ytcelebfaces/ytcelebrities_for_public.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import datetime\n",
    "import time\n",
    "import os\n",
    "import pickle\n",
    "import random\n",
    "from functools import partial, reduce\n",
    "import operator\n",
    "\n",
    "import numpy as np\n",
    "from scipy.misc import imresize\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score, precision_score, recall_score\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "import imageio\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as tnnf\n",
    "from torch.utils.data import DataLoader, TensorDataset, Dataset\n",
    "\n",
    "# need patched version\n",
    "from t3nsor import TTLinear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_random_seeds(seed_value, device=device):\n",
    "    '''source https://forums.fast.ai/t/solved-reproducibility-where-is-the-randomness-coming-in/31628/5.\n",
    "    No. Actually for me source is Seminar 12.'''\n",
    "    np.random.seed(seed_value)\n",
    "    torch.manual_seed(seed_value)\n",
    "    random.seed(seed_value)\n",
    "    if device != 'cpu': \n",
    "        torch.cuda.manual_seed(seed_value)\n",
    "        torch.cuda.manual_seed_all(seed_value)\n",
    "        torch.backends.cudnn.deterministic = True\n",
    "        torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED=0\n",
    "set_random_seeds(seed_value=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import HTML, display\n",
    "import imageio\n",
    "from base64 import b64encode\n",
    "\n",
    "def show_video(array, fps=25):\n",
    "    height = array.shape[1]\n",
    "    width = array.shape[2]\n",
    "    \n",
    "    scaling_coefficient = 320 / max(height, width)\n",
    "    if scaling_coefficient > 1:\n",
    "        height *= scaling_coefficient\n",
    "        width *= scaling_coefficient\n",
    "        \n",
    "    array = array.astype(np.uint8)\n",
    "    mp4_as_bytes = imageio.mimwrite(\n",
    "        imageio.RETURN_BYTES, array, format=\"mp4\", fps=fps\n",
    "    )\n",
    "    b64_encoded = b64encode(mp4_as_bytes)\n",
    "    display(HTML(\"\"\"\n",
    "    <video width=\"{}\" height=\"{}\" controls>\n",
    "      <source type=\"video/mp4\" src=\"data:video/mp4;base64,{}\">\n",
    "    </video>\n",
    "    \"\"\".format(width, height, b64_encoded.decode(\"utf-8\"), )\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_video_from_disk(filename, fps=25):\n",
    "    array = np.array(imageio.mimread(filename))\n",
    "    show_video(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "HOW_FAST = \"medium\" # \"slow\" or \"medium\" or \"fast\"\n",
    "LONG_SEQ = False\n",
    "\n",
    "RESIZING_SIZE = {\n",
    "    \"slow\": (160, 120),\n",
    "    \"medium\": (80, 60),\n",
    "    \"fast\": (29, 13)\n",
    "}[HOW_FAST]\n",
    "\n",
    "GLOBAL_MAX_LEN = 350 if LONG_SEQ else 85\n",
    "\n",
    "SHUFFLE_PIXELS = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_video(filename, resizing_size):\n",
    "    resized_frames = (\n",
    "        np.array(Image.fromarray(frame).resize(resizing_size, resample=Image.BICUBIC))\n",
    "        for frame in imageio.mimread(filename, \"ffmpeg\")\n",
    "    )\n",
    "    \n",
    "    return np.array(list(resized_frames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def permute_components(video: np.ndarray, permutation: np.ndarray) -> np.ndarray:\n",
    "    num_frames, frame_len = video.shape\n",
    "    assert permutation.shape == (frame_len, )\n",
    "    return video[:, permutation]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_v2(full_paths, resizing_size, maxlen, shuffle_pixels):\n",
    "    X = np.zeros((len(full_paths), maxlen, np.product(resizing_size) * 3), dtype=np.int8)\n",
    "    if shuffle_pixels:\n",
    "        permutation = np.arange(np.prod(RESIZING_SIZE) * 3)\n",
    "        np.random.shuffle(permutation)\n",
    "    for (n, path) in enumerate(tqdm(full_paths)):\n",
    "        this_clip = load_video(path, resizing_size)\n",
    "        this_clip = this_clip.reshape(this_clip.shape[0], -1).astype(np.int8)\n",
    "        this_clip = (this_clip - 128)\n",
    "        if shuffle_pixels:\n",
    "            this_clip = permute_components(this_clip, permutation)\n",
    "        X[n] = pad_sequences([this_clip], maxlen=maxlen, truncating=\"post\", dtype=np.int8)[0]\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '/mnt/hdd_1tb/datasets/youtube_celebrities_face_data/ytcelebrity/'\n",
    "SAVE_DIR = \"/mnt/hdd_1tb/datasets/youtube_celebrities_face_data/models\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir(data_path)\n",
    "random.shuffle(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = [''] * len(files)\n",
    "for l in range(len(files)):\n",
    "    # l = 0\n",
    "    this_file = files[l]\n",
    "    \n",
    "    # files are named like \n",
    "    this_file_split = this_file.split('_')\n",
    "    \n",
    "    # remove .avi or .whatever extension\n",
    "    this_file_split[-1] = this_file_split[-1].split('.')[0]\n",
    "    \n",
    "    # set targets (labels) to strings like \"vladimir_putin\"\n",
    "    targets[l] = this_file_split[-2] + '_' + this_file_split[-1]\n",
    "\n",
    "targets = np.array(targets)\n",
    "\n",
    "# np.array of unique target labels\n",
    "classes = np.unique(targets)\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_TRAIN, NUM_TEST = {\n",
    "    \"fast\": (32, 16),\n",
    "    \"medium\": (len(files) - 200, 200),\n",
    "    \"slow\": (len(files) - 200, 200)\n",
    "}[HOW_FAST]\n",
    "\n",
    "N = NUM_TRAIN + NUM_TEST\n",
    "\n",
    "files = files[:N]\n",
    "targets = targets[:N]\n",
    "y = y[:N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_paths = np.array([data_path + this_file for this_file in files])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_SAVE_PATH = os.path.join(SAVE_DIR, f\"Xy_seed_{SEED}_{HOW_FAST}_shuffle={SHUFFLE_PIXELS}.npy\")\n",
    "\n",
    "if os.path.exists(DATASET_SAVE_PATH):\n",
    "    X = np.load(DATASET_SAVE_PATH, allow_pickle=False)\n",
    "else:\n",
    "    X = load_data_v2(full_paths, resizing_size=RESIZING_SIZE, maxlen=GLOBAL_MAX_LEN, shuffle_pixels=SHUFFLE_PIXELS)\n",
    "    np.save(DATASET_SAVE_PATH, X, allow_pickle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X[:NUM_TRAIN]\n",
    "X_test = X[NUM_TRAIN:]\n",
    "\n",
    "y_train = y[:NUM_TRAIN]\n",
    "y_test = y[NUM_TRAIN:]\n",
    "\n",
    "full_paths_train = full_paths[:NUM_TRAIN]\n",
    "full_paths_test = full_paths[NUM_TRAIN:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_features = reduce(operator.mul, RESIZING_SIZE) * 3\n",
    "num_labels = len(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_random_sequence(what, with_original=True):\n",
    "    if what == \"train\":\n",
    "        X = X_train\n",
    "        y = y_train\n",
    "        full_paths = full_paths_train\n",
    "    elif what == \"test\":\n",
    "        X = X_test\n",
    "        y = y_test\n",
    "        full_paths = full_paths_test\n",
    "    else:\n",
    "        raise ValueError(\"what must be train or test\")\n",
    "        \n",
    "    index = random.randint(0, len(X)-1)\n",
    "    label_number = y[index]\n",
    "    label_name = label_encoder.inverse_transform([label_number])\n",
    "    print(label_name)\n",
    "    path = full_paths[index]\n",
    "    print(path)\n",
    "    x = X[index].reshape(GLOBAL_MAX_LEN, RESIZING_SIZE[1], RESIZING_SIZE[0], 3) + 128\n",
    "    show_video(x)\n",
    "    show_video_from_disk(os.path.join(data_path, path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (80, 60) to (80, 64) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['steven_spielberg']\n",
      "/mnt/hdd_1tb/datasets/youtube_celebrities_face_data/ytcelebrity/1771_03_013_steven_spielberg.avi\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <video width=\"320.0\" height=\"240.0\" controls>\n",
       "      <source type=\"video/mp4\" 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       "    </video>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_random_sequence(\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (80, 60) to (80, 64) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sandra_bullock']\n",
      "/mnt/hdd_1tb/datasets/youtube_celebrities_face_data/ytcelebrity/1659_01_009_sandra_bullock.avi\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <video width=\"320.0\" height=\"240.0\" controls>\n",
       "      <source type=\"video/mp4\" 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u+3Pk+uppOxtEeNcJd0Nq9UKlWCN6N1X5B4D/3vStw/qAI9b4rfAcFClzWTBTbiN02Ajlcb6hFqELCeZLdmifhkse6mGnor5sS+W8Iw/v8f2QPziZWWs0R1G/hAkEprmlh0PxM1DI3RUAQXNAlVNHgFRkPb8sCTblFB3zp9eIBu/2APmrvPodiKiPSQw03SP3wkedo3Qu+KbneAH7/eWHsKSjqJRsmnUdAZ0ytSs5JUbil0Ya+YaaSBcUdCzXLXohnSao1yj39FICzmwyLtLo/9ff2vBEk3e4CovH+Rx9pEhEvGo5nv2XZHErYk5LEzYjrmsi7pETi3qVufwtjFleW3+RbwuuToNdViRKz32TL9GzwLgOURPc3PCsZAVhEHLx7lbOzZsWUzOK8wKEUiwgb5WGx51XoFPzgEdWpQHp63YuGOUtmh65PttoUVJcXhwWvPLpY6V9gwxpm7oXHr7Wc8PWX+WUrp1u3E/kUey6OhsU1Rcx9eRVllEoc7izIfanhOj/03y9zbCESeLC3pgjMvM3D/TQ/LvbB99gKVKLNlPqmPqIJcWRT0cCxD52F39PiSu23jmTSf0r5r5PFYm4/xb8PBWmTcHasM0P2p/54Za8wnpYlyHiKJfIg03ml20P22CjFcwbRoGqYrV0FtzCFzJrR7zxtkZtBueOoqUAw3ykWWHscIKfmpAaUu77MsR1E3EQDMrkse7705uvQpwu7e3SKxAyNuXdO8QmdwHt+633mT9L2yt2nh40TRpnFibogy/fiDP8PvgS7LwkMTk2KazJmfhVp9DyLvEerALSm/Pf3LMJleWtAKRPUwhVwl9TAJ/ciQW87Zgan/XCcdbVAAAAVNBm1VJ4QpSZTAm/7bB6QnlOH3WkcxIvAR7COVI0jhibJA8hloYmR6903J973M2/2MEhSCKxGg23PHpo3RivqJ5VgYJ+PrSDhH+Acgy/LrNuxdTqt7xgJIPdtKZDbE8D5Oid+kZeBXAbeY0wORXnxdP/JxxKuvydESt+RGQXoXy+qvwTy/rleU+iP8JHtz0DauTJPxh3puKOUL6qLnbCU2JSlNlmn3Jwt+cIWD3uZ47xxE55MyNjodlKX+zCV8ToTnwq6l1f1JmIj7hIFn7MvUBaQvj/2eme7Cd3WB3kNiuyb60xj5kKKe8SCZ9lM8ZE63Z69kCPGLf932E8W1uEh3gtCe9xDGmugRDOTmPaKn/uQO4Cy6H4Kf9yDtO6u1hGZaNP/oAdug/si2GzoGVauciHajUKkCtOiClnJZ+0eXJsNnSliQNX5udcBuaN8WTI/IsKzEAAAAKQZ9zRTRMd+7c9AAAAAkBn5J0Ql/v/jwAAAAIAZ+UakJfXMEAAAGAQZuZSahBaJlMCb+2xHmhIbwFhCp2U/4rZ1OAm1YF3a7QxqWK5LyUEQJJwaS8nVsYCat4xO7/i6D5Uztsml85JNCSuF5Gqe+ckPsrq7LlDR7wZ1UDWAunBjoHx+PQW+W5oliN/AoK5FSCKqJnsYNVhQhIfTw5cuXRzkSlfgP9/P5cchjkdQN0KZKtXlpQQjSPRNN7vnJizuUh6DYw/wFUY4MDtZkud0jgw4Kuvv3ht/WicVQT9/elYlipqBNVQBMw+TJD6oGmVQb0Nmys7tAj3nedsrPKmc4lrmhvm9xuzsg3FIL1PKPjuxLUuPrIihrop/Jo9d5gOT9mv52blQe2z6XFCvniIEE+abHDM4tuaFlIRyONjDpDZDog2xaEw/7CEOj0DrYagkgRXw+rQAfYTKIfDHVmgrklt5NSlqVaPJJli2wWCkRUj3CAVKGZ2Bag+G8EXkZcwVX2qB/jb1Gu3Ldhv7lzX+WOyiSsOwtHzTAKWaAazfRi5cmHeT6RaGzAAAAACkGft0URLHfu3PUAAAAJAZ/WdEJf7/49AAAACAGf2GpCX1zAAAABjkGb3UuoQhBbIIwH1QH0wCb/+dA1OegyL4lA9+qrfwVj4qF9UIO6LA7+8Z1PB09ikKNPEWnOJjiuTxc5WwtR+QG08KkMQ+zV5On6DNdNcBFsmRr5xW/WAAJn4zIjXIo1e6ccFCpJZDCNGDjNRVwr4KuNgm580jcKsddGMIVRNPVumyx37Ahai6HAhL6eHeoK+8ZeizrAtm6eoKWQ8iJutEDvg8iLDYSX9b0jQNsarj6bdm/iExfivKevxgBSeFurdd1r9NnbNih2OOpl+qI7tKQ4oHH6qanuj8wyK/Rg/4AgC7pwPrYcJsOCxoQk/0FjNJ3tSWfwnt4oNMlqBTg6bS7JFyaReZXi13Jvhzx+BQaHlNSq3RT4iR2HtYRqIwsyUTYtflqJdqv6nSyST47eb3iy4o3nDdBLrFuvj6K7eEvkpwCpLq0XzlxtlFK/VAxeHAh9z5bFqMqfaJTdZfKDhaXwG4OlhSzMtKya5c77vAAwjU7Ga88MbwfySYU+PVwX9mnyHxrlFGVy6Epr2y6jAAAACkGf+0UVLHfu3PQAAAAJAZ4adEJf7/49AAAACAGeHGpCX1zBAAACUkGaAUuoQhBbJ0gAm/+8TfpoeyX/0YnRULq/En3v0hRHjBvlsPIk18eMYCbv8QJolL4BX4qTj2Hb7BedQK/m8PRtJgqr1tHQg044G7TabKeGPGkSKSIfiRBF4cHcxp8Hs8xhYWjwKFIkD3CLHgkJRj1t7O3SYu2w6HiFfsey+vMwIS+8zYVzqu6xcYsSWBuCAp1NoPQDXaE/W2A/qccquYy3CXNGXLqEoDNZAAnU6O2EjK65ThFu0nZs5T+pORU3v8uCJGe5WKG+7CZixgld22Za2cVNrViMt07AbPOoHIQvLyO4rK+qSRG5lGNIVSohVv/lIdupdSvZsDJX70vqyYfVNEZTW7cwp6l2wfi0I794n/Mz/FikFFt7l3hYEFdr5A6LUtYYrkEWMU56eeoBnJE/+OiyPrzRC+GgMJPoRyELEH1tVCN2E3Jppx7H/C6il59hcbJLIc7Z4E0YOJCasddh7iAd0gnx2xVycRpOGORzwr+2gHv/G8OrIt8+9rky+pkTU0fVzwnGgxQFFqLyERnSfRe3yg8PD3wx3r2kUkAUDPGKxdYcYs6AGBeJQO7MfQ5a4/73rtM7Hs/kM9nQYQzzVaTd9Fmd6Zh9x/wxsxK2nJdt5lKIhzG/54NMeuwneWn4hkSWzo7SsSgwrWGZ/HTUjosZ9Na8DN8vcov/3n2ixukfBAHZBPD/+G6K0q+EhfSoYGz5XsqIjoLSeZD9/Fbc+8LNEJbgBEruzBMfMt2eVYs+gY4sEuPx4mfn9Z1I2xPu0P81tuYUdLIR0G0nGls54AAAAF9Bnj9FFSx370DUl5uST7acR5iGSziW5d/T7StVQPB8qZTVIOwG2C7UVkV0dFJB4rN42BJGvAyQs3yAdGyHpR6qVuLsU9BBGVuJjpzWf7qPLar5owr23Vjadn97PJF10wAAAAkBnl50Ql/v/j0AAAAKAZ5AakJf3yGbkwAAAVVBmkVLqEIQWyCMB7UB7MAm//nQuMKAuMfKdU9WlW/xUm7ZHMWwj8m/1DP7VqS1KOcW8eHIIjsatdhHsDSUsOwtZloXiRBFYhTpML0gnVz527BZ7AOHk92lyU9i2yShlgu10qn+c15SwGybBecD75CarqhjfN1HwVE2l0QnIdzbR07e89+Fn/q4AeoUAfPAzg+uUh3aqIaFpvmCCHAWStYMdccaLUPEuWkBQ2KVmqMgvtib7MDxzTMjyLjbCZk4OmtO0+FyacaKIUfs1LH8+PtRPZp4whq5kpYlD+cIXYbuPcvtg2zPBlOi/GnGtByCM6K17rOwbRLIWPypyPWqFpoUoSGkYaEHqz3V/+ZNBZoPpujJ5qOq14g9lSCUB63WabUvQF0k1P7RuhItlS11lEJ4RSKevUTtavafrjVDt8dU3Q9Q1AnilUq415IEVjSE5KNDGbklAwAAAApBnmNFFSx37tz0AAAACQGegnRCX+/+PQAAAAgBnoRqQl9cwQAAAiBBmohLqEIQWydIAJv/2yz8i1kY4EZrhKTz29jX0hTaZHT4hdj1ziQ32q8fkDqHqkjxXzvWME3+13DSMzJEe336Myo8+AJXvH/fKlcmyTcF66ECQ2RVuKNyi1BHp0xYRtK9NjsEXrdyXflbcSMC1qjAroRrAfttNq4lrFV7zj9G4l7Xjdt+SmI1YKswkM1n9YuZJtWQD1dBLDRlsfNYIpC2+OXuwB8AZJoK/vSRbchk4LiYUJbwd0cd7jE3VyJ84Zr1GX9/Q9cOhT6BOrSMrw1lxY/2Fup/RVa23bK1Ok8PrvSZjZf43qOxW3LVJGUfHas8Yyy3K0FUIAMq4OUrQS0Qz3w3iu6I6MLvw+QY2YQKb2Fgk0D2VXxFruBEXfWuNpC1xYZrSyzqYI6i81aTRruQqzqZo2SFU3HxLRKPwZnCzNWJue8JoOCNpuQtF4+Vhjhw4SGZ9fIIl6QHono+FWLVHMeKEMeG19O5PwWLsPle2RH9K7f5g9pU8J2sI/Oci37uGB5miWoLMjxSbDpleDl0c3l017m1tsuJG+Bai2458lHjVrRuynLbdvQvd7k4idTZY3vTzX8iBbSTtaUC+i3BZP50TC3Tt7c3sbNbCO45WXWch+MxnQqwq0sK4xmXBX8auIdIJZltMNM8ctEiQdcOVk7kEpGn38SN431VgET8nHqpA556aAC/AOtLlNR0gQ/85UwlrikgaJescjZ6LLzXAAAAC0GepkUVLCX/8M/5AAAACAGex2pCX1zAAAABdkGazEuoQhBbIIwH9QH8wCb/+dGD52UCbrguunJayF9Lbm1OTjU5iPacBTRfMCGkkV42/oLoDuWTw6G9yL4Y7q0dKscsllwBJ90gXwnC7kGR/vGbcTxhf2XByDDEOHhzQM4ZQ2kcs2McHEMAnNpU7PbFjGwxWpsbow97btlm5DekVDZmzEIb4JM5D0370M2wimkATxRuWA5/q9q9v+/g78Ksx2GDR0xaEtrkCUTFIatWrsDpgXO5bG6MqkBgIp6/KVG9dI4VuBrum09jqt9opx2jVrTB9mDLYs1rz7luOACYrcXeWxvH48XaTkyuXY8DT6URx0PnPS5n7Sy2nJj9lMMQehZ+cLNFV25Qwbr6PaOGlqV+Z23Uycja9UE+Lkg0oDTadt+6k+yjzP4R+HJeI3eE/QcJ1h6oynR5SJ/APuteVLetBe9MsA/fF94yuDGQyR52ZchFjEo86AjG5iXdsa8Ffp7OIGM/zkR9AFKTpGuzgMw87Q+AAAAAC0Ge6kUVLCX/8M/5AAAACQGfCXRCX/jGPAAAAAgBnwtqQl9cwAAAAsxBmxBLqEIQWydIAJv/2yz3FZismvS7xVEN9G7+NnWN9ItWQEgKGP1MyNBw7S1MMJZKMNFzPzuZU4wrqxlO+rbFvnvPs0p58+BWEzyIYZRKrjlgiulz+Qkxm1IfdsAOK9HVVbpq2y/CztGLeW6x9abXBJtCVTeeEP146jWAzcA9zFp03bMR0tuxOd05K61OcQ1ORo3phQipwMJ6hPnu4QmavtjdDqYOBzq3GmlI+SsaeKbk3c84x4kG28/Fl/Fl2MCyIyqXh/+qaESmqeQOg+kCdmca7YgWqqpnBOzLi8ek94xdUVaIsTuRzeHZTYkwzgGxaF3J3cCDxL/bTsKyq04LyvQ6hROkEkx5eV+f4kTdSNifY1dY4UFIPANm/IKgETq9dpLLL+pTfepmjgr0jMbMAIDgv5BkdUiBEZGEDAGd1XkdWtBLPB1Pre1wj7/6n7sdK+PGrZRHinY6qi4CEqcTtcoAagrNKJU8EI1d13kkOkAxD1U2wS+a2DktD4pLl0ftFzphh+sKacTw48kbmSJwQp0mG9KtrjPzCEKAcgiMjJNtC2HRZxV6jgYZs9YBiEuC6kAxvu1dq3Xax6j5MIPG2aFa09FaK1uDOpCMZPSLG40tjsgxh+9qVA7s3Ma4PvJyFpH7XLlvpGz8Oxp5msRDVJH2JugDUgJSmWlbNEbtAaCuPufghS15+q30vehy7sVIYudMmUSYGxPPXWfDjwU940L33scDogUIxRDsmveh87ifF7QH45O4Ae+v2inztckauqklCqUxOLbPTbw6gfhiwgJGWPnT5VME+rFE64BkR5mu5nXBO17Eau+dsyQWj/zlaWvGazr/Jor/33jmrLCBC2Uhuvo+1UEoH6qdHmS/gNurScSrDflK/WTHRija6HRRfnrqUT8fvb4ftkTaQkmIVhWEeJOuqlciZPo+GFqfcrivPhB/xl6egh/ngQAAAAtBny5FFSwl//DP+QAAAA4Bn010Ql/4pyDmkK1LvQAAAAgBn09qQl9cwAAAAiVBm1RJqEFsmUwIj/GdDJsBNCq+hUlOCtMSYE0UELnbfEy/kayvEM+TulMBlIuBbNXgpFKlfH7nHLq1DijgvY0MUDUNyJjlJ2w2EvQzSiZjW59KoEHeV4Y4wICAPWk2Nza3A2gC7nkkKIqb80vrfupUX7w6z0lpBQNdWCQStZ5vcfPz+RnIVHH0ST1wJqCBJDg4qYBHhKtADxMWrHYAQaZzDvYo+GgIVSnEUOJPb7Dnst19pf9Cx3c3Y7IyrekXyXfSyOmg5vQpeNMuE0K1ZyhU6zzxiiOaDYaBOOF06DjabzJ10TdHeNSIxBbrs0HqYiDnda1//kcFORLRIBzkV8uj1TuiIMj0k+eaSYtYr7YwICoi/HQktkyxpYrx5J4F6mx7Drj9WKmUwIJdVxnyz+JA75QI5mM+5X72/QYdxZzF/eTr3mTxgLS6mYwT8+osiwQ5gF24ZsIUn2svbK4yKAyZTzV/ZguiZeWsB7JrRnUPusb7fksdqlFRqN/p4VvV4NWwZzhsGc3ROmCnPVjaz5+iHl66imlClOvC47iXDs0OXGiRuCTH+r6KflcKFoB34Gs/SjuFRUrBULAPMxt8DNx1D626dr+mxpJCGSnxkkXfxKPa+PQl8bLIytd4oOM+KxvYS4r0qFkppO8TvEpFA/6dGsmSrCsMfHEjJs8QuVcMBqMN9yCc8KoH+//A4w1Rtv3UK9Wq/zsYuSChE35F5fHCTZh+1DgAAAALQZ9yRRUsJf/wz/kAAAANAZ+RdEJf+MuxwlRQgAAAAAgBn5NqQl9cwAAAA7hBm5dLqEIQWyCMB/JAf0AiP7q3jiEhiI8Hy15n/eYUpSnJQHACy5Fr4EIlx8UezshefT2YQcUNfla7q1z6S2mLkeCKScxgz8gR9G/l1wnuxSL0mw6LzvnuDvvreI+JPqX9rmwru/rTygvQm2stEUF4YbFuQ+xv7kYJbfuhqFRtl6oY5kRR+yFSAA8TAzGwn4xieZULVbmNxFxJysh3etv418jOZfYzZnnxBM8XQtxYMvyCGBAoAIxZA3/MYpQ9OtWW1GikAuFHqLBDozD8zCFVQq9kaa4fXX6AmWKlFx7nqGPjAocIBLP6VRefjkiDFhHoXBJuJBDeQfha5TZGIibdzYew5g2Tsdwa1fb89YlWZ818r5PXfuzyigwYTviwcrBOGRshVnvb4wC3hgVOUk5I5iwdrflK80qNqf5Zn7FYlUisa/FJmZ2bd7dfJx+ivCcb3jXPnPcsoWyP6GK/HS5ovwLpLo1oysNeKASsp/qMTvYEMLFNqRg3m5pItKailgKigOJS2Cg2Ge8cBhuArGkdNHG2qFcIR96JH1Og+v4SVYQSGv9NZ0SDH+OEYk0pcGE0Hinizl4jqifoJangTULHFyM1721f+mzlb/oaEDDCmFSklkvoR/QKLkxUGSXgzsDTttAQl1N9MQeiPKv9gv4OH7mnZmC6LkzWdUP2IanYg0z9qGgUaLD2ambNjQ/IsEjjaRH+WA7yW+BKM8NE1FWkxfYu7jEPb6m9HEW8hALwz3VbWHS81xvxQcL7WxLxQknX/PafR72PFD7hvHnsfNyHftVOQnsVOdA2ugWlhQYlVF5aIBnwLOs5DgT+7kwbC0maT/Zvst1eWEc7RSi0C92PVOtgd5af38gTvBxLtByQEnQO20PhloptsyuSH+6FKGBam0QB9zXCqjEdPD2/ujEofeEM1NSibwQCN+zejaG/7Ql7FYVRbqcQ5A2dHLC92eBLiK0gshX8/nouN1xFGHuD/3rU8CMzyp8HqkhRfrrPfBs/YfZr73b7sxmPWcHdUfdITYexvO8PFJe55T/3Q37mOIbuwH+jyAShp91nXkLI3uP5rVV5Ypb9c8h6Wi32wK3L49okD6SWgDyIJTP3ITfR7dz1WKsXfupcvZMPOxsySfl3bvsq1LHzwJwfMfgXAcJSikvW0ab+4SZnTbMXqzdg3C85ej1fVUz699V0VArwKlr1J/eYRUmzzrX2xPrTuZVQmVCuSgj7A7NfSwemU6087O7VsWRQLhODwXcWbfeYXIufh182AeljAAAAEUGftUUVLCX/+GOpE5pDvS+IAAAACQGf1mpCX/DP+QAABgtBm9tJqEFsmUwIj7q8tyAyFaUaoBlx6vgostD1z8Av9U4o5027PK0iHyo8az40fydovT8Qn6XMd8hmJWKPa4hPGNLo0Cxq2lO6kG4cj4lhP5iCrPM3qAu/J5KzrRzP8lQH/9LwFuyurcariHBb8aozZvdAQhFtoMvtlCgF2DJO+QUROlCQ5pp+zw6VWzJYGXMTA7Z/9G20NZGV5D7F/mtTDeS4643Jr0oUv/xLQNWTRvLKnxElKH3YjV59wrJhlbd8TAkyZ35IqAySG/+uyWbEYKmFB0QvBkmLcap321Up278juYUKaDS/NcGcCMSaNcH7wI/liUOW0Wwt165IQKZY9NCJf050SHPeVeXABgqfNzd2Lp/M1bSCGjxxxI0fc/DkQvNpxOeSH9scWcLIMHZ+FqfnCSHQiARHRRCbBMLLHtof6YSGq+gTGaTfSEdwkzPJNIrSlXPTnW1nZk0wChQ03n94pklcWglYwqjodgmgSHM1DSGxBlJl5iq6V1zLE1B2u6jFoC6/SkeK+j8yp1oMvPvUn4/FySF0k0HUmp3ifK7cwDMcGjqyg0boD900SzbPAd39qEl5KpiMu3TQn+22YjCgX8++ZDwVZp+Ph1D14G8T3OK3F7/x1L4iG97vk10X7GrGJLvKBM8sIlbAATTxX6Kzyfjq34U7ZQ3cIx/hddzxvYxer8fMVxWtK+hg/gt60SU+7nDUSmdN6dljN7K0fYa3BeeB6WQWjR9HfKhNAVVYL5JgJaaFn78Po5dXqQDqmERURNFV7RyBYTwKlZQgiu51HQf4SvkvVk2Gt29ayrUlo7OBoRYj+vpgISml5ABXzF7KLHZtyRKFjznAvRt/oNNdiNWnFieL8ol/s6BakaBFq3i3+Pmb6kMOA0V70ynZte6CYFtn4YrXS4ULhB24cee/UBH2b+GgHxXPSYYCsxOKeDinN56MajJMN5GPvfsk25vw6/DBzaOiQfhMohhnmF9avhguq9gQMX7/drKEQJ38/uf6evypPmHbE3pbylCoejxPW1MWh1S6ZAniZDIXLs4ZUSS/3BDuJmmvkZUOn214zs2W6pRc0ireM8CuqXu84BVbnfPRPjArZRpxTTDNS7gq0ozPTJijXqE/OIsUW/osFx77K5fy3LN7SDu4XO+SDaL52zx8ajZFptPGsuaEfhvEsTua8+3f5PcmgSOQBYXGbWNzF46/haJGxfTBWZZCzOfegTBTWZH90pMQp/L7KrhU5GBhU4RoRieFe+cyTojy75EoKoFe770lI0bRs/oTFn2agw0Kl66pJkRMFQunBW4nNXql6ykjTihomBqJPb9XtIC334TTE3K1hGQ3eoxtKnXJlAp7jwI1+pchyrJR4pQE5i9BJxo/uQGH4MmlC8J4q5Chv/4tM7VcqXEUk3cjYGHHeQQ2UnWuQSOW3KqEIoMYrnkco/DbjHKsBGbf8n0ZhJ6b04AwVgUXzTswTr+VbWhOif/ALZI/JvUQB1U0ZzC+KVFwAP5+nVdZ8ZODZI8aeGMvYLGYtsz+KeTh/cfmKDCM51qXjg5OLm5jgdZqqGZi057WweePV0F/om33RufAxAKoUvT9HxzZrMQpqw2i1B1907HvtqBQtJhCIw2nP900WJRqnWIkQB0LI9AeGm2PsKFiV4UPqotUMT1/RspnGQWmzfz9ohwpYE32CmlsPjWRDxqFI8AhEChYiz/5ijxenPlrTIH9NP8+ym6hPLXixo9A3DY67x503pu7H4gGUWOpAMadp1W2bchiv7hRZLXVsYkpiBdD3kHi/lxSo6KcSdLDEpZxbuGZ/S9sojzz4wOV5RP7SvyPtaSU2sC0G6gm7CbSVyayio11eKxV/t2oKkqStSb0cjUW9EvAiCpRinMnuRK9ofZQkjTzLGxFWlMfx7HaaMEi0/HtgHbItXylkWXVdC9t+DQH2qo3yGKQq3pJz08uqN++bdnQVaXhFMWrrYY+vIUhUS0reft9ivmJv8JAdiDeyrVtUt8BNCJef1PwJVURz9zU/NSS24Q4G6pwtYQsCv08L9rUtLTrJxdvII4Nph+595HoIQAAAAtBn/lFFSwh//f8MgAAAAgBnhh0Ql9cwQAAAAkBnhpqQl/wz/gAAAXWQZofS6hCEFshsH1B8wCb//vxd8yyU6/2AVUj6MqcgApMiSiqpvrDDbB6l0AelfhH9ZElbr97VLqxAgR3HZ3gxKlaJI2XD6rxAjNNpRow7EYBraIFDwzUyoSPI0r0xF2UFWUJA7oKPXakr7TMog0vzhXbzEsroew9mP4VfjNnFoahqDHJV0SHZJ3c09cVg6iF9JB9/ltyEw264jLCACllg3iNaLwVtUZGGZh5Iv8frmp2Yoik4pkIEM5WMxfXyFmUYZ+hVplpSlg5lFaCrWvsdVEJLu04gf87X22RMmSxP3CORDI4FduFtpXN8zYqy33oQoyf5WUzpQB5AJej/jMAZxrT1XbWZHtphuiI6mK8g4BfBybli6KTNxSiASihAST0XwyNmEUspEmPxI7G2W8q2RWYdUdFihB4ZISJ3nO65LNwKZAUtt94jb567+FkICMLYQ6OqG97JKpB+Vc0HkukflETdbjXQ5m2efaFMEYBG7zpDXn9uRoUiuFm8z6TglBgxe3QY969JUew+w/JmN9O64Rlvrsq7pkuAkx6X9jeLSfq9bccPFWq3SWGmpkraqLhnoxO5Q61YkUycXUZJB4lYOBaQN97oZiQG1bh+YpDXqlcD1upIEsxTIAFT2LQN5Y29i74YTN+o0ZdGi3NaVy5UGZJ5uRuFb61ySr0GiYU8zR6pqSNDQKZyjSzLPzwsguFNETlzK7+2cmffbKJiWzM23lyEFIiQPw5bGo2DRYWBr1oEdZXOkZmqvUWQ9kseHLpew/IzjPsJ3Qd/NQE0h6zkfFY+qB8wHbCFhgwSM8AxmiiTxfxZSNZFEXVEskUlybUQhn7vIgr+TByHprpb41Zllu3XlkH7taGSRLhnKW/iOMAARcCArQIKuDM2xfwa0ml3TRRZUtQBCYphqwzJy/iCQId5nDFYnayqZ/imJ3RhQJ5vnfe8DT/k4/r9qFRf8WuMteypVZweNd7CBEri4s5yuKUqdmVGdLjHHUPA6imtO34AQr5ufdz+ueugir+hCeEP0Y6X8LjWEz8C5mpw7mx93FGTAXxMcfj6rItw4l43nkEEz0SbNvP0Jtq1k2HFZ9I4yj8uXEf/wx7qeGY8ifY9PeH6JoUc9M3OtM1q062W/aOebnY3Plt0vfnh9claVM7KncL6wvygl8fj8KKAju8pfDtMjwF/5KnJ7K1HF1d4cXz4sgizOe5JVN8rJqKsR7cEFgHC+c2yHn9urEqncJfol3JBssCpD4Yh4Fxy7wI34nwKg5DaaPBJOL+Wuprtmc+VMw/6i3VJ/GnMi5gSIEWkIba/CwkSX54coVNrOgMESxV449o8MQnN9qQd7eIeBzfLJ/mQrO8HMYBYaEhOZKuUMXbXH0hcXDxDd+Vo2Nd8QwYkzaMvplDEV964sOylmSrD/xAMkB5HzsGryf7Yus2BDIQOjX2EjWnzqJ3SEZFexICbbsq6Vx95lTbqVby2PyfLbzdcyP7C9blaOiE8PB0kuBhSUmF8dD/y9LhaywZlj/8RQ0dVHQ96SfNnvIOLdhulcYir2a50WIixX2DZ5j9zU86AZ01iOw0hPa2rJcvQafZkkLhsAskzWXy46tNI7Nuuea5Ih8Mcx+LPySZTMu8BVuM28pXnsjhk6TD5D4NBPHUCJ4oBq+l5ZMLufWm6A6eAhC92Ghe7avP0Qv+2o5GbLCcMvnwg9OA+VrcP7D+AcB8n8VRpdTXfsIDqcYylmy8RVhJnrQGxPZNz2anPwr3wZHjvbZkPLUQOjseqlL4GZj9R/TInsBZc7tN6P/LAmh+G5gQe7hc9EE2Ds0ast1VskDBrrEjmtrDMzdDJTvYSDgd7lJsGzOb7/58aIUI3AK54boa2hkmCtAVGinDL8gMhyQvtXyEcOK7TCnAcLynTyWFu8X9SyZ4kHK6exQcRjVwBxYqWCTQIC1rJmurhFmaJj+BPBwb0q4GhtEfp5Ez7/jeB/jMhLkOOineuE1NF/6F9pTe8kQJAAAAC0GePUUVLCX/+FZlAAAACAGeXHRCX1zAAAAACQGeXmpCX/DP+AAAAzZBmkNLqEIQWyCMB9JAfUAk/8g8/k29j7OwzMhaJdT5F/svi5/NtPlaRLThF3JHWyfy/ONsma7qzigOCHVcKmMuo+xmK9gpUJaiyK9zqwWy+rp+GnvJLzrLCqyQ1IHClYtBF1TzGxEc1vJPmWBDZBFfcpLJlF/Bte1iUAHl9I6sU0ttqC2kUoZQjLJ1EvsDzMWbPJxu6fdLcCGJbbjMkFcuHMWmbGcyzwPBvJeoRA2gQ6cfQgsynJzbSogz+x82ta7Lbk9vL0kRoKWEtbuUvuh1b6j5aO1+FZhQJhaKu8+6hcaS0rXEnkBt0JlM83ew1/LnFtRYMfLv6VFTwcav4Vb6O2q7tvQXkoCmiRlfcvadjAPk/bsZymPflg8UpIpfrQN/LwptZvHADtSfEAOf/yDR3vf7PZRo7I/01Y6ml66esMuFkFYK+xvujnEi85Eb/axJg8jnKmnXinQ+dTKjZmtlQVRn0ul31tX1gp56MqDOdIZIU5prHyXelCAkjP+K4zqAxhFHwOkARLgltiuYhNibGMUqByBv3KqvYVO/8+CY+j2moKnJAM0fr2VCEBZYSF+ZHwlAMVmh9SxckVUtz8RjTIqrZuU8wrrwcJnDqZq6jJqLV+WTJOMG46loXeEv6Ijzzv+TQ6IzIP5ZcFBhKjgf4GoXxLV0O/mcqwH1K/+7wgvpqSxOc4c6Va7joNQ0fDtidqQWi4uQZh9NR3SocZhALRVlH/E9RjUkFpIsdt6mf24+LgmrHyxL6+XtHuoUdYZginP8F+rntoDgXI3z/sCCOXCxSkd6Ep5m2o6FZZ0B4AnmfaX4eFgF8To/FIRiUo/oMFCr7wtx5zcI3CcwiSzgCqz++vKJu48mwuLx/+bGLLwOB9HxS1RUnHQ5JpG5VUDoYjYBEX/nPN613Wg8BzeeHVDUtKTvOzaICMsViEQQ2OrZ6d1kMym6bOx5Yra320zt8W21omJ7Llhw0+fdhdC8ZodDRHSFNtl/9sOvzQtLY0WoQWpF9yEm/j7FUrgmiyW/S2lsLDQOmUoUVSt6nspSylTRs5MOHngw7m+4Q4OKgh1lh3QEHM76j0ocS5SDBd26MnVjmeEAAAAVQZ5hRRUsd/e4hgsHiI7C0530qXRgAAAACgGegHRCX3KmcHEAAAAJAZ6CakJf8M/4AAAAD0GahEmoQWyZTAm/krRtUQAAAgZBmqhJ4QpSZTAm/7o3yUhYxrWVQZFBUpOWW+zwD+cV/4M2E6/F/4QZuRAAGVwjQzHXMyVyQXx8uvHoyfa09/xtDw1lROXFGmd8Een69ip85g8BkLDKNxdosOjOet+OP+RC8YVUEcFahcoiWNjVKG9EJEu3W743SufgaKimMRzleM5jjoPxOBC+/3+inE+IjuFGVDWKrLuZyxmoVtUOHeaat+5f07lPAVFcIdsmRVQThxkFJrgCHzbzPyIfpzBoMaukayUxwyTs8YlMfZgWqLQ9A2Y7Ap9c3+Z+uzoKHh3gnE3EQGAYXJ0RHQVy88zHRl8bVEWiLBeAaoxQVZmcuEoAofbjCvhWKYLiS+AWvC7HKuILmDqSLCsmMhu2japvvolEVxQFNDVLyQdDbWuYPQLZBVh5HnqWS8jJ/FcP/511iYrZyXED0WlPcURS0nLNyVAXIeqz7cdPaW7NVhC4Efl+rogdeAadh4n4FbmSe3tQnM+d/Qbs5mhQqXrtSGAozjzErX6wx36p7nj7gFfxOfk2TOY1mQlfleYui8ukB3oJGHZ0cEhRFaqwR7XOnAZi3Ct9oC94jGNcNhDNcr4oeLns2v8+6jBJgZGP94XU/uKtzL3zWmp+3iBoGrT5/YyK7ZWAOVbjgB2bbX6uwHeWLRKmuo13ozDfTvnK8mUhBcaUXP2XJR9QuQAAAApBnsZFNEx37tz1AAAADwGe5XRCX/jbpNufCp5vlQAAAAgBnudqQl9cwAAAAZxBmuxJqEFomUwIj7xGYpzzON1Cft0qRccfKtLQXbr36sNaAsEoYfEqNKlePwf5xbBQXORGuNGETDpMsdCSxF8nHFM+cS49Eev2B5eJrRMojiJm3mGkmDDVXD3w2yiI408whwRnx+IIwgGlPLGnWkEuJF4MYDw6go6WHXp9keI2s8F3XPiiMmxrKaMIbUiuciYFxOdaljDezXPPdv4/1yga4fo2XJK8rG/b2XH2JAJg445SBNqiVspq5ZHArlrVk07GW9XdfcATPdZkoeB6F2774DcrXkIgGwq4yB9DD8GuCLVWxYlTF22L9SlJGGn1Rf6JhztMsHoxJQHLc71zESFNYR6gh0RNpjAQP34jrlWyX+cgAnRI4TS+6jzfivLauYidujSoHWlRcDoKb7aPY2UeDbP/iiyB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       "    </video>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (320, 248) to (320, 256) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <video width=\"320\" height=\"248\" controls>\n",
       "      <source type=\"video/mp4\" 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hwezzZT5Jgq4Noi7tibtOpsCVljx3JqIWil1B2g1ZCCVC9qrImNdeSvGXCYeaX69KvFlTEc47avBlbHRaPu9cUnXUyMivCmvGoh3SXtks6I/WZwX9fVGevOYpHgd4q+1oAHO+s8Ex7NBiAE2gYJwGpaGkEWpHJgC/JltUlw3j757lLhYjpn1IqqvFsM+hGMyhpxwI4rZ8T6pD83r0Ozf1V1vjQ6kn4SW3gmvKIGsX8mOVAm8bUhsdnWmEuZpVHib+xQuJE4gOTX+wy68FEIml5aeXcoUIGQ16xrgIEi80aPwa4DsRXcMR7WPZAYNcAgWptENdTMZqnANPLwOFPGhfUYIf4H631ulauAOVqM5Vzk+FrfQNdF2aZz0CIn2BibAJyft7K6gVbGaRCiBfFtvDkwYYFhAqaMLaFP3Sxb7TKx7V5Vxjc3FnXFpD/zo6sa2BMDwfIxlesGIn7yE/TTZD682TU83JLlrXtD6Y0UJEbs9ogzoYJu/qgU+Pr5zRg5yXrwKmebaheHFf9T6b+7cJLG1vf+9+ONBhgzDV6Wl/ppNmwV2/581veWYIHBbeI5qaKpGBu/EDhH92NEQ1qK70X9svj4v0ILrcv/XBv0Mv7r3+pmB3M+ln8XgK/f06yY7m5lCNVlZguPVuZ7yBWNYM+qa9kIkoCfs84FU8kWPatg4LJ1zpFaskXPRQvp8/YqRn+vC6HLr4xF/FBs1cLPWct4QMjMFM3lZ3F37OvbfTajTBohA5pSMXKaQPwJETKPib6t/ylYEc/8+qJ7ahW3rCNc7KLl6MwZivlUgjOQcz50tgJP1pLubb1UyYh1j/8dSZc62jgS6tVgJaaRgLDdVupwXoETn2mzCsIuZODJtQUmlb6/OQmLHRordLi3f9PjqKvaudqa53ZRwm6V7IwKrgRMXdSZidd3UYVr4IlLDxJdg7z3g6woMuAXAakLzcnjPU0pO4bvHzwhB2dpKUTj7sD6H4fwnFb0ZseWgQ8qpLPQsqICWRGpT/PAQqPFnApM0AG3eXzrugjqfr74nV12blfkrs3sJUF6g90E1dapzIC/XEVjIF4g4GXElO20/GuHZc6aZZFo2XKLvC2+Dknl5SKGEHdiULXP1Rd/9h+fh0HcMHJrqF9LMRNHP6ixb8E4njiNFK6HHznU8eXfkwcBRv4T61wGBsPA4Wni1lQtIpCEsOi1Pt/i0qEBmmCuv7K0OMqQ/O22+RkCGlH+TqZMvnJban+geVh6OqXj7XW0vz+iQ3zSDsC8mew3mgLxu3Y19/kjxADGlp9RzMzuNwPWkjWSkfWRCDkPYabNYnmSLIbCZQ9iGE2YTcjb/e/6evjFZjpZEVJkxft3sbbSTFH7C8zjnAdtDx0kGDP1u6pMrCUgtOvJ5m7QpPe4dLsQotA96mFBneMKx4MHg7k/YxItimR59D1kIt717YRYweg18QOinNfK83wxeXMYzM47TGv3gSmPKfq16/d+oBgXLQU5LnaLOOvv8tQHj0Mk0Y9Ok0eqT9quqXSklMdyz1/jyr92W6Yqt3wnEqklsRinap6dftxk/1W5+hZNh3wkk5B4MhJdDqKM4PqykfX0ih0S89w/kZGBxOPd0GnNnRslCNffWRKyJ56GyAikU203E+ST1oQdbPiVsu7IxEXIqHN7a5kAjZ7jcAOmFDvWppkcPid57y8v0rsd5KZqU34GqfD1hFqa8JvXMS6JS7SxLUQMDO8LFAlxKGeV1USjjBaEPFhi1Lv8GgxLbizo79PO8AnZzdCFrwKeWRol/QV6wRN0dRrqK4uqZp0FdhaF6gzuphSfNqZN+7BcaB/1mTq0VhmnziaTw5YHOJbZjBAsbv2Z2PCHixHX+KejP3020VHTQt/k0A7T5wtkd1wiEKP+orKxJCBXw54TdMrgszGYEzP1B3QqAjZYLcSXqflbMsU9KfyITl/MxDO9kp/K1Zzgv4oYJO0pyIDnv+oNCqxgHI4C+s/a0hDFOWd6NGozDftepyuZLDb1IYeOu2O6zoUPAL76AE0Kor+8Z8JN8cPvSc63mQPoafxTp4D9JX0KuJt5X8N44FbBE+tp64KebFfywG9fAfpG7lJ+8r6LB5a+K7zCrIRI/q4L8dO9lEGCrS4X1nuMTrVuC3KvqosfTj+LbQyngmNdDHgqum1BAe3tQvErN4WWXIeqfRKIIrFHRum+oeHBYz/cxO69KXnQGfbou+H0HxOKwZ69oh/GyVAsuu6+3HenlLS7BMs4VgUN44h0IOdg1qcdsn2KADOiwsDz6v+loik2JpYfUn4mevV6HaPnvZhdA7QU5XXr22Xw9u/dNrfykCEgE5emwIDo8fdRnYVjiRflRMBkFcyDzDFxAs3P5pNE8uvUNGy+/EC+oWus9d191a3gYSsGlVRdmPJ9hsL0ii+DUEFHLvHTEVNECiCfx9IZqJ3+tw8bcfo2iSZ0yguX6TsZUdJ284Idw2s7zpDOh3vtUaWfAZwFwoyfvK937tx3O75ZqfEkizHUKCHaPGZYwTgZVsmaAMYrxa3BnyiJQ5D9GmOS/G9rgdxvI6lheVZqgrMHC6ZetH6fzRJxkk9QGfm/VNTnFG51/cKlTDF59bUo6q/vRlNoDrxppxH/vomKGM0SvpQfQ+IDu6Ay7luowur3gGbPINzeKq88NwraeZfAP+AURjM1gl9K9j7nENpNqlUd3Pj8rIpkrqEkCIJXPbFnAmznx2wlLx4NALJPcyO1zjYQXQKDeOfb++vSaL/CjeTZb4rq0yTOHP9Ytxi8aBI0ZhD4yeiH8r0zL9TkRgk1aAc+WBCmZwiR3XrlyYafVU/bQU1PNO+eHlra7CDDinpuf5xB2lkb9qlwUQmua2cXsLAdZH4RWQnNUqomabqEIM4Eycr64ZvxOY2SB6CvtnvdidOZsSiFv0gWVOWEsoKB6QyY9cxxwGmY2sY6EsmfRDYPjEzsh9xe31NDGpbYFTbeJAQG2oUHEOEMSJcZagd72CRV6cFd9BgJDWEmjZCYLOQsEPD8meS3JeRQhkVWShEx5hYDWGfUEyXdTkbmEkGXz0eKaywbDTLYCNz9tJ6FcXszBXYkmqcAlB/bXyGsNKliIoQD4DFvAgJBz5vR+WQMHtOFQiScZhaf67Jr2jfEmEFXhuOFYlRAJ6n5qvBwzDxzPy/gTx5iE/17wtPXlie8gDReCcHRvFjNw1GxBTS5z2oCu1mCGBEKTbRd+mcBA6ABRHymK+sX97B4cBbzshS64oS9a4NHi82ht3+ohXrC1pEeb3JOGo7MjG3C7l2UeEjZAL8XgnXcIavF5bef+K3LgW1+oIIuYehi0nG16TTEOp3Dpj8qyyoOXfRNY9RetKbzGjtIykrdKt1C3UaL87XNhthqouCGLpKOveLGfBKq+lwLxjus9a9FJzlvlPmj/RXIEHqkO8lH+bG3EuvfPwq/w4+eHx1qbp8ySAWxlj3rulBhAb7vCbb3wxcssekyE1/RDmlA7ht/D+7wMci6VCccsoy7EttEFgU2EkbLEODgVM4TSbHjBr6fjSovJ18KaaVkcq9eLgmxDcUSuhDMo/AKCPNbKSQUCqO+Ac5wy7KUx/L4Dr2b7icY8DhTfwBsIfarnoeLru9U8uSGQcK2g47y8xLEN+QmsbQ8uAEsSxEC8ITcZSw8ayDTGg3ZgiBowi4zD2uc3tqHxyDmziHrIDw64U48+AklDzzwoVtNBfjySgPirvB5DOhRcPv/iqRiRe4c1CHiEHIwWjlKVbhSWvIt813RI6KhbU3DII8Cov8B35ECHnCTjaNR7AR4nXttARBL1hvhizfP2aK8HoEfyDT44lB4tuy24LFIVJ7Id8r5Uui/eZIFJTtLY4Iy/4Zi/jFoRQQ/J/8w3cwwkuWdaEzU3xYdq/W/rKTwbgl1VVXzS8dwOeH43BjuwGNvpYbFTUJXYCgCw5MoSttXS6KVrxyqVyjjcC1R3EfiTjz7shGwjLQs0H+/I6RxV2r1BGcvLajFn1snXqj4JFtWVBhFGdqbChCEylGR/6dpLZc633bqQp4LGzDjayDG5AlVtPoOApp0nk7MTgdw28oAzSUO2ZqixeptpRs/7/RFVWF2eIqm9kioYbp/MbHe4uYXndfjqe4j8X9SCr0bfjA6KEKJI361i530N4NoyKAOz43BcmNoPjBg6iySLxP/BkhUynLoEP8Lo5u/Qb629E+5AASKA/trnvG2m2NFZDPNcbrUI9e1dry2QJzPghway3judUyixrUzwicR0cGVgLLLhXEar0R3J0xR3XJDFZcqsNY8J816SmNAdLVPSBpJqcVxCPCd6oItzgBze+SFnO6h9cEvOkXctCkG1qwwCrh3Z0Lf3LQWuco20ophJR/EV0cQEkBltCLhWYttK1AtN7aiVjtpMt9AIbK/PgY32dcMtP2wyR6F8eD2L06X3EpeuQTw3XgamwxwFXjzX5heq1Mrs4+1hHWGeW1mMfEqatmBmnVWBBJTJscas0gSHBcZPU7VgEF08Vq2sPVlrX+aEeLqIXlV99FwQjYj/wCpNY0C6ZP6xJ8CHdFijQp6FFBhlfs29ySsMEXeOLhZPhKa59OJ798J6Jitj4nUHuSYQiIXBzPVt3ggre8RTNEz8EzSwerb9MXj85pCesGrbLvSp0dH82S12HU6v4FYQ1H8cWSAxPwnmX4LxJYFuJscYlapSIcNGHMrDXft2YVqFjX1FCnEPiQYFU+PyFSoQcEHIREkCCFpJBLJq41MAiKBHkKwLvVy9R5iWBCo2SSjihqSdhIio/qgqzxf4kdexQB/DfL9NORYxAHJp1uX4hY/pJIN4J2kgbNgqnIlz2CHj6TfGWblb40bGat01lip1Gdpgpron23yN1U6TPUmxkmO3XjTIEzlAzD+GPNDtV5jEi2CAR//ucARO8zRuNwy9chMUUXapAVulOqzVXJPA0XSk6c+BozUfBJE4mHdhfTOT4gWHgMEV+QeUb84lHS/6sNqshe7JZ7ljMTjzuALzwC5kf9hkL8JwzeRcwfMh5ZqFiDPWTnn0KekaLJRi+jmaYXZGYujQNArOo2bg0taJ3nK+byyMM6lND3UCNbqgPS4mT1ILn4JAhlSns03/N9I/RQ/OOEtYnnSoXQjhl1GavozOQZwZJe558spV9ZmUROijJMTNVMKNB9QIjxu3UYiYNAAIYTDNZRUVoBmsPLHbrQu/8FTqo606ZnyCuNqyO6fTbU3r8+VoqTvTto/VqyTG+8NcFFfIIupWs0e/kj5bxafJT6por4J8Hpy4otLebfwJNJbCDWhzSL0s6z/29dH638hw/i10EJ1QviCJI67t3QMon5mtGkl5gF9lMf4c2OXgWixthfwe0XQRv5im1+T0KGan8RUk6DT5YUy+XyBd3GpWYjKrLncXCztKTn1CWOjM7p+5R1y5ZhlutrNk/d8IWzcOQBvvTTNqIKGZbnY88+HzI3QMGfNLUX6tw9m0eoDthQoiv06tI6ZL2xOPyqNT6NhtNSkvkOmxkSpGlLC4uGe0i162HsoMgQn/pxNGM9zwFvCZcu58f6PlyHUmXZc/PTEoacIlQwe2a901p84k0S2TQbFl6ZhGHp+CMVVB/lErU3oxApvy8lbDLbnKazzMNq0+Kg8iC7vjZ+xO6nbOG8eim5bLXd6ayOeWd5j6MLtDPDWxqnj+NSDhtHHr6ulGLTge9eI1jyJpVqO4ZQL9+DdxjrXitKFTyPx9QbcDKd9352Ebdy/AZMgYhIrpAMV32HfjKzCj32cT4aDt6pqLbgWcHKk58TYTY/u0LLBqpMqEme/yih8Rv58nfH71zqLqTvAwSnExhtC3Fr+23s1wPpQQT/2XuGlniBElZA54xVB0YF7C8LRh1LJmDFSBRSZsFz/1pGfe4PsdVQAz/zha/NYhSnO+4wzJMrLM5kFJgs0M5Ijmr+lEV7EEKo+1jEbUm35Z23kAggZ6DuALLpPCj61yF3HqgG5YFKiOk2CL85sQNIoq7uhW8BnZv7pnwUiVVLyndo0SMg98sJbMwtvzOJ5GQuyNtXWGXJG8CG/JnixdR9F4FnQQHe9Uwq0exk/mp6CHthX/4jybOQmqVUcGmWIDEiWHUnUiqptDbs06+d+w23DW6aplVu3SyVzib7RI1KAj5oKRRpVHjvoERDPD+Etluy/pwRphAAAJ6kGaIWxH/+cwaFELxZy0xFja9X8aYQgwr8LBOMexBERU0H1mZpQnxR+qQyiLGwzgxJy+mLnYULHvBJztvCHguZqlzlbY3dOSDfgvqINMRQzn+p21SidJ89SrNdwwAQyPKMCoqyWPpKixK1+zPNeRE8EibMn42xw/U3Qb4RrLMqC3u5VhFh5KRc+4UUlJWgAN4+6M8lqYTa+y2TB9cwH7FOoMNkxZQ9Pb4wpnZPL9T15P9IQTcEFKnGtRsXF2xVtmubEPNCAxihQDZr7Uo/5eNpVf2cjm5rvwa6N3nCEK7pWfc74ykCLo44954sTMcsTLHuxBPWjibo8hJZ0K2Gy6RYgh2G6+yCMEcrOSPiT74AdaTayCdWu4+VHnm3p3j4FGsVOtZBhnmijakoVTmILjoTp4Wd8mPvd4xebuV+hjcK74Txc+VW4dL5dkt4Amb9kXDxnD1QZHcON6lxuU2aKZLfP2yeAUOxsb6DOu5C+pVSwQkOtVuApYOQXRxpCBdK4d7XNvoJSFt1g/fFviZKKrQGGYc+5dazPlTvHDQmPTvS+/ncMyoVcFiAeQABUhiScKXS9T6bfbmc7mBpLd/dxL4gdGaRa2RptEVBZ29nadOeUqP3Hd8C3d53WNBwWqXWNACOH8SWBE7IowQouYAwfKxlL2AuVkKSWTTJRwvmYioYb2b9PYdKdLhcm9Gdg7NO1mk6WKKVhXtTgsUxxpPLRVRf3UwNYgWPlJOEUykTK/gk77k1TjlXDqAKIFXjKcnxqGu73/jJTqIe3xg0IrrUFM+pzB+AwQZGHehn4PhPVnmRa1jyFMWi5XTZAdzSrtamY8mKmR/W5XfuOm3ORxJeg7IU02mXaoRpVg4/HRRZc7CztbslxqduQC8pxIkO9EVmRpDEaAIMHMgSyw9JWAXryUCxTJFWjtWzpnbS/b+dbOHMRIDhUkGOCuVhho0Jul1v6YnBb3DKsmS3G5PeSqKmCyv2Llu8e3RBLNx30GDueAclGCrftNHGafHciWG1/J8vVpa6o2CI5SAMDZd0zYdCMQ7JHdNyxjSL1Otc/L7f3Xt696+80u0jdFTh7wWPWfGVNFuOFxcWjwjhgqFGYQwsSWHgGCma4aBHZXBF7HcpWkIds6sM9ZD4R2r20qmuOmJk/F4RqKRXls717u35d1CEjI9Je+FhowTp6dcLVW627qIrgEAXwk9ytIljf/Ccq3OUNXvgKDTcyCz7UfQDw0CCOQzlTcRMc4tRVlqGkL9yoC8AdCOWsnsttBsvAFV6RHDonpH9ekEc0sEwTFt3sQYnK47nq+So+0cskFTmIAI6a72MHZmcmf1jKnyvave88pagkZ8dFQWFAPIYagQgvVq1yUdCBrA2K5jBcsMC61vi/UG/2RdJJVQtk3s6M9OqVL2MGqcaUMRbmVq129LiVS31saI/kKNlZq/I0VubE4chdXR0dVSWgSJtGYXci7FiwxvQ6jD8xzYCBtCB/jdO5T3IGvD5Db/U2bm32XNH+HlN7rba1vfb8Yxk2AUnc0e8pHCvLU7pZOJ/CF0nfnnOTRYVM7qntH0yFjmdTGzwo4zCQBuGtELq0zas/SHGT/szhr1z9cMzD3hYTM71O3yqOcU2jang/gY3vHU1gtFip8koXk/nMR6YbF+G4Hto0jLjWAklAYIxSDcb2PzUb6zPNMIQTovEd9cpjX802XSyMPKSJGgyriOZ3W9kWkeMKKLf84mrpQDuIjYDJOVwX+9V+au2XfVM0QH7OSMiTh/s4FqcZKfLxWiIdwR4gVFU7A2XRpF8Vxl6fUoi+S01ZtwQ6U0+BcGEWwYmthD/rsHqJ77NHUe613pdV3Gt4M658TfVWB2lPUPjsTQFY6H7CUzRsotGzGaIGdvK5ZaP8nH3YO3aTHsjdGt/TsKO09JR9yg/4uyI3O/Dh10jNmN8gN8sM2NDU2iXxKn92j5L30baCnRPE+REAYK/onCpoW8OGALcTz+bJLT71PL28g0ZTLzpz1hAnKpPd3zMjrUaLh4OdB5sRhGMrmUbRrv2aJY5IJY2ADf9HQp61T/WQG21r5Lj53lJZIu0B2sEB8TPkenChEqKEP7GG2NKi2SlIWDV7W6TVt+nGrS9JWPwhfyn6Rwy80hQQ33hdz6l1xIzcmj2Y2dAb4sM4NO+wvIbe5Rjcs7xSiXxDScmrkM3scXyN/7r2ri7HJ2I7PcnBwWykJFFxFjyroZWMjOoD6kIRkUZqzF1tHSk401eswSyO5+nr6lXAorioiL2keegoNfZUlUSeoMqEApn4hhm1DZzYUGJtwUC/0zk8KLnJSepKKVUQLY8Mjla4J2ncyTnre8j2Tx2xeXZHoALJtniFhAQJixcyLsuv/lRIcZRFm/lcSB0ezsqfxIO5ySjgQtQvHE2J2oVCrr2isNaDkJAtpT1J1xK55IK0xUTY4pAa+GsXItMdLO7a6qVbZhFrBMJU0ioDqGngs0rWR35CBN/2qpHpn0nw86KCiVAbtp3uFxMUWGwuuWHAAHTU1vG/hmVnkbZ6VaS46mHXornBEvMufhgMx5PVgZbXAb2g0JYj8FB4dXC6cMEl4t38sFbdM/lWUrObj9zZ8qYylI57CF+UshCgoXnYMkl9WqIP4sKeR2UUT3HqCgApENT3u+xpNMQ03kyhUsCGvqPCBNHKK1IHhfIqqPxoLp1Kj87GA8p7Wkxhq3yGgl+1Rq1mFr7EUGjMYiK6fLSp3xjbVcOGsZ013qNEEXUpqinwvyXmAg/QnG7wKXbTvzRL0jhnf9RNvZEMQMDApF613lq2OYt1e2ddEkxIvDHtDhUc+26JfjyAG6GfaBMLx051XolstEP1XImDAiQwD+52v+rJka1J8l8VRX0OdNrqZLHTsJvN0s71FDEtj96fC0OERtrWYveWYYR4uiVFw1gXR67hxqrIwyXCanIPWSSEb6IiRfwnOA0a5mFnPPmLqvZqt6SrbFjjcvy3cOVBlEvJcSf7lz4HZivU5MKO4rkpyfVPBtYJBAw2xgTXy7DUWDZkItxht2tMR8W6pUmuKpq7r6Qy9RvPGJoLNUd3aUCZi2fB+ZURA0521RabZc/TifmibgmWYQlwC4Ee1GeA+vcM4VBievI3U0DEV1JMEu8HtSgR4yICP8kWSCQHJOwd1IfTtmcj8eWllzszm6LZ5TpQjIWSG4FmFN1gT+X2vY711WXwVPvX7N6tZfrP615Qcc2PW7YN1PYVFFxvQ1oqtt1pUGj7SsI4oBK8Emx5LmkIIWWXjhl94WpGs1fq3qxUY85NVEajBOi4vW8eMElsfB89iAPDqJ0VvzxBcW5YU70G++oXYo7DUu6bN5EvefyEjnjd1PCrpXs7lYvyZdBmdl8btq6FxS9jjNTz1SuwSixiXgAAAA0NBmkI8IZMphH+PCG0QfYVyVstO7t9OqNAflqhAaXndJBIFHtPAxh0HdoVLgfVMNStexkKT2wHA9A/sWgcebpBlhvcCi/lpRXl8JpoNWUYmvuXqZNixlnGXW1UP4qQTQuflH4fzCRjWHF9EzAoOEDzUW730NC6f2wheqCrFkQoak/E9FsUOh6jo/vNTFvjXuSXcs9HkDIkzeq6MM7saY3LeljuplFppx2eCsHpc1wleUDRd2Nm1Cl6IvdwvbKYCcN6FRgJNDCefSCMx00EHxmiNutL/29OssTfRlZe9pKxFNmJTiSrrd3pFe9ZIUJ6tpNG3OZoWo4AJc3Zz2/rsZSeTh985Nb89afnuXdyNZNTrDhXdC8Z4mDMKgcC7Qfe/KXJLoULRJxf4Xojnimij8S33iGucpoDG1PjsbjD3w45JIMZ2cxox2G4z7RsinZx0WGBzGBPv5uUzABt0+exR6xM6KFwKfdvsqRssCV1qoKil29m5N5+yrzTTbD/ZFsyfiuxv08xfkFgTC4SD/lTyieKOkAlMBBV+Iggnxffts9Z/4oYI4JnuyBBoFSKlUC3+4uL+qyCos7iIj2qBN5rD5MBc1BE0SfYDNfhZiskVS3HUiCV5C4EvQYrPia4nWztXrwsnZC5/x+Ab3CFYvXcUAcwAltsvz/KRaVu0xiaYGLtsstXaX9RzNaBacA8DB8XUSntu/rhRK71P66XsgMHEA7fJzbM9l0eFwMfU742hHb7pkuocPo476wLET4hpW40yQ6lx+VupVz6UAzzMRXPsmJBSLrGNPW+n/+2//u3UlGWDsWFSjcT5h9l0xCxErhrwxve+5+Uuw8dEUUv/lTcx8YfWJgfRn5xiEBsmkn8J332wZBskw8HPQL9Q6e4hvNHTgbMwW8JjKXai8OOegooSgfNF4UU0Pj5lTUMQZuYDuuLjkv9uaOKIx+41TSWNgBbaP0tMt3r9MON8lIJa4bnk7zK5E/5BGf5C83bTcxKVHm1TR7kPoe5p9XOSd4NF12O45m7vYak+SzuguO9jq18TGtiJCRmEQP1A6+DcZDZYDlDBdERVPdUu9OUG93w4QaliTEPULJbPkGdV1VWytaee/jkkkYw1AAABFkGaY0nhDyZTAj//9BnzD9S2EJ918ucFNy+aJ32zMQJMkHmF+8RE/gpt0QbxTUlbd6KOhyYgczszWZiVy5DgmEOkjqBJjVsBGZXV83wWutQL/CfKFaCjKUgy0k+NgJCaT6auDT0U0Qhry73+gZiG9Fs9531HGUCaO6ngluBC8WMv420ff0qdFAaBPMReVLWqNqyn+ZmKkxTeMH+VE/bXhKZNVTMVzGRgOhNyloFslsaCOxdt5CFbZaJSBtoFNv5sguAs0AYriJDZV3WIYY4Ydz1R04hSHZ2e+LuJ8ZQ0ItX25XXWvYapCwt78raCCCmt5543c1UOLa16MKfoqJLDR8LKSbSF9DvO9uV6ySvB7Zyi4o4YbxCAAAAKLkGahknhDyZTAhH/Z62Inpd2CxjBvwqRbaU5nebmjx+JDh/yahB9gDtFLmoXBP4wSiUffP+2Ui1CFc/bN/5BMHq7pQ+KxFxorZ5htcw9rzbWjubgOtSc7oa5mtwORCKVWSMY1GSdJPX+dEdVPXQKMSbWNpvKBAaP2/Yb+NXrj8qePOGJGKnup1H5ImqTlXEqAW2Vj0U6d+09kZfeTPRMNoXT3P6Wb45wf3cQ0nVaLYx7EtWuJPRzGSU8iuXMoBVDxxhk5zEyE90T9w3dMx1VbDgdJOJG17W+RSrlJFWYznUZ7F2xK3pU9TmFWtVUFLjpIb+xP0FFJzPaCPbml8z9xOy2hDEZEIJEyayBVLV5h2Eu8ocZlju0CwBdKLBIUCtGYU9kOl1RbyVJFbaaMC693v+Y8q5ce++uZdLs1s5BjnWggyYweBktVtdufZniHofe0VDEzr/4kGcEdsbzl7Y8ssCRyHalLmm5wNlP7cs+PrpLXE603GEjTAK9sMm39MKxTvtT0OrlH4p4WudfOMMJxnbgtDxeJY9C11TsgPLLSeHd/470s1VQd+vky+7+eVM1QCyfU9bK86PTyWRzkPxGjxgycMQqgeqq4bDaJdtatKZz3JeqnrNnmNt6+1E45YCmSSKmAw9a714rVmVARQ7cxP8XI1Nums31DXEX8IiTDW75BejMJi9ZWNlXLL9Jknv8n/JJJBdqvuh2cJslLwFxLx/eTMItpuPh7zZnB5204dYwAxCx4V0rIFm9Jyp3I4z8rtTbOxEoxpZPhglSffIe9E+x4MrQdZbLg3i20v1EQZb/N+irbN+wjBTA4DMGA0bqu2apl15W8rnfXLAIUQIkjqy80HGmspmeEG/TIEcY2VdspLWHwV4MYGGaaR3s/8E7NUr2kFrcy571A3uXXVjb0x7ES007s6n5utza1dEO2bsWnyTkNP4NXrhxmypAb8mUccRdFQZxjK0GfcQlMiUpp8JHHPlGu7F/Ejj08qm8OcybjMKrbnXp2WTxiJ7/XvK2USAgsCYf6RaUJUDmuctKrc6xJAVBKqFCeK/Fx9+7raV9CJW7GsNCo1boJ37gOdulZbNNTGfL1IKYRH5lxb8NN85TFs7EbgZKJHQk7rAOEpd3lvSOO3NzA5ub0EGnKV9iAPJXJzifME4ib7cbHwKttzJ6BOCCvRYyN7qxpUXbC09aZ/Q03OHAFsxSfjU+rC+jmxa+kVwUUZb3ooLSMG80A0ixmnNfXduCUr9QUOr+rcX4d2rHdxs1NBS9chGrgfxiSA/j9hbJcuQSjX7mZ4J3LjLbKgEDuRIc4LoNduWeUAdnPr6B1CduDNNFsPBMqcm105DltgyPl8JIq4I5dGMqSy+e7HsnXjXUTqVfJgEZ6qOV7wJHgMsHw2uYXpw60Lm65eMHoekKngTpWQ3KPjOocElip1NVBjhUzdl/Cc7x1BkU+5KKSNJNGqrSnq/HcmSe7ZS+7MvfM78oFa8G0FWbZ15NuMhLIzFtelLEvIfYWXorGwfSBEG26ir8hxLKWVUXUB5Bsqvvit4wFXkrUnDorhukI7cPqWqDbH3teaz0HXuHxlmUSPsFvUB6uE++uE8ARIvdPnaO1Hw3HMOBriyy3eyHhyzCPU5c8T/kZ0rnxsu8fopVsE3YzM9jeHoImbXyP1hACPHBZs1lk8enHd0XinLYgGm7eEpCSZ52IARAg/OqQ+mqVrBrMC0/R1KQdOhzvsplS4Dw7hV6IUCJrnwQrS0TYfk1Byx9OEPH4T1LOsWa81gWkhDxfaZAj6DHltbPntKvQImA+W4fRS/bx+YXxeC9FGFrwDVfpf2h4iIxJEZhrWDyKU4Y5brzdfz/Im3LURNgvpnQjmWhk1+X6XHkGj+mUvh3/I4j0pPN+ROgZec8PLvLMHDZPstlFCOO0aFFRKS8ZFSpK17Ycrhoh0m6e9KClyHGvPyQ5n8TnYixRORjRsV9oj6X8bgZYYja0s2fCvWT2baoZud6uv1GRmQA8Qgf8eMxaYC2m/YzFKHeUwBMXC8jeEFoWYF4jcWi2jKBGl6LPjTKIxeDrrV0qTFi9TP/YBeDxJ0IeK+HydwUXgFeinvl336Ydt62wDEjU2iAkZXn2jVeidlxWchfSIP/ESAuMG58s99JcOAHxEqNK9COsSI8XhYucRp+7n7LM10vduuj+f8/jwztbv5l8F1V3qPP+wpCrBs/Ac3Ha7RSGXN/W7Sxp7t259kawtDM7wwMf3IiJCNg6XfzchJYBg4xL0BgJ8ZCTGVrODhsmciutZ8ckHRe0Lpt8d4HXOarxSZAbgUQTa9twutoXc4flP8oYD1m0994wbHA9SeAy3mMP/xIrtc9ZHSFVYCev4+FS1WDa7ehXfCIRzeqqeUoj3poNgG4NspbsjCebXqfSf84G/xAYie9UHWJgLlSz97Wfis1lpNumjzRxk/Lz5zkcPZ4b3jpuJc2xD+GSAeTR6ZG+nh5Z1K29dvO4lrj2BgNt7jGRJvtkjQKiq65AVfvu0GCvGQDhgjfbve2VZ10WGMCowXuRz5xJ4pAcv7hqy4g/hKs+GNmSLH6Dq/bfxEejFKvPgvYU8NeqR+d07KkhnZwKHOG+hYWHK06haVQYmDzKgaSVQ6DbVyjMTImzR73wHc/AKhQjR7OV/YfPXLomch3XaqOzViyHWTIm5p5VLZ/A0nt+qRGsQIW6QZNeNX1m3zLumh/FRg1XALKGQj/EvJFWWBPU85/RE39V/35Jxmhjk/BgD5XzNQ5JclSX3OkvoR7e6ZVfpVnjGv7pD/rbhYq38ahEJUjLbCblYJdmj1jMYrOQJbRJpU+BBfZEFIzChqa8Y4AyERmGdXZUbmanFCGagEIUDynsdbpoH2ne3HkQWP1TUAKm71hy92RkKpCcSRa7JBwUhlV50gh+V6aSf2UmbGXJGSDGm3EyKOu3/4HKZU7FtvXeCGmQhh6VbrvJM6ptSjX1JNbLA6s+KoZBQMgBrmrKJeqCcEIcaMHMHXlLWO34d8AcxRxBE/NWPEfAdFYII8mwSkpBLwF7celjZTWMn8LwPGmfU3xgRb+fcIJiHFHtTsneNM97Uh4Er/b65EL8ScSk+AUKv3JrBAOq3nPniGWCA/IOxC7zxhyhpLnD6Wn3pG9KoNSf6wfMfkYP8uYeGQthX42JzwSjxQlv8mgFrwjYX5B9wdHli/QyHTlb4nxUregZgnj9DA7vApLO0y52q6L12iMMgB9CeXN/YrZXsC3LzCvihU5I3gXRhJpKSUeAs3lX7U78WB+RWXyf9zU5dhMP93icz69xyj7HPgK+vqINImDjqIizOuCkQCouhPKdNSndlq2lJHc0cbIZ/O8blRLWr5UljSKAlLb2tzpk7jCk/4vZYcEUiNrcZW7LrmMePjDNQOrAb3xL/FjrI1f5kgPX0itwarQg20U/in3JScF4M33jnOZArCHvNwupVwjQ2feKmAX2mYBoc9JnPqBAAAAUEGepEURPL/mVhWsCfM7XLGQEtMwoxuACbFb/uLNHT9YwI35HwqEHhoV2IFLeG9iY5Tt+jOuBK/1Hp1g3Mc3ml/nzziB/cg3vryWtFCmLYTBAAAAVQGexWpL/5zegReNtSXlNAY/Ze2kFJlt7ziiLRBPrLpwSvrHjFGa07Xp2JBLDdRc1o4X+jbBe0k1SdvAUB7whUd2y0ywmFMfReVGvGDOoPlkBjp5rlkAAAnmQZrKSahBaJlMCP/Glu6ybpH4b2MhroG6hz/uJ0hq9vQXpkCznSxU6upyJjtsLCULomX6E8MRRYI9hKBwFrfTrmIgPv/+a03NM3QEDBSDJ6YGSbS/5gyxZhF1u1OW3cQNhpdqXeRPFH7sOx0qG6FDLRNhVGxUuyfv6TRybTJ7jEQ2xlSaeqJ/q7Yr2P528UhzvsT0AlhUz0Fy3qFsmHYcp02ANHGuMD2d+du3T8jCH9EdfyYSvtpfodqPGLTjrOoSStIule6OI7ETaSrfY0qD2767RhqJ7qHL8lse8MWXDx5NfPUGr6B1TtSDb3jvXYgAg1TnrCxjEs/YWcrcfrfoyuwxH2BBDS/vHn6oV57tjV6sMumfcxzqPx/MEsd4tuuTYh4I+/pPGb5oj/dP/W7+US5/c4yImMhthu1rivrZ65val6/ML1ULAjjr4yFm86oMSYWzsxZ9DMB3/V83Jj5lnnTgNbRxoZkBcjYrFWZHUsEVb5Jj2VVuh5yNQzcqZKpRL8VPt16zwCgDf08eou6O2D+hPoW7ppF9eePWXoYQGH6zchTzZhITegIsHQXQXiDZOruKKlYxyax84mqym9P7ca63bmwKBnaVWY5GrA1JhkPlyKeO8X1H/atrYG++CEcqiJS29gRtVF7N4r0bIKQ+w16yUpMJSCP2HMJb5YiqVeNJUJSVjV8Q+XGBN/yZ2JJZqIkVb5383F9F7W2l8g4oviDR3wRCAj38f1b8jGc6nTXdhX+xqepmP2kAiEdkcoS9hT+Sv2RZjnr4DyhoYymadDylK7AHz2b4szQYJnFMWKarm43oiyGA1W77qqk/A18P+85GUEhYCGIhjCM+JYXh5jeVfS4nOWfdKhOO1PdJoZtBRRL6NBlFrszHxx+L7L9R3RdpTm3hLbOi0GF8KPWCoVCzxw+cQf3OW2iW61xWYGNQ00iKHantHZNnqa1hvS+x4kijYyQntm3XU/ndoPOGL9HbiUnDGlPL9GCj7Qs51rvQike+brkyyv75UqjTXyldMznsgDU2L0CaFEARfVHpqpCTKcqyxYTrKFDluKWdHkv+HwOpzOlLYbYUu+jbiluX6rvrwxgYFLI7MgLkpjYLeExoeExMGLgqTOQPluVDNgoOXTIO7WFzgVnWrnJ6QHeTAVvKNU3X2VcEpYlwmKKQ7IW6Fjgy5glHkXu2wnRRbpJ7aNO3A0PaahyVl9zpGLOVTwlaZdyNVBrJbBHHNyVOThRM0Wl3DmwrfHDISnjTIWTrAIHVKTMsnZtrVsMRDAVU4ljPdG5wEt2s3kSBR0q3kfvAG2srRwxB3kafYzIvrCLdEEFIVfbQqmRpXj0UbSHygp2d1Rj/Fh0lLGdnz87kyOeLwZ5z1nlYo6cFsJvAe6RnQFr2SexhPh14cPmFpEF1/ZMn2D6fzGJUmu7srJA4nIK/q7RdYyXhHKfOwShTc8gnmdCW4BWPaert86os0LeMqOkEv4QWjjfFJZMmyJ32oolJap8PTeV+wQdVfVTjatWu1AkLoQLdwCZ9ZSbqHtbf/bnjLnaK8ABnYoEl8kFzQrlRoduLL3V/dt4TmCe5MzXjl2dYWZUvnj/UFEgasoapSJqiF7Rt72+r3qC2JbPNVcu/4HQJP+Z7Yt+x/RNeZkZDb2R7bxD9tMn/0ojSXce/nYNd+inEDVhks2IsAttM4ckVM50GbFRZnNaKuaJuILQU1XcB0r8hKzjyUxewGVa/ZWk0RvdwzEYlE/XBB26zsQTCjRB3qqtb7EHuhDGHbXUcT0RZAsC3D4O9HnrYgIOEOQTej+qSAeEMn/UjGVljRzoeqFYPQS6YVUHYbvZoNvCWNyp9xSS4ItgJSmvEy0GB6315w14Kh/+4zin6y2aup1VZFMUD4WHGkRP++iCwHRTO3H0j0KL7FPsZnqJ8Qu7GPHXjJPl9p00F3zP/rI0voN1bDY2hHY24auT0g7rsxQWylPTSwbcUJfY+yunAGrTwzDyujJl3jl7rN2q0kY+4pr0MxUmySJJ28f8TFBhlSxLR+BXDCxwHIEgatvonH5/brpOVbL6/fyAJtq1RkyoiAWBZsNJXljFscfPyAnYA0ImYYHFot++FXRsO/YkoPUoIG6NZOHQPJeYY4esgQ14p57nk99SRgiIIwTiNT/GhcbpYxayLSyaHHJO9kl3IXRbCNLpRPjkfKEX4JED5FNOCMCbgCC2Yzn7Bj7/UXSYifO51HFhH6sQLTiOqyeWrs7OyQtM7fcuo2kQXHwalWEDZwnEMh3zgeKCiOZWNaSBGIc630G5Y28shP+YOfhEWQtxpsUsKNuLtP3JmKhWh+MIKyHc9U/6Xo8eASxQ1Sz5h0G5wPN5kF6L7f130vq53T3+FRT6c1/Domx9UR0uV2nLdA+Zol4c8wqpykzperhZ3XRBodQ4nZKZ8ZZWMlV0JB3TUsiLyyffzW4l3Ju/vMHfj5YBwniN1XMeWzUYQbF6PZfQ+/9oA3ODOdMz1ssuePDZh9isQcJVXhCkTeQgoIT1dKwXhfuprTY/+exQL4DIfgu6jfQY/PFkoVMC0pM5tNkX0w/RuTJSWmDL6CiVweM27M+ZRtL1Tc1aFuok5CzZFuqEbfz6d/pWH/Dd1cMYsB47cfz3DnZU1GQxhh5VmLhwVxWAcpzvYxcysexHvly7ReG0pok73PsQ1jfcSEv8kzU9XGNwx7s2+AreSPBPJ3WsrbMQuGyOKcw2wfyCDgmvJLs2nptiyD+pNEyBzkQu87WJ0rNTsf0ckA8PK7ZqEZH5ZU9qPTOjg9h9zFTonphWMJtKoqn5r6FMLgp0Ipe29ckqJQBJxSEI4EaWxNiaTkiOgn34XbF6gkYE1Wb3KFjFB5cxE8jNDGDMhQYrmkTIG8lGwjRHTVK+kysbH2ns+AlV/QPev6DUwmzrKAudqQOiXfbBj8eeavlM4Rpg7i735yp9DwWQ3BWFf21ULQw4Yb5+al8TWWVW0hiATVBFSGnNi70p/9mls1IBiLIgLI7xfpGwZ3mr9BSaa3c1CoD1UDH538qsc4rrlI4ypIybdAGGbH7ixh1LrfB/cGR5n2V1qd/dq5TkT3ulN5lB4QHgOqtccr4AWVp21oW12fNM6sLfOe+7Qt4MKZadO+pt4HAozLDohpcSUtI+2TfTRFsWKtJzAprB9oqI+UsnmfPue3vuYyuM4u4KkImyG0JMTCXtPoIE5LOpxs5dpW8ldrzOjzxS4e8YaDXIWDonLXj6wcOWRhJ+ah5DUQpP1ffBJ6wuHHgW9O3FDo0x/Xn9bbIDLUVrEYfuE+coUwf2LH9UQPXtre0GHOCYKTwVK8MUY70QDC/GBIVE5qxc8ffHvNGxGijdEWrOtYtOZuHM4EEBg4syJ42lzrqLgciMAAABRQZ7oRREsT6BtwgtXcgrILDIbNnb51En6RciaH9ase9Xy9Bjwh/lx3ferjX9NU59yNh6f+ycNelcUjgNi33VcwZoj/RRPgATcIqbbuZKEah6IAAAAFAGfB3RL/5UfUjgSdcoMA8BqhD/cAAAAOgGfCWpL/1PkMAEdgDm7cgQlNagkl8gVgMI7ifccSwPek6IcIEb1dNSEd2yhd7EW6TSfrjG8dV5Y0fsAAAc6QZsNSahBbJlMCN+8AOsulLEOqUHJ1CxMpV/mjx85O7ZoN21812kTk7r+c/wJVGEHrnjSrPjaBHPzaVD3tinsuPt7XPHXAJq/MktwrHjhpdLOLtzBPdDkUXI7hezNm8wHwq9qBNr+evqL1fC6ViqFvdPufGgv/oQC07QSyFThmYbdfwGdm680HHVB+bp1gIGuXjV7F/8eyaLERujXfZ/FRb4zvmTsyDsMEAHWU470Fec/Uj9WZNUWgh6FQ95fjMdW/P2nR3k3tVKXqPVgEW+P0JJYLFcHAas8N1a0mWbHGoHINruqHaifgPiZt083e0kaOJ8YChDfG+erZD5fIMyJap3e/8uS0u9sN2V3Cu2+cX2tYdUxcuIq6TyanV2CcMg1tzbQu5aNk/wA3ZxiFwcmRtjX5Ex7uAgMEC7KdwG5L5NZFwfSRml7qtg8Dx3BGupebSicdFDOu/agSDP1sJC5sNGR+QDtnK5bpZdsGWaZGGqZEg/Ps5wD/XrCFvHeXc2iz37+7cqB+K+5v8OKTH2LtPoLwykT/2Uhg3BBxUdxV8sgKsNfeGhml4CwE02UO/uYE4tgKq/oq0EwlSiRsZtCNPRm5HGl4S4T/kzfgH26qipcl1/Sg2tHNLJOhLbbkcOcP9lQmh7virxnWeELh7HhvYyWUgGaKgKblXVNiyJDEk9E6mDd9BDVVmeTQfZ97kEMlBMTiRc32bOxDD1+q+LoKXXub0fcyKw5B+v+/qpweSef/f6nWl9ENDNFoFowUyWkk/AeP/Aj5F81yE11RwyuIhMngBm+E0f3G5t4QawMalPTtyqg/UdrT3SLais91R31Lx6S5TWKq3FJWazdKOH5hYIUa8XVOpfR8YXlW1wx3s9KUSRwSkicX3KhbVc5dQGBhxruRrwCXWuT0tXQTbzZrP75cLSay/0UgA6zYtrG8BG9YgryrYWN+tSEY7x259Bkf0w95VrOcV29Y1xJc1HloASlPFLo585Ba5NbJxxzf6cYaueTtry+rUMw6uv/6rVFE73HQ+xymfmVUrPVlyKH8BTcygLnVsdI+BoVTSH390ilHQJ+CKYD7PNwC6KrKWaDEu160oSpx38kM266oI8FqirwqCxbrHt+nQP+Ybr8c7h9tIV8d2cmMLrDnp5lX22jo++ru/VXcHuobBpdYKvJOaj/80HgKT37HbGF+GPugOtlVc0azaIIpywudfU+jpnbP7xz6wfiUjLyrFqJ1055gl8RatZlnV1LPNcZUMR5XgRYxqePuO2EgRIuKoTSyhYYwVvjFAtOxhYDJ/UfyU55+PZGlnLsCyXvgnIm8gMD8C7oHgGNDGrrY3D0+fogSU+dmaRAGByxw2PhhU9VgdAvH+OE7c7S8uda8PbJKe5M0ljnHTOLljZQPAkrJkkE3GsMnxVocoX1mTVT0KsEgVmwpEpTGv/rARfJ0vrHfvCPCjTFHjRVdOYe/3uqZlAoZ5ofespzM3ZUhieNXGTxAg42wFdHw9aKnaeUl8YiSyUjvbXQuo2AK+T0CtoOzJ1wK1ozSCcArs/EBBqjghBfc6unEHuTf0SoLs+ZSCNsynR0Pvr58ys3+VEt+HAaxHTjdTZgBPuhMh+uMwbBqQfJChQ75FxLxEhNXNj682Igmpb2RBIpuph2bMtbtxteSdE3g4tEygG44nFuVWBRcFd9JfpnXS8pioaspmgNGTyak5CqYbROEfJgekkxh274InqO1kepUXVVKnQRNJxuY8BETTQnksjD69qpdjw9fD+QVH9UWFH9FdLk+LAztWQqpH50bc3iIZpTltx4vIIGu9b5a/4DAPfCkQgaMuVUj1NuYkFMDmCRNPMoTKQjHGsCt6ELEKyf7cnqSiY2l5eYgoMWbSEa4gsQ7gvvWv9fDSX6SoBuYFi8DmUrNV6N02ZK8HZSlEI62Hgpl4dJ+ecQe2l++RB1wYEVGZYk4wHEj8TPcXDAui2tmDFRVIOxn/LDJjSIBhiutJp+mWcNgQrJVjoZWBb7S20LUZaOPVhOlr+1NzZFn9pdBg5GdfxnUy6aVPE5z+ft70GQ20g12D80JklAc10ty1FYtJgoq3W73wAGiYBgppO67UEljuPQpmvZa8Ek3l06XOkMOF1Yb9b1DeHlniCbpnlaQRiGBT3TtqFqbCKp6kGPBa+DQL4zNAAHfUP1Mq6z9US7k5Q5kGmTobxSBM8hprZCWVbxagPJc9Qetu8A6PSgMCdj8Ln0XKdEBTKir54mXSbNWNOhiHP2puZzU7n9DpXNKumjzzoxGvIdX9o4j6QlsCjCIA1AEIACdo6tLZNVMU2PuG4WxatQTJ5QISERfetDU6zVRTy9TdUtw/HU+SXXn8ebAJMbfqxvbVeLSNQZwsT548ITQmeZGJ12VpnioeTZ9IENWN6zwOvxEGKhRcYVSfncz4lIALGkQHXNQpnLYvUu0g9ERJEIX6uVai9iuZwtAJuemt7XMOQAAABKQZ8rRRUs/x1jGlzG60+wCHAEkhKXPW2Y2XWjaDywUYK6IJdR+ML9XuW7y57QoSB4NvEuD8ZuXADptd4bBkmwFRQcWn/rnUofmWAAAAA0AZ9Makv/Wxy0CfG1K4A0JEnhmqTi7+AItX6oV+rdBAUoBFzj/PJ6YtjTyOyMIXiBFNYHmQAACKJBm1FJqEFsmUwJ/7/OVP8NVXaeCZPYSr6Wc8Wpz8BDkw28IV7bqNePIbr0hxrIxF8eEWY5In/90eNoC0l09Af7FAXYvxajLJbelKpyYO/LJUOMMo2Tp/YwuiMS8OY6d0NbvDafHr8OYtWBmXpN3pEQhg+hAgN9Q13gW6jCY22kclyqs6LDo+MbJwTjnfn1oK0fTUOtqQqemZX0CO6/nLoUY9ttMFkw2E7oAG2EFLWe2Nlh9NaIBsbLr2WCnQJdtiv14/z7V2/6yRDXMKcKkiT1F8GzdGALK5eBYDh5769le/cAyTGdUjcTg7d8t5R9j7sQ1DVtPpmwLyu/Hl+gSwxQc9+D847SFNNWsGRtfsjEhwYbEYii5yx5XZ5iuXxDLt1M8OCmQjSO4Z/oJQ6hFfhJyVWzVhli0atKW9S2MQOp7FaKecsl7jknqIX0WwTiup67ofcbyL8RVb7NUt3Ik1vVvPSQVLO+saNJlHKz6obs1a531K0sGXvnh6z1CmTzpVGnsIwu9kdw8Y4uhWrVZ4I2FQMYijdzv0HA77ZbqS2l358zi7lV9lNTGYcgbfeMZhaPZKtIDpS7u+G/wieoQAqRq/rBZfAx/gP4U8XyLLO9bSPH5iNcwzhNop/Uj6Gq1K3JOhnYKPqkjrW3gWXrsETqm6mh5jW0C+m5kowmiX9Vk+qJXHXHw5XdB+tEjhGzamZeP8c4NqIgCzW2Ot8GjYz1HRfVxFWej+DUIaGZKKjmMfOLhLZvjjyVCIgCxhslVE9oOeveCmSYn8JsLrds9xtiKH8JgKCcgH0T66h5ZQK8gsn625qS0oXZItqwYqu900hMawKt6kDEh7mJgLuZskQGEY+FtzGh2qk55ErIoo338opWGZVpUM9tPc41r2nR52FK3GMNjZXLTtZWsWlrqsdGynDBjsUD5JzgFlb+Ok/t9OkfE/Ov/ULdOOYv/Wi4radQbuuDVLsqrA4uUnJW0najfUHT4NAWW32kDBLw+KRb4yYoUtNZ0Ikmu42gBTxvSrzeJkZya41c0TMtOy39yBPho/tvuWHTcKw4+ckGAQzz2cEkzAcVlyXB+Lutv/FWRH8CA01RFvWWkx1K6LKj9JQlM5Z8gkW2iA1gaJ1rCeDfcKeXy2CyYfKcFDqVbrT5Nkop3ZM/6h6258XMzjoLAImqkrqtI5E1GbwP1Ali6SAdUI/6li6c2NGGHEZyaWv9q5MxnQjMGeGpV/eTTIneSn4UhqwZC79JTnnLM2tcHtCbC2/XFF7eVmXxamfqA56PVNltpS7Tv5kb97BNuHYje1CsAu6KsDUe6w4cY1jDMRoFckfbPzGv2TMamIYQUPr3tRngwDWJnR4KbKXLBweVdqieg8zUPerJHyEYgr2V6xWcYKR36SfIDPLcFw/hc4DfjKqJ/232b2/26ZWkxH12JVTLOwcxRPlHjke/0EpEVwBMsJvlBy+WsN7D6EtesJgJaDoLUSY/3g6KE7w0FTbWWvp/rb6tw6EZUoC8SgxKBhpAG9VvPnhyWdSzf2mVy5ybSR60MxFyA7OzwlOpT7UJK66WOh0E/3PW6eHByJxF5n1Bz5b/LErRMceLYOSuOhkqyi0vGYyvzmHDwmi3TRBP5JvC+OVCae3FQJvcKyTYwsNRIK+M+PTg8fIReQCOpVOz/rJsN/ijzAvLnao3DA2p4SMDOKb8Wtbc5sFoJKz2iwIyXfiAUMsRV09oZWYLmK+cKvKxAtDa3bbZS6vqFPu7GAmn1CeWovSEOWR7vvvpo+B6OiZkNFtCikEXHKwXOfdok7A5cIBi2jzBgDxgn1VMEoh2DYR3iaILqHjC7b67zBH401hkluXhx/c6372hBO0eyaB8GtyO9QmCD6hRuMYaDsDokkDZ8T9QvYPZvfnNTDc4uWzVQqkDKpcplZcM57cNuoAHwfZK+oTr+3MXYBos6ukvmnCE3gQbNzeVBtLUctv185XyxA1QTUbL5se4xCyGOVrW1CN2Mt7SLFOKJYztH54ZhphjfGd6ZVQjUmfPVsXOkhmC0u+UKCEu4X7LyKtmDV/EZD3h+VDZnzU4pelVxGAmFrUE4AIV6mUSIbZNCIGzSZ5jfeUFr+YgPB7KIOARswyMBdIK6WWi4d8zTlyrNz96oJS5tPC4N0c/NzYh6/Drh5NxBF1Z/w30Ej/eitsIvmHoqphofDZ9bKQkHhVBbB6gvwn4f86Wsv1t8fH8mkJ2/mT6B6yV47IRiiqmfvhDJaz0Rvs+N8m/fbUKwKYI2Qgx09aYq1rNeyTb1X9UF1YYyQt3bQxqvAGN9xmITfIjnff08O1XGr6003LGg4x5siqeA2dhn2v3ea1laycL3x9kvT6G9h/h661rtz0CUS8Wu7lZJsI1HN52rdX/5xNF6A+fjZ8jafdY0sj4A1t00QVnDskct0aI2hHuMmEIF9j1OplMJvwHDszOixuvbF6DS0iahDuHhY1Kzyg8ldwbxzJcR0cPDihdUXM2CcMNWg06rrJMfrzfUd0yvf+yAI5KU2yU8+bJttFvM2igNwgGeWGXOzK5/Mz2+oEinEoTFy0XhwxVvrd42IxGQoq+qFk9Tw4TMqrNY5wf7qiDzBzwX6ta4phjQHqUTpyH8SPSbKfF/5L/VeKwIyOK/pbhb9+Tg9VSTRG2NfDixsD4lfOLYivEeQvxXYs/ZzbAe8qBdIJfuHkR52KdCvRtsfICHPtNNLyXOZo46//3CnuyppyCmusV3K8xuAT7aoV6FXQ2KHHgdrpJWPA5lXY5xO9jZZ13dJiEPG/Lx2+nsdyNSsIChRzEJUqQ52SrU1NCEwH6UIvpzZMUFlhnEGwJ9Zw+rpuLIV6/716SwsUdc2TycXZw7xjEHQSjhje4ao+Zl38agMzdF6Oc23UQgbtc5BDzx6UgpbsMO6JtE35Uuy5OXNsIvW3D1JOEigIX4TlTbLyQsL/WLmGTQQAAAiJBn29FFSxP+LLVl5JketxuIy5vvEAqBAytHoMqW9wYFzOoizl2PmctzuywYQzVCsMnyXXKoDvD0/Ya4Q68cbWAH1jhIzUWMYA+8FcKQ41OwEVrqglGgQkbv1GOF1/PQGaKOwX0Deotp2L5iKNcewjPCVKaoCZlM7x/drla8fq2807StJdSpHwsQEuFY9ecWecVF1dYer+cJYhZ6jzdDDsMjOUPGdy+E0mCHyrLlVt6ojcJ8fgahNBUQESRVVkx//gFISXl8Bf1wnjW1aerWb60e64xKYBHTtyP3DPmAjDpfS/bpnYUeIftcgFRuPCxVCKHQ4+ymshaiFzciC3IhEMMVIH52V8l6ZZ8fCKpIezZqC3RV7Nu6qUcd/VQp3+sHpHfhigq3N25PAVBsWIoHlC4nBk4Xx5IE6UwanmsRt+VqUT7zfPpCsbSR8syD1s4vh8NXsL/6kAsvT2FpLiQJFSP7NT98iKjLlnhoXBC5QYsZNJWFxQIYpbYz/e2Pr+3grJHUZot7y200Jb6l3IWiJp+grQtRqQWqq4AnZk7iV3Z434Sq6JiiA4r8a8hhDgrelELv7Jq66a4H1onxHkEDMENFYstrnCiqcX3MdFLqvgX/rRNqZo+hSK27/wyf3numjqv0aeaJ1fX0+kMyXZvH1IHNnnsfXhoGU3tEc1uPeuIKlxfnUoj5Bj4XUjq0px7LDBrfY/bqZzU5oJql30edpJ2Q9sAAABIAZ+OdEv/l7PbpwSHH3GtwQ+rsgAAvV4BkN7QqahjQ5bqcJgYt4YNWwHktkhEGy5KwDi8hrYZGE7DnuEpBQMPnzHTJHu+kB/0AAAATQGfkGpL/+xl6K9zhCnST3I5TClsYZhSD1yMdwwo9F9MQqu0pG4AgVmYibKuIZGwozvhoAneOb8CSx8y6+/QqajUAgxd5gV0oMqpteVgAAAC5kGbkkmoQWyZTAjfX2K59giWkB6cymWhmRm5G2SR4lCKda1mdSlube/SgKgRfjCeaxrmETcda7fvZ/uvmZFVpym9fS3Ugh0lmaflPXummGrZBVt6LlQ7QCwS0x1uErbj8plA5mieUJKmnX7ky86Z60rwniHzjl/hN3wyWrxRpHcBopo2ugDhn2IpNGREQMg/HabIvsZ032WzqMlMSmx8DbN6sNVMIiVYwS6lBcsheDQJGsmqVXfqiIGw8XeKMgwve4SuFVvG2kZYZ7HG48q3fHmHryTIHW/7SFY5UIEsEYE92+n5GyjwIZMPbOsmAOatI7dAOeJfSdgvWpvJ6mPEndbFAjgoFlR+rONBVB0jFkx6+FRR6BLcUgKpP65A6bpUjEIUdb+aBeCTc4iVJvNH+dK/lEnM3UCw3/S9mef75NarW4054AC6LCOptHhHm2zvM3Hl3AaO6RaPxELaa8jjbGagQSfQmyukhWfj0aWmXyRnTlqw24ctOKxtUWcPkeQf230bJG4pttxT7FeBzVsgR8nDiVb9KHQYLkyuNGbRKSMwR6r144acyYF4RKm8BYmN7ugFIkjGUTYZZTEBZx33cx19X3KN0wHW8teyLRAa2kGiPuqbr1827LUVhbkwzEPqjNaMBAoc0PFX+a8TVzF6BAagEnxJtj2GQuqDFo9jEiw6bdwoBnNVAxaBvm4j95TEctMCxx86JgDLKo4HJTnoTrwhw0N8OAuyyg6cja0NLD651DGFvxWLy3BRykvTJRa9YNdReY5VkjqzA5+50fciQ+XHVU9yW9UvlZyifsPH11SPimt0+buB9DqOq6Bnh8756Dea/cbWTumUHpdIdePFNR33JC40Qampq8+deY+kOgIn3Hi9nfksm8g12Swe8p3iwj9bVnq7Wnxx9w5rF9RWx/vfJs6uM+amc5xqxg5j/JmiUB99zC8Ko1nl2PVvCTKQvL0LN+PbMo9g1EX9oo7kmtr+g5ZP5WkAAAhOQZu2SeEKUmUwIv/xZbke+dg8wVg5D4h3z9JMr9ZHS05dL/6qN13AyvcAGbP1t6Tbm0GW0sXZ2KIi/WIgTS/+1gUWevgQ0yQ/8hcjy4QlSGvyGK9nblymSzKRa8xbtot8gBz89MqJlHVRXHEjAfMrjqr68TzdR4LXETagYo/7kiI+l8vMvqIK+xsyzTxDac8TF1f4fPWpU5igv04gFC5JO0E9aeEEMGh2QBtpEYL+nRi0aG27lFMOHIOmpyzuqzsg/us2/2NeIW0tgWFOPKixqEEHsHVB+8GRzrZu0YUwK5FVrRi1AeHVnghhLCQCuLuTYCa9KFtmof9PepyJgJm1ZKd6x0ju85WIT5uaxW4L81q87dtWzAxCjWxcl5UkoVTQDtRfZQKkx2arqfmRSqE7sMAyuYKx2oyF2IB/UkjfuKR2DhzTpkO49cSEUqT8LUKZTCTO+rCbuqJq4B1wz7pnLllVmhvb5pinNj4ssgI/is8W4p89ygKYef4WtrE42aJi6QO5EPpzM3D3ag29QlCVyDMS8imKZ1pQqQ0bbXRsXtLPVDYbac+3+E35I89dEBapDDzknkJG+3ltk3j7137bgiz2y8Jdxk4WrzoRezNLjcw/vNVYrcF/efCqNLXYryyMBKLxw6MKoOaG0yWn39/1+qcIW4rroyDw1RdE4EWFkRC/zpoIst9s2Pjiw+LPtDQF3nIf+RdXWxN7ZsJycuOvG/NskkhB4u1Oo6SFRnmUko9YCXc9NIdFlZectyhgF+TJXK8WMgq/AZB3kfPHbguMrNcCu+nigNSuA+BST+5hFQ5bngT2exPfAAPWd3HpNQ42Rv3AR1AYHY1biZs1MlznLu8slqc1OWI180RT4KoL61D7LITVYII8N+vGBp4dKE7Pc4tITlIzpx37ZvMXdyziNmCSiHHYWSLn1k9o9/o4WDU0KdgJXKzkbWNtsqa8+z6cwyPfjzzAI/+DvsmZZ9+CFsQAyxLnYSjhqOCUaJM1GA7rZyJ66rd4QuT9XuxcsQWQzaH6rRjsExE5ghxc3CGn3rdoGRMV1+4fpyiYJS+mHzBi0w8pBLFXxlvXaE0diV2MxSImeebk5f4XBZRudikJr01H9XGu1XOUq3/Wc8XGlDbwG+8Kp7txkVvIdsmNuH46le12Uv7r7KI+Y4r0t4cJATmwPSFOGRxJbhbjNOCWOU9knWbMfF9iatGSoMx0EbklGr9z5n+i18FBxe+L8rJOzlZkct6PlmQGmvzwYTwznCR3S3UF0jxX2AdnIZ2JNR/hWTNi4ZtwedSesSwR+3TnLSHgwrpyiwFUELmRVG6upBKtZTVs+Uh+HOorwCcFSGFQIE391YyNvPo7FMGaidJN2cc3m6GFcmnOO8ZphrED+Yvdhzj8KlMF2R5ZaZODR7FA2H/0/CW+t+MBVJEJqbkeahbwxCZmGw1/fgcpNBSBvbcWfIXA8pCQNVhsMp997UeGemmipOEbVc/9KA6dDnG+77ITmsB8bLSls9Ra4OrnEbrRe3bWpmVFjJC1ETKoWBVIfIuXmFrvf4idWKcC4TE405fH5kMqJpfKD6EyGt1gI+znUiIU3UMyarmcEY1F4XENF/CjJca4GwLZH2G4CSmmc69ahfTuwPAULKFoOj7cAMkCFKxelqm+9H7CuBVded0qLs6wW63UP4s7bChev7z4TJqE8b8LBTpYB5cuzsyocglrCvlYwdswMI+OCUS9c+yTIk94fcRPj2rnAZsfIQtO3zwkPThIgi1Fu1q7AnEG7UQuoSdTnmY+GOaCKl4e9iCWwwOPiv5eFa2CyMYGU7t2tC2RjDjFF65Ai3LYTebGgPFbDfkWPQtylQx6K/RSRerAsK5T45BCR4yxV2APmH8bkwZLENp540i3WDk0EmUuDO7EmrN4jB+UriWZCJGjV4FaL7JSjXGfa6MZcuZX8QP2u3/oqhD2+kit+Hiq/T+1Rni5TXkfFhhJ/a7Gz0BaznOjlqZXRjQWS6KaVLUq0ZjIPmfacN3auhibkxkVFJia1R8Pc5iKmP0dlBb7AwKZSTLkIc/YHKnWdy//UTUb36GxQtSLGGsVVI4oy61FEkLTRYGKJ9xJmZe7F2yBWt8XVnZKOxKOESBU/ybdU5OVEYV+uekkn9CSVUgDk148+PcLFLfb7NCdG21XmDiFJp3A+ZPswpYZci9UPQV/KnMPoMnoYlIGRVbZpLT2WsyEZ9eaH4twDmiT4yDOQquuWjCG0vqZ7xzOyKlpiq1St4kiqA04Zm5K5HwNfvoiP2ES2MdyvXGzTSI+QE0tpiFvhSuur4qM3QKrvmZfrRKFx2POjFRxH/CU/pOzceKPvtDt8lzx0TXTay4/dHVdJ462DHj6Xj3hbI930ax1gYciItOTu6tz5DUbN79ozr3wYTTPu+pdwTPTJLMQIsjTkHB665g+RJHFwsL8grbKKxH1jaxGiY99AOTqgPf9Uh7aOKqDwNynp5H305QSWw+Q3j4w0ohl3XYPwwNXgUwCOW8M7AalHQ51clI51WNasth6fgwtPBaaaPNbp+HVjSxQzA19kJfQOPVO0eY9RyKMK+G9MMm7YMFyUn1LacmMMc9Fphtav++IFd1G0ACWpMANsJ48Hz4uCcPdER3Qr7e5wtblNAWSoi1SttmvadFFFg0YWVFEiJ2BN1S6kRJ+orbnfRXbFbVyJiwPP+offu2Dhscs+sIrH7yIA8BwsMzpEcL1yJEzBvqLu2/S+JEJyy+veXDtfahm+1p6qPtLYtOifvetq5siNJ0zLNvmVsZOujhYzrvJS4gxipVFIV+4SCRTtyfPZNDutMOTPDfyzLlOUI8hHQ9RpzoAAABPQZ/URTRMX3QsvtX5iSj2kMANUgoG02TllwmjVK4+PYlDebfT+WOec616NntdvNWYehxPw88xeyzuVgD6+HJA6nrGjQKIOHEvO6fiUv/LHQAAABcBn/N0T/8CFJ6Uy89aoLVDZ94RZBvegQAAAEQBn/VqT/95r64KA7FObEAKfZk+FiMchEpfE4jkYlqAbity9vcZ1nUd9EIk998XTLGMfCAv/PC4L8rqEJoZNEXx8zMAwAAACDVBm/pJqEFomUwIT//GG5JeV7lhySt6918cpQY40iOrE+CZxBg+wQU7h4TarKjWp2oeMfS8Idxb+rFMZK7noxvB3C2GsddGsuL/oxBUztmPSLQExxkYkiVbifRch60/PWs8MTlcwH3EPadkAenqZ3/1pb0DO1ozVEyJMstRB9+42riAm1CbcIA1ulhX62QLFkuiwNZd2CHPH7CYgkSKhrEc9c1yFuv/tUTWzuJD01FLh8lmjan7XB2gkRAvQgjgm0Yvl3O5kIV+pVZFmXdO3yYzl0oKwXIk0B0iP8By12iXu4vXS+wvGKWnQUrK82uTDVbnVrxqKG5fRUk4q+6bLRj9MV3rU/+EsOekkTrV36aCmJk3r2ryLqXZ2kxITym6XI6Xwh89KqgRYNfDVXbwckgY81zgjE7DnEwKQA/JzJN5CFm1MdsGK+V1035ye66YYYuo9CX9A+CbfyjRfRY8tJojPz0CcXvZSn6lpcBfeXQi8Dm69x8M03+EKzClpbZpUDxAwwRVa4IpuO/KweyFhtw2jqM+INdmqk/PZcMQ52Z8CjeSGAYN+5A42Pz3tDjEGh/FGOQ9teDeW86uUsMgkYbVxtQFRK4Fx1//OLp/LuzMOtTQ0mUH4vZppn8RyFhykflZ1+Dn6CLA05g52Y5cFr07JdKIDtELEQqsqjr3zNCLobvBbh7YuJrrz8gml2m/WjIr7vT1oc4JCKaQ9CsyfzUBxe6J9oDtk/u09D0EWx//NXMZMtg55YgpZngVj42EkBI3Hv+kEcP17yjIBKeK50VHW2EkEemFYgkyHCTLNNFkHZ/+CM5LVELWeEerRWPuF5hcL5ot92C6NfJ01UrM95dUqOqKyCByQwoJJEoBkcPgzSlyM3PfNvFiNEMwOhF+VL7MRH1P9Qvy5YdEvY+CZlXMObtWp+A8P7A5H+yZ8aNifZy0kdHSanMcwRYcYfAUWj/HdN6FCviXfS7nqLZU0K/6PUyMQsOlnvmUkCEfkA1YP2nQfgI3wtQ2wrMUkMueaPU2ugII6Shp6JUwQE+E6IiJDKYGExAFsqyYypysrOmktLTChuKfpMo34Xcmz4OQpFN/l/ApsU1t1UwPZwTLbCHBE5yLfNCxr/7q4pOhUNc5PQKtWOUV6HjFcvH0kd2+PN0j3H52wVpPmzIjy4csDpkn5+Uzsa+wHI7liEREJxZdoAWRAUCCsBGKYm8//4p9AyHd42auH3WH8KheFQl35EIK0KVOCM8ZIHOX5RuGgpTYezVoL5KxbA9wwYRjHlORF3E1MQBVYIJ4qniSDfZPae9J5bdG/B7imE3x5rcBGOr+9v6wBxhoAYl+6p4FUypE3zsCRsMxBePexNEDR8EOjjYAay2rHdIQf3Gt6984/0G9fUToKZU6oDVBaQFGtqclNPduC9jBQAZW9lqLc+HksYYKmtEnY3f+2tYJEfzbgFIOCHCYtfBqPa9OaibDFEiXf3wD3br0wrhdvuIt7101K0bcUHWlkstTjVInKmOgqtWVvQodn/7Wlyxd1Q71M4ruMn3PxcvoZlHVUggwAyV01Ga2SornuskZ+awj+eIUJn/hHfuMaH+OYsS3VAcd+d7FywzLAxLDfIVi8n1gRxAne8qdm3C9PTWAxLU3HSTrqK+FIrue7KWOycx9M4BfQWTDfJ4m4EA/eLs9FofclboQc+SztaHAarTtrjzvMtA5AQ501HFmI9aMLeVIbf1Qw8cJEZ03tsamEFpiu+JtXl1JQW0/cpas16UXWgjszkbKPtxuCMYvc+yE2XNTBVFa/8jv66ylACjW0NuM6gDaaTLsS6+D0eYhw0MmrjtKTCCdoYmx6i0eXI3C5Ya13/2GPPTI2E58DMa4lcog3b9SYhx8HryOAdi1ys8CfVymXskRUvNcMVJ2+c0CkTJxyuwuz1fTijb1sp51/LojpZ9qOMZ/3CXfFVFlqNas/r2LD7Ym9pyoKYgeSmnUysW9uMoCwohCWX2FLzrqPm4W0x8A9mPhrMrB+e3JO0CktvkXwpgB5/U/Y0e/IOPWtSMgPpHlPX3OM44jzuYAkiTOzvjxXLzMsilOX1L3wVIufCZvo4uYVdf667Z5K4k5aGjYjf+MYNXy5psvG0s6qkZpvNMVHLUjfU97ax1zoWszXIdQegXDF1J8Ex1cPygDlQcG36OUJY2D9bF2+EXA7RUs2md9+8KImRFHwmKGpxKVjVRdgixSQNNFyNSLYvj+VGxP29tzd0BCx0jXtAEx0uJsxfEB+T6VckFz+SOa6Ldt05BQyJh7HxdXwn8es9c1CyYm0orRcKBesaqViccqB70oFQAlpRrJqwJxlvwUuGdVJUztDHWLalbNSoD39+LZgiOaZB9sy668IT9sdZiQ7T5zqSaObRtUEgegKdx65elRYy8cjiQgRickmPrq6DDICigcFK6+Jzei9xwR09SjusHGFTJGi/9Y97pTDW1kNGFW+3s/jKlEhIjymnRyOwh/6Tt1rp0DEZHGGWCLGtUpD3dYZB+s5YGOQRGn9oGPJlAdMDpb164o+eNbLqweErMUJsoBQuWPdl6zCrbUAcuY+HOiIo+PT/Gc4xnhSh+poKj0gwj/MzsdctDZACHZD2FIZSs9Lw7krXsVymHVgnDpAZbehKchTafTTpBWwv0HBK1k+96P87nh2vhHFW+7XLSOuK0kkM84uCUYT6UTsZCBZsjPZIAMIGhuH1VjfbzysEl1qqge017d36L6krZyU0VocQpC+uJun5JtVk5//LfTUZd2ysX7P38YQLwrLylYSPZKFCxXH51x29y6ckwkFQuBAAAAYEGeGEURLE/VB69knzuCmHHandpaXDwT95TpOyY/wg/Hi/+q8lgGDsPeJnIu2IaCHwh0HsB2/yQKORQlG5+Qy2eky21EMGrpE6MKe8TE+xqvSTN34WasJtq2dTDriOciMQAAAB0Bnjd0T//Cmo5HJnIZt+Xzf0SvRWVvJxWn3sK8fgAAAE4BnjlqT/+FoIFvIXSnrDm4UHae4V93DjIFxC53jPKtnDYAHYF9SR+PS8Gc/0qBAzQnDrpE2bzbi/ls82/X8E8SrI2CSDmC28bWUoO96rEAAAhvQZo+SahBbJlMCE//dY5PCp/UPG+qFNVUBN+Cufrobt2pYfxKGNRbqqUKHlK/T8lKsGtTlxWVfJkGM7kSYGKKbO2xG81ykXanLVWZ6RKpD3wDLXulNrE8o22NaIMmBGGkn8FemwHFj9Fg/NzN7qRV9Ruq7quCEqyqWFC9GgcgbZzsB7ylJ02t1oozzm8AjWHaB24/c0WIor04qol7vIkuQbWbwdvFzO7ruDDQJ+m0BadshOEbC7IXnfnGLUh4LZLvairdzsiiCML0UnZMhVuJ5Sx4b6ryC6OAPg92nRuDje/3NNtiiyrP1SGuBswTI5IcQIaNXI7PB3sP3i3qwij9F49c8h86y+O0stE+y7n54mzai/3ETgn3W6G+o/sVE6NJ9kSB4X16smrcpe6aGKsfTnNSBh+znoRhCbaULHMhfZeDNyqXRfNVlJ9E6PgkcOufGqy8E9b8Edm6gnmZc/cEIZxUVCEtCAKSrOjuODQukReXjz4QHuqrs7cs2eXo13iunE4jRjmsHrhbqsGobSuEVNLGgQHiv2g7SukPvjJC7EiYUuOeQOtCOT5aDkNw27fLi9oo5u9FP2Dc8EnB0q2W8RjKtDyAH320l1wouiF87pa4ngxxK1Kkzd72/kBWZC5TSlKQ/j7pZjAZnvkWmVmUTBsKzezb7CQHHjQePYWOIU/GqMzt9Vb1Gw0kDtOnv+ljrZwfCQ4AwoG/pw4Xp2cJ4EnfqBeNn8AgDMVYHgXM7lUkcIDIc46VmPCYJDbipX99h8hQFsSxNcXmLyr0lTSbBvycS/VgCXIaOzYEKah+LL6RDMb966VTCuKo0t+/X5I/PUqKYAizrsYKNQxhvW4rFpfTXU7AAozom5Gi0n4hF9dbR4Q5Dk2YbyOLkD1rb6NPCI/zPeKWRW15vAsMG5H1L1pGxFsHHw1uW3Ubm5HjDzBaXRyeFSAYhwI5HrOWDzFoLn1T9MP0zABtzOHlQeUuSsyf6aeArJ1EyZeWdfLe1opbek94p0ar8u/TGSfmk+hBcwGb8PXWH5UzB0g1m09PHAe6nwpSEahdfixuK651i/zBL+FaSGADHMx2OyulhjvyP5FKcnWpCe0mVo9C7nYyrxgac+LhkCUn3NyCd+eeNmpQXKhvyVnYV6KiO70MZI1aVOCjF7cINVUMAPpMBKLo/qPEEBnoZ3O1nK7rkmYCTWx8IS9ozYCbFi/l2ESQQe7LVv2mgbrRaPPda0io8mZcJ/bjTrGJWxtuj/iSdAbgNAncLWSLlqP037+cfIx7LexzhhGteMA8YWVKykJAyyZokz97pUcoWQKqh49hPoyXxKlPWIv1o6HDGhT4RGkPHx6ZLmunApAtIZdkzLq1rMZa4lU/eyvT5AHWoBAF6GmWy+4lLHYFHAzysJ7oeYmN2bvgtkvjryGDBWx/mD18QtsQ62l5iv/7xlrJGKTOxoZymx1MRqwUBOYGbvVZ2nKUV2WE9eyP6kNGHZUrepoBr26ss1TdFYhN3vD9PTvNoQPuVWJrU2OlmDlupAyUOe0VGv5tYebBrXcOlzJ6+HDog42QVt7sOUaI20l1mcJdDjrRRk3D23t5I63jsfiiVu0nRlPb5wpVDb0mJYBvaisYVAKFh/voocUA0Fr1zv4a+RemWKQBW+uxEbwUNlhJT2WW/KLlslmZdsUGnDFUhMBCwbcjhrEPICG5sD8juWMAkGcRhLcmzf+uCl0LW/kDpJbMDUahqCBJS5WUChroFDZ4IbokTa2iPBSt9wyNaEzi3xfDJBhwYcNVljN35DKxOhnGaqadjTD8GeTHkbilaNGYHOfnDA02JaZz6pF1UszqNIpBxPW8b6W4RcIKU5rO6xQKqzOFqy6oVeUt/v9XxW3+qAMglnrOyfuf54AMENTYU1L2iRxrppfOsl4NinG5eNqLlpCTIbJyD/MaXzU0yL9atjdxVWemnfll41xe2PeULV7GiiQ/2oIE/fpoNjp+YAXxTCK3gTTrLIpY1LgYyPjDsiuQMago4AXDYr2klhwSN3uGjGyxPMOP1cLfJr+didD2mFmNS1G92kW6ThHyX5eKHp9rafr5yTRbl45oX72dloTwDKPI4vCdsaF5EuFeDCSj8MIw9hlo/l/mFhA70bZhpMggbsY/cZPT35pvOOW5RB3EE3d/ZEj+th8IDHUvA3sLAILFF4/+yoWUQ6YjDHqtcN5jGdpE2JBhRf2pxJAUHhmPl8jLu3baRKdxwmeYwVhTrYrP+6I/kn86XRSDfWVo4zGP/sYluXpuv4VaSLVfmM6/KW6DkVGwxVnicNLGFVfiME121gBtMFmBWc1ydY5p4CCNVVn0bW8yaOcW7LLkFRCMZzzCgeOa2Cy/TXIxQuYTHqOkEV0sGcSAEwNoDIYBkP0bjB51FYU6Dfsu6yxkdjWvuIrDGMon7OFKUnT9Fs4rhZpjVh/pKiKDunJSfAIba/nWAGWarA5YL/Q/qZoeTQfL2G0cE5xGtZ1765ZlmZSWm2RLYIPgK/wKSc34OGK6sWVv4hxQHM8o2gEs2/jxtS1QoQg4B42y/02cJddP9RIWkHDv7nWo2I1XXfcjQyMtSifLdXKtedd3r02JfPOVjs0GqAHMBGxw6pwOCbl/5o+cU9KsOFk3wz/AfYtb3/LxvgT5fcUastw78t1tO1pb8f8QJOFBdRYpD9mJGJdf5C7J5/HqWJ9Q/1OVdA2wLbr/9OGI5Qcs4zkMvP+etOW8UAVReaEQ8A0zBOmbtFW/mreizLhc2u1UASAlEtB+jj4nQzFPmA/gVyeL6dOlZXxx+65xAe3lhKb/HNtmUS50pJSjCNpETB+YNAEDqaAyP9C/qZXH3c7PHdc4yc0gBAPGjv7sgm63+nH8v7GLNngD0mv6vIAAAABiQZ5cRRUsT/M9yTPz32d+eq4BIEYSNXAs8++OUvhwbQe4DCrLOiRwWkohJkJ3tuFU2H1QK6/g3TEoPGL9hG6z/sz8QsbvBfc205VC/jEae4tlFcPX3/cHRhxZX0XjRmSlI0EAAAAbAZ57dE//CQ2hqvrgGHlNg6r6XxiQyQ4g2ZfxAAAAQQGefWpP/5mLU3F1ieOFQJjlDyMUAFO49NlyAXqngcnhx5hf7OEyEQcYa9CMWdCnlRnOY7nJ5qU5lOJ8JBvofT9aAAAIu0GaYkmoQWyZTAhP/27CjSEy6GwfQx8Pjbuf7zdCAQoT6qDX1UBgYk43d3MCUySHwx8XAz3Cai6BUH1g6z8Vb9BNWDx7nUBgGqHx9PkTbttnFVYALWPrxMjBZlC+uKJWDoC7h9+gInMQ1Yuk1xxnjULzd/XVWtMIABWGdf/B28NGw83Y/2EESGAhKoJaJ3UZKcmRE3fe88RX3ljm7rR9bHUqUFSqrFzKLp0K2/XoXGwrGKB9WbfEaFaTPdu1EVnBBzXHJpzKBFQvGNmMNyBfQHn2zHzRsk3opQFgQgFvcN1COKzUV9Knkm59kUvd5vqOhw8J1O2wL40qJrsmYhJoRU3Cja08ZGn3kX1vk2Ch+kdOQ0q7JAJvcvdxh7rNBzAeJOn31uwqWBjvh+2WktAPDzIcjQVn4ZuGfGPbDyrdgkIzNuLRa8j68tTkYx9iJVk8iWwdMhi+4215IEwdH5awHOjPfMlVyxoE/cNwdTK0K2Mp/CaojyTb2sFJTQiaKTxoF+b/v89cRMwjCZA0ngGRarNLM9q0UhpRjOFlX/p14esIEdTDFnxq61Aab/vk79RtKW8FhOTwrj81SC73cwBipjM3ZlAvMtdwBsR0QwIaFWB28+EQFnpm3taPS7SpXvyVndaHwcAG/xoQkSGSPDP6TVncZMwoITEp0MF26eoiH4zJkN/KRSSecYmAhCrkI3atGYVEEkoOghEsL7c664peYYDCftYfxWPCZSr2pL2aCw4/87XEHCuAPndWdzA2XzTrNDeAdmxoB2/Aw+Rbd91aga2jITu+U30iKUjTjfpmZmG0fptS68LsWv0MSaL+0s/p/qA1+NtNb2oN9t8LAXYQk4bh3x0a6zGPeZj5yLQLx2mdr/jMMjRklUAQfqCSQN8ENHdTZAU+jAH6SBf8Ll0OWViUEEvgBSmDZn7mWfCFW1ULU1TJLG3UHIy4bsTMd5G5ZALIKmdqwr5uW4zesZvmThSlUAXQJixjQ07ZYsxVucmjLHj6uEVUQU+OQMszwbTjPlubLF1m3I63n3JTIinK/tRGT9SZDhuL7Lzur1hZ0whKZkklI1d4JfwdCTi41RqQKXLY+zogyWUED5ByGrnOaDKuwi5omYQRgexyMmjJiofTBPO18ooXuQHRvrMQZoKtrzDUuITNolWzSEyR9Y/A4RrA/QYTKieEKUFkPQQWqv6Cm9XxSeIg1hujAf3zQeWUrlyCIrrjGdjqjpKsqMjvPbTDoHb7dM/4w2FMoeFB3wM+8QWmfRz4OOKZUjc9gSoqDY1ViYyGgxFTAEnTIl0pVs/jkaMnGEdzpcAmnYk0s9U/z+ACewMpRI6MFysexFpYjOWP69KQnU+AVz/iDbkyug2gCUwrADRAV5fFXTf+v5PJ9JswTydhLdxRzGH3/41HyfHC6D1CRUu1CyhkSb6qQYhgbEsRFP+XgzH2GYJ/bwecKxfdk0vpC6jIUkcC5CBjQVTyQPGb4Evk0DPAEyxjjcDO7x/ORT5dGqOL+XwXHyvHLrxszjaTqDxzUwwE9LQK608R/L16tAEugR1QbEKRBPhzj3BPjpKKzPdPko5OOoebm2dp91hB52AWHtlHpEooeLClUSbDgYbXzlRPALVcQILvCRmWTO3KP4mTVLn0aPAY7xqgnCZoki+zB0aBXMrRwkfgoEhapHPe4tMAVfbN0diEM5EzkjP+nUijvglTIAhO3+Js5DoehVjZ2tT3nNaspvlkOZkq3LmAohUPT8uF/I2YThJ6DVgHAdVs+Fi043EeTBtKrf9nh1DNgQHsVc/7WMxWl7t2uhn/MbIJTCtOKFGjIT8bUfcKRW774zr0+XZ9wE7etrBIP8ozkyhz4knE9284tOy5cFiLhHLUhWRs0fttSyI7HHXe011BJr1MakIroc/Qvocxl8I7BbF88M0VuW3aW+sMuasVb4iPqKgzE5kUZVVV5VkQLQ2Lw7BwD5UzQUNlr+xVX1tt//Uu+krBXKJtoPnml85ou9OAmuB90/+hzhIRoA2Mmyu9TuAxlMkJWxzTc4sTbx0KxRzb1xVGZPn8vZHv5gluk2Fz2X9YiOdZcyraYgecrMcBJ7HvkfXixfdcd/CBHNRMdUu1sgNAPhhcYL3tZemEUVoMhC9uGcdSKI6SVfzyEBRYB9yu4176mbGsXgae40czaRPVhBL4FRV8hsoPrr00Erg6MvUzHWdalhJJaQn9IrWKF5FDITEd7Yzwgq40iNfvk/VuDgWVVNGxCuufvg9Kcm7h4yMrXF7LkorPMX1ZRTvHw79U8yov+JJb1sVFWtm05yYyF9HIcHcQYqOWLECKCrctdgHhfp7qF0WAQsxktLjPL6/jAtggFIwoFjfC79628xkcY7q5rYTGYwOLHowW29urFhBQU4sshS1V4QZJzEZbeE0zgYJdqgS8N55HcVIseLykt1hdVIOZnK1QIDjKzj/KRc+rWnD2GdMAjJjsIKHGe46QLfDds+YXHmj/OEez0vOHnsjYMfAp8eC4R8BMAJK1rLI7kUW3m1rKmsraY7lnLg0Q11h9DcpwQzD/zHhkZdoz6lxOJQWI0haHvlWPyNLzpyvoem7sUlPewGYUsxlaRHVDCpn67uCg7k9Kx6LaHY/UwDeRKbLpS4ki7kbkcGhVHTtbnUnG/h2q+k39DEQc/04qZYgoaodn7NlcRlSxNbqMNVFzFoqNtxXEkYdiTWTLVsKokU4VAVhPmz1ZjanJ802OcTPgtUzojCPWjZsR2xVJev4MK3M5sCJzL9DoGdS1KA5+oYPIY81CecCg85PgT/H2bEyVaYkBCKzkjWWChyDpdq9HFXT49vyaUn/wbr6SuCyJTu6k0YqTv9DfnaZ3Wfm9Sejdi2/LtDDkJqSwAxqzHtq7bFBR5XSDMRSJ4X+r2TWe1o1iCgYiULEY7Qx82s1kK9O/j+vv27hKOZguijP6mlLQTvpDTI8Dd6jH3SH3GirPlH54mWmUgLRua03umAAAAFRBnoBFFSxPmmHkhtyJxucQWlGHK4XpzKU6DoeFjNPJDMtBg9TsOANyQBs72835my9AEl87+/30xRXmWotC2GaNEIfUtYjOpZ4utXdK5h9RcwfuWy8AAAAQAZ6/dE//JtCXdnDfz4MecAAAAEIBnqFqT/+UKI4DkFL94rHf0n2Usb6pPdC6HxVYaQCQaR9GicJTGJhOAIqLK5GJAhil8gYcgDTPCFMIomz/Mi4fnkEAAAksQZqmSahBbJlMCEf/5yyrR8u/VCHM2I2zpGwTIpTt2bJhWSDyGkMboihn/8oQ3t67te+ZfO7/aPB/6iHyw3s+X8LR7gObaj4ED8eIK8OUlFWcTuXUAWlg/WRzKEeF7OLQ2oydJ2YE3bM8up3ADmy0Us5IR70AG+Gh5zli5Yt6GXheRshNzqcGKtjITHTbZ59CU5/+dcq45PXF6tWFX4JY8buns6vKj2EQkuyNk0avd7uOF+49DIPsiv3AVtvNb0FCsgBEllIKRYgoLxuRuTX7x0TTj9YY42GbvXSzovPCtotKq5XVpAeos2s8G7k5SDwApHXF1S+azSNik/I3hJOVeokwbpxNRoj5G+pEsSa02yYxg4mbqtvOTIDO//3gHF82EOqd2gqplP8eJJ+cURUNm1dea+pKJvHnn7Uzixv+r8RnNFugDw9r53zOrKlRZs2lVmfQcqakb8ISFj3neBDQv62gyILPA/9Q/9eD2d2v1arYx7GyW1v6uBNBnocPSRfalq6on4fKA/6SZ71vqhjd7EgTCpqySdjnsTLoZFBMmfVqKD0Pt/QF7N2qtj7ExM2tlwmmJ+YJOyDYlnttr5d1rZbdRHHXijiAnHa5/UIafH402hyHVRyAlFR8vKm2yr5uGgvxf9w4MqgnhUfae1BvIJtJALAy9PV9kndjTxsxLv7ro6NZSswLfbcoAZF/EhaAC2MIbsTGl2kThiEPd2hfVSZNUx/pG2m6gnTNNNEnYN4jjYWWfw0hKjOPdbL6LRLawMKTGGpVWt6LjR+RFz39InUxJbBazGx5wD60IgzGhPmbDk6tY/DdWAvG0gZyECZylLgNYOMXORxRpz+fjN+tOD0KaYwqXY4mwR14dK3JizwXj3ZGGVxhXHStHVzmj3C18y6YlOGelb1jriBFJrZQxgJU6lKWfmFJNmYacvJKHrJ20FQmHNC6IIsUq9kk2/UANWK7EHVwX9QaVhbKH3uZbdJEd3nU9pc5h8uR/tMecboV2FDKDWnBJqNC4RHINyDTMO1JuTnyuCRW5Xbzm7DfBSJQAjNdD45975JbQNB/hTsqGFT49nmM238hZYokcQK1uXGOstjCvFK1OwE1Ct7w7odo/7WPsyqOghCYIUVjQcZfK9k8BG7TWmIlyX9bxCCFAs6uoXm9eBK5LMkvMSrV8/Jc+dBDMP412nuqKOYkVeV5TT/fqC1t+k/7mSDzBEd4NgWk3/y18LZkY1sfGLYF9FS3jCu2ZMhdKOx1MVre1ZJzUwZB81XBfwY0yEkvYPwXi2BFoLS1mq74O84OZKIR6eX7I3mt72oYCcNOZH/W4vhnmsh4xpFaHNKThsD3yxmSAOsMK8XiTEQAMQmMPbiHQfJCuhq7C2766ggMvu7+0k3HOmon3TdXnPFNQGJBw9VaVlgdh5nr2BU67b08xCHRIof1lHE+GKzt+aLkPa6/okhuf8i13Xl/K4f9MY39Fbpdywpa1ldKNPESq3zoIWck1zYrGLGAhsoFB3XxDV2nVO56ZPQjtsw++pA1lae/MBG5JYVIdqNNhibwI1YijHqVhRIWJVpkG7GIp+lNJItwTaaUGN3tAv2axxuCDYe2XWYBKkBqP/C6HzaR78UdQWLYagpXiz9rdWzAS+1qSiWSZPK7AhlmWsZE/Pa20huKdgBgOlosqf+C6ZtMk77rjnG6/2w/sNPALDLUZpCDI7P0MeNqmQL8u6xxyRKajoi6zBVS12PDvNEi69HBSJkN/PU75sxJ6Etc7XFxfShLUe/SMDUacoDc+x8aqe1boqv5nRQ0BUEkHIGxskuAAMNEPYs4Zr76WSRBFiBfDkFCWLC3OWA934nzjdaRqMUanxgYL2i7XkrF97tTwYqWum96cpCWYqhf7tFq206xl1/sxYZW3qJS95QAqGbwpbS4+3VlcXvh7SsjqJNqKVO4CX6jQl0NNyxO8oZUlsA1Bymc+/E4gnMgCLEv/Ur5kV7y585QNXQWv/Lo6tIUZQNijmKxcxp/FLVguMhudg8Ogx84XbjindUZv4gBCgKYFiz/qCKV8FOKEQYGEGX7tbGJM/c0K8tXnTAFeHirnrS/HIpu5v3nNTpd1mpKaVfFu/Ih+JeOjAu7/e/YOmQKupUMsw9QK0wRaS8VOCaAFY7YlZjkTvoUX91gDfWyUdur+MEDR1ARXank5tTWfyXFKxW0PkKWq2pyANH6cRpGeY0RQ6fvWx8xBaGNMFtkYjhx1nUNiGTSrPMRXj6RkPpU2joN7eLwTwwZ1bia3DHWqmQvxCe6tPbR/y9W78IekzHu10BAfMer6l+rVKiBcLGBQ2SGSsI+PSgn94VDfRPLeJZpUSfjZLxryeAKX8MqhMV3Y4qTj8z7Da3LZIH+TOMdHpio1IkhiPdmvfZltcE7WZqrdrEqLUxgP6msgtwWJwX3EwcFHqgnduIEvWoA4RLT4gVob6Mki1RyC8h+umQ5XD/YD2+iOq/NoxmCs8fsumNIq+fcJFHrUWPlnOGQNe5BgJV/Vk+nJzG3QbPdxnOxq8WZ33Ko3fcNHn0wmLTpSF8gn6IH5Bv3t2V4m5fRkj//8+k9T6I8JSafRVEE8ahaXCqsVvuWmi5X+4RsRb39pSyBlGPtNwZuL5Rgj0p5Vll7LfZ59TJszfuGL6TF/JGbSsAg1UbpROz5w5aY4hK7Q4cNBqE4jjIB5u2ig6RtiE1YFt9hik2WXOEGuPcenXR1Rnza2KVNdFy9ITfGOYq37jwajxrWdvODBrscxaJApyM2AZvo6iuxOjcv9TbMPu/+2XK9zdlwMTcFkl5nizgwJBhFIEz3kUaeLWDsMwC+YsxkfGWlQXFIi3RH07DmApNJaumu/uy7bJnpFMkM5cpkm3mmEuiiAsjR6ztKEpkQAgK2ag4W/dCFfUhSiE7CpRlYpeUyXcrjt7VNEuf4xQPV9QLfIcTL+S32QWvBo+0PHlJel96MUPKBDY/PvxY//qFZPzktY/4mqTIyiy6YSa50cB51EOpNDr6aRo/D5RTWfZUeg8Tp77cQZ273eJDZZW4hP4VvdU48EwYJ1CrmYF0m5/pdM8cMy18Wyd9r5xr5RVOGwu7w915lim2gjB0vSIuBjdk9cwlEptUvRRILgoLaroBLe4dMVQMAAABOQZ7ERRUsT6cXIbh3//BdS3PLTvNebl4OdsW2X4YSNI/t0QGPQ02CJKCgZRmEtuji2bh4Z2aRcddcfsIDXwdB9Jc/iACVNC/whznEV7yJAAAAGAGe43RP/62oNguZ4foBo1SHafy8gsjEzwAAAD8BnuVqT/+ZgSg9hbjTb27LzEtwqlUJ0xNEqi81FsuZP8CU5gpLJWeuvweQDA8yc76VGqNTQVvExwgaJPNOs0EAAAucQZrpSahBbJlMCP/5NaFRhd8MuATTfEbeDxuyr0Fn4pMVldFIVo+AZTVZQAHjt8HuAdRHHckq8C7VIXLtWcOPwpU2rNIKF10m49U8Vj+nSTMbZumL7rk++LFnyQG2A8QgKLEnKkbdouy/KNdfShBX39ZaiL6eqiGE5pvwjiDk/uh57e3iHJAXXBYrOCKDlER7ZLsCujr/Vk+aDfJYDU4x1JEn0OuVDlXVp6uLG0kItD0j3ynEDppJsbalE3h3M+pDD2RqbUsfDq8kaZZ61z/sYxIcoB4thgccYGe5xT5dmhV3jaELjbJhV9KTdF0kdxuGkpRaABXkwjw0u86ZHPcnXIGB0buATQ0rXmpnFl9Fj02B1W+1EWbc/kD10YRgkKoQpz3lfF5HdCYyFWApbgQpFZoeNTLCA/jvSwraVr+Z8PSJDzR6E+D5UDk3phZfcaFo2pZ7tjGfRgrmCV6JmeiwmREKyirjI6qL2X4RBMS3JKdnF1Jp6fAwh5EoHICE/zamTS1MY6E0BtnGAS55pjSl1w5us/kPxQVIKswUtCI12/ZBYoi5ySMxuGqN1K6ZQ2YSpIh0x4ZaTNfEWp3KlhCkuVIlkk4nUKbaZHMNM4trjcuBkdeFZ8IxpFSOsbAs6RD8AeEGIiYnVg9c7XoWNMnV6oeXLOXDLgSO7NMfUilGkd724WuIfhnUsJ1z+TKCKU6vt7q/oieKeNWHpKe6iHcpRD7ubgx8UnKBcx3cvA7p97VQ6XYRv9IV43Ob6krz4dht/AkaFVOx4rtTUdf61jNS6V0KvCVzTPeqLYopXAXTUMlDQOXMG+tkBClX/Eq2Is4SLL1mFJgzQ205chFevUU7ZIPrKCj8smQM9Ks+WCwjkunNh4CIJcLqT7AMkyy0Q/frE+gFza2EJvngnC+GN9/mjjzZOW8vzEGjv9QIz9iBM23ognaw6b0ocUKwJ7DHg6amDP8W8rGbAienYWPD3zTkVhA0f53M9yNsbcK0tgwQ9W8P5cjSTvDvtsry4Gg9L+Hb3crTmXxqDC7Ws+Fo9sma/AP+PcxuhbUvKMXYBJH5sBIIQBSUMndFiW8UBkUcW+lX+6sA4HCd8uTq85K0lMCj8ziJ3md10dTRKINbOoPBwdWwWVbT7ObC+xsla1qyv3hPoqCyxtqQE1PCPvEBL+9zCToU2XLFTl8u3m5/iUIHPKai7fKQEM9t1je6YLAND0mewzuvG6HfMERQ7izKp42bSD0bF7WyyC7H+FibqtSTfcUakPc1wq8XOOd7+7T3YuBpsJ5baD1bfW2/d1FXIzZ1+bnOyy8c6lDGvntFBXp4SNuuu1v2uuXse8yd4BKj5H88MtU57dpDkyvyxX55RaiuZ5p40GoQq0qy+otXKBDiRtSLLo82OSJne+fJCsYIEedNXKKFnR/7XVChRpgu9GJZ2BjiOx7tYMN5AoSCLys7779dqJJ+619cmoglbjlWVayRaBE5V1ZIfQqh2nEpLM08Tm7KGey6ai5K8IiqtrkgqVcvDZKdihmcy/zkyk9IFa8nVL/IplqeY78umdlcCo3LUo6iReZILrgnbAPxcgZzTYX3iqnscNqxVZc3HKTPNCEf38VEBwaC9C3CBU/9D3sOx9AbHjmR8ZbrPUVi1KiCHcaIqoVCP3tVqIdeZS8Gb+PAVf5DnOpjtLd5RO+PmFTRR2c8X7kBIjb1wGtIHRkJ8qOEqZaSdbww3JDl3fDuzA4Hm2jgEJEAZjeunCa0a69tIfRNJ03eRKDiPy1jPOYipRsCtZ1niKSPeMhKQTg9W6Q6GSPNA0AfNi+jGLLotkZJhRSGq1+wgWCWR+R9fbwI0hB8wYoAMGiWJlel3zDOQR3JwgYXLG5DCp5nC5MfkgQWp52vnFltnRaQ0GmCpRSttjVKBaoJcWJgwcX+miZQ6391ZKVdHs6VLkYa37GbwGa174ZOhHTutLEEE+QoFKT2cKFuMhwYCtFiXACUlbpOsCngbF+sivtyXrb7DMCxk8Qi7ZAsRp/0mZW4uHfM7pdLdH8EBTTYCH08lvYBup8AqlVJq/OsU/Xew3X5F9kY5izfKk2AooCqXVhIOpMWOa7CO+369SRVMskdl0fUt4W9eFfpPEiIQWd4oQrAfC5CRGVKk4e89QMHdGzP0q8adE8UalwUbSbIGPLkJ4WcdZkP4uWQrHEqEiEf2Yarbc+pWJ3OJgjfM5rMw3VZymut/AzNcg6cIZiaRqfln7xJvHtWznyV81mbWkiluFl6PoDrT10mLUnAVP4s754i+AOhhB048yhToT4IcAlQXhC6rhOYGkknEdh2N3BrKOb7S0z0ggC2b+bFay7HodZ6GeIpoyG4/u1MBzj/LhgEw55GgQcLNmLQI9VdcECCKvlFgFECv3zjZWj/d1G238ipQi8Zpt1CcyHtGOkXVvsCQ7nC9buW6gwkHFmwJTl6LPOCBwINNgIk0EfwxJWgUpvfbL7/g6CHKR5LsZgLk659kRWES0mK4i7z39+KPl2H6jHi5UpliURgMIS+kPsbMB8yHIPVPNjjbQTpb1ZoTwMy9CKCEnLefhfHBnKFKC8HBvQ14WYAWBnJ6S1hsz2ppHYhxPbzUGRUz1vJw2K5fe3eFk/kZgjyX9chLb2RlwO2Oh1R4rZe4RwnyCzoFRb0vrMcwdmJLjopXBdi+ZdkYMGWu7hWRTQ0yEmvzSFDvgDXuk9ersR958DS73FU1FbJ1Ln//a/yq6/XTwj4jA0SzGzCJnpkFBCY9a/nHeUTd4hjYF/D+k7mbFLcfDit3Tk9KsdvD+1EmQb9kAP55JYteuLJOGZOiGd2TZYPHUVLZAN9P9T9BY/FSXpMQ7U5ZAdBuyLBCtVZz4dHlCZ1fHcZxhKzLxXkrbQN7DO8Y9J3l9ERCSQZS/7dqzpnctsIlOpgdyM57uE+BwgJ5Z/eJz7ou6djVIOyvWiAqxKOEzhfWFsbQalYXH9tt3BzdTjOJX7Kzav8rf4+SWS8G+ubGd1uBKEf3Bgyjkgxno/Cj7Dh9S/J7Y9qiEqSfytOrefIJiK0lXoIaypm2Iusse9vx8C3jdQOXSFCScs/olMds1GQ2Y/GeqTKjFFDqC8p0QFDpVEsdfJAv0dyLNVKzFoIuVBLrkasXx1LiiHlv/fa+Txho7htuPGP2XW1m2lETnlimAROd9G5Db7jMXucNVPchGAVd8lo2VXx8L9cM+Ox0wCUT1J+4/9l1VSFKvTA8L1tPiZrLKgJLHV4dmHyxnWRHN0Gyvwb+VizzKYPy+xYQAOosPgEZknf1U5lp+yxw0/M6yN7TfqoUwgNSI9hahpJCpZIkPCH7IETpjqDMa3dZsZrr0351YY1tGrXZMe4ZrTZL+8sAv2sEo1zVdxMKmPQBL/OP1mcbkzJ48NnSb4sDCl+5a+5Ju6Z+WQ6NyIqRRPDpo16h9VcyQ4Hls/q29i+4/xGUGpHIAC8dVpadUiKsVlwGHCzKHxEjritxcW0xwDLD7HpZxNvct3EI8g8wNpFInOR1NCh7+voZXI0Vbh+mjgrCMkHiAVvv0+lKJ6neyGoG+DI+hcgixvmMp0D0+Qer4jOgBYEZPZchXZfGQr1zntoBg7e5V3dS3eQIE9vHOE6zC9RdhGM9rdJQo9SyW8Nsh1T/VaGo71C/+gknIdGrjFywit9sjqZUbpthXVx2S2S5NnHNBU39eKOAsxmE9cDYozOToPm3Zvbur2c/Kss/8CUYND9w79x1P3koSSoVjo1XpxPV8fYdmw2hVzbCyL0KfaEmQtMv4UVypTgB+qkcHOXUvsB406eAjz7OeE1KDssdH6n3G/PzAMTkh54BMtog73bFGIukvl7PJKElwIZqaYdTcPdguMHlJ3drnU7OwoCidfL2p8BvdMvyQLWlWjE+3B6dV5BG2+ANwfns08ZZyi9eWutI10GrZl3g388gblVwu4G22SvAnBcfCuy5NmKkrsoe/DL8kRxN4EAAABYQZ8HRRUsTyq7MNkMMYfRmYA9HDADDvl3ybiODV86wXatu3tKa82D/jGcXVtdiP4YX4fjyXvkasO3MSV50HDJtZP1VXyuvi0CGVfgPXrKOrJmj7b/7NX8IAAAADMBnyhqT/+C5tAykJvEjmLbmv9+GUkAKwReWjm++DaJg0sBsm1dW1pHmBuJasCKuP/+BXUAAAI9QZsqSahBbJlMCP/0GwrCqENVJ7bLKfJD91W+qmmxfK/VLVaAqahc67p36IbozYDO3M1JcxoElkevBLvD21x1Ej9HadA42BVzkoSJXJodhnYayfSZKHpbT//ARpKUnH1ssE+Biad1oS/APeQqP+CjrUzg2Ky0pQr0c5caCN9WXXBnveDKaMhsOeed3T2R01a1kv5ncWeSDTp1gtjxHJxeMfArR/Va4Ma3vSPqUH1ODc0lE9f1t65493RHSB4FVmhgouJKw9U8FFb/a+hPkeYvXTbGbeXMfJmtXk5leS6DH5llzBWsKsISiGDoAkGv8iKjHronIbi6d5VUbmnQl+RuflCnhCDRV5S8j79ZHvVsUKzz2z2RZQEkXE/J6uGqKO+Zw5fHhyWmrYbIXxcym0l+QBXiKLTSHjGhRabfYjWWe9idxXdi987/nRnyYSqb5vefABdEyGYNNOknSU/gB+qj3bLXktz3qzXM3N6fBN0276yhBUwuZyPJBDIFQyi8flMVF29PtPgmgNQmpUtgu7E9PiDbynkX5aLf59mPenXuOPA0mrwFWwZB0k8SCFqRcXO1cgkUFZxWWTocQswpnVP/JthcyHlzgYyGtsUdJhSkDeTMPzRnEDIME50dvoDNlOce9oxG6NWZ5M+J++c40m+1L7O7hlE7T6Hdl0b0rWnK7IdG66TwNjRTy20yIjHJp+eNk0hj+ZzLYglUcTIRgrJoyavMqpobo4gjxm26xuNRR60a16EXuAjOiYEsyk4hAAAMMUGbTknhClJlMCFfbomBwQdA42BbnRzmAhIJfr1/UGKlnt5NYABv1ANW5CrceoZ0GTO6rOs0gI8JqtqujQOnw3bO6duuW5iG+m4Wmmd2GuQGNpwtwvNq+H+tsTYDmh+Q6lJnol0m2nrI9eLfI7C2xhvJzUWZAWrbkzPh+ZVysmBN3ULGe769Zug5F45rQ+UewN46D4+t50aF3+O+Ehgm9lUdwpzaEshAj2YRyNapgNjBfnfdA9POJXxVbteceWqPWhn/6ASWKDtqE3R0OWL388kaBtUJYrpi4af60nWzyEdhNgZs2r1AeEAu86szGN7larfHHhLJ5aCPPM8btRS2jfza6Ki2FfEOWTjAU17dYmitZL3gsi6SkwPwIklV0SpAJd1FcUK5vo/4DnLzsJ8cEraOsa+Zd92YipGjOIVbubqzrqxQ9KamMdC8VnF25lmtKORPyAzbZqtEAOzDvkUJEVuK/085Bv/2RcvRFui1oQnOlig0Ppo0+hln85wfGkVHKGYhb0iWO6x4+Li3bijQfUHgtQkz4RolKXVrlUvZvlqhkOKIO0B3TOoF+UJBmN4TPdKlGjcd3H0jTUi6D1cKlGY52YaWAE4WggoEMCkz8jZt8/M7m1ZiV5yVMpW4CO4mlixwMAmfXsXUlj7M/0BzW5unVSDaR9/WQwtcdl8vt591JE4lo0n10WG0fKk+yE0N7tcsFhbbNp2ocAxqrJ5EFVJS4j2E1rgS1CxDcAI1CFL2WncfVXB0W8QeJeT9Dndhvm5A/mep92jQhB/4YqV7X8o7YqRsqc25MqN/zScq8e0MAwCO7Np2yI62Llzf/74+IwnA8epN8Hz085McFSYzscWG/7kRZ95yRuUUBPytMFMbprIcj2TIdS10/dl0pDfxPE7RD3KRnPxvRYw4SEobBebnGDyr+b5uABZXW3jBYOxPssznIAIT0SIImjtwjolOhxKjBeqF9BPzloMjaEMQbi3WN1PokQeZUrhPAPb7wPbF8CnLNfwf3wo8yRHVqO9cGUWWFTrJOGO+4QVqAc3EsTKmb2++APwJzcEQ67D34qxTejUn30l9/1b3tkB8evD/O2OnGsUeWk6nxZSfW8c/GbwbmF4aZDZ2I84JshreXs1pc9b3MLgFjrzS2gXBVdDN+3EgUt9eNU8aR9nKdKnBF79ULz9BrtBBalaWlDi6Ve8c1kuGgspxkl8hxHPPFj+AkY0zDvtXBbb4YNKfq1/w1Pt2UdWG6rGrjQR8P8nSvPJMmYPnU7RGRyg6UB1blYe0lkUiZiW9frgGi0RaM8OCS6sZ4ud3DHjfEg9WcrPmuynjr9fYiWO87QerQ8OHYLuRwjfnmTyXvB9rHQj4ff/RB4y9SEWn0hUCUFyFkpAYfj/JABVx8RMo5PeKEJZ/26chUgs0lDHIKnqO1eld4IeM7j+O5FMHEnBtmjfIiIA4161UfXntszvwzJ3KeFcFS9R1/mKeY0qAjvCtmDXXJQV7N7Ens5JHTd7nr9atIRY8cB1F4//eZdwSkrhELs+/ExcYmsmu813JngIPLV5lsJwnQMh9+dDxOSnSZxM7JUiJfLpwV+9t5giqWhvTq8CSaQBpD0dsjALnNmMiVIKxkdzcds0CTXGB5EW1hodgQ/Q/Xa9+5JhO3XA6b4HOTAb64yIzBLpS9hf7fzNsc8xWbLxvLewmMm6bH4ocur0yxUfD+m2VHXIYojoneOr3potYdYI4EBl9qgCvX89U2BQzvf7OZmIWBEVRXEfmouzM3KvngZlD+qZtbNwjQ1UiXyCC+ZRSfhiai+3LXitX0aRauAUMKkBoxFl8OgjyBXILXs5aVoWFtUWOIVvJFjfQtPnFkOXNA9JTn5JpVrGXrn+Xr8gsMp6RkU8dROECMs5ayVgwnJR0iHG7zvnKNiugi3+uLBwtTU3rShDXPUmXl7UZI7g/ErQ1mTX+/dOX7a3tNR6vvpioYawWEUD0r21TrbKAe/gylUFJ/QcA7FAtP9yUo+yNIP4wjawcEBOHfqJCAn//RI2W1otU2P+z1eenfb8YLV2rdKK2TF46CGg+jtOWcy3fCpKjj0dKazwWbNGssFbMEzbJEkMMVd6g4rIt7wcPKaBdDQLhhxPCzKCrU91IIat2770Mz51BIyJUYVwYThqMDK1GVN7+8ZIMqgP8s95icScwl75JI/Xr8YY3+jXextyUZWn0mWZfnNCPFnVlcpxTrHMnTCu+2jCLWQB2dkocvSPYKrUJjSNxMBthAygFQXEp08PhYoc1qNnYEF//V8m/zmxkr0+tCV1TZjXvqX2peNm7qbj0fnU/r4Hg1Wet/2KZjCVTdPtbG79aHmg61J8w3BpouoXW2HxTQdm5IBz35BjXI37Q0cNlPdTy5QdOQTFo/N0+J2TPFxWVEKooKNhN22rgzZypqla4cYlYTx2jOuum2UN85wCqPMe9ETAu5UlfFvvi3xenqLa149oQptdZ0AAVVfE2Q1Vb7y94dSR1c0biz4HCYBnzVzE2O5d7aFGkNXvSm4fDhJXjOSiTWeUGm2k5Dmy7Ew2o6LnYlyVEUZGqgN8JZ/NBdIIgzE7xqMMpC2wYfJNupc/kVPFbVnB5GBKh8sYEMWxTugyCrDWw8DD5aTzqKX2Ah69r7/BFujOGHv4Wtr0NsbXMZkD9mD4AJvR4+sLX9EECZrFOZJvVk0qvnGPlfpce30uZ92KrMCoTDCBqIIOpTKjbVOjAtvSHmoqZ8Iv8oTqbbNhpnCx3h7A6TF7SVYI7IEsD26ZgHtgR0dprk0IImU2W0MpmMIYWqJ65OFtB8MiA/8VwPbGe7dK8UuRUvOyAqDY9CRxI+MCd5j97hZLJU0KEffeyW2aa6+WnJd/7sV2TWQ76h+x7UpwV3ws+gEH/5qXsAjuba29ElW762pL96hoPkXUtIjyzXP2fZQo/AvQZeIi/u0u8eCef/85/nVEg5cabBQbFBDLAiagTHZjiAUQpIziIB9KRvGdndQFtC92UJyU/7LuSsLw5XnJc/vFPxNKpuS8V0jmgdxrmh9xIZFKj6eg44B2ZZLCfURpvWiQJJpK5Pmq4MqwPRgVGjUug91c9nxjxBJ6S+KxIRQYaKIRp13hQ8OAc06Fqc6R/RyHxZEOWH072vC3rPPLBF9OeNGTlCjDAKyZkP/Nv+wT0qEqwDUpTQtovtxGGhvPZPiauUIzvTxC4d28theLiHqOd7gGqzDjn3rsQLVvEGGJ4pQrxuJkyAPFrMAoRWkJ5x+dmIIXc+wJZQBmIxIbU2POUfB9HCCy8pRjMrQ/QIm6kIczKSNsxu6nvNFzsCHgSU1TN98sxPKJpeqW4Ox6t6xV9mDuHS/UHteCCtfmKKrXBPTcFdGVvG1OGG9bVWitxxZTlqY5NojVP8dW7X5R/pTspL0VwDa4C7GvNLy8nZSR94MDwqmUvpIFuvpqIRXa85J4Mx1q0/YmxqxKdnDfQklG0m3y+m+j2P+g/6f9jExeSuypkhFw7XXsGiXGVHb4mobNLFwTxNJhEEUXkF1PSZvNCaofcYHrSLOYyFN1wDWSUSGzKVCyumdwM27Noei+iLcxRUQ4DbRT/rLVL+c3oZBsFmsKEBdaGQWPgMVyTp6w6x4oNHJHJDxqCBFSwf+M5ETMz0xp2DS4Kty5c5vX+T0983E7QxBFNhjg3PY57pIeGgorpHrnsw+r5bAIBqFsOZgH1H2ZHpv+bdNQeyH+1wk9+qOernQPA/b69peecN1wIShhIIzyvq9qhb1pmw/xsJkdGs0iZqady8SskTswWtWEUl9fC5qWPOxaX/H5qalc+9Clu529EOLdgqbyQdBqs33L/EyBC0EHwFvc5zb5TaG27H2d8w/W3vmpjh8SNW996mGfgzLDHjC+L5ERpPTZw8heKl9gKT7RvTaX8NHw2UgqSaxhbRQugjQPhFM85ZSkZgk5LJPIAicBlxxa7ZaApq1ZX7iEYcH+qPm+YKeWYguRHzhnrK/nS2vlnlgapAS8NhW61cLNHOploTUPFhR8eKIdcXiKZx5eQieAd2+ApdNlySMkQfYqAcguzS70wu1QW5HpGy5ILbLAAtLWaV51uZnJ5wjYEqdsBlykx87uesaW+/3sl7o5aG/HIMi7ecjasuWM/o/N4yfB2rKnz1oWaWZ6aOjpPdO/2hdAPx+AAAABSQZ9sRTRMT56WAV2NPhfilSrAHaqkFUisaIYTeG7lY/C35XYN2U3uJJ1Ly2eDRtXJo1wyZvya+y7eXHQO4FwB1qLrpkNXKwWvl2aQYos9cU2dOAAAABIBn4t0T/8dFxpitBfTAhwRkdEAAABHAZ+Nak//mEKAwF90PPmhfvTTlfKlPKrY74MUzEmwtS94k9LGSQjIGXOmwwb92wk6AOe1P452bBF/kb0Qt3ywuoZtHCAAXUEAAAqUQZuSSahBaJlMCFf/1AmwTh35cN+mj2ge1OY7FLjbrZ1vNKGtuXykgw93SbjDolioN4YA2nlmDJGIU4aVA/wP+PvakGbz7e7R5NuGB84NVzM7gig8GsO2qs+UKLj4/4YGpoTYP97JPjk5MI9HF65OwbPQcvNznRmmwa3IP2olCR0/fk1nz31dDOz4f1aJoF7MSi5MEIcJoeLixPezlOhC7AM1AEnS+zUnjLiMXWGg+xcUEx0Le8oRUpbmN0wliMzLKFI/ZJyn3w5+dWOrnPz7Vshkz1HF/b0owx9up5T9V9TSaa0mtBMG5jwQruDUlVrMQMT77koRebQvj+5/yPSIC1oc6NlTxuP1ha7IRzAB5p8+18sXH9Btwny4DEE9YFP7/8fmvUNCBGi07dZRccm/2Y00jbofI5RgfAtHFvvIZTLEli0Esx41NQ2Q3jD3W9tvEh9lc77mvQTvjNy148l7rzXEgY2zp2Wcjw+ihYh9SaZUQdakQ/AnuHijEqULjz/QUTd3SL9ueVn5fP1Bc5gr5XJJDsB/6wJhQqqWjvTzyTT+H59uNP7HfMiwrfsrHn9y7R31f0CV7dp0QznKaLH7grSiim7C4KXxm8PVhNARad7PTf9wDnP+EMaIfFFmW5TdROwJ0gZ5J5+Wf/KxYKs2xayBG5ydHiInOmzdb0bxWS4b5MPEvAEH1Bzb3JSUNDRCHJo/FgRNtevYu09sYd5c5ZD/5krphi3T0TatKtrZBiX6YQQC7AEq3w2W5Wd68cPUFqHgGvvj8KxfKnb7XOlu7aLTO8hhreZRu2QzUXCv4UywGtFWDWCCu+2/GOZqxGTYMUC+vMaCCzUKp3ZXTkqloccAh+vFEoPU9+56avjJTc9T/EKdEHiCyRhi7wbkp2Zxnza7UTmqZHXas4J0ZBPAioiUZZJtjurZ1Snr/WAUaO80MNyenzRs1jUCjbMEez2DkmupsnAMVeS3U/a/iUUKVnkogfc4FEUtWut3uoG7YXkJh7eAEDZeqjU0H7H2gzsUtw2g90ayKfnRaUaJyhMciOEX2zuVWRUe2Ra7lI87uFeOlv2GqBgAR7PfVkXC1ifz9zenYDO4PnUGsZWs/SMOSxe6bIOAiJ7AADVq8Zy/B70bOXMc5SHLXsqkQwDWMyVvmOqk98qTNAeXyp+UfXnyWmmyPBxIxo1IO3JpJOY6cMGfozueBEMbAHM7bdp4pFXzqCdNhJWuYDwJcF5wKorhbSjaygPmIOag1BdvFWDIVfpQYw9eYooGaKU7YxLzIDO/stCfAuxFhkofIKOO9lqNTKUozRCV41/eS7WpI7bnSSjSQxlg3sDOvqHi2U5O/7czCt7sH6KKw5DydRc9YVCGjHJ6Fj2y8rNBlOhW/SVvvcaGvdP529Uj8B9fwuUS8C27U9TIXp9mCouqZHteU2Zf928a9hl1Mz9h+DaXzF+AH+eh3dXe4ZQX4dWbM7d2KUD9oMmJ2qwH5Y7ek/7WNgOLKvSzxzynrO9AX1/xANzBV0glQcllb+Zfr/fpuSUCcq2GGpSmHvSP2XcH6y4s1evWJBrfp22lWzNiEhX9U47VhOJ1STPclx03uoR9wFe7eL4SPKy6YDyE8WY7i+Pvm5/93rYw+j7VbB+qzgjPqY+1KMcB3tLSdtb8p/qpukuJoLpw2BkZrVrAhiRGhkE4HyJC4qck2Y76Zp2MzRu1sEZFkadjMSR0QHTCQbrQ+7hTmsNhgxugpNsKg+9nbUD4mAcp8idF/uv9c6M4eVz00jGzvF6Zpr9oVoxA9+kxheeVL73vCDgROBgG3q8Hgbdy4mQfmXRT1EW4qYaDrD19F5u4hHMt2MbedkIb+Jw4C6Ys2bnUj6ap62JMSTIY8r2hAe4bptJUr8SAkSyqy0owggNJy/WQrQgullAoX7EjrZjIjldRGsrcLY76oHEegHBudnS36qqh9IHiOej750K2HRqi9htvwcDxdXaXYZyzk9Z+9ykLnS6bmIDJ2QSpUnlkEKO1PjhF7vCdtQ1QcC/ewhOtDdAfNB22mzPktGH9UxhdlA49UdHvFqHwxy8MHNFkURqYw2XFKjgm2KeRIcjVnYoj3y7SVKb5VydMGvlDUtHt1c+YPs8SMRM0qdqAgN34veylwYedIn9lAUtsIXrbePP4xDaj5yoeCoLklW5Qp3v/tc4uehEGVi7/IOF+ZwzSTyctlLpMMFI5UGtll8TQNCVZGmQe0LeKe9QGa4B4evaxIs+Oo2XSnR+/bo1Bgz8ltDwYlX2TUAQmZeS+ecgrBQS0NUsQmikx3kt5YJBKH010OR7ZLAGT0pKnLNHWVtDxzsawxIEiJrqacf/mU+GLouHB33JvMNAW/qoIMAue58gnGbAPlDJ56a15Zpu6//lu5eTP28/aHInKAmb1qOEq3yk4TCwbS6RoLfr6OHs7PYSGsLt10terb3wUGv/QVn5bPIgaMBHb3XXi8VeK2R5hw95fCfCscxccPpHnG2PSLmTdLiYkGleGBVQ8UN7yIJOk2/X5XOEQPrPEmhF1g3P1Yrvu8E2Ca3nFLbGXRdm8GZbW6z+1jxnVl+a0tBvUfULmyh0EyollYZnvF0EO0Y2I+EuRXrvLTU+Sx4NhsEeXXCk6RUB4HIXg3+J96QnHDvx1cc9MuE3AzcdQpzDlkGQq1rvzAf+YrnIqx5lovYIN3hBRo/7CLboykQUPFUWIDYwXqzPghLGAxIz8DJ69QmZIZb1eDlGnFoRImsuzwST+MykNLImEG8T8uOj9FB3Qts2QRKICyioY94olb5p8NYYS4MPlqNSo+lteGaXiKvv3lU8gzG7v2Aj0qzFlIU1q2fuSGktnHE9/VNMwJ0bbw5zTJxTP+0Hfgkopn94QlWS8JjBL9w+As6m0KV+Ic+f+WMjvGZGgsvcsLIoM/RbLRsLpEOXkajxvocrNCbo95XjvZOh0vxrUyDNxBuFf4vi5aoy0JN3FPXqplSlSt4PHalxRmu/ViRIu5BnWzeyBdag2xg2jOuOz0t4KbXsS3W4t3q18ck860e35IeI9sfWJpgTYAtNcUy6Rc6jCrhseb2irkoacfvP/PlxnCIEdQnKkPZgYkm1dYhcu0LEWM27wQjn4lbXd6bIWGLGst9jLjxgsVpUGi5+4Eehvw9rhziCFf21WE42x8cTU5muM0h3+1eLd3eDwB8J8Qge9uGRYxmXLZgXfBPfOFsHudGTCse7qsL26n4ruufLnsOIpI4YL3IGgkG7F7a3GggzTD5Qr2ZgGttGcSbJ91SEOjL93rjFVUH0ksqC+6fwEEcWSnj/2GSHVbs6F8lpwsLvzqsvb0mnuwri32IgYcDt/WGlfppP3mB1zrtde1+UPUf8p1GsNmOqUK866pxNmgAVB+ytmwfbP5ZTI0z3tvaxpmq7bPCJosW5ipt4ZQs1kOWgaWLv7bmWeYvtHvxTZaxSiYZGaRN2q+7a+6otxpPZJC3b6oMQ7rebAzTWh0GeOPJeI2gpPYflYgFSfySfvGP5GtwM3f6G7l55rZN60KC08iJ6jt1sDUVTHSOsQNSmOpX5cMuC/NgPyjGfcpRLE5bbSEvkpn6ePB22UpMXEdp4nK/64Ap8oqaXl0u9tPBLlKF0AAABdQZ+wRREsX5tTv0LMmvWTzTNeYKR1enz/74EqvAVoA8zBnRLD95OlFjf5YJIyCKcwIHSPEhCVuDsiWnMs0IjQNM0c+dFb9EXLLJFQP5jhApEob1zlRtRKRxtYKJFEAAAAFQGfz3RP/w7vfak7vSzBk8IchpGU7AAAAEEBn9FqT/+rGTdrOqJXylRn36jW5erbKD4IWV5rpEBO5IiUGcb64W+0ZfiJOEvCSqfUa8pUQCoughLPoGtwTPShMQAACJZBm9ZJqEFsmUwIT//cnkSRQNSEwpo1SE213wqzVVTsaZPJCuVeynGYJesUeGYAbNuAaeU0rFEpnreVnrN1yuVKd3fRRuFh/yw2xiS/O3rBokw9jkqzCIVz2sp4dSIw7iSmjmKBfpppVjYevtGKrOhFBOKlG6vG5itnqSxzRZuJFyWgDYPZkAqB7fpECfZjT24OihRFs/iNuC2l5UQA50dfSz0/qTh2HDqf1zd9MTD9/kka7paJOvdYLdFoXJoJGXsLk68w1+l0x4/OfGlxesj9NL0ibma9kemtu+kqclqkQGNJ089dv3jcHGF33RLaPiwoGIRZOQmCOL2ywp/BNoSO1e+jspCrnmpOC6WccNkje1Z6f8M/9NzgHeIOJXkMJ/xnU+z1FkRJNQQy/0CIXzvopjfgslCVFF3p5TbTVJLz/kYpMzNk9v5ho3UBlwiANiRMoEsT5N/1gdKQcWS+wU3TqllLix0x/u2T1pZbMhQHV74jR5+EyDF1PXrGsLdFDg5l6x9CcIs0myKuRAK0b38IDK0isF0d8IaKfydEUq/zRZWeOd0P4OPxl/iwsPaopUI6kJ6loyPIRMBvRl/tHAf2C+O1/t+/7hltVQRR9e8y5cjHJ+/U/F/pADAerpsHV8B6TIHKXmeAa39HatK+Spqpqnd/id9wY8y2xTepWTDTiiQ+qWpKm+wEenFtAw8kxI58xLSFaikYGekwF76ntXnR8oDkD2nmjjzrl9h50xGDq/giZeaKjMwFJoAKYWLTeg1Z4WZsQRSeMz4mC4HOhld9/a6JIPdTJU1X4F0FHpTx0fXkZCbu0lpARGSdTpfxYbTPoyiujEWkbj8jOvB/Ab5ehbCu4BQOU82AgGAfkcfbs2/7Y73HtUpY2dy1MKlxSxZWs1RZEbXe3wrtUTnm6UyTo4hqc6aPUntZUgEix4Oiew71A/3skVbR4/FmR77A9tSI3gdDzg6gmmt2W0MitAUQOTjemkLhcEIUYjMtWNXCWC+GSWEKx9DNk9nTVnwA6eI04Ed9AYGyuBe+cjzDbdfdCyZw3yaPaf40wmYMFo4shM2hkolfmL7st8rLm0QSVR4PDpz8jTRgqFln4/s+ifwX5EJolIFhP1BanQ+gITBw/qEly11o/we4EZcMSdNlozsYV33rL5y5Xahc4RN2EhhnkD6tBy7v4GRCGgdrYsLkudCCawttvA3VQ57kHDZu+I8LELsXhEX5v0QgQrC3xDbJKGiA4ziFmD4j8HmWHYu7KtKcZThi5AHYZHXB4lWCa7PftdpoCBNHKbY0Q2QINEUj2GQUsyuWM/o/lE/EbaeGbeW/NwNXRYWyHgzKeZAjM9AzCJmz+yyD6psE48fp1A6UwjXHQzfHA2fTUCD2ZwCTukDza67u+OcMgpcMp+MyW9rCCSPz3Ao7mibMHBoPSQt9gX9BKz7RKNmIoH/rZ/j5jaa4LOGLE09JEAMi6JQ78pknSTJLgVTeKgzl4NInxYswrYVjonv8G4TiBwUWTKTKhHjnPTp4XiL/ERdvHQg5eM4tb7oANziwHvPyE8Bo2BXHDxJl50VgEo4IlSUa1K3EibAtq2Dheo6Yn4WThriIRk1FymQ1HC/QH1QGzRedxjKwEdyW2ulItwH688J9K3CC69Rmh3/XSCGXKtXKw2gD3/SXKz57uBHfgX7rL4o41sukO2yLSXzyun3lIvchr9Elt9mPPKO+eNo53g/wibrZ0l7s3F14+OQQIk5rkGCDoKFHCmgJlYqDhkXfBlt8W/Yp+0CYV6hSw3CaiasQap1TkbghqQfZ7G+Kvli7gtUEn7dcy8Ofne9V+v2y8NCwjifAyEByPiZZeQTUEqQXE9OZRqZrYDEnJ8ztmbg9al/KjRaZjjyVh8t2lznG2xN9VqTl7NbILZazKIykP+2Tflu0qb/UZTrsOvXbqQcMEa7rBbji7nTgSbRmz4GTf0fBJ6mDFaoN3LLy9gffZaeg3fnItvuzDFiIEE+lCxNYOPoUDvyHqc+qHDsHiIDmep7pT4ajASOB3K/YFhNpbKatyec73X+sfdYPJvJxML2DROwZf8NSt0QqmMfyYroy74SPw+tHBvV6m8uwcBIE8U5ReWPI017hjgaDjqxxwgmiE87L4pdskNAlSbMsP+uam07SqnUBvikkjETfjM+mdH4n0ia9FxhUIUYnxbY+WrY1dDIJtsOo6hPcHyOMRkARiRmRYY6P++4HD4sgFQo0l5t6j/+o0I5eUQSGSrEQ8HnZqgJ/GxORrLWgt9+MfjziBHjtsJ+Hx0OXXIdW1BgrZvod14a/0QlU1E5+abFmK/sw8Vgco7JSQPRQRU+ufDHE2fQDqCtq09r+UgZA/gbD3OPA2d0bxIXTO0JlpzU9inKMuS1YXVrKH3oVoHT7vMiFy4peFdGqrHZiXPjX/enOmF+qp7GnfCc+joNTAVLpXbt6XGmWULVckOhj6VNJXCZOWPE7fpDnTcFNxcfH5Tw42naYuvQO61Sv4M37xAlVofti6m4qoM5C+Be9Ae2jK0yF4TsV9au8gsFHPO5wrDOIgOpoTMnIYdAOQ9SZvNaU+v6JdXe1OR9NOrMGVIGAcjXuzwdZuyJZ+qjNI/6uclvP/WBp+C9MxgS/eTGfGNtxWm7E6EZ/nIaFnaKvbVO8C91oZggmit/WFHYnQTujUX5D13DGuj27ep9E66d2Ft5asvwZb1JyzKY/ZW5YlZnNYdbWmq4+RJEw0S3jQM8FYPfIuHPPGOKTZ0SdHse9EMNg+BxMS6T3N2KRPjubuQ0xxcarYadlHIG1IqofaThRIFZ1vhaAnlRC0LNC44pCaD4K7PfPVcS1yN0onKBH8kCcNDEdFXD6WS8v3DbQE0d4FmBnppFbQHfyT/isZrQTMTJG8MCjUBvB9yJfy7BJPKhrrewN6Jn18nFpHVdNzrJMEAAAAFlBn/RFFSxf0pv/nymHsirA0ZFzyMbZyPWC3DmhSobTDulT65wTMSONYGXU8XTHcDdgKvB33nyCkeRqmRji8I5hST1GV3holF4EiEf/FyHumbU0WcC+f7ilgAAAABwBnhN0T/8abxfyPU48l+5RocaAH5M9WXvLuka5AAAALwGeFWpP/6zcl8k21zP1pXYAGg4nI9KGVw1+f0vfaQnzSk1GzWnRlgcnMMWrqfWYAAAHNEGaGkmoQWyZTAhP/8qe/fmTDF2ds1dU7+R3fa4NwZA/EiugqBpvjeHNhLfcg6vYRWJ7HWcZSGYS1OXV+BXTbM9eP2rls99grHDbwOWL0BFj0/p/7rP0v+raMT9dW8PjJmw25rgLPd9+YMjnvnKlCctoEIZUBwlG6Dsyua00aIvRdAHI7E6UikF+dtGWrkZIJdOXFUFYuFJGfXQZFQOZs7J3j/rtr+y+2WZVJviUgUDyv+XIsq54454maZmrBMVgQkTk3wY2tbTJm+BkOciosrMUqPZWcFpjBVzvksZcLtdOz87n1kMHiM/O5Iko/nQmxWx5rMoc8e111hIJziAadXpY81LmBQ8kcyIWLgOiFxAPksyhBFpHwje1XV8B+EkvwJdQCKkMmegI6jGhWeVT+S6fwedxIsErPV7MvbSj/HXn9uWqQ6XG2XA6MLP0sdvHQl9uS+crqeinIMji/iyZYK0m3qx12MOQw4hZ2SCnsyPq8+cdAZtMYOF7lPrvji93MosDvJoO8CghXcf5zwdZzsNNGrhbGigQLNkh6SQFgS4SR2qCoy0MIYkvn43ypVuYWfKWsiApdO0XtajgK+wg1uAP+r2IJ47xDP/9ciC5J8YjEwP/eXj0/vOQ7Bf/yULdiu4ILZPImOpNSYkcyfwdEqeU77YRZrVASodybKNi++gWN1xlqVoKVgIzhR9a5iLP1QwH2MfcyT4OYoMbhtq/fHHbWWPZXGGm3c2mvjHCD0qygPxuztETZx4zUmS/h++l1JCBrIc19fS6dsgg8B81eD3cCPGQe5fyZYSMI0v+wRHIln+wLaLrhCqZxSZAgYeb9xEQ/JLcOYtbYmQ3TyjCQyfroyAp9xbX90UdKkrxTAusDXJsekPu+BwAQdtyWDkRiMuG0BjT4vrLb7trDRQv4fvDLXdg6jFtY73Hb56JRSIvSd1AyxEuT/k1WAqtk7bovIMMzzXsQECoukv4803D8SSn3NotFSVgKxytGpQfD0bprpVgAXxOzjAlYkbRqu2evQv0Lfsil7U9/CLISq9myhb2NeCdvKJN8e7Cr3Alp5arte1etNagaFWyelvemA8Wy5qM14VmXSlryfHYKL1ewqr8TxmeSml3PTpTZiBLkeK+qM2peTZUCG+qwNIvX4uAW2btrA/BVMx/W1Uhq+9qPePh3abVDVcCRGYP5TNGiwlXDbU+wYY7/8yH8fWNaHLJi9PjOIt8dssFTxqz06obj7J4LX2m5U94H5AR39DjLhvxvfcV6i3D4CvePpnWxrQ9HlFv2j9OQWhHnsxqYEA7iZT2uWKIRq2L78X5B5BT5siPyc5vQklQcblhwZAOZ1WVNrH5KaVPPn/CVgw7UCRqSu7Ui2tOQHa/fBUpxCDZFDei8vTEbLW+9iQWLE/8MQ/h+BGIKJ0KRcdmeaA0sqMyp8NDEDiIfm9GG9d3IVvbg644f6If1izZ4+GK71QhdgsrkM6YXyCDzg+GteyHUF6auczf0zLS1ZHBlydsInJBh0i6b79jq/FaJtOvBWz0IrC0TIlxgU5YN3RPnuIIRZj0C41D4i4AO0JrRRj4LAlOZoC+5iXRm45nvJvnQnovNcYRkbXl5MPuwteXtI4S3LqFpEgAGnsTzFtO0VTOqIZaDNBLbREhwIRAOt55k+rahj0Z1zoVHMSe1YBTXKrlXuUZ+PJ8ESZnhElfTNaeeyeJ2+ofF2FCOKsfFGrJDmqeEuxHM1flXWCoSkfXZ2xHY6MJR19jmgGU4wmtXCwdhGQyJW3YFQctyJoyX7iCN/i3k7meVsbkIY3wQbwgYP7znYLkXoTIZamonQiNDLfgAI2KjUlj1ICDaHgRqOp/vCbYcLPGClDiedH97U5XrnZsKu0ehTx9lpC1InSkjqlmuv84LQI/H1yak1j8g8bBRcGpjj4/uSNUB8POcqaPXfOycKrJroPNURoOUBt+TPcrUSzDa89fI4mTdUMlcSef0qOsNxOvYE4NHnPfZVfjABsH1eQBt5qZsxovt/lQcM+aklbAzlUnxHzoNW1bDauiQB73av6j7TTmQuWJOWFnl2lGXllBsQAqqSDyrGQZ5f+9ihVP+sPjTKx/l7QYUXLGn9LufroV13fsHjgA8Y/2ctKTZDvvNf0feBYDMcNlQ8ZKftV0CtpkoibltpoGbttx2l3y2daVL51VA3f8ULw35bB7iHckOSczxRXXRMeeLmiV7d8e23CnN86DxigOX9HRin98cpr0rPF+lbRLlpQmy5cjK+6e1/bQ9sfPp/YR7PlG+nJ5kdUuIcoP5AYNxiG0dub/Pl0Ru0bO1omNRvYbouzYjSOQKzboXzfxm+zcpHS1aSp09APYzjli6m8vQ256jduSraSSYRdhAk0x9sLR5tj01SRAex3XemZ+2Erqec1Lv5q8onkVHknQjHxDzSrhbDMIa0wPZ0QQUc2JNMeDyxBz9x/BtszOQN65AAAAg0GeOEUVLF/N5ykMR7X0e7P2ReOvYJLxSylI+DxL2CBmaKsE60qgnPdC9qTUC2j/vry46yc3DCMz6bpJzyv4226Yg++TNcU5kYFIghuOunilNH4J8wjo81Q2haQRW0+Czv+FPrGVhEFq/gcTNBqK8jSXAL36owkiAnYJIlYR0GbNQzqBAAAAJwGeV3RP/8KahAdMS0IVGxQhr3xYu05mYr+EWcxLKiam2g13rdPxQAAAADABnllqT/8sxR3uwXUy2Dz1IRxZkYSf9eaNIlTKoBjZw4ILFm+PGSqSINeDYZvaQncAAAcOQZpeSahBbJlMCP/q8GZL5iTFQvSmCL9OLA/bWTMG4WGV1+quTnoTk4i0urxda4t+L3FHje9UoOnDTb+pb50z83KGsedBKkwQhjIGcv28CyZmqLGCF7y2WMl0qVk+2LakBVzCmc0P74MY7ZTPDBX5coZXNoIShJupPrbYqK9mnIt2upsPmTwOYfrUge6wb6CZvcvrtIBPPhceVGSyH60a5RAma/vg5AaNlijTghWkpXQ7yUAnp3UxKuyyT0HtbPOl/9/onqMDHOnFXmz6DFFhDgTTEtSfjTd+aQqimzWz/iJ0Z/hIGoRJCDl/hWMxZFcHbbl6n1Vy0VlNrC9XxlhtqksQOrkpIEo0Y0P/CLUAUhMWEqnkxWEUv0kL6TbZjnfogVvLtjNaMftOigNlLtG+RxbuyRwlC41CNgHAsrzxixk9bSWlrw6AhLnzkSLjMEVA928jNwW1XcjVZwtEKmxF94Mv8asuqdtJP1V9lIQ033DJxCTu5kw4O7vg/ihaHtOxWXVYhdFfE5IF05o7OuCodNYvzPa1KsKmHOK+bn7O++uGpDyoBdfPGAHSsPZrAfWAr5MQ8bB66L1IulhgceF6zGO8Z2d4MJj3oWc0jJv0Ju+3UhFBxZsGIqEXKR5kVm99mBUCDDX3CR5uc48ZvNxmtDRATs0ONmHulgEpiVCsnOJngy/d/Dub3xNQSq/YxBszm0SpfvovKfPQ7MevI1iYlZC4Y72Xr3HzRQJ35TnzrR2YweSNlgPrk2PvvMZM0gGFe9naqmQGkU4Y2ugVMaVlXQL7CG1D9C5mRwdYy9IapdnedIxW3q1Zsk5v+3a2c9l/HbtE1XigrlBtZo1WsM6rVmANyWfZKvFyJHnlEcCQlKRfVA/qLhbmv3m1+jAdRYma90iTSqABhJhZZDSiPGCnqPxAU8sO87L34L5PMLMpZLeOiMhDankegE0HYdbquf2+XqRbjEb6jLu1WpxB/qJqKno739Td66zh63d/FMVSc0kQblv9sFWUh3J8bRs6DdR/xANK93CcK9Qy1SVuklNNHFjwticQNPFElWZEK4NmwTHq2kYsIz3MxONE1p1EpswS4dP02PBfvTsSKfxw0Qd1zobSZNqBvtjUR2/rp6/LZzR+wIqlRINuBN5SoBvmDjnGvucJBJJT4SFqaDHUyLmzb2dkkxoL+ARNL/1s1k9CTF3FN7vxVYGSmEN033q7rpxzoHj4I0W3s6qEd8oSC7fRwEK6ETdzUabe2Klrcj8rBzcRMUFr+imiAnPd6fuDIxy1Avu2avulBJ8QYT6FsVnYTSIBUIYx3MPTrfK9/y3FLsaHXvHHItwmvpJgCLQc+wbZYEbpy3VHC53F1ERamkDXPuqL0eHQDmt6L8MDUQsZChZA1M1gyrRHD9xieI1vbflrbSTg0iMC4s+guSpqnvVVPwkgEcxK3oERnUiGQCa8s1UMjRZKkfX1hqIW0NqMlE9y3r5eaVVehlnUXJ2HtIoxGQO0F5OYrUz27MS++A/JxU4LPJpcjXSpcij4gg/BeJxlqBgEZcz7AIHBhIu0aG+6+oBT3ZVSKcNzwsYXcidYDHuZ1JPNxa8mtokDnpDDkM/0SDOgRPSoep5Cef4nwNsmkFFdUflxNxQrgQLYN9wLbxGoDwx2jYOCHyipEiFpWkDdgjQg97cLMKwU3RYIlGlDbw4WG1eYYMvS/NHAFbGwgyHpA00VebV1TcnzCz6ULgztx5z7EpkIcZMntkU8D3+RoaVj5Tz8gwY5vUC2v5tS+FHVSBflhMBtoTXuRDiV9XYL7KC9YINEHz//ur4lNlYJyjbstb42HxjS05QnAFbe8X00lUmIiQkfqx5f6vDha6SLc9R82KLDURFBORLbm0c9oXrU4FvShEiSF1JoFZdwtxXlTaqxSHsBvqcg26TgDqIYYTYjXXZddZiPo1Og1g1M3vOrMSJ7ibem5vZzXw02Md6vt2FEV2sE9vfE3HAFiIJqJ8S24lxx/4eT4lIzwinhBFfpHArLjBBas4zRV0PkEemN+khmNNzEIwzNh+gnjtlCavPiAQyO5lIgHGEizroSd/XyaXr5cuLgvD1RdfT3CiFtfF6YOsyhz7QhfxY7rdsqPG+WZ7YLY2Vu4FuISGOy3JG4EWWEIkkAhf+KLYf5o8BVjFbu/C1IETv6qky38hV0ifuXSurB4QBIzZJTk/GLd6kodHEb4IkBqqytpXLIr/eXbqC/hPSSSR8PCkt1w7nYowIpQptQV/tFc9FVcM3mgB2frwSzWM7bflvXXaA5me6/kh/athLu1rqfLi7pZkJsxP8JCjDi44zOiyybFJNEv1QDJuXjIpi/gxuASZIq4LiN8CQaMey8CbonOZarUw57zdsvkZkfChqTUUSG9P5sLQCvcrxHffUtDa2CZnCcAAAAZkGefEUVLF+zFLDd6pzuxyU1htMH2iuAahf66mpYlJVi2LekRRI+0ZjQUeZgpRGvw9AL0dRSS334zymiJfQwoj3RduGM0nwfasqequpxOWDzsSZjePd9b58B/p2m9FGGdZqTogM9ZwAAABwBnpt0T/8kYXeRIhfFf7bx2/b0rkX4S0EPdbBxAAAAUwGenWpP/yzGgEmTU8NFJpYd0CVga7KADc1iUVdQXlpS+YDcndZAqSLahP1nlo1BG9m/8h4G8ESk62hAXokxpWc+L0wcXnxyNr1CaYyFc28TZ5jAAAAAuEGan0moQWyZTAj/5nCAPQLvFwAuwFzlVaUXnpDYs8103vuOggdyoi/gUe8JRM1iLrM8FvsJgtZXxXocOLQlpb4VaDzzXAfJ5BP5Sw2ww4SRe6SaiiO/FZhAbt+wrw/2LFTnOljYknnQ8ELDg9IHsprKNdgWElNR+YlV14Iii4ieEgHt9dSnLSqw+j/5yXwu6djvpJEf9pC6F/gH3AQvl3E5DbGrteliQToAjFSjGIb8hrhoJgc5WVAAAATWQZqiSeEKUmUwIv+44Ra/FK9DV8EAkxu+OSBMm6C9RKzrjxfGOlI1Dg57djdEQk/eK5Iqj/c7Y9bczIefVjP90aFLWDQekTU0A7x7bOicTRo2bxr62VfkaIKPz3cvF8HjdXt7hcjpTECHaMd9ezvv445pNVqgADXU8WXbCnHjXnOnIyh6yS9cCIrMwlmEnrI7C+Zna3ZHsEr/L+cN4qFq1z5siIbtzXOfQ00JbXRhVltu8LV7YY0Nlm1M+693cIdr0WeHLwuzVQXHuB4LssHQMPv0NroVePwnr4GBexonC95KAExeINriVFlTSmYYqpZ4L/OvSGDDPN4wiqDGsLdR8cTLLvZsVqlfelqlUv5eTIqAQ0inDRuBoYDuMknqjWX9qB/oMODEWxRllU2e/nLmgOaKAfDPkOdduadLauCFT0FNxAJ6G16gfoVsAUzMs2KnpJ18bBXDXjViyzaoUwZkJ5AyuNBEfXS6iaWAyJ1z6VtSyVhFKIZ/z5MLZFtqsAZhtaODcMNbAcpoCoifgjaRhbu+hXu2YXF+tCPB1fPOgSUNFf4X1EJUQWV5COntWpKlWIWQ2XrJAiTXdN3laqckfNYYGdJ0nMON/15zNXO4hzgZnDW/cdMadvUb1R7/WnTzlU7lK6Kd8Y632MXri6xtKc984sET6feFzGAiQTUNoV9N+CuUUSa97k4z0Vjtu9lyte1hodSsemRQuEh6+4WqMBnOpROMqtwR87rVLbaDqYj5jtH394fOfbScU140zLJxJH7goOn/9gQ8/uweO1XZ/du7JsbaE508rp2VKKjvqzW+EAxjMlaNLeieYPfG6Ha9Exgqh2Ad256nciHInsq1sfjnUTtWd6n041s8TtNNGcN0iuDNlH7jFubpNUc0stXixdLkGC727mm/lqU97lMUuYP8a5vtnR6VKA/oQSBz97WrHYEK0GEW6U7/LhQ3qtmtOJtyog/pnQHFQ4mGcE2jo3uRweY+5u6dfS+jy5/RpiKzWUqRkHowMaBXV8RhYdMcZyJsAX5lCBcGxXdMsMoU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       "    </video>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_random_sequence(\"test\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Define the model -----------------------------------------------------------------------------------------------------\n",
    "dropoutRate = 0.25\n",
    "alpha = 1e-2\n",
    "\n",
    "tt_input_shape = {\n",
    "    \"slow\": (4, 20, 20, 36),\n",
    "    \"medium\": (4, 10, 20, 18),\n",
    "    \"fast\": (3, 13, 29, 1)\n",
    "}[HOW_FAST]\n",
    "tt_output_shape = [4, 4, 4, 4]\n",
    "tt_ranks = [1, 4, 4, 4, 1]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "X_train, X_test, y_train, y_test = (\n",
    "    torch.from_numpy(array)\n",
    "    for array in (X_train, X_test, y_train, y_test)\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "X_train_cuda, X_test_cuda = (\n",
    "    torch.tensor(array.astype(np.float32), device=device)\n",
    "    for array in (X_train, X_test)\n",
    ")\n",
    "y_train_cuda, y_test_cuda = (\n",
    "    torch.tensor(array, device=device)\n",
    "    for array in (y_train, y_test)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GRU(nn.Module):\n",
    "    def __init__(\n",
    "        self, in_features, out_features, sequence_length, hidden_state_length,\n",
    "        linear_layer_maker, dropout_prob, updated_hidden_state_activation_maker=torch.nn.Tanh\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.hidden_state_length = hidden_state_length\n",
    "        self.sequence_length = sequence_length\n",
    "        \n",
    "        #initialize components required for calculating $ d^{(t)} $\n",
    "        #i.e. updated hidden value, which gets elementwisely multiplied by (1 - u{(t-1)})\n",
    "        # where u is update gate\n",
    "        # remark: hidden state is h^{(t)},\n",
    "        # updated hidden value is d^{(t)}\n",
    "        self.input_to_updated_hidden_value = linear_layer_maker(\n",
    "            in_features, hidden_state_length, bias=True\n",
    "        )\n",
    "        self.hidden_state_to_updated_hidden_value = nn.Linear(\n",
    "            hidden_state_length, hidden_state_length, bias=False\n",
    "        )\n",
    "        self.reset_gate = None\n",
    "        self.updated_hidden_value_dropout = nn.Dropout(p=dropout_prob)\n",
    "        self.updated_hidden_state_activation = updated_hidden_state_activation_maker()\n",
    "        \n",
    "        # initialize components required for calculating $ h^{(t)}, i.e.\n",
    "        # hidden state\n",
    "        # here u^{(t-1)} is update gate which, if takes value one,\n",
    "        # means that new value of h should just equal prev value of h`\n",
    "        self.hidden_state = None\n",
    "        self.update_gate = None\n",
    "        \n",
    "        # initialize components for calculating update gate\n",
    "        self.input_to_update_gate = linear_layer_maker(\n",
    "            in_features, hidden_state_length, bias=True\n",
    "        )\n",
    "        self.hidden_state_to_update_gate = nn.Linear(\n",
    "            hidden_state_length, hidden_state_length, bias=False\n",
    "        )\n",
    "        \n",
    "        # initialize components for calculating reset gate\n",
    "        self.input_to_reset_gate = linear_layer_maker(\n",
    "            in_features, hidden_state_length, bias=True\n",
    "        )\n",
    "        self.hidden_state_to_reset_gate = nn.Linear(\n",
    "            hidden_state_length, hidden_state_length, bias=False\n",
    "        )\n",
    "        \n",
    "        # initialize the classifier which performs classification\n",
    "        # of last hidden state\n",
    "        self.last_state_to_output = nn.Linear(hidden_state_length, out_features, bias=True)\n",
    "    \n",
    "    def forward(self, X):\n",
    "        assert len(X.shape) == 3\n",
    "        num_samples = X.shape[0]\n",
    "        assert X.shape[1] == self.sequence_length\n",
    "        \n",
    "        self.hidden_state, self.reset_gate, self.update_gate = [\n",
    "            torch.zeros(num_samples, self.hidden_state_length, device=X.device)\n",
    "            for i in range(3)\n",
    "        ]\n",
    "        \n",
    "        for part_index in range(self.sequence_length):\n",
    "            X_part = X[:, part_index].contiguous()\n",
    "            \n",
    "            # this is also called d^{(t)}\n",
    "            updated_hidden_state_value = self.updated_hidden_value_dropout(self.updated_hidden_state_activation(\n",
    "                self.input_to_updated_hidden_value(X_part)\n",
    "                + self.hidden_state_to_updated_hidden_value(self.hidden_state * self.reset_gate)\n",
    "            ))\n",
    "            \n",
    "            # calculate hidden state h^{(t)}\n",
    "            self.hidden_state = self.update_gate * self.hidden_state \\\n",
    "                + (1 - self.update_gate) * updated_hidden_state_value\n",
    "            \n",
    "            # calculate update gate u^{(t)} which will affect the next iteration\n",
    "            self.update_gate = torch.sigmoid(\n",
    "                self.input_to_update_gate(X_part)\n",
    "                + self.hidden_state_to_update_gate(self.hidden_state)\n",
    "            )\n",
    "            \n",
    "            # calculate reset gate r^{(t)} which will affect the next iteration\n",
    "            self.reset_gate = torch.sigmoid(\n",
    "                self.input_to_reset_gate(X_part)\n",
    "                + self.hidden_state_to_reset_gate(self.hidden_state)\n",
    "            )\n",
    "        \n",
    "        # we have calculated the last hidden state\n",
    "        # now we calculate logits\n",
    "        return self.last_state_to_output(self.hidden_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BuiltinGRU(nn.Module):\n",
    "    def __init__(self, in_features, out_features, hidden_state_length):\n",
    "        super().__init__()\n",
    "        self.hidden_state_length = hidden_state_length\n",
    "        self.gru = nn.GRU(in_features, hidden_state_length, batch_first=True)\n",
    "        self.linear = nn.Linear(hidden_state_length, out_features)\n",
    "\n",
    "    def forward(self, X):\n",
    "        gru_last_hidden = self.gru(X)[0][:, -1]\n",
    "        return self.linear(gru_last_hidden)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NumpyArrayDataset(Dataset):\n",
    "    def __init__(self, *arrays):\n",
    "        self.arrays = arrays\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.arrays[0])\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        return tuple(\n",
    "            torch.from_numpy(array[idx].astype(np.float32)) if len(array[idx].shape) != 0 else torch.tensor(array[idx])\n",
    "            for array in self.arrays\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset_train = TensorDataset(X_train, y_train)\n",
    "# dataset_test = TensorDataset(X_test, y_test)\n",
    "dataset_train = NumpyArrayDataset(X_train, y_train)\n",
    "dataset_test = NumpyArrayDataset(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = {\"fast\": 32, \"medium\": 32, \"slow\": 8}[HOW_FAST]\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset_train, batch_size=min(BATCH_SIZE, NUM_TRAIN), shuffle=True, drop_last=True,\n",
    "    num_workers=1, pin_memory=True\n",
    ")\n",
    "val_loader = DataLoader(\n",
    "    dataset_test, batch_size=min(BATCH_SIZE, NUM_TEST), shuffle=True, drop_last=False,\n",
    "    num_workers=1, pin_memory=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainingHistory(object):\n",
    "    \"\"\"History of how {train,test} {loss,accuracy} changes as training goes.\"\"\"\n",
    "    def __init__(self):\n",
    "        self.epochs = []\n",
    "        self.train_losses = []\n",
    "        self.train_accuracies = []\n",
    "        self.validation_losses = []\n",
    "        self.validation_accuracies = []\n",
    "    \n",
    "    def show_plots(self, clear):\n",
    "        if clear:\n",
    "            clear_output(wait=True)\n",
    "        fig, axes = plt.subplots(ncols=2, figsize=(14, 5))\n",
    "        axes = axes.flatten()\n",
    "        \n",
    "        axes[0].set_title(\"Loss\")\n",
    "        axes[0].plot(self.epochs, self.train_losses, label=\"train loss\")\n",
    "        axes[0].plot(self.epochs, self.validation_losses, label=\"validation loss\")\n",
    "        axes[0].legend()\n",
    "        \n",
    "        axes[1].set_title(\n",
    "            \"Accuracy. Last val: {}, best val: {}\".format(\n",
    "                self.validation_accuracies[-1],\n",
    "                max(self.validation_accuracies)\n",
    "            )\n",
    "        )\n",
    "        axes[1].plot(self.epochs, self.train_accuracies, label=\"train accuracy\")\n",
    "        axes[1].plot(\n",
    "            self.epochs, self.validation_accuracies,\n",
    "            label=\"validation accuracy\"\n",
    "        )\n",
    "        axes[1].legend()\n",
    "        \n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ignite.engine import (\n",
    "    create_supervised_evaluator, create_supervised_trainer,\n",
    "    Events\n",
    ")\n",
    "from ignite.metrics import Accuracy, Loss\n",
    "from ignite.handlers import EarlyStopping, ModelCheckpoint\n",
    "import functools\n",
    "from IPython.display import clear_output\n",
    "import pickle\n",
    "\n",
    "def do_every_num_epochs(num_epochs):\n",
    "    \"\"\"This must be written after @trainer.on, not before. Run function\n",
    "    every few epochs instead of every epoch.\"\"\"\n",
    "    def decorate(func):\n",
    "        def decorated(engine, *args, **kwargs):\n",
    "            if engine.state.epoch % num_epochs == 0:\n",
    "                return func(engine, *args, **kwargs)\n",
    "        return functools.update_wrapper(decorated, func)\n",
    "    return decorate\n",
    "                      \n",
    "def train_and_evaluate(\n",
    "    model, optimizer,\n",
    "    train_loader, validation_loader,\n",
    "    eval_every_num_epochs, plot_every_num_epochs,\n",
    "    num_epochs, early_stopping_epochs,\n",
    "    save_dir, save_prefix\n",
    "):\n",
    "    \"\"\"The main neural network training function. Trains a nn,\n",
    "    evaluates it as it goes. Saves the best model in save_dir.\n",
    "    The filename will have prefix save_prefix. Performs training\n",
    "    for at most num_epochs. If validation accuracy doesn't improve for\n",
    "    early_stopping_epochs, stops training process. Plots training\n",
    "    history during training.\n",
    "    \n",
    "    eval_every_num_epochs - how often to evaluate.\n",
    "    \n",
    "    plot_every_num_epochs - how often to update plots.\"\"\"\n",
    "    trainer = create_supervised_trainer(\n",
    "        model=model, optimizer=optimizer,\n",
    "        loss_fn=criterion, device=device\n",
    "    )\n",
    "    \n",
    "    evaluator = create_supervised_evaluator(\n",
    "        model,\n",
    "        metrics={\n",
    "            \"accuracy\": Accuracy(),\n",
    "            \"loss\": Loss(criterion)\n",
    "        },\n",
    "        device=device\n",
    "    )\n",
    "    \n",
    "    history = TrainingHistory()\n",
    "    \n",
    "    def evaluate(loader, loss_log, accuracy_log):\n",
    "        model.train(False)\n",
    "        evaluator.run(loader)\n",
    "        loss_log.append(evaluator.state.metrics[\"loss\"])\n",
    "        accuracy_log.append(evaluator.state.metrics[\"accuracy\"])\n",
    "\n",
    "    @trainer.on(Events.EPOCH_COMPLETED)\n",
    "    @do_every_num_epochs(eval_every_num_epochs)\n",
    "    def evaluate_on_train_and_test(engine):\n",
    "        evaluate(train_loader, history.train_losses, history.train_accuracies)\n",
    "        evaluate(val_loader, history.validation_losses, history.validation_accuracies)\n",
    "        assert not isinstance(engine.state.epoch, torch.Tensor)\n",
    "        history.epochs.append(engine.state.epoch)\n",
    "    \n",
    "    @trainer.on(Events.EPOCH_COMPLETED)\n",
    "    @do_every_num_epochs(plot_every_num_epochs)\n",
    "    def update_plot(*args):\n",
    "        history.show_plots(clear=True)\n",
    "    \n",
    "    trainer.add_event_handler(Events.EPOCH_COMPLETED, EarlyStopping(\n",
    "        patience=early_stopping_epochs, # wait this many epochs before stopping\n",
    "        score_function=lambda engine: history.validation_accuracies[-1],\n",
    "        trainer=trainer\n",
    "    ))\n",
    "    \n",
    "    # Add handler which saves model to disk whenever it achieves\n",
    "    # new best result\n",
    "    trainer.add_event_handler(\n",
    "        Events.EPOCH_COMPLETED,\n",
    "        ModelCheckpoint(\n",
    "            save_dir, save_prefix,\n",
    "            score_function=lambda engine: history.validation_accuracies[-1],\n",
    "            n_saved=1, atomic=True, require_empty=False,\n",
    "            save_as_state_dict=True\n",
    "        ),\n",
    "        {\"model\": model} # what should be saved\n",
    "    )\n",
    "        \n",
    "    trainer.run(train_loader, max_epochs=num_epochs)\n",
    "    \n",
    "    # save training history to disk as well\n",
    "    with open(os.path.join(save_dir, f\"{save_prefix}_history.pkl\"), \"wb\") as history_f:\n",
    "        pickle.dump(history, history_f)\n",
    "    \n",
    "    # usually we will not be using return values, because it returns\n",
    "    # last model, not the best model\n",
    "    return (\n",
    "        model,\n",
    "        history\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-1781ecb272a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mearly_stopping_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0msave_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSAVE_DIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0msave_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"tt_gru_activation_elu_adam_seed_{SEED}_{HOW_FAST}_dropout_prob_{dropout_prob}_rank_{tt_rank}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m )\n\u001b[1;32m     28\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-0b71b1ef1f5e>\u001b[0m in \u001b[0;36mtrain_and_evaluate\u001b[0;34m(model, optimizer, train_loader, validation_loader, eval_every_num_epochs, plot_every_num_epochs, num_epochs, early_stopping_epochs, save_dir, save_prefix)\u001b[0m\n\u001b[1;32m     90\u001b[0m     )\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m     \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;31m# save training history to disk as well\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, data, max_epochs)\u001b[0m\n\u001b[1;32m    357\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Engine run is terminating due to exception: %s.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36m_handle_exception\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEXCEPTION_RAISED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, data, max_epochs)\u001b[0m\n\u001b[1;32m    348\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_terminate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEPOCH_COMPLETED\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOMPLETED\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36m_fire_event\u001b[0;34m(self, event_name, *event_args, **event_kwargs)\u001b[0m\n\u001b[1;32m    257\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event_handlers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mevent_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 259\u001b[0;31m                 \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-0b71b1ef1f5e>\u001b[0m in \u001b[0;36mdecorated\u001b[0;34m(engine, *args, **kwargs)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mnum_epochs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecorated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-0b71b1ef1f5e>\u001b[0m in \u001b[0;36mevaluate_on_train_and_test\u001b[0;34m(engine)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdo_every_num_epochs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_every_num_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_on_train_and_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_accuracies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidation_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidation_accuracies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-0b71b1ef1f5e>\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(loader, loss_log, accuracy_log)\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_log\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy_log\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mevaluator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m         \u001b[0mloss_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"loss\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0maccuracy_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accuracy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, data, max_epochs)\u001b[0m\n\u001b[1;32m    357\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Engine run is terminating due to exception: %s.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36m_handle_exception\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEXCEPTION_RAISED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, data, max_epochs)\u001b[0m\n\u001b[1;32m    344\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEPOCH_STARTED\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m                 \u001b[0mhours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_once_on_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Epoch[%s] Complete. Time taken: %02d:%02d:%02d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_terminate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36m_run_once_on_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    311\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Current run is terminating due to exception: %s.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0mtime_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36m_handle_exception\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEXCEPTION_RAISED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36m_run_once_on_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    303\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteration\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mITERATION_STARTED\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fire_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mITERATION_COMPLETED\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_terminate\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_terminate_single_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/ignite/engine/__init__.py\u001b[0m in \u001b[0;36m_inference\u001b[0;34m(engine, batch)\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepare_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-22-1971d550b578>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     81\u001b[0m             \u001b[0;31m# calculate reset gate r^{(t)} which will affect the next iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             self.reset_gate = torch.sigmoid(\n\u001b[0;32m---> 83\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_to_reset_gate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_part\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m                 \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhidden_state_to_reset_gate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhidden_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m             )\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/t3nsor/layers.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    130\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mt3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtt_dense_matmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mt3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtt_dense_matmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/t3nsor/ops.py\u001b[0m in \u001b[0;36mtt_dense_matmul\u001b[0;34m(tt_matrix_a, matrix_b)\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0;31m# On the k = core_idx iteration, after applying einsum the shape of data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;31m# becomes ik x (ik-1..., id-1, K, j0, ..., jk-1) x rank_k\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meinsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'aijb,rjb->ira'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_core\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcore_idx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m           \u001b[0;31m# After reshape the shape of data becomes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/soft/conda/envs/pytorch/lib/python3.7/site-packages/torch/functional.py\u001b[0m in \u001b[0;36meinsum\u001b[0;34m(equation, *operands)\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;31m# the old interface of passing the operands as one list argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moperands\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_VariableFunctions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meinsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mequation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "dropout_prob = 0.4\n",
    "tt_rank = 3\n",
    "model = GRU(\n",
    "    num_features, num_labels,\n",
    "    sequence_length=GLOBAL_MAX_LEN,\n",
    "    hidden_state_length=256,\n",
    "    linear_layer_maker=partial(TTLinear, d=4, tt_rank=tt_rank),\n",
    "    dropout_prob=dropout_prob,\n",
    "    updated_hidden_state_activation_maker=torch.nn.ELU\n",
    ").to(device)\n",
    "optimizer = torch.optim.Adam(\n",
    "    model.parameters(),\n",
    "    lr=1e-5, weight_decay=1e-2, amsgrad=True\n",
    ")\n",
    "criterion = tnnf.cross_entropy\n",
    "train_and_evaluate(\n",
    "    model,\n",
    "    optimizer,\n",
    "    train_loader,\n",
    "    val_loader,\n",
    "    eval_every_num_epochs=1,\n",
    "    plot_every_num_epochs=1,\n",
    "    num_epochs=5000,\n",
    "    early_stopping_epochs=100,\n",
    "    save_dir=SAVE_DIR,\n",
    "    save_prefix=f\"tt_gru_activation_elu_adam_seed_{SEED}_{HOW_FAST}_dropout_prob_{dropout_prob}_rank_{tt_rank}\"\n",
    ")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import gc;\n",
    "# gc.collect()\n",
    "# torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # I want to continue training, maybe something will come out of it\n",
    "# optimizer = torch.optim.Adam(\n",
    "#     model.parameters(),\n",
    "#     lr=3 * 1e-4, weight_decay=1e-2, amsgrad=True\n",
    "# )\n",
    "# train_and_evaluate(\n",
    "#     model,\n",
    "#     optimizer,\n",
    "#     eval_every_num_epochs=1,\n",
    "#     plot_every_num_epochs=1,\n",
    "#     num_epochs=500,\n",
    "#     early_stopping_epochs=500,\n",
    "#     save_dir=SAVE_DIR,\n",
    "#     save_prefix=f\"tt_gru_adam_activation_refactored_seed_{SEED}_{HOW_FAST}_dropout_prob_{dropout_prob}_rank_{tt_rank}_continued\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ls $SAVE_DIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(os.path.join(\n",
    "#     SAVE_DIR,\n",
    "#     \"tt_gru_activation_elu_adam_seed_555_medium_dropout_prob_0.4_rank_3_history.pkl\"\n",
    "# ), 'br') as f:\n",
    "#     history = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# history.show_plots(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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