ter_VGG16_imagenet_Final.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!wget https://huggingface.co/datasets/OpenRL/DeepFakeFace/resolve/main/inpainting.zip\n",
        "!wget https://huggingface.co/datasets/OpenRL/DeepFakeFace/resolve/main/wiki.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGoTrkdr92AZ",
        "outputId": "ab68b694-f5a5-43a6-ad5a-b66b4fe92b84",
        "execution": {
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          "iopub.execute_input": "2024-04-23T16:53:01.965367Z",
          "iopub.status.idle": "2024-04-23T16:53:14.428791Z",
          "shell.execute_reply.started": "2024-04-23T16:53:01.965334Z",
          "shell.execute_reply": "2024-04-23T16:53:14.427532Z"
        },
        "trusted": true
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-12 18:10:08--  https://huggingface.co/datasets/OpenRL/DeepFakeFace/resolve/main/inpainting.zip\n",
            "Resolving huggingface.co (huggingface.co)... 18.238.109.52, 18.238.109.92, 18.238.109.102, ...\n",
            "Connecting to huggingface.co (huggingface.co)|18.238.109.52|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://cdn-lfs.huggingface.co/repos/43/54/4354080778713efff252f32d0ea8cda241f8af84fe016fdd4e0d57db34b94861/795b751f0967b2d940fbd9825e05c541666ec6f8c634acc7640044542897b7b2?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27inpainting.zip%3B+filename%3D%22inpainting.zip%22%3B&response-content-type=application%2Fzip&Expires=1715796608&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNTc5NjYwOH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy80My81NC80MzU0MDgwNzc4NzEzZWZmZjI1MmYzMmQwZWE4Y2RhMjQxZjhhZjg0ZmUwMTZmZGQ0ZTBkNTdkYjM0Yjk0ODYxLzc5NWI3NTFmMDk2N2IyZDk0MGZiZDk4MjVlMDVjNTQxNjY2ZWM2ZjhjNjM0YWNjNzY0MDA0NDU0Mjg5N2I3YjI%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=PSbfSHeKzF9BQFxNW59PQmpnnpb%7ER1i6YboB5lgHwTsjrtyCkiKhxWEhOFZAB3Fe3oLpRK-arkYUcIEytbhcZU3oh8BeYOTS6Hw-idGZ4PnjZspNE5aNgiMnuLij8jTi%7EtEzsvmRlZLCEs6oftcM17dwZNM7NFImeraAnxssEnCsvhEiuHAqgliVj7OEA3J7Wo8qEEcUok-1R-E-MlehnFhxFJ9r6ktg3sqxg5mhiw%7ERQu3w%7EHOc2JJ351NkcbkNqfph7fyAfGHuBgNIG6CKYwKhPOamuGQDpVGLj%7EKi16lxdTJnY7dFDsh0DBghmz9zLrglE181odSWdqJkh6Ykkw__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
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            "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.155.173.61, 18.155.173.8, 18.155.173.127, ...\n",
            "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.155.173.61|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1082027665 (1.0G) [application/zip]\n",
            "Saving to: ‘inpainting.zip’\n",
            "\n",
            "inpainting.zip      100%[===================>]   1.01G  52.1MB/s    in 20s     \n",
            "\n",
            "2024-05-12 18:10:28 (52.6 MB/s) - ‘inpainting.zip’ saved [1082027665/1082027665]\n",
            "\n",
            "--2024-05-12 18:10:28--  https://huggingface.co/datasets/OpenRL/DeepFakeFace/resolve/main/wiki.zip\n",
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            "Connecting to huggingface.co (huggingface.co)|18.164.174.118|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://cdn-lfs.huggingface.co/repos/43/54/4354080778713efff252f32d0ea8cda241f8af84fe016fdd4e0d57db34b94861/e83b55d6692d54e5aad38427531fa831bf30d1051ccc4267d4db90f03aaa7136?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27wiki.zip%3B+filename%3D%22wiki.zip%22%3B&response-content-type=application%2Fzip&Expires=1715796628&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNTc5NjYyOH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy80My81NC80MzU0MDgwNzc4NzEzZWZmZjI1MmYzMmQwZWE4Y2RhMjQxZjhhZjg0ZmUwMTZmZGQ0ZTBkNTdkYjM0Yjk0ODYxL2U4M2I1NWQ2NjkyZDU0ZTVhYWQzODQyNzUzMWZhODMxYmYzMGQxMDUxY2NjNDI2N2Q0ZGI5MGYwM2FhYTcxMzY%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=u4B3%7EZuvgEVorPaD%7E%7EckLJohLDSbzyk6Dveny3fe6Gx-aG1e7XE6xBJYDUUqbw5mJWDcUgU%7EbmrNHZbEvLaW5e2K920683dzt%7ETdZ9n2qhK3FO-FrKb2sZudtIo1A687lSqufrt27RVz3FS0mx8orRtSVmddzD7Ve6jXu0v8YFXOC6HkpVm1zeBMro3qcRSJ317jyAcAKgAv87hNV7d3Gktu0JqZrzv%7EqEOWaUV-CIbPLkaHgx7%7EEJIB55TEK9b-dvQcDvDM7ROcf2inHet%7EkWHGV4xW2YdaL7B3SS8vJp6zMj5cjFHSeTWQWNWqe1egg42CdfPk3yvXRNRDW5XiOA__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n",
            "--2024-05-12 18:10:28--  https://cdn-lfs.huggingface.co/repos/43/54/4354080778713efff252f32d0ea8cda241f8af84fe016fdd4e0d57db34b94861/e83b55d6692d54e5aad38427531fa831bf30d1051ccc4267d4db90f03aaa7136?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27wiki.zip%3B+filename%3D%22wiki.zip%22%3B&response-content-type=application%2Fzip&Expires=1715796628&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNTc5NjYyOH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy80My81NC80MzU0MDgwNzc4NzEzZWZmZjI1MmYzMmQwZWE4Y2RhMjQxZjhhZjg0ZmUwMTZmZGQ0ZTBkNTdkYjM0Yjk0ODYxL2U4M2I1NWQ2NjkyZDU0ZTVhYWQzODQyNzUzMWZhODMxYmYzMGQxMDUxY2NjNDI2N2Q0ZGI5MGYwM2FhYTcxMzY%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=u4B3%7EZuvgEVorPaD%7E%7EckLJohLDSbzyk6Dveny3fe6Gx-aG1e7XE6xBJYDUUqbw5mJWDcUgU%7EbmrNHZbEvLaW5e2K920683dzt%7ETdZ9n2qhK3FO-FrKb2sZudtIo1A687lSqufrt27RVz3FS0mx8orRtSVmddzD7Ve6jXu0v8YFXOC6HkpVm1zeBMro3qcRSJ317jyAcAKgAv87hNV7d3Gktu0JqZrzv%7EqEOWaUV-CIbPLkaHgx7%7EEJIB55TEK9b-dvQcDvDM7ROcf2inHet%7EkWHGV4xW2YdaL7B3SS8vJp6zMj5cjFHSeTWQWNWqe1egg42CdfPk3yvXRNRDW5XiOA__&Key-Pair-Id=KVTP0A1DKRTAX\n",
            "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.65.25.122, 18.65.25.124, 18.65.25.40, ...\n",
            "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.65.25.122|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1766572515 (1.6G) [application/zip]\n",
            "Saving to: ‘wiki.zip’\n",
            "\n",
            "wiki.zip            100%[===================>]   1.64G  47.2MB/s    in 34s     \n",
            "\n",
            "2024-05-12 18:11:03 (49.5 MB/s) - ‘wiki.zip’ saved [1766572515/1766572515]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bx-oQM5MVmML",
        "outputId": "fadc0988-e6c9-49d2-8e5c-242cfcfb97c2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -q inpainting.zip\n",
        "!unzip -q wiki.zip\n",
        "\n",
        "\n",
        "!rm -r inpainting.zip\n",
        "!rm -r wiki.zip"
      ],
      "metadata": {
        "id": "ytSg3-FW95a5",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:53:14.432799Z",
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          "shell.execute_reply": "2024-04-23T16:53:42.419729Z"
        },
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "def copy_images(source_folder, destination_folder):\n",
        "    # Create the destination folder if it doesn't exist\n",
        "    if not os.path.exists(destination_folder):\n",
        "        os.makedirs(destination_folder)\n",
        "\n",
        "    # Walk through each subfolder in the source folder\n",
        "    for root, dirs, files in os.walk(source_folder):\n",
        "        for file in files:\n",
        "            # Check if the file is an image\n",
        "            if file.lower().endswith(('.jpg', '.jpeg', '.png', '.gif')):\n",
        "                # Get the full path of the image\n",
        "                source_path = os.path.join(root, file)\n",
        "                # Copy the image to the destination folder\n",
        "                shutil.copy(source_path, destination_folder)"
      ],
      "metadata": {
        "id": "YWT3XPHSd0J2",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:53:42.422861Z",
          "iopub.execute_input": "2024-04-23T16:53:42.423221Z",
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          "shell.execute_reply": "2024-04-23T16:53:42.429510Z"
        },
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r real\n",
        "!rm -r fake\n",
        "!rm -r real_vs_fake"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-23T16:53:42.431793Z",
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          "shell.execute_reply": "2024-04-23T16:53:45.368389Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2yn6zmsAeQG",
        "outputId": "9d9775b0-2727-4da7-da4a-e3bbc7dbf33a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'real': No such file or directory\n",
            "rm: cannot remove 'fake': No such file or directory\n",
            "rm: cannot remove 'real_vs_fake': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the source folder containing subfolders with images\n",
        "destination_folder = 'real'\n",
        "# Specify the destination folder where images will be copied\n",
        "source_folder = 'wiki'\n",
        "\n",
        "# Call the function to copy images\n",
        "copy_images(source_folder, destination_folder)"
      ],
      "metadata": {
        "id": "9SAyKGqpgEE8",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:53:45.371207Z",
          "iopub.execute_input": "2024-04-23T16:53:45.371572Z",
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        },
        "trusted": true
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the source folder containing subfolders with images\n",
        "destination_folder = 'fake'\n",
        "# Specify the destination folder where images will be copied\n",
        "source_folder = 'inpainting'\n",
        "\n",
        "# Call the function to copy images\n",
        "copy_images(source_folder, destination_folder)"
      ],
      "metadata": {
        "id": "87pAn6w4gL80",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:53:49.993711Z",
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        },
        "trusted": true
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir real_vs_fake"
      ],
      "metadata": {
        "id": "nvdkod0AikXo",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:53:54.150674Z",
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        },
        "trusted": true
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "\n",
        "# Define paths\n",
        "source_folder = \"real\"\n",
        "destination_folder = \"real_vs_fake\"\n",
        "\n",
        "# Create destination folder if it doesn't exist\n",
        "if not os.path.exists(destination_folder):\n",
        "    os.makedirs(destination_folder)\n",
        "\n",
        "# Create test, train, and valid folders within destination folder\n",
        "folders = ['test', 'train', 'valid']\n",
        "for folder in folders:\n",
        "    os.makedirs(os.path.join(destination_folder, folder, 'real'), exist_ok=True)\n",
        "\n",
        "# List all images in source folder\n",
        "images = os.listdir(source_folder)\n",
        "random.shuffle(images)\n",
        "\n",
        "# Calculate the number of images for each category\n",
        "total_images = len(images)\n",
        "test_count = 5000\n",
        "train_count = int(0.8 * (total_images - test_count))\n",
        "valid_count = total_images - test_count - train_count\n",
        "\n",
        "# Move images to respective folders\n",
        "for folder, count in [('test', test_count), ('train', train_count), ('valid', valid_count)]:\n",
        "    for i in range(count):\n",
        "        image = images.pop()\n",
        "        shutil.copy(os.path.join(source_folder, image), os.path.join(destination_folder, folder, 'real'))\n",
        "\n",
        "# Copy train, valid, and test folders to real_vs_fake\n",
        "# for folder in folders:\n",
        "#     shutil.copytree(os.path.join(destination_folder, folder), os.path.join(destination_folder, 'real_vs_fake', folder))\n"
      ],
      "metadata": {
        "id": "oAlhHhhJgoRy",
        "execution": {
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          "shell.execute_reply": "2024-04-23T16:53:59.743550Z"
        },
        "trusted": true
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "import random"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-04-23T16:53:59.746991Z",
          "iopub.status.idle": "2024-04-23T16:53:59.751119Z",
          "shell.execute_reply.started": "2024-04-23T16:53:59.746967Z",
          "shell.execute_reply": "2024-04-23T16:53:59.750124Z"
        },
        "trusted": true,
        "id": "zXQVL3OMAeQH"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define paths\n",
        "source_folder = \"fake\"\n",
        "destination_folder = \"real_vs_fake\"\n",
        "\n",
        "# Create destination folder if it doesn't exist\n",
        "if not os.path.exists(destination_folder):\n",
        "    os.makedirs(destination_folder)\n",
        "\n",
        "# Create test, train, and valid folders within destination folder\n",
        "folders = ['test', 'train', 'valid']\n",
        "for folder in folders:\n",
        "    os.makedirs(os.path.join(destination_folder, folder, 'fake'), exist_ok=True)\n",
        "\n",
        "# List all images in source folder\n",
        "images = os.listdir(source_folder)\n",
        "random.shuffle(images)\n",
        "\n",
        "# Calculate the number of images for each category\n",
        "total_images = len(images)\n",
        "test_count = 5000\n",
        "train_count = int(0.8 * (total_images - test_count))\n",
        "valid_count = total_images - test_count - train_count\n",
        "\n",
        "# Move images to respective folders\n",
        "for folder, count in [('test', test_count), ('train', train_count), ('valid', valid_count)]:\n",
        "    for i in range(count):\n",
        "        image = images.pop()\n",
        "        shutil.copy(os.path.join(source_folder, image), os.path.join(destination_folder, folder, 'fake'))\n",
        "\n",
        "# Copy train, valid, and test folders to real_vs_fake\n",
        "# for folder in folders:\n",
        "#     shutil.copytree(os.path.join(destination_folder, folder), os.path.join(destination_folder, 'real_vs_fake', folder))\n"
      ],
      "metadata": {
        "id": "oy3dkHMMijDG",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:53:59.752343Z",
          "iopub.execute_input": "2024-04-23T16:53:59.752897Z",
          "iopub.status.idle": "2024-04-23T16:54:05.304861Z",
          "shell.execute_reply.started": "2024-04-23T16:53:59.752863Z",
          "shell.execute_reply": "2024-04-23T16:54:05.303874Z"
        },
        "trusted": true
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "uyZAyhowAeQH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -r /kaggle/working/real_vs_fake\n",
        "# !rm -r /kaggle/working/fake\n",
        "# !rm -r /kaggle/working/real"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:05.306059Z",
          "iopub.execute_input": "2024-04-23T16:54:05.306371Z",
          "iopub.status.idle": "2024-04-23T16:54:05.311117Z",
          "shell.execute_reply.started": "2024-04-23T16:54:05.306343Z",
          "shell.execute_reply": "2024-04-23T16:54:05.309855Z"
        },
        "trusted": true,
        "id": "uBjpMy86AeQI"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def count_files(folder_path):\n",
        "    # Initialize count\n",
        "    file_count = 0\n",
        "\n",
        "    # Walk through the folder and count files\n",
        "    for _, _, files in os.walk(folder_path):\n",
        "        file_count += len(files)\n",
        "\n",
        "    return file_count"
      ],
      "metadata": {
        "id": "isumAViQgyZo",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:05.312727Z",
          "iopub.execute_input": "2024-04-23T16:54:05.313070Z",
          "iopub.status.idle": "2024-04-23T16:54:05.324651Z",
          "shell.execute_reply.started": "2024-04-23T16:54:05.313037Z",
          "shell.execute_reply": "2024-04-23T16:54:05.323699Z"
        },
        "trusted": true
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the folder path\n",
        "folders = ['test', 'train', 'valid']\n",
        "folder_path = 'real_vs_fake/'\n",
        "\n",
        "for i in folders:\n",
        "  print(\"Number of files in fake/\"+str(i)+\" :\", count_files(folder_path+i+'/fake'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WIUyoGpcguam",
        "outputId": "9a64084d-307a-4cb6-8a52-584cf44d0500",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:05.326287Z",
          "iopub.execute_input": "2024-04-23T16:54:05.326845Z",
          "iopub.status.idle": "2024-04-23T16:54:05.398787Z",
          "shell.execute_reply.started": "2024-04-23T16:54:05.326819Z",
          "shell.execute_reply": "2024-04-23T16:54:05.397626Z"
        },
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of files in fake/test : 5000\n",
            "Number of files in fake/train : 20000\n",
            "Number of files in fake/valid : 5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the folder path\n",
        "folders = ['test', 'train', 'valid']\n",
        "folder_path = 'real_vs_fake/'\n",
        "\n",
        "for i in folders:\n",
        "  print(\"Number of files in real/\"+str(i)+\" :\", count_files(folder_path+i+'/real'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3oU59HAYgvfk",
        "outputId": "cabbd088-f527-4f49-d04f-fdc6a58254c2",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:05.400092Z",
          "iopub.execute_input": "2024-04-23T16:54:05.402050Z",
          "iopub.status.idle": "2024-04-23T16:54:05.448247Z",
          "shell.execute_reply.started": "2024-04-23T16:54:05.402013Z",
          "shell.execute_reply": "2024-04-23T16:54:05.447155Z"
        },
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of files in real/test : 5000\n",
            "Number of files in real/train : 20000\n",
            "Number of files in real/valid : 5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.applications import DenseNet121, VGG16\n",
        "from tensorflow.keras.callbacks import Callback, ModelCheckpoint\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import tensorflow as tf\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "yoI0F3p--Ajt",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:05.452847Z",
          "iopub.execute_input": "2024-04-23T16:54:05.453355Z",
          "iopub.status.idle": "2024-04-23T16:54:19.276062Z",
          "shell.execute_reply.started": "2024-04-23T16:54:05.453309Z",
          "shell.execute_reply": "2024-04-23T16:54:19.275231Z"
        },
        "trusted": true
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import keras"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:19.277163Z",
          "iopub.execute_input": "2024-04-23T16:54:19.277833Z",
          "iopub.status.idle": "2024-04-23T16:54:19.282992Z",
          "shell.execute_reply.started": "2024-04-23T16:54:19.277801Z",
          "shell.execute_reply": "2024-04-23T16:54:19.282102Z"
        },
        "trusted": true,
        "id": "pAfGqiNeAeQI"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_path = 'real_vs_fake/'\n",
        "image_gen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_flow = image_gen.flow_from_directory(\n",
        "    base_path + 'train/',\n",
        "    target_size=(224, 224),\n",
        "    batch_size=64,\n",
        "    class_mode='binary'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGkuJqoa-jcn",
        "outputId": "d9388e9e-c60e-4188-d47c-59ca77742800",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:20.805257Z",
          "iopub.execute_input": "2024-04-23T16:54:20.805570Z",
          "iopub.status.idle": "2024-04-23T16:54:26.329284Z",
          "shell.execute_reply.started": "2024-04-23T16:54:20.805543Z",
          "shell.execute_reply": "2024-04-23T16:54:26.328285Z"
        },
        "trusted": true
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 40000 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_gen1 = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "valid_flow = image_gen1.flow_from_directory(\n",
        "    base_path + 'valid/',\n",
        "    target_size=(224, 224),\n",
        "    batch_size=64,\n",
        "    class_mode='binary'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEs3NcBb-to7",
        "outputId": "82399da8-7ae3-4711-b01e-b6dbedeb0f1c",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:26.330620Z",
          "iopub.execute_input": "2024-04-23T16:54:26.331354Z",
          "iopub.status.idle": "2024-04-23T16:54:26.598751Z",
          "shell.execute_reply.started": "2024-04-23T16:54:26.331316Z",
          "shell.execute_reply": "2024-04-23T16:54:26.597947Z"
        },
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 10000 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_gen1 = ImageDataGenerator(rescale=1./255.)\n",
        "test_flow = image_gen.flow_from_directory(\n",
        "    base_path + 'test/',\n",
        "    target_size=(224, 224),\n",
        "    batch_size=64,\n",
        "    class_mode='binary'\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:26.599847Z",
          "iopub.execute_input": "2024-04-23T16:54:26.600141Z",
          "iopub.status.idle": "2024-04-23T16:54:26.870601Z",
          "shell.execute_reply.started": "2024-04-23T16:54:26.600114Z",
          "shell.execute_reply": "2024-04-23T16:54:26.869665Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyvDWe78AeQJ",
        "outputId": "7757e00c-4ff6-412f-9557-edf89fbbe388"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 10000 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "strategy = tf.distribute.MirroredStrategy()\n",
        "print('Number of devices: {}'.format(strategy.num_replicas_in_sync))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:26.871924Z",
          "iopub.execute_input": "2024-04-23T16:54:26.872670Z",
          "iopub.status.idle": "2024-04-23T16:54:27.489551Z",
          "shell.execute_reply.started": "2024-04-23T16:54:26.872632Z",
          "shell.execute_reply": "2024-04-23T16:54:27.488642Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XplMyFO5AeQJ",
        "outputId": "422dbf27-9051-4205-90bc-df7dd203147e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of devices: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(pretrained, learning_rate=0.0001):\n",
        "    model = Sequential([\n",
        "        pretrained,\n",
        "        layers.GlobalAveragePooling2D(),\n",
        "        layers.Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "    model.compile(\n",
        "        loss='binary_crossentropy',\n",
        "        optimizer=Adam(learning_rate=learning_rate),\n",
        "        metrics=['accuracy', keras.metrics.AUC(), keras.metrics.Precision(), keras.metrics.Recall()]\n",
        "    )\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "g73ZLa6g-YnK",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:19.287678Z",
          "iopub.execute_input": "2024-04-23T16:54:19.288554Z",
          "iopub.status.idle": "2024-04-23T16:54:20.804217Z",
          "shell.execute_reply.started": "2024-04-23T16:54:19.288517Z",
          "shell.execute_reply": "2024-04-23T16:54:20.803133Z"
        },
        "trusted": true
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "densenet = VGG16(\n",
        "    weights=\"imagenet\",\n",
        "    include_top=False,\n",
        "    input_shape=(224,224,3)\n",
        ")\n",
        "model = build_model(densenet)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "le_GVn1M-vND",
        "outputId": "1706471e-246c-402c-e9cd-8c4c660b92ad",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:27.490975Z",
          "iopub.execute_input": "2024-04-23T16:54:27.491725Z",
          "iopub.status.idle": "2024-04-23T16:54:30.437842Z",
          "shell.execute_reply.started": "2024-04-23T16:54:27.491695Z",
          "shell.execute_reply": "2024-04-23T16:54:30.436914Z"
        },
        "trusted": true
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " vgg16 (Functional)          (None, 7, 7, 512)         14714688  \n",
            "                                                                 \n",
            " global_average_pooling2d_1  (None, 512)               0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 513       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14715201 (56.13 MB)\n",
            "Trainable params: 14715201 (56.13 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=5)  # Stop training if no improvement after 3 epochs\n",
        "checkpoint = ModelCheckpoint('VGGImagenet.model.keras', monitor='val_loss', save_best_only=True)  # Save the best model"
      ],
      "metadata": {
        "id": "9sN7fm9x_aRA",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:30.438896Z",
          "iopub.execute_input": "2024-04-23T16:54:30.439149Z",
          "iopub.status.idle": "2024-04-23T16:54:30.443933Z",
          "shell.execute_reply.started": "2024-04-23T16:54:30.439125Z",
          "shell.execute_reply": "2024-04-23T16:54:30.443076Z"
        },
        "trusted": true
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm /kaggle/working/DenseNetNoW.keras"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:30.444958Z",
          "iopub.execute_input": "2024-04-23T16:54:30.445207Z",
          "iopub.status.idle": "2024-04-23T16:54:30.459822Z",
          "shell.execute_reply.started": "2024-04-23T16:54:30.445184Z",
          "shell.execute_reply": "2024-04-23T16:54:30.458881Z"
        },
        "trusted": true,
        "id": "2zDoEV2LAeQJ"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_flow,\n",
        "    epochs = 5,\n",
        "    validation_data =valid_flow,\n",
        "    callbacks=[checkpoint]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulDvC1XO-wdH",
        "outputId": "59f78b4a-eb80-447e-b5dc-d5f9934e8a41",
        "execution": {
          "iopub.status.busy": "2024-04-23T16:54:30.461213Z",
          "iopub.execute_input": "2024-04-23T16:54:30.461573Z",
          "iopub.status.idle": "2024-04-23T17:17:46.802078Z",
          "shell.execute_reply.started": "2024-04-23T16:54:30.461543Z",
          "shell.execute_reply": "2024-04-23T17:17:46.801283Z"
        },
        "trusted": true
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "544/625 [=========================>....] - ETA: 1:04 - loss: 0.6597 - accuracy: 0.5942 - auc_1: 0.6396 - precision_1: 0.5850 - recall_1: 0.6572"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/TiffImagePlugin.py:858: UserWarning: Corrupt EXIF data.  Expecting to read 2 bytes but only got 0. \n",
            "  warnings.warn(str(msg))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "625/625 [==============================] - 555s 877ms/step - loss: 0.6463 - accuracy: 0.6099 - auc_1: 0.6638 - precision_1: 0.5986 - recall_1: 0.6671 - val_loss: 0.5494 - val_accuracy: 0.7435 - val_auc_1: 0.8240 - val_precision_1: 0.7382 - val_recall_1: 0.7546\n",
            "Epoch 2/5\n",
            "625/625 [==============================] - 556s 890ms/step - loss: 0.4334 - accuracy: 0.7983 - auc_1: 0.8809 - precision_1: 0.7962 - recall_1: 0.8019 - val_loss: 0.3591 - val_accuracy: 0.8413 - val_auc_1: 0.9366 - val_precision_1: 0.7903 - val_recall_1: 0.9292\n",
            "Epoch 3/5\n",
            "625/625 [==============================] - 557s 890ms/step - loss: 0.3076 - accuracy: 0.8693 - auc_1: 0.9422 - precision_1: 0.8694 - recall_1: 0.8691 - val_loss: 0.4254 - val_accuracy: 0.8052 - val_auc_1: 0.9562 - val_precision_1: 0.9754 - val_recall_1: 0.6262\n",
            "Epoch 4/5\n",
            "625/625 [==============================] - 554s 887ms/step - loss: 0.2116 - accuracy: 0.9131 - auc_1: 0.9726 - precision_1: 0.9131 - recall_1: 0.9131 - val_loss: 0.2259 - val_accuracy: 0.9054 - val_auc_1: 0.9761 - val_precision_1: 0.8642 - val_recall_1: 0.9620\n",
            "Epoch 5/5\n",
            "625/625 [==============================] - 553s 885ms/step - loss: 0.1764 - accuracy: 0.9300 - auc_1: 0.9808 - precision_1: 0.9292 - recall_1: 0.9309 - val_loss: 0.1761 - val_accuracy: 0.9337 - val_auc_1: 0.9806 - val_precision_1: 0.9364 - val_recall_1: 0.9306\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('trainHistoryVGGImgNet.pkl', 'wb') as file_pi:\n",
        "    pickle.dump(history.history, file_pi)"
      ],
      "metadata": {
        "id": "XjPE08fCbHRR",
        "execution": {
          "iopub.status.busy": "2024-04-23T17:27:52.156861Z",
          "iopub.execute_input": "2024-04-23T17:27:52.157232Z",
          "iopub.status.idle": "2024-04-23T17:27:52.162569Z",
          "shell.execute_reply.started": "2024-04-23T17:27:52.157200Z",
          "shell.execute_reply": "2024-04-23T17:27:52.161645Z"
        },
        "trusted": true
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix, roc_auc_score, f1_score, mean_absolute_error, mean_squared_error, accuracy_score\n",
        "\n",
        "# Accuracy and loss history\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation precision values\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['precision_1'])\n",
        "plt.plot(history.history['val_precision_1'])\n",
        "plt.title('Model precision')\n",
        "plt.ylabel('Precision')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation recall values\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['recall_1'])\n",
        "plt.plot(history.history['val_recall_1'])\n",
        "plt.title('Model recall')\n",
        "plt.ylabel('Recall')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation AUC values\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['auc_1'])\n",
        "plt.plot(history.history['val_auc_1'])\n",
        "plt.title('Model AUC')\n",
        "plt.ylabel('AUC')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "_OucIqlKCoW-",
        "execution": {
          "iopub.status.busy": "2024-04-23T17:17:46.810005Z",
          "iopub.execute_input": "2024-04-23T17:17:46.810617Z",
          "iopub.status.idle": "2024-04-23T17:17:47.586589Z",
          "shell.execute_reply.started": "2024-04-23T17:17:46.810583Z",
          "shell.execute_reply": "2024-04-23T17:17:47.585592Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e2aabc6b-3a02-4d84-8c61-68b57541bf3e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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EBgZy9OjRcqtTREQqueQ9YM+DsOZWVyIiIlJlKciXA5vNVm5T3K3i7+9f5PjBBx/k+++/56WXXqJp06b4+vpy++23k5ube877eHp6Fjm22Ww4HI5yr1dERCqhnUvh40GQnw3Rg+DaJyEg3OqqREREqhzXTp9SYX777TeGDx/OLbfcApgj9Hv37rW2KBERqby2LYH5Q8Be8Avf9XPgz8XQ5RHodA94eFlbn4iISBWiNfJSrGbNmrFo0SLWr1/Phg0bGDhwoEbWRUSkeH99CfMGmyH+kj4wYgnU6wC5J+G78fDW5eZovYiIiJQLBXkp1ssvv0xISAiXX345ffr0oUePHnTo0MHqskREpLLZ8iksGAaOPGh1K9w+HRp2hlHL4OY3wD8Mju+A2bfBRwPNNfQiIiJSJjbjQjcjr0bS0tIICgoiNTWVwMDAIo9lZ2ezZ88eGjVqhI+Pj0UVSkXQn62ISAltXACf3g2GA9r2h5vfBPe/rdrLToWfX4CVb4MjH9y94fJ74aqx4OVf/H1FRESqoXPl0L/TiLyIiIiU3Pq5sGi0GeKjB0Pft84M8QA+QdDjWfjn79C4K9hz4NeXYGpH2PQJaDxBRESkxBTkRUREpGTWzIDP/gUYEDMcbnod3M6zJWpYCxjyKfSfA8ENIC0RFo6E6b3h8KaLULSIiEjVoSAvIiIiF27Ve/DFfwADOt0NN04Btwv8OGGzwSU3wr9XQdfx4OEL+3+Hd66Grx6AzOSKrFxERKTKUJAXERGRC7PiTfj6QfP7zmOg1wtmOC8pT1+45iEYsxpa3WJOz1/9Przewfynw16+dYuIiFQxCvIiIiJyfsunwLfjzO+v/C9c/0zpQvzpgiPhjhkw7Euo3QqyTpgj8+9cA3t/K2vFIiIiVZaCvIiIiJzbzy/C0ifN7695BK59suwh/nSNroJ7foHeL4FPMBzZBDN6wycjITWx/F5HRESkilCQFxERkeIZBvzwLPz4jHncdTx0fax8Q/wp7h7QaTTcuxZiRgA22PwJTI2FX16CvOzyf00REREXpSAvIiIiZzIMWDYJfnnBPO4+yVzXXtH8a0KfKXDPzxB5GeRlwg9Pw5txsPVrbVcnIiKCgryIiIj8nWHAd+Nh+SvmcY/JcOX9F7eGuu3griVw6/sQUBdO7IWPB8Ds2+DY9otbi4iISCWjIC8XrEuXLtx///3O46ioKKZMmXLO59hsNj777LMyv3Z53UdERM7DMOCbh2HFVPO490vQ+V/W1GKzQds7YEw8XDkW3L1g1zJ4q7P5i4bsNGvqEhERsZiCfDXRp08fevbsWexjv/76KzabjY0bN5bonqtXr+buu+8uj/KcJk6cSHR09BnnDx06RK9evcr1tURE5G8cDvjyflj1LmCDPq+Z69at5l0Duj8J//oDmvcERz78/jq8HgPr55p1i4iIVCMK8tXEyJEj+f777zlw4MAZj02fPp3Y2Fjatm1bonuGhYXh5+dXXiWeU506dfD29r4oryUiUi057LB4DKyZAdig75sQM8zqqoqq2QQGzoOBCyC0CWQchc/+CR9cB4lrrK5ORETkolGQLw+GAbkZF/+rBA1/brzxRsLCwpgxY0aR8+np6SxYsIC+ffsyYMAAIiIi8PPzo02bNnz00UfnvOffp9bv2LGDq6++Gh8fHy699FK+//77M57zyCOP0Lx5c/z8/GjcuDFPPPEEeXl5AMyYMYNJkyaxYcMGbDYbNpvNWe/fp9Zv2rSJbt264evrS82aNbn77rtJT093Pj58+HD69u3LSy+9RN26dalZsyb//ve/na8lIiKnsefDp/+A9XPA5ga3vgfRA62u6uyaX2+Ozl/3FHjVgMR4eO9a+HwMpB+zujoREZEK52F1AVVCXiY8V+/iv+5jB8HL/4Iu9fDwYOjQocyYMYPHH38cW8HWQQsWLMButzN48GAWLFjAI488QmBgIF999RVDhgyhSZMmdOrU6bz3dzgc3HrrrYSHh7Ny5UpSU1OLrKc/JSAggBkzZlCvXj02bdrE6NGjCQgI4OGHH6Z///5s3ryZJUuWsHTpUgCCgoLOuEdGRgY9evSgc+fOrF69mqNHjzJq1CjGjBlT5BcVP/74I3Xr1uXHH39k586d9O/fn+joaEaPrgTTREVEKgt7Hiy6G7YsAjcPuO19aHWL1VWdn4cXXPEfaNsfvn8SNn4M62bBn59Dl3HmkgB3T6urFBERqRAaka9G7rrrLnbt2sXPP//sPDd9+nRuu+02GjZsyIMPPkh0dDSNGzfm3nvvpWfPnsyfP/+C7r106VK2bt3KzJkzadeuHVdffTXPPffcGdeNHz+eyy+/nKioKPr06cODDz7ofA1fX19q1KiBh4cHderUoU6dOvj6+p5xj7lz55Kdnc3MmTNp3bo13bp1Y+rUqcyaNYsjR444rwsJCWHq1Km0bNmSG2+8kRtuuIFly5aV9McmIlJ15efCJyMKQrwn3PGha4T40wXUgVvfgbu+g7rRkJMG346Dt66AXT9aXZ2IiEiF0Ih8efD0M0fHrXjdEmjZsiWXX34506ZNo0uXLuzcuZNff/2Vp556CrvdznPPPcf8+fNJTEwkNzeXnJycC14D/9dffxEZGUm9eoUzEzp37nzGdfPmzeO1115j165dpKenk5+fT2BgYInex19//UW7du3w9y+cjXDFFVfgcDjYtm0b4eHhALRq1Qp3d3fnNXXr1mXTpk0lei0RkSorPwfmD4Pt35jd4PvPhuY9rK6q9BrEwegfYN1sWDYJkrbBrL7Q8kbo8SyERFldoYiISLnRiHx5sNnMKe4X+6tgenxJjBw5koULF3Ly5EmmT59OkyZNuOaaa3jxxRd59dVXeeSRR/jxxx9Zv349PXr0IDc3t9x+TCtWrGDQoEH07t2bL7/8knXr1vH444+X62ucztOz6JRKm82GQ52NRUQgLws+HmiGeA8fGPCRa4f4U9zczQZ9966BuH+CzR22fglvxMGPz0FuptUVioiIlAsF+WqmX79+uLm5MXfuXGbOnMldd92FzWbjt99+4+abb2bw4MG0a9eOxo0bs3379gu+7yWXXEJCQgKHDh1ynvvjjz+KXPP777/TsGFDHn/8cWJjY2nWrBn79u0rco2Xlxd2u/28r7VhwwYyMjKc53777Tfc3Nxo0aLFBdcsIlIt5WbC3P6wc6k5s2vgfGja3eqqypdvCPT6H/zzN2h0NeRnw8/Pw9SOsOXTEjWLFRERqYwU5KuZGjVq0L9/f8aNG8ehQ4cYPnw4AM2aNeP777/n999/56+//uKee+4pst78fLp3707z5s0ZNmwYGzZs4Ndff+Xxxx8vck2zZs3Yv38/H3/8Mbt27eK1117j008/LXJNVFQUe/bsYf369SQlJZGTk3PGaw0aNAgfHx+GDRvG5s2b+fHHH7n33nsZMmSIc1q9iIgUIycd5twBe342u70P+gQaX2N1VRWn9iUwdDH0mwlBkZB2ABYMhw/7wJEtVlcnIiJSagry1dDIkSM5ceIEPXr0cK5pHz9+PB06dKBHjx506dKFOnXq0Ldv3wu+p5ubG59++ilZWVl06tSJUaNG8eyzzxa55qabbuK///0vY8aMITo6mt9//50nnniiyDW33XYbPXv2pGvXroSFhRW7BZ6fnx/ffvstycnJdOzYkdtvv51rr72WqVOnlvyHISJSXWSnwezbYN9y8A6EwYsg6gqrq6p4NhtcejP8e5XZzd7DB/b+Cm9fBV8/DFknrK5QRESkxGyGYe38sjfeeIMXX3yRw4cP065dO15//fWzbneWl5fH5MmT+fDDD0lMTKRFixY8//zz9OzZ03nNxIkTmTRpUpHntWjRgq1bt15wTWlpaQQFBZGamnpGI7bs7Gz27NlDo0aN8PHxKcE7lcpOf7YiUmVlpZghPjEefIJg8KdQP8bqqqyRsh++fRz+Wmwe+4bCtROgw1Bzjb2IiIhFzpVD/87SEfl58+YxduxYnnzySdauXUu7du3o0aMHR48eLfb68ePH88477/D666/z559/8o9//INbbrmFdevWFbmuVatWHDp0yPm1fPnyi/F2REREKp/MZJh5kxnifUPMqebVNcQDBDeA/rNg6OcQdglkJcOX98O7XWD/H+d7toiISKVgaZB/+eWXGT16NCNGjODSSy/l7bffxs/Pj2nTphV7/axZs3jsscfo3bs3jRs35p///Ce9e/fm//7v/4pcd/o+5HXq1KFWrVoX4+2IiIhULhlJ8OFNcGgD+NWEYV9CvWirq6ocGneBf/wKPZ8H7yA4vBGm9YCFoyHNgi1lRURESsCyIJ+bm8uaNWvo3r2wU66bmxvdu3dnxYoVxT4nJyfnjCnPvr6+Z4y479ixg3r16tG4cWMGDRrE/v37z1lLTk4OaWlpRb5ERERcWvpRmHEjHNkE/rVh+FdQp7XVVVUu7p5w2T/gvrXm1HpssGk+vB4Ly1+B/DMbroqIiFQGlgX5pKQk7Hb7GV3Gw8PDOXz4cLHP6dGjBy+//DI7duzA4XDw/fffs2jRoiJbnsXFxTFjxgyWLFnCW2+9xZ49e7jqqqs4efLkWWuZPHkyQUFBzq/IyMjyeZMiIiJWSDsEM26AY39BQF0Y8bXZwV2K518LbnodRv8A9TtBXgYsnQhvXgbbv7W6OhERkTO4VNf6V199lWbNmtGyZUu8vLwYM2YMI0aMwM2t8G306tWLO+64g7Zt29KjRw++/vprUlJSmD9//lnvO27cOFJTU51fCQkJ563F4h6BUgH0ZyoiVULqAZjRG5K2Q2B9cyS+VjOrq3INER3grm/hlnegRjgk74a5/cwt+5J2Wl2diIiIk2VBvlatWri7u5+xV/mRI0eoU6dOsc8JCwvjs88+IyMjg3379rF161Zq1KhB48aNz/o6wcHBNG/enJ07z/4XsLe3N4GBgUW+zsbT0xOAzMzMc709cUGn/kxP/RmLiLicE/tgem8zgAY3gBFfQc0mVlflWtzcoN2dcO8auOI/4OYJO74zR+e/fxJyzj7DT0RE5GLxsOqFvby8iImJYdmyZc79yh0OB8uWLWPMmDHnfK6Pjw8RERHk5eWxcOFC+vXrd9Zr09PT2bVrF0OGDCmXut3d3QkODnZ21vfz88Nms5XLvcUahmGQmZnJ0aNHCQ4Oxt1d2w+JiAtK3m02tktNgJBGMOwLCNZSsVLzDoDrnoL2Q2HJo7Dze/htCmz42Dzftp+5R72IiIgFLAvyAGPHjmXYsGHExsbSqVMnpkyZQkZGBiNGjABg6NChREREMHnyZABWrlxJYmIi0dHRJCYmMnHiRBwOBw8//LDzng8++CB9+vShYcOGHDx4kCeffBJ3d3cGDBhQbnWfmjFwtm3yxDUFBwefdTaIiEillrQTPuwDJw9CzWYwbDEE1rO6qqqhVlMYtMBcK7/kUTixBz69G+I/gF7PQ732VlcoIiLVkKVBvn///hw7dowJEyZw+PBhoqOjWbJkibMB3v79+4usf8/Ozmb8+PHs3r2bGjVq0Lt3b2bNmkVwcLDzmgMHDjBgwACOHz9OWFgYV155JX/88QdhYWHlVrfNZqNu3brUrl2bvLy8cruvWMfT01Mj8SLimo5tM0N8+hEIa2nuEx8Qfv7nyYWz2aBFT2jSFVa8Ab+8BAkr4d2uZrf7ayeYDfNEREQuEpuhDl9nSEtLIygoiNTU1HOulxcREbHUkS3mdPrMJAhvDUM/V6C8GNIOwvcTYNMC89gnCLo+DrEjwd3SMRIREXFhJcmhLtW1XkRERAoc2mDuE5+ZBHXammviFeIvjsB6cNv7MGIJ1GkD2anwzcPwzlWw+2erqxMRkWpAQV5ERMTVJK4xp9NnJUO9DuaaeL9Qq6uqfhp2hrt/hhtfAd9QOPonzLwJ5g+FlP1WVyciIlWYgryIiIgrSVgFM/uao8CRcTD0M/ANsbqq6svNHWLvMrer63Q32Nzgz89haif46XnIy7K6QhERqYIU5EVERFzFvhUw6xbISYOGV8Dgheb6bLGeXyj0fhHu+RUaXgn5WfDTc2ag/3MxqCWRiIiUIwV5ERERV7DnV5h9K+SmQ6OrzS3RvAOsrkr+rk5rGP4l3D4dAiMgdT/MHwIzb4ajf1ldnYiIVBEK8iIiIpXdrh9gzh2QlwlNusHA+eDlb3VVcjY2G7S+FcashqsfBndv2PMzvHUFfPMoZKVYXaGIiLg4BXkREZHKbPt3MPdOc6p2sx5w50fg6Wt1VXIhvPyh2+MwZhW0vBEMO6x8C16PgbUzweGwukIRkerBMCA3w+oqypWCvIiISGW19Sv4eCDYc8wg2H82ePpYXZWUVEgU3DkHBi+CWs3NLQMX3wvvdzObF4qISMU4eRiWT4E3OsE3j1hdTbnysLoAERERKcafn8Mnd4EjHy7ta+5b7u5pdVVSFk2vhX/+DqvehZ/+BwfXwQfXQbsB0H0iBNSxukIREdeXnws7voV1s2HH9+ZsKDCXNdnzwb1qRGCbYaiN6t+lpaURFBREamoqgYGBVpcjIiLVzaZPYNHd5oePNndA37erzAcPKZB+FJZNMj9oAngFwDUPQdw/wcPL2tpERFzRkS2wbg5s/Bgyjxeer98J2g+GVreAT+XOdiXJoQryxVCQFxERy6z/CD7/FxgOaDcQbp5q7lUuVdOBNfDNQ5C4xjyu2RR6Pg/Nultbl4iIK8g6AZsXmr8UPbiu8HyNcGh3J0QPhrDm1tVXQgryZaQgLyIillg7ExbfBxjQYSjc+Cq4qZ1NledwwIaPYOmTkHHMPNe8F/R4Fmo2sbY2EZHKxuGAPT+Zo+9/fWH2kQFw84AWvczw3rS7S85kU5AvIwV5ERG56FZ/AF+NNb/vOAp6vagQX91kp8LPL8DKt83eCO5e0HkMXPUAeNewujoREWud2Avr55pfqQmF52u3gvaDoG1/8K9lWXnlQUG+jBTkRUTkovrjbVhS0E33sn9Bj+fMvcilejq23fz3YdcP5nFAPbj+aWh9m/69EJHqJTcT/lpsTp3f+2vheZ8gs4dM9CCo177K/L9RQb6MFORFROSi+f11+G68+f0V/4Huk6rMBxIpA8OAbV/DknGQss8816Az9HoB6ra1tjYRkYpkGHAgHtbNgs2LIPdkwQM2aNzFbFzX8sYquR2rgnwZKciLiMhF8ev/wbKnzO+vfgi6Pq4QL0XlZcOK1+HXlyEvE2xuEDMcuj0BfqFWVyciUn5OHjE7zq+bDUnbC8+HRJkj7+0GQHBkkac4HAapWXkcz8jleHrO3/6Zy/GMHJLSc0nOyKVpWA3eHhJzcd9TCZUkh7peBwARERFXZxjw8/Pw02TzuOvjcM3D1tYklZOnj/lLnnYD4LsnYMsiiJ9mjlJ1Gw8xI1yyoZOICHDanu9zYMd3zj3fHR4+nGjYm931+7LLrx3HM/NJ+jWN4+nrSM7IJakgrCdn5GJ3XNi4tHsV+0W5RuSLoRF5ERGpMIYBPzxtjsYDXPskXDXW2prEdexdDt88Akc2m8fhraHX8xB1pbV1iYicRXae/YyRcsfhLTTcv4hLk5YQYE9xXrvWaMa8/C58ZY8jHb8Lfo1AHw9q1fCmZg0vQv29qFnDm1oF/zx1rnaAD01rV+7GoZpaX0YK8iIiUiEMA75/wlwXD2ZTu87/trYmcT32fFgzHX54BrJTzHOtbjUb4gXVt7Q0Ean68uwOTmTkFjN9PYfj6bkkFZxLLng8PScfgEAyuMn9d+5w/5l2brud9ztqBLPIfhUL7Fezy4gAwM/LvSCAnwrkBaH81Pf+ZkCvVcObED8vvDyqxi4vCvJlpCAvIiLlzjBgyaPm1mIAvV+CTqOtrUlcW2ayGebXTAfDAR6+5lZ1l99bJZtAiUjFKFxnbgby4wWh/FQgP55eGNiPZ+SSkpl3wfe24eAKty309/iZ691W4435XDvu7Ai+kt31byGjQRdqBvpR09+7YDTdCz+v6rlkSEG+jBTkRUSkXDkc8PWDEP+BeXzjFIgdYWlJUoUc2mhOt9//u3kc3BB6ToYWvdU8UaQaMgyD9Jz8wvCdnuuc2n6q8dvx00bPT2Re+DrzU9xsmKHbv3Dqei3niLk39W1HaJq4mNq7F+JxMrHwibUvNbvOt+kHNcLK+Z27PgX5MlKQFxGRcuOwwxf/MbfRwQY3TzU/xIiUJ8OAzQvNhngnD5rnmnSDnv+DsBbW1iYiZZadZycpvXC6+unN3pLSc84I7bn5jhK/RpCvZ8G09cKA/vfp7LUKzgX5euLu9rdfFFazPd8rgoJ8GSnIi4hIuXDY4bN/mdvp2Nyg79vQrr/VVUlVlpMOy182+zDYc8HNA+L+Ye6K4BNkdXUiUuDUOvOkYkbN/75t2vH0HDJy7SV+DX8vd0JPD+D+3gXHXs7GcKcCe6nXmV/Qnu83gKdvye9dDSnIl5GCvIiIlJk9Hz692xwltbnDbe9B69usrkqqi+TdsOQx2P6NeewfBt0nQruB4FY1mkKJVCYOh0FKVt4Z09fNNeenjaQXhPbUrAtfZ36Kl7tbwSh5QQA/RxO4mv7e+Hq5V8A7LXC2Pd+DG5rhvZg93+X8FOTLSEFeRETKxJ4Hn9xlTjF084Q7psMlfayuSqqjHd+bTRaP7zSPI2Kg14tQP8baukQqOcMwOFmwzjz5tCZwzi3U/jZ6npyRSwmXmResMzdHy09tmWaOlpvd2s2u7IXhvIa3BzYrp6WfZc93PHzh0pvNAN/wCv2ysAwU5MtIQV5EREotPwcWjIBtX4G7F/SbCS16WV2VVGf5ueZuCT8/D7np5rnowdD9SahR29raRC6irFz7adPYi05dN0fLi46e59pLvs482M/TbPzmXGPu5QzrhaPlZmgP9vXE7e/rzCujI3+aI+8b50FmUuH5+p2g/SBz+0sfZabyoCBfRgryIiJSKnnZMH+IOVLh7g13zoVm3a2uSsR08jAsnQQb5prH3oFwzSMQdw+4e1pbm0gp5OY7OJFZ2OzN2fityGh54Rr0zFKuM695+nry06azO0fSC4J6iL8Xnu5VZDQ6KwU2f2IG+IPrCs/XCId2d5qN69RIs9wpyJeRgryIiJRYbiZ8PBB2/2hOMxzwETTpanVVImdKWA3fPFT44bxWc+j1vNnlXsRCdodBSuapaeun72GeQ1JGLsnpp2+blkNadn6JX8PLw41aBSPip/YsP7VtWujpTeAKzvl4VuA688rG4YA9P5lT5//6Auw55nk3D2jeE9oPgabdwb167vF+MSjIl5GCvIiIlEhOOnx0p7ndjqc/DJoPUVdaXZXI2TkcsH62OUJ/aqpsyxvh+mcgtJG1tUmVYRgGadn5zunrp3do//u2ackFW6mVdJ25u5uNEL+CteTFTF8v+s9KsM68MjqxF9bPNb9SEwrPa8/3i05BvowU5EVE5ILlnIQ5d8D+FeAVAIM/gQaXWV2VyIXJSjHXzq98x2xc5e4NV9wHV/4XvPytrk4qoaxce9Hp6xlFm8Cdvtf58Ywc8uwljxrBfp7OAP736eunRtJPrTkPcpV15pWN9nyvlBTky0hBXkRELkh2Ksy+DQ6sBu8gGLII6sdaXZVIyR3dCt88DHt+No8DI+D6p80mVvogX6Xl5juKrC1Pdk5dL2z8lnTamvOsvJKvM6/h7VHQ9O30QF50q7RTjeFC/KrQOvPKRnu+V3oK8mWkIC8iIueVdQJm3WKuM/YJhqGfmaMXIq7KMMx1sd89Din7zXMNrzTXz9dpbW1tcsHsDoMTmX+bul7slmnm92VdZ17sdPYaZtf20IJz1WqdeWWkPd9dhoJ8GSnIi4jIOWUch1k3w+FN4FcThn4OddpYXZVI+cjLgt9eg+UvQ3422NwgdiR0fQz8Qq2urto5tc78+GnT2U/fNu3vW6YlZ+ZS0k/37m62gtFys9nb35vAFZnOXsMbfy93rTOv7Ox5sP1bM7xrz3eXoSBfRgryIiJyVunHYOZNcPRP8K9thvjwS62uSqT8peyH78bDn5+bx74h0O0JiBkObhphrQjZeXY2JKQQv+8Ea/ad4M+DaaVeZx7i53nGevLimsDVquFFoI/WmVcZR/6E9XNgw8fa890FKciXkYK8iIgU6+Rh+PAmSNoGNerAsC8grLnVVYlUrN0/wzePwLG/zOM6baDXi9Cws7V1VQHHTuawZl8y8XtPEL/vBFsOpp41tAd4ezinqv+9CZxz9LzgXKifFx5aZ159OPd8nwMH1xae157vLkdBvowU5EVE5AypifBhH0jeZTYCG/YF1GxidVUiF4c9H+I/gB+fNZs8gtnZ+rqnILCetbW5CIfDYOex9ILQnsyafSfYdzzzjOtqB3gTGxVCTMNQoiODqRvkQ6jWmcvfORxmc8p1s2Hrl+YyGNCe7y5OQb6MFORFRKSIlP1miD+xF4IawPAvICTK6qpELr6MJPjhaVjzIWCApz9c/QB0HgMe3lZXV6lk5drZcCCFNftOEL/XDO5/byxns0GL8ABiGoYQGxVCbMNQ6of4av25nJ32fK/SFOTLSEFeRESckveY0+lT95vhfdgXENzA6qpErHVwvbldXcJK8zikEfScbI4EVtMQevRkNmsKpsjH7zvBlsRU8h1FP2b7eroTHRlcMOIeQvsGIQT5elpUsbgM7flebSjIl5GCvIiIAHB8lzkSn5YINZvC0MUQFGF1VSKVg2HApgXw3ROQftg817Q79Pwf1GpmbW0VzOEw2H70JPF7zaZ08fuSSUjOOuO68EBvYhuGOkfcL6kbqD3S5cKc2vN9/Wxzz/ectIIHtOd7VaYgX0YK8iIiwrHtZohPPwy1WsCwxRBQx+qqRCqfnJPwy0uw4g1w5IGbJ1z2T7j6oSrTHTsr1876hBSzMV1BR/mTZ5kmf2qKfEzDEE2Tl5Jz7vk+x2yseor2fK8WFOTLSEFeRKSaO/KnucVcxjGo3crcYk5rDkXO7fguWPKouWc1mB2zu0+Ctv1dbq/qo2nZ5hT5vSdYsy+ZLQfTzpgm7+dVME2+YQgxUaG0bxBMoI+myUspaM93KaAgX0YK8iIi1djhTTDzZsg8bm6zNeRz8K9pdVUirmP7t2agT95tHtfvBL2eh4gO1tZ1Fhc6Tb5OoA8xUSHENjRH3C+pG6At3qRstOe7/I2CfBkpyIuIVFMH18HMvpCdYjYOGrwI/EKtrkrE9eTnwB9vws8vQl4GYIMOQ6DbBMtnt2Tm5rN+f4pzivza/cVPk29ZJ9AM7QWN6SKCNU1eyoH2fJdzUJAvIwV5EZFq6EA8zLoVclKhfkcYvNDsCCwipZd2CJY+CRvnmcfeQdB1HHQcBe4XZxr64dRs4vclO0fc/zyUhr2YafLtGwQT0zCU2IYhtG8QTICmyUt50Z7vcoEU5MtIQV5EpJrZ/wfMvh1yT0KDzjBoAXgHWF2VSNWx/w/4+iE4vNE8DmtpTrdv3KVcX8buMNh+5KQ52r43mdV7T5CYcuY0+bpBPmYn+YYhxEaF0rKOpslLBdCe71JCLhXk33jjDV588UUOHz5Mu3bteP311+nUqVOx1+bl5TF58mQ+/PBDEhMTadGiBc8//zw9e/Ys9T2LoyAvIlKN7F0Oc/qZ03+jroKB88DL3+qqRKoehx3WzoRlT0FWsnnukj5w/bMQ0rBUtzx9mnz8vhOs23eCkzlFp8m7nZomXzBFPjYqlIhgbdklFeRse757B0Gb280Arz3f5SxcJsjPmzePoUOH8vbbbxMXF8eUKVNYsGAB27Zto3bt2mdc/8gjjzB79mzee+89WrZsybfffsvYsWP5/fffad++fanuWRwFeRGRamL3TzD3TsjPgsZd4c654OVndVUiVVvWCfhxMqx+3+zO7eEDV9wPV/znvP/9Xcg0eX8vd9o3CHHu3R4dqWnyUsG057uUE5cJ8nFxcXTs2JGpU6cC4HA4iIyM5N577+XRRx894/p69erx+OOP8+9//9t57rbbbsPX15fZs2eX6p7FUZAXEakGdiyFeYPMtYrNrod+s8DTx+qqRKqPI1vgm0cKRy2DIuH6Z8zttmw27A6DrYfTzE7yBcG9uGny9YJ8iIky17bHNAzRNHm5eLTnu5SzkuRQyzoq5ObmsmbNGsaNG+c85+bmRvfu3VmxYkWxz8nJycHHp+iHLF9fX5YvX17qe566b05OjvM4LS3trNeKiEgVsO0bmD8U7LnQ4ga4Yzp4eFtdlUj1Et4Khn0Bf34O34031xAvGMb+oFje8hnNF4dDSC9mmvwldQOde7fHNgyhnqbJy8V03j3fB0HDK7Xnu1Q4y4J8UlISdrud8PDwIufDw8PZunVrsc/p0aMHL7/8MldffTVNmjRh2bJlLFq0CLvdXup7AkyePJlJkyaV8R2JiIhL+HMxfDICHPnmh67bPrho3bNFpNDBlKyCpnRN2eT2Cl3y53KP+xc0SI3n6ZS1NLdfx3ve/WnSoH5BY7pQohsEU8Nbnb3FAmfd872jOfre6hbtdCIXlUv9n/DVV19l9OjRtGzZEpvNRpMmTRgxYgTTpk0r033HjRvH2LFjncdpaWlERmoajIhIlbN5ISwcbY6gtL4dbnlH2/2IXASnpsnH7z3h7Ch/MDW7yDVruZ1f/K5nvNdcotN/YYTHtwz3i8cWPcHcnsvN3aLqpdo6257v/rXNPd/bD9ae72IZyz691KpVC3d3d44cOVLk/JEjR6hTp06xzwkLC+Ozzz4jOzub48ePU69ePR599FEaN25c6nsCeHt74+2tKZUiIlXahnnw2T/AcEDbO6HvmwoGIhUkPSefdfvNde1r9p1g3f6UYqfJX1ovkNiGoc7GdHWDfIEBsOtH+OYRbEnb4Iv/QPw06PUiNIiz5g1J9XHePd8HF+z5rplcYi3LgryXlxcxMTEsW7aMvn37AmZjumXLljFmzJhzPtfHx4eIiAjy8vJYuHAh/fr1K/M9RUSkCls3Bz7/N2CYI3t9XlWIFylHiSlZxO9Ndjam23o4jb81k6eGtwftGwQT2zDU2U3e/2zT5Jt0hX/+Bqveg58mw6ENMO16aNsfuk+CwLoV/6aketGe7+JiLJ1POHbsWIYNG0ZsbCydOnViypQpZGRkMGLECACGDh1KREQEkydPBmDlypUkJiYSHR1NYmIiEydOxOFw8PDDD1/wPUVEpJqJnw5f3m9+HzsSer+kJkQiZZBvd7D18Eni9yab0+T3neDQ36bJA0QE+xIbFVLQTT6UFnUCcHcrwd7Z7p7Q+V/Q5g5YNskcId04D7Z+BVc/BJf9U00qpWxyM+GvL2DdLO35Li7H0iDfv39/jh07xoQJEzh8+DDR0dEsWbLE2axu//79uJ32YSs7O5vx48eze/duatSoQe/evZk1axbBwcEXfE8REalGVr4L3zxkfh/3T+g5WR/IREroZHYe6/anFIT2ZNbvTyEj117kGnc3G5fWDXROkY9tGEqdoHLazrFGGNw8FWJHwNcPQ2I8LH0S1s6Env+D5teXz+tI9aA936WKsHQf+cpK+8iLiFQBK96Abx8zv7/8XrjuaYV4kQtwapr8qcZ024qZJh/g7UH7huZoe2zDENqda5p8eXI4zFH57ydAxlHzXLMe5i/pajap+NcX16U938UFlCSHKsgXQ0FeRMTFLX8Flk40v7/qAej2hEK8SDHy7Q7+OnSS+H3mNPm1Z5kmXz/Et8je7c3DSzhNvrxlp8EvL8Afb5lbSbp7Qed/w1UPgncN6+qSyuXUnu/r55j/1J7vUskpyJeRgryIiAv7+QX48Vnz+y7j4JpHFOJFCpzMzmPt/hTWFKxvX5+QQmYx0+Rb1Qt07t0eGxVCeGA5TZMvb8e2w5JHYdcy8zigLlz3lLmuXv/dV1+n9nzfOA8yjhWe157vUskpyJeRgryIiAsyDDPA//KiedztCbj6QWtrErGQYRgcOJFldpLfZ06V33bkJH//5Bfg40GHBgVN6Qq6yft5WdpGqWQMA7YvMQP9ib3mucjLoNfzUC/aysrkYspKgc0LzaaI2vNdXJSCfBkpyIuIuBjDMJtf/faqeXz9M+a6eJFqJN/u4M9DacTvPeEM70fScs64LjLUt8je7c1rB+Bm5TT58pKXDSumwq//B3mZgA1ihkG3CeBf0+rqpCKc2vN9/Ryz+7z2fBcXpyBfRgryIiIuxDDMpnZ/vGke93oB4u6xtiaRiyAtO4+1Bdu/xe81p8ln5RWdJu/hnCYf6twKrnZlnSZfXlITzWZ4mz8xj32CoOt4iL0L3F1opoGcnXPP948gdX/hee35Li5OQb6MFORFRFyEw2FuL7f6ffP4hpeh40hraxKpAKemyZ+aIr9m39mnycc0LNy7PToyGF8vd2uKttq+383t6o5sMo9rX2pOt290tbV1Seloz3epBhTky0hBXkTEBTgc8OX9sPZDwAY3vQ4dhlhdlUi5yLM7+PNgmnPv9jX7ThQ7Tb5BqJ9zbXtsw1Ca1a5RNabJlxeHHdZMhx+egawT5rlL+5rLb7TVWOWnPd+lmlGQLyMFeRGRSs5hh8X3musibW7Q9y2zmZGIi0rNymPt/hOs2Wuubd+QkFr8NPmIIOfe7THVYZp8eclMNpthxk8Dw2FuP3bVWLOXhkJg5aM936WaUpAvIwV5EZFKzJ4Pn/0DNi0Amzvc+q45rVLERRiGQUJylnPv9jV7T7D96JnT5ANPTZOPMhvTtatfjafJl5fDm+CbR2Dfb+ZxcAPo8Ry0vFFTsq2mPd9FFOTLSkFeRKSSsufBwlHw52dmV+Lbp5kf8EQqsTy7gy0H04jfm1zQTf4Ex06eOU2+YU2/Inu3Nw3TNPkKYRiwZRF89wSkJZrnGneBns9D7ZaWllYtac93EScF+TJSkBcRqYTyc+GTEbD1S3DzhH4zoWVvq6sSOUNqpjlN/lRjug0HUsjOcxS5xtPdRqt6BdPko0Lo0DCE2gGaJn9R5WbAry/D76+BPdec4RN3D3R5VMGxomnPd5FiKciXkYK8iEglk5cN84fCjm/B3Rv6z4bm11tdlQiGYbA/OZP4vSecjel2HE0/Y5p8kK8nMQXr2mMbhtAuMhgfT02TrxSSd8O342HbV+axfxhc+yRED9I07vKkPd9FzktBvowU5EVEKpG8LPh4EOxaBh4+MOAjaNLN6qqkmsrNd7DlYKpz7/Y1+4ufJh9V06/I3u1NNE2+8tu5FL55FI7vMI/rdYBeL0BkR2vrcnXa813kginIl5GCvIhIJZGbAR/dCXt+AU8/GDhPe0DLRZWamcea/cnOEfeNZ5km37qgm3xMQ7MxXViAt0UVS5nk58Kqd+Cn5yH3pHmu3UDoPhECwi0tzaVoz3eRUlGQLyMFeRGRSiDnJMzpB/t/B68aMOgTaNjZ6qqkCjMMg33HM51T5OP3nmDH0fQzrgv28ySmQeHe7W3rB2mafFVz8ggsm2ROAwfwCoAuj0Cne8DDy9raKqtz7vl+DbQfoj3fRc5DQb6MFORFRCyWnQqzb4cDq8A7EAYv0vRWKXe5+Q42H0x17t2+Zl8KSelnTpNvVMvfubY9NiqExrU0Tb7aOBAPXz9U2JCtZjPo9T9zLbeYzrXne/QgiB5gbvMnIuelIF9GCvIiIhbKOgGzb4PENWbn6CGfQUQHq6uSKiAlM9e5/duagm7yOflFp8l7ubvROiLQuXd7TMMQatXQNPlqzeGADXNh6cTC7dFa9Db3nw9tZGlpltGe7yIVQkG+jBTkRUQskpkMs/rCoQ3gGwpDP4O67ayuSlyQYRjsPZ5ZZO/2ncVMkw/xO9VN3mxM1yZC0+TlLLJTzbXzq94BR765g8bl98JVY8HL3+rqLo6jf5lbxmnPd5EKoSBfRgryIiIWSD9mhvgjm83tn4Z+DuGtrK5KXEROvp3NiWnOte1r958gKT33jOsan5omH2WG9yZh/tjUcEtK4uhWWPII7P7JPA6oB9c/Da1vq5rN207t+b5+jjlT6hTt+S5S7hTky0hBXkTkIjt5BGbeBMe2Qo1wGPaFPhjKOZ3IOG2a/L5kNhxIJbeYafJt6p/qJm9+1dQ0eSkPhgFbv4Jvx0FKwZZqDS6H3i9AnTbW1lYeHA7Y+4s5+q4930UuGgX5MlKQFxG5iNIOwod94PhOc2Rr2BdQq6nVVUklYhgGe5IynGvb4/cls+tYxhnXmdPkC/dub61p8lLR8rLg99fh15chPwtsbhAzArqNB79Qq6sruRP7CvZ8n1t0z/ewS8zw3ra/9nwXqUAK8mWkIC8icpGkJJgh/sQeCIqEYYshtLHVVYnFzGnyqc6929fuO8HxjGKmyYf5m53kG4YSExVC41qaJi8WSUmA75+ALZ+ax74hZpiPGQFulfyXSaf2fF8/G/b8Unjeuef7IKjXoWouGxCpZBTky0hBXkTkIjixDz680ZyWGtzQHIkPaWh1VWKBZOc0+WTW7D3BxsTip8m3rR/k3Ls9pmEIof7az1sqmT2/wjePwNEt5nF4G+j1PERdYW1df2cY5nr3dbO057tIJaIgX0YK8iIiFez4LvjwJkg7AKFNzJH4oPpWVyUXgWEY7E7KcE6Rj993gt3FTJMP9fcqsnd764ggvD0q+cimCIA9H9ZMhx+egewU81zr2+C6pyEowtLSSD8KGz42175rz3eRSkdBvowU5EVEKlDSDnM6/clDUKs5DF0MgXWtrkoqSE6+nU0HUonfd8LZTT65mGnyTcL8nVPkYxuG0EjT5MXVZRyHH56GNTMAAzz94KoHoPMY8PS5eHVoz3cRl6EgX0YK8iIiFeToVjPEZxw1mycNWww1altdlZSj1Mw8Vu457uwov+lAKrn2v02T93CjXf0gszFdQTf5EE2Tl6rq0Ab4+mFI+MM8DomCHpOhRa+KXXeuPd9FXI6CfBkpyIuIVIDDm2HmzZCZZK4bHfoZ+NeyuiopBw6HwW+7kpi3OoHvthw5I7jXPDVNvmDv9tYRgZomL9WLYcCmT8yGeCcPmeeaXAs9/wdhzcvvdbTnu4hLU5AvIwV5EZFydnA9zOoLWSegbjQM+dQ1t2aSIg6cyGRB/AE+WXOAxJQs5/nGtfzp1Ci0ILyHElXTT9PkRQBy0uHXl2DFG2DPNfdlj/sHXPMI+JTyM6f2fBepMhTky0hBXkSkHCWugVm3QHYqRMTC4IXgG2x1VVJK2Xl2vv/zCPPjE1i+M4lTnyICfDzoGx1B/46RtI7QdF2Rczq+C759DLYvMY/9a8N1k6DtnRe+Vl17votUOQryZaQgLyJSTvavhDm3m1sbRV4GgxaUftRJLPXnwTTmxyfw2fpEUjLznOcvb1KT/h0j6dGqDj6emi4vUiLbv4Mlj0LyLvM4IhZ6vwARMcVfrz3fRao0BfkyUpAXESkHe3+Duf0gN93siDxwHnjXsLoqKYHUrDwWr09kfvwBNiWmOs/XDfLh9pj63BETSYOafhZWKFIF5OfCH2/CLy+a/78Ec0T92ifNZqDa812k2lCQLyMFeRGRMtr9M3x0J+RlQuMucOdH4KXA5wocDoM/9hxn/uoEvtl8mJx8s3Gdp7uN6y4Np19sJFc1C8PdTSN+IuUq7RAsnQgbPzaPvQOh3QDY/ZP2fBepJhTky0hBXkSkDHYug48Hmg2XmnaH/rM1UuQCDqVmsXDNAebHH2B/cqbzfPPwGvSLjeSW9hHUrOFtYYUi1cT+lfDNQ+a2dadoz3eRaqEkOdTjItUkIiLVwfZvYd5gsxtz817Q70PwUPirrHLzHSz76wjz4hP4ZfsxHAW/2q/h7UGfdvXo3zGSdvWD1HFe5GJqEAejfzS70O/9FRpeAa1v1Z7vIlKEgryIiJSPv76EBcPBkQeX9IHbpoGHl9VVSTG2HznJ/NUJfLoukeMZuc7znRqF0i82kt5t6uDnpY8IIpZxc4eYYeaXiEgx9Le0iIiU3ZZPYeEocORDq1vh1ne1Z3ElczI7jy83HmLe6gTWJ6Q4z9cO8Oa2mPr0i42kUS1/6woUERGRC6YgLyIiZbNxAXx6NxgOc9/im98Ed/31UhkYhsHqvSeYtzqBrzcdIivPDoCHm41uLWvTLzaSLi3C8HDXelsRERFXok9aIiJSeuvnwuf/NkN89GC46TVzSqhY6ujJbBauSWRBfAK7kzKc5xuH+dM/NpJbO9QnLEC9C0RERFyVgryIiJTOmg/hi/8ABsQMhxteUSdlC+XZHfy49Sjz4w/w47aj2As61/l5uXNj27r0i40kpmGIGteJiIhUAQryIiJScqveg68fNL/vdDf0egEUEC2x61g68+MTWLgmkaT0HOf5Dg2C6d8xkhva1qOGt/66FxERqUr0N7uIiJTMijfh23Hm953HwPXPKMRfZBk5+Xy16RDzVycQv++E83ytGl7c2qE+/WLr07R2gIUVioiISEVSkBcRkQv326vw/QTz+yv/C9c+qRB/kRiGwbqEFOavTuCLDQfJyDUb17nZoEsLs3HdtZfUxlON60RERKo8BXkREbkwv7wIPzxjfn/NI9BlnEL8RZCUnsOnaxOZH5/AjqPpzvNRNf24IzaS22PqEx7oY2GFIiIicrEpyIuIyLkZBvw0GX5+3jzuOh6uecjamqo4u8Pgl+3HmLc6gaV/HSG/oHGdj6cbvVvXpV/HSOIahapxnYiISDWlIC8iImdnGLDsKVj+snl83VNwxX+srakK23c8w9m47nBatvN8u/pB9OsYSZ929Qj08bSwQhEREakMLF9I98YbbxAVFYWPjw9xcXGsWrXqnNdPmTKFFi1a4OvrS2RkJP/973/Jzi78sDNx4kRsNluRr5YtW1b02xARqXoMA74bXxjie/5PIb4CZOXa+XTdAe58dwXXvPgTb/y4i8Np2YT4eTLiiiiW3H8Vn4+5kkFxDRXiRUREBLB4RH7evHmMHTuWt99+m7i4OKZMmUKPHj3Ytm0btWvXPuP6uXPn8uijjzJt2jQuv/xytm/fzvDhw7HZbLz88svO61q1asXSpUudxx4emnggIlIihgHfPAyr3jWPe78EnUZbW1MVYhgGmxJTmbc6gcUbDnIyOx8wWw5c1SyM/rGRdL+0Nt4e7hZXKiIiIpWRpQn35ZdfZvTo0YwYMQKAt99+m6+++opp06bx6KOPnnH977//zhVXXMHAgQMBiIqKYsCAAaxcubLIdR4eHtSpU6fi34CISFXkcMBXY2HNdMAGfV6FmGFWV1UlnMjI5bP1icxbncDWwyed5+uH+HJHTCS3x9YnItjXwgpFRETEFVgW5HNzc1mzZg3jxo1znnNzc6N79+6sWLGi2OdcfvnlzJ49m1WrVtGpUyd2797N119/zZAhQ4pct2PHDurVq4ePjw+dO3dm8uTJNGjQ4Ky15OTkkJOT4zxOS0sr47sTEXFRDjssvg/WzwZs0PdNiB5odVUuzeEwWL4ziXnxCXy/5Qi5dgcAXh5u9GxVh/4dI+ncuCZubmpcJyIiIhfGsiCflJSE3W4nPDy8yPnw8HC2bt1a7HMGDhxIUlISV155JYZhkJ+fzz/+8Q8ee+wx5zVxcXHMmDGDFi1acOjQISZNmsRVV13F5s2bCQgIKPa+kydPZtKkSeX35kREXJE9Hz7/F2ycBzZ3uOUdaHuH1VW5rAMnMlkQf4BP1hwgMSXLeb5VvUD6d4zkpnb1CPbzsrBCERERcVUutXj8p59+4rnnnuPNN98kLi6OnTt38p///Ienn36aJ554AoBevXo5r2/bti1xcXE0bNiQ+fPnM3LkyGLvO27cOMaOHes8TktLIzIysmLfjIhIZWLPg0V3w5ZF4OYBt70PrW6xuiqXk51n57s/jzB/dQK/7UrCMHeNI9DHg77tI+gXG0nriCBrixQRERGXZ1mQr1WrFu7u7hw5cqTI+SNHjpx1ffsTTzzBkCFDGDVqFABt2rQhIyODu+++m8cffxw3tzOb8AcHB9O8eXN27tx51lq8vb3x9vYuw7sREXFh+bmw8C746wtw84Q7ZsAlN1pdlUvZcjCVBfEH+HRdIqlZec7zVzStSb/YSHq0qoOPpxrXiYiISPmwLMh7eXkRExPDsmXL6Nu3LwAOh4Nly5YxZsyYYp+TmZl5Rlh3dzc/GBmnhj3+Jj09nV27dp2xjl5ERID8HJg/DLZ/A+5e0H82NO9hdVUuITUrj8XrE5kXn8DmxMLeKnWDfLgjpj53xEYSGepnYYUiIiJSVVk6tX7s2LEMGzaM2NhYOnXqxJQpU8jIyHB2sR86dCgRERFMnjwZgD59+vDyyy/Tvn1759T6J554gj59+jgD/YMPPkifPn1o2LAhBw8e5Mknn8Td3Z0BAwZY9j5FRCqlvCyYNxh2LgUPH7hzDjTtbnVVlZrDYfDH7uPMi09gyebD5OSbjes83W1cf2kd+nWM5MqmtXBX4zoRERGpQJYG+f79+3Ps2DEmTJjA4cOHiY6OZsmSJc4GePv37y8yAj9+/HhsNhvjx48nMTGRsLAw+vTpw7PPPuu85sCBAwwYMIDjx48TFhbGlVdeyR9//EFYWNhFf38iIpVWbiZ8PAB2/wSefjDgY2h8jdVVVVqHUrP4JP4AC9YcYH9ypvN8i/AA+nWM5Jb2EYT6q3GdiIiIXBw242xz0quxtLQ0goKCSE1NJTAw0OpyRETKV046fHQn7P0VvGrAoAXQ8HKrq6p0cvMdLP3rCPPjE/hl+zEcBX9bBnh70Ce6Hv1jI2lbPwibTaPvIiIiUnYlyaEu1bVeRETKKDsN5twBCX+AdyAM+gQaxFldVaWy/chJ5q1O4NN1iSRn5DrPd2oUSv/YSHq3qYuvlxrXiYiIiHUU5EVEqousFJh9GyTGg08QDP4U6sdYXVWlcDI7jy82HGJefAIbElKc52sHeHN7QeO6RrX8rStQRERE5DQK8iIi1UFmMsy6BQ6tB98QGPIZ1Iu2uChrGYbB6r0nmLc6ga83HSIrzw6Ah5uNbi1r079jJNc0D8PD/cytTUVERESspCAvIlLVZRyHmTfDkU3gVwuGfg51WltdlWWOpmXzydoDLIg/wJ6kDOf5JmH+9O8YyS3t6xMW4G1hhSIiIiLnpiAvIlKVpR81Q/zRP8G/Ngz7Amq3tLqqiy7P7uDHrUeZH5/Aj9uOYS/oXOfn5U6ftvXo17E+HRqEqHGdiIiIuAQFeRGRqirtEMy8CZK2Q0BdM8TXamZ1VRfVzqPpLIhPYOHaRJLSc5znYxqG0D82khva1sXfW38VioiIiGvRpxcRkaoo9QB82AeSd0NgfRi2GGo2sbqqiyIjJ5+vNh1i/uoE4vedcJ6vVcOLWzvUp19sfZrWDrCwQhEREZGyUZAXEalqTuwzQ3zKPghuYI7Eh0RZXVWFMgyDtftTmL86gS83HiQj12xc52aDri1q069jJN1a1sZTjetERESkClCQFxGpSpL3mCE+NQFCGpkhPjjS6qoqTFJ6Dp+uTWRefAI7j6Y7z0fV9KNfx0hu61Cf8EAfCysUERERKX8K8iIiVcXxXTDjRjh5EGo2M6fTB9azuqpyl2938MuOY8xbncCyv46SX9C4zsfTjd5t6tI/NpJOjULVuE5ERESqLAV5EZGq4Ng2cyQ+/QiEtYShiyEg3OqqytXepAwWrEngkzUHOJJW2LiuXWQw/WMjubFdXQJ9PC2sUEREROTiUJAXEXF1R7bAhzdBZhKEtzb3ifevZXVV5SIr1843mw8xb3UCK/ckO8+H+HlyS/v69O8YSYs6alwnIiIi1YuCvIiIKzu00dwnPisZ6rQ1Q7xfqNVVlYlhGGw8kMq8+AS+WH+Qkzn5ANhscHWzMPp3jOTaS2rj7eFucaUiIiIi1lCQFxFxVYlrYdYtkJ0CETEweCH4hlhdVamdyMjl03WJzI9PYOvhk87z9UN86Rcbye0x9akX7GthhSIiIiKVg4K8iIgrSlgNs2+FnDSIjINBC8AnyOqqSszuMFi+M4n58Ql8v+UIuXYHAF4ebvRqXYf+sZFc1rgmbm5qXCciIiJyioK8iIir2bcC5twOuenQ8AoYOA+8XWudeEJyJgvWHOCT+AQOpmY7z7eOCKRfbCQ3t4sgyE+N60RERESKoyAvIuJK9vwKc/tBXiY0uhoGfAxe/lZXdUGy8+x8u+UwC+IP8NuuJAxz1ziCfD3pG12Pfh0jaVXP9WYViIiIiFxsCvIiIq5i1w/w0UDIz4Im3eDOueBZ+deMbzmYyvzVCXy2/iCpWXnO81c0rUm/2Eh6tKqDj6ca14mIiIhcKAV5ERFXsON7+HgQ2HOgWQ/oNxM8fayu6qxSM/P4fEMi81YnsOVgmvN8vSAfbo+N5I6Y+kSG+llYoYiIiIjrUpAXEanstn4NC4aBPRda3gi3TwcPL6urOoPDYfDH7uPMi09gyebD5OQXNK5zd+O6VuH0i43kyqa1cFfjOhEREZEyUZAXEanM/vwcPrkLHPlwaV+47X1wr1xN4A6mZPHJmgMsWJNAQnKW83zLOgH0i42kb/sIQv0r3y8eRERERFyVgryISGW16RNYdDcYdmhzB/R9G9wrx/+2c/MdLP3rCPNWJ/DLjmPOxnUB3h7cFF2PfrGRtK0fhM2m0XcRERGR8lY5PhGKiEhRGz6Gz/4JhgPaDYSbp4Kb9Q3hth0+ybzVCXy2PpHkjFzn+bhGofTvGEmv1nXx9bK+ThEREZGqTEFeRKSyWTsLFt8LGNBhKNz4Kri5WVbOyew8vthwiHnxCWxISHGeDw/05vaY+twRE0lULdfYAk9ERESkKlCQFxGpTOKnwZf/Nb/vOAp6vWhJiDcMg1V7kpkXn8DXmw6RnWc2rvNws3HtJbXp3zGSq5uF4eFu3S8YRERERKorBXkRkcpi5TvwzcPm95f9C3o8Bxd5jfnRtGw+WXuABfEH2JOU4TzftHYN+hc0rgsL8L6oNYmIiIhIUQryIiKVwe+vw3fjze+v+A90n3TRQnye3cEPW48yf3UCP20/ht1hdq7z93Lnxrb16Ncxkg4NgtW4TkRERKSSUJAXEbHar/8Hy54yv7/6Iej6+EUJ8TuPprMgPoGFaxNJSs9xno9tGEK/jpHc0KYu/t76a0JERESkstEnNBERqxgG/Pw8/DTZPO76OFzzcIW+ZEZOPl9tNBvXrdl3wnm+Vg0vbutQnztiI2lau0aF1iAiIiIiZaMgLyJiBcOAH56BX18yj7tPhCv/W0EvZbB2/wnmrz7AlxsPkpFrB8DdzUbXFmH0i42ka8vaeKpxnYiIiIhLUJAXEbnYDAO+nwC/v2Ye93gOOv+73F8mKT2HRWsPMD/+ADuPpjvPN6rlzx2x9bm9Q31qB/qU++uKiIiISMVSkBcRuZgMA5aMg5Vvmce9X4JOo8vt9vl2B7/sOMa81Qks++so+QWN63w93endpi79O0bSMSpEjetEREREXJiCvIjIxeJwwNcPQvwH5vGNUyB2RLncem9SBvPjE1i49gBH0gob10VHBtO/YyQ3tq1LgI9nubyWiIiIiFhLQV5E5GJwOOCL+2DdLMAGN0+F9oPLdMusXDtfbzrE/PgEVu5Jdp4P9ffilvYR9IuNpEWdgDIWLiIiIiKVjYK8iEhFc9jh83/Dho/A5gZ934Z2/Ut1K8Mw2HgglXnxCXyx/iAnc/IBcLPB1c3NxnXdLwnHy0ON60RERESqKgV5EZGKZM+HT++BzZ+AzR1uex9a31ri2yRn5PLZukTmxyew9fBJ5/nIUF/6xURye2x96gb5lmflIiIiIlJJlSrI2+12ZsyYwbJlyzh69CgOh6PI4z/88EO5FCci4tLsebBwJPz5Obh5wh3T4ZI+F/50h8HynUnMX53A938eIddu/r/W28ONXq3r0C82kssa18TNTY3rRERERKqTUgX5//znP8yYMYMbbriB1q1bq/uxiMjf5efAghGw7Stw94J+M6FFrwt6akJyJgviE/hkzQEOpmY7z7eOCKR/bCQ3tYsgyE+N60RERESqq1IF+Y8//pj58+fTu3fv8q5HRMT15WXD/CGw4ztw94Y750Kz7ud8SnaenW+3HGZ+fAK/7TzuPB/k68kt7SO4I7Y+reoFVXTlIiIiIuICShXkvby8aNq0aXnXIiLi+nIz4eOBsPtH8PCFgR9D4y5nvXxzYirz4xP4bF0iadn5zvNXNq1Fv46RXH9pOD6e7hehcBERERFxFaUK8g888ACvvvoqU6dO1bR6EZFTcjNgbn/Y+yt4+sOg+RB15RmXpWbm8fmGROatTmDLwTTn+YhgX26Pqc/tMfWJDPW7mJWLiIiIiAspVZBfvnw5P/74I9988w2tWrXC07PoWs1FixaVS3EiIi4j5yTM6Qf7fwevABj8CTS4zPmww2GwYvdx5q1OYMmWw+Tmm43rvNzduK5VOP1jI7miaS3c1bhORERERM6jVEE+ODiYW265pbxrERFxTdmpMPt2OLAKvINgyCKoHwvAwZQsPllzgAVrEkhIznI+pWWdAPp3jKRvdAQh/l5WVS4iIiIiLqhUQX769OnlXYeIiGvKOgGzboGD68AnGIZ+Rk7ttizdeIh58Qn8uuMYhmFeGuDtwU3R9ejfMZI2EUFamiQiIiIipeJWlicfO3aM5cuXs3z5co4dO1aqe7zxxhtERUXh4+NDXFwcq1atOuf1U6ZMoUWLFvj6+hIZGcl///tfsrOzi1xT0nuKiJRKxnH4sI8Z4v1qsufGeTy1xpvLnlvGv+eu5ZftZoi/rHEor/Rvx6rHu/PsLW1oWz9YIV5ERERESq1UI/IZGRnce++9zJw5E4fDXOfp7u7O0KFDef311/Hzu7AmTfPmzWPs2LG8/fbbxMXFMWXKFHr06MG2bduoXbv2GdfPnTuXRx99lGnTpnH55Zezfft2hg8fjs1m4+WXXy7VPUVESiX9GMy8GY5uIcurJg95Pc2Xs48D5tZxdQJ9nI3romr5W1uriIiIiFQpNsM4Nenzwt1zzz0sXbqUqVOncsUVVwBmA7z77ruP6667jrfeeuuC7hMXF0fHjh2ZOnUqAA6Hg8jISO69914effTRM64fM2YMf/31F8uWLXOee+CBB1i5ciXLly8v1T2Lk5aWRlBQEKmpqQQGBl7Qc0Sk+jDSDpH1wY34pe7kqBHCgNzH2GVE4OFmo/sl4fTvGMnVzcPUuE5ERERELlhJcmipRuQXLlzIJ598QpcuXZznevfuja+vL/369bugIJ+bm8uaNWsYN26c85ybmxvdu3dnxYoVxT7n8ssvZ/bs2axatYpOnTqxe/duvv76a4YMGVLqewLk5OSQk5PjPE5LSzvrtSJSfR1Jy+ab39fSbeVIGhgHOWiEMjD3cTzCmvF4bCS3dIigVg1vq8sUERERkSquVEE+MzOT8PDwM87Xrl2bzMzMC7pHUlISdrv9jPuEh4ezdevWYp8zcOBAkpKSuPLKKzEMg/z8fP7xj3/w2GOPlfqeAJMnT2bSpEkXVLeIVC95dgc/bD3K/NUJbNu2hTmez9LA7SiJRhizW07l5SviaB+pNe8iIiIicvGUqtld586defLJJ4s0mcvKymLSpEl07ty53Ir7u59++onnnnuON998k7Vr17Jo0SK++uornn766TLdd9y4caSmpjq/EhISyqliEXFVO4+m89zXf9F58jLumbWGbds287HX0zR0O0q6X31C/v09jwzoSYcGIQrxIiIiInJRlWpE/tVXX6VHjx7Ur1+fdu3aAbBhwwZ8fHz49ttvL+getWrVwt3dnSNHjhQ5f+TIEerUqVPsc5544gmGDBnCqFGjAGjTpg0ZGRncfffdPP7446W6J4C3tzfe3poOK1LdZeTk8+XGg8yPP8CafSec56P9k5np/hyBuUlQsyk1hn0BgfUsrFREREREqrNSjci3bt2aHTt2MHnyZKKjo4mOjuZ///sfO3bsoFWrVhd0Dy8vL2JiYoo0rnM4HCxbtuyso/qZmZm4uRUt2d3dHQDDMEp1TxGR1Mw8xi3aSMdnl/LIwk2s2XcC94LGdXP6hvCp7zME5h6FWi1g+FcK8SIiIiJiqVKNyAP4+fkxevToMr342LFjGTZsGLGxsXTq1IkpU6aQkZHBiBEjABg6dCgRERFMnjwZgD59+vDyyy/Tvn174uLi2LlzJ0888QR9+vRxBvrz3VNE5HQns/MYOn0VGxJSAGhcy587YiO5rUMEtbN2w8wBkHEUareCoZ9DjTBrCxYRERGRau+Cg/zixYvp1asXnp6eLF68+JzX3nTTTRd0z/79+3Ps2DEmTJjA4cOHiY6OZsmSJc5mdfv37y8yAj9+/HhsNhvjx48nMTGRsLAw+vTpw7PPPnvB9xQROSUr187IGfFsSEghxM+TNwZ2oHOTmuaa98ObzH3iM49DnTYw5HPwr2l1ySIiIiIiF76PvJubG4cPH6Z27dpnTG8vckObDbvdXm4FWkH7yItUfdl5dkbPjOfXHUkE+Hjw0ejLaB0RZD54cB3M7AvZKVCvPQxeBH6hVpYrIiIiIlVchewj73A4iv1eRMTV5NkdjJm7ll93JOHn5c6MEZ0KQ/yBeJh1K+SkQv1OMPgT8AmytmARERERkdOUqtldcVJSUsrrViIiFSbf7uD+j9ez9K+jeHu48cGwjsQ0DDEf3P+HORKfkwoNOsOQRQrxIiIiIlLplCrIP//888ybN895fMcddxAaGkpERAQbNmwot+JERMqTw2Hw8MKNfLXpEJ7uNt4ZEkPnJgXr3vf+Zo7E556EqKtg8ELwDrC2YBERERGRYpQqyL/99ttERkYC8P3337N06VKWLFlCr169eOihh8q1QBGR8mAYBk98vplFaxNxd7MxdWAHuoTnwJoPYf4wmH0r5GVA464wcD54+VtdsoiIiIhIsUq1/dzhw4edQf7LL7+kX79+XH/99URFRREXF1euBYqIlJVhGDzz1V98unI73dz/4vEWh2jywwQ4vqPohc17wh0fgqePNYWKiIiIiFyAUgX5kJAQEhISiIyMZMmSJTzzzDOA+WHZ1TvWi0gV4nDA4Y0sXzKfa/f8yCPe2/Cy2WF3weM2N6jfEZp0M7/qdwSbzdKSRURERETOp1RB/tZbb2XgwIE0a9aM48eP06tXLwDWrVtH06ZNy7VAEZESSTsEu3+EXT/Arh8hM4mrANwLHg9uAE2uNYN7o6vBN9i6WkVERERESqFUQf6VV14hKiqKhIQEXnjhBWrUqAHAoUOH+Ne//lWuBYqInFNeFuz7vSC4/wBH/yzycLrhwwpHK3xbdufKnv0htLFG3UVERETEpdkMwzCsLqKySUtLIygoiNTUVAIDA60uR0ROZxhmWD8V3Pf9DvnZp11gg3rt2ewbw1N/1mGt0Ywx3S/h/u7NLStZREREROR8SpJDL3hEfvHixfTq1QtPT08WL158zmtvuummC72tiMj5pR+D3T8Vhvf0w0UfD6gHTQvWuTfqwqfbsxg7fwOGAfdc05j/XNvMiqpFRERERCrEBY/Iu7m5cfjwYWrXro2b29l3rbPZbC7f8E4j8iIWy8+BhJWFwf3QhqKPe/hC1JWFTerCWjiny3+96RBj5q7FYcCwzg2ZeFMrbJpKLyIiIiKVXIWMyDscjmK/FxEpM8OApB2FwX3vcnNP99PVaVMY3CMvK3aLuB+2HuG+j9bhMKBfbH2e7KMQLyIiIiJVT6ma3YmIlFlmMuz5ubC7fGpC0cf9axcG98ZdICD8nLf7bWcS/5i9lnyHwU3t6jH51ra4uSnEi4iIiEjVU6ogf99999G0aVPuu+++IuenTp3Kzp07mTJlSnnUJiJViT0PDsQXjrofXAvGabN73L2gQWdoWrA1XO1WcI5lPKdbvTeZUR/Gk5vv4PpLw/m/fu1wV4gXERERkSqqVF3rIyIiWLx4MTExMUXOr127lptuuokDBw6UW4FW0Bp5kXKSvAd2LTNH3Pf8AjlpRR8Pa1m4p3vDy8HLr8QvsSEhhUHvryQ9J59rmofx7tAYvD3cz/9EEREREZFKpELWyJ/u+PHjBAUFnXE+MDCQpKSk0txSRKqC7DTY+yvsXGaOup/YU/Rx31Bo0rVgunxXCIoo08v9dSiNodNWkZ6Tz2WNQ3lniEK8iIiIiFR9pQryTZs2ZcmSJYwZM6bI+W+++YbGjRuXS2Ei4gIcdji4vmDU/QdIWAXGabtWuHlAZFzhWve67cCtfIL2zqPpDH5/JalZeXRoEMz7wzri46kQLyIiIiJVX6mC/NixYxkzZgzHjh2jW7duACxbtoz/+7//0/p4kaou9UDhiPvunyA7pejjoU0K17lHXQneAeVewr7jGQx6/w+OZ+TSOiKQ6SM6UcNbvTtFREREpHoo1Sffu+66i5ycHJ599lmefvppAKKionjrrbcYOnRouRYoIhbLzYC9vxWOuidtL/q4dxA0vrpgrXtXCImq0HIOpmQx8L2VHEnLoXl4DWbeFUeQr2eFvqaIiIiISGVSqmZ3pzt27Bi+vr7UqFGjvGqynJrdSbXmcMCRTYWj7vv/AEde4eM2N4iILRx1r9cB3C/OaPjRk9n0f+cP9iRl0KiWP/PuuYzaAWfuJy8iIiIi4moqvNkdQH5+Pj/99BO7du1i4MCBABw8eJDAwMAqFepFqoWTh83O8qc6zGf+rWllUANo2s0cdW90NfgGX/QSkzNyGfz+SvYkZVA/xJc5o+IU4kVERESkWipVkN+3bx89e/Zk//795OTkcN111xEQEMDzzz9PTk4Ob7/9dnnXKSLlKS8L9q8wR9x3/gBHtxR93KsGRF1VOOoe2hhs1u3LnpqVx5APVrL9SDp1An2YO+oy6gX7WlaPiIiIiIiVShXk//Of/xAbG8uGDRuoWbOm8/wtt9zC6NGjy604ESknhgFH/zKD+65lsO93yM8+7QIb1Isu6C5/LdTvCB5eVlVbRHpOPsOnr2LLwTRq1fBi9qg4GtQs+X7zIiIiIiJVRamC/K+//srvv/+Ol1fRD/pRUVEkJiaWS2EiUkYZSWZX+V0/mF8nDxV9PKBeQXDvau7p7l+z2NtYKSvXzqgPV7NufwpBvp7MGhlH09pauiMiIiIi1VupgrzD4cBut59x/sCBAwQElP9WUyJyAfJzIWFlYXA/tAE4rZelhy9EXVE46h7WwtLp8ueTk2/nntlr+GN3MgHeHswa2YlL6qr5pIiIiIhIqYL89ddfz5QpU3j33XcBsNlspKen8+STT9K7d+9yLVBEzsIw4PjOwuC+51fIyyh6TXgbc8S9STdo0Bk8XaM5XJ7dwZi56/hl+zF8Pd2ZPqIjbesHW12WiIiIiEilUKrt5xISEujZsyeGYbBjxw5iY2PZsWMHtWrV4pdffqF27doVUetFo+3npNLKOgG7fy4I7z9C6v6ij/uHFYy4dzOnyweEW1NnGdgdBvfPW88XGw7i5eHG9OEduaJpLavLEhERERGpUBW+/VxkZCQbNmxg3rx5bNiwgfT0dEaOHMmgQYPw9VUnaZFyY8+HxPiC7vLL4OBaMByFj7t7mSPtTbqZHeZrtwI3N+vqLSOHw+DRhRv5YsNBPN1tvDM4RiFeRERERORvSjwin5eXR8uWLfnyyy+55JJLKqouS2lEXiyVvOe06fK/QE5a0cfDWhaOuje8AryqRgd3wzB4cvEWZq7Yh7ubjakD2tOrTV2ryxIRERERuSgqdETe09OT7Ozs818oIhcmOw32/loY3pN3F33cN8ScJn+qw3xQfWvqrECGYTD5m63MXLEPmw3+7452CvEiIiIiImdRqqn1//73v3n++ed5//338fAo1S1Eqi+HHQ6uLwzuB1aBI7/wcTcPiIwrbFJXNxrc3K2q9qKYsnQH7/5i/gLjuVva0Ld9hMUViYiIiIhUXqVK4atXr2bZsmV89913tGnTBn9//yKPL1q0qFyKE6kyUg8UBvfdP5lN604X2qRwunzUleBTfZZ0vP3zLl5dtgOAJ/tcyoBODSyuSERERESkcitVkA8ODua2224r71pEqo7cDNj7W2F4T9pW9HHvIGh8dWF4D4mypEyrffj7Xv73zVYAHu7ZghFXNLK4IhERERGRyq9EQd7hcPDiiy+yfft2cnNz6datGxMnTlSnehGHA45shl3LzOC+/w+w5xY+bnODiBhocq0Z3CNiwL16L0uZvzqBJxdvAeC+bk35V5emFlckIiIiIuIaSpQknn32WSZOnEj37t3x9fXltdde49ixY0ybNq2i6hOpvE4eOW26/I+Qcazo40ENoGnBiHujq82mdQLA5+sTeWTRRgBGXdmI/17X3OKKRERERERcR4m2n2vWrBkPPvgg99xzDwBLly7lhhtuICsrCzcX3rv677T9nBQrLxv2/14Q3n80R+BP5+lvBvZT0+VrNgGbzZpaK7Elmw/z77lrsTsMBsU14Jm+rbHp5yQiIiIi1VyFbT+3f/9+evfu7Tzu3r07NpuNgwcPUr9+1dsSS6o5w4BjW2FnwXT5fb9B/ulbL9qgXnRhcK/fCTy8rKrWJfy47Sj3fmSG+Ns61OfpmxXiRURERERKqkRBPj8/Hx8fnyLnPD09ycvLK9eiRCyTcdycJn9qyvzJQ0UfD6hbsM69KzTuAv61LCnTFf2+K4l/zFpDnt3ghrZ1eeH2tri5KcSLiIiIiJRUiYK8YRgMHz4cb29v57ns7Gz+8Y9/FNmCTtvPicvIzzX3cT816n5oA3DaahMPH2h4BTQtaFIX1lLT5Uthzb5kRn0YT06+g+6XhDOlfzTuCvEiIiIiIqVSoiA/bNiwM84NHjy43IoRqXCGAcd3FYy4L4M9v0JeRtFrwlsXTpdv0Bk8fYq/l1yQTQdSGT5tNZm5dq5qVoupA9vj6V51emqIiIiIiFxsJQry06dPr6g6RCpO1gnY84sZ3nf+AKn7iz7uHwaNu5qj7o27QEAdS8qsirYdPsmQaSs5mZNPp0ahvDskFh9Pd6vLEhERERFxadV7I2upmuz5kLimcNQ9cQ0YjsLH3b2gwWWFe7qHt4YqtOtCZbH7WDqD3l9JSmYe0ZHBTBveEV8vhXgRERERkbJSkJeq4cTe0/Z0/wVyUos+XquFGdqbXgsNLwcv/2JvI+UjITmTQe+vJCk9h0vrBvLhiE7U8Nb/bkREREREyoM+WYtryjlprm8/NeqevLvo474h5jT5U2vdg7Q94sVyKDWLge//waHUbJrVrsGskZ0I8vO0uiwRERERkSpDQV5cg8MOh9YXBPcfIWElOPILH3fzMPdxb9INmnaDutHgpmncF9uxkzkMem8lCclZRNX0Y86oOGrW8D7/E0VERERE5IJVioXBb7zxBlFRUfj4+BAXF8eqVavOem2XLl2w2WxnfN1www3Oa4YPH37G4z179rwYb0XKU2oirJ0FC0bAi03hvW7wwzOw7zczxIc2ho6j4M6P4OE9cNc3cM1DEBGjEG+BExm5DPlgJbuTMogI9mXO6MuoHaiO/yIiIiIi5c3yEfl58+YxduxY3n77beLi4pgyZQo9evRg27Zt1K5d+4zrFy1aRG5urvP4+PHjtGvXjjvuuKPIdT179izSZd/bW6OClV5uBuz7vaC7/DJI2lb0ce9AaHR14XT50EbW1ClnSMvOY+i0VWw9fJLaAd7MGRVHRLCv1WWJiIiIiFRJlgf5l19+mdGjRzNixAgA3n77bb766iumTZvGo48+esb1oaGhRY4//vhj/Pz8zgjy3t7e1KmjbcQqNYcDjmwubFK3fwXYC39Jg83NHF1v0s3sMB8RA+6W/ysrf5ORk8+I6avZlJhKTX8v5o6OI6qWmgmKiIiIiFQUS1NRbm4ua9asYdy4cc5zbm5udO/enRUrVlzQPT744APuvPNO/P2LBoeffvqJ2rVrExISQrdu3XjmmWeoWbNmsffIyckhJyfHeZyWllaKdyMX5OQR2P1j4Vr3jKNFHw+KLBxxb3yN2bROKq3sPDujZ8azZt8JAn08mDUyjqa1A6wuS0RERESkSrM0yCclJWG32wkPDy9yPjw8nK1bt573+atWrWLz5s188MEHRc737NmTW2+9lUaNGrFr1y4ee+wxevXqxYoVK3B3P3Pt9OTJk5k0aVLZ3owULy/bHGk/FdyPbCr6uKc/NLqqMLzXbAo2mzW1Sonk5Nv5x+w1/L7rODW8PZg5Mo5L6wVaXZaIiIiISJXn0vOUP/jgA9q0aUOnTp2KnL/zzjud37dp04a2bdvSpEkTfvrpJ6699toz7jNu3DjGjh3rPE5LSyMyMrLiCq/KDAOObS2cLr/3N8jPKnpN3ejC4B4ZBx5elpQqpZdvd/Cfj9bz07Zj+Hi6MW14R6Ijg60uS0RERESkWrA0yNeqVQt3d3eOHDlS5PyRI0fOu749IyODjz/+mKeeeuq8r9O4cWNq1arFzp07iw3y3t7eaoZXFhnHC6bLF0yZP3mw6OMBdU+bLt8F/GtZUqaUD7vD4IEFG1iy5TBe7m68NzSWTo1Cz/9EEREREREpF5YGeS8vL2JiYli2bBl9+/YFwOFwsGzZMsaMGXPO5y5YsICcnBwGDx583tc5cOAAx48fp27duuVRtuTnwoHVsGuZGdwPrgeMwsc9fKDhFYXhvfYlmi5fRRiGweOfbuLz9QfxcLPx5qAOXNUszOqyRERERESqFcun1o8dO5Zhw4YRGxtLp06dmDJlChkZGc4u9kOHDiUiIoLJkycXed4HH3xA3759z2hgl56ezqRJk7jtttuoU6cOu3bt4uGHH6Zp06b06NHjor2vKsUwIHm3uSXcrh9g76+Qm170mtqtoGlBcG/QGTy19VhVYxgGk774k49XJ+Bmg1fvbE/3S8PP/0QRERERESlXlgf5/v37c+zYMSZMmMDhw4eJjo5myZIlzgZ4+/fvx83Nrchztm3bxvLly/nuu+/OuJ+7uzsbN27kww8/JCUlhXr16nH99dfz9NNPa/p8SWSlwJ6fC9e6p+wv+rhfrcIR9yZdIUBb/VVlhmHw/JJtzPh9LwAv3t6OG9pqhouIiIiIiBVshmEY57+seklLSyMoKIjU1FQCA6tJF257PhxcWzjqnhgPhqPwcXcvaHBZYXgPbwN/+wWLVF2vLdvBy99vB+DZW1ozKK6hxRWJiIiIiFQtJcmhlo/Ii4VO7CsYcV8Gu3+BnNSij9dqURjco64AL39r6hRLvffLbmeIH3/DJQrxIiIiIiIWU5CvTnJOwt7lhaPuybuKPu4TbE6Tb9INGneFYG3BV93NWrGXZ7/+C4AHr2/OqKsaW1yRiIiIiIgoyFdlDgccWl+4zj1hJTjyCx+3uUNkJ2hyrRne60WDm7tV1UolsyA+gSc+3wLAv7s2YUy3ZhZXJCIiIiIioCBf9aQmmnu671wGu3+CrOSij4c0gqYFwT3qKvCpJj0ApES+2HCQRxZuBGDEFVE8eH0LiysSEREREZFTFORdXW4m7Pu9cK37sa1FH/cOhEZXF3aXD9XUaDm377Yc5r/z1uMwYECnBky48VJsNpvVZYmIiIiISAEFeVc2bwhs/xbsOYXnbG5Qr0PhqHtEDLh7WlejuJSftx9jzNx15DsMbm0fwbN9WyvEi4iIiIhUMgryrsxwmCE+sD407WaudW90NfiFWl2ZuKA/dh/nnlnx5Nod9G5Thxdub4ubm0K8iIiIiEhloyDvyro+Bt0nQs2moFFTKYO1+08wcsZqsvMcdGtZmyn92+Ph7mZ1WSIiIiIiUgwFeVcW3srqCqQK2JyYyvBpq8jItXNl01q8OagDXh4K8SIiIiIilZU+rYtUY9uPnGTotFWkZefTMSqEd4fG4OOpLQhFRERERCozBXmRampPUgaD3l9JckYu7eoHMW14R/y8NElHRERERKSyU5AXqYYOnMhk0Ht/cOxkDi3rBPDhXZ0I8NHuBiIiIiIirkBBXqSaOZyazcD3VnIwNZsmYf7MHhVHsJ+X1WWJiIiIiMgFUpAXqUaS0nMY9P4f7E/OpEGoH3NGXUatGt5WlyUiIiIiIiWgIC9STaRk5jLkg1XsOpZBvSAf5oyKo06Qj9VliYiIiIhICSnIi1QDJ7PzGDZtFX8dSiMswJs5oy8jMtTP6rJERERERKQUFORFqrjM3HzumrGaDQdSCfHzZM6oOBrV8re6LBERERERKSUFeZEqLDvPzt0z17B67wkCfDyYNTKO5uEBVpclIiIiIiJloCAvUkXl5jv415y1LN+ZhL+XOx/e1YnWEUFWlyUiIiIiImWkIC9SBeXbHdw/bx0/bD2Kt4cbHwzvSIcGIVaXJSIiIiIi5UBBXqSKcTgMHv5kI19vOoyXuxvvDo3lssY1rS5LRERERETKiYK8SBViGAbjP9/MonWJuLvZmDqwPdc0D7O6LBERERERKUcK8iJVhGEYPP3lX8xduR+bDV7pH831repYXZaIiIiIiJQzBXmRKuL/vtvOtN/2APD8bW25qV09iysSEREREZGKoCAvUgW88eNOpv64E4Cnb25Fv9hIiysSEREREZGKoiAv4uI+WL6HF7/dBsBjvVsypHOUtQWJiIiIiEiFUpAXcWFzVu7j6S//BOC/3Ztz99VNLK5IREREREQqmoK8iItatPYA4z/bDMA/rmnCfdc2tbgiERERERG5GBTkRVzQVxsP8eCCDRgGDL88ikd6tsBms1ldloiIiIiIXAQK8iIuZtlfR/jPx+twGNA/NpIJN16qEC8iIiIiUo0oyIu4kOU7kvjn7LXkOwxujq7Hc7e2wc1NIV5EREREpDpRkBdxEav2JDN6Zjy5dgc9WoXzf3e0w10hXkRERESk2lGQF3EB6xNSuGvGarLy7HRpEcZrA9rj4a7/fEVEREREqiMlAZFK7s+DaQybtor0nHw6N67J24Nj8PZwt7osERERERGxiIK8SCW28+hJhnywktSsPDo0COb9YbH4eCrEi4iIiIhUZwryIpXUvuMZDHxvJcczcmkTEcSMuzrh7+1hdVkiIiIiImIxBXmRSigxJYuB763k6MkcWoQHMPOuTgT6eFpdloiIiIiIVAIK8iKVzNG0bAa99weJKVk0ruXPrFGdCPH3srosERERERGpJBTkRSqR4+k5DHp/JXuPZxIZ6suc0XHUDvCxuiwREREREalEFORFKonUzDyGfLCKHUfTqRPow9xRl1E3yNfqskREREREpJJRkBepBNJz8hk2fRV/HkqjVg0v5oyOIzLUz+qyRERERESkElKQF7FYVq6du2asZn1CCsF+nsweFUeTsBpWlyUiIiIiIpWUgryIhXLy7dw9K55Ve5IJ8PZg1l1xtKwTaHVZIiIiIiJSiSnIi1gkz+7g33PW8euOJPy83JlxV0fa1A+yuiwREREREankFORFLGB3GPx33nqW/nUELw833h8aS0zDUKvLEhERERERF1Apgvwbb7xBVFQUPj4+xMXFsWrVqrNe26VLF2w22xlfN9xwg/MawzCYMGECdevWxdfXl+7du7Njx46L8VZEzsvhMHhk4Ua+3HgIT3cb7wyO4fKmtawuS0REREREXITlQX7evHmMHTuWJ598krVr19KuXTt69OjB0aNHi71+0aJFHDp0yPm1efNm3N3dueOOO5zXvPDCC7z22mu8/fbbrFy5En9/f3r06EF2dvbFelsixTIMgwmLN/PJmgO4u9l4fUB7urasbXVZIiIiIiLiQmyGYRhWFhAXF0fHjh2ZOnUqAA6Hg8jISO69914effTR8z5/ypQpTJgwgUOHDuHv749hGNSrV48HHniABx98EIDU1FTCw8OZMWMGd95553nvmZaWRlBQEKmpqQQGqvGYlA/DMHju679479c92GwwpX80N0dHWF2WiIiIiIhUAiXJoZaOyOfm5rJmzRq6d+/uPOfm5kb37t1ZsWLFBd3jgw8+4M4778Tf3x+APXv2cPjw4SL3DAoKIi4u7qz3zMnJIS0trciXSHl7ZekO3vt1DwD/u7WNQryIiIiIiJSKpUE+KSkJu91OeHh4kfPh4eEcPnz4vM9ftWoVmzdvZtSoUc5zp55XkntOnjyZoKAg51dkZGRJ34rIOb310y5eW2b2aZjY51L6d2xgcUUiIiIiIuKqLF8jXxYffPABbdq0oVOnTmW6z7hx40hNTXV+JSQklFOFIjDjtz08v2QrAI/0bMnwKxpZXJGIiIiIiLgyS4N8rVq1cHd358iRI0XOHzlyhDp16pzzuRkZGXz88ceMHDmyyPlTzyvJPb29vQkMDCzyJVIePl61n4lf/AnAfdc2459dmlhckYiIiIiIuDpLg7yXlxcxMTEsW7bMec7hcLBs2TI6d+58zucuWLCAnJwcBg8eXOR8o0aNqFOnTpF7pqWlsXLlyvPeU6Q8fbYukXGfbgLg7qsb89/uzSyuSEREREREqgIPqwsYO3Ysw4YNIzY2lk6dOjFlyhQyMjIYMWIEAEOHDiUiIoLJkycXed4HH3xA3759qVmzZpHzNpuN+++/n2eeeYZmzZrRqFEjnnjiCerVq0ffvn0v1tuSam7J5kM8sGADhgFDLmvIuF4tsdlsVpclIiIiIiJVgOVBvn///hw7dowJEyZw+PBhoqOjWbJkibNZ3f79+3FzKzpxYNu2bSxfvpzvvvuu2Hs+/PDDZGRkcPfdd5OSksKVV17JkiVL8PHxqfD3I/Lj1qPc+9E67A6D22PqM+mmVgrxIiIiIiJSbizfR74y0j7yUlq/70xi+IzV5OY7uLFtXV69sz3ubgrxIiIiIiJybi6zj7xIVRK/N5lRM+PJzXdw3aXhvNI/WiFeRERERETKnYK8SDnYeCCFEdNXk5lr56pmtZg6sD2e7vrPS0REREREyp+ShkgZbT2cxtBpqziZk0+nRqG8OyQWbw93q8sSEREREZEqSkFepAx2HUtn8PsrScnMIzoymGnDO+LrpRAvIiIiIiIVR0FepJQSkjMZ9N5KktJzaVUvkA/v6kQNb8s3ghARERERkSpOQV6kFA6lZjHgvT84nJZNs9o1mDUyjiBfT6vLEhERERGRakBBXqSEjp7MZtB7KzlwIouomn7MGRVHqL+X1WWJiIiIiEg1oSAvUgInMnIZ8v4qdidlEBHsy5zRl1E70MfqskREREREpBpRkBe5QKlZeQyZtpJtR04SHujN3NFxRAT7Wl2WiIiIiIhUMwryIhcgIyefEdNXsTkxjZr+XswZFUfDmv5WlyUiIiIiItWQgrzIeWTn2Rn1YTxr96cQ5OvJrJFxNK0dYHVZIiIiIiJSTSnIi5xDTr6de2atYcXu49Tw9uDDuzpxab1Aq8sSEREREZFqTEFe5Czy7Q7u+2gdP28/hq+nO9NHdCQ6MtjqskREREREpJpTkBcpht1h8MCCDXy75QheHm68NzSWjlGhVpclIiIiIiKiIC/ydw6HwWOLNvH5+oN4uNl4a1AHrmxWy+qyREREREREAAV5kSIMw2DSF1uYF5+Amw1eG9Ceay8Jt7osERERERERJwV5kQKGYfC/JVv5cMU+bDZ46Y529G5T1+qyREREREREilCQFynw2rKdvPPzbgCe7duGWzvUt7giERERERGRMynIiwDv/rKLV5ZuB+CJGy9lYFwDiysSEREREREpnoK8VHuzVuzlua+3AvBQjxaMvLKRxRWJiIiIiIicnYK8VGvz4xN44vMtAIzp2pR/d21qcUUiIiIiIiLnpiAv1dbiDQd5ZOFGAO66ohEPXN/c4opERERERETOT0FeqqVvtxzmv/PWYxgwMK4BT9x4CTabzeqyREREREREzktBXqqdn7Yd5d6567A7DG5tH8EzN7dWiBcREREREZehIC/Vyopdx7ln1hpy7Q5uaFOXF25vi5ubQryIiIiIiLgOBXmpNtbsO8HID1eTk+/g2pa1eaV/NB7u+k9ARERERERci1KMVAubE1MZPn0Vmbl2rmxaizcGdcDLQ//6i4iIiIiI61GSkSpv+5GTDPlgJSez8+kYFcK7Q2Pw8XS3uiwREREREZFSUZCXKm33sXQGvreSE5l5tKsfxLThHfHz8rC6LBERERERkVJTkJcqKyE5k0HvryQpPYdL6gby4V2dCPDxtLosERERERGRMlGQlyrpcGo2g95fyaHUbJqE+TNrZCeC/bysLktERERERKTMFOSlyjl2MoeB7//B/uRMGtb0Y+7oy6hVw9vqskRERERERMqFgrxUKSmZuQz5YCW7j2VQL8iHOaPiCA/0sbosERERERGRcqMgL1VGWnYeQ6etYuvhk4QFeDNn9GXUD/GzuiwREREREZFypSAvVUJmbj53TV/NxgOphPp7MXdUHI1q+VtdloiIiIiISLlTkBeXl51nZ/TMeOL3nSDQx4OZd3WiWXiA1WWJiIiIiIhUCAV5cWm5+Q7+NWctv+08jr+XOzPu6kTriCCryxIREREREakwCvLisvLtDv7z8Tp+2HoUH083PhjekQ4NQqwuS0REREREpEIpyItLcjgMHvpkI99sPoyXuxvvDonlssY1rS5LRERERESkwinIi8sxDIPHP9vEp+sS8XCz8cagDlzdPMzqskRERETk/9u797iq6nz/4+/NBjZ4ATUEt0qameQlMFEJbcZKHDJPv+zU0Yy8pZkO+sP8NYnNlHZytDmVziVHTfNSOnmhn+ZJ05RSJ28YipKR9wumgGYCkoLtvX5/zK/dQREBdS8WvJ6Px3o82Gt/1+Kz/Ph9rMd7r70WALyCIA9LMQxD//nJN/owLVs+Nml6/47q1S7M7LIAAAAAwGsI8rCUtz7br/lbjkmS/uvJKD0a1dTcggAAAADAywjysIx3Pj+oGV8cliS93reDnoxubnJFAAAAAOB9BHlYwtx/HtFbnx2QJP3+kbYaeF8LkysCAAAAAHMQ5FHtLdp+XJNXZ0mSxvVqo+d+3crkigAAAADAPKYH+RkzZqhly5YKCAhQTEyM0tLSyh1//vx5JSYmyul0yuFwqE2bNlqzZo3n/UmTJslms5Va7r777lt9GLhFUtJP6g8rv5YkjXrgTo15qLXJFQEAAACAuXzN/OVLly7VuHHjNGvWLMXExOjPf/6z4uPjtX//foWGhl41vqSkRL169VJoaKhSUlLUrFkzHT9+XA0aNCg1rn379tqwYYPnta+vqYeJKvpk7ym9lLJHkjSkW0u9FB8hm81mclUAAAAAYC5TE+60adP03HPPaejQoZKkWbNmafXq1Zo3b56Sk5OvGj9v3jydO3dOW7dulZ+fnySpZcuWV43z9fVVkyZNKlxHcXGxiouLPa8LCgoqeSS42TZ8k6uxSzLkNqSnuoTr1X9rR4gHAAAAAJn41fqSkhKlp6crLi7ul2J8fBQXF6dt27aVuc2qVasUGxurxMREhYWFqUOHDpoyZYpcLlepcQcPHlTTpk3VqlUrJSQk6MSJE+XWMnXqVAUHB3uW8PDwGz9AVNk/D57Rbxfv0k9uQ307NtUfH79HPj6EeAAAAACQTAzyZ8+elcvlUlhYWKn1YWFhysnJKXObI0eOKCUlRS6XS2vWrNErr7yit99+W5MnT/aMiYmJ0YIFC7R27VrNnDlTR48e1a9+9SsVFhZes5YJEyYoPz/fs2RnZ9+cg0Sl7TjyvZ57/yuVuNx6uH0TvfUfUbIT4gEAAADAw1I3j7vdboWGhurdd9+V3W5XdHS0vvvuO7355puaOHGiJKl3796e8ZGRkYqJiVGLFi20bNkyDRs2rMz9OhwOORwOrxwDrm33iR/07IKdunTZrQcjGuuvA+6Vr9305zECAAAAQLViWpAPCQmR3W5Xbm5uqfW5ubnXvL/d6XTKz89Pdrvds65t27bKyclRSUmJ/P39r9qmQYMGatOmjQ4dOnRzDwA31b5T+Ro8L01FJS51u/M2zXwmWv6+hHgAAAAAuJJpScnf31/R0dFKTU31rHO73UpNTVVsbGyZ23Tv3l2HDh2S2+32rDtw4ICcTmeZIV6SLly4oMOHD8vpdN7cA8BNczC3UAPfS1PBpZ8U3aKh5gzqrAA/+/U3BAAAAIBayNRLnuPGjdOcOXO0cOFCZWVladSoUSoqKvI8xX7QoEGaMGGCZ/yoUaN07tw5JSUl6cCBA1q9erWmTJmixMREz5gXX3xRmzZt0rFjx7R161Y9/vjjstvtGjBggNePD9d37GyREubu0LmiEt3TLFjzh3ZRXYel7vgAAAAAAK8yNTH1799fZ86c0auvvqqcnBx17NhRa9eu9TwA78SJE/Lx+eWzhvDwcK1bt04vvPCCIiMj1axZMyUlJWn8+PGeMSdPntSAAQP0/fffq3Hjxrr//vu1fft2NW7c2OvHh/Kd/OFHJczdobzCYkWE1df7z3ZVUICf2WUBAAAAQLVmMwzDMLuI6qagoEDBwcHKz89XUFCQ2eXUSLkFl9Rv9jYd//5HtQqpq6XPx6pxfR44CAAAAKB2qkwO5Wli8LrvLxQrYe4OHf/+R4U3CtTi52II8QAAAABQQQR5eFX+j5f1zHtpOpR3Qc7gAP1j+H1yBgeaXRYAAAAAWAZBHl5TeOmyBs1PU9bpAoXUc2jx8BiFN6pjdlkAAAAAYCkEeXjFxRKXhi34Snuyz6thHT8tHh6jVo3rmV0WAAAAAFgOQR633KXLLo344CulHTun+g5fvf9sjCKa1De7LAAAAACwJII8bqnLLrdG/2OX/nnwrOr427Xg2S66p3mw2WUBAAAAgGUR5HHLuNyGxi7N0IasPDl8fTR3cGdFt2hkdlkAAAAAYGkEedwSbrehl1L2avXe0/Kz2zR7YLS63RlidlkAAAAAYHkEedx0hmHolY+/1ke7TsruY9PfBnTSAxGhZpcFAAAAADUCQR43lWEY+uPqLC3ecUI2mzStX5Qe7tDE7LIAAAAAoMYgyOOmmr7+gOZ+eVSS9Kd/j9RjHZuZXBEAAAAA1CwEedw0M744pL9+fkiS9Nr/aq9+XcJNrggAAAAAah6CPG6KeV8e1Zvr9kuSknvfrcHdWppbEAAAAADUUAR53LAP007oPz/5RpKU1PMujexxp8kVAQAAAEDNRZDHDVmx+6ReXpEpSXr+1600Nu4ukysCAAAAgJqNII8q+zTztP7Psj0yDGlQbAsl975bNpvN7LIAAAAAoEYjyKNKPv82V/97yW65Dek/optr0qPtCfEAAAAA4AUEeVTalkNnNXLRLl12GXo0qqneeCJSPj6EeAAAAADwBoI8KuWrY+c0fOFXKvnJrV7twjStX5TshHgAAAAA8BqCPCpsT/Z5DZm/Uxcvu/TrNo31ztP3ys/OfyEAAAAA8CZSGCok63SBBs1L04XinxRzRyPNfiZaDl+72WUBAAAAQK1DkMd1Hcq7oIHv7VD+xcu69/YGem9IFwX6E+IBAAAAwAwEeZTr+PdFSpi7XWcvlKh90yAtGNpV9Ry+ZpcFAAAAALUWQR7XdOr8RT09Z4dyC4rVJqyePhgWo+BAP7PLAgAAAIBajSCPMuUVXlLC3B367vxF3RFSV4uGxahRXX+zywIAAACAWo8gj6ucKyrRM3N36OjZIjVrEKjFw2MUGhRgdlkAAAAAABHkcYX8i5c18L0dOpB7QWFBDn343H1q2iDQ7LIAAAAAAP8fQR4eF4p/0pD5adp3qkC31fXX4uH36fbb6phdFgAAAADgfyDIQ5J0scSl4Qt3aveJ8woO9NOi4TFqHVrP7LIAAAAAAFcgyEPFP7n0/KJ0bT9yTvUcvnr/2a5q6wwyuywAAAAAQBkI8rXcZZdbY/6xW5sPnFGgn13zh3ZRVHgDs8sCAAAAAFwDQb4Wc7kNjVu2R599kyt/Xx/NHdxZXVo2MrssAAAAAEA5CPK1lNttKPmjvfrvPafkZ7dp1jOd1L11iNllAQAAAACugyBfCxmGoUn/vU/L00/Kxyb99al79dDdYWaXBQAAAACoAIJ8LWMYht749Fu9v+24bDbp7X5R6n2P0+yyAAAAAAAVRJCvZf6SelCzNx+RJP2x7z16/N7mJlcEAAAAAKgMgnwtMmvTYf15w0FJ0qv/1k5Px9xuckUAAAAAgMoiyNcSC7ce0xuffitJ+l18hJ69/w6TKwIAAAAAVAVBvhZYtjNbE1ftkySNeai1Eh9sbXJFAAAAAICqIsjXcB9nfKfx/3evJGn4/XdoXK82JlcEAAAAALgRBPkabO3XORq3bI8MQ0qIuV2/79NWNpvN7LIAAAAAADeAIF9DbdyfpzEf7pLLbejfOzXT6491IMQDAAAAQA1AkK+Bth3+Xs9/kK7LLkN97nHqv56IlI8PIR4AAAAAagKCfA2TfvwHDVu4U8U/uRXXNlTT+3eUr502AwAAAEBNYXrCmzFjhlq2bKmAgADFxMQoLS2t3PHnz59XYmKinE6nHA6H2rRpozVr1tzQPmuKzJP5GjIvTT+WuPSru0L0ztOd5O9reosBAAAAADeRqSlv6dKlGjdunCZOnKhdu3YpKipK8fHxysvLK3N8SUmJevXqpWPHjiklJUX79+/XnDlz1KxZsyrvs6bYn1OogfN2qLD4J3Vt2UjvDuysAD+72WUBAAAAAG4ym2EYhlm/PCYmRl26dNE777wjSXK73QoPD9eYMWOUnJx81fhZs2bpzTff1Lfffis/P7+bss+yFBQUKDg4WPn5+QoKCqri0XnPkTMX1G/2dp29UKyo8AZaNKyr6geU/e8DAAAAAKh+KpNDTbsiX1JSovT0dMXFxf1SjI+P4uLitG3btjK3WbVqlWJjY5WYmKiwsDB16NBBU6ZMkcvlqvI+Jam4uFgFBQWlFqvIPvejEubu0NkLxWrnDNL7QwnxAAAAAFCTmRbkz549K5fLpbCwsFLrw8LClJOTU+Y2R44cUUpKilwul9asWaNXXnlFb7/9tiZPnlzlfUrS1KlTFRwc7FnCw8Nv8Oi843T+RT09d7tO519S69B6+mBYVwXXIcQDAAAAQE1mqSehud1uhYaG6t1331V0dLT69++v3//+95o1a9YN7XfChAnKz8/3LNnZ2Tep4lvn0mWXnpm7Q9nnLqrFbXW0eHiMbqvnMLssAAAAAMAt5mvWLw4JCZHdbldubm6p9bm5uWrSpEmZ2zidTvn5+clu/+Uhbm3btlVOTo5KSkqqtE9JcjgccjisFYID/Owa2v0Ozdp0WIuHxygsKMDskgAAAAAAXmDaFXl/f39FR0crNTXVs87tdis1NVWxsbFlbtO9e3cdOnRIbrfbs+7AgQNyOp3y9/ev0j6t7Jn7Wmj9Cz3UvGEds0sBAAAAAHiJqV+tHzdunObMmaOFCxcqKytLo0aNUlFRkYYOHSpJGjRokCZMmOAZP2rUKJ07d05JSUk6cOCAVq9erSlTpigxMbHC+6xpAv35E3MAAAAAUJuY9tV6Serfv7/OnDmjV199VTk5OerYsaPWrl3reVjdiRMn5OPzy2cN4eHhWrdunV544QVFRkaqWbNmSkpK0vjx4yu8TwAAAAAArMzUvyNfXVnt78gDAAAAAKzNEn9HHgAAAAAAVB5BHgAAAAAACyHIAwAAAABgIQR5AAAAAAAshCAPAAAAAICFEOQBAAAAALAQgjwAAAAAABZCkAcAAAAAwEII8gAAAAAAWAhBHgAAAAAACyHIAwAAAABgIQR5AAAAAAAshCAPAAAAAICFEOQBAAAAALAQgjwAAAAAABbia3YB1ZFhGJKkgoICkysBAAAAANQGP+fPn/NoeQjyZSgsLJQkhYeHm1wJAAAAAKA2KSwsVHBwcLljbEZF4n4t43a7derUKdWvX182m83scq6poKBA4eHhys7OVlBQkNnl4BrokzXQp+qPHlkDfbIG+lT90SNroE/WYJU+GYahwsJCNW3aVD4+5d8FzxX5Mvj4+Kh58+Zml1FhQUFB1fo/JP6FPlkDfar+6JE10CdroE/VHz2yBvpkDVbo0/WuxP+Mh90BAAAAAGAhBHkAAAAAACyEIG9hDodDEydOlMPhMLsUlIM+WQN9qv7okTXQJ2ugT9UfPbIG+mQNNbFPPOwOAAAAAAAL4Yo8AAAAAAAWQpAHAAAAAMBCCPIAAAAAAFgIQR4AAAAAAAshyFdzM2bMUMuWLRUQEKCYmBilpaWVO3758uW6++67FRAQoHvuuUdr1qzxUqW1W2X6tGDBAtlstlJLQECAF6utfTZv3qxHH31UTZs2lc1m08qVK6+7zcaNG9WpUyc5HA61bt1aCxYsuOV11naV7dPGjRuvmks2m005OTneKbgWmjp1qrp06aL69esrNDRUffv21f79+6+7Hecm76pKnzg3ed/MmTMVGRmpoKAgBQUFKTY2Vp9++mm52zCXvKuyPWIeVQ9vvPGGbDabxo4dW+44q88ngnw1tnTpUo0bN04TJ07Url27FBUVpfj4eOXl5ZU5fuvWrRowYICGDRum3bt3q2/fvurbt6++/vprL1deu1S2T5IUFBSk06dPe5bjx497seLap6ioSFFRUZoxY0aFxh89elR9+vTRgw8+qIyMDI0dO1bDhw/XunXrbnGltVtl+/Sz/fv3l5pPoaGht6hCbNq0SYmJidq+fbvWr1+vy5cv6ze/+Y2KioquuQ3nJu+rSp8kzk3e1rx5c73xxhtKT0/XV199pYceekiPPfaY9u3bV+Z45pL3VbZHEvPIbDt37tTs2bMVGRlZ7rgaMZ8MVFtdu3Y1EhMTPa9dLpfRtGlTY+rUqWWO79evn9GnT59S62JiYoznn3/+ltZZ21W2T/PnzzeCg4O9VB2uJMlYsWJFuWNeeuklo3379qXW9e/f34iPj7+FleF/qkifvvjiC0OS8cMPP3ilJlwtLy/PkGRs2rTpmmM4N5mvIn3i3FQ9NGzY0Jg7d26Z7zGXqofyesQ8MldhYaFx1113GevXrzd69OhhJCUlXXNsTZhPXJGvpkpKSpSenq64uDjPOh8fH8XFxWnbtm1lbrNt27ZS4yUpPj7+muNx46rSJ0m6cOGCWrRoofDw8Ot+sgvvYy5ZS8eOHeV0OtWrVy9t2bLF7HJqlfz8fElSo0aNrjmG+WS+ivRJ4txkJpfLpSVLlqioqEixsbFljmEumasiPZKYR2ZKTExUnz59rponZakJ84kgX02dPXtWLpdLYWFhpdaHhYVd8/7PnJycSo3HjatKnyIiIjRv3jx9/PHHWrRokdxut7p166aTJ096o2RUwLXmUkFBgS5evGhSVbiS0+nUrFmz9NFHH+mjjz5SeHi4HnjgAe3atcvs0moFt9utsWPHqnv37urQocM1x3FuMldF+8S5yRyZmZmqV6+eHA6HRo4cqRUrVqhdu3ZljmUumaMyPWIemWfJkiXatWuXpk6dWqHxNWE++ZpdAFDbxMbGlvokt1u3bmrbtq1mz56t119/3cTKAGuJiIhQRESE53W3bt10+PBhTZ8+XR988IGJldUOiYmJ+vrrr/Xll1+aXQrKUdE+cW4yR0REhDIyMpSfn6+UlBQNHjxYmzZtumZQhPdVpkfMI3NkZ2crKSlJ69evr1UPFyTIV1MhISGy2+3Kzc0ttT43N1dNmjQpc5smTZpUajxuXFX6dCU/Pz/de++9OnTo0K0oEVVwrbkUFBSkwMBAk6pCRXTt2pVg6QWjR4/WJ598os2bN6t58+bljuXcZJ7K9OlKnJu8w9/fX61bt5YkRUdHa+fOnfrLX/6i2bNnXzWWuWSOyvToSswj70hPT1deXp46derkWedyubR582a98847Ki4ult1uL7VNTZhPfLW+mvL391d0dLRSU1M969xut1JTU695X05sbGyp8ZK0fv36cu/jwY2pSp+u5HK5lJmZKafTeavKRCUxl6wrIyODuXQLGYah0aNHa8WKFfr88891xx13XHcb5pP3VaVPV+LcZA63263i4uIy32MuVQ/l9ehKzCPv6NmzpzIzM5WRkeFZOnfurISEBGVkZFwV4qUaMp/Mftoerm3JkiWGw+EwFixYYHzzzTfGiBEjjAYNGhg5OTmGYRjGwIEDjeTkZM/4LVu2GL6+vsZbb71lZGVlGRMnTjT8/PyMzMxMsw6hVqhsn1577TVj3bp1xuHDh4309HTjqaeeMgICAox9+/aZdQg1XmFhobF7925j9+7dhiRj2rRpxu7du43jx48bhmEYycnJxsCBAz3jjxw5YtSpU8f43e9+Z2RlZRkzZsww7Ha7sXbtWrMOoVaobJ+mT59urFy50jh48KCRmZlpJCUlGT4+PsaGDRvMOoQab9SoUUZwcLCxceNG4/Tp057lxx9/9Izh3GS+qvSJc5P3JScnG5s2bTKOHj1q7N2710hOTjZsNpvx2WefGYbBXKoOKtsj5lH1ceVT62vifCLIV3N/+9vfjNtvv93w9/c3unbtamzfvt3zXo8ePYzBgweXGr9s2TKjTZs2hr+/v9G+fXtj9erVXq64dqpMn8aOHesZGxYWZjzyyCPGrl27TKi69vj5z5Rdufzcl8GDBxs9evS4apuOHTsa/v7+RqtWrYz58+d7ve7aprJ9+tOf/mTceeedRkBAgNGoUSPjgQceMD7//HNziq8lyuqPpFLzg3OT+arSJ85N3vfss88aLVq0MPz9/Y3GjRsbPXv29AREw2AuVQeV7RHzqPq4MsjXxPlkMwzD8N71fwAAAAAAcCO4Rx4AAAAAAAshyAMAAAAAYCEEeQAAAAAALIQgDwAAAACAhRDkAQAAAACwEII8AAAAAAAWQpAHAAAAAMBCCPIAAAAAAFgIQR4AAJjOZrNp5cqVZpcBAIAlEOQBAKjlhgwZIpvNdtXy8MMPm10aAAAog6/ZBQAAAPM9/PDDmj9/fql1DofDpGoAAEB5uCIPAADkcDjUpEmTUkvDhg0l/etr7zNnzlTv3r0VGBioVq1aKSUlpdT2mZmZeuihhxQYGKjbbrtNI0aM0IULF0qNmTdvntq3by+HwyGn06nRo0eXev/s2bN6/PHHVadOHd11111atWrVrT1oAAAsiiAPAACu65VXXtETTzyhPXv2KCEhQU899ZSysrIkSUVFRYqPj1fDhg21c+dOLV++XBs2bCgV1GfOnKnExESNGDFCmZmZWrVqlVq3bl3qd7z22mvq16+f9u7dq0ceeUQJCQk6d+6cV48TAAArsBmGYZhdBAAAMM+QIUO0aNEiBQQElFr/8ssv6+WXX5bNZtPIkSM1c+ZMz3v33XefOnXqpL///e+aM2eOxo8fr+zsbNWtW1eStGbNGj366KM6deqUwsLC1KxZMw0dOlSTJ08uswabzaY//OEPev311yX968OBevXq6dNPP+VefQAArsA98gAAQA8++GCpoC5JjRo18vwcGxtb6r3Y2FhlZGRIkrKyshQVFeUJ8ZLUvXt3ud1u7d+/XzabTadOnVLPnj3LrSEyMtLzc926dRUUFKS8vLyqHhIAADUWQR4AAKhu3bpXfdX9ZgkMDKzQOD8/v1KvbTab3G73rSgJAABL4x55AABwXdu3b7/qddu2bSVJbdu21Z49e1RUVOR5f8uWLfLx8VFERITq16+vli1bKjU11as1AwBQU3FFHgAAqLi4WDk5OaXW+fr6KiQkRJK0fPlyde7cWffff78WL16stLQ0vffee5KkhIQETZw4UYMHD9akSZN05swZjRkzRgMHDlRYWJgkadKkSRo5cqRCQ0PVu3dvFRYWasuWLRozZox3DxQAgBqAIA8AALR27Vo5nc5S6yIiIvTtt99K+tcT5ZcsWaLf/va3cjqd+vDDD9WuXTtJUp06dbRu3TolJSWpS5cuqlOnjp544glNmzbNs6/Bgwfr0qVLmj59ul588UWFhIToySef9N4BAgBQg/DUegAAUC6bzaYVK1aob9++ZpcCAADEPfIAAAAAAFgKQR4AAAAAAAvhHnkAAFAu7sIDAKB64Yo8AAAAAAAWQpAHAAAAAMBCCPIAAAAAAFgIQR4AAAAAAAshyAMAAAAAYCEEeQAAAAAALIQgDwAAAACAhRDkAQAAAACwkP8HAbDnzdCmLv8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from tensorflow.keras.models import load_model\n",
        "# model2 = build_model(densenet)\n",
        "# model2.compile()\n",
        "# # Load the model\n",
        "# model2.load_weights('/kaggle/working/DenseNetNoW.weights.h5')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-23T17:17:47.587760Z",
          "iopub.execute_input": "2024-04-23T17:17:47.588050Z",
          "iopub.status.idle": "2024-04-23T17:17:47.592505Z",
          "shell.execute_reply.started": "2024-04-23T17:17:47.588024Z",
          "shell.execute_reply": "2024-04-23T17:17:47.591520Z"
        },
        "trusted": true,
        "id": "0oRGeHT3AeQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from tensorflow.keras.models import load_model\n",
        "\n",
        "\n",
        "# model2 = build_model(densenet)\n",
        "\n",
        "# # Load the model\n",
        "# model2.load_weights('DenseNetImagenet.model.keras')"
      ],
      "metadata": {
        "id": "U84IWlpaAeQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Valid"
      ],
      "metadata": {
        "id": "pm-7f1gpA0gE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy, auc, precision, recall = model.evaluate(test_flow)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxpN5II2Q0X5",
        "outputId": "82af29c9-357a-4388-d545-ed55ed5ff53e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 47s 296ms/step - loss: 0.1773 - accuracy: 0.9283 - auc_1: 0.9810 - precision_1: 0.9267 - recall_1: 0.9302\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/VGGImagenet.model.keras /content/drive/MyDrive/UPC/TER"
      ],
      "metadata": {
        "id": "v77czOmmViij"
      },
      "execution_count": 54,
      "outputs": []
    }
  ]
}