image_modeling_feature_extraction.ipynb

{"cells":[{"cell_type":"code","source":["!pip install pandarallel torchvision -q"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vqKcDxmj6zdU","outputId":"d39b4051-3fa2-4ac5-bb23-2d8f5a52a7f8","executionInfo":{"status":"ok","timestamp":1684958235110,"user_tz":-180,"elapsed":7310,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"vqKcDxmj6zdU","execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/110.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Building wheel for pandarallel (setup.py) ... \u001b[?25l\u001b[?25hdone\n"]}]},{"cell_type":"code","source":["ROOT_PATH = \"/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project\""],"metadata":{"id":"bnk4okW-VGCO","executionInfo":{"status":"ok","timestamp":1684958235111,"user_tz":-180,"elapsed":7,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"bnk4okW-VGCO","execution_count":3,"outputs":[]},{"cell_type":"code","source":["import os\n","\n","def count_files(directory):\n","    file_count = 0\n","\n","    for root, dirs, files in os.walk(directory):\n","        current_folder_files = len(files)\n","        print(f\"Folder: {root} - Number of files: {current_folder_files}\")\n","        file_count += current_folder_files\n","\n","    return file_count\n","\n","directory_path = '/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed'\n","num_files = count_files(directory_path)\n","print(f\"Total number of files in {directory_path} and its subdirectories: {num_files}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zhOdh75eCqDD","executionInfo":{"status":"ok","timestamp":1684953489485,"user_tz":-180,"elapsed":2642,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}},"outputId":"63090f6f-d413-4f69-f822-4feda87fa1fa"},"id":"zhOdh75eCqDD","execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["Folder: /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed - Number of files: 3\n","Folder: /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed/eval - Number of files: 0\n","Folder: /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed/eval/hateful - Number of files: 250\n","Folder: /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed/eval/non_hateful - Number of files: 250\n","Folder: /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed/train - Number of files: 0\n","Folder: /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed/train/hateful - Number of files: 3036\n","Folder: /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed/train/non_hateful - Number of files: 5450\n","Total number of files in /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed and its subdirectories: 8989\n"]}]},{"cell_type":"code","execution_count":4,"id":"e9feb072-1695-4159-b98a-17a749132f22","metadata":{"id":"e9feb072-1695-4159-b98a-17a749132f22","executionInfo":{"status":"ok","timestamp":1684958235501,"user_tz":-180,"elapsed":396,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[],"source":["import os\n","import shutil\n","import numpy as np\n","import pandas as pd\n","import json\n"]},{"cell_type":"code","source":["from google.colab import drive\n","\n","drive.mount(\"/content/drive\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IHQrKTOY5jTc","outputId":"d266414c-b158-4ab0-9074-ad759a6b6d06","executionInfo":{"status":"ok","timestamp":1684958271401,"user_tz":-180,"elapsed":35903,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"IHQrKTOY5jTc","execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":6,"id":"245fcd52-5278-4d40-ab88-57ce6241c152","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"245fcd52-5278-4d40-ab88-57ce6241c152","outputId":"8e70478e-aa42-4caf-bde0-07337b8f9b22","executionInfo":{"status":"ok","timestamp":1684958271402,"user_tz":-180,"elapsed":7,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Available CPUs: 4\n","INFO: Pandarallel will run on 4 workers.\n","INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n"]}],"source":["from pandarallel import pandarallel\n","import multiprocessing\n","\n","num_processors = multiprocessing.cpu_count()\n","print(f'Available CPUs: {num_processors}')\n","\n","pandarallel.initialize(nb_workers=num_processors, use_memory_fs=False, progress_bar=True)"]},{"cell_type":"code","execution_count":7,"id":"6d5a7c2d-ed85-4b50-a041-793e3d62b4d7","metadata":{"id":"6d5a7c2d-ed85-4b50-a041-793e3d62b4d7","executionInfo":{"status":"ok","timestamp":1684958271402,"user_tz":-180,"elapsed":5,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[],"source":["TRAIN_JSONL_PATH = os.path.join(ROOT_PATH, \"data_raw/train.jsonl\")\n","EVAL_JSONL_PATH =  os.path.join(ROOT_PATH, \"data_raw/dev.jsonl\")\n","TEST_JSONL_PATH =  os.path.join(ROOT_PATH, \"data_raw/test.jsonl\")"]},{"cell_type":"markdown","id":"2e8549ba-e0d6-41b1-9051-1131ee3b524c","metadata":{"id":"2e8549ba-e0d6-41b1-9051-1131ee3b524c"},"source":["### Read the Data"]},{"cell_type":"code","execution_count":41,"id":"95da5af7-f876-4b32-b51b-3d00123a1c04","metadata":{"tags":[],"id":"95da5af7-f876-4b32-b51b-3d00123a1c04","executionInfo":{"status":"ok","timestamp":1684958955895,"user_tz":-180,"elapsed":2,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[],"source":["def read_jsonl(path):\n","    # Open the JSONL file\n","    with open(path, 'r') as file:\n","        data_list = []\n","        \n","        # Read each line in the file\n","        for line in file:\n","            # Parse the JSON data from each line\n","            data = json.loads(line)\n","            \n","            # Append the JSON data to the list\n","            data_list.append(data)\n","\n","    # Create a Pandas DataFrame from the list of JSON objects\n","    df = pd.DataFrame(data_list)\n","    return df\n","\n","train = read_jsonl(TRAIN_JSONL_PATH)\n","eval_df = read_jsonl(EVAL_JSONL_PATH)\n","test = read_jsonl(TEST_JSONL_PATH)"]},{"cell_type":"code","source":["train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uiAMxoQ02wYD","outputId":"3f8add87-0986-4883-b15e-9a7510cfcb94","executionInfo":{"status":"ok","timestamp":1684958956155,"user_tz":-180,"elapsed":5,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"uiAMxoQ02wYD","execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8500, 4)"]},"metadata":{},"execution_count":42}]},{"cell_type":"code","source":["eval_df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aC7Uyjbw2xnp","outputId":"7b6030c8-4938-4e4c-ca34-895efeed6bac","executionInfo":{"status":"ok","timestamp":1684958956156,"user_tz":-180,"elapsed":4,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"aC7Uyjbw2xnp","execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(500, 4)"]},"metadata":{},"execution_count":43}]},{"cell_type":"code","execution_count":44,"id":"0e7a53fb-eb68-426b-b175-b50121e3e1e3","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"0e7a53fb-eb68-426b-b175-b50121e3e1e3","outputId":"79575a11-f41c-4ba3-e975-bcb3944290be","executionInfo":{"status":"ok","timestamp":1684958957539,"user_tz":-180,"elapsed":4,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["      id            img  label  \\\n","0  42953  img/42953.png    0.0   \n","1  23058  img/23058.png    0.0   \n","2  13894  img/13894.png    0.0   \n","3  37408  img/37408.png    0.0   \n","4  82403  img/82403.png    0.0   \n","\n","                                                text dataset  \n","0   its their character not their color that matters   train  \n","1  don't be afraid to love again everyone is not ...   train  \n","2                           putting bows on your pet   train  \n","3  i love everything and everybody! except for sq...   train  \n","4  everybody loves chocolate chip cookies, even h...   train  "],"text/html":["\n","  <div id=\"df-c37674db-6ee4-4c7d-9f2d-1974ead69683\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>img</th>\n","      <th>label</th>\n","      <th>text</th>\n","      <th>dataset</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42953</td>\n","      <td>img/42953.png</td>\n","      <td>0.0</td>\n","      <td>its their character not their color that matters</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>23058</td>\n","      <td>img/23058.png</td>\n","      <td>0.0</td>\n","      <td>don't be afraid to love again everyone is not ...</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13894</td>\n","      <td>img/13894.png</td>\n","      <td>0.0</td>\n","      <td>putting bows on your pet</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>37408</td>\n","      <td>img/37408.png</td>\n","      <td>0.0</td>\n","      <td>i love everything and everybody! except for sq...</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>82403</td>\n","      <td>img/82403.png</td>\n","      <td>0.0</td>\n","      <td>everybody loves chocolate chip cookies, even h...</td>\n","      <td>train</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c37674db-6ee4-4c7d-9f2d-1974ead69683')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-c37674db-6ee4-4c7d-9f2d-1974ead69683');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":44}],"source":["train[\"dataset\"] = \"train\"\n","eval_df[\"dataset\"] = \"eval\"\n","test[\"dataset\"] = \"test\"\n","\n","main_df = pd.concat([train, eval_df, test], ignore_index=True)\n","main_df.head()"]},{"cell_type":"code","source":["df = main_df.copy()"],"metadata":{"id":"_KLz0a3hYSG_","executionInfo":{"status":"ok","timestamp":1684958957806,"user_tz":-180,"elapsed":1,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"_KLz0a3hYSG_","execution_count":45,"outputs":[]},{"cell_type":"code","source":["del train\n","del eval_df\n","del test"],"metadata":{"id":"7L4niL36Vm7e","executionInfo":{"status":"ok","timestamp":1684958972227,"user_tz":-180,"elapsed":2,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"7L4niL36Vm7e","execution_count":46,"outputs":[]},{"cell_type":"code","execution_count":13,"id":"6334aa09-0ca3-4fde-b42f-d5219ffa2b1e","metadata":{"id":"6334aa09-0ca3-4fde-b42f-d5219ffa2b1e","executionInfo":{"status":"ok","timestamp":1684958273729,"user_tz":-180,"elapsed":8,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[],"source":["# replace the values in the label column from 0,1 to non_hateful and hateful\n","df[\"label\"] = df[\"label\"].replace({0.0: \"non_hateful\", 1.0: \"hateful\"})"]},{"cell_type":"code","execution_count":14,"id":"374ba8cf-173f-4887-ab8d-a094deea50f7","metadata":{"id":"374ba8cf-173f-4887-ab8d-a094deea50f7","executionInfo":{"status":"ok","timestamp":1684958273730,"user_tz":-180,"elapsed":8,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[],"source":["df[\"img\"] = os.path.join(ROOT_PATH, \"data_raw/\") + df[\"img\"].astype(str)"]},{"cell_type":"code","source":["df[\"img\"][1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"HPxoU180Qquu","outputId":"a5c74884-558a-44da-e30e-3e886826b1df","executionInfo":{"status":"ok","timestamp":1684958273730,"user_tz":-180,"elapsed":8,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"HPxoU180Qquu","execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_raw/img/23058.png'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["### Organize the data"],"metadata":{"id":"aYgbzRPz9quL"},"id":"aYgbzRPz9quL"},{"cell_type":"code","source":["import os\n","\n","# Authenticate to GCS.\n","from google.colab import auth\n","auth.authenticate_user()"],"metadata":{"id":"vaVJ8VZA_U-t"},"id":"vaVJ8VZA_U-t","execution_count":null,"outputs":[]},{"cell_type":"code","source":["ROOT_PATH"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"YQZLBoiABFXH","outputId":"618445ee-9644-428b-9656-78d50b684a47"},"id":"YQZLBoiABFXH","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["print(f\"gsutil -m cp -r gs://machine-learning-spring-2023/data_preprocessed {ROOT_PATH}/\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rSbjcSACBqzz","outputId":"6d9f2b17-4b8b-4d6c-8e6d-2a30f48ecc5c"},"id":"rSbjcSACBqzz","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["gsutil -m cp -r gs://machine-learning-spring-2023/data_preprocessed /content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/\n"]}]},{"cell_type":"code","source":["## TAKES APPROX. 3 MINS\n","# Copy the files from GCS to the current working directory in colab\n","os.system(f'gsutil -m cp -r gs://machine-learning-spring-2023/data_preprocessed {ROOT_PATH}/')\n","# os.system('gsutil cp -r gs://mybucket/folder2 .')\n","# os.system('gsutil cp -r gs://mybucket/folder3 .')"],"metadata":{"id":"bDyBpB2wWazI","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5e1004a3-258c-4ac5-9916-307eccb72311"},"id":"bDyBpB2wWazI","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["TARGET_MODEL_DATAPATH = \"/content/drive/MyDrive/UChicago_MachineLearning\""],"metadata":{"id":"Y7AhI2JFWaop"},"id":"Y7AhI2JFWaop","execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[(df['dataset'] == \"train\") & (df['label'] == \"hateful\")]['img'].iloc[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"wRvFI-EIXgo4","outputId":"a9e715a3-7621-42db-84c8-d8b9ed28521a"},"id":"wRvFI-EIXgo4","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_raw/img/79351.png'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":56}]},{"cell_type":"code","execution_count":null,"id":"acc826a2-549a-4963-8c88-38a564ee9b11","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"acc826a2-549a-4963-8c88-38a564ee9b11","outputId":"235b4da0-1487-40ae-c1fe-0dce32ae07c7"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/UChicago_MachineLearning/data_preprocessed/train\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-51-cd17501b54f3>:11: FutureWarning: Automatic reindexing on DataFrame vs Series comparisons is deprecated and will raise ValueError in a future version. Do `left, right = left.align(right, axis=1, copy=False)` before e.g. `left == right`\n","  dataset_classes = df[df['dataset'] == df]['label'].unique()\n"]},{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/UChicago_MachineLearning/data_preprocessed/eval\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-51-cd17501b54f3>:11: FutureWarning: Automatic reindexing on DataFrame vs Series comparisons is deprecated and will raise ValueError in a future version. Do `left, right = left.align(right, axis=1, copy=False)` before e.g. `left == right`\n","  dataset_classes = df[df['dataset'] == df]['label'].unique()\n"]},{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/UChicago_MachineLearning/data_preprocessed/test\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-51-cd17501b54f3>:11: FutureWarning: Automatic reindexing on DataFrame vs Series comparisons is deprecated and will raise ValueError in a future version. Do `left, right = left.align(right, axis=1, copy=False)` before e.g. `left == right`\n","  dataset_classes = df[df['dataset'] == df]['label'].unique()\n"]},{"output_type":"stream","name":"stdout","text":["Image copying completed successfully.\n"]}],"source":["import os\n","import shutil\n","\n","unique_datasets = df['dataset'].unique()\n","\n","for dataset in unique_datasets:\n","    dataset_folder = os.path.join(TARGET_MODEL_DATAPATH, \"data_preprocessed\", dataset)\n","    print(dataset_folder)\n","    os.makedirs(dataset_folder, exist_ok=True)\n","\n","    dataset_classes = df[df['dataset'] == df]['label'].unique()\n","    if len(dataset_classes) == 1:\n","        continue\n","\n","    for class_name in dataset_classes:\n","        print(class_name)\n","        class_folder = os.path.join(dataset_folder, str(class_name))\n","        os.makedirs(class_folder, exist_ok=True)\n","\n","        class_images = df[(df['dataset'] == dataset) & (df['label'] == class_name)]['img']\n","        print(class_images.shape)\n","        for image_path in class_images:\n","            image_name = os.path.basename(image_path)\n","            destination_path = os.path.join(class_folder, image_name)\n","            shutil.copy(image_path, destination_path)\n","        \n","\n","print(\"Image copying completed successfully.\")"]},{"cell_type":"code","source":["gsutil cp -r gs://machine-learning-spring-2023/data_preprocessed .\n"],"metadata":{"id":"YkEphE0e_KfT"},"id":"YkEphE0e_KfT","execution_count":null,"outputs":[]},{"cell_type":"code","source":["%ls \"/content/drive/MyDrive/UChicago_MachineLearning/data_preprocessed/train\""],"metadata":{"id":"bcWm1sXvXJaG"},"id":"bcWm1sXvXJaG","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"id":"93bcea2d-3717-4a6e-bbc1-c9d3b09e35b9","metadata":{"id":"93bcea2d-3717-4a6e-bbc1-c9d3b09e35b9"},"outputs":[],"source":["df = df.head(1000)"]},{"cell_type":"markdown","id":"65bc5e41-73e5-4511-8bee-9322c2cdc6a1","metadata":{"id":"65bc5e41-73e5-4511-8bee-9322c2cdc6a1"},"source":["### Pixel Processing"]},{"cell_type":"code","source":["def crop_center(img):\n","    y, x, _ = img.shape\n","    startx = x//2-(224//2)\n","    starty = y//2-(224//2)    \n","    return img[starty:starty+224, startx:startx+224, :]"],"metadata":{"id":"IW-K9_VPETFX"},"id":"IW-K9_VPETFX","execution_count":null,"outputs":[]},{"cell_type":"code","source":["%ls -al"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n1cGK_RfPG_U","outputId":"35e5a7c3-353d-4dff-c2cd-a7b2614ff1e6"},"id":"n1cGK_RfPG_U","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["total 20\n","drwxr-xr-x 1 root root 4096 May 23 20:19 \u001b[0m\u001b[01;34m.\u001b[0m/\n","drwxr-xr-x 1 root root 4096 May 23 20:03 \u001b[01;34m..\u001b[0m/\n","drwxr-xr-x 4 root root 4096 May 22 13:32 \u001b[01;34m.config\u001b[0m/\n","drwx------ 6 root root 4096 May 23 20:19 \u001b[01;34mdrive\u001b[0m/\n","drwxr-xr-x 1 root root 4096 May 22 13:33 \u001b[01;34msample_data\u001b[0m/\n"]}]},{"cell_type":"code","execution_count":null,"id":"31d3d893-5d7c-4c7e-9bc3-8a74743ad62f","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":517,"referenced_widgets":["b67a9ff3226843fca838cebac473faad","cdc18d7d4bf445ae916d8c5b30f05540","0cbc3114d44c461da6629e6b026948fa","b3c6cf8fde0d4be9b60bbd318be24ade","a2461a430e8f4f0cb79e8dc6175db6b0","83752beba9684ae491eab04385852e77","b423dc3ccd634f318ea50f3da91f344b","472980bfa3b14afe8194a633d7eac69e","a5edbeebc11c46439c2b43014b044c65","8779f5365e0f4ac1a423c84e530c4d3e","a682c3da40f74a46b0b28ebdaf465ddf","d8c5801e6e12404f827adf35b9f6b6f6","f7ff0739e2854eefa5e471d54521779d","8545446acd284ac5aef965a1e342220c","b877d81e10d542a4aa152df3bb8e81b4","d2cbd554d48d469d8ce5f94e034e02fb","fa1c49476ace49f3ac2e693687ac23ed","3cff5c9551f3428d9a324ecb634ee58e","1e7f3b03ffc54554a044124cf12a952c","9b06c3b8d0664e3ea2067b6f59709105","908c8cd2b5af47debf3828cc991cf34c","59aed129cfd542b6b717c00b19f03687","fa8512537e1848a0b901cf12bcc09b3d","437360534c36450ba9d9aa57de8adbd1","2b4dda2ff6534297a55bc6ec4835f93e","b551a7c779b04d06af38167bf7770e23","e9a3ead26d974cc6a34234d0e6bc0833","b18032b4a9ca45278f0d0a154f8101f8","d2a9d219ae8344b582c174f311dfbb14","85f0a50e029e44cbb3bf7adce3bbe035","103d3b09b2bc4395b24ce6c7a6266889","7c4b38956b3f4579b80663a633b2f2ff","ee5c1db647a247539964ce9755d7ca13","7b826b61733a4b3db0cf3f0678a042c1"]},"id":"31d3d893-5d7c-4c7e-9bc3-8a74743ad62f","outputId":"0314da69-780c-406a-be61-dfc2f4604b0d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=2500), Label(value='0 / 2500'))), …"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b67a9ff3226843fca838cebac473faad"}},"metadata":{}},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-16-d68beccb9c99>\u001b[0m in \u001b[0;36m<cell line: 21>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# Add a new column 'pixels' to the DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pixels'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWorkerStatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaster_workers_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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    429\u001b[0m             \u001b[0mworker_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworker_status\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpayload\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m             \u001b[0mworkers_status\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworker_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworker_status\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<string>\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, *args, **kwds)\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/multiprocessing/managers.py\u001b[0m in \u001b[0;36m_callmethod\u001b[0;34m(self, methodname, args, kwds)\u001b[0m\n\u001b[1;32m    816\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    817\u001b[0m         \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethodname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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--> 818\u001b[0;31m         \u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\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    819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    820\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'#RETURN'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/multiprocessing/connection.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\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    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_readable\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--> 250\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv_bytes\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    251\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_ForkingPickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetbuffer\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    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_recv_bytes\u001b[0;34m(self, maxsize)\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_recv_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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--> 414\u001b[0;31m         \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\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    415\u001b[0m         \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstruct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"!i\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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    416\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m==\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[0;32m/usr/lib/python3.10/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_recv\u001b[0;34m(self, size, read)\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0mremaining\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mremaining\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--> 379\u001b[0;31m             \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremaining\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    380\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\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;31mKeyboardInterrupt\u001b[0m: "]}],"source":["from PIL import Image\n","\n","def load_image_pixels(image_path):\n","    # Open the image file\n","    try:\n","        with Image.open(image_path) as img:\n","            # Convert the image to RGB (if it's not already in that format)\n","            img = img.convert('RGB')\n","            # Get the pixel values as a numpy array and flatten it\n","            pixels = np.array(img)\n","            pixels = crop_center(pixels)\n","            pixels = np.divide(pixels, 255)\n","            pixels = np.squeeze(pixels\n","                                )\n","            return pixels\n","    except:\n","        return \"\"\n","\n","\n","# Add a new column 'pixels' to the DataFrame\n","df['pixels'] = df['img'].parallel_apply(load_image_pixels)"]},{"cell_type":"code","source":["output_path = \"/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/image_pixels.pkl\""],"metadata":{"id":"ATEiA9b4_SsR"},"id":"ATEiA9b4_SsR","execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.to_pickle(output_path)"],"metadata":{"id":"BYK3hKJZ_cSq"},"id":"BYK3hKJZ_cSq","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Read the Pixel Data"],"metadata":{"id":"Gpbo46VXChGi"},"id":"Gpbo46VXChGi"},{"cell_type":"code","source":["output_path = \"/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/image_pixels.pkl\""],"metadata":{"id":"VVcyRWW_Clgc","executionInfo":{"status":"ok","timestamp":1684958274005,"user_tz":-180,"elapsed":3,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"VVcyRWW_Clgc","execution_count":16,"outputs":[]},{"cell_type":"code","source":["df = pd.read_pickle(output_path)"],"metadata":{"id":"5Pqbe_A9UOkJ","executionInfo":{"status":"ok","timestamp":1684958305777,"user_tz":-180,"elapsed":31775,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"5Pqbe_A9UOkJ","execution_count":17,"outputs":[]},{"cell_type":"code","source":["print(\"Length before processing: \", df.shape)\n","df = df[df.pixels != \"\"]\n","print(\"Length after processing: \", df.shape)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V6JGGvscURXT","outputId":"5cbc1cd9-ed3c-42df-dff4-97ef93cce1a5","executionInfo":{"status":"ok","timestamp":1684958305778,"user_tz":-180,"elapsed":6,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"V6JGGvscURXT","execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Length before processing:  (10000, 6)\n","Length after processing:  (4042, 6)\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/pandas/core/ops/array_ops.py:75: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n","  result = libops.scalar_compare(x.ravel(), y, op)\n"]}]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":337},"id":"ESLhZvml9N-F","executionInfo":{"status":"ok","timestamp":1684958314338,"user_tz":-180,"elapsed":8562,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}},"outputId":"6f35029e-d320-4199-8c6e-42f8daac95f4"},"id":"ESLhZvml9N-F","execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       id                                                img        label  \\\n","0   42953  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","3   37408  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","4   82403  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","7   70914  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","12  25489  /content/drive/MyDrive/UChicago_MachineLearnin...      hateful   \n","\n","                                                 text dataset  \\\n","0    its their character not their color that matters   train   \n","3   i love everything and everybody! except for sq...   train   \n","4   everybody loves chocolate chip cookies, even h...   train   \n","7   tattoos are bad for your health i know 5 milli...   train   \n","12   brother... a day without a blast is a day wasted   train   \n","\n","                                               pixels  \n","0   [[[0.3137254901960784, 0.30196078431372547, 0....  \n","3   [[[0.5490196078431373, 0.5450980392156862, 0.5...  \n","4   [[[0.043137254901960784, 0.043137254901960784,...  \n","7   [[[0.8313725490196079, 0.8, 0.7529411764705882...  \n","12  [[[0.7294117647058823, 0.5490196078431373, 0.4...  "],"text/html":["\n","  <div id=\"df-be974fab-172b-43d7-b649-99339b3251e1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>img</th>\n","      <th>label</th>\n","      <th>text</th>\n","      <th>dataset</th>\n","      <th>pixels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42953</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>its their character not their color that matters</td>\n","      <td>train</td>\n","      <td>[[[0.3137254901960784, 0.30196078431372547, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>37408</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>i love everything and everybody! except for sq...</td>\n","      <td>train</td>\n","      <td>[[[0.5490196078431373, 0.5450980392156862, 0.5...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>82403</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>everybody loves chocolate chip cookies, even h...</td>\n","      <td>train</td>\n","      <td>[[[0.043137254901960784, 0.043137254901960784,...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>70914</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>tattoos are bad for your health i know 5 milli...</td>\n","      <td>train</td>\n","      <td>[[[0.8313725490196079, 0.8, 0.7529411764705882...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>25489</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>hateful</td>\n","      <td>brother... a day without a blast is a day wasted</td>\n","      <td>train</td>\n","      <td>[[[0.7294117647058823, 0.5490196078431373, 0.4...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be974fab-172b-43d7-b649-99339b3251e1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-be974fab-172b-43d7-b649-99339b3251e1 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-be974fab-172b-43d7-b649-99339b3251e1');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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yorulmaz","userId":"14901711814338625122"}},"outputId":"5dbc65c4-62ff-4414-9c3a-6ae4ad08fb24"},"id":"Hi0InH_c9ZFe","execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["train    3399\n","test      423\n","eval      220\n","Name: dataset, dtype: int64"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["# df = df.head(1000)"],"metadata":{"id":"6z1Qtb_7UfRK","executionInfo":{"status":"ok","timestamp":1684951288036,"user_tz":-180,"elapsed":3,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"6z1Qtb_7UfRK","execution_count":17,"outputs":[]},{"cell_type":"markdown","id":"77759b8d-7a29-406b-aef8-5b8a0e25648f","metadata":{"id":"77759b8d-7a29-406b-aef8-5b8a0e25648f"},"source":["### Modeling"]},{"cell_type":"code","execution_count":21,"id":"5ed6b578-1e92-49c9-986c-75b7657c39f7","metadata":{"id":"5ed6b578-1e92-49c9-986c-75b7657c39f7","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0d3481d0-a83d-40ea-e1ed-a46d548142de","executionInfo":{"status":"ok","timestamp":1684958318892,"user_tz":-180,"elapsed":4569,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<contextlib.ExitStack at 0x7fd1db30f8e0>"]},"metadata":{},"execution_count":21}],"source":["from __future__ import print_function, division\n","\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","from torch.optim import lr_scheduler\n","import torch.backends.cudnn as cudnn\n","import numpy as np\n","import torchvision\n","from torchvision import datasets, models, transforms\n","import matplotlib.pyplot as plt\n","import time\n","import os\n","import copy\n","\n","cudnn.benchmark = True\n","plt.ion()   # interactive mode"]},{"cell_type":"code","source":["df[\"pixels\"][0].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JGg_6m48xAvw","outputId":"04f6de70-beab-40db-e452-0dab35928b72","executionInfo":{"status":"ok","timestamp":1684958318892,"user_tz":-180,"elapsed":15,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"JGg_6m48xAvw","execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(224, 224, 3)"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","execution_count":23,"id":"75c6eed6-62fb-4f96-bc2b-4eca8f6bccb4","metadata":{"id":"75c6eed6-62fb-4f96-bc2b-4eca8f6bccb4","colab":{"base_uri":"https://localhost:8080/"},"outputId":"46c856be-6a22-4fcf-a420-f06f024c1442","executionInfo":{"status":"ok","timestamp":1684958321971,"user_tz":-180,"elapsed":3092,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Error with Index 2906\n"]}],"source":["import numpy as np\n","from torchvision import transforms\n","\n","def calculate_mean_std(dataset):\n","    \"\"\"\n","    Calculate the mean and standard deviation of a dataset.\n","    \n","    Args:\n","        dataset: PyTorch dataset object.\n","        \n","    Returns:\n","        mean: List of mean values for each channel.\n","        std: List of standard deviation values for each channel.\n","    \"\"\"\n","    # Initialize lists to store channel-wise mean and std\n","    mean = [0.0, 0.0, 0.0]\n","    std = [0.0, 0.0, 0.0]\n","\n","    # Loop over the dataset to calculate mean and std\n","    for idx, image in enumerate(dataset):\n","        try:\n","            for i in range(3):  # Assuming 3 channels (RGB)\n","                mean[i] += image[:, :, i].mean()\n","                std[i] += image[:, :, i].std()\n","        except:\n","            print(f\"Error with Index {idx}\")\n","\n","\n","    # Calculate the average mean and std across the dataset\n","    mean = [m / len(dataset) for m in mean]\n","    std = [s / len(dataset) for s in std]\n","\n","    return mean, std\n","\n","# Calculate the mean and std for the training set\n","mean, std = calculate_mean_std(df['pixels'])"]},{"cell_type":"code","execution_count":25,"id":"6f9bf37c-6da5-47aa-969b-e6dedcf673da","metadata":{"id":"6f9bf37c-6da5-47aa-969b-e6dedcf673da","colab":{"base_uri":"https://localhost:8080/"},"outputId":"fbe58a83-8ce4-4a84-d5b9-8891d851331b","executionInfo":{"status":"ok","timestamp":1684958353931,"user_tz":-180,"elapsed":2,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["([0.5149958556130754, 0.45956921375635357, 0.42985640392557245],\n"," [0.24312528523774807, 0.2351713949596656, 0.23576431541623782])"]},"metadata":{},"execution_count":25}],"source":["mean, std"]},{"cell_type":"markdown","id":"d7dbc01d-df28-4d27-ab25-523be60dfbfb","metadata":{"id":"d7dbc01d-df28-4d27-ab25-523be60dfbfb"},"source":["### Transformation"]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"c8U13mUcdAFP","executionInfo":{"status":"ok","timestamp":1684960236018,"user_tz":-180,"elapsed":5,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}},"outputId":"44bb937b-f380-4d95-eb4c-060ab2613de7"},"id":"c8U13mUcdAFP","execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         id            img  label  \\\n","0     42953  img/42953.png    0.0   \n","1     23058  img/23058.png    0.0   \n","2     13894  img/13894.png    0.0   \n","3     37408  img/37408.png    0.0   \n","4     82403  img/82403.png    0.0   \n","...     ...            ...    ...   \n","9995   3869  img/03869.png    NaN   \n","9996  23817  img/23817.png    NaN   \n","9997  56280  img/56280.png    NaN   \n","9998  29384  img/29384.png    NaN   \n","9999  34127  img/34127.png    NaN   \n","\n","                                                   text dataset  \n","0      its their character not their color that matters   train  \n","1     don't be afraid to love again everyone is not ...   train  \n","2                              putting bows on your pet   train  \n","3     i love everything and everybody! except for sq...   train  \n","4     everybody loves chocolate chip cookies, even h...   train  \n","...                                                 ...     ...  \n","9995    a mother's love for the child is a divine thing    test  \n","9996                                        sea monkeys    test  \n","9997               little miss muffet sat on her tuffet    test  \n","9998                                   they're in a row    test  \n","9999  that feeling when you win a fifa game after be...    test  \n","\n","[10000 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-325112c3-4a33-49e1-a7e9-3c5144871c19\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>img</th>\n","      <th>label</th>\n","      <th>text</th>\n","      <th>dataset</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42953</td>\n","      <td>img/42953.png</td>\n","      <td>0.0</td>\n","      <td>its their character not their color that matters</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>23058</td>\n","      <td>img/23058.png</td>\n","      <td>0.0</td>\n","      <td>don't be afraid to love again everyone is not ...</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13894</td>\n","      <td>img/13894.png</td>\n","      <td>0.0</td>\n","      <td>putting bows on your pet</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>37408</td>\n","      <td>img/37408.png</td>\n","      <td>0.0</td>\n","      <td>i love everything and everybody! except for sq...</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>82403</td>\n","      <td>img/82403.png</td>\n","      <td>0.0</td>\n","      <td>everybody loves chocolate chip cookies, even h...</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>3869</td>\n","      <td>img/03869.png</td>\n","      <td>NaN</td>\n","      <td>a mother's love for the child is a divine thing</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>23817</td>\n","      <td>img/23817.png</td>\n","      <td>NaN</td>\n","      <td>sea monkeys</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>56280</td>\n","      <td>img/56280.png</td>\n","      <td>NaN</td>\n","      <td>little miss muffet sat on her tuffet</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>29384</td>\n","      <td>img/29384.png</td>\n","      <td>NaN</td>\n","      <td>they're in a row</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>34127</td>\n","      <td>img/34127.png</td>\n","      <td>NaN</td>\n","      <td>that feeling when you win a fifa game after be...</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10000 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-325112c3-4a33-49e1-a7e9-3c5144871c19')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 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tuple_with_path = (original_tuple + (path,))\n","        return tuple_with_path\n"],"metadata":{"id":"jWf0MUv-Wwpo","executionInfo":{"status":"ok","timestamp":1684960460574,"user_tz":-180,"elapsed":281,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"jWf0MUv-Wwpo","execution_count":75,"outputs":[]},{"cell_type":"code","execution_count":76,"id":"15b8cc70-95a9-47fe-bae3-b3a606d0e916","metadata":{"id":"15b8cc70-95a9-47fe-bae3-b3a606d0e916","executionInfo":{"status":"ok","timestamp":1684960461013,"user_tz":-180,"elapsed":1,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"outputs":[],"source":["BATCH_SIZE = 32\n","\n","# Data augmentation and normalization for training\n","# Just normalization for validation\n","data_transforms = {\n","    'train': transforms.Compose([\n","        transforms.RandomResizedCrop(224),\n","        transforms.RandomHorizontalFlip(),\n","        transforms.Resize((224, 224)),  # Resize all images to 224 x 224\n","\n","        transforms.ToTensor(),\n","        transforms.Normalize(mean, std)\n","    ]),\n","    'eval': transforms.Compose([\n","        transforms.Resize((224, 224)),  # Resize all images to 224 x 224\n","        transforms.ToTensor(),\n","        transforms.Normalize(mean, std)]),\n","}\n","\n","data_dir = \"/content/drive/MyDrive/UChicago_MachineLearning/Machine_Learning/Final_Project/data_preprocessed/\"\n","\n","image_datasets = {x: ImageFolderWithPaths(data_dir + x, data_transforms[x])\n","                  for x in ['train', 'eval']}\n","\n","\n","dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=BATCH_SIZE,\n","                                             shuffle=False, num_workers=4)\n","              for x in ['train', 'eval']}\n","              \n","dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'eval']}\n","class_names = image_datasets['train'].classes\n","\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"]},{"cell_type":"code","source":["dataset_sizes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mqRCkCF5XTrc","executionInfo":{"status":"ok","timestamp":1684960050308,"user_tz":-180,"elapsed":3,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}},"outputId":"3a6ebfe1-a301-4c22-edf1-16e050a53941"},"id":"mqRCkCF5XTrc","execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'train': 8486, 'eval': 500}"]},"metadata":{},"execution_count":62}]},{"cell_type":"code","execution_count":null,"id":"e60e36cc-33f1-49d4-9085-32e5720c3219","metadata":{"id":"e60e36cc-33f1-49d4-9085-32e5720c3219","colab":{"base_uri":"https://localhost:8080/","height":207},"outputId":"7f1041e7-e14d-4fcb-da9b-6640be46cdc1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}],"source":["def imshow(inp, mean, std, title=None):\n","    \"\"\"Display image for Tensor.\"\"\"\n","    inp = inp.numpy().transpose((1, 2, 0))\n","    inp = std * inp + mean\n","    inp = np.clip(inp, 0, 1)\n","    plt.imshow(inp)\n","    if title is not None:\n","        plt.title(title)\n","    plt.pause(0.001)  # pause a bit so that plots are updated\n","\n","\n","# Get a batch of training data\n","inputs, classes = next(iter(dataloaders['train']))\n","\n","# Make a grid from batch\n","out = torchvision.utils.make_grid(inputs)\n","\n","imshow(out, mean, std, title=[class_names[x] for x in classes])"]},{"cell_type":"markdown","id":"8b1c9c97-7aa0-4509-b498-9587e7aa8550","metadata":{"id":"8b1c9c97-7aa0-4509-b498-9587e7aa8550"},"source":["### Training"]},{"cell_type":"code","execution_count":null,"id":"d226922b-41bc-4fb8-bc9b-88743c942cac","metadata":{"id":"d226922b-41bc-4fb8-bc9b-88743c942cac"},"outputs":[],"source":["def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n","    since = time.time()\n","\n","    best_model_wts = copy.deepcopy(model.state_dict())\n","    best_acc = 0.0\n","\n","    train_loss_per_epoch = []\n","    eval_loss_per_epoch = []\n","\n","    train_acc_per_epoch = []\n","    eval_acc_per_epoch = []\n","\n","    for epoch in range(num_epochs):\n","        print(f'Epoch {epoch}/{num_epochs - 1}')\n","        print('-' * 10)\n","\n","        # Each epoch has a training and validation phase\n","        for phase in ['train', 'eval']:\n","            if phase == 'train':\n","                model.train()  # Set model to training mode\n","            else:\n","                model.eval()   # Set model to evaluate mode\n","\n","            running_loss = 0.0\n","            running_corrects = 0\n","\n","            # Iterate over data.\n","            for inputs, labels in dataloaders[phase]:\n","                inputs = inputs.to(device)\n","                labels = labels.to(device)\n","\n","                # zero the parameter gradients\n","                optimizer.zero_grad()\n","\n","                # forward\n","                # track history if only in train\n","                with torch.set_grad_enabled(phase == 'train'):\n","                    outputs = model(inputs)\n","                    _, preds = torch.max(outputs, 1)\n","                    loss = criterion(outputs, labels)\n","\n","                    # backward + optimize only if in training phase\n","                    if phase == 'train':\n","                        loss.backward()\n","                        optimizer.step()\n","\n","                # statistics\n","                running_loss += loss.item() * inputs.size(0)\n","                running_corrects += torch.sum(preds == labels.data)\n","            if phase == 'train':\n","                scheduler.step()\n","\n","            epoch_loss = running_loss / dataset_sizes[phase]\n","            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n","            if phase == 'train':\n","                train_loss_per_epoch.append(epoch_loss)\n","                train_acc_per_epoch.append(epoch_acc.item())\n","            elif phase == 'eval':\n","                eval_loss_per_epoch.append(epoch_loss)\n","                eval_acc_per_epoch.append(epoch_acc.item())\n","\n","            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n","\n","            # deep copy the model\n","            if phase == 'eval' and epoch_acc > best_acc:\n","                best_acc = epoch_acc\n","                best_model_wts = copy.deepcopy(model.state_dict())\n","\n","        print()\n","\n","    time_elapsed = time.time() - since\n","    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n","    print(f'Best val Acc: {best_acc:4f}')\n","\n","    # load best model weights\n","    model.load_state_dict(best_model_wts)\n","    return model, {\"train_losses\": train_loss_per_epoch, \n","                   \"test_losses\": eval_loss_per_epoch,\n","                   \"train_accuracies\": train_acc_per_epoch,\n","                   \"eval_accuracies\": eval_acc_per_epoch\n","                   }"]},{"cell_type":"code","execution_count":null,"id":"60748e86-c737-434b-8c58-aa3250a0fae3","metadata":{"id":"60748e86-c737-434b-8c58-aa3250a0fae3"},"outputs":[],"source":["model_ft = models.resnet18(weights='IMAGENET1K_V1')\n","num_ftrs = model_ft.fc.in_features\n","# Here the size of each output sample is set to 2.\n","# Alternatively, it can be generalized to ``nn.Linear(num_ftrs, len(class_names))``.\n","model_ft.fc = nn.Linear(num_ftrs, 2)\n","\n","model_ft = model_ft.to(device)\n","\n","criterion = nn.CrossEntropyLoss()\n","\n","# Observe that all parameters are being optimized\n","optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n","\n","# Decay LR by a factor of 0.1 every 7 epochs\n","exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"]},{"cell_type":"code","source":["device"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZzNLLvThDHo4","outputId":"2e1da9e7-431e-4209-e177-8d80717974f2"},"id":"ZzNLLvThDHo4","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["device(type='cuda', index=0)"]},"metadata":{},"execution_count":67}]},{"cell_type":"code","execution_count":null,"id":"cbeb3a50-31bf-4225-a38b-85fa841cf0d1","metadata":{"id":"cbeb3a50-31bf-4225-a38b-85fa841cf0d1","colab":{"base_uri":"https://localhost:8080/"},"outputId":"08b8f5a8-4dbc-45d2-abc0-baf73b598b5c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 0/19\n","----------\n","train Loss: 0.8379 Acc: 0.5698\n","eval Loss: 0.7174 Acc: 0.5104\n","\n","Epoch 1/19\n","----------\n","train Loss: 0.7186 Acc: 0.6016\n","eval Loss: 0.6572 Acc: 0.6455\n","\n","Epoch 2/19\n","----------\n","train Loss: 0.7006 Acc: 0.6111\n","eval Loss: 0.6615 Acc: 0.6257\n","\n","Epoch 3/19\n","----------\n","train Loss: 0.6910 Acc: 0.6142\n","eval Loss: 0.6979 Acc: 0.6439\n","\n","Epoch 4/19\n","----------\n","train Loss: 0.6741 Acc: 0.6260\n","eval Loss: 0.6712 Acc: 0.6521\n","\n","Epoch 5/19\n","----------\n","train Loss: 0.6755 Acc: 0.6247\n","eval Loss: 0.6377 Acc: 0.6588\n","\n","Epoch 6/19\n","----------\n","train Loss: 0.6686 Acc: 0.6263\n","eval Loss: 0.6458 Acc: 0.6411\n","\n","Epoch 7/19\n","----------\n","train Loss: 0.6393 Acc: 0.6530\n","eval Loss: 0.6303 Acc: 0.6630\n","\n","Epoch 8/19\n","----------\n","train Loss: 0.6390 Acc: 0.6507\n","eval Loss: 0.6386 Acc: 0.6607\n","\n","Epoch 9/19\n","----------\n","train Loss: 0.6347 Acc: 0.6578\n","eval Loss: 0.6354 Acc: 0.6634\n","\n","Epoch 10/19\n","----------\n","train Loss: 0.6341 Acc: 0.6587\n","eval Loss: 0.6421 Acc: 0.6583\n","\n","Epoch 11/19\n","----------\n","train Loss: 0.6331 Acc: 0.6594\n","eval Loss: 0.6272 Acc: 0.6639\n","\n","Epoch 12/19\n","----------\n","train Loss: 0.6337 Acc: 0.6576\n","eval Loss: 0.6234 Acc: 0.6678\n","\n","Epoch 13/19\n","----------\n","train Loss: 0.6344 Acc: 0.6559\n","eval Loss: 0.6173 Acc: 0.6720\n","\n","Epoch 14/19\n","----------\n","train Loss: 0.6289 Acc: 0.6624\n","eval Loss: 0.6169 Acc: 0.6697\n","\n","Epoch 15/19\n","----------\n","train Loss: 0.6295 Acc: 0.6596\n","eval Loss: 0.6220 Acc: 0.6685\n","\n","Epoch 16/19\n","----------\n","train Loss: 0.6279 Acc: 0.6624\n","eval Loss: 0.6253 Acc: 0.6697\n","\n","Epoch 17/19\n","----------\n","train Loss: 0.6297 Acc: 0.6617\n","eval Loss: 0.6217 Acc: 0.6686\n","\n","Epoch 18/19\n","----------\n","train Loss: 0.6303 Acc: 0.6619\n","eval Loss: 0.6221 Acc: 0.6718\n","\n","Epoch 19/19\n","----------\n","train Loss: 0.6261 Acc: 0.6626\n","eval Loss: 0.6175 Acc: 0.6700\n","\n","Training complete in 46m 24s\n","Best val Acc: 0.672048\n","{'train_losses': [0.8378587017180034, 0.7185671639743066, 0.7006370623087, 0.6910241164957159, 0.6741008267575718, 0.6755293594577136, 0.6686075033162131, 0.6393027293802909, 0.6390367304972537, 0.6346590471750944, 0.6341406968357483, 0.6331265978365891, 0.6337438616484593, 0.634378069601277, 0.6288583653520475, 0.6295346915511177, 0.6278542980618447, 0.6296825431131007, 0.6303081947018621, 0.6261240330867376], 'test_losses': [0.7174013494995723, 0.6571713373075531, 0.6615399996570729, 0.6978588148077057, 0.6711742162016128, 0.637704769536175, 0.6457576219298888, 0.6302652476658778, 0.6385800142895633, 0.6354402702722295, 0.6421354471084899, 0.6271654008807592, 0.623437797702935, 0.6173080711566472, 0.6168943388743584, 0.6219512342620003, 0.6253251989734024, 0.6217427474272973, 0.6220892845193592, 0.6175460426798413], 'train_accuracies': [0.5697619608767381, 0.601579071411737, 0.6111242045722366, 0.6141880744756069, 0.6259721894885695, 0.6246759368371435, 0.6263257129389583, 0.6529578128682536, 0.6507188310157908, 0.6577893000235683, 0.6587320292246053, 0.659439076125383, 0.657553617723309, 0.6559038416214943, 0.6623851048786237, 0.6595569172755127, 0.6623851048786237, 0.6616780579778458, 0.6619137402781051, 0.6626207871788828], 'eval_accuracies': [0.5103700212114071, 0.6455338204100872, 0.6257365071883102, 0.6438840443082725, 0.6521329248173462, 0.6588498703747349, 0.6410558567051615, 0.6629743106292718, 0.6607353287768089, 0.6634456752297903, 0.6582606646240867, 0.6639170398303088, 0.6678057977845864, 0.6720480791892529, 0.6696912561866604, 0.6685128446853642, 0.6696912561866604, 0.6686306858354938, 0.6718123968889936, 0.6700447796370493]}\n","CPU times: user 20min 1s, sys: 1min 37s, total: 21min 38s\n","Wall time: 46min 24s\n"]}],"source":["%%time\n","\n","try:\n","    model_ft, metrics = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n","                       num_epochs=20)\n","except Exception as e:\n","    raise Exception(e)\n","finally:\n","    print(metrics)"]},{"cell_type":"code","execution_count":null,"id":"94ed4a87-a5fd-4b85-a143-93cc203293a4","metadata":{"id":"94ed4a87-a5fd-4b85-a143-93cc203293a4"},"outputs":[],"source":["def visualize_model(model, mean, std, num_images=6):\n","    was_training = model.training\n","    model.eval()\n","    images_so_far = 0\n","    fig = plt.figure()\n","\n","    with torch.no_grad():\n","        for i, (inputs, labels) in enumerate(dataloaders['eval']):\n","            inputs = inputs.to(device)\n","            labels = labels.to(device)\n","\n","            outputs = model(inputs)\n","            _, preds = torch.max(outputs, 1)\n","\n","            for j in range(inputs.size()[0]):\n","                images_so_far += 1\n","                ax = plt.subplot(num_images//2, 2, images_so_far)\n","                ax.axis('off')\n","                ax.set_title(f'predicted: {class_names[preds[j]]}')\n","                imshow(inputs.cpu().data[j], mean=mean, std=std)\n","                if images_so_far == num_images:\n","                    model.train(mode=was_training)\n","                    return\n","        model.train(mode=was_training)"]},{"cell_type":"code","execution_count":null,"id":"7a85cb29-310c-4575-bd34-492b42df6024","metadata":{"id":"7a85cb29-310c-4575-bd34-492b42df6024","colab":{"base_uri":"https://localhost:8080/","height":923},"outputId":"da2380ab-e804-43e2-fca5-e199485703f5"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAM8AAACXCAYAAACyXA/FAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABvqElEQVR4nO39ebxtZ1XnC3+fbs65mt2cffbp0pw0J4GEELogvYTQBkqRjyBSqBD6Uhr1c1Ws960r+FYVWFCvJYJV12gJiFhXkPeWci+iIqgI0gUIgRDStyc5/e5WM+d8nme8fzzPXPuEoEAMkOza4/NJzt5rr2auteaYY4zf+I3fUCIibNu2bdt3bfoHfQDbtm0PVNt2nm3btntp286zbdt2L23bebZt2+6lbTvPtm3bvbRt59m2bbuXtu0827Zt99K2nWfbtu1e2rbzbNu23Ut7wDnP3/7t36KU4m//9m9nt1122WWceeaZP7Bj+mb7Vse4Vax7b3/6p3/6gz6Ue9jb3/52zj77bIwxPOIRj/iuHvue97wHpRQ333zzd/yYB5zz3Jf2lre8hf/5P//nD/ow/pe3j3zkI7z5zW/+Fz3HX/3VX/Erv/IrPPGJT+Td7343b3nLW+6bg/tnzH7PX+H7YL/3e79HjPG7ftxb3vIWXvCCF/C85z3vvj+obfuO7SMf+Qi/8zu/8y9yoI9//ONorfnv//2/UxTFfXdw/4x935wnxkjTNFRVdZ8/t3PuPn/ObXtg2eHDh+n1et83x4HvMm1785vfjFKKa665hhe+8IXMz8+zc+dOfv7nf57pdHq3+yqleN3rXsf73/9+LrjgAsqy5KMf/SgAd9xxBy9/+cvZs2cPZVlywQUX8Ad/8Af3eL3bb7+d5z3veQwGA3bv3s0v/uIvUtf1Pe73rWqeGCPveMc7uPDCC6mqil27dnHppZfyhS98YXZ8o9GI9773vSilUEpx2WWXzR5/Xx/jeDzmmmuu4ejRo9/2c37KU57CQx/6UK6++mouueQS+v0+p556Km9729vucd/Dhw/zile8gj179lBVFQ9/+MN573vfe7f73HzzzSil+M//+T9z+eWXc+DAAcqy5Id+6If4/Oc//22P51tZjJH/+B//I6eddhpVVfG0pz2N66+//m73+eQnP8lP/MRPsH//fsqy5PTTT+cXf/EXmUwms/tcdtll/M7v/A7A7HtQSt3tdX7rt36LCy64gKqq2LNnD695zWs4ceLE7D5KKd797nczGo1mj3/Pe94ze9/vec977nH8Sql/cap4ryLPC1/4Qs4880ze+ta38pnPfIbf/u3f5sSJE/zhH/7h3e738Y9/nA984AO87nWvY3l5mTPPPJNDhw7xuMc9buZcu3bt4i/+4i94xStewdraGr/wC78AwGQy4WlPexq33norb3jDGzjllFN43/vex8c//vHv6Bhf8YpX8J73vIdnP/vZvPKVr8R7zyc/+Uk+85nP8OhHP5r3ve99vPKVr+Qxj3kMr371qwE4cOAAwPfkGD/3uc9xySWX8KY3vek7+tJOnDjBpZdeyo//+I/zwhe+kD/90z/ljW98IxdeeCHPfvazZ6//lKc8heuvv57Xve51nHXWWXzwgx/ksssuY2VlhZ//+Z+/23P+8R//Mevr67zmNa9BKcXb3vY2fvzHf5wbb7zxu47ev/Ebv4HWml/6pV9idXWVt73tbfzUT/0Un/3sZ2f3+eAHP8h4POZnf/Zn2blzJ5/73Od45zvfye23384HP/hBAF7zmtdw8OBB/vqv/5r3ve9993id17zmNbznPe/hZS97GW94wxu46aabeNe73sWXvvQlPvWpT+Gc433vex+XX345n/vc5/j93/99AJ7whCd8V+/nXpl8F/amN71JAHnuc597t9t/7ud+TgC58sorZ7cBorWWr33ta3e77yte8QrZt2+fHD169G63v+hFL5KFhQUZj8ciIvJbv/VbAsgHPvCB2X1Go5Gcc845AsgnPvGJ2e0vfelL5Ywzzpj9/vGPf1wAecMb3nCP9xBjnP08GAzkpS996T3u8704xk984hMCyJve9KZ7vN4328UXXyyA/OEf/uHstrquZe/evfL85z9/dlv3+n/0R380u61pGnn84x8vw+FQ1tbWRETkpptuEkB27twpx48fn933z/7szwSQD3/4w9/2mL75fZx//vlS1/Xs9ne84x0CyFVXXTW7rfucTra3vvWtopSSW265ZXbba1/7WvlWp+InP/lJAeT973//3W7/6Ec/eo/bX/rSl8pgMLjb/br3/e53v/sez/3N38W73/1uAeSmm276J9/7N9u9Qtte+9rX3u3317/+9UAq/E62iy++mIc85CEnOyof+tCH+NEf/VFEhKNHj87+e9aznsXq6ipf/OIXZ8+1b98+XvCCF8we3+/3Z1Hin7MPfehDKKV405vedI+/nZwSfCv7Xh3jU57yFETkO04VhsMhP/3TPz37vSgKHvOYx3DjjTfObvvIRz7C3r17+df/+l/PbnPO8YY3vIGNjQ3+7u/+7m7P+ZM/+ZPs2LFj9vsP//APA9ztOb9Te9nLXna3+uJbPVev15v9PBqNOHr0KE94whMQEb70pS9929f44Ac/yMLCAs94xjPu9j1cdNFFDIdDPvGJT3zXx31f2r1K284999y7/X7gwAG01vfAyM8666y7/X7kyBFWVla4/PLLufzyy7/lcx8+fBiAW265hXPOOeceJ/uDH/zgb3t8N9xwA6eccgpLS0vf9r7fbN+vY/x2dtppp93jeXfs2MFXvvKV2e+33HIL5557Llrf/Rp4/vnnz/5+su3fv/8ezwfcrX74Tu07ea5bb72VX/u1X+PP//zP7/Eaq6ur3/Y1rrvuOlZXV9m9e/e3/Hv3Pfyg7D5B2/6pq/nJVx5gBif/9E//NC996Uu/5WMe9rCH3ReHdK/t/nKMxphvebv8C6bm78vn/HbPFULgGc94BsePH+eNb3wj5513HoPBgDvuuIPLLrvsO2otxBjZvXs373//+7/l33ft2vXPPv6fOi9DCN/2tb8Tu1fOc911190tqlx//fXEGL9tl3/Xrl3Mzc0RQuDpT3/6P3vfM844g69+9auIyN0+hG984xvf9vgOHDjAX/7lX3L8+PF/Nvp8qw/3+3WM94WdccYZfOUrXyHGeLfoc80118z+/oOyq666imuvvZb3vve9vOQlL5nd/td//df3uO8/dZIfOHCAj33sYzzxiU+8x4X4O7EuGq6srNzt9m+OyPfW7lXN00GLnb3zne8EmKFA/5QZY3j+85/Phz70Ib761a/e4+9HjhyZ/fyc5zyHgwcP3o0GMh6P/8lU6mR7/vOfj4jw67/+6/f428lX2cFgcI8P9nt1jN8NVP2d2nOe8xzuuusu/uRP/mR2m/eed77znQyHQy6++OL77LW+W+si08mft4jwjne84x73HQwGwD1P8he+8IWEEPj3//7f3+Mx3vt73P+bbX5+nuXlZf7+7//+brf/1//6X7+Tt/Bt7V5FnptuuonnPve5XHrppfzjP/4jf/RHf8SLX/xiHv7wh3/bx/7Gb/wGn/jEJ3jsYx/Lq171Kh7ykIdw/PhxvvjFL/Kxj32M48ePA/CqV72Kd73rXbzkJS/hiiuuYN++fbzvfe+j3+9/29e45JJL+Jmf+Rl++7d/m+uuu45LL72UGCOf/OQnueSSS3jd614HwEUXXcTHPvYxfvM3f5NTTjmFs846i8c+9rHfk2P8bqHq78Re/epX87u/+7tcdtllXHHFFZx55pn86Z/+KZ/61Kf4rd/6Lebm5u6T17k3dt5553HgwAF+6Zd+iTvuuIP5+Xk+9KEPfcv66qKLLgLgDW94A8961rMwxvCiF72Iiy++mNe85jW89a1v5ctf/jLPfOYzcc5x3XXX8cEPfpB3vOMddwNrvpW98pWv5Dd+4zd45StfyaMf/Wj+/u//nmuvvfa+eZPfMS4nm1D11VdfLS94wQtkbm5OduzYIa973etkMpncAwp87Wtf+y2f59ChQ/La175WTj/9dHHOyd69e+VpT3uaXH755Xe73y233CLPfe5zpd/vy/Lysvz8z//8DKb856BqERHvvbz97W+X8847T4qikF27dsmzn/1sueKKK2b3ueaaa+TJT36y9Ho9Ae4GW9/Xx/jdQtUXXHDBPW7/Vu/z0KFD8rKXvUyWl5elKAq58MIL7wHNdpDt29/+9ns853d6TN/8Pj74wQ9+y9c4+bWvvvpqefrTny7D4VCWl5flVa96lVx55ZX3uJ/3Xl7/+tfLrl27RCl1D9j68ssvl4suukh6vZ7Mzc3JhRdeKL/yK78iBw8evNtn881QtUiCy1/xilfIwsKCzM3NyQtf+EI5fPjwfQJVK5HvvFp885vfzK//+q9z5MgRlpeX7xvv3bZte4Da/9Ks6m3btn+JbQlW9bb9y61pmlkt90/ZwsLCvUK9tqptO8+2AfDpT3+aSy655J+9z7vf/e67kWf/V7fvqubZtq1rJ06c4Iorrvhn73PBBRewb9++79MR3f9t23m2bdvupW0DBtu2bffStp1n27btXtqWAAwec04fV2gQUIBGoYBEmep4ZwoEoCMFKkQBEVC6uzNCBCWkP6aHIRqlBEEQgdYrvAghpt9Rm6+rtUIRQSCKIKgZRUUUWAVOK6xWaA1agaj8PGhEID8KUESl8ruRdPgiaCxR0htUAEpQKJSK6Px6SkVEKRCNViY/OuT3qYgx4qOhDYEQAlFAKYPSEaM1Tmu0Tp9DiJ42aKJEoiiUAoXgjMIZi7Vg8vMqBKU7ak4AFVEYIHLyBMxffnGT5vRAtS3hPIXTVFaBSqeIySe+6jxIgUay8yg6Pq86KfAKmuxJQBrlFUBJdyKDiCYiKAMmaLyE9JB8f63I/2qIkYgiSno+EUkOphWFUVgtGK1RCkRFEMknVz5OlY5TxCRHkPQ+lOiUL0jy8fQmdR4/1ogolHiUcqA1SMAQiMrli4sm5ueLMVJ7wevubQhaKVCGoiwxCtpmgjEKbXS6WCCIGLQWnFaUVuEsaKXTJ6vSsSTHzVcWMYBNTi6SP+cHvm2NtE2BFgWyeY1O/6rZSSaiiGiiaFCWKJooClGWmB0nosAUKFOmx2bHiMog2hJQBBQojQAxpsejLDFGQo4aSIpvogwoQ4wQoyYdogZToXRJRCenEp3+jkaZgmgKgmiU6OT0aDAO0UU6xuRVoEKKXEK6XTtQZvbuU/SDYCpQFsGk9xyFKIZIQRCIGGJUoBzoAtNb5nX/9h28/t+9C9PvZmkk/6dRSifHRSGzKG3S+5Uc4aNKDi8uX5hI98Gk27eAbYnIQxSiE1RMeVu6ApMjhqCUAYnUHs551DN5ylOfyf/1gT9kvLHGC3/mNVx55Re54tMf5WmXvoBzz384dV3T1mP+9qP/P+645ToufcGrOH3/GfyPP/oD9u49lac/6zl89rP/yKc/+Te89JWvR2nNFz73WSQGvv6Fj6K04OZO4aWv+gXW1ta4+uqvceL4MY7ddTP/6nkvYsfSLqqyZOXEUf7qz97H2tE7OPcRP8wTLr4UHwSl4LOf/Cuuu/IfmNt5Gk//kRexsGMnIsLB227ibz/yJ8jkKApDFGHptPO45Nk/TtUbooBrr76Sz//dhzGuxyWXPp99p52JMZZ6ssFfffgDHDp4C8998c+yuLiDf/iHTzIYDvji5z7Ny1/9Onr9AZ/73Od4wpOejNaa/1YNmI5zyoeenfiiAAmofFFA2uT8dL/Hu8X49KA2/7o1rtlb412gQVS+6M2qhVRrSKbFq4gzKVX6sRe8mMc86RnsO+M8nv+il7Bn334e+uin80v/77ewuLSLT3/qU/zUZf+Gn/uVtxDcAk95+nN4wYtewu59p3PeQx/Bi3765Vz0mCcxGC7wYz/+k7zy1a/l3735LZx59jmzSDe/uMwLfvJn+Dev/UV+5VffxK49p3Pp817CT730NVz5la+wPprw6tf+bzz9x14G/b388q/9Js989vP46Ef+b84+5zxe8/P/O3p+P8/7qdfyqp/7RQ7eeYhp4/nFN76Zpz/vMmqfTuBaHK94/b/jsle+ji9e8UUaL7z69W9k7zmP5oLHPpOXvvoN3HnXEW659VZ++uU/y/Nf8lqC7fHM5zyPl7z8Nbz5P7yN8x7ycM678DH8zGWv4owzD3DHbbee9NlKjhzZcSRC/my7tFRyuqyI6XIlOTimCig9hwiIzv/dN8NoP2jbGpEn1yTpm1O5uE/lQFe3CAqthJuu/RJ33XUnD7nwkRw5voZSiptuuIZ9pyZ5pNWVFQ7ddTsiwu49ezGmBLIsUq6HOOn/Simm0ylvefMb+eoXPp5PIMmAhWLlxAn+7f/2s9zw9S9yw6nncNedd1BWQ848K42uL+1cphwsMD8/z3Qy4fhdN/E7v/FG+oMKwjp7952KtZYLH/YINjbW+drXvooyjqXlJQyBtdqxc1dKrY4fOcgH/+Bv+Js/ezdHDh/myB038Z/+469TDeZ51KMuwhjD0s5llLIoBW3b8ju/9Tb+4WP/F4960nNQSrGxscHRI3fhvc8aBV3d0kUfEIkonZxB0CARUTYDBLm+zI6T0jtBZSdLtl3z3I9MMpqUIkyGrGZfXPonfZH12lG+dMXnueChF/LYxz2e6WTCwVtvYJaFn5SOS+yet3uVb22TyYRbbr4OCdNULMvmndc31jl42w0UOnLJ05/Dr/6/fo3lnYtceWUSEbHOUhQu1zBgrGP/OQ/loic8E+XmiBIJIfDJv/sb/sf7/oAvfP6z3HLzjQwHFaeftpu9e3bkExtcYdmxZz9PuORHWdxzJuc/4nH88r/9Nc4//zy+/MXPE0LAGIPS6U02TcPNN16L+DFafet3J/kDiWxejFI475LiVCsKAWhBfAI+RFDiEWJCMLv/VKoct4JtCefZdJ10hYwZbk5RJ92j+7/D85lPfpx9+/bxYz/2Yxw6fIiNE4c4eNuNrK+vcdpp+3n4Ix+DiPD1q6+irddYX19Ha82DHnwBe/adikhOQ3I6IjFB05oEESfki9n9ylJTDRx7TjmFxcVFlFIsLCzm51C09Qa33HIz/f6AhzzyCTz/xa/kCRdfSgzCNVdfRV3XnHHWAZ76zGfzr1/80ywv7+LQ0RPcdWiVyWjEN675GjFGHnHR4/mxF17G81/8cpZ27mR59x527tyJMYYdOxZnn1dRlCm1ihGRiEYYjzbw3rNr927OOufBOOcymJEeY4yiLIv8OXbwe0xOJBlilw6oCCkaRVASgIioAOLTtW2LJDxbgp7zhAcP6LkM5bLZ+9A539aqSxkUk9jnR/7163jJy1+NSORP3v9e/vy9b0dpxTkPeyJnHngwKM3KscNc9YVPMl47xClnP4JHPvbJ3H7rLayvrXHa/jO47rprufbrX+HJT3sOWik++YmP4OIqViusUQS3xFOe+WM0TcuXP/s3ON1Qzu/losc+lbW1de48eDtnnX0WN994Pcfv+Br9xVM4cN6j6PUHjEfrXPG5T3P00I30enNc9Ngns2ff6Qhw+6038eUv/AM6ThlUDmsU0S7wiB+6mN179lHXUw7efA2jozdCuYOHPfpixuMJ1157DQ+54HwO3XUX13zlCzzs0U9kOJzns5/6G1RzFFPN8/DHPhO040tXfIFzz30QWmv+4WP/Exs2MNYRJDKaJjhfETAaCmtwVmMUKJWhfmVQaJT4DKXH1JOSmMOX5s8/d9+No/+gbEs4zxMfPKAo0leqAVEql7igRUDnhqgoGinYe8a5WGuoJxNOHLod4oSUo8fUfJyBq3qW7ndZPFFoBbxADBAyPG4Lw3BQEHyLBEFZS9WrKKoBhdFMpnXuNUVQDmMUTT2hCR5jDL4NTJuaIBqNog2RGCKFcywt9NEqECTgW6Fp8wmsoFcWKA1WO0pnUSrirKJXVfSqAleUjMdTNkY183MFZeUYrY8zxuKIMaJUpG3ScdRtoGmFtbV1EMH7lhAiWhvG04YmZ1wKwWpNYcEZhck5TEoJDYqIiiFB5yqg0KmHRCSK4cOf226S3k8sxRglMmMVZOyA7jqZAw+Fajh221fpehYdPNQBADo7Uer6R0xG8XRuUooGJQoV1Sy6aQNFqehVBa03gAFtiUDbChrBWst0Okod+9BibUKdvBdaP6VtwVmHxEjbBJQiNTOB8aSl37MYpVEWRDytD0ymHpRlUDmM0hglaKNxLjmr0gkV1irSrwzWWPYuL3BXzJ+JUUzqBq0sIUSs0VSlpeqVrM+XrK6OWNvYwEaLtQXjaZM+RFGgcj0jhig6d8AEokXpQMfg0MTUJFWSG8V61nR+oNuWcB4hZuQnw6oyIxagsiNpJZu9RTYL4c2Ykh4kkvoZnUNJRoq6lBBAx/zcWiERrDVopXGuRJQniiLECGhCqJnGVJy3PjU1p9MaYzRFYZnUgRgiogzGkKkyqYYKQfCqpQkaJp4ggtYWqx3TtqXxgTICyuDxGDFYDONJQBOY1hrvA1o0Zc/Rti2+bShtRJQmYiidBYHKOZRSlIXF2YK5vqLfqxisV4zHE7RxTJqWdn1M7FoAGS5ItWY+lVT3t5w0ZwBHiQYVcxTfbpLeb+xk6tqMazbrgKf7RLgbor35U5fSyQzqTk+6CUd3TAVyWudVimRGK6xVOT2y1E1DxGCtIYaASKRuPSC0vqVuAoIQokZHCLGlbSEEDcrTeo/JkdMHRd0qlLLQCOPQUJSWECb0iojRFmsNxlna0GJE4QxMm4TOEYVSkpcHX9NrapRS3HDLBKM1VVkmcMOYWc/Sh4gNQvSBthWMUQyqkkG/om0BLN4LG5NpAki0AVItM3MOpTOXTefIZBN83yEPSqWL0RawLeE8qSmXr3UnQ64zR4gzaHXTdRQdGTRFmU3ypcr1kXR3m11dk0MZrTCFoyhKnCuxRoEEBE3tI9PpCO89IVOGYuyioaVtp0imBW00gRATOmVMqmECgtWK1if+mVLCZFqDEfy4JQZBScBZgIhvG6bjSOEUMURChOgbUIY2eLRyND5QNxqlhRgaSucY9hr6VZ+gFM4a6rqlDe2MR+eDJ4T0nrUCqxU7FnuEuMgddx6lbck8te4z9Smnjd1Vp2XWOqBrroIibEee+5VJAga6r0TlaCL5yqtO/rIkAQoqYavJU6KA0okfdlK9pESIaAyp4YnSGK3RxjAczmGLirqdoLRBoqZpm3TVDhBiSpliCN0RkcifGQyIAWLE2MR49q1gnc6Ho/G0OKsJIdB6j80ghVLpeX0MEBQSAtO2pSxL6sx+8W1DURQEMUiY4gWCOLQK1NOWxiVu27SdopTgDIToMcYSPExDg1IJnhbRiRCghLYOLM73OX7CUdcNKLA6pWEpSe0aqd98AZMZaZXMQtgKtjWch9ztVqm33/EABEmFPszSsM0ULUcpSUxfUTHXS+mrnVEZFShtUdpincaYRBIVpWibFomBUd2kK34UiJEQ20T6jCnNCyHmn4UYNSoqnFGITqlN6cDr1NW1VhGjUFpD4RTee5zVqR+jFSb3j3QMiDIpFYyathHENImEKh5lFFGE1nu0KjAmAQ0+CipEZDrBtAVaaSS2WBUZzpWMpoEQPIVVWKPRWmOczf0swceAcz2smiAEogJDx6hOgIAQUWLois9IRAupkapgqzAMtoTznNTQR2WWr8o5VqpqVL7qp0JWS9dETS4WFYDJNJ6uDlKIsWjjiEpR2JKiTF2kJkCUiISADy0hCCFupk0iiigtMaYTLkTpspaZrrVWYKwiRKEoLKUomjbglEIXBqMzaIAhmogPHUWoe48gMdVQxiiMSq8lkiHhGPBBEuKlBO8jMfgUATA0TUCbgMQWRcugsrRtQ9Mo0A4RTet9Al5qj7agMNRtcmLlNITkKnr2QSeEUolJIwnEPNLBrOaRDIZsBdsSztPVK11oUZIo+omurzL1RDbBg1yUz/hXABJzQpFCVDSKiEEpjdYJNq59iiJGpzEDCZHGtzQeYpDkLAha6xSFAKs1RncntppliiKRsrAYZSiLHiEG5vsKosc5R+U0dSvECHXboCRFpOABJam/FDVRaRAPWATBIUhMtZYxOjcsIxIT61lEiDHVToXWlDbgjEEBdZN5GrpBosWoBMMbAxJcuvhEIQpUVQ8/rVPk7krHoEGnz0xlWo6c1BJQKjk7924t1P3OtobzqDwtGjcxAkRmBT8zeDSlRl3vB9l0KCWSnE1DUAofFFZHiC2NWKzWOBFCCPgwRSuLD5rYQgjQhHRlTz6s8jGl6FI4jUanyUutsMahNAyqMjU3iwRxp459kVA0pYkhUjctk2lNFE/beHyItDkF1BiCKELwBHGkRNUTgiHmqdEoMc0rSYKNlTJYHTFOo1Uq8kU0USCGSBRP6VJZLyJYKQgxoEyqtyKKoqyw1hAKzcZoTAgemy9ISkKCpyF/GTqDBuSBPr35HT3AbUs4T54yyz9nwE2TgshsxiedWikSdZnDSbiP0mgFHrITqjQoJoI2Bk2gaVua1qOIRGnwnpQOZecUlUBaCYI24AwUxjBXFgxLx7As6FUl/bJPr+codGqeGmOwhU2oVq4ztCTGtm9b2qahbgMhCK2PTH2qq+o6pP+8T+PhIRJCTZBIiJYolto3BCyI0EZPEE/wmrb1KG0SzcZFDAptDEopQtBEwBqFF0UMikIryNOvFoWyjug0rYd2OiLS5hieG8/kqVEViTgSGTQmdvUWydu2hvMAogXdFT8n9T9FkXUATnKUTlMAElOAlJN7IIqijppWa1wu0BFPG5JToNIEaQIIyDUOSIwYndAnVxr6hWau59g5HLI07DPXr6icpSoLql6BNRqrFc5atEljzxIjIgElqU4xyqIQgvd4LxlFjPgIURnGkzFrq2MmTYsP0AZP3da0wROjJogwbR3TRmhDy7Q11D7gVUoHE1vCdLgJgUiMLUE5VBCiNWjdohQ0WKzSlDmKRoGyKOn3PFMVmExBosdlZK1DOKMkTYeuFoWA2iLDcFvCeQRBxZP6Mif1O5WcJKAhm4UrpOiSL5KzmZ+xh1aziQ5hISoiYdb9D4k0jI9prkVj0EYojGFQWJbnepy6NMfy4jzzg5JeVVIUJdoorLYYrTFaZaESjVGKwnUt2cRyTsuqFLFN7ANVpMggKtVdWkEYVDTzc/gY8FExbRpGkyk+RLS1iCgm0zGjOjBuPOvjCaPJhDZ4muBpW6EJeYRNApEGYzXOZIaGBmUNIgGtgeBpQkNZDjAmPW7aNLTeU5Z9pvUGQRROMrVIbbI0ZowNpbcK2LZVnGcz1HTd685HOgdRM8rApuJN95gOJGg81CEpwYhKV++YuWFaaZRViTkQQqqbsmNaHekVjuW5Pmfv2cm+nYsM+gW9ytHvD7FliSt6WGdTSqY0ElqIgTCdQl0T6zweoBTaKCIerQyEgCbx4GL0EMHZAmMKFJFhUYDWBBLaF41JDdqYQIzgp/goNFEY1TXTpmF1NGE0nrK+0bA2njJpprQ+zRJFNFEZYhTaGAiNZJi9wSiFMaBUQ1kWRIn0exWN7voABX7adryozGvTM2ETyUo62zXP/chmmFmuddKJnU5uJCYVGXLNA7P7pjoop20C0wDa5tpF26Qa00lw6MS0boJgjQGtcSbgrGZYWU7ducTZe3exe+c8VVlQlBVFVVENhljnsLZAW4uxlqo/R6jHNOMRk5UT1JMJ+BatNIFIaD3apNdXkuonOn0AAGVTjQZorRFtcKoTGBGsCuiiSE4YDcELohSe/iyVrZvAaDJlfTJldTRhZW2dExtjNsZjAoldbY1j0rb43Pz0MQmatK0QZYKzJWXh0DrN/dT1lGh0Vg9K9WKHZG5CcqmduhVsSzhPJ9gimXM2Q9lIP3SDax2U3YHUM7UdBRFLyMwDnRupWixagzVpJigGwRoLGXq2tmBhULF/zxIHTt3Ljvk5CmfpDeawRYmxDmMtkFA6ZS39+QXmd+zEty0bx47SjMZo64g+JHjZWrBmJiWFaAIZ7sagdCLABvGEqDAxpJNXp9FqJZIauSLEECEqjCmAgEVT9ocoHROUrhU+CtNpzWg6ZWV9xKGjJzh8/Dgro5qNekpQoFVg3HpC7tFIHKGdQmuDipZm6hPrIaTGrI8BIycxNdSMspsaqVsDL9gazkMAcRnF6fiHHYlghu1kSo466Xc6vlvshKeI2qTZFJUaoUSdU5A04CXSoEiNzbn+HKcuz7F/3x52Li8npIzcfNUa7Rzet2ilaCZTbNuysHOZtRPHUUqxfuwok7U1Wh+wrkzOrRU6s4+7oTIQYtYMQOtZfYYCrEVUJPpUYxij00mtNMGHlG7qTNhUCqVVjoIF5fw8rt8nSmQ6GjM6cZRT9uzk+Ik1brvrKLfceYTDKxustU0iKemIDw0hRirXzzQhwRUWlKJQnslEEyRuNnRz1dO1BgCSjtsD37aG82jZ7Otki6qLLqQTUOU5HdlMGjrVF4lQR6hbiDEQrcGqiHIKE0l9CiU4U2Byk6+0BUvDHnuWFhj0epRVj6o3YDraYDwaYZoG6yYULo0ua+dQtmLl6HE04Nspa4cP46djtDEElZQ2VWHJlLy05VoBys7QqpA3X6vMsUMEZQxaqzTIZ1J3X2uLsczUPdN4eHrPURSESAwtvcUlyoUFgo+s3nU7xeFDOO2orGHYK9A334E/6vGZrS1ErLYgFqKkOkgpxFkmjSZKALFEiVirIOQCqOv95B7YVrAt4TwSVRbtkxnCJh2rQOdKQbp4k6mJSmYFf5DEYq5bQUtKkXAqNQdVwClH5RwSEv2lLCzLCwN27ZjDGUs9mrLCMaxbSxK6xuKnU6bjEcTkoMP5BZb2DGmnY0IbqMfrWYVGIyEm1oNJ1ZtxLl0PQpr9V8pAiChjcYXCWJdRq6QLgHEAFEWR0zXQRZFOWO8hhqQgajXlYICKSS00+sj4+JGE7BnLrodcRDF/M2H6Fdppzb7FQHGuRWnFrYdPsF5DE5NwpJKGKAaiRomjbRtEBKMcHk1Qgo2gdZYAy1O50AlUPfBtSzhPVGpW92jpQAHS/yMzyHTWashf4my4TYHRZOZvB69qnLEUJtU8oiLGKirnWOgNmetVFE4jocEDo9EYpRSusFjnMFZjtKGpJ6A049EIfeQQWkHZ61NUFU3dgDaIeIy1xBAIKjmNVobZoRJRRqUuvyYTNTVRa5TYHIUspkhfpwDWFemHIqJtSuEUGlsOiL7GiNC2HlPXjE8cy8chNMeOoCRQOIvXisXS8fCzT0Upww0HDxGalB7GGJEWCtfitEGsofGGWqb44LFK8FrQXQ1JivCd3vdWsC3hPHl/cvpFJ5nXGXAg3R1Uuvh16V3GEJLegeQTX+GcwTqNVVnUAkErTaFTEV9aR1VaXFnOcnqRNDymtEKyakzw6UUkBpxzaA2tb+n3h6DSKLayGhXSyV04s5nZREGrNtN8FCojWBIjaEfwnhDT6xiTapnQNiCJX6dIA21lb5CPCVx/QPQtxrnUiLWWwXxFMRhgrCEGz+joYcbHjxCaCZrEJtACw6riIWftIxK59fAK49ajlUJpjTHpwqNDohOBRatEmPVesCY1STsByruJ6D/AbUs4z+aUYkrhFJvRZgaRdlw2yF6l87S1JBqO0lRFSrmQXGtIQNNtNCgpLQx7FcN+Sb+0FMbMKPvWuDxyDL6Z5PmfmKJJDBhXMr9jicHCPH5a40URfMeFS2icjiE7A3RiJtY6jCuAhGQZU6TpT6kJumNKp4jb1s3sOU3hUEpjbIqmrt8jRsV0tIF1Ja7s4ap+OpG1hSCExuPrOvWH2gZ8xOkUteZdwTmn7mFlY0K9kno5hdWUZYm1CSYpbJFgbCO0rSLGhMCl61oeSFRJK3sr2JZwnjR+oLNzpNpnsweavzSTrsgyEzLIsz8CTYRGIETBSJqfCTHgrEHpJIIeg0flE9H7BM22GkpbUfUcVpdJ/C9EgmpSUaxsov2jqOspx+46xHh1BedKiJ7gPTqmmiZIi+iOCa2JPqnnaCdoCSApFTPWJiDD9YgxRRpbOsQH2ukIVRTEENBaE0KDbwNaKcYnQnJIpdBEYmsT16312F4fCZHpaJ3pZIJEj2/bDKgk5zRKs9Qv2L93J1PvmdQ1zqaVKj4kyhCZ/zdtEts65GgTQtYu6DKEWYh9YNvWcJ7cS0iRJ5M/c4ogmRSqZDNdULkAUghRQwiKphXQltIqrDXYPJvifYvkJmsRSkIkQcYxonVJJNLWLUHFdE5oSYiYsYmJ4BuK3gAJnqkPxLbFuilahaQNGCIqNjhrKAdDEEVhCqwTlI1EiXjvc78ooE3EuSoJwhswkqZRo9LEWNLWaZrVOYcEnxyzblE+IW4Ym0D5okBR4ooi9YWMxpY9bNVjujbBlOm9KdGokARFCq04ddciq5MJR06sgjIMSosXaMctELDOYkJS94lBE8PdZ5ky5Pf9P0m+B7YlnAdU5qJ1W0E21XA6IRDoWkBdrwEk7+dIw2uRykWM0gQvTNsGrcljBBFn0n4ZoyOKkDTLjJkJdoTgU2c9pEgX8Sit6c/PY01B9D6hZ0Si9/iQru7e+9zMjPSmHmdLnNHMzw2pqhKjXa51bE45Y4o+1hB8M1s2ZV1BOx7jm5bgGyQGjE08OiVCiPVMZjc2DX4yQRuDtVVSFoqBonK4oqBxFb6ZEn0ktEKMHqVTIllZy86leZo2TZEWpcMWllG9ytqoZjpNA3dGaaLys4ua6kAaddJ38AC3LeI8qX8tdF9UQnm+OTlQSjZTuw7HUqn2qQqDRNgYt4kd7JJDxk4EEzBaQwiENsyo+USPIjEDJEaUdRRlRdXvUfYqrDJE74kh4EOgndb4uiF6n9G+RPURCaycWE1TpgKrx46zsDDPwtIS1joiHldYdIjosgBR+LbBGIsyCokJNFAxKesoSZRvkcxozlWU1hplTGqougJlUkTyzYS2Tk4XQ0s9ndD6ljZ0gh1pKs45zfzcHBvjmrpp6PcdxlkW5wccPTFibb1ObIoc2Z0BZbsKLgvEsx157jemMs2mEyOf9bSFPPbcZQrpC8wdoEx9gVYkzcJE8C0URVpbqERjNRSFwmidnMa5BBj0DGWhULrEOJvRJ0UKEgULO5awVhPalqgVMdgkUqI0rnBIqJAQKNJGLGKEsStoplNiFMZ1iz+2RttGBsNBHlEQFncuIsHTNC3T8Yi5xR24wYDR+hpBBFsWgCa0NcaVGGOJ7RRXVShTJITMVZiiQluXajoJBB+YrK1Qj9bAT9EklkBiZ4c0Vm1SwR98i3UqLQgLgaYN1FOPMRrXs4SJRwk4nTbhGTazNYPZrnnuT5akpwDSySt5N2cnzpc2uEnmZSWmAYDSkRZN7dNV0hiF1hpn08/OCFZpiIKShDz1qoJB1Sc2gYYWYkz0msJRVhXDHYss7dqLs5YQasb1hMloQtsGWp/StLZtaeomCX0YQ2EtzjmKfontF8SY6iEtUAdBRuNUcLdTJEacLWjaltHqCs4VBBE2jq1Q14GiLGibFq01vf4A39RJLLEapAtAFAICrWe8toaxhtBMaCYjCCFdeMQnpNEqTIiEqPAC3gfEJTDFKE1RlGyMJhxfH7M68oQgWIlpclbSHrgUSfNkLR3PbZuec7+xNm2uyDVB5xoZIDiJz5aGzdJjREhsZauoCpfEOwRcoTODWtIVOQtqFAbmhwVzw16ORiXTyZSyKhkuzFP2Cnrzcyws7iBK4ODBOzh2fIVjKxusjcesjmpijBijicETYpoMNdpidMRZR2GTrG1VJjESjWbQq3ACFk0RE7csSsBHmK6to0RjXMl4PKGoKkIMHLnjDvr9PjFGmukUV1jMeJxe26e429YTYhC0DmiTSKhKK6KvUcqhraDaGmUcSgfEQ5CAtZZ+r0fjG6Z1Q93UrKxPObExYZyVd5TSSJ7MnRFyu9UvZKb7FrAt4TxNVomRPBUaMyTaTSQo3ekWkNU/ImjSODEJoQuiyITmxHrRmWkgkV5VMjfoMSwdpVHMz83TG/ZTwW3SyVuUDozh+NEjHDx4F3cdOcbRlTGrownr45pJ42e0FGuSAKGEjt2QaoIQUpqps8a0VZr5ylFoTb9wDJxh78KAiGE6qSk09EZTlA1srKyxY2eium6MkmO0AdqmZjg3xDdhxvLWSqW6K8YkyqFU2oJtOrGUNPpgjMufjUHpQOkcprSUddJlWF1rWd9o2Zg0TOpI27SgVaICKgiZkZ58pRv92Jy5eqDblnAe72VW3yQtATqJnNTUnvHeJMPJpHRCJyeRqCjdZn9IkwrwKAFnFc5UVLZkYTBgx44dzM3P5YG11NzENEzbyMqRE9x5+2Fuu/MIx1c32KgDjQSMNvTKCmMS4yDESNumSOgl9UhUrgeipIinspjgWhNwWjOxDRMnaPHUwSLthJ3DHqsbI8QYfN0waKZoLayPGqIIDTAZT1kdjzHW4HoV2mrKsmLH/BxVby6hgwoIbebSebRWWJ0YDRKSwKJxCuUMylraVmibwInVNVY2JrRNqsF8K3gRnFUom1ZedVu8U48nMby3I8/9yLq5EeAkDlvKt9PfYt5yoBATc7Mwr3K3Gnsy9T8mgXVoKayhcIZeYdixMMfy0gJzcwO0UUkrLYsfhiicOHqcG2+6g7sOn+D4xpjGB5Q2LFYFC8M+w96QojBIDPjW0wZJ4u+tp2lq6rrFh+yMUSGktfUSIyFPgqoYObSywYmNQM9GmsZTR0VZOpzSTEYjHIHxdErbTrGNZ208Zdq0aOuooyfkSdVBv2J+bsDSwhzLO3cyrHqUpcFphYQGJRHxDVFptFhUO4XQMhkFJAYmk5rjKxNan1R2fIQ2R3zv079VL2+zz3VmGktQbJWBni3hPJ3wxmYfp1sLkkcSsshgJudDjjpapSI4KoU2CZULMaZJUqVwVjMoLDvmeuzcMc/i0g6qoiJK2g8QvEcZy3Q6ZX19nfWNMZOmARUpCsuw1+eU3UvM90vatmVtY8J42jCuW+rW49s00GZ1GrYrnM6DdpEQNXUWUwwCOgYiiiCR1tdIr2CoHFhLNSgpjAGrmdQ1detZ3Wgw44baB6aNR/SUVgyr4zF18IhEqsqwtDDPzuUdLM4PWZgbsHN+jkFZUhqL0xZMQPm09drXDUEcripoQqRuI+O6Zdp4ohd8SBHFap0bO3dP0ToZ663hOlvEeQqb0i6RTNREzfoM3XSpzs26TpZXK1AGokr9mRg0ShmMFowVnHFUhWFhOGR+OGRhOKSq+hSlSxyyGNN6dt8yndZ4n/QH5nt9FoYDirJAIxy58xhXr45YndYEEhVoHCI+JNKks9AvSgbGIr7BEFFBKB2URUEQxbhpMUqY7xfM9woqU9ErHTvmCnYvD1hamqcse9SjMUeOrtB6T916pPH46PEhUY+8aJykftM4RJroGdfHOba2QdUrGfT7FIWiX/XoVz1O37XIzrk+vcoQUUymntZqvAjra1NqH/M2h4j3qYYjw9NOKyT6rFzdcdw7EZDtmud+Y4WB0qq7SVCr3NWeteSS7mzWpU4cr6hJ48JZhFwrwRhLr3BYbRhUhsI55voVc3OdgIfCOQdt5m7FmFcLGvbs2oE1DuMM6xsjbr71dtYbT39QUFUOZSy1wHrd4COIBJwzLPb6LFUlGxsbbGxMEAM+R7bSWapCYY1lcW7A4qDCZVmssjRUpWGu32PH3lNYO3acYytjjLPQNkwbYdpAG9MYQJQGJWAV9EyBl0jb1Gy0no1xw+r6hKIs6A8CpRszmkw5Y98yO4Ylg6pMaxXXJ6w2LeujaQIJYl7oq4SgBIOm5xSl6fKAjr7efR+yPc9zfzKrE/M5ptCT07WTKTndwHVC4IaDHruXdmCN5fhowh1HV6hDilTaqCSpq4SysCwMS4ZliZIs3B4TW9hag8QW5yzeNywuDtm5YwFQrK6uMFWKM07dS1GU+DokwqWkcCdKMNZRlJZe6ej3ykRCRdF4z2g8ZWM0Ytqm7dUxeqbjCc4mYXarYFAWLC3MsTA/xFmBdsKwX7Bz15AbDhasnNhgXDdpjkeZWUqrEbRKHL6BtWhtZhQfUQYfItP1Ea0xTMcNvm04fd8yS3MBWlhd2+DYKD13E3yCp2OaelVRURZQmAQWdLLHCb/LyyhUGlHYCrYlnEdOInqqbiNzrnHSMtkckYD5Xsl5e3azY3GJvaftQxnDddddz1dvuYsT4zGiIxGPiEtJupekTR0izbRJDUygrBzagA5QVQWVUints5bFpQW0ssTYMh1N2BhNWV3dYH11nWZagwRKbei7SL+yDPtpUZU1BmN7uN5e2sazsj5hbWNCU48ZuRIfAzqkWRqDR+ORuiFYS6g9IkLlCubn54h3raEt1H5M06TtcwEYSyTIFIWi7wrm+wWLg4Ldi3PMzQ1BK0YbNSvrY6at58SxNQqJqF0LVNZQt2kjRF23RC+0jRB1msZ1RuiViVHQqagCm2MhGSjYInjB1nAegM1GTpeodTWOOsmXFEtlj+PXH+aa8R0snHY7DzpwCsPK8aBTdnJsveDQ+ohx4ykM+DZQVSWDQR+lApPxhCiCLRxMI1VZMb8whys0ISQGNgISkiqmclXq0VQlu5bmEL9M8AmqDgF8aEFiipyFwxqbtrS1DU5rShXpGU+vNJTKMZ5EQpP6P5qcbqqkiyaSZKGM0py6bxExisPHRhw+ssLa+pRgI8o5JtMpkxbG45YyRo61U3bUDasbE+bLFU7ZtcTp+5Y57dRdHDx8jPH6GsOyoDAWTdr4MGla2pARtpgXJ6sUdSpLbkxv/qsyCXe2Xmy7z3P/sbtzpjukDbrhN6VSPaS1ohBhElqmXhgdWSfqO9m3o4dExVzVow6K2q8SY6QqSxYX5hCJTOs2MaetRVtLVfRS7UNEYuqLYAxKFNpYVJbgcSSaCzGiKDKXqE17S0XyFTr1p0xW5yQqppOG2EwwIaC1oomCCakXpE2BKRzWFKDShKstC3zjUTGyd2mB3ftOYX3sWTt+nMkkQeKtb5jEyOq45vjGmLFXtI1nh7HE6QYnVjb4xvgQ62vrPOTc0znztGXuvNVTOcfcsIcKAWXHjKctoybiJffOBOZ6UJnkSKJ1XjWSvp1uUVgCCrYFQO5XlvQl1OaEbx5PyLs8IOslW6AqDbGyzFnYdeounFWMpy29siC0NaWCHVUfWxXsXpqnX7g0mEZEu6QpXRZlqk+mDfXYE2ObHDSm3lLqAaUrrZY05mCVQVuHVqnmsJlwmUQZDSHXIiEGxvWEjdGIyXic+k7eExqPBUxpmVvcwfziPAObgJLCGcp+j3Z6DC0BqyzWKQaLA5YX51g7scp0MqFpkxPtX8oOq9MkrBJFnXXb7jxylOPHV7n11js4/6Hnsrw8j7OGfr8iNEkpdDJJwvMCVNZgjVA5SZsScrMa6cQnNwcUNWl/6RYhVW8N50kToh0c2t2YHEfloThIiNXc3BDGaQ3Hqbt3QAxMRTEZrdNMWyQIe3fOsfe0UymUMBmNiIWj7JU47bBoYhOY+Bo/bWmbCdG3uaEpaStbnmexTmUYvaIsSsoqiXAUVUWSHzFo0Zl5rNM4uPfU05Z62uK9SnJUxtBfnEur3gvHsN9j4ApKoFcYKltg0VS9BaZrE2hBh4A1nrmFRRZ2LHDi+DprJ1aYTurUJwqeqj9MO4I0IAP27V3mwJl7OXpijZWjRxn0eywuLyUun1FMjWVpKclwhehRaAY9yVqmMXlLBh/ULF/rFo1tdni2BUDuRyYnJW5pWDEzqLsvsPurUhhbUJYFu085hYV+nxMnThC9Z2NlnSCR+YUB5z74AFXV5/DBg/imTSe3T9OjsU3piJ9O0yBb9GlYLbO5I4J1BVFCFiLRFJ2KohiMMxnVyyeW0ihTJC252CYVG2UQrXBlibEapQ1Vr0dhDM5o5voDbN1SKIWNBiMWvzFhbnmZGCKj48eIjcf0HDpEej3L8IxTme7dw8b6OvVkiveJQe2MAmkTLUkncujy7l3cNd/HKmFuUKQlVzEQo0UZQRdJb9pZScOAIdUxUUlOyTItp5sR6cZD7j6O+IC3LeE8m5eydHXrvrNOsEXnvymTWAPV8iKDSnHXHXeyOpqysr5B7eFRj3ko5zzoTExZcNvNtzJtGpBIrBsa36JFURaTNCTXBrzvRhvUbFguSqCNeSV8MKigmap6xmiQWBJ9xFqTNd4MMbYQFW0QxuOGNnjQGmNCEuJA47WiNxgyv7iUCK0+pBTQWrCWMPWAY355N6GpqesRsalpRitoqxgO5xksLjKc7zFZX01jGiEtu9I6UXEkpKG9CMyddw6T0QahrgntFJFIiJ4QQgIstM56CKSlxZIXjGUwQKskCUYndTzLAPSW8Z4t4TxC6ids1jxqFoWEbpZHYbVJCp6x4djhY6yub9D4iNGKU/fv5az9+4ihZbw6YX1lBd/k9dLExIRGkmSUKGKTxqmTlprNU5okZ/OCIa1ftNrR1jV+OqHpDTBmgrMGVxYYrWaNV7RhPK6ZTFum9Zi2bgBFf26Bsiwoqj5FWRCVZTLewESPdg6DQqYN2oD4Fjc3jzElijFtPUXREiaOMB1hrMIaGAyT4IcIKJM7yaEg+hSBfDuhbhrcoIKeY2OtZTppab3n+MqYto1UhSIGaGoPPtGLZmqsKs9QsTnFmr4oNWsrbAXbEs4DJ2dnnW5bukEDSeM54mMiUzpjUK6kKCIQUNpTaOHE0UPosmA8aainNdEHlE7KoUhqYopKa0ZiiEj06fWiQtMk9c88yEaMWKMwqqVuDdOpphp5itJRlo6y9ThnKGOJUpY2BjbGNePplGldEySwML+DqupTtzUn7jrCaHWdtm5wSrMwrJjr96iqHnNzAxaXlwhKQT0F5THGpUVVbUtoPe1kinUZBUwruvPwX0LvFEI71YR6nP4mCt9MMpRuiRF8EFZWR/S04pFnnMZ1B49waNTkklJhuhEq8rxuvngpUTngbBJ1toJtHeeBmdpn9/vJf0MUbduy0dQs9nvsWF5CqzU2NkaYkEQFR+Ma3TZM61T0Wws+hLQdIGwKwPu0bzGtbQ+CBE9ayBhyBmnSdmqV+hsSQdoWpTyiyzTT33Z7QltCCEynDZOmzly0xJs7dvgox05ssLoxYuLTqIIBCmMZFI65qmJQFfSqgp1HV1g+vkp/WNFOxhTOYGxJDOPk7N4TfEg6cCoiJvWitBhi42mnI5rxFD+dEEJL8B6JdZrn8Um3oa4b6umE/TsXOP+03RidekmS9okgXRTrIM/8uXf9t64y3Z7nuR/ZN5U8M3KBUuTRkbTeovWRWiUPGM4NUVHQKtLENPtfT6aMVie4qoc2FmsV2ntUa4jIjIUdQhrASc6TlG8iSbkTdCKhGpO2yEnazemVIihLUl8HZTRRK0bjCZNpjfcgxiCqINJy7K7jHDp0hLVxy8G1hsOTiHKaHZVmR2XpGU3PmrTjtHAcPrbB4tFV+kNH5UxiSQ/mqPplFhlUhNYT/QjxHqUjrhoQpU21ThMIdZtYFOIRCQQfE5M6BoL3rG+ME5OhHnPD12/kpmMbeRapG+jIn/vsS9lM2XJwSrjJ9+vE+B7blnAelEp7ZE76VpRA7PLtTEhUEglEyl4BRrO8d5leZVKBbm0aK1gbMdBF2hCgkyMIYdaMxCgUPtUHSqGtS7SZDNjqvMdUqYjGpLqnMFSlo3SWoldhtMF7GK2PaEJAWUPZr7BlmWaJppq6VayPIpM2y/0qYdoEVhrPdOpZ7DnmypZx27BhLcc3xsQja0y80FPC/sWSs05Z5vQz97G4PI8Zb+DbFmUN7WSc6qNilNYjZuleXyeNhSCeGOs0MtG2M5b2aNxQthBiw1fWGm45tkanW6RmUHRG2lCb/sNJ5A+9DVXfr6zjtKmum52veJ3wYacmCjD1kYWdO9O4wME7OXL4KE0MadJUW/q9QRIRyc+rjcZJkTbGETFB0YohEPJK9yyUoZKqprMGaxMcrRQYbRn0KwpnksKO0jR1QzNt8NGjXcHccECvXyXSaIR+v0+/12NxaY7jq2tMmsCkDaxPatbXx6yNa8aNR4mmX6YjbXxExLOy3jBqPWbkGGjFntOWaVvPdGOEdS3a2eQQdcN0NE27gGJKedO/ES8BET+TCm4aTxsi80VFtbzELasjbj5+FB/jSeta8iCi6no5nWxwLoRmm+Fkmxh6f7KT8Bw6WPRue0dPutStb4wZN4HpiVU21saIKyiNoTcoKKqS6aRmdVKnDryC4GPaeqAVIba5e5525Bir0AFQGmc1zoJzFudKjI4UhcNal4pypREfCW0ShfdKsP0ecwsLVGVqwKLTVKrSiqIaMpjrc5pvGE8bNjZGTGrPdNSyvr7OiRNrTJuGQa/HKbt3sXffMnPz8zQbY1bvOohB8ZCHncfufTtpJqOkfCMNuvVpLCAEfNtm2aEsxxUCQlpaHBVpYRaCF0XhCvYMC/Rggeunt9Dmddrp+iSbn3VU6WKFnETPOSkyzQi7D3zbEs7TxZrZMFz3pUkXlUAMSFQcOXKCL331WuaMwbpcM/QrrBE2VjY4dPgoY7EsLC6ijUEpn9I3rWhjF00ghLQKHp2iS1k6rDN507WmKgucq9L0iggSPU2bpHlj8BT9kl5/CALTaUOtWpSxeQFxXqIbIko7ClOyc17jvafY5Sh6Z1GUBaPRFI0w6PUoKkdZFihZpjr/TPpzA8rSoSXiSkM9GiNB8E0zu7i03kNIUTciSEgbH0L0CDqvXgyI0lRGM52OsTh2zs2l5m5OhaPOm99mFnP52bGpyWtJ062y7Tz3IxNmWtSzYHPSclJRMls74n3g5oN3UrqKyiY1nMIZCEI9bVib1rSuYL8PWGMxOqnNoJIwiCfv5QwpHbRWUziblGVM2gtalDZtJ4C8EEvh26RdECViqgqFYWN9RFQ60XmcBRqEvC9UKZwr6FlPVVVUZQ+jFUVZUlR9lNY4tZpY3lbRK3v0emUS+si7gSQElBGstYSyTGIfKNq6wbc+rUKJMTtMes2QOWto0pa3kDfU5VFrSsvxlVVU7IQ8dCdGlP7XTSDOIg/pe+l6cCdlcA902yLOk658m/VproHylxc7lk6WoJIotPWY9Yli4oW61WjlOGv/boZLmvXxmPVxzcAljQBj0z4drUxWtRFMqTCqwNik16wRRDyuLECT2Afik3ZCVAm5agFjaeqWkZ8QBLwEUCov5E2ghNGKwmoUkagLiAYJGmNKECH6tIvHGFJPCTBWJ20Fn7YhBFoMkSYGNKRmKoItLT400ARExdl4RB5aT4vCJElShSwZNej3KZWhFsPhOnDNLQeRkNRPJeqk6yagI7MR69m1qzOVdaozMrcVbEs4T1eCdkwCPZsc6fAfnR0sjyd0zqWEwsGoFbCa3aftYjqeMm2FxguSh9tE0lZppdIsDcam2sRqjEvMZGLaDYpRJxEk08iCb6fUdU3dQmg1k6ZhY9qk9R5WU1QlxgjEQGjr3H9RiSEgitIZBmXJjoU55hfn6Q9Kyl4SX3QuM7UlEn2LD5HGmKTRIAEdBacFm6OCxBZNA5kBEW1IovNBMjCZUjERjRdYmJvntP2nsXZ8hVuPbfBXX76aw6sjQlIuBpLge9Rppic9Xs8WJHctndwnzS66NULPlnCeZN3e5a6r3e0nZVaninSQ9SY6ZEWxs68Y7Ogx3ytYPb6GipHCalrvcQ6cMWnteqPyVraA4NJWhMymBo22ltBGok5In5KIBM9k0jCa1tRBqL0wqSOjaZOoPtoi64HJeMp0WtO0HvEgojA6YpTCoujZitIepF85FgZ9lncuUzmNMYai1PTn+/T6JbYqMYUlqiRZZRSUWuOUUBUGTURrC9TE6DHGYEwkxgal0yJgLxBCEklcWJhDW8Pxcc3fXX0d1x88lKDtjjkgSQsv1WrpA58tSs5ctk7FKH1NJzVQH+C2JZyna8p1umCJIpIiy0xeV23Cz52bnazq0i8cw7JgrtD0pICg82YDCD7g6xrfxpRKRZ/JpwVRJG0akCalJmnXYVL9DEIzaRjVNXWITOvA6njKytqEE6OG6TQQQ9LHNiZR+60xDAYFg96AuX5JvyjpO4NTBt8mTYF6UnPozqOIttT1lOlkjHGaQb9kYa7Pzt1L7Ng9j+unRb8TCVTGMZ0mByoKhzEq6auRdq0qUyCQ5KOUBoksLvSZmy8YT0Zcef2tXH/7XcQAiJ61Pmefa5JmnV2UdNxUMTp5Ml6USiKUW8C2hPNEmLlEN7uTpjOT5FSWq85jjipJ6eY5ky7l01pTFQX7ds4xWhvRtmOmbR9UTB35KHgfAE8nz+NDg8EyaWusTcNxiOBscoS2mSZR96al9YHxpOXE+oRJ6xkUFUs9GPQtyzuX6ZVlEsfIw3MKjVGWnjMMexWDsqBXVQm0CKnY17aiblpW18bcdfwIN958G7ccWWPu0ApLw4Kzzj2V+V1DmrrGoOkXJs0DVZ7CaZRKUoTaAjEtokIngZMk8t6wfuwo60Fz/R0HEzon6ZPtlEDJn7naDO10K5VTRMr36RC52H0zD3zbEs7TQa9xBvTk/D12IEKmH5ykVpmg7XRlNCqJvevMvF5YGBK9Z30yIbZJcjat8UknQIgR4yJGcpEeFG0MxFijooA2xNYTQ4tLa7aJEigKy77lBYSAjop+r8jqNy7tRvWJ2t82DXUrRB8ZiWd1xVBqQ1mUFIVjOBwwHA7p9y2LSwOWdy6wa9eQvbsW+PqNd3DzwSMcX1tnFFrO9KclDW6tkEGZLhdRsPMlSmdh7o4hoBOVSKGpSkfVG2CMZmMy5djKegIU6HwkOUcK9pIUeHSWKp5dulQHvKE7+WN9Ugr3ALct4TwBCN2Xd9KY78xXuvWKclKxmvkiKSApev2SENPczXhtRFWUKPFs1GOUJAewRqfhMTSxFcREdIx4X9OKJF3n1ie5pZjGpqUAUQ5rSqrK5NTSpw3YRUHwacyh9Z6mbmh9S+2FEGQmZauUQsWA+IgmUpqSflky6Fl2LAzYsTDH3LDPGactsmvngAcf280tB49z8OhxvvK1G9i1c549uxeoSpuE5UOkbdKSK5U1CMhi88SIUZGesRRFRZBI5ZK6T/exJS3wTUKhSK53TsqRO/UvLZqoUhM25mJzGzC4H1lLEi/UIW8+yIqgHbdKQVLuh9mX2n35CoXRUBYOZRSTyQYhCusb65RlxcpGO9tXY7TCGY2xJkHTukSHGu89ddskQKBuc/+kwWpLv1VUDopCMDHJ+NqiTOs8gtA2CSRofaRtW3xoaWOWboqSRq+rin7RQ0KkLAqsq4ghbW07NhrREJn4hoX5HmVZcuq+IXt29zi+usi1tx3mzsPHKa1iYVAkXWKdGOYmRwitLT6m/pKgqMcNx0/UNAs10TmuveMoG+vjnK3mFE0LXRhSOiGTm+7EDLkTnbNcyP0eSQSELWBbw3lEoUWwpO/TkLTDusGs7suLJzNEcnoBCtGKwpUoMqtZJmlJ7bShcpq7jq/i0ZRWU1hHoQNl4dA6gNEE0vh1PW0YN562Wx+PxztFrCLgsNaincuM7EgIgdZ72rYhislHYyh0SBsKlMZpQyEBi1BUjl7lUhRQKm3Dzqvjp22LHaeGcK9MUsE7zYBHLZ7NidE+fNNSFnr2AUx9g0EwRqMly2FFRROEq284xHhlgu0XHJ003Hj4KKON6aZX5Agz6+mQarXkIB2GvYl+QodynlQnbQHbEs4TVKTVSRDdxLRUyZLUXEzuaG92uHPSoDo2QpJ/atoapSLWWrw1xDZBuaH1GGM5sraBMZq+s/RdmYiPBMpqgLVpYW9pLT537X0UtI1YV6FdD+2SrlpemI1WFjGSawYDSnDaEPAQJa1q1AVlUVA4gzOG0rq0Sa60RIm0TUPUBmWSWHwUT1srSqtxpmRubsAQYX5+wMZ4zJFjK0RRDHspzTU6oWM+hjQfh+LYRs2nbriT1fURSkHMlKT8USUNSUVWGU0AQLpiqeQ3CCgDEjOro2N40DFHt4z/bAnn8cFktCd1r1UUHEJUaXGUzQ08HbuTlZRuZEg7KsWR9Q1O2b1MjAFjLNYVNJMJMYKKmlI7VqeeUV0zcA2tVPhYIEwxJqKNoywtSqcIha4wWuh3J781GAMKTchDbc6WFHMuydVKlqiNPUKIaG2obElVOnpFlUaxnUt1lyswTtM0U0brG7QhoE3i3CkghEgIDYVYgoA0LUYig8phtSbkKBBi2sIgeQNeK4Erb7iVE+sbhI65gKBM3mWU68dZGhZlti4Rcq9H5zWKdPfPOg+zBqraKpvkt4bztG1EK5NAg4yvtQKFgiJTTpzK6QXQXT6j0njtaGyfm4+O2b1zxGJZIGEDawzBFah2RGmT6n/faEbes1YLbaypyzTaXRUKqwSlHEaDqxxFVabdnBmoUOQdQEowKvHPiiItmnJFkWHqdGgikeADWjT4tAvVAkYCVili21CYPrsWd7FzaQcbG6tpDsd7fAg461Ba0foW0MTQEtqGQiVHicEAgomC0obGe2rvuenQcb524+HEraMjoqXPONEE8+c3myjIwx9db0fIF6icwnUO13EO1SbzYyvY1nAeDzPtfaVnTbhoyAiBYKwiZsQ6Xf00jS6pTR8fA+2o5svfuJXHPOgUKuuopyOMMpRVHy81pZ0ybZLGdO0D46mn9pFRG1gcuAQlW4/TYE1BmExoGrLQusXoNOtTWkPl0qhC0wYUHgUUrsh7hDQhCNKkHaBJhERTVD2UhbI3wLgSC7jCURR95hYGtO2U6bimmdaJFR0T7B0ljXmHtqFuJIk3moDVGh0MGgiiufXICf7xqzcxnk5Bcu8LEs1GTrrsqI4GpTkp5JAbPJ1oxEy5SDJRNA/0zriBW8G2hPMYkq6AxI45kK+ECDYm8CDotAEhZXea1hZMTYGXZuZ4x9Y3OLY+4YzlIVL1GK9vgER6TtNzJRvaoyQ5iA6K8aRl6iNtDFTGomPSarN6itJpnSLi6ZWOXuGoioLgIIrBhzqxnX1C6KxpKIzGGI0SgxBRWhOBySRtwS5Lh58G+lVFVZX4okhpnDWgHapnMCiadkrt0+h5Qsg0EZWADB+wJmD7PaJA27TcdOgE//iVG1lbn6QmqDKJMZD5aScPFM5Sr84BTkrlUhO6I+l230WG2juQIYsibgXbEs4zXypagUkrtD7ic8/HBhCTv+aOPW8Mra2oVUGIILSZgZCojCtYLnnUY5G1E9x2/fUcPXyY0DbMVwXEliPrDdMsbWVRTKfCWhMYW4BADKCoU/PVaPpVPnF9oG49U1vSK5MgutY1NhokSlqqZQqcUWidRgOUNnk61TFtAr3WU09qpsUEax3FsRPMLw6Ymx9S9BxKk6R+vckzRw1KkvyvtQ7nhDLXX8pZgmi+ftsRvviNm1gfTYh0O1ljjhQqO5/arBXJgFrooMuExgTIK0WSJnjuEmxa1xsVUHprYNVbwnmWBpo2KtYmgRFC9BkEEpUX5KafW+PwtqJFZ6XPPLuCQmJKl6786s2MasVPv+hHeOpFj+b2a7/OjVd/jWN3HkZiS4hDjo/GTL1QKYVuFaM6EtoINtF8RDTRB0yMWAtjPLEwxBiZtp5RPcLpiNURpwqMSiKBWo8pbIlVhhiFum2RGBj0hjhrGIY+ldVM24izLc6atBDYt/SGxWZkDTEtC/ZpVCCIwhrFoKoIYZoibyOsrKxx1XU3MZpMiB0HTWWRdmbgWMrGhBnAMtsedlLw0R1lBwUSZ6teZj21k8KNbBERAyVb4J38yCOXaSWwOvasTYT1NjLxKZ0bWEVVWVzPIbYgiN50mm5eP4B4IUhMRE+tWFic59JnPJlnPfUJLA4HrB8/ysEbb+TGb1zPzTfdyuEjx1gbtTRA3XgmE6GNqTmLUogRjDXJMRAKDYUpUkREUCpiDBS2xGmNocVoRVlU9KzBWMd0OkZCguCVEub6Q3YO+wx7BYVNkiO90jG30KN0Se3HmJQ0BQyTpqFumvS+YlqUJQi+EcI0cN2dh/jSzQcJQWhECDHNy8VwErOBvO5edTSjSBtmDChENKiYIqdKS8Z0Iioww6fz8acGdcoK/vHGjR/Y+XJf2ZZwnhc97lQikZXxhNVpYGUaWZsKaFgYlrhelXL2mKY7o9SIpEjgIzO96Rg0ia9gERGsUzzkwQd49rMu5rxzzqLfrzBaM15f5/abbuFrX/4q137jBo6cWGU8DkyahqYVOh0mDWk026mZnnXMxbdRgjFpI7czGqdiXkxcUBYurZ0PCb4Okk5GZxQL/ZKFKu09LZ2icpaqLOhVBucM1qYlWx5hYzJhfWPCpE6EzsJZ5sqCvnEcX9/gU9ffyonRlEjeWZp37iC5flSpftE6O48kSN1nBsTJkcea7GQ61ZgJpZZ7wtIZmfv0TaPvy7nxvbQtkbaVpkQbwcdAwNPGkK6GvQpbmFlqFmizQoxCYki6axJP4ltlgqNK052+Fa766nXcfOud/PATH82THvdIdu5YwFnD/gc9iDMefC6PO3qMq7/yda695jpuvfVOVk6sMa493keUgtAGgldoB9Zk3YOQqoskLKrTCaktOkasbmlpE71Ip4E6CUnZRoXA2HvWJ57VpmZQGHqupN+rGNSKqrBpOM5YRMPauOb4qKGuPYtFwe7FAfOlYzT13HDkBCvjaRop6DwhR8l40lpKOo16FVPtk3ikM5P8uJjndiQ3RQGk86Jc73S9ntxNfcDblnCeoDXaRKzVlE7TqxSxZ/HKJtazCDH4RKKJIdUkMZ0kKqt8Ksn7aiLpMSelHcdPrPP//MXfcfU1N/CMpz6WBx84i8K5tNy3rLjg4ReyvHuZsw7exdEjx7j99js5dPAIx1dHtD4P3zURMWAMqWHqFMaYVFdILnpU2lIgSgjRZBAjZOVRMiMgUhMJdcMkWCoPZRsYTg2DfklZCEUptDEwmia1n1075jhn5yIDq2mawJH1NW4+emIGQ+tZJFFJCCTP5QDpxJ/RAzbZ0kok7xvd7E+lzE5mzqNltgsugw4nYdZbwLaE83zl6Do693QiCh8UYjQmXeQR0n5QyROQIQiRzcRdo1M+HoUYI1HSxGUnHIKAxMANN9zGwTuP8PCHns+Tn/gITtmzBGgkCMP5efpraxQbI84483T27Vvm0KGj3HLbUdZWx/gQ0SGNL0TvcUFDAc6mzQ3aFGAiBpeJrcl5ugakUomCY4zgTN7vozQtIUlGtRE/FfpEKh0pnWZ+UGB1ya65ORaHFaH2BAKr07RvNE0dxRkyIFmzoIOfgU0eGzJr8qbaBzJMkMCEDPgnyGBznqpzE5XDz7bQ+/3M1uuGGNK6DmMABcHXSUVGaZKOdOKQiSRxd0jrNdJUmcpX1+5qmteU5Pa5kjRqLToyGo/5x89+iWuvv5HHPeZh/NAjL2BxOEArzZ69e/Ct5+Ybb6FuGubm53nQ2SXHjq+wtr7BeFzTNoEQoG2Tdlq0GjEKaycYo4lWsLagLDWFK5Pkr9Y5e0oo2iwNQqG1ozCK0pI3azuMcXlhltB3lkFVYZ1FAhgbaaPQaTp0zqAk9YVixwLonJY0Zq2MykRQnR03PYeW7khmnITc7pEZXapLm3MYmzVcH+i2JZzHGEXh8kVSg/dCU0fqiVD1FM6qvBjAZ+pJ0lKLXeLhQ9JWy1dzpQQJOiNviVaf/rY57HXs2Aof+ctP86UvX8fjH/swHnnBAcrCsffUfXjfcsP1NzOd1iAwNz+Pc4rFuX7Sy562jMc1dd1mJ2qwUeG0xfkWdMNoEpkaS1H2qMoiDeuZJEqIShpxzjoMQmk0CwPHwrCkXxX0yjIjxpFeUTKoXBYrsRQxUhV5cXDMpFS6DXrZWZQkFK1rhWbe4CajutOB2FT+lE4sootc6Nke0oRq52ijOxd74NuWcB6JgC1m9Yt1CmchBEXdtjSNQpvUf0DyBmnIFDedexcpbVNkmLW7uiKbkrIwi1BISr9uv+NO/ueHj3DlV67hhx//SA6ceQqnnH46IobrvnED4/GIGARrLFpprDVUZWRuvk/wgWldU0+bpCQaPCGm7QyFSQBBPRoxWR8RURhnmesX9JwQ6kijNYNeiS4qrLGUZY9+r2RuWNLvFYnq06a9pz5mHoUhXwyYLT4WOoXPTrwjvUeRvJBLFFELWoccgVVmIrA5jp3CWPqgus183Z7YmGOT7rRJt4bzbAmo+syzF9OEp0560Gl2xOODoa4944nHWU1ZpQI9dhBSTJJSaEEyhT7VQal4n9FTkByZIhJNclLySScdmif0ewXnPfgsHnfRBezducjxI8e56YYbOH5sDR8CadM1gAEV8nMnpxYfqdspzbhm2vrMMFCUxiZHkpbgE5ihnWM46DGoXF532GK1Zr5fMj/oMTes6PX6DId9jKRRiaosUFFR7Vii3nUav3f5HzBaW0VlsMDnWr4NkRjTMcXsZUoprE0XlhCFGLoLCXSLxAygrWA7ofscqWIX1RDE5G1xCj573QO/z7MlIo9RBUrFlJpJRKnUpwlR0+3siVFoGsFanWn06SobO4q9iihtiTGNIwuxY9N3Mmzp6qkyuCAxtY4QNAFEMZ1M+fKV13DdDbdxwYPP5ImPeRgPf/SjOHjbbRy8/SDr61NCqJMIRm4cgkZri7aKolfAECS2NHXNZJyUcsZeKI2h3zNY5dME6mjMRq0Z9CoGlWN5sc+eXYvsmF/AOY21FmscRqdFVUY7RBv2P/JR7H/M0/jy1dfyt3/xEUTFGXmT2C2gTEz0TZsVWTO9O4k5+tBFsXyHmCLOrGjKgitCHkZUCpT5/pwY32PbEs5jXSJHhphoNzF2Xe0445gpLWitiD7ibPoOo85rQcjpnJCiAKnPg05XSh0kz9sYTmJAJqROsoflyU6tNKONDT73xa9y86138uQnXsSF553DGWfu547b7+T22+5gYz2t+4g5bUwPT6qh2kas0vR7PXYspknRum5Z39ig8S39smKhqBgUmt07F1lcWGA02aBpGrSoJA3sc+PXgK1cIqhqw85zzmH/Ix9Pb24npx94EOi/zCMQqabrQISZM8Hd5nVS6qUgbI4WpFUuzPpkXfoW1UmORVf7dBH9AZ/sAFvEeYw2aUWggqBtboDqPMCVVgKG4GdXymktuCLBtLNORDe0pVVO31K/Ja0pScN2KkYEmyJGl+PrpHGmiMxWx5Eg7kOHj/NnH/kEt91+kGc97Qk85GEXcPoZp3Hj9Tdx1513MZ22RN8mLhpC0mpKRbwx4FzBsD/Pjp1L7Nm7l1179xKj4sj1N9AevYu+g7lBj3275sAo6iYQ2jZt0E56XPg24quSsx/9WM55wlOpFk8hRiEogyr7CBNUaFExJqRNSZLQzaMcXUSRk3s6m0EFgdnwGxkgEBLLPaLT4zO9J1Fz1N14bg9k2xLOozRosznFGKLBxEAkpI3TEhhPTV5XmKJJ3UacS+sUtQqIWJTOSpgEwMyK4Zx05AI4qZoryU6UAQglbUZi203FXYS2bvn8FV/j0NETPOtpT+ChDz6Li5aWuOvgXdx2yy2sr43wbc10OsW3Eas1g0GPPft2cdaBczj73APsPfUUFpd20R/MYwtH2zQcu+MObvvaV7nz6i8yWTtBv2cZ9kvqCTRNSwyB2sNgbi8Pf9rTOOORj8f0llFZtVq5Ib63hLgW/BR8i6oblPeoGGaFvSJdlGbRZwYGkOs3ddJWsQT5BwEJWabK6NRH0wptYFtu935mCpu+TeWTULqyoBUGSzTJYYQx40kCBcpCp9HjEFE29S80GTCAVDPlSLOp92ZmAhYqx6woPqV5SuW16kJUOp9Pm03GKMKtt97B//jgh3nUox7Ks57yw5x17gFO3X8qd91xB8eOHCWESL/XY+8p+zjtjP0s797N3PwivUGfsurhbIGxDoWi1x9w2vmPYOn0c5k7fR/t0bsYH7yN0Z13EZQnaIXqV5z1sAs58KgfYucZD0IX86SyPmlrn3322WhjmbYBZSq07aPKNF1q/YTQNETfEH2bWdHMEMku5MycQBRKdyhkdiyTpmjFqJwyJ4aEUha/Reawt4TzpIanIzU/c6tTSUK3VGqeDoa9RPNvUo/H2sTuDU3AFolMCSoV/9k9JE+TKWWSkHrsLrZdr8Omf7NazKaQr05omsisL4TRTOuaz3z2y9xx+2H+1bOewg896qHsP+MMrLM4V1AUJUYbtDUY57DGJMWdXIAnJU+LMpZpM+Efv/hXHLztWs44fT+nPuWJ+JUNjt54E6aqYNDj9Ac9lIW9B9DFIumr7sAQxXkPvoA9u3dy0023JFBFu9TgNBpl+9hykD7P2CA+qabStsTQQCuoEEFJhvU3ZY2VTp9rVCrz85jNGXWf8VYZxt4SziNKEnFREhKQhAUzg7HrzWnDYNhH1se0PhAyj0zZpEPgcsrWFbhKxXxCaJBAmLXcdebCKVCBjjmpVCSISaRSNqdZlRY0Omm+WYM1hmPHjvOhP/sox9fG/PSLf4IzTj8N5xwxRpp6QtM2xJA3uOVxApEGi0Upx7Sp+Ycr/pav3fRlrBZuPHIjt6/cRtWvKPeW9PsFdT3ixE2fYXjwes4+4yL27XlQWlGSj2vXrn380GMfy8233A5BCCEkUZCQUESjTRJ+NxXKaFTZMQoC2guhnSC1T8BHCCAZvjfdY+NMukgpM2uiRkmjH1vBtkSf56EPPZ2iKFK0UOnKL6EhRYNEydEiRBXxXhiNpon1bEg9Hp+cwFoDUdLMjyJJ0WbQIBGKNWCzekzeY5q6HwkOz4Wz0pmNoMmrPRxatShtUMomF1UO5yznnHMmL37R83nKk5/EcDBM3XiJBN/S+rypmog2lrLogxL+7rN/z9999m/yCkfFwuKQ4Vzaa1o4g9aGsioYDAb0qh6j1Zqe3sWF5z+FwWBn/tQin/nsJ/mVX/5l6vEYpTUhpB2rkGscrdDKYJSgjCVp6aR6UCmf8Y30+UlbI75Gx2YGqKRmdBI/iSQGewyCF/jKtSe+n6fI98S2iPOcgSu6dmY+OSWnbYCQt0BJJGIIIVDXNW3bkPt3tAG0sTkCZQZCSAugZgRKACxJHUYyHA6djJVSBdoIxiSnVSrz6EIBqk3bFIBUe6RaQGFYmB/y1Eueyk+9+PmcfeYZCVrPELaIEKNHG0sU+IfPfpL/80N/xGTa0J+rWNoxpNevmFuYoyotReno9yvKXsn8/JC5uSFKW7565TW4dsiPPOslLO08HVCcWDnCL/7Cz/KlL36ZouyhtSG0LZ38faLo5AuCSSseTa7pOhHD5CjdKZTGO5R4iGl9pASft20LPiTnCcHytZuOfu9PjO+xbYm0TemkyBklbSnr1v2lWfnN9X8JVgVMQVVGnFO0bUhqNRJp2hblFFXpUgSIiY/gRaFEZ2dM9U2eIEBrR6p/Ohp/txogT6UqA8qjlU31UHdsKs6ef2VljT//8F/w1a9dzUt+5l/ztEueRL/XJ0YPaKwriWi+8OUreN//+YdMJutoa5mOYc0YvA+0vk1OU6Taqa8tSiX0q209x46e4Gtf/DzHDh7jZ176enYs7WfQn+fJT34qV1zxZSaTMWXVwxiLD3mFik6xO0QgpEgTdV6fohRCWsLVAS1aCaisuqo92qYIZmLA+xbVTJFYIyp8H8+O751tCeeRmK7UetaESJEmKbWkk1ap1EtJ2m4typgk6FemGinEiKsD06ZNe0OV48CBh3Daaadxww3Xc+ddt6SlUOIhgwqJxp80p5VKMlIxGkRFzjjjPM4//wKuv+5arr/uKpTN0liZxt9V2J0wrfcNN95wI//f3/wvXHnVV3nQgfNY3rUPpTTr68dZX59y8PAhphs9RtMVXAn1tKWZeibDEnMCyspRVSVRkmdXVUHwMY1fhwB14IrPXkGvuJz+4oPYv/9c1tZbrCsYjdeJk0ivGmBMis4iyXmTLkOC7r2fps9ap2nX0J1CKvfGoqBNREXPphJCkhkuXUnZF4Jvvt+nyPfEtobzSMrV03bDrjueSfzi0arIy6h05qUpTJ5NCVkAUFuLMxFrYDKNROOZGy5y6ilncufBw4gY9u45nVNPORWthePHT3DLbTfgmzqtRBTYsXQKZ531YE6srvKsZ/4rXvtzr+P3fu93+c3/8nUGwx2ceebZDAYDNjbWuPmma9kYrQEFu/fuY//pZ6KV4tDhQ3z4w3/Nvr3f4D3v+WOMMbzrXW/n5psPsn//GURfcezwmAc96AKK0nD0yCF2L+8nBM+dt9+ItmtsrFn8eIl2HVy5weJOy44dO6jsPEtn7ef6G49y2v49/NRPXcx4POFBDzqPK6/8IqGdMhFNWRWp/pNEo0nTtpIBeptG1qWFWGC0QnRMg3uSNmeLh5TeZtRSBAiZsW4wxZY47bbGbtUoMRE7g6FrNySURyeNMWkTRJu1k5Vk+V1ToLSeNUGVUpSlpSgMIUT+1b/6EX71V3+Viy++mLPOOMDv/957+Tf/5nVcdNHjeec7L+e5P/LCtM1AaVCON77x33P55e/l2c/6Ufbs3psmRZWm31vkP/z7/8xvv+P/4OEP+yHe/rZ38Z/+039jbriLH3r0k3jvez7Aq1/9ei6++Jn8/u/9EU+95NmMx+O8wl5z1ZVX8ahHPopf+ZVf5dJnPZvKLfDvfvU3+A+/9k4OnHERL/upX+Atb/6vnLbnYfjxHD972a/xY5dehh8PecaTXoKenM2VV9zOJc/6Kf73N/8XLnj4xcwvLqF1WnPyzGc8g93LO0Bb2tjS1A1NHXM0N7l+JDM0JEu4BVoJtKEhhs2RDonpu0gfaSBKi9Bm9VAhRE8IW6PPsyWcJ02BmvTFZP2CVODH9LNKdY+m6zXk8ewYEwPBOFJ2n5p4ZZFkc4uioN/v5x5MxcLCAoPBkC9/+cv8+v/n1/j0P/597pykTdannnoqxhg+85lPcfvtt3ZHx/Lybp7whCdirePQobvw3vO4xz2e0/efwyMf+WjOPutsvvbVq/jvf/B/8IEP/DFr62tptXu29dEIY206FmtppzX9Xg+tNddccxV3HLyN+fl5lhaX2X/qAR7ykAvwwXPs+FGKouRhF17EbTeeYNeuvTjn+MqVV3Dwju74FI965EW8/udewf5TT0kMDR+omymT6QQhUrgCZ4u0P5U066O1zUNvMWtexzzCDUIgSFrNMgP/O8F40cS4XfPcbyzGSAxt4hwqhRKTa5wMFEim16hMFiVsUktIS29Fp9ECEYsxLa4wM06KUnDd9VfxC7/ws1x66XP55V9+I9N6ynvf8/scvuv2zEmznERiudvxpUW5SQ9tbXWF3/3d/8ZgMOTWW2/BPPkpQLoAHD18kA//+YcYTyaU1SA/Wmjblpi6kWit6VUVHRUmxqy3QGpwFqVLxXyMrI9WeNd/fQdN3bAxWk/ETNIuoqJw3aeHKxw/8twXcNqpp/Hffu+9fP6LXyHm1SdaCb2yxFmL0YY25LWOojOZVRKXMMTEh8vSwilhC6gASvuMYMacEWxHnvuNCTKTj5LYZma1oJRN4AEmU0oyW1JcQoUwebwgoWZaJw0ypS1lWTFbQiKwa9fpvPjFL+Haa7/BW9/6Hzhw9gGe//yfRFuHZDnb7gQvy5KiKGbH1zY1k8mEum742tVX8aQn/TCvfOUr2bt3N2trq8QY6fV6nHX2+fze77+fy172s3ljdc5+ZFPd2VrH8q7ds+fWJ63siDGyvr6G95719TVuuvEbPPdHnsdP/MRPMr84n5E+GMwPGcwNAfBty8qxWynKIY969CN561vexE/8+I/S6/WBgroJTOvEVNfWYo2icBbTkWCVSkRUk0GFGNO28CgZkSOBKJm9vfnDA9+2RORxRmeJ18S7krzGQsSjdCbIqDCredJS3jQ4l1YgmnxfmxkC6bp588038bnPfZbbb7+V1ZUj3HrbbTzxiU/i/PPP56qrvsKHPvSBhBypCBL4+Mf/mqoqecQjHsl0OuXzn/8ct956K0eP3Mm73vVfeNrTL+U1r3kdS0tLfOCD/4ODB2/mE59Y4yEPOZ+zzjqLl7/8lXz961/jI//3h5iM1/nSl65AK81kPOa2227h85//HNdedy233HoHV111Jb1eD4ktN99yI1++8grW1o5zbPVm/sefvIcHnfsQfvx5P0PdTPjCl/6eU84Y8qUrP8u+ffs459wHc+jQIb529Ve44YZrqTdu4JGPugilNMs7F/jlX/5lzjzzHP7gPe/l6NGjTNskNt/rVakHFRVahaTIGj0hpIiiU2d11uhFNik5SVxFQHdNgwe+bZEm6WkUZZG2VWeiZkKtI8YUWcEyC5B3fE1Sgy/tqYl59sduMqhDeiw6SU9NpxMKVzI37GNd6oXU9QSn09yPIBhd0usNqZsR0XuKskdd1yiS7nRV9XC2JASYTFYBj2CwxlFWPRQwHo9p6jEhBsqqD2im0zHOOFzp8D4Nw+3atZsfevQFDPuGlZV1QuEp+xofGzbWpljXo9+rmF/ss//s3Sil+MbXb0Jax/zSgOFcn9NO3cttN9zOwa/fzIt/5hU89BEXs7ZyO4tLewnR8dGPfoT/8lu/yR0H70IrS68wVL1BHl1PKJpkJaKYo41WqW0QQpuiZ6dzLZmblwXsr/jiDT+w8+W+si3hPOedv4eq18fopEuWWAMBrZNous1sZOi0pPOUaQx0CZEEn7/o3OgL2YmIeK+ZTNaZTFpaH3N6lp7bKJVWE9pE7zFW4XSKhFn5AmeLPImaUSnpxh08KJ0G2EhN06SlJlmwJBXfKbcMM0Kq0RVGO3YsLfDIh5/HqacuMfWrxF6gbYWNtSnj8ZTBsKQ36LFrzxL79+9jOplw6M4jVIMeVVGxsDjHjqVFDt58EDu2PPfHXk6IFonrLO7ch/eav/3EJ3j72/8Tt912G2hLUfTplZZOilc6ZSEiIeap1KjwIckHG61BmRntRyT1u760BZxnS6RtISh826CLEmsMrU+UmijpRAw+YJ1LKV3u71irkGiJknlpJIfqds1oo9Kkp6QNBsNhj36/IHhD09Y0TYsPMek8e09oMkIuaXLVWpPY0jZpVqeUxqIJRBWJIavRK0fUaaRBGYMOHVNB0Lrj52Wp26jSyRcDQYSV46v842eu4twHncY5D9pHbAKucAwHGgjUkwZrHccPHaMsHDt3LnD6WadTT2qqfkE9mbA+Muw9Yy+jExscWbmVU/c9imOHDjGcG2GLPpc89RJ27Vrm93//cj716c8wrWu0ilRlATqlcGLiTKcg8dJTbWmMzdOjJpGmJGb9iK2Btm2JyHPOOXtxRepZGAVBJBE/8/4ljUYZhzGpZ6F1N5djQHfTpDot2fUxO5QClUelJUWEmGk6RgltaAiisih66iXFoBJ/K0racN22DPolOxYrwKYx60yIixKIIRDyakKtbIJyVa7ZRCOxzZEqjVjEmMU5uglYXaC1pSoVe/bt4qGPPovBgmEyShviJuMxRimWds7Ttp6zHrSfQb9HURQM5vtMRlNQiqoqGK2NmJvbwbmnPZpTls+kcBrrstaAKlld3eDjH/tr/uj97+P6G2+msCVFmUiyoVMUyjB2UzegBGMSSRU6DmDIG7fhC1+47vt/otzHtiWc58A5y1hXUrgica1MAPF4n8U+VKKIWJsEEJWyuQFpcqGb6iGl066cEEJyCtWC6Kw0GhLdpDtPdLeKI831pBqA1BBE0TQt6+sT5oZD+n03mydL6ZfKDOsUuTpuHaoTJynSuEPH4pFAiD4zJ3LHPppU22HQNu3E2bE8x+MuvoCq59gYjRHxjNYben3H0tIcxhpOPX0PvapkbmGeO245yLET67S+wWjLaaftZm44z4XnPp4D+y/E6JIkGBlT6hUNt95yK+973x/wl3/517QhpsVaotJ2hdgmAmsMSEyMApGQU+XNPaUhwhVXfOMHcq7cl7YlnOeMM5ewtkiKMVmpRRtFCKThLWVSbUFiThMVynQ1kMHkmQONSqh2TKPKHXOBPHUqKkC0hKyPoE2Souo67xIVSrVE0rLd6aRhbthP3DJFcgrxyRGIiR9mbUKsfEuMhsR0gE5sXsTmVC2XPtJmDRKduWRAHoFQSjEcDnjUE85nuGgYbYxSU7UNFIXBFY7l3UtUlWPXriXm5gYcPXKCtbWNFKE1lKVj0F/gwgOPZvfO0zG2B8oliDlviJuMa/7yL/4f3vdHf0zTeLTWqTpMe0UyIzxuHjfdNjgF0iLRcOVXrv9BnCr3qW0J59m2bftB2JZokm7btv0gbNt5tm3b7qVtO8+2bdu9tG3n2bZtu5e27Tzbtm330radZ9u27V7atvNs27bdS9t2nm3btntp286zbdt2L+3/D3f011L/ApITAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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J0Xrk840BYb+TcsaZYdBGI15GJ7yca8MM1FBEhAIpZGmsprggzsiM0QhpkELgB/46A4Sh0fm+AmPQxj0mwbkehHDnXwqEu/oCd7FD33pvbX8R92OSQUwap9ZbGlMaqRDSemBTfriLP3VxfTHalAYsKH0jOs/t50iIqiFhJUTnOUmc2lg2SZFSkmfanQfcsZp130MpyTXXH+at997N/LatVKoVtNY2rjWa1bVVnnjyKR79xuOsrbUJqxX8MGTQH1CpRkhPYHRu31cY0jSzcbTWLu4VSCnQ7m/lFR72NedBh3GXlKJ8RJiEQwe288xzR9m7bxcnjh/n7rvezF/99VdRykVVUiCRlhFujEsmBCbP0UYjhUQbg1JD1rjyPEyu0cbGVWDc5zqDMgblSTxfkacZ/e6APLOJTK41WZrhRwE61xgXk6VJzESjhs41FxZWbGJir7LdCSRIYfiJd/w4X/7Sl5iemebEyTNoAxqDH3j4oU/NQKMact+99/Bf/vC/0e+nqNI47Hv5vuJnfvpdfPVrj9BsrvLZz3ya2elppmZn2TQ7y+zcFhYuLfLoI49x4eIim6fHuXrPDr757AtE9TpZmhL3u3i+ssacZyAMOssxAhfLg3Zhk5QSlIcQOVoI1GvNQEsnBEgh7a6NIAwDrr3mEM21VX7tV/8xf/RHf0Ke5+zaNcdb33oXCHjgS1+l2epy7aGrOLh/H73+gEe+8Q3uetMb+cpXvsIdt9/O1x99lNfdfjsrKysY4MixY9z1hjvJc83nH/wC119zmDDwOXLiKN045pbD15FnKcdPneCW626gEka8ePwYq6tr3HjddTz2+BNElQrXXXOYdqfD33zxK+g856f+j/egjeaP//j/5p3vuM9qMn35a1xcXEEA05MN7r7rzTz5zSe45vAhWq0Wd911F+fPn2fP7j185rN/xTvffi9rq6vcfvut/NmffxbV7PKWe+5kx45tXFpc4tOf/jy+73HjjTdw7txZavUGzz33PLt27GDxwgXuftMb+d3f+wMOHzrE1Xt2Q5YzMzXJrTffxPTkJCfPvERvMOCGgwdI0pSHn3iCmw4fYnV1ld0Hd/GVb3yDXIDONEJIlCcw2hqqQKKEcMjG5V8biIMOY0gpBWjr1arVKnfe+Xqq9TH6/QGXFtf4i7/8Igf272N8rM7pU6dcDKW59eabaK6tsbq6Qhj43HLTjWyaneHGG65ndnqa6669FgTcfNONXHNgP5s3bWJ2Zppbb7yBgwf2c++P/zi+F5CnOdccOsQ9P3YPnlCM1xssLl7iTXe+gZnpad7+trcxN7eFt/7Y3XQ6He583euYmZwkzw2tVpvV1TWuu+4wmzbNEkUhd9x2M9LFc0lij7UxPsbrX/96du3eyS0334QxmhtuuI75uU3ceMP1LFy6hDEGpSSVesTVhw5w7NhR3nbfW6lWQ4zWKKV429vuo16rs3XrPLfecivLy2t0Ol26nR633nIzzzz3LDu3b0XrjG63i9aaq/bs5pqD+1FSsnvHDuZnZzi4/wAY2Lt7D7UwIkszF4OqMnGyIY314J6/Mb5s44F6G16R53rEZK1X1W7bETLgiSefxfN9rr76IEIqQPDFL3+Fw4cPMTM9jVfK3xRZrM2KkyRFCEEQhEyMj3PkyBGOvHAEYwxfeeghllvNEnP98te+QnPQ4fjJE8xvnbctvcZw/MQJHn3iSaq1GpMTk3zhi19kaWnJxozaYqC1Wo0sy0jSlDAMbejismOtNUEUIKQs0Yc0tVl0EVcPBoMyWcqyjMcff4Kdu3YShhGN8Tr1iTqe57F37176gwEWkRD28/McpTykEJw4eYbP/OVfk+eGNEmJBzFCgO95jI+P8Y3HvsGpEycxRjOIBw6Os6iE1gKDxVuV7+OHAWElIqxWCSo/fMmi72VtrIGa4Z8CPC8SjCKXldJacKNR5atf/RrT09NctWc3WZYRhQGf+6u/4tZbb2GsVgUh8DxVpu9CCDyXdadZyoWLF0nTjC1btiCEhWEqtUpp3EmSMFZrcM+P3cPJkyftcRj7OMIazgsvvsjMzAz1et0aHNbrZVnmsEpJkiYAeL6i1rCdrWIUOB9JiKWyhuZ51kP1ewMEgvf85Ls5ffoMWZ7heZIg8kjShM9//vMcuvoAnjd8EyEEeW4Tuq1bNvGud96HlMUptlBWmmaceekloihicnJyeP6xiZ3WxiWS1jiDKKIxOcHM/Bybt29jboPkLTfMQAvbBJup28pFzmAQ8/yRF1hdbfLEN59kemoCIUAoxQ03XE+z2eKlc+eRUqE8n5tvvpljx45zbuESZ86cYWpqimPHjpPrHOUp6vU6L7x4lGdeeIE0zdi3by9LqyucO3+OxaUlt5VpTp85TafXRviChUsLhFHEiRMnWF5e5sSJk6Rpxhe/9BUO7N8PCDrdPgAvvngMow1HXniBLM8Iw5Anvvk0lVpIY6qGJufosWN0ul2OHjvGWrPFsWPHWW2uceTIC7z00gVePHqMscYYzz77HMdfOM6RZ57nuWefZ6xhY01NjvIkx48f57EnHmetuUa70+L4ieOstpq8dPYcg0Gfbz75FHfcfhtnz1/g0tIyJ0+d4vz5C7x09hzfeOJJJsbHmZ6ZYbXd5uTJUywtL3P02HFa7Y7dwQocVto/yvMYn5zg6qsP8mNveMOG2MmG0e3Or6wwNTGB0ZrPfO5zfPrPH6C50ibLEoJQ0m33SPoDW9URipktE4Shh5CCNM1thSPLaYxXMMaQaY0w1stJIYgCn/179/LE00/jRxFCSTxpiwJCCYzWFqSPAvL+Ikq0UcEElYmttJbb9LoD8syQJAm+7yOkJE1ShAPrC+hJa40UgjTLCHwPqSSVeoWxqTpRJXSl2gxtNFmqybIcz1MsXlwh7mckSUq/2+fS+UsM+n3a7TZSSaLIJ01S/CBgftc29uzfRRQFFg81kCYpnudbw9KGNMkAi4ikaW7hOneuvcDHCwJ8TyGVQihbacqyHCmlK4JEeL7FgYMgYGpqigP7r+LQwQNsnp5m0OuzufC8l3FtaBav7QZEmmRkaUqWZ+S5od+JEUgQEm0kfuDh+R7S8zAYpDToXOOHPrjSYx4nLqYLwMAg1zz5wov4lSpSSYsWKEmuDXlst+xKzUPnmkpNE3kdUBFplmOEwg8jDCmRZ40ujRMbPwphY+A8dxitvcCeZ/HU+kSVxkSNSj3C8xRB5BP3E7qtHgaB8nyWF5v0ezmDfszSxWWaK02SNLHHKBVpEhMP+oBEDlIunF5AEaCUZGJ2gsZEnbBSRQiBjlOX4EhAoI3dkQwgPb8sEkgp7bmSFmZSUqD8ADBl7BuGIdu3bePwoas5sG8vU+PjKKXIdU6z1XxtGWgSD8jzOgbr9dIkRec5WZJidE6lHpFmKUaDUq+sYggp0Dpj68wcFxcWePMdt9Jstfnmc0cQUjlQ3oL6WZ6xZ/s8l5aX0EIwNzvLjdddx+ce+BukpzCVSbRuIP2IQX9AGrtadJbjhwFJHHPbddfRabf5+jeetJmtLVhi8gytDZ6v8EIPhCZLE3TugaeI+zG9do80yWg1u3RaPdI0p73W5tKFRYvd5hlZkuAHHlJAFIXEcUIYhIyPjzM2OYnv+0gl6bd7mFwjPQ/PV6iiemaKEm/OLTdew9TkJF/42qOA9fIit6GMEAIjIC9qdK4oUK1G3P3mN3Dj9dfTaDTAGFrtNi+dO8uLx45x9Phx/n//9P97OU0E2EAD/YPf/7/Ytn0bmzbNcObMWVunznL8wCMII1ud0RpjLDnChkc26RFCIDKBQvD3/97P8sADDzA3N8e+vRWuveYwD3zhS0xOTnD44AG0gb/43Gf5wM/+LJ//m7/hoW8+weTEBDdcdx1Ga1qdNi+eOM6dt91OGAQ88MUvsX3bHAf27aPT7fI3X/kqeZJyx6238vgTT7B18wxvfcs9aK35X3/xOe583e288MIRrrn2Gp5++mluuvFGOt0OL5w4yqAXs3d+J7tu3Emz1ebBB7/Em956Gw8//Chvuv0mXnrpHPfc82aarTb/+WN/wv/5//n7XFy4wNVXX83v/94f8p73/B3GJ8Z4/siLPPT1JwkDn3fffy9TkxMcPX6SU6dOc9eb30Cr1WIQJ7z44jHees9dzggF5quPgjEII9C5wRh7M0klUb5EuXOJMXQ7Pb705a9y/vx5duzcwcLFS5w6dYa1tTXiJGHQ626InWxYkvT8c8f44gNf45P//dN887HnrIHmOUXpPc9zW/bD4Pk29hRCoHwP5Su8wMMIQb/fZ63VRkrF0tISjVqdXTu2s21uC08//TTXX3sN05OTxIMBa60WQtisfhDHrDXXuPbwYcZrNRYvXUIpxeGrr+baQ4dYWFjgzJkzJIMYbaDv/n7TG9/AYNBndmaG66+9msOHr2bb9m1cf+21zG3dwvXXX8/r7riDQT+hvdrh9be/jhdfOMqtN9/Eppkprr/2Gvbv28PWrVt473vvp9vrcscdt3L7HTdx2203A3DttYe55dbrecObXs9TTz1JlqV4UjHWqDI+3uDEiZPcfdeb2DQ7zcED++n3+9x4/XXs2b2TyalJ1ppNe3EdNU8IgZCuBKwNeZqT9FJHrUsd6UazutLk0Ue+yf/4s8/wla88zEtnztJca9Hv9Bj0Bt/+Yv4I18Zl8cbWsNM0I8sywLgSImRpxqA7AFcG9UaqGMrBMmAZQ9rR7YQQJElKmtma9nPPH2FuyxaiKCIIQ0vJc68pboA4ThBCcmlpiTzXTE5MEoYhX3noIa655jDbt29HSBu/2RJpTrVaJUlSl7FHBYWgZE3lecYXvvxlWp0+zZUuvu/T6XYBQafdQ2vNzl3bOfPSWYIwZGpqigcf/ALLy0sgBJmD2gZJype+9BXe+RPvpFFv8I6/cy+HrzvM6VOn2bNnN77vAfb58SBGKonnKbIsJ8tSpJLUJ8eojtcJqhGe75cJUsFBzRPNoB3Ta/XpdwakSUquNWmakiYZSZySxqkjtvxwxMC+37VhBqqkcsQEW6lwdCMG3QFGG6JqaH+vhrgmWGaQ51liSFFDl0Ks0yeSQvD2++6j0+vR7fXKbFaV5GU5QiY2HNy/nx07dnDh4kXLj1SKz/zFX3Lj9dczOzVpPbl7+zRN8X0PpRRZ7sB230dKVYLwqystFi82SQY2bLFYrGZ5eZU4Sbnuuus4c/ocaZLy4ovHmJubY25+znm74lgFg36fb37zm/yd+9/Jm+66k9ffeRvveMfbubR4ydLulPWO0nOEbEfqUMpDYP/thwFhNSKsVQgqlowtSq/qDDXTDLoJndUe3WaXuJeQuZzA/rEcho1YGxaDHjp0kG6nR6fdodXukJoUJSVhLcIPPOK+rZZYtlFx11ujKsBnrQ1PPfMsUeBz4sQJur2e5UBeukRUqTA1OcmpU6dotts89czTVMKQPEtptVucOHWSldVVTp85w+LyMrt37qbd7nDx0gJ+GHLjDTdw4tQplldWSZOUkydPsri0zJmz5/ixN7+Rfr/PiTOnmZyaZGJ8nPPnz3Ph/AIvvHCUpcUle7wKvvbQw9xww/U88cRTvPjCSZ5+6lluvfUmLlxY5MEvfpk7br8ZAyyvNjl+/ASdTsfW6fft4eD+/eg8Z+HSJbbs2M6lxSXIMrQxnDh1kla3w4mTJ1leXeXkqdOcvXCB/VddRRzHvHT2bHnDCCgJNmAJIHmWI4xNmIRzFFmakbRj8qyHkOAFCs+3OQAbAkZuIA565uJFgjCi2+7w1Yce4i8+8wBrK22q9Qijc1rNDlmqaUzULD0t8gmrIdV6RL/bJ3f8zCxNSeOEPMutJym4o6GPcoYtXWtEGsdI5aGUIKqEqEBicks563UG1nsbW482uSbPc7QR5FkOOsMLPXzfw/Mkvu8RVEKSQcygn9Bc7dBt9QkCH+Ur/CBEKpuACAyrl5qcPPYSlUrIprkZ6uNjRJUQ31cIqYiiiD17dnDTzdezY+dOxsbGyLKMs2fPsrC4xOnT53jp3Dn6vS5JkgLGFiN8b8g+srELWWaTIT+MUJ6HUo6Moy3tL8+KPw6tSHNXhfPJM0232SFNknJXsgwozV9/9pPf/oL+iNaGedAwDBkfH2NifIxde7YTViPkWo88zewd7bargocopHCEZePockNGvOcHSGXpckU1xPM9lGcxwCD0rUGFAVIKwkpApRahc02v20dkxsaayiu3So2A3NLRfGdwXmAZ59JBO912n15nQLcTo3NDEIb4gU3iyh4iIdC5JqxW2LpjK43xOtVaBc/zUMqj0ahx4OA+7nj9rVy1fz9RFKGNZmlpmWeeeY6nnn6OpeUV26LiK8JaHRVk5HlqbyCtUUUMLCVCKYRSNmvPc5I0czGydP1YxiagqcWe0yTDGMtRrU9WbJuJgtZyyyZRSUqvk7s84fKvDTPQUWRTupYMIa1x+KEPHWeALlaS0m41WWq3OEs+N44gjNuCXPKkpPWm0hqq9aIS0GRJglQgco3JDb1+nzw3JIMEnWvX74QtvWoNwhBWfDyHOwoBSZISCkWn3cNDoYQhqIRkaUaaJGye3Uy70yXNNWmuSQYpQkpmtkzjex6e5zHWaHD42gO87s7b2b1nN2FU4fTJk5w/e5bTZ89z5uwFmq02uc5tFcwVAnDk7rwfY4zrdWLIay0wUTMSo2dpVrbB5Jk1aiklUTVkYnaCoBK4m85yIsZnG4TVgOZSk06zQzKALEvZiLXhhGVwd74UKCmIaiGD7oA807YCoty27Qw1dT1BYEm2ZQ256DcTjoCBTbC8wMPzFJ7vkaOZm9jCtq3zrK2tsm/PPr7w1a+wsLjMWKVCM26S64y5zZvp9/o0ux1qUZWpqXHW2m0yrSHXjFdqvOXuuzhy5EUunD/H2QsLjE9MUKlEnD9/kb/70+/jkUce5djxUywtrVCJPGrVKr1+n4lGlfpYjUOH9nP14UM0xsZ49qmnSJKUEydOcOdtt/PFL3yJTmw7WafGGyR5TnfQJ6pUWFpaJooipqoRa90u2g9L5lfhGQsMuSDg2O3cgvQ2TKrgh54LZ+wOlWeaNE4Y9GPi3oA0yVC+YHZ+Gs9XDPobAzO9KjyoEK4RDkjjDOV7IHAZpyMuKFHW3xlJkkpGTmmklsdoe51k6WAK77x502b27t7D4088zlijwd9529v52sMPc8/dluv5tYce4tZbbsX3Pf78Lz7DT7z9HaysrJCkKZ/6X3/BgT17uO+tbyHLMsIg4P777+fBL36Re37sbs6cOcORF14k8H127NjGffe+lS99+cv4ns+hQ4d49NFHOXzNYZIkxvd86lGFbzz0NebntpL0epw98xLRm97MLTdej/I8Ll5aZMf8ViYmJvjzv/ws/+iDf5+HH3qIqFKh1++SZJovPf5NhC/LLb7YAbQBYQwSu/MElZCwEuB5NozRRpOnGYNunzROGfRi0iQBLLOqWo/wfK9k4Ct/YwCfVwWbSWtNnuYWFnHbqBCCILIZvIVS7PZul0sIjK3ll9butn4bo1JCKTaxUQRhQJIldHpdvMBnZXWN1bU1KlEFpTwee+KbjI9PUKlWyLKMeqXG3JYt5HmOJz2aKx085dPpdGg2m7ZzMwxc46nh/LnzPPP0EXKtOX36JTzPo9vpMT+/lZmZabZtm0dKybHjJzhx8iSVSoWzZ89TrVR5+JFHaXd7xPGAc+fPU61U2DwzQxgE5FmGLyVGax5/5jmSLAMj+Nojj9oWjWyY+BhtE6awGtGYHmNyyzTjsxNU6pHL1FN6nS6dlRa9ZodskNg+qUpApRpRqYUEkU0wbXiQ0+/0aS23Lqt9FGvDPKg2mswxgbIsJ3NiC1lu6HVjpCo6LIXrFfJIBvFIjKXLWLQAyXF4aoFLGG0o4Duda7IkK0OComPS0oNsjHX+0gIH9l3F+fPn+cZjj7G2ukaSpHz5y1+l4gi7Rf+UwZSiDJ1un//23z7B/ff/BMrzy+YzhKDV7nDttdfywgsvcNNNN/H1Rx9l3759LC0ukqYpp86c5fMPPMB7330/n/nLz9qv4wgo2mhOnDrNS2dOs7bapNPtsrTW5vMPfok33H4r737HO/h/PvOXGF+WsXaROBbYsM41WZYRdwdkSQrGENVCJmbHiaohySAhjVN6nUEZCmRp4uLpFJ1bvmvZbXiZ14YZ6Be+9BU2z80xPtbg0uKy7Y40kBuQnofULgaVlP0weebiKq3Xe8qyRdnaa1HiK/BTz2Xzg35Mr9sjCkLiwaAkGec6J01t//ulpUW2z2/lLffcw59/+i9YXl7m1ltuIYhCnnvxFINBTK1eswynNCNNU8LA541vu5e11TUqlciWbV1FptvvI6XkxMmTHDx4kIuXFpmfn2d1ZZU8y9i6ZROHD11Nr99HecpWcZxxLCxcYn7rHAf37+cvPvfXriypeeOdd5BnKWEYMLVpgqAakSWZK13a7FxnOXFvQJZkllro4vwgCpjaMkW1XkUYQ9yLGXRjOmtd2qsd8kzbc+Z7RFGFaq3C+OQYY2OXafThy9aG4aA/9ff/EZVqFd/3McKwutSk1+5TG6sy6PXJM834dB0hBbUx21obd60sTpEA5Lkuy3ZF34z0JL6DlcLIx/M9qrWQLNOsLq6RdPuM1+ssXrpEpVq1hhvHjNXrrLbaZGnGpslpOu0eZ85dYMumaTZvmuHCpUv04wxPCmanpojjAd1ej/HxMdrtHldffRU33nQ97XaHOO7z9a8/RhCGNHsdtm3dwvLaKtWgwqVLy0xOTaKcEbT7A7ZuniXPDecuXKQaBCwtLlGJKnQHCbu2zxOnKcvNJls2TdHLMyYnGzTCiNV2m0wK4kHCoDcgi1O0wzbtVu/6itw5ssoogko9QmeafqdPp9klc1xSJSVRJWJiYpwtWzaxc/cOtu+YZ3Jygm63y52XYT78y9eGedCbb7iGNNN0u31anZbTGpKWfpbl+IGFVYrtvdfuOYUPU7YoFOVR4Xq4pScJosAG904ixhJNJP1ul17bJgRxsoZXqZIrDy0lIghp9WMMgvZan4Vzx6nWQsanxukOYp574QRhGGAMdNOcfu8SQeATVSpMzmzibT9xK9ffcB1jY2OsNps8d+R5tl29hhaGKaNZW17DeAHL7T54AYkQRFGECXxqYcRiq0sySMlyxdJqj4yATpzjhxVWBwMak2Ps3bEJPwqYFNDr9Fhot4l7Awbdvg198pyhCICw7dMubIp7iev3d1t8NSKqhgSBz9TMFGNjDTZvmmXH9m1snZ+jMVYnThJanS6rvR4nFy6xtLL62jLQv/sz/wfVWp00TXj2yPP8X3/0/5IOEgc+m2H26FmhsGI7B4ZiCsKUnMYi6/cdAmAw5JkmCAW57bwjzzTJICGqRYTVCARln3u/O2BtuYUEJqYbFi/MtM16TUK31UdIGxdXqxX27dvFG978Oq659hqqtSoXL13iG08+wZnzZ8mFpjZdJ01Sli4u017toI0VN6tUI+qTjZIVP3DGkznisZCCxniN8ZkxJmYmqY3VENKytjrNNp1mm16rU1bP7PatMMZixcbhnr1OyqDbd6JnirASMjkzwfhEg9nZaebmtrBtfiszszNEUUSSZ3T6PVa6PS5eaHH2zHk832frzh1Enkc12xiyyIYZaK4NURQSRgHjkxOurdUG+QZc/OmwzzhdR8rNM4v5SSkcPm8vjqXg4bKkYW+NFe9SpGmOHwXUJ+uu6gRZmtNc7tBpdvF9j0ottAoluUUWjO/TafVRSlGvVzh87QHu+rE3cOiaQ8RJzOLSIk996VkuLl3CKKiN1wmkpNPq0Fxu0lppgxDUx+tElQp5ais3cT9h0B1Yr6Y1US1iYnqM8ZkxamM1pOeRJAmttSatlSadNVvZKaAP4YQddG7Z/mls2Ud5liGE7UKoj9fxfI/p6Un27NnNnr27mJ2dIYhCMp3TTxJW4wEmtbpNUilUrUqQ50jPs1UlKfGkIii7Zi/v2jADvXDxEjMz04DDO1VRpmQobePgo9Rln1prRyfLnUe17B/lSSr1KtV61fFIcyvjJQSDXoxQitZqEz/0qI9XEdJK1sS9hOWLq8T9mEolsLS6zMoppnFKmuaohr1QjUadt7/rrbzx7jcQBCHPPn+ER77+dTzPJ/U0tYkaYcX2l6+srJR8gSDyqTaqhNWIuJcw6PXJUm0LEUqyeds0E7MT1CcaNh7u9VlbXqO53KTTbJPFieu4LKAJe17iTkwSW7aUUjaebYzXbP9RGKAC3/IOpGTLti3M7d6OqldZTQYonVs9LN+2hFhxM9vJkGa54yAYIj8kDCKUzIjCjWk73jADfeLJZxifHKdSrdDp9shSW4Kzupm2yzNLUrLEUr+EGAL1mSvX+a7zUCpFY6JOdazKoG91lQDiOCF1Zb40SQmrAUIKsjSn0+zSWukgjCGKgrJnCSPotixFr16vMT45hjESrQ0PP/w4J868RGUsotvr0V5ZpVqtM71jGm0MzdUWA2eYnu8TViJLZevHtFfb5EmO8gT18SqNiTpjU+OEUUi/22fx7AIrCyt0mm3yzLYQF7JAWttyaRrbUEd5ijAKmN00ydT0FJu3WPmbQZzw4vFTZEC1USeqVRlr1JnbtoVKpVKC9BRAfW5hNwSOV6tJs8yVeXPCMLQ9TUoSheGG2MmGGehXv/oIL509hwp90iyj3+vb/vU4tYKp3QFJnAzZ4IiynJelNvbCGMLIs9m8y96LLsc8z0jTjLgXk7r38XyPfi+mudwhi1M8V+P3fM8K0nYTao0qM7NT3HDTNdzxulswIue5Z17kmSMvMrFp0uoYedCYqtNZbSKVorXSRnhuO/QVYRRiwAncpvieojFWJapFhYoZcT/h3LGzrC016bW7FqMEgooVckgGKekgwRjLgY0qIZNbJti8eYat83Ns3TrH+OQEfmA/K9U5SabZsncPQiriPKU7GCCM1Ss1GDJddCJZvSmjrUFqbVlh2uhSxa+o16dpQrvd4sJLGzM3fsMMdMv8Zv7uT70Hg+Hk6dOcPnaGNE7pdwYoz261aO3+bcgLoN15TyEEeZpz8fSi7TzMNK3xZsnW0VoT9y0IXWTzrdUOeWYIqhWCwEcnCUmc0W0PwAimZia57Y4buP22W5mb38r5C+d57JtPsNRrsv/GqxjEMVmmqTWq6CxnfvtWJsbHOXXuXFmpKvqnhID6WJUgGC/FYNcWm3RW26wtrjnFaBj0kvLGTOIU5fDVMAyY2TzFzl3b2bFzG1vmNlOr15HKI81zqxwaVcmVItU5gzynl6X0Bn1ynTvPKPCRlqWfQ9G1oLU9R8YZqu0EyF0DnW3/MBg85VGNqvRbMaFX2xA72TADjZOEeqPO1NQkGo3nKXSWU6lZHmUBHZVk+gL/dHd+IdWY9C3et3Q2YeWCcElUbtUFHaMcAb12n7BWYWrrDH4UkPQGxL0Y4XnU6zV27drOW9/yJmZnp7m0tMhzX3yR555/kagaUGlUabdaeL6V2hZCksYx+6++CiUVq/02RoDnujKNscfQXm3Ta/VoLjVZOLvEoDtgbKKOHyqkJ8kSy6gSQlCrVJiYGMM4qp+NT7eyefs2+rnh+ZNnrMqdiuh3+vjVgF2H9qKNJk4TstxKKOaOFleo+qW5IXAaqEIMOQtaG4wwaJ07Vn1WPm6lIm0BI0lSwmpIY6KxEWaycQa6tLjCV77+DXbunGdlbZUsywmrIWWt0pXWRk9qod1eYJ/252GPkiXkatfBaDCut6nftRBREEVobYi7A0ySMTe/mR27tqOFYZCmfO2JJ0i6PZqrbSamJoiTmNxk9PoDKjVBY3IW6QX0O31bA5eSfm/Atp3z9GMrvtBcadFe7dDv9EkGCbV6ZHuNkpRkkLrkSBF6ATt2bWL7znm2zm9hfHKCfn/AqdPnSDJNu9Nh4dIiz794DCElnbUu3dU+QkoqtQqbd25maudm28OVF/G7LoUkLEQ89IhFWbcwXK2tRHDutnJw/VoYx3qKyHRGrq2k5XrlrMu3NsxA+4OYp557gbNLS8RJzPjWGWozY8zPzjJRr7O0vMzxk2dI+jH9dseB83p9eVOMNKzJQmTVAtXGlk0ATbVWJYlTBt0Bg1aPublZbrrpGm666UaElDz3/HMcP3WaM0dPgjbMbZ1zKrWGoBI4NlCF5mpMErdBQr1WRShBvz9geXmZc2fP01peKxM525SW0Wtb6qBUHruv2sm1Nxxkz1U7GZuYIKxY4x3EKe1en1Z3QKYkg36f1VYT6Um8ICBLMlpLPcAwOdagUokwuSGOB7av30kl2jZtg8beqAaQbjsf7cGyqnX2/ElsSdgUcQlWb7/X7kEiqI/3nU79a4ywbDBMb57l4HWHWF1ZZqW1BjriqkP7md+ymaMnTnKp18VkOavnITeGpJ+QpSkCrK59gXM6sL2xadKqBTtl5GoUMTs9Ra/b4+yZi2gpqdVr3Pa6m6k3anzj8Sd4/pkX2HdoH3GckQ0SjIYdu7fT7XRRvqafDOistek2z9sGucjHaOg16uzcuZN2p8PJ42cYDAYMurHtqwoCGmMNZq6aYnJmgtVWh1wIJiYn2LZrOzoMWE5TdBzT7/VpN9t0210G/T7xYIAUOGA/wA8DlPKZ3bIFoTRzM7N0un1qtSpZlqOgFPItCN6e8lCB7XtP4ni4yziMeEhmdq91hQ3hbNQC/kVmb1xy+hprmpucm+bc+fPIyCfNEvwoZLxW44bDhxDC8JVHH7FBfhQwNj1OP8nwqzbDLKZceL7dtvM0Y+3iEsIPUMpQqXtMjjc4sH8P81vnOfPSWXYfPohUiscfeYIHHvgqlTBgamqc1eVVpO+xc9c2WkvL9HsDauM1okpIu7NGGluYK+nFdJpd2ms9hBTsOriDXrdrJRc7A0yWc+DgPg4euorNWzZRG6uj/IAkz0ky+2dhYRERhuAp8iRhEKcMkgwjJX4Y2mw6ywCNF3goz8MLfITQhJFEqJyxySlU0CWoWa2AKAgsiO6MU2u7paeplYLM0hwT2pi92G7KTgUlEbkoKY1CijKJUkrQXFkl7tsQqbW2Bve/47LbyYYZ6NhEA1/5zG3ZzNLSIpeSFDUmGaQxzVaLZqdrZbdVQFSr0Bu07NYJGClAeEgxQPoV8lxQqVcxCHbumOONd9zK2FiDx596hiefO8Ltt95MfazOF770NcKaFYPdsn0Lc1tmqY7XqU820HHGxNbNyFabdrfLWFSh041JM4NQHn4UEuWgVEC33aXX7XPiuaPs2rmTN775dpJcs3l+jrGZCZCSWCmEMWgBrXhAu9ej1e0Q1Wt4wkdJW5bNc995M5tJq9gjzzPCKCSMIqJqhNCSnkjJdcKl5UusrqwSjUfs2VwnSVOSNLERiSi6OG2IU3QqSCftWMTzgpJlWEJKOtdInIE7xtiJF44R9wdE1YjaxGsMqI+qEctnV7hw4SLLS7ZevWd+G/3+gEe/+RTLC6v02l2kMfi+JO7H6Dy3W7opNOYD6Nsq09Zd82gNu3fuYK3d5fzyClOzm5nfHnDm4gWOP3yalZVVvNAnHSSMTY4zMT3FvgNXsdRcZa3dZWLrZjJP8uLTz7H/wH5a7T7GSCanJth/YIaZqSmEVJw6e56lhYsIJQgqIcdPnKXSaDATeCR5bpvVsqzcTuMkJs0szmiMYdDv02q2SmPIc9slEEURUsJg0MdTHlElREir9Hfy6HGSLHUCYxJVVaUiiO2HFwhhB5D5nuuLl5IsTksidwmJuN2+KE6U6iPCKk4Vwrtj0w0qjVmUkvS6GyN9s3FdnZEFj3vtHp3VNq2lNfrdAQtLK5w5cZbVhRXi/sBqMVV91xKs8X0wniTpCzprXWpjda4+tJeJqXGMUfTSnHHfh0HM1x95jFarjQEqlYBt83Nsm9vM2QsXidOEpeVlnjt+jP7qGldffYC13gA9iNFaE1Ui5ua3sHvfHqrVCngeCEl/MGBs0wyttSWMAU/ZNuQ0T+kPBrbz1BZaAeuhMocx+mGIlJIgCBx2m9PrD4gHseuvysuhZHESgwLlB6R5jhcGTM5NU/MipFJMbBrD92wYIHBdA0oRJwlhGEGhuOKKETgEpKhQGWH16DGa4RwpuwpbjmqBjVcxNKbGNsJMNrBpTkiqE3Wi8Rq7Jvexfe9OtBIcXbhIdXaSWQm9dodKFBKFHvWJmp3ZI6ycdRCGaAyb57dQn5qgFw8YpDb2On3mJZprTdLUlu2U7xFVKmS54cjRE6wsr+KjqV+1i+bCItJ5nUYQMghCBgaWLq3geQHRxAS5EK5GbasulWqF+sQ0/dYqJ46fYZDlhIGFbLTbQkslDlFMtLO9+bbMaLMR48bggFUskUogZc22RBs3kwnACHbu24P04cDevXTbPbTMML7FeQVWqaUwPpvBD/u11s04ch2f9p8FpCdKr+qsGADpe1QaVSukMXiNNc1JY5ieHqcS+ASVqBQe0BgqUcT45BittSYmTdFZSmNm3JFIDLt27GBiepJWv8fk9BSe79NcvITnQ9LXCO1T98etIEK1glSKNEnomYxwrMZsFNBdXCTPNNX6OHGvw9nT5/EnJhjfvo1ozKPb6TN/1UFyKAW2siyj22qjhKQ6PkatrsgGmrgbI7xCysex+43FIKEwEOMEFbTTOJU4/pYdd6gUSZrYKpHBtcPYMqcf+vgTVfr9rp0y53mAxhaH7KcUnq5keFHkRbLEOcG4+L3wpKAptvdhe5oQo5KOmjhOSAbJj94ovsXaOA/ai3ndG16H8hSXVpY4f+E8a0ur5GmOHwTkOqd9aZXVxTbTWycZm5qwLHSlIFAYT7JpbjPK80mThH53QDg1gfQ9sqpVyvN8n6hSLSdXeFJRDTySeEB3ok5ndY091xxi7dJ5uitN9u3dwyDPuNRbIVMZWZ7RaXdB2GEHSkpXSYI0TzADy+T3wwA/CNxsJBsHGmNlD40x5bzNopuyYCcVYxyH8Z8oW1mM1qCGSimWyZVZD6g1cRwTVKOhYQlbqLDbtsVwTWlow6JHSYcyhZ5A2RZbAvvuR9IkQVilc5LXmgdFG7Zs2YLveyRkLK0uW8W5fp9tO7YTxwNefOIop19cYGrLJLObZ22ikecE1QrS98ANgzVKWUnsPHMjapygl1Ik8YBqrY7WGYuLi5h+F09KqjWbIefJAJ1bonB7dZVmt02v3SEZxDzz6JPk2tCYGWdirAra2MG3lSoKyLDGVa1XLc3Nzbo0jGKNhXCsE0mDsivA/rHkFuH+ozAqhkTswmjzTJdVI8/zyLXGM0ODMsbK4Qg3Zc8V3YZeXRQiawLj5Bi1Hpm4jPX8UlhBtjRO3cAvQRrHl9U8irVxXZ1ulHbhRaxUtefmdtp+9smZBsZo2ist8twC12Cb6oQTX7UXxQoUZGnGoB+7kp+7EEbTbXVtzbrbJ+508ICO51Grh5x4/gyNqQqeVHzjiw9RqUf4AQSBR3tlDeH7BDWf82srmFwTqItEUQWpYGK6TtLPiNd6jM2Mryu9Gm3njQrXYC0wZZIyLEtacBzjGqkNNrnRGm1yjBlenmIsdub0+dvdHo1Gw80nxVHzZAkvlbNDHey0LgsqvaTztIUnLWJVd0PE3YEdYCsEWfoa2+Jzbb2BZwpIxG6Xuc45dfw4RudEtZBpNcHqUgcMrqVXlIK2pacqNyYoaslCmlKBJMttmU5IC1clmSYVCiVg8eIa0jPUahVeOnaeLTtmmZiugOtlkkoS9wZ0llfBgK8CKpWMLE9ojEe0Wz3OHD3PodsOltCOjfUk6JHBsFB6TRSlUZhCIMBYD2oHIiSuY8DehLnWZLEu6XtSCKpR6DidRZ1djnzQegkcUY6atDG+cJWqop+/UGQu3qAY1JD0E7SxgxZec9I3fs3n1EunCXwPrQRC2SRD55o4s/FOMXxVSus1LaQyzEqL+rGN8YaxWp47LXZtykQB93MySCDNMUphTESaWLmXWq0CAqeaB7VqlTDsoT1Fnub02317QSNBJdJO70hbfflCHbo8NgFoxEgv+VBZLkMIQTwYcOH0OQatPtVqnXq9hjaGbq+HNinkmkGvRTNJbCEhF6BTmmurtlY+6BOaED/wgSJEADCY3LC20iLPNZ4n8fZsozE+VpY6ixgU52ExhoVz5x1ZOgcJ7bUWUcVHa6vu4uXrXPBlWxtmoMeeO05zpc3WXVuY37m9FAcDnMCqnY5YUMHWLlzED23VxfMVQeBTnxgnqlSJBwP67Q6Zo7kJYQcRVBpVqpWKbQrLMjrtDvHKKkvnliAIqDWq+L5Ht9ljz77t1MdqtqwZZ4yPN5jdlLO4vGYf68d2vE1YsZ4wz2kuraG1Bbu7ax1WLy7SqFfxA9sBmiUpx547SrvZsb33SUK/3bc0wSSl2+zQb3dtpq0saz/u9fGjsLxZ7SQRqzGvc83RJ54sQ6NBu4uQkqhuh4XZaMnV2DONUJLGWJ1KFNIYH7NG7EKQYsK00PbGvfjSBS6evUAaW6WRYd3eOgdjXmNNc4svLbLrql2MT4zZrcq1OUARGwnXh2RI4piFk2fsCGtHD7PiAoFLKjS9dhc/WCsTkLAasXnbZsKxKmFgL37kC2qVkIU4QVUC8ixndm6KlaVVVhbXmJwYY3VlFU95nDt7kSiosHb+kuV5YnunPE+wvLBo2yKyATPzm5jdPE5vtcW5Y6eZmJpgZsusTTKSlOPPHGVpYQk/sN2m7ZUWfsW2RueZzcYLAjZA2h8gPOGSQFNSDJWSqIIulwkkEITKoQjesDokrRR4Mf8ojx0DrGB9uf/MSEdsgYcWaoF5rsuGQ+HCDK3Nt7qMP/K1YQbaXm3y/GPP01xZ4/rX3wgMdT+NGc6KL5KDJE4tcG0MlHdz4uJMA0YzvnmGqFopOx4r9boVoNUG3/MhrNBaadNpdpiYGScepNTqFZu1JxnX33SIJ594mjhO6HZ77D0wzu6DO1i5tEa/1aXX6kKuydKcIAzodQYkgxiEIskyer24nNMJglq9xi1vupVHvvSwZScFipmtM4TVkKQ/IHTHakYufjmXvWAgFVzYAhrCSkPiEiMhpX2983gFUmC7YTPOvnC2bIOxhSNdhKklHgqQxjn5wLD7wF6kkqwurtBcXcX3LZ0vy15jBqp1Tq/To9PsopOMeqWCJ+ysS40BbU92rd5AsIDJNekgHiYbIxieweD7iu17dzG5aWqkopK7pi/PbaH5EN7JNUbDxFSDxQtLtoc+CpiameTM6ZcIo5A4TpmaGae92mFm6xTdVh8p7HS6LNekSd91VUqMMKRpzOKFCyROBbnd7rK6tEK32aXd6oCAmS0zDLoD1pZW0drqihqtSdMcz1dUx6ulOnQ5LGIkuwZKkbAiifOcOBpCoIRrH1YSrWzL9fCGt6+3QyGcd861YzdJQq/CvsMHLb/gmaM0l1eRQlAba7Bl09bLaB3DtWEGGkQhm7fNWSJCZ4AGwqhCGKbUxiJ63R7LFxbZNL+FIAjcNLqMIsyCYcxV0MeKzDXPNSsLy6RxQqVeQWcZtfE6cZywc/9O5nZvAXCJiWbr7jnCIGBh8RJxkpAlGbVajaSf0G92OfL4C9TGqlTHqqhQIZTBk5DEhniQMLt1jOZyC5NlHHvuRSqVCCEE/X5Mr2f13qWS5FnOysKKRR2020YTC87nqZ1VirET9PzAXhrjvmgZpzpNgDzPS2jI8xWe5+F4I+Xz4kFCt9N3QyCGFSeth5qqhc6VXSNih46eZwsMigvnF370RvEt1oYZaJokKCVZXVzl5AsnqU9YcaokiVF9G79Jz3cTQOxcyYIxbl622xTTQgoozxjDkcePcP70eQvI1yIaEw3L86xGBBU7j7I+XrcEX0/iScmgNyDNEhoTDfzQww9s39B1tx+ycZkSw7AjzwmjgEG3T7fV4fypc+hcM7Vpkqhmp2nozJTb6eT0OFlmX5sMYpvNSxh0BiVpWElJOohZOLdAmqbrp5JIq9vpeZ5VKKlFTvFZEoQ+0niuqVCXROQkta3Kxr2/FAJtRkB5IawgoDYEUUhYDcjyHOKYMIjYvn0njYkqvY5lkm3E2jADrY3VSNIBQeSDNNTGK05XfhKlFNLzSOMEhGD3NXt56dhZOmstZ6jry3eWjCHKPm+jNVnmsuRO15KcfUtfC6KAqc0TCCGYmZsmjMISWF84u0jcT/EDj6nZafZctRsBdLs9er0+/b5thc5cz34/DOi1e8S9mM3zsyRxSuASICEFvVaLhXNL7D60hzS1s4akkoSVgDx11DjfyjUqz8rqxP2Yo88co9fpWQNSoiyTFi3SfuAThAHVagU/CAgqAVvm54imKo7QYqE5IQWDfoLBsLq8iueKIH7glf/WrsjRmGiQpXbiHEC702FxaYXz5xao1eq0mu0NsZMNM9Buq8PFU+cZmxxn795dzGzdRG8wII5ju/UAYSVCScn2vTvZumsbx58/wfkTL9Fvd502kyjBZpu923grzTKHhdqs1uRW7S3Lczxtt8fWSpdOM6FS8532vdVIivsxaZxz9vg5zh4/x5b5GbI0p9ftkySpE45w2GeWlb0/FhCnzIKjIOCq63bSXGnhBx5j0xMEkaXbpUlKZ61DntoBZgLKbT0ZJBy84QBBGNg2ZqWQQhL3B+S5JqxGjE00CMIAT0ord5NrvMC3UpJOGUQpRaUaEUQhnhIsnDpPlqYo3yOMQqRSZElKt92j3+3TXG6zcmmVpx9+iizN6Hf7VlInsb35pdrLZV4bZqCz85uY2jRNfbwBvqTT6dJut1lbaTov4ZVluniQYISgMdlgbvdWLpw4R7fVcfQwrGEYaxhaa1qrTbqtDn7oU2vU6LTa5LooNWpwVavBoEuuPQa9nEFvgFK+5WTmIEXKxQsLjM00mNw8hagGeP0B/XaPvG9nDNm6te25jyoRofvjB4EdzS3g4PWHiOp2d0jjjMEgoVqvMb9tnrAaonNNGtuBs91mh7iVMNYYszuD51GtVajVq0SVCmElRCmPQW9Ar9sDAZVGSFSNCKMQz/NI4oR+p8ugZ8fjdNo9sixFuxszzzKyLCfu26ERSZzZwV1p5nYey13NkpQs0w4hGHaDXu61cU1zxtBZ69DvDGzikBsnu1IE/0WcbuMlz1OEFc+JgWXDQaijPEZX+qzWKgShj84zwmrIII5JO30KjUzjcMdN89NUahGdVo9eu0drpcOg1yeJM2oNC7avLK5Sm2y4tzfD8qoelhLTOEPIjLCiqNXqTM5OMTk5jh/61mB6fZqrbVYvrdBfG7B0Zoksy6g2rLTOxNQ4mzbPMn71PvwwIO7HtNdaNNdajvmkiZOU3mAAeU6SJCTOu6VJ5niveenRC2nKQrRWCAgqdjwO2LlPaZqWEF2RdBkHR2mRF9X6ciqdkfJbX8gf8dowA20uNQmCvs0+GckuHXxS6IJ6nsIPgzL28gMPsclHSOXmIHn0uz16rXYJLvuhT2OywcrCEiuXbDYPwr1ekQ4SQLO21KTf6ZImNq6t1iPCSsDY5DhL5xZI05wsyawcT65LJMF66mHSprVhdWmFxQuXOKaNJSeHAWEUUmvUaIw3mJyZYtf+ncy84UYG/QFrK2v0Oj2SJCEexJw98xLHXjjKoB+TJAlZbNWbs9SSYLSb4SldXKrc3E2glDkfTR61MQhjEMiyslnoBaSJVQbUgHBsJqUkmbHSjX6gQI5yBiwMtRFr45rmJhtU63UqtQq1ulWGk56y9XdfltlqAXV4LjFQvsL3A+r1GoMkJc1zkkHMxZNniOOUpQuLjnmT05io0e/2CcIqWhuq9Spaa1YvNUEIKrWQXqvrkpeQXA+QUjE1O2V7xePUysi4uDNNbYdnntpsueiAFAL8wCvZVribrd/v0et2WDh/ERBElYAdV82DEAy6ffrdgdOiyoZsJcdYyh1rqWj3tfG0kzN3Q8qEkuuIxjiyiZX+GWosCQRpmkNqydI6d2INDpKzSi62GlXIWiLBGEd4EZaGtxFrwwz0xrtuIapWqY2NMbd1jkoYsdZu0Wq13QQ0my0ncUqapiR9jZESk2RATJykNutXHvVGnfl9e0jimCSOqY/VqU/USeOE2ngNMPR7MXmWApqoFpJnhsnZMaSE1aU27VXrzYLA5+zx0yWmiLZeJcsyZ5h2wGohw1O0UBSYrGW0O9U9OewDKtowVpfWCKIAjEH5EmM8y99MHZVO4/T6TTlfs6yfSzcDSsoh8UMYS5K2mjvko+iGcsNpy387Qk0xDMxYlRPjGRcyWb1U4eJ6CRhpE1EtX2OVpKKb0BhNksRkeUacJAgBYWQTjdXlNaT0CCI7ZjpJUuuxsBc9CAMCX1OJIiphQBonzrs22LZnB3mW01lr2Sa4akiWZG5koSLBXoggtPBTr51R82VJ3MszR9LQmjxLXXasy61WO+Sg4KSWXCKn8iGFtEp4DggtmtfCakgYhZa/muUIUhvXxpmL9wpWFFjLYt0UZIQojU4oWfJnbT3dGq9SEuPbJj/p4vSwZnefZJDQaw/cOJoBUWSVU4rRk17gYfIcoSwuW3w3+VrzoFYy0cPzfXzfd0NPPajXHazTQ2pBp9UnCOyMoEJkVSowA0Gc5yAMK2cNYClw8SDlQq7RQtpat5KsLqyQDLLSoLTWKCXI0gylbHLleZIgDCyR2gAmdbNCbZuGKUTLnCMR4MbeFEQPUVZmtAYhbbG7MDQDJSw2LN1YLymwntEzAqMl4A8rPGL4dDvzyc4e9TzPUhCVKitTeW7QqU1wdK5LsB8JSIOMBYkbtJAmCSuXFpjeNEOtUbczTCM7Vjzu91GOXVK0orzmxnGHKsAMcgZJl0udhCRJ6HV7FgDvD6wMTBy70X5OclE5Vo9nZ8YrZ0C2J9zGY1prK5Pjko8gCtm8cyvt1TYrF5ZKwnNYrZDGcdmnY0zuQHtIYlsp8lwjnMkd0dhhnUIP2ygwljUkXlY8KFAI+wBl+wWliToOp8AlhArPs94vzwupLuF4rLYUWjC1MJbgnaep1RUVuJq7g9HcB/qBQzkcL0E7wofnKYzxiSoRQeijPMuB8EKPMAeM8+YUFSf52oOZnv76E3ieX5KOlbIXx04dLjoR3YBTLdBaWEEvg5thPpyDJJXVIZJKQZJCblDahgFJbAH4IArZunee1kqLfrtrQ4fQNoNJ5bFpboY07SMl5Dq3I7cDj4LUawqmvkvabEOHc3LGwu2WID1swYAS/SqQs9JopZQIbyhDo5SV8skyq5kf92MHA1mPr7O8LIcqTw77jpzRK085ppOj0XnSMfJlSbWzpGnX5iGs9qod1aNQUlGpVTAa4m7fSu8oW66VnnztlTqFkgQVW7MuYCQ7m9NepDTNLFljkJLGdiy0EHZkn1BuS3QqbVo77qIRmCJWEqKkpBkMg14fow21sRqTs1OsLa0y6A3s5qs02mQ2eREQxxYzVb5yN41A6KFmqTUMOWqdNusdwUYt05/CYdpDwhqm7/t2ApzOhiN1AC8IkEojaKOk5YQWPAMx+rej2Y2uwnsrVZCScV2mQylGO3Iyc4wu2+AXVAI8zwcDQRRgNChflbAexiCVLQBsxNq4JMlTViYwzWwyIbCNcMb1xAiJ8BR+5AO2R1wXDCBh+0CEFLaz0xsaklL2NRXPp73axGjn9Yw1qHSQIBDM792GlMKC550uWrvZTNKOq8mSrJy1ZGEXgVYS7SnbzvmyrFZog85xSVax/TuaG7iKlwBjK0eFahxQZv5xf0C/07eJVMVpwpdxqyh7i9zbDSNZY0oYSHpuFHnBJ7WnEozd5qVwE/qEcefNRpte6BOEPsZQjh73A89xTtVrcF68km7+uXDGZrdsUQg4OC8ppR3TbZRwVeth7R0cPli+q82Wq9UK83t2snB+gXgwsJxQh5/6gYd0rSV+4LN52xxryyssnr1oG9KkJAhDpOq7LFnZRMpIJ+9tBcEMDtnBdmZmmUaU6E9R1bJ/2221yMaLlgsAicG2JGtjSrVl37eG8gp1kJd5Y3suivNhDdEPVDlip4C4bEVOYodB2B4rmVnmvcVVfcLI9jcZPSRNl90Lo/H0ZV4bZ6Bi6BEK2EhrjRJ2aIKnrMfK7JMtfMIwtgOGvNCRns7i97V6jZ37dpbzLYcJjDMaUwxa1UxvmmVyepqVxSVay6vovPCmylWzPCAjz2ysLIWPwVDmScbFdKY4Elkah9EaLZyaiBIu0bM3m8QauxYaocE4zqf0ivh7qONpN5VXWol9jr1jlGczcc+zu4DWBbxlz5Ule0tyKUuZdS/wCKMAqex31bku+6OMtrGsFfANfjgX/vtcGyfcQMm3LW9PW+fWTjaweM7QsIQqujdNSbgtfldkq0Y472o0gW/ZQKmbYDFaC1RCYJRBqtxNeIPN83PMbN7Ewrnz5Jmm3+2jnBeXQhCEPhgf5fsEkfU6QRTieYpzJ88R93oghIWpXA6stSYXtt9KKUkQeEOBWDRCuwY2adDGoRTKenhLLB4R9nLfcZ03M8Z9nrExpfOgBUFauDJmOcanYHkJy5KSLkH1Qx/lKyvk4PDVWsMKUmhtSNLXmHCDu58ZZqLD7czoodcoVJRFYY8FhkOxvdnXjGbNtl/cXhxP+XjKs6ok66AS+2KlFL7nWbKEsQnC/O4dzG7ZxKVzF2ktrzA+PUGlZrs1Pd93Fawh1JVnGcsXl8iSxMI/mpIFJJUs42epLLm4gIWEkJZXKO2XyXPKWNqYQvEDF6APYSrH4XZnzyDdNi8QCFcKlZ7tfi1OSlFZknlOllnidaHZ5AVWKNdoQ56l1N1AMMt2SpCeIqq9xqZ8wHCbLz1C6eGcgQpX2nPQzvoNTgzjz3WQjsvepXT9SAIlbUuEJTTnZYZRbMgWuFcljphlOZValT0H95EmVtbbEosl5QRhMewVStMU3/ccRisR+TBOFlJglFf26isXZ2vpwH8zsv8KXXrrwltKKcgL7qv7zCJBKsVqsQkaDq4qYmXjEsPhyXQQFJIoijh4437bzBeF5LndMbI0wwvtELJKvUalXiOqVsu+/8u9NjYGFcVGKEZq2kXZ0JQECalcKc8Gq0Mnaveq9YZbamm5Ph6XmPhK4nvKyuToYUyqtQY1fLnWGuy4TpSUTIxZXczi2Eb/YAoNJYfDlqNzik5N47zd8PGi/z/PATe8oATXyUtstzBOY8DpkVmWvBjW9wtIzXrroREXu4MwlIlnFIW2TdvYOalJnJDpLtkgsQ17gU91rEq1XiOqValUKvhu0pyQFmHYiLWB+qCUKPa6OZQOU4TiYriTXrbTOo4iIx628MJ2fx9CPUWC4TBR6eSwcycWa9wWX4xtwRiM6wMvvaurq9vjGJJChoaqXcwmS+MrmtaKxMhojUitV7R9RMJ+SVfH17kz6jLbH4YwdksXI9m4TSLRTvpHj4RJwnp3Ja1ko+f7FB3Jea5prrYdn8G+T1ipMD5jS53VRt2y9D3f6Q6MqC4LAa85D+qyVK1BFXe9MOV275yjA6ftOJnCSApPNarHycsCALAXOHd94EoqC0JLYevTMBR6dU8uqkUISlzRipu5evtIQlIY6VBC0RqpLVv6ENo4z2hNlqTrFOaElGX/pMbNJyqTPofzuk9Zh1oUBgpWeMHFpgjL11SenWKSpzmdVs8SmV0RQCqPsBLRmJykUq9Tq9eIqhXXiKfcHM+h9zbrkFbhZCUv/9pYD7qOCSFGsnH7OzFihBqLhxaNZGW3I0MvWzzPVnTsGxlt0Jaai1cIPzCcclFodRZYoTU8nAMeetESdC8ijaJa7ryWdMcjlSqpbcZYUa6MDG2GnlEKgXbYj9SQU2h5urhTORyzrNgP4bHyGIRE+UVs7kbF6JxL55cwxpT82VqlQrVRo1qvE1VrhFFop4coWwotKlVQfD4jlSpX5RIj3IPLvDbOQAtoRMkS4F63pWBPlHT0MYHF+QQg1IiScZHlu8DUFCfVDEMHa1vaKrh5Q08wjPMKDwkIGxfmujDIYQ2+uICFF7OkYGvAlXqNTrs94vFcrFj+e72CcZmGl8/GJXcOH3bhTmGWUliOqR9Y5MBobZX64oGt/gQBQaVGEIVUahUq1YrtkYoigjByxijLVpnR1mNjhgmYRR1Gf1/Imb/GGPW47Xu01lxc/FHHakY8LAikcq8TI0bq3O5owlpMAjFgy6fakAsQuc2k3T6JEK4E44ym8FSqTGAoa+u4LX1U/MA4xGDbnp14gcfC2XNuZLbbpt1BCTmiEDLyFa2BU9bxtZtDaqs3ijD0SiQDo8m1ZtC3Rik9j0a9RhhFVGpV5yWrBGGIct6x2JnkSNhQXgJ3TkfuqQK7sj+64og0ozLil3dtaAw6zHqHsZYx2tZYTJGQFBl98bN8pdc0BXaqAcvntAbktitReAo3FlBQxlSygLqGsYU9PkHpHY1xDHfHQC7EvoRwpU9t8PyAqc2zZDpjbWmFrGjsG6kT2n+7DBz73nlpmJa57ymJEuAHjniMJk1y11pi5cYbkzUq1QpRrUIYhkSVCkEYoZTnPLXbgeTQAEtBNqwN6jx3IsAFkuK+bwHxATgR3FcgJZdxbZyBSpyXWG9sFMlT7qAgN9mDQtc9FxQy7FrbbVaKrGyHsNt8YbA2tlt/eocGVnju4iKOZu84QNwOt8KSQdC2EU1YCKrwpsZ5Ns8P2LR1jmqjztrSCt1mG6Mdu94x+I025Ea7BM648eE2kbJdAj5ZmpLnrmkw8IlqFaKq3bZte3OFILC6+Eqqkr8gnGsWiBFPOcL6L88x4CaIjDrNgutQJmlF4rju/S7v2lAcFDE8oeBOyIhanX2eI2VIXPkzx8qmC4zJUBKELxHKatZbDFKUW2V5Tcq6qv13wW8UQtkkowDKi+RNGBebDTFJMYI72mEI1jBzbdnrmZOzqTfqRJWIzkSH5tIqvWbbAd2ibCXJHQ/Aas9nJZfUxpLWK9pYskqlViUKIzzfL1ENgXBs+kKXapjUuW9QnuPi9ixCoCL5L8558friHA1x2aEw7iiYcDnXBg6TLdbw5FhYppBtsUx1rXXJ+LZwnCF3MVGRMUtflbidU0MqSSIYNfJpzlcIl5nrfCTpcdu3wxkFQy9SZPeFJzLCJlyjBJYii86yzCU0PuMTE1SqVTrNFs2lFeJe37VlZHbmkwtzvIpVAIkqI7FkFBKEEZ5SLlZ3lMIigF23K4jh8b3sN4WB2lgcXBfyK35ffLciNrdnaxgzb9Ta0Cxea0s4M8IgcpBIjAJtCjaQ3W6sbI0zYCOcWpywZUxPjngIF8/lmjRJSJKUMBSoYvz0SKwFrnc81w4eGtniNSDt46OUN3DexpU7S7jIxbq+p6xgl5MEl1IQBCFTs9PUxxp0mi1WF5bQSYrvWxgocl4yjCL3d6XMtG0yKMtkch1CYP+ikBYtvbv7gsXf9nmWy4Aw5XuUIhZFUOMQhHKVocJ6tOFyrw2txVNkwcZWXDxfWuwQKLyCFMLpyVvvysiWNLKjUYx8MVqjjc10o2ps6XF+4La1YSgxhKXAYp/DGniRdNnxMeJlzsoavDEgZZHRG6T00Uriex5plrshr5ZFpaSiUq0TRhU8Jem1OqWnrNTqZSxpuQPK7RavhNxGduHiYPCEm1w3WtIfPccjiIg7/PK8iaKluIyl7fOLySFgva4ZSWIv99rAJEmU8ElJUtauylLGknpYYDGyhH4Qjpcph1BOoSCM+3eapCRxXKq4ediBDCWkRHEhh3GpvT4Ovio/uPj1EJi3SZXdL43RSIcwGKXQRuN5hjAIyXIrMWOnjAgqUUR9e2R1nZQqP1t5nu01Gv3uZujZ3Ac7mGokOHI3qizQjtHte8QIyxCK0e9Qvq27se1ONvyFM+TSojfGjW4gzDTs9y5qvyWj3p3Q4npoB2rbeFGWWb5Savh7gHyomwSmVAMRUkAQ4FEMqRrGlOJl3qHwsoXGe5l4iOG2CjgMVVhIbATGUSiMsscREEAUlZdWOiMsboyinl+ch9EkZ/h7m7AMSSJivTcrdoPyMZd1F+FjcRKLHeRb7NjCxii4gtswXhVDf/yak74ZXumRkzsCZNsTWcxCgnL/oQDIlcvUbRIkEG72T5Fs6SFc5TQwjbRtD4VxlkdSGJ+LyYrpwIVqsz2koUCDNYih4dr4rXiWXaPer/B7BemkJGIwhHIY8ZilZLcYNgEOk6PhMRjKr18chjtGdwyGMkwY3mjrD65wksIAaoTFL4bQkhEvuyku49pQdTuj7Z1rjLFNvPkIlQ5AF60Z9iIOMW/7BNvSoEdOXrmJlYZpOaCeJVMohbbFbxfrjZz4kZiv/M/9W494tmFcOGRAveLaOcOwkzSGMWRhZIYRKkbpma0IhEW4VGloxbZblGLNiFcrDNN95dFb3v1uGM0XFrxu12Zor2b0bmJo0GXStc67X761sS0fznNYyFKPbHf2DGpjhhpIgBbD8maB+xntwkolLKRkDJh8GDYZyNIMKaSb2y6dlLdxiY4st7+C+FEeX+kkRSnZWDwuhAXxv5UiTNmZ9LJtsYCWMKCFGSkfDg1uXe2f4riG72E9uTPy4mYVxTNHtnR3GxQhgjYvT3RGKHxIipmM7h5hiIeuO8LLvjbWgxYXxBkmchiHFf3ixVYNWO9TejBTXtAy3ZFg8iImXX/Ha2PKKW8Fe1675Lcsc7qMvQgVKDP7oeEOEQB3SKURmnUGPUy0h17a3mjOyMv3Kr5Y4Q6H1Zvhtl68F2XyVhrqiEFTvvvQSIff0AyPsQwHhiokw9Ly8HwNIYEikb38a+PG0BRyMva2L5Pm0jO62Gxkcx25qMPsfx12555cZp9lHFVwT00p0V3U4rUxqNIzmvJv4S6sjUWdly0gnRKPceZQZNOjWXaxpTPiUV0CuK7kOPK5w/d6mWEW301QEpDXhRfFDWNc8DASBo2ysIZ/r2d7FvezjfdN+T2G58G87Ha/fGsD5yRpZyguYSjaGXhlljkMmtyFdSdZFzM55ehJN+tFvoRwQq/WC4oRaMqKvMKw356y1FnYOFBWcqy31y70GIncCohqxNuuM6LC2sT67Xp4M47Eki/b0kfjQUYMrSBXryc0j75wmNxRxqKOhONsuOgezQvOJ5a/aseDWxqkNsVwte9wMX+Ea8OTJI0usTprgyNbTbnd6/L2NwI7WNXR9Oy2aWOogiBilZeVE4PwSm9ZkJwR0saDWlCw5YvWD4HlRI7eFIVasV1229fC0vlKbz6Su1A+07zMaES5TZT3mymcvSi9XXF+1mXfbnsuEqzR+vtodag4bUNYygyTNvs27rnDU1zclOUE5MLLFqEEIy+8zGsDS50uM9TWAI3TCnWBaLmdG6cUXCQBxokRGEPJyczJSxy1Vq8RRLY9WBY69oDn+SN0NIcACFNenOLPaJbOCHG4JJ/AMK4sPWUBFQ2ZWWXHpSl6qFzS4YyshLEo4tgisaKcAmc/yhqa+3iK8uPQXF55ZwhGRMzc83M93NZF+azhf8WuwUgIVbC+NiqDhw32oEMPidu3hpmjcRn8cI5lEeQLN7HDTliLahWqY5Y95IdW7CrpxaXined5KN+qhBS6ToYR8jBFxmwTCpseiXJrNgxnqxdZsgDK5nTWey9Rvta9uyxuuOFFHs3ui7iUEcMrPrfMprUZqZqZIj90nncUdRjCdIXXK2v1I8HTED1Yb9wSMZJgrjf619wWX3pQUcSU6+NIT9nYMU2LIbPWgPwooNaoWaOs2vEuUhbgu4utagI/iogqtilMKa/sGTLYZjPhwPhyKJgW5dmwnoORhGaY2AwjSlESnQvjGyZ3DLP/kS3SwlrDBEnAMLErvJgZiR2h3D2KNazNv9JoygSo2OoZQQlGMngLdzHcESgM3qyroBVtzy/P1y7n2tBSJwWUQeExAWHVjqWyM9KVUgSliIBl/Xi+VxqB5XUOCcie51EZHxthmAuX4BQXcLgFS1e8zrVGSbulWUBflltvkSULKdfBQusiS3eh89LrFqRoO2RWOnHd4vVFjGg9oR4xtPJkrMNX1xOpCyMdbtllvFoeiygNvghAyvcpdq3R/4n132dUy2qoxvIai0FNEZ+5rbyUCNQGrRS+H1GbqBJVI9uF6HulERozzKCF0HhegHJTf8MwJAwjgrCybrssiCcGyrbj0tM4ooSLMjA4gnKRUYtRdzUsAY5eVnvDWIN0LZzDzyiT+OIfYEbeb7ghuyMWRTw8fPMiJBmtahXg+1DtefiXcdl7WX8Xw0h7NPGCwghHbqqR6/TajUG1cUOnNB4eIvCsl6xV7MjAYkoGlDKNlhOpkZJSUdkPfMIgdBr3iiAIkdIbQlZFKOG2ZGc2JayFGDHU4j+jKeVGRjxSYauj2bZ7obVhh+gIqUbtbhjK2B+Kl7gjGRpBWTAYiQMLbzl0dqL8PkUOV3xG8We4rb/S743GsMUxrveqI6MdnedeD3td3rWBpU47d7JSr1JpFP3aFh7CUErAYAxKCTsA1bdz1D3PK8VXPS/AU15J0xsSMURZubFGLsq2ijK5cFnx6BWwXlMOsfjCSJxxFNipEEVDnizjTps8iXWJ3To/OYItFUnLaFZdbtsjCVxpTJRPLJc1MlNOoyuohEIMK0HFto+xDXGlAbt7oEAUcLF5AdaPfuTLwuDLujbMQDft3Eq1XreKFk4yRhReSoJSVuzfDwK8IMB3g1VtO61yWkijTPmitdb+WztFkcIYrYrxCLgtREkMBsqWktHOy4ITMLxcQ8MrQwdjyp8Lj1R4nnVeb5gGrTPK0s+NZOSvKCq62ByGySTuu9mpIjl5nuL7IbibSxc3I0PvqoviQmG4xTcyo3CTC1WGkaubcvIai0F95w3tFdIIY6dveH6hWR+4wQL2b9/3h5Mn3BZW3OiFw3kFHCSKOM7Fac4jD39XeNORWNIVEIakC+ddxJBIbD8L2+Epi+yedftpaXpFkgUjl1wMvbf7eX3tuzgW913kUGRsdOsd3YqV8stjGh0ZU9hVecOOXIOXj5Yx7kuMvncRArzm2EyWiY6bpuHjBT5+GNpt3PMdsC7t7CQojbNoUygzYWeosjBeZyxyxDsKij54V1sWjHiEwqRZZ0iFMWo0Sgzr9qPBQCGWK0ZeC8XWW1SYCr+ph7FsmTc5WogYJkGj4YJxFmqJRkVMOky+hzdLUTfXGAoZG11KJpaMrXWhsBn5u7gBKb3rMCYuT/YPfrH/FmvjPGjgEUQBYSXCC0KCMHTi/Z6TaVEloLyuj70wOjMEnIfb9jCJEWJYqVGymCAnyhitEIktsMOhgY0kMyPJU/HyEgd1HrBMgNa9B+sqXmUddNTYxLByUxA8Cs9fWOA6lbtRKMgYdHkM0iIhQCFhYz2tJs8Td46s7KKUYmjMJb5rX2eMGYlbhxk9FEourzE2kzXQkCCK8MMQPwhKUnFx8Yq7WUpV/gyUBlYqgkCpZ1TI4gzJxzjDKIzTGVVhUEKMGDojsNBIyZOiTl3ATsOt8hUEFyHc7iDK3+OOodizTXGjlMdA6V2H33HoUcslhsZqjcyVSIVEyuE2XpCfPS9w5BiJEKbIi+z7MvS+ZTVq5CYbDWk2jsu0gQZadZqUfhCVXtMYQ65x4wmHRmeXcOwbF8jLoWxLiQMOraa8iMXWX2rXOy9bwEfDiWrrCcRFIgXFtIshLllctOL1xUeXCZdQ646niGc19vCLG0gzNPDyc0fy/pLgjB5u6/YAyg5UPeKpLXnGbdnKK1tWjHFxrHaCunoYakg372ldIiiEE80d3jS5Xh+vXq61gWNoPLShHNpV7IRFDX0I7cjSABMo+8OtYQ5/V9ztqpCBKaUOi4toa+LFxRTCaoaObnVF9lrQ5YqYb+iNh1FmyQGVo78rqHMj1So5NBL7jgwNdzSmtG9LrvPSMxZe27jEp9ARHaIHQ+iq8I+FAb8cq315kmPj0+HzCnip8MxlMcRY8dtet/u3udw/8BJmI8sEV9aV9V3WxkS+V9aV9T2uKwZ6Zb2q1xUDvbJe1euKgV5Zr+p1xUCvrFf1umKgV9arel0x0CvrVb2uGOiV9apeVwz0ynpVr/8/L2e1cXZrOwgAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":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\n"},"metadata":{}},{"output_type":"display_data","data":{"text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size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["visualize_model(model_ft, mean=mean, std=std)"]},{"cell_type":"code","source":["metrics"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rzZrs_0yZ6sx","outputId":"828a461c-527e-4026-c34d-b43544fcbfab"},"id":"rzZrs_0yZ6sx","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'train_losses': [0.8378587017180034,\n","  0.7185671639743066,\n","  0.7006370623087,\n","  0.6910241164957159,\n","  0.6741008267575718,\n","  0.6755293594577136,\n","  0.6686075033162131,\n","  0.6393027293802909,\n","  0.6390367304972537,\n","  0.6346590471750944,\n","  0.6341406968357483,\n","  0.6331265978365891,\n","  0.6337438616484593,\n","  0.634378069601277,\n","  0.6288583653520475,\n","  0.6295346915511177,\n","  0.6278542980618447,\n","  0.6296825431131007,\n","  0.6303081947018621,\n","  0.6261240330867376],\n"," 'test_losses': [0.7174013494995723,\n","  0.6571713373075531,\n","  0.6615399996570729,\n","  0.6978588148077057,\n","  0.6711742162016128,\n","  0.637704769536175,\n","  0.6457576219298888,\n","  0.6302652476658778,\n","  0.6385800142895633,\n","  0.6354402702722295,\n","  0.6421354471084899,\n","  0.6271654008807592,\n","  0.623437797702935,\n","  0.6173080711566472,\n","  0.6168943388743584,\n","  0.6219512342620003,\n","  0.6253251989734024,\n","  0.6217427474272973,\n","  0.6220892845193592,\n","  0.6175460426798413],\n"," 'train_accuracies': [0.5697619608767381,\n","  0.601579071411737,\n","  0.6111242045722366,\n","  0.6141880744756069,\n","  0.6259721894885695,\n","  0.6246759368371435,\n","  0.6263257129389583,\n","  0.6529578128682536,\n","  0.6507188310157908,\n","  0.6577893000235683,\n","  0.6587320292246053,\n","  0.659439076125383,\n","  0.657553617723309,\n","  0.6559038416214943,\n","  0.6623851048786237,\n","  0.6595569172755127,\n","  0.6623851048786237,\n","  0.6616780579778458,\n","  0.6619137402781051,\n","  0.6626207871788828],\n"," 'eval_accuracies': [0.5103700212114071,\n","  0.6455338204100872,\n","  0.6257365071883102,\n","  0.6438840443082725,\n","  0.6521329248173462,\n","  0.6588498703747349,\n","  0.6410558567051615,\n","  0.6629743106292718,\n","  0.6607353287768089,\n","  0.6634456752297903,\n","  0.6582606646240867,\n","  0.6639170398303088,\n","  0.6678057977845864,\n","  0.6720480791892529,\n","  0.6696912561866604,\n","  0.6685128446853642,\n","  0.6696912561866604,\n","  0.6686306858354938,\n","  0.6718123968889936,\n","  0.6700447796370493]}"]},"metadata":{},"execution_count":78}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","epochs = range(1, len(metrics['train_losses']) + 1)\n","\n","plt.plot(list(epochs), metrics['train_losses'], 'r--', label='Train Losses')\n","plt.plot(list(epochs), metrics['test_losses'], 'b--', label='Eval Losses')\n","plt.plot(list(epochs), metrics['train_accuracies'], 'r:', label='Train Accuracies')\n","plt.plot(list(epochs), metrics['eval_accuracies'], 'b:', label='Eval Accuracies')\n","\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss/Accuracy')\n","plt.title('Training and Evaluation Metrics')\n","plt.legend()\n","\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"7g4iPUQlY04y","outputId":"e057ee69-ecdd-441b-bd91-da018117b4d8"},"id":"7g4iPUQlY04y","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkAAAAHHCAYAAABXx+fLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACffUlEQVR4nOzdd1QUVxsH4N/SliaggBRFsKBiwYIlFtREDFiIJVEiFvBTk1hiDNFEY8QWJVFjjzH2Eo3GEjV2RI29Yq+gIliQooAUKbv3++Nmy8CC9NmF9zlnDrt378zcWRbm3VsljDEGQgghhJBKRE/sAhBCCCGElDcKgAghhBBS6VAARAghhJBKhwIgQgghhFQ6FAARQgghpNKhAIgQQgghlQ4FQIQQQgipdCgAIoQQQkilQwEQIYQQQiodCoAIKQOBgYFwcXEp1r7Tp0+HRCIp3QJpmaioKEgkEqxfv17souTLxcUFgYGBopxbF96f8laSvylCNKEAiFQqEomkUNuJEyfELioBcOLEiQJ/T1u3bhW7iCWyZcsWLFq0SOxiCAQGBkIikcDCwgIZGRl5Xo+IiFC+//Pnzy/y8dPT0zF9+nT6GyOiMxC7AISUp02bNgmeb9y4EaGhoXnS3dzcSnSeVatWQS6XF2vfH374AZMmTSrR+SuacePGoXXr1nnS27VrJ0JpSs+WLVtw69YtjB8/XpDu7OyMjIwMGBoailIuAwMDpKen459//sGAAQMEr23evBnGxsZ4+/ZtsY6dnp6OGTNmAAC6dOlS6P1K8jdFiCYUAJFKZfDgwYLn58+fR2hoaJ703NLT02Fqalro85TkxmVgYAADA/rTVOfp6YlPPvlE7GKUG4lEAmNjY9HOL5VK0aFDB/z55595AqAtW7agZ8+e2LlzZ7mUJS0tDWZmZqIFg6TioiYwQnLp0qULmjRpgitXrqBTp04wNTXF999/DwDYs2cPevbsCUdHR0ilUtStWxezZs2CTCYTHCN3fwVFn4758+dj5cqVqFu3LqRSKVq3bo1Lly4J9tXUB0gikWDs2LHYvXs3mjRpAqlUisaNG+PQoUN5yn/ixAm0atUKxsbGqFu3Ln7//fdC9ys6deoU+vfvj1q1akEqlcLJyQlff/11nqaQwMBAmJub49mzZ+jTpw/Mzc1ha2uLCRMm5HkvkpKSEBgYCEtLS1hZWSEgIABJSUnvLEtRNGnSBO+//36edLlcjho1agiCp/nz56N9+/awtraGiYkJPDw8sGPHjneeI7/3cP369ZBIJIiKilKmFeZz0qVLF+zfvx9PnjxRNikpPjP59QE6duwYPD09YWZmBisrK/Tu3Rt3797VWM7IyEgEBgbCysoKlpaWGDZsGNLT0995nQr+/v44ePCg4Hd16dIlREREwN/fX+M+SUlJGD9+PJycnCCVSlGvXj38/PPPypqbqKgo2NraAgBmzJihvO7p06cDUH2uHj58iB49eqBKlSoYNGiQ8rXcfYDkcjkWL16Mpk2bwtjYGLa2tvDx8cHly5eVeUJDQ9GxY0dYWVnB3NwcDRo0UP49k8qNvmYSokFiYiK6d++OTz/9FIMHD4adnR0AfrMzNzdHUFAQzM3NcezYMQQHByMlJQXz5s1753G3bNmCN2/e4PPPP4dEIsHcuXPRr18/PHr06J3fcE+fPo1du3Zh9OjRqFKlCpYsWYKPP/4Y0dHRsLa2BgBcvXoVPj4+cHBwwIwZMyCTyTBz5kzlTeddtm/fjvT0dIwaNQrW1ta4ePEili5diqdPn2L79u2CvDKZDN7e3mjbti3mz5+Po0eP4pdffkHdunUxatQoAABjDL1798bp06fxxRdfwM3NDX///TcCAgIKVR6FN2/eICEhIU+6tbU1JBIJ/Pz8MH36dMTGxsLe3l7wnj1//hyffvqpMm3x4sX46KOPMGjQIGRlZWHr1q3o378/9u3bh549exapXPkpzOdkypQpSE5OxtOnT7Fw4UIAgLm5eb7HPHr0KLp37446depg+vTpyMjIwNKlS9GhQweEh4fnCQ4GDBiA2rVrIyQkBOHh4Vi9ejWqV6+On3/+uVDX0K9fP3zxxRfYtWsX/ve//wHgn9+GDRuiZcuWefKnp6ejc+fOePbsGT7//HPUqlULZ8+exeTJk/HixQssWrQItra2+O233zBq1Cj07dsX/fr1AwC4u7srj5OTkwNvb2907NgR8+fPL7Dmdfjw4Vi/fj26d++OESNGICcnB6dOncL58+fRqlUr3L59G7169YK7uztmzpwJqVSKyMhInDlzplDvAangGCGV2JgxY1juP4POnTszAGzFihV58qenp+dJ+/zzz5mpqSl7+/atMi0gIIA5Ozsrnz9+/JgBYNbW1uzVq1fK9D179jAA7J9//lGmTZs2LU+ZADAjIyMWGRmpTLt+/ToDwJYuXapM8/X1ZaampuzZs2fKtIiICGZgYJDnmJpour6QkBAmkUjYkydPBNcHgM2cOVOQt0WLFszDw0P5fPfu3QwAmzt3rjItJyeHeXp6MgBs3bp1BZbn+PHjDEC+24sXLxhjjN2/fz/Pe8EYY6NHj2bm5uaC68p9jVlZWaxJkybsgw8+EKQ7OzuzgIAA5XNNvxfGGFu3bh0DwB4/fpzvORjT/Dnp2bOn4HOioPi8qL8/zZs3Z9WrV2eJiYnKtOvXrzM9PT02dOjQPOX83//+Jzhm3759mbW1dZ5z5RYQEMDMzMwYY4x98sknrGvXrowxxmQyGbO3t2czZsxQlm/evHnK/WbNmsXMzMzYgwcPBMebNGkS09fXZ9HR0YwxxuLj4xkANm3aNI3nBsAmTZqk8TX19+rYsWMMABs3blyevHK5nDHG2MKFCxkAFh8f/87rJpUPNYERooFUKsWwYcPypJuYmCgfK2olPD09kZ6ejnv37r3zuH5+fqhataryuaenJwDg0aNH79zXy8sLdevWVT53d3eHhYWFcl+ZTIajR4+iT58+cHR0VOarV68eunfv/s7jA8LrS0tLQ0JCAtq3bw/GGK5evZon/xdffCF47unpKbiWAwcOwMDAQFkjBAD6+vr48ssvC1UeheDgYISGhubZqlWrBgCoX78+mjdvjm3btin3kclk2LFjB3x9fQXXpf749evXSE5OhqenJ8LDw4tUpoKU9HOS24sXL3Dt2jUEBgYqrxngn4Fu3brhwIEDefbR9LtJTExESkpKoc/r7++PEydOIDY2FseOHUNsbGy+zV/bt2+Hp6cnqlatioSEBOXm5eUFmUyGkydPFvq86p+X/OzcuRMSiQTTpk3L85qiqdLKygoAb5KkDtQkN2oCI0SDGjVqwMjIKE/67du38cMPP+DYsWN5biTJycnvPG6tWrUEzxXB0OvXr4u8r2J/xb5xcXHIyMhAvXr18uTTlKZJdHQ0goODsXfv3jxlyn19ij4X+ZUHAJ48eQIHB4c8TTsNGjQoVHkUmjZtCi8vrwLz+Pn54fvvv8ezZ89Qo0YNnDhxAnFxcfDz8xPk27dvH3788Udcu3YNmZmZyvTSnHuppJ+T3J48eQJA8/vm5uaGw4cPKzsLKxT0WbOwsCjUeRX9cLZt24Zr166hdevWqFevnqC/k0JERARu3LiRb3NrXFxcoc5pYGCAmjVrvjPfw4cP4ejoKAgIc/Pz88Pq1asxYsQITJo0CV27dkW/fv3wySefQE+Pvv9XdhQAEaKB+jd4haSkJHTu3BkWFhaYOXMm6tatC2NjY4SHh+O7774r1DdMfX19jemMsTLdtzBkMhm6deuGV69e4bvvvkPDhg1hZmaGZ8+eITAwMM/15Vcesfj5+WHy5MnYvn07xo8fj7/++guWlpbw8fFR5jl16hQ++ugjdOrUCcuXL4eDgwMMDQ2xbt06bNmypcDj5xcgaer0XdLPSWkojc+LVCpFv379sGHDBjx69EjZWVkTuVyObt264dtvv9X4ev369Qt9ztIKTkxMTHDy5EkcP34c+/fvx6FDh7Bt2zZ88MEHOHLkiNZ9hkn5ogCIkEI6ceIEEhMTsWvXLnTq1EmZ/vjxYxFLpVK9enUYGxsjMjIyz2ua0nK7efMmHjx4gA0bNmDo0KHK9NDQ0GKXydnZGWFhYUhNTRXUAt2/f7/Yx8xP7dq10aZNG2zbtg1jx47Frl270KdPH0ilUmWenTt3wtjYGIcPHxakr1u37p3HV9SgJCUlKZtWAFXtjEJRPieFrXVydnYGoPl9u3fvHmxsbAS1P6XJ398fa9euhZ6enqAzeW5169ZFamrqO2vqSqumrW7dujh8+DBevXpVYC2Qnp4eunbtiq5du2LBggWYM2cOpkyZguPHj7+zrKRiozpAQgpJ8W1R/Rt0VlYWli9fLlaRBPT19eHl5YXdu3fj+fPnyvTIyEgcPHiwUPsDwutjjGHx4sXFLlOPHj2Qk5OD3377TZkmk8mwdOnSYh+zIH5+fjh//jzWrl2LhISEPM1f+vr6kEgkglqbqKgo7N69+53HVvS/Uu/LkpaWhg0bNuQ5B1C4z4mZmVmhmsQcHBzQvHlzbNiwQTAs/datWzhy5Ah69OjxzmMU1/vvv49Zs2Zh2bJlghF2uQ0YMADnzp3D4cOH87yWlJSEnJwcAFCO6irpVAgff/wxGGPKSRXVKd77V69e5XmtefPmACBo/iSVE9UAEVJI7du3R9WqVREQEIBx48ZBIpFg06ZNpdYEVRqmT5+OI0eOoEOHDhg1ahRkMhmWLVuGJk2a4Nq1awXu27BhQ9StWxcTJkzAs2fPYGFhgZ07dxaqf1J+fH190aFDB0yaNAlRUVFo1KgRdu3aVeR+MKdOndI487C7u7tgCPWAAQMwYcIETJgwAdWqVcvzDb9nz55YsGABfHx84O/vj7i4OPz666+oV68ebty4UWAZPvzwQ9SqVQvDhw/HxIkToa+vj7Vr18LW1hbR0dHKfEX5nHh4eGDbtm0ICgpC69atYW5uDl9fX43nnzdvHrp374527dph+PDhymHwlpaWBTZNlZSenh5++OGHd+abOHEi9u7di169eiEwMBAeHh5IS0vDzZs3sWPHDkRFRcHGxgYmJiZo1KgRtm3bhvr166NatWpo0qQJmjRpUqRyvf/++xgyZAiWLFmCiIgI+Pj4QC6X49SpU3j//fcxduxYzJw5EydPnkTPnj3h7OyMuLg4LF++HDVr1kTHjh2L+5aQikKcwWeEaIf8hsE3btxYY/4zZ86w9957j5mYmDBHR0f27bffssOHDzMA7Pjx48p8+Q2DVx82rIBcQ4LzGwY/ZsyYPPvmHqrNGGNhYWGsRYsWzMjIiNWtW5etXr2affPNN8zY2Difd0Hlzp07zMvLi5mbmzMbGxs2cuRI5XB79SHZ6kOl1Wkqe2JiIhsyZAizsLBglpaWbMiQIezq1aulMgxe01DqDh06MABsxIgRGo+5Zs0a5urqyqRSKWvYsCFbt26dxnJrem+vXLnC2rZty4yMjFitWrXYggULNA6DL+znJDU1lfn7+zMrKysGQPmZ0TQMnjHGjh49yjp06MBMTEyYhYUF8/X1ZXfu3BHkUVxL7qHfmsqpSX6/W3X5fZ7fvHnDJk+ezOrVq8eMjIyYjY0Na9++PZs/fz7LyspS5jt79izz8PBgRkZGgt9jQefO/TfFGJ9SYd68eaxhw4bMyMiI2drasu7du7MrV64wxvjfQu/evZmjoyMzMjJijo6ObODAgXmG6pPKScKYFn19JYSUiT59+uD27duIiIgQuyiEEKIVqA8QIRVM7mUrIiIicODAgSItPEkIIRUd1QARUsE4ODggMDAQderUwZMnT/Dbb78hMzMTV69ehaurq9jFI4QQrUCdoAmpYHx8fPDnn38iNjYWUqkU7dq1w5w5cyj4IYQQNVQDRAghhJBKh/oAEUIIIaTSoQCIEEIIIZUO9QHSQC6X4/nz56hSpUqpLpBICCGEkLLDGMObN2/g6Oj47jXlRJyDiDHG2LJly5izszOTSqWsTZs27MKFCwXmX7hwIatfvz4zNjZmNWvWZOPHj2cZGRnK1xWTgKlvDRo0KFKZYmJiCpx8jTbaaKONNtpo094tJibmnfd6UWuAFFPAr1ixAm3btsWiRYvg7e2N+/fvo3r16nnyb9myBZMmTcLatWvRvn17PHjwAIGBgZBIJFiwYIEyX+PGjXH06FHlcwODol1mlSpVAAAxMTGwsLAo5tURQgghpDylpKTAyclJeR8viKgB0IIFCzBy5EgMGzYMALBixQrs378fa9euxaRJk/LkP3v2LDp06AB/f38AgIuLCwYOHIgLFy4I8hkYGBS4aN+7KJq9LCwsKAAihBBCdExhuq+I1gk6KysLV65cESxWqKenBy8vL5w7d07jPu3bt8eVK1dw8eJFAMCjR49w4MCBPCshR0REwNHREXXq1MGgQYMECxVqkpmZiZSUFMFGCCGEkIpLtBqghIQEyGQy2NnZCdLt7Oxw7949jfv4+/sjISEBHTt2BGMMOTk5+OKLL/D9998r87Rt2xbr169HgwYN8OLFC8yYMQOenp64detWvlViISEhmDFjRuldHCGEEEK0mk4Ngz9x4gTmzJmD5cuXIzw8HLt27cL+/fsxa9YsZZ7u3bujf//+cHd3h7e3Nw4cOICkpCT89ddf+R538uTJSE5OVm4xMTHlcTmEEEIIEYloNUA2NjbQ19fHy5cvBekvX77Mt//O1KlTMWTIEIwYMQIA0LRpU6SlpeGzzz7DlClTNA55s7KyQv369REZGZlvWaRSKaRSaQmuhhBCKg+ZTIbs7Gyxi0EqIUNDQ+jr65fKsUQLgIyMjODh4YGwsDD06dMHAJ9/JywsDGPHjtW4T3p6ep4gR/FGsHxW9EhNTcXDhw8xZMiQ0is8IYRUQowxxMbGIikpSeyikErMysoK9vb2JZ6nT9RRYEFBQQgICECrVq3Qpk0bLFq0CGlpacpRYUOHDkWNGjUQEhICAPD19cWCBQvQokULtG3bFpGRkZg6dSp8fX2VgdCECRPg6+sLZ2dnPH/+HNOmTYO+vj4GDhwo2nUSQkhFoAh+qlevDlNTU5oolpQrxhjS09MRFxcHAHBwcCjR8UQNgPz8/BAfH4/g4GDExsaiefPmOHTokLJjdHR0tKDG54cffoBEIsEPP/yAZ8+ewdbWFr6+vpg9e7Yyz9OnTzFw4EAkJibC1tYWHTt2xPnz52Fra1vu10cIIRWFTCZTBj/W1tZiF4dUUiYmJgCAuLg4VK9evUTNYbQavAYpKSmwtLREcnIyzQNECCEA3r59i8ePH8PFxUV5EyJEDBkZGYiKikLt2rVhbGwseK0o92+dGgVGCCFEXNTsRcRWWp9BCoAIIYQQUulQAEQIIYQUkYuLCxYtWiR2MUgJUABECCGkwpJIJAVu06dPL9ZxL126hM8++6xEZevSpQvGjx9fomOQ4hN1FBghhBBSll68eKF8vG3bNgQHB+P+/fvKNHNzc+VjxhhkMhkMDN59a6SRxbqPaoDK26tXQAGzUhNCCCk99vb2ys3S0hISiUT5/N69e6hSpQoOHjwIDw8PSKVSnD59Gg8fPkTv3r1hZ2cHc3NztG7dGkePHhUcN3cTmEQiwerVq9G3b1+YmprC1dUVe/fuLVHZd+7cicaNG0MqlcLFxQW//PKL4PXly5fD1dUVxsbGsLOzwyeffKJ8bceOHWjatClMTExgbW0NLy8vpKWlKV9fvXo13NzcYGxsjIYNG2L58uXK17KysjB27Fg4ODjA2NgYzs7Oyvn4KhKqASpPW7cCAwcCXboAx4+LXRpCCCkdajfWPPT1AfWhygXl1dMD1IfY55fXzKxo5XuHSZMmYf78+ahTpw6qVq2KmJgY9OjRA7Nnz4ZUKsXGjRvh6+uL+/fvo1atWvkeZ8aMGZg7dy7mzZuHpUuXYtCgQXjy5AmqVatW5DJduXIFAwYMwPTp0+Hn54ezZ89i9OjRsLa2RmBgIC5fvoxx48Zh06ZNaN++PV69eoVTp04B4LVeAwcOxNy5c9G3b1+8efMGp06dUq6YsHnzZgQHB2PZsmVo0aIFrl69ipEjR8LMzAwBAQFYsmQJ9u7di7/++gu1atVCTExMxVwjk5E8kpOTGQCWnJxcuge+do0xgDFLS8bk8tI9NiGElKGMjAx2584dlpGRkfdFIP+tRw9hXlPT/PN27izMa2OjOV8xrVu3jllaWiqfHz9+nAFgu3fvfue+jRs3ZkuXLlU+d3Z2ZgsXLlQ+B8B++OEH5fPU1FQGgB08eDDfY3bu3Jl99dVXGl/z9/dn3bp1E6RNnDiRNWrUiDHG2M6dO5mFhQVLSUnJs++VK1cYABYVFaXx2HXr1mVbtmwRpM2aNYu1a9eOMcbYl19+yT744AMm19L7VEGfxaLcv6kJrDw1agQYGQHJycDjx2KXhhBCCIBWrVoJnqempmLChAlwc3ODlZUVzM3NcffuXURHRxd4HHd3d+VjMzMzWFhYKJdtKKq7d++iQ4cOgrQOHTogIiICMpkM3bp1g7OzM+rUqYMhQ4Zg8+bNSE9PBwA0a9YMXbt2RdOmTdG/f3+sWrUKr1+/BgCkpaXh4cOHGD58OMzNzZXbjz/+iIcPHwIAAgMDce3aNTRo0ADjxo3DkSNHinUN2o6awMqToSHQtClw5QoQHg7UqSN2iQghpORSU/N/LfdSBQUFBLkWu0ZUVLGLVBRmuZrUJkyYgNDQUMyfPx/16tWDiYkJPvnkE2RlZRV4HENDQ8FziUQCuVxe6uUFgCpVqiA8PBwnTpzAkSNHEBwcjOnTp+PSpUuwsrJCaGgozp49iyNHjmDp0qWYMmUKLly4AFNTUwDAqlWr0LZtW8ExFctKtGzZEo8fP8bBgwdx9OhRDBgwAF5eXtixY0eZXItYqAaovLVsyX9evSpuOQghpLSYmeW/5VqqoMC8uZfYyC9fGTtz5gwCAwPRt29fNG3aFPb29ogqp2BMwc3NDWfOnMlTrvr16ysDFQMDA3h5eWHu3Lm4ceMGoqKicOzYMQA8+OrQoQNmzJiBq1evwsjICH///Tfs7Ozg6OiIR48eoV69eoKtdu3aynNZWFjAz88Pq1atwrZt27Bz5068evWq/N6AckA1QOVNEQCFh4tbDkIIIRq5urpi165d8PX1hUQiwdSpU8usJic+Ph7Xrl0TpDk4OOCbb75B69atMWvWLPj5+eHcuXNYtmyZcrTWvn378OjRI3Tq1AlVq1bFgQMHIJfL0aBBA1y4cAFhYWH48MMPUb16dVy4cAHx8fFwc3MDwDtrjxs3DpaWlvDx8UFmZiYuX76M169fIygoCAsWLICDgwNatGgBPT09bN++Hfb29rCysiqT90AsFACVtxYt+M/wcN6dj9bVIYQQrbJgwQL873//Q/v27WFjY4PvvvsOKSkpZXKuLVu2YMuWLYK0WbNm4YcffsBff/2F4OBgzJo1Cw4ODpg5cyYCAwMBAFZWVti1axemT5+Ot2/fwtXVFX/++ScaN26Mu3fv4uTJk1i0aBFSUlLg7OyMX375Bd27dwcAjBgxAqamppg3bx4mTpwIMzMzNG3aVDkpY5UqVTB37lxERERAX18frVu3xoEDB6CXu4lSx9Fq8BqU6WrwGRnA11/zQGjEiLzt44QQooUUq8FrWoGbkPJU0GexKPdvqgEqbyYmwIoVYpeCEEIIqdQqVn0WIYQQQkghUAAkhsxM4NIl4NAhsUtCCCGEVErUBCaG8+f5chi1agFPnohdGkIIIaTSoRogMTRvzn9GRwOJiaIWhRBCCKmMKAASg6UlUK8ef0wTIhJCCCHljgIgsajPB0QIIYSQckUBkFhoSQxCCCFENBQAiYWWxCCEEEJEQwGQWBRNYBERwJs34paFEEJIiURFRUEikeRZ14toLwqAxGJrCyxbBoSGAlKp2KUhhJAKKzAwEBKJJM/m4+NTruXo0qWLcr0tIj6aB0hMY8aIXQJCCKkUfHx8sG7dOkGalL58VmpUA0QIIaTCk0qlsLe3F2xVq1YFAPj7+8PPz0+QPzs7GzY2Nti4cSMA4NChQ+jYsSOsrKxgbW2NXr164eHDh6Vaxp07d6Jx48aQSqVwcXHBL7/8Inh9+fLlcHV1hbGxMezs7PDJJ58oX9uxYweaNm0KExMTWFtbw8vLC2lpacrXV69eDTc3NxgbG6Nhw4ZYvny58rWsrCyMHTsWDg4OMDY2hrOzM0JCQkr12rQR1QCJKSUF2L8fePYMmDBB7NIQQkixqN1n89DXB9QX7C4or54eXy/6XXnNzIpWvncZNGgQ+vfvj9TUVJibmwMADh8+jPT0dPTt2/e/sqQhKCgI7u7uSE1NRXBwMPr27Ytr165BT6/kdQlXrlzBgAEDMH36dPj5+eHs2bMYPXo0rK2tERgYiMuXL2PcuHHYtGkT2rdvj1evXuHUqVMAgBcvXmDgwIGYO3cu+vbtizdv3uDUqVNgjAEANm/ejODgYCxbtgwtWrTA1atXMXLkSJiZmSEgIABLlizB3r178ddff6FWrVqIiYlBTExMia9J6zGSR3JyMgPAkpOTy/ZE0dGMAYzp6zOWkVG25yKEkBLIyMhgd+7cYRka/lcB+W89egjzmprmn7dzZ2FeGxvN+YoqICCA6evrMzMzM8E2e/Zsxhhj2dnZzMbGhm3cuFG5z8CBA5mfn1++x4yPj2cA2M2bNxljjD1+/JgBYFevXs13n86dO7OvvvpK42v+/v6sW7dugrSJEyeyRo0aMcYY27lzJ7OwsGApKSl59r1y5QoDwKKiojQeu27dumzLli2CtFmzZrF27doxxhj78ssv2QcffMDkcnm+ZdcmBX0Wi3L/piYwMdWsCdjYADIZcPOm2KUhhJAK6/3338e1a9cE2xdffAEAMDAwwIABA7B582YAvLZnz549GDRokHL/iIgIDBw4EHXq1IGFhQVcXFwAANHR0aVSvrt376JDhw6CtA4dOiAiIgIymQzdunWDs7Mz6tSpgyFDhmDz5s1IT08HADRr1gxdu3ZF06ZN0b9/f6xatQqvX79WXsvDhw8xfPhwmJubK7cff/xR2YQXGBiIa9euoUGDBhg3bhyOHDlSKtek7agJTEwSCR8OHxrKJ0Rs3VrsEhFCSJGlpub/mr6+8HlcXP55c7ckRUUVu0h5mJmZoZ5iCSINBg0ahM6dOyMuLg6hoaEwMTERjBLz9fWFs7MzVq1aBUdHR8jlcjRp0gRZWVmlV8gCVKlSBeHh4Thx4gSOHDmC4OBgTJ8+HZcuXYKVlRVCQ0Nx9uxZHDlyBEuXLsWUKVNw4cIFmJqaAgBWrVqFtm3bCo6p/98vp2XLlnj8+DEOHjyIo0ePYsCAAfDy8sKOHTvK5drEQgGQ2Fq25AEQTYhICNFRRemTU1Z5S6p9+/ZwcnLCtm3bcPDgQfTv3x+GhoYAgMTERNy/fx+rVq2Cp6cnAOD06dOlen43NzecOXNGkHbmzBnUr19fGagYGBjAy8sLXl5emDZtGqysrHDs2DH069cPEokEHTp0QIcOHRAcHAxnZ2f8/fffCAoKgqOjIx49eiSo0crNwsICfn5+8PPzwyeffAIfHx+8evUK1apVK9Xr1CYUAImNlsQghJAyl5mZidjYWEGagYEBbGxslM/9/f2xYsUKPHjwAMePH1emV61aFdbW1li5ciUcHBwQHR2NSZMmFasc8fHxeSZLdHBwwDfffIPWrVtj1qxZ8PPzw7lz57Bs2TLlaK19+/bh0aNH6NSpE6pWrYoDBw5ALpejQYMGuHDhAsLCwvDhhx+ievXquHDhAuLj4+Hm5gYAmDFjBsaNGwdLS0v4+PggMzMTly9fxuvXrxEUFIQFCxbAwcEBLVq0gJ6eHrZv3w57e3tYWVkV6xp1Rll0UNJ15dYJmjHGIiJ4rz5jY8ays8v+fIQQUgwFdTzVdgEBAQxAnq1BgwaCfHfu3GEAmLOzc54OwaGhoczNzY1JpVLm7u7OTpw4wQCwv//+mzFW+E7Qmsoxa9YsxhhjO3bsYI0aNWKGhoasVq1abN68ecp9T506xTp37syqVq3KTExMmLu7O9u2bZuy3N7e3szW1pZJpVJWv359tnTpUsG5N2/ezJo3b86MjIxY1apVWadOndiuXbsYY4ytXLmSNW/enJmZmTELCwvWtWtXFh4eXqz3ujyUVidoCWP/jZMjSikpKbC0tERycjIsLCzK9mRyOWBlxZfDuHkTaNKkbM9HCCHF8PbtWzx+/Bi1a9eGsfq4dkLKWUGfxaLcv6kJTGx6esC+fYCLC+DkJHZpCCGEkEqBAiBt0KmT2CUghBBCKhWaB4gQQgghlQ4FQNogLQ2YNQv49FPeJ4gQQgghZYoCIG0glQJz5gDbtgGlvLgeIYQQQvISPQD69ddf4eLiAmNjY7Rt2xYXL14sMP+iRYvQoEEDmJiYwMnJCV9//TXevn1bomOKzsAAcHfnj2lCREIIIaTMiRoAbdu2DUFBQZg2bRrCw8PRrFkzeHt7Iy6fudK3bNmCSZMmYdq0abh79y7WrFmDbdu24fvvvy/2MbVGixb8J02ISAghhJQ5UQOgBQsWYOTIkRg2bBgaNWqEFStWwNTUFGvXrtWY/+zZs+jQoQP8/f3h4uKCDz/8EAMHDhTU8BT1mFpDMSM01QARQgghZU60ACgrKwtXrlyBl5eXqjB6evDy8sK5c+c07tO+fXtcuXJFGfA8evQIBw4cQI8ePYp9TK2hviQGzU1JCCGElCnRAqCEhATIZDLY2dkJ0u3s7PKs16Lg7++PmTNnomPHjjA0NETdunXRpUsXZRNYcY4J8DViUlJSBFu5a9KE9wVKSACePi3/8xNCCCk0FxcXLFq0SOxiaK3AwED06dNH7GIUSPRO0EVx4sQJzJkzB8uXL0d4eDh27dqF/fv3Y9asWSU6bkhICCwtLZWbkxgzMhsbA40a8Z80EowQQkqFRCIpcJs+fXqxjnvp0iV89tlnpVLGP//8E/r6+hgzZkypHE8bLF68GOvXrxe7GAUSbSZoGxsb6Ovr4+XLl4L0ly9fwt7eXuM+U6dOxZAhQzBixAgAQNOmTZGWlobPPvsMU6ZMKdYxAWDy5MkICgpSPk9JSREnCDp4EKhendcEEUIIKbEXL14oH2/btg3BwcG4f/++Ms3c3Fz5mDEGmUwGg0L8D7a1tS21Mq5Zswbffvstfv/9d/zyyy+irrWWlZUFIyOjEh/H0tKyFEpTtkSrATIyMoKHhwfCwsKUaXK5HGFhYWjXrp3GfdLT06GnJyyyvr4+AP7BLc4xAUAqlcLCwkKwicLRkYIfQggpRfb29srN0tISEolE+fzevXuoUqUKDh48CA8PD0ilUpw+fRoPHz5E7969YWdnB3Nzc7Ru3RpHjx4VHDd3E5hEIsHq1avRt29fmJqawtXVFXv37n1n+R4/foyzZ89i0qRJqF+/Pnbt2pUnz9q1a9G4cWNIpVI4ODhg7NixyteSkpLw+eefw87ODsbGxmjSpAn27dsHAJg+fTqaN28uONaiRYvg4uKifK5oqpo9ezYcHR3RoEEDAMCmTZvQqlUrVKlSBfb29vD3988zmvr27dvo1asXLCwsUKVKFXh6euLhfy0YuZvA5HI5QkJCULt2bZiYmKBZs2bYsWOH8vXXr19j0KBBsLW1hYmJCVxdXbFu3bp3vn8lIerdNigoCAEBAWjVqhXatGmDRYsWIS0tDcOGDQMADB06FDVq1EBISAgAwNfXFwsWLECLFi3Qtm1bREZGYurUqfD19VUGQu86JiGEkFKWlsZ/mpoCEgl/nJUFZGfzL3VSad68JiZ8MWiA58vKAvT1eTeAd+U1NCzV4k+aNAnz589HnTp1ULVqVcTExKBHjx6YPXs2pFIpNm7cCF9fX9y/fx+1atXK9zgzZszA3LlzMW/ePCxduhSDBg3CkydPUK1atXz3WbduHXr27AlLS0sMHjwYa9asgb+/v/L13377DUFBQfjpp5/QvXt3JCcn48yZMwB4UNG9e3e8efMGf/zxB+rWrYs7d+4o74eFFRYWBgsLC4SGhirTsrOzMWvWLDRo0ABxcXEICgpCYGAgDhw4AAB49uwZOnXqhC5duuDYsWOwsLDAmTNnkJOTo/EcISEh+OOPP7BixQq4urri5MmTGDx4MGxtbdG5c2dMnToVd+7cwcGDB2FjY4PIyEhkZGQU6TqKjIls6dKlrFatWszIyIi1adOGnT9/Xvla586dWUBAgPJ5dnY2mz59Oqtbty4zNjZmTk5ObPTo0ez169eFPmZhJCcnMwAsOTm5JJdWdDIZY4GBjDVtytjLl+V7bkIIKUBGRga7c+cOy8jIyPsiH7vKWFycKu3HH3naiBHCvKamPP3xY1XawoU8zd9fmNfGhqffuqVKW7my2Newbt06ZmlpqXx+/PhxBoDt3r37nfs2btyYLV26VPnc2dmZLVy4UPkcAPvhhx+Uz1NTUxkAdvDgwXyPKZPJmJOTk/L88fHxzMjIiD169EiZx9HRkU2ZMkXj/ocPH2Z6enrs/v37Gl+fNm0aa9asmSBt4cKFzNnZWfk8ICCA2dnZsczMzHzLyRhjly5dYgDYmzdvGGOMTZ48mdWuXZtlZWVpzB8QEMB69+7NGGPs7du3zNTUlJ09e1aQZ/jw4WzgwIGMMcZ8fX3ZsGHDCiyDQkGfxaLcv0Vvbxk7dqygOk/diRMnBM8NDAwwbdo0TJs2rdjH1Gp6esDZs8CDB3w4vLe32CUihJAKr1WrVoLnqampmD59Ovbv348XL14gJycHGRkZiI6OLvA47ooZ/QGYmZnBwsKiwEl4Q0NDkZaWppzKxcbGBt26dcPatWsxa9YsxMXF4fnz5+jatavG/a9du4aaNWuifv36hb1UjZo2bZqn38+VK1cwffp0XL9+Ha9fv4b8v3Uqo6Oj0ahRI1y7dg2enp4wLERtXGRkJNLT09GtWzdBelZWFlr8NwnwqFGj8PHHHyM8PBwffvgh+vTpg/bt25fout5F9ACI5NKyJQ+AwsMpACKE6IbUVP7T1FSVNnEiMH583n6NioDAxESVNmYMMHIkbwJTFxWVN29gYCkUWMjMzEzwfMKECQgNDcX8+fNRr149mJiY4JNPPkFWVlaBx8kdDEgkEmXgoMmaNWvw6tUrmKhdn1wux40bNzBjxgxBuibvel1PTw8s17xy2dnZefLlvv60tDR4e3vD29sbmzdvhq2tLaKjo+Ht7a18D951bnWp/30+9u/fjxo1aghek/7XPNq9e3c8efIEBw4cQGhoKLp27YoxY8Zg/vz5hT5PUVEApG1atAC2bqUlMQghuiPXDRQAYGTEt8LkNTTU3K8nv7xl7MyZMwgMDETfvn0B8Bt4lCIYKyWJiYnYs2cPtm7disaNGyvTZTIZOnbsiCNHjsDHxwcuLi4ICwvD+++/n+cY7u7uePr0KR48eKCxFsjW1haxsbFgjEHyX9+sa9euvbNs9+7dQ2JiIn766SfliOjLly/nOfeGDRuQnZ39zlqgRo0aQSqVIjo6Gp07d843n62tLQICAhAQEABPT09MnDiRAqBKhZbEIIQQUbm6umLXrl3w9fWFRCLB1KlTC6zJKY5NmzbB2toaAwYMUAYnCj169MCaNWvg4+OD6dOn44svvkD16tWVHZ7PnDmDL7/8Ep07d0anTp3w8ccfY8GCBahXrx7u3bsHiUQCHx8fdOnSBfHx8Zg7dy4++eQTHDp0CAcPHnznSOdatWrByMgIS5cuxRdffIFbt27lmW9v7NixWLp0KT799FNMnjwZlpaWOH/+PNq0aaMcSaZQpUoVTJgwAV9//TXkcjk6duyo7MxtYWGBgIAABAcHw8PDA40bN0ZmZib27dsHNze30nmz86FTEyFWCopFUR8+BJKTxS0LIYRUQgsWLEDVqlXRvn17+Pr6wtvbGy0VX05Lydq1a9G3b988wQ8AfPzxx9i7dy8SEhIQEBCARYsWYfny5WjcuDF69eqFiIgIZd6dO3eidevWGDhwIBo1aoRvv/0WMpkMAODm5obly5fj119/RbNmzXDx4kVMmDDhnWWztbXF+vXrsX37djRq1Ag//fRTnpoYa2trHDt2DKmpqejcuTM8PDywatWqfGuDZs2ahalTpyIkJARubm7w8fHB/v37Ubt2bQB8apzJkyfD3d0dnTp1gr6+PrZu3Vro97M4JCx3AyFBSkoKLC0tkZycLM6cQM7OQHQ0cOIEUEB1ISGElJe3b9/i8ePHqF27tqgT9RFS0GexKPdvagLTRq1a8c6E6elil4QQQgipkCgA0kbbt6sm/SKEEEJIqaO7rDai4IcQQggpU3Sn1WYyGVDKIw8IIYQQQgGQ9urbF7C0BC5dErskhBBCSIVDAZC2evuWLwRI8wERQgghpY4CIG1FEyISQgghZYYCIG2lmBCRlsQghBBCSh0FQNpKUQN08ybwjgX4CCGEEFI0FABpq9q1eSforCzgzh2xS0MIIaQAUVFRkEgkhVpstCIKDAxEnz59xC5GkVAApK0kElUzGPUDIoSQYgsMDIREIsmz+fj4iFKeP//8E/r6+hgzZowo5y8Lixcvxvr168UuRpHQTNDarFs3oEoVwN5e7JIQQohO8/Hxwbp16wRpUqlUlLKsWbMG3377LX7//Xf88ssvoq6tlpWVBSMjoxIfx9LSshRKU76oBkibff89sHcv0KOH2CUhhBCdJpVKYW9vL9iqVq0KAPD394efn58gf3Z2NmxsbLBx40YAwKFDh9CxY0dYWVnB2toavXr1wsOHD4tcjsePH+Ps2bOYNGkS6tevj127duXJs3btWjRu3BhSqRQODg4YO3as8rWkpCR8/vnnsLOzg7GxMZo0aYJ9+/YBAKZPn47mzZsLjrVo0SK4uLgonyuaqmbPng1HR0c0aNAAALBp0ya0atUKVapUgb29Pfz9/REXFyc41u3bt9GrVy9YWFigSpUq8PT0VL4HuZvA5HI5QkJCULt2bZiYmKBZs2bYsWOH8vXXr19j0KBBsLW1hYmJCVxdXfMEqGWNaoAIIYSUSFoa/2lqylvvAd59MTsbMDAA1CtaFHlNTFSr/mRn8/z6+oB6ZUh+eQ0NS7f8gwYNQv/+/ZGamgpzc3MAwOHDh5Geno6+ffv+V5Y0BAUFwd3dHampqQgODkbfvn1x7do16BVh+aJ169ahZ8+esLS0xODBg7FmzRr4+/srX//tt98QFBSEn376Cd27d0dycjLOnDkDgAcV3bt3x5s3b/DHH3+gbt26uHPnDvT19Yt0vWFhYbCwsEBoaKgyLTs7G7NmzUKDBg0QFxeHoKAgBAYG4sCBAwCAZ8+eoVOnTujSpQuOHTsGCwsLnDlzBjk5ORrPERISgj/++AMrVqyAq6srTp48icGDB8PW1hadO3fG1KlTcefOHRw8eBA2NjaIjIxERkZGka6jxBjJIzk5mQFgycnJYheFMbmcsWfPGEtJEbskhJBKLCMjg925c4dlZGTkeQ3gW1ycKu3HH3naiBHCvKamPP3xY1XawoU8zd9fmNfGhqffuqVKW7my6GUPCAhg+vr6zMzMTLDNnj2bMcZYdnY2s7GxYRs3blTuM3DgQObn55fvMePj4xkAdvPmTcYYY48fP2YA2NWrV/PdRyaTMScnJ7Z7927lMYyMjNijR4+UeRwdHdmUKVM07n/48GGmp6fH7t+/r/H1adOmsWbNmgnSFi5cyJydnQXvhZ2dHcvMzMy3nIwxdunSJQaAvXnzhjHG2OTJk1nt2rVZVlaWxvwBAQGsd+/ejDHG3r59y0xNTdnZs2cFeYYPH84GDhzIGGPM19eXDRs2rMAy5Kegz2JR7t/UBKbtevUCatQA/qviJIQQUnTvv/8+rl27Jti++OILAICBgQEGDBiAzZs3A+C1PXv27MGgQYOU+0dERGDgwIGoU6cOLCwslM1K0dHRhS5DaGgo0tLS0OO/bg02Njbo1q0b1q5dCwCIi4vD8+fP0bVrV437X7t2DTVr1kT9+vWLfP3qmjZtmqffz5UrV+Dr64tatWqhSpUq6Ny5MwDV9V27dg2enp4wLET1W2RkJNLT09GtWzeYm5srt40bNyqbzEaNGoWtW7eiefPm+Pbbb3H27NkSXVNxUBOYtlO03YaHAwMHiloUQgjRJDWV/zQ1VaVNnAiMH8+bwNQpupWYmKjSxowBRo7kTWDqoqLy5g0MLF4ZzczMUK9evXxfHzRoEDp37oy4uDiEhobCxMREMErM19cXzs7OWLVqFRwdHSGXy9GkSRNkFWGetjVr1uDVq1cwUbsguVyOGzduYMaMGYJ0Td71up6eHhhjgrTs7Ow8+czMzATP09LS4O3tDW9vb2zevBm2traIjo6Gt7e38vredW51qf99IPbv348aNWoIXlN0PO/evTuePHmCAwcOIDQ0FF27dsWYMWMwf/78Qp+npCgA0na0JAYhRMvlup8CAIyM+FaYvIaGmvv15Je3LLRv3x5OTk7Ytm0bDh48iP79+ytrOxITE3H//n2sWrUKnp6eAIDTp08X6fiJiYnYs2cPtm7disaNGyvTZTIZOnbsiCNHjsDHxwcuLi4ICwvD+++/n+cY7u7uePr0KR48eKCxFsjW1haxsbFgjEHyX2eswsxLdO/ePSQmJuKnn36Ck5MTAODy5ct5zr1hwwZkZ2e/sxaoUaNGkEqliI6OVtYkaWJra4uAgAAEBATA09MTEydOpACIqFFfEoMxVQ9DQgghhZaZmYnY2FhBmoGBAWxsbJTP/f39sWLFCjx48ADHjx9XpletWhXW1tZYuXIlHBwcEB0djUmTJhXp/Js2bYK1tTUGDBigDE4UevTogTVr1sDHxwfTp0/HF198gerVqys7PJ85cwZffvklOnfujE6dOuHjjz/GggULUK9ePdy7d085p1GXLl0QHx+PuXPn4pNPPsGhQ4dw8OBBWFhYFFi2WrVqwcjICEuXLsUXX3yBW7duYdasWYI8Y8eOxdKlS/Hpp59i8uTJsLS0xPnz59GmTRvlSDKFKlWqYMKECfj6668hl8vRsWNHZWduCwsLBAQEIDg4GB4eHmjcuDEyMzOxb98+uLm5Fek9LbFi9UCq4LSqE/Tbt4wZGubtNUgIIeWooI6n2i4gIIAByLM1aNBAkO/OnTsMAHN2dmZyuVzwWmhoKHNzc2NSqZS5u7uzEydOMADs77//Zoy9uxN006ZN2ejRozW+tm3bNmZkZMTi4+MZY4ytWLGCNWjQgBkaGjIHBwf25ZdfKvMmJiayYcOGMWtra2ZsbMyaNGnC9u3bp3z9t99+Y05OTszMzIwNHTqUzZ49O08naEVnZXVbtmxhLi4uTCqVsnbt2rG9e/fmuZ7r16+zDz/8kJmamrIqVaowT09P9vDhQ43HlcvlbNGiRcrrsLW1Zd7e3uzff/9ljDE2a9Ys5ubmxkxMTFi1atVY7969BZ3BC1JanaAljOVqMCRISUmBpaUlkpOT3xk5l4uWLXkN0M6dQL9+YpeGEFIJvX37Fo8fP0bt2rVFnbiPkII+i0W5f9MoMF1AS2IQQgghpYr6AOmCjz7iC6N+8IHYJSGEEEIqBAqAdEHv3nwjhBBCSKmgJjBCCCGEVDoUAOmKlBTg5Eng8WOxS0IIqcRo3AwRW2l9BikA0hWjRgGdOwNbtohdEkJIJaSY/C49PV3kkpDKTvEZLMyyHAWhPkC6omVLHvzQSDBCiAj09fVhZWWFuP/WsjA1Nc0zoR8hZYkxhvT0dMTFxcHKygr6uddOKSIKgHQFLYlBCBGZvb09ACiDIELEYGVlpfwslgQFQLqieXP+MyoKeP0aqFpVzNIQQiohiUQCBwcHVK9eXeMim4SUNUNDwxLX/ChQAKQrqlYFatfmnaCvXqU5gQghotHX1y+1mxAhYqFO0LqEmsEIIYSQUkEBkC5RXxmeEEIIIcVGTWC6xNeXN4W1by92SQghhBCdRgGQLnF35xshhBBCSoSawAghhBBS6WhFAPTrr7/CxcUFxsbGaNu2LS5evJhv3i5dukAikeTZevbsqcwTGBiY53UfH5/yuJSyFxkJrF0L/Puv2CUhhBBCdJboAdC2bdsQFBSEadOmITw8HM2aNYO3t3e+E23t2rULL168UG63bt2Cvr4++vfvL8jn4+MjyPfnn3+Wx+WUvY0bgeHD+U9CCCGEFIvoAdCCBQswcuRIDBs2DI0aNcKKFStgamqKtWvXasxfrVo12NvbK7fQ0FCYmprmCYCkUqkgX9WKMnEgDYUnhBBCSkzUACgrKwtXrlyBl5eXMk1PTw9eXl44d+5coY6xZs0afPrppzAzMxOknzhxAtWrV0eDBg0watQoJCYm5nuMzMxMpKSkCDatpRgKf+sWkJkpblkIIYQQHSVqAJSQkACZTAY7OztBup2dHWJjY9+5/8WLF3Hr1i2MGDFCkO7j44ONGzciLCwMP//8M/799190794dMplM43FCQkJgaWmp3JycnIp/UWWtVi2gWjUgJwe4fVvs0hBCCCE6SfQmsJJYs2YNmjZtijZt2gjSP/30U3z00Udo2rQp+vTpg3379uHSpUs4ceKExuNMnjwZycnJyi0mJqYcSl9MEomqFoiawQghhJBiETUAsrGxgb6+Pl6+fClIf/ny5TtXek1LS8PWrVsxfPjwd56nTp06sLGxQWRkpMbXpVIpLCwsBJtWo35AhBBCSImIGgAZGRnBw8MDYWFhyjS5XI6wsDC0a9euwH23b9+OzMxMDB48+J3nefr0KRITE+Hg4FDiMmsFWhKDEEIIKRHRm8CCgoKwatUqbNiwAXfv3sWoUaOQlpaGYcOGAQCGDh2KyZMn59lvzZo16NOnD6ytrQXpqampmDhxIs6fP4+oqCiEhYWhd+/eqFevHry9vcvlmsqclxdw+DCwd6/YJSGEEEJ0kuhLYfj5+SE+Ph7BwcGIjY1F8+bNcejQIWXH6OjoaOjpCeO0+/fv4/Tp0zhy5Eie4+nr6+PGjRvYsGEDkpKS4OjoiA8//BCzZs2CVCotl2sqc7a2wIcfil0KQgghRGdJGGNM7EJom5SUFFhaWiI5OVn7+wMRQgghBEDR7t+i1wCRYrp6Fdi+HahdGxg5UuzSEEIIITpF9D5ApJiuXwdCQoAtW8QuCSGEEKJzKADSVepzAcnl4paFEEII0TEUAOmqRo0AIyMgJQV4/Fjs0hBCCCE6hQIgXWVoCLi788c0HxAhhBBSJBQA6TJaEoMQQggpFgqAdBktiUEIIYQUCwVAukxRA/TokbjlIIQQQnQMzQOky1q0AB4+5HMBEUIIIaTQKADSZUZGQJ06YpeCEEII0TnUBEYIIYSQSocCIF136RIwYAAwdqzYJSGEEEJ0BgVAui4jg68J9s8/YpeEEEII0RkUAOm65s35z+hoICFB1KIQQgghuoICIF1nYQHUq8cf04zQhBBCSKFQAFQRKCZEpACIEEIIKRQKgCoCWhKDEEIIKRIKgCoCWhKDEEIIKRIKgCqCFi34pIiWlkB2ttilIYQQQrQezQRdEdjaAm/e8CCIEEIIIe9ENUAVBQU/hBBCSKFRAFTRyOVil4AQQgjRehQAVRTXr/PO0G3bil0SQgghROtRH6CKwtqazwOkrw+8fQsYG4tdIkIIIURrUQ1QRVGjBmBjA8hkwPnzYpeGEEII0WrFCoA6d+6MjRs3IiMjo7TLQ4pLIgE6d+aPe/cG9uwRtzyEEEKIFitWANSiRQtMmDAB9vb2GDlyJM5TjUOhJCQAP/8MfP11GZ1g6VKgQwcgJQXo0weYPBnIySmjkxFCCCG6q1gB0KJFi/D8+XOsW7cOcXFx6NSpExo1aoT58+fj5cuXpV3GCuPlS2DSJGDZMv641Dk4AMePA+PH8+c//QScPVsGJyKEEEJ0W7H7ABkYGKBfv37Ys2cPnj59Cn9/f0ydOhVOTk7o06cPjh07VprlrBAaN+aDtHJygE2byugkhobAwoXAtm3ArFlAp05ldCJCCCFEd5W4E/TFixcxbdo0/PLLL6hevTomT54MGxsb9OrVCxMmTCiNMlYow4fzn2vXAoyV4YkGDAB++EH1/MkTYPnyMj4pIYQQohskjBX9jhgXF4dNmzZh3bp1iIiIgK+vL0aMGAFvb29IJBIAwOnTp+Hj44PU1NRSL3RZS0lJgaWlJZKTk2FhYVHKxwbs7YGMDODcOeC990r18JplZQEdOwKXLgGffgqsWgWYm5fDiQkhhJDyU5T7d7FqgGrWrInVq1cjICAAT58+xY4dO+Dj46MMfgDA3d0drVu3Ls7hKzQLC6B/f/54zZpyOqmhITBoEGBgAGzdytvh7t0rp5MTQggh2qdYNUCnTp2Cp6dnWZRHK5RlDRAA/Psv0KULr4SJjQXMzEr9FJqdOcObxp4/5ydfu1YVjRFCCCE6rlxqgCIiIvKkR0REICoqqjiHrFQ6dQKaNuXT9SQnl+OJO3QAwsN59JWayoOhoCAgO7scC0EIIYSIr1gBUGBgIM5qGF594cIFBAYGlrRMFZ5Ewlet+OMPwNGxnE9uZweEhgLffsufHz9OcwURQgipdIoVAF29ehUdOnTIk/7ee+/h2rVrJS1TpaCvL+LJDQz4jIx//w3s3AmYmIhYGEIIIaT8FSsAkkgkePPmTZ705ORkyGSyEheqMrl1C9i/X6ST9+kD1Kmjej5nDjBvHg2VJ4QQUuEVKwDq1KkTQkJCBMGOTCZDSEgIOnbsWGqFq+iOHOF9gT77TAtaoa5f5/MGffst8MknfLw+IYQQUkEVaxTYnTt30KlTJ1hZWSlHg506dQopKSk4duwYmjRpUuoFLU9lPQpMISuLL+KekADs2wf07Flmp3o3xoAVK4CvvuKdol1dgV27AB3/XRJCCKk8ynwUWKNGjXDjxg0MGDAAcXFxePPmDYYOHYp79+7pfPBTnoyMgMGD+eO1a8UtCyQSYNQo4NQpoGZNICKCzxe0ZYvIBSOEEEJKX7GXwnB0dMScOXOwf/9+7NixA8HBwahWrVqxjvXrr7/CxcUFxsbGaNu2LS5evJhv3i5dukAikeTZeqpVnzDGEBwcDAcHB5iYmMDLy0vjsH1toFgaY+9eIC5O3LIA4EFPeDjg5QWkp/MJFGlJE0IIIRVMidYCS09Px71793Djxg3BVhTbtm1DUFAQpk2bhvDwcDRr1gze3t6Iyyca2LVrF168eKHcbt26BX19ffRXm9Bv7ty5WLJkCVasWIELFy7AzMwM3t7eePv2bUkut0w0aQK0bs37AP3xh9il+Y+tLXDoEDBlCn/u5iZueQghhJDSxoohLi6O9ezZk+np6WnciqJNmzZszJgxyucymYw5OjqykJCQQu2/cOFCVqVKFZaamsoYY0wulzN7e3s2b948ZZ6kpCQmlUrZn3/+WahjJicnMwAsOTm5CFdSfCtWMAYw1qgRY3J5uZyy8K5eFT5PStLCQhJCCCFFu38XqwZo/PjxSEpKwoULF2BiYoJDhw5hw4YNcHV1xd69ewt9nKysLFy5cgVeXl7KND09PXh5eeHcuXOFOsaaNWvw6aefwuy/9SQeP36M2NhYwTEtLS3Rtm3bfI+ZmZmJlJQUwVaePv0UMDYG4uOBZ8/K9dTv1ry56nFiItCsGdCiBbByJZCWJlqxCCGEkJIoVgB07NgxLFiwAK1atYKenh6cnZ0xePBgzJ07FyEhIYU+TkJCAmQyGezs7ATpdnZ2iI2Nfef+Fy9exK1btzBixAhlmmK/ohwzJCQElpaWys3JyanQ11AaLC153+OnT3n/Y621dy/w8iUfMv/553wI2/jxwP37YpeMEEIIKZJiBUBpaWmoXr06AKBq1aqIj48HADRt2hTh4eGlV7p3WLNmDZo2bYo2bdqU6DiTJ09GcnKycouJiSmlEhZeq1Z8VJhWGzaMV1H98gtQrx5fyGzxYqBhQ95p+tatYh+aMT4xdWRkKZaXEEIIyUexAqAGDRrg/n/f+ps1a4bff/8dz549w4oVK+Dg4FDo49jY2EBfXx8vX74UpL98+RL29vYF7puWloatW7diuGIY1X8U+xXlmFKpFBYWFoJNLHK5lowGy0+1anwB1fv3eUfpjz4C9PT4mmLq75tcXqTDLl0K9OvHO4TfvVvKZSaEEEJyKVYA9NVXX+HFixcAgGnTpuHgwYOoVasWlixZgjlz5hT6OEZGRvDw8EBYWJgyTS6XIywsDO3atStw3+3btyMzMxODFRPp/Kd27dqwt7cXHDMlJQUXLlx45zHFduoUX5nCz0/skhSCnh7g7Q3s2QM8egSsWwfUqqV6vV8/3rnp5Ml3Lq2RmgpMm8YfJyUBPXrwljZCCCGkzJRGr+u0tDR25coVFh8fX+R9t27dyqRSKVu/fj27c+cO++yzz5iVlRWLjY1ljDE2ZMgQNmnSpDz7dezYkfn5+Wk85k8//cSsrKzYnj172I0bN1jv3r1Z7dq1WUZGRqHKVN6jwBRiYhiTSPiIsMjIcj116VK/EICxJk0YW76csZSUfHe5fp2xzz9nrF49vkvr1oylpZVjmQkhhOi8oty/ixwAZWVlsTp16rA7d+4Uq3CaLF26lNWqVYsZGRmxNm3asPPnzytf69y5MwsICBDkv3fvHgPAjhw5ovF4crmcTZ06ldnZ2TGpVMq6du3K7t+/X+jyiBUAMcaYjw8PAKZMKfdTl67wcMZGjGDMxEQVCFWpwtiYMYzdu5fvbg8eMGZtzbP36cNYTk45lpkQQohOK8r9u1hrgdWoUQNHjx6FWwWdIK+81gLTZPt2YMAAPsDqyRNAX79cT1/6kpKADRuA5cuBBw942sqVwMiRePUKiI4WjrQHgDNngK5dgcaNgdBQ3u2IEEIIeZcyXwtszJgx+Pnnn5Ej+hLmFc9HHwHW1nyw1ZEjYpemFFhZ8QVW793j0czAgYC/PwC+wkYrDzmWdj8gmACpQwfg8GHg338p+CGEEFI2ilUD1LdvX4SFhcHc3BxNmzZVTkKosGvXrlIroBjErAEC+NQ6ixcDn3zCa4QqomPHeC0PAJxGB3TQvwD06QN8/z3QsmWe/C9eAEUYYEgIIaQSKvMaICsrK3z88cfw9vaGo6OjYBJBS0vLYhWaqAwbxn/u2QMkJIhblrKQkQF89hl/PMorAh089QGZDNi5ky/GOn++chg9Y8Ds2YCrK3DpkoiFJoQQUqEUqwaoohO7BggAZswAunUD2rUDJBJRilBmJk8GfvoJcHQE7tzhM2Hjxg1g+nQ+GyIA+PgAGzYgp1p1+PryKYfs7IDz5wEXFxELTwghRGsV5f5NAZAG2hAAVVTXrwMeHrzC5++/eauXEmPAqlW8z9Dbt8CBA0D37njzBvD05Pu6uQFnz/KuRYQQQoi6oty/DYpzgtq1a0NSQLXEo0ePinNYUsHJZMDIkfxnv365gh+AV3V99hnQvj3vMN29OwCgShVg3z7gvff4LNH9+vEaIa1fOoQQQojWKlYANH78eMHz7OxsXL16FYcOHcLEiRNLo1wEfNT4woV8hYmffxa7NKVj6FDeoXnp0gIyNWnCN4UnT1Bz1CjsX7kKHf1q4PhxHkitX1/xmgcJIYSUj1JtAvv1119x+fJlrFu3rrQOKQptaQILC+NrjFpY8KDB1FS0opSqrKwi1t706gXs3w9YWODQ2H/Q6+dOkMmAo0dVI8kIIYSQMh8Flp/u3btj586dpXnISu3993mH35QUQJdnFmCMd+lRKHLT1bJlvDd4Sgp85nTGivYbsXJZJgU/hBBCiq1UA6AdO3agGs1cV2r09FRD4teuFbcsJbFtG5/VWW192qJxceGzIn7/PSCRYMSpAIxc3hK4das0i0kIIaQSKVYTWIsWLQSdoBljiI2NRXx8PJYvX47PFJO86ChtaQID+HIYtWvzWpSHD/lq8brk1Ss+cisuDpg5E5g6tYQHDAsDBg8GYmMBY2Mk/HUMASvaYd48oFGjUikyIYQQHVXmo8D65Bq+o6enB1tbW3Tp0gUNGzYsziFJPpydeT+g0FDe6XfmTLFLVDQTJvDgp3Fj4LvvSuGAXbvy8fCBgcCzZwja2gYHDgC3b/M5guztS+EchBBCKjyaB0gDbaoBAngT0qefAjVrAlFRurNAqqITt0TCFzht164UDy6XA4mJSJDYon17ICICaNUoHScumiLXyiyEEEIqiTLvBH3gwAEcPnw4T/rhw4dx8ODB4hySFKB3b6BpUyAgQNiZWJtlZACff84fjx5dysEPwDtI2drCxobPl2htmo7Ld0wxqPV9yLJkpXwyQgghFU2xAqBJkyZBJst7k2GMYdKkSSUuFBEyNuatPj/+CJ2p3Zgxg/dZqlEDmDOnbM9Vry7D3k6/QIq32HO3Ab6puxt4/rxsT0oIIUSnFSsAioiIQCMNPU4bNmyIyMjIEheK5KVLE/4xxpukAGD5cj6PUZmSSND+wA/YOOo8AGDx04+xvP4iXjVECCGEaFCsAMjS0lLjcheRkZEw05UqCh2Uk8OXhND2+7pEAuzYAZw+DXz0UfmddMDyLvh5QjzqGUWjW9rfQM+ewDff8JkXCSGEEDXFCoB69+6N8ePH4+HDh8q0yMhIfPPNN/io3O54lc+aNYCvL19NXdu7rkskQIcO5X/eiXNtEf60OlzH9eAJy5fztriSYIx3anrxgi9Gdu4ckJ6uev34cWDSJD7G/+RJHqkSQgjRasUaBZacnAwfHx9cvnwZNWvWBAA8ffoUnp6e2LVrF6x0fKlubRsFpvDqFeDoCGRmApcv81XVtUlUFDB7NhASAtjYiF0aAP/8g9CTRnAd4w0XFwBv3vCJlZKSVFtysurxd9+pCr5kCZ+BWvFadrbw2FevAs2b88dz5gBTpqheq1aNL+Taqxfg40NL1xNCSDkp83mALC0tcfbsWYSGhuL69eswMTGBu7s7OnXqVKwCk8KpVg3o2xfYupXPDK1NARBjwKhRfJX2xETtWLpjZ5Yv/BYC9fcDZ88CVndv8Wqp/GL+wYNVAVBqqqojk4KeHmBpyQMa9Vqe994Dxo8H4uOBgwd5pLp5M9/09XmtkatrWVwiIYSQYqJ5gDTQ1hoggC8A2q0bvwc/fw6YmIhdIm7LFmDQIEAq5SPWGjQQu0TAs2dA27b85/td5DiU0gFG0ZH8zdO0ffklUKsW3zkqCoiJEb5ubv7u3ug5OXxGxn/+4R22UlKA6GjVfj/8wOcy6NUL6NgRMCjWdxBCCCEaFOX+XawAaNy4cahXrx7GjRsnSF+2bBkiIyOxaNGioh5Sq2hzACSX86UxoqN5BYO/v9glAhIS+HIXCQnArFn8Hq8trl/ncUZqKjB0KJ9Nu1xH1L1+DVStyh/LZICdHa8iA3hQ1b0779jl46PKR4gWys7mXd8sLfnzuDg+P9mbN7yS09mZp589yyc+bdMG6NxZvPKSonn8GAgPB5yc+O9OYcwY/sV28mTA1panxcby//cODoC1tTjlzU+ZT4S4c+dOdNDQw7V9+/bYsWNHcQ5JCkkbF0j95hv+x9CkCfDtt2KXRqhZM2D7dt4StXEjMH9+ORdAPaiRy4EVK3gkZm3N+xb9+SePYm1tAR1fQ49UHAkJ/OOqMG8en4MsOFiVZmvL+yNmZKhujABvBv/2Wz6DvQJj/GbZsCEfS6Bw4wbw11/AnTtldy1EKDsbmD4dGDJEOLHu2rXAJ58I7yuKf1kLFwpb/Tdt4sHv+PHCY3/4IdCjB691V7h3j/8Pvn69LK6mZIoVACUmJsJS8TVAjYWFBRISEkpcKFKwYcN4LcarV+LPDB0aygMLiQRYtQowMhK3PJr4+ABLl/LHU6aI+IdoaMj/w2zYALx8yecJmDSJL5QmkwHVq6vypqfzyPLYsbwdsAkpJTk5qgpJgAcq9erxgObxY1V69er8Y6jeLU4i4QMiHz4ETE1V6e7uvDtd+/aqtJQUXmtw/75wTMD27YCfHx9voF6GVq0Ab28eiClcvAisXAlcuCC8hocP+bHVA7aywhj/00xNFaYfPMj7ZuYeHDpuHK91VrdvH28hT0pSpWVk8P/nJZmxQybjYzzUA5UtW/hYjYkTVWkGBsCCBcAffwh/x+7uvMuAi4vwmCEhPKBVr+mRSHh3SfV/WXI5X/7o4EH+RV3hn3+AAQOAX34Rlnfx4uJfa6lhxdC4cWO2dOnSPOlLlixhbm5uxTmkVklOTmYAWHJysthFydeDB2KXgOvQgTGAsbFjxS5JweRyxnr35mWdMEHs0mjw6BFjz56pnu/ZwwsLMGZhwdiAAYyFhYlXPqLzXr9mLCND9XzbNsaMjRnr0UOYz8ODf+wOHBDu+/gx/zsqjpwcxu7fZ+zff4Xpv/3GWMeOjC1fLjyX4qOfnq5KnzyZp40bp0qTyxmTSHj6ixeq9OXLGWvYkLEZM4Tn++Ybxr77jrHERFXamTOMzZ7N2D//CI/brRtjbdowlpCgSp8zh59r2DDhcatU4ekREaq0Zct4Wv/+wryOjjw9PFyVtmEDT/P2Fubt3p3/Pq5eVaVducLfgxUrhOWtVo0f4/59VfqaNTztww+Fx50+nbGQEMaeP2elJieHsb17GVu7lrGsLFX6hg2MeXoy9tNPwrwLFpTeudUV5f5drABozZo1zMTEhAUHB7MTJ06wEydOsKlTpzJTU1O2cuXK4hxSq+hCAKQtXr/mAUVKitglebeXL/kfY3H/iZery5cZCwxkzNZWdTfQ12fs2DGxS0a0XEYGY1FRwrSuXflH6OBBVdqZMzytfn1h3shIcf+e375l7MgRxjZtEqZv2MDYRx/xm7pCWhoPPiQSxlJTVenff8+v7csvVWlyOWN6ejxd/cb/4488bfhw4fnMzXl6ZKQqbckSnvbJJ8K8Pj6MdenC2MOHqrTLl3k5tmwR5vX15YHV48eqtN9+48f9+GNh3po1efrly6q0TZt4Wrduwrzu7owZGgr/RcTE8MBOPTDTBjJZ2X3GinL/LvYosN9++w2zZ8/G8//WXHJxccH06dMxdOjQUqudEos2d4LO7c0bPpXNf9MxkYpGJuN1/z/9BOzdy9smrlzhPRVJpZaZCTx4wJsh7Ox42tmzgKcnUKeOsLnKz4/3tVm2jHdqVewfE8Pz6hWrM4T2kMt5s4xigEN0NG8as7fnAzQA/qc0cyb/nzl7tmoE7cGDfOb6Tp34gtMKO3bwVuuuXfkAUIA3VeXk8P5Qpf2eZWfzTb058cwZ3lTm6alaUig8HNi5k//ehg9X5U1I4F0O9fVLt1y6psxHgamLj4+HiYkJzP/7hLx69QrVqlUrySFFpysB0B9/8BXXfX15+3N5yckB9u/ny1zo0hpl6l6/5gu2zpxZDmuVlYb0dD6H0bVrfMKl5cvFLhEpJzk5vO9MdDQfNKjQpw+wZw+fs/PLL3labKxqZM6LF/wGDgBPn/KbOM3JSSq6Mh8Fps7W1hbm5uY4cuQIBgwYgBo1apT0kKSQGjXi98W//+Yd6MrLkiX8n++nn5bfOUvbRx/xTnhffy12SQrJ1JTPLjlhAqDj00yQ/D15wr/dh4er0p494yMse/cW9odv1IgH7+odb+3seP74eFXwA/AaYgp+CBEqUQD05MkTTJs2DS4uLujfvz/09PSwcePG0iobeYeWLXkP/6ws3tu/PDx+zJe8AviQR101ezavvVq7ln+L1gm1a/PxyNo41I4UCWPArVvA6tXCicmXLFENFFSoVYsvgdOypXDE1rRpqhVcFCQSnldXa2YJKU9FDoCysrKwdetWeHl5oWHDhggPD8fTp09x+vRpbN26Ff379y+LcpJ8/O9//Gd5zAnEGPDFF/wbZ5cuqnProk6d+ChzABg5kk/qplNycvgQ+osXS+Vwjx/z6RVCQoTp8fHCGgYx3L3L+2mkpKjSduzg8WDuiUBnz+ZzPanPQ6INZDLhkO6MDB7QjBwpHIrs4QG0bi3s4iWR8Cas8+d5nxYFqZQCHa1W2RdZYIy320ZE8D8AhYgIPjb+wQPxyqZQlN7VY8eOZdbW1uy9995jy5YtYwn/jQ80MDBgt2/fLnp3bS2lS6PAEhIYMzLKO6yytMlkjK1ezc8jlWrPMPySyMhgrEkTfk29e+vI6DCFGTN4wWvW5MPbSujwYX64Jk2E6d268XT1ETlRUYx9/bVw6DJjfEROUd7D7Gw+HFbh8mXGRo/mw3PVubjwMpw5o0r7+2+e1qaNKk0mY6xqVZ5+6ZIq/c4dxv76SzjLQHnauZMxS0s++kfdhx8y5uXF2LVrohRLO8lk/IOgPpZbJmOsdm3G2rcXjl+Pjmbs7l3hWPny9vgxY6GhwjH4p04xVqsWY++9J8zbpw9jJiaMbdyoSnvwgLHmzfPORbBkCWMBAcKpL1694n8cy5YJ8544wf8537ypSktKYmzhQsYWLxbm/ecfxqZNE85H8OYNY0FBjI0fL8z755+MDR3K/3jU87Zty1izZsKx7lOn8jkVJk5UpWVnq0awqv/eFP+75sxhZaHMhsHr6+uz77//nqXkGr9GAZC4Bgwo3bl43r4VDhNNTlbNcwHwOTMqimvX+NBRgM9foTOSkxlr0IAXvEsX/s+miGQy1eOICP57zf2/tVUrforDh1VpimCpaVNh3m7deDC+fbsqLSaGsZkzhXOsMMbnftHXf3dQwxgPHJo1E/7PTkzk+6oHNRkZjM2dy4coq/9vnjaNH3fwYOFxr15lLDOTlarly/n7cPKkKu3iRX5+Z2dhgKhTAXd5CQrib9bo0aq0p09V00Cof84nTODp6jdumYynL1kinPSoONR/QY8eMfbtt3xcu7rOnXkZNm9WpV29ytNsbYV5NX2buHSJp9WqJczbpw9PV5/s5+5dnla1qjCvvz9P/+UXVVpUFE8zNhbmHTmSp8+apUqLj1f9c1e/5vHjedp336nSMjJUedXjgB9+0HwTMjXl0b96gLhuHf8jV5/PoBSVWQC0ZcsW5uXlxczMzNiAAQPYP//8w3JycigAEpnihmRjU/T74KtXjB0/zr8sBATwuSQMDPJOnFWjBq/5GThQeHOpCEJC+PvXsKGwRkLr3bmjmqzkm2+KtOv167y25969gvPJ5TzWevtWlXb7Nv+ipz6xGWM8IAL4HC4Khw5pDmo6deLp6nOkPHrE2JQpjP3xR5Eu5Z2WLWOsRQvGfv9dlZaYyM9vYiL8P17Yz3ZmJv9yvmSJMF1xLwoOVqVlZ/NKjTx/m5U9Apo2jc/ypz55zp49/DM9Zowq7e1bxi5c4LPsqQsK4t/MFi1SpT17pjlYmjGDR9GrV6vSZDIeRZ84ITzuZ58xZm1duKBm1CjG3Nx4bYlCRgZjZ8/mrXJMSOA1RuofuKQk/kcSGirMu3s3Yz//LJwBMSaGsf/9j59T3fz5jPXqxb9BqJ/L35/PJaZu82a+v/osl2lpPMiZPFn4rSg0lH+jUP/mIZfz39Hhw8I/loQEHnQlJTGxlflEiI8ePWLBwcGsVq1azMbGhunp6bHt6l/7dJyuBUA5OfxvIDo6/zxyubAWkjF+U1AE87m3Bg2EeaOiKl7go5CTw2dGzf3+FJao97GdO1W/tK1bC72blxffJXezTEm8fcs/J2lpqrTwcMZGjGDs88+FeSMj+f1BrIDz0iU+c65yEsC0NMbi4thgfxmrXfu/Wqxnz/iN4uxZlpKidj9btYrFTloorN2/cIGxIUNY6NCNbOlStSbigQP5t4pz51QnP3CAf5to21ZYKD8/fjNVn63w+XPejLF7tzCv+o1KFzx5IqxGZIxPDwwI21KzsopWLSeXCz9EL17w9tmRI4X5+vfn51q4UJWmqFmqXl2Yd+jQvE00ycl8VsVffqHAVcuVeQCkIJfL2aFDh1j//v2ZVCplNWrUYF+qT72po3QtAMotO5s3B2/axL8kffAB/2dfp44wX5cu/O/cxYXXuE6fzoP7J0/ob7wwLlzgU9UPHSpML/f3btIk/os0NRX2AyhAXBz/cvj6ddkWrVxcv86/2ef+Fv3114wNGSJsz922jQcZo0czuVyt+1SNGowBzLVWBgP4l3LF+gSX3hvL9PR4DSljjFcVAqyrxyvm7//frMu7dvHfQfv2wjIo2hD371elKapsmzUT5m3ZMm/eo0d5WuPGwrze3rxpYccOVdqTJ/ybvHqzCWP823lKinh/1IraEwsL4beo3bv5e1wKfdjeKTKSv6/qtU03bvB2ydatheW6d49/pt68KftykVJXbgGQusTERLZw4ULm7u5eWocUjS4HQAEB/MulplodQ0PhdPGRkbwJjKjI5YytX583jlA0eTx6pEo7fz7v//Vnz3jNmnofkDKXk8P7F5iZ5e1soyY+vhzLVJbS04U1IIpOPgMGCPPZ2/N09V7Ga9fytNydTl1dGQNY0qFz7MiR/1opDh1irGVLtsTzL2VXK8YYb6cbMYI3QSpERDA2b17eWrjTp3mboPqbn57Oq2vVF5lijPfxCAsTpl++zGsvcndQVVTfqgdLoaGagyVNfU/u3ePVf19/Lcx79iw/TmwsK5YDB/i6EOo92XNyGLOz48FhTEzxjktIIZVZANSxY0c2b948dl+9h34FpMsB0IgR/H9dlSq8o+mXX/K+ZuHhwn4cRLOff1Z9OVeviR84kKdPm6ZKUyzod+uW6sv1//7H87VsWc6tFAkJBdb+/PUXj48OHSrHMpWFIUN4x50LF1Rp167xGpHcnZIWL+ZBiXoHzOfPeac39eCFMd4Elk97XEyMFt63ExN5wKTen+TWLca++oovbqWuTZv8g6Xcw/4UbaPqwVJ4OG8mUkaA/9mwgfdwV69GVKy+mbt5r6QdkgkppDILgDZs2MD69evHzM3NWcOGDdm3337LTp8+zeQVrL1ElwOgyEj+ZVTXughoi5gYfn8FhF+O16zhX2Jz31tyS03l3Q9EnyZAvSMOY+zTT/k1/e9/IpWnqORyHqTk7hGtGPJYkYYiljW5nH8e1CP6mBjGVq7M+/6OHMmDIvXh10eOaB72p1hhVb3/5/PnPOgsZFMsIaWtzBdDzczMRFhYGPbs2YN//vkHMpkMPXv2xEcffQRvb2+YKFaZ01G6shYYKbmEBD4RXevWqrQaNYDnz/kkc6dPA+3b8yUI9PWLtwDinj180j5399Ird4FOn+arX65cCfTsCYDPFr5qFZ/IUicWS3zyBHBx4YWNiwMU6wveuMFXvmzWjGYBLC9paXxlUZkMaNFClb5kCXDzJtCtGzBggHjlI0RNke7fpRFxnT9/nn3//fesSZMmzMTEhPXs2ZOdPn26UPsuW7aMOTs7M6lUytq0acMuqFdta/D69Ws2evRoZm9vz4yMjJirqyvbr1a1O23aNAZAsDXIPaTpHXS5BogU3r//MiaR8E7g6pWYmzYx1qED/3Jbp07J+kLevs37JhsbM3blSsnLXChjxzIGsNNmH/LqwHfJyeGdg9U7C8tk5ddp9vJl3rSVe44VDw/en0T06jRCiK4oyv27xIuhAkDbtm0xe/Zs3Lx5Ezdv3kTXrl3x4sWLd+63bds2BAUFYdq0aQgPD0ezZs3g7e2NuHzWJcjKykK3bt0QFRWFHTt24P79+1i1alWeBVgbN26MFy9eKLfTp0+XxmUSHXbmDDBiBLB5syrNw4Mvq2VhIVxMdvBgvtq9kxPw6JFqyYzisLPjy2507MgrLcoDm/8LJjtuQMe0w1jsuYN/g883MwO++orXGL18qUrfvJkvHz50qDD/6tXA+vXCdR2K6ulT4PVr1fOXL4FNm4CNG3l5FC5c4GtguLoW/1yEEJKf4kRY0dHRLEatV+CFCxfYV199xX5Xn2msENq0acPGqE14JZPJmKOjIwvJPRf+f3777TdWp04dllXAhDTTpk1jzXIPLy0iqgHSfXK5sAJj5kxeo9OzpzBf7oE46sLC+D4SCe9vWlwyGZ9GRL1sZb0sw4wJyQxgbD6CeAeg/GpzMjP5iCiAV1cpzJ2refpkW9u8I6s2b+bDyNUnr2OMj4j6+29hJ1nFTLRLl6rSMjL4zIr//kud1wghJVLmNUD+/v44fvw4ACA2NhZeXl64ePEipkyZgpkzZxbqGFlZWbhy5Qq8vLyUaXp6evDy8sK5c+c07rN37160a9cOY8aMgZ2dHZo0aYI5c+ZApr7QGoCIiAg4OjqiTp06GDRoEKKjo4tzmURH/fIL0LAhXzxSYdAgYPRoYMIEYV5r6/yP88EHfHHQ48f58YpLT4/XMinMmwc0bszXAywrU+da4Nxv1/CNwRJg61Zg0SLNGY2MeCelI0eARo1U6WPH8kULZ81SpTEG+PoC3t5AzZqq9JgYvvqo+mqlimP07csXRFSoX5/33Xn4UJVmbAzMncuryorTyYoQQoqjOBGWlZUVu/ffHPqLFy9m7f+b/Ovw4cOsdu3ahTrGs2fPGAB29uxZQfrEiRNZm9zz5v+nQYMGTCqVsv/973/s8uXLbOvWraxatWps+vTpyjwHDhxgf/31F7t+/To7dOgQa9euHatVq1ae9cvUvX37liUnJyu3mJgYqgHSIbl/TYMH80oGbZyTMyeHT08ACJdmKKnMTL4iQJ6R3EuWqJYGUO+XV5ozICYm8gWv1GuQ5HLG+vblQ7DV58BJTCyfie8IIZVSmU+EaGZmxh4/fswYY8zX15f99N/8G0+ePGHGuRdfy0dxAiBXV1fm5OTEctT+y//yyy/M3t4+3/O8fv2aWVhYsNXqa8DkoqnjNAVA2u/tWz4Ts1QqnLft4kW+4HIBMW+xREQIVykorsxM3tG6tPoYy+V8NXsg75ITTC7nHYz791f15o6M5PO6/PQTTflNCKlQyrwJrHHjxlixYgVOnTqF0NBQ+Pj4AACeP38O64LaFNTY2NhAX18fL9U7XgJ4+fIl7O3tNe7j4OCA+vXrQ19tHK+bmxtiY2ORlZWlcR8rKyvUr18fkZGR+ZZl8uTJSE5OVm4xMTGFugZSvnJygHv3VM+lUt55OTMTOHpUld66NTBkCFClSumd+8oV3on50095i09JGBnxjtaKUdxZWbyl6NSp4h1PIlFd78cfa3hx9Wpg2zbeqRkAdu7kQ8v/+ou/eYQQUgkVKwD6+eef8fvvv6NLly4YOHAgmv03vGXv3r1o06ZNoY5hZGQEDw8PhIWFKdPkcjnCwsLQrl07jft06NABkZGRkMvlyrQHDx7AwcEBRkZGGvdJTU3Fw4cP4eDgkG9ZpFIpLCwsBBvRLnfvAo6OvJtIdrYqffFi4P593senLDVrxufxSU4GAgP5VDSlZd48YPduHrwUNGCrIB9/zOcz6tZNw4tGRqpoizF+IatWAfv28f43hBBSGRW3miknJ4e9yrWQ1OPHj9nLIrTvb926lUmlUrZ+/Xp2584d9tlnnzErKysW+197xpAhQ9ikSZOU+aOjo1mVKlXY2LFj2f3799m+fftY9erV2Y9q0/N+88037MSJE+zx48fszJkzzMvLi9nY2LC4uLhCl4tGgYnvwQO+1pZCVhYfgGRrm3cVg/Isk6kpb2patKj0jvvmDe+3tG+fMF0m4912Ro9mzNGRL6+haDF+8YK3bCUlFfIkcjk/oJ8fv4BVq0rvAgghREuUeR+g9PR0lqY21X5UVBRbuHAhO1SMhYaWLl3KatWqxYyMjFibNm3YebW7XufOnVmAcglm7uzZs6xt27ZMKpWyOnXqsNmzZwv6BPn5+TEHBwdmZGTEatSowfz8/FhkZGSRyqSrAdCJE3yxTl0fSbxpk+blhO7cES7aLIbly3nZjI3LLhC7cYOv1K5Yy1N9s7fna2kqJmr85JNCHvT77/lBZ8zgOxoZCdfTIoSQCqDMA6Bu3bqx3377jTHGOxnb2dmxmjVrMmNjY7Z8+fLiHFKr6GIAlJOjWsPq/n3Gnjzhq5rrCvXRSy9e8Ptz9+7at4aiXM4nJwb4RMVlEZApRrEB/Hc6dChje/fynzt28DxXr/IFwR8+LMQB793jo8AAvlp8nz78cc2aNCKLEFKhlHkAZG1tzW7dusUYY2zVqlXM3d2dyWQy9tdff7GGDRsW55BaRRcDoLg4xtzdGbOw4MPCmzbl97jFi8UuWcEyMhgbPpyvYq+u0E07Inj2jLGqVfPO51dUMTGMzZ/PA6mLF1Xpe/bwhVdtbRl7+lSVnrtmb9s2xr77rpBB4oEDjCkmGE1OZqx+fX4BXbowlp1d/IsghBAtUuYBkImJCXvy5AljjLH+/fsr5+GJjo5mJiYmxTmkVtHFAEghJ4fXUkyfzvuNREeLXaKCnTrFmJ4e39QnF9Z227Yx9sMPwgW2CyMhgbEVKxjr3JnPMK2o6VFfeZ4xHuyo/+4uXGCsVi3V0l7JyYxZW/N9GzUSBlCFcvs2Y+bm/ADffFPEnSu22Fhee9q/Pw/OCSG6o8wDoKZNm7LFixez6OhoZmFhoZzL5/Lly8zOzq44h9QquhwAqcs9112eSfK0xJIljB05InYpytbLl3zFCQMDYZ+ejh0Z+/XXd7dE/f03zz9ggCpt925eU6SY53DyZD43EmOMVx35+RW81seOHXxnQ8NCtqVVTDk5vMN9cDBjrVoJfz8WFqpmztev+e+w3Ba1JYQUWVHu3xLG1FcfLJwdO3bA398fMpkMH3zwAUJDQwEAISEhOHnyJA4ePFgqI9TEkpKSAktLSyQnJ1eYIfEXLvDh29u3A02aiFcOxoBlywB//4KXodAlWVl8JYlevVRpb9/ylSSaNuXPZTK+ekRsLNC8OTBwIJ9TqFatwp1jxAg+RH7VKtV0PgCQmAh8+SXw55/8eePGwPp1DK0mvg/8+y/QuzcfY5+fn38GPD2B9u358127+JIW+vp5N3Nz4Tj7K1eA1FT+mp6eMK+RES+MQkICUK2aVi510aMHX3NVnYcHT+/RA2jThhf7q6+AJUv45U2cCAQHAyYm4pSZEKJZke7fxY2yXrx4wcLDw5lMrWPChQsX2N2SrBqpJXSxBqh7d8Z69eL9XXOTyxnz9OTfaAcOLP+yqfvqK14Ob2/dH63GGO9/4+HBr2n/fl6TNWwYY5aWfMSWeq3bgQMlW1S1IDt38smdAV7LFHPoFl+G4r8Z2wvNzS3v0DPF5uwszJu7ukR9s7FhjDH26hVjK1cyFmB3gH1n9AtbWe9ndrLfQsbWrmUsPLzobYjFJJfzjuOzZ/O/BfXa0VmzeE1P//6MrVvHO+FrEhvLa+AUl+jqytdvJYRojzKvAVL39OlTAEBN9cURdZyu1QBlZwNmZvznkyeaaxUSE4EffuCT7qnXIJS3GzeALl14xcOIEar5+XTZl1/yWq3catQATp4E6tQpn3IkJPD1R62sgBUrwO/TRX2Dv/iCL14qk+Xd7O35LNIKAwYAN29qzLvbsD82tVqMfft4DZm6JriJm3DnTwwNMaban5B83A916kpQty5Q1ykLdRoawdS0JO8Gr8g6ehQ4cIBvL16oXvvrL6B/f/44NZXPKm5oWLjj7tnDF9Z9/pw/HzUK+Okn4YK3hBBxlHkNkEwmYzNmzGAWFhZMT0+P6enpMUtLSzZz5kxBjZCu0rUaoOxsPgfQr78WbWmno0fLpxYmd/8WbR7hVRxpaYw1bMhrBaytGfviC14zUO5/CitWMHbvnmBQV1QUn/qnrCtacn/u2rdX1ZQ0bcrYD5Nz2NiB8ay7+1M2ptkpxt5/nzErKyYHmLleqsZKJAfjRDa0+TU+B0BMDGNyObt2jX+e3vU53707b38rU1PGPvqIv0351fIU1uvXjI0cqTr2uHElOx4hpHSUeSfoSZMmMVtbW7Z8+XJ2/fp1dv36dfbrr78yW1tb9v333xfnkFpF1wKg4ti6lf/j7tu37EZBZ2fzAUZWVnz9zYosIYHP2izaRI27dvFfaNWqfJw+40GClxdPbtaMNwGVtjt3+ByLrq7C/tZbtjA2cSJj168XsLNczrIjo9iqqU/YpEm8CcqjpYxZ4ZUysPDFHmWUIa9mzaoYpjOAD2Bzd2esbx85mzCBsVGjGNu8WXXoJ0/4bvXrMzZ+PG+aVHYQL0XHjjHWrp1wwXtCiHjKvAnM0dERK1aswEcffSRI37NnD0aPHo1nz54V9ZBaRdeawIpj61YgIAAYMwZYsKBszpGVBXTuDJw/DyxdyptnSBmJjwc++gho107wC/3rL95ck5gIGBjwZtDvvy98c48mL17wz88ffwDh4ar05ct5c1CJvXqFV6du49GJaOhH3EOL6D3AnTtIkZmiiekjPM2wgab/Wj1q3sD+Xr/xDthGRnji0hnOY/7rmZ6TA/z6q/I1GBqqHhsZ8R7qzZurDnbtmvB1ExPezmxikqdZUb2lkTFg5Ejggw94R3eda+KVy4Ud1V+84G2EmZl5N339fBafI0Q8Rbl/FysAMjY2xo0bN1C/fn1B+v3799G8eXNkZGQU9ZBaRdcCoH/+4f+X27QpWj+EmzeBRo34/7Gy8uwZcPEiX+2clLGMDH6zzvULffmSB0G7dvHnLVoA69fzNVGL4t49PhLq6FHVYrAGBkD37nx1e1/fMhwV9fYtcPs2YGiIt/XdERUFPDr5FA8//xkPURcMEvTAAXjjiGqfYcOAtWv54zdvCv7jGDAA2LaNP5bL8/+jkEh4oKk+sq5rV55uZoZ9SR3he3IiAKBnvXv4bcxtOI3/WJX36FH+ppmZqTZTU953Sk8PqF5dlffCBSA9XRVwZGWpHlerJvyj+ukn3glMPUBR5K9Zkw9fU+jViw9R1BTU1KkDPHigytusGe+4p4mDg6ojFABMmcLfZ09Pvtnb5/9+E1JGyjwAatu2Ldq2bYsl6n9UAL788ktcvHgRFy5cKOohtYquBUD16/P/Z6GhgJdX8Y7BGK+h8fUFfHyKf4wVK/iX6xEjincMUgRxccCdO7xX+Tswxu/vY8YAr17x39HBg/zenZ+cHB481ajBn8fH83ueTMYrmgYP5nGDjU3pXE6RyeVAZCRw9SoQE8NHAWRl8S07G2jVihcQ4LUYI0eqXlPPl5XFazJ+/JHnzcoCXFxUeRSBhMLHHwM7dvDHjPFg6b9/o1kwxM/4Dj/iB2RBiir6afh5qRk+//y/ihVLS947W5P33weOHVM9t7bmvyxNWrfm3ywUnJ1553VN3Nz450ShcWPhc3W1avGRFAqenjwAkkrzbtWrA4cOqd6HGjWEPc3r1VMFQ506AXXraj4nIaWozAOgf//9Fz179kStWrXQrl07AMC5c+cQExODAwcOwNPTs3gl1xK6FAAxBvj5Adev82lfivula/16/oVZKgUePQIcHYt+jP37+ZdLQ0P+P7Nhw+KVhRRCRga/YV65AmzerLrRv0NsLG+mun+fN18ZGwtfZwy4fJk3b23dyn+H//6ren3zZuC99yrhvUwu57UxaWk8krG1VaXv3cvT1bY7UaYYsb8PzsXyIYCensDq1UD9gHZAcjIPyBT5FcFVly7A8eOqc7Zvz/MaGeUNPho0AObPV+WdNYsHVpoCFRsb4JNPVHnPn+dBnaa8Jia8dqmoZDI+QvDUKT708eZNCNopcwdsDx7wD1FZVj+TSqnMAyAAeP78OX799Vfcu3cPAODm5obPPvsMP/74I1auXFmcQ2oNXQqASktWFvDZZ/x/7mefFe8YimCsdWtgwgQd7P+gSzIzgf/9j1fjnDvHb4iFxBivWFBMRJmTA6xZw2t4/viDB0cK1avz2sVK8mdQqmQy3i9q8mQe57i5AbduaZgLUi7nfywV6Q8mKQk4c4YHRKdO8RqgkBD+Wmoqn6vB3Bzo0EFVS9SqFQ/CSPmSyfI2hSpqPmvU4L8rHVIuAZAm169fR8uWLSGTyUrrkKKojAEQkHfamFev+P8jM7P89zl3jvc9UnyRK87UM6SY5HIgKqrEEw39/DMwaZLquYkJ0KcPb+Lq1q1kHaYJ/xV98QX/UlDcJuoKJTycj45ITRWmGxvzfyajR/NvUkXFGL95v32r+aezs6qP1fPnvLYtO5tH99bWfKtWjf/UtkAsPZ039yYk8G8qCQl8S0nhgUrv3qoP1717QFCQ5j5emZnA+PF8A3hVfbNm+Z83MBBYt44/zs4G/v6bdx50ddXa2rui3L8NyqlMpIyUZsChfpy3b3lfz4wM3slaU5PYvHnAd9/xm+ecOXmPQcrAnTu85zrAqxJKYZZFJyd+b3B1BYYM4X1rq1Qp8WHJf1xceEWd+t/GihW8q02lXE6jZUvg9Wvebn/yJK8hOn2a39hPnhQ21z1+zDuu5RfYLFvGb/4Ab4JTzG6pyerVwPDh/PGNGzzCz8/8+cA33/DHDx7w4ZOK4Eg9UKpWjfd1srMr+vsQH89ryRTBjHpgk5DAgxhFIHjuXMHRc82aqtdTU/Ou7aIuMVH12Mgo7+uK5lADA2FwdO+eqjzGxnxNJXd3nsfdnY+i1LHaIgqAdFzfvvzvc9Ei4MMPS++4jx/zppDsbN4NQVMA5OTEA7DXr6nmp1z88w+vmvnmGz7qp5TW1fL35xspO+p/GwkJfC2x1FQ+Mm/1at4CVKkYGPAF1zw8gK+/5v9A7t/nwZB6z/xjxwq+mScnqx7n7tBmbMxv5Oo/FezseMBgYMCP8eoVDwxeveI1q5aWqrxPnvBFFPPz88/At9/yx9evA/36qQIkY2N+TEVwExKiGiFy40bBw2MfPlQ9rl6db7a2vE+XYrOw4NemWMsPAGrX5rU2mvp4SaU8WFKoV4+XTfGaoaHww6reQJSeDrRty/t3pafzzoKXL6tenz2bz7EB8AEax47xwKh+ff4+ayHtLBUptGvX+N9nSZcNyM3NjY/CffaMP1ZQnybk00/531rbtqV7bpKPe/f4L+D1a4o2dZiNDbBpE2/pefCAd49p0oT/nbm58UFmRZ2iQOdJJLzHfe6RE+7uqpu5poCmdm1V3g8/5MGMsXHeG3luLVrwYbO5yeW8WUm9ZqR+fT6NgCJASkwUPlb/dhgXx0eRPHqk+bwvX6oe16jBRxSoBzTqAY5iJWWAP1bftyDW1rzpqjAMDAoexqn+HrZtyzvQy2T8+q5f50HcjRv8sXpt0ZkzfCIsgP+uGjfmv0tFjZGHhzDIFEmR+gD169evwNeTkpLw77//Uh+gcvT0KZ8epUOHsl/ja88ePtjo1auC+wVppdOnebW2mRkfyqQQGso7abRvr1q9PCeHD+e1sCjdP1LG+D/olBThgm0HD/JvUu+/D3TsyNOePuXfhLOyeHWcwpEjPB91zNF5SUm84mDVKmH6H38Agwbxx2fO8AoGRXCk2LT831LllZzM/yG/egWWkIjMN1kwtrdSBTc1a+pcM1Gx7NvH+0XcvJm3rxfAh5gWp59XIZRZHyDLd9wMLC0tMXTo0KIckpRQzZrCGs2ykpzMa6qzsvjIlokTy/6cpapqVT5cWX2iOYB/u/zzT2DhQlUA9OwZ77hhbMw7QSnMnMmDlTFjVP0H0tJ4ZyhLS96xUPGNackSHqz873+8ShzgVdqurjxSffNGdVxFO8isWaoAyNRUNSFdVpbqG2lptnMSUVlZAStX8n5AN24Ad+/yzcNDlefyZd7y+c8/wn0dHXkg9OOPvBIBUM2lSJWD5YexXHNsWlris/XtcfEiHz2Zns4rehSVW40a8Zq/Cq9XL74pBmooaosUPwvqeF2OihQArVP0BieVjoUFMG0av99//rnYpXkHxniwk5Cg6vTYuDGwcWPeu4OHB78o9WHkaWm8hiX3t4d793gVsPo3l4QEYMYMXs379deq9Js3+cRI772nCoAU86vI5bxzlaIWp3NnfudSX4rByopPwFOtmtaOtiClQ/ElpkePvK95e/MVPO7cUQVIL17wQUzPn6vmbgR4MPXDD6paokaN+M8mTcrnS1JFlpTEv49ERPBN8fjBA9VUEQo3b/L7vMKzZ3wLC+NjFtQDoBEj+L8bRYDUoAFvcSvtLg2iUQzUqFNHK5cDKNVh8BWFrjSB7dzJ+69161YJJ6YryMGD/G5iYcG/fVStWrzjqNe8APy/2uPHPJhydeVpsbE8AJLLgd9/V+X9918+bLVNG1VbvmKYbu7OmoQUQVKSKhgaMEDV9D1uHF9zT5M6dfjk1S1alFsxdU5qqirASUgQBipt2gCXLmnez8iIBzGKfr6HD/PaOFdX/q8nMpJ/d7p3j7fAT52q2tfWlp8rN2dn/r1owwZV2qtX/HhUw1cw0eYBqih0JQDy8uLfKtas4S0tlVbumf3kct6U1KULH6evBZ3tCClr6em8RkK9tujuXT64Si7nN1pFJeSaNTyvlxf/U6l0Q/HBl4YJC+PvT0SEcBUPQ0P+fiqCmsGD+bRBrq68hsbVVfW4Tp3ifadhjDdt3r+vCpDu3VOtftKtG29FV3B05EGaoqaoYUNe+5STwwMm9RrE2bN5JXNOTt6tQQPgyy9VeYcP5814ufPJZLz2cPHiol+bmCgAKiFdCYBCQnjf3tmzha0nlUpEBI/+kpJ4DY1iiBqNyycEAO9zf/Uqr1FQ8PTk/zsA1ShqLy++eXhUrFbXly/5iNbwcN6Mr/i30L+/akk3BVtbVXCzeLHqu5NMVn7vSUICD4T09fmae4Bq8uz8xhf17i1cn9fQkAcxmnh5CQfAVa3K/31q0rkzcOKE6nmvXjy/hwefzql5c+3rkE8BUAnpSgBEwIeE167NJ0U7f74SR4KEFN5ff/F1TEND+YBDdc7OvKVXF78/ZGXxqUHOn1dt6oMoIyL41DcA70IQHq5q0XZ11e4BWpmZfByFeo1RUhIPdlq35pXdCmPH8u+ABgY8kDIwUG116wIBAaq8v//Og6XceQFeqd69O3+ckqK5Mt3VlQdEPXrwiVTFRgFQCVEApMUuXOD9axQTjwG8z4+7u2rZckJIoTDGg4KjR/l27BifZeHvv1V5FAu5e3nxmRmKu+ByaWMMiInhtTaKJrxJk/i0AeokEt4h/L33+OuKAIgUzdu3vMkwPJyvwRwezt9/heHD+WBWgAeiQ4bw76MtW/JNsX5wWaMAqIR0IQB684aPFKhIVdXv9OgR/08skfChlE2aiF0iQiqUnBxeq6CYG0/xJ6eucWNVc1nnzuW3bEpaGr/xqtfuvHjBa7K8vXmev/8GRo7kwY5ia92augGWlfh4HgiFh/MgR/F7uHqVP1fn5KQKhnr04GvflgUKgEpIFwKgUaOA9ev5ighffSV2acrQ69fCUVyffsojvxkz+F8UIaTMvH3LV6dQ1BBdvSpcHWHMGL4cF8BfGz2aN58YGub9OWSIatmuJ0+AuXOFr6s/9vRUTYl1+jQf4XbjRt4+MAYGfOTbF1/w5zQXknZ49oxPr6aoLVJMaaYwZw4weXLZnJsWQ60EHjzg/5wKmsVcp71+DXz2Ga+Tf/RI9RVuy5ZSWwOLEFIwY2M+GqlbN/48MZGPhlIEROrrc75+zWtl8qOYsBHgs0csX55/3unTVQGQhQUPrgA+EqpdO1XtTsuWwjlzKlWNuBarUQOYMEH1PCWFj1FR1Bapd8gXE9UAaaALNUByOe/cZ22t3R33ik0m4/Pn3LvHe2yqrxBNCNEK6oMt4+P5ouXZ2aoh2OqP27VTzUMUE8P7i6jnU//Zuzfg68vzymS8aeu992hCR/Ju1ARWQroQAFU4P//Mx6v+8ovqP+qZM7zmh/r6EEIIKYSi3L+pLYGUnbdv+ZhN9fW09u7l68Dknrnxt9/4elzqk1l06EDBDyGEkDJBfYB00N9/88qRXr34ZMeie/aMdwwwNhY2VTVsyHs7nj2rmtGLMd6bUX2JCYD36s7MpDU9CCGElAsKgHTQP//wRcyrVBEhAJo1iy+KM2uWakXfixf5EI/WrYUBUO3avNdkYqIqrX174MCBvJNxqM/iRQghhJQxCoB0UO/efAHE998vh5O9eSOc6CMsjE9E+OmnqgCoYUNemNyzMO/bx4doqI9JtbVVTS1KCCGEiIQ6QWtAnaABxMUBw4YBly8D0dF8wSCAj8h69YqWoCeEEKJ1aB4gUjzqK/5Vq8b76sTFASdPqiYCGTBAvPIRQgghpYRGgemYuDheIVOq9XbR0cCgQXz6VcWBDQx4R6MHD1TBDyGEEFJBUACkY9as4as15x5FXiKmpnxp5HPngJs3VeleXnypX0IIIaSCoQBIx7x+zStn6tcv5gGiovgCPp9/rkqzseHz0l++zFdVJ4QQQio46gStgbZ3gs7K4pu5eTF2Dg8HPDz4ioNPnwLVq5d6+QghhBAxUCfoCs7IKO88gho9fcpnWLa1BcaP52ktWwKTJvHmLVvbsiwmIYQQorVEbwL79ddf4eLiAmNjY7Rt2xYXL14sMH9SUhLGjBkDBwcHSKVS1K9fHwcOHCjRMSuss2eBOXOAuXP5KoMKISFA167C+XkIIYSQSkTUAGjbtm0ICgrCtGnTEB4ejmbNmsHb2xtxcXEa82dlZaFbt26IiorCjh07cP/+faxatQo1atQo9jF1yZEjQJ8+wMqV+WQ4eJBPPqjQpw+fmXnpUgp2CCGEEDWi9gFq27YtWrdujWXLlgEA5HI5nJyc8OWXX2LSpEl58q9YsQLz5s3DvXv3YGhoWCrH1ERb+wDNmAFMn85HgK1Zk+vFBw9481azZsDp0xTwEEIIqXR0YjX4rKwsXLlyBV5eXqrC6OnBy8sL586d07jP3r170a5dO4wZMwZ2dnZo0qQJ5syZA5lMVuxjAkBmZiZSUlIEmzbq2xdYvJivQpFHdjbg5MQnMpTLy71shBBCiC4RrRN0QkICZDIZ7OzsBOl2dna4d++exn0ePXqEY8eOYdCgQThw4AAiIyMxevRoZGdnY9q0acU6JgCEhIRgxowZJb+oMubuXsAo9caNgStXgNRU1WzOhBBCCNFI9E7QRSGXy1G9enWsXLkSHh4e8PPzw5QpU7BixYoSHXfy5MlITk5WbjExMaVU4nKQk6N6bGpKw9oJIYSQQhAtALKxsYG+vj5evnwpSH/58iXs7e017uPg4ID69etDX62Gw83NDbGxscjKyirWMQFAKpXCwsJCsGmbuDjg2DH+U+nFC6BRI2D7dtHKRQghhOgi0QIgIyMjeHh4ICwsTJkml8sRFhaGdu3aadynQ4cOiIyMhFytj8uDBw/g4OAAIyOjYh1TV4SG8pHrH3+sljh/PhARAfz4o7AmiBBCCCEFErUJLCgoCKtWrcKGDRtw9+5djBo1CmlpaRg2bBgAYOjQoZg8ebIy/6hRo/Dq1St89dVXePDgAfbv3485c+ZgzJgxhT6mrpLJgLp1c/UB+uknYOpUYOtWvj4GIYQQQgpF1Lumn58f4uPjERwcjNjYWDRv3hyHDh1SdmKOjo6Gnp4qRnNycsLhw4fx9ddfw93dHTVq1MBXX32F7777rtDH1FVDh/JNMGmBoSEwc6ZoZSKEEEJ0Fa0FpoG2zgMEAEhJAXbtAgICaK4fQgghRI1OzANEioExYNQoYNgwYNw4sUtDCCGE6CwKgHTAlStAkybAmDEAWrfmw90HDhS7WIQQQojOogBIB9y4Ady+Ddx/IOGrukdFAe3bi10sQgghRGfR0CEd4Oudhf179SE1/W/+I1tbcQtECCGE6DiqAdIBNr9MRo/5H6BrfR2aoZoQQgjRYlQDpO3i4oDVq/nor2vX+IKnhBBCCCkRCoC0XIpxdWydGIGmCcfRztdX7OIQQgghFQI1gWm569eBz6dWx6d/+4ldFEIIIaTCoBogbbVxI9CqFQwMGsHbG3BwELtAhBBCSMVBAZA2unQJGD4cMDREu6tXcehQA7FLRAghhFQoFABpIycn4IMPAEtLoH59sUtDCCGEVDgUAGkje3vg4EGwjLdgTAI9WvKLEEIIKVXUCVqbJCWpHuvp4eUbU5ibA23aADKZaKUihBBCKhwKgLTFgwdA7drA7NnKaOfWLSAjg8dF+vriFo8QQgipSCgA0hZ//skjndBQZdL77wP37wMbNohXLEIIIaQioj5A2iI4GKhTh0c9/1X36OtTH2hCCCGkLFAApC0kEmDIELFLQQghhFQK1AQmpthYYMoUIDMzz0uMAd9/z+dDfPtWhLIRQgghFRjVAImFMSAwEDh8GHjyBPjjD8HLMTFASAhgaAgMHChOEQkhhJCKigIgsUgkwNixwN27vKpHgzFjgLQ0HgQRQgghpPRIGGNM7EJom5SUFFhaWiI5ORkWFhZle7LsbIpwCCGEkFJQlPs39QEqb2/eACkpqucU/BBCCCHljgKg8jZ6NNCiBXD5coHZ4uN5NyFCCCGElD4KgMpTQgJw6hTv9JyVlW82mYyvh1q1KvD0aTmWjxBCCKkkqBN0ebKxAa5dA44fB9q3zzdbTAyQk8NjJAeH8iseIYQQUllQAFTerKyAvn0LzOLiwkd/PXlCa4ARQgghZYGawLSUVErLYBBCCCFlhQIgQgghhFQ61ASmhb76ireUjRoF2NuLXRpCCCGk4qEASMtkZQHLl/NO0CNGiF0aQgghpGKiAEjL5OQA8+YBDx4ANWuKXRpCCCGkYqIASMuYmgLjx4tdCkIIIaRio07QhBBCCKl0KADSMrdvA4mJYpeCEEIIqdgoANIyvXvzCaP//VfskhBCCCEVFwVAWiQnB5BI+GM3N3HLQgghhFRk1AlaixgYABERQGoqYG4udmkIIYSQiotqgLQQBT+EEEJI2aIAiBBCCCGVDgVAWuTLL4FBg4CrV8UuCSGEEFKxaUUA9Ouvv8LFxQXGxsZo27YtLl68mG/e9evXQyKRCDZjY2NBnsDAwDx5fHx8yvoySmzPHmDLFiA9XeySEEIIIRWb6J2gt23bhqCgIKxYsQJt27bFokWL4O3tjfv376N69eoa97GwsMD9+/eVzyWKoVNqfHx8sG7dOuVzqVRa+oUvRYwBv/0G3LoFNG0qdmkIIYSQik30AGjBggUYOXIkhg0bBgBYsWIF9u/fj7Vr12LSpEka95FIJLB/xzLpUqn0nXm0iUQC9OzJN0IIIYSULVGbwLKysnDlyhV4eXkp0/T09ODl5YVz587lu19qaiqcnZ3h5OSE3r174/bt23nynDhxAtWrV0eDBg0watQoJBYwvXJmZiZSUlIEGyGEEEIqLlEDoISEBMhkMtjZ2QnS7ezsEBsbq3GfBg0aYO3atdizZw/++OMPyOVytG/fHk+fPlXm8fHxwcaNGxEWFoaff/4Z//77L7p37w6ZTKbxmCEhIbC0tFRuTk5OpXeRhXT+PHDhApCWVu6nJoQQQiodCWOMiXXy58+fo0aNGjh79izatWunTP/222/x77//4sKFC+88RnZ2Ntzc3DBw4EDMmjVLY55Hjx6hbt26OHr0KLp27Zrn9czMTGRmZiqfp6SkwMnJCcnJybCwsCjGlRVd167AsWPAunVAYGC5nJIQQgipUFJSUmBpaVmo+7eoNUA2NjbQ19fHy5cvBekvX74sdP8dQ0NDtGjRApGRkfnmqVOnDmxsbPLNI5VKYWFhIdjKW/XqgIMD0KRJuZ+aEEIIqXREDYCMjIzg4eGBsLAwZZpcLkdYWJigRqggMpkMN2/ehIODQ755nj59isTExALziO3PP4HnzwEPD7FLQgghhFR8os8DFBQUhFWrVmHDhg24e/cuRo0ahbS0NOWosKFDh2Ly5MnK/DNnzsSRI0fw6NEjhIeHY/DgwXjy5AlGjBgBgHeQnjhxIs6fP4+oqCiEhYWhd+/eqFevHry9vUW5xqLQMKKfEEIIIaVM9GHwfn5+iI+PR3BwMGJjY9G8eXMcOnRI2TE6OjoaenqqOO3169cYOXIkYmNjUbVqVXh4eODs2bNo1KgRAEBfXx83btzAhg0bkJSUBEdHR3z44YeYNWuW1s8FRAghhJDyIWonaG1VlE5UpeGbb4DTp4HvvgP69Svz0xFCCCEVks50gibchQvAxYuA2kA0QgghhJQh0ZvACLB2LXDjBtChg9glIYQQQioHCoC0QP36fCOEEEJI+aAmMEIIIYRUOlQDJLJTp4DoaKBdO6BOHbFLQwghhFQOVAMksrVrgcGDgU2bxC4JIYQQUnlQACQyNzfA0xNo1UrskhBCCCGVB80DpEF5zwNECCGEkJKjeYAIIYQQQgpAAZCIsrMBqn8jhBBCyh8FQCKaOxewsQF+/FHskhBCCCGVCwVAIrp9G3j1CjA0FLskhBBCSOVC8wCJaO1a4NtveS0QIYQQQsoPBUAiMjYGmjcXuxSEEEJI5UNNYIQQQgipdCgAEsm5c8CMGcDx42KXhBBCCKl8KAASyaFDwPTpwObNYpeEEEIIqXwoABJJq1bAsGGAl5fYJSGEEEIqH+oELRJfX74RQgghpPxRDRAhhBBCKh0KgESQmsonQCSEEEKIOCgAEsHu3YC1NdCvn9glIYQQQionCoBEEB3Nfzo4iFsOQgghpLKSMEbrkeeWkpICS0tLJCcnw8LCoozOAbx9C1SvXiaHJ4QQQiqdoty/aRSYSCws+EYIIYSQ8kdNYIQQQgipdCgAKmc3bwJDhgArVohdEkIIIaTyogConF28CPzxB7Brl9glIYQQQiov6gNUztq0AWbPBpycxC4JIYQQUnlRAFTOmjblGyGEEELEQ01ghBBCCKl0KAAqR+npwKVLQFqa2CUhhBBCKjcKgMrR5cu8D1CTJmKXhBBCCKncKAAqR69fA3Z2QOPGYpeEEEIIqdyoE3Q56t2bb2/fil0SQgghpHKjGiARGBuLXQJCCCGkcqMAiBBCCCGVDgVAhBBCCKl0KAAihBBCSKVDARAhhBBCKh2tCIB+/fVXuLi4wNjYGG3btsXFixfzzbt+/XpIJBLBZpyrVzFjDMHBwXBwcICJiQm8vLwQERFR1pdBCCGEEB0hegC0bds2BAUFYdq0aQgPD0ezZs3g7e2NuLi4fPexsLDAixcvlNuTJ08Er8+dOxdLlizBihUrcOHCBZiZmcHb2xtvafw5IYQQQqAFAdCCBQswcuRIDBs2DI0aNcKKFStgamqKtWvX5ruPRCKBvb29crOzs1O+xhjDokWL8MMPP6B3795wd3fHxo0b8fz5c+zevbscrogQQggh2k7UACgrKwtXrlyBl5eXMk1PTw9eXl44d+5cvvulpqbC2dkZTk5O6N27N27fvq187fHjx4iNjRUc09LSEm3bti3wmIQQQgipPEQNgBISEiCTyQQ1OABgZ2eH2NhYjfs0aNAAa9euxZ49e/DHH39ALpejffv2ePr0KQAo9yvKMTMzM5GSkiLYCCGEEFJxid4EVlTt2rXD0KFD0bx5c3Tu3Bm7du2Cra0tfv/992IfMyQkBJaWlsrNycmpFEtMCCGEEG0jagBkY2MDfX19vHz5UpD+8uVL2NvbF+oYhoaGaNGiBSIjIwFAuV9Rjjl58mQkJycrt5iYmKJeCiGEEEJ0iKgBkJGRETw8PBAWFqZMk8vlCAsLQ7t27Qp1DJlMhps3b8LBwQEAULt2bdjb2wuOmZKSggsXLuR7TKlUCgsLC8FGCCGEkIpL9NXgg4KCEBAQgFatWqFNmzZYtGgR0tLSMGzYMADA0KFDUaNGDYSEhAAAZs6ciffeew/16tVDUlIS5s2bhydPnmDEiBEA+Aix8ePH48cff4Srqytq166NqVOnwtHREX369BHrMgkhhBCiRUQPgPz8/BAfH4/g4GDExsaiefPmOHTokLITc3R0NPT0VBVVr1+/xsiRIxEbG4uqVavCw8MDZ8+eRaNGjZR5vv32W6SlpeGzzz5DUlISOnbsiEOHDuWZMJEQQgghlZOEMcbELoS2SU5OhpWVFWJiYqg5jBBCCNERKSkpcHJyQlJSEiwtLQvMK3oNkDZ68+YNANBoMEIIIUQHvXnz5p0BENUAaSCXy/H8+XNUqVIFEolE7OKUGUWkXFlquirT9dK1VkyV6VqBynW9dK2lgzGGN2/ewNHRUdB9RhOqAdJAT08PNWvWFLsY5aayjXyrTNdL11oxVaZrBSrX9dK1lty7an4UdG4iREIIIYSQkqIAiBBCCCGVDgVAlZhUKsW0adMglUrFLkq5qEzXS9daMVWmawUq1/XStZY/6gRNCCGEkEqHaoAIIYQQUulQAEQIIYSQSocCIEIIIYRUOhQAEUIIIaTSoQCoggoJCUHr1q1RpUoVVK9eHX369MH9+/cL3Gf9+vWQSCSCTVcWkJ0+fXqesjds2LDAfbZv346GDRvC2NgYTZs2xYEDB8qptCXj4uKS51olEgnGjBmjMb8u/V5PnjwJX19fODo6QiKRYPfu3YLXGWMIDg6Gg4MDTExM4OXlhYiIiHce99dff4WLiwuMjY3Rtm1bXLx4sYyuoGgKut7s7Gx89913aNq0KczMzODo6IihQ4fi+fPnBR6zOH8L5eFdv9vAwMA85fbx8XnncbXxd/uua9X09yuRSDBv3rx8j6mtv9fC3Gvevn2LMWPGwNraGubm5vj444/x8uXLAo9b3L/1oqAAqIL6999/MWbMGJw/fx6hoaHIzs7Ghx9+iLS0tAL3s7CwwIsXL5TbkydPyqnEJde4cWNB2U+fPp1v3rNnz2LgwIEYPnw4rl69ij59+qBPnz64detWOZa4eC5duiS4ztDQUABA//79891HV36vaWlp/2/v/mOirv84gD8PPa6DGaD8ziAwJKJgRckOcy1gwukSjYa0mzv7RSA4rdysFaFrrV/OWq5dugnWaJA0UZOUAQJrDLUAlZSYOsI5QdLCQAQc9/r+4ddbJ9zBIT/uuOdj+2z3+Xzen/e93vfivc+Lz31uH0RHR+Prr78ecf9nn32Gr776Ct988w2OHz8Od3d3JCUlob+/32KfP/zwA9566y3k5eWhsbER0dHRSEpKQldX12QNY8ysjbevrw+NjY3Izc1FY2Mj9u3bh9bWVqxYsWLUfm2ZC1NltNwCQHJyslncRUVFVvu019yONtb/jrGjowP5+flQKBRITU212q895nUs55o333wTP/30E0pKSlBbW4vLly/jhRdesNrveOa6zYScQldXlwCQ2tpai20KCgrEw8Nj6oKaQHl5eRIdHT3m9mlpabJ8+XKzbbGxsfLGG29McGSTb8OGDbJgwQIxGo0j7nfUvAKQ0tJS07rRaBR/f3/5/PPPTdu6u7tFpVJJUVGRxX4WLVok2dnZpvWhoSEJDAyUjz/+eFLiHq+7xzuSEydOCABpb2+32MbWuTAdRhqrXq+XlJQUm/pxhNyOJa8pKSkSHx9vtY0j5FVk+Lmmu7tblEqllJSUmNq0tLQIAKmvrx+xj/HOdVvxCpCTuH79OgBg7ty5Vtv19vYiODgYDz74IFJSUnDmzJmpCG9CnDt3DoGBgQgNDYVOp8PFixcttq2vr0diYqLZtqSkJNTX1092mBNqcHAQhYWFeOWVV6w+uNeR83pHW1sbOjs7zfLm4eGB2NhYi3kbHBxEQ0OD2TEuLi5ITEx0uFwDt+exQqGAp6en1Xa2zAV7UlNTA19fX4SHhyMrKwvXrl2z2Ham5PbKlSsoKyvDq6++OmpbR8jr3eeahoYG3Lp1yyxPjzzyCIKCgizmaTxzfTxYADkBo9GIjRs3YvHixXjssccstgsPD0d+fj4OHDiAwsJCGI1GxMXF4dKlS1MY7fjExsZiz549OHLkCAwGA9ra2rBkyRL09PSM2L6zsxN+fn5m2/z8/NDZ2TkV4U6Y/fv3o7u7G2vXrrXYxpHz+l93cmNL3q5evYqhoaEZkev+/n5s3rwZL730ktUHSNo6F+xFcnIyvvvuO1RVVeHTTz9FbW0ttFothoaGRmw/U3L77bffYs6cOaN+JeQIeR3pXNPZ2QlXV9dhRbu1PI1nro8HnwbvBLKzs/H777+P+n2xRqOBRqMxrcfFxSEiIgI7d+7Ehx9+ONlh3hOtVmt6HRUVhdjYWAQHB2Pv3r1j+s/KUe3evRtarRaBgYEW2zhyXum2W7duIS0tDSICg8Fgta2jzoX09HTT68cffxxRUVFYsGABampqkJCQMI2RTa78/HzodLpRf5jgCHkd67nGXvAK0AyXk5ODQ4cOobq6GvPnz7fpWKVSiSeeeALnz5+fpOgmj6enJxYuXGgxdn9//2G/Qrhy5Qr8/f2nIrwJ0d7ejsrKSrz22ms2Heeoeb2TG1vy5u3tjVmzZjl0ru8UP+3t7aioqLB69Wcko80FexUaGgpvb2+Lcc+E3P7yyy9obW21eQ4D9pdXS+caf39/DA4Ooru726y9tTyNZ66PBwugGUpEkJOTg9LSUhw9ehQhISE29zE0NITm5mYEBARMQoSTq7e3FxcuXLAYu0ajQVVVldm2iooKsysl9q6goAC+vr5Yvny5Tcc5al5DQkLg7+9vlrd///0Xx48ft5g3V1dXxMTEmB1jNBpRVVXlELm+U/ycO3cOlZWVmDdvns19jDYX7NWlS5dw7do1i3E7em6B21dwY2JiEB0dbfOx9pLX0c41MTExUCqVZnlqbW3FxYsXLeZpPHN9vMHTDJSVlSUeHh5SU1MjHR0dpqWvr8/UZs2aNfLOO++Y1rdu3Srl5eVy4cIFaWhokPT0dLnvvvvkzJkz0zEEm7z99ttSU1MjbW1tUldXJ4mJieLt7S1dXV0iMnysdXV1Mnv2bNm2bZu0tLRIXl6eKJVKaW5unq4h2GRoaEiCgoJk8+bNw/Y5cl57enqkqalJmpqaBIBs375dmpqaTL96+uSTT8TT01MOHDggp0+flpSUFAkJCZGbN2+a+oiPj5cdO3aY1ouLi0WlUsmePXvk7NmzkpGRIZ6entLZ2Tnl47ubtfEODg7KihUrZP78+XLy5EmzeTwwMGDq4+7xjjYXpou1sfb09MimTZukvr5e2trapLKyUp588kkJCwuT/v5+Ux+OktvR/o5FRK5fvy5ubm5iMBhG7MNR8jqWc01mZqYEBQXJ0aNH5bfffhONRiMajcasn/DwcNm3b59pfSxz/V6xAJqhAIy4FBQUmNo8++yzotfrTesbN26UoKAgcXV1FT8/P1m2bJk0NjZOffDjsHr1agkICBBXV1d54IEHZPXq1XL+/HnT/rvHKiKyd+9eWbhwobi6ukpkZKSUlZVNcdTjV15eLgCktbV12D5Hzmt1dfWIf7d3xmM0GiU3N1f8/PxEpVJJQkLCsM8gODhY8vLyzLbt2LHD9BksWrRIjh07NkUjss7aeNva2izO4+rqalMfd493tLkwXayNta+vT5YuXSo+Pj6iVColODhYXn/99WGFjKPkdrS/YxGRnTt3ilqtlu7u7hH7cJS8juVcc/PmTVm3bp14eXmJm5ubrFq1Sjo6Oob1899jxjLX75Xi/29MRERE5DR4DxARERE5HRZARERE5HRYABEREZHTYQFERERETocFEBERETkdFkBERETkdFgAERERkdNhAUREZIFCocD+/funOwwimgQsgIjILq1duxYKhWLYkpycPN2hEdEMMHu6AyAisiQ5ORkFBQVm21Qq1TRFQ0QzCa8AEZHdUqlU8Pf3N1u8vLwA3P56ymAwQKvVQq1WIzQ0FD/++KPZ8c3NzYiPj4darca8efOQkZGB3t5eszb5+fmIjIyESqVCQEAAcnJyzPZfvXoVq1atgpubG8LCwnDw4EHTvn/++Qc6nQ4+Pj5Qq9UICwsbVrARkX1iAUREDis3Nxepqak4deoUdDod0tPT0dLSAgC4ceMGkpKS4OXlhV9//RUlJSWorKw0K3AMBgOys7ORkZGB5uZmHDx4EA8//LDZe2zduhVpaWk4ffo0li1bBp1Oh7///tv0/mfPnsXhw4fR0tICg8EAb2/vqfsAiGj8JvTRqkREE0Sv18usWbPE3d3dbPnoo49E5PbTozMzM82OiY2NlaysLBER2bVrl3h5eUlvb69pf1lZmbi4uJieMh4YGCjvvfeexRgAyPvvv29a7+3tFQBy+PBhERF5/vnn5eWXX56YARPRlOI9QERkt5577jkYDAazbXPnzjW91mg0Zvs0Gg1OnjwJAGhpaUF0dDTc3d1N+xcvXgyj0YjW1lYoFApcvnwZCQkJVmOIiooyvXZ3d8f999+Prq4uAEBWVhZSU1PR2NiIpUuXYuXKlYiLixvXWIloarEAIiK75e7uPuwrqYmiVqvH1E6pVJqtKxQKGI1GAIBWq0V7ezt+/vlnVFRUICEhAdnZ2di2bduEx0tEE4v3ABGRwzp27Niw9YiICABAREQETp06hRs3bpj219XVwcXFBeHh4ZgzZw4eeughVFVV3VMMPj4+0Ov1KCwsxJdffoldu3bdU39ENDV4BYiI7NbAwAA6OzvNts2ePdt0o3FJSQmeeuopPPPMM/j+++9x4sQJ7N69GwCg0+mQl5cHvV6PLVu24K+//sL69euxZs0a+Pn5AQC2bNmCzMxM+Pr6QqvVoqenB3V1dVi/fv2Y4vvggw8QExODyMhIDAwM4NChQ6YCjIjsGwsgIrJbR44cQUBAgNm28PBw/PHHHwBu/0KruLgY69atQ0BAAIqKivDoo48CANzc3FBeXo4NGzbg6aefhpubG1JTU7F9+3ZTX3q9Hv39/fjiiy+wadMmeHt748UXXxxzfK6urnj33Xfx559/Qq1WY8mSJSguLp6AkRPRZFOIiEx3EEREtlIoFCgtLcXKlSunOxQickC8B4iIiIicDgsgIiIicjq8B4iIHBK/vSeie8ErQEREROR0WAARERGR02EBRERERE6HBRARERE5HRZARERE5HRYABEREZHTYQFERERETocFEBERETkdFkBERETkdP4HlNcYt6f2L/gAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### Feature Extraction"],"metadata":{"id":"P3V_gY3ca4bv"},"id":"P3V_gY3ca4bv"},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":337},"id":"BBJTig_hmjZW","outputId":"30e58633-5aa7-443e-8b24-c601953d92d5","executionInfo":{"status":"ok","timestamp":1684958451312,"user_tz":-180,"elapsed":9197,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"BBJTig_hmjZW","execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       id                                                img        label  \\\n","0   42953  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","3   37408  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","4   82403  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","7   70914  /content/drive/MyDrive/UChicago_MachineLearnin...  non_hateful   \n","12  25489  /content/drive/MyDrive/UChicago_MachineLearnin...      hateful   \n","\n","                                                 text dataset  \\\n","0    its their character not their color that matters   train   \n","3   i love everything and everybody! except for sq...   train   \n","4   everybody loves chocolate chip cookies, even h...   train   \n","7   tattoos are bad for your health i know 5 milli...   train   \n","12   brother... a day without a blast is a day wasted   train   \n","\n","                                               pixels  \n","0   [[[0.3137254901960784, 0.30196078431372547, 0....  \n","3   [[[0.5490196078431373, 0.5450980392156862, 0.5...  \n","4   [[[0.043137254901960784, 0.043137254901960784,...  \n","7   [[[0.8313725490196079, 0.8, 0.7529411764705882...  \n","12  [[[0.7294117647058823, 0.5490196078431373, 0.4...  "],"text/html":["\n","  <div id=\"df-863ea0ba-d550-47cd-9cee-74d8603207fe\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>img</th>\n","      <th>label</th>\n","      <th>text</th>\n","      <th>dataset</th>\n","      <th>pixels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42953</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>its their character not their color that matters</td>\n","      <td>train</td>\n","      <td>[[[0.3137254901960784, 0.30196078431372547, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>37408</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>i love everything and everybody! except for sq...</td>\n","      <td>train</td>\n","      <td>[[[0.5490196078431373, 0.5450980392156862, 0.5...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>82403</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>everybody loves chocolate chip cookies, even h...</td>\n","      <td>train</td>\n","      <td>[[[0.043137254901960784, 0.043137254901960784,...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>70914</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>non_hateful</td>\n","      <td>tattoos are bad for your health i know 5 milli...</td>\n","      <td>train</td>\n","      <td>[[[0.8313725490196079, 0.8, 0.7529411764705882...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>25489</td>\n","      <td>/content/drive/MyDrive/UChicago_MachineLearnin...</td>\n","      <td>hateful</td>\n","      <td>brother... a day without a blast is a day wasted</td>\n","      <td>train</td>\n","      <td>[[[0.7294117647058823, 0.5490196078431373, 0.4...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-863ea0ba-d550-47cd-9cee-74d8603207fe')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-863ea0ba-d550-47cd-9cee-74d8603207fe button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n","  warnings.warn(msg)\n"]}]},{"cell_type":"code","source":["train_ids = list(df[df.dataset==\"train\"][\"id\"])\n","eval_ids = list(df[df.dataset==\"eval\"][\"id\"])"],"metadata":{"id":"rf-Fww0K91co","executionInfo":{"status":"ok","timestamp":1684960060049,"user_tz":-180,"elapsed":2,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"rf-Fww0K91co","execution_count":64,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import os\n","\n","# Create directory for features if not exists\n","os.makedirs(\"/content/features\", exist_ok=True)\n","\n","# No need for gradients as we're not training\n","with torch.no_grad():\n","    for phase in ['train', 'eval']:  # Loop over both train and eval dataloaders\n","        features_list = []\n","        labels_list = []\n","        paths_list = []\n","        for i, (inputs, labels, paths) in enumerate(dataloaders[phase]):\n","            print(\"i: \", i)\n","            inputs = inputs.to(device)\n","            labels = labels.to(device)\n","\n","            features = model_conv(inputs)\n","            for j in range(features.size(0)):  # Loop over samples within a batch\n","                features_list.append(features[j].cpu().numpy())\n","                labels_list.append(labels[j].cpu().numpy())\n","\n","            paths_list += paths\n","            \n","            # Save every 100 batches\n","            if i % 100 == 0 and i > 0:  # Save only if we've accumulated enough data\n","                # Convert lists to numpy arrays\n","                features_array = np.array(features_list)\n","                labels_array = np.array(labels_list)\n","                paths_list = [img_file_name.split(\"/\")[-1] for img_file_name in paths_list]\n","                paths_array = np.array(paths_list)\n","\n","                # Save to disk\n","                np.save(f\"/content/features/features_{phase}_b{i}.npy\", features_array)\n","                np.save(f\"/content/features/labels_{phase}_b{i}.npy\", labels_array)\n","                np.save(f\"/content/features/paths_{phase}_b{i}.npy\", paths_array)\n","\n","                # Clear lists to free memory\n","                features_list = []\n","                labels_list = []\n","                paths_list = []\n","\n","        # Save the remaining features, labels and ids if necessary\n","        if len(features_list) > 0:\n","            # Convert lists to numpy arrays\n","            features_array = np.array(features_list)\n","            labels_array = np.array(labels_list)\n","            paths_list = [img_file_name.split(\"/\")[-1] for img_file_name in paths_list]\n","            paths_array = np.array(paths_list)\n","\n","            # Save to disk\n","            np.save(f\"/content/features/features_{phase}_b{i}.npy\", features_array)\n","            np.save(f\"/content/features/labels_{phase}_b{i}.npy\", labels_array)\n","            np.save(f\"/content/features/paths_{phase}_b{i}.npy\", paths_array)\n","\n"],"metadata":{"id":"vy9BVn8Nq_gW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1684961070573,"user_tz":-180,"elapsed":556071,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}},"outputId":"db807474-ac1b-498f-9a66-1dfa5e227279"},"id":"vy9BVn8Nq_gW","execution_count":78,"outputs":[{"output_type":"stream","name":"stdout","text":["i:  0\n","i:  1\n","i:  2\n","i:  3\n","i:  4\n","i:  5\n","i:  6\n","i:  7\n","i:  8\n","i:  9\n","i:  10\n","i:  11\n","i:  12\n","i:  13\n","i:  14\n","i:  15\n","i:  16\n","i:  17\n","i:  18\n","i:  19\n","i:  20\n","i:  21\n","i:  22\n","i:  23\n","i:  24\n","i:  25\n","i:  26\n","i:  27\n","i:  28\n","i:  29\n","i:  30\n","i:  31\n","i:  32\n","i:  33\n","i:  34\n","i:  35\n","i:  36\n","i:  37\n","i:  38\n","i:  39\n","i:  40\n","i: 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paths\n","    npy_files = glob.glob(\"/content/features/*.npy\")\n","    \n","    # Separate the file paths based on type: features, labels, and paths\n","    features_files = sorted([file for file in npy_files if 'features_' in file])\n","    labels_files = sorted([file for file in npy_files if 'labels' in file])\n","    paths_files = sorted([file for file in npy_files if 'paths' in file])\n","    \n","    print(len(features_files))\n","    print(len(labels_files))\n","    print(len(paths_files))\n","\n","    # Initialize empty lists to store all the data\n","    all_features = []\n","    all_labels = []\n","    all_paths = []\n","\n","    # Read and append all the data\n","    for f_file, l_file, p_file in zip(features_files, labels_files, paths_files):\n","        features = np.load(f_file)\n","        labels = np.load(l_file)\n","        paths = np.load(p_file)\n","\n","        # Assuming that the feature vectors are flattened\n","        # and each feature vector has a length of 20\n","        all_features.extend(features)\n","        all_labels.extend(labels)\n","        all_paths.extend(paths)\n","\n","    # Convert the lists to a DataFrame\n","    df = pd.DataFrame(all_features, columns=[f'feature_{i}' for i in range(20)])\n","    df['label'] = all_labels\n","    df['path'] = all_paths\n","\n","    return df\n","\n","feature_df = create_dataframe_from_npy_files()"],"metadata":{"id":"9Z4-hBpX2Cor","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1684962286129,"user_tz":-180,"elapsed":314,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}},"outputId":"c68b5200-92eb-471a-c885-b266c63dadd6"},"id":"9Z4-hBpX2Cor","execution_count":86,"outputs":[{"output_type":"stream","name":"stdout","text":["4\n","4\n","4\n"]}]},{"cell_type":"code","source":["feature_df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4n-Y_67h2JUC","outputId":"aa7e7a4c-f585-46be-dbe0-9ce050f3b014","executionInfo":{"status":"ok","timestamp":1684962288140,"user_tz":-180,"elapsed":441,"user":{"displayName":"egehan yorulmaz","userId":"14901711814338625122"}}},"id":"4n-Y_67h2JUC","execution_count":87,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8986, 22)"]},"metadata":{},"execution_count":87}]},{"cell_type":"code","source":["feature_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"s6Pd8_GTknQ2","executionInfo":{"status":"ok","timestamp":1684962423448,"user_tz":-180,"elapsed":321,"user":{"displayName":"egehan 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except for sq...</td>\n","      <td>train</td>\n","      <td>37408.png</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>82403</td>\n","      <td>img/82403.png</td>\n","      <td>0.0</td>\n","      <td>everybody loves chocolate chip cookies, even h...</td>\n","      <td>train</td>\n","      <td>82403.png</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>3869</td>\n","      <td>img/03869.png</td>\n","      <td>NaN</td>\n","      <td>a mother's love for the child is a divine thing</td>\n","      <td>test</td>\n","      <td>03869.png</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>23817</td>\n","      <td>img/23817.png</td>\n","      <td>NaN</td>\n","      <td>sea monkeys</td>\n","      <td>test</td>\n","      <td>23817.png</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>56280</td>\n","      <td>img/56280.png</td>\n","      <td>NaN</td>\n","      <td>little miss muffet sat on her tuffet</td>\n","      <td>test</td>\n","      <td>56280.png</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>29384</td>\n","      <td>img/29384.png</td>\n","      <td>NaN</td>\n","      <td>they're in a row</td>\n","      <td>test</td>\n","      <td>29384.png</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>34127</td>\n","      <td>img/34127.png</td>\n","      <td>NaN</td>\n","      <td>that feeling when you win a fifa game after be...</td>\n","      <td>test</td>\n","      <td>34127.png</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10000 rows × 6 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e0a0e68-86c8-4c4c-bcdd-9afbaed305d5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0e0a0e68-86c8-4c4c-bcdd-9afbaed305d5 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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                 who farted ?  \n","8982  98751  \"the other day i was sucking my sister and she...  \n","8983  98752                                    shut up ontario  \n","8984  98762              child in poverty with empty water jug  \n","8985  98764               african kids dancing and celebrating  \n","\n","[8986 rows x 24 columns]"],"text/html":["\n","  <div id=\"df-c34da417-e73b-4485-8efe-390151eb38c2\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>path</th>\n","      <th>feature_0</th>\n","      <th>feature_1</th>\n","      <th>feature_2</th>\n","      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