fix: Print error for schema classes

PiperOrigin-RevId: 487277666

google/cloud/aiplatform/metadata/schema/base_artifact.py

@@ -17,10 +17,9 @@
 
 import abc
 
-from typing import Optional, Dict
+from typing import Any, Optional, Dict
 
 from google.auth import credentials as auth_credentials
-
 from google.cloud.aiplatform.compat.types import artifact as gca_artifact
 from google.cloud.aiplatform.metadata import artifact
 from google.cloud.aiplatform.constants import base as base_constants
@@ -58,7 +57,7 @@ def __init__(
         various structure and field requirements for the metadata field.
 
         Args:
-            resource_id (str):
+            artifact_id (str):
                 Optional. The <resource_id> portion of the Artifact name with
                 the following format, this is globally unique in a metadataStore:
                 projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id>.
@@ -82,6 +81,8 @@ def __init__(
                 Pipelines), and the system does not prescribe or
                 check the validity of state transitions.
         """
+        # initialize the exception to resolve the FutureManager exception.
+        self._exception = None
         # resource_id is not stored in the proto. Create method uses the
         # resource_id along with project_id and location to construct an
         # resource_name which is stored in the proto message.
@@ -178,3 +179,53 @@ def create(
         # Reinstantiate this class using the newly created resource.
         self._init_with_resource_name(artifact_name=new_artifact_instance.resource_name)
         return self
+
+    def sync_resource(self):
+        """Syncs local resource with the resource in metadata store.
+
+        Raises:
+            RuntimeError: if the artifact resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            super().sync_resource()
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def update(
+        self,
+        metadata: Optional[Dict[str, Any]] = None,
+        description: Optional[str] = None,
+        credentials: Optional[auth_credentials.Credentials] = None,
+    ):
+        """Updates an existing Artifact resource with new metadata.
+
+        Args:
+            metadata (Dict):
+                Optional. metadata contains the updated metadata information.
+            description (str):
+                Optional. Description describes the resource to be updated.
+            credentials (auth_credentials.Credentials):
+                Custom credentials to use to update this resource. Overrides
+                credentials set in aiplatform.init.
+
+        Raises:
+            RuntimeError: if the artifact resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            super().update(
+                metadata=metadata,
+                description=description,
+                credentials=credentials,
+            )
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def __repr__(self) -> str:
+        if self._gca_resource.name:
+            return super().__repr__()
+        else:
+            return f"{object.__repr__(self)}\nschema_title: {self.schema_title}"

google/cloud/aiplatform/metadata/schema/base_context.py

@@ -17,11 +17,14 @@
 
 import abc
 
-from typing import Optional, Dict
+from typing import Dict, List, Optional, Sequence
 
 from google.auth import credentials as auth_credentials
 
 from google.cloud.aiplatform.compat.types import context as gca_context
+from google.cloud.aiplatform.compat.types import (
+    lineage_subgraph as gca_lineage_subgraph,
+)
 from google.cloud.aiplatform.constants import base as base_constants
 from google.cloud.aiplatform.metadata import constants
 from google.cloud.aiplatform.metadata import context
@@ -64,6 +67,8 @@ def __init__(
             description (str):
                 Optional. Describes the purpose of the Context to be created.
         """
+        # initialize the exception to resolve the FutureManager exception.
+        self._exception = None
         # resource_id is not stored in the proto. Create method uses the
         # resource_id along with project_id and location to construct an
         # resource_name which is stored in the proto message.
@@ -154,3 +159,70 @@ def create(
         # Reinstantiate this class using the newly created resource.
         self._init_with_resource_name(context_name=new_context.resource_name)
         return self
+
+    def add_artifacts_and_executions(
+        self,
+        artifact_resource_names: Optional[Sequence[str]] = None,
+        execution_resource_names: Optional[Sequence[str]] = None,
+    ):
+        """Associate Executions and attribute Artifacts to a given Context.
+
+        Args:
+            artifact_resource_names (Sequence[str]):
+                Optional. The full resource name of Artifacts to attribute to
+                the Context.
+            execution_resource_names (Sequence[str]):
+                Optional. The full resource name of Executions to associate with
+                the Context.
+
+        Raises:
+            RuntimeError: if Context resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            super().add_artifacts_and_executions(
+                artifact_resource_names=artifact_resource_names,
+                execution_resource_names=execution_resource_names,
+            )
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def add_context_children(self, contexts: List[context.Context]):
+        """Adds the provided contexts as children of this context.
+
+        Args:
+            contexts (List[_Context]): Contexts to add as children.
+
+        Raises:
+            RuntimeError: if Context resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            super().add_context_children(contexts)
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def query_lineage_subgraph(self) -> gca_lineage_subgraph.LineageSubgraph:
+        """Queries lineage subgraph of this context.
+
+        Returns:
+            lineage subgraph(gca_lineage_subgraph.LineageSubgraph):
+            Lineage subgraph of this Context.
+
+        Raises:
+            RuntimeError: if Context resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            return super().query_lineage_subgraph()
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def __repr__(self) -> str:
+        if self._gca_resource.name:
+            return super().__repr__()
+        else:
+            return f"{object.__repr__(self)}\nschema_title: {self.schema_title}"

google/cloud/aiplatform/metadata/schema/base_execution.py

@@ -17,12 +17,14 @@
 
 import abc
 
-from typing import Optional, Dict
+from typing import Any, Dict, List, Optional, Union
 
 from google.auth import credentials as auth_credentials
 
+from google.cloud.aiplatform import models
 from google.cloud.aiplatform.compat.types import execution as gca_execution
 from google.cloud.aiplatform.constants import base as base_constants
+from google.cloud.aiplatform.metadata import artifact
 from google.cloud.aiplatform.metadata import constants
 from google.cloud.aiplatform.metadata import execution
 from google.cloud.aiplatform.metadata import metadata
@@ -70,7 +72,8 @@ def __init__(
             description (str):
                 Optional. Describes the purpose of the Execution to be created.
         """
-
+        # initialize the exception to resolve the FutureManager exception.
+        self._exception = None
         # resource_id is not stored in the proto. Create method uses the
         # resource_id along with project_id and location to construct an
         # resource_name which is stored in the proto message.
@@ -249,3 +252,110 @@ def start_execution(
             execution_name=new_execution_instance.resource_name
         )
         return self
+
+    def assign_input_artifacts(
+        self, artifacts: List[Union[artifact.Artifact, models.Model]]
+    ):
+        """Assigns Artifacts as inputs to this Executions.
+
+        Args:
+            artifacts (List[Union[artifact.Artifact, models.Model]]):
+                Required. Artifacts to assign as input.
+
+        Raises:
+            RuntimeError: if Execution resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            super().assign_input_artifacts(artifacts)
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def assign_output_artifacts(
+        self, artifacts: List[Union[artifact.Artifact, models.Model]]
+    ):
+        """Assigns Artifacts as outputs to this Executions.
+
+        Args:
+            artifacts (List[Union[artifact.Artifact, models.Model]]):
+                Required. Artifacts to assign as input.
+
+        Raises:
+            RuntimeError: if Execution resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            super().assign_output_artifacts(artifacts)
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def get_input_artifacts(self) -> List[artifact.Artifact]:
+        """Get the input Artifacts of this Execution.
+
+        Returns:
+            List of input Artifacts.
+
+        Raises:
+            RuntimeError: if Execution resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            return super().get_input_artifacts()
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def get_output_artifacts(self) -> List[artifact.Artifact]:
+        """Get the output Artifacts of this Execution.
+
+        Returns:
+            List of output Artifacts.
+
+        Raises:
+            RuntimeError: if Execution resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            return super().get_output_artifacts()
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def update(
+        self,
+        state: Optional[gca_execution.Execution.State] = None,
+        description: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+    ):
+        """Update this Execution.
+
+        Args:
+            state (gca_execution.Execution.State):
+                    Optional. State of this Execution.
+            description (str):
+                Optional. Describes the purpose of the Execution to be created.
+            metadata (Dict[str, Any):
+                Optional. Contains the metadata information that will be stored
+                in the Execution.
+
+        Raises:
+            RuntimeError: if Execution resource hasn't been created.
+        """
+        if self._gca_resource.name:
+            super().update(
+                state=state,
+                description=description,
+                metadata=metadata,
+            )
+        else:
+            raise RuntimeError(
+                f"{self.__class__.__name__} resource has not been created."
+            )
+
+    def __repr__(self) -> str:
+        if self._gca_resource.name:
+            return super().__repr__()
+        else:
+            return f"{object.__repr__(self)}\nschema_title: {self.schema_title}"