diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index a36238fca4a874..9a6aba6a88971e 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -66,44 +66,23 @@ jobs:
     INSTALL_DIR: $(WORK_DIR)\install_pkg
     INSTALL_TEST_DIR: $(INSTALL_DIR)\tests
     SETUPVARS: $(INSTALL_DIR)\setupvars.bat
-    PYTHON_DIR: C:\hostedtoolcache\windows\Python\3.11.2\x64
     CMAKE_VERSION: 3.24.0
     CMAKE_CMD: $(WORK_DIR)\cmake-$(CMAKE_VERSION)-windows-x86_64\cmake-$(CMAKE_VERSION)-windows-x86_64\bin\cmake.exe
     OV_CMAKE_TOOLCHAIN_FILE: $(REPO_DIR)\cmake\toolchains\mt.runtime.win32.toolchain.cmake
-    PYTHON_VENV_DIR: $(WORK_DIR)\.venv
-
+    PYTHON_EXE: C:\hostedtoolcache\windows\Python\3.8.2\x64\python.exe
 
   steps:
   - script: |
       rd /Q /S $(WORK_DIR) & mkdir $(WORK_DIR)
       rd /Q /S $(BUILD_DIR) & mkdir $(BUILD_DIR)
-      rd /Q /S $(WORK_DIR) & mkdir C:\hostedtoolcache\windows\Python\3.11.2
-      rd /Q /S $(BUILD_DIR) & mkdir C:\hostedtoolcache\windows\Python\3.11.2\x64
       rd /Q /S $(BUILD_SAMPLES_DIR) & mkdir $(BUILD_SAMPLES_DIR)
       rd /Q /S $(BUILD_SAMPLES_TESTS_DIR) & mkdir $(BUILD_SAMPLES_TESTS_DIR)
     displayName: 'Make dir'
 
-  - script: curl -O https://www.python.org/ftp/python/3.11.2/python-3.11.2-amd64.exe
-    displayName: 'Download Python'
-    workingDirectory: $(WORK_DIR)
-
-  - script: |
-      python-3.11.2-amd64.exe /passive InstallAllUsers=0 Include_launcher=0 TargetDir=C:\hostedtoolcache\windows\Python\3.11.2\x64 && ^
-      cp C:\hostedtoolcache\windows\Python\3.8.2\x64.complete C:\hostedtoolcache\windows\Python\3.11.2\x64.complete
-    displayName: 'Install Python'
-    workingDirectory: $(WORK_DIR)
-
-  - task: UsePythonVersion@0
-    displayName: 'Use Python'
-    inputs:
-      versionSpec: '3.11.2'
-      disableDownloadFromRegistry: true
-
   - script: |
       powershell -command "Invoke-RestMethod -Headers @{\"Metadata\"=\"true\"} -Method GET -Uri http://169.254.169.254/metadata/instance/compute?api-version=2019-06-01 | format-custom"
-      tree C:\hostedtoolcache\windows\Python
-      where python
-      python --version
+      where $(PYTHON_EXE)
+      $(PYTHON_EXE) --version
       where java
       java -version
       wmic computersystem get TotalPhysicalMemory
@@ -124,20 +103,20 @@ jobs:
     path: openvino_contrib
 
   - script: |
-      python -m pip install --upgrade pip
+      $(PYTHON_EXE) -m pip install --upgrade pip
       rem For running Python API tests
-      python -m pip install -r $(REPO_DIR)\src\bindings\python\src\compatibility\openvino\requirements-dev.txt
-      python -m pip install -r $(REPO_DIR)\src\bindings\python\wheel\requirements-dev.txt
-      python -m pip install -r $(REPO_DIR)\src\bindings\python\requirements.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\src\bindings\python\src\compatibility\openvino\requirements-dev.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\src\bindings\python\wheel\requirements-dev.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\src\bindings\python\requirements.txt
       rem For running Paddle frontend unit tests
-      python -m pip install -r $(REPO_DIR)\src\frontends\paddle\tests\requirements.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\src\frontends\paddle\tests\requirements.txt
       rem For running ONNX frontend unit tests
-      python -m pip install -r $(REPO_DIR)\src\frontends\onnx\tests\requirements.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\src\frontends\onnx\tests\requirements.txt
       rem For running TensorFlow frontend unit tests
-      python -m pip install -r $(REPO_DIR)\src\frontends\tensorflow\tests\requirements.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\src\frontends\tensorflow\tests\requirements.txt
       rem For MO unit tests
-      python -m pip install -r $(REPO_DIR)\tools\mo\requirements.txt
-      python -m pip install -r $(REPO_DIR)\tools\mo\requirements_dev.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\tools\mo\requirements.txt
+      $(PYTHON_EXE) -m pip install -r $(REPO_DIR)\tools\mo\requirements_dev.txt
       rem Speed up build
       powershell -command "Invoke-WebRequest https://github.com/Kitware/CMake/releases/download/v$(CMAKE_VERSION)/cmake-$(CMAKE_VERSION)-windows-x86_64.zip -OutFile cmake-$(CMAKE_VERSION)-windows-x86_64.zip"
       powershell -command "Expand-Archive -Force cmake-$(CMAKE_VERSION)-windows-x86_64.zip"
@@ -162,10 +141,10 @@ jobs:
         -DENABLE_TESTS=ON ^
         -DCMAKE_COMPILE_WARNING_AS_ERROR=ON ^
         -DENABLE_STRICT_DEPENDENCIES=OFF ^
+        -DPython3_EXECUTABLE=$(PYTHON_EXE) ^
         -DENABLE_PYTHON=ON ^
         -DBUILD_nvidia_plugin=OFF ^
         -DCUSTOM_OPERATIONS="calculate_grid;complex_mul;fft;grid_sample;sparse_conv;sparse_conv_transpose" ^
-        -DPython3_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.11.2\x64\python.exe" ^
         -DOPENVINO_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules ^
         -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" ^
         -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" ^
@@ -190,7 +169,7 @@ jobs:
   - script: dir $(INSTALL_DIR) /s
     displayName: 'List install files'
 
-  - script: python -m pip install openvino-dev --find-links=$(INSTALL_DIR)\tools
+  - script: $(PYTHON_EXE) -m pip install openvino-dev --find-links=$(INSTALL_DIR)\tools
     displayName: 'Install Wheels'
 
   - script: |
@@ -215,12 +194,12 @@ jobs:
       if not exist %USERPROFILE%\Documents\Intel\OpenVINO\openvino_c_samples_build\ exit 1
     displayName: 'Build c samples'
 
-  - script: python -m pip install -r $(INSTALL_TEST_DIR)\smoke_tests\requirements.txt
+  - script: $(PYTHON_EXE) -m pip install -r $(INSTALL_TEST_DIR)\smoke_tests\requirements.txt
     displayName: 'Install dependencies for samples smoke tests'
 
   - script: |
       call $(SETUPVARS) && ^
-      python -m pytest $(INSTALL_DIR)\tests\smoke_tests\  --env_conf $(INSTALL_TEST_DIR)\smoke_tests\env_config.yml -s --junitxml=$(INSTALL_TEST_DIR)/TEST-SamplesSmokeTests.xml
+      $(PYTHON_EXE) -m pytest $(INSTALL_DIR)\tests\smoke_tests\  --env_conf $(INSTALL_TEST_DIR)\smoke_tests\env_config.yml -s --junitxml=$(INSTALL_TEST_DIR)/TEST-SamplesSmokeTests.xml
     env:
       IE_APP_PATH: $(INSTALL_DIR)\samples_bin
       IE_APP_PYTHON_PATH: $(INSTALL_DIR)\samples\python\
diff --git a/.ci/azure/windows_conditional_compilation.yml b/.ci/azure/windows_conditional_compilation.yml
index 70c7ebfff239e6..3d2df492194950 100644
--- a/.ci/azure/windows_conditional_compilation.yml
+++ b/.ci/azure/windows_conditional_compilation.yml
@@ -64,15 +64,6 @@ jobs:
     SETUPVARS: $(INSTALL_DIR)\setupvars.bat
 
   steps:
-  - task: UsePythonVersion@0
-    inputs:
-      versionSpec: '3.11.2'
-      addToPath: true
-      architecture: 'x64'
-      githubToken: $(auth_token)
-    displayName: Setup Python 3.11
-    name: setupPython
-
   - script: |
       powershell -command "Invoke-RestMethod -Headers @{\"Metadata\"=\"true\"} -Method GET -Uri http://169.254.169.254/metadata/instance/compute?api-version=2019-06-01 | format-custom"
       where python
diff --git a/.github/workflows/build_doc.yml b/.github/workflows/build_doc.yml
index b7e688d55f8c4a..fca75e99dc4109 100644
--- a/.github/workflows/build_doc.yml
+++ b/.github/workflows/build_doc.yml
@@ -17,8 +17,8 @@ jobs:
       - name: Clone OpenVINO
         uses: actions/checkout@v4
         with:
-          submodules: true
-          lfs: true
+          submodules: 'true'
+          lfs: 'true'
 
       - name: Install apt-get dependencies
         uses: awalsh128/cache-apt-pkgs-action@v1.3.0
diff --git a/.github/workflows/linux.yml b/.github/workflows/linux.yml
index bf602387ae8c6d..733dfed4c09d14 100644
--- a/.github/workflows/linux.yml
+++ b/.github/workflows/linux.yml
@@ -1,21 +1,25 @@
-name: Tests on Linux (Ubuntu 20.04, Python 3.11)
+name: Linux (Ubuntu 20.04, Python 3.11)
 on:
   schedule:
     # at 00:00 on Wednesday and Saturday
     - cron: '0 0 * * 3,6'
   workflow_dispatch:
   pull_request:
-    paths-ignore:
-      - '**/docs/**'
-      - 'docs/**'
-      - '**/**.md'
-      - '**.md'
+    paths:
+      - '**'
+      - '!**/docs/**'
+      - '!docs/**'
+      - 'docs/snippets/**'
+      - '!**/**.md'
+      - '!**.md'
   push:
-    paths-ignore:
-      - '**/docs/**'
-      - 'docs/**'
-      - '**/**.md'
-      - '**.md'
+    paths:
+      - '**'
+      - '!docs/**'
+      - '!**/docs/**'
+      - 'docs/snippets/**'
+      - '!**/**.md'
+      - '!**.md'
     branches:
       - master
       - 'releases/**'
@@ -50,6 +54,7 @@ jobs:
       OPENVINO_CONTRIB_REPO: /__w/openvino/openvino/openvino_contrib
       INSTALL_DIR: /__w/openvino/openvino/openvino_install
       INSTALL_TEST_DIR: /__w/openvino/openvino/tests_install
+      DEVELOPER_PACKAGE_DIR: /__w/openvino/openvino/developer_package_install
       BUILD_DIR: /__w/openvino/openvino/openvino_build
       CCACHE_DIR: /mount/caches/ccache/ubuntu20_x86_64_Release
       CCACHE_TEMPDIR: /__w/openvino/openvino/ccache_temp
@@ -74,6 +79,7 @@ jobs:
           repository: 'openvinotoolkit/openvino_contrib'
           path: ${{ env.OPENVINO_CONTRIB_REPO }}
           submodules: 'true'
+          ref: 'master'
 
       #
       # Dependencies
@@ -150,6 +156,7 @@ jobs:
         run: |
           cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_DIR} -P ${BUILD_DIR}/cmake_install.cmake
           cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_TEST_DIR} -DCOMPONENT=tests -P ${BUILD_DIR}/cmake_install.cmake
+          cmake -DCMAKE_INSTALL_PREFIX=${DEVELOPER_PACKAGE_DIR} -DCOMPONENT=developer_package -P ${BUILD_DIR}/cmake_install.cmake
           cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_DIR} -DCOMPONENT=python_wheels -P ${BUILD_DIR}/cmake_install.cmake
 
       - name: Pack Artifacts
@@ -164,6 +171,10 @@ jobs:
             tar -czvf ${BUILD_DIR}/openvino_package.tar.gz *
           popd
 
+          pushd ${DEVELOPER_PACKAGE_DIR}
+            tar -czvf ${BUILD_DIR}/openvino_developer_package.tar.gz *
+          popd
+
           pushd ${INSTALL_TEST_DIR}
             tar -czvf ${BUILD_DIR}/openvino_tests.tar.gz *
           popd
@@ -203,6 +214,14 @@ jobs:
           path: ${{ env.BUILD_DIR }}/openvino_package.tar.gz
           if-no-files-found: 'error'
 
+      - name: Upload openvino developer package
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v3
+        with:
+          name: openvino_developer_package
+          path: ${{ env.BUILD_DIR }}/openvino_developer_package.tar.gz
+          if-no-files-found: 'error'
+
       - name: Upload openvino debian packages
         if: ${{ always() }}
         uses: actions/upload-artifact@v3
@@ -493,7 +512,6 @@ jobs:
       ONNX_RUNTIME_BUILD_DIR: /__w/openvino/openvino/onnxruntime/build
 
     steps:
-
       - name: Fetch install_build_dependencies.sh
         uses: actions/checkout@v4
         with:
@@ -501,6 +519,7 @@ jobs:
             install_build_dependencies.sh
           sparse-checkout-cone-mode: false
           path: ${{ env.OPENVINO_REPO }}
+          ref: 'master'
 
       - name: Install git
         run: |
@@ -932,6 +951,16 @@ jobs:
           TEST_DEVICE: CPU
           TEST_PRECISION: FP16
 
+      - name: ONNX Layer Tests
+        run: |
+          python3 -m pip install -r ${LAYER_TESTS_INSTALL_DIR}/requirements.txt
+          # requires 'unit_tests' from 'tools/mo'
+          export PYTHONPATH=${OPENVINO_REPO}/tools/mo/:$PYTHONPATH
+          python3 -m pytest ${LAYER_TESTS_INSTALL_DIR}/onnx_tests -m "not launch_only_if_manually_specified and precommit" --junitxml=${INSTALL_TEST_DIR}/TEST-onnx.xml
+        env:
+          TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
+
       - name: TensorFlow 1 Layer Tests - TF FE
         run: |
           python3 -m pip install -r ${LAYER_TESTS_INSTALL_DIR}/requirements.txt
@@ -1111,13 +1140,14 @@ jobs:
         with:
           name: test-results-functional-cpu
           path: |
-            ${{ env.INSTALL_TEST_DIR }}/TEST*.xml
+            ${{ env.INSTALL_TEST_DIR }}/temp/*.log
+            ${{ env.INSTALL_TEST_DIR }}/logs/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/failed/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/crashed/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/hanged/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/interapted/*.log
-            ${{ env.INSTALL_TEST_DIR }}/logs/disabled_tests.log
             ${{ env.INSTALL_TEST_DIR }}/logs/hash_table.csv
+            ${{ env.PARALLEL_TEST_CACHE }}
           if-no-files-found: 'error'
 
   TensorFlow_Hub_Models_Tests:
@@ -1292,3 +1322,122 @@ jobs:
           path: |
             ${{ env.INSTALL_TEST_DIR }}/TEST*.html
           if-no-files-found: 'error'
+
+  NVIDIA_Plugin:
+    name: NVIDIA plugin
+    needs: Build
+    defaults:
+      run:
+        shell: bash
+    runs-on: aks-linux-16-cores
+    container:
+      image: openvinogithubactions.azurecr.io/dockerhub/nvidia/cuda:11.8.0-runtime-ubuntu20.04
+      volumes:
+        - /mount/caches:/mount/caches
+    env:
+      CMAKE_BUILD_TYPE: 'Release'
+      CMAKE_GENERATOR: 'Ninja Multi-Config'
+      CMAKE_CUDA_COMPILER_LAUNCHER: ccache
+      CMAKE_CXX_COMPILER_LAUNCHER: ccache
+      CMAKE_C_COMPILER_LAUNCHER: ccache
+      INSTALL_DIR: /__w/openvino/openvino/install
+      OPENVINO_DEVELOPER_PACKAGE: /__w/openvino/openvino/install/developer_package
+      OPENVINO_REPO: /__w/openvino/openvino/openvino
+      OPENVINO_CONTRIB_REPO: /__w/openvino/openvino/openvino_contrib
+      NVIDIA_BUILD_DIR: /__w/openvino/openvino/nvidia_plugin_build
+      DEBIAN_FRONTEND: 'noninteractive'
+      CCACHE_DIR: /mount/caches/ccache/ubuntu20_x86_64_Release
+      CCACHE_TEMPDIR: /__w/openvino/openvino/ccache_temp
+      CCACHE_MAXSIZE: 50G
+
+    steps:
+      - name: Install Prerequisites
+        run: apt update && apt install -y git ca-certificates
+
+      - name: Download OpenVINO package
+        uses: actions/download-artifact@v3
+        with:
+          name: openvino_package
+          path: ${{ env.INSTALL_DIR }}
+
+      - name: Download OpenVINO Developer package
+        uses: actions/download-artifact@v3
+        with:
+          name: openvino_developer_package
+          path: ${{ env.INSTALL_DIR }}
+
+      - name: Extract OpenVINO packages
+        run: |
+          pushd ${INSTALL_DIR}
+            tar -xzf openvino_package.tar.gz -C ${INSTALL_DIR}
+          popd
+
+          pushd ${INSTALL_DIR}
+            tar -xzf openvino_developer_package.tar.gz -C ${INSTALL_DIR}
+          popd
+
+      # TODO: replace with sparse checkout below
+      - name: Clone OpenVINO
+        uses: actions/checkout@v4
+        with:
+          path: ${{ env.OPENVINO_REPO }}
+
+      - name: Fetch install_build_dependencies.sh
+        if: ${{ 'false' }}
+        uses: actions/checkout@v4
+        with:
+          sparse-checkout: |
+            install_build_dependencies.sh
+          sparse-checkout-cone-mode: false
+          path: ${{ env.OPENVINO_REPO }}
+
+      - name: Clone OpenVINO Contrib
+        uses: actions/checkout@v4
+        with:
+          repository: 'openvinotoolkit/openvino_contrib'
+          path: ${{ env.OPENVINO_CONTRIB_REPO }}
+          ref: 'master'
+
+      #
+      # Dependencies
+      #
+
+      - name: Install build dependencies
+        run: |
+          ${OPENVINO_REPO}/install_build_dependencies.sh
+          apt -y --no-install-recommends install software-properties-common
+
+      - name: Install CUDA
+        run: |
+          wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
+          mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
+
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
+          add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
+          apt update
+          apt install -y \
+            libcudnn8=8.9.4.*-1+cuda11.8 \
+            libcudnn8-dev=8.9.4.*-1+cuda11.8 \
+            libcudnn8-samples=8.9.4.*-1+cuda11.8 \
+            cuda-runtime-11-8 \
+            cuda-11-8 \
+            libcutensor1=1.6.1.5-1 \
+            libcutensor-dev=1.6.1.5-1 \
+            cuda-drivers=520.61.05-1
+
+      #
+      # Build
+      #
+
+      - name: Cmake & Build - NVIDIA Plugin
+        run: |
+          source ${INSTALL_DIR}/setupvars.sh
+          cmake \
+            -DOpenVINODeveloperPackage_DIR=${OPENVINO_DEVELOPER_PACKAGE}/cmake \
+            -DCMAKE_COMPILE_WARNING_AS_ERROR=OFF \
+            -S ${OPENVINO_CONTRIB_REPO}/modules/nvidia_plugin \
+            -B ${NVIDIA_BUILD_DIR}
+          cmake --build ${NVIDIA_BUILD_DIR} --parallel --config ${{ env.CMAKE_BUILD_TYPE }} --verbose -- ov_nvidia_func_tests ov_nvidia_unit_tests
+
+      - name: Show ccache stats
+        run: ccache --show-stats
diff --git a/.github/workflows/linux_conditional_compilation.yml b/.github/workflows/linux_conditional_compilation.yml
index b8567d57a6a6cd..c8fb34cca85244 100644
--- a/.github/workflows/linux_conditional_compilation.yml
+++ b/.github/workflows/linux_conditional_compilation.yml
@@ -73,6 +73,7 @@ jobs:
           repository: 'openvinotoolkit/testdata'
           path: ${{ env.MODELS_PATH }}
           lfs: 'true'
+          ref: 'master'
 
       #
       # Dependencies
@@ -110,6 +111,9 @@ jobs:
 
           # For running Paddle frontend unit tests
           python3 -m pip install -r ${OPENVINO_REPO}/src/frontends/paddle/tests/requirements.txt
+          # see https://github.com/PaddlePaddle/Paddle/issues/55597#issuecomment-1718131420
+          wget http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
+          apt-get install ./libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
 
       #
       # Build
@@ -230,6 +234,7 @@ jobs:
           repository: 'openvinotoolkit/testdata'
           path: ${{ env.MODELS_PATH }}
           lfs: 'true'
+          ref: 'master'
 
       - name: Download selective build statistics package
         uses: actions/download-artifact@v3
diff --git a/.github/workflows/linux_cuda.yml b/.github/workflows/linux_cuda.yml
deleted file mode 100644
index 7fd90dac00dcf4..00000000000000
--- a/.github/workflows/linux_cuda.yml
+++ /dev/null
@@ -1,143 +0,0 @@
-name: Linux NVIDIA Plugin (Ubuntu 20.04)
-on:
-  workflow_dispatch:
-  pull_request:
-    paths-ignore:
-      - '**/docs/**'
-      - 'docs/**'
-      - '**/**.md'
-      - '**.md'
-      - '**/layer_tests_summary/**'
-      - '**/conformance/**'
-  push:
-    paths-ignore:
-      - '**/docs/**'
-      - 'docs/**'
-      - '**/**.md'
-      - '**.md'
-      - '**/layer_tests_summary/**'
-      - '**/conformance/**'
-    branches:
-      - master
-
-concurrency:
-  # github.ref is not unique in post-commit
-  group: ${{ github.event_name == 'push' && github.run_id || github.ref }}-linux-nvidia
-  cancel-in-progress: true
-
-jobs:
-  Build:
-    defaults:
-      run:
-        shell: bash
-    runs-on: aks-linux-16-cores
-    container:
-      image: openvinogithubactions.azurecr.io/dockerhub/nvidia/cuda:11.8.0-runtime-ubuntu20.04
-      volumes:
-        - /mount/caches:/mount/caches
-    env:
-      CMAKE_BUILD_TYPE: 'Release'
-      CMAKE_GENERATOR: 'Ninja Multi-Config'
-      CMAKE_CUDA_COMPILER_LAUNCHER: ccache
-      CMAKE_CXX_COMPILER_LAUNCHER: ccache
-      CMAKE_C_COMPILER_LAUNCHER: ccache
-      OPENVINO_REPO: /__w/openvino/openvino/openvino
-      OPENVINO_CONTRIB_REPO: /__w/openvino/openvino/openvino_contrib
-      OV_BUILD_DIR: /__w/openvino/openvino/openvino_build
-      NVIDIA_BUILD_DIR: /__w/openvino/openvino/nvidia_plugin_build
-      DEBIAN_FRONTEND: 'noninteractive'
-      CCACHE_DIR: /mount/caches/ccache/ubuntu20_x86_64_Release
-      CCACHE_TEMPDIR: /__w/openvino/openvino/ccache_temp
-      CCACHE_MAXSIZE: 50G
-    steps:
-
-      - name: Install Prerequisites
-        run: |
-          apt update
-          apt install -y git curl git git-lfs unzip wget
-
-      - name: Clone OpenVINO
-        uses: actions/checkout@v4
-        with:
-          path: ${{ env.OPENVINO_REPO }}
-          submodules: 'true'
-
-      - name: Clone OpenVINO Contrib
-        uses: actions/checkout@v4
-        with:
-          repository: 'openvinotoolkit/openvino_contrib'
-          path: ${{ env.OPENVINO_CONTRIB_REPO }}
-          ref: 'master'
-
-      #
-      # Dependencies
-      #
-
-      - name: Install build dependencies
-        run: |
-          ${OPENVINO_REPO}/install_build_dependencies.sh
-          
-          apt -y --no-install-recommends install unzip wget software-properties-common
-
-      - name: Install CUDA
-        run: |
-         wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin 
-         mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
-         
-         apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
-         add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
-         apt update
-         apt install -y \
-            libcudnn8=8.9.4.*-1+cuda11.8 \
-            libcudnn8-dev=8.9.4.*-1+cuda11.8 \
-            libcudnn8-samples=8.9.4.*-1+cuda11.8 \
-            cuda-runtime-11-8 \
-            cuda-11-8 \
-            libcutensor1=1.6.1.5-1 \
-            libcutensor-dev=1.6.1.5-1 \
-            cuda-drivers=520.61.05-1
-
-      #
-      # Build
-      #
-
-      - name: CMake configure
-        run: |
-          cmake \
-            -G "${{ env.CMAKE_GENERATOR }}" \
-            -DENABLE_CPPLINT=OFF \
-            -DENABLE_NCC_STYLE=OFF \
-            -DENABLE_SYSTEM_PUGIXML=ON \
-            -DENABLE_SYSTEM_OPENCL=ON \
-            -DENABLE_STRICT_DEPENDENCIES=OFF \
-            -DCMAKE_BUILD_TYPE=${{ env.CMAKE_BUILD_TYPE }} \
-            -DENABLE_INTEL_CPU=OFF \
-            -DENABLE_INTEL_GPU=OFF \
-            -DENABLE_INTEL_GNA=OFF \
-            -DENABLE_OV_TF_FRONTEND=OFF \
-            -DENABLE_OV_TF_LITE_FRONTEND=OFF \
-            -DENABLE_OV_PADDLE_FRONTEND=OFF \
-            -DENABLE_OV_PYTORCH_FRONTEND=OFF \
-            -DENABLE_OV_ONNX_FRONTEND=OFF \
-            -DENABLE_PYTHON=OFF \
-            -DENABLE_TESTS=ON \
-            -DCPACK_GENERATOR=TGZ \
-            -DCMAKE_COMPILE_WARNING_AS_ERROR=ON \
-            -S ${OPENVINO_REPO} \
-            -B ${OV_BUILD_DIR}
-
-      - name: Build - OpenVINO
-        run: |
-          cmake --build ${OV_BUILD_DIR} --parallel --config ${{ env.CMAKE_BUILD_TYPE }} --verbose --target ov_dev_targets
-
-      - name: Cmake & Build - NVIDIA Plugin
-        run: |
-          cmake \
-            -DOpenVINODeveloperPackage_DIR=${OV_BUILD_DIR} \
-            -DCMAKE_COMPILE_WARNING_AS_ERROR=OFF \
-            -S ${OPENVINO_CONTRIB_REPO}/modules/nvidia_plugin \
-            -B ${NVIDIA_BUILD_DIR}
-          cmake --build ${NVIDIA_BUILD_DIR} --parallel --config ${{ env.CMAKE_BUILD_TYPE }} --verbose -- ov_nvidia_func_tests ov_nvidia_unit_tests
-
-      - name: Show ccache stats
-        run: ccache --show-stats
diff --git a/.github/workflows/mac.yml b/.github/workflows/mac.yml
index f1318923b3e0c7..d5084d7a5d19c6 100644
--- a/.github/workflows/mac.yml
+++ b/.github/workflows/mac.yml
@@ -360,7 +360,7 @@ jobs:
           ${{ env.INSTALL_TEST_DIR }}/ov_tensorflow_lite_frontend_tests --gtest_print_time=1 \
                 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-TensorFlowLiteFrontend.xml
 
-      - name: Transformations Tests
+      - name: Transformations func tests
         run: |
           source ${{ env.INSTALL_DIR }}/setupvars.sh
           ${{ env.INSTALL_TEST_DIR }}/ov_transformations_tests --gtest_print_time=1 \
@@ -372,6 +372,12 @@ jobs:
           ${{ env.INSTALL_TEST_DIR }}/ov_util_tests --gtest_print_time=1 \
                 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-commonUtilsTests.xml
 
+      - name: Snippets func tests
+        run: |
+          source ${{ env.INSTALL_DIR }}/setupvars.sh
+          ${{ env.INSTALL_TEST_DIR }}/ov_snippets_func_tests --gtest_print_time=1 \
+                --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-SnippetsFuncTests.xml
+
       - name: CPU plugin unit tests
         run: |
           source ${{ env.INSTALL_DIR }}/setupvars.sh
@@ -384,7 +390,13 @@ jobs:
           ${{ env.INSTALL_TEST_DIR }}/ov_auto_unit_tests --gtest_print_time=1 \
                 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_unit_tests.xml
 
-      - name: Template plugin tests
+      - name: AUTO func Tests
+        run: |
+          source ${{ env.INSTALL_DIR }}/setupvars.sh
+          ${{ env.INSTALL_TEST_DIR }}/ov_auto_func_tests --gtest_print_time=1 \
+                --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_func_tests.xml
+
+      - name: Template plugin func tests
         run: |
           source ${{ env.INSTALL_DIR }}/setupvars.sh
           ${{ env.INSTALL_TEST_DIR }}/ov_template_func_tests --gtest_print_time=1 \
@@ -403,11 +415,31 @@ jobs:
           ${{ env.INSTALL_TEST_DIR }}/ov_capi_test --gtest_print_time=1 \
                 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-OpenVINOCAPITests.xml
 
+      - name: AutoBatch unit tests
+        run: |
+          source ${{ env.INSTALL_DIR }}/setupvars.sh
+          ${{ env.INSTALL_TEST_DIR }}/ov_auto_batch_unit_tests --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_batch_unit_tests.xml
+
       - name: AutoBatch func tests
         run: |
           source ${{ env.INSTALL_DIR }}/setupvars.sh
           ${{ env.INSTALL_TEST_DIR }}/ov_auto_batch_func_tests --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_batch_func_tests.xml
 
+      - name: Proxy Plugin func tests
+        run: |
+          source ${{ env.INSTALL_DIR }}/setupvars.sh
+          ${{ env.INSTALL_TEST_DIR }}/ov_proxy_plugin_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-OVProxyTests.xml
+
+      - name: Hetero unit tests
+        run: |
+          source ${{ env.INSTALL_DIR }}/setupvars.sh
+          ${{ env.INSTALL_TEST_DIR }}/ov_hetero_unit_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-OVHeteroUnitTests.xml
+
+      - name: Hetero func tests
+        run: |
+          source ${{ env.INSTALL_DIR }}/setupvars.sh
+          ${{ env.INSTALL_TEST_DIR }}/ov_hetero_func_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-OVHeteroFuncTests.xml
+
       - name: Upload Test Results
         uses: actions/upload-artifact@v3
         if: ${{ always() }}
@@ -485,7 +517,7 @@ jobs:
             python3 -m pip install $ov_dev_wheel_name[mxnet,caffe,kaldi,onnx,tensorflow2,pytorch]
           popd
 
-      - name: nGraph and IE Python Bindings Tests
+      - name: Python API 1.0 Tests
         run: |
           python3 -m pytest -s ${{ env.INSTALL_TEST_DIR }}/pyngraph \
             --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-Pyngraph.xml \
@@ -501,9 +533,7 @@ jobs:
 
           python3 -m pytest -sv ${{ env.INSTALL_TEST_DIR }}/pyopenvino \
             --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-Pyngraph.xml \
-            --ignore=${{ env.INSTALL_TEST_DIR }}/pyopenvino/tests/test_utils/test_utils.py \
-            --ignore=${{ env.INSTALL_TEST_DIR }}/pyopenvino/tests/test_onnx/test_zoo_models.py \
-            --ignore=${{ env.INSTALL_TEST_DIR }}/pyopenvino/tests/test_onnx/test_backend.py
+            --ignore=${{ env.INSTALL_TEST_DIR }}/pyopenvino/tests/test_utils/test_utils.py
 
       - name: MO Python API Tests
         run: |
@@ -517,10 +547,22 @@ jobs:
           TEST_DEVICE: CPU
           TEST_PRECISION: FP16
 
+      - name: OVC Python API Tests
+        run: |
+          python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
+          # TODO: remove setupvars.sh from here; currently, it's used for 'test_utils' installed in '<package>/python/openvino'
+          source ${{ env.INSTALL_DIR }}/setupvars.sh
+
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/ovc_python_api_tests --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-test_ovc_convert.xml
+        env:
+          TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
+
       - name: Model Optimizer unit tests
         run: |
           export PYTHONPATH=${{ env.INSTALL_TEST_DIR }}:$PYTHONPATH
           python3 -m pytest -s ${{ env.INSTALL_TEST_DIR }}/mo/unit_tests \
+              --ignore=${{ env.INSTALL_TEST_DIR }}/mo/unit_tests/mo/front/mxnet \
               --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-ModelOptimizer.xml
 
       - name: PyTorch Layer Tests
@@ -533,6 +575,17 @@ jobs:
           TEST_DEVICE: CPU
           TEST_PRECISION: FP16
 
+      - name: ONNX Layer Tests
+        run: |
+          python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
+          
+          export PYTHONPATH=${{ env.OPENVINO_REPO }}/tools/mo/:${{ env.LAYER_TESTS_INSTALL_DIR }}:$PYTHONPATH
+          
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/onnx_tests -m "not launch_only_if_manually_specified and precommit" --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-onnx.xml
+        env:
+          TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
+
       - name: TensorFlow 1 Layer Tests - TF FE
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
@@ -568,6 +621,7 @@ jobs:
               --ir_version=11 --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf2_Activation.xml -k "sigmoid"
         env:
           TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
 
       - name: TensorFlow Lite Layer Tests - TFL FE
         run: |
@@ -577,6 +631,14 @@ jobs:
           python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_lite_tests/ --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tfl_fe.xml
         env:
           TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
+
+      - name: Python ONNX operators tests
+        run: |
+          # Skip test_onnx/test_zoo_models and test_onnx/test_backend due to long execution time - ONNX Model Zoo tests are run separately
+          python3 -m pytest -sv ${{ env.OPENVINO_REPO }}/src/frontends/onnx/tests -k 'not cuda' \
+            --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-onnx_frontend.xml \
+            --ignore=${{ env.OPENVINO_REPO }}/src/frontends/onnx/tests/test_python/test_zoo_models.py
 
       - name: Python Frontend tests
         run: |
@@ -587,6 +649,10 @@ jobs:
 
           python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/py_frontend_tests --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-test_py_fontend.xml
 
+      # TODO: install to 'tests' component via cpack
+      - name: OVC unit tests
+        run: python3 -m pytest -s ${{ env.OPENVINO_REPO }}/tools/ovc/unit_tests --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-OpenVinoConversion.xml
+
       - name: Upload Test Results
         uses: actions/upload-artifact@v3
         if: ${{ always() }}
diff --git a/.github/workflows/windows.yml b/.github/workflows/windows.yml
index 493a1e47ba6e0a..6ce891e6767698 100644
--- a/.github/workflows/windows.yml
+++ b/.github/workflows/windows.yml
@@ -1,24 +1,27 @@
 name: Windows (VS 2022, Python 3.11)
 on:
-  workflow_dispatch:
-  pull_request:
-    paths-ignore:
-      - '**/docs/**'
-      - 'docs/**'
-      - '**/**.md'
-      - '**.md'
-      - '**/layer_tests_summary/**'
-      - '**/conformance/**'
-  push:
-    paths-ignore:
-      - '**/docs/**'
-      - 'docs/**'
-      - '**/**.md'
-      - '**.md'
-      - '**/layer_tests_summary/**'
-      - '**/conformance/**'
-    branches:
-      - master
+  schedule:
+    # at 00:00 on workdays
+    - cron: '0 0 * * 1,2,3,4,5'
+#  workflow_dispatch:
+#  pull_request:
+#    paths-ignore:
+#      - '**/docs/**'
+#      - 'docs/**'
+#      - '**/**.md'
+#      - '**.md'
+#      - '**/layer_tests_summary/**'
+#      - '**/conformance/**'
+# push:
+#   paths-ignore:
+#     - '**/docs/**'
+#     - 'docs/**'
+#     - '**/**.md'
+#     - '**.md'
+#     - '**/layer_tests_summary/**'
+#     - '**/conformance/**'
+#  branches:
+#    - master
 
 concurrency:
   # github.ref is not unique in post-commit
@@ -56,6 +59,7 @@ jobs:
         with:
           repository: 'openvinotoolkit/openvino_contrib'
           path: 'openvino_contrib'
+          ref: 'master'
 
       #
       # Dependencies
@@ -300,7 +304,7 @@ jobs:
           # Find and install the core OV wheel
           $ovCoreWheelPath=Get-ChildItem -Path "${{ env.INSTALL_DIR }}\tools" -Filter openvino-*.whl | % { $_.FullName }
           python3 -m pip install "$ovCoreWheelPath"
-          
+
           # Find and install the dev OV wheel
           $ovDevWheelPath=Get-ChildItem -Path "${{ env.INSTALL_DIR }}\tools" -Filter openvino_dev*.whl | % { $_.FullName }
           python3 -m pip install "$ovDevWheelPath[mxnet,caffe,kaldi,onnx,tensorflow2,pytorch]"
@@ -308,21 +312,18 @@ jobs:
       - name: Python API 1.0 Tests
         shell: cmd
         run: |
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest -s ${{ env.INSTALL_TEST_DIR }}/pyngraph ${{ env.PYTHON_STATIC_ARGS }} --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-Pyngraph.xml --ignore=${{ env.INSTALL_TEST_DIR }}/pyngraph/tests_compatibility/test_onnx/test_zoo_models.py --ignore=${{ env.INSTALL_TEST_DIR }}/pyngraph/tests_compatibility/test_onnx/test_backend.py
+          python3 -m pytest -s ${{ env.INSTALL_TEST_DIR }}/pyngraph ${{ env.PYTHON_STATIC_ARGS }} --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-Pyngraph.xml --ignore=${{ env.INSTALL_TEST_DIR }}/pyngraph/tests_compatibility/test_onnx/test_zoo_models.py
 
       - name: Python API 2.0 Tests
         shell: cmd
         run: |
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest -sv ${{ env.INSTALL_TEST_DIR }}/pyopenvino ${{ env.PYTHON_STATIC_ARGS }} --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-Pyngraph.xml --ignore=${{ env.INSTALL_TEST_DIR }}/pyopenvino/tests/test_utils/test_utils.py
+          set PYTHONPATH=${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
+          python3 -m pytest -sv ${{ env.INSTALL_TEST_DIR }}/pyopenvino ${{ env.PYTHON_STATIC_ARGS }} --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-Pyngraph.xml --ignore=${{ env.INSTALL_TEST_DIR }}/pyopenvino/tests/test_utils/test_utils.py
 
       - name: Model Optimizer UT
         shell: cmd
         run: |
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};${{ env.INSTALL_TEST_DIR }};${{ env.INSTALL_DIR }}\python\python${{ env.PYTHON_VERSION }};%PYTHONPATH%
-
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest -s ${{ env.INSTALL_TEST_DIR }}/mo/unit_tests --ignore=${{ env.INSTALL_TEST_DIR }}/mo/unit_tests/mo/front/mxnet --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-ModelOptimizer.xml
+          python3 -m pytest -s ${{ env.INSTALL_TEST_DIR }}/mo/unit_tests --ignore=${{ env.INSTALL_TEST_DIR }}/mo/unit_tests/mo/front/mxnet --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-ModelOptimizer.xml
 
       # Ticket - 115085
       - name: PyTorch Layer Tests
@@ -330,32 +331,43 @@ jobs:
         shell: cmd
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/pytorch_tests -m precommit --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-pytorch.xml
+        env:
+          TEST_DEVICE: CPU
 
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
+      - name: ONNX Layer Tests
+        shell: cmd
+        run: |
+          python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
 
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/pytorch_tests -m precommit --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-pytorch.xml
+          :: requires 'unit_tests' from 'tools/mo'
+          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/onnx_tests -m "not launch_only_if_manually_specified and precommit" --junitxml=${INSTALL_TEST_DIR}/TEST-onnx.xml
         env:
           TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
 
       - name: TensorFlow 1 Layer Tests - TF FE
         shell: cmd
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
 
+          :: requires 'unit_tests' from 'tools/mo'
           set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_tests/ --use_new_frontend -m precommit_tf_fe --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf_fe.xml
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_tests/ --use_new_frontend -m precommit_tf_fe  --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf_fe.xml
         env:
           TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
 
       - name: TensorFlow 2 Layer Tests - TF FE
         shell: cmd
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
 
+          :: requires 'unit_tests' from 'tools/mo'
           set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
 
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow2_keras_tests/ --use_new_frontend -m precommit_tf_fe --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf2_fe.xml
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow2_keras_tests/ --use_new_frontend -m precommit_tf_fe --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf2_fe.xml
         env:
           TEST_DEVICE: CPU
 
@@ -363,42 +375,30 @@ jobs:
         shell: cmd
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
-
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_tests/test_tf_Roll.py --ir_version=10 --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf_Roll.xml
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_tests/test_tf_Roll.py --ir_version=10  --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf_Roll.xml
 
       - name: TensorFlow 2 Layer Tests - Legacy FE
         shell: cmd
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
-
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow2_keras_tests/test_tf2_keras_activation.py --ir_version=11 --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf2_Activation.xml -k "sigmoid"
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow2_keras_tests/test_tf2_keras_activation.py --ir_version=11 --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf2_Activation.xml -k "sigmoid"
         env:
           TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
 
       - name: TensorFlow Lite Layer Tests - TFL FE
         shell: cmd
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
-
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
-          call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_lite_tests/ --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tfl_fe.xml
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_lite_tests/ --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tfl_fe.xml
         env:
           TEST_DEVICE: CPU
           TEST_PRECISION: FP16
 
-      - name: TensorFlow Lite Layer Tests - TFL FE
+      - name: Python ONNX operators tests
         shell: cmd
         run: |
           :: Skip test_onnx/test_zoo_models and test_onnx/test_backend due to long execution time - ONNX Model Zoo tests are run separately
-          python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
-
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
           python3 -m pytest ${{ env.OPENVINO_REPO }}/src/frontends/onnx/tests -k "not cuda" ^
           --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-onnx_frontend.xml ^
           --ignore=${{ env.OPENVINO_REPO }}/src/frontends/onnx/tests/test_python/test_zoo_models.py
@@ -408,8 +408,7 @@ jobs:
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
 
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
+          :: TODO: remove setupvars.bat from here; currently, it's used for 'test_utils' installed in '<package>/python/openvino'
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/mo_python_api_tests --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-test_mo_convert.xml
         env:
           TEST_DEVICE: CPU
@@ -420,8 +419,7 @@ jobs:
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
 
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
+          :: TODO: remove setupvars.sh from here; currently, it's used for 'test_utils' installed in '<package>/python/openvino'
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/ovc_python_api_tests --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-test_ovc_convert.xml
         env:
           TEST_DEVICE: CPU
@@ -432,10 +430,13 @@ jobs:
         run: |
           python3 -m pip install -r ${{ env.LAYER_TESTS_INSTALL_DIR }}/requirements.txt
 
-          set PYTHONPATH=${{ env.OPENVINO_REPO }}\tools\mo;${{ env.LAYER_TESTS_INSTALL_DIR }};%PYTHONPATH%
-
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/py_frontend_tests --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-test_py_fontend.xml
 
+      # TODO: install to 'tests' component via cpack
+      - name: OVC unit tests
+        shell: cmd
+        run: python3 -m pytest -s ${{ env.OPENVINO_REPO }}/tools/ovc/unit_tests --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-OpenVinoConversion.xml
+
       - name: Upload Test Results
         uses: actions/upload-artifact@v3
         if: ${{ !cancelled() }}
@@ -518,7 +519,7 @@ jobs:
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_onnx_frontend_tests --gtest_print_time=1 --gtest_filter=-*IE_GPU* --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ONNXFrontend.xml
 
-      - name: TensorFlow Common tests
+      - name: TensorFlow Common frontend tests
         shell: cmd
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_tensorflow_common_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-TensorFlowCommonFrontend.xml
@@ -533,7 +534,7 @@ jobs:
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_tensorflow_lite_frontend_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-TensorFlowLiteFrontend.xml
 
-      - name: Transformations Tests
+      - name: Transformations func tests
         shell: cmd
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_transformations_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-Transformations.xml
@@ -568,17 +569,17 @@ jobs:
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_gna_unit_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-GNAUnitTests.xml
 
-      - name: AUTO UT
+      - name: AUTO unit tests
         shell: cmd
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_auto_unit_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_unit_tests.xml
 
-      - name: AUTO FuncTests
+      - name: AUTO func Tests
         shell: cmd
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_auto_func_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_func_tests.xml
 
-      - name: Template plugin tests
+      - name: Template plugin func tests
         shell: cmd
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_template_func_tests --gtest_print_time=1 --gtest_filter=*smoke* --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-TemplateFuncTests.xml
@@ -598,12 +599,12 @@ jobs:
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_auto_batch_unit_tests --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_batch_unit_tests.xml
 
-      - name: AutoBatch FuncTests
+      - name: AutoBatch func tests
         shell: cmd
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_auto_batch_func_tests --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-ov_auto_batch_func_tests.xml
 
-      - name: Proxy Plugin Tests
+      - name: Proxy Plugin func tests
         shell: cmd
         run: |
           call "${{ env.INSTALL_DIR }}\\setupvars.bat" && ${{ env.INSTALL_TEST_DIR }}/ov_proxy_plugin_tests --gtest_print_time=1 --gtest_output=xml:${{ env.INSTALL_TEST_DIR }}/TEST-OVProxyTests.xml
@@ -697,9 +698,11 @@ jobs:
           name: test-results-functional-cpu
           path: |
             ${{ env.INSTALL_TEST_DIR }}/temp/*.log
+            ${{ env.INSTALL_TEST_DIR }}/logs/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/failed/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/crashed/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/hanged/*.log
             ${{ env.INSTALL_TEST_DIR }}/logs/interapted/*.log
-            ${{ env.INSTALL_TEST_DIR }}/logs/*.log
+            ${{ env.INSTALL_TEST_DIR }}/logs/hash_table.csv
+            ${{ env.PARALLEL_TEST_CACHE }}
           if-no-files-found: 'error'
diff --git a/.github/workflows/windows_conditional_compilation.yml b/.github/workflows/windows_conditional_compilation.yml
index fabd763e1a3f2d..93f947ee071df1 100644
--- a/.github/workflows/windows_conditional_compilation.yml
+++ b/.github/workflows/windows_conditional_compilation.yml
@@ -67,6 +67,7 @@ jobs:
           repository: 'openvinotoolkit/testdata'
           path: 'testdata'
           lfs: 'true'
+          ref: 'master'
 
       #
       # Dependencies
diff --git a/CMakeLists.txt b/CMakeLists.txt
index ea3de7994f722e..82277e5c875cfb 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -81,54 +81,55 @@ file(REMOVE "${CMAKE_BINARY_DIR}/ngraphTargets.cmake")
 file(REMOVE "${CMAKE_BINARY_DIR}/InferenceEngineTargets.cmake")
 file(REMOVE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
 
-# remove exported developer targets to force its regeneration
-macro(ov_clean_dev_targets)
-    foreach(component IN LISTS openvino_export_components)
-        file(REMOVE "${CMAKE_BINARY_DIR}/${component}_dev_targets.cmake")
-        file(REMOVE "${CMAKE_BINARY_DIR}/ov_${component}_dev_targets.cmake")
-        unset(${component} CACHE)
-    endforeach()
-    unset(openvino_export_components CACHE)
+# remove exported developer targets files to force its regeneration
+macro(ov_clean_developer_package_targets)
+    file(REMOVE "${CMAKE_BINARY_DIR}/inference_engine_developer_package_targets.cmake")
+    file(REMOVE "${CMAKE_BINARY_DIR}/openvino_developer_package_targets.cmake")
+    unset(_OPENVINO_DEVELOPER_PACKAGE_TARGETS CACHE)
     unset(openvino_installed_targets CACHE)
 endmacro()
-ov_clean_dev_targets()
+ov_clean_developer_package_targets()
 
-#
-# Build
-#
+function(ov_developer_package_export_targets)
+    cmake_parse_arguments(EXPORT "" "TARGET;INSTALL_DESTIONATION" "INSTALL_INCLUDE_DIRECTORIES" ${ARGN})
 
-function(openvino_developer_export_targets)
-    cmake_parse_arguments(EXPORT "" "COMPONENT" "TARGETS" ${ARGN})
+    # to allow exporting of aliased targets with the original names
+    if(TARGET "${EXPORT_TARGET}")
+        get_target_property(original_name ${EXPORT_TARGET} ALIASED_TARGET)
+        if(TARGET "${original_name}")
+            # replace target with its original name
+            set(EXPORT_TARGET ${original_name})
+        endif()
+        list(APPEND _OPENVINO_DEVELOPER_PACKAGE_TARGETS ${EXPORT_TARGET})
 
-    if(EXPORT_UNPARSED_ARGUMENTS)
-        message(FATAL_ERROR "openvino_developer_export_targets has unparsed arguments: ${EXPORT_UNPARSED_ARGUMENTS}")
-    endif()
+        if(EXPORT_INSTALL_INCLUDE_DIRECTORIES)
+            if(NOT EXPORT_INSTALL_DESTIONATION)
+                set(EXPORT_INSTALL_DESTIONATION "developer_package/include/${EXPORT_TARGET}")
+            endif()
 
-    set(${EXPORT_COMPONENT} "${${EXPORT_COMPONENT}};${EXPORT_TARGETS}")
+            target_include_directories(${EXPORT_TARGET} INTERFACE "$<INSTALL_INTERFACE:${EXPORT_INSTALL_DESTIONATION}>")
 
-    # to allow exporting of aliased targets with the original names
-    foreach(target_name IN LISTS ${EXPORT_COMPONENT})
-        if(TARGET "${target_name}")
-            get_target_property(original_name ${target_name} ALIASED_TARGET)
-            if(TARGET "${original_name}")
-                list(REMOVE_ITEM ${EXPORT_COMPONENT} ${target_name})
-                list(APPEND ${EXPORT_COMPONENT} ${original_name})
-            endif()
+            foreach(install_dir IN LISTS EXPORT_INSTALL_INCLUDE_DIRECTORIES)
+                install(DIRECTORY "${install_dir}"
+                        DESTINATION "${EXPORT_INSTALL_DESTIONATION}"
+                        COMPONENT developer_package EXCLUDE_FROM_ALL)
+            endforeach()
         endif()
-    endforeach()
-
-    list(REMOVE_DUPLICATES ${EXPORT_COMPONENT})
-    set(${EXPORT_COMPONENT} "${${EXPORT_COMPONENT}}" CACHE INTERNAL
-        "A list of OpenVINO ${EXPORT_COMPONENT} exported targets" FORCE)
+    else()
+        message(FATAL_ERROR "Internal error: ${target_name} does not represent a cmake target")
+    endif()
 
-    list(APPEND openvino_export_components ${EXPORT_COMPONENT})
-    list(REMOVE_DUPLICATES openvino_export_components)
-    set(openvino_export_components "${openvino_export_components}" CACHE INTERNAL
-        "A list of OpenVINO exported components" FORCE)
+    list(REMOVE_DUPLICATES _OPENVINO_DEVELOPER_PACKAGE_TARGETS)
+    set(_OPENVINO_DEVELOPER_PACKAGE_TARGETS "${_OPENVINO_DEVELOPER_PACKAGE_TARGETS}" CACHE INTERNAL
+        "A list of OpenVINO Developer Package exported targets" FORCE)
 endfunction()
 
-# add target with processed tests model zoo
+#
+# Build
+#
+
 if(ENABLE_TESTS)
+    # add target with processed tests model zoo
     include(cmake/test_model_zoo.cmake)
 endif()
 
diff --git a/README.md b/README.md
index adc6f9f2b965ea..bfc4a722c2680d 100644
--- a/README.md
+++ b/README.md
@@ -33,7 +33,7 @@ OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
  - Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud
 
 
-This open-source version includes several components: namely [Model Optimizer], [OpenVINO™ Runtime], [Post-Training Optimization Tool], as well as CPU, GPU, GNA, multi device and heterogeneous plugins to accelerate deep learning inference on Intel® CPUs and Intel® Processor Graphics.
+This open-source version includes several components: namely [OpenVINO Model Converter (OVC)], [OpenVINO™ Runtime], as well as CPU, GPU, GNA, multi device and heterogeneous plugins to accelerate deep learning inference on Intel® CPUs and Intel® Processor Graphics.
 It supports pre-trained models from [Open Model Zoo], along with 100+ open
 source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.
 
@@ -48,8 +48,7 @@ source and public models in popular formats such as TensorFlow, ONNX, PaddlePadd
         * [python](./src/bindings/python) - Python API for OpenVINO™ Runtime
 * [Plugins](./src/plugins) - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the [list of supported devices](#supported-hardware-matrix).
 * [Frontends](./src/frontends) - contains available OpenVINO frontends that allow reading models from the native framework format.
-* [Model Optimizer] - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
-* [Post-Training Optimization Tool] - is designed to accelerate the inference of deep learning models by applying special methods without model retraining or fine-tuning, for example, post-training 8-bit quantization. 
+* [OpenVINO Model Converter (OVC)] - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, and adjusts deep learning models for optimal execution on end-point target devices.
 * [Samples] - applications in C, C++ and Python languages that show basic OpenVINO use cases.
 
 ## Supported Hardware matrix
@@ -62,7 +61,7 @@ The OpenVINO™ Runtime can infer models on different hardware devices. This sec
             <th>Device</th>
             <th>Plugin</th>
             <th>Library</th>
-            <th>ShortDescription</th>
+            <th>Short Description</th>
         </tr>
     </thead>
     <tbody>
@@ -70,7 +69,7 @@ The OpenVINO™ Runtime can infer models on different hardware devices. This sec
             <td rowspan=2>CPU</td>
             <td> <a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_CPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-c-p-u">Intel CPU</a></tb>
             <td><b><i><a href="./src/plugins/intel_cpu">openvino_intel_cpu_plugin</a></i></b></td>
-            <td>Intel Xeon with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel Core Processors with Intel AVX2, Intel Atom Processors with Intel® Streaming SIMD Extensions (Intel® SSE)</td>
+            <td>Intel Xeon with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel Core Processors with Intel AVX2, Intel Atom Processors with Intel® Streaming SIMD Extensions (Intel® SSE), Intel® Advanced Matrix Extensions (Intel® AMX)</td>
         </tr>
         <tr>
             <td> <a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_CPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-c-p-u">ARM CPU</a></tb>
@@ -98,7 +97,7 @@ OpenVINO™ Toolkit also contains several plugins which simplify loading models
         <tr>
             <th>Plugin</th>
             <th>Library</th>
-            <th>ShortDescription</th>
+            <th>Short Description</th>
         </tr>
     </thead>
     <tbody>
@@ -196,6 +195,5 @@ Report questions, issues and suggestions, using:
 
 [Open Model Zoo]:https://github.com/openvinotoolkit/open_model_zoo
 [OpenVINO™ Runtime]:https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_OV_Runtime_User_Guide.html
-[Model Optimizer]:https://docs.openvino.ai/2023.1/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html
-[Post-Training Optimization Tool]:https://docs.openvino.ai/2023.1/pot_introduction.html
+[OpenVINO Model Converter (OVC)]:https://docs.openvino.ai/2023.1/openvino_docs_model_processing_introduction.html#convert-a-model-in-cli-ovc
 [Samples]:https://github.com/openvinotoolkit/openvino/tree/master/samples
diff --git a/cmake/dependencies.cmake b/cmake/dependencies.cmake
index 1d4210f300b058..5c86bdea57620c 100644
--- a/cmake/dependencies.cmake
+++ b/cmake/dependencies.cmake
@@ -5,7 +5,7 @@
 cmake_policy(SET CMP0054 NEW)
 
 # TODO: fix it, outside of source dir MO cannot find TBB dependency
-set_temp_directory(TEMP "${CMAKE_SOURCE_DIR}")
+ov_set_temp_directory(TEMP "${CMAKE_SOURCE_DIR}")
 
 ## Intel OMP package
 if(THREADING STREQUAL "OMP")
@@ -71,12 +71,16 @@ function(ov_download_tbb)
 
     if(NOT DEFINED ENV{TBBROOT} AND (DEFINED ENV{TBB_DIR} OR DEFINED TBB_DIR))
         if(DEFINED ENV{TBB_DIR})
-            set(TEMP_ROOT $ENV{TBB_DIR})
-        elseif (DEFINED TBB_DIR)
-            set(TEMP_ROOT ${TBB_DIR})
+            set(TBB_DIR "$ENV{TBB_DIR}")
         endif()
+        set(TEMP_ROOT "${TBB_DIR}")
         while(NOT EXISTS "${TEMP_ROOT}/include")
-            get_filename_component(TEMP_ROOT ${TEMP_ROOT} PATH)
+            get_filename_component(TEMP_ROOT_PARENT ${TEMP_ROOT} PATH)
+            if(TEMP_ROOT_PARENT STREQUAL TEMP_ROOT)
+                # to prevent recursion
+                message(FATAL_ERROR "${TBB_DIR} does not contain 'include' folder. Please, unset TBB_DIR")
+            endif()
+            set(TEMP_ROOT "${TEMP_ROOT_PARENT}")
         endwhile()
         set(TBBROOT ${TEMP_ROOT})
     endif()
diff --git a/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake b/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake
index 3996f373156d89..bc512b9b229b02 100644
--- a/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake
+++ b/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake
@@ -27,11 +27,12 @@ endmacro()
 set(OLD_CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH})
 set(CMAKE_MODULE_PATH "${OpenVINODeveloperScripts_DIR}")
 
-function(set_ci_build_number)
-    set(repo_root "${CMAKE_SOURCE_DIR}")
+function(ov_set_ci_build_number)
     include(version)
-    foreach(var CI_BUILD_NUMBER OpenVINO_VERSION OpenVINO_SOVERSION OpenVINO_VERSION_SUFFIX OpenVINO_VERSION_BUILD
-                OpenVINO_VERSION_MAJOR OpenVINO_VERSION_MINOR OpenVINO_VERSION_PATCH)
+    ov_parse_ci_build_number("${CMAKE_SOURCE_DIR}")
+
+    foreach(var CI_BUILD_NUMBER OpenVINO_VERSION OpenVINO_SOVERSION OpenVINO_VERSION_SUFFIX
+                OpenVINO_VERSION_MAJOR OpenVINO_VERSION_MINOR OpenVINO_VERSION_PATCH OpenVINO_VERSION_BUILD)
         if(NOT DEFINED ${var})
             message(FATAL_ERROR "${var} version component is not defined")
         endif()
@@ -44,7 +45,7 @@ ov_set_if_not_defined(Python3_FIND_STRATEGY LOCATION)
 
 include(features)
 
-set_ci_build_number()
+ov_set_ci_build_number()
 
 #
 # Detect target
@@ -139,10 +140,13 @@ endif()
 
 # allow to override default OUTPUT_ROOT root
 if(NOT DEFINED OUTPUT_ROOT)
-    if(NOT DEFINED OpenVINO_SOURCE_DIR)
-        message(FATAL_ERROR "OpenVINO_SOURCE_DIR is not defined")
+    if(DEFINED OpenVINO_SOURCE_DIR)
+        # For BW compatiblity, when extra modules are built separately
+        # but still write its artifacts to OpenVINO source directory
+        set(OUTPUT_ROOT ${OpenVINO_SOURCE_DIR})
+    else()
+        set(OUTPUT_ROOT ${CMAKE_SOURCE_DIR})
     endif()
-    set(OUTPUT_ROOT ${OpenVINO_SOURCE_DIR})
 endif()
 
 # Enable postfixes for Debug/Release builds
diff --git a/cmake/developer_package/add_target_helpers.cmake b/cmake/developer_package/add_target_helpers.cmake
index c52b393d7bbe74..92f4afbc23bbbe 100644
--- a/cmake/developer_package/add_target_helpers.cmake
+++ b/cmake/developer_package/add_target_helpers.cmake
@@ -9,7 +9,6 @@ ov_add_target(
    NAME core_lib
    ADD_CPPLINT
    ADD_CLANG_FORMAT
-   DEVELOPER_PACKAGE <component>
    TYPE <SHARED / STATIC / EXECUTABLE>
    ROOT ${CMAKE_CURRENT_SOURCE_DIR}
    ADDITIONAL_SOURCE_DIRS
@@ -44,9 +43,6 @@ function(ov_add_target)
         NAME # name of target
         ROOT # root directory to be used for recursive search of source files
         )
-    set(oneValueOptionalArgs
-        DEVELOPER_PACKAGE             # Enables exporting of the target through the developer package
-        )
     set(multiValueArgs
         INCLUDES                      # Extra include directories
         LINK_LIBRARIES                # Link libraries (in form of target name or file name)
@@ -58,7 +54,7 @@ function(ov_add_target)
         LINK_LIBRARIES_WHOLE_ARCHIVE  # list of static libraries to link, each object file should be used and not discarded
         LINK_FLAGS                    # list of extra commands to linker
         )
-    cmake_parse_arguments(ARG "${options}" "${oneValueRequiredArgs};${oneValueOptionalArgs}" "${multiValueArgs}" ${ARGN} )
+    cmake_parse_arguments(ARG "${options}" "${oneValueRequiredArgs}" "${multiValueArgs}" ${ARGN} )
 
     # sanity checks
     foreach(argName IN LISTS oneValueRequiredArgs)
@@ -128,11 +124,6 @@ function(ov_add_target)
         # code style
         ov_add_clang_format_target(${ARG_NAME}_clang FOR_TARGETS ${ARG_NAME})
     endif()
-    if (ARG_DEVELOPER_PACKAGE)
-        # developer package
-        openvino_developer_export_targets(COMPONENT ${ARG_DEVELOPER_PACKAGE}
-                                          TARGETS ${ARG_NAME})
-    endif()
     if(WIN32)
         # Provide default compile pdb name equal to target name
         set_target_properties(${ARG_NAME} PROPERTIES COMPILE_PDB_NAME ${ARG_NAME})
@@ -181,7 +172,9 @@ function(ov_add_test_target)
     else()
         add_test(NAME ${ARG_NAME} COMMAND ${ARG_NAME})
     endif()
-    set_property(TEST ${ARG_NAME} PROPERTY LABELS ${ARG_LABELS})
+    if(ARG_LABELS)
+        set_property(TEST ${ARG_NAME} PROPERTY LABELS ${ARG_LABELS})
+    endif()
 
     install(TARGETS ${ARG_NAME}
             RUNTIME DESTINATION tests
diff --git a/cmake/developer_package/compile_flags/os_flags.cmake b/cmake/developer_package/compile_flags/os_flags.cmake
index 7d98b40c3ce81d..c0c878e0183eb0 100644
--- a/cmake/developer_package/compile_flags/os_flags.cmake
+++ b/cmake/developer_package/compile_flags/os_flags.cmake
@@ -329,15 +329,15 @@ endif()
 
 file(RELATIVE_PATH OV_RELATIVE_BIN_PATH ${CMAKE_CURRENT_BINARY_DIR} ${CMAKE_SOURCE_DIR})
 
-if(${CMAKE_VERSION} VERSION_LESS "3.20")
-    file(TO_NATIVE_PATH ${OpenVINO_SOURCE_DIR} OV_NATIVE_PROJECT_ROOT_DIR)
+if(CMAKE_VERSION VERSION_LESS 3.20)
+    file(TO_NATIVE_PATH ${CMAKE_SOURCE_DIR} OV_NATIVE_PROJECT_ROOT_DIR)
     file(TO_NATIVE_PATH ${OV_RELATIVE_BIN_PATH} NATIVE_OV_RELATIVE_BIN_PATH)
 else()
-    cmake_path(NATIVE_PATH OpenVINO_SOURCE_DIR OV_NATIVE_PROJECT_ROOT_DIR)
+    cmake_path(NATIVE_PATH CMAKE_SOURCE_DIR OV_NATIVE_PROJECT_ROOT_DIR)
     cmake_path(NATIVE_PATH OV_RELATIVE_BIN_PATH NATIVE_OV_RELATIVE_BIN_PATH)
 endif()
 
-file(RELATIVE_PATH OV_NATIVE_PARENT_PROJECT_ROOT_DIR "${OpenVINO_SOURCE_DIR}/.." ${OpenVINO_SOURCE_DIR})
+file(RELATIVE_PATH OV_NATIVE_PARENT_PROJECT_ROOT_DIR "${CMAKE_SOURCE_DIR}/.." ${CMAKE_SOURCE_DIR})
 
 if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
     #
@@ -392,7 +392,7 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
     if(NOT DEFINED ENV{GITHUB_ACTIONS})
         add_compile_options(
             "$<$<COMPILE_LANGUAGE:CXX>:/d1trimfile:${OV_NATIVE_PROJECT_ROOT_DIR}\\>"
-            "$<$<COMPILE_LANGUAGE:CXX>:/d1trimfile:${OpenVINO_SOURCE_DIR}/>")
+            "$<$<COMPILE_LANGUAGE:CXX>:/d1trimfile:${CMAKE_SOURCE_DIR}/>")
     endif()
 
     #
diff --git a/cmake/developer_package/options.cmake b/cmake/developer_package/options.cmake
index 75b9c886894631..4506d85a027f92 100644
--- a/cmake/developer_package/options.cmake
+++ b/cmake/developer_package/options.cmake
@@ -43,7 +43,7 @@ macro(ov_option_enum variable description value)
 endmacro()
 
 function (ov_print_enabled_features)
-    if(NOT COMMAND set_ci_build_number)
+    if(NOT COMMAND ov_set_ci_build_number)
         message(FATAL_ERROR "CI_BUILD_NUMBER is not set yet")
     endif()
 
diff --git a/cmake/developer_package/packaging/nsis.cmake b/cmake/developer_package/packaging/nsis.cmake
index 901e34f97820bb..4174037af74f39 100644
--- a/cmake/developer_package/packaging/nsis.cmake
+++ b/cmake/developer_package/packaging/nsis.cmake
@@ -5,6 +5,8 @@
 macro(ov_nsis_specific_settings)
     # installation directory
     set(CPACK_PACKAGE_INSTALL_DIRECTORY "Intel")
+    # License to be embedded in the installer
+    set(CPACK_RESOURCE_FILE_LICENSE "${OpenVINO_SOURCE_DIR}/LICENSE")
 
     # TODO: provide icons
     # set(CPACK_NSIS_MUI_ICON "")
diff --git a/cmake/developer_package/packaging/packaging.cmake b/cmake/developer_package/packaging/packaging.cmake
index 505565f55da5d7..2279580040f736 100644
--- a/cmake/developer_package/packaging/packaging.cmake
+++ b/cmake/developer_package/packaging/packaging.cmake
@@ -193,8 +193,6 @@ macro(ov_cpack)
     set(CPACK_PACKAGE_CONTACT "OpenVINO Developers <openvino@intel.com>")
     set(CPACK_VERBATIM_VARIABLES ON)
     set(CPACK_COMPONENTS_ALL ${ARGN})
-    # TODO: set proper license file for Windows installer
-    set(CPACK_RESOURCE_FILE_LICENSE "${OpenVINO_SOURCE_DIR}/LICENSE")
 
     # default permissions for directories creation
     set(CMAKE_INSTALL_DEFAULT_DIRECTORY_PERMISSIONS
diff --git a/cmake/developer_package/version.cmake b/cmake/developer_package/version.cmake
index 0353e3a52a8617..1b71befe448b76 100644
--- a/cmake/developer_package/version.cmake
+++ b/cmake/developer_package/version.cmake
@@ -4,50 +4,51 @@
 
 find_package(Git QUIET)
 
-function (branchName VAR)
-    if(NOT DEFINED repo_root)
-        message(FATAL_ERROR "repo_root is not defined")
-    endif()
+function(ov_branch_name VAR REPO_ROOT)
     if(GIT_FOUND)
         execute_process(
                 COMMAND ${GIT_EXECUTABLE} rev-parse --abbrev-ref HEAD
-                WORKING_DIRECTORY ${repo_root}
+                WORKING_DIRECTORY ${REPO_ROOT}
                 OUTPUT_VARIABLE GIT_BRANCH
                 OUTPUT_STRIP_TRAILING_WHITESPACE)
         set (${VAR} ${GIT_BRANCH} PARENT_SCOPE)
     endif()
 endfunction()
 
-function (commitHash VAR)
-    if(NOT DEFINED repo_root)
-        message(FATAL_ERROR "repo_root is not defined")
-    endif()
+function(ov_commit_hash VAR REPO_ROOT)
     if(GIT_FOUND)
         execute_process(
                 COMMAND ${GIT_EXECUTABLE} rev-parse --short=11 HEAD
-                WORKING_DIRECTORY ${repo_root}
+                WORKING_DIRECTORY ${REPO_ROOT}
                 OUTPUT_VARIABLE GIT_COMMIT_HASH
                 OUTPUT_STRIP_TRAILING_WHITESPACE)
         set (${VAR} ${GIT_COMMIT_HASH} PARENT_SCOPE)
     endif()
 endfunction()
 
-function (commitNumber VAR)
-    if(NOT DEFINED repo_root)
-        message(FATAL_ERROR "repo_root is not defined")
-    endif()
+function(ov_commit_number VAR REPO_ROOT)
     if(GIT_FOUND)
         execute_process(
                 COMMAND ${GIT_EXECUTABLE} rev-list --count --first-parent HEAD
-                WORKING_DIRECTORY ${repo_root}
+                WORKING_DIRECTORY ${REPO_ROOT}
                 OUTPUT_VARIABLE GIT_COMMIT_NUMBER
                 OUTPUT_STRIP_TRAILING_WHITESPACE)
         set (${VAR} ${GIT_COMMIT_NUMBER} PARENT_SCOPE)
+    else()
+        # set zeros since git is not available
+        set (${VAR} "000" PARENT_SCOPE)
     endif()
 endfunction()
 
-macro(ov_parse_ci_build_number)
-    set(OpenVINO_VERSION_BUILD 000)
+macro(ov_parse_ci_build_number repo_root)
+    # provides OpenVINO version
+    # 1. If CI_BUILD_NUMBER is defined, parses this information
+    # 2. Otherwise, either:
+    #  - parses openvino/core/version.hpp
+    #  - takes from OpenVINOConfig-version.cmake in case of relocatable Developer package
+    if (DEFINED ENV{CI_BUILD_NUMBER})
+        set(CI_BUILD_NUMBER $ENV{CI_BUILD_NUMBER})
+    endif()
 
     if(CI_BUILD_NUMBER MATCHES "^([0-9]+)\.([0-9]+)\.([0-9]+)\-([0-9]+)\-.*")
         set(OpenVINO_VERSION_MAJOR ${CMAKE_MATCH_1})
@@ -63,12 +64,9 @@ macro(ov_parse_ci_build_number)
         message(FATAL_ERROR "Failed to parse CI_BUILD_NUMBER which is ${CI_BUILD_NUMBER}")
     endif()
 
-    if(NOT DEFINED repo_root)
-        message(FATAL_ERROR "repo_root is not defined")
-    endif()
-
-    macro(ov_get_hpp_version)
+    function(ov_compare_version_with_headers)
         if(NOT DEFINED OpenVINO_SOURCE_DIR)
+            # if we are not in OpenVINO source tree, let's ignore this comparison
             return()
         endif()
 
@@ -101,30 +99,29 @@ macro(ov_parse_ci_build_number)
             endif()
         endforeach()
 
-        # detect commit number
-        commitNumber(OpenVINO_VERSION_BUILD_HPP)
-        if(OpenVINO_VERSION_BUILD STREQUAL "000" AND DEFINED OpenVINO_VERSION_BUILD_HPP)
-            set(OpenVINO_VERSION_BUILD "${OpenVINO_VERSION_BUILD_HPP}")
-        else()
-            set(OpenVINO_VERSION_BUILD_HPP "${OpenVINO_VERSION_BUILD}")
-        endif()
-
-        set(ov_hpp_version_is_found ON)
-    endmacro()
-
-    # detect OpenVINO version via openvino/core/version.hpp and ie_version.hpp
-    ov_get_hpp_version()
-
-    if(ov_hpp_version_is_found)
-        foreach(var OpenVINO_VERSION_MAJOR OpenVINO_VERSION_MINOR OpenVINO_VERSION_PATCH OpenVINO_VERSION_BUILD)
+        foreach(var OpenVINO_VERSION_MAJOR OpenVINO_VERSION_MINOR OpenVINO_VERSION_PATCH)
             if(DEFINED ${var} AND NOT ${var} EQUAL ${var}_HPP)
                 message(FATAL_ERROR "${var} parsed from CI_BUILD_NUMBER (${${var}}) \
                     and from openvino/core/version.hpp (${${var}_HPP}) are different")
             else()
                 # CI_BUILD_NUMBER is not defined well, take info from openvino/core/version.hpp as a baseline
-                set(${var} ${${var}_HPP})
+                set(${var} ${${var}_HPP} PARENT_SCOPE)
             endif()
         endforeach()
+    endfunction()
+
+    # detect OpenVINO version via openvino/core/version.hpp and ie_version.hpp
+    ov_compare_version_with_headers()
+
+    # detect commit number
+    ov_commit_number(OpenVINO_VERSION_BUILD_FROM_GIT "${repo_root}")
+
+    if(OpenVINO_VERSION_BUILD AND NOT OpenVINO_VERSION_BUILD STREQUAL OpenVINO_VERSION_BUILD_FROM_GIT)
+        # TODO: replace with FATAL_ERROR once NPU version will be discussed
+        message(WARNING "OpenVINO_VERSION_BUILD parsed from CI_BUILD_NUMBER (${OpenVINO_VERSION_BUILD}) \
+            and determined by git (${OpenVINO_VERSION_BUILD_FROM_GIT}) are different")
+    else()
+        set(OpenVINO_VERSION_BUILD "${OpenVINO_VERSION_BUILD_FROM_GIT}")
     endif()
 
     set(OpenVINO_SOVERSION "${OpenVINO_VERSION_MAJOR}${OpenVINO_VERSION_MINOR}${OpenVINO_VERSION_PATCH}")
@@ -140,8 +137,8 @@ macro(ov_parse_ci_build_number)
     if(NOT the_whole_version_is_defined_by_ci)
         # create CI_BUILD_NUMBER
 
-        branchName(GIT_BRANCH)
-        commitHash(GIT_COMMIT_HASH)
+        ov_branch_name(GIT_BRANCH "${repo_root}")
+        ov_commit_hash(GIT_COMMIT_HASH "${repo_root}")
 
         if(NOT GIT_BRANCH STREQUAL "master")
             set(GIT_BRANCH_POSTFIX "-${GIT_BRANCH}")
@@ -157,14 +154,6 @@ macro(ov_parse_ci_build_number)
     endif()
 endmacro()
 
-# provides OpenVINO version
-# 1. If CI_BUILD_NUMBER is defined, parses this information
-# 2. Otherwise, parses openvino/core/version.hpp
-if (DEFINED ENV{CI_BUILD_NUMBER})
-    set(CI_BUILD_NUMBER $ENV{CI_BUILD_NUMBER})
-endif()
-ov_parse_ci_build_number()
-
 macro (addVersionDefines FILE)
     message(WARNING "'addVersionDefines' is deprecated. Please, use 'ov_add_version_defines'")
 
diff --git a/cmake/extra_modules.cmake b/cmake/extra_modules.cmake
index afc1dc335b3e55..6c392fcc6eed12 100644
--- a/cmake/extra_modules.cmake
+++ b/cmake/extra_modules.cmake
@@ -9,13 +9,10 @@ function(ie_generate_dev_package_config)
         set(OpenCV_FOUND OFF)
     endif()
 
-    foreach(component IN LISTS openvino_export_components)
-        # export all targets with prefix and use them during extra modules build
-        export(TARGETS ${${component}} NAMESPACE IE::
-               APPEND FILE "${CMAKE_BINARY_DIR}/${component}_dev_targets.cmake")
-        list(APPEND all_dev_targets ${${component}})
-    endforeach()
-    add_custom_target(ie_dev_targets DEPENDS ${all_dev_targets})
+    # export all targets with prefix and use them during extra modules build
+    export(TARGETS ${_OPENVINO_DEVELOPER_PACKAGE_TARGETS} NAMESPACE IE::
+           APPEND FILE "${CMAKE_BINARY_DIR}/inference_engine_developer_package_targets.cmake")
+    add_custom_target(ie_dev_targets DEPENDS ${_OPENVINO_DEVELOPER_PACKAGE_TARGETS})
 
     set(PATH_VARS "OpenVINO_SOURCE_DIR")
     if(ENABLE_SAMPLES OR ENABLE_TESTS)
@@ -44,20 +41,20 @@ function(ov_generate_dev_package_config)
         set(OpenCV_FOUND OFF)
     endif()
 
-    foreach(component IN LISTS openvino_export_components)
-        # filter out targets which are installed by OpenVINOConfig.cmake static build case
-        set(exported_targets)
-        foreach(target IN LISTS ${component})
-            if(NOT target IN_LIST openvino_installed_targets)
-                list(APPEND exported_targets ${target})
-            endif()
-        endforeach()
-        # export all developer targets with prefix and use them during extra modules build
-        export(TARGETS ${exported_targets} NAMESPACE openvino::
-               APPEND FILE "${CMAKE_BINARY_DIR}/ov_${component}_dev_targets.cmake")
-        list(APPEND all_dev_targets ${${component}})
-    endforeach()
-    add_custom_target(ov_dev_targets DEPENDS ${all_dev_targets})
+    # create a helper target to build all developer package targets
+    add_custom_target(ov_dev_targets DEPENDS ${_OPENVINO_DEVELOPER_PACKAGE_TARGETS})
+
+    # filter out targets which are installed by OpenVINOConfig.cmake static build case
+    if(openvino_installed_targets)
+        list(REMOVE_ITEM _OPENVINO_DEVELOPER_PACKAGE_TARGETS ${openvino_installed_targets})
+    endif()
+    # export all developer targets with prefix and use them during extra modules build
+    export(TARGETS ${_OPENVINO_DEVELOPER_PACKAGE_TARGETS} NAMESPACE openvino::
+           APPEND FILE "${CMAKE_BINARY_DIR}/openvino_developer_package_targets.cmake")
+
+    #
+    # OpenVINODeveloperPackageConfig.cmake for build tree
+    #
 
     set(PATH_VARS "OpenVINO_SOURCE_DIR")
     if(ENABLE_SAMPLES OR ENABLE_TESTS)
@@ -77,38 +74,91 @@ function(ov_generate_dev_package_config)
     configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig-version.cmake.in"
                    "${CMAKE_BINARY_DIR}/OpenVINODeveloperPackageConfig-version.cmake" 
                    @ONLY)
+
+    #
+    # OpenVINODeveloperPackageConfig.cmake for installation tree
+    #
+
+    set(DEV_PACKAGE_ROOT_DIR developer_package)
+    set(DEV_PACKAGE_CMAKE_DIR ${DEV_PACKAGE_ROOT_DIR}/cmake)
+    set(DEVELOPER_PACKAGE_COMPONENT developer_package)
+    set(DEVELOPER_PACKAGE_EXPORT_SET OpenVINODeveloperTargets)
+
+    # create and install main developer package config files
+    configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in"
+                                  "${OpenVINO_BINARY_DIR}/share/OpenVINODeveloperPackageConfig.cmake"
+                                  INSTALL_DESTINATION ${DEV_PACKAGE_CMAKE_DIR}
+                                  NO_CHECK_REQUIRED_COMPONENTS_MACRO)
+
+    configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig-version.cmake.in"
+                   "${OpenVINO_BINARY_DIR}/share/OpenVINODeveloperPackageConfig-version.cmake" 
+                   @ONLY)
+
+    install(FILES "${OpenVINO_BINARY_DIR}/share/OpenVINODeveloperPackageConfig.cmake"
+                  "${OpenVINO_BINARY_DIR}/share/OpenVINODeveloperPackageConfig-version.cmake"
+            DESTINATION ${DEV_PACKAGE_CMAKE_DIR}
+            COMPONENT ${DEVELOPER_PACKAGE_COMPONENT}
+            EXCLUDE_FROM_ALL)
+
+    # Install whole 'cmake/developer_package' folder
+    install(DIRECTORY "${OpenVINODeveloperScripts_DIR}/"
+            DESTINATION "${DEV_PACKAGE_CMAKE_DIR}"
+            COMPONENT ${DEVELOPER_PACKAGE_COMPONENT}
+            EXCLUDE_FROM_ALL)
+
+    # Install CMakeLists.txt to read cache variables from
+    install(FILES "${OpenVINO_BINARY_DIR}/CMakeCache.txt"
+            DESTINATION ${DEV_PACKAGE_CMAKE_DIR}
+            COMPONENT ${DEVELOPER_PACKAGE_COMPONENT}
+            EXCLUDE_FROM_ALL)
+
+    # install developer package targets
+    install(TARGETS ${_OPENVINO_DEVELOPER_PACKAGE_TARGETS} EXPORT ${DEVELOPER_PACKAGE_EXPORT_SET}
+            RUNTIME DESTINATION ${DEV_PACKAGE_ROOT_DIR}/bin COMPONENT ${DEVELOPER_PACKAGE_COMPONENT} EXCLUDE_FROM_ALL
+            ARCHIVE DESTINATION ${DEV_PACKAGE_ROOT_DIR}/lib COMPONENT ${DEVELOPER_PACKAGE_COMPONENT} EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION ${DEV_PACKAGE_ROOT_DIR}/lib COMPONENT ${DEVELOPER_PACKAGE_COMPONENT} EXCLUDE_FROM_ALL)
+
+    install(EXPORT ${DEVELOPER_PACKAGE_EXPORT_SET}
+            FILE OpenVINODeveloperPackageTargets.cmake
+            NAMESPACE openvino::
+            DESTINATION ${DEV_PACKAGE_ROOT_DIR}/cmake
+            COMPONENT ${DEVELOPER_PACKAGE_COMPONENT}
+            EXCLUDE_FROM_ALL)
+
+    # Note: that OpenCV and gflags are explicitly not installed to simplify relocatable
+    # OpenVINO Developer package maintainance. OpenVINO_SOURCE_DIR is also unvailable, because
+    # relocatable developer package can be used on a different machine where OpenVINO repo is not available
 endfunction()
 
 #
 # Add extra modules
 #
 
-function(register_extra_modules)
+function(_ov_register_extra_modules)
     set(InferenceEngineDeveloperPackage_DIR "${CMAKE_CURRENT_BINARY_DIR}/build-modules")
     set(OpenVINODeveloperPackage_DIR "${CMAKE_BINARY_DIR}/build-modules")
     set(OpenVINO_DIR "${CMAKE_BINARY_DIR}")
 
-    function(generate_fake_dev_package NS)
+    function(_ov_generate_fake_developer_package NS)
         if(NS STREQUAL "openvino")
             set(devconfig_file "${OpenVINODeveloperPackage_DIR}/OpenVINODeveloperPackageConfig.cmake")
         else()
             set(devconfig_file "${InferenceEngineDeveloperPackage_DIR}/InferenceEngineDeveloperPackageConfig.cmake")
         endif()
-        file(REMOVE "${devconfig_file}")
 
+        file(REMOVE "${devconfig_file}")
         file(WRITE "${devconfig_file}" "\# !! AUTOGENERATED: DON'T EDIT !!\n\n")
 
-        foreach(targets_list IN LISTS ${openvino_export_components})
-            foreach(target IN LISTS targets_list)
-                file(APPEND "${devconfig_file}" "if(NOT TARGET ${NS}::${target})
-    add_library(${NS}::${target} ALIAS ${target})
+        foreach(exported_target IN LISTS _OPENVINO_DEVELOPER_PACKAGE_TARGETS)
+            file(APPEND "${devconfig_file}" "if(NOT TARGET ${NS}::${exported_target})
+    add_library(${NS}::${exported_target} ALIAS ${exported_target})
 endif()\n")
-            endforeach()
         endforeach()
     endfunction()
 
-    generate_fake_dev_package("openvino")
-    generate_fake_dev_package("IE")
+    _ov_generate_fake_developer_package("openvino")
+    # TODO: remove with API 1.0 removal
+    _ov_generate_fake_developer_package("IE")
 
     # detect where OPENVINO_EXTRA_MODULES contains folders with CMakeLists.txt
     # other folders are supposed to have sub-folders with CMakeLists.txt
@@ -155,21 +205,18 @@ endfunction()
 # Extra modules support
 #
 
-# this InferenceEngineDeveloperPackageConfig.cmake is not used
-# during extra modules build since it's generated after modules
-# are configured
+# this OpenVINODeveloperPackageConfig.cmake is not used during extra modules build
+# since it's generated after modules are configured
 ie_generate_dev_package_config()
 ov_generate_dev_package_config()
 
 # extra modules must be registered after inference_engine library
 # and all other OpenVINO Core libraries are creared
-# because 'register_extra_modules' creates fake InferenceEngineDeveloperPackageConfig.cmake
+# because '_ov_register_extra_modules' creates fake InferenceEngineDeveloperPackageConfig.cmake
 # with all imported developer targets
-register_extra_modules()
+_ov_register_extra_modules()
 
-# for static libraries case we need to generate final ov_plugins.hpp
-# with all the information about plugins
+# we need to generate final ov_plugins.hpp with all the information about plugins
 ov_generate_plugins_hpp()
-
-# used for static build
+# we need to generate final ov_frontends.hpp with all the information about frontends
 ov_generate_frontends_hpp()
diff --git a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
index e197597487b61c..a98b4207e285d2 100644
--- a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
+++ b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
@@ -88,9 +88,7 @@ endif()
 
 _ov_find_tbb()
 
-foreach(component @openvino_export_components@)
-    include("${CMAKE_CURRENT_LIST_DIR}/${component}_dev_targets.cmake")
-endforeach()
+include("${CMAKE_CURRENT_LIST_DIR}/inference_engine_developer_package_targets.cmake")
 
 if(TARGET IE::ov_core_dev AND NOT TARGET openvino::core::dev)
     add_library(openvino::core::dev INTERFACE IMPORTED)
diff --git a/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
index f78e31ce635d81..3620bcd091dab5 100644
--- a/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
+++ b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in
@@ -77,13 +77,7 @@ find_dependency(OpenVINO
 _ov_find_tbb()
 _ov_find_pugixml()
 
-foreach(component @openvino_export_components@)
-    # TODO: remove legacy targets from some tests
-    # string(FIND "${component}" "_legacy" index)
-    # if (index EQUAL -1)
-    include("${CMAKE_CURRENT_LIST_DIR}/ov_${component}_dev_targets.cmake")
-    # endif()
-endforeach()
+include("${CMAKE_CURRENT_LIST_DIR}/openvino_developer_package_targets.cmake")
 
 # inherit OpenCV from main OpenVINO project if enabled
 if("@OpenCV_FOUND@")
diff --git a/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in b/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in
new file mode 100644
index 00000000000000..a4cdb93d387c58
--- /dev/null
+++ b/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in
@@ -0,0 +1,74 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+@PACKAGE_INIT@
+
+include(CMakeFindDependencyMacro)
+
+# Variables to export in plugin's projects
+
+set(ov_options "@OV_OPTIONS@")
+list(APPEND ov_options CPACK_GENERATOR)
+
+if(APPLE)
+    list(APPEND ov_options CMAKE_OSX_ARCHITECTURES CMAKE_OSX_DEPLOYMENT_TARGET)
+endif()
+
+get_property(_OV_GENERATOR_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
+if(_OV_GENERATOR_MULTI_CONFIG)
+    list(APPEND ov_options CMAKE_CONFIGURATION_TYPES)
+    if(CMAKE_GENERATOR MATCHES "^Ninja Multi-Config$")
+        list(APPEND ov_options CMAKE_DEFAULT_BUILD_TYPE)
+    endif()
+else()
+    list(APPEND ov_options CMAKE_BUILD_TYPE)
+endif()
+unset(_OV_GENERATOR_MULTI_CONFIG)
+
+file(TO_CMAKE_PATH "${CMAKE_CURRENT_LIST_DIR}" cache_path)
+
+message(STATUS "The following CMake options are exported from OpenVINO Developer package")
+message(" ")
+foreach(option IN LISTS ov_options)
+    if(NOT DEFINED "${option}")
+        load_cache("${cache_path}" READ_WITH_PREFIX "" ${option})
+    endif()
+    message("    ${option}: ${${option}}")
+endforeach()
+message(" ")
+
+# Restore TBB installation directory (requires for proper LC_RPATH on macOS with SIP)
+load_cache("${cache_path}" READ_WITH_PREFIX "" TBB_INSTALL_DIR)
+
+# activate generation of plugins.xml
+set(ENABLE_PLUGINS_XML ON)
+
+# Disable warning as error for private components
+set(CMAKE_COMPILE_WARNING_AS_ERROR OFF)
+
+#
+# Content
+#
+
+# OpenVINO_DIR is supposed to be set as an environment variable
+find_dependency(OpenVINO)
+
+find_dependency(OpenVINODeveloperScripts
+                PATHS "${CMAKE_CURRENT_LIST_DIR}"
+                NO_CMAKE_FIND_ROOT_PATH
+                NO_DEFAULT_PATH)
+
+_ov_find_tbb()
+_ov_find_pugixml()
+
+include("${CMAKE_CURRENT_LIST_DIR}/OpenVINODeveloperPackageTargets.cmake")
+#
+# Extra Compile Flags
+#
+
+# don't fail on strict compilation options in 3rd party modules
+ov_dev_package_no_errors()
+
+# Don't threat deprecated API warnings as errors in 3rd party apps
+ov_deprecated_no_errors()
diff --git a/docs/OV_Runtime_UG/auto_device_selection.md b/docs/OV_Runtime_UG/auto_device_selection.md
index 6b2385683494da..234197a6488b3e 100644
--- a/docs/OV_Runtime_UG/auto_device_selection.md
+++ b/docs/OV_Runtime_UG/auto_device_selection.md
@@ -56,11 +56,10 @@ The logic behind the choice is as follows:
 To put it simply, when loading the model to the first device on the list fails, AUTO will try to load it to the next device in line, until one of them succeeds.
 What is important, **AUTO starts inference with the CPU of the system by default**, as it provides very low latency and can start inference with no additional delays.
 While the CPU is performing inference, AUTO continues to load the model to the device best suited for the purpose and transfers the task to it when ready.
-This way, the devices which are much slower in compiling models, GPU being the best example, do not impede inference at its initial stages.
+This way, the devices which are much slower in compiling models, GPU being the best example, do not impact inference at its initial stages.
 For example, if you use a CPU and a GPU, the first-inference latency of AUTO will be better than that of using GPU alone.
 
-Note that if you choose to exclude CPU from the priority list or disable the initial CPU acceleration feature via ``ov::intel_auto::enable_startup_fallback``, it will be unable to support the initial model compilation stage.
-
+Note that if you choose to exclude CPU from the priority list or disable the initial CPU acceleration feature via ``ov::intel_auto::enable_startup_fallback``, it will be unable to support the initial model compilation stage. The models with dynamic input/output or stateful :doc:`stateful<openvino_docs_OV_UG_model_state_intro>` operations will be loaded to the CPU if it is in the candidate list. Otherwise, these models will follow the normal flow and be loaded to the device based on priority.
 
 .. image:: _static/images/autoplugin_accelerate.svg
 
@@ -91,7 +90,7 @@ Following the OpenVINO™ naming convention, the Automatic Device Selection mode
 
 
 +----------------------------------------------+--------------------------------------------------------------------+
-| Property                                     | Values and Description                                             |
+| Property(C++ version)                        | Values and Description                                             |
 +==============================================+====================================================================+
 | <device candidate list>                      | **Values**:                                                        |
 |                                              |                                                                    |
@@ -170,6 +169,25 @@ Following the OpenVINO™ naming convention, the Automatic Device Selection mode
 Inference with AUTO is configured similarly to when device plugins are used:
 you compile the model on the plugin with configuration and execute inference.
 
+The code samples on this page assume following import(Python)/using (C++) are included at the beginning of code snippets. 
+
+.. tab-set::
+
+    .. tab-item:: Python
+        :sync: py
+
+        .. doxygensnippet:: docs/snippets/ov_auto.py
+           :language: python
+           :fragment: [py_ov_property_import_header]
+
+    .. tab-item:: C++
+        :sync: cpp
+
+        .. doxygensnippet:: docs/snippets/AUTO0.cpp
+            :language: cpp
+            :fragment: [py_ov_property_import_header]
+
+
 
 Device Candidates and Priority
 ++++++++++++++++++++++++++++++
@@ -303,7 +321,7 @@ If device priority is specified when using CUMULATIVE_THROUGHPUT, AUTO will run
 
         .. code-block:: sh
          
-           compiled_model = core.compile_model(model, "AUTO:GPU,CPU", {"PERFORMANCE_HINT" : {"CUMULATIVE_THROUGHPUT"}})
+           compiled_model = core.compile_model(model, "AUTO:GPU,CPU", {hints.performance_mode: hints.PerformanceMode.CUMULATIVE_THROUGHPUT})
 
     .. tab-item:: C++
         :sync: cpp
@@ -322,7 +340,7 @@ If AUTO is used without specifying any device names, and if there are multiple G
 
         .. code-block:: sh
          
-           compiled_model = core.compile_model(model, "AUTO:GPU.1,GPU.0", {"PERFORMANCE_HINT" : {"CUMULATIVE_THROUGHPUT"})
+           compiled_model = core.compile_model(model, "AUTO:GPU.1,GPU.0", {hints.performance_mode: hints.PerformanceMode.CUMULATIVE_THROUGHPUT})
 
     .. tab-item:: C++
         :sync: cpp
diff --git a/docs/_static/selector-tool/assets/selector-56fddec6.js b/docs/_static/selector-tool/assets/selector-114afa0d.js
similarity index 51%
rename from docs/_static/selector-tool/assets/selector-56fddec6.js
rename to docs/_static/selector-tool/assets/selector-114afa0d.js
index 3437a6b4b55dbc..2878b10357074f 100644
--- a/docs/_static/selector-tool/assets/selector-56fddec6.js
+++ b/docs/_static/selector-tool/assets/selector-114afa0d.js
@@ -1,4 +1,4 @@
-var of=Object.defineProperty;var sf=(e,t,n)=>t in e?of(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var De=(e,t,n)=>(sf(e,typeof t!="symbol"?t+"":t,n),n);function lf(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var Ar={},af={get exports(){return Ar},set exports(e){Ar=e}},Ti={},D={},uf={get exports(){return D},set exports(e){D=e}},j={};/**
+var af=Object.defineProperty;var uf=(e,t,n)=>t in e?af(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var je=(e,t,n)=>(uf(e,typeof t!="symbol"?t+"":t,n),n);function cf(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var Ar={},df={get exports(){return Ar},set exports(e){Ar=e}},Ti={},j={},pf={get exports(){return j},set exports(e){j=e}},D={};/**
  * @license React
  * react.production.min.js
  *
@@ -6,7 +6,7 @@ var of=Object.defineProperty;var sf=(e,t,n)=>t in e?of(e,t,{enumerable:!0,config
  *
  * This source code is licensed under the MIT license found in the
  * LICENSE file in the root directory of this source tree.
- */var ro=Symbol.for("react.element"),cf=Symbol.for("react.portal"),df=Symbol.for("react.fragment"),pf=Symbol.for("react.strict_mode"),ff=Symbol.for("react.profiler"),hf=Symbol.for("react.provider"),mf=Symbol.for("react.context"),gf=Symbol.for("react.forward_ref"),vf=Symbol.for("react.suspense"),yf=Symbol.for("react.memo"),_f=Symbol.for("react.lazy"),Aa=Symbol.iterator;function kf(e){return e===null||typeof e!="object"?null:(e=Aa&&e[Aa]||e["@@iterator"],typeof e=="function"?e:null)}var Nc={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},Ec=Object.assign,xc={};function ur(e,t,n){this.props=e,this.context=t,this.refs=xc,this.updater=n||Nc}ur.prototype.isReactComponent={};ur.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};ur.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function Pc(){}Pc.prototype=ur.prototype;function Vl(e,t,n){this.props=e,this.context=t,this.refs=xc,this.updater=n||Nc}var Fl=Vl.prototype=new Pc;Fl.constructor=Vl;Ec(Fl,ur.prototype);Fl.isPureReactComponent=!0;var za=Array.isArray,Cc=Object.prototype.hasOwnProperty,Dl={current:null},Rc={key:!0,ref:!0,__self:!0,__source:!0};function Tc(e,t,n){var r,o={},i=null,s=null;if(t!=null)for(r in t.ref!==void 0&&(s=t.ref),t.key!==void 0&&(i=""+t.key),t)Cc.call(t,r)&&!Rc.hasOwnProperty(r)&&(o[r]=t[r]);var l=arguments.length-2;if(l===1)o.children=n;else if(1<l){for(var a=Array(l),u=0;u<l;u++)a[u]=arguments[u+2];o.children=a}if(e&&e.defaultProps)for(r in l=e.defaultProps,l)o[r]===void 0&&(o[r]=l[r]);return{$$typeof:ro,type:e,key:i,ref:s,props:o,_owner:Dl.current}}function wf(e,t){return{$$typeof:ro,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function jl(e){return typeof e=="object"&&e!==null&&e.$$typeof===ro}function Sf(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var Ma=/\/+/g;function Xi(e,t){return typeof e=="object"&&e!==null&&e.key!=null?Sf(""+e.key):t.toString(36)}function Bo(e,t,n,r,o){var i=typeof e;(i==="undefined"||i==="boolean")&&(e=null);var s=!1;if(e===null)s=!0;else switch(i){case"string":case"number":s=!0;break;case"object":switch(e.$$typeof){case ro:case cf:s=!0}}if(s)return s=e,o=o(s),e=r===""?"."+Xi(s,0):r,za(o)?(n="",e!=null&&(n=e.replace(Ma,"$&/")+"/"),Bo(o,t,n,"",function(u){return u})):o!=null&&(jl(o)&&(o=wf(o,n+(!o.key||s&&s.key===o.key?"":(""+o.key).replace(Ma,"$&/")+"/")+e)),t.push(o)),1;if(s=0,r=r===""?".":r+":",za(e))for(var l=0;l<e.length;l++){i=e[l];var a=r+Xi(i,l);s+=Bo(i,t,n,a,o)}else if(a=kf(e),typeof a=="function")for(e=a.call(e),l=0;!(i=e.next()).done;)i=i.value,a=r+Xi(i,l++),s+=Bo(i,t,n,a,o);else if(i==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). If you meant to render a collection of children, use an array instead.");return s}function ho(e,t,n){if(e==null)return e;var r=[],o=0;return Bo(e,r,"","",function(i){return t.call(n,i,o++)}),r}function Of(e){if(e._status===-1){var t=e._result;t=t(),t.then(function(n){(e._status===0||e._status===-1)&&(e._status=1,e._result=n)},function(n){(e._status===0||e._status===-1)&&(e._status=2,e._result=n)}),e._status===-1&&(e._status=0,e._result=t)}if(e._status===1)return e._result.default;throw e._result}var Ne={current:null},Ko={transition:null},Nf={ReactCurrentDispatcher:Ne,ReactCurrentBatchConfig:Ko,ReactCurrentOwner:Dl};j.Children={map:ho,forEach:function(e,t,n){ho(e,function(){t.apply(this,arguments)},n)},count:function(e){var t=0;return ho(e,function(){t++}),t},toArray:function(e){return ho(e,function(t){return t})||[]},only:function(e){if(!jl(e))throw Error("React.Children.only expected to receive a single React element child.");return e}};j.Component=ur;j.Fragment=df;j.Profiler=ff;j.PureComponent=Vl;j.StrictMode=pf;j.Suspense=vf;j.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=Nf;j.cloneElement=function(e,t,n){if(e==null)throw Error("React.cloneElement(...): The argument must be a React element, but you passed "+e+".");var r=Ec({},e.props),o=e.key,i=e.ref,s=e._owner;if(t!=null){if(t.ref!==void 0&&(i=t.ref,s=Dl.current),t.key!==void 0&&(o=""+t.key),e.type&&e.type.defaultProps)var l=e.type.defaultProps;for(a in t)Cc.call(t,a)&&!Rc.hasOwnProperty(a)&&(r[a]=t[a]===void 0&&l!==void 0?l[a]:t[a])}var a=arguments.length-2;if(a===1)r.children=n;else if(1<a){l=Array(a);for(var u=0;u<a;u++)l[u]=arguments[u+2];r.children=l}return{$$typeof:ro,type:e.type,key:o,ref:i,props:r,_owner:s}};j.createContext=function(e){return e={$$typeof:mf,_currentValue:e,_currentValue2:e,_threadCount:0,Provider:null,Consumer:null,_defaultValue:null,_globalName:null},e.Provider={$$typeof:hf,_context:e},e.Consumer=e};j.createElement=Tc;j.createFactory=function(e){var t=Tc.bind(null,e);return t.type=e,t};j.createRef=function(){return{current:null}};j.forwardRef=function(e){return{$$typeof:gf,render:e}};j.isValidElement=jl;j.lazy=function(e){return{$$typeof:_f,_payload:{_status:-1,_result:e},_init:Of}};j.memo=function(e,t){return{$$typeof:yf,type:e,compare:t===void 0?null:t}};j.startTransition=function(e){var t=Ko.transition;Ko.transition={};try{e()}finally{Ko.transition=t}};j.unstable_act=function(){throw Error("act(...) is not supported in production builds of React.")};j.useCallback=function(e,t){return Ne.current.useCallback(e,t)};j.useContext=function(e){return Ne.current.useContext(e)};j.useDebugValue=function(){};j.useDeferredValue=function(e){return Ne.current.useDeferredValue(e)};j.useEffect=function(e,t){return Ne.current.useEffect(e,t)};j.useId=function(){return Ne.current.useId()};j.useImperativeHandle=function(e,t,n){return Ne.current.useImperativeHandle(e,t,n)};j.useInsertionEffect=function(e,t){return Ne.current.useInsertionEffect(e,t)};j.useLayoutEffect=function(e,t){return Ne.current.useLayoutEffect(e,t)};j.useMemo=function(e,t){return Ne.current.useMemo(e,t)};j.useReducer=function(e,t,n){return Ne.current.useReducer(e,t,n)};j.useRef=function(e){return Ne.current.useRef(e)};j.useState=function(e){return Ne.current.useState(e)};j.useSyncExternalStore=function(e,t,n){return Ne.current.useSyncExternalStore(e,t,n)};j.useTransition=function(){return Ne.current.useTransition()};j.version="18.2.0";(function(e){e.exports=j})(uf);const Ef=lf(D);/**
+ */var ro=Symbol.for("react.element"),ff=Symbol.for("react.portal"),hf=Symbol.for("react.fragment"),mf=Symbol.for("react.strict_mode"),gf=Symbol.for("react.profiler"),vf=Symbol.for("react.provider"),yf=Symbol.for("react.context"),_f=Symbol.for("react.forward_ref"),kf=Symbol.for("react.suspense"),wf=Symbol.for("react.memo"),Sf=Symbol.for("react.lazy"),$a=Symbol.iterator;function Of(e){return e===null||typeof e!="object"?null:(e=$a&&e[$a]||e["@@iterator"],typeof e=="function"?e:null)}var Rc={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},Tc=Object.assign,bc={};function ur(e,t,n){this.props=e,this.context=t,this.refs=bc,this.updater=n||Rc}ur.prototype.isReactComponent={};ur.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};ur.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function Lc(){}Lc.prototype=ur.prototype;function Vl(e,t,n){this.props=e,this.context=t,this.refs=bc,this.updater=n||Rc}var Fl=Vl.prototype=new Lc;Fl.constructor=Vl;Tc(Fl,ur.prototype);Fl.isPureReactComponent=!0;var Ba=Array.isArray,Ic=Object.prototype.hasOwnProperty,jl={current:null},Vc={key:!0,ref:!0,__self:!0,__source:!0};function Fc(e,t,n){var r,o={},i=null,s=null;if(t!=null)for(r in t.ref!==void 0&&(s=t.ref),t.key!==void 0&&(i=""+t.key),t)Ic.call(t,r)&&!Vc.hasOwnProperty(r)&&(o[r]=t[r]);var l=arguments.length-2;if(l===1)o.children=n;else if(1<l){for(var a=Array(l),u=0;u<l;u++)a[u]=arguments[u+2];o.children=a}if(e&&e.defaultProps)for(r in l=e.defaultProps,l)o[r]===void 0&&(o[r]=l[r]);return{$$typeof:ro,type:e,key:i,ref:s,props:o,_owner:jl.current}}function Nf(e,t){return{$$typeof:ro,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function Dl(e){return typeof e=="object"&&e!==null&&e.$$typeof===ro}function Ef(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var Ka=/\/+/g;function Xi(e,t){return typeof e=="object"&&e!==null&&e.key!=null?Ef(""+e.key):t.toString(36)}function Bo(e,t,n,r,o){var i=typeof e;(i==="undefined"||i==="boolean")&&(e=null);var s=!1;if(e===null)s=!0;else switch(i){case"string":case"number":s=!0;break;case"object":switch(e.$$typeof){case ro:case ff:s=!0}}if(s)return s=e,o=o(s),e=r===""?"."+Xi(s,0):r,Ba(o)?(n="",e!=null&&(n=e.replace(Ka,"$&/")+"/"),Bo(o,t,n,"",function(u){return u})):o!=null&&(Dl(o)&&(o=Nf(o,n+(!o.key||s&&s.key===o.key?"":(""+o.key).replace(Ka,"$&/")+"/")+e)),t.push(o)),1;if(s=0,r=r===""?".":r+":",Ba(e))for(var l=0;l<e.length;l++){i=e[l];var a=r+Xi(i,l);s+=Bo(i,t,n,a,o)}else if(a=Of(e),typeof a=="function")for(e=a.call(e),l=0;!(i=e.next()).done;)i=i.value,a=r+Xi(i,l++),s+=Bo(i,t,n,a,o);else if(i==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). If you meant to render a collection of children, use an array instead.");return s}function ho(e,t,n){if(e==null)return e;var r=[],o=0;return Bo(e,r,"","",function(i){return t.call(n,i,o++)}),r}function xf(e){if(e._status===-1){var t=e._result;t=t(),t.then(function(n){(e._status===0||e._status===-1)&&(e._status=1,e._result=n)},function(n){(e._status===0||e._status===-1)&&(e._status=2,e._result=n)}),e._status===-1&&(e._status=0,e._result=t)}if(e._status===1)return e._result.default;throw e._result}var Ne={current:null},Ko={transition:null},Pf={ReactCurrentDispatcher:Ne,ReactCurrentBatchConfig:Ko,ReactCurrentOwner:jl};D.Children={map:ho,forEach:function(e,t,n){ho(e,function(){t.apply(this,arguments)},n)},count:function(e){var t=0;return ho(e,function(){t++}),t},toArray:function(e){return ho(e,function(t){return t})||[]},only:function(e){if(!Dl(e))throw Error("React.Children.only expected to receive a single React element child.");return e}};D.Component=ur;D.Fragment=hf;D.Profiler=gf;D.PureComponent=Vl;D.StrictMode=mf;D.Suspense=kf;D.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=Pf;D.cloneElement=function(e,t,n){if(e==null)throw Error("React.cloneElement(...): The argument must be a React element, but you passed "+e+".");var r=Tc({},e.props),o=e.key,i=e.ref,s=e._owner;if(t!=null){if(t.ref!==void 0&&(i=t.ref,s=jl.current),t.key!==void 0&&(o=""+t.key),e.type&&e.type.defaultProps)var l=e.type.defaultProps;for(a in t)Ic.call(t,a)&&!Vc.hasOwnProperty(a)&&(r[a]=t[a]===void 0&&l!==void 0?l[a]:t[a])}var a=arguments.length-2;if(a===1)r.children=n;else if(1<a){l=Array(a);for(var u=0;u<a;u++)l[u]=arguments[u+2];r.children=l}return{$$typeof:ro,type:e.type,key:o,ref:i,props:r,_owner:s}};D.createContext=function(e){return e={$$typeof:yf,_currentValue:e,_currentValue2:e,_threadCount:0,Provider:null,Consumer:null,_defaultValue:null,_globalName:null},e.Provider={$$typeof:vf,_context:e},e.Consumer=e};D.createElement=Fc;D.createFactory=function(e){var t=Fc.bind(null,e);return t.type=e,t};D.createRef=function(){return{current:null}};D.forwardRef=function(e){return{$$typeof:_f,render:e}};D.isValidElement=Dl;D.lazy=function(e){return{$$typeof:Sf,_payload:{_status:-1,_result:e},_init:xf}};D.memo=function(e,t){return{$$typeof:wf,type:e,compare:t===void 0?null:t}};D.startTransition=function(e){var t=Ko.transition;Ko.transition={};try{e()}finally{Ko.transition=t}};D.unstable_act=function(){throw Error("act(...) is not supported in production builds of React.")};D.useCallback=function(e,t){return Ne.current.useCallback(e,t)};D.useContext=function(e){return Ne.current.useContext(e)};D.useDebugValue=function(){};D.useDeferredValue=function(e){return Ne.current.useDeferredValue(e)};D.useEffect=function(e,t){return Ne.current.useEffect(e,t)};D.useId=function(){return Ne.current.useId()};D.useImperativeHandle=function(e,t,n){return Ne.current.useImperativeHandle(e,t,n)};D.useInsertionEffect=function(e,t){return Ne.current.useInsertionEffect(e,t)};D.useLayoutEffect=function(e,t){return Ne.current.useLayoutEffect(e,t)};D.useMemo=function(e,t){return Ne.current.useMemo(e,t)};D.useReducer=function(e,t,n){return Ne.current.useReducer(e,t,n)};D.useRef=function(e){return Ne.current.useRef(e)};D.useState=function(e){return Ne.current.useState(e)};D.useSyncExternalStore=function(e,t,n){return Ne.current.useSyncExternalStore(e,t,n)};D.useTransition=function(){return Ne.current.useTransition()};D.version="18.2.0";(function(e){e.exports=D})(pf);const Cf=cf(j);/**
  * @license React
  * react-jsx-runtime.production.min.js
  *
@@ -14,12 +14,12 @@ var of=Object.defineProperty;var sf=(e,t,n)=>t in e?of(e,t,{enumerable:!0,config
  *
  * This source code is licensed under the MIT license found in the
  * LICENSE file in the root directory of this source tree.
- */var xf=D,Pf=Symbol.for("react.element"),Cf=Symbol.for("react.fragment"),Rf=Object.prototype.hasOwnProperty,Tf=xf.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner,Lf={key:!0,ref:!0,__self:!0,__source:!0};function Lc(e,t,n){var r,o={},i=null,s=null;n!==void 0&&(i=""+n),t.key!==void 0&&(i=""+t.key),t.ref!==void 0&&(s=t.ref);for(r in t)Rf.call(t,r)&&!Lf.hasOwnProperty(r)&&(o[r]=t[r]);if(e&&e.defaultProps)for(r in t=e.defaultProps,t)o[r]===void 0&&(o[r]=t[r]);return{$$typeof:Pf,type:e,key:i,ref:s,props:o,_owner:Tf.current}}Ti.Fragment=Cf;Ti.jsx=Lc;Ti.jsxs=Lc;(function(e){e.exports=Ti})(af);const ni=Ar.Fragment,_=Ar.jsx,I=Ar.jsxs;document.body.style.cssText+=`
+ */var Rf=j,Tf=Symbol.for("react.element"),bf=Symbol.for("react.fragment"),Lf=Object.prototype.hasOwnProperty,If=Rf.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner,Vf={key:!0,ref:!0,__self:!0,__source:!0};function jc(e,t,n){var r,o={},i=null,s=null;n!==void 0&&(i=""+n),t.key!==void 0&&(i=""+t.key),t.ref!==void 0&&(s=t.ref);for(r in t)Lf.call(t,r)&&!Vf.hasOwnProperty(r)&&(o[r]=t[r]);if(e&&e.defaultProps)for(r in t=e.defaultProps,t)o[r]===void 0&&(o[r]=t[r]);return{$$typeof:Tf,type:e,key:i,ref:s,props:o,_owner:If.current}}Ti.Fragment=bf;Ti.jsx=jc;Ti.jsxs=jc;(function(e){e.exports=Ti})(df);const ni=Ar.Fragment,_=Ar.jsx,L=Ar.jsxs;document.body.style.cssText+=`
 overflow: hidden;
-`;const If=()=>{const e={type:"size",height:document.body.offsetHeight};window.parent.postMessage(e)};new ResizeObserver(If).observe(document.body);function fe(e){return fe=typeof Symbol=="function"&&typeof Symbol.iterator=="symbol"?function(t){return typeof t}:function(t){return t&&typeof Symbol=="function"&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},fe(e)}function ct(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function bf(e,t){if(fe(e)!=="object"||e===null)return e;var n=e[Symbol.toPrimitive];if(n!==void 0){var r=n.call(e,t||"default");if(fe(r)!=="object")return r;throw new TypeError("@@toPrimitive must return a primitive value.")}return(t==="string"?String:Number)(e)}function Ic(e){var t=bf(e,"string");return fe(t)==="symbol"?t:String(t)}function $a(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,Ic(r.key),r)}}function dt(e,t,n){return t&&$a(e.prototype,t),n&&$a(e,n),Object.defineProperty(e,"prototype",{writable:!1}),e}function Wt(e){if(e===void 0)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}function Vs(e,t){return Vs=Object.setPrototypeOf?Object.setPrototypeOf.bind():function(r,o){return r.__proto__=o,r},Vs(e,t)}function Li(e,t){if(typeof t!="function"&&t!==null)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),Object.defineProperty(e,"prototype",{writable:!1}),t&&Vs(e,t)}function oo(e,t){if(t&&(fe(t)==="object"||typeof t=="function"))return t;if(t!==void 0)throw new TypeError("Derived constructors may only return object or undefined");return Wt(e)}function St(e){return St=Object.setPrototypeOf?Object.getPrototypeOf.bind():function(n){return n.__proto__||Object.getPrototypeOf(n)},St(e)}function qe(e,t,n){return t=Ic(t),t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function bc(e){if(Array.isArray(e))return e}function Vf(e){if(typeof Symbol<"u"&&e[Symbol.iterator]!=null||e["@@iterator"]!=null)return Array.from(e)}function Ba(e,t){(t==null||t>e.length)&&(t=e.length);for(var n=0,r=new Array(t);n<t;n++)r[n]=e[n];return r}function Vc(e,t){if(e){if(typeof e=="string")return Ba(e,t);var n=Object.prototype.toString.call(e).slice(8,-1);if(n==="Object"&&e.constructor&&(n=e.constructor.name),n==="Map"||n==="Set")return Array.from(e);if(n==="Arguments"||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n))return Ba(e,t)}}function Fc(){throw new TypeError(`Invalid attempt to destructure non-iterable instance.
-In order to be iterable, non-array objects must have a [Symbol.iterator]() method.`)}function Ff(e){return bc(e)||Vf(e)||Vc(e)||Fc()}function Ka(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function Ha(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Ka(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Ka(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var Df={type:"logger",log:function(t){this.output("log",t)},warn:function(t){this.output("warn",t)},error:function(t){this.output("error",t)},output:function(t,n){console&&console[t]&&console[t].apply(console,n)}},jf=function(){function e(t){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};ct(this,e),this.init(t,n)}return dt(e,[{key:"init",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.prefix=r.prefix||"i18next:",this.logger=n||Df,this.options=r,this.debug=r.debug}},{key:"setDebug",value:function(n){this.debug=n}},{key:"log",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"log","",!0)}},{key:"warn",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"warn","",!0)}},{key:"error",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"error","")}},{key:"deprecate",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"warn","WARNING DEPRECATED: ",!0)}},{key:"forward",value:function(n,r,o,i){return i&&!this.debug?null:(typeof n[0]=="string"&&(n[0]="".concat(o).concat(this.prefix," ").concat(n[0])),this.logger[r](n))}},{key:"create",value:function(n){return new e(this.logger,Ha(Ha({},{prefix:"".concat(this.prefix,":").concat(n,":")}),this.options))}},{key:"clone",value:function(n){return n=n||this.options,n.prefix=n.prefix||this.prefix,new e(this.logger,n)}}]),e}(),yt=new jf,tn=function(){function e(){ct(this,e),this.observers={}}return dt(e,[{key:"on",value:function(n,r){var o=this;return n.split(" ").forEach(function(i){o.observers[i]=o.observers[i]||[],o.observers[i].push(r)}),this}},{key:"off",value:function(n,r){if(this.observers[n]){if(!r){delete this.observers[n];return}this.observers[n]=this.observers[n].filter(function(o){return o!==r})}}},{key:"emit",value:function(n){for(var r=arguments.length,o=new Array(r>1?r-1:0),i=1;i<r;i++)o[i-1]=arguments[i];if(this.observers[n]){var s=[].concat(this.observers[n]);s.forEach(function(a){a.apply(void 0,o)})}if(this.observers["*"]){var l=[].concat(this.observers["*"]);l.forEach(function(a){a.apply(a,[n].concat(o))})}}}]),e}();function hr(){var e,t,n=new Promise(function(r,o){e=r,t=o});return n.resolve=e,n.reject=t,n}function Wa(e){return e==null?"":""+e}function Uf(e,t,n){e.forEach(function(r){t[r]&&(n[r]=t[r])})}function Ul(e,t,n){function r(l){return l&&l.indexOf("###")>-1?l.replace(/###/g,"."):l}function o(){return!e||typeof e=="string"}for(var i=typeof t!="string"?[].concat(t):t.split(".");i.length>1;){if(o())return{};var s=r(i.shift());!e[s]&&n&&(e[s]=new n),Object.prototype.hasOwnProperty.call(e,s)?e=e[s]:e={}}return o()?{}:{obj:e,k:r(i.shift())}}function Ya(e,t,n){var r=Ul(e,t,Object),o=r.obj,i=r.k;o[i]=n}function Af(e,t,n,r){var o=Ul(e,t,Object),i=o.obj,s=o.k;i[s]=i[s]||[],r&&(i[s]=i[s].concat(n)),r||i[s].push(n)}function ri(e,t){var n=Ul(e,t),r=n.obj,o=n.k;if(r)return r[o]}function Ga(e,t,n){var r=ri(e,n);return r!==void 0?r:ri(t,n)}function Dc(e,t,n){for(var r in t)r!=="__proto__"&&r!=="constructor"&&(r in e?typeof e[r]=="string"||e[r]instanceof String||typeof t[r]=="string"||t[r]instanceof String?n&&(e[r]=t[r]):Dc(e[r],t[r],n):e[r]=t[r]);return e}function bn(e){return e.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}var zf={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;","/":"&#x2F;"};function Mf(e){return typeof e=="string"?e.replace(/[&<>"'\/]/g,function(t){return zf[t]}):e}var Ii=typeof window<"u"&&window.navigator&&typeof window.navigator.userAgentData>"u"&&window.navigator.userAgent&&window.navigator.userAgent.indexOf("MSIE")>-1,$f=[" ",",","?","!",";"];function Bf(e,t,n){t=t||"",n=n||"";var r=$f.filter(function(l){return t.indexOf(l)<0&&n.indexOf(l)<0});if(r.length===0)return!0;var o=new RegExp("(".concat(r.map(function(l){return l==="?"?"\\?":l}).join("|"),")")),i=!o.test(e);if(!i){var s=e.indexOf(n);s>0&&!o.test(e.substring(0,s))&&(i=!0)}return i}function Qa(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function mo(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Qa(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Qa(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Kf(e){var t=Hf();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function Hf(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}function jc(e,t){var n=arguments.length>2&&arguments[2]!==void 0?arguments[2]:".";if(e){if(e[t])return e[t];for(var r=t.split(n),o=e,i=0;i<r.length;++i){if(!o||typeof o[r[i]]=="string"&&i+1<r.length)return;if(o[r[i]]===void 0){for(var s=2,l=r.slice(i,i+s).join(n),a=o[l];a===void 0&&r.length>i+s;)s++,l=r.slice(i,i+s).join(n),a=o[l];if(a===void 0)return;if(a===null)return null;if(t.endsWith(l)){if(typeof a=="string")return a;if(l&&typeof a[l]=="string")return a[l]}var u=r.slice(i+s).join(n);return u?jc(a,u,n):void 0}o=o[r[i]]}return o}}var Wf=function(e){Li(n,e);var t=Kf(n);function n(r){var o,i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{ns:["translation"],defaultNS:"translation"};return ct(this,n),o=t.call(this),Ii&&tn.call(Wt(o)),o.data=r||{},o.options=i,o.options.keySeparator===void 0&&(o.options.keySeparator="."),o.options.ignoreJSONStructure===void 0&&(o.options.ignoreJSONStructure=!0),o}return dt(n,[{key:"addNamespaces",value:function(o){this.options.ns.indexOf(o)<0&&this.options.ns.push(o)}},{key:"removeNamespaces",value:function(o){var i=this.options.ns.indexOf(o);i>-1&&this.options.ns.splice(i,1)}},{key:"getResource",value:function(o,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{},a=l.keySeparator!==void 0?l.keySeparator:this.options.keySeparator,u=l.ignoreJSONStructure!==void 0?l.ignoreJSONStructure:this.options.ignoreJSONStructure,f=[o,i];s&&typeof s!="string"&&(f=f.concat(s)),s&&typeof s=="string"&&(f=f.concat(a?s.split(a):s)),o.indexOf(".")>-1&&(f=o.split("."));var d=ri(this.data,f);return d||!u||typeof s!="string"?d:jc(this.data&&this.data[o]&&this.data[o][i],s,a)}},{key:"addResource",value:function(o,i,s,l){var a=arguments.length>4&&arguments[4]!==void 0?arguments[4]:{silent:!1},u=this.options.keySeparator;u===void 0&&(u=".");var f=[o,i];s&&(f=f.concat(u?s.split(u):s)),o.indexOf(".")>-1&&(f=o.split("."),l=i,i=f[1]),this.addNamespaces(i),Ya(this.data,f,l),a.silent||this.emit("added",o,i,s,l)}},{key:"addResources",value:function(o,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{silent:!1};for(var a in s)(typeof s[a]=="string"||Object.prototype.toString.apply(s[a])==="[object Array]")&&this.addResource(o,i,a,s[a],{silent:!0});l.silent||this.emit("added",o,i,s)}},{key:"addResourceBundle",value:function(o,i,s,l,a){var u=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{silent:!1},f=[o,i];o.indexOf(".")>-1&&(f=o.split("."),l=s,s=i,i=f[1]),this.addNamespaces(i);var d=ri(this.data,f)||{};l?Dc(d,s,a):d=mo(mo({},d),s),Ya(this.data,f,d),u.silent||this.emit("added",o,i,s)}},{key:"removeResourceBundle",value:function(o,i){this.hasResourceBundle(o,i)&&delete this.data[o][i],this.removeNamespaces(i),this.emit("removed",o,i)}},{key:"hasResourceBundle",value:function(o,i){return this.getResource(o,i)!==void 0}},{key:"getResourceBundle",value:function(o,i){return i||(i=this.options.defaultNS),this.options.compatibilityAPI==="v1"?mo(mo({},{}),this.getResource(o,i)):this.getResource(o,i)}},{key:"getDataByLanguage",value:function(o){return this.data[o]}},{key:"hasLanguageSomeTranslations",value:function(o){var i=this.getDataByLanguage(o),s=i&&Object.keys(i)||[];return!!s.find(function(l){return i[l]&&Object.keys(i[l]).length>0})}},{key:"toJSON",value:function(){return this.data}}]),n}(tn),Uc={processors:{},addPostProcessor:function(t){this.processors[t.name]=t},handle:function(t,n,r,o,i){var s=this;return t.forEach(function(l){s.processors[l]&&(n=s.processors[l].process(n,r,o,i))}),n}};function qa(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function we(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?qa(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):qa(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Yf(e){var t=Gf();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function Gf(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}var Xa={},Ja=function(e){Li(n,e);var t=Yf(n);function n(r){var o,i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};return ct(this,n),o=t.call(this),Ii&&tn.call(Wt(o)),Uf(["resourceStore","languageUtils","pluralResolver","interpolator","backendConnector","i18nFormat","utils"],r,Wt(o)),o.options=i,o.options.keySeparator===void 0&&(o.options.keySeparator="."),o.logger=yt.create("translator"),o}return dt(n,[{key:"changeLanguage",value:function(o){o&&(this.language=o)}},{key:"exists",value:function(o){var i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}};if(o==null)return!1;var s=this.resolve(o,i);return s&&s.res!==void 0}},{key:"extractFromKey",value:function(o,i){var s=i.nsSeparator!==void 0?i.nsSeparator:this.options.nsSeparator;s===void 0&&(s=":");var l=i.keySeparator!==void 0?i.keySeparator:this.options.keySeparator,a=i.ns||this.options.defaultNS||[],u=s&&o.indexOf(s)>-1,f=!this.options.userDefinedKeySeparator&&!i.keySeparator&&!this.options.userDefinedNsSeparator&&!i.nsSeparator&&!Bf(o,s,l);if(u&&!f){var d=o.match(this.interpolator.nestingRegexp);if(d&&d.length>0)return{key:o,namespaces:a};var h=o.split(s);(s!==l||s===l&&this.options.ns.indexOf(h[0])>-1)&&(a=h.shift()),o=h.join(l)}return typeof a=="string"&&(a=[a]),{key:o,namespaces:a}}},{key:"translate",value:function(o,i,s){var l=this;if(fe(i)!=="object"&&this.options.overloadTranslationOptionHandler&&(i=this.options.overloadTranslationOptionHandler(arguments)),i||(i={}),o==null)return"";Array.isArray(o)||(o=[String(o)]);var a=i.returnDetails!==void 0?i.returnDetails:this.options.returnDetails,u=i.keySeparator!==void 0?i.keySeparator:this.options.keySeparator,f=this.extractFromKey(o[o.length-1],i),d=f.key,h=f.namespaces,g=h[h.length-1],v=i.lng||this.language,k=i.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(v&&v.toLowerCase()==="cimode"){if(k){var O=i.nsSeparator||this.options.nsSeparator;return a?{res:"".concat(g).concat(O).concat(d),usedKey:d,exactUsedKey:d,usedLng:v,usedNS:g}:"".concat(g).concat(O).concat(d)}return a?{res:d,usedKey:d,exactUsedKey:d,usedLng:v,usedNS:g}:d}var p=this.resolve(o,i),c=p&&p.res,m=p&&p.usedKey||d,y=p&&p.exactUsedKey||d,S=Object.prototype.toString.apply(c),w=["[object Number]","[object Function]","[object RegExp]"],E=i.joinArrays!==void 0?i.joinArrays:this.options.joinArrays,x=!this.i18nFormat||this.i18nFormat.handleAsObject,V=typeof c!="string"&&typeof c!="boolean"&&typeof c!="number";if(x&&c&&V&&w.indexOf(S)<0&&!(typeof E=="string"&&S==="[object Array]")){if(!i.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");var P=this.options.returnedObjectHandler?this.options.returnedObjectHandler(m,c,we(we({},i),{},{ns:h})):"key '".concat(d," (").concat(this.language,")' returned an object instead of string.");return a?(p.res=P,p):P}if(u){var K=S==="[object Array]",Ce=K?[]:{},Ot=K?y:m;for(var Ze in c)if(Object.prototype.hasOwnProperty.call(c,Ze)){var Tn="".concat(Ot).concat(u).concat(Ze);Ce[Ze]=this.translate(Tn,we(we({},i),{joinArrays:!1,ns:h})),Ce[Ze]===Tn&&(Ce[Ze]=c[Ze])}c=Ce}}else if(x&&typeof E=="string"&&S==="[object Array]")c=c.join(E),c&&(c=this.extendTranslation(c,o,i,s));else{var ft=!1,et=!1,R=i.count!==void 0&&typeof i.count!="string",b=n.hasDefaultValue(i),F=R?this.pluralResolver.getSuffix(v,i.count,i):"",A=i["defaultValue".concat(F)]||i.defaultValue;!this.isValidLookup(c)&&b&&(ft=!0,c=A),this.isValidLookup(c)||(et=!0,c=d);var q=i.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey,Nt=q&&et?void 0:c,Ve=b&&A!==c&&this.options.updateMissing;if(et||ft||Ve){if(this.logger.log(Ve?"updateKey":"missingKey",v,g,d,Ve?A:c),u){var Ln=this.resolve(d,we(we({},i),{},{keySeparator:!1}));Ln&&Ln.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}var Fe=[],Et=this.languageUtils.getFallbackCodes(this.options.fallbackLng,i.lng||this.language);if(this.options.saveMissingTo==="fallback"&&Et&&Et[0])for(var Gi=0;Gi<Et.length;Gi++)Fe.push(Et[Gi]);else this.options.saveMissingTo==="all"?Fe=this.languageUtils.toResolveHierarchy(i.lng||this.language):Fe.push(i.lng||this.language);var Da=function(In,qi,ja){var Ua=b&&ja!==c?ja:Nt;l.options.missingKeyHandler?l.options.missingKeyHandler(In,g,qi,Ua,Ve,i):l.backendConnector&&l.backendConnector.saveMissing&&l.backendConnector.saveMissing(In,g,qi,Ua,Ve,i),l.emit("missingKey",In,g,qi,c)};this.options.saveMissing&&(this.options.saveMissingPlurals&&R?Fe.forEach(function(Qi){l.pluralResolver.getSuffixes(Qi,i).forEach(function(In){Da([Qi],d+In,i["defaultValue".concat(In)]||A)})}):Da(Fe,d,A))}c=this.extendTranslation(c,o,i,p,s),et&&c===d&&this.options.appendNamespaceToMissingKey&&(c="".concat(g,":").concat(d)),(et||ft)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?c=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(g,":").concat(d):d,ft?c:void 0):c=this.options.parseMissingKeyHandler(c))}return a?(p.res=c,p):c}},{key:"extendTranslation",value:function(o,i,s,l,a){var u=this;if(this.i18nFormat&&this.i18nFormat.parse)o=this.i18nFormat.parse(o,we(we({},this.options.interpolation.defaultVariables),s),l.usedLng,l.usedNS,l.usedKey,{resolved:l});else if(!s.skipInterpolation){s.interpolation&&this.interpolator.init(we(we({},s),{interpolation:we(we({},this.options.interpolation),s.interpolation)}));var f=typeof o=="string"&&(s&&s.interpolation&&s.interpolation.skipOnVariables!==void 0?s.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables),d;if(f){var h=o.match(this.interpolator.nestingRegexp);d=h&&h.length}var g=s.replace&&typeof s.replace!="string"?s.replace:s;if(this.options.interpolation.defaultVariables&&(g=we(we({},this.options.interpolation.defaultVariables),g)),o=this.interpolator.interpolate(o,g,s.lng||this.language,s),f){var v=o.match(this.interpolator.nestingRegexp),k=v&&v.length;d<k&&(s.nest=!1)}s.nest!==!1&&(o=this.interpolator.nest(o,function(){for(var c=arguments.length,m=new Array(c),y=0;y<c;y++)m[y]=arguments[y];return a&&a[0]===m[0]&&!s.context?(u.logger.warn("It seems you are nesting recursively key: ".concat(m[0]," in key: ").concat(i[0])),null):u.translate.apply(u,m.concat([i]))},s)),s.interpolation&&this.interpolator.reset()}var O=s.postProcess||this.options.postProcess,p=typeof O=="string"?[O]:O;return o!=null&&p&&p.length&&s.applyPostProcessor!==!1&&(o=Uc.handle(p,o,i,this.options&&this.options.postProcessPassResolved?we({i18nResolved:l},s):s,this)),o}},{key:"resolve",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},l,a,u,f,d;return typeof o=="string"&&(o=[o]),o.forEach(function(h){if(!i.isValidLookup(l)){var g=i.extractFromKey(h,s),v=g.key;a=v;var k=g.namespaces;i.options.fallbackNS&&(k=k.concat(i.options.fallbackNS));var O=s.count!==void 0&&typeof s.count!="string",p=O&&!s.ordinal&&s.count===0&&i.pluralResolver.shouldUseIntlApi(),c=s.context!==void 0&&(typeof s.context=="string"||typeof s.context=="number")&&s.context!=="",m=s.lngs?s.lngs:i.languageUtils.toResolveHierarchy(s.lng||i.language,s.fallbackLng);k.forEach(function(y){i.isValidLookup(l)||(d=y,!Xa["".concat(m[0],"-").concat(y)]&&i.utils&&i.utils.hasLoadedNamespace&&!i.utils.hasLoadedNamespace(d)&&(Xa["".concat(m[0],"-").concat(y)]=!0,i.logger.warn('key "'.concat(a,'" for languages "').concat(m.join(", "),`" won't get resolved as namespace "`).concat(d,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(function(S){if(!i.isValidLookup(l)){f=S;var w=[v];if(i.i18nFormat&&i.i18nFormat.addLookupKeys)i.i18nFormat.addLookupKeys(w,v,S,y,s);else{var E;O&&(E=i.pluralResolver.getSuffix(S,s.count,s));var x="".concat(i.options.pluralSeparator,"zero");if(O&&(w.push(v+E),p&&w.push(v+x)),c){var V="".concat(v).concat(i.options.contextSeparator).concat(s.context);w.push(V),O&&(w.push(V+E),p&&w.push(V+x))}}for(var P;P=w.pop();)i.isValidLookup(l)||(u=P,l=i.getResource(S,y,P,s))}}))})}}),{res:l,usedKey:a,exactUsedKey:u,usedLng:f,usedNS:d}}},{key:"isValidLookup",value:function(o){return o!==void 0&&!(!this.options.returnNull&&o===null)&&!(!this.options.returnEmptyString&&o==="")}},{key:"getResource",value:function(o,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(o,i,s,l):this.resourceStore.getResource(o,i,s,l)}}],[{key:"hasDefaultValue",value:function(o){var i="defaultValue";for(var s in o)if(Object.prototype.hasOwnProperty.call(o,s)&&i===s.substring(0,i.length)&&o[s]!==void 0)return!0;return!1}}]),n}(tn);function Ji(e){return e.charAt(0).toUpperCase()+e.slice(1)}var Za=function(){function e(t){ct(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=yt.create("languageUtils")}return dt(e,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var r=n.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var r=n.split("-");return this.formatLanguageCode(r[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var r=["hans","hant","latn","cyrl","cans","mong","arab"],o=n.split("-");return this.options.lowerCaseLng?o=o.map(function(i){return i.toLowerCase()}):o.length===2?(o[0]=o[0].toLowerCase(),o[1]=o[1].toUpperCase(),r.indexOf(o[1].toLowerCase())>-1&&(o[1]=Ji(o[1].toLowerCase()))):o.length===3&&(o[0]=o[0].toLowerCase(),o[1].length===2&&(o[1]=o[1].toUpperCase()),o[0]!=="sgn"&&o[2].length===2&&(o[2]=o[2].toUpperCase()),r.indexOf(o[1].toLowerCase())>-1&&(o[1]=Ji(o[1].toLowerCase())),r.indexOf(o[2].toLowerCase())>-1&&(o[2]=Ji(o[2].toLowerCase()))),o.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var o;return n.forEach(function(i){if(!o){var s=r.formatLanguageCode(i);(!r.options.supportedLngs||r.isSupportedCode(s))&&(o=s)}}),!o&&this.options.supportedLngs&&n.forEach(function(i){if(!o){var s=r.getLanguagePartFromCode(i);if(r.isSupportedCode(s))return o=s;o=r.options.supportedLngs.find(function(l){if(l.indexOf(s)===0)return l})}}),o||(o=this.getFallbackCodes(this.options.fallbackLng)[0]),o}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var o=n[r];return o||(o=n[this.getScriptPartFromCode(r)]),o||(o=n[this.formatLanguageCode(r)]),o||(o=n[this.getLanguagePartFromCode(r)]),o||(o=n.default),o||[]}},{key:"toResolveHierarchy",value:function(n,r){var o=this,i=this.getFallbackCodes(r||this.options.fallbackLng||[],n),s=[],l=function(u){u&&(o.isSupportedCode(u)?s.push(u):o.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof n=="string"&&n.indexOf("-")>-1?(this.options.load!=="languageOnly"&&l(this.formatLanguageCode(n)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&l(this.getScriptPartFromCode(n)),this.options.load!=="currentOnly"&&l(this.getLanguagePartFromCode(n))):typeof n=="string"&&l(this.formatLanguageCode(n)),i.forEach(function(a){s.indexOf(a)<0&&l(o.formatLanguageCode(a))}),s}}]),e}(),Qf=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],qf={1:function(t){return+(t>1)},2:function(t){return+(t!=1)},3:function(t){return 0},4:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},5:function(t){return t==0?0:t==1?1:t==2?2:t%100>=3&&t%100<=10?3:t%100>=11?4:5},6:function(t){return t==1?0:t>=2&&t<=4?1:2},7:function(t){return t==1?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},8:function(t){return t==1?0:t==2?1:t!=8&&t!=11?2:3},9:function(t){return+(t>=2)},10:function(t){return t==1?0:t==2?1:t<7?2:t<11?3:4},11:function(t){return t==1||t==11?0:t==2||t==12?1:t>2&&t<20?2:3},12:function(t){return+(t%10!=1||t%100==11)},13:function(t){return+(t!==0)},14:function(t){return t==1?0:t==2?1:t==3?2:3},15:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&(t%100<10||t%100>=20)?1:2},16:function(t){return t%10==1&&t%100!=11?0:t!==0?1:2},17:function(t){return t==1||t%10==1&&t%100!=11?0:1},18:function(t){return t==0?0:t==1?1:2},19:function(t){return t==1?0:t==0||t%100>1&&t%100<11?1:t%100>10&&t%100<20?2:3},20:function(t){return t==1?0:t==0||t%100>0&&t%100<20?1:2},21:function(t){return t%100==1?1:t%100==2?2:t%100==3||t%100==4?3:0},22:function(t){return t==1?0:t==2?1:(t<0||t>10)&&t%10==0?2:3}},Xf=["v1","v2","v3"],eu={zero:0,one:1,two:2,few:3,many:4,other:5};function Jf(){var e={};return Qf.forEach(function(t){t.lngs.forEach(function(n){e[n]={numbers:t.nr,plurals:qf[t.fc]}})}),e}var Zf=function(){function e(t){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};ct(this,e),this.languageUtils=t,this.options=n,this.logger=yt.create("pluralResolver"),(!this.options.compatibilityJSON||this.options.compatibilityJSON==="v4")&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=Jf()}return dt(e,[{key:"addRule",value:function(n,r){this.rules[n]=r}},{key:"getRule",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(n,{type:r.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[n]||this.rules[this.languageUtils.getLanguagePartFromCode(n)]}},{key:"needsPlural",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},o=this.getRule(n,r);return this.shouldUseIntlApi()?o&&o.resolvedOptions().pluralCategories.length>1:o&&o.numbers.length>1}},{key:"getPluralFormsOfKey",value:function(n,r){var o=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(n,o).map(function(i){return"".concat(r).concat(i)})}},{key:"getSuffixes",value:function(n){var r=this,o=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},i=this.getRule(n,o);return i?this.shouldUseIntlApi()?i.resolvedOptions().pluralCategories.sort(function(s,l){return eu[s]-eu[l]}).map(function(s){return"".concat(r.options.prepend).concat(s)}):i.numbers.map(function(s){return r.getSuffix(n,s,o)}):[]}},{key:"getSuffix",value:function(n,r){var o=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i=this.getRule(n,o);return i?this.shouldUseIntlApi()?"".concat(this.options.prepend).concat(i.select(r)):this.getSuffixRetroCompatible(i,r):(this.logger.warn("no plural rule found for: ".concat(n)),"")}},{key:"getSuffixRetroCompatible",value:function(n,r){var o=this,i=n.noAbs?n.plurals(r):n.plurals(Math.abs(r)),s=n.numbers[i];this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1&&(s===2?s="plural":s===1&&(s=""));var l=function(){return o.options.prepend&&s.toString()?o.options.prepend+s.toString():s.toString()};return this.options.compatibilityJSON==="v1"?s===1?"":typeof s=="number"?"_plural_".concat(s.toString()):l():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1?l():this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString()}},{key:"shouldUseIntlApi",value:function(){return!Xf.includes(this.options.compatibilityJSON)}}]),e}();function tu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function tt(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?tu(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):tu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var eh=function(){function e(){var t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};ct(this,e),this.logger=yt.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||function(n){return n},this.init(t)}return dt(e,[{key:"init",value:function(){var n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};n.interpolation||(n.interpolation={escapeValue:!0});var r=n.interpolation;this.escape=r.escape!==void 0?r.escape:Mf,this.escapeValue=r.escapeValue!==void 0?r.escapeValue:!0,this.useRawValueToEscape=r.useRawValueToEscape!==void 0?r.useRawValueToEscape:!1,this.prefix=r.prefix?bn(r.prefix):r.prefixEscaped||"{{",this.suffix=r.suffix?bn(r.suffix):r.suffixEscaped||"}}",this.formatSeparator=r.formatSeparator?r.formatSeparator:r.formatSeparator||",",this.unescapePrefix=r.unescapeSuffix?"":r.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":r.unescapeSuffix||"",this.nestingPrefix=r.nestingPrefix?bn(r.nestingPrefix):r.nestingPrefixEscaped||bn("$t("),this.nestingSuffix=r.nestingSuffix?bn(r.nestingSuffix):r.nestingSuffixEscaped||bn(")"),this.nestingOptionsSeparator=r.nestingOptionsSeparator?r.nestingOptionsSeparator:r.nestingOptionsSeparator||",",this.maxReplaces=r.maxReplaces?r.maxReplaces:1e3,this.alwaysFormat=r.alwaysFormat!==void 0?r.alwaysFormat:!1,this.resetRegExp()}},{key:"reset",value:function(){this.options&&this.init(this.options)}},{key:"resetRegExp",value:function(){var n="".concat(this.prefix,"(.+?)").concat(this.suffix);this.regexp=new RegExp(n,"g");var r="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(r,"g");var o="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(o,"g")}},{key:"interpolate",value:function(n,r,o,i){var s=this,l,a,u,f=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function d(O){return O.replace(/\$/g,"$$$$")}var h=function(p){if(p.indexOf(s.formatSeparator)<0){var c=Ga(r,f,p);return s.alwaysFormat?s.format(c,void 0,o,tt(tt(tt({},i),r),{},{interpolationkey:p})):c}var m=p.split(s.formatSeparator),y=m.shift().trim(),S=m.join(s.formatSeparator).trim();return s.format(Ga(r,f,y),S,o,tt(tt(tt({},i),r),{},{interpolationkey:y}))};this.resetRegExp();var g=i&&i.missingInterpolationHandler||this.options.missingInterpolationHandler,v=i&&i.interpolation&&i.interpolation.skipOnVariables!==void 0?i.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables,k=[{regex:this.regexpUnescape,safeValue:function(p){return d(p)}},{regex:this.regexp,safeValue:function(p){return s.escapeValue?d(s.escape(p)):d(p)}}];return k.forEach(function(O){for(u=0;l=O.regex.exec(n);){var p=l[1].trim();if(a=h(p),a===void 0)if(typeof g=="function"){var c=g(n,l,i);a=typeof c=="string"?c:""}else if(i&&Object.prototype.hasOwnProperty.call(i,p))a="";else if(v){a=l[0];continue}else s.logger.warn("missed to pass in variable ".concat(p," for interpolating ").concat(n)),a="";else typeof a!="string"&&!s.useRawValueToEscape&&(a=Wa(a));var m=O.safeValue(a);if(n=n.replace(l[0],m),v?(O.regex.lastIndex+=a.length,O.regex.lastIndex-=l[0].length):O.regex.lastIndex=0,u++,u>=s.maxReplaces)break}}),n}},{key:"nest",value:function(n,r){var o=this,i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},s,l,a;function u(g,v){var k=this.nestingOptionsSeparator;if(g.indexOf(k)<0)return g;var O=g.split(new RegExp("".concat(k,"[ ]*{"))),p="{".concat(O[1]);g=O[0],p=this.interpolate(p,a);var c=p.match(/'/g),m=p.match(/"/g);(c&&c.length%2===0&&!m||m.length%2!==0)&&(p=p.replace(/'/g,'"'));try{a=JSON.parse(p),v&&(a=tt(tt({},v),a))}catch(y){return this.logger.warn("failed parsing options string in nesting for key ".concat(g),y),"".concat(g).concat(k).concat(p)}return delete a.defaultValue,g}for(;s=this.nestingRegexp.exec(n);){var f=[];a=tt({},i),a=a.replace&&typeof a.replace!="string"?a.replace:a,a.applyPostProcessor=!1,delete a.defaultValue;var d=!1;if(s[0].indexOf(this.formatSeparator)!==-1&&!/{.*}/.test(s[1])){var h=s[1].split(this.formatSeparator).map(function(g){return g.trim()});s[1]=h.shift(),f=h,d=!0}if(l=r(u.call(this,s[1].trim(),a),a),l&&s[0]===n&&typeof l!="string")return l;typeof l!="string"&&(l=Wa(l)),l||(this.logger.warn("missed to resolve ".concat(s[1]," for nesting ").concat(n)),l=""),d&&(l=f.reduce(function(g,v){return o.format(g,v,i.lng,tt(tt({},i),{},{interpolationkey:s[1].trim()}))},l.trim())),n=n.replace(s[0],l),this.regexp.lastIndex=0}return n}}]),e}();function nu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function xt(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?nu(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):nu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function th(e){var t=e.toLowerCase().trim(),n={};if(e.indexOf("(")>-1){var r=e.split("(");t=r[0].toLowerCase().trim();var o=r[1].substring(0,r[1].length-1);if(t==="currency"&&o.indexOf(":")<0)n.currency||(n.currency=o.trim());else if(t==="relativetime"&&o.indexOf(":")<0)n.range||(n.range=o.trim());else{var i=o.split(";");i.forEach(function(s){if(s){var l=s.split(":"),a=Ff(l),u=a[0],f=a.slice(1),d=f.join(":").trim().replace(/^'+|'+$/g,"");n[u.trim()]||(n[u.trim()]=d),d==="false"&&(n[u.trim()]=!1),d==="true"&&(n[u.trim()]=!0),isNaN(d)||(n[u.trim()]=parseInt(d,10))}})}}return{formatName:t,formatOptions:n}}function Vn(e){var t={};return function(r,o,i){var s=o+JSON.stringify(i),l=t[s];return l||(l=e(o,i),t[s]=l),l(r)}}var nh=function(){function e(){var t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};ct(this,e),this.logger=yt.create("formatter"),this.options=t,this.formats={number:Vn(function(n,r){var o=new Intl.NumberFormat(n,xt({},r));return function(i){return o.format(i)}}),currency:Vn(function(n,r){var o=new Intl.NumberFormat(n,xt(xt({},r),{},{style:"currency"}));return function(i){return o.format(i)}}),datetime:Vn(function(n,r){var o=new Intl.DateTimeFormat(n,xt({},r));return function(i){return o.format(i)}}),relativetime:Vn(function(n,r){var o=new Intl.RelativeTimeFormat(n,xt({},r));return function(i){return o.format(i,r.range||"day")}}),list:Vn(function(n,r){var o=new Intl.ListFormat(n,xt({},r));return function(i){return o.format(i)}})},this.init(t)}return dt(e,[{key:"init",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}},o=r.interpolation;this.formatSeparator=o.formatSeparator?o.formatSeparator:o.formatSeparator||","}},{key:"add",value:function(n,r){this.formats[n.toLowerCase().trim()]=r}},{key:"addCached",value:function(n,r){this.formats[n.toLowerCase().trim()]=Vn(r)}},{key:"format",value:function(n,r,o){var i=this,s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{},l=r.split(this.formatSeparator),a=l.reduce(function(u,f){var d=th(f),h=d.formatName,g=d.formatOptions;if(i.formats[h]){var v=u;try{var k=s&&s.formatParams&&s.formatParams[s.interpolationkey]||{},O=k.locale||k.lng||s.locale||s.lng||o;v=i.formats[h](u,O,xt(xt(xt({},g),s),k))}catch(p){i.logger.warn(p)}return v}else i.logger.warn("there was no format function for ".concat(h));return u},n);return a}}]),e}();function ru(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ou(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?ru(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):ru(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function rh(e){var t=oh();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function oh(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}function ih(e,t){e.pending[t]!==void 0&&(delete e.pending[t],e.pendingCount--)}var sh=function(e){Li(n,e);var t=rh(n);function n(r,o,i){var s,l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return ct(this,n),s=t.call(this),Ii&&tn.call(Wt(s)),s.backend=r,s.store=o,s.services=i,s.languageUtils=i.languageUtils,s.options=l,s.logger=yt.create("backendConnector"),s.waitingReads=[],s.maxParallelReads=l.maxParallelReads||10,s.readingCalls=0,s.maxRetries=l.maxRetries>=0?l.maxRetries:5,s.retryTimeout=l.retryTimeout>=1?l.retryTimeout:350,s.state={},s.queue=[],s.backend&&s.backend.init&&s.backend.init(i,l.backend,l),s}return dt(n,[{key:"queueLoad",value:function(o,i,s,l){var a=this,u={},f={},d={},h={};return o.forEach(function(g){var v=!0;i.forEach(function(k){var O="".concat(g,"|").concat(k);!s.reload&&a.store.hasResourceBundle(g,k)?a.state[O]=2:a.state[O]<0||(a.state[O]===1?f[O]===void 0&&(f[O]=!0):(a.state[O]=1,v=!1,f[O]===void 0&&(f[O]=!0),u[O]===void 0&&(u[O]=!0),h[k]===void 0&&(h[k]=!0)))}),v||(d[g]=!0)}),(Object.keys(u).length||Object.keys(f).length)&&this.queue.push({pending:f,pendingCount:Object.keys(f).length,loaded:{},errors:[],callback:l}),{toLoad:Object.keys(u),pending:Object.keys(f),toLoadLanguages:Object.keys(d),toLoadNamespaces:Object.keys(h)}}},{key:"loaded",value:function(o,i,s){var l=o.split("|"),a=l[0],u=l[1];i&&this.emit("failedLoading",a,u,i),s&&this.store.addResourceBundle(a,u,s),this.state[o]=i?-1:2;var f={};this.queue.forEach(function(d){Af(d.loaded,[a],u),ih(d,o),i&&d.errors.push(i),d.pendingCount===0&&!d.done&&(Object.keys(d.loaded).forEach(function(h){f[h]||(f[h]={});var g=d.loaded[h];g.length&&g.forEach(function(v){f[h][v]===void 0&&(f[h][v]=!0)})}),d.done=!0,d.errors.length?d.callback(d.errors):d.callback())}),this.emit("loaded",f),this.queue=this.queue.filter(function(d){return!d.done})}},{key:"read",value:function(o,i,s){var l=this,a=arguments.length>3&&arguments[3]!==void 0?arguments[3]:0,u=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,f=arguments.length>5?arguments[5]:void 0;if(!o.length)return f(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:o,ns:i,fcName:s,tried:a,wait:u,callback:f});return}this.readingCalls++;var d=function(k,O){if(l.readingCalls--,l.waitingReads.length>0){var p=l.waitingReads.shift();l.read(p.lng,p.ns,p.fcName,p.tried,p.wait,p.callback)}if(k&&O&&a<l.maxRetries){setTimeout(function(){l.read.call(l,o,i,s,a+1,u*2,f)},u);return}f(k,O)},h=this.backend[s].bind(this.backend);if(h.length===2){try{var g=h(o,i);g&&typeof g.then=="function"?g.then(function(v){return d(null,v)}).catch(d):d(null,g)}catch(v){d(v)}return}return h(o,i,d)}},{key:"prepareLoading",value:function(o,i){var s=this,l=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},a=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),a&&a();typeof o=="string"&&(o=this.languageUtils.toResolveHierarchy(o)),typeof i=="string"&&(i=[i]);var u=this.queueLoad(o,i,l,a);if(!u.toLoad.length)return u.pending.length||a(),null;u.toLoad.forEach(function(f){s.loadOne(f)})}},{key:"load",value:function(o,i,s){this.prepareLoading(o,i,{},s)}},{key:"reload",value:function(o,i,s){this.prepareLoading(o,i,{reload:!0},s)}},{key:"loadOne",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:"",l=o.split("|"),a=l[0],u=l[1];this.read(a,u,"read",void 0,void 0,function(f,d){f&&i.logger.warn("".concat(s,"loading namespace ").concat(u," for language ").concat(a," failed"),f),!f&&d&&i.logger.log("".concat(s,"loaded namespace ").concat(u," for language ").concat(a),d),i.loaded(o,f,d)})}},{key:"saveMissing",value:function(o,i,s,l,a){var u=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{},f=arguments.length>6&&arguments[6]!==void 0?arguments[6]:function(){};if(this.services.utils&&this.services.utils.hasLoadedNamespace&&!this.services.utils.hasLoadedNamespace(i)){this.logger.warn('did not save key "'.concat(s,'" as the namespace "').concat(i,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");return}if(!(s==null||s==="")){if(this.backend&&this.backend.create){var d=ou(ou({},u),{},{isUpdate:a}),h=this.backend.create.bind(this.backend);if(h.length<6)try{var g;h.length===5?g=h(o,i,s,l,d):g=h(o,i,s,l),g&&typeof g.then=="function"?g.then(function(v){return f(null,v)}).catch(f):f(null,g)}catch(v){f(v)}else h(o,i,s,l,f,d)}!o||!o[0]||this.store.addResource(o[0],i,s,l)}}}]),n}(tn);function iu(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(fe(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),fe(t[2])==="object"||fe(t[3])==="object"){var r=t[3]||t[2];Object.keys(r).forEach(function(o){n[o]=r[o]})}return n},interpolation:{escapeValue:!0,format:function(t,n,r,o){return t},prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function su(e){return typeof e.ns=="string"&&(e.ns=[e.ns]),typeof e.fallbackLng=="string"&&(e.fallbackLng=[e.fallbackLng]),typeof e.fallbackNS=="string"&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function lu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ht(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?lu(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):lu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function lh(e){var t=ah();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function ah(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}function go(){}function uh(e){var t=Object.getOwnPropertyNames(Object.getPrototypeOf(e));t.forEach(function(n){typeof e[n]=="function"&&(e[n]=e[n].bind(e))})}var oi=function(e){Li(n,e);var t=lh(n);function n(){var r,o=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},i=arguments.length>1?arguments[1]:void 0;if(ct(this,n),r=t.call(this),Ii&&tn.call(Wt(r)),r.options=su(o),r.services={},r.logger=yt,r.modules={external:[]},uh(Wt(r)),i&&!r.isInitialized&&!o.isClone){if(!r.options.initImmediate)return r.init(o,i),oo(r,Wt(r));setTimeout(function(){r.init(o,i)},0)}return r}return dt(n,[{key:"init",value:function(){var o=this,i=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},s=arguments.length>1?arguments[1]:void 0;typeof i=="function"&&(s=i,i={}),!i.defaultNS&&i.defaultNS!==!1&&i.ns&&(typeof i.ns=="string"?i.defaultNS=i.ns:i.ns.indexOf("translation")<0&&(i.defaultNS=i.ns[0]));var l=iu();this.options=ht(ht(ht({},l),this.options),su(i)),this.options.compatibilityAPI!=="v1"&&(this.options.interpolation=ht(ht({},l.interpolation),this.options.interpolation)),i.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=i.keySeparator),i.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=i.nsSeparator);function a(p){return p?typeof p=="function"?new p:p:null}if(!this.options.isClone){this.modules.logger?yt.init(a(this.modules.logger),this.options):yt.init(null,this.options);var u;this.modules.formatter?u=this.modules.formatter:typeof Intl<"u"&&(u=nh);var f=new Za(this.options);this.store=new Wf(this.options.resources,this.options);var d=this.services;d.logger=yt,d.resourceStore=this.store,d.languageUtils=f,d.pluralResolver=new Zf(f,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),u&&(!this.options.interpolation.format||this.options.interpolation.format===l.interpolation.format)&&(d.formatter=a(u),d.formatter.init(d,this.options),this.options.interpolation.format=d.formatter.format.bind(d.formatter)),d.interpolator=new eh(this.options),d.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},d.backendConnector=new sh(a(this.modules.backend),d.resourceStore,d,this.options),d.backendConnector.on("*",function(p){for(var c=arguments.length,m=new Array(c>1?c-1:0),y=1;y<c;y++)m[y-1]=arguments[y];o.emit.apply(o,[p].concat(m))}),this.modules.languageDetector&&(d.languageDetector=a(this.modules.languageDetector),d.languageDetector.init&&d.languageDetector.init(d,this.options.detection,this.options)),this.modules.i18nFormat&&(d.i18nFormat=a(this.modules.i18nFormat),d.i18nFormat.init&&d.i18nFormat.init(this)),this.translator=new Ja(this.services,this.options),this.translator.on("*",function(p){for(var c=arguments.length,m=new Array(c>1?c-1:0),y=1;y<c;y++)m[y-1]=arguments[y];o.emit.apply(o,[p].concat(m))}),this.modules.external.forEach(function(p){p.init&&p.init(o)})}if(this.format=this.options.interpolation.format,s||(s=go),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var h=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);h.length>0&&h[0]!=="dev"&&(this.options.lng=h[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var g=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];g.forEach(function(p){o[p]=function(){var c;return(c=o.store)[p].apply(c,arguments)}});var v=["addResource","addResources","addResourceBundle","removeResourceBundle"];v.forEach(function(p){o[p]=function(){var c;return(c=o.store)[p].apply(c,arguments),o}});var k=hr(),O=function(){var c=function(y,S){o.isInitialized&&!o.initializedStoreOnce&&o.logger.warn("init: i18next is already initialized. You should call init just once!"),o.isInitialized=!0,o.options.isClone||o.logger.log("initialized",o.options),o.emit("initialized",o.options),k.resolve(S),s(y,S)};if(o.languages&&o.options.compatibilityAPI!=="v1"&&!o.isInitialized)return c(null,o.t.bind(o));o.changeLanguage(o.options.lng,c)};return this.options.resources||!this.options.initImmediate?O():setTimeout(O,0),k}},{key:"loadResources",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:go,l=s,a=typeof o=="string"?o:this.language;if(typeof o=="function"&&(l=o),!this.options.resources||this.options.partialBundledLanguages){if(a&&a.toLowerCase()==="cimode")return l();var u=[],f=function(g){if(g){var v=i.services.languageUtils.toResolveHierarchy(g);v.forEach(function(k){u.indexOf(k)<0&&u.push(k)})}};if(a)f(a);else{var d=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);d.forEach(function(h){return f(h)})}this.options.preload&&this.options.preload.forEach(function(h){return f(h)}),this.services.backendConnector.load(u,this.options.ns,function(h){!h&&!i.resolvedLanguage&&i.language&&i.setResolvedLanguage(i.language),l(h)})}else l(null)}},{key:"reloadResources",value:function(o,i,s){var l=hr();return o||(o=this.languages),i||(i=this.options.ns),s||(s=go),this.services.backendConnector.reload(o,i,function(a){l.resolve(),s(a)}),l}},{key:"use",value:function(o){if(!o)throw new Error("You are passing an undefined module! Please check the object you are passing to i18next.use()");if(!o.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return o.type==="backend"&&(this.modules.backend=o),(o.type==="logger"||o.log&&o.warn&&o.error)&&(this.modules.logger=o),o.type==="languageDetector"&&(this.modules.languageDetector=o),o.type==="i18nFormat"&&(this.modules.i18nFormat=o),o.type==="postProcessor"&&Uc.addPostProcessor(o),o.type==="formatter"&&(this.modules.formatter=o),o.type==="3rdParty"&&this.modules.external.push(o),this}},{key:"setResolvedLanguage",value:function(o){if(!(!o||!this.languages)&&!(["cimode","dev"].indexOf(o)>-1))for(var i=0;i<this.languages.length;i++){var s=this.languages[i];if(!(["cimode","dev"].indexOf(s)>-1)&&this.store.hasLanguageSomeTranslations(s)){this.resolvedLanguage=s;break}}}},{key:"changeLanguage",value:function(o,i){var s=this;this.isLanguageChangingTo=o;var l=hr();this.emit("languageChanging",o);var a=function(h){s.language=h,s.languages=s.services.languageUtils.toResolveHierarchy(h),s.resolvedLanguage=void 0,s.setResolvedLanguage(h)},u=function(h,g){g?(a(g),s.translator.changeLanguage(g),s.isLanguageChangingTo=void 0,s.emit("languageChanged",g),s.logger.log("languageChanged",g)):s.isLanguageChangingTo=void 0,l.resolve(function(){return s.t.apply(s,arguments)}),i&&i(h,function(){return s.t.apply(s,arguments)})},f=function(h){!o&&!h&&s.services.languageDetector&&(h=[]);var g=typeof h=="string"?h:s.services.languageUtils.getBestMatchFromCodes(h);g&&(s.language||a(g),s.translator.language||s.translator.changeLanguage(g),s.services.languageDetector&&s.services.languageDetector.cacheUserLanguage&&s.services.languageDetector.cacheUserLanguage(g)),s.loadResources(g,function(v){u(v,g)})};return!o&&this.services.languageDetector&&!this.services.languageDetector.async?f(this.services.languageDetector.detect()):!o&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(f):this.services.languageDetector.detect(f):f(o),l}},{key:"getFixedT",value:function(o,i,s){var l=this,a=function u(f,d){var h;if(fe(d)!=="object"){for(var g=arguments.length,v=new Array(g>2?g-2:0),k=2;k<g;k++)v[k-2]=arguments[k];h=l.options.overloadTranslationOptionHandler([f,d].concat(v))}else h=ht({},d);h.lng=h.lng||u.lng,h.lngs=h.lngs||u.lngs,h.ns=h.ns||u.ns,h.keyPrefix=h.keyPrefix||s||u.keyPrefix;var O=l.options.keySeparator||".",p;return h.keyPrefix&&Array.isArray(f)?p=f.map(function(c){return"".concat(h.keyPrefix).concat(O).concat(c)}):p=h.keyPrefix?"".concat(h.keyPrefix).concat(O).concat(f):f,l.t(p,h)};return typeof o=="string"?a.lng=o:a.lngs=o,a.ns=i,a.keyPrefix=s,a}},{key:"t",value:function(){var o;return this.translator&&(o=this.translator).translate.apply(o,arguments)}},{key:"exists",value:function(){var o;return this.translator&&(o=this.translator).exists.apply(o,arguments)}},{key:"setDefaultNamespace",value:function(o){this.options.defaultNS=o}},{key:"hasLoadedNamespace",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;var l=this.resolvedLanguage||this.languages[0],a=this.options?this.options.fallbackLng:!1,u=this.languages[this.languages.length-1];if(l.toLowerCase()==="cimode")return!0;var f=function(g,v){var k=i.services.backendConnector.state["".concat(g,"|").concat(v)];return k===-1||k===2};if(s.precheck){var d=s.precheck(this,f);if(d!==void 0)return d}return!!(this.hasResourceBundle(l,o)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||f(l,o)&&(!a||f(u,o)))}},{key:"loadNamespaces",value:function(o,i){var s=this,l=hr();return this.options.ns?(typeof o=="string"&&(o=[o]),o.forEach(function(a){s.options.ns.indexOf(a)<0&&s.options.ns.push(a)}),this.loadResources(function(a){l.resolve(),i&&i(a)}),l):(i&&i(),Promise.resolve())}},{key:"loadLanguages",value:function(o,i){var s=hr();typeof o=="string"&&(o=[o]);var l=this.options.preload||[],a=o.filter(function(u){return l.indexOf(u)<0});return a.length?(this.options.preload=l.concat(a),this.loadResources(function(u){s.resolve(),i&&i(u)}),s):(i&&i(),Promise.resolve())}},{key:"dir",value:function(o){if(o||(o=this.resolvedLanguage||(this.languages&&this.languages.length>0?this.languages[0]:this.language)),!o)return"rtl";var i=["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"],s=this.services&&this.services.languageUtils||new Za(iu());return i.indexOf(s.getLanguagePartFromCode(o))>-1||o.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}},{key:"cloneInstance",value:function(){var o=this,i=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:go,l=ht(ht(ht({},this.options),i),{isClone:!0}),a=new n(l);(i.debug!==void 0||i.prefix!==void 0)&&(a.logger=a.logger.clone(i));var u=["store","services","language"];return u.forEach(function(f){a[f]=o[f]}),a.services=ht({},this.services),a.services.utils={hasLoadedNamespace:a.hasLoadedNamespace.bind(a)},a.translator=new Ja(a.services,a.options),a.translator.on("*",function(f){for(var d=arguments.length,h=new Array(d>1?d-1:0),g=1;g<d;g++)h[g-1]=arguments[g];a.emit.apply(a,[f].concat(h))}),a.init(l,s),a.translator.options=a.options,a.translator.backendConnector.services.utils={hasLoadedNamespace:a.hasLoadedNamespace.bind(a)},a}},{key:"toJSON",value:function(){return{options:this.options,store:this.store,language:this.language,languages:this.languages,resolvedLanguage:this.resolvedLanguage}}}]),n}(tn);qe(oi,"createInstance",function(){var e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},t=arguments.length>1?arguments[1]:void 0;return new oi(e,t)});var ce=oi.createInstance();ce.createInstance=oi.createInstance;ce.createInstance;ce.dir;ce.init;ce.loadResources;ce.reloadResources;ce.use;ce.changeLanguage;ce.getFixedT;ce.t;ce.exists;ce.setDefaultNamespace;ce.hasLoadedNamespace;ce.loadNamespaces;ce.loadLanguages;function ch(e,t){if(e==null)return{};var n={},r=Object.keys(e),o,i;for(i=0;i<r.length;i++)o=r[i],!(t.indexOf(o)>=0)&&(n[o]=e[o]);return n}function Al(e,t){if(e==null)return{};var n=ch(e,t),r,o;if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(o=0;o<i.length;o++)r=i[o],!(t.indexOf(r)>=0)&&Object.prototype.propertyIsEnumerable.call(e,r)&&(n[r]=e[r])}return n}var dh={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},ph=/\s([^'"/\s><]+?)[\s/>]|([^\s=]+)=\s?(".*?"|'.*?')/g;function au(e){var t={type:"tag",name:"",voidElement:!1,attrs:{},children:[]},n=e.match(/<\/?([^\s]+?)[/\s>]/);if(n&&(t.name=n[1],(dh[n[1]]||e.charAt(e.length-2)==="/")&&(t.voidElement=!0),t.name.startsWith("!--"))){var r=e.indexOf("-->");return{type:"comment",comment:r!==-1?e.slice(4,r):""}}for(var o=new RegExp(ph),i=null;(i=o.exec(e))!==null;)if(i[0].trim())if(i[1]){var s=i[1].trim(),l=[s,""];s.indexOf("=")>-1&&(l=s.split("=")),t.attrs[l[0]]=l[1],o.lastIndex--}else i[2]&&(t.attrs[i[2]]=i[3].trim().substring(1,i[3].length-1));return t}var fh=/<[a-zA-Z0-9\-\!\/](?:"[^"]*"|'[^']*'|[^'">])*>/g,hh=/^\s*$/,mh=Object.create(null);function Ac(e,t){switch(t.type){case"text":return e+t.content;case"tag":return e+="<"+t.name+(t.attrs?function(n){var r=[];for(var o in n)r.push(o+'="'+n[o]+'"');return r.length?" "+r.join(" "):""}(t.attrs):"")+(t.voidElement?"/>":">"),t.voidElement?e:e+t.children.reduce(Ac,"")+"</"+t.name+">";case"comment":return e+"<!--"+t.comment+"-->"}}var gh={parse:function(e,t){t||(t={}),t.components||(t.components=mh);var n,r=[],o=[],i=-1,s=!1;if(e.indexOf("<")!==0){var l=e.indexOf("<");r.push({type:"text",content:l===-1?e:e.substring(0,l)})}return e.replace(fh,function(a,u){if(s){if(a!=="</"+n.name+">")return;s=!1}var f,d=a.charAt(1)!=="/",h=a.startsWith("<!--"),g=u+a.length,v=e.charAt(g);if(h){var k=au(a);return i<0?(r.push(k),r):((f=o[i]).children.push(k),r)}if(d&&(i++,(n=au(a)).type==="tag"&&t.components[n.name]&&(n.type="component",s=!0),n.voidElement||s||!v||v==="<"||n.children.push({type:"text",content:e.slice(g,e.indexOf("<",g))}),i===0&&r.push(n),(f=o[i-1])&&f.children.push(n),o[i]=n),(!d||n.voidElement)&&(i>-1&&(n.voidElement||n.name===a.slice(2,-1))&&(i--,n=i===-1?r:o[i]),!s&&v!=="<"&&v)){f=i===-1?r:o[i].children;var O=e.indexOf("<",g),p=e.slice(g,O===-1?void 0:O);hh.test(p)&&(p=" "),(O>-1&&i+f.length>=0||p!==" ")&&f.push({type:"text",content:p})}}),r},stringify:function(e){return e.reduce(function(t,n){return t+Ac("",n)},"")}};function Ho(){if(console&&console.warn){for(var e,t=arguments.length,n=new Array(t),r=0;r<t;r++)n[r]=arguments[r];typeof n[0]=="string"&&(n[0]="react-i18next:: ".concat(n[0])),(e=console).warn.apply(e,n)}}var uu={};function ii(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];typeof t[0]=="string"&&uu[t[0]]||(typeof t[0]=="string"&&(uu[t[0]]=new Date),Ho.apply(void 0,t))}function cu(e,t,n){e.loadNamespaces(t,function(){if(e.isInitialized)n();else{var r=function o(){setTimeout(function(){e.off("initialized",o)},0),n()};e.on("initialized",r)}})}function vh(e,t){var n=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},r=t.languages[0],o=t.options?t.options.fallbackLng:!1,i=t.languages[t.languages.length-1];if(r.toLowerCase()==="cimode")return!0;var s=function(a,u){var f=t.services.backendConnector.state["".concat(a,"|").concat(u)];return f===-1||f===2};return n.bindI18n&&n.bindI18n.indexOf("languageChanging")>-1&&t.services.backendConnector.backend&&t.isLanguageChangingTo&&!s(t.isLanguageChangingTo,e)?!1:!!(t.hasResourceBundle(r,e)||!t.services.backendConnector.backend||t.options.resources&&!t.options.partialBundledLanguages||s(r,e)&&(!o||s(i,e)))}function yh(e,t){var n=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};if(!t.languages||!t.languages.length)return ii("i18n.languages were undefined or empty",t.languages),!0;var r=t.options.ignoreJSONStructure!==void 0;return r?t.hasLoadedNamespace(e,{precheck:function(i,s){if(n.bindI18n&&n.bindI18n.indexOf("languageChanging")>-1&&i.services.backendConnector.backend&&i.isLanguageChangingTo&&!s(i.isLanguageChangingTo,e))return!1}}):vh(e,t,n)}var _h=/&(?:amp|#38|lt|#60|gt|#62|apos|#39|quot|#34|nbsp|#160|copy|#169|reg|#174|hellip|#8230|#x2F|#47);/g,kh={"&amp;":"&","&#38;":"&","&lt;":"<","&#60;":"<","&gt;":">","&#62;":">","&apos;":"'","&#39;":"'","&quot;":'"',"&#34;":'"',"&nbsp;":" ","&#160;":" ","&copy;":"©","&#169;":"©","&reg;":"®","&#174;":"®","&hellip;":"…","&#8230;":"…","&#x2F;":"/","&#47;":"/"},wh=function(t){return kh[t]},Sh=function(t){return t.replace(_h,wh)};function du(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function pu(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?du(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):du(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var Fs={bindI18n:"languageChanged",bindI18nStore:"",transEmptyNodeValue:"",transSupportBasicHtmlNodes:!0,transWrapTextNodes:"",transKeepBasicHtmlNodesFor:["br","strong","i","p"],useSuspense:!0,unescape:Sh};function Oh(){var e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};Fs=pu(pu({},Fs),e)}function zc(){return Fs}var Mc;function Nh(e){Mc=e}function zl(){return Mc}var Eh=["format"],xh=["children","count","parent","i18nKey","context","tOptions","values","defaults","components","ns","i18n","t","shouldUnescape"];function fu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ie(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?fu(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):fu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Zi(e,t){if(!e)return!1;var n=e.props?e.props.children:e.children;return t?n.length>0:!!n}function es(e){return e?e.props?e.props.children:e.children:[]}function Ph(e){return Object.prototype.toString.call(e)!=="[object Array]"?!1:e.every(function(t){return D.isValidElement(t)})}function Er(e){return Array.isArray(e)?e:[e]}function Ch(e,t){var n=ie({},t);return n.props=Object.assign(e.props,t.props),n}function $c(e,t){if(!e)return"";var n="",r=Er(e),o=t.transSupportBasicHtmlNodes&&t.transKeepBasicHtmlNodesFor?t.transKeepBasicHtmlNodesFor:[];return r.forEach(function(i,s){if(typeof i=="string")n+="".concat(i);else if(D.isValidElement(i)){var l=Object.keys(i.props).length,a=o.indexOf(i.type)>-1,u=i.props.children;if(!u&&a&&l===0)n+="<".concat(i.type,"/>");else if(!u&&(!a||l!==0))n+="<".concat(s,"></").concat(s,">");else if(i.props.i18nIsDynamicList)n+="<".concat(s,"></").concat(s,">");else if(a&&l===1&&typeof u=="string")n+="<".concat(i.type,">").concat(u,"</").concat(i.type,">");else{var f=$c(u,t);n+="<".concat(s,">").concat(f,"</").concat(s,">")}}else if(i===null)Ho("Trans: the passed in value is invalid - seems you passed in a null child.");else if(fe(i)==="object"){var d=i.format,h=Al(i,Eh),g=Object.keys(h);if(g.length===1){var v=d?"".concat(g[0],", ").concat(d):g[0];n+="{{".concat(v,"}}")}else Ho("react-i18next: the passed in object contained more than one variable - the object should look like {{ value, format }} where format is optional.",i)}else Ho("Trans: the passed in value is invalid - seems you passed in a variable like {number} - please pass in variables for interpolation as full objects like {{number}}.",i)}),n}function Rh(e,t,n,r,o,i){if(t==="")return[];var s=r.transKeepBasicHtmlNodesFor||[],l=t&&new RegExp(s.join("|")).test(t);if(!e&&!l)return[t];var a={};function u(O){var p=Er(O);p.forEach(function(c){typeof c!="string"&&(Zi(c)?u(es(c)):fe(c)==="object"&&!D.isValidElement(c)&&Object.assign(a,c))})}u(e);var f=gh.parse("<0>".concat(t,"</0>")),d=ie(ie({},a),o);function h(O,p,c){var m=es(O),y=v(m,p.children,c);return Ph(m)&&y.length===0?m:y}function g(O,p,c,m,y){O.dummy&&(O.children=p),c.push(D.cloneElement(O,ie(ie({},O.props),{},{key:m}),y?void 0:p))}function v(O,p,c){var m=Er(O),y=Er(p);return y.reduce(function(S,w,E){var x=w.children&&w.children[0]&&w.children[0].content&&n.services.interpolator.interpolate(w.children[0].content,d,n.language);if(w.type==="tag"){var V=m[parseInt(w.name,10)];!V&&c.length===1&&c[0][w.name]&&(V=c[0][w.name]),V||(V={});var P=Object.keys(w.attrs).length!==0?Ch({props:w.attrs},V):V,K=D.isValidElement(P),Ce=K&&Zi(w,!0)&&!w.voidElement,Ot=l&&fe(P)==="object"&&P.dummy&&!K,Ze=fe(e)==="object"&&e!==null&&Object.hasOwnProperty.call(e,w.name);if(typeof P=="string"){var Tn=n.services.interpolator.interpolate(P,d,n.language);S.push(Tn)}else if(Zi(P)||Ce){var ft=h(P,w,c);g(P,ft,S,E)}else if(Ot){var et=v(m,w.children,c);S.push(D.cloneElement(P,ie(ie({},P.props),{},{key:E}),et))}else if(Number.isNaN(parseFloat(w.name)))if(Ze){var R=h(P,w,c);g(P,R,S,E,w.voidElement)}else if(r.transSupportBasicHtmlNodes&&s.indexOf(w.name)>-1)if(w.voidElement)S.push(D.createElement(w.name,{key:"".concat(w.name,"-").concat(E)}));else{var b=v(m,w.children,c);S.push(D.createElement(w.name,{key:"".concat(w.name,"-").concat(E)},b))}else if(w.voidElement)S.push("<".concat(w.name," />"));else{var F=v(m,w.children,c);S.push("<".concat(w.name,">").concat(F,"</").concat(w.name,">"))}else if(fe(P)==="object"&&!K){var A=w.children[0]?x:null;A&&S.push(A)}else w.children.length===1&&x?S.push(D.cloneElement(P,ie(ie({},P.props),{},{key:E}),x)):S.push(D.cloneElement(P,ie(ie({},P.props),{},{key:E})))}else if(w.type==="text"){var q=r.transWrapTextNodes,Nt=i?r.unescape(n.services.interpolator.interpolate(w.content,d,n.language)):n.services.interpolator.interpolate(w.content,d,n.language);q?S.push(D.createElement(q,{key:"".concat(w.name,"-").concat(E)},Nt)):S.push(Nt)}return S},[])}var k=v([{dummy:!0,children:e||[]}],f,Er(e||[]));return es(k[0])}function Th(e){var t=e.children,n=e.count,r=e.parent,o=e.i18nKey,i=e.context,s=e.tOptions,l=s===void 0?{}:s,a=e.values,u=e.defaults,f=e.components,d=e.ns,h=e.i18n,g=e.t,v=e.shouldUnescape,k=Al(e,xh),O=h||zl();if(!O)return ii("You will need to pass in an i18next instance by using i18nextReactModule"),t;var p=g||O.t.bind(O)||function(Ce){return Ce};i&&(l.context=i);var c=ie(ie({},zc()),O.options&&O.options.react),m=d||p.ns||O.options&&O.options.defaultNS;m=typeof m=="string"?[m]:m||["translation"];var y=u||$c(t,c)||c.transEmptyNodeValue||o,S=c.hashTransKey,w=o||(S?S(y):y),E=a?l.interpolation:{interpolation:ie(ie({},l.interpolation),{},{prefix:"#$?",suffix:"?$#"})},x=ie(ie(ie(ie({},l),{},{count:n},a),E),{},{defaultValue:y,ns:m}),V=w?p(w,x):y,P=Rh(f||t,V,O,c,x,v),K=r!==void 0?r:c.defaultTransParent;return K?D.createElement(K,k,P):P}var Lh={type:"3rdParty",init:function(t){Oh(t.options.react),Nh(t)}},Bc=D.createContext(),Ih=function(){function e(){ct(this,e),this.usedNamespaces={}}return dt(e,[{key:"addUsedNamespaces",value:function(n){var r=this;n.forEach(function(o){r.usedNamespaces[o]||(r.usedNamespaces[o]=!0)})}},{key:"getUsedNamespaces",value:function(){return Object.keys(this.usedNamespaces)}}]),e}(),bh=["children","count","parent","i18nKey","context","tOptions","values","defaults","components","ns","i18n","t","shouldUnescape"];function hu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function Vh(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?hu(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):hu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function W(e){var t=e.children,n=e.count,r=e.parent,o=e.i18nKey,i=e.context,s=e.tOptions,l=s===void 0?{}:s,a=e.values,u=e.defaults,f=e.components,d=e.ns,h=e.i18n,g=e.t,v=e.shouldUnescape,k=Al(e,bh),O=D.useContext(Bc)||{},p=O.i18n,c=O.defaultNS,m=h||p||zl(),y=g||m&&m.t.bind(m);return Th(Vh({children:t,count:n,parent:r,i18nKey:o,context:i,tOptions:l,values:a,defaults:u,components:f,ns:d||y&&y.ns||c||m&&m.options&&m.options.defaultNS,i18n:m,t:g,shouldUnescape:v},k))}function Fh(e,t){var n=e==null?null:typeof Symbol<"u"&&e[Symbol.iterator]||e["@@iterator"];if(n!=null){var r,o,i,s,l=[],a=!0,u=!1;try{if(i=(n=n.call(e)).next,t===0){if(Object(n)!==n)return;a=!1}else for(;!(a=(r=i.call(n)).done)&&(l.push(r.value),l.length!==t);a=!0);}catch(f){u=!0,o=f}finally{try{if(!a&&n.return!=null&&(s=n.return(),Object(s)!==s))return}finally{if(u)throw o}}return l}}function Dh(e,t){return bc(e)||Fh(e,t)||Vc(e,t)||Fc()}function mu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ts(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?mu(Object(n),!0).forEach(function(r){qe(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):mu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var jh=function(t,n){var r=D.useRef();return D.useEffect(function(){r.current=n?r.current:t},[t,n]),r.current};function J(e){var t=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},n=t.i18n,r=D.useContext(Bc)||{},o=r.i18n,i=r.defaultNS,s=n||o||zl();if(s&&!s.reportNamespaces&&(s.reportNamespaces=new Ih),!s){ii("You will need to pass in an i18next instance by using initReactI18next");var l=function(V,P){return typeof P=="string"?P:P&&fe(P)==="object"&&typeof P.defaultValue=="string"?P.defaultValue:Array.isArray(V)?V[V.length-1]:V},a=[l,{},!1];return a.t=l,a.i18n={},a.ready=!1,a}s.options.react&&s.options.react.wait!==void 0&&ii("It seems you are still using the old wait option, you may migrate to the new useSuspense behaviour.");var u=ts(ts(ts({},zc()),s.options.react),t),f=u.useSuspense,d=u.keyPrefix,h=e||i||s.options&&s.options.defaultNS;h=typeof h=="string"?[h]:h||["translation"],s.reportNamespaces.addUsedNamespaces&&s.reportNamespaces.addUsedNamespaces(h);var g=(s.isInitialized||s.initializedStoreOnce)&&h.every(function(x){return yh(x,s,u)});function v(){return s.getFixedT(null,u.nsMode==="fallback"?h:h[0],d)}var k=D.useState(v),O=Dh(k,2),p=O[0],c=O[1],m=h.join(),y=jh(m),S=D.useRef(!0);D.useEffect(function(){var x=u.bindI18n,V=u.bindI18nStore;S.current=!0,!g&&!f&&cu(s,h,function(){S.current&&c(v)}),g&&y&&y!==m&&S.current&&c(v);function P(){S.current&&c(v)}return x&&s&&s.on(x,P),V&&s&&s.store.on(V,P),function(){S.current=!1,x&&s&&x.split(" ").forEach(function(K){return s.off(K,P)}),V&&s&&V.split(" ").forEach(function(K){return s.store.off(K,P)})}},[s,m]);var w=D.useRef(!0);D.useEffect(function(){S.current&&!w.current&&c(v),w.current=!1},[s,d]);var E=[p,s,g];if(E.t=p,E.i18n=s,E.ready=g,g||!g&&!f)return E;throw new Promise(function(x){cu(s,h,function(){x()})})}const Uh={installationInstructions:"安装说明",getStarted:"入门指南",notebooks:"笔记本电脑",troubleshooting:"故障排除指南"},Ah={copy:"复制",copied:"已复制"},zh={distributionLinks:Uh,copyCommand:Ah},Mh={titles:{recommended:"推荐",latestLTS:"最新的 LTS 版本",hddlSupport:"包含 NCS2/HDDL 支持",previousLTS:"旧 LTS 版本",LTS:"LTS"}},$h={devTools:{title:"开发工具",subtitle:"开发和优化深度学习模型的最佳选择。"},runtime:{title:"运行时",subtitle:"您已拥有模型，并且希望对其运行推理。"}},Bh={common:{requirements:{win10:"Windows* 10（64 位）",win11:"Windows® 11（推荐用于第 12 代英特尔® 酷睿™ 处理器）",ubuntu18:"Ubuntu 18.04 长期支持 (LTS)（64 位）",ubuntu20:"Ubuntu 20.04 长期支持 (LTS)（64 位）",ubuntu22:"Ubuntu 22.04 长期支持 (LTS)（64 位）",redHat8:"Red Hat Enterprise Linux 8（64 位）",centOS7:"CentOS 7（64 位）",armPluginFootnote:"搭载 M1 处理器的 Macintosh 计算机针对 OpenVINO 工具套件 2022.3.0 LTS 及更高版本使用 ARM 插件。此插件是社区提供支持的。由于英特尔不提供任何支持，它不适用 LTS 的两年支持政策。"},modules:{runtime:"运行时/推理引擎",modelOptimizer:"模型优化器",benchmarkTool:"基准测试工具",accuracyChecker:"准确性检查程序",annotationConverter:"批注转换器",pot:"训练后优化工具",openCVPython:"OpenCV Python",omz:"模型下载程序和其他 Open Model Zoo 工具"}},openVINOArchives:"OpenVINO 归档文件",source:"源",offlineInstaller:"离线安装程序",recommendedOption:"推荐选项",pythonAPIOnly:"仅限 Python API",CAPIOnly:"仅限 C++ API"},Kh={versions:Mh,environment:$h,distributions:Bh},Hh={titles:{envinronment:"环境",os:"操作系统",version:"版本",distribution:"分发",frameworks:"框架"},prevReleases:"旧版",devCloud:{label:"在英特尔® Developer Cloud 中试用",href:"https://www.intel.cn/content/www/cn/zh/developer/tools/devcloud/overview.html"},nncf:"高级优化工具可单独购买：<1>了解 NNCF</1>",simplifiedInstallOptions:"我们简化了安装选项（例如：运行时和开发的整合）: <1>了解详情</1>"},Wh={learnMore:"了解详情",title:"系统要求",fullRequirements:{text:"如需查看支持硬件的完整列表，请参见<1>系统要求</1>。",href:"https://www.intel.cn/content/www/cn/zh/developer/tools/openvino-toolkit/system-requirements.html"},supportedOS:"支持的操作系统",pythonVersion:"Python 版本（64 位）",dockerDocs:"请访问我们<1>有关 Docker 安装的文档</1>。"},Yh={apt:{title:"从 APT 安装",downloadTitle:"下载",steps:{download:"<0>第 1 步：</0>下载 <2>{{filename}}</2>。您也可以使用以下命令",addKey:"<0>第 2 步：</0>将此密钥添加到系统密钥环。",addRepository:"<0>第 3 步：</0>通过以下命令添加存储库。",updateList:"<0>第 4 步：</0>使用更新命令更新软件包列表。",verify:"<0>第 5 步：</0>验证 APT 存储库的设置是否正确。使用 apt-cache 命令查看包含所有可用 OpenVINO 程序包和组件的列表",install:"<0>第 6 步：</0>安装 OpenVINO 运行时。"}},download:{title:"下载",download:"下载",downloadArchives:"下载归档文件",gotoDocker:"转到 Docker"},githubGitee:{openSource:"使用开源存储库，在发布版本之间获取最新版本，并为开源社区做出贡献。",installationInstructions:"安装说明",githubRepository:"OpenVINO 工具套件 GitHub 存储库",giteeRepository:"OpenVINO 工具套件 Gitee 存储库",titleGitee:"基于 Gitee 上的源构建",titleGithub:"基于 GitHub 上的源构建",steps:{useGitClone:"<0>第 1 步：</0>运用 Git 克隆 OpenVINO 工具套件存储库。",buildInstructions:"<0>第 2 步：</0>按照<2>说明从源代码构建</2>"}},pip:{downloadRuntime:"下载 OpenVINO™ 运行时",downloadDevtools:"下载 OpenVINO™ 开发工具",install:"从 PIP 安装",steps:{createVenv:"<0>第 1 步：</0>创建虚拟环境。",activateVenv:"<0>第 2 步：</0>激活虚拟环境。",upgradePip:"<0>第 3 步：</0>将 pip 升级到最新版本。",install:"<0>第 4 步：</0>下载并安装程序包。"}},conda:{download:"下载 OpenVINO™ 运行时",install:"从 Conda 安装",steps:{createEnv:"<0>第 1 步：</0>创建 Anaconda 环境（以 Python 3.10 为例）",activateEnv:"<0>第 2 步：</0>激活 Anaconda 环境",update:"<0>第 3 步：</0>将 Anaconda 环境更新至最新版本",install:"<0>第 4 步：</0>下载并安装程序包。"}},brew:{download:"下载 OpenVINO™ 运行时",install:"从 Homebrew 安装",steps:{install:"下载并安装程序包"}},vcpkg:{install:"从 vcpkg 安装",steps:{install:"下载并安装程序包"}},yum:{title:"从 YUM 安装",downloadTitle:"下载",steps:{createRepoFile:"<0>第 1 步：</0>以正常用户身份在 /tmp 目录中创建 YUM 存储库文件。",moveFile:"<0>第 2 步：</0>将新的 openvino-{{year}}.repo 文件移动到 YUM 配置目录 <4>{{directory}}</4>。",verify:"<0>第 3 步：</0>使用以下命令验证新建存储库的设置是否正确。",install:"<0>第 4 步：</0>安装 OpenVINO 运行时。"}}},Gh={frameworks:{install:"安装框架",tooltip:`我们假设所需的框架已经安装到您的环境中。如果没有，您可以使用 OpenVINO 工具套件安装该框架。
-可用框架：Caffe、Kaldi、Apache MXNet、PyTorch、ONNX、TensorFlow。`},selectorForm:{titles:{install:"安装"}},distributions:{aptYum:{followInstructions:"按照<1>安装说明进行操作</1>"},download:{useFollowingLink:"使用下方链接："}}},Qh={selectorForm:Hh,systemRequirements:Wh,distributions:Yh,compact:Gh},qh={translation:Qh,common:zh,core:Kh},Xh={installationInstructions:"Installation Instructions",getStarted:"Get Started Guide",notebooks:"Notebooks",troubleshooting:"Troubleshooting Guide"},Jh={copy:"Copy",copied:"Copied"},Zh={distributionLinks:Xh,copyCommand:Jh},em={titles:{recommended:"Recommended",latestLTS:"Latest LTS release",hddlSupport:"Includes NCS2/HDDL support",previousLTS:"Previous LTS release",LTS:"LTS"}},tm={devTools:{title:"Development Tools",subtitle:"Your best option to develop and optimize deep learning models."},runtime:{title:"Runtime",subtitle:"You already have a model and want to run inference on it."}},nm={common:{requirements:{win10:"Windows® 10 (64 bit)",win11:"Windows® 11 (recommended for 12th Generation Intel® Core™ processors)",ubuntu18:"Ubuntu 18.04 long-term support (LTS) (64 bit)",ubuntu20:"Ubuntu 20.04 long-term support (LTS) (64 bit)",ubuntu22:"Ubuntu 22.04 long-term support (LTS) (64 bit)",redHat8:"Red Hat Enterprise Linux 8 (64 bit)",centOS7:"CentOS 7 (64 bit)",armPluginFootnote:"Macintosh computers using the M1 processor use the ARM Plug-in on OpenVINO toolkit 2022.3.0 LTS and later. This plugin is community supported; no support is provided by Intel and it doesn't fall under the LTS 2 year support policy."},modules:{runtime:"Runtime/Inference Engine",modelOptimizer:"Model Optimizer",benchmarkTool:"Benchmark Tool",accuracyChecker:"Accuracy Checker",annotationConverter:"Annotation Converter",pot:"Post-Training Optimization Tool",openCVPython:"OpenCV Python",omz:"Model Downloader and other Open Model Zoo Tools"}},openVINOArchives:"OpenVINO Archives",source:"Source",offlineInstaller:"Offline Installer",recommendedOption:"Recommended option",pythonAPIOnly:"Python API only",CAPIOnly:"C++ API only"},rm={versions:em,environment:tm,distributions:nm},om={titles:{envinronment:"Environment",os:"Operating System",version:"Version",distribution:"Distribution",frameworks:"Frameworks"},prevReleases:"Previous Releases",devCloud:{label:"Try in the Intel® Developer Cloud",href:"https://www.intel.com/content/www/us/en/developer/tools/devcloud/overview.html"},nncf:"Advanced Optimization tool available separately: <1>Learn about NNCF</1>",simplifiedInstallOptions:"We have simplified the install options (example: consolidation of Runtime and Development Tools): <1>Learn more</1>"},im={learnMore:"Learn more",title:"System Requirements",fullRequirements:{text:"For a complete list of supported hardware, see the <1>system requirements</1>.",href:"https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html"},supportedOS:"Supported Operating System",pythonVersion:"Python Version (64 bit)",dockerDocs:"Visit our <1>documentation on Docker Installation</1>."},sm={apt:{title:"Install with APT",downloadTitle:"Download",steps:{download:"<0>Step 1:</0> Download the <2>{{filename}}</2>. You can also use the following command",addKey:"<0>Step 2:</0> Add this key to the system keyring",addRepository:"<0>Step 3:</0> Add the repository via the following command",updateList:"<0>Step 4:</0> Update the list of packages via the update command",verify:"<0>Step 5:</0> Verify that the APT repository is properly set up. Run the apt-cache command to see a list of all available OpenVINO packages and components",install:"<0>Step 6:</0> Install OpenVINO Runtime"}},download:{title:"Download",download:"Download",downloadArchives:"Download Archives",gotoDocker:"Go to Docker"},githubGitee:{openSource:"Use an open source repository, get the most recent version in between releases, and contribute to the open source community.",installationInstructions:"Installation instructions",githubRepository:"OpenVINO Toolkit GitHub Repository",giteeRepository:"OpenVINO Toolkit Gitee Repository",titleGitee:"Build from Source on Gitee",titleGithub:"Build from Source on GitHub",steps:{useGitClone:"<0>Step 1:</0> Use Git to clone the OpenVINO toolkit repository",buildInstructions:"<0>Step 2:</0> Follow the <2>instructions to build from source</2>"}},pip:{downloadRuntime:"Download OpenVINO™ Runtime",downloadDevtools:"Download OpenVINO™ Development Tools",install:"Install with PIP",steps:{createVenv:"<0>Step 1:</0> Create virtual environment",activateVenv:"<0>Step 2:</0> Activate virtual environment",upgradePip:"<0>Step 3:</0> Upgrade pip to latest version",install:"<0>Step 4:</0> Download and install the package"}},conda:{download:"Download OpenVINO™ Runtime",install:"Install with Conda",steps:{createEnv:"<0>Step 1:</0> Create the Anaconda environment (Python 3.10 used as an example)",activateEnv:"<0>Step 2:</0> Activate the Anaconda environment",update:"<0>Step 3:</0> Update the Anaconda environment to the latest version",install:"<0>Step 4:</0> Download and install the package"}},brew:{download:"Download OpenVINO™ Runtime",install:"Install with Homebrew",steps:{install:"Download and install the package"}},vcpkg:{install:"Install with vcpkg",steps:{install:"Download and install the package"}},yum:{title:"Install with YUM",downloadTitle:"Download",steps:{createRepoFile:"<0>Step 1:</0> Create the YUM repo file in the /tmp directory as a normal user",moveFile:"<0>Step 2:</0> Move the new openvino-{{year}}.repo file to the YUM configuration directory <4>{{directory}}</4>",verify:"<0>Step 3:</0> Verify that the new repo is properly set up by running the following command",install:"<0>Step 4:</0> Install OpenVINO Runtime"}}},lm={frameworks:{install:"Install Frameworks",tooltip:`We assume that needed frameworks are already installed in your environment. If not, you can install them with OpenVINO toolkit.
-Available frameworks: Caffe, Kaldi, Apache MXNet, PyTorch, ONNX, TensorFlow.`},selectorForm:{titles:{install:"Install"}},distributions:{aptYum:{followInstructions:"Follow the <1>Installation Instructions</1>"},download:{useFollowingLink:"Use the following link:"}}},am={selectorForm:om,systemRequirements:im,distributions:sm,compact:lm},um={translation:am,common:Zh,core:rm},tr={en:um,cn:qh},Ds="en";if(!(Ds in tr))throw new Error(`'${Ds}' language is not supported`);ce.use(Lh).init({debug:!1,resources:tr,lng:Ds,fallbackLng:"en",interpolation:{escapeValue:!1},supportedLngs:Object.keys(tr),returnObjects:!1,returnNull:!1});ce.t;var js={},Us={},cm={get exports(){return Us},set exports(e){Us=e}},$e={},As={},dm={get exports(){return As},set exports(e){As=e}},Kc={};/**
+`;const Ff=()=>{const e={type:"size",height:document.body.offsetHeight};window.parent.postMessage(e)};new ResizeObserver(Ff).observe(document.body);function fe(e){return fe=typeof Symbol=="function"&&typeof Symbol.iterator=="symbol"?function(t){return typeof t}:function(t){return t&&typeof Symbol=="function"&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},fe(e)}function dt(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function jf(e,t){if(fe(e)!=="object"||e===null)return e;var n=e[Symbol.toPrimitive];if(n!==void 0){var r=n.call(e,t||"default");if(fe(r)!=="object")return r;throw new TypeError("@@toPrimitive must return a primitive value.")}return(t==="string"?String:Number)(e)}function Dc(e){var t=jf(e,"string");return fe(t)==="symbol"?t:String(t)}function Ha(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,Dc(r.key),r)}}function pt(e,t,n){return t&&Ha(e.prototype,t),n&&Ha(e,n),Object.defineProperty(e,"prototype",{writable:!1}),e}function Gt(e){if(e===void 0)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}function Vs(e,t){return Vs=Object.setPrototypeOf?Object.setPrototypeOf.bind():function(r,o){return r.__proto__=o,r},Vs(e,t)}function bi(e,t){if(typeof t!="function"&&t!==null)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),Object.defineProperty(e,"prototype",{writable:!1}),t&&Vs(e,t)}function oo(e,t){if(t&&(fe(t)==="object"||typeof t=="function"))return t;if(t!==void 0)throw new TypeError("Derived constructors may only return object or undefined");return Gt(e)}function St(e){return St=Object.setPrototypeOf?Object.getPrototypeOf.bind():function(n){return n.__proto__||Object.getPrototypeOf(n)},St(e)}function Je(e,t,n){return t=Dc(t),t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function Uc(e){if(Array.isArray(e))return e}function Df(e){if(typeof Symbol<"u"&&e[Symbol.iterator]!=null||e["@@iterator"]!=null)return Array.from(e)}function Wa(e,t){(t==null||t>e.length)&&(t=e.length);for(var n=0,r=new Array(t);n<t;n++)r[n]=e[n];return r}function Ac(e,t){if(e){if(typeof e=="string")return Wa(e,t);var n=Object.prototype.toString.call(e).slice(8,-1);if(n==="Object"&&e.constructor&&(n=e.constructor.name),n==="Map"||n==="Set")return Array.from(e);if(n==="Arguments"||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n))return Wa(e,t)}}function zc(){throw new TypeError(`Invalid attempt to destructure non-iterable instance.
+In order to be iterable, non-array objects must have a [Symbol.iterator]() method.`)}function Uf(e){return Uc(e)||Df(e)||Ac(e)||zc()}function Ya(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function Ga(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Ya(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Ya(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var Af={type:"logger",log:function(t){this.output("log",t)},warn:function(t){this.output("warn",t)},error:function(t){this.output("error",t)},output:function(t,n){console&&console[t]&&console[t].apply(console,n)}},zf=function(){function e(t){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};dt(this,e),this.init(t,n)}return pt(e,[{key:"init",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.prefix=r.prefix||"i18next:",this.logger=n||Af,this.options=r,this.debug=r.debug}},{key:"setDebug",value:function(n){this.debug=n}},{key:"log",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"log","",!0)}},{key:"warn",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"warn","",!0)}},{key:"error",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"error","")}},{key:"deprecate",value:function(){for(var n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return this.forward(r,"warn","WARNING DEPRECATED: ",!0)}},{key:"forward",value:function(n,r,o,i){return i&&!this.debug?null:(typeof n[0]=="string"&&(n[0]="".concat(o).concat(this.prefix," ").concat(n[0])),this.logger[r](n))}},{key:"create",value:function(n){return new e(this.logger,Ga(Ga({},{prefix:"".concat(this.prefix,":").concat(n,":")}),this.options))}},{key:"clone",value:function(n){return n=n||this.options,n.prefix=n.prefix||this.prefix,new e(this.logger,n)}}]),e}(),yt=new zf,rn=function(){function e(){dt(this,e),this.observers={}}return pt(e,[{key:"on",value:function(n,r){var o=this;return n.split(" ").forEach(function(i){o.observers[i]=o.observers[i]||[],o.observers[i].push(r)}),this}},{key:"off",value:function(n,r){if(this.observers[n]){if(!r){delete this.observers[n];return}this.observers[n]=this.observers[n].filter(function(o){return o!==r})}}},{key:"emit",value:function(n){for(var r=arguments.length,o=new Array(r>1?r-1:0),i=1;i<r;i++)o[i-1]=arguments[i];if(this.observers[n]){var s=[].concat(this.observers[n]);s.forEach(function(a){a.apply(void 0,o)})}if(this.observers["*"]){var l=[].concat(this.observers["*"]);l.forEach(function(a){a.apply(a,[n].concat(o))})}}}]),e}();function hr(){var e,t,n=new Promise(function(r,o){e=r,t=o});return n.resolve=e,n.reject=t,n}function qa(e){return e==null?"":""+e}function Mf(e,t,n){e.forEach(function(r){t[r]&&(n[r]=t[r])})}function Ul(e,t,n){function r(l){return l&&l.indexOf("###")>-1?l.replace(/###/g,"."):l}function o(){return!e||typeof e=="string"}for(var i=typeof t!="string"?[].concat(t):t.split(".");i.length>1;){if(o())return{};var s=r(i.shift());!e[s]&&n&&(e[s]=new n),Object.prototype.hasOwnProperty.call(e,s)?e=e[s]:e={}}return o()?{}:{obj:e,k:r(i.shift())}}function Qa(e,t,n){var r=Ul(e,t,Object),o=r.obj,i=r.k;o[i]=n}function $f(e,t,n,r){var o=Ul(e,t,Object),i=o.obj,s=o.k;i[s]=i[s]||[],r&&(i[s]=i[s].concat(n)),r||i[s].push(n)}function ri(e,t){var n=Ul(e,t),r=n.obj,o=n.k;if(r)return r[o]}function Xa(e,t,n){var r=ri(e,n);return r!==void 0?r:ri(t,n)}function Mc(e,t,n){for(var r in t)r!=="__proto__"&&r!=="constructor"&&(r in e?typeof e[r]=="string"||e[r]instanceof String||typeof t[r]=="string"||t[r]instanceof String?n&&(e[r]=t[r]):Mc(e[r],t[r],n):e[r]=t[r]);return e}function Vn(e){return e.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}var Bf={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;","/":"&#x2F;"};function Kf(e){return typeof e=="string"?e.replace(/[&<>"'\/]/g,function(t){return Bf[t]}):e}var Li=typeof window<"u"&&window.navigator&&typeof window.navigator.userAgentData>"u"&&window.navigator.userAgent&&window.navigator.userAgent.indexOf("MSIE")>-1,Hf=[" ",",","?","!",";"];function Wf(e,t,n){t=t||"",n=n||"";var r=Hf.filter(function(l){return t.indexOf(l)<0&&n.indexOf(l)<0});if(r.length===0)return!0;var o=new RegExp("(".concat(r.map(function(l){return l==="?"?"\\?":l}).join("|"),")")),i=!o.test(e);if(!i){var s=e.indexOf(n);s>0&&!o.test(e.substring(0,s))&&(i=!0)}return i}function Ja(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function mo(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Ja(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Ja(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Yf(e){var t=Gf();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function Gf(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}function $c(e,t){var n=arguments.length>2&&arguments[2]!==void 0?arguments[2]:".";if(e){if(e[t])return e[t];for(var r=t.split(n),o=e,i=0;i<r.length;++i){if(!o||typeof o[r[i]]=="string"&&i+1<r.length)return;if(o[r[i]]===void 0){for(var s=2,l=r.slice(i,i+s).join(n),a=o[l];a===void 0&&r.length>i+s;)s++,l=r.slice(i,i+s).join(n),a=o[l];if(a===void 0)return;if(a===null)return null;if(t.endsWith(l)){if(typeof a=="string")return a;if(l&&typeof a[l]=="string")return a[l]}var u=r.slice(i+s).join(n);return u?$c(a,u,n):void 0}o=o[r[i]]}return o}}var qf=function(e){bi(n,e);var t=Yf(n);function n(r){var o,i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{ns:["translation"],defaultNS:"translation"};return dt(this,n),o=t.call(this),Li&&rn.call(Gt(o)),o.data=r||{},o.options=i,o.options.keySeparator===void 0&&(o.options.keySeparator="."),o.options.ignoreJSONStructure===void 0&&(o.options.ignoreJSONStructure=!0),o}return pt(n,[{key:"addNamespaces",value:function(o){this.options.ns.indexOf(o)<0&&this.options.ns.push(o)}},{key:"removeNamespaces",value:function(o){var i=this.options.ns.indexOf(o);i>-1&&this.options.ns.splice(i,1)}},{key:"getResource",value:function(o,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{},a=l.keySeparator!==void 0?l.keySeparator:this.options.keySeparator,u=l.ignoreJSONStructure!==void 0?l.ignoreJSONStructure:this.options.ignoreJSONStructure,f=[o,i];s&&typeof s!="string"&&(f=f.concat(s)),s&&typeof s=="string"&&(f=f.concat(a?s.split(a):s)),o.indexOf(".")>-1&&(f=o.split("."));var d=ri(this.data,f);return d||!u||typeof s!="string"?d:$c(this.data&&this.data[o]&&this.data[o][i],s,a)}},{key:"addResource",value:function(o,i,s,l){var a=arguments.length>4&&arguments[4]!==void 0?arguments[4]:{silent:!1},u=this.options.keySeparator;u===void 0&&(u=".");var f=[o,i];s&&(f=f.concat(u?s.split(u):s)),o.indexOf(".")>-1&&(f=o.split("."),l=i,i=f[1]),this.addNamespaces(i),Qa(this.data,f,l),a.silent||this.emit("added",o,i,s,l)}},{key:"addResources",value:function(o,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{silent:!1};for(var a in s)(typeof s[a]=="string"||Object.prototype.toString.apply(s[a])==="[object Array]")&&this.addResource(o,i,a,s[a],{silent:!0});l.silent||this.emit("added",o,i,s)}},{key:"addResourceBundle",value:function(o,i,s,l,a){var u=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{silent:!1},f=[o,i];o.indexOf(".")>-1&&(f=o.split("."),l=s,s=i,i=f[1]),this.addNamespaces(i);var d=ri(this.data,f)||{};l?Mc(d,s,a):d=mo(mo({},d),s),Qa(this.data,f,d),u.silent||this.emit("added",o,i,s)}},{key:"removeResourceBundle",value:function(o,i){this.hasResourceBundle(o,i)&&delete this.data[o][i],this.removeNamespaces(i),this.emit("removed",o,i)}},{key:"hasResourceBundle",value:function(o,i){return this.getResource(o,i)!==void 0}},{key:"getResourceBundle",value:function(o,i){return i||(i=this.options.defaultNS),this.options.compatibilityAPI==="v1"?mo(mo({},{}),this.getResource(o,i)):this.getResource(o,i)}},{key:"getDataByLanguage",value:function(o){return this.data[o]}},{key:"hasLanguageSomeTranslations",value:function(o){var i=this.getDataByLanguage(o),s=i&&Object.keys(i)||[];return!!s.find(function(l){return i[l]&&Object.keys(i[l]).length>0})}},{key:"toJSON",value:function(){return this.data}}]),n}(rn),Bc={processors:{},addPostProcessor:function(t){this.processors[t.name]=t},handle:function(t,n,r,o,i){var s=this;return t.forEach(function(l){s.processors[l]&&(n=s.processors[l].process(n,r,o,i))}),n}};function Za(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function we(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Za(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Za(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Qf(e){var t=Xf();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function Xf(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}var eu={},tu=function(e){bi(n,e);var t=Qf(n);function n(r){var o,i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};return dt(this,n),o=t.call(this),Li&&rn.call(Gt(o)),Mf(["resourceStore","languageUtils","pluralResolver","interpolator","backendConnector","i18nFormat","utils"],r,Gt(o)),o.options=i,o.options.keySeparator===void 0&&(o.options.keySeparator="."),o.logger=yt.create("translator"),o}return pt(n,[{key:"changeLanguage",value:function(o){o&&(this.language=o)}},{key:"exists",value:function(o){var i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}};if(o==null)return!1;var s=this.resolve(o,i);return s&&s.res!==void 0}},{key:"extractFromKey",value:function(o,i){var s=i.nsSeparator!==void 0?i.nsSeparator:this.options.nsSeparator;s===void 0&&(s=":");var l=i.keySeparator!==void 0?i.keySeparator:this.options.keySeparator,a=i.ns||this.options.defaultNS||[],u=s&&o.indexOf(s)>-1,f=!this.options.userDefinedKeySeparator&&!i.keySeparator&&!this.options.userDefinedNsSeparator&&!i.nsSeparator&&!Wf(o,s,l);if(u&&!f){var d=o.match(this.interpolator.nestingRegexp);if(d&&d.length>0)return{key:o,namespaces:a};var h=o.split(s);(s!==l||s===l&&this.options.ns.indexOf(h[0])>-1)&&(a=h.shift()),o=h.join(l)}return typeof a=="string"&&(a=[a]),{key:o,namespaces:a}}},{key:"translate",value:function(o,i,s){var l=this;if(fe(i)!=="object"&&this.options.overloadTranslationOptionHandler&&(i=this.options.overloadTranslationOptionHandler(arguments)),i||(i={}),o==null)return"";Array.isArray(o)||(o=[String(o)]);var a=i.returnDetails!==void 0?i.returnDetails:this.options.returnDetails,u=i.keySeparator!==void 0?i.keySeparator:this.options.keySeparator,f=this.extractFromKey(o[o.length-1],i),d=f.key,h=f.namespaces,g=h[h.length-1],v=i.lng||this.language,k=i.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(v&&v.toLowerCase()==="cimode"){if(k){var O=i.nsSeparator||this.options.nsSeparator;return a?{res:"".concat(g).concat(O).concat(d),usedKey:d,exactUsedKey:d,usedLng:v,usedNS:g}:"".concat(g).concat(O).concat(d)}return a?{res:d,usedKey:d,exactUsedKey:d,usedLng:v,usedNS:g}:d}var p=this.resolve(o,i),c=p&&p.res,m=p&&p.usedKey||d,y=p&&p.exactUsedKey||d,S=Object.prototype.toString.apply(c),w=["[object Number]","[object Function]","[object RegExp]"],E=i.joinArrays!==void 0?i.joinArrays:this.options.joinArrays,P=!this.i18nFormat||this.i18nFormat.handleAsObject,V=typeof c!="string"&&typeof c!="boolean"&&typeof c!="number";if(P&&c&&V&&w.indexOf(S)<0&&!(typeof E=="string"&&S==="[object Array]")){if(!i.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");var C=this.options.returnedObjectHandler?this.options.returnedObjectHandler(m,c,we(we({},i),{},{ns:h})):"key '".concat(d," (").concat(this.language,")' returned an object instead of string.");return a?(p.res=C,p):C}if(u){var W=S==="[object Array]",Ce=W?[]:{},Nt=W?y:m;for(var et in c)if(Object.prototype.hasOwnProperty.call(c,et)){var bn="".concat(Nt).concat(u).concat(et);Ce[et]=this.translate(bn,we(we({},i),{joinArrays:!1,ns:h})),Ce[et]===bn&&(Ce[et]=c[et])}c=Ce}}else if(P&&typeof E=="string"&&S==="[object Array]")c=c.join(E),c&&(c=this.extendTranslation(c,o,i,s));else{var ft=!1,tt=!1,R=i.count!==void 0&&typeof i.count!="string",I=n.hasDefaultValue(i),F=R?this.pluralResolver.getSuffix(v,i.count,i):"",z=i["defaultValue".concat(F)]||i.defaultValue;!this.isValidLookup(c)&&I&&(ft=!0,c=z),this.isValidLookup(c)||(tt=!0,c=d);var X=i.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey,Et=X&&tt?void 0:c,Ve=I&&z!==c&&this.options.updateMissing;if(tt||ft||Ve){if(this.logger.log(Ve?"updateKey":"missingKey",v,g,d,Ve?z:c),u){var Ln=this.resolve(d,we(we({},i),{},{keySeparator:!1}));Ln&&Ln.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}var Fe=[],xt=this.languageUtils.getFallbackCodes(this.options.fallbackLng,i.lng||this.language);if(this.options.saveMissingTo==="fallback"&&xt&&xt[0])for(var Gi=0;Gi<xt.length;Gi++)Fe.push(xt[Gi]);else this.options.saveMissingTo==="all"?Fe=this.languageUtils.toResolveHierarchy(i.lng||this.language):Fe.push(i.lng||this.language);var Aa=function(In,Qi,za){var Ma=I&&za!==c?za:Et;l.options.missingKeyHandler?l.options.missingKeyHandler(In,g,Qi,Ma,Ve,i):l.backendConnector&&l.backendConnector.saveMissing&&l.backendConnector.saveMissing(In,g,Qi,Ma,Ve,i),l.emit("missingKey",In,g,Qi,c)};this.options.saveMissing&&(this.options.saveMissingPlurals&&R?Fe.forEach(function(qi){l.pluralResolver.getSuffixes(qi,i).forEach(function(In){Aa([qi],d+In,i["defaultValue".concat(In)]||z)})}):Aa(Fe,d,z))}c=this.extendTranslation(c,o,i,p,s),tt&&c===d&&this.options.appendNamespaceToMissingKey&&(c="".concat(g,":").concat(d)),(tt||ft)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?c=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(g,":").concat(d):d,ft?c:void 0):c=this.options.parseMissingKeyHandler(c))}return a?(p.res=c,p):c}},{key:"extendTranslation",value:function(o,i,s,l,a){var u=this;if(this.i18nFormat&&this.i18nFormat.parse)o=this.i18nFormat.parse(o,we(we({},this.options.interpolation.defaultVariables),s),l.usedLng,l.usedNS,l.usedKey,{resolved:l});else if(!s.skipInterpolation){s.interpolation&&this.interpolator.init(we(we({},s),{interpolation:we(we({},this.options.interpolation),s.interpolation)}));var f=typeof o=="string"&&(s&&s.interpolation&&s.interpolation.skipOnVariables!==void 0?s.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables),d;if(f){var h=o.match(this.interpolator.nestingRegexp);d=h&&h.length}var g=s.replace&&typeof s.replace!="string"?s.replace:s;if(this.options.interpolation.defaultVariables&&(g=we(we({},this.options.interpolation.defaultVariables),g)),o=this.interpolator.interpolate(o,g,s.lng||this.language,s),f){var v=o.match(this.interpolator.nestingRegexp),k=v&&v.length;d<k&&(s.nest=!1)}s.nest!==!1&&(o=this.interpolator.nest(o,function(){for(var c=arguments.length,m=new Array(c),y=0;y<c;y++)m[y]=arguments[y];return a&&a[0]===m[0]&&!s.context?(u.logger.warn("It seems you are nesting recursively key: ".concat(m[0]," in key: ").concat(i[0])),null):u.translate.apply(u,m.concat([i]))},s)),s.interpolation&&this.interpolator.reset()}var O=s.postProcess||this.options.postProcess,p=typeof O=="string"?[O]:O;return o!=null&&p&&p.length&&s.applyPostProcessor!==!1&&(o=Bc.handle(p,o,i,this.options&&this.options.postProcessPassResolved?we({i18nResolved:l},s):s,this)),o}},{key:"resolve",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},l,a,u,f,d;return typeof o=="string"&&(o=[o]),o.forEach(function(h){if(!i.isValidLookup(l)){var g=i.extractFromKey(h,s),v=g.key;a=v;var k=g.namespaces;i.options.fallbackNS&&(k=k.concat(i.options.fallbackNS));var O=s.count!==void 0&&typeof s.count!="string",p=O&&!s.ordinal&&s.count===0&&i.pluralResolver.shouldUseIntlApi(),c=s.context!==void 0&&(typeof s.context=="string"||typeof s.context=="number")&&s.context!=="",m=s.lngs?s.lngs:i.languageUtils.toResolveHierarchy(s.lng||i.language,s.fallbackLng);k.forEach(function(y){i.isValidLookup(l)||(d=y,!eu["".concat(m[0],"-").concat(y)]&&i.utils&&i.utils.hasLoadedNamespace&&!i.utils.hasLoadedNamespace(d)&&(eu["".concat(m[0],"-").concat(y)]=!0,i.logger.warn('key "'.concat(a,'" for languages "').concat(m.join(", "),`" won't get resolved as namespace "`).concat(d,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(function(S){if(!i.isValidLookup(l)){f=S;var w=[v];if(i.i18nFormat&&i.i18nFormat.addLookupKeys)i.i18nFormat.addLookupKeys(w,v,S,y,s);else{var E;O&&(E=i.pluralResolver.getSuffix(S,s.count,s));var P="".concat(i.options.pluralSeparator,"zero");if(O&&(w.push(v+E),p&&w.push(v+P)),c){var V="".concat(v).concat(i.options.contextSeparator).concat(s.context);w.push(V),O&&(w.push(V+E),p&&w.push(V+P))}}for(var C;C=w.pop();)i.isValidLookup(l)||(u=C,l=i.getResource(S,y,C,s))}}))})}}),{res:l,usedKey:a,exactUsedKey:u,usedLng:f,usedNS:d}}},{key:"isValidLookup",value:function(o){return o!==void 0&&!(!this.options.returnNull&&o===null)&&!(!this.options.returnEmptyString&&o==="")}},{key:"getResource",value:function(o,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(o,i,s,l):this.resourceStore.getResource(o,i,s,l)}}],[{key:"hasDefaultValue",value:function(o){var i="defaultValue";for(var s in o)if(Object.prototype.hasOwnProperty.call(o,s)&&i===s.substring(0,i.length)&&o[s]!==void 0)return!0;return!1}}]),n}(rn);function Ji(e){return e.charAt(0).toUpperCase()+e.slice(1)}var nu=function(){function e(t){dt(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=yt.create("languageUtils")}return pt(e,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var r=n.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var r=n.split("-");return this.formatLanguageCode(r[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var r=["hans","hant","latn","cyrl","cans","mong","arab"],o=n.split("-");return this.options.lowerCaseLng?o=o.map(function(i){return i.toLowerCase()}):o.length===2?(o[0]=o[0].toLowerCase(),o[1]=o[1].toUpperCase(),r.indexOf(o[1].toLowerCase())>-1&&(o[1]=Ji(o[1].toLowerCase()))):o.length===3&&(o[0]=o[0].toLowerCase(),o[1].length===2&&(o[1]=o[1].toUpperCase()),o[0]!=="sgn"&&o[2].length===2&&(o[2]=o[2].toUpperCase()),r.indexOf(o[1].toLowerCase())>-1&&(o[1]=Ji(o[1].toLowerCase())),r.indexOf(o[2].toLowerCase())>-1&&(o[2]=Ji(o[2].toLowerCase()))),o.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var o;return n.forEach(function(i){if(!o){var s=r.formatLanguageCode(i);(!r.options.supportedLngs||r.isSupportedCode(s))&&(o=s)}}),!o&&this.options.supportedLngs&&n.forEach(function(i){if(!o){var s=r.getLanguagePartFromCode(i);if(r.isSupportedCode(s))return o=s;o=r.options.supportedLngs.find(function(l){if(l.indexOf(s)===0)return l})}}),o||(o=this.getFallbackCodes(this.options.fallbackLng)[0]),o}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var o=n[r];return o||(o=n[this.getScriptPartFromCode(r)]),o||(o=n[this.formatLanguageCode(r)]),o||(o=n[this.getLanguagePartFromCode(r)]),o||(o=n.default),o||[]}},{key:"toResolveHierarchy",value:function(n,r){var o=this,i=this.getFallbackCodes(r||this.options.fallbackLng||[],n),s=[],l=function(u){u&&(o.isSupportedCode(u)?s.push(u):o.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof n=="string"&&n.indexOf("-")>-1?(this.options.load!=="languageOnly"&&l(this.formatLanguageCode(n)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&l(this.getScriptPartFromCode(n)),this.options.load!=="currentOnly"&&l(this.getLanguagePartFromCode(n))):typeof n=="string"&&l(this.formatLanguageCode(n)),i.forEach(function(a){s.indexOf(a)<0&&l(o.formatLanguageCode(a))}),s}}]),e}(),Jf=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],Zf={1:function(t){return+(t>1)},2:function(t){return+(t!=1)},3:function(t){return 0},4:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},5:function(t){return t==0?0:t==1?1:t==2?2:t%100>=3&&t%100<=10?3:t%100>=11?4:5},6:function(t){return t==1?0:t>=2&&t<=4?1:2},7:function(t){return t==1?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},8:function(t){return t==1?0:t==2?1:t!=8&&t!=11?2:3},9:function(t){return+(t>=2)},10:function(t){return t==1?0:t==2?1:t<7?2:t<11?3:4},11:function(t){return t==1||t==11?0:t==2||t==12?1:t>2&&t<20?2:3},12:function(t){return+(t%10!=1||t%100==11)},13:function(t){return+(t!==0)},14:function(t){return t==1?0:t==2?1:t==3?2:3},15:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&(t%100<10||t%100>=20)?1:2},16:function(t){return t%10==1&&t%100!=11?0:t!==0?1:2},17:function(t){return t==1||t%10==1&&t%100!=11?0:1},18:function(t){return t==0?0:t==1?1:2},19:function(t){return t==1?0:t==0||t%100>1&&t%100<11?1:t%100>10&&t%100<20?2:3},20:function(t){return t==1?0:t==0||t%100>0&&t%100<20?1:2},21:function(t){return t%100==1?1:t%100==2?2:t%100==3||t%100==4?3:0},22:function(t){return t==1?0:t==2?1:(t<0||t>10)&&t%10==0?2:3}},eh=["v1","v2","v3"],ru={zero:0,one:1,two:2,few:3,many:4,other:5};function th(){var e={};return Jf.forEach(function(t){t.lngs.forEach(function(n){e[n]={numbers:t.nr,plurals:Zf[t.fc]}})}),e}var nh=function(){function e(t){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};dt(this,e),this.languageUtils=t,this.options=n,this.logger=yt.create("pluralResolver"),(!this.options.compatibilityJSON||this.options.compatibilityJSON==="v4")&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=th()}return pt(e,[{key:"addRule",value:function(n,r){this.rules[n]=r}},{key:"getRule",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(n,{type:r.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[n]||this.rules[this.languageUtils.getLanguagePartFromCode(n)]}},{key:"needsPlural",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},o=this.getRule(n,r);return this.shouldUseIntlApi()?o&&o.resolvedOptions().pluralCategories.length>1:o&&o.numbers.length>1}},{key:"getPluralFormsOfKey",value:function(n,r){var o=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(n,o).map(function(i){return"".concat(r).concat(i)})}},{key:"getSuffixes",value:function(n){var r=this,o=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},i=this.getRule(n,o);return i?this.shouldUseIntlApi()?i.resolvedOptions().pluralCategories.sort(function(s,l){return ru[s]-ru[l]}).map(function(s){return"".concat(r.options.prepend).concat(s)}):i.numbers.map(function(s){return r.getSuffix(n,s,o)}):[]}},{key:"getSuffix",value:function(n,r){var o=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i=this.getRule(n,o);return i?this.shouldUseIntlApi()?"".concat(this.options.prepend).concat(i.select(r)):this.getSuffixRetroCompatible(i,r):(this.logger.warn("no plural rule found for: ".concat(n)),"")}},{key:"getSuffixRetroCompatible",value:function(n,r){var o=this,i=n.noAbs?n.plurals(r):n.plurals(Math.abs(r)),s=n.numbers[i];this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1&&(s===2?s="plural":s===1&&(s=""));var l=function(){return o.options.prepend&&s.toString()?o.options.prepend+s.toString():s.toString()};return this.options.compatibilityJSON==="v1"?s===1?"":typeof s=="number"?"_plural_".concat(s.toString()):l():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1?l():this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString()}},{key:"shouldUseIntlApi",value:function(){return!eh.includes(this.options.compatibilityJSON)}}]),e}();function ou(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function nt(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?ou(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):ou(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var rh=function(){function e(){var t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};dt(this,e),this.logger=yt.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||function(n){return n},this.init(t)}return pt(e,[{key:"init",value:function(){var n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};n.interpolation||(n.interpolation={escapeValue:!0});var r=n.interpolation;this.escape=r.escape!==void 0?r.escape:Kf,this.escapeValue=r.escapeValue!==void 0?r.escapeValue:!0,this.useRawValueToEscape=r.useRawValueToEscape!==void 0?r.useRawValueToEscape:!1,this.prefix=r.prefix?Vn(r.prefix):r.prefixEscaped||"{{",this.suffix=r.suffix?Vn(r.suffix):r.suffixEscaped||"}}",this.formatSeparator=r.formatSeparator?r.formatSeparator:r.formatSeparator||",",this.unescapePrefix=r.unescapeSuffix?"":r.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":r.unescapeSuffix||"",this.nestingPrefix=r.nestingPrefix?Vn(r.nestingPrefix):r.nestingPrefixEscaped||Vn("$t("),this.nestingSuffix=r.nestingSuffix?Vn(r.nestingSuffix):r.nestingSuffixEscaped||Vn(")"),this.nestingOptionsSeparator=r.nestingOptionsSeparator?r.nestingOptionsSeparator:r.nestingOptionsSeparator||",",this.maxReplaces=r.maxReplaces?r.maxReplaces:1e3,this.alwaysFormat=r.alwaysFormat!==void 0?r.alwaysFormat:!1,this.resetRegExp()}},{key:"reset",value:function(){this.options&&this.init(this.options)}},{key:"resetRegExp",value:function(){var n="".concat(this.prefix,"(.+?)").concat(this.suffix);this.regexp=new RegExp(n,"g");var r="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(r,"g");var o="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(o,"g")}},{key:"interpolate",value:function(n,r,o,i){var s=this,l,a,u,f=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function d(O){return O.replace(/\$/g,"$$$$")}var h=function(p){if(p.indexOf(s.formatSeparator)<0){var c=Xa(r,f,p);return s.alwaysFormat?s.format(c,void 0,o,nt(nt(nt({},i),r),{},{interpolationkey:p})):c}var m=p.split(s.formatSeparator),y=m.shift().trim(),S=m.join(s.formatSeparator).trim();return s.format(Xa(r,f,y),S,o,nt(nt(nt({},i),r),{},{interpolationkey:y}))};this.resetRegExp();var g=i&&i.missingInterpolationHandler||this.options.missingInterpolationHandler,v=i&&i.interpolation&&i.interpolation.skipOnVariables!==void 0?i.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables,k=[{regex:this.regexpUnescape,safeValue:function(p){return d(p)}},{regex:this.regexp,safeValue:function(p){return s.escapeValue?d(s.escape(p)):d(p)}}];return k.forEach(function(O){for(u=0;l=O.regex.exec(n);){var p=l[1].trim();if(a=h(p),a===void 0)if(typeof g=="function"){var c=g(n,l,i);a=typeof c=="string"?c:""}else if(i&&Object.prototype.hasOwnProperty.call(i,p))a="";else if(v){a=l[0];continue}else s.logger.warn("missed to pass in variable ".concat(p," for interpolating ").concat(n)),a="";else typeof a!="string"&&!s.useRawValueToEscape&&(a=qa(a));var m=O.safeValue(a);if(n=n.replace(l[0],m),v?(O.regex.lastIndex+=a.length,O.regex.lastIndex-=l[0].length):O.regex.lastIndex=0,u++,u>=s.maxReplaces)break}}),n}},{key:"nest",value:function(n,r){var o=this,i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},s,l,a;function u(g,v){var k=this.nestingOptionsSeparator;if(g.indexOf(k)<0)return g;var O=g.split(new RegExp("".concat(k,"[ ]*{"))),p="{".concat(O[1]);g=O[0],p=this.interpolate(p,a);var c=p.match(/'/g),m=p.match(/"/g);(c&&c.length%2===0&&!m||m.length%2!==0)&&(p=p.replace(/'/g,'"'));try{a=JSON.parse(p),v&&(a=nt(nt({},v),a))}catch(y){return this.logger.warn("failed parsing options string in nesting for key ".concat(g),y),"".concat(g).concat(k).concat(p)}return delete a.defaultValue,g}for(;s=this.nestingRegexp.exec(n);){var f=[];a=nt({},i),a=a.replace&&typeof a.replace!="string"?a.replace:a,a.applyPostProcessor=!1,delete a.defaultValue;var d=!1;if(s[0].indexOf(this.formatSeparator)!==-1&&!/{.*}/.test(s[1])){var h=s[1].split(this.formatSeparator).map(function(g){return g.trim()});s[1]=h.shift(),f=h,d=!0}if(l=r(u.call(this,s[1].trim(),a),a),l&&s[0]===n&&typeof l!="string")return l;typeof l!="string"&&(l=qa(l)),l||(this.logger.warn("missed to resolve ".concat(s[1]," for nesting ").concat(n)),l=""),d&&(l=f.reduce(function(g,v){return o.format(g,v,i.lng,nt(nt({},i),{},{interpolationkey:s[1].trim()}))},l.trim())),n=n.replace(s[0],l),this.regexp.lastIndex=0}return n}}]),e}();function iu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function Pt(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?iu(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):iu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function oh(e){var t=e.toLowerCase().trim(),n={};if(e.indexOf("(")>-1){var r=e.split("(");t=r[0].toLowerCase().trim();var o=r[1].substring(0,r[1].length-1);if(t==="currency"&&o.indexOf(":")<0)n.currency||(n.currency=o.trim());else if(t==="relativetime"&&o.indexOf(":")<0)n.range||(n.range=o.trim());else{var i=o.split(";");i.forEach(function(s){if(s){var l=s.split(":"),a=Uf(l),u=a[0],f=a.slice(1),d=f.join(":").trim().replace(/^'+|'+$/g,"");n[u.trim()]||(n[u.trim()]=d),d==="false"&&(n[u.trim()]=!1),d==="true"&&(n[u.trim()]=!0),isNaN(d)||(n[u.trim()]=parseInt(d,10))}})}}return{formatName:t,formatOptions:n}}function Fn(e){var t={};return function(r,o,i){var s=o+JSON.stringify(i),l=t[s];return l||(l=e(o,i),t[s]=l),l(r)}}var ih=function(){function e(){var t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};dt(this,e),this.logger=yt.create("formatter"),this.options=t,this.formats={number:Fn(function(n,r){var o=new Intl.NumberFormat(n,Pt({},r));return function(i){return o.format(i)}}),currency:Fn(function(n,r){var o=new Intl.NumberFormat(n,Pt(Pt({},r),{},{style:"currency"}));return function(i){return o.format(i)}}),datetime:Fn(function(n,r){var o=new Intl.DateTimeFormat(n,Pt({},r));return function(i){return o.format(i)}}),relativetime:Fn(function(n,r){var o=new Intl.RelativeTimeFormat(n,Pt({},r));return function(i){return o.format(i,r.range||"day")}}),list:Fn(function(n,r){var o=new Intl.ListFormat(n,Pt({},r));return function(i){return o.format(i)}})},this.init(t)}return pt(e,[{key:"init",value:function(n){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}},o=r.interpolation;this.formatSeparator=o.formatSeparator?o.formatSeparator:o.formatSeparator||","}},{key:"add",value:function(n,r){this.formats[n.toLowerCase().trim()]=r}},{key:"addCached",value:function(n,r){this.formats[n.toLowerCase().trim()]=Fn(r)}},{key:"format",value:function(n,r,o){var i=this,s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{},l=r.split(this.formatSeparator),a=l.reduce(function(u,f){var d=oh(f),h=d.formatName,g=d.formatOptions;if(i.formats[h]){var v=u;try{var k=s&&s.formatParams&&s.formatParams[s.interpolationkey]||{},O=k.locale||k.lng||s.locale||s.lng||o;v=i.formats[h](u,O,Pt(Pt(Pt({},g),s),k))}catch(p){i.logger.warn(p)}return v}else i.logger.warn("there was no format function for ".concat(h));return u},n);return a}}]),e}();function su(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function lu(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?su(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):su(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function sh(e){var t=lh();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function lh(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}function ah(e,t){e.pending[t]!==void 0&&(delete e.pending[t],e.pendingCount--)}var uh=function(e){bi(n,e);var t=sh(n);function n(r,o,i){var s,l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return dt(this,n),s=t.call(this),Li&&rn.call(Gt(s)),s.backend=r,s.store=o,s.services=i,s.languageUtils=i.languageUtils,s.options=l,s.logger=yt.create("backendConnector"),s.waitingReads=[],s.maxParallelReads=l.maxParallelReads||10,s.readingCalls=0,s.maxRetries=l.maxRetries>=0?l.maxRetries:5,s.retryTimeout=l.retryTimeout>=1?l.retryTimeout:350,s.state={},s.queue=[],s.backend&&s.backend.init&&s.backend.init(i,l.backend,l),s}return pt(n,[{key:"queueLoad",value:function(o,i,s,l){var a=this,u={},f={},d={},h={};return o.forEach(function(g){var v=!0;i.forEach(function(k){var O="".concat(g,"|").concat(k);!s.reload&&a.store.hasResourceBundle(g,k)?a.state[O]=2:a.state[O]<0||(a.state[O]===1?f[O]===void 0&&(f[O]=!0):(a.state[O]=1,v=!1,f[O]===void 0&&(f[O]=!0),u[O]===void 0&&(u[O]=!0),h[k]===void 0&&(h[k]=!0)))}),v||(d[g]=!0)}),(Object.keys(u).length||Object.keys(f).length)&&this.queue.push({pending:f,pendingCount:Object.keys(f).length,loaded:{},errors:[],callback:l}),{toLoad:Object.keys(u),pending:Object.keys(f),toLoadLanguages:Object.keys(d),toLoadNamespaces:Object.keys(h)}}},{key:"loaded",value:function(o,i,s){var l=o.split("|"),a=l[0],u=l[1];i&&this.emit("failedLoading",a,u,i),s&&this.store.addResourceBundle(a,u,s),this.state[o]=i?-1:2;var f={};this.queue.forEach(function(d){$f(d.loaded,[a],u),ah(d,o),i&&d.errors.push(i),d.pendingCount===0&&!d.done&&(Object.keys(d.loaded).forEach(function(h){f[h]||(f[h]={});var g=d.loaded[h];g.length&&g.forEach(function(v){f[h][v]===void 0&&(f[h][v]=!0)})}),d.done=!0,d.errors.length?d.callback(d.errors):d.callback())}),this.emit("loaded",f),this.queue=this.queue.filter(function(d){return!d.done})}},{key:"read",value:function(o,i,s){var l=this,a=arguments.length>3&&arguments[3]!==void 0?arguments[3]:0,u=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,f=arguments.length>5?arguments[5]:void 0;if(!o.length)return f(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:o,ns:i,fcName:s,tried:a,wait:u,callback:f});return}this.readingCalls++;var d=function(k,O){if(l.readingCalls--,l.waitingReads.length>0){var p=l.waitingReads.shift();l.read(p.lng,p.ns,p.fcName,p.tried,p.wait,p.callback)}if(k&&O&&a<l.maxRetries){setTimeout(function(){l.read.call(l,o,i,s,a+1,u*2,f)},u);return}f(k,O)},h=this.backend[s].bind(this.backend);if(h.length===2){try{var g=h(o,i);g&&typeof g.then=="function"?g.then(function(v){return d(null,v)}).catch(d):d(null,g)}catch(v){d(v)}return}return h(o,i,d)}},{key:"prepareLoading",value:function(o,i){var s=this,l=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},a=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),a&&a();typeof o=="string"&&(o=this.languageUtils.toResolveHierarchy(o)),typeof i=="string"&&(i=[i]);var u=this.queueLoad(o,i,l,a);if(!u.toLoad.length)return u.pending.length||a(),null;u.toLoad.forEach(function(f){s.loadOne(f)})}},{key:"load",value:function(o,i,s){this.prepareLoading(o,i,{},s)}},{key:"reload",value:function(o,i,s){this.prepareLoading(o,i,{reload:!0},s)}},{key:"loadOne",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:"",l=o.split("|"),a=l[0],u=l[1];this.read(a,u,"read",void 0,void 0,function(f,d){f&&i.logger.warn("".concat(s,"loading namespace ").concat(u," for language ").concat(a," failed"),f),!f&&d&&i.logger.log("".concat(s,"loaded namespace ").concat(u," for language ").concat(a),d),i.loaded(o,f,d)})}},{key:"saveMissing",value:function(o,i,s,l,a){var u=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{},f=arguments.length>6&&arguments[6]!==void 0?arguments[6]:function(){};if(this.services.utils&&this.services.utils.hasLoadedNamespace&&!this.services.utils.hasLoadedNamespace(i)){this.logger.warn('did not save key "'.concat(s,'" as the namespace "').concat(i,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");return}if(!(s==null||s==="")){if(this.backend&&this.backend.create){var d=lu(lu({},u),{},{isUpdate:a}),h=this.backend.create.bind(this.backend);if(h.length<6)try{var g;h.length===5?g=h(o,i,s,l,d):g=h(o,i,s,l),g&&typeof g.then=="function"?g.then(function(v){return f(null,v)}).catch(f):f(null,g)}catch(v){f(v)}else h(o,i,s,l,f,d)}!o||!o[0]||this.store.addResource(o[0],i,s,l)}}}]),n}(rn);function au(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(fe(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),fe(t[2])==="object"||fe(t[3])==="object"){var r=t[3]||t[2];Object.keys(r).forEach(function(o){n[o]=r[o]})}return n},interpolation:{escapeValue:!0,format:function(t,n,r,o){return t},prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function uu(e){return typeof e.ns=="string"&&(e.ns=[e.ns]),typeof e.fallbackLng=="string"&&(e.fallbackLng=[e.fallbackLng]),typeof e.fallbackNS=="string"&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function cu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ht(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?cu(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):cu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function ch(e){var t=dh();return function(){var r=St(e),o;if(t){var i=St(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return oo(this,o)}}function dh(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}function go(){}function ph(e){var t=Object.getOwnPropertyNames(Object.getPrototypeOf(e));t.forEach(function(n){typeof e[n]=="function"&&(e[n]=e[n].bind(e))})}var oi=function(e){bi(n,e);var t=ch(n);function n(){var r,o=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},i=arguments.length>1?arguments[1]:void 0;if(dt(this,n),r=t.call(this),Li&&rn.call(Gt(r)),r.options=uu(o),r.services={},r.logger=yt,r.modules={external:[]},ph(Gt(r)),i&&!r.isInitialized&&!o.isClone){if(!r.options.initImmediate)return r.init(o,i),oo(r,Gt(r));setTimeout(function(){r.init(o,i)},0)}return r}return pt(n,[{key:"init",value:function(){var o=this,i=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},s=arguments.length>1?arguments[1]:void 0;typeof i=="function"&&(s=i,i={}),!i.defaultNS&&i.defaultNS!==!1&&i.ns&&(typeof i.ns=="string"?i.defaultNS=i.ns:i.ns.indexOf("translation")<0&&(i.defaultNS=i.ns[0]));var l=au();this.options=ht(ht(ht({},l),this.options),uu(i)),this.options.compatibilityAPI!=="v1"&&(this.options.interpolation=ht(ht({},l.interpolation),this.options.interpolation)),i.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=i.keySeparator),i.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=i.nsSeparator);function a(p){return p?typeof p=="function"?new p:p:null}if(!this.options.isClone){this.modules.logger?yt.init(a(this.modules.logger),this.options):yt.init(null,this.options);var u;this.modules.formatter?u=this.modules.formatter:typeof Intl<"u"&&(u=ih);var f=new nu(this.options);this.store=new qf(this.options.resources,this.options);var d=this.services;d.logger=yt,d.resourceStore=this.store,d.languageUtils=f,d.pluralResolver=new nh(f,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),u&&(!this.options.interpolation.format||this.options.interpolation.format===l.interpolation.format)&&(d.formatter=a(u),d.formatter.init(d,this.options),this.options.interpolation.format=d.formatter.format.bind(d.formatter)),d.interpolator=new rh(this.options),d.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},d.backendConnector=new uh(a(this.modules.backend),d.resourceStore,d,this.options),d.backendConnector.on("*",function(p){for(var c=arguments.length,m=new Array(c>1?c-1:0),y=1;y<c;y++)m[y-1]=arguments[y];o.emit.apply(o,[p].concat(m))}),this.modules.languageDetector&&(d.languageDetector=a(this.modules.languageDetector),d.languageDetector.init&&d.languageDetector.init(d,this.options.detection,this.options)),this.modules.i18nFormat&&(d.i18nFormat=a(this.modules.i18nFormat),d.i18nFormat.init&&d.i18nFormat.init(this)),this.translator=new tu(this.services,this.options),this.translator.on("*",function(p){for(var c=arguments.length,m=new Array(c>1?c-1:0),y=1;y<c;y++)m[y-1]=arguments[y];o.emit.apply(o,[p].concat(m))}),this.modules.external.forEach(function(p){p.init&&p.init(o)})}if(this.format=this.options.interpolation.format,s||(s=go),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var h=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);h.length>0&&h[0]!=="dev"&&(this.options.lng=h[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var g=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];g.forEach(function(p){o[p]=function(){var c;return(c=o.store)[p].apply(c,arguments)}});var v=["addResource","addResources","addResourceBundle","removeResourceBundle"];v.forEach(function(p){o[p]=function(){var c;return(c=o.store)[p].apply(c,arguments),o}});var k=hr(),O=function(){var c=function(y,S){o.isInitialized&&!o.initializedStoreOnce&&o.logger.warn("init: i18next is already initialized. You should call init just once!"),o.isInitialized=!0,o.options.isClone||o.logger.log("initialized",o.options),o.emit("initialized",o.options),k.resolve(S),s(y,S)};if(o.languages&&o.options.compatibilityAPI!=="v1"&&!o.isInitialized)return c(null,o.t.bind(o));o.changeLanguage(o.options.lng,c)};return this.options.resources||!this.options.initImmediate?O():setTimeout(O,0),k}},{key:"loadResources",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:go,l=s,a=typeof o=="string"?o:this.language;if(typeof o=="function"&&(l=o),!this.options.resources||this.options.partialBundledLanguages){if(a&&a.toLowerCase()==="cimode")return l();var u=[],f=function(g){if(g){var v=i.services.languageUtils.toResolveHierarchy(g);v.forEach(function(k){u.indexOf(k)<0&&u.push(k)})}};if(a)f(a);else{var d=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);d.forEach(function(h){return f(h)})}this.options.preload&&this.options.preload.forEach(function(h){return f(h)}),this.services.backendConnector.load(u,this.options.ns,function(h){!h&&!i.resolvedLanguage&&i.language&&i.setResolvedLanguage(i.language),l(h)})}else l(null)}},{key:"reloadResources",value:function(o,i,s){var l=hr();return o||(o=this.languages),i||(i=this.options.ns),s||(s=go),this.services.backendConnector.reload(o,i,function(a){l.resolve(),s(a)}),l}},{key:"use",value:function(o){if(!o)throw new Error("You are passing an undefined module! Please check the object you are passing to i18next.use()");if(!o.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return o.type==="backend"&&(this.modules.backend=o),(o.type==="logger"||o.log&&o.warn&&o.error)&&(this.modules.logger=o),o.type==="languageDetector"&&(this.modules.languageDetector=o),o.type==="i18nFormat"&&(this.modules.i18nFormat=o),o.type==="postProcessor"&&Bc.addPostProcessor(o),o.type==="formatter"&&(this.modules.formatter=o),o.type==="3rdParty"&&this.modules.external.push(o),this}},{key:"setResolvedLanguage",value:function(o){if(!(!o||!this.languages)&&!(["cimode","dev"].indexOf(o)>-1))for(var i=0;i<this.languages.length;i++){var s=this.languages[i];if(!(["cimode","dev"].indexOf(s)>-1)&&this.store.hasLanguageSomeTranslations(s)){this.resolvedLanguage=s;break}}}},{key:"changeLanguage",value:function(o,i){var s=this;this.isLanguageChangingTo=o;var l=hr();this.emit("languageChanging",o);var a=function(h){s.language=h,s.languages=s.services.languageUtils.toResolveHierarchy(h),s.resolvedLanguage=void 0,s.setResolvedLanguage(h)},u=function(h,g){g?(a(g),s.translator.changeLanguage(g),s.isLanguageChangingTo=void 0,s.emit("languageChanged",g),s.logger.log("languageChanged",g)):s.isLanguageChangingTo=void 0,l.resolve(function(){return s.t.apply(s,arguments)}),i&&i(h,function(){return s.t.apply(s,arguments)})},f=function(h){!o&&!h&&s.services.languageDetector&&(h=[]);var g=typeof h=="string"?h:s.services.languageUtils.getBestMatchFromCodes(h);g&&(s.language||a(g),s.translator.language||s.translator.changeLanguage(g),s.services.languageDetector&&s.services.languageDetector.cacheUserLanguage&&s.services.languageDetector.cacheUserLanguage(g)),s.loadResources(g,function(v){u(v,g)})};return!o&&this.services.languageDetector&&!this.services.languageDetector.async?f(this.services.languageDetector.detect()):!o&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(f):this.services.languageDetector.detect(f):f(o),l}},{key:"getFixedT",value:function(o,i,s){var l=this,a=function u(f,d){var h;if(fe(d)!=="object"){for(var g=arguments.length,v=new Array(g>2?g-2:0),k=2;k<g;k++)v[k-2]=arguments[k];h=l.options.overloadTranslationOptionHandler([f,d].concat(v))}else h=ht({},d);h.lng=h.lng||u.lng,h.lngs=h.lngs||u.lngs,h.ns=h.ns||u.ns,h.keyPrefix=h.keyPrefix||s||u.keyPrefix;var O=l.options.keySeparator||".",p;return h.keyPrefix&&Array.isArray(f)?p=f.map(function(c){return"".concat(h.keyPrefix).concat(O).concat(c)}):p=h.keyPrefix?"".concat(h.keyPrefix).concat(O).concat(f):f,l.t(p,h)};return typeof o=="string"?a.lng=o:a.lngs=o,a.ns=i,a.keyPrefix=s,a}},{key:"t",value:function(){var o;return this.translator&&(o=this.translator).translate.apply(o,arguments)}},{key:"exists",value:function(){var o;return this.translator&&(o=this.translator).exists.apply(o,arguments)}},{key:"setDefaultNamespace",value:function(o){this.options.defaultNS=o}},{key:"hasLoadedNamespace",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;var l=this.resolvedLanguage||this.languages[0],a=this.options?this.options.fallbackLng:!1,u=this.languages[this.languages.length-1];if(l.toLowerCase()==="cimode")return!0;var f=function(g,v){var k=i.services.backendConnector.state["".concat(g,"|").concat(v)];return k===-1||k===2};if(s.precheck){var d=s.precheck(this,f);if(d!==void 0)return d}return!!(this.hasResourceBundle(l,o)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||f(l,o)&&(!a||f(u,o)))}},{key:"loadNamespaces",value:function(o,i){var s=this,l=hr();return this.options.ns?(typeof o=="string"&&(o=[o]),o.forEach(function(a){s.options.ns.indexOf(a)<0&&s.options.ns.push(a)}),this.loadResources(function(a){l.resolve(),i&&i(a)}),l):(i&&i(),Promise.resolve())}},{key:"loadLanguages",value:function(o,i){var s=hr();typeof o=="string"&&(o=[o]);var l=this.options.preload||[],a=o.filter(function(u){return l.indexOf(u)<0});return a.length?(this.options.preload=l.concat(a),this.loadResources(function(u){s.resolve(),i&&i(u)}),s):(i&&i(),Promise.resolve())}},{key:"dir",value:function(o){if(o||(o=this.resolvedLanguage||(this.languages&&this.languages.length>0?this.languages[0]:this.language)),!o)return"rtl";var i=["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"],s=this.services&&this.services.languageUtils||new nu(au());return i.indexOf(s.getLanguagePartFromCode(o))>-1||o.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}},{key:"cloneInstance",value:function(){var o=this,i=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:go,l=ht(ht(ht({},this.options),i),{isClone:!0}),a=new n(l);(i.debug!==void 0||i.prefix!==void 0)&&(a.logger=a.logger.clone(i));var u=["store","services","language"];return u.forEach(function(f){a[f]=o[f]}),a.services=ht({},this.services),a.services.utils={hasLoadedNamespace:a.hasLoadedNamespace.bind(a)},a.translator=new tu(a.services,a.options),a.translator.on("*",function(f){for(var d=arguments.length,h=new Array(d>1?d-1:0),g=1;g<d;g++)h[g-1]=arguments[g];a.emit.apply(a,[f].concat(h))}),a.init(l,s),a.translator.options=a.options,a.translator.backendConnector.services.utils={hasLoadedNamespace:a.hasLoadedNamespace.bind(a)},a}},{key:"toJSON",value:function(){return{options:this.options,store:this.store,language:this.language,languages:this.languages,resolvedLanguage:this.resolvedLanguage}}}]),n}(rn);Je(oi,"createInstance",function(){var e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},t=arguments.length>1?arguments[1]:void 0;return new oi(e,t)});var ce=oi.createInstance();ce.createInstance=oi.createInstance;ce.createInstance;ce.dir;ce.init;ce.loadResources;ce.reloadResources;ce.use;ce.changeLanguage;ce.getFixedT;ce.t;ce.exists;ce.setDefaultNamespace;ce.hasLoadedNamespace;ce.loadNamespaces;ce.loadLanguages;function fh(e,t){if(e==null)return{};var n={},r=Object.keys(e),o,i;for(i=0;i<r.length;i++)o=r[i],!(t.indexOf(o)>=0)&&(n[o]=e[o]);return n}function Al(e,t){if(e==null)return{};var n=fh(e,t),r,o;if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(o=0;o<i.length;o++)r=i[o],!(t.indexOf(r)>=0)&&Object.prototype.propertyIsEnumerable.call(e,r)&&(n[r]=e[r])}return n}var hh={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},mh=/\s([^'"/\s><]+?)[\s/>]|([^\s=]+)=\s?(".*?"|'.*?')/g;function du(e){var t={type:"tag",name:"",voidElement:!1,attrs:{},children:[]},n=e.match(/<\/?([^\s]+?)[/\s>]/);if(n&&(t.name=n[1],(hh[n[1]]||e.charAt(e.length-2)==="/")&&(t.voidElement=!0),t.name.startsWith("!--"))){var r=e.indexOf("-->");return{type:"comment",comment:r!==-1?e.slice(4,r):""}}for(var o=new RegExp(mh),i=null;(i=o.exec(e))!==null;)if(i[0].trim())if(i[1]){var s=i[1].trim(),l=[s,""];s.indexOf("=")>-1&&(l=s.split("=")),t.attrs[l[0]]=l[1],o.lastIndex--}else i[2]&&(t.attrs[i[2]]=i[3].trim().substring(1,i[3].length-1));return t}var gh=/<[a-zA-Z0-9\-\!\/](?:"[^"]*"|'[^']*'|[^'">])*>/g,vh=/^\s*$/,yh=Object.create(null);function Kc(e,t){switch(t.type){case"text":return e+t.content;case"tag":return e+="<"+t.name+(t.attrs?function(n){var r=[];for(var o in n)r.push(o+'="'+n[o]+'"');return r.length?" "+r.join(" "):""}(t.attrs):"")+(t.voidElement?"/>":">"),t.voidElement?e:e+t.children.reduce(Kc,"")+"</"+t.name+">";case"comment":return e+"<!--"+t.comment+"-->"}}var _h={parse:function(e,t){t||(t={}),t.components||(t.components=yh);var n,r=[],o=[],i=-1,s=!1;if(e.indexOf("<")!==0){var l=e.indexOf("<");r.push({type:"text",content:l===-1?e:e.substring(0,l)})}return e.replace(gh,function(a,u){if(s){if(a!=="</"+n.name+">")return;s=!1}var f,d=a.charAt(1)!=="/",h=a.startsWith("<!--"),g=u+a.length,v=e.charAt(g);if(h){var k=du(a);return i<0?(r.push(k),r):((f=o[i]).children.push(k),r)}if(d&&(i++,(n=du(a)).type==="tag"&&t.components[n.name]&&(n.type="component",s=!0),n.voidElement||s||!v||v==="<"||n.children.push({type:"text",content:e.slice(g,e.indexOf("<",g))}),i===0&&r.push(n),(f=o[i-1])&&f.children.push(n),o[i]=n),(!d||n.voidElement)&&(i>-1&&(n.voidElement||n.name===a.slice(2,-1))&&(i--,n=i===-1?r:o[i]),!s&&v!=="<"&&v)){f=i===-1?r:o[i].children;var O=e.indexOf("<",g),p=e.slice(g,O===-1?void 0:O);vh.test(p)&&(p=" "),(O>-1&&i+f.length>=0||p!==" ")&&f.push({type:"text",content:p})}}),r},stringify:function(e){return e.reduce(function(t,n){return t+Kc("",n)},"")}};function Ho(){if(console&&console.warn){for(var e,t=arguments.length,n=new Array(t),r=0;r<t;r++)n[r]=arguments[r];typeof n[0]=="string"&&(n[0]="react-i18next:: ".concat(n[0])),(e=console).warn.apply(e,n)}}var pu={};function ii(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];typeof t[0]=="string"&&pu[t[0]]||(typeof t[0]=="string"&&(pu[t[0]]=new Date),Ho.apply(void 0,t))}function fu(e,t,n){e.loadNamespaces(t,function(){if(e.isInitialized)n();else{var r=function o(){setTimeout(function(){e.off("initialized",o)},0),n()};e.on("initialized",r)}})}function kh(e,t){var n=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},r=t.languages[0],o=t.options?t.options.fallbackLng:!1,i=t.languages[t.languages.length-1];if(r.toLowerCase()==="cimode")return!0;var s=function(a,u){var f=t.services.backendConnector.state["".concat(a,"|").concat(u)];return f===-1||f===2};return n.bindI18n&&n.bindI18n.indexOf("languageChanging")>-1&&t.services.backendConnector.backend&&t.isLanguageChangingTo&&!s(t.isLanguageChangingTo,e)?!1:!!(t.hasResourceBundle(r,e)||!t.services.backendConnector.backend||t.options.resources&&!t.options.partialBundledLanguages||s(r,e)&&(!o||s(i,e)))}function wh(e,t){var n=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};if(!t.languages||!t.languages.length)return ii("i18n.languages were undefined or empty",t.languages),!0;var r=t.options.ignoreJSONStructure!==void 0;return r?t.hasLoadedNamespace(e,{precheck:function(i,s){if(n.bindI18n&&n.bindI18n.indexOf("languageChanging")>-1&&i.services.backendConnector.backend&&i.isLanguageChangingTo&&!s(i.isLanguageChangingTo,e))return!1}}):kh(e,t,n)}var Sh=/&(?:amp|#38|lt|#60|gt|#62|apos|#39|quot|#34|nbsp|#160|copy|#169|reg|#174|hellip|#8230|#x2F|#47);/g,Oh={"&amp;":"&","&#38;":"&","&lt;":"<","&#60;":"<","&gt;":">","&#62;":">","&apos;":"'","&#39;":"'","&quot;":'"',"&#34;":'"',"&nbsp;":" ","&#160;":" ","&copy;":"©","&#169;":"©","&reg;":"®","&#174;":"®","&hellip;":"…","&#8230;":"…","&#x2F;":"/","&#47;":"/"},Nh=function(t){return Oh[t]},Eh=function(t){return t.replace(Sh,Nh)};function hu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function mu(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?hu(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):hu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var Fs={bindI18n:"languageChanged",bindI18nStore:"",transEmptyNodeValue:"",transSupportBasicHtmlNodes:!0,transWrapTextNodes:"",transKeepBasicHtmlNodesFor:["br","strong","i","p"],useSuspense:!0,unescape:Eh};function xh(){var e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};Fs=mu(mu({},Fs),e)}function Hc(){return Fs}var Wc;function Ph(e){Wc=e}function zl(){return Wc}var Ch=["format"],Rh=["children","count","parent","i18nKey","context","tOptions","values","defaults","components","ns","i18n","t","shouldUnescape"];function gu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ie(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?gu(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):gu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Zi(e,t){if(!e)return!1;var n=e.props?e.props.children:e.children;return t?n.length>0:!!n}function es(e){return e?e.props?e.props.children:e.children:[]}function Th(e){return Object.prototype.toString.call(e)!=="[object Array]"?!1:e.every(function(t){return j.isValidElement(t)})}function Er(e){return Array.isArray(e)?e:[e]}function bh(e,t){var n=ie({},t);return n.props=Object.assign(e.props,t.props),n}function Yc(e,t){if(!e)return"";var n="",r=Er(e),o=t.transSupportBasicHtmlNodes&&t.transKeepBasicHtmlNodesFor?t.transKeepBasicHtmlNodesFor:[];return r.forEach(function(i,s){if(typeof i=="string")n+="".concat(i);else if(j.isValidElement(i)){var l=Object.keys(i.props).length,a=o.indexOf(i.type)>-1,u=i.props.children;if(!u&&a&&l===0)n+="<".concat(i.type,"/>");else if(!u&&(!a||l!==0))n+="<".concat(s,"></").concat(s,">");else if(i.props.i18nIsDynamicList)n+="<".concat(s,"></").concat(s,">");else if(a&&l===1&&typeof u=="string")n+="<".concat(i.type,">").concat(u,"</").concat(i.type,">");else{var f=Yc(u,t);n+="<".concat(s,">").concat(f,"</").concat(s,">")}}else if(i===null)Ho("Trans: the passed in value is invalid - seems you passed in a null child.");else if(fe(i)==="object"){var d=i.format,h=Al(i,Ch),g=Object.keys(h);if(g.length===1){var v=d?"".concat(g[0],", ").concat(d):g[0];n+="{{".concat(v,"}}")}else Ho("react-i18next: the passed in object contained more than one variable - the object should look like {{ value, format }} where format is optional.",i)}else Ho("Trans: the passed in value is invalid - seems you passed in a variable like {number} - please pass in variables for interpolation as full objects like {{number}}.",i)}),n}function Lh(e,t,n,r,o,i){if(t==="")return[];var s=r.transKeepBasicHtmlNodesFor||[],l=t&&new RegExp(s.join("|")).test(t);if(!e&&!l)return[t];var a={};function u(O){var p=Er(O);p.forEach(function(c){typeof c!="string"&&(Zi(c)?u(es(c)):fe(c)==="object"&&!j.isValidElement(c)&&Object.assign(a,c))})}u(e);var f=_h.parse("<0>".concat(t,"</0>")),d=ie(ie({},a),o);function h(O,p,c){var m=es(O),y=v(m,p.children,c);return Th(m)&&y.length===0?m:y}function g(O,p,c,m,y){O.dummy&&(O.children=p),c.push(j.cloneElement(O,ie(ie({},O.props),{},{key:m}),y?void 0:p))}function v(O,p,c){var m=Er(O),y=Er(p);return y.reduce(function(S,w,E){var P=w.children&&w.children[0]&&w.children[0].content&&n.services.interpolator.interpolate(w.children[0].content,d,n.language);if(w.type==="tag"){var V=m[parseInt(w.name,10)];!V&&c.length===1&&c[0][w.name]&&(V=c[0][w.name]),V||(V={});var C=Object.keys(w.attrs).length!==0?bh({props:w.attrs},V):V,W=j.isValidElement(C),Ce=W&&Zi(w,!0)&&!w.voidElement,Nt=l&&fe(C)==="object"&&C.dummy&&!W,et=fe(e)==="object"&&e!==null&&Object.hasOwnProperty.call(e,w.name);if(typeof C=="string"){var bn=n.services.interpolator.interpolate(C,d,n.language);S.push(bn)}else if(Zi(C)||Ce){var ft=h(C,w,c);g(C,ft,S,E)}else if(Nt){var tt=v(m,w.children,c);S.push(j.cloneElement(C,ie(ie({},C.props),{},{key:E}),tt))}else if(Number.isNaN(parseFloat(w.name)))if(et){var R=h(C,w,c);g(C,R,S,E,w.voidElement)}else if(r.transSupportBasicHtmlNodes&&s.indexOf(w.name)>-1)if(w.voidElement)S.push(j.createElement(w.name,{key:"".concat(w.name,"-").concat(E)}));else{var I=v(m,w.children,c);S.push(j.createElement(w.name,{key:"".concat(w.name,"-").concat(E)},I))}else if(w.voidElement)S.push("<".concat(w.name," />"));else{var F=v(m,w.children,c);S.push("<".concat(w.name,">").concat(F,"</").concat(w.name,">"))}else if(fe(C)==="object"&&!W){var z=w.children[0]?P:null;z&&S.push(z)}else w.children.length===1&&P?S.push(j.cloneElement(C,ie(ie({},C.props),{},{key:E}),P)):S.push(j.cloneElement(C,ie(ie({},C.props),{},{key:E})))}else if(w.type==="text"){var X=r.transWrapTextNodes,Et=i?r.unescape(n.services.interpolator.interpolate(w.content,d,n.language)):n.services.interpolator.interpolate(w.content,d,n.language);X?S.push(j.createElement(X,{key:"".concat(w.name,"-").concat(E)},Et)):S.push(Et)}return S},[])}var k=v([{dummy:!0,children:e||[]}],f,Er(e||[]));return es(k[0])}function Ih(e){var t=e.children,n=e.count,r=e.parent,o=e.i18nKey,i=e.context,s=e.tOptions,l=s===void 0?{}:s,a=e.values,u=e.defaults,f=e.components,d=e.ns,h=e.i18n,g=e.t,v=e.shouldUnescape,k=Al(e,Rh),O=h||zl();if(!O)return ii("You will need to pass in an i18next instance by using i18nextReactModule"),t;var p=g||O.t.bind(O)||function(Ce){return Ce};i&&(l.context=i);var c=ie(ie({},Hc()),O.options&&O.options.react),m=d||p.ns||O.options&&O.options.defaultNS;m=typeof m=="string"?[m]:m||["translation"];var y=u||Yc(t,c)||c.transEmptyNodeValue||o,S=c.hashTransKey,w=o||(S?S(y):y),E=a?l.interpolation:{interpolation:ie(ie({},l.interpolation),{},{prefix:"#$?",suffix:"?$#"})},P=ie(ie(ie(ie({},l),{},{count:n},a),E),{},{defaultValue:y,ns:m}),V=w?p(w,P):y,C=Lh(f||t,V,O,c,P,v),W=r!==void 0?r:c.defaultTransParent;return W?j.createElement(W,k,C):C}var Vh={type:"3rdParty",init:function(t){xh(t.options.react),Ph(t)}},Gc=j.createContext(),Fh=function(){function e(){dt(this,e),this.usedNamespaces={}}return pt(e,[{key:"addUsedNamespaces",value:function(n){var r=this;n.forEach(function(o){r.usedNamespaces[o]||(r.usedNamespaces[o]=!0)})}},{key:"getUsedNamespaces",value:function(){return Object.keys(this.usedNamespaces)}}]),e}(),jh=["children","count","parent","i18nKey","context","tOptions","values","defaults","components","ns","i18n","t","shouldUnescape"];function vu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function Dh(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?vu(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):vu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function B(e){var t=e.children,n=e.count,r=e.parent,o=e.i18nKey,i=e.context,s=e.tOptions,l=s===void 0?{}:s,a=e.values,u=e.defaults,f=e.components,d=e.ns,h=e.i18n,g=e.t,v=e.shouldUnescape,k=Al(e,jh),O=j.useContext(Gc)||{},p=O.i18n,c=O.defaultNS,m=h||p||zl(),y=g||m&&m.t.bind(m);return Ih(Dh({children:t,count:n,parent:r,i18nKey:o,context:i,tOptions:l,values:a,defaults:u,components:f,ns:d||y&&y.ns||c||m&&m.options&&m.options.defaultNS,i18n:m,t:g,shouldUnescape:v},k))}function Uh(e,t){var n=e==null?null:typeof Symbol<"u"&&e[Symbol.iterator]||e["@@iterator"];if(n!=null){var r,o,i,s,l=[],a=!0,u=!1;try{if(i=(n=n.call(e)).next,t===0){if(Object(n)!==n)return;a=!1}else for(;!(a=(r=i.call(n)).done)&&(l.push(r.value),l.length!==t);a=!0);}catch(f){u=!0,o=f}finally{try{if(!a&&n.return!=null&&(s=n.return(),Object(s)!==s))return}finally{if(u)throw o}}return l}}function Ah(e,t){return Uc(e)||Uh(e,t)||Ac(e,t)||zc()}function yu(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ts(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?yu(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):yu(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var zh=function(t,n){var r=j.useRef();return j.useEffect(function(){r.current=n?r.current:t},[t,n]),r.current};function q(e){var t=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},n=t.i18n,r=j.useContext(Gc)||{},o=r.i18n,i=r.defaultNS,s=n||o||zl();if(s&&!s.reportNamespaces&&(s.reportNamespaces=new Fh),!s){ii("You will need to pass in an i18next instance by using initReactI18next");var l=function(V,C){return typeof C=="string"?C:C&&fe(C)==="object"&&typeof C.defaultValue=="string"?C.defaultValue:Array.isArray(V)?V[V.length-1]:V},a=[l,{},!1];return a.t=l,a.i18n={},a.ready=!1,a}s.options.react&&s.options.react.wait!==void 0&&ii("It seems you are still using the old wait option, you may migrate to the new useSuspense behaviour.");var u=ts(ts(ts({},Hc()),s.options.react),t),f=u.useSuspense,d=u.keyPrefix,h=e||i||s.options&&s.options.defaultNS;h=typeof h=="string"?[h]:h||["translation"],s.reportNamespaces.addUsedNamespaces&&s.reportNamespaces.addUsedNamespaces(h);var g=(s.isInitialized||s.initializedStoreOnce)&&h.every(function(P){return wh(P,s,u)});function v(){return s.getFixedT(null,u.nsMode==="fallback"?h:h[0],d)}var k=j.useState(v),O=Ah(k,2),p=O[0],c=O[1],m=h.join(),y=zh(m),S=j.useRef(!0);j.useEffect(function(){var P=u.bindI18n,V=u.bindI18nStore;S.current=!0,!g&&!f&&fu(s,h,function(){S.current&&c(v)}),g&&y&&y!==m&&S.current&&c(v);function C(){S.current&&c(v)}return P&&s&&s.on(P,C),V&&s&&s.store.on(V,C),function(){S.current=!1,P&&s&&P.split(" ").forEach(function(W){return s.off(W,C)}),V&&s&&V.split(" ").forEach(function(W){return s.store.off(W,C)})}},[s,m]);var w=j.useRef(!0);j.useEffect(function(){S.current&&!w.current&&c(v),w.current=!1},[s,d]);var E=[p,s,g];if(E.t=p,E.i18n=s,E.ready=g,g||!g&&!f)return E;throw new Promise(function(P){fu(s,h,function(){P()})})}const Mh={installationInstructions:"安装说明",getStarted:"入门指南",notebooks:"笔记本电脑",troubleshooting:"故障排除指南"},$h={copy:"复制",copied:"已复制"},Bh={distributionLinks:Mh,copyCommand:$h},Kh={titles:{recommended:"推荐",latestLTS:"最新的 LTS 版本",hddlSupport:"包含 NCS2/HDDL 支持",previousLTS:"旧 LTS 版本",LTS:"LTS"}},Hh={devTools:{title:"开发工具",subtitle:"开发和优化深度学习模型的最佳选择。"},runtime:{title:"运行时",subtitle:"您已拥有模型，并且希望对其运行推理。"}},Wh={common:{requirements:{win10:"Windows* 10（64 位）",win11:"Windows® 11（推荐用于第 12 代英特尔® 酷睿™ 处理器）",ubuntu18:"Ubuntu 18.04 长期支持 (LTS)（64 位）",ubuntu20:"Ubuntu 20.04 长期支持 (LTS)（64 位）",ubuntu22:"Ubuntu 22.04 长期支持 (LTS)（64 位）",redHat8:"Red Hat Enterprise Linux 8（64 位）",centOS7:"CentOS 7（64 位）",armPluginFootnote:"搭载 M1 处理器的 Macintosh 计算机针对 OpenVINO 工具套件 2022.3.0 LTS 及更高版本使用 ARM 插件。此插件是社区提供支持的。由于英特尔不提供任何支持，它不适用 LTS 的两年支持政策。"},modules:{runtime:"运行时/推理引擎",modelOptimizer:"模型优化器",benchmarkTool:"基准测试工具",accuracyChecker:"准确性检查程序",annotationConverter:"批注转换器",pot:"训练后优化工具",openCVPython:"OpenCV Python",omz:"模型下载程序和其他 Open Model Zoo 工具"}},openVINOArchives:"OpenVINO 归档文件",source:"源",offlineInstaller:"离线安装程序",recommendedOption:"推荐选项",pythonAPIOnly:"仅限 Python API",CAPIOnly:"仅限 C++ API",archives:{includesNPUPlugin:"包括NPU"}},Yh={versions:Kh,environment:Hh,distributions:Wh},Gh={titles:{envinronment:"环境",os:"操作系统",version:"版本",distribution:"分发",frameworks:"框架"},prevReleases:"旧版",devCloud:{label:"在英特尔® Developer Cloud 中试用",href:"https://www.intel.cn/content/www/cn/zh/developer/tools/devcloud/overview.html"},nncf:"高级优化工具可单独购买：<1>了解 NNCF</1>",simplifiedInstallOptions:"我们简化了安装选项（示例：运行时工具和开发工具整合）：<1>了解详情</1>"},qh={learnMore:"了解详情",title:"系统要求",fullRequirements:{text:"如需查看支持硬件的完整列表，请参见<1>系统要求</1>。",href:"https://www.intel.cn/content/www/cn/zh/developer/tools/openvino-toolkit/system-requirements.html"},supportedOS:"支持的操作系统",pythonVersion:"Python 版本（64 位）",dockerDocs:"请访问我们<1>有关 Docker 安装的文档</1>。"},Qh={apt:{title:"使用 APT 安装",downloadTitle:"下载",steps:{download:"<0>第 1 步：</0>下载 <2>{{filename}}</2>。您也可以使用以下命令",addKey:"<0>第 2 步：</0>将此密钥添加到系统密钥环。",addRepository:"<0>第 3 步：</0>通过以下命令添加存储库。",updateList:"<0>第 4 步：</0>使用更新命令更新软件包列表。",verify:"<0>第 5 步：</0>验证 APT 存储库的设置是否正确。使用 apt-cache 命令查看包含所有可用 OpenVINO 程序包和组件的列表",install:"<0>第 6 步：</0>安装 OpenVINO 运行时。"}},download:{title:"下载",download:"下载",downloadArchives:"下载归档文件",gotoDocker:"转到 Docker"},githubGitee:{openSource:"使用开源存储库，在发布版本之间获取最新版本，并为开源社区做出贡献。",installationInstructions:"安装说明",githubRepository:"OpenVINO 工具套件 GitHub 存储库",giteeRepository:"OpenVINO 工具套件 Gitee 存储库",titleGitee:"基于 Gitee 上的源构建",titleGithub:"基于 GitHub 上的源构建",steps:{useGitClone:"<0>第 1 步：</0>运用 Git 克隆 OpenVINO 工具套件存储库。",buildInstructions:"<0>第 2 步：</0>按照<2>说明从源代码构建</2>"}},pip:{downloadRuntime:"下载 OpenVINO™ 运行时",downloadDevtools:"下载 OpenVINO™ 开发工具",install:"使用 PIP 安装",steps:{createVenv:"<0>第 1 步：</0>创建虚拟环境。",activateVenv:"<0>第 2 步：</0>激活虚拟环境。",upgradePip:"<0>第 3 步：</0>将 pip 升级到最新版本。",install:"<0>第 4 步：</0>下载并安装程序包。"}},conda:{download:"下载 OpenVINO™ 运行时",install:"使用 Conda 安装",steps:{createEnv:"<0>第 1 步：</0>创建 Anaconda 环境（以 Python 3.10 为例）",activateEnv:"<0>第 2 步：</0>激活 Anaconda 环境",update:"<0>第 3 步：</0>将 Anaconda 环境更新至最新版本",install:"<0>第 4 步：</0>下载并安装程序包。"}},conan:{install:"使用 Conan 安装",steps:{createConanFile:'<0>第 1 步：</0>在OpenVINO项目中创建<2>{{txtFilename}}</2>文件，并加入"openvino"作为依赖库。',install:"<0>第 2 步：</0>运行以下命令来创建<2>{{cmakeFilename}}</2>文件，此文件会被用来将你的项目与OpenVINO一起进行编译。",compile:"<0>第 3 步：</0>配置并编译你的项目。"}},brew:{download:"下载 OpenVINO™ 运行时",install:"使用 Homebrew 安装",steps:{install:"下载并安装程序包"}},vcpkg:{install:"使用 vcpkg 安装",steps:{install:"下载并安装程序包"}},yum:{title:"使用 YUM 安装",downloadTitle:"下载",steps:{createRepoFile:"<0>第 1 步：</0>以正常用户身份在 /tmp 目录中创建 YUM 存储库文件。",moveFile:"<0>第 2 步：</0>将新的 openvino-{{year}}.repo 文件移动到 YUM 配置目录 <4>{{directory}}</4>。",verify:"<0>第 3 步：</0>使用以下命令验证新建存储库的设置是否正确。",install:"<0>第 4 步：</0>安装 OpenVINO 运行时。"}}},Xh={frameworks:{install:"安装框架",tooltip:`我们假设所需的框架已经安装到您的环境中。如果没有，您可以使用 OpenVINO 工具套件安装该框架。
+可用框架：Caffe、Kaldi、Apache MXNet、PyTorch、ONNX、TensorFlow。`},selectorForm:{titles:{install:"安装"}},distributions:{aptYum:{followInstructions:"按照<1>安装说明进行操作</1>"},download:{useFollowingLink:"使用下方链接："}}},Jh={selectorForm:Gh,systemRequirements:qh,distributions:Qh,compact:Xh},Zh={translation:Jh,common:Bh,core:Yh},em={installationInstructions:"Installation Instructions",getStarted:"Get Started Guide",notebooks:"Notebooks",troubleshooting:"Troubleshooting Guide"},tm={copy:"Copy",copied:"Copied"},nm={distributionLinks:em,copyCommand:tm},rm={titles:{recommended:"Recommended",latestLTS:"Latest LTS release",hddlSupport:"Includes NCS2/HDDL support",previousLTS:"Previous LTS release",LTS:"LTS"}},om={devTools:{title:"Development Tools",subtitle:"Your best option to develop and optimize deep learning models."},runtime:{title:"Runtime",subtitle:"You already have a model and want to run inference on it."}},im={common:{requirements:{win10:"Windows® 10 (64 bit)",win11:"Windows® 11 (recommended for 12th Generation Intel® Core™ processors)",ubuntu18:"Ubuntu 18.04 long-term support (LTS) (64 bit)",ubuntu20:"Ubuntu 20.04 long-term support (LTS) (64 bit)",ubuntu22:"Ubuntu 22.04 long-term support (LTS) (64 bit)",redHat8:"Red Hat Enterprise Linux 8 (64 bit)",centOS7:"CentOS 7 (64 bit)",armPluginFootnote:"Macintosh computers using the M1 processor use the ARM Plug-in on OpenVINO toolkit 2022.3.0 LTS and later. This plugin is community supported; no support is provided by Intel and it doesn't fall under the LTS 2 year support policy."},modules:{runtime:"Runtime/Inference Engine",modelOptimizer:"Model Optimizer",benchmarkTool:"Benchmark Tool",accuracyChecker:"Accuracy Checker",annotationConverter:"Annotation Converter",pot:"Post-Training Optimization Tool",openCVPython:"OpenCV Python",omz:"Model Downloader and other Open Model Zoo Tools"}},openVINOArchives:"OpenVINO Archives",source:"Source",offlineInstaller:"Offline Installer",recommendedOption:"Recommended option",pythonAPIOnly:"Python API only",CAPIOnly:"C++ API only",archives:{includesNPUPlugin:"Includes NPU plugin"}},sm={versions:rm,environment:om,distributions:im},lm={titles:{envinronment:"Environment",os:"Operating System",version:"Version",distribution:"Distribution",frameworks:"Frameworks"},prevReleases:"Previous Releases",devCloud:{label:"Try in the Intel® Developer Cloud",href:"https://www.intel.com/content/www/us/en/developer/tools/devcloud/overview.html"},nncf:"Advanced Optimization tool available separately: <1>Learn about NNCF</1>",simplifiedInstallOptions:"We have simplified the install options (example: consolidation of Runtime and Development Tools): <1>Learn more</1>"},am={learnMore:"Learn more",title:"System Requirements",fullRequirements:{text:"For a complete list of supported hardware, see the <1>system requirements</1>.",href:"https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html"},supportedOS:"Supported Operating System",pythonVersion:"Python Version (64 bit)",dockerDocs:"Visit our <1>documentation on Docker Installation</1>."},um={apt:{title:"Install with APT",downloadTitle:"Download",steps:{download:"<0>Step 1:</0> Download the <2>{{filename}}</2>. You can also use the following command",addKey:"<0>Step 2:</0> Add this key to the system keyring",addRepository:"<0>Step 3:</0> Add the repository via the following command",updateList:"<0>Step 4:</0> Update the list of packages via the update command",verify:"<0>Step 5:</0> Verify that the APT repository is properly set up. Run the apt-cache command to see a list of all available OpenVINO packages and components",install:"<0>Step 6:</0> Install OpenVINO Runtime"}},download:{title:"Download",download:"Download",downloadArchives:"Download Archives",gotoDocker:"Go to Docker"},githubGitee:{openSource:"Use an open source repository, get the most recent version in between releases, and contribute to the open source community.",installationInstructions:"Installation instructions",githubRepository:"OpenVINO Toolkit GitHub Repository",giteeRepository:"OpenVINO Toolkit Gitee Repository",titleGitee:"Build from Source on Gitee",titleGithub:"Build from Source on GitHub",steps:{useGitClone:"<0>Step 1:</0> Use Git to clone the OpenVINO toolkit repository",buildInstructions:"<0>Step 2:</0> Follow the <2>instructions to build from source</2>"}},pip:{downloadRuntime:"Download OpenVINO™ Runtime",downloadDevtools:"Download OpenVINO™ Development Tools",install:"Install with PIP",steps:{createVenv:"<0>Step 1:</0> Create virtual environment",activateVenv:"<0>Step 2:</0> Activate virtual environment",upgradePip:"<0>Step 3:</0> Upgrade pip to latest version",install:"<0>Step 4:</0> Download and install the package"}},conda:{download:"Download OpenVINO™ Runtime",install:"Install with Conda",steps:{createEnv:"<0>Step 1:</0> Create the Anaconda environment (Python 3.10 used as an example)",activateEnv:"<0>Step 2:</0> Activate the Anaconda environment",update:"<0>Step 3:</0> Update the Anaconda environment to the latest version",install:"<0>Step 4:</0> Download and install the package"}},conan:{install:"Install with Conan",steps:{createConanFile:"<0>Step 1:</0> Create a <2>{{txtFilename}}</2> file for your OpenVINO project and add “openvino” dependency in there",install:"<0>Step 2:</0> Run the command below to create <2>{{cmakeFilename}}</2> file, which will be used to compile your project with OpenVINO",compile:"<0>Step 3:</0> Configure and compile your project with OpenVINO"}},brew:{download:"Download OpenVINO™ Runtime",install:"Install with Homebrew",steps:{install:"Download and install the package"}},vcpkg:{install:"Install with vcpkg",steps:{install:"Download and install the package"}},yum:{title:"Install with YUM",downloadTitle:"Download",steps:{createRepoFile:"<0>Step 1:</0> Create the YUM repo file in the /tmp directory as a normal user",moveFile:"<0>Step 2:</0> Move the new openvino-{{year}}.repo file to the YUM configuration directory <4>{{directory}}</4>",verify:"<0>Step 3:</0> Verify that the new repo is properly set up by running the following command",install:"<0>Step 4:</0> Install OpenVINO Runtime"}}},cm={frameworks:{install:"Install Frameworks",tooltip:`We assume that needed frameworks are already installed in your environment. If not, you can install them with OpenVINO toolkit.
+Available frameworks: Caffe, Kaldi, Apache MXNet, PyTorch, ONNX, TensorFlow.`},selectorForm:{titles:{install:"Install"}},distributions:{aptYum:{followInstructions:"Follow the <1>Installation Instructions</1>"},download:{useFollowingLink:"Use the following link:"}}},dm={selectorForm:lm,systemRequirements:am,distributions:um,compact:cm},pm={translation:dm,common:nm,core:sm},tr={en:pm,cn:Zh},js="en";if(!(js in tr))throw new Error(`'${js}' language is not supported`);ce.use(Vh).init({debug:!1,resources:tr,lng:js,fallbackLng:"en",interpolation:{escapeValue:!1},supportedLngs:Object.keys(tr),returnObjects:!1,returnNull:!1});ce.t;var Ds={},Us={},fm={get exports(){return Us},set exports(e){Us=e}},$e={},As={},hm={get exports(){return As},set exports(e){As=e}},qc={};/**
  * @license React
  * scheduler.production.min.js
  *
@@ -27,7 +27,7 @@ Available frameworks: Caffe, Kaldi, Apache MXNet, PyTorch, ONNX, TensorFlow.`},s
  *
  * This source code is licensed under the MIT license found in the
  * LICENSE file in the root directory of this source tree.
- */(function(e){function t(R,b){var F=R.length;R.push(b);e:for(;0<F;){var A=F-1>>>1,q=R[A];if(0<o(q,b))R[A]=b,R[F]=q,F=A;else break e}}function n(R){return R.length===0?null:R[0]}function r(R){if(R.length===0)return null;var b=R[0],F=R.pop();if(F!==b){R[0]=F;e:for(var A=0,q=R.length,Nt=q>>>1;A<Nt;){var Ve=2*(A+1)-1,Ln=R[Ve],Fe=Ve+1,Et=R[Fe];if(0>o(Ln,F))Fe<q&&0>o(Et,Ln)?(R[A]=Et,R[Fe]=F,A=Fe):(R[A]=Ln,R[Ve]=F,A=Ve);else if(Fe<q&&0>o(Et,F))R[A]=Et,R[Fe]=F,A=Fe;else break e}}return b}function o(R,b){var F=R.sortIndex-b.sortIndex;return F!==0?F:R.id-b.id}if(typeof performance=="object"&&typeof performance.now=="function"){var i=performance;e.unstable_now=function(){return i.now()}}else{var s=Date,l=s.now();e.unstable_now=function(){return s.now()-l}}var a=[],u=[],f=1,d=null,h=3,g=!1,v=!1,k=!1,O=typeof setTimeout=="function"?setTimeout:null,p=typeof clearTimeout=="function"?clearTimeout:null,c=typeof setImmediate<"u"?setImmediate:null;typeof navigator<"u"&&navigator.scheduling!==void 0&&navigator.scheduling.isInputPending!==void 0&&navigator.scheduling.isInputPending.bind(navigator.scheduling);function m(R){for(var b=n(u);b!==null;){if(b.callback===null)r(u);else if(b.startTime<=R)r(u),b.sortIndex=b.expirationTime,t(a,b);else break;b=n(u)}}function y(R){if(k=!1,m(R),!v)if(n(a)!==null)v=!0,ft(S);else{var b=n(u);b!==null&&et(y,b.startTime-R)}}function S(R,b){v=!1,k&&(k=!1,p(x),x=-1),g=!0;var F=h;try{for(m(b),d=n(a);d!==null&&(!(d.expirationTime>b)||R&&!K());){var A=d.callback;if(typeof A=="function"){d.callback=null,h=d.priorityLevel;var q=A(d.expirationTime<=b);b=e.unstable_now(),typeof q=="function"?d.callback=q:d===n(a)&&r(a),m(b)}else r(a);d=n(a)}if(d!==null)var Nt=!0;else{var Ve=n(u);Ve!==null&&et(y,Ve.startTime-b),Nt=!1}return Nt}finally{d=null,h=F,g=!1}}var w=!1,E=null,x=-1,V=5,P=-1;function K(){return!(e.unstable_now()-P<V)}function Ce(){if(E!==null){var R=e.unstable_now();P=R;var b=!0;try{b=E(!0,R)}finally{b?Ot():(w=!1,E=null)}}else w=!1}var Ot;if(typeof c=="function")Ot=function(){c(Ce)};else if(typeof MessageChannel<"u"){var Ze=new MessageChannel,Tn=Ze.port2;Ze.port1.onmessage=Ce,Ot=function(){Tn.postMessage(null)}}else Ot=function(){O(Ce,0)};function ft(R){E=R,w||(w=!0,Ot())}function et(R,b){x=O(function(){R(e.unstable_now())},b)}e.unstable_IdlePriority=5,e.unstable_ImmediatePriority=1,e.unstable_LowPriority=4,e.unstable_NormalPriority=3,e.unstable_Profiling=null,e.unstable_UserBlockingPriority=2,e.unstable_cancelCallback=function(R){R.callback=null},e.unstable_continueExecution=function(){v||g||(v=!0,ft(S))},e.unstable_forceFrameRate=function(R){0>R||125<R?console.error("forceFrameRate takes a positive int between 0 and 125, forcing frame rates higher than 125 fps is not supported"):V=0<R?Math.floor(1e3/R):5},e.unstable_getCurrentPriorityLevel=function(){return h},e.unstable_getFirstCallbackNode=function(){return n(a)},e.unstable_next=function(R){switch(h){case 1:case 2:case 3:var b=3;break;default:b=h}var F=h;h=b;try{return R()}finally{h=F}},e.unstable_pauseExecution=function(){},e.unstable_requestPaint=function(){},e.unstable_runWithPriority=function(R,b){switch(R){case 1:case 2:case 3:case 4:case 5:break;default:R=3}var F=h;h=R;try{return b()}finally{h=F}},e.unstable_scheduleCallback=function(R,b,F){var A=e.unstable_now();switch(typeof F=="object"&&F!==null?(F=F.delay,F=typeof F=="number"&&0<F?A+F:A):F=A,R){case 1:var q=-1;break;case 2:q=250;break;case 5:q=1073741823;break;case 4:q=1e4;break;default:q=5e3}return q=F+q,R={id:f++,callback:b,priorityLevel:R,startTime:F,expirationTime:q,sortIndex:-1},F>A?(R.sortIndex=F,t(u,R),n(a)===null&&R===n(u)&&(k?(p(x),x=-1):k=!0,et(y,F-A))):(R.sortIndex=q,t(a,R),v||g||(v=!0,ft(S))),R},e.unstable_shouldYield=K,e.unstable_wrapCallback=function(R){var b=h;return function(){var F=h;h=b;try{return R.apply(this,arguments)}finally{h=F}}}})(Kc);(function(e){e.exports=Kc})(dm);/**
+ */(function(e){function t(R,I){var F=R.length;R.push(I);e:for(;0<F;){var z=F-1>>>1,X=R[z];if(0<o(X,I))R[z]=I,R[F]=X,F=z;else break e}}function n(R){return R.length===0?null:R[0]}function r(R){if(R.length===0)return null;var I=R[0],F=R.pop();if(F!==I){R[0]=F;e:for(var z=0,X=R.length,Et=X>>>1;z<Et;){var Ve=2*(z+1)-1,Ln=R[Ve],Fe=Ve+1,xt=R[Fe];if(0>o(Ln,F))Fe<X&&0>o(xt,Ln)?(R[z]=xt,R[Fe]=F,z=Fe):(R[z]=Ln,R[Ve]=F,z=Ve);else if(Fe<X&&0>o(xt,F))R[z]=xt,R[Fe]=F,z=Fe;else break e}}return I}function o(R,I){var F=R.sortIndex-I.sortIndex;return F!==0?F:R.id-I.id}if(typeof performance=="object"&&typeof performance.now=="function"){var i=performance;e.unstable_now=function(){return i.now()}}else{var s=Date,l=s.now();e.unstable_now=function(){return s.now()-l}}var a=[],u=[],f=1,d=null,h=3,g=!1,v=!1,k=!1,O=typeof setTimeout=="function"?setTimeout:null,p=typeof clearTimeout=="function"?clearTimeout:null,c=typeof setImmediate<"u"?setImmediate:null;typeof navigator<"u"&&navigator.scheduling!==void 0&&navigator.scheduling.isInputPending!==void 0&&navigator.scheduling.isInputPending.bind(navigator.scheduling);function m(R){for(var I=n(u);I!==null;){if(I.callback===null)r(u);else if(I.startTime<=R)r(u),I.sortIndex=I.expirationTime,t(a,I);else break;I=n(u)}}function y(R){if(k=!1,m(R),!v)if(n(a)!==null)v=!0,ft(S);else{var I=n(u);I!==null&&tt(y,I.startTime-R)}}function S(R,I){v=!1,k&&(k=!1,p(P),P=-1),g=!0;var F=h;try{for(m(I),d=n(a);d!==null&&(!(d.expirationTime>I)||R&&!W());){var z=d.callback;if(typeof z=="function"){d.callback=null,h=d.priorityLevel;var X=z(d.expirationTime<=I);I=e.unstable_now(),typeof X=="function"?d.callback=X:d===n(a)&&r(a),m(I)}else r(a);d=n(a)}if(d!==null)var Et=!0;else{var Ve=n(u);Ve!==null&&tt(y,Ve.startTime-I),Et=!1}return Et}finally{d=null,h=F,g=!1}}var w=!1,E=null,P=-1,V=5,C=-1;function W(){return!(e.unstable_now()-C<V)}function Ce(){if(E!==null){var R=e.unstable_now();C=R;var I=!0;try{I=E(!0,R)}finally{I?Nt():(w=!1,E=null)}}else w=!1}var Nt;if(typeof c=="function")Nt=function(){c(Ce)};else if(typeof MessageChannel<"u"){var et=new MessageChannel,bn=et.port2;et.port1.onmessage=Ce,Nt=function(){bn.postMessage(null)}}else Nt=function(){O(Ce,0)};function ft(R){E=R,w||(w=!0,Nt())}function tt(R,I){P=O(function(){R(e.unstable_now())},I)}e.unstable_IdlePriority=5,e.unstable_ImmediatePriority=1,e.unstable_LowPriority=4,e.unstable_NormalPriority=3,e.unstable_Profiling=null,e.unstable_UserBlockingPriority=2,e.unstable_cancelCallback=function(R){R.callback=null},e.unstable_continueExecution=function(){v||g||(v=!0,ft(S))},e.unstable_forceFrameRate=function(R){0>R||125<R?console.error("forceFrameRate takes a positive int between 0 and 125, forcing frame rates higher than 125 fps is not supported"):V=0<R?Math.floor(1e3/R):5},e.unstable_getCurrentPriorityLevel=function(){return h},e.unstable_getFirstCallbackNode=function(){return n(a)},e.unstable_next=function(R){switch(h){case 1:case 2:case 3:var I=3;break;default:I=h}var F=h;h=I;try{return R()}finally{h=F}},e.unstable_pauseExecution=function(){},e.unstable_requestPaint=function(){},e.unstable_runWithPriority=function(R,I){switch(R){case 1:case 2:case 3:case 4:case 5:break;default:R=3}var F=h;h=R;try{return I()}finally{h=F}},e.unstable_scheduleCallback=function(R,I,F){var z=e.unstable_now();switch(typeof F=="object"&&F!==null?(F=F.delay,F=typeof F=="number"&&0<F?z+F:z):F=z,R){case 1:var X=-1;break;case 2:X=250;break;case 5:X=1073741823;break;case 4:X=1e4;break;default:X=5e3}return X=F+X,R={id:f++,callback:I,priorityLevel:R,startTime:F,expirationTime:X,sortIndex:-1},F>z?(R.sortIndex=F,t(u,R),n(a)===null&&R===n(u)&&(k?(p(P),P=-1):k=!0,tt(y,F-z))):(R.sortIndex=X,t(a,R),v||g||(v=!0,ft(S))),R},e.unstable_shouldYield=W,e.unstable_wrapCallback=function(R){var I=h;return function(){var F=h;h=I;try{return R.apply(this,arguments)}finally{h=F}}}})(qc);(function(e){e.exports=qc})(hm);/**
  * @license React
  * react-dom.production.min.js
  *
@@ -35,14 +35,21 @@ Available frameworks: Caffe, Kaldi, Apache MXNet, PyTorch, ONNX, TensorFlow.`},s
  *
  * This source code is licensed under the MIT license found in the
  * LICENSE file in the root directory of this source tree.
- */var Hc=D,Me=As;function N(e){for(var t="https://reactjs.org/docs/error-decoder.html?invariant="+e,n=1;n<arguments.length;n++)t+="&args[]="+encodeURIComponent(arguments[n]);return"Minified React error #"+e+"; visit "+t+" for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}var Wc=new Set,zr={};function Pn(e,t){nr(e,t),nr(e+"Capture",t)}function nr(e,t){for(zr[e]=t,e=0;e<t.length;e++)Wc.add(t[e])}var It=!(typeof window>"u"||typeof window.document>"u"||typeof window.document.createElement>"u"),zs=Object.prototype.hasOwnProperty,pm=/^[:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD][:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD\-.0-9\u00B7\u0300-\u036F\u203F-\u2040]*$/,gu={},vu={};function fm(e){return zs.call(vu,e)?!0:zs.call(gu,e)?!1:pm.test(e)?vu[e]=!0:(gu[e]=!0,!1)}function hm(e,t,n,r){if(n!==null&&n.type===0)return!1;switch(typeof t){case"function":case"symbol":return!0;case"boolean":return r?!1:n!==null?!n.acceptsBooleans:(e=e.toLowerCase().slice(0,5),e!=="data-"&&e!=="aria-");default:return!1}}function mm(e,t,n,r){if(t===null||typeof t>"u"||hm(e,t,n,r))return!0;if(r)return!1;if(n!==null)switch(n.type){case 3:return!t;case 4:return t===!1;case 5:return isNaN(t);case 6:return isNaN(t)||1>t}return!1}function Ee(e,t,n,r,o,i,s){this.acceptsBooleans=t===2||t===3||t===4,this.attributeName=r,this.attributeNamespace=o,this.mustUseProperty=n,this.propertyName=e,this.type=t,this.sanitizeURL=i,this.removeEmptyString=s}var me={};"children dangerouslySetInnerHTML defaultValue defaultChecked innerHTML suppressContentEditableWarning suppressHydrationWarning style".split(" ").forEach(function(e){me[e]=new Ee(e,0,!1,e,null,!1,!1)});[["acceptCharset","accept-charset"],["className","class"],["htmlFor","for"],["httpEquiv","http-equiv"]].forEach(function(e){var t=e[0];me[t]=new Ee(t,1,!1,e[1],null,!1,!1)});["contentEditable","draggable","spellCheck","value"].forEach(function(e){me[e]=new Ee(e,2,!1,e.toLowerCase(),null,!1,!1)});["autoReverse","externalResourcesRequired","focusable","preserveAlpha"].forEach(function(e){me[e]=new Ee(e,2,!1,e,null,!1,!1)});"allowFullScreen async autoFocus autoPlay controls default defer disabled disablePictureInPicture disableRemotePlayback formNoValidate hidden loop noModule noValidate open playsInline readOnly required reversed scoped seamless itemScope".split(" ").forEach(function(e){me[e]=new Ee(e,3,!1,e.toLowerCase(),null,!1,!1)});["checked","multiple","muted","selected"].forEach(function(e){me[e]=new Ee(e,3,!0,e,null,!1,!1)});["capture","download"].forEach(function(e){me[e]=new Ee(e,4,!1,e,null,!1,!1)});["cols","rows","size","span"].forEach(function(e){me[e]=new Ee(e,6,!1,e,null,!1,!1)});["rowSpan","start"].forEach(function(e){me[e]=new Ee(e,5,!1,e.toLowerCase(),null,!1,!1)});var Ml=/[\-:]([a-z])/g;function $l(e){return e[1].toUpperCase()}"accent-height alignment-baseline arabic-form baseline-shift cap-height clip-path clip-rule color-interpolation color-interpolation-filters color-profile color-rendering dominant-baseline enable-background fill-opacity fill-rule flood-color flood-opacity font-family font-size font-size-adjust font-stretch font-style font-variant font-weight glyph-name glyph-orientation-horizontal glyph-orientation-vertical horiz-adv-x horiz-origin-x image-rendering letter-spacing lighting-color marker-end marker-mid marker-start overline-position overline-thickness paint-order panose-1 pointer-events rendering-intent shape-rendering stop-color stop-opacity strikethrough-position strikethrough-thickness stroke-dasharray stroke-dashoffset stroke-linecap stroke-linejoin stroke-miterlimit stroke-opacity stroke-width text-anchor text-decoration text-rendering underline-position underline-thickness unicode-bidi unicode-range units-per-em v-alphabetic v-hanging v-ideographic v-mathematical vector-effect vert-adv-y vert-origin-x vert-origin-y word-spacing writing-mode xmlns:xlink x-height".split(" ").forEach(function(e){var t=e.replace(Ml,$l);me[t]=new Ee(t,1,!1,e,null,!1,!1)});"xlink:actuate xlink:arcrole xlink:role xlink:show xlink:title xlink:type".split(" ").forEach(function(e){var t=e.replace(Ml,$l);me[t]=new Ee(t,1,!1,e,"http://www.w3.org/1999/xlink",!1,!1)});["xml:base","xml:lang","xml:space"].forEach(function(e){var t=e.replace(Ml,$l);me[t]=new Ee(t,1,!1,e,"http://www.w3.org/XML/1998/namespace",!1,!1)});["tabIndex","crossOrigin"].forEach(function(e){me[e]=new Ee(e,1,!1,e.toLowerCase(),null,!1,!1)});me.xlinkHref=new Ee("xlinkHref",1,!1,"xlink:href","http://www.w3.org/1999/xlink",!0,!1);["src","href","action","formAction"].forEach(function(e){me[e]=new Ee(e,1,!1,e.toLowerCase(),null,!0,!0)});function Bl(e,t,n,r){var o=me.hasOwnProperty(t)?me[t]:null;(o!==null?o.type!==0:r||!(2<t.length)||t[0]!=="o"&&t[0]!=="O"||t[1]!=="n"&&t[1]!=="N")&&(mm(t,n,o,r)&&(n=null),r||o===null?fm(t)&&(n===null?e.removeAttribute(t):e.setAttribute(t,""+n)):o.mustUseProperty?e[o.propertyName]=n===null?o.type===3?!1:"":n:(t=o.attributeName,r=o.attributeNamespace,n===null?e.removeAttribute(t):(o=o.type,n=o===3||o===4&&n===!0?"":""+n,r?e.setAttributeNS(r,t,n):e.setAttribute(t,n))))}var Dt=Hc.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED,vo=Symbol.for("react.element"),jn=Symbol.for("react.portal"),Un=Symbol.for("react.fragment"),Kl=Symbol.for("react.strict_mode"),Ms=Symbol.for("react.profiler"),Yc=Symbol.for("react.provider"),Gc=Symbol.for("react.context"),Hl=Symbol.for("react.forward_ref"),$s=Symbol.for("react.suspense"),Bs=Symbol.for("react.suspense_list"),Wl=Symbol.for("react.memo"),At=Symbol.for("react.lazy"),Qc=Symbol.for("react.offscreen"),yu=Symbol.iterator;function mr(e){return e===null||typeof e!="object"?null:(e=yu&&e[yu]||e["@@iterator"],typeof e=="function"?e:null)}var ee=Object.assign,ns;function xr(e){if(ns===void 0)try{throw Error()}catch(n){var t=n.stack.trim().match(/\n( *(at )?)/);ns=t&&t[1]||""}return`
+ */var Qc=j,Me=As;function N(e){for(var t="https://reactjs.org/docs/error-decoder.html?invariant="+e,n=1;n<arguments.length;n++)t+="&args[]="+encodeURIComponent(arguments[n]);return"Minified React error #"+e+"; visit "+t+" for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}var Xc=new Set,zr={};function Cn(e,t){nr(e,t),nr(e+"Capture",t)}function nr(e,t){for(zr[e]=t,e=0;e<t.length;e++)Xc.add(t[e])}var It=!(typeof window>"u"||typeof window.document>"u"||typeof window.document.createElement>"u"),zs=Object.prototype.hasOwnProperty,mm=/^[:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD][:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD\-.0-9\u00B7\u0300-\u036F\u203F-\u2040]*$/,_u={},ku={};function gm(e){return zs.call(ku,e)?!0:zs.call(_u,e)?!1:mm.test(e)?ku[e]=!0:(_u[e]=!0,!1)}function vm(e,t,n,r){if(n!==null&&n.type===0)return!1;switch(typeof t){case"function":case"symbol":return!0;case"boolean":return r?!1:n!==null?!n.acceptsBooleans:(e=e.toLowerCase().slice(0,5),e!=="data-"&&e!=="aria-");default:return!1}}function ym(e,t,n,r){if(t===null||typeof t>"u"||vm(e,t,n,r))return!0;if(r)return!1;if(n!==null)switch(n.type){case 3:return!t;case 4:return t===!1;case 5:return isNaN(t);case 6:return isNaN(t)||1>t}return!1}function Ee(e,t,n,r,o,i,s){this.acceptsBooleans=t===2||t===3||t===4,this.attributeName=r,this.attributeNamespace=o,this.mustUseProperty=n,this.propertyName=e,this.type=t,this.sanitizeURL=i,this.removeEmptyString=s}var me={};"children dangerouslySetInnerHTML defaultValue defaultChecked innerHTML suppressContentEditableWarning suppressHydrationWarning style".split(" ").forEach(function(e){me[e]=new Ee(e,0,!1,e,null,!1,!1)});[["acceptCharset","accept-charset"],["className","class"],["htmlFor","for"],["httpEquiv","http-equiv"]].forEach(function(e){var t=e[0];me[t]=new Ee(t,1,!1,e[1],null,!1,!1)});["contentEditable","draggable","spellCheck","value"].forEach(function(e){me[e]=new Ee(e,2,!1,e.toLowerCase(),null,!1,!1)});["autoReverse","externalResourcesRequired","focusable","preserveAlpha"].forEach(function(e){me[e]=new Ee(e,2,!1,e,null,!1,!1)});"allowFullScreen async autoFocus autoPlay controls default defer disabled disablePictureInPicture disableRemotePlayback formNoValidate hidden loop noModule noValidate open playsInline readOnly required reversed scoped seamless itemScope".split(" ").forEach(function(e){me[e]=new Ee(e,3,!1,e.toLowerCase(),null,!1,!1)});["checked","multiple","muted","selected"].forEach(function(e){me[e]=new Ee(e,3,!0,e,null,!1,!1)});["capture","download"].forEach(function(e){me[e]=new Ee(e,4,!1,e,null,!1,!1)});["cols","rows","size","span"].forEach(function(e){me[e]=new Ee(e,6,!1,e,null,!1,!1)});["rowSpan","start"].forEach(function(e){me[e]=new Ee(e,5,!1,e.toLowerCase(),null,!1,!1)});var Ml=/[\-:]([a-z])/g;function $l(e){return e[1].toUpperCase()}"accent-height alignment-baseline arabic-form baseline-shift cap-height clip-path clip-rule color-interpolation color-interpolation-filters color-profile color-rendering dominant-baseline enable-background fill-opacity fill-rule flood-color flood-opacity font-family font-size font-size-adjust font-stretch font-style font-variant font-weight glyph-name glyph-orientation-horizontal glyph-orientation-vertical horiz-adv-x horiz-origin-x image-rendering letter-spacing lighting-color marker-end marker-mid marker-start overline-position overline-thickness paint-order panose-1 pointer-events rendering-intent shape-rendering stop-color stop-opacity strikethrough-position strikethrough-thickness stroke-dasharray stroke-dashoffset stroke-linecap stroke-linejoin stroke-miterlimit stroke-opacity stroke-width text-anchor text-decoration text-rendering underline-position underline-thickness unicode-bidi unicode-range units-per-em v-alphabetic v-hanging v-ideographic v-mathematical vector-effect vert-adv-y vert-origin-x vert-origin-y word-spacing writing-mode xmlns:xlink x-height".split(" ").forEach(function(e){var t=e.replace(Ml,$l);me[t]=new Ee(t,1,!1,e,null,!1,!1)});"xlink:actuate xlink:arcrole xlink:role xlink:show xlink:title xlink:type".split(" ").forEach(function(e){var t=e.replace(Ml,$l);me[t]=new Ee(t,1,!1,e,"http://www.w3.org/1999/xlink",!1,!1)});["xml:base","xml:lang","xml:space"].forEach(function(e){var t=e.replace(Ml,$l);me[t]=new Ee(t,1,!1,e,"http://www.w3.org/XML/1998/namespace",!1,!1)});["tabIndex","crossOrigin"].forEach(function(e){me[e]=new Ee(e,1,!1,e.toLowerCase(),null,!1,!1)});me.xlinkHref=new Ee("xlinkHref",1,!1,"xlink:href","http://www.w3.org/1999/xlink",!0,!1);["src","href","action","formAction"].forEach(function(e){me[e]=new Ee(e,1,!1,e.toLowerCase(),null,!0,!0)});function Bl(e,t,n,r){var o=me.hasOwnProperty(t)?me[t]:null;(o!==null?o.type!==0:r||!(2<t.length)||t[0]!=="o"&&t[0]!=="O"||t[1]!=="n"&&t[1]!=="N")&&(ym(t,n,o,r)&&(n=null),r||o===null?gm(t)&&(n===null?e.removeAttribute(t):e.setAttribute(t,""+n)):o.mustUseProperty?e[o.propertyName]=n===null?o.type===3?!1:"":n:(t=o.attributeName,r=o.attributeNamespace,n===null?e.removeAttribute(t):(o=o.type,n=o===3||o===4&&n===!0?"":""+n,r?e.setAttributeNS(r,t,n):e.setAttribute(t,n))))}var Dt=Qc.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED,vo=Symbol.for("react.element"),Dn=Symbol.for("react.portal"),Un=Symbol.for("react.fragment"),Kl=Symbol.for("react.strict_mode"),Ms=Symbol.for("react.profiler"),Jc=Symbol.for("react.provider"),Zc=Symbol.for("react.context"),Hl=Symbol.for("react.forward_ref"),$s=Symbol.for("react.suspense"),Bs=Symbol.for("react.suspense_list"),Wl=Symbol.for("react.memo"),Mt=Symbol.for("react.lazy"),ed=Symbol.for("react.offscreen"),wu=Symbol.iterator;function mr(e){return e===null||typeof e!="object"?null:(e=wu&&e[wu]||e["@@iterator"],typeof e=="function"?e:null)}var ee=Object.assign,ns;function xr(e){if(ns===void 0)try{throw Error()}catch(n){var t=n.stack.trim().match(/\n( *(at )?)/);ns=t&&t[1]||""}return`
 `+ns+e}var rs=!1;function os(e,t){if(!e||rs)return"";rs=!0;var n=Error.prepareStackTrace;Error.prepareStackTrace=void 0;try{if(t)if(t=function(){throw Error()},Object.defineProperty(t.prototype,"props",{set:function(){throw Error()}}),typeof Reflect=="object"&&Reflect.construct){try{Reflect.construct(t,[])}catch(u){var r=u}Reflect.construct(e,[],t)}else{try{t.call()}catch(u){r=u}e.call(t.prototype)}else{try{throw Error()}catch(u){r=u}e()}}catch(u){if(u&&r&&typeof u.stack=="string"){for(var o=u.stack.split(`
 `),i=r.stack.split(`
 `),s=o.length-1,l=i.length-1;1<=s&&0<=l&&o[s]!==i[l];)l--;for(;1<=s&&0<=l;s--,l--)if(o[s]!==i[l]){if(s!==1||l!==1)do if(s--,l--,0>l||o[s]!==i[l]){var a=`
-`+o[s].replace(" at new "," at ");return e.displayName&&a.includes("<anonymous>")&&(a=a.replace("<anonymous>",e.displayName)),a}while(1<=s&&0<=l);break}}}finally{rs=!1,Error.prepareStackTrace=n}return(e=e?e.displayName||e.name:"")?xr(e):""}function gm(e){switch(e.tag){case 5:return xr(e.type);case 16:return xr("Lazy");case 13:return xr("Suspense");case 19:return xr("SuspenseList");case 0:case 2:case 15:return e=os(e.type,!1),e;case 11:return e=os(e.type.render,!1),e;case 1:return e=os(e.type,!0),e;default:return""}}function Ks(e){if(e==null)return null;if(typeof e=="function")return e.displayName||e.name||null;if(typeof e=="string")return e;switch(e){case Un:return"Fragment";case jn:return"Portal";case Ms:return"Profiler";case Kl:return"StrictMode";case $s:return"Suspense";case Bs:return"SuspenseList"}if(typeof e=="object")switch(e.$$typeof){case Gc:return(e.displayName||"Context")+".Consumer";case Yc:return(e._context.displayName||"Context")+".Provider";case Hl:var t=e.render;return e=e.displayName,e||(e=t.displayName||t.name||"",e=e!==""?"ForwardRef("+e+")":"ForwardRef"),e;case Wl:return t=e.displayName||null,t!==null?t:Ks(e.type)||"Memo";case At:t=e._payload,e=e._init;try{return Ks(e(t))}catch{}}return null}function vm(e){var t=e.type;switch(e.tag){case 24:return"Cache";case 9:return(t.displayName||"Context")+".Consumer";case 10:return(t._context.displayName||"Context")+".Provider";case 18:return"DehydratedFragment";case 11:return e=t.render,e=e.displayName||e.name||"",t.displayName||(e!==""?"ForwardRef("+e+")":"ForwardRef");case 7:return"Fragment";case 5:return t;case 4:return"Portal";case 3:return"Root";case 6:return"Text";case 16:return Ks(t);case 8:return t===Kl?"StrictMode":"Mode";case 22:return"Offscreen";case 12:return"Profiler";case 21:return"Scope";case 13:return"Suspense";case 19:return"SuspenseList";case 25:return"TracingMarker";case 1:case 0:case 17:case 2:case 14:case 15:if(typeof t=="function")return t.displayName||t.name||null;if(typeof t=="string")return t}return null}function nn(e){switch(typeof e){case"boolean":case"number":case"string":case"undefined":return e;case"object":return e;default:return""}}function qc(e){var t=e.type;return(e=e.nodeName)&&e.toLowerCase()==="input"&&(t==="checkbox"||t==="radio")}function ym(e){var t=qc(e)?"checked":"value",n=Object.getOwnPropertyDescriptor(e.constructor.prototype,t),r=""+e[t];if(!e.hasOwnProperty(t)&&typeof n<"u"&&typeof n.get=="function"&&typeof n.set=="function"){var o=n.get,i=n.set;return Object.defineProperty(e,t,{configurable:!0,get:function(){return o.call(this)},set:function(s){r=""+s,i.call(this,s)}}),Object.defineProperty(e,t,{enumerable:n.enumerable}),{getValue:function(){return r},setValue:function(s){r=""+s},stopTracking:function(){e._valueTracker=null,delete e[t]}}}}function yo(e){e._valueTracker||(e._valueTracker=ym(e))}function Xc(e){if(!e)return!1;var t=e._valueTracker;if(!t)return!0;var n=t.getValue(),r="";return e&&(r=qc(e)?e.checked?"true":"false":e.value),e=r,e!==n?(t.setValue(e),!0):!1}function si(e){if(e=e||(typeof document<"u"?document:void 0),typeof e>"u")return null;try{return e.activeElement||e.body}catch{return e.body}}function Hs(e,t){var n=t.checked;return ee({},t,{defaultChecked:void 0,defaultValue:void 0,value:void 0,checked:n??e._wrapperState.initialChecked})}function _u(e,t){var n=t.defaultValue==null?"":t.defaultValue,r=t.checked!=null?t.checked:t.defaultChecked;n=nn(t.value!=null?t.value:n),e._wrapperState={initialChecked:r,initialValue:n,controlled:t.type==="checkbox"||t.type==="radio"?t.checked!=null:t.value!=null}}function Jc(e,t){t=t.checked,t!=null&&Bl(e,"checked",t,!1)}function Ws(e,t){Jc(e,t);var n=nn(t.value),r=t.type;if(n!=null)r==="number"?(n===0&&e.value===""||e.value!=n)&&(e.value=""+n):e.value!==""+n&&(e.value=""+n);else if(r==="submit"||r==="reset"){e.removeAttribute("value");return}t.hasOwnProperty("value")?Ys(e,t.type,n):t.hasOwnProperty("defaultValue")&&Ys(e,t.type,nn(t.defaultValue)),t.checked==null&&t.defaultChecked!=null&&(e.defaultChecked=!!t.defaultChecked)}function ku(e,t,n){if(t.hasOwnProperty("value")||t.hasOwnProperty("defaultValue")){var r=t.type;if(!(r!=="submit"&&r!=="reset"||t.value!==void 0&&t.value!==null))return;t=""+e._wrapperState.initialValue,n||t===e.value||(e.value=t),e.defaultValue=t}n=e.name,n!==""&&(e.name=""),e.defaultChecked=!!e._wrapperState.initialChecked,n!==""&&(e.name=n)}function Ys(e,t,n){(t!=="number"||si(e.ownerDocument)!==e)&&(n==null?e.defaultValue=""+e._wrapperState.initialValue:e.defaultValue!==""+n&&(e.defaultValue=""+n))}var Pr=Array.isArray;function Qn(e,t,n,r){if(e=e.options,t){t={};for(var o=0;o<n.length;o++)t["$"+n[o]]=!0;for(n=0;n<e.length;n++)o=t.hasOwnProperty("$"+e[n].value),e[n].selected!==o&&(e[n].selected=o),o&&r&&(e[n].defaultSelected=!0)}else{for(n=""+nn(n),t=null,o=0;o<e.length;o++){if(e[o].value===n){e[o].selected=!0,r&&(e[o].defaultSelected=!0);return}t!==null||e[o].disabled||(t=e[o])}t!==null&&(t.selected=!0)}}function Gs(e,t){if(t.dangerouslySetInnerHTML!=null)throw Error(N(91));return ee({},t,{value:void 0,defaultValue:void 0,children:""+e._wrapperState.initialValue})}function wu(e,t){var n=t.value;if(n==null){if(n=t.children,t=t.defaultValue,n!=null){if(t!=null)throw Error(N(92));if(Pr(n)){if(1<n.length)throw Error(N(93));n=n[0]}t=n}t==null&&(t=""),n=t}e._wrapperState={initialValue:nn(n)}}function Zc(e,t){var n=nn(t.value),r=nn(t.defaultValue);n!=null&&(n=""+n,n!==e.value&&(e.value=n),t.defaultValue==null&&e.defaultValue!==n&&(e.defaultValue=n)),r!=null&&(e.defaultValue=""+r)}function Su(e){var t=e.textContent;t===e._wrapperState.initialValue&&t!==""&&t!==null&&(e.value=t)}function ed(e){switch(e){case"svg":return"http://www.w3.org/2000/svg";case"math":return"http://www.w3.org/1998/Math/MathML";default:return"http://www.w3.org/1999/xhtml"}}function Qs(e,t){return e==null||e==="http://www.w3.org/1999/xhtml"?ed(t):e==="http://www.w3.org/2000/svg"&&t==="foreignObject"?"http://www.w3.org/1999/xhtml":e}var _o,td=function(e){return typeof MSApp<"u"&&MSApp.execUnsafeLocalFunction?function(t,n,r,o){MSApp.execUnsafeLocalFunction(function(){return e(t,n,r,o)})}:e}(function(e,t){if(e.namespaceURI!=="http://www.w3.org/2000/svg"||"innerHTML"in e)e.innerHTML=t;else{for(_o=_o||document.createElement("div"),_o.innerHTML="<svg>"+t.valueOf().toString()+"</svg>",t=_o.firstChild;e.firstChild;)e.removeChild(e.firstChild);for(;t.firstChild;)e.appendChild(t.firstChild)}});function Mr(e,t){if(t){var n=e.firstChild;if(n&&n===e.lastChild&&n.nodeType===3){n.nodeValue=t;return}}e.textContent=t}var Tr={animationIterationCount:!0,aspectRatio:!0,borderImageOutset:!0,borderImageSlice:!0,borderImageWidth:!0,boxFlex:!0,boxFlexGroup:!0,boxOrdinalGroup:!0,columnCount:!0,columns:!0,flex:!0,flexGrow:!0,flexPositive:!0,flexShrink:!0,flexNegative:!0,flexOrder:!0,gridArea:!0,gridRow:!0,gridRowEnd:!0,gridRowSpan:!0,gridRowStart:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnSpan:!0,gridColumnStart:!0,fontWeight:!0,lineClamp:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,tabSize:!0,widows:!0,zIndex:!0,zoom:!0,fillOpacity:!0,floodOpacity:!0,stopOpacity:!0,strokeDasharray:!0,strokeDashoffset:!0,strokeMiterlimit:!0,strokeOpacity:!0,strokeWidth:!0},_m=["Webkit","ms","Moz","O"];Object.keys(Tr).forEach(function(e){_m.forEach(function(t){t=t+e.charAt(0).toUpperCase()+e.substring(1),Tr[t]=Tr[e]})});function nd(e,t,n){return t==null||typeof t=="boolean"||t===""?"":n||typeof t!="number"||t===0||Tr.hasOwnProperty(e)&&Tr[e]?(""+t).trim():t+"px"}function rd(e,t){e=e.style;for(var n in t)if(t.hasOwnProperty(n)){var r=n.indexOf("--")===0,o=nd(n,t[n],r);n==="float"&&(n="cssFloat"),r?e.setProperty(n,o):e[n]=o}}var km=ee({menuitem:!0},{area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0});function qs(e,t){if(t){if(km[e]&&(t.children!=null||t.dangerouslySetInnerHTML!=null))throw Error(N(137,e));if(t.dangerouslySetInnerHTML!=null){if(t.children!=null)throw Error(N(60));if(typeof t.dangerouslySetInnerHTML!="object"||!("__html"in t.dangerouslySetInnerHTML))throw Error(N(61))}if(t.style!=null&&typeof t.style!="object")throw Error(N(62))}}function Xs(e,t){if(e.indexOf("-")===-1)return typeof t.is=="string";switch(e){case"annotation-xml":case"color-profile":case"font-face":case"font-face-src":case"font-face-uri":case"font-face-format":case"font-face-name":case"missing-glyph":return!1;default:return!0}}var Js=null;function Yl(e){return e=e.target||e.srcElement||window,e.correspondingUseElement&&(e=e.correspondingUseElement),e.nodeType===3?e.parentNode:e}var Zs=null,qn=null,Xn=null;function Ou(e){if(e=lo(e)){if(typeof Zs!="function")throw Error(N(280));var t=e.stateNode;t&&(t=ji(t),Zs(e.stateNode,e.type,t))}}function od(e){qn?Xn?Xn.push(e):Xn=[e]:qn=e}function id(){if(qn){var e=qn,t=Xn;if(Xn=qn=null,Ou(e),t)for(e=0;e<t.length;e++)Ou(t[e])}}function sd(e,t){return e(t)}function ld(){}var is=!1;function ad(e,t,n){if(is)return e(t,n);is=!0;try{return sd(e,t,n)}finally{is=!1,(qn!==null||Xn!==null)&&(ld(),id())}}function $r(e,t){var n=e.stateNode;if(n===null)return null;var r=ji(n);if(r===null)return null;n=r[t];e:switch(t){case"onClick":case"onClickCapture":case"onDoubleClick":case"onDoubleClickCapture":case"onMouseDown":case"onMouseDownCapture":case"onMouseMove":case"onMouseMoveCapture":case"onMouseUp":case"onMouseUpCapture":case"onMouseEnter":(r=!r.disabled)||(e=e.type,r=!(e==="button"||e==="input"||e==="select"||e==="textarea")),e=!r;break e;default:e=!1}if(e)return null;if(n&&typeof n!="function")throw Error(N(231,t,typeof n));return n}var el=!1;if(It)try{var gr={};Object.defineProperty(gr,"passive",{get:function(){el=!0}}),window.addEventListener("test",gr,gr),window.removeEventListener("test",gr,gr)}catch{el=!1}function wm(e,t,n,r,o,i,s,l,a){var u=Array.prototype.slice.call(arguments,3);try{t.apply(n,u)}catch(f){this.onError(f)}}var Lr=!1,li=null,ai=!1,tl=null,Sm={onError:function(e){Lr=!0,li=e}};function Om(e,t,n,r,o,i,s,l,a){Lr=!1,li=null,wm.apply(Sm,arguments)}function Nm(e,t,n,r,o,i,s,l,a){if(Om.apply(this,arguments),Lr){if(Lr){var u=li;Lr=!1,li=null}else throw Error(N(198));ai||(ai=!0,tl=u)}}function Cn(e){var t=e,n=e;if(e.alternate)for(;t.return;)t=t.return;else{e=t;do t=e,t.flags&4098&&(n=t.return),e=t.return;while(e)}return t.tag===3?n:null}function ud(e){if(e.tag===13){var t=e.memoizedState;if(t===null&&(e=e.alternate,e!==null&&(t=e.memoizedState)),t!==null)return t.dehydrated}return null}function Nu(e){if(Cn(e)!==e)throw Error(N(188))}function Em(e){var t=e.alternate;if(!t){if(t=Cn(e),t===null)throw Error(N(188));return t!==e?null:e}for(var n=e,r=t;;){var o=n.return;if(o===null)break;var i=o.alternate;if(i===null){if(r=o.return,r!==null){n=r;continue}break}if(o.child===i.child){for(i=o.child;i;){if(i===n)return Nu(o),e;if(i===r)return Nu(o),t;i=i.sibling}throw Error(N(188))}if(n.return!==r.return)n=o,r=i;else{for(var s=!1,l=o.child;l;){if(l===n){s=!0,n=o,r=i;break}if(l===r){s=!0,r=o,n=i;break}l=l.sibling}if(!s){for(l=i.child;l;){if(l===n){s=!0,n=i,r=o;break}if(l===r){s=!0,r=i,n=o;break}l=l.sibling}if(!s)throw Error(N(189))}}if(n.alternate!==r)throw Error(N(190))}if(n.tag!==3)throw Error(N(188));return n.stateNode.current===n?e:t}function cd(e){return e=Em(e),e!==null?dd(e):null}function dd(e){if(e.tag===5||e.tag===6)return e;for(e=e.child;e!==null;){var t=dd(e);if(t!==null)return t;e=e.sibling}return null}var pd=Me.unstable_scheduleCallback,Eu=Me.unstable_cancelCallback,xm=Me.unstable_shouldYield,Pm=Me.unstable_requestPaint,ne=Me.unstable_now,Cm=Me.unstable_getCurrentPriorityLevel,Gl=Me.unstable_ImmediatePriority,fd=Me.unstable_UserBlockingPriority,ui=Me.unstable_NormalPriority,Rm=Me.unstable_LowPriority,hd=Me.unstable_IdlePriority,bi=null,_t=null;function Tm(e){if(_t&&typeof _t.onCommitFiberRoot=="function")try{_t.onCommitFiberRoot(bi,e,void 0,(e.current.flags&128)===128)}catch{}}var st=Math.clz32?Math.clz32:bm,Lm=Math.log,Im=Math.LN2;function bm(e){return e>>>=0,e===0?32:31-(Lm(e)/Im|0)|0}var ko=64,wo=4194304;function Cr(e){switch(e&-e){case 1:return 1;case 2:return 2;case 4:return 4;case 8:return 8;case 16:return 16;case 32:return 32;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:return e&4194240;case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:return e&130023424;case 134217728:return 134217728;case 268435456:return 268435456;case 536870912:return 536870912;case 1073741824:return 1073741824;default:return e}}function ci(e,t){var n=e.pendingLanes;if(n===0)return 0;var r=0,o=e.suspendedLanes,i=e.pingedLanes,s=n&268435455;if(s!==0){var l=s&~o;l!==0?r=Cr(l):(i&=s,i!==0&&(r=Cr(i)))}else s=n&~o,s!==0?r=Cr(s):i!==0&&(r=Cr(i));if(r===0)return 0;if(t!==0&&t!==r&&!(t&o)&&(o=r&-r,i=t&-t,o>=i||o===16&&(i&4194240)!==0))return t;if(r&4&&(r|=n&16),t=e.entangledLanes,t!==0)for(e=e.entanglements,t&=r;0<t;)n=31-st(t),o=1<<n,r|=e[n],t&=~o;return r}function Vm(e,t){switch(e){case 1:case 2:case 4:return t+250;case 8:case 16:case 32:case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:return t+5e3;case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:return-1;case 134217728:case 268435456:case 536870912:case 1073741824:return-1;default:return-1}}function Fm(e,t){for(var n=e.suspendedLanes,r=e.pingedLanes,o=e.expirationTimes,i=e.pendingLanes;0<i;){var s=31-st(i),l=1<<s,a=o[s];a===-1?(!(l&n)||l&r)&&(o[s]=Vm(l,t)):a<=t&&(e.expiredLanes|=l),i&=~l}}function nl(e){return e=e.pendingLanes&-1073741825,e!==0?e:e&1073741824?1073741824:0}function md(){var e=ko;return ko<<=1,!(ko&4194240)&&(ko=64),e}function ss(e){for(var t=[],n=0;31>n;n++)t.push(e);return t}function io(e,t,n){e.pendingLanes|=t,t!==536870912&&(e.suspendedLanes=0,e.pingedLanes=0),e=e.eventTimes,t=31-st(t),e[t]=n}function Dm(e,t){var n=e.pendingLanes&~t;e.pendingLanes=t,e.suspendedLanes=0,e.pingedLanes=0,e.expiredLanes&=t,e.mutableReadLanes&=t,e.entangledLanes&=t,t=e.entanglements;var r=e.eventTimes;for(e=e.expirationTimes;0<n;){var o=31-st(n),i=1<<o;t[o]=0,r[o]=-1,e[o]=-1,n&=~i}}function Ql(e,t){var n=e.entangledLanes|=t;for(e=e.entanglements;n;){var r=31-st(n),o=1<<r;o&t|e[r]&t&&(e[r]|=t),n&=~o}}var M=0;function gd(e){return e&=-e,1<e?4<e?e&268435455?16:536870912:4:1}var vd,ql,yd,_d,kd,rl=!1,So=[],Yt=null,Gt=null,Qt=null,Br=new Map,Kr=new Map,Mt=[],jm="mousedown mouseup touchcancel touchend touchstart auxclick dblclick pointercancel pointerdown pointerup dragend dragstart drop compositionend compositionstart keydown keypress keyup input textInput copy cut paste click change contextmenu reset submit".split(" ");function xu(e,t){switch(e){case"focusin":case"focusout":Yt=null;break;case"dragenter":case"dragleave":Gt=null;break;case"mouseover":case"mouseout":Qt=null;break;case"pointerover":case"pointerout":Br.delete(t.pointerId);break;case"gotpointercapture":case"lostpointercapture":Kr.delete(t.pointerId)}}function vr(e,t,n,r,o,i){return e===null||e.nativeEvent!==i?(e={blockedOn:t,domEventName:n,eventSystemFlags:r,nativeEvent:i,targetContainers:[o]},t!==null&&(t=lo(t),t!==null&&ql(t)),e):(e.eventSystemFlags|=r,t=e.targetContainers,o!==null&&t.indexOf(o)===-1&&t.push(o),e)}function Um(e,t,n,r,o){switch(t){case"focusin":return Yt=vr(Yt,e,t,n,r,o),!0;case"dragenter":return Gt=vr(Gt,e,t,n,r,o),!0;case"mouseover":return Qt=vr(Qt,e,t,n,r,o),!0;case"pointerover":var i=o.pointerId;return Br.set(i,vr(Br.get(i)||null,e,t,n,r,o)),!0;case"gotpointercapture":return i=o.pointerId,Kr.set(i,vr(Kr.get(i)||null,e,t,n,r,o)),!0}return!1}function wd(e){var t=gn(e.target);if(t!==null){var n=Cn(t);if(n!==null){if(t=n.tag,t===13){if(t=ud(n),t!==null){e.blockedOn=t,kd(e.priority,function(){yd(n)});return}}else if(t===3&&n.stateNode.current.memoizedState.isDehydrated){e.blockedOn=n.tag===3?n.stateNode.containerInfo:null;return}}}e.blockedOn=null}function Wo(e){if(e.blockedOn!==null)return!1;for(var t=e.targetContainers;0<t.length;){var n=ol(e.domEventName,e.eventSystemFlags,t[0],e.nativeEvent);if(n===null){n=e.nativeEvent;var r=new n.constructor(n.type,n);Js=r,n.target.dispatchEvent(r),Js=null}else return t=lo(n),t!==null&&ql(t),e.blockedOn=n,!1;t.shift()}return!0}function Pu(e,t,n){Wo(e)&&n.delete(t)}function Am(){rl=!1,Yt!==null&&Wo(Yt)&&(Yt=null),Gt!==null&&Wo(Gt)&&(Gt=null),Qt!==null&&Wo(Qt)&&(Qt=null),Br.forEach(Pu),Kr.forEach(Pu)}function yr(e,t){e.blockedOn===t&&(e.blockedOn=null,rl||(rl=!0,Me.unstable_scheduleCallback(Me.unstable_NormalPriority,Am)))}function Hr(e){function t(o){return yr(o,e)}if(0<So.length){yr(So[0],e);for(var n=1;n<So.length;n++){var r=So[n];r.blockedOn===e&&(r.blockedOn=null)}}for(Yt!==null&&yr(Yt,e),Gt!==null&&yr(Gt,e),Qt!==null&&yr(Qt,e),Br.forEach(t),Kr.forEach(t),n=0;n<Mt.length;n++)r=Mt[n],r.blockedOn===e&&(r.blockedOn=null);for(;0<Mt.length&&(n=Mt[0],n.blockedOn===null);)wd(n),n.blockedOn===null&&Mt.shift()}var Jn=Dt.ReactCurrentBatchConfig,di=!0;function zm(e,t,n,r){var o=M,i=Jn.transition;Jn.transition=null;try{M=1,Xl(e,t,n,r)}finally{M=o,Jn.transition=i}}function Mm(e,t,n,r){var o=M,i=Jn.transition;Jn.transition=null;try{M=4,Xl(e,t,n,r)}finally{M=o,Jn.transition=i}}function Xl(e,t,n,r){if(di){var o=ol(e,t,n,r);if(o===null)gs(e,t,r,pi,n),xu(e,r);else if(Um(o,e,t,n,r))r.stopPropagation();else if(xu(e,r),t&4&&-1<jm.indexOf(e)){for(;o!==null;){var i=lo(o);if(i!==null&&vd(i),i=ol(e,t,n,r),i===null&&gs(e,t,r,pi,n),i===o)break;o=i}o!==null&&r.stopPropagation()}else gs(e,t,r,null,n)}}var pi=null;function ol(e,t,n,r){if(pi=null,e=Yl(r),e=gn(e),e!==null)if(t=Cn(e),t===null)e=null;else if(n=t.tag,n===13){if(e=ud(t),e!==null)return e;e=null}else if(n===3){if(t.stateNode.current.memoizedState.isDehydrated)return t.tag===3?t.stateNode.containerInfo:null;e=null}else t!==e&&(e=null);return pi=e,null}function Sd(e){switch(e){case"cancel":case"click":case"close":case"contextmenu":case"copy":case"cut":case"auxclick":case"dblclick":case"dragend":case"dragstart":case"drop":case"focusin":case"focusout":case"input":case"invalid":case"keydown":case"keypress":case"keyup":case"mousedown":case"mouseup":case"paste":case"pause":case"play":case"pointercancel":case"pointerdown":case"pointerup":case"ratechange":case"reset":case"resize":case"seeked":case"submit":case"touchcancel":case"touchend":case"touchstart":case"volumechange":case"change":case"selectionchange":case"textInput":case"compositionstart":case"compositionend":case"compositionupdate":case"beforeblur":case"afterblur":case"beforeinput":case"blur":case"fullscreenchange":case"focus":case"hashchange":case"popstate":case"select":case"selectstart":return 1;case"drag":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"mousemove":case"mouseout":case"mouseover":case"pointermove":case"pointerout":case"pointerover":case"scroll":case"toggle":case"touchmove":case"wheel":case"mouseenter":case"mouseleave":case"pointerenter":case"pointerleave":return 4;case"message":switch(Cm()){case Gl:return 1;case fd:return 4;case ui:case Rm:return 16;case hd:return 536870912;default:return 16}default:return 16}}var Kt=null,Jl=null,Yo=null;function Od(){if(Yo)return Yo;var e,t=Jl,n=t.length,r,o="value"in Kt?Kt.value:Kt.textContent,i=o.length;for(e=0;e<n&&t[e]===o[e];e++);var s=n-e;for(r=1;r<=s&&t[n-r]===o[i-r];r++);return Yo=o.slice(e,1<r?1-r:void 0)}function Go(e){var t=e.keyCode;return"charCode"in e?(e=e.charCode,e===0&&t===13&&(e=13)):e=t,e===10&&(e=13),32<=e||e===13?e:0}function Oo(){return!0}function Cu(){return!1}function Be(e){function t(n,r,o,i,s){this._reactName=n,this._targetInst=o,this.type=r,this.nativeEvent=i,this.target=s,this.currentTarget=null;for(var l in e)e.hasOwnProperty(l)&&(n=e[l],this[l]=n?n(i):i[l]);return this.isDefaultPrevented=(i.defaultPrevented!=null?i.defaultPrevented:i.returnValue===!1)?Oo:Cu,this.isPropagationStopped=Cu,this}return ee(t.prototype,{preventDefault:function(){this.defaultPrevented=!0;var n=this.nativeEvent;n&&(n.preventDefault?n.preventDefault():typeof n.returnValue!="unknown"&&(n.returnValue=!1),this.isDefaultPrevented=Oo)},stopPropagation:function(){var n=this.nativeEvent;n&&(n.stopPropagation?n.stopPropagation():typeof n.cancelBubble!="unknown"&&(n.cancelBubble=!0),this.isPropagationStopped=Oo)},persist:function(){},isPersistent:Oo}),t}var cr={eventPhase:0,bubbles:0,cancelable:0,timeStamp:function(e){return e.timeStamp||Date.now()},defaultPrevented:0,isTrusted:0},Zl=Be(cr),so=ee({},cr,{view:0,detail:0}),$m=Be(so),ls,as,_r,Vi=ee({},so,{screenX:0,screenY:0,clientX:0,clientY:0,pageX:0,pageY:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,getModifierState:ea,button:0,buttons:0,relatedTarget:function(e){return e.relatedTarget===void 0?e.fromElement===e.srcElement?e.toElement:e.fromElement:e.relatedTarget},movementX:function(e){return"movementX"in e?e.movementX:(e!==_r&&(_r&&e.type==="mousemove"?(ls=e.screenX-_r.screenX,as=e.screenY-_r.screenY):as=ls=0,_r=e),ls)},movementY:function(e){return"movementY"in e?e.movementY:as}}),Ru=Be(Vi),Bm=ee({},Vi,{dataTransfer:0}),Km=Be(Bm),Hm=ee({},so,{relatedTarget:0}),us=Be(Hm),Wm=ee({},cr,{animationName:0,elapsedTime:0,pseudoElement:0}),Ym=Be(Wm),Gm=ee({},cr,{clipboardData:function(e){return"clipboardData"in e?e.clipboardData:window.clipboardData}}),Qm=Be(Gm),qm=ee({},cr,{data:0}),Tu=Be(qm),Xm={Esc:"Escape",Spacebar:" ",Left:"ArrowLeft",Up:"ArrowUp",Right:"ArrowRight",Down:"ArrowDown",Del:"Delete",Win:"OS",Menu:"ContextMenu",Apps:"ContextMenu",Scroll:"ScrollLock",MozPrintableKey:"Unidentified"},Jm={8:"Backspace",9:"Tab",12:"Clear",13:"Enter",16:"Shift",17:"Control",18:"Alt",19:"Pause",20:"CapsLock",27:"Escape",32:" ",33:"PageUp",34:"PageDown",35:"End",36:"Home",37:"ArrowLeft",38:"ArrowUp",39:"ArrowRight",40:"ArrowDown",45:"Insert",46:"Delete",112:"F1",113:"F2",114:"F3",115:"F4",116:"F5",117:"F6",118:"F7",119:"F8",120:"F9",121:"F10",122:"F11",123:"F12",144:"NumLock",145:"ScrollLock",224:"Meta"},Zm={Alt:"altKey",Control:"ctrlKey",Meta:"metaKey",Shift:"shiftKey"};function eg(e){var t=this.nativeEvent;return t.getModifierState?t.getModifierState(e):(e=Zm[e])?!!t[e]:!1}function ea(){return eg}var tg=ee({},so,{key:function(e){if(e.key){var t=Xm[e.key]||e.key;if(t!=="Unidentified")return t}return e.type==="keypress"?(e=Go(e),e===13?"Enter":String.fromCharCode(e)):e.type==="keydown"||e.type==="keyup"?Jm[e.keyCode]||"Unidentified":""},code:0,location:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,repeat:0,locale:0,getModifierState:ea,charCode:function(e){return e.type==="keypress"?Go(e):0},keyCode:function(e){return e.type==="keydown"||e.type==="keyup"?e.keyCode:0},which:function(e){return e.type==="keypress"?Go(e):e.type==="keydown"||e.type==="keyup"?e.keyCode:0}}),ng=Be(tg),rg=ee({},Vi,{pointerId:0,width:0,height:0,pressure:0,tangentialPressure:0,tiltX:0,tiltY:0,twist:0,pointerType:0,isPrimary:0}),Lu=Be(rg),og=ee({},so,{touches:0,targetTouches:0,changedTouches:0,altKey:0,metaKey:0,ctrlKey:0,shiftKey:0,getModifierState:ea}),ig=Be(og),sg=ee({},cr,{propertyName:0,elapsedTime:0,pseudoElement:0}),lg=Be(sg),ag=ee({},Vi,{deltaX:function(e){return"deltaX"in e?e.deltaX:"wheelDeltaX"in e?-e.wheelDeltaX:0},deltaY:function(e){return"deltaY"in e?e.deltaY:"wheelDeltaY"in e?-e.wheelDeltaY:"wheelDelta"in e?-e.wheelDelta:0},deltaZ:0,deltaMode:0}),ug=Be(ag),cg=[9,13,27,32],ta=It&&"CompositionEvent"in window,Ir=null;It&&"documentMode"in document&&(Ir=document.documentMode);var dg=It&&"TextEvent"in window&&!Ir,Nd=It&&(!ta||Ir&&8<Ir&&11>=Ir),Iu=String.fromCharCode(32),bu=!1;function Ed(e,t){switch(e){case"keyup":return cg.indexOf(t.keyCode)!==-1;case"keydown":return t.keyCode!==229;case"keypress":case"mousedown":case"focusout":return!0;default:return!1}}function xd(e){return e=e.detail,typeof e=="object"&&"data"in e?e.data:null}var An=!1;function pg(e,t){switch(e){case"compositionend":return xd(t);case"keypress":return t.which!==32?null:(bu=!0,Iu);case"textInput":return e=t.data,e===Iu&&bu?null:e;default:return null}}function fg(e,t){if(An)return e==="compositionend"||!ta&&Ed(e,t)?(e=Od(),Yo=Jl=Kt=null,An=!1,e):null;switch(e){case"paste":return null;case"keypress":if(!(t.ctrlKey||t.altKey||t.metaKey)||t.ctrlKey&&t.altKey){if(t.char&&1<t.char.length)return t.char;if(t.which)return String.fromCharCode(t.which)}return null;case"compositionend":return Nd&&t.locale!=="ko"?null:t.data;default:return null}}var hg={color:!0,date:!0,datetime:!0,"datetime-local":!0,email:!0,month:!0,number:!0,password:!0,range:!0,search:!0,tel:!0,text:!0,time:!0,url:!0,week:!0};function Vu(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return t==="input"?!!hg[e.type]:t==="textarea"}function Pd(e,t,n,r){od(r),t=fi(t,"onChange"),0<t.length&&(n=new Zl("onChange","change",null,n,r),e.push({event:n,listeners:t}))}var br=null,Wr=null;function mg(e){Ud(e,0)}function Fi(e){var t=$n(e);if(Xc(t))return e}function gg(e,t){if(e==="change")return t}var Cd=!1;if(It){var cs;if(It){var ds="oninput"in document;if(!ds){var Fu=document.createElement("div");Fu.setAttribute("oninput","return;"),ds=typeof Fu.oninput=="function"}cs=ds}else cs=!1;Cd=cs&&(!document.documentMode||9<document.documentMode)}function Du(){br&&(br.detachEvent("onpropertychange",Rd),Wr=br=null)}function Rd(e){if(e.propertyName==="value"&&Fi(Wr)){var t=[];Pd(t,Wr,e,Yl(e)),ad(mg,t)}}function vg(e,t,n){e==="focusin"?(Du(),br=t,Wr=n,br.attachEvent("onpropertychange",Rd)):e==="focusout"&&Du()}function yg(e){if(e==="selectionchange"||e==="keyup"||e==="keydown")return Fi(Wr)}function _g(e,t){if(e==="click")return Fi(t)}function kg(e,t){if(e==="input"||e==="change")return Fi(t)}function wg(e,t){return e===t&&(e!==0||1/e===1/t)||e!==e&&t!==t}var ut=typeof Object.is=="function"?Object.is:wg;function Yr(e,t){if(ut(e,t))return!0;if(typeof e!="object"||e===null||typeof t!="object"||t===null)return!1;var n=Object.keys(e),r=Object.keys(t);if(n.length!==r.length)return!1;for(r=0;r<n.length;r++){var o=n[r];if(!zs.call(t,o)||!ut(e[o],t[o]))return!1}return!0}function ju(e){for(;e&&e.firstChild;)e=e.firstChild;return e}function Uu(e,t){var n=ju(e);e=0;for(var r;n;){if(n.nodeType===3){if(r=e+n.textContent.length,e<=t&&r>=t)return{node:n,offset:t-e};e=r}e:{for(;n;){if(n.nextSibling){n=n.nextSibling;break e}n=n.parentNode}n=void 0}n=ju(n)}}function Td(e,t){return e&&t?e===t?!0:e&&e.nodeType===3?!1:t&&t.nodeType===3?Td(e,t.parentNode):"contains"in e?e.contains(t):e.compareDocumentPosition?!!(e.compareDocumentPosition(t)&16):!1:!1}function Ld(){for(var e=window,t=si();t instanceof e.HTMLIFrameElement;){try{var n=typeof t.contentWindow.location.href=="string"}catch{n=!1}if(n)e=t.contentWindow;else break;t=si(e.document)}return t}function na(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return t&&(t==="input"&&(e.type==="text"||e.type==="search"||e.type==="tel"||e.type==="url"||e.type==="password")||t==="textarea"||e.contentEditable==="true")}function Sg(e){var t=Ld(),n=e.focusedElem,r=e.selectionRange;if(t!==n&&n&&n.ownerDocument&&Td(n.ownerDocument.documentElement,n)){if(r!==null&&na(n)){if(t=r.start,e=r.end,e===void 0&&(e=t),"selectionStart"in n)n.selectionStart=t,n.selectionEnd=Math.min(e,n.value.length);else if(e=(t=n.ownerDocument||document)&&t.defaultView||window,e.getSelection){e=e.getSelection();var o=n.textContent.length,i=Math.min(r.start,o);r=r.end===void 0?i:Math.min(r.end,o),!e.extend&&i>r&&(o=r,r=i,i=o),o=Uu(n,i);var s=Uu(n,r);o&&s&&(e.rangeCount!==1||e.anchorNode!==o.node||e.anchorOffset!==o.offset||e.focusNode!==s.node||e.focusOffset!==s.offset)&&(t=t.createRange(),t.setStart(o.node,o.offset),e.removeAllRanges(),i>r?(e.addRange(t),e.extend(s.node,s.offset)):(t.setEnd(s.node,s.offset),e.addRange(t)))}}for(t=[],e=n;e=e.parentNode;)e.nodeType===1&&t.push({element:e,left:e.scrollLeft,top:e.scrollTop});for(typeof n.focus=="function"&&n.focus(),n=0;n<t.length;n++)e=t[n],e.element.scrollLeft=e.left,e.element.scrollTop=e.top}}var Og=It&&"documentMode"in document&&11>=document.documentMode,zn=null,il=null,Vr=null,sl=!1;function Au(e,t,n){var r=n.window===n?n.document:n.nodeType===9?n:n.ownerDocument;sl||zn==null||zn!==si(r)||(r=zn,"selectionStart"in r&&na(r)?r={start:r.selectionStart,end:r.selectionEnd}:(r=(r.ownerDocument&&r.ownerDocument.defaultView||window).getSelection(),r={anchorNode:r.anchorNode,anchorOffset:r.anchorOffset,focusNode:r.focusNode,focusOffset:r.focusOffset}),Vr&&Yr(Vr,r)||(Vr=r,r=fi(il,"onSelect"),0<r.length&&(t=new Zl("onSelect","select",null,t,n),e.push({event:t,listeners:r}),t.target=zn)))}function No(e,t){var n={};return n[e.toLowerCase()]=t.toLowerCase(),n["Webkit"+e]="webkit"+t,n["Moz"+e]="moz"+t,n}var Mn={animationend:No("Animation","AnimationEnd"),animationiteration:No("Animation","AnimationIteration"),animationstart:No("Animation","AnimationStart"),transitionend:No("Transition","TransitionEnd")},ps={},Id={};It&&(Id=document.createElement("div").style,"AnimationEvent"in window||(delete Mn.animationend.animation,delete Mn.animationiteration.animation,delete Mn.animationstart.animation),"TransitionEvent"in window||delete Mn.transitionend.transition);function Di(e){if(ps[e])return ps[e];if(!Mn[e])return e;var t=Mn[e],n;for(n in t)if(t.hasOwnProperty(n)&&n in Id)return ps[e]=t[n];return e}var bd=Di("animationend"),Vd=Di("animationiteration"),Fd=Di("animationstart"),Dd=Di("transitionend"),jd=new Map,zu="abort auxClick cancel canPlay canPlayThrough click close contextMenu copy cut drag dragEnd dragEnter dragExit dragLeave dragOver dragStart drop durationChange emptied encrypted ended error gotPointerCapture input invalid keyDown keyPress keyUp load loadedData loadedMetadata loadStart lostPointerCapture mouseDown mouseMove mouseOut mouseOver mouseUp paste pause play playing pointerCancel pointerDown pointerMove pointerOut pointerOver pointerUp progress rateChange reset resize seeked seeking stalled submit suspend timeUpdate touchCancel touchEnd touchStart volumeChange scroll toggle touchMove waiting wheel".split(" ");function sn(e,t){jd.set(e,t),Pn(t,[e])}for(var fs=0;fs<zu.length;fs++){var hs=zu[fs],Ng=hs.toLowerCase(),Eg=hs[0].toUpperCase()+hs.slice(1);sn(Ng,"on"+Eg)}sn(bd,"onAnimationEnd");sn(Vd,"onAnimationIteration");sn(Fd,"onAnimationStart");sn("dblclick","onDoubleClick");sn("focusin","onFocus");sn("focusout","onBlur");sn(Dd,"onTransitionEnd");nr("onMouseEnter",["mouseout","mouseover"]);nr("onMouseLeave",["mouseout","mouseover"]);nr("onPointerEnter",["pointerout","pointerover"]);nr("onPointerLeave",["pointerout","pointerover"]);Pn("onChange","change click focusin focusout input keydown keyup selectionchange".split(" "));Pn("onSelect","focusout contextmenu dragend focusin keydown keyup mousedown mouseup selectionchange".split(" "));Pn("onBeforeInput",["compositionend","keypress","textInput","paste"]);Pn("onCompositionEnd","compositionend focusout keydown keypress keyup mousedown".split(" "));Pn("onCompositionStart","compositionstart focusout keydown keypress keyup mousedown".split(" "));Pn("onCompositionUpdate","compositionupdate focusout keydown keypress keyup mousedown".split(" "));var Rr="abort canplay canplaythrough durationchange emptied encrypted ended error loadeddata loadedmetadata loadstart pause play playing progress ratechange resize seeked seeking stalled suspend timeupdate volumechange waiting".split(" "),xg=new Set("cancel close invalid load scroll toggle".split(" ").concat(Rr));function Mu(e,t,n){var r=e.type||"unknown-event";e.currentTarget=n,Nm(r,t,void 0,e),e.currentTarget=null}function Ud(e,t){t=(t&4)!==0;for(var n=0;n<e.length;n++){var r=e[n],o=r.event;r=r.listeners;e:{var i=void 0;if(t)for(var s=r.length-1;0<=s;s--){var l=r[s],a=l.instance,u=l.currentTarget;if(l=l.listener,a!==i&&o.isPropagationStopped())break e;Mu(o,l,u),i=a}else for(s=0;s<r.length;s++){if(l=r[s],a=l.instance,u=l.currentTarget,l=l.listener,a!==i&&o.isPropagationStopped())break e;Mu(o,l,u),i=a}}}if(ai)throw e=tl,ai=!1,tl=null,e}function H(e,t){var n=t[dl];n===void 0&&(n=t[dl]=new Set);var r=e+"__bubble";n.has(r)||(Ad(t,e,2,!1),n.add(r))}function ms(e,t,n){var r=0;t&&(r|=4),Ad(n,e,r,t)}var Eo="_reactListening"+Math.random().toString(36).slice(2);function Gr(e){if(!e[Eo]){e[Eo]=!0,Wc.forEach(function(n){n!=="selectionchange"&&(xg.has(n)||ms(n,!1,e),ms(n,!0,e))});var t=e.nodeType===9?e:e.ownerDocument;t===null||t[Eo]||(t[Eo]=!0,ms("selectionchange",!1,t))}}function Ad(e,t,n,r){switch(Sd(t)){case 1:var o=zm;break;case 4:o=Mm;break;default:o=Xl}n=o.bind(null,t,n,e),o=void 0,!el||t!=="touchstart"&&t!=="touchmove"&&t!=="wheel"||(o=!0),r?o!==void 0?e.addEventListener(t,n,{capture:!0,passive:o}):e.addEventListener(t,n,!0):o!==void 0?e.addEventListener(t,n,{passive:o}):e.addEventListener(t,n,!1)}function gs(e,t,n,r,o){var i=r;if(!(t&1)&&!(t&2)&&r!==null)e:for(;;){if(r===null)return;var s=r.tag;if(s===3||s===4){var l=r.stateNode.containerInfo;if(l===o||l.nodeType===8&&l.parentNode===o)break;if(s===4)for(s=r.return;s!==null;){var a=s.tag;if((a===3||a===4)&&(a=s.stateNode.containerInfo,a===o||a.nodeType===8&&a.parentNode===o))return;s=s.return}for(;l!==null;){if(s=gn(l),s===null)return;if(a=s.tag,a===5||a===6){r=i=s;continue e}l=l.parentNode}}r=r.return}ad(function(){var u=i,f=Yl(n),d=[];e:{var h=jd.get(e);if(h!==void 0){var g=Zl,v=e;switch(e){case"keypress":if(Go(n)===0)break e;case"keydown":case"keyup":g=ng;break;case"focusin":v="focus",g=us;break;case"focusout":v="blur",g=us;break;case"beforeblur":case"afterblur":g=us;break;case"click":if(n.button===2)break e;case"auxclick":case"dblclick":case"mousedown":case"mousemove":case"mouseup":case"mouseout":case"mouseover":case"contextmenu":g=Ru;break;case"drag":case"dragend":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"dragstart":case"drop":g=Km;break;case"touchcancel":case"touchend":case"touchmove":case"touchstart":g=ig;break;case bd:case Vd:case Fd:g=Ym;break;case Dd:g=lg;break;case"scroll":g=$m;break;case"wheel":g=ug;break;case"copy":case"cut":case"paste":g=Qm;break;case"gotpointercapture":case"lostpointercapture":case"pointercancel":case"pointerdown":case"pointermove":case"pointerout":case"pointerover":case"pointerup":g=Lu}var k=(t&4)!==0,O=!k&&e==="scroll",p=k?h!==null?h+"Capture":null:h;k=[];for(var c=u,m;c!==null;){m=c;var y=m.stateNode;if(m.tag===5&&y!==null&&(m=y,p!==null&&(y=$r(c,p),y!=null&&k.push(Qr(c,y,m)))),O)break;c=c.return}0<k.length&&(h=new g(h,v,null,n,f),d.push({event:h,listeners:k}))}}if(!(t&7)){e:{if(h=e==="mouseover"||e==="pointerover",g=e==="mouseout"||e==="pointerout",h&&n!==Js&&(v=n.relatedTarget||n.fromElement)&&(gn(v)||v[bt]))break e;if((g||h)&&(h=f.window===f?f:(h=f.ownerDocument)?h.defaultView||h.parentWindow:window,g?(v=n.relatedTarget||n.toElement,g=u,v=v?gn(v):null,v!==null&&(O=Cn(v),v!==O||v.tag!==5&&v.tag!==6)&&(v=null)):(g=null,v=u),g!==v)){if(k=Ru,y="onMouseLeave",p="onMouseEnter",c="mouse",(e==="pointerout"||e==="pointerover")&&(k=Lu,y="onPointerLeave",p="onPointerEnter",c="pointer"),O=g==null?h:$n(g),m=v==null?h:$n(v),h=new k(y,c+"leave",g,n,f),h.target=O,h.relatedTarget=m,y=null,gn(f)===u&&(k=new k(p,c+"enter",v,n,f),k.target=m,k.relatedTarget=O,y=k),O=y,g&&v)t:{for(k=g,p=v,c=0,m=k;m;m=Fn(m))c++;for(m=0,y=p;y;y=Fn(y))m++;for(;0<c-m;)k=Fn(k),c--;for(;0<m-c;)p=Fn(p),m--;for(;c--;){if(k===p||p!==null&&k===p.alternate)break t;k=Fn(k),p=Fn(p)}k=null}else k=null;g!==null&&$u(d,h,g,k,!1),v!==null&&O!==null&&$u(d,O,v,k,!0)}}e:{if(h=u?$n(u):window,g=h.nodeName&&h.nodeName.toLowerCase(),g==="select"||g==="input"&&h.type==="file")var S=gg;else if(Vu(h))if(Cd)S=kg;else{S=yg;var w=vg}else(g=h.nodeName)&&g.toLowerCase()==="input"&&(h.type==="checkbox"||h.type==="radio")&&(S=_g);if(S&&(S=S(e,u))){Pd(d,S,n,f);break e}w&&w(e,h,u),e==="focusout"&&(w=h._wrapperState)&&w.controlled&&h.type==="number"&&Ys(h,"number",h.value)}switch(w=u?$n(u):window,e){case"focusin":(Vu(w)||w.contentEditable==="true")&&(zn=w,il=u,Vr=null);break;case"focusout":Vr=il=zn=null;break;case"mousedown":sl=!0;break;case"contextmenu":case"mouseup":case"dragend":sl=!1,Au(d,n,f);break;case"selectionchange":if(Og)break;case"keydown":case"keyup":Au(d,n,f)}var E;if(ta)e:{switch(e){case"compositionstart":var x="onCompositionStart";break e;case"compositionend":x="onCompositionEnd";break e;case"compositionupdate":x="onCompositionUpdate";break e}x=void 0}else An?Ed(e,n)&&(x="onCompositionEnd"):e==="keydown"&&n.keyCode===229&&(x="onCompositionStart");x&&(Nd&&n.locale!=="ko"&&(An||x!=="onCompositionStart"?x==="onCompositionEnd"&&An&&(E=Od()):(Kt=f,Jl="value"in Kt?Kt.value:Kt.textContent,An=!0)),w=fi(u,x),0<w.length&&(x=new Tu(x,e,null,n,f),d.push({event:x,listeners:w}),E?x.data=E:(E=xd(n),E!==null&&(x.data=E)))),(E=dg?pg(e,n):fg(e,n))&&(u=fi(u,"onBeforeInput"),0<u.length&&(f=new Tu("onBeforeInput","beforeinput",null,n,f),d.push({event:f,listeners:u}),f.data=E))}Ud(d,t)})}function Qr(e,t,n){return{instance:e,listener:t,currentTarget:n}}function fi(e,t){for(var n=t+"Capture",r=[];e!==null;){var o=e,i=o.stateNode;o.tag===5&&i!==null&&(o=i,i=$r(e,n),i!=null&&r.unshift(Qr(e,i,o)),i=$r(e,t),i!=null&&r.push(Qr(e,i,o))),e=e.return}return r}function Fn(e){if(e===null)return null;do e=e.return;while(e&&e.tag!==5);return e||null}function $u(e,t,n,r,o){for(var i=t._reactName,s=[];n!==null&&n!==r;){var l=n,a=l.alternate,u=l.stateNode;if(a!==null&&a===r)break;l.tag===5&&u!==null&&(l=u,o?(a=$r(n,i),a!=null&&s.unshift(Qr(n,a,l))):o||(a=$r(n,i),a!=null&&s.push(Qr(n,a,l)))),n=n.return}s.length!==0&&e.push({event:t,listeners:s})}var Pg=/\r\n?/g,Cg=/\u0000|\uFFFD/g;function Bu(e){return(typeof e=="string"?e:""+e).replace(Pg,`
-`).replace(Cg,"")}function xo(e,t,n){if(t=Bu(t),Bu(e)!==t&&n)throw Error(N(425))}function hi(){}var ll=null,al=null;function ul(e,t){return e==="textarea"||e==="noscript"||typeof t.children=="string"||typeof t.children=="number"||typeof t.dangerouslySetInnerHTML=="object"&&t.dangerouslySetInnerHTML!==null&&t.dangerouslySetInnerHTML.__html!=null}var cl=typeof setTimeout=="function"?setTimeout:void 0,Rg=typeof clearTimeout=="function"?clearTimeout:void 0,Ku=typeof Promise=="function"?Promise:void 0,Tg=typeof queueMicrotask=="function"?queueMicrotask:typeof Ku<"u"?function(e){return Ku.resolve(null).then(e).catch(Lg)}:cl;function Lg(e){setTimeout(function(){throw e})}function vs(e,t){var n=t,r=0;do{var o=n.nextSibling;if(e.removeChild(n),o&&o.nodeType===8)if(n=o.data,n==="/$"){if(r===0){e.removeChild(o),Hr(t);return}r--}else n!=="$"&&n!=="$?"&&n!=="$!"||r++;n=o}while(n);Hr(t)}function qt(e){for(;e!=null;e=e.nextSibling){var t=e.nodeType;if(t===1||t===3)break;if(t===8){if(t=e.data,t==="$"||t==="$!"||t==="$?")break;if(t==="/$")return null}}return e}function Hu(e){e=e.previousSibling;for(var t=0;e;){if(e.nodeType===8){var n=e.data;if(n==="$"||n==="$!"||n==="$?"){if(t===0)return e;t--}else n==="/$"&&t++}e=e.previousSibling}return null}var dr=Math.random().toString(36).slice(2),vt="__reactFiber$"+dr,qr="__reactProps$"+dr,bt="__reactContainer$"+dr,dl="__reactEvents$"+dr,Ig="__reactListeners$"+dr,bg="__reactHandles$"+dr;function gn(e){var t=e[vt];if(t)return t;for(var n=e.parentNode;n;){if(t=n[bt]||n[vt]){if(n=t.alternate,t.child!==null||n!==null&&n.child!==null)for(e=Hu(e);e!==null;){if(n=e[vt])return n;e=Hu(e)}return t}e=n,n=e.parentNode}return null}function lo(e){return e=e[vt]||e[bt],!e||e.tag!==5&&e.tag!==6&&e.tag!==13&&e.tag!==3?null:e}function $n(e){if(e.tag===5||e.tag===6)return e.stateNode;throw Error(N(33))}function ji(e){return e[qr]||null}var pl=[],Bn=-1;function ln(e){return{current:e}}function G(e){0>Bn||(e.current=pl[Bn],pl[Bn]=null,Bn--)}function $(e,t){Bn++,pl[Bn]=e.current,e.current=t}var rn={},_e=ln(rn),Le=ln(!1),wn=rn;function rr(e,t){var n=e.type.contextTypes;if(!n)return rn;var r=e.stateNode;if(r&&r.__reactInternalMemoizedUnmaskedChildContext===t)return r.__reactInternalMemoizedMaskedChildContext;var o={},i;for(i in n)o[i]=t[i];return r&&(e=e.stateNode,e.__reactInternalMemoizedUnmaskedChildContext=t,e.__reactInternalMemoizedMaskedChildContext=o),o}function Ie(e){return e=e.childContextTypes,e!=null}function mi(){G(Le),G(_e)}function Wu(e,t,n){if(_e.current!==rn)throw Error(N(168));$(_e,t),$(Le,n)}function zd(e,t,n){var r=e.stateNode;if(t=t.childContextTypes,typeof r.getChildContext!="function")return n;r=r.getChildContext();for(var o in r)if(!(o in t))throw Error(N(108,vm(e)||"Unknown",o));return ee({},n,r)}function gi(e){return e=(e=e.stateNode)&&e.__reactInternalMemoizedMergedChildContext||rn,wn=_e.current,$(_e,e),$(Le,Le.current),!0}function Yu(e,t,n){var r=e.stateNode;if(!r)throw Error(N(169));n?(e=zd(e,t,wn),r.__reactInternalMemoizedMergedChildContext=e,G(Le),G(_e),$(_e,e)):G(Le),$(Le,n)}var Ct=null,Ui=!1,ys=!1;function Md(e){Ct===null?Ct=[e]:Ct.push(e)}function Vg(e){Ui=!0,Md(e)}function an(){if(!ys&&Ct!==null){ys=!0;var e=0,t=M;try{var n=Ct;for(M=1;e<n.length;e++){var r=n[e];do r=r(!0);while(r!==null)}Ct=null,Ui=!1}catch(o){throw Ct!==null&&(Ct=Ct.slice(e+1)),pd(Gl,an),o}finally{M=t,ys=!1}}return null}var Kn=[],Hn=0,vi=null,yi=0,Ke=[],He=0,Sn=null,Rt=1,Tt="";function fn(e,t){Kn[Hn++]=yi,Kn[Hn++]=vi,vi=e,yi=t}function $d(e,t,n){Ke[He++]=Rt,Ke[He++]=Tt,Ke[He++]=Sn,Sn=e;var r=Rt;e=Tt;var o=32-st(r)-1;r&=~(1<<o),n+=1;var i=32-st(t)+o;if(30<i){var s=o-o%5;i=(r&(1<<s)-1).toString(32),r>>=s,o-=s,Rt=1<<32-st(t)+o|n<<o|r,Tt=i+e}else Rt=1<<i|n<<o|r,Tt=e}function ra(e){e.return!==null&&(fn(e,1),$d(e,1,0))}function oa(e){for(;e===vi;)vi=Kn[--Hn],Kn[Hn]=null,yi=Kn[--Hn],Kn[Hn]=null;for(;e===Sn;)Sn=Ke[--He],Ke[He]=null,Tt=Ke[--He],Ke[He]=null,Rt=Ke[--He],Ke[He]=null}var ze=null,Ae=null,Q=!1,it=null;function Bd(e,t){var n=We(5,null,null,0);n.elementType="DELETED",n.stateNode=t,n.return=e,t=e.deletions,t===null?(e.deletions=[n],e.flags|=16):t.push(n)}function Gu(e,t){switch(e.tag){case 5:var n=e.type;return t=t.nodeType!==1||n.toLowerCase()!==t.nodeName.toLowerCase()?null:t,t!==null?(e.stateNode=t,ze=e,Ae=qt(t.firstChild),!0):!1;case 6:return t=e.pendingProps===""||t.nodeType!==3?null:t,t!==null?(e.stateNode=t,ze=e,Ae=null,!0):!1;case 13:return t=t.nodeType!==8?null:t,t!==null?(n=Sn!==null?{id:Rt,overflow:Tt}:null,e.memoizedState={dehydrated:t,treeContext:n,retryLane:1073741824},n=We(18,null,null,0),n.stateNode=t,n.return=e,e.child=n,ze=e,Ae=null,!0):!1;default:return!1}}function fl(e){return(e.mode&1)!==0&&(e.flags&128)===0}function hl(e){if(Q){var t=Ae;if(t){var n=t;if(!Gu(e,t)){if(fl(e))throw Error(N(418));t=qt(n.nextSibling);var r=ze;t&&Gu(e,t)?Bd(r,n):(e.flags=e.flags&-4097|2,Q=!1,ze=e)}}else{if(fl(e))throw Error(N(418));e.flags=e.flags&-4097|2,Q=!1,ze=e}}}function Qu(e){for(e=e.return;e!==null&&e.tag!==5&&e.tag!==3&&e.tag!==13;)e=e.return;ze=e}function Po(e){if(e!==ze)return!1;if(!Q)return Qu(e),Q=!0,!1;var t;if((t=e.tag!==3)&&!(t=e.tag!==5)&&(t=e.type,t=t!=="head"&&t!=="body"&&!ul(e.type,e.memoizedProps)),t&&(t=Ae)){if(fl(e))throw Kd(),Error(N(418));for(;t;)Bd(e,t),t=qt(t.nextSibling)}if(Qu(e),e.tag===13){if(e=e.memoizedState,e=e!==null?e.dehydrated:null,!e)throw Error(N(317));e:{for(e=e.nextSibling,t=0;e;){if(e.nodeType===8){var n=e.data;if(n==="/$"){if(t===0){Ae=qt(e.nextSibling);break e}t--}else n!=="$"&&n!=="$!"&&n!=="$?"||t++}e=e.nextSibling}Ae=null}}else Ae=ze?qt(e.stateNode.nextSibling):null;return!0}function Kd(){for(var e=Ae;e;)e=qt(e.nextSibling)}function or(){Ae=ze=null,Q=!1}function ia(e){it===null?it=[e]:it.push(e)}var Fg=Dt.ReactCurrentBatchConfig;function rt(e,t){if(e&&e.defaultProps){t=ee({},t),e=e.defaultProps;for(var n in e)t[n]===void 0&&(t[n]=e[n]);return t}return t}var _i=ln(null),ki=null,Wn=null,sa=null;function la(){sa=Wn=ki=null}function aa(e){var t=_i.current;G(_i),e._currentValue=t}function ml(e,t,n){for(;e!==null;){var r=e.alternate;if((e.childLanes&t)!==t?(e.childLanes|=t,r!==null&&(r.childLanes|=t)):r!==null&&(r.childLanes&t)!==t&&(r.childLanes|=t),e===n)break;e=e.return}}function Zn(e,t){ki=e,sa=Wn=null,e=e.dependencies,e!==null&&e.firstContext!==null&&(e.lanes&t&&(Te=!0),e.firstContext=null)}function Ge(e){var t=e._currentValue;if(sa!==e)if(e={context:e,memoizedValue:t,next:null},Wn===null){if(ki===null)throw Error(N(308));Wn=e,ki.dependencies={lanes:0,firstContext:e}}else Wn=Wn.next=e;return t}var vn=null;function ua(e){vn===null?vn=[e]:vn.push(e)}function Hd(e,t,n,r){var o=t.interleaved;return o===null?(n.next=n,ua(t)):(n.next=o.next,o.next=n),t.interleaved=n,Vt(e,r)}function Vt(e,t){e.lanes|=t;var n=e.alternate;for(n!==null&&(n.lanes|=t),n=e,e=e.return;e!==null;)e.childLanes|=t,n=e.alternate,n!==null&&(n.childLanes|=t),n=e,e=e.return;return n.tag===3?n.stateNode:null}var zt=!1;function ca(e){e.updateQueue={baseState:e.memoizedState,firstBaseUpdate:null,lastBaseUpdate:null,shared:{pending:null,interleaved:null,lanes:0},effects:null}}function Wd(e,t){e=e.updateQueue,t.updateQueue===e&&(t.updateQueue={baseState:e.baseState,firstBaseUpdate:e.firstBaseUpdate,lastBaseUpdate:e.lastBaseUpdate,shared:e.shared,effects:e.effects})}function Lt(e,t){return{eventTime:e,lane:t,tag:0,payload:null,callback:null,next:null}}function Xt(e,t,n){var r=e.updateQueue;if(r===null)return null;if(r=r.shared,U&2){var o=r.pending;return o===null?t.next=t:(t.next=o.next,o.next=t),r.pending=t,Vt(e,n)}return o=r.interleaved,o===null?(t.next=t,ua(r)):(t.next=o.next,o.next=t),r.interleaved=t,Vt(e,n)}function Qo(e,t,n){if(t=t.updateQueue,t!==null&&(t=t.shared,(n&4194240)!==0)){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,Ql(e,n)}}function qu(e,t){var n=e.updateQueue,r=e.alternate;if(r!==null&&(r=r.updateQueue,n===r)){var o=null,i=null;if(n=n.firstBaseUpdate,n!==null){do{var s={eventTime:n.eventTime,lane:n.lane,tag:n.tag,payload:n.payload,callback:n.callback,next:null};i===null?o=i=s:i=i.next=s,n=n.next}while(n!==null);i===null?o=i=t:i=i.next=t}else o=i=t;n={baseState:r.baseState,firstBaseUpdate:o,lastBaseUpdate:i,shared:r.shared,effects:r.effects},e.updateQueue=n;return}e=n.lastBaseUpdate,e===null?n.firstBaseUpdate=t:e.next=t,n.lastBaseUpdate=t}function wi(e,t,n,r){var o=e.updateQueue;zt=!1;var i=o.firstBaseUpdate,s=o.lastBaseUpdate,l=o.shared.pending;if(l!==null){o.shared.pending=null;var a=l,u=a.next;a.next=null,s===null?i=u:s.next=u,s=a;var f=e.alternate;f!==null&&(f=f.updateQueue,l=f.lastBaseUpdate,l!==s&&(l===null?f.firstBaseUpdate=u:l.next=u,f.lastBaseUpdate=a))}if(i!==null){var d=o.baseState;s=0,f=u=a=null,l=i;do{var h=l.lane,g=l.eventTime;if((r&h)===h){f!==null&&(f=f.next={eventTime:g,lane:0,tag:l.tag,payload:l.payload,callback:l.callback,next:null});e:{var v=e,k=l;switch(h=t,g=n,k.tag){case 1:if(v=k.payload,typeof v=="function"){d=v.call(g,d,h);break e}d=v;break e;case 3:v.flags=v.flags&-65537|128;case 0:if(v=k.payload,h=typeof v=="function"?v.call(g,d,h):v,h==null)break e;d=ee({},d,h);break e;case 2:zt=!0}}l.callback!==null&&l.lane!==0&&(e.flags|=64,h=o.effects,h===null?o.effects=[l]:h.push(l))}else g={eventTime:g,lane:h,tag:l.tag,payload:l.payload,callback:l.callback,next:null},f===null?(u=f=g,a=d):f=f.next=g,s|=h;if(l=l.next,l===null){if(l=o.shared.pending,l===null)break;h=l,l=h.next,h.next=null,o.lastBaseUpdate=h,o.shared.pending=null}}while(1);if(f===null&&(a=d),o.baseState=a,o.firstBaseUpdate=u,o.lastBaseUpdate=f,t=o.shared.interleaved,t!==null){o=t;do s|=o.lane,o=o.next;while(o!==t)}else i===null&&(o.shared.lanes=0);Nn|=s,e.lanes=s,e.memoizedState=d}}function Xu(e,t,n){if(e=t.effects,t.effects=null,e!==null)for(t=0;t<e.length;t++){var r=e[t],o=r.callback;if(o!==null){if(r.callback=null,r=n,typeof o!="function")throw Error(N(191,o));o.call(r)}}}var Yd=new Hc.Component().refs;function gl(e,t,n,r){t=e.memoizedState,n=n(r,t),n=n==null?t:ee({},t,n),e.memoizedState=n,e.lanes===0&&(e.updateQueue.baseState=n)}var Ai={isMounted:function(e){return(e=e._reactInternals)?Cn(e)===e:!1},enqueueSetState:function(e,t,n){e=e._reactInternals;var r=Oe(),o=Zt(e),i=Lt(r,o);i.payload=t,n!=null&&(i.callback=n),t=Xt(e,i,o),t!==null&&(lt(t,e,o,r),Qo(t,e,o))},enqueueReplaceState:function(e,t,n){e=e._reactInternals;var r=Oe(),o=Zt(e),i=Lt(r,o);i.tag=1,i.payload=t,n!=null&&(i.callback=n),t=Xt(e,i,o),t!==null&&(lt(t,e,o,r),Qo(t,e,o))},enqueueForceUpdate:function(e,t){e=e._reactInternals;var n=Oe(),r=Zt(e),o=Lt(n,r);o.tag=2,t!=null&&(o.callback=t),t=Xt(e,o,r),t!==null&&(lt(t,e,r,n),Qo(t,e,r))}};function Ju(e,t,n,r,o,i,s){return e=e.stateNode,typeof e.shouldComponentUpdate=="function"?e.shouldComponentUpdate(r,i,s):t.prototype&&t.prototype.isPureReactComponent?!Yr(n,r)||!Yr(o,i):!0}function Gd(e,t,n){var r=!1,o=rn,i=t.contextType;return typeof i=="object"&&i!==null?i=Ge(i):(o=Ie(t)?wn:_e.current,r=t.contextTypes,i=(r=r!=null)?rr(e,o):rn),t=new t(n,i),e.memoizedState=t.state!==null&&t.state!==void 0?t.state:null,t.updater=Ai,e.stateNode=t,t._reactInternals=e,r&&(e=e.stateNode,e.__reactInternalMemoizedUnmaskedChildContext=o,e.__reactInternalMemoizedMaskedChildContext=i),t}function Zu(e,t,n,r){e=t.state,typeof t.componentWillReceiveProps=="function"&&t.componentWillReceiveProps(n,r),typeof t.UNSAFE_componentWillReceiveProps=="function"&&t.UNSAFE_componentWillReceiveProps(n,r),t.state!==e&&Ai.enqueueReplaceState(t,t.state,null)}function vl(e,t,n,r){var o=e.stateNode;o.props=n,o.state=e.memoizedState,o.refs=Yd,ca(e);var i=t.contextType;typeof i=="object"&&i!==null?o.context=Ge(i):(i=Ie(t)?wn:_e.current,o.context=rr(e,i)),o.state=e.memoizedState,i=t.getDerivedStateFromProps,typeof i=="function"&&(gl(e,t,i,n),o.state=e.memoizedState),typeof t.getDerivedStateFromProps=="function"||typeof o.getSnapshotBeforeUpdate=="function"||typeof o.UNSAFE_componentWillMount!="function"&&typeof o.componentWillMount!="function"||(t=o.state,typeof o.componentWillMount=="function"&&o.componentWillMount(),typeof o.UNSAFE_componentWillMount=="function"&&o.UNSAFE_componentWillMount(),t!==o.state&&Ai.enqueueReplaceState(o,o.state,null),wi(e,n,o,r),o.state=e.memoizedState),typeof o.componentDidMount=="function"&&(e.flags|=4194308)}function kr(e,t,n){if(e=n.ref,e!==null&&typeof e!="function"&&typeof e!="object"){if(n._owner){if(n=n._owner,n){if(n.tag!==1)throw Error(N(309));var r=n.stateNode}if(!r)throw Error(N(147,e));var o=r,i=""+e;return t!==null&&t.ref!==null&&typeof t.ref=="function"&&t.ref._stringRef===i?t.ref:(t=function(s){var l=o.refs;l===Yd&&(l=o.refs={}),s===null?delete l[i]:l[i]=s},t._stringRef=i,t)}if(typeof e!="string")throw Error(N(284));if(!n._owner)throw Error(N(290,e))}return e}function Co(e,t){throw e=Object.prototype.toString.call(t),Error(N(31,e==="[object Object]"?"object with keys {"+Object.keys(t).join(", ")+"}":e))}function ec(e){var t=e._init;return t(e._payload)}function Qd(e){function t(p,c){if(e){var m=p.deletions;m===null?(p.deletions=[c],p.flags|=16):m.push(c)}}function n(p,c){if(!e)return null;for(;c!==null;)t(p,c),c=c.sibling;return null}function r(p,c){for(p=new Map;c!==null;)c.key!==null?p.set(c.key,c):p.set(c.index,c),c=c.sibling;return p}function o(p,c){return p=en(p,c),p.index=0,p.sibling=null,p}function i(p,c,m){return p.index=m,e?(m=p.alternate,m!==null?(m=m.index,m<c?(p.flags|=2,c):m):(p.flags|=2,c)):(p.flags|=1048576,c)}function s(p){return e&&p.alternate===null&&(p.flags|=2),p}function l(p,c,m,y){return c===null||c.tag!==6?(c=Es(m,p.mode,y),c.return=p,c):(c=o(c,m),c.return=p,c)}function a(p,c,m,y){var S=m.type;return S===Un?f(p,c,m.props.children,y,m.key):c!==null&&(c.elementType===S||typeof S=="object"&&S!==null&&S.$$typeof===At&&ec(S)===c.type)?(y=o(c,m.props),y.ref=kr(p,c,m),y.return=p,y):(y=ti(m.type,m.key,m.props,null,p.mode,y),y.ref=kr(p,c,m),y.return=p,y)}function u(p,c,m,y){return c===null||c.tag!==4||c.stateNode.containerInfo!==m.containerInfo||c.stateNode.implementation!==m.implementation?(c=xs(m,p.mode,y),c.return=p,c):(c=o(c,m.children||[]),c.return=p,c)}function f(p,c,m,y,S){return c===null||c.tag!==7?(c=kn(m,p.mode,y,S),c.return=p,c):(c=o(c,m),c.return=p,c)}function d(p,c,m){if(typeof c=="string"&&c!==""||typeof c=="number")return c=Es(""+c,p.mode,m),c.return=p,c;if(typeof c=="object"&&c!==null){switch(c.$$typeof){case vo:return m=ti(c.type,c.key,c.props,null,p.mode,m),m.ref=kr(p,null,c),m.return=p,m;case jn:return c=xs(c,p.mode,m),c.return=p,c;case At:var y=c._init;return d(p,y(c._payload),m)}if(Pr(c)||mr(c))return c=kn(c,p.mode,m,null),c.return=p,c;Co(p,c)}return null}function h(p,c,m,y){var S=c!==null?c.key:null;if(typeof m=="string"&&m!==""||typeof m=="number")return S!==null?null:l(p,c,""+m,y);if(typeof m=="object"&&m!==null){switch(m.$$typeof){case vo:return m.key===S?a(p,c,m,y):null;case jn:return m.key===S?u(p,c,m,y):null;case At:return S=m._init,h(p,c,S(m._payload),y)}if(Pr(m)||mr(m))return S!==null?null:f(p,c,m,y,null);Co(p,m)}return null}function g(p,c,m,y,S){if(typeof y=="string"&&y!==""||typeof y=="number")return p=p.get(m)||null,l(c,p,""+y,S);if(typeof y=="object"&&y!==null){switch(y.$$typeof){case vo:return p=p.get(y.key===null?m:y.key)||null,a(c,p,y,S);case jn:return p=p.get(y.key===null?m:y.key)||null,u(c,p,y,S);case At:var w=y._init;return g(p,c,m,w(y._payload),S)}if(Pr(y)||mr(y))return p=p.get(m)||null,f(c,p,y,S,null);Co(c,y)}return null}function v(p,c,m,y){for(var S=null,w=null,E=c,x=c=0,V=null;E!==null&&x<m.length;x++){E.index>x?(V=E,E=null):V=E.sibling;var P=h(p,E,m[x],y);if(P===null){E===null&&(E=V);break}e&&E&&P.alternate===null&&t(p,E),c=i(P,c,x),w===null?S=P:w.sibling=P,w=P,E=V}if(x===m.length)return n(p,E),Q&&fn(p,x),S;if(E===null){for(;x<m.length;x++)E=d(p,m[x],y),E!==null&&(c=i(E,c,x),w===null?S=E:w.sibling=E,w=E);return Q&&fn(p,x),S}for(E=r(p,E);x<m.length;x++)V=g(E,p,x,m[x],y),V!==null&&(e&&V.alternate!==null&&E.delete(V.key===null?x:V.key),c=i(V,c,x),w===null?S=V:w.sibling=V,w=V);return e&&E.forEach(function(K){return t(p,K)}),Q&&fn(p,x),S}function k(p,c,m,y){var S=mr(m);if(typeof S!="function")throw Error(N(150));if(m=S.call(m),m==null)throw Error(N(151));for(var w=S=null,E=c,x=c=0,V=null,P=m.next();E!==null&&!P.done;x++,P=m.next()){E.index>x?(V=E,E=null):V=E.sibling;var K=h(p,E,P.value,y);if(K===null){E===null&&(E=V);break}e&&E&&K.alternate===null&&t(p,E),c=i(K,c,x),w===null?S=K:w.sibling=K,w=K,E=V}if(P.done)return n(p,E),Q&&fn(p,x),S;if(E===null){for(;!P.done;x++,P=m.next())P=d(p,P.value,y),P!==null&&(c=i(P,c,x),w===null?S=P:w.sibling=P,w=P);return Q&&fn(p,x),S}for(E=r(p,E);!P.done;x++,P=m.next())P=g(E,p,x,P.value,y),P!==null&&(e&&P.alternate!==null&&E.delete(P.key===null?x:P.key),c=i(P,c,x),w===null?S=P:w.sibling=P,w=P);return e&&E.forEach(function(Ce){return t(p,Ce)}),Q&&fn(p,x),S}function O(p,c,m,y){if(typeof m=="object"&&m!==null&&m.type===Un&&m.key===null&&(m=m.props.children),typeof m=="object"&&m!==null){switch(m.$$typeof){case vo:e:{for(var S=m.key,w=c;w!==null;){if(w.key===S){if(S=m.type,S===Un){if(w.tag===7){n(p,w.sibling),c=o(w,m.props.children),c.return=p,p=c;break e}}else if(w.elementType===S||typeof S=="object"&&S!==null&&S.$$typeof===At&&ec(S)===w.type){n(p,w.sibling),c=o(w,m.props),c.ref=kr(p,w,m),c.return=p,p=c;break e}n(p,w);break}else t(p,w);w=w.sibling}m.type===Un?(c=kn(m.props.children,p.mode,y,m.key),c.return=p,p=c):(y=ti(m.type,m.key,m.props,null,p.mode,y),y.ref=kr(p,c,m),y.return=p,p=y)}return s(p);case jn:e:{for(w=m.key;c!==null;){if(c.key===w)if(c.tag===4&&c.stateNode.containerInfo===m.containerInfo&&c.stateNode.implementation===m.implementation){n(p,c.sibling),c=o(c,m.children||[]),c.return=p,p=c;break e}else{n(p,c);break}else t(p,c);c=c.sibling}c=xs(m,p.mode,y),c.return=p,p=c}return s(p);case At:return w=m._init,O(p,c,w(m._payload),y)}if(Pr(m))return v(p,c,m,y);if(mr(m))return k(p,c,m,y);Co(p,m)}return typeof m=="string"&&m!==""||typeof m=="number"?(m=""+m,c!==null&&c.tag===6?(n(p,c.sibling),c=o(c,m),c.return=p,p=c):(n(p,c),c=Es(m,p.mode,y),c.return=p,p=c),s(p)):n(p,c)}return O}var ir=Qd(!0),qd=Qd(!1),ao={},kt=ln(ao),Xr=ln(ao),Jr=ln(ao);function yn(e){if(e===ao)throw Error(N(174));return e}function da(e,t){switch($(Jr,t),$(Xr,e),$(kt,ao),e=t.nodeType,e){case 9:case 11:t=(t=t.documentElement)?t.namespaceURI:Qs(null,"");break;default:e=e===8?t.parentNode:t,t=e.namespaceURI||null,e=e.tagName,t=Qs(t,e)}G(kt),$(kt,t)}function sr(){G(kt),G(Xr),G(Jr)}function Xd(e){yn(Jr.current);var t=yn(kt.current),n=Qs(t,e.type);t!==n&&($(Xr,e),$(kt,n))}function pa(e){Xr.current===e&&(G(kt),G(Xr))}var X=ln(0);function Si(e){for(var t=e;t!==null;){if(t.tag===13){var n=t.memoizedState;if(n!==null&&(n=n.dehydrated,n===null||n.data==="$?"||n.data==="$!"))return t}else if(t.tag===19&&t.memoizedProps.revealOrder!==void 0){if(t.flags&128)return t}else if(t.child!==null){t.child.return=t,t=t.child;continue}if(t===e)break;for(;t.sibling===null;){if(t.return===null||t.return===e)return null;t=t.return}t.sibling.return=t.return,t=t.sibling}return null}var _s=[];function fa(){for(var e=0;e<_s.length;e++)_s[e]._workInProgressVersionPrimary=null;_s.length=0}var qo=Dt.ReactCurrentDispatcher,ks=Dt.ReactCurrentBatchConfig,On=0,Z=null,se=null,ae=null,Oi=!1,Fr=!1,Zr=0,Dg=0;function ge(){throw Error(N(321))}function ha(e,t){if(t===null)return!1;for(var n=0;n<t.length&&n<e.length;n++)if(!ut(e[n],t[n]))return!1;return!0}function ma(e,t,n,r,o,i){if(On=i,Z=t,t.memoizedState=null,t.updateQueue=null,t.lanes=0,qo.current=e===null||e.memoizedState===null?zg:Mg,e=n(r,o),Fr){i=0;do{if(Fr=!1,Zr=0,25<=i)throw Error(N(301));i+=1,ae=se=null,t.updateQueue=null,qo.current=$g,e=n(r,o)}while(Fr)}if(qo.current=Ni,t=se!==null&&se.next!==null,On=0,ae=se=Z=null,Oi=!1,t)throw Error(N(300));return e}function ga(){var e=Zr!==0;return Zr=0,e}function gt(){var e={memoizedState:null,baseState:null,baseQueue:null,queue:null,next:null};return ae===null?Z.memoizedState=ae=e:ae=ae.next=e,ae}function Qe(){if(se===null){var e=Z.alternate;e=e!==null?e.memoizedState:null}else e=se.next;var t=ae===null?Z.memoizedState:ae.next;if(t!==null)ae=t,se=e;else{if(e===null)throw Error(N(310));se=e,e={memoizedState:se.memoizedState,baseState:se.baseState,baseQueue:se.baseQueue,queue:se.queue,next:null},ae===null?Z.memoizedState=ae=e:ae=ae.next=e}return ae}function eo(e,t){return typeof t=="function"?t(e):t}function ws(e){var t=Qe(),n=t.queue;if(n===null)throw Error(N(311));n.lastRenderedReducer=e;var r=se,o=r.baseQueue,i=n.pending;if(i!==null){if(o!==null){var s=o.next;o.next=i.next,i.next=s}r.baseQueue=o=i,n.pending=null}if(o!==null){i=o.next,r=r.baseState;var l=s=null,a=null,u=i;do{var f=u.lane;if((On&f)===f)a!==null&&(a=a.next={lane:0,action:u.action,hasEagerState:u.hasEagerState,eagerState:u.eagerState,next:null}),r=u.hasEagerState?u.eagerState:e(r,u.action);else{var d={lane:f,action:u.action,hasEagerState:u.hasEagerState,eagerState:u.eagerState,next:null};a===null?(l=a=d,s=r):a=a.next=d,Z.lanes|=f,Nn|=f}u=u.next}while(u!==null&&u!==i);a===null?s=r:a.next=l,ut(r,t.memoizedState)||(Te=!0),t.memoizedState=r,t.baseState=s,t.baseQueue=a,n.lastRenderedState=r}if(e=n.interleaved,e!==null){o=e;do i=o.lane,Z.lanes|=i,Nn|=i,o=o.next;while(o!==e)}else o===null&&(n.lanes=0);return[t.memoizedState,n.dispatch]}function Ss(e){var t=Qe(),n=t.queue;if(n===null)throw Error(N(311));n.lastRenderedReducer=e;var r=n.dispatch,o=n.pending,i=t.memoizedState;if(o!==null){n.pending=null;var s=o=o.next;do i=e(i,s.action),s=s.next;while(s!==o);ut(i,t.memoizedState)||(Te=!0),t.memoizedState=i,t.baseQueue===null&&(t.baseState=i),n.lastRenderedState=i}return[i,r]}function Jd(){}function Zd(e,t){var n=Z,r=Qe(),o=t(),i=!ut(r.memoizedState,o);if(i&&(r.memoizedState=o,Te=!0),r=r.queue,va(np.bind(null,n,r,e),[e]),r.getSnapshot!==t||i||ae!==null&&ae.memoizedState.tag&1){if(n.flags|=2048,to(9,tp.bind(null,n,r,o,t),void 0,null),ue===null)throw Error(N(349));On&30||ep(n,t,o)}return o}function ep(e,t,n){e.flags|=16384,e={getSnapshot:t,value:n},t=Z.updateQueue,t===null?(t={lastEffect:null,stores:null},Z.updateQueue=t,t.stores=[e]):(n=t.stores,n===null?t.stores=[e]:n.push(e))}function tp(e,t,n,r){t.value=n,t.getSnapshot=r,rp(t)&&op(e)}function np(e,t,n){return n(function(){rp(t)&&op(e)})}function rp(e){var t=e.getSnapshot;e=e.value;try{var n=t();return!ut(e,n)}catch{return!0}}function op(e){var t=Vt(e,1);t!==null&&lt(t,e,1,-1)}function tc(e){var t=gt();return typeof e=="function"&&(e=e()),t.memoizedState=t.baseState=e,e={pending:null,interleaved:null,lanes:0,dispatch:null,lastRenderedReducer:eo,lastRenderedState:e},t.queue=e,e=e.dispatch=Ag.bind(null,Z,e),[t.memoizedState,e]}function to(e,t,n,r){return e={tag:e,create:t,destroy:n,deps:r,next:null},t=Z.updateQueue,t===null?(t={lastEffect:null,stores:null},Z.updateQueue=t,t.lastEffect=e.next=e):(n=t.lastEffect,n===null?t.lastEffect=e.next=e:(r=n.next,n.next=e,e.next=r,t.lastEffect=e)),e}function ip(){return Qe().memoizedState}function Xo(e,t,n,r){var o=gt();Z.flags|=e,o.memoizedState=to(1|t,n,void 0,r===void 0?null:r)}function zi(e,t,n,r){var o=Qe();r=r===void 0?null:r;var i=void 0;if(se!==null){var s=se.memoizedState;if(i=s.destroy,r!==null&&ha(r,s.deps)){o.memoizedState=to(t,n,i,r);return}}Z.flags|=e,o.memoizedState=to(1|t,n,i,r)}function nc(e,t){return Xo(8390656,8,e,t)}function va(e,t){return zi(2048,8,e,t)}function sp(e,t){return zi(4,2,e,t)}function lp(e,t){return zi(4,4,e,t)}function ap(e,t){if(typeof t=="function")return e=e(),t(e),function(){t(null)};if(t!=null)return e=e(),t.current=e,function(){t.current=null}}function up(e,t,n){return n=n!=null?n.concat([e]):null,zi(4,4,ap.bind(null,t,e),n)}function ya(){}function cp(e,t){var n=Qe();t=t===void 0?null:t;var r=n.memoizedState;return r!==null&&t!==null&&ha(t,r[1])?r[0]:(n.memoizedState=[e,t],e)}function dp(e,t){var n=Qe();t=t===void 0?null:t;var r=n.memoizedState;return r!==null&&t!==null&&ha(t,r[1])?r[0]:(e=e(),n.memoizedState=[e,t],e)}function pp(e,t,n){return On&21?(ut(n,t)||(n=md(),Z.lanes|=n,Nn|=n,e.baseState=!0),t):(e.baseState&&(e.baseState=!1,Te=!0),e.memoizedState=n)}function jg(e,t){var n=M;M=n!==0&&4>n?n:4,e(!0);var r=ks.transition;ks.transition={};try{e(!1),t()}finally{M=n,ks.transition=r}}function fp(){return Qe().memoizedState}function Ug(e,t,n){var r=Zt(e);if(n={lane:r,action:n,hasEagerState:!1,eagerState:null,next:null},hp(e))mp(t,n);else if(n=Hd(e,t,n,r),n!==null){var o=Oe();lt(n,e,r,o),gp(n,t,r)}}function Ag(e,t,n){var r=Zt(e),o={lane:r,action:n,hasEagerState:!1,eagerState:null,next:null};if(hp(e))mp(t,o);else{var i=e.alternate;if(e.lanes===0&&(i===null||i.lanes===0)&&(i=t.lastRenderedReducer,i!==null))try{var s=t.lastRenderedState,l=i(s,n);if(o.hasEagerState=!0,o.eagerState=l,ut(l,s)){var a=t.interleaved;a===null?(o.next=o,ua(t)):(o.next=a.next,a.next=o),t.interleaved=o;return}}catch{}finally{}n=Hd(e,t,o,r),n!==null&&(o=Oe(),lt(n,e,r,o),gp(n,t,r))}}function hp(e){var t=e.alternate;return e===Z||t!==null&&t===Z}function mp(e,t){Fr=Oi=!0;var n=e.pending;n===null?t.next=t:(t.next=n.next,n.next=t),e.pending=t}function gp(e,t,n){if(n&4194240){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,Ql(e,n)}}var Ni={readContext:Ge,useCallback:ge,useContext:ge,useEffect:ge,useImperativeHandle:ge,useInsertionEffect:ge,useLayoutEffect:ge,useMemo:ge,useReducer:ge,useRef:ge,useState:ge,useDebugValue:ge,useDeferredValue:ge,useTransition:ge,useMutableSource:ge,useSyncExternalStore:ge,useId:ge,unstable_isNewReconciler:!1},zg={readContext:Ge,useCallback:function(e,t){return gt().memoizedState=[e,t===void 0?null:t],e},useContext:Ge,useEffect:nc,useImperativeHandle:function(e,t,n){return n=n!=null?n.concat([e]):null,Xo(4194308,4,ap.bind(null,t,e),n)},useLayoutEffect:function(e,t){return Xo(4194308,4,e,t)},useInsertionEffect:function(e,t){return Xo(4,2,e,t)},useMemo:function(e,t){var n=gt();return t=t===void 0?null:t,e=e(),n.memoizedState=[e,t],e},useReducer:function(e,t,n){var r=gt();return t=n!==void 0?n(t):t,r.memoizedState=r.baseState=t,e={pending:null,interleaved:null,lanes:0,dispatch:null,lastRenderedReducer:e,lastRenderedState:t},r.queue=e,e=e.dispatch=Ug.bind(null,Z,e),[r.memoizedState,e]},useRef:function(e){var t=gt();return e={current:e},t.memoizedState=e},useState:tc,useDebugValue:ya,useDeferredValue:function(e){return gt().memoizedState=e},useTransition:function(){var e=tc(!1),t=e[0];return e=jg.bind(null,e[1]),gt().memoizedState=e,[t,e]},useMutableSource:function(){},useSyncExternalStore:function(e,t,n){var r=Z,o=gt();if(Q){if(n===void 0)throw Error(N(407));n=n()}else{if(n=t(),ue===null)throw Error(N(349));On&30||ep(r,t,n)}o.memoizedState=n;var i={value:n,getSnapshot:t};return o.queue=i,nc(np.bind(null,r,i,e),[e]),r.flags|=2048,to(9,tp.bind(null,r,i,n,t),void 0,null),n},useId:function(){var e=gt(),t=ue.identifierPrefix;if(Q){var n=Tt,r=Rt;n=(r&~(1<<32-st(r)-1)).toString(32)+n,t=":"+t+"R"+n,n=Zr++,0<n&&(t+="H"+n.toString(32)),t+=":"}else n=Dg++,t=":"+t+"r"+n.toString(32)+":";return e.memoizedState=t},unstable_isNewReconciler:!1},Mg={readContext:Ge,useCallback:cp,useContext:Ge,useEffect:va,useImperativeHandle:up,useInsertionEffect:sp,useLayoutEffect:lp,useMemo:dp,useReducer:ws,useRef:ip,useState:function(){return ws(eo)},useDebugValue:ya,useDeferredValue:function(e){var t=Qe();return pp(t,se.memoizedState,e)},useTransition:function(){var e=ws(eo)[0],t=Qe().memoizedState;return[e,t]},useMutableSource:Jd,useSyncExternalStore:Zd,useId:fp,unstable_isNewReconciler:!1},$g={readContext:Ge,useCallback:cp,useContext:Ge,useEffect:va,useImperativeHandle:up,useInsertionEffect:sp,useLayoutEffect:lp,useMemo:dp,useReducer:Ss,useRef:ip,useState:function(){return Ss(eo)},useDebugValue:ya,useDeferredValue:function(e){var t=Qe();return se===null?t.memoizedState=e:pp(t,se.memoizedState,e)},useTransition:function(){var e=Ss(eo)[0],t=Qe().memoizedState;return[e,t]},useMutableSource:Jd,useSyncExternalStore:Zd,useId:fp,unstable_isNewReconciler:!1};function lr(e,t){try{var n="",r=t;do n+=gm(r),r=r.return;while(r);var o=n}catch(i){o=`
+`+o[s].replace(" at new "," at ");return e.displayName&&a.includes("<anonymous>")&&(a=a.replace("<anonymous>",e.displayName)),a}while(1<=s&&0<=l);break}}}finally{rs=!1,Error.prepareStackTrace=n}return(e=e?e.displayName||e.name:"")?xr(e):""}function _m(e){switch(e.tag){case 5:return xr(e.type);case 16:return xr("Lazy");case 13:return xr("Suspense");case 19:return xr("SuspenseList");case 0:case 2:case 15:return e=os(e.type,!1),e;case 11:return e=os(e.type.render,!1),e;case 1:return e=os(e.type,!0),e;default:return""}}function Ks(e){if(e==null)return null;if(typeof e=="function")return e.displayName||e.name||null;if(typeof e=="string")return e;switch(e){case Un:return"Fragment";case Dn:return"Portal";case Ms:return"Profiler";case Kl:return"StrictMode";case $s:return"Suspense";case Bs:return"SuspenseList"}if(typeof e=="object")switch(e.$$typeof){case Zc:return(e.displayName||"Context")+".Consumer";case Jc:return(e._context.displayName||"Context")+".Provider";case Hl:var t=e.render;return e=e.displayName,e||(e=t.displayName||t.name||"",e=e!==""?"ForwardRef("+e+")":"ForwardRef"),e;case Wl:return t=e.displayName||null,t!==null?t:Ks(e.type)||"Memo";case Mt:t=e._payload,e=e._init;try{return Ks(e(t))}catch{}}return null}function km(e){var t=e.type;switch(e.tag){case 24:return"Cache";case 9:return(t.displayName||"Context")+".Consumer";case 10:return(t._context.displayName||"Context")+".Provider";case 18:return"DehydratedFragment";case 11:return e=t.render,e=e.displayName||e.name||"",t.displayName||(e!==""?"ForwardRef("+e+")":"ForwardRef");case 7:return"Fragment";case 5:return t;case 4:return"Portal";case 3:return"Root";case 6:return"Text";case 16:return Ks(t);case 8:return t===Kl?"StrictMode":"Mode";case 22:return"Offscreen";case 12:return"Profiler";case 21:return"Scope";case 13:return"Suspense";case 19:return"SuspenseList";case 25:return"TracingMarker";case 1:case 0:case 17:case 2:case 14:case 15:if(typeof t=="function")return t.displayName||t.name||null;if(typeof t=="string")return t}return null}function on(e){switch(typeof e){case"boolean":case"number":case"string":case"undefined":return e;case"object":return e;default:return""}}function td(e){var t=e.type;return(e=e.nodeName)&&e.toLowerCase()==="input"&&(t==="checkbox"||t==="radio")}function wm(e){var t=td(e)?"checked":"value",n=Object.getOwnPropertyDescriptor(e.constructor.prototype,t),r=""+e[t];if(!e.hasOwnProperty(t)&&typeof n<"u"&&typeof n.get=="function"&&typeof n.set=="function"){var o=n.get,i=n.set;return Object.defineProperty(e,t,{configurable:!0,get:function(){return o.call(this)},set:function(s){r=""+s,i.call(this,s)}}),Object.defineProperty(e,t,{enumerable:n.enumerable}),{getValue:function(){return r},setValue:function(s){r=""+s},stopTracking:function(){e._valueTracker=null,delete e[t]}}}}function yo(e){e._valueTracker||(e._valueTracker=wm(e))}function nd(e){if(!e)return!1;var t=e._valueTracker;if(!t)return!0;var n=t.getValue(),r="";return e&&(r=td(e)?e.checked?"true":"false":e.value),e=r,e!==n?(t.setValue(e),!0):!1}function si(e){if(e=e||(typeof document<"u"?document:void 0),typeof e>"u")return null;try{return e.activeElement||e.body}catch{return e.body}}function Hs(e,t){var n=t.checked;return ee({},t,{defaultChecked:void 0,defaultValue:void 0,value:void 0,checked:n??e._wrapperState.initialChecked})}function Su(e,t){var n=t.defaultValue==null?"":t.defaultValue,r=t.checked!=null?t.checked:t.defaultChecked;n=on(t.value!=null?t.value:n),e._wrapperState={initialChecked:r,initialValue:n,controlled:t.type==="checkbox"||t.type==="radio"?t.checked!=null:t.value!=null}}function rd(e,t){t=t.checked,t!=null&&Bl(e,"checked",t,!1)}function Ws(e,t){rd(e,t);var n=on(t.value),r=t.type;if(n!=null)r==="number"?(n===0&&e.value===""||e.value!=n)&&(e.value=""+n):e.value!==""+n&&(e.value=""+n);else if(r==="submit"||r==="reset"){e.removeAttribute("value");return}t.hasOwnProperty("value")?Ys(e,t.type,n):t.hasOwnProperty("defaultValue")&&Ys(e,t.type,on(t.defaultValue)),t.checked==null&&t.defaultChecked!=null&&(e.defaultChecked=!!t.defaultChecked)}function Ou(e,t,n){if(t.hasOwnProperty("value")||t.hasOwnProperty("defaultValue")){var r=t.type;if(!(r!=="submit"&&r!=="reset"||t.value!==void 0&&t.value!==null))return;t=""+e._wrapperState.initialValue,n||t===e.value||(e.value=t),e.defaultValue=t}n=e.name,n!==""&&(e.name=""),e.defaultChecked=!!e._wrapperState.initialChecked,n!==""&&(e.name=n)}function Ys(e,t,n){(t!=="number"||si(e.ownerDocument)!==e)&&(n==null?e.defaultValue=""+e._wrapperState.initialValue:e.defaultValue!==""+n&&(e.defaultValue=""+n))}var Pr=Array.isArray;function qn(e,t,n,r){if(e=e.options,t){t={};for(var o=0;o<n.length;o++)t["$"+n[o]]=!0;for(n=0;n<e.length;n++)o=t.hasOwnProperty("$"+e[n].value),e[n].selected!==o&&(e[n].selected=o),o&&r&&(e[n].defaultSelected=!0)}else{for(n=""+on(n),t=null,o=0;o<e.length;o++){if(e[o].value===n){e[o].selected=!0,r&&(e[o].defaultSelected=!0);return}t!==null||e[o].disabled||(t=e[o])}t!==null&&(t.selected=!0)}}function Gs(e,t){if(t.dangerouslySetInnerHTML!=null)throw Error(N(91));return ee({},t,{value:void 0,defaultValue:void 0,children:""+e._wrapperState.initialValue})}function Nu(e,t){var n=t.value;if(n==null){if(n=t.children,t=t.defaultValue,n!=null){if(t!=null)throw Error(N(92));if(Pr(n)){if(1<n.length)throw Error(N(93));n=n[0]}t=n}t==null&&(t=""),n=t}e._wrapperState={initialValue:on(n)}}function od(e,t){var n=on(t.value),r=on(t.defaultValue);n!=null&&(n=""+n,n!==e.value&&(e.value=n),t.defaultValue==null&&e.defaultValue!==n&&(e.defaultValue=n)),r!=null&&(e.defaultValue=""+r)}function Eu(e){var t=e.textContent;t===e._wrapperState.initialValue&&t!==""&&t!==null&&(e.value=t)}function id(e){switch(e){case"svg":return"http://www.w3.org/2000/svg";case"math":return"http://www.w3.org/1998/Math/MathML";default:return"http://www.w3.org/1999/xhtml"}}function qs(e,t){return e==null||e==="http://www.w3.org/1999/xhtml"?id(t):e==="http://www.w3.org/2000/svg"&&t==="foreignObject"?"http://www.w3.org/1999/xhtml":e}var _o,sd=function(e){return typeof MSApp<"u"&&MSApp.execUnsafeLocalFunction?function(t,n,r,o){MSApp.execUnsafeLocalFunction(function(){return e(t,n,r,o)})}:e}(function(e,t){if(e.namespaceURI!=="http://www.w3.org/2000/svg"||"innerHTML"in e)e.innerHTML=t;else{for(_o=_o||document.createElement("div"),_o.innerHTML="<svg>"+t.valueOf().toString()+"</svg>",t=_o.firstChild;e.firstChild;)e.removeChild(e.firstChild);for(;t.firstChild;)e.appendChild(t.firstChild)}});function Mr(e,t){if(t){var n=e.firstChild;if(n&&n===e.lastChild&&n.nodeType===3){n.nodeValue=t;return}}e.textContent=t}var Tr={animationIterationCount:!0,aspectRatio:!0,borderImageOutset:!0,borderImageSlice:!0,borderImageWidth:!0,boxFlex:!0,boxFlexGroup:!0,boxOrdinalGroup:!0,columnCount:!0,columns:!0,flex:!0,flexGrow:!0,flexPositive:!0,flexShrink:!0,flexNegative:!0,flexOrder:!0,gridArea:!0,gridRow:!0,gridRowEnd:!0,gridRowSpan:!0,gridRowStart:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnSpan:!0,gridColumnStart:!0,fontWeight:!0,lineClamp:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,tabSize:!0,widows:!0,zIndex:!0,zoom:!0,fillOpacity:!0,floodOpacity:!0,stopOpacity:!0,strokeDasharray:!0,strokeDashoffset:!0,strokeMiterlimit:!0,strokeOpacity:!0,strokeWidth:!0},Sm=["Webkit","ms","Moz","O"];Object.keys(Tr).forEach(function(e){Sm.forEach(function(t){t=t+e.charAt(0).toUpperCase()+e.substring(1),Tr[t]=Tr[e]})});function ld(e,t,n){return t==null||typeof t=="boolean"||t===""?"":n||typeof t!="number"||t===0||Tr.hasOwnProperty(e)&&Tr[e]?(""+t).trim():t+"px"}function ad(e,t){e=e.style;for(var n in t)if(t.hasOwnProperty(n)){var r=n.indexOf("--")===0,o=ld(n,t[n],r);n==="float"&&(n="cssFloat"),r?e.setProperty(n,o):e[n]=o}}var Om=ee({menuitem:!0},{area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0});function Qs(e,t){if(t){if(Om[e]&&(t.children!=null||t.dangerouslySetInnerHTML!=null))throw Error(N(137,e));if(t.dangerouslySetInnerHTML!=null){if(t.children!=null)throw Error(N(60));if(typeof t.dangerouslySetInnerHTML!="object"||!("__html"in t.dangerouslySetInnerHTML))throw Error(N(61))}if(t.style!=null&&typeof t.style!="object")throw Error(N(62))}}function Xs(e,t){if(e.indexOf("-")===-1)return typeof t.is=="string";switch(e){case"annotation-xml":case"color-profile":case"font-face":case"font-face-src":case"font-face-uri":case"font-face-format":case"font-face-name":case"missing-glyph":return!1;default:return!0}}var Js=null;function Yl(e){return e=e.target||e.srcElement||window,e.correspondingUseElement&&(e=e.correspondingUseElement),e.nodeType===3?e.parentNode:e}var Zs=null,Qn=null,Xn=null;function xu(e){if(e=lo(e)){if(typeof Zs!="function")throw Error(N(280));var t=e.stateNode;t&&(t=Di(t),Zs(e.stateNode,e.type,t))}}function ud(e){Qn?Xn?Xn.push(e):Xn=[e]:Qn=e}function cd(){if(Qn){var e=Qn,t=Xn;if(Xn=Qn=null,xu(e),t)for(e=0;e<t.length;e++)xu(t[e])}}function dd(e,t){return e(t)}function pd(){}var is=!1;function fd(e,t,n){if(is)return e(t,n);is=!0;try{return dd(e,t,n)}finally{is=!1,(Qn!==null||Xn!==null)&&(pd(),cd())}}function $r(e,t){var n=e.stateNode;if(n===null)return null;var r=Di(n);if(r===null)return null;n=r[t];e:switch(t){case"onClick":case"onClickCapture":case"onDoubleClick":case"onDoubleClickCapture":case"onMouseDown":case"onMouseDownCapture":case"onMouseMove":case"onMouseMoveCapture":case"onMouseUp":case"onMouseUpCapture":case"onMouseEnter":(r=!r.disabled)||(e=e.type,r=!(e==="button"||e==="input"||e==="select"||e==="textarea")),e=!r;break e;default:e=!1}if(e)return null;if(n&&typeof n!="function")throw Error(N(231,t,typeof n));return n}var el=!1;if(It)try{var gr={};Object.defineProperty(gr,"passive",{get:function(){el=!0}}),window.addEventListener("test",gr,gr),window.removeEventListener("test",gr,gr)}catch{el=!1}function Nm(e,t,n,r,o,i,s,l,a){var u=Array.prototype.slice.call(arguments,3);try{t.apply(n,u)}catch(f){this.onError(f)}}var br=!1,li=null,ai=!1,tl=null,Em={onError:function(e){br=!0,li=e}};function xm(e,t,n,r,o,i,s,l,a){br=!1,li=null,Nm.apply(Em,arguments)}function Pm(e,t,n,r,o,i,s,l,a){if(xm.apply(this,arguments),br){if(br){var u=li;br=!1,li=null}else throw Error(N(198));ai||(ai=!0,tl=u)}}function Rn(e){var t=e,n=e;if(e.alternate)for(;t.return;)t=t.return;else{e=t;do t=e,t.flags&4098&&(n=t.return),e=t.return;while(e)}return t.tag===3?n:null}function hd(e){if(e.tag===13){var t=e.memoizedState;if(t===null&&(e=e.alternate,e!==null&&(t=e.memoizedState)),t!==null)return t.dehydrated}return null}function Pu(e){if(Rn(e)!==e)throw Error(N(188))}function Cm(e){var t=e.alternate;if(!t){if(t=Rn(e),t===null)throw Error(N(188));return t!==e?null:e}for(var n=e,r=t;;){var o=n.return;if(o===null)break;var i=o.alternate;if(i===null){if(r=o.return,r!==null){n=r;continue}break}if(o.child===i.child){for(i=o.child;i;){if(i===n)return Pu(o),e;if(i===r)return Pu(o),t;i=i.sibling}throw Error(N(188))}if(n.return!==r.return)n=o,r=i;else{for(var s=!1,l=o.child;l;){if(l===n){s=!0,n=o,r=i;break}if(l===r){s=!0,r=o,n=i;break}l=l.sibling}if(!s){for(l=i.child;l;){if(l===n){s=!0,n=i,r=o;break}if(l===r){s=!0,r=i,n=o;break}l=l.sibling}if(!s)throw Error(N(189))}}if(n.alternate!==r)throw Error(N(190))}if(n.tag!==3)throw Error(N(188));return n.stateNode.current===n?e:t}function md(e){return e=Cm(e),e!==null?gd(e):null}function gd(e){if(e.tag===5||e.tag===6)return e;for(e=e.child;e!==null;){var t=gd(e);if(t!==null)return t;e=e.sibling}return null}var vd=Me.unstable_scheduleCallback,Cu=Me.unstable_cancelCallback,Rm=Me.unstable_shouldYield,Tm=Me.unstable_requestPaint,ne=Me.unstable_now,bm=Me.unstable_getCurrentPriorityLevel,Gl=Me.unstable_ImmediatePriority,yd=Me.unstable_UserBlockingPriority,ui=Me.unstable_NormalPriority,Lm=Me.unstable_LowPriority,_d=Me.unstable_IdlePriority,Ii=null,_t=null;function Im(e){if(_t&&typeof _t.onCommitFiberRoot=="function")try{_t.onCommitFiberRoot(Ii,e,void 0,(e.current.flags&128)===128)}catch{}}var lt=Math.clz32?Math.clz32:jm,Vm=Math.log,Fm=Math.LN2;function jm(e){return e>>>=0,e===0?32:31-(Vm(e)/Fm|0)|0}var ko=64,wo=4194304;function Cr(e){switch(e&-e){case 1:return 1;case 2:return 2;case 4:return 4;case 8:return 8;case 16:return 16;case 32:return 32;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:return e&4194240;case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:return e&130023424;case 134217728:return 134217728;case 268435456:return 268435456;case 536870912:return 536870912;case 1073741824:return 1073741824;default:return e}}function ci(e,t){var n=e.pendingLanes;if(n===0)return 0;var r=0,o=e.suspendedLanes,i=e.pingedLanes,s=n&268435455;if(s!==0){var l=s&~o;l!==0?r=Cr(l):(i&=s,i!==0&&(r=Cr(i)))}else s=n&~o,s!==0?r=Cr(s):i!==0&&(r=Cr(i));if(r===0)return 0;if(t!==0&&t!==r&&!(t&o)&&(o=r&-r,i=t&-t,o>=i||o===16&&(i&4194240)!==0))return t;if(r&4&&(r|=n&16),t=e.entangledLanes,t!==0)for(e=e.entanglements,t&=r;0<t;)n=31-lt(t),o=1<<n,r|=e[n],t&=~o;return r}function Dm(e,t){switch(e){case 1:case 2:case 4:return t+250;case 8:case 16:case 32:case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:return t+5e3;case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:return-1;case 134217728:case 268435456:case 536870912:case 1073741824:return-1;default:return-1}}function Um(e,t){for(var n=e.suspendedLanes,r=e.pingedLanes,o=e.expirationTimes,i=e.pendingLanes;0<i;){var s=31-lt(i),l=1<<s,a=o[s];a===-1?(!(l&n)||l&r)&&(o[s]=Dm(l,t)):a<=t&&(e.expiredLanes|=l),i&=~l}}function nl(e){return e=e.pendingLanes&-1073741825,e!==0?e:e&1073741824?1073741824:0}function kd(){var e=ko;return ko<<=1,!(ko&4194240)&&(ko=64),e}function ss(e){for(var t=[],n=0;31>n;n++)t.push(e);return t}function io(e,t,n){e.pendingLanes|=t,t!==536870912&&(e.suspendedLanes=0,e.pingedLanes=0),e=e.eventTimes,t=31-lt(t),e[t]=n}function Am(e,t){var n=e.pendingLanes&~t;e.pendingLanes=t,e.suspendedLanes=0,e.pingedLanes=0,e.expiredLanes&=t,e.mutableReadLanes&=t,e.entangledLanes&=t,t=e.entanglements;var r=e.eventTimes;for(e=e.expirationTimes;0<n;){var o=31-lt(n),i=1<<o;t[o]=0,r[o]=-1,e[o]=-1,n&=~i}}function ql(e,t){var n=e.entangledLanes|=t;for(e=e.entanglements;n;){var r=31-lt(n),o=1<<r;o&t|e[r]&t&&(e[r]|=t),n&=~o}}var $=0;function wd(e){return e&=-e,1<e?4<e?e&268435455?16:536870912:4:1}var Sd,Ql,Od,Nd,Ed,rl=!1,So=[],qt=null,Qt=null,Xt=null,Br=new Map,Kr=new Map,Bt=[],zm="mousedown mouseup touchcancel touchend touchstart auxclick dblclick pointercancel pointerdown pointerup dragend dragstart drop compositionend compositionstart keydown keypress keyup input textInput copy cut paste click change contextmenu reset submit".split(" ");function Ru(e,t){switch(e){case"focusin":case"focusout":qt=null;break;case"dragenter":case"dragleave":Qt=null;break;case"mouseover":case"mouseout":Xt=null;break;case"pointerover":case"pointerout":Br.delete(t.pointerId);break;case"gotpointercapture":case"lostpointercapture":Kr.delete(t.pointerId)}}function vr(e,t,n,r,o,i){return e===null||e.nativeEvent!==i?(e={blockedOn:t,domEventName:n,eventSystemFlags:r,nativeEvent:i,targetContainers:[o]},t!==null&&(t=lo(t),t!==null&&Ql(t)),e):(e.eventSystemFlags|=r,t=e.targetContainers,o!==null&&t.indexOf(o)===-1&&t.push(o),e)}function Mm(e,t,n,r,o){switch(t){case"focusin":return qt=vr(qt,e,t,n,r,o),!0;case"dragenter":return Qt=vr(Qt,e,t,n,r,o),!0;case"mouseover":return Xt=vr(Xt,e,t,n,r,o),!0;case"pointerover":var i=o.pointerId;return Br.set(i,vr(Br.get(i)||null,e,t,n,r,o)),!0;case"gotpointercapture":return i=o.pointerId,Kr.set(i,vr(Kr.get(i)||null,e,t,n,r,o)),!0}return!1}function xd(e){var t=vn(e.target);if(t!==null){var n=Rn(t);if(n!==null){if(t=n.tag,t===13){if(t=hd(n),t!==null){e.blockedOn=t,Ed(e.priority,function(){Od(n)});return}}else if(t===3&&n.stateNode.current.memoizedState.isDehydrated){e.blockedOn=n.tag===3?n.stateNode.containerInfo:null;return}}}e.blockedOn=null}function Wo(e){if(e.blockedOn!==null)return!1;for(var t=e.targetContainers;0<t.length;){var n=ol(e.domEventName,e.eventSystemFlags,t[0],e.nativeEvent);if(n===null){n=e.nativeEvent;var r=new n.constructor(n.type,n);Js=r,n.target.dispatchEvent(r),Js=null}else return t=lo(n),t!==null&&Ql(t),e.blockedOn=n,!1;t.shift()}return!0}function Tu(e,t,n){Wo(e)&&n.delete(t)}function $m(){rl=!1,qt!==null&&Wo(qt)&&(qt=null),Qt!==null&&Wo(Qt)&&(Qt=null),Xt!==null&&Wo(Xt)&&(Xt=null),Br.forEach(Tu),Kr.forEach(Tu)}function yr(e,t){e.blockedOn===t&&(e.blockedOn=null,rl||(rl=!0,Me.unstable_scheduleCallback(Me.unstable_NormalPriority,$m)))}function Hr(e){function t(o){return yr(o,e)}if(0<So.length){yr(So[0],e);for(var n=1;n<So.length;n++){var r=So[n];r.blockedOn===e&&(r.blockedOn=null)}}for(qt!==null&&yr(qt,e),Qt!==null&&yr(Qt,e),Xt!==null&&yr(Xt,e),Br.forEach(t),Kr.forEach(t),n=0;n<Bt.length;n++)r=Bt[n],r.blockedOn===e&&(r.blockedOn=null);for(;0<Bt.length&&(n=Bt[0],n.blockedOn===null);)xd(n),n.blockedOn===null&&Bt.shift()}var Jn=Dt.ReactCurrentBatchConfig,di=!0;function Bm(e,t,n,r){var o=$,i=Jn.transition;Jn.transition=null;try{$=1,Xl(e,t,n,r)}finally{$=o,Jn.transition=i}}function Km(e,t,n,r){var o=$,i=Jn.transition;Jn.transition=null;try{$=4,Xl(e,t,n,r)}finally{$=o,Jn.transition=i}}function Xl(e,t,n,r){if(di){var o=ol(e,t,n,r);if(o===null)gs(e,t,r,pi,n),Ru(e,r);else if(Mm(o,e,t,n,r))r.stopPropagation();else if(Ru(e,r),t&4&&-1<zm.indexOf(e)){for(;o!==null;){var i=lo(o);if(i!==null&&Sd(i),i=ol(e,t,n,r),i===null&&gs(e,t,r,pi,n),i===o)break;o=i}o!==null&&r.stopPropagation()}else gs(e,t,r,null,n)}}var pi=null;function ol(e,t,n,r){if(pi=null,e=Yl(r),e=vn(e),e!==null)if(t=Rn(e),t===null)e=null;else if(n=t.tag,n===13){if(e=hd(t),e!==null)return e;e=null}else if(n===3){if(t.stateNode.current.memoizedState.isDehydrated)return t.tag===3?t.stateNode.containerInfo:null;e=null}else t!==e&&(e=null);return pi=e,null}function Pd(e){switch(e){case"cancel":case"click":case"close":case"contextmenu":case"copy":case"cut":case"auxclick":case"dblclick":case"dragend":case"dragstart":case"drop":case"focusin":case"focusout":case"input":case"invalid":case"keydown":case"keypress":case"keyup":case"mousedown":case"mouseup":case"paste":case"pause":case"play":case"pointercancel":case"pointerdown":case"pointerup":case"ratechange":case"reset":case"resize":case"seeked":case"submit":case"touchcancel":case"touchend":case"touchstart":case"volumechange":case"change":case"selectionchange":case"textInput":case"compositionstart":case"compositionend":case"compositionupdate":case"beforeblur":case"afterblur":case"beforeinput":case"blur":case"fullscreenchange":case"focus":case"hashchange":case"popstate":case"select":case"selectstart":return 1;case"drag":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"mousemove":case"mouseout":case"mouseover":case"pointermove":case"pointerout":case"pointerover":case"scroll":case"toggle":case"touchmove":case"wheel":case"mouseenter":case"mouseleave":case"pointerenter":case"pointerleave":return 4;case"message":switch(bm()){case Gl:return 1;case yd:return 4;case ui:case Lm:return 16;case _d:return 536870912;default:return 16}default:return 16}}var Wt=null,Jl=null,Yo=null;function Cd(){if(Yo)return Yo;var e,t=Jl,n=t.length,r,o="value"in Wt?Wt.value:Wt.textContent,i=o.length;for(e=0;e<n&&t[e]===o[e];e++);var s=n-e;for(r=1;r<=s&&t[n-r]===o[i-r];r++);return Yo=o.slice(e,1<r?1-r:void 0)}function Go(e){var t=e.keyCode;return"charCode"in e?(e=e.charCode,e===0&&t===13&&(e=13)):e=t,e===10&&(e=13),32<=e||e===13?e:0}function Oo(){return!0}function bu(){return!1}function Be(e){function t(n,r,o,i,s){this._reactName=n,this._targetInst=o,this.type=r,this.nativeEvent=i,this.target=s,this.currentTarget=null;for(var l in e)e.hasOwnProperty(l)&&(n=e[l],this[l]=n?n(i):i[l]);return this.isDefaultPrevented=(i.defaultPrevented!=null?i.defaultPrevented:i.returnValue===!1)?Oo:bu,this.isPropagationStopped=bu,this}return ee(t.prototype,{preventDefault:function(){this.defaultPrevented=!0;var n=this.nativeEvent;n&&(n.preventDefault?n.preventDefault():typeof n.returnValue!="unknown"&&(n.returnValue=!1),this.isDefaultPrevented=Oo)},stopPropagation:function(){var n=this.nativeEvent;n&&(n.stopPropagation?n.stopPropagation():typeof n.cancelBubble!="unknown"&&(n.cancelBubble=!0),this.isPropagationStopped=Oo)},persist:function(){},isPersistent:Oo}),t}var cr={eventPhase:0,bubbles:0,cancelable:0,timeStamp:function(e){return e.timeStamp||Date.now()},defaultPrevented:0,isTrusted:0},Zl=Be(cr),so=ee({},cr,{view:0,detail:0}),Hm=Be(so),ls,as,_r,Vi=ee({},so,{screenX:0,screenY:0,clientX:0,clientY:0,pageX:0,pageY:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,getModifierState:ea,button:0,buttons:0,relatedTarget:function(e){return e.relatedTarget===void 0?e.fromElement===e.srcElement?e.toElement:e.fromElement:e.relatedTarget},movementX:function(e){return"movementX"in e?e.movementX:(e!==_r&&(_r&&e.type==="mousemove"?(ls=e.screenX-_r.screenX,as=e.screenY-_r.screenY):as=ls=0,_r=e),ls)},movementY:function(e){return"movementY"in e?e.movementY:as}}),Lu=Be(Vi),Wm=ee({},Vi,{dataTransfer:0}),Ym=Be(Wm),Gm=ee({},so,{relatedTarget:0}),us=Be(Gm),qm=ee({},cr,{animationName:0,elapsedTime:0,pseudoElement:0}),Qm=Be(qm),Xm=ee({},cr,{clipboardData:function(e){return"clipboardData"in e?e.clipboardData:window.clipboardData}}),Jm=Be(Xm),Zm=ee({},cr,{data:0}),Iu=Be(Zm),eg={Esc:"Escape",Spacebar:" ",Left:"ArrowLeft",Up:"ArrowUp",Right:"ArrowRight",Down:"ArrowDown",Del:"Delete",Win:"OS",Menu:"ContextMenu",Apps:"ContextMenu",Scroll:"ScrollLock",MozPrintableKey:"Unidentified"},tg={8:"Backspace",9:"Tab",12:"Clear",13:"Enter",16:"Shift",17:"Control",18:"Alt",19:"Pause",20:"CapsLock",27:"Escape",32:" ",33:"PageUp",34:"PageDown",35:"End",36:"Home",37:"ArrowLeft",38:"ArrowUp",39:"ArrowRight",40:"ArrowDown",45:"Insert",46:"Delete",112:"F1",113:"F2",114:"F3",115:"F4",116:"F5",117:"F6",118:"F7",119:"F8",120:"F9",121:"F10",122:"F11",123:"F12",144:"NumLock",145:"ScrollLock",224:"Meta"},ng={Alt:"altKey",Control:"ctrlKey",Meta:"metaKey",Shift:"shiftKey"};function rg(e){var t=this.nativeEvent;return t.getModifierState?t.getModifierState(e):(e=ng[e])?!!t[e]:!1}function ea(){return rg}var og=ee({},so,{key:function(e){if(e.key){var t=eg[e.key]||e.key;if(t!=="Unidentified")return t}return e.type==="keypress"?(e=Go(e),e===13?"Enter":String.fromCharCode(e)):e.type==="keydown"||e.type==="keyup"?tg[e.keyCode]||"Unidentified":""},code:0,location:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,repeat:0,locale:0,getModifierState:ea,charCode:function(e){return e.type==="keypress"?Go(e):0},keyCode:function(e){return e.type==="keydown"||e.type==="keyup"?e.keyCode:0},which:function(e){return e.type==="keypress"?Go(e):e.type==="keydown"||e.type==="keyup"?e.keyCode:0}}),ig=Be(og),sg=ee({},Vi,{pointerId:0,width:0,height:0,pressure:0,tangentialPressure:0,tiltX:0,tiltY:0,twist:0,pointerType:0,isPrimary:0}),Vu=Be(sg),lg=ee({},so,{touches:0,targetTouches:0,changedTouches:0,altKey:0,metaKey:0,ctrlKey:0,shiftKey:0,getModifierState:ea}),ag=Be(lg),ug=ee({},cr,{propertyName:0,elapsedTime:0,pseudoElement:0}),cg=Be(ug),dg=ee({},Vi,{deltaX:function(e){return"deltaX"in e?e.deltaX:"wheelDeltaX"in e?-e.wheelDeltaX:0},deltaY:function(e){return"deltaY"in e?e.deltaY:"wheelDeltaY"in e?-e.wheelDeltaY:"wheelDelta"in e?-e.wheelDelta:0},deltaZ:0,deltaMode:0}),pg=Be(dg),fg=[9,13,27,32],ta=It&&"CompositionEvent"in window,Lr=null;It&&"documentMode"in document&&(Lr=document.documentMode);var hg=It&&"TextEvent"in window&&!Lr,Rd=It&&(!ta||Lr&&8<Lr&&11>=Lr),Fu=String.fromCharCode(32),ju=!1;function Td(e,t){switch(e){case"keyup":return fg.indexOf(t.keyCode)!==-1;case"keydown":return t.keyCode!==229;case"keypress":case"mousedown":case"focusout":return!0;default:return!1}}function bd(e){return e=e.detail,typeof e=="object"&&"data"in e?e.data:null}var An=!1;function mg(e,t){switch(e){case"compositionend":return bd(t);case"keypress":return t.which!==32?null:(ju=!0,Fu);case"textInput":return e=t.data,e===Fu&&ju?null:e;default:return null}}function gg(e,t){if(An)return e==="compositionend"||!ta&&Td(e,t)?(e=Cd(),Yo=Jl=Wt=null,An=!1,e):null;switch(e){case"paste":return null;case"keypress":if(!(t.ctrlKey||t.altKey||t.metaKey)||t.ctrlKey&&t.altKey){if(t.char&&1<t.char.length)return t.char;if(t.which)return String.fromCharCode(t.which)}return null;case"compositionend":return Rd&&t.locale!=="ko"?null:t.data;default:return null}}var vg={color:!0,date:!0,datetime:!0,"datetime-local":!0,email:!0,month:!0,number:!0,password:!0,range:!0,search:!0,tel:!0,text:!0,time:!0,url:!0,week:!0};function Du(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return t==="input"?!!vg[e.type]:t==="textarea"}function Ld(e,t,n,r){ud(r),t=fi(t,"onChange"),0<t.length&&(n=new Zl("onChange","change",null,n,r),e.push({event:n,listeners:t}))}var Ir=null,Wr=null;function yg(e){Bd(e,0)}function Fi(e){var t=$n(e);if(nd(t))return e}function _g(e,t){if(e==="change")return t}var Id=!1;if(It){var cs;if(It){var ds="oninput"in document;if(!ds){var Uu=document.createElement("div");Uu.setAttribute("oninput","return;"),ds=typeof Uu.oninput=="function"}cs=ds}else cs=!1;Id=cs&&(!document.documentMode||9<document.documentMode)}function Au(){Ir&&(Ir.detachEvent("onpropertychange",Vd),Wr=Ir=null)}function Vd(e){if(e.propertyName==="value"&&Fi(Wr)){var t=[];Ld(t,Wr,e,Yl(e)),fd(yg,t)}}function kg(e,t,n){e==="focusin"?(Au(),Ir=t,Wr=n,Ir.attachEvent("onpropertychange",Vd)):e==="focusout"&&Au()}function wg(e){if(e==="selectionchange"||e==="keyup"||e==="keydown")return Fi(Wr)}function Sg(e,t){if(e==="click")return Fi(t)}function Og(e,t){if(e==="input"||e==="change")return Fi(t)}function Ng(e,t){return e===t&&(e!==0||1/e===1/t)||e!==e&&t!==t}var ct=typeof Object.is=="function"?Object.is:Ng;function Yr(e,t){if(ct(e,t))return!0;if(typeof e!="object"||e===null||typeof t!="object"||t===null)return!1;var n=Object.keys(e),r=Object.keys(t);if(n.length!==r.length)return!1;for(r=0;r<n.length;r++){var o=n[r];if(!zs.call(t,o)||!ct(e[o],t[o]))return!1}return!0}function zu(e){for(;e&&e.firstChild;)e=e.firstChild;return e}function Mu(e,t){var n=zu(e);e=0;for(var r;n;){if(n.nodeType===3){if(r=e+n.textContent.length,e<=t&&r>=t)return{node:n,offset:t-e};e=r}e:{for(;n;){if(n.nextSibling){n=n.nextSibling;break e}n=n.parentNode}n=void 0}n=zu(n)}}function Fd(e,t){return e&&t?e===t?!0:e&&e.nodeType===3?!1:t&&t.nodeType===3?Fd(e,t.parentNode):"contains"in e?e.contains(t):e.compareDocumentPosition?!!(e.compareDocumentPosition(t)&16):!1:!1}function jd(){for(var e=window,t=si();t instanceof e.HTMLIFrameElement;){try{var n=typeof t.contentWindow.location.href=="string"}catch{n=!1}if(n)e=t.contentWindow;else break;t=si(e.document)}return t}function na(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return t&&(t==="input"&&(e.type==="text"||e.type==="search"||e.type==="tel"||e.type==="url"||e.type==="password")||t==="textarea"||e.contentEditable==="true")}function Eg(e){var t=jd(),n=e.focusedElem,r=e.selectionRange;if(t!==n&&n&&n.ownerDocument&&Fd(n.ownerDocument.documentElement,n)){if(r!==null&&na(n)){if(t=r.start,e=r.end,e===void 0&&(e=t),"selectionStart"in n)n.selectionStart=t,n.selectionEnd=Math.min(e,n.value.length);else if(e=(t=n.ownerDocument||document)&&t.defaultView||window,e.getSelection){e=e.getSelection();var o=n.textContent.length,i=Math.min(r.start,o);r=r.end===void 0?i:Math.min(r.end,o),!e.extend&&i>r&&(o=r,r=i,i=o),o=Mu(n,i);var s=Mu(n,r);o&&s&&(e.rangeCount!==1||e.anchorNode!==o.node||e.anchorOffset!==o.offset||e.focusNode!==s.node||e.focusOffset!==s.offset)&&(t=t.createRange(),t.setStart(o.node,o.offset),e.removeAllRanges(),i>r?(e.addRange(t),e.extend(s.node,s.offset)):(t.setEnd(s.node,s.offset),e.addRange(t)))}}for(t=[],e=n;e=e.parentNode;)e.nodeType===1&&t.push({element:e,left:e.scrollLeft,top:e.scrollTop});for(typeof n.focus=="function"&&n.focus(),n=0;n<t.length;n++)e=t[n],e.element.scrollLeft=e.left,e.element.scrollTop=e.top}}var xg=It&&"documentMode"in document&&11>=document.documentMode,zn=null,il=null,Vr=null,sl=!1;function $u(e,t,n){var r=n.window===n?n.document:n.nodeType===9?n:n.ownerDocument;sl||zn==null||zn!==si(r)||(r=zn,"selectionStart"in r&&na(r)?r={start:r.selectionStart,end:r.selectionEnd}:(r=(r.ownerDocument&&r.ownerDocument.defaultView||window).getSelection(),r={anchorNode:r.anchorNode,anchorOffset:r.anchorOffset,focusNode:r.focusNode,focusOffset:r.focusOffset}),Vr&&Yr(Vr,r)||(Vr=r,r=fi(il,"onSelect"),0<r.length&&(t=new Zl("onSelect","select",null,t,n),e.push({event:t,listeners:r}),t.target=zn)))}function No(e,t){var n={};return n[e.toLowerCase()]=t.toLowerCase(),n["Webkit"+e]="webkit"+t,n["Moz"+e]="moz"+t,n}var Mn={animationend:No("Animation","AnimationEnd"),animationiteration:No("Animation","AnimationIteration"),animationstart:No("Animation","AnimationStart"),transitionend:No("Transition","TransitionEnd")},ps={},Dd={};It&&(Dd=document.createElement("div").style,"AnimationEvent"in window||(delete Mn.animationend.animation,delete Mn.animationiteration.animation,delete Mn.animationstart.animation),"TransitionEvent"in window||delete Mn.transitionend.transition);function ji(e){if(ps[e])return ps[e];if(!Mn[e])return e;var t=Mn[e],n;for(n in t)if(t.hasOwnProperty(n)&&n in Dd)return ps[e]=t[n];return e}var Ud=ji("animationend"),Ad=ji("animationiteration"),zd=ji("animationstart"),Md=ji("transitionend"),$d=new Map,Bu="abort auxClick cancel canPlay canPlayThrough click close contextMenu copy cut drag dragEnd dragEnter dragExit dragLeave dragOver dragStart drop durationChange emptied encrypted ended error gotPointerCapture input invalid keyDown keyPress keyUp load loadedData loadedMetadata loadStart lostPointerCapture mouseDown mouseMove mouseOut mouseOver mouseUp paste pause play playing pointerCancel pointerDown pointerMove pointerOut pointerOver pointerUp progress rateChange reset resize seeked seeking stalled submit suspend timeUpdate touchCancel touchEnd touchStart volumeChange scroll toggle touchMove waiting wheel".split(" ");function an(e,t){$d.set(e,t),Cn(t,[e])}for(var fs=0;fs<Bu.length;fs++){var hs=Bu[fs],Pg=hs.toLowerCase(),Cg=hs[0].toUpperCase()+hs.slice(1);an(Pg,"on"+Cg)}an(Ud,"onAnimationEnd");an(Ad,"onAnimationIteration");an(zd,"onAnimationStart");an("dblclick","onDoubleClick");an("focusin","onFocus");an("focusout","onBlur");an(Md,"onTransitionEnd");nr("onMouseEnter",["mouseout","mouseover"]);nr("onMouseLeave",["mouseout","mouseover"]);nr("onPointerEnter",["pointerout","pointerover"]);nr("onPointerLeave",["pointerout","pointerover"]);Cn("onChange","change click focusin focusout input keydown keyup selectionchange".split(" "));Cn("onSelect","focusout contextmenu dragend focusin keydown keyup mousedown mouseup selectionchange".split(" "));Cn("onBeforeInput",["compositionend","keypress","textInput","paste"]);Cn("onCompositionEnd","compositionend focusout keydown keypress keyup mousedown".split(" "));Cn("onCompositionStart","compositionstart focusout keydown keypress keyup mousedown".split(" "));Cn("onCompositionUpdate","compositionupdate focusout keydown keypress keyup mousedown".split(" "));var Rr="abort canplay canplaythrough durationchange emptied encrypted ended error loadeddata loadedmetadata loadstart pause play playing progress ratechange resize seeked seeking stalled suspend timeupdate volumechange waiting".split(" "),Rg=new Set("cancel close invalid load scroll toggle".split(" ").concat(Rr));function Ku(e,t,n){var r=e.type||"unknown-event";e.currentTarget=n,Pm(r,t,void 0,e),e.currentTarget=null}function Bd(e,t){t=(t&4)!==0;for(var n=0;n<e.length;n++){var r=e[n],o=r.event;r=r.listeners;e:{var i=void 0;if(t)for(var s=r.length-1;0<=s;s--){var l=r[s],a=l.instance,u=l.currentTarget;if(l=l.listener,a!==i&&o.isPropagationStopped())break e;Ku(o,l,u),i=a}else for(s=0;s<r.length;s++){if(l=r[s],a=l.instance,u=l.currentTarget,l=l.listener,a!==i&&o.isPropagationStopped())break e;Ku(o,l,u),i=a}}}if(ai)throw e=tl,ai=!1,tl=null,e}function Y(e,t){var n=t[dl];n===void 0&&(n=t[dl]=new Set);var r=e+"__bubble";n.has(r)||(Kd(t,e,2,!1),n.add(r))}function ms(e,t,n){var r=0;t&&(r|=4),Kd(n,e,r,t)}var Eo="_reactListening"+Math.random().toString(36).slice(2);function Gr(e){if(!e[Eo]){e[Eo]=!0,Xc.forEach(function(n){n!=="selectionchange"&&(Rg.has(n)||ms(n,!1,e),ms(n,!0,e))});var t=e.nodeType===9?e:e.ownerDocument;t===null||t[Eo]||(t[Eo]=!0,ms("selectionchange",!1,t))}}function Kd(e,t,n,r){switch(Pd(t)){case 1:var o=Bm;break;case 4:o=Km;break;default:o=Xl}n=o.bind(null,t,n,e),o=void 0,!el||t!=="touchstart"&&t!=="touchmove"&&t!=="wheel"||(o=!0),r?o!==void 0?e.addEventListener(t,n,{capture:!0,passive:o}):e.addEventListener(t,n,!0):o!==void 0?e.addEventListener(t,n,{passive:o}):e.addEventListener(t,n,!1)}function gs(e,t,n,r,o){var i=r;if(!(t&1)&&!(t&2)&&r!==null)e:for(;;){if(r===null)return;var s=r.tag;if(s===3||s===4){var l=r.stateNode.containerInfo;if(l===o||l.nodeType===8&&l.parentNode===o)break;if(s===4)for(s=r.return;s!==null;){var a=s.tag;if((a===3||a===4)&&(a=s.stateNode.containerInfo,a===o||a.nodeType===8&&a.parentNode===o))return;s=s.return}for(;l!==null;){if(s=vn(l),s===null)return;if(a=s.tag,a===5||a===6){r=i=s;continue e}l=l.parentNode}}r=r.return}fd(function(){var u=i,f=Yl(n),d=[];e:{var h=$d.get(e);if(h!==void 0){var g=Zl,v=e;switch(e){case"keypress":if(Go(n)===0)break e;case"keydown":case"keyup":g=ig;break;case"focusin":v="focus",g=us;break;case"focusout":v="blur",g=us;break;case"beforeblur":case"afterblur":g=us;break;case"click":if(n.button===2)break e;case"auxclick":case"dblclick":case"mousedown":case"mousemove":case"mouseup":case"mouseout":case"mouseover":case"contextmenu":g=Lu;break;case"drag":case"dragend":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"dragstart":case"drop":g=Ym;break;case"touchcancel":case"touchend":case"touchmove":case"touchstart":g=ag;break;case Ud:case Ad:case zd:g=Qm;break;case Md:g=cg;break;case"scroll":g=Hm;break;case"wheel":g=pg;break;case"copy":case"cut":case"paste":g=Jm;break;case"gotpointercapture":case"lostpointercapture":case"pointercancel":case"pointerdown":case"pointermove":case"pointerout":case"pointerover":case"pointerup":g=Vu}var k=(t&4)!==0,O=!k&&e==="scroll",p=k?h!==null?h+"Capture":null:h;k=[];for(var c=u,m;c!==null;){m=c;var y=m.stateNode;if(m.tag===5&&y!==null&&(m=y,p!==null&&(y=$r(c,p),y!=null&&k.push(qr(c,y,m)))),O)break;c=c.return}0<k.length&&(h=new g(h,v,null,n,f),d.push({event:h,listeners:k}))}}if(!(t&7)){e:{if(h=e==="mouseover"||e==="pointerover",g=e==="mouseout"||e==="pointerout",h&&n!==Js&&(v=n.relatedTarget||n.fromElement)&&(vn(v)||v[Vt]))break e;if((g||h)&&(h=f.window===f?f:(h=f.ownerDocument)?h.defaultView||h.parentWindow:window,g?(v=n.relatedTarget||n.toElement,g=u,v=v?vn(v):null,v!==null&&(O=Rn(v),v!==O||v.tag!==5&&v.tag!==6)&&(v=null)):(g=null,v=u),g!==v)){if(k=Lu,y="onMouseLeave",p="onMouseEnter",c="mouse",(e==="pointerout"||e==="pointerover")&&(k=Vu,y="onPointerLeave",p="onPointerEnter",c="pointer"),O=g==null?h:$n(g),m=v==null?h:$n(v),h=new k(y,c+"leave",g,n,f),h.target=O,h.relatedTarget=m,y=null,vn(f)===u&&(k=new k(p,c+"enter",v,n,f),k.target=m,k.relatedTarget=O,y=k),O=y,g&&v)t:{for(k=g,p=v,c=0,m=k;m;m=jn(m))c++;for(m=0,y=p;y;y=jn(y))m++;for(;0<c-m;)k=jn(k),c--;for(;0<m-c;)p=jn(p),m--;for(;c--;){if(k===p||p!==null&&k===p.alternate)break t;k=jn(k),p=jn(p)}k=null}else k=null;g!==null&&Hu(d,h,g,k,!1),v!==null&&O!==null&&Hu(d,O,v,k,!0)}}e:{if(h=u?$n(u):window,g=h.nodeName&&h.nodeName.toLowerCase(),g==="select"||g==="input"&&h.type==="file")var S=_g;else if(Du(h))if(Id)S=Og;else{S=wg;var w=kg}else(g=h.nodeName)&&g.toLowerCase()==="input"&&(h.type==="checkbox"||h.type==="radio")&&(S=Sg);if(S&&(S=S(e,u))){Ld(d,S,n,f);break e}w&&w(e,h,u),e==="focusout"&&(w=h._wrapperState)&&w.controlled&&h.type==="number"&&Ys(h,"number",h.value)}switch(w=u?$n(u):window,e){case"focusin":(Du(w)||w.contentEditable==="true")&&(zn=w,il=u,Vr=null);break;case"focusout":Vr=il=zn=null;break;case"mousedown":sl=!0;break;case"contextmenu":case"mouseup":case"dragend":sl=!1,$u(d,n,f);break;case"selectionchange":if(xg)break;case"keydown":case"keyup":$u(d,n,f)}var E;if(ta)e:{switch(e){case"compositionstart":var P="onCompositionStart";break e;case"compositionend":P="onCompositionEnd";break e;case"compositionupdate":P="onCompositionUpdate";break e}P=void 0}else An?Td(e,n)&&(P="onCompositionEnd"):e==="keydown"&&n.keyCode===229&&(P="onCompositionStart");P&&(Rd&&n.locale!=="ko"&&(An||P!=="onCompositionStart"?P==="onCompositionEnd"&&An&&(E=Cd()):(Wt=f,Jl="value"in Wt?Wt.value:Wt.textContent,An=!0)),w=fi(u,P),0<w.length&&(P=new Iu(P,e,null,n,f),d.push({event:P,listeners:w}),E?P.data=E:(E=bd(n),E!==null&&(P.data=E)))),(E=hg?mg(e,n):gg(e,n))&&(u=fi(u,"onBeforeInput"),0<u.length&&(f=new Iu("onBeforeInput","beforeinput",null,n,f),d.push({event:f,listeners:u}),f.data=E))}Bd(d,t)})}function qr(e,t,n){return{instance:e,listener:t,currentTarget:n}}function fi(e,t){for(var n=t+"Capture",r=[];e!==null;){var o=e,i=o.stateNode;o.tag===5&&i!==null&&(o=i,i=$r(e,n),i!=null&&r.unshift(qr(e,i,o)),i=$r(e,t),i!=null&&r.push(qr(e,i,o))),e=e.return}return r}function jn(e){if(e===null)return null;do e=e.return;while(e&&e.tag!==5);return e||null}function Hu(e,t,n,r,o){for(var i=t._reactName,s=[];n!==null&&n!==r;){var l=n,a=l.alternate,u=l.stateNode;if(a!==null&&a===r)break;l.tag===5&&u!==null&&(l=u,o?(a=$r(n,i),a!=null&&s.unshift(qr(n,a,l))):o||(a=$r(n,i),a!=null&&s.push(qr(n,a,l)))),n=n.return}s.length!==0&&e.push({event:t,listeners:s})}var Tg=/\r\n?/g,bg=/\u0000|\uFFFD/g;function Wu(e){return(typeof e=="string"?e:""+e).replace(Tg,`
+`).replace(bg,"")}function xo(e,t,n){if(t=Wu(t),Wu(e)!==t&&n)throw Error(N(425))}function hi(){}var ll=null,al=null;function ul(e,t){return e==="textarea"||e==="noscript"||typeof t.children=="string"||typeof t.children=="number"||typeof t.dangerouslySetInnerHTML=="object"&&t.dangerouslySetInnerHTML!==null&&t.dangerouslySetInnerHTML.__html!=null}var cl=typeof setTimeout=="function"?setTimeout:void 0,Lg=typeof clearTimeout=="function"?clearTimeout:void 0,Yu=typeof Promise=="function"?Promise:void 0,Ig=typeof queueMicrotask=="function"?queueMicrotask:typeof Yu<"u"?function(e){return Yu.resolve(null).then(e).catch(Vg)}:cl;function Vg(e){setTimeout(function(){throw e})}function vs(e,t){var n=t,r=0;do{var o=n.nextSibling;if(e.removeChild(n),o&&o.nodeType===8)if(n=o.data,n==="/$"){if(r===0){e.removeChild(o),Hr(t);return}r--}else n!=="$"&&n!=="$?"&&n!=="$!"||r++;n=o}while(n);Hr(t)}function Jt(e){for(;e!=null;e=e.nextSibling){var t=e.nodeType;if(t===1||t===3)break;if(t===8){if(t=e.data,t==="$"||t==="$!"||t==="$?")break;if(t==="/$")return null}}return e}function Gu(e){e=e.previousSibling;for(var t=0;e;){if(e.nodeType===8){var n=e.data;if(n==="$"||n==="$!"||n==="$?"){if(t===0)return e;t--}else n==="/$"&&t++}e=e.previousSibling}return null}var dr=Math.random().toString(36).slice(2),vt="__reactFiber$"+dr,Qr="__reactProps$"+dr,Vt="__reactContainer$"+dr,dl="__reactEvents$"+dr,Fg="__reactListeners$"+dr,jg="__reactHandles$"+dr;function vn(e){var t=e[vt];if(t)return t;for(var n=e.parentNode;n;){if(t=n[Vt]||n[vt]){if(n=t.alternate,t.child!==null||n!==null&&n.child!==null)for(e=Gu(e);e!==null;){if(n=e[vt])return n;e=Gu(e)}return t}e=n,n=e.parentNode}return null}function lo(e){return e=e[vt]||e[Vt],!e||e.tag!==5&&e.tag!==6&&e.tag!==13&&e.tag!==3?null:e}function $n(e){if(e.tag===5||e.tag===6)return e.stateNode;throw Error(N(33))}function Di(e){return e[Qr]||null}var pl=[],Bn=-1;function un(e){return{current:e}}function G(e){0>Bn||(e.current=pl[Bn],pl[Bn]=null,Bn--)}function H(e,t){Bn++,pl[Bn]=e.current,e.current=t}var sn={},_e=un(sn),be=un(!1),Sn=sn;function rr(e,t){var n=e.type.contextTypes;if(!n)return sn;var r=e.stateNode;if(r&&r.__reactInternalMemoizedUnmaskedChildContext===t)return r.__reactInternalMemoizedMaskedChildContext;var o={},i;for(i in n)o[i]=t[i];return r&&(e=e.stateNode,e.__reactInternalMemoizedUnmaskedChildContext=t,e.__reactInternalMemoizedMaskedChildContext=o),o}function Le(e){return e=e.childContextTypes,e!=null}function mi(){G(be),G(_e)}function qu(e,t,n){if(_e.current!==sn)throw Error(N(168));H(_e,t),H(be,n)}function Hd(e,t,n){var r=e.stateNode;if(t=t.childContextTypes,typeof r.getChildContext!="function")return n;r=r.getChildContext();for(var o in r)if(!(o in t))throw Error(N(108,km(e)||"Unknown",o));return ee({},n,r)}function gi(e){return e=(e=e.stateNode)&&e.__reactInternalMemoizedMergedChildContext||sn,Sn=_e.current,H(_e,e),H(be,be.current),!0}function Qu(e,t,n){var r=e.stateNode;if(!r)throw Error(N(169));n?(e=Hd(e,t,Sn),r.__reactInternalMemoizedMergedChildContext=e,G(be),G(_e),H(_e,e)):G(be),H(be,n)}var Rt=null,Ui=!1,ys=!1;function Wd(e){Rt===null?Rt=[e]:Rt.push(e)}function Dg(e){Ui=!0,Wd(e)}function cn(){if(!ys&&Rt!==null){ys=!0;var e=0,t=$;try{var n=Rt;for($=1;e<n.length;e++){var r=n[e];do r=r(!0);while(r!==null)}Rt=null,Ui=!1}catch(o){throw Rt!==null&&(Rt=Rt.slice(e+1)),vd(Gl,cn),o}finally{$=t,ys=!1}}return null}var Kn=[],Hn=0,vi=null,yi=0,We=[],Ye=0,On=null,Tt=1,bt="";function hn(e,t){Kn[Hn++]=yi,Kn[Hn++]=vi,vi=e,yi=t}function Yd(e,t,n){We[Ye++]=Tt,We[Ye++]=bt,We[Ye++]=On,On=e;var r=Tt;e=bt;var o=32-lt(r)-1;r&=~(1<<o),n+=1;var i=32-lt(t)+o;if(30<i){var s=o-o%5;i=(r&(1<<s)-1).toString(32),r>>=s,o-=s,Tt=1<<32-lt(t)+o|n<<o|r,bt=i+e}else Tt=1<<i|n<<o|r,bt=e}function ra(e){e.return!==null&&(hn(e,1),Yd(e,1,0))}function oa(e){for(;e===vi;)vi=Kn[--Hn],Kn[Hn]=null,yi=Kn[--Hn],Kn[Hn]=null;for(;e===On;)On=We[--Ye],We[Ye]=null,bt=We[--Ye],We[Ye]=null,Tt=We[--Ye],We[Ye]=null}var ze=null,Ae=null,Q=!1,st=null;function Gd(e,t){var n=Ge(5,null,null,0);n.elementType="DELETED",n.stateNode=t,n.return=e,t=e.deletions,t===null?(e.deletions=[n],e.flags|=16):t.push(n)}function Xu(e,t){switch(e.tag){case 5:var n=e.type;return t=t.nodeType!==1||n.toLowerCase()!==t.nodeName.toLowerCase()?null:t,t!==null?(e.stateNode=t,ze=e,Ae=Jt(t.firstChild),!0):!1;case 6:return t=e.pendingProps===""||t.nodeType!==3?null:t,t!==null?(e.stateNode=t,ze=e,Ae=null,!0):!1;case 13:return t=t.nodeType!==8?null:t,t!==null?(n=On!==null?{id:Tt,overflow:bt}:null,e.memoizedState={dehydrated:t,treeContext:n,retryLane:1073741824},n=Ge(18,null,null,0),n.stateNode=t,n.return=e,e.child=n,ze=e,Ae=null,!0):!1;default:return!1}}function fl(e){return(e.mode&1)!==0&&(e.flags&128)===0}function hl(e){if(Q){var t=Ae;if(t){var n=t;if(!Xu(e,t)){if(fl(e))throw Error(N(418));t=Jt(n.nextSibling);var r=ze;t&&Xu(e,t)?Gd(r,n):(e.flags=e.flags&-4097|2,Q=!1,ze=e)}}else{if(fl(e))throw Error(N(418));e.flags=e.flags&-4097|2,Q=!1,ze=e}}}function Ju(e){for(e=e.return;e!==null&&e.tag!==5&&e.tag!==3&&e.tag!==13;)e=e.return;ze=e}function Po(e){if(e!==ze)return!1;if(!Q)return Ju(e),Q=!0,!1;var t;if((t=e.tag!==3)&&!(t=e.tag!==5)&&(t=e.type,t=t!=="head"&&t!=="body"&&!ul(e.type,e.memoizedProps)),t&&(t=Ae)){if(fl(e))throw qd(),Error(N(418));for(;t;)Gd(e,t),t=Jt(t.nextSibling)}if(Ju(e),e.tag===13){if(e=e.memoizedState,e=e!==null?e.dehydrated:null,!e)throw Error(N(317));e:{for(e=e.nextSibling,t=0;e;){if(e.nodeType===8){var n=e.data;if(n==="/$"){if(t===0){Ae=Jt(e.nextSibling);break e}t--}else n!=="$"&&n!=="$!"&&n!=="$?"||t++}e=e.nextSibling}Ae=null}}else Ae=ze?Jt(e.stateNode.nextSibling):null;return!0}function qd(){for(var e=Ae;e;)e=Jt(e.nextSibling)}function or(){Ae=ze=null,Q=!1}function ia(e){st===null?st=[e]:st.push(e)}var Ug=Dt.ReactCurrentBatchConfig;function ot(e,t){if(e&&e.defaultProps){t=ee({},t),e=e.defaultProps;for(var n in e)t[n]===void 0&&(t[n]=e[n]);return t}return t}var _i=un(null),ki=null,Wn=null,sa=null;function la(){sa=Wn=ki=null}function aa(e){var t=_i.current;G(_i),e._currentValue=t}function ml(e,t,n){for(;e!==null;){var r=e.alternate;if((e.childLanes&t)!==t?(e.childLanes|=t,r!==null&&(r.childLanes|=t)):r!==null&&(r.childLanes&t)!==t&&(r.childLanes|=t),e===n)break;e=e.return}}function Zn(e,t){ki=e,sa=Wn=null,e=e.dependencies,e!==null&&e.firstContext!==null&&(e.lanes&t&&(Te=!0),e.firstContext=null)}function Qe(e){var t=e._currentValue;if(sa!==e)if(e={context:e,memoizedValue:t,next:null},Wn===null){if(ki===null)throw Error(N(308));Wn=e,ki.dependencies={lanes:0,firstContext:e}}else Wn=Wn.next=e;return t}var yn=null;function ua(e){yn===null?yn=[e]:yn.push(e)}function Qd(e,t,n,r){var o=t.interleaved;return o===null?(n.next=n,ua(t)):(n.next=o.next,o.next=n),t.interleaved=n,Ft(e,r)}function Ft(e,t){e.lanes|=t;var n=e.alternate;for(n!==null&&(n.lanes|=t),n=e,e=e.return;e!==null;)e.childLanes|=t,n=e.alternate,n!==null&&(n.childLanes|=t),n=e,e=e.return;return n.tag===3?n.stateNode:null}var $t=!1;function ca(e){e.updateQueue={baseState:e.memoizedState,firstBaseUpdate:null,lastBaseUpdate:null,shared:{pending:null,interleaved:null,lanes:0},effects:null}}function Xd(e,t){e=e.updateQueue,t.updateQueue===e&&(t.updateQueue={baseState:e.baseState,firstBaseUpdate:e.firstBaseUpdate,lastBaseUpdate:e.lastBaseUpdate,shared:e.shared,effects:e.effects})}function Lt(e,t){return{eventTime:e,lane:t,tag:0,payload:null,callback:null,next:null}}function Zt(e,t,n){var r=e.updateQueue;if(r===null)return null;if(r=r.shared,U&2){var o=r.pending;return o===null?t.next=t:(t.next=o.next,o.next=t),r.pending=t,Ft(e,n)}return o=r.interleaved,o===null?(t.next=t,ua(r)):(t.next=o.next,o.next=t),r.interleaved=t,Ft(e,n)}function qo(e,t,n){if(t=t.updateQueue,t!==null&&(t=t.shared,(n&4194240)!==0)){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,ql(e,n)}}function Zu(e,t){var n=e.updateQueue,r=e.alternate;if(r!==null&&(r=r.updateQueue,n===r)){var o=null,i=null;if(n=n.firstBaseUpdate,n!==null){do{var s={eventTime:n.eventTime,lane:n.lane,tag:n.tag,payload:n.payload,callback:n.callback,next:null};i===null?o=i=s:i=i.next=s,n=n.next}while(n!==null);i===null?o=i=t:i=i.next=t}else o=i=t;n={baseState:r.baseState,firstBaseUpdate:o,lastBaseUpdate:i,shared:r.shared,effects:r.effects},e.updateQueue=n;return}e=n.lastBaseUpdate,e===null?n.firstBaseUpdate=t:e.next=t,n.lastBaseUpdate=t}function wi(e,t,n,r){var o=e.updateQueue;$t=!1;var i=o.firstBaseUpdate,s=o.lastBaseUpdate,l=o.shared.pending;if(l!==null){o.shared.pending=null;var a=l,u=a.next;a.next=null,s===null?i=u:s.next=u,s=a;var f=e.alternate;f!==null&&(f=f.updateQueue,l=f.lastBaseUpdate,l!==s&&(l===null?f.firstBaseUpdate=u:l.next=u,f.lastBaseUpdate=a))}if(i!==null){var d=o.baseState;s=0,f=u=a=null,l=i;do{var h=l.lane,g=l.eventTime;if((r&h)===h){f!==null&&(f=f.next={eventTime:g,lane:0,tag:l.tag,payload:l.payload,callback:l.callback,next:null});e:{var v=e,k=l;switch(h=t,g=n,k.tag){case 1:if(v=k.payload,typeof v=="function"){d=v.call(g,d,h);break e}d=v;break e;case 3:v.flags=v.flags&-65537|128;case 0:if(v=k.payload,h=typeof v=="function"?v.call(g,d,h):v,h==null)break e;d=ee({},d,h);break e;case 2:$t=!0}}l.callback!==null&&l.lane!==0&&(e.flags|=64,h=o.effects,h===null?o.effects=[l]:h.push(l))}else g={eventTime:g,lane:h,tag:l.tag,payload:l.payload,callback:l.callback,next:null},f===null?(u=f=g,a=d):f=f.next=g,s|=h;if(l=l.next,l===null){if(l=o.shared.pending,l===null)break;h=l,l=h.next,h.next=null,o.lastBaseUpdate=h,o.shared.pending=null}}while(1);if(f===null&&(a=d),o.baseState=a,o.firstBaseUpdate=u,o.lastBaseUpdate=f,t=o.shared.interleaved,t!==null){o=t;do s|=o.lane,o=o.next;while(o!==t)}else i===null&&(o.shared.lanes=0);En|=s,e.lanes=s,e.memoizedState=d}}function ec(e,t,n){if(e=t.effects,t.effects=null,e!==null)for(t=0;t<e.length;t++){var r=e[t],o=r.callback;if(o!==null){if(r.callback=null,r=n,typeof o!="function")throw Error(N(191,o));o.call(r)}}}var Jd=new Qc.Component().refs;function gl(e,t,n,r){t=e.memoizedState,n=n(r,t),n=n==null?t:ee({},t,n),e.memoizedState=n,e.lanes===0&&(e.updateQueue.baseState=n)}var Ai={isMounted:function(e){return(e=e._reactInternals)?Rn(e)===e:!1},enqueueSetState:function(e,t,n){e=e._reactInternals;var r=Oe(),o=tn(e),i=Lt(r,o);i.payload=t,n!=null&&(i.callback=n),t=Zt(e,i,o),t!==null&&(at(t,e,o,r),qo(t,e,o))},enqueueReplaceState:function(e,t,n){e=e._reactInternals;var r=Oe(),o=tn(e),i=Lt(r,o);i.tag=1,i.payload=t,n!=null&&(i.callback=n),t=Zt(e,i,o),t!==null&&(at(t,e,o,r),qo(t,e,o))},enqueueForceUpdate:function(e,t){e=e._reactInternals;var n=Oe(),r=tn(e),o=Lt(n,r);o.tag=2,t!=null&&(o.callback=t),t=Zt(e,o,r),t!==null&&(at(t,e,r,n),qo(t,e,r))}};function tc(e,t,n,r,o,i,s){return e=e.stateNode,typeof e.shouldComponentUpdate=="function"?e.shouldComponentUpdate(r,i,s):t.prototype&&t.prototype.isPureReactComponent?!Yr(n,r)||!Yr(o,i):!0}function Zd(e,t,n){var r=!1,o=sn,i=t.contextType;return typeof i=="object"&&i!==null?i=Qe(i):(o=Le(t)?Sn:_e.current,r=t.contextTypes,i=(r=r!=null)?rr(e,o):sn),t=new t(n,i),e.memoizedState=t.state!==null&&t.state!==void 0?t.state:null,t.updater=Ai,e.stateNode=t,t._reactInternals=e,r&&(e=e.stateNode,e.__reactInternalMemoizedUnmaskedChildContext=o,e.__reactInternalMemoizedMaskedChildContext=i),t}function nc(e,t,n,r){e=t.state,typeof t.componentWillReceiveProps=="function"&&t.componentWillReceiveProps(n,r),typeof t.UNSAFE_componentWillReceiveProps=="function"&&t.UNSAFE_componentWillReceiveProps(n,r),t.state!==e&&Ai.enqueueReplaceState(t,t.state,null)}function vl(e,t,n,r){var o=e.stateNode;o.props=n,o.state=e.memoizedState,o.refs=Jd,ca(e);var i=t.contextType;typeof i=="object"&&i!==null?o.context=Qe(i):(i=Le(t)?Sn:_e.current,o.context=rr(e,i)),o.state=e.memoizedState,i=t.getDerivedStateFromProps,typeof i=="function"&&(gl(e,t,i,n),o.state=e.memoizedState),typeof t.getDerivedStateFromProps=="function"||typeof o.getSnapshotBeforeUpdate=="function"||typeof o.UNSAFE_componentWillMount!="function"&&typeof o.componentWillMount!="function"||(t=o.state,typeof o.componentWillMount=="function"&&o.componentWillMount(),typeof o.UNSAFE_componentWillMount=="function"&&o.UNSAFE_componentWillMount(),t!==o.state&&Ai.enqueueReplaceState(o,o.state,null),wi(e,n,o,r),o.state=e.memoizedState),typeof o.componentDidMount=="function"&&(e.flags|=4194308)}function kr(e,t,n){if(e=n.ref,e!==null&&typeof e!="function"&&typeof e!="object"){if(n._owner){if(n=n._owner,n){if(n.tag!==1)throw Error(N(309));var r=n.stateNode}if(!r)throw Error(N(147,e));var o=r,i=""+e;return t!==null&&t.ref!==null&&typeof t.ref=="function"&&t.ref._stringRef===i?t.ref:(t=function(s){var l=o.refs;l===Jd&&(l=o.refs={}),s===null?delete l[i]:l[i]=s},t._stringRef=i,t)}if(typeof e!="string")throw Error(N(284));if(!n._owner)throw Error(N(290,e))}return e}function Co(e,t){throw e=Object.prototype.toString.call(t),Error(N(31,e==="[object Object]"?"object with keys {"+Object.keys(t).join(", ")+"}":e))}function rc(e){var t=e._init;return t(e._payload)}function ep(e){function t(p,c){if(e){var m=p.deletions;m===null?(p.deletions=[c],p.flags|=16):m.push(c)}}function n(p,c){if(!e)return null;for(;c!==null;)t(p,c),c=c.sibling;return null}function r(p,c){for(p=new Map;c!==null;)c.key!==null?p.set(c.key,c):p.set(c.index,c),c=c.sibling;return p}function o(p,c){return p=nn(p,c),p.index=0,p.sibling=null,p}function i(p,c,m){return p.index=m,e?(m=p.alternate,m!==null?(m=m.index,m<c?(p.flags|=2,c):m):(p.flags|=2,c)):(p.flags|=1048576,c)}function s(p){return e&&p.alternate===null&&(p.flags|=2),p}function l(p,c,m,y){return c===null||c.tag!==6?(c=Es(m,p.mode,y),c.return=p,c):(c=o(c,m),c.return=p,c)}function a(p,c,m,y){var S=m.type;return S===Un?f(p,c,m.props.children,y,m.key):c!==null&&(c.elementType===S||typeof S=="object"&&S!==null&&S.$$typeof===Mt&&rc(S)===c.type)?(y=o(c,m.props),y.ref=kr(p,c,m),y.return=p,y):(y=ti(m.type,m.key,m.props,null,p.mode,y),y.ref=kr(p,c,m),y.return=p,y)}function u(p,c,m,y){return c===null||c.tag!==4||c.stateNode.containerInfo!==m.containerInfo||c.stateNode.implementation!==m.implementation?(c=xs(m,p.mode,y),c.return=p,c):(c=o(c,m.children||[]),c.return=p,c)}function f(p,c,m,y,S){return c===null||c.tag!==7?(c=wn(m,p.mode,y,S),c.return=p,c):(c=o(c,m),c.return=p,c)}function d(p,c,m){if(typeof c=="string"&&c!==""||typeof c=="number")return c=Es(""+c,p.mode,m),c.return=p,c;if(typeof c=="object"&&c!==null){switch(c.$$typeof){case vo:return m=ti(c.type,c.key,c.props,null,p.mode,m),m.ref=kr(p,null,c),m.return=p,m;case Dn:return c=xs(c,p.mode,m),c.return=p,c;case Mt:var y=c._init;return d(p,y(c._payload),m)}if(Pr(c)||mr(c))return c=wn(c,p.mode,m,null),c.return=p,c;Co(p,c)}return null}function h(p,c,m,y){var S=c!==null?c.key:null;if(typeof m=="string"&&m!==""||typeof m=="number")return S!==null?null:l(p,c,""+m,y);if(typeof m=="object"&&m!==null){switch(m.$$typeof){case vo:return m.key===S?a(p,c,m,y):null;case Dn:return m.key===S?u(p,c,m,y):null;case Mt:return S=m._init,h(p,c,S(m._payload),y)}if(Pr(m)||mr(m))return S!==null?null:f(p,c,m,y,null);Co(p,m)}return null}function g(p,c,m,y,S){if(typeof y=="string"&&y!==""||typeof y=="number")return p=p.get(m)||null,l(c,p,""+y,S);if(typeof y=="object"&&y!==null){switch(y.$$typeof){case vo:return p=p.get(y.key===null?m:y.key)||null,a(c,p,y,S);case Dn:return p=p.get(y.key===null?m:y.key)||null,u(c,p,y,S);case Mt:var w=y._init;return g(p,c,m,w(y._payload),S)}if(Pr(y)||mr(y))return p=p.get(m)||null,f(c,p,y,S,null);Co(c,y)}return null}function v(p,c,m,y){for(var S=null,w=null,E=c,P=c=0,V=null;E!==null&&P<m.length;P++){E.index>P?(V=E,E=null):V=E.sibling;var C=h(p,E,m[P],y);if(C===null){E===null&&(E=V);break}e&&E&&C.alternate===null&&t(p,E),c=i(C,c,P),w===null?S=C:w.sibling=C,w=C,E=V}if(P===m.length)return n(p,E),Q&&hn(p,P),S;if(E===null){for(;P<m.length;P++)E=d(p,m[P],y),E!==null&&(c=i(E,c,P),w===null?S=E:w.sibling=E,w=E);return Q&&hn(p,P),S}for(E=r(p,E);P<m.length;P++)V=g(E,p,P,m[P],y),V!==null&&(e&&V.alternate!==null&&E.delete(V.key===null?P:V.key),c=i(V,c,P),w===null?S=V:w.sibling=V,w=V);return e&&E.forEach(function(W){return t(p,W)}),Q&&hn(p,P),S}function k(p,c,m,y){var S=mr(m);if(typeof S!="function")throw Error(N(150));if(m=S.call(m),m==null)throw Error(N(151));for(var w=S=null,E=c,P=c=0,V=null,C=m.next();E!==null&&!C.done;P++,C=m.next()){E.index>P?(V=E,E=null):V=E.sibling;var W=h(p,E,C.value,y);if(W===null){E===null&&(E=V);break}e&&E&&W.alternate===null&&t(p,E),c=i(W,c,P),w===null?S=W:w.sibling=W,w=W,E=V}if(C.done)return n(p,E),Q&&hn(p,P),S;if(E===null){for(;!C.done;P++,C=m.next())C=d(p,C.value,y),C!==null&&(c=i(C,c,P),w===null?S=C:w.sibling=C,w=C);return Q&&hn(p,P),S}for(E=r(p,E);!C.done;P++,C=m.next())C=g(E,p,P,C.value,y),C!==null&&(e&&C.alternate!==null&&E.delete(C.key===null?P:C.key),c=i(C,c,P),w===null?S=C:w.sibling=C,w=C);return e&&E.forEach(function(Ce){return t(p,Ce)}),Q&&hn(p,P),S}function O(p,c,m,y){if(typeof m=="object"&&m!==null&&m.type===Un&&m.key===null&&(m=m.props.children),typeof m=="object"&&m!==null){switch(m.$$typeof){case vo:e:{for(var S=m.key,w=c;w!==null;){if(w.key===S){if(S=m.type,S===Un){if(w.tag===7){n(p,w.sibling),c=o(w,m.props.children),c.return=p,p=c;break e}}else if(w.elementType===S||typeof S=="object"&&S!==null&&S.$$typeof===Mt&&rc(S)===w.type){n(p,w.sibling),c=o(w,m.props),c.ref=kr(p,w,m),c.return=p,p=c;break e}n(p,w);break}else t(p,w);w=w.sibling}m.type===Un?(c=wn(m.props.children,p.mode,y,m.key),c.return=p,p=c):(y=ti(m.type,m.key,m.props,null,p.mode,y),y.ref=kr(p,c,m),y.return=p,p=y)}return s(p);case Dn:e:{for(w=m.key;c!==null;){if(c.key===w)if(c.tag===4&&c.stateNode.containerInfo===m.containerInfo&&c.stateNode.implementation===m.implementation){n(p,c.sibling),c=o(c,m.children||[]),c.return=p,p=c;break e}else{n(p,c);break}else t(p,c);c=c.sibling}c=xs(m,p.mode,y),c.return=p,p=c}return s(p);case Mt:return w=m._init,O(p,c,w(m._payload),y)}if(Pr(m))return v(p,c,m,y);if(mr(m))return k(p,c,m,y);Co(p,m)}return typeof m=="string"&&m!==""||typeof m=="number"?(m=""+m,c!==null&&c.tag===6?(n(p,c.sibling),c=o(c,m),c.return=p,p=c):(n(p,c),c=Es(m,p.mode,y),c.return=p,p=c),s(p)):n(p,c)}return O}var ir=ep(!0),tp=ep(!1),ao={},kt=un(ao),Xr=un(ao),Jr=un(ao);function _n(e){if(e===ao)throw Error(N(174));return e}function da(e,t){switch(H(Jr,t),H(Xr,e),H(kt,ao),e=t.nodeType,e){case 9:case 11:t=(t=t.documentElement)?t.namespaceURI:qs(null,"");break;default:e=e===8?t.parentNode:t,t=e.namespaceURI||null,e=e.tagName,t=qs(t,e)}G(kt),H(kt,t)}function sr(){G(kt),G(Xr),G(Jr)}function np(e){_n(Jr.current);var t=_n(kt.current),n=qs(t,e.type);t!==n&&(H(Xr,e),H(kt,n))}function pa(e){Xr.current===e&&(G(kt),G(Xr))}var J=un(0);function Si(e){for(var t=e;t!==null;){if(t.tag===13){var n=t.memoizedState;if(n!==null&&(n=n.dehydrated,n===null||n.data==="$?"||n.data==="$!"))return t}else if(t.tag===19&&t.memoizedProps.revealOrder!==void 0){if(t.flags&128)return t}else if(t.child!==null){t.child.return=t,t=t.child;continue}if(t===e)break;for(;t.sibling===null;){if(t.return===null||t.return===e)return null;t=t.return}t.sibling.return=t.return,t=t.sibling}return null}var _s=[];function fa(){for(var e=0;e<_s.length;e++)_s[e]._workInProgressVersionPrimary=null;_s.length=0}var Qo=Dt.ReactCurrentDispatcher,ks=Dt.ReactCurrentBatchConfig,Nn=0,Z=null,se=null,ae=null,Oi=!1,Fr=!1,Zr=0,Ag=0;function ge(){throw Error(N(321))}function ha(e,t){if(t===null)return!1;for(var n=0;n<t.length&&n<e.length;n++)if(!ct(e[n],t[n]))return!1;return!0}function ma(e,t,n,r,o,i){if(Nn=i,Z=t,t.memoizedState=null,t.updateQueue=null,t.lanes=0,Qo.current=e===null||e.memoizedState===null?Bg:Kg,e=n(r,o),Fr){i=0;do{if(Fr=!1,Zr=0,25<=i)throw Error(N(301));i+=1,ae=se=null,t.updateQueue=null,Qo.current=Hg,e=n(r,o)}while(Fr)}if(Qo.current=Ni,t=se!==null&&se.next!==null,Nn=0,ae=se=Z=null,Oi=!1,t)throw Error(N(300));return e}function ga(){var e=Zr!==0;return Zr=0,e}function gt(){var e={memoizedState:null,baseState:null,baseQueue:null,queue:null,next:null};return ae===null?Z.memoizedState=ae=e:ae=ae.next=e,ae}function Xe(){if(se===null){var e=Z.alternate;e=e!==null?e.memoizedState:null}else e=se.next;var t=ae===null?Z.memoizedState:ae.next;if(t!==null)ae=t,se=e;else{if(e===null)throw Error(N(310));se=e,e={memoizedState:se.memoizedState,baseState:se.baseState,baseQueue:se.baseQueue,queue:se.queue,next:null},ae===null?Z.memoizedState=ae=e:ae=ae.next=e}return ae}function eo(e,t){return typeof t=="function"?t(e):t}function ws(e){var t=Xe(),n=t.queue;if(n===null)throw Error(N(311));n.lastRenderedReducer=e;var r=se,o=r.baseQueue,i=n.pending;if(i!==null){if(o!==null){var s=o.next;o.next=i.next,i.next=s}r.baseQueue=o=i,n.pending=null}if(o!==null){i=o.next,r=r.baseState;var l=s=null,a=null,u=i;do{var f=u.lane;if((Nn&f)===f)a!==null&&(a=a.next={lane:0,action:u.action,hasEagerState:u.hasEagerState,eagerState:u.eagerState,next:null}),r=u.hasEagerState?u.eagerState:e(r,u.action);else{var d={lane:f,action:u.action,hasEagerState:u.hasEagerState,eagerState:u.eagerState,next:null};a===null?(l=a=d,s=r):a=a.next=d,Z.lanes|=f,En|=f}u=u.next}while(u!==null&&u!==i);a===null?s=r:a.next=l,ct(r,t.memoizedState)||(Te=!0),t.memoizedState=r,t.baseState=s,t.baseQueue=a,n.lastRenderedState=r}if(e=n.interleaved,e!==null){o=e;do i=o.lane,Z.lanes|=i,En|=i,o=o.next;while(o!==e)}else o===null&&(n.lanes=0);return[t.memoizedState,n.dispatch]}function Ss(e){var t=Xe(),n=t.queue;if(n===null)throw Error(N(311));n.lastRenderedReducer=e;var r=n.dispatch,o=n.pending,i=t.memoizedState;if(o!==null){n.pending=null;var s=o=o.next;do i=e(i,s.action),s=s.next;while(s!==o);ct(i,t.memoizedState)||(Te=!0),t.memoizedState=i,t.baseQueue===null&&(t.baseState=i),n.lastRenderedState=i}return[i,r]}function rp(){}function op(e,t){var n=Z,r=Xe(),o=t(),i=!ct(r.memoizedState,o);if(i&&(r.memoizedState=o,Te=!0),r=r.queue,va(lp.bind(null,n,r,e),[e]),r.getSnapshot!==t||i||ae!==null&&ae.memoizedState.tag&1){if(n.flags|=2048,to(9,sp.bind(null,n,r,o,t),void 0,null),ue===null)throw Error(N(349));Nn&30||ip(n,t,o)}return o}function ip(e,t,n){e.flags|=16384,e={getSnapshot:t,value:n},t=Z.updateQueue,t===null?(t={lastEffect:null,stores:null},Z.updateQueue=t,t.stores=[e]):(n=t.stores,n===null?t.stores=[e]:n.push(e))}function sp(e,t,n,r){t.value=n,t.getSnapshot=r,ap(t)&&up(e)}function lp(e,t,n){return n(function(){ap(t)&&up(e)})}function ap(e){var t=e.getSnapshot;e=e.value;try{var n=t();return!ct(e,n)}catch{return!0}}function up(e){var t=Ft(e,1);t!==null&&at(t,e,1,-1)}function oc(e){var t=gt();return typeof e=="function"&&(e=e()),t.memoizedState=t.baseState=e,e={pending:null,interleaved:null,lanes:0,dispatch:null,lastRenderedReducer:eo,lastRenderedState:e},t.queue=e,e=e.dispatch=$g.bind(null,Z,e),[t.memoizedState,e]}function to(e,t,n,r){return e={tag:e,create:t,destroy:n,deps:r,next:null},t=Z.updateQueue,t===null?(t={lastEffect:null,stores:null},Z.updateQueue=t,t.lastEffect=e.next=e):(n=t.lastEffect,n===null?t.lastEffect=e.next=e:(r=n.next,n.next=e,e.next=r,t.lastEffect=e)),e}function cp(){return Xe().memoizedState}function Xo(e,t,n,r){var o=gt();Z.flags|=e,o.memoizedState=to(1|t,n,void 0,r===void 0?null:r)}function zi(e,t,n,r){var o=Xe();r=r===void 0?null:r;var i=void 0;if(se!==null){var s=se.memoizedState;if(i=s.destroy,r!==null&&ha(r,s.deps)){o.memoizedState=to(t,n,i,r);return}}Z.flags|=e,o.memoizedState=to(1|t,n,i,r)}function ic(e,t){return Xo(8390656,8,e,t)}function va(e,t){return zi(2048,8,e,t)}function dp(e,t){return zi(4,2,e,t)}function pp(e,t){return zi(4,4,e,t)}function fp(e,t){if(typeof t=="function")return e=e(),t(e),function(){t(null)};if(t!=null)return e=e(),t.current=e,function(){t.current=null}}function hp(e,t,n){return n=n!=null?n.concat([e]):null,zi(4,4,fp.bind(null,t,e),n)}function ya(){}function mp(e,t){var n=Xe();t=t===void 0?null:t;var r=n.memoizedState;return r!==null&&t!==null&&ha(t,r[1])?r[0]:(n.memoizedState=[e,t],e)}function gp(e,t){var n=Xe();t=t===void 0?null:t;var r=n.memoizedState;return r!==null&&t!==null&&ha(t,r[1])?r[0]:(e=e(),n.memoizedState=[e,t],e)}function vp(e,t,n){return Nn&21?(ct(n,t)||(n=kd(),Z.lanes|=n,En|=n,e.baseState=!0),t):(e.baseState&&(e.baseState=!1,Te=!0),e.memoizedState=n)}function zg(e,t){var n=$;$=n!==0&&4>n?n:4,e(!0);var r=ks.transition;ks.transition={};try{e(!1),t()}finally{$=n,ks.transition=r}}function yp(){return Xe().memoizedState}function Mg(e,t,n){var r=tn(e);if(n={lane:r,action:n,hasEagerState:!1,eagerState:null,next:null},_p(e))kp(t,n);else if(n=Qd(e,t,n,r),n!==null){var o=Oe();at(n,e,r,o),wp(n,t,r)}}function $g(e,t,n){var r=tn(e),o={lane:r,action:n,hasEagerState:!1,eagerState:null,next:null};if(_p(e))kp(t,o);else{var i=e.alternate;if(e.lanes===0&&(i===null||i.lanes===0)&&(i=t.lastRenderedReducer,i!==null))try{var s=t.lastRenderedState,l=i(s,n);if(o.hasEagerState=!0,o.eagerState=l,ct(l,s)){var a=t.interleaved;a===null?(o.next=o,ua(t)):(o.next=a.next,a.next=o),t.interleaved=o;return}}catch{}finally{}n=Qd(e,t,o,r),n!==null&&(o=Oe(),at(n,e,r,o),wp(n,t,r))}}function _p(e){var t=e.alternate;return e===Z||t!==null&&t===Z}function kp(e,t){Fr=Oi=!0;var n=e.pending;n===null?t.next=t:(t.next=n.next,n.next=t),e.pending=t}function wp(e,t,n){if(n&4194240){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,ql(e,n)}}var Ni={readContext:Qe,useCallback:ge,useContext:ge,useEffect:ge,useImperativeHandle:ge,useInsertionEffect:ge,useLayoutEffect:ge,useMemo:ge,useReducer:ge,useRef:ge,useState:ge,useDebugValue:ge,useDeferredValue:ge,useTransition:ge,useMutableSource:ge,useSyncExternalStore:ge,useId:ge,unstable_isNewReconciler:!1},Bg={readContext:Qe,useCallback:function(e,t){return gt().memoizedState=[e,t===void 0?null:t],e},useContext:Qe,useEffect:ic,useImperativeHandle:function(e,t,n){return n=n!=null?n.concat([e]):null,Xo(4194308,4,fp.bind(null,t,e),n)},useLayoutEffect:function(e,t){return Xo(4194308,4,e,t)},useInsertionEffect:function(e,t){return Xo(4,2,e,t)},useMemo:function(e,t){var n=gt();return t=t===void 0?null:t,e=e(),n.memoizedState=[e,t],e},useReducer:function(e,t,n){var r=gt();return t=n!==void 0?n(t):t,r.memoizedState=r.baseState=t,e={pending:null,interleaved:null,lanes:0,dispatch:null,lastRenderedReducer:e,lastRenderedState:t},r.queue=e,e=e.dispatch=Mg.bind(null,Z,e),[r.memoizedState,e]},useRef:function(e){var t=gt();return e={current:e},t.memoizedState=e},useState:oc,useDebugValue:ya,useDeferredValue:function(e){return gt().memoizedState=e},useTransition:function(){var e=oc(!1),t=e[0];return e=zg.bind(null,e[1]),gt().memoizedState=e,[t,e]},useMutableSource:function(){},useSyncExternalStore:function(e,t,n){var r=Z,o=gt();if(Q){if(n===void 0)throw Error(N(407));n=n()}else{if(n=t(),ue===null)throw Error(N(349));Nn&30||ip(r,t,n)}o.memoizedState=n;var i={value:n,getSnapshot:t};return o.queue=i,ic(lp.bind(null,r,i,e),[e]),r.flags|=2048,to(9,sp.bind(null,r,i,n,t),void 0,null),n},useId:function(){var e=gt(),t=ue.identifierPrefix;if(Q){var n=bt,r=Tt;n=(r&~(1<<32-lt(r)-1)).toString(32)+n,t=":"+t+"R"+n,n=Zr++,0<n&&(t+="H"+n.toString(32)),t+=":"}else n=Ag++,t=":"+t+"r"+n.toString(32)+":";return e.memoizedState=t},unstable_isNewReconciler:!1},Kg={readContext:Qe,useCallback:mp,useContext:Qe,useEffect:va,useImperativeHandle:hp,useInsertionEffect:dp,useLayoutEffect:pp,useMemo:gp,useReducer:ws,useRef:cp,useState:function(){return ws(eo)},useDebugValue:ya,useDeferredValue:function(e){var t=Xe();return vp(t,se.memoizedState,e)},useTransition:function(){var e=ws(eo)[0],t=Xe().memoizedState;return[e,t]},useMutableSource:rp,useSyncExternalStore:op,useId:yp,unstable_isNewReconciler:!1},Hg={readContext:Qe,useCallback:mp,useContext:Qe,useEffect:va,useImperativeHandle:hp,useInsertionEffect:dp,useLayoutEffect:pp,useMemo:gp,useReducer:Ss,useRef:cp,useState:function(){return Ss(eo)},useDebugValue:ya,useDeferredValue:function(e){var t=Xe();return se===null?t.memoizedState=e:vp(t,se.memoizedState,e)},useTransition:function(){var e=Ss(eo)[0],t=Xe().memoizedState;return[e,t]},useMutableSource:rp,useSyncExternalStore:op,useId:yp,unstable_isNewReconciler:!1};function lr(e,t){try{var n="",r=t;do n+=_m(r),r=r.return;while(r);var o=n}catch(i){o=`
 Error generating stack: `+i.message+`
-`+i.stack}return{value:e,source:t,stack:o,digest:null}}function Os(e,t,n){return{value:e,source:null,stack:n??null,digest:t??null}}function yl(e,t){try{console.error(t.value)}catch(n){setTimeout(function(){throw n})}}var Bg=typeof WeakMap=="function"?WeakMap:Map;function vp(e,t,n){n=Lt(-1,n),n.tag=3,n.payload={element:null};var r=t.value;return n.callback=function(){xi||(xi=!0,Cl=r),yl(e,t)},n}function yp(e,t,n){n=Lt(-1,n),n.tag=3;var r=e.type.getDerivedStateFromError;if(typeof r=="function"){var o=t.value;n.payload=function(){return r(o)},n.callback=function(){yl(e,t)}}var i=e.stateNode;return i!==null&&typeof i.componentDidCatch=="function"&&(n.callback=function(){yl(e,t),typeof r!="function"&&(Jt===null?Jt=new Set([this]):Jt.add(this));var s=t.stack;this.componentDidCatch(t.value,{componentStack:s!==null?s:""})}),n}function rc(e,t,n){var r=e.pingCache;if(r===null){r=e.pingCache=new Bg;var o=new Set;r.set(t,o)}else o=r.get(t),o===void 0&&(o=new Set,r.set(t,o));o.has(n)||(o.add(n),e=rv.bind(null,e,t,n),t.then(e,e))}function oc(e){do{var t;if((t=e.tag===13)&&(t=e.memoizedState,t=t!==null?t.dehydrated!==null:!0),t)return e;e=e.return}while(e!==null);return null}function ic(e,t,n,r,o){return e.mode&1?(e.flags|=65536,e.lanes=o,e):(e===t?e.flags|=65536:(e.flags|=128,n.flags|=131072,n.flags&=-52805,n.tag===1&&(n.alternate===null?n.tag=17:(t=Lt(-1,1),t.tag=2,Xt(n,t,1))),n.lanes|=1),e)}var Kg=Dt.ReactCurrentOwner,Te=!1;function Se(e,t,n,r){t.child=e===null?qd(t,null,n,r):ir(t,e.child,n,r)}function sc(e,t,n,r,o){n=n.render;var i=t.ref;return Zn(t,o),r=ma(e,t,n,r,i,o),n=ga(),e!==null&&!Te?(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~o,Ft(e,t,o)):(Q&&n&&ra(t),t.flags|=1,Se(e,t,r,o),t.child)}function lc(e,t,n,r,o){if(e===null){var i=n.type;return typeof i=="function"&&!xa(i)&&i.defaultProps===void 0&&n.compare===null&&n.defaultProps===void 0?(t.tag=15,t.type=i,_p(e,t,i,r,o)):(e=ti(n.type,null,r,t,t.mode,o),e.ref=t.ref,e.return=t,t.child=e)}if(i=e.child,!(e.lanes&o)){var s=i.memoizedProps;if(n=n.compare,n=n!==null?n:Yr,n(s,r)&&e.ref===t.ref)return Ft(e,t,o)}return t.flags|=1,e=en(i,r),e.ref=t.ref,e.return=t,t.child=e}function _p(e,t,n,r,o){if(e!==null){var i=e.memoizedProps;if(Yr(i,r)&&e.ref===t.ref)if(Te=!1,t.pendingProps=r=i,(e.lanes&o)!==0)e.flags&131072&&(Te=!0);else return t.lanes=e.lanes,Ft(e,t,o)}return _l(e,t,n,r,o)}function kp(e,t,n){var r=t.pendingProps,o=r.children,i=e!==null?e.memoizedState:null;if(r.mode==="hidden")if(!(t.mode&1))t.memoizedState={baseLanes:0,cachePool:null,transitions:null},$(Gn,je),je|=n;else{if(!(n&1073741824))return e=i!==null?i.baseLanes|n:n,t.lanes=t.childLanes=1073741824,t.memoizedState={baseLanes:e,cachePool:null,transitions:null},t.updateQueue=null,$(Gn,je),je|=e,null;t.memoizedState={baseLanes:0,cachePool:null,transitions:null},r=i!==null?i.baseLanes:n,$(Gn,je),je|=r}else i!==null?(r=i.baseLanes|n,t.memoizedState=null):r=n,$(Gn,je),je|=r;return Se(e,t,o,n),t.child}function wp(e,t){var n=t.ref;(e===null&&n!==null||e!==null&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function _l(e,t,n,r,o){var i=Ie(n)?wn:_e.current;return i=rr(t,i),Zn(t,o),n=ma(e,t,n,r,i,o),r=ga(),e!==null&&!Te?(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~o,Ft(e,t,o)):(Q&&r&&ra(t),t.flags|=1,Se(e,t,n,o),t.child)}function ac(e,t,n,r,o){if(Ie(n)){var i=!0;gi(t)}else i=!1;if(Zn(t,o),t.stateNode===null)Jo(e,t),Gd(t,n,r),vl(t,n,r,o),r=!0;else if(e===null){var s=t.stateNode,l=t.memoizedProps;s.props=l;var a=s.context,u=n.contextType;typeof u=="object"&&u!==null?u=Ge(u):(u=Ie(n)?wn:_e.current,u=rr(t,u));var f=n.getDerivedStateFromProps,d=typeof f=="function"||typeof s.getSnapshotBeforeUpdate=="function";d||typeof s.UNSAFE_componentWillReceiveProps!="function"&&typeof s.componentWillReceiveProps!="function"||(l!==r||a!==u)&&Zu(t,s,r,u),zt=!1;var h=t.memoizedState;s.state=h,wi(t,r,s,o),a=t.memoizedState,l!==r||h!==a||Le.current||zt?(typeof f=="function"&&(gl(t,n,f,r),a=t.memoizedState),(l=zt||Ju(t,n,l,r,h,a,u))?(d||typeof s.UNSAFE_componentWillMount!="function"&&typeof s.componentWillMount!="function"||(typeof s.componentWillMount=="function"&&s.componentWillMount(),typeof s.UNSAFE_componentWillMount=="function"&&s.UNSAFE_componentWillMount()),typeof s.componentDidMount=="function"&&(t.flags|=4194308)):(typeof s.componentDidMount=="function"&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=a),s.props=r,s.state=a,s.context=u,r=l):(typeof s.componentDidMount=="function"&&(t.flags|=4194308),r=!1)}else{s=t.stateNode,Wd(e,t),l=t.memoizedProps,u=t.type===t.elementType?l:rt(t.type,l),s.props=u,d=t.pendingProps,h=s.context,a=n.contextType,typeof a=="object"&&a!==null?a=Ge(a):(a=Ie(n)?wn:_e.current,a=rr(t,a));var g=n.getDerivedStateFromProps;(f=typeof g=="function"||typeof s.getSnapshotBeforeUpdate=="function")||typeof s.UNSAFE_componentWillReceiveProps!="function"&&typeof s.componentWillReceiveProps!="function"||(l!==d||h!==a)&&Zu(t,s,r,a),zt=!1,h=t.memoizedState,s.state=h,wi(t,r,s,o);var v=t.memoizedState;l!==d||h!==v||Le.current||zt?(typeof g=="function"&&(gl(t,n,g,r),v=t.memoizedState),(u=zt||Ju(t,n,u,r,h,v,a)||!1)?(f||typeof s.UNSAFE_componentWillUpdate!="function"&&typeof s.componentWillUpdate!="function"||(typeof s.componentWillUpdate=="function"&&s.componentWillUpdate(r,v,a),typeof s.UNSAFE_componentWillUpdate=="function"&&s.UNSAFE_componentWillUpdate(r,v,a)),typeof s.componentDidUpdate=="function"&&(t.flags|=4),typeof s.getSnapshotBeforeUpdate=="function"&&(t.flags|=1024)):(typeof s.componentDidUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=4),typeof s.getSnapshotBeforeUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=v),s.props=r,s.state=v,s.context=a,r=u):(typeof s.componentDidUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=4),typeof s.getSnapshotBeforeUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=1024),r=!1)}return kl(e,t,n,r,i,o)}function kl(e,t,n,r,o,i){wp(e,t);var s=(t.flags&128)!==0;if(!r&&!s)return o&&Yu(t,n,!1),Ft(e,t,i);r=t.stateNode,Kg.current=t;var l=s&&typeof n.getDerivedStateFromError!="function"?null:r.render();return t.flags|=1,e!==null&&s?(t.child=ir(t,e.child,null,i),t.child=ir(t,null,l,i)):Se(e,t,l,i),t.memoizedState=r.state,o&&Yu(t,n,!0),t.child}function Sp(e){var t=e.stateNode;t.pendingContext?Wu(e,t.pendingContext,t.pendingContext!==t.context):t.context&&Wu(e,t.context,!1),da(e,t.containerInfo)}function uc(e,t,n,r,o){return or(),ia(o),t.flags|=256,Se(e,t,n,r),t.child}var wl={dehydrated:null,treeContext:null,retryLane:0};function Sl(e){return{baseLanes:e,cachePool:null,transitions:null}}function Op(e,t,n){var r=t.pendingProps,o=X.current,i=!1,s=(t.flags&128)!==0,l;if((l=s)||(l=e!==null&&e.memoizedState===null?!1:(o&2)!==0),l?(i=!0,t.flags&=-129):(e===null||e.memoizedState!==null)&&(o|=1),$(X,o&1),e===null)return hl(t),e=t.memoizedState,e!==null&&(e=e.dehydrated,e!==null)?(t.mode&1?e.data==="$!"?t.lanes=8:t.lanes=1073741824:t.lanes=1,null):(s=r.children,e=r.fallback,i?(r=t.mode,i=t.child,s={mode:"hidden",children:s},!(r&1)&&i!==null?(i.childLanes=0,i.pendingProps=s):i=Bi(s,r,0,null),e=kn(e,r,n,null),i.return=t,e.return=t,i.sibling=e,t.child=i,t.child.memoizedState=Sl(n),t.memoizedState=wl,e):_a(t,s));if(o=e.memoizedState,o!==null&&(l=o.dehydrated,l!==null))return Hg(e,t,s,r,l,o,n);if(i){i=r.fallback,s=t.mode,o=e.child,l=o.sibling;var a={mode:"hidden",children:r.children};return!(s&1)&&t.child!==o?(r=t.child,r.childLanes=0,r.pendingProps=a,t.deletions=null):(r=en(o,a),r.subtreeFlags=o.subtreeFlags&14680064),l!==null?i=en(l,i):(i=kn(i,s,n,null),i.flags|=2),i.return=t,r.return=t,r.sibling=i,t.child=r,r=i,i=t.child,s=e.child.memoizedState,s=s===null?Sl(n):{baseLanes:s.baseLanes|n,cachePool:null,transitions:s.transitions},i.memoizedState=s,i.childLanes=e.childLanes&~n,t.memoizedState=wl,r}return i=e.child,e=i.sibling,r=en(i,{mode:"visible",children:r.children}),!(t.mode&1)&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function _a(e,t){return t=Bi({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function Ro(e,t,n,r){return r!==null&&ia(r),ir(t,e.child,null,n),e=_a(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function Hg(e,t,n,r,o,i,s){if(n)return t.flags&256?(t.flags&=-257,r=Os(Error(N(422))),Ro(e,t,s,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(i=r.fallback,o=t.mode,r=Bi({mode:"visible",children:r.children},o,0,null),i=kn(i,o,s,null),i.flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,t.mode&1&&ir(t,e.child,null,s),t.child.memoizedState=Sl(s),t.memoizedState=wl,i);if(!(t.mode&1))return Ro(e,t,s,null);if(o.data==="$!"){if(r=o.nextSibling&&o.nextSibling.dataset,r)var l=r.dgst;return r=l,i=Error(N(419)),r=Os(i,r,void 0),Ro(e,t,s,r)}if(l=(s&e.childLanes)!==0,Te||l){if(r=ue,r!==null){switch(s&-s){case 4:o=2;break;case 16:o=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:o=32;break;case 536870912:o=268435456;break;default:o=0}o=o&(r.suspendedLanes|s)?0:o,o!==0&&o!==i.retryLane&&(i.retryLane=o,Vt(e,o),lt(r,e,o,-1))}return Ea(),r=Os(Error(N(421))),Ro(e,t,s,r)}return o.data==="$?"?(t.flags|=128,t.child=e.child,t=ov.bind(null,e),o._reactRetry=t,null):(e=i.treeContext,Ae=qt(o.nextSibling),ze=t,Q=!0,it=null,e!==null&&(Ke[He++]=Rt,Ke[He++]=Tt,Ke[He++]=Sn,Rt=e.id,Tt=e.overflow,Sn=t),t=_a(t,r.children),t.flags|=4096,t)}function cc(e,t,n){e.lanes|=t;var r=e.alternate;r!==null&&(r.lanes|=t),ml(e.return,t,n)}function Ns(e,t,n,r,o){var i=e.memoizedState;i===null?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:o}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=o)}function Np(e,t,n){var r=t.pendingProps,o=r.revealOrder,i=r.tail;if(Se(e,t,r.children,n),r=X.current,r&2)r=r&1|2,t.flags|=128;else{if(e!==null&&e.flags&128)e:for(e=t.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&cc(e,n,t);else if(e.tag===19)cc(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if($(X,r),!(t.mode&1))t.memoizedState=null;else switch(o){case"forwards":for(n=t.child,o=null;n!==null;)e=n.alternate,e!==null&&Si(e)===null&&(o=n),n=n.sibling;n=o,n===null?(o=t.child,t.child=null):(o=n.sibling,n.sibling=null),Ns(t,!1,o,n,i);break;case"backwards":for(n=null,o=t.child,t.child=null;o!==null;){if(e=o.alternate,e!==null&&Si(e)===null){t.child=o;break}e=o.sibling,o.sibling=n,n=o,o=e}Ns(t,!0,n,null,i);break;case"together":Ns(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Jo(e,t){!(t.mode&1)&&e!==null&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Ft(e,t,n){if(e!==null&&(t.dependencies=e.dependencies),Nn|=t.lanes,!(n&t.childLanes))return null;if(e!==null&&t.child!==e.child)throw Error(N(153));if(t.child!==null){for(e=t.child,n=en(e,e.pendingProps),t.child=n,n.return=t;e.sibling!==null;)e=e.sibling,n=n.sibling=en(e,e.pendingProps),n.return=t;n.sibling=null}return t.child}function Wg(e,t,n){switch(t.tag){case 3:Sp(t),or();break;case 5:Xd(t);break;case 1:Ie(t.type)&&gi(t);break;case 4:da(t,t.stateNode.containerInfo);break;case 10:var r=t.type._context,o=t.memoizedProps.value;$(_i,r._currentValue),r._currentValue=o;break;case 13:if(r=t.memoizedState,r!==null)return r.dehydrated!==null?($(X,X.current&1),t.flags|=128,null):n&t.child.childLanes?Op(e,t,n):($(X,X.current&1),e=Ft(e,t,n),e!==null?e.sibling:null);$(X,X.current&1);break;case 19:if(r=(n&t.childLanes)!==0,e.flags&128){if(r)return Np(e,t,n);t.flags|=128}if(o=t.memoizedState,o!==null&&(o.rendering=null,o.tail=null,o.lastEffect=null),$(X,X.current),r)break;return null;case 22:case 23:return t.lanes=0,kp(e,t,n)}return Ft(e,t,n)}var Ep,Ol,xp,Pp;Ep=function(e,t){for(var n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Ol=function(){};xp=function(e,t,n,r){var o=e.memoizedProps;if(o!==r){e=t.stateNode,yn(kt.current);var i=null;switch(n){case"input":o=Hs(e,o),r=Hs(e,r),i=[];break;case"select":o=ee({},o,{value:void 0}),r=ee({},r,{value:void 0}),i=[];break;case"textarea":o=Gs(e,o),r=Gs(e,r),i=[];break;default:typeof o.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=hi)}qs(n,r);var s;n=null;for(u in o)if(!r.hasOwnProperty(u)&&o.hasOwnProperty(u)&&o[u]!=null)if(u==="style"){var l=o[u];for(s in l)l.hasOwnProperty(s)&&(n||(n={}),n[s]="")}else u!=="dangerouslySetInnerHTML"&&u!=="children"&&u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&u!=="autoFocus"&&(zr.hasOwnProperty(u)?i||(i=[]):(i=i||[]).push(u,null));for(u in r){var a=r[u];if(l=o!=null?o[u]:void 0,r.hasOwnProperty(u)&&a!==l&&(a!=null||l!=null))if(u==="style")if(l){for(s in l)!l.hasOwnProperty(s)||a&&a.hasOwnProperty(s)||(n||(n={}),n[s]="");for(s in a)a.hasOwnProperty(s)&&l[s]!==a[s]&&(n||(n={}),n[s]=a[s])}else n||(i||(i=[]),i.push(u,n)),n=a;else u==="dangerouslySetInnerHTML"?(a=a?a.__html:void 0,l=l?l.__html:void 0,a!=null&&l!==a&&(i=i||[]).push(u,a)):u==="children"?typeof a!="string"&&typeof a!="number"||(i=i||[]).push(u,""+a):u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&(zr.hasOwnProperty(u)?(a!=null&&u==="onScroll"&&H("scroll",e),i||l===a||(i=[])):(i=i||[]).push(u,a))}n&&(i=i||[]).push("style",n);var u=i;(t.updateQueue=u)&&(t.flags|=4)}};Pp=function(e,t,n,r){n!==r&&(t.flags|=4)};function wr(e,t){if(!Q)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function ve(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var o=e.child;o!==null;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags&14680064,r|=o.flags&14680064,o.return=e,o=o.sibling;else for(o=e.child;o!==null;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags,r|=o.flags,o.return=e,o=o.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yg(e,t,n){var r=t.pendingProps;switch(oa(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return ve(t),null;case 1:return Ie(t.type)&&mi(),ve(t),null;case 3:return r=t.stateNode,sr(),G(Le),G(_e),fa(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),(e===null||e.child===null)&&(Po(t)?t.flags|=4:e===null||e.memoizedState.isDehydrated&&!(t.flags&256)||(t.flags|=1024,it!==null&&(Ll(it),it=null))),Ol(e,t),ve(t),null;case 5:pa(t);var o=yn(Jr.current);if(n=t.type,e!==null&&t.stateNode!=null)xp(e,t,n,r,o),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(t.stateNode===null)throw Error(N(166));return ve(t),null}if(e=yn(kt.current),Po(t)){r=t.stateNode,n=t.type;var i=t.memoizedProps;switch(r[vt]=t,r[qr]=i,e=(t.mode&1)!==0,n){case"dialog":H("cancel",r),H("close",r);break;case"iframe":case"object":case"embed":H("load",r);break;case"video":case"audio":for(o=0;o<Rr.length;o++)H(Rr[o],r);break;case"source":H("error",r);break;case"img":case"image":case"link":H("error",r),H("load",r);break;case"details":H("toggle",r);break;case"input":_u(r,i),H("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!i.multiple},H("invalid",r);break;case"textarea":wu(r,i),H("invalid",r)}qs(n,i),o=null;for(var s in i)if(i.hasOwnProperty(s)){var l=i[s];s==="children"?typeof l=="string"?r.textContent!==l&&(i.suppressHydrationWarning!==!0&&xo(r.textContent,l,e),o=["children",l]):typeof l=="number"&&r.textContent!==""+l&&(i.suppressHydrationWarning!==!0&&xo(r.textContent,l,e),o=["children",""+l]):zr.hasOwnProperty(s)&&l!=null&&s==="onScroll"&&H("scroll",r)}switch(n){case"input":yo(r),ku(r,i,!0);break;case"textarea":yo(r),Su(r);break;case"select":case"option":break;default:typeof i.onClick=="function"&&(r.onclick=hi)}r=o,t.updateQueue=r,r!==null&&(t.flags|=4)}else{s=o.nodeType===9?o:o.ownerDocument,e==="http://www.w3.org/1999/xhtml"&&(e=ed(n)),e==="http://www.w3.org/1999/xhtml"?n==="script"?(e=s.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=s.createElement(n,{is:r.is}):(e=s.createElement(n),n==="select"&&(s=e,r.multiple?s.multiple=!0:r.size&&(s.size=r.size))):e=s.createElementNS(e,n),e[vt]=t,e[qr]=r,Ep(e,t,!1,!1),t.stateNode=e;e:{switch(s=Xs(n,r),n){case"dialog":H("cancel",e),H("close",e),o=r;break;case"iframe":case"object":case"embed":H("load",e),o=r;break;case"video":case"audio":for(o=0;o<Rr.length;o++)H(Rr[o],e);o=r;break;case"source":H("error",e),o=r;break;case"img":case"image":case"link":H("error",e),H("load",e),o=r;break;case"details":H("toggle",e),o=r;break;case"input":_u(e,r),o=Hs(e,r),H("invalid",e);break;case"option":o=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},o=ee({},r,{value:void 0}),H("invalid",e);break;case"textarea":wu(e,r),o=Gs(e,r),H("invalid",e);break;default:o=r}qs(n,o),l=o;for(i in l)if(l.hasOwnProperty(i)){var a=l[i];i==="style"?rd(e,a):i==="dangerouslySetInnerHTML"?(a=a?a.__html:void 0,a!=null&&td(e,a)):i==="children"?typeof a=="string"?(n!=="textarea"||a!=="")&&Mr(e,a):typeof a=="number"&&Mr(e,""+a):i!=="suppressContentEditableWarning"&&i!=="suppressHydrationWarning"&&i!=="autoFocus"&&(zr.hasOwnProperty(i)?a!=null&&i==="onScroll"&&H("scroll",e):a!=null&&Bl(e,i,a,s))}switch(n){case"input":yo(e),ku(e,r,!1);break;case"textarea":yo(e),Su(e);break;case"option":r.value!=null&&e.setAttribute("value",""+nn(r.value));break;case"select":e.multiple=!!r.multiple,i=r.value,i!=null?Qn(e,!!r.multiple,i,!1):r.defaultValue!=null&&Qn(e,!!r.multiple,r.defaultValue,!0);break;default:typeof o.onClick=="function"&&(e.onclick=hi)}switch(n){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}t.ref!==null&&(t.flags|=512,t.flags|=2097152)}return ve(t),null;case 6:if(e&&t.stateNode!=null)Pp(e,t,e.memoizedProps,r);else{if(typeof r!="string"&&t.stateNode===null)throw Error(N(166));if(n=yn(Jr.current),yn(kt.current),Po(t)){if(r=t.stateNode,n=t.memoizedProps,r[vt]=t,(i=r.nodeValue!==n)&&(e=ze,e!==null))switch(e.tag){case 3:xo(r.nodeValue,n,(e.mode&1)!==0);break;case 5:e.memoizedProps.suppressHydrationWarning!==!0&&xo(r.nodeValue,n,(e.mode&1)!==0)}i&&(t.flags|=4)}else r=(n.nodeType===9?n:n.ownerDocument).createTextNode(r),r[vt]=t,t.stateNode=r}return ve(t),null;case 13:if(G(X),r=t.memoizedState,e===null||e.memoizedState!==null&&e.memoizedState.dehydrated!==null){if(Q&&Ae!==null&&t.mode&1&&!(t.flags&128))Kd(),or(),t.flags|=98560,i=!1;else if(i=Po(t),r!==null&&r.dehydrated!==null){if(e===null){if(!i)throw Error(N(318));if(i=t.memoizedState,i=i!==null?i.dehydrated:null,!i)throw Error(N(317));i[vt]=t}else or(),!(t.flags&128)&&(t.memoizedState=null),t.flags|=4;ve(t),i=!1}else it!==null&&(Ll(it),it=null),i=!0;if(!i)return t.flags&65536?t:null}return t.flags&128?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,t.mode&1&&(e===null||X.current&1?le===0&&(le=3):Ea())),t.updateQueue!==null&&(t.flags|=4),ve(t),null);case 4:return sr(),Ol(e,t),e===null&&Gr(t.stateNode.containerInfo),ve(t),null;case 10:return aa(t.type._context),ve(t),null;case 17:return Ie(t.type)&&mi(),ve(t),null;case 19:if(G(X),i=t.memoizedState,i===null)return ve(t),null;if(r=(t.flags&128)!==0,s=i.rendering,s===null)if(r)wr(i,!1);else{if(le!==0||e!==null&&e.flags&128)for(e=t.child;e!==null;){if(s=Si(e),s!==null){for(t.flags|=128,wr(i,!1),r=s.updateQueue,r!==null&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;n!==null;)i=n,e=r,i.flags&=14680066,s=i.alternate,s===null?(i.childLanes=0,i.lanes=e,i.child=null,i.subtreeFlags=0,i.memoizedProps=null,i.memoizedState=null,i.updateQueue=null,i.dependencies=null,i.stateNode=null):(i.childLanes=s.childLanes,i.lanes=s.lanes,i.child=s.child,i.subtreeFlags=0,i.deletions=null,i.memoizedProps=s.memoizedProps,i.memoizedState=s.memoizedState,i.updateQueue=s.updateQueue,i.type=s.type,e=s.dependencies,i.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return $(X,X.current&1|2),t.child}e=e.sibling}i.tail!==null&&ne()>ar&&(t.flags|=128,r=!0,wr(i,!1),t.lanes=4194304)}else{if(!r)if(e=Si(s),e!==null){if(t.flags|=128,r=!0,n=e.updateQueue,n!==null&&(t.updateQueue=n,t.flags|=4),wr(i,!0),i.tail===null&&i.tailMode==="hidden"&&!s.alternate&&!Q)return ve(t),null}else 2*ne()-i.renderingStartTime>ar&&n!==1073741824&&(t.flags|=128,r=!0,wr(i,!1),t.lanes=4194304);i.isBackwards?(s.sibling=t.child,t.child=s):(n=i.last,n!==null?n.sibling=s:t.child=s,i.last=s)}return i.tail!==null?(t=i.tail,i.rendering=t,i.tail=t.sibling,i.renderingStartTime=ne(),t.sibling=null,n=X.current,$(X,r?n&1|2:n&1),t):(ve(t),null);case 22:case 23:return Na(),r=t.memoizedState!==null,e!==null&&e.memoizedState!==null!==r&&(t.flags|=8192),r&&t.mode&1?je&1073741824&&(ve(t),t.subtreeFlags&6&&(t.flags|=8192)):ve(t),null;case 24:return null;case 25:return null}throw Error(N(156,t.tag))}function Gg(e,t){switch(oa(t),t.tag){case 1:return Ie(t.type)&&mi(),e=t.flags,e&65536?(t.flags=e&-65537|128,t):null;case 3:return sr(),G(Le),G(_e),fa(),e=t.flags,e&65536&&!(e&128)?(t.flags=e&-65537|128,t):null;case 5:return pa(t),null;case 13:if(G(X),e=t.memoizedState,e!==null&&e.dehydrated!==null){if(t.alternate===null)throw Error(N(340));or()}return e=t.flags,e&65536?(t.flags=e&-65537|128,t):null;case 19:return G(X),null;case 4:return sr(),null;case 10:return aa(t.type._context),null;case 22:case 23:return Na(),null;case 24:return null;default:return null}}var To=!1,ye=!1,Qg=typeof WeakSet=="function"?WeakSet:Set,T=null;function Yn(e,t){var n=e.ref;if(n!==null)if(typeof n=="function")try{n(null)}catch(r){te(e,t,r)}else n.current=null}function Nl(e,t,n){try{n()}catch(r){te(e,t,r)}}var dc=!1;function qg(e,t){if(ll=di,e=Ld(),na(e)){if("selectionStart"in e)var n={start:e.selectionStart,end:e.selectionEnd};else e:{n=(n=e.ownerDocument)&&n.defaultView||window;var r=n.getSelection&&n.getSelection();if(r&&r.rangeCount!==0){n=r.anchorNode;var o=r.anchorOffset,i=r.focusNode;r=r.focusOffset;try{n.nodeType,i.nodeType}catch{n=null;break e}var s=0,l=-1,a=-1,u=0,f=0,d=e,h=null;t:for(;;){for(var g;d!==n||o!==0&&d.nodeType!==3||(l=s+o),d!==i||r!==0&&d.nodeType!==3||(a=s+r),d.nodeType===3&&(s+=d.nodeValue.length),(g=d.firstChild)!==null;)h=d,d=g;for(;;){if(d===e)break t;if(h===n&&++u===o&&(l=s),h===i&&++f===r&&(a=s),(g=d.nextSibling)!==null)break;d=h,h=d.parentNode}d=g}n=l===-1||a===-1?null:{start:l,end:a}}else n=null}n=n||{start:0,end:0}}else n=null;for(al={focusedElem:e,selectionRange:n},di=!1,T=t;T!==null;)if(t=T,e=t.child,(t.subtreeFlags&1028)!==0&&e!==null)e.return=t,T=e;else for(;T!==null;){t=T;try{var v=t.alternate;if(t.flags&1024)switch(t.tag){case 0:case 11:case 15:break;case 1:if(v!==null){var k=v.memoizedProps,O=v.memoizedState,p=t.stateNode,c=p.getSnapshotBeforeUpdate(t.elementType===t.type?k:rt(t.type,k),O);p.__reactInternalSnapshotBeforeUpdate=c}break;case 3:var m=t.stateNode.containerInfo;m.nodeType===1?m.textContent="":m.nodeType===9&&m.documentElement&&m.removeChild(m.documentElement);break;case 5:case 6:case 4:case 17:break;default:throw Error(N(163))}}catch(y){te(t,t.return,y)}if(e=t.sibling,e!==null){e.return=t.return,T=e;break}T=t.return}return v=dc,dc=!1,v}function Dr(e,t,n){var r=t.updateQueue;if(r=r!==null?r.lastEffect:null,r!==null){var o=r=r.next;do{if((o.tag&e)===e){var i=o.destroy;o.destroy=void 0,i!==void 0&&Nl(t,n,i)}o=o.next}while(o!==r)}}function Mi(e,t){if(t=t.updateQueue,t=t!==null?t.lastEffect:null,t!==null){var n=t=t.next;do{if((n.tag&e)===e){var r=n.create;n.destroy=r()}n=n.next}while(n!==t)}}function El(e){var t=e.ref;if(t!==null){var n=e.stateNode;switch(e.tag){case 5:e=n;break;default:e=n}typeof t=="function"?t(e):t.current=e}}function Cp(e){var t=e.alternate;t!==null&&(e.alternate=null,Cp(t)),e.child=null,e.deletions=null,e.sibling=null,e.tag===5&&(t=e.stateNode,t!==null&&(delete t[vt],delete t[qr],delete t[dl],delete t[Ig],delete t[bg])),e.stateNode=null,e.return=null,e.dependencies=null,e.memoizedProps=null,e.memoizedState=null,e.pendingProps=null,e.stateNode=null,e.updateQueue=null}function Rp(e){return e.tag===5||e.tag===3||e.tag===4}function pc(e){e:for(;;){for(;e.sibling===null;){if(e.return===null||Rp(e.return))return null;e=e.return}for(e.sibling.return=e.return,e=e.sibling;e.tag!==5&&e.tag!==6&&e.tag!==18;){if(e.flags&2||e.child===null||e.tag===4)continue e;e.child.return=e,e=e.child}if(!(e.flags&2))return e.stateNode}}function xl(e,t,n){var r=e.tag;if(r===5||r===6)e=e.stateNode,t?n.nodeType===8?n.parentNode.insertBefore(e,t):n.insertBefore(e,t):(n.nodeType===8?(t=n.parentNode,t.insertBefore(e,n)):(t=n,t.appendChild(e)),n=n._reactRootContainer,n!=null||t.onclick!==null||(t.onclick=hi));else if(r!==4&&(e=e.child,e!==null))for(xl(e,t,n),e=e.sibling;e!==null;)xl(e,t,n),e=e.sibling}function Pl(e,t,n){var r=e.tag;if(r===5||r===6)e=e.stateNode,t?n.insertBefore(e,t):n.appendChild(e);else if(r!==4&&(e=e.child,e!==null))for(Pl(e,t,n),e=e.sibling;e!==null;)Pl(e,t,n),e=e.sibling}var de=null,ot=!1;function Ut(e,t,n){for(n=n.child;n!==null;)Tp(e,t,n),n=n.sibling}function Tp(e,t,n){if(_t&&typeof _t.onCommitFiberUnmount=="function")try{_t.onCommitFiberUnmount(bi,n)}catch{}switch(n.tag){case 5:ye||Yn(n,t);case 6:var r=de,o=ot;de=null,Ut(e,t,n),de=r,ot=o,de!==null&&(ot?(e=de,n=n.stateNode,e.nodeType===8?e.parentNode.removeChild(n):e.removeChild(n)):de.removeChild(n.stateNode));break;case 18:de!==null&&(ot?(e=de,n=n.stateNode,e.nodeType===8?vs(e.parentNode,n):e.nodeType===1&&vs(e,n),Hr(e)):vs(de,n.stateNode));break;case 4:r=de,o=ot,de=n.stateNode.containerInfo,ot=!0,Ut(e,t,n),de=r,ot=o;break;case 0:case 11:case 14:case 15:if(!ye&&(r=n.updateQueue,r!==null&&(r=r.lastEffect,r!==null))){o=r=r.next;do{var i=o,s=i.destroy;i=i.tag,s!==void 0&&(i&2||i&4)&&Nl(n,t,s),o=o.next}while(o!==r)}Ut(e,t,n);break;case 1:if(!ye&&(Yn(n,t),r=n.stateNode,typeof r.componentWillUnmount=="function"))try{r.props=n.memoizedProps,r.state=n.memoizedState,r.componentWillUnmount()}catch(l){te(n,t,l)}Ut(e,t,n);break;case 21:Ut(e,t,n);break;case 22:n.mode&1?(ye=(r=ye)||n.memoizedState!==null,Ut(e,t,n),ye=r):Ut(e,t,n);break;default:Ut(e,t,n)}}function fc(e){var t=e.updateQueue;if(t!==null){e.updateQueue=null;var n=e.stateNode;n===null&&(n=e.stateNode=new Qg),t.forEach(function(r){var o=iv.bind(null,e,r);n.has(r)||(n.add(r),r.then(o,o))})}}function nt(e,t){var n=t.deletions;if(n!==null)for(var r=0;r<n.length;r++){var o=n[r];try{var i=e,s=t,l=s;e:for(;l!==null;){switch(l.tag){case 5:de=l.stateNode,ot=!1;break e;case 3:de=l.stateNode.containerInfo,ot=!0;break e;case 4:de=l.stateNode.containerInfo,ot=!0;break e}l=l.return}if(de===null)throw Error(N(160));Tp(i,s,o),de=null,ot=!1;var a=o.alternate;a!==null&&(a.return=null),o.return=null}catch(u){te(o,t,u)}}if(t.subtreeFlags&12854)for(t=t.child;t!==null;)Lp(t,e),t=t.sibling}function Lp(e,t){var n=e.alternate,r=e.flags;switch(e.tag){case 0:case 11:case 14:case 15:if(nt(t,e),mt(e),r&4){try{Dr(3,e,e.return),Mi(3,e)}catch(k){te(e,e.return,k)}try{Dr(5,e,e.return)}catch(k){te(e,e.return,k)}}break;case 1:nt(t,e),mt(e),r&512&&n!==null&&Yn(n,n.return);break;case 5:if(nt(t,e),mt(e),r&512&&n!==null&&Yn(n,n.return),e.flags&32){var o=e.stateNode;try{Mr(o,"")}catch(k){te(e,e.return,k)}}if(r&4&&(o=e.stateNode,o!=null)){var i=e.memoizedProps,s=n!==null?n.memoizedProps:i,l=e.type,a=e.updateQueue;if(e.updateQueue=null,a!==null)try{l==="input"&&i.type==="radio"&&i.name!=null&&Jc(o,i),Xs(l,s);var u=Xs(l,i);for(s=0;s<a.length;s+=2){var f=a[s],d=a[s+1];f==="style"?rd(o,d):f==="dangerouslySetInnerHTML"?td(o,d):f==="children"?Mr(o,d):Bl(o,f,d,u)}switch(l){case"input":Ws(o,i);break;case"textarea":Zc(o,i);break;case"select":var h=o._wrapperState.wasMultiple;o._wrapperState.wasMultiple=!!i.multiple;var g=i.value;g!=null?Qn(o,!!i.multiple,g,!1):h!==!!i.multiple&&(i.defaultValue!=null?Qn(o,!!i.multiple,i.defaultValue,!0):Qn(o,!!i.multiple,i.multiple?[]:"",!1))}o[qr]=i}catch(k){te(e,e.return,k)}}break;case 6:if(nt(t,e),mt(e),r&4){if(e.stateNode===null)throw Error(N(162));o=e.stateNode,i=e.memoizedProps;try{o.nodeValue=i}catch(k){te(e,e.return,k)}}break;case 3:if(nt(t,e),mt(e),r&4&&n!==null&&n.memoizedState.isDehydrated)try{Hr(t.containerInfo)}catch(k){te(e,e.return,k)}break;case 4:nt(t,e),mt(e);break;case 13:nt(t,e),mt(e),o=e.child,o.flags&8192&&(i=o.memoizedState!==null,o.stateNode.isHidden=i,!i||o.alternate!==null&&o.alternate.memoizedState!==null||(Sa=ne())),r&4&&fc(e);break;case 22:if(f=n!==null&&n.memoizedState!==null,e.mode&1?(ye=(u=ye)||f,nt(t,e),ye=u):nt(t,e),mt(e),r&8192){if(u=e.memoizedState!==null,(e.stateNode.isHidden=u)&&!f&&e.mode&1)for(T=e,f=e.child;f!==null;){for(d=T=f;T!==null;){switch(h=T,g=h.child,h.tag){case 0:case 11:case 14:case 15:Dr(4,h,h.return);break;case 1:Yn(h,h.return);var v=h.stateNode;if(typeof v.componentWillUnmount=="function"){r=h,n=h.return;try{t=r,v.props=t.memoizedProps,v.state=t.memoizedState,v.componentWillUnmount()}catch(k){te(r,n,k)}}break;case 5:Yn(h,h.return);break;case 22:if(h.memoizedState!==null){mc(d);continue}}g!==null?(g.return=h,T=g):mc(d)}f=f.sibling}e:for(f=null,d=e;;){if(d.tag===5){if(f===null){f=d;try{o=d.stateNode,u?(i=o.style,typeof i.setProperty=="function"?i.setProperty("display","none","important"):i.display="none"):(l=d.stateNode,a=d.memoizedProps.style,s=a!=null&&a.hasOwnProperty("display")?a.display:null,l.style.display=nd("display",s))}catch(k){te(e,e.return,k)}}}else if(d.tag===6){if(f===null)try{d.stateNode.nodeValue=u?"":d.memoizedProps}catch(k){te(e,e.return,k)}}else if((d.tag!==22&&d.tag!==23||d.memoizedState===null||d===e)&&d.child!==null){d.child.return=d,d=d.child;continue}if(d===e)break e;for(;d.sibling===null;){if(d.return===null||d.return===e)break e;f===d&&(f=null),d=d.return}f===d&&(f=null),d.sibling.return=d.return,d=d.sibling}}break;case 19:nt(t,e),mt(e),r&4&&fc(e);break;case 21:break;default:nt(t,e),mt(e)}}function mt(e){var t=e.flags;if(t&2){try{e:{for(var n=e.return;n!==null;){if(Rp(n)){var r=n;break e}n=n.return}throw Error(N(160))}switch(r.tag){case 5:var o=r.stateNode;r.flags&32&&(Mr(o,""),r.flags&=-33);var i=pc(e);Pl(e,i,o);break;case 3:case 4:var s=r.stateNode.containerInfo,l=pc(e);xl(e,l,s);break;default:throw Error(N(161))}}catch(a){te(e,e.return,a)}e.flags&=-3}t&4096&&(e.flags&=-4097)}function Xg(e,t,n){T=e,Ip(e)}function Ip(e,t,n){for(var r=(e.mode&1)!==0;T!==null;){var o=T,i=o.child;if(o.tag===22&&r){var s=o.memoizedState!==null||To;if(!s){var l=o.alternate,a=l!==null&&l.memoizedState!==null||ye;l=To;var u=ye;if(To=s,(ye=a)&&!u)for(T=o;T!==null;)s=T,a=s.child,s.tag===22&&s.memoizedState!==null?gc(o):a!==null?(a.return=s,T=a):gc(o);for(;i!==null;)T=i,Ip(i),i=i.sibling;T=o,To=l,ye=u}hc(e)}else o.subtreeFlags&8772&&i!==null?(i.return=o,T=i):hc(e)}}function hc(e){for(;T!==null;){var t=T;if(t.flags&8772){var n=t.alternate;try{if(t.flags&8772)switch(t.tag){case 0:case 11:case 15:ye||Mi(5,t);break;case 1:var r=t.stateNode;if(t.flags&4&&!ye)if(n===null)r.componentDidMount();else{var o=t.elementType===t.type?n.memoizedProps:rt(t.type,n.memoizedProps);r.componentDidUpdate(o,n.memoizedState,r.__reactInternalSnapshotBeforeUpdate)}var i=t.updateQueue;i!==null&&Xu(t,i,r);break;case 3:var s=t.updateQueue;if(s!==null){if(n=null,t.child!==null)switch(t.child.tag){case 5:n=t.child.stateNode;break;case 1:n=t.child.stateNode}Xu(t,s,n)}break;case 5:var l=t.stateNode;if(n===null&&t.flags&4){n=l;var a=t.memoizedProps;switch(t.type){case"button":case"input":case"select":case"textarea":a.autoFocus&&n.focus();break;case"img":a.src&&(n.src=a.src)}}break;case 6:break;case 4:break;case 12:break;case 13:if(t.memoizedState===null){var u=t.alternate;if(u!==null){var f=u.memoizedState;if(f!==null){var d=f.dehydrated;d!==null&&Hr(d)}}}break;case 19:case 17:case 21:case 22:case 23:case 25:break;default:throw Error(N(163))}ye||t.flags&512&&El(t)}catch(h){te(t,t.return,h)}}if(t===e){T=null;break}if(n=t.sibling,n!==null){n.return=t.return,T=n;break}T=t.return}}function mc(e){for(;T!==null;){var t=T;if(t===e){T=null;break}var n=t.sibling;if(n!==null){n.return=t.return,T=n;break}T=t.return}}function gc(e){for(;T!==null;){var t=T;try{switch(t.tag){case 0:case 11:case 15:var n=t.return;try{Mi(4,t)}catch(a){te(t,n,a)}break;case 1:var r=t.stateNode;if(typeof r.componentDidMount=="function"){var o=t.return;try{r.componentDidMount()}catch(a){te(t,o,a)}}var i=t.return;try{El(t)}catch(a){te(t,i,a)}break;case 5:var s=t.return;try{El(t)}catch(a){te(t,s,a)}}}catch(a){te(t,t.return,a)}if(t===e){T=null;break}var l=t.sibling;if(l!==null){l.return=t.return,T=l;break}T=t.return}}var Jg=Math.ceil,Ei=Dt.ReactCurrentDispatcher,ka=Dt.ReactCurrentOwner,Ye=Dt.ReactCurrentBatchConfig,U=0,ue=null,re=null,he=0,je=0,Gn=ln(0),le=0,no=null,Nn=0,$i=0,wa=0,jr=null,Re=null,Sa=0,ar=1/0,Pt=null,xi=!1,Cl=null,Jt=null,Lo=!1,Ht=null,Pi=0,Ur=0,Rl=null,Zo=-1,ei=0;function Oe(){return U&6?ne():Zo!==-1?Zo:Zo=ne()}function Zt(e){return e.mode&1?U&2&&he!==0?he&-he:Fg.transition!==null?(ei===0&&(ei=md()),ei):(e=M,e!==0||(e=window.event,e=e===void 0?16:Sd(e.type)),e):1}function lt(e,t,n,r){if(50<Ur)throw Ur=0,Rl=null,Error(N(185));io(e,n,r),(!(U&2)||e!==ue)&&(e===ue&&(!(U&2)&&($i|=n),le===4&&$t(e,he)),be(e,r),n===1&&U===0&&!(t.mode&1)&&(ar=ne()+500,Ui&&an()))}function be(e,t){var n=e.callbackNode;Fm(e,t);var r=ci(e,e===ue?he:0);if(r===0)n!==null&&Eu(n),e.callbackNode=null,e.callbackPriority=0;else if(t=r&-r,e.callbackPriority!==t){if(n!=null&&Eu(n),t===1)e.tag===0?Vg(vc.bind(null,e)):Md(vc.bind(null,e)),Tg(function(){!(U&6)&&an()}),n=null;else{switch(gd(r)){case 1:n=Gl;break;case 4:n=fd;break;case 16:n=ui;break;case 536870912:n=hd;break;default:n=ui}n=zp(n,bp.bind(null,e))}e.callbackPriority=t,e.callbackNode=n}}function bp(e,t){if(Zo=-1,ei=0,U&6)throw Error(N(327));var n=e.callbackNode;if(er()&&e.callbackNode!==n)return null;var r=ci(e,e===ue?he:0);if(r===0)return null;if(r&30||r&e.expiredLanes||t)t=Ci(e,r);else{t=r;var o=U;U|=2;var i=Fp();(ue!==e||he!==t)&&(Pt=null,ar=ne()+500,_n(e,t));do try{tv();break}catch(l){Vp(e,l)}while(1);la(),Ei.current=i,U=o,re!==null?t=0:(ue=null,he=0,t=le)}if(t!==0){if(t===2&&(o=nl(e),o!==0&&(r=o,t=Tl(e,o))),t===1)throw n=no,_n(e,0),$t(e,r),be(e,ne()),n;if(t===6)$t(e,r);else{if(o=e.current.alternate,!(r&30)&&!Zg(o)&&(t=Ci(e,r),t===2&&(i=nl(e),i!==0&&(r=i,t=Tl(e,i))),t===1))throw n=no,_n(e,0),$t(e,r),be(e,ne()),n;switch(e.finishedWork=o,e.finishedLanes=r,t){case 0:case 1:throw Error(N(345));case 2:hn(e,Re,Pt);break;case 3:if($t(e,r),(r&130023424)===r&&(t=Sa+500-ne(),10<t)){if(ci(e,0)!==0)break;if(o=e.suspendedLanes,(o&r)!==r){Oe(),e.pingedLanes|=e.suspendedLanes&o;break}e.timeoutHandle=cl(hn.bind(null,e,Re,Pt),t);break}hn(e,Re,Pt);break;case 4:if($t(e,r),(r&4194240)===r)break;for(t=e.eventTimes,o=-1;0<r;){var s=31-st(r);i=1<<s,s=t[s],s>o&&(o=s),r&=~i}if(r=o,r=ne()-r,r=(120>r?120:480>r?480:1080>r?1080:1920>r?1920:3e3>r?3e3:4320>r?4320:1960*Jg(r/1960))-r,10<r){e.timeoutHandle=cl(hn.bind(null,e,Re,Pt),r);break}hn(e,Re,Pt);break;case 5:hn(e,Re,Pt);break;default:throw Error(N(329))}}}return be(e,ne()),e.callbackNode===n?bp.bind(null,e):null}function Tl(e,t){var n=jr;return e.current.memoizedState.isDehydrated&&(_n(e,t).flags|=256),e=Ci(e,t),e!==2&&(t=Re,Re=n,t!==null&&Ll(t)),e}function Ll(e){Re===null?Re=e:Re.push.apply(Re,e)}function Zg(e){for(var t=e;;){if(t.flags&16384){var n=t.updateQueue;if(n!==null&&(n=n.stores,n!==null))for(var r=0;r<n.length;r++){var o=n[r],i=o.getSnapshot;o=o.value;try{if(!ut(i(),o))return!1}catch{return!1}}}if(n=t.child,t.subtreeFlags&16384&&n!==null)n.return=t,t=n;else{if(t===e)break;for(;t.sibling===null;){if(t.return===null||t.return===e)return!0;t=t.return}t.sibling.return=t.return,t=t.sibling}}return!0}function $t(e,t){for(t&=~wa,t&=~$i,e.suspendedLanes|=t,e.pingedLanes&=~t,e=e.expirationTimes;0<t;){var n=31-st(t),r=1<<n;e[n]=-1,t&=~r}}function vc(e){if(U&6)throw Error(N(327));er();var t=ci(e,0);if(!(t&1))return be(e,ne()),null;var n=Ci(e,t);if(e.tag!==0&&n===2){var r=nl(e);r!==0&&(t=r,n=Tl(e,r))}if(n===1)throw n=no,_n(e,0),$t(e,t),be(e,ne()),n;if(n===6)throw Error(N(345));return e.finishedWork=e.current.alternate,e.finishedLanes=t,hn(e,Re,Pt),be(e,ne()),null}function Oa(e,t){var n=U;U|=1;try{return e(t)}finally{U=n,U===0&&(ar=ne()+500,Ui&&an())}}function En(e){Ht!==null&&Ht.tag===0&&!(U&6)&&er();var t=U;U|=1;var n=Ye.transition,r=M;try{if(Ye.transition=null,M=1,e)return e()}finally{M=r,Ye.transition=n,U=t,!(U&6)&&an()}}function Na(){je=Gn.current,G(Gn)}function _n(e,t){e.finishedWork=null,e.finishedLanes=0;var n=e.timeoutHandle;if(n!==-1&&(e.timeoutHandle=-1,Rg(n)),re!==null)for(n=re.return;n!==null;){var r=n;switch(oa(r),r.tag){case 1:r=r.type.childContextTypes,r!=null&&mi();break;case 3:sr(),G(Le),G(_e),fa();break;case 5:pa(r);break;case 4:sr();break;case 13:G(X);break;case 19:G(X);break;case 10:aa(r.type._context);break;case 22:case 23:Na()}n=n.return}if(ue=e,re=e=en(e.current,null),he=je=t,le=0,no=null,wa=$i=Nn=0,Re=jr=null,vn!==null){for(t=0;t<vn.length;t++)if(n=vn[t],r=n.interleaved,r!==null){n.interleaved=null;var o=r.next,i=n.pending;if(i!==null){var s=i.next;i.next=o,r.next=s}n.pending=r}vn=null}return e}function Vp(e,t){do{var n=re;try{if(la(),qo.current=Ni,Oi){for(var r=Z.memoizedState;r!==null;){var o=r.queue;o!==null&&(o.pending=null),r=r.next}Oi=!1}if(On=0,ae=se=Z=null,Fr=!1,Zr=0,ka.current=null,n===null||n.return===null){le=1,no=t,re=null;break}e:{var i=e,s=n.return,l=n,a=t;if(t=he,l.flags|=32768,a!==null&&typeof a=="object"&&typeof a.then=="function"){var u=a,f=l,d=f.tag;if(!(f.mode&1)&&(d===0||d===11||d===15)){var h=f.alternate;h?(f.updateQueue=h.updateQueue,f.memoizedState=h.memoizedState,f.lanes=h.lanes):(f.updateQueue=null,f.memoizedState=null)}var g=oc(s);if(g!==null){g.flags&=-257,ic(g,s,l,i,t),g.mode&1&&rc(i,u,t),t=g,a=u;var v=t.updateQueue;if(v===null){var k=new Set;k.add(a),t.updateQueue=k}else v.add(a);break e}else{if(!(t&1)){rc(i,u,t),Ea();break e}a=Error(N(426))}}else if(Q&&l.mode&1){var O=oc(s);if(O!==null){!(O.flags&65536)&&(O.flags|=256),ic(O,s,l,i,t),ia(lr(a,l));break e}}i=a=lr(a,l),le!==4&&(le=2),jr===null?jr=[i]:jr.push(i),i=s;do{switch(i.tag){case 3:i.flags|=65536,t&=-t,i.lanes|=t;var p=vp(i,a,t);qu(i,p);break e;case 1:l=a;var c=i.type,m=i.stateNode;if(!(i.flags&128)&&(typeof c.getDerivedStateFromError=="function"||m!==null&&typeof m.componentDidCatch=="function"&&(Jt===null||!Jt.has(m)))){i.flags|=65536,t&=-t,i.lanes|=t;var y=yp(i,l,t);qu(i,y);break e}}i=i.return}while(i!==null)}jp(n)}catch(S){t=S,re===n&&n!==null&&(re=n=n.return);continue}break}while(1)}function Fp(){var e=Ei.current;return Ei.current=Ni,e===null?Ni:e}function Ea(){(le===0||le===3||le===2)&&(le=4),ue===null||!(Nn&268435455)&&!($i&268435455)||$t(ue,he)}function Ci(e,t){var n=U;U|=2;var r=Fp();(ue!==e||he!==t)&&(Pt=null,_n(e,t));do try{ev();break}catch(o){Vp(e,o)}while(1);if(la(),U=n,Ei.current=r,re!==null)throw Error(N(261));return ue=null,he=0,le}function ev(){for(;re!==null;)Dp(re)}function tv(){for(;re!==null&&!xm();)Dp(re)}function Dp(e){var t=Ap(e.alternate,e,je);e.memoizedProps=e.pendingProps,t===null?jp(e):re=t,ka.current=null}function jp(e){var t=e;do{var n=t.alternate;if(e=t.return,t.flags&32768){if(n=Gg(n,t),n!==null){n.flags&=32767,re=n;return}if(e!==null)e.flags|=32768,e.subtreeFlags=0,e.deletions=null;else{le=6,re=null;return}}else if(n=Yg(n,t,je),n!==null){re=n;return}if(t=t.sibling,t!==null){re=t;return}re=t=e}while(t!==null);le===0&&(le=5)}function hn(e,t,n){var r=M,o=Ye.transition;try{Ye.transition=null,M=1,nv(e,t,n,r)}finally{Ye.transition=o,M=r}return null}function nv(e,t,n,r){do er();while(Ht!==null);if(U&6)throw Error(N(327));n=e.finishedWork;var o=e.finishedLanes;if(n===null)return null;if(e.finishedWork=null,e.finishedLanes=0,n===e.current)throw Error(N(177));e.callbackNode=null,e.callbackPriority=0;var i=n.lanes|n.childLanes;if(Dm(e,i),e===ue&&(re=ue=null,he=0),!(n.subtreeFlags&2064)&&!(n.flags&2064)||Lo||(Lo=!0,zp(ui,function(){return er(),null})),i=(n.flags&15990)!==0,n.subtreeFlags&15990||i){i=Ye.transition,Ye.transition=null;var s=M;M=1;var l=U;U|=4,ka.current=null,qg(e,n),Lp(n,e),Sg(al),di=!!ll,al=ll=null,e.current=n,Xg(n),Pm(),U=l,M=s,Ye.transition=i}else e.current=n;if(Lo&&(Lo=!1,Ht=e,Pi=o),i=e.pendingLanes,i===0&&(Jt=null),Tm(n.stateNode),be(e,ne()),t!==null)for(r=e.onRecoverableError,n=0;n<t.length;n++)o=t[n],r(o.value,{componentStack:o.stack,digest:o.digest});if(xi)throw xi=!1,e=Cl,Cl=null,e;return Pi&1&&e.tag!==0&&er(),i=e.pendingLanes,i&1?e===Rl?Ur++:(Ur=0,Rl=e):Ur=0,an(),null}function er(){if(Ht!==null){var e=gd(Pi),t=Ye.transition,n=M;try{if(Ye.transition=null,M=16>e?16:e,Ht===null)var r=!1;else{if(e=Ht,Ht=null,Pi=0,U&6)throw Error(N(331));var o=U;for(U|=4,T=e.current;T!==null;){var i=T,s=i.child;if(T.flags&16){var l=i.deletions;if(l!==null){for(var a=0;a<l.length;a++){var u=l[a];for(T=u;T!==null;){var f=T;switch(f.tag){case 0:case 11:case 15:Dr(8,f,i)}var d=f.child;if(d!==null)d.return=f,T=d;else for(;T!==null;){f=T;var h=f.sibling,g=f.return;if(Cp(f),f===u){T=null;break}if(h!==null){h.return=g,T=h;break}T=g}}}var v=i.alternate;if(v!==null){var k=v.child;if(k!==null){v.child=null;do{var O=k.sibling;k.sibling=null,k=O}while(k!==null)}}T=i}}if(i.subtreeFlags&2064&&s!==null)s.return=i,T=s;else e:for(;T!==null;){if(i=T,i.flags&2048)switch(i.tag){case 0:case 11:case 15:Dr(9,i,i.return)}var p=i.sibling;if(p!==null){p.return=i.return,T=p;break e}T=i.return}}var c=e.current;for(T=c;T!==null;){s=T;var m=s.child;if(s.subtreeFlags&2064&&m!==null)m.return=s,T=m;else e:for(s=c;T!==null;){if(l=T,l.flags&2048)try{switch(l.tag){case 0:case 11:case 15:Mi(9,l)}}catch(S){te(l,l.return,S)}if(l===s){T=null;break e}var y=l.sibling;if(y!==null){y.return=l.return,T=y;break e}T=l.return}}if(U=o,an(),_t&&typeof _t.onPostCommitFiberRoot=="function")try{_t.onPostCommitFiberRoot(bi,e)}catch{}r=!0}return r}finally{M=n,Ye.transition=t}}return!1}function yc(e,t,n){t=lr(n,t),t=vp(e,t,1),e=Xt(e,t,1),t=Oe(),e!==null&&(io(e,1,t),be(e,t))}function te(e,t,n){if(e.tag===3)yc(e,e,n);else for(;t!==null;){if(t.tag===3){yc(t,e,n);break}else if(t.tag===1){var r=t.stateNode;if(typeof t.type.getDerivedStateFromError=="function"||typeof r.componentDidCatch=="function"&&(Jt===null||!Jt.has(r))){e=lr(n,e),e=yp(t,e,1),t=Xt(t,e,1),e=Oe(),t!==null&&(io(t,1,e),be(t,e));break}}t=t.return}}function rv(e,t,n){var r=e.pingCache;r!==null&&r.delete(t),t=Oe(),e.pingedLanes|=e.suspendedLanes&n,ue===e&&(he&n)===n&&(le===4||le===3&&(he&130023424)===he&&500>ne()-Sa?_n(e,0):wa|=n),be(e,t)}function Up(e,t){t===0&&(e.mode&1?(t=wo,wo<<=1,!(wo&130023424)&&(wo=4194304)):t=1);var n=Oe();e=Vt(e,t),e!==null&&(io(e,t,n),be(e,n))}function ov(e){var t=e.memoizedState,n=0;t!==null&&(n=t.retryLane),Up(e,n)}function iv(e,t){var n=0;switch(e.tag){case 13:var r=e.stateNode,o=e.memoizedState;o!==null&&(n=o.retryLane);break;case 19:r=e.stateNode;break;default:throw Error(N(314))}r!==null&&r.delete(t),Up(e,n)}var Ap;Ap=function(e,t,n){if(e!==null)if(e.memoizedProps!==t.pendingProps||Le.current)Te=!0;else{if(!(e.lanes&n)&&!(t.flags&128))return Te=!1,Wg(e,t,n);Te=!!(e.flags&131072)}else Te=!1,Q&&t.flags&1048576&&$d(t,yi,t.index);switch(t.lanes=0,t.tag){case 2:var r=t.type;Jo(e,t),e=t.pendingProps;var o=rr(t,_e.current);Zn(t,n),o=ma(null,t,r,e,o,n);var i=ga();return t.flags|=1,typeof o=="object"&&o!==null&&typeof o.render=="function"&&o.$$typeof===void 0?(t.tag=1,t.memoizedState=null,t.updateQueue=null,Ie(r)?(i=!0,gi(t)):i=!1,t.memoizedState=o.state!==null&&o.state!==void 0?o.state:null,ca(t),o.updater=Ai,t.stateNode=o,o._reactInternals=t,vl(t,r,e,n),t=kl(null,t,r,!0,i,n)):(t.tag=0,Q&&i&&ra(t),Se(null,t,o,n),t=t.child),t;case 16:r=t.elementType;e:{switch(Jo(e,t),e=t.pendingProps,o=r._init,r=o(r._payload),t.type=r,o=t.tag=lv(r),e=rt(r,e),o){case 0:t=_l(null,t,r,e,n);break e;case 1:t=ac(null,t,r,e,n);break e;case 11:t=sc(null,t,r,e,n);break e;case 14:t=lc(null,t,r,rt(r.type,e),n);break e}throw Error(N(306,r,""))}return t;case 0:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:rt(r,o),_l(e,t,r,o,n);case 1:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:rt(r,o),ac(e,t,r,o,n);case 3:e:{if(Sp(t),e===null)throw Error(N(387));r=t.pendingProps,i=t.memoizedState,o=i.element,Wd(e,t),wi(t,r,null,n);var s=t.memoizedState;if(r=s.element,i.isDehydrated)if(i={element:r,isDehydrated:!1,cache:s.cache,pendingSuspenseBoundaries:s.pendingSuspenseBoundaries,transitions:s.transitions},t.updateQueue.baseState=i,t.memoizedState=i,t.flags&256){o=lr(Error(N(423)),t),t=uc(e,t,r,n,o);break e}else if(r!==o){o=lr(Error(N(424)),t),t=uc(e,t,r,n,o);break e}else for(Ae=qt(t.stateNode.containerInfo.firstChild),ze=t,Q=!0,it=null,n=qd(t,null,r,n),t.child=n;n;)n.flags=n.flags&-3|4096,n=n.sibling;else{if(or(),r===o){t=Ft(e,t,n);break e}Se(e,t,r,n)}t=t.child}return t;case 5:return Xd(t),e===null&&hl(t),r=t.type,o=t.pendingProps,i=e!==null?e.memoizedProps:null,s=o.children,ul(r,o)?s=null:i!==null&&ul(r,i)&&(t.flags|=32),wp(e,t),Se(e,t,s,n),t.child;case 6:return e===null&&hl(t),null;case 13:return Op(e,t,n);case 4:return da(t,t.stateNode.containerInfo),r=t.pendingProps,e===null?t.child=ir(t,null,r,n):Se(e,t,r,n),t.child;case 11:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:rt(r,o),sc(e,t,r,o,n);case 7:return Se(e,t,t.pendingProps,n),t.child;case 8:return Se(e,t,t.pendingProps.children,n),t.child;case 12:return Se(e,t,t.pendingProps.children,n),t.child;case 10:e:{if(r=t.type._context,o=t.pendingProps,i=t.memoizedProps,s=o.value,$(_i,r._currentValue),r._currentValue=s,i!==null)if(ut(i.value,s)){if(i.children===o.children&&!Le.current){t=Ft(e,t,n);break e}}else for(i=t.child,i!==null&&(i.return=t);i!==null;){var l=i.dependencies;if(l!==null){s=i.child;for(var a=l.firstContext;a!==null;){if(a.context===r){if(i.tag===1){a=Lt(-1,n&-n),a.tag=2;var u=i.updateQueue;if(u!==null){u=u.shared;var f=u.pending;f===null?a.next=a:(a.next=f.next,f.next=a),u.pending=a}}i.lanes|=n,a=i.alternate,a!==null&&(a.lanes|=n),ml(i.return,n,t),l.lanes|=n;break}a=a.next}}else if(i.tag===10)s=i.type===t.type?null:i.child;else if(i.tag===18){if(s=i.return,s===null)throw Error(N(341));s.lanes|=n,l=s.alternate,l!==null&&(l.lanes|=n),ml(s,n,t),s=i.sibling}else s=i.child;if(s!==null)s.return=i;else for(s=i;s!==null;){if(s===t){s=null;break}if(i=s.sibling,i!==null){i.return=s.return,s=i;break}s=s.return}i=s}Se(e,t,o.children,n),t=t.child}return t;case 9:return o=t.type,r=t.pendingProps.children,Zn(t,n),o=Ge(o),r=r(o),t.flags|=1,Se(e,t,r,n),t.child;case 14:return r=t.type,o=rt(r,t.pendingProps),o=rt(r.type,o),lc(e,t,r,o,n);case 15:return _p(e,t,t.type,t.pendingProps,n);case 17:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:rt(r,o),Jo(e,t),t.tag=1,Ie(r)?(e=!0,gi(t)):e=!1,Zn(t,n),Gd(t,r,o),vl(t,r,o,n),kl(null,t,r,!0,e,n);case 19:return Np(e,t,n);case 22:return kp(e,t,n)}throw Error(N(156,t.tag))};function zp(e,t){return pd(e,t)}function sv(e,t,n,r){this.tag=e,this.key=n,this.sibling=this.child=this.return=this.stateNode=this.type=this.elementType=null,this.index=0,this.ref=null,this.pendingProps=t,this.dependencies=this.memoizedState=this.updateQueue=this.memoizedProps=null,this.mode=r,this.subtreeFlags=this.flags=0,this.deletions=null,this.childLanes=this.lanes=0,this.alternate=null}function We(e,t,n,r){return new sv(e,t,n,r)}function xa(e){return e=e.prototype,!(!e||!e.isReactComponent)}function lv(e){if(typeof e=="function")return xa(e)?1:0;if(e!=null){if(e=e.$$typeof,e===Hl)return 11;if(e===Wl)return 14}return 2}function en(e,t){var n=e.alternate;return n===null?(n=We(e.tag,t,e.key,e.mode),n.elementType=e.elementType,n.type=e.type,n.stateNode=e.stateNode,n.alternate=e,e.alternate=n):(n.pendingProps=t,n.type=e.type,n.flags=0,n.subtreeFlags=0,n.deletions=null),n.flags=e.flags&14680064,n.childLanes=e.childLanes,n.lanes=e.lanes,n.child=e.child,n.memoizedProps=e.memoizedProps,n.memoizedState=e.memoizedState,n.updateQueue=e.updateQueue,t=e.dependencies,n.dependencies=t===null?null:{lanes:t.lanes,firstContext:t.firstContext},n.sibling=e.sibling,n.index=e.index,n.ref=e.ref,n}function ti(e,t,n,r,o,i){var s=2;if(r=e,typeof e=="function")xa(e)&&(s=1);else if(typeof e=="string")s=5;else e:switch(e){case Un:return kn(n.children,o,i,t);case Kl:s=8,o|=8;break;case Ms:return e=We(12,n,t,o|2),e.elementType=Ms,e.lanes=i,e;case $s:return e=We(13,n,t,o),e.elementType=$s,e.lanes=i,e;case Bs:return e=We(19,n,t,o),e.elementType=Bs,e.lanes=i,e;case Qc:return Bi(n,o,i,t);default:if(typeof e=="object"&&e!==null)switch(e.$$typeof){case Yc:s=10;break e;case Gc:s=9;break e;case Hl:s=11;break e;case Wl:s=14;break e;case At:s=16,r=null;break e}throw Error(N(130,e==null?e:typeof e,""))}return t=We(s,n,t,o),t.elementType=e,t.type=r,t.lanes=i,t}function kn(e,t,n,r){return e=We(7,e,r,t),e.lanes=n,e}function Bi(e,t,n,r){return e=We(22,e,r,t),e.elementType=Qc,e.lanes=n,e.stateNode={isHidden:!1},e}function Es(e,t,n){return e=We(6,e,null,t),e.lanes=n,e}function xs(e,t,n){return t=We(4,e.children!==null?e.children:[],e.key,t),t.lanes=n,t.stateNode={containerInfo:e.containerInfo,pendingChildren:null,implementation:e.implementation},t}function av(e,t,n,r,o){this.tag=t,this.containerInfo=e,this.finishedWork=this.pingCache=this.current=this.pendingChildren=null,this.timeoutHandle=-1,this.callbackNode=this.pendingContext=this.context=null,this.callbackPriority=0,this.eventTimes=ss(0),this.expirationTimes=ss(-1),this.entangledLanes=this.finishedLanes=this.mutableReadLanes=this.expiredLanes=this.pingedLanes=this.suspendedLanes=this.pendingLanes=0,this.entanglements=ss(0),this.identifierPrefix=r,this.onRecoverableError=o,this.mutableSourceEagerHydrationData=null}function Pa(e,t,n,r,o,i,s,l,a){return e=new av(e,t,n,l,a),t===1?(t=1,i===!0&&(t|=8)):t=0,i=We(3,null,null,t),e.current=i,i.stateNode=e,i.memoizedState={element:r,isDehydrated:n,cache:null,transitions:null,pendingSuspenseBoundaries:null},ca(i),e}function uv(e,t,n){var r=3<arguments.length&&arguments[3]!==void 0?arguments[3]:null;return{$$typeof:jn,key:r==null?null:""+r,children:e,containerInfo:t,implementation:n}}function Mp(e){if(!e)return rn;e=e._reactInternals;e:{if(Cn(e)!==e||e.tag!==1)throw Error(N(170));var t=e;do{switch(t.tag){case 3:t=t.stateNode.context;break e;case 1:if(Ie(t.type)){t=t.stateNode.__reactInternalMemoizedMergedChildContext;break e}}t=t.return}while(t!==null);throw Error(N(171))}if(e.tag===1){var n=e.type;if(Ie(n))return zd(e,n,t)}return t}function $p(e,t,n,r,o,i,s,l,a){return e=Pa(n,r,!0,e,o,i,s,l,a),e.context=Mp(null),n=e.current,r=Oe(),o=Zt(n),i=Lt(r,o),i.callback=t??null,Xt(n,i,o),e.current.lanes=o,io(e,o,r),be(e,r),e}function Ki(e,t,n,r){var o=t.current,i=Oe(),s=Zt(o);return n=Mp(n),t.context===null?t.context=n:t.pendingContext=n,t=Lt(i,s),t.payload={element:e},r=r===void 0?null:r,r!==null&&(t.callback=r),e=Xt(o,t,s),e!==null&&(lt(e,o,s,i),Qo(e,o,s)),s}function Ri(e){if(e=e.current,!e.child)return null;switch(e.child.tag){case 5:return e.child.stateNode;default:return e.child.stateNode}}function _c(e,t){if(e=e.memoizedState,e!==null&&e.dehydrated!==null){var n=e.retryLane;e.retryLane=n!==0&&n<t?n:t}}function Ca(e,t){_c(e,t),(e=e.alternate)&&_c(e,t)}function cv(){return null}var Bp=typeof reportError=="function"?reportError:function(e){console.error(e)};function Ra(e){this._internalRoot=e}Hi.prototype.render=Ra.prototype.render=function(e){var t=this._internalRoot;if(t===null)throw Error(N(409));Ki(e,t,null,null)};Hi.prototype.unmount=Ra.prototype.unmount=function(){var e=this._internalRoot;if(e!==null){this._internalRoot=null;var t=e.containerInfo;En(function(){Ki(null,e,null,null)}),t[bt]=null}};function Hi(e){this._internalRoot=e}Hi.prototype.unstable_scheduleHydration=function(e){if(e){var t=_d();e={blockedOn:null,target:e,priority:t};for(var n=0;n<Mt.length&&t!==0&&t<Mt[n].priority;n++);Mt.splice(n,0,e),n===0&&wd(e)}};function Ta(e){return!(!e||e.nodeType!==1&&e.nodeType!==9&&e.nodeType!==11)}function Wi(e){return!(!e||e.nodeType!==1&&e.nodeType!==9&&e.nodeType!==11&&(e.nodeType!==8||e.nodeValue!==" react-mount-point-unstable "))}function kc(){}function dv(e,t,n,r,o){if(o){if(typeof r=="function"){var i=r;r=function(){var u=Ri(s);i.call(u)}}var s=$p(t,r,e,0,null,!1,!1,"",kc);return e._reactRootContainer=s,e[bt]=s.current,Gr(e.nodeType===8?e.parentNode:e),En(),s}for(;o=e.lastChild;)e.removeChild(o);if(typeof r=="function"){var l=r;r=function(){var u=Ri(a);l.call(u)}}var a=Pa(e,0,!1,null,null,!1,!1,"",kc);return e._reactRootContainer=a,e[bt]=a.current,Gr(e.nodeType===8?e.parentNode:e),En(function(){Ki(t,a,n,r)}),a}function Yi(e,t,n,r,o){var i=n._reactRootContainer;if(i){var s=i;if(typeof o=="function"){var l=o;o=function(){var a=Ri(s);l.call(a)}}Ki(t,s,e,o)}else s=dv(n,t,e,o,r);return Ri(s)}vd=function(e){switch(e.tag){case 3:var t=e.stateNode;if(t.current.memoizedState.isDehydrated){var n=Cr(t.pendingLanes);n!==0&&(Ql(t,n|1),be(t,ne()),!(U&6)&&(ar=ne()+500,an()))}break;case 13:En(function(){var r=Vt(e,1);if(r!==null){var o=Oe();lt(r,e,1,o)}}),Ca(e,1)}};ql=function(e){if(e.tag===13){var t=Vt(e,134217728);if(t!==null){var n=Oe();lt(t,e,134217728,n)}Ca(e,134217728)}};yd=function(e){if(e.tag===13){var t=Zt(e),n=Vt(e,t);if(n!==null){var r=Oe();lt(n,e,t,r)}Ca(e,t)}};_d=function(){return M};kd=function(e,t){var n=M;try{return M=e,t()}finally{M=n}};Zs=function(e,t,n){switch(t){case"input":if(Ws(e,n),t=n.name,n.type==="radio"&&t!=null){for(n=e;n.parentNode;)n=n.parentNode;for(n=n.querySelectorAll("input[name="+JSON.stringify(""+t)+'][type="radio"]'),t=0;t<n.length;t++){var r=n[t];if(r!==e&&r.form===e.form){var o=ji(r);if(!o)throw Error(N(90));Xc(r),Ws(r,o)}}}break;case"textarea":Zc(e,n);break;case"select":t=n.value,t!=null&&Qn(e,!!n.multiple,t,!1)}};sd=Oa;ld=En;var pv={usingClientEntryPoint:!1,Events:[lo,$n,ji,od,id,Oa]},Sr={findFiberByHostInstance:gn,bundleType:0,version:"18.2.0",rendererPackageName:"react-dom"},fv={bundleType:Sr.bundleType,version:Sr.version,rendererPackageName:Sr.rendererPackageName,rendererConfig:Sr.rendererConfig,overrideHookState:null,overrideHookStateDeletePath:null,overrideHookStateRenamePath:null,overrideProps:null,overridePropsDeletePath:null,overridePropsRenamePath:null,setErrorHandler:null,setSuspenseHandler:null,scheduleUpdate:null,currentDispatcherRef:Dt.ReactCurrentDispatcher,findHostInstanceByFiber:function(e){return e=cd(e),e===null?null:e.stateNode},findFiberByHostInstance:Sr.findFiberByHostInstance||cv,findHostInstancesForRefresh:null,scheduleRefresh:null,scheduleRoot:null,setRefreshHandler:null,getCurrentFiber:null,reconcilerVersion:"18.2.0-next-9e3b772b8-20220608"};if(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__<"u"){var Io=__REACT_DEVTOOLS_GLOBAL_HOOK__;if(!Io.isDisabled&&Io.supportsFiber)try{bi=Io.inject(fv),_t=Io}catch{}}$e.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=pv;$e.createPortal=function(e,t){var n=2<arguments.length&&arguments[2]!==void 0?arguments[2]:null;if(!Ta(t))throw Error(N(200));return uv(e,t,null,n)};$e.createRoot=function(e,t){if(!Ta(e))throw Error(N(299));var n=!1,r="",o=Bp;return t!=null&&(t.unstable_strictMode===!0&&(n=!0),t.identifierPrefix!==void 0&&(r=t.identifierPrefix),t.onRecoverableError!==void 0&&(o=t.onRecoverableError)),t=Pa(e,1,!1,null,null,n,!1,r,o),e[bt]=t.current,Gr(e.nodeType===8?e.parentNode:e),new Ra(t)};$e.findDOMNode=function(e){if(e==null)return null;if(e.nodeType===1)return e;var t=e._reactInternals;if(t===void 0)throw typeof e.render=="function"?Error(N(188)):(e=Object.keys(e).join(","),Error(N(268,e)));return e=cd(t),e=e===null?null:e.stateNode,e};$e.flushSync=function(e){return En(e)};$e.hydrate=function(e,t,n){if(!Wi(t))throw Error(N(200));return Yi(null,e,t,!0,n)};$e.hydrateRoot=function(e,t,n){if(!Ta(e))throw Error(N(405));var r=n!=null&&n.hydratedSources||null,o=!1,i="",s=Bp;if(n!=null&&(n.unstable_strictMode===!0&&(o=!0),n.identifierPrefix!==void 0&&(i=n.identifierPrefix),n.onRecoverableError!==void 0&&(s=n.onRecoverableError)),t=$p(t,null,e,1,n??null,o,!1,i,s),e[bt]=t.current,Gr(e),r)for(e=0;e<r.length;e++)n=r[e],o=n._getVersion,o=o(n._source),t.mutableSourceEagerHydrationData==null?t.mutableSourceEagerHydrationData=[n,o]:t.mutableSourceEagerHydrationData.push(n,o);return new Hi(t)};$e.render=function(e,t,n){if(!Wi(t))throw Error(N(200));return Yi(null,e,t,!1,n)};$e.unmountComponentAtNode=function(e){if(!Wi(e))throw Error(N(40));return e._reactRootContainer?(En(function(){Yi(null,null,e,!1,function(){e._reactRootContainer=null,e[bt]=null})}),!0):!1};$e.unstable_batchedUpdates=Oa;$e.unstable_renderSubtreeIntoContainer=function(e,t,n,r){if(!Wi(n))throw Error(N(200));if(e==null||e._reactInternals===void 0)throw Error(N(38));return Yi(e,t,n,!1,r)};$e.version="18.2.0-next-9e3b772b8-20220608";(function(e){function t(){if(!(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__>"u"||typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE!="function"))try{__REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE(t)}catch(n){console.error(n)}}t(),e.exports=$e})(cm);var wc=Us;js.createRoot=wc.createRoot,js.hydrateRoot=wc.hydrateRoot;const La=ce.createInstance(),Il="en";if(!(Il in tr))throw new Error(`'${Il}' language is not supported`);La.init({debug:!1,resources:tr,lng:Il,fallbackLng:"en",supportedLngs:Object.keys(tr)});const oe=La.getFixedT(null,"core"),hv=e=>La.getFixedT(null,"core",e);var L=(e=>(e.ROOT="ROOT",e.VERSION="VERSION",e.ENVIRONMENT="ENVIRONMENT",e.OP_SYSTEM="OP_SYSTEM",e.DISTRIBUTION="DISTRIBUTION",e.FRAMEWORK="FRAMEWORK",e))(L||{}),Kp=(e=>(e.ROOT="ROOT",e))(Kp||{}),Rn=(e=>(e.v_2021_4_2="v_2021_4_2",e.v_2022_3_1="v_2022_3_1",e.v_2023_1_0="v_2023_1_0",e))(Rn||{}),uo=(e=>(e.DEV_TOOLS="DEV_TOOLS",e.RUNTIME="RUNTIME",e))(uo||{}),un=(e=>(e.WINDOWS="WINDOWS",e.MACOS="MACOS",e.LINUX="LINUX",e))(un||{});const mv={title:"Windows"},gv={title:"macOS"},vv={title:"Linux"};var z=(e=>(e.PIP="PIP",e.ARCHIVE="ARCHIVE",e.OFFLINE_INSTALLER="OFFLINE_INSTALLER",e.GITHUB="GITHUB",e.GITEE="GITEE",e.DOCKER="DOCKER",e.APT="APT",e.YUM="YUM",e.CONDA="CONDA",e.BREW="BREW",e.VCPKG="VCPKG",e))(z||{}),Ue=(e=>(e.CAFFE="CAFFE",e.PYTORCH="PYTORCH",e.TENSORFLOW_1="TENSORFLOW_1",e.TENSORFLOW_2="TENSORFLOW_2",e.ONNX="ONNX",e.KALDI="KALDI",e.MXNET="MXNET",e))(Ue||{});class pe{constructor(t,n=[]){De(this,"level");De(this,"key");De(this,"metadata");De(this,"children",[]);De(this,"isDefault",!1);De(this,"checked",!1);De(this,"childrenSelector");this.level=t.level,this.key=t.key,this.metadata=t.metadata,this.children=n,this.childrenSelector=t.childrenSelector}default(){return this.isDefault=!0,this.checked=!0,this}toOption(){return{level:this.level,key:this.key,metadata:this.metadata,checked:this.checked,hasFootnote:!1}}}const Bt=hv("distributions.common"),C={V_3_6:"3.6",V_3_7:"3.7",V_3_8:"3.8",V_3_9:"3.9",V_3_10:"3.10",V_3_11:"3.11"},on={V_10:Bt("requirements.win10"),V_11:Bt("requirements.win11")},at={V_10_15_X:"macOS 10.15.x",V_11:"macOS 11",V_12:"macOS 12"},B={UBUNTU_18:Bt("requirements.ubuntu18"),UBUNTU_20:Bt("requirements.ubuntu20"),UBUNTU_22:Bt("requirements.ubuntu22"),RED_HAT_8:Bt("requirements.redHat8"),CENT_OS_7:Bt("requirements.centOS7")},Hp={text:`* ${Bt("requirements.armPluginFootnote")}`,learnMoreLink:"https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_supported_plugins_ARM_CPU.html"};class pt extends pe{constructor(n,r=[]){super(n,r);De(this,"footnoteLevel",null)}toOption(){return{...super.toOption(),key:this.key,data:this.data}}addFootnote(n){return this.footnoteLevel=n,this}}class Wp extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.APT,metadata:{title:"APT"}}),this._data=t}get data(){return{...this._data,commands:wv}}cApiOnly(){return this.metadata.subtitle=oe("distributions.CAPIOnly"),this}}const bl="GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB",Sc=`https://apt.repos.intel.com/intel-gpg-keys/${bl}`,yv={alias:{22:"jammy",20:"focal",18:"bionic"},numberName:{22:"ubuntu22",20:"ubuntu20",18:"ubuntu18"}},_v=new Set([Rn.v_2023_1_0]),kv=(e,t)=>{const n=_v.has(e.key)?"numberName":"alias";return yv[n][t]},wv={keyFilename:bl,keyHref:Sc,downloadKeyCommand:`wget ${Sc}`,addKeyCommand:`sudo apt-key add ${bl}`,getAddRepositoryCommand:(e,t)=>{const n=kv(e,t);return`echo "deb https://apt.repos.intel.com/openvino/${e.metadata.aptYear} ${n} main" | sudo tee /etc/apt/sources.list.d/intel-openvino-${e.metadata.aptYear}.list`},updatePackagesCommand:"sudo apt update",verifyAptCacheCommand:"apt-cache search openvino",getInstallCommand:e=>`sudo apt install openvino-${e.metadata.aptVersion}`};class pr extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.ARCHIVE,metadata:{title:oe("distributions.openVINOArchives")}}),this.data=t}}class Yp extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.BREW,metadata:{title:"Homebrew"}}),this._data=t}get data(){return{...this._data,commands:Sv}}}const Sv={install:"brew install openvino"};class Ia extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.CONDA,metadata:{title:"Conda"}}),this._data=t}get data(){return{...this._data,commands:Ov}}}const Ov={createEnv:"conda create --name py310 python=3.10",activateEnv:"conda activate py310",update:"conda update --all",getInstall:({metadata:e})=>`conda install -c conda-forge openvino=${e.condaVersion}`};class cn extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.DOCKER,metadata:{title:"Docker"}}),this.data=t}}class xe extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.GITEE,metadata:{title:"Gitee",subtitle:oe("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Nv,links:Ev}}}const Nv={getCloneCommand(e){return`git clone -b ${e.metadata.giteeVersion} https://gitee.com/openvinotoolkit-prc/openvino.git`}},Ev={build:"https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md",getRepositoryLink:e=>`https://gitee.com/openvinotoolkit-prc/openvino/tree/${e.metadata.giteeVersion}`};class Pe extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.GITHUB,metadata:{title:"GitHub",subtitle:oe("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:xv,links:Pv}}}const xv={getCloneCommand(e){return`git clone -b ${e.metadata.githubVersion} https://github.com/openvinotoolkit/openvino.git`}},Pv={build:"https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md",getRepositoryLink:e=>`https://github.com/openvinotoolkit/openvino/releases/tag/${e.metadata.githubVersion}`};class fr extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.OFFLINE_INSTALLER,metadata:{title:oe("distributions.offlineInstaller"),subtitle:oe("distributions.recommendedOption")}}),this.data=t}}const Cv=(e,t)=>{e.forEach(n=>{n.key===Ue.TENSORFLOW_1&&n.checked&&t===Ue.TENSORFLOW_2||n.key===Ue.TENSORFLOW_2&&n.checked&&t===Ue.TENSORFLOW_1?n.checked=!1:t===n.key&&(n.checked=!n.checked)})},ba=(e,t)=>{e.forEach(n=>{n.checked=n.key===t,n.key===z.PIP&&n.checked&&n.children.forEach(r=>r.checked=r.isDefault)})},Rv=()=>[new pe({level:L.FRAMEWORK,key:Ue.CAFFE,metadata:{title:"Caffe",pipKey:"caffe"}}),new pe({level:L.FRAMEWORK,key:Ue.KALDI,metadata:{title:"Kaldi",pipKey:"kaldi"}}),new pe({level:L.FRAMEWORK,key:Ue.MXNET,metadata:{title:"Apache MXNet",pipKey:"mxnet"}}),new pe({level:L.FRAMEWORK,key:Ue.ONNX,metadata:{title:"Open Neural Network Exchange (ONNX)",pipKey:"ONNX"}}),new pe({level:L.FRAMEWORK,key:Ue.PYTORCH,metadata:{title:"PyTorch",pipKey:"pytorch"}}),new pe({level:L.FRAMEWORK,key:Ue.TENSORFLOW_1,metadata:{title:"TensorFlow 1.x",pipKey:"tensorflow"}}),new pe({level:L.FRAMEWORK,key:Ue.TENSORFLOW_2,metadata:{title:"TensorFlow 2.x",pipKey:"tensorflow2"}})];class ke extends pt{constructor(t,{hasFrameworks:n,pythonAPI:r}={}){super({level:L.DISTRIBUTION,key:z.PIP,metadata:{title:"PIP",subtitle:r?oe("distributions.pythonAPIOnly"):void 0},childrenSelector:Cv},n?Rv():[]),this._data=t}get data(){return{...this._data,commands:bv,frameworks:this.children.filter(({checked:t})=>t).map(t=>t.toOption())}}}function Tv(e){return e.key===un.WINDOWS?"python -m venv openvino_env":"python3 -m venv openvino_env"}function Lv(e){return e.key===un.WINDOWS?"openvino_env\\Scripts\\activate":"source openvino_env/bin/activate"}function Iv({environment:e,os:t,version:n,distribution:r}){const o=r.data.frameworks.map(a=>a.metadata.pipKey),i=o.length?`[${o.join(",")}]`:"";let l=`${(e==null?void 0:e.key)===uo.DEV_TOOLS?"openvino-dev":"openvino"}${i}==${n.metadata.pipVersion}`;return t.key===un.MACOS&&(l=`'${l}'`),`pip install ${l}`}const bv={getCreateVenvCommand:Tv,getActivateVenvCommand:Lv,upgradeCommand:"python -m pip install --upgrade pip",getInstallCommand:Iv};class Va extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.VCPKG,metadata:{title:"vcpkg",subtitle:oe("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Vv}}}const Vv={install:"vcpkg install openvino"};class Gp extends pt{constructor(t){super({level:L.DISTRIBUTION,key:z.YUM,metadata:{title:"YUM",subtitle:oe("distributions.CAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:Fv}}}const Oc="/etc/yum.repos.d",Fv={directory:Oc,getCreateRepoCommand:e=>`tee > /tmp/openvino-${e.metadata.yumYear}.repo << EOF
+`+i.stack}return{value:e,source:t,stack:o,digest:null}}function Os(e,t,n){return{value:e,source:null,stack:n??null,digest:t??null}}function yl(e,t){try{console.error(t.value)}catch(n){setTimeout(function(){throw n})}}var Wg=typeof WeakMap=="function"?WeakMap:Map;function Sp(e,t,n){n=Lt(-1,n),n.tag=3,n.payload={element:null};var r=t.value;return n.callback=function(){xi||(xi=!0,Cl=r),yl(e,t)},n}function Op(e,t,n){n=Lt(-1,n),n.tag=3;var r=e.type.getDerivedStateFromError;if(typeof r=="function"){var o=t.value;n.payload=function(){return r(o)},n.callback=function(){yl(e,t)}}var i=e.stateNode;return i!==null&&typeof i.componentDidCatch=="function"&&(n.callback=function(){yl(e,t),typeof r!="function"&&(en===null?en=new Set([this]):en.add(this));var s=t.stack;this.componentDidCatch(t.value,{componentStack:s!==null?s:""})}),n}function sc(e,t,n){var r=e.pingCache;if(r===null){r=e.pingCache=new Wg;var o=new Set;r.set(t,o)}else o=r.get(t),o===void 0&&(o=new Set,r.set(t,o));o.has(n)||(o.add(n),e=sv.bind(null,e,t,n),t.then(e,e))}function lc(e){do{var t;if((t=e.tag===13)&&(t=e.memoizedState,t=t!==null?t.dehydrated!==null:!0),t)return e;e=e.return}while(e!==null);return null}function ac(e,t,n,r,o){return e.mode&1?(e.flags|=65536,e.lanes=o,e):(e===t?e.flags|=65536:(e.flags|=128,n.flags|=131072,n.flags&=-52805,n.tag===1&&(n.alternate===null?n.tag=17:(t=Lt(-1,1),t.tag=2,Zt(n,t,1))),n.lanes|=1),e)}var Yg=Dt.ReactCurrentOwner,Te=!1;function Se(e,t,n,r){t.child=e===null?tp(t,null,n,r):ir(t,e.child,n,r)}function uc(e,t,n,r,o){n=n.render;var i=t.ref;return Zn(t,o),r=ma(e,t,n,r,i,o),n=ga(),e!==null&&!Te?(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~o,jt(e,t,o)):(Q&&n&&ra(t),t.flags|=1,Se(e,t,r,o),t.child)}function cc(e,t,n,r,o){if(e===null){var i=n.type;return typeof i=="function"&&!xa(i)&&i.defaultProps===void 0&&n.compare===null&&n.defaultProps===void 0?(t.tag=15,t.type=i,Np(e,t,i,r,o)):(e=ti(n.type,null,r,t,t.mode,o),e.ref=t.ref,e.return=t,t.child=e)}if(i=e.child,!(e.lanes&o)){var s=i.memoizedProps;if(n=n.compare,n=n!==null?n:Yr,n(s,r)&&e.ref===t.ref)return jt(e,t,o)}return t.flags|=1,e=nn(i,r),e.ref=t.ref,e.return=t,t.child=e}function Np(e,t,n,r,o){if(e!==null){var i=e.memoizedProps;if(Yr(i,r)&&e.ref===t.ref)if(Te=!1,t.pendingProps=r=i,(e.lanes&o)!==0)e.flags&131072&&(Te=!0);else return t.lanes=e.lanes,jt(e,t,o)}return _l(e,t,n,r,o)}function Ep(e,t,n){var r=t.pendingProps,o=r.children,i=e!==null?e.memoizedState:null;if(r.mode==="hidden")if(!(t.mode&1))t.memoizedState={baseLanes:0,cachePool:null,transitions:null},H(Gn,De),De|=n;else{if(!(n&1073741824))return e=i!==null?i.baseLanes|n:n,t.lanes=t.childLanes=1073741824,t.memoizedState={baseLanes:e,cachePool:null,transitions:null},t.updateQueue=null,H(Gn,De),De|=e,null;t.memoizedState={baseLanes:0,cachePool:null,transitions:null},r=i!==null?i.baseLanes:n,H(Gn,De),De|=r}else i!==null?(r=i.baseLanes|n,t.memoizedState=null):r=n,H(Gn,De),De|=r;return Se(e,t,o,n),t.child}function xp(e,t){var n=t.ref;(e===null&&n!==null||e!==null&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function _l(e,t,n,r,o){var i=Le(n)?Sn:_e.current;return i=rr(t,i),Zn(t,o),n=ma(e,t,n,r,i,o),r=ga(),e!==null&&!Te?(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~o,jt(e,t,o)):(Q&&r&&ra(t),t.flags|=1,Se(e,t,n,o),t.child)}function dc(e,t,n,r,o){if(Le(n)){var i=!0;gi(t)}else i=!1;if(Zn(t,o),t.stateNode===null)Jo(e,t),Zd(t,n,r),vl(t,n,r,o),r=!0;else if(e===null){var s=t.stateNode,l=t.memoizedProps;s.props=l;var a=s.context,u=n.contextType;typeof u=="object"&&u!==null?u=Qe(u):(u=Le(n)?Sn:_e.current,u=rr(t,u));var f=n.getDerivedStateFromProps,d=typeof f=="function"||typeof s.getSnapshotBeforeUpdate=="function";d||typeof s.UNSAFE_componentWillReceiveProps!="function"&&typeof s.componentWillReceiveProps!="function"||(l!==r||a!==u)&&nc(t,s,r,u),$t=!1;var h=t.memoizedState;s.state=h,wi(t,r,s,o),a=t.memoizedState,l!==r||h!==a||be.current||$t?(typeof f=="function"&&(gl(t,n,f,r),a=t.memoizedState),(l=$t||tc(t,n,l,r,h,a,u))?(d||typeof s.UNSAFE_componentWillMount!="function"&&typeof s.componentWillMount!="function"||(typeof s.componentWillMount=="function"&&s.componentWillMount(),typeof s.UNSAFE_componentWillMount=="function"&&s.UNSAFE_componentWillMount()),typeof s.componentDidMount=="function"&&(t.flags|=4194308)):(typeof s.componentDidMount=="function"&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=a),s.props=r,s.state=a,s.context=u,r=l):(typeof s.componentDidMount=="function"&&(t.flags|=4194308),r=!1)}else{s=t.stateNode,Xd(e,t),l=t.memoizedProps,u=t.type===t.elementType?l:ot(t.type,l),s.props=u,d=t.pendingProps,h=s.context,a=n.contextType,typeof a=="object"&&a!==null?a=Qe(a):(a=Le(n)?Sn:_e.current,a=rr(t,a));var g=n.getDerivedStateFromProps;(f=typeof g=="function"||typeof s.getSnapshotBeforeUpdate=="function")||typeof s.UNSAFE_componentWillReceiveProps!="function"&&typeof s.componentWillReceiveProps!="function"||(l!==d||h!==a)&&nc(t,s,r,a),$t=!1,h=t.memoizedState,s.state=h,wi(t,r,s,o);var v=t.memoizedState;l!==d||h!==v||be.current||$t?(typeof g=="function"&&(gl(t,n,g,r),v=t.memoizedState),(u=$t||tc(t,n,u,r,h,v,a)||!1)?(f||typeof s.UNSAFE_componentWillUpdate!="function"&&typeof s.componentWillUpdate!="function"||(typeof s.componentWillUpdate=="function"&&s.componentWillUpdate(r,v,a),typeof s.UNSAFE_componentWillUpdate=="function"&&s.UNSAFE_componentWillUpdate(r,v,a)),typeof s.componentDidUpdate=="function"&&(t.flags|=4),typeof s.getSnapshotBeforeUpdate=="function"&&(t.flags|=1024)):(typeof s.componentDidUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=4),typeof s.getSnapshotBeforeUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=v),s.props=r,s.state=v,s.context=a,r=u):(typeof s.componentDidUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=4),typeof s.getSnapshotBeforeUpdate!="function"||l===e.memoizedProps&&h===e.memoizedState||(t.flags|=1024),r=!1)}return kl(e,t,n,r,i,o)}function kl(e,t,n,r,o,i){xp(e,t);var s=(t.flags&128)!==0;if(!r&&!s)return o&&Qu(t,n,!1),jt(e,t,i);r=t.stateNode,Yg.current=t;var l=s&&typeof n.getDerivedStateFromError!="function"?null:r.render();return t.flags|=1,e!==null&&s?(t.child=ir(t,e.child,null,i),t.child=ir(t,null,l,i)):Se(e,t,l,i),t.memoizedState=r.state,o&&Qu(t,n,!0),t.child}function Pp(e){var t=e.stateNode;t.pendingContext?qu(e,t.pendingContext,t.pendingContext!==t.context):t.context&&qu(e,t.context,!1),da(e,t.containerInfo)}function pc(e,t,n,r,o){return or(),ia(o),t.flags|=256,Se(e,t,n,r),t.child}var wl={dehydrated:null,treeContext:null,retryLane:0};function Sl(e){return{baseLanes:e,cachePool:null,transitions:null}}function Cp(e,t,n){var r=t.pendingProps,o=J.current,i=!1,s=(t.flags&128)!==0,l;if((l=s)||(l=e!==null&&e.memoizedState===null?!1:(o&2)!==0),l?(i=!0,t.flags&=-129):(e===null||e.memoizedState!==null)&&(o|=1),H(J,o&1),e===null)return hl(t),e=t.memoizedState,e!==null&&(e=e.dehydrated,e!==null)?(t.mode&1?e.data==="$!"?t.lanes=8:t.lanes=1073741824:t.lanes=1,null):(s=r.children,e=r.fallback,i?(r=t.mode,i=t.child,s={mode:"hidden",children:s},!(r&1)&&i!==null?(i.childLanes=0,i.pendingProps=s):i=Bi(s,r,0,null),e=wn(e,r,n,null),i.return=t,e.return=t,i.sibling=e,t.child=i,t.child.memoizedState=Sl(n),t.memoizedState=wl,e):_a(t,s));if(o=e.memoizedState,o!==null&&(l=o.dehydrated,l!==null))return Gg(e,t,s,r,l,o,n);if(i){i=r.fallback,s=t.mode,o=e.child,l=o.sibling;var a={mode:"hidden",children:r.children};return!(s&1)&&t.child!==o?(r=t.child,r.childLanes=0,r.pendingProps=a,t.deletions=null):(r=nn(o,a),r.subtreeFlags=o.subtreeFlags&14680064),l!==null?i=nn(l,i):(i=wn(i,s,n,null),i.flags|=2),i.return=t,r.return=t,r.sibling=i,t.child=r,r=i,i=t.child,s=e.child.memoizedState,s=s===null?Sl(n):{baseLanes:s.baseLanes|n,cachePool:null,transitions:s.transitions},i.memoizedState=s,i.childLanes=e.childLanes&~n,t.memoizedState=wl,r}return i=e.child,e=i.sibling,r=nn(i,{mode:"visible",children:r.children}),!(t.mode&1)&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function _a(e,t){return t=Bi({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function Ro(e,t,n,r){return r!==null&&ia(r),ir(t,e.child,null,n),e=_a(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function Gg(e,t,n,r,o,i,s){if(n)return t.flags&256?(t.flags&=-257,r=Os(Error(N(422))),Ro(e,t,s,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(i=r.fallback,o=t.mode,r=Bi({mode:"visible",children:r.children},o,0,null),i=wn(i,o,s,null),i.flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,t.mode&1&&ir(t,e.child,null,s),t.child.memoizedState=Sl(s),t.memoizedState=wl,i);if(!(t.mode&1))return Ro(e,t,s,null);if(o.data==="$!"){if(r=o.nextSibling&&o.nextSibling.dataset,r)var l=r.dgst;return r=l,i=Error(N(419)),r=Os(i,r,void 0),Ro(e,t,s,r)}if(l=(s&e.childLanes)!==0,Te||l){if(r=ue,r!==null){switch(s&-s){case 4:o=2;break;case 16:o=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:o=32;break;case 536870912:o=268435456;break;default:o=0}o=o&(r.suspendedLanes|s)?0:o,o!==0&&o!==i.retryLane&&(i.retryLane=o,Ft(e,o),at(r,e,o,-1))}return Ea(),r=Os(Error(N(421))),Ro(e,t,s,r)}return o.data==="$?"?(t.flags|=128,t.child=e.child,t=lv.bind(null,e),o._reactRetry=t,null):(e=i.treeContext,Ae=Jt(o.nextSibling),ze=t,Q=!0,st=null,e!==null&&(We[Ye++]=Tt,We[Ye++]=bt,We[Ye++]=On,Tt=e.id,bt=e.overflow,On=t),t=_a(t,r.children),t.flags|=4096,t)}function fc(e,t,n){e.lanes|=t;var r=e.alternate;r!==null&&(r.lanes|=t),ml(e.return,t,n)}function Ns(e,t,n,r,o){var i=e.memoizedState;i===null?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:o}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=o)}function Rp(e,t,n){var r=t.pendingProps,o=r.revealOrder,i=r.tail;if(Se(e,t,r.children,n),r=J.current,r&2)r=r&1|2,t.flags|=128;else{if(e!==null&&e.flags&128)e:for(e=t.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&fc(e,n,t);else if(e.tag===19)fc(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(H(J,r),!(t.mode&1))t.memoizedState=null;else switch(o){case"forwards":for(n=t.child,o=null;n!==null;)e=n.alternate,e!==null&&Si(e)===null&&(o=n),n=n.sibling;n=o,n===null?(o=t.child,t.child=null):(o=n.sibling,n.sibling=null),Ns(t,!1,o,n,i);break;case"backwards":for(n=null,o=t.child,t.child=null;o!==null;){if(e=o.alternate,e!==null&&Si(e)===null){t.child=o;break}e=o.sibling,o.sibling=n,n=o,o=e}Ns(t,!0,n,null,i);break;case"together":Ns(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Jo(e,t){!(t.mode&1)&&e!==null&&(e.alternate=null,t.alternate=null,t.flags|=2)}function jt(e,t,n){if(e!==null&&(t.dependencies=e.dependencies),En|=t.lanes,!(n&t.childLanes))return null;if(e!==null&&t.child!==e.child)throw Error(N(153));if(t.child!==null){for(e=t.child,n=nn(e,e.pendingProps),t.child=n,n.return=t;e.sibling!==null;)e=e.sibling,n=n.sibling=nn(e,e.pendingProps),n.return=t;n.sibling=null}return t.child}function qg(e,t,n){switch(t.tag){case 3:Pp(t),or();break;case 5:np(t);break;case 1:Le(t.type)&&gi(t);break;case 4:da(t,t.stateNode.containerInfo);break;case 10:var r=t.type._context,o=t.memoizedProps.value;H(_i,r._currentValue),r._currentValue=o;break;case 13:if(r=t.memoizedState,r!==null)return r.dehydrated!==null?(H(J,J.current&1),t.flags|=128,null):n&t.child.childLanes?Cp(e,t,n):(H(J,J.current&1),e=jt(e,t,n),e!==null?e.sibling:null);H(J,J.current&1);break;case 19:if(r=(n&t.childLanes)!==0,e.flags&128){if(r)return Rp(e,t,n);t.flags|=128}if(o=t.memoizedState,o!==null&&(o.rendering=null,o.tail=null,o.lastEffect=null),H(J,J.current),r)break;return null;case 22:case 23:return t.lanes=0,Ep(e,t,n)}return jt(e,t,n)}var Tp,Ol,bp,Lp;Tp=function(e,t){for(var n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Ol=function(){};bp=function(e,t,n,r){var o=e.memoizedProps;if(o!==r){e=t.stateNode,_n(kt.current);var i=null;switch(n){case"input":o=Hs(e,o),r=Hs(e,r),i=[];break;case"select":o=ee({},o,{value:void 0}),r=ee({},r,{value:void 0}),i=[];break;case"textarea":o=Gs(e,o),r=Gs(e,r),i=[];break;default:typeof o.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=hi)}Qs(n,r);var s;n=null;for(u in o)if(!r.hasOwnProperty(u)&&o.hasOwnProperty(u)&&o[u]!=null)if(u==="style"){var l=o[u];for(s in l)l.hasOwnProperty(s)&&(n||(n={}),n[s]="")}else u!=="dangerouslySetInnerHTML"&&u!=="children"&&u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&u!=="autoFocus"&&(zr.hasOwnProperty(u)?i||(i=[]):(i=i||[]).push(u,null));for(u in r){var a=r[u];if(l=o!=null?o[u]:void 0,r.hasOwnProperty(u)&&a!==l&&(a!=null||l!=null))if(u==="style")if(l){for(s in l)!l.hasOwnProperty(s)||a&&a.hasOwnProperty(s)||(n||(n={}),n[s]="");for(s in a)a.hasOwnProperty(s)&&l[s]!==a[s]&&(n||(n={}),n[s]=a[s])}else n||(i||(i=[]),i.push(u,n)),n=a;else u==="dangerouslySetInnerHTML"?(a=a?a.__html:void 0,l=l?l.__html:void 0,a!=null&&l!==a&&(i=i||[]).push(u,a)):u==="children"?typeof a!="string"&&typeof a!="number"||(i=i||[]).push(u,""+a):u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&(zr.hasOwnProperty(u)?(a!=null&&u==="onScroll"&&Y("scroll",e),i||l===a||(i=[])):(i=i||[]).push(u,a))}n&&(i=i||[]).push("style",n);var u=i;(t.updateQueue=u)&&(t.flags|=4)}};Lp=function(e,t,n,r){n!==r&&(t.flags|=4)};function wr(e,t){if(!Q)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function ve(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var o=e.child;o!==null;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags&14680064,r|=o.flags&14680064,o.return=e,o=o.sibling;else for(o=e.child;o!==null;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags,r|=o.flags,o.return=e,o=o.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Qg(e,t,n){var r=t.pendingProps;switch(oa(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return ve(t),null;case 1:return Le(t.type)&&mi(),ve(t),null;case 3:return r=t.stateNode,sr(),G(be),G(_e),fa(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),(e===null||e.child===null)&&(Po(t)?t.flags|=4:e===null||e.memoizedState.isDehydrated&&!(t.flags&256)||(t.flags|=1024,st!==null&&(bl(st),st=null))),Ol(e,t),ve(t),null;case 5:pa(t);var o=_n(Jr.current);if(n=t.type,e!==null&&t.stateNode!=null)bp(e,t,n,r,o),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(t.stateNode===null)throw Error(N(166));return ve(t),null}if(e=_n(kt.current),Po(t)){r=t.stateNode,n=t.type;var i=t.memoizedProps;switch(r[vt]=t,r[Qr]=i,e=(t.mode&1)!==0,n){case"dialog":Y("cancel",r),Y("close",r);break;case"iframe":case"object":case"embed":Y("load",r);break;case"video":case"audio":for(o=0;o<Rr.length;o++)Y(Rr[o],r);break;case"source":Y("error",r);break;case"img":case"image":case"link":Y("error",r),Y("load",r);break;case"details":Y("toggle",r);break;case"input":Su(r,i),Y("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!i.multiple},Y("invalid",r);break;case"textarea":Nu(r,i),Y("invalid",r)}Qs(n,i),o=null;for(var s in i)if(i.hasOwnProperty(s)){var l=i[s];s==="children"?typeof l=="string"?r.textContent!==l&&(i.suppressHydrationWarning!==!0&&xo(r.textContent,l,e),o=["children",l]):typeof l=="number"&&r.textContent!==""+l&&(i.suppressHydrationWarning!==!0&&xo(r.textContent,l,e),o=["children",""+l]):zr.hasOwnProperty(s)&&l!=null&&s==="onScroll"&&Y("scroll",r)}switch(n){case"input":yo(r),Ou(r,i,!0);break;case"textarea":yo(r),Eu(r);break;case"select":case"option":break;default:typeof i.onClick=="function"&&(r.onclick=hi)}r=o,t.updateQueue=r,r!==null&&(t.flags|=4)}else{s=o.nodeType===9?o:o.ownerDocument,e==="http://www.w3.org/1999/xhtml"&&(e=id(n)),e==="http://www.w3.org/1999/xhtml"?n==="script"?(e=s.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=s.createElement(n,{is:r.is}):(e=s.createElement(n),n==="select"&&(s=e,r.multiple?s.multiple=!0:r.size&&(s.size=r.size))):e=s.createElementNS(e,n),e[vt]=t,e[Qr]=r,Tp(e,t,!1,!1),t.stateNode=e;e:{switch(s=Xs(n,r),n){case"dialog":Y("cancel",e),Y("close",e),o=r;break;case"iframe":case"object":case"embed":Y("load",e),o=r;break;case"video":case"audio":for(o=0;o<Rr.length;o++)Y(Rr[o],e);o=r;break;case"source":Y("error",e),o=r;break;case"img":case"image":case"link":Y("error",e),Y("load",e),o=r;break;case"details":Y("toggle",e),o=r;break;case"input":Su(e,r),o=Hs(e,r),Y("invalid",e);break;case"option":o=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},o=ee({},r,{value:void 0}),Y("invalid",e);break;case"textarea":Nu(e,r),o=Gs(e,r),Y("invalid",e);break;default:o=r}Qs(n,o),l=o;for(i in l)if(l.hasOwnProperty(i)){var a=l[i];i==="style"?ad(e,a):i==="dangerouslySetInnerHTML"?(a=a?a.__html:void 0,a!=null&&sd(e,a)):i==="children"?typeof a=="string"?(n!=="textarea"||a!=="")&&Mr(e,a):typeof a=="number"&&Mr(e,""+a):i!=="suppressContentEditableWarning"&&i!=="suppressHydrationWarning"&&i!=="autoFocus"&&(zr.hasOwnProperty(i)?a!=null&&i==="onScroll"&&Y("scroll",e):a!=null&&Bl(e,i,a,s))}switch(n){case"input":yo(e),Ou(e,r,!1);break;case"textarea":yo(e),Eu(e);break;case"option":r.value!=null&&e.setAttribute("value",""+on(r.value));break;case"select":e.multiple=!!r.multiple,i=r.value,i!=null?qn(e,!!r.multiple,i,!1):r.defaultValue!=null&&qn(e,!!r.multiple,r.defaultValue,!0);break;default:typeof o.onClick=="function"&&(e.onclick=hi)}switch(n){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}t.ref!==null&&(t.flags|=512,t.flags|=2097152)}return ve(t),null;case 6:if(e&&t.stateNode!=null)Lp(e,t,e.memoizedProps,r);else{if(typeof r!="string"&&t.stateNode===null)throw Error(N(166));if(n=_n(Jr.current),_n(kt.current),Po(t)){if(r=t.stateNode,n=t.memoizedProps,r[vt]=t,(i=r.nodeValue!==n)&&(e=ze,e!==null))switch(e.tag){case 3:xo(r.nodeValue,n,(e.mode&1)!==0);break;case 5:e.memoizedProps.suppressHydrationWarning!==!0&&xo(r.nodeValue,n,(e.mode&1)!==0)}i&&(t.flags|=4)}else r=(n.nodeType===9?n:n.ownerDocument).createTextNode(r),r[vt]=t,t.stateNode=r}return ve(t),null;case 13:if(G(J),r=t.memoizedState,e===null||e.memoizedState!==null&&e.memoizedState.dehydrated!==null){if(Q&&Ae!==null&&t.mode&1&&!(t.flags&128))qd(),or(),t.flags|=98560,i=!1;else if(i=Po(t),r!==null&&r.dehydrated!==null){if(e===null){if(!i)throw Error(N(318));if(i=t.memoizedState,i=i!==null?i.dehydrated:null,!i)throw Error(N(317));i[vt]=t}else or(),!(t.flags&128)&&(t.memoizedState=null),t.flags|=4;ve(t),i=!1}else st!==null&&(bl(st),st=null),i=!0;if(!i)return t.flags&65536?t:null}return t.flags&128?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,t.mode&1&&(e===null||J.current&1?le===0&&(le=3):Ea())),t.updateQueue!==null&&(t.flags|=4),ve(t),null);case 4:return sr(),Ol(e,t),e===null&&Gr(t.stateNode.containerInfo),ve(t),null;case 10:return aa(t.type._context),ve(t),null;case 17:return Le(t.type)&&mi(),ve(t),null;case 19:if(G(J),i=t.memoizedState,i===null)return ve(t),null;if(r=(t.flags&128)!==0,s=i.rendering,s===null)if(r)wr(i,!1);else{if(le!==0||e!==null&&e.flags&128)for(e=t.child;e!==null;){if(s=Si(e),s!==null){for(t.flags|=128,wr(i,!1),r=s.updateQueue,r!==null&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;n!==null;)i=n,e=r,i.flags&=14680066,s=i.alternate,s===null?(i.childLanes=0,i.lanes=e,i.child=null,i.subtreeFlags=0,i.memoizedProps=null,i.memoizedState=null,i.updateQueue=null,i.dependencies=null,i.stateNode=null):(i.childLanes=s.childLanes,i.lanes=s.lanes,i.child=s.child,i.subtreeFlags=0,i.deletions=null,i.memoizedProps=s.memoizedProps,i.memoizedState=s.memoizedState,i.updateQueue=s.updateQueue,i.type=s.type,e=s.dependencies,i.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return H(J,J.current&1|2),t.child}e=e.sibling}i.tail!==null&&ne()>ar&&(t.flags|=128,r=!0,wr(i,!1),t.lanes=4194304)}else{if(!r)if(e=Si(s),e!==null){if(t.flags|=128,r=!0,n=e.updateQueue,n!==null&&(t.updateQueue=n,t.flags|=4),wr(i,!0),i.tail===null&&i.tailMode==="hidden"&&!s.alternate&&!Q)return ve(t),null}else 2*ne()-i.renderingStartTime>ar&&n!==1073741824&&(t.flags|=128,r=!0,wr(i,!1),t.lanes=4194304);i.isBackwards?(s.sibling=t.child,t.child=s):(n=i.last,n!==null?n.sibling=s:t.child=s,i.last=s)}return i.tail!==null?(t=i.tail,i.rendering=t,i.tail=t.sibling,i.renderingStartTime=ne(),t.sibling=null,n=J.current,H(J,r?n&1|2:n&1),t):(ve(t),null);case 22:case 23:return Na(),r=t.memoizedState!==null,e!==null&&e.memoizedState!==null!==r&&(t.flags|=8192),r&&t.mode&1?De&1073741824&&(ve(t),t.subtreeFlags&6&&(t.flags|=8192)):ve(t),null;case 24:return null;case 25:return null}throw Error(N(156,t.tag))}function Xg(e,t){switch(oa(t),t.tag){case 1:return Le(t.type)&&mi(),e=t.flags,e&65536?(t.flags=e&-65537|128,t):null;case 3:return sr(),G(be),G(_e),fa(),e=t.flags,e&65536&&!(e&128)?(t.flags=e&-65537|128,t):null;case 5:return pa(t),null;case 13:if(G(J),e=t.memoizedState,e!==null&&e.dehydrated!==null){if(t.alternate===null)throw Error(N(340));or()}return e=t.flags,e&65536?(t.flags=e&-65537|128,t):null;case 19:return G(J),null;case 4:return sr(),null;case 10:return aa(t.type._context),null;case 22:case 23:return Na(),null;case 24:return null;default:return null}}var To=!1,ye=!1,Jg=typeof WeakSet=="function"?WeakSet:Set,T=null;function Yn(e,t){var n=e.ref;if(n!==null)if(typeof n=="function")try{n(null)}catch(r){te(e,t,r)}else n.current=null}function Nl(e,t,n){try{n()}catch(r){te(e,t,r)}}var hc=!1;function Zg(e,t){if(ll=di,e=jd(),na(e)){if("selectionStart"in e)var n={start:e.selectionStart,end:e.selectionEnd};else e:{n=(n=e.ownerDocument)&&n.defaultView||window;var r=n.getSelection&&n.getSelection();if(r&&r.rangeCount!==0){n=r.anchorNode;var o=r.anchorOffset,i=r.focusNode;r=r.focusOffset;try{n.nodeType,i.nodeType}catch{n=null;break e}var s=0,l=-1,a=-1,u=0,f=0,d=e,h=null;t:for(;;){for(var g;d!==n||o!==0&&d.nodeType!==3||(l=s+o),d!==i||r!==0&&d.nodeType!==3||(a=s+r),d.nodeType===3&&(s+=d.nodeValue.length),(g=d.firstChild)!==null;)h=d,d=g;for(;;){if(d===e)break t;if(h===n&&++u===o&&(l=s),h===i&&++f===r&&(a=s),(g=d.nextSibling)!==null)break;d=h,h=d.parentNode}d=g}n=l===-1||a===-1?null:{start:l,end:a}}else n=null}n=n||{start:0,end:0}}else n=null;for(al={focusedElem:e,selectionRange:n},di=!1,T=t;T!==null;)if(t=T,e=t.child,(t.subtreeFlags&1028)!==0&&e!==null)e.return=t,T=e;else for(;T!==null;){t=T;try{var v=t.alternate;if(t.flags&1024)switch(t.tag){case 0:case 11:case 15:break;case 1:if(v!==null){var k=v.memoizedProps,O=v.memoizedState,p=t.stateNode,c=p.getSnapshotBeforeUpdate(t.elementType===t.type?k:ot(t.type,k),O);p.__reactInternalSnapshotBeforeUpdate=c}break;case 3:var m=t.stateNode.containerInfo;m.nodeType===1?m.textContent="":m.nodeType===9&&m.documentElement&&m.removeChild(m.documentElement);break;case 5:case 6:case 4:case 17:break;default:throw Error(N(163))}}catch(y){te(t,t.return,y)}if(e=t.sibling,e!==null){e.return=t.return,T=e;break}T=t.return}return v=hc,hc=!1,v}function jr(e,t,n){var r=t.updateQueue;if(r=r!==null?r.lastEffect:null,r!==null){var o=r=r.next;do{if((o.tag&e)===e){var i=o.destroy;o.destroy=void 0,i!==void 0&&Nl(t,n,i)}o=o.next}while(o!==r)}}function Mi(e,t){if(t=t.updateQueue,t=t!==null?t.lastEffect:null,t!==null){var n=t=t.next;do{if((n.tag&e)===e){var r=n.create;n.destroy=r()}n=n.next}while(n!==t)}}function El(e){var t=e.ref;if(t!==null){var n=e.stateNode;switch(e.tag){case 5:e=n;break;default:e=n}typeof t=="function"?t(e):t.current=e}}function Ip(e){var t=e.alternate;t!==null&&(e.alternate=null,Ip(t)),e.child=null,e.deletions=null,e.sibling=null,e.tag===5&&(t=e.stateNode,t!==null&&(delete t[vt],delete t[Qr],delete t[dl],delete t[Fg],delete t[jg])),e.stateNode=null,e.return=null,e.dependencies=null,e.memoizedProps=null,e.memoizedState=null,e.pendingProps=null,e.stateNode=null,e.updateQueue=null}function Vp(e){return e.tag===5||e.tag===3||e.tag===4}function mc(e){e:for(;;){for(;e.sibling===null;){if(e.return===null||Vp(e.return))return null;e=e.return}for(e.sibling.return=e.return,e=e.sibling;e.tag!==5&&e.tag!==6&&e.tag!==18;){if(e.flags&2||e.child===null||e.tag===4)continue e;e.child.return=e,e=e.child}if(!(e.flags&2))return e.stateNode}}function xl(e,t,n){var r=e.tag;if(r===5||r===6)e=e.stateNode,t?n.nodeType===8?n.parentNode.insertBefore(e,t):n.insertBefore(e,t):(n.nodeType===8?(t=n.parentNode,t.insertBefore(e,n)):(t=n,t.appendChild(e)),n=n._reactRootContainer,n!=null||t.onclick!==null||(t.onclick=hi));else if(r!==4&&(e=e.child,e!==null))for(xl(e,t,n),e=e.sibling;e!==null;)xl(e,t,n),e=e.sibling}function Pl(e,t,n){var r=e.tag;if(r===5||r===6)e=e.stateNode,t?n.insertBefore(e,t):n.appendChild(e);else if(r!==4&&(e=e.child,e!==null))for(Pl(e,t,n),e=e.sibling;e!==null;)Pl(e,t,n),e=e.sibling}var de=null,it=!1;function Ut(e,t,n){for(n=n.child;n!==null;)Fp(e,t,n),n=n.sibling}function Fp(e,t,n){if(_t&&typeof _t.onCommitFiberUnmount=="function")try{_t.onCommitFiberUnmount(Ii,n)}catch{}switch(n.tag){case 5:ye||Yn(n,t);case 6:var r=de,o=it;de=null,Ut(e,t,n),de=r,it=o,de!==null&&(it?(e=de,n=n.stateNode,e.nodeType===8?e.parentNode.removeChild(n):e.removeChild(n)):de.removeChild(n.stateNode));break;case 18:de!==null&&(it?(e=de,n=n.stateNode,e.nodeType===8?vs(e.parentNode,n):e.nodeType===1&&vs(e,n),Hr(e)):vs(de,n.stateNode));break;case 4:r=de,o=it,de=n.stateNode.containerInfo,it=!0,Ut(e,t,n),de=r,it=o;break;case 0:case 11:case 14:case 15:if(!ye&&(r=n.updateQueue,r!==null&&(r=r.lastEffect,r!==null))){o=r=r.next;do{var i=o,s=i.destroy;i=i.tag,s!==void 0&&(i&2||i&4)&&Nl(n,t,s),o=o.next}while(o!==r)}Ut(e,t,n);break;case 1:if(!ye&&(Yn(n,t),r=n.stateNode,typeof r.componentWillUnmount=="function"))try{r.props=n.memoizedProps,r.state=n.memoizedState,r.componentWillUnmount()}catch(l){te(n,t,l)}Ut(e,t,n);break;case 21:Ut(e,t,n);break;case 22:n.mode&1?(ye=(r=ye)||n.memoizedState!==null,Ut(e,t,n),ye=r):Ut(e,t,n);break;default:Ut(e,t,n)}}function gc(e){var t=e.updateQueue;if(t!==null){e.updateQueue=null;var n=e.stateNode;n===null&&(n=e.stateNode=new Jg),t.forEach(function(r){var o=av.bind(null,e,r);n.has(r)||(n.add(r),r.then(o,o))})}}function rt(e,t){var n=t.deletions;if(n!==null)for(var r=0;r<n.length;r++){var o=n[r];try{var i=e,s=t,l=s;e:for(;l!==null;){switch(l.tag){case 5:de=l.stateNode,it=!1;break e;case 3:de=l.stateNode.containerInfo,it=!0;break e;case 4:de=l.stateNode.containerInfo,it=!0;break e}l=l.return}if(de===null)throw Error(N(160));Fp(i,s,o),de=null,it=!1;var a=o.alternate;a!==null&&(a.return=null),o.return=null}catch(u){te(o,t,u)}}if(t.subtreeFlags&12854)for(t=t.child;t!==null;)jp(t,e),t=t.sibling}function jp(e,t){var n=e.alternate,r=e.flags;switch(e.tag){case 0:case 11:case 14:case 15:if(rt(t,e),mt(e),r&4){try{jr(3,e,e.return),Mi(3,e)}catch(k){te(e,e.return,k)}try{jr(5,e,e.return)}catch(k){te(e,e.return,k)}}break;case 1:rt(t,e),mt(e),r&512&&n!==null&&Yn(n,n.return);break;case 5:if(rt(t,e),mt(e),r&512&&n!==null&&Yn(n,n.return),e.flags&32){var o=e.stateNode;try{Mr(o,"")}catch(k){te(e,e.return,k)}}if(r&4&&(o=e.stateNode,o!=null)){var i=e.memoizedProps,s=n!==null?n.memoizedProps:i,l=e.type,a=e.updateQueue;if(e.updateQueue=null,a!==null)try{l==="input"&&i.type==="radio"&&i.name!=null&&rd(o,i),Xs(l,s);var u=Xs(l,i);for(s=0;s<a.length;s+=2){var f=a[s],d=a[s+1];f==="style"?ad(o,d):f==="dangerouslySetInnerHTML"?sd(o,d):f==="children"?Mr(o,d):Bl(o,f,d,u)}switch(l){case"input":Ws(o,i);break;case"textarea":od(o,i);break;case"select":var h=o._wrapperState.wasMultiple;o._wrapperState.wasMultiple=!!i.multiple;var g=i.value;g!=null?qn(o,!!i.multiple,g,!1):h!==!!i.multiple&&(i.defaultValue!=null?qn(o,!!i.multiple,i.defaultValue,!0):qn(o,!!i.multiple,i.multiple?[]:"",!1))}o[Qr]=i}catch(k){te(e,e.return,k)}}break;case 6:if(rt(t,e),mt(e),r&4){if(e.stateNode===null)throw Error(N(162));o=e.stateNode,i=e.memoizedProps;try{o.nodeValue=i}catch(k){te(e,e.return,k)}}break;case 3:if(rt(t,e),mt(e),r&4&&n!==null&&n.memoizedState.isDehydrated)try{Hr(t.containerInfo)}catch(k){te(e,e.return,k)}break;case 4:rt(t,e),mt(e);break;case 13:rt(t,e),mt(e),o=e.child,o.flags&8192&&(i=o.memoizedState!==null,o.stateNode.isHidden=i,!i||o.alternate!==null&&o.alternate.memoizedState!==null||(Sa=ne())),r&4&&gc(e);break;case 22:if(f=n!==null&&n.memoizedState!==null,e.mode&1?(ye=(u=ye)||f,rt(t,e),ye=u):rt(t,e),mt(e),r&8192){if(u=e.memoizedState!==null,(e.stateNode.isHidden=u)&&!f&&e.mode&1)for(T=e,f=e.child;f!==null;){for(d=T=f;T!==null;){switch(h=T,g=h.child,h.tag){case 0:case 11:case 14:case 15:jr(4,h,h.return);break;case 1:Yn(h,h.return);var v=h.stateNode;if(typeof v.componentWillUnmount=="function"){r=h,n=h.return;try{t=r,v.props=t.memoizedProps,v.state=t.memoizedState,v.componentWillUnmount()}catch(k){te(r,n,k)}}break;case 5:Yn(h,h.return);break;case 22:if(h.memoizedState!==null){yc(d);continue}}g!==null?(g.return=h,T=g):yc(d)}f=f.sibling}e:for(f=null,d=e;;){if(d.tag===5){if(f===null){f=d;try{o=d.stateNode,u?(i=o.style,typeof i.setProperty=="function"?i.setProperty("display","none","important"):i.display="none"):(l=d.stateNode,a=d.memoizedProps.style,s=a!=null&&a.hasOwnProperty("display")?a.display:null,l.style.display=ld("display",s))}catch(k){te(e,e.return,k)}}}else if(d.tag===6){if(f===null)try{d.stateNode.nodeValue=u?"":d.memoizedProps}catch(k){te(e,e.return,k)}}else if((d.tag!==22&&d.tag!==23||d.memoizedState===null||d===e)&&d.child!==null){d.child.return=d,d=d.child;continue}if(d===e)break e;for(;d.sibling===null;){if(d.return===null||d.return===e)break e;f===d&&(f=null),d=d.return}f===d&&(f=null),d.sibling.return=d.return,d=d.sibling}}break;case 19:rt(t,e),mt(e),r&4&&gc(e);break;case 21:break;default:rt(t,e),mt(e)}}function mt(e){var t=e.flags;if(t&2){try{e:{for(var n=e.return;n!==null;){if(Vp(n)){var r=n;break e}n=n.return}throw Error(N(160))}switch(r.tag){case 5:var o=r.stateNode;r.flags&32&&(Mr(o,""),r.flags&=-33);var i=mc(e);Pl(e,i,o);break;case 3:case 4:var s=r.stateNode.containerInfo,l=mc(e);xl(e,l,s);break;default:throw Error(N(161))}}catch(a){te(e,e.return,a)}e.flags&=-3}t&4096&&(e.flags&=-4097)}function ev(e,t,n){T=e,Dp(e)}function Dp(e,t,n){for(var r=(e.mode&1)!==0;T!==null;){var o=T,i=o.child;if(o.tag===22&&r){var s=o.memoizedState!==null||To;if(!s){var l=o.alternate,a=l!==null&&l.memoizedState!==null||ye;l=To;var u=ye;if(To=s,(ye=a)&&!u)for(T=o;T!==null;)s=T,a=s.child,s.tag===22&&s.memoizedState!==null?_c(o):a!==null?(a.return=s,T=a):_c(o);for(;i!==null;)T=i,Dp(i),i=i.sibling;T=o,To=l,ye=u}vc(e)}else o.subtreeFlags&8772&&i!==null?(i.return=o,T=i):vc(e)}}function vc(e){for(;T!==null;){var t=T;if(t.flags&8772){var n=t.alternate;try{if(t.flags&8772)switch(t.tag){case 0:case 11:case 15:ye||Mi(5,t);break;case 1:var r=t.stateNode;if(t.flags&4&&!ye)if(n===null)r.componentDidMount();else{var o=t.elementType===t.type?n.memoizedProps:ot(t.type,n.memoizedProps);r.componentDidUpdate(o,n.memoizedState,r.__reactInternalSnapshotBeforeUpdate)}var i=t.updateQueue;i!==null&&ec(t,i,r);break;case 3:var s=t.updateQueue;if(s!==null){if(n=null,t.child!==null)switch(t.child.tag){case 5:n=t.child.stateNode;break;case 1:n=t.child.stateNode}ec(t,s,n)}break;case 5:var l=t.stateNode;if(n===null&&t.flags&4){n=l;var a=t.memoizedProps;switch(t.type){case"button":case"input":case"select":case"textarea":a.autoFocus&&n.focus();break;case"img":a.src&&(n.src=a.src)}}break;case 6:break;case 4:break;case 12:break;case 13:if(t.memoizedState===null){var u=t.alternate;if(u!==null){var f=u.memoizedState;if(f!==null){var d=f.dehydrated;d!==null&&Hr(d)}}}break;case 19:case 17:case 21:case 22:case 23:case 25:break;default:throw Error(N(163))}ye||t.flags&512&&El(t)}catch(h){te(t,t.return,h)}}if(t===e){T=null;break}if(n=t.sibling,n!==null){n.return=t.return,T=n;break}T=t.return}}function yc(e){for(;T!==null;){var t=T;if(t===e){T=null;break}var n=t.sibling;if(n!==null){n.return=t.return,T=n;break}T=t.return}}function _c(e){for(;T!==null;){var t=T;try{switch(t.tag){case 0:case 11:case 15:var n=t.return;try{Mi(4,t)}catch(a){te(t,n,a)}break;case 1:var r=t.stateNode;if(typeof r.componentDidMount=="function"){var o=t.return;try{r.componentDidMount()}catch(a){te(t,o,a)}}var i=t.return;try{El(t)}catch(a){te(t,i,a)}break;case 5:var s=t.return;try{El(t)}catch(a){te(t,s,a)}}}catch(a){te(t,t.return,a)}if(t===e){T=null;break}var l=t.sibling;if(l!==null){l.return=t.return,T=l;break}T=t.return}}var tv=Math.ceil,Ei=Dt.ReactCurrentDispatcher,ka=Dt.ReactCurrentOwner,qe=Dt.ReactCurrentBatchConfig,U=0,ue=null,oe=null,he=0,De=0,Gn=un(0),le=0,no=null,En=0,$i=0,wa=0,Dr=null,Re=null,Sa=0,ar=1/0,Ct=null,xi=!1,Cl=null,en=null,bo=!1,Yt=null,Pi=0,Ur=0,Rl=null,Zo=-1,ei=0;function Oe(){return U&6?ne():Zo!==-1?Zo:Zo=ne()}function tn(e){return e.mode&1?U&2&&he!==0?he&-he:Ug.transition!==null?(ei===0&&(ei=kd()),ei):(e=$,e!==0||(e=window.event,e=e===void 0?16:Pd(e.type)),e):1}function at(e,t,n,r){if(50<Ur)throw Ur=0,Rl=null,Error(N(185));io(e,n,r),(!(U&2)||e!==ue)&&(e===ue&&(!(U&2)&&($i|=n),le===4&&Kt(e,he)),Ie(e,r),n===1&&U===0&&!(t.mode&1)&&(ar=ne()+500,Ui&&cn()))}function Ie(e,t){var n=e.callbackNode;Um(e,t);var r=ci(e,e===ue?he:0);if(r===0)n!==null&&Cu(n),e.callbackNode=null,e.callbackPriority=0;else if(t=r&-r,e.callbackPriority!==t){if(n!=null&&Cu(n),t===1)e.tag===0?Dg(kc.bind(null,e)):Wd(kc.bind(null,e)),Ig(function(){!(U&6)&&cn()}),n=null;else{switch(wd(r)){case 1:n=Gl;break;case 4:n=yd;break;case 16:n=ui;break;case 536870912:n=_d;break;default:n=ui}n=Hp(n,Up.bind(null,e))}e.callbackPriority=t,e.callbackNode=n}}function Up(e,t){if(Zo=-1,ei=0,U&6)throw Error(N(327));var n=e.callbackNode;if(er()&&e.callbackNode!==n)return null;var r=ci(e,e===ue?he:0);if(r===0)return null;if(r&30||r&e.expiredLanes||t)t=Ci(e,r);else{t=r;var o=U;U|=2;var i=zp();(ue!==e||he!==t)&&(Ct=null,ar=ne()+500,kn(e,t));do try{ov();break}catch(l){Ap(e,l)}while(1);la(),Ei.current=i,U=o,oe!==null?t=0:(ue=null,he=0,t=le)}if(t!==0){if(t===2&&(o=nl(e),o!==0&&(r=o,t=Tl(e,o))),t===1)throw n=no,kn(e,0),Kt(e,r),Ie(e,ne()),n;if(t===6)Kt(e,r);else{if(o=e.current.alternate,!(r&30)&&!nv(o)&&(t=Ci(e,r),t===2&&(i=nl(e),i!==0&&(r=i,t=Tl(e,i))),t===1))throw n=no,kn(e,0),Kt(e,r),Ie(e,ne()),n;switch(e.finishedWork=o,e.finishedLanes=r,t){case 0:case 1:throw Error(N(345));case 2:mn(e,Re,Ct);break;case 3:if(Kt(e,r),(r&130023424)===r&&(t=Sa+500-ne(),10<t)){if(ci(e,0)!==0)break;if(o=e.suspendedLanes,(o&r)!==r){Oe(),e.pingedLanes|=e.suspendedLanes&o;break}e.timeoutHandle=cl(mn.bind(null,e,Re,Ct),t);break}mn(e,Re,Ct);break;case 4:if(Kt(e,r),(r&4194240)===r)break;for(t=e.eventTimes,o=-1;0<r;){var s=31-lt(r);i=1<<s,s=t[s],s>o&&(o=s),r&=~i}if(r=o,r=ne()-r,r=(120>r?120:480>r?480:1080>r?1080:1920>r?1920:3e3>r?3e3:4320>r?4320:1960*tv(r/1960))-r,10<r){e.timeoutHandle=cl(mn.bind(null,e,Re,Ct),r);break}mn(e,Re,Ct);break;case 5:mn(e,Re,Ct);break;default:throw Error(N(329))}}}return Ie(e,ne()),e.callbackNode===n?Up.bind(null,e):null}function Tl(e,t){var n=Dr;return e.current.memoizedState.isDehydrated&&(kn(e,t).flags|=256),e=Ci(e,t),e!==2&&(t=Re,Re=n,t!==null&&bl(t)),e}function bl(e){Re===null?Re=e:Re.push.apply(Re,e)}function nv(e){for(var t=e;;){if(t.flags&16384){var n=t.updateQueue;if(n!==null&&(n=n.stores,n!==null))for(var r=0;r<n.length;r++){var o=n[r],i=o.getSnapshot;o=o.value;try{if(!ct(i(),o))return!1}catch{return!1}}}if(n=t.child,t.subtreeFlags&16384&&n!==null)n.return=t,t=n;else{if(t===e)break;for(;t.sibling===null;){if(t.return===null||t.return===e)return!0;t=t.return}t.sibling.return=t.return,t=t.sibling}}return!0}function Kt(e,t){for(t&=~wa,t&=~$i,e.suspendedLanes|=t,e.pingedLanes&=~t,e=e.expirationTimes;0<t;){var n=31-lt(t),r=1<<n;e[n]=-1,t&=~r}}function kc(e){if(U&6)throw Error(N(327));er();var t=ci(e,0);if(!(t&1))return Ie(e,ne()),null;var n=Ci(e,t);if(e.tag!==0&&n===2){var r=nl(e);r!==0&&(t=r,n=Tl(e,r))}if(n===1)throw n=no,kn(e,0),Kt(e,t),Ie(e,ne()),n;if(n===6)throw Error(N(345));return e.finishedWork=e.current.alternate,e.finishedLanes=t,mn(e,Re,Ct),Ie(e,ne()),null}function Oa(e,t){var n=U;U|=1;try{return e(t)}finally{U=n,U===0&&(ar=ne()+500,Ui&&cn())}}function xn(e){Yt!==null&&Yt.tag===0&&!(U&6)&&er();var t=U;U|=1;var n=qe.transition,r=$;try{if(qe.transition=null,$=1,e)return e()}finally{$=r,qe.transition=n,U=t,!(U&6)&&cn()}}function Na(){De=Gn.current,G(Gn)}function kn(e,t){e.finishedWork=null,e.finishedLanes=0;var n=e.timeoutHandle;if(n!==-1&&(e.timeoutHandle=-1,Lg(n)),oe!==null)for(n=oe.return;n!==null;){var r=n;switch(oa(r),r.tag){case 1:r=r.type.childContextTypes,r!=null&&mi();break;case 3:sr(),G(be),G(_e),fa();break;case 5:pa(r);break;case 4:sr();break;case 13:G(J);break;case 19:G(J);break;case 10:aa(r.type._context);break;case 22:case 23:Na()}n=n.return}if(ue=e,oe=e=nn(e.current,null),he=De=t,le=0,no=null,wa=$i=En=0,Re=Dr=null,yn!==null){for(t=0;t<yn.length;t++)if(n=yn[t],r=n.interleaved,r!==null){n.interleaved=null;var o=r.next,i=n.pending;if(i!==null){var s=i.next;i.next=o,r.next=s}n.pending=r}yn=null}return e}function Ap(e,t){do{var n=oe;try{if(la(),Qo.current=Ni,Oi){for(var r=Z.memoizedState;r!==null;){var o=r.queue;o!==null&&(o.pending=null),r=r.next}Oi=!1}if(Nn=0,ae=se=Z=null,Fr=!1,Zr=0,ka.current=null,n===null||n.return===null){le=1,no=t,oe=null;break}e:{var i=e,s=n.return,l=n,a=t;if(t=he,l.flags|=32768,a!==null&&typeof a=="object"&&typeof a.then=="function"){var u=a,f=l,d=f.tag;if(!(f.mode&1)&&(d===0||d===11||d===15)){var h=f.alternate;h?(f.updateQueue=h.updateQueue,f.memoizedState=h.memoizedState,f.lanes=h.lanes):(f.updateQueue=null,f.memoizedState=null)}var g=lc(s);if(g!==null){g.flags&=-257,ac(g,s,l,i,t),g.mode&1&&sc(i,u,t),t=g,a=u;var v=t.updateQueue;if(v===null){var k=new Set;k.add(a),t.updateQueue=k}else v.add(a);break e}else{if(!(t&1)){sc(i,u,t),Ea();break e}a=Error(N(426))}}else if(Q&&l.mode&1){var O=lc(s);if(O!==null){!(O.flags&65536)&&(O.flags|=256),ac(O,s,l,i,t),ia(lr(a,l));break e}}i=a=lr(a,l),le!==4&&(le=2),Dr===null?Dr=[i]:Dr.push(i),i=s;do{switch(i.tag){case 3:i.flags|=65536,t&=-t,i.lanes|=t;var p=Sp(i,a,t);Zu(i,p);break e;case 1:l=a;var c=i.type,m=i.stateNode;if(!(i.flags&128)&&(typeof c.getDerivedStateFromError=="function"||m!==null&&typeof m.componentDidCatch=="function"&&(en===null||!en.has(m)))){i.flags|=65536,t&=-t,i.lanes|=t;var y=Op(i,l,t);Zu(i,y);break e}}i=i.return}while(i!==null)}$p(n)}catch(S){t=S,oe===n&&n!==null&&(oe=n=n.return);continue}break}while(1)}function zp(){var e=Ei.current;return Ei.current=Ni,e===null?Ni:e}function Ea(){(le===0||le===3||le===2)&&(le=4),ue===null||!(En&268435455)&&!($i&268435455)||Kt(ue,he)}function Ci(e,t){var n=U;U|=2;var r=zp();(ue!==e||he!==t)&&(Ct=null,kn(e,t));do try{rv();break}catch(o){Ap(e,o)}while(1);if(la(),U=n,Ei.current=r,oe!==null)throw Error(N(261));return ue=null,he=0,le}function rv(){for(;oe!==null;)Mp(oe)}function ov(){for(;oe!==null&&!Rm();)Mp(oe)}function Mp(e){var t=Kp(e.alternate,e,De);e.memoizedProps=e.pendingProps,t===null?$p(e):oe=t,ka.current=null}function $p(e){var t=e;do{var n=t.alternate;if(e=t.return,t.flags&32768){if(n=Xg(n,t),n!==null){n.flags&=32767,oe=n;return}if(e!==null)e.flags|=32768,e.subtreeFlags=0,e.deletions=null;else{le=6,oe=null;return}}else if(n=Qg(n,t,De),n!==null){oe=n;return}if(t=t.sibling,t!==null){oe=t;return}oe=t=e}while(t!==null);le===0&&(le=5)}function mn(e,t,n){var r=$,o=qe.transition;try{qe.transition=null,$=1,iv(e,t,n,r)}finally{qe.transition=o,$=r}return null}function iv(e,t,n,r){do er();while(Yt!==null);if(U&6)throw Error(N(327));n=e.finishedWork;var o=e.finishedLanes;if(n===null)return null;if(e.finishedWork=null,e.finishedLanes=0,n===e.current)throw Error(N(177));e.callbackNode=null,e.callbackPriority=0;var i=n.lanes|n.childLanes;if(Am(e,i),e===ue&&(oe=ue=null,he=0),!(n.subtreeFlags&2064)&&!(n.flags&2064)||bo||(bo=!0,Hp(ui,function(){return er(),null})),i=(n.flags&15990)!==0,n.subtreeFlags&15990||i){i=qe.transition,qe.transition=null;var s=$;$=1;var l=U;U|=4,ka.current=null,Zg(e,n),jp(n,e),Eg(al),di=!!ll,al=ll=null,e.current=n,ev(n),Tm(),U=l,$=s,qe.transition=i}else e.current=n;if(bo&&(bo=!1,Yt=e,Pi=o),i=e.pendingLanes,i===0&&(en=null),Im(n.stateNode),Ie(e,ne()),t!==null)for(r=e.onRecoverableError,n=0;n<t.length;n++)o=t[n],r(o.value,{componentStack:o.stack,digest:o.digest});if(xi)throw xi=!1,e=Cl,Cl=null,e;return Pi&1&&e.tag!==0&&er(),i=e.pendingLanes,i&1?e===Rl?Ur++:(Ur=0,Rl=e):Ur=0,cn(),null}function er(){if(Yt!==null){var e=wd(Pi),t=qe.transition,n=$;try{if(qe.transition=null,$=16>e?16:e,Yt===null)var r=!1;else{if(e=Yt,Yt=null,Pi=0,U&6)throw Error(N(331));var o=U;for(U|=4,T=e.current;T!==null;){var i=T,s=i.child;if(T.flags&16){var l=i.deletions;if(l!==null){for(var a=0;a<l.length;a++){var u=l[a];for(T=u;T!==null;){var f=T;switch(f.tag){case 0:case 11:case 15:jr(8,f,i)}var d=f.child;if(d!==null)d.return=f,T=d;else for(;T!==null;){f=T;var h=f.sibling,g=f.return;if(Ip(f),f===u){T=null;break}if(h!==null){h.return=g,T=h;break}T=g}}}var v=i.alternate;if(v!==null){var k=v.child;if(k!==null){v.child=null;do{var O=k.sibling;k.sibling=null,k=O}while(k!==null)}}T=i}}if(i.subtreeFlags&2064&&s!==null)s.return=i,T=s;else e:for(;T!==null;){if(i=T,i.flags&2048)switch(i.tag){case 0:case 11:case 15:jr(9,i,i.return)}var p=i.sibling;if(p!==null){p.return=i.return,T=p;break e}T=i.return}}var c=e.current;for(T=c;T!==null;){s=T;var m=s.child;if(s.subtreeFlags&2064&&m!==null)m.return=s,T=m;else e:for(s=c;T!==null;){if(l=T,l.flags&2048)try{switch(l.tag){case 0:case 11:case 15:Mi(9,l)}}catch(S){te(l,l.return,S)}if(l===s){T=null;break e}var y=l.sibling;if(y!==null){y.return=l.return,T=y;break e}T=l.return}}if(U=o,cn(),_t&&typeof _t.onPostCommitFiberRoot=="function")try{_t.onPostCommitFiberRoot(Ii,e)}catch{}r=!0}return r}finally{$=n,qe.transition=t}}return!1}function wc(e,t,n){t=lr(n,t),t=Sp(e,t,1),e=Zt(e,t,1),t=Oe(),e!==null&&(io(e,1,t),Ie(e,t))}function te(e,t,n){if(e.tag===3)wc(e,e,n);else for(;t!==null;){if(t.tag===3){wc(t,e,n);break}else if(t.tag===1){var r=t.stateNode;if(typeof t.type.getDerivedStateFromError=="function"||typeof r.componentDidCatch=="function"&&(en===null||!en.has(r))){e=lr(n,e),e=Op(t,e,1),t=Zt(t,e,1),e=Oe(),t!==null&&(io(t,1,e),Ie(t,e));break}}t=t.return}}function sv(e,t,n){var r=e.pingCache;r!==null&&r.delete(t),t=Oe(),e.pingedLanes|=e.suspendedLanes&n,ue===e&&(he&n)===n&&(le===4||le===3&&(he&130023424)===he&&500>ne()-Sa?kn(e,0):wa|=n),Ie(e,t)}function Bp(e,t){t===0&&(e.mode&1?(t=wo,wo<<=1,!(wo&130023424)&&(wo=4194304)):t=1);var n=Oe();e=Ft(e,t),e!==null&&(io(e,t,n),Ie(e,n))}function lv(e){var t=e.memoizedState,n=0;t!==null&&(n=t.retryLane),Bp(e,n)}function av(e,t){var n=0;switch(e.tag){case 13:var r=e.stateNode,o=e.memoizedState;o!==null&&(n=o.retryLane);break;case 19:r=e.stateNode;break;default:throw Error(N(314))}r!==null&&r.delete(t),Bp(e,n)}var Kp;Kp=function(e,t,n){if(e!==null)if(e.memoizedProps!==t.pendingProps||be.current)Te=!0;else{if(!(e.lanes&n)&&!(t.flags&128))return Te=!1,qg(e,t,n);Te=!!(e.flags&131072)}else Te=!1,Q&&t.flags&1048576&&Yd(t,yi,t.index);switch(t.lanes=0,t.tag){case 2:var r=t.type;Jo(e,t),e=t.pendingProps;var o=rr(t,_e.current);Zn(t,n),o=ma(null,t,r,e,o,n);var i=ga();return t.flags|=1,typeof o=="object"&&o!==null&&typeof o.render=="function"&&o.$$typeof===void 0?(t.tag=1,t.memoizedState=null,t.updateQueue=null,Le(r)?(i=!0,gi(t)):i=!1,t.memoizedState=o.state!==null&&o.state!==void 0?o.state:null,ca(t),o.updater=Ai,t.stateNode=o,o._reactInternals=t,vl(t,r,e,n),t=kl(null,t,r,!0,i,n)):(t.tag=0,Q&&i&&ra(t),Se(null,t,o,n),t=t.child),t;case 16:r=t.elementType;e:{switch(Jo(e,t),e=t.pendingProps,o=r._init,r=o(r._payload),t.type=r,o=t.tag=cv(r),e=ot(r,e),o){case 0:t=_l(null,t,r,e,n);break e;case 1:t=dc(null,t,r,e,n);break e;case 11:t=uc(null,t,r,e,n);break e;case 14:t=cc(null,t,r,ot(r.type,e),n);break e}throw Error(N(306,r,""))}return t;case 0:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:ot(r,o),_l(e,t,r,o,n);case 1:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:ot(r,o),dc(e,t,r,o,n);case 3:e:{if(Pp(t),e===null)throw Error(N(387));r=t.pendingProps,i=t.memoizedState,o=i.element,Xd(e,t),wi(t,r,null,n);var s=t.memoizedState;if(r=s.element,i.isDehydrated)if(i={element:r,isDehydrated:!1,cache:s.cache,pendingSuspenseBoundaries:s.pendingSuspenseBoundaries,transitions:s.transitions},t.updateQueue.baseState=i,t.memoizedState=i,t.flags&256){o=lr(Error(N(423)),t),t=pc(e,t,r,n,o);break e}else if(r!==o){o=lr(Error(N(424)),t),t=pc(e,t,r,n,o);break e}else for(Ae=Jt(t.stateNode.containerInfo.firstChild),ze=t,Q=!0,st=null,n=tp(t,null,r,n),t.child=n;n;)n.flags=n.flags&-3|4096,n=n.sibling;else{if(or(),r===o){t=jt(e,t,n);break e}Se(e,t,r,n)}t=t.child}return t;case 5:return np(t),e===null&&hl(t),r=t.type,o=t.pendingProps,i=e!==null?e.memoizedProps:null,s=o.children,ul(r,o)?s=null:i!==null&&ul(r,i)&&(t.flags|=32),xp(e,t),Se(e,t,s,n),t.child;case 6:return e===null&&hl(t),null;case 13:return Cp(e,t,n);case 4:return da(t,t.stateNode.containerInfo),r=t.pendingProps,e===null?t.child=ir(t,null,r,n):Se(e,t,r,n),t.child;case 11:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:ot(r,o),uc(e,t,r,o,n);case 7:return Se(e,t,t.pendingProps,n),t.child;case 8:return Se(e,t,t.pendingProps.children,n),t.child;case 12:return Se(e,t,t.pendingProps.children,n),t.child;case 10:e:{if(r=t.type._context,o=t.pendingProps,i=t.memoizedProps,s=o.value,H(_i,r._currentValue),r._currentValue=s,i!==null)if(ct(i.value,s)){if(i.children===o.children&&!be.current){t=jt(e,t,n);break e}}else for(i=t.child,i!==null&&(i.return=t);i!==null;){var l=i.dependencies;if(l!==null){s=i.child;for(var a=l.firstContext;a!==null;){if(a.context===r){if(i.tag===1){a=Lt(-1,n&-n),a.tag=2;var u=i.updateQueue;if(u!==null){u=u.shared;var f=u.pending;f===null?a.next=a:(a.next=f.next,f.next=a),u.pending=a}}i.lanes|=n,a=i.alternate,a!==null&&(a.lanes|=n),ml(i.return,n,t),l.lanes|=n;break}a=a.next}}else if(i.tag===10)s=i.type===t.type?null:i.child;else if(i.tag===18){if(s=i.return,s===null)throw Error(N(341));s.lanes|=n,l=s.alternate,l!==null&&(l.lanes|=n),ml(s,n,t),s=i.sibling}else s=i.child;if(s!==null)s.return=i;else for(s=i;s!==null;){if(s===t){s=null;break}if(i=s.sibling,i!==null){i.return=s.return,s=i;break}s=s.return}i=s}Se(e,t,o.children,n),t=t.child}return t;case 9:return o=t.type,r=t.pendingProps.children,Zn(t,n),o=Qe(o),r=r(o),t.flags|=1,Se(e,t,r,n),t.child;case 14:return r=t.type,o=ot(r,t.pendingProps),o=ot(r.type,o),cc(e,t,r,o,n);case 15:return Np(e,t,t.type,t.pendingProps,n);case 17:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:ot(r,o),Jo(e,t),t.tag=1,Le(r)?(e=!0,gi(t)):e=!1,Zn(t,n),Zd(t,r,o),vl(t,r,o,n),kl(null,t,r,!0,e,n);case 19:return Rp(e,t,n);case 22:return Ep(e,t,n)}throw Error(N(156,t.tag))};function Hp(e,t){return vd(e,t)}function uv(e,t,n,r){this.tag=e,this.key=n,this.sibling=this.child=this.return=this.stateNode=this.type=this.elementType=null,this.index=0,this.ref=null,this.pendingProps=t,this.dependencies=this.memoizedState=this.updateQueue=this.memoizedProps=null,this.mode=r,this.subtreeFlags=this.flags=0,this.deletions=null,this.childLanes=this.lanes=0,this.alternate=null}function Ge(e,t,n,r){return new uv(e,t,n,r)}function xa(e){return e=e.prototype,!(!e||!e.isReactComponent)}function cv(e){if(typeof e=="function")return xa(e)?1:0;if(e!=null){if(e=e.$$typeof,e===Hl)return 11;if(e===Wl)return 14}return 2}function nn(e,t){var n=e.alternate;return n===null?(n=Ge(e.tag,t,e.key,e.mode),n.elementType=e.elementType,n.type=e.type,n.stateNode=e.stateNode,n.alternate=e,e.alternate=n):(n.pendingProps=t,n.type=e.type,n.flags=0,n.subtreeFlags=0,n.deletions=null),n.flags=e.flags&14680064,n.childLanes=e.childLanes,n.lanes=e.lanes,n.child=e.child,n.memoizedProps=e.memoizedProps,n.memoizedState=e.memoizedState,n.updateQueue=e.updateQueue,t=e.dependencies,n.dependencies=t===null?null:{lanes:t.lanes,firstContext:t.firstContext},n.sibling=e.sibling,n.index=e.index,n.ref=e.ref,n}function ti(e,t,n,r,o,i){var s=2;if(r=e,typeof e=="function")xa(e)&&(s=1);else if(typeof e=="string")s=5;else e:switch(e){case Un:return wn(n.children,o,i,t);case Kl:s=8,o|=8;break;case Ms:return e=Ge(12,n,t,o|2),e.elementType=Ms,e.lanes=i,e;case $s:return e=Ge(13,n,t,o),e.elementType=$s,e.lanes=i,e;case Bs:return e=Ge(19,n,t,o),e.elementType=Bs,e.lanes=i,e;case ed:return Bi(n,o,i,t);default:if(typeof e=="object"&&e!==null)switch(e.$$typeof){case Jc:s=10;break e;case Zc:s=9;break e;case Hl:s=11;break e;case Wl:s=14;break e;case Mt:s=16,r=null;break e}throw Error(N(130,e==null?e:typeof e,""))}return t=Ge(s,n,t,o),t.elementType=e,t.type=r,t.lanes=i,t}function wn(e,t,n,r){return e=Ge(7,e,r,t),e.lanes=n,e}function Bi(e,t,n,r){return e=Ge(22,e,r,t),e.elementType=ed,e.lanes=n,e.stateNode={isHidden:!1},e}function Es(e,t,n){return e=Ge(6,e,null,t),e.lanes=n,e}function xs(e,t,n){return t=Ge(4,e.children!==null?e.children:[],e.key,t),t.lanes=n,t.stateNode={containerInfo:e.containerInfo,pendingChildren:null,implementation:e.implementation},t}function dv(e,t,n,r,o){this.tag=t,this.containerInfo=e,this.finishedWork=this.pingCache=this.current=this.pendingChildren=null,this.timeoutHandle=-1,this.callbackNode=this.pendingContext=this.context=null,this.callbackPriority=0,this.eventTimes=ss(0),this.expirationTimes=ss(-1),this.entangledLanes=this.finishedLanes=this.mutableReadLanes=this.expiredLanes=this.pingedLanes=this.suspendedLanes=this.pendingLanes=0,this.entanglements=ss(0),this.identifierPrefix=r,this.onRecoverableError=o,this.mutableSourceEagerHydrationData=null}function Pa(e,t,n,r,o,i,s,l,a){return e=new dv(e,t,n,l,a),t===1?(t=1,i===!0&&(t|=8)):t=0,i=Ge(3,null,null,t),e.current=i,i.stateNode=e,i.memoizedState={element:r,isDehydrated:n,cache:null,transitions:null,pendingSuspenseBoundaries:null},ca(i),e}function pv(e,t,n){var r=3<arguments.length&&arguments[3]!==void 0?arguments[3]:null;return{$$typeof:Dn,key:r==null?null:""+r,children:e,containerInfo:t,implementation:n}}function Wp(e){if(!e)return sn;e=e._reactInternals;e:{if(Rn(e)!==e||e.tag!==1)throw Error(N(170));var t=e;do{switch(t.tag){case 3:t=t.stateNode.context;break e;case 1:if(Le(t.type)){t=t.stateNode.__reactInternalMemoizedMergedChildContext;break e}}t=t.return}while(t!==null);throw Error(N(171))}if(e.tag===1){var n=e.type;if(Le(n))return Hd(e,n,t)}return t}function Yp(e,t,n,r,o,i,s,l,a){return e=Pa(n,r,!0,e,o,i,s,l,a),e.context=Wp(null),n=e.current,r=Oe(),o=tn(n),i=Lt(r,o),i.callback=t??null,Zt(n,i,o),e.current.lanes=o,io(e,o,r),Ie(e,r),e}function Ki(e,t,n,r){var o=t.current,i=Oe(),s=tn(o);return n=Wp(n),t.context===null?t.context=n:t.pendingContext=n,t=Lt(i,s),t.payload={element:e},r=r===void 0?null:r,r!==null&&(t.callback=r),e=Zt(o,t,s),e!==null&&(at(e,o,s,i),qo(e,o,s)),s}function Ri(e){if(e=e.current,!e.child)return null;switch(e.child.tag){case 5:return e.child.stateNode;default:return e.child.stateNode}}function Sc(e,t){if(e=e.memoizedState,e!==null&&e.dehydrated!==null){var n=e.retryLane;e.retryLane=n!==0&&n<t?n:t}}function Ca(e,t){Sc(e,t),(e=e.alternate)&&Sc(e,t)}function fv(){return null}var Gp=typeof reportError=="function"?reportError:function(e){console.error(e)};function Ra(e){this._internalRoot=e}Hi.prototype.render=Ra.prototype.render=function(e){var t=this._internalRoot;if(t===null)throw Error(N(409));Ki(e,t,null,null)};Hi.prototype.unmount=Ra.prototype.unmount=function(){var e=this._internalRoot;if(e!==null){this._internalRoot=null;var t=e.containerInfo;xn(function(){Ki(null,e,null,null)}),t[Vt]=null}};function Hi(e){this._internalRoot=e}Hi.prototype.unstable_scheduleHydration=function(e){if(e){var t=Nd();e={blockedOn:null,target:e,priority:t};for(var n=0;n<Bt.length&&t!==0&&t<Bt[n].priority;n++);Bt.splice(n,0,e),n===0&&xd(e)}};function Ta(e){return!(!e||e.nodeType!==1&&e.nodeType!==9&&e.nodeType!==11)}function Wi(e){return!(!e||e.nodeType!==1&&e.nodeType!==9&&e.nodeType!==11&&(e.nodeType!==8||e.nodeValue!==" react-mount-point-unstable "))}function Oc(){}function hv(e,t,n,r,o){if(o){if(typeof r=="function"){var i=r;r=function(){var u=Ri(s);i.call(u)}}var s=Yp(t,r,e,0,null,!1,!1,"",Oc);return e._reactRootContainer=s,e[Vt]=s.current,Gr(e.nodeType===8?e.parentNode:e),xn(),s}for(;o=e.lastChild;)e.removeChild(o);if(typeof r=="function"){var l=r;r=function(){var u=Ri(a);l.call(u)}}var a=Pa(e,0,!1,null,null,!1,!1,"",Oc);return e._reactRootContainer=a,e[Vt]=a.current,Gr(e.nodeType===8?e.parentNode:e),xn(function(){Ki(t,a,n,r)}),a}function Yi(e,t,n,r,o){var i=n._reactRootContainer;if(i){var s=i;if(typeof o=="function"){var l=o;o=function(){var a=Ri(s);l.call(a)}}Ki(t,s,e,o)}else s=hv(n,t,e,o,r);return Ri(s)}Sd=function(e){switch(e.tag){case 3:var t=e.stateNode;if(t.current.memoizedState.isDehydrated){var n=Cr(t.pendingLanes);n!==0&&(ql(t,n|1),Ie(t,ne()),!(U&6)&&(ar=ne()+500,cn()))}break;case 13:xn(function(){var r=Ft(e,1);if(r!==null){var o=Oe();at(r,e,1,o)}}),Ca(e,1)}};Ql=function(e){if(e.tag===13){var t=Ft(e,134217728);if(t!==null){var n=Oe();at(t,e,134217728,n)}Ca(e,134217728)}};Od=function(e){if(e.tag===13){var t=tn(e),n=Ft(e,t);if(n!==null){var r=Oe();at(n,e,t,r)}Ca(e,t)}};Nd=function(){return $};Ed=function(e,t){var n=$;try{return $=e,t()}finally{$=n}};Zs=function(e,t,n){switch(t){case"input":if(Ws(e,n),t=n.name,n.type==="radio"&&t!=null){for(n=e;n.parentNode;)n=n.parentNode;for(n=n.querySelectorAll("input[name="+JSON.stringify(""+t)+'][type="radio"]'),t=0;t<n.length;t++){var r=n[t];if(r!==e&&r.form===e.form){var o=Di(r);if(!o)throw Error(N(90));nd(r),Ws(r,o)}}}break;case"textarea":od(e,n);break;case"select":t=n.value,t!=null&&qn(e,!!n.multiple,t,!1)}};dd=Oa;pd=xn;var mv={usingClientEntryPoint:!1,Events:[lo,$n,Di,ud,cd,Oa]},Sr={findFiberByHostInstance:vn,bundleType:0,version:"18.2.0",rendererPackageName:"react-dom"},gv={bundleType:Sr.bundleType,version:Sr.version,rendererPackageName:Sr.rendererPackageName,rendererConfig:Sr.rendererConfig,overrideHookState:null,overrideHookStateDeletePath:null,overrideHookStateRenamePath:null,overrideProps:null,overridePropsDeletePath:null,overridePropsRenamePath:null,setErrorHandler:null,setSuspenseHandler:null,scheduleUpdate:null,currentDispatcherRef:Dt.ReactCurrentDispatcher,findHostInstanceByFiber:function(e){return e=md(e),e===null?null:e.stateNode},findFiberByHostInstance:Sr.findFiberByHostInstance||fv,findHostInstancesForRefresh:null,scheduleRefresh:null,scheduleRoot:null,setRefreshHandler:null,getCurrentFiber:null,reconcilerVersion:"18.2.0-next-9e3b772b8-20220608"};if(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__<"u"){var Lo=__REACT_DEVTOOLS_GLOBAL_HOOK__;if(!Lo.isDisabled&&Lo.supportsFiber)try{Ii=Lo.inject(gv),_t=Lo}catch{}}$e.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=mv;$e.createPortal=function(e,t){var n=2<arguments.length&&arguments[2]!==void 0?arguments[2]:null;if(!Ta(t))throw Error(N(200));return pv(e,t,null,n)};$e.createRoot=function(e,t){if(!Ta(e))throw Error(N(299));var n=!1,r="",o=Gp;return t!=null&&(t.unstable_strictMode===!0&&(n=!0),t.identifierPrefix!==void 0&&(r=t.identifierPrefix),t.onRecoverableError!==void 0&&(o=t.onRecoverableError)),t=Pa(e,1,!1,null,null,n,!1,r,o),e[Vt]=t.current,Gr(e.nodeType===8?e.parentNode:e),new Ra(t)};$e.findDOMNode=function(e){if(e==null)return null;if(e.nodeType===1)return e;var t=e._reactInternals;if(t===void 0)throw typeof e.render=="function"?Error(N(188)):(e=Object.keys(e).join(","),Error(N(268,e)));return e=md(t),e=e===null?null:e.stateNode,e};$e.flushSync=function(e){return xn(e)};$e.hydrate=function(e,t,n){if(!Wi(t))throw Error(N(200));return Yi(null,e,t,!0,n)};$e.hydrateRoot=function(e,t,n){if(!Ta(e))throw Error(N(405));var r=n!=null&&n.hydratedSources||null,o=!1,i="",s=Gp;if(n!=null&&(n.unstable_strictMode===!0&&(o=!0),n.identifierPrefix!==void 0&&(i=n.identifierPrefix),n.onRecoverableError!==void 0&&(s=n.onRecoverableError)),t=Yp(t,null,e,1,n??null,o,!1,i,s),e[Vt]=t.current,Gr(e),r)for(e=0;e<r.length;e++)n=r[e],o=n._getVersion,o=o(n._source),t.mutableSourceEagerHydrationData==null?t.mutableSourceEagerHydrationData=[n,o]:t.mutableSourceEagerHydrationData.push(n,o);return new Hi(t)};$e.render=function(e,t,n){if(!Wi(t))throw Error(N(200));return Yi(null,e,t,!1,n)};$e.unmountComponentAtNode=function(e){if(!Wi(e))throw Error(N(40));return e._reactRootContainer?(xn(function(){Yi(null,null,e,!1,function(){e._reactRootContainer=null,e[Vt]=null})}),!0):!1};$e.unstable_batchedUpdates=Oa;$e.unstable_renderSubtreeIntoContainer=function(e,t,n,r){if(!Wi(n))throw Error(N(200));if(e==null||e._reactInternals===void 0)throw Error(N(38));return Yi(e,t,n,!1,r)};$e.version="18.2.0-next-9e3b772b8-20220608";(function(e){function t(){if(!(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__>"u"||typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE!="function"))try{__REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE(t)}catch(n){console.error(n)}}t(),e.exports=$e})(fm);var Nc=Us;Ds.createRoot=Nc.createRoot,Ds.hydrateRoot=Nc.hydrateRoot;const ba=ce.createInstance(),Ll="en";if(!(Ll in tr))throw new Error(`'${Ll}' language is not supported`);ba.init({debug:!1,resources:tr,lng:Ll,fallbackLng:"en",supportedLngs:Object.keys(tr)});const re=ba.getFixedT(null,"core"),vv=e=>ba.getFixedT(null,"core",e);var b=(e=>(e.ROOT="ROOT",e.VERSION="VERSION",e.ENVIRONMENT="ENVIRONMENT",e.OP_SYSTEM="OP_SYSTEM",e.DISTRIBUTION="DISTRIBUTION",e.FRAMEWORK="FRAMEWORK",e))(b||{}),qp=(e=>(e.ROOT="ROOT",e))(qp||{}),Tn=(e=>(e.v_2021_4_2="v_2021_4_2",e.v_2022_3_1="v_2022_3_1",e.v_2023_1_0="v_2023_1_0",e))(Tn||{}),uo=(e=>(e.DEV_TOOLS="DEV_TOOLS",e.RUNTIME="RUNTIME",e))(uo||{}),dn=(e=>(e.WINDOWS="WINDOWS",e.MACOS="MACOS",e.LINUX="LINUX",e))(dn||{});const yv={title:"Windows"},_v={title:"macOS"},kv={title:"Linux"};var A=(e=>(e.PIP="PIP",e.ARCHIVE="ARCHIVE",e.OFFLINE_INSTALLER="OFFLINE_INSTALLER",e.GITHUB="GITHUB",e.GITEE="GITEE",e.DOCKER="DOCKER",e.APT="APT",e.YUM="YUM",e.CONDA="CONDA",e.BREW="BREW",e.VCPKG="VCPKG",e.CONAN="CONAN",e))(A||{}),Ue=(e=>(e.CAFFE="CAFFE",e.PYTORCH="PYTORCH",e.TENSORFLOW_1="TENSORFLOW_1",e.TENSORFLOW_2="TENSORFLOW_2",e.ONNX="ONNX",e.KALDI="KALDI",e.MXNET="MXNET",e))(Ue||{});class pe{constructor(t,n=[]){je(this,"level");je(this,"key");je(this,"metadata");je(this,"children",[]);je(this,"isDefault",!1);je(this,"checked",!1);je(this,"childrenSelector");this.level=t.level,this.key=t.key,this.metadata=t.metadata,this.children=n,this.childrenSelector=t.childrenSelector}default(){return this.isDefault=!0,this.checked=!0,this}toOption(){return{level:this.level,key:this.key,metadata:this.metadata,checked:this.checked,hasFootnote:!1}}}const Ht=vv("distributions.common"),x={V_3_6:"3.6",V_3_7:"3.7",V_3_8:"3.8",V_3_9:"3.9",V_3_10:"3.10",V_3_11:"3.11"},ln={V_10:Ht("requirements.win10"),V_11:Ht("requirements.win11")},ut={V_10_15_X:"macOS 10.15.x",V_11:"macOS 11",V_12:"macOS 12"},M={UBUNTU_18:Ht("requirements.ubuntu18"),UBUNTU_20:Ht("requirements.ubuntu20"),UBUNTU_22:Ht("requirements.ubuntu22"),RED_HAT_8:Ht("requirements.redHat8"),CENT_OS_7:Ht("requirements.centOS7")},Qp={text:`* ${Ht("requirements.armPluginFootnote")}`,learnMoreLink:"https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_supported_plugins_ARM_CPU.html"};class Ze extends pe{constructor(n,r=[]){super(n,r);je(this,"footnoteLevel",null)}toOption(){return{...super.toOption(),key:this.key,data:this.data}}addFootnote(n){return this.footnoteLevel=n,this}}class La extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.APT,metadata:{title:"APT"}}),this._data=t}get data(){return{...this._data,commands:Nv}}cApiOnly(){return this.metadata.subtitle=re("distributions.CAPIOnly"),this}}const Il="GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB",Ec=`https://apt.repos.intel.com/intel-gpg-keys/${Il}`,wv={alias:{22:"jammy",20:"focal",18:"bionic"},numberName:{22:"ubuntu22",20:"ubuntu20",18:"ubuntu18"}},Sv=new Set([Tn.v_2023_1_0]),Ov=(e,t)=>{const n=Sv.has(e.key)?"numberName":"alias";return wv[n][t]},Nv={keyFilename:Il,keyHref:Ec,downloadKeyCommand:`wget ${Ec}`,addKeyCommand:`sudo apt-key add ${Il}`,getAddRepositoryCommand:(e,t)=>{const n=Ov(e,t);return`echo "deb https://apt.repos.intel.com/openvino/${e.metadata.aptYear} ${n} main" | sudo tee /etc/apt/sources.list.d/intel-openvino-${e.metadata.aptYear}.list`},updatePackagesCommand:"sudo apt update",verifyAptCacheCommand:"apt-cache search openvino",getInstallCommand:e=>`sudo apt install openvino-${e.metadata.aptVersion}`};class pr extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.ARCHIVE,metadata:{title:re("distributions.openVINOArchives")}}),this.data=t}includesNPUPlugin(){return this.metadata.subtitle=re("distributions.archives.includesNPUPlugin"),this}}class Xp extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.BREW,metadata:{title:"Homebrew"}}),this._data=t}get data(){return{...this._data,commands:Ev}}}const Ev={install:"brew install openvino"};class Ia extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.CONAN,metadata:{title:"Conan"}}),this._data=t}get data(){return{...this._data,commands:Pv}}}const xc="conanfile.txt",Pc="conan_toolchain.cmake",xv=e=>`[requires]
+openvino/${e.metadata.conanVersion}
+[generators]
+CMakeDeps
+CMakeToolchain
+[layout]
+cmake_layout`,Pv={conanTXTContent:xv,txtFilename:xc,cmakeFilename:Pc,install:`conan install ${xc} --build=missing`,compile:`cmake -DCMAKE_TOOLCHAIN_FILE=<path to ${Pc}> -DCMAKE_BUILD_TYPE=Release -S <path to CMakeLists.txt of your project> -B <build dir>
+cmake --build <build dir> --parallel`};class Va extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.CONDA,metadata:{title:"Conda"}}),this._data=t}get data(){return{...this._data,commands:Cv}}}const Cv={createEnv:"conda create --name py310 python=3.10",activateEnv:"conda activate py310",update:"conda update --all",getInstall:({metadata:e})=>`conda install -c conda-forge openvino=${e.condaVersion}`};class pn extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.DOCKER,metadata:{title:"Docker"}}),this.data=t}}class xe extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.GITEE,metadata:{title:"Gitee",subtitle:re("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Rv,links:Tv}}}const Rv={getCloneCommand(e){return`git clone -b ${e.metadata.giteeVersion} https://gitee.com/openvinotoolkit-prc/openvino.git`}},Tv={build:"https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md",getRepositoryLink:e=>`https://gitee.com/openvinotoolkit-prc/openvino/tree/${e.metadata.giteeVersion}`};class Pe extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.GITHUB,metadata:{title:"GitHub",subtitle:re("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:bv,links:Lv}}}const bv={getCloneCommand(e){return`git clone -b ${e.metadata.githubVersion} https://github.com/openvinotoolkit/openvino.git`}},Lv={build:"https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/build.md",getRepositoryLink:e=>`https://github.com/openvinotoolkit/openvino/releases/tag/${e.metadata.githubVersion}`};class fr extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.OFFLINE_INSTALLER,metadata:{title:re("distributions.offlineInstaller"),subtitle:re("distributions.recommendedOption")}}),this.data=t}}const Iv=(e,t)=>{e.forEach(n=>{n.key===Ue.TENSORFLOW_1&&n.checked&&t===Ue.TENSORFLOW_2||n.key===Ue.TENSORFLOW_2&&n.checked&&t===Ue.TENSORFLOW_1?n.checked=!1:t===n.key&&(n.checked=!n.checked)})},Fa=(e,t)=>{e.forEach(n=>{n.checked=n.key===t,n.key===A.PIP&&n.checked&&n.children.forEach(r=>r.checked=r.isDefault)})},Vv=()=>[new pe({level:b.FRAMEWORK,key:Ue.CAFFE,metadata:{title:"Caffe",pipKey:"caffe"}}),new pe({level:b.FRAMEWORK,key:Ue.KALDI,metadata:{title:"Kaldi",pipKey:"kaldi"}}),new pe({level:b.FRAMEWORK,key:Ue.MXNET,metadata:{title:"Apache MXNet",pipKey:"mxnet"}}),new pe({level:b.FRAMEWORK,key:Ue.ONNX,metadata:{title:"Open Neural Network Exchange (ONNX)",pipKey:"ONNX"}}),new pe({level:b.FRAMEWORK,key:Ue.PYTORCH,metadata:{title:"PyTorch",pipKey:"pytorch"}}),new pe({level:b.FRAMEWORK,key:Ue.TENSORFLOW_1,metadata:{title:"TensorFlow 1.x",pipKey:"tensorflow"}}),new pe({level:b.FRAMEWORK,key:Ue.TENSORFLOW_2,metadata:{title:"TensorFlow 2.x",pipKey:"tensorflow2"}})];class ke extends Ze{constructor(t,{hasFrameworks:n,pythonAPI:r}={}){super({level:b.DISTRIBUTION,key:A.PIP,metadata:{title:"PIP",subtitle:r?re("distributions.pythonAPIOnly"):void 0},childrenSelector:Iv},n?Vv():[]),this._data=t}get data(){return{...this._data,commands:Uv,frameworks:this.children.filter(({checked:t})=>t).map(t=>t.toOption())}}}function Fv(e){return e.key===dn.WINDOWS?"python -m venv openvino_env":"python3 -m venv openvino_env"}function jv(e){return e.key===dn.WINDOWS?"openvino_env\\Scripts\\activate":"source openvino_env/bin/activate"}function Dv({environment:e,os:t,version:n,distribution:r}){const o=r.data.frameworks.map(a=>a.metadata.pipKey),i=o.length?`[${o.join(",")}]`:"";let l=`${(e==null?void 0:e.key)===uo.DEV_TOOLS?"openvino-dev":"openvino"}${i}==${n.metadata.pipVersion}`;return t.key===dn.MACOS&&(l=`'${l}'`),`pip install ${l}`}const Uv={getCreateVenvCommand:Fv,getActivateVenvCommand:jv,upgradeCommand:"python -m pip install --upgrade pip",getInstallCommand:Dv};class ja extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.VCPKG,metadata:{title:"vcpkg",subtitle:re("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Av}}}const Av={install:"vcpkg install openvino"};class Da extends Ze{constructor(t){super({level:b.DISTRIBUTION,key:A.YUM,metadata:{title:"YUM",subtitle:re("distributions.CAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:zv}}}const Cc="/etc/yum.repos.d",zv={directory:Cc,getCreateRepoCommand:e=>`tee > /tmp/openvino-${e.metadata.yumYear}.repo << EOF
 [OpenVINO]
 name=Intel(R) Distribution of OpenVINO ${e.metadata.yumYear}
 baseurl=https://yum.repos.intel.com/openvino/${e.metadata.yumYear}
@@ -50,5 +57,5 @@ enabled=1
 gpgcheck=1
 repo_gpgcheck=1
 gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-EOF`,getMoveRepoFileCommand:e=>`sudo mv /tmp/openvino-${e.metadata.yumYear}.repo ${Oc}`,verifyRepoCommand:"yum repolist | grep -i openvino",getInstallCommand:e=>`sudo yum install openvino-${e.metadata.yumVersion}`},Dv={title:`2023.1.0 (${oe("versions.titles.recommended")})`,compactTitle:"2023.1.0",compactSubtitle:oe("versions.titles.recommended"),pipVersion:"2023.1.0",githubVersion:"2023.1.0",giteeVersion:"2023.1.0",aptYear:2023,aptVersion:"2023.1.0",yumYear:2023,yumVersion:"2023.1.0",condaVersion:"2023.1.0"};class Qp extends pe{constructor(t){super({level:L.VERSION,key:Rn.v_2023_1_0,metadata:Dv},t)}}const jv={title:"2022.3.1",subtitle:`${oe("versions.titles.latestLTS")}
-${oe("versions.titles.hddlSupport")}`,compactTitle:"2022.3.1",compactSubtitle:oe("versions.titles.LTS"),pipVersion:"2022.3.1",githubVersion:"2022.3.1",giteeVersion:"2022.3.1",aptYear:2022,aptVersion:"2022.3.1",yumYear:2022,yumVersion:"2022.3.1"};class Uv extends pe{constructor(t){super({level:L.VERSION,key:Rn.v_2022_3_1,metadata:jv},t)}}const Av={title:"2021.4.2",subtitle:oe("versions.titles.previousLTS"),compactTitle:"2021.4.2",compactSubtitle:oe("versions.titles.LTS"),pipVersion:"2021.4.2",githubVersion:"2021.4.2",giteeVersion:"2021.4.2"};class zv extends pe{constructor(t){super({level:L.VERSION,key:Rn.v_2021_4_2,metadata:Av},t)}}class qp extends pe{constructor(t){super({level:L.ENVIRONMENT,key:uo.RUNTIME,metadata:{title:oe("environment.runtime.title"),subtitle:oe("environment.runtime.subtitle")}},t)}}class Xp extends pe{constructor(t){super({level:L.ENVIRONMENT,key:uo.DEV_TOOLS,metadata:{title:oe("environment.devTools.title"),subtitle:oe("environment.devTools.subtitle")}},t)}}class co extends pe{constructor(t){super({level:L.OP_SYSTEM,key:un.WINDOWS,metadata:mv,childrenSelector:ba},t)}}class po extends pe{constructor(t){super({level:L.OP_SYSTEM,key:un.MACOS,metadata:gv,childrenSelector:ba},t)}}class fo extends pe{constructor(t){super({level:L.OP_SYSTEM,key:un.LINUX,metadata:vv,childrenSelector:ba},t)}}const bo={os:[B.UBUNTU_18,B.UBUNTU_20,B.RED_HAT_8,B.CENT_OS_7],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},Mv=new fo([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18319/l_openvino_toolkit_p_2021.4.752.tgz",systemRequirements:bo}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:bo,frameworks:[]},{hasFrameworks:!0}),new Pe({systemRequirements:bo}),new xe({systemRequirements:bo}),new cn({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html#troubleshooting"},downloadLink:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),Vo={os:[at.V_10_15_X],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},$v=new po([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_macos.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Vo,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18317/m_openvino_toolkit_p_2021.4.752.dmg"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Vo,frameworks:[]},{hasFrameworks:!0}),new Pe({systemRequirements:Vo}),new xe({systemRequirements:Vo})]),Fo={os:[on.V_10],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},Bv=new co([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_windows.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Fo,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18320/w_openvino_toolkit_p_2021.4.752.exe"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Fo,frameworks:[]},{hasFrameworks:!0}),new Pe({systemRequirements:Fo}),new xe({systemRequirements:Fo})]),Kv=new Xp([Bv.default(),$v,Mv]),Do={os:[B.UBUNTU_18,B.UBUNTU_20,B.RED_HAT_8,B.CENT_OS_7],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},Hv=new fo([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18319/l_openvino_toolkit_p_2021.4.752.tgz",systemRequirements:Do}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Do,frameworks:[]},{pythonAPI:!0}),new Wp({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_apt.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/latest/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:{os:[B.UBUNTU_18,B.UBUNTU_20],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},docsOnly:!0}).cApiOnly(),new Gp({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_yum.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/latest/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:{os:[B.RED_HAT_8,B.CENT_OS_7],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},docsOnly:!0}),new Pe({systemRequirements:Do}),new xe({systemRequirements:Do}),new cn({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html#troubleshooting"},downloadLink:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),jo={os:[at.V_10_15_X],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},Wv=new po([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_macos.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:jo,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18317/m_openvino_toolkit_p_2021.4.752.dmg"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:jo,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:jo}),new xe({systemRequirements:jo})]),Uo={os:[on.V_10],pythonVersions:[C.V_3_6,C.V_3_7,C.V_3_8]},Yv=new co([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_windows.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Uo,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18320/w_openvino_toolkit_p_2021.4.752.exe"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Uo,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:Uo}),new xe({systemRequirements:Uo}),new cn({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_windows.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},downloadLink:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_windows.html"})]),Gv=new qp([Yv.default(),Wv,Hv]),Qv=new zv([Kv.default(),Gv]),Ps={os:[B.UBUNTU_18,B.UBUNTU_20,B.RED_HAT_8],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10]},qv=new fo([new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Ps,frameworks:[]},{hasFrameworks:!0}).default(),new Pe({systemRequirements:Ps}),new xe({systemRequirements:Ps}),new cn({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),Cs={os:[at.V_10_15_X,at.V_11,at.V_12],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10],note:Hp},Xv=new po([new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Cs,frameworks:[]},{hasFrameworks:!0}).addFootnote(L.OP_SYSTEM).default(),new Pe({systemRequirements:Cs}).addFootnote(L.OP_SYSTEM),new xe({systemRequirements:Cs}).addFootnote(L.OP_SYSTEM)]),Rs={os:[on.V_10,on.V_11],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10]},Jv=new co([new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Rs,frameworks:[]},{hasFrameworks:!0}).default(),new Pe({systemRequirements:Rs}),new xe({systemRequirements:Rs})]),Zv=new Xp([Jv.default(),Xv,qv]),Ao={os:[B.UBUNTU_18,B.UBUNTU_20,B.RED_HAT_8],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10]},ey=new fo([new pr({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_from_archive_linux.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3.1/linux",systemRequirements:Ao}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Ao,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:Ao}),new xe({systemRequirements:Ao}),new cn({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),zo={os:[at.V_10_15_X,at.V_11,at.V_12],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10],note:Hp},ty=new po([new pr({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_from_archive_macos.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3.1/macos",systemRequirements:zo}).addFootnote(L.OP_SYSTEM).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:zo,frameworks:[]},{pythonAPI:!0}).addFootnote(L.OP_SYSTEM),new Pe({systemRequirements:zo}).addFootnote(L.OP_SYSTEM),new xe({systemRequirements:zo}).addFootnote(L.OP_SYSTEM)]),Mo={os:[on.V_10,on.V_11],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10]},ny=new co([new pr({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_from_archive_windows.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3.1/windows",systemRequirements:Mo}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Mo,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:Mo}),new xe({systemRequirements:Mo}),new cn({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_windows.html",getStarted:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_windows.html"})]),ry=new qp([ny.default(),ty,ey]),oy=new Uv([Zv.default(),ry]),dn={os:[B.UBUNTU_18,B.UBUNTU_20,B.UBUNTU_22,B.RED_HAT_8],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10,C.V_3_11]},iy=new fo([new pr({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_from_archive_linux.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/linux",systemRequirements:dn}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:dn,frameworks:[]},{pythonAPI:!0}),new Wp({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_apt.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:{os:[B.UBUNTU_18,B.UBUNTU_20,B.UBUNTU_22],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10,C.V_3_11]}}),new Gp({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_yum.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:{os:[B.RED_HAT_8],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10,C.V_3_11]}}),new Pe({systemRequirements:dn}),new xe({systemRequirements:dn}),new cn({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html"}),new Ia({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conda.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:dn}),new Yp({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_brew.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:dn}),new Va({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_vcpkg.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:dn})]),pn={os:[at.V_10_15_X,at.V_11,at.V_12],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10,C.V_3_11]},sy=new po([new pr({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_from_archive_macos.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/macos",systemRequirements:pn}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:pn,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:pn}),new xe({systemRequirements:pn}),new Ia({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conda.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:pn}),new Yp({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_brew.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:pn}),new Va({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_vcpkg.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:pn})]),Dn={os:[on.V_10,on.V_11],pythonVersions:[C.V_3_7,C.V_3_8,C.V_3_9,C.V_3_10,C.V_3_11]},ly=new co([new pr({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_from_archive_windows.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/windows",systemRequirements:Dn}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:Dn,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:Dn}),new xe({systemRequirements:Dn}),new cn({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html"}),new Ia({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conda.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:Dn}),new Va({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_vcpkg.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:Dn})]),ay=new Qp([ly.default(),sy,iy]),uy=new pe({level:L.ROOT,key:Kp.ROOT,metadata:{title:"ROOT"}},[ay.default(),oy,Qv]).default();function cy(e,t){var o,i;if(t.key===z.DOCKER||!t.footnoteLevel)return e;const n=(o=e[t.footnoteLevel])==null?void 0:o.selected,r=(i=e[t.footnoteLevel])==null?void 0:i.nodes;return!n||!r||((Array.isArray(n)?[...n,...r]:[n]).forEach(s=>s.hasFootnote=!0),r.forEach(s=>s.checked&&(s.hasFootnote=!0))),e}class dy{constructor(){De(this,"_root",uy)}getState(){try{return this._getState()}catch(t){return console.error(t),this._selectDefaults(this._root),this._getState()}}_getState(){const t=this._root.children,n=this._get_selected(t),{environments:r,environment:o,systems:i,system:s}=this._processVersion(n),l=s.children,a=this._get_selected(l),u=this._processDistribution(a),f={[L.VERSION]:{nodes:t.map(d=>d.toOption()),selected:n.toOption()},[L.ENVIRONMENT]:o?{nodes:r.map(d=>d.toOption()),selected:o.toOption()}:null,[L.OP_SYSTEM]:{nodes:i.map(d=>d.toOption()),selected:s.toOption()},[L.DISTRIBUTION]:{nodes:l.map(d=>d.toOption()),selected:a.toOption()},[L.FRAMEWORK]:u!=null&&u.length?{nodes:u.map(d=>d.toOption()),selected:u.filter(({checked:d})=>d).map(d=>d.toOption())}:null};return cy(f,a)}_get_selected(t){t.some(({checked:r})=>r)||this._selectDefaultsForLevel(t[0].level);const n=t.find(({checked:r})=>r);if(!n)throw new Error("Not valid tree");return n}_processVersion(t){if(t instanceof Qp){const s=t.children,l=this._get_selected(s);return{environments:null,environment:null,systems:s,system:l}}const n=t.children,r=this._get_selected(n),o=r.children,i=this._get_selected(o);return{environments:n,environment:r,systems:o,system:i}}_processDistribution(t){return t instanceof ke?t.children:null}setState(t){this._setState(t)}_setState(t,n=this._root){if(!n.children.length)return;const r=n.children[0].level,o=py(t[r]);n.children.forEach(i=>i.checked=o.includes(i.key)),n.children.forEach(i=>this._setState(t,i))}select(t,n){return this._select(t,n),this.getState()}_select(t,n,r=this._root){var o;if(((o=r.children[0])==null?void 0:o.level)!==t){r.children.forEach(i=>this._select(t,n,i));return}if(r.childrenSelector){r.childrenSelector(r.children,n);return}r.children.forEach(i=>i.checked=i.key===n)}_selectDefaultsForLevel(t,n=this._root){if(n.children.length){if(n.children[0].level!==t){n.children.forEach(r=>this._selectDefaultsForLevel(t,r));return}this._selectDefaults(n)}}_selectDefaults(t){t.children.forEach(n=>{n.checked=n.isDefault,this._selectDefaults(n)})}}const xn=new dy;function py(e){const t=[];return Array.isArray(e)?t.push(...e):e&&t.push(e),t}function Jp(e){var r,o;const t={[L.VERSION]:e.VERSION.selected.key,[L.ENVIRONMENT]:(r=e.ENVIRONMENT)==null?void 0:r.selected.key,[L.OP_SYSTEM]:e.OP_SYSTEM.selected.key,[L.DISTRIBUTION]:e.DISTRIBUTION.selected.key,[L.FRAMEWORK]:(o=e.FRAMEWORK)==null?void 0:o.selected.map(i=>i.key).join(",")},n=new URLSearchParams;for(const[i,s]of Object.entries(t))s&&n.set(i,s);return n}function Zp(e){function t(r,o){const i=e.get(r);if(!i)throw new Error(`Cannot extract value for: ${r}`);if(!o[i])throw new Error(`Bad node key for: ${r}`);return o[i]}function n(r,o){const i=e.get(r);return i?i.split(",").map(l=>{if(!l||!o[l])throw new Error(`Bad node key for: ${r}`);return o[l]}):null}try{return{[L.VERSION]:t(L.VERSION,Rn),[L.ENVIRONMENT]:e.has(L.ENVIRONMENT)?t(L.ENVIRONMENT,uo):null,[L.OP_SYSTEM]:t(L.OP_SYSTEM,un),[L.DISTRIBUTION]:t(L.DISTRIBUTION,z),[L.FRAMEWORK]:n(L.FRAMEWORK,Ue)}}catch(r){return console.log(`Cannot restore state from url due to error "${r}"`),null}}function fy(){const e=window.parent;if(!e.location.search)return null;const t=new URLSearchParams(e.location.search);return Zp(t)}function hy(e,t){D.useEffect(()=>{const n=window.parent,r=Jp(e).toString(),o=new URL(n.location.toString());if(!o.search){o.search=r,n.history.replaceState(null,"",o);return}o.search.slice(1)!==r&&(o.search=r,n.history.pushState(null,"",o))}),parent.onpopstate=()=>{const n=window.parent,r=new URLSearchParams(n.location.search),o=Zp(r);o&&(xn.setState(o),t(xn.getState()))}}const Ts=function(e){let t,n=!1;return function(...r){return n||(t=e(r),n=!0),t}};function my(e){var t,n;return typeof((n=(t=e.wap_tms)==null?void 0:t.custom)==null?void 0:n.trackComponentClick)!="function"?null:e.wap_tms.custom.trackComponentClick.bind(e.wap_tms.custom)}class gy{constructor(){De(this,"_window");De(this,"_consoleNotification",{notInitialized:Ts(()=>console.log("Adobe analytics is not initialized")),notFound:Ts(()=>console.log("Adobe analytics not found on a page")),devMode:Ts(()=>console.log("Analytics in dev mode"))});De(this,"_send",t=>{if(!this._window){this._consoleNotification.notInitialized();return}const n=Jp(xn.getState()).toString(),r=my(this._window);if(!r){this._consoleNotification.notFound();return}try{r(t,n)}catch(o){console.error(o)}})}initialize(t){this._window=t}install(){this._send("install")}combinationView(){this._send("combination-view")}}const Xe=new gy;function vy(){const e=fy();e&&xn.setState(e)}const Fa=D.createContext((e,t)=>{xn.select(e,t)});function yy(){const[e,t]=D.useState(xn.getState());return hy(e,t),[e,(n,r)=>t(xn.select(n,r))]}async function _y(e){e&&(navigator.clipboard?await navigator.clipboard.writeText(e):ky(e))}function ky(e){const t=wy(e);document.body.append(t),t.select(),document.execCommand("copy"),t.remove()}function wy(e){const t=document.createElement("textarea");t.style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style.left="-9999px";const n=window.pageYOffset||document.documentElement.scrollTop;return t.style.top=`${n}px`,t.setAttribute("readonly",""),t.value=e,t}const Or={copyButton:"spark-button spark-button-ghost spark-button-size-m spark-button-icon-only spark-focus-visible spark-focus-visible-self spark-focus-visible-snap spark-code-snippet-is-visible",copyButtonContent:"spark-button-content",regularIcon:"spark-icon icon regular",copyIcon:"copy",copiedIcon:"check"},Y=({comment:e,command:t,onCopy:n})=>{const[r,o]=D.useState(!1),i=async()=>{r||(await _y(t),o(!0),setTimeout(()=>o(!1),1500),n==null||n())};return I("div",{className:"st-code-snippet","data-cy":"instructions-step",children:[e&&_(ef,{children:e}),I("div",{"data-cy":"command",children:[t&&_("code",{className:"st-code-snippet-content",children:t}),t&&_("button",{className:`copy-button ${Or.copyButton}`,type:"button","aria-label":"Copy","data-cy":"copy",onClick:i,children:_("span",{className:Or.copyButtonContent,children:_("span",{"aria-hidden":"true",role:"img",className:`${Or.regularIcon} ${r?Or.copiedIcon:Or.copyIcon}`})})})]})]})},ef=({children:e})=>I("pre",{className:"st-code-snippet-comment",children:["# ",e]}),Sy=({comment:e,snippets:t})=>I("div",{className:"st-code-snippet-multi-line","data-cy":"command",children:[e&&_(ef,{children:e}),t.map(n=>_(Y,{...n},n.command))]});function Oy(e){return e.host==="docs.openvino.ai"}const Ls={link:"spark-hyperlink spark-hyperlink-primary spark-hyperlink-standard spark-focus-visible spark-focus-visible-self spark-focus-visible-snap spark-focus-visible-background",button:"spark-button spark-button-action spark-button-size-m spark-focus-visible spark-focus-visible-self spark-focus-visible-snap",buttonContent:"spark-button-content"},wt=({href:e,children:t,type:n="link",testId:r="link",onClick:o})=>{const i=Oy(new URL(e))?"_parent":"_blank";return n==="link"?_("a",{href:e,className:Ls.link,target:i,rel:"noreferrer noopener","data-cy":r,onClick:()=>o==null?void 0:o(),children:t}):_("span",{className:Ls.button,children:_("span",{className:Ls.buttonContent,children:_("a",{href:e,target:i,rel:"noreferrer noopener","data-cy":r,onClick:()=>o==null?void 0:o(),children:t})})})},tf=({children:e})=>{const{t}=J("translation",{keyPrefix:"selectorForm"});return I("div",{"data-cy":"link-set",children:[I("div",{className:"st-distribution-links",children:[e,_(wt,{href:"https://github.com/openvinotoolkit/openvino/releases",testId:"previous-releases-link",children:t("prevReleases")})]}),_("div",{className:"nncf-link",children:I(W,{ns:"translation",i18nKey:"selectorForm.nncf",children:["Advanced Optimization tool available separately:",_(wt,{href:"https://docs.openvino.ai/2023.0/tmo_introduction.html",testId:"nncf-link",children:"Learn about NNCF"})]})})]})},jt=({installUrl:e})=>{const{t}=J("common",{keyPrefix:"distributionLinks"});return _(tf,{children:_(wt,{href:e,testId:"install-instructions-link",children:t("installationInstructions")})})};const Ny={heading:"spark-heading spark-font-200"},Je=({title:e,accent:t=!1,children:n,testId:r})=>I("div",{className:`st-section ${t?"st-section-accent":""}`,"data-cy":r,children:[_("span",{className:`st-section-title ${Ny.heading}`,children:e}),_("div",{className:"st-section-content",children:D.Children.map(n,o=>_(Ey,{children:o}))})]}),Ey=({children:e})=>_("div",{className:"st-section-content-row",children:e}),xy=({version:e,distribution:t})=>{if(t.data.docsOnly)return _(Py,{distribution:t});const{t:n}=J("translation",{keyPrefix:"distributions.apt"}),{t:r}=J("translation",{keyPrefix:"compact.selectorForm.titles"}),{commands:o}=t.data,i={comment:I(W,{ns:"translation",i18nKey:"distributions.apt.steps.addRepository",children:[_("b",{children:"Step 3:"})," Add the repository via the following command"]}),snippets:[{comment:"Ubuntu 20",command:o.getAddRepositoryCommand(e,20)},{comment:"Ubuntu 18",command:o.getAddRepositoryCommand(e,18)}]};e.key===Rn.v_2023_1_0&&i.snippets.unshift({comment:"Ubuntu 22",command:o.getAddRepositoryCommand(e,22)});const s={downloadKey:{comment:I(W,{ns:"translation",i18nKey:"distributions.apt.steps.download",values:{filename:o.keyFilename},children:[_("b",{children:"Step 1:"})," Download the ",_(wt,{href:o.keyHref,children:o.keyFilename}),". You can also use the following command"]}),command:o.downloadKeyCommand},addKey:{comment:I(W,{t:n,i18nKey:"steps.addKey",children:[_("b",{children:"Step 2:"})," Add this key to the system keyring"]}),command:o.addKeyCommand},addRepository:i,updatePackages:{comment:I(W,{t:n,i18nKey:"steps.updateList",children:[_("b",{children:"Step 4:"})," Update the list of packages via the update command"]}),command:o.updatePackagesCommand},verifyAptCache:{comment:I(W,{t:n,i18nKey:"steps.verify",children:[_("b",{children:"Step 5:"})," Verify that the APT repository is properly set up. Run the apt-cache command to see a list of all available OpenVINO packages and components"]}),command:o.verifyAptCacheCommand},install:{comment:I(W,{t:n,i18nKey:"steps.install",children:[_("b",{children:"Step 6:"})," Install OpenVINO Runtime"]}),command:o.getInstallCommand(e),onCopy:()=>Xe.install()}};return I(Je,{title:r("install"),accent:!0,testId:"instructions",children:[_(Y,{...s.downloadKey}),_(Y,{...s.addKey}),_(Sy,{...s.addRepository}),_(Y,{...s.updatePackages}),_(Y,{...s.verifyAptCache}),_(Y,{...s.install}),_(jt,{installUrl:t.data.linksSet.installation})]})},Py=({distribution:e})=>{const{t}=J("translation",{keyPrefix:"compact"}),n=I(W,{t,i18nKey:"distributions.aptYum.followInstructions",children:["Follow the",_(wt,{href:e.data.linksSet.installation,testId:"install-instructions-button",onClick:()=>Xe.install(),children:"Installation instructions"})]});return I(Je,{title:t("selectorForm.titles.install"),accent:!0,testId:"instructions",children:[_(Y,{comment:n}),_(jt,{installUrl:e.data.linksSet.installation})]})},Cy=({distribution:e})=>{const{t}=J("translation",{keyPrefix:"distributions.brew.steps"}),{t:n}=J("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:r,commands:o}=e.data,i={install:{comment:_(W,{t,i18nKey:"install",children:"Download and install the package"}),command:o.install,onCopy:()=>Xe.install()}};return I(Je,{title:n("install"),accent:!0,testId:"instructions",children:[_(Y,{...i.install}),_(jt,{installUrl:r.installation})]})},Ry=({version:e,distribution:t})=>{const{t:n}=J("translation",{keyPrefix:"distributions.conda.steps"}),{t:r}=J("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:o,commands:i}=t.data,s={createEnv:{comment:I(W,{t:n,i18nKey:"createEnv",children:[_("b",{children:"Step 1:"})," Create the Anaconda environment (Python 3.10 used as an example)"]}),command:i.createEnv},activateEnv:{comment:I(W,{t:n,i18nKey:"activateEnv",children:[_("b",{children:"Step 2:"})," Activate the Anaconda environment"]}),command:i.activateEnv},upgradePip:{comment:I(W,{t:n,i18nKey:"update",children:[_("b",{children:"Step 3:"})," Update the Anaconda to latest version"]}),command:i.update},install:{comment:I(W,{t:n,i18nKey:"install",children:[_("b",{children:"Step 4:"})," Download and install the package"]}),command:i.getInstall(e),onCopy:()=>Xe.install()}};return I(Je,{title:r("install"),accent:!0,testId:"instructions",children:[_(Y,{...s.createEnv}),_(Y,{...s.activateEnv}),_(Y,{...s.upgradePip}),_(Y,{...s.install}),_(jt,{installUrl:o.installation})]})},Is=({distribution:e})=>{const{t}=J("translation",{keyPrefix:"distributions.download"}),{t:n}=J("translation",{keyPrefix:"compact"}),r={[z.ARCHIVE]:t("downloadArchives"),[z.DOCKER]:t("gotoDocker"),[z.OFFLINE_INSTALLER]:t("download")}[e.key],o=I(ni,{children:[n("distributions.download.useFollowingLink"),_("br",{}),_("b",{children:_(wt,{href:e.data.downloadLink,testId:"download-button",onClick:()=>Xe.install(),children:r})})]});return I(Je,{title:n("selectorForm.titles.install"),accent:!0,testId:"instructions",children:[_(Y,{comment:o}),_(jt,{installUrl:e.data.linksSet.installation})]})},Ty=({version:e,distribution:t})=>{const{t:n}=J("common",{keyPrefix:"distributionLinks"}),{t:r}=J("translation",{keyPrefix:"distributions.githubGitee"}),{t:o}=J("translation",{keyPrefix:"compact.selectorForm.titles"}),i={build:{href:t.data.links.build,text:n("installationInstructions")},repository:{href:t.data.links.getRepositoryLink(e),text:t.key===z.GITHUB?r("githubRepository"):r("giteeRepository")}},s={clone:{comment:I(W,{t:r,i18nKey:"steps.useGitClone",children:[_("b",{children:"Step 1:"})," Use Git to clone the OpenVINO toolkit repository"]}),command:t.data.commands.getCloneCommand(e),onCopy:()=>Xe.install()},build:{comment:I(W,{t:r,i18nKey:"steps.buildInstructions",children:[_("b",{children:"Step 2:"})," Follow the",_(wt,{href:i.build.href,testId:"build-instructions-link",children:i.build.text})]})}};return I(Je,{title:o("install"),accent:!0,testId:"instructions",children:[_(Y,{...s.clone}),_(Y,{...s.build}),I(tf,{children:[_(wt,{href:i.build.href,testId:"install-instructions-link",children:i.build.text}),_(wt,{href:i.repository.href,testId:"repository-link",children:i.repository.text})]})]})},Ly=({environment:e,os:t,version:n,distribution:r})=>{const{t:o}=J("translation",{keyPrefix:"distributions.pip.steps"}),{t:i}=J("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:s,commands:l}=r.data,a=l.getCreateVenvCommand(t),u=l.getActivateVenvCommand(t),f=l.getInstallCommand({environment:e,os:t,version:n,distribution:r}),d={createEnv:{comment:I(W,{t:o,i18nKey:"createVenv",children:[_("b",{children:"Step 1:"})," Create virtual environment"]}),command:a},activateEnv:{comment:I(W,{t:o,i18nKey:"activateVenv",children:[_("b",{children:"Step 2:"})," Activate virtual environment"]}),command:u},upgradePip:{comment:I(W,{t:o,i18nKey:"upgradePip",children:[_("b",{children:"Step 3:"})," Upgrade pip to latest version"]}),command:l.upgradeCommand},install:{comment:I(W,{t:o,i18nKey:"install",children:[_("b",{children:"Step 4:"})," Download and install the package"]}),command:f,onCopy:()=>Xe.install()}};return I(Je,{title:i("install"),accent:!0,testId:"instructions",children:[_(Y,{...d.createEnv}),_(Y,{...d.activateEnv}),_(Y,{...d.upgradePip}),_(Y,{...d.install}),_(jt,{installUrl:s.installation})]})},Iy=({distribution:e})=>{const{t}=J("translation",{keyPrefix:"distributions.vcpkg.steps"}),{t:n}=J("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:r,commands:o}=e.data,i={install:{comment:_(W,{t,i18nKey:"install",children:"Download and install the package"}),command:o.install,onCopy:()=>Xe.install()}};return I(Je,{title:n("install"),accent:!0,testId:"instructions",children:[_(Y,{...i.install}),_(jt,{installUrl:r.installation})]})},by=({version:e,distribution:t})=>{if(t.data.docsOnly)return _(Vy,{distribution:t});const{t:n}=J("translation",{keyPrefix:"distributions.yum"}),{t:r}=J("translation",{keyPrefix:"compact.selectorForm.titles"}),{yumYear:o}=e.metadata,{commands:i}=t.data,s={createRepo:{comment:I(W,{ns:"translation",i18nKey:"distributions.yum.steps.createRepoFile",children:[_("b",{children:"Step 1:"})," Create the YUM repo file in the /tmp directory as a normal user"]}),command:i.getCreateRepoCommand(e)},moveRepoFile:{comment:I(W,{ns:"translation",i18nKey:"distributions.yum.steps.moveFile",values:{year:o,directory:i.directory},children:[_("b",{children:"Step 2:"})," Move the new openvino-",{year:o},".repo file to the YUM configuration directory ",_("b",{children:i.directory})]}),command:i.getMoveRepoFileCommand(e)},verifyRepo:{comment:I(W,{t:n,i18nKey:"steps.verify",children:[_("b",{children:"Step 3:"})," Verify that the new repo is properly setup by running the following command"]}),command:i.verifyRepoCommand},install:{comment:I(W,{t:n,i18nKey:"steps.install",children:[_("b",{children:"Step 4:"})," Install OpenVINO Runtime"]}),command:i.getInstallCommand(e),onCopy:()=>Xe.install()}};return I(Je,{title:r("install"),accent:!0,testId:"instructions",children:[_(Y,{...s.createRepo}),_(Y,{...s.moveRepoFile}),_(Y,{...s.verifyRepo}),_(Y,{...s.install}),_(jt,{installUrl:t.data.linksSet.installation})]})},Vy=({distribution:e})=>{const{t}=J("translation",{keyPrefix:"compact"}),n=I(W,{t,i18nKey:"distributions.aptYum.followInstructions",children:["Follow the",_(wt,{href:e.data.linksSet.installation,testId:"install-instructions-button",onClick:()=>Xe.install(),children:"Installation instructions"})]});return I(Je,{title:t("selectorForm.titles.install"),accent:!0,testId:"instructions",children:[_(Y,{comment:n}),_(jt,{installUrl:e.data.linksSet.installation})]})},Fy=({state:e})=>{var r;const t={environment:(r=e.ENVIRONMENT)==null?void 0:r.selected,os:e.OP_SYSTEM.selected,version:e.VERSION.selected,distribution:e.DISTRIBUTION.selected};if(t.distribution.key===z.PIP)return _(Ly,{...t,distribution:t.distribution});if(t.distribution.key===z.ARCHIVE)return _(Is,{...t,distribution:t.distribution});if(t.distribution.key===z.DOCKER)return _(Is,{...t,distribution:t.distribution});if(t.distribution.key===z.OFFLINE_INSTALLER)return _(Is,{...t,distribution:t.distribution});if(t.distribution.key===z.GITHUB||t.distribution.key===z.GITEE)return _(Ty,{...t,distribution:t.distribution});if(t.distribution.key===z.APT)return _(xy,{...t,distribution:t.distribution});if(t.distribution.key===z.YUM)return _(by,{...t,distribution:t.distribution});if(t.distribution.key===z.CONDA)return _(Ry,{...t,distribution:t.distribution});if(t.distribution.key===z.BREW)return _(Cy,{...t,distribution:t.distribution});if(t.distribution.key===z.VCPKG)return _(Iy,{...t,distribution:t.distribution});const n=t.distribution;throw new Error(`${n}`)};const Nr={helpIcon:"spark-icon icon help-circle regular",tooltipToggle:"spark-tooltip-toggle",tooltipPopover:"spark-tooltip spark-tooltip-size-m spark-shadow spark-tooltip-bottom",tooltipLabel:"spark-tooltip-label",tooltipArrow:"spark-tooltip-tip"},Dy=({content:e})=>{const[t,n]=D.useState(!1);return _("div",{className:"help-tooltip",children:I("div",{className:Nr.tooltipToggle,children:[_("span",{"aria-hidden":"true",role:"img",className:`help-tooltip-icon ${Nr.helpIcon}`,onMouseEnter:()=>n(!0),onMouseLeave:()=>n(!1)}),t&&I("div",{className:`help-tooltip-popover ${Nr.tooltipPopover}`,role:"tooltip",children:[_("span",{className:`help-tooltip-popover-content ${Nr.tooltipLabel}`,children:e}),_("span",{className:Nr.tooltipArrow})]})]})})};const bs={toggleSwitchField:"spark-fieldlabel spark-fieldlabel-size-m spark-toggle-switch spark-toggle-switch-action spark-toggle-switch-size-l",toggleSwitchInput:"spark-focus-visible spark-focus-visible-suppress spark-focus-visible-adjacent",toggleSwitchSelector:"spark-toggle-switch-selector spark-focus-visible spark-focus-visible-snap"},jy=({label:e,checked:t=!1,onClick:n,testId:r})=>I("label",{className:bs.toggleSwitchField,"data-cy":r,children:[_("input",{type:"checkbox",className:bs.toggleSwitchInput,role:"switch",checked:t,onChange:()=>n==null?void 0:n()}),_("span",{className:bs.toggleSwitchSelector}),_("div",{children:e})]});const mn={toggleButton:"spark-button spark-button-size-l spark-focus-visible spark-focus-visible-self spark-focus-visible-snap",toggleButtonGroup:"spark-button-group spark-button-group-orientation-horizontal spark-button-group-align-start spark-button-group-spacing-l",actionButton:"spark-button-action",secondaryButton:"spark-button-secondary",disabledButton:"spark-button-disabled",buttonContent:"spark-button-content",fontXs:"spark-font-25"},nf=({onClick:e,checked:t=!1,disabled:n=!1,title:r,subtitle:o,value:i})=>_("button",{className:`${mn.toggleButton} ${t?mn.actionButton:mn.secondaryButton} ${n&&mn.disabledButton}`,type:"button",role:"radio","aria-checked":t,onClick:()=>e==null?void 0:e(),"data-cy":i,"aria-label":r,children:I("span",{className:mn.buttonContent,children:[_("span",{className:"title",children:r}),o&&_("span",{className:`${mn.fontXs} subtitle`,children:o})]})}),rf=({children:e,className:t})=>_("div",{className:`option-button-group ${t||""} ${mn.toggleButtonGroup}`,children:e});function Uy({title:e,options:t,level:n}){const r=D.useContext(Fa),o=t.some(({checked:u})=>u),[i,s]=D.useState(o),l=t.map(({level:u,key:f,checked:d,metadata:h})=>_(nf,{value:`${u}_${f}`,checked:d,title:h.title,onClick:()=>r(u,f)},f)),{t:a}=J("translation",{keyPrefix:"compact.frameworks"});return I(Je,{title:e,testId:n,children:[I(ni,{children:[_(jy,{label:a("install"),checked:i,onClick:()=>s(!i),testId:"frameworks-toggle-switch"}),_(Dy,{content:a("tooltip")})]}),_(ni,{children:i&&_(rf,{children:l})})]})}function $o({title:e,options:t,level:n}){const r=D.useContext(Fa),o=t.map(({level:i,key:s,checked:l,metadata:a})=>_(nf,{value:`${i}_${s}`,checked:l,title:a.compactTitle||a.title,subtitle:a.compactSubtitle||a.subtitle,onClick:()=>r(i,s)},s));return _(Je,{title:e,testId:n,children:_(rf,{children:o})})}function Ay({state:e}){var l,a;const t=(l=e.ENVIRONMENT)==null?void 0:l.nodes.map(u=>({...u,metadata:{...u.metadata,subtitle:void 0}})),n=e.OP_SYSTEM.nodes,r=e.VERSION.nodes.map(u=>({...u,metadata:{...u.metadata,subtitle:void 0}})),o=e.DISTRIBUTION.nodes,i=(a=e.FRAMEWORK)==null?void 0:a.nodes;D.useEffect(()=>Xe.combinationView(),[e]);const{t:s}=J("translation",{keyPrefix:"selectorForm.titles"});return I(ni,{children:[_($o,{title:s("version"),options:r,level:L.VERSION}),t&&_($o,{title:s("envinronment"),options:t,level:L.ENVIRONMENT}),_($o,{title:s("os"),options:n,level:L.OP_SYSTEM}),_($o,{title:s("distribution"),options:o,level:L.DISTRIBUTION}),i&&_(Uy,{title:s("frameworks"),options:i,level:L.FRAMEWORK})]})}vy();Xe.initialize(window.parent);function zy(){const[e,t]=yy();return _("div",{className:"st-responsive-container",children:I(Fa.Provider,{value:t,children:[_(Ay,{state:e}),_(Fy,{state:e})]})})}js.createRoot(document.getElementById("root")).render(_(Ef.StrictMode,{children:_(zy,{})}));
+EOF`,getMoveRepoFileCommand:e=>`sudo mv /tmp/openvino-${e.metadata.yumYear}.repo ${Cc}`,verifyRepoCommand:"yum repolist | grep -i openvino",getInstallCommand:e=>`sudo yum install openvino-${e.metadata.yumVersion}`},Mv={title:`2023.1 (${re("versions.titles.recommended")})`,compactTitle:"2023.1",compactSubtitle:re("versions.titles.recommended"),pipVersion:"2023.1.0",githubVersion:"2023.1.0",giteeVersion:"2023.1.0",aptYear:2023,aptVersion:"2023.1.0",yumYear:2023,yumVersion:"2023.1.0",condaVersion:"2023.1.0",conanVersion:"2023.1.0"};class Jp extends pe{constructor(t){super({level:b.VERSION,key:Tn.v_2023_1_0,metadata:Mv},t)}}const $v={title:"2022.3.1",subtitle:`${re("versions.titles.latestLTS")}
+${re("versions.titles.hddlSupport")}`,compactTitle:"2022.3.1",compactSubtitle:re("versions.titles.LTS"),pipVersion:"2022.3.1",githubVersion:"2022.3.1",giteeVersion:"2022.3.1",aptYear:2022,aptVersion:"2022.3.1",yumYear:2022,yumVersion:"2022.3.1"};class Bv extends pe{constructor(t){super({level:b.VERSION,key:Tn.v_2022_3_1,metadata:$v},t)}}const Kv={title:"2021.4.2",subtitle:re("versions.titles.previousLTS"),compactTitle:"2021.4.2",compactSubtitle:re("versions.titles.LTS"),pipVersion:"2021.4.2",githubVersion:"2021.4.2",giteeVersion:"2021.4.2"};class Hv extends pe{constructor(t){super({level:b.VERSION,key:Tn.v_2021_4_2,metadata:Kv},t)}}class Zp extends pe{constructor(t){super({level:b.ENVIRONMENT,key:uo.RUNTIME,metadata:{title:re("environment.runtime.title"),subtitle:re("environment.runtime.subtitle")}},t)}}class ef extends pe{constructor(t){super({level:b.ENVIRONMENT,key:uo.DEV_TOOLS,metadata:{title:re("environment.devTools.title"),subtitle:re("environment.devTools.subtitle")}},t)}}class co extends pe{constructor(t){super({level:b.OP_SYSTEM,key:dn.WINDOWS,metadata:yv,childrenSelector:Fa},t)}}class po extends pe{constructor(t){super({level:b.OP_SYSTEM,key:dn.MACOS,metadata:_v,childrenSelector:Fa},t)}}class fo extends pe{constructor(t){super({level:b.OP_SYSTEM,key:dn.LINUX,metadata:kv,childrenSelector:Fa},t)}}const Io={os:[M.UBUNTU_18,M.UBUNTU_20,M.RED_HAT_8,M.CENT_OS_7],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},Wv=new fo([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18319/l_openvino_toolkit_p_2021.4.752.tgz",systemRequirements:Io}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Io,frameworks:[]},{hasFrameworks:!0}),new Pe({systemRequirements:Io}),new xe({systemRequirements:Io}),new pn({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html#troubleshooting"},downloadLink:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),Vo={os:[ut.V_10_15_X],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},Yv=new po([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_macos.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Vo,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18317/m_openvino_toolkit_p_2021.4.752.dmg"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Vo,frameworks:[]},{hasFrameworks:!0}),new Pe({systemRequirements:Vo}),new xe({systemRequirements:Vo})]),Fo={os:[ln.V_10],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},Gv=new co([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_windows.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Fo,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18320/w_openvino_toolkit_p_2021.4.752.exe"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Fo,frameworks:[]},{hasFrameworks:!0}),new Pe({systemRequirements:Fo}),new xe({systemRequirements:Fo})]),qv=new ef([Gv.default(),Yv,Wv]),jo={os:[M.UBUNTU_18,M.UBUNTU_20,M.RED_HAT_8,M.CENT_OS_7],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},Qv=new fo([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18319/l_openvino_toolkit_p_2021.4.752.tgz",systemRequirements:jo}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:jo,frameworks:[]},{pythonAPI:!0}),new La({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_apt.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/latest/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:{os:[M.UBUNTU_18,M.UBUNTU_20],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},docsOnly:!0}).cApiOnly(),new Da({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_yum.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/latest/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:{os:[M.RED_HAT_8,M.CENT_OS_7],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},docsOnly:!0}),new Pe({systemRequirements:jo}),new xe({systemRequirements:jo}),new pn({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html#troubleshooting"},downloadLink:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),Do={os:[ut.V_10_15_X],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},Xv=new po([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_macos.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Do,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18317/m_openvino_toolkit_p_2021.4.752.dmg"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Do,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:Do}),new xe({systemRequirements:Do})]),Uo={os:[ln.V_10],pythonVersions:[x.V_3_6,x.V_3_7,x.V_3_8]},Jv=new co([new fr({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_windows.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Uo,downloadLink:"https://registrationcenter-download.intel.com/akdlm/irc_nas/18320/w_openvino_toolkit_p_2021.4.752.exe"}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2021.4/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},systemRequirements:Uo,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:Uo}),new xe({systemRequirements:Uo}),new pn({linksSet:{installation:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_windows.html",getStarted:"https://docs.openvino.ai/2021.4/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:null},downloadLink:"https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_docker_windows.html"})]),Zv=new Zp([Jv.default(),Xv,Qv]),ey=new Hv([qv.default(),Zv]),Ps={os:[M.UBUNTU_18,M.UBUNTU_20,M.RED_HAT_8],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10]},ty=new fo([new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Ps,frameworks:[]},{hasFrameworks:!0}).default(),new Pe({systemRequirements:Ps}),new xe({systemRequirements:Ps}),new pn({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),Cs={os:[ut.V_10_15_X,ut.V_11,ut.V_12],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10],note:Qp},ny=new po([new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Cs,frameworks:[]},{hasFrameworks:!0}).addFootnote(b.OP_SYSTEM).default(),new Pe({systemRequirements:Cs}).addFootnote(b.OP_SYSTEM),new xe({systemRequirements:Cs}).addFootnote(b.OP_SYSTEM)]),Rs={os:[ln.V_10,ln.V_11],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10]},ry=new co([new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Rs,frameworks:[]},{hasFrameworks:!0}).default(),new Pe({systemRequirements:Rs}),new xe({systemRequirements:Rs})]),oy=new ef([ry.default(),ny,ty]),Ao={os:[M.UBUNTU_18,M.UBUNTU_20,M.RED_HAT_8],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10]},iy=new fo([new pr({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_from_archive_linux.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3.1/linux",systemRequirements:Ao}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Ao,frameworks:[]},{pythonAPI:!0}),new La({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_apt.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:{os:[M.UBUNTU_18,M.UBUNTU_20],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10]}}),new Da({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_yum.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:{os:[M.RED_HAT_8],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10]}}),new Pe({systemRequirements:Ao}),new xe({systemRequirements:Ao}),new pn({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),zo={os:[ut.V_10_15_X,ut.V_11,ut.V_12],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10],note:Qp},sy=new po([new pr({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_from_archive_macos.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3.1/macos",systemRequirements:zo}).addFootnote(b.OP_SYSTEM).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:zo,frameworks:[]},{pythonAPI:!0}).addFootnote(b.OP_SYSTEM),new Pe({systemRequirements:zo}).addFootnote(b.OP_SYSTEM),new xe({systemRequirements:zo}).addFootnote(b.OP_SYSTEM)]),Mo={os:[ln.V_10,ln.V_11],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10]},ly=new co([new pr({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_from_archive_windows.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3.1/windows",systemRequirements:Mo}).default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:Mo,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:Mo}),new xe({systemRequirements:Mo}),new pn({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_windows.html",getStarted:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_windows.html"})]),ay=new Zp([ly.default(),sy,iy]),uy=new Bv([oy.default(),ay]),At={os:[M.UBUNTU_18,M.UBUNTU_20,M.UBUNTU_22,M.RED_HAT_8],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10,x.V_3_11]},cy=new fo([new pr({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_from_archive_linux.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/linux",systemRequirements:At}).includesNPUPlugin().default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:At,frameworks:[]},{pythonAPI:!0}),new La({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_apt.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:{os:[M.UBUNTU_18,M.UBUNTU_20,M.UBUNTU_22],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10,x.V_3_11]}}),new Da({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_yum.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:{os:[M.RED_HAT_8],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10,x.V_3_11]}}),new Pe({systemRequirements:At}),new xe({systemRequirements:At}),new pn({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html"}),new Va({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conda.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:At}),new Xp({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_brew.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:At}),new ja({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_vcpkg.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:At}),new Ia({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conan.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:At})]),zt={os:[ut.V_10_15_X,ut.V_11,ut.V_12],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10,x.V_3_11]},dy=new po([new pr({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_from_archive_macos.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/macos",systemRequirements:zt}).includesNPUPlugin().default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:zt,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:zt}),new xe({systemRequirements:zt}),new Va({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conda.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:zt}),new Xp({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_brew.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:zt}),new ja({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_vcpkg.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:zt}),new Ia({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conan.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:zt})]),fn={os:[ln.V_10,ln.V_11],pythonVersions:[x.V_3_7,x.V_3_8,x.V_3_9,x.V_3_10,x.V_3_11]},py=new co([new pr({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_from_archive_windows.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.1/windows",systemRequirements:fn}).includesNPUPlugin().default(),new ke({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:fn,frameworks:[]},{pythonAPI:!0}),new Pe({systemRequirements:fn}),new xe({systemRequirements:fn}),new pn({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},downloadLink:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_docker.html"}),new Va({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conda.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:fn}),new ja({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_vcpkg.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:fn}),new Ia({linksSet:{installation:"https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_conan.html",getStarted:"https://docs.openvino.ai/2023.1/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html"},systemRequirements:fn})]),fy=new Jp([py.default(),dy,cy]),hy=new pe({level:b.ROOT,key:qp.ROOT,metadata:{title:"ROOT"}},[fy.default(),uy,ey]).default();function my(e,t){var o,i;if(t.key===A.DOCKER||!t.footnoteLevel)return e;const n=(o=e[t.footnoteLevel])==null?void 0:o.selected,r=(i=e[t.footnoteLevel])==null?void 0:i.nodes;return!n||!r||((Array.isArray(n)?[...n,...r]:[n]).forEach(s=>s.hasFootnote=!0),r.forEach(s=>s.checked&&(s.hasFootnote=!0))),e}class gy{constructor(){je(this,"_root",hy)}getState(){try{return this._getState()}catch(t){return console.error(t),this._selectDefaults(this._root),this._getState()}}_getState(){const t=this._root.children,n=this._get_selected(t),{environments:r,environment:o,systems:i,system:s}=this._processVersion(n),l=s.children,a=this._get_selected(l),u=this._processDistribution(a),f={[b.VERSION]:{nodes:t.map(d=>d.toOption()),selected:n.toOption()},[b.ENVIRONMENT]:o?{nodes:r.map(d=>d.toOption()),selected:o.toOption()}:null,[b.OP_SYSTEM]:{nodes:i.map(d=>d.toOption()),selected:s.toOption()},[b.DISTRIBUTION]:{nodes:l.map(d=>d.toOption()),selected:a.toOption()},[b.FRAMEWORK]:u!=null&&u.length?{nodes:u.map(d=>d.toOption()),selected:u.filter(({checked:d})=>d).map(d=>d.toOption())}:null};return my(f,a)}_get_selected(t){t.some(({checked:r})=>r)||this._selectDefaultsForLevel(t[0].level);const n=t.find(({checked:r})=>r);if(!n)throw new Error("Not valid tree");return n}_processVersion(t){if(t instanceof Jp){const s=t.children,l=this._get_selected(s);return{environments:null,environment:null,systems:s,system:l}}const n=t.children,r=this._get_selected(n),o=r.children,i=this._get_selected(o);return{environments:n,environment:r,systems:o,system:i}}_processDistribution(t){return t instanceof ke?t.children:null}setState(t){this._setState(t)}_setState(t,n=this._root){if(!n.children.length)return;const r=n.children[0].level,o=vy(t[r]);n.children.forEach(i=>i.checked=o.includes(i.key)),n.children.forEach(i=>this._setState(t,i))}select(t,n){return this._select(t,n),this.getState()}_select(t,n,r=this._root){var o;if(((o=r.children[0])==null?void 0:o.level)!==t){r.children.forEach(i=>this._select(t,n,i));return}if(r.childrenSelector){r.childrenSelector(r.children,n);return}r.children.forEach(i=>i.checked=i.key===n)}_selectDefaultsForLevel(t,n=this._root){if(n.children.length){if(n.children[0].level!==t){n.children.forEach(r=>this._selectDefaultsForLevel(t,r));return}this._selectDefaults(n)}}_selectDefaults(t){t.children.forEach(n=>{n.checked=n.isDefault,this._selectDefaults(n)})}}const Pn=new gy;function vy(e){const t=[];return Array.isArray(e)?t.push(...e):e&&t.push(e),t}function tf(e){var r,o;const t={[b.VERSION]:e.VERSION.selected.key,[b.ENVIRONMENT]:(r=e.ENVIRONMENT)==null?void 0:r.selected.key,[b.OP_SYSTEM]:e.OP_SYSTEM.selected.key,[b.DISTRIBUTION]:e.DISTRIBUTION.selected.key,[b.FRAMEWORK]:(o=e.FRAMEWORK)==null?void 0:o.selected.map(i=>i.key).join(",")},n=new URLSearchParams;for(const[i,s]of Object.entries(t))s&&n.set(i,s);return n}function nf(e){function t(r,o){const i=e.get(r);if(!i)throw new Error(`Cannot extract value for: ${r}`);if(!o[i])throw new Error(`Bad node key for: ${r}`);return o[i]}function n(r,o){const i=e.get(r);return i?i.split(",").map(l=>{if(!l||!o[l])throw new Error(`Bad node key for: ${r}`);return o[l]}):null}try{return{[b.VERSION]:t(b.VERSION,Tn),[b.ENVIRONMENT]:e.has(b.ENVIRONMENT)?t(b.ENVIRONMENT,uo):null,[b.OP_SYSTEM]:t(b.OP_SYSTEM,dn),[b.DISTRIBUTION]:t(b.DISTRIBUTION,A),[b.FRAMEWORK]:n(b.FRAMEWORK,Ue)}}catch(r){return console.log(`Cannot restore state from url due to error "${r}"`),null}}function yy(){const e=window.parent;if(!e.location.search)return null;const t=new URLSearchParams(e.location.search);return nf(t)}function _y(e,t){j.useEffect(()=>{const n=window.parent,r=tf(e).toString(),o=new URL(n.location.toString());if(!o.search){o.search=r,n.history.replaceState(null,"",o);return}o.search.slice(1)!==r&&(o.search=r,n.history.pushState(null,"",o))}),parent.onpopstate=()=>{const n=window.parent,r=new URLSearchParams(n.location.search),o=nf(r);o&&(Pn.setState(o),t(Pn.getState()))}}const Ts=function(e){let t,n=!1;return function(...r){return n||(t=e(r),n=!0),t}};function ky(e){var t,n;return typeof((n=(t=e.wap_tms)==null?void 0:t.custom)==null?void 0:n.trackComponentClick)!="function"?null:e.wap_tms.custom.trackComponentClick.bind(e.wap_tms.custom)}class wy{constructor(){je(this,"_window");je(this,"_consoleNotification",{notInitialized:Ts(()=>console.log("Adobe analytics is not initialized")),notFound:Ts(()=>console.log("Adobe analytics not found on a page")),devMode:Ts(()=>console.log("Analytics in dev mode"))});je(this,"_send",t=>{if(!this._window){this._consoleNotification.notInitialized();return}const n=tf(Pn.getState()).toString(),r=ky(this._window);if(!r){this._consoleNotification.notFound();return}try{r(t,n)}catch(o){console.error(o)}})}initialize(t){this._window=t}install(){this._send("install")}combinationView(){this._send("combination-view")}}const Ke=new wy;function Sy(){const e=yy();e&&Pn.setState(e)}const Ua=j.createContext((e,t)=>{Pn.select(e,t)});function Oy(){const[e,t]=j.useState(Pn.getState());return _y(e,t),[e,(n,r)=>t(Pn.select(n,r))]}async function Ny(e){e&&(navigator.clipboard?await navigator.clipboard.writeText(e):Ey(e))}function Ey(e){const t=xy(e);document.body.append(t),t.select(),document.execCommand("copy"),t.remove()}function xy(e){const t=document.createElement("textarea");t.style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style.left="-9999px";const n=window.pageYOffset||document.documentElement.scrollTop;return t.style.top=`${n}px`,t.setAttribute("readonly",""),t.value=e,t}const Or={copyButton:"spark-button spark-button-ghost spark-button-size-m spark-button-icon-only spark-focus-visible spark-focus-visible-self spark-focus-visible-snap spark-code-snippet-is-visible",copyButtonContent:"spark-button-content",regularIcon:"spark-icon icon regular",copyIcon:"copy",copiedIcon:"check"},K=({comment:e,command:t,onCopy:n})=>{const[r,o]=j.useState(!1),i=async()=>{r||(await Ny(t),o(!0),setTimeout(()=>o(!1),1500),n==null||n())};return L("div",{className:"st-code-snippet","data-cy":"instructions-step",children:[e&&_(rf,{children:e}),L("div",{"data-cy":"command",children:[t&&_("code",{className:"st-code-snippet-content",children:t}),t&&_("button",{className:`copy-button ${Or.copyButton}`,type:"button","aria-label":"Copy","data-cy":"copy",onClick:i,children:_("span",{className:Or.copyButtonContent,children:_("span",{"aria-hidden":"true",role:"img",className:`${Or.regularIcon} ${r?Or.copiedIcon:Or.copyIcon}`})})})]})]})},rf=({children:e})=>L("pre",{className:"st-code-snippet-comment",children:["# ",e]}),Py=({comment:e,snippets:t})=>L("div",{className:"st-code-snippet-multi-line","data-cy":"command",children:[e&&_(rf,{children:e}),t.map(n=>_(K,{...n},n.command))]});function Cy(e){return e.host==="docs.openvino.ai"}const bs={link:"spark-hyperlink spark-hyperlink-primary spark-hyperlink-standard spark-focus-visible spark-focus-visible-self spark-focus-visible-snap spark-focus-visible-background",button:"spark-button spark-button-action spark-button-size-m spark-focus-visible spark-focus-visible-self spark-focus-visible-snap",buttonContent:"spark-button-content"},wt=({href:e,children:t,type:n="link",testId:r="link",onClick:o})=>{const i=Cy(new URL(e))?"_parent":"_blank";return n==="link"?_("a",{href:e,className:bs.link,target:i,rel:"noreferrer noopener","data-cy":r,onClick:()=>o==null?void 0:o(),children:t}):_("span",{className:bs.button,children:_("span",{className:bs.buttonContent,children:_("a",{href:e,target:i,rel:"noreferrer noopener","data-cy":r,onClick:()=>o==null?void 0:o(),children:t})})})},of=({children:e})=>{const{t}=q("translation",{keyPrefix:"selectorForm"});return L("div",{"data-cy":"link-set",children:[L("div",{className:"st-distribution-links",children:[e,_(wt,{href:"https://github.com/openvinotoolkit/openvino/releases",testId:"previous-releases-link",children:t("prevReleases")})]}),_("div",{className:"nncf-link",children:L(B,{ns:"translation",i18nKey:"selectorForm.nncf",children:["Advanced Optimization tool available separately:",_(wt,{href:"https://docs.openvino.ai/2023.1/tmo_introduction.html",testId:"nncf-link",children:"Learn about NNCF"})]})})]})},Ot=({installUrl:e})=>{const{t}=q("common",{keyPrefix:"distributionLinks"});return _(of,{children:_(wt,{href:e,testId:"install-instructions-link",children:t("installationInstructions")})})};const Ry={heading:"spark-heading spark-font-200"},He=({title:e,accent:t=!1,children:n,testId:r})=>L("div",{className:`st-section ${t?"st-section-accent":""}`,"data-cy":r,children:[_("span",{className:`st-section-title ${Ry.heading}`,children:e}),_("div",{className:"st-section-content",children:j.Children.map(n,o=>_(Ty,{children:o}))})]}),Ty=({children:e})=>_("div",{className:"st-section-content-row",children:e}),by=({version:e,distribution:t})=>{if(t.data.docsOnly)return _(Ly,{distribution:t});const{t:n}=q("translation",{keyPrefix:"distributions.apt"}),{t:r}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),{commands:o}=t.data,i={comment:L(B,{ns:"translation",i18nKey:"distributions.apt.steps.addRepository",children:[_("b",{children:"Step 3:"})," Add the repository via the following command"]}),snippets:[{comment:"Ubuntu 20",command:o.getAddRepositoryCommand(e,20)},{comment:"Ubuntu 18",command:o.getAddRepositoryCommand(e,18)}]};e.key===Tn.v_2023_1_0&&i.snippets.unshift({comment:"Ubuntu 22",command:o.getAddRepositoryCommand(e,22)});const s={downloadKey:{comment:L(B,{ns:"translation",i18nKey:"distributions.apt.steps.download",values:{filename:o.keyFilename},children:[_("b",{children:"Step 1:"})," Download the ",_(wt,{href:o.keyHref,children:o.keyFilename}),". You can also use the following command"]}),command:o.downloadKeyCommand},addKey:{comment:L(B,{t:n,i18nKey:"steps.addKey",children:[_("b",{children:"Step 2:"})," Add this key to the system keyring"]}),command:o.addKeyCommand},addRepository:i,updatePackages:{comment:L(B,{t:n,i18nKey:"steps.updateList",children:[_("b",{children:"Step 4:"})," Update the list of packages via the update command"]}),command:o.updatePackagesCommand},verifyAptCache:{comment:L(B,{t:n,i18nKey:"steps.verify",children:[_("b",{children:"Step 5:"})," Verify that the APT repository is properly set up. Run the apt-cache command to see a list of all available OpenVINO packages and components"]}),command:o.verifyAptCacheCommand},install:{comment:L(B,{t:n,i18nKey:"steps.install",children:[_("b",{children:"Step 6:"})," Install OpenVINO Runtime"]}),command:o.getInstallCommand(e),onCopy:()=>Ke.install()}};return L(He,{title:r("install"),accent:!0,testId:"instructions",children:[_(K,{...s.downloadKey}),_(K,{...s.addKey}),_(Py,{...s.addRepository}),_(K,{...s.updatePackages}),_(K,{...s.verifyAptCache}),_(K,{...s.install}),_(Ot,{installUrl:t.data.linksSet.installation})]})},Ly=({distribution:e})=>{const{t}=q("translation",{keyPrefix:"compact"}),n=L(B,{t,i18nKey:"distributions.aptYum.followInstructions",children:["Follow the",_(wt,{href:e.data.linksSet.installation,testId:"install-instructions-button",onClick:()=>Ke.install(),children:"Installation instructions"})]});return L(He,{title:t("selectorForm.titles.install"),accent:!0,testId:"instructions",children:[_(K,{comment:n}),_(Ot,{installUrl:e.data.linksSet.installation})]})},Iy=({distribution:e})=>{const{t}=q("translation",{keyPrefix:"distributions.brew.steps"}),{t:n}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:r,commands:o}=e.data,i={install:{comment:_(B,{t,i18nKey:"install",children:"Download and install the package"}),command:o.install,onCopy:()=>Ke.install()}};return L(He,{title:n("install"),accent:!0,testId:"instructions",children:[_(K,{...i.install}),_(Ot,{installUrl:r.installation})]})},Vy=({version:e,distribution:t})=>{const{t:n}=q("translation",{keyPrefix:"distributions.conan.steps"}),{t:r}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),{commands:o}=t.data,{txtFilename:i,cmakeFilename:s}=o,l={createConanFile:{comment:L(B,{t:n,i18nKey:"createConanFile",values:{txtFilename:i},children:[_("b",{children:"Step 1:"})," Create a ",_("b",{children:i})," file for your OpenVINO project and add “openvino” dependency in there"]}),command:o.conanTXTContent(e)},install:{comment:L(B,{t:n,i18nKey:"install",values:{cmakeFilename:s},children:[_("b",{children:"Step 2:"})," Run the command below to create ",_("b",{children:s})," file, which will be used to compile your project with OpenVINO"]}),command:o.install,onCopy:()=>Ke.install()},compile:{comment:L(B,{t:n,i18nKey:"compile",children:[_("b",{children:"Step 3:"})," Configure and compile your project with OpenVINO"]}),command:o.compile}};return L(He,{title:r("install"),accent:!0,testId:"instructions",children:[_(K,{...l.createConanFile}),_(K,{...l.install}),_(K,{...l.compile}),_(Ot,{installUrl:t.data.linksSet.installation})]})},Fy=({version:e,distribution:t})=>{const{t:n}=q("translation",{keyPrefix:"distributions.conda.steps"}),{t:r}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:o,commands:i}=t.data,s={createEnv:{comment:L(B,{t:n,i18nKey:"createEnv",children:[_("b",{children:"Step 1:"})," Create the Anaconda environment (Python 3.10 used as an example)"]}),command:i.createEnv},activateEnv:{comment:L(B,{t:n,i18nKey:"activateEnv",children:[_("b",{children:"Step 2:"})," Activate the Anaconda environment"]}),command:i.activateEnv},upgradePip:{comment:L(B,{t:n,i18nKey:"update",children:[_("b",{children:"Step 3:"})," Update the Anaconda to latest version"]}),command:i.update},install:{comment:L(B,{t:n,i18nKey:"install",children:[_("b",{children:"Step 4:"})," Download and install the package"]}),command:i.getInstall(e),onCopy:()=>Ke.install()}};return L(He,{title:r("install"),accent:!0,testId:"instructions",children:[_(K,{...s.createEnv}),_(K,{...s.activateEnv}),_(K,{...s.upgradePip}),_(K,{...s.install}),_(Ot,{installUrl:o.installation})]})},Ls=({distribution:e})=>{const{t}=q("translation",{keyPrefix:"distributions.download"}),{t:n}=q("translation",{keyPrefix:"compact"}),r={[A.ARCHIVE]:t("downloadArchives"),[A.DOCKER]:t("gotoDocker"),[A.OFFLINE_INSTALLER]:t("download")}[e.key],o=L(ni,{children:[n("distributions.download.useFollowingLink"),_("br",{}),_("b",{children:_(wt,{href:e.data.downloadLink,testId:"download-button",onClick:()=>Ke.install(),children:r})})]});return L(He,{title:n("selectorForm.titles.install"),accent:!0,testId:"instructions",children:[_(K,{comment:o}),_(Ot,{installUrl:e.data.linksSet.installation})]})},jy=({version:e,distribution:t})=>{const{t:n}=q("common",{keyPrefix:"distributionLinks"}),{t:r}=q("translation",{keyPrefix:"distributions.githubGitee"}),{t:o}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),i={build:{href:t.data.links.build,text:n("installationInstructions")},repository:{href:t.data.links.getRepositoryLink(e),text:t.key===A.GITHUB?r("githubRepository"):r("giteeRepository")}},s={clone:{comment:L(B,{t:r,i18nKey:"steps.useGitClone",children:[_("b",{children:"Step 1:"})," Use Git to clone the OpenVINO toolkit repository"]}),command:t.data.commands.getCloneCommand(e),onCopy:()=>Ke.install()},build:{comment:L(B,{t:r,i18nKey:"steps.buildInstructions",children:[_("b",{children:"Step 2:"})," Follow the",_(wt,{href:i.build.href,testId:"build-instructions-link",children:i.build.text})]})}};return L(He,{title:o("install"),accent:!0,testId:"instructions",children:[_(K,{...s.clone}),_(K,{...s.build}),L(of,{children:[_(wt,{href:i.build.href,testId:"install-instructions-link",children:i.build.text}),_(wt,{href:i.repository.href,testId:"repository-link",children:i.repository.text})]})]})},Dy=({environment:e,os:t,version:n,distribution:r})=>{const{t:o}=q("translation",{keyPrefix:"distributions.pip.steps"}),{t:i}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:s,commands:l}=r.data,a=l.getCreateVenvCommand(t),u=l.getActivateVenvCommand(t),f=l.getInstallCommand({environment:e,os:t,version:n,distribution:r}),d={createEnv:{comment:L(B,{t:o,i18nKey:"createVenv",children:[_("b",{children:"Step 1:"})," Create virtual environment"]}),command:a},activateEnv:{comment:L(B,{t:o,i18nKey:"activateVenv",children:[_("b",{children:"Step 2:"})," Activate virtual environment"]}),command:u},upgradePip:{comment:L(B,{t:o,i18nKey:"upgradePip",children:[_("b",{children:"Step 3:"})," Upgrade pip to latest version"]}),command:l.upgradeCommand},install:{comment:L(B,{t:o,i18nKey:"install",children:[_("b",{children:"Step 4:"})," Download and install the package"]}),command:f,onCopy:()=>Ke.install()}};return L(He,{title:i("install"),accent:!0,testId:"instructions",children:[_(K,{...d.createEnv}),_(K,{...d.activateEnv}),_(K,{...d.upgradePip}),_(K,{...d.install}),_(Ot,{installUrl:s.installation})]})},Uy=({distribution:e})=>{const{t}=q("translation",{keyPrefix:"distributions.vcpkg.steps"}),{t:n}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),{linksSet:r,commands:o}=e.data,i={install:{comment:_(B,{t,i18nKey:"install",children:"Download and install the package"}),command:o.install,onCopy:()=>Ke.install()}};return L(He,{title:n("install"),accent:!0,testId:"instructions",children:[_(K,{...i.install}),_(Ot,{installUrl:r.installation})]})},Ay=({version:e,distribution:t})=>{if(t.data.docsOnly)return _(zy,{distribution:t});const{t:n}=q("translation",{keyPrefix:"distributions.yum"}),{t:r}=q("translation",{keyPrefix:"compact.selectorForm.titles"}),{yumYear:o}=e.metadata,{commands:i}=t.data,s={createRepo:{comment:L(B,{ns:"translation",i18nKey:"distributions.yum.steps.createRepoFile",children:[_("b",{children:"Step 1:"})," Create the YUM repo file in the /tmp directory as a normal user"]}),command:i.getCreateRepoCommand(e)},moveRepoFile:{comment:L(B,{ns:"translation",i18nKey:"distributions.yum.steps.moveFile",values:{year:o,directory:i.directory},children:[_("b",{children:"Step 2:"})," Move the new openvino-",{year:o},".repo file to the YUM configuration directory ",_("b",{children:i.directory})]}),command:i.getMoveRepoFileCommand(e)},verifyRepo:{comment:L(B,{t:n,i18nKey:"steps.verify",children:[_("b",{children:"Step 3:"})," Verify that the new repo is properly setup by running the following command"]}),command:i.verifyRepoCommand},install:{comment:L(B,{t:n,i18nKey:"steps.install",children:[_("b",{children:"Step 4:"})," Install OpenVINO Runtime"]}),command:i.getInstallCommand(e),onCopy:()=>Ke.install()}};return L(He,{title:r("install"),accent:!0,testId:"instructions",children:[_(K,{...s.createRepo}),_(K,{...s.moveRepoFile}),_(K,{...s.verifyRepo}),_(K,{...s.install}),_(Ot,{installUrl:t.data.linksSet.installation})]})},zy=({distribution:e})=>{const{t}=q("translation",{keyPrefix:"compact"}),n=L(B,{t,i18nKey:"distributions.aptYum.followInstructions",children:["Follow the",_(wt,{href:e.data.linksSet.installation,testId:"install-instructions-button",onClick:()=>Ke.install(),children:"Installation instructions"})]});return L(He,{title:t("selectorForm.titles.install"),accent:!0,testId:"instructions",children:[_(K,{comment:n}),_(Ot,{installUrl:e.data.linksSet.installation})]})},My=({state:e})=>{var r;const t={environment:(r=e.ENVIRONMENT)==null?void 0:r.selected,os:e.OP_SYSTEM.selected,version:e.VERSION.selected,distribution:e.DISTRIBUTION.selected};if(t.distribution.key===A.PIP)return _(Dy,{...t,distribution:t.distribution});if(t.distribution.key===A.ARCHIVE)return _(Ls,{...t,distribution:t.distribution});if(t.distribution.key===A.DOCKER)return _(Ls,{...t,distribution:t.distribution});if(t.distribution.key===A.OFFLINE_INSTALLER)return _(Ls,{...t,distribution:t.distribution});if(t.distribution.key===A.GITHUB||t.distribution.key===A.GITEE)return _(jy,{...t,distribution:t.distribution});if(t.distribution.key===A.APT)return _(by,{...t,distribution:t.distribution});if(t.distribution.key===A.YUM)return _(Ay,{...t,distribution:t.distribution});if(t.distribution.key===A.CONDA)return _(Fy,{...t,distribution:t.distribution});if(t.distribution.key===A.BREW)return _(Iy,{...t,distribution:t.distribution});if(t.distribution.key===A.VCPKG)return _(Uy,{...t,distribution:t.distribution});if(t.distribution.key===A.CONAN)return _(Vy,{...t,distribution:t.distribution});const n=t.distribution;throw new Error(`${n}`)};const Nr={helpIcon:"spark-icon icon help-circle regular",tooltipToggle:"spark-tooltip-toggle",tooltipPopover:"spark-tooltip spark-tooltip-size-m spark-shadow spark-tooltip-bottom",tooltipLabel:"spark-tooltip-label",tooltipArrow:"spark-tooltip-tip"},$y=({content:e})=>{const[t,n]=j.useState(!1);return _("div",{className:"help-tooltip",children:L("div",{className:Nr.tooltipToggle,children:[_("span",{"aria-hidden":"true",role:"img",className:`help-tooltip-icon ${Nr.helpIcon}`,onMouseEnter:()=>n(!0),onMouseLeave:()=>n(!1)}),t&&L("div",{className:`help-tooltip-popover ${Nr.tooltipPopover}`,role:"tooltip",children:[_("span",{className:`help-tooltip-popover-content ${Nr.tooltipLabel}`,children:e}),_("span",{className:Nr.tooltipArrow})]})]})})};const Is={toggleSwitchField:"spark-fieldlabel spark-fieldlabel-size-m spark-toggle-switch spark-toggle-switch-action spark-toggle-switch-size-l",toggleSwitchInput:"spark-focus-visible spark-focus-visible-suppress spark-focus-visible-adjacent",toggleSwitchSelector:"spark-toggle-switch-selector spark-focus-visible spark-focus-visible-snap"},By=({label:e,checked:t=!1,onClick:n,testId:r})=>L("label",{className:Is.toggleSwitchField,"data-cy":r,children:[_("input",{type:"checkbox",className:Is.toggleSwitchInput,role:"switch",checked:t,onChange:()=>n==null?void 0:n()}),_("span",{className:Is.toggleSwitchSelector}),_("div",{children:e})]});const gn={toggleButton:"spark-button spark-button-size-l spark-focus-visible spark-focus-visible-self spark-focus-visible-snap",toggleButtonGroup:"spark-button-group spark-button-group-orientation-horizontal spark-button-group-align-start spark-button-group-spacing-l",actionButton:"spark-button-action",secondaryButton:"spark-button-secondary",disabledButton:"spark-button-disabled",buttonContent:"spark-button-content",fontXs:"spark-font-25"},sf=({onClick:e,checked:t=!1,disabled:n=!1,title:r,subtitle:o,value:i})=>_("button",{className:`${gn.toggleButton} ${t?gn.actionButton:gn.secondaryButton} ${n&&gn.disabledButton}`,type:"button",role:"radio","aria-checked":t,onClick:()=>e==null?void 0:e(),"data-cy":i,"aria-label":r,children:L("span",{className:gn.buttonContent,children:[_("span",{className:"title",children:r}),o&&_("span",{className:`${gn.fontXs} subtitle`,children:o})]})}),lf=({children:e,className:t})=>_("div",{className:`option-button-group ${t||""} ${gn.toggleButtonGroup}`,children:e});function Ky({title:e,options:t,level:n}){const r=j.useContext(Ua),o=t.some(({checked:u})=>u),[i,s]=j.useState(o),l=t.map(({level:u,key:f,checked:d,metadata:h})=>_(sf,{value:`${u}_${f}`,checked:d,title:h.title,onClick:()=>r(u,f)},f)),{t:a}=q("translation",{keyPrefix:"compact.frameworks"});return L(He,{title:e,testId:n,children:[L(ni,{children:[_(By,{label:a("install"),checked:i,onClick:()=>s(!i),testId:"frameworks-toggle-switch"}),_($y,{content:a("tooltip")})]}),_(ni,{children:i&&_(lf,{children:l})})]})}function $o({title:e,options:t,level:n}){const r=j.useContext(Ua),o=t.map(({level:i,key:s,checked:l,metadata:a})=>_(sf,{value:`${i}_${s}`,checked:l,title:a.compactTitle||a.title,subtitle:a.compactSubtitle||a.subtitle,onClick:()=>r(i,s)},s));return _(He,{title:e,testId:n,children:_(lf,{children:o})})}function Hy({state:e}){var l,a;const t=(l=e.ENVIRONMENT)==null?void 0:l.nodes.map(u=>({...u,metadata:{...u.metadata,subtitle:void 0}})),n=e.OP_SYSTEM.nodes,r=e.VERSION.nodes.map(u=>({...u,metadata:{...u.metadata,subtitle:void 0}})),o=e.DISTRIBUTION.nodes,i=(a=e.FRAMEWORK)==null?void 0:a.nodes;j.useEffect(()=>Ke.combinationView(),[e]);const{t:s}=q("translation",{keyPrefix:"selectorForm.titles"});return L(ni,{children:[_($o,{title:s("version"),options:r,level:b.VERSION}),t&&_($o,{title:s("envinronment"),options:t,level:b.ENVIRONMENT}),_($o,{title:s("os"),options:n,level:b.OP_SYSTEM}),_($o,{title:s("distribution"),options:o,level:b.DISTRIBUTION}),i&&_(Ky,{title:s("frameworks"),options:i,level:b.FRAMEWORK})]})}Sy();Ke.initialize(window.parent);function Wy(){const[e,t]=Oy();return _("div",{className:"st-responsive-container",children:L(Ua.Provider,{value:t,children:[_(Hy,{state:e}),_(My,{state:e})]})})}Ds.createRoot(document.getElementById("root")).render(_(Cf.StrictMode,{children:_(Wy,{})}));
diff --git a/docs/_static/selector-tool/selector-12b6d7d.html b/docs/_static/selector-tool/selector-68d2f71.html
similarity index 76%
rename from docs/_static/selector-tool/selector-12b6d7d.html
rename to docs/_static/selector-tool/selector-68d2f71.html
index 3fddc9cffb3342..32e4c35b2f2c5c 100644
--- a/docs/_static/selector-tool/selector-12b6d7d.html
+++ b/docs/_static/selector-tool/selector-68d2f71.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html lang="en">
   <head>
-    <meta name="version" content="12b6d7d" />
+    <meta name="version" content="68d2f71" />
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Download Intel® Distribution of OpenVINO™ Toolkit</title>
@@ -9,14 +9,11 @@
       name="description"
       content="Download a version of the Intel® Distribution of OpenVINO™ toolkit for Linux, Windows, or macOS."
     />
-    <script
-      type="module"
-      crossorigin
-      src="./assets/selector-56fddec6.js"
-    ></script>
-    <link rel="stylesheet" href="./assets/selector-5c3f26d1.css" />
+    <script type="module" crossorigin src="./assets/selector-114afa0d.js"></script>
+    <link rel="stylesheet" href="./assets/selector-5c3f26d1.css">
   </head>
   <body>
     <div id="root"></div>
+    
   </body>
 </html>
diff --git a/docs/articles_en/about_openvino.md b/docs/articles_en/about_openvino.md
new file mode 100644
index 00000000000000..37545223e8b6f3
--- /dev/null
+++ b/docs/articles_en/about_openvino.md
@@ -0,0 +1,26 @@
+# About OpenVINO {#about_openvino}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_docs_performance_benchmarks
+   compatibility_and_support
+   Release Notes <https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino/2023-1.html>
+   prerelease_information
+   system_requirements
+   Additional Resources <resources>
+
+OpenVINO is a toolkit for simple and efficient deployment of various deep learning models.
+In this section you will find information on the product itself, as well as the software
+and hardware solutions it supports.
+
+
+
+
+
+
+
+@endsphinxdirective
diff --git a/docs/resources/resources.md b/docs/articles_en/about_openvino/additional_resources.md
similarity index 50%
rename from docs/resources/resources.md
rename to docs/articles_en/about_openvino/additional_resources.md
index bc4a8240875a82..f824cbff435810 100644
--- a/docs/resources/resources.md
+++ b/docs/articles_en/about_openvino/additional_resources.md
@@ -1,4 +1,4 @@
-# Resources {#resources}
+# Additional Resources {#resources}
 
 
 @sphinxdirective
@@ -7,21 +7,6 @@
    :description: Learn more about OpenVINO from benchmark results, case studies 
                  and lists of supported models, operations and devices.
 
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   openvino_docs_performance_benchmarks
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   openvino_docs_OV_UG_supported_plugins_Supported_Devices
-   openvino_supported_models
-   openvino_resources_supported_operations
-   openvino_resources_supported_operations_frontend
-
 .. toctree::
    :maxdepth: 1
    :hidden:
@@ -32,19 +17,8 @@
    Case Studies <https://www.intel.com/openvino-success-stories>
 
 
-This section includes a variety of reference information focusing mostly on describing OpenVINO 
-and its proprietary model format, OpenVINO IR.
-
 :doc:`Performance Benchmarks <openvino_docs_performance_benchmarks>` contain results from benchmarking models with OpenVINO on Intel hardware.
 
-:doc:`Supported Devices <openvino_docs_OV_UG_supported_plugins_Supported_Devices>` is compatibility information about supported hardware accelerators.
-
-:doc:`Supported Models <openvino_supported_models>` is a table of models officially supported by OpenVINO.
-
-:doc:`Supported Operations <openvino_resources_supported_operations>` is a listing of framework layers supported by OpenVINO. 
-
-:doc:`Supported Operations <openvino_resources_supported_operations_frontend>` is a listing of layers supported by OpenVINO inference devices.
-
 :doc:`Glossary <openvino_docs_OV_Glossary>` contains terms used in OpenVINO.
 
 :doc:`Legal Information <openvino_docs_Legal_Information>` has trademark information and other legal statements.
diff --git a/docs/resources/telemetry_information.md b/docs/articles_en/about_openvino/additional_resources/telemetry_information.md
similarity index 100%
rename from docs/resources/telemetry_information.md
rename to docs/articles_en/about_openvino/additional_resources/telemetry_information.md
diff --git a/docs/resources/tensorflow_frontend.md b/docs/articles_en/about_openvino/additional_resources/tensorflow_frontend.md
similarity index 100%
rename from docs/resources/tensorflow_frontend.md
rename to docs/articles_en/about_openvino/additional_resources/tensorflow_frontend.md
diff --git a/docs/articles_en/about_openvino/compatibility_and_support.md b/docs/articles_en/about_openvino/compatibility_and_support.md
new file mode 100644
index 00000000000000..904c4b7337d2ba
--- /dev/null
+++ b/docs/articles_en/about_openvino/compatibility_and_support.md
@@ -0,0 +1,25 @@
+# Compatibility and Support {#compatibility_and_support}
+
+@sphinxdirective
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   openvino_supported_models
+   openvino_docs_OV_UG_supported_plugins_Supported_Devices
+   openvino_resources_supported_operations
+   openvino_resources_supported_operations_frontend
+
+
+:doc:`Supported Devices <openvino_docs_OV_UG_supported_plugins_Supported_Devices>` - compatibility information for supported hardware accelerators.
+
+:doc:`Supported Models <openvino_supported_models>` - a table of models officially supported by OpenVINO.
+
+:doc:`Supported Operations <openvino_resources_supported_operations>` - a listing of framework layers supported by OpenVINO. 
+
+:doc:`Supported Operations <openvino_resources_supported_operations_frontend>` - a listing of layers supported by OpenVINO inference devices.
+
+
+
+@endsphinxdirective
diff --git a/docs/benchmarks/performance_benchmarks.md b/docs/articles_en/about_openvino/performance_benchmarks.md
similarity index 100%
rename from docs/benchmarks/performance_benchmarks.md
rename to docs/articles_en/about_openvino/performance_benchmarks.md
diff --git a/docs/MO_DG/prepare_model/Getting_performance_numbers.md b/docs/articles_en/about_openvino/performance_benchmarks/Getting_performance_numbers.md
similarity index 100%
rename from docs/MO_DG/prepare_model/Getting_performance_numbers.md
rename to docs/articles_en/about_openvino/performance_benchmarks/Getting_performance_numbers.md
diff --git a/docs/benchmarks/performance_benchmarks_faq.md b/docs/articles_en/about_openvino/performance_benchmarks/performance_benchmarks_faq.md
similarity index 100%
rename from docs/benchmarks/performance_benchmarks_faq.md
rename to docs/articles_en/about_openvino/performance_benchmarks/performance_benchmarks_faq.md
diff --git a/docs/benchmarks/performance_int8_vs_fp32.md b/docs/articles_en/about_openvino/performance_benchmarks/performance_int8_vs_fp32.md
similarity index 100%
rename from docs/benchmarks/performance_int8_vs_fp32.md
rename to docs/articles_en/about_openvino/performance_benchmarks/performance_int8_vs_fp32.md
diff --git a/docs/articles_en/about_openvino/release_notes.md b/docs/articles_en/about_openvino/release_notes.md
deleted file mode 100644
index 9d7427f919d6ad..00000000000000
--- a/docs/articles_en/about_openvino/release_notes.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Release Notes {#release_notes}
-
-@sphinxdirective
-
-.. raw:: html
-
-   <meta http-equiv="Refresh" content="0; url='https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino/2023-1.html'" />
-
-
-.. toctree::
-   :hidden:
-
-   prerelease_information
-
-The official OpenVINO Release Notes are published at `www.intel.com <https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino/2023-1.html>`__
-
-
-@endsphinxdirective
\ No newline at end of file
diff --git a/docs/articles_en/about_openvino/system_requirements.md b/docs/articles_en/about_openvino/system_requirements.md
new file mode 100644
index 00000000000000..11cb8333265153
--- /dev/null
+++ b/docs/articles_en/about_openvino/system_requirements.md
@@ -0,0 +1,151 @@
+# System Requirements {#system_requirements}
+
+@sphinxdirective
+
+
+Certain hardware (including but not limited to GPU and GNA) requires manual
+installation of specific drivers to work correctly. The drivers may also require
+updates to the operating system, including Linux kernel. These updates need to
+be handled by the user and are not part of OpenVINO installation. Refer to your
+system's documentation for updating instructions.
+
+
+Intel CPU processors 
+#####################
+
+.. tab-set::
+
+   .. tab-item:: Supported Hardware
+
+      * Intel Atom® processor with Intel® SSE4.2 support
+      * Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics 
+      * 6th - 13th generation Intel® Core™ processors 
+      * Intel® Xeon® Scalable Processors (code name Skylake)
+      * 2nd Generation Intel® Xeon® Scalable Processors (code name Cascade Lake) 
+      * 3rd Generation Intel® Xeon® Scalable Processors (code name Cooper Lake and Ice Lake) 
+      * 4th Generation Intel® Xeon® Scalable Processors (code name Sapphire Rapids) 
+
+   .. tab-item:: Required Operating Systems
+
+      * Ubuntu 22.04 long-term support (LTS), 64-bit (Kernel 5.15+)
+      * Ubuntu 20.04 long-term support (LTS), 64-bit (Kernel 5.15+)
+      * Ubuntu 18.04 long-term support (LTS) with limitations, 64-bit (Kernel 5.4+)
+      * Windows* 10 
+      * Windows* 11 
+      * macOS* 10.15 and above, 64-bit 
+      * Red Hat Enterprise Linux* 8, 64-bit
+
+Intel® Processor Graphics
+###########################################
+
+.. tab-set::
+
+   .. tab-item:: Supported Hardware
+
+      * Intel® HD Graphics 
+      * Intel® UHD Graphics
+      * Intel® Iris® Pro Graphics
+      * Intel® Iris® Xe Graphics
+      * Intel® Iris® Xe Max Graphics
+      * Intel® Arc ™ GPU Series
+      * Intel® Data Center GPU Flex Series
+
+   .. tab-item:: Required Operating Systems
+
+      * Ubuntu* 22.04 long-term support (LTS), 64-bit
+      * Ubuntu* 20.04 long-term support (LTS), 64-bit
+      * Windows* 10, 64-bit
+      * Windows* 11, 64-bit
+      * Red Hat Enterprise Linux* 8, 64-bit
+
+.. note::
+
+   | Using a GPU requires installing drivers that are not included in the Intel® Distribution of OpenVINO™ toolkit. 
+   | Not all Intel CPUs include the integrated graphics processor. See `Product Specifications <https://ark.intel.com/>`__
+     for information about your processor. 
+   | Although this release works with Ubuntu 20.04 for discrete graphic cards, the support is limited 
+     due to discrete graphics drivers. 
+   | Recommended `OpenCL™ driver <https://github.com/intel/compute-runtime>`__ versions:
+     22.43 for Ubuntu 22.04, 22.41 for Ubuntu 20.04 and 22.28 for Red Hat Enterprise Linux 8 
+
+
+Intel® Gaussian & Neural Accelerator
+###########################################
+
+Operating Systems:
+
+Ubuntu* 22.04 long-term support (LTS), 64-bit
+Ubuntu* 20.04 long-term support (LTS), 64-bit
+Windows* 10, 64-bit 
+Windows* 11, 64-bit
+ 
+
+Operating system and developer environment requirements
+############################################################
+
+.. tab-set::
+
+   .. tab-item:: Linux OS
+
+      * Ubuntu 22.04 with Linux kernel 5.15+
+      * Ubuntu 20.04 with Linux kernel 5.15+
+      * RHEL 8 with Linux kernel 5.4
+
+      A Linux OS build environment requires:
+      
+      * Python* 3.7-3.11
+      * `Intel® HD Graphics Driver <https://downloadcenter.intel.com/product/80939/Graphics-Drivers>`__ 
+        for inference on a GPU.
+
+      GNU Compiler Collection and CMake are needed for building from source:
+
+      * `GNU Compiler Collection (GCC) <https://www.gnu.org/software/gcc/>`__
+        8.4 (RHEL 8) 9.3 (Ubuntu 20)
+      * `CMake <https://cmake.org/download/>`__ 3.10 or higher
+
+      To support CPU, GPU, GNA, or hybrid-core CPU capabilities, higher versions of kernel 
+      might be required for 10th Gen Intel® Core™ Processor, 
+      11th Gen Intel® Core™ Processors, 11th Gen Intel® Core™ Processors S-Series Processors, 
+      12th Gen Intel® Core™ Processors, 13th Gen Intel® Core™ Processors,  or 4th Gen 
+      Intel® Xeon® Scalable Processors.
+
+   .. tab-item:: Windows* 10 and 11
+
+      A Windows OS build environment requires:
+
+      * `Microsoft Visual Studio 2019 <https://visualstudio.microsoft.com/vs/older-downloads/>`__
+      * `CMake <https://cmake.org/download/>`__ 3.14 or higher
+      * `Python 3.7-3.11 <http://www.python.org/downloads/>`__
+      * `Intel® HD Graphics Driver <https://downloadcenter.intel.com/product/80939/Graphics-Drivers>`__ for inference on a GPU.
+
+   .. tab-item:: macOS* 10.15 and above
+
+      A macOS build environment requires:
+
+      * `Xcode 10.3 <https://developer.apple.com/xcode/>`__
+      * `Python 3.7-3.11 <http://www.python.org/downloads/>`__
+      * `CMake 3.13 or higher <https://cmake.org/download/>`__
+
+   .. tab-item:: DL framework versions
+
+      * TensorFlow 1.15, 2.12
+      * MxNet 1.9
+      * ONNX 1.13
+      * PaddlePaddle* 2.4
+
+      Other DL Framework versions may be compatible with the current OpenVINO
+      release, but only the versions listed here are fully validated.
+
+
+.. note::
+
+   OpenVINO Python binaries and binaries on Windows/CentOS7/MACOS(x86) are built
+   with oneTBB libraries. Other binaries on Ubuntu and Redhat OSes are built with
+   legacy TBB which is released by OS distribution. OpenVINO can be built with 
+   either oneTBB or legacy TBB by the user on all OS systems listed. System 
+   compatibility and performance are improved on Hybrid CPUs, 
+   such as 12th Gen Intel Core and above.
+
+
+
+@endsphinxdirective
\ No newline at end of file
diff --git a/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition.md b/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition.md
index 9de12249a341f8..e45eac3b3a3c67 100644
--- a/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition.md
+++ b/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition.md
@@ -624,6 +624,233 @@ Here is the guide to transition from legacy model preprocessing to new API prepr
 
             - Not available in OVC tool. Please check Python API.
 
+Cutting Off Parts of a Model
+############################
+
+Performing surgery by cutting model inputs and outputs from a model is no longer available in the new conversion API. Instead, we recommend performing the cut in the original framework. 
+Below are examples of model cutting of TensorFlow protobuf, TensorFlow SavedModel, and ONNX formats with the legacy conversion API, compared to achieving the same cut with tools provided by the Tensorflow and ONNX frameworks.
+For PyTorch, TensorFlow 2 Keras, and PaddlePaddle, we recommend changing the original model code to perform the model cut.
+
+Note: This guide does not cover the cutting a model by input port of an operation that MO tool provides using `input` and `output` options, for example, `--input 1:name_op`. 
+
+``PyTorch``
+###########
+
+Model cut for PyTorch is not available in legacy API.  
+
+When it is needed to remove a whole module from the model it is possible to replace such modules with `Identity`. Below is the example of removing `conv1` and `bn1` modules at the input and `fc` module at the output of the resnet50 model.
+
+.. code-block:: py
+   :force:
+
+   import openvino as ov
+   import torch
+   import torchvision
+   from torch.nn import Identity
+     
+   # Load pretrained model
+   model = torchvision.models.resnet50(weights='DEFAULT')
+     
+   # input cut
+   model.conv1 = Identity()
+   model.bn1 = Identity()
+     
+   # output cut
+   model.fc = Identity()
+     
+   # convert and compile the model
+   ov_model = ov.convert_model(model, input=([-1,64,-1,-1], torch.float32))
+   compiled_model = ov.compile_model(ov_model)
+
+When it is needed to remove one or more outputs from the model it is possible to create a wrapper for the model and only output the needed output. Below is the example of removing second output from the model.
+
+.. code-block:: py
+   :force:
+
+   import openvino as ov
+   import torch
+
+   # Example of model with multiple outputs
+   class Model(torch.nn.Module):
+       def __init__(self):
+           super(Model, self).__init__() 
+           self.linear1 = torch.nn.Linear(100, 200)
+           self.activation1 = torch.nn.ReLU()
+           self.linear2 = torch.nn.Linear(200, 10)
+           self.activation2 = torch.nn.Sigmoid()
+
+       def forward(self, x):
+           x = self.linear1(x)
+           x = self.activation1(x)
+           y = self.linear2(x)
+           y = self.activation2(y)
+           return x, y
+     
+   # New model, where some outputs are cut
+   class CutModel(torch.nn.Module):
+       def __init__(self):
+           super(CutModel, self).__init__()
+           self.model = Model()
+
+       def forward(self, x): 
+
+           # get first output
+           x, _ = self.model(x)
+
+           return x
+     
+   # Model with output cut
+   cut_model = CutModel()
+     
+   # convert and compile the model
+   ov_model = ov.convert_model(cut_model, input=([-1,-1,-1], torch.float32))
+   compiled_model = ov.compile_model(ov_model)
+
+
+``TensorFlow protobuf format / tf.Graph / tf.GraphDef``
+#######################################################
+
+Legacy API.
+
+.. code-block:: py
+   :force:
+
+   import openvino as ov
+   import openvino.tools.mo as mo
+         
+   import tensorflow as tf
+    
+   def load_graph(model_path):
+       graph_def = tf.compat.v1.GraphDef()
+       with open(model_path, "rb") as f:
+           graph_def.ParseFromString(f.read())
+       with tf.compat.v1.Graph().as_default() as graph:
+           tf.graph_util.import_graph_def(graph_def, name="")
+           return graph
+           
+   # Load TF model
+   graph = load_graph("/path_to_model/HugeCTR.pb")
+     
+   # Convert the model with input and output cut
+   input_name = "concat"
+   output_name = "MatVec_3/Squeeze"
+   ov_model = mo.convert_model(graph, input=(input_name, [-1, -1]), output=output_name)
+     
+   # Compile the model
+   compiled_model = ov.compile_model(ov_model)
+
+Model cut in original FW.
+
+.. code-block:: py
+   :force:
+
+   import openvino as ov
+   import tensorflow as tf
+     
+   from tensorflow.python.tools.strip_unused_lib import strip_unused
+     
+   def load_graph(model_path):
+       graph_def = tf.compat.v1.GraphDef()
+       with open(model_path, "rb") as f:
+           graph_def.ParseFromString(f.read())
+       with tf.compat.v1.Graph().as_default() as graph:
+           tf.graph_util.import_graph_def(graph_def, name="")
+           return graph
+     
+   # Load TF model
+   graph = load_graph("/path_to_model/HugeCTR.pb")
+     
+   # Cut the model
+   input_name = "concat"
+   output_name = "MatVec_3/Squeeze"
+   graph_def = graph.as_graph_def()
+   new_graph_def = strip_unused(graph_def, [input_name], [output_name], tf.float32.as_datatype_enum)
+     
+   # Convert and compile model
+   ov_model = ov.convert_model(new_graph_def, input=[-1, -1])
+   cmp_model = ov.compile_model(ov_model)
+
+
+``TensorFlow SavedModel format``
+################################
+
+Model cut for SavedModel format is not available in legacy API.  
+
+Example of model cut in original FW.
+
+.. code-block:: py
+   :force:
+
+   import openvino as ov
+   import tensorflow_hub as hub
+     
+   import tensorflow as tf
+   from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+   from tensorflow.python.tools.strip_unused_lib import strip_unused
+     
+   # Load TF model
+   model = hub.load("https://tfhub.dev/svampeatlas/vision/embedder/fungi_V2/1?tf-hub-format=compressed")
+     
+   # Convert model to GraphDef
+   model_func = model.signatures["default"]
+   frozen_func = convert_variables_to_constants_v2(model_func)
+   graph_def = frozen_func.graph.as_graph_def()
+     
+   # Cut the model
+   input_name = 'InceptionV4/InceptionV4/Conv2d_2b_3x3/Relu'
+   output_name = 'InceptionV4/InceptionV4/Mixed_7c/concat'
+   new_graph_def = strip_unused(graph_def, [input_name], [output_name], tf.float32.as_datatype_enum)
+     
+   # Convert and compile the model
+   ov_model = ov.convert_model(new_graph_def)
+   compiled_model = ov.compile_model(ov_model)
+
+
+``ONNX``
+########
+
+
+Legacy API.
+
+.. code-block:: py
+   :force:
+
+   import openvino as ov
+   import openvino.tools.mo as mo
+          
+   input_path = "/path_to_model/yolov8x.onnx"
+          
+   # Convert model and perform input and output cut
+   input_name = "/model.2/Concat_output_0"
+   output_name = "/model.22/Concat_3_output_0"
+   ov_model = mo.convert_model(input_path, input=input_name, output=output_name)
+          
+   # Compile model
+   ov.compile_model(ov_model)
+
+Model cut in original FW.
+
+.. code-block:: py
+   :force:
+
+   import onnx
+   import openvino as ov
+          
+   input_path = "/path_to_model/yolov8x.onnx"
+          
+   # Cut the model
+   input_name = "/model.2/Concat_output_0"
+   output_name = "/model.22/Concat_3_output_0"
+   cut_model_path = "/path_to_model/yolov8x_cut.onnx"
+   onnx.utils.extract_model(input_path, cut_model_path, [input_name], [output_name])
+          
+   # Convert model
+   ov_model = ov.convert_model(cut_model_path)
+          
+   # Compile model
+   ov.compile_model(ov_model)
+
+
 Supported Frameworks in MO vs OVC
 #################################
 
diff --git a/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api.md b/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api.md
index dce063b625fd24..86ef2bdda17d22 100644
--- a/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api.md
+++ b/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api.md
@@ -2,8 +2,6 @@
 
 @sphinxdirective
 
-.. _deep learning model optimizer:
-
 .. toctree::
    :maxdepth: 1
    :hidden:
diff --git a/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/setting_input_shapes.md b/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/setting_input_shapes.md
index e962dc8babb472..3de90266af87db 100644
--- a/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/setting_input_shapes.md
+++ b/docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/setting_input_shapes.md
@@ -8,9 +8,6 @@ With model conversion API you can increase your model's efficiency by providing
    :description: Learn how to increase the efficiency of a model with MO by providing an additional shape definition with the input_shape and static_shape parameters.
 
 
-.. _when_to_specify_input_shapes:
-
-
 Specifying input_shape parameter
 ################################
 
diff --git a/docs/articles_en/get started/installing-openvino-overview.md b/docs/articles_en/get started/installing-openvino-overview.md
index f0c6e61981635f..f1d222c3589dca 100644
--- a/docs/articles_en/get started/installing-openvino-overview.md	
+++ b/docs/articles_en/get started/installing-openvino-overview.md	
@@ -21,7 +21,7 @@
 
    <script type="module" crossorigin src="_static/selector-tool/assets/index-f34d1fad.js"></script>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-   <iframe id="selector" src="_static/selector-tool/selector-12b6d7d.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
+   <iframe id="selector" src="_static/selector-tool/selector-68d2f71.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
 
 .. warning::
    
diff --git a/docs/articles_en/get_started.md b/docs/articles_en/get_started.md
index d414afd80d8b04..54a6be18958710 100644
--- a/docs/articles_en/get_started.md
+++ b/docs/articles_en/get_started.md
@@ -14,6 +14,7 @@
    Install OpenVINO <openvino_docs_install_guides_overview>
    Additional Hardware Setup <openvino_docs_install_guides_configurations_header>
    Troubleshooting <openvino_docs_get_started_guide_troubleshooting>
+   System Requirements <system_requirements>
 
 
 .. raw:: html
diff --git a/docs/articles_en/learn_openvino/openvino_samples/cpp_benchmark_tool.md b/docs/articles_en/learn_openvino/openvino_samples/cpp_benchmark_tool.md
index 168d3b85ba898a..9a056ffc79a4cd 100644
--- a/docs/articles_en/learn_openvino/openvino_samples/cpp_benchmark_tool.md
+++ b/docs/articles_en/learn_openvino/openvino_samples/cpp_benchmark_tool.md
@@ -24,7 +24,7 @@ To use the C++ benchmark_app, you must first build it following the :doc:`Build
 
    If you installed OpenVINO Runtime using PyPI or Anaconda Cloud, only the :doc:`Benchmark Python Tool <openvino_inference_engine_tools_benchmark_tool_README>` is available, and you should follow the usage instructions on that page instead.
 
-The benchmarking application works with models in the OpenVINO IR (``model.xml`` and ``model.bin``) and ONNX (``model.onnx``) formats. Make sure to :doc:`convert your models <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` if necessary.
+The benchmarking application works with models in the OpenVINO IR, TensorFlow, TensorFlow Lite, PaddlePaddle, PyTorch and ONNX formats. If you need it, OpenVINO also allows you to :doc:`convert your models <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
 
 To run benchmarking with default options on a model, use the following command:
 
diff --git a/docs/articles_en/openvino_workflow.md b/docs/articles_en/openvino_workflow.md
index 3d617a35155a66..5453c03426cd3f 100644
--- a/docs/articles_en/openvino_workflow.md
+++ b/docs/articles_en/openvino_workflow.md
@@ -20,29 +20,39 @@
    pytorch_2_0_torch_compile
    
 
+.. image:: ./_static/images/model_conversion_diagram.svg
+   :alt: model conversion diagram
+
+OpenVINO offers multiple workflows, depending on the use case and personal or project preferences.
+The diagram above is only a rough representation of the available options, but this section will
+give you a detailed view of how you can go from preparing your model, through optimizing it,
+to executing inference, and deploying your solution. 
+
+
 | :doc:`Model Preparation <openvino_docs_model_processing_introduction>`
-| With model conversion API guide, you will learn to convert pre-trained models for use with OpenVINO™. You can use your own models or choose some from a broad selection in online databases, such as `TensorFlow Hub <https://tfhub.dev/>`__, `Hugging Face <https://huggingface.co/>`__, `Torchvision models <https://pytorch.org/hub/>`__..
+|    Learn how to convert pre-trained models to OpenVINO IR, using different approaches for more convenience or higher performance.
+
 
 | :doc:`Model Optimization and Compression <openvino_docs_model_optimization_guide>`
-| In this section you will find out how to optimize a model to achieve better inference performance. It describes multiple optimization methods for both the training and post-training stages. 
+|    Find out how to optimize a model to achieve better inference performance, utilizing multiple optimization methods for both in-training compression and post-training quantization. 
+
 
 | :doc:`Running Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
-| This section explains describes how to run inference which is the most basic form of deployment and the quickest way of launching inference.
+|    See how to run inference with OpenVINO, which is the most basic form of deployment, and the quickest way of running a deep learning model.
 
+| :doc:`Deployment Option 1. Using OpenVINO Runtime <openvino_deployment_guide>` 
+|    Deploy a model locally, reading the file directly from your application and utilizing resources available to the system.
+|    Deployment on a local system uses the steps described in the section on running inference.
 
-Once you have a model that meets both OpenVINO™ and your requirements, you can choose how to deploy it with your application. 
 
+| :doc:`Deployment Option 2. Using Model Server <ovms_what_is_openvino_model_server>`
+|    Deploy a model remotely, connecting your application to an inference server and utilizing external resources, with no impact on the app's performance.
+|    Deployment on OpenVINO Model Server is quick and does not require any additional steps described in the section on running inference.
 
-| :doc:`Option 1. Deployment via OpenVINO Runtime <openvino_deployment_guide>` 
-| Local deployment uses OpenVINO Runtime that is called from, and linked to, the application directly. 
-| It utilizes resources available to the system and provides the quickest way of launching inference.
-| Deployment on a local system requires performing the steps from the running inference section.
 
+| :doc:`Deployment Option 3. Using torch.compile for PyTorch 2.0  <pytorch_2_0_torch_compile>`
+|    Deploy a PyTorch model using OpenVINO in a PyTorch-native application.
 
-| :doc:`Option 2. Deployment via Model Server <ovms_what_is_openvino_model_server>`
-| Deployment via OpenVINO Model Server allows the application to connect to the inference server set up remotely. 
-| This way inference can use external resources instead of those available to the application itself. 
-| Deployment on a model server can be done quickly and without performing any additional steps described in the running inference section.
 
 
 @endsphinxdirective
\ No newline at end of file
diff --git a/docs/articles_en/openvino_workflow/model_introduction.md b/docs/articles_en/openvino_workflow/model_introduction.md
deleted file mode 100644
index c11beb17e67764..00000000000000
--- a/docs/articles_en/openvino_workflow/model_introduction.md
+++ /dev/null
@@ -1,258 +0,0 @@
-# Model Preparation {#openvino_docs_model_processing_introduction}
-
-@sphinxdirective
-
-.. meta::
-   :description: Preparing models for OpenVINO Runtime. Learn about the methods
-                 used to read, convert and compile models from different frameworks.
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   Supported_Model_Formats
-   openvino_docs_OV_Converter_UG_Conversion_Options
-   openvino_docs_OV_Converter_UG_prepare_model_convert_model_Converting_Model
-
-
-Every deep learning workflow begins with obtaining a model. You can choose to prepare
-a custom one, use a ready-made solution and adjust it to your needs, or even download
-and run a pre-trained network from an online database, such as 
-`TensorFlow Hub <https://tfhub.dev/>`__, `Hugging Face <https://huggingface.co/>`__,
-or `Torchvision models <https://pytorch.org/hub/>`__.
-
-If your selected model is in one of the :doc:`OpenVINO™ supported model formats <Supported_Model_Formats>`,
-you can use it directly, without the need to save as OpenVINO IR
-(`openvino.Model <api/ie_python_api/_autosummary/openvino.Model.html>`__ -
-`ov.Model <api/ie_python_api/_autosummary/openvino.Model.html>`__). 
-For this purpose, you can use ``openvino.Core.read_model`` and ``openvino.Core.compile_model`` 
-methods, so that conversion is performed automatically before inference, for 
-maximum convenience. Note that for PyTorch models, Python API
-is the only conversion option. TensorFlow may present additional considerations
-:doc:`TensorFlow Frontend Capabilities and Limitations <openvino_docs_MO_DG_TensorFlow_Frontend>`.
-
-
-For better performance and more optimization options, OpenVINO also offers a conversion
-API with two possible approaches: the Python API functions (``openvino.convert_model`` 
-and ``openvino.save_model``) and the ``ovc`` command line tool, which are described in detail in this article. 
-
-.. note::
-   
-   Model conversion API prior to OpenVINO 2023.1 is considered deprecated. 
-   Both existing and new projects are recommended to transition to the new
-   solutions, keeping in mind that they are not fully backwards compatible 
-   with ``openvino.tools.mo.convert_model`` or the ``mo`` CLI tool.
-   For more details, see the :doc:`Model Conversion API Transition Guide <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`.
-
-
-
-
-Convert a Model in Python: ``convert_model``
-##############################################
-
-You can use the Model conversion API in Python with the ``openvino.convert_model`` function. This function converts a model from its original framework representation, for example PyTorch or TensorFlow, to the object of type ``openvino.Model``. The resulting ``openvino.Model`` can be compiled with ``openvino.compile_model`` and inferred in the same application (Python script or Jupyter Notebook) or saved into a file using``openvino.save_model`` for future use. Below, there are examples of how to use the ``openvino.convert_model`` with models from popular public repositories:
-
-
-.. tab-set::
-
-   .. tab-item:: Torchvision
-
-      .. code-block:: py
-         :force:
-
-            import openvino as ov
-            import torch
-            from torchvision.models import resnet50
-
-            model = resnet50(weights='DEFAULT')
-
-            # prepare input_data
-            input_data = torch.rand(1, 3, 224, 224)
-
-            ov_model = ov.convert_model(model, example_input=input_data)
-
-            ###### Option 1: Save to OpenVINO IR:
-
-            # save model to OpenVINO IR for later use
-            ov.save_model(ov_model, 'model.xml')
-
-            ###### Option 2: Compile and infer with OpenVINO:
-
-            # compile model
-            compiled_model = ov.compile_model(ov_model)
-
-            # run inference
-            result = compiled_model(input_data)
-
-   .. tab-item:: Hugging Face Transformers
-
-      .. code-block:: py
-
-         from transformers import BertTokenizer, BertModel
-
-         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-         model = BertModel.from_pretrained("bert-base-uncased")
-         text = "Replace me by any text you'd like."
-         encoded_input = tokenizer(text, return_tensors='pt')
-
-         import openvino as ov
-         ov_model = ov.convert_model(model, example_input={**encoded_input})
-
-         ###### Option 1: Save to OpenVINO IR:
-
-         # save model to OpenVINO IR for later use
-         ov.save_model(ov_model, 'model.xml')
-
-         ###### Option 2: Compile and infer with OpenVINO:
-
-         # compile model
-         compiled_model = ov.compile_model(ov_model)
-
-         # prepare input_data using HF tokenizer or your own tokenizer
-         # encoded_input is reused here for simplicity
-
-         # run inference
-         result = compiled_model({**encoded_input})
-
-   .. tab-item:: Keras Applications
-
-      .. code-block:: py
-
-         import tensorflow as tf
-         import openvino as ov
-
-         tf_model = tf.keras.applications.ResNet50(weights="imagenet")
-         ov_model = ov.convert_model(tf_model)
-
-         ###### Option 1: Save to OpenVINO IR:
-
-         # save model to OpenVINO IR for later use
-         ov.save_model(ov_model, 'model.xml')
-
-         ###### Option 2: Compile and infer with OpenVINO:
-
-         # compile model
-         compiled_model = ov.compile_model(ov_model)
-
-         # prepare input_data
-         import numpy as np
-         input_data = np.random.rand(1, 224, 224, 3)
-
-         # run inference
-         result = compiled_model(input_data)
-
-   .. tab-item:: TensorFlow Hub
-
-      .. code-block:: py
-
-         import tensorflow as tf
-         import tensorflow_hub as hub
-         import openvino as ov
-
-         model = tf.keras.Sequential([
-               hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/5")
-         ])
-
-         # Check model page for information about input shape: https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/5
-         model.build([None, 224, 224, 3])
-
-         model.save('mobilenet_v1_100_224')  # use a temporary directory
-         ov_model = ov.convert_model('mobilenet_v1_100_224')
-
-         ###### Option 1: Save to OpenVINO IR:
-
-         ov.save_model(ov_model, 'model.xml')
-
-         ###### Option 2: Compile and infer with OpenVINO:
-
-         compiled_model = ov.compile_model(ov_model)
-
-         # prepare input_data
-         import numpy as np
-         input_data = np.random.rand(1, 224, 224, 3)
-
-         # run inference
-         result = compiled_model(input_data)
-
-   .. tab-item:: ONNX Model Hub
-
-      .. code-block:: py
-
-         import onnx
-
-         model = onnx.hub.load("resnet50")
-         onnx.save(model, 'resnet50.onnx')  # use a temporary file for model
-
-         import openvino as ov
-         ov_model = ov.convert_model('resnet50.onnx')
-
-         ###### Option 1: Save to OpenVINO IR:
-
-         # save model to OpenVINO IR for later use
-         ov.save_model(ov_model, 'model.xml')
-
-         ###### Option 2: Compile and infer with OpenVINO:
-
-         # compile model
-         compiled_model = ov.compile_model(ov_model)
-
-         # prepare input_data
-         import numpy as np
-         input_data = np.random.rand(1, 3, 224, 224)
-
-         # run inference
-         result = compiled_model(input_data)
-
-In Option 1, where the ``openvino.save_model`` function is used, an OpenVINO model is serialized in the file system as two files with ``.xml`` and ``.bin`` extensions. This pair of files is called OpenVINO Intermediate Representation format (OpenVINO IR, or just IR) and useful for efficient model deployment. OpenVINO IR can be loaded into another application for inference using the ``openvino.Core.read_model`` function. For more details, refer to the :doc:`OpenVINO™ Runtime documentation <openvino_docs_OV_UG_OV_Runtime_User_Guide>`.
-
-Option 2, where ``openvino.compile_model`` is used, provides a convenient way to quickly switch from framework-based code to OpenVINO-based code in your existing Python inference application. In this case, the converted model is not saved to IR. Instead, the model is compiled and used for inference within the same application.
-
-Option 1 separates model conversion and model inference into two different applications. This approach is useful for deployment scenarios requiring fewer extra dependencies and faster model loading in the end inference application. 
-
-For example, converting a PyTorch model to OpenVINO usually demands the ``torch`` Python module and Python. This process can take extra time and memory. But, after the converted model is saved as OpenVINO IR with ``openvino.save_model``, it can be loaded in a separate application without requiring the ``torch`` dependency and the time-consuming conversion. The inference application can be written in other languages supported by OpenVINO, for example, in C++, and Python installation is not necessary for it to run.
-
-Before saving the model to OpenVINO IR, consider applying :doc:`Post-training Optimization <ptq_introduction>` to enable more efficient inference and smaller model size.
-
-The figure below illustrates the typical workflow for deploying a trained deep-learning model.
-
-.. image:: ./_static/images/model_conversion_diagram.svg
-   :alt: model conversion diagram
-
-Convert a Model in CLI: ``ovc``
-###############################
-
-Another option for model conversion is to use ``ovc`` command-line tool, which stands for OpenVINO Model Converter. The tool combines both ``openvino.convert_model`` and ``openvino.save_model`` functionalities. It is convenient to use when the original model is ready for inference and is in one of the supported file formats: ONNX, TensorFlow, TensorFlow Lite, or PaddlePaddle. As a result, ``ovc`` produces an OpenVINO IR, consisting of ``.xml`` and ``.bin`` files, which needs to be read with the ``openvino.Core.read_model`` method. You can compile and infer the ``ov.Model`` later with :doc:`OpenVINO™ Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
-
-.. note::
-   PyTorch models cannot be converted with ``ovc``, use ``openvino.convert_model`` instead.
-
-The results of both ``ovc`` and ``openvino.convert_model``/``openvino.save_model`` conversion methods are the same. You can choose either of them based on your convenience. Note that there should not be any differences in the results of model conversion if the same set of parameters is used and the model is saved into OpenVINO IR.
-
-
-
-
-Additional Resources
-####################
-
-The following articles describe in details how to obtain and prepare your model depending on the source model type:
-
-* :doc:`Convert different model formats to the ov.Model format <Supported_Model_Formats>`.
-* :doc:`Review all available conversion parameters <openvino_docs_OV_Converter_UG_Conversion_Options>`.
-
-To achieve the best model inference performance and more compact OpenVINO IR representation follow:
-
-* :doc:`Post-training optimization <ptq_introduction>`
-* :doc:`Model inference in OpenVINO Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
-
-If you are using legacy conversion API (``mo`` or ``openvino.tools.mo.convert_model``), please refer to the following materials:
-
-* :doc:`Transition from legacy mo and ov.tools.mo.convert_model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`
-* :doc:`Legacy Model Conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
-
-
-
-
-.. api/ie_python_api/_autosummary/openvino.Model.html is a broken link for some reason - need to investigate python api article generation
-
-
-@endsphinxdirective
diff --git a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats.md b/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats.md
deleted file mode 100644
index 6ff5e620f10d78..00000000000000
--- a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Supported Model Formats {#Supported_Model_Formats}
-
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch
-   openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow
-   openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX
-   openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite
-   openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_Paddle
-
-
-**OpenVINO IR (Intermediate Representation)** - the proprietary format of OpenVINO™, benefiting from the full extent of its features. The result of running ``ovc`` CLI tool or ``openvino.save_model`` is OpenVINO IR. All other supported formats can be converted to the IR, refer to the following articles for details on conversion:
-
-* :doc:`How to convert PyTorch <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch>`
-* :doc:`How to convert ONNX <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX>`
-* :doc:`How to convert TensorFlow <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`
-* :doc:`How to convert TensorFlow Lite <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>`
-* :doc:`How to convert PaddlePaddle <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_Paddle>`
-
-To choose the best workflow for your application, read the :doc:`Model Preparation section <openvino_docs_model_processing_introduction>`
-
-Refer to the list of all supported conversion options in :doc:`Conversion Parameters <openvino_docs_OV_Converter_UG_Conversion_Options>`
-
-Additional Resources
-####################
-
-* :doc:`Transition guide from the legacy to new conversion API <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`
-
-@endsphinxdirective
diff --git a/docs/articles_en/openvino_workflow/model_preparation.md b/docs/articles_en/openvino_workflow/model_preparation.md
new file mode 100644
index 00000000000000..dbc34640a992cc
--- /dev/null
+++ b/docs/articles_en/openvino_workflow/model_preparation.md
@@ -0,0 +1,263 @@
+# Model Preparation {#openvino_docs_model_processing_introduction}
+
+@sphinxdirective
+
+.. meta::
+   :description: Preparing models for OpenVINO Runtime. Learn about the methods
+                 used to read, convert and compile models from different frameworks.
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+
+   Conversion Parameters <openvino_docs_OV_Converter_UG_Conversion_Options>
+   Setting Input Shapes <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Converting_Model>
+   Convert from PyTorch <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch>
+   Convert from TensorFlow <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow>
+   Convert from ONNX <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX>
+   Convert from TensorFlow_Lite <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>
+   Convert from PaddlePaddle <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_Paddle>
+   Supported_Model_Formats
+
+
+To start working with OpenVINO, you need to obtain a model in one of the
+:doc:`supported model formats <Supported_Model_Formats>`. The easiest way
+to do so is to download a pre-trained network from an online database, such as 
+`TensorFlow Hub <https://tfhub.dev/>`__,
+`Hugging Face <https://huggingface.co/>`__, or 
+`Torchvision models <https://pytorch.org/hub/>`__.
+
+The OpenVINO workflow starts with converting the selected model to its
+proprietary format, :doc:`OpenVINO IR <openvino_ir>`
+(`openvino.Model <api/ie_python_api/_autosummary/openvino.runtime.Model.html>`__ -
+`ov.Model <api/ie_python_api/_autosummary/openvino.runtime.Model.html>`__).
+Although in most cases it can be done automatically, under the hood, explicit 
+conversion may enable more optimization options and better performance. 
+It is done in one of two ways:
+
+   * the Python API functions (``openvino.convert_model`` and ``openvino.save_model``) 
+   * the ``ovc`` command line tool. 
+
+   .. note::
+      
+      Model conversion API prior to OpenVINO 2023.1 is considered deprecated. 
+      Existing and new projects are recommended to transition to the new
+      solutions, keeping in mind that they are not fully backwards compatible 
+      with ``openvino.tools.mo.convert_model`` or the ``mo`` CLI tool.
+      For more details, see the :doc:`Model Conversion API Transition Guide <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`.
+
+
+Convert a Model in Python: ``convert_model``
+##############################################
+
+The Model conversion API in Python uses the ``openvino.convert_model`` function,
+turning a given model to the ``openvino.Model`` object. The object may be used
+further, compiled and inferred, or saved to a drive as :doc:`OpenVINO IR <openvino_ir>`
+(``openvino.save_model`` produces a set of ``.xml`` and ``.bin`` files).
+
+See how to use ``openvino.convert_model`` with models from some of the most popular
+public repositories:
+
+.. tab-set::
+
+   .. tab-item:: Torchvision
+
+      .. code-block:: py
+         :force:
+
+            import openvino as ov
+            import torch
+            from torchvision.models import resnet50
+
+            model = resnet50(weights='DEFAULT')
+
+            # prepare input_data
+            input_data = torch.rand(1, 3, 224, 224)
+
+            ov_model = ov.convert_model(model, example_input=input_data)
+
+            ###### Option 1: Save to OpenVINO IR:
+
+            # save model to OpenVINO IR for later use
+            ov.save_model(ov_model, 'model.xml')
+
+            ###### Option 2: Compile and infer with OpenVINO:
+
+            # compile model
+            compiled_model = ov.compile_model(ov_model)
+
+            # run inference
+            result = compiled_model(input_data)
+
+   .. tab-item:: Hugging Face Transformers
+
+      .. code-block:: py
+
+         from transformers import BertTokenizer, BertModel
+
+         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+         model = BertModel.from_pretrained("bert-base-uncased")
+         text = "Replace me by any text you'd like."
+         encoded_input = tokenizer(text, return_tensors='pt')
+
+         import openvino as ov
+         ov_model = ov.convert_model(model, example_input={**encoded_input})
+
+         ###### Option 1: Save to OpenVINO IR:
+
+         # save model to OpenVINO IR for later use
+         ov.save_model(ov_model, 'model.xml')
+
+         ###### Option 2: Compile and infer with OpenVINO:
+
+         # compile model
+         compiled_model = ov.compile_model(ov_model)
+
+         # prepare input_data using HF tokenizer or your own tokenizer
+         # encoded_input is reused here for simplicity
+
+         # run inference
+         result = compiled_model({**encoded_input})
+
+   .. tab-item:: Keras Applications
+
+      .. code-block:: py
+
+         import tensorflow as tf
+         import openvino as ov
+
+         tf_model = tf.keras.applications.ResNet50(weights="imagenet")
+         ov_model = ov.convert_model(tf_model)
+
+         ###### Option 1: Save to OpenVINO IR:
+
+         # save model to OpenVINO IR for later use
+         ov.save_model(ov_model, 'model.xml')
+
+         ###### Option 2: Compile and infer with OpenVINO:
+
+         # compile model
+         compiled_model = ov.compile_model(ov_model)
+
+         # prepare input_data
+         import numpy as np
+         input_data = np.random.rand(1, 224, 224, 3)
+
+         # run inference
+         result = compiled_model(input_data)
+
+   .. tab-item:: TensorFlow Hub
+
+      .. code-block:: py
+
+         import tensorflow as tf
+         import tensorflow_hub as hub
+         import openvino as ov
+
+         model = tf.keras.Sequential([
+               hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/5")
+         ])
+
+         # Check model page for information about input shape: https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/5
+         model.build([None, 224, 224, 3])
+
+         model.save('mobilenet_v1_100_224')  # use a temporary directory
+         ov_model = ov.convert_model('mobilenet_v1_100_224')
+
+         ###### Option 1: Save to OpenVINO IR:
+
+         ov.save_model(ov_model, 'model.xml')
+
+         ###### Option 2: Compile and infer with OpenVINO:
+
+         compiled_model = ov.compile_model(ov_model)
+
+         # prepare input_data
+         import numpy as np
+         input_data = np.random.rand(1, 224, 224, 3)
+
+         # run inference
+         result = compiled_model(input_data)
+
+   .. tab-item:: ONNX Model Hub
+
+      .. code-block:: py
+
+         import onnx
+
+         model = onnx.hub.load("resnet50")
+         onnx.save(model, 'resnet50.onnx')  # use a temporary file for model
+
+         import openvino as ov
+         ov_model = ov.convert_model('resnet50.onnx')
+
+         ###### Option 1: Save to OpenVINO IR:
+
+         # save model to OpenVINO IR for later use
+         ov.save_model(ov_model, 'model.xml')
+
+         ###### Option 2: Compile and infer with OpenVINO:
+
+         # compile model
+         compiled_model = ov.compile_model(ov_model)
+
+         # prepare input_data
+         import numpy as np
+         input_data = np.random.rand(1, 3, 224, 224)
+
+         # run inference
+         result = compiled_model(input_data)
+
+
+* Saving the model, **Option 1**, is used as a separate step, outside of deployment. 
+  The file it provides is then used in the final software solution, resulting in
+  maximum performance due to fewer dependencies and faster model loading. 
+
+* Compiling the model, **Option 2**, provides a convenient way to quickly switch from 
+  framework-based code to OpenVINO-based code in your existing Python inference application. 
+  The converted model is not saved to IR but compiled and used for inference within the same application.
+
+Before saving the model to OpenVINO IR, consider :doc:`Post-training Optimization <ptq_introduction>` 
+to achieve more efficient inference and smaller model size.
+
+
+Convert a Model in CLI: ``ovc``
+###############################
+
+``ovc`` is a command-line model converter, combining the ``openvino.convert_model``
+and ``openvino.save_model`` functionalities, providing the exact same results, if the same set of 
+parameters is used for saving into OpenVINO IR. It converts files from one of the 
+:doc:`supported model formats <Supported_Model_Formats>` to :doc:`OpenVINO IR <openvino_ir>`, which can then be read, compiled,
+and run by the final inference application.
+
+.. note::
+   PyTorch models cannot be converted with ``ovc``, use ``openvino.convert_model`` instead.
+
+
+
+Additional Resources
+####################
+
+The following articles describe in detail how to obtain and prepare your model depending on the source model type:
+
+* :doc:`Convert different model formats to the ov.Model format <Supported_Model_Formats>`.
+* :doc:`Review all available conversion parameters <openvino_docs_OV_Converter_UG_Conversion_Options>`.
+
+To achieve the best model inference performance and more compact OpenVINO IR representation follow:
+
+* :doc:`Post-training optimization <ptq_introduction>`
+* :doc:`Model inference in OpenVINO Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
+
+If you are still using the legacy conversion API (``mo`` or ``openvino.tools.mo.convert_model``), please refer to the following materials:
+
+* :doc:`Transition from legacy mo and ov.tools.mo.convert_model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`
+* :doc:`Legacy Model Conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
+
+
+
+
+.. need to investigate python api article generation - api/ie_python_api/_autosummary/openvino.Model.html does not exist, api/ie_python_api/_autosummary/openvino.runtime.Model.html does.
+
+
+@endsphinxdirective
diff --git a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_ONNX.md b/docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_ONNX.md
similarity index 100%
rename from docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_ONNX.md
rename to docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_ONNX.md
diff --git a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_Paddle.md b/docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_Paddle.md
similarity index 100%
rename from docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_Paddle.md
rename to docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_Paddle.md
diff --git a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_PyTorch.md b/docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_PyTorch.md
similarity index 100%
rename from docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_PyTorch.md
rename to docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_PyTorch.md
diff --git a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_TensorFlow.md b/docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_TensorFlow.md
similarity index 99%
rename from docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_TensorFlow.md
rename to docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_TensorFlow.md
index bec51f537cd541..e74b45cbc82a91 100644
--- a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_TensorFlow.md
+++ b/docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_TensorFlow.md
@@ -3,8 +3,8 @@
 @sphinxdirective
 
 .. meta::
-   :description: Learn how to convert a model from a
-                 TensorFlow format to the OpenVINO Model.
+   :description: Learn how to convert a model from a TensorFlow format to the OpenVINO Model.
+
 
 This page provides general instructions on how to run model conversion from a TensorFlow format to the OpenVINO IR format. The instructions are different depending on whether your model was created with TensorFlow v1.X or TensorFlow v2.X.
 
diff --git a/docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_TensorFlow_Lite.md b/docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_TensorFlow_Lite.md
similarity index 100%
rename from docs/articles_en/openvino_workflow/model_introduction/supported_model_formats/Convert_Model_From_TensorFlow_Lite.md
rename to docs/articles_en/openvino_workflow/model_preparation/Convert_Model_From_TensorFlow_Lite.md
diff --git a/docs/articles_en/openvino_workflow/model_introduction/Deep_Learning_Model_Optimizer_DevGuide.md b/docs/articles_en/openvino_workflow/model_preparation/conversion_parameters.md
similarity index 99%
rename from docs/articles_en/openvino_workflow/model_introduction/Deep_Learning_Model_Optimizer_DevGuide.md
rename to docs/articles_en/openvino_workflow/model_preparation/conversion_parameters.md
index 94b01c4299179d..ca562ab46ac948 100644
--- a/docs/articles_en/openvino_workflow/model_introduction/Deep_Learning_Model_Optimizer_DevGuide.md
+++ b/docs/articles_en/openvino_workflow/model_preparation/conversion_parameters.md
@@ -2,8 +2,6 @@
 
 @sphinxdirective
 
-.. _deep learning model optimizer:
-
 .. meta::
    :description: Model Conversion API provides several parameters to adjust model conversion.
 
diff --git a/docs/articles_en/openvino_workflow/model_introduction/Converting_Model.md b/docs/articles_en/openvino_workflow/model_preparation/setting_input_shapes.md
similarity index 82%
rename from docs/articles_en/openvino_workflow/model_introduction/Converting_Model.md
rename to docs/articles_en/openvino_workflow/model_preparation/setting_input_shapes.md
index 24fa33c17f4a94..0a4b25000904d3 100644
--- a/docs/articles_en/openvino_workflow/model_introduction/Converting_Model.md
+++ b/docs/articles_en/openvino_workflow/model_preparation/setting_input_shapes.md
@@ -1,26 +1,25 @@
 # Setting Input Shapes {#openvino_docs_OV_Converter_UG_prepare_model_convert_model_Converting_Model}
 
-With model conversion API you can increase your model's efficiency by providing an additional shape definition using the ``input`` parameter.
-
 @sphinxdirective
 
 .. meta::
    :description: Learn how to increase the efficiency of a model by providing an additional shape definition with the ``input`` parameter of ``openvino.convert_model`` and ``ovc``.
 
-.. _when_to_specify_input_shapes:
 
-Specifying Shapes in the ``input`` Parameter
-#####################################################
+``openvino.convert_model`` supports conversion of models with dynamic input shapes that
+contain undefined dimensions. However, if the shape of data is not going to change from
+one inference request to another, it is recommended to **set up static shapes**
+(all dimensions are fully defined) for the inputs, using the the ``input`` parameter.
+Doing so at the model preparation stage, not at runtime, can be beneficial in terms of
+performance and memory consumption.
 
-``openvino.convert_model`` supports conversion of models with dynamic input shapes that contain undefined dimensions.
-However, if the shape of data is not going to change from one inference request to another,
-it is recommended to set up static shapes (when all dimensions are fully defined) for the inputs.
-Doing it at this stage, instead of during inference in runtime, can be beneficial in terms of performance and memory consumption.
-To set up static shapes, model conversion API provides the ``input`` parameter.
-For more information on changing input shapes in runtime, refer to the :doc:`Changing input shapes <openvino_docs_OV_UG_ShapeInference>` guide.
-To learn more about dynamic shapes in runtime, refer to the :doc:`Dynamic Shapes <openvino_docs_OV_UG_DynamicShapes>` guide.
+For more information on changing input shapes in runtime, refer to the 
+:doc:`Changing input shapes <openvino_docs_OV_UG_ShapeInference>` guide.
+To learn more about dynamic shapes in runtime, refer to the 
+:doc:`Dynamic Shapes <openvino_docs_OV_UG_DynamicShapes>` guide.
 
-The OpenVINO Runtime API may present certain limitations in inferring models with undefined dimensions on some hardware. See the :doc:`Features support matrix <openvino_docs_OV_UG_Working_with_devices>` for reference.
+The OpenVINO Runtime API may present certain limitations in inferring models with undefined dimensions on some hardware. 
+See the :doc:`Feature support matrix <openvino_docs_OV_UG_Working_with_devices>` for reference.
 In this case, the ``input`` parameter and the :doc:`reshape method <openvino_docs_OV_UG_ShapeInference>` can help to resolve undefined dimensions.
 
 For example, run model conversion for the TensorFlow MobileNet model with the single input
@@ -138,4 +137,4 @@ In practice, not every model is designed in a way that allows change of input sh
 For more information about shape follow the :doc:`inference troubleshooting <troubleshooting_reshape_errors>`
 and :ref:`ways to relax shape inference flow <how-to-fix-non-reshape-able-model>` guides.
 
-@endsphinxdirective
+@endsphinxdirective
\ No newline at end of file
diff --git a/docs/articles_en/openvino_workflow/model_preparation/supported_model_formats.md b/docs/articles_en/openvino_workflow/model_preparation/supported_model_formats.md
new file mode 100644
index 00000000000000..7688568e218f04
--- /dev/null
+++ b/docs/articles_en/openvino_workflow/model_preparation/supported_model_formats.md
@@ -0,0 +1,607 @@
+# Supported Model Formats {#Supported_Model_Formats}
+
+@sphinxdirective
+
+.. meta::
+   :description: Learn about supported model formats and the methods used to convert, read, and compile them in OpenVINO™.
+
+
+| **OpenVINO IR (Intermediate Representation)**
+| The proprietary format of OpenVINO™, benefiting from the full extent of its features. 
+  It is obtained by :doc:`converting a model <openvino_docs_model_processing_introduction>` 
+  from one of the remaining supported formats using the Python model conversion API or the 
+  OpenVINO Converter.
+| Consider storing your model in this format to minimize first-inference latency, 
+  perform model optimizations, and save space on your drive, in some cases.
+
+
+| **PyTorch, TensorFlow, TensorFlow Lite, ONNX, and PaddlePaddle**
+| These supported model formats can be read, compiled, and converted to OpenVINO IR,
+  either automatically or explicitly.
+
+
+In the Python API, these options are provided as three separate methods: 
+``read_model()``, ``compile_model()``, and ``convert_model()``.
+
+The ``convert_model()`` method enables you to perform additional adjustments 
+to the model, such as setting shapes, changing model input types or layouts, 
+cutting parts of the model, freezing inputs, etc. For a detailed description 
+of the conversion process, see the 
+:doc:`model conversion guide <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
+
+
+
+
+
+Note that for PyTorch models, Python API
+is the only conversion option. 
+
+TensorFlow may present additional considerations
+:doc:`TensorFlow Frontend Capabilities and Limitations <openvino_docs_MO_DG_TensorFlow_Frontend>`.
+
+
+
+
+
+
+
+
+
+
+Here are code examples of how to use these methods with different model formats:
+
+.. tab-set::
+
+   .. tab-item:: PyTorch
+      :sync: torch
+
+      .. tab-set::
+
+         .. tab-item:: Python
+            :sync: py
+
+            * The ``convert_model()`` method:
+
+              This is the only method applicable to PyTorch models.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Python objects**:
+
+                   * ``torch.nn.Module``
+                   * ``torch.jit.ScriptModule``
+                   * ``torch.jit.ScriptFunction``
+
+              .. code-block:: py
+                 :force:
+
+                 model = torchvision.models.resnet50(weights='DEFAULT')
+                 ov_model = convert_model(model)
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+              For more details on conversion, refer to the
+              :doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch>`
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
+              on this topic.
+
+   .. tab-item:: TensorFlow
+      :sync: tf
+
+      .. tab-set::
+
+         .. tab-item:: Python
+            :sync: py
+
+            * The ``convert_model()`` method:
+
+              When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
+                   * Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
+                   * MetaGraph - ``<INPUT_META_GRAPH>.meta``
+
+                 * **Python objects**:
+
+                   * ``tf.keras.Model``
+                   * ``tf.keras.layers.Layer``
+                   * ``tf.Module``
+                   * ``tf.compat.v1.Graph``
+                   * ``tf.compat.v1.GraphDef``
+                   * ``tf.function``
+                   * ``tf.compat.v1.session``
+                   * ``tf.train.checkpoint``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = convert_model("saved_model.pb")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+              For more details on conversion, refer to the
+              :doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/101-tensorflow-to-openvino-with-output.html>`__
+              on this topic.
+
+            * The ``read_model()`` and ``compile_model()`` methods:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
+                   * Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
+                   * MetaGraph - ``<INPUT_META_GRAPH>.meta``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = read_model("saved_model.pb")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+              For TensorFlow format, see :doc:`TensorFlow Frontend Capabilities and Limitations <openvino_docs_MO_DG_TensorFlow_Frontend>`.
+
+         .. tab-item:: C++
+            :sync: cpp
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
+                   * Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
+                   * MetaGraph - ``<INPUT_META_GRAPH>.meta``
+
+              .. code-block:: cpp
+
+                 ov::CompiledModel compiled_model = core.compile_model("saved_model.pb", "AUTO");
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: C
+            :sync: c
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
+                   * Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
+                   * MetaGraph - ``<INPUT_META_GRAPH>.meta``
+
+              .. code-block:: c
+
+                 ov_compiled_model_t* compiled_model = NULL;
+                 ov_core_compile_model_from_file(core, "saved_model.pb", "AUTO", 0, &compiled_model);
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: CLI
+            :sync: cli
+
+            You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
+
+            .. code-block:: sh
+
+               mo --input_model <INPUT_MODEL>.pb
+
+            For details on the conversion, refer to the
+            :doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`.
+
+   .. tab-item:: TensorFlow Lite
+      :sync: tflite
+
+      .. tab-set::
+
+         .. tab-item:: Python
+            :sync: py
+
+            * The ``convert_model()`` method:
+
+              When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.tflite``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = convert_model("<INPUT_MODEL>.tflite")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+              For more details on conversion, refer to the
+              :doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/119-tflite-to-openvino-with-output.html>`__
+              on this topic.
+
+
+            * The ``read_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.tflite``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = read_model("<INPUT_MODEL>.tflite")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.tflite``
+
+              .. code-block:: py
+                 :force:
+
+                 compiled_model = core.compile_model("<INPUT_MODEL>.tflite", "AUTO")
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+
+         .. tab-item:: C++
+            :sync: cpp
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.tflite``
+
+              .. code-block:: cpp
+
+                 ov::CompiledModel compiled_model = core.compile_model("<INPUT_MODEL>.tflite", "AUTO");
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: C
+            :sync: c
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.tflite``
+
+              .. code-block:: c
+
+                 ov_compiled_model_t* compiled_model = NULL;
+                 ov_core_compile_model_from_file(core, "<INPUT_MODEL>.tflite", "AUTO", 0, &compiled_model);
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: CLI
+            :sync: cli
+
+            * The ``convert_model()`` method:
+
+              You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.tflite``
+
+              .. code-block:: sh
+
+                 mo --input_model <INPUT_MODEL>.tflite
+
+              For details on the conversion, refer to the
+              :doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>`.
+
+   .. tab-item:: ONNX
+      :sync: onnx
+
+      .. tab-set::
+
+         .. tab-item:: Python
+            :sync: py
+
+            * The ``convert_model()`` method:
+
+              When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.onnx``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = convert_model("<INPUT_MODEL>.onnx")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+              For more details on conversion, refer to the
+              :doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>`
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
+              on this topic.
+
+
+            * The ``read_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.onnx``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = read_model("<INPUT_MODEL>.onnx")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.onnx``
+
+              .. code-block:: py
+                 :force:
+
+                 compiled_model = core.compile_model("<INPUT_MODEL>.onnx", "AUTO")
+
+              For a guide on how to run inference, see how to :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+
+         .. tab-item:: C++
+            :sync: cpp
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.onnx``
+
+              .. code-block:: cpp
+
+                 ov::CompiledModel compiled_model = core.compile_model("<INPUT_MODEL>.onnx", "AUTO");
+
+              For a guide on how to run inference, see how to :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: C
+            :sync: c
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.onnx``
+
+              .. code-block:: c
+
+                 ov_compiled_model_t* compiled_model = NULL;
+                 ov_core_compile_model_from_file(core, "<INPUT_MODEL>.onnx", "AUTO", 0, &compiled_model);
+
+              For details on the conversion, refer to the :doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>`
+
+         .. tab-item:: CLI
+            :sync: cli
+
+            * The ``convert_model()`` method:
+
+              You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.onnx``
+
+              .. code-block:: sh
+
+                 mo --input_model <INPUT_MODEL>.onnx
+
+              For details on the conversion, refer to the
+              :doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>`
+
+   .. tab-item:: PaddlePaddle
+      :sync: pdpd
+
+      .. tab-set::
+
+         .. tab-item:: Python
+            :sync: py
+
+            * The ``convert_model()`` method:
+
+              When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.pdmodel``
+
+                 * **Python objects**:
+
+                   * ``paddle.hapi.model.Model``
+                   * ``paddle.fluid.dygraph.layers.Layer``
+                   * ``paddle.fluid.executor.Executor``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = convert_model("<INPUT_MODEL>.pdmodel")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+              For more details on conversion, refer to the
+              :doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle>`
+              and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/103-paddle-to-openvino-classification-with-output.html>`__
+              on this topic.
+
+            * The ``read_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.pdmodel``
+
+              .. code-block:: py
+                 :force:
+
+                 ov_model = read_model("<INPUT_MODEL>.pdmodel")
+                 compiled_model = core.compile_model(ov_model, "AUTO")
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.pdmodel``
+
+              .. code-block:: py
+                 :force:
+
+                 compiled_model = core.compile_model("<INPUT_MODEL>.pdmodel", "AUTO")
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: C++
+            :sync: cpp
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.pdmodel``
+
+              .. code-block:: cpp
+
+                 ov::CompiledModel compiled_model = core.compile_model("<INPUT_MODEL>.pdmodel", "AUTO");
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: C
+            :sync: c
+
+            * The ``compile_model()`` method:
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.pdmodel``
+
+              .. code-block:: c
+
+                 ov_compiled_model_t* compiled_model = NULL;
+                 ov_core_compile_model_from_file(core, "<INPUT_MODEL>.pdmodel", "AUTO", 0, &compiled_model);
+
+              For a guide on how to run inference, see how to
+              :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
+
+         .. tab-item:: CLI
+            :sync: cli
+
+            * The ``convert_model()`` method:
+
+              You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
+
+              .. dropdown:: List of supported formats:
+
+                 * **Files**:
+
+                   * ``<INPUT_MODEL>.pdmodel``
+
+              .. code-block:: sh
+
+                 mo --input_model <INPUT_MODEL>.pdmodel
+
+              For details on the conversion, refer to the
+              :doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle>`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+* :doc:`How to convert PyTorch <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch>`
+* :doc:`How to convert ONNX <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX>`
+* :doc:`How to convert TensorFlow <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`
+* :doc:`How to convert TensorFlow Lite <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>`
+* :doc:`How to convert PaddlePaddle <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_Paddle>`
+
+To choose the best workflow for your application, read the :doc:`Model Preparation section <openvino_docs_model_processing_introduction>`
+
+Refer to the list of all supported conversion options in :doc:`Conversion Parameters <openvino_docs_OV_Converter_UG_Conversion_Options>`
+
+Additional Resources
+####################
+
+* :doc:`Transition guide from the legacy to new conversion API <openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition>`
+
+@endsphinxdirective
diff --git a/docs/articles_en/openvino_workflow/openvino_intro/ShapeInference.md b/docs/articles_en/openvino_workflow/openvino_intro/ShapeInference.md
index b6e95b74ca8421..c6e4fd8b6882e7 100644
--- a/docs/articles_en/openvino_workflow/openvino_intro/ShapeInference.md
+++ b/docs/articles_en/openvino_workflow/openvino_intro/ShapeInference.md
@@ -9,7 +9,8 @@
    troubleshooting_reshape_errors
 
 .. meta::
-   :description: OpenVINO™ allows changing model input shape during the runtime when the provided input has a different size than the model's input shape.
+   :description: OpenVINO™ allows changing model input shape during the runtime when the provided
+                 input has a different size than the model's input shape.
 
 
 OpenVINO™ enables you to change model input shape during the application runtime.
@@ -24,7 +25,7 @@ The following instructions are for cases where you need to change the model inpu
 
 
 The reshape method
-++++++++++++++++++++
+########################
 
 The reshape method is used as ``ov::Model::reshape`` in C++ and
 `Model.reshape <api/ie_python_api/_autosummary/openvino.runtime.Model.html#openvino.runtime.Model.reshape>`__
@@ -166,7 +167,7 @@ You can find the usage scenarios of the ``reshape`` method in
    nor the ``reshape`` method.
 
 The set_batch method
-++++++++++++++++++++
+########################
 
 The meaning of the model batch may vary depending on the model design.
 To change the batch dimension of the model, :ref:`set the layout <declare_model_s_layout>` and call the ``set_batch`` method.
diff --git a/docs/articles_en/openvino_workflow/openvino_intro/dldt_deployment_optimization_guide/preprocessing_overview/preprocessing_usecase_save.md b/docs/articles_en/openvino_workflow/openvino_intro/dldt_deployment_optimization_guide/preprocessing_overview/preprocessing_usecase_save.md
index 71de4a7e5cc82f..2cb7270c14daf0 100644
--- a/docs/articles_en/openvino_workflow/openvino_intro/dldt_deployment_optimization_guide/preprocessing_overview/preprocessing_usecase_save.md
+++ b/docs/articles_en/openvino_workflow/openvino_intro/dldt_deployment_optimization_guide/preprocessing_overview/preprocessing_usecase_save.md
@@ -68,14 +68,14 @@ in the model preparation script for such a case.
 
       .. doxygensnippet:: docs/snippets/ov_preprocessing.py
          :language: Python
-         :fragment: ov:preprocess:save
+         :fragment: ov:preprocess:save_model
 
    .. tab-item:: C++
       :sync: cpp
 
       .. doxygensnippet:: docs/snippets/ov_preprocessing.cpp
          :language: cpp
-         :fragment: ov:preprocess:save
+         :fragment: ov:preprocess:save_model
 
 
 Application Code - Load Model to Target Device
diff --git a/docs/articles_en/openvino_workflow/openvino_intro.md b/docs/articles_en/openvino_workflow/running_inference_with_openvino.md
similarity index 58%
rename from docs/articles_en/openvino_workflow/openvino_intro.md
rename to docs/articles_en/openvino_workflow/running_inference_with_openvino.md
index 2937189c136a01..20dfce6162042d 100644
--- a/docs/articles_en/openvino_workflow/openvino_intro.md
+++ b/docs/articles_en/openvino_workflow/running_inference_with_openvino.md
@@ -1,9 +1,7 @@
-# Inference with OpenVINO Runtime {#openvino_docs_OV_UG_OV_Runtime_User_Guide}
+# Running Inference with OpenVINO™ {#openvino_docs_OV_UG_OV_Runtime_User_Guide}
 
 @sphinxdirective
 
-.. _deep learning openvino runtime:
-
 .. toctree::
    :maxdepth: 1
    :hidden:
@@ -17,12 +15,21 @@
    Optimize Inference <openvino_docs_deployment_optimization_guide_dldt_optimization_guide>
 
 .. meta::
-   :description: OpenVINO Runtime is an API comprised of a set of C++ libraries 
-                 with C and Python bindings and it delivers inference solutions 
-                 on different platforms.
+   :description: Learn how to run inference using OpenVINO.
+
 
+OpenVINO Runtime is a set of C++ libraries with C and Python bindings providing a common API
+to deploy inference on the platform of your choice. You can run any of the 
+:doc:`supported model formats <Supported_Model_Formats>` directly or convert the model
+and save it to the :doc:`OpenVINO IR <openvino_ir>` format, for maximum performance.
 
-OpenVINO Runtime is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice. Use the OpenVINO Runtime API to read PyTorch, TensorFlow, TensorFlow Lite, ONNX, and PaddlePaddle models and execute them on preferred devices. OpenVINO gives you the option to use these models directly or convert them to the OpenVINO IR (Intermediate Representation) format explicitly, for maximum performance.
+Why is OpenVINO IR inference faster? Even if you run a supported model directly, it is
+converted before inference. It may happen automatically, under the hood, for maximum convenience,
+but it is not suited for the most performance-oriented use cases. For example, converting PyTorch
+usually requires Python and the ``torch`` module, which take extra time and memory, on top the
+conversion process itself. If OpenVINO IR is used instead, it does not require any conversion,
+nor the additional dependencies, as the inference application can be written in C or C++.
+OpenVINO IR provides by far the best first-inference latency scores.
 
 
 .. note::
@@ -33,7 +40,7 @@ OpenVINO Runtime is a set of C++ libraries with C and Python bindings providing
    Note that TensorFlow models can be run using the
    :doc:`torch.compile feature <pytorch_2_0_torch_compile>`, as well as the standard ways of
    :doc:`converting TensorFlow <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch>`
-   or reading them directly.
+   or running its inference.
 
 OpenVINO Runtime uses a plugin architecture. Its plugins are software components that contain complete implementation for inference on a particular Intel® hardware device: CPU, GPU, GNA, etc. Each plugin implements the unified API and provides additional hardware-specific APIs for configuring devices or API interoperability between OpenVINO Runtime and underlying plugin backend.
 
diff --git a/docs/dev/cmake_options_for_custom_compilation.md b/docs/dev/cmake_options_for_custom_compilation.md
index 847997e067d5c3..f8cfa3255ead49 100644
--- a/docs/dev/cmake_options_for_custom_compilation.md
+++ b/docs/dev/cmake_options_for_custom_compilation.md
@@ -157,8 +157,6 @@ In this case OpenVINO CMake scripts take `TBBROOT` environment variable into acc
 * `ENABLE_CLANG_FORMAT` enables [Clang format] code style check:
     * `ON` is default.
     * Used only for ngraph component.
-* `TREAT_WARNING_AS_ERROR` treats all warnings as an error:
-    * `OFF` is default.
 * `ENABLE_FASTER_BUILD` enables [precompiled headers] and [unity build] using CMake:
     * `OFF` is default.
 * `ENABLE_INTEGRITYCHECK` builds DLLs with [/INTEGRITYCHECK] flag:
diff --git a/docs/home.rst b/docs/home.rst
index dc3f721856c5bf..9019b1600a2e8b 100644
--- a/docs/home.rst
+++ b/docs/home.rst
@@ -143,6 +143,4 @@ Feature Overview
    LEARN OPENVINO <learn_openvino>
    OPENVINO WORKFLOW <openvino_workflow>
    DOCUMENTATION <documentation>
-   RESOURCES <resources>
-   RELEASE NOTES <release_notes>
-
+   ABOUT OPENVINO <about_openvino>
\ No newline at end of file
diff --git a/docs/install_guides/pypi-openvino-dev.md b/docs/install_guides/pypi-openvino-dev.md
index 8d53e6488f1602..08a16318b42d92 100644
--- a/docs/install_guides/pypi-openvino-dev.md
+++ b/docs/install_guides/pypi-openvino-dev.md
@@ -3,6 +3,7 @@
 <!--- The note below is intended for master branch only for pre-release purpose. Remove it for official releases. --->
 > **NOTE**: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.
 
+> **NOTE**: OpenVINO™ Development Tools package has been deprecated and will be discontinued with 2024.0 release. To learn more, refer to the [OpenVINO Legacy Features and Components page](https://docs.openvino.ai/2023.1/openvino_legacy_features.html).
 
 Intel® Distribution of OpenVINO™ toolkit is an open-source toolkit for optimizing and deploying AI inference. It can be used to develop applications and solutions based on deep learning tasks, such as: emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, etc. It provides high-performance and rich deployment options, from edge to cloud.
 
diff --git a/docs/snippets/AUTO0.cpp b/docs/snippets/AUTO0.cpp
index 54d720eb4bcc0d..124bdd900970a7 100644
--- a/docs/snippets/AUTO0.cpp
+++ b/docs/snippets/AUTO0.cpp
@@ -1,4 +1,6 @@
+//! [py_ov_property_import_header]
 #include <openvino/openvino.hpp>
+//! [py_ov_property_import_header]
 
 int main() {
 {
diff --git a/docs/snippets/ov_auto.py b/docs/snippets/ov_auto.py
index a665b509713f6e..47d8d877ecda24 100644
--- a/docs/snippets/ov_auto.py
+++ b/docs/snippets/ov_auto.py
@@ -2,10 +2,13 @@
 # SPDX-License-Identifier: Apache-2.0
 #
  
+#! [py_ov_property_import_header]
 import openvino as ov
 import openvino.properties as properties
 import openvino.properties.device as device
 import openvino.properties.hint as hints
+import openvino.properties.streams as streams
+#! [py_ov_property_import_header]
 import openvino.properties.log as log
 
 from openvino.inference_engine import IECore
@@ -156,8 +159,23 @@ def part5():
     core = ov.Core()
 
     # gpu_config and cpu_config will load during compile_model()
-    compiled_model = core.compile_model(model=model)
-    compiled_model = core.compile_model(model=model, device_name="AUTO")
+    gpu_config = {
+        hints.performance_mode: hints.PerformanceMode.THROUGHPUT,
+        streams.num: 4
+    }
+    cpu_config = {
+        hints.performance_mode: hints.PerformanceMode.LATENCY,
+        streams.num: 8,
+        properties.enable_profiling: True
+    }
+    compiled_model = core.compile_model(
+        model=model,
+        device_name="AUTO",
+        config={
+            device.priorities: "GPU,CPU",
+            device.properties: {'CPU': cpu_config, 'GPU': gpu_config}
+        }
+    )
     #! [part5]
 
 
diff --git a/docs/snippets/ov_multi.py b/docs/snippets/ov_multi.py
index 1f852faea94c9c..e3ee1aa7bf7158 100644
--- a/docs/snippets/ov_multi.py
+++ b/docs/snippets/ov_multi.py
@@ -4,6 +4,7 @@
 import openvino as ov
 import openvino.properties as properties
 import openvino.properties.device as device
+import openvino.properties.streams as streams
 from utils import get_model
 
 model = get_model()
@@ -96,15 +97,17 @@ def available_devices_2():
 def MULTI_4():
     #! [MULTI_4]
     core = ov.Core()
-    cpu_config = {}
-    gpu_config = {}
+    cpu_config = {streams.num : 4}
+    gpu_config = {streams.num : 8}
 
     # When compiling the model on MULTI, configure CPU and GPU
     # (devices, priorities, and device configurations; gpu_config and cpu_config will load during compile_model() ):
     compiled_model = core.compile_model(
         model=model,
         device_name="MULTI:GPU,CPU",
-        config={"CPU": "NUM_STREAMS 4", "GPU": "NUM_STREAMS 8"},
+        config={
+            device.properties: {'CPU': cpu_config, 'GPU': gpu_config}
+        }
     )
 
     # Optionally, query the optimal number of requests:
diff --git a/docs/snippets/ov_preprocessing.cpp b/docs/snippets/ov_preprocessing.cpp
index f559a7a5a1aef4..176953f46691f1 100644
--- a/docs/snippets/ov_preprocessing.cpp
+++ b/docs/snippets/ov_preprocessing.cpp
@@ -165,7 +165,7 @@ int main() {
  //! [ov:preprocess:save_headers]
 
 void save_example() {
- //! [ov:preprocess:save]
+ //! [ov:preprocess:save_model]
  // ========  Step 0: read original model =========
  ov::Core core;
  std::shared_ptr<ov::Model> model = core.read_model("/path/to/some_model.onnx");
@@ -200,7 +200,7 @@ void save_example() {
  std::string xml = "/path/to/some_model_saved.xml";
  std::string bin = "/path/to/some_model_saved.bin";
  ov::serialize(model, xml, bin);
- //! [ov:preprocess:save]
+ //! [ov:preprocess:save_model]
 
 }
 
diff --git a/docs/snippets/ov_preprocessing.py b/docs/snippets/ov_preprocessing.py
index 23cd30548115ad..8a8f4ce212b4f7 100644
--- a/docs/snippets/ov_preprocessing.py
+++ b/docs/snippets/ov_preprocessing.py
@@ -184,7 +184,7 @@ def custom_abs(output: Output):
 model_path = get_path_to_model()
 serialized_model_path = get_path_to_model()
 
-# ! [ov:preprocess:save]
+# ! [ov:preprocess:save_model]
 # ========  Step 0: read original model =========
 core = Core()
 model = core.read_model(model=model_path)
@@ -219,7 +219,7 @@ def custom_abs(output: Output):
 
 # ======== Step 3: Save the model ================
 serialize(model, serialized_model_path)
-# ! [ov:preprocess:save]
+# ! [ov:preprocess:save_model]
 
 path_to_cache_dir = get_temp_dir()
 
diff --git a/samples/CMakeLists.txt b/samples/CMakeLists.txt
index ab5abf8024c045..e2aeebc9c35e7f 100644
--- a/samples/CMakeLists.txt
+++ b/samples/CMakeLists.txt
@@ -5,7 +5,8 @@
 add_subdirectory(cpp)
 add_subdirectory(c)
 
-openvino_developer_export_targets(COMPONENT samples TARGETS format_reader ie_samples_utils)
+ov_developer_package_export_targets(TARGET format_reader)
+ov_developer_package_export_targets(TARGET ie_samples_utils)
 
 #
 # Install
diff --git a/samples/cpp/common/format_reader/CMakeLists.txt b/samples/cpp/common/format_reader/CMakeLists.txt
index 89732ca039a363..7be5f6af757501 100644
--- a/samples/cpp/common/format_reader/CMakeLists.txt
+++ b/samples/cpp/common/format_reader/CMakeLists.txt
@@ -30,7 +30,7 @@ else()
     target_compile_definitions(${TARGET_NAME} PRIVATE USE_OPENCV)
 endif()
 
-target_include_directories(${TARGET_NAME} PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/include"
+target_include_directories(${TARGET_NAME} PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
                                           PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src")
 
 set_target_properties(${TARGET_NAME} PROPERTIES FOLDER cpp_samples)
diff --git a/samples/cpp/common/utils/CMakeLists.txt b/samples/cpp/common/utils/CMakeLists.txt
index 108818b94c0c23..f7dd66d67b58fd 100644
--- a/samples/cpp/common/utils/CMakeLists.txt
+++ b/samples/cpp/common/utils/CMakeLists.txt
@@ -11,7 +11,7 @@ add_library(${TARGET_NAME} STATIC EXCLUDE_FROM_ALL ${SOURCES})
 set_target_properties(${TARGET_NAME} PROPERTIES FOLDER "src")
 
 target_include_directories(${TARGET_NAME}
-    PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/include")
+    PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>")
 
 find_package(OpenVINO REQUIRED COMPONENTS Runtime)
 
diff --git a/src/bindings/c/src/CMakeLists.txt b/src/bindings/c/src/CMakeLists.txt
index 8f5eaeac581735..a6a649ada6e990 100644
--- a/src/bindings/c/src/CMakeLists.txt
+++ b/src/bindings/c/src/CMakeLists.txt
@@ -46,6 +46,7 @@ export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
        APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
 
 # install
+
 ov_cpack_add_component(${OV_CPACK_COMP_CORE_C} HIDDEN)
 ov_cpack_add_component(${OV_CPACK_COMP_CORE_C_DEV} HIDDEN)
 
diff --git a/src/bindings/python/CMakeLists.txt b/src/bindings/python/CMakeLists.txt
index 8a572f89a0f47e..a2e8945a807776 100644
--- a/src/bindings/python/CMakeLists.txt
+++ b/src/bindings/python/CMakeLists.txt
@@ -20,7 +20,20 @@ project(OpenVINOPython DESCRIPTION "OpenVINO Runtime Python bindings")
 if(NOT DEFINED OpenVINO_SOURCE_DIR)
     find_package(OpenVINODeveloperPackage REQUIRED
                  PATHS "${InferenceEngineDeveloperPackage_DIR}")
+
+    # we assume that OpenVINODeveloperPackage is generated in OpenVINO build tree
     set(OpenVINO_BINARY_DIR "${OpenVINODeveloperPackage_DIR}")
+    # but this can be invalid for cases of OpenVINODeveloperPackage relocatable installation
+    # so, we need to disable wheen generation for this case
+    if(NOT EXISTS "${OpenVINO_BINARY_DIR}/cmake_install.cmake")
+        set(OpenVINODeveloperPackage_RELOCATABLE ON)
+    endif()
+
+    set(OpenVINO_SOURCE_DIR "${OpenVINOPython_SOURCE_DIR}/../../../")
+endif()
+
+if(NOT DEFINED OpenVINODeveloperPackage_RELOCATABLE)
+    set(OpenVINODeveloperPackage_RELOCATABLE OFF)
 endif()
 
 #
@@ -141,11 +154,10 @@ function(ov_check_init_files_alignment init_files)
     endforeach()
 endfunction()
 
-set(INIT_FILES_RUNTIME
-"${OpenVINOPython_SOURCE_DIR}/src/openvino/__init__.py"
-"${OpenVINOPython_SOURCE_DIR}/src/compatibility/openvino/__init__.py"
-"${OpenVINO_SOURCE_DIR}/tools/ovc/openvino/__init__.py"
-"${OpenVINO_SOURCE_DIR}/tools/benchmark_tool/openvino/__init__.py")
+set(INIT_FILES_RUNTIME "${OpenVINOPython_SOURCE_DIR}/src/openvino/__init__.py"
+                       "${OpenVINOPython_SOURCE_DIR}/src/compatibility/openvino/__init__.py"
+                       "${OpenVINO_SOURCE_DIR}/tools/ovc/openvino/__init__.py"
+                       "${OpenVINO_SOURCE_DIR}/tools/benchmark_tool/openvino/__init__.py")
 
 ov_check_init_files_alignment("${INIT_FILES_RUNTIME}")
 
@@ -193,7 +205,7 @@ endif()
 
 # this option should not be a part of OpenVINODeveloperPackage
 # since wheels can be built only together with main OV build
-ov_dependent_option(ENABLE_WHEEL "Build wheel packages for PyPI" ${ENABLE_WHEEL_DEFAULT} "ENABLE_PYTHON" OFF)
+ov_dependent_option(ENABLE_WHEEL "Build wheel packages for PyPI" ${ENABLE_WHEEL_DEFAULT} "ENABLE_PYTHON;NOT OpenVINODeveloperPackage_RELOCATABLE" OFF)
 
 if(NOT ENABLE_PYTHON)
     if(CMAKE_SOURCE_DIR STREQUAL OpenVINOPython_SOURCE_DIR)
diff --git a/src/bindings/python/constraints.txt b/src/bindings/python/constraints.txt
index 9db99017681f4f..20e888bde84984 100644
--- a/src/bindings/python/constraints.txt
+++ b/src/bindings/python/constraints.txt
@@ -5,7 +5,7 @@ numpy>=1.16.6,<1.27  # Python bindings, frontends
 pytest>=5.0,<7.5
 pytest-dependency==0.5.1
 pytest-html==3.2.0
-pytest-timeout==2.1.0
+pytest-timeout==2.2.0
 
 # Python bindings
 py>=1.9.0
diff --git a/src/bindings/python/src/compatibility/openvino/cmake/CythonConfig.cmake b/src/bindings/python/src/compatibility/openvino/cmake/CythonConfig.cmake
index 8d02cf9890a5be..8eeabf849f49c5 100644
--- a/src/bindings/python/src/compatibility/openvino/cmake/CythonConfig.cmake
+++ b/src/bindings/python/src/compatibility/openvino/cmake/CythonConfig.cmake
@@ -36,9 +36,12 @@ function( _find_cython_executable )
     get_filename_component( _python_path ${Python3_EXECUTABLE} PATH )
     file(TO_CMAKE_PATH "$ENV{HOME}" ENV_HOME)
     find_host_program( CYTHON_EXECUTABLE
-      NAMES cython cython.bat cython3
-      HINTS ${_python_path} ${ENV_HOME}/.local/bin $ENV{HOMEBREW_OPT}/cython/bin
+      NAMES cython cython.exe cython.bat cython3
+      HINTS ${_python_path}
+            ${ENV_HOME}/.local/bin
+            $ENV{HOMEBREW_OPT}/cython/bin
             ${ENV_HOME}/Library/Python/${Python3_VERSION_MAJOR}.${Python3_VERSION_MINOR}/bin
+            ${_python_path}/Scripts
       )
   else()
     find_host_program( CYTHON_EXECUTABLE
diff --git a/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt b/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
index ba20fd76055cac..8b68d5dde8a5e9 100644
--- a/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
+++ b/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
@@ -7,27 +7,25 @@ cmake_minimum_required (VERSION 3.13)
 project (pyngraph)
 
 if(NOT DEFINED OpenVINO_SOURCE_DIR)
+    find_package(OpenVINO REQUIRED)
     find_package(OpenVINODeveloperPackage QUIET
                  PATHS "${InferenceEngineDeveloperPackage_DIR}")
-    find_package(OpenVINO REQUIRED)
 endif()
 
 # Python3_VERSION_MAJOR and Python3_VERSION_MINOR are defined in FindPython3
 set(pyversion python${Python3_VERSION_MAJOR}.${Python3_VERSION_MINOR})
 
-if(OpenVINO_SOURCE_DIR)
-    if(OV_GENERATOR_MULTI_CONFIG)
-        set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/$<CONFIG>/python/)
-    else()
-        set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/python/)
-    endif()
-
-    set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
-    set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
-    set(CMAKE_COMPILE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
-    set(CMAKE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+if(OV_GENERATOR_MULTI_CONFIG)
+    set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/$<CONFIG>/python/)
+else()
+    set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/python/)
 endif()
 
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+set(CMAKE_COMPILE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+set(CMAKE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+
 # compile options
 
 if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
@@ -54,43 +52,39 @@ file(GLOB_RECURSE SOURCES *.cpp)
 
 pybind11_add_module(_${PROJECT_NAME} MODULE NO_EXTRAS ${SOURCES})
 
-target_include_directories(_${PROJECT_NAME} PRIVATE "../" "${OpenVINO_SOURCE_DIR}/src/common/transformations/include")
+target_include_directories(_${PROJECT_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/../")
 
-target_link_libraries(_${PROJECT_NAME} PRIVATE openvino::runtime)
+target_link_libraries(_${PROJECT_NAME} PRIVATE openvino::runtime openvino::core::dev)
 
 set_target_properties(_${PROJECT_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
 # perform copy
-if(OpenVINO_SOURCE_DIR)
-    add_custom_command(TARGET _${PROJECT_NAME}
-        POST_BUILD
-        COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/../ngraph ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/ngraph
-    )
-endif()
+add_custom_command(TARGET _${PROJECT_NAME}
+    POST_BUILD
+    COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/../ngraph ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/ngraph
+)
 
 ov_set_apple_rpath(_${PROJECT_NAME} ${OV_CPACK_PYTHONDIR} ${OV_CPACK_RUNTIMEDIR})
 
 # Install
 
-if(OpenVINO_SOURCE_DIR OR OpenVINODeveloperPackage_FOUND)
-    ov_python_minimal_api(_${PROJECT_NAME})
-    ov_add_clang_format_target(_${PROJECT_NAME}_clang FOR_TARGETS _${PROJECT_NAME})
+ov_python_minimal_api(_${PROJECT_NAME})
+ov_add_clang_format_target(_${PROJECT_NAME}_clang FOR_TARGETS _${PROJECT_NAME})
 
-    ov_cpack_add_component(${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion} HIDDEN)
+ov_cpack_add_component(${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion} HIDDEN)
 
-    install(TARGETS _${PROJECT_NAME}
-            DESTINATION ${OV_CPACK_PYTHONDIR}
-            COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
-            ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL})
+install(TARGETS _${PROJECT_NAME}
+        DESTINATION ${OV_CPACK_PYTHONDIR}
+        COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
+        ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL})
 
-    install(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/../ngraph
-            DESTINATION ${OV_CPACK_PYTHONDIR}
-            COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
-            ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL}
-            USE_SOURCE_PERMISSIONS)
+install(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/../ngraph
+        DESTINATION ${OV_CPACK_PYTHONDIR}
+        COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
+        ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL}
+        USE_SOURCE_PERMISSIONS)
 
-    install(DIRECTORY ${OpenVINOPython_SOURCE_DIR}/tests_compatibility
-            DESTINATION tests/${PROJECT_NAME}
-            COMPONENT tests
-            EXCLUDE_FROM_ALL)
-endif()
+install(DIRECTORY ${OpenVINOPython_SOURCE_DIR}/tests_compatibility
+        DESTINATION tests/${PROJECT_NAME}
+        COMPONENT tests
+        EXCLUDE_FROM_ALL)
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py b/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py
index 45a662e4e45fd1..a79892b3e4d6f5 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py
@@ -370,7 +370,7 @@ def inlined_inputs(self, index):
         return result
 
     def may_produce_alias(self, in_index: int, out_index: int) -> bool:
-        if self.get_op_type() in ["aten::conv1d", "aten::conv2d", "aten::conv3d"]:
+        if self.get_op_type() in ["aten::conv1d", "aten::conv2d", "aten::conv3d", "aten::matmul"]:
             # AliasDB::may_contain_alias sometimes return True for tensors produced by convnd, we have to workaround that
             return False
         try:
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/gptq.py b/src/bindings/python/src/openvino/frontend/pytorch/gptq.py
new file mode 100644
index 00000000000000..b4bd06552b2a1e
--- /dev/null
+++ b/src/bindings/python/src/openvino/frontend/pytorch/gptq.py
@@ -0,0 +1,140 @@
+
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# flake8: noqa
+# mypy: ignore-errors
+
+import torch
+from functools import partial
+
+# Wraps a single tensor to a module to prevent it from jit.freezing
+# It depends on a tensor dtype whether it will be preserved from freezing. Refer to the decoder code to learn which types will be preserved.
+class KeepWeight(torch.nn.Module):
+
+    def __init__(self, weight):
+        super().__init__()
+        self.weight = torch.nn.Parameter(weight, requires_grad=False)
+
+    def forward(self):
+        return self.weight
+
+
+# Produces a pattern that can be captured later and represented as a single u4 constant node
+def decompression_pattern(weights):
+    mask = torch.tensor(15, dtype=torch.uint8).to(weights.device)
+    return torch.stack((torch.bitwise_and(weights, mask), torch.bitwise_right_shift(weights, 4)), dim=-1)
+
+
+def patched_forward(self, *args, **kwargs):
+    if hasattr(self, '_hf_hook'):
+        args, kwargs = self._hf_hook.pre_forward(self, *args, **kwargs)
+
+    x = args[0]
+    dtype = x.dtype
+    outshape = x.shape[:-1] + (self.width,)
+    x = x.view(-1, x.shape[-1])
+    groups = self.qzeros.shape[0]
+    height = self.qweight.shape[0]
+
+    unpacked_weights = decompression_pattern(
+        self._openvino_u4_compression_submodule_qweights()).contiguous().view(height, -1, 8)
+    unpacked_weights = torch.transpose(
+        unpacked_weights, 1, 2).contiguous().view(-1, self.group_size, self.width)
+    unpacked_zp = decompression_pattern(
+        self._openvino_u4_compression_submodule_qzeros()).contiguous().view(groups, 1, -1)
+
+    unpacked_zp = unpacked_zp.to(dtype) + 1
+
+    unpacked_weights = (unpacked_weights.to(dtype) - unpacked_zp) * self.scales
+    unpacked_weights = unpacked_weights.view(-1, self.width)
+
+    out = x @ unpacked_weights
+
+    out = out.view(outshape)
+    if self.bias is not None:
+        out.add_(self.bias)
+
+    if hasattr(self, '_hf_hook'):
+        out = self._hf_hook.post_forward(self, out)
+    return out
+
+
+# All the following AutoGPTQ's quant types are supposed to have the same weights packing schema
+supported_quant_types = ['triton', 'exllama', 'cuda', 'exllamav2', 'cuda-old']
+
+
+def patch_model(model):
+    for name, m in model.named_modules():
+        if hasattr(m, '_openvino_patch_orig_forward'):
+            # already patched, skipping
+            continue
+        # TODO: Check module type
+        is_quantized = getattr(m, 'is_quantized', None)
+        if is_quantized is not None:
+            m.is_quantized = False
+        m.float()  # enables tracing on CPU, applied for all modules
+        if hasattr(m, 'QUANT_TYPE'):
+            if m.QUANT_TYPE not in supported_quant_types:
+                raise ValueError(
+                    f'Unsupported QUANT_TYPE == {m.QUANT_TYPE} is discovered for AutoGPTQ model, only the following types are supported: {supported_quant_types}')
+            if m.bits != 4:
+                raise ValueError(
+                    f'Unsupported bits == {m.bits} is discovered in module {name} in AutoGPTQ model, only bits == 4 is supported.')
+
+            int4_in_int32 = 8
+            groups = m.qzeros.shape[0]
+            m.width = m.qweight.shape[1]
+            assert m.group_size == m.qweight.shape[0] * int4_in_int32 // groups
+
+            m._openvino_patch_orig_forward = m.forward
+            m.forward = partial(patched_forward, m)
+
+            # Keep original field properties to be used when model is returned back to its original state
+            m._openvino_patch_orig_qweights_type = m.qweight.dtype
+            m._openvino_patch_orig_qzeros_type = m.qzeros.dtype
+            m._openvino_patch_orig_scale_shape = m.scales.shape
+
+            m.qweight = m.qweight.view(dtype=torch.uint8)
+            m.qzeros = m.qzeros.view(dtype=torch.uint8)
+
+            # TODO: Redundant tensor copy? Try to remove m.qweigh and m.qzeros after keeping modified values as submodules
+            m.add_module(
+                '_openvino_u4_compression_submodule_qweights', KeepWeight(m.qweight))
+            m.add_module('_openvino_u4_compression_submodule_qzeros',
+                         KeepWeight(m.qzeros))
+
+            m.scales = m.scales.view(-1, 1, m.width)
+
+
+def unpatch_model(model):
+    for _, m in model.named_modules():
+        if hasattr(m, '_openvino_patch_orig_forward'):
+            try:
+                m.forward = m._openvino_patch_orig_forward
+                del m._openvino_patch_orig_forward
+
+                m.qweight = m.qweight.view(
+                    dtype=m._openvino_patch_orig_qweights_type)
+                del m._openvino_patch_orig_qweights_type
+
+                m.qzeros = m.qzeros.view(
+                    dtype=m._openvino_patch_orig_qzeros_type)
+                del m._openvino_patch_orig_qzeros_type
+
+                m.scales = m.scales.view(m._openvino_patch_orig_scale_shape)
+                del m._openvino_patch_orig_scale_shape
+
+                del m._openvino_u4_compression_submodule_qweights
+                del m._openvino_u4_compression_submodule_qzeros
+            except Exception as error:
+                print('[ WARNING ] Exception raised during GPTQ model unpatching. Depending on the exact issue it may lead to broken original model')
+                print(error)
+
+
+def detect_gptq_model_raw(model):
+    return model and getattr(model, 'config', None) and getattr(model.config, 'quantization_config', None) and model.config.quantization_config.quant_method == 'gptq'
+
+
+def detect_gptq_model(model):
+    return detect_gptq_model_raw(model) or getattr(model, 'model', None) and detect_gptq_model_raw(model.model)
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py b/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py
index b6caf22cfc7b68..11d5991e700c42 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py
@@ -9,6 +9,7 @@
 from openvino.runtime import op, PartialShape, Type as OVType, OVAny
 from openvino.frontend.pytorch.utils import ivalue_to_constant, get_value_from_getattr, pt_to_ov_type_map, prepare_example_inputs_and_model, convert_quantized_tensor
 from openvino.runtime import opset11 as ops
+from openvino.frontend.pytorch import gptq
 
 import typing
 import torch
@@ -84,8 +85,27 @@ def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False)
             if example_inputs is None:
                 scripted = torch.jit.script(pt_module)
             else:
-                input_parameters, input_signature, pt_module, self._input_is_list = prepare_example_inputs_and_model(example_inputs, input_params, pt_module)
-                scripted = torch.jit.trace(pt_module, **input_parameters, strict=False)
+                input_parameters, input_signature, pt_module, self._input_is_list = prepare_example_inputs_and_model(
+                    example_inputs, input_params, pt_module)
+                gptq_patched = False
+
+                if gptq.detect_gptq_model(pt_module):
+                    try:
+                        gptq.patch_model(pt_module)
+                        gptq_patched = True
+                    except Exception as error:
+                        print('[ WARNING ] Failed patching of AutoGPTQ model. Error message:\n', error)
+                        print('[ WARNING ] Tracing of the model will likely be unsuccesfull or incorrect')
+                        gptq.unpatch_model(pt_module)
+                        gptq_patched = False
+
+                try:
+                    scripted = torch.jit.trace(
+                        pt_module, **input_parameters, strict=False)
+                finally:
+                    if gptq_patched:
+                        gptq.unpatch_model(pt_module)
+
             if not skip_freeze:
                 for n in scripted.inlined_graph.nodes():
                     # TODO: switch off freezing for all traced models
@@ -341,7 +361,7 @@ def input_is_none(self, index: int) -> bool:
         return False
 
     def may_produce_alias(self, in_index: int, out_index: int) -> bool:
-        if self.get_op_type() in ["aten::conv1d", "aten::conv2d", "aten::conv3d"]:
+        if self.get_op_type() in ["aten::conv1d", "aten::conv2d", "aten::conv3d", "aten::matmul"]:
             # AliasDB::may_contain_alias sometimes return True for tensors produced by convnd, we have to workaround that
             return False
         try:
diff --git a/src/bindings/python/src/openvino/runtime/opset13/__init__.py b/src/bindings/python/src/openvino/runtime/opset13/__init__.py
index 4ea991bf77b9ec..9cdb7149569ebb 100644
--- a/src/bindings/python/src/openvino/runtime/opset13/__init__.py
+++ b/src/bindings/python/src/openvino/runtime/opset13/__init__.py
@@ -106,6 +106,7 @@
 from openvino.runtime.opset4.ops import mish
 from openvino.runtime.opset1.ops import mod
 from openvino.runtime.opset9.ops import multiclass_nms
+from openvino.runtime.opset13.ops import multinomial
 from openvino.runtime.opset1.ops import multiply
 from openvino.runtime.opset6.ops import mvn
 from openvino.runtime.opset1.ops import negative
diff --git a/src/bindings/python/src/openvino/runtime/opset13/ops.py b/src/bindings/python/src/openvino/runtime/opset13/ops.py
index f864d7fccca0ea..fff95b33d234d6 100644
--- a/src/bindings/python/src/openvino/runtime/opset13/ops.py
+++ b/src/bindings/python/src/openvino/runtime/opset13/ops.py
@@ -110,6 +110,47 @@ def bitwise_xor(
     )
 
 
+@nameable_op
+def multinomial(
+    probs: NodeInput,
+    num_samples: NodeInput,
+    convert_type: str,
+    with_replacement: bool,
+    log_probs: bool,
+    global_seed: int = 0,
+    op_seed: int = 0,
+) -> Node:
+    """Return a node which generates a sequence of class indices sampled from the multinomial distribution.
+
+    :param probs: Tensor with probabilities of floating-point type, and shape [class_size] or [batch_size, class_size].
+    :param num_samples: Tensor (scalar or 1D) a single element of type i32 or i64,
+                        specifying the number of samples to draw from the multinomial distribution.
+    :param convert_type: Specifies the output tensor type, possible values: 'i64', 'i32'.
+    :param with_replacement: Flag that specifies whether to sample with replacement.
+    :param log_probs: Flag that specifies whether *probs* should be treated as unnormalized log probabilities.
+    :param global_seed: Specifies global seed value. Required to be a positive integer or 0.
+    :param op_seed: Specifies operational seed value. Required to be a positive integer or 0.
+
+    :return: The new node performing Multinomial operation.
+    """
+    inputs = as_nodes(probs, num_samples)
+
+    if global_seed < 0:
+        raise RuntimeError(f"global_seed should be positive or 0. Got: {global_seed}")
+
+    if op_seed < 0:
+        raise RuntimeError(f"op_seed should be positive or 0. Got: {op_seed}")
+
+    attributes = {
+        "convert_type": convert_type,
+        "with_replacement": with_replacement,
+        "log_probs": log_probs,
+        "global_seed": global_seed,
+        "op_seed": op_seed,
+    }
+    return _get_node_factory_opset13().create("Multinomial", inputs, attributes)
+
+
 @nameable_op
 def nms_rotated(
     boxes: NodeInput,
diff --git a/src/bindings/python/src/pyopenvino/CMakeLists.txt b/src/bindings/python/src/pyopenvino/CMakeLists.txt
index 99ae9983ee82c5..5566c961d1a57b 100644
--- a/src/bindings/python/src/pyopenvino/CMakeLists.txt
+++ b/src/bindings/python/src/pyopenvino/CMakeLists.txt
@@ -10,19 +10,17 @@ endif()
 # Python3_VERSION_MAJOR and Python3_VERSION_MINOR are defined by FindPython3
 set(pyversion python${Python3_VERSION_MAJOR}.${Python3_VERSION_MINOR})
 
-if(OpenVINO_SOURCE_DIR)
-    if(OV_GENERATOR_MULTI_CONFIG)
-        set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/$<CONFIG>/python/openvino)
-    else()
-        set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/python/openvino)
-    endif()
-
-    set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
-    set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
-    set(CMAKE_COMPILE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
-    set(CMAKE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+if(OV_GENERATOR_MULTI_CONFIG)
+    set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/$<CONFIG>/python/openvino)
+else()
+    set(PYTHON_BRIDGE_OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/python/openvino)
 endif()
 
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+set(CMAKE_COMPILE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+set(CMAKE_PDB_OUTPUT_DIRECTORY ${PYTHON_BRIDGE_OUTPUT_DIRECTORY})
+
 # compile options
 
 if(OV_COMPILER_IS_APPLECLANG)
@@ -64,15 +62,8 @@ list(FILTER SOURCES EXCLUDE REGEX ".*(frontend/(onnx|tensorflow|paddle|pytorch))
 
 pybind11_add_module(${PROJECT_NAME} MODULE NO_EXTRAS ${SOURCES})
 
-if(TARGET offline_transformations)
-    set(OFFLINE_TRANSFORMATIONS_LIB offline_transformations)
-else()
-    set(OFFLINE_TRANSFORMATIONS_LIB openvino::offline_transformations)
-endif()
-
 target_include_directories(${PROJECT_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/..")
-target_link_libraries(${PROJECT_NAME} PRIVATE
-    openvino::core::dev openvino::runtime ${OFFLINE_TRANSFORMATIONS_LIB})
+target_link_libraries(${PROJECT_NAME} PRIVATE openvino::core::dev openvino::runtime openvino::offline_transformations)
 set_target_properties(${PROJECT_NAME} PROPERTIES
     INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO}
     OUTPUT_NAME "_pyopenvino")
@@ -96,58 +87,56 @@ if(OV_GENERATOR_MULTI_CONFIG)
 endif()
 
 # perform copy
-if(OpenVINO_SOURCE_DIR)
-    add_custom_command(TARGET ${PROJECT_NAME}
-            POST_BUILD
-            COMMAND ${CMAKE_COMMAND} -E copy_directory ${OpenVINOPython_SOURCE_DIR}/src/openvino ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}
-            COMMAND ${CMAKE_COMMAND} -E copy ${OpenVINOPython_SOURCE_DIR}/requirements.txt ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/../requirements.txt
-            )
-endif()
-
-if(OpenVINO_SOURCE_DIR OR OpenVINODeveloperPackage_FOUND)
-    ov_python_minimal_api(${PROJECT_NAME})
-    ov_add_clang_format_target(${PROJECT_NAME}_clang FOR_TARGETS ${PROJECT_NAME})
-
-    ov_cpack_add_component(${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
-                           HIDDEN)
-
-    install(DIRECTORY ${OpenVINOPython_SOURCE_DIR}/src/openvino
-            DESTINATION ${OV_CPACK_PYTHONDIR}
-            COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
-            ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL}
-            USE_SOURCE_PERMISSIONS
-            PATTERN "test_utils" EXCLUDE
-            PATTERN "torchvision/requirements.txt" EXCLUDE)
-
-    install(TARGETS ${PROJECT_NAME}
-            DESTINATION ${OV_CPACK_PYTHONDIR}/openvino
-            COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
-            ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL})
-
-    ov_set_apple_rpath(${PROJECT_NAME} ${OV_CPACK_PYTHONDIR}/openvino
-        # path to OpenVINO C++ libraries
-        ${OV_CPACK_RUNTIMEDIR}
-        # pyopenvino also depends on TBB because of:
-        # pyopenvino => openvino::offline_transformations => TBB optimized openvino::reference
-        ${TBB_LIB_INSTALL_DIR})
-
-    ov_cpack_add_component(${OV_CPACK_COMP_OPENVINO_REQ_FILES} HIDDEN)
-
-    install(FILES ${OpenVINOPython_SOURCE_DIR}/requirements.txt
-            DESTINATION ${OV_CPACK_PYTHONDIR}
-            COMPONENT ${OV_CPACK_COMP_OPENVINO_REQ_FILES}
-            ${OV_CPACK_COMP_OPENVINO_REQ_FILES_EXCLUDE_ALL})
-    
-    install(FILES ${OpenVINOPython_SOURCE_DIR}/src/openvino/preprocess/torchvision/requirements.txt
-            DESTINATION ${OV_CPACK_PYTHONDIR}/openvino/preprocess/torchvision
-            COMPONENT ${OV_CPACK_COMP_OPENVINO_REQ_FILES}
-            ${OV_CPACK_COMP_OPENVINO_REQ_FILES_EXCLUDE_ALL})
-
-    install(DIRECTORY ${OpenVINOPython_SOURCE_DIR}/tests
-            DESTINATION tests/${PROJECT_NAME}
-            COMPONENT tests
-            EXCLUDE_FROM_ALL)
-endif()
+add_custom_command(TARGET ${PROJECT_NAME}
+        POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_directory ${OpenVINOPython_SOURCE_DIR}/src/openvino ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}
+        COMMAND ${CMAKE_COMMAND} -E copy ${OpenVINOPython_SOURCE_DIR}/requirements.txt ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/../requirements.txt
+        )
+
+ov_python_minimal_api(${PROJECT_NAME})
+ov_add_clang_format_target(${PROJECT_NAME}_clang FOR_TARGETS ${PROJECT_NAME})
+
+# install steps
+
+ov_cpack_add_component(${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
+                        HIDDEN)
+
+install(DIRECTORY ${OpenVINOPython_SOURCE_DIR}/src/openvino
+        DESTINATION ${OV_CPACK_PYTHONDIR}
+        COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
+        ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL}
+        USE_SOURCE_PERMISSIONS
+        PATTERN "test_utils" EXCLUDE
+        PATTERN "torchvision/requirements.txt" EXCLUDE)
+
+install(TARGETS ${PROJECT_NAME}
+        DESTINATION ${OV_CPACK_PYTHONDIR}/openvino
+        COMPONENT ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion}
+        ${OV_CPACK_COMP_PYTHON_OPENVINO_EXCLUDE_ALL})
+
+ov_set_apple_rpath(${PROJECT_NAME} ${OV_CPACK_PYTHONDIR}/openvino
+    # path to OpenVINO C++ libraries
+    ${OV_CPACK_RUNTIMEDIR}
+    # pyopenvino also depends on TBB because of:
+    # pyopenvino => openvino::offline_transformations => TBB optimized openvino::reference
+    ${TBB_LIB_INSTALL_DIR})
+
+ov_cpack_add_component(${OV_CPACK_COMP_OPENVINO_REQ_FILES} HIDDEN)
+
+install(FILES ${OpenVINOPython_SOURCE_DIR}/requirements.txt
+        DESTINATION ${OV_CPACK_PYTHONDIR}
+        COMPONENT ${OV_CPACK_COMP_OPENVINO_REQ_FILES}
+        ${OV_CPACK_COMP_OPENVINO_REQ_FILES_EXCLUDE_ALL})
+
+install(FILES ${OpenVINOPython_SOURCE_DIR}/src/openvino/preprocess/torchvision/requirements.txt
+        DESTINATION ${OV_CPACK_PYTHONDIR}/openvino/preprocess/torchvision
+        COMPONENT ${OV_CPACK_COMP_OPENVINO_REQ_FILES}
+        ${OV_CPACK_COMP_OPENVINO_REQ_FILES_EXCLUDE_ALL})
+
+install(DIRECTORY ${OpenVINOPython_SOURCE_DIR}/tests
+        DESTINATION tests/${PROJECT_NAME}
+        COMPONENT tests
+        EXCLUDE_FROM_ALL)
 
 if(TARGET ie_wheel)
     add_dependencies(ie_wheel ${PROJECT_NAME})
diff --git a/src/bindings/python/tests/test_graph/test_multinomial.py b/src/bindings/python/tests/test_graph/test_multinomial.py
new file mode 100644
index 00000000000000..a1275837cc39d8
--- /dev/null
+++ b/src/bindings/python/tests/test_graph/test_multinomial.py
@@ -0,0 +1,78 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+import openvino.runtime.opset13 as ops
+from openvino.runtime import PartialShape, Dimension, Type
+
+
+@pytest.mark.parametrize(
+    ("probs_shape", "num_samples_shape", "convert_type", "with_replacement", "log_probs", "global_seed", "op_seed", "expected_out_shape"),
+    [
+        ([4, 16], [], "i32", False, True, 7461, 1546, PartialShape([4, -1])),
+        ([8], [1], "i64", True, False, 0, 0, PartialShape([-1])),
+    ],
+)
+def test_multinomial_param_inputs(probs_shape, num_samples_shape, convert_type, with_replacement, log_probs, global_seed, op_seed, expected_out_shape):
+    probs = ops.parameter(probs_shape, dtype=np.float32)
+    num_samples = ops.parameter(num_samples_shape, dtype=np.int32)
+
+    op = ops.multinomial(probs, num_samples,
+                         convert_type=convert_type,
+                         with_replacement=with_replacement,
+                         log_probs=log_probs,
+                         global_seed=global_seed,
+                         op_seed=op_seed)
+    assert op.get_output_size() == 1
+    assert op.get_type_name() == "Multinomial"
+    assert op.get_output_element_type(0) == Type.i32 if convert_type == "i32" else Type.i64
+    assert op.get_output_partial_shape(0) == expected_out_shape
+
+
+@pytest.mark.parametrize(
+    ("probs_array", "num_samples_val", "convert_type", "with_replacement", "log_probs", "global_seed", "op_seed", "expected_out_shape"),
+    [
+        (np.array([0.7, 0.3, 0.6, 0.5]), 3, "i32", False, True, 111, 222, PartialShape([3])),
+        (np.array([[0.7, 0.3], [0.6, 0.5]]), 2, "i64", True, False, 111, 222, PartialShape([2, 2])),
+    ],
+)
+def test_multinomial_const_inputs(probs_array, num_samples_val, convert_type, with_replacement, log_probs, global_seed, op_seed, expected_out_shape):
+    probs = ops.constant(probs_array, dtype=np.float32)
+    num_samples = ops.constant(num_samples_val, dtype=np.int32)
+
+    op = ops.multinomial(probs, num_samples,
+                         convert_type=convert_type,
+                         with_replacement=with_replacement,
+                         log_probs=log_probs,
+                         global_seed=global_seed,
+                         op_seed=op_seed)
+
+    assert op.get_output_size() == 1
+    assert op.get_type_name() == "Multinomial"
+    assert op.get_output_element_type(0) == Type.i32 if convert_type == "i32" else Type.i64
+    assert op.get_output_partial_shape(0) == expected_out_shape
+
+
+@pytest.mark.parametrize(
+    ("probs_shape", "num_samples_shape", "convert_type", "with_replacement", "log_probs", "expected_out_shape"),
+    [
+        ([10], [1], "i32", True, True, PartialShape([-1])),
+        ([2, 16], [], "i64", False, False, PartialShape([2, -1])),
+    ],
+)
+def test_multinomial_default_attrs(probs_shape, num_samples_shape, convert_type, with_replacement, log_probs, expected_out_shape):
+    probs = ops.parameter(probs_shape, dtype=np.float32)
+    num_samples = ops.parameter(num_samples_shape, dtype=np.int32)
+
+    op = ops.multinomial(probs, num_samples,
+                         convert_type=convert_type,
+                         with_replacement=with_replacement,
+                         log_probs=log_probs)
+
+    assert op.get_output_size() == 1
+    assert op.get_type_name() == "Multinomial"
+    assert op.get_output_element_type(0) == Type.i32 if convert_type == "i32" else Type.i64
+    assert op.get_output_partial_shape(0) == expected_out_shape
diff --git a/src/cmake/openvino.cmake b/src/cmake/openvino.cmake
index ba3786cd697a75..7fb6e2fd77bd6b 100644
--- a/src/cmake/openvino.cmake
+++ b/src/cmake/openvino.cmake
@@ -107,27 +107,27 @@ install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
 # Add openvino::runtine::dev target
 #
 
-add_library(${TARGET_NAME}_dev INTERFACE)
-add_library(openvino::runtime::dev ALIAS ${TARGET_NAME}_dev)
+add_library(openvino_runtime_dev INTERFACE)
+add_library(openvino::runtime::dev ALIAS openvino_runtime_dev)
 
-target_include_directories(${TARGET_NAME}_dev INTERFACE
+target_include_directories(openvino_runtime_dev INTERFACE
     $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/inference/dev_api>
     $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/common/low_precision_transformations/include>
-    $<TARGET_PROPERTY:openvino_gapi_preproc,INTERFACE_INCLUDE_DIRECTORIES>)
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:openvino_gapi_preproc,INTERFACE_INCLUDE_DIRECTORIES>>)
 
-target_compile_definitions(${TARGET_NAME}_dev INTERFACE
+target_compile_definitions(openvino_runtime_dev INTERFACE
     $<TARGET_PROPERTY:openvino_gapi_preproc,INTERFACE_COMPILE_DEFINITIONS>)
 
-target_link_libraries(${TARGET_NAME}_dev INTERFACE ${TARGET_NAME} openvino::core::dev)
+target_link_libraries(openvino_runtime_dev INTERFACE ${TARGET_NAME} openvino::core::dev)
 
-# TODO: remove once NPU will use explicltly `ov_set_threading_interface_for`
-ov_set_threading_interface_for(${TARGET_NAME}_dev)
-set_target_properties(${TARGET_NAME}_dev PROPERTIES EXPORT_NAME runtime::dev)
+ov_set_threading_interface_for(openvino_runtime_dev)
+set_target_properties(openvino_runtime_dev PROPERTIES EXPORT_NAME runtime::dev)
 
-openvino_developer_export_targets(COMPONENT core TARGETS openvino::runtime::dev)
+ov_developer_package_export_targets(TARGET openvino::runtime::dev
+                                    INSTALL_INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/inference/dev_api/")
 
 # Install static libraries for case BUILD_SHARED_LIBS=OFF
-ov_install_static_lib(${TARGET_NAME}_dev ${OV_CPACK_COMP_CORE})
+ov_install_static_lib(openvino_runtime_dev ${OV_CPACK_COMP_CORE})
 
 #
 # Install OpenVINO runtime
diff --git a/src/common/conditional_compilation/CMakeLists.txt b/src/common/conditional_compilation/CMakeLists.txt
index 876558cea5e474..8f5cd90fe22d21 100644
--- a/src/common/conditional_compilation/CMakeLists.txt
+++ b/src/common/conditional_compilation/CMakeLists.txt
@@ -2,11 +2,12 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME conditional_compilation)
+set(TARGET_NAME openvino_conditional_compilation)
 
 add_library(${TARGET_NAME} INTERFACE)
 
 add_library(openvino::conditional_compilation ALIAS ${TARGET_NAME})
+set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME conditional_compilation)
 
 target_link_libraries(${TARGET_NAME} INTERFACE openvino::itt)
 
@@ -23,9 +24,7 @@ elseif(SELECTIVE_BUILD STREQUAL "ON")
     find_host_package (Python3 REQUIRED COMPONENTS Interpreter)
 
     file(TO_CMAKE_PATH ${SELECTIVE_BUILD_STAT} CMAKE_SELECTIVE_BUILD_STAT)
-
     file(GLOB STAT_FILES ${CMAKE_SELECTIVE_BUILD_STAT})
-
     if(NOT STAT_FILES)
         message(FATAL_ERROR "SELECTIVE_BUILD_STAT (${SELECTIVE_BUILD_STAT}) path doesn't contain valid csv files!")
     endif()
@@ -56,12 +55,16 @@ elseif(SELECTIVE_BUILD STREQUAL "ON")
     ov_force_include(${TARGET_NAME} INTERFACE ${GENERATED_HEADER})
 endif()
 
-ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
-
 file(GLOB_RECURSE hdrs ${CMAKE_CURRENT_SOURCE_DIR}/include/*.h ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_SOURCES ${hdrs})
-openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::conditional_compilation)
 
 if(ENABLE_TESTS)
     add_subdirectory(tests)
 endif()
+
+# install & export
+
+ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
+
+ov_developer_package_export_targets(TARGET openvino::conditional_compilation
+                                    INSTALL_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/include/")
diff --git a/src/common/itt/CMakeLists.txt b/src/common/itt/CMakeLists.txt
index 512574fd89bc06..4541fa112755a7 100644
--- a/src/common/itt/CMakeLists.txt
+++ b/src/common/itt/CMakeLists.txt
@@ -36,7 +36,11 @@ endif()
 target_include_directories(${TARGET_NAME} PUBLIC
     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
 
+ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
+
+# install & export
+
 ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
 
-ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
-openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::itt)
+ov_developer_package_export_targets(TARGET openvino::itt
+                                    INSTALL_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/include/")
diff --git a/src/common/low_precision_transformations/include/itt.hpp b/src/common/low_precision_transformations/include/itt.hpp
index 37e01b9cfc4162..f9388c2facc557 100644
--- a/src/common/low_precision_transformations/include/itt.hpp
+++ b/src/common/low_precision_transformations/include/itt.hpp
@@ -23,12 +23,12 @@ namespace domains {
 }   // namespace itt
 }   // namespace low_precision
 }   // namespace pass
-}   // namespace ngraph
+}   // namespace ov
 
 /*
  * RUN_ON_FUNCTION_SCOPE macro allows to disable the run_on_function pass
  * MATCHER_SCOPE macro allows to disable the MatcherPass if matcher isn't applied
- * INTERNAL_OP_SCOPE macro allows to disable parts of internal nGraph operations if they are not used
+ * INTERNAL_OP_SCOPE macro allows to disable parts of internal openvino operations if they are not used
  */
 #if defined(SELECTIVE_BUILD_ANALYZER)
 
diff --git a/src/common/low_precision_transformations/include/low_precision/network_helper.hpp b/src/common/low_precision_transformations/include/low_precision/network_helper.hpp
index d3c5a04d14df6d..83e486af697ff7 100644
--- a/src/common/low_precision_transformations/include/low_precision/network_helper.hpp
+++ b/src/common/low_precision_transformations/include/low_precision/network_helper.hpp
@@ -29,7 +29,7 @@ namespace pass {
 namespace low_precision {
 
 /**
-* @brief NetworkHelper class encapsulates manipulations with nGraph function.
+* @brief NetworkHelper class encapsulates manipulations with ov::Model.
 */
 class LP_TRANSFORMATIONS_API NetworkHelper {
 public:
diff --git a/src/common/low_precision_transformations/src/batch_to_space.cpp b/src/common/low_precision_transformations/src/batch_to_space.cpp
index b136d284ed5902..cc80f95707eb70 100644
--- a/src/common/low_precision_transformations/src/batch_to_space.cpp
+++ b/src/common/low_precision_transformations/src/batch_to_space.cpp
@@ -5,10 +5,9 @@
 #include "low_precision/batch_to_space.hpp"
 
 #include <memory>
-#include <ngraph/ngraph.hpp>
-#include <openvino/op/batch_to_space.hpp>
-#include <ngraph/pattern/op/wrap_type.hpp>
 
+#include "openvino/op/batch_to_space.hpp"
+#include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "low_precision/network_helper.hpp"
 #include "itt.hpp"
 
@@ -20,7 +19,7 @@ BatchToSpaceTransformation::BatchToSpaceTransformation(const Params& params) : L
     MATCHER_SCOPE(BatchToSpaceTransformation);
     auto matcher = pattern::wrap_type<ov::op::v1::BatchToSpace>();
 
-    ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
+    ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
         auto op = m.get_match_root();
         if (transformation_callback(op)) {
             return false;
@@ -28,7 +27,7 @@ BatchToSpaceTransformation::BatchToSpaceTransformation(const Params& params) : L
         return transform(*context, m);
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(matcher, matcher_name);
+    auto m = std::make_shared<ov::pass::pattern::Matcher>(matcher, matcher_name);
     this->register_matcher(m, callback);
 }
 
@@ -45,7 +44,7 @@ bool BatchToSpaceTransformation::canBeTransformed(const TransformationContext& c
     return dequantization.isPerTensor();
 }
 
-bool BatchToSpaceTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher& m) {
+bool BatchToSpaceTransformation::transform(TransformationContext& context, ov::pass::pattern::Matcher& m) {
     if (!canBeTransformed(context, m.get_match_root())) {
         return false;
     }
diff --git a/src/common/low_precision_transformations/src/network_helper.cpp b/src/common/low_precision_transformations/src/network_helper.cpp
index 29d7c9670bb362..b3925c850c5673 100644
--- a/src/common/low_precision_transformations/src/network_helper.cpp
+++ b/src/common/low_precision_transformations/src/network_helper.cpp
@@ -195,7 +195,7 @@ size_t NetworkHelper::getGroupsCount(std::shared_ptr<Node> layer) {
 }
 
 void NetworkHelper::removeLayer(std::shared_ptr<Node> layer) {
-    ngraph::replace_output_update_name(layer->output(0), layer->input_value(0));
+    ov::replace_output_update_name(layer->output(0), layer->input_value(0));
 }
 
 std::shared_ptr<Node> NetworkHelper::swapMultiplyAndAdd(std::shared_ptr<ov::opset1::Add> addAfterMultiply, const int multiplyBranch) {
diff --git a/src/common/low_precision_transformations/src/reshape.cpp b/src/common/low_precision_transformations/src/reshape.cpp
index 487139077f5c69..0c5f83502df4e8 100644
--- a/src/common/low_precision_transformations/src/reshape.cpp
+++ b/src/common/low_precision_transformations/src/reshape.cpp
@@ -200,7 +200,7 @@ bool ReshapeTransformation::canBeTransformed(const TransformationContext& contex
         const auto inputs = op->get_output_target_inputs(0);
         if (inputs.size() == 1ul) {
             const auto consumer = inputs.begin()->get_node();
-            ignorePerTensorQuantizationCheck = ngraph::as_type<ov::opset1::MatMul>(consumer) != nullptr;
+            ignorePerTensorQuantizationCheck = ov::as_type<ov::opset1::MatMul>(consumer) != nullptr;
         }
     }
 
diff --git a/src/common/low_precision_transformations/src/space_to_batch.cpp b/src/common/low_precision_transformations/src/space_to_batch.cpp
index 0c9200a2f061eb..75bf0f9dbbc559 100644
--- a/src/common/low_precision_transformations/src/space_to_batch.cpp
+++ b/src/common/low_precision_transformations/src/space_to_batch.cpp
@@ -5,11 +5,9 @@
 #include "low_precision/space_to_batch.hpp"
 
 #include <memory>
-#include <ngraph/ngraph.hpp>
-#include <openvino/op/space_to_batch.hpp>
-
-#include <ngraph/pattern/op/wrap_type.hpp>
 
+#include "openvino/op/space_to_batch.hpp"
+#include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "low_precision/network_helper.hpp"
 #include "itt.hpp"
 
@@ -21,7 +19,7 @@ SpaceToBatchTransformation::SpaceToBatchTransformation(const Params& params) : L
     MATCHER_SCOPE(SpaceToBatchTransformation);
     auto matcher = pattern::wrap_type<ov::op::v1::SpaceToBatch>();
 
-    ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
+    ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
         auto op = m.get_match_root();
         if (transformation_callback(op)) {
             return false;
@@ -29,7 +27,7 @@ SpaceToBatchTransformation::SpaceToBatchTransformation(const Params& params) : L
         return transform(*context, m);
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(matcher, matcher_name);
+    auto m = std::make_shared<ov::pass::pattern::Matcher>(matcher, matcher_name);
     this->register_matcher(m, callback);
 }
 
@@ -46,7 +44,7 @@ bool SpaceToBatchTransformation::canBeTransformed(const TransformationContext& c
     return dequantization.isPerTensor();
 }
 
-bool SpaceToBatchTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher& m) {
+bool SpaceToBatchTransformation::transform(TransformationContext& context, ov::pass::pattern::Matcher& m) {
     if (!canBeTransformed(context, m.get_match_root())) {
         return false;
     }
diff --git a/src/common/low_precision_transformations/src/transpose.cpp b/src/common/low_precision_transformations/src/transpose.cpp
index e2c098103a5677..eb40b4e183abaa 100644
--- a/src/common/low_precision_transformations/src/transpose.cpp
+++ b/src/common/low_precision_transformations/src/transpose.cpp
@@ -116,7 +116,7 @@ bool TransposeTransformation::canBeTransformed(const TransformationContext& cont
             }
         }
         if (dequantization.multiply != nullptr) {
-            const auto mulConst = ov::as_type_ptr<ngraph::op::v0::Constant>(dequantization.multiplyConstant);
+            const auto mulConst = ov::as_type_ptr<ov::op::v0::Constant>(dequantization.multiplyConstant);
             if (!NetworkHelper::isScalarLike(mulConst)) {
                 return false;
             }
diff --git a/src/common/low_precision_transformations/src/weightable_layer_transformation.cpp b/src/common/low_precision_transformations/src/weightable_layer_transformation.cpp
index d3dd47d2107737..dd6995efc6e957 100644
--- a/src/common/low_precision_transformations/src/weightable_layer_transformation.cpp
+++ b/src/common/low_precision_transformations/src/weightable_layer_transformation.cpp
@@ -163,7 +163,7 @@ bool WeightableLayerTransformation::canBeTransformed(const TransformationContext
     //
     // [1] no other consumers for FQ sitting on weights (neither Result node, nor any others -
     // original code includes separate checks for node being output and other consumers present; for
-    // ngraph it is a single check for number of consumers).
+    // openvino it is a single check for number of consumers).
     //
     // [2] if weights is anything except a constant with data_type other than i8; this check is overriden by
     // stronger check from Convolution patter which expects FQ only on weights
diff --git a/src/common/low_precision_transformations/tests/get_dequantization_transformation.cpp b/src/common/low_precision_transformations/tests/get_dequantization_transformation.cpp
index 0d5bad38902cb4..9c378f33b69406 100644
--- a/src/common/low_precision_transformations/tests/get_dequantization_transformation.cpp
+++ b/src/common/low_precision_transformations/tests/get_dequantization_transformation.cpp
@@ -26,7 +26,7 @@ using namespace ngraph::builder::subgraph;
 class GetDequantizationTestValues {
 public:
     FakeQuantizeOnData fakeQuantize;
-    // actual dequantization to create nGraph function to run NetworkHelper::getDequantization
+    // actual dequantization to create ov::Model to run NetworkHelper::getDequantization
     DequantizationOperations actualDequantization;
     DequantizationOperations expectedDequantization;
 };
diff --git a/src/common/offline_transformations/CMakeLists.txt b/src/common/offline_transformations/CMakeLists.txt
index 6712f2f28586e3..69335b19be4e7a 100644
--- a/src/common/offline_transformations/CMakeLists.txt
+++ b/src/common/offline_transformations/CMakeLists.txt
@@ -2,13 +2,13 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME "offline_transformations")
-
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
+set(TARGET_NAME "openvino_offline_transformations")
 
 set(PUBLIC_HEADERS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+file(GLOB_RECURSE PUBLIC_HEADERS ${PUBLIC_HEADERS_DIR}/*.hpp)
+
 # Create named folders for the sources within the .vcproj
 # Empty name lists them directly under the .vcproj
 
@@ -19,15 +19,20 @@ source_group("include" FILES ${PUBLIC_HEADERS})
 
 add_library(${TARGET_NAME} STATIC ${LIBRARY_SRC} ${PUBLIC_HEADERS})
 
+add_library(openvino::offline_transformations ALIAS ${TARGET_NAME})
+set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME offline_transformations)
+
 target_link_libraries(${TARGET_NAME} PRIVATE openvino::core::dev openvino::reference openvino::runtime)
 
-target_include_directories(${TARGET_NAME} PUBLIC ${PUBLIC_HEADERS_DIR}
-                                          PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src")
+target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
+                                          PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src"
+                                                  $<TARGET_PROPERTY:openvino::pugixml,INTERFACE_INCLUDE_DIRECTORIES>)
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
 
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
-# developer package
+# install & export
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/include/")
diff --git a/src/common/preprocessing/src/CMakeLists.txt b/src/common/preprocessing/src/CMakeLists.txt
index ccab33a652c633..9e3fd2d3789a02 100644
--- a/src/common/preprocessing/src/CMakeLists.txt
+++ b/src/common/preprocessing/src/CMakeLists.txt
@@ -211,7 +211,7 @@ endif()
 
 # developer package
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
+ov_developer_package_export_targets(TARGET ${TARGET_NAME})
 
 # install
 
diff --git a/src/common/snippets/CMakeLists.txt b/src/common/snippets/CMakeLists.txt
index fdc1c83889423d..b3d2db77b77241 100644
--- a/src/common/snippets/CMakeLists.txt
+++ b/src/common/snippets/CMakeLists.txt
@@ -46,8 +46,9 @@ endif()
 
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
-# install
+# install & export
 
 ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
 
-openvino_developer_export_targets(COMPONENT ${OV_CPACK_COMP_CORE} TARGETS ${TARGET_NAME})
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${PUBLIC_HEADERS_DIR}/")
diff --git a/src/common/snippets/docs/snippets_design_guide.md b/src/common/snippets/docs/snippets_design_guide.md
index 83adce2171cccc..2f157b620b9574 100644
--- a/src/common/snippets/docs/snippets_design_guide.md
+++ b/src/common/snippets/docs/snippets_design_guide.md
@@ -236,7 +236,7 @@ Finally, the `Backend` uses the optimized `IR` to produce executable code.
 As shown on the figure below, `Snippets` are organized in a very similar way.
 ```mermaid
  graph LR
-   Source[nGraph \n model]
+   Source[OpenVINO \n model]
    subgraph Snippets
       direction LR
       subgraph Optimizer[Optimizer]
@@ -244,10 +244,10 @@ As shown on the figure below, `Snippets` are organized in a very similar way.
        Data[Data flow \n optimizations]
        Converter[Convert \n IR]
        Control[Control flow \n optimizations]
-       Data-->|nGraph \nIR|Converter
+       Data-->|OpenVINO \nIR|Converter
        Converter-->|Linear \nIR|Control
       end
-      Frontend[Tokenizer]-->|nGraph \nIR|Data
+      Frontend[Tokenizer]-->|OpenVINO \nIR|Data
       Control-->|Linear \nIR|Backend[Generator]
    end
    Source --> Frontend
@@ -258,13 +258,13 @@ classDef daisy1 fill:#FFE17A, stroke: #FEC91B, color: #262626
 class Frontend,Optimizer,Backend steel1
 class Source,Executable daisy1
 ```
-Instead of a source code, `Snippets` take `nGraph` model as an input. 
-Then the `Tokenizer` (which is essentially a `Snippets` `Frontend`) parses an input `nGraph model`, and tries to find a part of the model that could be processed by `Snippets`. 
-If such a part is found, `Tokenizer` converts it to an `nGraph IR` and stores inside a `Subgraph` node. 
-`nGraph IR` - is one of the two `IR` types used by `Snippets`, it is simply a small `nGraph model` that can contain `Snippets`-specific operations. 
+Instead of a source code, `Snippets` take `OpenVINO` model as an input. 
+Then the `Tokenizer` (which is essentially a `Snippets` `Frontend`) parses an input `OpenVINO model`, and tries to find a part of the model that could be processed by `Snippets`. 
+If such a part is found, `Tokenizer` converts it to an `OpenVINO IR` and stores inside a `Subgraph` node. 
+`OpenVINO IR` - is one of the two `IR` types used by `Snippets`, it is simply a small `OpenVINO model` that can contain `Snippets`-specific operations. 
 
-`nGraph IR` is then passed to the `Optimizer` unit that in turn consists of three subunits. 
-The purpose of the first subunit is to perform data flow optimizations. The second subunit converts `nGraph IR` (data-flow-oriented representation) to `Linear IR` (control-flow-focused IR). Finally, the third subunit is dedicated to control flow optimizations.
+`OpenVINO IR` is then passed to the `Optimizer` unit that in turn consists of three subunits. 
+The purpose of the first subunit is to perform data flow optimizations. The second subunit converts `OpenVINO IR` (data-flow-oriented representation) to `Linear IR` (control-flow-focused IR). Finally, the third subunit is dedicated to control flow optimizations.
 
 After all optimizations, the `Linear IR` is used by the `Generator` (which is `Snippets` `Backend`) to produce executable code, which we will refer to as `Kernel`. 
 As discussed in the Introduction, the purpose of the `Kernel` is to process a part of the initial tensor, and several `Kernels` are usually executed in parallel to process the whole tensor. 
@@ -280,7 +280,7 @@ The `Snippets` integration into the plugin pipeline is schematically depicted be
  graph LR
    subgraph Plugin[<font size=+2> Plugin pipeline </font>]
     direction LR
-    subgraph ngraph[<b> Transformations on nGraph model </b>]
+    subgraph openvino[<b> Transformations on OpenVINO model </b>]
         direction LR
         common[Common \n Transformations]
         lpt[Low \n Precision]
@@ -305,7 +305,7 @@ The `Snippets` integration into the plugin pipeline is schematically depicted be
          create-->execute
     end
    end
-   Source[nGraph \n model]-->|Main \n flow|common
+   Source[OpenVINO \n model]-->|Main \n flow|common
    convert~~~internal
 classDef no-bg-color fill:none,stroke-width:0px
 classDef steel1 fill:#B9D6E5, stroke: #86B3CA, color: #262626
@@ -315,15 +315,15 @@ class tokenize,optimize,generate steel1
 class Source,Executable daisy1
 class create,execute dafault_node1
 ```
-As one can see from the picture, overall plugin pipeline consists of two major blocks: the first block applies transformations to `nGraph model` while the second one works with the internal plugin graph representation. Since `Snippets` is a backend-independent framework, it can't work with the plugin graph or plugin-specific `Ops` directly, so the tokenization is performed immediately before plugin-specific operations are introduced into the graph (`Conversion to Plugin opset`). 
-`Tokenizer` replaces parts of the `nGraph model` that can be executed by `Snippets` with `ov::op::Subgraph` nGraph nodes. 
-Each of the nodes stores a piece of the initial `nGraph model` that was replaced by the node. 
-This piece is stored as an nGraph model itself, which we refer to  as `nGraph IR` to distinguish from the original `nGraph model`. 
+As one can see from the picture, overall plugin pipeline consists of two major blocks: the first block applies transformations to `OpenVINO model` while the second one works with the internal plugin graph representation. Since `Snippets` is a backend-independent framework, it can't work with the plugin graph or plugin-specific `Ops` directly, so the tokenization is performed immediately before plugin-specific operations are introduced into the graph (`Conversion to Plugin opset`). 
+`Tokenizer` replaces parts of the `OpenVINO model` that can be executed by `Snippets` with `ov::op::Subgraph` OpenVINO nodes. 
+Each of the nodes stores a piece of the initial `OpenVINO model` that was replaced by the node. 
+This piece is stored as an OpenVINO model itself, which we refer to  as `OpenVINO IR` to distinguish from the original `OpenVINO model`. 
 Note that sometimes the exact type of `IR` is not important in our discussion. 
-In such cases, we will refer to the `IR` (`nGraph` or `Linear`) as `body function`, or simply `body`. 
+In such cases, we will refer to the `IR` (`OpenVINO` or `Linear`) as `body function`, or simply `body`. 
 
-When the plugin finalizes all `nGraph model` transformations, the model is converted to an internal plugin graph representation. 
-At this point `ov::op::Subgraph` is converted to `ov::intel_cpu::node::Snippet` which still retains the `nGraph IR`. 
+When the plugin finalizes all `OpenVINO model` transformations, the model is converted to an internal plugin graph representation. 
+At this point `ov::op::Subgraph` is converted to `ov::intel_cpu::node::Snippet` which still retains the `OpenVINO IR`. 
 This IR is then optimized and an executable `Kernel` is produced during the `CreateComputePrimitive` stage (`CreatePrimitive()` stage in CPU plugin). 
 Finally, multiple copies of the produced kernel executed in parallel during the `Execute` stage.
 
@@ -332,7 +332,7 @@ To summarize, `Snippets` workflow consists of three major blocks: `Tokenizer`, `
 
 ### Tokenizer
 
-`Tokenizer` is run on an `nGraph model` and its main purpose is to identify subgraphs that are suitable for code generation. 
+`Tokenizer` is run on an `OpenVINO model` and its main purpose is to identify subgraphs that are suitable for code generation. 
 These subgraphs are then replaced with the `ov::op::Subgraph` node. 
 This stage is called tokenization because the `Tokenizer` employs a greedy  algorithm similar to the ones used for parsing input stream of characters into tokens. 
 One of the distinctive features of this algorithm is its flexibility, so it can seamlessly handle arbitrary operations' patterns. 
@@ -371,8 +371,8 @@ The tokenization algorithm is depicted on the flowchart below.
 ```
 Let us briefly describe the process:
 1. If a Node is not supported by `Snippets`, then ignore it and proceed to the next one.
-2. If a Node has no `Subgraph`  parents, then replace it with `Subgraph` node and copy the initial Node to the `Subgraph's` body (which is in the `nGraph IR` form).
-3. If a Node has a single `Subgraph` parent, then attach it to the `Subgraph`. It means copy the Node to the `Subgraph's` body, and remove it from the original `nGraph model`. Note that if the Node has more than one parent, corresponding parents' outputs will be connected with the updated `Subgraph` as shown on the diagram below.
+2. If a Node has no `Subgraph`  parents, then replace it with `Subgraph` node and copy the initial Node to the `Subgraph's` body (which is in the `OpenVINO IR` form).
+3. If a Node has a single `Subgraph` parent, then attach it to the `Subgraph`. It means copy the Node to the `Subgraph's` body, and remove it from the original `OpenVINO model`. Note that if the Node has more than one parent, corresponding parents' outputs will be connected with the updated `Subgraph` as shown on the diagram below.
 4. If a Node has multiple `Subgraph` parents, then they will be merged into a single `Subgraph` and the Node will be attached to it.
 ```mermaid
  graph LR
@@ -409,7 +409,7 @@ If a `Constant` is not scalar, then it can't be tokenized since storing `Constan
 Please refer to the [collapse_subgraph.cpp](../src/pass/collapse_subgraph.cpp) to gain more insights on the tokenization process.
 
 There is however one more aspect of the tokenization process that is worth covering here. 
-As discussed in the **Plugin integration** section above, the `Tokenizer` is executed before the plugin converts the `nGraph model` to an internal graph representation. 
+As discussed in the **Plugin integration** section above, the `Tokenizer` is executed before the plugin converts the `OpenVINO model` to an internal graph representation. 
 It means that the tokenized nodes will not be visible to the plugin (since they are hidden inside `Subrgaphs'` body functions), so they will be ignored by plugin optimization passes. 
 In particular, the plugin won't be able to fuse the nodes using the OneDNN post-ops mechanism. 
 This type of fusings is backend-specific, therefore can't be supported by `Snippets` directly, but it's still important from the performance perspective.
@@ -424,15 +424,15 @@ Please, refer to the [snippets_mark_skipped.cpp](../../../plugins/intel_cpu/src/
 As briefly discussed in the ***Architecture*** section, `Optimizer` consists of two major units: the first one performs data flow optimization, and the second one is focused on control flow. 
 Note however that some data-flow-related passes can be performed only after the control flow optimizations, so the second unit modifies the dataflow as well. 
 Nevertheless, we will refer to the units as `Data flow optimizer` and `Control flow optimizer` to reflect their main purpose. 
-Keep in mind that, as discussed above, the `Data flow optimizer` operates exclusively on the `nGraph IR`, while the `Control flow optimizer` works with the `Linear IR`. 
+Keep in mind that, as discussed above, the `Data flow optimizer` operates exclusively on the `OpenVINO IR`, while the `Control flow optimizer` works with the `Linear IR`. 
 We will discuss these units in more detail below.
 
 #### Data flow optimizer
 
  Before `Data flow optimizer` can modify data flow, it needs to perform a preliminary stage called `Canonicalization`. 
  To understand the stage's purpose we need to make a step back to the tokenization. 
- The `Tokenizer` saves a part of the initial `nGraph function` in `Subgraph's` body. 
- The problem is that the `nGraph function` has no information about data layouts that will be used by the `Subgraph's` parents during the `Execution` stage. 
+ The `Tokenizer` saves a part of the initial `OpenVINO function` in `Subgraph's` body. 
+ The problem is that the `OpenVINO function` has no information about data layouts that will be used by the `Subgraph's` parents during the `Execution` stage. 
  This happens because the plugin assigns layouts on internal graph representation well after the tokenization is finished. 
  The purpose of `Canonicalization` is to incorporate the plugin-defined input layouts into the body function. 
  If an input's layout was changed to a blocked one, then the corresponding body input `Parameter` will be reshaped, and new shapes will be propagated through the body function. 
@@ -485,17 +485,17 @@ The managers will be executed on different stages of the pipeline to enable more
 #### Control flow optimizer
 
 As follows from its name, the main objective of `Control flow optimizer` is to manage and optimize control flow of the kernel. 
-Since the `nGraph IR` doesn't have an explicit control flow representation, a special control-flow-oriented `IR` was developed. 
+Since the `OpenVINO IR` doesn't have an explicit control flow representation, a special control-flow-oriented `IR` was developed. 
 It is called `Linear IR` (or simply `LIR`), let's discuss it first, before we consider the transformation pipeline.
 
 ##### Linear Intermediate Representation
 
 `Linear IR` is specially designed to facilitate manipulations with control flow. 
-It is called linear, because it is essentially a sequence of `Expressions` (an analog of nGraph `Op`) that represents control flow. 
+It is called linear, because it is essentially a sequence of `Expressions` (an analog of OpenVINO `Op`) that represents control flow. 
 So if `Expression 1` is followed by `Expression 2` in `LIR` then the code for `Expression 1` will be emitted before the code for `Expression 2`. 
 Note that this doesn't necessarily mean that the `Expression 2` uses the result of `Expression 1`, they can be completely unrelated from the data flow standpoint. 
 The only restriction here is that all the `Expression's` inputs must be ready by the time it is executed. 
-This restriction is the same as in `nGraph IR`, but an important distinction here is that `LIR` allows to permute `Expressions` while this data-dependency condition is fulfilled. 
+This restriction is the same as in `OpenVINO IR`, but an important distinction here is that `LIR` allows to permute `Expressions` while this data-dependency condition is fulfilled. 
 So the `LIR` preserves data dependencies, but also allows for a more control on expressions' order that represents control flow. 
 This is a brief rationale behind the linear `IR`, now let's move to the implementation.
 
@@ -536,13 +536,13 @@ flowchart LR
    class consumers no-bg
 ```
 
-`LinearIR` is our graph representation, it's an analog to an nGraph model. 
+`LinearIR` is our graph representation, it's an analog to an OpenVINO model. 
 It is simply a container for `Expressions`, the order of `Expressions` represents control flow. 
-`LIR` also incorporates a range of useful methods to manage the `Expressions`, for example `create_expression(...)` to build `Expressions` from nGraph nodes, or `replace_input(...)` to modify data dependencies between `Expressions`. 
+`LIR` also incorporates a range of useful methods to manage the `Expressions`, for example `create_expression(...)` to build `Expressions` from OpenVINO nodes, or `replace_input(...)` to modify data dependencies between `Expressions`. 
 Please refer to the implementation in [linear_ir.cpp](../src/lowered/linear_ir.cpp) for more details.
 
 `Expression` is the main building block of a `Linear IR`. 
-It contains a pointer to the nGraph node it was created from and a pointer to the emitter it will be mapped to (which is null until `Expression::init_emitter(...)` is called). 
+It contains a pointer to the OpenVINO node it was created from and a pointer to the emitter it will be mapped to (which is null until `Expression::init_emitter(...)` is called). 
 An `Expression` can have an arbitrary number of inputs and outputs, we will refer to them simply as ports. 
 Every port can be uniquely identified by the `ExpressionPort` class. 
 The `ExpressionPort` contains a pointer to the `Expression` which port it represents, the port type (`input` or `output`) and its index (input/output number). 
@@ -556,7 +556,7 @@ This information will be used by the control flow optimization pipeline to deter
 An `Expression` internally stores two separate vectors of input and output `PortDescriptors` which could be accessed by calling `get_input_port_descriptors()` or `get_input_port_descriptor(i)` (and similar for outputs).
 
 Finally, `PortConnectors` specify how the `Expression's` ports are connected. 
-Note that an `Expression` output can be connected to several inputs (like with nGraph nodes), So every `PortConnector` stores one source `ExpressionPort` and a set of consumer `ExpressionPorts` that can be accessed by the `get_source()` or `get_consumers()` methods, respectively. 
+Note that an `Expression` output can be connected to several inputs (like with OpenVINO nodes), So every `PortConnector` stores one source `ExpressionPort` and a set of consumer `ExpressionPorts` that can be accessed by the `get_source()` or `get_consumers()` methods, respectively. 
 Like with `PortDescriptors`, an `Expression` stores input and output `PortConnectors` in two separate vectors accessed via `get_input_port_connector(i)` (or its output twin).
 
 An example on how `PortConnectors` can be used to move between `Expressions` is given on the right side of the above picture. 
@@ -622,7 +622,7 @@ Please see [assign_registers.cpp](../src/lowered/pass/assign_registers.cpp) and
 When the `Preparation` is finished, the `Generator` constructs target-specific emitters by calling `init_emitter(target)` method for every `Expression` in the `LinearIR`, where the `target` is a `TargetMachine` instance.
 
 The `TargetMachine` is a class that provides generator with target-specific information, such as supported instruction sets, vector register size etc. 
-`TargetMachine` also maps the nGraph's `DiscreteTypeInfo` (stored in the `Expression`) to the emitter that actually implements the operation. 
+`TargetMachine` also maps the OpenVINO's `DiscreteTypeInfo` (stored in the `Expression`) to the emitter that actually implements the operation. 
 The mapping is done using the `jitters` map defined in [target_machine.hpp](../include/snippets/target_machine.hpp). 
 In order for this mechanism to work, every `Snippets'` code generation backend should create emitter implementations derived from the `Emitter` base class defined in [emitter.hpp](../include/snippets/emitter.hpp). 
 The backend then should create its own target machine class (derived from the common `TargetMachine`) and populate the `jitters` map, see the [cpu_generator.cpp](../../../plugins/intel_cpu/src/emitters/x64/cpu_generator.cpp) for an implementation example.
diff --git a/src/common/snippets/include/snippets/itt.hpp b/src/common/snippets/include/snippets/itt.hpp
index 0c594165ab5776..4a617f5a06e645 100644
--- a/src/common/snippets/include/snippets/itt.hpp
+++ b/src/common/snippets/include/snippets/itt.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 
-#include <openvino/cc/ngraph/itt.hpp>
+#include <openvino/cc/pass/itt.hpp>
 
 namespace ov {
 namespace pass {
@@ -26,7 +26,7 @@ OV_CC_DOMAINS(internal_op);
 /*
  * RUN_ON_FUNCTION_SCOPE macro allows to disable the run_on_function pass
  * MATCHER_SCOPE macro allows to disable the MatcherPass if matcher isn't applied
- * INTERNAL_OP_SCOPE macro allows to disable parts of internal nGraph operations if they are not used
+ * INTERNAL_OP_SCOPE macro allows to disable parts of internal openvino operations if they are not used
  */
 #if defined(SELECTIVE_BUILD_ANALYZER)
 
diff --git a/src/common/snippets/include/snippets/op/loop.hpp b/src/common/snippets/include/snippets/op/loop.hpp
index fefc1368bb4307..1fd51649fc65d1 100644
--- a/src/common/snippets/include/snippets/op/loop.hpp
+++ b/src/common/snippets/include/snippets/op/loop.hpp
@@ -6,7 +6,7 @@
 
 #include "openvino/op/op.hpp"
 #include "snippets/emitter.hpp"
-#include "ngraph/op/parameter.hpp"
+#include "openvino/op/parameter.hpp"
 
 namespace ov {
 namespace snippets {
diff --git a/src/common/snippets/include/snippets/op/powerstatic.hpp b/src/common/snippets/include/snippets/op/powerstatic.hpp
index 5a1d0abb23ffb4..d76fa48e0601aa 100644
--- a/src/common/snippets/include/snippets/op/powerstatic.hpp
+++ b/src/common/snippets/include/snippets/op/powerstatic.hpp
@@ -5,7 +5,6 @@
 #pragma once
 
 #include "openvino/op/op.hpp"
-#include <ngraph/op/power.hpp>
 #include <snippets/snippets_isa.hpp>
 
 namespace ov {
diff --git a/src/common/snippets/include/snippets/op/scalar.hpp b/src/common/snippets/include/snippets/op/scalar.hpp
index 43ecb1aad671cc..2720ffdc062091 100644
--- a/src/common/snippets/include/snippets/op/scalar.hpp
+++ b/src/common/snippets/include/snippets/op/scalar.hpp
@@ -5,7 +5,7 @@
 #pragma once
 
 #include "openvino/op/op.hpp"
-#include "ngraph/op/constant.hpp"
+#include "openvino/op/constant.hpp"
 
 namespace ov {
 namespace snippets {
diff --git a/src/common/snippets/include/snippets/op/subgraph.hpp b/src/common/snippets/include/snippets/op/subgraph.hpp
index dab2de53e56d47..a9321e957e273c 100644
--- a/src/common/snippets/include/snippets/op/subgraph.hpp
+++ b/src/common/snippets/include/snippets/op/subgraph.hpp
@@ -190,10 +190,10 @@ class Subgraph : public ov::op::util::SubGraphOp {
 
     std::shared_ptr<ShapeInferSnippetsNode> m_shape_infer = nullptr;
 
-    class NgraphShapeInfer : public ShapeInferSnippetsNode {
-        std::shared_ptr<ov::Model> m_ngraph_body;
+    class OVShapeInfer : public ShapeInferSnippetsNode {
+        std::shared_ptr<ov::Model> m_ov_body;
     public:
-        explicit NgraphShapeInfer(const std::shared_ptr<ov::Model>& body);
+        explicit OVShapeInfer(const std::shared_ptr<ov::Model>& body);
         Result infer(const std::vector<VectorDimsRef>& input_shapes) override;
     };
 };
diff --git a/src/common/snippets/include/snippets/pass/propagate_precision.hpp b/src/common/snippets/include/snippets/pass/propagate_precision.hpp
index 1f5bd0cf9542bf..6f805cb1b68808 100644
--- a/src/common/snippets/include/snippets/pass/propagate_precision.hpp
+++ b/src/common/snippets/include/snippets/pass/propagate_precision.hpp
@@ -5,7 +5,7 @@
 #pragma once
 
 #include <memory>
-#include <ngraph/pass/pass.hpp>
+#include "openvino/pass/pass.hpp"
 #include "snippets/generator.hpp"
 
 namespace ov {
diff --git a/src/common/snippets/include/snippets/shape_inference/shape_inference.hpp b/src/common/snippets/include/snippets/shape_inference/shape_inference.hpp
index af7d29f8e3f3c3..9066d571cbb4e6 100644
--- a/src/common/snippets/include/snippets/shape_inference/shape_inference.hpp
+++ b/src/common/snippets/include/snippets/shape_inference/shape_inference.hpp
@@ -38,7 +38,7 @@ class IShapeInferSnippets {
 };
 
 /**
- * Shape inference class for Subgraph node (both nGraph and Linear IRs).
+ * Shape inference class for Subgraph node (both openvino and Linear IRs).
  * It stores the result of the last shape inference, so it can be reused in optimization pipeline.
  *
  */
diff --git a/src/common/snippets/src/lowered/expression_factory.cpp b/src/common/snippets/src/lowered/expression_factory.cpp
index 34651fd6dbbbd2..cd5cfe0db74c53 100644
--- a/src/common/snippets/src/lowered/expression_factory.cpp
+++ b/src/common/snippets/src/lowered/expression_factory.cpp
@@ -69,7 +69,7 @@ ExpressionPtr LinearIR::ExpressionFactory::create(const std::shared_ptr<ov::op::
     OPENVINO_ASSERT(model != nullptr, "To create IOExpression from Result there must be inited model!");
     auto expr = std::shared_ptr<IOExpression>(new IOExpression(res, model->get_result_index(res), linear_ir.m_shape_infer_factory));
     create_expression_inputs(linear_ir, expr);
-    // The Result node don't need output port (because of sense of the node). But each node in ngraph must have one output at least.
+    // The Result node don't need output port (because of sense of the node). But each node in openvino must have one output at least.
     // The port descriptors are automatically created in constructor. We manually clean output ports.
     expr->m_output_port_descriptors.clear();
     expr->validate();
@@ -110,7 +110,7 @@ ExpressionPtr LinearIR::ExpressionFactory::create(const std::shared_ptr<op::Loop
     OPENVINO_ASSERT(ov::is_type<op::LoopBegin>(last_input.get_expr()->get_node()), "LoopEnd expression expects LoopBegin on last input");
     expr->m_input_port_descriptors[inputs.size() - 1] = last_input.get_descriptor_ptr()->clone();
     init_expression_inputs(expr, inputs);
-    // The LoopEnd node don't need output port (because of sense of the node). But each node in ngraph must have one output at least.
+    // The LoopEnd node don't need output port (because of sense of the node). But each node in openvino must have one output at least.
     // The port descriptors are automatically created in constructor. We manually clean output ports.
     expr->m_output_port_descriptors.clear();
     expr->validate();
diff --git a/src/common/snippets/src/lowered/pass/identify_buffers.cpp b/src/common/snippets/src/lowered/pass/identify_buffers.cpp
index 02aabc93ead6ac..d411da67af38d6 100644
--- a/src/common/snippets/src/lowered/pass/identify_buffers.cpp
+++ b/src/common/snippets/src/lowered/pass/identify_buffers.cpp
@@ -36,7 +36,7 @@ std::vector<bool> IdentifyBuffers::create_adjacency_matrix(const LinearIR& linea
 
     auto get_buffer_idx = [&](const std::shared_ptr<op::Buffer>& buffer) {
         const auto iter = std::find(buffers.cbegin(), buffers.cend(), buffer);
-        NGRAPH_CHECK(iter != buffers.cend(), "Buffer wasn't find in Buffer system of Subgraph");
+        OPENVINO_ASSERT(iter != buffers.cend(), "Buffer wasn't find in Buffer system of Subgraph");
         return std::distance(buffers.cbegin(), iter);
     };
 
diff --git a/src/common/snippets/src/op/brgemm.cpp b/src/common/snippets/src/op/brgemm.cpp
index b64a4328a83b1c..5cce5d85c13a82 100644
--- a/src/common/snippets/src/op/brgemm.cpp
+++ b/src/common/snippets/src/op/brgemm.cpp
@@ -127,7 +127,7 @@ ov::PartialShape Brgemm::get_planar_output_shape(const ov::PartialShape& output_
 }
 
 ov::PartialShape Brgemm::get_output_partial_shape(const std::vector<ov::PartialShape>& input_shapes) const {
-    NGRAPH_CHECK(input_shapes.size() == 2, "BRGEMM expects 2 input shapes for shape inference");
+    OPENVINO_ASSERT(input_shapes.size() == 2, "BRGEMM expects 2 input shapes for shape inference");
 
     // Note: All majors checks are missed because Brgemm is transformed from MatMul with whole shape infer support
 
diff --git a/src/common/snippets/src/op/fill.cpp b/src/common/snippets/src/op/fill.cpp
index 437f594cdfc519..05f79495ae1748 100644
--- a/src/common/snippets/src/op/fill.cpp
+++ b/src/common/snippets/src/op/fill.cpp
@@ -32,7 +32,7 @@ std::shared_ptr<Node> Fill::clone_with_new_inputs(const OutputVector& new_args)
 void Fill::validate_and_infer_types() {
     INTERNAL_OP_SCOPE(Fill_validate_and_infer_types);
     const auto in_type = get_input_element_type(0);
-    NGRAPH_CHECK(in_type.size() == 4, "Fill operation supports only element types with 4 byte size but got:" + std::to_string(in_type.size()));
+    OPENVINO_ASSERT(in_type.size() == 4, "Fill operation supports only element types with 4 byte size but got:" + std::to_string(in_type.size()));
     set_output_type(0, get_input_element_type(0), get_input_partial_shape(0));
 }
 
diff --git a/src/common/snippets/src/op/load.cpp b/src/common/snippets/src/op/load.cpp
index d1a7d0f2cb523e..868ed4294e6dab 100644
--- a/src/common/snippets/src/op/load.cpp
+++ b/src/common/snippets/src/op/load.cpp
@@ -40,13 +40,13 @@ std::shared_ptr<Node> Load::clone_with_new_inputs(const OutputVector& new_args)
 LoadReshape::LoadReshape(const Output<ov::Node>& x, const size_t count, const size_t offset, std::vector<size_t> order)
                             : Load(x, count, offset), m_order(std::move(order)) {
     const auto& in_shape = x.get_partial_shape();
-    NGRAPH_CHECK(in_shape.is_static(), "LoadReshape supports only static input shapes");
+    OPENVINO_ASSERT(in_shape.is_static(), "LoadReshape supports only static input shapes");
     const auto in_shape_size = in_shape.size();
-    NGRAPH_CHECK(m_order.size() == in_shape_size, "LoadReshape got new_order of invalid size");
-    NGRAPH_CHECK(*std::max_element(m_order.begin(), m_order.end()) == in_shape_size - 1 &&
+    OPENVINO_ASSERT(m_order.size() == in_shape_size, "LoadReshape got new_order of invalid size");
+    OPENVINO_ASSERT(*std::max_element(m_order.begin(), m_order.end()) == in_shape_size - 1 &&
                  *std::min_element(m_order.begin(), m_order.end()) == 0, "LoadReshape detected invalid values in new_order");
     const std::set<size_t> unique_dims(order.begin(), order.end());
-    NGRAPH_CHECK(unique_dims.size() == order.size(), "LoadReshape order must not contain repeated elements");
+    OPENVINO_ASSERT(unique_dims.size() == order.size(), "LoadReshape order must not contain repeated elements");
     constructor_validate_and_infer_types();
 }
 
diff --git a/src/common/snippets/src/op/memory_access.cpp b/src/common/snippets/src/op/memory_access.cpp
index 117c1bd14e2e7f..f98d72be7f94f5 100644
--- a/src/common/snippets/src/op/memory_access.cpp
+++ b/src/common/snippets/src/op/memory_access.cpp
@@ -73,25 +73,25 @@ bool MemoryAccess::is_memory_access_output_port(size_t idx) const {
 
 void MemoryAccess::set_input_port_descriptor(const PortDescriptor& desc, const size_t i) {
     const auto it = m_input_ports.find(i);
-    NGRAPH_CHECK(it != m_input_ports.end(), "Index of input port descriptor should be less than count of input ports");
+    OPENVINO_ASSERT(it != m_input_ports.end(), "Index of input port descriptor should be less than count of input ports");
     (*it).second = { desc.count, desc.offset, i};
 }
 
 void MemoryAccess::set_output_port_descriptor(const PortDescriptor& desc, const size_t i) {
     const auto it = m_output_ports.find(i);
-    NGRAPH_CHECK(it != m_output_ports.end(), "Index of output port descriptor should be less than count of output ports");
+    OPENVINO_ASSERT(it != m_output_ports.end(), "Index of output port descriptor should be less than count of output ports");
     (*it).second = { desc.count, desc.offset, i};
 }
 
 const MemoryAccess::PortDescriptor& MemoryAccess::get_input_port_descriptor(const size_t i) const {
     const auto it = m_input_ports.find(i);
-    NGRAPH_CHECK(it != m_input_ports.end(), "Index of input port descriptor should be less than count of input ports");
+    OPENVINO_ASSERT(it != m_input_ports.end(), "Index of input port descriptor should be less than count of input ports");
     return (*it).second;
 }
 
 const MemoryAccess::PortDescriptor& MemoryAccess::get_output_port_descriptor(const size_t i) const {
     const auto it = m_output_ports.find(i);
-    NGRAPH_CHECK(it != m_output_ports.end(), "Index of output port descriptor should be less than count of output ports");
+    OPENVINO_ASSERT(it != m_output_ports.end(), "Index of output port descriptor should be less than count of output ports");
     return (*it).second;
 }
 
diff --git a/src/common/snippets/src/op/subgraph.cpp b/src/common/snippets/src/op/subgraph.cpp
index 5de4dae47a95a4..dc13bb3e8bb716 100644
--- a/src/common/snippets/src/op/subgraph.cpp
+++ b/src/common/snippets/src/op/subgraph.cpp
@@ -160,7 +160,7 @@ Subgraph::Subgraph(const OutputVector& args, const std::shared_ptr<ov::Model>& b
     for (size_t i = 0; i < body->get_output_size(); ++i)
         m_output_descriptions[0].push_back(std::make_shared<BodyOutputDescription>(i, i));
     m_transformations_allowed = false;
-    m_shape_infer = std::make_shared<NgraphShapeInfer>(body);
+    m_shape_infer = std::make_shared<OVShapeInfer>(body);
 }
 
 Subgraph::Subgraph(const NodeVector& args, const std::shared_ptr<ov::Model>& body)
@@ -292,7 +292,7 @@ auto Subgraph::wrap_node_as_subgraph(const std::shared_ptr<ov::Node>& node) -> s
 }
 
 void Subgraph::fill_empty_output_names(const Output<Node>& target_output_node, const Output<Node>& replacement_output_node) {
-    NGRAPH_SUPPRESS_DEPRECATED_START
+    OPENVINO_SUPPRESS_DEPRECATED_START
     auto& out_tensor = target_output_node.get_tensor();
     const std::string new_name = ov::op::util::get_ie_output_name(replacement_output_node);
     if (ov::descriptor::get_ov_tensor_legacy_name(out_tensor).empty()) {
@@ -301,7 +301,7 @@ void Subgraph::fill_empty_output_names(const Output<Node>& target_output_node, c
     if (!replacement_output_node.get_names().empty()) {
         out_tensor.set_names(replacement_output_node.get_names());
     }
-    NGRAPH_SUPPRESS_DEPRECATED_END
+    OPENVINO_SUPPRESS_DEPRECATED_END
 }
 
 auto Subgraph::constant_input_should_be_inside_body(const std::shared_ptr<ov::Node>& node) -> bool {
@@ -484,18 +484,18 @@ IShapeInferSnippets::Result Subgraph::shape_infer(const std::vector<VectorDimsRe
     return m_shape_infer->infer(input_shapes);
 }
 
-Subgraph::NgraphShapeInfer::NgraphShapeInfer(const std::shared_ptr<ov::Model>& body) :
-    m_ngraph_body(body) {
-    OPENVINO_ASSERT(m_ngraph_body, "Can't initialize shape infer with empty body");
+Subgraph::OVShapeInfer::OVShapeInfer(const std::shared_ptr<ov::Model>& body) :
+    m_ov_body(body) {
+    OPENVINO_ASSERT(m_ov_body, "Can't initialize shape infer with empty body");
 }
 
-IShapeInferSnippets::Result Subgraph::NgraphShapeInfer::infer(const std::vector<VectorDimsRef>& input_shapes) {
-    const ParameterVector& parameters = m_ngraph_body->get_parameters();
-    const ResultVector& results = m_ngraph_body->get_results();
+IShapeInferSnippets::Result Subgraph::OVShapeInfer::infer(const std::vector<VectorDimsRef>& input_shapes) {
+    const ParameterVector& parameters = m_ov_body->get_parameters();
+    const ResultVector& results = m_ov_body->get_results();
     OPENVINO_ASSERT(parameters.size() == input_shapes.size(), "Got invalid number of input shapes to reshape subgraph body");
     for (size_t i = 0; i < parameters.size(); ++i)
         parameters[i]->set_partial_shape(utils::vdims_to_pshape(input_shapes[i].get()));
-    m_ngraph_body->validate_nodes_and_infer_types();
+    m_ov_body->validate_nodes_and_infer_types();
     std::vector<VectorDims> outputDims;
     for (const auto& res : results)
         outputDims.emplace_back(utils::pshape_to_vdims(res->get_input_partial_shape(0)));
@@ -702,7 +702,7 @@ snippets::Schedule Subgraph::generate(const std::vector<pass::Manager::Positione
                                       const void* compile_params) {
     INTERNAL_OP_SCOPE(Subgraph);
     OV_ITT_SCOPED_TASK(ov::pass::itt::domains::SnippetsTransform, "Snippets::op::generate")
-    NGRAPH_CHECK(m_generator != nullptr, "generate is called while generator is not set");
+    OPENVINO_ASSERT(m_generator != nullptr, "generate is called while generator is not set");
 
     data_flow_transformations(data_flow_passes);
 
diff --git a/src/common/snippets/src/pass/collapse_subgraph.cpp b/src/common/snippets/src/pass/collapse_subgraph.cpp
index 35aa8f14308d99..6ed1054adac40c 100644
--- a/src/common/snippets/src/pass/collapse_subgraph.cpp
+++ b/src/common/snippets/src/pass/collapse_subgraph.cpp
@@ -16,7 +16,7 @@
 #include "openvino/opsets/opset1.hpp"
 #include "openvino/core/rt_info.hpp"
 #include "transformations/utils/utils.hpp"
-#include "ngraph/op/util/attr_types.hpp"
+#include "openvino/op/util/attr_types.hpp"
 #include "openvino/core/validation_util.hpp"
 
 #include <memory>
diff --git a/src/common/snippets/src/pass/hash.cpp b/src/common/snippets/src/pass/hash.cpp
index 48dd9586ae4337..2f975ef2cbccee 100644
--- a/src/common/snippets/src/pass/hash.cpp
+++ b/src/common/snippets/src/pass/hash.cpp
@@ -10,8 +10,6 @@
 #include <unordered_map>
 #include <unordered_set>
 
-#include "ngraph/ops.hpp"
-#include "ngraph/opsets/opset.hpp"
 #include "openvino/core/except.hpp"
 #include "openvino/core/meta_data.hpp"
 #include "openvino/core/model.hpp"
@@ -169,7 +167,7 @@ class SnippetsHasher : public ov::AttributeVisitor {
           m_node_type_name(node_type_name) {}
 
     void on_adapter(const std::string& name, ov::ValueAccessor<void>& adapter) override {
-        if (const auto& a = ov::as_type<ov::AttributeAdapter<std::shared_ptr<ngraph::Variable>>>(&adapter)) {
+        if (const auto& a = ov::as_type<ov::AttributeAdapter<std::shared_ptr<ov::op::util::Variable>>>(&adapter)) {
             m_hash = hash_combine(hash_combine(m_hash, name), a->get()->get_info().variable_id);
         } else if (const auto& a =
                        ov::as_type<ov::AttributeAdapter<std::shared_ptr<ngraph::runtime::AlignedBuffer>>>(&adapter)) {
diff --git a/src/common/snippets/src/pass/propagate_precision.cpp b/src/common/snippets/src/pass/propagate_precision.cpp
index 6ba1f5f3d09ad1..568db74d6a5c0a 100644
--- a/src/common/snippets/src/pass/propagate_precision.cpp
+++ b/src/common/snippets/src/pass/propagate_precision.cpp
@@ -32,7 +32,7 @@ bool ov::snippets::pass::PropagatePrecision::run_on_model(const std::shared_ptr<
         auto type_info = op->get_type_info();
         std::set<ov::element::TypeVector> supported_precisions;
         // TODO: At the moment Softmax is decomposed on Linear IR level.
-        //       When Softmax will be decomposed on NGraph level, remove it
+        //       When Softmax will be decomposed on openvino level, remove it
         if (type_info.is_castable(ov::op::v1::Softmax::get_type_info_static())) {
             supported_precisions = {{ov::element::f32}};
         } else {
diff --git a/src/common/snippets/src/pass/softmax_reshape_elimination.cpp b/src/common/snippets/src/pass/softmax_reshape_elimination.cpp
index 2f60f1e1155c76..36a0afb7c11325 100644
--- a/src/common/snippets/src/pass/softmax_reshape_elimination.cpp
+++ b/src/common/snippets/src/pass/softmax_reshape_elimination.cpp
@@ -10,7 +10,7 @@
 
 #include "openvino/core/rt_info.hpp"
 #include "openvino/pass/pattern/op/wrap_type.hpp"
-#include <ngraph/validation_util.hpp>
+#include "openvino/core/validation_util.hpp"
 
 ov::snippets::pass::SoftmaxReshapeElimination::SoftmaxReshapeElimination() {
     MATCHER_SCOPE(SoftmaxReshapeElimination);
diff --git a/src/common/transformations/CMakeLists.txt b/src/common/transformations/CMakeLists.txt
index 164daec54c2f18..e7d365ca32492e 100644
--- a/src/common/transformations/CMakeLists.txt
+++ b/src/common/transformations/CMakeLists.txt
@@ -4,11 +4,11 @@
 
 set(TARGET_NAME "inference_engine_transformations")
 
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
 set(PUBLIC_HEADERS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+file(GLOB_RECURSE PUBLIC_HEADERS ${PUBLIC_HEADERS_DIR}/*.hpp)
+
 # Create named folders for the sources within the .vcproj
 # Empty name lists them directly under the .vcproj
 
@@ -27,8 +27,8 @@ ov_build_target_faster(${TARGET_NAME}_obj
 
 target_link_libraries(${TARGET_NAME}_obj PRIVATE openvino::reference openvino::itt openvino::builders openvino::core::dev openvino::shape_inference)
 
-target_include_directories(${TARGET_NAME}_obj PRIVATE $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
-                                                     "${CMAKE_CURRENT_SOURCE_DIR}/src")
+target_include_directories(${TARGET_NAME}_obj PRIVATE "${PUBLIC_HEADERS_DIR}"
+                                                      "${CMAKE_CURRENT_SOURCE_DIR}/src")
 
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}_obj)
 
@@ -43,14 +43,14 @@ endif()
 add_library(${TARGET_NAME} INTERFACE)
 
 target_include_directories(${TARGET_NAME} INTERFACE
-    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+    $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
     $<BUILD_INTERFACE:$<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>>)
 
 target_link_libraries(${TARGET_NAME} INTERFACE openvino::runtime)
 
 # even the Transformations library is supposed to be Plugin API
 # we still have some code compiled as transformations, but headers are
-# part of ngraph core API
+# part of openvino core API
 # so, we need to mark this library as important for ABI free
 ov_abi_free_target(${TARGET_NAME}_obj)
 
diff --git a/src/common/transformations/tests/CMakeLists.txt b/src/common/transformations/tests/CMakeLists.txt
index 84a4fb6e900edf..7091f5162fea8d 100644
--- a/src/common/transformations/tests/CMakeLists.txt
+++ b/src/common/transformations/tests/CMakeLists.txt
@@ -15,7 +15,7 @@ ov_add_test_target(
     LINK_LIBRARIES
         gmock
         func_test_utils
-        offline_transformations
+        openvino::offline_transformations
         sharedTestClasses
         ov_lpt_models
     ADD_CLANG_FORMAT
diff --git a/src/common/transformations/tests/common_optimizations/fq_mul_fusion_test.cpp b/src/common/transformations/tests/common_optimizations/fq_mul_fusion_test.cpp
index 61f43c937be16e..7dcf6a9c44b3c3 100644
--- a/src/common/transformations/tests/common_optimizations/fq_mul_fusion_test.cpp
+++ b/src/common/transformations/tests/common_optimizations/fq_mul_fusion_test.cpp
@@ -13,7 +13,6 @@
 #include "common_test_utils/ov_test_utils.hpp"
 #include "common_test_utils/test_common.hpp"
 #include "functional_test_utils/plugin_cache.hpp"
-#include "ie_core.hpp"
 #include "openvino/core/model.hpp"
 #include "openvino/opsets/opset4.hpp"
 #include "openvino/pass/manager.hpp"
diff --git a/src/common/transformations/tests/common_optimizations/fq_reshape_fusion.cpp b/src/common/transformations/tests/common_optimizations/fq_reshape_fusion.cpp
index 8e92a5e3f7797f..8127ad129ef34b 100644
--- a/src/common/transformations/tests/common_optimizations/fq_reshape_fusion.cpp
+++ b/src/common/transformations/tests/common_optimizations/fq_reshape_fusion.cpp
@@ -10,7 +10,6 @@
 #include <memory>
 #include <string>
 
-#include "cnn_network_ngraph_impl.hpp"
 #include "common_test_utils/ov_test_utils.hpp"
 #include "openvino/core/model.hpp"
 #include "openvino/opsets/opset4.hpp"
@@ -19,7 +18,6 @@
 
 using namespace ov;
 using namespace testing;
-using namespace InferenceEngine;
 
 namespace {
 
@@ -32,8 +30,8 @@ struct FQReshapeFusionTestCase {
     bool is_negative;
 };
 
-class nGraphFQReshapeFusionTests : public ov::test::TestsCommon,
-                                   public testing::WithParamInterface<std::tuple<FQReshapeFusionTestCase>> {
+class FQReshapeFusionTests : public ov::test::TestsCommon,
+                             public testing::WithParamInterface<std::tuple<FQReshapeFusionTestCase>> {
 public:
     std::shared_ptr<ov::Model> f, ref_f;
 
@@ -115,7 +113,7 @@ class nGraphFQReshapeFusionTests : public ov::test::TestsCommon,
     }
 };
 
-TEST_P(nGraphFQReshapeFusionTests, ReshapeMatMul) {
+TEST_P(FQReshapeFusionTests, ReshapeMatMul) {
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     pass::Manager manager;
     manager.register_pass<ov::pass::InitUniqueNames>(unh);
@@ -134,7 +132,7 @@ TEST_P(nGraphFQReshapeFusionTests, ReshapeMatMul) {
 
 INSTANTIATE_TEST_SUITE_P(
     NGraph,
-    nGraphFQReshapeFusionTests,
+    FQReshapeFusionTests,
     testing::Values(
         // positive
         FQReshapeFusionTestCase{{1, 2, 1, 3},
diff --git a/src/common/transformations/tests/common_optimizations/mish_fusion_test.cpp b/src/common/transformations/tests/common_optimizations/mish_fusion_test.cpp
index 61d236a6355628..4fa1af8088d6b2 100644
--- a/src/common/transformations/tests/common_optimizations/mish_fusion_test.cpp
+++ b/src/common/transformations/tests/common_optimizations/mish_fusion_test.cpp
@@ -19,7 +19,7 @@
 using namespace ov;
 using namespace testing;
 
-// LPT to nGraph migration: temporary disabling unexpected not reproduced fails on CI:
+// LPT to openvino migration: temporary disabling unexpected not reproduced fails on CI:
 // https://openvino-ci.intel.com/job/private-ci/job/ie/job/build-linux-ubuntu18_i386/478/
 TEST_F(TransformationTestsF, MishFusing) {
     {
diff --git a/src/common/transformations/tests/smart_reshape/sr_mimicking_sbs.cpp b/src/common/transformations/tests/smart_reshape/sr_mimicking_sbs.cpp
index 5b11259cbaf998..40f954312b7f76 100644
--- a/src/common/transformations/tests/smart_reshape/sr_mimicking_sbs.cpp
+++ b/src/common/transformations/tests/smart_reshape/sr_mimicking_sbs.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <cpp/ie_cnn_network.h>
 #include <gtest/gtest.h>
 
 #include "common_test_utils/ov_test_utils.hpp"
@@ -20,15 +19,9 @@ TEST(SmartReshapeTests, MimickingSBS) {
         f = std::make_shared<ov::Model>(NodeVector{reshape}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({12, 4}));
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 2, 3, 4}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, MimickingSBS_1) {
@@ -40,15 +33,9 @@ TEST(SmartReshapeTests, MimickingSBS_1) {
         f = std::make_shared<ov::Model>(NodeVector{reshape}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({2, 24}));
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 2, 3, 4}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, MimickingSBS_2) {
@@ -60,13 +47,7 @@ TEST(SmartReshapeTests, MimickingSBS_2) {
         f = std::make_shared<ov::Model>(NodeVector{reshape}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(1));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({6, 4}));
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 2, 3, 4}));
+    EXPECT_ANY_THROW(set_batch(f, 1));
 }
diff --git a/src/common/transformations/tests/smart_reshape/sr_proposal_scales.cpp b/src/common/transformations/tests/smart_reshape/sr_proposal_scales.cpp
index 5e8088a9f2371d..06408dc2807d36 100644
--- a/src/common/transformations/tests/smart_reshape/sr_proposal_scales.cpp
+++ b/src/common/transformations/tests/smart_reshape/sr_proposal_scales.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <cpp/ie_cnn_network.h>
 #include <gtest/gtest.h>
 
 #include "common_test_utils/ov_test_utils.hpp"
@@ -39,12 +38,9 @@ TEST(SmartReshapeTests, Proposal1Scales) {
         f = std::make_shared<ov::Model>(NodeVector{proposal}, ParameterVector{input_0, input_1, input_2});
     }
 
-    InferenceEngine::CNNNetwork network(f);
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({600, 5}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, Proposal1Scales_WithConvert) {
@@ -75,12 +71,9 @@ TEST(SmartReshapeTests, Proposal1Scales_WithConvert) {
         f = std::make_shared<ov::Model>(NodeVector{proposal}, ParameterVector{input_0, input_1, input_2});
     }
 
-    InferenceEngine::CNNNetwork network(f);
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({600, 5}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, Proposal4Scales) {
@@ -110,14 +103,9 @@ TEST(SmartReshapeTests, Proposal4Scales) {
         f = std::make_shared<ov::Model>(NodeVector{proposal}, ParameterVector{input_0, input_1, input_2});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({600, 5}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, Proposal4Scales_WithConvert) {
@@ -148,12 +136,7 @@ TEST(SmartReshapeTests, Proposal4Scales_WithConvert) {
         f = std::make_shared<ov::Model>(NodeVector{proposal}, ParameterVector{input_0, input_1, input_2});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({600, 5}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
diff --git a/src/common/transformations/tests/smart_reshape/sr_reshape_1d.cpp b/src/common/transformations/tests/smart_reshape/sr_reshape_1d.cpp
index bad3962e3fd080..d98cb32f258f4d 100644
--- a/src/common/transformations/tests/smart_reshape/sr_reshape_1d.cpp
+++ b/src/common/transformations/tests/smart_reshape/sr_reshape_1d.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <cpp/ie_cnn_network.h>
 #include <gtest/gtest.h>
 
 #include "common_test_utils/ov_test_utils.hpp"
@@ -19,20 +18,16 @@ TEST(SmartReshapeTests, Reshape1d) {
         f = std::make_shared<ov::Model>(NodeVector{reshape}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({5}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({5}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {1, 3, 300, 300}}}));
+    ASSERT_NO_THROW(f->reshape({{1, 3, 300, 300}}));
     check_unique_names(f, unh);
 
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({270000}));
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3, 300, 300}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({270000}));
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 3, 300, 300}));
 }
 
 TEST(SmartReshapeTests, Reshape1d_negative) {
@@ -44,19 +39,10 @@ TEST(SmartReshapeTests, Reshape1d_negative) {
         f = std::make_shared<ov::Model>(NodeVector{reshape}, ParameterVector{input, pattern});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().is_dynamic());
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().is_dynamic());
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {1, 3, 300, 300}}}));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({270000}));
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3, 300, 300}));
-    ASSERT_FALSE(network.getFunction()->get_parameters()[1]->get_output_target_inputs(0).empty());
+    EXPECT_ANY_THROW(f->reshape({{1, 3, 300, 300}}));
 }
diff --git a/src/common/transformations/tests/smart_reshape/sr_strided_slice_squeeze.cpp b/src/common/transformations/tests/smart_reshape/sr_strided_slice_squeeze.cpp
index 002dc860dfffba..3c9053594ff68c 100644
--- a/src/common/transformations/tests/smart_reshape/sr_strided_slice_squeeze.cpp
+++ b/src/common/transformations/tests/smart_reshape/sr_strided_slice_squeeze.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <cpp/ie_cnn_network.h>
 #include <gtest/gtest.h>
 
 #include "common_test_utils/ov_test_utils.hpp"
@@ -27,19 +26,13 @@ TEST(SmartReshapeTests, SS_Squeeze) {
         f = std::make_shared<ov::Model>(NodeVector{relu}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({3}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 3}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, SS_Squeeze_partial_begin_end_mask) {
@@ -59,21 +52,19 @@ TEST(SmartReshapeTests, SS_Squeeze_partial_begin_end_mask) {
         f = std::make_shared<ov::Model>(NodeVector{relu}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({1, 768}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 128, 768}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({1, 768}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 128, 768}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    auto inputname = network.getFunction()->get_parameters()[0]->get_friendly_name();
-    ASSERT_NO_THROW(network.reshape(InferenceEngine::ICNNNetwork::InputShapes{{inputname, {2, 128, 768}}}));
+    auto inputname = f->get_parameters()[0]->get_friendly_name();
+    ASSERT_NO_THROW(f->reshape({{2, 128, 768}}));
     check_unique_names(f, unh);
 
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({2, 768}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 128, 768}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({2, 768}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({2, 128, 768}));
 }
 
 TEST(SmartReshapeTests, SS_Squeeze_partial_begin_end) {
@@ -95,21 +86,19 @@ TEST(SmartReshapeTests, SS_Squeeze_partial_begin_end) {
         f = std::make_shared<ov::Model>(NodeVector{relu}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({1, 768}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 1, 768}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({1, 768}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 1, 768}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    auto inputname = network.getFunction()->get_parameters()[0]->get_friendly_name();
-    ASSERT_NO_THROW(network.reshape(InferenceEngine::ICNNNetwork::InputShapes{{inputname, {2, 1, 768}}}));
+    auto inputname = f->get_parameters()[0]->get_friendly_name();
+    ASSERT_NO_THROW(f->reshape({{2, 1, 768}}));
     check_unique_names(f, unh);
 
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({2, 768}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 1, 768}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({2, 768}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({2, 1, 768}));
 }
 
 TEST(SmartReshapeTests, SS_Squeeze_mask_use_negative) {
@@ -128,15 +117,13 @@ TEST(SmartReshapeTests, SS_Squeeze_mask_use_negative) {
         f = std::make_shared<ov::Model>(NodeVector{squeeze}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({1, 3}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({1, 3}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_ANY_THROW(network.setBatchSize(2));
+    ASSERT_ANY_THROW(set_batch(f, 2));
     check_unique_names(f, unh);
 }
 
@@ -156,15 +143,13 @@ TEST(SmartReshapeTests, SS_Squeeze_negative_stride_negative) {
         f = std::make_shared<ov::Model>(NodeVector{relu}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({3}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_ANY_THROW(network.setBatchSize(2));
+    ASSERT_ANY_THROW(set_batch(f, 2));
     check_unique_names(f, unh);
 }
 
@@ -185,20 +170,13 @@ TEST(SmartReshapeTests, SS_SharedSqueezes) {
         f = std::make_shared<ov::Model>(NodeVector{relu_1, relu_2}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({3}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 3}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 3}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, SS_SqueezeNegativeAxes) {
@@ -218,20 +196,13 @@ TEST(SmartReshapeTests, SS_SqueezeNegativeAxes) {
         f = std::make_shared<ov::Model>(NodeVector{relu}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3, 8, 2}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3, 1, 8, 1, 2}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({3, 8, 2}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 3, 1, 8, 1, 2}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3, 8, 2}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 3, 1, 8, 1, 2}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
 
 TEST(SmartReshapeTests, Squeeze_SSNegativeAxes) {
@@ -250,18 +221,11 @@ TEST(SmartReshapeTests, Squeeze_SSNegativeAxes) {
         f = std::make_shared<ov::Model>(NodeVector{ss}, ParameterVector{input});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3, 8, 2}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({1, 3, 1, 8, 1, 2}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({3, 8, 2}))
+        << f->get_results()[0]->get_output_partial_shape(0);
+    ASSERT_TRUE(f->get_parameters()[0]->get_partial_shape().compatible({1, 3, 1, 8, 1, 2}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.setBatchSize(2));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({3, 8, 2}))
-        << network.getFunction()->get_results()[0]->get_output_partial_shape(0);
-    ASSERT_TRUE(network.getFunction()->get_parameters()[0]->get_partial_shape().compatible({2, 3, 1, 8, 1, 2}));
+    EXPECT_ANY_THROW(set_batch(f, 2));
 }
diff --git a/src/common/transformations/tests/smart_reshape/sr_sub_graph_ops.cpp b/src/common/transformations/tests/smart_reshape/sr_sub_graph_ops.cpp
index 9ff6aa84ca7419..25c30db2fa4339 100644
--- a/src/common/transformations/tests/smart_reshape/sr_sub_graph_ops.cpp
+++ b/src/common/transformations/tests/smart_reshape/sr_sub_graph_ops.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <cpp/ie_cnn_network.h>
 #include <gtest/gtest.h>
 
 #include "common_test_utils/ov_test_utils.hpp"
@@ -49,26 +48,17 @@ TEST(SmartReshapeTests, TensorIteratorStaticParameters) {
         f = std::make_shared<Model>(OutputVector{out0, out1, out2, out3}, ParameterVector{X, Y, M});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({}));
+    ASSERT_TRUE(f->get_results()[1]->get_output_partial_shape(0).compatible({1, 1, 1}));
     // concat output (seq len = 1, so it means num_iter = 1)
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({1, 1, 1}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[2]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[3]->get_output_partial_shape(0).compatible({1, 1, 1}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
-                                                  {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
-                                                  {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({32, 1, 10}));
-    // concat output
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({32, 10, 10}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({32, 1, 1}));
+    EXPECT_ANY_THROW(f->reshape({{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
+                                 {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
+                                 {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
 }
 
 TEST(SmartReshapeTests, TensorIteratorDynamicParameters) {
@@ -109,26 +99,17 @@ TEST(SmartReshapeTests, TensorIteratorDynamicParameters) {
         f = std::make_shared<Model>(OutputVector{out0, out1, out2, out3}, ParameterVector{X, Y, M});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({}));
+    ASSERT_TRUE(f->get_results()[1]->get_output_partial_shape(0).compatible({1, 1, 1}));
     // concat output (seq len = 1, so it means num_iter = 1)
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({1, 1, 1}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[2]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[3]->get_output_partial_shape(0).compatible({1, 1, 1}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
-                                                  {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
-                                                  {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({32, 1, 10}));
-    // concat output
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({32, 10, 10}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({32, 1, 1}));
+    EXPECT_ANY_THROW(f->reshape({{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
+                                 {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
+                                 {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
 }
 
 TEST(SmartReshapeTests, LoopStaticParameters) {
@@ -174,29 +155,17 @@ TEST(SmartReshapeTests, LoopStaticParameters) {
         f = std::make_shared<Model>(OutputVector{out0, out1, out2, out3}, ParameterVector{X, Y, M});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({}));
+    ASSERT_TRUE(f->get_results()[1]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
     // concat output
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[2]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[3]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
-                                                  {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
-                                                  {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({32, 1, 10}));
-    // concat output
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({32, 10, 10}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({32, 1, 1}));
+    EXPECT_ANY_THROW(f->reshape({{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
+                                 {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
+                                 {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
 }
 
 TEST(SmartReshapeTests, LoopDynamicParameters) {
@@ -242,29 +211,17 @@ TEST(SmartReshapeTests, LoopDynamicParameters) {
         f = std::make_shared<Model>(OutputVector{out0, out1, out2, out3}, ParameterVector{X, Y, M});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({}));
+    ASSERT_TRUE(f->get_results()[1]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
     // concat output
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[2]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[3]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
-                                                  {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
-                                                  {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({32, 1, 10}));
-    // concat output
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({32, 10, 10}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({32, 1, 1}));
+    EXPECT_ANY_THROW(f->reshape({{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
+                                 {f->get_parameters()[1]->get_friendly_name(), {32, 10, 1}},
+                                 {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
 }
 
 TEST(SmartReshapeTests, LoopParentParametersUsedInBody) {
@@ -314,29 +271,17 @@ TEST(SmartReshapeTests, LoopParentParametersUsedInBody) {
         f = std::make_shared<Model>(OutputVector{out0, out1, out2, out3}, ParameterVector{X, Y, M});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({}));
+    ASSERT_TRUE(f->get_results()[1]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
     // concat output
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
-    ASSERT_TRUE(
-        network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[2]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
+    ASSERT_TRUE(f->get_results()[3]->get_output_partial_shape(0).compatible(PartialShape::dynamic()));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {4, 3, 2}},
-                                                  {f->get_parameters()[1]->get_friendly_name(), {4, 3, 2}},
-                                                  {f->get_parameters()[2]->get_friendly_name(), {4, 3, 2}}}));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({4, 3, 2}));
-    // concat output
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({4, 30, 2}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({4, 3, 2}));
+    EXPECT_ANY_THROW(f->reshape({{f->get_parameters()[0]->get_friendly_name(), {4, 3, 2}},
+                                 {f->get_parameters()[1]->get_friendly_name(), {4, 3, 2}},
+                                 {f->get_parameters()[2]->get_friendly_name(), {4, 3, 2}}}));
 }
 
 TEST(SmartReshapeTests, TensorIteratorParentParameterUsedInBody) {
@@ -381,24 +326,15 @@ TEST(SmartReshapeTests, TensorIteratorParentParameterUsedInBody) {
         f = std::make_shared<Model>(OutputVector{out0, out1, out2, out3}, ParameterVector{X, Y, M});
     }
 
-    InferenceEngine::CNNNetwork network(f);
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[0]->get_output_partial_shape(0).compatible({}));
+    ASSERT_TRUE(f->get_results()[1]->get_output_partial_shape(0).compatible({1, 1, 1}));
     // concat output (seq len = 1, so it means num_iter = 1)
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({1, 1, 1}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[2]->get_output_partial_shape(0).compatible({1, 1, 1}));
+    ASSERT_TRUE(f->get_results()[3]->get_output_partial_shape(0).compatible({1, 1, 1}));
 
     auto unh = std::make_shared<ov::pass::UniqueNamesHolder>();
     init_unique_names(f, unh);
-    ASSERT_NO_THROW(network.reshape(
-        InferenceEngine::ICNNNetwork::InputShapes{{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
-                                                  {f->get_parameters()[1]->get_friendly_name(), {1, 1, 1}},
-                                                  {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
-    check_unique_names(f, unh);
-
-    ASSERT_TRUE(network.getFunction()->get_results()[0]->get_output_partial_shape(0).compatible({}));
-    ASSERT_TRUE(network.getFunction()->get_results()[1]->get_output_partial_shape(0).compatible({32, 1, 10}));
-    // concat output
-    ASSERT_TRUE(network.getFunction()->get_results()[2]->get_output_partial_shape(0).compatible({32, 10, 10}));
-    ASSERT_TRUE(network.getFunction()->get_results()[3]->get_output_partial_shape(0).compatible({32, 1, 1}));
+    EXPECT_ANY_THROW(f->reshape({{f->get_parameters()[0]->get_friendly_name(), {32, 1, 10}},
+                                 {f->get_parameters()[1]->get_friendly_name(), {1, 1, 1}},
+                                 {f->get_parameters()[2]->get_friendly_name(), {32, 1, 10}}}));
 }
diff --git a/src/common/transformations/tests/utils/convert_precision.cpp b/src/common/transformations/tests/utils/convert_precision.cpp
index 6c0da965f9bfad..1dac080461d16b 100644
--- a/src/common/transformations/tests/utils/convert_precision.cpp
+++ b/src/common/transformations/tests/utils/convert_precision.cpp
@@ -1188,7 +1188,7 @@ void constant_convert_test(element::Type type_from,
     }
     ASSERT_TRUE(actual.size() >= expected.size());
     for (size_t i = 0; i < expected.size(); i++) {
-        ASSERT_EQ(expected[i], actual[i]);
+        EXPECT_EQ(expected[i], actual[i]) << "Elements with index " << i << " are not equal.";
     }
 }
 
@@ -1378,7 +1378,7 @@ TEST(TransformationTests, ConvertPrecision_ConstantConversion_U1ToU4) {
     constant_convert_test<uint8_t, uint8_t>(element::u1,
                                             element::u4,
                                             std::vector<uint8_t>{171},
-                                            {1, 0, 1, 0, 1, 0, 1, 1});
+                                            {0, 1, 0, 1, 0, 1, 1, 1});
 }
 
 TEST(TransformationTests, ConvertPrecision_keep_precission_sensitive_fp32_with_exp) {
diff --git a/src/common/transformations/tests/utils/primitives_priority_test.cpp b/src/common/transformations/tests/utils/primitives_priority_test.cpp
index a748477e2b9137..64f6330a1da188 100644
--- a/src/common/transformations/tests/utils/primitives_priority_test.cpp
+++ b/src/common/transformations/tests/utils/primitives_priority_test.cpp
@@ -2,7 +2,6 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <cpp/ie_cnn_network.h>
 #include <gtest/gtest.h>
 
 #include <fstream>
@@ -22,8 +21,6 @@
 
 using namespace ov;
 using namespace testing;
-using namespace InferenceEngine;
-using namespace InferenceEngine::details;
 
 TEST(TransformationTests, ConvBiasFusion) {
     std::shared_ptr<ov::Model> f(nullptr);
@@ -46,12 +43,7 @@ TEST(TransformationTests, ConvBiasFusion) {
 
     std::unordered_map<std::string, std::string> pp;
 
-    InferenceEngine::CNNNetwork network(f);
-
-    // Set PrimitivesPriority to all Convolutions
-    auto model = network.getFunction();
-    ASSERT_NE(nullptr, model);
-    for (auto& op : model->get_ops()) {
+    for (auto& op : f->get_ops()) {
         if (auto conv = std::dynamic_pointer_cast<opset1::Convolution>(op)) {
             auto& rtInfo = conv->get_rt_info();
             rtInfo[ov::PrimitivesPriority::get_type_info_static()] = ov::PrimitivesPriority("test");
@@ -59,8 +51,7 @@ TEST(TransformationTests, ConvBiasFusion) {
         }
     }
 
-    auto clonedNetwork = InferenceEngine::details::cloneNetwork(network);
-    auto funcs = clonedNetwork.getFunction();
+    auto funcs = f->clone();
 
     for (auto& op : funcs->get_ops()) {
         if (auto conv = std::dynamic_pointer_cast<opset1::Convolution>(op)) {
diff --git a/src/common/util/CMakeLists.txt b/src/common/util/CMakeLists.txt
index faaab5c26d22bc..49f9d1e19cf163 100644
--- a/src/common/util/CMakeLists.txt
+++ b/src/common/util/CMakeLists.txt
@@ -4,11 +4,11 @@
 
 set(TARGET_NAME openvino_util)
 
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
 set(UTIL_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include/)
 
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+file(GLOB_RECURSE PUBLIC_HEADERS ${UTIL_INCLUDE_DIR}/*.hpp)
+
 if (WIN32)
     # Remove linux specific files
     file(GLOB_RECURSE LIN_FILES ${CMAKE_CURRENT_SOURCE_DIR}/src/os/lin/*.cpp
@@ -41,13 +41,15 @@ target_link_libraries(${TARGET_NAME} PRIVATE ${CMAKE_DL_LIBS})
 if (WIN32)
     target_link_libraries(${TARGET_NAME} PRIVATE Shlwapi)
 endif()
-target_include_directories(${TARGET_NAME} PUBLIC
-    $<BUILD_INTERFACE:${UTIL_INCLUDE_DIR}>)
-
-ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
+target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${UTIL_INCLUDE_DIR}>)
 
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
                     SOURCE_DIRECTORIES ${UTIL_INCLUDE_DIR})
 
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
+# install & export
+
+ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${UTIL_INCLUDE_DIR}/")
diff --git a/src/core/CMakeLists.txt b/src/core/CMakeLists.txt
index 869b9a02c49272..d389c1862703bf 100644
--- a/src/core/CMakeLists.txt
+++ b/src/core/CMakeLists.txt
@@ -36,25 +36,29 @@ source_group("src" FILES ${LIBRARY_SRC})
 source_group("include" FILES ${PUBLIC_HEADERS})
 
 #
-# Create ov_core_dev library
+# Create openvino_core_dev library
 #
 
-add_library(ov_core_dev INTERFACE)
-add_library(openvino::core::dev ALIAS ov_core_dev)
+add_library(openvino_core_dev INTERFACE)
+add_library(openvino::core::dev ALIAS openvino_core_dev)
 
-target_include_directories(ov_core_dev INTERFACE
+target_include_directories(openvino_core_dev INTERFACE
     $<BUILD_INTERFACE:${OV_CORE_INCLUDE_PATH}>
     $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/core/dev_api>
     $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/frontends/common/include>
     $<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/common/transformations/include>)
 
-target_link_libraries(ov_core_dev INTERFACE openvino::itt openvino::util)
+target_link_libraries(openvino_core_dev INTERFACE openvino::itt openvino::util)
 
-set_target_properties(ov_core_dev PROPERTIES EXPORT_NAME core::dev)
-openvino_developer_export_targets(COMPONENT core TARGETS openvino::core::dev)
+set_target_properties(openvino_core_dev PROPERTIES EXPORT_NAME core::dev)
+ov_developer_package_export_targets(TARGET openvino::core::dev
+                                    INSTALL_INCLUDE_DIRECTORIES
+                                        "${OV_CORE_DEV_API_PATH}/"
+                                        "${OpenVINO_SOURCE_DIR}/src/common/transformations/include/"
+                                        "${OpenVINO_SOURCE_DIR}/src/common/low_precision_transformations/include/")
 
 # Install interface libraries for case BUILD_SHARED_LIBS=OFF
-ov_install_static_lib(ov_core_dev ${OV_CPACK_COMP_CORE})
+ov_install_static_lib(openvino_core_dev ${OV_CPACK_COMP_CORE})
 
 # Fix error LNK1248: image size (...) exceeds maximum allowable size (FFFFFFFF)
 # the symbolic debugging information will be stored in a separate .pdb file.
diff --git a/src/core/builder/CMakeLists.txt b/src/core/builder/CMakeLists.txt
index 64ce45a4870921..ee87ece0365d60 100644
--- a/src/core/builder/CMakeLists.txt
+++ b/src/core/builder/CMakeLists.txt
@@ -4,11 +4,11 @@
 
 set(TARGET_NAME "openvino_builders")
 
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
 set(BUILDER_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include/)
 
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+file(GLOB_RECURSE PUBLIC_HEADERS ${BUILDER_INCLUDE_DIR}/*.hpp)
+
 # Create named folders for the sources within the .vcproj
 # Empty name lists them directly under the .vcproj
 
@@ -35,8 +35,9 @@ endif()
 
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
-ov_install_static_lib(openvino_builders ${OV_CPACK_COMP_CORE})
+# install & export
 
-# developer package
+ov_install_static_lib(openvino_builders ${OV_CPACK_COMP_CORE})
 
-openvino_developer_export_targets(COMPONENT core TARGETS openvino::builders)
+ov_developer_package_export_targets(TARGET openvino::builders
+                                    INSTALL_INCLUDE_DIRECTORIES "${BUILDER_INCLUDE_DIR}/")
diff --git a/src/core/dev_api/validation_util.hpp b/src/core/dev_api/validation_util.hpp
index c214b404798a9c..e93fefd1411eb9 100644
--- a/src/core/dev_api/validation_util.hpp
+++ b/src/core/dev_api/validation_util.hpp
@@ -78,5 +78,10 @@ bool try_apply_auto_padding(const PartialShape& image_shape,
                             CoordinateDiff& padding_above,
                             CoordinateDiff& padding_below);
 
+/// @brief Get the tensors shapes as ov::PartialShape.
+///
+/// @param tensors  Input tensors vector to get their shapes.
+/// @return Vector of partial shapes same size as input tensor vector.
+OPENVINO_API std::vector<PartialShape> get_tensors_partial_shapes(const TensorVector& tensors);
 }  // namespace util
 }  // namespace ov
diff --git a/src/core/include/openvino/core/node.hpp b/src/core/include/openvino/core/node.hpp
index 860290617709a7..ac1c61ce0f18d3 100644
--- a/src/core/include/openvino/core/node.hpp
+++ b/src/core/include/openvino/core/node.hpp
@@ -554,21 +554,17 @@ OPENVINO_API void NodeValidationFailure::create(const CheckLocInfo& check_loc_in
     NODE_VALIDATION_CHECK(std::make_pair(static_cast<const ::ov::Node*>((node)), &(input_shapes)), __VA_ARGS__)
 
 namespace ov {
-template <typename T>
-void check_new_args_count(const Node* node, T new_args) {
-    NODE_VALIDATION_CHECK(node,
-                          new_args.size() == node->input_values().size(),
-                          "clone_with_new_inputs() expected ",
-                          node->input_values().size(),
-                          " argument",
-                          (node->input_values().size() == 1 ? "" : "s"),
-                          " but got ",
-                          new_args.size());
-}
 
-}  // namespace ov
+/**
+ * @brief Check new arguments size if match node inputs count.
+ *
+ * This check is required in cloning ov::Node.
+ *
+ * @param node      Pointer to node.
+ * @param new_args  Vector with new outputs to check.
+ */
+void OPENVINO_API check_new_args_count(const Node* const node, const OutputVector& new_args);
 
-namespace ov {
 /// \brief Visits a reference to a node that has been registered with the visitor.
 template <>
 class OPENVINO_API AttributeAdapter<std::shared_ptr<ov::Node>> : public VisitorAdapter {
diff --git a/src/core/include/openvino/core/shape.hpp b/src/core/include/openvino/core/shape.hpp
index 392bd9c48bd9d5..a04a864a8394fb 100644
--- a/src/core/include/openvino/core/shape.hpp
+++ b/src/core/include/openvino/core/shape.hpp
@@ -42,19 +42,6 @@ class Shape : public std::vector<size_t> {
     OPENVINO_API std::string to_string() const;
 };
 
-/**
- * @brief Number of elements in spanned by a shape
- * @ingroup ov_model_cpp_api
- */
-template <typename SHAPE_TYPE>
-size_t shape_size(const SHAPE_TYPE& shape) {
-    size_t size = 1;
-    for (auto d : shape) {
-        size *= d;
-    }
-    return size;
-}
-
 /**
  * Number of elements in a subset of dimensions of a shape.
  * Returns a product of dimensions in a range [start_dim;end_dim)
@@ -72,6 +59,15 @@ size_t shape_size(ForwardIt start_dim, const ForwardIt end_dim) {
                            std::multiplies<typename std::iterator_traits<ForwardIt>::value_type>());
 }
 
+/**
+ * @brief Number of elements in spanned by a shape
+ * @ingroup ov_model_cpp_api
+ */
+template <typename SHAPE_TYPE>
+size_t shape_size(const SHAPE_TYPE& shape) {
+    return shape_size(shape.begin(), shape.end());
+}
+
 /// Row-major strides for a shape
 template <typename SHAPE_TYPE>
 std::vector<size_t> row_major_strides(const SHAPE_TYPE& shape) {
diff --git a/src/core/include/openvino/op/batch_to_space.hpp b/src/core/include/openvino/op/batch_to_space.hpp
index 6609e539087628..2dbbf018913fd3 100644
--- a/src/core/include/openvino/op/batch_to_space.hpp
+++ b/src/core/include/openvino/op/batch_to_space.hpp
@@ -37,9 +37,7 @@ class OPENVINO_API BatchToSpace : public Op {
                  const Output<Node>& block_shape,
                  const Output<Node>& crops_begin,
                  const Output<Node>& crops_end);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
 
     void validate_and_infer_types() override;
diff --git a/src/core/include/openvino/op/constant.hpp b/src/core/include/openvino/op/constant.hpp
index 14ee7b3313490e..e122d36a8223d1 100644
--- a/src/core/include/openvino/op/constant.hpp
+++ b/src/core/include/openvino/op/constant.hpp
@@ -426,7 +426,7 @@ class OPENVINO_API Constant : public Op {
               typename StorageDataType = fundamental_type_for<Type>,
               typename std::enable_if<Type == element::Type_t::u4, bool>::type = true>
     StorageDataType get_element_value(size_t index) const {
-        return (get_data_ptr<uint8_t>()[index / 2] >> (index % 2 ? 0 : 4)) & 0x0F;
+        return (get_data_ptr<uint8_t>()[index / 2] >> (index % 2 ? 4 : 0)) & 0x0F;
     }
 
     template <element::Type_t Type,
@@ -440,7 +440,7 @@ class OPENVINO_API Constant : public Op {
               typename StorageDataType = fundamental_type_for<Type>,
               typename std::enable_if<Type == element::Type_t::i4, bool>::type = true>
     StorageDataType get_element_value(size_t index) const {
-        const uint8_t i4data = (get_data_ptr<uint8_t>()[index / 2] >> (index % 2 ? 0 : 4)) & 0x0F;
+        const uint8_t i4data = (get_data_ptr<uint8_t>()[index / 2] >> (index % 2 ? 4 : 0)) & 0x0F;
         const bool is_negative_number = (i4data >> 3) & 0x01;
         const int8_t data = is_negative_number ? i4data | 0xF0 : i4data;
         return data;
@@ -530,7 +530,7 @@ class OPENVINO_API Constant : public Op {
         const auto round_element_no = element_number % 2 ? element_number + 1 : element_number;
         output.reserve(round_element_no);  // adds 1 more elements here?
         std::for_each(source_begin, source_end, [&](IN_T c) {
-            for (const auto i : {4, 0}) {
+            for (const auto i : {0, 4}) {
                 const uint8_t data = (c >> i) & 0x0F;
                 output.push_back(data);
             }
@@ -548,7 +548,7 @@ class OPENVINO_API Constant : public Op {
         const auto round_element_no = element_number % 2 ? element_number + 1 : element_number;
         output.reserve(round_element_no);  // adds 1 more elements here?
         std::for_each(source_begin, source_end, [&](IN_T c) {
-            for (const auto i : {4, 0}) {
+            for (const auto i : {0, 4}) {
                 const uint8_t i4data = (c >> i) & 0x0F;
                 const bool is_negative_number = (i4data >> 3) & 0x01;
                 const int8_t data = is_negative_number ? i4data | 0xF0 : i4data;
@@ -663,27 +663,9 @@ class OPENVINO_API Constant : public Op {
     template <element::Type_t Type,
               typename T,
               typename StorageDataType = fundamental_type_for<Type>,
-              typename std::enable_if<Type == element::Type_t::u4 || Type == element::Type_t::i4, bool>::type = true>
-    void write_buffer(const std::vector<T>& source) {
-        auto p = get_data_ptr_nc<Type>();
-        size_t i = 0;
-        for (; i < source.size() / 2; i++) {
-            const auto v1 = value_in_range<Type>(source[i * 2]) & 0x0F;
-            const auto v2 = value_in_range<Type>(source[i * 2 + 1]) & 0x0F;
-            const auto v = (v1 << 4) | v2;
-            p[i] = static_cast<StorageDataType>(v);
-        }
-        if (source.size() % 2) {
-            const auto v1 = value_in_range<Type>(source[i * 2]) & 0x0F;
-            const auto v = v1 << 4;
-            p[i] = static_cast<StorageDataType>(v);
-        }
-    }
-
-    template <element::Type_t Type,
-              typename T,
-              typename StorageDataType = fundamental_type_for<Type>,
-              typename std::enable_if<Type == element::Type_t::nf4 && std::is_integral<T>::value, bool>::type = true>
+              typename std::enable_if<Type == element::Type_t::u4 || Type == element::Type_t::i4 ||
+                                          (Type == element::Type_t::nf4 && std::is_integral<T>::value),
+                                      bool>::type = true>
     void write_buffer(const std::vector<T>& source) {
         auto p = get_data_ptr_nc<Type>();
         size_t i = 0;
diff --git a/src/core/include/openvino/op/logical_and.hpp b/src/core/include/openvino/op/logical_and.hpp
index 6d55f8f3585e0f..382679d16b78e1 100644
--- a/src/core/include/openvino/op/logical_and.hpp
+++ b/src/core/include/openvino/op/logical_and.hpp
@@ -35,10 +35,7 @@ class OPENVINO_API LogicalAnd : public util::BinaryElementwiseLogical {
                const AutoBroadcastSpec& auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY));
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
-    bool visit_attributes(AttributeVisitor& visitor) override;
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
 };
 }  // namespace v1
diff --git a/src/core/include/openvino/op/logical_not.hpp b/src/core/include/openvino/op/logical_not.hpp
index c5421b8db14a47..052aed0a09ad24 100644
--- a/src/core/include/openvino/op/logical_not.hpp
+++ b/src/core/include/openvino/op/logical_not.hpp
@@ -24,9 +24,7 @@ class OPENVINO_API LogicalNot : public Op {
     void validate_and_infer_types() override;
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
 };
 }  // namespace v1
diff --git a/src/core/include/openvino/op/logical_or.hpp b/src/core/include/openvino/op/logical_or.hpp
index 15c00eea04baf3..1dab36217b175a 100644
--- a/src/core/include/openvino/op/logical_or.hpp
+++ b/src/core/include/openvino/op/logical_or.hpp
@@ -34,9 +34,7 @@ class OPENVINO_API LogicalOr : public util::BinaryElementwiseLogical {
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
 };
 }  // namespace v1
diff --git a/src/core/include/openvino/op/max_pool.hpp b/src/core/include/openvino/op/max_pool.hpp
index c1741eef6cb717..534f8b1d067397 100644
--- a/src/core/include/openvino/op/max_pool.hpp
+++ b/src/core/include/openvino/op/max_pool.hpp
@@ -43,13 +43,8 @@ class OPENVINO_API MaxPool : public op::util::MaxPoolBase {
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
-
-private:
-    bool evaluate_maxpool(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
 };
 }  // namespace v1
 
@@ -119,10 +114,8 @@ class OPENVINO_API MaxPool : public op::util::MaxPoolBase {
         m_axis = axis;
     }
 
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector&, const HostTensorVector&) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
 
 private:
     Strides m_dilations;
diff --git a/src/core/include/openvino/op/split.hpp b/src/core/include/openvino/op/split.hpp
index 918457c0d84a05..6137f0591cfba1 100644
--- a/src/core/include/openvino/op/split.hpp
+++ b/src/core/include/openvino/op/split.hpp
@@ -39,9 +39,8 @@ class OPENVINO_API Split : public Op {
     void set_num_splits(const size_t num_splits) {
         m_num_splits = num_splits;
     }
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool evaluate_lower(TensorVector& outputs) const override;
     bool evaluate_upper(TensorVector& outputs) const override;
     bool has_evaluate() const override;
diff --git a/src/core/include/openvino/op/squeeze.hpp b/src/core/include/openvino/op/squeeze.hpp
index 28f098be406bf7..e66cfb3d27667a 100644
--- a/src/core/include/openvino/op/squeeze.hpp
+++ b/src/core/include/openvino/op/squeeze.hpp
@@ -20,11 +20,8 @@ class OPENVINO_API Squeeze : public Op {
     Squeeze(const Output<Node>& data, const Output<Node>& axes);
     Squeeze(const Output<Node>& data);
 
-    bool visit_attributes(AttributeVisitor& visitor) override;
     void validate_and_infer_types() override;
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
     bool evaluate_lower(TensorVector& outputs) const override;
     bool evaluate_upper(TensorVector& outputs) const override;
diff --git a/src/core/include/openvino/op/topk.hpp b/src/core/include/openvino/op/topk.hpp
index 9c2ec7a9ce1492..cfc6ccd6cc5462 100644
--- a/src/core/include/openvino/op/topk.hpp
+++ b/src/core/include/openvino/op/topk.hpp
@@ -36,7 +36,7 @@ class OPENVINO_API TopK : public util::TopKBase {
     ///             the biggest element of two.
     /// \param sort Specifies order of output elements and/or indices
     ///             Accepted values: none, index, value
-    /// \param index_element_type Specyfies type of produced indices
+    /// \param index_element_type Specifies type of produced indices
     TopK(const Output<Node>& data,
          const Output<Node>& k,
          const int64_t axis,
@@ -53,9 +53,7 @@ class OPENVINO_API TopK : public util::TopKBase {
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
 
 protected:
@@ -83,7 +81,7 @@ class OPENVINO_API TopK : public util::TopKBase {
     ///             the biggest element of two.
     /// \param sort Specifies order of output elements and/or indices
     ///             Accepted values: none, index, value
-    /// \param index_element_type Specyfies type of produced indices
+    /// \param index_element_type Specifies type of produced indices
     TopK(const Output<Node>& data,
          const Output<Node>& k,
          const int64_t axis,
@@ -99,9 +97,7 @@ class OPENVINO_API TopK : public util::TopKBase {
          const element::Type& index_element_type = element::i32);
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
 };
 }  // namespace v3
@@ -153,9 +149,7 @@ class OPENVINO_API TopK : public util::TopKBase {
     bool visit_attributes(AttributeVisitor& visitor) override;
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
 
     bool get_stable() const {
diff --git a/src/core/include/openvino/op/util/evaluate_helpers.hpp b/src/core/include/openvino/op/util/evaluate_helpers.hpp
deleted file mode 100644
index 616528adf60d08..00000000000000
--- a/src/core/include/openvino/op/util/evaluate_helpers.hpp
+++ /dev/null
@@ -1,23 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include "openvino/core/partial_shape.hpp"
-#include "openvino/runtime/tensor.hpp"
-
-namespace ov {
-namespace op {
-namespace util {
-
-/**
- * @brief Get the tensors shapes as ov::PartialShape.
- *
- * @param tensors  Input tensors vector to get its shapes.
- * @return Vector of partial shapes sam size as input tensor vector.
- */
-std::vector<PartialShape> get_tensors_partial_shapes(const TensorVector& tensors);
-}  // namespace util
-}  // namespace op
-}  // namespace ov
diff --git a/src/core/include/openvino/op/variadic_split.hpp b/src/core/include/openvino/op/variadic_split.hpp
index 2d6f751d48d3ba..49cb5dcc714502 100644
--- a/src/core/include/openvino/op/variadic_split.hpp
+++ b/src/core/include/openvino/op/variadic_split.hpp
@@ -29,25 +29,17 @@ class OPENVINO_API VariadicSplit : public Op {
     /// outputs. The sum of split_lengths must match data.shape[axis]
     VariadicSplit(const Output<Node>& data, const Output<Node>& axis, const Output<Node>& split_lengths);
 
-    bool visit_attributes(AttributeVisitor& visitor) override;
-
     void validate_and_infer_types() override;
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
     size_t get_default_output_index() const override {
         return no_default_index();
     }
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
-    OPENVINO_SUPPRESS_DEPRECATED_END
 
+    bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool evaluate_lower(TensorVector& outputs) const override;
     bool evaluate_upper(TensorVector& outputs) const override;
     bool has_evaluate() const override;
     bool evaluate_label(TensorLabelVector& output_labels) const override;
-
-private:
-    bool evaluate_variadic_split(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
-    bool has_axis_and_splits_bound_set() const;
 };
 }  // namespace v1
 }  // namespace op
diff --git a/src/core/reference/CMakeLists.txt b/src/core/reference/CMakeLists.txt
index 4154a1455ffef0..e868c07c391e96 100644
--- a/src/core/reference/CMakeLists.txt
+++ b/src/core/reference/CMakeLists.txt
@@ -4,11 +4,11 @@
 
 set(TARGET_NAME "openvino_reference")
 
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
 set(REF_IMPL_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+file(GLOB_RECURSE PUBLIC_HEADERS ${REF_IMPL_INCLUDE_DIR}/*.hpp)
+
 # Create named folders for the sources within the .vcproj
 # Empty name lists them directly under the .vcproj
 
@@ -46,7 +46,9 @@ target_link_libraries(${TARGET_NAME} PRIVATE Threads::Threads openvino::core::de
 
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
+# install & export
+
 ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
 
-# developer package
-openvino_developer_export_targets(COMPONENT core TARGETS openvino::reference)
+ov_developer_package_export_targets(TARGET openvino::reference
+                                    INSTALL_INCLUDE_DIRECTORIES "${REF_IMPL_INCLUDE_DIR}/")
diff --git a/src/core/reference/include/openvino/reference/and.hpp b/src/core/reference/include/openvino/reference/and.hpp
index 326e4b59d773af..8f43b045d6398c 100644
--- a/src/core/reference/include/openvino/reference/and.hpp
+++ b/src/core/reference/include/openvino/reference/and.hpp
@@ -4,31 +4,37 @@
 
 #pragma once
 
-#include <cstddef>
+#include <algorithm>
+#include <functional>
 
 #include "openvino/core/shape.hpp"
-#include "openvino/op/util/attr_types.hpp"
 #include "openvino/reference/autobroadcast_binop.hpp"
 
 namespace ov {
 namespace reference {
-template <typename T>
+template <class T>
 void logical_and(const T* arg0, const T* arg1, T* out, size_t count) {
-    for (size_t i = 0; i < count; i++) {
-        out[i] = static_cast<T>(arg0[i] && arg1[i]);
-    }
+    std::transform(arg0, std::next(arg0, count), arg1, out, std::logical_and<T>());
 }
 
-template <typename T>
+/**
+ * @brief Reference implementation of binary elementwise LogicalAnd operator.
+ *
+ * @param arg0            Pointer to input 0 data.
+ * @param arg1            Pointer to input 1 data.
+ * @param out             Pointer to output data.
+ * @param arg_shape0      Input 0 shape.
+ * @param arg_shape1      Input 1 shape.
+ * @param broadcast_spec  Broadcast specification mode.
+ */
+template <class T>
 void logical_and(const T* arg0,
                  const T* arg1,
                  T* out,
                  const Shape& arg0_shape,
                  const Shape& arg1_shape,
                  const op::AutoBroadcastSpec& broadcast_spec) {
-    autobroadcast_binop(arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
-        return static_cast<T>(x && y);
-    });
+    autobroadcast_binop(arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, std::logical_and<T>());
 }
 }  // namespace reference
 }  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/convert.hpp b/src/core/reference/include/openvino/reference/convert.hpp
index e943e548a8fa4e..bd36b50b03301d 100644
--- a/src/core/reference/include/openvino/reference/convert.hpp
+++ b/src/core/reference/include/openvino/reference/convert.hpp
@@ -14,7 +14,7 @@ namespace reference {
 namespace detail {
 inline void set_u1(uint8_t* buf, size_t idx, uint8_t val) {
     const size_t byte_idx = idx / 8;
-    const uint8_t bit_idx = 7 - (idx % 8);
+    const uint8_t bit_idx = 7 - (idx % 8);  // Reversed order of bits
     if (val) {
         buf[byte_idx] |= (1 << bit_idx);
     } else {
@@ -24,33 +24,33 @@ inline void set_u1(uint8_t* buf, size_t idx, uint8_t val) {
 
 inline uint8_t get_u1(const uint8_t* buf, size_t idx) {
     const size_t byte_idx = idx / 8;
-    const uint8_t bit_idx = 7 - (idx % 8);
+    const uint8_t bit_idx = 7 - (idx % 8);  // Reversed order of bits
     return (buf[byte_idx] & (1 << bit_idx)) ? 1 : 0;
 }
 
 inline void set_u4(uint8_t* buf, size_t idx, uint8_t val) {
     const size_t byte_idx = idx / 2;
-    const uint8_t bit_shift = 4 * (++idx % 2);
+    const uint8_t bit_shift = 4 * (idx % 2);
     buf[byte_idx] &= ~(0xF << bit_shift);         // half byte zeroed
     buf[byte_idx] |= ((val & 0xF) << bit_shift);  // set 1's
 }
 
 inline uint8_t get_u4(const uint8_t* buf, size_t idx) {
     const size_t byte_idx = idx / 2;
-    const uint8_t bit_shift = 4 * (++idx % 2);
+    const uint8_t bit_shift = 4 * (idx % 2);
     return (buf[byte_idx] >> bit_shift) & 0xF;
 }
 
 inline void set_i4(uint8_t* buf, size_t idx, int8_t val) {
     const size_t byte_idx = idx / 2;
-    const uint8_t bit_shift = 4 * (++idx % 2);
+    const uint8_t bit_shift = 4 * (idx % 2);
     buf[byte_idx] &= ~(0xF << bit_shift);         // half byte zeroed
     buf[byte_idx] |= ((val & 0xF) << bit_shift);  // set 1's
 }
 
 inline int8_t get_i4(const uint8_t* buf, size_t idx) {
     const size_t byte_idx = idx / 2;
-    const uint8_t bit_shift = 4 * (++idx % 2);
+    const uint8_t bit_shift = 4 * (idx % 2);
     uint8_t val = (buf[byte_idx] >> bit_shift) & 0xF;
     if (val & 0x08) {  // negative number
         val |= 0xF0;
diff --git a/src/core/reference/include/openvino/reference/logical_not.hpp b/src/core/reference/include/openvino/reference/logical_not.hpp
new file mode 100644
index 00000000000000..ca31a824b50d5f
--- /dev/null
+++ b/src/core/reference/include/openvino/reference/logical_not.hpp
@@ -0,0 +1,25 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <algorithm>
+#include <functional>
+
+namespace ov {
+namespace reference {
+
+/**
+ * @brief Reference implementation of LogicalNot operator.
+ *
+ * @param arg    Pointer to input data.
+ * @param out    Pointer to output data.
+ * @param count  Number of elements in input buffer.
+ */
+template <class T>
+void logical_not(const T* arg, T* out, const size_t count) {
+    std::transform(arg, std::next(arg, count), out, std::logical_not<T>());
+}
+}  // namespace reference
+}  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/not.hpp b/src/core/reference/include/openvino/reference/not.hpp
deleted file mode 100644
index e0444a8eb73a2a..00000000000000
--- a/src/core/reference/include/openvino/reference/not.hpp
+++ /dev/null
@@ -1,18 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <cstddef>
-
-namespace ov {
-namespace reference {
-template <typename T>
-void logical_not(const T* arg, T* out, size_t count) {
-    for (size_t i = 0; i < count; i++) {
-        out[i] = static_cast<T>(!(arg[i]));
-    }
-}
-}  // namespace reference
-}  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/or.hpp b/src/core/reference/include/openvino/reference/or.hpp
index 7e821de63e3c03..4b0d760ec41349 100644
--- a/src/core/reference/include/openvino/reference/or.hpp
+++ b/src/core/reference/include/openvino/reference/or.hpp
@@ -4,31 +4,38 @@
 
 #pragma once
 
-#include <cstddef>
+#include <algorithm>
+#include <functional>
 
 #include "openvino/core/shape.hpp"
-#include "openvino/op/util/attr_types.hpp"
 #include "openvino/reference/autobroadcast_binop.hpp"
 
 namespace ov {
 namespace reference {
-template <typename T>
-void logical_or(const T* arg0, const T* arg1, T* out, size_t count) {
-    for (size_t i = 0; i < count; i++) {
-        out[i] = static_cast<T>(arg0[i] || arg1[i]);
-    }
+
+template <class T>
+void logical_or(const T* arg0, const T* arg1, T* out, const size_t count) {
+    std::transform(arg0, std::next(arg0, count), arg1, out, std::logical_or<T>());
 }
 
-template <typename T>
+/**
+ * @brief Reference implementation of binary elementwise LogicalOr operator.
+ *
+ * @param arg0            Pointer to input 0 data.
+ * @param arg1            Pointer to input 1 data.
+ * @param out             Pointer to output data.
+ * @param arg_shape0      Input 0 shape.
+ * @param arg_shape1      Input 1 shape.
+ * @param broadcast_spec  Broadcast specification mode.
+ */
+template <class T>
 void logical_or(const T* arg0,
                 const T* arg1,
                 T* out,
                 const Shape& arg0_shape,
                 const Shape& arg1_shape,
                 const op::AutoBroadcastSpec& broadcast_spec) {
-    autobroadcast_binop(arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, [](T x, T y) -> T {
-        return static_cast<T>(x || y);
-    });
+    autobroadcast_binop(arg0, arg1, out, arg0_shape, arg1_shape, broadcast_spec, std::logical_or<T>());
 }
 }  // namespace reference
 }  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/split.hpp b/src/core/reference/include/openvino/reference/split.hpp
index dcbede1883a409..6e3564ed035bb1 100644
--- a/src/core/reference/include/openvino/reference/split.hpp
+++ b/src/core/reference/include/openvino/reference/split.hpp
@@ -4,17 +4,28 @@
 
 #pragma once
 
-#include <cmath>
+#include <cstddef>
 
-#include "openvino/reference/slice.hpp"
+#include "openvino/core/shape.hpp"
 
 namespace ov {
 namespace reference {
+
+/**
+ * @brief Reference implementation of Split operator.
+ *
+ * @param data        Pointer to input data.
+ * @param data_shape  Input data shape.
+ * @param elem_size   Size of single element type.
+ * @param axis        Axis used for split input data.
+ * @param num_splits  Number of splits
+ * @param out_data    Pointer to output data pointers (must have size of num_splits)
+ */
 void split(const char* data,
            const Shape& data_shape,
            size_t elem_size,
            int64_t axis,
            size_t num_splits,
            char** out_data);
-}
+}  // namespace reference
 }  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/topk.hpp b/src/core/reference/include/openvino/reference/topk.hpp
index c84fb54e9962bb..76ce901eb27f9a 100644
--- a/src/core/reference/include/openvino/reference/topk.hpp
+++ b/src/core/reference/include/openvino/reference/topk.hpp
@@ -8,7 +8,7 @@
 #include <cmath>
 #include <numeric>
 
-#include "openvino/op/topk.hpp"
+#include "openvino/op/util/attr_types.hpp"
 #include "openvino/reference/utils/coordinate_index.hpp"
 #include "openvino/reference/utils/coordinate_transform.hpp"
 
@@ -17,23 +17,11 @@ namespace reference {
 // This used to be lambda expressions but MSVC had difficulty compiling it. This way is more explicit.
 template <bool D, typename T, typename U>
 inline bool compare_max(const std::tuple<T, U>& a, const std::tuple<T, U>& b) {
-// this is intentional to be able to compare floats directly
-// without using relative or absolute tolerance
-#if defined(__GNUC__)
-#    pragma GCC diagnostic push
-#    pragma GCC diagnostic ignored "-Wfloat-equal"
-#endif
-    if (std::get<0>(a) == std::get<0>(b)) {
+    if (std::get<0>(a) != std::get<0>(b)) {
+        return D ? std::get<0>(a) > std::get<0>(b) : std::get<0>(a) < std::get<0>(b);
+    } else {
         return std::get<1>(a) < std::get<1>(b);
     }
-#if defined(__GNUC__)
-#    pragma GCC diagnostic pop
-#endif
-
-    if (D)
-        return std::get<0>(a) > std::get<0>(b);
-    else
-        return std::get<0>(a) < std::get<0>(b);
 }
 
 template <typename T, typename U>
@@ -41,63 +29,76 @@ inline bool compare_indices_ascending(const std::tuple<T, U>& a, const std::tupl
     return std::get<1>(a) < std::get<1>(b);
 }
 
-// TopK reference implementation provides stable indices output
+/**
+ * @brief Reference implementation fo TopK operator
+ *
+ * @param arg         Pointer to input data.
+ * @param out_indices Pointer to output indicies.
+ * @param out_values  Pointer to output values.
+ * @param in_shape    Input data shape.
+ * @param out_shape   Output data (values, indicies) shape.
+ * @param axis        Axis for search of top K elements.
+ * @param k           Number to find of top elements.
+ * @param compute_max Select mode of find max or min.
+ * @param sort        Sorting type.
+ */
 template <typename T, typename U>
 void topk(const T* arg,
           U* out_indices,
           T* out_values,
           const Shape& in_shape,
           const Shape& out_shape,
-          size_t axis,
-          size_t k,
-          bool compute_max,
-          op::TopKSortType sort = op::TopKSortType::NONE) {
-    using namespace std;
+          const size_t axis,
+          const size_t k,
+          const bool compute_max,
+          const op::TopKSortType sort = op::TopKSortType::NONE) {
     // Create temp vector for sorting.
-    vector<tuple<T, U>> workspace(in_shape[axis]);
-    vector<size_t> in_strides = row_major_strides(in_shape);
-    vector<size_t> out_strides = row_major_strides(out_shape);
-    auto in_axis_stride = in_strides[axis];
-    auto out_axis_stride = out_strides[axis];
+    std::vector<std::tuple<T, U>> workspace(in_shape[axis]);
+    const auto in_strides = row_major_strides(in_shape);
+    const auto out_strides = row_major_strides(out_shape);
+    const auto in_axis_stride = in_strides[axis];
+    const auto out_axis_stride = out_strides[axis];
 
     // Iterate over elements with 0 index at "axis" dimension
     auto traverse_shape = in_shape;
     traverse_shape[axis] = 1;
     CoordinateTransformBasic traverse_transform(traverse_shape);
-    for (const Coordinate& coord : traverse_transform) {
+    for (const auto& coord : traverse_transform) {
         auto arg_index = coordinate_index(coord, in_shape);
         auto out_index = coordinate_index(coord, out_shape);
         // Fill the temp vector
         U i = 0;
-        for (tuple<T, U>& entry : workspace) {
-            get<0>(entry) = arg[arg_index];
-            get<1>(entry) = i;
+        for (auto& entry : workspace) {
+            std::get<0>(entry) = arg[arg_index];
+            std::get<1>(entry) = i;
             arg_index += in_axis_stride;
-            i++;
-        }
-        // Sort the temp vector
-        if (compute_max) {
-            nth_element(workspace.begin(), workspace.begin() + k, workspace.end(), compare_max<true, T, U>);
-        } else {
-            nth_element(workspace.begin(), workspace.begin() + k, workspace.end(), compare_max<false, T, U>);
+            ++i;
         }
-        // Write temp vector to output
+
+        const auto cmp_func = compute_max ? compare_max<true, T, U> : compare_max<false, T, U>;
+
+        typename std::decay<decltype(cmp_func)>::type sort_func;
         switch (sort) {
-        case op::TopKSortType::NONE:
-            break;
         case op::TopKSortType::SORT_INDICES:
-            std::sort(workspace.begin(), workspace.begin() + k, compare_indices_ascending<T, U>);
+            sort_func = compare_indices_ascending<T, U>;
             break;
         case op::TopKSortType::SORT_VALUES:
-            if (compute_max)
-                std::sort(workspace.begin(), workspace.begin() + k, compare_max<true, T, U>);
-            else
-                std::sort(workspace.begin(), workspace.begin() + k, compare_max<false, T, U>);
+            sort_func = cmp_func;
+            break;
+        default:
+            sort_func = nullptr;
+            break;
         }
-        for (size_t j = 0; j < k; j++) {
+
+        std::nth_element(workspace.begin(), workspace.begin() + k, workspace.end(), cmp_func);
+        if (sort_func) {
+            std::sort(workspace.begin(), workspace.begin() + k, sort_func);
+        }
+
+        for (size_t j = 0; j < k; ++j) {
             const auto& entry = workspace[j];
-            out_values[out_index] = get<0>(entry);
-            out_indices[out_index] = get<1>(entry);
+            out_values[out_index] = std::get<0>(entry);
+            out_indices[out_index] = std::get<1>(entry);
             out_index += out_axis_stride;
         }
     }
diff --git a/src/core/reference/src/op/split.cpp b/src/core/reference/src/op/split.cpp
index 6186bdd5af941d..855fc29c4a1be9 100644
--- a/src/core/reference/src/op/split.cpp
+++ b/src/core/reference/src/op/split.cpp
@@ -6,35 +6,43 @@
 
 #include <stdio.h>
 
-#include <cmath>
+#include <iterator>
 
-using namespace ov;
+#include "openvino/core/coordinate.hpp"
+#include "openvino/reference/slice.hpp"
 
-void reference::split(const char* data,
-                      const Shape& data_shape,
-                      size_t elem_size,
-                      int64_t axis,
-                      size_t num_splits,
-                      char** out_data) {
+namespace ov {
+namespace reference {
+
+void split(const char* data,
+           const Shape& data_shape,
+           const size_t elem_size,
+           const int64_t axis,
+           const size_t num_splits,
+           char** out_data) {
     const size_t part_length = data_shape.at(axis) / num_splits;
 
-    Shape output_shape = data_shape;
-    output_shape.at(axis) = part_length;
+    auto output_shape = data_shape;
+    output_shape[axis] = part_length;
 
-    std::vector<size_t> lower_bounds(data_shape.size(), 0);
-    std::vector<size_t> upper_bounds = data_shape;
-    upper_bounds.at(axis) = part_length;
+    Coordinate lower_bounds(data_shape.size(), 0);
+    Coordinate upper_bounds = output_shape;
+    auto& lb_at_axis = lower_bounds[axis];
+    auto& ub_at_axis = upper_bounds[axis];
 
-    for (size_t i = 0; i < num_splits; ++i) {
+    const auto out_last = std::next(out_data, num_splits);
+    for (auto out_first = out_data; out_first != out_last; ++out_first) {
         reference::slice(data,
-                         out_data[i],
+                         *out_first,
                          data_shape,
                          lower_bounds,
                          upper_bounds,
                          Strides(lower_bounds.size(), 1),
                          output_shape,
                          elem_size);
-        lower_bounds.at(axis) += part_length;
-        upper_bounds.at(axis) += part_length;
+        lb_at_axis += part_length;
+        ub_at_axis += part_length;
     }
 }
+}  // namespace reference
+}  // namespace ov
diff --git a/src/core/shape_inference/CMakeLists.txt b/src/core/shape_inference/CMakeLists.txt
index b04f0cf8573b85..db862ac520d0b5 100644
--- a/src/core/shape_inference/CMakeLists.txt
+++ b/src/core/shape_inference/CMakeLists.txt
@@ -4,11 +4,11 @@
 
 set(TARGET_NAME "openvino_shape_inference")
 
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
 set(SHAPE_INFER_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 
+file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+file(GLOB_RECURSE PUBLIC_HEADERS ${SHAPE_INFER_INCLUDE_DIR}/*.hpp)
+
 # Create named folders for the sources within the .vcproj
 # Empty name lists them directly under the .vcproj
 
@@ -24,7 +24,7 @@ set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME shape_inference)
 target_include_directories(${TARGET_NAME} PUBLIC
     $<BUILD_INTERFACE:${SHAPE_INFER_INCLUDE_DIR}>
     $<BUILD_INTERFACE:${OV_CORE_INCLUDE_PATH}>
-    $<BUILD_INTERFACE:$<TARGET_PROPERTY:openvino::core::dev,INTERFACE_INCLUDE_DIRECTORIES>>)
+    $<TARGET_PROPERTY:openvino::core::dev,INTERFACE_INCLUDE_DIRECTORIES>)
 
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
@@ -32,7 +32,9 @@ if(NOT BUILD_SHARED_LIBS)
     target_compile_definitions(${TARGET_NAME} PUBLIC OPENVINO_STATIC_LIBRARY)
 endif()
 
+# developer package
+
 ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
 
-# developer package
-openvino_developer_export_targets(COMPONENT core TARGETS ${TARGET_NAME})
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${SHAPE_INFER_INCLUDE_DIR}/")
diff --git a/src/core/src/bound_evaluate.cpp b/src/core/src/bound_evaluate.cpp
index cf3dc5bf21e3da..1b1093b871c657 100644
--- a/src/core/src/bound_evaluate.cpp
+++ b/src/core/src/bound_evaluate.cpp
@@ -479,13 +479,13 @@ bool ov::interval_bound_evaluator(const Node* node,
     return fully_defined;
 }
 
-bool ov::tensor_is_positive(const Tensor& bound) {
+bool ov::tensor_is_non_negative(const Tensor& bound) {
     const auto bound_constant =
         std::make_shared<op::v0::Constant>(bound.get_element_type(), bound.get_shape(), bound.data());
     const auto zero_constant = op::v0::Constant::create(bound.get_element_type(), {1}, {0});
     OutputVector greater(1);
 
-    bool folded = std::make_shared<op::v1::Greater>(bound_constant, zero_constant)
+    bool folded = std::make_shared<op::v1::GreaterEqual>(bound_constant, zero_constant)
                       ->constant_fold(greater, {bound_constant, zero_constant});
     OPENVINO_ASSERT(folded);
 
@@ -500,6 +500,50 @@ bool ov::tensor_is_positive(const Tensor& bound) {
     return std::dynamic_pointer_cast<op::v0::Constant>(all[0].get_node_shared_ptr())->cast_vector<bool>()[0];
 }
 
+bool ov::tensor_has_max_value(const Tensor& bound) {
+    const auto bound_constant =
+        std::make_shared<op::v0::Constant>(bound.get_element_type(), bound.get_shape(), bound.data());
+    OPENVINO_SUPPRESS_DEPRECATED_START
+    auto max_constant = ngraph::get_constant_max_of_type(bound.get_element_type());
+    OPENVINO_SUPPRESS_DEPRECATED_END
+    OutputVector equal(1);
+
+    bool folded = std::make_shared<op::v1::Equal>(bound_constant, max_constant)
+                      ->constant_fold(equal, {bound_constant, max_constant});
+    OPENVINO_ASSERT(folded);
+
+    auto axes_vector = std::vector<int64_t>(equal[0].get_shape().size());
+    std::iota(axes_vector.begin(), axes_vector.end(), 0);
+    const auto axes = op::v0::Constant::create(element::i64, {axes_vector.size()}, axes_vector);
+
+    OutputVector all(1);
+    folded = std::make_shared<op::v1::ReduceLogicalOr>(equal[0], axes)->constant_fold(all, {equal[0], axes});
+    OPENVINO_ASSERT(folded && ov::is_type<op::v0::Constant>(all[0].get_node_shared_ptr()));
+    OPENVINO_ASSERT(all[0].get_shape() == Shape{});
+    return std::dynamic_pointer_cast<op::v0::Constant>(all[0].get_node_shared_ptr())->cast_vector<bool>()[0];
+}
+
+bool ov::tensor_has_zero_value(const Tensor& bound) {
+    const auto bound_constant =
+        std::make_shared<op::v0::Constant>(bound.get_element_type(), bound.get_shape(), bound.data());
+    const auto zero_constant = op::v0::Constant::create(bound.get_element_type(), {1}, {0});
+    OutputVector equal(1);
+
+    bool folded = std::make_shared<op::v1::Equal>(bound_constant, zero_constant)
+                      ->constant_fold(equal, {bound_constant, zero_constant});
+    OPENVINO_ASSERT(folded);
+
+    auto axes_vector = std::vector<int64_t>(equal[0].get_shape().size());
+    std::iota(axes_vector.begin(), axes_vector.end(), 0);
+    const auto axes = op::v0::Constant::create(element::i64, {axes_vector.size()}, axes_vector);
+
+    OutputVector all(1);
+    folded = std::make_shared<op::v1::ReduceLogicalOr>(equal[0], axes)->constant_fold(all, {equal[0], axes});
+    OPENVINO_ASSERT(folded && ov::is_type<op::v0::Constant>(all[0].get_node_shared_ptr()));
+    OPENVINO_ASSERT(all[0].get_shape() == Shape{});
+    return std::dynamic_pointer_cast<op::v0::Constant>(all[0].get_node_shared_ptr())->cast_vector<bool>()[0];
+}
+
 bool ov::has_and_set_equal_bounds(const Output<Node>& source) {
     if (op::util::is_constant(source.get_node_shared_ptr()))
         return true;
@@ -509,7 +553,7 @@ bool ov::has_and_set_equal_bounds(const Output<Node>& source) {
 }
 
 bool ov::have_node_inputs_bounds_set(const Node* const node, const size_t first_idx, const size_t last_idx) {
-    bool have_bound_set = last_idx <= node->get_input_size();
+    bool have_bound_set = last_idx < node->get_input_size();
     for (size_t i = first_idx; have_bound_set && (i <= last_idx); ++i) {
         have_bound_set = node->get_input_tensor(i).has_and_set_bound();
     }
diff --git a/src/core/src/bound_evaluate.hpp b/src/core/src/bound_evaluate.hpp
index 297f69d661f131..952343a7d0076b 100644
--- a/src/core/src/bound_evaluate.hpp
+++ b/src/core/src/bound_evaluate.hpp
@@ -9,8 +9,14 @@
 namespace ov {
 // bool could_propagate(const Output<Node>& output, std::vector<Node*>& order);
 
-/// \brief Checks if all the elements of the bound Tensor are positive
-bool tensor_is_positive(const Tensor& bound);
+/// \brief Checks if all the elements of the bound Tensor are non-negative
+bool tensor_is_non_negative(const Tensor& bound);
+
+/// \brief Checks if any element of the bound Tensor has max possible value
+bool tensor_has_max_value(const Tensor& bound);
+
+/// \brief Checks if any element of the bound Tensor has zero value
+bool tensor_has_zero_value(const Tensor& bound);
 
 /// \brief Estimates upper bound for node output tensors using only upper bounds of the nodes
 /// inputs.
diff --git a/src/core/src/node.cpp b/src/core/src/node.cpp
index ee2c454bb6a235..492f0dec1e3a04 100644
--- a/src/core/src/node.cpp
+++ b/src/core/src/node.cpp
@@ -844,6 +844,17 @@ bool ov::Node::visit_attributes(AttributeVisitor&) {
 }
 
 namespace ov {
+void check_new_args_count(const Node* const node, const OutputVector& new_args) {
+    NODE_VALIDATION_CHECK(node,
+                          new_args.size() == node->input_values().size(),
+                          "clone_with_new_inputs() expected ",
+                          node->input_values().size(),
+                          " argument",
+                          (node->input_values().size() == 1 ? "" : "s"),
+                          " but got ",
+                          new_args.size());
+}
+
 AttributeAdapter<std::shared_ptr<Node>>::AttributeAdapter(std::shared_ptr<Node>& value) : m_ref(value) {}
 
 bool AttributeAdapter<std::shared_ptr<Node>>::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/batch_to_space.cpp b/src/core/src/op/batch_to_space.cpp
index da2c2c5fa703a1..0b522b5156b017 100644
--- a/src/core/src/op/batch_to_space.cpp
+++ b/src/core/src/op/batch_to_space.cpp
@@ -2,33 +2,23 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph/op/batch_to_space.hpp"
-
-#include <batch_to_space_shape_inference.hpp>
-#include <cmath>
-#include <cstddef>
-#include <memory>
-#include <ngraph/ops.hpp>
-#include <ngraph/validation_util.hpp>
-#include <numeric>
+#include "openvino/op/batch_to_space.hpp"
 
+#include "batch_to_space_shape_inference.hpp"
 #include "itt.hpp"
-#include "ngraph/builder/make_constant.hpp"
-#include "ngraph/node.hpp"
-#include "ngraph/opsets/opset3.hpp"
-#include "ngraph/shape.hpp"
 #include "openvino/op/util/precision_sensitive_attribute.hpp"
 #include "openvino/op/util/slice_plan.hpp"
 #include "openvino/reference/reshape.hpp"
 #include "openvino/reference/strided_slice.hpp"
 
-using namespace std;
-using namespace ngraph;
+namespace ov {
+namespace op {
+namespace v1 {
 
-ngraph::op::v1::BatchToSpace::BatchToSpace(const ngraph::Output<ngraph::Node>& data,
-                                           const ngraph::Output<ngraph::Node>& block_shape,
-                                           const ngraph::Output<ngraph::Node>& crops_begin,
-                                           const ngraph::Output<ngraph::Node>& crops_end)
+BatchToSpace::BatchToSpace(const Output<Node>& data,
+                           const Output<Node>& block_shape,
+                           const Output<Node>& crops_begin,
+                           const Output<Node>& crops_end)
     : Op({data, block_shape, crops_begin, crops_end}) {
     ov::mark_as_precision_sensitive(input(1));
     ov::mark_as_precision_sensitive(input(2));
@@ -36,7 +26,7 @@ ngraph::op::v1::BatchToSpace::BatchToSpace(const ngraph::Output<ngraph::Node>& d
     constructor_validate_and_infer_types();
 }
 
-void op::v1::BatchToSpace::validate_and_infer_types() {
+void BatchToSpace::validate_and_infer_types() {
     OV_OP_SCOPE(v1_BatchToSpace_validate_and_infer_types);
 
     const auto& data_et = get_input_element_type(0);
@@ -66,30 +56,29 @@ void op::v1::BatchToSpace::validate_and_infer_types() {
     set_output_type(0, data_et, output_shape);
 }
 
-std::shared_ptr<ngraph::Node> ngraph::op::v1::BatchToSpace::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> BatchToSpace::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_BatchToSpace_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<BatchToSpace>(new_args.at(0), new_args.at(1), new_args.at(2), new_args.at(3));
+    return std::make_shared<BatchToSpace>(new_args.at(0), new_args.at(1), new_args.at(2), new_args.at(3));
 }
 
-bool ngraph::op::v1::BatchToSpace::visit_attributes(ngraph::AttributeVisitor& visitor) {
+bool BatchToSpace::visit_attributes(AttributeVisitor& visitor) {
     OV_OP_SCOPE(v1_BatchToSpace_visit_attributes);
     return true;
 }
 
-OPENVINO_SUPPRESS_DEPRECATED_START
 namespace {
-bool batch_to_space_evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) {
-    auto data = inputs[0];
-    const auto elem_size = data->get_element_type().size();
+bool batch_to_space_evaluate(TensorVector& outputs, const TensorVector& inputs) {
+    const auto& in = inputs[0];
+    const auto elem_size = in.get_element_type().size();
 
-    auto data_shape = data->get_shape();
+    auto data_shape = in.get_shape();
 
-    auto const block_values_size = shape_size(inputs[1]->get_shape());
+    auto const block_values_size = shape_size(inputs[1].get_shape());
 
-    const auto* block_values = inputs[1]->get_data_ptr<int64_t>();
-    const auto* crops_begin_values = inputs[2]->get_data_ptr<int64_t>();
-    const auto* crops_end_values = inputs[3]->get_data_ptr<int64_t>();
+    const auto* block_values = inputs[1].data<int64_t>();
+    const auto* crops_begin_values = inputs[2].data<int64_t>();
+    const auto* crops_end_values = inputs[3].data<int64_t>();
 
     ov::Shape dispersed_shape(1);
     dispersed_shape.insert(dispersed_shape.end(), data_shape.begin(), data_shape.end());
@@ -101,7 +90,13 @@ bool batch_to_space_evaluate(const HostTensorVector& outputs, const HostTensorVe
         return false;
     }
 
-    auto* flat_data = data->get_data_ptr<char>();
+    auto* in_first = static_cast<const char*>(in.data());
+
+    // Copy input tensor to not overwrite evaluate's inputs tensors passed as const.
+    // The evaluate algorithm should be improved to avoid additional data copy.
+    auto flat_in = Tensor(in.get_element_type(), data_shape);
+    auto* flat_data = static_cast<char*>(flat_in.data());
+    std::memcpy(flat_data, in_first, flat_in.get_byte_size());
     std::vector<char> dispersed_data(shape_size(data_shape) * elem_size);
 
     ov::Shape post_transpose_shape(axes_order.size());
@@ -117,15 +112,15 @@ bool batch_to_space_evaluate(const HostTensorVector& outputs, const HostTensorVe
                                dispersed_shape,
                                elem_size);
 
-        size_t val = 1;
-        for (size_t axis_idx = 0; axis_idx <= block_values_size; ++axis_idx) {
+        for (size_t axis_idx = 0, val = 1; axis_idx <= block_values_size; ++axis_idx) {
             if ((block_idx + 1) == axis_idx) {
                 axes_order[axis_idx] = 0;
             } else {
                 axes_order[axis_idx] = val;
-                val++;
+                ++val;
             }
         }
+
         for (size_t axis_idx = 0; axis_idx < axes_order.size(); ++axis_idx) {
             post_transpose_shape[axis_idx] = dispersed_shape[axes_order[axis_idx]];
         }
@@ -148,61 +143,52 @@ bool batch_to_space_evaluate(const HostTensorVector& outputs, const HostTensorVe
         data_shape = squeezed_shape;
     }
 
-    std::vector<int64_t> upperbounds_values(data_shape.size());
+    std::vector<int64_t> upper_bounds_values(data_shape.size());
     for (size_t i = 0; i < data_shape.size(); ++i) {
-        upperbounds_values[i] = data_shape[i] - crops_end_values[i];
+        upper_bounds_values[i] = data_shape[i] - crops_end_values[i];
     }
 
     std::vector<size_t> begin_mask(data_shape.size(), 0);
     std::vector<size_t> end_mask(data_shape.size(), 0);
 
-    std::vector<int64_t> begins(shape_size(inputs[2]->get_shape()));
-    begins.assign(crops_begin_values, crops_begin_values + shape_size(inputs[2]->get_shape()));
+    std::vector<int64_t> begins(shape_size(inputs[2].get_shape()));
+    begins.assign(crops_begin_values, crops_begin_values + shape_size(inputs[2].get_shape()));
 
     std::vector<int64_t> default_strides(begins.size(), 1);
     const auto slice_plan = ov::op::util::make_slice_plan(data_shape,
                                                           begins,
-                                                          upperbounds_values,
+                                                          upper_bounds_values,
                                                           default_strides,
                                                           begin_mask,
                                                           end_mask,
                                                           AxisSet(),
                                                           AxisSet(),
                                                           AxisSet());
-    ov::reference::strided_slice(flat_data, outputs[0]->get_data_ptr<char>(), data_shape, slice_plan, elem_size);
+    ov::reference::strided_slice(flat_data, static_cast<char*>(outputs[0].data()), data_shape, slice_plan, elem_size);
     return true;
 }
 }  // namespace
 
-bool ngraph::op::v1::BatchToSpace::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool BatchToSpace::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v1_BatchToSpace_evaluate);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    OPENVINO_ASSERT(validate_host_tensor_vector(inputs, 4));
-    OPENVINO_ASSERT(validate_host_tensor_vector(outputs, 1));
-    OPENVINO_SUPPRESS_DEPRECATED_END
-
-    if (outputs[0]->get_partial_shape().is_dynamic()) {
-        std::vector<ov::PartialShape> input_shapes;
-        input_shapes.reserve(inputs.size());
+    OPENVINO_ASSERT(outputs.size() == 1);
 
-        for (size_t i = 0; i < inputs.size(); ++i) {
-            input_shapes.push_back(inputs[i]->get_partial_shape());
-            if (input_shapes.back().is_dynamic()) {
-                return false;
-            }
-        }
-
-        const auto output_shape = shape_infer(this, input_shapes, ov::make_tensor_accessor(inputs)).front().to_shape();
-
-        outputs[0]->set_element_type(inputs[0]->get_element_type());
-        outputs[0]->set_shape(output_shape);
+    std::vector<ov::PartialShape> input_shapes;
+    for (const auto& in : inputs) {
+        input_shapes.emplace_back(in.get_shape());
     }
 
+    const auto output_shape = shape_infer(this, input_shapes, ov::make_tensor_accessor(inputs)).front().to_shape();
+    outputs[0].set_shape(output_shape);
+
     return batch_to_space_evaluate(outputs, inputs);
 }
 
-bool ngraph::op::v1::BatchToSpace::has_evaluate() const {
+bool BatchToSpace::has_evaluate() const {
     OV_OP_SCOPE(v1_BatchToSpace_has_evaluate);
     return !get_input_partial_shape(0).is_dynamic() && get_input_shape(0).size() >= 2 &&
            get_input_shape(0).size() <= shape_size(get_input_shape(1));
 }
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/ceiling.cpp b/src/core/src/op/ceiling.cpp
index 8b8f9e96f2503f..c46ed21ae03ebc 100644
--- a/src/core/src/op/ceiling.cpp
+++ b/src/core/src/op/ceiling.cpp
@@ -44,7 +44,7 @@ bool Ceiling::evaluate(TensorVector& outputs, const TensorVector& inputs) const
     outputs[0].set_shape(inputs[0].get_shape());
 
     using namespace ov::element;
-    return IfTypeOf<f32, i8, i16, i32, i64, u8, u16, u32, u64>::apply<ceiling::Evaluate>(
+    return IfTypeOf<f16, f32, i8, i16, i32, i64, u8, u16, u32, u64>::apply<ceiling::Evaluate>(
         inputs[0].get_element_type(),
         inputs[0],
         outputs[0],
@@ -62,6 +62,7 @@ bool Ceiling::has_evaluate() const {
     case element::u16:
     case element::u32:
     case element::u64:
+    case element::f16:
     case element::f32:
         return true;
     default:
diff --git a/src/core/src/op/eye.cpp b/src/core/src/op/eye.cpp
index edf9abbb06f4c4..4f1ecca6d47ad7 100644
--- a/src/core/src/op/eye.cpp
+++ b/src/core/src/op/eye.cpp
@@ -8,7 +8,6 @@
 #include "eye_shape_inference.hpp"
 #include "itt.hpp"
 #include "openvino/core/validation_util.hpp"
-#include "openvino/op/util/evaluate_helpers.hpp"
 #include "openvino/reference/eye.hpp"
 
 namespace ov {
@@ -107,7 +106,7 @@ bool Eye::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OPENVINO_ASSERT(outputs.size() == 1);
 
     // Inputs size and shapes checked by shape_infer
-    const auto input_shapes = util::get_tensors_partial_shapes(inputs);
+    const auto input_shapes = ov::util::get_tensors_partial_shapes(inputs);
     const auto output_shape = shape_infer(this, input_shapes, make_tensor_accessor(inputs)).front().to_shape();
 
     int64_t diagonal_index;
diff --git a/src/core/src/op/logical_and.cpp b/src/core/src/op/logical_and.cpp
index d6f451715a564d..fe8bd612ed2d85 100644
--- a/src/core/src/op/logical_and.cpp
+++ b/src/core/src/op/logical_and.cpp
@@ -2,83 +2,53 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/op/logical_and.hpp"
+
 #include "itt.hpp"
-#include "ngraph/op/and.hpp"
-#include "ngraph/runtime/host_tensor.hpp"
-#include "ngraph/validation_util.hpp"
 #include "openvino/reference/and.hpp"
+#include "utils.hpp"
 
-using namespace std;
-using namespace ngraph;
-
-op::v1::LogicalAnd::LogicalAnd(const Output<Node>& arg0,
-                               const Output<Node>& arg1,
-                               const AutoBroadcastSpec& auto_broadcast)
+namespace ov {
+namespace op {
+namespace v1 {
+LogicalAnd::LogicalAnd(const Output<Node>& arg0, const Output<Node>& arg1, const AutoBroadcastSpec& auto_broadcast)
     : BinaryElementwiseLogical(arg0, arg1, auto_broadcast) {
     constructor_validate_and_infer_types();
 }
 
-bool op::v1::LogicalAnd::visit_attributes(AttributeVisitor& visitor) {
-    OV_OP_SCOPE(v1_LogicalAnd_visit_attributes);
-    BinaryElementwiseLogical::visit_attributes(visitor);
-    return true;
-}
-
-shared_ptr<Node> op::v1::LogicalAnd::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> LogicalAnd::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_LogicalAnd_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v1::LogicalAnd>(new_args.at(0), new_args.at(1), this->get_autob());
+    return std::make_shared<LogicalAnd>(new_args.at(0), new_args.at(1), get_autob());
 }
 
-OPENVINO_SUPPRESS_DEPRECATED_START
-namespace logand {
-namespace {
-template <element::Type_t ET>
-bool evaluate(const HostTensorPtr& arg0,
-              const HostTensorPtr& arg1,
-              const HostTensorPtr& out,
-              const op::AutoBroadcastSpec& broadcast_spec) {
-    ov::reference::logical_and(arg0->get_data_ptr<ET>(),
-                               arg1->get_data_ptr<ET>(),
-                               out->get_data_ptr<ET>(),
-                               arg0->get_shape(),
-                               arg1->get_shape(),
-                               broadcast_spec);
-    return true;
-}
-
-bool evaluate_logand(const HostTensorPtr& arg0,
-                     const HostTensorPtr& arg1,
-                     const HostTensorPtr& out,
-                     const op::AutoBroadcastSpec& broadcast_spec) {
-    bool rc = true;
-    out->set_broadcast(broadcast_spec, arg0, arg1);
-    switch (arg0->get_element_type()) {
-        OPENVINO_TYPE_CASE(evaluate_logand, boolean, arg0, arg1, out, broadcast_spec);
-    default:
-        rc = false;
-        break;
-    }
-    return rc;
-}
-}  // namespace
-}  // namespace logand
-
-bool op::v1::LogicalAnd::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool LogicalAnd::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v1_LogicalAnd_evaluate);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    OPENVINO_ASSERT(validate_host_tensor_vector(outputs, 1) && validate_host_tensor_vector(inputs, 2));
-    OPENVINO_SUPPRESS_DEPRECATED_END
-    return logand::evaluate_logand(inputs[0], inputs[1], outputs[0], get_autob());
+    OPENVINO_ASSERT(outputs.size() == 1);
+    OPENVINO_ASSERT(inputs.size() == 2);
+
+    const auto& shape_0 = inputs[0].get_shape();
+    const auto& shape_1 = inputs[1].get_shape();
+    outputs[0].set_shape(infer_broadcast_shape(this, shape_0, shape_1));
+
+    if (inputs[0].get_element_type() == element::boolean) {
+        using T = fundamental_type_for<element::boolean>;
+        reference::logical_and(inputs[0].data<const T>(),
+                               inputs[1].data<const T>(),
+                               outputs[0].data<T>(),
+                               shape_0,
+                               shape_1,
+                               get_autob());
+        return true;
+    } else {
+        return false;
+    }
 }
 
-bool op::v1::LogicalAnd::has_evaluate() const {
+bool LogicalAnd::has_evaluate() const {
     OV_OP_SCOPE(v1_LogicalAnd_has_evaluate);
-    switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
-        return true;
-    default:
-        break;
-    }
-    return false;
+    return get_input_element_type(0) == element::boolean;
 }
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/logical_not.cpp b/src/core/src/op/logical_not.cpp
index 7ed4971861766a..db9f939463651a 100644
--- a/src/core/src/op/logical_not.cpp
+++ b/src/core/src/op/logical_not.cpp
@@ -2,22 +2,34 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/op/logical_not.hpp"
+
+#include "element_visitor.hpp"
 #include "itt.hpp"
-#include "ngraph/op/not.hpp"
-#include "ngraph/op/op.hpp"
-#include "ngraph/op/util/elementwise_args.hpp"
-#include "ngraph/runtime/host_tensor.hpp"
-#include "ngraph/validation_util.hpp"
-#include "openvino/reference/not.hpp"
+#include "openvino/reference/logical_not.hpp"
+
+namespace ov {
+namespace op {
+namespace logical_not {
 
-using namespace ngraph;
-using namespace std;
+struct Evaluate : element::NoAction<bool> {
+    using element::NoAction<bool>::visit;
 
-op::v1::LogicalNot::LogicalNot(const Output<Node>& arg) : Op({arg}) {
+    template <element::Type_t ET, class T = fundamental_type_for<ET>>
+    static result_type visit(const Tensor& in, Tensor& out, const size_t count) {
+        reference::logical_not(in.data<const T>(), out.data<T>(), count);
+        return true;
+    }
+};
+}  // namespace logical_not
+
+namespace v1 {
+
+LogicalNot::LogicalNot(const Output<Node>& arg) : Op({arg}) {
     constructor_validate_and_infer_types();
 }
 
-void op::v1::LogicalNot::validate_and_infer_types() {
+void LogicalNot::validate_and_infer_types() {
     OV_OP_SCOPE(v1_LogicalNot_validate_and_infer_types);
     const auto& element_type = get_input_element_type(0);
     // No boolean element_type validation for backward compatibility
@@ -25,64 +37,43 @@ void op::v1::LogicalNot::validate_and_infer_types() {
     set_output_type(0, element_type, arg_pshape);
 }
 
-shared_ptr<Node> op::v1::LogicalNot::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> LogicalNot::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_LogicalNot_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v1::LogicalNot>(new_args.at(0));
-}
-
-OPENVINO_SUPPRESS_DEPRECATED_START
-namespace notop {
-namespace {
-template <element::Type_t ET>
-inline bool evaluate(const HostTensorPtr& arg0, const HostTensorPtr& out, const size_t count) {
-    using T = typename element_type_traits<ET>::value_type;
-    ov::reference::logical_not<T>(arg0->get_data_ptr<ET>(), out->get_data_ptr<ET>(), count);
-    return true;
+    return std::make_shared<LogicalNot>(new_args.at(0));
 }
 
-bool evaluate_not(const HostTensorPtr& arg0, const HostTensorPtr& out, const size_t count) {
-    bool rc = true;
-    out->set_unary(arg0);
+bool LogicalNot::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
+    OV_OP_SCOPE(v1_LogicalNot_evaluate);
+    OPENVINO_ASSERT(outputs.size() == 1);
+    OPENVINO_ASSERT(inputs.size() == 1);
 
-    switch (arg0->get_element_type()) {
-        OPENVINO_TYPE_CASE(evaluate_not, boolean, arg0, out, count);
-        OPENVINO_TYPE_CASE(evaluate_not, i32, arg0, out, count);
-        OPENVINO_TYPE_CASE(evaluate_not, i64, arg0, out, count);
-        OPENVINO_TYPE_CASE(evaluate_not, u32, arg0, out, count);
-        OPENVINO_TYPE_CASE(evaluate_not, u64, arg0, out, count);
-        OPENVINO_TYPE_CASE(evaluate_not, f16, arg0, out, count);
-        OPENVINO_TYPE_CASE(evaluate_not, f32, arg0, out, count);
-    default:
-        rc = false;
-        break;
-    }
-    return rc;
-}
-}  // namespace
-}  // namespace notop
+    outputs[0].set_shape(inputs[0].get_shape());
 
-bool op::v1::LogicalNot::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
-    OV_OP_SCOPE(v1_LogicalNot_evaluate);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    OPENVINO_ASSERT(validate_host_tensor_vector(outputs, 1) && validate_host_tensor_vector(inputs, 1));
-    OPENVINO_SUPPRESS_DEPRECATED_END
-    return notop::evaluate_not(inputs[0], outputs[0], inputs[0]->get_element_count());
+    using namespace ov::element;
+    return IfTypeOf<boolean, i32, i64, u32, u64, f16, f32>::apply<logical_not::Evaluate>(
+        inputs[0].get_element_type(),
+        inputs[0],
+        outputs[0],
+        shape_size(inputs[0].get_shape()));
 }
 
-bool op::v1::LogicalNot::has_evaluate() const {
+bool LogicalNot::has_evaluate() const {
     OV_OP_SCOPE(v1_LogicalNot_has_evaluate);
     switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
-    case ngraph::element::i32:
-    case ngraph::element::i64:
-    case ngraph::element::u32:
-    case ngraph::element::u64:
-    case ngraph::element::f16:
-    case ngraph::element::f32:
+    case element::boolean:
+    case element::f16:
+    case element::f32:
+    case element::i32:
+    case element::i64:
+    case element::u32:
+    case element::u64:
         return true;
     default:
-        break;
+        return false;
     }
-    return false;
 }
+
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/logical_or.cpp b/src/core/src/op/logical_or.cpp
index c473e6c12e385f..403089318de314 100644
--- a/src/core/src/op/logical_or.cpp
+++ b/src/core/src/op/logical_or.cpp
@@ -2,77 +2,54 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/op/logical_or.hpp"
+
 #include "itt.hpp"
-#include "ngraph/op/or.hpp"
-#include "ngraph/runtime/host_tensor.hpp"
-#include "ngraph/validation_util.hpp"
 #include "openvino/reference/or.hpp"
+#include "utils.hpp"
 
-using namespace std;
-using namespace ngraph;
+namespace ov {
+namespace op {
+namespace v1 {
 
-op::v1::LogicalOr::LogicalOr(const Output<Node>& arg0,
-                             const Output<Node>& arg1,
-                             const AutoBroadcastSpec& auto_broadcast)
+LogicalOr::LogicalOr(const Output<Node>& arg0, const Output<Node>& arg1, const AutoBroadcastSpec& auto_broadcast)
     : BinaryElementwiseLogical(arg0, arg1, auto_broadcast) {
     constructor_validate_and_infer_types();
 }
 
-shared_ptr<Node> op::v1::LogicalOr::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> LogicalOr::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_LogicalOr_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v1::LogicalOr>(new_args.at(0), new_args.at(1), this->get_autob());
-}
-
-OPENVINO_SUPPRESS_DEPRECATED_START
-namespace logor {
-namespace {
-template <element::Type_t ET>
-bool evaluate(const HostTensorPtr& arg0,
-              const HostTensorPtr& arg1,
-              const HostTensorPtr& out,
-              const op::AutoBroadcastSpec& broadcast_spec) {
-    ov::reference::logical_or(arg0->get_data_ptr<ET>(),
-                              arg1->get_data_ptr<ET>(),
-                              out->get_data_ptr<ET>(),
-                              arg0->get_shape(),
-                              arg1->get_shape(),
-                              broadcast_spec);
-    return true;
-}
-
-bool evaluate_logor(const HostTensorPtr& arg0,
-                    const HostTensorPtr& arg1,
-                    const HostTensorPtr& out,
-                    const op::AutoBroadcastSpec& broadcast_spec) {
-    bool rc = true;
-    out->set_broadcast(broadcast_spec, arg0, arg1);
-    switch (arg0->get_element_type()) {
-        OPENVINO_TYPE_CASE(evaluate_logor, boolean, arg0, arg1, out, broadcast_spec);
-    default:
-        rc = false;
-        break;
-    }
-    return rc;
+    return std::make_shared<LogicalOr>(new_args.at(0), new_args.at(1), get_autob());
 }
-}  // namespace
-}  // namespace logor
 
-bool op::v1::LogicalOr::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool LogicalOr::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v1_LogicalOr_evaluate);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    OPENVINO_ASSERT(validate_host_tensor_vector(outputs, 1) && validate_host_tensor_vector(inputs, 2));
-    OPENVINO_SUPPRESS_DEPRECATED_END
-    return logor::evaluate_logor(inputs[0], inputs[1], outputs[0], get_autob());
+    OPENVINO_ASSERT(outputs.size() == 1);
+    OPENVINO_ASSERT(inputs.size() == 2);
+
+    const auto& shape_0 = inputs[0].get_shape();
+    const auto& shape_1 = inputs[1].get_shape();
+    outputs[0].set_shape(infer_broadcast_shape(this, shape_0, shape_1));
+
+    if (inputs[0].get_element_type() == element::boolean) {
+        using T = fundamental_type_for<element::boolean>;
+        reference::logical_or(inputs[0].data<const T>(),
+                              inputs[1].data<const T>(),
+                              outputs[0].data<T>(),
+                              shape_0,
+                              shape_1,
+                              get_autob());
+        return true;
+    } else {
+        return false;
+    }
 }
 
-bool op::v1::LogicalOr::has_evaluate() const {
+bool LogicalOr::has_evaluate() const {
     OV_OP_SCOPE(v1_LogicalOr_has_evaluate);
-    switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
-        return true;
-    default:
-        break;
-    }
-    return false;
+    return get_input_element_type(0) == element::boolean;
 }
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/max_pool.cpp b/src/core/src/op/max_pool.cpp
index d40c13644cd3cd..df74c1e6a105b6 100644
--- a/src/core/src/op/max_pool.cpp
+++ b/src/core/src/op/max_pool.cpp
@@ -2,32 +2,30 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph/op/max_pool.hpp"
+#include "openvino/op/max_pool.hpp"
 
 #include "itt.hpp"
 #include "max_pool_shape_inference.hpp"
-#include "ngraph/attribute_visitor.hpp"
-#include "ngraph/op/add.hpp"
-#include "ngraph/op/constant.hpp"
-#include "ngraph/runtime/host_tensor.hpp"
-#include "ngraph/validation_util.hpp"
+#include "openvino/core/attribute_visitor.hpp"
+#include "openvino/core/validation_util.hpp"
 #include "openvino/reference/max_pool.hpp"
 
-using namespace std;
-using namespace ngraph;
+namespace ov {
+namespace op {
+namespace v1 {
 
-op::v1::MaxPool::MaxPool(const Output<Node>& arg,
-                         const Strides& strides,
-                         const ov::Shape& pads_begin,
-                         const ov::Shape& pads_end,
-                         const ov::Shape& kernel,
-                         const op::RoundingType rounding_type,
-                         const PadType auto_pad)
-    : op::util::MaxPoolBase(arg, strides, pads_begin, pads_end, kernel, rounding_type, auto_pad) {
+MaxPool::MaxPool(const Output<Node>& arg,
+                 const Strides& strides,
+                 const Shape& pads_begin,
+                 const Shape& pads_end,
+                 const Shape& kernel,
+                 const RoundingType rounding_type,
+                 const PadType auto_pad)
+    : util::MaxPoolBase(arg, strides, pads_begin, pads_end, kernel, rounding_type, auto_pad) {
     constructor_validate_and_infer_types();
 }
 
-bool ngraph::op::v1::MaxPool::visit_attributes(AttributeVisitor& visitor) {
+bool MaxPool::visit_attributes(AttributeVisitor& visitor) {
     OV_OP_SCOPE(v1_MaxPool_visit_attributes);
     visitor.on_attribute("strides", m_strides);
     visitor.on_attribute("pads_begin", m_pads_begin);
@@ -38,7 +36,7 @@ bool ngraph::op::v1::MaxPool::visit_attributes(AttributeVisitor& visitor) {
     return true;
 }
 
-void op::v1::MaxPool::validate_and_infer_types() {
+void MaxPool::validate_and_infer_types() {
     OV_OP_SCOPE(v1_MaxPool_validate_and_infer_types);
 
     OPENVINO_SUPPRESS_DEPRECATED_START
@@ -47,219 +45,105 @@ void op::v1::MaxPool::validate_and_infer_types() {
     set_output_type(0, get_input_element_type(0), output_shapes.front());
 }
 
-shared_ptr<Node> op::v1::MaxPool::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> MaxPool::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_MaxPool_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v1::MaxPool>(new_args.at(0),
-                                    m_strides,
-                                    m_pads_begin,
-                                    m_pads_end,
-                                    m_kernel,
-                                    m_rounding_type,
-                                    m_auto_pad);
+    return std::make_shared<v1::MaxPool>(new_args.at(0),
+                                         m_strides,
+                                         m_pads_begin,
+                                         m_pads_end,
+                                         m_kernel,
+                                         m_rounding_type,
+                                         m_auto_pad);
 }
 
-OPENVINO_SUPPRESS_DEPRECATED_START
 namespace maxpool {
-namespace {
-template <element::Type_t ET>
-inline bool evaluate(const HostTensorPtr& arg,
-                     const HostTensorPtr& out,
-                     const ov::Shape& out_shape,
-                     const ov::Shape& window_shape,
-                     const Strides& window_movement_strides,
-                     const ov::Shape& padding_below,
-                     const ov::Shape& padding_above) {
-    using T = typename element_type_traits<ET>::value_type;
-    out->set_shape(out_shape);
-    ov::reference::max_pool<T>(arg->get_data_ptr<ET>(),
-                               out->get_data_ptr<ET>(),
-                               arg->get_shape(),
-                               out_shape,
-                               window_shape,
-                               window_movement_strides,
-                               padding_below,
-                               padding_above);
-    return true;
-}
-
-bool evaluate_maxpool(const HostTensorPtr& arg,
-                      const HostTensorPtr& out,
-                      const ov::Shape& out_shape,
-                      const ov::Shape& kernel,
-                      const Strides& strides,
-                      const ov::Shape& pad_begin,
-                      const ov::Shape& pad_end) {
-    bool rc = true;
-    auto arg_shape = arg->get_shape();
+struct Evaluate : element::NoAction<bool> {
+    using element::NoAction<bool>::visit;
 
-    switch (out->get_element_type()) {
-        OPENVINO_TYPE_CASE(evaluate_maxpool, i32, arg, out, out_shape, kernel, strides, pad_begin, pad_end);
-        OPENVINO_TYPE_CASE(evaluate_maxpool, i64, arg, out, out_shape, kernel, strides, pad_begin, pad_end);
-        OPENVINO_TYPE_CASE(evaluate_maxpool, u32, arg, out, out_shape, kernel, strides, pad_begin, pad_end);
-        OPENVINO_TYPE_CASE(evaluate_maxpool, u64, arg, out, out_shape, kernel, strides, pad_begin, pad_end);
-        OPENVINO_TYPE_CASE(evaluate_maxpool, f16, arg, out, out_shape, kernel, strides, pad_begin, pad_end);
-        OPENVINO_TYPE_CASE(evaluate_maxpool, f32, arg, out, out_shape, kernel, strides, pad_begin, pad_end);
-    default:
-        rc = false;
-        break;
+    template <element::Type_t ET, class T = fundamental_type_for<ET>>
+    static result_type visit(const Tensor& in,
+                             Tensor& out,
+                             const Shape& in_shape,
+                             const Shape& out_shape,
+                             const Shape& kernel,
+                             const Strides& strides,
+                             const Shape& pads_begin,
+                             const Shape& pads_end) {
+        reference::max_pool(in.data<const T>(),
+                            out.data<T>(),
+                            in_shape,
+                            out_shape,
+                            kernel,
+                            strides,
+                            pads_begin,
+                            pads_end);
+        return true;
     }
-    return rc;
-}
-}  // namespace
+};
 }  // namespace maxpool
 
-bool op::v1::MaxPool::evaluate_maxpool(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
-    const auto input_shapes = std::vector<PartialShape>{inputs[0]->get_partial_shape()};
+bool MaxPool::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
+    OV_OP_SCOPE(v1_MaxPool_evaluate);
+    const auto input_shapes = std::vector<PartialShape>{inputs[0].get_shape()};
     auto pads_begin = m_pads_begin;
     auto pads_end = m_pads_end;
-    auto out_shape = shape_infer(this, input_shapes, pads_begin, pads_end).front();
+    const auto output_shape = shape_infer(this, input_shapes, pads_begin, pads_end).front();
 
-    return maxpool::evaluate_maxpool(inputs[0],
-                                     outputs[0],
-                                     out_shape.get_shape(),
-                                     get_kernel(),
-                                     get_strides(),
-                                     get_pads_begin(),
-                                     get_pads_end());
-}
-bool op::v1::MaxPool::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
-    OV_OP_SCOPE(v1_MaxPool_evaluate);
-    return evaluate_maxpool(outputs, inputs);
+    outputs[0].set_shape(output_shape.get_shape());
+    using namespace ov::element;
+    return IfTypeOf<f16, f32, i32, i64, u32, u64>::apply<maxpool::Evaluate>(inputs[0].get_element_type(),
+                                                                            inputs[0],
+                                                                            outputs[0],
+                                                                            inputs[0].get_shape(),
+                                                                            outputs[0].get_shape(),
+                                                                            get_kernel(),
+                                                                            get_strides(),
+                                                                            get_pads_begin(),
+                                                                            get_pads_end());
 }
 
-bool op::v1::MaxPool::has_evaluate() const {
+bool MaxPool::has_evaluate() const {
     OV_OP_SCOPE(v1_MaxPool_has_evaluate);
     switch (get_input_element_type(0)) {
-    case ngraph::element::i32:
-    case ngraph::element::i64:
-    case ngraph::element::u32:
-    case ngraph::element::u64:
-    case ngraph::element::f16:
-    case ngraph::element::f32:
+    case element::i32:
+    case element::i64:
+    case element::u32:
+    case element::u64:
+    case element::f16:
+    case element::f32:
         return true;
     default:
-        break;
+        return false;
     }
-    return false;
 }
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
 
 // ------------------------------ V8 ------------------------------
+namespace ov {
+namespace op {
+namespace v8 {
 
-namespace maxpool_v8 {
-namespace {
-template <element::Type_t Values, element::Type_t Indices>
-inline bool evaluate(const HostTensorPtr& data,
-                     const HostTensorPtr& values,
-                     const HostTensorPtr& indices,
-                     const ov::Shape& out_shape,
-                     const ov::Shape& kernel,
-                     const Strides& strides,
-                     const Strides& dilations,
-                     const ov::Shape& pads_begin,
-                     const ov::Shape& pads_end,
-                     const int64_t axis) {
-    using Values_t = typename element_type_traits<Values>::value_type;
-    using Indices_t = typename element_type_traits<Indices>::value_type;
-    ov::reference::max_pool<Values_t, Indices_t>(data->get_data_ptr<Values_t>(),
-                                                 values->get_data_ptr<Values_t>(),
-                                                 indices->get_data_ptr<Indices_t>(),
-                                                 data->get_shape(),
-                                                 out_shape,
-                                                 kernel,
-                                                 strides,
-                                                 dilations,
-                                                 pads_begin,
-                                                 pads_end,
-                                                 axis);
-    return true;
-}
-
-bool evaluate_maxpool(const HostTensorPtr& data,
-                      const HostTensorPtr& values,
-                      const HostTensorPtr& indices,
-                      const ov::Shape& out_shape,
-                      const ov::Shape& kernel,
-                      const Strides& strides,
-                      const Strides& dilations,
-                      const ov::Shape& pads_begin,
-                      const ov::Shape& pads_end,
-                      const int64_t axis) {
-#define EVAL_MAX_POOL_8(data_et, index_et)              \
-    OPENVINO_2_TYPES_CASE(maxpool_v8::evaluate_maxpool, \
-                          data_et,                      \
-                          index_et,                     \
-                          data,                         \
-                          values,                       \
-                          indices,                      \
-                          out_shape,                    \
-                          kernel,                       \
-                          strides,                      \
-                          dilations,                    \
-                          pads_begin,                   \
-                          pads_end,                     \
-                          axis)
-
-    bool rc = true;
-    switch (indices->get_element_type()) {
-    case element::Type_t::i32: {
-        switch (data->get_element_type()) {
-            EVAL_MAX_POOL_8(i8, i32);
-            EVAL_MAX_POOL_8(i32, i32);
-            EVAL_MAX_POOL_8(i64, i32);
-            EVAL_MAX_POOL_8(u8, i32);
-            EVAL_MAX_POOL_8(u32, i32);
-            EVAL_MAX_POOL_8(u64, i32);
-            EVAL_MAX_POOL_8(f16, i32);
-            EVAL_MAX_POOL_8(f32, i32);
-        default:
-            rc = false;
-            break;
-        }
-    } break;
-    case element::Type_t::i64: {
-        switch (data->get_element_type()) {
-            EVAL_MAX_POOL_8(i8, i64);
-            EVAL_MAX_POOL_8(i32, i64);
-            EVAL_MAX_POOL_8(i64, i64);
-            EVAL_MAX_POOL_8(u8, i64);
-            EVAL_MAX_POOL_8(u32, i64);
-            EVAL_MAX_POOL_8(u64, i64);
-            EVAL_MAX_POOL_8(f16, i64);
-            EVAL_MAX_POOL_8(f32, i64);
-        default:
-            rc = false;
-            break;
-        }
-    } break;
-    default:
-        rc = false;
-        break;
-    }
-
-    return rc;
-}
-}  // namespace
-}  // namespace maxpool_v8
-
-op::v8::MaxPool::MaxPool(const Output<Node>& arg,
-                         const Strides& strides,
-                         const Strides& dilations,
-                         const ov::Shape& pads_begin,
-                         const ov::Shape& pads_end,
-                         const ov::Shape& kernel,
-                         const op::RoundingType rounding_type,
-                         const PadType auto_pad,
-                         const element::Type index_element_type,
-                         const int64_t axis)
-    : op::util::MaxPoolBase(arg, strides, pads_begin, pads_end, kernel, rounding_type, auto_pad),
+MaxPool::MaxPool(const Output<Node>& arg,
+                 const Strides& strides,
+                 const Strides& dilations,
+                 const Shape& pads_begin,
+                 const Shape& pads_end,
+                 const Shape& kernel,
+                 const RoundingType rounding_type,
+                 const PadType auto_pad,
+                 const element::Type index_element_type,
+                 const int64_t axis)
+    : util::MaxPoolBase(arg, strides, pads_begin, pads_end, kernel, rounding_type, auto_pad),
       m_dilations{dilations},
       m_index_element_type{index_element_type},
       m_axis{axis} {
     constructor_validate_and_infer_types();
 }
 
-bool ngraph::op::v8::MaxPool::visit_attributes(AttributeVisitor& visitor) {
+bool MaxPool::visit_attributes(AttributeVisitor& visitor) {
     OV_OP_SCOPE(v8_MaxPool_visit_attributes);
     visitor.on_attribute("strides", m_strides);
     visitor.on_attribute("dilations", m_dilations);
@@ -273,13 +157,13 @@ bool ngraph::op::v8::MaxPool::visit_attributes(AttributeVisitor& visitor) {
     return true;
 }
 
-void op::v8::MaxPool::validate_and_infer_types() {
+void MaxPool::validate_and_infer_types() {
     OV_OP_SCOPE(v8_MaxPool_validate_and_infer_types);
 
     const auto input_shape = get_input_partial_shape(0);
     if (input_shape.rank().is_static()) {
         OPENVINO_SUPPRESS_DEPRECATED_START
-        m_axis = ngraph::normalize_axis(this, m_axis, input_shape.rank());
+        m_axis = normalize_axis(this, m_axis, input_shape.rank());
         OPENVINO_SUPPRESS_DEPRECATED_END
     }
 
@@ -290,55 +174,126 @@ void op::v8::MaxPool::validate_and_infer_types() {
     set_output_type(1, m_index_element_type, output_shapes[1]);
 }
 
-shared_ptr<Node> op::v8::MaxPool::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> MaxPool::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v8_MaxPool_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v8::MaxPool>(new_args.at(0),
-                                    m_strides,
-                                    m_dilations,
-                                    m_pads_begin,
-                                    m_pads_end,
-                                    m_kernel,
-                                    m_rounding_type,
-                                    m_auto_pad,
-                                    m_index_element_type,
-                                    m_axis);
+    return std::make_shared<v8::MaxPool>(new_args.at(0),
+                                         m_strides,
+                                         m_dilations,
+                                         m_pads_begin,
+                                         m_pads_end,
+                                         m_kernel,
+                                         m_rounding_type,
+                                         m_auto_pad,
+                                         m_index_element_type,
+                                         m_axis);
 }
 
-bool op::v8::MaxPool::has_evaluate() const {
-    OV_OP_SCOPE(v8_MaxPool_has_evaluate);
-    switch (get_input_element_type(0)) {
-    case ngraph::element::i8:
-    case ngraph::element::i32:
-    case ngraph::element::i64:
-    case ngraph::element::u8:
-    case ngraph::element::u32:
-    case ngraph::element::u64:
-    case ngraph::element::f16:
-    case ngraph::element::f32:
-        return true;
-    default:
-        break;
+namespace maxpool {
+struct Evaluate : element::NoAction<bool> {
+    using element::NoAction<bool>::visit;
+
+    template <element::Type_t ET, class T = fundamental_type_for<ET>>
+    static result_type visit(const Tensor& in,
+                             Tensor& out_values,
+                             Tensor& out_indices,
+                             const Shape& in_shape,
+                             const Shape& out_shape,
+                             const Shape& kernel,
+                             const Strides& strides,
+                             const Strides& dilations,
+                             const Shape& pads_begin,
+                             const Shape& pads_end,
+                             const int64_t axis) {
+        using namespace ov::element;
+        return IfTypeOf<i32, i64>::apply<EvalByIdxType>(out_indices.get_element_type(),
+                                                        in.data<const T>(),
+                                                        out_values.data<T>(),
+                                                        out_indices,
+                                                        in_shape,
+                                                        out_shape,
+                                                        kernel,
+                                                        strides,
+                                                        dilations,
+                                                        pads_begin,
+                                                        pads_end,
+                                                        axis);
     }
-    return false;
-}
 
-bool op::v8::MaxPool::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+private:
+    struct EvalByIdxType : public element::NoAction<bool> {
+        using element::NoAction<bool>::visit;
+
+        template <element::Type_t ET, class T, class I = fundamental_type_for<ET>>
+        static result_type visit(const T* in_data,
+                                 T* out_values_data,
+                                 Tensor& out_indices,
+                                 const Shape& in_shape,
+                                 const Shape& out_shape,
+                                 const Shape& kernel,
+                                 const Strides& strides,
+                                 const Strides& dilations,
+                                 const Shape& pads_begin,
+                                 const Shape& pads_end,
+                                 const int64_t axis) {
+            reference::max_pool(in_data,
+                                out_values_data,
+                                out_indices.data<I>(),
+                                in_shape,
+                                out_shape,
+                                kernel,
+                                strides,
+                                dilations,
+                                pads_begin,
+                                pads_end,
+                                axis);
+            return true;
+        }
+    };
+};
+}  // namespace maxpool
+
+bool MaxPool::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v8_MaxPool_evaluate);
 
-    const auto input_shapes = std::vector<PartialShape>{inputs[0]->get_partial_shape()};
+    const auto input_shapes = std::vector<PartialShape>{inputs[0].get_shape()};
     auto pads_begin = m_pads_begin;
     auto pads_end = m_pads_end;
-    auto out_shape = shape_infer(this, input_shapes, pads_begin, pads_end).front();
+    const auto output_shape = shape_infer(this, input_shapes, pads_begin, pads_end).front();
 
-    return maxpool_v8::evaluate_maxpool(inputs[0],
-                                        outputs[0],
-                                        outputs[1],
-                                        out_shape.get_shape(),
-                                        get_kernel(),
-                                        get_strides(),
-                                        get_dilations(),
-                                        get_pads_begin(),
-                                        get_pads_end(),
-                                        get_axis());
+    outputs[0].set_shape(output_shape.get_shape());
+    using namespace ov::element;
+    return IfTypeOf<f16, f32, i8, i32, i64, u8, u32, u64>::apply<maxpool::Evaluate>(inputs[0].get_element_type(),
+                                                                                    inputs[0],
+                                                                                    outputs[0],
+                                                                                    outputs[1],
+                                                                                    inputs[0].get_shape(),
+                                                                                    outputs[0].get_shape(),
+                                                                                    get_kernel(),
+                                                                                    get_strides(),
+                                                                                    get_dilations(),
+                                                                                    get_pads_begin(),
+                                                                                    get_pads_end(),
+                                                                                    get_axis());
 }
+
+bool MaxPool::has_evaluate() const {
+    OV_OP_SCOPE(v8_MaxPool_has_evaluate);
+    switch (get_input_element_type(0)) {
+    case element::i8:
+    case element::i32:
+    case element::i64:
+    case element::u8:
+    case element::u32:
+    case element::u64:
+    case element::f16:
+    case element::f32:
+        return true;
+    default:
+        return false;
+    }
+}
+
+}  // namespace v8
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/multinomial.cpp b/src/core/src/op/multinomial.cpp
index 90f41369364879..d8e6a967f38b58 100644
--- a/src/core/src/op/multinomial.cpp
+++ b/src/core/src/op/multinomial.cpp
@@ -6,11 +6,9 @@
 
 #include <cstring>
 
-#include "bound_evaluate.hpp"
 #include "itt.hpp"
 #include "multinomial_shape_inference.hpp"
 #include "openvino/core/attribute_visitor.hpp"
-#include "openvino/op/constant.hpp"
 #include "openvino/op/util/op_types.hpp"
 #include "openvino/reference/multinomial.hpp"
 
@@ -60,7 +58,7 @@ void op::v13::Multinomial::validate_and_infer_types() {
 
 std::shared_ptr<Node> op::v13::Multinomial::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v13_Multinomial_clone_with_new_inputs);
-    check_new_args_count<OutputVector>(this, new_args);
+    check_new_args_count(this, new_args);
 
     return std::make_shared<op::v13::Multinomial>(new_args.at(0),
                                                   new_args.at(1),
diff --git a/src/core/src/op/random_uniform.cpp b/src/core/src/op/random_uniform.cpp
index 296b115979c8f7..b07044960f44c9 100644
--- a/src/core/src/op/random_uniform.cpp
+++ b/src/core/src/op/random_uniform.cpp
@@ -92,6 +92,8 @@ bool RandomUniform::evaluate(TensorVector& outputs, const TensorVector& inputs)
     const auto& t_out = get_out_type();
     OPENVINO_ASSERT(validate::out_et(t_out), "Unsupported type of RandomUniform: " + t_out.get_type_name());
 
+    outputs[0].set_shape(out_shape);
+
     auto state = ov::reference::random_uniform(out_dims.data(),
                                                static_cast<const char*>(inputs[1].data()),
                                                static_cast<const char*>(inputs[2].data()),
diff --git a/src/core/src/op/reduce_prod.cpp b/src/core/src/op/reduce_prod.cpp
index 9d345ae63cf301..9d2c4dee4a8c51 100644
--- a/src/core/src/op/reduce_prod.cpp
+++ b/src/core/src/op/reduce_prod.cpp
@@ -7,6 +7,7 @@
 #include "bound_evaluate.hpp"
 #include "element_visitor.hpp"
 #include "itt.hpp"
+#include "ngraph/validation_util.hpp"
 #include "openvino/core/shape_util.hpp"
 #include "openvino/op/util/axes_util.hpp"
 #include "openvino/reference/reduce_prod.hpp"
@@ -15,11 +16,11 @@ namespace ov {
 namespace op {
 namespace reduce_prod {
 namespace {
-bool has_positive_bounds_on_data(const Node* const op) {
+bool has_non_negative_bounds_on_data(const Node* const op) {
     const auto& lb = op->get_input_tensor(0).get_lower_value();
     const auto& ub = op->get_input_tensor(0).get_upper_value();
 
-    return lb && ub && tensor_is_positive(lb) && tensor_is_positive(ub);
+    return lb && ub && tensor_is_non_negative(lb) && tensor_is_non_negative(ub);
 }
 }  // namespace
 
@@ -78,14 +79,34 @@ bool ReduceProd::has_evaluate() const {
 }
 
 bool ReduceProd::evaluate_lower(ov::TensorVector& output_values) const {
-    return reduce_prod::has_positive_bounds_on_data(this) && get_input_tensor(1).has_and_set_bound() &&
+    return reduce_prod::has_non_negative_bounds_on_data(this) && get_input_tensor(1).has_and_set_bound() &&
            default_lower_bound_evaluator(this, output_values);
 }
 
 bool ReduceProd::evaluate_upper(ov::TensorVector& output_values) const {
-    return reduce_prod::has_positive_bounds_on_data(this) && get_input_tensor(1).has_and_set_bound() &&
-           default_upper_bound_evaluator(this, output_values);
+    if (!reduce_prod::has_non_negative_bounds_on_data(this) || !get_input_tensor(1).has_and_set_bound())
+        return false;
+    // We need to cover a case: if an Upper Bound comes from ShapeOf and contains
+    // dynamic dimension (-1) - it has a value max_of_type, which points on
+    // a maximum possible value. For example, Upper Bound of shape [-1, 12] is
+    // [max_of_type, 12].
+    // In such case we shouldn't evaluate a real ReduceProd because it'll cause an
+    // overflow and returns wrong value. We should return an Upper Bound as for [-1],
+    // which will be evaluated as [max_of_type]
+    // In case dimensions has a zero dimension - it should return 0 in any case
+    if (tensor_has_max_value(get_input_tensor(0).get_upper_value()) &&
+        !tensor_has_zero_value(get_input_tensor(0).get_upper_value())) {
+        OPENVINO_SUPPRESS_DEPRECATED_START
+        auto max_constant = ngraph::get_constant_max_of_type(get_output_element_type(0));
+        OPENVINO_SUPPRESS_DEPRECATED_END
+        OPENVINO_ASSERT(max_constant->get_byte_size() <= output_values[0].get_byte_size());
+        memcpy(output_values[0].data(), max_constant->get_data_ptr(), max_constant->get_byte_size());
+        return true;
+    }
+
+    return default_upper_bound_evaluator(this, output_values);
 }
+
 }  // namespace v1
 }  // namespace op
 }  // namespace ov
diff --git a/src/core/src/op/split.cpp b/src/core/src/op/split.cpp
index dc2ac72b509db7..4c5563e892321c 100644
--- a/src/core/src/op/split.cpp
+++ b/src/core/src/op/split.cpp
@@ -2,42 +2,46 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "openvino/reference/split.hpp"
+#include "openvino/op/split.hpp"
 
 #include <numeric>
-#include <split_shape_inference.hpp>
 
 #include "bound_evaluate.hpp"
 #include "itt.hpp"
-#include "ngraph/attribute_visitor.hpp"
-#include "ngraph/builder/split.hpp"
-#include "ngraph/op/constant.hpp"
-#include "ngraph/op/split.hpp"
-#include "ngraph/op/util/op_types.hpp"
-#include "ngraph/runtime/host_tensor.hpp"
-#include "ngraph/validation_util.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-op::v1::Split::Split(const Output<Node>& data, const Output<Node>& axis, const size_t num_splits)
+#include "openvino/core/validation_util.hpp"
+#include "openvino/reference/split.hpp"
+#include "split_shape_inference.hpp"
+
+namespace ov {
+namespace op {
+
+namespace v1 {
+namespace validate {
+namespace {
+bool axis_type(const element::Type& et) {
+    return et.is_integral_number();
+}
+}  // namespace
+}  // namespace validate
+
+Split::Split(const Output<Node>& data, const Output<Node>& axis, const size_t num_splits)
     : Op({data, axis}),
       m_num_splits{num_splits} {
     constructor_validate_and_infer_types();
 }
 
-bool ngraph::op::v1::Split::visit_attributes(AttributeVisitor& visitor) {
+bool Split::visit_attributes(AttributeVisitor& visitor) {
     OV_OP_SCOPE(v1_Split_visit_attributes);
     visitor.on_attribute("num_splits", m_num_splits);
     return true;
 }
 
-void op::v1::Split::validate_and_infer_types() {
+void Split::validate_and_infer_types() {
     OV_OP_SCOPE(v1_Split_validate_and_infer_types);
     const auto& axis_et = get_input_element_type(1);
 
     NODE_VALIDATION_CHECK(this,
-                          axis_et.is_integral_number(),
+                          validate::axis_type(axis_et),
                           "Element type of 'axis' input must be integer. Got: ",
                           axis_et);
 
@@ -58,72 +62,70 @@ void op::v1::Split::validate_and_infer_types() {
     set_input_is_relevant_to_shape(0);
 }
 
-shared_ptr<Node> op::v1::Split::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> Split::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_Split_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v1::Split>(new_args.at(0), new_args.at(1), m_num_splits);
+    return std::make_shared<Split>(new_args.at(0), new_args.at(1), m_num_splits);
 }
 
-OPENVINO_SUPPRESS_DEPRECATED_START
-bool op::v1::Split::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool Split::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v1_Split_evaluate);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    OPENVINO_ASSERT(validate_host_tensor_vector(outputs, m_num_splits) && validate_host_tensor_vector(inputs, 2));
-    OPENVINO_SUPPRESS_DEPRECATED_END
+    OPENVINO_ASSERT(outputs.size() == m_num_splits);
 
-    if (has_evaluate()) {
+    const auto output_shapes =
+        shape_infer(this, ov::util::get_tensors_partial_shapes(inputs), make_tensor_accessor(inputs));
+    const auto& axis_tensor = inputs[1];
+    const auto result = validate::axis_type(axis_tensor.get_element_type());
+    if (result) {
         const auto& data_tensor = inputs[0];
-        const auto& axis_tensor = inputs[1];
-
-        const auto input_shapes =
-            std::vector<PartialShape>{data_tensor->get_partial_shape(), axis_tensor->get_partial_shape()};
-
-        auto output_shapes = shape_infer(this, input_shapes, make_tensor_accessor(inputs));
 
         auto outputs_data = std::vector<char*>(m_num_splits);
-        for (size_t i = 0; i < m_num_splits; ++i) {
-            outputs[i]->set_shape(output_shapes[i].get_shape());
-            outputs_data[i] = outputs[i]->get_data_ptr<char>();
+        {
+            auto outputs_it = outputs.begin();
+            auto outputs_data_it = outputs_data.begin();
+            for (const auto& p_shape : output_shapes) {
+                outputs_it->set_shape(p_shape.get_shape());
+                *outputs_data_it = static_cast<char*>(outputs_it->data());
+                ++outputs_it, ++outputs_data_it;
+            }
         }
 
-        OPENVINO_SUPPRESS_DEPRECATED_START
-        auto axis = host_tensor_2_vector<int64_t>(axis_tensor)[0];
-        axis = normalize_axis(this, axis, data_tensor->get_partial_shape().rank());
-        OPENVINO_SUPPRESS_DEPRECATED_END
+        auto axis = get_tensor_data_as<int64_t>(axis_tensor).front();
+        axis = ov::util::normalize(axis, data_tensor.get_shape().size());
 
-        ov::reference::split(data_tensor->get_data_ptr<char>(),
-                             data_tensor->get_shape(),
-                             data_tensor->get_element_type().size(),
+        ov::reference::split(static_cast<char*>(data_tensor.data()),
+                             data_tensor.get_shape(),
+                             data_tensor.get_element_type().size(),
                              axis,
                              m_num_splits,
                              outputs_data.data());
-        return true;
     }
-    return false;
+
+    return result;
 }
-OPENVINO_SUPPRESS_DEPRECATED_END
 
-bool op::v1::Split::has_evaluate() const {
+bool Split::has_evaluate() const {
     OV_OP_SCOPE(v1_Split_has_evaluate);
-    return get_input_element_type(1).is_integral_number();
+    return validate::axis_type(get_input_element_type(1));
 }
 
-bool op::v1::Split::evaluate_lower(ov::TensorVector& output_values) const {
+bool Split::evaluate_lower(ov::TensorVector& output_values) const {
     OV_OP_SCOPE(v1_Split_evaluate_lower);
-
-    return input(1).get_tensor().has_and_set_bound() && default_lower_bound_evaluator(this, output_values);
+    return get_input_tensor(1).has_and_set_bound() && default_lower_bound_evaluator(this, output_values);
 }
 
-bool op::v1::Split::evaluate_upper(ov::TensorVector& output_values) const {
+bool Split::evaluate_upper(ov::TensorVector& output_values) const {
     OV_OP_SCOPE(v1_Split_evaluate_upper);
-
-    return input(1).get_tensor().has_and_set_bound() && default_upper_bound_evaluator(this, output_values);
+    return get_input_tensor(1).has_and_set_bound() && default_upper_bound_evaluator(this, output_values);
 }
 
-bool op::v1::Split::evaluate_label(TensorLabelVector& output_labels) const {
+bool Split::evaluate_label(TensorLabelVector& output_labels) const {
     OPENVINO_ASSERT(output_labels.size() == get_num_splits());
 
     OPENVINO_SUPPRESS_DEPRECATED_START
-    return input(1).get_tensor().has_and_set_bound() && default_label_evaluator(this, output_labels);
+    return get_input_tensor(1).has_and_set_bound() && default_label_evaluator(this, output_labels);
     OPENVINO_SUPPRESS_DEPRECATED_END
 }
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/squeeze.cpp b/src/core/src/op/squeeze.cpp
index 50bf9af02d00a5..5e47edae2c1cc9 100644
--- a/src/core/src/op/squeeze.cpp
+++ b/src/core/src/op/squeeze.cpp
@@ -2,34 +2,39 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph/op/squeeze.hpp"
+#include "openvino/op/squeeze.hpp"
 
-#include <algorithm>
-#include <cstddef>
-#include <functional>
-#include <set>
+#include <cstring>
 
 #include "bound_evaluate.hpp"
 #include "itt.hpp"
-#include "ngraph/op/constant.hpp"
-#include "ngraph/validation_util.hpp"
-#include "openvino/reference/copy.hpp"
+#include "openvino/core/validation_util.hpp"
+#include "openvino/op/constant.hpp"
 #include "squeeze_shape_inference.hpp"
 
-using namespace std;
-using namespace ngraph;
+namespace ov {
+namespace op {
+namespace v0 {
+namespace validate {
+namespace {
 
-op::Squeeze::Squeeze() : Op() {}
+bool axes_has_and_set_bound(const Node& op) {
+    return (op.get_input_size() < 2) || op.get_input_tensor(1).has_and_set_bound();
+}
+}  // namespace
+}  // namespace validate
+
+Squeeze::Squeeze() : Op() {}
 
-op::Squeeze::Squeeze(const Output<Node>& data, const Output<Node>& axes) : Op({data, axes}) {
+Squeeze::Squeeze(const Output<Node>& data, const Output<Node>& axes) : Op({data, axes}) {
     constructor_validate_and_infer_types();
 }
 
-op::Squeeze::Squeeze(const Output<Node>& data) : Op({data}) {
+Squeeze::Squeeze(const Output<Node>& data) : Op({data}) {
     constructor_validate_and_infer_types();
 }
 
-void op::Squeeze::validate_and_infer_types() {
+void Squeeze::validate_and_infer_types() {
     OV_OP_SCOPE(v0_Squeeze_validate_and_infer_types);
 
     OPENVINO_SUPPRESS_DEPRECATED_START
@@ -40,115 +45,86 @@ void op::Squeeze::validate_and_infer_types() {
     set_output_type(0, get_input_element_type(0), output_shapes[0]);
 }
 
-bool ngraph::op::v0::Squeeze::visit_attributes(AttributeVisitor& visitor) {
-    OV_OP_SCOPE(v0_Squeeze_visit_attributes);
-    return true;
-}
-
-shared_ptr<Node> op::Squeeze::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> Squeeze::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v0_Squeeze_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    if (new_args.size() == 1) {
-        return make_shared<Squeeze>(new_args.at(0));
-    } else if (new_args.size() == 2) {
-        return make_shared<Squeeze>(new_args.at(0), new_args.at(1));
-    } else {
+
+    switch (new_args.size()) {
+    case 1:
+        return std::make_shared<Squeeze>(new_args[0]);
+    case 2:
+        return std::make_shared<Squeeze>(new_args[0], new_args[1]);
+    default:
         OPENVINO_THROW("Incorrect number of new arguments");
     }
 }
 
-OPENVINO_SUPPRESS_DEPRECATED_START
-bool op::v0::Squeeze::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool Squeeze::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v0_Squeeze_evaluate);
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    OPENVINO_ASSERT(validate_host_tensor_vector(inputs, inputs.size()));
-    OPENVINO_ASSERT(validate_host_tensor_vector(outputs, 1));
-    OPENVINO_SUPPRESS_DEPRECATED_END
-
-    if (has_evaluate()) {
-        auto input_shapes = std::vector<PartialShape>{inputs[0]->get_partial_shape()};
-
-        if (inputs.size() == 2) {
-            input_shapes.push_back(inputs[1]->get_partial_shape());
-        }
+    OPENVINO_ASSERT(outputs.size() == 1);
 
-        auto output_shapes = shape_infer(this, input_shapes, make_tensor_accessor(inputs));
+    const auto output_shapes =
+        shape_infer(this, ov::util::get_tensors_partial_shapes(inputs), make_tensor_accessor(inputs));
+    outputs[0].set_shape(output_shapes.front().get_shape());
 
-        auto out_shape = output_shapes[0].get_shape();
-        outputs[0]->set_shape(out_shape);
-
-        ov::reference::copy(inputs[0]->get_data_ptr<char>(),
-                            outputs[0]->get_data_ptr<char>(),
-                            shape_size(out_shape) * outputs[0]->get_element_type().size());
-
-        return true;
-    }
-    return false;
+    std::memcpy(outputs[0].data(), inputs[0].data(), outputs[0].get_byte_size());
+    return true;
 }
-OPENVINO_SUPPRESS_DEPRECATED_END
 
-bool op::v0::Squeeze::has_evaluate() const {
+bool Squeeze::has_evaluate() const {
     OV_OP_SCOPE(v0_Squeeze_has_evaluate);
-
-    if (get_input_size() == 2) {
-        switch (get_input_element_type(1)) {
-        case ngraph::element::i8:
-        case ngraph::element::i16:
-        case ngraph::element::i32:
-        case ngraph::element::i64:
-        case ngraph::element::u8:
-        case ngraph::element::u16:
-        case ngraph::element::u32:
-        case ngraph::element::u64:
+    const auto validate_axes_type = [](const element::Type& et) -> bool {
+        switch (et) {
+        case element::i8:
+        case element::i16:
+        case element::i32:
+        case element::i64:
+        case element::u8:
+        case element::u16:
+        case element::u32:
+        case element::u64:
             return true;
         default:
-            break;
+            return false;
         }
-        return false;
-    } else if (get_input_size() == 1) {
-        return true;
-    } else {
-        return false;
-    }
+    };
+
+    return (get_input_size() < 2) || validate_axes_type(get_input_element_type(1));
 }
 
-bool op::v0::Squeeze::evaluate_lower(ov::TensorVector& output_values) const {
+bool Squeeze::evaluate_lower(TensorVector& output_values) const {
     OV_OP_SCOPE(v0_Squeeze_evaluate_lower);
-    if (inputs().size() > 1 && !input_value(1).get_tensor().has_and_set_bound())
-        return false;
-    return default_lower_bound_evaluator(this, output_values);
+    return validate::axes_has_and_set_bound(*this) && default_lower_bound_evaluator(this, output_values);
 }
 
-bool op::v0::Squeeze::evaluate_upper(ov::TensorVector& output_values) const {
+bool Squeeze::evaluate_upper(TensorVector& output_values) const {
     OV_OP_SCOPE(v0_Squeeze_evaluate_upper);
-    if (inputs().size() > 1 && !input_value(1).get_tensor().has_and_set_bound())
-        return false;
-    return default_upper_bound_evaluator(this, output_values);
+    return validate::axes_has_and_set_bound(*this) && default_upper_bound_evaluator(this, output_values);
 }
 
-bool op::v0::Squeeze::evaluate_label(TensorLabelVector& output_labels) const {
-    if (get_input_size() > 1 && !get_input_tensor(1).has_and_set_bound())
-        return false;
+bool Squeeze::evaluate_label(TensorLabelVector& output_labels) const {
     OPENVINO_SUPPRESS_DEPRECATED_START
-    return default_label_evaluator(this, output_labels);
+    return validate::axes_has_and_set_bound(*this) && default_label_evaluator(this, output_labels);
     OPENVINO_SUPPRESS_DEPRECATED_END
 }
 
-bool op::v0::Squeeze::constant_fold(OutputVector& output_values, const OutputVector& inputs_values) {
+bool Squeeze::constant_fold(OutputVector& output_values, const OutputVector& inputs_values) {
     OV_OP_SCOPE(v0_Squeeze_constant_fold);
     if (get_output_partial_shape(0).is_dynamic() || is_const_fold_disabled()) {
         return false;
     }
 
-    const auto& shape = get_output_shape(0);
-
-    if (auto data_const = std::dynamic_pointer_cast<op::v0::Constant>(inputs_values[0].get_node_shared_ptr())) {
-        output_values[0] = std::make_shared<op::v0::Constant>(*data_const, shape);
+    if (auto data_const = std::dynamic_pointer_cast<Constant>(inputs_values[0].get_node_shared_ptr())) {
+        const auto& shape = get_output_shape(0);
+        output_values[0] = std::make_shared<Constant>(*data_const, shape);
         return true;
     }
     return false;
 }
 
-bool op::v0::Squeeze::is_dynamic() const {
+bool Squeeze::is_dynamic() const {
     return get_output_partial_shape(0).is_dynamic();
 }
+}  // namespace v0
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/op/topk.cpp b/src/core/src/op/topk.cpp
index da56c6bb7494c7..a84d0490d9bebd 100644
--- a/src/core/src/op/topk.cpp
+++ b/src/core/src/op/topk.cpp
@@ -4,163 +4,153 @@
 
 #include "openvino/op/topk.hpp"
 
-#include <memory>
-#include <topk_shape_inference.hpp>
-
+#include "element_visitor.hpp"
 #include "itt.hpp"
-#include "openvino/core/attribute_visitor.hpp"
-#include "openvino/core/axis_vector.hpp"
-#include "openvino/core/dimension_tracker.hpp"
-#include "openvino/core/shape.hpp"
 #include "openvino/core/validation_util.hpp"
-#include "openvino/op/constant.hpp"
-#include "openvino/op/util/op_types.hpp"
 #include "openvino/reference/topk.hpp"
-
-using namespace std;
+#include "topk_shape_inference.hpp"
 
 namespace ov {
-OPENVINO_SUPPRESS_DEPRECATED_START
+namespace op {
 namespace topk {
+namespace validate {
 namespace {
-template <element::Type_t INPUT_ET, element::Type_t INDEX_ET>
-inline bool evaluate_execute(const ngraph::HostTensorPtr& arg0,
-                             const ngraph::HostTensorPtr& out_indices,
-                             const ngraph::HostTensorPtr& out_values,
-                             const ov::Shape out_shape,
-                             const size_t axis,
-                             const size_t k,
-                             const bool compute_max,
-                             const op::v1::TopK::SortType sort) {
-    using T = typename element_type_traits<INPUT_ET>::value_type;
-    using U = typename element_type_traits<INDEX_ET>::value_type;
-    const ov::Shape in_shape = arg0->get_shape();
-    out_indices->set_shape(out_shape);
-    out_indices->set_element_type(INDEX_ET);
-
-    out_values->set_shape(out_shape);
-    out_values->set_element_type(arg0->get_element_type());
-
-    ov::reference::topk<T, U>(arg0->get_data_ptr<INPUT_ET>(),
-                              out_indices->get_data_ptr<INDEX_ET>(),
-                              out_values->get_data_ptr<INPUT_ET>(),
-                              in_shape,
-                              out_shape,
-                              axis,
-                              k,
-                              compute_max,
-                              sort);
-    return true;
-}
-
-#define EXECUTE_EVALUATE_TOPK(a, ...)                                     \
-    case element::Type_t::a: {                                            \
-        OV_OP_SCOPE(OV_PP_CAT3(exec_topk_eval, _, a));                    \
-        rc = evaluate_execute<INPUT_ET, element::Type_t::a>(__VA_ARGS__); \
-    } break
-
-template <element::Type_t INPUT_ET>
-bool evaluate(const ngraph::HostTensorPtr& arg,
-              const ngraph::HostTensorPtr& out_indices,
-              const ngraph::HostTensorPtr& out_values,
-              const ov::Shape out_shape,
-              const size_t axis,
-              const size_t k,
-              const bool max,
-              const op::v1::TopK::SortType sort,
-              const element::Type index_et) {
-    bool rc = true;
-    switch (index_et) {
-        EXECUTE_EVALUATE_TOPK(i32, arg, out_indices, out_values, out_shape, axis, k, max, sort);
-        EXECUTE_EVALUATE_TOPK(i64, arg, out_indices, out_values, out_shape, axis, k, max, sort);
+bool data_type(const element::Type& et) {
+    switch (et) {
+    case element::f16:
+    case element::f32:
+    case element::i32:
+    case element::i64:
+    case element::u32:
+    case element::u64:
+        return true;
     default:
-        rc = false;
-        break;
+        return false;
     }
-    return rc;
 }
 
-bool evaluate_topk(const ngraph::HostTensorPtr& arg,
-                   const ngraph::HostTensorPtr& out_indices,
-                   const ngraph::HostTensorPtr& out_values,
-                   const ov::Shape out_shape,
-                   const size_t axis,
-                   const size_t k,
-                   const bool max,
-                   const op::v1::TopK::SortType sort,
-                   const element::Type index_et) {
-    bool rc = true;
-    switch (arg->get_element_type()) {
-        OPENVINO_TYPE_CASE(evaluate_topk, i32, arg, out_indices, out_values, out_shape, axis, k, max, sort, index_et);
-        OPENVINO_TYPE_CASE(evaluate_topk, i64, arg, out_indices, out_values, out_shape, axis, k, max, sort, index_et);
-        OPENVINO_TYPE_CASE(evaluate_topk, u32, arg, out_indices, out_values, out_shape, axis, k, max, sort, index_et);
-        OPENVINO_TYPE_CASE(evaluate_topk, u64, arg, out_indices, out_values, out_shape, axis, k, max, sort, index_et);
-        OPENVINO_TYPE_CASE(evaluate_topk, f16, arg, out_indices, out_values, out_shape, axis, k, max, sort, index_et);
-        OPENVINO_TYPE_CASE(evaluate_topk, f32, arg, out_indices, out_values, out_shape, axis, k, max, sort, index_et);
+bool k_type(const element::Type& et) {
+    switch (et) {
+    case element::i8:
+    case element::i16:
+    case element::i32:
+    case element::i64:
+    case element::u8:
+    case element::u16:
+    case element::u32:
+    case element::u64:
+        return true;
     default:
-        rc = false;
-        break;
+        return false;
     }
-    return rc;
 }
-bool TopK_evaluate(const ov::op::util::TopKBase* const node,
-                   const HostTensorVector& outputs,
-                   const HostTensorVector& inputs) {
-    const auto& arg_shape = inputs[0]->get_shape();
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    const auto axis = normalize_axis(node, node->get_provided_axis(), arg_shape.size());
-    OPENVINO_SUPPRESS_DEPRECATED_END
-    const auto compute_max = node->get_mode() == ov::op::TopKMode::MAX;
-    const auto sort_type = node->get_sort_type();
-
-    const auto input_shapes = vector<PartialShape>{inputs[0]->get_partial_shape(), inputs[1]->get_partial_shape()};
-    auto output_shape = shape_infer(node, input_shapes, ov::make_tensor_accessor(inputs)).front().to_shape();
+}  // namespace
+}  // namespace validate
+
+struct Evaluate : public element::NoAction<bool> {
+    using element::NoAction<bool>::visit;
+
+    template <element::Type_t ET, class T = fundamental_type_for<ET>>
+    static result_type visit(const Tensor& in,
+                             Tensor& out_values,
+                             Tensor& out_indices,
+                             const Shape& out_shape,
+                             const size_t axis,
+                             const bool compute_max,
+                             const TopKSortType sort) {
+        using namespace ov::element;
+        return IfTypeOf<i32, i64>::apply<EvalByIdxType>(out_indices.get_element_type(),
+                                                        in.data<const T>(),
+                                                        out_values.data<T>(),
+                                                        out_indices,
+                                                        in.get_shape(),
+                                                        out_shape,
+                                                        axis,
+                                                        out_shape[axis],
+                                                        compute_max,
+                                                        sort);
+    }
+
+private:
+    struct EvalByIdxType : public element::NoAction<bool> {
+        using element::NoAction<bool>::visit;
+
+        template <element::Type_t ET, class T, class I = fundamental_type_for<ET>>
+        static result_type visit(const T* in_first,
+                                 T* out_first,
+                                 Tensor& out_indices,
+                                 const Shape& in_shape,
+                                 const Shape& out_shape,
+                                 const size_t axis,
+                                 const size_t k,
+                                 const bool compute_max,
+                                 const TopKSortType sort) {
+            reference::topk(in_first,
+                            out_indices.data<I>(),
+                            out_first,
+                            in_shape,
+                            out_shape,
+                            axis,
+                            k,
+                            compute_max,
+                            sort);
+            return true;
+        }
+    };
+};
+
+namespace {
+bool evaluate(const util::TopKBase* const node, TensorVector& outputs, const TensorVector& inputs) {
+    auto output_shapes = shape_infer(node, ov::util::get_tensors_partial_shapes(inputs), make_tensor_accessor(inputs));
+    OPENVINO_ASSERT(outputs.size() == output_shapes.size());
 
+    auto output_shape = output_shapes.front().get_shape();
+    const auto axis = ov::util::normalize(node->get_provided_axis(), output_shape.size());
     if (output_shape[axis] == 0) {
         // the kernel can't handle K (output_shape[axis]) equal 0, use arg_shape[axis] instead.
-        output_shape[axis] = arg_shape[axis];
+        output_shape[axis] = inputs[0].get_shape()[axis];
     }
 
-    const size_t k = output_shape[axis];
-    OPENVINO_ASSERT(k <= arg_shape[axis], "'K' exceeds the dimension of top_k_axis");
-
-    // TopK reference implementation provides stable indices output so this parameter is not passed on
-    return evaluate_topk(inputs[0],
-                         outputs[1],
-                         outputs[0],
-                         output_shape,
-                         axis,
-                         k,
-                         compute_max,
-                         sort_type,
-                         node->get_index_element_type());
+    for (auto& t : outputs) {
+        t.set_shape(output_shape);
+    }
+
+    using namespace ov::element;
+    return IfTypeOf<f16, f32, i32, i64, u32, u64>::apply<topk::Evaluate>(inputs[0].get_element_type(),
+                                                                         inputs[0],
+                                                                         outputs[0],
+                                                                         outputs[1],
+                                                                         output_shape,
+                                                                         axis,
+                                                                         (node->get_mode() == ov::op::TopKMode::MAX),
+                                                                         node->get_sort_type());
 }
 }  // namespace
 }  // namespace topk
 
 // v1 version starts
-
-op::v1::TopK::TopK(const Output<Node>& data,
-                   const Output<Node>& k,
-                   const int64_t axis,
-                   const std::string& mode,
-                   const std::string& sort,
-                   const element::Type& index_element_type)
+namespace v1 {
+TopK::TopK(const Output<Node>& data,
+           const Output<Node>& k,
+           const int64_t axis,
+           const std::string& mode,
+           const std::string& sort,
+           const element::Type& index_element_type)
     : util::TopKBase(data, k, axis, mode, sort, index_element_type) {
     constructor_validate_and_infer_types();
 }
 
-op::v1::TopK::TopK(const Output<Node>& data,
-                   const Output<Node>& k,
-                   const int64_t axis,
-                   const Mode mode,
-                   const SortType sort,
-                   const element::Type& index_element_type)
+TopK::TopK(const Output<Node>& data,
+           const Output<Node>& k,
+           const int64_t axis,
+           const Mode mode,
+           const SortType sort,
+           const element::Type& index_element_type)
     : util::TopKBase(data, k, axis, mode, sort, index_element_type) {
     constructor_validate_and_infer_types();
 }
 
-void op::v1::TopK::k_type_check(const element::Type& k_element_type) const {
+void TopK::k_type_check(const element::Type& k_element_type) const {
     NODE_VALIDATION_CHECK(
         this,
         k_element_type == element::i8 || k_element_type == element::i32 || k_element_type == element::i64,
@@ -169,156 +159,84 @@ void op::v1::TopK::k_type_check(const element::Type& k_element_type) const {
         ").");
 }
 
-shared_ptr<Node> op::v1::TopK::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> TopK::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_TopK_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v1::TopK>(new_args.at(0), new_args.at(1), m_axis, m_mode, m_sort, m_index_element_type);
+    return std::make_shared<TopK>(new_args.at(0), new_args.at(1), m_axis, m_mode, m_sort, m_index_element_type);
 }
 
-bool op::v1::TopK::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool TopK::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v1_TopK_evaluate);
-    return topk::TopK_evaluate(this, outputs, inputs);
+    return topk::evaluate(this, outputs, inputs);
 }
 
-bool op::v1::TopK::has_evaluate() const {
+bool TopK::has_evaluate() const {
     OV_OP_SCOPE(v1_TopK_has_evaluate);
-
-    switch (get_input_element_type(0)) {
-    case element::i32:
-    case element::i64:
-    case element::u32:
-    case element::u64:
-    case element::f16:
-    case element::f32:
-        break;
-    default:
-        return false;
-    }
-
-    if (op::util::is_constant(input_value(1).get_node())) {
-        switch (get_input_element_type(1)) {
-        case element::i8:
-        case element::i32:
-        case element::i64:
-            break;
-        default:
-            return false;
-        }
-    } else {
-        switch (get_input_element_type(1)) {
-        case element::i8:
-        case element::i16:
-        case element::i32:
-        case element::i64:
-        case element::u8:
-        case element::u16:
-        case element::u32:
-        case element::u64:
-            break;
-        default:
-            return false;
-        }
-    }
-
-    return true;
+    return topk::validate::data_type(get_input_element_type(0)) && topk::validate::k_type(get_input_element_type(1));
 }
+}  // namespace v1
 
 // v3 version starts
-op::v3::TopK::TopK(const Output<Node>& data,
-                   const Output<Node>& k,
-                   const int64_t axis,
-                   const std::string& mode,
-                   const std::string& sort,
-                   const element::Type& index_element_type)
+namespace v3 {
+TopK::TopK(const Output<Node>& data,
+           const Output<Node>& k,
+           const int64_t axis,
+           const std::string& mode,
+           const std::string& sort,
+           const element::Type& index_element_type)
     : TopK(data, k, axis, as_enum<Mode>(mode), as_enum<SortType>(sort), index_element_type) {}
 
-op::v3::TopK::TopK(const Output<Node>& data,
-                   const Output<Node>& k,
-                   const int64_t axis,
-                   const Mode mode,
-                   const SortType sort,
-                   const element::Type& index_element_type)
+TopK::TopK(const Output<Node>& data,
+           const Output<Node>& k,
+           const int64_t axis,
+           const Mode mode,
+           const SortType sort,
+           const element::Type& index_element_type)
     : util::TopKBase{data, k, axis, mode, sort, index_element_type} {
     constructor_validate_and_infer_types();
 }
 
-shared_ptr<Node> op::v3::TopK::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> TopK::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v3_TopK_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v3::TopK>(new_args.at(0), new_args.at(1), m_axis, m_mode, m_sort, m_index_element_type);
+    return std::make_shared<TopK>(new_args.at(0), new_args.at(1), m_axis, m_mode, m_sort, m_index_element_type);
 }
 
-bool op::v3::TopK::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool TopK::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v3_TopK_evaluate);
-    return topk::TopK_evaluate(this, outputs, inputs);
+    return topk::evaluate(this, outputs, inputs);
 }
 
-bool op::v3::TopK::has_evaluate() const {
+bool TopK::has_evaluate() const {
     OV_OP_SCOPE(v3_TopK_has_evaluate);
-
-    switch (get_input_element_type(0)) {
-    case element::i32:
-    case element::i64:
-    case element::u32:
-    case element::u64:
-    case element::f16:
-    case element::f32:
-        break;
-    default:
-        return false;
-    }
-
-    if (op::util::is_constant(input_value(1).get_node())) {
-        switch (get_input_element_type(1)) {
-        case element::i8:
-        case element::i32:
-        case element::i64:
-            break;
-        default:
-            return false;
-        }
-    } else {
-        switch (get_input_element_type(1)) {
-        case element::i8:
-        case element::i16:
-        case element::i32:
-        case element::i64:
-        case element::u8:
-        case element::u16:
-        case element::u32:
-        case element::u64:
-            break;
-        default:
-            return false;
-        }
-    }
-
-    return true;
+    return topk::validate::data_type(get_input_element_type(0)) && topk::validate::k_type(get_input_element_type(1));
 }
+}  // namespace v3
 
 // =============== V11 ===============
-ov::op::v11::TopK::TopK(const Output<Node>& data,
-                        const Output<Node>& k,
-                        const int64_t axis,
-                        const std::string& mode,
-                        const std::string& sort,
-                        const element::Type& index_element_type,
-                        const bool stable)
+namespace v11 {
+TopK::TopK(const Output<Node>& data,
+           const Output<Node>& k,
+           const int64_t axis,
+           const std::string& mode,
+           const std::string& sort,
+           const element::Type& index_element_type,
+           const bool stable)
     : TopK(data, k, axis, as_enum<TopKMode>(mode), as_enum<TopKSortType>(sort), index_element_type, stable) {}
 
-ov::op::v11::TopK::TopK(const Output<Node>& data,
-                        const Output<Node>& k,
-                        const int64_t axis,
-                        const TopKMode mode,
-                        const TopKSortType sort,
-                        const element::Type& index_element_type,
-                        const bool stable)
+TopK::TopK(const Output<Node>& data,
+           const Output<Node>& k,
+           const int64_t axis,
+           const TopKMode mode,
+           const TopKSortType sort,
+           const element::Type& index_element_type,
+           const bool stable)
     : util::TopKBase{data, k, axis, mode, sort, index_element_type},
       m_stable{stable} {
     constructor_validate_and_infer_types();
 }
 
-void ov::op::v11::TopK::validate_and_infer_types() {
+void TopK::validate_and_infer_types() {
     OV_OP_SCOPE(v11_TopK_validate_and_infer_types);
 
     if (m_stable) {
@@ -331,44 +249,34 @@ void ov::op::v11::TopK::validate_and_infer_types() {
     util::TopKBase::validate_and_infer_types();
 }
 
-bool ov::op::v11::TopK::visit_attributes(AttributeVisitor& visitor) {
+bool TopK::visit_attributes(AttributeVisitor& visitor) {
     OV_OP_SCOPE(v11_TopK_visit_attributes);
     util::TopKBase::visit_attributes(visitor);
     visitor.on_attribute("stable", m_stable);
     return true;
 }
 
-std::shared_ptr<Node> ov::op::v11::TopK::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> TopK::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v11_TopK_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<ov::op::v11::TopK>(new_args.at(0),
-                                          new_args.at(1),
-                                          m_axis,
-                                          m_mode,
-                                          m_sort,
-                                          m_index_element_type,
-                                          m_stable);
+    return std::make_shared<TopK>(new_args.at(0),
+                                  new_args.at(1),
+                                  m_axis,
+                                  m_mode,
+                                  m_sort,
+                                  m_index_element_type,
+                                  m_stable);
 }
 
-bool ov::op::v11::TopK::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+bool TopK::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v11_TopK_evaluate);
-    return topk::TopK_evaluate(this, outputs, inputs);
+    return topk::evaluate(this, outputs, inputs);
 }
 
-bool ov::op::v11::TopK::has_evaluate() const {
+bool TopK::has_evaluate() const {
     OV_OP_SCOPE(v11_TopK_has_evaluate);
-
-    switch (get_input_element_type(0)) {
-    case element::i32:
-    case element::i64:
-    case element::u32:
-    case element::u64:
-    case element::f16:
-    case element::f32:
-        break;
-    default:
-        return false;
-    }
-    return true;
+    return topk::validate::data_type(get_input_element_type(0));
 }
+}  // namespace v11
+}  // namespace op
 }  // namespace ov
diff --git a/src/core/src/op/util/evaluate_helpers.cpp b/src/core/src/op/util/evaluate_helpers.cpp
index 4e21da40bfe013..cffc57e6fbd87c 100644
--- a/src/core/src/op/util/evaluate_helpers.cpp
+++ b/src/core/src/op/util/evaluate_helpers.cpp
@@ -4,8 +4,6 @@
 
 #include "ngraph/op/util/evaluate_helpers.hpp"
 
-#include "openvino/op/util/evaluate_helpers.hpp"
-
 namespace ngraph {
 AxisSet get_normalized_axes_from_tensor(const HostTensorPtr tensor,
                                         const ngraph::Rank& rank,
@@ -17,18 +15,3 @@ AxisSet get_normalized_axes_from_tensor(const HostTensorPtr tensor,
     return AxisSet{normalized_axes};
 }
 }  // namespace ngraph
-
-namespace ov {
-namespace op {
-namespace util {
-std::vector<PartialShape> get_tensors_partial_shapes(const TensorVector& tensors) {
-    std::vector<PartialShape> shapes;
-    shapes.reserve(tensors.size());
-    for (const auto& t : tensors) {
-        shapes.emplace_back(t.get_shape());
-    }
-    return shapes;
-}
-}  // namespace util
-}  // namespace op
-}  // namespace ov
diff --git a/src/core/src/op/variadic_split.cpp b/src/core/src/op/variadic_split.cpp
index ab94af3fd8345f..38b309325fe16a 100644
--- a/src/core/src/op/variadic_split.cpp
+++ b/src/core/src/op/variadic_split.cpp
@@ -2,33 +2,69 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph/op/variadic_split.hpp"
+#include "openvino/op/variadic_split.hpp"
 
 #include <numeric>
 
 #include "bound_evaluate.hpp"
 #include "compare.hpp"
 #include "itt.hpp"
-#include "ngraph/validation_util.hpp"
+#include "openvino/core/validation_util.hpp"
 #include "openvino/reference/slice.hpp"
 #include "variadic_split_shape_inference.hpp"
 
-using namespace std;
-using namespace ngraph;
+namespace ov {
+namespace op {
+namespace variadic_split {
+namespace {
 
-op::v1::VariadicSplit::VariadicSplit(const Output<Node>& data,
-                                     const Output<Node>& axis,
-                                     const Output<Node>& split_lengths)
-    : Op({data, axis, split_lengths}) {
-    constructor_validate_and_infer_types();
+bool has_axis_and_splits_bound_set(const Node* const node) {
+    return have_node_inputs_bounds_set(node, 1, 2);
 }
 
-bool ngraph::op::v1::VariadicSplit::visit_attributes(AttributeVisitor& visitor) {
-    OV_OP_SCOPE(v1_VariadicSplit_visit_attributes);
+bool evaluate(TensorVector& outputs, const TensorVector& inputs) {
+    const auto& data_tensor = inputs[0];
+    const auto& axis_tensor = inputs[1];
+    const auto axis =
+        ov::util::normalize(get_tensor_data_as<int64_t>(axis_tensor).front(), data_tensor.get_shape().size());
+
+    ov::Coordinate upper_bounds(data_tensor.get_shape());
+    ov::Coordinate lower_bounds(upper_bounds.size());
+    upper_bounds[axis] = 0;
+
+    const Strides default_strides(upper_bounds.size(), 1);
+    constexpr auto is_zero_dim = ov::cmp::Equal<size_t>(0);
+
+    for (auto& output : outputs) {
+        const auto& out_shape = output.get_shape();
+        upper_bounds[axis] += out_shape[axis];
+
+        if (std::none_of(out_shape.cbegin(), out_shape.cend(), is_zero_dim)) {
+            reference::slice(static_cast<const char*>(data_tensor.data()),
+                             static_cast<char*>(output.data()),
+                             data_tensor.get_shape(),
+                             lower_bounds,
+                             upper_bounds,
+                             default_strides,
+                             out_shape,
+                             data_tensor.get_element_type().size());
+        }
+
+        lower_bounds[axis] = upper_bounds[axis];
+    }
+
     return true;
 }
+}  // namespace
+}  // namespace variadic_split
 
-void ngraph::op::v1::VariadicSplit::validate_and_infer_types() {
+namespace v1 {
+VariadicSplit::VariadicSplit(const Output<Node>& data, const Output<Node>& axis, const Output<Node>& split_lengths)
+    : Op({data, axis, split_lengths}) {
+    constructor_validate_and_infer_types();
+}
+
+void VariadicSplit::validate_and_infer_types() {
     OV_OP_SCOPE(v1_VariadicSplit_validate_and_infer_types);
     for (size_t i = 0; i < get_input_size(); ++i) {
         set_input_is_relevant_to_value(i);
@@ -45,107 +81,52 @@ void ngraph::op::v1::VariadicSplit::validate_and_infer_types() {
     }
 }
 
-shared_ptr<Node> op::v1::VariadicSplit::clone_with_new_inputs(const OutputVector& new_args) const {
+std::shared_ptr<Node> VariadicSplit::clone_with_new_inputs(const OutputVector& new_args) const {
     OV_OP_SCOPE(v1_VariadicSplit_clone_with_new_inputs);
     check_new_args_count(this, new_args);
-    return make_shared<v1::VariadicSplit>(new_args.at(0), new_args.at(1), new_args.at(2));
+    return std::make_shared<VariadicSplit>(new_args.at(0), new_args.at(1), new_args.at(2));
 }
 
-OPENVINO_SUPPRESS_DEPRECATED_START
-namespace variadic_split {
-namespace {
-inline bool evaluate(const HostTensorPtr& in,
-                     const HostTensorPtr& out,
-                     const Coordinate& lower_bounds,
-                     const Coordinate& upper_bounds) {
-    const auto& output_shape = out->get_shape();
-    const auto has_nonzero_dims = std::none_of(output_shape.begin(), output_shape.end(), ov::cmp::Equal<size_t>(0));
-
-    if (has_nonzero_dims) {
-        ov::reference::slice(in->get_data_ptr<const char>(),
-                             out->get_data_ptr<char>(),
-                             in->get_shape(),
-                             lower_bounds,
-                             upper_bounds,
-                             Strides(lower_bounds.size(), 1),
-                             out->get_shape(),
-                             in->get_element_type().size());
-        return true;
-    }
-    return false;
-}
-}  // namespace
-}  // namespace variadic_split
-
-bool op::v1::VariadicSplit::evaluate_variadic_split(const HostTensorVector& inputs,
-                                                    const HostTensorVector& outputs) const {
-    const auto& data_tensor = inputs[0];
-    const auto& axis_tensor = inputs[1];
-    const auto& split_lengths_tensor = inputs[2];
-    OPENVINO_ASSERT(axis_tensor->get_element_type().is_integral_number(),
-                    "axis element type is not integral data type");
-    OPENVINO_ASSERT(split_lengths_tensor->get_element_type().is_integral_number(),
-                    "split_lengths element type is not integral data type");
-
-    OPENVINO_SUPPRESS_DEPRECATED_START
-    int64_t axis = host_tensor_2_vector<int64_t>(axis_tensor)[0];
-    axis = ngraph::normalize_axis(this, axis, data_tensor->get_partial_shape().rank());
-    OPENVINO_SUPPRESS_DEPRECATED_END
+bool VariadicSplit::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
+    OV_OP_SCOPE(v1_VariadicSplit_evaluate);
 
-    std::vector<ov::PartialShape> input_shapes = {data_tensor->get_partial_shape(),
-                                                  axis_tensor->get_partial_shape(),
-                                                  split_lengths_tensor->get_partial_shape()};
-    auto output_shapes = shape_infer(this, input_shapes, make_tensor_accessor(inputs));
+    if (inputs[1].get_element_type().is_integral_number() && inputs[2].get_element_type().is_integral_number()) {
+        const auto output_shapes =
+            shape_infer(this, ov::util::get_tensors_partial_shapes(inputs), make_tensor_accessor(inputs));
+        OPENVINO_ASSERT(outputs.size() == output_shapes.size());
 
-    const auto data_shape = data_tensor->get_shape();
-    std::vector<size_t> lower_bounds(data_shape.size(), 0);
-    std::vector<size_t> upper_bounds = data_shape;
-    upper_bounds[axis] = 0;
+        auto out_partial_shape = output_shapes.cbegin();
+        for (auto& output : outputs) {
+            output.set_shape(out_partial_shape->to_shape());
+            ++out_partial_shape;
+        }
 
-    size_t split_pos = 0;
-    for (const auto& output : outputs) {
-        ov::Shape output_shape = output_shapes[split_pos++].get_shape();
-        upper_bounds[axis] += output_shape[axis];
-        output->set_shape(output_shape);
-        variadic_split::evaluate(data_tensor, output, lower_bounds, upper_bounds);
-        lower_bounds.at(axis) = upper_bounds.at(axis);
+        return variadic_split::evaluate(outputs, inputs);
+    } else {
+        return false;
     }
-
-    return true;
-}
-bool op::v1::VariadicSplit::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
-    OV_OP_SCOPE(v1_VariadicSplit_evaluate);
-    return evaluate_variadic_split(inputs, outputs);
 }
 
-bool op::v1::VariadicSplit::has_evaluate() const {
+bool VariadicSplit::has_evaluate() const {
     OV_OP_SCOPE(v1_VariadicSplit_has_evaluate);
     return get_input_element_type(1).is_integral_number() && get_input_element_type(2).is_integral_number();
 }
 
-bool op::v1::VariadicSplit::has_axis_and_splits_bound_set() const {
-    for (size_t i = 1; i < get_input_size(); ++i) {
-        if (!get_input_tensor(i).has_and_set_bound()) {
-            return false;
-        }
-    }
-    return true;
-}
-
-bool op::v1::VariadicSplit::evaluate_lower(ov::TensorVector& output_values) const {
+bool VariadicSplit::evaluate_lower(TensorVector& output_values) const {
     OV_OP_SCOPE(v1_Split_evaluate_lower);
-
-    return has_axis_and_splits_bound_set() && default_lower_bound_evaluator(this, output_values);
+    return variadic_split::has_axis_and_splits_bound_set(this) && default_lower_bound_evaluator(this, output_values);
 }
 
-bool op::v1::VariadicSplit::evaluate_upper(ov::TensorVector& output_values) const {
+bool VariadicSplit::evaluate_upper(TensorVector& output_values) const {
     OV_OP_SCOPE(v1_Split_evaluate_upper);
-
-    return has_axis_and_splits_bound_set() && default_upper_bound_evaluator(this, output_values);
+    return variadic_split::has_axis_and_splits_bound_set(this) && default_upper_bound_evaluator(this, output_values);
 }
 
-bool op::v1::VariadicSplit::evaluate_label(TensorLabelVector& output_labels) const {
+bool VariadicSplit::evaluate_label(TensorLabelVector& output_labels) const {
     OPENVINO_SUPPRESS_DEPRECATED_START
-    return has_axis_and_splits_bound_set() && default_label_evaluator(this, output_labels);
+    return variadic_split::has_axis_and_splits_bound_set(this) && default_label_evaluator(this, output_labels);
     OPENVINO_SUPPRESS_DEPRECATED_END
 }
+}  // namespace v1
+}  // namespace op
+}  // namespace ov
diff --git a/src/core/src/validation_util.cpp b/src/core/src/validation_util.cpp
index 2e1db9dd6864f1..803364b289008d 100644
--- a/src/core/src/validation_util.cpp
+++ b/src/core/src/validation_util.cpp
@@ -1384,5 +1384,14 @@ std::shared_ptr<Constant> get_constant_from_source(const Output<Node>& source) {
         return {};
     }
 }
+
+std::vector<PartialShape> get_tensors_partial_shapes(const TensorVector& tensors) {
+    std::vector<PartialShape> shapes;
+    shapes.reserve(tensors.size());
+    for (const auto& t : tensors) {
+        shapes.emplace_back(t.get_shape());
+    }
+    return shapes;
+}
 }  // namespace util
 }  // namespace ov
diff --git a/src/core/tests/constant.cpp b/src/core/tests/constant.cpp
index 45ad60d153627a..0feefb84bed8a5 100644
--- a/src/core/tests/constant.cpp
+++ b/src/core/tests/constant.cpp
@@ -266,8 +266,8 @@ TEST(constant, int4_string) {
     EXPECT_EQ(v[2], -1);
 
     const auto p = c.get_data_ptr<uint8_t>();
-    EXPECT_EQ(0x10, p[0]);
-    EXPECT_EQ(0xF0, p[1] & 0xF0);
+    EXPECT_EQ(0x01, p[0]);
+    EXPECT_EQ(0x0F, p[1] & 0x0F);
 
     EXPECT_EQ(input, c.get_value_strings());
 
@@ -318,8 +318,8 @@ TEST(constant, int4_vector_negative_number) {
     EXPECT_EQ(v[2], int8_t(-1));
 
     const auto p = c.get_data_ptr<uint8_t>();
-    EXPECT_EQ(0xFE, p[0]);
-    EXPECT_EQ(0xF0, p[1] & 0xF0);
+    EXPECT_EQ(0xEF, p[0]);
+    EXPECT_EQ(0x0F, p[1] & 0x0F);
 }
 
 TEST(constant, int4_vector_positive_number) {
@@ -332,8 +332,8 @@ TEST(constant, int4_vector_positive_number) {
     EXPECT_EQ(v[2], int8_t(5));
 
     const auto p = c.get_data_ptr<uint8_t>();
-    EXPECT_EQ(0x12, p[0]);
-    EXPECT_EQ(0x50, p[1] & 0xF0);
+    EXPECT_EQ(0x21, p[0]);
+    EXPECT_EQ(0x05, p[1] & 0x0F);
 }
 
 TEST(constant, int4_vector_broadcast_negative_number) {
@@ -795,8 +795,8 @@ TEST(constant, uint4_string) {
     EXPECT_EQ(v[3], 0);
 
     const auto p = c.get_data_ptr<uint8_t>();
-    EXPECT_EQ(p[0], 0x10);
-    EXPECT_EQ(p[1], 0x10);
+    EXPECT_EQ(p[0], 0x01);
+    EXPECT_EQ(p[1], 0x01);
 
     EXPECT_EQ(input, c.get_value_strings());
 
@@ -831,8 +831,8 @@ TEST(constant, uint4_vector) {
     EXPECT_EQ(v[3], 0);
 
     const auto p = c.get_data_ptr<uint8_t>();
-    EXPECT_EQ(p[0], 0x10);
-    EXPECT_EQ(p[1], 0x10);
+    EXPECT_EQ(p[0], 0x01);
+    EXPECT_EQ(p[1], 0x01);
 }
 
 TEST(constant, uint4_vector_broadcast) {
diff --git a/src/core/tests/copy.cpp b/src/core/tests/copy.cpp
index d7b2b4256f4aa3..f902d7485a1932 100644
--- a/src/core/tests/copy.cpp
+++ b/src/core/tests/copy.cpp
@@ -447,12 +447,12 @@ TEST(copy, random_uniform) {
 
     const auto min_val_param = make_shared<ov::op::v0::Parameter>(element::f32, Shape{1});
     const auto max_val_param = make_shared<ov::op::v0::Parameter>(element::f32, Shape{1});
-    auto out_shape = make_shared<ov::op::v0::Constant>(element::i64, Shape{3}, std::vector<int64_t>{1, 2, 3});
+    auto out_shape = make_shared<ov::op::v0::Constant>(element::i64, Shape{3}, shape);
     auto ru =
         std::make_shared<ov::op::v8::RandomUniform>(out_shape, min_val_param, max_val_param, element::f32, 150, 10);
 
     // Call `evaluate` to update m_state
-    auto outputs = ov::TensorVector{{element::i64, out_shape->get_shape(), shape.data()}};
+    auto outputs = ov::TensorVector{{element::i64, {1lu, 2lu, 3lu}}};
     ru->evaluate(outputs,
                  ov::TensorVector{{element::i64, out_shape->get_shape(), shape.data()},
                                   {element::f32, min_val_param->get_shape(), &min},
diff --git a/src/core/tests/int4.cpp b/src/core/tests/int4.cpp
index 2edb82dda0183c..d9a20fbf3649b2 100644
--- a/src/core/tests/int4.cpp
+++ b/src/core/tests/int4.cpp
@@ -15,9 +15,9 @@ TEST(int4, convert_i4_to_string) {
     vector<uint8_t> values{171, 16};
     auto constant = make_shared<ov::op::v0::Constant>(element::i4, Shape{3}, &values[0]);
 
-    vector<string> ref{"-6", "-5", "1"};
+    vector<string> ref{"-5", "-6", "0"};
     for (size_t i = 0; i < 3; ++i) {
-        ASSERT_EQ(constant->convert_value_to_string(i), ref[i]);
+        EXPECT_EQ(constant->convert_value_to_string(i), ref[i]);
     }
 }
 
diff --git a/src/core/tests/type_prop/reduce_prod.cpp b/src/core/tests/type_prop/reduce_prod.cpp
index 20cc6699a6b61b..4def461f847a69 100644
--- a/src/core/tests/type_prop/reduce_prod.cpp
+++ b/src/core/tests/type_prop/reduce_prod.cpp
@@ -11,6 +11,9 @@
 using Type = ::testing::Types<ov::op::v1::ReduceProd>;
 INSTANTIATE_TYPED_TEST_SUITE_P(type_prop_reduce_prod, ReduceTest, Type);
 INSTANTIATE_TYPED_TEST_SUITE_P(type_prop_reduce_prod_et, ReduceArithmeticTest, Type);
+INSTANTIATE_TYPED_TEST_SUITE_P(type_prop_reduce_prod_dynamic, ReduceTest, Type);
+INSTANTIATE_TYPED_TEST_SUITE_P(type_prop_reduce_prod_dynamic_zero, ReduceTest, Type);
+INSTANTIATE_TYPED_TEST_SUITE_P(type_prop_reduce_prod_scalar, ReduceTest, Type);
 
 TEST(type_prop, reduce_prod_value_propagation) {
     const auto param = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{{1, 8}, {2, 3}, 6});
@@ -22,3 +25,36 @@ TEST(type_prop, reduce_prod_value_propagation) {
     EXPECT_EQ(reshape->get_element_type(), ov::element::f32);
     EXPECT_EQ(reshape->get_output_partial_shape(0), (PartialShape{{12, 144}}));
 }
+
+TEST(type_prop, reduce_prod_value_propagation_dynamic) {
+    const auto param = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, 12, 32, 32});
+    const auto shape_of = std::make_shared<op::v3::ShapeOf>(param);
+    const auto reduce_prod =
+        std::make_shared<op::v1::ReduceProd>(shape_of, ov::op::v0::Constant::create(element::i64, {1}, {0}), true);
+    const auto reshape = std::make_shared<op::v1::Reshape>(param, reduce_prod, false);
+
+    EXPECT_EQ(reshape->get_element_type(), ov::element::f32);
+    EXPECT_EQ(reshape->get_output_partial_shape(0), (PartialShape{-1}));
+}
+
+TEST(type_prop, reduce_prod_value_propagation_dynamic_zero) {
+    const auto param = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, 12, 0, -1});
+    const auto shape_of = std::make_shared<op::v3::ShapeOf>(param);
+    const auto reduce_prod =
+        std::make_shared<op::v1::ReduceProd>(shape_of, ov::op::v0::Constant::create(element::i64, {1}, {0}), true);
+    const auto reshape = std::make_shared<op::v1::Reshape>(param, reduce_prod, false);
+
+    EXPECT_EQ(reshape->get_element_type(), ov::element::f32);
+    EXPECT_EQ(reshape->get_output_partial_shape(0), (PartialShape{0}));
+}
+
+TEST(type_prop, reduce_prod_value_propagation_scalar) {
+    const auto param = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{0});
+    const auto shape_of = std::make_shared<op::v3::ShapeOf>(param);
+    const auto reduce_prod =
+        std::make_shared<op::v1::ReduceProd>(shape_of, ov::op::v0::Constant::create(element::i64, {1}, {0}), true);
+    const auto reshape = std::make_shared<op::v1::Reshape>(param, reduce_prod, false);
+
+    EXPECT_EQ(reshape->get_element_type(), ov::element::f32);
+    EXPECT_EQ(reshape->get_output_partial_shape(0), (PartialShape{0}));
+}
diff --git a/src/core/tests/uint4.cpp b/src/core/tests/uint4.cpp
index 5c3b0a5e06af20..8285fdb3cd5e1c 100644
--- a/src/core/tests/uint4.cpp
+++ b/src/core/tests/uint4.cpp
@@ -13,9 +13,9 @@ TEST(uint4, convert_u4_to_string) {
     vector<uint8_t> values{171, 16};
     auto constant = make_shared<ov::op::v0::Constant>(element::u4, Shape{3}, &values[0]);
 
-    vector<string> ref{"10", "11", "1"};
+    vector<string> ref{"11", "10", "0"};
     for (size_t i = 0; i < 3; ++i) {
-        ASSERT_EQ(constant->convert_value_to_string(i), ref[i]);
+        EXPECT_EQ(constant->convert_value_to_string(i), ref[i]);
     }
 }
 
diff --git a/src/frontends/onnx/frontend/src/op/random_uniform.cpp b/src/frontends/onnx/frontend/src/op/random_uniform.cpp
index 6215dcc491c07d..a26ed672a0cc15 100644
--- a/src/frontends/onnx/frontend/src/op/random_uniform.cpp
+++ b/src/frontends/onnx/frontend/src/op/random_uniform.cpp
@@ -29,6 +29,7 @@ OutputVector random_uniform(const Node& node) {
 
     const auto target_type = common::get_ngraph_element_type(dtype);
     const uint64_t global_seed = 0;
+    // TODO: This multiplication leads to a mismatch in accuracy. Issue: 123003
     const auto seed_uint64 = static_cast<uint64_t>(seed * 1000);
 
     return {std::make_shared<ngraph::opset8::RandomUniform>(target_shape_const,
diff --git a/src/frontends/paddle/src/op/slice_ops.hpp b/src/frontends/paddle/src/op/slice_ops.hpp
index dc2a609ba18513..1f8798bc00e91f 100644
--- a/src/frontends/paddle/src/op/slice_ops.hpp
+++ b/src/frontends/paddle/src/op/slice_ops.hpp
@@ -33,63 +33,16 @@ NamedOutputs slice_op(const NodeContext& node, const bool& stride_input) {
     Output<Node> start_idx_node = idx_node("StartsTensor", "StartsTensorList", "starts", node);
     Output<Node> end_idx_node = idx_node("EndsTensor", "EndsTensorList", "ends", node);
     Output<Node> strides_idx_node;
-    if (stride_input)
-        strides_idx_node = idx_node("StridesTensor", "StridesTensorList", "strides", node);
-
-    // The following process is:
-    // Given:
-    // data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] // shape is: [2, 4]
-    // axes = [0]
-    // starts = [1]
-    // ends = [2]
-    // Our process is:
-    //  1. Get 'axes': [0, 1], 'starts', 'ends'
-    //  2. Get data shape: [2,4] and dims: 2
-    //  3. Create two tensor t1 and t2, shape is the dims from step2: 2. t1: [0, 0], t2: [INT_MAX, INT_MAX]
-    //  4. Use 'ScatterNDUpdate' to update some elements in t1, the updated indexes are coming from 'axes', the contents
-    //  are coming from 'starts', t1: [1, 0]; apply the similar process to t2
-    //  5. Call 'StrideSlice' with t1 and t2
-    // Why using ScatterNDUpdate is that 'axes' may be discontinuous.
-
-    // the shape of input, such as [2, 4]
-    const auto shape_node = std::make_shared<default_opset::ShapeOf>(data, element::Type_t::i32);
-    // the input dim, such as [2]
-    const auto rank_node = std::make_shared<default_opset::ShapeOf>(shape_node, element::i32);
-    const auto const_0_node = default_opset::Constant::create(element::i32, {}, {0});
-    const auto const_max_node = default_opset::Constant::create(element::i32, {}, {INT_MAX});
-    const auto const_1_node = default_opset::Constant::create(element::i32, {}, {1});
-    // t1: [0, 0]
-    const auto start_node = std::make_shared<default_opset::Broadcast>(const_0_node, rank_node);
-    // t2: [INT_MAX, INT_MAX]
-    const auto end_node = std::make_shared<default_opset::Broadcast>(const_max_node, rank_node);
-    const auto strides_node = std::make_shared<default_opset::Broadcast>(const_1_node, rank_node);
-    const auto axes_node = default_opset::Constant::create(element::i32, {axes.size(), 1}, axes);
-    // update t1
-    const auto fixed_start_node =
-        std::make_shared<default_opset::ScatterNDUpdate>(start_node, axes_node, start_idx_node);
-    // update t2
-    const auto fixed_end_node = std::make_shared<default_opset::ScatterNDUpdate>(end_node, axes_node, end_idx_node);
-    std::shared_ptr<Node> stride_slice_node;
     if (stride_input) {
-        const auto fixed_strides_node =
-            std::make_shared<default_opset::ScatterNDUpdate>(strides_node, axes_node, strides_idx_node);
-
-        stride_slice_node = std::make_shared<default_opset::StridedSlice>(data,
-                                                                          fixed_start_node,
-                                                                          fixed_end_node,
-                                                                          fixed_strides_node,
-                                                                          std::vector<int64_t>{0},
-                                                                          std::vector<int64_t>{0});
+        strides_idx_node = idx_node("StridesTensor", "StridesTensorList", "strides", node);
     } else {
-        stride_slice_node = std::make_shared<default_opset::StridedSlice>(data,
-                                                                          fixed_start_node,
-                                                                          fixed_end_node,
-                                                                          std::vector<int64_t>{0},
-                                                                          std::vector<int64_t>{0});
+        strides_idx_node =
+            default_opset::Constant::create(element::i32, start_idx_node.get_shape(), std::vector<int32_t>{1});
     }
-
+    const auto axes_node = default_opset::Constant::create(element::i32, {axes.size()}, axes);
+    const auto slice_node =
+        std::make_shared<default_opset::Slice>(data, start_idx_node, end_idx_node, strides_idx_node, axes_node);
     const auto decrease_axis = node.get_attribute<std::vector<int32_t>>("decrease_axis");
-
     if (decrease_axis.size() > 0) {
         PartialShape input_shape = data.get_partial_shape();
         PADDLE_OP_CHECK(node,
@@ -99,18 +52,19 @@ NamedOutputs slice_op(const NodeContext& node, const bool& stride_input) {
             // according to paddle slice_op, when all axes are decreased, output shape is [1], instead of scalar.
             // Ref: paddle/fluid/operators/slice_op.h
             auto decreased_node = std::make_shared<default_opset::Reshape>(
-                stride_slice_node,
+                slice_node,
                 std::make_shared<default_opset::Constant>(element::i64, Shape{1}, 1),
                 false);
             return node.default_single_output_mapping({decreased_node}, {"Out"});
         }
+
         const auto squeeze_index_node =
             default_opset::Constant::create(element::i32, {decrease_axis.size()}, decrease_axis);
-        const auto decreased_node = std::make_shared<default_opset::Squeeze>(stride_slice_node, squeeze_index_node);
+        const auto decreased_node = std::make_shared<default_opset::Squeeze>(slice_node, squeeze_index_node);
         return node.default_single_output_mapping({decreased_node}, {"Out"});
+    } else {
+        return node.default_single_output_mapping({slice_node}, {"Out"});
     }
-
-    return node.default_single_output_mapping({stride_slice_node}, {"Out"});
 }
 }  // namespace
 }  // namespace op
diff --git a/src/frontends/paddle/tests/CMakeLists.txt b/src/frontends/paddle/tests/CMakeLists.txt
index 82a68ce62bc206..6d373e67c0a663 100644
--- a/src/frontends/paddle/tests/CMakeLists.txt
+++ b/src/frontends/paddle/tests/CMakeLists.txt
@@ -4,25 +4,6 @@
 
 set(TARGET_NAME "paddle_tests")
 
-ov_add_test_target(
-    NAME ${TARGET_NAME}
-        ROOT ${CMAKE_CURRENT_SOURCE_DIR}
-        DEPENDENCIES
-            paddle_test_models
-            openvino_paddle_frontend
-            paddle_fe_standalone_build_test
-        LINK_LIBRARIES
-            cnpy
-            frontend_shared_test_classes
-            openvino_paddle_frontend
-            openvino::runtime
-            gtest_main_manifest
-            func_test_utils
-        ADD_CLANG_FORMAT
-        # LABELS
-        #     OV UNIT PADDLE_FE
-)
-
 # Test model generating
 set(PADDLE_REQ "${CMAKE_CURRENT_SOURCE_DIR}/requirements.txt")
 if(Python3_Interpreter_FOUND)
@@ -43,6 +24,29 @@ endif()
 # PDPD 2.5.1 is not compatible with tests models we use
 set(paddlepaddle_FOUND OFF)
 
+if(paddlepaddle_FOUND)
+    set(ctest_labels OV UNIT)
+endif()
+
+ov_add_test_target(
+    NAME ${TARGET_NAME}
+        ROOT ${CMAKE_CURRENT_SOURCE_DIR}
+        DEPENDENCIES
+            paddle_test_models
+            openvino_paddle_frontend
+            paddle_fe_standalone_build_test
+        LINK_LIBRARIES
+            cnpy
+            frontend_shared_test_classes
+            openvino_paddle_frontend
+            openvino::runtime
+            gtest_main_manifest
+            func_test_utils
+        ADD_CLANG_FORMAT
+        LABELS
+            ${ctest_labels} PADDLE_FE
+)
+
 set(TEST_PADDLE_MODELS_DIRNAME ${TEST_MODEL_ZOO}/paddle_test_models)
 target_compile_definitions(${TARGET_NAME} PRIVATE -D TEST_PADDLE_MODELS_DIRNAME=\"${TEST_PADDLE_MODELS_DIRNAME}/\")
 
diff --git a/src/frontends/paddle/tests/test_models/gen_scripts/generate_slice.py b/src/frontends/paddle/tests/test_models/gen_scripts/generate_slice.py
index f89e18d7500c65..f2a6d1a8769295 100644
--- a/src/frontends/paddle/tests/test_models/gen_scripts/generate_slice.py
+++ b/src/frontends/paddle/tests/test_models/gen_scripts/generate_slice.py
@@ -20,7 +20,7 @@ def slice(name : str, x, axes : list, start : list, end : list):
 
     with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
         node_x = paddle.static.data(name='x', shape=x.shape, dtype = data_type)
-        out = paddle.fluid.layers.slice(node_x, axes = axes, starts = start, ends = end)
+        out = paddle.slice(node_x, axes = axes, starts = start, ends = end)
 
         cpu = paddle.static.cpu_places(1)
         exe = paddle.static.Executor(cpu[0])
diff --git a/src/frontends/pytorch/src/frontend.cpp b/src/frontends/pytorch/src/frontend.cpp
index 0910aa3e057e72..14c1094b3d098f 100644
--- a/src/frontends/pytorch/src/frontend.cpp
+++ b/src/frontends/pytorch/src/frontend.cpp
@@ -41,6 +41,7 @@
 #include "transforms/softmax_reshape_elimination.hpp"
 #include "transforms/string_equality_replacer.hpp"
 #include "transforms/tuple_unpack_replacer.hpp"
+#include "transforms/u4_block_repack.hpp"
 #include "translate_session.hpp"
 
 namespace ov {
@@ -200,6 +201,7 @@ void FrontEnd::normalize(const std::shared_ptr<ov::Model>& model) const {
     manager.register_pass<ov::frontend::pytorch::pass::IndexLoopGetitemReplacer>();
     manager.register_pass<ov::frontend::pytorch::pass::QuantizedNodeRemover>();
     manager.register_pass<ov::frontend::pytorch::pass::SoftmaxReshapeElimination>();
+    manager.register_pass<ov::frontend::pytorch::pass::U4BlockRepack>();
     manager.register_pass<ov::pass::RemoveMultiSubGraphOpDanglingParamsResults>();
     manager.register_pass<ov::pass::ReverseShapeAndTypeInfer>();
     manager.register_pass<ov::pass::ResolveNameCollisions>();
diff --git a/src/frontends/pytorch/src/op/full.cpp b/src/frontends/pytorch/src/op/full.cpp
index cf60d096555007..e8bfa1c7ce99d7 100644
--- a/src/frontends/pytorch/src/op/full.cpp
+++ b/src/frontends/pytorch/src/op/full.cpp
@@ -3,10 +3,19 @@
 //
 
 #include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/add.hpp"
 #include "openvino/op/broadcast.hpp"
 #include "openvino/op/constant.hpp"
 #include "openvino/op/convert_like.hpp"
+#include "openvino/op/divide.hpp"
+#include "openvino/op/gather.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/power.hpp"
+#include "openvino/op/range.hpp"
+#include "openvino/op/reshape.hpp"
+#include "openvino/op/scatter_elements_update.hpp"
 #include "openvino/op/shape_of.hpp"
+#include "openvino/op/squeeze.hpp"
 #include "utils.hpp"
 
 namespace ov {
@@ -71,12 +80,17 @@ OutputVector translate_full_like(const NodeContext& context) {
     return {base_translate_full_with_convertlike(context, sizes, value, out)};
 };
 
-OutputVector translate_fill_(const NodeContext& context) {
-    num_inputs_check(context, 2, 2);
+OutputVector translate_fill(const NodeContext& context) {
+    num_inputs_check(context, 2, 3);
     auto input = context.get_input(0);
     auto value = context.get_input(1);
     auto sizes = context.mark_node(std::make_shared<v3::ShapeOf>(input, element::i32));
-    return {base_translate_full_with_convertlike(context, sizes, value, input)};
+    auto out = context.input_is_none(2) ? input : context.get_input(2);
+    auto result = base_translate_full_with_convertlike(context, sizes, value, out);
+    if (!context.input_is_none(2)) {
+        context.mutate_input(2, result);
+    }
+    return {result};
 };
 
 OutputVector translate_new_full(const NodeContext& context) {
@@ -187,6 +201,67 @@ OutputVector translate_empty(const NodeContext& context) {
     }
     return {empty};
 };
+
+OutputVector translate_fill_diagonal(const NodeContext& context) {
+    // aten::fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!)
+    // realization inspired by numpy:
+    // https://github.com/numpy/numpy/blob/c236e694d222ae6b812cb8dab54471bc4c912f0f/numpy/lib/_index_tricks_impl.py#L787-L918
+    num_inputs_check(context, 3, 3);
+    auto input_tensor = context.get_input(0);
+    auto fill_value = context.get_input(1);
+    auto input_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input_tensor, element::i32));
+    auto input_rank = input_tensor.get_partial_shape().rank();
+    auto const_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {1}));
+    auto const_zero = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
+    auto const_one_s = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
+    auto const_zero_s = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
+    auto const_neg_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
+    if (input_rank.is_dynamic() || input_rank.get_length() < 2) {
+        FRONT_END_OP_CONVERSION_CHECK(false, "aten::fill_diagonal_ required tensor with static rank >= 2 ");
+    }
+    auto flatten_input = context.mark_node(std::make_shared<v1::Reshape>(input_tensor, const_neg_one, false));
+    auto wrap = context.const_input<bool>(2);
+    Output<Node> step;
+    // default value for end - number of elements in input tensor
+    Output<Node> end;
+    auto flatten_shape = context.mark_node(std::make_shared<v3::ShapeOf>(flatten_input, element::i32));
+    end = context.mark_node(std::make_shared<v8::Gather>(flatten_shape, const_neg_one, const_zero));
+    auto last_dim = context.mark_node(std::make_shared<v8::Gather>(input_shape, const_neg_one, const_zero));
+    if (input_rank.get_length() == 2) {
+        // step = a.shape[1] + 1
+        step = context.mark_node(std::make_shared<v1::Add>(last_dim, const_one_s));
+        if (!wrap) {
+            // if not wrap. and non squared matrix, do not fill tail by cutting end to square
+            end = context.mark_node(std::make_shared<v1::Multiply>(last_dim, last_dim));
+        }
+    } else {
+        // step = 1 + (cumprod(a.shape[:-1])).sum()
+        // cumprod operation is not supported by ov, but with condition that >2D tensors supported only if all dims
+        // equals cumprod can be represented as finite geometric serial and its sum can be found by formula
+        // b0 * (bn * q - 1) / (q - 1), where in this particual case q = b0, bn = b0 ^ n
+        auto rank_minus_one =
+            context.mark_node(v0::Constant::create(element::i32, Shape{}, {input_rank.get_length() - 1}));
+        auto dim_power = context.mark_node(std::make_shared<v1::Power>(last_dim, rank_minus_one));
+        auto dim_power_minus_one = context.mark_node(std::make_shared<v1::Add>(dim_power, const_neg_one));
+        auto dim_minus_one = context.mark_node(std::make_shared<v1::Add>(last_dim, const_neg_one));
+        auto q = context.mark_node(std::make_shared<v1::Divide>(dim_power_minus_one, dim_minus_one, true));
+        auto cumprod_sum = context.mark_node(std::make_shared<v1::Multiply>(last_dim, q));
+        step = context.mark_node(std::make_shared<v1::Add>(const_one_s, cumprod_sum));
+        // wrap parameter is not applicable in this case as supported only equal dims on pytorch side
+    }
+    step = context.mark_node(std::make_shared<v0::Squeeze>(step, const_zero));
+    end = context.mark_node(std::make_shared<v0::Squeeze>(end, const_zero));
+    auto indices = context.mark_node(std::make_shared<v4::Range>(const_zero_s, end, step, element::i32));
+    auto indices_shape = context.mark_node(std::make_shared<v3::ShapeOf>(indices, element::i32));
+    fill_value = context.mark_node(std::make_shared<v1::ConvertLike>(fill_value, input_tensor));
+    fill_value = context.mark_node(std::make_shared<v1::Broadcast>(fill_value, indices_shape));
+    // fill values
+    auto filled_tensor =
+        context.mark_node(std::make_shared<v12::ScatterElementsUpdate>(flatten_input, indices, fill_value, const_zero));
+    // reshape back to original shape
+    filled_tensor = context.mark_node(std::make_shared<v1::Reshape>(filled_tensor, input_shape, false));
+    return {filled_tensor};
+}
 }  // namespace op
 }  // namespace pytorch
 }  // namespace frontend
diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
index 4254ba85130afd..5614a3881c3573 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -66,7 +66,8 @@ OP_CONVERTER(translate_expand_as);
 OP_CONVERTER(translate_eye);
 OP_CONVERTER(translate_fake_quantize_per_channel_affine);
 OP_CONVERTER(translate_fake_quantize_per_tensor_affine);
-OP_CONVERTER(translate_fill_);
+OP_CONVERTER(translate_fill);
+OP_CONVERTER(translate_fill_diagonal);
 OP_CONVERTER(translate_flatten);
 OP_CONVERTER(translate_flip);
 OP_CONVERTER(translate_floor_divide);
@@ -324,7 +325,9 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::fake_quantize_per_channel_affine", op::translate_fake_quantize_per_channel_affine},
         {"aten::fake_quantize_per_tensor_affine", op::translate_fake_quantize_per_tensor_affine},
         {"aten::feature_dropout", op::skip_node},
-        {"aten::fill_", op::inplace_op<op::translate_fill_>},
+        {"aten::fill", op::translate_fill},
+        {"aten::fill_", op::inplace_op<op::translate_fill>},
+        {"aten::fill_diagonal_", op::inplace_op<op::translate_fill_diagonal>},
         {"aten::flatten", op::quantizable_op<op::translate_flatten>},
         {"aten::flip", op::translate_flip},
         {"aten::floor", op::translate_1to1_match_1_inputs<opset10::Floor>},
diff --git a/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp b/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp
index f8de5275b69ae8..67ea5f4f9e1ff9 100644
--- a/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp
+++ b/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp
@@ -12,6 +12,7 @@
 #include "openvino/pass/pattern/matcher.hpp"
 #include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "utils.hpp"
+#include "utils_quantize.hpp"
 
 namespace ov {
 namespace frontend {
@@ -38,22 +39,31 @@ AtenStackListConstructReplacer::AtenStackListConstructReplacer() {
         auto axis_node = pattern_map.at(axis).get_node_shared_ptr();
         auto axis_const = std::dynamic_pointer_cast<v0::Constant>(axis_node);
         auto axis = axis_const->cast_vector<int64_t>();
+        if (axis.size() != 1) {
+            add_exception_to_fw_node(stack, "aten::stack has multiple axes, only one is supported.");
+            return false;
+        }
         // Check if ListConstruct is an input
         if (auto list_construct_node = cast_fw_node(input_node, "prim::ListConstruct")) {
             const auto& list_inputs = list_construct_node->input_values();
-            OutputVector node_vector;
-            auto zero = v0::Constant::create(element::i32, Shape{}, {0});
-            // Iterate over values in ListConstruct
-            for (const auto& list_input : list_inputs) {
-                auto node = concat_list_construct(list_input);
-                auto unsqueezed_node = std::make_shared<v0::Unsqueeze>(node, axis_const);
-                node_vector.push_back(unsqueezed_node);
+            std::shared_ptr<Node> node;
+            if (auto compression = u4_compression_stack(list_inputs, axis[0])) {
+                node = compression;
+            } else {
+                OutputVector node_vector;
+                auto zero = v0::Constant::create(element::i32, Shape{}, {0});
+                // Iterate over values in ListConstruct
+                for (const auto& list_input : list_inputs) {
+                    auto node = concat_list_construct(list_input);
+                    auto unsqueezed_node = std::make_shared<v0::Unsqueeze>(node, axis_const);
+                    node_vector.push_back(unsqueezed_node);
+                }
+                // Concat vectors on provided axis
+                node = std::make_shared<v0::Concat>(node_vector, axis[0]);
             }
-            // Concat vectors on provided axis
-            auto concat = std::make_shared<v0::Concat>(node_vector, axis[0]);
 
-            copy_runtime_info_and_name(stack, {concat}, {input_node});
-            replace_node(stack, concat);
+            copy_runtime_info_and_name(stack, {node}, {input_node});
+            replace_node(stack, node);
             return true;
         }
         add_exception_to_fw_node(stack, "Unsupported case of aten::stack.");
diff --git a/src/frontends/pytorch/src/transforms/u4_block_repack.cpp b/src/frontends/pytorch/src/transforms/u4_block_repack.cpp
new file mode 100644
index 00000000000000..e08ebd728b050e
--- /dev/null
+++ b/src/frontends/pytorch/src/transforms/u4_block_repack.cpp
@@ -0,0 +1,98 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "u4_block_repack.hpp"
+
+#include "openvino/core/rt_info.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/reshape.hpp"
+#include "openvino/op/transpose.hpp"
+#include "openvino/pass/pattern/matcher.hpp"
+#include "openvino/pass/pattern/op/wrap_type.hpp"
+#include "utils.hpp"
+#include "utils_quantize.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace pass {
+
+using namespace ov::op;
+using namespace ov::pass::pattern;
+
+U4BlockRepack::U4BlockRepack() {
+    const auto m_constant = ov::pass::pattern::wrap_type<ov::op::v0::Constant>();
+    const auto m_reshape1 = ov::pass::pattern::wrap_type<ov::op::v1::Reshape>({m_constant, any_input()});
+    const auto m_transpose = ov::pass::pattern::wrap_type<ov::op::v1::Transpose>({m_reshape1, any_input()});
+    const auto m_reshape2 = ov::pass::pattern::wrap_type<ov::op::v1::Reshape>({m_transpose, any_input()});
+
+    auto pack_byte = [](uint8_t lo, uint8_t hi) {
+        return (hi << 4) | (lo & 0x0F);
+    };  // swap halfs because Convert op assumes this layout
+
+    auto get_u4 = [](const uint8_t* src, size_t idx) {
+        const size_t byte_idx = idx / 2;
+        const uint8_t bit_shift = 4 * (idx % 2);
+        return (src[byte_idx] >> bit_shift) & 0xF;
+    };
+
+    register_matcher(
+        std::make_shared<ov::pass::pattern::Matcher>(m_reshape2, "ov::frontend::pytorch::pass::U4BlockRepack"),
+        [=](ov::pass::pattern::Matcher& m) {
+            auto& pattern_to_output = m.get_pattern_value_map();
+            auto constant =
+                std::dynamic_pointer_cast<ov::op::v0::Constant>(pattern_to_output[m_constant].get_node_shared_ptr());
+            if (!constant)
+                return false;
+            auto reshape1 = pattern_to_output[m_reshape1].get_node_shared_ptr();
+            auto transpose = pattern_to_output[m_transpose].get_node_shared_ptr();
+            auto reshape2 = pattern_to_output[m_reshape2].get_node_shared_ptr();
+
+            if (constant->get_element_type() != element::u4)
+                return false;
+
+            // FIXME: Check reshape/transpose/reshape target shapes and axes permutation; now they are supposed to be
+            // always in expected form
+
+            auto source_shape = reshape1->get_output_shape(0);
+
+            if (source_shape.size() != 3)
+                return false;
+
+            auto destination_shape = reshape2->get_output_shape(0);
+
+            size_t n_blocks = source_shape[0];
+            size_t block_height = source_shape[1];
+            size_t lane_size = source_shape[2];                // size in u4 units
+            size_t block_size = block_height * lane_size / 2;  // size in bytes
+
+            auto src = constant->get_data_ptr<uint8_t>();
+
+            auto new_const = std::make_shared<v0::Constant>(element::u4, destination_shape);
+            auto dst = const_cast<uint8_t*>(                                   // const_cast?
+                reinterpret_cast<const uint8_t*>(new_const->get_data_ptr()));  // TODO: How to better accees u4 data?
+
+            for (size_t iblock = 0; iblock < n_blocks; ++iblock) {
+                auto src_block = src + iblock * block_size;
+                auto dst_block = dst + iblock * block_size;
+                for (size_t i = 0; i < lane_size; ++i) {
+                    for (size_t j = 0; j < block_height / 2; ++j) {  // /2 because we handle two bytes at once
+                        uint8_t lo = get_u4(src_block, 2 * j * lane_size + i);
+                        uint8_t hi = get_u4(src_block, (2 * j + 1) * lane_size + i);
+                        dst_block[i * block_height / 2 + j] = pack_byte(lo, hi);
+                    }
+                }
+            }
+
+            copy_runtime_info(NodeVector{constant, reshape1, transpose, reshape2}, new_const);
+            replace_node(reshape2, new_const);
+
+            return true;
+        });
+};
+
+}  // namespace pass
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/pytorch/src/transforms/u4_block_repack.hpp b/src/frontends/pytorch/src/transforms/u4_block_repack.hpp
new file mode 100644
index 00000000000000..aa6e00f70e564c
--- /dev/null
+++ b/src/frontends/pytorch/src/transforms/u4_block_repack.hpp
@@ -0,0 +1,24 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/pass/graph_rewrite.hpp"
+#include "openvino/pass/pass.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace pass {
+
+class U4BlockRepack : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("ov::frontend::pytorch::pass::U4BlockRepack");
+    U4BlockRepack();
+};
+
+}  // namespace pass
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/pytorch/src/utils_quantize.cpp b/src/frontends/pytorch/src/utils_quantize.cpp
index 5af546f3d5be5d..1346fd76971fcc 100644
--- a/src/frontends/pytorch/src/utils_quantize.cpp
+++ b/src/frontends/pytorch/src/utils_quantize.cpp
@@ -6,6 +6,7 @@
 
 #include "openvino/frontend/pytorch/node_context.hpp"
 #include "openvino/op/broadcast.hpp"
+#include "openvino/op/constant.hpp"
 #include "openvino/op/convert.hpp"
 #include "openvino/op/convert_like.hpp"
 #include "openvino/op/fake_quantize.hpp"
@@ -13,6 +14,7 @@
 #include "openvino/op/reshape.hpp"
 #include "openvino/op/scatter_elements_update.hpp"
 #include "openvino/op/subtract.hpp"
+#include "transformations/utils/utils.hpp"
 
 namespace ov {
 namespace frontend {
@@ -168,6 +170,53 @@ std::shared_ptr<QuantizedPtNode> cast_quantized_fw_node(std::shared_ptr<Node> no
     return quant_node;
 }
 
+std::shared_ptr<Node> u4_compression_stack(const OutputVector& list_elems, int64_t axis) {
+    // Part 1: Detect pattern
+
+    if (list_elems.size() != 2)
+        return nullptr;
+    auto bitwise_and = cast_fw_node(list_elems[0].get_node_shared_ptr(), "aten::bitwise_and");
+    if (!bitwise_and)
+        return nullptr;
+    auto bitwise_shift = cast_fw_node(list_elems[1].get_node_shared_ptr(), "aten::bitwise_right_shift");
+    if (!bitwise_shift)
+        return nullptr;
+
+    auto weights_u8 = std::dynamic_pointer_cast<v0::Constant>(bitwise_and->get_input_node_shared_ptr(0));
+    if (weights_u8 != std::dynamic_pointer_cast<v0::Constant>(bitwise_shift->get_input_node_shared_ptr(0)))
+        return nullptr;
+
+    if (weights_u8->get_output_element_type(0) != element::u8)
+        return nullptr;
+
+    if (axis != -1 && static_cast<uint64_t>(axis) != weights_u8->get_shape().size() - 1)
+        return nullptr;
+
+    if (!ov::op::util::has_constant_value<uint64_t>(bitwise_and->get_input_node_shared_ptr(1), 0x0F))
+        return nullptr;
+
+    if (!ov::op::util::has_constant_value<uint64_t>(bitwise_shift->get_input_node_shared_ptr(1), 4))
+        return nullptr;
+
+    // Pattern detected, weights_u8 is target u8 packed constant with weights
+
+    // Part 2: Form u4 constant by repacking of the original weights_u8
+    // Repacking transformes half of lanes to interleaved representation.
+
+    auto u8_shape = weights_u8->get_shape();
+    size_t full_size = shape_size(u8_shape);
+    auto src = weights_u8->get_data_ptr<uint8_t>();
+
+    auto u4_shape = u8_shape;
+    u4_shape.push_back(2);
+    auto new_const = std::make_shared<v0::Constant>(element::u4, u4_shape);
+    auto dst = const_cast<uint8_t*>(reinterpret_cast<const uint8_t*>(new_const->get_data_ptr()));
+
+    std::copy(src, src + full_size, dst);  // TODO: Avoid copying, reuse the same constant
+    copy_runtime_info_and_name(weights_u8, {new_const}, {weights_u8, bitwise_and, bitwise_shift});
+    return new_const;
+}
+
 }  // namespace pytorch
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/pytorch/src/utils_quantize.hpp b/src/frontends/pytorch/src/utils_quantize.hpp
index 69917e7b8bce3e..e02bce880d2480 100644
--- a/src/frontends/pytorch/src/utils_quantize.hpp
+++ b/src/frontends/pytorch/src/utils_quantize.hpp
@@ -166,6 +166,12 @@ OutputVector quantizable_op(const NodeContext& context) {
 }
 }  // namespace op
 
+/**
+ * Captures aten::stack([aten::bitwise_and(Constant(u8)), aten::bitwise_right_shift(Constant(u8))], dim=-1).
+ * This pattern is transformed to a single Constant with element_type=u4.
+ */
+std::shared_ptr<Node> u4_compression_stack(const OutputVector& list_elems, int64_t axis);
+
 }  // namespace pytorch
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/tensorflow/src/op_table.cpp b/src/frontends/tensorflow/src/op_table.cpp
index fce3af3f0a235b..bc1a657faf54fb 100644
--- a/src/frontends/tensorflow/src/op_table.cpp
+++ b/src/frontends/tensorflow/src/op_table.cpp
@@ -130,6 +130,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"AvgPool3D", CreatorFunction(translate_avg_pool_op)},
         {"BatchMatMul", CreatorFunction(translate_batch_mat_mul_op)},
         {"BatchMatMulV2", CreatorFunction(translate_batch_mat_mul_op)},
+        {"BatchMatMulV3", CreatorFunction(translate_batch_mat_mul_with_type_op)},
         {"BatchToSpaceND", CreatorFunction(translate_batch_to_space_nd_op)},
         {"BroadcastArgs", CreatorFunction(translate_broadcast_args_op)},
         {"BroadcastTo", CreatorFunction(translate_broadcast_to_op)},
@@ -286,6 +287,8 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"While", CreatorFunction(translate_while_op)},
         {"Where", CreatorFunction(translate_where_op)},
         {"Xdivy", CreatorFunction(translate_x_div_y_op)},
+        {"Xlog1py", CreatorFunction(translate_xlog1py_op)},
+        {"Xlogy", CreatorFunction(translate_xlogy_op)},
         {"ZerosLike", CreatorFunction(translate_zeros_like_op)},
 
         // Translators for SavedModel and MetaGraph
diff --git a/src/frontends/tensorflow/tests/CMakeLists.txt b/src/frontends/tensorflow/tests/CMakeLists.txt
index 45e64536b60f23..a532441ac393eb 100644
--- a/src/frontends/tensorflow/tests/CMakeLists.txt
+++ b/src/frontends/tensorflow/tests/CMakeLists.txt
@@ -10,6 +10,15 @@ if (NOT ENABLE_INTEL_CPU)
     set(EXCLUDED_TESTS ${CMAKE_CURRENT_SOURCE_DIR}/compilation.cpp)
 endif()
 
+ov_check_pip_packages(REQUIREMENTS_FILE "${CMAKE_CURRENT_SOURCE_DIR}/requirements.txt"
+                      MESSAGE_MODE WARNING
+                      WARNING_MESSAGE "TensorFlow testing models weren't generated, some tests will fail due models not found"
+                      RESULT_VAR tensorflow_FOUND)
+
+if(tensorflow_FOUND)
+    set(ctest_labels OV UNIT)
+endif()
+
 ov_add_test_target(
     NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
@@ -22,16 +31,11 @@ ov_add_test_target(
             openvino_tensorflow_common
         ADD_CLANG_FORMAT
         LABELS
-            OV UNIT TF_FE
+            ${ctest_labels} TF_FE
 )
 
 # Test model generating
 
-ov_check_pip_packages(REQUIREMENTS_FILE "${CMAKE_CURRENT_SOURCE_DIR}/requirements.txt"
-                      MESSAGE_MODE WARNING
-                      WARNING_MESSAGE "TensorFlow testing models weren't generated, some tests will fail due models not found"
-                      RESULT_VAR tensorflow_FOUND)
-
 set(TEST_TENSORFLOW_MODELS_DIRNAME ${TEST_MODEL_ZOO}/tensorflow_test_models)
 target_compile_definitions(${TARGET_NAME} PRIVATE -D TEST_TENSORFLOW_MODELS_DIRNAME=\"${TEST_TENSORFLOW_MODELS_DIRNAME}/\")
 
diff --git a/src/frontends/tensorflow_common/include/common_op_table.hpp b/src/frontends/tensorflow_common/include/common_op_table.hpp
index 17a865acfb0e99..54f1dff243efd1 100644
--- a/src/frontends/tensorflow_common/include/common_op_table.hpp
+++ b/src/frontends/tensorflow_common/include/common_op_table.hpp
@@ -38,6 +38,7 @@ OP_CONVERTER(translate_arg_max_op);
 OP_CONVERTER(translate_arg_min_op);
 OP_CONVERTER(translate_avg_pool_op);
 OP_CONVERTER(translate_batch_mat_mul_op);
+OP_CONVERTER(translate_batch_mat_mul_with_type_op);
 OP_CONVERTER(translate_batch_to_space_nd_op);
 OP_CONVERTER(translate_bias_add_op);
 OP_CONVERTER(translate_broadcast_args_op);
@@ -149,6 +150,8 @@ OP_CONVERTER(translate_unravel_index_op);
 OP_CONVERTER(translate_unsorted_segment_sum_op);
 OP_CONVERTER(translate_where_op);
 OP_CONVERTER(translate_x_div_y_op);
+OP_CONVERTER(translate_xlog1py_op);
+OP_CONVERTER(translate_xlogy_op);
 OP_CONVERTER(translate_zeros_like_op);
 
 // Translators for internal operations
diff --git a/src/frontends/tensorflow_common/src/op/matmul.cpp b/src/frontends/tensorflow_common/src/op/matmul.cpp
index dd3aba71ddf5cd..21a0591d109b69 100644
--- a/src/frontends/tensorflow_common/src/op/matmul.cpp
+++ b/src/frontends/tensorflow_common/src/op/matmul.cpp
@@ -35,6 +35,26 @@ OutputVector translate_batch_mat_mul_op(const NodeContext& node) {
     set_node_name(node.get_name(), result);
     return result->outputs();
 }
+
+OutputVector translate_batch_mat_mul_with_type_op(const NodeContext& node) {
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+
+    auto input_type = x.get_element_type();
+
+    auto adj_x = node.get_attribute<bool>("adj_x", false);
+    auto adj_y = node.get_attribute<bool>("adj_y", false);
+    auto t_out = node.get_attribute<ov::element::Type>("Tout", input_type);
+
+    auto result = make_shared<MatMul>(x, y, adj_x, adj_y)->output(0);
+
+    if (t_out != input_type) {
+        result = make_shared<Convert>(result, t_out);
+    }
+
+    set_node_name(node.get_name(), result.get_node_shared_ptr());
+    return {result};
+}
 }  // namespace op
 }  // namespace tensorflow
 }  // namespace frontend
diff --git a/src/frontends/tensorflow_common/src/op/xlog1py.cpp b/src/frontends/tensorflow_common/src/op/xlog1py.cpp
new file mode 100644
index 00000000000000..cf24fec54382d3
--- /dev/null
+++ b/src/frontends/tensorflow_common/src/op/xlog1py.cpp
@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_op_table.hpp"
+#include "openvino/op/add.hpp"
+#include "openvino/op/equal.hpp"
+#include "openvino/op/log.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/select.hpp"
+
+using namespace std;
+using namespace ov::opset10;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+OutputVector translate_xlog1py_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"Xlog1py"});
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+
+    // prepare auxiliary constants of the same type as the input
+    auto zero = create_same_type_const_scalar<int32_t>(x, 0);
+    auto one = create_same_type_const_scalar<int32_t>(y, 1);
+
+    // compute a mask to identify where x is equal to 0
+    auto is_zero = make_shared<Equal>(x, zero);
+
+    // compute x * log(y + 1) elementwise
+    auto xlog1py = make_shared<Multiply>(x, make_shared<Log>(make_shared<Add>(y, one)));
+
+    // create the output tensor using Select to handle the x == 0 condition
+    auto result = make_shared<Select>(is_zero, zero, xlog1py);
+
+    set_node_name(node.get_name(), result);
+    return result->outputs();
+}
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/tensorflow_common/src/op/xlogy.cpp b/src/frontends/tensorflow_common/src/op/xlogy.cpp
new file mode 100644
index 00000000000000..8b4f9da063a27e
--- /dev/null
+++ b/src/frontends/tensorflow_common/src/op/xlogy.cpp
@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_op_table.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/convert_like.hpp"
+#include "openvino/op/equal.hpp"
+#include "openvino/op/log.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/select.hpp"
+
+using namespace std;
+using namespace ov::opset10;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+OutputVector translate_xlogy_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"Xlogy"});
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+
+    // prepare auxiliary zero constant of the same type as the input
+    auto zero = create_same_type_const_scalar<int32_t>(x, 0);
+
+    // compute a mask to identify where x is equal to 0
+    auto is_zero = make_shared<Equal>(x, zero);
+
+    // compute x * log(y) elementwise
+    auto xlog_y = make_shared<Multiply>(x, make_shared<Log>(y));
+
+    // create the output tensor using Select to handle the x == 0 condition
+    auto result = make_shared<Select>(is_zero, zero, xlog_y);
+
+    set_node_name(node.get_name(), result);
+    return result->outputs();
+}
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/tensorflow_lite/tests/CMakeLists.txt b/src/frontends/tensorflow_lite/tests/CMakeLists.txt
index ddb2fba4d9c429..ef6ca4ee670732 100644
--- a/src/frontends/tensorflow_lite/tests/CMakeLists.txt
+++ b/src/frontends/tensorflow_lite/tests/CMakeLists.txt
@@ -4,6 +4,15 @@
 
 set(TARGET_NAME "ov_tensorflow_lite_frontend_tests")
 
+ov_check_pip_packages(REQUIREMENTS_FILE "${CMAKE_CURRENT_SOURCE_DIR}/requirements.txt"
+                      MESSAGE_MODE WARNING
+                      WARNING_MESSAGE "TensorFlow Lite testing models weren't generated, some tests will fail due models not found"
+                      RESULT_VAR tensorflow_FOUND)
+
+if(tensorflow_FOUND)
+    set(ctest_labels OV UNIT)
+endif()
+
 ov_add_test_target(
     NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
@@ -16,16 +25,11 @@ ov_add_test_target(
             openvino_tensorflow_lite_frontend
         ADD_CLANG_FORMAT
         LABELS
-            OV UNIT TFL_FE
+            ${ctest_labels} TFL_FE
 )
 
 # Test model generating
 
-ov_check_pip_packages(REQUIREMENTS_FILE "${CMAKE_CURRENT_SOURCE_DIR}/requirements.txt"
-                      MESSAGE_MODE WARNING
-                      WARNING_MESSAGE "TensorFlow Lite testing models weren't generated, some tests will fail due models not found"
-                      RESULT_VAR tensorflow_FOUND)
-
 set(TEST_TENSORFLOW_LITE_MODELS_DIRNAME ${TEST_MODEL_ZOO}/tensorflow_lite_test_models)
 target_compile_definitions(${TARGET_NAME} PRIVATE -D TEST_TENSORFLOW_LITE_MODELS_DIRNAME=\"${TEST_TENSORFLOW_LITE_MODELS_DIRNAME}/\")
 
diff --git a/src/frontends/tests/frontend/shared/CMakeLists.txt b/src/frontends/tests/frontend/shared/CMakeLists.txt
index f5d2809205db0e..f413e359afb738 100644
--- a/src/frontends/tests/frontend/shared/CMakeLists.txt
+++ b/src/frontends/tests/frontend/shared/CMakeLists.txt
@@ -17,8 +17,10 @@ target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/../
 
 target_link_libraries(${TARGET_NAME}
         PUBLIC
-                offline_transformations
-                common_test_utils func_test_utils openvino::util
+                openvino::offline_transformations
+                common_test_utils
+                func_test_utils
+                openvino::util
                 openvino::runtime
         PRIVATE
                 cnpy)
diff --git a/src/inference/CMakeLists.txt b/src/inference/CMakeLists.txt
index 3e61c370d20482..11ad85b3740d6a 100644
--- a/src/inference/CMakeLists.txt
+++ b/src/inference/CMakeLists.txt
@@ -199,8 +199,8 @@ if(WIN32)
     set_target_properties(${TARGET_NAME}_s PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}_s)
 endif()
 
-target_link_libraries(${TARGET_NAME}_s PRIVATE openvino::itt ${CMAKE_DL_LIBS} ngraph
-    frontend_common::static inference_engine_transformations openvino::pugixml)
+target_link_libraries(${TARGET_NAME}_s PRIVATE openvino::itt ${CMAKE_DL_LIBS}
+    openvino::runtime::dev openvino::pugixml)
 
 target_compile_definitions(${TARGET_NAME}_s PUBLIC USE_STATIC_IE)
 
@@ -215,7 +215,8 @@ set_target_properties(${TARGET_NAME}_obj
 
 # Export for developer package
 
-openvino_developer_export_targets(COMPONENT core_legacy TARGETS ${TARGET_NAME}_plugin_api)
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}_s)
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}_plugin_api)
 
 # Install static libraries for case BUILD_SHARED_LIBS=OFF
 
diff --git a/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp b/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp
index 7936130997b6a2..d8f65b086fcbe0 100644
--- a/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp
+++ b/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp
@@ -131,23 +131,25 @@ void reserve_cpu_by_streams_info(const std::vector<std::vector<int>> _streams_in
     }
 
     for (size_t i = 0; i < _cpu_mapping_table.size(); i++) {
-        std::string cpu_string = std::to_string(_cpu_mapping_table[i][CPU_MAP_CORE_TYPE]) +
-                                 std::to_string(_cpu_mapping_table[i][CPU_MAP_NUMA_NODE_ID]) +
-                                 std::to_string(_cpu_mapping_table[i][CPU_MAP_SOCKET_ID]);
-        for (size_t j = 0; j < stream_conditions.size(); j++) {
-            if (std::find(stream_conditions[j].begin(), stream_conditions[j].end(), cpu_string) !=
-                stream_conditions[j].end()) {
-                _stream_processors[stream_pos[j]].push_back(_cpu_mapping_table[i][CPU_MAP_PROCESSOR_ID]);
-                _cpu_mapping_table[i][CPU_MAP_USED_FLAG] = _cpu_status;
-                if (static_cast<int>(_stream_processors[stream_pos[j]].size()) ==
-                    streams_table[j][THREADS_PER_STREAM]) {
-                    stream_pos[j]++;
-                    stream_num[j]++;
-                }
-                if (stream_num[j] >= streams_table[j][NUMBER_OF_STREAMS]) {
-                    stream_conditions[j].clear();
+        if (_cpu_mapping_table[i][CPU_MAP_USED_FLAG] == NOT_USED) {
+            std::string cpu_string = std::to_string(_cpu_mapping_table[i][CPU_MAP_CORE_TYPE]) +
+                                     std::to_string(_cpu_mapping_table[i][CPU_MAP_NUMA_NODE_ID]) +
+                                     std::to_string(_cpu_mapping_table[i][CPU_MAP_SOCKET_ID]);
+            for (size_t j = 0; j < stream_conditions.size(); j++) {
+                if (std::find(stream_conditions[j].begin(), stream_conditions[j].end(), cpu_string) !=
+                    stream_conditions[j].end()) {
+                    _stream_processors[stream_pos[j]].push_back(_cpu_mapping_table[i][CPU_MAP_PROCESSOR_ID]);
+                    _cpu_mapping_table[i][CPU_MAP_USED_FLAG] = _cpu_status;
+                    if (static_cast<int>(_stream_processors[stream_pos[j]].size()) ==
+                        streams_table[j][THREADS_PER_STREAM]) {
+                        stream_pos[j]++;
+                        stream_num[j]++;
+                    }
+                    if (stream_num[j] >= streams_table[j][NUMBER_OF_STREAMS]) {
+                        stream_conditions[j].clear();
+                    }
+                    break;
                 }
-                break;
             }
         }
     }
diff --git a/src/inference/tests/unit/cpu_reserve_test.cpp b/src/inference/tests/unit/cpu_reserve_test.cpp
index 7a5427d777bea7..e5fe6b40abdf7b 100644
--- a/src/inference/tests/unit/cpu_reserve_test.cpp
+++ b/src/inference/tests/unit/cpu_reserve_test.cpp
@@ -44,6 +44,12 @@ class LinuxCpuReserveTests : public ov::test::TestsCommon,
                                                    test_data._cpu_status);
 
         ASSERT_EQ(test_data._stream_processors, test_processors);
+        for (size_t i = 0; i < test_processors.size(); i++) {
+            for (size_t j = 0; j < test_processors[i].size(); j++) {
+                ASSERT_EQ(test_data._cpu_mapping_table[test_processors[i][j]][CPU_MAP_USED_FLAG],
+                          test_data._cpu_status);
+            }
+        }
     }
 };
 
@@ -869,8 +875,6 @@ LinuxCpuReserveTestCase _1socket_16cores_hyper_4streams_ecoreonly = {
     },
     NOT_USED,
 };
-// streams_info_table={1, MAIN_CORE_PROC, 36}, but the number of physical cores is 18,
-// in this case, threads are assigned on physical and logical cores.
 LinuxCpuReserveTestCase _1socket_18cores_hyper_1streams = {
     36,
     1,
@@ -933,6 +937,38 @@ LinuxCpuReserveTestCase _1socket_18cores_hyper_2streams = {
     },
     NOT_USED,
 };
+// other plugin reserved 2 threads
+LinuxCpuReserveTestCase _1socket_18cores_hyper_plugin_reserve_2threads = {
+    36,
+    1,
+    {{36, 18, 0, 18, 0, 0}},
+    {
+        {0, 0, 0, 0, HYPER_THREADING_PROC, 0, -1},  {1, 0, 0, 0, HYPER_THREADING_PROC, 0, -1},
+        {2, 0, 0, 1, HYPER_THREADING_PROC, 1, -1},  {3, 0, 0, 1, HYPER_THREADING_PROC, 1, -1},
+        {4, 0, 0, 2, HYPER_THREADING_PROC, 2, -1},  {5, 0, 0, 2, HYPER_THREADING_PROC, 2, -1},
+        {6, 0, 0, 3, HYPER_THREADING_PROC, 3, -1},  {7, 0, 0, 3, HYPER_THREADING_PROC, 3, -1},
+        {8, 0, 0, 4, HYPER_THREADING_PROC, 4, -1},  {9, 0, 0, 4, HYPER_THREADING_PROC, 4, -1},
+        {10, 0, 0, 5, HYPER_THREADING_PROC, 5, -1}, {11, 0, 0, 5, HYPER_THREADING_PROC, 5, -1},
+        {12, 0, 0, 6, HYPER_THREADING_PROC, 6, -1}, {13, 0, 0, 6, HYPER_THREADING_PROC, 6, -1},
+        {14, 0, 0, 7, HYPER_THREADING_PROC, 7, -1}, {15, 0, 0, 7, HYPER_THREADING_PROC, 7, -1},
+        {16, 0, 0, 8, HYPER_THREADING_PROC, 8, -1}, {17, 0, 0, 9, HYPER_THREADING_PROC, 8, -1},
+        {18, 0, 0, 0, MAIN_CORE_PROC, 0, CPU_USED}, {19, 0, 0, 1, MAIN_CORE_PROC, 1, CPU_USED},
+        {20, 0, 0, 2, MAIN_CORE_PROC, 2, -1},       {21, 0, 0, 3, MAIN_CORE_PROC, 3, -1},
+        {22, 0, 0, 4, MAIN_CORE_PROC, 4, -1},       {23, 0, 0, 5, MAIN_CORE_PROC, 5, -1},
+        {24, 0, 0, 6, MAIN_CORE_PROC, 6, -1},       {25, 0, 0, 7, MAIN_CORE_PROC, 7, -1},
+        {26, 0, 0, 8, MAIN_CORE_PROC, 8, -1},       {27, 0, 0, 9, MAIN_CORE_PROC, 9, -1},
+        {28, 0, 0, 10, MAIN_CORE_PROC, 10, -1},     {29, 0, 0, 11, MAIN_CORE_PROC, 11, -1},
+        {30, 0, 0, 12, MAIN_CORE_PROC, 12, -1},     {31, 0, 0, 13, MAIN_CORE_PROC, 13, -1},
+        {32, 0, 0, 14, MAIN_CORE_PROC, 14, -1},     {33, 0, 0, 15, MAIN_CORE_PROC, 15, -1},
+        {34, 0, 0, 16, MAIN_CORE_PROC, 16, -1},     {35, 0, 0, 17, MAIN_CORE_PROC, 17, -1},
+    },
+    {{16, MAIN_CORE_PROC, 1, 0, 0}, {16, HYPER_THREADING_PROC, 1, 0, 0}},
+    {
+        {20}, {21}, {22}, {23}, {24}, {25}, {26}, {27}, {28}, {29}, {30}, {31}, {32}, {33}, {34}, {35},
+        {0},  {1},  {2},  {3},  {4},  {5},  {6},  {7},  {8},  {9},  {10}, {11}, {12}, {13}, {14}, {15},
+    },
+    NOT_USED,
+};
 LinuxCpuReserveTestCase _1socket_32cores_hyper_1streams = {
     32,
     1,
@@ -986,6 +1022,7 @@ INSTANTIATE_TEST_SUITE_P(CPUReserve,
                                          _1socket_16cores_hyper_4streams_ecoreonly,
                                          _1socket_18cores_hyper_1streams,
                                          _1socket_18cores_hyper_2streams,
+                                         _1socket_18cores_hyper_plugin_reserve_2threads,
                                          _1socket_32cores_hyper_1streams));
 #endif
 }  // namespace
diff --git a/src/plugins/auto/tests/functional/CMakeLists.txt b/src/plugins/auto/tests/functional/CMakeLists.txt
index cd239db8806120..b15afe68b96660 100644
--- a/src/plugins/auto/tests/functional/CMakeLists.txt
+++ b/src/plugins/auto/tests/functional/CMakeLists.txt
@@ -31,4 +31,4 @@ ov_add_test_target(
 )
 
 target_compile_definitions(${TARGET_NAME} PRIVATE ${COMPILE_DEFINITIONS})
-set_ie_threading_interface_for(${TARGET_NAME})
\ No newline at end of file
+ov_set_threading_interface_for(${TARGET_NAME})
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/CMakeLists.txt b/src/plugins/intel_cpu/CMakeLists.txt
index 10b82b2b75ffcb..8276d5a3188970 100644
--- a/src/plugins/intel_cpu/CMakeLists.txt
+++ b/src/plugins/intel_cpu/CMakeLists.txt
@@ -27,7 +27,7 @@ elseif(AARCH64)
     if(APPLE)
         # Apple M1 / M2 is assumed
         set(OV_CPU_ARM_TARGET_ARCH_DEFAULT arm64-v8.2-a)
-        add_definitions(-DOV_CPU_ARM_ENABLE_FP16)
+        add_compile_definitions(OV_CPU_ARM_ENABLE_FP16)
     else()
         set(OV_CPU_ARM_TARGET_ARCH_DEFAULT arm64-v8a)
     endif()
diff --git a/src/plugins/intel_cpu/src/config.cpp b/src/plugins/intel_cpu/src/config.cpp
index 277bdb412563e7..90e65a15495719 100644
--- a/src/plugins/intel_cpu/src/config.cpp
+++ b/src/plugins/intel_cpu/src/config.cpp
@@ -255,10 +255,16 @@ void Config::readProperties(const std::map<std::string, std::string> &prop, cons
     // when both execution_mode and inference_precision are specified
     if (!inferencePrecisionSetExplicitly) {
         if (executionMode == ov::hint::ExecutionMode::PERFORMANCE) {
+            inferencePrecision = ov::element::f32;
+#if defined(OV_CPU_ARM_ENABLE_FP16)
+            //fp16 precision is used as default precision on ARM for non-convolution networks
+            //fp16 ACL convolution is slower than fp32
+            if (modelType != ModelType::CNN)
+                inferencePrecision = ov::element::f16;
+#else
             if (mayiuse(avx512_core_bf16))
                 inferencePrecision = ov::element::bf16;
-            else
-                inferencePrecision = ov::element::f32;
+#endif
         } else {
             inferencePrecision = ov::element::f32;
         }
diff --git a/src/plugins/intel_cpu/src/cpu_types.cpp b/src/plugins/intel_cpu/src/cpu_types.cpp
index 03fbe1a9923b7a..6f5a84701b184d 100644
--- a/src/plugins/intel_cpu/src/cpu_types.cpp
+++ b/src/plugins/intel_cpu/src/cpu_types.cpp
@@ -123,6 +123,7 @@ static const TypeToNameMap& get_type_to_name_tbl() {
             { "ScatterElementsUpdate", Type::ScatterElementsUpdate},
             { "ScatterNDUpdate", Type::ScatterNDUpdate},
             { "Interpolate", Type::Interpolate},
+            { "RandomUniform", Type::RandomUniform},
             { "ReduceL1", Type::Reduce},
             { "ReduceL2", Type::Reduce},
             { "ReduceLogicalAnd", Type::Reduce},
@@ -317,6 +318,7 @@ std::string NameFromType(const Type type) {
         CASE(PriorBox);
         CASE(PriorBoxClustered)
         CASE(MHA);
+        CASE(RandomUniform);
         CASE(Unique);
         CASE(Ngram);
         CASE(Unknown);
diff --git a/src/plugins/intel_cpu/src/cpu_types.h b/src/plugins/intel_cpu/src/cpu_types.h
index 403ed62d482f8b..9afbe2d7485ddd 100644
--- a/src/plugins/intel_cpu/src/cpu_types.h
+++ b/src/plugins/intel_cpu/src/cpu_types.h
@@ -110,6 +110,7 @@ enum class Type {
     PriorBoxClustered,
     Interaction,
     MHA,
+    RandomUniform,
     Unique,
     Ngram
 };
diff --git a/src/plugins/intel_cpu/src/graph.cpp b/src/plugins/intel_cpu/src/graph.cpp
index ed18c01c848af1..5275259886ed91 100644
--- a/src/plugins/intel_cpu/src/graph.cpp
+++ b/src/plugins/intel_cpu/src/graph.cpp
@@ -1709,7 +1709,10 @@ void Graph::EnforceInferencePrecision() {
 
     if (inferPrec == Precision::FP32)
         return; // nothing to do, only precision reduction is currently allowed
-
+#if defined(OV_CPU_ARM_ENABLE_FP16)
+    if (inferPrec == Precision::FP16)
+        return; // precision of configured by ov::pass::ConvertPrecision
+#endif
     std::function<void(const NodePtr&, std::unordered_set<NodePtr>& skipNodes)> searchForNodesToSkip;
     searchForNodesToSkip = [&](const NodePtr& node, std::unordered_set<NodePtr>& skipNodes) -> void {
         for (size_t i = 0; i < node->getParentEdges().size(); i++) {
diff --git a/src/plugins/intel_cpu/src/node.cpp b/src/plugins/intel_cpu/src/node.cpp
index e8fe6b89a00afc..ab02ae44dd6ce2 100644
--- a/src/plugins/intel_cpu/src/node.cpp
+++ b/src/plugins/intel_cpu/src/node.cpp
@@ -95,8 +95,6 @@ Node::Node(const std::shared_ptr<ngraph::Node>& op,
       typeStr(op->get_type_name()),
       type(TypeFromName(op->get_type_name())),
       profiling(op->get_friendly_name()) {
-    const std::string errorPrefix = "Ngraph operation " + std::string(op->get_type_name()) + " with name " + op->get_friendly_name();
-
     for (size_t i = 0; i < op->get_input_size(); i++) {
         const auto &shape = op->get_input_partial_shape(i);
         if (shape.rank().is_dynamic()) {
@@ -480,6 +478,8 @@ std::string Node::getPrimitiveDescriptorType() const {
     SEARCH_TYPE(_dw);
     SEARCH_TYPE(_1x1);
 
+#undef SEARCH_TYPE
+
     if (type == impl_desc_type::unknown)
         str_type = "unknown";
     else if (str_type.empty())
diff --git a/src/plugins/intel_cpu/src/node.h b/src/plugins/intel_cpu/src/node.h
index 5becbfa9863f70..864c08a95b04c6 100644
--- a/src/plugins/intel_cpu/src/node.h
+++ b/src/plugins/intel_cpu/src/node.h
@@ -41,6 +41,8 @@
 #include "nodes/executors/mvn_list.hpp"
 #include "nodes/executors/executor.hpp"
 
+#define THROW_CPU_NODE_ERR(...) OPENVINO_THROW(getTypeStr(), " node with name '", getName(), "' ", __VA_ARGS__)
+
 namespace ov {
 namespace intel_cpu {
 
@@ -353,7 +355,7 @@ class Node {
         inplace = InPlaceType::Unknown;
     }
 
-    std::string getPrimitiveDescriptorType() const;
+    virtual std::string getPrimitiveDescriptorType() const;
 
     PerfCount &PerfCounter() { return perfCounter; }
 
diff --git a/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder.cpp b/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder.cpp
index 494fb6176dd65e..0b2ee382e2a2a9 100644
--- a/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder.cpp
+++ b/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder.cpp
@@ -56,11 +56,11 @@ void CTCGreedyDecoder::initSupportedPrimitiveDescriptors() {
         return;
 
     Precision inDataPrecision = getOriginalInputPrecisionAtPort(DATA_INDEX);
-    if (inDataPrecision != Precision::FP32 && inDataPrecision != Precision::BF16)
+    if (!one_of(inDataPrecision, Precision::FP32, Precision::BF16, Precision::FP16))
         IE_THROW() << errorPrefix << "has unsupported 'data' input precision: " << inDataPrecision;
 
     Precision seqLenPrecision = getOriginalInputPrecisionAtPort(SEQUENCE_LENGTH_INDEX);
-    if (seqLenPrecision != Precision::FP32 && seqLenPrecision != Precision::BF16)
+    if (!one_of(inDataPrecision, Precision::FP32, Precision::BF16, Precision::FP16))
         IE_THROW() << errorPrefix << "has unsupported 'sequence_length' input precision: " << seqLenPrecision;
 
     addSupportedPrimDesc({{LayoutType::ncsp, Precision::FP32},
diff --git a/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder_seq_len.cpp b/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder_seq_len.cpp
index be695d85b8c6f2..8e6e0617cd75f2 100644
--- a/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder_seq_len.cpp
+++ b/src/plugins/intel_cpu/src/nodes/ctc_greedy_decoder_seq_len.cpp
@@ -55,7 +55,7 @@ void CTCGreedyDecoderSeqLen::initSupportedPrimitiveDescriptors() {
         return;
 
     Precision inDataPrecision = getOriginalInputPrecisionAtPort(DATA_INDEX);
-    if (inDataPrecision != Precision::FP32 && inDataPrecision != Precision::BF16)
+    if (!one_of(inDataPrecision, Precision::FP32, Precision::BF16, Precision::FP16))
         IE_THROW() << errorPrefix << "has unsupported 'data' input precision: " << inDataPrecision;
 
     Precision seqLenPrecision = getOriginalInputPrecisionAtPort(SEQUENCE_LENGTH_INDEX);
diff --git a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_eltwise.cpp b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_eltwise.cpp
index cb5911e90836fb..f22004a0d3e154 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_eltwise.cpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_eltwise.cpp
@@ -10,6 +10,11 @@ namespace intel_cpu {
 
 using namespace arm_compute;
 
+static std::mutex & get_mtx_ifunc() {
+    static std::mutex mtx_ifunc;
+    return mtx_ifunc;
+}
+
 inline VectorDims reshape_sizes(VectorDims dims) {
     const size_t MAX_NUM_SHAPE = arm_compute::MAX_DIMS;
     VectorDims result_dims(MAX_NUM_SHAPE - 1);
@@ -494,6 +499,11 @@ bool AclEltwiseExecutor::init(const EltwiseAttrs &eltwiseAttrs, const std::vecto
         default:
             IE_THROW() << "Unsupported operation type for ACL Eltwise executor: " << static_cast<int>(aclEltwiseAttrs.algorithm);
     }
+
+    // We get a problem (seg. faults, data race etc) for eltwise operations when we use several configure(...) functions in parallel.
+    // We created issue about this problem here: https://github.com/ARM-software/ComputeLibrary/issues/1073
+    // TODO: change it when we will get an answer to our question in issue
+    std::lock_guard<std::mutex> _lock {get_mtx_ifunc()};
     ifunc = exec_func();
     return true;
 }
diff --git a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_ie_scheduler.cpp b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_ie_scheduler.cpp
new file mode 100644
index 00000000000000..c617363aefebf6
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_ie_scheduler.cpp
@@ -0,0 +1,77 @@
+// Copyright (C) 2020-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "acl_ie_scheduler.hpp"
+
+#include "arm_compute/core/CPP/ICPPKernel.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include <ie_parallel.hpp>
+
+namespace ov {
+namespace intel_cpu {
+
+using namespace arm_compute;
+
+ACLScheduler::ACLScheduler() = default;
+
+unsigned int ACLScheduler::num_threads() const {
+    return parallel_get_num_threads();
+}
+
+void ACLScheduler::set_num_threads(unsigned int num_threads) {}
+
+void ACLScheduler::schedule_custom(ICPPKernel *kernel, const Hints &hints, const Window &window, ITensorPack &tensors) {
+    const Window & max_window = window;
+    const unsigned int num_iterations = max_window.num_iterations_total();
+    const auto _num_threads = std::min(num_iterations, static_cast<unsigned int>(parallel_get_num_threads()));
+
+    if (num_iterations == 0) {
+        return;
+    }
+
+    std::function<void(const Window &window, const ThreadInfo &info)> main_run;
+    if (tensors.empty()) {
+        main_run = [&](const Window &window, const ThreadInfo &info) {
+            kernel->run(window, info);
+        };
+    } else {
+        main_run = [&](const Window &window, const ThreadInfo &info) {
+            kernel->run_op(tensors, window, info);
+        };
+    }
+
+    if (!kernel->is_parallelisable() || _num_threads == 1) {
+        ThreadInfo info;
+        info.cpu_info = &cpu_info();
+        main_run(max_window, info);
+    } else {
+        const auto num_windows = _num_threads;
+        const auto hints_split_dimension = hints.split_dimension();
+
+        InferenceEngine::parallel_for(num_windows, [&](int wid) {
+            Window win = max_window.split_window(hints_split_dimension, wid, num_windows);
+            win.validate();
+            main_run(win, {wid, static_cast<int>(_num_threads), &cpu_info()});
+        });
+    }
+}
+
+void ACLScheduler::schedule(ICPPKernel *kernel, const Hints &hints) {
+    ITensorPack tensors;
+    schedule_custom(kernel, hints, kernel->window(), tensors);
+}
+
+void ACLScheduler::schedule_op(ICPPKernel *kernel, const Hints &hints, const Window &window, ITensorPack &tensors) {
+    schedule_custom(kernel, hints, window, tensors);
+}
+
+void ACLScheduler::run_workloads(std::vector<arm_compute::IScheduler::Workload> &workloads) {
+    InferenceEngine::parallel_for(workloads.size(), [&](int wid) {
+        workloads[wid]({wid, static_cast<int>(parallel_get_num_threads()), &cpu_info()});
+    });
+}
+
+} // namespace intel_cpu
+} // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_ie_scheduler.hpp b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_ie_scheduler.hpp
new file mode 100644
index 00000000000000..1148f4ad5edd69
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_ie_scheduler.hpp
@@ -0,0 +1,31 @@
+// Copyright (C) 2020-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <arm_compute/runtime/Scheduler.h>
+#include <arm_compute/core/CPP/ICPPKernel.h>
+#include <arm_compute/core/ITensorPack.h>
+#include "support/Mutex.h"
+
+namespace ov {
+namespace intel_cpu {
+
+using namespace arm_compute;
+
+class ACLScheduler final : public IScheduler {
+public:
+    ACLScheduler();
+    ~ACLScheduler() override = default;
+    std::uint32_t num_threads() const override;
+    void set_num_threads(unsigned int num_threads) override;
+    void schedule(ICPPKernel *kernel, const Hints &hints) override;
+    void schedule_op(ICPPKernel *kernel, const Hints &hints, const Window &window, ITensorPack &tensors) override;
+protected:
+    void run_workloads(std::vector<Workload> &workloads) override;
+private:
+    void schedule_custom(ICPPKernel *kernel, const Hints &hints, const Window &window, ITensorPack &tensors);
+};
+}  //  namespace intel_cpu
+}  //  namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.cpp b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.cpp
index 72021a5c6c5812..9cd6e3c43df7b0 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.cpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.cpp
@@ -20,7 +20,8 @@ bool AclPoolingExecutor::isSupported(const TensorInfo& srcTensorInfo,
                                      DataLayout dataLayout,
                                      const VectorDims* indDims,
                                      PoolingLayerInfo* pool_info,
-                                     Pooling3dLayerInfo* pool3d_info) {
+                                     Pooling3dLayerInfo* pool3d_info,
+                                     bool ignoreOutShapeErrors) {
     unsigned int pad_left   = (poolingAttrs.data_pad_begin.size() >= 2u) ? poolingAttrs.data_pad_begin[1] : poolingAttrs.data_pad_begin[0];
     unsigned int pad_right  = (poolingAttrs.data_pad_end.size() >= 2u) ?   poolingAttrs.data_pad_end[1]   : poolingAttrs.data_pad_end[0];
     unsigned int pad_top    = (poolingAttrs.data_pad_begin.size() >= 2u) ? poolingAttrs.data_pad_begin[0] : 0;
@@ -46,7 +47,12 @@ bool AclPoolingExecutor::isSupported(const TensorInfo& srcTensorInfo,
     // The combination of parameters: NCHW + CEIL gives an accuracy problem in AvgPool.
     // One workaround is to disable the ACL executor for these parameters.
     // Then OneDNN will run this case in ACL backend as reorder -> NHWC -> reorder
-    if (dataLayout == arm_compute::DataLayout::NCHW && poolingAttrs.rounding == op::RoundingType::CEIL) return false;
+    if (pool_type == PoolingType::AVG &&
+        dataLayout == arm_compute::DataLayout::NCHW &&
+        poolingAttrs.rounding == op::RoundingType::CEIL) {
+        DEBUG_LOG("NCHW + CEIL gives an accuracy problem in ACL AvgPool. ACL executor will not be created.");
+        return false;
+    }
     DimensionRoundingType round = (poolingAttrs.rounding == op::RoundingType::CEIL) ?
                                    DimensionRoundingType::CEIL : DimensionRoundingType::FLOOR;
 
@@ -82,12 +88,22 @@ bool AclPoolingExecutor::isSupported(const TensorInfo& srcTensorInfo,
             arm_compute::Status s = arm_compute::NEPoolingLayer::validate(&srcTensorInfo, &dstTensorInfo, *pool_info, &indTensorInfo);
             if (!s) {
                 DEBUG_LOG("NEPoolingLayer validation with indices failed: ", s.error_description());
+                if (ignoreOutShapeErrors &&
+                    s.error_description().find("Tensors have different shapes") != std::string::npos) {
+                    DEBUG_LOG("Ignore shape error because the flag ignoreOutShapeErrors is set");
+                    return true;
+                }
                 return false;
             }
         } else {
             arm_compute::Status s = arm_compute::NEPoolingLayer::validate(&srcTensorInfo, &dstTensorInfo, *pool_info);
             if (!s) {
                 DEBUG_LOG("NEPoolingLayer validation without indices failed: ", s.error_description());
+                if (ignoreOutShapeErrors &&
+                    s.error_description().find("Tensors have different shapes") != std::string::npos) {
+                    DEBUG_LOG("Ignore shape error because the flag ignoreOutShapeErrors is set");
+                    return true;
+                }
                 return false;
             }
         }
diff --git a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.hpp b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.hpp
index 44a2e999057d32..2525ccb490468a 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.hpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_pooling.hpp
@@ -31,7 +31,8 @@ class AclPoolingExecutor : public PoolingExecutor {
                             arm_compute::DataLayout dataLayout,
                             const VectorDims* indDims,
                             arm_compute::PoolingLayerInfo* pool_info,
-                            arm_compute::Pooling3dLayerInfo* pool3d_info);
+                            arm_compute::Pooling3dLayerInfo* pool3d_info,
+                            bool ignoreOutShapeErrors = false);
 
     impl_desc_type getImplType() const override {
         return implType;
@@ -79,8 +80,8 @@ class AclPoolingExecutorBuilder : public PoolingExecutorBuilder {
 
         if (dstDescs.size() == 2u &&
             dstDescs[1]->getPrecision() != InferenceEngine::Precision::U32) {
-            DEBUG_LOG("AclPoolingExecutor does not support precisions:",
-                      " dst[1]=", dstDescs[1]->getPrecision());
+            DEBUG_LOG("AclPoolingExecutor supports U32 as indices precisions only. ",
+                      "Passed indices precision: ", dstDescs[1]->getPrecision());
                 return false;
             }
 
diff --git a/src/plugins/intel_cpu/src/nodes/grid_sample.cpp b/src/plugins/intel_cpu/src/nodes/grid_sample.cpp
index 798b04078352bf..6868e907fa7ae8 100644
--- a/src/plugins/intel_cpu/src/nodes/grid_sample.cpp
+++ b/src/plugins/intel_cpu/src/nodes/grid_sample.cpp
@@ -10,9 +10,11 @@
 #include <ngraph/opsets/opset1.hpp>
 
 using namespace InferenceEngine;
-using namespace dnnl::impl::cpu;
 using namespace ov::intel_cpu;
 using namespace ov::intel_cpu::node;
+#if defined(OPENVINO_ARCH_X86_64)
+using namespace dnnl::impl::cpu;
+#endif // OPENVINO_ARCH_X86_64
 
 #define THROW_ERROR IE_THROW() << getTypeStr() << " node with name '" << getName() << "' "
 
@@ -23,10 +25,14 @@ bool GridSample::isSupportedOperation(const std::shared_ptr<const ov::Node>& op,
             errorMessage = "Not supported GridSample operation version. CPU plug-in supports only 9th version.";
             return false;
         }
+#if defined(OPENVINO_ARCH_X86_64)
         if (!x64::mayiuse(x64::sse41)) {
             errorMessage = "Not supported CPU instructions set.";
             return false;
         }
+#else
+        return false;
+#endif // OPENVINO_ARCH_X86_64
     } catch (...) {
         return false;
     }
@@ -34,6 +40,8 @@ bool GridSample::isSupportedOperation(const std::shared_ptr<const ov::Node>& op,
     return true;
 }
 
+#if defined(OPENVINO_ARCH_X86_64)
+
 GridSample::GridSample(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr context)
     : Node(op, context, NgraphShapeInferFactory(op, PortMask(1))) {
     std::string errorMessage;
@@ -110,7 +118,7 @@ void GridSample::initSupportedPrimitiveDescriptors() {
 }
 
 void GridSample::createPrimitive() {
-    GridSampleKernelConfParams jcp;
+    kernel::GridSampleKernelConfParams jcp;
 
     jcp.inDataPrc     = dataPrecision;
     jcp.gridPrc       = gridPrecision;
@@ -133,15 +141,13 @@ void GridSample::createPrimitive() {
         jcp.cannelNum      = jcp.dynamicChannel ? 1lu : srcDataDims[1];
     }
 
-#if defined(OPENVINO_ARCH_X86_64)
     if (x64::mayiuse(x64::avx512_core)) {
-        jitKernel.reset(new GridSampleKernel<x64::avx512_core>(jcp));
+        jitKernel.reset(new kernel::GridSampleKernel<x64::avx512_core>(jcp));
     } else if (x64::mayiuse(x64::avx2)) {
-        jitKernel.reset(new GridSampleKernel<x64::avx2>(jcp));
+        jitKernel.reset(new kernel::GridSampleKernel<x64::avx2>(jcp));
     } else if (x64::mayiuse(x64::sse41)) {
-        jitKernel.reset(new GridSampleKernel<x64::sse41>(jcp));
+        jitKernel.reset(new kernel::GridSampleKernel<x64::sse41>(jcp));
     }
-#endif // OPENVINO_ARCH_X86_64
     if (!jitKernel) {
         THROW_ERROR << " could not create JIT kernel.";
     }
@@ -268,7 +274,7 @@ void GridSample::execute(dnnl::stream strm) {
 
     auto threadBody = [&](const int ithr, const int nthr) {
         const auto& p = execParamsPerThread[ithr];
-        auto arg = GridSamplesKernelExecArgs();
+        auto arg = kernel::GridSamplesKernelExecArgs();
         if (p.workAmount == 0lu) {
             return;
         }
@@ -311,3 +317,5 @@ void GridSample::executeDynamicImpl(dnnl::stream strm) {
 bool GridSample::created() const {
     return getType() == Type::GridSample;
 }
+
+#endif // OPENVINO_ARCH_X86_64
diff --git a/src/plugins/intel_cpu/src/nodes/grid_sample.hpp b/src/plugins/intel_cpu/src/nodes/grid_sample.hpp
index 89a1a409764615..78b5f9d66710ca 100644
--- a/src/plugins/intel_cpu/src/nodes/grid_sample.hpp
+++ b/src/plugins/intel_cpu/src/nodes/grid_sample.hpp
@@ -72,7 +72,7 @@ class GridSample : public Node {
     static constexpr size_t IN_DATA = 0;
     static constexpr size_t IN_GRID = 1;
 
-    std::shared_ptr<GridSampleKernelBase> jitKernel;
+    std::shared_ptr<kernel::GridSampleKernelBase> jitKernel;
 };
 
 }   // namespace node
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp b/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp
index 7501dd606427ce..89e658a7d6a6fc 100644
--- a/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp
+++ b/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp
@@ -1,4 +1,4 @@
-// Copyright (C) 2022 Intel Corporation
+// Copyright (C) 2018-2023 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
 
@@ -8,12 +8,13 @@ using namespace dnnl::impl::cpu;
 
 namespace ov {
 namespace intel_cpu {
+namespace kernel {
 
 #define GET_OFF(field) offsetof(GridSamplesKernelExecArgs, field)
 
 template <x64::cpu_isa_t isa>
 GridSampleKernel<isa>::GridSampleKernel(const GridSampleKernelConfParams& jcp) :
-        GridSampleKernelBase(jit_name(), jcp) {
+        GridSampleKernelBase(jit_name(), jcp, isa) {
     vlen = x64::cpu_isa_traits<isa>::vlen;
     dataTypeSize = jcp.inDataPrc.size();
     gridTypeSize = jcp.gridPrc.size();
@@ -2085,5 +2086,6 @@ template class GridSampleKernel<x64::avx512_core>;
 template class GridSampleKernel<x64::avx2>;
 template class GridSampleKernel<x64::sse41>;
 
+}   // namespace kernel
 }   // namespace intel_cpu
 }   // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.hpp b/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.hpp
index c24100259cd5bb..295c715fb8146b 100644
--- a/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.hpp
+++ b/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.hpp
@@ -1,4 +1,4 @@
-// Copyright (C) 2022 Intel Corporation
+// Copyright (C) 2018-2023 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
 
@@ -14,6 +14,12 @@ namespace intel_cpu {
 enum class GridSampleInterpolationMode { BILINEAR, BICUBIC, NEAREST };
 enum class GridSamplePaddingMode { ZEROS, BORDER, REFLECTION };
 
+namespace kernel {
+
+class GridSampleKernelBase;
+
+#if defined(OPENVINO_ARCH_X86_64)
+
 struct GridSampleKernelConfParams {
     bool dynamicShapes  = false;
     bool dynamicBatch   = false;
@@ -66,7 +72,8 @@ class GridSampleKernelBase: public JitKernelBase {
         assert(ker_);
         ker_(args);
     }
-    explicit GridSampleKernelBase(const char* name, const GridSampleKernelConfParams& jcp) : JitKernelBase(name), ker_(nullptr), jcp(jcp) {}
+    explicit GridSampleKernelBase(const char* name, const GridSampleKernelConfParams& jcp, dnnl::impl::cpu::x64::cpu_isa_t isa)
+        : JitKernelBase(name, isa), ker_(nullptr), jcp(jcp) {}
 
     virtual void create_ker() = 0;
     uint64_t getVecLen() {
@@ -173,5 +180,8 @@ class GridSampleKernel : public GridSampleKernelBase {
     void hwShiftPs2dq(const Vmm& vDst, const Vmm& vHCoord, const Vmm& vWCoord, const Vmm& vWidth);
 };
 
+#endif // OPENVINO_ARCH_X86_64
+
+}   // namespace kernel
 }   // namespace intel_cpu
 }   // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.cpp b/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.cpp
index 6afbecf143f27b..bc0daaf6e33e2a 100644
--- a/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.cpp
+++ b/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.cpp
@@ -1,172 +1,243 @@
-// Copyright (C) 2022 Intel Corporation
+// Copyright (C) 2018-2023 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
 
 #include "jit_kernel_base.hpp"
 
-using namespace ov;
-using namespace intel_cpu;
 using namespace dnnl::impl::cpu;
 
+namespace ov {
+namespace intel_cpu {
+namespace kernel {
 
-void JitKernelBase::uni_vfmsub132ps(const Xbyak::Xmm& vDst,
-                                    const Xbyak::Xmm& vSrc,
+JitKernelBase::JitKernelBase(const char* name, x64::cpu_isa_t isa)
+        : x64::jit_generator(name, nullptr, x64::MAX_CODE_SIZE, true, isa), m_isa(isa) {
+    vlen = x64::isa_max_vlen(isa);
+}
+
+void JitKernelBase::uni_vfmsub132ps(const Xbyak::Xmm& v_dst,
+                                    const Xbyak::Xmm& v_src,
                                     const Xbyak::Operand& op) {
     if (isValidIsa(x64::avx2)) {
-        vfmsub132ps(vDst, vSrc, op);
+        vfmsub132ps(v_dst, v_src, op);
     } else if (isValidIsa(x64::avx)) {
-        assert(vDst.getIdx() != vSrc.getIdx());
-        vmulps(vDst, vDst, op);
-        vsubps(vDst, vDst, vSrc);
+        assert(v_dst.getIdx() != v_src.getIdx());
+        vmulps(v_dst, v_dst, op);
+        vsubps(v_dst, v_dst, v_src);
     } else {
-        assert(vDst.getIdx() != vSrc.getIdx());
-        mulps(vDst, op);
-        subps(vDst, vSrc);
+        assert(v_dst.getIdx() != v_src.getIdx());
+        mulps(v_dst, op);
+        subps(v_dst, v_src);
     }
 }
 
-void JitKernelBase::uni_vfnmadd132ps(const Xbyak::Xmm& vDst,
-                                     const Xbyak::Xmm& vSrc,
+void JitKernelBase::uni_vfnmadd132ps(const Xbyak::Xmm& v_dst,
+                                     const Xbyak::Xmm& v_src,
                                      const Xbyak::Operand& op) {
     if (isValidIsa(x64::avx2)) {
-        vfnmadd132ps(vDst, vSrc, op);
+        vfnmadd132ps(v_dst, v_src, op);
     } else if (isValidIsa(x64::avx)) {
-        assert(vDst.getIdx() != vSrc.getIdx());
-        vmulps(vDst, vDst, op);
-        vsubps(vDst, vSrc, vDst);
+        assert(v_dst.getIdx() != v_src.getIdx());
+        vmulps(v_dst, v_dst, op);
+        vsubps(v_dst, v_src, v_dst);
     } else {
-        assert(vDst.getIdx() != vSrc.getIdx());
-        mulps(vDst, op);
-        subps(vSrc, vDst);
-        movups(vDst, vSrc);
+        assert(v_dst.getIdx() != v_src.getIdx());
+        mulps(v_dst, op);
+        subps(v_src, v_dst);
+        movups(v_dst, v_src);
     }
 }
 
-void JitKernelBase::uni_vfmsub231ps(const Xbyak::Xmm& vDst,
-                                    const Xbyak::Xmm& vSrc,
+void JitKernelBase::uni_vfmsub231ps(const Xbyak::Xmm& v_dst,
+                                    const Xbyak::Xmm& v_src,
                                     const Xbyak::Operand& op) {
     if (isValidIsa(x64::avx2)) {
-        vfmsub231ps(vDst, vSrc, op);
+        vfmsub231ps(v_dst, v_src, op);
     } else if (isValidIsa(x64::avx)) {
-        assert(!vDst.isEqualIfNotInherited(op));
-        vmulps(vSrc, vSrc, op);
-        vsubps(vDst, vSrc, vDst);
+        assert(!v_dst.isEqualIfNotInherited(op));
+        vmulps(v_src, v_src, op);
+        vsubps(v_dst, v_src, v_dst);
     } else {
-        assert(!vDst.isEqualIfNotInherited(op));
-        mulps(vSrc, op);
-        subps(vSrc, vDst);
-        movups(vDst, vSrc);
+        assert(!v_dst.isEqualIfNotInherited(op));
+        mulps(v_src, op);
+        subps(v_src, v_dst);
+        movups(v_dst, v_src);
     }
 }
 
-void JitKernelBase::uni_vpaddd(const Xbyak::Ymm& vDst,
-                               const Xbyak::Ymm& vSrc,
+void JitKernelBase::uni_vpaddd(const Xbyak::Ymm& v_dst,
+                               const Xbyak::Ymm& v_src,
                                const Xbyak::Operand& op) {
     if (isValidIsa(x64::avx2)) {
-        vpaddd(vDst, vSrc, op);
+        vpaddd(v_dst, v_src, op);
     } else if (isValidIsa(x64::avx)) {
-        Xbyak::Xmm xmmDst(vDst.getIdx());
-        vmovups(vDst, vSrc);
+        Xbyak::Xmm xmmDst(v_dst.getIdx());
+        vmovups(v_dst, v_src);
         if (op.isYMM()) {
             Xbyak::Ymm ymmOp(op.getIdx());
             Xbyak::Xmm xmmOp(op.getIdx());
             paddd(xmmDst, xmmOp);
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
             vperm2f128(ymmOp, ymmOp, ymmOp, 0x1);
             paddd(xmmDst, xmmOp);
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
             vperm2f128(ymmOp, ymmOp, ymmOp, 0x1);
         } else if (op.isMEM()) {
             const int vlen = x64::cpu_isa_traits<x64::sse41>::vlen;
             paddd(xmmDst, op.getAddress());
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
             paddd(xmmDst, ptr[op.getAddress().getRegExp() + vlen]);
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
         } else {
             IE_THROW() << "Not supported operand type.";
         }
     } else if (isValidIsa(x64::sse41)) {
-        assert(vDst.getIdx() != vSrc.getIdx());
-        paddd(vDst, op);
+        assert(v_dst.getIdx() != v_src.getIdx());
+        paddd(v_dst, op);
     } else {
         IE_THROW() << "Not defined behavior for instruction 'vpaddd' in current instructions set.";
     }
 }
 
-void JitKernelBase::uni_vpsubd(const Xbyak::Ymm& vDst,
-                               const Xbyak::Ymm& vSrc,
+void JitKernelBase::uni_vpaddq(const Xbyak::Xmm& v_dst,
+                               const Xbyak::Xmm& v_src,
+                               const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx2)) {
+        vpaddq(v_dst, v_src, op);
+    } else {
+        if (v_dst.getIdx() != v_src.getIdx()) {
+            movups(v_dst, v_src);
+        }
+        paddq(v_dst, op);
+    }
+}
+
+void JitKernelBase::uni_vpsubd(const Xbyak::Ymm& v_dst,
+                               const Xbyak::Ymm& v_src,
                                const Xbyak::Operand& op) {
     if (isValidIsa(x64::avx2)) {
-        vpsubd(vDst, vSrc, op);
+        vpsubd(v_dst, v_src, op);
     } else if (isValidIsa(x64::avx)) {
-        Xbyak::Xmm xmmDst(vDst.getIdx());
-        vmovups(vDst, vSrc);
+        Xbyak::Xmm xmmDst(v_dst.getIdx());
+        vmovups(v_dst, v_src);
         if (op.isYMM()) {
             Xbyak::Ymm ymmOp(op.getIdx());
             Xbyak::Xmm xmmOp(op.getIdx());
             psubd(xmmDst, xmmOp);
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
             vperm2f128(ymmOp, ymmOp, ymmOp, 0x1);
             psubd(xmmDst, xmmOp);
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
             vperm2f128(ymmOp, ymmOp, ymmOp, 0x1);
         } else if (op.isMEM()) {
             const int vlen = x64::cpu_isa_traits<x64::sse41>::vlen;
             psubd(xmmDst, op.getAddress());
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
             psubd(xmmDst, ptr[op.getAddress().getRegExp() + vlen]);
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
         } else {
             IE_THROW() << "Not supported operand type.";
         }
     } else if (isValidIsa(x64::sse41)) {
-        assert(vDst.getIdx() != vSrc.getIdx());
-        psubd(vDst, op);
+        assert(v_dst.getIdx() != v_src.getIdx());
+        psubd(v_dst, op);
     } else {
         IE_THROW() << "Not defined behavior for instruction 'vpsubd' in current instructions set.";
     }
 }
 
-void JitKernelBase::uni_vdivps(const Xbyak::Xmm& vDst,
+void JitKernelBase::uni_vsubpd(const Xbyak::Xmm& v_dst,
+                               const Xbyak::Xmm& v_src,
+                               const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx)) {
+        vsubpd(v_dst, v_src, op);
+    } else {
+        if (v_dst.getIdx() != v_src.getIdx()) {
+            movups(v_dst, v_src);
+        }
+        subpd(v_dst, op);
+    }
+}
+
+void JitKernelBase::uni_vmulpd(const Xbyak::Xmm& v_dst,
+                               const Xbyak::Xmm& v_src,
+                               const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx)) {
+        vmulpd(v_dst, v_src, op);
+    } else {
+        if (v_dst.getIdx() != v_src.getIdx()) {
+            movups(v_dst, v_src);
+        }
+        mulpd(v_dst, op);
+    }
+}
+
+void JitKernelBase::uni_vpmuludq(const Xbyak::Xmm& v_dst,
+                                 const Xbyak::Xmm& v_src,
+                                 const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx2)) {
+        vpmuludq(v_dst, v_src, op);
+    } else {
+        if (v_dst.getIdx() != v_src.getIdx()) {
+            movups(v_dst, v_src);
+        }
+        pmuludq(v_dst, op);
+    }
+}
+
+void JitKernelBase::uni_vdivps(const Xbyak::Xmm& v_dst,
                                const Xbyak::Operand& op1,
                                const Xbyak::Operand& op2) {
     if (isValidIsa(x64::avx)) {
-        vdivps(vDst, op1, op2);
+        vdivps(v_dst, op1, op2);
+    } else {
+        if (!v_dst.isEqualIfNotInherited(op1)) {
+            movups(v_dst, op1);
+        }
+        divps(v_dst, op2);
+    }
+}
+
+void JitKernelBase::uni_vdivpd(const Xbyak::Xmm& v_dst,
+                               const Xbyak::Xmm& v_src,
+                               const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx)) {
+        vdivpd(v_dst, v_src, op);
     } else {
-        if (!vDst.isEqualIfNotInherited(op1)) {
-            movups(vDst, op1);
+        if (v_dst.getIdx() != v_src.getIdx()) {
+            movups(v_dst, v_src);
         }
-        divps(vDst, op2);
+        divpd(v_dst, op);
     }
 }
 
-void JitKernelBase::uni_vandps(const Xbyak::Xmm& vDst,
+void JitKernelBase::uni_vandps(const Xbyak::Xmm& v_dst,
                                const Xbyak::Xmm& vSrs,
                                const Xbyak::Operand &op) {
     if (isValidIsa(x64::avx)) {
-        vandps(vDst, vSrs, op);
+        vandps(v_dst, vSrs, op);
     } else {
-        if (!vDst.isEqualIfNotInherited(vSrs)) {
-            movups(vDst, vSrs);
+        if (!v_dst.isEqualIfNotInherited(vSrs)) {
+            movups(v_dst, vSrs);
         }
-        andps(vDst, op);
+        andps(v_dst, op);
     }
 }
 
-void JitKernelBase::uni_vandnps(const Xbyak::Xmm& vDst,
+void JitKernelBase::uni_vandnps(const Xbyak::Xmm& v_dst,
                                 const Xbyak::Xmm& vSrs,
                                 const Xbyak::Operand &op) {
     if (isValidIsa(x64::avx)) {
-        vandnps(vDst, vSrs, op);
+        vandnps(v_dst, vSrs, op);
     } else {
-        if (!vDst.isEqualIfNotInherited(vSrs)) {
-            movups(vDst, vSrs);
+        if (!v_dst.isEqualIfNotInherited(vSrs)) {
+            movups(v_dst, vSrs);
         }
-        andnps(vDst, op);
+        andnps(v_dst, op);
     }
 }
 
-void JitKernelBase::gatherdd(const Xbyak::Xmm&    vDst,
+void JitKernelBase::gatherdd(const Xbyak::Xmm&    v_dst,
                              const Xbyak::Reg64&  rSrcPtr,
                              const Xbyak::Xmm&    vSrcShift,
                              const Xbyak::Opmask& kReadMask,
@@ -178,28 +249,28 @@ void JitKernelBase::gatherdd(const Xbyak::Xmm&    vDst,
     if (!useMask)
         kxnord(kReadMask, kReadMask, kReadMask);
     if (zeroFill)
-        uni_vpxor(vDst, vDst, vDst);
+        uni_vpxor(v_dst, v_dst, v_dst);
 
-    vpgatherdd(vDst | kReadMask, ptr[rSrcPtr + vSrcShift]);
+    vpgatherdd(v_dst | kReadMask, ptr[rSrcPtr + vSrcShift]);
 }
 
-void JitKernelBase::gatherdd(const Xbyak::Xmm&   vDst,
+void JitKernelBase::gatherdd(const Xbyak::Xmm&   v_dst,
                              const Xbyak::Reg64& rSrcPtr,
                              const Xbyak::Xmm&   vSrcShift,
                              const Xbyak::Xmm&   vReadMask,
                              const bool useMask,
                              const bool zeroFill) {
-    if (vDst.getIdx() == vSrcShift.getIdx() || vDst.getIdx() == vReadMask.getIdx() || vSrcShift.getIdx() == vReadMask.getIdx()) {
+    if (v_dst.getIdx() == vSrcShift.getIdx() || v_dst.getIdx() == vReadMask.getIdx() || vSrcShift.getIdx() == vReadMask.getIdx()) {
         IE_THROW() << "Any pair of the index, mask, or destination registers cannot be the same.";
     }
     if (zeroFill)
-        pxor(vDst, vDst); // Don't use vpxor. It zeros the rest of the YMM register.
+        pxor(v_dst, v_dst); // Don't use vpxor. It zeros the rest of the YMM register.
 
     if (isValidIsa(x64::avx2)) {
         if (!useMask)
             uni_vpcmpeqd(vReadMask, vReadMask, vReadMask);
 
-        vpgatherdd(vDst, ptr[rSrcPtr + vSrcShift], vReadMask);
+        vpgatherdd(v_dst, ptr[rSrcPtr + vSrcShift], vReadMask);
     } else {
         auto rAux = getReg64();
         Xbyak::Reg32 r32Aux = Xbyak::Reg32(rAux.getIdx());
@@ -213,7 +284,7 @@ void JitKernelBase::gatherdd(const Xbyak::Xmm&   vDst,
                 je(lLoopNext, T_NEAR);
             }
             uni_vpextrd(r32Aux, vSrcShift, i);
-            pinsrd(vDst, ptr[rSrcPtr + rAux], i);
+            pinsrd(v_dst, ptr[rSrcPtr + rAux], i);
 
             if (useMask)
                 L(lLoopNext);
@@ -221,30 +292,30 @@ void JitKernelBase::gatherdd(const Xbyak::Xmm&   vDst,
     }
 }
 
-void JitKernelBase::gatherdd(const Xbyak::Ymm&   vDst,
+void JitKernelBase::gatherdd(const Xbyak::Ymm&   v_dst,
                              const Xbyak::Reg64& rSrcPtr,
                              const Xbyak::Ymm&   vSrcShift,
                              const Xbyak::Ymm&   vReadMask,
                              const bool useMask,
                              const bool zeroFill) {
-    if (vDst.getIdx() == vSrcShift.getIdx() || vDst.getIdx() == vReadMask.getIdx() || vSrcShift.getIdx() == vReadMask.getIdx()) {
+    if (v_dst.getIdx() == vSrcShift.getIdx() || v_dst.getIdx() == vReadMask.getIdx() || vSrcShift.getIdx() == vReadMask.getIdx()) {
         IE_THROW() << "Any pair of the index, mask, or destination registers cannot be the same.";
     }
     if (isValidIsa(x64::avx2)) {
         if (!useMask)
             uni_vpcmpeqd(vReadMask, vReadMask, vReadMask);
         if (zeroFill)
-            uni_vpxor(vDst, vDst, vDst);
+            uni_vpxor(v_dst, v_dst, v_dst);
 
-        vpgatherdd(vDst, ptr[rSrcPtr + vSrcShift], vReadMask);
+        vpgatherdd(v_dst, ptr[rSrcPtr + vSrcShift], vReadMask);
     } else {
-        Xbyak::Xmm xmmDst      = Xbyak::Xmm(vDst.getIdx()),
+        Xbyak::Xmm xmmDst      = Xbyak::Xmm(v_dst.getIdx()),
                    xmmSrcShft  = Xbyak::Xmm(vSrcShift.getIdx()),
                    xmmReadMask = Xbyak::Xmm(vReadMask.getIdx());
         for (uint8_t i = 0; i < 2; i++) {
             gatherdd(xmmDst, rSrcPtr, xmmSrcShft, xmmReadMask, useMask, zeroFill);
 
-            vperm2f128(vDst, vDst, vDst, 0x1);
+            vperm2f128(v_dst, v_dst, v_dst, 0x1);
             vperm2f128(vSrcShift, vSrcShift, vSrcShift, 0x1);
             if (useMask)
                 vperm2f128(vReadMask, vReadMask, vReadMask, 0x1);
@@ -252,6 +323,15 @@ void JitKernelBase::gatherdd(const Xbyak::Ymm&   vDst,
     }
 }
 
+void JitKernelBase::uni_vpbroadcastq(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+    if (isValidIsa(x64::avx2)) {
+        vpbroadcastq(x, op);
+    } else {
+        movsd(x, op);
+        shufpd(x, x, 0x0);
+    }
+}
+
 void JitKernelBase::uni_vpbroadcastd(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
     if (isValidIsa(x64::avx2)) {
         vpbroadcastd(x, op);
@@ -285,6 +365,57 @@ void JitKernelBase::uni_vpbroadcastd(const Xbyak::Ymm &x, const Xbyak::Operand &
     }
 }
 
+void JitKernelBase::uni_vroundpd(const Xbyak::Xmm& v_dst, const Xbyak::Operand& op, const uint8_t imm) {
+    if (isValidIsa(x64::avx512_core)) {
+        vrndscalepd(v_dst, op, imm & 0x3);
+    } else if (isValidIsa(x64::avx)) {
+        vroundpd(v_dst, op, imm);
+    } else {
+        roundpd(v_dst, op, imm);
+    }
+}
+
+void JitKernelBase::uni_vcvtdq2pd(const Xbyak::Xmm& v_dst,
+                                  const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx)) {
+        vcvtdq2pd(v_dst, op);
+    } else {
+        cvtdq2pd(v_dst, op);
+    }
+}
+
+void JitKernelBase::uni_vcvtpd2dq(const Xbyak::Xmm& v_dst,
+                                  const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx)) {
+        vcvtpd2dq(v_dst, op);
+    } else {
+        cvtpd2dq(v_dst, op);
+    }
+}
+
+void JitKernelBase::uni_vpmovzxdq(const Xbyak::Xmm& v_dst,
+                                  const Xbyak::Operand& op) {
+    if (isValidIsa(x64::avx2)) {
+        vpmovzxdq(v_dst, op);
+    } else {
+        pmovzxdq(v_dst, op);
+    }
+}
+
+void JitKernelBase::uni_vshufpd(const Xbyak::Xmm& v_dst,
+                                const Xbyak::Xmm& v_src,
+                                const Xbyak::Operand& op,
+                                uint8_t imm) {
+    if (isValidIsa(x64::avx)) {
+        vshufpd(v_dst, v_src, op, imm);
+    } else {
+        if (v_dst.getIdx() != v_src.getIdx()) {
+            movups(v_dst, v_src);
+        }
+        shufpd(v_dst, op, imm);
+    }
+}
+
 void JitKernelBase::fillRestWorkMask(const Xbyak::Opmask& dstMask,
                                      const Xbyak::Reg64& rWorkRest) {
     auto rOnes = getReg64();
@@ -362,7 +493,7 @@ void JitKernelBase::fillRestWorkMask(const Xbyak::Ymm& ymmDstMask,
     L(lEnd);
 }
 
-void JitKernelBase::load(const Xbyak::Xmm&     vDst,
+void JitKernelBase::load(const Xbyak::Xmm&     v_dst,
                          const Xbyak::Address& srcAddr,
                          const Xbyak::Reg64&   rLoadNum,
                          const size_t          typeSize,
@@ -373,7 +504,7 @@ void JitKernelBase::load(const Xbyak::Xmm&     vDst,
     const uint8_t elPerVec = x64::cpu_isa_traits<x64::sse41>::vlen / typeSize;
     Xbyak::Label lEnd;
     if (zeroFilling)
-        pxor(vDst, vDst);
+        pxor(v_dst, v_dst);
 
     for (uint8_t i = 0; i < elPerVec; i++) {
         cmp(rLoadNum, i);
@@ -381,18 +512,18 @@ void JitKernelBase::load(const Xbyak::Xmm&     vDst,
 
         const size_t offset = i * typeSize;
         if (typeSize == 1)
-            pinsrb(vDst, ptr[srcAddr.getRegExp() + offset], i);
+            pinsrb(v_dst, ptr[srcAddr.getRegExp() + offset], i);
         else if (typeSize == 2)
-            pinsrw(vDst, ptr[srcAddr.getRegExp() + offset], i);
+            pinsrw(v_dst, ptr[srcAddr.getRegExp() + offset], i);
         else if (typeSize == 4)
-            pinsrd(vDst, ptr[srcAddr.getRegExp() + offset], i);
+            pinsrd(v_dst, ptr[srcAddr.getRegExp() + offset], i);
         else if (typeSize == 8)
-            pinsrq(vDst, ptr[srcAddr.getRegExp() + offset], i);
+            pinsrq(v_dst, ptr[srcAddr.getRegExp() + offset], i);
     }
     L(lEnd);
 }
 
-void JitKernelBase::load(const Xbyak::Ymm&     vDst,
+void JitKernelBase::load(const Xbyak::Ymm&     v_dst,
                          const Xbyak::Address& srcAddr,
                          const Xbyak::Reg64&   rLoadNum,
                          const size_t          typeSize,
@@ -403,8 +534,8 @@ void JitKernelBase::load(const Xbyak::Ymm&     vDst,
     const size_t elPerXmm = x64::cpu_isa_traits<x64::sse41>::vlen / typeSize;
     Xbyak::Label lEnd;
     if (zeroFilling)
-        uni_vpxor(vDst, vDst, vDst);
-    Xbyak::Xmm xmmDst(vDst.getIdx());
+        uni_vpxor(v_dst, v_dst, v_dst);
+    Xbyak::Xmm xmmDst(v_dst.getIdx());
 
     for (size_t i = 0lu; i < 2lu; i++) {
         Xbyak::Label lPerm;
@@ -427,13 +558,13 @@ void JitKernelBase::load(const Xbyak::Ymm&     vDst,
         }
 
         L(lPerm);
-        vperm2f128(vDst, vDst, vDst, 0x1);
+        vperm2f128(v_dst, v_dst, v_dst, 0x1);
     }
     L(lEnd);
 }
 
 void JitKernelBase::store(const Xbyak::Address& dstAddr,
-                          const Xbyak::Xmm&     vSrc,
+                          const Xbyak::Xmm&     v_src,
                           const Xbyak::Reg64&   rToStoreNum,
                           const size_t          typeSize) {
     if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
@@ -448,27 +579,27 @@ void JitKernelBase::store(const Xbyak::Address& dstAddr,
 
         const size_t offset = i * typeSize;
         if (typeSize == 1) {
-            uni_vpextrb(ptr[dstAddr.getRegExp() + offset], vSrc, i);
+            uni_vpextrb(ptr[dstAddr.getRegExp() + offset], v_src, i);
         } else if (typeSize == 2) {
-            uni_vpextrw(ptr[dstAddr.getRegExp() + offset], vSrc, i);
+            uni_vpextrw(ptr[dstAddr.getRegExp() + offset], v_src, i);
         } else if (typeSize == 4) {
-            uni_vpextrd(ptr[dstAddr.getRegExp() + offset], vSrc, i);
+            uni_vpextrd(ptr[dstAddr.getRegExp() + offset], v_src, i);
         } else if (typeSize == 8) {
-            uni_vpextrq(ptr[dstAddr.getRegExp() + offset], vSrc, i);
+            uni_vpextrq(ptr[dstAddr.getRegExp() + offset], v_src, i);
         }
     }
     L(lEnd);
 }
 
 void JitKernelBase::store(const Xbyak::Address& dstAddr,
-                          const Xbyak::Ymm&     vSrc,
+                          const Xbyak::Ymm&     v_src,
                           const Xbyak::Reg64&   rToStoreNum,
                           const size_t          typeSize) {
     if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
         IE_THROW() << "Could not store data with type size " << typeSize;
     }
     Xbyak::Label lEnd;
-    Xbyak::Xmm xmmSrc(vSrc.getIdx());
+    Xbyak::Xmm xmmSrc(v_src.getIdx());
     const size_t elPerXmm = x64::cpu_isa_traits<x64::sse41>::vlen / typeSize;
 
     for (int i = 0; i < 2; i++) {
@@ -493,7 +624,7 @@ void JitKernelBase::store(const Xbyak::Address& dstAddr,
         }
 
         L(lPerm);
-        vperm2f128(vSrc, vSrc, vSrc, 0x1);
+        vperm2f128(v_src, v_src, v_src, 0x1);
     }
     L(lEnd);
 }
@@ -575,3 +706,7 @@ void JitKernelBase::memMovDD(const Xbyak::Reg64& rDst,
     }
     L(lEnd);
 }
+
+} // namespace kernel
+} // namespace intel_cpu
+} // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.hpp b/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.hpp
index e39efde753bbbc..f17eb9a02d8771 100644
--- a/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.hpp
+++ b/src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.hpp
@@ -1,14 +1,23 @@
-// Copyright (C) 2022 Intel Corporation
+// Copyright (C) 2018-2023 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
 
 #pragma once
 
+#include "openvino/core/visibility.hpp"
+
+#if defined(OPENVINO_ARCH_X86_64)
 #include "cpu/x64/jit_generator.hpp"
 #include "registers_pool.hpp"
+#endif // OPENVINO_ARCH_X86_64
 
 namespace ov {
 namespace intel_cpu {
+namespace kernel {
+
+class JitKernelBase;
+
+#if defined(OPENVINO_ARCH_X86_64)
 
 #define getReg64() RegistersPool::Reg<Xbyak::Reg64>(registersPool)
 #define getReg32() RegistersPool::Reg<Xbyak::Reg32>(registersPool)
@@ -17,7 +26,11 @@ namespace intel_cpu {
 
 class JitKernelBase: public dnnl::impl::cpu::x64::jit_generator {
 public:
-    JitKernelBase(const char* name) : dnnl::impl::cpu::x64::jit_generator(name) {}
+    JitKernelBase(const char* name, dnnl::impl::cpu::x64::cpu_isa_t max_cpu_isa);
+
+    dnnl::impl::cpu::x64::cpu_isa_t getIsa() { return m_isa; }
+
+    size_t getVectorLen() { return vlen; }
 
     void uni_vfmsub132ps(const Xbyak::Xmm& vDst, const Xbyak::Xmm& vSrc, const Xbyak::Operand& op);
 
@@ -31,14 +44,24 @@ class JitKernelBase: public dnnl::impl::cpu::x64::jit_generator {
 
     void uni_vpaddd(const Xbyak::Ymm& vDst, const Xbyak::Ymm& vSrc, const Xbyak::Operand& op);
 
+    void uni_vpaddq(const Xbyak::Xmm& vDst, const Xbyak::Xmm& vSrc, const Xbyak::Operand& op);
+
     void uni_vpsubd(const Xbyak::Xmm& vDst, const Xbyak::Xmm& vSrc, const Xbyak::Operand& op) {
         jit_generator::uni_vpsubd(vDst, vSrc, op);
     }
 
     void uni_vpsubd(const Xbyak::Ymm& vDst, const Xbyak::Ymm& vSrc, const Xbyak::Operand& op);
 
+    void uni_vsubpd(const Xbyak::Xmm& v_dst, const Xbyak::Xmm& v_src, const Xbyak::Operand& op);
+
+    void uni_vmulpd(const Xbyak::Xmm& v_dst, const Xbyak::Xmm& v_src, const Xbyak::Operand& op);
+
+    void uni_vpmuludq(const Xbyak::Xmm& v_dst, const Xbyak::Xmm& op_1, const Xbyak::Operand& op_2);
+
     void uni_vdivps(const Xbyak::Xmm& vDst, const Xbyak::Operand& op1, const Xbyak::Operand& op2);
 
+    void uni_vdivpd(const Xbyak::Xmm& v_dst, const Xbyak::Xmm& v_src, const Xbyak::Operand& op2);
+
     void uni_vandps(const Xbyak::Xmm& vDst, const Xbyak::Xmm& vSrs, const Xbyak::Operand &op);
 
     void uni_vandnps(const Xbyak::Xmm& vDst, const Xbyak::Xmm& vSrs, const Xbyak::Operand &op);
@@ -63,6 +86,18 @@ class JitKernelBase: public dnnl::impl::cpu::x64::jit_generator {
 
     void uni_vpbroadcastd(const Xbyak::Ymm &x, const Xbyak::Operand &op);
 
+    void uni_vpbroadcastq(const Xbyak::Xmm &x, const Xbyak::Operand &op);
+
+    void uni_vroundpd(const Xbyak::Xmm& v_dst, const Xbyak::Operand& op, const uint8_t imm);
+
+    void uni_vcvtdq2pd(const Xbyak::Xmm& v_dst, const Xbyak::Operand& op);
+
+    void uni_vcvtpd2dq(const Xbyak::Xmm& v_dst, const Xbyak::Operand& op);
+
+    void uni_vpmovzxdq(const Xbyak::Xmm& v_dst, const Xbyak::Operand& op);
+
+    void uni_vshufpd(const Xbyak::Xmm& v_dst, const Xbyak::Xmm& v_srs, const Xbyak::Operand& op, uint8_t imm);
+
     void gatherdd(const Xbyak::Xmm&    vDst,
                   const Xbyak::Reg64&  rSrcPtr,
                   const Xbyak::Xmm&    vSrcShift,
@@ -140,7 +175,9 @@ class JitKernelBase: public dnnl::impl::cpu::x64::jit_generator {
         return dnnl::impl::cpu::x64::mayiuse(isa);
     }
 
+    const dnnl::impl::cpu::x64::cpu_isa_t m_isa;
     RegistersPool::Ptr registersPool;
+    size_t vlen;
 
     enum {
         // Comparison predicate operand (immediate byte) for single-precision floating-point values.
@@ -155,5 +192,70 @@ class JitKernelBase: public dnnl::impl::cpu::x64::jit_generator {
     };
 };
 
+template<typename CompileParams, typename CallArgs>
+class JitKernel : public JitKernelBase {
+public:
+    using KernelFunc = void (*)(const CallArgs *);
+
+    explicit JitKernel(const char* name, const CompileParams& jcp, dnnl::impl::cpu::x64::cpu_isa_t max_cpu_isa)
+        : JitKernelBase{name, max_cpu_isa}, m_jcp{jcp}, m_func{nullptr} {}
+
+    ~JitKernel() override = default;
+
+    dnnl::impl::status_t create_kernel() override {
+        const dnnl::impl::status_t code = jit_generator::create_kernel();
+        if (code != dnnl::impl::status::success) {
+            OPENVINO_THROW("Could not create kernel. Error code: ", std::to_string(code), ". ",
+                       "Xbyak error code: ", Xbyak::ConvertErrorToString(Xbyak::GetError()));
+        }
+        m_func = (decltype(m_func))jit_ker();
+        return code;
+    }
+
+    void operator()(const CallArgs* args) const {
+        assert(m_func);
+        m_func(args);
+    }
+
+    void operator()(const CallArgs& args) const {
+        this->operator()(&args);
+    }
+
+    template <template<dnnl::impl::cpu::x64::cpu_isa_t isa> class KernelT>
+    static std::shared_ptr<JitKernel<CompileParams, CallArgs>> createInstance(const CompileParams& jcp) {
+        std::shared_ptr<JitKernel<CompileParams, CallArgs>> res;
+
+        try {
+#define IF_ISA_CASE(ISA)                                \
+            if (dnnl::impl::cpu::x64::mayiuse(ISA))     \
+                res.reset(new KernelT<ISA>(jcp));       \
+            else
+
+            IF_ISA_CASE(dnnl::impl::cpu::x64::avx512_core)
+            IF_ISA_CASE(dnnl::impl::cpu::x64::avx2)
+            IF_ISA_CASE(dnnl::impl::cpu::x64::sse41);
+
+#undef IF_ISA_CASE
+
+            if (res) {
+                res->create_kernel();
+            }
+        } catch (...) {
+            return nullptr;
+        }
+
+        return res;
+    }
+
+protected:
+    CompileParams m_jcp;
+
+private:
+    KernelFunc m_func;
+};
+
+#endif // OPENVINO_ARCH_X86_64
+
+} // namespace kernel
 } // namespace intel_cpu
 } // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/x64/random_uniform.cpp b/src/plugins/intel_cpu/src/nodes/kernels/x64/random_uniform.cpp
new file mode 100644
index 00000000000000..301c2f7e08ff69
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/kernels/x64/random_uniform.cpp
@@ -0,0 +1,635 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "random_uniform.hpp"
+
+using namespace dnnl::impl::cpu;
+
+namespace ov {
+namespace intel_cpu {
+namespace kernel {
+
+#define GET_OFF(field) offsetof(RandomUniformCallArgs, field)
+
+template <x64::cpu_isa_t isa>
+RandomUniform<isa>::RandomUniform(const RandomUniformCompileParams& jcp) :
+        JitKernel(jit_name(), jcp, isa) {
+}
+
+template <x64::cpu_isa_t isa>
+void RandomUniform<isa>::generate() {
+    this->preamble();
+    registersPool = RegistersPool::create(isa, {rax, rcx, rsp, rdi, k0});
+
+    r64_dst = getReg64();
+    r64_work_amount = getReg64();
+
+    mov(r64_work_amount, ptr[r64_params + GET_OFF(work_amount)]);
+    mov(r64_dst,  ptr[r64_params + GET_OFF(dst_ptr)]);
+
+    initVectors();
+    process();
+
+    registersPool.reset();
+    this->postamble();
+}
+
+template <>
+void RandomUniform<x64::avx512_core>::initVectors() {
+    const auto r64_aux = getReg64();
+    const auto r32_aux = Xbyak::Reg32(r64_aux.getIdx());
+    const auto r16_aux = Xbyak::Reg16(r64_aux.getIdx());
+
+    v_max_mul_n_64 = getVmm();
+    v_max_mul_c_64 = getVmm();
+    v_add_low_k    = getVmm();
+    v_add_up_k     = getVmm();
+    v_n_inc        = getVmm();
+    v_range        = getVmm();
+    v_min          = getVmm();
+    v_key_64       = getVmm();
+    v_counter_64   = getVmm();
+    v_n_64         = getVmm();
+    v_res_perm     = getVmm();
+
+    if (m_jcp.out_data_type.is_real()) {
+        v_convert_0 = getVmm();
+        v_convert_1 = getVmm();
+    }
+
+    // Initialize constants.
+#define BROADCAST_R(F, V, R, C)           \
+    mov(R, C);                            \
+    F(V, R);
+#define BROADCAST_P(F, V, R, C)           \
+    mov(R, ptr[r64_params + GET_OFF(C)]);  \
+    F(V, ptr[R]);
+
+    BROADCAST_R(vpbroadcastq, v_max_mul_n_64, r64_aux, STATISTIC_MAXIMIZING_MULTIPLIER_N)
+    BROADCAST_R(vpbroadcastq, v_max_mul_c_64, r64_aux, STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER)
+    BROADCAST_R(vpbroadcastd, v_add_low_k,    r32_aux, CRUSH_RESISTANCE_CONST_LOWER_VALUE)
+    BROADCAST_R(vpbroadcastd, v_add_up_k,     r32_aux, CRUSH_RESISTANCE_CONST_UPPER_VALUE)
+    BROADCAST_R(vpbroadcastq, v_n_inc,        r64_aux, 0x00000008)
+
+    if (m_jcp.out_data_type == element::f32) {
+        BROADCAST_R(vpbroadcastd, v_convert_0, r32_aux, 0x3f800000)
+        BROADCAST_R(vpbroadcastd, v_convert_1, r32_aux, 0x007fffff)
+        BROADCAST_P(vpbroadcastd, v_range,     r64_aux, range_ptr)
+        BROADCAST_P(vpbroadcastd, v_min,       r64_aux, min_ptr)
+    } else if (m_jcp.out_data_type == element::f16 && x64::mayiuse(x64::avx512_core_fp16)) {
+        BROADCAST_R(vpbroadcastw, v_convert_0, r16_aux, 0x3c00)
+        BROADCAST_R(vpbroadcastw, v_convert_1, r16_aux, 0x03ff)
+        BROADCAST_P(vpbroadcastw, v_range,     r64_aux, range_ptr)
+        BROADCAST_P(vpbroadcastw, v_min,       r64_aux, min_ptr)
+    } else if (m_jcp.out_data_type == element::bf16 && x64::mayiuse(x64::avx512_core_bf16)) {
+        v_convert_2 = getVmm();
+        const auto ymm_min = Xbyak::Ymm(v_min.getIdx());
+        const auto ymm_range = Xbyak::Ymm(v_range.getIdx());
+
+        BROADCAST_R(vpbroadcastw, v_convert_0, r16_aux, 0x3f80)
+        BROADCAST_R(vpbroadcastw, v_convert_1, r16_aux, 0x007f)
+        BROADCAST_R(vpbroadcastd, v_convert_2, r32_aux, 0x3f800000)
+
+        BROADCAST_P(vpbroadcastw, v_range, r64_aux, range_ptr)
+        vpmovzxwd(v_range, ymm_range);
+        uni_vpslld(v_range, v_range, 16);
+
+        BROADCAST_P(vpbroadcastw, v_min, r64_aux, min_ptr)
+        vpmovzxwd(v_min, ymm_min);
+        uni_vpslld(v_min, v_min, 16);
+    } else if (m_jcp.out_data_type == element::i32) {
+        const auto ymm_range = Xbyak::Ymm(v_range.getIdx());
+
+        BROADCAST_P(vpbroadcastd, v_range, r64_aux, range_ptr)
+        BROADCAST_P(vpbroadcastd, v_min,   r64_aux, min_ptr)
+
+        uni_vcvtdq2pd(v_range, ymm_range);
+    } else {
+        OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+    }
+
+    // Initialize inputs.
+    BROADCAST_P(vpbroadcastq, v_key_64,     r64_aux, key_ptr)
+    BROADCAST_P(vpbroadcastq, v_counter_64, r64_aux, counter_ptr)
+    BROADCAST_P(vpbroadcastq, v_n_64,       r64_aux, n_ptr)
+
+    if (m_jcp.out_data_type.size() <= 4) {
+        static const uint64_t n_inc_arr[8]  = { 0, 1, 2, 3, 4, 5, 6, 7 };
+        mov(r64_aux, reinterpret_cast<uintptr_t>(n_inc_arr));
+    } else {
+        static const uint64_t n_inc_arr[8]  = { 0, 1, 2, 3, 4, 5, 6, 7 }; // TODO: i64
+        mov(r64_aux, reinterpret_cast<uintptr_t>(n_inc_arr));
+    }
+    uni_vpaddq(v_n_64, v_n_64, ptr[r64_aux]);
+
+    // Initialize auxiliary vectors.
+    static const uint32_t res_perm_mask[16] = { 0b00000000, 0b00010000, 0b00001000, 0b00011000, 0b00000010, 0b00010010, 0b00001010, 0b00011010,
+                                                0b00000100, 0b00010100, 0b00001100, 0b00011100, 0b00000110, 0b00010110, 0b00001110, 0b00011110 };
+    mov(r64_aux, reinterpret_cast<uintptr_t>(res_perm_mask));
+    uni_vmovups(v_res_perm, ptr[r64_aux]);
+
+    if (m_jcp.out_data_type == element::f16 && x64::mayiuse(x64::avx512_core_fp16)) {
+        v_perm_16 = getVmm();
+        static const uint16_t perm_16[32] = { 0b00000000, 0b00000010, 0b00000100, 0b00000110, 0b00001000, 0b00001010, 0b00001100, 0b00001110,
+                                              0b00010000, 0b00010010, 0b00010100, 0b00010110, 0b00011000, 0b00011010, 0b00011100, 0b00011110,
+                                              0b00100000, 0b00100010, 0b00100100, 0b00100110, 0b00101000, 0b00101010, 0b00101100, 0b00101110,
+                                              0b00110000, 0b00110010, 0b00110100, 0b00110110, 0b00111000, 0b00111010, 0b00111100, 0b00111110 };
+        mov(r64_aux, reinterpret_cast<uintptr_t>(perm_16));
+        uni_vmovups(v_perm_16, ptr[r64_aux]);
+    }
+
+#undef BROADCAST_R
+#undef BROADCAST_P
+}
+
+template <x64::cpu_isa_t isa> // Works for AVX2, SSE41
+void RandomUniform<isa>::initVectors() {
+    const auto r64_aux = getReg64();
+
+    v_max_mul_n_64 = getVmm();
+    v_max_mul_c_64 = getVmm();
+    v_add_low_k    = getVmm();
+    v_add_up_k     = getVmm();
+    v_range        = getVmm();
+    v_key_64       = getVmm();
+    v_counter_64   = getVmm();
+    v_n_64         = getVmm();
+
+    r64_n_inc      = getReg64();
+    r64_min        = getReg64();
+
+#define INIT_ARR(A, V, R, T)                                                                \
+    static const T A[8] = { V, V, V, V, V, V, V, V };                                       \
+    if (isa == x64::avx2) {                                                                 \
+        mov(R, reinterpret_cast<uintptr_t>(A));                                             \
+    } else {                                                                                \
+        static const T* A##_aligned = A + (reinterpret_cast<int64_t>(A) % 16) / sizeof(T);  \
+        mov(R, reinterpret_cast<uintptr_t>(A##_aligned));                                   \
+    }
+
+    // Initialize constants.
+    INIT_ARR(max_mul_n_64, STATISTIC_MAXIMIZING_MULTIPLIER_N, r64_aux, uint64_t);
+    uni_vmovups(v_max_mul_n_64, ptr[r64_aux]);
+
+    INIT_ARR(max_mul_c_64, STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER, r64_aux, uint64_t);
+    uni_vmovups(v_max_mul_c_64, ptr[r64_aux]);
+
+    INIT_ARR(add_low_k, CRUSH_RESISTANCE_CONST_LOWER_VALUE, r64_aux, uint32_t);
+    uni_vmovups(v_add_low_k, ptr[r64_aux]);
+
+    INIT_ARR(add_up_k, CRUSH_RESISTANCE_CONST_UPPER_VALUE, r64_aux, uint32_t);
+    uni_vmovups(v_add_up_k, ptr[r64_aux]);
+
+    INIT_ARR(n_inc_step, isa == x64::avx2 ? 4 : 2, r64_n_inc, uint64_t);
+
+    if (m_jcp.out_data_type == element::f32) {
+        r64_convert_0  = getReg64();
+        r64_convert_1  = getReg64();
+
+        INIT_ARR(convert_0, 0x3f800000, r64_convert_0, uint32_t);
+        INIT_ARR(convert_1, 0x007fffff, r64_convert_1, uint32_t);
+
+        mov(r64_aux, ptr[r64_params + GET_OFF(range_ptr)]);
+        uni_vpbroadcastd(v_range, ptr[r64_aux]);
+
+        auto v_aux = getVmm();
+        mov(r64_aux, ptr[r64_params + GET_OFF(min_ptr)]);
+        uni_vpbroadcastd(v_aux, ptr[r64_aux]);
+        static uint32_t min_arr[8];
+        mov(r64_min, reinterpret_cast<uintptr_t>(min_arr));
+        uni_vmovups(ptr[r64_min], v_aux);
+    } else if (m_jcp.out_data_type == element::i32) {
+        r64_f64_pow_52 = getReg64();
+        const auto v_aux = getVmm();
+        const auto xmm_range = Xbyak::Xmm(v_range.getIdx());
+
+        INIT_ARR(f64_pow_52, 0x4330000000000000, r64_f64_pow_52, uint64_t);
+
+        mov(r64_aux, ptr[r64_params + GET_OFF(range_ptr)]);
+        uni_vpbroadcastd(v_range, ptr[r64_aux]);
+
+        mov(r64_aux, ptr[r64_params + GET_OFF(min_ptr)]);
+        uni_vpbroadcastd(v_aux, ptr[r64_aux]);
+        static uint32_t min_arr[8];
+        mov(r64_min, reinterpret_cast<uintptr_t>(min_arr));
+        uni_vmovups(ptr[r64_min], v_aux);
+
+        uni_vcvtdq2pd(v_range, xmm_range);
+    } else {
+        OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+    }
+
+    // Initialize inputs.
+    mov(r64_aux, ptr[r64_params + GET_OFF(key_ptr)]);
+    uni_vpbroadcastq(v_key_64, ptr[r64_aux]);
+
+    mov(r64_aux, ptr[r64_params + GET_OFF(counter_ptr)]);
+    uni_vpbroadcastq(v_counter_64, ptr[r64_aux]);
+
+    mov(r64_aux, ptr[r64_params + GET_OFF(n_ptr)]);
+    uni_vpbroadcastq(v_n_64, ptr[r64_aux]);
+
+    if (m_jcp.out_data_type.size() <= 4) {
+        if (isa == x64::avx2) {
+            static const uint64_t n_inc_arr[4]  = { 0, 1, 2, 3 };
+            mov(r64_aux, reinterpret_cast<uintptr_t>(n_inc_arr));
+        } else {
+            static uint64_t n_inc_arr[4];
+            static uint64_t* n_inc_arr_aligned = n_inc_arr + (reinterpret_cast<int64_t>(n_inc_arr) % 16) / sizeof(uint64_t);
+            n_inc_arr_aligned[0] = 0;
+            n_inc_arr_aligned[1] = 1;
+            mov(r64_aux, reinterpret_cast<uintptr_t>(n_inc_arr_aligned));
+        }
+    } else {
+        static const uint64_t n_inc_arr[4]  = { 0, 1, 2, 3 }; // TODO: i64
+        mov(r64_aux, reinterpret_cast<uintptr_t>(n_inc_arr));
+    }
+
+    uni_vpaddq(v_n_64, v_n_64, ptr[r64_aux]);
+
+#undef INIT_ARR
+}
+
+template <x64::cpu_isa_t isa>
+void RandomUniform<isa>::process() {
+    auto v_dst_0 = getVmm();
+    auto v_dst_1 = getVmm();
+    std::vector<Vmm> v_res{ v_dst_0, v_dst_1 };
+
+    auto step = vlen;
+    if (one_of(m_jcp.out_data_type.size(), 2lu, 4lu)) {
+        step = vlen * 2 / sizeof(uint32_t);
+    } else if (m_jcp.out_data_type.size() == 8) {
+        step = vlen / sizeof(uint32_t);
+    }
+
+    Xbyak::Label l_loop, l_tail;
+    L(l_loop); {
+        cmp(r64_work_amount, step);
+        jl(l_tail, T_NEAR);
+
+        runPhilox(v_res, v_key_64, v_counter_64, v_n_64);
+        convert(v_res, v_res);
+
+        uni_vmovups(ptr[r64_dst], v_dst_0);
+        add(r64_dst, vlen);
+        if (one_of(m_jcp.out_data_type.size(), 4lu, 8lu)) {
+            uni_vmovups(ptr[r64_dst], v_dst_1);
+            add(r64_dst, vlen);
+        }
+
+        if (isa == x64::avx512_core) {
+            uni_vpaddd(v_n_64, v_n_64, v_n_inc);
+        } else {
+            uni_vpaddd(v_n_64, v_n_64, ptr[r64_n_inc]);
+        }
+
+        sub(r64_work_amount, step);
+        jmp(l_loop, T_NEAR);
+    }
+
+    L(l_tail);
+    tail(v_res);
+}
+
+template <x64::cpu_isa_t isa>
+void RandomUniform<isa>::calculateRound(const Vmm& vmm_k_0, const Vmm& vmm_k_1, const Vmm& vmm_c_0, const Vmm& vmm_c_1,
+                                        const Vmm& vmm_n_0, const Vmm& vmm_n_1, const Vmm& vmm_aux_0, const Vmm& vmm_aux_1) {
+    uni_vpmuludq(vmm_aux_0, vmm_n_0, v_max_mul_n_64);  // {p0,p1,p0,p1} = {n0,_,n0,_} * {m0,_,m0,_}
+    uni_vpmuludq(vmm_aux_1, vmm_c_0, v_max_mul_c_64);  // {r0,r1,r0,r1} = {c0,_,c0,_} * {m0,_,m0,_}
+
+    uni_vpshufd(vmm_c_0, vmm_aux_0, 0b10110001);       // {p1,p0,p1,p0} = shuf {p0,p1,p0,p1}
+    uni_vxorps(vmm_c_0, vmm_c_0, vmm_c_1);             // {c0,_,c0,_} = {p1,_,p1,_} ^ {c1,_,c1,_}
+    uni_vxorps(vmm_c_0, vmm_c_0, vmm_k_1);             // {c0,_,c0,_} = {c0,_,c0,_} ^ {k1,_,k1,_}
+
+    uni_vpshufd(vmm_n_0, vmm_aux_1, 0b10110001);       // {r1,r0,r1,r0} = shuf {r0,r1,r0,r1}
+    uni_vxorps(vmm_n_0, vmm_n_0, vmm_n_1);             // {n0,_,n0,_} = {r1,_,r1,_} ^ {n1,_,n1,_}
+    uni_vxorps(vmm_n_0, vmm_n_0, vmm_k_0);             // {n0,_,n0,_} = {n0,_,n0,_} ^ {k0,_,k0,_}
+}
+
+template <x64::cpu_isa_t isa>
+void RandomUniform<isa>::runPhilox(const std::vector<Vmm>& vmm_dst, const Vmm& vmm_key, const Vmm& vmm_counter, const Vmm& vmm_n) {
+    auto vmm_k_0 = getVmm();
+    auto vmm_k_1 = getVmm();
+    auto vmm_n_0 = getVmm();
+    auto vmm_n_1 = vmm_dst[0];
+    auto vmm_c_0 = getVmm();
+    auto vmm_c_1 = getVmm();
+    auto vmm_aux_0 = getVmm();
+    auto vmm_aux_1 = vmm_dst[1];
+
+    uni_vmovups(vmm_k_0, vmm_key);                        // {k0,k1,k0,k1} -> {k0,_,k0,_}
+    uni_vpshufd(vmm_k_1, vmm_key, 0b10110001);            // {k0,k1,k0,k1} -> {k1,_,k1,_}
+
+    uni_vpmuludq(vmm_aux_0, vmm_n, v_max_mul_n_64);       // {p0,p1,p0,p1} = {n0,_,n0,_} * {m0,_,m0,_}
+    uni_vpmuludq(vmm_aux_1, vmm_counter, v_max_mul_c_64); // {r0,r1,r0,r1} = {c0,_,c0,_} * {m0,_,m0,_}
+
+    uni_vxorps(vmm_c_0, vmm_aux_0, vmm_counter);          // {_,c0,_,c0} = {_,p1,_,p1} ^ {_,c1,_,c1}
+    uni_vxorps(vmm_c_0, vmm_c_0, vmm_key);                // {_,c0,_,c0} = {_,c0,_,c0} ^ {_,k1,_,k1}
+    uni_vpshufd(vmm_c_0, vmm_c_0, 0b10110001);            // {_,c0,_,c0} -> {c0,_,c0,_}
+
+    uni_vxorps(vmm_n_0, vmm_aux_1, vmm_n);                // {_,n0,_,n0} = {_,r1,_,r1} ^ {_,n1,_,n1}
+    uni_vpshufd(vmm_n_0, vmm_n_0, 0b10110001);            // {_,n0,_,n0} -> {n0,_,n0,_}
+    uni_vxorps(vmm_n_0, vmm_n_0, vmm_key);                // {n0,_,n0,_} = {n0,_,n0,_} ^ {k0,_,k0,_}
+
+    for (size_t i = 0lu; i < ROUNDS_NUMBER - 1; i++) {
+        raiseKey(vmm_k_0, vmm_k_1);
+
+        std::swap(vmm_c_1, vmm_aux_0);
+        std::swap(vmm_n_1, vmm_aux_1);
+        calculateRound(vmm_k_0, vmm_k_1, vmm_c_0, vmm_c_1, vmm_n_0, vmm_n_1, vmm_aux_0, vmm_aux_1);
+    }
+    std::swap(vmm_c_1, vmm_aux_0);
+    std::swap(vmm_n_1, vmm_aux_1);
+
+    if (isa == x64::avx512_core) {
+        vpermt2d(vmm_n_0, v_res_perm, vmm_n_1);             // {n0,n1,n0,n1} = perm {n0,_,n0,_} {n1,_,n1,_}
+        vpermt2d(vmm_c_0, v_res_perm, vmm_c_1);             // {c0,c1,c0,c1} = perm {c0,_,c0,_} {c1,_,c1,_}
+        vshufpd(vmm_dst[0], vmm_n_0, vmm_c_0, 0b00000000);  // {n0,n1,c0,c1} = shuf {n0,n1,n0,n1} {c0,c1,c0,c1}
+        vshufpd(vmm_dst[1], vmm_n_0, vmm_c_0, 0b11111111);  // {n0,n1,c0,c1} = shuf {n0,n1,n0,n1} {c0,c1,c0,c1}
+    } else if (isa == x64::avx2) {
+        auto ymm_dst_0 = Xbyak::Ymm(vmm_dst[0].getIdx());
+        auto ymm_dst_1 = Xbyak::Ymm(vmm_dst[1].getIdx());
+        auto ymm_c_0 = Xbyak::Ymm(vmm_c_0.getIdx());
+
+        uni_vshufps(vmm_n_0, vmm_n_0, vmm_n_1, 0b10001000);   // {n0,n0,n1,n1} = shuf {n0,_,n0,_} {n1,_,n1,_}
+        uni_vshufps(vmm_c_0, vmm_c_0, vmm_c_1, 0b10001000);   // {c0,c0,c1,c1} = shuf {c0,_,c0,_} {c1,_,c1,_}
+        uni_vshufps(ymm_dst_1, vmm_n_0, vmm_c_0, 0b10001000); // {n0,n1,c0,c1} = shuf {n0,n0,n1,n1} {c0,c0,c1,c1}
+        uni_vshufps(vmm_c_0, vmm_n_0, vmm_c_0, 0b11011101);   // {n0,n1,c0,c1} = shuf {n0,n0,n1,n1} {c0,c0,c1,c1}
+        vperm2f128(ymm_dst_0, ymm_dst_1, ymm_c_0, 0b00100000);
+        vperm2f128(ymm_dst_1, ymm_dst_1, ymm_c_0, 0b00110001);
+    } else {
+        uni_vshufps(vmm_n_0, vmm_n_0, vmm_n_1, 0b10001000);
+        uni_vshufps(vmm_c_0, vmm_c_0, vmm_c_1, 0b10001000);
+        uni_vshufps(vmm_dst[0], vmm_n_0, vmm_c_0, 0b10001000);
+        uni_vshufps(vmm_dst[1], vmm_n_0, vmm_c_0, 0b11011101);
+    }
+}
+
+template <x64::cpu_isa_t isa>
+void RandomUniform<isa>::raiseKey(const Vmm& vmm_k_0, const Vmm& vmm_k_1) {
+    uni_vpaddd(vmm_k_0, vmm_k_0, v_add_low_k); // {k0,_,k0,_} + {l0,_,l0,_}
+    uni_vpaddd(vmm_k_1, vmm_k_1, v_add_up_k);  // {k1,_,k1,_} + {u0,_,u0,_}
+}
+
+template <>
+void RandomUniform<x64::avx512_core>::convert(const std::vector<Vmm>& v_dst, const std::vector<Vmm>& v_src) {
+    if (m_jcp.out_data_type.size() == 4) {
+        for (size_t i = 0lu; i < v_src.size(); i++) {
+            const auto& vmm_src = v_src[i];
+            const auto& vmm_dst = v_dst[i];
+
+            if (m_jcp.out_data_type == element::f32) {
+                uni_vandps(vmm_dst, vmm_src, v_convert_1);
+                uni_vorps(vmm_dst, vmm_dst, v_convert_0);
+                uni_vsubps(vmm_dst, vmm_dst, v_convert_0);
+                vfmadd132ps(vmm_dst, v_min, v_range);
+            } else if (m_jcp.out_data_type == element::i32) {
+                // x % (max - min) + min
+                const auto v_aux_0 = getVmm();
+                const auto v_aux_1 = getVmm();
+                const auto ymm_src = Xbyak::Ymm(vmm_src.getIdx());
+                const auto ymm_dst = Xbyak::Ymm(vmm_dst.getIdx());
+                const auto ymm_aux_1 = Xbyak::Ymm(v_aux_1.getIdx());
+
+                // Divide in the f64 due to the f32 loses accuracy here.
+                vcvtudq2pd(v_aux_0, ymm_src);
+                uni_vdivpd(v_aux_1, v_aux_0, v_range);
+                uni_vroundpd(v_aux_1, v_aux_1, 3);
+                vfnmadd132pd(v_aux_1, v_aux_0, v_range);
+
+                vextractf64x4(ymm_dst, vmm_src, 1);
+                vcvtudq2pd(v_aux_0, ymm_dst);
+                uni_vcvtpd2dq(ymm_dst, v_aux_1);
+                uni_vdivpd(v_aux_1, v_aux_0, v_range);
+                uni_vroundpd(v_aux_1, v_aux_1, 3);
+                vfnmadd132pd(v_aux_1, v_aux_0, v_range);
+                uni_vcvtpd2dq(ymm_aux_1, v_aux_1);
+                vshuff64x2(vmm_dst, vmm_dst, v_aux_1, 0b01000100);
+
+                uni_vpaddd(vmm_dst, vmm_dst, v_min);
+            } else {
+                OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+            }
+        }
+    } else if (m_jcp.out_data_type.size() == 2) {
+        if (m_jcp.out_data_type == element::f16 && x64::mayiuse(x64::avx512_core_fp16)) {
+            const auto& vmm_dst = v_dst[0];
+
+            if (v_src[0].getIdx() != vmm_dst.getIdx()) {
+                uni_vmovups(vmm_dst, v_src[0]);
+            }
+            vpermt2w(vmm_dst, v_perm_16, v_src[1]);
+
+            uni_vandps(vmm_dst, vmm_dst, v_convert_1);
+            uni_vorps(vmm_dst, vmm_dst, v_convert_0);
+            vsubph(vmm_dst, vmm_dst, v_convert_0);
+            vfmadd132ph(vmm_dst, v_min, v_range);
+        } else if (m_jcp.out_data_type == element::bf16 && x64::mayiuse(x64::avx512_core_bf16)) {
+            for (size_t i = 0lu; i < v_src.size(); i++) {
+                const auto& vmm_dst = v_dst[i];
+
+                uni_vandps(vmm_dst, v_src[i], v_convert_1);
+                uni_vorps(vmm_dst, vmm_dst, v_convert_0);
+                uni_vpslld(vmm_dst, vmm_dst, 16);
+
+                uni_vsubps(vmm_dst, vmm_dst, v_convert_2);
+                vfmadd132ps(vmm_dst, v_min, v_range);
+            }
+
+            vcvtne2ps2bf16(v_dst[0], v_dst[1], v_dst[0]);
+        } else {
+            OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+        }
+    } else if (m_jcp.out_data_type.size() == 8) {
+        if (m_jcp.out_data_type == element::i64) {
+            // TODO: in scope of i64 enabling.
+        }
+        OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+    } else {
+        OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+    }
+}
+
+template <x64::cpu_isa_t isa> // Works for AVX2, SSE41
+void RandomUniform<isa>::convert(const std::vector<Vmm>& v_dst, const std::vector<Vmm>& v_src) {
+    if (m_jcp.out_data_type.size() == 4) {
+        for (size_t i = 0lu; i < v_src.size(); i++) {
+            auto vmm_src = v_src[i];
+            auto vmm_dst = v_dst[i];
+
+            if (m_jcp.out_data_type == element::f32) {
+                uni_vandps(vmm_dst, vmm_src, ptr[r64_convert_1]);
+                uni_vorps(vmm_dst, vmm_dst, ptr[r64_convert_0]);
+                uni_vsubps(vmm_dst, vmm_dst, ptr[r64_convert_0]);
+                if (isa == x64::avx2) {
+                    vfmadd213ps(vmm_dst, v_range, ptr[r64_min]);
+                } else {
+                    uni_vmulps(vmm_dst, vmm_dst, v_range);
+                    uni_vaddps(vmm_dst, vmm_dst, ptr[r64_min]);
+                }
+            } else if (m_jcp.out_data_type == element::i32) {
+                // x % (max - min) + min
+                const auto v_aux_0 = getVmm();
+                const auto v_aux_1 = getVmm();
+                const auto xmm_dst = Xbyak::Xmm(vmm_dst.getIdx());
+                const auto ymm_dst = Xbyak::Ymm(vmm_dst.getIdx());
+                const auto xmm_aux_1 = Xbyak::Xmm(v_aux_1.getIdx());
+
+                // Convert u32->f64. TODO: move to convert emitter after i64 enabling.
+                uni_vpmovzxdq(v_aux_0, xmm_dst);
+                uni_vorps(v_aux_0, v_aux_0, ptr[r64_f64_pow_52]);
+                uni_vsubpd(v_aux_0, v_aux_0, ptr[r64_f64_pow_52]);
+
+                // Divide in the f64 due to the f32 loses accuracy here.
+                uni_vdivpd(v_aux_1, v_aux_0, v_range);
+                uni_vroundpd(v_aux_1, v_aux_1, 3);
+                if (isa == x64::avx2) {
+                    vfnmadd132pd(v_aux_1, v_aux_0, v_range);
+                } else {
+                    uni_vmulpd(v_aux_1, v_aux_1, v_range);
+                    uni_vsubpd(v_aux_0, v_aux_0, v_aux_1);
+                    uni_vmovups(v_aux_1, v_aux_0);
+                }
+
+                if (isa == x64::avx2) {
+                    vperm2f128(ymm_dst, ymm_dst, ymm_dst, 0b00000001);
+                } else {
+                    uni_vshufpd(vmm_dst, vmm_dst, vmm_dst, 0b00000001);
+                }
+                // Convert u32->f64. TODO: move to convert emitter after i64 enabling.
+                uni_vpmovzxdq(v_aux_0, xmm_dst);
+                uni_vorps(v_aux_0, v_aux_0, ptr[r64_f64_pow_52]);
+                uni_vsubpd(v_aux_0, v_aux_0, ptr[r64_f64_pow_52]);
+
+                uni_vcvtpd2dq(xmm_dst, v_aux_1);
+                uni_vdivpd(v_aux_1, v_aux_0, v_range);
+                uni_vroundpd(v_aux_1, v_aux_1, 3);
+                if (isa == x64::avx2) {
+                    vfnmadd132pd(v_aux_1, v_aux_0, v_range);
+                } else {
+                    uni_vmulpd(v_aux_1, v_aux_1, v_range);
+                    uni_vsubpd(v_aux_0, v_aux_0, v_aux_1);
+                    uni_vmovups(v_aux_1, v_aux_0);
+                }
+                uni_vcvtpd2dq(xmm_aux_1, v_aux_1);
+                if (isa == x64::avx2) {
+                    vperm2f128(ymm_dst, ymm_dst, v_aux_1, 0b00100000);
+                } else {
+                    uni_vshufpd(vmm_dst, vmm_dst, v_aux_1, 0b00000000);
+                }
+
+                uni_vpaddd(vmm_dst, vmm_dst, ptr[r64_min]);
+            } else {
+                OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+            }
+        }
+    } else if (m_jcp.out_data_type.size() == 8) {
+        if (m_jcp.out_data_type == element::i64) {
+            // TODO: in scope of i64 enabling.
+        }
+        OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+    } else {
+        OPENVINO_THROW("RandomUniform kernel does not support precision ", m_jcp.out_data_type, " for ", x64::get_isa_info());
+    }
+}
+
+template <>
+void RandomUniform<x64::avx512_core>::tail(const std::vector<Vmm>& vmm_dst) {
+    Xbyak::Label l_end;
+    const auto k_rest_mask = getMask();
+
+    cmp(r64_work_amount, 0);
+    jle(l_end, T_NEAR);
+
+    runPhilox(vmm_dst, v_key_64, v_counter_64, v_n_64);
+    convert(vmm_dst, vmm_dst);
+
+    if (m_jcp.out_data_type.size() == 4) {
+        Xbyak::Label l_0;
+        const auto step = vlen / sizeof(uint32_t);
+
+        cmp(r64_work_amount, step);
+        jl(l_0, T_NEAR);
+
+        uni_vmovups(ptr[r64_dst], vmm_dst[0]);
+        add(r64_dst, vlen);
+        sub(r64_work_amount, step);
+        fillRestWorkMask(k_rest_mask, r64_work_amount);
+        uni_vmovups(ptr[r64_dst] | k_rest_mask, vmm_dst[1]);
+        jmp(l_end, T_NEAR);
+
+        L(l_0);
+        fillRestWorkMask(k_rest_mask, r64_work_amount);
+        uni_vmovups(ptr[r64_dst] | k_rest_mask, vmm_dst[0]);
+    } else if (m_jcp.out_data_type.size() == 2) {
+        fillRestWorkMask(k_rest_mask, r64_work_amount);
+        vmovdqu16(ptr[r64_dst] | k_rest_mask, vmm_dst[0]);
+    }
+
+    L(l_end);
+}
+
+template <>
+void RandomUniform<x64::avx2>::tail(const std::vector<Vmm>& vmm_dst) {
+    Xbyak::Label l_0, l_end;
+    const auto step = vlen / sizeof(uint32_t);
+
+    cmp(r64_work_amount, 0);
+    jle(l_end, T_NEAR);
+
+    runPhilox(vmm_dst, v_key_64, v_counter_64, v_n_64);
+    convert(vmm_dst, vmm_dst);
+    const auto v_rest_mask = getVmm();
+
+    cmp(r64_work_amount, step);
+    jl(l_0, T_NEAR);
+
+    uni_vmovups(ptr[r64_dst], vmm_dst[0]);
+    add(r64_dst, vlen);
+    sub(r64_work_amount, step);
+    fillRestWorkMask(v_rest_mask, r64_work_amount, m_jcp.out_data_type.size());
+    vmaskmovps(ptr[r64_dst], v_rest_mask, vmm_dst[1]);
+    jmp(l_end, T_NEAR);
+
+    L(l_0);
+    fillRestWorkMask(v_rest_mask, r64_work_amount, m_jcp.out_data_type.size());
+    vmaskmovps(ptr[r64_dst],  v_rest_mask, vmm_dst[0]);
+
+    L(l_end);
+}
+
+template <x64::cpu_isa_t isa>
+void RandomUniform<isa>::tail(const std::vector<Vmm>& vmm_dst) {
+    Xbyak::Label l_0, l_end;
+    const auto step = vlen / sizeof(uint32_t);
+
+    cmp(r64_work_amount, 0);
+    jle(l_end, T_NEAR);
+
+    runPhilox(vmm_dst, v_key_64, v_counter_64, v_n_64);
+    convert(vmm_dst, vmm_dst);
+
+    cmp(r64_work_amount, step);
+    jl(l_0, T_NEAR);
+
+    uni_vmovups(ptr[r64_dst], vmm_dst[0]);
+    add(r64_dst, vlen);
+    sub(r64_work_amount, step);
+    store(ptr[r64_dst], vmm_dst[1], r64_work_amount, m_jcp.out_data_type.size());
+    jmp(l_end, T_NEAR);
+
+    L(l_0);
+    store(ptr[r64_dst], vmm_dst[0], r64_work_amount, m_jcp.out_data_type.size());
+
+    L(l_end);
+}
+
+template class RandomUniform<x64::avx512_core>;
+template class RandomUniform<x64::avx2>;
+template class RandomUniform<x64::sse41>;
+
+}   // namespace kernel
+}   // namespace intel_cpu
+}   // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/x64/random_uniform.hpp b/src/plugins/intel_cpu/src/nodes/kernels/x64/random_uniform.hpp
new file mode 100644
index 00000000000000..366be4c3a132ce
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/kernels/x64/random_uniform.hpp
@@ -0,0 +1,99 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "jit_kernel_base.hpp"
+
+#if defined(OPENVINO_ARCH_X86_64)
+
+namespace ov {
+namespace intel_cpu {
+namespace kernel {
+
+struct RandomUniformCompileParams {
+    element::Type out_data_type = element::f32;
+};
+
+struct RandomUniformCallArgs {
+    void* dst_ptr;
+    const void* key_ptr;
+    const void* counter_ptr;
+    const void* n_ptr;
+    const void* min_ptr;
+    const void* range_ptr;
+    uint64_t work_amount = 0lu;
+};
+
+template <dnnl::impl::cpu::x64::cpu_isa_t isa>
+class RandomUniform : public JitKernel<RandomUniformCompileParams, RandomUniformCallArgs> {
+public:
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(RandomUniform)
+
+    explicit RandomUniform(const RandomUniformCompileParams& jcp);
+
+    void generate() override;
+
+private:
+    using Vmm   = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::avx512_core, Xbyak::Zmm,
+                                                           isa == dnnl::impl::cpu::x64::sse41,       Xbyak::Xmm,
+                                                                                                     Xbyak::Ymm>::type;
+    using Vmask = typename dnnl::impl::utils::conditional3<isa == dnnl::impl::cpu::x64::avx512_core, Xbyak::Opmask,
+                                                           isa == dnnl::impl::cpu::x64::sse41,       Xbyak::Xmm,
+                                                                                                     Xbyak::Ymm>::type;
+
+    RegistersPool::Reg<Xbyak::Reg64> r64_dst;
+    RegistersPool::Reg<Xbyak::Reg64> r64_work_amount;
+    RegistersPool::Reg<Xbyak::Reg64> r64_n_inc;
+    RegistersPool::Reg<Xbyak::Reg64> r64_convert_0;
+    RegistersPool::Reg<Xbyak::Reg64> r64_convert_1;
+    RegistersPool::Reg<Xbyak::Reg64> r64_min;
+    RegistersPool::Reg<Xbyak::Reg64> r64_f64_pow_52;
+
+    const Xbyak::Reg64 r64_params = Xbyak::Reg64(dnnl::impl::cpu::x64::abi_param_regs[0]);
+
+    // Vector registers.
+    RegistersPool::Reg<Vmm> v_max_mul_n_64;
+    RegistersPool::Reg<Vmm> v_max_mul_c_64;
+    RegistersPool::Reg<Vmm> v_add_low_k;
+    RegistersPool::Reg<Vmm> v_add_up_k;
+    RegistersPool::Reg<Vmm> v_convert_0;
+    RegistersPool::Reg<Vmm> v_convert_1;
+    RegistersPool::Reg<Vmm> v_convert_2;
+    RegistersPool::Reg<Vmm> v_n_inc;
+    RegistersPool::Reg<Vmm> v_key_64;
+    RegistersPool::Reg<Vmm> v_counter_64;
+    RegistersPool::Reg<Vmm> v_n_64;
+    RegistersPool::Reg<Vmm> v_min;
+    RegistersPool::Reg<Vmm> v_range;
+    RegistersPool::Reg<Vmm> v_res_perm;
+    RegistersPool::Reg<Vmm> v_perm_16;
+
+    void initVectors();
+
+    void process();
+
+    void runPhilox(const std::vector<Vmm>& vmm_res, const Vmm& vmm_key, const Vmm& vmm_counter, const Vmm& vmm_n);
+
+    void calculateRound(const Vmm& vmm_k_0, const Vmm& vmm_k_1, const Vmm& vmm_c_0, const Vmm& vmm_c_1,
+                        const Vmm& vmm_n_0, const Vmm& vmm_n_1, const Vmm& vmm_aux_0, const Vmm& vmm_aux_1);
+
+    void raiseKey(const Vmm& vmm_k_0, const Vmm& vmm_k_1);
+
+    void convert(const std::vector<Vmm>& vmm_dst, const std::vector<Vmm>& vmm_src);
+
+    void tail(const std::vector<Vmm>& vmm_dst);
+
+    static constexpr uint64_t ROUNDS_NUMBER = 10lu;
+    static constexpr uint32_t CRUSH_RESISTANCE_CONST_LOWER_VALUE = 0x9E3779B9;
+    static constexpr uint32_t CRUSH_RESISTANCE_CONST_UPPER_VALUE = 0xBB67AE85;
+    static constexpr uint64_t STATISTIC_MAXIMIZING_MULTIPLIER_N = 0xD2511F53;
+    static constexpr uint64_t STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER = 0xCD9E8D57;
+};
+
+}   // namespace kernel
+}   // namespace intel_cpu
+}   // namespace ov
+
+#endif // OPENVINO_ARCH_X86_64
diff --git a/src/plugins/intel_cpu/src/nodes/non_max_suppression.cpp b/src/plugins/intel_cpu/src/nodes/non_max_suppression.cpp
index bfd1d8fa982901..d2a46ac97da017 100644
--- a/src/plugins/intel_cpu/src/nodes/non_max_suppression.cpp
+++ b/src/plugins/intel_cpu/src/nodes/non_max_suppression.cpp
@@ -626,7 +626,7 @@ void NonMaxSuppression::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    const std::vector<Precision> supportedFloatPrecision = {Precision::FP32, Precision::BF16};
+    const std::vector<Precision> supportedFloatPrecision = {Precision::FP32, Precision::BF16, Precision::FP16};
     const std::vector<Precision> supportedIntOutputPrecision = {Precision::I32, Precision::I64};
 
     checkPrecision(getOriginalInputPrecisionAtPort(NMS_BOXES), supportedFloatPrecision, "boxes", inType);
diff --git a/src/plugins/intel_cpu/src/nodes/non_zero.cpp b/src/plugins/intel_cpu/src/nodes/non_zero.cpp
index 4571eaa9e8c998..be2a8d894fc7f8 100644
--- a/src/plugins/intel_cpu/src/nodes/non_zero.cpp
+++ b/src/plugins/intel_cpu/src/nodes/non_zero.cpp
@@ -57,7 +57,7 @@ void NonZero::initSupportedPrimitiveDescriptors() {
         return;
 
     const auto &inPrc = getOriginalInputPrecisionAtPort(0);
-    if (!one_of(inPrc, Precision::FP32, Precision::BF16, Precision::I32, Precision::U32, Precision::I8,  Precision::U8)) {
+    if (!one_of(inPrc, Precision::FP32, Precision::BF16, Precision::FP16, Precision::I32, Precision::U32, Precision::I8,  Precision::U8)) {
         IE_THROW() << "Can't create primitive descriptor for NonZero layer with name: " << getName() << " doesn't support "
                    << inPrc.name() << " precision on 0 port";
     }
@@ -123,6 +123,7 @@ void NonZero::execute(dnnl::stream strm) {
     OV_SWITCH(intel_cpu, NonZeroExecute, ctx, inputPrec,
               OV_CASE(Precision::FP32, float),
               OV_CASE(Precision::BF16, bfloat16_t),
+              OV_CASE(Precision::FP16, float16),
               OV_CASE(Precision::I32, int),
               OV_CASE(Precision::U32, uint32_t),
               OV_CASE(Precision::I8, int8_t),
diff --git a/src/plugins/intel_cpu/src/nodes/normalize.cpp b/src/plugins/intel_cpu/src/nodes/normalize.cpp
index c4fc60a9d9e855..12f3ecf397764a 100644
--- a/src/plugins/intel_cpu/src/nodes/normalize.cpp
+++ b/src/plugins/intel_cpu/src/nodes/normalize.cpp
@@ -796,10 +796,14 @@ void NormalizeL2::initSupportedPrimitiveDescriptors() {
             inputPrecision = outputPrecision = Precision::BF16;
     }
 
-    if (!one_of(inputPrecision, Precision::FP32, Precision::BF16, Precision::I8, Precision::U8)) {
+    if (one_of(Precision::FP16, inputPrecision, outputPrecision) && mayiuse(cpu::x64::sse41)) {
+        inputPrecision = outputPrecision = Precision::FP32;
+    }
+
+    if (!one_of(inputPrecision, Precision::FP32, Precision::BF16, Precision::FP16, Precision::I8, Precision::U8)) {
         THROW_ERROR << "has unsupported input precision: " << inputPrecision;
     }
-    if (!one_of(outputPrecision, Precision::FP32, Precision::BF16, Precision::I8, Precision::U8)) {
+    if (!one_of(outputPrecision, Precision::FP32, Precision::BF16, Precision::FP16, Precision::I8, Precision::U8)) {
         THROW_ERROR << "has unsupported output precision: " << outputPrecision;
     }
 
@@ -1483,7 +1487,8 @@ std::shared_ptr<NormalizeL2::NormalizeL2Executor> NormalizeL2::NormalizeL2Execut
               OV_CASE2(Precision::U8, Precision::FP32, uint8_t, float),
               OV_CASE2(Precision::I8, Precision::FP32, int8_t, float),
               OV_CASE2(Precision::FP32, Precision::FP32, float, float),
-              OV_CASE2(Precision::BF16, Precision::BF16, bfloat16_t, bfloat16_t));
+              OV_CASE2(Precision::BF16, Precision::BF16, bfloat16_t, bfloat16_t),
+              OV_CASE2(Precision::FP16, Precision::FP16, float16_t, float16_t));
 
     return ctx.executor;
 }
diff --git a/src/plugins/intel_cpu/src/nodes/pooling.cpp b/src/plugins/intel_cpu/src/nodes/pooling.cpp
index 0bd6f3208c1e87..42aa97d062702b 100644
--- a/src/plugins/intel_cpu/src/nodes/pooling.cpp
+++ b/src/plugins/intel_cpu/src/nodes/pooling.cpp
@@ -271,14 +271,31 @@ void Pooling::getSupportedDescriptors() {
     const auto &childShape = getOutputShapeAtPort(0);
     const size_t inputRank = getInputShapeAtPort(0).getRank();
 
+    if (isDynamicNode()) {
+        inShape = MemoryDescUtils::makeDummyShape(parentShape);
+        const auto& origDims = parentShape.getDims();
+        const auto& origMaxDims = parentShape.getMaxDims();
+
+        auto inDims = inShape.getStaticDims();
+        for (size_t i = 0; i < inDims.size() - 2; i++) {
+            if (origDims[i + 2] == Shape::UNDEFINED_DIM) {
+                inDims[i + 2] = std::min<Dim>(origMaxDims[i + 2], std::max<Dim>(inDims[i + 2], poolingAttrs.kernel[i]));
+            }
+        }
+        inShape = Shape(inDims);
+    } else {
+        inShape = parentShape;
+    }
+
 #if defined(OV_CPU_WITH_ACL)
     // WA: we may specify any layout here (NCHW or NHWC) since both are supported by ACL
-    arm_compute::DataLayout dataLayout = (parentShape.getDims().size() == 5) ? arm_compute::DataLayout::NDHWC : arm_compute::DataLayout::NCHW;
-    arm_compute::TensorInfo srcTensorInfo = arm_compute::TensorInfo(shapeCast(parentShape.getDims()),
+    arm_compute::DataLayout dataLayout = (inShape.getDims().size() == 5) ? arm_compute::DataLayout::NDHWC : arm_compute::DataLayout::NCHW;
+    arm_compute::TensorInfo srcTensorInfo = arm_compute::TensorInfo(shapeCast(inShape.getDims()),
                                                                     1,
                                                                     precisionToAclDataType(inputPrecision),
                                                                     dataLayout);
-    arm_compute::TensorInfo dstTensorInfo = arm_compute::TensorInfo(shapeCast(childShape.getDims()),
+    arm_compute::TensorInfo dstTensorInfo = arm_compute::TensorInfo(shapeCast(isDynamicNode() ? MemoryDescUtils::makeDummyShape(childShape).getDims() :
+                                                                                                childShape.getDims()),
                                                                     1,
                                                                     precisionToAclDataType(outputPrecision),
                                                                     dataLayout);
@@ -287,16 +304,19 @@ void Pooling::getSupportedDescriptors() {
     useACL = AclPoolingExecutor::isSupported(srcTensorInfo,
                                              dstTensorInfo,
                                              poolingAttrs,
-                                             parentShape.getDims().size(),
+                                             inShape.getDims().size(),
                                              getOriginalOutputsNumber(),
                                              dataLayout,
                                              (getOriginalOutputsNumber() > 1) ? &getOutputShapeAtPort(1).getDims() : nullptr,
                                              &pool_info,
-                                             &pool3d_info);
+                                             &pool3d_info,
+                                             isDynamicNode());
     //FIXME: 5D tensors case is not assigned to ACL because there is no way to check layout here
     //NEPooling3dLayer supports NDHWC only
-    if (parentShape.getDims().size() == 5)
+    if (inShape.getDims().size() == 5) {
         useACL = false;
+        DEBUG_LOG("FIXME: 5D tensors case is not assigned to ACL because there is no way to check layout in getSupportedDescriptors()");
+    }
 #endif
     if (useACL) return;
 
@@ -324,19 +344,7 @@ void Pooling::getSupportedDescriptors() {
     if ((inputRank < 3) || (inputRank > 5))
         IE_THROW() << "Pooling layer. Unsupported mode. Only 3D, 4D and 5D blobs are supported as input.";
 
-    inShape = MemoryDescUtils::makeDummyShape(parentShape);
-    if (isDynamicNode()) {
-        const auto& origDims = parentShape.getDims();
-        const auto& origMaxDims = parentShape.getMaxDims();
 
-        auto inDims = inShape.getStaticDims();
-        for (size_t i = 0; i < inDims.size() - 2; i++) {
-            if (origDims[i + 2] == Shape::UNDEFINED_DIM) {
-                inDims[i + 2] = std::min<Dim>(origMaxDims[i + 2], std::max<Dim>(inDims[i + 2], poolingAttrs.kernel[i]));
-            }
-        }
-        inShape = Shape(inDims);
-    }
 
     initEffectiveAttributes(inShape,
                             MemoryDescUtils::makeDummyShape(childShape));
@@ -386,7 +394,12 @@ void Pooling::prepareParams() {
     } else {
         attr = initPrimitiveAttr();
     }
-
+    if (isDynamicNode()) {
+        if (poolingAttrs.auto_pad) {
+            poolingAttrs.data_pad_begin = shapeInference->get_pads_begin();
+            poolingAttrs.data_pad_end = shapeInference->get_pads_end();
+        }
+    }
     if (useACL) {
         auto dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
         auto srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
@@ -414,10 +427,6 @@ void Pooling::prepareParams() {
         auto outDesc = getChildEdgesAtPort(0)[0]->getMemory().getDescWithType<DnnlMemoryDesc>();
 
         if (isDynamicNode()) {
-            if (poolingAttrs.auto_pad) {
-                poolingAttrs.data_pad_begin = shapeInference->get_pads_begin();
-                poolingAttrs.data_pad_end = shapeInference->get_pads_end();
-            }
             initEffectiveAttributes(inDesc->getShape(), outDesc->getShape());
         }
 
@@ -593,18 +602,17 @@ void Pooling::initSupportedPrimitiveDescriptors() {
             config.inConfs.resize(getParentEdges().size());
             config.outConfs.resize(getOriginalOutputsNumber());
 
-            config.inConfs[0].setMemDesc(
-                creatorsMap.at(format)->createSharedDesc(getOriginalInputPrecisionAtPort(0), getInputShapeAtPort(0)));
-            config.outConfs[0].setMemDesc(
-                creatorsMap.at(format)->createSharedDesc(getOriginalOutputPrecisionAtPort(0), getOutputShapeAtPort(0)));
-
             std::vector<MemoryDescPtr> srcMemoryDescs;
-            for (const auto& inConf : config.inConfs) {
-                srcMemoryDescs.push_back(inConf.getMemDesc());
+            for (size_t i = 0; i < config.inConfs.size(); i++) {
+                config.inConfs[i].setMemDesc(
+                    creatorsMap.at(format)->createSharedDesc(getOriginalInputPrecisionAtPort(i), getInputShapeAtPort(i)));
+                srcMemoryDescs.push_back(config.inConfs[i].getMemDesc());
             }
             std::vector<MemoryDescPtr> dstMemoryDescs;
-            for (const auto& outConf : config.outConfs) {
-                dstMemoryDescs.push_back(outConf.getMemDesc());
+            for (size_t i = 0; i < config.outConfs.size(); i++) {
+                config.outConfs[i].setMemDesc(
+                    creatorsMap.at(format)->createSharedDesc(getOriginalOutputPrecisionAtPort(i), getOutputShapeAtPort(i)));
+                dstMemoryDescs.push_back(config.outConfs[i].getMemDesc());
             }
 
             auto factory = std::make_shared<PoolingExecutorFactory>(
diff --git a/src/plugins/intel_cpu/src/nodes/random_uniform.cpp b/src/plugins/intel_cpu/src/nodes/random_uniform.cpp
new file mode 100644
index 00000000000000..77d823710c942f
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/random_uniform.cpp
@@ -0,0 +1,532 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "random_uniform.hpp"
+
+#include "ie_parallel.hpp"
+#include "ie_ngraph_utils.hpp"
+#include <openvino/op/constant.hpp>
+#include <openvino/op/random_uniform.hpp>
+#include "shape_inference/custom/random_uniform.hpp"
+
+namespace ov {
+namespace intel_cpu {
+namespace node {
+
+bool RandomUniform::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
+    try {
+        if (op->get_type_info() != op::v8::RandomUniform::get_type_info_static()) {
+            errorMessage = "Only RandomUniform operation from the opset8 is supported by the CPU plugin.";
+            return false;
+        }
+    } catch (...) {
+        return false;
+    }
+    return true;
+}
+
+RandomUniform::RandomUniform(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr& context)
+        : Node(op, context, RandomUniformShapeInferFactory(op)) {
+    std::string errorMessage;
+    if (!isSupportedOperation(op, errorMessage)) {
+        THROW_CPU_NODE_ERR(errorMessage);
+    }
+
+    // RandomUniform should generate new sequence each run even if all inputs are constants. So that method Node::IsConstant()
+    // doesn't return 'True' for RandomUniform with all constant inputs and the node generates new values for each inference,
+    // we set 'NoConst' value for 'ConstantType' in ctor.
+    constant = ConstantType::NoConst;
+
+    auto rnd_op = as_type_ptr<op::v8::RandomUniform>(op);
+    m_global_seed = rnd_op->get_global_seed();
+    m_op_seed = rnd_op->get_op_seed();
+
+    m_output_prc = op->get_output_element_type(0);
+
+    for (size_t i = 0lu; i < op->get_input_size(); i++) {
+        if (is_type<op::v0::Constant>(op->get_input_node_ptr(i))) {
+            m_const_inputs[i] = true;
+        }
+    }
+
+    if (m_algo == STL) {
+        m_generator = std::default_random_engine{static_cast<uint32_t>(m_op_seed)};
+    }
+}
+
+void RandomUniform::getSupportedDescriptors() {
+    if (getParentEdges().size() != 3) {
+        THROW_CPU_NODE_ERR("has incorrect number of input edges.");
+    }
+    if (getChildEdges().empty()) {
+        THROW_CPU_NODE_ERR("has incorrect number of output edges.");
+    }
+}
+
+void RandomUniform::initSupportedPrimitiveDescriptors() {
+    auto shape_prc = getOriginalInputPrecisionAtPort(SHAPE);
+    if (!one_of(shape_prc, InferenceEngine::Precision::I32, InferenceEngine::Precision::I64)) {
+        shape_prc = InferenceEngine::Precision::I32;
+    }
+
+    auto out_prc = getOriginalOutputPrecisionAtPort(0);
+    if (out_prc.is_float() && ((m_algo == PHILOX &&
+            !one_of(out_prc, InferenceEngine::Precision::FP32, InferenceEngine::Precision::FP16, InferenceEngine::Precision::BF16)) ||
+            (m_algo == STL && !one_of(out_prc, InferenceEngine::Precision::FP32)))) {
+        out_prc = InferenceEngine::Precision::FP32;
+    }
+    if (!out_prc.is_float() && !one_of(out_prc, InferenceEngine::Precision::I32, InferenceEngine::Precision::I64)) {
+        out_prc = InferenceEngine::Precision::I32;
+    }
+    m_output_prc = InferenceEngine::details::convertPrecision(out_prc);
+
+    addSupportedPrimDesc({{LayoutType::ncsp, shape_prc, m_const_inputs[SHAPE]},
+                          {LayoutType::ncsp, out_prc, m_const_inputs[MIN_VAL]},
+                          {LayoutType::ncsp, out_prc, m_const_inputs[MAX_VAL]}},
+                         {{LayoutType::ncsp, out_prc}},
+                         ref_any);
+}
+
+void RandomUniform::createPrimitive() {
+    if (m_const_inputs[MIN_VAL]) {
+        initEdgeValues(m_min_val, getParentEdgeAt(MIN_VAL)->getMemoryPtr()->getData(), m_output_prc);
+    }
+    if (m_const_inputs[MAX_VAL]) {
+        initEdgeValues(m_max_val, getParentEdgeAt(MAX_VAL)->getMemoryPtr()->getData(), m_output_prc);
+        evalRange();
+    }
+
+    if (m_algo == PHILOX) {
+#if defined(OPENVINO_ARCH_X86_64)
+        kernel::RandomUniformCompileParams jcp;
+
+        jcp.out_data_type = m_output_prc;
+
+        m_jit_kernel = kernel::JitKernel<kernel::RandomUniformCompileParams, kernel::RandomUniformCallArgs>::createInstance<kernel::RandomUniform>(jcp);
+
+        if (m_jit_kernel) {
+            if (auto selected_pd = getSelectedPrimitiveDescriptor()) {
+                using namespace dnnl::impl::cpu;
+                if (m_jit_kernel->getIsa() == x64::avx512_core) {
+                    selected_pd->setImplementationType(jit_avx512);
+                } else if (m_jit_kernel->getIsa() == x64::avx2) {
+                    selected_pd->setImplementationType(jit_avx2);
+                } else if (m_jit_kernel->getIsa() == x64::sse41) {
+                    selected_pd->setImplementationType(jit_sse42);
+                }
+            }
+        }
+#endif // OPENVINO_ARCH_X86_64
+    }
+
+    if (m_const_inputs[SHAPE]) {
+        Node::createPrimitive();
+    }
+}
+
+bool RandomUniform::needPrepareParams() const {
+    if (m_out_shape != getChildEdgeAt(0)->getMemoryPtr()->getShape().getStaticDims()) {
+        return true;
+    }
+    return false;
+}
+
+void RandomUniform::prepareParams() {
+    m_out_shape = getChildEdgeAt(0)->getMemoryPtr()->getShape().getStaticDims();
+    m_out_el_num = std::accumulate(m_out_shape.begin(), m_out_shape.end(), 1lu, std::multiplies<Dim>());
+
+    if (m_algo == PHILOX) {
+        m_skip_count = m_out_el_num * SKIP_CONST;
+
+        if (m_out_el_num < PHILOX_PARALLEL_EXECUTION_THRESHOLD) {
+            m_threads_num = 1;
+        } else {
+            m_threads_num = parallel_get_max_threads();
+        }
+        m_thread_params.resize(m_threads_num);
+
+        parallel_nt(m_threads_num, [&](const int ithr, const int nthr) {
+            auto& p = m_thread_params[ithr];
+            uint64_t start = 0lu, end = 0lu;
+
+            if (m_jit_kernel) {
+#if defined(OPENVINO_ARCH_X86_64)
+                const auto block_size = (m_jit_kernel->getVectorLen() / m_output_prc.size()) * 2;
+                const auto blocks_num = (m_out_el_num + block_size - 1) / block_size;
+                const auto blocks_per_thr = (blocks_num + nthr - 1) / nthr;
+
+                start = ithr * blocks_per_thr * block_size;
+                end = (ithr + 1) * blocks_per_thr * block_size;
+#endif // OPENVINO_ARCH_X86_64
+            } else {
+                const auto groups_num = (m_out_el_num + PHILOX_GROUP_SIZE - 1) / PHILOX_GROUP_SIZE;
+                const auto groups_per_thr = (groups_num + nthr - 1) / nthr;
+
+                start = ithr * groups_per_thr * PHILOX_GROUP_SIZE;
+                end = (ithr + 1) * groups_per_thr * PHILOX_GROUP_SIZE;
+
+                p.step = m_output_prc.size() > 4 ? 2 : 4;
+            }
+
+            if (end > m_out_el_num) {
+                end = m_out_el_num;
+            }
+            if (start > end) {
+                start = end;
+            }
+            p.work_amount = end - start;
+            p.n_shift = start / PHILOX_GROUP_SIZE;
+            p.dst_shift = start * m_output_prc.size();
+        });
+    }
+}
+
+void RandomUniform::execute(dnnl::stream strm) {
+    if (!m_const_inputs[MIN_VAL]) {
+        initEdgeValues(m_min_val, getParentEdgeAt(MIN_VAL)->getMemoryPtr()->getData(), m_output_prc);
+        if (m_const_inputs[MAX_VAL]) {
+            evalRange();
+        }
+    }
+    if (!m_const_inputs[MAX_VAL]) {
+        initEdgeValues(m_max_val, getParentEdgeAt(MAX_VAL)->getMemoryPtr()->getData(), m_output_prc);
+        evalRange();
+    }
+
+    auto data = getChildEdgeAt(0)->getMemoryPtr()->getData();
+
+    if (m_algo == PHILOX) {
+        m_state = computePhilox(data, m_out_el_num, m_state);
+    } else if (m_algo == STL) {
+        computeStl(data, m_out_el_num);
+    } else {
+        THROW_CPU_NODE_ERR("unsupported algorithm.");
+    }
+}
+
+void RandomUniform::executeDynamicImpl(dnnl::stream strm) {
+    execute(strm);
+}
+
+////////////// PHILOX algo ///////////////
+
+namespace {
+// Following const values are taken from the original paper:
+// https://www.thesalmons.org/john/random123/papers/random123sc11.pdf
+constexpr uint32_t CRUSH_RESISTANCE_CONST_LOWER_VALUE = 0x9E3779B9;
+constexpr uint32_t CRUSH_RESISTANCE_CONST_UPPER_VALUE = 0xBB67AE85;
+constexpr uint64_t STATISTIC_MAXIMIZING_MULTIPLIER_N = 0xD2511F53;
+constexpr uint64_t STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER = 0xCD9E8D57;
+constexpr uint64_t ROUNDS_NUMBER = 10llu;
+
+inline void calculateRound(const uint32_t* key, uint32_t* counter, uint32_t* n) {
+    uint64_t prod_0 = STATISTIC_MAXIMIZING_MULTIPLIER_N * n[0];
+    uint64_t prod_1 = STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER * counter[0];
+    n[0] = static_cast<uint32_t>(prod_1 >> 32) ^ n[1] ^ key[0];
+    n[1] = static_cast<uint32_t>(prod_1);
+    counter[0] = static_cast<uint32_t>(prod_0 >> 32) ^ counter[1] ^ key[1];
+    counter[1] = static_cast<uint32_t>(prod_0);
+}
+
+inline void raiseKey(uint32_t* key) {
+    key[0] += CRUSH_RESISTANCE_CONST_LOWER_VALUE;
+    key[1] += CRUSH_RESISTANCE_CONST_UPPER_VALUE;
+}
+
+inline void runPhilox(uint64_t key, uint64_t counter, uint64_t n, uint32_t* res) {
+    uint32_t* key_32 = reinterpret_cast<uint32_t*>(&key);
+    uint32_t* counter_32 = reinterpret_cast<uint32_t*>(&counter);
+    uint32_t* n_32 = reinterpret_cast<uint32_t*>(&n);
+
+    for (size_t i = 0lu; i < ROUNDS_NUMBER; i++) {
+        calculateRound(key_32, counter_32, n_32);
+        if (i < ROUNDS_NUMBER - 1)
+            raiseKey(key_32);
+    }
+
+    res[0] = n_32[0];
+    res[1] = n_32[1];
+    res[2] = counter_32[0];
+    res[3] = counter_32[1];
+}
+
+inline void convertToOutputType(const uint32_t* in,
+                                float min,
+                                float range,
+                                float* out,
+                                size_t el_to_copy) {
+    RandomUniform::OutputType out_val;
+
+    for (size_t i = 0lu; i < el_to_copy; i++) {
+        out_val.u32 = 0x3f800000 | (in[i] & 0x7fffffu);
+        out[i] = (out_val.f32 - 1.f) * range + min;
+    }
+}
+
+inline void convertToOutputType(const uint32_t* in,
+                                float16 min,
+                                float16 range,
+                                float16* out,
+                                size_t el_to_copy) {
+    RandomUniform::OutputType out_val;
+
+    for (size_t i = 0lu; i < el_to_copy; i++) {
+        uint16_t x_uint16 = static_cast<uint16_t>(in[i]);
+        out_val.u16 = 0x3c00 | (x_uint16 & 0x03ffu);
+        out[i] = (out_val.f16 - static_cast<float16>(1)) * range + min;
+    }
+}
+
+inline void convertToOutputType(const uint32_t* in,
+                                bfloat16 min,
+                                bfloat16 range,
+                                bfloat16* out,
+                                size_t el_to_copy) {
+    RandomUniform::OutputType out_val;
+
+    for (size_t i = 0lu; i < el_to_copy; i++) {
+        uint16_t x_uint16 = static_cast<uint16_t>(in[i]);
+        out_val.u16 = 0x3f80 | (x_uint16 & 0x7fu);
+        out[i] = (out_val.bf16 - static_cast<bfloat16>(1)) * range + min;
+    }
+}
+
+inline void convertToOutputType(const uint32_t* in,
+                                int32_t min,
+                                int32_t range,
+                                int32_t* out,
+                                size_t el_to_copy) {
+    for (size_t i = 0lu; i < el_to_copy; i++) {
+        out[i] = static_cast<int32_t>(in[i] % range + min);
+    }
+}
+
+inline void convertToOutputType(const uint32_t* in,
+                                int64_t min,
+                                int64_t range,
+                                int64_t* out,
+                                size_t el_to_copy) {
+    for (size_t i = 0lu; i < el_to_copy; i++) {
+        out[i] = static_cast<int64_t>(((static_cast<uint64_t>(in[i * 2]) << 32) + in[i * 2 + 1]) % range + min);
+    }
+}
+
+}  // namespace
+
+std::pair<uint64_t, uint64_t> RandomUniform::computePhilox(void* out, size_t out_el_num, const std::pair<uint64_t, uint64_t>& prev_state) {
+    // When both seed values are equal to zero RandomUniform should generate non-deterministic sequence.
+    if (m_global_seed == 0lu && m_op_seed == 0lu) {
+        std::srand(static_cast<unsigned int>(std::time(nullptr)));
+        m_global_seed = std::rand();
+    }
+
+    uint64_t n_state = prev_state.first;
+    uint64_t counter_state = prev_state.second;
+
+    uint64_t counter = counter_state > 0 ? counter_state : m_op_seed;
+
+    auto out_u8 = reinterpret_cast<uint8_t*>(out);
+
+    if (m_jit_kernel) {
+#if defined(OPENVINO_ARCH_X86_64)
+        parallel_nt(m_threads_num, [&](const int ithr, const int nthr) {
+                auto& p = m_thread_params[ithr];
+                if (p.work_amount == 0lu) {
+                    return;
+                }
+                auto n = n_state + p.n_shift;
+
+                kernel::RandomUniformCallArgs args;
+
+                args.dst_ptr     = (out_u8 + p.dst_shift);
+                args.key_ptr     = &m_global_seed;
+                args.counter_ptr = &counter;
+                args.n_ptr       = &n;
+                args.min_ptr     = &m_min_val;
+                args.range_ptr   = &m_range_val;
+                args.work_amount = p.work_amount;
+
+                (*m_jit_kernel)(&args);
+            });
+#endif // OPENVINO_ARCH_X86_64
+    } else {
+        auto threadBody = [&](const int ithr, const int nthr) {
+            auto& p = m_thread_params[ithr];
+            if (p.work_amount == 0lu) {
+                return;
+            }
+            auto n = n_state + p.n_shift;
+            auto out_cur = out_u8 + p.dst_shift;
+            auto work_rest = static_cast<int64_t>(p.work_amount);
+            uint32_t res[4];
+
+#define EXEC_CASE(P)                                                                                    \
+            case element::P: {                                                                          \
+                auto out_t = reinterpret_cast<element_type_traits<element::P>::value_type *>(out_cur);  \
+                for (; work_rest > 0l; work_rest -= p.step, out_t += p.step) {                          \
+                    runPhilox(m_global_seed, counter, n, res);                                          \
+                    auto el_to_copy = std::min(p.step, static_cast<uint64_t>(work_rest));               \
+                    convertToOutputType(res, m_min_val.P, m_range_val.P, out_t, el_to_copy);            \
+                    if (++n == 0) {                                                                     \
+                        counter++;                                                                      \
+                    }                                                                                   \
+                }                                                                                       \
+            } break;
+
+            switch (m_output_prc) {
+                EXEC_CASE(f32)
+                EXEC_CASE(f16)
+                EXEC_CASE(bf16)
+                EXEC_CASE(i32)
+                EXEC_CASE(i64)
+                default: THROW_CPU_NODE_ERR("Unsupported type of RandomUniform: ", m_output_prc.to_string());
+            }
+
+#undef EXEC_CASE
+        };
+
+        parallel_nt(m_threads_num, threadBody);
+    }
+
+    // Calculate counter values for next RandomUniform run.
+    n_state += m_skip_count;
+    if (n_state < m_skip_count) {
+        counter_state++;
+    }
+
+    return { n_state, counter_state };
+}
+
+////////////// STL algo ///////////////
+void RandomUniform::computeStl(void* out, size_t work_amount) {
+    switch (m_output_prc) {
+        case element::f32: {
+            generateData<float, std::uniform_real_distribution<float>>(
+                    std::uniform_real_distribution<float>{m_min_val.f32, m_max_val.f32}, out, work_amount);
+        } break;
+        case element::i32: {
+            generateData<int32_t, std::uniform_int_distribution<int32_t>>(
+                    std::uniform_int_distribution<int32_t>{m_min_val.i32, m_max_val.i32}, out, work_amount);
+        } break;
+        case element::i64: {
+            generateData<int64_t, std::uniform_int_distribution<int64_t>>(
+                    std::uniform_int_distribution<int64_t>{m_min_val.i64, m_max_val.i64}, out, work_amount);
+        } break;
+        default:
+            THROW_CPU_NODE_ERR("has unsupported output type: ", m_output_prc);
+    }
+}
+
+template <typename T, typename DISTR_TYPE>
+void RandomUniform::generateData(DISTR_TYPE distribution, void* out, size_t work_amount) {
+    auto dst = reinterpret_cast<T*>(out);
+    for (size_t i = 0; i < work_amount; i++) {
+        *dst = distribution(m_generator);
+        dst++;
+    }
+}
+//////////////////////////////////
+
+void RandomUniform::initEdgeValues(OutputType& dst, const void* src, const element::Type& output_type) {
+#define EL_CASE(E) \
+    case element::E: \
+        dst.E = *reinterpret_cast<const element_type_traits<element::E>::value_type *>(src); \
+        break;
+
+    switch (output_type) {
+        EL_CASE(f32)
+        EL_CASE(f16)
+        EL_CASE(bf16)
+        EL_CASE(i32)
+        EL_CASE(i64)
+        EL_CASE(f64)
+        default:
+            THROW_CPU_NODE_ERR("has unsupported output precision: ", output_type);
+    }
+
+#undef EL_CASE
+}
+
+void RandomUniform::evalRange() {
+#define EL_CASE(E) \
+    case element::E: \
+        m_range_val.E = m_max_val.E - m_min_val.E; \
+        break;
+
+    switch (m_output_prc) {
+        EL_CASE(f32)
+        EL_CASE(f16)
+        EL_CASE(bf16)
+        EL_CASE(i32)
+        EL_CASE(i64)
+        EL_CASE(f64)
+        default:
+            THROW_CPU_NODE_ERR("has unsupported output precision: ", m_output_prc);
+    }
+
+#undef EL_CASE
+}
+
+std::string RandomUniform::getPrimitiveDescriptorType() const {
+    auto selectedPrimitiveDesc = getSelectedPrimitiveDescriptor();
+
+    impl_desc_type type = impl_desc_type::undef;
+    if (selectedPrimitiveDesc) {
+        type = selectedPrimitiveDesc->getImplementationType();
+    }
+
+    std::string str_type;
+
+    auto add_type = [&](std::string t) {
+        if (!str_type.empty() && t.c_str()[0] != '_')
+            str_type += "_";
+        str_type += t;
+    };
+
+#define SEARCH_TYPE(_type)                                          \
+    if ((type & impl_desc_type::_type) == impl_desc_type::_type)    \
+        add_type(#_type)
+
+    SEARCH_TYPE(undef);
+    SEARCH_TYPE(jit);
+    SEARCH_TYPE(ref);
+
+    SEARCH_TYPE(avx512);
+    SEARCH_TYPE(avx2);
+    SEARCH_TYPE(sse42);
+    SEARCH_TYPE(any);
+
+#undef SEARCH_TYPE
+
+    if (type == impl_desc_type::unknown)
+        str_type = "unknown";
+    else if (str_type.empty())
+        str_type = "undef";
+
+    if (selectedPrimitiveDesc) {
+        if (selectedPrimitiveDesc->getConfig().outConfs[0].getMemDesc()->getPrecision() != InferenceEngine::Precision::U8) {
+            str_type += "_" + std::string(selectedPrimitiveDesc->getConfig().outConfs[0].getMemDesc()->getPrecision().name());
+        } else {
+            str_type += "_I8";
+        }
+    }
+
+    return str_type;
+}
+
+bool RandomUniform::needShapeInfer() const {
+    return !m_const_inputs[SHAPE];
+}
+
+bool RandomUniform::isExecutable() const {
+    return !isInputTensorAtPortEmpty(SHAPE);
+}
+
+bool RandomUniform::created() const {
+    return getType() == Type::RandomUniform;
+}
+
+}   // namespace node
+}   // namespace intel_cpu
+}   // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/random_uniform.hpp b/src/plugins/intel_cpu/src/nodes/random_uniform.hpp
new file mode 100644
index 00000000000000..ecbfebdf5d79c6
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/random_uniform.hpp
@@ -0,0 +1,120 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <node.h>
+#include <random>
+#include "kernels/x64/random_uniform.hpp"
+
+namespace ov {
+namespace intel_cpu {
+namespace node {
+
+class RandomUniform : public Node {
+public:
+    union OutputType {
+        float    f32;
+        float16  f16;
+        bfloat16 bf16;
+        double   f64;
+        int32_t  i32;
+        uint32_t u32;
+        uint16_t u16;
+        int64_t  i64;
+    };
+
+    RandomUniform(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr& context);
+
+    void getSupportedDescriptors() override;
+
+    void initSupportedPrimitiveDescriptors() override;
+
+    bool needPrepareParams() const override;
+
+    void prepareParams() override;
+
+    void execute(dnnl::stream strm) override;
+
+    void executeDynamicImpl(dnnl::stream strm) override;
+
+    bool isExecutable() const override;
+
+    void createPrimitive() override;
+
+    bool created() const override;
+
+    bool canBeInPlace() const override { return false; }
+
+    static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
+
+    std::string getPrimitiveDescriptorType() const override;
+
+protected:
+    bool needShapeInfer() const override;
+
+private:
+    void computeStl(void* out, size_t work_amount);
+
+    std::pair<uint64_t, uint64_t> computePhilox(void* out, size_t work_amount, const std::pair<uint64_t, uint64_t>& prev_state);
+
+    template <typename T, typename DISTR_TYPE>
+    void generateData(DISTR_TYPE distribution, void* out, size_t work_amount);
+
+    void initOutShape(VectorDims& dst, const void* src, const element::Type& shape_type, size_t len);
+
+    void initEdgeValues(OutputType& dst, const void* src, const element::Type& output_type);
+
+    void evalRange();
+
+    enum { SHAPE = 0, MIN_VAL, MAX_VAL };
+    enum AlgoType { STL, PHILOX };
+
+    bool m_const_inputs[3] = {false, false, false};
+
+    ov::element::Type m_output_prc;
+    uint64_t m_global_seed = 0lu;
+    uint64_t m_op_seed = 0lu;
+    std::pair<uint64_t, uint64_t> m_state {0lu, 0lu};
+
+    VectorDims m_out_shape = {};
+    uint64_t m_out_el_num = 1lu;
+    OutputType m_min_val;
+    OutputType m_max_val;
+    OutputType m_range_val;
+    AlgoType m_algo = PHILOX;
+
+    std::default_random_engine m_generator;
+
+    struct ThreadParams {
+        uint64_t work_amount = 0lu;
+        uint64_t dst_shift = 0lu;
+        uint64_t n_shift = 0lu;
+        uint64_t step = 0lu;
+    };
+
+    uint64_t m_threads_num = 0lu;
+    std::vector<ThreadParams> m_thread_params;
+
+    ///// PHILOX constants /////
+
+    // Determines how many sequence elements of RNG sequence are skipped between runs.
+    // Can be any positive value, 256 is chosen for parity with Tensorflow.
+    static constexpr uint64_t SKIP_CONST = 256lu;
+
+    // Philox algorithm returns 4 elements of RNG sequence per each invocation
+    static constexpr uint64_t PHILOX_GROUP_SIZE = 4lu;
+
+    // Output elements number threshold to execute on one thread.
+    static constexpr uint64_t PHILOX_PARALLEL_EXECUTION_THRESHOLD = 1000lu;
+
+    uint64_t m_skip_count = 0lu;
+    /////////////////////////////////////////////////////////////////////////////////
+
+    std::shared_ptr<kernel::JitKernelBase> m_jit_kernel;
+};
+
+}   // namespace node
+}   // namespace intel_cpu
+}   // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/reference.cpp b/src/plugins/intel_cpu/src/nodes/reference.cpp
index b42dc99b390fb4..091e31813125cf 100644
--- a/src/plugins/intel_cpu/src/nodes/reference.cpp
+++ b/src/plugins/intel_cpu/src/nodes/reference.cpp
@@ -2,18 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <dnnl_extension_utils.h>
-
-#include <ie_ngraph_utils.hpp>
-#include <ngraph/opsets/opset1.hpp>
+#include "reference.h"
 
-#include "common/blocked_desc_creator.h"
 #include "common/cpu_memcpy.h"
+#include <ie_ngraph_utils.hpp>
 #include "openvino/core/shape_util.hpp"
-#include "openvino/runtime/tensor.hpp"
-#include "reference.h"
 
-using namespace dnnl;
 using namespace InferenceEngine;
 using namespace InferenceEngine::details;
 
@@ -21,21 +15,15 @@ namespace ov {
 namespace intel_cpu {
 namespace node {
 
-Reference::Reference(const std::shared_ptr<ngraph::Node>& op, const GraphContext::CPtr context,
+Reference::Reference(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr& context,
                                          const std::string& errorMessage) :
-        Node(op, context, NgraphShapeInferFactory(op, FULL_PORT_MASK)), ngraphOp(op), additionalErrorMessage(errorMessage) {
+        Node(op, context, NgraphShapeInferFactory(op, FULL_PORT_MASK)), ovCoreNode(op), additionalErrorMessage(errorMessage) {
     if (!op->has_evaluate()) {
         IE_THROW(NotImplemented) << "Cannot fallback on ngraph reference implementation (Ngraph::Node::evaluate() is not implemented)";
     }
+
     setType(Type::Reference);
     setTypeStr("Reference");
-
-    // RandomUniform should generate new sequence each run even if all inputs are constants. So that method Node::IsConstant()
-    // doesn't return 'True' for RandomUniform with all constant inputs and the node generates new values for each inference,
-    // we set 'NoConst' value for 'ConstantType' in ctor
-    if (ov::is_type<ngraph::op::v8::RandomUniform>(ngraphOp)) {
-        constant = ConstantType::NoConst;
-    }
 }
 
 void Reference::getSupportedDescriptors() {}
@@ -47,13 +35,13 @@ void Reference::initSupportedPrimitiveDescriptors() {
     std::vector<PortConfigurator> inputConfigurators;
     inputConfigurators.reserve(inputShapes.size());
     for (size_t i = 0; i < inputShapes.size(); i++) {
-        inputConfigurators.emplace_back(LayoutType::ncsp, convertPrecision(ngraphOp->get_input_element_type(i)), inputShapes[i]);
+        inputConfigurators.emplace_back(LayoutType::ncsp, convertPrecision(ovCoreNode->get_input_element_type(i)), inputShapes[i]);
     }
 
     std::vector<PortConfigurator> outputConfigurators;
     outputConfigurators.reserve(inputShapes.size());
     for (size_t i = 0; i < outputShapes.size(); i++) {
-        outputConfigurators.emplace_back(LayoutType::ncsp, convertPrecision(ngraphOp->get_output_element_type(i)), outputShapes[i]);
+        outputConfigurators.emplace_back(LayoutType::ncsp, convertPrecision(ovCoreNode->get_output_element_type(i)), outputShapes[i]);
     }
 
     addSupportedPrimDesc(inputConfigurators, outputConfigurators, impl_desc_type::ref);
@@ -64,8 +52,8 @@ void Reference::createPrimitive() {}
 void Reference::execute(dnnl::stream strm) {
     auto inputs = prepareInputs();
     auto outputs = prepareOutputs();
-    if (!ngraphOp->evaluate(outputs, inputs)) {
-        IE_THROW() << "Evaluation failed on node of type: " << std::string(ngraphOp->get_type_name()) << " name: " << getName();
+    if (!ovCoreNode->evaluate(outputs, inputs)) {
+        THROW_CPU_NODE_ERR("evaluation failed for core operation: ", std::string(ovCoreNode->get_type_name()));
     }
 }
 
@@ -81,18 +69,16 @@ void Reference::executeDynamicImpl(dnnl::stream strm) {
         for (size_t i = 0; i < outputShapes.size(); ++i) {
             auto mem_desc = getBaseMemDescAtOutputPort(i);
             if (mem_desc->isDefined()) {
-                outputs.emplace_back(ngraphOp->get_output_element_type(i), mem_desc->getShape().getStaticDims());
+                outputs.emplace_back(ovCoreNode->get_output_element_type(i), mem_desc->getShape().getStaticDims());
             } else {
-                outputs.emplace_back(ngraphOp->get_output_element_type(i), ov::util::make_dynamic_shape());
+                outputs.emplace_back(ovCoreNode->get_output_element_type(i), ov::util::make_dynamic_shape());
             }
         }
     } else {
-         IE_THROW(Unexpected) <<
-            "Unexpected shape infer result status during the inference of a node with type " <<
-            getTypeStr() << " and name " << getName();
+         THROW_CPU_NODE_ERR("got unexpected shape infer result status during the inference.");
     }
-    if (!ngraphOp->evaluate(outputs, inputs)) {
-        IE_THROW() << "Evaluation failed on node of type: " << std::string(ngraphOp->get_type_name()) << " name: " << getName();
+    if (!ovCoreNode->evaluate(outputs, inputs)) {
+        THROW_CPU_NODE_ERR("evaluation failed for core operation: ", std::string(ovCoreNode->get_type_name()));
     }
     if (ShapeInferStatus::skip == result.status) {
         std::vector<VectorDims> newOutputDims;
@@ -105,8 +91,7 @@ void Reference::executeDynamicImpl(dnnl::stream strm) {
             auto memory = getChildEdgesAtPort(i)[0]->getMemoryPtr();
             auto& tensor = outputs[i];
             if (memory->getSize() != tensor.get_byte_size()) {
-                IE_THROW(Unexpected) << "Output tensor data size mismatch occurred during the inference of a node with type " <<
-                getTypeStr() << " and name " << getName() << " on output port number " << i;
+                THROW_CPU_NODE_ERR("output tensor data size mismatch occurred during the inference on output port number ", i);
             }
             cpu_memcpy(memory->getData(), tensor.data(), tensor.get_byte_size());
         }
@@ -125,9 +110,9 @@ ov::TensorVector Reference::prepareInputs() const {
     ov::TensorVector inputs;
     for (size_t i = 0; i < inputShapes.size(); i++) {
         void *srcDataPtr = getParentEdgesAtPort(i)[0]->getMemory().getData();
-        ov::Shape shape = ngraphOp->get_input_partial_shape(i).rank().get_length() == 0 ?
+        ov::Shape shape = ovCoreNode->get_input_partial_shape(i).rank().get_length() == 0 ?
                 ov::Shape{} : getParentEdgesAtPort(i)[0]->getMemory().getStaticDims();
-        inputs.push_back(ov::Tensor(ngraphOp->get_input_element_type(i), shape, srcDataPtr));
+        inputs.push_back(ov::Tensor(ovCoreNode->get_input_element_type(i), shape, srcDataPtr));
     }
     return inputs;
 }
@@ -136,9 +121,9 @@ ov::TensorVector Reference::prepareOutputs() const {
     ov::TensorVector outputs;
     for (size_t i = 0; i < outputShapes.size(); i++) {
         void *dstDataPtr = getChildEdgesAtPort(i)[0]->getMemory().getData();
-        ov::Shape shape = ngraphOp->get_output_partial_shape(i).rank().get_length() == 0 ?
+        ov::Shape shape = ovCoreNode->get_output_partial_shape(i).rank().get_length() == 0 ?
                 ov::Shape{} : getChildEdgesAtPort(i)[0]->getMemory().getStaticDims();
-        outputs.push_back(ov::Tensor(ngraphOp->get_output_element_type(i), shape, dstDataPtr));
+        outputs.push_back(ov::Tensor(ovCoreNode->get_output_element_type(i), shape, dstDataPtr));
     }
     return outputs;
 }
diff --git a/src/plugins/intel_cpu/src/nodes/reference.h b/src/plugins/intel_cpu/src/nodes/reference.h
index 4c2a8a1310806f..c2453835229138 100644
--- a/src/plugins/intel_cpu/src/nodes/reference.h
+++ b/src/plugins/intel_cpu/src/nodes/reference.h
@@ -12,7 +12,7 @@ namespace node {
 
 class Reference : public Node {
 public:
-    Reference(const std::shared_ptr<ngraph::Node>& op, const GraphContext::CPtr context, const std::string& errorMessage);
+    Reference(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr& context, const std::string& errorMessage);
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
@@ -29,7 +29,7 @@ class Reference : public Node {
     ov::TensorVector prepareOutputs() const;
 
 private:
-    const std::shared_ptr<ngraph::Node> ngraphOp;
+    const std::shared_ptr<ov::Node> ovCoreNode;
     const std::string additionalErrorMessage;
 };
 
diff --git a/src/plugins/intel_cpu/src/nodes/rnn.cpp b/src/plugins/intel_cpu/src/nodes/rnn.cpp
index 9992f0f392b893..f453b7a5a51e0b 100644
--- a/src/plugins/intel_cpu/src/nodes/rnn.cpp
+++ b/src/plugins/intel_cpu/src/nodes/rnn.cpp
@@ -133,6 +133,7 @@ inline bool haveAttention(const dnnl::algorithm& alg) {
 const std::map<memory::data_type, memory::data_type> RNN::weightsByinputDataType {
     // layer data type        weights data type
     {memory::data_type::f32,  memory::data_type::f32},
+    {memory::data_type::f16,  memory::data_type::f16},
     {memory::data_type::bf16, memory::data_type::bf16},
     {memory::data_type::u8,   memory::data_type::s8},
     {memory::data_type::s8,   memory::data_type::s8},
@@ -505,6 +506,10 @@ void RNN::configurePortDataTypes() {
     if (one_of(memory::data_type::bf16, inDataTypes[xIdx], inDataTypes[hIdx]))
         inDataTypes[xIdx] = outDataTypes[yIdx] = outDataTypes[hoIdx] = inDataTypes[hIdx] = memory::data_type::bf16; // required by oneDNN.
 
+    if (one_of(memory::data_type::f16, inDataTypes[xIdx], inDataTypes[hIdx]))
+        // onednn doesn't have fp16 instance
+        inDataTypes[xIdx] = outDataTypes[yIdx] = outDataTypes[hoIdx] = inDataTypes[hIdx] = memory::data_type::f32; // required by oneDNN.
+
     if (outDataTypes[yIdx] == memory::data_type::bf16 && one_of(inDataTypes[xIdx], memory::data_type::s8, memory::data_type::u8))
         outDataTypes[yIdx] = memory::data_type::f32; // oneDNN does not support bf16 output precision for quantized rnn primitive yet
 }
@@ -882,7 +887,7 @@ void RNN::copyWeightsData() {
     }
 
     const auto& dataType = inDataTypes[xIdx];
-    if (dataType == memory::data_type::bf16) {
+    if (one_of(dataType, memory::data_type::bf16, memory::data_type::f16)) {
         fillWeights<uint16_t>(gate_map, wIdx, rIdx);
     } else if (dataType == memory::data_type::f32) {
         // WA To avoid different weights layer and iter formats in FP32 case
diff --git a/src/plugins/intel_cpu/src/nodes_factory.cpp b/src/plugins/intel_cpu/src/nodes_factory.cpp
index 3afe8aaa32c1d9..7add05741f04e1 100644
--- a/src/plugins/intel_cpu/src/nodes_factory.cpp
+++ b/src/plugins/intel_cpu/src/nodes_factory.cpp
@@ -79,6 +79,7 @@
 #include "nodes/experimental_detectron_generate_proposals_single_image.h"
 #include "nodes/generate_proposals.h"
 #include "nodes/embedding_bag_packed_sum.h"
+#include "nodes/random_uniform.hpp"
 #include "nodes/reduce.h"
 #include "nodes/if.h"
 #include "nodes/ctc_greedy_decoder.h"
@@ -180,6 +181,7 @@ Node::NodesFactory::NodesFactory()
     INTEL_CPU_NODE(Unique, Type::Unique);
     INTEL_CPU_NODE(Ngram, Type::Ngram);
     INTEL_CPU_NODE(Interpolate, Type::Interpolate);
+    INTEL_CPU_NODE(RandomUniform, Type::RandomUniform);
     INTEL_CPU_NODE(Reduce, Type::Reduce);
     INTEL_CPU_NODE(Gather, Type::Gather);
     INTEL_CPU_NODE(NonMaxSuppression, Type::NonMaxSuppression);
diff --git a/src/plugins/intel_cpu/src/plugin.cpp b/src/plugins/intel_cpu/src/plugin.cpp
index ddf14ef59a7eab..96be8734ec0dce 100644
--- a/src/plugins/intel_cpu/src/plugin.cpp
+++ b/src/plugins/intel_cpu/src/plugin.cpp
@@ -38,6 +38,11 @@
 #include <cpu/x64/cpu_isa_traits.hpp>
 #include <itt.h>
 
+#if defined(OV_CPU_WITH_ACL)
+#include "nodes/executors/acl/acl_ie_scheduler.hpp"
+#include "arm_compute/runtime/CPP/CPPScheduler.h"
+#endif
+
 using namespace InferenceEngine;
 
 #define IE_CPU_PLUGIN_THROW(...) IE_THROW(__VA_ARGS__) << "CPU plugin: "
@@ -137,11 +142,44 @@ class CPUSpecialSetup {
 };
 #endif // __linux__
 
+#if defined(OV_CPU_WITH_ACL)
+std::mutex Engine::SchedulerGuard::mutex;
+std::weak_ptr<Engine::SchedulerGuard> Engine::SchedulerGuard::ptr;
+
+Engine::SchedulerGuard::SchedulerGuard() {
+#if IE_THREAD == IE_THREAD_SEQ
+    // To save state for ACL cores in single-thread mode
+    arm_compute::Scheduler::set(arm_compute::Scheduler::Type::ST);
+#else
+    arm_compute::Scheduler::set(std::make_shared<ACLScheduler>());
+#endif
+}
+
+std::shared_ptr<Engine::SchedulerGuard> Engine::SchedulerGuard::instance() {
+    std::lock_guard<std::mutex> lock{SchedulerGuard::mutex};
+    auto scheduler_guard_ptr = SchedulerGuard::ptr.lock();
+    if (scheduler_guard_ptr == nullptr) {
+        SchedulerGuard::ptr = scheduler_guard_ptr = std::make_shared<SchedulerGuard>();
+    }
+    return scheduler_guard_ptr;
+}
+
+Engine::SchedulerGuard::~SchedulerGuard() {
+    // To save the state of scheduler after ACLScheduler has been executed
+    // TODO: find out the cause of the state
+    std::lock_guard<std::mutex> lock{this->dest_mutex};
+    arm_compute::Scheduler::set(arm_compute::Scheduler::Type::ST);
+}
+#endif
+
 Engine::Engine() :
     deviceFullName(getDeviceFullName()),
     specialSetup(new CPUSpecialSetup) {
     _pluginName = "CPU";
     extensionManager->AddExtension(std::make_shared<Extension>());
+#if defined(OV_CPU_WITH_ACL)
+    scheduler_guard = SchedulerGuard::instance();
+#endif
 }
 
 Engine::~Engine() {
diff --git a/src/plugins/intel_cpu/src/plugin.h b/src/plugins/intel_cpu/src/plugin.h
index 20c6d315a2c623..3e9d616dcec02c 100644
--- a/src/plugins/intel_cpu/src/plugin.h
+++ b/src/plugins/intel_cpu/src/plugin.h
@@ -63,6 +63,20 @@ class Engine : public InferenceEngine::IInferencePlugin {
     const std::string deviceFullName;
 
     std::shared_ptr<void> specialSetup;
+
+#if defined(OV_CPU_WITH_ACL)
+    struct SchedulerGuard {
+        SchedulerGuard();
+        ~SchedulerGuard();
+        static std::shared_ptr<SchedulerGuard> instance();
+        static std::mutex mutex;
+        // separate mutex for saving ACLScheduler state in destructor
+        mutable std::mutex dest_mutex;
+        static std::weak_ptr<SchedulerGuard> ptr;
+    };
+
+    std::shared_ptr<SchedulerGuard> scheduler_guard;
+#endif
 };
 
 }   // namespace intel_cpu
diff --git a/src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.cpp b/src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.cpp
new file mode 100644
index 00000000000000..cca3c74cce86b0
--- /dev/null
+++ b/src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.cpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "random_uniform.hpp"
+#include <openvino/op/random_uniform.hpp>
+
+namespace ov {
+namespace intel_cpu {
+namespace node {
+
+// TODO: remove after fixing the issue 123011
+IShapeInfer::Result RandomUniformShapeInfer::infer(
+        const std::vector<std::reference_wrapper<const VectorDims>>& input_shapes,
+        const std::unordered_map<size_t, MemoryPtr>& data_dependency) {
+    VectorDims dims;
+    const auto& mem = data_dependency.at(0);
+    const auto rank = mem->getShape().getElementsCount();
+    auto shape_prc = mem->getDesc().getPrecision();
+    switch (shape_prc) {
+        case InferenceEngine::Precision::I32: {
+            auto data = reinterpret_cast<const int32_t*>(mem->getData());
+            dims.assign(data, data + rank);
+        } break;
+        case InferenceEngine::Precision::I64: {
+            auto data = reinterpret_cast<const int64_t*>(mem->getData());
+            dims.assign(data, data + rank);
+        } break;
+        default:
+            OPENVINO_THROW("Unexpected Shape input precision: ", shape_prc);
+    }
+
+    return {{dims}, ShapeInferStatus::success};
+}
+
+RandomUniformShapeInferFactory::RandomUniformShapeInferFactory(const std::shared_ptr<ov::Node>& op) : m_op(op) {
+    OPENVINO_ASSERT(ov::is_type<const op::v8::RandomUniform>(m_op),
+            "Unexpected op type in RandomUniform shape inference factory: ", m_op->get_type_name());
+}
+
+ShapeInferPtr RandomUniformShapeInferFactory::makeShapeInfer() const {
+    return std::make_shared<RandomUniformShapeInfer>();
+}
+
+} // namespace node
+} // namespace intel_cpu
+} // namespace ov
diff --git a/src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.hpp b/src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.hpp
new file mode 100644
index 00000000000000..ce87a966a9cbc9
--- /dev/null
+++ b/src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.hpp
@@ -0,0 +1,37 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shape_inference/shape_inference_cpu.hpp"
+#include <node.h>
+
+#pragma once
+
+namespace ov {
+namespace intel_cpu {
+namespace node {
+
+class RandomUniformShapeInfer : public ShapeInferEmptyPads {
+public:
+    explicit RandomUniformShapeInfer() {}
+    IShapeInfer::Result infer(
+        const std::vector<std::reference_wrapper<const VectorDims>>& input_shapes,
+        const std::unordered_map<size_t, MemoryPtr>& data_dependency) override;
+
+    port_mask_t get_port_mask() const override {
+        return PortMask(0);
+    }
+};
+
+class RandomUniformShapeInferFactory : public ShapeInferFactory {
+public:
+    explicit RandomUniformShapeInferFactory(const std::shared_ptr<ov::Node>& op);
+    ShapeInferPtr makeShapeInfer() const override;
+
+private:
+    std::shared_ptr<ov::Node> m_op;
+};
+
+} // namespace node
+} // namespace intel_cpu
+} // namespace ov
diff --git a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp
index 28756ba21664e1..f87bfb4f1b055f 100644
--- a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp
+++ b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp
@@ -195,14 +195,6 @@ void Transformations::PreLpt(const std::vector<ov::element::Type>& defaultPrecis
     CPU_REGISTER_PASS_COMMON(manager, ov::pass::InitNodeInfo);
     CPU_REGISTER_PASS_COMMON(manager, ov::pass::MarkShapeOfSubgraphs);
 
-    CPU_REGISTER_PASS_COMMON(manager, ov::pass::KeepConstAndDecompression);
-    CPU_SET_CALLBACK_COMMON(manager,
-        [](const_node_ptr &node) -> bool {
-            const auto outputs = node->get_output_target_inputs(0);
-            return outputs.size() != 1 || !is_type<ov::op::v0::MatMul>(outputs.begin()->get_node());
-        },
-        ov::pass::KeepConstAndDecompression);
-
     const bool useLpt = !defaultPrecisions.empty();
     if (useLpt) {
         CPU_REGISTER_PASS_COMMON(manager, ov::pass::MarkDequantizationSubgraph, defaultPrecisions);
@@ -233,17 +225,23 @@ void Transformations::PreLpt(const std::vector<ov::element::Type>& defaultPrecis
                 return false;
             }
             // TODO: Uncomment when group decompression is supported
-            // else if (ov::is_type<ov::opset1::Reshape>(consumer)) {
+            // if (ov::is_type<ov::opset1::Reshape>(consumer)) {
             //     consumer = get_single_consumer(consumer);
             //     if (consumer != nullptr && ov::is_type<ov::opset1::MatMul>(consumer)) {
             //         return false;
             //     }
             // }
+            if (ov::is_type<ov::opset1::Convert>(consumer)) {
+                consumer = get_single_consumer(consumer);
+                if (consumer != nullptr && ov::is_type<ov::opset1::MatMul>(consumer)) {
+                    return false;
+                }
+            }
             return true;
         }, ov::pass::MarkDequantizationSubgraph);
     }
 
-    auto get_convert_precisions = []() {
+    auto get_convert_precisions = [&]() {
         precisions_map map = {
             {ov::element::i64,     ov::element::i32},
             {ov::element::u64,     ov::element::i32},
@@ -251,7 +249,6 @@ void Transformations::PreLpt(const std::vector<ov::element::Type>& defaultPrecis
             {ov::element::u16,     ov::element::i32},
             {ov::element::u32,     ov::element::i32},
             {ov::element::f64,     ov::element::f32},
-            {ov::element::f16,     ov::element::f32},
             {ov::element::boolean, ov::element::u8},
             {ov::element::i4,      ov::element::i8},
             {ov::element::u4,      ov::element::u8}
@@ -259,12 +256,37 @@ void Transformations::PreLpt(const std::vector<ov::element::Type>& defaultPrecis
         // @todo should we always convert to f32 regardless of hardware support, as it is done for f16?
         if (!dnnl::impl::cpu::x64::mayiuse(dnnl::impl::cpu::x64::avx512_core))
             map.insert({ov::element::bf16, ov::element::f32});
-
+#if defined(OV_CPU_ARM_ENABLE_FP16)
+        if (inferencePrecision != ov::element::f16)
+            map.insert({ov::element::f16, ov::element::f32});
+#else
+        map.insert({ov::element::f16, ov::element::f32});
+#endif
         return map;
     };
     static const auto precisions = get_convert_precisions();
     type_to_fuse_map type_to_fuse = {{ov::opset10::Convert::get_type_info_static(), fuse_type_to_convert}};
 
+#if defined(OV_CPU_ARM_ENABLE_FP16)
+    if (inferencePrecision == ov::element::f16) {
+        precisions_map fp_convert_precision_map = {
+                {ov::element::f32, ov::element::f16}
+        };
+        type_to_fuse_map empty_fuse_map = {};
+        const bool keep_precision_sensitive_in_fp32 = true;
+        CPU_REGISTER_PASS_COMMON(manager, ov::pass::ConvertPrecision, fp_convert_precision_map,
+                                                                      empty_fuse_map,
+                                                                      keep_precision_sensitive_in_fp32);
+    }
+#endif
+    CPU_REGISTER_PASS_COMMON(manager, ov::pass::KeepConstAndDecompression);
+    CPU_SET_CALLBACK_COMMON(manager,
+        [](const_node_ptr &node) -> bool {
+            const auto outputs = node->get_output_target_inputs(0);
+            return outputs.size() != 1 || !is_type<ov::op::v0::MatMul>(outputs.begin()->get_node());
+        },
+        ov::pass::KeepConstAndDecompression);
+
     CPU_REGISTER_PASS_COMMON(manager, ov::pass::AUGRUCellFusion);
     CPU_REGISTER_PASS_COMMON(manager, ov::pass::CommonOptimizations);
     CPU_REGISTER_PASS_COMMON(manager, ov::pass::WrapInterpolateIntoTransposes);
diff --git a/src/plugins/intel_cpu/tests/functional/behavior/ov_plugin/properties.cpp b/src/plugins/intel_cpu/tests/functional/behavior/ov_plugin/properties.cpp
index 0e959ab865a6ed..10c0a244fcca31 100644
--- a/src/plugins/intel_cpu/tests/functional/behavior/ov_plugin/properties.cpp
+++ b/src/plugins/intel_cpu/tests/functional/behavior/ov_plugin/properties.cpp
@@ -173,13 +173,18 @@ TEST_F(OVClassConfigTestCPU, smoke_PluginSetConfigAffinityCore) {
     ASSERT_EQ(false, value);
 }
 
+#if defined(OV_CPU_ARM_ENABLE_FP16)
+    const auto expected_precision_for_performance_mode = ov::element::f16;
+#else
+    const auto expected_precision_for_performance_mode = InferenceEngine::with_cpu_x86_bfloat16() ? ov::element::bf16 : ov::element::f32;
+#endif
+
 TEST_F(OVClassConfigTestCPU, smoke_PluginSetConfigHintInferencePrecision) {
     ov::Core ie;
     auto value = ov::element::f32;
-    const auto precision = InferenceEngine::with_cpu_x86_bfloat16() ? ov::element::bf16 : ov::element::f32;
 
     ASSERT_NO_THROW(value = ie.get_property("CPU", ov::hint::inference_precision));
-    ASSERT_EQ(precision, value);
+    ASSERT_EQ(expected_precision_for_performance_mode, value);
 
     const auto forcedPrecision = ov::element::f32;
 
@@ -210,8 +215,6 @@ TEST_F(OVClassConfigTestCPU, smoke_PluginSetConfigEnableProfiling) {
     ASSERT_EQ(enableProfiling, value);
 }
 
-const auto expected_precision_for_performance_mode = InferenceEngine::with_cpu_x86_bfloat16() ? ov::element::bf16 : ov::element::f32;
-
 const auto bf16_if_can_be_emulated = InferenceEngine::with_cpu_x86_avx512_core() ? ov::element::bf16 : ov::element::f32;
 using ExpectedModeAndType = std::pair<ov::hint::ExecutionMode, ov::element::Type>;
 
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
index fa66f4a2c7801d..9cbe69255db0e0 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/infer_request_dynamic.cpp
@@ -11,11 +11,12 @@ using namespace ov::test::behavior;
 namespace {
 
 const std::vector<ov::AnyMap> configs = {
-    {}
+    {{ov::hint::inference_precision.name(), ov::element::f32}}
 };
 
 const std::vector<ov::AnyMap> HeteroConfigs = {
-    {ov::device::priorities(ov::test::utils::DEVICE_CPU)}
+    {{ov::hint::inference_precision.name(), ov::element::f32},
+     {ov::device::priorities(ov::test::utils::DEVICE_CPU)}},
 };
 
 std::shared_ptr<ngraph::Function> getFunction1() {
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/iteration_chaining.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/iteration_chaining.cpp
index 2c8678165426b3..567c877be8e8ab 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/iteration_chaining.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/iteration_chaining.cpp
@@ -10,11 +10,12 @@ using namespace ov::test::behavior;
 namespace {
 
 const std::vector<ov::AnyMap> configs = {
-    {}
+    {{ov::hint::inference_precision.name(), ov::element::f32}}
 };
 
 const std::vector<ov::AnyMap> HeteroConfigs = {
-    {ov::device::priorities(ov::test::utils::DEVICE_CPU)}
+    {{ov::hint::inference_precision.name(), ov::element::f32},
+     {ov::device::priorities(ov::test::utils::DEVICE_CPU)}},
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVIterationChaining,
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/pooling.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/pooling.cpp
index c57694a072f9e7..6d4b6a72e5819a 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/pooling.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/pooling.cpp
@@ -4,209 +4,184 @@
 
 #include <vector>
 
-#include "single_layer_tests/pooling.hpp"
+#include "single_op_tests/pooling.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
-
 namespace {
-// Common params
-const std::vector<InferenceEngine::Precision> inputPrecisions = {
-        InferenceEngine::Precision::FP32,
-//         InferenceEngine::Precision::FP16, // "[NOT_IMPLEMENTED] Input image format FP16 is not supported yet...
-        InferenceEngine::Precision::U8,
-//         InferenceEngine::Precision::I8 // Too much cases
-};
+using ov::test::PoolingLayerTest;
+using ov::test::MaxPoolingV8LayerTest;
+using ov::test::utils::PoolingTypes;
+using ov::test::poolSpecificParams;
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP16
+const std::vector<ov::element::Type> model_types = {
+        ov::element::f16
 };
 
 const std::vector<std::vector<size_t >> kernels = {{3, 3},
                                                           {3, 5}};
-const std::vector<std::vector<size_t >> kernel3D = {{2, 2, 2}};
+const std::vector<std::vector<size_t >> kernel_3d = {{2, 2, 2}};
 
 const std::vector<std::vector<size_t>> strides = {{1, 1},
                                                   {1, 2},
                                                   {2, 1},
                                                   {2, 2}};
-const std::vector<std::vector<size_t >> strides3D = {{1, 1, 1},
-                                                          {2, 2, 2}};
-const std::vector<std::vector<size_t >> stridess3D = {{2, 2, 2}};
-const std::vector<std::vector<size_t >> padBegins = {{0, 0},
-                                                            {0, 2}};
-const std::vector<std::vector<size_t >> padBegins3D = {{0, 0, 0}};
-const std::vector<std::vector<size_t >> padEnds = {{0, 0},
-                                                          {0, 2}};
-const std::vector<std::vector<size_t >> padEnds3D = {{0, 0, 0}};
-const std::vector<ngraph::op::RoundingType> roundingTypes = {ngraph::op::RoundingType::CEIL,
-                                                             ngraph::op::RoundingType::FLOOR};
+
+const std::vector<std::vector<size_t >> strides_3d = {{1, 1, 1},
+                                                      {2, 2, 2}};
+
+const std::vector<std::vector<size_t >> pad_begins = {{0, 0},
+                                                      {0, 2}};
+
+const std::vector<std::vector<size_t >> pad_begins_3d = {{0, 0, 0}};
+
+const std::vector<std::vector<size_t >> pad_ends = {{0, 0},
+                                                    {0, 2}};
+
+const std::vector<std::vector<size_t >> pad_ends_3d = {{0, 0, 0}};
+
 ////* ========== Max Polling ========== */
 /* +========== Explicit Pad Floor Rounding ========== */
+std::vector<ov::Shape> input_shapes_static = {{1, 3, 30, 30}};
+
 const auto maxPool_ExplicitPad_FloorRounding_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX),
+        ::testing::Values(PoolingTypes::MAX),
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::EXPLICIT),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_FloorRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_ExplicitPad_FloorRounding_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* +========== Same Upper Pad Floor Rounding ========== */
 const auto maxPool_SameUpperPad_FloorRounding_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX),
+        ::testing::Values(PoolingTypes::MAX),
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::SAME_UPPER),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::SAME_UPPER),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_SameUpperPad_FloorRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_SameUpperPad_FloorRounding_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* +========== Same Lower Pad Floor Rounding ========== */
 const auto maxPool_SameLowerPad_FloorRounding_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX),
+        ::testing::Values(PoolingTypes::MAX),
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::SAME_LOWER),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::SAME_LOWER),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_SameLowerPad_FloorRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_SameUpperPad_FloorRounding_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* ========== Explicit Pad Floor Rounding 5D input========== */
+
+std::vector<ov::Shape> input_shapes_5d_static = {{32, 32, 2, 2, 2}};
+
 const auto maxPool_ExplicitPad_FloorRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX),
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT),
+        ::testing::Values(PoolingTypes::MAX),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::EXPLICIT),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_FloorRounding_5Dinput, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_ExplicitPad_FloorRounding_5Dinput_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* ========== Same Upper Pad Floor Rounding 5D input========== */
 const auto maxPool_SameUpperPad_FloorRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX),
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::SAME_UPPER),
+        ::testing::Values(PoolingTypes::MAX),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::SAME_UPPER),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_SameUpperPad_FloorRounding_5Dinput, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_SameUpperPad_FloorRounding_5Dinput_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* ========== Same Lower Pad Ceil Rounding 5D input========== */
 const auto maxPool_SameLowerPad_CeilRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX),
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::op::RoundingType::CEIL),
-        ::testing::Values(ngraph::op::PadType::SAME_LOWER),
+        ::testing::Values(PoolingTypes::MAX),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::op::RoundingType::CEIL),
+        ::testing::Values(ov::op::PadType::SAME_LOWER),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_SameLowerPad_CeilRounding_5Dinput, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_SameUpperPad_FloorRounding_5Dinput_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* ========== Explicit Pad Ceil Rounding ========== */
 const auto maxPool_ExplicitPad_CeilRounding_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX),
+        ::testing::Values(PoolingTypes::MAX),
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::op::RoundingType::CEIL),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::op::RoundingType::CEIL),
+        ::testing::Values(ov::op::PadType::EXPLICIT),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_CeilRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 maxPool_ExplicitPad_CeilRounding_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
@@ -214,55 +189,49 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_ExplicitPad_CeilRounding, PoolingLayerTes
 ////* ========== Avg Pooling ========== */
 /* +========== Explicit Pad Ceil Rounding ========== */
 const auto avgPoolExplicitPadCeilRoundingParams = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::AVG),
+        ::testing::Values(PoolingTypes::AVG),
         ::testing::ValuesIn(kernels),
-        // TODO: Non 1 strides fails in ngraph reference implementation with error "The end corner is out of bounds at axis 3" thrown in the test body.
+        // TODO: Non 1 strides fails in reference implementation with error "The end corner is out of bounds at axis 3" thrown in the test body.
         ::testing::ValuesIn(strides),
         ::testing::ValuesIn(std::vector<std::vector<size_t>>({{0, 0}, {1, 1}, {0, 1}})),
         ::testing::ValuesIn(std::vector<std::vector<size_t>>({{0, 0}, {1, 1}, {0, 1}})),
-        ::testing::Values(ngraph::op::RoundingType::CEIL),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT),
+        ::testing::Values(ov::op::RoundingType::CEIL),
+        ::testing::Values(ov::op::PadType::EXPLICIT),
         ::testing::Values(true, false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_ExplicitPad_CeilRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 avgPoolExplicitPadCeilRoundingParams,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
-std::vector<poolSpecificParams> psParams({poolSpecificParams(ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {0, 0}, {0, 0},
-                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false),
-    poolSpecificParams(ngraph::helpers::PoolingTypes::AVG, {7, 7}, {1, 1}, {0, 0}, {1, 1},
-                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false)});
+std::vector<poolSpecificParams> psParams({poolSpecificParams(PoolingTypes::AVG, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                        ov::op::RoundingType::CEIL, ov::op::PadType::EXPLICIT, false),
+    poolSpecificParams(PoolingTypes::AVG, {7, 7}, {1, 1}, {0, 0}, {1, 1},
+                        ov::op::RoundingType::CEIL, ov::op::PadType::EXPLICIT, false)});
+
+std::vector<ov::Shape> input_shapes_explicit_pad_ceil_rounding_corner_static = {{1, 3, 30, 30}};
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_ExplicitPad_CeilRounding_corner, PoolingLayerTest,
                         ::testing::Combine(
                                 ::testing::ValuesIn(psParams),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 1024, 6, 6})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_explicit_pad_ceil_rounding_corner_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* +========== Explicit Pad Floor Rounding ========== */
 const auto avgPoolExplicitPadFloorRoundingParams = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::AVG),
+        ::testing::Values(PoolingTypes::AVG),
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
         ::testing::ValuesIn(std::vector<std::vector<size_t>>({{0, 0}, {1, 1}})),
         ::testing::ValuesIn(std::vector<std::vector<size_t>>({{0, 0}, {1, 1}})),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::EXPLICIT),
         ::testing::Values(true, false)
 );
 
@@ -270,114 +239,96 @@ const auto avgPoolExplicitPadFloorRoundingParams = ::testing::Combine(
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_ExplicitPad_FloorRounding, PoolingLayerTest,
                         ::testing::Combine(
                                 avgPoolExplicitPadFloorRoundingParams,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* ========== Explicit Pad Floor Rounding 5D input========== */
 const auto avgPool_ExplicitPad_FloorRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::AVG),
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT),
+        ::testing::Values(PoolingTypes::AVG),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::EXPLICIT),
         ::testing::Values(true, false)
 );
 
+std::vector<ov::Shape> input_shapes_5d_2_static = {{32, 32, 2, 2, 4}};
+
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_ExplicitPad_FloorRounding_5Dinput, PoolingLayerTest,
                         ::testing::Combine(
                                 avgPool_ExplicitPad_FloorRounding_5Dinput_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 4})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_2_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* ========== Same Upper Pad Floor Rounding 5D input========== */
 const auto avgPool_SameUpperPad_FloorRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::AVG),
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::SAME_UPPER),
+        ::testing::Values(PoolingTypes::AVG),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::SAME_UPPER),
         ::testing::Values(true)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_SameUpperPad_FloorRounding_5Dinput, PoolingLayerTest,
                         ::testing::Combine(
                                 avgPool_SameUpperPad_FloorRounding_5Dinput_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 4})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_2_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 /* ========== Same Lower Pad Ceil Rounding 5D input========== */
 const auto avgPool_SameLowerPad_CeilRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::AVG),
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::op::RoundingType::CEIL),
-        ::testing::Values(ngraph::op::PadType::SAME_LOWER),
+        ::testing::Values(PoolingTypes::AVG),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::op::RoundingType::CEIL),
+        ::testing::Values(ov::op::PadType::SAME_LOWER),
         ::testing::Values(true)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_SameLowerPad_CeilRounding_5Dinput, PoolingLayerTest,
                         ::testing::Combine(
                                 avgPool_SameLowerPad_CeilRounding_5Dinput_Params,
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
 ////* ========== Max Pooling V8 ========== */
 
 const std::vector<std::vector<size_t>> dilation = {{1, 1}, {2, 2}};
-const std::vector<std::vector<size_t >> dilation3D = {{1, 1, 1}, {2, 2, 2}};
+const std::vector<std::vector<size_t >> dilation_3d = {{1, 1, 1}, {2, 2, 2}};
 
 /* ========== Explicit Pad Floor Rounding ========== */
 const auto maxPoolv8_ExplicitPad_FloorRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
         ::testing::ValuesIn(dilation),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::EXPLICIT)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_ExplicitPad_FloorRounding, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_ExplicitPad_FloorRounding_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
@@ -386,23 +337,19 @@ const auto maxPoolv8_SameUpperPad_FloorRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
         ::testing::ValuesIn(dilation),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::SAME_UPPER)
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::SAME_UPPER)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameUpperPad_FloorRounding, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_SameUpperPad_FloorRounding_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
@@ -411,98 +358,82 @@ const auto maxPoolv8_SameLowerPad_FloorRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
         ::testing::ValuesIn(dilation),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::SAME_LOWER)
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::SAME_LOWER)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameLowerPad_FloorRounding, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_SameLowerPad_FloorRounding_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
 /* ========= Explicit Pad Floor Rounding 5D input========== */
 const auto maxPoolv8_ExplicitPad_FloorRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::Values(dilation3D[0]),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::Values(dilation_3d[0]),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::EXPLICIT)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_ExplicitPad_FloorRounding_5Dinput, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_ExplicitPad_FloorRounding_5Dinput_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
 /* ========= Same Upper Pad Floor Rounding 5D input========== */
 const auto maxPoolv8_SameUpperPad_FloorRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(dilation3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(dilation_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),
-        ::testing::Values(ngraph::op::PadType::SAME_UPPER)
+        ::testing::Values(ov::op::RoundingType::FLOOR),
+        ::testing::Values(ov::op::PadType::SAME_UPPER)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameUpperPad_FloorRounding_5Dinput, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_SameUpperPad_FloorRounding_5Dinput_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
 /* ========= Same Lower Pad Ceil Rounding 5D input========== */
 const auto maxPoolv8_SameLowerPad_CeilRounding_5Dinput_Params = ::testing::Combine(
-        ::testing::ValuesIn(kernel3D),
-        ::testing::ValuesIn(strides3D),
-        ::testing::ValuesIn(dilation3D),
-        ::testing::ValuesIn(padBegins3D),
-        ::testing::ValuesIn(padEnds3D),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::ValuesIn(kernel_3d),
+        ::testing::ValuesIn(strides_3d),
+        ::testing::ValuesIn(dilation_3d),
+        ::testing::ValuesIn(pad_begins_3d),
+        ::testing::ValuesIn(pad_ends_3d),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::CEIL),
-        ::testing::Values(ngraph::op::PadType::SAME_LOWER)
+        ::testing::Values(ov::op::RoundingType::CEIL),
+        ::testing::Values(ov::op::PadType::SAME_LOWER)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameLowerPad_CeilRounding_5Dinput, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_SameLowerPad_CeilRounding_5Dinput_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
@@ -511,49 +442,41 @@ const auto maxPoolv8_ExplicitPad_CeilRounding_Params = ::testing::Combine(
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
         ::testing::ValuesIn(dilation),
-        ::testing::ValuesIn(padBegins),
-        ::testing::ValuesIn(padEnds),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::ValuesIn(pad_begins),
+        ::testing::ValuesIn(pad_ends),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::CEIL),
-        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+        ::testing::Values(ov::op::RoundingType::CEIL),
+        ::testing::Values(ov::op::PadType::EXPLICIT)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_ExplicitPad_CeilRounding, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_ExplicitPad_CeilRounding_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
 ////* ========== Avg and Max Polling Cases ========== */
 /* ========== Valid Pad Rounding Not Applicable ========== */
 const auto allPools_ValidPad_Params = ::testing::Combine(
-        ::testing::Values(ngraph::helpers::PoolingTypes::MAX, ngraph::helpers::PoolingTypes::AVG),
+        ::testing::Values(PoolingTypes::MAX, PoolingTypes::AVG),
         ::testing::ValuesIn(kernels),
         ::testing::ValuesIn(strides),
         ::testing::Values(std::vector<size_t>({0, 0})),
         ::testing::Values(std::vector<size_t>({0, 0})),
         ::testing::Values(
-                ngraph::op::RoundingType::FLOOR),  // placeholder value - Rounding Type not applicable for Valid pad type
-        ::testing::Values(ngraph::op::PadType::VALID),
+                ov::op::RoundingType::FLOOR),  // placeholder value - Rounding Type not applicable for Valid pad type
+        ::testing::Values(ov::op::PadType::VALID),
         ::testing::Values(false)  // placeholder value - exclude pad not applicable for max pooling
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MAX_and_AVGPool_ValidPad, PoolingLayerTest,
                          ::testing::Combine(
                                  allPools_ValidPad_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::ValuesIn(model_types),
+                                ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          PoolingLayerTest::getTestCaseName);
 
@@ -563,21 +486,17 @@ const auto maxPoolv8_ValidPad_Params = ::testing::Combine(
         ::testing::ValuesIn(dilation),
         ::testing::Values(std::vector<size_t>({0, 0})),
         ::testing::Values(std::vector<size_t>({0, 0})),
-        ::testing::Values(ngraph::element::Type_t::i32),
+        ::testing::Values(ov::element::i32),
         ::testing::Values(0),
-        ::testing::Values(ngraph::op::RoundingType::FLOOR),  // placeholder value - Rounding Type not applicable for Valid pad type
-        ::testing::Values(ngraph::op::PadType::VALID)
+        ::testing::Values(ov::op::RoundingType::FLOOR),  // placeholder value - Rounding Type not applicable for Valid pad type
+        ::testing::Values(ov::op::PadType::VALID)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_MAXPoolv8_ValidPad, MaxPoolingV8LayerTest,
                          ::testing::Combine(
                                  maxPoolv8_ValidPad_Params,
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(InferenceEngine::Layout::ANY),
-                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::ValuesIn(model_types),
+                                 ::testing::Values(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                  ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MaxPoolingV8LayerTest::getTestCaseName);
 
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_align.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_align.cpp
index 7009da247635e1..58eaa5174ee107 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_align.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_align.cpp
@@ -4,30 +4,32 @@
 
 #include <vector>
 
-#include "single_layer_tests/roi_align.hpp"
+#include "single_op_tests/roi_align.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
+using ov::test::ROIAlignLayerTest;
 
+namespace {
 
-const std::vector<InferenceEngine::Precision> netPRCs = {
-    InferenceEngine::Precision::FP16,
-    InferenceEngine::Precision::FP32
+const std::vector<ov::element::Type> model_types = {
+    ov::element::f16,
+    ov::element::f32
 };
 
 const auto ROIAlignCases_average = ::testing::Combine(
         ::testing::ValuesIn(
-    std::vector<std::vector<size_t>> {
-            { 3, 8, 16, 16 },
-            { 2, 1, 16, 16 },
-            { 2, 1, 8, 16 }}),
-        ::testing::Values(std::vector<size_t>{ 2, 4 }),
+    ov::test::static_shapes_to_test_representation(
+        std::vector<std::vector<ov::Shape>>{
+            {{ 3, 8, 16, 16 }},
+            {{ 2, 1, 16, 16 }},
+            {{ 2, 1, 8, 16 }}})),
+        ::testing::Values(ov::Shape{ 2, 4 }),
         ::testing::Values(2),
         ::testing::Values(2),
         ::testing::ValuesIn(std::vector<float> { 1, 0.625 }),
         ::testing::Values(2),
         ::testing::Values("avg"),
-        ::testing::ValuesIn(netPRCs),
+        ::testing::ValuesIn(model_types),
         ::testing::Values(ov::test::utils::DEVICE_CPU)
 );
 
@@ -35,18 +37,21 @@ INSTANTIATE_TEST_SUITE_P(smoke_TestsROIAlign_average, ROIAlignLayerTest, ROIAlig
 
 const auto ROIAlignCases_max = ::testing::Combine(
         ::testing::ValuesIn(
-        std::vector<std::vector<size_t>> {
-            { 2, 8, 20, 20 },
-            { 2, 1, 20, 20 },
-            { 2, 1, 10, 20 }}),
-        ::testing::Values(std::vector<size_t>{ 2, 4 }),
+        ov::test::static_shapes_to_test_representation(
+            std::vector<std::vector<ov::Shape>>{
+            {{ 2, 8, 20, 20 }},
+            {{ 2, 1, 20, 20 }},
+            {{ 2, 1, 10, 20 }}})),
+        ::testing::Values(ov::Shape{ 2, 4 }),
         ::testing::Values(2),
         ::testing::Values(2),
         ::testing::ValuesIn(std::vector<float> { 1, 0.625 }),
         ::testing::Values(2),
         ::testing::Values("max"),
-        ::testing::ValuesIn(netPRCs),
+        ::testing::ValuesIn(model_types),
         ::testing::Values(ov::test::utils::DEVICE_CPU)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_TestsROIAlign_max, ROIAlignLayerTest, ROIAlignCases_max, ROIAlignLayerTest::getTestCaseName);
+
+}  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_pooling.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_pooling.cpp
index ff2be946948c6a..ca68bc6fa4d5ea 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_pooling.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/roi_pooling.cpp
@@ -4,62 +4,72 @@
 
 #include <vector>
 
-#include "single_layer_tests/roi_pooling.hpp"
+#include "single_op_tests/roi_pooling.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
+using ov::test::ROIPoolingLayerTest;
 
-const std::vector<std::vector<size_t>> inShapes = {
-    {1, 3, 8, 8},
-    {3, 4, 50, 50}
+namespace {
+
+const std::vector<ov::Shape> param_shapes = {
+    {{1, 3, 8, 8}},
+    {{3, 4, 50, 50}}
 };
 
-const std::vector<std::vector<size_t>> pooledShapes_max = {
-    {1, 1},
-    {2, 2},
-    {3, 3},
-    {6, 6}
+const std::vector<ov::Shape> coord_shapes = {
+    {{1, 5}},
+    {{3, 5}},
+    {{5, 5}}
 };
 
-const std::vector<std::vector<size_t>> pooledShapes_bilinear = {
-    {1, 1},
-    {2, 2},
-    {3, 3},
-    {6, 6}
+auto input_shapes = [](const std::vector<ov::Shape>& in1, const std::vector<ov::Shape>& in2) {
+    std::vector<std::vector<ov::test::InputShape>> res;
+    for (const auto& sh1 : in1)
+        for (const auto& sh2 : in2)
+            res.push_back(ov::test::static_shapes_to_test_representation({sh1, sh2}));
+    return res;
+}(param_shapes, coord_shapes);
+
+const std::vector<ov::Shape> pooled_shapes_max = {
+    {{1, 1}},
+    {{2, 2}},
+    {{3, 3}},
+    {{6, 6}}
 };
 
-const std::vector<std::vector<size_t>> coordShapes = {
-    {1, 5},
-    {3, 5},
-    {5, 5}
+const std::vector<ov::Shape> pooled_shapes_bilinear = {
+    {{1, 1}},
+    {{2, 2}},
+    {{3, 3}},
+    {{6, 6}}
 };
 
-const std::vector<InferenceEngine::Precision> netPRCs = {
-    InferenceEngine::Precision::FP16,
-    InferenceEngine::Precision::FP32
+const std::vector<ov::element::Type> model_types = {
+    ov::element::f16,
+    ov::element::f32
 };
 
 const std::vector<float> spatial_scales = {0.625f, 1.f};
 
 const auto test_ROIPooling_max = ::testing::Combine(
-    ::testing::ValuesIn(inShapes),
-    ::testing::ValuesIn(coordShapes),
-    ::testing::ValuesIn(pooledShapes_max),
+    ::testing::ValuesIn(input_shapes),
+    ::testing::ValuesIn(pooled_shapes_max),
     ::testing::ValuesIn(spatial_scales),
-    ::testing::Values(ngraph::helpers::ROIPoolingTypes::ROI_MAX),
-    ::testing::ValuesIn(netPRCs),
+    ::testing::Values(ov::test::utils::ROIPoolingTypes::ROI_MAX),
+    ::testing::ValuesIn(model_types),
     ::testing::Values(ov::test::utils::DEVICE_CPU)
 );
 
 const auto test_ROIPooling_bilinear = ::testing::Combine(
-    ::testing::ValuesIn(inShapes),
-    ::testing::ValuesIn(coordShapes),
-    ::testing::ValuesIn(pooledShapes_bilinear),
+    ::testing::ValuesIn(input_shapes),
+    ::testing::ValuesIn(pooled_shapes_bilinear),
     ::testing::Values(spatial_scales[1]),
-    ::testing::Values(ngraph::helpers::ROIPoolingTypes::ROI_BILINEAR),
-    ::testing::ValuesIn(netPRCs),
+    ::testing::Values(ov::test::utils::ROIPoolingTypes::ROI_BILINEAR),
+    ::testing::ValuesIn(model_types),
     ::testing::Values(ov::test::utils::DEVICE_CPU)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_TestsROIPooling_max, ROIPoolingLayerTest, test_ROIPooling_max, ROIPoolingLayerTest::getTestCaseName);
 INSTANTIATE_TEST_SUITE_P(smoke_TestsROIPooling_bilinear, ROIPoolingLayerTest, test_ROIPooling_bilinear, ROIPoolingLayerTest::getTestCaseName);
+
+}  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
index e629a715b69890..4eb40365fa95d7 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
@@ -24,8 +24,8 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*(QuantGroupConv3D).*)",
         // TODO: Issue: 34518
         R"(.*RangeLayerTest.*)",
-        R"(.*(RangeAddSubgraphTest).*Start=1.2.*Stop=(5.2|-5.2).*Step=(0.1|-0.1).*netPRC=FP16.*)",
-        R"(.*(RangeNumpyAddSubgraphTest).*netPRC=FP16.*)",
+        R"(.*(RangeAddSubgraphTest).*Start=1.2.*Stop=(5.2|-5.2).*Step=(0.1|-0.1).*ET=f16.*)",
+        R"(.*(RangeNumpyAddSubgraphTest).*ET=f16.*)",
         // TODO: Issue: 43793
         R"(.*InferRequestPreprocessDynamicallyInSetBlobTest.*iPRC=0.*_iLT=1.*)",
         R"(.*InferRequestPreprocessDynamicallyInSetBlobTest.*oPRC=0.*_oLT=1.*)",
@@ -189,16 +189,9 @@ std::vector<std::string> disabledTestPatterns() {
         R"(smoke_LSTMSequenceCommon.*LSTMSequenceTest.Inference.*CONVERT_TO_TI.*)",
         // Issue: 122094
         R"(smoke_Interpolate_Basic_Down_Sample_Tail/InterpolateLayerTest.Inference.*(asymmetric|align_corners).*f16.*)",
+        // Need to generate sequence exactly in the i64 data type. Enable in scope of i64 enabling.
+        R"(.*RandomUniformLayerTestCPU.*OutPrc=i64.*)",
     };
-#if defined(__APPLE__) && defined(OPENVINO_ARCH_ARM64)
-    // Issue: 120950
-    retVector.emplace_back(R"(.*smoke_TensorIteratorCommon/TensorIteratorTest.Inference.*_modelType=f16_targetDevice=CPU.*)");
-    retVector.emplace_back(R"(.*smoke_CtcGreedyDecoderBasic/CTCGreedyDecoderLayerTest.Inference.*netPRC=f16.*trgDev=CPU.*)");
-    retVector.emplace_back(R"(.*CTCGreedyDecoderSeqLenLayerTest.Inference.*dataPRC=f16.*trgDev=CPU.*)");
-    // Issue: 122177
-    retVector.emplace_back(R"(.*smoke_LSTMCellCommon/LSTMCellTest.Inference.*_modelType=f16.*)");
-    retVector.emplace_back(R"(.*smoke_LSTMSequenceCommonZeroClip/LSTMSequenceTest.Inference.*_modelType=f16.*)");
-#endif
 
 #if defined(OPENVINO_ARCH_X86)
     retVector.emplace_back(R"(.*DetectionOutputLayerTest.*)");
@@ -227,6 +220,18 @@ std::vector<std::string> disabledTestPatterns() {
     retVector.emplace_back(R"(smoke_NegativeQuantizedMatMulMultiplyFusion.*)");
     // int8 specific
     retVector.emplace_back(R"(smoke_Quantized.*)");
+
+#if defined(OV_CPU_ARM_ENABLE_FP16)
+    // Issue: 123019
+    retVector.emplace_back(R"(smoke_AvgPool_ExplicitPad_CeilRounding.*modelType=f16.*)");
+    retVector.emplace_back(R"(smoke_AvgPool_ExplicitPad_FloorRounding_5Dinput/PoolingLayerTest.*modelType=f16.*)");
+    retVector.emplace_back(R"(smoke_AvgPool_SameUpperPad_FloorRounding_5Dinput/PoolingLayerTest.*modelType=f16.*)");
+    retVector.emplace_back(R"(smoke_AvgPool_SameLowerPad_CeilRounding_5Dinput/PoolingLayerTest.*modelType=f16.*)");
+    retVector.emplace_back(R"(smoke_CompareWithRefs_Mvn.*INFERENCE_PRECISION_HINT=f16.*)");
+    retVector.emplace_back(R"(smoke_staticShapes4D.*INFERENCE_PRECISION_HINT=f16.*)");
+    retVector.emplace_back(R"(smoke_dynamicShapes4D.*INFERENCE_PRECISION_HINT=f16.*)");
+#endif
+
 #endif
 
 #if defined(OPENVINO_ARCH_ARM)
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mul_conv_fusion.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mul_conv_fusion.cpp
index 480a538c6c68ad..4ab6ddfbaefdb9 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mul_conv_fusion.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mul_conv_fusion.cpp
@@ -2,263 +2,268 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "subgraph_tests/mul_conv_fusion.hpp"
+
 #include <vector>
 
-#include "subgraph_tests/mul_conv_fusion.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-#include <ngraph/opsets/opset8.hpp>
-
-using namespace SubgraphTestsDefinitions;
+using namespace ov::test;
 
 namespace {
-    const std::vector<ngraph::element::Type> types{ngraph::element::f32};
+const std::vector<ov::element::Type> types{ov::element::f32};
 
-    const std::vector<ngraph::Shape> const_shapes_fprop_1d{
-        {},
-        {1},
-        {1, 1},
-        {8, 1},
-        {1, 1, 1},
-        {1, 8, 1},
-    };
+const std::vector<ov::Shape> const_shapes_fprop_1d{
+    {},
+    {1},
+    {1, 1},
+    {8, 1},
+    {1, 1, 1},
+    {1, 8, 1},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_Convolution_1D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::Convolution::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{1, 8, 64}),
-                                    ::testing::Values(ngraph::Shape{64, 8, 1}),
-                                    ::testing::ValuesIn(const_shapes_fprop_1d),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(false), // Positive test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_Convolution_1D,
+                         MulConvFusion,
+                         ::testing::Combine(::testing::Values(ov::op::v1::Convolution::get_type_info_static()),
+                                            ::testing::Values(ov::Shape{1, 8, 64}),
+                                            ::testing::Values(ov::Shape{64, 8, 1}),
+                                            ::testing::ValuesIn(const_shapes_fprop_1d),
+                                            ::testing::ValuesIn(types),
+                                            ::testing::Values(false),  // Positive test
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> const_shapes_fprop_2d{
-        {},
-        {1},
-        {1, 1},
-        {1, 1, 1},
-        {8, 1, 1},
-        {1, 1, 1, 1},
-        {1, 8, 1, 1},
-    };
+const std::vector<ov::Shape> const_shapes_fprop_2d{
+    {},
+    {1},
+    {1, 1},
+    {1, 1, 1},
+    {8, 1, 1},
+    {1, 1, 1, 1},
+    {1, 8, 1, 1},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_Convolution_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::Convolution::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 8, 14, 14}),
-                                    ::testing::Values(ngraph::Shape{2, 8, 7, 7}),
-                                    ::testing::ValuesIn(const_shapes_fprop_2d),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(false), // Positive test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_Convolution_2D,
+                         MulConvFusion,
+                         ::testing::Combine(::testing::Values(ov::op::v1::Convolution::get_type_info_static()),
+                                            ::testing::Values(ov::Shape{2, 8, 14, 14}),
+                                            ::testing::Values(ov::Shape{2, 8, 7, 7}),
+                                            ::testing::ValuesIn(const_shapes_fprop_2d),
+                                            ::testing::ValuesIn(types),
+                                            ::testing::Values(false),  // Positive test
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> const_shapes_fprop_2d_kernel_same_as_input{
-        {7},
-        {1, 7},
-        {1, 1, 7},
-        {8, 1, 7},
-        {1, 1, 1, 7},
-        {1, 8, 1, 7},
-        {7, 1},
-        {1, 7, 1},
-        {8, 7, 1},
-        {1, 1, 7, 1},
-        {1, 8, 7, 1},
-        {1, 1, 7, 7},
-        {1, 8, 7, 7},
-    };
+const std::vector<ov::Shape> const_shapes_fprop_2d_kernel_same_as_input{
+    {7},
+    {1, 7},
+    {1, 1, 7},
+    {8, 1, 7},
+    {1, 1, 1, 7},
+    {1, 8, 1, 7},
+    {7, 1},
+    {1, 7, 1},
+    {8, 7, 1},
+    {1, 1, 7, 1},
+    {1, 8, 7, 1},
+    {1, 1, 7, 7},
+    {1, 8, 7, 7},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_Convolution_2D_kernel_same_as_input, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::Convolution::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 8, 7, 7}),
-                                    ::testing::Values(ngraph::Shape{3, 8, 7, 7}),
-                                    ::testing::ValuesIn(const_shapes_fprop_2d_kernel_same_as_input),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(false), // Positive test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_Convolution_2D_kernel_same_as_input,
+                         MulConvFusion,
+                         ::testing::Combine(::testing::Values(ov::op::v1::Convolution::get_type_info_static()),
+                                            ::testing::Values(ov::Shape{2, 8, 7, 7}),
+                                            ::testing::Values(ov::Shape{3, 8, 7, 7}),
+                                            ::testing::ValuesIn(const_shapes_fprop_2d_kernel_same_as_input),
+                                            ::testing::ValuesIn(types),
+                                            ::testing::Values(false),  // Positive test
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> const_shapes_conv_bprop{
-        {},
-        {1},
-        {1, 1},
-        {1, 1, 1},
-        {1, 1, 1, 1},
-        {3, 1, 1},
-        {1, 3, 1, 1},
-    };
-    INSTANTIATE_TEST_SUITE_P(smoke_ConvolutionBackpropData_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::ConvolutionBackpropData::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{4, 3, 64, 64}),
-                                    ::testing::Values(ngraph::Shape{3, 20, 3, 3}),
-                                    ::testing::ValuesIn(const_shapes_conv_bprop),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(false), // Positive test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+const std::vector<ov::Shape> const_shapes_conv_bprop{
+    {},
+    {1},
+    {1, 1},
+    {1, 1, 1},
+    {1, 1, 1, 1},
+    {3, 1, 1},
+    {1, 3, 1, 1},
+};
+INSTANTIATE_TEST_SUITE_P(
+    smoke_ConvolutionBackpropData_2D,
+    MulConvFusion,
+    ::testing::Combine(::testing::Values(ov::op::v1::ConvolutionBackpropData::get_type_info_static()),
+                       ::testing::Values(ov::Shape{4, 3, 64, 64}),
+                       ::testing::Values(ov::Shape{3, 20, 3, 3}),
+                       ::testing::ValuesIn(const_shapes_conv_bprop),
+                       ::testing::ValuesIn(types),
+                       ::testing::Values(false),  // Positive test
+                       ::testing::Values(ov::test::utils::DEVICE_CPU)),
+    MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> const_shapes_group_conv{
-        {},
-        {1},
-        {1, 1},
-        {1, 1, 1},
-        {12, 1, 1},
-        {1, 1, 1, 1},
-        {1, 12, 1, 1},
-    };
+const std::vector<ov::Shape> const_shapes_group_conv{
+    {},
+    {1},
+    {1, 1},
+    {1, 1, 1},
+    {12, 1, 1},
+    {1, 1, 1, 1},
+    {1, 12, 1, 1},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_GroupConvolution_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::GroupConvolution::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 14, 14}),
-                                    ::testing::Values(ngraph::Shape{4, 5, 3, 7, 7}),
-                                    ::testing::ValuesIn(const_shapes_group_conv),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(false), // Positive test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_GroupConvolution_2D,
+                         MulConvFusion,
+                         ::testing::Combine(::testing::Values(ov::op::v1::GroupConvolution::get_type_info_static()),
+                                            ::testing::Values(ov::Shape{2, 12, 14, 14}),
+                                            ::testing::Values(ov::Shape{4, 5, 3, 7, 7}),
+                                            ::testing::ValuesIn(const_shapes_group_conv),
+                                            ::testing::ValuesIn(types),
+                                            ::testing::Values(false),  // Positive test
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> const_shapes_group_conv_kernel_same_as_input{
-        {14},
-        {1, 14},
-        {1, 1, 14},
-        {12, 1, 14},
-        {1, 1, 1, 14},
-        {1, 12, 1, 14},
-        {14, 1},
-        {1, 14, 1},
-        {12, 14, 1},
-        {1, 1, 14, 1},
-        {1, 12, 14, 1},
-        {1, 1, 14, 14},
-        {1, 12, 14, 14},
-    };
+const std::vector<ov::Shape> const_shapes_group_conv_kernel_same_as_input{
+    {14},
+    {1, 14},
+    {1, 1, 14},
+    {12, 1, 14},
+    {1, 1, 1, 14},
+    {1, 12, 1, 14},
+    {14, 1},
+    {1, 14, 1},
+    {12, 14, 1},
+    {1, 1, 14, 1},
+    {1, 12, 14, 1},
+    {1, 1, 14, 14},
+    {1, 12, 14, 14},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_GroupConvolution_2D_kernel_same_as_input, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::GroupConvolution::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 14, 14}),
-                                    ::testing::Values(ngraph::Shape{4, 5, 3, 14, 14}),
-                                    ::testing::ValuesIn(const_shapes_group_conv_kernel_same_as_input),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(false), // Positive test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_GroupConvolution_2D_kernel_same_as_input,
+                         MulConvFusion,
+                         ::testing::Combine(::testing::Values(ov::op::v1::GroupConvolution::get_type_info_static()),
+                                            ::testing::Values(ov::Shape{2, 12, 14, 14}),
+                                            ::testing::Values(ov::Shape{4, 5, 3, 14, 14}),
+                                            ::testing::ValuesIn(const_shapes_group_conv_kernel_same_as_input),
+                                            ::testing::ValuesIn(types),
+                                            ::testing::Values(false),  // Positive test
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> const_shapes_group_conv_bprop{
-        {},
-        {1},
-        {1, 1},
-        {1, 1, 1},
-        {12, 1, 1},
-        {1, 1, 1, 1},
-        {1, 12, 1, 1},
-    };
+const std::vector<ov::Shape> const_shapes_group_conv_bprop{
+    {},
+    {1},
+    {1, 1},
+    {1, 1, 1},
+    {12, 1, 1},
+    {1, 1, 1, 1},
+    {1, 12, 1, 1},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_GroupConvolutionBackpropData_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::GroupConvolutionBackpropData::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 10, 10}),
-                                    ::testing::Values(ngraph::Shape{4, 3, 5, 2, 2}),
-                                    ::testing::ValuesIn(const_shapes_group_conv_bprop),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(false), // Positive test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(
+    smoke_GroupConvolutionBackpropData_2D,
+    MulConvFusion,
+    ::testing::Combine(::testing::Values(ov::op::v1::GroupConvolutionBackpropData::get_type_info_static()),
+                       ::testing::Values(ov::Shape{2, 12, 10, 10}),
+                       ::testing::Values(ov::Shape{4, 3, 5, 2, 2}),
+                       ::testing::ValuesIn(const_shapes_group_conv_bprop),
+                       ::testing::ValuesIn(types),
+                       ::testing::Values(false),  // Positive test
+                       ::testing::Values(ov::test::utils::DEVICE_CPU)),
+    MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> negative_const_shapes{
-        {12, 64, 64},
-        {2, 1, 1, 1},
-        {1, 1, 64, 64},
-        {1, 12, 64, 64},
-        {2, 12, 64, 64},
-    };
+const std::vector<ov::Shape> negative_const_shapes{
+    {12, 64, 64},
+    {2, 1, 1, 1},
+    {1, 1, 64, 64},
+    {1, 12, 64, 64},
+    {2, 12, 64, 64},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_NegativeConvolution_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::Convolution::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 64, 64}),
-                                    ::testing::Values(ngraph::Shape{20, 12, 1, 1}),
-                                    ::testing::ValuesIn(negative_const_shapes),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(true), // Negative test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_NegativeConvolution_2D,
+                         MulConvFusion,
+                         ::testing::Combine(::testing::Values(ov::op::v1::Convolution::get_type_info_static()),
+                                            ::testing::Values(ov::Shape{2, 12, 64, 64}),
+                                            ::testing::Values(ov::Shape{20, 12, 1, 1}),
+                                            ::testing::ValuesIn(negative_const_shapes),
+                                            ::testing::ValuesIn(types),
+                                            ::testing::Values(true),  // Negative test
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MulConvFusion::getTestCaseName);
 
-    INSTANTIATE_TEST_SUITE_P(smoke_NegativeConvolutionBackpropData_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::ConvolutionBackpropData::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 64, 64}),
-                                    ::testing::Values(ngraph::Shape{12, 20, 3, 3}),
-                                    ::testing::ValuesIn(negative_const_shapes),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(true), // Negative test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(
+    smoke_NegativeConvolutionBackpropData_2D,
+    MulConvFusion,
+    ::testing::Combine(::testing::Values(ov::op::v1::ConvolutionBackpropData::get_type_info_static()),
+                       ::testing::Values(ov::Shape{2, 12, 64, 64}),
+                       ::testing::Values(ov::Shape{12, 20, 3, 3}),
+                       ::testing::ValuesIn(negative_const_shapes),
+                       ::testing::ValuesIn(types),
+                       ::testing::Values(true),  // Negative test
+                       ::testing::Values(ov::test::utils::DEVICE_CPU)),
+    MulConvFusion::getTestCaseName);
 
-    INSTANTIATE_TEST_SUITE_P(smoke_NegativeGroupConvolution_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::GroupConvolution::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 64, 64}),
-                                    ::testing::Values(ngraph::Shape{4, 5, 3, 1, 2}),
-                                    ::testing::ValuesIn(negative_const_shapes),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(true), // Negative test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_NegativeGroupConvolution_2D,
+                         MulConvFusion,
+                         ::testing::Combine(::testing::Values(ov::op::v1::GroupConvolution::get_type_info_static()),
+                                            ::testing::Values(ov::Shape{2, 12, 64, 64}),
+                                            ::testing::Values(ov::Shape{4, 5, 3, 1, 2}),
+                                            ::testing::ValuesIn(negative_const_shapes),
+                                            ::testing::ValuesIn(types),
+                                            ::testing::Values(true),  // Negative test
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MulConvFusion::getTestCaseName);
 
-    INSTANTIATE_TEST_SUITE_P(smoke_NegativeGroupConvolutionBackpropData_2D, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::GroupConvolutionBackpropData::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 64, 64}),
-                                    ::testing::Values(ngraph::Shape{4, 3, 5, 1, 1}),
-                                    ::testing::ValuesIn(negative_const_shapes),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(true), // Negative test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(
+    smoke_NegativeGroupConvolutionBackpropData_2D,
+    MulConvFusion,
+    ::testing::Combine(::testing::Values(ov::op::v1::GroupConvolutionBackpropData::get_type_info_static()),
+                       ::testing::Values(ov::Shape{2, 12, 64, 64}),
+                       ::testing::Values(ov::Shape{4, 3, 5, 1, 1}),
+                       ::testing::ValuesIn(negative_const_shapes),
+                       ::testing::ValuesIn(types),
+                       ::testing::Values(true),  // Negative test
+                       ::testing::Values(ov::test::utils::DEVICE_CPU)),
+    MulConvFusion::getTestCaseName);
 
-    const std::vector<ngraph::Shape> negative_const_shapes_kernel_same_as_input{
-        {7},
-        {1, 7},
-        {1, 1, 7},
-        {12, 1, 7},
-        {1, 1, 1, 7},
-        {1, 12, 1, 7},
-        {7, 1},
-        {1, 7, 1},
-        {12, 7, 1},
-        {1, 1, 7, 1},
-        {1, 12, 7, 1},
-        {1, 1, 7, 7},
-        {1, 12, 7, 7},
-    };
+const std::vector<ov::Shape> negative_const_shapes_kernel_same_as_input{
+    {7},
+    {1, 7},
+    {1, 1, 7},
+    {12, 1, 7},
+    {1, 1, 1, 7},
+    {1, 12, 1, 7},
+    {7, 1},
+    {1, 7, 1},
+    {12, 7, 1},
+    {1, 1, 7, 1},
+    {1, 12, 7, 1},
+    {1, 1, 7, 7},
+    {1, 12, 7, 7},
+};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_NegativeConvolutionBackpropData_2D_kernel_same_as_input, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::ConvolutionBackpropData::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 7, 7}),
-                                    ::testing::Values(ngraph::Shape{12, 20, 7, 7}),
-                                    ::testing::ValuesIn(negative_const_shapes_kernel_same_as_input),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(true), // Negative test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(
+    smoke_NegativeConvolutionBackpropData_2D_kernel_same_as_input,
+    MulConvFusion,
+    ::testing::Combine(::testing::Values(ov::op::v1::ConvolutionBackpropData::get_type_info_static()),
+                       ::testing::Values(ov::Shape{2, 12, 7, 7}),
+                       ::testing::Values(ov::Shape{12, 20, 7, 7}),
+                       ::testing::ValuesIn(negative_const_shapes_kernel_same_as_input),
+                       ::testing::ValuesIn(types),
+                       ::testing::Values(true),  // Negative test
+                       ::testing::Values(ov::test::utils::DEVICE_CPU)),
+    MulConvFusion::getTestCaseName);
 
-    INSTANTIATE_TEST_SUITE_P(smoke_NegativeGroupConvolutionBackpropData_2D_kernel_same_as_input, MulConvFusion,
-                            ::testing::Combine(
-                                    ::testing::Values(ngraph::opset8::GroupConvolutionBackpropData::get_type_info_static()),
-                                    ::testing::Values(ngraph::Shape{2, 12, 7, 7}),
-                                    ::testing::Values(ngraph::Shape{4, 3, 5, 7, 7}),
-                                    ::testing::ValuesIn(negative_const_shapes_kernel_same_as_input),
-                                    ::testing::ValuesIn(types),
-                                    ::testing::Values(true), // Negative test
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MulConvFusion::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(
+    smoke_NegativeGroupConvolutionBackpropData_2D_kernel_same_as_input,
+    MulConvFusion,
+    ::testing::Combine(::testing::Values(ov::op::v1::GroupConvolutionBackpropData::get_type_info_static()),
+                       ::testing::Values(ov::Shape{2, 12, 7, 7}),
+                       ::testing::Values(ov::Shape{4, 3, 5, 7, 7}),
+                       ::testing::ValuesIn(negative_const_shapes_kernel_same_as_input),
+                       ::testing::ValuesIn(types),
+                       ::testing::Values(true),  // Negative test
+                       ::testing::Values(ov::test::utils::DEVICE_CPU)),
+    MulConvFusion::getTestCaseName);
 
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp
index f28990117a39d3..c46e92708949b0 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp
@@ -2,31 +2,29 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
-
 #include "subgraph_tests/multiply_add.hpp"
 
-using namespace SubgraphTestsDefinitions;
+#include <vector>
+
+using namespace ov::test;
 
 namespace {
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP32
-};
+const std::vector<ov::element::Type> input_type = {ov::element::f32};
 
-const std::vector<std::vector<size_t>> inputShapes = {
-        {1, 3, 2, 2, 4, 5},
-        {1, 3, 2, 2, 2, 4, 5},
-        {1, 3, 2, 2, 2, 2, 4, 5},
-        {1, 3, 2, 2, 2, 2, 2, 4, 5},
-        {1, 3, 2, 2, 2, 2, 2, 2, 4, 5},
+const std::vector<ov::Shape> inputShapes = {
+    {1, 3, 2, 2, 4, 5},
+    {1, 3, 2, 2, 2, 4, 5},
+    {1, 3, 2, 2, 2, 2, 4, 5},
+    {1, 3, 2, 2, 2, 2, 2, 4, 5},
+    {1, 3, 2, 2, 2, 2, 2, 2, 4, 5},
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_MultipleAdd_Nd, MultiplyAddLayerTest,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(inputShapes),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        MultiplyAddLayerTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_MultipleAdd_Nd,
+                         MultiplyAddLayerTest,
+                         ::testing::Combine(::testing::ValuesIn(inputShapes),
+                                            ::testing::ValuesIn(input_type),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MultiplyAddLayerTest::getTestCaseName);
 
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mvn_multiply_add.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mvn_multiply_add.cpp
index 4fe0990bfba66a..f27050440a7cf9 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mvn_multiply_add.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/mvn_multiply_add.cpp
@@ -4,91 +4,75 @@
 
 #include "subgraph_tests/mvn_multiply_add.hpp"
 
-using namespace SubgraphTestsDefinitions;
-using namespace InferenceEngine;
+using namespace ov::test;
 
 namespace {
 
-const std::vector<Precision> netPrecision = {
-        Precision::FP32
-};
+const std::vector<ov::element::Type> netPrecision = {ov::element::f32};
 
-std::vector<Precision> idxPrecision = {
-        Precision::I64
-};
+std::vector<ov::element::Type> idxPrecision = {ov::element::i64};
 
-const std::vector<bool> acrossChannels = {
-        true,
-        false
-};
+const std::vector<bool> acrossChannels = {true, false};
 
-const std::vector<bool> normalizeVariance = {
-        true,
-        false
-};
+const std::vector<bool> normalizeVariance = {true, false};
 
-const std::vector<float> epsilon = {
-        0.000000001f
-};
+const std::vector<float> epsilon = {0.000000001f};
 
-const std::vector<std::string> epsMode = {
-        "inside_sqrt",
-        "outside_sqrt"
-};
+const std::vector<std::string> epsMode = {"inside_sqrt", "outside_sqrt"};
 
-const std::vector<std::pair<SizeVector, SizeVector>> shapes_1D = {
-        std::pair<SizeVector, SizeVector>{ SizeVector{5}, SizeVector{5}},
-        std::pair<SizeVector, SizeVector>{ SizeVector{64}, SizeVector{64}},
+const std::vector<std::pair<ov::Shape, ov::Shape>> shapes_1D = {
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{5}, ov::Shape{5}},
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{64}, ov::Shape{64}},
 };
 
-const std::vector<std::pair<SizeVector, SizeVector>> shapes_2D = {
-        std::pair<SizeVector, SizeVector>{ SizeVector{1, 5}, SizeVector{1, 5}},
-        std::pair<SizeVector, SizeVector>{ SizeVector{2, 17}, SizeVector{1, 17}},
-        std::pair<SizeVector, SizeVector>{ SizeVector{9, 64}, SizeVector{1, 64}},
-        std::pair<SizeVector, SizeVector>{ SizeVector{5, 15}, SizeVector{1, 15}},
+const std::vector<std::pair<ov::Shape, ov::Shape>> shapes_2D = {
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5}, ov::Shape{1, 5}},
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{2, 17}, ov::Shape{1, 17}},
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{9, 64}, ov::Shape{1, 64}},
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{5, 15}, ov::Shape{1, 15}},
 };
 
-const std::vector<std::pair<SizeVector, SizeVector>> shapes_3D = {
-        std::pair<SizeVector, SizeVector>{ SizeVector{1, 5, 8}, SizeVector{1, 5, 8}},
-        std::pair<SizeVector, SizeVector>{ SizeVector{2, 17, 9}, SizeVector{1, 1, 9}},
-        std::pair<SizeVector, SizeVector>{ SizeVector{1, 1, 10}, SizeVector{1, 1, 10}},
-        std::pair<SizeVector, SizeVector>{ SizeVector{2, 3, 3}, SizeVector{2, 3, 3}},
+const std::vector<std::pair<ov::Shape, ov::Shape>> shapes_3D = {
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5, 8}, ov::Shape{1, 5, 8}},
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{2, 17, 9}, ov::Shape{1, 1, 9}},
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 1, 10}, ov::Shape{1, 1, 10}},
+    std::pair<ov::Shape, ov::Shape>{ov::Shape{2, 3, 3}, ov::Shape{2, 3, 3}},
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_MVNMultiplyAdd_1D, MVNMultiplyAdd,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(shapes_1D),
-                                ::testing::ValuesIn(netPrecision),
-                                ::testing::ValuesIn(idxPrecision),
-                                ::testing::Values(std::vector<int>{0}),
-                                ::testing::ValuesIn(normalizeVariance),
-                                ::testing::ValuesIn(epsilon),
-                                ::testing::ValuesIn(epsMode),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        MVNMultiplyAdd::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_MVNMultiplyAdd_2D, MVNMultiplyAdd,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(shapes_2D),
-                                ::testing::ValuesIn(netPrecision),
-                                ::testing::ValuesIn(idxPrecision),
-                                ::testing::Values(std::vector<int>{1}),
-                                ::testing::ValuesIn(normalizeVariance),
-                                ::testing::ValuesIn(epsilon),
-                                ::testing::ValuesIn(epsMode),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        MVNMultiplyAdd::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_MVNMultiplyAdd_3D, MVNMultiplyAdd,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(shapes_3D),
-                                ::testing::ValuesIn(netPrecision),
-                                ::testing::ValuesIn(idxPrecision),
-                                ::testing::Values(std::vector<int>{2}),
-                                ::testing::ValuesIn(normalizeVariance),
-                                ::testing::ValuesIn(epsilon),
-                                ::testing::ValuesIn(epsMode),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            MVNMultiplyAdd::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_MVNMultiplyAdd_1D,
+                         MVNMultiplyAdd,
+                         ::testing::Combine(::testing::ValuesIn(shapes_1D),
+                                            ::testing::ValuesIn(netPrecision),
+                                            ::testing::ValuesIn(idxPrecision),
+                                            ::testing::Values(std::vector<int>{0}),
+                                            ::testing::ValuesIn(normalizeVariance),
+                                            ::testing::ValuesIn(epsilon),
+                                            ::testing::ValuesIn(epsMode),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MVNMultiplyAdd::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MVNMultiplyAdd_2D,
+                         MVNMultiplyAdd,
+                         ::testing::Combine(::testing::ValuesIn(shapes_2D),
+                                            ::testing::ValuesIn(netPrecision),
+                                            ::testing::ValuesIn(idxPrecision),
+                                            ::testing::Values(std::vector<int>{1}),
+                                            ::testing::ValuesIn(normalizeVariance),
+                                            ::testing::ValuesIn(epsilon),
+                                            ::testing::ValuesIn(epsMode),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MVNMultiplyAdd::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MVNMultiplyAdd_3D,
+                         MVNMultiplyAdd,
+                         ::testing::Combine(::testing::ValuesIn(shapes_3D),
+                                            ::testing::ValuesIn(netPrecision),
+                                            ::testing::ValuesIn(idxPrecision),
+                                            ::testing::Values(std::vector<int>{2}),
+                                            ::testing::ValuesIn(normalizeVariance),
+                                            ::testing::ValuesIn(epsilon),
+                                            ::testing::ValuesIn(epsMode),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MVNMultiplyAdd::getTestCaseName);
 
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/parameter_result.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/parameter_result.cpp
index 7f25b2ef54ef44..a70b3c7bbc3659 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/parameter_result.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/parameter_result.cpp
@@ -11,12 +11,6 @@ using namespace ov::test;
 
 namespace {
 
-INSTANTIATE_TEST_SUITE_P(smoke_Check,
-                         ParameterResultSubgraphTestLegacyApi,
-                         ::testing::Combine(::testing::Values(ov::test::InputShape{{1, 3, 10, 10}, {}}),
-                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                         ParameterResultSubgraphTestBase::getTestCaseName);
-
 const std::vector<ov::test::InputShape> inputShapes = {
     ov::test::InputShape{{1, 3, 10, 10}, {{1, 3, 10, 10}, {1, 3, 10, 10}}},
     ov::test::InputShape{{-1, -1, -1, -1}, {{1, 3, 10, 10}, {2, 5, 3, 10}, {1, 3, 10, 10}, {1, 3, 10, 10}}},
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
index 4fe713ebc44187..8617d1e68c2742 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
@@ -2,42 +2,45 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
 #include "subgraph_tests/perm_conv_perm_concat.hpp"
-#include "common_test_utils/test_constants.hpp"
+
+#include <vector>
+
 namespace {
-std::vector<std::array<size_t, 4>> input_shapes {
+std::vector<ov::Shape> input_shapes{
     {1, 1, 7, 32},
     {1, 1, 8, 16},
 };
 
-std::vector<std::array<size_t, 2>> kernel_shapes {
+std::vector<ov::Shape> kernel_shapes{
     {1, 3},
     {1, 5},
 };
 
-std::vector<size_t> output_channels {
+std::vector<size_t> output_channels{
     32,
     64,
 };
 
-std::vector<InferenceEngine::Precision> netPrecisions = {
-    InferenceEngine::Precision::FP32,
-    InferenceEngine::Precision::FP16,
+std::vector<ov::element::Type> netPrecisions = {
+    ov::element::f32,
+    ov::element::f16,
 };
 
-std::map<std::string, std::string> additional_config = {
-};
-} // namespace
-
-namespace SubgraphTestsDefinitions {
-INSTANTIATE_TEST_SUITE_P(smoke_basic, PermConvPermConcat,
-    ::testing::Combine(
-        ::testing::ValuesIn(netPrecisions),
-        ::testing::Values(ov::test::utils::DEVICE_CPU),
-        ::testing::ValuesIn(input_shapes),
-        ::testing::ValuesIn(kernel_shapes),
-        ::testing::ValuesIn(output_channels),
-        ::testing::Values(additional_config)),
-    PermConvPermConcat::getTestCaseName);
-} // namespace SubgraphTestsDefinitions
+ov::AnyMap additional_config = {};
+}  // namespace
+
+namespace ov {
+namespace test {
+
+INSTANTIATE_TEST_SUITE_P(smoke_basic,
+                         PermConvPermConcat,
+                         ::testing::Combine(::testing::ValuesIn(netPrecisions),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                            ::testing::ValuesIn(input_shapes),
+                                            ::testing::ValuesIn(kernel_shapes),
+                                            ::testing::ValuesIn(output_channels),
+                                            ::testing::Values(additional_config)),
+                         PermConvPermConcat::getTestCaseName);
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/range_add.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/range_add.cpp
index 409ba17f27e68f..5b19f8b34d7f81 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/range_add.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/range_add.cpp
@@ -2,97 +2,76 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
-
 #include "subgraph_tests/range_add.hpp"
-#include "common_test_utils/test_constants.hpp"
 
-using namespace SubgraphTestsDefinitions;
+#include <vector>
+
+using namespace ov::test;
 
 namespace {
 
-const std::vector<float> positiveStart = { 1.0f, 1.2f };
-const std::vector<float> positiveStop = { 5.0f, 5.2f };
-const std::vector<float> positiveStep = { 1.0f, 0.1f };
+const std::vector<float> positiveStart = {1.0f, 1.2f};
+const std::vector<float> positiveStop = {5.0f, 5.2f};
+const std::vector<float> positiveStep = {1.0f, 0.1f};
 
-const std::vector<float> negativeStart = { 1.0f, 1.2f };
-const std::vector<float> negativeStop = { -5.0f, -5.2f };
-const std::vector<float> negativeStep = { -1.0f, -0.1f };
+const std::vector<float> negativeStart = {1.0f, 1.2f};
+const std::vector<float> negativeStop = {-5.0f, -5.2f};
+const std::vector<float> negativeStep = {-1.0f, -0.1f};
 
-const std::vector<float> trunc_start = { 1.2f, 1.9f };
-const std::vector<float> trunc_stop = { 11.4f, 11.8f };
-const std::vector<float> trunc_step = { 1.3f, 2.8f };
+const std::vector<float> trunc_start = {1.2f, 1.9f};
+const std::vector<float> trunc_stop = {11.4f, 11.8f};
+const std::vector<float> trunc_step = {1.3f, 2.8f};
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::FP16        // "[NOT_IMPLEMENTED] Input image format FP16 is not supported yet...
+const std::vector<ov::element::Type> element_types = {
+    ov::element::f32,
+    ov::element::f16  // "[NOT_IMPLEMENTED] Input image format FP16 is not supported yet...
 };
 
 // ------------------------------ V0 ------------------------------
 
-INSTANTIATE_TEST_SUITE_P(smoke_BasicPositive, RangeAddSubgraphTest,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(positiveStart),
-                                ::testing::ValuesIn(positiveStop),
-                                ::testing::ValuesIn(positiveStep),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        RangeAddSubgraphTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_BasicPositive,
+                         RangeAddSubgraphTest,
+                         ::testing::Combine(::testing::ValuesIn(positiveStart),
+                                            ::testing::ValuesIn(positiveStop),
+                                            ::testing::ValuesIn(positiveStep),
+                                            ::testing::ValuesIn(element_types),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         RangeAddSubgraphTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BasicNegative, RangeAddSubgraphTest,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(negativeStart),
-                                ::testing::ValuesIn(negativeStop),
-                                ::testing::ValuesIn(negativeStep),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        RangeAddSubgraphTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_BasicNegative,
+                         RangeAddSubgraphTest,
+                         ::testing::Combine(::testing::ValuesIn(negativeStart),
+                                            ::testing::ValuesIn(negativeStop),
+                                            ::testing::ValuesIn(negativeStep),
+                                            ::testing::ValuesIn(element_types),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         RangeAddSubgraphTest::getTestCaseName);
 
 // ------------------------------ V4 ------------------------------
-INSTANTIATE_TEST_SUITE_P(smoke_BasicPositive, RangeNumpyAddSubgraphTest,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(positiveStart),
-                                ::testing::ValuesIn(positiveStop),
-                                ::testing::ValuesIn(positiveStep),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        RangeNumpyAddSubgraphTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_BasicPositive,
+                         RangeNumpyAddSubgraphTest,
+                         ::testing::Combine(::testing::ValuesIn(positiveStart),
+                                            ::testing::ValuesIn(positiveStop),
+                                            ::testing::ValuesIn(positiveStep),
+                                            ::testing::ValuesIn(element_types),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         RangeNumpyAddSubgraphTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BasicNegative, RangeNumpyAddSubgraphTest,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(negativeStart),
-                                ::testing::ValuesIn(negativeStop),
-                                ::testing::ValuesIn(negativeStep),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        RangeNumpyAddSubgraphTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_BasicNegative,
+                         RangeNumpyAddSubgraphTest,
+                         ::testing::Combine(::testing::ValuesIn(negativeStart),
+                                            ::testing::ValuesIn(negativeStop),
+                                            ::testing::ValuesIn(negativeStep),
+                                            ::testing::ValuesIn(element_types),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         RangeNumpyAddSubgraphTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BasicTruncateInputs, RangeNumpyAddSubgraphTest,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(trunc_start),
-                                ::testing::ValuesIn(trunc_stop),
-                                ::testing::ValuesIn(trunc_step),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::I32),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        RangeNumpyAddSubgraphTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_BasicTruncateInputs,
+                         RangeNumpyAddSubgraphTest,
+                         ::testing::Combine(::testing::ValuesIn(trunc_start),
+                                            ::testing::ValuesIn(trunc_stop),
+                                            ::testing::ValuesIn(trunc_step),
+                                            ::testing::ValuesIn(element_types),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         RangeNumpyAddSubgraphTest::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/relu_shape_of.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/relu_shape_of.cpp
index 18ad81aecc25d2..00559d09144d2d 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/relu_shape_of.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/relu_shape_of.cpp
@@ -2,22 +2,20 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
-
 #include "subgraph_tests/relu_shape_of.hpp"
-#include "common_test_utils/test_constants.hpp"
 
-using namespace SubgraphTestsDefinitions;
+#include <vector>
+
+using namespace ov::test;
 
 namespace {
-    const std::vector<InferenceEngine::Precision> netPrecisions = {
-            InferenceEngine::Precision::I32
-    };
+const std::vector<ov::element::Type> input_types = {ov::element::i32};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_Check, ReluShapeOfSubgraphTest,
-                            ::testing::Combine(
-                                    ::testing::ValuesIn(netPrecisions),
-                                    ::testing::Values(std::vector<size_t>({20, 10, 10, 10})),
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                            ReluShapeOfSubgraphTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_Check,
+                         ReluShapeOfSubgraphTest,
+                         ::testing::Combine(::testing::ValuesIn(input_types),
+                                            ::testing::Values(ov::element::i64),
+                                            ::testing::Values(ov::Shape{20, 10, 10, 10}),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         ReluShapeOfSubgraphTest::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
index 79743431999170..fa82dc5baa7f1e 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
@@ -2,47 +2,46 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
 #include "subgraph_tests/reshape_squeeze_reshape_relu.hpp"
-#include "common_test_utils/test_constants.hpp"
 
-using namespace SubgraphTestsDefinitions;
+#include <vector>
+
+using namespace ov::test;
 
 namespace {
-    std::vector<ShapeAxesTuple> inputs{
-            {{1, 1, 3}, {0, 1}},
-            {{1, 1, 3}, {0}},
-            {{1, 1, 3}, {1}},
-            {{1, 3, 1}, {0, 2}},
-            {{1, 3, 1}, {0}},
-            {{1, 3, 1}, {2}},
-            {{3, 1, 1}, {1, 2}},
-            {{3, 1, 1}, {1}},
-            {{3, 1, 1}, {2}},
-            {{4, 1, 3, 1}, {1, 3}},
-            {{4, 1, 1, 3}, {1, 2}},
-            {{1, 4, 1, 3}, {0, 2}},
-            {{1, 3, 5, 2, 1}, {0, 4}},
-            {{3, 1, 2, 4, 4, 3}, {1}},
-            {{1, 1, 1, 1, 1, 3}, {0, 1, 2, 3, 4}},
-            {{1, 1, 1, 1, 1, 3}, {1, 3}},
-            {{1}, {0}},
-    };
+std::vector<ShapeAxesTuple> inputs{
+    {{1, 1, 3}, {0, 1}},
+    {{1, 1, 3}, {0}},
+    {{1, 1, 3}, {1}},
+    {{1, 3, 1}, {0, 2}},
+    {{1, 3, 1}, {0}},
+    {{1, 3, 1}, {2}},
+    {{3, 1, 1}, {1, 2}},
+    {{3, 1, 1}, {1}},
+    {{3, 1, 1}, {2}},
+    {{4, 1, 3, 1}, {1, 3}},
+    {{4, 1, 1, 3}, {1, 2}},
+    {{1, 4, 1, 3}, {0, 2}},
+    {{1, 3, 5, 2, 1}, {0, 4}},
+    {{3, 1, 2, 4, 4, 3}, {1}},
+    {{1, 1, 1, 1, 1, 3}, {0, 1, 2, 3, 4}},
+    {{1, 1, 1, 1, 1, 3}, {1, 3}},
+    {{1}, {0}},
+};
 
-    std::vector<InferenceEngine::Precision> netPrecisions = {InferenceEngine::Precision::FP32,
-                                                             InferenceEngine::Precision::FP16,
-    };
+std::vector<ov::element::Type> input_types = {
+    ov::element::f32,
+    ov::element::f16,
+};
 
-    const std::vector<ngraph::helpers::SqueezeOpType> opTypes = {
-            ngraph::helpers::SqueezeOpType::SQUEEZE,
-            ngraph::helpers::SqueezeOpType::UNSQUEEZE
-    };
+const std::vector<ov::test::utils::SqueezeOpType> opTypes = {ov::test::utils::SqueezeOpType::SQUEEZE,
+                                                             ov::test::utils::SqueezeOpType::UNSQUEEZE};
 
-    INSTANTIATE_TEST_SUITE_P(smoke_reshape_squeeze_reshape_relu, ReshapeSqueezeReshapeRelu,
-                            ::testing::Combine(
-                                    ::testing::ValuesIn(inputs),
-                                    ::testing::ValuesIn(netPrecisions),
-                                    ::testing::Values(ov::test::utils::DEVICE_CPU),
-                                    ::testing::ValuesIn(opTypes)),
-                            ReshapeSqueezeReshapeRelu::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_reshape_squeeze_reshape_relu,
+                         ReshapeSqueezeReshapeRelu,
+                         ::testing::Combine(::testing::ValuesIn(inputs),
+                                            ::testing::ValuesIn(input_types),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                            ::testing::ValuesIn(opTypes)),
+                         ReshapeSqueezeReshapeRelu::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
index 8e347ad75c31d4..1d60e68187709e 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
@@ -2,28 +2,20 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
-
 #include "subgraph_tests/split_conv_concat.hpp"
-#include "common_test_utils/test_constants.hpp"
 
-using namespace SubgraphTestsDefinitions;
+#include <vector>
+
+using namespace ov::test;
 
 namespace {
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::FP16
-};
+const std::vector<ov::element::Type> input_types = {ov::element::f32, ov::element::f16};
 
-INSTANTIATE_TEST_SUITE_P(smoke_NoReshape, SplitConvConcat,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::SizeVector({1, 6, 40, 40})),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        SplitConvConcat::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_NoReshape,
+                         SplitConvConcat,
+                         ::testing::Combine(::testing::ValuesIn(input_types),
+                                            ::testing::Values(ov::Shape{1, 6, 40, 40}),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         SplitConvConcat::getTestCaseName);
 }  // namespace
-
-
-
-
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/variadic_split_pad.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/variadic_split_pad.cpp
index 7386a46ef3868a..27d63ad27ff852 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/variadic_split_pad.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/subgraph_tests/variadic_split_pad.cpp
@@ -4,60 +4,52 @@
 
 #include "subgraph_tests/variadic_split_pad.hpp"
 
-using namespace SubgraphTestsDefinitions;
+using namespace ov::test;
 
 namespace {
 
-const std::vector<InferenceEngine::Precision> netPrecision = {
-        InferenceEngine::Precision::FP32
-};
+const std::vector<ov::element::Type> input_type = {ov::element::f32};
 
-const std::vector<InferenceEngine::SizeVector> shapes = {
-        {1, 8, 3, 2},
-        {3, 8, 8, 8},
+const std::vector<ov::Shape> shapes = {
+    {1, 8, 3, 2},
+    {3, 8, 8, 8},
 };
 
 const std::vector<std::vector<size_t>> connectedIndexes = {
-        {0},
-        {0, 2},
-        {0, 1, 3},
-        {0, 1, 1, 0},
-        {0, 0, 0, 1},
+    {0},
+    {0, 2},
+    {0, 1, 3},
+    {0, 1, 1, 0},
+    {0, 0, 0, 1},
 };
 
-const std::vector<std::vector<size_t>> numSplits = {
-        {2, 2, 2, 2},
-        {1, 2, 4, 1},
-        {3, 2, 2, 1}
-};
+const std::vector<std::vector<size_t>> numSplits = {{2, 2, 2, 2}, {1, 2, 4, 1}, {3, 2, 2, 1}};
 
 const std::vector<std::vector<int64_t>> padsBegin = {
-        {0, 0, 0, 0},
-        {0, 0, 1, 1},
+    {0, 0, 0, 0},
+    {0, 0, 1, 1},
 };
 
 const std::vector<std::vector<int64_t>> padsEnd = {
-        {0, 0, 0, 0},
-        {0, 0, 1, 1},
-};
-
-const std::vector<ngraph::helpers::PadMode> padMode = {
-        ngraph::helpers::PadMode::CONSTANT,
-        ngraph::helpers::PadMode::EDGE,
-        ngraph::helpers::PadMode::REFLECT,
-        ngraph::helpers::PadMode::SYMMETRIC
+    {0, 0, 0, 0},
+    {0, 0, 1, 1},
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_CPU, VariadicSplitPad,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(shapes),
-                                ::testing::Values(1),
-                                ::testing::ValuesIn(numSplits),
-                                ::testing::ValuesIn(connectedIndexes),
-                                ::testing::ValuesIn(padsBegin),
-                                ::testing::ValuesIn(padsEnd),
-                                ::testing::ValuesIn(padMode),
-                                ::testing::ValuesIn(netPrecision),
-                                ::testing::Values(ov::test::utils::DEVICE_CPU)),
-                        VariadicSplitPad::getTestCaseName);
+const std::vector<ov::op::PadMode> padMode = {ov::op::PadMode::CONSTANT,
+                                              ov::op::PadMode::EDGE,
+                                              ov::op::PadMode::REFLECT,
+                                              ov::op::PadMode::SYMMETRIC};
+
+INSTANTIATE_TEST_SUITE_P(smoke_CPU,
+                         VariadicSplitPad,
+                         ::testing::Combine(::testing::ValuesIn(shapes),
+                                            ::testing::Values(1),
+                                            ::testing::ValuesIn(numSplits),
+                                            ::testing::ValuesIn(connectedIndexes),
+                                            ::testing::ValuesIn(padsBegin),
+                                            ::testing::ValuesIn(padsEnd),
+                                            ::testing::ValuesIn(padMode),
+                                            ::testing::ValuesIn(input_type),
+                                            ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         VariadicSplitPad::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/pooling.cpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/pooling.cpp
new file mode 100644
index 00000000000000..e40771146cd0ec
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/pooling.cpp
@@ -0,0 +1,464 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "pooling.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ngraph::helpers;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+std::string PoolingLayerCPUTest::getTestCaseName(const testing::TestParamInfo<poolLayerCpuTestParamsSet>& obj) {
+    LayerTestsDefinitions::poolSpecificParams basicParamsSet;
+    InputShape inputShapes;
+    ElementType inPrc;
+    bool isInt8;
+    CPUSpecificParams cpuParams;
+    fusingSpecificParams fusingParams;
+    std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = obj.param;
+
+    ngraph::helpers::PoolingTypes poolType;
+    std::vector<size_t> kernel, stride;
+    std::vector<size_t> padBegin, padEnd;
+    ngraph::op::PadType padType;
+    ngraph::op::RoundingType roundingType;
+    bool excludePad;
+    std::tie(poolType, kernel, stride, padBegin, padEnd, roundingType, padType, excludePad) = basicParamsSet;
+
+    std::ostringstream results;
+    results << "IS=(";
+    results << ov::test::utils::partialShape2str({inputShapes.first}) << ")_";
+    results << "TS=";
+    for (const auto& shape : inputShapes.second) {
+        results << ov::test::utils::vec2str(shape) << "_";
+    }
+    results << "Prc=" << inPrc << "_";
+    switch (poolType) {
+    case ngraph::helpers::PoolingTypes::MAX:
+        results << "MaxPool_";
+        break;
+    case ngraph::helpers::PoolingTypes::AVG:
+        results << "AvgPool_";
+        results << "ExcludePad=" << excludePad << "_";
+        break;
+    }
+    results << "K" << ov::test::utils::vec2str(kernel) << "_";
+    results << "S" << ov::test::utils::vec2str(stride) << "_";
+    results << "PB" << ov::test::utils::vec2str(padBegin) << "_";
+    results << "PE" << ov::test::utils::vec2str(padEnd) << "_";
+    results << "Rounding=" << roundingType << "_";
+    results << "AutoPad=" << padType << "_";
+    results << "INT8=" << isInt8 << "_";
+
+    results << CPUTestsBase::getTestCaseName(cpuParams);
+    results << CpuTestWithFusing::getTestCaseName(fusingParams);
+    return results.str();
+}
+
+void PoolingLayerCPUTest::SetUp() {
+    targetDevice = ov::test::utils::DEVICE_CPU;
+
+    LayerTestsDefinitions::poolSpecificParams basicParamsSet;
+    InputShape inputShapes;
+    ElementType inPrc;
+    bool isInt8;
+    CPUSpecificParams cpuParams;
+    fusingSpecificParams fusingParams;
+    std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = this->GetParam();
+
+    ngraph::helpers::PoolingTypes poolType;
+    std::vector<size_t> kernel, stride;
+    std::vector<size_t> padBegin, padEnd;
+    ngraph::op::PadType padType;
+    ngraph::op::RoundingType roundingType;
+    bool excludePad;
+    std::tie(poolType, kernel, stride, padBegin, padEnd, roundingType, padType, excludePad) = basicParamsSet;
+
+    std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+    std::tie(postOpMgrPtr, fusedOps) = fusingParams;
+
+    if (selectedType.empty()) {
+        selectedType = getPrimitiveType();
+    }
+    if (isInt8)
+        selectedType = selectedType + "_I8";
+    else
+        selectedType = makeSelectedTypeStr(selectedType, inPrc);
+
+    init_input_shapes({inputShapes});
+
+    ov::ParameterVector params;
+    for (auto&& shape : inputDynamicShapes) {
+        params.push_back(std::make_shared<ov::op::v0::Parameter>(inPrc, shape));
+    }
+
+    std::shared_ptr<ngraph::Node> poolInput = params[0];
+    if (isInt8) {
+        ov::Shape newShape(poolInput->get_output_partial_shape(0).size(), 1);
+        poolInput = ngraph::builder::makeFakeQuantize(poolInput, inPrc, 256, newShape);
+    }
+
+    std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makePooling(poolInput,
+                                                                         stride,
+                                                                         padBegin,
+                                                                         padEnd,
+                                                                         kernel,
+                                                                         roundingType,
+                                                                         padType,
+                                                                         excludePad,
+                                                                         poolType);
+
+    function = makeNgraphFunction(inPrc, params, pooling, "PoolingCPU");
+}
+
+std::string MaxPoolingV8LayerCPUTest::getTestCaseName(
+    const testing::TestParamInfo<maxPoolV8LayerCpuTestParamsSet>& obj) {
+    LayerTestsDefinitions::maxPoolV8SpecificParams basicParamsSet;
+    InputShape inputShapes;
+    ElementType inPrc;
+    CPUSpecificParams cpuParams;
+    std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = obj.param;
+
+    std::vector<size_t> kernel, stride, dilation;
+    std::vector<size_t> padBegin, padEnd;
+    ngraph::op::PadType padType;
+    ngraph::op::RoundingType roundingType;
+    ngraph::element::Type indexElementType;
+    int64_t axis;
+    std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) =
+        basicParamsSet;
+
+    std::ostringstream results;
+    results << "IS=(";
+    results << ov::test::utils::partialShape2str({inputShapes.first}) << ")_";
+    results << "TS=";
+    for (const auto& shape : inputShapes.second) {
+        results << ov::test::utils::vec2str(shape) << "_";
+    }
+    results << "Prc=" << inPrc << "_";
+    results << "MaxPool_";
+    results << "K" << ov::test::utils::vec2str(kernel) << "_";
+    results << "S" << ov::test::utils::vec2str(stride) << "_";
+    results << "D" << ov::test::utils::vec2str(dilation) << "_";
+    results << "PB" << ov::test::utils::vec2str(padBegin) << "_";
+    results << "PE" << ov::test::utils::vec2str(padEnd) << "_";
+    results << "Rounding=" << roundingType << "_";
+    results << "AutoPad=" << padType << "_";
+
+    results << CPUTestsBase::getTestCaseName(cpuParams);
+    return results.str();
+}
+
+void MaxPoolingV8LayerCPUTest::SetUp() {
+    targetDevice = ov::test::utils::DEVICE_CPU;
+
+    LayerTestsDefinitions::maxPoolV8SpecificParams basicParamsSet;
+    InputShape inputShapes;
+    ElementType inPrc;
+    CPUSpecificParams cpuParams;
+    std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = this->GetParam();
+
+    std::vector<size_t> kernel, stride, dilation;
+    std::vector<size_t> padBegin, padEnd;
+    ngraph::op::PadType padType;
+    ngraph::op::RoundingType roundingType;
+    ngraph::element::Type indexElementType;
+    int64_t axis;
+    std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) =
+        basicParamsSet;
+    std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+    if (selectedType.empty()) {
+        selectedType = getPrimitiveType();
+    }
+    selectedType = makeSelectedTypeStr(selectedType, inPrc);
+
+    init_input_shapes({inputShapes});
+
+    ov::ParameterVector params;
+    for (auto&& shape : inputDynamicShapes) {
+        params.push_back(std::make_shared<ov::op::v0::Parameter>(inPrc, shape));
+    }
+    std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makeMaxPoolingV8(params[0],
+                                                                              stride,
+                                                                              dilation,
+                                                                              padBegin,
+                                                                              padEnd,
+                                                                              kernel,
+                                                                              roundingType,
+                                                                              padType,
+                                                                              indexElementType,
+                                                                              axis);
+    pooling->get_rt_info() = getCPUInfo();
+    ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(pooling->output(0))};
+    function = std::make_shared<ngraph::Function>(results, params, "MaxPooling");
+}
+
+TEST_P(PoolingLayerCPUTest, CompareWithRefs) {
+    run();
+    CheckPluginRelatedResults(compiledModel, "Pooling");
+}
+
+TEST_P(MaxPoolingV8LayerCPUTest, CompareWithRefs) {
+    run();
+    CheckPluginRelatedResults(compiledModel, "Pooling");
+}
+
+namespace Pooling {
+
+// The combination of parameters: NCHW + CEIL gives an accuracy problem in ACL AvgPool
+const ngraph::op::RoundingType expectedAvgRoundingType() {
+#if defined(OPENVINO_ARCH_ARM) || defined(OPENVINO_ARCH_ARM64)
+    return ngraph::op::RoundingType::FLOOR;
+#else
+    return ngraph::op::RoundingType::CEIL;
+#endif
+}
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsMax3D() {
+    static const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax3D = {
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2}, {2}, {0}, {0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4}, {2}, {0}, {0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2}, {1}, {0}, {0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
+    };
+    return paramsMax3D;
+}
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg3D() {
+    static const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg3D = {
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3}, {1}, {1}, {0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_UPPER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3}, {1}, {1}, {0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4}, {4}, {2}, {2},
+                                expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, true },
+    };
+    return paramsAvg3D;
+}
+
+const std::vector<ElementType>& inpOutPrecision() {
+    static const std::vector<ElementType> inpOutPrecision = {ElementType::f32/*, ElementType::bf16*/};
+    return inpOutPrecision;
+}
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsMax4D() {
+    static const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax4D = {
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 2}, {0, 0}, {0, 0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 1}, {0, 0}, {0, 0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
+    };
+    return paramsMax4D;
+}
+
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams>& paramsMaxV84D() {
+    static const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D = {
+            LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {1, 1}, {0, 0}, {0, 0},
+                                                            ngraph::element::Type_t::i32, 0,
+                                                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
+    };
+    return paramsMaxV84D;
+}
+
+const std::vector<InputShape>& inputShapes3D() {
+    static const std::vector<InputShape> inputShapes3D = {
+            { {}, {{3, 4, 64}} },
+            { {}, {{2, 8, 12}} },
+            { {}, {{1, 16, 12}} },
+            { {}, {{1, 21, 4}} },
+            { {}, {{1, 32, 8}} },
+            {
+                // dynamic
+                {-1, -1, -1},
+                // target
+                {
+                    {1, 32, 8},
+                    {1, 21, 4},
+                    {2, 8, 12}
+                }
+            },
+            {
+                // dynamic
+                {{1, 5}, {4, 32}, {1, 64}},
+                // target
+                {
+                    {3, 4, 64},
+                    {1, 16, 12},
+                    {1, 32, 8}
+                }
+            }
+    };
+    return inputShapes3D;
+}
+
+const std::vector<InputShape>& inputShapes4D() {
+    static const std::vector<InputShape> inputShapes4D = {
+            { {}, {{3, 4, 64, 64}} },
+            { {}, {{2, 8, 8, 12}} },
+            { {}, {{1, 16, 16, 12}} },
+            { {}, {{1, 21, 8, 4}} },
+            { {}, {{1, 32, 8, 8}} },
+            {
+                // dynamic
+                {-1, -1, -1, -1},
+                // target
+                {
+                    {1, 32, 8, 8},
+                    {1, 21, 8, 4},
+                    {2, 8, 8, 12},
+                    {1, 96, 125, 125}
+                }
+            },
+            {
+                // dynamic
+                {{1, 5}, {4, 32}, {1, 64}, {1, 64}},
+                // target
+                {
+                    {3, 4, 64, 64},
+                    {1, 16, 16, 12},
+                    {1, 32, 8, 8}
+                }
+            },
+            {
+                // dynamic
+                {{1, 10}, 16, 8, 8},
+                // target
+                {
+                    {1, 16, 8, 8},
+                    {2, 16, 8, 8},
+                }
+            }
+    };
+    return inputShapes4D;
+}
+
+const std::vector<InputShape>& inputShapes5D() {
+    static const std::vector<InputShape> inputShapes5D = {
+            { {}, {{1, 4, 16, 16, 16}} },
+            { {}, {{2, 8, 8, 8, 8}} },
+            { {}, {{2, 16, 12, 16, 20}} },
+            { {}, {{1, 19, 16, 20, 8}} },
+            { {}, {{1, 32, 16, 8, 12}} },
+            {
+                // dynamic
+                {-1, -1, -1, -1, -1},
+                // target
+                {
+                    {2, 8, 8, 8, 8},
+                    {1, 19, 16, 20, 8},
+                    {1, 4, 16, 16, 16}
+                }
+            },
+            {
+                // dynamic
+                {{1, 5}, {4, 32}, {1, 64}, {1, 64}, {1, 25}},
+                // target
+                {
+                    {1, 4, 16, 16, 16},
+                    {1, 32, 16, 8, 12},
+                    {3, 16, 4, 8, 3}
+                }
+            }
+    };
+    return inputShapes5D;
+}
+
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams>& paramsMaxV85D() {
+    static const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D = {
+            LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
+                                                            ngraph::element::Type_t::i32, 0,
+                                                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
+    };
+    return paramsMaxV85D;
+}
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg4D() {
+    static const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D = {
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_LOWER, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_UPPER, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_LOWER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_UPPER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4}, {4, 4}, {2, 2}, {2, 2},
+                                expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, true },
+    };
+    return paramsAvg4D;
+}
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg5D() {
+    static const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D = {
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_LOWER, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_UPPER, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_LOWER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::SAME_UPPER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3, 3, 3}, {3, 3, 3}, {1, 1, 1}, {0, 0, 0},
+                                expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, true },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4, 4}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2},
+                                expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, true },
+    };
+    return paramsAvg5D;
+}
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsMax5D() {
+    static const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax5D = {
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {3, 3, 3}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1},
+                                ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
+    };
+    return paramsMax5D;
+}
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg4D_Large() {
+    static const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D_Large = {
+            LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {65, 65}, {65, 65}, {0, 0}, {0, 0},
+                                ngraph::op::RoundingType::FLOOR, ngraph::op::PadType::VALID, true },
+    };
+    return paramsAvg4D_Large;
+}
+
+const std::vector<InputShape>& inputShapes4D_Large() {
+    static const std::vector<InputShape> inputShapes4D_Large = {
+            {
+                // dynamic
+                {-1, -1, -1, -1},
+                // target
+                {
+                    {1, 16, 65, 65},
+                    {1, 8, 130, 130},
+                    {1, 16, 65, 65}
+                }
+            },
+    };
+    return inputShapes4D_Large;
+}
+
+
+}  // namespace Pooling
+}  // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/pooling.hpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/pooling.hpp
new file mode 100644
index 00000000000000..ecf12a0360de1f
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/pooling.hpp
@@ -0,0 +1,69 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "ov_models/builders.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+#include "test_utils/fusing_test_utils.hpp"
+#include "shared_test_classes/single_layer/pooling.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+using namespace ov::test;
+using namespace CPUTestUtils;
+
+namespace CPULayerTestsDefinitions {
+
+using poolLayerCpuTestParamsSet = std::tuple<LayerTestsDefinitions::poolSpecificParams,
+                                             InputShape,
+                                             ElementType,
+                                             bool,
+                                             CPUSpecificParams,
+                                             fusingSpecificParams>;
+
+using maxPoolV8LayerCpuTestParamsSet = std::tuple<LayerTestsDefinitions::maxPoolV8SpecificParams,
+        InputShape,
+        ElementType,
+        CPUSpecificParams>;
+
+class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestParamsSet>,
+                            virtual public SubgraphBaseTest, public CpuTestWithFusing {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<poolLayerCpuTestParamsSet>& obj);
+
+protected:
+    void SetUp() override;
+};
+
+class MaxPoolingV8LayerCPUTest : public testing::WithParamInterface<maxPoolV8LayerCpuTestParamsSet>,
+                                 virtual public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<maxPoolV8LayerCpuTestParamsSet>& obj);
+
+protected:
+    void SetUp() override;
+};
+
+namespace Pooling {
+const std::vector<ElementType>& inpOutPrecision();
+const ngraph::op::RoundingType expectedAvgRoundingType();
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsMax3D();
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg3D();
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsMax4D();
+
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams>& paramsMaxV84D();
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams>& paramsMaxV85D();
+
+const std::vector<InputShape>& inputShapes3D();
+const std::vector<InputShape>& inputShapes4D();
+const std::vector<InputShape>& inputShapes4D_Large();
+const std::vector<InputShape>& inputShapes5D();
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg4D();
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg4D_Large();
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsAvg5D();
+const std::vector<LayerTestsDefinitions::poolSpecificParams>& paramsMax5D();
+} // namespace Pooling
+} // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/random_uniform.cpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/random_uniform.cpp
new file mode 100644
index 00000000000000..2f9706e7d2562e
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/random_uniform.cpp
@@ -0,0 +1,265 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "random_uniform.hpp"
+#include "ov_models/builders.hpp"
+
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+std::string RandomUniformLayerTestCPU::getTestCaseName(const testing::TestParamInfo<RandomUniformLayerTestCPUParamSet>& obj) {
+    const auto& out_shape = std::get<0>(obj.param);
+    const auto& min_max   = std::get<1>(obj.param);
+
+    std::ostringstream result;
+
+    result << "IS={"              << out_shape.size();
+    result << "}_OS="             << out_shape;
+    result << "_Min="             << std::get<0>(min_max);
+    result << "_Max="             << std::get<1>(min_max);
+    result << "_ShapePrc="        << std::get<2>(obj.param);
+    result << "_OutPrc="          << std::get<3>(obj.param);
+    result << "_GlobalSeed="      << std::get<4>(obj.param);
+    result << "_OperationalSeed=" << std::get<5>(obj.param);
+    result << "_ConstIn={"        << utils::bool2str(std::get<6>(obj.param)) << ","
+                                  << utils::bool2str(std::get<7>(obj.param)) << ","
+                                  << utils::bool2str(std::get<8>(obj.param)) << "}";
+
+    result << CPUTestsBase::getTestCaseName(std::get<9>(obj.param));
+
+    const auto& config = std::get<10>(obj.param);
+    if (!config.empty()) {
+        result << "_PluginConf={";
+        for (const auto& conf_item : config) {
+            result << "_" << conf_item.first << "=";
+            conf_item.second.print(result);
+        }
+        result << "}";
+    }
+
+    return result.str();
+}
+
+void RandomUniformLayerTestCPU::SetUp() {
+    targetDevice = utils::DEVICE_CPU;
+
+    const auto& params     = this->GetParam();
+    m_output_shape         = std::get<0>(params);
+    const auto& min_max    = std::get<1>(params);
+    const auto& shape_prc  = std::get<2>(params);
+    const auto& output_prc = std::get<3>(params);
+    m_global_seed          = std::get<4>(params);
+    m_operational_seed     = std::get<5>(params);
+    const auto& const_in_1 = std::get<6>(params);
+    const auto& const_in_2 = std::get<7>(params);
+    const auto& const_in_3 = std::get<8>(params);
+    const auto& cpu_params = std::get<9>(params);
+    configuration          = std::get<10>(params);
+
+    m_min_val = std::get<0>(min_max);
+    m_max_val = std::get<1>(min_max);
+    std::tie(inFmts, outFmts, priority, selectedType) = cpu_params;
+
+#if defined(OV_CPU_WITH_ACL)
+    updateSelectedType("ref_any", output_prc, configuration);
+#else
+    if (output_prc == ElementType::i64) {
+        updateSelectedType(getPrimitiveType(), ElementType::i32, configuration);
+    } else if (output_prc == ElementType::f64) {
+        updateSelectedType(getPrimitiveType(), ElementType::f32, configuration);
+    } else if (output_prc == ElementType::f16) {
+        if (InferenceEngine::with_cpu_x86_avx512_core_fp16()) {
+            updateSelectedType(getPrimitiveType(), ElementType::f16, configuration);
+        } else {
+            updateSelectedType(getPrimitiveType(), ElementType::f32, configuration);
+        }
+    } else if (output_prc == ElementType::bf16) {
+        if (InferenceEngine::with_cpu_x86_bfloat16()) {
+            updateSelectedType(getPrimitiveType(), ElementType::bf16, configuration);
+        } else {
+            updateSelectedType("ref_any", ElementType::bf16, configuration);
+        }
+    } else {
+        updateSelectedType(getPrimitiveType(), output_prc, configuration);
+    }
+#endif
+
+    std::vector<InputShape> in_shapes;
+    ov::ParameterVector in_params;
+    std::vector<std::shared_ptr<ov::Node>> inputs;
+
+    if (!const_in_1) {
+        in_shapes.push_back({{}, {{m_output_shape.size()}}});
+        in_params.push_back(std::make_shared<ov::op::v0::Parameter>(shape_prc, ov::PartialShape{static_cast<int64_t>(m_output_shape.size())}));
+        in_params.back()->set_friendly_name("shape");
+        inputs.push_back(in_params.back());
+    } else {
+        inputs.push_back(ngraph::builder::makeConstant(shape_prc, {m_output_shape.size()}, m_output_shape));
+    }
+    if (!const_in_2) {
+        in_shapes.push_back({{}, {{1}}});
+        in_params.push_back(std::make_shared<ov::op::v0::Parameter>(output_prc, ov::PartialShape{1}));
+        in_params.back()->set_friendly_name("minval");
+        inputs.push_back(in_params.back());
+    } else {
+        inputs.push_back(ngraph::builder::makeConstant(output_prc, {1}, std::vector<double>{m_min_val}));
+    }
+    if (!const_in_3) {
+        in_shapes.push_back({{}, {{1}}});
+        in_params.push_back(std::make_shared<ov::op::v0::Parameter>(output_prc, ov::PartialShape{1}));
+        in_params.back()->set_friendly_name("maxval");
+        inputs.push_back(in_params.back());
+    } else {
+        inputs.push_back(ngraph::builder::makeConstant(output_prc, {1}, std::vector<double>{m_max_val}));
+    }
+
+    init_input_shapes(in_shapes);
+
+    const auto rnd_op = std::make_shared<ov::op::v8::RandomUniform>(inputs[0], inputs[1], inputs[2], output_prc, m_global_seed, m_operational_seed);
+    const ov::ResultVector results{std::make_shared<ov::op::v0::Result>(rnd_op)};
+
+    function = std::make_shared<ov::Model>(results, in_params, "RandomUniformLayerTestCPU");
+}
+
+template<typename TD, typename TS>
+void fill_data(TD* dst, const TS* src, size_t len) {
+    for (size_t i = 0llu; i < len; i++) {
+        dst[i] = static_cast<TD>(src[i]);
+    }
+}
+
+void RandomUniformLayerTestCPU::generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) {
+    inputs.clear();
+    const auto& func_inputs = function->inputs();
+
+    for (size_t i = 0llu; i < func_inputs.size(); ++i) {
+        const auto& func_input = func_inputs[i];
+        const auto& name = func_input.get_node()->get_friendly_name();
+        const auto& in_prc = func_input.get_element_type();
+        auto tensor = ov::Tensor(in_prc, targetInputStaticShapes[i]);
+
+#define CASE(P, S, L)                                                              \
+case P :                                                                           \
+fill_data(tensor.data<ov::element_type_traits<P>::value_type>(), S, L); break;
+
+        if (name == "shape") {
+            switch (in_prc) {
+                CASE(ElementType::i32, m_output_shape.data(), m_output_shape.size())
+                CASE(ElementType::i64, m_output_shape.data(), m_output_shape.size())
+                default:
+                    OPENVINO_THROW("RandomUniform does not support precision ", in_prc, " for the Shape input.");
+            }
+        } else if (name == "minval") {
+            switch (in_prc) {
+                CASE(ElementType::f32,  &m_min_val, 1)
+                CASE(ElementType::f16,  &m_min_val, 1)
+                CASE(ElementType::bf16, &m_min_val, 1)
+                CASE(ElementType::i32,  &m_min_val, 1)
+                CASE(ElementType::i64,  &m_min_val, 1)
+                CASE(ElementType::f64,  &m_min_val, 1)
+                default:
+                    OPENVINO_THROW("RandomUniform does not support precision ", in_prc, " for the Minval input.");
+            }
+        } else if (name == "maxval") {
+            switch (in_prc) {
+                CASE(ElementType::f32,  &m_max_val, 1)
+                CASE(ElementType::f16,  &m_max_val, 1)
+                CASE(ElementType::bf16, &m_max_val, 1)
+                CASE(ElementType::i32,  &m_max_val, 1)
+                CASE(ElementType::i64,  &m_max_val, 1)
+                CASE(ElementType::f64,  &m_max_val, 1)
+                default:
+                    OPENVINO_THROW("RandomUniform does not support precision ", in_prc, " for the Maxval input.");
+            }
+        }
+
+#undef CASE
+
+        inputs.insert({func_input.get_node_shared_ptr(), tensor});
+    }
+}
+
+void RandomUniformLayerTestCPU::compare(const std::vector<ov::Tensor>& expected, const std::vector<ov::Tensor>& actual) {
+    if (m_global_seed != 0lu || m_operational_seed != 0lu) {
+        SubgraphBaseTest::compare(expected, actual);
+        return;
+    }
+
+    // When both seed values are equal to zero, RandomUniform should generate non-deterministic sequence.
+    // In this case will use Mean and Variance metrics.
+
+#define CASE(X) case X : rndUCompare<ov::element_type_traits<X>::value_type>(expected[0], actual[0]); break;
+
+    switch (expected[0].get_element_type()) {
+        CASE(ElementType::f32)
+        CASE(ElementType::i32)
+        CASE(ElementType::f16)
+        CASE(ElementType::bf16)
+        CASE(ElementType::i64)
+        CASE(ElementType::f64)
+        default: OPENVINO_THROW("Unsupported element type: ", expected[0].get_element_type());
+    }
+
+#undef CASE
+}
+
+precisions_map RandomUniformLayerTestCPU::get_ref_precisions_convert_map() {
+    precisions_map precisions;
+
+    if (!InferenceEngine::with_cpu_x86_avx512_core()) {
+        precisions.insert({ ov::element::bf16, ov::element::f32 });
+    }
+    if (!InferenceEngine::with_cpu_x86_avx512_core_fp16()) {
+        precisions.insert({ ov::element::f16, ov::element::f32 });
+    }
+
+    return precisions;
+}
+
+inline double less_or_equal(double a, double b) {
+    return (b - a) >= (std::fmax(std::fabs(a), std::fabs(b)) * std::numeric_limits<double>::epsilon());
+}
+
+template<typename T>
+void RandomUniformLayerTestCPU::rndUCompare(const ov::Tensor& expected, const ov::Tensor& actual) {
+    auto actual_data = actual.data<T>();
+    size_t shape_size_cnt = ov::shape_size(expected.get_shape());
+    double act_mean = 0.0;
+    double act_variance = 0.0;
+    const double exp_mean = (m_max_val + m_min_val) / 2.0;
+    const double exp_variance = std::pow(m_max_val - m_min_val, 2) / 12.0;
+
+    for (size_t i = 0; i < shape_size_cnt; ++i) {
+        auto actual_value = static_cast<double>(actual_data[i]);
+        if (std::isnan(actual_value)) {
+            std::ostringstream out_stream;
+            out_stream << "Actual value is NAN on coordinate: " << i;
+            throw std::runtime_error(out_stream.str());
+        }
+        act_mean += actual_value;
+        act_variance += std::pow(actual_value - exp_mean, 2);
+    }
+    act_mean /= shape_size_cnt;
+    act_variance /= shape_size_cnt;
+
+    auto rel_mean = (exp_mean - act_mean) / (m_max_val - m_min_val);
+    auto rel_variance = (exp_variance - act_variance) / std::pow(m_max_val - m_min_val, 2);
+
+    if (!(less_or_equal(rel_mean, m_mean_threshold) && less_or_equal(rel_variance, m_variance_threshold))) {
+        std::ostringstream out_stream;
+        out_stream << "rel_mean < m_mean_threshold && rel_variance < m_variance_threshold" <<
+                "\n\t rel_mean: " << rel_mean <<
+                "\n\t rel_variance: " << rel_variance;
+        throw std::runtime_error(out_stream.str());
+    }
+}
+
+TEST_P(RandomUniformLayerTestCPU, CompareWithRefs) {
+    run();
+    CheckPluginRelatedResults(compiledModel, "RandomUniform");
+}
+
+} // namespace CPULayerTestsDefinitions
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/random_uniform.hpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/random_uniform.hpp
new file mode 100644
index 00000000000000..1cb9f5fccc451a
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/classes/random_uniform.hpp
@@ -0,0 +1,53 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+namespace CPULayerTestsDefinitions {
+
+typedef std::tuple<
+        ov::Shape,                        // Output shapes
+        std::tuple<double, double>,       // Min and Max values
+        ov::test::ElementType,            // Shape precision
+        ov::test::ElementType,            // Output precision
+        uint64_t,                         // Global seed
+        uint64_t,                         // Operational seed
+        bool,                             // Is 1st input constant
+        bool,                             // Is 2nd input constant
+        bool,                             // Is 3rd input constant
+        CPUTestUtils::CPUSpecificParams,  // CPU specific params
+        ov::AnyMap                        // Additional plugin configuration
+> RandomUniformLayerTestCPUParamSet;
+
+class RandomUniformLayerTestCPU : public testing::WithParamInterface<RandomUniformLayerTestCPUParamSet>,
+                                  public ov::test::SubgraphBaseTest, public CPUTestUtils::CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<RandomUniformLayerTestCPUParamSet>& obj);
+
+protected:
+    void SetUp() override;
+
+    void generate_inputs(const std::vector<ov::Shape>& target_shapes) override;
+
+    void compare(const std::vector<ov::Tensor>& expected, const std::vector<ov::Tensor>& actual) override;
+
+    precisions_map get_ref_precisions_convert_map() override;
+
+    template<typename T>
+    void rndUCompare(const ov::Tensor& expected, const ov::Tensor& actual);
+
+private:
+    ov::Shape m_output_shape;
+    uint64_t m_global_seed;
+    uint64_t m_operational_seed;
+    double m_min_val;
+    double m_max_val;
+    static constexpr double m_mean_threshold = 0.05;
+    static constexpr double m_variance_threshold = 0.1;
+};
+
+} // namespace CPULayerTestsDefinitions
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/common/pooling.cpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/common/pooling.cpp
new file mode 100644
index 00000000000000..e15408a6085b9d
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/common/pooling.cpp
@@ -0,0 +1,181 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "single_layer_tests/classes/pooling.hpp"
+#include "shared_test_classes/single_layer/pooling.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+#include "test_utils/fusing_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ngraph::helpers;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+namespace Pooling {
+
+static CPUSpecificParams expectedCpuConfig() {
+#if defined(OPENVINO_ARCH_ARM) || defined(OPENVINO_ARCH_ARM64)
+    return CPUSpecificParams{{}, {}, {"acl"}, "acl"};
+#else
+    return CPUSpecificParams{{}, {}, {"ref_any"}, "ref_any"};
+#endif
+}
+const std::vector<CPUSpecificParams> vecCpuConfigs = {expectedCpuConfig()};
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg3D_RefOnly = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2}, {2}, {2}, {2},
+                            expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, false },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_3D, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMax3D()),
+                                 ::testing::ValuesIn(inputShapes3D()),
+                                 ::testing::ValuesIn((inpOutPrecision())),
+                                 ::testing::Values(false),
+                                 ::testing::ValuesIn(vecCpuConfigs),
+                                 ::testing::Values(emptyFusingSpec)),
+                         PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsAvg3D()),
+                                 ::testing::ValuesIn(inputShapes3D()),
+                                 ::testing::ValuesIn((inpOutPrecision())),
+                                 ::testing::Values(false),
+                                 ::testing::ValuesIn(vecCpuConfigs),
+                                 ::testing::Values(emptyFusingSpec)),
+                         PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D_NotOptimized, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsAvg3D_RefOnly),
+                                 ::testing::ValuesIn(inputShapes3D()),
+                                 ::testing::ValuesIn((inpOutPrecision())),
+                                 ::testing::Values(false),
+                                 ::testing::Values(expectedCpuConfig()),
+                                 ::testing::Values(emptyFusingSpec)),
+                         PoolingLayerCPUTest::getTestCaseName);
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D_RefOnly = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {2, 2}, {2, 2},
+                            expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, false },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_4D, PoolingLayerCPUTest,
+                            ::testing::Combine(
+                            ::testing::ValuesIn(paramsMax4D()),
+                            ::testing::ValuesIn(inputShapes4D()),
+                            ::testing::ValuesIn((inpOutPrecision())),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(vecCpuConfigs),
+                            ::testing::Values(emptyFusingSpec)),
+                        PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV84D()),
+                                 ::testing::ValuesIn(inputShapes4D()),
+                                 ::testing::ValuesIn((inpOutPrecision())),
+                                 ::testing::ValuesIn(vecCpuConfigs)),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D, PoolingLayerCPUTest,
+                        ::testing::Combine(
+                            ::testing::ValuesIn(paramsAvg4D()),
+                            ::testing::ValuesIn(inputShapes4D()),
+                            ::testing::ValuesIn((inpOutPrecision())),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(vecCpuConfigs),
+                            ::testing::Values(emptyFusingSpec)),
+                        PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_NotOptimized, PoolingLayerCPUTest,
+                        ::testing::Combine(
+                            ::testing::ValuesIn(paramsAvg4D_RefOnly),
+                            ::testing::ValuesIn(inputShapes4D()),
+                            ::testing::ValuesIn((inpOutPrecision())),
+                            ::testing::Values(false),
+                            ::testing::Values(expectedCpuConfig()),
+                            ::testing::Values(emptyFusingSpec)),
+                        PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_Large, PoolingLayerCPUTest,
+                        ::testing::Combine(
+                            ::testing::ValuesIn(paramsAvg4D_Large()),
+                            ::testing::ValuesIn(inputShapes4D_Large()),
+                            ::testing::ValuesIn((inpOutPrecision())),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(vecCpuConfigs),
+                            ::testing::Values(emptyFusingSpec)),
+                        PoolingLayerCPUTest::getTestCaseName);
+
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D_ref = {
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1},
+                                                        ngraph::element::Type_t::i32, 0,
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 3, 4}, {2, 2, 2}, {2, 1, 1}, {1, 1, 1}, {1, 2, 2},
+                                                        ngraph::element::Type_t::i32, 0,
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+};
+
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D_RefOnly = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2},
+                            expectedAvgRoundingType(), ngraph::op::PadType::EXPLICIT, false },
+};
+
+//FIXME: 5D cases are temporarly disabled on ARM because ACL support check in Pooling::getSupportedDescriptors() can't check layout
+#if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                             ::testing::ValuesIn(paramsMax5D()),
+                             ::testing::ValuesIn(inputShapes5D()),
+                             ::testing::ValuesIn((inpOutPrecision())),
+                             ::testing::Values(false),
+                             ::testing::ValuesIn(vecCpuConfigs),
+                             ::testing::Values(emptyFusingSpec)),
+                         PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV85D()),
+                                 ::testing::ValuesIn(inputShapes5D()),
+                                 ::testing::ValuesIn((inpOutPrecision())),
+                                 ::testing::ValuesIn(vecCpuConfigs)),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D_ref, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV85D_ref),
+                                 ::testing::ValuesIn(inputShapes5D()),
+                                 ::testing::ValuesIn((inpOutPrecision())),
+                                 ::testing::Values(expectedCpuConfig())),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg5D()),
+                              ::testing::ValuesIn(inputShapes5D()),
+                              ::testing::ValuesIn((inpOutPrecision())),
+                              ::testing::Values(false),
+                              ::testing::ValuesIn(vecCpuConfigs),
+                              ::testing::Values(emptyFusingSpec)),
+                          PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_NotOptimized, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg5D_RefOnly),
+                              ::testing::ValuesIn(inputShapes5D()),
+                              ::testing::ValuesIn((inpOutPrecision())),
+                              ::testing::Values(false),
+                              ::testing::Values(expectedCpuConfig()),
+                              ::testing::Values(emptyFusingSpec)),
+                          PoolingLayerCPUTest::getTestCaseName);
+#endif
+} // namespace Pooling
+} // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/common/random_uniform.cpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/common/random_uniform.cpp
new file mode 100644
index 00000000000000..f319fb6ada2719
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/common/random_uniform.cpp
@@ -0,0 +1,68 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "single_layer_tests/classes/random_uniform.hpp"
+
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+namespace RandomUniform {
+
+static const std::vector<ElementType> shape_prc = {
+        ElementType::i32,
+        ElementType::i64
+};
+
+static const std::vector<ov::Shape> output_shapes = {
+        {500},
+        {4, 3, 210}
+};
+
+static const std::vector<uint64_t> global_seed = {
+        0, 8
+};
+
+static const std::vector<uint64_t> operational_seed = {
+        0, 3, 5
+};
+
+static const std::vector<std::tuple<double, double>> min_max = {
+        {0, 50},
+        {-50, 50},
+        {-50, 0}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Param, RandomUniformLayerTestCPU,
+        ::testing::Combine(
+                ::testing::ValuesIn(output_shapes),
+                ::testing::ValuesIn(min_max),
+                ::testing::ValuesIn(shape_prc),
+                ::testing::Values(ElementType::f32, ElementType::i32),
+                ::testing::ValuesIn(global_seed),
+                ::testing::ValuesIn(operational_seed),
+                ::testing::Values(false),
+                ::testing::Values(false),
+                ::testing::Values(false),
+                ::testing::Values(emptyCPUSpec),
+                ::testing::Values(empty_plugin_config)),
+        RandomUniformLayerTestCPU::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_ParamConst, RandomUniformLayerTestCPU,
+        ::testing::Combine(
+                ::testing::Values(output_shapes[0]),
+                ::testing::Values(min_max[0]),
+                ::testing::Values(ElementType::i32),
+                ::testing::Values(ElementType::f32),
+                ::testing::Values(1),
+                ::testing::Values(0),
+                ::testing::Values(true, false),
+                ::testing::Values(true, false),
+                ::testing::Values(true, false),
+                ::testing::Values(emptyCPUSpec),
+                ::testing::Values(empty_plugin_config)),
+        RandomUniformLayerTestCPU::getTestCaseName);
+
+} // namespace RandomUniform
+} // namespace CPULayerTestsDefinitions
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/x64/pooling.cpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/x64/pooling.cpp
new file mode 100644
index 00000000000000..89331ea284d49a
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/x64/pooling.cpp
@@ -0,0 +1,148 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "single_layer_tests/classes/pooling.hpp"
+#include "shared_test_classes/single_layer/pooling.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+#include "test_utils/fusing_test_utils.hpp"
+#include <ov_models/builders.hpp>
+#include <common_test_utils/ov_tensor_utils.hpp>
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ngraph::helpers;
+using namespace ov::test;
+
+
+namespace CPULayerTestsDefinitions {
+namespace Pooling {
+namespace {
+
+const auto ref = CPUSpecificParams{{}, {}, {"ref_any"}, "ref_any"};
+const auto avx512 = CPUSpecificParams{{}, {}, {"jit_avx512"}, "jit_avx512"};
+const auto avx = CPUSpecificParams{{}, {}, {"jit_avx"}, "jit_avx"};
+const auto sse42 = CPUSpecificParams{{}, {}, {"jit_sse42"}, "jit_sse42"};
+
+const std::vector<CPUSpecificParams> vecCpuConfigs = {sse42, avx, avx512};
+
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D_ref = {
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 2}, {1, 2}, {0, 0}, {0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 1}, {2, 2}, {0, 0}, {0, 0},
+                                                        ngraph::element::Type_t::i32, 0,
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D_ref, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV84D_ref),
+                                 ::testing::ValuesIn(inputShapes4D()),
+                                 ::testing::ValuesIn((inpOutPrecision())),
+                                 ::testing::Values(ref)),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
+const auto avx512_nhwc = CPUSpecificParams{{nhwc}, {nhwc}, {"jit_avx512"}, "jit_avx512"};
+const auto avx512_ndhwc = CPUSpecificParams{{ndhwc}, {ndhwc}, {"jit_avx512"}, "jit_avx512"};
+
+const auto avx2_nhwc = CPUSpecificParams{{nhwc}, {nhwc}, {"jit_avx2"}, "jit_avx2"};
+const auto avx2_ndhwc = CPUSpecificParams{{ndhwc}, {ndhwc}, {"jit_avx2"}, "jit_avx2"};
+
+const auto sse42_nhwc = CPUSpecificParams{{nhwc}, {nhwc}, {"jit_sse42"}, "jit_sse42"};
+const auto sse42_ndhwc = CPUSpecificParams{{ndhwc}, {ndhwc}, {"jit_sse42"}, "jit_sse42"};
+
+const std::vector<CPUSpecificParams> vecCpuConfigsFusing_4D = {sse42_nhwc, avx2_nhwc, avx512_nhwc};
+const std::vector<CPUSpecificParams> vecCpuConfigsFusing_5D = {sse42_ndhwc, avx2_ndhwc, avx512_ndhwc};
+
+std::vector<fusingSpecificParams> fusingParamsSet {
+    emptyFusingSpec,
+    fusingFakeQuantizePerTensor,
+    fusingFakeQuantizePerChannel,
+};
+
+const std::vector<InputShape> inputShapes4D_int8 = {
+        { {}, {{3, 4, 64, 64}} },
+        { {}, {{2, 8, 8, 12}} },
+        { {}, {{1, 16, 16, 12}} },
+        { {}, {{1, 21, 8, 4}} },
+        { {}, {{1, 32, 8, 8}} },
+        {
+            // dynamic
+            {-1, 32, -1, -1},
+            // target
+            {
+                {1, 32, 8, 8},
+                {1, 32, 8, 4},
+                {2, 32, 8, 12},
+                {1, 32, 8, 8}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 64}, {1, 64}},
+            // target
+            {
+                {3, 16, 32, 32},
+                {1, 16, 16, 12},
+                {1, 16, 8, 8},
+                {3, 16, 32, 32},
+            }
+        }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_I8, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg4D()),
+                              ::testing::ValuesIn(inputShapes4D_int8),
+                              ::testing::Values(ElementType::f32),
+                              ::testing::Values(true),
+                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_4D)),
+                              ::testing::ValuesIn(fusingParamsSet)),
+                          PoolingLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes5D_int8 = {
+        { {}, {{1, 4, 16, 16, 16}} },
+        { {}, {{2, 8, 8, 8, 8}} },
+        { {}, {{2, 16, 12, 16, 20}} },
+        { {}, {{1, 19, 16, 20, 8}} },
+        { {}, {{1, 32, 16, 8, 12}} },
+        {
+            // dynamic
+            {-1, 32, -1, -1, -1},
+            // target
+            {
+                {2, 32, 8, 8, 8},
+                {1, 32, 16, 20, 8},
+                {1, 32, 16, 16, 16},
+                {2, 32, 8, 8, 8}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 64}, {1, 64}, {1, 25}},
+            // target
+            {
+                {1, 16, 16, 16, 16},
+                {1, 16, 16, 8, 12},
+                {2, 16, 8, 8, 8},
+                {1, 16, 16, 16, 16},
+            }
+        }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_I8, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg5D()),
+                              ::testing::ValuesIn(inputShapes5D_int8),
+                              ::testing::Values(ElementType::f32),
+                              ::testing::Values(true),
+                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D)),
+                              ::testing::ValuesIn(fusingParamsSet)),
+                          PoolingLayerCPUTest::getTestCaseName);
+} // namespace
+} // namespace Pooling
+} // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/x64/random_uniform.cpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/x64/random_uniform.cpp
new file mode 100644
index 00000000000000..8fec42f382464d
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/single_layer_tests/instances/x64/random_uniform.cpp
@@ -0,0 +1,46 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "single_layer_tests/classes/random_uniform.hpp"
+
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+namespace RandomUniform {
+
+static const std::vector<ElementType> output_prc_nightly = {
+        ElementType::f32,
+        ElementType::f16,
+        ElementType::bf16,
+        ElementType::i32,
+        ElementType::i64
+};
+
+// Need to validate the Kernel corner cases.
+static const std::vector<ov::Shape> output_shapes_nightly = {
+        {1}, {2}, {3}, {2, 2}, {5}, {2, 3}, {7}, {2, 2, 2}, {3, 3}, {2, 5}, {11}, {2, 3, 2}, {13}, {2, 7}, {3, 5},
+        {4, 4}, {1, 17}, {2, 9}, {19}, {4, 5}, {21}, {11, 2}, {23, 1}, {4, 2, 3}, {5, 5}, {26}, {1, 27}, {14, 2},
+        {29}, {10, 3}, {31}, {2, 8, 2}, {33}, {17, 2}, {5, 7}, {2, 3, 2, 3}, {37}, {2, 19}, {2, 20}, {41}, {42},
+        {43}, {22, 2}, {3, 5, 3}, {5, 2, 5}, {1, 3, 1, 17, 1}, {26, 2}, {53}, {54}, {55}, {56}, {57}, {58}, {59},
+        {2, 32}, {99}, {127}, {128}, {129}, {199}, {255}, {499}, {997}, {1753}, {2899}
+};
+
+INSTANTIATE_TEST_SUITE_P(nightly_Param, RandomUniformLayerTestCPU,
+        ::testing::Combine(
+                ::testing::ValuesIn(output_shapes_nightly),
+                ::testing::Values(std::tuple<double, double>{-31, 17}),
+                ::testing::Values(ElementType::i32),
+                ::testing::ValuesIn(output_prc_nightly),
+                ::testing::Values(3),
+                ::testing::Values(1),
+                ::testing::Values(true, false),
+                ::testing::Values(true, false),
+                ::testing::Values(true, false),
+                ::testing::Values(emptyCPUSpec),
+                ::testing::Values(empty_plugin_config)),
+        RandomUniformLayerTestCPU::getTestCaseName);
+
+} // namespace RandomUniform
+} // namespace CPULayerTestsDefinitions
diff --git a/src/plugins/intel_cpu/tests/functional/single_layer_tests/pooling.cpp b/src/plugins/intel_cpu/tests/functional/single_layer_tests/pooling.cpp
deleted file mode 100644
index c6a76f7fee9fad..00000000000000
--- a/src/plugins/intel_cpu/tests/functional/single_layer_tests/pooling.cpp
+++ /dev/null
@@ -1,704 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "ov_models/builders.hpp"
-#include "test_utils/cpu_test_utils.hpp"
-#include "test_utils/fusing_test_utils.hpp"
-#include "shared_test_classes/single_layer/pooling.hpp"
-#include "shared_test_classes/base/ov_subgraph.hpp"
-
-using namespace ov::test;
-using namespace CPUTestUtils;
-
-namespace CPULayerTestsDefinitions {
-
-using poolLayerCpuTestParamsSet = std::tuple<LayerTestsDefinitions::poolSpecificParams,
-                                             InputShape,
-                                             ElementType,
-                                             bool,
-                                             CPUSpecificParams,
-                                             fusingSpecificParams>;
-
-using maxPoolV8LayerCpuTestParamsSet = std::tuple<LayerTestsDefinitions::maxPoolV8SpecificParams,
-        InputShape,
-        ElementType,
-        CPUSpecificParams>;
-
-class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestParamsSet>,
-                            virtual public SubgraphBaseTest, public CpuTestWithFusing {
-public:
-    static std::string getTestCaseName(const testing::TestParamInfo<poolLayerCpuTestParamsSet>& obj) {
-        LayerTestsDefinitions::poolSpecificParams basicParamsSet;
-        InputShape inputShapes;
-        ElementType inPrc;
-        bool isInt8;
-        CPUSpecificParams cpuParams;
-        fusingSpecificParams fusingParams;
-        std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = obj.param;
-
-        ngraph::helpers::PoolingTypes poolType;
-        std::vector<size_t> kernel, stride;
-        std::vector<size_t> padBegin, padEnd;
-        ngraph::op::PadType padType;
-        ngraph::op::RoundingType roundingType;
-        bool excludePad;
-        std::tie(poolType, kernel, stride, padBegin, padEnd, roundingType, padType, excludePad) = basicParamsSet;
-
-        std::ostringstream results;
-        results << "IS=(";
-        results << ov::test::utils::partialShape2str({inputShapes.first}) << ")_";
-        results << "TS=";
-        for (const auto& shape : inputShapes.second) {
-            results << ov::test::utils::vec2str(shape) << "_";
-        }
-        results << "Prc=" << inPrc << "_";
-        switch (poolType) {
-            case ngraph::helpers::PoolingTypes::MAX:
-                results << "MaxPool_";
-                break;
-            case ngraph::helpers::PoolingTypes::AVG:
-                results << "AvgPool_";
-                results << "ExcludePad=" << excludePad << "_";
-                break;
-        }
-        results << "K" << ov::test::utils::vec2str(kernel) << "_";
-        results << "S" << ov::test::utils::vec2str(stride) << "_";
-        results << "PB" << ov::test::utils::vec2str(padBegin) << "_";
-        results << "PE" << ov::test::utils::vec2str(padEnd) << "_";
-        results << "Rounding=" << roundingType << "_";
-        results << "AutoPad=" << padType << "_";
-        results << "INT8=" << isInt8 << "_";
-
-        results << CPUTestsBase::getTestCaseName(cpuParams);
-        results << CpuTestWithFusing::getTestCaseName(fusingParams);
-        return results.str();
-    }
-
-protected:
-    void SetUp() override {
-        targetDevice = ov::test::utils::DEVICE_CPU;
-
-        LayerTestsDefinitions::poolSpecificParams basicParamsSet;
-        InputShape inputShapes;
-        ElementType inPrc;
-        bool isInt8;
-        CPUSpecificParams cpuParams;
-        fusingSpecificParams fusingParams;
-        std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = this->GetParam();
-
-        ngraph::helpers::PoolingTypes poolType;
-        std::vector<size_t> kernel, stride;
-        std::vector<size_t> padBegin, padEnd;
-        ngraph::op::PadType padType;
-        ngraph::op::RoundingType roundingType;
-        bool excludePad;
-        std::tie(poolType, kernel, stride, padBegin, padEnd, roundingType, padType, excludePad) = basicParamsSet;
-
-        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-        std::tie(postOpMgrPtr, fusedOps) = fusingParams;
-
-        if (selectedType.empty()) {
-            selectedType = getPrimitiveType();
-        }
-        if (isInt8)
-            selectedType = selectedType + "_I8";
-        else
-            selectedType = makeSelectedTypeStr(selectedType, inPrc);
-
-        init_input_shapes({inputShapes});
-
-        ov::ParameterVector params;
-        for (auto&& shape : inputDynamicShapes) {
-            params.push_back(std::make_shared<ov::op::v0::Parameter>(inPrc, shape));
-        }
-        std::shared_ptr<ngraph::Node> poolInput = params[0];
-        if (isInt8) {
-            ov::Shape newShape(poolInput->get_output_partial_shape(0).size(), 1);
-            poolInput = ngraph::builder::makeFakeQuantize(poolInput, inPrc, 256, newShape);
-        }
-
-        std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makePooling(poolInput,
-                                                                             stride,
-                                                                             padBegin,
-                                                                             padEnd,
-                                                                             kernel,
-                                                                             roundingType,
-                                                                             padType,
-                                                                             excludePad,
-                                                                             poolType);
-
-        function = makeNgraphFunction(inPrc, params, pooling, "PoolingCPU");
-    }
-};
-
-class MaxPoolingV8LayerCPUTest : public testing::WithParamInterface<maxPoolV8LayerCpuTestParamsSet>,
-                                 virtual public SubgraphBaseTest, public CPUTestsBase {
-public:
-    static std::string getTestCaseName(const testing::TestParamInfo<maxPoolV8LayerCpuTestParamsSet>& obj) {
-        LayerTestsDefinitions::maxPoolV8SpecificParams basicParamsSet;
-        InputShape inputShapes;
-        ElementType inPrc;
-        CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = obj.param;
-
-        std::vector<size_t> kernel, stride, dilation;
-        std::vector<size_t> padBegin, padEnd;
-        ngraph::op::PadType padType;
-        ngraph::op::RoundingType roundingType;
-        ngraph::element::Type indexElementType;
-        int64_t axis;
-        std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) = basicParamsSet;
-
-        std::ostringstream results;
-        results << "IS=(";
-        results << ov::test::utils::partialShape2str({inputShapes.first}) << ")_";
-        results << "TS=";
-        for (const auto& shape : inputShapes.second) {
-            results << ov::test::utils::vec2str(shape) << "_";
-        }
-        results << "Prc=" << inPrc << "_";
-        results << "MaxPool_";
-        results << "K" << ov::test::utils::vec2str(kernel) << "_";
-        results << "S" << ov::test::utils::vec2str(stride) << "_";
-        results << "D" << ov::test::utils::vec2str(dilation) << "_";
-        results << "PB" << ov::test::utils::vec2str(padBegin) << "_";
-        results << "PE" << ov::test::utils::vec2str(padEnd) << "_";
-        results << "Rounding=" << roundingType << "_";
-        results << "AutoPad=" << padType << "_";
-
-        results << CPUTestsBase::getTestCaseName(cpuParams);
-        return results.str();
-    }
-
-protected:
-    void SetUp() override {
-        targetDevice = ov::test::utils::DEVICE_CPU;
-
-        LayerTestsDefinitions::maxPoolV8SpecificParams basicParamsSet;
-        InputShape inputShapes;
-        ElementType inPrc;
-        CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = this->GetParam();
-
-        std::vector<size_t> kernel, stride, dilation;
-        std::vector<size_t> padBegin, padEnd;
-        ngraph::op::PadType padType;
-        ngraph::op::RoundingType roundingType;
-        ngraph::element::Type indexElementType;
-        int64_t axis;
-        std::tie(kernel, stride, dilation, padBegin, padEnd, indexElementType, axis, roundingType, padType) = basicParamsSet;
-        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-        if (selectedType.empty()) {
-            selectedType = getPrimitiveType();
-        }
-        selectedType = makeSelectedTypeStr(selectedType, inPrc);
-
-        init_input_shapes({inputShapes});
-
-        ov::ParameterVector params;
-        for (auto&& shape : inputDynamicShapes) {
-            params.push_back(std::make_shared<ov::op::v0::Parameter>(inPrc, shape));
-        }
-        std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makeMaxPoolingV8(params[0], stride, dilation, padBegin, padEnd,
-                                                                                  kernel, roundingType, padType,
-                                                                                  indexElementType, axis);
-        pooling->get_rt_info() = getCPUInfo();
-        ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(pooling->output(0))};
-        function = std::make_shared<ngraph::Function>(results, params, "MaxPooling");
-    }
-};
-
-TEST_P(PoolingLayerCPUTest, CompareWithRefs) {
-    run();
-    CheckPluginRelatedResults(compiledModel, "Pooling");
-}
-
-TEST_P(MaxPoolingV8LayerCPUTest, CompareWithRefs) {
-    run();
-    CheckPluginRelatedResults(compiledModel, "Pooling");
-}
-
-namespace {
-
-const auto avx512 = CPUSpecificParams{{}, {}, {"jit_avx512"}, "jit_avx512"};
-const auto avx = CPUSpecificParams{{}, {}, {"jit_avx"}, "jit_avx"};
-const auto sse42 = CPUSpecificParams{{}, {}, {"jit_sse42"}, "jit_sse42"};
-const auto ref = CPUSpecificParams{{}, {}, {"ref_any"}, "ref_any"};
-
-const std::vector<CPUSpecificParams> vecCpuConfigs = {ref, sse42, avx, avx512};
-const std::vector<ElementType> inpOutPrecision = {ElementType::f32/*, ElementType::bf16*/};
-
-const std::vector<InputShape> inputShapes3D = {
-        { {}, {{3, 4, 64}} },
-        { {}, {{2, 8, 12}} },
-        { {}, {{1, 16, 12}} },
-        { {}, {{1, 21, 4}} },
-        { {}, {{1, 32, 8}} },
-        {
-            // dynamic
-            {-1, -1, -1},
-            // target
-            {
-                {1, 32, 8},
-                {1, 21, 4},
-                {2, 8, 12}
-            }
-        },
-        {
-            // dynamic
-            {{1, 5}, {4, 32}, {1, 64}},
-            // target
-            {
-                {3, 4, 64},
-                {1, 16, 12},
-                {1, 32, 8}
-            }
-        }
-};
-
-const std::vector<InputShape> inputShapes4D = {
-        { {}, {{3, 4, 64, 64}} },
-        { {}, {{2, 8, 8, 12}} },
-        { {}, {{1, 16, 16, 12}} },
-        { {}, {{1, 21, 8, 4}} },
-        { {}, {{1, 32, 8, 8}} },
-        {
-            // dynamic
-            {-1, -1, -1, -1},
-            // target
-            {
-                {1, 32, 8, 8},
-                {1, 21, 8, 4},
-                {2, 8, 8, 12},
-                {1, 96, 125, 125}
-            }
-        },
-        {
-            // dynamic
-            {{1, 5}, {4, 32}, {1, 64}, {1, 64}},
-            // target
-            {
-                {3, 4, 64, 64},
-                {1, 16, 16, 12},
-                {1, 32, 8, 8}
-            }
-        },
-        {
-            // dynamic
-            {{1, 10}, 16, 8, 8},
-            // target
-            {
-                {1, 16, 8, 8},
-                {2, 16, 8, 8},
-            }
-        }
-};
-
-const std::vector<InputShape> inputShapes5D = {
-        { {}, {{1, 4, 16, 16, 16}} },
-        { {}, {{2, 8, 8, 8, 8}} },
-        { {}, {{2, 16, 12, 16, 20}} },
-        { {}, {{1, 19, 16, 20, 8}} },
-        { {}, {{1, 32, 16, 8, 12}} },
-        {
-            // dynamic
-            {-1, -1, -1, -1, -1},
-            // target
-            {
-                {2, 8, 8, 8, 8},
-                {1, 19, 16, 20, 8},
-                {1, 4, 16, 16, 16}
-            }
-        },
-        {
-            // dynamic
-            {{1, 5}, {4, 32}, {1, 64}, {1, 64}, {1, 25}},
-            // target
-            {
-                {1, 4, 16, 16, 16},
-                {1, 32, 16, 8, 12},
-                {3, 16, 4, 8, 3}
-            }
-        }
-};
-
-/* ============= Pooling (1D) ============= */
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax3D = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2}, {2}, {0}, {0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4}, {2}, {0}, {0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2}, {1}, {0}, {0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-};
-
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg3D = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3}, {1}, {1}, {0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3}, {1}, {1}, {0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4}, {4}, {2}, {2},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-};
-
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg3D_RefOnly = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2}, {2}, {2}, {2},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-};
-
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_3D, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::ValuesIn(paramsMax3D),
-                                 ::testing::ValuesIn(inputShapes3D),
-                                 ::testing::ValuesIn(inpOutPrecision),
-                                 ::testing::Values(false),
-                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                                 ::testing::Values(emptyFusingSpec)),
-                         PoolingLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::ValuesIn(paramsAvg3D),
-                                 ::testing::ValuesIn(inputShapes3D),
-                                 ::testing::ValuesIn(inpOutPrecision),
-                                 ::testing::Values(false),
-                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                                 ::testing::Values(emptyFusingSpec)),
-                         PoolingLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D_NotOptimized, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::ValuesIn(paramsAvg3D_RefOnly),
-                                 ::testing::ValuesIn(inputShapes3D),
-                                 ::testing::ValuesIn(inpOutPrecision),
-                                 ::testing::Values(false),
-                                 ::testing::Values(ref),
-                                 ::testing::Values(emptyFusingSpec)),
-                         PoolingLayerCPUTest::getTestCaseName);
-
-/* ============= Pooling (2D) ============= */
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax4D = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2}, {2, 2}, {0, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2}, {2, 2}, {0, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 2}, {0, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 1}, {0, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-};
-
-const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D = {
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {1, 1}, {0, 0}, {0, 0},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
-};
-
-const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D_ref = {
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {2, 2}, {0, 0}, {0, 0},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 2}, {1, 2}, {0, 0}, {0, 0},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 1}, {2, 2}, {0, 0}, {0, 0},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
-};
-
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {0, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4}, {4, 4}, {2, 2}, {2, 2},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-};
-
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D_RefOnly = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {2, 2}, {2, 2},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-};
-
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_4D, PoolingLayerCPUTest,
-                            ::testing::Combine(
-                            ::testing::ValuesIn(paramsMax4D),
-                            ::testing::ValuesIn(inputShapes4D),
-                            ::testing::ValuesIn(inpOutPrecision),
-                            ::testing::Values(false),
-                            ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                            ::testing::Values(emptyFusingSpec)),
-                        PoolingLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D, MaxPoolingV8LayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::ValuesIn(paramsMaxV84D),
-                                 ::testing::ValuesIn(inputShapes4D),
-                                 ::testing::ValuesIn(inpOutPrecision),
-                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs))),
-                         MaxPoolingV8LayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D_ref, MaxPoolingV8LayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::ValuesIn(paramsMaxV84D_ref),
-                                 ::testing::ValuesIn(inputShapes4D),
-                                 ::testing::ValuesIn(inpOutPrecision),
-                                 ::testing::Values(ref)),
-                         MaxPoolingV8LayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                            ::testing::ValuesIn(paramsAvg4D),
-                            ::testing::ValuesIn(inputShapes4D),
-                            ::testing::ValuesIn(inpOutPrecision),
-                            ::testing::Values(false),
-                            ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                            ::testing::Values(emptyFusingSpec)),
-                        PoolingLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_NotOptimized, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                            ::testing::ValuesIn(paramsAvg4D_RefOnly),
-                            ::testing::ValuesIn(inputShapes4D),
-                            ::testing::ValuesIn(inpOutPrecision),
-                            ::testing::Values(false),
-                            ::testing::Values(ref),
-                            ::testing::Values(emptyFusingSpec)),
-                        PoolingLayerCPUTest::getTestCaseName);
-
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D_Large = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {65, 65}, {65, 65}, {0, 0}, {0, 0},
-                            ngraph::op::RoundingType::FLOOR, ngraph::op::PadType::VALID, true },
-};
-
-const std::vector<InputShape> inputShapes4D_Large = {
-        {
-            // dynamic
-            {-1, -1, -1, -1},
-            // target
-            {
-                {1, 16, 65, 65},
-                {1, 8, 130, 130},
-                {1, 16, 65, 65}
-            }
-        },
-};
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_Large, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                            ::testing::ValuesIn(paramsAvg4D_Large),
-                            ::testing::ValuesIn(inputShapes4D_Large),
-                            ::testing::ValuesIn(inpOutPrecision),
-                            ::testing::Values(false),
-                            ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                            ::testing::Values(emptyFusingSpec)),
-                        PoolingLayerCPUTest::getTestCaseName);
-
-/* ============= Pooling (3D) ============= */
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax5D = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {3, 3, 3}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-};
-
-const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D = {
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
-};
-
-const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D_ref = {
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
-        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 3, 4}, {2, 2, 2}, {2, 1, 1}, {1, 1, 1}, {1, 2, 2},
-                                                        ngraph::element::Type_t::i32, 0,
-                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
-};
-
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3, 3, 3}, {3, 3, 3}, {1, 1, 1}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4, 4}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-};
-
-const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D_RefOnly = {
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-};
-
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                             ::testing::ValuesIn(paramsMax5D),
-                             ::testing::ValuesIn(inputShapes5D),
-                             ::testing::ValuesIn(inpOutPrecision),
-                             ::testing::Values(false),
-                             ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                             ::testing::Values(emptyFusingSpec)),
-                         PoolingLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D, MaxPoolingV8LayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::ValuesIn(paramsMaxV85D),
-                                 ::testing::ValuesIn(inputShapes5D),
-                                 ::testing::ValuesIn(inpOutPrecision),
-                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs))),
-                         MaxPoolingV8LayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D_ref, MaxPoolingV8LayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::ValuesIn(paramsMaxV85D_ref),
-                                 ::testing::ValuesIn(inputShapes5D),
-                                 ::testing::ValuesIn(inpOutPrecision),
-                                 ::testing::Values(ref)),
-                         MaxPoolingV8LayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                              ::testing::ValuesIn(paramsAvg5D),
-                              ::testing::ValuesIn(inputShapes5D),
-                              ::testing::ValuesIn(inpOutPrecision),
-                              ::testing::Values(false),
-                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                              ::testing::Values(emptyFusingSpec)),
-                          PoolingLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_NotOptimized, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                              ::testing::ValuesIn(paramsAvg5D_RefOnly),
-                              ::testing::ValuesIn(inputShapes5D),
-                              ::testing::ValuesIn(inpOutPrecision),
-                              ::testing::Values(false),
-                              ::testing::Values(ref),
-                              ::testing::Values(emptyFusingSpec)),
-                          PoolingLayerCPUTest::getTestCaseName);
-
-/* === Fusing === */
-
-const auto avx512_nhwc = CPUSpecificParams{{nhwc}, {nhwc}, {"jit_avx512"}, "jit_avx512"};
-const auto avx512_ndhwc = CPUSpecificParams{{ndhwc}, {ndhwc}, {"jit_avx512"}, "jit_avx512"};
-
-const auto avx2_nhwc = CPUSpecificParams{{nhwc}, {nhwc}, {"jit_avx2"}, "jit_avx2"};
-const auto avx2_ndhwc = CPUSpecificParams{{ndhwc}, {ndhwc}, {"jit_avx2"}, "jit_avx2"};
-
-const auto sse42_nhwc = CPUSpecificParams{{nhwc}, {nhwc}, {"jit_sse42"}, "jit_sse42"};
-const auto sse42_ndhwc = CPUSpecificParams{{ndhwc}, {ndhwc}, {"jit_sse42"}, "jit_sse42"};
-
-const std::vector<CPUSpecificParams> vecCpuConfigsFusing_4D = {sse42_nhwc, avx2_nhwc, avx512_nhwc};
-const std::vector<CPUSpecificParams> vecCpuConfigsFusing_5D = {sse42_ndhwc, avx2_ndhwc, avx512_ndhwc};
-
-std::vector<fusingSpecificParams> fusingParamsSet {
-    emptyFusingSpec,
-    fusingFakeQuantizePerTensor,
-    fusingFakeQuantizePerChannel,
-};
-
-const std::vector<InputShape> inputShapes4D_int8 = {
-        { {}, {{3, 4, 64, 64}} },
-        { {}, {{2, 8, 8, 12}} },
-        { {}, {{1, 16, 16, 12}} },
-        { {}, {{1, 21, 8, 4}} },
-        { {}, {{1, 32, 8, 8}} },
-        {
-            // dynamic
-            {-1, 32, -1, -1},
-            // target
-            {
-                {1, 32, 8, 8},
-                {1, 32, 8, 4},
-                {2, 32, 8, 12},
-                {1, 32, 8, 8}
-            }
-        },
-        {
-            // dynamic
-            {{1, 5}, 16, {1, 64}, {1, 64}},
-            // target
-            {
-                {3, 16, 32, 32},
-                {1, 16, 16, 12},
-                {1, 16, 8, 8},
-                {3, 16, 32, 32},
-            }
-        }
-};
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_I8, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                              ::testing::ValuesIn(paramsAvg4D),
-                              ::testing::ValuesIn(inputShapes4D_int8),
-                              ::testing::Values(ElementType::f32),
-                              ::testing::Values(true),
-                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_4D)),
-                              ::testing::ValuesIn(fusingParamsSet)),
-                          PoolingLayerCPUTest::getTestCaseName);
-
-const std::vector<InputShape> inputShapes5D_int8 = {
-        { {}, {{1, 4, 16, 16, 16}} },
-        { {}, {{2, 8, 8, 8, 8}} },
-        { {}, {{2, 16, 12, 16, 20}} },
-        { {}, {{1, 19, 16, 20, 8}} },
-        { {}, {{1, 32, 16, 8, 12}} },
-        {
-            // dynamic
-            {-1, 32, -1, -1, -1},
-            // target
-            {
-                {2, 32, 8, 8, 8},
-                {1, 32, 16, 20, 8},
-                {1, 32, 16, 16, 16},
-                {2, 32, 8, 8, 8}
-            }
-        },
-        {
-            // dynamic
-            {{1, 5}, 16, {1, 64}, {1, 64}, {1, 25}},
-            // target
-            {
-                {1, 16, 16, 16, 16},
-                {1, 16, 16, 8, 12},
-                {2, 16, 8, 8, 8},
-                {1, 16, 16, 16, 16},
-            }
-        }
-};
-
-INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_I8, PoolingLayerCPUTest,
-                         ::testing::Combine(
-                              ::testing::ValuesIn(paramsAvg5D),
-                              ::testing::ValuesIn(inputShapes5D_int8),
-                              ::testing::Values(ElementType::f32),
-                              ::testing::Values(true),
-                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D)),
-                              ::testing::ValuesIn(fusingParamsSet)),
-                          PoolingLayerCPUTest::getTestCaseName);
-
-} // namespace
-
-} // namespace CPULayerTestsDefinitions
diff --git a/src/plugins/intel_cpu/tests/functional/subgraph_tests/src/matmul_weights_decompression.cpp b/src/plugins/intel_cpu/tests/functional/subgraph_tests/src/matmul_weights_decompression.cpp
index e29ed1f4bfce94..b107b406cd833a 100644
--- a/src/plugins/intel_cpu/tests/functional/subgraph_tests/src/matmul_weights_decompression.cpp
+++ b/src/plugins/intel_cpu/tests/functional/subgraph_tests/src/matmul_weights_decompression.cpp
@@ -14,18 +14,26 @@ using namespace ov::test;
 
 namespace SubgraphTestsDefinitions {
 /*
- *                        Subtract_const(U8/NF4)
+ * WP - weights precision
+ * DP - decompression precision
+ * IP - input precision
+ * Opt - optional
+ *                        Subtract_const(WP)
  *                           /
- *    Weights(U8/NF4)     Convert(F32)
+ *    Weights(WP)     Convert(DP)
  *       |               /
- *    Convert(F32)   Reshape
- *            \        /       Multiply_const(F32)
- *            Subtract(opt)     /
- *                  \       Reshape
- *                   \       /
- *                   Multiply
+ *    Convert(DP)   Reshape (Opt)
+ *            \        /          Multiply_const(DP)
+ *            Subtract(Opt)       /
+ *                  \         Reshape (Opt)
+ *                   \         /
+ *                    Multiply
  *                      |
- *      Data(F32)   Transpose(opt)
+ *                   Reshape (in case of group decompression)
+ *                      |
+ *                   Convert (if IP != DP)
+ *                      |
+ *      Data(IP)   Transpose(Opt)
  *            \     /
  *             Matmul
  *               |
@@ -46,6 +54,7 @@ struct ShapeParams {
 };
 using MatmulWeightsDecompressionParams = std::tuple<ShapeParams,
                                                     ov::test::ElementType,  // weights precision
+                                                    ov::test::ElementType,  // decompression precision
                                                     bool,                   // transpose on weights
                                                     bool,                   // decompression subtract
                                                     bool,                   // reshape on decompression constants
@@ -60,6 +69,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
     static std::string getTestCaseName(testing::TestParamInfo<MatmulWeightsDecompressionParams> obj) {
         ShapeParams shape_params;
         ov::test::ElementType weights_precision;
+        ov::test::ElementType decompression_precision;
         bool transpose;
         bool decompression_sub;
         bool reshape_on_decompression;
@@ -69,6 +79,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
 
         std::tie(shape_params,
                  weights_precision,
+                 decompression_precision,
                  transpose,
                  decompression_sub,
                  reshape_on_decompression,
@@ -81,6 +92,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
         result << "weights_shape=" << shape_params.weights_shape << "_";
         result << "group_size=" << shape_params.weights_group_size << "_";
         result << "weights_precision=" << weights_precision << "_";
+        result << "decompression_precision=" << decompression_precision << "_";
         result << "transpose_weights=" << transpose << "_";
         result << "decompression_subtract=" << decompression_sub << "_";
         result << "reshape_on_decompression=" << reshape_on_decompression << "_";
@@ -100,6 +112,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
                                                        const int group_size,
                                                        const ov::element::Type data_precision,
                                                        const ov::element::Type weights_precision,
+                                                       const ov::element::Type decompression_precision,
                                                        const bool transpose_weights,
                                                        const bool add_subtract,
                                                        const bool reshape_on_decompression_constant) {
@@ -131,7 +144,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
         }
         auto weights = ngraph::builder::makeConstant<uint8_t>(weights_precision, transformed_weights_shape, {}, true);
         weights->set_friendly_name("Compressed_weights");
-        auto weights_convert = std::make_shared<ngraph::opset1::Convert>(weights, data_precision);
+        auto weights_convert = std::make_shared<ngraph::opset1::Convert>(weights, decompression_precision);
 
         std::shared_ptr<ov::Node> mul_parent = weights_convert;
         auto output_channels = *weights_shape.rbegin();
@@ -152,7 +165,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
             scaleshift_const_shape.erase(std::remove(scaleshift_const_shape.begin(), scaleshift_const_shape.end(), 1), scaleshift_const_shape.end());
         if (add_subtract) {
             auto shift_const = ngraph::builder::makeConstant<uint8_t>(weights_precision, scaleshift_const_shape, {}, true);
-            std::shared_ptr<ov::Node> shift_convert = std::make_shared<ngraph::opset1::Convert>(shift_const, data_precision);
+            std::shared_ptr<ov::Node> shift_convert = std::make_shared<ngraph::opset1::Convert>(shift_const, decompression_precision);
             if (reshape_on_decompression_constant) {
                 auto shift_reshape_const = ov::opset10::Constant::create(ov::element::i32, {scaleshift_target_shape.size()}, scaleshift_target_shape);
                 auto shift_reshape = std::make_shared<ov::opset10::Reshape>(shift_convert, shift_reshape_const, false);
@@ -161,7 +174,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
             mul_parent = std::make_shared<ov::opset10::Subtract>(weights_convert, shift_convert);
         }
 
-        std::shared_ptr<ov::Node> scale_const = ngraph::builder::makeConstant<float>(data_precision, scaleshift_const_shape, {}, true);
+        std::shared_ptr<ov::Node> scale_const = ngraph::builder::makeConstant<float>(decompression_precision, scaleshift_const_shape, {}, true);
         if (reshape_on_decompression_constant) {
             auto scale_reshape_const = ov::opset10::Constant::create(ov::element::i32, {scaleshift_target_shape.size()}, scaleshift_target_shape);
             auto scale_reshape = std::make_shared<ov::opset10::Reshape>(scale_const, scale_reshape_const, false);
@@ -175,6 +188,9 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
             auto target_shape_node = ov::opset10::Constant::create(ov::element::i32, {reshape_target_shape.size()}, reshape_target_shape);
             last_node = std::make_shared<ov::opset10::Reshape>(last_node, target_shape_node, false);
         }
+        if (decompression_precision != data_precision) {
+            last_node = std::make_shared<ov::opset10::Convert>(last_node, data_precision);
+        }
         if (transpose_weights) {
             const size_t rank = last_node->get_output_partial_shape(0).size();
             std::vector<int> order(rank);
@@ -191,6 +207,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
                                             const int group_size,
                                             const ov::element::Type data_precision,
                                             const ov::element::Type weights_precision,
+                                            const ov::element::Type decompression_precision,
                                             const bool transpose_weights,
                                             const bool add_subtract,
                                             const bool reshape_on_decompression) {
@@ -199,6 +216,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
                                                                group_size,
                                                                data_precision,
                                                                weights_precision,
+                                                               decompression_precision,
                                                                transpose_weights,
                                                                add_subtract,
                                                                reshape_on_decompression);
@@ -211,6 +229,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
 
         ShapeParams shape_params;
         ov::test::ElementType weights_precision;
+        ov::test::ElementType decompression_precision;
         bool transpose_weights;
         bool decompression_sub;
         bool reshape_on_decompression;
@@ -220,6 +239,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
 
         std::tie(shape_params,
                  weights_precision,
+                 decompression_precision,
                  transpose_weights,
                  decompression_sub,
                  reshape_on_decompression,
@@ -239,6 +259,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
                                 shape_params.weights_group_size,
                                 netType,
                                 weights_precision,
+                                decompression_precision,
                                 transpose_weights,
                                 decompression_sub,
                                 reshape_on_decompression);
@@ -260,7 +281,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
         }
         ASSERT_TRUE(weights_found);
 
-        const bool should_fuse = std::get<7>(test_param);
+        const bool should_fuse = std::get<8>(test_param);
         const size_t expected_count = should_fuse ? 0 : 1;
         CheckNumberOfNodesWithType(compiledModel, "Convert", expected_count);
         CheckNumberOfNodesWithType(compiledModel, "Eltwise", expected_count);
@@ -304,6 +325,7 @@ bool shouldUseDecompressionKernelBasic() {
 }
 
 const std::vector<ov::test::ElementType> weights_precisions = {ov::element::u8, ov::element::nf4};
+const std::vector<ov::test::ElementType> decompression_precisions = {ov::element::f32};
 const std::vector<ShapeParams> input_shapes_basic = {
     {{{-1, -1, -1}, {{1, 4, 16}, {10, 16, 16}}}, {16, 32}},
     {{{}, {{1, 4, 16}}}, {16, 32}, 2ul},
@@ -331,6 +353,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_basic,
                          MatmulWeightsDecompression,
                          ::testing::Combine(::testing::ValuesIn(input_shapes_basic),
                                             ::testing::ValuesIn(weights_precisions),
+                                            ::testing::ValuesIn(decompression_precisions),
                                             ::testing::Values(true),
                                             ::testing::Values(true),
                                             ::testing::Values(true),
@@ -343,6 +366,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_big,
                          MatmulWeightsDecompression,
                          ::testing::Combine(::testing::ValuesIn(input_shapes_big),
                                             ::testing::ValuesIn(weights_precisions),
+                                            ::testing::ValuesIn(decompression_precisions),
                                             ::testing::Values(true),
                                             ::testing::Values(true),
                                             ::testing::Values(true),
@@ -364,11 +388,13 @@ const std::vector<ShapeParams> input_shapes_corner_cases_big = {
 const std::vector<bool> transpose_weights = {true, false};
 const std::vector<bool> add_decompression_sub = {true, false};
 const std::vector<bool> reshape_on_decompression = {true, false};
+const std::vector<ov::test::ElementType> decompression_precisions_corner_cases = {ov::element::f16, ov::element::f32};
 
 INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_corner_cases_basic,
                          MatmulWeightsDecompression,
                          ::testing::Combine(::testing::ValuesIn(input_shapes_corner_cases_basic),
                                             ::testing::ValuesIn(weights_precisions),
+                                            ::testing::ValuesIn(decompression_precisions_corner_cases),
                                             ::testing::ValuesIn(transpose_weights),
                                             ::testing::ValuesIn(add_decompression_sub),
                                             ::testing::ValuesIn(reshape_on_decompression),
@@ -381,6 +407,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_corner_cases_big,
                          MatmulWeightsDecompression,
                          ::testing::Combine(::testing::ValuesIn(input_shapes_corner_cases_big),
                                             ::testing::ValuesIn(weights_precisions),
+                                            ::testing::ValuesIn(decompression_precisions_corner_cases),
                                             ::testing::ValuesIn(transpose_weights),
                                             ::testing::ValuesIn(add_decompression_sub),
                                             ::testing::ValuesIn(reshape_on_decompression),
diff --git a/src/plugins/intel_cpu/tests/functional/test_utils/cpu_test_utils.hpp b/src/plugins/intel_cpu/tests/functional/test_utils/cpu_test_utils.hpp
index d8deddfebe5d69..fff65f9e1c442f 100644
--- a/src/plugins/intel_cpu/tests/functional/test_utils/cpu_test_utils.hpp
+++ b/src/plugins/intel_cpu/tests/functional/test_utils/cpu_test_utils.hpp
@@ -170,6 +170,7 @@ class CPUTestsBase {
 // common parameters
 const auto emptyCPUSpec = CPUSpecificParams{{}, {}, {}, {}};
 const std::map<std::string, std::string> cpuEmptyPluginConfig;
+const ov::AnyMap empty_plugin_config{};
 const std::map<std::string, std::string> cpuFP32PluginConfig =
         { { InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16, InferenceEngine::PluginConfigParams::NO } };
 const std::map<std::string, std::string> cpuBF16PluginConfig =
diff --git a/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py b/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py
index c30c5773467b4f..3adf6e65025af4 100644
--- a/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py
+++ b/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py
@@ -191,14 +191,10 @@ def runCommandList(commit, cfgData, enforceClean=False):
         )
         proc = subprocess.Popen(
             formattedCmd, cwd=cwd, stdout=subprocess.PIPE,
-            stderr=subprocess.STDOUT
+            stderr=subprocess.STDOUT,
+            encoding="utf-8", errors="replace"
         )
         for line in proc.stdout:
-            # decode if line is byte-type
-            try:
-                line = line.decode("utf-8")
-            except (UnicodeDecodeError, AttributeError):
-                pass
             sys.stdout.write(line)
             commitLogger.info(line)
             if "catchMsg" in cmd:
diff --git a/src/plugins/intel_gna/tests/deprecated/helpers/single_layer_common.hpp b/src/plugins/intel_gna/tests/deprecated/helpers/single_layer_common.hpp
index 03cf9af92fbaa7..765846056930f8 100644
--- a/src/plugins/intel_gna/tests/deprecated/helpers/single_layer_common.hpp
+++ b/src/plugins/intel_gna/tests/deprecated/helpers/single_layer_common.hpp
@@ -20,10 +20,6 @@
 # include <regex>
 # define REPLACE_WITH_STR(SRC, PATTERN, STR) SRC = std::regex_replace(SRC, std::regex(PATTERN), STR)
 # define FIND_STR(SRC, PATTERN) std::regex_search(SRC, std::regex(PATTERN))
-#elif defined USE_BOOST_RE
-# include <boost/regex.hpp>
-# define REPLACE_WITH_STR(SRC, PATTERN, STR) SRC = boost::regex_replace(SRC, boost::regex(PATTERN), STR)
-# define FIND_STR(SRC, PATTERN) boost::regex_search(SRC, boost::regex(PATTERN))
 #else
 # error "Cannot implement regex"
 # define REPLACE_WITH_STR(SRC, PATTERN, STR)
diff --git a/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp b/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
deleted file mode 100644
index 299935865f1ca5..00000000000000
--- a/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
+++ /dev/null
@@ -1,45 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "subgraph_tests/perm_conv_perm_concat.hpp"
-
-#include <vector>
-
-#include "common_test_utils/test_constants.hpp"
-namespace {
-std::vector<std::array<size_t, 4>> input_shapes{
-    {1, 1, 7, 32},
-    {1, 1, 8, 16},
-};
-
-std::vector<std::array<size_t, 2>> kernel_shapes{
-    {1, 3},
-    {1, 5},
-};
-
-std::vector<size_t> output_channels{
-    32,
-    64,
-};
-
-std::vector<InferenceEngine::Precision> netPrecisions = {
-    InferenceEngine::Precision::FP32,
-    InferenceEngine::Precision::FP16,
-};
-
-std::vector<std::map<std::string, std::string>> configs = {{{"GNA_DEVICE_MODE", "GNA_SW_EXACT"}},
-                                                           {{"GNA_DEVICE_MODE", "GNA_SW_FP32"}}};
-}  // namespace
-
-namespace SubgraphTestsDefinitions {
-INSTANTIATE_TEST_SUITE_P(smoke_basic,
-                         PermConvPermConcat,
-                         ::testing::Combine(::testing::ValuesIn(netPrecisions),
-                                            ::testing::Values(ov::test::utils::DEVICE_GNA),
-                                            ::testing::ValuesIn(input_shapes),
-                                            ::testing::ValuesIn(kernel_shapes),
-                                            ::testing::ValuesIn(output_channels),
-                                            ::testing::ValuesIn(configs)),
-                         PermConvPermConcat::getTestCaseName);
-}  // namespace SubgraphTestsDefinitions
diff --git a/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp b/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
deleted file mode 100644
index f702ba9b3ec5f7..00000000000000
--- a/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
+++ /dev/null
@@ -1,49 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "subgraph_tests/reshape_squeeze_reshape_relu.hpp"
-
-#include <vector>
-
-#include "common_test_utils/test_constants.hpp"
-
-using namespace SubgraphTestsDefinitions;
-
-namespace {
-std::vector<ShapeAxesTuple> inputs{
-    {{1, 1, 3}, {0, 1}},
-    {{1, 1, 3}, {0}},
-    {{1, 1, 3}, {1}},
-    {{1, 3, 1}, {0, 2}},
-    {{1, 3, 1}, {0}},
-    {{1, 3, 1}, {2}},
-    {{3, 1, 1}, {1, 2}},
-    {{3, 1, 1}, {1}},
-    {{3, 1, 1}, {2}},
-    {{4, 1, 3, 1}, {1, 3}},
-    {{4, 1, 1, 3}, {1, 2}},
-    {{1, 4, 1, 3}, {0, 2}},
-    {{1, 3, 5, 2, 1}, {0, 4}},
-    {{3, 1, 2, 4, 4, 3}, {1}},
-    {{1, 1, 1, 1, 1, 3}, {0, 1, 2, 3, 4}},
-    {{1, 1, 1, 1, 1, 3}, {1, 3}},
-    {{1}, {0}},
-};
-
-std::vector<InferenceEngine::Precision> netPrecisions = {
-    InferenceEngine::Precision::FP32,
-    InferenceEngine::Precision::FP16,
-};
-
-const std::vector<ngraph::helpers::SqueezeOpType> opTypes = {ngraph::helpers::SqueezeOpType::SQUEEZE,
-                                                             ngraph::helpers::SqueezeOpType::UNSQUEEZE};
-
-INSTANTIATE_TEST_SUITE_P(smoke_reshape_squeeze_reshape_relu,
-                         ReshapeSqueezeReshapeRelu,
-                         ::testing::Combine(::testing::ValuesIn(inputs),
-                                            ::testing::ValuesIn(netPrecisions),
-                                            ::testing::Values(ov::test::utils::DEVICE_GNA),
-                                            ::testing::ValuesIn(opTypes)),
-                         ReshapeSqueezeReshapeRelu::getTestCaseName);
-}  // namespace
diff --git a/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp b/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
deleted file mode 100644
index 1d9cea34c783ac..00000000000000
--- a/src/plugins/intel_gna/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
+++ /dev/null
@@ -1,22 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "subgraph_tests/split_conv_concat.hpp"
-
-#include <vector>
-
-#include "common_test_utils/test_constants.hpp"
-
-using namespace SubgraphTestsDefinitions;
-const std::vector<InferenceEngine::Precision> netPrecisions = {InferenceEngine::Precision::FP32,
-                                                               InferenceEngine::Precision::FP16};
-
-std::vector<std::vector<size_t>> inputShapes = {{1, 32, 1, 130}, {1, 64, 1, 170}, {1, 32, 1, 1026}};
-
-INSTANTIATE_TEST_SUITE_P(smoke_SplitConvConcat,
-                         SplitConvConcat,
-                         ::testing::Combine(::testing::ValuesIn(netPrecisions),
-                                            ::testing::ValuesIn(inputShapes),
-                                            ::testing::Values(ov::test::utils::DEVICE_GNA)),
-                         SplitConvConcat::getTestCaseName);
diff --git a/src/plugins/intel_gpu/docs/memory_allocation_gpu_plugin.md b/src/plugins/intel_gpu/docs/memory_allocation_gpu_plugin.md
index eb548eb386abe4..1ad9039435fb56 100644
--- a/src/plugins/intel_gpu/docs/memory_allocation_gpu_plugin.md
+++ b/src/plugins/intel_gpu/docs/memory_allocation_gpu_plugin.md
@@ -20,7 +20,7 @@ calls the corresponding memory object wrapper for each allocation type: [gpu_buf
 
 ## Dump memory allocation history
 
-The memory allocation history is being managed by the `engine`, which can be dumped by setting the environment variable `OV_GPU_Verbose=1` if OpenVino is built with the cmake configuration `ENABLE_DEBUG_CAPS=ON`.
+The memory allocation history is being managed by the `engine`, which can be dumped by setting the environment variable `OV_GPU_Verbose=2` if OpenVINO is built with the cmake configuration `ENABLE_DEBUG_CAPS=ON`.
 ```cpp
 ...
 GPU_Debug: Allocate 58982400 bytes of usm_host allocation type (current=117969612; max=117969612)
diff --git a/src/plugins/intel_gpu/include/intel_gpu/graph/network.hpp b/src/plugins/intel_gpu/include/intel_gpu/graph/network.hpp
index 1474543428a7b7..ab5d6b5e0af140 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/graph/network.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/graph/network.hpp
@@ -283,6 +283,8 @@ struct network {
     size_t _weights_cache_capacity = 1;
 
     std::unordered_map<primitive_id, event::ptr> _events;
+    // This map is used to temporarily hold events that will be deallocated later
+    std::unordered_map<primitive_id, event::ptr> _old_events;
     output_chains_map _output_chains;
 
     std::unique_ptr<ShapePredictor> _shape_predictor;
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
index 1ae7ef4f76618e..17e62ca926397b 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp
@@ -257,6 +257,7 @@ REGISTER_FACTORY(v11, Interpolate);
 REGISTER_FACTORY(v11, TopK);
 
 // ------------------------------ Supported v12 ops ----------------------------- //
+REGISTER_FACTORY(v12, GroupNormalization);
 REGISTER_FACTORY(v12, Pad);
 REGISTER_FACTORY(v12, ScatterElementsUpdate);
 
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_allocators.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_allocators.hpp
deleted file mode 100644
index 877c2c707f1791..00000000000000
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_allocators.hpp
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright (C) 2022 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include "openvino/runtime/so_ptr.hpp"
-
-#include <memory>
-
-namespace ov {
-namespace intel_gpu {
-
-class RemoteTensorImpl;
-class RemoteContextImpl;
-
-class USMHostAllocator final {
-private:
-    ov::SoPtr<RemoteTensorImpl> _usm_host_tensor = { nullptr, nullptr };
-    std::shared_ptr<RemoteContextImpl> _context = nullptr;
-
-public:
-    using Ptr = std::shared_ptr<USMHostAllocator>;
-
-    explicit USMHostAllocator(std::shared_ptr<RemoteContextImpl> context) : _context(context) { }
-
-    /**
-    * @brief Allocates memory
-    * @param size The size in bytes to allocate
-    * @return Handle to the allocated resource
-    */
-    void* allocate(const size_t bytes, const size_t alignment = alignof(max_align_t)) noexcept;
-    /**
-    * @brief Releases handle and all associated memory resources which invalidates the handle.
-    * @return false if handle cannot be released, otherwise - true.
-    */
-    bool deallocate(void* handle, const size_t bytes, size_t alignment = alignof(max_align_t)) noexcept;
-
-    bool is_equal(const USMHostAllocator& other) const;
-};
-
-}  // namespace intel_gpu
-}  // namespace ov
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_tensor.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_tensor.hpp
index 74a07bbcbf38bf..f7f72cc77a16a3 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_tensor.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/remote_tensor.hpp
@@ -15,6 +15,7 @@
 #endif
 #include "openvino/runtime/iremote_tensor.hpp"
 
+#include "intel_gpu/runtime/memory_caps.hpp"
 #include "intel_gpu/runtime/memory.hpp"
 #include "intel_gpu/runtime/engine.hpp"
 #include "intel_gpu/plugin/common_utils.hpp"
@@ -56,6 +57,8 @@ class RemoteTensorImpl : public ov::IRemoteTensor {
     cldnn::memory::ptr get_memory() const;
     cldnn::memory::ptr get_original_memory() const;
 
+    void set_memory(cldnn::memory::ptr memory, size_t actual_size);
+
     std::shared_ptr<RemoteContextImpl> get_context() const;
 
 private:
@@ -76,8 +79,11 @@ class RemoteTensorImpl : public ov::IRemoteTensor {
     size_t m_hash = 0;
 
     bool supports_caching() const;
+    void update_hash();
     void update_strides();
-    void init_properties();
+    void update_properties();
+
+    static TensorType allocation_type_to_tensor_type(cldnn::allocation_type t);
 };
 
 }  // namespace intel_gpu
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/sync_infer_request.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/sync_infer_request.hpp
index 1fd6d035dd48af..3050846e2c2354 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/sync_infer_request.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/sync_infer_request.hpp
@@ -90,9 +90,7 @@ class SyncInferRequest : public ov::ISyncInferRequest {
                                                 bool need_lockable_mem) const;
     std::shared_ptr<ov::ITensor> reinterpret_device_tensor(std::shared_ptr<RemoteTensorImpl> tensor, const ov::Shape new_shape) const;
     std::shared_ptr<ov::ITensor> create_host_tensor(const ov::PartialShape& port_shape, const ov::element::Type& port_element_type) const;
-    std::shared_ptr<ov::ITensor> create_device_tensor(const ov::Shape& pshape, ov::element::Type element_type,
-                                                      bool need_lockable_memory = false, void* mem_ptr = nullptr) const;
-    std::shared_ptr<ov::ITensor> create_shared_device_tensor(const ov::Shape& pshape, ov::element::Type element_type, void* usm_host_mem) const;
+    std::shared_ptr<ov::ITensor> create_device_tensor(const ov::PartialShape& pshape, ov::element::Type element_type, bool need_lockable_memory = false) const;
 
     void allocate_inputs();
     void allocate_outputs();
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/usm_host_tensor.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/usm_host_tensor.hpp
new file mode 100644
index 00000000000000..d410fa046651e5
--- /dev/null
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/usm_host_tensor.hpp
@@ -0,0 +1,42 @@
+// Copyright (C) 2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/runtime/itensor.hpp"
+
+#include <memory>
+
+namespace ov {
+namespace intel_gpu {
+
+class RemoteContextImpl;
+class RemoteTensorImpl;
+
+class USMHostTensor : public ov::ITensor {
+public:
+    USMHostTensor(std::shared_ptr<RemoteContextImpl> context, const element::Type element_type, const Shape& shape);
+    explicit USMHostTensor(std::shared_ptr<RemoteTensorImpl> tensor);
+
+    ~USMHostTensor() override = default;
+
+    void* data(const element::Type& element_type) const override;
+    const element::Type& get_element_type() const override;
+
+    const Shape& get_shape() const override;
+
+    const Strides& get_strides() const override;
+
+    void set_shape(ov::Shape new_shape) override;
+
+    void set_memory(std::shared_ptr<RemoteTensorImpl> tensor);
+
+    std::shared_ptr<RemoteTensorImpl> get_impl() const;
+
+private:
+    std::shared_ptr<RemoteTensorImpl> m_impl;
+};
+
+}  // namespace intel_gpu
+}  // namespace ov
diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/condition.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/condition.hpp
index 5ad37c7dfa55bf..4301f7cc10d31a 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/primitives/condition.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/condition.hpp
@@ -54,8 +54,8 @@ struct condition : public primitive_base<condition> {
             const std::vector<input_info>& inputs,
             const branch& branch_true,
             const branch& branch_false,
-            const padding& output_padding = padding())
-        : primitive_base(id, inputs, {output_padding}),
+            const size_t num_outputs = 1)
+        : primitive_base(id, inputs, {padding()}, {optional_data_type()}, num_outputs),
         branch_true(branch_true),
         branch_false(branch_false) {}
 
diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/gather.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/gather.hpp
index 2a7dad7fe4774d..cbc64a0e143ec2 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/primitives/gather.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/gather.hpp
@@ -21,6 +21,7 @@ struct gather : public primitive_base<gather> {
     /// @param dict Input dictionary primitive id.
     /// @param idx Input indexes primitive id.
     /// @param axis Gathering axis.
+    /// @param input_rank Input rank.
     /// @param output_shape Output shape.
     /// @param batch_dim Batch_dim
     /// @param support_neg_ind Support negative indexes
@@ -28,18 +29,22 @@ struct gather : public primitive_base<gather> {
            const input_info& dict,
            const input_info& idx,
            const int64_t axis,
+           const int64_t input_rank,
            const ov::Shape& output_shape,
            const int64_t batch_dim = 0,
            const bool support_neg_ind = false,
            const padding& output_padding = padding())
         : primitive_base(id, {dict, idx}, {output_padding})
         , axis(axis)
+        , input_rank(input_rank)
         , output_shape(output_shape)
         , batch_dim(batch_dim)
         , support_neg_ind(support_neg_ind) {}
 
     /// @brief Gathering axis
     int64_t axis = 0;
+    /// @brief Gather input rank
+    int64_t input_rank;
     /// @brief Gather output shape
     ov::Shape output_shape;
     /// @brief Gathering batch_dim
@@ -69,6 +74,7 @@ struct gather : public primitive_base<gather> {
     void save(BinaryOutputBuffer& ob) const override {
         primitive_base<gather>::save(ob);
         ob << axis;
+        ob << input_rank;
         ob << output_shape;
         ob << batch_dim;
         ob << support_neg_ind;
@@ -77,6 +83,7 @@ struct gather : public primitive_base<gather> {
     void load(BinaryInputBuffer& ib) override {
         primitive_base<gather>::load(ib);
         ib >> axis;
+        ib >> input_rank;
         ib >> output_shape;
         ib >> batch_dim;
         ib >> support_neg_ind;
diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/group_normalization.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/group_normalization.hpp
new file mode 100644
index 00000000000000..d8c7e385f4c9a7
--- /dev/null
+++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/group_normalization.hpp
@@ -0,0 +1,73 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "primitive.hpp"
+
+namespace cldnn {
+
+/// @brief Performs the following transformation of the input tensor:
+/// y = scale * (x - mean) / sqrt(variance + epsilon) + bias
+/// The operation is applied per batch, per group of channels.
+struct group_normalization : public primitive_base<group_normalization> {
+    CLDNN_DECLARE_PRIMITIVE(group_normalization)
+
+    group_normalization() : primitive_base("", {}) {}
+
+    /// @brief Constructs group_normalization primitive.
+    /// @param id This primitive id.
+    /// @param data The input tensor to be normalized.
+    /// @param scale Scale values tensor.
+    /// @param bias Bias values.
+    /// @param num_groups Number of groups the channel dimension will be divided into.
+    /// @param epsilon A value added to the variance which ensures that division by zero.
+    /// does not occur for any normalized element.
+    group_normalization(const primitive_id& id,
+                        const input_info& data,
+                        const input_info& scale,
+                        const input_info& bias,
+                        std::int64_t num_groups,
+                        double epsilon,
+                        const padding& output_padding = padding())
+    : primitive_base(id, {data, scale, bias}, {output_padding}), num_groups{num_groups}, epsilon{epsilon} {}
+
+    /// @brief Number of groups the channel dimension will be divided into
+    /// @details
+    /// Specifies the number of groups G that the channel dimension will be divided into.
+    std::int64_t num_groups{};
+
+    /// @brief A value added to the variance which ensures that division by zero.
+    /// @details
+    /// A very small value added to the variance for numerical stability.
+    /// Ensures that division by zero does not occur for any normalized element.
+    double epsilon{};
+
+    std::size_t hash() const override {
+        size_t seed = primitive::hash();
+        seed = hash_combine(seed, num_groups);
+        return hash_combine(seed, epsilon);
+    }
+
+    bool operator==(const primitive& rhs) const override {
+        if (!compare_common_params(rhs))
+            return false;
+
+        const auto& rhs_casted = downcast<const group_normalization>(rhs);
+
+        return num_groups == rhs_casted.num_groups && epsilon == rhs_casted.epsilon;
+    }
+
+    void save(BinaryOutputBuffer& ob) const override {
+        primitive_base<group_normalization>::save(ob);
+        ob << num_groups;
+        ob << epsilon;
+    }
+
+    void load(BinaryInputBuffer& ib) override {
+        primitive_base<group_normalization>::load(ib);
+        ib >> num_groups;
+        ib >> epsilon;
+    }
+};
+
+} // namespace cldnn
diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/loop.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/loop.hpp
index c27a88e1975f1c..282147cc9e9d3d 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/primitives/loop.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/loop.hpp
@@ -7,8 +7,8 @@
 #include <functional>
 #include "primitive.hpp"
 #include "intel_gpu/graph/topology.hpp"
+#include "intel_gpu/graph/program.hpp"
 
-#define DEFAULT_MAX_NUM_ITERATION 256
 namespace cldnn {
 
 ///
@@ -53,18 +53,35 @@ struct loop : public primitive_base<loop> {
     CLDNN_DECLARE_PRIMITIVE(loop)
 
     loop() : primitive_base("", {}),
-             max_iteration(0) {}
+             max_num_iterations(0) {}
 
     struct io_primitive_map {
         /// @brief Constructs a mapping from external input/output primitive to input/output primitive in body topology
-        ///
+        ///         or a mapping from output of body topology to input of body topology for the next iteration.
+        /// @param external_id Primitive id of input of loop or output of body network.
+        /// @param internal_id Primitive id of input of body network.
+        /// @param axis Axis to iterate through. Negative value means the axis will not iterate through and start, end, stride arguments will be ignored.
+        /// @param start Index where the iteration starts from. Applies only when axis >=0.
+        /// @param end Index where iteration ends. Negative value means counting indexes from the end. Applies only when axis >=0.
+        /// @param stride Step of iteration. Negative value means backward iteration. Applies only when axis >=0.
+        io_primitive_map(primitive_id external_id, primitive_id internal_id,
+            int64_t axis = -1, int64_t start = 0, int64_t end = -1, int64_t stride = 1) :
+            external_id(external_id, 0),
+            internal_id(internal_id, 0),
+            axis(axis),
+            start(start),
+            end(end),
+            stride(stride) {}
+
+        /// @brief Constructs a mapping from external input/output primitive to input/output primitive in body topology
+        ///         or a mapping from output of body topology to input of body topology for the next iteration.
         /// @param external_id Primitive id of input of loop or output of body network.
         /// @param internal_id Primitive id of input of body network.
         /// @param axis Axis to iterate through. Negative value means the axis will not iterate through and start, end, stride arguments will be ignored.
         /// @param start Index where the iteration starts from. Applies only when axis >=0.
         /// @param end Index where iteration ends. Negative value means counting indexes from the end. Applies only when axis >=0.
         /// @param stride Step of iteration. Negative value means backward iteration. Applies only when axis >=0.
-        io_primitive_map(primitive_id external_id = "", primitive_id internal_id = "",
+        io_primitive_map(input_info external_id = input_info(), input_info internal_id = input_info(),
             int64_t axis = -1, int64_t start = 0, int64_t end = -1, int64_t stride = 1) :
             external_id(std::move(external_id)),
             internal_id(std::move(internal_id)),
@@ -73,8 +90,8 @@ struct loop : public primitive_base<loop> {
             end(end),
             stride(stride) {}
 
-        primitive_id external_id;
-        primitive_id internal_id;
+        input_info external_id;
+        input_info internal_id;
         int64_t axis;
         int64_t start;
         int64_t end;
@@ -125,68 +142,69 @@ struct loop : public primitive_base<loop> {
     ///
     /// @param id This primitive id.
     /// @param inputs Input data primitive ids.
-    /// @param body Topology to be recurrently executed.
+    /// @param body_program body program to be recurrently executed.
     /// @param trip_count_id Data primitive id in external topology specifying maximum number of iterations.
     ///                      Its data primitive should have 1 integer element. Negative value means infinite
     ///                      number of iteration.
-    /// @param initial_condition_id Data primitive id in external topology specifying initial execution
+    /// @param first_execution_condition_id Data primitive id in external topology specifying initial execution
     ///                                       condition. Its data primitive should have 1 integer element. Zero means
     ///                                       loop will not be executed, otherwise loop will be executed.
     /// @param num_iteration_id mutable_data primitive id to get the actual number of loop iterations.
-    /// @param current_iteration_id Optional data primitive id in the body network to specify current iteration.
-    ///                             If current_iteration_id is specified but body does not have data whose primitive
-    ///                             id is same as current_iteration_id, data primitive will be added in the body network.
-    /// @param condition_id Optional data primitive id in the body network to specify execution condition
+    /// @param body_current_iteration_id Optional data primitive id in the body network to specify current iteration.
+    ///                             If body_current_iteration_id is specified but body does not have data whose primitive
+    ///                             id is same as body_current_iteration_id, data primitive will be added in the body network.
+    /// @param body_execution_condition_id Optional data primitive id in the body network to specify execution condition
     ///                               for the next iteration. Its data primitive should have 1 integer element. Zero means
-    ///                               loop will not be executed, otherwise loop will be executed.  If condition_id
-    ///                               is specified but body does not have data whose primitive id is same as condition_id,
+    ///                               loop will not be executed, otherwise loop will be executed.  If body_execution_condition_id
+    ///                               is specified but body does not have data whose primitive id is same as body_execution_condition_id,
     ///                               data primitive will be added in the body network.
     /// @param primitive_map Rules to map input of loop or output of body topology to input of the body topology
     /// @param back_edges Output data primitive id.
     /// @param output_padding     Optional padding for output from primitive.
     loop(const primitive_id& id,
          const std::vector<input_info>& inputs,
-         const topology& body,
+         const program::ptr body_program,
          const primitive_id& trip_count_id,
-         const primitive_id& initial_condition_id,
+         const primitive_id& first_execution_condition_id,
          const primitive_id& num_iteration_id,
          const std::vector<io_primitive_map>& input_primitive_maps,
          const std::vector<io_primitive_map>& output_primitive_maps,
          const std::vector<backedge_mapping>& back_edges,
-         int64_t max_iteration = -1,
-         const primitive_id& current_iteration_id = primitive_id(),
-         const primitive_id& condition_id = primitive_id(),
-         const padding& output_padding = padding())
-            : primitive_base(id, inputs, {output_padding}),
-              body(body),
+         int64_t max_num_iterations = -1,
+         const primitive_id& body_current_iteration_id = primitive_id(),
+         const primitive_id& body_execution_condition_id = primitive_id(),
+         const size_t num_outputs = 1)
+            : primitive_base(id, inputs, {padding()}, {optional_data_type()}, num_outputs),
+              body_program(std::move(body_program)),
               trip_count_id(trip_count_id),
-              initial_execution_id(initial_condition_id),
+              first_execution_condition_id(first_execution_condition_id),
               num_iteration_id(num_iteration_id),
-              current_iteration_id(current_iteration_id),
-              condition_id(condition_id),
+              body_current_iteration_id(body_current_iteration_id),
+              body_execution_condition_id(body_execution_condition_id),
               input_primitive_maps(input_primitive_maps),
               output_primitive_maps(output_primitive_maps),
               back_edges(back_edges),
-              max_iteration(max_iteration)
-              {}
+              max_num_iterations(max_num_iterations) {
+        OPENVINO_ASSERT(inputs.front().pid == num_iteration_id, "first input of inputs should be num_iteration_id");
+    }
 
-    /// @brief Topology to be recurrently executed.
-    topology body;
+    /// @brief Body program to be recurrently executed.
+    program::ptr body_program;
 
     /// @brief Data primitive id in external topology specifying maximum number of iterations.
     primitive_id trip_count_id;
 
     /// @brief Data primitive id in external topology specifying initial execution condition.
-    primitive_id initial_execution_id;
+    primitive_id first_execution_condition_id;
 
     /// @brief mutable_data primitive id to get the actual number of loop iterations.
     primitive_id num_iteration_id;
 
     /// @brief Data primitive id in the body network to store current iteration
-    primitive_id current_iteration_id;
+    primitive_id body_current_iteration_id;
 
     /// @brief Data primitive id in the body network to store execution condition
-    primitive_id condition_id;
+    primitive_id body_execution_condition_id;
 
     /// @brief Rules to map input or output data of loop layer onto input or output data of body topology.
     std::vector<io_primitive_map> input_primitive_maps;
@@ -195,7 +213,7 @@ struct loop : public primitive_base<loop> {
     /// @brief Rules to transfer data from body outputs at one iteration to body input at the next iteration.
     std::vector<backedge_mapping> back_edges;
 
-    int64_t max_iteration;
+    int32_t max_num_iterations;
 
     size_t hash() const override {
         size_t seed = primitive::hash();
@@ -206,42 +224,43 @@ struct loop : public primitive_base<loop> {
     void save(BinaryOutputBuffer& ob) const override {
         primitive_base<loop>::save(ob);
         ob << trip_count_id;
-        ob << initial_execution_id;
+        ob << first_execution_condition_id;
         ob << num_iteration_id;
-        ob << current_iteration_id;
-        ob << condition_id;
+        ob << body_current_iteration_id;
+        ob << body_execution_condition_id;
         ob << input_primitive_maps;
         ob << output_primitive_maps;
         ob << back_edges;
-        ob << max_iteration;
+        ob << max_num_iterations;
     }
 
     void load(BinaryInputBuffer& ib) override {
         primitive_base<loop>::load(ib);
         ib >> trip_count_id;
-        ib >> initial_execution_id;
+        ib >> first_execution_condition_id;
         ib >> num_iteration_id;
-        ib >> current_iteration_id;
-        ib >> condition_id;
+        ib >> body_current_iteration_id;
+        ib >> body_execution_condition_id;
         ib >> input_primitive_maps;
         ib >> output_primitive_maps;
         ib >> back_edges;
-        ib >> max_iteration;
+        ib >> max_num_iterations;
     }
 
 protected:
     std::vector<std::reference_wrapper<const primitive_id>> get_dependencies() const override {
-        std::vector<std::reference_wrapper<const primitive_id>> ret{
-            std::ref(trip_count_id), std::ref(initial_execution_id), std::ref(num_iteration_id)
-        };
+        std::vector<std::reference_wrapper<const primitive_id>> ret;
+        ret.push_back(std::ref(num_iteration_id));
+        if (!trip_count_id.empty()) ret.push_back(std::ref(trip_count_id));
+        if (!first_execution_condition_id.empty()) ret.push_back(std::ref(first_execution_condition_id));
+
         // add external_id in dependencies if not exist
         for (const auto& mapping : input_primitive_maps) {
             auto target = std::find_if(input.begin(), input.end(),
-                                       [&](const input_info& info) {
-                                           return info.pid == mapping.external_id;
-                                       });
+                                    [&](const input_info& info) {
+                                        return info.pid == mapping.external_id.pid;});
             if (target == input.end()) {
-                ret.push_back(std::ref(mapping.external_id));
+                ret.push_back(std::ref(mapping.external_id.pid));
             }
         }
         return ret;
diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/primitive.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/primitive.hpp
index bc55ed80e4f362..72c841a7578ab4 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/primitives/primitive.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/primitive.hpp
@@ -70,8 +70,19 @@ struct input_info {
         ib >> pid;
         ib >> idx;
     }
+
+    std::string to_string() const {
+        std::stringstream ss;
+        ss << "input_info(pid:" << pid << ",idx:" << idx << ")";
+        return ss.str();
+    }
 };
 
+static inline std::ostream& operator<< (std::ostream& os, input_info& info) {
+    os << info.to_string();
+    return os;
+}
+
 struct prim_map_storage {
     static prim_map_storage& instance() {
         static prim_map_storage instance;
diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/format.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/format.hpp
index 9e38a8b99c7b5e..ea3e2aec0274d0 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/runtime/format.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/format.hpp
@@ -139,6 +139,7 @@ struct format {
         oyix,
         oxiy,
         os_iyx_osv16,                                 ///< format used only for convolution weights
+        o_is_yx_isv2,                                 ///< format used only for convolution weights
         o_is_yx_isv4,                                 ///< format used only for convolution weights
         o_is_yx_isv16,                                ///< format used only for convolution weights
         o_is_zyx_isv16,                               ///< format used only for convolution weights
diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/layout.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/layout.hpp
index 3dad0cea4e008b..679f4c51ea6881 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/runtime/layout.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/layout.hpp
@@ -13,10 +13,10 @@
 #include <limits>
 #include <string>
 #include <functional>
-#include <set>
 
-#include <openvino/core/partial_shape.hpp>
-#include <openvino/core/type/element_type.hpp>
+#include "openvino/core/partial_shape.hpp"
+#include "openvino/core/type/element_type.hpp"
+#include "openvino/core/type/element_type_traits.hpp"
 
 #include "intel_gpu/graph/serialization/binary_buffer.hpp"
 #include "intel_gpu/graph/serialization/vector_serializer.hpp"
@@ -28,33 +28,9 @@ namespace cldnn {
 /// @addtogroup cpp_memory Memory description and management
 /// @{
 
-constexpr size_t float_type_mask = 0x80;
-constexpr size_t uint_type_mask = 0x40;
-constexpr size_t bin_type_mask = 0x20;
-
 /// @brief Possible data types could be stored in memory.
 using data_types = ov::element::Type_t;
 
-/// Converts @ref data_types to C++ type.
-template <data_types Data_Type>
-struct data_type_to_type;
-#ifndef DOXYGEN_SHOULD_SKIP_THIS
-template <>
-struct data_type_to_type<data_types::u1> { typedef uint32_t type; };
-template <>
-struct data_type_to_type<data_types::u8> { typedef uint8_t type; };
-template <>
-struct data_type_to_type<data_types::i8> { typedef int8_t type; };
-template <>
-struct data_type_to_type<data_types::i32> { typedef int32_t type; };
-template <>
-struct data_type_to_type<data_types::i64> { typedef int64_t type; };
-template <>
-struct data_type_to_type<data_types::f16> { typedef ov::float16 type; };
-template <>
-struct data_type_to_type<data_types::f32> { typedef float type; };
-#endif
-
 /// Helper class to identify key properties for data_types.
 struct data_type_traits {
     static size_t size_of(data_types data_type) {
@@ -72,52 +48,27 @@ struct data_type_traits {
         return et.is_quantized() && et.bitwidth() == 8;
     }
 
-    static size_t align_of(data_types data_type) {
-        switch (data_type) {
-            case data_types::u1:
-                return alignof(data_type_to_type<data_types::u1>::type);
-            case data_types::i8:
-                return alignof(data_type_to_type<data_types::i8>::type);
-            case data_types::u8:
-                return alignof(data_type_to_type<data_types::u8>::type);
-            case data_types::i32:
-                return alignof(data_type_to_type<data_types::i32>::type);
-            case data_types::i64:
-                return alignof(data_type_to_type<data_types::i64>::type);
-            case data_types::f16:
-                return alignof(data_type_to_type<data_types::f16>::type);
-            case data_types::f32:
-                return alignof(data_type_to_type<data_types::f32>::type);
-            default:
-                return size_t(1);
-        }
-    }
-
-    static std::string name(data_types data_type) {
-        return ov::element::Type(data_type).get_type_name();
-    }
+    static ov::element::Type max_type(ov::element::Type t1, ov::element::Type t2) {
+        if (t1 == ov::element::u1)
+            return t2;
 
-    static data_types max_type(data_types dt1, data_types dt2) {
-        if (dt1 == data_types::u1)
-            return dt2;
+        if (t2 == ov::element::u1)
+            return t1;
 
-        if (dt2 == data_types::u1)
-            return dt1;
+        if (t1.bitwidth() < t2.bitwidth())
+            return t2;
 
-        if (size_of(dt1) < size_of(dt2))
-            return dt2;
+        if (t1.bitwidth() > t2.bitwidth())
+            return t1;
 
-        if (size_of(dt1) > size_of(dt2))
-            return dt1;
+        if (t2.is_real())
+            return t2;
 
-        if (is_floating_point(dt2))
-            return dt2;
-
-        return dt1;
+        return t1;
     }
 
-    static bool is_quantized(data_types dt) {
-        return is_i8_u8(dt);
+    static bool is_quantized(ov::element::Type t) {
+        return t.is_quantized();
     }
 
     template <typename T>
@@ -132,7 +83,7 @@ struct data_type_traits {
             case data_types::i64:
                 return static_cast<T>(std::numeric_limits<int64_t>::max());
             case data_types::f16:
-                return static_cast<T>(65504);
+                return static_cast<T>(std::numeric_limits<ov::float16>::max());
             case data_types::f32:
                 return static_cast<T>(std::numeric_limits<float>::max());
             default:
@@ -152,7 +103,7 @@ struct data_type_traits {
             case data_types::i64:
                 return static_cast<T>(std::numeric_limits<int64_t>::lowest());
             case data_types::f16:
-                return static_cast<T>(-65504);
+                return static_cast<T>(std::numeric_limits<ov::float16>::lowest());
             case data_types::f32:
                 return static_cast<T>(std::numeric_limits<float>::lowest());
             default:
@@ -170,44 +121,17 @@ inline data_types element_type_to_data_type(ov::element::Type t) {
     switch (t) {
     case ov::element::Type_t::i16:
     case ov::element::Type_t::u16:
-    case ov::element::Type_t::f32:
     case ov::element::Type_t::f64:
         return cldnn::data_types::f32;
-    case ov::element::Type_t::f16:
-        return cldnn::data_types::f16;
-    case ov::element::Type_t::u8:
-        return cldnn::data_types::u8;
-    case ov::element::Type_t::i8:
-        return cldnn::data_types::i8;
-    case ov::element::Type_t::i32:
     case ov::element::Type_t::u32:
     case ov::element::Type_t::u64:
         return cldnn::data_types::i32;
-    case ov::element::Type_t::i64:
-        return cldnn::data_types::i64;
     case ov::element::Type_t::boolean:
         return cldnn::data_types::u8;
-    case ov::element::Type_t::u1:
-        return cldnn::data_types::u1;
-    default:
-        throw std::runtime_error("Can't convert " + t.get_type_name() + " element type");
+    default: return t;
     }
 }
 
-/// Helper function to get both data_types and format::type in a single, unique value. Useable in 'case' statement.
-constexpr auto fuse(data_types dt, cldnn::format::type fmt) -> decltype(static_cast<std::underlying_type<data_types>::type>(dt) |
-                                                                        static_cast<std::underlying_type<format::type>::type>(fmt)) {
-    using dt_type = std::underlying_type<data_types>::type;
-    using fmt_type = std::underlying_type<cldnn::format::type>::type;
-    using fmt_narrow_type = int16_t;
-
-    return static_cast<fmt_type>(fmt) <= std::numeric_limits<fmt_narrow_type>::max() &&
-                   static_cast<dt_type>(dt) <= (std::numeric_limits<dt_type>::max() >> (sizeof(fmt_narrow_type) * 8))
-               ? (static_cast<dt_type>(dt) << (sizeof(fmt_narrow_type) * 8)) |
-                     (static_cast<fmt_type>(fmt) >= 0 ? static_cast<fmt_narrow_type>(fmt) : static_cast<fmt_narrow_type>(-1))
-               : throw std::invalid_argument("data_type and/or format values are too big to be fused into single value");
-}
-
 /// @brief Represents data padding information.
 struct padding {
     /// @brief Filling value for padding area.
diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/shape_predictor.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/shape_predictor.hpp
index 2f2e614c29f2c6..51f09989502a13 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/runtime/shape_predictor.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/shape_predictor.hpp
@@ -50,7 +50,7 @@ struct ShapePredictor {
 ///         says if shape is successfully predicted and can be preallocated, and the second element is ov::Shape itself.
     std::pair<bool, ov::Shape> predict_preallocation_shape(const std::string& id,
                                                            const ov::Shape& current_shape,
-                                                           size_t dt_size,
+                                                           size_t dt_bitwidth,
                                                            bool can_reuse_buffer);
     bool can_preallocate(size_t desired_buffer_size);
 
diff --git a/src/plugins/intel_gpu/src/graph/condition.cpp b/src/plugins/intel_gpu/src/graph/condition.cpp
index 842495e0b24e0d..8da80347ea66fd 100644
--- a/src/plugins/intel_gpu/src/graph/condition.cpp
+++ b/src/plugins/intel_gpu/src/graph/condition.cpp
@@ -215,7 +215,7 @@ std::string condition_inst::to_string(condition_node const& node) {
 }
 
 /*
-Condition primitive is resuing memory with the input.
+Condition primitive is reusing memory with the input.
 */
 condition_inst::typed_primitive_inst(network& network, condition_node const& node)
     : parent(network, node),
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp
index 537fa7412b09f5..9c3eb7c813045d 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp
@@ -14,6 +14,7 @@
 #include "arg_max_min_inst.h"
 #include "fully_connected_inst.h"
 #include "condition_inst.h"
+#include "loop_inst.h"
 #include "program_node.h"
 
 #include <iostream>
@@ -74,7 +75,7 @@ void compile_graph::run(program& p) {
         if (node->is_dynamic() && !is_planar)
             can_select_impl = false;
 
-        if (node->is_type<condition>() || node->is_type<proposal>())
+        if (node->is_type<condition>() || node->is_type<loop>() || node->is_type<proposal>())
             can_select_impl = true;
 
         if (can_select_impl) {
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/concat_input_order.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/concat_input_order.cpp
index 1f2016e8d6706e..de6d6c62859bd9 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/concat_input_order.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/concat_input_order.cpp
@@ -23,6 +23,8 @@ bool can_shuffle_features(program_node& node, stream& stream) {
     if (node.is_type<convolution>()) {
         auto& conv_node = node.as<convolution>();
         auto& wei_node = conv_node.weights();
+        if (ov::element::Type(wei_node.get_output_layout().data_type).bitwidth() < 8)
+            return false;
 
         return conv_node.get_groups() == 1 &&
             conv_node.get_deformable_groups() == 1 && !conv_node.get_transposed() &&
@@ -32,6 +34,8 @@ bool can_shuffle_features(program_node& node, stream& stream) {
     if (node.is_type<fully_connected>()) {
         auto& fc_node = node.as<fully_connected>();
         auto& wei_node = fc_node.weights();
+        if (ov::element::Type(wei_node.get_output_layout().data_type).bitwidth() < 8)
+            return false;
 
         return wei_node.is_type<data>() && wei_node.is_constant() && !wei_node.is_output();
     }
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/graph_initializations.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/graph_initializations.cpp
index 8a031e45582314..cf9f44a9a59686 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/graph_initializations.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/graph_initializations.cpp
@@ -402,6 +402,7 @@ void graph_initializations::set_outputs(program& p) {
     auto custom_outputs = p.get_config().get_property(ov::intel_gpu::custom_outputs);
     if (!custom_outputs.empty()) {
         for (auto const& output : custom_outputs) {
+            OPENVINO_ASSERT(p.has_node(output), "not found custom output node in current cldnn::program: ", output);
             auto o_node = p.get_node_ptr(output);
             o_node->set_output(true);
             p.outputs.push_back(o_node.get());
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp
index d1b125aa8f1df5..f55d99b6a5fa80 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp
@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "fully_connected_inst.h"
 #include "pooling_inst.h"
 #include "quantize_inst.h"
 #include "reorder_inst.h"
@@ -847,6 +848,42 @@ bool prepare_quantization::optimize_quantize(program &p, quantize_node& quantize
     return true;
 }
 
+static void optimize_weights_decompression_parameters(fully_connected_node& fc_node, program& p) {
+    auto fc_prim = fc_node.get_primitive();
+    if (!fc_prim->compressed_weights)
+        return;
+
+    auto reorder_bfyx_to_fbyx = [&](size_t dep_id) {
+        auto& dep = fc_node.get_dependency(dep_id);
+        auto target_layout = dep.get_output_layout();
+        target_layout.format = format::fbyx;
+        auto reorder_prim = std::make_shared<reorder>(dep.id() + "_reorder", dep.id(), target_layout);
+        p.add_intermediate(reorder_prim, fc_node, dep_id, true);
+        fc_node.get_dependency(dep_id).recalc_output_layout(false);
+    };
+
+    auto need_reorder = [&](size_t dep_id) {
+        auto dep_layout = fc_node.get_input_layout(dep_id);
+        auto dep_pshape = dep_layout.get_partial_shape();
+
+        auto groups_count = dep_pshape[dep_pshape.size() - 1].get_length();
+
+        return groups_count > 1;
+    };
+
+    auto decompression_scale_idx = !fc_node.bias_term() ? 2 : 3;
+    if (need_reorder(decompression_scale_idx)) {
+        reorder_bfyx_to_fbyx(decompression_scale_idx);
+    }
+
+    if (!fc_prim->decompression_zero_point.empty()) {
+        auto decompression_zp_idx = decompression_scale_idx + 1;
+        if (need_reorder(decompression_zp_idx)) {
+            reorder_bfyx_to_fbyx(decompression_zp_idx);
+        }
+    }
+}
+
 void prepare_quantization::run(program& p) {
     auto itr = p.get_processing_order().begin();
     while (itr != p.get_processing_order().end()) {
@@ -859,6 +896,8 @@ void prepare_quantization::run(program& p) {
             remove_fake_reorders(p, node->as<reorder>());
         } else if (node->is_type<convolution>()) {
             prepare_asymmetric_quantization(p, node->as<convolution>());
+        } else if (node->is_type<fully_connected>()) {
+            optimize_weights_decompression_parameters(node->as<fully_connected>(), p);
         }
     }
 }
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/reorder_inputs.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/reorder_inputs.cpp
index 094e645a69e05f..769134e440b848 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/reorder_inputs.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/reorder_inputs.cpp
@@ -121,15 +121,10 @@ struct travel_direction_wrapper<direction_e::backwards> {
 static format get_target_output_format(layout_optimizer& lo, const std::map<program_node*, format::type>& fmt_map, program_node *node, program_node *next) {
     auto user_idx = node->get_user_index(*next);
 
-    bool allow_new_shape_infer = node->get_program().get_config().get_property(ov::intel_gpu::allow_new_shape_infer);
     // 1. Check selected preferred_output_format
-    if (lo.get_optimization_attributes().use_onednn_impls || allow_new_shape_infer) {
-        // If onednn is not used, need to ignore get_preferred_output_fmt result as it is from onednn
-        auto ret = node->get_preferred_output_fmt(user_idx);
-
-        if (ret != format::any)
-            return ret;
-    }
+    auto ret = node->get_preferred_output_fmt(user_idx);
+    if (ret != format::any)
+        return ret;
 
     // 2. Check fmt
     if (fmt_map.count(node) > 0)
@@ -142,14 +137,10 @@ static format get_target_output_format(layout_optimizer& lo, const std::map<prog
 static format get_target_input_format(layout_optimizer& lo, const std::map<program_node*, format::type>& fmt_map, program_node *node, program_node *prev) {
     auto dep_idx = node->get_dependency_index(*prev);
 
-    bool allow_new_shape_infer = node->get_program().get_config().get_property(ov::intel_gpu::allow_new_shape_infer);
     // 1. Check selected preferred_input_format
-    if (lo.get_optimization_attributes().use_onednn_impls || allow_new_shape_infer) {
-        // If onednn is not used, need to ignore get_preferred_input_fmt result as it is from onednn
-        auto ret = node->get_preferred_input_fmt(dep_idx);
-        if (ret != format::any)
-            return ret;
-    }
+    auto ret = node->get_preferred_input_fmt(dep_idx);
+    if (ret != format::any)
+        return ret;
 
     // 2. Check fmt
     if (fmt_map.count(node) > 0)
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/select_preferred_formats.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/select_preferred_formats.cpp
index 05dacd336a43f5..8b2b3a118a501f 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/select_preferred_formats.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/select_preferred_formats.cpp
@@ -32,11 +32,21 @@ void select_preferred_formats::run(program& p) {
         return;
 
 #ifdef ENABLE_ONEDNN_FOR_GPU
+    auto forcing_map = _lo.get_implementation_forcing();
+
     engine.create_onednn_engine(p.get_config());
     for (auto n : p.get_processing_order()) {
-        if (n->is_input() || !_lo.are_data_types_suitable_for_onednn(*n)) {
+        if (n->is_input() || !layout_optimizer::is_node_suitable_for_onednn(*n)) {
             continue;
         }
+
+        // skip to set preferred_formats if forcing_impl is not onednn.
+        if (std::find_if(forcing_map.begin(), forcing_map.end(),
+                [&n](std::map<primitive_id, std::pair<format::type, impl_types>>::value_type const& it) {
+                    return (it.first == n->id() && it.second.second != impl_types::onednn);
+                }) != forcing_map.end())
+            continue;
+
         // Onednn primitive descriptor creation may fail, for example, due to asymmetric weight.
         try {
             if (n->is_type<convolution>()) {
diff --git a/src/plugins/intel_gpu/src/graph/group_normalization.cpp b/src/plugins/intel_gpu/src/graph/group_normalization.cpp
new file mode 100644
index 00000000000000..69b06343362570
--- /dev/null
+++ b/src/plugins/intel_gpu/src/graph/group_normalization.cpp
@@ -0,0 +1,42 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "group_normalization_inst.h"
+#include "primitive_type_base.h"
+#include "json_object.h"
+
+namespace cldnn {
+GPU_DEFINE_PRIMITIVE_TYPE_ID(group_normalization)
+
+layout group_normalization_inst::calc_output_layout(group_normalization_node const& node, kernel_impl_params const& impl_param) {
+    assert(static_cast<bool>(impl_param.desc->output_data_types[0]) == false &&
+        "Output data type forcing is not supported for group_normalization_node!");
+    auto output_layout = impl_param.get_input_layout();
+
+    if (impl_param.has_fused_primitives())
+        output_layout.data_type = impl_param.get_fused_output_layout().data_type;
+
+    return output_layout;
+}
+
+std::string group_normalization_inst::to_string(group_normalization_node const& node) {
+    auto desc = node.get_primitive();
+    auto node_info = node.desc_to_json();
+
+    std::stringstream primitive_description;
+
+    json_composite group_normalization_info;
+    group_normalization_info.add("dimension", desc->num_groups);
+    group_normalization_info.add("epsilon", desc->epsilon);
+
+    node_info->add("group_normalization_info", group_normalization_info);
+    node_info->dump(primitive_description);
+
+    return primitive_description.str();
+}
+
+group_normalization_inst::typed_primitive_inst(network& network, group_normalization_node const& node) : parent(network, node) {
+}
+
+} // namespace cldnn
diff --git a/src/plugins/intel_gpu/src/graph/impls/common/loop.cpp b/src/plugins/intel_gpu/src/graph/impls/common/loop.cpp
index 81f244051a25aa..b774b72dd506ec 100644
--- a/src/plugins/intel_gpu/src/graph/impls/common/loop.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/common/loop.cpp
@@ -12,6 +12,77 @@
 
 namespace cldnn {
 namespace common {
+
+// read scala value from data primitive
+static int64_t read_scalar_value(memory::ptr mem, stream& stream) {
+    int64_t trip_count = 0;
+    const layout& prim_layout = mem->get_layout();
+
+    switch (prim_layout.data_type) {
+    case data_types::u8: {
+        mem_lock<uint8_t> lock_prim_output{mem, stream};
+        trip_count = *lock_prim_output.data();
+        break;
+    }
+    case data_types::i8: {
+        mem_lock<int8_t> lock_prim_output{mem, stream};
+        trip_count = *lock_prim_output.data();
+        break;
+    }
+    case data_types::i32: {
+        mem_lock<int32_t> lock_prim_output{mem, stream};
+        trip_count = *lock_prim_output.data();
+        break;
+    }
+    case data_types::i64: {
+        mem_lock<int64_t> lock_prim_output{mem, stream};
+        trip_count = *lock_prim_output.data();
+        break;
+    }
+    default:
+        OPENVINO_THROW("Invalid data type : ",  ov::element::Type(prim_layout.data_type).get_type_name());
+    }
+    return trip_count;
+}
+
+template<typename T>
+static inline void validate_input_value(int64_t input) {
+    OPENVINO_ASSERT((input >= std::numeric_limits<T>::min() && input <= std::numeric_limits<T>::max()),
+                "Invalid data value : ", input);
+}
+
+static void write_scalar_value(memory::ptr mem, stream& stream, int64_t input) {
+    const layout& prim_layout = mem->get_layout();
+
+    switch (prim_layout.data_type) {
+    case data_types::u8: {
+        validate_input_value<uint8_t>(input);
+        mem_lock<uint8_t> lock_prim_output{mem, stream};
+        lock_prim_output[0] = static_cast<uint8_t>(input);
+        break;
+    }
+    case data_types::i8: {
+        validate_input_value<int8_t>(input);
+        mem_lock<int8_t> lock_prim_output{mem, stream};
+        lock_prim_output[0] = static_cast<int8_t>(input);
+        break;
+    }
+    case data_types::i32: {
+        validate_input_value<int32_t>(input);
+        mem_lock<int32_t> lock_prim_output{mem, stream};
+        lock_prim_output[0] = static_cast<int32_t>(input);
+        break;
+    }
+    case data_types::i64: {
+        mem_lock<int64_t> lock_prim_output{mem, stream};
+        lock_prim_output[0] = input;
+        break;
+    }
+    default:
+        OPENVINO_THROW("Invalid data type : ",  ov::element::Type(prim_layout.data_type).get_type_name());
+    }
+}
+
 struct loop_impl : typed_primitive_impl<loop> {
     using parent = typed_primitive_impl<loop>;
     using parent::parent;
@@ -27,7 +98,6 @@ struct loop_impl : typed_primitive_impl<loop> {
     loop_impl() : parent() {}
 
     loop_impl(const loop_impl& other) : typed_primitive_impl<loop>(other),
-        _max_iteration(other._max_iteration),
         _back_edges(other._back_edges) {}
 
     explicit loop_impl(const loop_node& node) {
@@ -37,63 +107,169 @@ struct loop_impl : typed_primitive_impl<loop> {
     void set_node_params(const program_node& arg) override {
         OPENVINO_ASSERT(arg.is_type<loop>());
         const auto& node = arg.as<loop>();
-        _max_iteration = node.get_max_iteration();
         _back_edges = node.get_back_edges();
     }
 
+    void set_memory_in_body_network(cldnn::network::ptr body_network,
+                    const std::shared_ptr<cldnn::primitive_inst>& inst, memory::ptr mem) const {
+        if (inst->is_input()) {
+            body_network->set_input_data(inst->id(), mem);
+        } else if (inst->is_output()) {
+            body_network->set_output_memory(inst->id(), mem);
+        } else {
+            inst->set_output_memory(mem, false);
+        }
+    }
+
+    std::vector<event::ptr> handle_buffers_for_next_iteration(const loop_inst::backedge_memory_mapping& mapping,
+                                                    network::ptr body_network, int64_t iter, bool is_dynamic) const {
+        std::vector<event::ptr> event_vec;
+        OPENVINO_ASSERT(iter >= 0, "iteration should not be negative : ", iter);
+        if (mapping.type == loop_inst::backedge_memory_mapping::CONCAT_OUTPUT) {
+            if (iter == 0) {
+                set_memory_in_body_network(body_network, mapping.to_primitive, mapping.initial_mem);
+            } else if (iter > 0) {
+                if (is_dynamic) {
+                    auto from_id = mapping.from_primitive->id();
+                    if (body_network->has_event(from_id)) {
+                        auto ev = body_network->get_primitive_event(from_id);
+                        if (ev) ev->wait();
+                    }
+                    // In dynamic model, just copy data from inner body output to inner body input in back_edges.
+                    memory::ptr mem1 = mapping.to_primitive->output_memory_ptr();
+                    memory::ptr mem2 = mapping.from_primitive->output_memory_ptr();
+                    auto ev = mem1->copy_from(body_network->get_stream(), *(mem2));
+                    if (ev) event_vec = {ev};
+                } else {
+                    auto mem = mapping.concat_mem_mapping->get_sliced_mems().at(iter - 1);
+                    set_memory_in_body_network(body_network, mapping.to_primitive, mem);
+                }
+            }
+        } else if (mapping.type ==  loop_inst::backedge_memory_mapping::SINGLE_SHARED) {
+            if (iter == 0) {
+                if (mapping.from_mem != nullptr) {
+                    auto ev = mapping.from_mem->copy_from(body_network->get_stream(), *(mapping.initial_mem));
+                    if (ev) event_vec = {ev};
+                }
+            } else {
+                // In dynamic model, output memory is not defined before execution.
+                // After body network execution, replace input memory from initial_mem(external input memory) to output memory.
+                if (mapping.from_mem == nullptr) {
+                    mapping.from_mem = mapping.from_primitive->output_memory_ptr();
+                    OPENVINO_ASSERT(mapping.from_mem != nullptr, "from_mem should not be null");
+                    set_memory_in_body_network(body_network, mapping.to_primitive, mapping.from_mem);
+                }
+            }
+        } else if (mapping.type ==  loop_inst::backedge_memory_mapping::SINGLE) {
+            memory::ptr mem1 = mapping.to_primitive->output_memory_ptr();
+            if (iter == 0) {
+                auto ev = mem1->copy_from(body_network->get_stream(), *(mapping.initial_mem));
+                if (ev) event_vec = {ev};
+            } else {
+                if (is_dynamic) {
+                    // In dynamic model, do not set memory buffer between input and output in inner body network.
+                    // Just copy data from input buffer memory to output buffer memory.
+                    auto from_id = mapping.from_primitive->id();
+                    if (body_network->has_event(from_id)) {
+                        auto ev = body_network->get_primitive_event(from_id);
+                        if (ev) ev->wait();
+                    }
+                    memory::ptr mem2 = mapping.from_primitive->output_memory_ptr();
+                    auto ev = mem1->copy_from(body_network->get_stream(), *(mem2));
+                    if (ev) event_vec = {ev};
+                } else {
+                    // In static model, swap memory buffer between output and input in inner body network
+                    memory::ptr mem2 = mapping.from_primitive->output_memory_ptr();
+                    set_memory_in_body_network(body_network, mapping.to_primitive, std::move(mem2));
+                    set_memory_in_body_network(body_network, mapping.from_primitive, std::move(mem1));
+                }
+            }
+        }
+        return event_vec;
+    }
+
     event::ptr execute_impl(const std::vector<event::ptr>& events, loop_inst& instance) override {
-        const auto& primitive = instance.get_typed_desc<loop>();
+        const auto& impl_params = instance.get_impl_params();
+        const auto& primitive = impl_params->typed_desc<loop>();
         auto& outer_network = instance.get_network();
         auto& stream = outer_network.get_stream();
 
+        const auto max_num_iterations = primitive->max_num_iterations;
         auto body_network = instance.get_body_network();
+        int64_t current_iteration_idx = 0;
 
         auto ev = stream.create_user_event(false);
 
-        if (!instance.preproc_memories_done) {
-            instance.preprocess_output_memory();
-            instance.preprocess_input_memory();
-            instance.preprocess_backedge_memory();
-
-            // set input data for current_iteration primitive if current_iteration is used
-            if (!primitive->current_iteration_id.empty()) {
-                auto current_iteration_prim = body_network->get_primitive(primitive->current_iteration_id);
-                auto input_layout_prim = std::dynamic_pointer_cast<input_layout_inst>(current_iteration_prim);
-                if (input_layout_prim == nullptr) {
-                    CLDNN_ERROR_MESSAGE(instance.id(), "current_iteration primitive is not input_layout");
-                } else {
-                    const auto& backedge_mapping = instance.get_current_iteration_backedge_mapping();
-                    input_layout_prim->set_data(backedge_mapping.initial_mem);
-                }
-            }
-            instance.preproc_memories_done = true;
-        }
+        OPENVINO_ASSERT(!primitive->num_iteration_id.empty(), "loop operation should have num_iteration_id");
 
+        //////////////////////////////////////////
+        // memory pointers for outer network
+        //////////////////////////////////////////
         // read trip_count from outer network
-        bool update_num_iterations = false;
-        memory::ptr trip_count_mem = outer_network.get_primitive(primitive->trip_count_id)->output_memory_ptr();
-        int64_t trip_count = loop_node::read_scalar_value(std::move(trip_count_mem), stream);
-        if (trip_count < 0) {
-            trip_count = _max_iteration;
-            update_num_iterations = true;
+        int64_t trip_count = -1;
+        if (!primitive->trip_count_id.empty()) {
+            memory::ptr trip_count_mem = outer_network.get_primitive(primitive->trip_count_id)->output_memory_ptr();
+            trip_count = read_scalar_value(std::move(trip_count_mem), stream);
+        } else {
+            trip_count = max_num_iterations;
         }
 
         // read initial execution condition from outer network
-        memory::ptr initial_execution_mem = outer_network.get_primitive(primitive->initial_execution_id)->output_memory_ptr();
-        int64_t execution_condition = loop_node::read_scalar_value(initial_execution_mem, stream);
+        int64_t execution_condition = 1;
+        if (!primitive->first_execution_condition_id.empty()) {
+            memory::ptr first_execution_condition_mem = outer_network.get_primitive(primitive->first_execution_condition_id)->output_memory_ptr();
+            execution_condition = read_scalar_value(first_execution_condition_mem, stream);
+        }
+
+        // When execution_condition is false or trip_count is zero, return execute_impl without any body_network execution.
+        if (!execution_condition || trip_count == 0) {
+            // Update num_iterations (actual number of iterations)
+            memory::ptr num_actual_iterations_mem = outer_network.get_primitive(primitive->num_iteration_id)->output_memory_ptr();
+            write_scalar_value(num_actual_iterations_mem, stream, current_iteration_idx);
+
+            instance.update_output_layout();
+            ev->set();
+            return ev;
+        }
 
+        //////////////////////////////////////////
+        // memory pointers for body network
+        //////////////////////////////////////////
         // shortcut of execution_condition memory in body network
-        memory::ptr execution_condition_mem = nullptr;
-        if (!primitive->condition_id.empty()) {
-            execution_condition_mem = body_network->get_primitive(primitive->condition_id)->output_memory_ptr();
+        memory::ptr body_execution_condition_mem = nullptr;
+        if (!primitive->body_execution_condition_id.empty()) {
+            body_execution_condition_mem = body_network->get_primitive(primitive->body_execution_condition_id)->output_memory_ptr();
+        }
+
+        // shortcut of current_iteration memory in body network
+        if (!primitive->body_current_iteration_id.empty()) {
+            memory::ptr body_current_iteration_mem = body_network->get_primitive(primitive->body_current_iteration_id)->output_memory_ptr();
+            write_scalar_value(body_current_iteration_mem, body_network->get_stream(), 0);
+        }
+
+        const auto is_dynamic = instance.is_dynamic();
+        if (is_dynamic) {
+            instance.update_shape();
+            if (instance.shape_changed()) {
+                instance.preproc_memories_done = false;
+                instance.reset_memory();
+            }
+        }
+
+        if (!instance.preproc_memories_done) {
+            instance.preprocess_output_memory(trip_count);
+            instance.preprocess_input_memory(trip_count);
+            instance.preprocess_backedge_memory();
+            instance.preproc_memories_done = true;
         }
 
         const auto& concatenated_input_mem_mappings = instance.concatenated_input_mem_mappings;
         const auto& concatenated_output_mem_mappings = instance.concatenated_output_mem_mappings;
+        const auto& backedge_memory_mappings = instance.backedge_memory_mappings;
 
         // If there are concatenated_output_mem_mappings or backedge_memory_mappings we need to wait for
         // previous tasks before accessing memory in get_sliced_mem() and setup_iteration() functions
-        if (!concatenated_input_mem_mappings.empty() || !instance.backedge_memory_mappings.empty()) {
+        if (!concatenated_input_mem_mappings.empty() || !backedge_memory_mappings.empty()) {
             for (auto& e : events) {
                 e->wait();
             }
@@ -102,37 +278,36 @@ struct loop_impl : typed_primitive_impl<loop> {
         // Set sliced input data
         for (size_t i = 0; i < concatenated_input_mem_mappings.size(); ++i) {
             const auto& concatenated_input = concatenated_input_mem_mappings.at(i);
-            memory::ptr mem = concatenated_input.get_sliced_mem(0);
-            if (mem) {
-                body_network->set_input_data(concatenated_input.sliced_data_prim->id(), mem);
-            } else {
-                CLDNN_ERROR_MESSAGE(instance.id(), "sliced input memory of loop is not allocated properly");
-            }
+            memory::ptr mem = concatenated_input->get_sliced_mem(0);
+            OPENVINO_ASSERT(mem != nullptr, instance.id(), "sliced input memory of loop is not allocated properly");
+            body_network->set_input_data(concatenated_input->sliced_data_prim->id(), mem);
         }
 
         std::vector<event::ptr> all_events;
         std::vector<event::ptr> loop_carried_dep(events.begin(), events.end());
-        int64_t current_iteration_idx = 0;
-        while (current_iteration_idx < trip_count && execution_condition) {
+        while (((trip_count <= 0) || (current_iteration_idx < trip_count)) && execution_condition) {
             // Copy & Set sliced input memory
             for (size_t i = 0; i < concatenated_input_mem_mappings.size(); ++i) {
                 const auto& concatenated_input = concatenated_input_mem_mappings.at(i);
-                memory::ptr mem = concatenated_input.get_sliced_mem(current_iteration_idx);
-                if (mem) {
-                    concatenated_input.sliced_data_prim->set_output_memory(mem);
-                } else {
-                    CLDNN_ERROR_MESSAGE(instance.id(), "sliced input memory of loop is not allocated properly");
-                }
+                memory::ptr mem = concatenated_input->get_sliced_mem(current_iteration_idx);
+                OPENVINO_ASSERT(mem != nullptr, instance.id(), " sliced input memory of loop is not allocated properly");
+                concatenated_input->sliced_data_prim->set_output_memory(mem);
             }
 
-            // Set backedges
-            for (const auto& backedge_memory_mapping : instance.backedge_memory_mappings) {
-                backedge_memory_mapping.setup_iteration(current_iteration_idx);
+            // Set backedges and output memory
+            for (auto& backedge_memory_mapping : backedge_memory_mappings) {
+                auto event_vec = handle_buffers_for_next_iteration(backedge_memory_mapping, body_network, current_iteration_idx, is_dynamic);
+                for (auto ev : event_vec) {
+                    loop_carried_dep.push_back(ev);
+                }
             }
 
-            // Set sliced output memory
-            for (const auto& concat_output_mem_mapping : concatenated_output_mem_mappings) {
-                concat_output_mem_mapping.setup_sliced_output_memory(current_iteration_idx);
+            if (!is_dynamic) {
+                // Set sliced output memory for static shape model
+                // because body network generate output memory during the body network execution in dynamic model
+                for (const auto& concat_output_mem_mapping : concatenated_output_mem_mappings) {
+                    concat_output_mem_mapping->setup_sliced_output_memory(current_iteration_idx);
+                }
             }
 
             // execute body network
@@ -141,9 +316,10 @@ struct loop_impl : typed_primitive_impl<loop> {
             loop_carried_dep.clear();
             for (const auto& backedge : _back_edges) {
                 event::ptr body_event;
-                if (body_network->has_event(backedge.from))
+                if (body_network->has_event(backedge.from)) {
                     body_event = body_network->get_primitive_event(backedge.from);
-                loop_carried_dep.emplace_back(body_event);
+                    loop_carried_dep.emplace_back(body_event);
+                }
             }
 
             // Collect output events for waiting for all iterations finishing
@@ -155,42 +331,59 @@ struct loop_impl : typed_primitive_impl<loop> {
                 }
             }
 
-            //TODO: execution_condition is prepared as they are presented in the
-            //      ngraph opset document for loop operation.
-            // However they are not being used yet and only TensorIterator which
-            // has fixed sequence length is being validated.
-            if (!primitive->condition_id.empty()) {
-                execution_condition = loop_node::read_scalar_value(execution_condition_mem, stream);
+            // Store output of sliced_data_prim to sliced mems vector
+            // After execution of body network, sliced_data_prim will has output memory buffer
+            // current memory buffer move to sliced_mems and new memory buffer will be allocated in sliced_data_prim
+            if (is_dynamic) {
+                for (const auto& concat_output_mem_mapping : concatenated_output_mem_mappings) {
+                    auto sliced_data_prim = concat_output_mem_mapping->sliced_data_prim;
+                    auto output_mem_ptr = sliced_data_prim->output_memory_ptr();
+
+                    auto sliced_id = sliced_data_prim->id();
+                    if (body_network->has_event(sliced_id)) {
+                        auto ev = body_network->get_primitive_event(sliced_id);
+                        if (ev) ev->wait();
+                    }
+                    memory::ptr new_sliced_mem = concat_output_mem_mapping->get_or_create_sliced_mem(current_iteration_idx,
+                                                                                                output_mem_ptr->get_layout());
+                    auto ev = new_sliced_mem->copy_from(body_network->get_stream(), *output_mem_ptr);
+                    if (ev) {
+                        loop_carried_dep.push_back(ev);
+                        all_events.push_back(ev);
+                    }
+                }
             }
 
-            // update index & execution condition for the next iteration
-            ++current_iteration_idx;
+            // execution condition is the result of body network execution
+            if (body_execution_condition_mem != nullptr) {
+                auto execution_id = primitive->body_execution_condition_id;
+                if (body_network->has_event(execution_id)) {
+                    auto ev = body_network->get_primitive_event(execution_id);
+                    if (ev) ev->wait();
+                }
+                execution_condition = read_scalar_value(body_execution_condition_mem, body_network->get_stream());
+            }
+            GPU_DEBUG_IF(!execution_condition) {
+                GPU_DEBUG_LOG << "body_exec_condition is false at "<< current_iteration_idx << " iterations" << std::endl;
+            }
+
+            current_iteration_idx++;
         }
 
         // Reset network and wait for all collected events
         body_network->reset_execution(false);
         stream.wait_for_events(all_events);
 
-        // Concatenate sliced output to the outer network
-        for (size_t i = 0; i < concatenated_output_mem_mappings.size(); ++i) {
-            const auto& concat_output = concatenated_output_mem_mappings.at(i);
-            concat_output.restore_concatenated_mem();
-        }
+        // Update actual num iteration
+        // update num_iterations (actual number of iterations)
+        memory::ptr num_actual_iterations_mem = outer_network.get_primitive(primitive->num_iteration_id)->output_memory_ptr();
+        write_scalar_value(num_actual_iterations_mem, stream, current_iteration_idx);
+        GPU_DEBUG_LOG << "current_iteration(" << primitive->num_iteration_id << ", "
+                        << num_actual_iterations_mem << ")  : " << current_iteration_idx << std::endl;
 
-        if (update_num_iterations) {
-            // update num_iterations (actual number of iterations)
-            int64_t actual_iterations = 0;
-            if (!primitive->current_iteration_id.empty()) {
-                const auto& backedge_mapping = instance.get_current_iteration_backedge_mapping();
-                auto current_iteration_mem = backedge_mapping.from_primitive->output_memory_ptr();
-                actual_iterations = loop_node::read_scalar_value(current_iteration_mem, stream);
-            } else {
-                actual_iterations = current_iteration_idx;
-            }
-
-            memory::ptr num_actual_iterations_mem = outer_network.get_primitive(primitive->num_iteration_id)->output_memory_ptr();
-            loop_node::write_scalar_value(num_actual_iterations_mem, stream, actual_iterations);
-        }
+        if (is_dynamic)
+            instance.update_output_layout();
+        instance.postprocess_output_memory(is_dynamic);
 
         ev->set();
         return ev;
@@ -202,23 +395,25 @@ struct loop_impl : typed_primitive_impl<loop> {
 
     void save(BinaryOutputBuffer& ob) const override {
         parent::save(ob);
-        ob << _max_iteration;
         ob << _back_edges;
     }
 
     void load(BinaryInputBuffer& ib) override {
         parent::load(ib);
-        ib >> _max_iteration;
         ib >> _back_edges;
     }
 
 private:
-    int64_t _max_iteration = 0;
     std::vector<cldnn::loop::backedge_mapping> _back_edges;
 };
 
 namespace detail {
 attach_loop_common::attach_loop_common() {
+    implementation_map<loop>::add(impl_types::common,
+                                    shape_types::dynamic_shape,
+                                    loop_impl::create,
+                                    {},
+                                    {});
     implementation_map<loop>::add(impl_types::common, loop_impl::create, {});
 }
 }  // namespace detail
diff --git a/src/plugins/intel_gpu/src/graph/impls/cpu/activation.cpp b/src/plugins/intel_gpu/src/graph/impls/cpu/activation.cpp
index 57c0f057455ba7..7f1e7abcb9b580 100644
--- a/src/plugins/intel_gpu/src/graph/impls/cpu/activation.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/cpu/activation.cpp
@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/core/type/element_type_traits.hpp"
 #include "register.hpp"
 #include "activation_inst.h"
 #include "implementation_map.hpp"
@@ -108,7 +109,7 @@ struct activation_impl : public typed_primitive_impl<activation> {
             input_host_tensors.push_back(make_tensor(params->input_layouts[i], input_mem_ptrs[i]->lock(stream, mem_lock_type::read)));
 
         // Most of the evaluate functions expect same data type for all inputs, so we need to convert params from float
-        typename data_type_to_type<DT>::type param_a = static_cast<typename data_type_to_type<DT>::type>(additional_params.a);
+        auto param_a = static_cast<typename ov::element_type_traits<DT>::value_type>(additional_params.a);
 
         auto input_dt = instance.get_input_layout().data_type;
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/cpu/detection_output.cpp b/src/plugins/intel_gpu/src/graph/impls/cpu/detection_output.cpp
index 9e9cd8b1c93389..f15d143e28539c 100644
--- a/src/plugins/intel_gpu/src/graph/impls/cpu/detection_output.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/cpu/detection_output.cpp
@@ -839,11 +839,11 @@ struct detection_output_impl : typed_primitive_impl<detection_output> {
 
         std::vector<std::vector<std::pair<float, std::pair<int, int>>>> scoreIndexPairs;
         if (instance.location_memory()->get_layout().data_type == data_types::f32) {
-            prepare_data<data_type_to_type<data_types::f32>::type>(stream, instance, bboxes, confidences, scoreIndexPairs);
-            generate_detections<data_type_to_type<data_types::f32>::type>(stream, instance, num_of_images, bboxes, confidences, scoreIndexPairs);
+            prepare_data<ov::element_type_traits<data_types::f32>::value_type>(stream, instance, bboxes, confidences, scoreIndexPairs);
+            generate_detections<ov::element_type_traits<data_types::f32>::value_type>(stream, instance, num_of_images, bboxes, confidences, scoreIndexPairs);
         } else {
-            prepare_data<data_type_to_type<data_types::f16>::type>(stream, instance, bboxes, confidences, scoreIndexPairs);
-            generate_detections<data_type_to_type<data_types::f16>::type>(stream, instance, num_of_images, bboxes, confidences, scoreIndexPairs);
+            prepare_data<ov::element_type_traits<data_types::f16>::value_type>(stream, instance, bboxes, confidences, scoreIndexPairs);
+            generate_detections<ov::element_type_traits<data_types::f16>::value_type>(stream, instance, num_of_images, bboxes, confidences, scoreIndexPairs);
         }
 
         ev->set();
diff --git a/src/plugins/intel_gpu/src/graph/impls/cpu/non_max_suppression.cpp b/src/plugins/intel_gpu/src/graph/impls/cpu/non_max_suppression.cpp
index 7afca0cb91c91f..cfb05c176c06ca 100644
--- a/src/plugins/intel_gpu/src/graph/impls/cpu/non_max_suppression.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/cpu/non_max_suppression.cpp
@@ -149,9 +149,9 @@ vector2D<bounding_box> load_boxes(stream& stream, memory::ptr mem, bool center_p
     auto data_type = mem->get_layout().data_type;
     switch (data_type) {
     case cldnn::data_types::f16:
-        return load_boxes_impl<data_type_to_type<data_types::f16>::type>(stream, mem, center_point);
+        return load_boxes_impl<ov::element_type_traits<data_types::f16>::value_type>(stream, mem, center_point);
     case cldnn::data_types::f32:
-        return load_boxes_impl<data_type_to_type<data_types::f32>::type>(stream, mem, center_point);
+        return load_boxes_impl<ov::element_type_traits<data_types::f32>::value_type>(stream, mem, center_point);
     default:
         throw std::runtime_error("Non max suppression - unsupported boxes data type");
     }
@@ -186,9 +186,9 @@ vector3D<float> load_scores(stream& stream, memory::ptr mem) {
     auto data_type = mem->get_layout().data_type;
     switch (data_type) {
     case cldnn::data_types::f16:
-        return load_scores_impl<data_type_to_type<data_types::f16>::type>(stream, mem);
+        return load_scores_impl<ov::element_type_traits<data_types::f16>::value_type>(stream, mem);
     case cldnn::data_types::f32:
-        return load_scores_impl<data_type_to_type<data_types::f32>::type>(stream, mem);
+        return load_scores_impl<ov::element_type_traits<data_types::f32>::value_type>(stream, mem);
     default:
         throw std::runtime_error("Non max suppression - unsupported scores data type");
     }
@@ -207,11 +207,11 @@ T load_scalar(stream& stream, memory::ptr mem) {
     auto data_type = mem->get_layout().data_type;
     switch (data_type) {
     case cldnn::data_types::i32:
-        return load_scalar_impl<T, data_type_to_type<data_types::i32>::type>(stream, mem);
+        return load_scalar_impl<T, ov::element_type_traits<data_types::i32>::value_type>(stream, mem);
     case cldnn::data_types::f16:
-        return load_scalar_impl<T, data_type_to_type<data_types::f16>::type>(stream, mem);
+        return load_scalar_impl<T, ov::element_type_traits<data_types::f16>::value_type>(stream, mem);
     case cldnn::data_types::f32:
-        return load_scalar_impl<T, data_type_to_type<data_types::f32>::type>(stream, mem);
+        return load_scalar_impl<T, ov::element_type_traits<data_types::f32>::value_type>(stream, mem);
     default:
         throw std::runtime_error("Non max suppression - unsupported data type");
     }
@@ -244,13 +244,13 @@ void store_result(stream& stream, memory::ptr mem, const std::vector<result_indi
     auto data_type = mem->get_layout().data_type;
     switch (data_type) {
     case cldnn::data_types::i32:
-        store_result_impl<data_type_to_type<data_types::i32>::type>(stream, mem, result);
+        store_result_impl<ov::element_type_traits<data_types::i32>::value_type>(stream, mem, result);
         break;
     case cldnn::data_types::f16:
-        store_result_impl<data_type_to_type<data_types::f16>::type>(stream, mem, result);
+        store_result_impl<ov::element_type_traits<data_types::f16>::value_type>(stream, mem, result);
         break;
     case cldnn::data_types::f32:
-        store_result_impl<data_type_to_type<data_types::f32>::type>(stream, mem, result);
+        store_result_impl<ov::element_type_traits<data_types::f32>::value_type>(stream, mem, result);
         break;
     default:
         throw std::runtime_error("Non max suppression - unsupported output data type");
@@ -261,10 +261,10 @@ void store_first_output(stream& stream, memory::ptr mem, const std::vector<resul
     auto data_type = mem->get_layout().data_type;
     switch (data_type) {
     case cldnn::data_types::i32:
-        store_result_impl<data_type_to_type<data_types::i32>::type>(stream, mem, result);
+        store_result_impl<ov::element_type_traits<data_types::i32>::value_type>(stream, mem, result);
         break;
     case cldnn::data_types::i64:
-        store_result_impl<data_type_to_type<data_types::i32>::type>(stream, mem, result);
+        store_result_impl<ov::element_type_traits<data_types::i32>::value_type>(stream, mem, result);
         break;
     default:
         throw std::runtime_error("Non max suppression - unsupported output data type");
@@ -298,10 +298,10 @@ void store_second_output(stream& stream, memory::ptr mem, const std::vector<resu
     auto data_type = mem->get_layout().data_type;
     switch (data_type) {
     case cldnn::data_types::f16:
-        store_second_output_impl<data_type_to_type<data_types::f16>::type>(stream, mem, result);
+        store_second_output_impl<ov::element_type_traits<data_types::f16>::value_type>(stream, mem, result);
         break;
     case cldnn::data_types::f32:
-        store_second_output_impl<data_type_to_type<data_types::f32>::type>(stream, mem, result);
+        store_second_output_impl<ov::element_type_traits<data_types::f32>::value_type>(stream, mem, result);
         break;
     default:
         throw std::runtime_error("Non max suppression - unsupported second output data type");
@@ -319,10 +319,10 @@ void store_third_output(stream& stream, memory::ptr mem, const std::vector<resul
     auto data_type = mem->get_layout().data_type;
     switch (data_type) {
     case cldnn::data_types::i32:
-        store_third_output_impl<data_type_to_type<data_types::i32>::type>(stream, mem, result);
+        store_third_output_impl<ov::element_type_traits<data_types::i32>::value_type>(stream, mem, result);
         break;
     case cldnn::data_types::i64:
-        store_third_output_impl<data_type_to_type<data_types::i32>::type>(stream, mem, result);
+        store_third_output_impl<ov::element_type_traits<data_types::i32>::value_type>(stream, mem, result);
         break;
     default:
         throw std::runtime_error("Non max suppression - unsupported third output data type");
diff --git a/src/plugins/intel_gpu/src/graph/impls/cpu/proposal.cpp b/src/plugins/intel_gpu/src/graph/impls/cpu/proposal.cpp
index 461035c1defd75..2670949f8e9284 100644
--- a/src/plugins/intel_gpu/src/graph/impls/cpu/proposal.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/cpu/proposal.cpp
@@ -396,9 +396,9 @@ struct proposal_impl : typed_primitive_impl<proposal> {
         auto ev = instance.get_network().get_stream().create_user_event(false);
         im_info_t im_info;
         if (instance.dep_memory(proposal_inst::image_info_index).get_layout().data_type == data_types::f16) {
-            read_image_info<data_type_to_type<data_types::f16>::type>(stream, instance, im_info);
+            read_image_info<ov::element_type_traits<data_types::f16>::value_type>(stream, instance, im_info);
         } else {
-            read_image_info<data_type_to_type<data_types::f32>::type>(stream, instance, im_info);
+            read_image_info<ov::element_type_traits<data_types::f32>::value_type>(stream, instance, im_info);
         }
 
         if (instance.dep_memory(proposal_inst::cls_scores_index).get_layout().data_type !=
@@ -408,26 +408,26 @@ struct proposal_impl : typed_primitive_impl<proposal> {
         if (instance.dependencies().size() == 4) {
             auto proposal_probabilities = instance.dep_memory_ptr(proposal_inst::proposal_probabilities_out);
             if (instance.dep_memory(proposal_inst::cls_scores_index).get_layout().data_type == data_types::f16) {
-                mem_lock<data_type_to_type<data_types::f16>::type, mem_lock_type::read> proposal_prob_ptr{proposal_probabilities, stream};
-                execute<data_type_to_type<data_types::f16>::type>(stream, instance, im_info, proposal_prob_ptr.data());
+                mem_lock<ov::element_type_traits<data_types::f16>::value_type, mem_lock_type::read> proposal_prob_ptr{proposal_probabilities, stream};
+                execute<ov::element_type_traits<data_types::f16>::value_type>(stream, instance, im_info, proposal_prob_ptr.data());
             } else {
-                mem_lock<data_type_to_type<data_types::f32>::type, mem_lock_type::read> proposal_prob_ptr{proposal_probabilities, stream};
-                execute<data_type_to_type<data_types::f32>::type>(stream, instance, im_info, proposal_prob_ptr.data());
+                mem_lock<ov::element_type_traits<data_types::f32>::value_type, mem_lock_type::read> proposal_prob_ptr{proposal_probabilities, stream};
+                execute<ov::element_type_traits<data_types::f32>::value_type>(stream, instance, im_info, proposal_prob_ptr.data());
             }
         } else if (instance.outputs_memory_count() == 2) {
             auto proposal_probabilities = instance.output_memory_ptr(1);
             if (instance.dep_memory(proposal_inst::cls_scores_index).get_layout().data_type == data_types::f16) {
-                mem_lock<data_type_to_type<data_types::f16>::type, mem_lock_type::write> proposal_prob_ptr{proposal_probabilities, stream};
-                execute<data_type_to_type<data_types::f16>::type>(stream, instance, im_info, proposal_prob_ptr.data());
+                mem_lock<ov::element_type_traits<data_types::f16>::value_type, mem_lock_type::write> proposal_prob_ptr{proposal_probabilities, stream};
+                execute<ov::element_type_traits<data_types::f16>::value_type>(stream, instance, im_info, proposal_prob_ptr.data());
             } else {
-                mem_lock<data_type_to_type<data_types::f32>::type, mem_lock_type::write> proposal_prob_ptr{proposal_probabilities, stream};
-                execute<data_type_to_type<data_types::f32>::type>(stream, instance, im_info, proposal_prob_ptr.data());
+                mem_lock<ov::element_type_traits<data_types::f32>::value_type, mem_lock_type::write> proposal_prob_ptr{proposal_probabilities, stream};
+                execute<ov::element_type_traits<data_types::f32>::value_type>(stream, instance, im_info, proposal_prob_ptr.data());
             }
         } else {
             if (instance.dep_memory(proposal_inst::cls_scores_index).get_layout().data_type == data_types::f16) {
-                execute<data_type_to_type<data_types::f16>::type>(stream, instance, im_info);
+                execute<ov::element_type_traits<data_types::f16>::value_type>(stream, instance, im_info);
             } else {
-                execute<data_type_to_type<data_types::f32>::type>(stream, instance, im_info);
+                execute<ov::element_type_traits<data_types::f32>::value_type>(stream, instance, im_info);
             }
         }
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp
index 43ce081d2f69ea..19007a481579f6 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp
@@ -110,20 +110,13 @@ struct fully_connected_impl : typed_primitive_impl_ocl<fully_connected> {
             bool has_scale = !primitive->decompression_scale.empty();
 
             size_t offset = primitive->bias.empty() ? 2 : 3;
-            const auto& weights_pshape = input1_layout.get_partial_shape();
             if (has_scale) {
                 auto scale_layout = input_layouts[offset++];
-                if (input1_pshape.size() != 2) {
-                    scale_layout.set_partial_shape(reshape_to_2d(scale_layout.get_partial_shape(), weights_pshape[0], primitive->weights_rank));
-                }
                 layouts.push_back(scale_layout);
             }
 
             if (has_zp) {
                 auto zp_layout = input_layouts[offset];
-                if (input1_pshape.size() != 2) {
-                    zp_layout.set_partial_shape(reshape_to_2d(zp_layout.get_partial_shape(), weights_pshape[0], primitive->weights_rank));
-                }
                 layouts.push_back(zp_layout);
             }
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/group_normalization.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/group_normalization.cpp
new file mode 100644
index 00000000000000..5296c1dda7e7aa
--- /dev/null
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/group_normalization.cpp
@@ -0,0 +1,71 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "primitive_base.hpp"
+#include "group_normalization_inst.h"
+#include "group_normalization/group_normalization_kernel_ref.h"
+#include "group_normalization/group_normalization_kernel_selector.h"
+
+namespace cldnn {
+namespace ocl {
+
+struct group_normalization_impl : typed_primitive_impl_ocl<group_normalization> {
+    using parent = typed_primitive_impl_ocl<group_normalization>;
+    using parent::parent;
+    using kernel_selector_t = kernel_selector::group_normalization_kernel_selector;
+    using kernel_params_t = std::pair<kernel_selector::group_normalization_params, kernel_selector::group_normalization_optional_params>;
+
+    DECLARE_OBJECT_TYPE_SERIALIZATION(cldnn::ocl::group_normalization_impl)
+
+    std::unique_ptr<primitive_impl> clone() const override {
+        return make_unique<group_normalization_impl>(*this);
+    }
+
+    static kernel_params_t get_kernel_params(const kernel_impl_params& impl_param, bool is_shape_agnostic = false) {
+        const auto& primitive = impl_param.typed_desc<group_normalization>();
+        auto params = get_default_params<kernel_selector::group_normalization_params>(impl_param, is_shape_agnostic);
+        params.inputs.push_back(convert_data_tensor(impl_param.get_input_layout(1)));
+        params.inputs.push_back(convert_data_tensor(impl_param.get_input_layout(2)));
+        auto optional_params = get_default_optional_params<kernel_selector::group_normalization_optional_params>(impl_param.get_program());
+        params.num_groups = primitive->num_groups;
+        params.epsilon = primitive->epsilon;
+        return {params, optional_params};
+    }
+
+    void update_dispatch_data(const kernel_impl_params& impl_param) override {
+        auto kernel_params = get_kernel_params(impl_param, true);
+        (_kernel_data.update_dispatch_data_func)(kernel_params.first, _kernel_data);
+    }
+};
+
+namespace detail {
+
+attach_group_normalization_impl::attach_group_normalization_impl() {
+    auto types = {data_types::f16, data_types::f32};
+    auto formats = {
+            format::bfyx,
+            format::byxf,
+            format::yxfb,
+            format::bfzyx,
+            format::b_fs_yx_fsv2,
+            format::b_fs_zyx_fsv2,
+            format::b_fs_yx_fsv4,
+            format::b_fs_zyx_fsv4,
+            format::b_fs_yx_fsv16,
+            format::b_fs_yx_fsv32,
+            format::b_fs_zyx_fsv16,
+            format::b_fs_zyx_fsv32,
+    };
+
+    implementation_map<group_normalization>::add(impl_types::ocl, shape_types::static_shape,
+                                     typed_primitive_impl_ocl<group_normalization>::create<group_normalization_impl>,
+                                     types,
+                                     formats);
+}
+
+}  // namespace detail
+}  // namespace ocl
+}  // namespace cldnn
+
+BIND_BINARY_BUFFER_WITH_TYPE(cldnn::ocl::group_normalization_impl)
+BIND_BINARY_BUFFER_WITH_TYPE(cldnn::group_normalization)
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp
index 3392a7e42b2363..f0872d3702970e 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp
@@ -422,6 +422,8 @@ kernel_selector::weights_layout to_weights_layout(format f, bool is_grouped) {
             return kernel_selector::weights_layout::yxio;
         case format::os_yxi_osv16:
             return kernel_selector::weights_layout::os_yxi_osv16;
+        case format::o_is_yx_isv2:
+            return kernel_selector::weights_layout::o_is_yx_isv2;
         case format::o_is_yx_isv4:
             return kernel_selector::weights_layout::o_is_yx_isv4;
         case format::o_is_yx_isv16:
@@ -741,6 +743,8 @@ cldnn::format::type from_weights_layout(kernel_selector::weights_layout l) {
             return cldnn::format::yxio;
         case kernel_selector::weights_layout::os_yxi_osv16:
             return cldnn::format::os_yxi_osv16;
+        case kernel_selector::weights_layout::o_is_yx_isv2:
+            return cldnn::format::o_is_yx_isv2;
         case kernel_selector::weights_layout::o_is_yx_isv4:
             return cldnn::format::o_is_yx_isv4;
         case kernel_selector::weights_layout::o_is_yx_isv16:
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/register.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/register.cpp
index 3a287bdeda4f7d..6b35b9cdfb16ce 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/register.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/register.cpp
@@ -40,6 +40,7 @@ void register_implementations() {
     REGISTER_OCL(gemm);
     REGISTER_OCL(generate_proposals);
     REGISTER_OCL(grid_sample);
+    REGISTER_OCL(group_normalization);
     REGISTER_OCL(lrn);
     REGISTER_OCL(lstm_gemm);
     REGISTER_OCL(lstm_elt);
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/register.hpp b/src/plugins/intel_gpu/src/graph/impls/ocl/register.hpp
index a5fb5a5817e395..45f4018bf90dac 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/register.hpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/register.hpp
@@ -35,6 +35,7 @@
 #include "intel_gpu/primitives/gemm.hpp"
 #include "intel_gpu/primitives/grid_sample.hpp"
 #include "intel_gpu/primitives/grn.hpp"
+#include "intel_gpu/primitives/group_normalization.hpp"
 #include "intel_gpu/primitives/lrn.hpp"
 #include "intel_gpu/primitives/lstm.hpp"
 #include "intel_gpu/primitives/lstm_dynamic.hpp"
@@ -120,6 +121,7 @@ REGISTER_OCL(gather_elements);
 REGISTER_OCL(gemm);
 REGISTER_OCL(generate_proposals);
 REGISTER_OCL(grid_sample);
+REGISTER_OCL(group_normalization);
 REGISTER_OCL(lrn);
 REGISTER_OCL(lstm_gemm);
 REGISTER_OCL(lstm_elt);
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp
index 075929afa765fb..aa11884b2445bc 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp
@@ -121,9 +121,9 @@ struct convolution_onednn : typed_primitive_onednn_impl<convolution> {
             }
 
             if (a_zp_dtype == data_types::i8) {
-                set_activation_zero_points_attr<data_type_to_type<data_types::i8>::type>(attrs, a_zp.as<data>(), zero_point_mask);
+                set_activation_zero_points_attr<ov::element_type_traits<data_types::i8>::value_type>(attrs, a_zp.as<data>(), zero_point_mask);
             } else { // if (a_zp_dtype == data_types::u8)
-                set_activation_zero_points_attr<data_type_to_type<data_types::u8>::type>(attrs, a_zp.as<data>(), zero_point_mask);
+                set_activation_zero_points_attr<ov::element_type_traits<data_types::u8>::value_type>(attrs, a_zp.as<data>(), zero_point_mask);
             }
         }
 
diff --git a/src/plugins/intel_gpu/src/graph/include/group_normalization_inst.h b/src/plugins/intel_gpu/src/graph/include/group_normalization_inst.h
new file mode 100644
index 00000000000000..27fe382146999b
--- /dev/null
+++ b/src/plugins/intel_gpu/src/graph/include/group_normalization_inst.h
@@ -0,0 +1,39 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "intel_gpu/primitives/group_normalization.hpp"
+#include "primitive_inst.h"
+
+namespace cldnn {
+template <>
+struct typed_program_node<group_normalization> : public typed_program_node_base<group_normalization> {
+    using parent = typed_program_node_base<group_normalization>;
+
+public:
+    using parent::parent;
+
+    std::vector<size_t> get_shape_infer_dependencies() const override { return {}; }
+};
+using group_normalization_node = typed_program_node<group_normalization>;
+
+template <>
+class typed_primitive_inst<group_normalization> : public typed_primitive_inst_base<group_normalization> {
+    using parent = typed_primitive_inst_base<group_normalization>;
+    using parent::parent;
+
+public:
+    template<typename ShapeType>
+    static std::vector<layout> calc_output_layouts(group_normalization_node const& /*node*/, const kernel_impl_params& impl_param) {
+        return forward_input0_shape<ShapeType>(impl_param);
+    }
+
+    static layout calc_output_layout(group_normalization_node const& node, kernel_impl_params const& impl_param);
+    static std::string to_string(group_normalization_node const& node);
+
+    typed_primitive_inst(network& network, group_normalization_node const& desc);
+};
+
+using group_normalization_inst = typed_primitive_inst<group_normalization>;
+
+}  // namespace cldnn
diff --git a/src/plugins/intel_gpu/src/graph/include/layout_optimizer.h b/src/plugins/intel_gpu/src/graph/include/layout_optimizer.h
index fd048838c704ea..e9ed4ec959ae1c 100644
--- a/src/plugins/intel_gpu/src/graph/include/layout_optimizer.h
+++ b/src/plugins/intel_gpu/src/graph/include/layout_optimizer.h
@@ -169,6 +169,7 @@ class layout_optimizer {
     impl_types get_preferred_impl_type(program_node& node, format preferred_format);
 
     impl_types get_forced_impl_type_by_config(program_node& node);
+    static bool is_node_suitable_for_onednn(program_node& node);
     static bool are_data_types_suitable_for_onednn(program_node& node);
     bool are_layouts_suitable_for_onednn(program_node& node);
     static bool onednn_check_data_types_for_pooling(data_types in_dt, data_types out_dt);
@@ -188,6 +189,7 @@ class layout_optimizer {
     optimization_attributes get_optimization_attributes() { return _optimization_attributes; }
 
     void set_implementation_forcing(const ov::intel_gpu::ImplForcingMap& map);
+    const std::map<primitive_id, std::pair<format::type, impl_types>> get_implementation_forcing() const;
 
     void update_formats_map(const convolution_node& node);
     bool is_format_optimized(const convolution_node& node, const format& format, bool use_weak_restrictions = false);
diff --git a/src/plugins/intel_gpu/src/graph/include/loop_inst.h b/src/plugins/intel_gpu/src/graph/include/loop_inst.h
index 5d7dd710892181..22f4489ae507b5 100644
--- a/src/plugins/intel_gpu/src/graph/include/loop_inst.h
+++ b/src/plugins/intel_gpu/src/graph/include/loop_inst.h
@@ -21,163 +21,52 @@ template<>
 struct typed_program_node<loop> : public typed_program_node_base<loop> {
 private:
     using parent = typed_program_node_base<loop>;
-    mutable topology body;
 
-    std::vector<loop::io_primitive_map> input_primitive_maps;
-    std::vector<loop::io_primitive_map> output_primitive_maps;
-    mutable std::vector<loop::backedge_mapping> back_edges;
-    bool use_current_iteration;
-    bool use_execution_condition;
-    mutable program::ptr body_program;
+    std::vector<loop::io_primitive_map>& input_primitive_maps;
+    std::vector<loop::io_primitive_map>& output_primitive_maps;
+    std::vector<loop::backedge_mapping>& back_edges;
 
 public:
-    typed_program_node(std::shared_ptr<primitive> prim, program& prog) :
+    typed_program_node(std::shared_ptr<loop> prim, program& prog) :
         parent(prim, prog),
-        body(this->get_primitive()->body),
-        input_primitive_maps(this->get_primitive()->input_primitive_maps),
-        output_primitive_maps(this->get_primitive()->output_primitive_maps),
-        back_edges(this->get_primitive()->back_edges),
-        use_current_iteration(!this->get_primitive()->current_iteration_id.empty()),
-        use_execution_condition(!this->get_primitive()->condition_id.empty()),
-        iteration_axis(0),
-        max_iteration(this->get_primitive()->max_iteration < 0 ? DEFAULT_MAX_NUM_ITERATION : this->get_primitive()->max_iteration) {}
-
-    mutable size_t iteration_axis;
-    int64_t max_iteration;
-
-    int64_t get_max_iteration() const { return max_iteration; }
-    program::ptr get_body_program() const { return body_program; }
-    bool is_current_iteration_used() const { return use_current_iteration; }
-    bool is_execution_condition_used() const { return use_execution_condition; }
-
-    static size_t convert_to_raw_axis(size_t axis, size_t ndim) {
-        // convert between bfyx, bfzyx, bfzyxw and tensor.size.raw
-        if (axis >= ndim) {
-            throw std::runtime_error("axis should be less than ndim");
-        }
-
-        if (axis < 2) {
-            return axis;
-        }
-        return (ndim - 1) - (axis - 2);
-    }
-
-    // read scala value from data primitive
-    static int64_t read_scalar_value(memory::ptr mem, stream& stream) {
-        int64_t trip_count = 0;
-        const layout& prim_layout = mem->get_layout();
-
-        switch (prim_layout.data_type) {
-        case data_types::u8: {
-            mem_lock<uint8_t> lock_prim_output{mem, stream};
-            trip_count = *lock_prim_output.data();
-            break;
-        }
-        case data_types::i8: {
-            mem_lock<int8_t> lock_prim_output{mem, stream};
-            trip_count = *lock_prim_output.data();
-            break;
-        }
-        case data_types::i32: {
-            mem_lock<int32_t> lock_prim_output{mem, stream};
-            trip_count = *lock_prim_output.data();
-            break;
-        }
-        case data_types::i64: {
-            mem_lock<int64_t> lock_prim_output{mem, stream};
-            trip_count = *lock_prim_output.data();
-            break;
-        }
-        default:
-            throw std::runtime_error("Invalid data type : " + ov::element::Type(prim_layout.data_type).get_type_name());
-        }
-        return trip_count;
-    }
+        input_primitive_maps(prim->input_primitive_maps),
+        output_primitive_maps(prim->output_primitive_maps),
+        back_edges(prim->back_edges) {}
 
-    template<typename T>
-    static inline void validate_input_value(int64_t input) {
-        if (input < std::numeric_limits<T>::min() || input > std::numeric_limits<T>::max()) {
-            throw std::runtime_error("Invalid data value : " + std::to_string(input));
-        }
-    }
+    program::ptr get_body_program() const { return get_primitive()->body_program; }
 
-    static void write_scalar_value(memory::ptr mem, stream& stream, int64_t input) {
-        const layout& prim_layout = mem->get_layout();
-
-        switch (prim_layout.data_type) {
-        case data_types::u8: {
-            validate_input_value<uint8_t>(input);
-            mem_lock<uint8_t> lock_prim_output{mem, stream};
-            lock_prim_output[0] = static_cast<uint8_t>(input);
-            break;
-        }
-        case data_types::i8: {
-            validate_input_value<int8_t>(input);
-            mem_lock<int8_t> lock_prim_output{mem, stream};
-            lock_prim_output[0] = static_cast<int8_t>(input);
-            break;
-        }
-        case data_types::i32: {
-            validate_input_value<int32_t>(input);
-            mem_lock<int32_t> lock_prim_output{mem, stream};
-            lock_prim_output[0] = static_cast<int32_t>(input);
-            break;
-        }
-        case data_types::i64: {
-            mem_lock<int64_t> lock_prim_output{mem, stream};
-            lock_prim_output[0] = input;
-            break;
-        }
-        default:
-            throw std::runtime_error("Invalid data type : " + ov::element::Type(prim_layout.data_type).get_type_name());
-        }
-    }
-
-    layout calc_body_input_layout(const loop::io_primitive_map& inputDesc) const {
-        const auto& dependency_list = this->get_dependencies();
-        auto input = std::find_if(dependency_list.begin(), dependency_list.end(), [&inputDesc](const std::pair<program_node*, int32_t>& dep){
-            return dep.first->id() == inputDesc.external_id;
-        });
-        if (input == dependency_list.end()) {
-            throw std::runtime_error("Can't find input from dependency_list");
-        }
-        layout calculated_layout = (*input).first->get_output_layout();
-        auto shape = calculated_layout.get_tensor().sizes(calculated_layout.format);
-
-        if (inputDesc.axis >= 0) {
-            iteration_axis = convert_to_raw_axis(static_cast<size_t>(inputDesc.axis), shape.size());
-            auto calculated_size = calculated_layout.get_tensor();
-            calculated_size.raw[iteration_axis] = 1; // cropped inputs shape
-            calculated_layout.set_tensor(calculated_size);
-        }
-
-        return calculated_layout;
-    }
+    const primitive_id& get_trip_count_id() const { return get_primitive()->trip_count_id; }
+    const primitive_id& get_initial_execution_id() const { return get_primitive()->first_execution_condition_id; }
+    const primitive_id& get_current_iteration_id() const { return get_primitive()->body_current_iteration_id; }
+    const primitive_id& get_execution_condition_id() const { return get_primitive()->body_execution_condition_id; }
+    const primitive_id& get_num_iterations_id() const { return get_primitive()->num_iteration_id; }
+    const int32_t get_max_num_iteration() const { return get_primitive()->max_num_iterations; }
 
     const std::vector<loop::io_primitive_map>& get_input_primitive_maps() const { return input_primitive_maps; }
     const std::vector<loop::io_primitive_map>& get_output_primitive_maps() const { return output_primitive_maps; }
+    const std::vector<loop::backedge_mapping>& get_back_edges() const { return back_edges;}
 
     void update_primitive_map(const primitive_id& prevID, const primitive_id& newID, bool external_id = true) {
         if (external_id) {
             for (auto& pm : input_primitive_maps) {
-                if (pm.external_id == prevID) {
-                    pm.external_id = newID;
+                if (pm.external_id.pid == prevID) {
+                    pm.external_id.pid = newID;
                 }
             }
             for (auto& pm : output_primitive_maps) {
-                if (pm.external_id == prevID) {
-                    pm.external_id = newID;
+                if (pm.external_id.pid == prevID) {
+                    pm.external_id.pid = newID;
                 }
             }
         } else {
             for (auto& pm : input_primitive_maps) {
-                if (pm.internal_id == prevID) {
-                    pm.internal_id = newID;
+                if (pm.internal_id.pid == prevID) {
+                    pm.internal_id.pid = newID;
                 }
             }
             for (auto& pm : output_primitive_maps) {
-                if (pm.internal_id == prevID) {
-                    pm.internal_id = newID;
+                if (pm.internal_id.pid == prevID) {
+                    pm.internal_id.pid = newID;
                 }
             }
             for (auto& back_edge : back_edges) {
@@ -191,157 +80,266 @@ struct typed_program_node<loop> : public typed_program_node_base<loop> {
         }
     }
 
-    const std::vector<cldnn::loop::backedge_mapping>& get_back_edges() const { return back_edges;}
-
-    static bool is_integer(const data_types& data_type) {
-        switch (data_type) {
-            case data_types::u8:
-            case data_types::i8:
-            case data_types::i32:
-            case data_types::i64:
-                return true;
-            default:
-                return false;
-        }
+    // current_iteration is necessary to calculate output layout in dynamic shape
+    std::vector<size_t> get_shape_infer_dependencies() const override { return {0}; }
+
+    using parent::get_kernel_impl_params;
+    std::unique_ptr<kernel_impl_params> get_kernel_impl_params(const std::vector<layout>& in_layouts, const std::vector<layout>& out_layouts) const override {
+        auto params = parent::get_kernel_impl_params(in_layouts, out_layouts);
+        params->inner_progs = { get_primitive()->body_program };
+        // Set memory_deps using custom get_memory_deps to add current_iteration(mutable_data) into memory_deps
+        params->memory_deps = get_memory_deps();
+        return params;
     }
 
-    void process_current_iteration() const {
-        const primitive_id& current_iteration_id = get_current_iteration_id();
-        if (current_iteration_id.empty()) {
-            return;
-        }
+private:
+    std::map<size_t, memory::ptr> get_memory_deps() const;
+};
 
-        const topology_map& body_topology_map = body.get_primitives();
-        const layout body_input_layout(data_types::i64, format::bfyx, {1, 1, 1, 1});
+using loop_node = typed_program_node<loop>;
 
-        // add current_iteration primitive if current_iteration primitive is not exist in body
-        if (body_topology_map.find(current_iteration_id) == body_topology_map.end()) {
-            body.add_primitive(std::make_shared<input_layout>(current_iteration_id, body_input_layout));
-        } else {
-            const auto& body_input_prim = body.at(current_iteration_id);
-            const auto input_layout_prim = std::dynamic_pointer_cast<input_layout>(body_input_prim);
-            OPENVINO_ASSERT(input_layout_prim, "[GPU] current_iteration primitive should be cldnn::input_layout in node", this->id());
-            input_layout_prim->change_layout(body_input_layout);
+template <>
+class typed_primitive_inst<loop> : public typed_primitive_inst_base<loop> {
+    using parent = typed_primitive_inst_base<loop>;
+    using parent::parent;
+
+public:
+    struct concatenated_memory_mapping {
+        using ptr = std::shared_ptr<concatenated_memory_mapping>;
+        using cptr = std::shared_ptr<const concatenated_memory_mapping>;
+        concatenated_memory_mapping(int64_t axis,
+                                    memory::ptr concatenated_mem,
+                                    std::vector<memory::ptr> sliced_mems, // To change shared ptr vector
+                                    stream& stream,
+                                    engine& engine,
+                                    int64_t iteration_elements = 0,
+                                    int64_t stride = 0,
+                                    int64_t initial_offset = 0) :
+            axis(axis),
+            concatenated_mem(concatenated_mem),
+            sliced_mems(sliced_mems),
+            stream(stream),
+            engine(engine),
+            iteration_elements(iteration_elements),
+            stride(stride),
+            initial_offset(initial_offset) {
+                calculate_concatenated_mem();
+            }
+
+        concatenated_memory_mapping(const concatenated_memory_mapping& o) :
+            axis(o.axis),
+            concat_data_prim(o.concat_data_prim),
+            sliced_data_prim(o.sliced_data_prim),
+
+            concatenated_mem(o.concatenated_mem),
+            sliced_mems(o.sliced_mems),
+            stream(o.stream),
+            engine(o.engine),
+            iteration_elements(o.iteration_elements),
+            stride(o.stride),
+            initial_offset(o.initial_offset),
+
+            bytes_per_element(o.bytes_per_element),
+            batch_size(o.batch_size),
+            bytes_batch_stride(o.bytes_batch_stride),
+            bytes_iteration(o.bytes_iteration),
+            bytes_iteration_stride(o.bytes_iteration_stride),
+            bytes_iteration_initial_offset(o.bytes_iteration_initial_offset) {}
+
+
+        static int64_t get_batch_size(layout mem_layout, int64_t axis) {
+            if (axis < 0) {
+                throw std::runtime_error("axis should be positive integer or zero");
+            }
+
+            if (mem_layout.is_dynamic()) {
+                return -1;
+            }
+
+            int64_t batch_size = 1;
+            for (int64_t i = 0; i < axis; ++i) {
+                batch_size *= mem_layout.get_tensor().raw[i];
+            }
+            for (int64_t i = axis-1; i >= 2; --i) {
+                batch_size *= mem_layout.get_tensor().raw[i];
+            }
+            return batch_size;
         }
 
-        // add incremental data: 1
-        // it is used to update current_iteration in body network
-        const primitive_id increment_value_id = current_iteration_id + "_inc";
-        auto mem = get_program().get_engine().allocate_memory(body_input_layout);
-        auto& stream = get_program().get_stream();
-        write_scalar_value(mem, stream, 1);
-        body.add_primitive(std::make_shared<data>(increment_value_id, mem));
-
-        // add eltwise sum updating current_iteration with incremental data
-        const primitive_id updated_currnet_iteration_id = current_iteration_id + "_update";
-        body.add_primitive(std::make_shared<eltwise>(updated_currnet_iteration_id,
-            current_iteration_id, increment_value_id, eltwise_mode::sum));
-
-        // set backedge
-        back_edges.emplace_back(updated_currnet_iteration_id, current_iteration_id);
-    }
+        void calculate_concatenated_mem() const {
+            if (!sliced_mems.empty() && concatenated_mem != nullptr) {
+                auto& sliced_layout = sliced_mems.front()->get_layout();
+                const int64_t num_elements_batch = get_batch_size(sliced_layout, axis);
+                iteration_elements = sliced_layout.count() / num_elements_batch;
+                bytes_per_element = data_type_traits::size_of(concatenated_mem->get_layout().data_type);
+                batch_size = get_batch_size(concatenated_mem->get_layout(), axis);
+                bytes_batch_stride = (static_cast<int64_t>(concatenated_mem->get_layout().count()) / batch_size) * bytes_per_element;
+                bytes_iteration = iteration_elements * bytes_per_element;
+                bytes_iteration_stride = stride * bytes_iteration;
+                bytes_iteration_initial_offset = initial_offset * bytes_iteration;
+            }
+        }
 
-    void process_single_int_output(const primitive_id& id) const {
-        // add mutable if not exist
-        const topology_map& body_topology_map = body.get_primitives();
-        layout body_output_layout(data_types::i64, format::bfyx, {1, 1, 1, 1});
-        if (!id.empty()) {
-            auto body_output = body_topology_map.find(id);
-            if (body_output == body_topology_map.end()) {
-                auto mem = get_program().get_engine().allocate_memory(body_output_layout);
-                auto md = std::make_shared<data>(id, mem);
-                body.add_primitive(md);
+        void update_concatenated_mem(memory::ptr mem) {
+            if (concatenated_mem != nullptr && concatenated_mem->get_layout() == mem->get_layout()) {
+                concatenated_mem = mem;
             } else {
-                auto body_output_prim = body.at(body_output->first);
-                auto mem = get_program().get_engine().allocate_memory(body_output_layout);
-                body_output_prim.reset(new mutable_data(body_output->first, std::move(mem)));
+                concatenated_mem = mem;
+                calculate_concatenated_mem();
             }
         }
-    }
 
-    void build_body_program() const {
-        for (const auto& pm : input_primitive_maps) {
-            layout calculated_layout = calc_body_input_layout(pm);
-            const primitive_id& internal_input_id = pm.internal_id;
+        void restore_concatenated_mem() const {
+            OPENVINO_ASSERT(concatenated_mem != nullptr, "concatenated_mem should not be nullptr");
+            mem_lock<uint8_t> concat_mem_lock{ concatenated_mem, stream };
+            int64_t iteration_offset = bytes_iteration_initial_offset;
+            for (const auto& sliced_mem : sliced_mems) {
+                // To support multi-batch, just repeat memcpy for each batch
+                for (int64_t batch = 0; batch < batch_size; ++batch) {
+                    const int64_t src_offset = batch * bytes_iteration;
+                    const int64_t dst_offset = batch * bytes_batch_stride + iteration_offset;
+                    mem_lock<uint8_t> sliced_mem_lock{ sliced_mem, stream };
+                    uint8_t* src = sliced_mem_lock.data() + src_offset;
+                    uint8_t* dst = concat_mem_lock.data() + dst_offset;
+                    std::copy(src, src + bytes_iteration, dst);
+                }
+                iteration_offset += bytes_iteration_stride;
+            }
+        }
 
-            // add inputs for body network if not exist
-            if (body.get_primitives().count(internal_input_id) == 0) {
-                body.add_primitive(std::make_shared<input_layout>(internal_input_id, calculated_layout));
-            } else {
-                body.change_input_layout(internal_input_id, calculated_layout);
+        // Get sliced mem for the iteration idx and copy data from external input to sliced mem
+        // In the case of dynamic model, concatenated_mem is always non nullptr.
+        memory::ptr get_sliced_mem(int64_t iteration) const {
+            OPENVINO_ASSERT(!sliced_mems.empty(), "For input data, sliced_mems should not be empty");
+            mem_lock<uint8_t, mem_lock_type::read> from_lock{ concatenated_mem, stream };
+            int64_t batch_offset = 0;
+            auto sliced_mem = get_or_create_sliced_mem(iteration, sliced_mems.front()->get_layout());
+            const int64_t iteration_offset = bytes_iteration_initial_offset +
+                bytes_iteration_stride * iteration;
+            // To support multi-batch, just repeat memcpy for each batch
+            for (int64_t batch = 0; batch < batch_size; ++batch) {
+                const int64_t src_offset = batch_offset + iteration_offset;
+                const int64_t dst_offset = batch * bytes_iteration;
+                mem_lock<uint8_t> to_lock{ sliced_mem, stream };
+                const auto src = from_lock.begin() + src_offset;
+                const auto dst = to_lock.begin() + dst_offset;
+                std::copy(src, src + bytes_iteration, dst);
+                batch_offset += bytes_batch_stride;
             }
+            return sliced_mem;
         }
 
-        // setup internal output
-        OPENVINO_ASSERT(!output_primitive_maps.empty(), "[GPU] Output primitive map should have at least 1 mapping in primitive ", this->id());
-        std::set<primitive_id> output_names;
-        output_names.insert(output_primitive_maps.front().internal_id);
-
-        // add current_iteration_id in body network, condition_id if exist
-        process_current_iteration();
-        process_single_int_output(get_condition_id());
-
-        // setup outputs for backedges
-        for (auto& back_edge : back_edges) {
-            // check whether the back_edge.to has its corresponding io_primitive_map
-            const auto& input_map = std::find_if(input_primitive_maps.begin(), input_primitive_maps.end(),
-                [&](const loop::io_primitive_map& pm) {
-                    return pm.internal_id == back_edge.to;
-                });
-
-            // backedge which is current_iteration does not have
-            // input primitive map because its initial value is always
-            // zero and the value will be set in execute_impl()
-            if (back_edge.to != get_current_iteration_id() && input_map == input_primitive_maps.end()) {
-                std::string msg = "[GPU] No primitive mapping for backedge (internal_id: " + back_edge.to + ") for primitive " + this->id();
-                OPENVINO_ASSERT(false, msg.c_str());
+        memory::ptr get_or_create_sliced_mem(int64_t idx, const layout& mem_layout) const {
+            bool recalc_data = !sliced_mems.empty();
+            while (sliced_mems.size() <= static_cast<size_t>(idx)) {
+                memory::ptr sliced_mem = engine.allocate_memory(mem_layout, 0);
+                sliced_mems.push_back(sliced_mem);
             }
+            if (recalc_data) {
+                calculate_concatenated_mem();
+            }
+            return sliced_mems.at(idx);
+        }
 
-            output_names.insert(back_edge.from);
+        void setup_sliced_output_memory(uint64_t iteration) const {
+            if (sliced_data_prim) {
+                OPENVINO_ASSERT(iteration < sliced_mems.size(), "invalid index");
+                const auto& sliced_output_mem = sliced_mems.at(iteration);
+                sliced_data_prim->set_output_memory(sliced_output_mem);
+            }
         }
 
-        // if execution_condition_id is specified, we need to add the id in build_option::outputs
-        if (!get_condition_id().empty()) {
-            output_names.insert(get_condition_id());
+        std::vector<memory::ptr>& get_sliced_mems() const { return sliced_mems; }
+
+        void reset_data_for_shape_changed() {
+            bytes_per_element = 0;
+            batch_size = 0;
+            bytes_batch_stride = 0;
+            bytes_iteration = 0;
+            bytes_iteration_stride = 0;
+            bytes_iteration_initial_offset = 0;
+            if (concatenated_mem) concatenated_mem = nullptr;
+            iteration_elements = 0;
+            sliced_mems.clear();
         }
 
-        std::vector<primitive_id> output_names_vec(output_names.begin(), output_names.end());
-        auto config = get_program().get_config();
-        config.set_property(ov::intel_gpu::custom_outputs(output_names_vec));
-        body_program = program::build_program(get_program().get_engine(), body, config, get_program().get_task_executor(), false, false, true);
-    }
+        std::string to_string() const {
+            std::stringstream ss;
+            ss << "concatenated_memory_mapping [" << std::endl;
+            ss << "* axis                           : " << axis << std::endl;
+            ss << "* bytes_per_element              : " << bytes_per_element << std::endl;
+            ss << "* batch_size                     : " << batch_size << std::endl;
+            if (concatenated_mem != nullptr && concatenated_mem->get_layout().is_static()) {
+                ss << "* bytes_batch_stride             : " << bytes_batch_stride << " = (static_cast<int64_t>("
+                    << concatenated_mem->get_layout().count() << ") / batch_size:" << batch_size << ") * bytes_per_element:" << bytes_per_element << std::endl;
+            } else {
+                ss << "* bytes_batch_stride             : " << bytes_batch_stride << std::endl;
+            }
+            ss << "* bytes_iteration                : " << bytes_iteration << " = (iteration_elements:"
+                << iteration_elements << " * bytes_per_element:" << bytes_per_element << ")" << std::endl;
+            ss << "* bytes_iteration_stride         : " << bytes_iteration_stride << std::endl;
+            ss << "* bytes_iteration_initial_offset : " << bytes_iteration_initial_offset << std::endl;
+            ss << "* concat_data_prim               : " << ((concat_data_prim != nullptr)? concat_data_prim->id() : "nullptr") << std::endl;
+            ss << "* sliced_data_prim               : " << ((sliced_data_prim != nullptr)? sliced_data_prim->id()  : "nullptr") << std::endl;
+            if (concatenated_mem) {
+                ss << "* concatenated_mem               : " << concatenated_mem->get_layout().to_short_string() << std::endl;
+            } else {
+                ss << "* concatenated_mem               : nullptr" << std::endl;
+            }
+            ss << "* iteration_elements             : " << iteration_elements << std::endl;
+            ss << "* stride                         : " << stride << std::endl;
+            ss << "* initial_offset                 : " << initial_offset << std::endl;
+            ss << "* sliced_mems                    :{ ";
+            for (auto mem : sliced_mems) {
+                ss << mem->get_layout().to_short_string() << ",";
+            }
+            ss << "}]" << std::endl;
+            return ss.str();
+        }
 
-    const primitive_id& get_trip_count_id() const { return get_primitive()->trip_count_id; }
-    const primitive_id& get_initial_execution_id() const { return get_primitive()->initial_execution_id; }
-    const primitive_id& get_current_iteration_id() const { return get_primitive()->current_iteration_id; }
-    const primitive_id& get_condition_id() const { return get_primitive()->condition_id; }
-    const primitive_id& get_num_iteration_id() const { return get_primitive()->num_iteration_id; }
-    const topology& get_body_topology() const { return get_primitive()->body; }
-};
+        const int64_t axis;
+        std::shared_ptr<primitive_inst> concat_data_prim;
+        std::shared_ptr<primitive_inst> sliced_data_prim;
 
-using loop_node = typed_program_node<loop>;
+private:
+        mutable memory::ptr concatenated_mem;
+        mutable std::vector<memory::ptr> sliced_mems;
+        cldnn::stream& stream;
+        cldnn::engine& engine;
+        mutable int64_t iteration_elements = 0;
+        const int64_t stride = 0;
+        const int64_t initial_offset = 0;
 
-template <>
-class typed_primitive_inst<loop> : public typed_primitive_inst_base<loop> {
-    using parent = typed_primitive_inst_base<loop>;
-    using parent::parent;
+        // element size
+        mutable int64_t bytes_per_element;
+        // number of higher level of dimension of slicing axis
+        mutable int64_t batch_size;
+        // stride of batch in concatenated memory
+        mutable int64_t bytes_batch_stride;
+        // byte size of each iteration per batch in a sliced memory
+        mutable int64_t bytes_iteration;
+        // byte size of each iteration (bytes_iteration * batch_size) in a sliced memory
+        mutable int64_t bytes_iteration_stride;
+        // byte offset of 1st iteration in a batch in a sliced memory
+        mutable int64_t bytes_iteration_initial_offset;
+    };
 
-public:
     struct backedge_memory_mapping {
         enum backedge_type {
             // output memory(from_primitive) of body network needs to be concatenated
             CONCAT_OUTPUT,
-            // output memory(from_primitive) of body network does not need to be concateneated
+            // output memory(from_primitive) of body network does not need to be concatenated
             // input memory is shared by output memory
             SINGLE_SHARED,
-            // output memory(from_primitive) of body network does not need to be concateneated
-            // input memory is not shared by output memroy
+            // output memory(from_primitive) of body network does not need to be concatenated
+            // input memory is not shared by output memory
             // each iteration input memory and output memory are swapped
             SINGLE,
         };
         std::shared_ptr<primitive_inst> from_primitive;
         std::shared_ptr<primitive_inst> to_primitive;
-        std::vector<memory::ptr> from_mems;
+        std::shared_ptr<concatenated_memory_mapping> concat_mem_mapping;
+        mutable memory::ptr from_mem;
         memory::ptr initial_mem;
         cldnn::stream& stream;
         backedge_type type;
@@ -349,10 +347,11 @@ class typed_primitive_inst<loop> : public typed_primitive_inst_base<loop> {
 
         backedge_memory_mapping(
             std::shared_ptr<primitive_inst> _from_primitive, std::shared_ptr<primitive_inst> _to_primitive,
-            std::vector<memory::ptr> _from_mems, memory::ptr _initial_mem, cldnn::stream& _stream, backedge_type _type = CONCAT_OUTPUT):
+            std::shared_ptr<concatenated_memory_mapping> _concat_mem_mapping, memory::ptr _initial_mem,
+            cldnn::stream& _stream, backedge_type _type = CONCAT_OUTPUT):
             from_primitive(_from_primitive),
             to_primitive(std::move(_to_primitive)),
-            from_mems(_from_mems),
+            concat_mem_mapping(std::move(_concat_mem_mapping)),
             initial_mem(std::move(_initial_mem)),
             stream(_stream),
             type(_type),
@@ -365,7 +364,7 @@ class typed_primitive_inst<loop> : public typed_primitive_inst_base<loop> {
             memory::ptr _from_mem, memory::ptr _initial_mem, cldnn::stream& _stream, backedge_type _type = SINGLE_SHARED):
             from_primitive(_from_primitive),
             to_primitive(std::move(_to_primitive)),
-            from_mems{std::move(_from_mem)},
+            from_mem{std::move(_from_mem)},
             initial_mem(std::move(_initial_mem)),
             stream(_stream),
             type(_type),
@@ -385,161 +384,67 @@ class typed_primitive_inst<loop> : public typed_primitive_inst_base<loop> {
                 validate_backedge_memory();
             }
 
-        void setup_iteration(int64_t iter) const {
-            if (type == CONCAT_OUTPUT) {
-                if (iter == 0) {
-                    to_primitive->set_output_memory(initial_mem);
-                } else if (iter > 0) {
-                    to_primitive->set_output_memory(from_mems.at(iter - 1));
-                } else {
-                    throw std::runtime_error("Invalid iteraton count" + std::to_string(iter));
-                }
-            } else if (type == SINGLE_SHARED && iter == 0) {
-                from_mems.front()->copy_from(stream, *initial_mem);
-            } else if (type == SINGLE) {
-                memory::ptr mem1 = to_primitive->output_memory_ptr();
-                if (iter == 0) {
-                    mem1->copy_from(stream, *initial_mem);
-                } else {
-                    memory::ptr mem2 = from_primitive->output_memory_ptr();
-                    to_primitive->set_output_memory(std::move(mem2));
-                    from_primitive->set_output_memory(mem1);
-                }
-            }
-        }
-
 private:
         void validate_backedge_memory() {
-            for (const auto& from_mem : from_mems) {
+            if (from_mem) {
                 const size_t from_mem_bytes = from_mem->get_layout().bytes_count();
-                if (from_mem_bytes != total_bytes) {
-                    throw std::runtime_error("Invalid backedge memory layout: "
-                        "size not matched with that of initial_mem");
-                }
+                OPENVINO_ASSERT((from_mem_bytes == total_bytes), "Invalid backedge memory layout: size(",
+                        from_mem_bytes, ",", from_mem->get_layout().to_short_string(),
+                        ") not matched with that of initial_mem(", total_bytes,
+                        ",", initial_mem->get_layout().to_short_string(), ")");
             }
-        }
-    };
-
-    struct concatenated_memory_mapping {
-        concatenated_memory_mapping(int64_t axis,
-                                    memory::ptr concatenated_mem,
-                                    std::vector<memory::ptr> sliced_mems,
-                                    stream& stream,
-                                    int64_t iteration_elements = 0,
-                                    int64_t stride = 0,
-                                    int64_t initial_offset = 0) :
-            axis(axis),
-            concatenated_mem(concatenated_mem),
-            sliced_mems(sliced_mems),
-            stream(stream),
-            bytes_per_element(data_type_traits::size_of(concatenated_mem->get_layout().data_type)),
-            batch_size(get_batch_size(concatenated_mem->get_layout(), axis)),
-            bytes_batch_stride((static_cast<int64_t>(concatenated_mem->get_layout().count()) / batch_size) * bytes_per_element),
-            bytes_iteration(iteration_elements * bytes_per_element),
-            bytes_iteration_stride(stride * bytes_iteration),
-            bytes_iteration_initial_offset(initial_offset * bytes_iteration) {}
-
-        static int64_t get_batch_size(layout mem_layout, int64_t axis) {
-            if (axis < 0) {
-                throw std::runtime_error("axis should be positive integer or zero");
-            }
-
-            int64_t batch_size = 1;
-            for (int64_t i = 0; i < axis; ++i) {
-                batch_size *= mem_layout.get_tensor().raw[i];
-            }
-            for (int64_t i = axis-1; i >= 2; --i) {
-                batch_size *= mem_layout.get_tensor().raw[i];
-            }
-            return batch_size;
-        }
-
-        void restore_concatenated_mem() const {
-            mem_lock<uint8_t> concat_mem_lock{ concatenated_mem, stream };
-            int64_t iteration_offset = bytes_iteration_initial_offset;
-            for (const auto& sliced_mem : sliced_mems) {
-                for (int64_t batch = 0; batch < batch_size; ++batch) {
-                    const int64_t src_offset = batch * bytes_iteration;
-                    const int64_t dst_offset = batch * bytes_batch_stride + iteration_offset;
-                    mem_lock<uint8_t> sliced_mem_lock{ sliced_mem, stream };
-                    uint8_t* src = sliced_mem_lock.data() + src_offset;
-                    uint8_t* dst = concat_mem_lock.data() + dst_offset;
-                    std::copy(src, src + bytes_iteration, dst);
+            if (concat_mem_mapping) {
+                for (const auto& from_mem : concat_mem_mapping->get_sliced_mems()) {
+                    const size_t from_mem_bytes = from_mem->get_layout().bytes_count();
+                    OPENVINO_ASSERT((from_mem_bytes == total_bytes), "Invalid backedge memory layout: size(",
+                        from_mem_bytes, ",", from_mem->get_layout().to_short_string(),
+                        ") not matched with that of initial_mem(", total_bytes,
+                        ",", initial_mem->get_layout().to_short_string(), ")");
                 }
-                iteration_offset += bytes_iteration_stride;
             }
         }
-
-        void setup_sliced_output_memory(uint64_t iteration) const {
-            const auto& sliced_output_mem = sliced_mems.at(iteration);
-            sliced_data_prim->set_output_memory(sliced_output_mem);
-        }
-
-        memory::ptr get_sliced_mem(int64_t iteration) const {
-            mem_lock<uint8_t, mem_lock_type::read> from_lock{ concatenated_mem, stream };
-            int64_t batch_offset = 0;
-            const int64_t iteration_offset = bytes_iteration_initial_offset +
-                bytes_iteration_stride * iteration;
-            for (int64_t batch = 0; batch < batch_size; ++batch) {
-                const int64_t src_offset = batch_offset + iteration_offset;
-                const int64_t dst_offset = batch * bytes_iteration;
-                mem_lock<uint8_t> to_lock{ sliced_mems.at(iteration), stream };
-                const auto src = from_lock.begin() + src_offset;
-                const auto dst = to_lock.begin() + dst_offset;
-                std::copy(src, src + bytes_iteration, dst);
-                batch_offset += bytes_batch_stride;
-            }
-            return sliced_mems.at(iteration);
-        }
-
-        const int64_t axis;
-        std::shared_ptr<primitive_inst> concat_data_prim;
-        std::shared_ptr<primitive_inst> sliced_data_prim;
-        memory::ptr concatenated_mem;
-        std::vector<memory::ptr> sliced_mems;
-        cldnn::stream& stream;
-        // element size
-        const int64_t bytes_per_element;
-        // number of higher level of dimension of slicing axis
-        const int64_t batch_size;
-        // stride of batch in concatanated memory
-        const int64_t bytes_batch_stride;
-        // byte size of each iteration per batch in a sliced memory
-        const int64_t bytes_iteration;
-        // byte size of each iteration (bytes_iteration * batch_size) in a sliced memory
-        const int64_t bytes_iteration_stride;
-        // byte offset of 1st iteration in a batch in a sliced memory
-        const int64_t bytes_iteration_initial_offset;
     };
 
-    static layout calc_output_layout(const loop_node& node, kernel_impl_params const& impl_param);
+    template<typename ShapeType>
+    static std::vector<layout> calc_output_layouts(loop_node const& /*node*/, kernel_impl_params const& impl_param);
+    static layout calc_output_layout(const loop_node& /*node*/, kernel_impl_params const& impl_param);
     bool preproc_memories_done = false;
     std::vector<backedge_memory_mapping> backedge_memory_mappings;
-    std::vector<concatenated_memory_mapping> concatenated_input_mem_mappings;
-    std::vector<concatenated_memory_mapping> concatenated_output_mem_mappings;
+    std::vector<concatenated_memory_mapping::ptr> concatenated_input_mem_mappings;
+    std::vector<concatenated_memory_mapping::ptr> concatenated_output_mem_mappings;
 
     static std::string to_string(const loop_node& node);
-    size_t current_iteratoin_backedge_mapping_idx = 0;
 
 public:
     typed_primitive_inst(network& network, const loop_node& node);
     network::ptr get_body_network() const { return body_network; }
-    void preprocess_input_memory();
-    void preprocess_output_memory();
+    void preprocess_input_memory(const int64_t trip_count);
+    void preprocess_output_memory(const int64_t trip_count);
     void preprocess_backedge_memory();
     void update_mapped_memory();
+    void update_input_mapped_memory();
+    void update_output_mapped_memory();
+    void update_backedge_mapped_memory();
+    void postprocess_output_memory(bool is_dynamic);
+    concatenated_memory_mapping::ptr create_concat_memory_map(const input_info& id,
+                                                                const cldnn::loop::io_primitive_map& io_prim_map,
+                                                                memory::ptr mem_ptr,
+                                                                const int64_t trip_count);
     event::ptr set_output_memory(memory::ptr mem, bool check = true, size_t idx = 0) override;
-    const backedge_memory_mapping& get_current_iteration_backedge_mapping() const {
-        OPENVINO_ASSERT(node->is_current_iteration_used(), "[GPU] No backedge mapping for current_iteration for primitive ", node->id());
-        return backedge_memory_mappings.at(current_iteratoin_backedge_mapping_idx);
-    }
+    void reset_memory();
+
     void save(BinaryOutputBuffer& ob) const override;
     void load(BinaryInputBuffer& ib) override;
+    void validate_backedges(loop_node const & node) const;
+
+    void update_shape() override { primitive_inst::update_shape(); }
+    void update_output_layout();
 
 private:
     network::ptr body_network;
-    memory::ptr get_external_memory(const primitive_id& external_id) const;
-    std::vector<memory::ptr> get_sliced_mem(const primitive_id& internal_id) const;
+    memory::ptr get_external_memory(const primitive_id& external_id, size_t mem_idx = 0) const;
+    layout get_external_output_layout(const primitive_id& external_id, size_t mem_idx = 0) const;
+    std::shared_ptr<concatenated_memory_mapping> get_sliced_mem(const primitive_id& internal_id) const;
     std::vector<loop::io_primitive_map> _input_primitive_maps;
     std::vector<loop::io_primitive_map> _output_primitive_maps;
     std::vector<loop::backedge_mapping> _back_edges;
@@ -547,9 +452,13 @@ class typed_primitive_inst<loop> : public typed_primitive_inst_base<loop> {
     primitive_id _initial_execution_id;
     primitive_id _current_iteration_id;
     primitive_id _condition_id;
-    primitive_id _num_iteration_id;
-    int64_t _max_iteration = 0;
+    primitive_id _num_iterations_id;
 };
 
 using loop_inst = typed_primitive_inst<loop>;
+
+static inline std::ostream& operator<< (std::ostream& os, loop_inst::concatenated_memory_mapping& map) {
+    os << map.to_string();
+    return os;
+}
 }  // namespace cldnn
diff --git a/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp b/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp
index fabb1e53329293..69b1e12fa3b4ae 100644
--- a/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp
+++ b/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp
@@ -32,6 +32,7 @@
 #include "region_yolo_inst.h"
 #include "prior_box_inst.h"
 #include "scatter_nd_update_inst.h"
+#include "gather_inst.h"
 #include "to_string_utils.h"
 #include <vector>
 #include <memory>
@@ -306,7 +307,7 @@ bool layout_optimizer::can_fuse_reorder(program_node& prev, program_node& next,
          (fmt_prev == format::b_fs_yx_fsv4 &&
           prev_output_layout.feature() % 32 == 0 &&
           prev_output_layout.spatial(0) == 1 &&
-          prev_output_layout.spatial(1) == 1)))
+          prev_output_layout.spatial(1) == 1)) && is_input_reorder(prev, next))
         return true;
 
     if (next.is_type<convolution>() && fmt_prev == format::b_fs_yx_fsv16 && fmt_next == format::b_fs_yx_fsv4 && is_input_idx(0))
@@ -849,6 +850,18 @@ static bool is_node_for_onednn(reduce_node const& node, format preferred_format)
     return true;
 }
 
+static bool is_node_for_onednn(convolution_node const& node) {
+    if (!layout_optimizer::are_data_types_suitable_for_onednn((program_node&)node))
+        return false;
+
+    auto input_layout = node.get_input_layout(0);
+    auto output_layout = node.get_output_layout(0);
+    if (input_layout.is_dynamic() || output_layout.is_dynamic())
+        return false;
+
+    return true;
+}
+
 static bool is_node_for_onednn(deconvolution_node const& node) {
     auto prim = node.get_primitive();
     auto input_layout = node.get_input_layout(0);
@@ -871,6 +884,9 @@ static bool is_node_for_onednn(deconvolution_node const& node) {
 
 
 static bool is_node_for_onednn(fully_connected_node const& node) {
+    if (!layout_optimizer::are_data_types_suitable_for_onednn((program_node&)node))
+        return false;
+
     auto fc_prim = node.get_primitive();
     // onednn impl doesn't support compressed weights for now
     if (fc_prim->compressed_weights)
@@ -891,6 +907,10 @@ static bool is_node_for_onednn(fully_connected_node const& node) {
     return true;
 }
 
+static bool is_node_for_onednn(gemm_node const& node) {
+    return layout_optimizer::are_data_types_suitable_for_onednn((program_node&)node);
+}
+
 // This function is needed to avoid performance regressions for the convolutions with byxf layout
 // Previously some topologies had scale operations which prevented byxf usage
 // Now instead of scale we have eltwise + fused_ops which might enable byxf convolution in unexpected cases
@@ -1242,6 +1262,20 @@ format layout_optimizer::get_expected_format(quantize_node const& node) {
     return expected;
 }
 
+bool layout_optimizer::is_node_suitable_for_onednn(program_node& node) {
+    if (node.is_type<convolution>()) {
+        return is_node_for_onednn(node.as<convolution>());
+    } else if (node.is_type<deconvolution>()) {
+        return is_node_for_onednn(node.as<deconvolution>());
+    } else if (node.is_type<fully_connected>()) {
+        return is_node_for_onednn(node.as<fully_connected>());
+    } else if (node.is_type<gemm>()) {
+        return is_node_for_onednn(node.as<gemm>());
+    }
+
+    return false;
+}
+
 bool layout_optimizer::are_data_types_suitable_for_onednn(program_node& node) {
     auto in_dt = node.get_input_layout(0).data_type;
     auto out_dt = node.get_output_layout(false).data_type;
@@ -1770,6 +1804,10 @@ format layout_optimizer::get_preferred_format(program_node& node) {
                 node.set_preferred_input_fmt(0, format::bfyx);
             }
         }
+    } else if (node.is_type<gather>()) {
+        // Gather needs the original input/output rank because
+        // the parameters as indices, batch_dims and axis depend on the rank.
+        node.set_preferred_input_fmt(0, format::get_default_format(node.as<gather>().get_primitive()->input_rank));
     }
 
     if (allow_new_shape_infer && node.get_preferred_input_fmt() != format::any) {
@@ -2089,6 +2127,10 @@ void layout_optimizer::set_implementation_forcing(const ov::intel_gpu::ImplForci
     }
 }
 
+const std::map<primitive_id, std::pair<format::type, impl_types>> layout_optimizer::get_implementation_forcing() const {
+    return _forcing_map;
+}
+
 const std::vector<std::pair<format::type, bool>> layout_optimizer::optimized_formats = {
         {format::b_fs_yx_fsv16, true},
         {format::b_fs_yx_fsv16, false},
diff --git a/src/plugins/intel_gpu/src/graph/loop.cpp b/src/plugins/intel_gpu/src/graph/loop.cpp
index 4e33f14e507084..a51c2d0d85973e 100644
--- a/src/plugins/intel_gpu/src/graph/loop.cpp
+++ b/src/plugins/intel_gpu/src/graph/loop.cpp
@@ -3,6 +3,8 @@
 //
 #include "loop_inst.h"
 
+#include "data_inst.h"
+#include "mutable_data_inst.h"
 #include "json_object.h"
 #include "primitive_type_base.h"
 #include "intel_gpu/primitives/data.hpp"
@@ -15,6 +17,41 @@
 namespace cldnn {
 GPU_DEFINE_PRIMITIVE_TYPE_ID(loop)
 
+std::map<size_t, memory::ptr> loop_node::get_memory_deps() const {
+    auto memory_deps = get_const_memory_deps();
+    for (auto& i : get_shape_infer_dependencies()) {
+        auto& dep = get_dependency(i);
+        auto dep_id = dep.id();
+        if (memory_deps.count(i) > 0 || i >= get_dependencies().size()) {
+            continue;
+        }
+
+        memory::ptr mem_ptr = nullptr;
+        if (dep.is_type<data>()) {
+            mem_ptr = dep.as<data>().get_attached_memory_ptr();
+        } else if (dep.is_type<mutable_data>()) {
+            mem_ptr = dep.as<mutable_data>().get_attached_memory_ptr();
+        }
+
+        if (mem_ptr) {
+            memory_deps.insert({i, mem_ptr});
+        }
+    }
+    return memory_deps;
+}
+
+static size_t convert_to_raw_axis(size_t axis, size_t ndim) {
+    // convert between bfyx, bfzyx, bfzyxw and tensor.size.raw
+    if (axis >= ndim) {
+        throw std::runtime_error("axis should be less than ndim");
+    }
+
+    if (axis < 2) {
+        return axis;
+    }
+    return (ndim - 1) - (axis - 2);
+}
+
 static bool check_if_axis_is_set_properly(loop_node const & node) {
     const auto& input_primitive_maps = node.get_input_primitive_maps();
 
@@ -30,11 +67,11 @@ static bool check_if_axis_is_set_properly(loop_node const & node) {
     int32_t iteration_size = -1;
     for (const auto& pm : input_with_axis_iteration) {
         auto found = std::find_if(dependencies.begin(), dependencies.end(),
-            [&pm](const std::pair<program_node*, int32_t>& dep){ return dep.first->id() == pm.get().external_id; });
+            [&pm](const std::pair<program_node*, int32_t>& dep){ return dep.first->id() == pm.get().external_id.pid; });
         assert(found != dependencies.end());
         const layout input_layout = (*found).first->get_output_layout();
         const auto shape = input_layout.get_tensor().sizes(input_layout.format);
-        const size_t iteration_axis = node.convert_to_raw_axis(pm.get().axis, static_cast<int32_t>(shape.size()));
+        const size_t iteration_axis = convert_to_raw_axis(pm.get().axis, static_cast<int32_t>(shape.size()));
         if (iteration_size < 0) {
             iteration_size = shape[iteration_axis];
         } else {
@@ -48,7 +85,7 @@ static bool check_if_axis_is_set_properly(loop_node const & node) {
     for (const auto& input_ref : input_with_axis_iteration) {
         const loop::io_primitive_map& input = input_ref.get();
         auto dep = std::find_if(dependencies.begin(), dependencies.end(),
-            [&input](const std::pair<program_node*, int>& dep) { return input.external_id == dep.first->id(); });
+            [&input](const std::pair<program_node*, int>& dep) { return input.external_id.pid == dep.first->id(); });
 
         // if corresponding external id is not found
         if (dep == dependencies.end()) {
@@ -58,75 +95,104 @@ static bool check_if_axis_is_set_properly(loop_node const & node) {
     return true;
 }
 
-static void validate_backedges(loop_node const & node) {
-    const auto& back_edges = node.get_back_edges();
-    const auto& input_primitive_maps = node.get_input_primitive_maps();
+layout loop_inst::calc_output_layout(loop_node const& /*node*/, kernel_impl_params const& impl_param) {
+    auto prim = impl_param.typed_desc<loop>();
 
-    // check input with iteration axis has backedge
-    for (const auto& back_edge : back_edges) {
-        for (const auto& mapping : input_primitive_maps) {
-            if (mapping.internal_id == back_edge.to && mapping.axis >= 0) {
-                CLDNN_ERROR_MESSAGE(node.id(),
-                    "input with iteration axis should not have backedges");
-            }
-        }
-    }
-}
+    // finds internal output
+    const auto& output_primitive_maps = prim->output_primitive_maps;
+    const auto& output_mapping = output_primitive_maps.front();
 
-layout loop_inst::calc_output_layout(loop_node const & node, kernel_impl_params const& impl_param) {
-    // body program should be built here to calculate body input layout
-    // from outputs of loop's dependency and calculate loop output layout
-    // from the outputs of body program
-    if (!node.get_body_program()) {
-        const_cast<loop_node&>(node).build_body_program();
-    }
+    const auto& body_program = impl_param.inner_progs.front();
+    const auto& body_outputs = body_program->get_outputs();
 
-    // type checks
-    const primitive_id& num_iteration_id = node.get_num_iteration_id();
-    if (!node.get_program().get_node(num_iteration_id).is_type<mutable_data>()) {
-        CLDNN_ERROR_MESSAGE(node.id(), "num_iteration is not mutable_data");
+    const primitive_id& output_internal_id = output_mapping.internal_id.pid;
+    auto target = std::find_if(body_outputs.begin(), body_outputs.end(), [&](const cldnn::program_node * output) {
+        return output->id() == output_internal_id;
+    });
+    OPENVINO_ASSERT(target != body_outputs.end(), impl_param.desc->id, "output not found");
+
+    // set body output layout
+    layout loop_output_layout = (*target)->get_output_layout();
+    const int64_t axis_to_iterate_through = output_mapping.axis;
+    if (axis_to_iterate_through != -1) {
+        const size_t ndim = loop_output_layout.get_rank();
+        auto shape = loop_output_layout.get_dims();
+        shape[axis_to_iterate_through] = static_cast<int32_t>(prim->max_num_iterations);
+        loop_output_layout.set_tensor(tensor(format::get_default_format(ndim), shape));
     }
 
-    if (!check_if_axis_is_set_properly(node)) {
-        CLDNN_ERROR_MESSAGE(node.id(), "axis is not set properly");
-    }
+    return loop_output_layout;
+}
 
+template<typename T>
+static std::vector<layout> get_output_layouts(kernel_impl_params const& impl_param, std::vector<T> body_outputs, const int64_t num_iterations = -1) {
+    auto prim = impl_param.typed_desc<loop>();
+    std::vector<layout> output_layouts;
+
+    const auto& output_primitive_maps = prim->output_primitive_maps;
+    for (auto& output_mapping : output_primitive_maps) {
+        const primitive_id& output_internal_id = output_mapping.internal_id.pid;
+        auto target = std::find_if(body_outputs.begin(), body_outputs.end(), [&](const T output) {
+            return output->id() == output_internal_id;
+        });
+        OPENVINO_ASSERT(target != body_outputs.end(), impl_param.desc->id, "output not found");
 
-    // finds internal output
-    const auto& output_primitive_maps = node.get_output_primitive_maps();
-    const auto& output_mapping = output_primitive_maps.front();
-    const auto& body_outputs = node.get_body_program()->get_outputs();
-    const primitive_id& output_internal_id = output_mapping.internal_id;
-    auto target = std::find_if(body_outputs.begin(), body_outputs.end(), [&](const cldnn::program_node * output) {
-        return output->id() == output_internal_id;
-    });
-    layout loop_output_layout;
-    if (target == body_outputs.end()) {
-        CLDNN_ERROR_MESSAGE(impl_param.desc->id, "output not found");
-    } else {
         // set body output layout
-        loop_output_layout = (*target)->get_output_layout();
-        const int64_t axis_to_iterate_throgh = output_mapping.axis;
-        if (axis_to_iterate_throgh != -1) {
-            const size_t ndim = loop_output_layout.get_rank();
-            auto shape = loop_output_layout.get_dims();
-            shape[axis_to_iterate_throgh] = static_cast<int32_t>(node.get_max_iteration());
-            loop_output_layout.set_tensor(tensor(format::get_default_format(ndim), shape));
+        layout loop_output_layout = (*target)->get_output_layout();
+        const int64_t axis_to_iterate_through = output_mapping.axis;
+        if (axis_to_iterate_through != -1) {
+            auto shape = loop_output_layout.get_partial_shape();
+            shape[axis_to_iterate_through] = static_cast<int32_t>(num_iterations);
+            loop_output_layout.set_partial_shape(shape);
         }
+        output_layouts.push_back(loop_output_layout);
     }
-    return loop_output_layout;
+    return output_layouts;
+}
+
+template<typename ShapeType>
+std::vector<layout> loop_inst::calc_output_layouts(loop_node const& /*node*/, kernel_impl_params const& impl_param) {
+    std::vector<layout> output_layouts;
+    auto prim = impl_param.typed_desc<loop>();
+    if (impl_param.inner_nets.empty()) {
+        OPENVINO_ASSERT(impl_param.inner_progs.size() == 1, "Loop(", prim->id, ") should have only one inner network");
+        const auto& body_outputs = impl_param.inner_progs.front()->get_outputs();
+        output_layouts = get_output_layouts<program_node*>(impl_param, body_outputs);
+    } else {
+        auto& memory_deps = impl_param.memory_deps;
+        const size_t current_iteration_idx = 0;
+        OPENVINO_ASSERT(memory_deps.count(current_iteration_idx) > 0, "The count of memory deps(current_iteration) should not be zero");
+        cldnn::mem_lock<int64_t, mem_lock_type::read> current_iterations_lock(memory_deps.at(current_iteration_idx), impl_param.get_stream());
+        int64_t current_iteration = static_cast<int64_t>(*current_iterations_lock.data());
+        GPU_DEBUG_LOG << "* current_iteration(" << memory_deps.at(current_iteration_idx) << ")  : " << current_iteration << std::endl;
+
+        OPENVINO_ASSERT(impl_param.inner_nets.size() == 1, "Loop(", prim->id, ") should have only one inner program");
+        const auto& body_outputs = impl_param.inner_nets.front()->get_outputs();
+        output_layouts = get_output_layouts<std::shared_ptr<primitive_inst>>(impl_param, body_outputs, current_iteration);
+    }
+    return output_layouts;
 }
 
+template std::vector<layout> loop_inst::calc_output_layouts<ov::PartialShape>(loop_node const& node, const kernel_impl_params& impl_param);
+
+
 std::string loop_inst::to_string(const loop_node & node) {
     auto desc = node.get_primitive();
     auto node_info = node.desc_to_json();
 
+    std::vector<primitive_id> body_inputs;
+    {
+        for (auto& input : desc->body_program->get_inputs()) {
+            body_inputs.push_back(input->id());
+        }
+    }
+
     json_composite loop_info;
-    loop_info.add("body input id", desc->body.get_primitives_ids());
+    loop_info.add("body input id", body_inputs);
     loop_info.add("trip_count_id", desc->trip_count_id);
-    loop_info.add("initial_execution_id", desc->initial_execution_id);
-    loop_info.add("current_iteration_id", desc->current_iteration_id);
-    loop_info.add("condition_id", desc->condition_id);
+    loop_info.add("first_execution_condition_id", desc->first_execution_condition_id);
+    loop_info.add("body_current_iteration_id", desc->body_current_iteration_id);
+    loop_info.add("body_execution_condition_id", desc->body_execution_condition_id);
 
     std::stringstream primitive_description;
     node_info->add("loop info", loop_info);
@@ -142,23 +208,23 @@ static std::vector<const loop::io_primitive_map*> find_io_primitive_maps(
     std::vector<const loop::io_primitive_map*> ret;
     if (is_external) {
         for (const auto& it : input_primitive_maps) {
-            if (it.external_id == prim_id) {
+            if (it.external_id.pid == prim_id) {
                 ret.push_back(&it);
             }
         }
         for (const auto& it : output_primitive_maps) {
-            if (it.external_id == prim_id) {
+            if (it.external_id.pid == prim_id) {
                 ret.push_back(&it);
             }
         }
     } else {
         for (const auto& it : input_primitive_maps) {
-            if (it.internal_id == prim_id) {
+            if (it.internal_id.pid == prim_id) {
                 ret.push_back(&it);
             }
         }
         for (const auto& it : output_primitive_maps) {
-            if (it.internal_id == prim_id) {
+            if (it.internal_id.pid == prim_id) {
                 ret.push_back(&it);
             }
         }
@@ -175,24 +241,19 @@ static void validate_mappings(loop_node const & node) {
     for (const auto& id : outer_inputs) {
         if (id == node.get_trip_count_id() ||
             id == node.get_initial_execution_id() ||
-            id == node.get_num_iteration_id()) {
+            id == node.get_num_iterations_id()) {
             continue;
         }
         const auto results = find_io_primitive_maps(node.get_input_primitive_maps(),
                                                     node.get_output_primitive_maps(), id, true);
-        if (results.size() == 0) {
-            std::string msg = "outer input '" + id + "' does not have primitive map";
-            CLDNN_ERROR_MESSAGE(node.id(), msg.c_str());
-        }
+        OPENVINO_ASSERT(results.size() > 0, node.id(), " : outer input '", id, "' does not have primitive map");
     }
 
     // check all io_primitive_maps have their corresponding external id
     for (const auto& pm : input_primitive_maps) {
-        auto found = std::find(outer_inputs.begin(), outer_inputs.end(), pm.external_id);
-        if (found == outer_inputs.end()) {
-            std::string msg = "external id '" + pm.external_id + "' in primitive map cannot be found loop inputs";
-            CLDNN_ERROR_MESSAGE(node.id(), msg.c_str());
-        }
+        auto found = std::find(outer_inputs.begin(), outer_inputs.end(), pm.external_id.pid);
+        OPENVINO_ASSERT(found != outer_inputs.end(), node.id(),
+                        " : external id '", pm.external_id.pid, "' in primitive map cannot be found loop inputs");
     }
 
     const auto& nodes = node.get_body_program()->get_processing_order();
@@ -200,50 +261,25 @@ static void validate_mappings(loop_node const & node) {
     // check all io_primitive_maps have their corresponding interal id
     for (const auto& pm : input_primitive_maps) {
         auto found = std::find_if(nodes.begin(), nodes.end(), [&pm](const program_node* body_input) {
-            return body_input->id() == pm.internal_id;
+            return body_input->id() == pm.internal_id.pid;
         });
-        if (found == nodes.end()) {
-            std::string msg = "internal id '" + pm.internal_id + "' in primitive map cannot be found loop body";
-            CLDNN_ERROR_MESSAGE(node.id(), msg.c_str());
-        }
+        OPENVINO_ASSERT(found != nodes.end(), node.id(),
+                    " : internal id '", pm.internal_id.pid, "' in primitive map cannot be found loop body");
     }
     for (const auto& pm : output_primitive_maps) {
         auto found = std::find_if(nodes.begin(), nodes.end(), [&pm](const program_node* body_output) {
-            return body_output->id() == pm.internal_id;
+            return body_output->id() == pm.internal_id.pid;
         });
-        if (found == nodes.end()) {
-            std::string msg = "internal id '" + pm.internal_id + "' in primitive map cannot be found body body";
-            CLDNN_ERROR_MESSAGE(node.id(), msg.c_str());
-        }
+        OPENVINO_ASSERT(found != nodes.end(), node.id(),
+                    " : internal id '", pm.internal_id.pid, "' in primitive map cannot be found body body");
     }
 }
 
-void loop_inst::update_mapped_memory() {
-    if (!preproc_memories_done) {
-        return;
-    }
-    // update output memory
-    for (size_t i = 0; i < _output_primitive_maps.size(); ++i) {
-        const auto& output_mapping = _output_primitive_maps.at(i);
-        const primitive_id& external_id = output_mapping.external_id;
-        const primitive_id& internal_id = output_mapping.internal_id;
-        memory::ptr to_mem = get_external_memory(external_id);
-        if (output_mapping.axis < 0) {
-            body_network->get_primitive(internal_id)->set_output_memory(to_mem);
-        } else {
-            for (auto& mem_mapping : concatenated_output_mem_mappings) {
-                if (mem_mapping.sliced_data_prim->id() == internal_id) {
-                    mem_mapping.concatenated_mem = to_mem;
-                    break;
-                }
-            }
-        }
-    }
-    // update input memory
+void loop_inst::update_input_mapped_memory() {
     for (size_t memory_num = 0; memory_num < inputs_memory_count(); memory_num++) {
         const primitive_id& input_external_id = dependencies().at(memory_num).first->id();
         auto input_map_ptrs = find_io_primitive_maps(_input_primitive_maps,
-                                                     _output_primitive_maps, input_external_id, true);
+                                                    _output_primitive_maps, input_external_id, true);
         if (input_map_ptrs.empty()) {
             if (input_external_id == _trip_count_id ||
                 input_external_id == _initial_execution_id) {
@@ -257,36 +293,69 @@ void loop_inst::update_mapped_memory() {
             bool is_concatenated_input = (input_map->axis >= 0);
             if (is_concatenated_input) {
                 for (auto& mem_mapping : concatenated_input_mem_mappings) {
-                    if (mem_mapping.sliced_data_prim->id() == input_map->internal_id) {
-                        mem_mapping.concatenated_mem = memory;
+                    if (mem_mapping->sliced_data_prim->id() == input_map->internal_id.pid) {
+                        mem_mapping->update_concatenated_mem(memory);
                         break;
                     }
                 }
             } else {
-                body_network->set_input_data(input_map->internal_id, memory);
+                body_network->set_input_data(input_map->internal_id.pid, memory);
             }
         }
     }
-    //update backedges memory
+}
+
+void loop_inst::update_output_mapped_memory() {
+    if (is_dynamic()) {
+        if (!outputs_allocated()) {
+            _outputs = allocate_outputs(_impl_params.get(), true, true);
+        }
+    }
+
+    for (size_t i = 0; i < _output_primitive_maps.size(); ++i) {
+        const auto& output_mapping = _output_primitive_maps.at(i);
+        const primitive_id& external_id = output_mapping.external_id.pid;
+        const size_t external_mem_idx = output_mapping.external_id.idx;
+        const primitive_id& internal_id = output_mapping.internal_id.pid;
+        const size_t internal_mem_idx = output_mapping.internal_id.idx;
+
+        memory::ptr to_mem = get_external_memory(external_id, external_mem_idx);
+        if (to_mem) {
+            if (output_mapping.axis < 0) {
+                body_network->get_primitive(internal_id)->set_output_memory(to_mem, true, internal_mem_idx);
+            } else {
+                for (auto& mem_mapping : concatenated_output_mem_mappings) {
+                    if (mem_mapping->sliced_data_prim->id() == internal_id) {
+                        mem_mapping->update_concatenated_mem(to_mem);
+                        break;
+                    }
+                }
+            }
+        }
+    }
+}
+
+void loop_inst::update_backedge_mapped_memory() {
     // checking if memory is a destination of a backedge
     for (const auto& back_edge : _back_edges) {
         //find corresponding input of the backedge
         const auto input_map_ptrs = find_io_primitive_maps(_input_primitive_maps,
-                                                           _output_primitive_maps, back_edge.to, false);
+                                                            _output_primitive_maps, back_edge.to, false);
         assert(input_map_ptrs.size() == 1);
         const auto& input_map = input_map_ptrs.front();
-        auto backedged_sliced_output_mems = get_sliced_mem(back_edge.from);
+        auto backedged_sliced_output = get_sliced_mem(back_edge.from);
         const auto backedge_to_prim = body_network->get_primitive(back_edge.to);
         const auto backedge_from_prim = body_network->get_primitive(back_edge.from);
-        memory::ptr initial_mem = get_external_memory(input_map->external_id);
+
+        memory::ptr initial_mem = get_external_memory(input_map->external_id.pid, input_map->external_id.idx);
 
         for (auto& backedge_mapping : backedge_memory_mappings) {
             if (backedge_mapping.from_primitive->id() == backedge_from_prim->id() &&
                 backedge_mapping.to_primitive->id() == backedge_to_prim->id()) {
-                if (backedged_sliced_output_mems.empty()) {
+                if (backedged_sliced_output == nullptr) {
                     // backedge output which does not need concatenation
                     const auto output_mapping = find_io_primitive_maps(_input_primitive_maps,
-                                                                       _output_primitive_maps, back_edge.from, false);
+                                                                        _output_primitive_maps, back_edge.from, false);
                     memory::ptr backedge_mem;
                     if (output_mapping.empty()) {
                         // from and to primitives in backedge are connected directly
@@ -300,14 +369,15 @@ void loop_inst::update_mapped_memory() {
                             backedge_mem = body_network->get_engine().allocate_memory(output_layout, 0);
                         }
                     } else {
-                        backedge_mem = get_external_memory(output_mapping.front()->external_id);
+                        auto external_id = output_mapping.front()->external_id;
+                        backedge_mem = get_external_memory(external_id.pid, external_id.idx);
                     }
                     body_network->set_input_data(back_edge.to, backedge_mem);
                     body_network->set_output_memory(back_edge.from, backedge_mem);
-                    backedge_mapping.from_mems = { backedge_mem };
+                    backedge_mapping.from_mem = backedge_mem;
                     backedge_mapping.initial_mem = initial_mem;
                 } else {
-                    backedge_mapping.from_mems = backedged_sliced_output_mems;
+                    backedge_mapping.concat_mem_mapping = backedged_sliced_output;
                     backedge_mapping.initial_mem = initial_mem;
                 }
                 break;
@@ -316,92 +386,138 @@ void loop_inst::update_mapped_memory() {
     }
 }
 
+
+void loop_inst::update_mapped_memory() {
+    if (!preproc_memories_done) {
+        return;
+    }
+
+    update_output_mapped_memory();
+    update_input_mapped_memory();
+    update_backedge_mapped_memory();
+}
+
 event::ptr loop_inst::set_output_memory(memory::ptr mem, bool check, size_t idx) {
     auto ev = primitive_inst::set_output_memory(mem, check, idx);
     update_mapped_memory();
     return ev;
 }
 
-void loop_inst::preprocess_output_memory() {
-    auto& engine = _network.get_engine();
-    concatenated_output_mem_mappings.reserve(_output_primitive_maps.size());
-    for (size_t i = 0; i < _output_primitive_maps.size(); ++i) {
-        const auto& output_mapping = _output_primitive_maps.at(i);
-        const primitive_id& external_id = output_mapping.external_id;
-        const primitive_id& internal_id = output_mapping.internal_id;
-        if (output_mapping.axis < 0) {
-            memory::ptr memory = get_external_memory(external_id);
-            body_network->get_primitive(internal_id)->set_output_memory(memory);
+loop_inst::concatenated_memory_mapping::ptr loop_inst::create_concat_memory_map(const input_info& internal_id,
+                                        const cldnn::loop::io_primitive_map& io_prim_map,
+                                        memory::ptr mem_ptr,
+                                        const int64_t trip_count) {
+    auto& engine = body_network->get_engine();
+    auto& stream = body_network->get_stream();
+    auto prim = body_network->get_primitive(internal_id.pid);
+    const int64_t start = io_prim_map.start < 0? trip_count - 1: io_prim_map.start;
+
+    std::vector<memory::ptr> sliced_mems;
+    int64_t num_elements_iteration = 0;
+
+    // if memory is nullptr, that means memory is not allocated yet because current network is dynamic shape model.
+    // In dynamic model, we can't calculate num_element_iteration, start, and sliced_layout.
+    // will recalculate that parameters in backedge preprocessing map after first execution.
+    if (mem_ptr != nullptr) {
+        layout sliced_layout = prim->output_memory(internal_id.idx).get_layout();
+
+        // When trip_count is -1, allocate first sliced_mem and allocate sliced memory if additional sliced mem is required
+        if (trip_count < 0) {
+            memory::ptr sliced_mem = engine.allocate_memory(sliced_layout, 0);
+            sliced_mems.push_back(sliced_mem);
         } else {
-            memory::ptr to_mem = get_external_memory(external_id);
-            auto output_prim = body_network->get_primitive(internal_id);
-            layout sliced_layout = output_prim->output_memory().get_layout();
-
-            const int64_t max_iteration = _max_iteration;
-            std::vector<memory::ptr> sliced_mems;
-            sliced_mems.reserve(max_iteration);
-            for (int32_t j = 0; j < max_iteration; ++j) {
+            sliced_mems.reserve(trip_count);
+            for (int j=0; j < trip_count; ++j) {
                 memory::ptr sliced_mem = engine.allocate_memory(sliced_layout, 0);
                 sliced_mems.push_back(sliced_mem);
             }
+        }
 
-            const int64_t num_elements_batch = concatenated_memory_mapping::get_batch_size(
-                sliced_layout, output_mapping.axis);
-            const int64_t num_elements_iteration = sliced_layout.count() / num_elements_batch;
-            const int64_t start = output_mapping.start < 0? _max_iteration - 1: output_mapping.start;
-            concatenated_memory_mapping memory_mapping_info(
-                output_mapping.axis, std::move(to_mem), sliced_mems, _network.get_stream(),
-                num_elements_iteration, output_mapping.stride, start);
-            memory_mapping_info.sliced_data_prim = body_network->get_primitive(internal_id);
-            memory_mapping_info.concat_data_prim = get_network().get_primitive(external_id);
-            concatenated_output_mem_mappings.push_back(memory_mapping_info);
+        const int64_t num_elements_batch = concatenated_memory_mapping::get_batch_size(
+            sliced_layout, io_prim_map.axis);
+        num_elements_iteration = sliced_layout.count() / num_elements_batch;
+    }
+
+    auto concat_memory_mapping = std::make_shared<concatenated_memory_mapping>(
+                                                    io_prim_map.axis, mem_ptr, sliced_mems, stream,
+                                                    engine, num_elements_iteration, io_prim_map.stride, start);
+    concat_memory_mapping->sliced_data_prim = body_network->get_primitive(internal_id.pid);
+    return concat_memory_mapping;
+}
+
+void loop_inst::preprocess_output_memory(const int64_t trip_count) {
+    if (concatenated_output_mem_mappings.empty())
+        concatenated_output_mem_mappings.reserve(_output_primitive_maps.size());
+    for (size_t i = 0; i < _output_primitive_maps.size(); ++i) {
+        const auto& output_mapping = _output_primitive_maps.at(i);
+        const auto& external_id = output_mapping.external_id;
+        const auto& internal_id = output_mapping.internal_id;
+        GPU_DEBUG_LOG << i << ") output mapping - external " << external_id.to_string() << std::endl;
+        GPU_DEBUG_LOG << i << ") output mapping - internal " << internal_id.to_string() << std::endl;
+
+        memory::ptr memory = get_external_memory(external_id.pid, external_id.idx);
+        if (output_mapping.axis < 0) {
+            // In dynamic model, Don't get output memory of loop node because body network's output layouts are not calculated
+            if (memory != nullptr) {
+                body_network->get_primitive(internal_id.pid)->set_output_memory(memory, true, internal_id.idx);
+            }
+        } else {
+            auto iter = std::find_if(concatenated_output_mem_mappings.begin(), concatenated_output_mem_mappings.end(),
+                [&](loop_inst::concatenated_memory_mapping::ptr concat_mem_map) -> bool {
+                    return concat_mem_map->sliced_data_prim->id() == internal_id.pid;
+                });
+            if (iter == concatenated_output_mem_mappings.end()) {
+                auto memory_mapping_info = create_concat_memory_map(internal_id, output_mapping, memory, trip_count);
+                memory_mapping_info->concat_data_prim = get_network().get_primitive(external_id.pid);
+                concatenated_output_mem_mappings.push_back(memory_mapping_info);
+                GPU_DEBUG_LOG << i << ") generate concat output memory mapping: " << memory_mapping_info->to_string() << std::endl;
+            }
+            GPU_DEBUG_IF(iter != concatenated_output_mem_mappings.end()) {
+                GPU_DEBUG_LOG << i << ") memory_mapping_info is already existed : " << (*iter)->to_string() << std::endl;
+            }
         }
     }
 }
 
-void loop_inst::preprocess_input_memory() {
-    auto& engine = _network.get_engine();
-    auto& iteration_mem = concatenated_input_mem_mappings;
+void loop_inst::preprocess_input_memory(const int64_t trip_count) {
     for (size_t memory_num = 0; memory_num < inputs_memory_count(); memory_num++) {
         const primitive_id& input_external_id = dependencies().at(memory_num).first->id();
         auto input_map_ptrs = find_io_primitive_maps(_input_primitive_maps,
-                                                     _output_primitive_maps, input_external_id, true);
+                                                    _output_primitive_maps, input_external_id, true);
         if (input_map_ptrs.size() == 0) {
-            if (input_external_id == _trip_count_id ||
-                input_external_id == _initial_execution_id) {
-                continue;
-            }
-            CLDNN_ERROR_MESSAGE(id(), "loop primitive_map is incomplete");
+            OPENVINO_ASSERT((input_external_id == _trip_count_id
+                                || input_external_id == _num_iterations_id
+                                || input_external_id == _initial_execution_id),
+                                id(), "loop primitive_map is incomplete "
+                                "input_external_id(", input_external_id, ") != _trip_count_id(", _trip_count_id, ")",
+                                "input_external_id(", input_external_id, ") != _num_iterations_id(", _num_iterations_id, ")",
+                                " && input_external_id(", input_external_id, ") != _initial_execution_id(", _initial_execution_id, ")");
+            continue;
         }
 
         auto memory = input_memory_ptr(memory_num);
         for (size_t i = 0; i < input_map_ptrs.size(); ++i) {
             const auto input_map = input_map_ptrs.at(i);
-            bool is_concatenated_input = (input_map->axis >= 0);
-            if (is_concatenated_input) {
-                layout sliced_layout
-                    = body_network->get_primitive(input_map->internal_id)->output_memory().get_layout();
-                const int64_t max_iteration = _max_iteration;
-                std::vector<memory::ptr> sliced_mems;
-                sliced_mems.reserve(max_iteration);
-                for (int j=0; j < max_iteration; ++j) {
-                    memory::ptr sliced_mem = engine.allocate_memory(sliced_layout, 0);
-                    sliced_mems.push_back(sliced_mem);
-                }
-                const int64_t num_elements_batch = concatenated_memory_mapping::get_batch_size(
-                    sliced_layout, input_map->axis);
-                const int64_t num_elements_iteration = sliced_layout.count() / num_elements_batch;
-                const int64_t start = input_map->start < 0? _max_iteration - 1: input_map->start;
-                concatenated_memory_mapping concatenated_input_mem_mapping_info(
-                    input_map->axis, memory, sliced_mems, _network.get_stream(),
-                    num_elements_iteration, input_map->stride, start);
-                concatenated_input_mem_mapping_info.sliced_data_prim = body_network->get_primitive(input_map->internal_id);
-                iteration_mem.push_back(concatenated_input_mem_mapping_info);
+            const auto& external_id = input_map->external_id;
+            const auto& internal_id = input_map->internal_id;
+            GPU_DEBUG_LOG << i << ") input mapping - external " << external_id.to_string() << std::endl;
+            GPU_DEBUG_LOG << i << ") input mapping - internal " << internal_id.to_string() << std::endl;
+
+            if (input_map->axis >= 0) {
+                OPENVINO_ASSERT(trip_count > 0, "In preprocessing concat input mapping, trip_count should be positive");
+                OPENVINO_ASSERT(memory != nullptr, "In preprocessing concat input mapping, concat memory should be allocated");
+                auto memory_mapping_info = create_concat_memory_map(internal_id, *input_map, memory, trip_count);
+                concatenated_input_mem_mappings.push_back(memory_mapping_info);
+                GPU_DEBUG_LOG << i << ") generate concat input memory mapping: " << memory_mapping_info->to_string() << std::endl;
             } else {
-                if (memory->get_layout().data_type != body_network->get_primitive(input_map->internal_id)->output_memory().get_layout().data_type) {
-                    CLDNN_ERROR_MESSAGE(id(), "incompatible datatypes");
+                auto input_inst = body_network->get_primitive(internal_id.pid);
+                if (memory->get_layout() != input_inst->get_output_layout()) {
+                    input_inst->set_output_layout(memory->get_layout());
+                    GPU_DEBUG_LOG << input_inst->id() << " is changed memory because layout is changed from "
+                                        << input_inst->get_output_layout().to_short_string()
+                                        << " to " << memory->get_layout().to_short_string() << std::endl;
                 }
-                body_network->set_input_data(input_map->internal_id, memory);
+                body_network->set_input_data(internal_id.pid, memory);
             }
         }
     }
@@ -409,88 +525,141 @@ void loop_inst::preprocess_input_memory() {
 
 void loop_inst::preprocess_backedge_memory() {
     // checking if memory is a destination of a backedge
-    for (const auto& back_edge : _back_edges) {
+    for (size_t idx = 0; idx < _back_edges.size(); idx++) {
+        const auto& back_edge = _back_edges[idx];
         //find corresponding input of the backedge
         const auto input_map_ptrs = find_io_primitive_maps(_input_primitive_maps,
-                                                           _output_primitive_maps, back_edge.to, false);
+                                                            _output_primitive_maps, back_edge.to, false);
         const auto backedge_to_prim = body_network->get_primitive(back_edge.to);
         const auto backedge_from_prim = body_network->get_primitive(back_edge.from);
 
         memory::ptr initial_mem;
-        if (back_edge.to == _current_iteration_id) {
-            const layout current_iteration_layout = backedge_to_prim->output_memory().get_layout();
-            initial_mem = get_network().get_engine().allocate_memory(current_iteration_layout);
-            auto& stream = get_network().get_stream();
-            loop_node::write_scalar_value(initial_mem, stream, 0);
-            current_iteratoin_backedge_mapping_idx = backedge_memory_mappings.size();
+        OPENVINO_ASSERT(!input_map_ptrs.empty(), id(), " has no input_mapping for backedged input");
+        auto& external_id = input_map_ptrs.front()->external_id;
+        initial_mem = get_external_memory(external_id.pid, external_id.idx);
+
+        GPU_DEBUG_LOG << idx << ") back_edge mapping - back_edge.from " << back_edge.from << std::endl;
+        GPU_DEBUG_LOG << idx << ") back_edge mapping - back_edge.to   " << back_edge.to << std::endl;
+
+        auto backedged_sliced_output = get_sliced_mem(back_edge.from);
+        const auto output_mapping = find_io_primitive_maps(_input_primitive_maps,
+                                                            _output_primitive_maps, back_edge.from, false);
+        if (backedged_sliced_output != nullptr) {
+            // CONCAT_OUTPUT mode, backedge output which needs concatenation
+            backedge_memory_mappings.emplace_back(
+                backedge_from_prim, backedge_to_prim, backedged_sliced_output, initial_mem, body_network->get_stream());
+            GPU_DEBUG_LOG << idx << ") add back_edge mapping with CONCAT_OUTPUT type, backedged_sliced_output("
+                            << backedged_sliced_output << "), initial_mem(" << initial_mem << ")" << std::endl;
+        } else if (output_mapping.empty() && backedge_to_prim == backedge_from_prim->dependencies().front().first) {
+            // SINGLE mode, from and to primitives in backedge are connected directly
+            backedge_memory_mappings.emplace_back(
+                backedge_from_prim, backedge_to_prim, initial_mem, body_network->get_stream());
+            GPU_DEBUG_LOG << idx << ") add back_edge mapping with SINGLE type, initial_mem(" << initial_mem << ")" << std::endl;
         } else {
-            if (input_map_ptrs.empty()) {
-                CLDNN_ERROR_MESSAGE(id(), "no input_mapping for backedged input");
-            }
-            initial_mem = get_external_memory(input_map_ptrs.front()->external_id);
-        }
-
-        auto backedged_sliced_output_mems = get_sliced_mem(back_edge.from);
-        if (backedged_sliced_output_mems.empty()) {
-            // backedge output which does not need concatenation
-            const auto output_mapping = find_io_primitive_maps(_input_primitive_maps,
-                                                               _output_primitive_maps, back_edge.from, false);
+            // SINGLE_SHARED mode
             memory::ptr backedge_mem;
-            if (output_mapping.empty()) {
-                // from and to primitives in backedge are connected directly
-                if (backedge_to_prim == backedge_from_prim->dependencies().front().first) {
-                    backedge_memory_mappings.emplace_back(
-                        backedge_from_prim, backedge_to_prim, initial_mem, body_network->get_stream());
-                    continue;
+            auto output_prim = body_network->get_primitive(back_edge.from);
+
+            if (is_dynamic()) {
+                if (output_prim->outputs_allocated()) {
+                    auto internal_output_prim_mem = output_prim->output_memory_ptr();
+                    if (internal_output_prim_mem->get_layout() == initial_mem->get_layout()) {
+                        backedge_mem = internal_output_prim_mem;
+                        body_network->set_input_data(back_edge.to, backedge_mem);
+                        GPU_DEBUG_LOG << idx << ") Get backedge_mem(" << backedge_mem
+                                    << ") from back_edge.from(" << back_edge.from << ")" << std::endl;
+                    } else {
+                        // When input layout is changed or backedge_mem is null
+                        // because output layout of body network is not calculated yet,
+                        // Set backedge_mem to nullptr and update it after first execution.
+                        body_network->set_input_data(back_edge.to, initial_mem);
+                        GPU_DEBUG_LOG << idx << ") Just set input data using initial_mem because back_edge.from("
+                                                << back_edge.from << ") layout is changed or backedge_mem is nullptr" << std::endl;
+                    }
                 } else {
-                    auto output_prim = body_network->get_primitive(back_edge.from);
-                    layout output_layout = output_prim->output_memory().get_layout();
-                    backedge_mem = body_network->get_engine().allocate_memory(output_layout, 0);
+                    body_network->set_input_data(back_edge.to, initial_mem);
+                    GPU_DEBUG_LOG << idx << ") Just set input data using initial_mem because back_edge.from("
+                                            << back_edge.from << ") has dynamic layout now" << std::endl;
                 }
             } else {
-                backedge_mem = get_external_memory(output_mapping.front()->external_id);
+                if (output_mapping.empty()) {
+                    backedge_mem = output_prim->output_memory_ptr();
+                    body_network->set_input_data(back_edge.to, backedge_mem);
+                    GPU_DEBUG_LOG << idx << ") Get backedge_mem(" << backedge_mem
+                                    << ") from back_edge.from(" << back_edge.from << ")" << std::endl;
+                } else {
+                    // Set input and output memory for body_network using external output memory of loop op
+                    auto& out_mapping_ext_id = output_mapping.front()->external_id;
+                    backedge_mem = get_external_memory(out_mapping_ext_id.pid, out_mapping_ext_id.idx);
+                    GPU_DEBUG_LOG << idx << ") Get backedge_mem(" << backedge_mem << ") from output_mapping_external_id.pid("
+                                    << out_mapping_ext_id.pid << ")" << std::endl;
+
+                    body_network->set_input_data(back_edge.to, backedge_mem);
+                    body_network->set_output_memory(back_edge.from, backedge_mem);
+                }
             }
-            body_network->set_input_data(back_edge.to, backedge_mem);
-            body_network->set_output_memory(back_edge.from, backedge_mem);
+
             backedge_memory_mappings.emplace_back(
                 backedge_from_prim, backedge_to_prim, backedge_mem, initial_mem, body_network->get_stream());
-        } else {
-            // backedge output which needs concatenation
-            backedge_memory_mappings.emplace_back(
-                backedge_from_prim, backedge_to_prim, backedged_sliced_output_mems, initial_mem, body_network->get_stream());
+            GPU_DEBUG_LOG << idx << ") add back_edge mapping with SINGLE_SHARED type, backedge_mem("
+                            << backedge_mem << "), initial_mem(" << initial_mem << ")" << std::endl;
         }
     }
 }
 
-std::vector<memory::ptr> loop_inst::get_sliced_mem(const primitive_id& internal_id) const {
+std::shared_ptr<loop_inst::concatenated_memory_mapping> loop_inst::get_sliced_mem(const primitive_id& internal_id) const {
     for (const auto& mem_mapping : concatenated_input_mem_mappings) {
-        if (mem_mapping.sliced_data_prim->id() == internal_id) {
-            return mem_mapping.sliced_mems;
+        if (mem_mapping->sliced_data_prim->id() == internal_id) {
+            return mem_mapping;
         }
     }
     for (const auto& mem_mapping : concatenated_output_mem_mappings) {
-        if (mem_mapping.sliced_data_prim->id() == internal_id) {
-            return mem_mapping.sliced_mems;
+        if (mem_mapping->sliced_data_prim->id() == internal_id) {
+            return mem_mapping;
+        }
+    }
+    return nullptr; // not found
+}
+
+void loop_inst::validate_backedges(loop_node const & node) const {
+    const auto& back_edges = node.get_back_edges();
+    const auto& input_primitive_maps = node.get_input_primitive_maps();
+
+    // check input with iteration axis has backedge
+    for (const auto& back_edge : back_edges) {
+        for (const auto& mapping : input_primitive_maps) {
+            OPENVINO_ASSERT((mapping.internal_id.pid != back_edge.to || mapping.axis < 0),
+                node.id(), ": input with iteration axis should not have backedges");
         }
     }
-    return {}; // not found
 }
 
-memory::ptr loop_inst::get_external_memory(const primitive_id& external_id) const {
+memory::ptr loop_inst::get_external_memory(const primitive_id& external_id, size_t mem_idx) const {
     const auto outputPrim = _network.get_primitive(external_id);
-    return outputPrim->output_memory_ptr();
+    if (outputPrim->outputs_allocated()) {
+        return outputPrim->output_memory_ptr(mem_idx);
+    }
+    return nullptr;
+}
+
+layout loop_inst::get_external_output_layout(const primitive_id& external_id, size_t mem_idx) const {
+    const auto outputPrim = _network.get_primitive(external_id);
+    return outputPrim->get_output_layout(mem_idx);
 }
 
 loop_inst::typed_primitive_inst(network & network, loop_node const & node)
     : parent(network, node),
-      preproc_memories_done(false),
-      body_network(network::allocate_network(network.get_stream_ptr(),
-                                                  node.get_body_program(),
-                                                  false,
-                                                  network.is_primary_stream())) {
-    if (!check_if_axis_is_set_properly(node))
-        CLDNN_ERROR_MESSAGE(node.id(), "axis is not set properly");
-
+        preproc_memories_done(false),
+        body_network(network::allocate_network(network.get_stream_ptr(),
+                                                node.get_body_program(),
+                                                false,
+                                                network.is_primary_stream())) {
+    const primitive_id& num_iterations_id = node.get_num_iterations_id();
+    OPENVINO_ASSERT(node.get_program().get_node(num_iterations_id).is_type<mutable_data>(),
+                        node.id(), ": num_iterations is not mutable_data");
+    OPENVINO_ASSERT(check_if_axis_is_set_properly(node), node.id(), ": axis is not set properly");
+
+    set_inner_networks({body_network});
     validate_backedges(node);
     validate_mappings(node);
 
@@ -500,9 +669,8 @@ loop_inst::typed_primitive_inst(network & network, loop_node const & node)
     _trip_count_id = node.get_trip_count_id();
     _initial_execution_id = node.get_initial_execution_id();
     _current_iteration_id = node.get_current_iteration_id();
-    _condition_id = node.get_condition_id();
-    _num_iteration_id = node.get_num_iteration_id();
-    _max_iteration = node.get_max_iteration();
+    _condition_id = node.get_execution_condition_id();
+    _num_iterations_id = node.get_num_iterations_id();
 }
 
 void loop_inst::save(BinaryOutputBuffer& ob) const {
@@ -514,8 +682,7 @@ void loop_inst::save(BinaryOutputBuffer& ob) const {
     ob << _initial_execution_id;
     ob << _current_iteration_id;
     ob << _condition_id;
-    ob << _num_iteration_id;
-    ob << _max_iteration;
+    ob << _num_iterations_id;
     body_network->save(ob);
 }
 
@@ -529,9 +696,97 @@ void loop_inst::load(BinaryInputBuffer& ib) {
     ib >> _initial_execution_id;
     ib >> _current_iteration_id;
     ib >> _condition_id;
-    ib >> _num_iteration_id;
-    ib >> _max_iteration;
+    ib >> _num_iterations_id;
     body_network = std::make_shared<cldnn::network>(ib, get_network().get_stream_ptr(), get_network().get_engine(), get_network().is_primary_stream(), 0);
 }
 
+void loop_inst::postprocess_output_memory(bool is_dynamic) {
+    if (is_dynamic) {
+        for (size_t i = 0; i < _output_primitive_maps.size(); ++i) {
+            const auto& output_mapping = _output_primitive_maps.at(i);
+            const auto& external_id = output_mapping.external_id;
+            const auto& internal_id = output_mapping.internal_id;
+            if (output_mapping.axis < 0) {
+                auto internalOutputPrim = get_body_network()->get_primitive(internal_id.pid);
+                auto internal_mem = internalOutputPrim->output_memory_ptr(internal_id.idx);
+                if (internal_mem == nullptr) {
+                    continue;
+                }
+                auto externalOutputPrim = _network.get_primitive(external_id.pid);
+                if (!externalOutputPrim->outputs_allocated()) {
+                    externalOutputPrim->set_output_memory(internal_mem, external_id.idx);
+                } else {
+                    auto external_mem = externalOutputPrim->output_memory_ptr(external_id.idx);
+                    if (external_mem->get_layout() != internal_mem->get_layout()) {
+                        externalOutputPrim->set_output_memory(internal_mem, external_id.idx);
+                    } else if (external_mem != internal_mem) {
+                        external_mem->copy_from(get_network().get_stream(), *internal_mem);
+                    }
+                }
+            } else {
+                auto externalOutputPrim = _network.get_primitive(external_id.pid);
+                if (!externalOutputPrim->outputs_allocated() || shape_changed()) {
+                    auto concat_layout = _impl_params->get_output_layout(external_id.idx);
+                    auto concat_mem = _network.get_engine().allocate_memory(concat_layout, 0);
+                    externalOutputPrim->set_output_memory(concat_mem, external_id.idx);
+                    auto iter = std::find_if(concatenated_output_mem_mappings.begin(),
+                                                concatenated_output_mem_mappings.end(),
+                                                [&](std::shared_ptr<loop_inst::concatenated_memory_mapping> &concat_output){
+                                                    return concat_output->concat_data_prim->id() == external_id.pid;
+                                                });
+                    if (iter != concatenated_output_mem_mappings.end()) {
+                        (*iter)->update_concatenated_mem(concat_mem);
+                    }
+                }
+            }
+        }
+    }
+
+    for (size_t i = 0; i < concatenated_output_mem_mappings.size(); ++i) {
+        const auto& concat_output = concatenated_output_mem_mappings.at(i);
+        concat_output->restore_concatenated_mem();
+    }
+}
+
+void loop_inst::reset_memory() {
+    backedge_memory_mappings.clear();
+    concatenated_input_mem_mappings.clear();
+    for (auto concat_mem_map : concatenated_output_mem_mappings) {
+        concat_mem_map->reset_data_for_shape_changed();
+    }
+}
+
+
+void loop_inst::update_output_layout() {
+    if (_node == nullptr)
+        return;
+
+    auto memory_deps = _node->get_const_memory_deps();
+    for (auto& i : _node->get_shape_infer_dependencies()) {
+        auto dep_id = _node->get_dependency(i).id();
+        if (memory_deps.count(i) > 0 || i >= _node->get_dependencies().size()) {
+            continue;
+        }
+
+        auto dep_mem = _network.get_output_memory(dep_id);
+        memory_deps.insert({i, dep_mem});
+    }
+    _impl_params->memory_deps = memory_deps;
+
+    auto new_layouts = _node->type()->calc_output_layouts(*_node, *_impl_params);
+    if (new_layouts.empty()) {
+        auto new_layout = _node->type()->calc_output_layout(*_node, *_impl_params);
+        new_layout.data_padding = padding::max(_node->get_primitive()->output_paddings[0], new_layout.data_padding);
+        _impl_params->output_layouts[0] = new_layout;
+    } else {
+        if (_impl_params->output_layouts.size() < new_layouts.size()) {
+            _impl_params->output_layouts.resize(new_layouts.size());
+        }
+        for (size_t i = 0; i < new_layouts.size(); ++i) {
+            auto new_layout = new_layouts[i];
+            new_layout.data_padding = padding::max(_node->get_primitive()->output_paddings[i], new_layout.data_padding);
+            _impl_params->output_layouts[i] = new_layout;
+        }
+    }
+}
 }  // namespace cldnn
diff --git a/src/plugins/intel_gpu/src/graph/network.cpp b/src/plugins/intel_gpu/src/graph/network.cpp
index 51b988076d18f3..240db96d5b4988 100644
--- a/src/plugins/intel_gpu/src/graph/network.cpp
+++ b/src/plugins/intel_gpu/src/graph/network.cpp
@@ -758,7 +758,10 @@ void network::reset_execution(bool wait) {
             get_stream().wait_for_events(events);
         }
     }
-    _events.clear();
+
+    // Move events to temporarily map to deallocate them at the end of network::execute() call for better overlapping with
+    // kernels execution, since it may take significant time for high amount of events
+    _old_events = std::move(_events);
 }
 
 event::ptr network::set_input_data(const primitive_id& id, memory::ptr data) {
@@ -1457,6 +1460,9 @@ void network::execute_impl(const std::vector<event::ptr>& events) {
     // In scenarios with a big number of very small networks it can provide performance drop.
     get_stream().flush();
 
+    // Deallocate events from the previos iteration
+    _old_events.clear();
+
     GPU_DEBUG_IF(debug_config->dump_runtime_memory_pool > 0) {
         get_memory_pool().dump(get_id());
     }
diff --git a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
index eea18ca1fe6b79..6c1e88de349115 100644
--- a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
+++ b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
@@ -425,7 +425,7 @@ event::ptr primitive_inst::realloc_if_needed() {
 
     auto current_shape = actual_layout.get_shape();
     auto& sp = get_network().get_shape_predictor();
-    auto dt_size = data_type_traits::size_of(actual_layout.data_type);
+    auto dt_size = ov::element::Type(actual_layout.data_type).bitwidth();
     auto prealloc_info = sp.predict_preallocation_shape(id(), current_shape, dt_size, can_reuse_buffer);
     if (prealloc_info.first && sp.can_preallocate(ov::shape_size(prealloc_info.second) * dt_size)) {
         auto new_layout = actual_layout;
@@ -685,6 +685,7 @@ void primitive_inst::do_runtime_skip_reorder() {
             GPU_DEBUG_TRACE_DETAIL << "[do runtime skip reorder] update shape for user " << u->id() << std::endl;
             u->update_shape();
             u->update_shape_done_by_other = true;
+
             if (u->_impl_params->get_input_layout() == u->_impl_params->get_output_layout()) {
                 std::function<void(std::vector<std::shared_ptr<primitive_inst>>)> update_memory_dependencies;
                 update_memory_dependencies = [&](std::vector<std::shared_ptr<primitive_inst>> users) {
@@ -699,6 +700,10 @@ void primitive_inst::do_runtime_skip_reorder() {
                 update_memory_dependencies(u->get_user_insts());
 
                 u->set_can_be_optimized(true);
+                // Opt out reorder which has _needs_completion_event = true causes syncronization failed in dGPU.
+                if (_needs_completion_event == false && u->_needs_completion_event == true) {
+                    _needs_completion_event = true;
+                }
                 GPU_DEBUG_TRACE_DETAIL << "[do runtime skip reorder] set user " << u->id() << " as can_be_optimized" << std::endl;
             } else {
                 GPU_DEBUG_TRACE_DETAIL << "[do runtime skip reorder] user " << u->id() << " cannot be optimized" << std::endl;
diff --git a/src/plugins/intel_gpu/src/graph/prior_box.cpp b/src/plugins/intel_gpu/src/graph/prior_box.cpp
index 899f0db6f2ba4a..571a2c6d92c218 100644
--- a/src/plugins/intel_gpu/src/graph/prior_box.cpp
+++ b/src/plugins/intel_gpu/src/graph/prior_box.cpp
@@ -401,12 +401,12 @@ void prior_box_node::calc_result() {
 
     // perform calculations
     if (get_output_layout().data_type == data_types::f16)
-        calculate_prior_box_output<data_type_to_type<data_types::f16>::type>(result,
+        calculate_prior_box_output<ov::element_type_traits<data_types::f16>::value_type>(result,
                                                                              get_program().get_stream(),
                                                                              input().get_output_layout(),
                                                                              *typed_desc());
     else
-        calculate_prior_box_output<data_type_to_type<data_types::f32>::type>(result,
+        calculate_prior_box_output<ov::element_type_traits<data_types::f32>::value_type>(result,
                                                                              get_program().get_stream(),
                                                                              input().get_output_layout(),
                                                                              *typed_desc());
diff --git a/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp b/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp
index 684057ee4726f8..f8aa9fb1c08c60 100644
--- a/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp
+++ b/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp
@@ -188,6 +188,25 @@ void dump_graph_init(std::ofstream& graph,
 
         return out;
     };
+    const auto dump_mem_preferred_info = [](const program_node* ptr) {
+        std::string out = "";
+        auto input_fmts = ptr->get_preferred_input_fmts();
+        if (!input_fmts.empty()) {
+            out += "preferred_in_fmt";
+            for (auto& fmt : input_fmts) {
+                out += ":" + fmt_to_str(fmt);
+            }
+        }
+        auto output_fmts = ptr->get_preferred_output_fmts();
+        if (!output_fmts.empty()) {
+            out += "\npreferred_out_fmt";
+            for (auto& fmt : output_fmts) {
+                out += ":" + fmt_to_str(fmt);
+            }
+        }
+
+        return out;
+    };
 
     graph << "digraph cldnn_program {\n";
     for (auto& node : program.get_processing_order()) {
@@ -220,6 +239,7 @@ void dump_graph_init(std::ofstream& graph,
             }
         }
         graph << "\n" + dump_mem_info(node);
+        graph << "\n" + dump_mem_preferred_info(node);
         graph << "\"";
 #ifdef __clang__
 #pragma clang diagnostic pop
diff --git a/src/plugins/intel_gpu/src/graph/program_node.cpp b/src/plugins/intel_gpu/src/graph/program_node.cpp
index 62c11e2f7e8066..dc9b2029ff408c 100644
--- a/src/plugins/intel_gpu/src/graph/program_node.cpp
+++ b/src/plugins/intel_gpu/src/graph/program_node.cpp
@@ -208,7 +208,8 @@ std::unique_ptr<json_composite> program_node::desc_to_json() const {
 #endif
         impls.push_back(selected_impl->get_kernel_name());
 
-        if (get_preferred_impl_type() == impl_types::ocl) {
+        auto preferred_impl_type = get_preferred_impl_type();
+        if (preferred_impl_type != impl_types::onednn && preferred_impl_type != impl_types::cpu) {
             json_composite cl_dump_info;
             cl_dump_info.add("batch_hash", selected_impl->get_kernels_dump_info().first);
             cl_dump_info.add("kernel_entry", selected_impl->get_kernels_dump_info().second);
@@ -331,6 +332,8 @@ layout program_node::get_non_padded_output_layout(bool invalidate_users_if_chang
 
 bool program_node::set_output_layout(layout& new_layout, bool invalidate_users_if_changed, size_t idx) {
     merge_output_padding(new_layout.data_padding, idx);
+    OPENVINO_ASSERT(idx < output_layouts.size(), id(), " has invalid index : index is ", std::to_string(idx),
+                                        " but output_layouts length is ", std::to_string(output_layouts.size()));
     new_layout.data_padding = output_layouts[idx].data_padding;
     bool changed = (new_layout != output_layouts[idx]);
     if (changed && invalidate_users_if_changed)  // output_layout has changed! invalidate users
diff --git a/src/plugins/intel_gpu/src/graph/scatter_nd_update.cpp b/src/plugins/intel_gpu/src/graph/scatter_nd_update.cpp
index 40d2b48d8edfaf..0f64b64a0bcad7 100644
--- a/src/plugins/intel_gpu/src/graph/scatter_nd_update.cpp
+++ b/src/plugins/intel_gpu/src/graph/scatter_nd_update.cpp
@@ -69,8 +69,9 @@ scatter_nd_update_inst::typed_primitive_inst(network& network, scatter_nd_update
 void scatter_nd_update_inst::on_execute() {
     auto input1_shape = _impl_params->input_layouts[1].get_partial_shape();
     auto input2_shape = _impl_params->input_layouts[2].get_partial_shape();
+    auto same_layouts = _impl_params->input_layouts[0] == _impl_params->output_layouts[0];
 
-    if ((ov::shape_size(input1_shape.to_shape()) == 0) || (ov::shape_size(input2_shape.to_shape()) == 0))
+    if (same_layouts && ((ov::shape_size(input1_shape.to_shape()) == 0) || (ov::shape_size(input2_shape.to_shape()) == 0)))
         reuse_input();
 }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl
index d4992801a80447..f6dacec4a73c80 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl
@@ -120,7 +120,7 @@ KERNEL(fc)(
     uint input_offset = out_b * TILE_IN_B_PITCH + INPUT0_OFFSET;
     uint weights_offset = out_f * INPUT_ELEMENTS_COUNT;
 
-#if COMPRESSED_WEIGHTS
+#if COMPRESSED_WEIGHTS && DECOMPRESSION_SCALE_GROUPS_NUM == 1
     #if DECOMPRESSION_SCALE_LENGTH > 1 && DECOMPRESSION_SCALE_LENGTH % SIMD == 0
         ACCUMULATOR_VEC_TYPE d_scale = BLOCK_READN(ACCUMULATOR_TYPE, TILE_OFM, decompression_scale, out_f);
     #elif DECOMPRESSION_SCALE_LENGTH > 1 && DECOMPRESSION_SCALE_LENGTH % SIMD != 0
@@ -134,9 +134,11 @@ KERNEL(fc)(
         ACCUMULATOR_VEC_TYPE d_scale = decompression_scale[0];
     #endif
 
-    #if !DECOMPRESSION_ZP_TERM
-        ACCUMULATOR_VEC_TYPE d_zp = 0;
-    #elif DECOMPRESSION_ZP_LENGTH > 1 && DECOMPRESSION_ZP_LENGTH % SIMD == 0
+    ACCUMULATOR_TYPE* d_scales = (ACCUMULATOR_TYPE*)(&d_scale);
+#endif
+
+#if COMPRESSED_WEIGHTS && DECOMPRESSION_ZP_TERM && DECOMPRESSION_ZP_GROUPS_NUM == 1
+    #if DECOMPRESSION_ZP_LENGTH > 1 && DECOMPRESSION_ZP_LENGTH % SIMD == 0
         ACCUMULATOR_VEC_TYPE d_zp = BLOCK_READN(ACCUMULATOR_TYPE, TILE_OFM, decompression_zp, out_f);
     #elif DECOMPRESSION_ZP_LENGTH > 1 && DECOMPRESSION_ZP_LENGTH % SIMD != 0
         ACCUMULATOR_VEC_TYPE d_zp = 0;
@@ -148,9 +150,7 @@ KERNEL(fc)(
     #else
         ACCUMULATOR_VEC_TYPE d_zp = decompression_zp[0];
     #endif
-
-    ACCUMULATOR_TYPE* ds = (ACCUMULATOR_TYPE*)(&d_scale);
-    ACCUMULATOR_TYPE* dzp = (ACCUMULATOR_TYPE*)(&d_zp);
+    ACCUMULATOR_TYPE* d_zps = (ACCUMULATOR_TYPE*)(&d_zp);
 #endif
 
 #if REALIGN_FP16_OFFSET
@@ -193,7 +193,28 @@ KERNEL(fc)(
                 ACCUMULATOR_TYPE* w = (ACCUMULATOR_TYPE*)(&wei);
                 unroll_for(uint kii = 0; kii < TILE_K; ++kii) {
                     unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) {
-                        w[kii * TILE_OFM + fi] = (w[kii * TILE_OFM + fi] - dzp[fi]) * ds[fi];
+                        const uint w_idx = kii * TILE_OFM + fi;
+                        const uint offset_ofm = out_f + fi*SIMD + sglid;
+                        #if DECOMPRESSION_SCALE_GROUPS_NUM > 1
+                            const uint scale_offset = (offset_ofm % DECOMPRESSION_SCALE_BATCH_NUM) * DECOMPRESSION_SCALE_BATCH_PITCH  +
+                                                     ((kii + ki*TILE_K + ni*TILE_IFM*SIMD) / DECOMPRESSION_SCALE_GROUP_SIZE)*DECOMPRESSION_SCALE_FEATURE_PITCH;
+                            ACCUMULATOR_TYPE ds = decompression_scale[scale_offset];
+                        #else
+                            ACCUMULATOR_TYPE ds = d_scales[fi];
+                        #endif
+
+                        #if DECOMPRESSION_ZP_TERM
+                            #if DECOMPRESSION_ZP_GROUPS_NUM > 1
+                                const uint zp_offset = (offset_ofm % DECOMPRESSION_ZP_BATCH_NUM) * DECOMPRESSION_ZP_BATCH_PITCH +
+                                                    ((kii + ki*TILE_K + ni*TILE_IFM*SIMD) / DECOMPRESSION_ZP_GROUP_SIZE) * DECOMPRESSION_ZP_FEATURE_PITCH;
+                                ACCUMULATOR_TYPE dzp = decompression_zp[zp_offset];
+                            #else
+                                ACCUMULATOR_TYPE dzp = d_zps[fi];
+                            #endif
+                        #else
+                            ACCUMULATOR_TYPE dzp = ACCUMULATOR_VAL_ZERO;
+                        #endif
+                        w[w_idx] = (w[w_idx] - dzp) * ds;
                     }
                 }
             #endif
@@ -230,7 +251,28 @@ KERNEL(fc)(
                 ACCUMULATOR_TYPE* w = (ACCUMULATOR_TYPE*)(&wei);
                 unroll_for(uint kii = 0; kii < TILE_K; ++kii) {
                     unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) {
-                        w[kii * TILE_OFM + fi] = (w[kii * TILE_OFM + fi] - dzp[fi]) * ds[fi];
+                        const uint w_idx = kii * TILE_OFM + fi;
+                        uint offset_ofm = out_f + fi*SIMD + get_sub_group_local_id();
+                        #if DECOMPRESSION_SCALE_GROUPS_NUM > 1
+                            const uint scale_offset = (offset_ofm % DECOMPRESSION_SCALE_BATCH_NUM) * DECOMPRESSION_SCALE_BATCH_PITCH +
+                                                     ((kii + ki*TILE_K + ni*TILE_IFM*SIMD) / DECOMPRESSION_SCALE_GROUP_SIZE)*DECOMPRESSION_SCALE_FEATURE_PITCH;
+                            ACCUMULATOR_TYPE ds = decompression_scale[scale_offset];
+                        #else
+                            ACCUMULATOR_TYPE ds = d_scales[fi];
+                        #endif
+
+                        #if DECOMPRESSION_ZP_TERM
+                            #if DECOMPRESSION_ZP_GROUPS_NUM > 1
+                                const uint zp_offset = (offset_ofm % DECOMPRESSION_ZP_BATCH_NUM) * DECOMPRESSION_ZP_BATCH_PITCH +
+                                                    ((kii + ki*TILE_K + ni*TILE_IFM*SIMD) / DECOMPRESSION_ZP_GROUP_SIZE) * DECOMPRESSION_ZP_FEATURE_PITCH;
+                                ACCUMULATOR_TYPE dzp = decompression_zp[zp_offset];
+                            #else
+                                ACCUMULATOR_TYPE dzp = d_zps[fi];
+                            #endif
+                        #else
+                            ACCUMULATOR_TYPE dzp = ACCUMULATOR_VAL_ZERO;
+                        #endif
+                        w[w_idx] = (w[w_idx] - dzp) * ds;
                     }
                 }
             #endif
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bfyx_ref.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bfyx_ref.cl
index 72e8d6d7d3d855..6374e65c4f5fcc 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bfyx_ref.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bfyx_ref.cl
@@ -36,18 +36,24 @@ KERNEL(fc)(
         for (uint x = 0; x < INPUT0_SIZE_X; ++x)
         {
             const uint input0_idx = INPUT0_GET_INDEX(b, ofm, y, x);
-            const uint filter_idx = GET_FILTER_INDEX(FILTER, 0, oym, y, 0, 0);
             #if COMPRESSED_WEIGHTS
-                ACCUMULATOR_TYPE filter_compressed = TO_ACCUMULATOR_TYPE(weights[filter_idx]);
                 #if DECOMPRESSION_ZP_TERM
-                    ACCUMULATOR_TYPE zp = TO_ACCUMULATOR_TYPE(decompression_zp[DECOMPRESSION_ZP_GET_INDEX_SAFE(0, oym, 0, 0)]);
+                    const uint zp_offset = DECOMPRESSION_ZP_GET_INDEX_SAFE(oym, y / DECOMPRESSION_ZP_GROUP_SIZE, 0, 0);
+                    ACCUMULATOR_TYPE zp = TO_ACCUMULATOR_TYPE(decompression_zp[zp_offset]);
                 #else
                     ACCUMULATOR_TYPE zp = ACCUMULATOR_VAL_ZERO;
                 #endif
-                DECOMPRESSION_SCALE_TYPE scale = decompression_scale[DECOMPRESSION_SCALE_GET_INDEX_SAFE(0, oym, 0, 0)];
-                ACCUMULATOR_TYPE filter_val = (TO_ACCUMULATOR_TYPE(filter_compressed) - TO_ACCUMULATOR_TYPE(zp)) * scale;
+                const uint decomp_offset = DECOMPRESSION_SCALE_GET_INDEX_SAFE(oym, y / DECOMPRESSION_SCALE_GROUP_SIZE, 0, 0);
+                DECOMPRESSION_SCALE_TYPE scale = decompression_scale[decomp_offset];
+            #endif
+
+            #if COMPRESSED_WEIGHTS_INT8
+                const uint filter_idx = GET_FILTER_INDEX(FILTER, 0, oym, y, 0, 0);
+                ACCUMULATOR_TYPE filter_compressed = TO_ACCUMULATOR_TYPE(weights[filter_idx]);
+                ACCUMULATOR_TYPE filter_val = (filter_compressed - zp) * scale;
                 dotProd += (ACCUMULATOR_TYPE)(input[input0_idx]) * (ACCUMULATOR_TYPE)(filter_val);
             #else
+                const uint filter_idx = GET_FILTER_INDEX(FILTER, 0, oym, y, 0, 0);
                 dotProd += (ACCUMULATOR_TYPE)(input[input0_idx]) * (ACCUMULATOR_TYPE)(weights[filter_idx]);
             #endif
         }
@@ -67,19 +73,25 @@ KERNEL(fc)(
            for (uint x = 0; x < INPUT0_SIZE_X; ++x)
             {
                 const uint input0_idx = INPUT0_GET_INDEX(b, ifm, y, x);
-                const uint filter_idx = GET_FILTER_INDEX(FILTER, 0, ofm, ifm, y, x);
                 #if COMPRESSED_WEIGHTS
-                    FILTER_TYPE filter_compressed = weights[filter_idx];
                     #if DECOMPRESSION_ZP_TERM
-                        ACCUMULATOR_TYPE zp = decompression_zp[DECOMPRESSION_ZP_GET_INDEX_SAFE(0, ofm, 0, 0)];
+                        const uint zp_offset = DECOMPRESSION_ZP_GET_INDEX_SAFE(ofm, ifm / DECOMPRESSION_ZP_GROUP_SIZE, 0, 0);
+                        ACCUMULATOR_TYPE zp = TO_ACCUMULATOR_TYPE(decompression_zp[zp_offset]);
                     #else
                         ACCUMULATOR_TYPE zp = ACCUMULATOR_VAL_ZERO;
                     #endif
+                    const uint decomp_offset = DECOMPRESSION_SCALE_GET_INDEX_SAFE(ofm, ifm / DECOMPRESSION_SCALE_GROUP_SIZE, 0, 0);
+                    DECOMPRESSION_SCALE_TYPE scale = decompression_scale[decomp_offset];
+                #endif
 
-                    DECOMPRESSION_SCALE_TYPE scale = decompression_scale[DECOMPRESSION_SCALE_GET_INDEX_SAFE(0, ofm, 0, 0)];
-                    ACCUMULATOR_TYPE filter_val = (TO_ACCUMULATOR_TYPE(filter_compressed) - TO_ACCUMULATOR_TYPE(zp)) * scale;
+
+                #if COMPRESSED_WEIGHTS_INT8
+                    const uint filter_idx = GET_FILTER_INDEX(FILTER, 0, ofm, ifm, y, x);
+                    FILTER_TYPE filter_compressed = weights[filter_idx];
+                    ACCUMULATOR_TYPE filter_val = (TO_ACCUMULATOR_TYPE(filter_compressed) - zp) * scale;
                     dotProd += (ACCUMULATOR_TYPE)(input[input0_idx]) * (ACCUMULATOR_TYPE)(filter_val);
                 #else
+                    const uint filter_idx = GET_FILTER_INDEX(FILTER, 0, ofm, ifm, y, x);
                     dotProd += (ACCUMULATOR_TYPE)(input[input0_idx]) * (ACCUMULATOR_TYPE)(weights[filter_idx]);
                 #endif
             }
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/group_normalization_gpu_ref.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/group_normalization_gpu_ref.cl
new file mode 100644
index 00000000000000..2715f90780071d
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/group_normalization_gpu_ref.cl
@@ -0,0 +1,144 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "include/batch_headers/common.cl"
+
+#define NUM_CHANNELS_IN_GROUP (INPUT0_FEATURE_NUM / NUM_GROUPS)
+#define CHANNEL_SIZE (INPUT0_BATCH_PITCH / INPUT0_FEATURE_NUM)
+#define GROUP_SIZE (NUM_CHANNELS_IN_GROUP * CHANNEL_SIZE)
+
+#if MEAN_KERNEL_ENABLED || STANDARD_DEVIATION_KERNEL_ENABLED
+inline void FUNC(kahan_summation)(INPUT0_TYPE elem, __private float* compensation, __private float* sum) {
+    if (isfinite(elem) && isfinite(*sum)) {
+        float temp = *sum + (elem - *compensation);
+        *compensation = (temp - *sum) - (elem - *compensation);
+        *sum = temp;
+    } else {
+        *sum += elem;
+    }
+}
+#endif
+
+#if MEAN_KERNEL_ENABLED
+
+KERNEL (calc_mean_ref)(  __global INPUT0_TYPE* input
+                       , __global float* output
+#if HAS_FUSED_OPS_DECLS
+                       , FUSED_OPS_DECLS
+#endif
+)
+{
+    const int batch = get_global_id(0);
+    if (batch >= INPUT0_BATCH_NUM)
+        return;
+    const int group = get_global_id(1);
+    const int feature_begin = group * NUM_CHANNELS_IN_GROUP;
+    const int feature_end = group * NUM_CHANNELS_IN_GROUP + NUM_CHANNELS_IN_GROUP;
+    float variance = 0.f, error = 0.f, mean_value = 0.f;
+    for (int feature = feature_begin; feature < feature_end; feature++)
+    {
+        if (feature >= INPUT0_FEATURE_NUM)
+            continue;
+#if OUTPUT_DIMS > 4
+        for (int z = 0; z < INPUT0_SIZE_Z; z++)
+#endif
+        for (int y = 0; y < INPUT0_SIZE_Y; y++)
+            for (int x = 0; x < INPUT0_SIZE_X; x++)
+            {
+#if OUTPUT_DIMS == 5
+                size_t input_idx = INPUT0_GET_INDEX(batch, feature, z, y, x);
+#elif OUTPUT_DIMS == 4
+                size_t input_idx = INPUT0_GET_INDEX(batch, feature, y, x);
+#endif
+                FUNC_CALL(kahan_summation)(input[input_idx], &error, &mean_value);
+            }
+    }
+    mean_value /= GROUP_SIZE;
+    output[batch * NUM_GROUPS + group] = mean_value;
+}
+
+#elif STANDARD_DEVIATION_KERNEL_ENABLED
+
+KERNEL (calc_standard_deviation_ref)(  __global INPUT0_TYPE* input
+                                     , __global float* mean
+                                     , __global float* output
+#if HAS_FUSED_OPS_DECLS
+                                     , FUSED_OPS_DECLS
+#endif
+)
+{
+    const int batch = get_global_id(0);
+    if (batch >= INPUT0_BATCH_NUM)
+        return;
+    const int group = get_global_id(1);
+    const output_idx = batch * NUM_GROUPS + group;
+    const int feature_begin = group * NUM_CHANNELS_IN_GROUP;
+    const int feature_end = group * NUM_CHANNELS_IN_GROUP + NUM_CHANNELS_IN_GROUP;
+    float variance = 0.f, error = 0.f;
+    
+    for (int feature = feature_begin; feature < feature_end; feature++)
+    {
+        if (feature >= INPUT0_FEATURE_NUM)
+            continue;
+#if OUTPUT_DIMS > 4
+        for (int z = 0; z < INPUT0_SIZE_Z; z++)
+#endif
+        for (int y = 0; y < INPUT0_SIZE_Y; y++)
+            for (int x = 0; x < INPUT0_SIZE_X; x++)
+            {
+#if OUTPUT_DIMS == 5
+                size_t input_idx = INPUT0_GET_INDEX(batch, feature, z, y, x);
+#elif OUTPUT_DIMS == 4
+                size_t input_idx = INPUT0_GET_INDEX(batch, feature, y, x);
+#endif
+                FUNC_CALL(kahan_summation)(pow(input[input_idx] - mean[output_idx], 2), &error, &variance);
+            }
+    }
+    variance /= GROUP_SIZE;
+    float standard_deviation = sqrt(variance + EPSILON);
+    output[output_idx] = standard_deviation;
+}
+#elif NORMALIZE_KERNEL_ENABLED
+KERNEL (normalize_ref)(  __global INPUT0_TYPE* input
+                       , __global INPUT0_TYPE* scale_values
+                       , __global INPUT0_TYPE* bias_values
+                       , __global float* mean_values
+                       , __global float* standard_deviation_values
+                       , __global OUTPUT_TYPE* output
+#if HAS_FUSED_OPS_DECLS
+                       , FUSED_OPS_DECLS
+#endif
+)
+{
+    const int batch = get_global_id(0);
+#if OUTPUT_DIMS == 4
+    const int feature = get_global_id(1);
+#elif OUTPUT_DIMS == 5
+    const int feature = get_global_id(1) / OUTPUT_SIZE_Z;
+    const int z = get_global_id(1) % OUTPUT_SIZE_Z;
+#endif
+    const int y = get_global_id(2) / OUTPUT_SIZE_X;
+    const int x = get_global_id(2) % OUTPUT_SIZE_X;
+    const int group = feature / NUM_CHANNELS_IN_GROUP;
+    float mean = mean_values[batch * NUM_GROUPS + group];
+    float standard_deviation = standard_deviation_values[batch * NUM_GROUPS + group];
+#if OUTPUT_DIMS == 4
+    size_t output_idx = OUTPUT_GET_INDEX(batch, feature, y, x);
+#elif OUTPUT_DIMS == 5
+    size_t output_idx = OUTPUT_GET_INDEX(batch, feature, z, y, x);
+#endif
+    OUTPUT_TYPE res = ((input[output_idx] - mean) / standard_deviation) * scale_values[feature] + bias_values[feature];
+#if HAS_FUSED_OPS
+    FUSED_OPS;
+    output[output_idx] = FUSED_OPS_RESULT;
+#else
+    output[output_idx] = ACTIVATION(res, ACTIVATION_PARAMS);
+#endif
+}
+
+#endif
+
+#undef NUM_CHANNELS_IN_GROUP
+#undef CHANNEL_SIZE
+#undef GROUP_SIZE
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl
index 9003f23ad1ec8d..052c6721a88141 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl
@@ -280,6 +280,8 @@ inline uint FUNC(get_input_index)(uint g, uint o, uint i, uint z, uint y, uint x
     return GET_FILTER_OS_IYX_OSV_INDEX(INPUT0, o, i, y, x, 32);
 #elif defined INPUT0_LAYOUT_OS_IYX_OSV32__AI32
     return GET_FILTER_OS_IYX_OSV_INDEX(INPUT0, o, i, y, x, 32);
+#elif defined INPUT0_LAYOUT_O_IS_YX_ISV2
+    return GET_FILTER_O_IS_ZYX_ISV16_INDEX(INPUT0, o, i, 0, y, x, 2);
 #elif defined INPUT0_LAYOUT_O_IS_YX_ISV4
     return GET_FILTER_O_IS_ZYX_ISV16_INDEX(INPUT0, o, i, 0, y, x, 4);
 #elif defined INPUT0_LAYOUT_O_IS_YX_ISV16
@@ -501,6 +503,8 @@ inline uint FUNC(get_output_index)(uint g, uint o, uint i, uint z, uint y, uint
     return GET_FILTER_OS_IYX_OSV_INDEX(OUTPUT, o, i, y, x, 32);
 #elif defined OUTPUT_LAYOUT_OS_IYX_OSV64
     return GET_FILTER_OS_IYX_OSV_INDEX(OUTPUT, o, i, y, x, 64);
+#elif defined OUTPUT_LAYOUT_O_IS_YX_ISV2
+    return GET_FILTER_O_IS_ZYX_ISV16_INDEX(OUTPUT, o, i, 0, y, x, 2);
 #elif defined OUTPUT_LAYOUT_O_IS_YX_ISV4
     return GET_FILTER_O_IS_ZYX_ISV16_INDEX(OUTPUT, o, i, 0, y, x, 4);
 #elif defined OUTPUT_LAYOUT_O_IS_YX_ISV16
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/scatter_nd_update_ref.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/scatter_nd_update_ref.cl
index 4ec9b665760e34..8c48ad4d4e9979 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/scatter_nd_update_ref.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/scatter_nd_update_ref.cl
@@ -36,8 +36,10 @@
 
 KERNEL(scatter_nd_update_ref)(OPTIONAL_SHAPE_INFO_ARG
                    const __global INPUT0_TYPE* data,
+#ifdef IS_SECOND_ITER
                    const __global INPUT1_TYPE* indices,
                    const __global INPUT2_TYPE* updates,
+#endif
                    __global OUTPUT_TYPE* output
 #if HAS_FUSED_OPS_DECLS
                    , FUSED_OPS_DECLS
@@ -56,8 +58,9 @@ KERNEL(scatter_nd_update_ref)(OPTIONAL_SHAPE_INFO_ARG
     const uint f = dim2 % OUTPUT_FEATURE_NUM;
     const uint b = dim2 / OUTPUT_FEATURE_NUM;
 
+    const uint input_idx = GET_UPDATES_INDEX(INPUT0, ORDER);
     const uint output_idx = GET_OUTPUT_INDEX(ORDER);
-    INPUT0_TYPE val = data[output_idx];
+    INPUT0_TYPE val = data[input_idx];
     #if HAS_FUSED_OPS
         FUSED_OPS_FIRST_KERNEL;
         output[output_idx] = TO_OUTPUT_TYPE(FUSED_OPS_RESULT_FIRST_KERNEL);
diff --git a/src/plugins/intel_gpu/src/kernel_selector/common_types.h b/src/plugins/intel_gpu/src/kernel_selector/common_types.h
index 148b6c10e39183..7706da6003fe74 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/common_types.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/common_types.h
@@ -32,6 +32,7 @@ enum class KernelType {
     RESHAPE,
     COUNT_NONZERO,
     GATHER_NONZERO,
+    GROUP_NORMALIZATION,
     PERMUTE,
     CONCATENATION,
     RESAMPLE,
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernel_base_opencl.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernel_base_opencl.cpp
index d0d052b44c4ed3..b382561afdac34 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernel_base_opencl.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernel_base_opencl.cpp
@@ -80,6 +80,10 @@ std::string KernelBaseOpenCL::GetEntryPoint(const std::string& templateName,
     // UniqueID = program_id + processing_index + additional weight/reorder tag
     kernelID += "_" + params.uniqueID + "_" + std::to_string(partID);
 
+    // Add "__sa" suffix for shape agnostic kernels
+    if (params.is_shape_agnostic)
+        kernelID += "__sa";
+
     return kernelID;
 }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp
index 6e192c92bfb808..8c6d2af2fd8f69 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp
@@ -316,6 +316,7 @@ std::string toString(WeightsLayout layout) {
         case WeightsLayout::os_is_zyx_osv16_isv16:                       return "OS_IS_ZYX_OSV16_ISV16";
         case WeightsLayout::os_is_zyx_osv32_isv16:                       return "OS_IS_ZYX_OSV32_ISV16";
         case WeightsLayout::os_is_zyx_osv64_isv16:                       return "OS_IS_ZYX_OSV64_ISV16";
+        case WeightsLayout::o_is_yx_isv2:                                return "O_IS_YX_ISV2";
         case WeightsLayout::o_is_yx_isv4:                                return "O_IS_YX_ISV4";
         case WeightsLayout::o_is_yx_isv16:                               return "O_IS_YX_ISV16";
         case WeightsLayout::o_is_zyx_isv16:                              return "O_IS_ZYX_ISV16";
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_base.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_base.cpp
index 3e9eb35cdaaff0..a75d35469837f7 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_base.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_base.cpp
@@ -24,11 +24,23 @@ JitConstants FullyConnectedKernelBase::GetJitConstants(const fully_connected_par
 
     if (params.compressed) {
         jit.AddConstants({MakeJitConstant("COMPRESSED_WEIGHTS", 1)});
+        if (params.weights.GetDType() == WeightsType::INT8 || params.weights.GetDType() == WeightsType::UINT8) {
+            jit.AddConstants({MakeJitConstant("COMPRESSED_WEIGHTS_INT8", 1)});
+        }
+
+        const size_t scale_groups_num = params.decompression_scale.Feature().v;
+        const size_t scale_group_size = params.weights.IFM().v / params.decompression_scale.Feature().v;
         jit.AddConstants({MakeJitConstant("DECOMPRESSION_SCALE_TERM", 1)});
         jit.AddConstants({MakeJitConstant("DECOMPRESSION_SCALE", params.decompression_scale)});
+        jit.AddConstants({MakeJitConstant("DECOMPRESSION_SCALE_GROUPS_NUM", scale_groups_num)});
+        jit.AddConstants({MakeJitConstant("DECOMPRESSION_SCALE_GROUP_SIZE", scale_group_size)});
         if (params.has_decompression_zp) {
+            const size_t zp_groups_num = params.decompression_zero_point.Feature().v;
+            const size_t zp_group_size = params.weights.IFM().v / params.decompression_zero_point.Feature().v;
             jit.AddConstants({MakeJitConstant("DECOMPRESSION_ZP_TERM", 1)});
             jit.AddConstants({MakeJitConstant("DECOMPRESSION_ZP", params.decompression_zero_point)});
+            jit.AddConstants({MakeJitConstant("DECOMPRESSION_ZP_GROUPS_NUM", zp_groups_num)});
+            jit.AddConstants({MakeJitConstant("DECOMPRESSION_ZP_GROUP_SIZE", zp_group_size)});
         }
     }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp
index 6b0407f6580cad..c272124627db23 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp
@@ -241,6 +241,9 @@ FullyConnected_bf_tiled::GetAutoTuneParams(const fully_connected_params& params,
                     .Case(tune_params(8,  std::min(max_tile_ofm, 2u), 1, 1, 1,  1, EXE_MODE_AGE_BASED));
         }
 
+        if (params.compressed && batch == 1)
+            selector.Case(tune_params(1,  std::min(max_tile_ofm, 2u), 4, 2, 1, 1, EXE_MODE_AGE_BASED));
+
         selector.Case([&](const fully_connected_params&) -> tune_params {
             tune_params result(8, std::min(max_tile_ofm, 2u), 1, 2, 1, 1, EXE_MODE_DEFAULT);
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_ref.cpp
new file mode 100644
index 00000000000000..a6dd21c43fe4e3
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_ref.cpp
@@ -0,0 +1,170 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "group_normalization_kernel_ref.h"
+#include <kernel_selector_utils.h>
+
+namespace kernel_selector {
+
+ParamsKey GroupNormalizationKernelRef::GetSupportedKey() const {
+    ParamsKey k;
+    k.EnableInputDataType(Datatype::F16);
+    k.EnableInputDataType(Datatype::F32);
+    k.EnableOutputDataType(Datatype::F16);
+    k.EnableOutputDataType(Datatype::F32);
+    k.EnableAllInputLayout();
+    k.EnableAllOutputLayout();
+    k.EnableTensorOffset();
+    k.EnableTensorPitches();
+    k.EnableBatching();
+    k.EnableDifferentTypes();
+    return k;
+}
+
+static std::size_t InternalBufferSize(const group_normalization_params &params) {
+    const auto& output = params.outputs[0];
+    return output.Batch().v * params.num_groups * sizeof(float);
+}
+
+static GroupNormalizationKernelRef::KernelId operator++(GroupNormalizationKernelRef::KernelId& id) {
+    id = static_cast<GroupNormalizationKernelRef::KernelId>(static_cast<int>(id) + 1);
+    return id;
+}
+
+GroupNormalizationKernelRef::DispatchData GroupNormalizationKernelRef::SetDefault(
+    KernelId id, const group_normalization_params &params) const {
+    DispatchData dispatch_data;
+    auto& output = params.outputs[0];
+    switch (id) {
+    case eCalcMeanKernel:
+    case eCalcStandardDeviationKernel: {
+        auto maxWorkGroupSize = params.engineInfo.maxWorkGroupSize;
+        dispatch_data.gws = std::vector<std::size_t>{
+            output.Batch().v,
+            static_cast<std::size_t>(params.num_groups),
+            1
+        };
+        dispatch_data.lws = std::vector<std::size_t>{
+            output.Batch().v * params.num_groups > maxWorkGroupSize ? maxWorkGroupSize / params.num_groups : output.Batch().v,
+            static_cast<std::size_t>(params.num_groups),
+            1};
+        break;
+    }
+    case eNormalize: {
+        auto in_layout = params.inputs[0].GetLayout();
+        auto out_layout = output.GetLayout();
+        std::vector<std::vector<Tensor::DataChannelName>> dims_by_gws = {
+            { Tensor::DataChannelName::BATCH },
+            { Tensor::DataChannelName::FEATURE, Tensor::DataChannelName::Z  },
+            { Tensor::DataChannelName::X, Tensor::DataChannelName::Y }};
+        dispatch_data.gws = std::vector<std::size_t>{
+            output.Batch().v,
+            output.Feature().v * output.Z().v,
+            output.X().v * output.Y().v};
+        dispatch_data.lws = GetOptimalLocalWorkGroupSizes(dispatch_data.gws, params.engineInfo,
+            in_layout, out_layout, dims_by_gws);
+        break;
+    }
+    default:
+        assert(false);
+        break;
+    }
+    return dispatch_data;
+}
+
+JitConstants GroupNormalizationKernelRef::GetJitConstants(KernelId kernelId,
+                                                          const group_normalization_params &params) const {
+    auto jit = MakeBaseParamsJitConstants(params);
+    jit.AddConstant(MakeJitConstant("EPSILON", static_cast<float>(params.epsilon)));
+    jit.AddConstant(MakeJitConstant("NUM_GROUPS", params.num_groups));
+    switch (kernelId) {
+    case eCalcMeanKernel:
+        jit.AddConstant(MakeJitConstant("MEAN_KERNEL_ENABLED", true));
+        break;
+    case eCalcStandardDeviationKernel:
+        jit.AddConstant(MakeJitConstant("STANDARD_DEVIATION_KERNEL_ENABLED", true));
+        break;
+    case eNormalize: {
+        jit.AddConstant(MakeJitConstant("NORMALIZE_KERNEL_ENABLED", true));
+        jit.AddConstant(MakeJitConstant("INPUT_INDICES_ORDER", "batch, feature, z, y, x"));
+        if (!params.fused_ops.empty()) {
+            FusedOpsConfiguration conf{
+                "",
+                params.outputs[0].Dimentions() == 5 ? std::vector<std::string>{"batch", "feature", "z", "y", "x"} :
+                    std::vector<std::string>{"batch", "feature", "y", "x"},
+                "res",
+                params.outputs[0].GetDType()
+            };
+            jit.Merge(MakeFusedOpsJitConstants(params, {conf}));
+        }
+        break;
+    }
+    default:
+        assert(false);
+        break;
+    }
+    return jit;
+}
+
+void GroupNormalizationKernelRef::SetKernelArguments(const group_normalization_params& params,
+                                                     KernelId kernelId,
+                                                     cldnn::arguments_desc& arguments,
+                                                     std::vector<std::size_t>& internalBufferSizes) {
+    switch (kernelId) {
+    case eCalcMeanKernel: {
+        arguments.push_back({ArgumentDescriptor::Types::INPUT, 0});
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0});
+        internalBufferSizes.push_back(InternalBufferSize(params));
+        break;
+    }
+    case eCalcStandardDeviationKernel: {
+        arguments.push_back({ArgumentDescriptor::Types::INPUT, 0});
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0});
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1});
+        internalBufferSizes.push_back(InternalBufferSize(params));
+        break;
+    }
+    case eNormalize: {
+        arguments.push_back({ArgumentDescriptor::Types::INPUT, 0});
+        arguments.push_back({ArgumentDescriptor::Types::INPUT, 1});
+        arguments.push_back({ArgumentDescriptor::Types::INPUT, 2});
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0});
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1});
+        arguments.push_back({ArgumentDescriptor::Types::OUTPUT, 0});
+        break;
+    }
+    default:
+        assert(false);
+        break;
+    }
+}
+
+KernelsData GroupNormalizationKernelRef::GetKernelsData(const Params &params, const optional_params &options) const {
+    const group_normalization_params& parameters = static_cast<const group_normalization_params&>(params);
+    KernelData kd = KernelData::Default<group_normalization_params>(params, eKernelsNum);
+    kd.internalBufferDataType = Datatype::F32;
+    for (KernelId id = eCalcMeanKernel; id < eKernelsNum; ++id) {
+        auto& kernel = kd.kernels[id];
+        const auto entryPoint = GetEntryPoint(kernelName, parameters.layerID, params, options, id);
+        auto jitConstants = GetJitConstants(id, parameters);
+        const auto jit = CreateJit(kernelName, jitConstants, entryPoint);
+        const auto dispatchData = SetDefault(id, parameters);
+        FillCLKernelData(kernel,
+                         dispatchData,
+                         params.engineInfo,
+                         kernelName,
+                         jit,
+                         entryPoint,
+                         "",
+                         false,
+                         false,
+                         0,
+                         0,
+                         0);
+        SetKernelArguments(parameters, id, kernel.params.arguments, kd.internalBufferSizes);
+    }
+    return {kd};
+}
+
+} // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_ref.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_ref.h
new file mode 100644
index 00000000000000..0737c4d45089bf
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_ref.h
@@ -0,0 +1,60 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "kernel_base_opencl.h"
+#include "kernel_selector_params.h"
+
+namespace kernel_selector {
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// GroupNormalizationParams
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+struct group_normalization_params : public base_params {
+    group_normalization_params() : base_params(KernelType::GROUP_NORMALIZATION) {}
+
+    std::int64_t num_groups{};
+    double epsilon{};
+
+    ParamsKey GetParamsKey() const override {
+        return base_params::GetParamsKey();
+    }
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// group_normalization_optional_params
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+struct group_normalization_optional_params : optional_params {
+    group_normalization_optional_params() : optional_params(KernelType::GROUP_NORMALIZATION) {}
+};
+
+class GroupNormalizationKernelRef : public KernelBaseOpenCL {
+public:
+    using DispatchData = CommonDispatchData;
+    enum KernelId {
+        eCalcMeanKernel,
+        eCalcStandardDeviationKernel,
+        eNormalize,
+        eKernelsNum
+    };
+
+    GroupNormalizationKernelRef() : KernelBaseOpenCL{"group_normalization_gpu_ref"} {}
+    KernelsData GetKernelsData(const Params& params, const optional_params& options) const override;
+    ParamsKey GetSupportedKey() const override;
+    std::vector<FusedOpType> GetSupportedFusedOps() const override {
+        return {
+            FusedOpType::ACTIVATION,
+            FusedOpType::QUANTIZE,
+            FusedOpType::ELTWISE
+        };
+    }
+
+protected:
+    DispatchData SetDefault(KernelId id, const group_normalization_params& params) const;
+    JitConstants GetJitConstants(KernelId kernelId, const group_normalization_params& params) const;
+    static void SetKernelArguments(const group_normalization_params& params,
+                                   KernelId kernelId,
+                                   cldnn::arguments_desc& arguments,
+                                   std::vector<std::size_t>& internalBufferSizes);
+};
+
+}  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_selector.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_selector.cpp
new file mode 100644
index 00000000000000..40a5044c5216b5
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_selector.cpp
@@ -0,0 +1,18 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "group_normalization_kernel_selector.h"
+#include "group_normalization_kernel_ref.h"
+
+namespace kernel_selector {
+
+group_normalization_kernel_selector::group_normalization_kernel_selector() {
+    Attach<GroupNormalizationKernelRef>();
+}
+
+KernelsData group_normalization_kernel_selector::GetBestKernels(const Params &params,
+                                                                const optional_params &options) const {
+    return GetNaiveBestKernel(params, options, KernelType::GROUP_NORMALIZATION);
+}
+
+}  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_selector.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_selector.h
new file mode 100644
index 00000000000000..8e8579e61de41c
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_selector.h
@@ -0,0 +1,19 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "kernel_selector.h"
+
+namespace kernel_selector {
+class group_normalization_kernel_selector : public kernel_selector_base {
+public:
+    static group_normalization_kernel_selector& Instance() {
+        static group_normalization_kernel_selector instance_;
+        return instance_;
+    }
+
+    group_normalization_kernel_selector();
+
+    KernelsData GetBestKernels(const Params& params, const optional_params& options) const override;
+};
+}  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/scatter_update/scatter_nd_update_kernel_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/scatter_update/scatter_nd_update_kernel_ref.cpp
index 9fbe45f3da02a4..1680d39ca27bb6 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/scatter_update/scatter_nd_update_kernel_ref.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/scatter_update/scatter_nd_update_kernel_ref.cpp
@@ -170,6 +170,10 @@ KernelsData ScatterNDUpdateKernelRef::GetKernelsData(const Params& params, const
             kd.kernels[i].params.workGroups.global = dispatchData.gws;
             kd.kernels[i].params.workGroups.local = dispatchData.lws;
             kd.kernels[i].skip_execution = KernelData::SkipKernelExecution(prim_params);
+
+            // Do not skip copy stage if output buffer is not empty or requires modification
+            if (i == 0 && prim_params.outputs[0].LogicalSize() != 0 && prim_params.outputs[0] != prim_params.inputs[0])
+                kd.kernels[i].skip_execution = false;
         }
     };
 
@@ -178,6 +182,7 @@ KernelsData ScatterNDUpdateKernelRef::GetKernelsData(const Params& params, const
     for (int i = 0; i < 2; i++) {
         auto dispatchData = SetDefault(newParams, (i == 1));
         auto entry_point = GetEntryPoint(kernelName, newParams.layerID, params, options, i);
+        auto inputs_number = i == 0 ? 1 : 3;
 
         if (i == 1) {
             size_t input0_rank = newParams.inputs[0].LogicalDims().size();
@@ -213,7 +218,7 @@ KernelsData ScatterNDUpdateKernelRef::GetKernelsData(const Params& params, const
         clKernelData& kernel = kd.kernels[i];
 
         FillCLKernelData(kernel, dispatchData, params.engineInfo, kernelName, jit, entry_point,
-                         "", false, false, 3, GetFusedPrimitiveInputsCount(params), 1, newParams.has_dynamic_tensors());
+                         "", false, false, inputs_number, GetFusedPrimitiveInputsCount(params), 1, newParams.has_dynamic_tensors());
     }
 
     return {kd};
diff --git a/src/plugins/intel_gpu/src/kernel_selector/tensor_type.cpp b/src/plugins/intel_gpu/src/kernel_selector/tensor_type.cpp
index b352059d850dea..3fcd03bdece0db 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/tensor_type.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/tensor_type.cpp
@@ -86,6 +86,7 @@ WeightsTensor::WeightsChannelArray WeightsTensor::weightsChannelArray {{
     { WeightsLayout::os_iyx_osv32__ai32,                          {  0,  1, -1,   2,   3, -1 } },
     { WeightsLayout::os_iyx_osv64,                                {  0,  1, -1,   2,   3, -1 } },
     { WeightsLayout::os_iyx_osv16_rotate_180,                     {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::o_is_yx_isv2,                                {  0,  1, -1,   2,   3, -1 } },
     { WeightsLayout::o_is_yx_isv4,                                {  0,  1, -1,   2,   3, -1 } },
     { WeightsLayout::o_is_yx_isv16,                               {  0,  1, -1,   2,   3, -1 } },
     { WeightsLayout::o_is_zyx_isv16,                              {  0,  1,  2,   3,   4, -1 } },
@@ -617,6 +618,10 @@ NDims WeightsTensor::GetSimpleDims(const std::vector<size_t>& d, WeightsLayout l
 
     // TODO: It's not the right pitches. it's here in order to calculate physical size
     switch (l) {
+        case o_is_yx_isv2:
+            assert(newDims.size() == 4);
+            newDims[2] = RoundUp(newDims[2], 2);
+            break;
         case o_is_yx_isv4:
             assert(newDims.size() == 4);
             newDims[2] = RoundUp(newDims[2], 4);
diff --git a/src/plugins/intel_gpu/src/kernel_selector/tensor_type.h b/src/plugins/intel_gpu/src/kernel_selector/tensor_type.h
index 3d54dfabade1c0..205b3198a7a103 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/tensor_type.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/tensor_type.h
@@ -94,6 +94,7 @@ enum WeightsLayout {
     oxiy,
     iyxo,
     yxio,
+    o_is_yx_isv2,
     o_is_yx_isv4,
     o_is_yx_isv16,
     o_is_zyx_isv16,
@@ -621,6 +622,10 @@ struct TensorBaseT : public TensorBase {
         return same;
     }
 
+    bool operator!=(const TensorBaseT& t) const {
+        return !(*this == t);
+    }
+
     bool SameDims(const TensorBaseT& t) const {
         bool same = dtype == t.dtype && layout == t.layout && dims.size() == t.dims.size();
         if (same) {
diff --git a/src/plugins/intel_gpu/src/plugin/compiled_model.cpp b/src/plugins/intel_gpu/src/plugin/compiled_model.cpp
index 3b8581a1e2e34e..fc04e35748fe6c 100644
--- a/src/plugins/intel_gpu/src/plugin/compiled_model.cpp
+++ b/src/plugins/intel_gpu/src/plugin/compiled_model.cpp
@@ -35,6 +35,20 @@ std::shared_ptr<ov::threading::ITaskExecutor> create_task_executor(const std::sh
     if (config.get_property(ov::internal::exclusive_async_requests)) {
         //exclusive_async_requests essentially disables the streams (and hence should be checked first) => aligned with the CPU behavior
         return plugin->get_executor_manager()->get_executor("GPU");
+    } else if (config.get_property(ov::hint::enable_cpu_pinning)) {
+        auto executor_config =
+            ov::threading::IStreamsExecutor::Config{"Intel GPU plugin executor",
+                                                    0,
+                                                    0,
+                                                    ov::threading::IStreamsExecutor::ThreadBindingType::CORES,
+                                                    1,
+                                                    0,
+                                                    0,
+                                                    ov::threading::IStreamsExecutor::Config::PreferredCoreType::BIG,
+                                                    {{config.get_property(ov::num_streams), MAIN_CORE_PROC, 1, 0, 0}},
+                                                    true};
+        auto post_config = ov::threading::IStreamsExecutor::Config::reserve_cpu_threads(executor_config);
+        return std::make_shared<ov::threading::CPUStreamsExecutor>(post_config);
     } else {
         return std::make_shared<ov::threading::CPUStreamsExecutor>(
             ov::threading::IStreamsExecutor::Config{"Intel GPU plugin executor", config.get_property(ov::num_streams)});
@@ -250,6 +264,7 @@ ov::Any CompiledModel::get_property(const std::string& name) const {
 
             // Configs
             ov::PropertyName{ov::enable_profiling.name(), PropertyMutability::RO},
+            ov::PropertyName{ov::hint::enable_cpu_pinning.name(), PropertyMutability::RO},
             ov::PropertyName{ov::hint::model_priority.name(), PropertyMutability::RO},
             ov::PropertyName{ov::intel_gpu::hint::host_task_priority.name(), PropertyMutability::RO},
             ov::PropertyName{ov::intel_gpu::hint::queue_priority.name(), PropertyMutability::RO},
diff --git a/src/plugins/intel_gpu/src/plugin/ops/condition.cpp b/src/plugins/intel_gpu/src/plugin/ops/condition.cpp
index d9b4e77314e600..c25726f673a2f8 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/condition.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/condition.cpp
@@ -22,6 +22,12 @@ static cldnn::condition::branch gen_branch(ProgramBuilder& p, const std::shared_
                     << ", num inputs: " << op->get_input_size() << std::endl;
 
     auto config = p.get_config();
+    {
+        auto custom_outputs = config.get_property(ov::intel_gpu::custom_outputs);
+        if (!custom_outputs.empty()) {
+            config.set_property(ov::intel_gpu::custom_outputs(std::vector<std::string>({})));
+        }
+    }
     config.set_property(ov::intel_gpu::max_dynamic_batch(1));
     config.set_property(ov::intel_gpu::allow_new_shape_infer(op->is_dynamic()));
 
@@ -61,10 +67,13 @@ static void CreateIfOp(ProgramBuilder& p, const std::shared_ptr<ov::op::v8::If>&
     auto branch_true = gen_branch(p, op, idx_true);
     auto branch_false = gen_branch(p, op, idx_false);
 
+    const size_t num_outputs = op->get_output_size();
+
     const cldnn::condition conditionPrimitive(layerName,
                                 inputs,
                                 branch_true,
-                                branch_false);
+                                branch_false,
+                                num_outputs);
 
     p.add_primitive(*op, conditionPrimitive);
 }
diff --git a/src/plugins/intel_gpu/src/plugin/ops/constant.cpp b/src/plugins/intel_gpu/src/plugin/ops/constant.cpp
index b74d05e4f9aca6..b12536b10ccb9a 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/constant.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/constant.cpp
@@ -19,6 +19,8 @@
 #include "openvino/op/roi_align.hpp"
 #include "openvino/op/variadic_split.hpp"
 #include "openvino/op/util/op_types.hpp"
+#include "openvino/op/loop.hpp"
+#include "openvino/op/tensor_iterator.hpp"
 
 #include "intel_gpu/primitives/data.hpp"
 #include "intel_gpu/runtime/debug_configuration.hpp"
@@ -206,6 +208,13 @@ static void CreateConstantOp(ProgramBuilder& p, const std::shared_ptr<ov::op::v0
             }
         } else if (ov::is_type<ov::op::v3::ROIAlign>(outOp) || ov::is_type<ov::op::v9::ROIAlign>(outOp)) {
             consts[op].needsBatchInterpretation = constDims.size() == 1;
+        } else if ((ov::is_type<ov::op::v5::Loop>(outOp) || ov::is_type<ov::op::v0::TensorIterator>(outOp))) {
+            // when inner network has 1d parameter which is connected to outer loop's constant 1d data,
+            // outer constant 1d data and inner 1d parameter has same bytes_count but layout is different
+            // (outer constant is [1, N, 1, 1] but inner parameter is [N, 1, 1, 1]).
+            // To pass check_memory_to_set in input_layout::set_data for this case, Set constDims to [N, 1, 1, 1]
+            // when constDims is one dim and user op is Loop or TensorIterator.
+            consts[op].needsBatchInterpretation = constDims.size() == 1;
         }
     }
 
diff --git a/src/plugins/intel_gpu/src/plugin/ops/gather.cpp b/src/plugins/intel_gpu/src/plugin/ops/gather.cpp
index 883ebaba1a6dc2..7d941375d5ae14 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/gather.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/gather.cpp
@@ -119,6 +119,7 @@ void CreateGatherOpBase(ProgramBuilder& p, const std::shared_ptr<T>& op, const i
                                         reordered_inputs[0],
                                         reordered_inputs[1],
                                         axis,
+                                        input_rank,
                                         out_shape,
                                         batch_dim,
                                         support_neg_ind);
diff --git a/src/plugins/intel_gpu/src/plugin/ops/group_normalization.cpp b/src/plugins/intel_gpu/src/plugin/ops/group_normalization.cpp
new file mode 100644
index 00000000000000..8c17d111331a67
--- /dev/null
+++ b/src/plugins/intel_gpu/src/plugin/ops/group_normalization.cpp
@@ -0,0 +1,30 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/group_normalization.hpp"
+#include "intel_gpu/plugin/program_builder.hpp"
+#include "intel_gpu/primitives/group_normalization.hpp"
+
+namespace ov {
+namespace intel_gpu {
+
+static void CreateGroupNormalizationOp(ProgramBuilder& p, const std::shared_ptr<op::v12::GroupNormalization>& op) {
+    validate_inputs_count(op, {3});
+    auto inputs = p.GetInputInfo(op);
+    auto layerName = layer_type_name_ID(op);
+    cldnn::group_normalization groupNormalizationPrimitive {
+        layerName,
+        inputs[0],
+        inputs[1],
+        inputs[2],
+        op->get_num_groups(),
+        op->get_epsilon()
+    };
+    p.add_primitive(*op, groupNormalizationPrimitive);
+}
+
+REGISTER_FACTORY_IMPL(v12, GroupNormalization);
+
+}  // namespace intel_gpu
+}  // namespace ov
diff --git a/src/plugins/intel_gpu/src/plugin/ops/loop.cpp b/src/plugins/intel_gpu/src/plugin/ops/loop.cpp
index f44dddb26ba0e6..628b0d7c37d9aa 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/loop.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/loop.cpp
@@ -5,6 +5,7 @@
 #include "intel_gpu/plugin/common_utils.hpp"
 #include "intel_gpu/plugin/plugin.hpp"
 
+#include "openvino/op/tensor_iterator.hpp"
 #include "openvino/op/loop.hpp"
 #include "openvino/op/constant.hpp"
 #include "openvino/op/util/sub_graph_base.hpp"
@@ -20,13 +21,14 @@
 #include <algorithm>
 
 using Loop = ov::op::v5::Loop;
+using TensorIterator = ov::op::v0::TensorIterator;
 
 namespace ov {
 namespace intel_gpu {
 
 template<class DATA_TYPE>
-static DATA_TYPE CreateScalarData(ProgramBuilder &p, const cldnn::primitive_id& id, int64_t num) {
-    auto mem = p.get_engine().allocate_memory({ cldnn::data_types::i64, cldnn::format::bfyx, { 1, 1, 1, 1 } });
+static DATA_TYPE CreateScalarData(ProgramBuilder &p, const cldnn::primitive_id& id, ov::Shape& shape, cldnn::data_types dtype, int64_t num) {
+    auto mem = p.get_engine().allocate_memory({ shape, dtype, cldnn::format::bfyx });
     cldnn::mem_lock<int64_t> ptr{mem, p.get_engine().get_service_stream()};
     *ptr.begin() = num;
     return {id, mem};
@@ -40,52 +42,37 @@ static cldnn::mutable_data CreateAdditionalOutputData(ProgramBuilder &p, const s
     const auto tensor = tensor_from_dims(op->get_output_shape(output_idx));
     cldnn::layout output_layout = cldnn::layout(precision, format, tensor);
     auto mem = p.get_engine().allocate_memory(output_layout);
-    auto md = cldnn::mutable_data(id, {cldnn::input_info(input)}, mem); // cldnn::data cannot set dependency
+    auto md = cldnn::mutable_data(id, {cldnn::input_info(input)}, std::move(mem)); // cldnn::data cannot set dependency
     return md;
 }
 
-static void CreateLoopOp(ProgramBuilder& p, const std::shared_ptr<Loop>& op) {
+static void SetLoopInputOutputMap(ProgramBuilder& p,
+                                    const std::shared_ptr<ov::op::util::SubGraphOp>& op,
+                                    cldnn::primitive::input_info_arr& inputs,
+                                    std::vector<cldnn::loop::io_primitive_map>& input_primitive_maps,
+                                    std::vector<cldnn::loop::io_primitive_map>& output_primitive_maps,
+                                    std::vector<cldnn::loop::backedge_mapping>& back_edges_maps) {
     const std::string layerName = layer_type_name_ID(op);
-    auto inputs = p.GetInputInfo(op);
     const auto& loop_input_descs = op->get_input_descriptions();
     const auto& loop_output_descs = op->get_output_descriptions();
     const auto& body_inputs = op->get_function()->get_parameters();
     const auto& body_outputs = op->get_function()->get_results();
 
-    // Set special body ports: current_iteration input , execution condition output
-    auto special_body_ports = op->get_special_body_ports();
-
-    std::string body_current_iteration_id;
-    if (special_body_ports.current_iteration_input_idx >= 0) {
-        auto current_iteration_input = body_inputs.at(special_body_ports.current_iteration_input_idx);
-        body_current_iteration_id = layer_type_name_ID(current_iteration_input);
-        std::string input_name = ov::op::util::create_ie_output_name(current_iteration_input);
-    }
-
-    cldnn::primitive_id body_execution_condition_id;
-    if (special_body_ports.body_condition_output_idx >= 0) {
-        auto body_condition_output = body_outputs.at(special_body_ports.body_condition_output_idx)->get_input_node_shared_ptr(0);
-        body_execution_condition_id = layer_type_name_ID(body_condition_output);
-    }
-
-    // get body topology from ov::Model
-    ProgramBuilder body_program(op->get_function(), p.get_engine(), p.get_config(), true);
-    auto body_topology = *body_program.get_topology();
-
-    // setup input_primitive_maps/ output_primitive_maps and back_edges
-    std::vector<cldnn::loop::io_primitive_map> input_primitive_maps;
-    std::vector<cldnn::loop::io_primitive_map> output_primitive_maps;
-    std::vector<cldnn::loop::backedge_mapping> back_edges;
+    bool use_new_shape_infer = p.use_new_shape_infer();
 
     // set input mapping & back edges
     for (const auto& loop_input_desc : loop_input_descs) {
-        const cldnn::primitive_id& external_id = inputs.at(loop_input_desc->m_input_index).pid;
+        auto external_id = inputs.at(loop_input_desc->m_input_index);
         auto& body_input = body_inputs.at(loop_input_desc->m_body_parameter_index);
         cldnn::primitive_id internal_id = layer_type_name_ID(body_input);
 
+        GPU_DEBUG_LOG << "loop_input_descs[" << layerName << "] = {m_input_index:" << loop_input_desc->m_input_index << "(external_id: "
+                    << external_id << "), m_body_parameter_index:" << loop_input_desc->m_body_parameter_index
+                    << "(internal_id: " << internal_id << ")}" << std::endl;
+
         // set input mapping
         if (const auto& sliceInfo =
-            std::dynamic_pointer_cast<Loop::SliceInputDescription>(loop_input_desc)) {
+            std::dynamic_pointer_cast<ov::op::util::MultiSubGraphOp::SliceInputDescription>(loop_input_desc)) {
             // sliced input
             input_primitive_maps.emplace_back(external_id, internal_id, sliceInfo->m_axis,
                 sliceInfo->m_start, sliceInfo->m_end, sliceInfo->m_stride);
@@ -96,7 +83,7 @@ static void CreateLoopOp(ProgramBuilder& p, const std::shared_ptr<Loop>& op) {
 
         // set back edges
         if (const auto& mergedInput =
-            std::dynamic_pointer_cast<Loop::MergedInputDescription>(loop_input_desc)) {
+            std::dynamic_pointer_cast<ov::op::util::MultiSubGraphOp::MergedInputDescription>(loop_input_desc)) {
             // backedge
             const auto& to = body_inputs.at(mergedInput->m_body_parameter_index);
             const auto& from = body_outputs.at(mergedInput->m_body_value_index);
@@ -104,81 +91,234 @@ static void CreateLoopOp(ProgramBuilder& p, const std::shared_ptr<Loop>& op) {
             cldnn::primitive_id to_id = layer_type_name_ID(to);
             cldnn::primitive_id from_id = layer_type_name_ID(from);
 
-            // reset output data type because the data types of the outputs of the
-            // body topology are always FP32 regardless of element type
-            {
-                const auto from_prim = body_topology.at(from_id);
-                const auto to_cldnn_type = cldnn::element_type_to_data_type(to->get_element_type());
-                from_prim->output_data_types = {to_cldnn_type};
-            }
-            back_edges.emplace_back(from_id, to_id);
+            back_edges_maps.emplace_back(from_id, to_id);
         }
     }
 
-    // set trip count, initial execution condition, num iteration primitives
-    // they should be mutable_data to prevent from being optimized out
-    const cldnn::primitive_id trip_count_id = layer_type_name_ID(op->get_input_node_shared_ptr(0));
-    const cldnn::primitive_id execution_condition_id = layer_type_name_ID(op->get_input_node_shared_ptr(1));
-    const int64_t num_iterations = op->get_num_iterations();
-    if (num_iterations < 0) {
-        OPENVINO_THROW("loop's num_iteration cannot be negative");
+    // set output mapping
+    if (use_new_shape_infer) {
+        for (const auto& loop_output_desc : loop_output_descs) {
+            cldnn::input_info external_input_info(layerName, loop_output_desc->m_output_index);
+            p.primitive_ids[layerName] = layerName;
+
+            const auto& body_output = body_outputs.at(loop_output_desc->m_body_value_index);
+            cldnn::primitive_id internal_id = layer_type_name_ID(body_output);
+
+            // update primitive_map
+            if (const auto& concatOutput =
+                std::dynamic_pointer_cast<ov::op::util::MultiSubGraphOp::ConcatOutputDescription>(loop_output_desc)) {
+                // output which requires concatenation
+                output_primitive_maps.emplace_back(external_input_info, internal_id, concatOutput->m_axis,
+                    concatOutput->m_start, concatOutput->m_end, concatOutput->m_stride);
+                GPU_DEBUG_LOG << "loop_output_descs[" << layerName << "][ConcatOutputDescription] external:"
+                        << external_input_info << ", internal:"
+                        << internal_id << "(axis, start, end, stride)={"
+                        << concatOutput->m_axis << "," << concatOutput->m_start << ","
+                        << concatOutput->m_end << "," << concatOutput->m_stride << "}" << std::endl;
+            }
+            if (std::dynamic_pointer_cast<ov::op::util::MultiSubGraphOp::BodyOutputDescription>(loop_output_desc)) {
+                // output which requires no concatenation
+                output_primitive_maps.emplace_back(external_input_info, internal_id);
+                GPU_DEBUG_LOG << "loop_output_descs[" << layerName << "][BodyOutputDescription] external:"
+                        << external_input_info << ", internal:" << internal_id << std::endl;
+            }
+        }
+    } else {
+        for (const auto& loop_output_desc : loop_output_descs) {
+            const uint64_t output_idx = loop_output_desc->m_output_index;
+
+            // Add additional mutable_data for multiple outputs
+            // primitive ID should be <TI primitive ID>.<output_idx> if output_idx > 0
+            // otherwise primitive ID should be equals to TI primitive ID
+            const std::string layerNameWithIndex = layerName + ".out" + std::to_string(output_idx);
+            std::string external_id;
+            if (output_idx > 0) {
+                cldnn::mutable_data output_data = CreateAdditionalOutputData(p, op, layerNameWithIndex, layerName, output_idx);
+                p.add_primitive(*op, std::move(output_data));
+                external_id = layerNameWithIndex;
+            } else {
+                p.primitive_ids[layerNameWithIndex] = layerName;
+                p.primitive_ids[layerName] = layerName;
+                external_id = layerName;
+            }
+            const auto& body_output = body_outputs.at(loop_output_desc->m_body_value_index);
+            cldnn::primitive_id internal_id = layer_type_name_ID(body_output);
+
+            // update primitive_map
+            if (const auto& concatOutput =
+                std::dynamic_pointer_cast<ov::op::util::MultiSubGraphOp::ConcatOutputDescription>(loop_output_desc)) {
+                // output which requires concatenation
+                output_primitive_maps.emplace_back(external_id, internal_id, concatOutput->m_axis,
+                    concatOutput->m_start, concatOutput->m_end, concatOutput->m_stride);
+                GPU_DEBUG_LOG << "loop_output_descs[" << layerName << "][ConcatOutputDescription] external:"
+                        << external_id << ", internal:"
+                        << internal_id << "(axis, start, end, stride)={"
+                        << concatOutput->m_axis << "," << concatOutput->m_start << ","
+                        << concatOutput->m_end << "," << concatOutput->m_stride << "}" << std::endl;
+            }
+            if (std::dynamic_pointer_cast<ov::op::util::MultiSubGraphOp::BodyOutputDescription>(loop_output_desc)) {
+                // output which requires no concatenation
+                output_primitive_maps.emplace_back(external_id, internal_id);
+                GPU_DEBUG_LOG << "loop_output_descs[" << layerName << "][BodyOutputDescription] external:"
+                        << external_id << ", internal:" << internal_id << std::endl;
+            }
+        }
     }
-    const cldnn::primitive_id num_iteration_id = layerName + "_numIteration";
-    {
-        cldnn::mutable_data num_iteration = CreateScalarData<cldnn::mutable_data>(p, num_iteration_id, 0);
-        p.add_primitive(*op, std::move(num_iteration));
+}
+
+static std::vector<cldnn::primitive_id> GetOutputNames(const cldnn::primitive_id id,
+                                                        const cldnn::primitive_id body_execution_condition_id,
+                                                        const std::vector<cldnn::loop::io_primitive_map>& output_primitive_maps,
+                                                        const std::vector<cldnn::loop::backedge_mapping>& back_edges) {
+    std::vector<cldnn::primitive_id> output_names;
+    OPENVINO_ASSERT(!output_primitive_maps.empty(), "[GPU] Output primitive map should have at least 1 mapping in primitive ", id);
+    for (auto out_map : output_primitive_maps) {
+        output_names.push_back(out_map.internal_id.pid);
     }
 
-    // set output mapping
-    for (const auto& loop_output_desc : loop_output_descs) {
-        const uint64_t output_idx = loop_output_desc->m_output_index;
-
-        // Add additional mutable_data for multiple outputs
-        // primitive ID should be <TI primitive ID>.<output_idx> if output_idx > 0
-        // otherwise primitive ID should be equals to TI primitive ID
-        const std::string layerNameWithIndex = layerName + ".out" + std::to_string(output_idx);
-        std::string external_id;
-        if (output_idx > 0) {
-            cldnn::mutable_data output_data = CreateAdditionalOutputData(p, op, layerNameWithIndex, layerName, output_idx);
-            p.add_primitive(*op, std::move(output_data));
-            external_id = layerNameWithIndex;
-        } else {
-            external_id = layerName;
+    // setup outputs for backedges
+    for (auto& back_edge : back_edges) {
+        auto iter = std::find(output_names.begin(), output_names.end(), back_edge.from);
+        // Do not add duplicated output name
+        if (iter == output_names.end()) {
+            output_names.push_back(back_edge.from);
         }
-        const auto& body_output = body_outputs.at(loop_output_desc->m_body_value_index);
-        cldnn::primitive_id internal_id = layer_type_name_ID(body_output);
-
-        // update primitive_map
-        if (const auto& concatOutput =
-            std::dynamic_pointer_cast<Loop::ConcatOutputDescription>(loop_output_desc)) {
-            // output which requires concatenation
-            output_primitive_maps.emplace_back(external_id, internal_id, concatOutput->m_axis,
-                concatOutput->m_start, concatOutput->m_end, concatOutput->m_stride);
+    }
+
+    // if execution_condition_id is specified, we need to add the id in build_option::outputs
+    if (!body_execution_condition_id.empty()) {
+        output_names.push_back(body_execution_condition_id);
+    }
+
+    return output_names;
+}
+
+static void CreateCommonLoopOp(ProgramBuilder& p, const std::shared_ptr<ov::op::util::SubGraphOp>& op, bool is_loop_op) {
+    const std::string layerName = layer_type_name_ID(op);
+    auto inputs = p.GetInputInfo(op);
+    bool is_dynamic = p.use_new_shape_infer() || op->is_dynamic();
+
+    int64_t num_iterations = op->get_num_iterations();
+    OPENVINO_ASSERT((is_dynamic || num_iterations > 0), "loop's num_iteration should be positive on static shape model");
+
+    auto num_outputs = is_dynamic? op->get_output_size() : 1;
+    auto ov_model = op->get_function();
+
+    // Set special body ports: current_iteration input , execution condition output
+    cldnn::primitive_id body_current_iteration_id;
+    cldnn::primitive_id body_execution_condition_id;
+    cldnn::primitive_id trip_count_id;
+    cldnn::primitive_id first_execution_condition_id;
+    cldnn::primitive_id updated_current_iteration_id;
+
+    std::shared_ptr<ov::op::v0::Parameter> current_iteration_input_op;
+    if (is_loop_op) {
+        auto loop_op = std::dynamic_pointer_cast<Loop>(op);
+        auto special_body_ports = loop_op->get_special_body_ports();
+        if (special_body_ports.current_iteration_input_idx >= 0) {
+            const auto& body_inputs = loop_op->get_function()->get_parameters();
+            current_iteration_input_op = body_inputs.at(special_body_ports.current_iteration_input_idx);
+            body_current_iteration_id = layer_type_name_ID(current_iteration_input_op);
         }
-        if (std::dynamic_pointer_cast<Loop::BodyOutputDescription>(loop_output_desc)) {
-            // output which requires no concatenation
-            output_primitive_maps.emplace_back(external_id, internal_id);
+
+        if (special_body_ports.body_condition_output_idx >= 0) {
+            const auto& body_outputs = loop_op->get_function()->get_results();
+            auto body_condition_output = body_outputs.at(special_body_ports.body_condition_output_idx)->get_input_node_shared_ptr(0);
+            body_execution_condition_id = layer_type_name_ID(body_condition_output);
         }
+
+        trip_count_id = layer_type_name_ID(loop_op->get_input_node_shared_ptr(0));
+        first_execution_condition_id = layer_type_name_ID(loop_op->get_input_node_shared_ptr(1));
+    }
+
+    // setup input_primitive_maps/ output_primitive_maps and back_edges
+    std::vector<cldnn::loop::io_primitive_map> input_primitive_maps;
+    std::vector<cldnn::loop::io_primitive_map> output_primitive_maps;
+    std::vector<cldnn::loop::backedge_mapping> back_edges;
+
+    SetLoopInputOutputMap(p, op, inputs, input_primitive_maps, output_primitive_maps, back_edges);
+
+    auto shape = is_dynamic? ngraph::Shape{1} : ngraph::Shape{1, 1, 1, 1};
+    auto prec = ngraph::element::i64;
+    if (current_iteration_input_op) {
+        current_iteration_input_op->set_output_type(0, prec, shape);
+        current_iteration_input_op->set_partial_shape(shape);
+        current_iteration_input_op->set_element_type(prec);
+
+        auto increment_value_id = current_iteration_input_op->get_friendly_name() + "_inc";
+        auto increment_value_op = std::make_shared<op::v0::Constant>(prec, shape, 1);
+        increment_value_op->set_friendly_name(increment_value_id);
+
+        auto update_current_iter_op_id = current_iteration_input_op->get_friendly_name() + "_update";
+        auto update_current_iter_op = std::make_shared<op::v1::Add>(current_iteration_input_op, increment_value_op);
+        update_current_iter_op->set_friendly_name(update_current_iter_op_id);
+        updated_current_iteration_id = layer_type_name_ID(update_current_iter_op);
+
+        auto result = std::make_shared<ov::op::v0::Result>(update_current_iter_op);
+        ov_model->add_results({result});
+    }
+
+    // set trip count, num iteration primitives
+    // they should be mutable_data to prevent from being optimized out
+    const cldnn::primitive_id num_iteration_id = layerName + "_numIteration";
+    cldnn::mutable_data num_iteration_data = CreateScalarData<cldnn::mutable_data>(p, num_iteration_id, shape, prec, 0);
+
+    p.add_primitive(*op, std::move(num_iteration_data));
+    inputs.insert(inputs.begin(), cldnn::input_info(num_iteration_id, 0));
+
+    if (!body_current_iteration_id.empty()) {
+        // update input_primitive_maps and back_edges for current_iteration nodes
+        input_primitive_maps.emplace_back(cldnn::input_info(num_iteration_id), cldnn::input_info(body_current_iteration_id));
+        back_edges.emplace_back(updated_current_iteration_id, body_current_iteration_id);
     }
 
+    auto output_names_vec = GetOutputNames(layerName, body_execution_condition_id, output_primitive_maps, back_edges);
+
+    auto config = p.get_config();
+    config.set_property(ov::intel_gpu::custom_outputs(output_names_vec));
+    config.set_property(ov::intel_gpu::max_dynamic_batch(1));
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(is_dynamic));
+
+    // get body program from ov::Model
+    ProgramBuilder prog(ov_model, p.get_engine(), config, false, false, p.get_task_executor(), true);
+    auto body_program = prog.get_compiled_program();
+
+    GPU_DEBUG_LOG << "* trip_count_id                 : " << trip_count_id << std::endl;
+    GPU_DEBUG_LOG << "* num_iteration_id              : " << num_iteration_id << std::endl;
+    GPU_DEBUG_LOG << "* body_current_iteration_id     : " << body_current_iteration_id << std::endl;
+    GPU_DEBUG_LOG << "* first_execution_condition_id  : " << first_execution_condition_id << std::endl;
+    GPU_DEBUG_LOG << "* body_execution_condition_id   : " << body_execution_condition_id << std::endl;
+
     const cldnn::loop loopPrimitive(
-        layerName,              /* layer name of this primitive (output id) */
-        inputs,                 /* inputs of this layer */
-        body_topology,          /* body network */
-        trip_count_id,          /* trip_count data in outer network, always same as num_iterations in TI */
-        execution_condition_id, /* initial_execution_condition data in outer network, always true in TI */
-        num_iteration_id,       /* actual number of iteration data in body network */
-        input_primitive_maps,         /* input mappings connecting outer network and inner network */
-        output_primitive_maps,        /* output mappings connecting outer network and inner network */
-        back_edges,             /* back edge mapping */
-        num_iterations,         /* max iteration, i.e. length of iteration axis */
+        layerName,                      /* layer name of this primitive (output id) */
+        inputs,                         /* inputs of this layer */
+        body_program,                   /* body network */
+        trip_count_id,                  /* trip_count data in outer network, always same as num_iterations in TI */
+        first_execution_condition_id,   /* initial_execution_condition data in outer network, always true in TI */
+        num_iteration_id,               /* actual number of iteration data in body network */
+        input_primitive_maps,           /* input mappings connecting outer network and inner network */
+        output_primitive_maps,          /* output mappings connecting outer network and inner network */
+        back_edges,                     /* back edge mapping */
+        num_iterations,                 /* max iteration, i.e. length of iteration axis */
         body_current_iteration_id,
-        body_execution_condition_id);
+        body_execution_condition_id,
+        num_outputs);
 
     p.add_primitive(*op, loopPrimitive);
 }
 
+static void CreateLoopOp(ProgramBuilder& p, const std::shared_ptr<Loop>& op) {
+    CreateCommonLoopOp(p, op, true);
+}
+
+/* The above code is a comment in C++ programming language. It is not doing anything in terms of code
+execution. It is simply providing information or documentation about the code. */
+static void CreateTensorIteratorOp(ProgramBuilder& p, const std::shared_ptr<TensorIterator>& op) {
+    CreateCommonLoopOp(p, op, false);
+}
+
 REGISTER_FACTORY_IMPL(v5, Loop);
+REGISTER_FACTORY_IMPL(v0, TensorIterator);
 
 }  // namespace intel_gpu
 }  // namespace ov
diff --git a/src/plugins/intel_gpu/src/plugin/ops/tensor_iterator.cpp b/src/plugins/intel_gpu/src/plugin/ops/tensor_iterator.cpp
deleted file mode 100644
index 21c7d3a8167a91..00000000000000
--- a/src/plugins/intel_gpu/src/plugin/ops/tensor_iterator.cpp
+++ /dev/null
@@ -1,181 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "intel_gpu/plugin/program_builder.hpp"
-#include "intel_gpu/plugin/common_utils.hpp"
-#include "intel_gpu/plugin/plugin.hpp"
-
-#include <cpp/ie_cnn_network.h>
-
-#include "openvino/op/tensor_iterator.hpp"
-#include "openvino/op/constant.hpp"
-#include "openvino/op/util/sub_graph_base.hpp"
-
-#include "intel_gpu/primitives/loop.hpp"
-#include "intel_gpu/primitives/mutable_data.hpp"
-#include "intel_gpu/primitives/data.hpp"
-#include "intel_gpu/primitives/reorder.hpp"
-#include "intel_gpu/graph/topology.hpp"
-
-#include <vector>
-#include <algorithm>
-
-using TensorIterator = ov::op::v0::TensorIterator;
-
-namespace ov {
-namespace intel_gpu {
-
-template<class DATA_TYPE>
-static DATA_TYPE CreateScalarData(ProgramBuilder &p, const cldnn::primitive_id& id, int64_t num) {
-    auto mem = p.get_engine().allocate_memory({ cldnn::data_types::i64, cldnn::format::bfyx, { 1, 1, 1, 1 } });
-    cldnn::mem_lock<int64_t> ptr{mem, p.get_engine().get_service_stream()};
-    *ptr.begin() = num;
-    return {id, mem};
-}
-
-static cldnn::mutable_data CreateAdditionalOutputData(ProgramBuilder &p, const std::shared_ptr<ov::Node>& op,
-                                            const cldnn::primitive_id& id, const cldnn::primitive_id& input,
-                                            const int32_t output_idx) {
-    const auto precision = cldnn::element_type_to_data_type(op->get_output_element_type(output_idx));
-    const auto format = cldnn::format::get_default_format(op->get_output_shape(output_idx).size());
-    const auto tensor = tensor_from_dims(op->get_output_shape(output_idx));
-    cldnn::layout output_layout = cldnn::layout(precision, format, tensor);
-    auto mem = p.get_engine().allocate_memory(output_layout);
-    auto md = cldnn::mutable_data(id, {cldnn::input_info(input)}, std::move(mem)); // cldnn::data cannot set dependency
-    return md;
-}
-
-static void CreateTensorIteratorOp(ProgramBuilder &p, const std::shared_ptr<TensorIterator> &op) {
-    auto inputs = p.GetInputInfo(op);
-
-    ProgramBuilder body_program(op->get_body(), p.get_engine(), p.get_config(), true);
-    auto body_topology = *body_program.get_topology();
-
-    // setup input_primitive_maps/ output_primitive_maps and back_edges
-    const auto& loop_input_descs = op->get_input_descriptions();
-    const auto& loop_output_descs = op->get_output_descriptions();
-    const auto& body_inputs = op->get_body()->get_parameters();
-    const auto& body_outputs = op->get_body()->get_results();
-
-    std::vector<cldnn::loop::io_primitive_map> input_primitive_maps;
-    std::vector<cldnn::loop::io_primitive_map> output_primitive_maps;
-    std::vector<cldnn::loop::backedge_mapping> back_edges;
-    std::map<cldnn::primitive_id, cldnn::primitive_id> reordered_output_ids;
-
-    // set input mapping & back edges
-    for (const auto& loop_input_desc : loop_input_descs) {
-        const cldnn::primitive_id& external_id = inputs.at(loop_input_desc->m_input_index).pid;
-        auto& body_input = body_inputs.at(loop_input_desc->m_body_parameter_index);
-        cldnn::primitive_id internal_id = layer_type_name_ID(body_input);
-
-        // set input mapping
-        if (const auto& sliceInfo =
-            std::dynamic_pointer_cast<TensorIterator::SliceInputDescription>(loop_input_desc)) {
-            // sliced input
-            input_primitive_maps.emplace_back(external_id, internal_id, sliceInfo->m_axis,
-                sliceInfo->m_start, sliceInfo->m_end, sliceInfo->m_stride);
-        } else {
-            // input without slicing
-            input_primitive_maps.emplace_back(external_id, internal_id);
-        }
-
-        // set back edges
-        if (const auto& mergedInput =
-            std::dynamic_pointer_cast<TensorIterator::MergedInputDescription>(loop_input_desc)) {
-            // backedge
-            const auto& to = body_inputs.at(mergedInput->m_body_parameter_index);
-            const auto& from = body_outputs.at(mergedInput->m_body_value_index);
-
-            cldnn::primitive_id to_id = layer_type_name_ID(to);
-            cldnn::primitive_id from_id = layer_type_name_ID(from);
-
-            // reset output data type because the data types of the outputs of the
-            // body topology are always FP32 regardless of element type
-            {
-                const auto from_prim = body_topology.at(from_id);
-                const auto to_cldnn_type = cldnn::element_type_to_data_type(to->get_element_type());
-                from_prim->output_data_types = {to_cldnn_type};
-            }
-            back_edges.emplace_back(from_id, to_id);
-        }
-    }
-
-    // set trip count, initial execution condition, num iteration primitives
-    // they should be mutable_data to prevent from being optimized out
-    std::string layerName = layer_type_name_ID(op);
-    const cldnn::primitive_id trip_count_id = layerName + "_tripCount";
-    const int64_t num_iterations = op->get_num_iterations();
-    if (num_iterations < 0) {
-        throw std::runtime_error("tensor iterator's num_iteration cannot be negative");
-    }
-    {
-        cldnn::data trip_count = CreateScalarData<cldnn::data>(p, trip_count_id, num_iterations);
-        p.add_primitive(*op, trip_count);
-    }
-    const cldnn::primitive_id execution_condition_id = layerName + "_initialExecutionCondition";
-    {
-        cldnn::mutable_data execution_condition = CreateScalarData<cldnn::mutable_data>(p, execution_condition_id, 1);
-        p.add_primitive(*op, std::move(execution_condition));
-    }
-    const cldnn::primitive_id num_iteration_id = layerName + "_numIteration";
-    {
-        cldnn::mutable_data num_iteration = CreateScalarData<cldnn::mutable_data>(p, num_iteration_id, 0);
-        p.add_primitive(*op, num_iteration);
-    }
-
-    // set output mapping
-    for (const auto& loop_output_desc : loop_output_descs) {
-        const uint64_t output_idx = loop_output_desc->m_output_index;
-
-        // Add additional mutable_data for multiple outputs
-        // primitive ID should be <TI primitive ID>.<output_idx> if output_idx > 0
-        // otherwise primitive ID should be equals to TI primitive ID
-        const std::string layerNameWithIndex = layerName + ".out" + std::to_string(output_idx);
-        std::string external_id;
-        if (output_idx > 0) {
-            cldnn::mutable_data output_data = CreateAdditionalOutputData(p, op, layerNameWithIndex, layerName, output_idx);
-            p.add_primitive(*op, std::move(output_data));
-            external_id = layerNameWithIndex;
-        } else {
-            p.primitive_ids[layerNameWithIndex] = layerName;
-            p.primitive_ids[layerName] = layerName;
-            external_id = layerName;
-        }
-        const auto& body_output = body_outputs.at(loop_output_desc->m_body_value_index);
-        cldnn::primitive_id internal_id = layer_type_name_ID(body_output);
-
-        // update primitive_map
-        if (const auto& concatOutput =
-            std::dynamic_pointer_cast<TensorIterator::ConcatOutputDescription>(loop_output_desc)) {
-            // output which requires concatenation
-            output_primitive_maps.emplace_back(external_id, internal_id, concatOutput->m_axis,
-                concatOutput->m_start, concatOutput->m_end, concatOutput->m_stride);
-        }
-        if (std::dynamic_pointer_cast<TensorIterator::BodyOutputDescription>(loop_output_desc)) {
-            // output which requires no concatenation
-            output_primitive_maps.emplace_back(external_id, internal_id);
-        }
-    }
-
-    const cldnn::loop loopPrimitive(
-        layerName,              /* layer name of this primitive (output id) */
-        inputs,        /* inputs of this layer */
-        body_topology,          /* body network */
-        trip_count_id,          /* trip_count data in outer network, always same as num_iterations in TI */
-        execution_condition_id, /* initial_execution_condition data in outer network, always true in TI */
-        num_iteration_id,       /* actual number of iteration data in body network */
-        input_primitive_maps,         /* input mappings connecting outer network and inner network */
-        output_primitive_maps,        /* output mappings connecting outer network and inner network */
-        back_edges,             /* back edge mapping */
-        num_iterations,         /* max iteration, i.e. length of iteration axis */
-        "",
-        "");
-
-    p.add_primitive(*op, loopPrimitive);
-}
-
-REGISTER_FACTORY_IMPL(v0, TensorIterator);
-
-}  // namespace intel_gpu
-}  // namespace ov
diff --git a/src/plugins/intel_gpu/src/plugin/plugin.cpp b/src/plugins/intel_gpu/src/plugin/plugin.cpp
index 55f4f5e7a42065..388269ddbb424d 100644
--- a/src/plugins/intel_gpu/src/plugin/plugin.cpp
+++ b/src/plugins/intel_gpu/src/plugin/plugin.cpp
@@ -593,6 +593,7 @@ std::vector<ov::PropertyName> Plugin::get_supported_properties() const {
         ov::PropertyName{ov::num_streams.name(), PropertyMutability::RW},
         ov::PropertyName{ov::hint::num_requests.name(), PropertyMutability::RW},
         ov::PropertyName{ov::hint::inference_precision.name(), PropertyMutability::RW},
+        ov::PropertyName{ov::hint::enable_cpu_pinning.name(), PropertyMutability::RW},
         ov::PropertyName{ov::device::id.name(), PropertyMutability::RW},
     };
 
diff --git a/src/plugins/intel_gpu/src/plugin/program_builder.cpp b/src/plugins/intel_gpu/src/plugin/program_builder.cpp
index 404818ce92ce8c..a97b7e87a9e4b3 100644
--- a/src/plugins/intel_gpu/src/plugin/program_builder.cpp
+++ b/src/plugins/intel_gpu/src/plugin/program_builder.cpp
@@ -158,7 +158,7 @@ std::shared_ptr<cldnn::program> ProgramBuilder::build(const std::vector<std::sha
         OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "ProgramBuilder::CreateProgram");
         cldnn::program::ptr program;
         try {
-            program = cldnn::program::build_program(m_engine, *m_topology, m_config, get_task_executor());
+            program = cldnn::program::build_program(m_engine, *m_topology, m_config, get_task_executor(), false, false, is_inner_program);
         } catch (std::exception& e) {
             OPENVINO_ASSERT(false, "[GPU] ProgramBuilder build failed!\n", e.what());
         }
diff --git a/src/plugins/intel_gpu/src/plugin/remote_allocators.cpp b/src/plugins/intel_gpu/src/plugin/remote_allocators.cpp
deleted file mode 100644
index fe9ff746f04283..00000000000000
--- a/src/plugins/intel_gpu/src/plugin/remote_allocators.cpp
+++ /dev/null
@@ -1,37 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "intel_gpu/plugin/remote_allocators.hpp"
-#include "intel_gpu/plugin/remote_tensor.hpp"
-#include "intel_gpu/plugin/remote_context.hpp"
-#include <memory>
-
-namespace ov {
-namespace intel_gpu {
-
-void* USMHostAllocator::allocate(const size_t bytes, const size_t /* alignment */) noexcept {
-    try {
-        ov::AnyMap params = { ov::intel_gpu::shared_mem_type(ov::intel_gpu::SharedMemType::USM_HOST_BUFFER) };
-        _usm_host_tensor = _context->create_tensor(ov::element::u8, {bytes}, params);
-        if (auto casted = std::dynamic_pointer_cast<RemoteTensorImpl>(_usm_host_tensor._ptr)) {
-            return casted->get_original_memory()->get_internal_params().mem;
-        }
-        return nullptr;
-    } catch (std::exception&) {
-        return nullptr;
-    }
-}
-
-bool USMHostAllocator::deallocate(void* /* handle */, const size_t /* bytes */, size_t /* alignment */) noexcept {
-    try {
-        _usm_host_tensor = {nullptr, nullptr};
-    } catch (std::exception&) { }
-    return true;
-}
-
-bool USMHostAllocator::is_equal(const USMHostAllocator& other) const {
-    return other._usm_host_tensor != nullptr && _usm_host_tensor != nullptr && other._usm_host_tensor._ptr == _usm_host_tensor._ptr;
-}
-}  // namespace intel_gpu
-}  // namespace ov
diff --git a/src/plugins/intel_gpu/src/plugin/remote_context.cpp b/src/plugins/intel_gpu/src/plugin/remote_context.cpp
index 1b932226881db3..e4aefa00bb0f0f 100644
--- a/src/plugins/intel_gpu/src/plugin/remote_context.cpp
+++ b/src/plugins/intel_gpu/src/plugin/remote_context.cpp
@@ -6,7 +6,7 @@
 #include "openvino/runtime/make_tensor.hpp"
 #include "intel_gpu/plugin/remote_context.hpp"
 #include "intel_gpu/plugin/remote_tensor.hpp"
-#include "intel_gpu/plugin/remote_allocators.hpp"
+#include "intel_gpu/plugin/usm_host_tensor.hpp"
 #include "intel_gpu/runtime/itt.hpp"
 #include "intel_gpu/runtime/device_query.hpp"
 #include <memory>
@@ -111,8 +111,7 @@ std::shared_ptr<RemoteContextImpl> RemoteContextImpl::get_this_shared_ptr() {
 
 ov::SoPtr<ov::ITensor> RemoteContextImpl::create_host_tensor(const ov::element::Type type, const ov::Shape& shape) {
     if (m_engine->use_unified_shared_memory()) {
-        USMHostAllocator allocator(get_this_shared_ptr());
-        return { ov::make_tensor(type, shape, allocator), nullptr };
+        return { std::make_shared<USMHostTensor>(get_this_shared_ptr(), type, shape), nullptr };
     } else {
         return { ov::make_tensor(type, shape), nullptr };
     }
diff --git a/src/plugins/intel_gpu/src/plugin/remote_tensor.cpp b/src/plugins/intel_gpu/src/plugin/remote_tensor.cpp
index a7c68cd8f81107..cd1011ea153bfe 100644
--- a/src/plugins/intel_gpu/src/plugin/remote_tensor.cpp
+++ b/src/plugins/intel_gpu/src/plugin/remote_tensor.cpp
@@ -2,17 +2,29 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "intel_gpu/plugin/common_utils.hpp"
 #include "intel_gpu/plugin/remote_context.hpp"
 #include "intel_gpu/plugin/remote_tensor.hpp"
-#include "intel_gpu/plugin/remote_allocators.hpp"
 #include "intel_gpu/plugin/plugin.hpp"
 #include "intel_gpu/runtime/itt.hpp"
+#include "intel_gpu/runtime/memory_caps.hpp"
 
 #include <memory>
 
 namespace ov {
 namespace intel_gpu {
 
+TensorType RemoteTensorImpl::allocation_type_to_tensor_type(cldnn::allocation_type t) {
+    switch (t) {
+    case cldnn::allocation_type::cl_mem: return TensorType::BT_BUF_INTERNAL;
+    case cldnn::allocation_type::usm_host: return TensorType::BT_USM_HOST_INTERNAL;
+    case cldnn::allocation_type::usm_device: return TensorType::BT_USM_DEVICE_INTERNAL;
+    default: return TensorType::BT_EMPTY;
+    }
+
+    return TensorType::BT_EMPTY;
+}
+
 RemoteTensorImpl::RemoteTensorImpl(RemoteContextImpl::Ptr context,
                                    const ov::Shape& shape,
                                    const ov::element::Type& element_type,
@@ -28,20 +40,8 @@ RemoteTensorImpl::RemoteTensorImpl(RemoteContextImpl::Ptr context,
     , m_mem(mem)
     , m_surf(surf)
     , m_plane(plane) {
-    if (supports_caching()) {
-        m_hash = cldnn::hash_combine(0, m_mem);
-        m_hash = cldnn::hash_combine(m_hash, m_surf);
-        m_hash = cldnn::hash_combine(m_hash, plane);
-        m_hash = cldnn::hash_combine(m_hash, m_shape.size());
-        m_hash = cldnn::hash_combine(m_hash, element_type.hash());
-        for (const auto& d : m_shape) {
-            m_hash = cldnn::hash_combine(m_hash, d);
-        }
-    }
-
-    update_strides();
+    update_hash();
     allocate();
-    init_properties();
 }
 
 RemoteTensorImpl::~RemoteTensorImpl() {
@@ -82,12 +82,15 @@ const AnyMap& RemoteTensorImpl::get_properties() const {
     m_shape = shape;
 
     if (ov::shape_size(shape) > m_memory_object->count()) {
-        OPENVINO_ASSERT(!is_shared(), "Cannot call setShape for Tensor created on top of preallocated memory if shape was increased.");
+        GPU_DEBUG_TRACE_DETAIL << "Remote realloc" << std::endl;
+        OPENVINO_ASSERT(!is_shared(), "Cannot call set_shape for Tensor created on top of preallocated memory if shape was increased.");
         if (!deallocate()) {
-            OPENVINO_THROW("Cannot deallocate tensor while an attempt to enlarge tensor area in setShape.");
+            OPENVINO_THROW("Cannot deallocate tensor while an attempt to enlarge tensor area in set_shape.");
         }
 
         allocate();
+    } else {
+        update_strides();
     }
 }
 
@@ -108,23 +111,39 @@ void RemoteTensorImpl::allocate() {
 
     if (enable_caching) {
         m_memory_object = context->try_get_cached_memory(m_hash);
-        if (m_memory_object)
+        if (m_memory_object) {
+            update_properties();
+            update_strides();
             return;
+        }
     }
 
     auto& engine = context->get_engine();
 
+    // Currently, clDeviceMemAllocINTEL returns memory address allocated to other input blob if the current blob is empty
+    // W/A for this issue:
+    // Allocate with non-empty shape and then reinterprete with original shape
+    auto shape_copy = m_shape;
+    for (auto &i : shape_copy) {
+        if (i == 0)
+            i = 1;
+    }
+
+    m_layout.set_partial_shape(shape_copy);
+
+    const bool reset = false;
+
     switch (m_mem_type) {
     case TensorType::BT_BUF_INTERNAL: {
-        m_memory_object = engine.allocate_memory(m_layout, cldnn::allocation_type::cl_mem);
+        m_memory_object = engine.allocate_memory(m_layout, cldnn::allocation_type::cl_mem, reset);
         break;
     }
     case TensorType::BT_USM_HOST_INTERNAL: {
-        m_memory_object = engine.allocate_memory(m_layout, cldnn::allocation_type::usm_host);
+        m_memory_object = engine.allocate_memory(m_layout, cldnn::allocation_type::usm_host, reset);
         break;
     }
     case TensorType::BT_USM_DEVICE_INTERNAL: {
-        m_memory_object = engine.allocate_memory(m_layout, cldnn::allocation_type::usm_device);
+        m_memory_object = engine.allocate_memory(m_layout, cldnn::allocation_type::usm_device, reset);
         break;
     }
     case TensorType::BT_BUF_SHARED: {
@@ -161,6 +180,9 @@ void RemoteTensorImpl::allocate() {
         m_memory_object.reset();
     }
 
+    update_properties();
+    update_strides();
+
     if (enable_caching)
         context->add_to_cache(m_hash, m_memory_object);
 }
@@ -181,6 +203,19 @@ bool RemoteTensorImpl::supports_caching() const {
     return is_shared();
 }
 
+void RemoteTensorImpl::update_hash() {
+    if (supports_caching()) {
+        m_hash = cldnn::hash_combine(0, m_mem);
+        m_hash = cldnn::hash_combine(m_hash, m_surf);
+        m_hash = cldnn::hash_combine(m_hash, m_plane);
+        m_hash = cldnn::hash_combine(m_hash, m_shape.size());
+        m_hash = cldnn::hash_combine(m_hash, m_element_type.hash());
+        for (const auto& d : m_shape) {
+            m_hash = cldnn::hash_combine(m_hash, d);
+        }
+    }
+}
+
 bool RemoteTensorImpl::is_surface() const noexcept {
     return m_mem_type == TensorType::BT_SURF_SHARED ||
            m_mem_type == TensorType::BT_IMG_SHARED ||
@@ -196,11 +231,24 @@ cldnn::memory::ptr RemoteTensorImpl::get_original_memory() const {
     return m_memory_object;
 }
 
+void RemoteTensorImpl::set_memory(cldnn::memory::ptr memory, size_t actual_size) {
+    auto engine = m_memory_object->get_engine();
+    m_layout = memory->get_layout();
+    m_shape = m_layout.get_shape();
+
+    auto actual_layout = m_layout;
+    actual_layout.set_partial_shape({ov::Dimension(actual_size)});
+    m_memory_object = engine->reinterpret_buffer(*memory, actual_layout);
+
+    update_properties();
+    update_strides();
+}
+
 std::shared_ptr<RemoteContextImpl> RemoteTensorImpl::get_context() const {
     return m_context;
 }
 
-void RemoteTensorImpl::init_properties() {
+void RemoteTensorImpl::update_properties() {
     OPENVINO_ASSERT(is_allocated(), "[GPU] Can't initialize RemoteTensorImpl parameters as memory was not allocated");
     auto params = m_memory_object->get_internal_params();
 
diff --git a/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp b/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp
index 6e9e8bbf353803..9c097d222fdc1b 100644
--- a/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp
+++ b/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp
@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "intel_gpu/plugin/usm_host_tensor.hpp"
 #include "openvino/runtime/make_tensor.hpp"
 #include "openvino/core/preprocess/input_tensor_info.hpp"
 #include "openvino/core/parallel.hpp"
@@ -10,7 +11,6 @@
 
 #include "intel_gpu/plugin/sync_infer_request.hpp"
 #include "intel_gpu/plugin/remote_context.hpp"
-#include "intel_gpu/plugin/remote_allocators.hpp"
 #include "intel_gpu/plugin/remote_tensor.hpp"
 #include "intel_gpu/plugin/compiled_model.hpp"
 #include "intel_gpu/plugin/variable_state.hpp"
@@ -41,6 +41,15 @@ inline bool can_use_usm_host(const cldnn::engine& engine) {
     return can_use_usm;
 }
 
+inline ov::Shape get_tensor_shape(const ov::PartialShape& pshape) {
+    ov::Shape res(pshape.size());
+    for (size_t i = 0; i < pshape.size(); i++) {
+        res[i] = pshape[i].is_dynamic() ? 0 : pshape[i].get_length();
+    }
+
+    return res;
+}
+
 inline std::string get_port_name(const ov::Output<const ov::Node>& port, const bool is_legacy_api) {
     std::string name;
     // TODO: Should use tensor name as the port name, but many legacy tests still use legacy name
@@ -72,7 +81,7 @@ void convert_and_copy(const void* src_ptr, ov::element::Type src_et, void* dst_p
         return;
 
     if (src_et == dst_et) {
-        std::memcpy(dst_ptr, src_ptr, size);
+        std::memcpy(dst_ptr, src_ptr, size * src_et.size());
         return;
     }
 
@@ -167,11 +176,10 @@ bool same_host_mem(cldnn::memory::cptr memory, const uint8_t* host_ptr) {
 }
 
 ov::Shape predict_shape(const std::string& name, const ov::Shape current_shape, ov::element::Type element_type, cldnn::ShapePredictor& shape_predictor) {
-    auto et_size = cldnn::ceil_div(element_type.bitwidth(), 8);
-    auto prealloc_info = shape_predictor.predict_preallocation_shape(name, current_shape, et_size, false);
+    auto prealloc_info = shape_predictor.predict_preallocation_shape(name, current_shape, element_type.bitwidth(), false);
     const auto& preallocation_shape = prealloc_info.second;
     auto can_preallocate_buffer = prealloc_info.first &&
-                                    shape_predictor.can_preallocate(ov::shape_size(preallocation_shape) * et_size);
+                                    shape_predictor.can_preallocate(cldnn::ceil_div(ov::shape_size(preallocation_shape) * element_type.bitwidth(), 8));
     if (can_preallocate_buffer) {
         return preallocation_shape;
     }
@@ -426,6 +434,7 @@ void SyncInferRequest::wait() {
             OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "SyncInferRequest::wait::reinterpret_memory");
             OPENVINO_ASSERT(!output_memory->get_layout().data_padding, "[GPU] Unexpected padding in output buffer");
             output_memory = m_graph->get_engine().reinterpret_buffer(*output_memory, output_layout);
+            GPU_DEBUG_TRACE_DETAIL << name << " model output: " << output_memory->buffer_ptr() << std::endl;
         }
 
         OPENVINO_ASSERT(m_user_outputs.count(name) > 0, "[GPU] Output ", name, " is not found in output tensors map");
@@ -434,6 +443,12 @@ void SyncInferRequest::wait() {
         auto remote_ptr = std::dynamic_pointer_cast<RemoteTensorImpl>(output_tensor);
         bool is_remote = remote_ptr != nullptr;
 
+        if (is_remote) {
+            GPU_DEBUG_TRACE_DETAIL << name << " handle output tensor (remote): " << remote_ptr->get_original_memory()->buffer_ptr() << std::endl;
+        } else {
+            GPU_DEBUG_TRACE_DETAIL << name << " handle output tensor (host): " << output_tensor->data() << std::endl;
+        }
+
         bool need_output_update = output_layout.bytes_count() == 0 || (output_memory && output_tensor->get_byte_size() != output_memory->size());
         if (need_output_update) {
             OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "SyncInferRequest::wait::update_output");
@@ -445,6 +460,19 @@ void SyncInferRequest::wait() {
                 OPENVINO_ASSERT(ov::shape_size(port.get_shape()) == ov::shape_size(mem_shape), "[GPU] Unexpected elements count for output tensor");
                 mem_shape = port.get_shape();
             }
+            if (port.get_partial_shape().is_dynamic()) {
+                bool need_reallocate = true;
+                auto usm_host_tensor = std::dynamic_pointer_cast<USMHostTensor>(output_tensor);
+                if (usm_host_tensor && output_memory)
+                    need_reallocate = usm_host_tensor->get_impl()->get_original_memory()->size() < output_memory->size();
+
+                if (need_reallocate) {
+                    auto& shape_predictor = m_graph->get_network()->get_shape_predictor();
+                    auto actual_memory_shape = predict_shape(name, mem_shape, output_tensor->get_element_type(), shape_predictor);
+                    output_tensor->set_shape(actual_memory_shape);
+                }
+            }
+
             output_tensor->set_shape(mem_shape);
         }
 
@@ -454,6 +482,8 @@ void SyncInferRequest::wait() {
             auto dst_ptr = static_cast<uint8_t*>(output_tensor->data());
             bool same_mem = same_host_mem(output_memory, dst_ptr);
             if (!same_mem && output_memory->size()) {
+                GPU_DEBUG_TRACE_DETAIL << name << " copy from: " << output_memory->buffer_ptr() << " to "
+                                       << (!is_remote ? output_tensor->data() : remote_ptr->get_original_memory()->buffer_ptr()) << std::endl;
                 if (auto ev = copy_output_data(output_memory, *output_tensor)) {
                     copy_events.push_back(ev);
                 }
@@ -493,22 +523,13 @@ void SyncInferRequest::setup_stream_graph() {
 
 std::shared_ptr<ov::ITensor> SyncInferRequest::create_host_tensor(const ov::PartialShape& port_shape, const ov::element::Type& port_element_type) const {
     OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "SyncInferRequest::create_host_tensor");
-    // Disable USM usage as USMHostAllocator may fail for attempt to allocate 0 bytes
-    // If we add WA for such case to avoid driver call, then deallocate method will return false and Blob::setShape call will throw an exception
-    bool use_usm = m_graph->get_engine().use_unified_shared_memory() && !port_shape.is_dynamic();
-
-    auto shape = port_shape.is_static() ? port_shape.to_shape() : ov::Shape(port_shape.size(), 0);
-    auto usm_allocator = USMHostAllocator(m_context);
-    return use_usm ? ov::make_tensor(port_element_type, shape, usm_allocator)
-                   : ov::make_tensor(port_element_type, shape);
+    return m_context->create_host_tensor(port_element_type, get_tensor_shape(port_shape))._ptr;
 }
 
-std::shared_ptr<ov::ITensor> SyncInferRequest::create_device_tensor(const ov::Shape& shape, ov::element::Type element_type,
-                                                                    bool need_lockable_memory, void* mem_ptr) const {
+std::shared_ptr<ov::ITensor> SyncInferRequest::create_device_tensor(const ov::PartialShape& port_shape, ov::element::Type element_type,
+                                                                    bool need_lockable_memory) const {
     TensorType tensor_type = TensorType::BT_EMPTY;
-    if (mem_ptr) {
-        tensor_type = TensorType::BT_USM_SHARED;
-    } else if (m_graph->get_engine().use_unified_shared_memory()) {
+    if (m_graph->get_engine().use_unified_shared_memory()) {
         tensor_type = need_lockable_memory ? TensorType::BT_USM_HOST_INTERNAL : TensorType::BT_USM_DEVICE_INTERNAL;
     } else {
         tensor_type = TensorType::BT_BUF_INTERNAL;
@@ -518,24 +539,10 @@ std::shared_ptr<ov::ITensor> SyncInferRequest::create_device_tensor(const ov::Sh
     if (!can_use_usm_host(m_graph->get_engine()) && need_lockable_memory)
         tensor_type = TensorType::BT_BUF_INTERNAL;
 
-    // Currently, clDeviceMemAllocINTEL returns memory address allocated to other input blob if the current blob is empty
-    // W/A for this issue:
-    // Allocate with non-empty shape and then reinterprete with original shape
-    auto shape_copy = shape;
-    for (auto &i : shape_copy) {
-        if (i == 0)
-            i = 1;
-    }
-
     return std::make_shared<RemoteTensorImpl>(m_context,
-                                              shape_copy,
+                                              get_tensor_shape(port_shape),
                                               element_type,
-                                              tensor_type,
-                                              mem_ptr);
-}
-
-std::shared_ptr<ov::ITensor> SyncInferRequest::create_shared_device_tensor(const ov::Shape& shape, ov::element::Type element_type, void* usm_host_mem) const {
-    return create_device_tensor(shape, element_type, false, usm_host_mem);
+                                              tensor_type);
 }
 
 TensorWrapper SyncInferRequest::create_or_share_device_tensor(const TensorWrapper& user_tensor_wrapper,
@@ -547,17 +554,12 @@ TensorWrapper SyncInferRequest::create_or_share_device_tensor(const TensorWrappe
     auto tensor_shape = user_tensor->get_shape();
     bool is_dynamic = port_pshape.is_dynamic();
     OPENVINO_ASSERT(std::dynamic_pointer_cast<RemoteTensorImpl>(user_tensor) == nullptr, "[GPU] Unexpected remote tensor");
-    auto input_ptr = user_tensor->data();
-    const auto alloc_type = m_graph->get_engine().detect_usm_allocation_type(input_ptr);
-    const auto is_usm_host = alloc_type == cldnn::allocation_type::usm_host;
-    bool can_share = is_usm_host &&
-                     !is_convert_required(user_tensor->get_element_type(), element_type) &&
+    auto usm_host_tensor = std::dynamic_pointer_cast<USMHostTensor>(user_tensor);
+    bool can_share = usm_host_tensor != nullptr && !is_convert_required(user_tensor->get_element_type(), element_type) &&
                      can_use_usm_host(m_graph->get_engine());
 
     if (can_share) {
-        // For USM case we create host blob using custom USM host allocator
-        // and then create shared device blob on top of this buffer
-        return { create_shared_device_tensor(tensor_shape, element_type, input_ptr), user_tensor_wrapper.owner };
+        return { usm_host_tensor->get_impl(), user_tensor_wrapper.owner };
     }
 
     auto actual_memory_shape = tensor_shape;
@@ -690,13 +692,17 @@ std::vector<cldnn::event::ptr> SyncInferRequest::prepare_batched_input(const std
 std::vector<cldnn::event::ptr> SyncInferRequest::prepare_input(const std::string& name,
                                                                const ov::Output<const ov::Node>& port,
                                                                const TensorWrapper& user_tensor_wrapper) {
-    OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "SyncInferRequest::prepare_input");
+    OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, openvino::itt::handle("SyncInferRequest::prepare_input: " + name));
     auto pshape = port.get_partial_shape();
     auto is_dynamic = pshape.is_dynamic();
     auto user_tensor = user_tensor_wrapper.ptr;
     auto element_type = user_tensor->get_element_type();
+
     auto remote_ptr = std::dynamic_pointer_cast<RemoteTensorImpl>(user_tensor);
+    auto usm_host_ptr = std::dynamic_pointer_cast<USMHostTensor>(user_tensor);
     bool is_remote = remote_ptr != nullptr;
+    bool is_usm_host_tensor = usm_host_ptr != nullptr;
+
     GPU_DEBUG_TRACE_DETAIL << "Prepare input for " << name << " ( is_remote ? " << is_remote << ")" << std::endl;
     GPU_DEBUG_TRACE_DETAIL << "    port shape       : " << pshape.to_string() << std::endl;
     GPU_DEBUG_TRACE_DETAIL << "    user_tensor shape: " << user_tensor->get_shape().to_string() << std::endl;
@@ -714,12 +720,16 @@ std::vector<cldnn::event::ptr> SyncInferRequest::prepare_input(const std::string
                     user_tensor->get_shape(),
                     ") are incompatible");
 
+    auto device_tensor_et = convert_to_supported_device_type(element_type);
+    bool convert_needed = is_convert_required(element_type, device_tensor_et);
+
     if (is_remote) {
         m_plugin_inputs[name] = user_tensor_wrapper;
+    } else if (is_usm_host_tensor && !convert_needed) {
+        m_plugin_inputs[name] = {usm_host_ptr->get_impl(), user_tensor_wrapper.owner};
+        is_remote = true;
     }
 
-    auto device_tensor_et = convert_to_supported_device_type(element_type);
-    bool convert_needed = is_convert_required(element_type, device_tensor_et);
     bool update_device_tensor = m_plugin_inputs.count(name) == 0 || (m_plugin_inputs[name].owner == TensorOwner::USER && !is_remote);
 
     if (update_device_tensor) {
@@ -781,6 +791,7 @@ std::vector<cldnn::event::ptr> SyncInferRequest::prepare_input(const std::string
         }
     }
 
+    GPU_DEBUG_TRACE_DETAIL << name << " prepare input: " << memory->buffer_ptr() << std::endl;
     const cldnn::primitive_id internal_name = "parameter:" + name;
     network->set_input_data(internal_name, memory);
 
@@ -840,6 +851,7 @@ std::vector<cldnn::event::ptr> SyncInferRequest::prepare_output(const std::strin
 
     auto output_tensor = std::dynamic_pointer_cast<RemoteTensorImpl>(m_plugin_outputs.at(name).ptr);
     auto output_memory = output_tensor->get_memory();
+    GPU_DEBUG_TRACE_DETAIL << name << " prepare output: " << output_memory->buffer_ptr() << std::endl;
     return network->set_output_memory(internal_name, output_memory);
 }
 
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/convert_fc_to_compressed.cpp b/src/plugins/intel_gpu/src/plugin/transformations/convert_fc_to_compressed.cpp
index a1c4d60b81977c..0ff0e1fd0bf258 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/convert_fc_to_compressed.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/convert_fc_to_compressed.cpp
@@ -3,16 +3,19 @@
 //
 
 #include "convert_fc_to_compressed.hpp"
+#include <memory>
 
 #include "intel_gpu/op/fully_connected.hpp"
 #include "intel_gpu/op/fully_connected_compressed.hpp"
 
+#include "openvino/op/constant.hpp"
 #include "openvino/op/subtract.hpp"
 #include "openvino/op/matmul.hpp"
 #include "openvino/op/convert.hpp"
 #include "openvino/op/transpose.hpp"
 #include "openvino/op/reshape.hpp"
 #include "openvino/core/rt_info.hpp"
+#include "openvino/pass/pattern/op/pattern.hpp"
 #include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "openvino/pass/pattern/op/or.hpp"
 #include "transformations/utils/utils.hpp"
@@ -23,7 +26,19 @@ namespace intel_gpu {
 ConvertFullyConnectedToFullyConnectedCompressed::ConvertFullyConnectedToFullyConnectedCompressed() {
     using namespace ov::pass::pattern;
 
-    auto weights_m = wrap_type<ov::op::v0::Constant>(consumers_count(1));
+    auto compressed_constant = [](const ov::Output<ov::Node>& output) {
+        return (output.get_element_type() == ov::element::u8 ||
+                output.get_element_type() == ov::element::i8) &&
+               output.get_target_inputs().size() == 1;
+    };
+
+    auto reshape_3d_to_2d = [](const ov::Output<ov::Node>& output) {
+        auto in_ps = output.get_node()->get_input_partial_shape(0);
+        auto out_ps = output.get_node()->get_output_partial_shape(0);
+        return in_ps.rank().is_static() && out_ps.rank().is_static() && in_ps.size() == 3 && out_ps.size() == 2;
+    };
+
+    auto weights_m = wrap_type<ov::op::v0::Constant>(compressed_constant);
     auto convert_m = wrap_type<ov::op::v0::Convert>({weights_m});
 
     auto sub_const_m = wrap_type<ov::op::v0::Constant>(consumers_count(1));
@@ -34,11 +49,15 @@ ConvertFullyConnectedToFullyConnectedCompressed::ConvertFullyConnectedToFullyCon
     auto mul_no_sub_m = wrap_type<ov::op::v1::Multiply>({convert_m, mul_const_m});
     auto mul_m = std::make_shared<ov::pass::pattern::op::Or>(OutputVector{mul_with_sub_m, mul_no_sub_m});
 
+    auto reshape_const_m = wrap_type<ov::op::v0::Constant>();
+    auto reshape_m = wrap_type<ov::op::v1::Reshape>({mul_m, reshape_const_m}, reshape_3d_to_2d);
+
+    auto transpose_input = std::make_shared<ov::pass::pattern::op::Or>(OutputVector{reshape_m, mul_m});
     auto transpose_const_m = wrap_type<ov::op::v0::Constant>();
-    auto transpose_m = wrap_type<ov::op::v1::Transpose>({mul_m, transpose_const_m});
-    auto weights_input_m = std::make_shared<ov::pass::pattern::op::Or>(ov::OutputVector{mul_m, transpose_m});
+    auto transpose_m = wrap_type<ov::op::v1::Transpose>({transpose_input, transpose_const_m});
 
     auto data_m = any_input();
+    auto weights_input_m = std::make_shared<ov::pass::pattern::op::Or>(ov::OutputVector{reshape_m, transpose_m, mul_m});
     auto fully_connected_m = wrap_type<op::FullyConnected>({data_m, weights_input_m});
 
     ov::matcher_pass_callback callback = [=](ov::pass::pattern::Matcher& m) {
@@ -52,53 +71,73 @@ ConvertFullyConnectedToFullyConnectedCompressed::ConvertFullyConnectedToFullyCon
             return false;
         }
 
+        bool has_transpose = pattern_map.count(transpose_m);
+        auto scale_shape = pattern_map.at(mul_const_m).get_shape();
+        bool grouped = std::count_if(scale_shape.begin(), scale_shape.end(), [](size_t d) { return d > 1; }) > 1;
+
+        auto reshape_const_to_2d = [has_transpose, grouped](std::shared_ptr<ov::Node> node) {
+            auto constant = std::dynamic_pointer_cast<ov::op::v0::Constant>(node);
+            OPENVINO_ASSERT(constant != nullptr);
+            ov::Shape current_shape = constant->get_shape();
+            if (current_shape.size() == 2)
+                return constant;
+            OPENVINO_ASSERT(current_shape.size() == 3);
+
+            auto new_shape = (has_transpose || !grouped) ? ov::Shape{current_shape[0] * current_shape[1], current_shape[2]}
+                                                         : ov::Shape{current_shape[0], current_shape[1] * current_shape[2]};
+
+            return std::make_shared<ov::op::v0::Constant>(*constant, new_shape);
+        };
+
         const auto& fc_input_a = fc->get_input_node_shared_ptr(0);
-        const auto& scale = pattern_map.at(mul_const_m).get_node_shared_ptr();
+        const auto& scale = reshape_const_to_2d(pattern_map.at(mul_const_m).get_node_shared_ptr());
         std::shared_ptr<ov::Node> optional_zero_point = nullptr;
 
-        ov::NodeVector nodes_to_copy_info{pattern_map.at(fully_connected_m).get_node_shared_ptr(),
-                                          pattern_map.at(convert_m).get_node_shared_ptr()};
-        if (pattern_map.count(mul_no_sub_m)) {
-            nodes_to_copy_info.push_back(pattern_map.at(mul_no_sub_m).get_node_shared_ptr());
-        }
-        if (pattern_map.count(mul_with_sub_m)) {
-            nodes_to_copy_info.push_back(pattern_map.at(mul_with_sub_m).get_node_shared_ptr());
-        }
-
         const bool with_zero_point = pattern_map.count(subtract_m) > 0;
         if (with_zero_point) {
-            optional_zero_point = pattern_map.at(sub_const_m).get_node_shared_ptr();
-            nodes_to_copy_info.push_back(subtract_m);
+            optional_zero_point = reshape_const_to_2d(pattern_map.at(sub_const_m).get_node_shared_ptr());
         }
 
-        std::shared_ptr<ov::Node> fc_input_b = pattern_map.at(weights_m).get_node_shared_ptr();
-        if (pattern_map.count(transpose_m)) {
+        std::shared_ptr<ov::Node> fc_input_b = reshape_const_to_2d(pattern_map.at(weights_m).get_node_shared_ptr());
+        std::shared_ptr<ov::Node> fc_input_scale = scale;
+        std::shared_ptr<ov::Node> fc_input_zp = optional_zero_point;
+        if (has_transpose) {
             const auto& transpose = pattern_map.at(transpose_m).get_node_shared_ptr();
-            const auto& transpose_const = pattern_map.at(transpose_const_m).get_node_shared_ptr();
+            std::shared_ptr<ov::Node> transpose_const = pattern_map.at(transpose_const_m).get_node_shared_ptr();
+            if (ov::shape_size(transpose_const->get_shape()) != fc_input_b->get_output_partial_shape(0).size()) {
+                std::vector<int32_t> new_order(fc_input_b->get_output_partial_shape(0).size());
+                std::iota(new_order.begin(), new_order.end(), 0);
+                std::swap(new_order[new_order.size() - 1], new_order[new_order.size() - 2]);
+                transpose_const = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{new_order.size()}, new_order);
+            }
+
             fc_input_b = transpose->clone_with_new_inputs({ fc_input_b->output(0), transpose_const });
+            fc_input_scale = transpose->clone_with_new_inputs({ scale->output(0), transpose_const });
+            if (with_zero_point)
+                fc_input_zp = transpose->clone_with_new_inputs({ optional_zero_point->output(0), transpose_const });
         }
 
         std::shared_ptr<ov::Node> new_fc = nullptr;
         if (with_zero_point) {
             new_fc = std::make_shared<op::FullyConnectedCompressed>(fc_input_a,
                                                                     fc_input_b,
-                                                                    scale,
-                                                                    optional_zero_point,
+                                                                    fc_input_scale,
+                                                                    fc_input_zp,
                                                                     fc->get_output_type());
         } else {
             new_fc = std::make_shared<op::FullyConnectedCompressed>(fc_input_a,
                                                                     fc_input_b,
-                                                                    scale,
+                                                                    fc_input_scale,
                                                                     fc->get_output_type());
         }
 
         new_fc->set_friendly_name(fc->get_friendly_name());
-        ov::copy_runtime_info(nodes_to_copy_info, new_fc);
+        ov::copy_runtime_info(m.get_matched_nodes(), new_fc);
         ov::replace_node(fc, new_fc);
         return true;
     };
 
-    auto m = std::make_shared<ov::pass::pattern::Matcher>(fully_connected_m);
+    auto m = std::make_shared<ov::pass::pattern::Matcher>(fully_connected_m, "ConvertFullyConnectedToFullyConnectedCompressed");
     this->register_matcher(m, callback);
 }
 
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/convert_matmul_to_fc.cpp b/src/plugins/intel_gpu/src/plugin/transformations/convert_matmul_to_fc.cpp
index a30c88e7d1492d..2caf3cd4d69850 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/convert_matmul_to_fc.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/convert_matmul_to_fc.cpp
@@ -160,7 +160,7 @@ ConvertMatMulToFullyConnected::ConvertMatMulToFullyConnected() {
         return true;
     };
 
-    auto m = std::make_shared<ov::pass::pattern::Matcher>(matmul_m);
+    auto m = std::make_shared<ov::pass::pattern::Matcher>(matmul_m, "ConvertMatMulToFullyConnected");
     this->register_matcher(m, callback);
 }
 
diff --git a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
index 14122656fc5145..10275dae95d729 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
@@ -102,6 +102,7 @@
 #include "transformations/op_conversions/convert_prior_box_v8_to_v0.hpp"
 #include "transformations/op_conversions/convert_shapeof3.hpp"
 #include "transformations/op_conversions/convert_topk11_downgrade.hpp"
+#include "transformations/op_conversions/group_normalization_decomposition.hpp"
 #include "transformations/op_conversions/eye_decomposition.hpp"
 #include "transformations/op_conversions/convert_pad12_downgrade.hpp"
 #include "transformations/convert_precision.hpp"
@@ -488,6 +489,7 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Model> func) {
         pass_config->disable<ov::pass::ConvertGather8ToGather7>();
         pass_config->disable<ov::pass::ConvertGather7ToGather1>();
         pass_config->disable<ov::pass::ConvertTopK11ToTopK3>();
+        pass_config->disable<ov::pass::GroupNormalizationDecomposition>();
 
         pass_config->enable<ov::pass::ConvertInterpolate1ToInterpolate4>();
 
diff --git a/src/plugins/intel_gpu/src/plugin/usm_host_tensor.cpp b/src/plugins/intel_gpu/src/plugin/usm_host_tensor.cpp
new file mode 100644
index 00000000000000..bcb0877b521f20
--- /dev/null
+++ b/src/plugins/intel_gpu/src/plugin/usm_host_tensor.cpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "intel_gpu/plugin/usm_host_tensor.hpp"
+#include "intel_gpu/plugin/remote_tensor.hpp"
+#include "intel_gpu/plugin/remote_context.hpp"
+#include "openvino/runtime/make_tensor.hpp"
+#include <memory>
+
+namespace ov {
+namespace intel_gpu {
+
+USMHostTensor::USMHostTensor(std::shared_ptr<RemoteContextImpl> context, const element::Type element_type, const Shape& shape)
+    : m_impl(std::make_shared<RemoteTensorImpl>(context, shape, element_type, TensorType::BT_USM_HOST_INTERNAL)) {}
+
+USMHostTensor::USMHostTensor(std::shared_ptr<RemoteTensorImpl> tensor)
+    : m_impl(tensor) {}
+
+void* USMHostTensor::data(const element::Type& element_type) const {
+    return m_impl->get_original_memory()->buffer_ptr();
+}
+
+const element::Type& USMHostTensor::get_element_type() const {
+    return m_impl->get_element_type();
+}
+
+const Shape& USMHostTensor::get_shape() const {
+    return m_impl->get_shape();
+}
+
+const Strides& USMHostTensor::get_strides() const {
+    return m_impl->get_strides();
+}
+
+void USMHostTensor::set_shape(ov::Shape new_shape) {
+    m_impl->set_shape(new_shape);
+}
+
+void USMHostTensor::set_memory(std::shared_ptr<RemoteTensorImpl> tensor) {
+    OPENVINO_ASSERT(tensor->get_original_memory()->get_allocation_type() == cldnn::allocation_type::usm_host, "[GPU] Unexpected allocation type");
+    m_impl = tensor;
+}
+
+std::shared_ptr<RemoteTensorImpl> USMHostTensor::get_impl() const {
+    return m_impl;
+}
+
+}  // namespace intel_gpu
+}  // namespace ov
diff --git a/src/plugins/intel_gpu/src/runtime/execution_config.cpp b/src/plugins/intel_gpu/src/runtime/execution_config.cpp
index e1375ef14ddb47..8bcb853a4c3090 100644
--- a/src/plugins/intel_gpu/src/runtime/execution_config.cpp
+++ b/src/plugins/intel_gpu/src/runtime/execution_config.cpp
@@ -48,6 +48,7 @@ void ExecutionConfig::set_default() {
         std::make_tuple(ov::hint::performance_mode, ov::hint::PerformanceMode::LATENCY, PerformanceModeValidator()),
         std::make_tuple(ov::hint::execution_mode, ov::hint::ExecutionMode::PERFORMANCE),
         std::make_tuple(ov::hint::num_requests, 0),
+        std::make_tuple(ov::hint::enable_cpu_pinning, false),
 
         std::make_tuple(ov::intel_gpu::hint::host_task_priority, ov::hint::Priority::MEDIUM),
         std::make_tuple(ov::intel_gpu::hint::queue_throttle, ov::intel_gpu::hint::ThrottleLevel::MEDIUM),
@@ -86,7 +87,7 @@ void ExecutionConfig::set_property(const AnyMap& config) {
     for (auto& kv : config) {
         auto& name = kv.first;
         auto& val = kv.second;
-        OPENVINO_ASSERT(is_supported(kv.first), "[GPU] Attepmpt to set property ", name, " (", val.as<std::string>(), ") which was not registered!\n");
+        OPENVINO_ASSERT(is_supported(kv.first), "[GPU] Attempt to set property ", name, " (", val.as<std::string>(), ") which was not registered!\n");
         OPENVINO_ASSERT(property_validators.at(name)->is_valid(val), "[GPU] Invalid value for property ", name,  ": ", val.as<std::string>());
         internal_properties[name] = val;
     }
@@ -108,7 +109,7 @@ void ExecutionConfig::set_user_property(const AnyMap& config) {
         auto& name = kv.first;
         auto& val = kv.second;
         bool supported = is_supported(name) && supported_properties.at(name) == PropertyVisibility::PUBLIC;
-        OPENVINO_ASSERT(supported, "[GPU] Attepmpt to set user property ", name, " (", val.as<std::string>(), ") which was not registered or internal!\n");
+        OPENVINO_ASSERT(supported, "[GPU] Attempt to set user property ", name, " (", val.as<std::string>(), ") which was not registered or internal!\n");
         OPENVINO_ASSERT(property_validators.at(name)->is_valid(val), "[GPU] Invalid value for property ", name,  ": `", val.as<std::string>(), "`");
 
         user_properties[kv.first] = kv.second;
diff --git a/src/plugins/intel_gpu/src/runtime/format.cpp b/src/plugins/intel_gpu/src/runtime/format.cpp
index bd31583493ea71..095bda14bbf97d 100644
--- a/src/plugins/intel_gpu/src/runtime/format.cpp
+++ b/src/plugins/intel_gpu/src/runtime/format.cpp
@@ -85,6 +85,7 @@ static const std::map<format::type, format_traits> format_traits_map {
         FMT_TRAITS(oizyx,                                        1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx",  "oixyz", {}),
         FMT_TRAITS(iozyx,                                        1, 1, 3, 0, {1, 0, 2, 3, 4}, "iozyx",  "oixyz", {}),
         FMT_TRAITS(os_is_yx_isv16_osv16,                         1, 1, 2, 0, {0, 1, 2, 3},    "oiyx",   "oixy",  {{1, 16}, {0, 16}}),
+        FMT_TRAITS(o_is_yx_isv2,                                 1, 1, 2, 0, {0, 1, 2, 3},    "oiyx",   "oixy",  {{1, 2}}),
         FMT_TRAITS(o_is_yx_isv4,                                 1, 1, 2, 0, {0, 1, 2, 3},    "oiyx",   "oixy",  {{1, 4}}),
         FMT_TRAITS(o_is_yx_isv16,                                1, 1, 2, 0, {0, 1, 2, 3},    "oiyx",   "oixy",  {{1, 16}}),
         FMT_TRAITS(o_is_zyx_isv16,                               1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx",  "oixyz", {{1, 16}}),
diff --git a/src/plugins/intel_gpu/src/runtime/shape_predictor.cpp b/src/plugins/intel_gpu/src/runtime/shape_predictor.cpp
index 2d398ee89ff1de..1ff00c905bd073 100644
--- a/src/plugins/intel_gpu/src/runtime/shape_predictor.cpp
+++ b/src/plugins/intel_gpu/src/runtime/shape_predictor.cpp
@@ -58,7 +58,7 @@ bool ShapePredictor::can_preallocate(size_t desired_buffer_size) {
 
 std::pair<bool, ov::Shape> ShapePredictor::predict_preallocation_shape(const std::string& id,
                                                                        const ov::Shape& current_shape,
-                                                                       size_t dt_size,
+                                                                       size_t dt_bitwidth,
                                                                        bool can_reuse_buffer) {
     add_shape(id, current_shape);
 
@@ -110,7 +110,7 @@ std::pair<bool, ov::Shape> ShapePredictor::predict_preallocation_shape(const std
             for (size_t i = 0; i < current_shape.size(); ++i)
                 single_iter_shape.push_back(diffs[0][i] == 0 ? current_shape[i] : 1);
 
-            if (ov::shape_size(single_iter_shape) * dt_size > _max_per_iter_size)
+            if (ceil_div(ov::shape_size(single_iter_shape) * dt_bitwidth, 8) > _max_per_iter_size)
                 can_use_iterations_preallocation = false;
         }
 
diff --git a/src/plugins/intel_gpu/tests/common/subgraphs_builders.hpp b/src/plugins/intel_gpu/tests/common/subgraphs_builders.hpp
new file mode 100644
index 00000000000000..dea703cf7104b2
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/common/subgraphs_builders.hpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include "openvino/core/dimension.hpp"
+#include "openvino/core/model.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/transpose.hpp"
+#include "openvino/op/result.hpp"
+#include "openvino/op/parameter.hpp"
+#include "openvino/op/matmul.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/concat.hpp"
+
+namespace tests {
+
+inline std::shared_ptr<ov::Model> make_llm_kv_cache_pattern(ov::Dimension batch = ov::Dimension::dynamic(),
+                                                            ov::Dimension n_heads = ov::Dimension::dynamic(),
+                                                            ov::Dimension n_features = ov::Dimension::dynamic(),
+                                                            ov::element::Type_t element_type = ov::element::f32) {
+    ov::PartialShape kv_cache_size = {batch, n_heads, -1, n_features};
+    ov::PartialShape new_token_size = {batch, -1, n_heads, n_features};
+    ov::PartialShape matmul_in_size = {batch, n_heads, -1, -1};
+
+    auto in_kv_prev = std::make_shared<ov::op::v0::Parameter>(element_type, kv_cache_size);
+    in_kv_prev->set_friendly_name("past_key_values");
+    auto in_new_token = std::make_shared<ov::op::v0::Parameter>(element_type, new_token_size);
+    in_new_token->set_friendly_name("new_token_input");
+    auto in_matmul = std::make_shared<ov::op::v0::Parameter>(element_type, matmul_in_size);
+    in_matmul->set_friendly_name("in_matmul");
+
+    auto transpose_const = ov::op::v0::Constant::create(ov::element::i32, {new_token_size.size()}, {0, 2, 1, 3});
+    auto transpose = std::make_shared<ov::op::v1::Transpose>(in_new_token, transpose_const);
+    auto concat = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{in_kv_prev, transpose}, 2);
+    auto convert = std::make_shared<ov::op::v0::Convert>(concat, element_type);
+    auto kv_present = std::make_shared<ov::op::v0::Result>(convert);
+    kv_present->set_friendly_name("present_key_values");
+    auto matmul = std::make_shared<ov::op::v0::MatMul>(in_matmul, concat, false, false);
+    auto matmul_out = std::make_shared<ov::op::v0::Result>(matmul);
+    matmul_out->set_friendly_name("matmul_out");
+
+    ov::ParameterVector params{in_kv_prev, in_new_token, in_matmul};
+    ov::ResultVector results{kv_present, matmul_out};
+    return std::make_shared<ov::Model>(results, params, "LLM-KV-Cache");
+}
+
+} // namespace tests
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/iteration_chaining.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/iteration_chaining.cpp
new file mode 100644
index 00000000000000..4bcef9a7bedbe0
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/behavior/ov_infer_request/iteration_chaining.cpp
@@ -0,0 +1,23 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <limits.h>
+#include "behavior/ov_infer_request/iteration_chaining.hpp"
+#include "common_test_utils/test_constants.hpp"
+#include "openvino/runtime/properties.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+
+const std::vector<ov::AnyMap> configs = {
+    { ov::hint::inference_precision(ov::element::f32) }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVIterationChaining,
+                        ::testing::Combine(
+                                ::testing::Values(ov::test::utils::DEVICE_GPU),
+                                ::testing::ValuesIn(configs)),
+                        OVIterationChaining::getTestCaseName);
+
+}  // namespace
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp
index 31c4d4884f05d1..39711f74105596 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp
@@ -42,6 +42,7 @@ const std::vector<ov::AnyMap> gpu_setcore_properties = {
 const std::vector<ov::AnyMap> gpu_compileModel_properties = {
     {ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY),
      ov::hint::num_requests(10),
+     ov::hint::enable_cpu_pinning(true),
      ov::enable_profiling(true)}};
 
 INSTANTIATE_TEST_SUITE_P(smoke_gpuCompileModelBehaviorTests,
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/broadcast.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/broadcast.cpp
index 84234e94bbd347..3387375a0c07c1 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/broadcast.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/broadcast.cpp
@@ -4,25 +4,24 @@
 
 #include <vector>
 
-#include "single_layer_tests/broadcast.hpp"
+#include "single_op_tests/broadcast.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
-
 namespace {
-
-const std::vector<InferenceEngine::Precision> inputPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::FP16,
-        InferenceEngine::Precision::I32,
-        InferenceEngine::Precision::I8,
-        InferenceEngine::Precision::U8
+using ov::test::BroadcastLayerTest;
+using ov::test::BroadcastParamsTuple;
+const std::vector<ov::element::Type> inputPrecisions = {
+        ov::element::f32,
+        ov::element::f16,
+        ov::element::i32,
+        ov::element::i8,
+        ov::element::u8
 };
 
-const std::vector<InferenceEngine::Precision> inputTPrecisions = {
-        InferenceEngine::Precision::FP16,
-        InferenceEngine::Precision::I16,
-        InferenceEngine::Precision::BOOL
+const std::vector<ov::element::Type> inputTPrecisions = {
+        ov::element::f16,
+        ov::element::i16,
+        ov::element::boolean
 };
 
 // NUMPY MODE //////////////////////////////////////////
@@ -31,12 +30,16 @@ std::vector<std::vector<size_t>> targetShapesNumpy0D = {
         {},
 };
 
+std::vector<std::vector<ov::Shape>> input_shapes_0d_static = {
+        {{}}
+};
+
 INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast0D,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesNumpy0D),
-                                           ::testing::Values(ngraph::AxisSet{}),  // not used in numpy mode
-                                           ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-                                           ::testing::Values(std::vector<size_t>{}),
+                                           ::testing::Values(ov::AxisSet{}),  // not used in numpy mode
+                                           ::testing::Values(ov::op::BroadcastType::NUMPY),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_0d_static)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
@@ -53,12 +56,16 @@ std::vector<std::vector<size_t>> targetShapesNumpy1D = {
         {1, 4, 4},
 };
 
+std::vector<std::vector<ov::Shape>> input_shapes_1d_static = {
+        {{1}}
+};
+
 INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast1D,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesNumpy1D),
-                                           ::testing::Values(ngraph::AxisSet{}),  // not used in numpy mode
-                                           ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-                                           ::testing::Values(std::vector<size_t>{1}),
+                                           ::testing::Values(ov::AxisSet{}),  // not used in numpy mode
+                                           ::testing::Values(ov::op::BroadcastType::NUMPY),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_1d_static)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
@@ -66,9 +73,9 @@ INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast1D,
 INSTANTIATE_TEST_CASE_P(smoke_PrecTransformation, BroadcastLayerTest,
         ::testing::Combine(
             ::testing::Values(targetShapesNumpy1D[0]),
-            ::testing::Values(ngraph::AxisSet{}), //not used in numpy mode
-            ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-            ::testing::Values(std::vector<size_t>{1}),
+            ::testing::Values(ov::AxisSet{}), //not used in numpy mode
+            ::testing::Values(ov::op::BroadcastType::NUMPY),
+            ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_1d_static)),
             ::testing::ValuesIn(inputTPrecisions),
             ::testing::Values(ov::test::utils::DEVICE_GPU)),
         BroadcastLayerTest::getTestCaseName);
@@ -81,12 +88,16 @@ std::vector<std::vector<size_t>> targetShapesNumpy2D = {
         {2, 2, 3, 6},
 };
 
+std::vector<std::vector<ov::Shape>> input_shapes_2d_static = {
+        {{3, 1}}
+};
+
 INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast2D,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesNumpy2D),
                                            ::testing::Values(ngraph::AxisSet{}),  // not used in numpy mode
                                            ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-                                           ::testing::Values(std::vector<size_t>{3, 1}),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_2d_static)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
@@ -99,42 +110,63 @@ std::vector<std::vector<size_t>> targetShapesNumpy3D = {
         {2, 1, 1, 4, 4},
 };
 
+std::vector<std::vector<ov::Shape>> input_shapes_3d_static = {
+        {{1, 4, 1}}
+};
+
+
 INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast3D,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesNumpy3D),
-                                           ::testing::Values(ngraph::AxisSet{}),  // not used in numpy mode
-                                           ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-                                           ::testing::Values(std::vector<size_t>{1, 4, 1}),
+                                           ::testing::Values(ov::AxisSet{}),  // not used in numpy mode
+                                           ::testing::Values(ov::op::BroadcastType::NUMPY),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_3d_static)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
 
+std::vector<std::vector<size_t>> targetShapesNumpy6D = {
+        {1, 2, 3, 4, 5, 6},
+};
+
+std::vector<std::vector<ov::Shape>> input_shapes_6d_static = {
+        {{1, 2, 1, 4, 1, 6}}
+};
+
 INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast6D,
                         BroadcastLayerTest,
-                        ::testing::Combine(::testing::Values(std::vector<size_t>{1, 2, 3, 4, 5, 6}),
-                                           ::testing::Values(ngraph::AxisSet{}),  // not used in numpy mode
-                                           ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-                                           ::testing::Values(std::vector<size_t>{1, 2, 1, 4, 1, 6}),
+                        ::testing::Combine(::testing::ValuesIn(targetShapesNumpy6D),
+                                           ::testing::Values(ov::AxisSet{}),  // not used in numpy mode
+                                           ::testing::Values(ov::op::BroadcastType::NUMPY),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_6d_static)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
 
+std::vector<std::vector<size_t>> targetShapesNumpy5D = {
+        {1, 2, 3, 4, 5},
+};
+
+std::vector<std::vector<ov::Shape>> input_shapes_5d_static = {
+        {{1, 2, 1, 4, 1}}
+};
+
 INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast5D,
                         BroadcastLayerTest,
-                        ::testing::Combine(::testing::Values(std::vector<size_t>{1, 2, 3, 4, 5}),
-                                           ::testing::Values(ngraph::AxisSet{}),  // not used in numpy mode
-                                           ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-                                           ::testing::Values(std::vector<size_t>{1, 2, 1, 4, 1}),
+                        ::testing::Combine(::testing::ValuesIn(targetShapesNumpy5D),
+                                           ::testing::Values(ov::AxisSet{}),  // not used in numpy mode
+                                           ::testing::Values(ov::op::BroadcastType::NUMPY),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_5d_static)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
 // END NUMPY MODE //////////////////////////////////////
 
 // BIDIRECTIONAL MODE //////////////////////////////////
-std::vector<std::vector<size_t>> inShapesBidi = {
-        {4, 1},
-        {1, 4, 1},
-        {4, 1, 1}
+std::vector<std::vector<ov::Shape>> inShapesBidi = {
+        {{4, 1}},
+        {{1, 4, 1}},
+        {{4, 1, 1}}
 };
 
 std::vector<std::vector<size_t>> targetShapesBidi = {
@@ -146,25 +178,25 @@ std::vector<std::vector<size_t>> targetShapesBidi = {
 INSTANTIATE_TEST_CASE_P(smoke_TestBidirectionalBroadcast,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesBidi),
-                                           ::testing::Values(ngraph::AxisSet{}),  // not used in bidirectional mode
-                                           ::testing::Values(ngraph::op::BroadcastType::BIDIRECTIONAL),
-                                           ::testing::ValuesIn(inShapesBidi),
+                                           ::testing::Values(ov::AxisSet{}),  // not used in bidirectional mode
+                                           ::testing::Values(ov::op::BroadcastType::BIDIRECTIONAL),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inShapesBidi)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
 
 // EXPLICIT MODE ///////////////////////////////////////
 // 1D
-std::vector<std::vector<size_t>> inShapesExplicit1D = { {4} };
+std::vector<std::vector<ov::Shape>> inShapesExplicit1D = { {{4}} };
 std::vector<std::vector<size_t>> targetShapesExplicit1D = { {4, 2, 4}, {4, 2, 4, 1} };
-std::vector<ngraph::AxisSet> axes1D = { {0}, {2} };
+std::vector<ov::AxisSet> axes1D = { {0}, {2} };
 
 INSTANTIATE_TEST_CASE_P(smoke_TestExplicitBroadcast1D,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesExplicit1D),
                                            ::testing::ValuesIn(axes1D),
-                                           ::testing::Values(ngraph::op::BroadcastType::EXPLICIT),
-                                           ::testing::ValuesIn(inShapesExplicit1D),
+                                           ::testing::Values(ov::op::BroadcastType::EXPLICIT),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inShapesExplicit1D)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
@@ -172,18 +204,18 @@ INSTANTIATE_TEST_CASE_P(smoke_TestExplicitBroadcast1D,
 INSTANTIATE_TEST_SUITE_P(smoke_TestBidirectionalBroadcast3,
                          BroadcastLayerTest,
                          ::testing::Combine(::testing::Values(targetShapesBidi[2]),
-                                            ::testing::Values(ngraph::AxisSet{}),  // not used in bidirectional mode
-                                            ::testing::Values(ngraph::op::BroadcastType::BIDIRECTIONAL),
-                                            ::testing::Values(inShapesBidi[2]),
+                                            ::testing::Values(ov::AxisSet{}),  // not used in bidirectional mode
+                                            ::testing::Values(ov::op::BroadcastType::BIDIRECTIONAL),
+                                            ::testing::Values(ov::test::static_shapes_to_test_representation(inShapesBidi[2])),
                                             ::testing::ValuesIn(inputPrecisions),
                                             ::testing::Values(ov::test::utils::DEVICE_GPU)),
                          BroadcastLayerTest::getTestCaseName);
 
 // EXPLICIT MODE
 
-std::vector<std::vector<size_t>> inShapesExplicit = {
-        {3, 1},
-        {2, 4}
+std::vector<std::vector<ov::Shape>> inShapesExplicit = {
+        {{3, 1}},
+        {{2, 4}}
 };
 
 std::vector<std::vector<size_t>> targetShapesExplicit = {
@@ -192,34 +224,33 @@ std::vector<std::vector<size_t>> targetShapesExplicit = {
 };
 
 // 2D
-std::vector<std::vector<size_t>> inShapesExplicit2D = { {2, 4} };
+std::vector<std::vector<ov::Shape>> inShapesExplicit2D = { {{2, 4}} };
 std::vector<std::vector<size_t>> targetShapesExplicit2D = { {2, 2, 4}, {2, 2, 4, 1}};
-std::vector<ngraph::AxisSet> axes2D = { {1, 2}, {0, 2} };
+std::vector<ov::AxisSet> axes2D = { {1, 2}, {0, 2} };
 
 INSTANTIATE_TEST_CASE_P(smoke_TestExplicitBroadcast2D,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesExplicit2D),
                                            ::testing::ValuesIn(axes2D),
-                                           ::testing::Values(ngraph::op::BroadcastType::EXPLICIT),
-                                           ::testing::ValuesIn(inShapesExplicit2D),
+                                           ::testing::Values(ov::op::BroadcastType::EXPLICIT),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inShapesExplicit2D)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
 
 // 3D
-std::vector<std::vector<size_t>> inShapesExplicit3D = { {2, 2, 2} };
+std::vector<std::vector<ov::Shape>> inShapesExplicit3D = { {{2, 2, 2}} };
 std::vector<std::vector<size_t>> targetShapesExplicit3D = { {2, 2, 2, 2} };
-std::vector<ngraph::AxisSet> axes3D = { {0, 1, 2}, {0, 1, 3}, {0, 2, 3}, {1, 2, 3} };
+std::vector<ov::AxisSet> axes3D = { {0, 1, 2}, {0, 1, 3}, {0, 2, 3}, {1, 2, 3} };
 
 INSTANTIATE_TEST_CASE_P(smoke_TestExplicitBroadcast3D,
                         BroadcastLayerTest,
                         ::testing::Combine(::testing::ValuesIn(targetShapesExplicit3D),
                                            ::testing::ValuesIn(axes3D),
-                                           ::testing::Values(ngraph::op::BroadcastType::EXPLICIT),
-                                           ::testing::ValuesIn(inShapesExplicit3D),
+                                           ::testing::Values(ov::op::BroadcastType::EXPLICIT),
+                                           ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inShapesExplicit3D)),
                                            ::testing::ValuesIn(inputPrecisions),
                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         BroadcastLayerTest::getTestCaseName);
 // END EXPLICIT MODE ///////////////////////////////////
-
 }  // namespace
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/bucketize.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/bucketize.cpp
index b658cfc840fe06..89da385392745f 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/bucketize.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/bucketize.cpp
@@ -2,31 +2,37 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "single_layer_tests/bucketize.hpp"
+#include "single_op_tests/bucketize.hpp"
 
 #include <vector>
 
-using namespace LayerTestsDefinitions;
+using ov::test::BucketizeLayerTest;
 
 namespace {
 
-const std::vector<std::vector<size_t>> data_shapes = {
-    // No reason to test other ranks as logic is the same
-    {40, 22, 13, 9},     // 4D
-    {6, 7, 3, 2, 8}, // 5D
-    {6, 7, 3, 2, 8, 5},  // 6D
-};
-
-const std::vector<std::vector<size_t>> buckets_shapes = {
-    {5},
-    {100},
+const std::vector<std::vector<ov::Shape>> input_shapes_static  = {
+    {{40, 22, 13, 9}, {5}},
+    {{6, 7, 3, 2, 8}, {5}},
+    {{6, 7, 3, 2, 8, 5}, {5}},
+    {{40, 22, 13, 9}, {100}},
+    {{6, 7, 3, 2, 8}, {100}},
+    {{6, 7, 3, 2, 8, 5}, {100}},
 };
 
 const std::vector<bool> with_right_bound = {true, false};
 
-const std::vector<InferenceEngine::Precision> out_precision = {
-    InferenceEngine::Precision::I32,
-    InferenceEngine::Precision::I64,
+const std::vector<ov::element::Type> out_precision = {
+    ov::element::i32,
+    ov::element::i64
+};
+
+const std::vector<ov::element::Type> in_buckets_precision = {
+    ov::element::f16,
+    ov::element::f32,
+    ov::element::i32,
+    ov::element::i64,
+    ov::element::i8,
+    ov::element::u8
 };
 
 // We won't test FP32 and FP16 together as it won't make sense for now
@@ -34,94 +40,68 @@ const std::vector<InferenceEngine::Precision> out_precision = {
 
 INSTANTIATE_TEST_SUITE_P(smoke_Bucketize_input_fp16,
                          BucketizeLayerTest,
-                         testing::Combine(testing::ValuesIn(data_shapes),
-                                          testing::ValuesIn(buckets_shapes),
+                         testing::Combine(testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                           testing::ValuesIn(with_right_bound),
-                                          testing::Values(InferenceEngine::Precision::FP16),
-                                          testing::Values(InferenceEngine::Precision::FP16,
-                                                          InferenceEngine::Precision::I32,
-                                                          InferenceEngine::Precision::I64,
-                                                          InferenceEngine::Precision::I8,
-                                                          InferenceEngine::Precision::U8),
+                                          testing::Values(ov::element::f16),
+                                          testing::Values(ov::element::f16,
+                                                          ov::element::i32,
+                                                          ov::element::i64,
+                                                          ov::element::i8,
+                                                          ov::element::u8),
                                           testing::ValuesIn(out_precision),
                                           testing::Values(ov::test::utils::DEVICE_GPU)),
                          BucketizeLayerTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Bucketize_input_fp32,
                          BucketizeLayerTest,
-                         testing::Combine(testing::ValuesIn(data_shapes),
-                                          testing::ValuesIn(buckets_shapes),
+                         testing::Combine(testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                           testing::ValuesIn(with_right_bound),
-                                          testing::Values(InferenceEngine::Precision::FP32),
-                                          testing::Values(InferenceEngine::Precision::FP32,
-                                                          InferenceEngine::Precision::I32,
-                                                          InferenceEngine::Precision::I64,
-                                                          InferenceEngine::Precision::I8,
-                                                          InferenceEngine::Precision::U8),
+                                          testing::Values(ov::element::f32),
+                                          testing::Values(ov::element::f32,
+                                                          ov::element::i32,
+                                                          ov::element::i64,
+                                                          ov::element::i8,
+                                                          ov::element::u8),
                                           testing::ValuesIn(out_precision),
                                           testing::Values(ov::test::utils::DEVICE_GPU)),
                          BucketizeLayerTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Bucketize_input_i32,
                          BucketizeLayerTest,
-                         testing::Combine(testing::ValuesIn(data_shapes),
-                                          testing::ValuesIn(buckets_shapes),
+                         testing::Combine(testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                           testing::ValuesIn(with_right_bound),
-                                          testing::Values(InferenceEngine::Precision::I32),
-                                          testing::Values(InferenceEngine::Precision::FP16,
-                                                          InferenceEngine::Precision::FP32,
-                                                          InferenceEngine::Precision::I32,
-                                                          InferenceEngine::Precision::I64,
-                                                          InferenceEngine::Precision::I8,
-                                                          InferenceEngine::Precision::U8),
+                                          testing::Values(ov::element::i32),
+                                          testing::ValuesIn(in_buckets_precision),
                                           testing::ValuesIn(out_precision),
                                           testing::Values(ov::test::utils::DEVICE_GPU)),
                          BucketizeLayerTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Bucketize_input_i64,
                          BucketizeLayerTest,
-                         testing::Combine(testing::ValuesIn(data_shapes),
-                                          testing::ValuesIn(buckets_shapes),
+                         testing::Combine(testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                           testing::ValuesIn(with_right_bound),
-                                          testing::Values(InferenceEngine::Precision::I64),
-                                          testing::Values(InferenceEngine::Precision::FP16,
-                                                          InferenceEngine::Precision::FP32,
-                                                          InferenceEngine::Precision::I32,
-                                                          InferenceEngine::Precision::I64,
-                                                          InferenceEngine::Precision::I8,
-                                                          InferenceEngine::Precision::U8),
+                                          testing::Values(ov::element::i64),
+                                          testing::ValuesIn(in_buckets_precision),
                                           testing::ValuesIn(out_precision),
                                           testing::Values(ov::test::utils::DEVICE_GPU)),
                          BucketizeLayerTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Bucketize_input_i8,
                          BucketizeLayerTest,
-                         testing::Combine(testing::ValuesIn(data_shapes),
-                                          testing::ValuesIn(buckets_shapes),
+                         testing::Combine(testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                           testing::ValuesIn(with_right_bound),
-                                          testing::Values(InferenceEngine::Precision::I8),
-                                          testing::Values(InferenceEngine::Precision::FP16,
-                                                          InferenceEngine::Precision::FP32,
-                                                          InferenceEngine::Precision::I32,
-                                                          InferenceEngine::Precision::I64,
-                                                          InferenceEngine::Precision::I8,
-                                                          InferenceEngine::Precision::U8),
+                                          testing::Values(ov::element::i8),
+                                          testing::ValuesIn(in_buckets_precision),
                                           testing::ValuesIn(out_precision),
                                           testing::Values(ov::test::utils::DEVICE_GPU)),
                          BucketizeLayerTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Bucketize_input_u8,
                          BucketizeLayerTest,
-                         testing::Combine(testing::ValuesIn(data_shapes),
-                                          testing::ValuesIn(buckets_shapes),
+                         testing::Combine(testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                           testing::ValuesIn(with_right_bound),
-                                          testing::Values(InferenceEngine::Precision::U8),
-                                          testing::Values(InferenceEngine::Precision::FP16,
-                                                          InferenceEngine::Precision::FP32,
-                                                          InferenceEngine::Precision::I32,
-                                                          InferenceEngine::Precision::I64,
-                                                          InferenceEngine::Precision::I8,
-                                                          InferenceEngine::Precision::U8),
+                                          testing::Values(ov::element::u8),
+                                          testing::ValuesIn(in_buckets_precision),
                                           testing::ValuesIn(out_precision),
                                           testing::Values(ov::test::utils::DEVICE_GPU)),
                          BucketizeLayerTest::getTestCaseName);
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/concat.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/concat.cpp
index 0d0aa1fe704b77..a6dfee6b36d641 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/concat.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/concat.cpp
@@ -4,35 +4,30 @@
 
 #include <vector>
 
-#include "single_layer_tests/concat.hpp"
+#include "single_op_tests/concat.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
-
 namespace {
+using ov::test::ConcatLayerTest;
 
 std::vector<int> axes = {-3, -2, -1, 0, 1, 2, 3};
-std::vector<std::vector<std::vector<size_t>>> inShapes = {
+std::vector<std::vector<ov::Shape>> inShapes = {
         {{10, 10, 10, 10}},
         {{10, 10, 10, 10}, {10, 10, 10, 10}},
         {{10, 10, 10, 10}, {10, 10, 10, 10}, {10, 10, 10, 10}},
         {{10, 10, 10, 10}, {10, 10, 10, 10}, {10, 10, 10, 10}, {10, 10, 10, 10}},
         {{10, 10, 10, 10}, {10, 10, 10, 10}, {10, 10, 10, 10}, {10, 10, 10, 10}, {10, 10, 10, 10}}
 };
-std::vector<InferenceEngine::Precision> netPrecisions = {InferenceEngine::Precision::FP32,
-                                                         InferenceEngine::Precision::FP16,
-                                                         InferenceEngine::Precision::I64};
+std::vector<ov::element::Type> netPrecisions = {ov::element::f32,
+                                                ov::element::f16,
+                                                ov::element::i64};
 
 
 INSTANTIATE_TEST_SUITE_P(smoke_NoReshape, ConcatLayerTest,
                         ::testing::Combine(
                                 ::testing::ValuesIn(axes),
-                                ::testing::ValuesIn(inShapes),
+                                ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inShapes)),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
                                 ::testing::Values(ov::test::utils::DEVICE_GPU)),
                         ConcatLayerTest::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder.cpp
index 2206490567e7e2..b7c2807ce37086 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder.cpp
@@ -3,35 +3,33 @@
 //
 
 #include <vector>
-#include "single_layer_tests/ctc_greedy_decoder.hpp"
+#include "single_op_tests/ctc_greedy_decoder.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
-using namespace ngraph::helpers;
-
 namespace {
+using ov::test::CTCGreedyDecoderLayerTest;
+
 // Common params
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-    InferenceEngine::Precision::FP32,
-    InferenceEngine::Precision::FP16
+const std::vector<ov::element::Type> netPrecisions = {
+    ov::element::f32,
+    ov::element::f16
 };
 std::vector<bool> mergeRepeated{true, false};
 
+std::vector<std::vector<ov::Shape>> input_shapes_static = {
+    {{ 50, 3, 3 }},
+    {{ 50, 3, 7 }},
+    {{ 50, 3, 8 }},
+    {{ 50, 3, 16 }},
+    {{ 50, 3, 128 }},
+    {{ 50, 3, 49 }},
+    {{ 50, 3, 55 }},
+    {{ 1, 1, 16 }}};
+
 INSTANTIATE_TEST_SUITE_P(smoke_CtcGreedyDecoderBasic,
                          CTCGreedyDecoderLayerTest,
                          ::testing::Combine(::testing::ValuesIn(netPrecisions),
-                                            ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                            ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                            ::testing::Values(InferenceEngine::Layout::ANY),
-                                            ::testing::Values(InferenceEngine::Layout::ANY),
-                                            ::testing::Values(std::vector<size_t>({50, 3, 3}),
-                                                              std::vector<size_t>({50, 3, 7}),
-                                                              std::vector<size_t>({50, 3, 8}),
-                                                              std::vector<size_t>({50, 3, 16}),
-                                                              std::vector<size_t>({50, 3, 128}),
-                                                              std::vector<size_t>({50, 3, 49}),
-                                                              std::vector<size_t>({50, 3, 55}),
-                                                              std::vector<size_t>({1, 1, 16})),
+                                            ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(input_shapes_static)),
                                             ::testing::ValuesIn(mergeRepeated),
                                             ::testing::Values(ov::test::utils::DEVICE_GPU)),
                          CTCGreedyDecoderLayerTest::getTestCaseName);
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder_seq_len.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder_seq_len.cpp
index c015258d41ed24..12d318d107d342 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder_seq_len.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_greedy_decoder_seq_len.cpp
@@ -3,30 +3,28 @@
 //
 
 #include <vector>
-#include "single_layer_tests/ctc_greedy_decoder_seq_len.hpp"
+#include "single_op_tests/ctc_greedy_decoder_seq_len.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
-using namespace ngraph::helpers;
-
 namespace {
+using ov::test::CTCGreedyDecoderSeqLenLayerTest;
 
-std::vector<std::vector<size_t>> inputShape{{1, 1, 1}, {1, 6, 10}, {3, 3, 16}, {5, 3, 55}};
+std::vector<std::vector<ov::Shape>> inputShape{{{1, 1, 1}}, {{1, 6, 10}}, {{3, 3, 16}}, {{5, 3, 55}}};
 
-const std::vector<InferenceEngine::Precision> probPrecisions = {
-    InferenceEngine::Precision::FP32,
-    InferenceEngine::Precision::FP16
+const std::vector<ov::element::Type> probPrecisions = {
+    ov::element::f32,
+    ov::element::f16
 };
-const std::vector<InferenceEngine::Precision> idxPrecisions = {
-    InferenceEngine::Precision::I32,
-    InferenceEngine::Precision::I64
+const std::vector<ov::element::Type> idxPrecisions = {
+    ov::element::i32,
+    ov::element::i64
 };
 
 std::vector<bool> mergeRepeated{true, false};
 
 INSTANTIATE_TEST_SUITE_P(smoke_set1,
                          CTCGreedyDecoderSeqLenLayerTest,
-                         ::testing::Combine(::testing::ValuesIn(inputShape),
+                         ::testing::Combine(::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inputShape)),
                                             ::testing::Values(10),
                                             ::testing::ValuesIn(probPrecisions),
                                             ::testing::ValuesIn(idxPrecisions),
@@ -37,8 +35,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_set1,
 
 INSTANTIATE_TEST_SUITE_P(smoke_set2,
                          CTCGreedyDecoderSeqLenLayerTest,
-                         ::testing::Combine(::testing::ValuesIn(std::vector<std::vector<size_t>>{{2, 8, 11},
-                                                                                                 {4, 10, 55}}),
+                         ::testing::Combine(::testing::ValuesIn(ov::test::static_shapes_to_test_representation(std::vector<std::vector<ov::Shape>>{{{2, 8, 11}},
+                                                                                                 {{4, 10, 55}}})),
                                             ::testing::ValuesIn(std::vector<int>{5, 100}),
                                             ::testing::ValuesIn(probPrecisions),
                                             ::testing::ValuesIn(idxPrecisions),
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_loss.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_loss.cpp
index 9e71f45de30034..742b4974e6fc88 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_loss.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/ctc_loss.cpp
@@ -2,22 +2,21 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "single_layer_tests/ctc_loss.hpp"
+#include "single_op_tests/ctc_loss.hpp"
 
 #include <vector>
 
-using namespace LayerTestsDefinitions;
-
 namespace {
+using ov::test::CTCLossLayerTest;
 
-const std::vector<InferenceEngine::Precision> fPrecisions = {
-    InferenceEngine::Precision::FP32,
-    InferenceEngine::Precision::FP16,
+const std::vector<ov::element::Type> fPrecisions = {
+    ov::element::f32,
+    ov::element::f16,
 };
 
-const std::vector<InferenceEngine::Precision> iPrecisions = {
-    InferenceEngine::Precision::I32,
-    InferenceEngine::Precision::I64,
+const std::vector<ov::element::Type> iPrecisions = {
+    ov::element::i32,
+    ov::element::i64,
 };
 
 const std::vector<bool> preprocessCollapseRepeated = {true, false};
@@ -25,7 +24,6 @@ const std::vector<bool> ctcMergeRepeated = {true, false};
 const std::vector<bool> unique = {true, false};
 
 const auto ctcLossArgsSubset1 = testing::Combine(
-    testing::Values(std::vector<size_t>({2, 3, 3})),                     // logits shape
     testing::ValuesIn(std::vector<std::vector<int>>({{2, 3}, {3, 3}})),  // logits length
     testing::ValuesIn(
         std::vector<std::vector<std::vector<int>>>({{{0, 1, 0}, {1, 0, 1}}, {{0, 1, 2}, {1, 1, 1}}})),  // labels
@@ -38,14 +36,14 @@ const auto ctcLossArgsSubset1 = testing::Combine(
 INSTANTIATE_TEST_SUITE_P(smoke_CTCLoss_Set1,
                          CTCLossLayerTest,
                          testing::Combine(ctcLossArgsSubset1,
-                                          testing::ValuesIn(fPrecisions),
-                                          testing::ValuesIn(iPrecisions),
-                                          testing::Values(ov::test::utils::DEVICE_GPU)),
+                                        testing::Values(ov::test::static_shapes_to_test_representation({{2, 3, 3}})),  // logits shape
+                                        testing::ValuesIn(fPrecisions),
+                                        testing::ValuesIn(iPrecisions),
+                                        testing::Values(ov::test::utils::DEVICE_GPU)),
                          CTCLossLayerTest::getTestCaseName);
 
 const auto ctcLossArgsSubset2 =
-    testing::Combine(testing::Values(std::vector<size_t>({3, 6, 8})),                           // logits shape
-                     testing::ValuesIn(std::vector<std::vector<int>>({{6, 5, 6}, {5, 5, 5}})),  // logits length
+    testing::Combine(testing::ValuesIn(std::vector<std::vector<int>>({{6, 5, 6}, {5, 5, 5}})),  // logits length
                      testing::ValuesIn(std::vector<std::vector<std::vector<int>>>(
                          {{{4, 1, 2, 3, 4, 5}, {5, 4, 3, 0, 1, 0}, {2, 1, 3, 1, 3, 0}},
                           {{2, 1, 5, 3, 2, 6}, {3, 3, 3, 3, 3, 3}, {6, 5, 6, 5, 6, 5}}})),      // labels
@@ -58,8 +56,9 @@ const auto ctcLossArgsSubset2 =
 INSTANTIATE_TEST_SUITE_P(smoke_CTCLoss_Set2,
                          CTCLossLayerTest,
                          testing::Combine(ctcLossArgsSubset2,
-                                          testing::ValuesIn(fPrecisions),
-                                          testing::ValuesIn(iPrecisions),
-                                          testing::Values(ov::test::utils::DEVICE_GPU)),
+                                        testing::Values(ov::test::static_shapes_to_test_representation({{3, 6, 8}})),  // logits shape
+                                        testing::ValuesIn(fPrecisions),
+                                        testing::ValuesIn(iPrecisions),
+                                        testing::Values(ov::test::utils::DEVICE_GPU)),
                          CTCLossLayerTest::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/einsum.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/einsum.cpp
index 94555588b727bb..7303e7a396a2b2 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/einsum.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/einsum.cpp
@@ -4,35 +4,34 @@
 
 #include <vector>
 
-#include "single_layer_tests/einsum.hpp"
-
-using namespace ngraph::helpers;
-using namespace LayerTestsDefinitions;
+#include "single_op_tests/einsum.hpp"
 
 namespace {
-const std::vector<InferenceEngine::Precision> precisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::FP16
+using ov::test::EinsumLayerTest;
+
+const std::vector<ov::element::Type> model_types = {
+        ov::element::f32,
+        ov::element::f16
 };
 
-const std::vector<EinsumEquationWithInput> equationsWithInput = {
-    { "ij->ji", {{{1, 2}}} }, // transpose 2d
-    { "ijk->kij", { {1, 2, 3} } }, // transpose 3d
-    { "ij->i", { {2, 3} } }, // reduce
-    { "ab,cd->abcd", { { 1, 2}, {3, 4} } }, // no reduction
-    { "ab,bc->ac", { {2, 3}, {3, 2} } }, // matrix multiplication
-    { "ab,bcd,bc->ca", { {2, 4}, {4, 3, 1}, {4, 3} } },  // multiple multiplications
-    { "kii->ki", { {1, 3, 3} } }, // diagonal
-    { "abbac,bad->ad", { {2, 3, 3, 2, 4}, {3, 2, 1} } }, // diagonal and multiplication with repeated labels
-    { "a...->...a", { {2, 2, 3} } }, // transpose with ellipsis
-    { "a...->...", { {2, 2, 3} } }, // reduce with ellipsis
-    { "ab...,...->ab...", { {2, 2, 3}, {1} } }, // multiply by scalar
-    { "a...j,j...->a...", { {1, 1, 4, 3}, {3, 4, 2, 1} } } // complex multiplication
+const std::vector<ov::test::EinsumEquationWithInput> equationsWithInput = {
+    { "ij->ji",           ov::test::static_shapes_to_test_representation({ {1, 2} }) }, // transpose 2d
+    { "ijk->kij",         ov::test::static_shapes_to_test_representation({ {1, 2, 3} }) }, // transpose 3d
+    { "ij->i",            ov::test::static_shapes_to_test_representation({ {2, 3} }) }, // reduce
+    { "ab,cd->abcd",      ov::test::static_shapes_to_test_representation({ { 1, 2}, {3, 4} }) }, // no reduction
+    { "ab,bc->ac",        ov::test::static_shapes_to_test_representation({ {2, 3}, {3, 2} }) }, // matrix multiplication
+    { "ab,bcd,bc->ca",    ov::test::static_shapes_to_test_representation({ {2, 4}, {4, 3, 1}, {4, 3} }) },  // multiple multiplications
+    { "kii->ki",          ov::test::static_shapes_to_test_representation({ {1, 3, 3} }) }, // diagonal
+    { "abbac,bad->ad",    ov::test::static_shapes_to_test_representation({ {2, 3, 3, 2, 4}, {3, 2, 1} }) }, // diagonal and multiplication with repeated labels
+    { "a...->...a",       ov::test::static_shapes_to_test_representation({ {2, 2, 3} }) }, // transpose with ellipsis
+    { "a...->...",        ov::test::static_shapes_to_test_representation({ {2, 2, 3} }) }, // reduce with ellipsis
+    { "ab...,...->ab...", ov::test::static_shapes_to_test_representation({ {2, 2, 3}, {1} }) }, // multiply by scalar
+    { "a...j,j...->a...", ov::test::static_shapes_to_test_representation({ {1, 1, 4, 3}, {3, 4, 2, 1} }) } // complex multiplication
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_Einsum,
                          EinsumLayerTest,
-                         ::testing::Combine(::testing::ValuesIn(precisions),
+                         ::testing::Combine(::testing::ValuesIn(model_types),
                                             ::testing::ValuesIn(equationsWithInput),
                                             ::testing::Values(ov::test::utils::DEVICE_GPU)),
                          EinsumLayerTest::getTestCaseName);
diff --git a/src/plugins/intel_gpu/tests/functional/single_layer_tests/eye.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eye.cpp
similarity index 88%
rename from src/plugins/intel_gpu/tests/functional/single_layer_tests/eye.cpp
rename to src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eye.cpp
index a347ab6877b16d..d610fc923d8e72 100644
--- a/src/plugins/intel_gpu/tests/functional/single_layer_tests/eye.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eye.cpp
@@ -2,19 +2,18 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "shared_test_classes/single_layer/eye.hpp"
-
-using namespace LayerTestsDefinitions;
+#include "single_op_tests/eye.hpp"
 
 namespace {
+using ov::test::EyeLayerTest;
 
-TEST_P(EyeLayerTest, CompareWithRefs) {
-    SKIP_IF_CURRENT_TEST_IS_DISABLED()
-    Run();
-}
-
-const std::vector<ov::element::Type_t> netPrecisions =
-    {ElementType::f32, ElementType::f16, ElementType::i32, ElementType::i8, ElementType::u8, ElementType::i64};
+const std::vector<ov::element::Type> model_types = {
+    ov::element::f32,
+    ov::element::f16,
+    ov::element::i32,
+    ov::element::i8,
+    ov::element::u8,
+    ov::element::i64};
 
 const std::vector<std::vector<int>> eyePars = {
     // rows, cols, diag_shift
@@ -40,7 +39,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Eye2D_WithNonScalar_Test,
                          ::testing::Combine(::testing::ValuesIn(std::vector<std::vector<ov::Shape>>{{{1}, {1}, {1}}}),
                                             ::testing::ValuesIn(emptyBatchShape),
                                             ::testing::ValuesIn(eyePars),
-                                            ::testing::ValuesIn(netPrecisions),
+                                            ::testing::ValuesIn(model_types),
                                             ::testing::Values(ov::test::utils::DEVICE_GPU)),
                          EyeLayerTest::getTestCaseName);
 
@@ -50,7 +49,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Eye_1DBatch_Test,
                                                 {{1}, {1}, {1}, {1}}}),
                                             ::testing::ValuesIn(batchShapes1D),
                                             ::testing::ValuesIn(eyePars),
-                                            ::testing::ValuesIn(netPrecisions),
+                                            ::testing::ValuesIn(model_types),
                                             ::testing::Values(ov::test::utils::DEVICE_GPU)),
                          EyeLayerTest::getTestCaseName);
 
@@ -60,7 +59,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Eye_2DBatch_Test,
                                                 {{1}, {1}, {1}, {2}}}),
                                             ::testing::ValuesIn(batchShapes2D),
                                             ::testing::ValuesIn(eyePars),
-                                            ::testing::ValuesIn(netPrecisions),
+                                            ::testing::ValuesIn(model_types),
                                             ::testing::Values(ov::test::utils::DEVICE_GPU)),
                          EyeLayerTest::getTestCaseName);
 
@@ -70,7 +69,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Eye_3DBatch_Test,
                                                 {{1}, {1}, {1}, {3}}}),
                                             ::testing::ValuesIn(batchShapes3D),
                                             ::testing::ValuesIn(eyePars),
-                                            ::testing::ValuesIn(netPrecisions),
+                                            ::testing::ValuesIn(model_types),
                                             ::testing::Values(ov::test::utils::DEVICE_GPU)),
                          EyeLayerTest::getTestCaseName);
 
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/group_normalization.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/group_normalization.cpp
new file mode 100644
index 00000000000000..72bb27eba05c54
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/group_normalization.cpp
@@ -0,0 +1,41 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "single_layer_tests/group_normalization.hpp"
+
+using namespace ov::test::subgraph;
+
+namespace {
+
+const std::vector<ov::test::ElementType> netPrecisions = {
+    ov::element::f16,
+    ov::element::f32,
+};
+
+const std::vector<ov::Shape> inputShapes = {
+    {3, 8, 32, 64},
+    {3, 8, 28, 32, 12},
+};
+
+const std::vector<int64_t> numGroups = {
+    2, 4,
+};
+
+const std::vector<double> epsilon = {
+    0.0025
+};
+
+INSTANTIATE_TEST_SUITE_P(
+    smoke_GroupNormalization,
+    GroupNormalizationTest,
+    testing::Combine(testing::ValuesIn(netPrecisions),
+                     ::testing::Values(ov::element::undefined),
+                     ::testing::Values(ov::element::undefined),
+                     testing::ValuesIn(ov::test::static_shapes_to_test_representation(inputShapes)),
+                     testing::ValuesIn(numGroups),
+                     testing::ValuesIn(epsilon),
+                     testing::Values(ov::test::utils::DEVICE_GPU),
+                     testing::Values(ov::AnyMap())),
+                     GroupNormalizationTest::getTestCaseName);
+
+} // anonymous namespace
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp
index d60cbeb5875472..056a85a926aaa3 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/multiply_add.cpp
@@ -2,32 +2,29 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
-
 #include "subgraph_tests/multiply_add.hpp"
 
-using namespace SubgraphTestsDefinitions;
+#include <vector>
+
+using namespace ov::test;
 
 namespace {
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::FP16
-};
+const std::vector<ov::element::Type> input_type = {ov::element::f32, ov::element::f16};
 
-const std::vector<std::vector<size_t>> inputShapes = {
-        {1, 3},
-        {1, 3, 2},
-        {1, 3, 2, 5},
-        {1, 3, 2, 5, 4},
-        {1, 3, 2, 2, 4, 5},
+const std::vector<ov::Shape> inputShapes = {
+    {1, 3},
+    {1, 3, 2},
+    {1, 3, 2, 5},
+    {1, 3, 2, 5, 4},
+    {1, 3, 2, 2, 4, 5},
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_MultipleAdd_Nd, MultiplyAddLayerTest,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(inputShapes),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(ov::test::utils::DEVICE_GPU)),
-                        MultiplyAddLayerTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_MultipleAdd_Nd,
+                         MultiplyAddLayerTest,
+                         ::testing::Combine(::testing::ValuesIn(inputShapes),
+                                            ::testing::ValuesIn(input_type),
+                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
+                         MultiplyAddLayerTest::getTestCaseName);
 
 }  // namespace
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
index 105a37a30f2c37..5eda8a003d8971 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/perm_conv_perm_concat.cpp
@@ -2,42 +2,41 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
 #include "subgraph_tests/perm_conv_perm_concat.hpp"
-#include "common_test_utils/test_constants.hpp"
+
+#include <vector>
+
 namespace {
-std::vector<std::array<size_t, 4>> input_shapes {
+std::vector<ov::Shape> input_shapes{
     {1, 1, 7, 32},
     {1, 1, 8, 16},
 };
 
-std::vector<std::array<size_t, 2>> kernel_shapes {
+std::vector<ov::Shape> kernel_shapes{
     {1, 3},
     {1, 5},
 };
 
-std::vector<size_t> output_channels {
+std::vector<size_t> output_channels{
     32,
     64,
 };
 
-std::vector<InferenceEngine::Precision> netPrecisions = {
-    InferenceEngine::Precision::FP32,
-//    InferenceEngine::Precision::FP16,
-};
+std::vector<ov::element::Type> netPrecisions = {ov::element::f32};
 
-std::map<std::string, std::string> additional_config = {
-};
-} // namespace
-
-namespace SubgraphTestsDefinitions {
-    INSTANTIATE_TEST_SUITE_P(smoke_basic, PermConvPermConcat,
-        ::testing::Combine(
-            ::testing::ValuesIn(netPrecisions),
-            ::testing::Values(ov::test::utils::DEVICE_GPU),
-            ::testing::ValuesIn(input_shapes),
-            ::testing::ValuesIn(kernel_shapes),
-            ::testing::ValuesIn(output_channels),
-            ::testing::Values(additional_config)),
-        PermConvPermConcat::getTestCaseName);
-} // namespace SubgraphTestsDefinitions
+ov::AnyMap additional_config = {};
+}  // namespace
+
+namespace ov {
+namespace test {
+INSTANTIATE_TEST_SUITE_P(smoke_basic,
+                         PermConvPermConcat,
+                         ::testing::Combine(::testing::ValuesIn(netPrecisions),
+                                            ::testing::Values(ov::test::utils::DEVICE_GPU),
+                                            ::testing::ValuesIn(input_shapes),
+                                            ::testing::ValuesIn(kernel_shapes),
+                                            ::testing::ValuesIn(output_channels),
+                                            ::testing::Values(additional_config)),
+                         PermConvPermConcat::getTestCaseName);
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
index 872140ceea815f..78cef86f3b0c09 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/reshape_squeeze_reshape_relu.cpp
@@ -2,48 +2,48 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
 #include "subgraph_tests/reshape_squeeze_reshape_relu.hpp"
+
+#include <vector>
+
 #include "common_test_utils/test_constants.hpp"
 
-using namespace SubgraphTestsDefinitions;
+using namespace ov::test;
 
 namespace {
-    std::vector<ShapeAxesTuple> inputs_squeeze {
-            {{1, 1, 3}, {0, 1}},
-            {{1, 1, 3}, {1}},
-            {{1, 3, 1}, {0, 2}},
-            {{3, 1, 1}, {1}},
-            {{1, 4, 1, 3}, {0, 2}},
-            {{3, 1, 2, 4, 4, 3}, {1}},
-            {{1, 1, 1, 1, 1, 3}, {0, 1, 2, 3, 4}},
-            {{1}, {0}},
-    };
-
-    std::vector<ShapeAxesTuple> inputs_unsqueeze{
-            {{1}, {0}},
-            {{1}, {0, 1}},
-            {{1}, {0, 1, 2}},
-            {{1, 2, 3}, {0}},
-            {{1, 1, 3}, {1, 2}},
-            {{1, 4, 1, 3}, {0, 2}},
-    };
-
-    std::vector<InferenceEngine::Precision> netPrecisions = {InferenceEngine::Precision::FP32,
-                                                             InferenceEngine::Precision::FP16,
-    };
-
-
-    const std::vector<ngraph::helpers::SqueezeOpType> opTypes = {
-            ngraph::helpers::SqueezeOpType::SQUEEZE,
-            ngraph::helpers::SqueezeOpType::UNSQUEEZE
-    };
-
-    INSTANTIATE_TEST_SUITE_P(smoke_reshape_squeeze_reshape_relu, ReshapeSqueezeReshapeRelu,
-                            ::testing::Combine(
-                                    ::testing::ValuesIn(inputs_squeeze),
-                                    ::testing::ValuesIn(netPrecisions),
-                                    ::testing::Values(ov::test::utils::DEVICE_GPU),
-                                    ::testing::ValuesIn(opTypes)),
-                            ReshapeSqueezeReshapeRelu::getTestCaseName);
+std::vector<ShapeAxesTuple> inputs_squeeze{
+    {{1, 1, 3}, {0, 1}},
+    {{1, 1, 3}, {1}},
+    {{1, 3, 1}, {0, 2}},
+    {{3, 1, 1}, {1}},
+    {{1, 4, 1, 3}, {0, 2}},
+    {{3, 1, 2, 4, 4, 3}, {1}},
+    {{1, 1, 1, 1, 1, 3}, {0, 1, 2, 3, 4}},
+    {{1}, {0}},
+};
+
+std::vector<ShapeAxesTuple> inputs_unsqueeze{
+    {{1}, {0}},
+    {{1}, {0, 1}},
+    {{1}, {0, 1, 2}},
+    {{1, 2, 3}, {0}},
+    {{1, 1, 3}, {1, 2}},
+    {{1, 4, 1, 3}, {0, 2}},
+};
+
+std::vector<ov::element::Type> input_types = {
+    ov::element::f32,
+    ov::element::f16,
+};
+
+const std::vector<ov::test::utils::SqueezeOpType> opTypes = {ov::test::utils::SqueezeOpType::SQUEEZE,
+                                                             ov::test::utils::SqueezeOpType::UNSQUEEZE};
+
+INSTANTIATE_TEST_SUITE_P(smoke_reshape_squeeze_reshape_relu,
+                         ReshapeSqueezeReshapeRelu,
+                         ::testing::Combine(::testing::ValuesIn(inputs_squeeze),
+                                            ::testing::ValuesIn(input_types),
+                                            ::testing::Values(ov::test::utils::DEVICE_GPU),
+                                            ::testing::ValuesIn(opTypes)),
+                         ReshapeSqueezeReshapeRelu::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
index 3d5a1af905130e..5857c2188cfdb2 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/split_conv_concat.cpp
@@ -2,26 +2,21 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <vector>
-
 #include "subgraph_tests/split_conv_concat.hpp"
-#include "common_test_utils/test_constants.hpp"
 
-using namespace SubgraphTestsDefinitions;
+#include <vector>
+
+using namespace ov::test;
 
 namespace {
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::FP16
-};
+const std::vector<ov::element::Type> input_types = {ov::element::f32, ov::element::f16};
 
-INSTANTIATE_TEST_SUITE_P(smoke_NoReshape, SplitConvConcat,
-                        ::testing::Combine(
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t >({1, 6, 40, 40})),
-                                ::testing::Values(ov::test::utils::DEVICE_GPU)),
-                        SplitConvConcat::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_NoReshape,
+                         SplitConvConcat,
+                         ::testing::Combine(::testing::ValuesIn(input_types),
+                                            ::testing::Values(ov::Shape{1, 6, 40, 40}),
+                                            ::testing::Values(ov::test::utils::DEVICE_GPU)),
+                         SplitConvConcat::getTestCaseName);
 
 }  // namespace
 
-
diff --git a/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/kv_cache.cpp b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/kv_cache.cpp
new file mode 100644
index 00000000000000..a32e97d8e8e0fc
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/kv_cache.cpp
@@ -0,0 +1,217 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_test_utils/ov_tensor_utils.hpp"
+#include "openvino/core/node_vector.hpp"
+#include "openvino/core/partial_shape.hpp"
+#include "openvino/core/preprocess/pre_post_process.hpp"
+#include "openvino/op/concat.hpp"
+#include "openvino/op/matmul.hpp"
+#include "openvino/op/parameter.hpp"
+#include "openvino/op/transpose.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "shared_test_classes/base/utils/compare_results.hpp"
+#include "transformations/rt_info/decompression.hpp"
+#include "subgraphs_builders.hpp"
+
+using namespace ov::test;
+
+namespace SubgraphTestsDefinitions {
+
+using KVCacheTestParams = std::tuple<std::vector<InputShape>,  // input shapes
+                                     ov::element::Type,        // in/out precision
+                                     std::map<std::string, std::string>>;  // additional config
+
+class KVCacheTest : public testing::WithParamInterface<KVCacheTestParams>, public SubgraphBaseTest {
+public:
+    static std::string get_test_case_name(testing::TestParamInfo<KVCacheTestParams> obj) {
+        std::vector<InputShape> input_shapes;
+        ov::element::Type element_type;
+        std::map<std::string, std::string> additional_config;
+
+        std::tie(input_shapes, element_type, additional_config) = obj.param;
+
+        std::ostringstream result;
+        for (const auto& shape : input_shapes) {
+            result << ov::test::utils::partialShape2str({shape.first}) << "_";
+        }
+        result << "TS=";
+        for (const auto& shape : input_shapes) {
+            result << "(";
+            if (!shape.second.empty()) {
+                auto itr = shape.second.begin();
+                do {
+                    result << ov::test::utils::vec2str(*itr);
+                } while (++itr != shape.second.end() && result << "_");
+            }
+            result << ")_";
+        }
+        result << "precision=" << element_type << "_";
+        result << "config=(";
+        for (const auto& configEntry : additional_config) {
+            result << configEntry.first << ", " << configEntry.second << ":";
+        }
+        result << ")";
+
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = ov::test::utils::DEVICE_GPU;
+
+        std::vector<InputShape> input_shapes;
+        ov::element::Type element_type;
+        std::map<std::string, std::string> additional_config;
+
+        std::tie(input_shapes, element_type, additional_config) = GetParam();
+
+        configuration.insert(additional_config.begin(), additional_config.end());
+        init_input_shapes(input_shapes);
+
+        inType = outType = element_type;
+
+        function = tests::make_llm_kv_cache_pattern(inputDynamicShapes[0][0], inputDynamicShapes[0][1], inputDynamicShapes[0][3], element_type);
+    }
+};
+
+TEST_P(KVCacheTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+}
+
+namespace {
+
+const std::vector<ov::element::Type> precisions = {ov::element::f32, ov::element::f16};
+
+const std::vector<std::vector<InputShape>> input_shapes_basic = {
+    {
+        {{-1, 32, -1, 80}, { {1, 32, 0, 80}, {1, 32, 20, 80} }},
+        {{-1, -1, 32, 80}, { {1, 20, 32, 80}, {1, 1, 32, 80} }},
+        {{-1, 32, -1, -1}, { {1, 32, 1, 20}, {1, 32, 1, 21} }}
+    },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_GPU_Dynamic,
+                         KVCacheTest,
+                         ::testing::Combine(::testing::ValuesIn(input_shapes_basic),
+                                            ::testing::ValuesIn(precisions),
+                                            ::testing::Values(std::map<std::string, std::string>())),
+                         KVCacheTest::get_test_case_name);
+} // namespace
+
+TEST(KVCacheTest, smoke_multipleIterations) {
+#if defined(ANDROID)
+    GTEST_SKIP();
+#endif
+    auto core = ov::Core();
+
+    const size_t batch = 1;
+    const size_t n_heads = 32;
+    const size_t n_features = 80;
+    const size_t context_size = 20;
+    size_t cache_size = 0;
+
+    ov::element::Type element_type = ov::element::f16;
+
+    auto model = tests::make_llm_kv_cache_pattern(batch, n_heads, n_features, element_type);
+    auto compiled_model = core.compile_model(model, ov::test::utils::DEVICE_GPU, ov::hint::inference_precision(ov::element::f16));
+
+    auto input0 = model->get_parameters().at(0);
+    auto input1 = model->get_parameters().at(1);
+    auto input2 = model->get_parameters().at(2);
+    auto output0 = model->get_results().at(0);
+    auto output1 = model->get_results().at(1);
+
+    auto infer_request = compiled_model.create_infer_request();
+    auto input0_tensor_remote_io = infer_request.get_tensor(input0);
+    auto input1_tensor_remote_io = infer_request.get_tensor(input1);
+    auto input2_tensor_remote_io = infer_request.get_tensor(input2);
+    auto output0_tensor_remote_io = infer_request.get_tensor(output0);
+    auto output1_tensor_remote_io = infer_request.get_tensor(output1);
+
+    auto compare_tensors = [&model](const std::vector<ov::Tensor> expected, const std::vector<ov::Tensor>& actual) {
+            ASSERT_EQ(expected.size(), actual.size());
+            ASSERT_EQ(expected.size(), model->get_results().size());
+            auto compareMap = ov::test::utils::getCompareMap();
+            const auto& results = model->get_results();
+            for (size_t j = 0; j < results.size(); j++) {
+                const auto result = results[j];
+                for (size_t i = 0; i < result->get_input_size(); ++i) {
+                    std::shared_ptr<ov::Node> inputNode = result->get_input_node_shared_ptr(i);
+                    if (std::dynamic_pointer_cast<ov::op::v0::Convert>(inputNode)) {
+                        std::shared_ptr<ov::Node> nextNodePtr = inputNode->get_input_node_shared_ptr(0);
+                        if (!ngraph::is_type<ov::op::v0::Result>(nextNodePtr)) {
+                            inputNode = nextNodePtr;
+                        }
+                    }
+                    auto it = compareMap.find(inputNode->get_type_info());
+                    ASSERT_NE(it, compareMap.end());
+                    it->second(inputNode, i, expected[j], actual[j], 1e-4f, 1e-4f);
+                }
+            }
+    };
+
+    {
+        const ov::Shape kv_cache_size_initial = {batch, n_heads, cache_size, n_features};
+        const ov::Shape new_token_size_initial = {batch, context_size, n_heads, n_features};
+        const ov::Shape matmul_in_size_initial = {batch, n_heads, context_size, context_size};
+
+        auto new_token_data = ov::test::utils::create_and_fill_tensor(element_type, new_token_size_initial);
+        auto matmul_data = ov::test::utils::create_and_fill_tensor(element_type, matmul_in_size_initial);
+
+        auto kv_cache_input = infer_request.get_tensor(input0);
+        kv_cache_input.set_shape(kv_cache_size_initial);
+
+        auto ref_model = model->clone();
+        ngraph::helpers::resize_function(ref_model, {kv_cache_input.get_shape(), new_token_data.get_shape(), matmul_data.get_shape()});
+        auto results = ngraph::helpers::interpretFunction(ref_model, {{input0, kv_cache_input}, {input1, new_token_data}, {input2, matmul_data}});
+
+        infer_request.set_tensor(input0, kv_cache_input);
+        infer_request.set_tensor(input1, new_token_data);
+        infer_request.set_tensor(input2, matmul_data);
+
+        infer_request.infer();
+
+        compare_tensors(results, {infer_request.get_tensor(output0), infer_request.get_tensor(output1)});
+
+        cache_size += context_size;
+    }
+
+    const size_t input_tokens = 1;
+    const size_t niters = 10;
+    const ov::Shape new_token_size = {batch, input_tokens, n_heads, n_features};
+    size_t context_length = cache_size + input_tokens;
+    for (size_t i = 0; i < niters; i++, context_length += input_tokens) {
+        ov::Shape matmul_in_size_loop = {batch, n_heads, input_tokens, context_length};
+        auto new_token_data = ov::test::utils::create_and_fill_tensor(element_type, new_token_size);
+        auto matmul_data = ov::test::utils::create_and_fill_tensor(element_type, matmul_in_size_loop);
+
+        auto kv_cache_input = infer_request.get_tensor(output0);
+        auto kv_shape = kv_cache_input.get_shape();
+
+        auto ref_model = model->clone();
+        ngraph::helpers::resize_function(ref_model, {kv_shape, new_token_data.get_shape(), matmul_data.get_shape()});
+        auto results = ngraph::helpers::interpretFunction(ref_model, {{input0, kv_cache_input}, {input1, new_token_data}, {input2, matmul_data}});
+
+        auto new_token_input = infer_request.get_tensor(input1);
+        new_token_input.set_shape(new_token_data.get_shape());
+        auto matmul_input = infer_request.get_tensor(input2);
+        matmul_input.set_shape(matmul_data.get_shape());
+
+        new_token_data.copy_to(new_token_input);
+        matmul_data.copy_to(matmul_input);
+
+        infer_request.set_tensor(input0, kv_cache_input);
+        infer_request.set_tensor(input1, new_token_input);
+        infer_request.set_tensor(input2, matmul_input);
+
+        infer_request.infer();
+
+        compare_tensors(results, {infer_request.get_tensor(output0), infer_request.get_tensor(output1)});
+    }
+}
+
+} // namespace SubgraphTestsDefinitions
diff --git a/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/matmul_weights_decompression.cpp b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/matmul_weights_decompression.cpp
index 313015da3406ba..75bdb9f0ec71a7 100644
--- a/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/matmul_weights_decompression.cpp
+++ b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/matmul_weights_decompression.cpp
@@ -2,19 +2,21 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ov_models/builders.hpp"
+#include "common_test_utils/ov_tensor_utils.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/matmul.hpp"
 #include "shared_test_classes/base/layer_test_utils.hpp"
 #include "shared_test_classes/base/ov_subgraph.hpp"
 #include "transformations/rt_info/decompression.hpp"
 
-using namespace ngraph;
+using namespace ov;
 using namespace ov::test;
 
 namespace SubgraphTestsDefinitions {
 /*
- *                        Subtract_const(U8)
+ *                        Subtract_const(U8/NF4/U4)
  *                           /
- *    Weights(U8)       Convert(F32)
+ *    Weights(U8/NF4/U4)       Convert(F32)
  *       |               /
  *    Convert(F32)   Reshape(optional)
  *            \        /       Multiply_const(F32)
@@ -29,7 +31,20 @@ namespace SubgraphTestsDefinitions {
  *               |
  *              Bias
  */
-using MatmulWeightsDecompressionParams = std::tuple<std::vector<InputShape>,  // input shapes
+
+struct ShapeParams {
+    ShapeParams() = default;
+    ShapeParams(InputShape data_shape, ov::Shape weights_shape, int weights_group_size = -1)
+        : data_shape(std::move(data_shape)),
+          weights_shape(std::move(weights_shape)),
+          weights_group_size(weights_group_size) {}
+
+    InputShape data_shape;
+    ov::Shape weights_shape;
+    // Decompression group size. If the value is equal to -1, ordinary decompression is used
+    int weights_group_size;
+};
+using MatmulWeightsDecompressionParams = std::tuple<ShapeParams,              // input shapes
                                                     ov::test::ElementType,    // weights precision
                                                     ov::test::ElementType,    // activations precision
                                                     bool,                     // transpose on weights
@@ -40,7 +55,7 @@ using MatmulWeightsDecompressionParams = std::tuple<std::vector<InputShape>,  //
 class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeightsDecompressionParams>, public SubgraphBaseTest {
 public:
     static std::string get_test_case_name(testing::TestParamInfo<MatmulWeightsDecompressionParams> obj) {
-        std::vector<InputShape> inputShapes;
+        ShapeParams shape_params;
         ov::test::ElementType weights_precision;
         ov::test::ElementType activations_precision;
         bool transpose;
@@ -48,7 +63,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
         bool reshape_on_decompression;
         std::map<std::string, std::string> additional_config;
 
-        std::tie(inputShapes,
+        std::tie(shape_params,
                  weights_precision,
                  activations_precision,
                  transpose,
@@ -57,20 +72,9 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
                  additional_config) = obj.param;
 
         std::ostringstream result;
-        for (const auto& shape : inputShapes) {
-            result << ov::test::utils::partialShape2str({shape.first}) << "_";
-        }
-        result << "TS=";
-        for (const auto& shape : inputShapes) {
-            result << "(";
-            if (!shape.second.empty()) {
-                auto itr = shape.second.begin();
-                do {
-                    result << ov::test::utils::vec2str(*itr);
-                } while (++itr != shape.second.end() && result << "_");
-            }
-            result << ")_";
-        }
+        result << "data_shape=" << shape_params.data_shape << "_";
+        result << "weights_shape=" << shape_params.weights_shape << "_";
+        result << "group_size=" << shape_params.weights_group_size << "_";
         result << "weights_precision=" << weights_precision << "_";
         result << "activations_precision=" << activations_precision << "_";
         result << "transpose_weights=" << transpose << "_";
@@ -87,34 +91,87 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
     }
 
 protected:
-    std::shared_ptr<ov::Model> init_subgraph(std::vector<ov::PartialShape>& inputShapes,
-                                             const ov::element::Type data_precision,
-                                             const ov::element::Type weights_precision,
-                                             const bool transpose_weights,
-                                             const bool add_subtract,
-                                             const bool reshape_on_decompression) {
-        ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(data_precision, inputShapes[0])};
+    std::shared_ptr<ov::Model> init_subgraph(const ov::PartialShape& data_shape,
+                                              const ov::Shape& weights_shape,
+                                              const int group_size,
+                                              const ov::element::Type data_precision,
+                                              const ov::element::Type weights_precision,
+                                              const bool transpose_weights,
+                                              const bool add_subtract,
+                                              const bool reshape_on_decompression) {
+        ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(data_precision, data_shape)};
+        const auto weights_subgraph = init_compressed_weights_subgraph(weights_shape,
+                                                                       group_size,
+                                                                       data_precision,
+                                                                       weights_precision,
+                                                                       transpose_weights,
+                                                                       add_subtract,
+                                                                       reshape_on_decompression);
+
+        auto mat_mul = std::make_shared<ov::op::v0::MatMul>(params[0], weights_subgraph);
+        return std::make_shared<ov::Model>(NodeVector{mat_mul}, params, "MatmulWeightsDecompression");
+    }
+
+    std::shared_ptr<ov::Node> init_compressed_weights_subgraph(const ov::Shape& weights_shape,
+                                                               const int group_size,
+                                                               const ov::element::Type data_precision,
+                                                               const ov::element::Type weights_precision,
+                                                               const bool transpose_weights,
+                                                               const bool add_subtract,
+                                                               const bool reshape_on_decompression_constant) {
         auto transpose_if_necessary = [&](const ov::Shape& shape) {
-            if (!transpose_weights)
-                return shape;
-            auto transposed_shape = shape;
-            std::swap(*transposed_shape.rbegin(), *(transposed_shape.rbegin() + 1));
-            return transposed_shape;
+            auto result_shape = shape;
+            if (transpose_weights)
+                std::swap(*result_shape.rbegin(), *(result_shape.rbegin() + 1));
+            return result_shape;
         };
 
-        auto weights_shape = transpose_if_necessary(inputShapes[1].to_shape());
-        auto weights = ngraph::builder::makeConstant<uint8_t>(weights_precision, weights_shape, {}, true);
+        const bool group_decompression = group_size != -1;
+        // Weights has shape [I, O], where
+        // I - input channels
+        // O - output channels
+        // In case of group decompression, input channels dimension is split into 2: I -> [N, G], where
+        // N - number of groups
+        // G - group size
+        auto transformed_weights_shape = transpose_if_necessary(weights_shape);
+        if (group_decompression) {
+            OPENVINO_ASSERT(weights_shape[0] % group_size == 0,
+                            "Weights output channels count (",
+                            weights_shape[0],
+                            ") must be divisible by decompression group size (",
+                            group_size,
+                            ").");
+            auto in_channel_idx = transpose_weights ? transformed_weights_shape.size() - 1 : transformed_weights_shape.size() - 2;
+            transformed_weights_shape[in_channel_idx] = weights_shape[0] / group_size;
+            transformed_weights_shape.insert(transformed_weights_shape.begin() + in_channel_idx + 1, group_size);
+        }
+        auto weights_tensor = ov::test::utils::create_and_fill_tensor(weights_precision, transformed_weights_shape);
+        auto weights = std::make_shared<ov::op::v0::Constant>(weights_tensor);
         weights->set_friendly_name("Compressed_weights");
         auto weights_convert = std::make_shared<ngraph::opset1::Convert>(weights, data_precision);
 
         std::shared_ptr<ov::Node> mul_parent = weights_convert;
-        auto output_channels = transpose_weights ? *(weights_shape.rbegin() + 1) : *weights_shape.rbegin();
-        auto scaleshift_target_shape = transpose_if_necessary(ov::Shape{1, output_channels});
-        auto scaleshift_const_shape = reshape_on_decompression ? ov::Shape{output_channels} : scaleshift_target_shape;
+        auto output_channels = *weights_shape.rbegin();
+
+        // Decompression constants shape:
+        // Ordinary decompression: [O, 1]
+        // Group decompression: [O, N, 1]
+        ov::Shape scaleshift_target_shape{output_channels};
+        scaleshift_target_shape.insert(scaleshift_target_shape.begin(), group_decompression ? weights_shape[0] / group_size : 1);
+        scaleshift_target_shape = transpose_if_necessary(scaleshift_target_shape);
+        if (group_decompression) {
+            auto in_channel_idx = transpose_weights ? scaleshift_target_shape.size() - 1 : scaleshift_target_shape.size() - 2;
+            scaleshift_target_shape.insert(scaleshift_target_shape.begin() + in_channel_idx + 1, 1);
+        }
+
+        auto scaleshift_const_shape = scaleshift_target_shape;
+        if (reshape_on_decompression_constant)
+            scaleshift_const_shape.erase(std::remove(scaleshift_const_shape.begin(), scaleshift_const_shape.end(), 1), scaleshift_const_shape.end());
         if (add_subtract) {
-            auto shift_const = ngraph::builder::makeConstant<uint8_t>(weights_precision, scaleshift_const_shape, {}, true);
+            auto shift_tensor = ov::test::utils::create_and_fill_tensor(weights_precision, scaleshift_const_shape);
+            auto shift_const = std::make_shared<ov::op::v0::Constant>(shift_tensor);
             std::shared_ptr<ov::Node> shift_convert = std::make_shared<ngraph::opset1::Convert>(shift_const, data_precision);
-            if (reshape_on_decompression) {
+            if (reshape_on_decompression_constant) {
                 auto shift_reshape_const = ov::opset10::Constant::create(ov::element::i32, {scaleshift_target_shape.size()}, scaleshift_target_shape);
                 auto shift_reshape = std::make_shared<ov::opset10::Reshape>(shift_convert, shift_reshape_const, false);
                 shift_convert = shift_reshape;
@@ -122,32 +179,36 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
             mul_parent = std::make_shared<ov::opset10::Subtract>(weights_convert, shift_convert);
         }
 
-        std::shared_ptr<ov::Node> scale_const = ngraph::builder::makeConstant<float>(data_precision, scaleshift_const_shape, {}, true);
-        if (reshape_on_decompression) {
+        auto scale_tensor = ov::test::utils::create_and_fill_tensor(data_precision, scaleshift_const_shape, 1, -0.5, 10000);
+        std::shared_ptr<ov::Node> scale_const = std::make_shared<ov::op::v0::Constant>(scale_tensor);
+        if (reshape_on_decompression_constant) {
             auto scale_reshape_const = ov::opset10::Constant::create(ov::element::i32, {scaleshift_target_shape.size()}, scaleshift_target_shape);
             auto scale_reshape = std::make_shared<ov::opset10::Reshape>(scale_const, scale_reshape_const, false);
             scale_const = scale_reshape;
         }
-        auto multiply = std::make_shared<ov::opset10::Multiply>(mul_parent, scale_const);
+        std::shared_ptr<ov::Node> last_node = std::make_shared<ov::opset10::Multiply>(mul_parent, scale_const);
 
-        std::shared_ptr<ov::Node> matmul_weights = multiply;
+        if (group_decompression) {
+            auto reshape_target_shape = transpose_weights ? std::vector<int>{-1, static_cast<int>(weights_shape[0])}
+                                                          : std::vector<int>{static_cast<int>(weights_shape[0]), -1};
+            auto target_shape_node = ov::opset10::Constant::create(ov::element::i32, {reshape_target_shape.size()}, reshape_target_shape);
+            last_node = std::make_shared<ov::opset10::Reshape>(last_node, target_shape_node, false);
+        }
         if (transpose_weights) {
-            const size_t rank = matmul_weights->get_output_partial_shape(0).size();
+            const size_t rank = last_node->get_output_partial_shape(0).size();
             std::vector<int> order(rank);
             std::iota(order.begin(), order.end(), 0);
             std::swap(*order.rbegin(), *(order.rbegin() + 1));
             auto transpose_constant = ov::opset10::Constant::create(ov::element::i32, {rank}, order);
-            auto transpose = std::make_shared<ov::opset10::Transpose>(matmul_weights, transpose_constant);
-            matmul_weights = transpose;
+            last_node = std::make_shared<ov::opset10::Transpose>(last_node, transpose_constant);
         }
-        auto matMul = builder::makeMatMul(params[0], matmul_weights);
-        return std::make_shared<ov::Model>(NodeVector{matMul}, params, "MatmulWeightsDecompression");
+        return last_node;
     }
 
     void SetUp() override {
         targetDevice = ov::test::utils::DEVICE_GPU;
 
-        std::vector<InputShape> inputShapes;
+        ShapeParams shape_params;
         ov::test::ElementType weights_precision;
         ov::test::ElementType activations_precision;
         bool transpose_weights;
@@ -155,7 +216,7 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
         bool reshape_on_decompression;
         std::map<std::string, std::string> additional_config;
 
-        std::tie(inputShapes,
+        std::tie(shape_params,
                  weights_precision,
                  activations_precision,
                  transpose_weights,
@@ -164,14 +225,47 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
                  additional_config) = GetParam();
 
         configuration.insert(additional_config.begin(), additional_config.end());
-        init_input_shapes(inputShapes);
+        init_input_shapes({shape_params.data_shape, {{}, {{shape_params.weights_shape}}}});
 
         inType = outType = activations_precision;
 
-        function = init_subgraph(inputDynamicShapes, activations_precision, weights_precision, transpose_weights, decompression_sub, reshape_on_decompression);
+        function = init_subgraph(inputDynamicShapes[0],
+                                 shape_params.weights_shape,
+                                 shape_params.weights_group_size,
+                                 activations_precision,
+                                 weights_precision,
+                                 transpose_weights,
+                                 decompression_sub,
+                                 reshape_on_decompression);
+
+
+        if (activations_precision == ov::element::f16) {
+            auto weights_size = ov::shape_size(shape_params.weights_shape);
+            auto weights_input_channels = weights_size / (transpose_weights ? shape_params.weights_shape[0] : shape_params.weights_shape.back());
+            // Absolute values range during accumulation may be quite big ( > 200) so fp16 representation & math error is larger than default threshold
+            if (weights_input_channels > 2048) {
+                abs_threshold = 4.0f;
+            } else {
+                abs_threshold = 1.0f;
+            }
+        }
+    }
+
+    void generate_inputs(const std::vector<ngraph::Shape>& target_input_static_shapes) override {
+          inputs.clear();
+          const auto& model_inputs = function->inputs();
+          for (size_t i = 0; i < model_inputs.size(); ++i) {
+                const auto& model_input = model_inputs[i];
+                ov::Tensor tensor = ov::test::utils::create_and_fill_tensor(model_input.get_element_type(),
+                                                                            target_input_static_shapes[i],
+                                                                            2,
+                                                                            -1,
+                                                                            10000);
+                inputs.insert({model_input.get_node_shared_ptr(), tensor});
+          }
     }
 
-    void checkResults() {
+    void check_results() {
         const auto& test_param = GetParam();
         ov::test::ElementType weights_precision = std::get<1>(test_param);
         for (const auto& n : compiledModel.get_runtime_model()->get_ordered_ops()) {
@@ -185,24 +279,20 @@ class MatmulWeightsDecompression : public testing::WithParamInterface<MatmulWeig
 TEST_P(MatmulWeightsDecompression, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
     run();
-    checkResults();
+    check_results();
 }
 
 namespace {
 
 const std::vector<ov::test::ElementType> activations_precisions = {ov::element::f32, ov::element::f16};
 const std::vector<ov::test::ElementType> weights_precisions = {ov::element::u8};
-const std::vector<std::vector<InputShape>> input_shapes_basic = {
-    {{{-1, -1, -1}, {{1, 4, 16}, {10, 16, 16}}}, {{}, {{16, 32}}}},
-    {{{}, {{10, 40, 496}}}, {{}, {{1, 496, 240}}}},
-    {{{}, {{1, 4, 48}}}, {{}, {{48, 256}}}},
-    {{{}, {{11, 339, 377}}}, {{}, {{377, 335}}}},
-    {{{}, {{1, 4, 32}}}, {{}, {{32, 256}}}},
-    {{{}, {{1, 4, 512}}}, {{}, {{512, 256}}}},
-    {{{}, {{1, 16, 32}}}, {{}, {{32, 64}}}},
-    {{{}, {{2, 4, 32}}}, {{}, {{32, 65}}}},
-    {{{}, {{3, 12, 768}}}, {{}, {{768, 1024}}}},
-    {{{}, {{11, 339, 577}}}, {{}, {{577, 335}}}},
+const std::vector<ShapeParams> input_shapes_basic = {
+    {{{-1, -1, -1}, {{1, 4, 16}, {10, 16, 16}}}, {16, 32}},
+    {{{}, {{1, 4, 16}}}, {16, 32}, 2ul},
+    {{{}, {{1, 4, 16}}}, {1, 16, 32}},
+    {{{}, {{10, 40, 496}}}, {1, 496, 240}},
+    {{{}, {{1, 4, 48}}}, {48, 256}},
+    {{{}, {{11, 339, 377}}}, {377, 335}}
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_basic,
@@ -216,15 +306,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_basic,
                                             ::testing::Values(std::map<std::string, std::string>())),
                          MatmulWeightsDecompression::get_test_case_name);
 
-const std::vector<std::vector<InputShape>> input_shapes_corner_cases_basic = {
-    {{{-1, -1, -1}, {{1, 4, 16}}}, {{}, {{1, 16, 32}}}},
-    {{{}, {{1, 4, 16}}}, {{}, {{1, 16, 32}}}},
-    {{{-1, -1, -1}, {{1, 4, 16}}}, {{}, {{16, 32}}}},
-    {{{-1, -1, -1, -1}, {{1, 1, 4, 16}}}, {{}, {{1, 1, 16, 32}}}},
-    {{{}, {{1, 1, 4, 16}}}, {{}, {{1, 1, 16, 32}}}},
+const std::vector<ShapeParams> input_shapes_corner_cases_basic = {
+    {{{-1, -1, -1}, {{1, 4, 16}}}, {1, 16, 32}},
+    {{{-1, -1, -1}, {{1, 4, 16}}}, {16, 32}},
+    {{{-1, -1, 16}, {{1, 4, 16}}}, {16, 32}, 4},
 };
-const std::vector<std::vector<InputShape>> input_shapes_corner_cases_big = {
-    {{{-1, -1, -1}, {{10, 40, 480}, {11, 40, 480}}}, {{}, {{1, 480, 256}}}},
+const std::vector<ShapeParams> input_shapes_corner_cases_big = {
+    {{{-1, -1, -1}, {{10, 40, 480}, {11, 40, 480}}}, {1, 480, 256}},
+    {{{-1, -1, -1}, {{1, 1, 4096}}}, {4096, 4096}, 128},
+    {{{-1, -1, -1}, {{1, 1, 4096}}}, {4096, 4096}},
+    {{{-1, 4096}, {{1, 4096}}}, {4096, 4096}, 128},
 };
 
 const std::vector<bool> transpose_weights = {true, false};
@@ -242,7 +333,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_corner_cases_basic,
                                             ::testing::Values(std::map<std::string, std::string>{})),
                          MatmulWeightsDecompression::get_test_case_name);
 
-INSTANTIATE_TEST_SUITE_P(smoke_MatMulCompressedWeights_corner_cases_big,
+INSTANTIATE_TEST_SUITE_P(MatMulCompressedWeights_corner_cases_big,
                          MatmulWeightsDecompression,
                          ::testing::Combine(::testing::ValuesIn(input_shapes_corner_cases_big),
                                             ::testing::ValuesIn(weights_precisions),
diff --git a/src/plugins/intel_gpu/tests/functional/subgraph_tests/loop.cpp b/src/plugins/intel_gpu/tests/functional/subgraph_tests/loop.cpp
new file mode 100644
index 00000000000000..8c7de510531348
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/functional/subgraph_tests/loop.cpp
@@ -0,0 +1,316 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <tuple>
+#include <string>
+#include <vector>
+#include <memory>
+#include "ov_models/utils/ov_helpers.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "ov_models/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "common_test_utils/test_constants.hpp"
+#include "shared_test_classes/base/utils/ranges.hpp"
+#include <common_test_utils/ov_tensor_utils.hpp>
+#include "shared_test_classes/base/utils/compare_results.hpp"
+#include "openvino/pass/constant_folding.hpp"
+#include <transformations/control_flow/unroll_tensor_iterator.hpp>
+
+using namespace InferenceEngine;
+using namespace ov::test;
+
+namespace GPULayerTestsDefinitions {
+
+using DynamicShapeLoopParams = typename std::tuple<
+        bool,
+        std::tuple<
+            bool,
+            int64_t,
+            int64_t,
+            int64_t
+            >,
+        int64_t,
+        InputShape,
+        InferenceEngine::Precision,
+        std::string,
+        ov::AnyMap
+        >;
+
+/**
+ * Test case with Dynamic SHAPE version of loop operation.
+ * Total iteration count is dynamic.
+ */
+class DynamicShapeLoopTest : public testing::WithParamInterface<DynamicShapeLoopParams>,
+                            virtual public SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<DynamicShapeLoopParams> &obj) {
+        bool static_iter_num;
+        bool static_continue_cond;
+        int64_t max_iter_num;
+        int64_t dynamic_exit;
+        int64_t axis;
+        int64_t start_value;
+        InputShape data_shapes;
+        InferenceEngine::Precision data_prc;
+        std::string targetDevice;
+        auto args_pack = std::tie(static_iter_num, max_iter_num, dynamic_exit, axis);
+        ov::Any configuration;
+        std::tie(
+            static_continue_cond,
+            args_pack,
+            start_value,
+            data_shapes,
+            data_prc,
+            targetDevice,
+            configuration) = obj.param;
+
+        std::ostringstream result;
+        result << "static_iter_num=" << std::to_string(static_iter_num) << "_";
+        result << "static_continue_cond=" << std::to_string(static_continue_cond) << "_";
+        result << "max_iter_num=" << std::to_string(max_iter_num) << "_";
+        result << "dynamic_exit=" << std::to_string(dynamic_exit) << "_";
+        result << "axis=" << std::to_string(axis) << "_";
+        result << "start_value=" << std::to_string(start_value) << "_";
+        result << "max_iter_num=" << std::to_string(max_iter_num) << "_";
+        result << "IS=(";
+        result << ov::test::utils::partialShape2str({data_shapes.first}) << "_";
+        for (size_t i = 0lu; i < data_shapes.second.size(); i++) {
+            result << "{";
+            result << ov::test::utils::vec2str(data_shapes.second[i]) << "_";
+            result << "}_";
+        }
+        result << ")_";
+        result << "netPRC=" << data_prc << "_";
+        result << "targetDevice=" << targetDevice << "_";
+
+        auto res_str = result.str();
+        std::replace(res_str.begin(), res_str.end(), '-', '_');
+        return res_str;
+    }
+
+private:
+    bool static_iter_num;       // trip count provided by constant node
+    bool static_continue_cond;  // initial_cond provided by constant node
+    int64_t max_iter_num;       // -1 means infinity loop (expected dynamic exit condition in body)
+    int64_t dynamic_exit;       // -1 means always true
+    int64_t axis;               // -1 means no auto concatenation
+    int64_t start_value;
+    InputShape data_shapes;
+    InferenceEngine::Precision data_prc;
+
+protected:
+    void SetUp() override {
+        SKIP_IF_CURRENT_TEST_IS_DISABLED()
+        auto args_pack = std::tie(static_iter_num, max_iter_num, dynamic_exit, axis);
+        std::tie(
+            static_continue_cond,
+            args_pack,
+            start_value,
+            data_shapes,
+            data_prc,
+            targetDevice,
+            configuration) = GetParam();
+
+        const auto prc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(data_prc);
+        const auto inputShape = data_shapes.first;
+        const auto scalarShape = ngraph::Shape{};
+        init_input_shapes({data_shapes});
+
+        ngraph::ParameterVector params{};
+        auto cond_input_create = [&params] (ngraph::element::Type prc, const ov::PartialShape &shape, int value = 0, bool is_static = false)
+                -> std::shared_ptr<ngraph::Node> {
+            if (is_static)
+                return std::make_shared<ngraph::opset5::Constant>(prc, shape.to_shape(), value);
+
+            auto input = std::make_shared<ngraph::opset5::Parameter>(prc, shape);
+            params.push_back(input);
+            return input;
+        };
+
+        auto start = cond_input_create(prc, inputShape);
+        start->set_friendly_name("start");
+        auto count = cond_input_create(ngraph::element::i64, scalarShape, max_iter_num, static_iter_num);
+        count->set_friendly_name("count");
+        auto skip  = cond_input_create(ngraph::element::boolean, scalarShape, true, static_continue_cond);
+        skip->set_friendly_name("skip");
+
+        //
+        //      count skip  start         count skip      start
+        //                  /                             /
+        //          ___*___*____           __________*___*____       | idx  | data | out |
+        //         |  idx  in   |         | ex_val  idx  in   |      |  0   |  7   |  7  |
+        //         |   |  /     |         |   |   /  |  /     |      |  1   |  7   |  8  |
+        //         |   add      |         |   less   add      |      |  2   |  8   |  10 |
+        //         |   |   true |         |    |     |        |      |  3   |  10  |  13 |
+        //         |   |    |   |         |    |     |        |       ~~~~~  * * *  ~~~~~
+        //         |  out  cnd  |         |   cnd   out       |
+        //         |___*____*___|         |____*_____*________|
+        //           Full loop              Dynamic exit loop
+        //           n_iter = count         n_iter = ex_val
+        //
+        auto b_indx = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::i64, ngraph::Shape{});
+        b_indx->set_friendly_name("body_index");
+        auto b_data = std::make_shared<ngraph::opset5::Parameter>(prc, inputShape);
+        b_data->set_friendly_name("body_data");
+        auto b_indx_cast = std::make_shared<ngraph::opset5::Convert>(b_indx, prc);
+        b_indx_cast->set_friendly_name("body_index_cast");
+        auto b_add  = std::make_shared<ngraph::opset5::Add>(b_data, b_indx_cast);
+        b_add->set_friendly_name("body_addition");
+
+        std::shared_ptr<ngraph::Node> b_cond;
+        if (dynamic_exit == -1) {
+            b_cond = std::make_shared<ngraph::opset5::Constant>(ngraph::element::boolean, ngraph::Shape{}, true);
+            b_cond->set_friendly_name("body_condition");
+        } else {
+            auto b_exit_value = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, scalarShape, dynamic_exit);
+            b_exit_value->set_friendly_name("body_exit_value");
+            b_cond = std::make_shared<ngraph::opset5::Less>(b_indx, b_exit_value);
+            b_cond->set_friendly_name("body_condition_with_exit_value");
+        }
+
+        auto body = std::make_shared<ngraph::Function>(
+                ngraph::OutputVector    {b_cond, b_add},    // TODO: check with reverse
+                ngraph::ParameterVector {b_indx, b_data});  // TODO: check with reverse
+        body->set_friendly_name("body_network");
+
+        auto loop = std::make_shared<ngraph::opset5::Loop>(count, skip);
+        loop->set_friendly_name("loop");
+        loop->set_function(body);
+        loop->set_special_body_ports({0, 0});
+        loop->set_merged_input(b_data, start, b_add);
+        if (axis == -1)
+            loop->get_iter_value(b_add, -1);
+        else
+            loop->get_concatenated_slices(b_add, 0, 1, 1, -1, axis);
+
+        function = std::make_shared<ngraph::Function>(
+                ngraph::OutputVector {loop},
+                params);
+        function->set_friendly_name("outer_body_network");
+    }
+};
+
+
+TEST_P(DynamicShapeLoopTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+}
+
+std::vector<InferenceEngine::Precision> netPrecisions = {
+    InferenceEngine::Precision::FP32,
+    InferenceEngine::Precision::I32
+};
+
+ov::AnyMap netConfigurations = {
+    {GPUConfigParams::KEY_GPU_ENABLE_LOOP_UNROLLING, PluginConfigParams::NO}
+};
+
+static const std::vector<std::tuple<bool, int64_t, int64_t, int64_t>> dynamic_loop_types_axis_0 {
+    //  GCC4.8 limitation: have to specify type of each element in list
+    //                               static_trip_count |  max | dynamic_exit | axis
+    std::tuple<bool, int64_t, int64_t, int64_t>{  true ,  10, -1, 0 },  // n_iter 10, no dynamic exit
+};
+
+std::vector<InputShape> inputs_0 = {
+    InputShape(ov::PartialShape({1, -1, 2}), {{1, 4, 2}, {1, 5, 2}, {1, 10, 2}}),
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicShapeLoop_axis_0, DynamicShapeLoopTest,
+                        testing::Combine(
+                        /* static_continue_cond */ testing::Values(true),
+                        /* args_pack */ testing::ValuesIn(dynamic_loop_types_axis_0),
+                        /* start_value */ testing::Values<int64_t>(0),
+                        /* data_shape */ testing::ValuesIn(inputs_0),
+                        /* data_prc */ testing::ValuesIn(netPrecisions),
+                        /* device */ testing::Values<std::string>(ov::test::utils::DEVICE_GPU),
+                        /* configuration */ testing::Values<ov::AnyMap>(netConfigurations)),
+                        DynamicShapeLoopTest::getTestCaseName);
+
+static const std::vector<std::tuple<bool, int64_t, int64_t, int64_t>> dynamic_loop_types_1 {
+    //  GCC4.8 limitation: have to specify type of each element in list
+    //                               static_trip_count |  max | dynamic_exit | axis
+    std::tuple<bool, int64_t, int64_t, int64_t>{  true ,  5, -1,  1 },  // n_iter 5, no dynamic exit
+};
+
+std::vector<InputShape> inputs_1 = {
+    InputShape(ov::PartialShape({-1, 1, 4, -1}), {{2, 1, 4, 10}, {3, 1, 4, 14}, {6, 1, 4, 16}}),
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicShapeLoop_axis_1, DynamicShapeLoopTest,
+                        testing::Combine(
+                        /* static_continue_cond */ testing::Values(true),
+                        /* args_pack */ testing::ValuesIn(dynamic_loop_types_1),
+                        /* start_value */ testing::Values<int64_t>(0),
+                        /* data_shape */ testing::ValuesIn(inputs_1),
+                        /* data_prc */ testing::ValuesIn(netPrecisions),
+                        /* device */ testing::Values<std::string>(ov::test::utils::DEVICE_GPU),
+                        /* configuration */ testing::Values<ov::AnyMap>(netConfigurations)),
+                        DynamicShapeLoopTest::getTestCaseName);
+
+static const std::vector<std::tuple<bool, int64_t, int64_t, int64_t>> dynamic_loop_types_2 {
+    //  GCC4.8 limitation: have to specify type of each element in list
+    //                               static_trip_count |  max | dynamic_exit | axis
+    std::tuple<bool, int64_t, int64_t, int64_t>{  true ,  10, -1,  2 },  // n_iter 10, no dynamic exit
+};
+
+std::vector<InputShape> inputs_2 = {
+    InputShape(ov::PartialShape({-1, -1, 1, 6}), {{2, 4, 1, 6}, {10, 40, 1, 6}, {12, 16, 1, 6}}),
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicShapeLoop_axis_2, DynamicShapeLoopTest,
+                        testing::Combine(
+                        /* static_continue_cond */ testing::Values(true),
+                        /* args_pack */ testing::ValuesIn(dynamic_loop_types_2),
+                        /* start_value */ testing::Values<int64_t>(0),
+                        /* data_shape */ testing::ValuesIn(inputs_2),
+                        /* data_prc */ testing::ValuesIn(netPrecisions),
+                        /* device */ testing::Values<std::string>(ov::test::utils::DEVICE_GPU),
+                        /* configuration */ testing::Values<ov::AnyMap>(netConfigurations)),
+                        DynamicShapeLoopTest::getTestCaseName);
+
+static const std::vector<std::tuple<bool, int64_t, int64_t, int64_t>> dynamic_loop_types_no_auto_concat {
+    //  GCC4.8 limitation: have to specify type of each element in list
+    //                               static_trip_count |  max | dynamic_exit | axis
+    std::tuple<bool, int64_t, int64_t, int64_t>{  true ,  10, -1, -1 },  // n_iter 5, no dynamic exit
+};
+
+std::vector<InputShape> inputs_no_auto_concat = {
+    InputShape(ov::PartialShape({-1, 1, 6}), {{2, 1, 6}, {10, 1, 6}, {12, 1, 6}}),
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicShapeLoop_no_auto_concat, DynamicShapeLoopTest,
+                        testing::Combine(
+                        /* static_continue_cond */ testing::Values(true),
+                        /* args_pack */ testing::ValuesIn(dynamic_loop_types_no_auto_concat),
+                        /* start_value */ testing::Values<int64_t>(0),
+                        /* data_shape */ testing::ValuesIn(inputs_no_auto_concat),
+                        /* data_prc */ testing::ValuesIn(netPrecisions),
+                        /* device */ testing::Values<std::string>(ov::test::utils::DEVICE_GPU),
+                        /* configuration */ testing::Values<ov::AnyMap>(netConfigurations)),
+                        DynamicShapeLoopTest::getTestCaseName);
+
+static const std::vector<std::tuple<bool, int64_t, int64_t, int64_t>> dynamic_loop_types_dynamic_exit {
+    //  GCC4.8 limitation: have to specify type of each element in list
+    //                               static_trip_count |  max | dynamic_exit | axis
+    std::tuple<bool, int64_t, int64_t, int64_t>{  true ,  5,  3,  -1 },  // n_iter 3, dynamic exit on 3
+    std::tuple<bool, int64_t, int64_t, int64_t>{  true ,  5,  7,   1 },  // n_iter 5, dynamic exit not reached
+    std::tuple<bool, int64_t, int64_t, int64_t>{  true , -1,  5,  -1 },  // n_iter 5, inf loop with dynamic exit on 5
+};
+
+std::vector<InputShape> inputs_dynamic_exit = {
+    InputShape(ov::PartialShape({-1, 1, 2}), {{4, 1, 2}, {10, 1, 2}, {12, 1, 2}}),
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicShapeLoop_dynamic_exit, DynamicShapeLoopTest,
+                        testing::Combine(
+                        /* static_continue_cond */ testing::Values(true),
+                        /* args_pack */ testing::ValuesIn(dynamic_loop_types_dynamic_exit),
+                        /* start_value */ testing::Values<int64_t>(0),
+                        /* data_shape */ testing::ValuesIn(inputs_dynamic_exit),
+                        /* data_prc */ testing::ValuesIn(netPrecisions),
+                        /* device */ testing::Values<std::string>(ov::test::utils::DEVICE_GPU),
+                        /* configuration */ testing::Values<ov::AnyMap>(netConfigurations)),
+                        DynamicShapeLoopTest::getTestCaseName);
+
+} // namespace GPULayerTestsDefinitions
\ No newline at end of file
diff --git a/src/plugins/intel_gpu/tests/unit/CMakeLists.txt b/src/plugins/intel_gpu/tests/unit/CMakeLists.txt
index 3dda088627b833..1230e57effbd74 100644
--- a/src/plugins/intel_gpu/tests/unit/CMakeLists.txt
+++ b/src/plugins/intel_gpu/tests/unit/CMakeLists.txt
@@ -23,14 +23,12 @@ file(GLOB_RECURSE SOURCES_MAIN
   )
 
 if (NOT ENABLE_ONEDNN_FOR_GPU)
-    set(EXCLUDE_DIR "/onednn/")
-    foreach (SOURCE_FILE ${SOURCES_MAIN})
-        string (FIND ${SOURCE_FILE} ${EXCLUDE_DIR} EXCLUDE_DIR_FOUND)
-        if (NOT ${EXCLUDE_DIR_FOUND} EQUAL -1)
-            message (Exclude : ${SOURCE_FILE})
+    set(EXCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/onednn/")
+    foreach (SOURCE_FILE IN LISTS SOURCES_MAIN)
+        if (SOURCE_FILE MATCHES "${EXCLUDE_DIR}.*")
             list (REMOVE_ITEM SOURCES_MAIN ${SOURCE_FILE})
         endif ()
-    endforeach(SOURCE_FILE)
+    endforeach()
 endif()
 
 if (MSVC)
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/convolution_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/convolution_fusion_test.cpp
index aa5934a710f196..df20a50e33b9b2 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/convolution_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/convolution_fusion_test.cpp
@@ -625,6 +625,12 @@ TEST_P(conv_fp32_activation, basic) {
         reorder("reorder_bfyx", input_info("activation"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
     execute(p);
 }
@@ -635,9 +641,9 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_activation, ::testing::ValuesIn(
     convolution_test_params{ CASE_CONV_FP32_3, 2, 2, 3 },
     convolution_test_params{ CASE_CONV_FP32_4, 2, 2, 3 },
 
-    convolution_test_params{ CASE_CONV_FP16_4, 2, 2, 3 },
-    convolution_test_params{ CASE_CONV_FP16_4, 2, 2, 3 },
-    convolution_test_params{ CASE_CONV_FP16_4, 2, 2, 3 },
+    convolution_test_params{ CASE_CONV_FP16_1, 2, 2, 3 },
+    convolution_test_params{ CASE_CONV_FP16_2, 2, 2, 3 },
+    convolution_test_params{ CASE_CONV_FP16_3, 2, 2, 3 },
     convolution_test_params{ CASE_CONV_FP16_4, 2, 2, 3 },
 }));
 
@@ -655,6 +661,12 @@ TEST_P(conv_fp32_scale, basic) {
         reorder("reorder_bfyx", input_info("scale"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
     execute(p);
 }
@@ -685,6 +697,12 @@ TEST_P(conv_fp32_bias, basic) {
         reorder("reorder_bfyx", input_info("add_bias"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
     execute(p);
 }
@@ -700,7 +718,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_bias, ::testing::ValuesIn(std::v
     convolution_test_params{ CASE_CONV_FP16_2, 2, 2, 3 },
     convolution_test_params{ CASE_CONV_FP16_3, 2, 2, 3 },
     convolution_test_params{ CASE_CONV_FP16_4, 2, 2, 3 },
-    convolution_test_params{ CASE_CONV_FP16_10, 2, 2, 3 },
+    // convolution_test_params{ CASE_CONV_FP16_10, 2, 2, 3 }, // Issue: 94154
 }));
 
 class conv_fp32_double_bias : public ConvFusingTest {};
@@ -800,6 +818,12 @@ TEST_P(conv_fp32_prelu_eltwise, basic_sum) {
     );
 
     tolerance = default_tolerance(p.data_type);
+    if (engine.get_device_info().supports_immad && p.default_type == data_types::f16) {
+        tolerance *= 2;
+        if (p.weights_format == format::gs_oiyx_gsv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+    }
     execute(p);
 }
 
@@ -818,6 +842,12 @@ TEST_P(conv_fp32_prelu_eltwise, basic_sum_slope_2) {
     );
 
     tolerance = default_tolerance(p.data_type);
+    if (engine.get_device_info().supports_immad && p.default_type == data_types::f16) {
+        tolerance *= 2;
+        if (p.weights_format == format::gs_oiyx_gsv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+    }
     execute(p);
 }
 
@@ -835,6 +865,12 @@ TEST_P(conv_fp32_prelu_eltwise, basic_prod) {
         reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.data_type);
     execute(p);
 }
@@ -854,6 +890,12 @@ TEST_P(conv_fp32_prelu_eltwise, basic_prod_slope_2) {
     );
 
     tolerance = default_tolerance(p.data_type);
+    if (engine.get_device_info().supports_immad && p.default_type == data_types::f16) {
+        tolerance *= 4;
+        if (p.weights_format == format::gs_oiyx_gsv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+    }
     execute(p);
 }
 
@@ -873,6 +915,12 @@ TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_sum) {
     );
 
     tolerance = default_tolerance(p.data_type);
+    if (engine.get_device_info().supports_immad && p.default_type == data_types::f16) {
+        tolerance *= 2;
+        if (p.weights_format == format::gs_oiyx_gsv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+    }
     execute(p);
 }
 
@@ -891,6 +939,12 @@ TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_sum_slope_2) {
         reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.data_type);
     execute(p);
 }
@@ -911,6 +965,12 @@ TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_prod) {
     );
 
     tolerance = default_tolerance(p.data_type);
+    if (engine.get_device_info().supports_immad && p.default_type == data_types::f16) {
+        tolerance *= 4;
+        if (p.weights_format == format::gs_oiyx_gsv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+    }
     execute(p);
 }
 
@@ -929,6 +989,12 @@ TEST_P(conv_fp32_prelu_eltwise, eltw_broadcast_prod_slope_2) {
         reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.data_type);
     execute(p);
 }
@@ -1129,6 +1195,9 @@ TEST_P(conv_fp32_multi_eltwise_4_clamp, basic) {
     cfg_fused.set_property(ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ { "conv_prim", conv_impl } }));
 
     tolerance = default_tolerance(p.default_type);
+    if (p.default_type == data_types::f16) {
+        tolerance *= 4.f; // Issue: 94154
+    }
     execute(p);
 }
 
@@ -1168,6 +1237,9 @@ TEST_P(conv_fp32_eltwise_fusing_extend_ops, pattern01_simple_sub) {
     cfg_fused.set_property(ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ { "conv_prim", conv_impl } }));
 
     tolerance = default_tolerance(p.default_type);
+    if (p.default_type == data_types::f16) {
+        tolerance *= 8.f; // Issue: 94154
+    }
     execute(p);
 }
 
@@ -1335,7 +1407,16 @@ TEST_P(conv_fp32_multi_eltwise_quantization, basic) {
         reorder("reorder_bfyx", input_info("eltwise2"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = 1.f;
+    if (p.default_type == data_types::f16) {
+        tolerance *= 8.f; // Issue: 94154
+    }
     execute(p);
 }
 
@@ -1420,7 +1501,15 @@ TEST_P(conv_fp32_swish, basic) {
         reorder("reorder_bfyx", input_info("mul"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
+    if (p.default_type == data_types::f16) {
+        tolerance *= 3.f; // Issue: 94154
+    }
     execute(p);
 }
 
@@ -1784,6 +1873,11 @@ TEST_P(conv_swap_xy_with_eltwise_diff_sizes, basic) {
         reorder("reorder_bfyx", input_info("sum"), p.default_format, data_types::f16)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
     execute(p);
 }
@@ -3329,6 +3423,10 @@ TEST_P(conv_gen9_common_conv_fwd_data_1stconv, basic) {
     );
 
     tolerance = default_tolerance(p.default_type);
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16) {
+        tolerance *= 2; // Issue: 94154
+    }
     execute(p);
 }
 
@@ -3438,6 +3536,12 @@ TEST_P(conv_fp32_activation_abs_onednn, basic) {
         reorder("reorder_bfyx", input_info("activation"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
     execute(p);
 }
@@ -3462,6 +3566,12 @@ TEST_P(conv_fp32_activation_mish_onednn, basic) {
     );
 
     tolerance = default_tolerance(p.default_type);
+    if (engine.get_device_info().supports_immad && p.default_type == data_types::f16) {
+        tolerance *= 4;
+        if (p.weights_format == format::gs_oiyx_gsv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+    }
     execute(p);
 }
 
@@ -3484,6 +3594,12 @@ TEST_P(conv_fp32_activation_swish_onednn, basic) {
         reorder("reorder_bfyx", input_info("activation"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::gs_oiyx_gsv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
     execute(p);
 }
@@ -3508,6 +3624,12 @@ TEST_P(conv_fp32_activation_hswish_onednn, basic) {
     );
 
     tolerance = default_tolerance(p.default_type);
+    if (engine.get_device_info().supports_immad && p.default_type == data_types::f16) {
+        tolerance *= 8;
+        if (p.weights_format == format::gs_oiyx_gsv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+    }
     execute(p);
 }
 
@@ -3530,6 +3652,11 @@ TEST_P(conv_fp32_activation_exp_onednn, basic) {
         reorder("reorder_bfyx", input_info("activation"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = default_tolerance(p.default_type);
     execute(p);
 }
@@ -4440,6 +4567,8 @@ TEST_P(conv_after_permute_not_optimizing, basic) {
     if (!engine.get_device_info().supports_immad)
         return;
 
+    GTEST_SKIP(); // Issue: 94154
+
     auto p = GetParam();
 
     create_topologies(
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/deconvolution_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/deconvolution_fusion_test.cpp
index 5702b695d359a0..7dba6f2ca7e266 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/deconvolution_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/deconvolution_fusion_test.cpp
@@ -240,6 +240,13 @@ TEST_P(deconv_actv, basic) {
         activation("act", input_info("deconv"), activation_func::relu),
         reorder("out", input_info("act"), p.default_format, data_types::f32)
     );
+
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::is_os_yx_isv16_osv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     // Need much higher tolerance because of deconvolution -> convolution optimization
     tolerance = 1.f;
     execute(p);
@@ -335,6 +342,12 @@ TEST_P(deconv_bias, basic) {
         reorder("out", input_info("bias_add"), p.default_format, data_types::f32)
     );
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::is_os_yx_isv16_osv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     // Need much higher tolerance because of deconvolution -> convolution optimization
     tolerance = 1.f;
     execute(p);
@@ -457,6 +470,13 @@ class deconv_actv_eltw_actv : public DeconvolutionFusingTest {
             activation("act2", input_info("eltw"), activation_func::relu),
             reorder("out", input_info("act2"), p.default_format, data_types::f32)
         );
+
+        if (engine.get_device_info().supports_immad &&
+            p.default_type == data_types::f16 &&
+            p.weights_format == format::is_os_yx_isv16_osv16) {
+            GTEST_SKIP(); // Issue: 94154
+        }
+
         // Need much higher tolerance because of deconvolution -> convolution optimization
         tolerance = 1.f;
         execute(p, is_caching_test);
@@ -570,6 +590,12 @@ TEST_P(deconv_scale_actv_quant_i8, basic) {
     if (engine.get_device_info().supports_immad)
         p.expected_fused_primitives++;
 
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        p.weights_format == format::is_os_yx_isv16_osv16) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = 1.f;
     execute(p);
 }
@@ -681,6 +707,14 @@ TEST_P(deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8, basic) {
                  input_info("out2_lo"), input_info("out2_hi"), 255, data_types::i8),
         reorder("out", input_info("quant2"), p.default_format, data_types::f32)
     );
+
+    if (engine.get_device_info().supports_immad &&
+        p.default_type == data_types::f16 &&
+        (p.weights_format == format::is_os_yx_isv16_osv16 ||
+         p.weights_format == format::is_os_zyx_isv16_osv16)) {
+        GTEST_SKIP(); // Issue: 94154
+    }
+
     tolerance = 2.1f;
     execute(p);
 }
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp
index 50ddb9870e51e2..bf72af9c91837f 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp
@@ -124,6 +124,9 @@ TEST_P(eltwise_quantize, u8) {
     );
 
     tolerance = default_tolerance(data_types::i8);
+    if (p.default_type == data_types::f16 && p.default_format == format::b_fs_yx_fsv4) {
+        tolerance *= 2.f; // Issue: 94154
+    }
     execute(p);
 }
 
@@ -143,6 +146,9 @@ TEST_P(eltwise_quantize, i8_per_channel) {
     );
 
     tolerance = default_tolerance(data_types::i8);
+    if (p.default_type == data_types::f16 && p.default_format == format::b_fs_yx_fsv4) {
+        tolerance *= 11.f; // Issue: 94154
+    }
     execute(p);
 }
 
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/fusion_test_common.hpp b/src/plugins/intel_gpu/tests/unit/fusions/fusion_test_common.hpp
index 0eaf6cbee32543..58694f91d4c3ab 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/fusion_test_common.hpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/fusion_test_common.hpp
@@ -121,7 +121,7 @@ class BaseFusingTest : public ::testing::TestWithParam<T> {
             VF<uint8_t> rnd_vec = rg.generate_random_1d<uint8_t>(s.count(), min_random, max_random);
             set_values(prim, rnd_vec);
         } else if (l.data_type == data_types::f16) {
-            VF<uint16_t> rnd_vec = rg.generate_random_1d<uint16_t>(s.count(), -1, 1);
+            VF<ov::float16> rnd_vec = rg.generate_random_1d<ov::float16>(s.count(), -1, 1);
             set_values(prim, rnd_vec);
         } else {
             VF<float> rnd_vec = rg.generate_random_1d<float>(s.count(), -1, 1);
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp
index 39022f9f7b870d..c1af88852dde09 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp
@@ -129,7 +129,7 @@ TEST_P(gather_quantize, basic) {
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
         data("out_lo", get_mem(get_single_element_layout(p), -127)),
         data("out_hi", get_mem(get_single_element_layout(p), 127)),
-        gather("gather_prim", input_info("input"), input_info("gather_indices"), p.axis, p.out_shape),
+        gather("gather_prim", input_info("input"), input_info("gather_indices"), p.axis, p.dictionary_shape.size(), p.out_shape),
         quantize("quantize", input_info("gather_prim"), input_info("in_lo"), input_info("in_hi"),
                  input_info("out_lo"), input_info("out_hi"), 255, data_types::i8),
         reorder("reorder_bfyx", input_info("quantize"), p.default_format, data_types::f32)
@@ -172,7 +172,7 @@ TEST_P(gather_eltwise_activation, basic) {
         input_layout("input", get_input_layout(p)),
         data("gather_indices", get_mem(get_indices_layout(p), 0, static_cast<int>(get_axis_dim(p) - 1))),
         data("eltwise_data", get_mem(get_per_channel_layout(p), -10, 10)),
-        gather("gather_prim", input_info("input"), input_info("gather_indices"), p.axis, p.out_shape),
+        gather("gather_prim", input_info("input"), input_info("gather_indices"), p.axis, p.dictionary_shape.size(), p.out_shape),
         activation("activation", input_info("gather_prim"), activation_func::abs),
         eltwise("eltwise", { input_info("activation"), input_info("eltwise_data") }, eltwise_mode::prod),
         reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
@@ -220,7 +220,7 @@ TEST_P(gather_eltwise_activation_dynamic, basic) {
         input_layout("input", get_input_layout(p, true)),
         input_layout("gather_indices", layout{ ov::PartialShape::dynamic(p.indices_shape.size()), p.data_type, format::bfyx }),
         input_layout("eltwise_data", get_per_channel_layout(p, true)),
-        gather("gather_prim", input_info("input"), input_info("gather_indices"), p.axis, p.out_shape),
+        gather("gather_prim", input_info("input"), input_info("gather_indices"), p.axis, p.dictionary_shape.size(), p.out_shape),
         activation("activation", input_info("gather_prim"), activation_func::abs),
         eltwise("eltwise", { input_info("activation"), input_info("eltwise_data") }, eltwise_mode::prod),
         reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/gemm_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/gemm_fusion_test.cpp
index 19a9deccfc3e33..68d444094f331b 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/gemm_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/gemm_fusion_test.cpp
@@ -493,6 +493,9 @@ TEST_P(gemm_2in_act_scale_eltwise, basic) {
     );
 
     tolerance = default_tolerance(p.default_type);
+    if (p.default_type == data_types::f16 && p.kernel_name == "gemm_tiled_opt") {
+        tolerance *= 2.1f; // Issue: 94154
+    }
     execute(p, false);
 }
 
@@ -511,6 +514,9 @@ TEST_P(gemm_2in_act_scale_eltwise, broadcast_eltwise) {
     );
 
     tolerance = default_tolerance(p.default_type);
+    if (p.default_type == data_types::f16 && p.kernel_name == "gemm_tiled_opt") {
+        tolerance *= 2.1f; // Issue: 94154
+    }
     execute(p, false);
 }
 
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/loop_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/loop_fusion_test.cpp
index 6e980ac7d25d8b..a3635bb320f47f 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/loop_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/loop_fusion_test.cpp
@@ -31,6 +31,35 @@ struct loop_params {
     size_t expected_not_fused_primitives;
 };
 
+
+program::ptr build_program(engine& engine,
+                            topology& body_topology,
+                            primitive_id initial_condition_id,
+                            std::vector<loop::io_primitive_map> output_primitive_maps,
+                            std::vector<loop::backedge_mapping> back_edges) {
+    std::vector<primitive_id> output_names_vec;
+    for (auto out_map : output_primitive_maps) {
+        output_names_vec.push_back(out_map.internal_id.pid);
+    }
+
+    // setup outputs for backedges
+    for (auto& back_edge : back_edges) {
+        output_names_vec.push_back(back_edge.from);
+    }
+
+    // if execution_condition_id is specified, we need to add the id in build_option::outputs
+    if (!initial_condition_id.empty()) {
+        output_names_vec.push_back(initial_condition_id);
+    }
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::optimize_data(true));
+    config.set_property(ov::intel_gpu::custom_outputs(output_names_vec));
+    config.set_property(ov::intel_gpu::max_dynamic_batch(1));
+
+    return program::build_program(engine, body_topology, config, false, false, true);
+}
+
 class LoopFusingTest : public ::BaseFusingTest<loop_params> {
 public:
 
@@ -71,6 +100,8 @@ TEST_P(permute_eltwise_loop, basic) {
     std::vector<loop::io_primitive_map> output_primitive_maps {loop::io_primitive_map("loop", "body_eltwise", 2)};
     std::vector<loop::backedge_mapping> back_edges {loop::backedge_mapping("body_eltwise", "body_eltwise_operand")};
 
+    auto body_program = build_program(engine, body, "", output_primitive_maps, back_edges);
+
     create_topologies(
         input_layout("input", get_input_layout(p)),
         data("eltwise_data", get_mem(layout{p.data_type, p.default_format, p.loop_input_shape})),
@@ -80,7 +111,7 @@ TEST_P(permute_eltwise_loop, basic) {
         data("trip_count", trip_count_mem),
         data("initial_condition", initial_condition_mem),
         mutable_data("num_iteration", num_iteration_mem),
-        loop("loop", { input_info("eltwise"), input_info("loop_eltwise_init_values") }, body,
+        loop("loop", { input_info("num_iteration"), input_info("eltwise"), input_info("loop_eltwise_init_values") }, body_program,
              "trip_count", "initial_condition", "num_iteration",
              input_primitive_maps, output_primitive_maps, back_edges, p.loop_trip_count),
         reorder("output", input_info("loop"), format::bfyx, p.default_type)
diff --git a/src/plugins/intel_gpu/tests/unit/module_tests/primitive_comparison_test.cpp b/src/plugins/intel_gpu/tests/unit/module_tests/primitive_comparison_test.cpp
index 11d769f322be93..7fafb55beeccb8 100644
--- a/src/plugins/intel_gpu/tests/unit/module_tests/primitive_comparison_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/module_tests/primitive_comparison_test.cpp
@@ -89,11 +89,11 @@ TEST(primitive_comparison, fully_connected) {
 }
 
 TEST(primitive_comparison, gather) {
-    auto gather_prim = gather("gather", input_info("input0"), input_info("input1"), 2, {1, 3, 224, 224}, 1, true);
-    auto gather_prim_eq = gather("gather_eq", input_info("input0_eq"), input_info("input1_eq"), 2, {1, 3, 224, 224}, 1, true);
-    auto gather_prim_axis = gather("gather", input_info("input0"), input_info("input1"), 3, {1, 3, 224, 224}, 1, true);
-    auto gather_prim_batch_dim = gather("gather", input_info("input0"), input_info("input1"), 2, {1, 3, 224, 224}, 2, true);
-    auto gather_prim_support_neg_ind = gather("gather", input_info("input0"), input_info("input1"), 2, {1, 3, 224, 224}, 1, false);
+    auto gather_prim = gather("gather", input_info("input0"), input_info("input1"), 2, {}, {1, 3, 224, 224}, 1, true);
+    auto gather_prim_eq = gather("gather_eq", input_info("input0_eq"), input_info("input1_eq"), 2, {}, {1, 3, 224, 224}, 1, true);
+    auto gather_prim_axis = gather("gather", input_info("input0"), input_info("input1"), 3, {}, {1, 3, 224, 224}, 1, true);
+    auto gather_prim_batch_dim = gather("gather", input_info("input0"), input_info("input1"), 2, {}, {1, 3, 224, 224}, 2, true);
+    auto gather_prim_support_neg_ind = gather("gather", input_info("input0"), input_info("input1"), 2, {}, {1, 3, 224, 224}, 1, false);
 
     ASSERT_EQ(gather_prim, gather_prim_eq);
     ASSERT_NE(gather_prim, gather_prim_axis);
diff --git a/src/plugins/intel_gpu/tests/unit/module_tests/shape_predictor_test.cpp b/src/plugins/intel_gpu/tests/unit/module_tests/shape_predictor_test.cpp
index e6c78b4fd8513e..5e5caa91cd7d3f 100644
--- a/src/plugins/intel_gpu/tests/unit/module_tests/shape_predictor_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/module_tests/shape_predictor_test.cpp
@@ -25,10 +25,10 @@ TEST_P(shape_predictor_tests, prediction) {
 
     ShapePredictor sp(&engine, p.buffers_preallocation_ratio);
     std::pair<bool, ov::Shape> result;
-    const auto dt_size = 4;
+    const auto dt_bitwidth = ov::element::f32.bitwidth();
 
     for (auto& shape : in_shapes)
-        result = sp.predict_preallocation_shape("dummy_name", shape, dt_size, p.can_reuse_buffer);
+        result = sp.predict_preallocation_shape("dummy_name", shape, dt_bitwidth, p.can_reuse_buffer);
 
     ASSERT_TRUE(result.first == !expected_predicted_shape.empty());
     ASSERT_EQ(result.second, expected_predicted_shape);
diff --git a/src/plugins/intel_gpu/tests/unit/passes/add_required_reorders_test.cpp b/src/plugins/intel_gpu/tests/unit/passes/add_required_reorders_test.cpp
index 4ac5f414c70664..629769d086d9d1 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/add_required_reorders_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/add_required_reorders_test.cpp
@@ -41,11 +41,11 @@ TEST(add_required_reorders, input_reorder_inside_shape_of_subgraph) {
     topology.add(data("data_0", data_0));
     topology.add(data("data_1", data_1));
     topology.add(shape_of("shape_of", input_info("input"), 4, data_types::i32));
-    topology.add(gather("gather0", input_info("shape_of"), input_info("data_0"), 0, {}, 0, true));
+    topology.add(gather("gather0", input_info("shape_of"), input_info("data_0"), 0, {}, {}, 0, true));
     topology.add(eltwise("eltwise0", {input_info("gather0"), input_info("data_1")}, eltwise_mode::prod, data_types::f32));
     topology.add(reshape("reshape0", input_info("eltwise0"), false, {},
                          ov::PartialShape{1}, reshape::reshape_mode::unsqueeze));
-    topology.add(gather("gather1", input_info("shape_of"), input_info("data_0"), 0, {}, 0, true));
+    topology.add(gather("gather1", input_info("shape_of"), input_info("data_0"), 0, {}, {}, 0, true));
     topology.add(eltwise("eltwise1", {input_info("gather1"), input_info("data_1")}, eltwise_mode::prod, data_types::f32));
     topology.add(reshape("reshape1", input_info("eltwise1"), false, {},
                          ov::PartialShape{1}, reshape::reshape_mode::unsqueeze));
diff --git a/src/plugins/intel_gpu/tests/unit/passes/mark_shape_of_subgraphs_test.cpp b/src/plugins/intel_gpu/tests/unit/passes/mark_shape_of_subgraphs_test.cpp
index 4fe7598e8af3d8..6b66075c6db26d 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/mark_shape_of_subgraphs_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/mark_shape_of_subgraphs_test.cpp
@@ -62,7 +62,7 @@ TEST(mark_shape_of_subgraphs, simple_chain) {
     topology.add(data("data_0", data_0));
     topology.add(data("data_1", data_1));
     topology.add(shape_of("shape_of", input_info("input"), data_types::i64));
-    topology.add(gather("gather", input_info("shape_of"), input_info("data_0"), 0, {}));
+    topology.add(gather("gather", input_info("shape_of"), input_info("data_0"), 0, 0, {}));
     topology.add(eltwise("eltwise", input_info("gather"), input_info("data_1"), eltwise_mode::sum));
     topology.add(concatenation("concat", {input_info("eltwise"), input_info("data_1")}, 0));
     topology.add(broadcast("broadcast", input_info("input"), input_info("concat"), {}, ov::op::BroadcastType::BIDIRECTIONAL));
@@ -103,7 +103,7 @@ TEST(mark_shape_of_subgraphs, simple_chain_w_reshape_inside_subgraph) {
     topology.add(data("data_0", data_0));
     topology.add(data("data_1", data_1));
     topology.add(shape_of("shape_of", input_info("input"), data_types::i64));
-    topology.add(gather("gather", input_info("shape_of"), input_info("data_0"), 0, {1}));
+    topology.add(gather("gather", input_info("shape_of"), input_info("data_0"), 0, 1, {1}));
     topology.add(reshape("reshape", input_info("gather"), input_info("data_1"), false, ov::PartialShape{2}));
     topology.add(broadcast("broadcast", input_info("input"), input_info("reshape"), {}, ov::op::BroadcastType::BIDIRECTIONAL));
 
@@ -129,8 +129,8 @@ TEST(mark_shape_of_subgraphs, parallel_shape_of_subgraphs) {
     topology.add(data("data_0", data_0));
     topology.add(shape_of("shape_of_0", input_info("input"), data_types::i64));
     topology.add(shape_of("shape_of_1", input_info("input"), data_types::i64));
-    topology.add(gather("gather_0", input_info("shape_of_0"), input_info("data_0"), 0, {}));
-    topology.add(gather("gather_1", input_info("shape_of_1"), input_info("data_0"), 0, {}));
+    topology.add(gather("gather_0", input_info("shape_of_0"), input_info("data_0"), 0, 0, {}));
+    topology.add(gather("gather_1", input_info("shape_of_1"), input_info("data_0"), 0, 0, {}));
     topology.add(eltwise("eltwise", input_info("gather_0"), input_info("gather_1"), eltwise_mode::sum));
     topology.add(reshape("reshape", input_info("input"), input_info("eltwise"), false, ov::PartialShape()));
 
@@ -160,9 +160,9 @@ TEST(mark_shape_of_subgraphs, parallel_shape_of_subgraphs_cascade) {
     topology.add(data("data_1", data_1));
     topology.add(data("data_2", data_2));
     topology.add(shape_of("shape_of_0", input_info("input"), data_types::i64));
-    topology.add(gather("gather_0", input_info("shape_of_0"), input_info("data_0"), 0, {1}));
+    topology.add(gather("gather_0", input_info("shape_of_0"), input_info("data_0"), 0, 1, {1}));
     topology.add(shape_of("shape_of_1", input_info("input"), data_types::i64));
-    topology.add(gather("gather_1", input_info("shape_of_1"), input_info("data_0"), 0, {1}));
+    topology.add(gather("gather_1", input_info("shape_of_1"), input_info("data_0"), 0, 1, {1}));
     topology.add(scatter_update("scatter_update_0", input_info("gather_0"), input_info("data_0"), input_info("data_0"), 0));
     topology.add(scatter_update("scatter_update_1", input_info("gather_1"), input_info("data_0"), input_info("data_0"), 0));
     topology.add(strided_slice("strided_slice_1",
@@ -171,7 +171,7 @@ TEST(mark_shape_of_subgraphs, parallel_shape_of_subgraphs_cascade) {
                                input_info("scatter_update_1"),
                                input_info("data_0"), {}, {}, {}, {}, {}, {}));
     topology.add(shape_of("shape_of_2", input_info("input"), data_types::i64));
-    topology.add(gather("gather_2", input_info("shape_of_2"), input_info("data_0"), 0, {}));
+    topology.add(gather("gather_2", input_info("shape_of_2"), input_info("data_0"), 0, 0, {}));
     topology.add(scatter_update("scatter_update_2", input_info("gather_2"), input_info("data_0"), input_info("data_0"), 0));
     topology.add(strided_slice("strided_slice_2",
                                input_info("data_1"),
@@ -207,7 +207,7 @@ TEST(mark_shape_of_subgraphs, simple_chain_w_inserted_reorder) {
     topology.add(input_layout("input", input_layout_dynamic));
     topology.add(data("data_0", data_0));
     topology.add(shape_of("shape_of", input_info("input"), data_types::i64));
-    topology.add(gather("gather", input_info("shape_of"), input_info("data_0"), 0, {1}));
+    topology.add(gather("gather", input_info("shape_of"), input_info("data_0"), 0, 1, {1}));
     topology.add(reshape("reshape", input_info("gather"), true, {}, {}));
     topology.add(reorder("reorder", input_info("reshape"), format::bfyx, data_types::f16));
     topology.add(eltwise("eltwise", input_info("reorder"), input_info("data_0"), eltwise_mode::prod));
@@ -237,7 +237,7 @@ TEST(mark_shape_of_subgraphs, concat_with_empty_tensor_inputs) {
     topology.add(input_layout("input_empty", input_layout_empty));
     topology.add(data("data_0", data_0));
     topology.add(shape_of("shape_of_01", input_info("input"), data_types::i64));
-    topology.add(gather("gather01", input_info("shape_of_01"), input_info("data_0"), 0, {1}));
+    topology.add(gather("gather01", input_info("shape_of_01"), input_info("data_0"), 0, 1, {1}));
     topology.add(shape_of("shape_of_02", input_info("input_empty"), data_types::i64));
     topology.add(shape_of("shape_of_03", input_info("input_empty"), data_types::i64));
     topology.add(concatenation("concat", {input_info("gather01"), input_info("shape_of_02"), input_info("shape_of_03")}, 0));
diff --git a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
index 1866ddb6c19870..9d7aef3e2b68bf 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp
@@ -633,13 +633,13 @@ TEST(prepare_buffer_fusing, skip_in_place_concat_inside_shape_of_subgraph) {
     topology.add(data("data_1", data_1));
     topology.add(data("data_2", data_2));
     topology.add(shape_of("shape_of", input_info("input"), 4, data_types::i32));
-    topology.add(gather("gather0", input_info("shape_of"), input_info("data_0"), 0, {}, 0, true));
+    topology.add(gather("gather0", input_info("shape_of"), input_info("data_0"), 0, 0, {}, 0, true));
     topology.add(reorder("reorder0", input_info("gather0"), format::any, data_types::f32,
                          std::vector<float>(), reorder_mean_mode::subtract, padding(), true));
     topology.add(eltwise("eltwise0", input_info("reorder0"), input_info("data_1"), eltwise_mode::prod, broadcast_spec));
     topology.add(reshape("reshape0", input_info("eltwise0"), false, {},
                          ov::PartialShape{1}, reshape::reshape_mode::unsqueeze));
-    topology.add(gather("gather1", input_info("shape_of"), input_info("data_0"), 0, {}, 0, true));
+    topology.add(gather("gather1", input_info("shape_of"), input_info("data_0"), 0, 0, {}, 0, true));
     topology.add(reorder("reorder1", input_info("gather1"), format::any, data_types::f32,
                          std::vector<float>(), reorder_mean_mode::subtract, padding(), true));
     topology.add(eltwise("eltwise1", input_info("reorder1"), input_info("data_1"), eltwise_mode::prod, broadcast_spec));
@@ -693,7 +693,7 @@ TEST(prepare_buffer_fusing, test_implicit_crop_and_outerpadding) {
     topology.add(input_layout("Input", in_input->get_layout()));
     topology.add(input_layout("Input_idx_1", input_idx1->get_layout()));
     topology.add(reorder("reorder_input", input_info("Input"), format::bfzyx, data_types::f32));
-    topology.add(gather("gather1", input_info("reorder_input"), input_info("Input_idx_1"), axis, ov::Shape{1, 6, 2, 2, 2}));
+    topology.add(gather("gather1", input_info("reorder_input"), input_info("Input_idx_1"), axis, 5, ov::Shape{1, 6, 2, 2, 2}));
     topology.add(reorder("gather1_reorder", input_info("gather1"), reorder_layout));
     topology.add(reshape("reshape1", input_info("gather1_reorder"), tensor(6, 2, 2, 2)));
     topology.add(crop("crop", input_info("reorder_input"), tensor{1, 6, 2, 2, 2}, tensor(1, 0, 0, 0, 0)));
diff --git a/src/plugins/intel_gpu/tests/unit/passes/remove_redundant_reorders_tests.cpp b/src/plugins/intel_gpu/tests/unit/passes/remove_redundant_reorders_tests.cpp
index 1d5bdd88209668..ee454a74e96635 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/remove_redundant_reorders_tests.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/remove_redundant_reorders_tests.cpp
@@ -346,13 +346,13 @@ TEST(remove_redundant_reorders, not_to_fuse_concat_with_reorder_inside_shape_of_
     topology.add(data("data_1", data_1));
     topology.add(data("data_2", data_2));
     topology.add(shape_of("shape_of", input_info("input"), 4, data_types::i32));
-    topology.add(gather("gather0", input_info("shape_of"), input_info("data_0"), 0, {}, 0, true));
+    topology.add(gather("gather0", input_info("shape_of"), input_info("data_0"), 0, {}, {}, 0, true));
     topology.add(reorder("reorder0", input_info("gather0"), format::any, data_types::f32,
                          std::vector<float>(), reorder_mean_mode::subtract, padding(), true));
     topology.add(eltwise("eltwise0", input_info("reorder0"), input_info("data_1"), eltwise_mode::prod, broadcast_spec));
     topology.add(reshape("reshape0", input_info("eltwise0"), false, {},
                          ov::PartialShape{1}, reshape::reshape_mode::unsqueeze));
-    topology.add(gather("gather1", input_info("shape_of"), input_info("data_0"), 0, {}, 0, true));
+    topology.add(gather("gather1", input_info("shape_of"), input_info("data_0"), 0, {}, {}, 0, true));
     topology.add(reorder("reorder1", input_info("gather1"), format::any, data_types::f32,
                          std::vector<float>(), reorder_mean_mode::subtract, padding(), true));
     topology.add(eltwise("eltwise1", input_info("reorder1"), input_info("data_1"), eltwise_mode::prod, broadcast_spec));
diff --git a/src/plugins/intel_gpu/tests/unit/passes/reorder_inputs_test.cpp b/src/plugins/intel_gpu/tests/unit/passes/reorder_inputs_test.cpp
index a6efbbc98a5de3..4406605784a22f 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/reorder_inputs_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/reorder_inputs_test.cpp
@@ -136,8 +136,8 @@ TEST(reorder_inputs, mixed_ranks_gather) {
                              ov::CoordinateDiff{0, 0},
                              false));
     topology.add(border("pad", { input_info("conv") }, 0, ov::CoordinateDiff{0, 0, 1, 1}, ov::CoordinateDiff{0, 0, 1, 1}));
-    topology.add(gather("gather1", input_info("pad"), input_info("data1"), 2, { 1, 2, 3, 128, 57 }, 0, false));
-    topology.add(gather("gather2", input_info("gather1"), input_info("data2"), 4, { 1, 2, 3, 128, 3, 55 }, 0, false));
+    topology.add(gather("gather1", input_info("pad"), input_info("data1"), 2, 4, { 1, 2, 3, 128, 57 }, 0, false));
+    topology.add(gather("gather2", input_info("gather1"), input_info("data2"), 4, 5, { 1, 2, 3, 128, 3, 55 }, 0, false));
     topology.add(permute("permute", input_info("gather2"), {0, 1, 2, 4, 3, 5}));
 
     ExecutionConfig config = get_test_default_config(engine);
@@ -155,10 +155,10 @@ TEST(reorder_inputs, mixed_ranks_gather) {
     auto& gather1_node = prog_impl->get_node("gather1");
     auto& gather2_node = prog_impl->get_node("gather2");
 
-    ASSERT_EQ(gather1_node.get_input_layouts()[0].format, format::bfzyx);
+    ASSERT_EQ(gather1_node.get_input_layouts()[0].format, format::bfyx);
     ASSERT_EQ(gather1_node.get_output_layout().format, format::bfzyx);
 
-    ASSERT_EQ(gather2_node.get_input_layouts()[0].format, format::bfwzyx);
+    ASSERT_EQ(gather2_node.get_input_layouts()[0].format, format::bfzyx);
     ASSERT_EQ(gather2_node.get_output_layout().format, format::bfwzyx);
 }
 
diff --git a/src/plugins/intel_gpu/tests/unit/shape_infer/gather_si_test.cpp b/src/plugins/intel_gpu/tests/unit/shape_infer/gather_si_test.cpp
index aedcfb9d4dce5c..a1852c0c4561a5 100644
--- a/src/plugins/intel_gpu/tests/unit/shape_infer/gather_si_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/shape_infer/gather_si_test.cpp
@@ -37,7 +37,7 @@ TEST_P(gather_test, shape_infer) {
 
     auto input0_layout_prim = std::make_shared<input_layout>("input0", p.in0_layout);
     auto input1_layout_prim = std::make_shared<input_layout>("input1", p.in1_layout);
-    auto gather_prim = std::make_shared<gather>("output", input_info("input0"), input_info("input1"), p.axis, ov::Shape{}, p.batch_dim);
+    auto gather_prim = std::make_shared<gather>("output", input_info("input0"), input_info("input1"), p.axis, 0, ov::Shape{}, p.batch_dim);
 
     cldnn::program prog(engine);
 
diff --git a/src/plugins/intel_gpu/tests/unit/shape_infer/random_uniform_si_test.cpp b/src/plugins/intel_gpu/tests/unit/shape_infer/random_uniform_si_test.cpp
index 6597351a0c728c..f41cafc9e2d4f9 100644
--- a/src/plugins/intel_gpu/tests/unit/shape_infer/random_uniform_si_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/shape_infer/random_uniform_si_test.cpp
@@ -67,19 +67,19 @@ TEST_P(random_uniform_si_test, shape_infer) {
                 set_values(allocated_mem, {ov::float16(val).to_bits()});
                 break;
             case data_types::f32:
-                set_values(allocated_mem, {static_cast<data_type_to_type<data_types::f32>::type>(val)});
+                set_values(allocated_mem, {static_cast<ov::element_type_traits<data_types::f32>::value_type>(val)});
                 break;
             case data_types::i32:
-                set_values(allocated_mem, {static_cast<data_type_to_type<data_types::i32>::type>(val)});
+                set_values(allocated_mem, {static_cast<ov::element_type_traits<data_types::i32>::value_type>(val)});
                 break;
             case data_types::i64:
-                set_values(allocated_mem, {static_cast<data_type_to_type<data_types::i64>::type>(val)});
+                set_values(allocated_mem, {static_cast<ov::element_type_traits<data_types::i64>::value_type>(val)});
                 break;
             case data_types::i8:
-                set_values(allocated_mem, {static_cast<data_type_to_type<data_types::i8>::type>(val)});
+                set_values(allocated_mem, {static_cast<ov::element_type_traits<data_types::i8>::value_type>(val)});
                 break;
             case data_types::u8:
-                set_values(allocated_mem, {static_cast<data_type_to_type<data_types::u8>::type>(val)});
+                set_values(allocated_mem, {static_cast<ov::element_type_traits<data_types::u8>::value_type>(val)});
                 break;
             default:
                 break;
diff --git a/src/plugins/intel_gpu/tests/unit/shape_infer/range_si_test.cpp b/src/plugins/intel_gpu/tests/unit/shape_infer/range_si_test.cpp
index 2430d628aa2c42..b079017d5c12e0 100644
--- a/src/plugins/intel_gpu/tests/unit/shape_infer/range_si_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/shape_infer/range_si_test.cpp
@@ -66,19 +66,19 @@ TEST_P(range_si_test, shape_infer) {
                     set_values(prim_mem, {ov::float16(p.vals[idx]).to_bits()});
                     break;
                 case data_types::f32:
-                    set_values(prim_mem, {static_cast<data_type_to_type<data_types::f32>::type>(p.vals[idx])});
+                    set_values(prim_mem, {static_cast<ov::element_type_traits<data_types::f32>::value_type>(p.vals[idx])});
                     break;
                 case data_types::i32:
-                    set_values(prim_mem, {static_cast<data_type_to_type<data_types::i32>::type>(p.vals[idx])});
+                    set_values(prim_mem, {static_cast<ov::element_type_traits<data_types::i32>::value_type>(p.vals[idx])});
                     break;
                 case data_types::i64:
-                    set_values(prim_mem, {static_cast<data_type_to_type<data_types::i64>::type>(p.vals[idx])});
+                    set_values(prim_mem, {static_cast<ov::element_type_traits<data_types::i64>::value_type>(p.vals[idx])});
                     break;
                 case data_types::i8:
-                    set_values(prim_mem, {static_cast<data_type_to_type<data_types::i8>::type>(p.vals[idx])});
+                    set_values(prim_mem, {static_cast<ov::element_type_traits<data_types::i8>::value_type>(p.vals[idx])});
                     break;
                 case data_types::u8:
-                    set_values(prim_mem, {static_cast<data_type_to_type<data_types::u8>::type>(p.vals[idx])});
+                    set_values(prim_mem, {static_cast<ov::element_type_traits<data_types::u8>::value_type>(p.vals[idx])});
                     break;
                 default:
                     break;
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/canonicalization_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/canonicalization_gpu_test.cpp
index 400152887e6ce9..933161185c78a8 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/canonicalization_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/canonicalization_gpu_test.cpp
@@ -220,7 +220,7 @@ TEST(canonicalization, gather) {
         topology.add(input_layout("data", data_layout));
         topology.add(input_layout("indices", indices_layout));
         topology.add(gather("gather", input_info("data"), input_info("indices"), params.second.axis,
-                            ov::Shape{}, params.second.batch_dim, params.second.support_neg_ind));
+                            0, ov::Shape{}, params.second.batch_dim, params.second.support_neg_ind));
 
         canonicalization_test(topology, "gather", std::get<1>(params.first), std::get<2>(params.first));
     }
@@ -254,9 +254,9 @@ TEST(canonicalization, fusing_gather_eltwise) {
         topology.add(input_layout("indices_second", indices_layout_second));
         topology.add(input_layout("data", input_mul_layout));
         topology.add(gather("gather_first", input_info("input"), input_info("indices_first"), shapes.second.axis,
-                            shapes.second.out_shape, shapes.second.batch_dim, shapes.second.support_neg_ind));
+                            shapes.second.data_shape.rank().get_length(), shapes.second.out_shape, shapes.second.batch_dim, shapes.second.support_neg_ind));
         topology.add(gather("gather_second", input_info("input"), input_info("indices_second"), shapes.second.axis,
-                            shapes.second.out_shape, shapes.second.batch_dim, shapes.second.support_neg_ind));
+                            shapes.second.data_shape.rank().get_length(), shapes.second.out_shape, shapes.second.batch_dim, shapes.second.support_neg_ind));
         topology.add(eltwise("mul", {input_info("gather_first"), input_info("data")}, eltwise_mode::prod));
         topology.add(eltwise("add", {input_info("gather_second"), input_info("mul")}, eltwise_mode::sum));
         topology.add(reorder("out_reorder", input_info("add"), format::bfyx, data_types::f32));
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/condition_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/condition_gpu_test.cpp
index b42241b23f1e99..fda7c1c41e5c12 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/condition_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/condition_gpu_test.cpp
@@ -28,6 +28,7 @@ bool is_output_equal(const cldnn::memory::ptr mem, const std::vector<T>& ref)
     return true;
 }
 
+
 topology generate_simple_branch (bool branch_true_false, const primitive_id& id, const primitive_id& input_id, const data_types dt = data_types::f32)
 {
     topology branch;
@@ -86,14 +87,14 @@ class condition_gpu_basic_test : public ::testing::Test {
         condition::branch branch_true;
         {
             cldnn::topology branch_true_topology   = generate_simple_branch(true,  cond_id, branch_input_id, dat_dt);
-            branch_true.inner_program = program::build_program(engine, branch_true_topology, config, true);
+            branch_true.inner_program = program::build_program(engine, branch_true_topology, config, false, false, true);
             branch_true.input_map.insert({input_id, branch_input_id});
             branch_true.output_map.insert({0, "condi_when_true"});
         }
         condition::branch branch_false;
         {
             cldnn::topology branch_false_topology  = generate_simple_branch(false, cond_id, branch_input_id, dat_dt);
-            branch_false.inner_program = program::build_program(engine, branch_false_topology, config, true);
+            branch_false.inner_program = program::build_program(engine, branch_false_topology, config, false, false, true);
             branch_false.input_map.insert({input_id, branch_input_id});
             branch_false.output_map.insert({0, "condi_when_false"});
         }
@@ -178,14 +179,14 @@ TEST(condition_gpu, basic_range_equal_comp) {
     condition::branch branch_true;
     {
         cldnn::topology branch_true_topology  = generate_simple_branch(true,  condi_id, branch_input_id);
-        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, true);
+        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, false, false, true);
         branch_true.input_map.insert({concat_id, branch_input_id});
         branch_true.output_map.insert({0, "condi_when_true"});
     }
     condition::branch branch_false;
     {
         cldnn::topology branch_false_topology = generate_simple_branch(false, condi_id, branch_input_id);
-        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, true);
+        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, false, false, true);
         branch_false.input_map.insert({concat_id, branch_input_id});
         branch_false.output_map.insert({0, "condi_when_false"});
     }
@@ -258,8 +259,8 @@ TEST(condition_gpu, basic_stacked_ifs) {
     auto predicate2 = engine.allocate_memory({ data_types::f32, format::bfyx,{ 1, 1, 1, 1 } });
 
     primitive_id input_id           = "input";
-    primitive_id pred_id         = "predicate";
-    primitive_id predicate2_id        = "predicate2";
+    primitive_id pred_id            = "predicate";
+    primitive_id predicate2_id      = "predicate2";
     primitive_id branch_input_id    = "branch_input";
     primitive_id cond_id            = "condi";
     primitive_id cond2_id           = "condi2";
@@ -280,22 +281,22 @@ TEST(condition_gpu, basic_stacked_ifs) {
     );
 
     condition::branch branch_condi_1_true;
-    branch_condi_1_true.inner_program = program::build_program(engine, condi_1_true, config, true);
+    branch_condi_1_true.inner_program = program::build_program(engine, condi_1_true, config, false, false, true);
     branch_condi_1_true.input_map.insert({input_id, branch_input_id});
     branch_condi_1_true.output_map.insert({0, "condi_when_true"});
 
     condition::branch branch_condi_1_false;
-    branch_condi_1_false.inner_program = program::build_program(engine, condi_1_false, config, true);
+    branch_condi_1_false.inner_program = program::build_program(engine, condi_1_false, config, false, false, true);
     branch_condi_1_false.input_map.insert({input_id, branch_input_id});
     branch_condi_1_false.output_map.insert({0, "condi_when_false"});
 
     condition::branch branch_condi_2_true;
-    branch_condi_2_true.inner_program = program::build_program(engine, condi_2_true, config, true);
+    branch_condi_2_true.inner_program = program::build_program(engine, condi_2_true, config, false, false, true);
     branch_condi_2_true.input_map.insert({cond_id, branch_input_id});
     branch_condi_2_true.output_map.insert({0, "activ_when_true"});
 
     condition::branch branch_condi_2_false;
-    branch_condi_2_false.inner_program = program::build_program(engine, condi_2_false, config, true);
+    branch_condi_2_false.inner_program = program::build_program(engine, condi_2_false, config, false, false, true);
     branch_condi_2_false.input_map.insert({cond_id, branch_input_id});
     branch_condi_2_false.output_map.insert({0, "activ_when_false"});
 
@@ -373,7 +374,7 @@ TEST(condition_gpu, basic_nested_ifs) {
             data("scale_5_data", scale_5_mem),
             eltwise("scale_5", { input_info("branch_input1"), input_info("scale_5_data") }, eltwise_mode::prod)
         );
-        nested_true.inner_program = program::build_program(engine, nested_true_topology, config, true);
+        nested_true.inner_program = program::build_program(engine, nested_true_topology, config, false, false, true);
         nested_true.input_map.insert({"pooling_when_true", "branch_input1"});
         nested_true.output_map.insert({0, "scale_5"});
     }
@@ -385,7 +386,7 @@ TEST(condition_gpu, basic_nested_ifs) {
             data("scale_10_data", scale_10_mem),
             eltwise("scale_10", { input_info("branch_input2"), input_info("scale_10_data") }, eltwise_mode::prod)
         );
-        nested_false.inner_program = program::build_program(engine, nested_false_topology, config, true);
+        nested_false.inner_program = program::build_program(engine, nested_false_topology, config, false, false, true);
         nested_false.input_map.insert({"pooling_when_true", "branch_input2"});
         nested_false.output_map.insert({0, "scale_10"});
     }
@@ -399,7 +400,7 @@ TEST(condition_gpu, basic_nested_ifs) {
             input_layout("predicate2", predicate2->get_layout()),
             condition( "condi_nested", {input_info("predicate2"), input_info("pooling_when_true")}, nested_true, nested_false)
         );
-        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, true);
+        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, false, false, true);
         branch_true.input_map.insert({"input", "branch_input3"});
         branch_true.output_map.insert({0, "condi_nested"});
     }
@@ -411,7 +412,7 @@ TEST(condition_gpu, basic_nested_ifs) {
             input_layout("branch_input4", { data_types::f32, format::bfyx,{ 1, 1, 4, 1 } }),
             pooling("pooling_when_false", input_info("branch_input4"), cldnn::pooling_mode::average, { 1, 2 }, { 1, 2 })
         );
-        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, true);
+        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, false, false, true);
         branch_false.input_map.insert({"input", "branch_input4"});
         branch_false.output_map.insert({0, "pooling_when_false"});
     }
@@ -460,21 +461,21 @@ TEST(condition_gpu, negative_predicate_wrong_layout) {
     auto predicate = engine.allocate_memory({ data_types::f32, format::bfyx,{ 1, 1, 5, 1 } });
 
     primitive_id input_id           = "input";
-    primitive_id pred_id         = "predicate";
+    primitive_id pred_id            = "predicate";
     primitive_id branch_input_id    = "branch_input";
     primitive_id cond_id            = "condi";
 
     condition::branch branch_true;
     {
         cldnn::topology branch_true_topology   = generate_simple_branch(true,  cond_id, branch_input_id, data_types::f32);
-        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, true);
+        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, false, false, true);
         branch_true.input_map.insert({input_id, branch_input_id});
         branch_true.output_map.insert({0, "condi_when_true"});
     }
     condition::branch branch_false;
     {
         cldnn::topology branch_false_topology  = generate_simple_branch(false, cond_id, branch_input_id, data_types::f32);
-        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, true);
+        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, false, false, true);
         branch_false.input_map.insert({input_id, branch_input_id});
         branch_false.output_map.insert({0, "condi_when_false"});
     }
@@ -501,7 +502,7 @@ TEST(condition_gpu, negative_not_same_layouts) {
     auto predicate = engine.allocate_memory({ data_types::u8, format::bfyx,{ 1, 1, 1, 1 } });
 
     primitive_id input_id           = "input";
-    primitive_id pred_id         = "predicate";
+    primitive_id pred_id            = "predicate";
     primitive_id branch_input_id    = "branch_input";
     primitive_id cond_id            = "condi";
 
@@ -513,7 +514,7 @@ TEST(condition_gpu, negative_not_same_layouts) {
             input_layout(branch_input_id, { data_types::f32, format::bfyx,{ 1, 1, 4, 1 } }),
             pooling(pool_id, input_info(branch_input_id), cldnn::pooling_mode::max, { 1, 2 }, { 1, 2 })
         );
-        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, true);
+        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, false, false, true);
         branch_true.input_map.insert({input_id, branch_input_id});
         branch_true.output_map.insert({0, pool_id});
     }
@@ -526,7 +527,7 @@ TEST(condition_gpu, negative_not_same_layouts) {
             input_layout(branch_input_id, { data_types::f32, format::bfyx,{ 1, 1, 4, 1 } }),
             pooling(pool_id, input_info(branch_input_id), cldnn::pooling_mode::max, { 1, 4 }, { 1, 4 })
         );
-        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, true);
+        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, false, false, true);
         branch_false.input_map.insert({input_id, branch_input_id});
         branch_false.output_map.insert({0, pool_id});
     }
@@ -566,7 +567,7 @@ TEST(condition_gpu, negative_same_names_within_different_networks) {
             input_layout(branch_input_id, { data_types::f32, format::bfyx,{ 1, 1, 4, 1 } }),
             pooling(duplicated_id, input_info(branch_input_id), cldnn::pooling_mode::max, { 2, 1 }, { 2, 1 })
         );
-        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, true);
+        branch_true.inner_program = program::build_program(engine, branch_true_topology, config, false, false, true);
         branch_true.input_map.insert({input_id, branch_input_id});
         branch_true.output_map.insert({0, duplicated_id});
     }
@@ -578,7 +579,7 @@ TEST(condition_gpu, negative_same_names_within_different_networks) {
             input_layout(branch_input_id, { data_types::f32, format::bfyx,{ 1, 1, 4, 1 } }),
             pooling("pooling_when_false", input_info(branch_input_id), cldnn::pooling_mode::max, { 2, 1 }, { 2, 1 })
         );
-        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, true);
+        branch_false.inner_program = program::build_program(engine, branch_false_topology, config, false, false, true);
         branch_false.input_map.insert({input_id, branch_input_id});
         branch_false.output_map.insert({0, "pooling_when_false"});
     }
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp
index c1309b720daf16..5da00e8fb739f3 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp
@@ -2482,7 +2482,7 @@ TEST(eltwise_gpu_int, div_gather_fusing) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(input_layout("Input3", input3->get_layout()));
-    topology.add(gather("gather", input_info("InputDictionary"), input_info("InputText"), 0, ov::Shape{2, 2, 2, 2}));
+    topology.add(gather("gather", input_info("InputDictionary"), input_info("InputText"), 0, 4, ov::Shape{2, 2, 2, 2}));
     topology.add(reorder("gather_reorder", input_info("gather"), { data_types::i32, format::bfyx, { 2, 2, 2, 2 } }));
     topology.add(eltwise("eltwise",
                  { input_info("gather_reorder"), input_info("Input3") },
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
index 71301447bb28b9..dc23440c48af67 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
@@ -663,21 +663,22 @@ TEST(fully_connected_gpu, compressed_scale_zp_bias) {
     auto& engine = get_test_engine();
 
     auto input_mem = engine.allocate_memory({ {1, 2, 4}, data_types::f32, format::bfyx });
-    auto weights_mem = engine.allocate_memory({ {8, 4}, data_types::f32, format::bfyx });
+    auto weights_mem = engine.allocate_memory({ {8, 4}, data_types::u8, format::bfyx });
     auto bias_mem = engine.allocate_memory({ {1, 1, 8}, data_types::f32, format::bfyx });
-    auto scale_mem = engine.allocate_memory({ {1, 1, 8}, data_types::f32, format::bfyx });
-    auto zp_mem = engine.allocate_memory({ {1, 1, 8}, data_types::f32, format::bfyx });
+    auto scale_mem = engine.allocate_memory({ {8, 1}, data_types::f32, format::bfyx });
+    auto zp_mem = engine.allocate_memory({ {8, 1}, data_types::f32, format::bfyx });
 
     set_values(input_mem, { -0.5f, 2.0f, 0.5f, 1.0f,
                             0.5f, -2.0f, -0.5f, -1.0f });
-    set_values(weights_mem, { 1.5f,  1.0f,  0.5f, -1.0f,
-                              0.0f,  0.5f,  0.5f, -0.5f,
-                             -2.0f, -0.5f,  1.0f,  1.5f,
-                             -2.0f, -0.5f,  1.0f,  1.5f,
-                              2.0f,  0.5f, -1.0f, -1.5f,
-                              2.0f,  0.5f, -1.0f, -1.5f,
-                             -1.5f, -1.0f, -0.5f,  1.0f,
-                              0.0f, -0.5f, 0.5f, 0.5f });
+    set_values<uint8_t>(weights_mem, { 1, 2, 3, 4,
+                                       5, 6, 7, 8,
+                                       9, 10, 11, 12,
+                                       13, 14, 15, 0,
+                                       15, 14, 13, 12,
+                                       11, 10, 9, 8,
+                                       7, 6, 5, 4,
+                                       3, 2, 1, 0});
+
 
     set_values(bias_mem, { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, 2.0f  });
     set_values(scale_mem, { 2.0f, 4.0f, -2.0f, -4.0f, 0.5f, -0.5f, 2.0f, 2.0f  });
@@ -709,8 +710,7 @@ TEST(fully_connected_gpu, compressed_scale_zp_bias) {
     ov::PartialShape expected_shape{1, 2, 8};
     ASSERT_EQ(expected_shape, output_mem->get_layout().get_partial_shape());
 
-    std::vector<float> expected_result = {-4.0f, -23.0f, 11.0f, 0.0f, -2.0f, -3.5f, -30.0f, -10.5f,
-                                          6.0f, 19.0f, -5.0f, -8.0f, 12.0f, -8.5f, 44.0f, 14.5f};
+    std::vector<float> expected_result = {13.f, 58.f, -51.f, -108.f, 18.5f, -18.f, 1.f, -4.f, -11.f, -62.f, 57.f, 100.f, -8.5f, 6.f, 13.f, 8.f, };
 
     for (size_t i = 0; i < expected_result.size(); i++) {
         ASSERT_EQ(expected_result[i], output_ptr[i]) << "i = " << i;
@@ -721,20 +721,20 @@ TEST(fully_connected_gpu, compressed_scale_bias) {
     auto& engine = get_test_engine();
 
     auto input_mem = engine.allocate_memory({ {1, 2, 4}, data_types::f32, format::bfyx });
-    auto weights_mem = engine.allocate_memory({ {8, 4}, data_types::f32, format::bfyx });
+    auto weights_mem = engine.allocate_memory({ {8, 4}, data_types::u8, format::bfyx });
     auto bias_mem = engine.allocate_memory({ {1, 1, 8}, data_types::f32, format::bfyx });
     auto scale_mem = engine.allocate_memory({ {1, 1, 8}, data_types::f32, format::bfyx });
 
     set_values(input_mem, { -0.5f, 2.0f, 0.5f, 1.0f,
                             0.5f, -2.0f, -0.5f, -1.0f });
-    set_values(weights_mem, { 1.5f,  1.0f,  0.5f, -1.0f,
-                              0.0f,  0.5f,  0.5f, -0.5f,
-                             -2.0f, -0.5f,  1.0f,  1.5f,
-                             -2.0f, -0.5f,  1.0f,  1.5f,
-                              2.0f,  0.5f, -1.0f, -1.5f,
-                              2.0f,  0.5f, -1.0f, -1.5f,
-                             -1.5f, -1.0f, -0.5f,  1.0f,
-                              0.0f, -0.5f, 0.5f, 0.5f });
+    set_values<uint8_t>(weights_mem, { 1, 2, 3, 4,
+                                       5, 6, 7, 8,
+                                       9, 10, 11, 12,
+                                       13, 14, 15, 0,
+                                       15, 14, 13, 12,
+                                       11, 10, 9, 8,
+                                       7, 6, 5, 4,
+                                       3, 2, 1, 0});
 
     set_values(bias_mem, { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f });
     set_values(scale_mem, { 2.0f, 4.0f, -2.0f, -4.0f, 0.5f, -0.5f, 2.0f, 1.0f });
@@ -764,8 +764,7 @@ TEST(fully_connected_gpu, compressed_scale_bias) {
     ov::PartialShape expected_shape{1, 2, 8};
     ASSERT_EQ(expected_shape, output_mem->get_layout().get_partial_shape());
 
-    std::vector<float> expected_result = {2.0f, 1.0f, -1.0f, -12.0f, 4.0f, -5.0f, 6.0f, -8.25f,
-                                          0.0f, -5.0f, 7.0f, 4.0f, 6.0f, -7.0f, 8.0f, -7.75f};
+    std::vector<float> expected_result = {19.f, 40.f, 69.f, 54.f, 83.f, 48.f, 37.f, -2.f, -17.f, -44.f, -63.f, -62.f, -73.f, -60.f, -23.f, -14.f };
 
     for (size_t i = 0; i < expected_result.size(); i++) {
         ASSERT_EQ(expected_result[i], output_ptr[i]) << "i = " << i;
@@ -776,19 +775,19 @@ TEST(fully_connected_gpu, compressed_scale_fp16) {
     auto& engine = get_test_engine();
 
     auto input_mem = engine.allocate_memory({ { 2, 4}, data_types::f16, format::bfyx });
-    auto weights_mem = engine.allocate_memory({ {8, 4}, data_types::f16, format::bfyx });
-    auto scale_mem = engine.allocate_memory({ {1, 8}, data_types::f16, format::bfyx });
+    auto weights_mem = engine.allocate_memory({ {8, 4}, data_types::u8, format::bfyx });
+    auto scale_mem = engine.allocate_memory({ {8, 1}, data_types::f16, format::bfyx });
 
     set_values<ov::float16>(input_mem, { ov::float16(-0.5f), ov::float16(2.0f),  ov::float16(0.5f),  ov::float16(1.0f),
                                      ov::float16(0.5f),  ov::float16(-2.0f), ov::float16(-0.5f), ov::float16(-1.0f) });
-    set_values<ov::float16>(weights_mem, {ov::float16( 1.5f), ov::float16( 1.0f), ov::float16( 0.5f), ov::float16(-1.0f),
-                                      ov::float16( 0.0f), ov::float16( 0.5f), ov::float16( 0.5f), ov::float16(-0.5f),
-                                      ov::float16(-2.0f), ov::float16(-0.5f), ov::float16( 1.0f), ov::float16( 1.5f),
-                                      ov::float16(-2.0f), ov::float16(-0.5f), ov::float16( 1.0f), ov::float16( 1.5f),
-                                      ov::float16( 2.0f), ov::float16( 0.5f), ov::float16(-1.0f), ov::float16(-1.5f),
-                                      ov::float16( 2.0f), ov::float16( 0.5f), ov::float16(-1.0f), ov::float16(-1.5f),
-                                      ov::float16(-1.5f), ov::float16(-1.0f), ov::float16(-0.5f), ov::float16( 1.0f),
-                                      ov::float16( 0.0f), ov::float16(-0.5f), ov::float16(0.5f),  ov::float16( 0.5f) });
+    set_values<uint8_t>(weights_mem, { 1, 2, 3, 4,
+                                       5, 6, 7, 8,
+                                       9, 10, 11, 12,
+                                       13, 14, 15, 0,
+                                       15, 14, 13, 12,
+                                       11, 10, 9, 8,
+                                       7, 6, 5, 4,
+                                       3, 2, 1, 0});
 
     set_values<ov::float16>(scale_mem, {ov::float16(2.0f), ov::float16(4.0f), ov::float16(-2.0f), ov::float16(-4.0f), ov::float16(0.5f), ov::float16(-0.5f), ov::float16(2.0f), ov::float16(2.0f)});
 
@@ -817,8 +816,8 @@ TEST(fully_connected_gpu, compressed_scale_fp16) {
     ASSERT_EQ(expected_shape, output_mem->get_layout().get_partial_shape());
 
    std::vector<ov::float16> expected_result = {
-        ov::float16(1.0f), ov::float16( 3.0f), ov::float16(-4.0f), ov::float16(-8.0f), ov::float16(-1.0f), ov::float16( 1.0f), ov::float16(-1.0f), ov::float16(-0.5f),
-        ov::float16(-1.0f), ov::float16(-3.0f), ov::float16( 4.0f), ov::float16( 8.0f), ov::float16( 1.0f), ov::float16(-1.0f), ov::float16( 1.0f), ov::float16( 0.5f)};
+       ov::float16(18), ov::float16(84), ov::float16(-66), ov::float16(-116), ov::float16(19.5), ov::float16(-13.5), ov::float16(30), ov::float16(6),
+       ov::float16(-18), ov::float16(-84), ov::float16(66), ov::float16(116), ov::float16(-19.5), ov::float16(13.5), ov::float16(-30), ov::float16(-6) };
 
     for (size_t i = 0; i < expected_result.size(); i++) {
         ASSERT_FLOAT_EQ(expected_result[i], output_ptr[i]) << "i = " << i;
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp
index bbef9e78912d5b..75465b89ae51ff 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp
@@ -88,6 +88,7 @@ class gather8_test : public ::testing::TestWithParam<gather8_test_param> {
                                 input_info("reorder0"),
                                 input_info("reorder1"),
                                 axis,
+                                shape_in[0].size(),
                                 ov::Shape(shape_out.begin(), shape_out.end()),
                                 batch_dim,
                                 true));
@@ -108,7 +109,7 @@ class gather8_test : public ::testing::TestWithParam<gather8_test_param> {
         planar_topo.add(input_layout("input0", input0->get_layout()));
         planar_topo.add(input_layout("input1", input1->get_layout()));
         planar_topo.add(
-            gather("gather", input_info("input0"), input_info("input1"), axis, ov::Shape(shape_out.begin(), shape_out.end()), batch_dim, true));
+            gather("gather", input_info("input0"), input_info("input1"), axis, shape_in[0].size(), ov::Shape(shape_out.begin(), shape_out.end()), batch_dim, true));
         network planar_network(engine, planar_topo, get_test_default_config(engine));
         planar_network.set_input_data("input0", input0);
         planar_network.set_input_data("input1", input1);
@@ -408,7 +409,7 @@ TEST(gather8_gpu_fp16, d323_axisY_bdim_m1) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 2, 3, 3, 2}, batch_dim, negative_indexes)
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 5, ov::Shape{3, 2, 3, 3, 2}, batch_dim, negative_indexes)
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -515,7 +516,7 @@ TEST(gather7_gpu_fp16, d222_axisX_bdim_m1) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2, 2, 2}, batch_dim)
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 6, ov::Shape{2, 2, 2, 2, 2, 2}, batch_dim)
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -626,7 +627,7 @@ TEST(gather7_gpu_fp16, d323_axisY_bdim_m1) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 2, 3, 3, 2}, batch_dim)
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 5, ov::Shape{3, 2, 3, 3, 2}, batch_dim)
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -730,7 +731,7 @@ TEST(gather7_gpu_fp16, d44_axisY_bdim1) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{4, 3, 4, 1, 1, 1}, batch_dim)
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{4, 3, 4, 1, 1, 1}, batch_dim)
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -805,7 +806,7 @@ TEST(gather7_gpu_fp16, d32_axisF_bdim_m1) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 2, 1, 1}, batch_dim)
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{3, 2, 1, 1}, batch_dim)
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -868,7 +869,7 @@ TEST(gather7_gpu_fp16, d32_axisF_bdim1) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 2, 1, 1}, batch_dim)
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{3, 2, 1, 1}, batch_dim)
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -930,7 +931,7 @@ TEST(gather7_gpu_fp16, d32_axisF_bdim0) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 3, 2, 1}, batch_dim)
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{3, 3, 2, 1}, batch_dim)
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -998,7 +999,7 @@ TEST(gather_gpu_fp16, d14_axisB) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{1, 4, 2, 1})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{1, 4, 2, 1})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1060,7 +1061,7 @@ TEST(gather_gpu_fp16, d222_axisB) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1121,7 +1122,7 @@ TEST(gather_gpu_fp16, d22_axisY) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1182,7 +1183,7 @@ TEST(gather_gpu_fp16, d22_axisF) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1240,7 +1241,7 @@ TEST(gather_gpu_fp32, d14_axisB) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{1, 4, 2, 1})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{1, 4, 2, 1})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1301,7 +1302,7 @@ TEST(gather_gpu_fp32, d222_axisB) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1362,7 +1363,7 @@ TEST(gather_gpu_fp32, d22_axisY) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1423,7 +1424,7 @@ TEST(gather_gpu_fp32, d22_axisF) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1484,7 +1485,7 @@ TEST(gather_gpu_int32, d22_axisF) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1542,7 +1543,7 @@ TEST(gather_gpu_int32, d14_axisB) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{1, 4, 2, 1})
+            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{1, 4, 2, 1})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1603,7 +1604,7 @@ TEST(gather_gpu_int32, d222_axisB) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1664,7 +1665,7 @@ TEST(gather_gpu_int32, d22_axisY) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 2, 2})
+            gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 2, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1728,7 +1729,7 @@ TEST(gather_gpu_fp32, d41_axisB) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{4, 1, 2, 3})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{4, 1, 2, 3})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1791,7 +1792,7 @@ TEST(gather_gpu_fp32, d41_axisF) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 4, 1, 2})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 4, 1, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1850,7 +1851,7 @@ TEST(gather_gpu_fp32, d2_axisX) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{2, 2, 1, 2})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{2, 2, 1, 2})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1900,7 +1901,7 @@ TEST(gather_gpu_fp32, 322_axisF) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 2, 2, 1})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{3, 2, 2, 1})
     );
 
     network network(engine, topology, get_test_default_config(engine));
@@ -1940,7 +1941,7 @@ TEST(gather_gpu_fp32, dynamic_322_axisF) {
     topology topology;
     topology.add(input_layout("input1", in1_layout));
     topology.add(input_layout("input2", in2_layout));
-    topology.add(gather("gather", input_info("input1"), input_info("input2"), axis, ov::Shape{}));
+    topology.add(gather("gather", input_info("input1"), input_info("input2"), axis, 0, ov::Shape{}));
 
     ExecutionConfig config = get_test_default_config(engine);
     config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
@@ -1983,7 +1984,7 @@ TEST(gather_gpu_fp32, indice_out_of_bound) {
     topology topology;
     topology.add(input_layout("input1", in1_layout));
     topology.add(input_layout("input2", in2_layout));
-    topology.add(gather("gather", input_info("input1"), input_info("input2"), axis, ov::Shape{}, 0, true));
+    topology.add(gather("gather", input_info("input1"), input_info("input2"), axis, 0, ov::Shape{}, 0, true));
 
     ExecutionConfig config = get_test_default_config(engine);
     config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
@@ -2021,7 +2022,7 @@ TEST(gather_cpu_impl_fp32, dynamic_322_axisF) {
     topology topology;
     topology.add(input_layout("input1", in1_layout));
     topology.add(input_layout("input2", in2_layout));
-    topology.add(gather("gather", input_info("input1"), input_info("input2"), axis, ov::Shape{}));
+    topology.add(gather("gather", input_info("input1"), input_info("input2"), axis, 0, ov::Shape{}));
 
     auto config = get_test_default_config(engine);
     config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
@@ -2071,7 +2072,7 @@ void test_gather_gpu_u8_322_axisF(bool is_caching_test) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 2, 2, 1}));
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{3, 2, 2, 1}));
 
     cldnn::network::ptr network = get_network(engine, topology, get_test_default_config(engine), get_test_stream_ptr(), is_caching_test);
 
@@ -2121,7 +2122,7 @@ TEST(gather_single_axis, simple_Baxis) {
     topology.add(input_layout("InputDictionary", input1->get_layout()));
     topology.add(input_layout("InputText", input2->get_layout()));
     topology.add(
-        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{1, 2, 2, 1})
+        gather("gather", input_info("InputDictionary"), input_info("InputText"), axis, 4, ov::Shape{1, 2, 2, 1})
     );
     topology.add(reorder("reorder", input_info("gather"), format::bfyx, data_types::i8));
 
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/group_normalization_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/group_normalization_gpu_test.cpp
new file mode 100644
index 00000000000000..a13c1d1550882f
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/group_normalization_gpu_test.cpp
@@ -0,0 +1,146 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils.h"
+#include "random_generator.hpp"
+#include <intel_gpu/primitives/input_layout.hpp>
+#include <intel_gpu/primitives/group_normalization.hpp>
+#include "openvino/reference/group_normalization.hpp"
+#include "compilation_context.hpp"
+
+
+using namespace cldnn;
+using namespace ::tests;
+
+namespace {
+
+typedef std::tuple<
+std::vector<std::int32_t>,  // Input shape
+std::size_t,                // Number of groups
+double,                     // Epsilon
+format                      // First input layout
+>
+GroupNormalizationParams;
+
+class GroupNormalizationGPUTest : public ::testing::TestWithParam<GroupNormalizationParams> {
+public:
+    GroupNormalizationGPUTest() = default;
+
+    void SetUp() override {
+        std::vector<std::int32_t> input_shape;
+        const auto& params = GetParam();
+        std::tie(input_shape, num_groups_, epsilon_, format_) = params;
+        std::copy(std::begin(input_shape), std::end(input_shape), std::back_inserter(data_shape_));
+        tests::random_generator rg{"GroupNormalizationGPUTest"};
+        data_ = rg.generate_random_1d<float>(ov::shape_size(input_shape), -1, 1);
+        scale_ = rg.generate_random_1d<float>(input_shape[1], -1, 1);
+        bias_ = rg.generate_random_1d<float>(input_shape[1], -1, 1);
+        const auto planar_format = format::dimension(format_) == 4 ? format::bfyx : format::bfzyx;
+
+        topology tp;
+        auto &engine = get_test_engine();
+        data_layout_ = layout{data_types::f32, planar_format, tensor{input_shape}};
+        scale_bias_layout_ = layout{data_types::f32, planar_format, tensor{1,
+            static_cast<std::int32_t>(scale_.size()), 1, 1}};
+
+        primitive_id reordered_data_primitive = data_primitive_ + "_reordered";
+        tp.add(input_layout{data_primitive_, data_layout_});
+        tp.add(input_layout{scale_primitive_, scale_bias_layout_});
+        tp.add(input_layout{bias_primitive_, scale_bias_layout_});
+        tp.add(reorder{reordered_data_primitive, data_primitive_, format_, data_types::f32});
+
+        auto g = group_normalization{
+            "group_normalization_output",
+            input_info{reordered_data_primitive},
+            input_info{scale_primitive_},
+            input_info{bias_primitive_},
+            static_cast<std::int64_t>(num_groups_),
+            epsilon_
+        };
+        tp.add(g);
+        tp.add(reorder{"output", input_info("group_normalization_output"), planar_format, data_types::f32});
+
+        network_ = std::make_shared<cldnn::network>(engine, tp, get_test_default_config(engine));
+    }
+
+    void Test() {
+        auto &engine = get_test_engine();
+        auto data_gpu_mem = engine.allocate_memory(data_layout_);
+        auto scale_gpu_mem = engine.allocate_memory(scale_bias_layout_);
+        auto bias_gpu_mem = engine.allocate_memory(scale_bias_layout_);
+        set_values(data_gpu_mem, data_);
+        set_values(scale_gpu_mem, scale_);
+        set_values(bias_gpu_mem, bias_);
+        network_->set_input_data(data_primitive_, data_gpu_mem);
+        network_->set_input_data(scale_primitive_, scale_gpu_mem);
+        network_->set_input_data(bias_primitive_, bias_gpu_mem);
+        auto outputs = network_->execute();
+        auto output = outputs.at("output").get_memory();
+        cldnn::mem_lock<float> output_gpu_mem(output, get_test_stream());
+
+        std::vector<float> reference_output(data_.size());
+        ov::reference::group_normalization(data_.data(), scale_.data(), bias_.data(), reference_output.data(),
+                                           ov::Shape{data_shape_}, num_groups_, epsilon_);
+
+        ASSERT_EQ(output_gpu_mem.size(), reference_output.size());
+        for (std::size_t i = 0; i < reference_output.size(); i++) {
+            ASSERT_NEAR(output_gpu_mem[i], reference_output[i], 0.0001);
+        }
+    }
+
+private:
+    std::vector<float> data_{};
+    std::vector<float> scale_{};
+    std::vector<float> bias_{};
+    std::size_t num_groups_{};
+    double epsilon_{};
+    format format_{format::any};
+    network::ptr network_{};
+    layout data_layout_{};
+    layout scale_bias_layout_{};
+    std::vector<std::size_t> data_shape_;
+    static const primitive_id data_primitive_;
+    static const primitive_id scale_primitive_;
+    static const primitive_id bias_primitive_;
+};
+
+const primitive_id GroupNormalizationGPUTest::data_primitive_{"data"};
+const primitive_id GroupNormalizationGPUTest::scale_primitive_{"scale"};
+const primitive_id GroupNormalizationGPUTest::bias_primitive_{"bias"};
+
+TEST_P(GroupNormalizationGPUTest, blocked_layouts_support) {
+    Test();
+}
+
+const std::vector<cldnn::format> f_blocked_4d_formats {
+    format::b_fs_yx_fsv2,
+    format::b_fs_yx_fsv4,
+    format::b_fs_yx_fsv16,
+    format::b_fs_yx_fsv32,
+};
+
+const std::vector<cldnn::format> f_blocked_5d_formats {
+    format::b_fs_zyx_fsv2,
+    format::b_fs_zyx_fsv4,
+    format::b_fs_zyx_fsv16,
+    format::b_fs_zyx_fsv32,
+};
+
+INSTANTIATE_TEST_SUITE_P(
+    GroupNormalizationGPUTest_blocked_layouts_support_4d, GroupNormalizationGPUTest,
+    ::testing::Combine(
+        ::testing::Values(std::vector<int32_t>{3, 64, 32, 64}),
+        ::testing::Values(4),
+        ::testing::Values(0.0025),
+        ::testing::ValuesIn(f_blocked_4d_formats)));
+
+INSTANTIATE_TEST_SUITE_P(
+    GroupNormalizationGPUTest_blocked_layouts_support_5d, GroupNormalizationGPUTest,
+    ::testing::Combine(
+        ::testing::Values(std::vector<int32_t>{3, 64, 28, 32, 12}),
+        ::testing::Values(4),
+        ::testing::Values(0.0025),
+        ::testing::ValuesIn(f_blocked_5d_formats)));
+
+} // anonymous namespace
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/hash_key_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/hash_key_gpu_test.cpp
index a9c1e1262f3aff..68cfc54237737b 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/hash_key_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/hash_key_gpu_test.cpp
@@ -94,7 +94,7 @@ class check_hash_value: public ::testing::Test {
         topology.add(input_layout("InputDictionary", input1->get_layout()));
         topology.add(input_layout("InputText", input2->get_layout()));
         topology.add(
-            gather(key_prim_id, input_info("InputDictionary"), input_info("InputText"), axis, ov::Shape{3, 2, 3, 3, 2}, batch_dim, negative_indexes)
+            gather(key_prim_id, input_info("InputDictionary"), input_info("InputText"), axis, 5, ov::Shape{3, 2, 3, 3, 2}, batch_dim, negative_indexes)
         );
 
         cldnn::network::ptr net = get_network(engine, topology, get_test_default_config(engine), get_test_stream_ptr(), is_caching_test);
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp
index 94fb17104275e0..240f0df9980e97 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp
@@ -13,6 +13,7 @@
 #include <intel_gpu/primitives/loop.hpp>
 #include <intel_gpu/primitives/mutable_data.hpp>
 #include <intel_gpu/primitives/data.hpp>
+#include <intel_gpu/graph/program.hpp>
 
 #include <cassert>
 #include <cmath>
@@ -23,6 +24,34 @@ using namespace cldnn;
 using namespace tests;
 using namespace testing;
 
+static program::ptr build_program(engine& engine,
+                                    topology& body_topology,
+                                    primitive_id execution_condition_id,
+                                    std::vector<loop::io_primitive_map> output_primitive_maps,
+                                    std::vector<loop::backedge_mapping> back_edges) {
+    std::vector<cldnn::primitive_id> output_names_vec;
+    for (auto out_map : output_primitive_maps) {
+        output_names_vec.push_back(out_map.internal_id.pid);
+    }
+
+    // setup outputs for backedges
+    for (auto& back_edge : back_edges) {
+        output_names_vec.push_back(back_edge.from);
+    }
+
+    // if execution_condition_id is specified, we need to add the id in build_option::outputs
+    if (!execution_condition_id.empty()) {
+        output_names_vec.push_back(execution_condition_id);
+    }
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::optimize_data(true));
+    config.set_property(ov::intel_gpu::custom_outputs(output_names_vec));
+    config.set_property(ov::intel_gpu::max_dynamic_batch(1));
+
+    return program::build_program(engine, body_topology, config, false, false, true);
+}
+
 template <typename T>
 void test_loop_gpu_basic_no_concat(bool is_caching_test)
 {
@@ -52,23 +81,23 @@ void test_loop_gpu_basic_no_concat(bool is_caching_test)
     set_values(initial_condition_mem, {initial_condition});
 
     topology body(
+        input_layout("input", input_mem->get_layout()),
         data("eltwise_operand", operand_mem),
         eltwise("eltwise", input_info("input"), input_info("eltwise_operand"), eltwise_mode::sum)
     );
 
     std::vector<loop::io_primitive_map> input_primitive_maps { loop::io_primitive_map("input", "input") };
     std::vector<loop::io_primitive_map> output_primitive_maps { loop::io_primitive_map("loop", "eltwise") };
+    std::vector<loop::backedge_mapping> back_edges { loop::backedge_mapping("eltwise", "input") };
 
-    std::vector<loop::backedge_mapping> back_edges {
-        loop::backedge_mapping("eltwise", "input")
-    };
+    auto body_program = build_program(engine, body, "", output_primitive_maps, back_edges);
 
     topology topology(
         input_layout("input", input_mem->get_layout()),
         input_layout("trip_count", trip_count_mem->get_layout()),
         input_layout("initial_condition", initial_condition_mem->get_layout()),
         mutable_data("num_iteration", num_iteration_mem),
-        loop("loop", { input_info("input") }, body,
+        loop("loop", { input_info("num_iteration"), input_info("input") }, body_program,
              "trip_count", "initial_condition", "num_iteration",
              input_primitive_maps, output_primitive_maps, back_edges, 8)
     );
@@ -161,15 +190,16 @@ void test_loop_gpu_basic_concat(bool is_caching_test)
 
     std::vector<loop::io_primitive_map> input_primitive_maps { loop::io_primitive_map("input", "input", 2) };
     std::vector<loop::io_primitive_map> output_primitive_maps { loop::io_primitive_map("loop", "eltwise", 2) };
-
     std::vector<loop::backedge_mapping> back_edges {};
 
+    auto body_program = build_program(engine, body, "", output_primitive_maps, back_edges);
+
     topology topology(
         input_layout("input", input_mem->get_layout()),
         input_layout("trip_count", trip_count_mem->get_layout()),
         input_layout("initial_condition", initial_condition_mem->get_layout()),
         mutable_data("num_iteration", num_iteration_mem),
-        loop("loop", { input_info("input") }, body,
+        loop("loop", { input_info("num_iteration"), input_info("input") }, body_program,
              "trip_count", "initial_condition", "num_iteration",
              input_primitive_maps, output_primitive_maps, back_edges, trip_count)
     );
@@ -274,6 +304,8 @@ void test_loop_gpu_basic_concat_nested(bool is_caching_test)
     std::vector<loop::io_primitive_map> inner_output_primitive_maps { loop::io_primitive_map("inner_loop", "inner_eltwise", 2) };
     std::vector<loop::backedge_mapping> inner_back_edges {};
 
+    auto inner_body_program = build_program(engine, inner_loop_body, "", inner_output_primitive_maps, inner_back_edges);
+
     /////////////////////////////////
     // set outer loop body
     /////////////////////////////////
@@ -282,8 +314,8 @@ void test_loop_gpu_basic_concat_nested(bool is_caching_test)
         input_layout("trip_count", inner_trip_count_mem->get_layout()),
         input_layout("initial_condition", inner_initial_condition_mem->get_layout()),
         mutable_data("inner_num_iteration", inner_num_iteration_mem),
-        loop("inner_loop", { input_info("inner_input"), input_info("trip_count"), input_info("initial_condition") },
-            inner_loop_body, "trip_count", "initial_condition", "inner_num_iteration",
+        loop("inner_loop", { input_info("inner_num_iteration"), input_info("inner_input"), input_info("trip_count"), input_info("initial_condition") },
+            inner_body_program, "trip_count", "initial_condition", "inner_num_iteration",
             inner_input_primitive_maps, inner_output_primitive_maps, inner_back_edges, inner_trip_count)
     );
     std::vector<loop::io_primitive_map> outer_input_primitive_maps {
@@ -296,6 +328,8 @@ void test_loop_gpu_basic_concat_nested(bool is_caching_test)
     };
     std::vector<loop::backedge_mapping> outer_back_edges { {"inner_loop", "inner_input"} };
 
+    auto outer_body_program = build_program(engine, outer_loop_body, "", outer_output_primitive_maps, outer_back_edges);
+
     /////////////////////////////////
     // set main topology
     /////////////////////////////////
@@ -306,8 +340,8 @@ void test_loop_gpu_basic_concat_nested(bool is_caching_test)
         mutable_data("num_iteration", num_iteration_mem),
         input_layout("inner_trip_count", inner_trip_count_mem->get_layout()),
         input_layout("inner_initial_condition", inner_initial_condition_mem->get_layout()),
-        loop("loop", { input_info("input"), input_info("inner_trip_count"), input_info("inner_initial_condition") },
-            outer_loop_body, "trip_count", "initial_condition", "num_iteration",
+        loop("loop", { input_info("num_iteration"), input_info("input"), input_info("inner_trip_count"), input_info("inner_initial_condition") },
+            outer_body_program, "trip_count", "initial_condition", "num_iteration",
             outer_input_primitive_maps, outer_output_primitive_maps, outer_back_edges, outer_trip_count)
     );
 
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/permute_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/permute_gpu_test.cpp
index 7be1609c64a9c9..ac22cc773f885a 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/permute_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/permute_gpu_test.cpp
@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/core/type/element_type_traits.hpp"
 #include "test_utils.h"
 #include "random_generator.hpp"
 
@@ -1924,7 +1925,7 @@ void TiledPermuteTest::run_test(const std::vector<cldnn::tensor::value_type>& si
                                 const std::string & permute_opt, std::vector<uint16_t> permute_order, bool is_caching_test)
 {
     // convert ov::float16 to ov::float16
-    using type_ = typename data_type_to_type<Data_Type>::type;
+    using type_ = typename ov::element_type_traits<Data_Type>::value_type;
     using type = typename std::conditional<std::is_same<type_, ov::float16>::value, ov::float16, type_>::type;
 
     std::vector<cldnn::tensor::value_type> internal_sizes(sizes);
@@ -2318,7 +2319,7 @@ struct TiledPerformancePermuteTest : TiledPermuteTest
     {
         auto& engine = get_test_engine();
         // convert ov::float16 to ov::float16
-        using type_ = typename data_type_to_type<Data_Type>::type;
+        using type_ = typename ov::element_type_traits<Data_Type>::value_type;
         using type = typename std::conditional<std::is_same<type_, ov::float16>::value, ov::float16, type_>::type;
 
         std::vector<cldnn::tensor::value_type> internal_sizes(sizes);
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/scatter_nd_update_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/scatter_nd_update_gpu_test.cpp
index d905755e789a71..b3f68e0f00b349 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/scatter_nd_update_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/scatter_nd_update_gpu_test.cpp
@@ -4458,6 +4458,67 @@ TEST(scatter_nd_update_gpu, dynamic) {
     }
 }
 
+
+TEST(scatter_nd_update_gpu, dynamic_padded_output) {
+    //  Dictionary : 2x1x2x8
+    //  Indexes : 0x3
+    //  Updates : 0x8
+    //  Output : 2x1x2x8
+    //  Input values in fp32
+    //
+    auto& engine = get_test_engine();
+
+    auto input1_layout = layout{ ov::PartialShape::dynamic(4), data_types::f32, format::bfyx };
+    auto input2_layout = layout{ ov::PartialShape::dynamic(2), data_types::f32, format::bfyx };
+    auto input3_layout = layout{ ov::PartialShape::dynamic(2), data_types::f32, format::bfyx };
+
+    auto input1 = engine.allocate_memory({ { 1, 1, 2, 8 }, data_types::f32, format::bfyx }); // Dictionary
+    auto input2 = engine.allocate_memory({ { 0, 3 },       data_types::f32, format::bfyx }); // Indexes
+    auto input3 = engine.allocate_memory({ { 0, 8 },       data_types::f32, format::bfyx }); // Updates
+
+    set_values(input1, {
+        0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
+        8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
+    });
+
+    topology topology;
+    topology.add(input_layout("InputData", input1_layout));
+    topology.add(input_layout("InputIndices", input2_layout));
+    topology.add(input_layout("InputUpdates", input3_layout));
+    topology.add(
+        scatter_nd_update("scatter_nd_update", input_info("InputData"), input_info("InputIndices"), input_info("InputUpdates"), 2, padding({0, 0, 1, 1}))
+    );
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+    network network(engine, topology, config);
+
+    network.set_input_data("InputData", input1);
+    network.set_input_data("InputIndices", input2);
+    network.set_input_data("InputUpdates", input3);
+
+    auto inst = network.get_primitive("scatter_nd_update");
+    auto impl = inst->get_impl();
+    ASSERT_TRUE(impl != nullptr);
+    ASSERT_TRUE(impl->is_dynamic());
+
+    auto outputs = network.execute();
+
+    auto output = outputs.at("scatter_nd_update").get_memory();
+    cldnn::mem_lock<float> output_ptr(output, get_test_stream());
+
+    std::vector<float> expected_results = {
+        0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
+        0.f, 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 0.f,
+        0.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 0.f,
+        0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
+    };
+
+    for (size_t i = 0; i < expected_results.size(); ++i) {
+        ASSERT_EQ(expected_results[i], output_ptr[i]);
+    }
+}
+
 TEST(scatter_nd_update_gpu, dynamic_5d) {
     tests::random_generator rg(std::string(::testing::UnitTest::GetInstance()->current_test_info()->test_suite_name()) +
                                std::string(::testing::UnitTest::GetInstance()->current_test_info()->name()));
diff --git a/src/plugins/template/CMakeLists.txt b/src/plugins/template/CMakeLists.txt
index 4a3691186302aa..47cbb954d63dbd 100644
--- a/src/plugins/template/CMakeLists.txt
+++ b/src/plugins/template/CMakeLists.txt
@@ -7,8 +7,6 @@ cmake_minimum_required(VERSION 3.13)
 
 project(OpenVINOTemplatePlugin)
 
-set(TEMPLATE_PLUGIN_SOURCE_DIR ${OpenVINOTemplatePlugin_SOURCE_DIR})
-
 find_package(OpenVINODeveloperPackage REQUIRED)
 
 ov_option(ENABLE_TEMPLATE_REGISTRATION "Enables registration of TEMPLATE plugin" OFF)
diff --git a/src/plugins/template/backend/CMakeLists.txt b/src/plugins/template/backend/CMakeLists.txt
index 2836d0c34b6c4f..0dc03242b554af 100644
--- a/src/plugins/template/backend/CMakeLists.txt
+++ b/src/plugins/template/backend/CMakeLists.txt
@@ -2,12 +2,11 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
+set(TARGET_NAME openvino_interpreter_backend)
+
 add_definitions(-DIN_OV_COMPONENT)
-ov_deprecated_no_errors()
 
-file(GLOB OPS_SRC 
-    "${CMAKE_CURRENT_SOURCE_DIR}/ops/*.cpp"
-    )
+file(GLOB OPS_SRC "${CMAKE_CURRENT_SOURCE_DIR}/ops/*.cpp")
 
 set (SRC
     backend.cpp
@@ -24,31 +23,38 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
     ov_add_compiler_flags(/wd4018)
 endif()
 
-add_library(interpreter_backend STATIC EXCLUDE_FROM_ALL ${OPS_SRC} ${SRC})
-add_library(openvino::interpreter_backend ALIAS interpreter_backend)
+add_library(${TARGET_NAME} STATIC EXCLUDE_FROM_ALL ${OPS_SRC} ${SRC})
+
+add_library(openvino::interpreter_backend ALIAS ${TARGET_NAME})
+set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME interpreter_backend)
 
 if(CMAKE_COMPILER_IS_GNUCXX)
     ov_add_compiler_flags(-Wno-missing-declarations)
 endif()
 
-ov_build_target_faster(interpreter_backend UNITY)
+ov_build_target_faster(${TARGET_NAME} UNITY)
 
-target_compile_definitions(interpreter_backend
+target_compile_definitions(${TARGET_NAME}
     PRIVATE
         SHARED_LIB_PREFIX="${CMAKE_SHARED_LIBRARY_PREFIX}"
         SHARED_LIB_SUFFIX="${OV_BUILD_POSTFIX}${CMAKE_SHARED_LIBRARY_SUFFIX}"
 )
-target_link_libraries(interpreter_backend PRIVATE openvino::builders openvino::reference openvino::util openvino::runtime::dev openvino::shape_inference)
+target_link_libraries(${TARGET_NAME} PRIVATE openvino::builders openvino::reference openvino::util openvino::runtime::dev openvino::shape_inference)
 
-target_include_directories(interpreter_backend PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}> $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/ops/>)
+target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>
+                                                 $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/ops/>
+                                                 $<INSTALL_INTERFACE:developer_package/include/${TARGET_NAME}>)
 
 file(GLOB_RECURSE all_backends_src "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp" "${CMAKE_CURRENT_SOURCE_DIR}/*.hpp")
-ov_add_clang_format_target(interpreter_backend_clang FOR_SOURCES ${all_backends_src})
+ov_add_clang_format_target(${TARGET_NAME}_clang FOR_SOURCES ${all_backends_src})
 
-# developer package
+# install & export
 
-openvino_developer_export_targets(COMPONENT core TARGETS interpreter_backend)
+ov_install_static_lib(${TARGET_NAME} ${OV_CPACK_COMP_CORE})
 
-# install
+ov_developer_package_export_targets(TARGET openvino::interpreter_backend)
 
-ov_install_static_lib(interpreter_backend ${OV_CPACK_COMP_CORE})
+install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/"
+        DESTINATION developer_package/include/${TARGET_NAME}
+        COMPONENT developer_package EXCLUDE_FROM_ALL
+        FILES_MATCHING PATTERN "*.hpp")
diff --git a/src/plugins/template/src/CMakeLists.txt b/src/plugins/template/src/CMakeLists.txt
index f382f90d11b450..effc4831f18947 100644
--- a/src/plugins/template/src/CMakeLists.txt
+++ b/src/plugins/template/src/CMakeLists.txt
@@ -26,7 +26,7 @@ ov_mark_target_as_cc(${TARGET_NAME})
 
 target_include_directories(${TARGET_NAME} PRIVATE
     "${CMAKE_CURRENT_SOURCE_DIR}"
-    "${TEMPLATE_PLUGIN_SOURCE_DIR}/include")
+    "${OpenVINOTemplatePlugin_SOURCE_DIR}/include")
 
 # link common OpenVINO Runtime libraries
 target_link_libraries(${TARGET_NAME} PRIVATE
diff --git a/src/plugins/template/tests/functional/CMakeLists.txt b/src/plugins/template/tests/functional/CMakeLists.txt
index eb634d4f91f955..18296710d64a28 100644
--- a/src/plugins/template/tests/functional/CMakeLists.txt
+++ b/src/plugins/template/tests/functional/CMakeLists.txt
@@ -18,7 +18,7 @@ ov_add_test_target(
             openvino::funcSharedTests
             openvino::runtime::dev
         INCLUDES
-            "${TEMPLATE_PLUGIN_SOURCE_DIR}/include"
+            "${OpenVINOTemplatePlugin_SOURCE_DIR}/include"
             "${CMAKE_CURRENT_SOURCE_DIR}/op_reference"
         ADD_CLANG_FORMAT
         LABELS
diff --git a/src/plugins/template/tests/functional/op_reference/convert.cpp b/src/plugins/template/tests/functional/op_reference/convert.cpp
index 0f3e47148790be..b6195744c9c6f3 100644
--- a/src/plugins/template/tests/functional/op_reference/convert.cpp
+++ b/src/plugins/template/tests/functional/op_reference/convert.cpp
@@ -103,7 +103,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{2, 2},
                       ov::element::u4,
                       ov::element::f32,
-                      std::vector<uint8_t>{0xFB, 0x0A},
+                      std::vector<uint8_t>{0xBF, 0xA0},
                       std::vector<float>{15.0f, 11.0f, 0.0f, 10.0f},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -134,7 +134,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{2, 2},
                       ov::element::i4,
                       ov::element::f32,
-                      std::vector<uint8_t>{0xFE, 0xF2},
+                      std::vector<uint8_t>{0xEF, 0x2F},
                       std::vector<float>{-1.0f, -2.0f, -1.0f, 2.0f},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -245,7 +245,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u1,
                       ov::element::i4,
                       std::vector<uint8_t>{0xA0},
-                      std::vector<uint8_t>{0x10, 0x10},
+                      std::vector<uint8_t>{0x01, 0x01},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -261,7 +261,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u8,
                       ov::element::i4,
                       std::vector<uint8_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -269,7 +269,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u16,
                       ov::element::i4,
                       std::vector<uint16_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -277,7 +277,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u32,
                       ov::element::i4,
                       std::vector<uint32_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -285,7 +285,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u64,
                       ov::element::i4,
                       std::vector<uint64_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -301,7 +301,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i8,
                       ov::element::i4,
                       std::vector<int8_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -309,7 +309,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i16,
                       ov::element::i4,
                       std::vector<int16_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -317,7 +317,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i32,
                       ov::element::i4,
                       std::vector<int32_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -325,7 +325,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i64,
                       ov::element::i4,
                       std::vector<int64_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -333,7 +333,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f16,
                       ov::element::i4,
                       std::vector<ov::float16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -341,7 +341,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::bf16,
                       ov::element::i4,
                       std::vector<ov::bfloat16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -349,7 +349,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f32,
                       ov::element::i4,
                       std::vector<float>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         // destination i8
@@ -364,7 +364,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -395,7 +395,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -452,7 +452,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -483,7 +483,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -540,7 +540,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -571,7 +571,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -628,7 +628,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -659,7 +659,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -718,7 +718,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{8},
                       ov::element::u4,
                       ov::element::u1,
-                      std::vector<uint8_t>{0x10, 0x01, 0x00, 0x00},
+                      std::vector<uint8_t>{0x01, 0x10, 0x00, 0x00},
                       std::vector<uint8_t>{0x90},
                       8,
                       8),
@@ -758,7 +758,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{8},
                       ov::element::i4,
                       ov::element::u1,
-                      std::vector<uint8_t>{0x10, 0x01, 0x00, 0x00},
+                      std::vector<uint8_t>{0x01, 0x10, 0x00, 0x00},
                       std::vector<uint8_t>{0x90},
                       8,
                       8),
@@ -825,7 +825,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u1,
                       ov::element::u4,
                       std::vector<uint8_t>{0xA0},
-                      std::vector<uint8_t>{0x10, 0x10},
+                      std::vector<uint8_t>{0x01, 0x01},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -841,7 +841,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u8,
                       ov::element::u4,
                       std::vector<uint8_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -849,7 +849,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u16,
                       ov::element::u4,
                       std::vector<uint16_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -857,7 +857,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u32,
                       ov::element::u4,
                       std::vector<uint32_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -865,7 +865,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u64,
                       ov::element::u4,
                       std::vector<uint64_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -881,7 +881,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i8,
                       ov::element::u4,
                       std::vector<int8_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -889,7 +889,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i16,
                       ov::element::u4,
                       std::vector<int16_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -897,7 +897,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i32,
                       ov::element::u4,
                       std::vector<int32_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -905,7 +905,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i64,
                       ov::element::u4,
                       std::vector<int64_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -913,7 +913,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f16,
                       ov::element::u4,
                       std::vector<ov::float16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -921,7 +921,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::bf16,
                       ov::element::u4,
                       std::vector<ov::bfloat16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -929,7 +929,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f32,
                       ov::element::u4,
                       std::vector<float>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
 
@@ -945,7 +945,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -976,7 +976,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -1034,7 +1034,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -1065,7 +1065,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -1123,7 +1123,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -1154,7 +1154,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -1211,7 +1211,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
@@ -1242,7 +1242,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT,
diff --git a/src/plugins/template/tests/functional/op_reference/convert_like.cpp b/src/plugins/template/tests/functional/op_reference/convert_like.cpp
index b46fe98af030c2..4ddf3dda276b92 100644
--- a/src/plugins/template/tests/functional/op_reference/convert_like.cpp
+++ b/src/plugins/template/tests/functional/op_reference/convert_like.cpp
@@ -6,6 +6,8 @@
 
 #include <gtest/gtest.h>
 
+#include <tuple>
+
 #include "conversion.hpp"
 
 using namespace ov;
@@ -101,7 +103,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{2, 2},
                       ov::element::u4,
                       ov::element::f32,
-                      std::vector<uint8_t>{0xFB, 0x0A},
+                      std::vector<uint8_t>{0xBF, 0xA0},
                       std::vector<float>{15.0f, 11.0f, 0.0f, 10.0f},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -132,7 +134,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{2, 2},
                       ov::element::i4,
                       ov::element::f32,
-                      std::vector<uint8_t>{0xFE, 0xF2},
+                      std::vector<uint8_t>{0xEF, 0x2F},
                       std::vector<float>{-1.0f, -2.0f, -1.0f, 2.0f},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -243,7 +245,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u1,
                       ov::element::i4,
                       std::vector<uint8_t>{0xA0},
-                      std::vector<uint8_t>{0x10, 0x10},
+                      std::vector<uint8_t>{0x01, 0x01},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -259,7 +261,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u8,
                       ov::element::i4,
                       std::vector<uint8_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -267,7 +269,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u16,
                       ov::element::i4,
                       std::vector<uint16_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -275,7 +277,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u32,
                       ov::element::i4,
                       std::vector<uint32_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -283,7 +285,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u64,
                       ov::element::i4,
                       std::vector<uint64_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -299,7 +301,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i8,
                       ov::element::i4,
                       std::vector<int8_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -307,7 +309,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i16,
                       ov::element::i4,
                       std::vector<int16_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -315,7 +317,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i32,
                       ov::element::i4,
                       std::vector<int32_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -323,7 +325,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i64,
                       ov::element::i4,
                       std::vector<int64_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -331,7 +333,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f16,
                       ov::element::i4,
                       std::vector<ov::float16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -339,7 +341,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::bf16,
                       ov::element::i4,
                       std::vector<ov::bfloat16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -347,7 +349,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f32,
                       ov::element::i4,
                       std::vector<float>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         // destination i8
@@ -362,7 +364,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -393,7 +395,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -436,7 +438,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::i8,
-                      std::vector<float>{-1, -2, 2, 3},
+                      std::vector<float>{-1, -2, 2.2, 3.8},
                       std::vector<int8_t>{-1, -2, 2, 3}),
         // destination i16
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -450,7 +452,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -481,7 +483,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -524,7 +526,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::i16,
-                      std::vector<float>{-1, -2, 2, 3},
+                      std::vector<float>{-1, -2, 2.2, 3.8},
                       std::vector<int16_t>{-1, -2, 2, 3}),
         // destination i32
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -538,7 +540,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -569,7 +571,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -612,7 +614,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::i32,
-                      std::vector<float>{-1, -2, 2, 3},
+                      std::vector<float>{-1, -2, 2.2, 3.8},
                       std::vector<int32_t>{-1, -2, 2, 3}),
         // destination i64
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -626,7 +628,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::i64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -657,7 +659,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::i64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<int64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -700,7 +702,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::i64,
-                      std::vector<float>{-1, -2, 2, 3},
+                      std::vector<float>{-1, -2, 2.2, 3.8},
                       std::vector<int64_t>{-1, -2, 2, 3}),
 
         // destination u1
@@ -716,7 +718,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{8},
                       ov::element::u4,
                       ov::element::u1,
-                      std::vector<uint8_t>{0x10, 0x01, 0x00, 0x00},
+                      std::vector<uint8_t>{0x01, 0x10, 0x00, 0x00},
                       std::vector<uint8_t>{0x90},
                       8,
                       8),
@@ -756,7 +758,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{8},
                       ov::element::i4,
                       ov::element::u1,
-                      std::vector<uint8_t>{0x10, 0x01, 0x00, 0x00},
+                      std::vector<uint8_t>{0x01, 0x10, 0x00, 0x00},
                       std::vector<uint8_t>{0x90},
                       8,
                       8),
@@ -823,7 +825,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u1,
                       ov::element::u4,
                       std::vector<uint8_t>{0xA0},
-                      std::vector<uint8_t>{0x10, 0x10},
+                      std::vector<uint8_t>{0x01, 0x01},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -839,7 +841,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u8,
                       ov::element::u4,
                       std::vector<uint8_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -847,7 +849,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u16,
                       ov::element::u4,
                       std::vector<uint16_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -855,7 +857,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u32,
                       ov::element::u4,
                       std::vector<uint32_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -863,7 +865,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::u64,
                       ov::element::u4,
                       std::vector<uint64_t>{1, 2, 0, 3},
-                      std::vector<uint8_t>{0x12, 0x03},
+                      std::vector<uint8_t>{0x21, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -879,7 +881,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i8,
                       ov::element::u4,
                       std::vector<int8_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -887,7 +889,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i16,
                       ov::element::u4,
                       std::vector<int16_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -895,7 +897,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i32,
                       ov::element::u4,
                       std::vector<int32_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -903,7 +905,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::i64,
                       ov::element::u4,
                       std::vector<int64_t>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -911,7 +913,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f16,
                       ov::element::u4,
                       std::vector<ov::float16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -919,7 +921,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::bf16,
                       ov::element::u4,
                       std::vector<ov::bfloat16>{-1, -2, 0, 3},
-                      std::vector<uint8_t>{0xFE, 0x03},
+                      std::vector<uint8_t>{0xEF, 0x30},
                       4,
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -927,7 +929,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f32,
                       ov::element::u4,
                       std::vector<float>{-1, -2, 2, 3},
-                      std::vector<uint8_t>{0xFE, 0x23},
+                      std::vector<uint8_t>{0xEF, 0x32},
                       4,
                       4),
 
@@ -943,7 +945,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -974,7 +976,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u8,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint8_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1017,7 +1019,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::u8,
-                      std::vector<float>{1, 2, 2, 3},
+                      std::vector<float>{1, 2, 2.2, 3.8},
                       std::vector<uint8_t>{1, 2, 2, 3}),
 
         // destination u16
@@ -1032,7 +1034,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1063,7 +1065,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u16,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint16_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1106,7 +1108,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::u16,
-                      std::vector<float>{1, 2, 2, 3},
+                      std::vector<float>{1, 2, 2.2, 3.8},
                       std::vector<uint16_t>{1, 2, 2, 3}),
 
         // destination u32
@@ -1121,7 +1123,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1152,7 +1154,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u32,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint32_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1195,7 +1197,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::u32,
-                      std::vector<float>{1, 2, 2, 3},
+                      std::vector<float>{1, 2, 2.2, 3.8},
                       std::vector<uint32_t>{1, 2, 2, 3}),
         // destination u64
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1209,7 +1211,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::u4,
                       ov::element::u64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1240,7 +1242,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::i4,
                       ov::element::u64,
-                      std::vector<uint8_t>{0x21, 0x43},
+                      std::vector<uint8_t>{0x12, 0x34},
                       std::vector<uint64_t>{2, 1, 4, 3},
                       4),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -1283,7 +1285,7 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::PartialShape{4},
                       ov::element::f32,
                       ov::element::u64,
-                      std::vector<float>{1, 2, 2, 3},
+                      std::vector<float>{1, 2, 2.2, 3.8},
                       std::vector<uint64_t>{1, 2, 2, 3})),
     ReferenceConversionLayerTest::getTestCaseName);
 }  // namespace
diff --git a/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt b/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
index 6f981d0702c96d..027af903cff097 100644
--- a/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
+++ b/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
@@ -12,13 +12,13 @@ list(APPEND LIBRARIES
 )
 
 # add subgraphs_dumpers tool
-ov_add_target(
+ov_add_test_target(
         NAME ${TARGET_NAME}
         TYPE EXECUTABLE
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}/src
         INCLUDES
             PRIVATE
-                ${CMAKE_CURRENT_SOURCE_DIR}/include
+                "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
         LINK_LIBRARIES
             PRIVATE
                 ${LIBRARIES}
@@ -34,7 +34,7 @@ ov_add_target(
         ROOT "${CMAKE_CURRENT_SOURCE_DIR}/src"
         INCLUDES
             PUBLIC
-                ${CMAKE_CURRENT_SOURCE_DIR}/include
+                "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
         LINK_LIBRARIES
             PUBLIC
                 ${LIBRARIES}
@@ -46,4 +46,4 @@ ov_add_target(
         ADD_CPPLINT
 )
 
-ov_build_target_faster(${TARGET_NAME} UNITY)
\ No newline at end of file
+ov_build_target_faster(${TARGET_NAME} UNITY)
diff --git a/src/tests/functional/plugin/conformance/test_runner/conformance_infra/CMakeLists.txt b/src/tests/functional/plugin/conformance/test_runner/conformance_infra/CMakeLists.txt
index 0f25d3218e1b92..5f9dc01189a443 100644
--- a/src/tests/functional/plugin/conformance/test_runner/conformance_infra/CMakeLists.txt
+++ b/src/tests/functional/plugin/conformance/test_runner/conformance_infra/CMakeLists.txt
@@ -14,8 +14,8 @@ ov_add_target(
         ADD_CPPLINT
         INCLUDES
             PUBLIC
-                ${CMAKE_CURRENT_SOURCE_DIR}/include
-                ${OpenVINO_SOURCE_DIR}/src/tests/functional/plugin/conformance/subgraphs_dumper_new/include/cache/meta/
+                "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+                "$<BUILD_INTERFACE:${OpenVINO_SOURCE_DIR}/src/tests/functional/plugin/conformance/subgraphs_dumper_new/include/cache/meta>"
         LINK_LIBRARIES
             PUBLIC
                 gflags
diff --git a/src/tests/functional/plugin/shared/CMakeLists.txt b/src/tests/functional/plugin/shared/CMakeLists.txt
index c75d2938d6badf..3715abb9bb059d 100644
--- a/src/tests/functional/plugin/shared/CMakeLists.txt
+++ b/src/tests/functional/plugin/shared/CMakeLists.txt
@@ -52,13 +52,11 @@ ov_add_target(
         ADDITIONAL_SOURCE_DIRS
             ${CMAKE_CURRENT_SOURCE_DIR}/src
         ADD_CPPLINT
-        DEVELOPER_PACKAGE
-            tests
         EXCLUDED_SOURCE_PATHS ${EXCLUDED_SOURCE_PATHS}
         DEFINES ${DEFINES}
         INCLUDES
             PUBLIC
-                ${PUBLIC_HEADERS_DIR}
+                "$<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>"
         LINK_LIBRARIES
             PUBLIC
                 openvino::pugixml
@@ -86,3 +84,8 @@ if (ENABLE_INTEL_CPU)
         "${CMAKE_CURRENT_SOURCE_DIR}/src/behavior/ov_executable_network/get_metric.cpp"
         PROPERTIES COMPILE_DEFINITIONS ENABLE_INTEL_CPU=1)
 endif()
+
+# install & export
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${PUBLIC_HEADERS_DIR}/")
diff --git a/src/tests/functional/plugin/shared/include/single_layer_tests/group_normalization.hpp b/src/tests/functional/plugin/shared/include/single_layer_tests/group_normalization.hpp
new file mode 100644
index 00000000000000..8f080764adbced
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/single_layer_tests/group_normalization.hpp
@@ -0,0 +1,21 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "shared_test_classes/single_layer/group_normalization.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+TEST_P(GroupNormalizationTest, CompareWithRefs) {
+    run();
+}
+
+TEST_P(GroupNormalizationTest, CompareQueryModel) {
+    query_model();
+}
+
+}  // namespace subgraph
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/single_op_tests/einsum.hpp b/src/tests/functional/plugin/shared/include/single_op_tests/einsum.hpp
new file mode 100644
index 00000000000000..375a02f2ec65f4
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/single_op_tests/einsum.hpp
@@ -0,0 +1,15 @@
+// Copyright (C) 2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/single_op/einsum.hpp"
+
+namespace ov {
+namespace test {
+TEST_P(EinsumLayerTest, Inference) {
+    run();
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/single_op_tests/eye.hpp b/src/tests/functional/plugin/shared/include/single_op_tests/eye.hpp
new file mode 100644
index 00000000000000..ebacbba67b4844
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/single_op_tests/eye.hpp
@@ -0,0 +1,14 @@
+// Copyright (C) 2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+
+#include "shared_test_classes/single_op/eye.hpp"
+
+namespace ov {
+namespace test {
+TEST_P(EyeLayerTest, Inference) {
+    run();
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/single_op_tests/pooling.hpp b/src/tests/functional/plugin/shared/include/single_op_tests/pooling.hpp
new file mode 100644
index 00000000000000..495c551e9764eb
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/single_op_tests/pooling.hpp
@@ -0,0 +1,19 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/single_op/pooling.hpp"
+
+namespace ov {
+namespace test {
+TEST_P(PoolingLayerTest, Inference) {
+    run();
+}
+
+TEST_P(MaxPoolingV8LayerTest, Inference) {
+    run();
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/single_op_tests/roi_align.hpp b/src/tests/functional/plugin/shared/include/single_op_tests/roi_align.hpp
new file mode 100644
index 00000000000000..96e1bbeeeac639
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/single_op_tests/roi_align.hpp
@@ -0,0 +1,19 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/single_op/roi_align.hpp"
+
+namespace ov {
+namespace test {
+TEST_P(ROIAlignLayerTest, Inference) {
+    run();
+}
+
+TEST_P(ROIAlignV9LayerTest, Inference) {
+    run();
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/single_op_tests/roi_pooling.hpp b/src/tests/functional/plugin/shared/include/single_op_tests/roi_pooling.hpp
new file mode 100644
index 00000000000000..3b89b91aa15504
--- /dev/null
+++ b/src/tests/functional/plugin/shared/include/single_op_tests/roi_pooling.hpp
@@ -0,0 +1,15 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/single_op/roi_pooling.hpp"
+
+namespace ov {
+namespace test {
+TEST_P(ROIPoolingLayerTest, Inference) {
+    run();
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/mul_conv_fusion.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/mul_conv_fusion.hpp
index 46d65560b9e1ab..529b22c56e401e 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/mul_conv_fusion.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/mul_conv_fusion.hpp
@@ -6,9 +6,12 @@
 
 #include "shared_test_classes/subgraph/mul_conv_fusion.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
 TEST_P(MulConvFusion, CompareWithRefs) {
-    Run();
+    run();
 }
-} // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/multiply_add.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/multiply_add.hpp
index f3d65830592133..93f3600048a90d 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/multiply_add.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/multiply_add.hpp
@@ -6,10 +6,12 @@
 
 #include "shared_test_classes/subgraph/multiply_add.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
 TEST_P(MultiplyAddLayerTest, CompareWithRefs) {
-    Run();
+    run();
 };
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/mvn_multiply_add.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/mvn_multiply_add.hpp
index 8b118b1295f140..174dffd25ca873 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/mvn_multiply_add.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/mvn_multiply_add.hpp
@@ -6,10 +6,12 @@
 
 #include "shared_test_classes/subgraph/mvn_multiply_add.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
-TEST_P(MVNMultiplyAdd, CompareWithRefs){
-    Run();
+TEST_P(MVNMultiplyAdd, CompareWithRefs) {
+    run();
 };
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/perm_conv_perm_concat.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/perm_conv_perm_concat.hpp
index b4ad568c56b462..41582d13ffd009 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/perm_conv_perm_concat.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/perm_conv_perm_concat.hpp
@@ -6,10 +6,12 @@
 
 #include "shared_test_classes/subgraph/perm_conv_perm_concat.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
 TEST_P(PermConvPermConcat, CompareWithRefs) {
-    Run();
+    run();
 }
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/range_add.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/range_add.hpp
index 197fe81621e5f8..ce16e5850744a5 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/range_add.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/range_add.hpp
@@ -6,14 +6,16 @@
 
 #include "shared_test_classes/subgraph/range_add.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
 TEST_P(RangeAddSubgraphTest, CompareWithRefs) {
-    Run();
+    run();
 }
 
 TEST_P(RangeNumpyAddSubgraphTest, CompareWithRefs) {
-    Run();
+    run();
 }
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/relu_shape_of.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/relu_shape_of.hpp
index b2391ef6c04dd8..e26e8837f80279 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/relu_shape_of.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/relu_shape_of.hpp
@@ -6,10 +6,12 @@
 
 #include "shared_test_classes/subgraph/relu_shape_of.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
 TEST_P(ReluShapeOfSubgraphTest, CompareWithRefs) {
-    Run();
+    run();
 }
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/reshape_squeeze_reshape_relu.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/reshape_squeeze_reshape_relu.hpp
index 331ee30f7e515d..7b1c841d7d17b7 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/reshape_squeeze_reshape_relu.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/reshape_squeeze_reshape_relu.hpp
@@ -6,10 +6,12 @@
 
 #include "shared_test_classes/subgraph/reshape_squeeze_reshape_relu.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
-TEST_P(ReshapeSqueezeReshapeRelu, CompareWithRefs){
-    Run();
+TEST_P(ReshapeSqueezeReshapeRelu, CompareWithRefs) {
+    run();
 };
 
-} // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/split_conv_concat.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/split_conv_concat.hpp
index c121683977f898..5fd61dd48f738a 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/split_conv_concat.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/split_conv_concat.hpp
@@ -6,14 +6,17 @@
 
 #include "shared_test_classes/subgraph/split_conv_concat.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
 TEST_P(SplitConvConcat, CompareWithRefImpl) {
-    Run();
+    run();
 };
 
-TEST_P(SplitConvConcat, QueryNetwork) {
-    QueryNetwork();
+TEST_P(SplitConvConcat, QueryModel) {
+    query_model();
 }
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
+
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/variadic_split_pad.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/variadic_split_pad.hpp
index 9b9d4fb77839ea..8f1eec6cbf6214 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/variadic_split_pad.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/variadic_split_pad.hpp
@@ -6,10 +6,12 @@
 
 #include "shared_test_classes/subgraph/variadic_split_pad.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
-TEST_P(VariadicSplitPad, CompareWithRefs){
-    Run();
+TEST_P(VariadicSplitPad, CompareWithRefs) {
+    run();
 };
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp b/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp
index 6540664e3f4b11..e431c4d6eead2b 100644
--- a/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp
+++ b/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp
@@ -76,6 +76,14 @@ bool OVInferRequestDynamicTests::checkOutput(const ov::runtime::Tensor& in, cons
         tensor.data<float>()[i] = in.data<float>()[i];
     }
     req.infer();
+    const auto reqShape = req.get_output_tensor(0).get_shape();
+    const auto actualShape = actual.get_shape();
+    if (reqShape.size() != actualShape.size()) {
+        return false;
+    }
+    if (!std::equal(reqShape.cbegin(), reqShape.cend(), actualShape.cbegin())) {
+        return false;
+    }
     for (int i = 0; i < actual.get_size(); i++) {
         if (fabs(req.get_output_tensor(0).data<float>()[i] - actual.data<float>()[i]) > std::numeric_limits<float>::epsilon())
             return false;
diff --git a/src/tests/functional/shared_test_classes/CMakeLists.txt b/src/tests/functional/shared_test_classes/CMakeLists.txt
index 35d09840c09770..a4f46b241437b0 100644
--- a/src/tests/functional/shared_test_classes/CMakeLists.txt
+++ b/src/tests/functional/shared_test_classes/CMakeLists.txt
@@ -9,11 +9,9 @@ ov_add_target(
         TYPE STATIC
         ROOT "${CMAKE_CURRENT_SOURCE_DIR}/include"
         ADD_CPPLINT
-        DEVELOPER_PACKAGE
-            tests
         INCLUDES
             PUBLIC
-                "${CMAKE_CURRENT_SOURCE_DIR}/include"
+                "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
         ADDITIONAL_SOURCE_DIRS
             ${CMAKE_CURRENT_SOURCE_DIR}/src
         LINK_LIBRARIES
@@ -25,3 +23,8 @@ ov_add_target(
 ov_build_target_faster(${TARGET_NAME}
         PCH PRIVATE "src/precomp.hpp"
         )
+
+# install & export
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/include/")
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp
index 05867b81d67c8c..5ca0b6531a39f3 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp
@@ -20,6 +20,12 @@ using ElementType = ov::element::Type_t;
 using Config = ov::AnyMap;
 using TargetDevice = std::string;
 
+typedef std::tuple<ov::element::Type,  // Input element type
+                   ov::Shape,          // Input Shape
+                   TargetDevice        // Target Device
+                   >
+    BasicParams;
+
 class SubgraphBaseTest : public ov::test::TestsCommon {
 public:
     virtual void run();
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/group_normalization.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/group_normalization.hpp
new file mode 100644
index 00000000000000..759f47786d98be
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/group_normalization.hpp
@@ -0,0 +1,98 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "ov_models/builders.hpp"
+#include "common_test_utils/common_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+using GroupNormalizationTestParams = std::tuple<ElementType,   // netPrecision
+                                                ElementType,   // inPrecision
+                                                ElementType,   // outPrecision
+                                                InputShape,    // Dynamic shape + Target static shapes
+                                                std::int64_t,  // num_groups
+                                                double,        // epsilon
+                                                TargetDevice,  // targetDevice
+                                                Config         // config
+                                                >;
+
+class GroupNormalizationTest : public testing::WithParamInterface<GroupNormalizationTestParams>,
+                               virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<GroupNormalizationTestParams> &obj) {
+        ElementType netType, inType, outType;
+        InputShape shapes;
+        std::int64_t num_groups;
+        double epsilon;
+        TargetDevice targetDevice;
+        Config config;
+        std::tie(netType, inType, outType, shapes, num_groups, epsilon, targetDevice, config) = obj.param;
+
+        std::ostringstream result;
+        result << "NetType=" << netType << "_";
+        result << "InType=" << inType << "_";
+        result << "OutType=" << outType << "_";
+        result << "IS=" << ov::test::utils::partialShape2str({shapes.first}) << "_";
+        result << "TS=";
+        for (const auto& item : shapes.second) {
+            result << ov::test::utils::vec2str(item) << "_";
+        }
+        result << "NumGroups=" << num_groups << "_";
+        result << "Epsilon=" << epsilon << "_";
+        result << "Device=" << targetDevice;
+
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        InputShape shapes;
+        ElementType ngPrc;
+        std::int64_t num_groups;
+        double epsilon;
+
+        std::tie(ngPrc, inType, outType, shapes, num_groups, epsilon, targetDevice, configuration) = this->GetParam();
+        InputShape biasInputShape = ExtractBiasShape(shapes);
+        init_input_shapes({shapes, biasInputShape, biasInputShape});
+        ov::ParameterVector params;
+        for (auto&& shape : inputDynamicShapes) {
+            params.push_back(std::make_shared<ov::op::v0::Parameter>(ngPrc, shape));
+        }
+        const auto paramOuts =
+            ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+
+        const auto groupNormalization = std::make_shared<ov::op::v12::GroupNormalization>(
+            paramOuts.at(0),
+            paramOuts.at(1),
+            paramOuts.at(2),
+            num_groups,
+            epsilon);
+        const ngraph::ResultVector results{std::make_shared<ngraph::opset8::Result>(groupNormalization)};
+
+        // TODO: This workaround is needed as there is no full support for f16 type in the reference implementation
+        if (ngPrc == element::Type_t::f16) {
+            abs_threshold = 0.007;
+        }
+
+        function = std::make_shared<ngraph::Function>(results, params, "GroupNormalization");
+    }
+
+    InputShape ExtractBiasShape(const InputShape& shape) {
+        std::vector<ov::Shape> biasShape;
+        std::transform(shape.second.cbegin(), shape.second.cend(), std::back_inserter(biasShape),
+                       [](const ov::Shape& s)->ov::Shape { return {s[1]}; });
+        InputShape biasInputShape {
+            shape.first.is_dynamic() ? ov::PartialShape{shape.first[1]} : shape.first,
+            std::move(biasShape)
+        };
+        return biasInputShape;
+    }
+};
+
+}  // namespace subgraph
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/einsum.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/einsum.hpp
new file mode 100644
index 00000000000000..aeef17447e6a43
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/einsum.hpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <string>
+#include <tuple>
+#include <vector>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+typedef std::tuple<
+        std::string,              // Equation
+        std::vector<InputShape>   // Input shapes
+> EinsumEquationWithInput;
+
+typedef std::tuple<
+        ov::element::Type,         // Model type
+        EinsumEquationWithInput,   // Equation with corresponding input shapes
+        std::string                // Device name
+> EinsumLayerTestParamsSet;
+
+class EinsumLayerTest : public testing::WithParamInterface<EinsumLayerTestParamsSet>,
+                        virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<EinsumLayerTestParamsSet>& obj);
+protected:
+    void SetUp() override;
+};
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/eye.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/eye.hpp
new file mode 100644
index 00000000000000..1563a8a37f9cda
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/eye.hpp
@@ -0,0 +1,24 @@
+// Copyright (C) 2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+using EyeLayerTestParams = std::tuple<
+    std::vector<ov::Shape>,  // eye shape
+    std::vector<int>,        // output batch shape
+    std::vector<int>,        // eye params (rows, cols, diag_shift)
+    ov::element::Type,     // Model type
+    std::string>;            // Device name
+
+class EyeLayerTest : public testing::WithParamInterface<EyeLayerTestParams>,
+                     virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<EyeLayerTestParams> obj);
+    void SetUp() override;
+};
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/pooling.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/pooling.hpp
new file mode 100644
index 00000000000000..cfb57994b35b33
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/pooling.hpp
@@ -0,0 +1,71 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <tuple>
+#include <string>
+#include <vector>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "common_test_utils/test_enums.hpp"
+
+namespace ov {
+namespace test {
+typedef std::tuple<
+        ov::test::utils::PoolingTypes,  // Pooling type, max or avg
+        std::vector<size_t>,            // Kernel size
+        std::vector<size_t>,            // Stride
+        std::vector<size_t>,            // Pad begin
+        std::vector<size_t>,            // Pad end
+        ov::op::RoundingType,           // Rounding type
+        ov::op::PadType,                // Pad type
+        bool                            // Exclude pad
+> poolSpecificParams;
+
+typedef std::tuple<
+        poolSpecificParams,
+        ov::element::Type,              // Model type
+        std::vector<InputShape>,        // Input shape
+        std::string                     // Device name
+> poolLayerTestParamsSet;
+
+typedef std::tuple<
+        std::vector<size_t>,            // Kernel size
+        std::vector<size_t>,            // Stride
+        std::vector<size_t>,            // Dilation
+        std::vector<size_t>,            // Pad begin
+        std::vector<size_t>,            // Pad end
+        ov::element::Type,              // Index element type
+        int64_t,                        // Axis
+        ov::op::RoundingType,           // Rounding type
+        ov::op::PadType                 // Pad type
+> maxPoolV8SpecificParams;
+
+typedef std::tuple<
+        maxPoolV8SpecificParams,
+        ov::element::Type,              // Model type
+        std::vector<InputShape>,        // Input shape
+        std::string                     // Device name
+> maxPoolV8LayerTestParamsSet;
+
+class PoolingLayerTest : public testing::WithParamInterface<poolLayerTestParamsSet>,
+                         virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<poolLayerTestParamsSet>& obj);
+
+protected:
+    void SetUp() override;
+};
+
+class MaxPoolingV8LayerTest : public testing::WithParamInterface<maxPoolV8LayerTestParamsSet>,
+                              virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<maxPoolV8LayerTestParamsSet>& obj);
+
+protected:
+    void SetUp() override;
+};
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/roi_align.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/roi_align.hpp
new file mode 100644
index 00000000000000..57257c7154a829
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/roi_align.hpp
@@ -0,0 +1,54 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+using roialignParams = std::tuple<
+        std::vector<InputShape>,                 // Feature map shape
+        ov::Shape,                               // Proposal coords shape
+        int,                                     // Bin's row count
+        int,                                     // Bin's column count
+        float,                                   // Spatial scale
+        int,                                     // Pooling ratio
+        std::string,                             // Pooling mode
+        ov::element::Type,                       // Model type
+        ov::test::TargetDevice>;                 // Device name
+
+class ROIAlignLayerTest : public testing::WithParamInterface<roialignParams>,
+                              virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<roialignParams>& obj);
+    static void fillCoordTensor(std::vector<float>& coords, int height, int width,
+                                float spatialScale, int pooledRatio, int pooledH, int pooledW);
+    static void fillIdxTensor(std::vector<int>& idx, int batchSize);
+
+protected:
+    void SetUp() override;
+};
+
+using roialignV9Params = std::tuple<
+        std::vector<InputShape>,                 // Feature map shape
+        ov::Shape,                               // Proposal coords shape
+        int,                                     // Bin's row count
+        int,                                     // Bin's column count
+        float,                                   // Spatial scale
+        int,                                     // Pooling ratio
+        std::string,                             // Pooling mode
+        std::string,                             // ROI aligned mode
+        ov::element::Type,                       // Model type
+        ov::test::TargetDevice>;                 // Device name
+class ROIAlignV9LayerTest : public testing::WithParamInterface<roialignV9Params>,
+                            virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<roialignV9Params>& obj);
+
+protected:
+    void SetUp() override;
+};
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/roi_pooling.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/roi_pooling.hpp
new file mode 100644
index 00000000000000..ecd714f6ef511a
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/roi_pooling.hpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <tuple>
+#include <string>
+#include <vector>
+#include <memory>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "common_test_utils/test_enums.hpp"
+
+namespace ov {
+namespace test {
+using roiPoolingParamsTuple = std::tuple<
+        std::vector<InputShape>,                    // Input, coords shapes
+        ov::Shape,                                  // Pooled shape {pooled_h, pooled_w}
+        float,                                      // Spatial scale
+        ov::test::utils::ROIPoolingTypes,           // ROIPooling method
+        ov::element::Type,                          // Model type
+        ov::test::TargetDevice>;                    // Device name
+
+class ROIPoolingLayerTest : public testing::WithParamInterface<roiPoolingParamsTuple>,
+                            virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<roiPoolingParamsTuple>& obj);
+
+protected:
+    void SetUp() override;
+};
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mul_conv_fusion.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mul_conv_fusion.hpp
index 7486a950fffd6b..571a8903c32f75 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mul_conv_fusion.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mul_conv_fusion.hpp
@@ -4,32 +4,33 @@
 
 #pragma once
 
-#include <tuple>
 #include <string>
+#include <tuple>
 #include <vector>
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include <ngraph/shape.hpp>
-#include <ngraph/node.hpp>
-
-namespace SubgraphTestsDefinitions {
-
-typedef std::tuple<
-        ngraph::NodeTypeInfo,       // Convolution type
-        ngraph::Shape,              // Input shape
-        ngraph::Shape,              // Weights shape
-        ngraph::Shape,              // Const shape
-        ngraph::element::Type,      // Network precision
-        bool,                       // True if test is negative
-        std::string                 // Device name
-        > MulConvFusionParams;
-
-class MulConvFusion
-        : public testing::WithParamInterface<MulConvFusionParams>,
-          virtual public LayerTestsUtils::LayerTestsCommon {
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+
+typedef std::tuple<ov::NodeTypeInfo,   // Convolution type
+                   ov::Shape,          // Input shape
+                   ov::Shape,          // Weights shape
+                   ov::Shape,          // Const shape
+                   ov::element::Type,  // Network precision
+                   bool,               // True if test is negative
+                   std::string         // Device name
+                   >
+    MulConvFusionParams;
+
+class MulConvFusion : public testing::WithParamInterface<MulConvFusionParams>,
+                      virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<MulConvFusionParams> &obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<MulConvFusionParams>& obj);
 
 protected:
     void SetUp() override;
 };
-} // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/multiply_add.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/multiply_add.hpp
index 1e016857d4728f..fd93d5a01e3560 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/multiply_add.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/multiply_add.hpp
@@ -4,30 +4,28 @@
 
 #pragma once
 
-#include <tuple>
+#include <memory>
 #include <string>
+#include <tuple>
 #include <vector>
-#include <memory>
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ov_models/builders.hpp"
-#include "ov_models/utils/ov_helpers.hpp"
-#include "common_test_utils/test_constants.hpp"
 
-namespace SubgraphTestsDefinitions {
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
 
-using MultiplyAddParamsTuple = typename std::tuple<
-        std::vector<size_t>,              //input shapes
-        InferenceEngine::Precision,       //Network precision
-        std::string>;                     //Device name
+using MultiplyAddParamsTuple = typename std::tuple<ov::Shape,          // input shapes
+                                                   ov::element::Type,  // Input precision
+                                                   std::string>;       // Device name
 
-class MultiplyAddLayerTest:
-        public testing::WithParamInterface<MultiplyAddParamsTuple>,
-        virtual public LayerTestsUtils::LayerTestsCommon{
+class MultiplyAddLayerTest : public testing::WithParamInterface<MultiplyAddParamsTuple>,
+                             virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    std::shared_ptr<ngraph::Function> fn;
-    static std::string getTestCaseName(const testing::TestParamInfo<MultiplyAddParamsTuple> &obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<MultiplyAddParamsTuple>& obj);
+
 protected:
     void SetUp() override;
 };
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mvn_multiply_add.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mvn_multiply_add.hpp
index 800fc2cbb0caa1..f8218c2f04238f 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mvn_multiply_add.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/mvn_multiply_add.hpp
@@ -4,31 +4,34 @@
 
 #pragma once
 
-#include <tuple>
 #include <string>
+#include <tuple>
 #include <vector>
 
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ov_models/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
-typedef std::tuple<
-    std::pair<InferenceEngine::SizeVector, InferenceEngine::SizeVector>,  // Input shape, Constant shape
-    InferenceEngine::Precision,                                           // Data precision
-    InferenceEngine::Precision,                                           // Axes precision
-    std::vector<int>,                                                     // Axes
-    bool,                                                                 // Normalize variance
-    float,                                                                // Epsilon
-    std::string,                                                          // Epsilon mode
-    std::string                                                           // Device name
-> mvnMultiplyAddParams;
+typedef std::tuple<std::pair<ov::Shape, ov::Shape>,  // Input shape, Constant shape
+                   ov::element::Type,                // Data precision
+                   ov::element::Type,                // Axes precision
+                   std::vector<int>,                 // Axes
+                   bool,                             // Normalize variance
+                   float,                            // Epsilon
+                   std::string,                      // Epsilon mode
+                   std::string                       // Device name
+                   >
+    mvnMultiplyAddParams;
 
-class MVNMultiplyAdd: public testing::WithParamInterface<mvnMultiplyAddParams>,
-                      virtual public LayerTestsUtils::LayerTestsCommon{
+class MVNMultiplyAdd : public testing::WithParamInterface<mvnMultiplyAddParams>,
+                       virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<mvnMultiplyAddParams> &obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<mvnMultiplyAddParams>& obj);
+
 protected:
     void SetUp() override;
 };
-}  // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/perm_conv_perm_concat.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/perm_conv_perm_concat.hpp
index b8be31f93271e7..e9b47db5482dee 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/perm_conv_perm_concat.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/perm_conv_perm_concat.hpp
@@ -5,32 +5,29 @@
 #pragma once
 
 #include <tuple>
-#include <vector>
-#include <array>
-#include <string>
-#include <memory>
-
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ov_models/utils/ov_helpers.hpp"
-#include "ov_models/builders.hpp"
-
-namespace SubgraphTestsDefinitions {
-typedef std::tuple<
-    InferenceEngine::Precision,         // Network Precision
-    std::string,                        // Target Device
-    std::array<size_t, 4>,              // Input shape
-    std::array<size_t, 2>,              // Kernel shape
-    size_t,                             // Output channels
-    std::map<std::string, std::string>  // Configuration
-> PermConvPermConcatParams;
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+
+typedef std::tuple<ov::element::Type,  // Network Precision
+                   std::string,        // Target Device
+                   ov::Shape,          // Input shape
+                   ov::Shape,          // Kernel shape
+                   size_t,             // Output channels
+                   ov::AnyMap          // Configuration
+                   >
+    PermConvPermConcatParams;
 
 class PermConvPermConcat : public testing::WithParamInterface<PermConvPermConcatParams>,
-    virtual public LayerTestsUtils::LayerTestsCommon {
+                           virtual public ov::test::SubgraphBaseStaticTest {
 public:
     static std::string getTestCaseName(const testing::TestParamInfo<PermConvPermConcatParams>& obj);
 
 protected:
     void SetUp() override;
-    void Run() override;
 };
-}  // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/range_add.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/range_add.hpp
index 18fe4775ebbea7..6139db3244fc83 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/range_add.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/range_add.hpp
@@ -4,36 +4,45 @@
 
 #pragma once
 
-#include <tuple>
+#include <memory>
 #include <string>
+#include <tuple>
 #include <vector>
-#include <memory>
 
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ov_models/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-#include "shared_test_classes/single_layer/range.hpp"
+namespace ov {
+namespace test {
 
-namespace SubgraphTestsDefinitions {
+typedef std::tuple<float,              // start
+                   float,              // stop
+                   float,              // step
+                   ov::element::Type,  // Input type
+                   std::string         // Target device name
+                   >
+    RangeParams;
 
 // ------------------------------ V0 ------------------------------
 
-class RangeAddSubgraphTest : public testing::WithParamInterface<LayerTestsDefinitions::RangeParams>,
-                             virtual public LayerTestsUtils::LayerTestsCommon {
+class RangeAddSubgraphTest : public testing::WithParamInterface<RangeParams>,
+                             virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<LayerTestsDefinitions::RangeParams>& obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<RangeParams>& obj);
+
 protected:
     void SetUp() override;
 };
 
 // ------------------------------ V4 ------------------------------
 
-class RangeNumpyAddSubgraphTest : public testing::WithParamInterface<LayerTestsDefinitions::RangeParams>,
-                             virtual public LayerTestsUtils::LayerTestsCommon {
+class RangeNumpyAddSubgraphTest : public testing::WithParamInterface<RangeParams>,
+                                  virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<LayerTestsDefinitions::RangeParams>& obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<RangeParams>& obj);
+
 protected:
     void SetUp() override;
 };
 
-}  // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/relu_shape_of.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/relu_shape_of.hpp
index c84f0e2d8292e5..6127de67d74ab1 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/relu_shape_of.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/relu_shape_of.hpp
@@ -4,23 +4,26 @@
 
 #pragma once
 
-#include <tuple>
-#include <string>
-#include <vector>
-#include <memory>
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "shared_test_classes/single_layer/shape_of.hpp"
+namespace ov {
+namespace test {
 
-#include "ov_models/builders.hpp"
+typedef std::tuple<ov::element::Type,  // Input type
+                   ov::element::Type,  // Output type
+                   ov::Shape,          // Input shapes
+                   std::string         // Device name
+                   >
+    ShapeOfParams;
 
-namespace SubgraphTestsDefinitions {
-
-class ReluShapeOfSubgraphTest : public testing::WithParamInterface<LayerTestsDefinitions::shapeOfParamsCommon>,
-        virtual public LayerTestsUtils::LayerTestsCommon {
+class ReluShapeOfSubgraphTest : public testing::WithParamInterface<ov::test::ShapeOfParams>,
+                                virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<LayerTestsDefinitions::shapeOfParamsCommon>& obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<ov::test::ShapeOfParams>& obj);
+
 protected:
     void SetUp() override;
 };
-}  // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/reshape_squeeze_reshape_relu.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/reshape_squeeze_reshape_relu.hpp
index eb4811e08cf353..b2c69f96d946c0 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/reshape_squeeze_reshape_relu.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/reshape_squeeze_reshape_relu.hpp
@@ -4,30 +4,32 @@
 
 #pragma once
 
-#include <tuple>
 #include <string>
+#include <tuple>
 #include <vector>
-#include <memory>
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ov_models/builders.hpp"
-#include "ov_models/utils/ov_helpers.hpp"
-
-namespace SubgraphTestsDefinitions {
-using ShapeAxesTuple = std::pair<std::vector<size_t>, std::vector<int>>;
-
-using ReshapeSqueezeReshapeReluTuple = typename std::tuple<
-    ShapeAxesTuple,                     // Input shapes & squeeze_indices
-    InferenceEngine::Precision,       // Network precision
-    std::string,                      // Device name
-    ngraph::helpers::SqueezeOpType    // SqueezeOpType
->;
-
-class ReshapeSqueezeReshapeRelu
-        : public testing::WithParamInterface<ReshapeSqueezeReshapeReluTuple>,
-          virtual public LayerTestsUtils::LayerTestsCommon {
+
+#include "common_test_utils/test_enums.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+
+using ShapeAxesTuple = std::pair<ov::Shape, std::vector<int>>;
+
+using ReshapeSqueezeReshapeReluTuple = typename std::tuple<ShapeAxesTuple,     // Input shapes & squeeze_indices
+                                                           ov::element::Type,  // Input element type
+                                                           std::string,        // Device name
+                                                           ov::test::utils::SqueezeOpType  // SqueezeOpType
+                                                           >;
+
+class ReshapeSqueezeReshapeRelu : public testing::WithParamInterface<ReshapeSqueezeReshapeReluTuple>,
+                                  virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<ReshapeSqueezeReshapeReluTuple> &obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<ReshapeSqueezeReshapeReluTuple>& obj);
+
 protected:
     void SetUp() override;
 };
-} // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_conv_concat.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_conv_concat.hpp
index a02822a50b9374..d74865a6bb0c6b 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_conv_concat.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_conv_concat.hpp
@@ -4,19 +4,37 @@
 
 #pragma once
 
+#include <memory>
+#include <string>
 #include <tuple>
 #include <vector>
-#include <string>
-#include <memory>
 
 #include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ov_models/utils/ov_helpers.hpp"
-#include "ov_models/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+
+class SplitConvConcatBase : public ov::test::SubgraphBaseStaticTest {
+protected:
+    void configure_test(const ov::test::BasicParams& param);
+};
+
+class SplitConvConcat : public testing::WithParamInterface<ov::test::BasicParams>, virtual public SplitConvConcatBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ov::test::BasicParams>& obj);
+
+protected:
+    void SetUp() override;
+};
+
+}  // namespace test
+}  // namespace ov
 
 namespace SubgraphTestsDefinitions {
 
 class SplitConvConcat : public testing::WithParamInterface<LayerTestsUtils::basicParams>,
-                        virtual public LayerTestsUtils::LayerTestsCommon {
+                        virtual public ov::test::SplitConvConcatBase {
 public:
     static std::string getTestCaseName(const testing::TestParamInfo<LayerTestsUtils::basicParams>& obj);
 
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/variadic_split_pad.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/variadic_split_pad.hpp
index 6cfbf94286902d..d9744f8b2ab8cc 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/variadic_split_pad.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/variadic_split_pad.hpp
@@ -4,33 +4,35 @@
 
 #pragma once
 
-#include <tuple>
 #include <string>
+#include <tuple>
 #include <vector>
 
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ov_models/builders.hpp"
-
-namespace SubgraphTestsDefinitions {
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-typedef std::tuple<
-    InferenceEngine::SizeVector,       // Input shapes
-    size_t,                            // Axis
-    std::vector<size_t>,               // Split number
-    std::vector<size_t>,               // Index connected layer
-    std::vector<int64_t>,              // Pad begin
-    std::vector<int64_t>,              // Pad end
-    ngraph::helpers::PadMode,          // Pad mode
-    InferenceEngine::Precision,        // Network precision
-    std::string                        // Device name
-> SplitPadTuple;
+namespace ov {
+namespace test {
 
+typedef std::tuple<ov::Shape,             // Input shapes
+                   size_t,                // Axis
+                   std::vector<size_t>,   // Split number
+                   std::vector<size_t>,   // Index connected layer
+                   std::vector<int64_t>,  // Pad begin
+                   std::vector<int64_t>,  // Pad end
+                   ov::op::PadMode,       // Pad mode
+                   ov::element::Type,     // Input element type
+                   std::string            // Device name
+                   >
+    SplitPadTuple;
 
-class VariadicSplitPad: public testing::WithParamInterface<SplitPadTuple>,
-                        virtual public LayerTestsUtils::LayerTestsCommon{
+class VariadicSplitPad : public testing::WithParamInterface<SplitPadTuple>,
+                         virtual public ov::test::SubgraphBaseStaticTest {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<SplitPadTuple> &obj);
+    static std::string getTestCaseName(const testing::TestParamInfo<SplitPadTuple>& obj);
+
 protected:
     void SetUp() override;
 };
-}  // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp b/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp
index 139678a602fd21..0d8909c46581e0 100644
--- a/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp
+++ b/src/tests/functional/shared_test_classes/src/base/utils/generate_inputs.cpp
@@ -9,7 +9,7 @@
 
 #include "common_test_utils/ov_tensor_utils.hpp"
 
-#include "shared_test_classes/single_layer/roi_align.hpp"
+#include "shared_test_classes/single_op/roi_align.hpp"
 #include "shared_test_classes/single_layer/psroi_pooling.hpp"
 #include "shared_test_classes/base/utils/generate_inputs.hpp"
 #include "shared_test_classes/base/utils/ranges.hpp"
@@ -537,13 +537,13 @@ ov::runtime::Tensor generate(const std::shared_ptr<ov::op::v3::ROIAlign>& node,
             if (node->get_sampling_ratio() != 0) {
                 const auto &inputShape = node->get_input_shape(0);
                 std::vector<float> blobData(node->get_shape()[0] * 4);
-                LayerTestsDefinitions::ROIAlignLayerTest::fillCoordTensor(blobData,
-                                                                          inputShape[2],
-                                                                          inputShape[3],
-                                                                          node->get_spatial_scale(),
-                                                                          node->get_sampling_ratio(),
-                                                                          node->get_pooled_h(),
-                                                                          node->get_pooled_w());
+                ov::test::ROIAlignLayerTest::fillCoordTensor(blobData,
+                                                             inputShape[2],
+                                                             inputShape[3],
+                                                             node->get_spatial_scale(),
+                                                             node->get_sampling_ratio(),
+                                                             node->get_pooled_h(),
+                                                             node->get_pooled_w());
                 return ov::test::utils::create_tensor<float>(ov::element::f32, targetShape, blobData);
             } else {
                 return generate(std::dynamic_pointer_cast<ov::Node>(node), port, elemType, targetShape);
@@ -551,7 +551,7 @@ ov::runtime::Tensor generate(const std::shared_ptr<ov::op::v3::ROIAlign>& node,
         }
         case 2: {
             std::vector<int> roiIdxVector(node->get_shape()[0]);
-            LayerTestsDefinitions::ROIAlignLayerTest::fillIdxTensor(roiIdxVector, node->get_shape()[0]);
+            ov::test::ROIAlignLayerTest::fillIdxTensor(roiIdxVector, node->get_shape()[0]);
             return ov::test::utils::create_tensor<int>(elemType, targetShape, roiIdxVector);
         }
         default:
diff --git a/src/tests/functional/shared_test_classes/src/single_op/einsum.cpp b/src/tests/functional/shared_test_classes/src/single_op/einsum.cpp
new file mode 100644
index 00000000000000..4912bf6a0afd04
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/src/single_op/einsum.cpp
@@ -0,0 +1,61 @@
+// Copyright (C) 2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/single_op/einsum.hpp"
+
+namespace ov {
+namespace test {
+
+std::string EinsumLayerTest::getTestCaseName(const testing::TestParamInfo<EinsumLayerTestParamsSet>& obj) {
+    EinsumEquationWithInput equation_with_input;
+    ov::element::Type model_type;
+    std::string targetDevice;
+    std::tie(model_type, equation_with_input, targetDevice) = obj.param;
+    std::string equation;
+    std::vector<InputShape> shapes;
+    std::tie(equation, shapes) = equation_with_input;
+
+    std::ostringstream result;
+        result << "IS=(";
+    for (size_t i = 0lu; i < shapes.size(); i++) {
+        result << ov::test::utils::partialShape2str({shapes[i].first}) << (i < shapes.size() - 1lu ? "_" : "");
+    }
+    result << ")_TS=";
+    for (size_t i = 0lu; i < shapes.front().second.size(); i++) {
+        result << "{";
+        for (size_t j = 0lu; j < shapes.size(); j++) {
+            result << ov::test::utils::vec2str(shapes[j].second[i]) << (j < shapes.size() - 1lu ? "_" : "");
+        }
+        result << "}_";
+    }
+    result << "PRC=" << model_type.get_type_name() << "_";
+    result << "Eq=" << equation << "_";
+    result << "trgDev=" << targetDevice;
+    return result.str();
+}
+
+void EinsumLayerTest::SetUp() {
+    EinsumEquationWithInput equation_with_input;
+    ov::element::Type model_type;
+    std::tie(model_type, equation_with_input, targetDevice) = this->GetParam();
+    std::string equation;
+    std::vector<InputShape> shapes;
+    std::tie(equation, shapes) = equation_with_input;
+    init_input_shapes(shapes);
+
+    ov::ParameterVector params;
+    ov::OutputVector param_outs;
+    for (const auto& shape : inputDynamicShapes) {
+        auto param = std::make_shared<ov::op::v0::Parameter>(model_type, shape);
+        params.push_back(param);
+        param_outs.push_back(param);
+    }
+
+    auto einsum = std::make_shared<ov::op::v7::Einsum>(param_outs, equation);
+
+    auto result = std::make_shared<ov::op::v0::Result>(einsum);
+    function = std::make_shared<ov::Model>(result, params, "einsum");
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/single_op/eye.cpp b/src/tests/functional/shared_test_classes/src/single_op/eye.cpp
new file mode 100644
index 00000000000000..0aae5e1593ef43
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/src/single_op/eye.cpp
@@ -0,0 +1,74 @@
+// Copyright (C) 2022 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "shared_test_classes/single_op/eye.hpp"
+
+#include "common_test_utils/ov_tensor_utils.hpp"
+#include "openvino/pass/constant_folding.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/result.hpp"
+#include "openvino/op/eye.hpp"
+
+namespace ov {
+namespace test {
+std::string EyeLayerTest::getTestCaseName(testing::TestParamInfo<EyeLayerTestParams> obj) {
+    std::string td;
+    std::vector<ov::Shape> input_shapes;
+    ov::element::Type model_type;
+    std::vector<int> out_batch_shape;
+    std::vector<int> eye_par;
+    std::tie(input_shapes, out_batch_shape, eye_par, model_type, td) = obj.param;
+    std::ostringstream result;
+    result << "EyeTest_";
+    result << "IS=(";
+    for (const auto& shape : input_shapes) {
+        result << ov::test::utils::partialShape2str({shape}) << "_";
+    }
+    result << ")";
+    result << "rowNum=" << eye_par[0] << "_";
+    result << "colNum=" << eye_par[1] << "_";
+    result << "diagShift=" << eye_par[2] << "_";
+    result << "batchShape=" << ov::test::utils::vec2str(out_batch_shape) << "_";
+    result << model_type << "_";
+    result << std::to_string(obj.index);
+    return result.str();
+}
+
+void EyeLayerTest::SetUp() {
+    std::vector<ov::Shape> input_shapes;
+    int row_num, col_num;
+    int shift;
+    std::vector<int> out_batch_shape;
+    ov::element::Type model_type;
+    std::vector<int> eye_par;
+    std::tie(input_shapes, out_batch_shape, eye_par, model_type, targetDevice) = this->GetParam();
+    row_num = eye_par[0];
+    col_num = eye_par[1];
+    shift = eye_par[2];
+
+    std::shared_ptr<ov::op::v9::Eye> eye_operation;
+
+    auto rows_const = std::make_shared<ov::op::v0::Constant>(ov::element::i32, input_shapes[0], &row_num);
+    rows_const->set_friendly_name("rows");
+    auto cols_const = std::make_shared<ov::op::v0::Constant>(ov::element::i32, input_shapes[1], &col_num);
+    cols_const->set_friendly_name("cols");
+    auto diag_const = std::make_shared<ov::op::v0::Constant>(ov::element::i32, input_shapes[2], &shift);
+    diag_const->set_friendly_name("diagInd");
+
+    if (!out_batch_shape.empty() && out_batch_shape[0] != 0) {
+        auto batch_shape_par = std::make_shared<ov::op::v0::Constant>(ov::element::i32,
+                                                                      ov::Shape{out_batch_shape.size()},
+                                                                      out_batch_shape.data());
+        batch_shape_par->set_friendly_name("batchShape");
+        eye_operation = std::make_shared<ov::op::v9::Eye>(rows_const, cols_const, diag_const, batch_shape_par, model_type);
+    } else {
+        eye_operation = std::make_shared<ov::op::v9::Eye>(rows_const, cols_const, diag_const, model_type);
+    }
+
+    // Without this call the eye operation will be calculated by CPU and substituted by Constant operator
+    ov::pass::disable_constant_folding(eye_operation);
+    auto result = std::make_shared<ov::op::v0::Result>(eye_operation);
+    function = std::make_shared<ov::Model>(result, ov::ParameterVector(), "eye");
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/single_op/pooling.cpp b/src/tests/functional/shared_test_classes/src/single_op/pooling.cpp
new file mode 100644
index 00000000000000..c86f8a2217fee5
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/src/single_op/pooling.cpp
@@ -0,0 +1,170 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/single_op/pooling.hpp"
+
+#include "ov_models/builders.hpp"
+#include "openvino/op/parameter.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/result.hpp"
+#include "openvino/op/avg_pool.hpp"
+#include "openvino/op/max_pool.hpp"
+
+namespace ov {
+namespace test {
+using ov::test::utils::PoolingTypes;
+
+std::string PoolingLayerTest::getTestCaseName(const testing::TestParamInfo<poolLayerTestParamsSet>& obj) {
+    poolSpecificParams pool_params;
+    ov::element::Type model_type;
+    std::vector<InputShape> shapes;
+    std::string targetDevice;
+    std::tie(pool_params, model_type, shapes, targetDevice) = obj.param;
+    PoolingTypes pool_type;
+    std::vector<size_t> kernel, stride;
+    std::vector<size_t> pad_begin, pad_end;
+    ov::op::PadType pad_type;
+    ov::op::RoundingType rounding_type;
+    bool excludePad;
+    std::tie(pool_type, kernel, stride, pad_begin, pad_end, rounding_type, pad_type, excludePad) = pool_params;
+
+    std::ostringstream result;
+    result << "IS=(";
+    for (size_t i = 0lu; i < shapes.size(); i++) {
+        result << ov::test::utils::partialShape2str({shapes[i].first}) << (i < shapes.size() - 1lu ? "_" : "");
+    }
+    result << ")_TS=";
+    for (size_t i = 0lu; i < shapes.front().second.size(); i++) {
+        result << "{";
+        for (size_t j = 0lu; j < shapes.size(); j++) {
+            result << ov::test::utils::vec2str(shapes[j].second[i]) << (j < shapes.size() - 1lu ? "_" : "");
+        }
+        result << "}_";
+    }
+    switch (pool_type) {
+        case PoolingTypes::MAX:
+            result << "MaxPool_";
+            break;
+        case PoolingTypes::AVG:
+            result << "AvgPool_";
+            result << "ExcludePad=" << excludePad << "_";
+            break;
+    }
+    result << "K" << ov::test::utils::vec2str(kernel) << "_";
+    result << "S" << ov::test::utils::vec2str(stride) << "_";
+    result << "PB" << ov::test::utils::vec2str(pad_begin) << "_";
+    result << "PE" << ov::test::utils::vec2str(pad_end) << "_";
+    result << "Rounding=" << rounding_type << "_";
+    result << "AutoPad=" << pad_type << "_";
+    result << "modelType=" << model_type.get_type_name() << "_";
+    result << "trgDev=" << targetDevice;
+    return result.str();
+}
+
+void PoolingLayerTest::SetUp() {
+    poolSpecificParams pool_params;
+    std::vector<InputShape> shapes;
+    ov::element::Type model_type;
+    std::tie(pool_params, model_type, shapes, targetDevice) = this->GetParam();
+    PoolingTypes pool_type;
+    std::vector<size_t> kernel, stride;
+    std::vector<size_t> pad_begin, pad_end;
+    ov::op::PadType pad_type;
+    ov::op::RoundingType rounding_type;
+    bool excludePad;
+    std::tie(pool_type, kernel, stride, pad_begin, pad_end, rounding_type, pad_type, excludePad) = pool_params;
+    init_input_shapes(shapes);
+
+    auto param = std::make_shared<ov::op::v0::Parameter>(model_type, inputDynamicShapes.front());
+
+    std::shared_ptr<ov::Node> pooling = ngraph::builder::makePooling(param,
+                                                                         stride,
+                                                                         pad_begin,
+                                                                         pad_end,
+                                                                         kernel,
+                                                                         rounding_type,
+                                                                         pad_type,
+                                                                         excludePad,
+                                                                         pool_type);
+
+    auto result = std::make_shared<ov::op::v0::Result>(pooling);
+    function = std::make_shared<ov::Model>(result, ov::ParameterVector{param}, "pooling");
+}
+
+
+std::string MaxPoolingV8LayerTest::getTestCaseName(const testing::TestParamInfo<maxPoolV8LayerTestParamsSet>& obj) {
+    maxPoolV8SpecificParams pool_params;
+    ov::element::Type model_type;
+    std::vector<InputShape> shapes;
+    std::string target_device;
+    std::tie(pool_params, model_type, shapes, target_device) = obj.param;
+    std::vector<size_t> kernel, stride, dilation;
+    std::vector<size_t> pad_begin, pad_end;
+    ov::op::PadType pad_type;
+    ov::op::RoundingType rounding_type;
+    ov::element::Type index_element_type;
+    int64_t axis;
+    std::tie(kernel, stride, dilation, pad_begin, pad_end, index_element_type, axis, rounding_type, pad_type) = pool_params;
+
+    std::ostringstream result;
+    result << "IS=(";
+    for (size_t i = 0lu; i < shapes.size(); i++) {
+        result << ov::test::utils::partialShape2str({shapes[i].first}) << (i < shapes.size() - 1lu ? "_" : "");
+    }
+    result << ")_TS=";
+    for (size_t i = 0lu; i < shapes.front().second.size(); i++) {
+        result << "{";
+        for (size_t j = 0lu; j < shapes.size(); j++) {
+            result << ov::test::utils::vec2str(shapes[j].second[i]) << (j < shapes.size() - 1lu ? "_" : "");
+        }
+        result << "}_";
+    }
+    result << "K" << ov::test::utils::vec2str(kernel) << "_";
+    result << "S" << ov::test::utils::vec2str(stride) << "_";
+    result << "D" << ov::test::utils::vec2str(dilation) << "_";
+    result << "PB" << ov::test::utils::vec2str(pad_begin) << "_";
+    result << "PE" << ov::test::utils::vec2str(pad_end) << "_";
+    result << "IET" << index_element_type << "_";
+    result << "A" << axis << "_";
+    result << "Rounding=" << rounding_type << "_";
+    result << "AutoPad=" << pad_type << "_";
+    result << "modelType=" << model_type.get_type_name() << "_";
+    result << "trgDev=" << target_device;
+    return result.str();
+}
+
+void MaxPoolingV8LayerTest::SetUp() {
+    maxPoolV8SpecificParams pool_params;
+    ov::element::Type model_type;
+    std::vector<InputShape> shapes;
+    std::tie(pool_params, model_type, shapes, targetDevice) = this->GetParam();
+    std::vector<size_t> kernel, stride, dilation;
+    std::vector<size_t> pad_begin, pad_end;
+    ov::op::PadType pad_type;
+    ov::op::RoundingType rounding_type;
+    ov::element::Type index_element_type;
+    int64_t axis;
+    std::tie(kernel, stride, dilation, pad_begin, pad_end, index_element_type, axis, rounding_type, pad_type) = pool_params;
+    init_input_shapes(shapes);
+
+    auto param = std::make_shared<ov::op::v0::Parameter>(model_type, inputDynamicShapes.front());
+
+    std::shared_ptr<ov::Node> max_pool = ngraph::builder::makeMaxPoolingV8(param, stride, dilation, pad_begin, pad_end,
+                                                                              kernel, rounding_type, pad_type,
+                                                                              index_element_type, axis);
+
+    const auto max_pool_v8_second_output_is_supported = targetDevice == ov::test::utils::DEVICE_GPU;
+
+    ov::ResultVector results;
+    if (max_pool_v8_second_output_is_supported) {
+        results = {std::make_shared<ov::op::v0::Result>(max_pool->output(0)),
+                   std::make_shared<ov::op::v0::Result>(max_pool->output(1))};
+    } else {
+        results = { std::make_shared<ov::op::v0::Result>(max_pool->output(0)) };
+    }
+    function = std::make_shared<ov::Model>(max_pool->outputs(), ov::ParameterVector{param}, "MaxPoolV8");
+}
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/single_op/roi_align.cpp b/src/tests/functional/shared_test_classes/src/single_op/roi_align.cpp
new file mode 100644
index 00000000000000..d191e7dda9565f
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/src/single_op/roi_align.cpp
@@ -0,0 +1,203 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <random>
+
+#include "shared_test_classes/single_op/roi_align.hpp"
+
+#include "openvino/core/enum_names.hpp"
+
+namespace ov {
+namespace test {
+std::string ROIAlignLayerTest::getTestCaseName(const testing::TestParamInfo<roialignParams>& obj) {
+    std::vector<InputShape> input_shapes;
+    ov::Shape coords_shape;
+    int pooled_h;
+    int pooled_w;
+    float spatial_scale;
+    int pooling_ratio;
+    std::string pooling_mode;
+    ov::element::Type model_type;
+    std::string target_device;
+    std::tie(input_shapes, coords_shape, pooled_h, pooled_w, spatial_scale,
+    pooling_ratio, pooling_mode, model_type, target_device) = obj.param;
+
+    std::ostringstream result;
+    result << "IS=(";
+    for (size_t i = 0lu; i < input_shapes.size(); i++) {
+        result << ov::test::utils::partialShape2str({input_shapes[i].first})
+               << (i < input_shapes.size() - 1lu ? "_" : "");
+    }
+    result << ")_TS=";
+    for (size_t i = 0lu; i < input_shapes.front().second.size(); i++) {
+        result << "{";
+        for (size_t j = 0lu; j < input_shapes.size(); j++) {
+            result << ov::test::utils::vec2str(input_shapes[j].second[i]) << (j < input_shapes.size() - 1lu ? "_" : "");
+        }
+        result << "}_";
+    }
+    result << "coordShape=" << ov::test::utils::vec2str(coords_shape) << "_";
+    result << "pooledH=" << pooled_h << "_";
+    result << "pooledW=" << pooled_w << "_";
+    result << "spatialScale=" << spatial_scale << "_";
+    result << "poolingRatio=" << pooling_ratio << "_";
+    result << "poolingMode=" << pooling_mode << "_";
+    result << "modelType=" << model_type.to_string() << "_";
+    result << "trgDev=" << target_device;
+    return result.str();
+}
+
+static int randInt(int low, int high) {
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<int> dis(low, high);
+    return dis(gen);
+}
+
+void ROIAlignLayerTest::fillCoordTensor(std::vector<float>& coords, int height, int width,
+                                        float spatial_scale, int pooled_ratio, int pooled_h, int pooled_w) {
+    int min_roi_width = pooled_w;
+    int max_roi_width = width / pooled_ratio;
+    int min_roi_height = pooled_h;
+    int max_roi_height = height / pooled_ratio;
+
+    for (int i = 0; i < coords.size() / 4; i++) {
+        int size_x = std::min(width, randInt(min_roi_width, max_roi_width));
+        int size_y = std::min(height, randInt(min_roi_height, max_roi_height));
+        int start_x = randInt(0, std::max(1, width - size_x - 1));
+        int start_y = randInt(0, std::max(1, height - size_y - 1));
+
+        coords[i * 4] = start_x / spatial_scale;
+        coords[i * 4 + 1] = start_y / spatial_scale;
+        coords[i * 4 + 2] = (start_x + size_x - 1) / spatial_scale;
+        coords[i * 4 + 3] = (start_y + size_y - 1) / spatial_scale;
+    }
+}
+void ROIAlignLayerTest::fillIdxTensor(std::vector<int>& idx, int batch_size) {
+    int batch_id = 0;
+    for (int i = 0; i < idx.size(); i++) {
+        idx[i] = batch_id;
+        batch_id = (batch_id + 1) % batch_size;
+    }
+}
+
+void ROIAlignLayerTest::SetUp() {
+    std::vector<InputShape> input_shapes;
+    ov::Shape coords_shape;
+    int pooled_h;
+    int pooled_w;
+    float spatial_scale;
+    int pooling_ratio;
+    std::string pooling_mode;
+    ov::element::Type model_type;
+    std::tie(input_shapes, coords_shape, pooled_h, pooled_w, spatial_scale,
+    pooling_ratio, pooling_mode, model_type, targetDevice) = this->GetParam();
+
+    init_input_shapes(input_shapes);
+
+    auto param = std::make_shared<ov::op::v0::Parameter>(model_type, inputDynamicShapes[0]);
+    std::vector<float> proposal_vector;
+    std::vector<int> roi_idx_vector;
+    proposal_vector.resize(coords_shape[0] * 4);
+    roi_idx_vector.resize(coords_shape[0]);
+
+    fillCoordTensor(proposal_vector, inputDynamicShapes[0][2].get_length(), inputDynamicShapes[0][3].get_length(),
+                    spatial_scale, pooling_ratio, pooled_h, pooled_w);
+    fillIdxTensor(roi_idx_vector, inputDynamicShapes[0][0].get_length());
+    auto idx_shape = ov::Shape{coords_shape[0]};
+
+    auto coords = std::make_shared<ov::op::v0::Constant>(model_type, coords_shape, proposal_vector.data());
+    auto rois_Idx = std::make_shared<ov::op::v0::Constant>(ov::element::i32, idx_shape, roi_idx_vector.data());
+    auto roi_align = std::make_shared<ov::op::v3::ROIAlign>(param,
+                                                            coords,
+                                                            rois_Idx,
+                                                            pooled_h,
+                                                            pooled_w,
+                                                            pooling_ratio,
+                                                            spatial_scale,
+                                                            pooling_mode);
+    function = std::make_shared<ov::Model>(roi_align->outputs(), ov::ParameterVector{param}, "roi_align");
+}
+
+std::string ROIAlignV9LayerTest::getTestCaseName(const testing::TestParamInfo<roialignV9Params>& obj) {
+    std::vector<InputShape> input_shapes;
+    ov::Shape coords_shape;
+    int pooled_h;
+    int pooled_w;
+    float spatial_scale;
+    int pooling_ratio;
+    std::string pooling_mode;
+    std::string roi_aligned_mode;
+    ov::element::Type model_type;
+    std::string target_device;
+    std::tie(input_shapes, coords_shape, pooled_h, pooled_w, spatial_scale,
+    pooling_ratio, pooling_mode, roi_aligned_mode, model_type, target_device) = obj.param;
+
+    std::ostringstream result;
+    result << "IS=(";
+    for (size_t i = 0lu; i < input_shapes.size(); i++) {
+        result << ov::test::utils::partialShape2str({input_shapes[i].first})
+               << (i < input_shapes.size() - 1lu ? "_" : "");
+    }
+    result << ")_TS=";
+    for (size_t i = 0lu; i < input_shapes.front().second.size(); i++) {
+        result << "{";
+        for (size_t j = 0lu; j < input_shapes.size(); j++) {
+            result << ov::test::utils::vec2str(input_shapes[j].second[i]) << (j < input_shapes.size() - 1lu ? "_" : "");
+        }
+        result << "}_";
+    }
+    result << "coordShape=" << ov::test::utils::vec2str(coords_shape) << "_";
+    result << "pooledH=" << pooled_h << "_";
+    result << "pooledW=" << pooled_w << "_";
+    result << "spatialScale=" << spatial_scale << "_";
+    result << "poolingRatio=" << pooling_ratio << "_";
+    result << "poolingMode=" << pooling_mode << "_";
+    result << "ROIMode=" << roi_aligned_mode << "_";
+    result << "modelType=" << model_type.to_string() << "_";
+    result << "trgDev=" << target_device;
+    return result.str();
+}
+
+void ROIAlignV9LayerTest::SetUp() {
+    std::vector<InputShape> input_shapes;
+    ov::Shape coords_shape;
+    int pooled_h;
+    int pooled_w;
+    float spatial_scale;
+    int pooling_ratio;
+    std::string pooling_mode;
+    std::string roi_aligned_mode;
+    ov::element::Type model_type;
+    std::tie(input_shapes, coords_shape, pooled_h, pooled_w, spatial_scale,
+    pooling_ratio, pooling_mode, roi_aligned_mode, model_type, targetDevice) = this->GetParam();
+
+    init_input_shapes(input_shapes);
+
+    auto param = std::make_shared<ov::op::v0::Parameter>(model_type, inputDynamicShapes[0]);
+    std::vector<float> proposal_vector;
+    std::vector<int> roi_idx_vector;
+    proposal_vector.resize(coords_shape[0] * 4);
+    roi_idx_vector.resize(coords_shape[0]);
+
+    ROIAlignLayerTest::fillCoordTensor(proposal_vector, inputDynamicShapes[0][2].get_length(), inputDynamicShapes[0][3].get_length(),
+                                       spatial_scale, pooling_ratio, pooled_h, pooled_w);
+    ROIAlignLayerTest::fillIdxTensor(roi_idx_vector, inputDynamicShapes[0][0].get_length());
+    auto idx_shape = ov::Shape{coords_shape[0]};
+
+    auto coords = std::make_shared<ov::op::v0::Constant>(model_type, coords_shape, proposal_vector.data());
+    auto rois_Idx = std::make_shared<ov::op::v0::Constant>(ov::element::i32, idx_shape, roi_idx_vector.data());
+    auto roi_align = std::make_shared<ov::op::v9::ROIAlign>(param,
+                                                            coords,
+                                                            rois_Idx,
+                                                            pooled_h,
+                                                            pooled_w,
+                                                            pooling_ratio,
+                                                            spatial_scale,
+                                                            ov::EnumNames<ov::op::v9::ROIAlign::PoolingMode>::as_enum(pooling_mode),
+                                                            ov::EnumNames<ov::op::v9::ROIAlign::AlignedMode>::as_enum(roi_aligned_mode));
+    function = std::make_shared<ov::Model>(roi_align->outputs(), ov::ParameterVector{param}, "roi_align");
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/single_op/roi_pooling.cpp b/src/tests/functional/shared_test_classes/src/single_op/roi_pooling.cpp
new file mode 100644
index 00000000000000..f4867abe6629b7
--- /dev/null
+++ b/src/tests/functional/shared_test_classes/src/single_op/roi_pooling.cpp
@@ -0,0 +1,74 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/single_op/roi_pooling.hpp"
+
+namespace ov {
+namespace test {
+std::string ROIPoolingLayerTest::getTestCaseName(const testing::TestParamInfo<roiPoolingParamsTuple>& obj) {
+    std::vector<InputShape> input_shapes;
+    ov::Shape pool_shape;
+    float spatial_scale;
+    ov::test::utils::ROIPoolingTypes pool_method;
+    ov::element::Type model_type;
+    std::string target_device;
+    std::tie(input_shapes, pool_shape, spatial_scale, pool_method, model_type, target_device) = obj.param;
+
+    std::ostringstream result;
+    result << "IS=(";
+    for (size_t i = 0lu; i < input_shapes.size(); i++) {
+        result << ov::test::utils::partialShape2str({input_shapes[i].first})
+               << (i < input_shapes.size() - 1lu ? "_" : "");
+    }
+    result << ")_TS=";
+    for (size_t i = 0lu; i < input_shapes.front().second.size(); i++) {
+        result << "{";
+        for (size_t j = 0lu; j < input_shapes.size(); j++) {
+            result << ov::test::utils::vec2str(input_shapes[j].second[i]) << (j < input_shapes.size() - 1lu ? "_" : "");
+        }
+        result << "}_";
+    }
+    result << "PS=" << ov::test::utils::vec2str(pool_shape) << "_";
+    result << "Scale=" << spatial_scale << "_";
+    switch (pool_method) {
+        case utils::ROIPoolingTypes::ROI_MAX:
+            result << "Max_";
+            break;
+        case utils::ROIPoolingTypes::ROI_BILINEAR:
+            result << "Bilinear_";
+            break;
+    }
+    result << "modelType=" << model_type.to_string() << "_";
+    result << "trgDev=" << target_device;
+    return result.str();
+}
+
+void ROIPoolingLayerTest::SetUp() {
+    std::vector<InputShape> input_shapes;
+    ov::Shape pool_shape;
+    float spatial_scale;
+    ov::test::utils::ROIPoolingTypes pool_method;
+    ov::element::Type model_type;
+    std::string target_device;
+    std::tie(input_shapes, pool_shape, spatial_scale, pool_method, model_type, targetDevice) = this->GetParam();
+
+    abs_threshold = 0.08f;
+
+    init_input_shapes(input_shapes);
+
+    auto param = std::make_shared<ov::op::v0::Parameter>(model_type, inputDynamicShapes[0]);
+    auto coord_param = std::make_shared<ov::op::v0::Parameter>(model_type, inputDynamicShapes[1]);
+    std::string pool_method_str;
+    if (pool_method == ov::test::utils::ROIPoolingTypes::ROI_MAX) {
+        pool_method_str = "max";
+    } else if (pool_method == ov::test::utils::ROIPoolingTypes::ROI_BILINEAR) {
+        pool_method_str = "bilinear";
+    } else {
+        FAIL() << "Incorrect type of ROIPooling operation";
+    }
+    auto roi_pooling = std::make_shared<ov::op::v0::ROIPooling>(param, coord_param, pool_shape, spatial_scale, pool_method_str);
+    function = std::make_shared<ov::Model>(roi_pooling->outputs(), ov::ParameterVector{param, coord_param}, "roi_pooling");
+}
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/mul_conv_fusion.cpp b/src/tests/functional/shared_test_classes/src/subgraph/mul_conv_fusion.cpp
index d16090a2e1c819..411cff4a46ab21 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/mul_conv_fusion.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/mul_conv_fusion.cpp
@@ -2,17 +2,20 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "transformations/common_optimizations/mul_conv_fusion.hpp"
-#include "ngraph/pass/constant_folding.hpp"
 #include "shared_test_classes/subgraph/mul_conv_fusion.hpp"
+
+#include "common_test_utils/graph_comparator.hpp"
+#include "openvino/pass/manager.hpp"
 #include "ov_models/builders.hpp"
+#include "transformations/common_optimizations/mul_conv_fusion.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
-std::string MulConvFusion::getTestCaseName(const testing::TestParamInfo<MulConvFusionParams> &obj) {
-    ngraph::NodeTypeInfo conv_type;
-    ngraph::Shape input_shape, weights_shape, const_shape;
-    ngraph::element::Type precision;
+std::string MulConvFusion::getTestCaseName(const testing::TestParamInfo<MulConvFusionParams>& obj) {
+    ov::NodeTypeInfo conv_type;
+    ov::Shape input_shape, weights_shape, const_shape;
+    ov::element::Type precision;
     std::string device;
     std::tie(conv_type, input_shape, weights_shape, const_shape, precision, std::ignore, device) = obj.param;
     std::ostringstream results;
@@ -27,36 +30,43 @@ std::string MulConvFusion::getTestCaseName(const testing::TestParamInfo<MulConvF
 }
 
 void MulConvFusion::SetUp() {
-    ngraph::NodeTypeInfo conv_type;
-    ngraph::Shape input_shape, weights_shape, const_shape;
-    ngraph::element::Type precision;
+    ov::NodeTypeInfo conv_type;
+    ov::Shape input_shape, weights_shape, const_shape;
+    ov::element::Type precision;
     bool is_negative;
-    std::tie(conv_type, input_shape, weights_shape, const_shape, precision, is_negative, targetDevice) = this->GetParam();
-    auto param = std::make_shared<ngraph::opset8::Parameter>(precision, input_shape);
+    std::tie(conv_type, input_shape, weights_shape, const_shape, precision, is_negative, targetDevice) =
+        this->GetParam();
+    auto param = std::make_shared<ov::op::v0::Parameter>(precision, input_shape);
     auto spatial_dims = input_shape.size() - 2;
 
     auto mul_const = ngraph::builder::makeConstant<float>(precision, const_shape, {}, true);
-    auto mul = std::make_shared<ngraph::opset8::Multiply>(param, mul_const);
-    ngraph::Shape strides(spatial_dims, 1);
+    auto mul = std::make_shared<ov::op::v1::Multiply>(param, mul_const);
+    ov::Shape strides(spatial_dims, 1);
     std::vector<ptrdiff_t> pad_begin(spatial_dims, 0), pad_end(spatial_dims, 0);
     auto weights = ngraph::builder::makeConstant<float>(precision, weights_shape, {}, true);
-    std::shared_ptr<ngraph::Node> conv;
-    if (conv_type == ngraph::opset8::Convolution::get_type_info_static()) {
-        conv = std::make_shared<ngraph::opset8::Convolution>(mul, weights, strides, pad_begin, pad_end, strides);
-    } else if (conv_type == ngraph::opset8::GroupConvolution::get_type_info_static()) {
-        conv = std::make_shared<ngraph::opset8::GroupConvolution>(mul, weights, strides, pad_begin, pad_end, strides);
-    } else if (conv_type == ngraph::opset8::ConvolutionBackpropData::get_type_info_static()) {
-        conv = std::make_shared<ngraph::opset8::ConvolutionBackpropData>(mul, weights, strides, pad_begin, pad_end, strides);
-    } else if (conv_type == ngraph::opset8::GroupConvolutionBackpropData::get_type_info_static()) {
-        conv = std::make_shared<ngraph::opset8::GroupConvolutionBackpropData>(mul, weights, strides, pad_begin, pad_end, strides);
+    std::shared_ptr<ov::Node> conv;
+    if (conv_type == ov::op::v1::Convolution::get_type_info_static()) {
+        conv = std::make_shared<ov::op::v1::Convolution>(mul, weights, strides, pad_begin, pad_end, strides);
+    } else if (conv_type == ov::op::v1::GroupConvolution::get_type_info_static()) {
+        conv = std::make_shared<ov::op::v1::GroupConvolution>(mul, weights, strides, pad_begin, pad_end, strides);
+    } else if (conv_type == ov::op::v1::ConvolutionBackpropData::get_type_info_static()) {
+        conv =
+            std::make_shared<ov::op::v1::ConvolutionBackpropData>(mul, weights, strides, pad_begin, pad_end, strides);
+    } else if (conv_type == ov::op::v1::GroupConvolutionBackpropData::get_type_info_static()) {
+        conv = std::make_shared<ov::op::v1::GroupConvolutionBackpropData>(mul,
+                                                                          weights,
+                                                                          strides,
+                                                                          pad_begin,
+                                                                          pad_end,
+                                                                          strides);
     } else {
         OPENVINO_THROW("Unsupported type");
     }
 
-    function = std::make_shared<ngraph::Function>(ngraph::OutputVector{conv}, ngraph::ParameterVector{param});
-    auto cloned_function = ngraph::clone_function(*function);
+    function = std::make_shared<ov::Model>(ov::OutputVector{conv}, ov::ParameterVector{param});
+    auto cloned_function = function->clone();
 
-    ngraph::pass::Manager manager;
+    ov::pass::Manager manager;
     manager.register_pass<ov::pass::MultiplyConvolutionFusion>();
     manager.register_pass<ov::pass::MultiplyGroupConvolutionFusion>();
     manager.register_pass<ov::pass::MultiplyConvolutionBackpropDataFusion>();
@@ -65,58 +75,75 @@ void MulConvFusion::SetUp() {
 
     bool functions_equal = false;
     if (!is_negative) {
-        auto param = std::make_shared<ngraph::opset8::Parameter>(precision, input_shape);
-        ngraph::Shape strides(spatial_dims, 1);
+        auto param = std::make_shared<ov::op::v0::Parameter>(precision, input_shape);
+        ov::Shape strides(spatial_dims, 1);
         std::vector<ptrdiff_t> pad_begin(spatial_dims, 0), pad_end(spatial_dims, 0);
-        std::shared_ptr<ngraph::Node> conv;
-        if (conv_type == ngraph::opset8::Convolution::get_type_info_static()) {
-            weights = std::make_shared<ngraph::opset8::Multiply>(weights, mul_const);
+        std::shared_ptr<ov::Node> conv;
+        if (conv_type == ov::op::v1::Convolution::get_type_info_static()) {
+            weights = std::make_shared<ov::op::v1::Multiply>(weights, mul_const);
             weights = ov::get_constant_from_source(weights);
             ASSERT_NE(nullptr, weights);
-            conv = std::make_shared<ngraph::opset8::Convolution>(param, weights, strides, pad_begin, pad_end, strides);
-        } else if (conv_type == ngraph::opset8::GroupConvolution::get_type_info_static()) {
+            conv = std::make_shared<ov::op::v1::Convolution>(param, weights, strides, pad_begin, pad_end, strides);
+        } else if (conv_type == ov::op::v1::GroupConvolution::get_type_info_static()) {
             const_shape.insert(const_shape.begin(), weights_shape.size() - const_shape.size(), 1);
             auto G = const_shape[2] > 1 ? weights_shape[0] : 1;
             const_shape[0] = G;
             const_shape[2] /= G;
-            auto reshape = std::make_shared<ngraph::opset8::Reshape>(mul_const,
-                    ngraph::op::Constant::create(ngraph::element::u64, ngraph::Shape{const_shape.size()}, const_shape), false);
-            weights = std::make_shared<ngraph::opset8::Multiply>(weights, reshape);
+            auto reshape = std::make_shared<ov::op::v1::Reshape>(
+                mul_const,
+                ov::op::v0::Constant::create(ov::element::u64, ov::Shape{const_shape.size()}, const_shape),
+                false);
+            weights = std::make_shared<ov::op::v1::Multiply>(weights, reshape);
             weights = ov::get_constant_from_source(weights);
             ASSERT_NE(nullptr, weights);
-            conv = std::make_shared<ngraph::opset8::GroupConvolution>(param, weights, strides, pad_begin, pad_end, strides);
-        } else if (conv_type == ngraph::opset8::ConvolutionBackpropData::get_type_info_static()) {
+            conv = std::make_shared<ov::op::v1::GroupConvolution>(param, weights, strides, pad_begin, pad_end, strides);
+        } else if (conv_type == ov::op::v1::ConvolutionBackpropData::get_type_info_static()) {
             const_shape.insert(const_shape.begin(), weights_shape.size() - const_shape.size(), 1);
             const_shape[0] = const_shape[1];
             const_shape[1] = 1;
-            auto reshape = std::make_shared<ngraph::opset8::Reshape>(mul_const,
-                    ngraph::op::Constant::create(ngraph::element::u64, ngraph::Shape{const_shape.size()}, const_shape), false);
-            weights = std::make_shared<ngraph::opset8::Multiply>(weights, reshape);
+            auto reshape = std::make_shared<ov::op::v1::Reshape>(
+                mul_const,
+                ov::op::v0::Constant::create(ov::element::u64, ov::Shape{const_shape.size()}, const_shape),
+                false);
+            weights = std::make_shared<ov::op::v1::Multiply>(weights, reshape);
             weights = ov::get_constant_from_source(weights);
             ASSERT_NE(nullptr, weights);
-            conv = std::make_shared<ngraph::opset8::ConvolutionBackpropData>(param, weights, strides, pad_begin, pad_end, strides);
-        } else if (conv_type == ngraph::opset8::GroupConvolutionBackpropData::get_type_info_static()) {
+            conv = std::make_shared<ov::op::v1::ConvolutionBackpropData>(param,
+                                                                         weights,
+                                                                         strides,
+                                                                         pad_begin,
+                                                                         pad_end,
+                                                                         strides);
+        } else if (conv_type == ov::op::v1::GroupConvolutionBackpropData::get_type_info_static()) {
             const_shape.insert(const_shape.begin(), weights_shape.size() - const_shape.size(), 1);
             auto G = const_shape[2] > 1 ? weights_shape[0] : 1;
             const_shape[0] = G;
             const_shape[1] = const_shape[2] / G;
             const_shape[2] = 1;
-            auto reshape = std::make_shared<ngraph::opset8::Reshape>(mul_const,
-                    ngraph::op::Constant::create(ngraph::element::u64, ngraph::Shape{const_shape.size()}, const_shape), false);
-            weights = std::make_shared<ngraph::opset8::Multiply>(weights, reshape);
+            auto reshape = std::make_shared<ov::op::v1::Reshape>(
+                mul_const,
+                ov::op::v0::Constant::create(ov::element::u64, ov::Shape{const_shape.size()}, const_shape),
+                false);
+            weights = std::make_shared<ov::op::v1::Multiply>(weights, reshape);
             weights = ov::get_constant_from_source(weights);
             ASSERT_NE(nullptr, weights);
-            conv = std::make_shared<ngraph::opset8::GroupConvolutionBackpropData>(param, weights, strides, pad_begin, pad_end, strides);
+            conv = std::make_shared<ov::op::v1::GroupConvolutionBackpropData>(param,
+                                                                              weights,
+                                                                              strides,
+                                                                              pad_begin,
+                                                                              pad_end,
+                                                                              strides);
         } else {
             OPENVINO_THROW("Unsupported type");
         }
-        auto reference_function = std::make_shared<ngraph::Function>(ngraph::OutputVector{conv}, ngraph::ParameterVector{param});
+        auto reference_function = std::make_shared<ov::Model>(ov::OutputVector{conv}, ov::ParameterVector{param});
         std::tie(functions_equal, std::ignore) = compare_functions(cloned_function, reference_function, true);
         ASSERT_TRUE(functions_equal);
     } else {
-        auto reference_function = ngraph::clone_function(*function);
+        auto reference_function = function->clone();
         std::tie(functions_equal, std::ignore) = compare_functions(cloned_function, reference_function, true);
         ASSERT_TRUE(functions_equal);
     }
 }
-} // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/multiply_add.cpp b/src/tests/functional/shared_test_classes/src/subgraph/multiply_add.cpp
index 8b070a70026d15..dfc1dcdb5f7fd5 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/multiply_add.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/multiply_add.cpp
@@ -4,37 +4,43 @@
 
 #include "shared_test_classes/subgraph/multiply_add.hpp"
 
-namespace SubgraphTestsDefinitions {
-std::string MultiplyAddLayerTest::getTestCaseName(const testing::TestParamInfo<MultiplyAddParamsTuple> &obj) {
-    std::vector<size_t> inputShapes;
-    InferenceEngine::Precision netPrecision;
+#include "ov_models/builders.hpp"
+#include "ov_models/utils/ov_helpers.hpp"
+
+namespace ov {
+namespace test {
+
+std::string MultiplyAddLayerTest::getTestCaseName(const testing::TestParamInfo<MultiplyAddParamsTuple>& obj) {
+    ov::Shape inputShapes;
+    ov::element::Type element_type;
     std::string targetName;
-    std::tie(inputShapes, netPrecision, targetName) = obj.param;
+    std::tie(inputShapes, element_type, targetName) = obj.param;
     std::ostringstream results;
 
     results << "IS=" << ov::test::utils::vec2str(inputShapes) << "_";
-    results << "netPRC=" << netPrecision.name() << "_";
+    results << "ET=" << element_type << "_";
     results << "targetDevice=" << targetName << "_";
     return results.str();
 }
 
 void MultiplyAddLayerTest::SetUp() {
-    std::vector<size_t> inputShape;
-    auto netPrecision = InferenceEngine::Precision::UNSPECIFIED;
-    std::tie(inputShape, netPrecision, targetDevice) = this->GetParam();
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::Shape(inputShape))};
-    auto paramOuts = ngraph::helpers::convert2OutputVector(
-            ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    ov::Shape inputShape;
+    ov::element::Type element_type;
+    std::tie(inputShape, element_type, targetDevice) = this->GetParam();
+    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(element_type, ov::PartialShape(inputShape))};
+    auto paramOuts =
+        ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ov::op::v0::Parameter>(params));
 
     std::vector<size_t> constShape(inputShape.size(), 1);
     constShape[1] = inputShape[1];
 
-    auto const_mul = ngraph::builder::makeConstant<float>(ngPrc, constShape, {}, true);
-    auto mul = std::make_shared<ngraph::opset3::Multiply>(paramOuts[0], const_mul);
-    auto const_add = ngraph::builder::makeConstant<float>(ngPrc, constShape, {}, true);
-    auto add = std::make_shared<ngraph::opset3::Add>(mul, const_add);
-    ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(add)};
-    function = std::make_shared<ngraph::Function>(results, params, "multiplyAdd");
+    auto const_mul = ngraph::builder::makeConstant<float>(element_type, constShape, {}, true);
+    auto mul = std::make_shared<ov::op::v1::Multiply>(paramOuts[0], const_mul);
+    auto const_add = ngraph::builder::makeConstant<float>(element_type, constShape, {}, true);
+    auto add = std::make_shared<ov::op::v1::Add>(mul, const_add);
+    ov::ResultVector results{std::make_shared<ov::op::v0::Result>(add)};
+    function = std::make_shared<ov::Model>(results, params, "multiplyAdd");
 }
-} // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/mvn_multiply_add.cpp b/src/tests/functional/shared_test_classes/src/subgraph/mvn_multiply_add.cpp
index 2371182175c711..9ff6272b9ab529 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/mvn_multiply_add.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/mvn_multiply_add.cpp
@@ -4,12 +4,16 @@
 
 #include "shared_test_classes/subgraph/mvn_multiply_add.hpp"
 
-namespace SubgraphTestsDefinitions {
+#include "ov_models/builders.hpp"
+#include "ov_models/utils/ov_helpers.hpp"
 
-std::string MVNMultiplyAdd::getTestCaseName(const testing::TestParamInfo<mvnMultiplyAddParams> &obj) {
-    std::pair<InferenceEngine::SizeVector, InferenceEngine::SizeVector> shapes;
-    InferenceEngine::SizeVector inputShapes, constantShapes;
-    InferenceEngine::Precision dataPrecision, axesPrecision;
+namespace ov {
+namespace test {
+
+std::string MVNMultiplyAdd::getTestCaseName(const testing::TestParamInfo<mvnMultiplyAddParams>& obj) {
+    std::pair<ov::Shape, ov::Shape> shapes;
+    ov::Shape inputShapes, constantShapes;
+    ov::element::Type dataPrecision, axesPrecision;
     std::vector<int> axes;
     bool normalizeVariance;
     float eps;
@@ -20,8 +24,8 @@ std::string MVNMultiplyAdd::getTestCaseName(const testing::TestParamInfo<mvnMult
     std::ostringstream result;
     result << "IS=" << ov::test::utils::vec2str(inputShapes) << "_";
     result << "CS=" << ov::test::utils::vec2str(constantShapes) << "_";
-    result << "DataPrc=" << dataPrecision.name() << "_";
-    result << "AxPrc=" << axesPrecision.name() << "_";
+    result << "DataET=" << dataPrecision << "_";
+    result << "AxET=" << axesPrecision << "_";
     result << "Ax=" << ov::test::utils::vec2str(axes) << "_";
     result << "NormVariance=" << (normalizeVariance ? "TRUE" : "FALSE") << "_";
     result << "Eps=" << eps << "_";
@@ -31,29 +35,29 @@ std::string MVNMultiplyAdd::getTestCaseName(const testing::TestParamInfo<mvnMult
 }
 
 void MVNMultiplyAdd::SetUp() {
-    std::pair<InferenceEngine::SizeVector, InferenceEngine::SizeVector> shapes;
-    InferenceEngine::SizeVector inputShapes, constantShapes;
-    InferenceEngine::Precision dataPrecision, axesPrecision;
+    std::pair<ov::Shape, ov::Shape> shapes;
+    ov::Shape inputShapes, constantShapes;
+    ov::element::Type dataType, axesType;
     std::vector<int> axes;
     bool normalizeVariance;
     float eps;
     std::string epsMode;
-    std::tie(shapes, dataPrecision, axesPrecision, axes, normalizeVariance, eps, epsMode, targetDevice) = this->GetParam();
+    std::tie(shapes, dataType, axesType, axes, normalizeVariance, eps, epsMode, targetDevice) = this->GetParam();
     std::tie(inputShapes, constantShapes) = shapes;
 
-    auto dataType = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(dataPrecision);
-    auto axesType = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(axesPrecision);
-
-    ov::ParameterVector param {std::make_shared<ov::op::v0::Parameter>(dataType, ov::Shape(inputShapes))};
-    auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(param));
-    auto axesNode = ngraph::builder::makeConstant(axesType, ngraph::Shape{axes.size()}, axes);
+    ov::ParameterVector param{std::make_shared<ov::op::v0::Parameter>(dataType, ov::Shape(inputShapes))};
+    auto paramOuts =
+        ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ov::op::v0::Parameter>(param));
+    auto axesNode = ngraph::builder::makeConstant(axesType, ov::Shape{axes.size()}, axes);
     auto mvn = ngraph::builder::makeMVN6(paramOuts[0], axesNode, normalizeVariance, eps, epsMode);
     auto gamma = ngraph::builder::makeConstant<float>(dataType, constantShapes, {}, true);
-    auto mul = std::make_shared<ngraph::opset7::Multiply>(mvn, gamma);
+    auto mul = std::make_shared<ov::op::v1::Multiply>(mvn, gamma);
     auto beta = ngraph::builder::makeConstant<float>(dataType, constantShapes, {}, true);
-    auto add = std::make_shared<ngraph::opset7::Add>(mul, beta);
+    auto add = std::make_shared<ov::op::v1::Add>(mul, beta);
 
-    ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(add)};
-    function = std::make_shared<ngraph::Function>(results, param, "MVNMultiplyAdd");
+    ov::ResultVector results{std::make_shared<ov::op::v0::Result>(add)};
+    function = std::make_shared<ov::Model>(results, param, "MVNMultiplyAdd");
 }
-} // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/perm_conv_perm_concat.cpp b/src/tests/functional/shared_test_classes/src/subgraph/perm_conv_perm_concat.cpp
index b488166297f618..e51e30a6caaa49 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/perm_conv_perm_concat.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/perm_conv_perm_concat.cpp
@@ -4,105 +4,97 @@
 
 #include "shared_test_classes/subgraph/perm_conv_perm_concat.hpp"
 
-namespace SubgraphTestsDefinitions {
+#include "common_test_utils/data_utils.hpp"
+#include "functional_test_utils/skip_tests_config.hpp"
+#include "ov_models/builders.hpp"
+
+namespace ov {
+namespace test {
+
 std::string PermConvPermConcat::getTestCaseName(const testing::TestParamInfo<PermConvPermConcatParams>& obj) {
-    InferenceEngine::Precision netPrecision;
+    ov::element::Type element_type;
     std::string targetName;
-    std::array<size_t, 4> input_shape;
-    std::array<size_t, 2> kernel_shape;
+    ov::Shape input_shape;
+    ov::Shape kernel_shape;
     size_t output_channels;
-    std::map<std::string, std::string> configuration;
-
+    ov::AnyMap configuration;
 
-    std::tie(netPrecision, targetName, input_shape, kernel_shape, output_channels, configuration) = obj.param;
+    std::tie(element_type, targetName, input_shape, kernel_shape, output_channels, configuration) = obj.param;
     std::ostringstream results;
 
     results << "IS=" << ov::test::utils::vec2str(std::vector<size_t>(input_shape.begin(), input_shape.end())) << "_";
     results << "KS=" << ov::test::utils::vec2str(std::vector<size_t>(kernel_shape.begin(), kernel_shape.end())) << "_";
     results << "OC=" << output_channels << "_";
-    results << "netPRC=" << netPrecision.name() << "_";
+    results << "ET=" << element_type << "_";
     results << "targetDevice=" << targetName;
     for (auto const& configItem : configuration) {
-        results << "_configItem=" << configItem.first << "_" << configItem.second;
+        results << "_configItem=" << configItem.first << "_" << configItem.second.as<std::string>();
     }
     return results.str();
 }
 
 void PermConvPermConcat::SetUp() {
-    InferenceEngine::Precision netPrecision;
-    std::array<size_t, 4> input_shape;
-    std::array<size_t, 2> kernel_shape;
+    ov::element::Type element_type;
+    ov::Shape input_shape;
+    ov::Shape kernel_shape;
     size_t output_channels;
-    std::map<std::string, std::string> additional_config;
+    ov::AnyMap additional_config;
 
-    std::tie(netPrecision, targetDevice, input_shape, kernel_shape, output_channels, additional_config) = this->GetParam();
+    std::tie(element_type, targetDevice, input_shape, kernel_shape, output_channels, additional_config) =
+        this->GetParam();
 
     configuration.insert(additional_config.begin(), additional_config.end());
 
     const std::size_t input_dim = std::accumulate(input_shape.begin(), input_shape.end(), 1, std::multiplies<size_t>());
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
 
-    std::vector<size_t> input_dims { 1, input_dim };
+    std::vector<size_t> input_dims{1, input_dim};
     std::vector<size_t> reshape_in_dims = std::vector<size_t>(input_shape.begin(), input_shape.end());
-    std::vector<size_t> permute_in_order = { 0, 3, 1, 2 };
-    std::vector<size_t> permute_out_order = { 0, 2, 3, 1 };
+    std::vector<size_t> permute_in_order = {0, 3, 1, 2};
+    std::vector<size_t> permute_out_order = {0, 2, 3, 1};
 
-    ov::ParameterVector input_parameter {std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::Shape(input_dims))};
+    ov::ParameterVector input_parameter{std::make_shared<ov::op::v0::Parameter>(element_type, ov::Shape(input_dims))};
 
-    auto reshape_in_pattern = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
-        ngraph::Shape{4},
-        reshape_in_dims);
-    auto reshape_in = std::make_shared<ngraph::op::v1::Reshape>(input_parameter[0], reshape_in_pattern, false);
+    auto reshape_in_pattern = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, reshape_in_dims);
+    auto reshape_in = std::make_shared<ov::op::v1::Reshape>(input_parameter[0], reshape_in_pattern, false);
 
-    auto permute_in_params = std::make_shared<ngraph::opset1::Constant>(ngraph::element::i64,
-        ngraph::Shape{4},
-        ngraph::Shape{permute_in_order});
-    auto permute_in = std::make_shared<ngraph::opset1::Transpose>(reshape_in, permute_in_params);
+    auto permute_in_params =
+        std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, ov::Shape{permute_in_order});
+    auto permute_in = std::make_shared<ov::op::v1::Transpose>(reshape_in, permute_in_params);
     auto conv_in_shape = permute_in->get_output_shape(0);
     auto conv_weights_size = output_channels * (conv_in_shape[1]) * kernel_shape[0] * kernel_shape[1];
-    auto conv = ngraph::builder::makeConvolution(permute_in, ngPrc, {kernel_shape[0], kernel_shape[1]}, {1, 1}, {0, 0}, {0, 0}, {1, 1},
-        ngraph::op::PadType::VALID, output_channels, false, ov::test::utils::generate_float_numbers(conv_weights_size, -0.5f, 0.5f));
-
-    auto permute_out_params = std::make_shared<ngraph::opset1::Constant>(ngraph::element::i64,
-        ngraph::Shape{4},
-        permute_out_order);
-    auto permute_out = std::make_shared<ngraph::opset1::Transpose>(conv, permute_out_params);
+    auto conv =
+        ngraph::builder::makeConvolution(permute_in,
+                                         element_type,
+                                         {kernel_shape[0], kernel_shape[1]},
+                                         {1, 1},
+                                         {0, 0},
+                                         {0, 0},
+                                         {1, 1},
+                                         ov::op::PadType::VALID,
+                                         output_channels,
+                                         false,
+                                         ov::test::utils::generate_float_numbers(conv_weights_size, -0.5f, 0.5f));
+
+    auto permute_out_params = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, permute_out_order);
+    auto permute_out = std::make_shared<ov::op::v1::Transpose>(conv, permute_out_params);
 
     auto permute_out_shape = permute_out->get_output_shape(0);
 
-    auto concat_const = ngraph::builder::makeConstant(ngPrc, {1, 1, 1, permute_out_shape[3]},
-                                                      ov::test::utils::generate_float_numbers(permute_out_shape[3], -10, 10));
+    auto concat_const =
+        ngraph::builder::makeConstant(element_type,
+                                      {1, 1, 1, permute_out_shape[3]},
+                                      ov::test::utils::generate_float_numbers(permute_out_shape[3], -10, 10));
 
     auto concat = ngraph::builder::makeConcat({permute_out, concat_const}, 2);
 
-    auto reshape_out_pattern = std::make_shared<ngraph::opset1::Constant>(ngraph::element::i64,
-        ngraph::Shape{2},
+    auto reshape_out_pattern = std::make_shared<ov::op::v0::Constant>(
+        ov::element::i64,
+        ov::Shape{2},
         InferenceEngine::SizeVector({1, (permute_out_shape[2] + 1) * permute_out_shape[3]}));
-    auto reshape_out = std::make_shared<ngraph::op::v1::Reshape>(concat, reshape_out_pattern, false);
+    auto reshape_out = std::make_shared<ov::op::v1::Reshape>(concat, reshape_out_pattern, false);
 
-    function = std::make_shared<ngraph::Function>(reshape_out, input_parameter, "perm_conv_perm_concat");
+    function = std::make_shared<ov::Model>(reshape_out, input_parameter, "perm_conv_perm_concat");
 }
 
-void PermConvPermConcat::Run() {
-    SKIP_IF_CURRENT_TEST_IS_DISABLED()
-
-    LoadNetwork();
-
-    inferRequest = executableNetwork.CreateInferRequest();
-    inputs.clear();
-
-    for (const auto &input : cnnNetwork.getInputsInfo()) {
-        const auto &info = input.second;
-        auto tensorDesc = info->getTensorDesc();
-
-        auto blob = FuncTestUtils::createAndFillBlobFloat(tensorDesc, 2, -1, 100, 111);
-
-        FuncTestUtils::fillInputsBySinValues(blob);
-        inferRequest.SetBlob(info->name(), blob);
-        inputs.push_back(blob);
-    }
-    inferRequest.Infer();
-
-    Validate();
-}
-} // namespace SubgraphTestsDefinitions
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/range_add.cpp b/src/tests/functional/shared_test_classes/src/subgraph/range_add.cpp
index 86cfab9864b6cf..129b19667baca8 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/range_add.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/range_add.cpp
@@ -4,84 +4,79 @@
 
 #include "shared_test_classes/subgraph/range_add.hpp"
 
-namespace SubgraphTestsDefinitions {
+#include "ov_models/builders.hpp"
+
+namespace ov {
+namespace test {
 
 // ------------------------------ V0 ------------------------------
 
-std::string RangeAddSubgraphTest::getTestCaseName(const testing::TestParamInfo<LayerTestsDefinitions::RangeParams>& obj) {
-    InferenceEngine::Precision netPrecision;
-    InferenceEngine::Precision inPrc, outPrc;
-    InferenceEngine::Layout inLayout, outLayout;
+std::string RangeAddSubgraphTest::getTestCaseName(const testing::TestParamInfo<RangeParams>& obj) {
+    ov::element::Type input_type;
     float start, stop, step;
     std::string targetDevice;
-    std::tie(start, stop, step, netPrecision, inPrc, outPrc, inLayout, outLayout, targetDevice) = obj.param;
+    std::tie(start, stop, step, input_type, targetDevice) = obj.param;
 
     std::ostringstream result;
     const char separator = '_';
     result << "Start=" << start << separator;
     result << "Stop=" << stop << separator;
     result << "Step=" << step << separator;
-    result << "netPRC=" << netPrecision.name() << separator;
+    result << "ET=" << input_type << separator;
     result << "targetDevice=" << targetDevice;
     return result.str();
 }
 
 void RangeAddSubgraphTest::SetUp() {
-    InferenceEngine::Precision netPrecision;
+    ov::element::Type element_type;
     float start, stop, step;
-    std::tie(start, stop, step, netPrecision, inPrc, outPrc, inLayout, outLayout, targetDevice) = GetParam();
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-
-    auto startConstant = std::make_shared<ngraph::opset1::Constant>(ngPrc, ngraph::Shape{}, start);
-    auto stopConstant = std::make_shared<ngraph::opset1::Constant>(ngPrc, ngraph::Shape{}, stop);
-    auto stepConstant = std::make_shared<ngraph::opset1::Constant>(ngPrc, ngraph::Shape{}, step);
-    auto range = std::make_shared<ngraph::opset3::Range>(startConstant, stopConstant, stepConstant);
-
-    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(ngPrc, range->get_shape())};
-    auto eltwise = ngraph::builder::makeEltwise(params.front(), range, ngraph::helpers::EltwiseTypes::ADD);
-    const ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(eltwise)};
-    function = std::make_shared<ngraph::Function>(results, params, "RangeEltwise");
+    std::tie(start, stop, step, element_type, targetDevice) = GetParam();
+
+    auto startConstant = std::make_shared<ov::op::v0::Constant>(element_type, ov::Shape{}, start);
+    auto stopConstant = std::make_shared<ov::op::v0::Constant>(element_type, ov::Shape{}, stop);
+    auto stepConstant = std::make_shared<ov::op::v0::Constant>(element_type, ov::Shape{}, step);
+    auto range = std::make_shared<ov::op::v0::Range>(startConstant, stopConstant, stepConstant);
+
+    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(element_type, range->get_shape())};
+    auto eltwise = ngraph::builder::makeEltwise(params.front(), range, ov::test::utils::EltwiseTypes::ADD);
+    const ov::ResultVector results{std::make_shared<ov::op::v0::Result>(eltwise)};
+    function = std::make_shared<ov::Model>(results, params, "RangeEltwise");
 }
 
 // ------------------------------ V4 ------------------------------
 
-std::string RangeNumpyAddSubgraphTest::getTestCaseName(const testing::TestParamInfo<LayerTestsDefinitions::RangeParams>& obj) {
-    InferenceEngine::Precision netPrc;
-    InferenceEngine::Precision constPrc;
-    InferenceEngine::Precision outPrc;
-    InferenceEngine::Layout inLayout, outLayout;
+std::string RangeNumpyAddSubgraphTest::getTestCaseName(const testing::TestParamInfo<RangeParams>& obj) {
+    ov::element::Type element_type;
     float start, stop, step;
     std::string targetDevice;
-    std::tie(start, stop, step, constPrc, netPrc, outPrc, inLayout, outLayout, targetDevice) = obj.param;
+    std::tie(start, stop, step, element_type, targetDevice) = obj.param;
 
     std::ostringstream result;
     const char separator = '_';
     result << "Start=" << start << separator;
     result << "Stop=" << stop << separator;
     result << "Step=" << step << separator;
-    result << "constPRC=" << constPrc.name() << separator;
-    result << "netPRC=" << netPrc.name() << separator;
+    result << "ET=" << element_type << separator;
     result << "targetDevice=" << targetDevice;
     return result.str();
 }
 
 void RangeNumpyAddSubgraphTest::SetUp() {
-    InferenceEngine::Precision netPrc;
-    InferenceEngine::Precision constPrc;
+    ov::element::Type element_type;
     float start, stop, step;
-    std::tie(start, stop, step, constPrc, netPrc, outPrc, inLayout, outLayout, targetDevice) = GetParam();
-    auto ngConstPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(constPrc);
-    auto ngNetPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrc);
+    std::tie(start, stop, step, element_type, targetDevice) = GetParam();
 
-    auto startConstant = std::make_shared<ngraph::opset1::Constant>(ngConstPrc, ngraph::Shape{}, start);
-    auto stopConstant = std::make_shared<ngraph::opset1::Constant>(ngConstPrc, ngraph::Shape{}, stop);
-    auto stepConstant = std::make_shared<ngraph::opset1::Constant>(ngConstPrc, ngraph::Shape{}, step);
-    auto range = std::make_shared<ngraph::opset4::Range>(startConstant, stopConstant, stepConstant, ngNetPrc);
+    auto startConstant = std::make_shared<ov::op::v0::Constant>(element_type, ov::Shape{}, start);
+    auto stopConstant = std::make_shared<ov::op::v0::Constant>(element_type, ov::Shape{}, stop);
+    auto stepConstant = std::make_shared<ov::op::v0::Constant>(element_type, ov::Shape{}, step);
+    auto range = std::make_shared<ov::op::v4::Range>(startConstant, stopConstant, stepConstant, element_type);
 
-    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(ngNetPrc, range->get_shape())};
+    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(element_type, range->get_shape())};
 
-    auto eltwise = ngraph::builder::makeEltwise(params.front(), range, ngraph::helpers::EltwiseTypes::ADD);
-    const ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(eltwise)};
-    function = std::make_shared<ngraph::Function>(results, params, "RangeEltwise");
+    auto eltwise = ngraph::builder::makeEltwise(params.front(), range, ov::test::utils::EltwiseTypes::ADD);
+    const ov::ResultVector results{std::make_shared<ov::op::v0::Result>(eltwise)};
+    function = std::make_shared<ov::Model>(results, params, "RangeEltwise");
 }
-}  // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/relu_shape_of.cpp b/src/tests/functional/shared_test_classes/src/subgraph/relu_shape_of.cpp
index b23f32f4d5b99f..bbb78339d04e6f 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/relu_shape_of.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/relu_shape_of.cpp
@@ -4,29 +4,32 @@
 
 #include "shared_test_classes/subgraph/relu_shape_of.hpp"
 
-namespace SubgraphTestsDefinitions {
+namespace ov {
+namespace test {
 
-    std::string ReluShapeOfSubgraphTest::getTestCaseName(const testing::TestParamInfo<LayerTestsDefinitions::shapeOfParamsCommon>& obj) {
-        InferenceEngine::SizeVector inputShapes;
-        InferenceEngine::Precision inputPrecision;
-        std::string targetDevice;
-        std::tie(inputPrecision, inputShapes, targetDevice) = obj.param;
-        std::ostringstream result;
-        result << "IS=" << ov::test::utils::vec2str(inputShapes) << "_";
-        result << "Precision=" << inputPrecision.name() << "_";
-        result << "TargetDevice=" << targetDevice;
-        return result.str();
-    }
+std::string ReluShapeOfSubgraphTest::getTestCaseName(const testing::TestParamInfo<ov::test::ShapeOfParams>& obj) {
+    ov::Shape inputShapes;
+    ov::element::Type element_type, output_type;
+    std::string targetDevice;
+    std::tie(element_type, output_type, inputShapes, targetDevice) = obj.param;
+    std::ostringstream result;
+    result << "IS=" << ov::test::utils::vec2str(inputShapes) << "_";
+    result << "IET=" << element_type << "_";
+    result << "OET=" << output_type << "_";
+    result << "TargetDevice=" << targetDevice;
+    return result.str();
+}
 
-    void ReluShapeOfSubgraphTest::SetUp() {
-        InferenceEngine::SizeVector inputShapes;
-        InferenceEngine::Precision inputPrecision;
-        std::tie(inputPrecision, inputShapes, targetDevice) = this->GetParam();
-        auto inType = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inputPrecision);
-        ov::ParameterVector param {std::make_shared<ov::op::v0::Parameter>(inType, ov::Shape(inputShapes))};
-        auto relu = std::make_shared<ngraph::opset3::Relu>(param[0]);
-        auto shapeOf = std::make_shared<ngraph::opset3::ShapeOf>(relu, inType);
-        const ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(shapeOf)};
-        function = std::make_shared<ngraph::Function>(results, param, "ReluShapeOf");
-    }
-}  // namespace SubgraphTestsDefinitions
+void ReluShapeOfSubgraphTest::SetUp() {
+    ov::Shape inputShapes;
+    ov::element::Type element_type, output_type;
+    std::tie(element_type, output_type, inputShapes, targetDevice) = this->GetParam();
+    ov::ParameterVector param{std::make_shared<ov::op::v0::Parameter>(element_type, ov::Shape(inputShapes))};
+    auto relu = std::make_shared<ov::op::v0::Relu>(param[0]);
+    auto shapeOf = std::make_shared<ov::op::v3::ShapeOf>(relu, output_type);
+    const ov::ResultVector results{std::make_shared<ov::op::v0::Result>(shapeOf)};
+    function = std::make_shared<ov::Model>(results, param, "ReluShapeOf");
+}
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/reshape_squeeze_reshape_relu.cpp b/src/tests/functional/shared_test_classes/src/subgraph/reshape_squeeze_reshape_relu.cpp
index 098f1d12a5ed89..1d0c680a55408e 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/reshape_squeeze_reshape_relu.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/reshape_squeeze_reshape_relu.cpp
@@ -2,46 +2,50 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <debug.h>
 #include "shared_test_classes/subgraph/reshape_squeeze_reshape_relu.hpp"
 
-namespace SubgraphTestsDefinitions {
-    std::string ReshapeSqueezeReshapeRelu::getTestCaseName(const testing::TestParamInfo<ReshapeSqueezeReshapeReluTuple> &obj) {
-        ShapeAxesTuple squeezeShape;
-        InferenceEngine::Precision netPrecision;
-        std::string targetName;
-        ngraph::helpers::SqueezeOpType opType;
-        std::tie(squeezeShape, netPrecision, targetName, opType) = obj.param;
-        std::ostringstream results;
-        results << "OpType=" << opType;
-        results << "IS=" << ov::test::utils::vec2str(squeezeShape.first) << "_";
-        results << "indices=" << ov::test::utils::vec2str(squeezeShape.second) << "_";
-        results << "netPRC=" << netPrecision.name() << "_";
-        results << "targetDevice=" << targetName << "_";
-        return results.str();
-    }
+#include "ov_models/builders.hpp"
 
-    void ReshapeSqueezeReshapeRelu::SetUp() {
-        ShapeAxesTuple squeezeShape;
-        InferenceEngine::Precision netPrecision;
-        ngraph::helpers::SqueezeOpType opType;
-        std::tie(squeezeShape, netPrecision, targetDevice, opType) = this->GetParam();
-        const std::size_t input_dim = InferenceEngine::details::product(squeezeShape.first);
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        std::vector<size_t> shape_input{1, input_dim};
-        ov::ParameterVector input {std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::Shape(shape_input))};
-        auto reshape1_pattern = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
-                                                                       ngraph::Shape{squeezeShape.first.size()},
-                                                                       squeezeShape.first);
-        auto reshape1 = std::make_shared<ngraph::op::v1::Reshape>(input[0], reshape1_pattern, false);
-        auto squeeze = ngraph::builder::makeSqueezeUnsqueeze(reshape1, ngraph::element::i64, squeezeShape.second, opType);
-        auto reshape2_pattern = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
-                                                                       ngraph::Shape{2},
-                                                                       std::vector<size_t>{1, input_dim});
-        auto reshape2 = std::make_shared<ngraph::op::v1::Reshape>(squeeze, reshape2_pattern, false);
-        auto func = std::make_shared<ngraph::opset1::Relu>(reshape2);
-        std::string squeezeType;
+namespace ov {
+namespace test {
 
-        function = std::make_shared<ngraph::Function>(func, input, "reshape_squeeze_reshape_relu");
-    }
-} // namespace SubgraphTestsDefinitions
+std::string ReshapeSqueezeReshapeRelu::getTestCaseName(
+    const testing::TestParamInfo<ReshapeSqueezeReshapeReluTuple>& obj) {
+    ShapeAxesTuple squeezeShape;
+    ov::element::Type element_type;
+    std::string targetName;
+    ov::test::utils::SqueezeOpType opType;
+    std::tie(squeezeShape, element_type, targetName, opType) = obj.param;
+    std::ostringstream results;
+    results << "OpType=" << opType;
+    results << "IS=" << ov::test::utils::vec2str(squeezeShape.first) << "_";
+    results << "indices=" << ov::test::utils::vec2str(squeezeShape.second) << "_";
+    results << "netPRC=" << element_type << "_";
+    results << "targetDevice=" << targetName << "_";
+    return results.str();
+}
+
+void ReshapeSqueezeReshapeRelu::SetUp() {
+    ShapeAxesTuple squeezeShape;
+    ov::element::Type element_type;
+    ov::test::utils::SqueezeOpType opType;
+    std::tie(squeezeShape, element_type, targetDevice, opType) = this->GetParam();
+    const size_t input_dim = ov::shape_size(squeezeShape.first);
+    std::vector<size_t> shape_input{1, input_dim};
+    ov::ParameterVector input{std::make_shared<ov::op::v0::Parameter>(element_type, ov::Shape(shape_input))};
+    auto reshape1_pattern = std::make_shared<ov::op::v0::Constant>(ov::element::i64,
+                                                                   ov::Shape{squeezeShape.first.size()},
+                                                                   squeezeShape.first);
+    auto reshape1 = std::make_shared<ov::op::v1::Reshape>(input[0], reshape1_pattern, false);
+    auto squeeze = ngraph::builder::makeSqueezeUnsqueeze(reshape1, ov::element::i64, squeezeShape.second, opType);
+    auto reshape2_pattern =
+        std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, std::vector<size_t>{1, input_dim});
+    auto reshape2 = std::make_shared<ov::op::v1::Reshape>(squeeze, reshape2_pattern, false);
+    auto func = std::make_shared<ov::op::v0::Relu>(reshape2);
+    std::string squeezeType;
+
+    function = std::make_shared<ov::Model>(func, input, "reshape_squeeze_reshape_relu");
+}
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/split_conv_concat.cpp b/src/tests/functional/shared_test_classes/src/subgraph/split_conv_concat.cpp
index 60ad615b7567a1..7dc009d0022fff 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/split_conv_concat.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/split_conv_concat.cpp
@@ -4,30 +4,40 @@
 
 #include "shared_test_classes/subgraph/split_conv_concat.hpp"
 
-namespace SubgraphTestsDefinitions {
+#include "common_test_utils/data_utils.hpp"
+#include "ie_common.h"
+#include "ov_models/builders.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-std::string SplitConvConcat::getTestCaseName(const testing::TestParamInfo<LayerTestsUtils::basicParams>& obj) {
-    InferenceEngine::Precision netPrecision;
-    InferenceEngine::SizeVector inputShapes, newInputShapes;
+namespace ov {
+namespace test {
+
+std::string SplitConvConcat::getTestCaseName(const testing::TestParamInfo<ov::test::BasicParams>& obj) {
+    ov::element::Type element_type;
+    ov::Shape inputShapes;
     std::string targetDevice;
-    std::tie(netPrecision, inputShapes, targetDevice) = obj.param;
+    std::tie(element_type, inputShapes, targetDevice) = obj.param;
 
     std::ostringstream result;
     result << "IS=" << ov::test::utils::vec2str(inputShapes) << "_";
-    result << "netPRC=" << netPrecision.name() << "_";
+    result << "ET=" << element_type << "_";
     result << "targetDevice=" << targetDevice;
     return result.str();
 }
 
 void SplitConvConcat::SetUp() {
-    std::vector<size_t> inputShape;
-    InferenceEngine::Precision netPrecision;
-    std::tie(netPrecision, inputShape, targetDevice) = this->GetParam();
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+    configure_test(this->GetParam());
+}
+
+void SplitConvConcatBase::configure_test(const ov::test::BasicParams& param) {
+    ov::Shape inputShape;
+    ov::element::Type element_type;
+    std::tie(element_type, inputShape, targetDevice) = param;
 
-    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::Shape(inputShape))};
+    ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(element_type, ov::Shape(inputShape))};
 
-    auto split = ngraph::builder::makeSplit(params[0], ngPrc, 2, 1);
+    auto split = ngraph::builder::makeSplit(params[0], element_type, 2, 1);
 
     std::vector<float> filterWeights1;
     std::vector<float> filterWeights2;
@@ -35,17 +45,65 @@ void SplitConvConcat::SetUp() {
         filterWeights1 = ov::test::utils::generate_float_numbers(8 * inputShape[1] / 2 * 3, -0.2f, 0.2f);
         filterWeights2 = ov::test::utils::generate_float_numbers(8 * inputShape[1] / 2 * 3, -0.2f, 0.2f);
     }
-    auto conv1 = ngraph::builder::makeConvolution(split->output(0), ngPrc, {1, 3}, {1, 1}, {0, 0}, {0, 0}, {1, 1},
-                                                  ngraph::op::PadType::VALID, 8, false, filterWeights1);
-    auto relu1 = std::make_shared<ngraph::opset1::Relu>(conv1);
+    auto conv1 = ngraph::builder::makeConvolution(split->output(0),
+                                                  element_type,
+                                                  {1, 3},
+                                                  {1, 1},
+                                                  {0, 0},
+                                                  {0, 0},
+                                                  {1, 1},
+                                                  ov::op::PadType::VALID,
+                                                  8,
+                                                  false,
+                                                  filterWeights1);
+    auto relu1 = std::make_shared<ov::op::v0::Relu>(conv1);
+
+    auto conv2 = ngraph::builder::makeConvolution(split->output(1),
+                                                  element_type,
+                                                  {1, 3},
+                                                  {1, 1},
+                                                  {0, 0},
+                                                  {0, 0},
+                                                  {1, 1},
+                                                  ov::op::PadType::VALID,
+                                                  8,
+                                                  false,
+                                                  filterWeights2);
+    auto relu2 = std::make_shared<ov::op::v0::Relu>(conv2);
+    auto concat = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{relu1->output(0), relu2->output(0)}, 1);
+
+    ov::ResultVector results{std::make_shared<ov::op::v0::Result>(concat)};
+    function = std::make_shared<ov::Model>(results, params, "SplitConvConcat");
+}
+
+}  // namespace test
+}  // namespace ov
+
+namespace SubgraphTestsDefinitions {
 
-    auto conv2 = ngraph::builder::makeConvolution(split->output(1), ngPrc, {1, 3}, {1, 1}, {0, 0}, {0, 0}, {1, 1},
-                                                  ngraph::op::PadType::VALID, 8, false, filterWeights2);
-    auto relu2 = std::make_shared<ngraph::opset1::Relu>(conv2);
-    auto concat = std::make_shared<ngraph::opset1::Concat>(ngraph::OutputVector{relu1->output(0), relu2->output(0)}, 1);
+std::string SplitConvConcat::getTestCaseName(const testing::TestParamInfo<LayerTestsUtils::basicParams>& obj) {
+    InferenceEngine::Precision precision;
+    InferenceEngine::SizeVector inputShapes;
+    std::string targetDevice;
+    std::tie(precision, inputShapes, targetDevice) = obj.param;
+    auto element_type = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(precision);
+
+    std::ostringstream result;
+    result << "IS=" << ov::test::utils::vec2str(inputShapes) << "_";
+    result << "ET=" << element_type << "_";
+    result << "targetDevice=" << targetDevice;
+    return result.str();
+}
+
+void SplitConvConcat::SetUp() {
+    InferenceEngine::Precision precision;
+    InferenceEngine::SizeVector inputShapes;
+    std::tie(precision, inputShapes, targetDevice) = this->GetParam();
+    auto element_type = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(precision);
+    ov::Shape shape = inputShapes;
 
-    ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(concat)};
-    function = std::make_shared<ngraph::Function>(results, params, "SplitConvConcat");
+    ov::test::BasicParams param(element_type, shape, targetDevice);
+    configure_test(param);
 }
 
 }  // namespace SubgraphTestsDefinitions
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/variadic_split_pad.cpp b/src/tests/functional/shared_test_classes/src/subgraph/variadic_split_pad.cpp
index 13d1c9c542c5cb..8c7906c275e3c1 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/variadic_split_pad.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/variadic_split_pad.cpp
@@ -4,48 +4,54 @@
 
 #include "shared_test_classes/subgraph/variadic_split_pad.hpp"
 
-namespace SubgraphTestsDefinitions {
+#include "ov_models/builders.hpp"
 
-std::string VariadicSplitPad::getTestCaseName(const testing::TestParamInfo<SplitPadTuple> &obj) {
-    InferenceEngine::SizeVector inputShape;
+namespace ov {
+namespace test {
+
+std::string VariadicSplitPad::getTestCaseName(const testing::TestParamInfo<SplitPadTuple>& obj) {
+    ov::Shape input_shape;
     int64_t axis;
     std::vector<size_t> numSplits, connectIndexes;
     std::vector<int64_t> padsBegin, padsEnd;
-    ngraph::helpers::PadMode padMode;
-    InferenceEngine::Precision netPrecision;
+    ov::op::PadMode padMode;
+    ov::element::Type element_type;
     std::string targetName;
-    std::tie(inputShape, axis, numSplits, connectIndexes, padsBegin, padsEnd, padMode, netPrecision, targetName) = obj.param;
+    std::tie(input_shape, axis, numSplits, connectIndexes, padsBegin, padsEnd, padMode, element_type, targetName) =
+        obj.param;
     std::ostringstream results;
 
-    results << "IS=" << ov::test::utils::vec2str(inputShape) << "_";
+    results << "IS=" << ov::test::utils::vec2str(input_shape) << "_";
     results << "Axis=" << axis << "_";
     results << "NumSplits=" << ov::test::utils::vec2str(numSplits) << "_";
     results << "ConnectIndexes=" << ov::test::utils::vec2str(connectIndexes) << "_";
     results << "padsBegin=" << ov::test::utils::vec2str(padsBegin) << "_";
     results << "padsEnd=" << ov::test::utils::vec2str(padsEnd) << "_";
     results << "PadMode=" << padMode << "_";
-    results << "netPRC=" << netPrecision.name() << "_";
+    results << "netPRC=" << element_type << "_";
     results << "targetDevice=" << targetName << "_";
     return results.str();
 }
 
 void VariadicSplitPad::SetUp() {
-    InferenceEngine::SizeVector inputs;
+    ov::Shape input_shape;
     int64_t axis;
     std::vector<size_t> numSplits, connectIndexes;
     std::vector<int64_t> padBegin, padEnd;
-    ngraph::helpers::PadMode padMode;
-    InferenceEngine::Precision netPrecision;
-    std::tie(inputs, axis, numSplits, connectIndexes, padBegin, padEnd, padMode, netPrecision, targetDevice) = this->GetParam();
-    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-    ov::ParameterVector input {std::make_shared<ov::op::v0::Parameter>(ngPrc, ov::Shape(inputs))};
+    ov::op::PadMode padMode;
+    ov::element::Type element_type;
+    std::tie(input_shape, axis, numSplits, connectIndexes, padBegin, padEnd, padMode, element_type, targetDevice) =
+        this->GetParam();
+    ov::ParameterVector input{std::make_shared<ov::op::v0::Parameter>(element_type, ov::Shape(input_shape))};
     auto split = ngraph::builder::makeVariadicSplit(input[0], numSplits, axis);
-    ngraph::ResultVector results;
+    ov::ResultVector results;
 
     for (size_t i : connectIndexes) {
         auto pad = ngraph::builder::makePad(split->output(i), padBegin, padEnd, 0, padMode);
-        results.push_back(std::make_shared<ngraph::opset1::Result>(pad));
+        results.push_back(std::make_shared<ov::op::v0::Result>(pad));
     }
-    function = std::make_shared<ngraph::Function>(results, input, "variadic_split_pad");
+    function = std::make_shared<ov::Model>(results, input, "variadic_split_pad");
 }
-} // namespace SubgraphTestsDefinitions
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/ov_helpers/ov_lpt_models/CMakeLists.txt b/src/tests/ov_helpers/ov_lpt_models/CMakeLists.txt
index f2b4514c5b0d32..7eda3438659f0d 100644
--- a/src/tests/ov_helpers/ov_lpt_models/CMakeLists.txt
+++ b/src/tests/ov_helpers/ov_lpt_models/CMakeLists.txt
@@ -12,7 +12,7 @@ ov_add_target(
         ROOT ${PUBLIC_HEADERS_DIR}
         INCLUDES
             PUBLIC
-                ${PUBLIC_HEADERS_DIR}
+                "$<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>"
         ADDITIONAL_SOURCE_DIRS
             ${CMAKE_CURRENT_SOURCE_DIR}/src
         LINK_LIBRARIES
@@ -20,13 +20,14 @@ ov_add_target(
                 ov_models
                 openvino::runtime::dev
         ADD_CPPLINT
-        DEPENDENCIES
-            ov_models
-        DEVELOPER_PACKAGE
-            tests
 )
 
 ov_build_target_faster(${TARGET_NAME}
     UNITY
     PCH PRIVATE "src/precomp.hpp"
 )
+
+# install & export
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${PUBLIC_HEADERS_DIR}/")
diff --git a/src/tests/ov_helpers/ov_models/CMakeLists.txt b/src/tests/ov_helpers/ov_models/CMakeLists.txt
index 6d2989f94af734..69631bd82ba2a0 100644
--- a/src/tests/ov_helpers/ov_models/CMakeLists.txt
+++ b/src/tests/ov_helpers/ov_models/CMakeLists.txt
@@ -12,22 +12,25 @@ ov_add_target(
         ROOT ${PUBLIC_HEADERS_DIR}
         INCLUDES
             PUBLIC
-                ${PUBLIC_HEADERS_DIR}
+                "$<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>"
         ADDITIONAL_SOURCE_DIRS
             ${CMAKE_CURRENT_SOURCE_DIR}/src
         LINK_LIBRARIES
             PUBLIC
                 openvino::runtime
                 openvino::reference
-                interpreter_backend
+                openvino::interpreter_backend
                 openvino::runtime::dev
                 common_test_utils
         ADD_CLANG_FORMAT
-        DEVELOPER_PACKAGE
-            tests
 )
 
 ov_build_target_faster(${TARGET_NAME}
     UNITY
     PCH PRIVATE "src/precomp.hpp"
 )
+
+# install & export
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${PUBLIC_HEADERS_DIR}/")
diff --git a/src/tests/ov_helpers/ov_snippets_models/CMakeLists.txt b/src/tests/ov_helpers/ov_snippets_models/CMakeLists.txt
index 69cd602bb5eab5..24f1efae26a9e8 100644
--- a/src/tests/ov_helpers/ov_snippets_models/CMakeLists.txt
+++ b/src/tests/ov_helpers/ov_snippets_models/CMakeLists.txt
@@ -5,8 +5,6 @@
 set(TARGET_NAME ov_snippets_models)
 
 set(PUBLIC_HEADERS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
-set(SNIPPETS_INCLUDES "$<TARGET_PROPERTY:openvino::snippets,SOURCE_DIR>/include")
-set(COMMON_TEST_UTILS_INCLUDES "$<TARGET_PROPERTY:common_test_utils,INTERFACE_INCLUDE_DIRECTORIES>")
 
 ov_add_target(
         NAME ${TARGET_NAME}
@@ -14,11 +12,8 @@ ov_add_target(
         ROOT ${PUBLIC_HEADERS_DIR}
         INCLUDES
             PUBLIC
-                ${PUBLIC_HEADERS_DIR}
-                ${COMMON_TEST_UTILS_INCLUDES}
-            PRIVATE
-                ${SNIPPETS_INCLUDES}
-
+                "$<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>"
+                "$<BUILD_INTERFACE:${COMMON_TEST_UTILS_INCLUDES}>"
         ADDITIONAL_SOURCE_DIRS
             ${CMAKE_CURRENT_SOURCE_DIR}/src
         LINK_LIBRARIES
@@ -28,11 +23,14 @@ ov_add_target(
                 openvino::snippets
                 ov_lpt_models
         ADD_CPPLINT
-        DEVELOPER_PACKAGE
-            tests
 )
 
 ov_build_target_faster(${TARGET_NAME}
     UNITY
     PCH PRIVATE "src/precomp.hpp"
 )
+
+# install & export
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${PUBLIC_HEADERS_DIR}/")
diff --git a/src/tests/test_utils/common_test_utils/CMakeLists.txt b/src/tests/test_utils/common_test_utils/CMakeLists.txt
index abf36d4fa3864d..1112ccd08558af 100644
--- a/src/tests/test_utils/common_test_utils/CMakeLists.txt
+++ b/src/tests/test_utils/common_test_utils/CMakeLists.txt
@@ -23,8 +23,6 @@ function(add_common_utils ADD_TARGET_NAME)
             EXCLUDED_SOURCE_PATHS
                 ${TARGET_EXCLUDED_SOURCE_PATHS}
             ADD_CLANG_FORMAT
-            DEVELOPER_PACKAGE
-                tests
             LINK_LIBRARIES
                 PUBLIC
                     gtest
@@ -38,12 +36,14 @@ function(add_common_utils ADD_TARGET_NAME)
                     openvino::shape_inference
                 INCLUDES
                     PUBLIC
-                        "${CMAKE_CURRENT_SOURCE_DIR}/include"
+                        "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
                     PRIVATE
                         "${CMAKE_CURRENT_SOURCE_DIR}/src"
-
     )
 
+    ov_developer_package_export_targets(TARGET ${ADD_TARGET_NAME}
+                                        INSTALL_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/include/")
+
     if(ENABLE_CONFORMANCE_PGQL)
         target_compile_definitions(${ADD_TARGET_NAME} PUBLIC ENABLE_CONFORMANCE_PGQL)
     endif()
@@ -58,31 +58,14 @@ function(add_common_utils ADD_TARGET_NAME)
         PCH PRIVATE "src/precomp.hpp"
     )
 
-    # detecting regex support
-    if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
-        set(USE_BOOST_RE ON)
-    endif()
-    if (USE_BOOST_RE)
-        target_compile_definitions(${ADD_TARGET_NAME} PUBLIC USE_BOOST_RE)
-
-        debug_message(STATUS "Adding boost dependency")
-        if (CMAKE_VERBOSE_MAKEFILE)
-            set(Boost_DEBUG on)
-        endif ()
-        find_package(Boost REQUIRED COMPONENTS regex)
-        target_link_libraries(${ADD_TARGET_NAME} PUBLIC ${Boost_REGEX_LIBRARY})
-        target_include_directories(${ADD_TARGET_NAME} PUBLIC ${Boost_INCLUDE_DIRS})
-    endif ()
-
     target_include_directories(${ADD_TARGET_NAME}
         PUBLIC
             $<TARGET_PROPERTY:openvino::runtime,INTERFACE_INCLUDE_DIRECTORIES>
         PRIVATE
             $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>)
-    target_include_directories(${ADD_TARGET_NAME} SYSTEM PUBLIC ${OV_TESTS_ROOT}/test_utils)
+    target_include_directories(${ADD_TARGET_NAME} SYSTEM PUBLIC "$<BUILD_INTERFACE:${OV_TESTS_ROOT}/test_utils>")
 
     target_compile_definitions(${ADD_TARGET_NAME} PUBLIC ${ARGN})
-
 endfunction()
 
 # Keep old name so that library can be used from NPU repo
diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp
index cc45a47d779d57..0bd9f4845d481b 100644
--- a/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp
+++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp
@@ -115,6 +115,10 @@ inline std::string set2str(const std::set<vecElementType>& set) {
     return std::string("()");
 }
 
+inline std::string bool2str(const bool val) {
+    return val ? "True" : "False";
+}
+
 template <typename master, typename slave>
 std::vector<std::pair<master, slave>> combineParams(const std::map<master, std::vector<slave>>& keyValueSets) {
     std::vector<std::pair<master, slave>> resVec;
diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp
index 4d2e5006eeb72d..3b876a530cdf33 100644
--- a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp
+++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp
@@ -232,9 +232,9 @@ void inline fill_random_unique_sequence(T* rawBlobDataPtr,
         auto value = static_cast<float>(dist(generator));
         value /= static_cast<float>(k);
         if (std::is_same<ov::float16, T>::value) {
-            elems.insert(static_cast<T>(ov::float16(value).to_bits()));
+            elems.insert(static_cast<T>(ov::float16(value)));
         } else if (std::is_same<ov::bfloat16, T>::value) {
-            elems.insert(static_cast<T>(ov::bfloat16(value).to_bits()));
+            elems.insert(static_cast<T>(ov::bfloat16(value)));
         } else {
             elems.insert(static_cast<T>(value));
         }
diff --git a/src/tests/test_utils/functional_test_utils/CMakeLists.txt b/src/tests/test_utils/functional_test_utils/CMakeLists.txt
index c990febcd6a0b2..e1148d82ee1132 100644
--- a/src/tests/test_utils/functional_test_utils/CMakeLists.txt
+++ b/src/tests/test_utils/functional_test_utils/CMakeLists.txt
@@ -9,11 +9,9 @@ ov_add_target(
     TYPE STATIC
     ROOT ${CMAKE_CURRENT_SOURCE_DIR}
     ADD_CLANG_FORMAT
-    DEVELOPER_PACKAGE
-        tests
     INCLUDES
         PUBLIC
-        "${CMAKE_CURRENT_SOURCE_DIR}/include"
+            "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
     ADDITIONAL_SOURCE_DIRS
         ${CMAKE_CURRENT_SOURCE_DIR}/src
     LINK_LIBRARIES
@@ -24,13 +22,17 @@ ov_add_target(
         PRIVATE
             ov_models
             openvino::pugixml
-    INCLUDES
-        PUBLIC
-            $<TARGET_PROPERTY:openvino::runtime::dev,INTERFACE_INCLUDE_DIRECTORIES>
 )
 
-install(DIRECTORY layer_tests_summary DESTINATION tests/functional_test_utils COMPONENT tests EXCLUDE_FROM_ALL)
-
 ov_build_target_faster(${TARGET_NAME}
     PCH PRIVATE "src/precomp.hpp"
 )
+
+# install & export
+
+install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/layer_tests_summary"
+        DESTINATION tests/functional_test_utils
+        COMPONENT tests EXCLUDE_FROM_ALL)
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/include/")
diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/skip_configs/CPU/expected_failures_OP.csv b/src/tests/test_utils/functional_test_utils/layer_tests_summary/skip_configs/CPU/expected_failures_OP.csv
index 84d3e26eb35fc2..07f091dd7a222b 100644
--- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/skip_configs/CPU/expected_failures_OP.csv
+++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/skip_configs/CPU/expected_failures_OP.csv
@@ -1130,3 +1130,10 @@ conformance_PRelu/ReadIRTest.ImportExport/Op=PRelu.1_Type=f32_IR=20e7e74f55eb5fb
 conformance_RegionYolo/ReadIRTest.ImportExport/Op=RegionYolo.1_Type=f32_IR=RegionYolo-1_750_Device=CPU_Shape=static_Config=(),5.06332e-06
 conformance_Add/ReadIRTest.ImportExport/Op=Add.1_Type=i32_IR=28f23780d4ca0d40671caf79d5cd9223ad8f6dc2fa5ade2521f3d99586eeeb7f_Device=CPU_Shape=static_Config=(),9.72615e-07
 conformance_Convolution/ReadIRTest.Inference/Op=Convolution.1_Type=f32_IR=c301804445f273eef62f41f02204711d9d6e571da28c76ab447d7d90983b0032_Device=CPU_Shape=dynamic_Config=(),0.000113281
+conformance/OpImplCheckTest.checkPluginImplementation/Function=BitwiseAnd_opset13_Device=CPU_Config=(),1
+conformance/OpImplCheckTest.checkPluginImplementation/Function=BitwiseOr_opset13_Device=CPU_Config=(),1
+conformance/OpImplCheckTest.checkPluginImplementation/Function=BitwiseNot_opset13_Device=CPU_Config=(),1
+conformance/OpImplCheckTest.checkPluginImplementation/Function=Multinomial_opset13_Device=CPU_Config=(),1
+conformance/OpImplCheckTest.checkPluginImplementation/Function=NMSRotated_opset13_Device=CPU_Config=(),1
+conformance/OpImplCheckTest.checkPluginImplementation/Function=LSTMSequence_opset1_Device=CPU_Config=(),1
+conformance/OpImplCheckTest.checkPluginImplementation/Function=BitwiseXor_opset13_Device=CPU_Config=(),1
diff --git a/src/tests/test_utils/unit_test_utils/CMakeLists.txt b/src/tests/test_utils/unit_test_utils/CMakeLists.txt
index 7b8607d6bae28c..dd2bb77aa52224 100644
--- a/src/tests/test_utils/unit_test_utils/CMakeLists.txt
+++ b/src/tests/test_utils/unit_test_utils/CMakeLists.txt
@@ -15,14 +15,20 @@ ov_add_target(
         TYPE STATIC
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
         ADD_CLANG_FORMAT
-        DEVELOPER_PACKAGE
-            tests
         LINK_LIBRARIES
             PUBLIC
                 common_test_utils_s
                 inference_engine_s
                 gmock
+        DEPENDENCIES
+            mock_engine
         INCLUDES
             PUBLIC
-                "${CMAKE_CURRENT_SOURCE_DIR}/.."
+                "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/..>"
 )
+
+# install & export
+
+ov_developer_package_export_targets(TARGET ${TARGET_NAME}
+                                    INSTALL_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_SOURCE_DIR}/../unit_test_utils"
+                                    INSTALL_DESTIONATION "developer_package/include/unit_test_utils/unit_test_utils")
diff --git a/tests/layer_tests/onnx_tests/test_roi_align.py b/tests/layer_tests/onnx_tests/test_roi_align.py
index 392856eaff7b2d..13663808a3acd3 100644
--- a/tests/layer_tests/onnx_tests/test_roi_align.py
+++ b/tests/layer_tests/onnx_tests/test_roi_align.py
@@ -1,6 +1,8 @@
 # Copyright (C) 2018-2023 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import platform
+
 import numpy as np
 import pytest
 from common.layer_test_class import check_ir_version
@@ -133,6 +135,7 @@ def create_net(self, input_shape, rois_shape, indices_shape, output_shape,
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
     @pytest.mark.precommit
+    @pytest.mark.xfail(condition=platform.system() == 'Windows', reason="Ticket - 122731")
     def test_roi_alignv10(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
         # TODO: ticket for investigating GPU failures: CVS-86300
         if ie_device != "GPU":
diff --git a/tests/layer_tests/pytorch_tests/test_full.py b/tests/layer_tests/pytorch_tests/test_full.py
index 4ce42db7fa9167..c564b1bb3731b9 100644
--- a/tests/layer_tests/pytorch_tests/test_full.py
+++ b/tests/layer_tests/pytorch_tests/test_full.py
@@ -104,31 +104,94 @@ def test_full_out(self, shape, value, dtype, with_names, ie_device, precision, i
                    ir_version, kwargs_to_prepare_input={'value': value})
 
 class TestFill(PytorchLayerTest):
-    def _prepare_input(self, value, shape, input_dtype, value_dtype):
-        return (np.random.randn(*shape).astype(input_dtype), np.array(value, dtype=value_dtype),)
+    def _prepare_input(self, value, shape, input_dtype, value_dtype, out=False):
+        if not out:
+            return (np.random.randn(*shape).astype(input_dtype), np.array(value, dtype=value_dtype),)
+        return (np.random.randn(*shape).astype(input_dtype), np.array(value, dtype=value_dtype), np.zeros(shape, dtype=input_dtype))
 
-    def create_model(self):
+
+    def create_model(self, mode):
         import torch
 
         class aten_fill(torch.nn.Module):
+            def __init__(self, mode) -> None:
+                super().__init__()
+                if mode == "inplace":
+                    self.forward = self.forward_inplace
+                if mode == "out":
+                    self.forward = self.forward_out
 
-            def forward(self, input_t: torch.Tensor, x: float):
+
+            def forward_inplace(self, input_t: torch.Tensor, x: float):
                 return input_t.fill_(x)
+        
+            def forward_out(self, input_t: torch.Tensor, x: float, out: torch.Tensor):
+                return input_t.fill(x, out=out), out
+            
+            def forward(self, input_t: torch.Tensor, x:float):
+                return input_t.fill(x)
+    
         ref_net = None
 
-        model = aten_fill()
+        model = aten_fill(mode)
 
-        return model, ref_net, "aten::fill_"
+        return model, ref_net, "aten::fill_" if mode == "inplace" else "aten::fill"
 
     @pytest.mark.parametrize("shape", [[1], [1, 2], [1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5, 6]])
     @pytest.mark.parametrize("value", [0, 1, -1, 0.5])
     @pytest.mark.parametrize("input_dtype", ["int8", "int32", "int64", "float32", "float64"])
     @pytest.mark.parametrize("value_dtype", ["int8", "int32", "int64", "float32", "float64"])
+    @pytest.mark.parametrize("mode", ["", "inplace", "out"])
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_fill(self, shape, value, input_dtype, value_dtype, ie_device, precision, ir_version):
-        self._test(*self.create_model(), ie_device, precision, ir_version,
-                   kwargs_to_prepare_input={'value': value, 'shape': shape, "input_dtype": input_dtype, "value_dtype": value_dtype})
+    def test_fill(self, shape, value, input_dtype, value_dtype, mode, ie_device, precision, ir_version):
+        self._test(*self.create_model(mode), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input={
+                       'value': value, 
+                       'shape': shape, 
+                       "input_dtype": input_dtype, 
+                       "value_dtype": value_dtype,
+                       "out": mode == "out"
+                       })
+
+class TestFillDiagonal(PytorchLayerTest):
+    def _prepare_input(self, shape, input_dtype, value, value_dtype):
+        return np.zeros(shape).astype(input_dtype), np.array(value, dtype=value_dtype)
+
+    def create_model(self, shape, wrap):
+        import torch
+
+        class aten_fill_diagonal(torch.nn.Module):
+            def __init__(self, input_shape, wrap=False) -> None:
+                super().__init__()
+                self.wrap = wrap
+                self.input_shape = input_shape
+
+            def forward(self, x:torch.Tensor, y:float):
+                x = x.reshape(self.input_shape)
+                return x.fill_diagonal_(y, wrap=self.wrap), x
+        
+        ref_net = None
+
+        model = aten_fill_diagonal(shape, wrap)
+        return model, "aten::fill_diagonal_", ref_net
+
+    @pytest.mark.parametrize("shape", ([4, 4], [5, 4], [8, 4], [4, 3],  [5, 5, 5], [3, 3, 3, 3], [4, 4, 4, 4, 4]))
+    @pytest.mark.parametrize("value", [0, 1, -1, 2.5])
+    @pytest.mark.parametrize("input_dtype", ["int8", "int32", "int64", "float32", "float64"])
+    @pytest.mark.parametrize("value_dtype", ["int8", "int32", "int64", "float32", "float64"])
+    @pytest.mark.parametrize("wrap", [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_fill_diagonal(self, shape, value, input_dtype, value_dtype, wrap, ie_device, precision, ir_version):
+        self._test(*self.create_model(shape, wrap), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input={
+                       'value': value, 
+                       'shape': shape, 
+                       "input_dtype": input_dtype, 
+                       "value_dtype": value_dtype
+                       })
+
 
 class TestZero(PytorchLayerTest):
     def _prepare_input(self, shape, input_dtype):
diff --git a/tests/layer_tests/requirements.txt b/tests/layer_tests/requirements.txt
index b1b76c54b92abe..fd15d8bf32405d 100644
--- a/tests/layer_tests/requirements.txt
+++ b/tests/layer_tests/requirements.txt
@@ -1,6 +1,7 @@
 -c ../constraints.txt
 # paddlepaddle #  ticket 95904
 numpy
+onnxruntime
 requests
 torch
 torchvision
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_MatMul.py b/tests/layer_tests/tensorflow_tests/test_tf_MatMul.py
index 460afb662851e7..2a93291af28230 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_MatMul.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_MatMul.py
@@ -36,7 +36,7 @@ def create_net_with_matmul_op(self, x_shape, y_shape, x_bool, y_bool, op_type, i
             elif op_type == 'BatchMatMulV3':
                 op_type_to_tf[op_type](x=tf_x, y=tf_y, Tout=tf.float32, adj_x=x_bool, adj_y=y_bool, name='Operation')
             else:
-                raise RuntimeError("Undknown operation")
+                raise RuntimeError("Unknown operation")
 
             tf.compat.v1.global_variables_initializer()
             tf_net = sess.graph_def
@@ -53,7 +53,7 @@ def create_net_with_matmul_op(self, x_shape, y_shape, x_bool, y_bool, op_type, i
     @pytest.mark.parametrize("params", test_data_precommit)
     @pytest.mark.parametrize("op_type", ['BatchMatMul',
                                          'BatchMatMulV2',
-                                         #'BatchMatMulV3',      #Isn't supported
+                                         'BatchMatMulV3',
                                          'MatMul',
                                          ])
     @pytest.mark.precommit_tf_fe
@@ -72,7 +72,7 @@ def test_matmul_op_precommit(self, params, ie_device, precision, ir_version, tem
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.parametrize("op_type", ['BatchMatMul',
                                          'BatchMatMulV2',
-                                         #'BatchMatMulV3',      #Isn't supported
+                                         'BatchMatMulV3',
                                          'MatMul',
                                          ])
     @pytest.mark.parametrize("x_bool", [
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Xlog1py.py b/tests/layer_tests/tensorflow_tests/test_tf_Xlog1py.py
new file mode 100644
index 00000000000000..7c80fbdad88b09
--- /dev/null
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Xlog1py.py
@@ -0,0 +1,49 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+
+
+class TestXlog1py(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x' in inputs_info
+        assert 'y' in inputs_info
+        x_shape = inputs_info['x']
+        y_shape = inputs_info['y']
+        inputs_data = {}
+        # x = [-3 ,3] y = [1, 2]
+        # generate x in way to have zeros
+        inputs_data['x'] = (6 * np.random.random(size=x_shape).astype(np.float32) - 3) * \
+                            np.random.randint(2, size=x_shape).astype(np.float32)
+        inputs_data['y'] = np.random.random(size=y_shape).astype(np.float32) + 1
+        return inputs_data
+
+    def create_xlog1py_net(self, input_shape, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
+            y = tf.compat.v1.placeholder(input_type, input_shape, 'y')
+            tf.raw_ops.Xlog1py(x=x, y=y)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[10, 20], input_type=np.float32),
+        dict(input_shape=[2, 3, 4], input_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_xlog1py_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                         use_new_frontend, use_old_api):
+        self._test(*self.create_xlog1py_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Xlogy.py b/tests/layer_tests/tensorflow_tests/test_tf_Xlogy.py
new file mode 100644
index 00000000000000..6ecddeb439aed3
--- /dev/null
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Xlogy.py
@@ -0,0 +1,49 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+
+
+class TestXlogy(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x' in inputs_info
+        assert 'y' in inputs_info
+        x_shape = inputs_info['x']
+        y_shape = inputs_info['y']
+        inputs_data = {}
+        # x = [-3 ,3] y = [1, 2]
+        # generate x in way to have zeros
+        inputs_data['x'] = (6 * np.random.random(size=x_shape).astype(np.float32) - 3) * \
+                            np.random.randint(2, size=x_shape).astype(np.float32)
+        inputs_data['y'] = np.random.random(size=y_shape).astype(np.float32) + 1
+        return inputs_data
+
+    def create_xlogy_net(self, input_shape, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
+            y = tf.compat.v1.placeholder(input_type, input_shape, 'y')
+            tf.raw_ops.Xlogy(x=x, y=y)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[10, 20], input_type=np.float32),
+        dict(input_shape=[2, 3, 4], input_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_xlogy_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                         use_new_frontend, use_old_api):
+        self._test(*self.create_xlogy_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)
diff --git a/tests/model_hub_tests/torch_tests/hf_transformers_models b/tests/model_hub_tests/torch_tests/hf_transformers_models
index 112aedeb60de0c..0618d98a4d9f31 100644
--- a/tests/model_hub_tests/torch_tests/hf_transformers_models
+++ b/tests/model_hub_tests/torch_tests/hf_transformers_models
@@ -242,7 +242,7 @@ microsoft/deberta-base,deberta
 microsoft/git-large-coco,git,skip,Load problem
 microsoft/layoutlm-base-uncased,layoutlm
 microsoft/layoutlmv2-base-uncased,layoutlmv2,skip,Load problem
-microsoft/layoutlmv3-base,layoutlmv3,xfail,Unsupported op aten::amax aten::clip
+microsoft/layoutlmv3-base,layoutlmv3
 microsoft/markuplm-base,markuplm
 microsoft/resnet-50,resnet
 microsoft/speecht5_hifigan,hifigan,skip,Load problem
@@ -251,7 +251,7 @@ microsoft/swinv2-tiny-patch4-window8-256,swinv2,xfail,Unsupported op aten::adapt
 microsoft/table-transformer-detection,table-transformer
 microsoft/wavlm-large,wavlm,skip,Load problem
 microsoft/xclip-base-patch32,xclip,skip,Load problem
-microsoft/xprophetnet-large-wiki100-cased,xlm-prophetnet,xfail,Unsupported op aten::fill_diagonal_
+microsoft/xprophetnet-large-wiki100-cased,xlm-prophetnet
 miguelvictor/python-fromzero-lstmlm,lstmlm,skip,Load problem
 mingzi151/test-hf-wav2vec2bert,wav2vec2bert,skip,Load problem
 MIT/ast-finetuned-audioset-10-10-0.4593,audio-spectrogram-transformer,skip,Load problem
@@ -348,7 +348,7 @@ SteveZhan/my-resnet50d,resnet_steve,skip,Load problem
 suno/bark,bark,skip,Load problem
 surajnair/r3m-50,r3m,skip,Load problem
 susnato/clvp_dev,clvp,skip,Load problem
-Tanrei/GPTSAN-japanese,gptsan-japanese,xfail,Unsupported op aten::clip aten::index_put_ prim::TupleConstruct
+Tanrei/GPTSAN-japanese,gptsan-japanese,xfail,Unsupported op aten::index_put_ prim::TupleConstruct
 tau/bart-large-sled-govreport,tau/sled,skip,Load problem
 taufeeque/best-cb-model,codebook,skip,Load problem
 Team-PIXEL/pixel-base,pixel,skip,Load problem
@@ -357,7 +357,7 @@ thomwolf/vqgan_imagenet_f16_1024,vqgan_model,skip,Load problem
 thu-ml/zh-clip-vit-roberta-large-patch14,zhclip,skip,Load problem
 tifa-benchmark/promptcap-coco-vqa,ofa,skip,Load problem
 tli8hf/robertabase_snli,transformerfornli,skip,Load problem
-transfo-xl-wt103,transfo-xl,xfail,Unsupported op aten::clamp_ aten::index_copy_
+transfo-xl-wt103,transfo-xl,xfail,Unsupported op aten::index_copy_
 transZ/BART_shared_clean,shared_bart,skip,Load problem
 transZ/BART_shared_v2,shared_bart_v2,skip,Load problem
 transZ/misecom,misecom,skip,Load problem
diff --git a/tests/model_hub_tests/torch_tests/test_hf_transformers.py b/tests/model_hub_tests/torch_tests/test_hf_transformers.py
index 3a677353c86508..184e725a04f9b9 100644
--- a/tests/model_hub_tests/torch_tests/test_hf_transformers.py
+++ b/tests/model_hub_tests/torch_tests/test_hf_transformers.py
@@ -298,7 +298,10 @@ def teardown_method(self):
                                            ("google/tapas-large-finetuned-wtq", "tapas"),
                                            ("gpt2", "gpt2"),
                                            ("openai/clip-vit-large-patch14", "clip"),
-                                           ("RWKV/rwkv-4-169m-pile", "rwkv")])
+                                           ("RWKV/rwkv-4-169m-pile", "rwkv"),
+                                           ("microsoft/layoutlmv3-base", "layoutlmv3"),
+                                           ("microsoft/xprophetnet-large-wiki100-cased", "xlm-prophetnet"),
+                                           ])
     @pytest.mark.precommit
     def test_convert_model_precommit(self, name, type, ie_device):
         self.run(model_name=name, model_link=type, ie_device=ie_device)
diff --git a/tests/model_hub_tests/torch_tests/test_timm.py b/tests/model_hub_tests/torch_tests/test_timm.py
index d08f6a8c4a9a40..d6dd438df5c6c6 100644
--- a/tests/model_hub_tests/torch_tests/test_timm.py
+++ b/tests/model_hub_tests/torch_tests/test_timm.py
@@ -10,9 +10,28 @@
 from openvino import convert_model
 
 
+def filter_timm(timm_list: list) -> list:
+    unique_models = set()
+    filtered_list = []
+    ignore_set = {"base", "mini", "small", "xxtiny", "xtiny", "tiny", "lite", "nano", "pico", "medium", "big",
+                  "large", "xlarge", "xxlarge", "huge", "gigantic", "giant", "enormous", "xs", "xxs", "s", "m", "l", "xl"}
+    for name in timm_list:
+        # first: remove datasets
+        name_parts = name.split(".")
+        _name = "_".join(name.split(".")[:-1]) if len(name_parts) > 1 else name
+        # second: remove sizes
+        name_set = set([n for n in _name.split("_") if not n.isnumeric()])
+        name_set = name_set.difference(ignore_set)
+        name_join = "_".join(name_set)
+        if name_join not in unique_models:
+            unique_models.add(name_join)
+            filtered_list.append(name)
+    return filtered_list
+
+
 def get_all_models() -> list:
     m_list = timm.list_pretrained()
-    return m_list
+    return filter_timm(m_list)
 
 
 # To make tests reproducible we seed the random generator
@@ -61,6 +80,7 @@ def teardown_method(self):
     def test_convert_model_precommit(self, name, ie_device):
         self.run(name, None, ie_device)
 
+    @pytest.mark.nightly
     @pytest.mark.parametrize("name", get_all_models())
     def test_convert_model_all_models(self, name, ie_device):
         self.run(name, None, ie_device)
diff --git a/thirdparty/dependencies.cmake b/thirdparty/dependencies.cmake
index 8ef6c5bf1c51ea..fac4752c318250 100644
--- a/thirdparty/dependencies.cmake
+++ b/thirdparty/dependencies.cmake
@@ -51,9 +51,6 @@ if(X86_64 OR X86 OR UNIVERSAL2)
         # conan creates alias xbyak::xbyak, no extra steps are required
     else()
         add_subdirectory(thirdparty/xbyak EXCLUDE_FROM_ALL)
-        # export and install xbyak
-        openvino_developer_export_targets(COMPONENT openvino_common TARGETS xbyak::xbyak)
-        ov_install_static_lib(xbyak ${OV_CPACK_COMP_CORE})
     endif()
 endif()
 
@@ -269,12 +266,13 @@ if(NOT TARGET openvino::pugixml)
     function(ov_build_pugixml)
         function(ov_build_pugixml_static)
             set(BUILD_SHARED_LIBS OFF)
+            set(PUGIXML_INSTALL OFF CACHE BOOL "" FORCE)
             add_subdirectory(thirdparty/pugixml EXCLUDE_FROM_ALL)
         endfunction()
         ov_build_pugixml_static()
         set_property(TARGET pugixml-static PROPERTY EXPORT_NAME pugixml)
         add_library(openvino::pugixml ALIAS pugixml-static)
-        openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::pugixml)
+        ov_developer_package_export_targets(TARGET openvino::pugixml)
         ov_install_static_lib(pugixml-static ${OV_CPACK_COMP_CORE})
     endfunction()
 
@@ -300,7 +298,7 @@ if(ENABLE_GAPI_PREPROCESSING)
         add_subdirectory(thirdparty/ade EXCLUDE_FROM_ALL)
 
         set_target_properties(ade PROPERTIES FOLDER thirdparty)
-        openvino_developer_export_targets(COMPONENT openvino_common TARGETS ade)
+        ov_developer_package_export_targets(TARGET ade)
 
         ov_install_static_lib(ade ${OV_CPACK_COMP_CORE})
     endif()
@@ -316,7 +314,7 @@ if(ENABLE_GAPI_PREPROCESSING)
     endif()
 
     set_target_properties(fluid PROPERTIES FOLDER thirdparty)
-    openvino_developer_export_targets(COMPONENT openvino_common TARGETS fluid)
+    ov_developer_package_export_targets(TARGET fluid)
 
     ov_install_static_lib(fluid ${OV_CPACK_COMP_CORE})
 endif()
@@ -369,7 +367,7 @@ if(ENABLE_SAMPLES OR ENABLE_TESTS)
 
     if(NOT TARGET gflags)
         add_subdirectory(thirdparty/gflags EXCLUDE_FROM_ALL)
-        openvino_developer_export_targets(COMPONENT openvino_common TARGETS gflags)
+        ov_developer_package_export_targets(TARGET gflags)
     endif()
 endif()
 
@@ -391,8 +389,14 @@ if(ENABLE_TESTS)
         endforeach()
     else()
         add_subdirectory(thirdparty/gtest EXCLUDE_FROM_ALL)
-        openvino_developer_export_targets(COMPONENT tests
-                                          TARGETS gmock gmock_main gtest gtest_main)
+        # install & export
+        set(googletest_root "${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/gtest/gtest")
+        ov_developer_package_export_targets(TARGET gtest_main
+                                            INSTALL_INCLUDE_DIRECTORIES "${googletest_root}/googletest/include/")
+        ov_developer_package_export_targets(TARGET gtest
+                                            INSTALL_INCLUDE_DIRECTORIES "${googletest_root}/googletest/include/")
+        ov_developer_package_export_targets(TARGET gmock
+                                            INSTALL_INCLUDE_DIRECTORIES "${googletest_root}/googlemock/include/")
     endif()
 endif()
 
@@ -585,8 +589,9 @@ if(ENABLE_SAMPLES)
     else()
         add_subdirectory(thirdparty/json EXCLUDE_FROM_ALL)
 
-        # this is required only because of NPU plugin reused this
-        openvino_developer_export_targets(COMPONENT openvino_common TARGETS nlohmann_json)
+        # this is required only because of NPU plugin reused this: export & install
+        ov_developer_package_export_targets(TARGET nlohmann_json
+                                            INSTALL_INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/thirdparty/json/nlohmann_json/include")
 
         # for nlohmann library versions older than v3.0.0
         if(NOT TARGET nlohmann_json::nlohmann_json)
diff --git a/thirdparty/gtest/CMakeLists.txt b/thirdparty/gtest/CMakeLists.txt
index f527552903c1d7..585b80934bc4ce 100644
--- a/thirdparty/gtest/CMakeLists.txt
+++ b/thirdparty/gtest/CMakeLists.txt
@@ -14,16 +14,31 @@ set(INSTALL_GTEST OFF CACHE BOOL "" FORCE)
 
 add_subdirectory(gtest EXCLUDE_FROM_ALL)
 
-get_target_property(gtest_include_dirs gtest INTERFACE_INCLUDE_DIRECTORIES)
-set_target_properties(gtest PROPERTIES INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${gtest_include_dirs}")
+function(_ov_gtest_filter_install_interface TARGET TYPE)
+    set(final_include_dirs "$<INSTALL_INTERFACE:developer_package/include/${TYPE}>")
+
+    get_target_property(include_dirs ${TARGET} INTERFACE_INCLUDE_DIRECTORIES)
+    foreach(include_dir IN LISTS include_dirs)
+        if(NOT include_dir MATCHES ".*INSTALL_INTERFACE.*")
+            # remove leading and trailing parts of generator expressions
+            string(REPLACE "$<BUILD_INTERFACE:" "" include_dir "${include_dir}")
+            string(REPLACE ">" "" include_dir "${include_dir}")
+            # wrap to BUILD_INTERFACE again
+            list(APPEND final_include_dirs "$<BUILD_INTERFACE:${include_dir}>")
+        endif()
+    endforeach()
 
-get_target_property(gmock_include_dirs gtest INTERFACE_INCLUDE_DIRECTORIES)
-set_target_properties(gmock PROPERTIES INTERFACE_SYSTEM_INCLUDE_DIRECTORIES
-    "${gmock_include_dirs};${gmock_SOURCE_DIR}/include")
+    set_target_properties(${TARGET} PROPERTIES
+        INTERFACE_INCLUDE_DIRECTORIES "${final_include_dirs}"
+        INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${final_include_dirs}")
+endfunction()
 
-set(gtest_targets gtest gtest_main gmock gmock_main)
+_ov_gtest_filter_install_interface(gtest gtest)
+_ov_gtest_filter_install_interface(gtest_main gtest)
+_ov_gtest_filter_install_interface(gmock gmock)
+_ov_gtest_filter_install_interface(gmock_main gmock)
 
-foreach(target IN LISTS gtest_targets)
+foreach(target gtest gtest_main gmock gmock_main)
     # If we have specified /Z7 option, remove -Zi option which comes from gtest
     if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
         get_target_property(_target_cxx_flags ${target} COMPILE_OPTIONS)
@@ -41,8 +56,7 @@ foreach(target IN LISTS gtest_targets)
             target_compile_options(${target} PRIVATE -Wno-deprecated-copy)
         endif()
     endif()
+    # disable warnings
     ov_disable_all_warnings(${target})
+    set_target_properties(${target} PROPERTIES FOLDER thirdparty)
 endforeach()
-
-set_target_properties(${gtest_targets}
-                      PROPERTIES FOLDER thirdparty)
diff --git a/thirdparty/ittapi/CMakeLists.txt b/thirdparty/ittapi/CMakeLists.txt
index fe821f74feaaac..14e18f8bbced68 100644
--- a/thirdparty/ittapi/CMakeLists.txt
+++ b/thirdparty/ittapi/CMakeLists.txt
@@ -4,8 +4,8 @@
 
 if(DEFINED INTEL_VTUNE_DIR OR DEFINED ENV{INTEL_VTUNE_DIR})
     find_package(ITT
-                    PATHS "${OpenVINO_SOURCE_DIR}/src/common/itt/cmake"
-                    NO_DEFAULT_PATH)
+                 PATHS "${OpenVINO_SOURCE_DIR}/src/common/itt/cmake"
+                 NO_DEFAULT_PATH)
     if(NOT ITT_FOUND)
         message(WARNING "Profiling option enabled, but no ITT library was found under INTEL_VTUNE_DIR")
     endif()
@@ -25,6 +25,6 @@ else()
     # create alias ittapi::ittnotify
     add_library(ittapi::ittnotify ALIAS ittnotify)
 
-    openvino_developer_export_targets(COMPONENT openvino_common TARGETS ittapi::ittnotify)
+    ov_developer_package_export_targets(TARGET ittapi::ittnotify)
     ov_install_static_lib(ittnotify ${OV_CPACK_COMP_CORE})
 endif()
diff --git a/thirdparty/pugixml b/thirdparty/pugixml
index a0e064336317c9..2e357d19a3228c 160000
--- a/thirdparty/pugixml
+++ b/thirdparty/pugixml
@@ -1 +1 @@
-Subproject commit a0e064336317c9347a91224112af9933598714e9
+Subproject commit 2e357d19a3228c0a301727aac6bea6fecd982d21
diff --git a/tools/mo/automation/package_BOM.txt b/tools/mo/automation/package_BOM.txt
index 0780ce7eba8151..b9bc64d1c8bf08 100644
--- a/tools/mo/automation/package_BOM.txt
+++ b/tools/mo/automation/package_BOM.txt
@@ -928,6 +928,7 @@ openvino/tools/mo/ops/MatMul.py
 openvino/tools/mo/ops/memory.py
 openvino/tools/mo/ops/memoryoffset.py
 openvino/tools/mo/ops/merge.py
+openvino/tools/mo/ops/multinomial.py
 openvino/tools/mo/ops/mvn.py
 openvino/tools/mo/ops/mxfft.py
 openvino/tools/mo/ops/mxrepeat.py
@@ -1106,4 +1107,4 @@ openvino/tools/mo/utils/tensorboard_util.py
 openvino/tools/mo/utils/type_utils.py
 openvino/tools/mo/utils/unsupported_ops.py
 openvino/tools/mo/utils/utils.py
-openvino/tools/mo/utils/version.py
\ No newline at end of file
+openvino/tools/mo/utils/version.py
diff --git a/tools/mo/openvino/tools/mo/ops/Cast.py b/tools/mo/openvino/tools/mo/ops/Cast.py
index 77beb07c74122e..24409912429f07 100644
--- a/tools/mo/openvino/tools/mo/ops/Cast.py
+++ b/tools/mo/openvino/tools/mo/ops/Cast.py
@@ -36,12 +36,14 @@ def backend_attrs(self):
 
     @staticmethod
     def type_infer(node: Node):
-        assert node.has_valid('dst_type'), 'Destination type of "Cast" operation should be extracted earlier'
+        assert node.has_valid(
+            'dst_type'), 'Destination type of "Cast" operation should be extracted earlier'
         node.out_port(0).set_data_type(node.dst_type)
 
     @staticmethod
     def helper_value_propagation(node_name, value, dst_type):
-        new_blob, finite_match_count, zero_match_count = convert_blob(value, dst_type)
+        new_blob, finite_match_count, zero_match_count = convert_blob(
+            value, dst_type)
 
         if finite_match_count:
             log.error("{} elements of {} were clipped to infinity while converting an input blob for node '{}' to {}."
@@ -63,6 +65,10 @@ def custom_type_casting_and_packing(node: Node, value, dst_type):
             we would pad them to 6 element with the last element as zero and we would pack them into 3 uint8 values
         """
         assert dst_type in [packed_U4, packed_I4]
+        # TODO: Remove this comment when it's clear that we can fix it easily
+        # raise Exception("Packing of u4/i4 data is no longer supported in mo because it is now incompatible with the new "
+        #                 "order of the halfs of a byte that was introduced in OpenVINO runtime recently. Use ovc "
+        #                 "command line tool or openvino.convert_model python function instead.")
 
         minimum_regular_dtype = np.uint8 if dst_type == packed_U4 else np.int8
         # initial casing from the source type to the numpy-friendly type which could absorb all the values of dst_type
@@ -83,10 +89,12 @@ def custom_type_casting_and_packing(node: Node, value, dst_type):
         padded = np.concatenate((flattened, np.zeros([pad], dtype=minimum_regular_dtype)))
         assert np.prod(padded.shape) % num_values_fitting_into_uint8 == 0
 
-        bit_order_little = (padded[:, None] & (1 << np.arange(num_bits)) > 0).astype(np.uint8)
-        bit_order_big = np.flip(bit_order_little, axis=1)
-        bit_order_big_flattened = bit_order_big.flatten()
-        packed = np.packbits(bit_order_big_flattened)
+        bit_order_little = (padded[:, None] & (
+            1 << np.arange(num_bits)) > 0).astype(np.uint8)
+        bit_order_big_flattened = bit_order_little.flatten()
+        # u1 still has reversed bit order:
+        packed = np.packbits(bit_order_big_flattened,
+                             bitorder='little' if num_bits > 1 else 'big')
 
         node.out_node(0)['force_shape'] = data_shape.copy()
         node.out_node(0)['force_type'] = np_data_type_to_precision(dst_type)
diff --git a/tools/mo/openvino/tools/mo/ops/multinomial.py b/tools/mo/openvino/tools/mo/ops/multinomial.py
new file mode 100644
index 00000000000000..42f4b0d3eedbb9
--- /dev/null
+++ b/tools/mo/openvino/tools/mo/ops/multinomial.py
@@ -0,0 +1,69 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+
+from openvino.tools.mo.front.common.partial_infer.utils import dynamic_dimension
+from openvino.tools.mo.front.extractor import bool_to_str
+from openvino.tools.mo.graph.graph import Graph, Node
+
+from openvino.tools.mo.ops.op import Op
+
+
+class Multinomial(Op):
+    op = 'Multinomial'
+    enabled = False
+
+    def __init__(self, graph: Graph, attrs: dict):
+        super().__init__(graph, {
+            'type': self.op,
+            'op': self.op,
+            'version': 'opset13',
+            'infer': self.infer,
+            'in_ports_count': 2,
+            'out_ports_count': 1,
+            'type_infer': self.type_infer,
+            'with_replacement': False,
+            'log_probs': False,
+            'global_seed': 0,
+            'op_seed': 0,
+            'convert_type': np.int64,
+        }, attrs)
+
+    def backend_attrs(self):
+        return ['convert_type',
+                ('with_replacement', lambda node: bool_to_str(
+                    node, 'with_replacement')),
+                ('log_probs', lambda node: bool_to_str(node, 'log_probs')),
+                'global_seed',
+                'op_seed']
+
+    def supported_attrs(self):
+        return ['convert_type',
+                'with_replacement',
+                'log_probs',
+                'global_seed',
+                'op_seed']
+
+    @staticmethod
+    def type_infer(node: Node):
+        assert node.has_valid('convert_type')
+        if node['convert_type'] == 'i32':
+            node.out_port(0).set_data_type(np.int32)
+        else:
+            node.out_port(0).set_data_type(np.int64)
+
+    @staticmethod
+    def infer(node: Node):
+
+        input_shape = node.in_node(0).shape
+        output_shape = []
+        if input_shape is not None and input_shape.size == 2:
+            output_shape.append(input_shape[0])
+
+        num_samples = node.in_port(1).data.get_value()
+        if num_samples is not None:
+            output_shape.append(num_samples)
+        else:
+            output_shape.append(dynamic_dimension)
+        node.out_port(0).data.set_shape(output_shape)
diff --git a/tools/mo/unit_tests/mo/ops/cast_test.py b/tools/mo/unit_tests/mo/ops/cast_test.py
index 985a7276514235..73a468e9fb80fa 100644
--- a/tools/mo/unit_tests/mo/ops/cast_test.py
+++ b/tools/mo/unit_tests/mo/ops/cast_test.py
@@ -21,81 +21,20 @@
 class TestCastTest():
     """
     Example of checking:
-        7 == 0111,           padded to 0111 0000, results in 112
-        7 == 0111, 8 == 1000 packed to 0111 1000, results in 120
+        7 == 0111,           padded to 00000111, results in 7
+        7 == 0111, 8 == 1000 packed to 10000111, results in 7+16
 
-        -8 == 1000,          padded to 1000 0000, results in 128
+        -8 == 1000,          padded to 00001000, results in 8
     """
 
-    @pytest.mark.parametrize("value, expected, custom_dtype",[
-        ([0], [0], packed_U4),
-        ([1], [16], packed_U4),
-        ([2], [32], packed_U4),
-        ([3], [48], packed_U4),
-        ([4], [64], packed_U4),
-        ([5], [80], packed_U4),
-        ([6], [96], packed_U4),
-        ([7], [112], packed_U4),
-        ([8], [128], packed_U4),
-        ([9], [144], packed_U4),
-        ([10], [160], packed_U4),
-        ([11], [176], packed_U4),
-        ([12], [192], packed_U4),
-        ([13], [208], packed_U4),
-        ([14], [224], packed_U4),
-        ([15], [240], packed_U4),
-
-        ([0, 15], [15], packed_U4),
-        ([1, 14], [30], packed_U4),
-        ([2, 13], [45], packed_U4),
-        ([3, 12], [60], packed_U4),
-        ([4, 11], [75], packed_U4),
-        ([5, 10], [90], packed_U4),
-        ([6, 9], [105], packed_U4),
-        ([7, 8], [120], packed_U4),
-        ([8, 7], [135], packed_U4),
-        ([9, 6], [150], packed_U4),
-        ([10, 5], [165], packed_U4),
-        ([11, 4], [180], packed_U4),
-        ([12, 3], [195], packed_U4),
-        ([13, 2], [210], packed_U4),
-        ([14, 1], [225], packed_U4),
-        ([15, 0], [240], packed_U4),
-
-        ([-8], [128], packed_I4),
-        ([-7], [144], packed_I4),
-        ([-6], [160], packed_I4),
-        ([-5], [176], packed_I4),
-        ([-4], [192], packed_I4),
-        ([-3], [208], packed_I4),
-        ([-2], [224], packed_I4),
-        ([-1], [240], packed_I4),
-        ([0], [0], packed_I4),
-        ([1], [16], packed_I4),
-        ([2], [32], packed_I4),
-        ([3], [48], packed_I4),
-        ([4], [64], packed_I4),
-        ([5], [80], packed_I4),
-        ([6], [96], packed_I4),
-        ([7], [112], packed_I4),
-
-        ([-8, 7], [135], packed_I4),
-        ([-7, 6], [150], packed_I4),
-        ([-6, 5], [165], packed_I4),
-        ([-5, 4], [180], packed_I4),
-        ([-4, 3], [195], packed_I4),
-        ([-3, 2], [210], packed_I4),
-        ([-2, 1], [225], packed_I4),
-        ([-1, 0], [240], packed_I4),
-        ([0, -1], [15], packed_I4),
-        ([1, -2], [30], packed_I4),
-        ([2, -3], [45], packed_I4),
-        ([3, -4], [60], packed_I4),
-        ([4, -5], [75], packed_I4),
-        ([5, -6], [90], packed_I4),
-        ([6, -7], [105], packed_I4),
-        ([7, -8], [120], packed_I4),
-    ])
+    @pytest.mark.parametrize("value, expected, custom_dtype",
+        [([i], [i], packed_U4) for i in range(16)] +
+        [([i, 15-i], [i + (15-i)*16], packed_U4) for i in range(16)] +
+        [([-i], [16-i], packed_I4) for i in range(1, 8+1)] +
+        [([i], [i], packed_I4) for i in range(8)] +
+        [([-i-1, i], [16-i-1 + 16*i], packed_I4) for i in range(8)] +
+        [([i, -i-1], [i + 16*(16-i-1)], packed_I4) for i in range(8)]
+    )
     def test_custom_value_propagation(self, value, expected, custom_dtype):
         graph = build_graph(nodes(value, custom_dtype), [
             *connect('value', 'convert'), *connect('convert', 'output'),
diff --git a/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py b/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py
index 62cd013ad23093..3e5b35ef62fabb 100644
--- a/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py
+++ b/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py
@@ -10,7 +10,7 @@
 import openvino.runtime.opset12 as opset12
 import openvino.runtime.opset11 as opset11
 import openvino.runtime.opset10 as opset10
-from openvino.runtime import Model, serialize, Core, PartialShape, Dimension
+from openvino.runtime import Model, serialize, Core, PartialShape, Dimension, Type
 
 from openvino.tools.mo.utils.ir_reader.restore_graph import restore_graph_from_ir, save_restored_graph
 from openvino.tools.mo.utils.logger import init_logger
@@ -23,16 +23,22 @@ class TestOps(unittest.TestCase):
     @staticmethod
     def check_graph_can_save(model, name):
         with tempfile.TemporaryDirectory() as tmp:
-            model_xml = Path(tmp) / (name + '.xml')
-            model_bin = Path(tmp) / (name + '.bin')
+            tmp_path = Path(tmp)
+            model_xml = tmp_path / (name + '.xml')
+            model_bin = tmp_path / (name + '.bin')
             serialize(model, model_xml, model_bin)
             graph, _ = restore_graph_from_ir(model_xml, model_bin)
-            save_restored_graph(graph, tmp, {}, name)
+            save_restored_graph(graph, tmp, {}, name + '_restored')
             # restore 2 times to validate that after save graph doesn't lose attributes etc.
-            graph, _ = restore_graph_from_ir(model_xml, model_bin)
+            restored_model_xml = tmp_path / (name + '_restored.xml')
+            restored_model_bin = tmp_path / (name + '_restored.bin')
+            graph, _ = restore_graph_from_ir(
+                restored_model_xml, restored_model_bin)
+            core = Core()
+            core.set_property({"ENABLE_MMAP": False})
             # check that re-saved model can be read in runtime
-            Core().read_model(model_xml)
-            return graph
+            model = core.read_model(restored_model_xml)
+            return graph, model
 
     def test_topk_11(self):
         data_shape = [6, 12, 10, 24]
@@ -43,7 +49,7 @@ def test_topk_11(self):
         topk = opset11.topk(data_parameter, k_val, axis,
                             "max", "value", stable=True, name="TopK_11")
         model = Model(topk, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'topk_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'topk_model')
         topk_node = graph.get_op_nodes(op="TopK")[0]
         self.assertEqual(topk_node["version"], "opset11")
         self.assertTrue(topk_node["stable"])
@@ -56,7 +62,7 @@ def test_interpolate_11(self):
         interpolate = opset11.interpolate(data_parameter, np.int32(
             [20, 48]), "nearest", "sizes", axes=np.int32([2, 3]), name="Interpolate_11")
         model = Model(interpolate, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'interpolate_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'interpolate_model')
         interpolate_node = graph.get_op_nodes(op="Interpolate")[0]
         self.assertEqual(interpolate_node["version"], "opset11")
         self.assertTrue("force_precision_in_ports" in interpolate_node)
@@ -69,7 +75,7 @@ def test_interpolate_11_scales(self):
         interpolate = opset11.interpolate(data_parameter, np.float32(
             [2., 2.]), "nearest", "scales", axes=np.int32([2, 3]), name="Interpolate_11")
         model = Model(interpolate, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'interpolate_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'interpolate_model')
         interpolate_node = graph.get_op_nodes(op="Interpolate")[0]
         self.assertEqual(interpolate_node["version"], "opset11")
         self.assertTrue("force_precision_in_ports" not in interpolate_node)
@@ -81,7 +87,7 @@ def test_interpolate_11_no_axes(self):
         interpolate = opset11.interpolate(data_parameter, np.int32(
             [6, 12, 20, 48]), "nearest", "sizes", name="Interpolate_11")
         model = Model(interpolate, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'interpolate_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'interpolate_model')
         interpolate_node = graph.get_op_nodes(op="Interpolate")[0]
         self.assertEqual(interpolate_node["version"], "opset11")
         self.assertTrue("force_precision_in_ports" in interpolate_node)
@@ -94,7 +100,7 @@ def test_interpolate_4(self):
         interpolate = opset10.interpolate(data_parameter, np.int32([20, 48]), np.float32(
             [2, 2]), "nearest", "sizes", axes=np.int32([2, 3]), name="Interpolate_4")
         model = Model(interpolate, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'interpolate4_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'interpolate4_model')
         interpolate_node = graph.get_op_nodes(op="Interpolate")[0]
         self.assertEqual(interpolate_node["version"], "opset4")
 
@@ -105,7 +111,7 @@ def test_unique(self):
         unique = opset10.unique(data_parameter, axis=np.int32(
             [2]), sorted=True, name="Unique_10")
         model = Model(unique, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'unique_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'unique_model')
         unique_node = graph.get_op_nodes(op="Unique")[0]
         self.assertEqual(unique_node["version"], "opset10")
         self.assertListEqual(unique_node.out_port(
@@ -118,7 +124,7 @@ def test_is_finite(self):
             data_shape, name="Data", dtype=np.float32)
         is_finite = opset10.is_finite(data_parameter, name="Is_finite_10")
         model = Model(is_finite, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'is_finite_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'is_finite_model')
         is_finite_node = graph.get_op_nodes(op="IsFinite")[0]
         self.assertEqual(is_finite_node["version"], "opset10")
 
@@ -128,7 +134,7 @@ def test_is_inf(self):
             data_shape, name="Data", dtype=np.float32)
         is_inf = opset10.is_inf(data_parameter, name="Is_inf_10")
         model = Model(is_inf, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'is_inf_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'is_inf_model')
         is_inf_node = graph.get_op_nodes(op="IsInf")[0]
         self.assertEqual(is_inf_node["version"], "opset10")
 
@@ -138,7 +144,7 @@ def test_is_nan(self):
             data_shape, name="Data", dtype=np.float32)
         is_nan = opset10.is_nan(data_parameter, name="Is_nan_10")
         model = Model(is_nan, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'is_nan_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'is_nan_model')
         is_nan_node = graph.get_op_nodes(op="IsNaN")[0]
         self.assertEqual(is_nan_node["version"], "opset10")
 
@@ -177,7 +183,7 @@ def test_if(self):
         out2 = if_node.set_output(then_body_res_2, else_body_res_2)
 
         model = Model([out1, out2], [parameter_x, parameter_y])
-        graph = TestOps.check_graph_can_save(model, 'if_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'if_model')
         if_node = graph.get_op_nodes(op="If")[0]
         self.assertEqual(if_node["version"], "opset8")
         _, layer_info, _ = if_node['IE'][0]
@@ -192,7 +198,7 @@ def test_strided_slice_no_begin_end_mask(self):
         strided_slice = opset11.strided_slice(data_parameter, np.int32([1, 2, 3, 4]), np.int32(
             [3, 6, 9, 12]), np.int32([1, 1, 1, 1]), begin_mask=[], end_mask=[], name="StridedSlice_10")
         model = Model(strided_slice, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'strided_slice_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'strided_slice_model')
         strided_slice_node = graph.get_op_nodes(op="StridedSlice")[0]
         self.assertEqual(strided_slice_node["version"], "opset1")
 
@@ -206,7 +212,7 @@ def test_scatter_dynamic_shape(self):
         mul = opset11.multiply(scatter, np.int64([1, 2]))
         reshape = opset11.reshape(data_parameter, mul, True)
         model = Model(reshape, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'scatter_dynamic_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'scatter_dynamic_model')
         scatter_update_node = graph.get_op_nodes(op="ScatterUpdate")[0]
         self.assertListEqual(scatter_update_node.out_port(0).data.get_value().tolist(), [0, None])
 
@@ -214,7 +220,7 @@ def test_pad_12(self):
         data_parameter = opset12.parameter([6, 12, 10, 24], name="Data", dtype=np.float32)
         pad = opset12.pad(data_parameter, np.int64([0, 0, -1, -2]), np.int64([0, 0, -3, -4]), "constant")
         model = Model(pad, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'pad_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'pad_model')
         pad_node = graph.get_op_nodes(op="Pad")[0]
         self.assertEqual(pad_node["version"], "opset12")
         self.assertListEqual(pad_node.in_port(1).data.get_value().tolist(), [0, 0, -1, -2])
@@ -225,7 +231,7 @@ def test_scatter_elements_update_12(self):
         data_parameter = opset12.parameter([10], name="Data", dtype=np.float32)
         scatter = opset12.scatter_elements_update(data_parameter, np.int32([5, 0, 7, 5]), np.float32([5., 6., 1.5, -5.]), np.int32(0), "sum", False)
         model = Model(scatter, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'scatter_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'scatter_model')
         scatter_node = graph.get_op_nodes(op="ScatterElementsUpdate")[0]
         self.assertListEqual(scatter_node.out_port(0).data.get_shape().tolist(), [10])
         self.assertEqual(scatter_node["version"], "opset12")
@@ -240,7 +246,7 @@ def test_group_norm_12(self):
         epsilon = 1e-6
         node = opset12.group_normalization(data_parameter, scale, bias, num_groups, epsilon)
         model = Model(node, [data_parameter])
-        graph = TestOps.check_graph_can_save(model, 'group_norm_model')
+        graph, _ = TestOps.check_graph_can_save(model, 'group_norm_model')
         gn_node = graph.get_op_nodes(op="GroupNormalization")[0]
         self.assertListEqual(gn_node.out_port(0).data.get_shape().tolist(), [1, 3, 3, 3])
         self.assertEqual(gn_node["version"], "opset12")
@@ -253,7 +259,7 @@ def test_bitwise_and_13(self):
 
         op = opset13.bitwise_and(a, b)
         model = Model(op, [a, b])
-        graph = TestOps.check_graph_can_save(model, "bitwise_and_model")
+        graph, _ = TestOps.check_graph_can_save(model, "bitwise_and_model")
         op_node = graph.get_op_nodes(op="BitwiseAnd")[0]
         self.assertListEqual(op_node.out_port(0).data.get_shape().tolist(), [4, 2])
         self.assertEqual(op_node["version"], "opset13")
@@ -265,7 +271,7 @@ def test_bitwise_or_13(self):
 
         op = opset13.bitwise_or(a, b)
         model = Model(op, [a, b])
-        graph = TestOps.check_graph_can_save(model, "bitwise_or_model")
+        graph, _ = TestOps.check_graph_can_save(model, "bitwise_or_model")
         op_node = graph.get_op_nodes(op="BitwiseOr")[0]
         self.assertListEqual(op_node.out_port(0).data.get_shape().tolist(), [4, 2])
         self.assertEqual(op_node["version"], "opset13")
@@ -277,7 +283,7 @@ def test_bitwise_xor_13(self):
 
         op = opset13.bitwise_xor(a, b)
         model = Model(op, [a, b])
-        graph = TestOps.check_graph_can_save(model, "bitwise_xor_model")
+        graph, _ = TestOps.check_graph_can_save(model, "bitwise_xor_model")
         op_node = graph.get_op_nodes(op="BitwiseXor")[0]
         self.assertListEqual(op_node.out_port(0).data.get_shape().tolist(), [4, 2])
         self.assertEqual(op_node["version"], "opset13")
@@ -288,7 +294,66 @@ def test_bitwise_not_13(self):
 
         op = opset13.bitwise_not(a)
         model = Model(op, [a])
-        graph = TestOps.check_graph_can_save(model, "bitwise_not_model")
+        graph, _ = TestOps.check_graph_can_save(model, "bitwise_not_model")
         op_node = graph.get_op_nodes(op="BitwiseNot")[0]
         self.assertListEqual(op_node.out_port(0).data.get_shape().tolist(), [4, 2])
         self.assertEqual(op_node["version"], "opset13")
+
+    def test_multinomial_13_param_inputs(self):
+        data_shape = [2, 8]
+        probs = opset13.parameter(
+            data_shape, name="probs", dtype=np.float32)
+        num_samples = opset13.parameter(
+            [1], name="num_samples", dtype=np.int32)
+
+        op = opset13.multinomial(probs, num_samples,
+                                 convert_type="i32",
+                                 with_replacement=True,
+                                 log_probs=True,
+                                 global_seed=456,
+                                 op_seed=213)
+
+        model = Model(op, [probs, num_samples])
+        graph, loaded_model = TestOps.check_graph_can_save(
+            model, 'multinomial_param_model')
+        graph_node = graph.get_op_nodes(op="Multinomial")[0]
+
+        self.assertEqual(graph_node["version"], "opset13")
+        self.assertListEqual(graph_node.out_port(
+            0).data.get_shape().tolist(), [2, None])
+        self.assertEqual(graph_node["convert_type"], "i32")
+        self.assertTrue(graph_node["with_replacement"])
+        self.assertTrue(graph_node["log_probs"])
+        self.assertEqual(graph_node["global_seed"], 456)
+        self.assertEqual(graph_node["op_seed"], 213)
+        self.assertEqual(loaded_model.get_output_element_type(0), Type.i32)
+        self.assertEqual(loaded_model.get_output_partial_shape(
+            0), PartialShape([2, -1]))
+
+    def test_multinomial_13_const_inputs(self):
+        probs = opset13.constant(
+            [[0.4, 0.5, 0.1], [0.3, 0.2, 0.5]], name="probs", dtype=np.float32)
+        num_samples = opset13.constant(
+            [3], name="num_samples", dtype=np.int64)
+
+        op = opset13.multinomial(probs, num_samples,
+                                 convert_type="i64",
+                                 with_replacement=False,
+                                 log_probs=False)
+
+        model = Model(op, [])
+        graph, loaded_model = TestOps.check_graph_can_save(
+            model, 'multinomial_const_model')
+        graph_node = graph.get_op_nodes(op="Multinomial")[0]
+
+        self.assertEqual(graph_node["version"], "opset13")
+        self.assertListEqual(graph_node.out_port(
+            0).data.get_shape().tolist(), [2, 3])
+        self.assertEqual(graph_node["convert_type"], "i64")
+        self.assertFalse(graph_node["with_replacement"])
+        self.assertFalse(graph_node["log_probs"])
+        self.assertEqual(graph_node["global_seed"], 0)
+        self.assertEqual(graph_node["op_seed"], 0)
+        self.assertEqual(loaded_model.get_output_element_type(0), Type.i64)
+        self.assertEqual(loaded_model.get_output_partial_shape(
+            0), PartialShape([2, 3]))