kcf_ai.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "A100",
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# 데이터셋 경로 설정 (./archive에 있는 데이터셋)\n",
        "data_dir = './archive/'\n",
        "\n",
        "# 데이터 전처리 설정\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "# trainset 설정 (train 데이터 로드)\n",
        "trainset = torchvision.datasets.ImageFolder(root=data_dir + 'train', transform=transform)\n",
        "\n",
        "# trainloader 설정\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)\n",
        "\n",
        "# testset 설정 (test 데이터 로드)\n",
        "testset = torchvision.datasets.ImageFolder(root=data_dir + 'test', transform=transform)\n",
        "\n",
        "# testloader 설정\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)\n",
        "\n",
        "# 클래스 레이블 설정\n",
        "classes = ('Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral')\n"
      ],
      "metadata": {
        "id": "kypKlwN10CHc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "c2762629-adf1-46a2-eeb0-f373e33362dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-a320659b6b40>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# trainset 설정 (train 데이터 로드)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtrainset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImageFolder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_dir\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform\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     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# trainloader 설정\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, transform, target_transform, loader, is_valid_file)\u001b[0m\n\u001b[1;32m    307\u001b[0m         \u001b[0mis_valid_file\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\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[0m\n\u001b[1;32m    308\u001b[0m     ):\n\u001b[0;32m--> 309\u001b[0;31m         super().__init__(\n\u001b[0m\u001b[1;32m    310\u001b[0m             \u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, loader, extensions, transform, target_transform, is_valid_file)\u001b[0m\n\u001b[1;32m    142\u001b[0m     ) -> None:\n\u001b[1;32m    143\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_transform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget_transform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m         \u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_to_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\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    145\u001b[0m         \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_to_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextensions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_valid_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mfind_classes\u001b[0;34m(self, directory)\u001b[0m\n\u001b[1;32m    216\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m \u001b[0mof\u001b[0m \u001b[0mall\u001b[0m \u001b[0mclasses\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdictionary\u001b[0m \u001b[0mmapping\u001b[0m \u001b[0meach\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mto\u001b[0m \u001b[0man\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m         \"\"\"\n\u001b[0;32m--> 218\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfind_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\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    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\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/usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mfind_classes\u001b[0;34m(directory)\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0mSee\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;32mclass\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDatasetFolder\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdetails\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \"\"\"\n\u001b[0;32m---> 40\u001b[0;31m     \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mentry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscandir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mentry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_dir\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[0m\u001b[1;32m     41\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Couldn't find any class folder in {directory}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './archive/train'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "y97TCd7Y5gBB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# 데이터셋 경로 설정 (./archive에 있는 데이터셋)\n",
        "data_dir = './'\n",
        "\n",
        "# 데이터 전처리 설정\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])\n",
        "\n",
        "# trainset 설정 (train 데이터 로드)\n",
        "trainset = torchvision.datasets.ImageFolder(root=data_dir + 'train', transform=transform)\n",
        "\n",
        "# trainloader 설정\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)\n",
        "\n",
        "# 클래스 레이블 설정\n",
        "classes = ('Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral')\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = next(dataiter)\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "1QcArqjy0Tnh",
        "outputId": "ae5d2fca-55cf-4e70-c77b-e79873c83b88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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BaxMRAPlyE/Wn369ESyUsa3zcE9ec4fOZXGR6xQ2x5w65znUnUGVFRG0MxPOdnZ2Ynp6uRSgVfRG44JIoI5/T09O1ucb7+/1+zM7O1pKa1RbKq/rAZ8fHx2Nrayt2d3dLfzRv3RZIL0xMTMTU1FTMzMyUCKMiWso707IOQZHretkoyip5yITcS9Htl0PHAqCI3EMg0Vg3KUsXWDfoVJT0uDPA0+ZRNSn5pjbyN/874kAZ6u82gEJw0ARS+ByVceax+ce9M++fG7UrFWDnp//GfjUBG28vk4NF3DlFhRdx2BjRW5GxkMJhIhlBW9NyDpWbwIQSD10u1RbndQZuCSY9upR9nI9t8kmeNv3WVE4T0bAzEVbJsFLM+sgT9SWdpnawPe4c+L1Zv1zW3Gg6sHGFz4/GkPOH7crqI2VAMRvLJrDfVCbrbwKk/qxkWOOlshygsI42anNQmnQSeSbgwfmT2QPWwQgcoyBNc01zXeVRPzNKrt/9WkR9xxIjHUqgV0RVOprpBNnuM31UFkEc25jN60x/O2+df5lDcy3o2AAUKtzM0OqbDNTgOgJsMrwyDNxhQQMjpEqlyDa1eRsRB8oq26fO9rtRzyZi1o+s/mxyEpAwpJm1mXxgX52vuk/l0GNz3ngdTk3G0aNdmeGhwmTol33XWRosWwpgamqq7ByJiKJEFOpVYrSWbeTFV1UV8/PzsbW1VXIj2F7J197eXqyurtbO81CbB4NB8YLEb2X77+zsFB50OheXiZQXpax+3aP7aADpgblXrX45T5sMdQaA+fdRERM+I7nQmvvq6mr5rKyslJ0OTIrlFnFGTdyIZkZXylr1ygnhUoC3nUaAS3w+vqqLc4flaqdXllegdvlOG+cTc+AYCXO+crw4bv57E2X8dABEmep2u3HhwoWYmpqKkydP1o54YDuGrd/rEy85HzMD7ePg/c92VSoCx91QPF5CEdW5ubmYnp4uc5T2heWpzQJtkm8C2G73YMeSnB3Og/Pnz8f6+npsb2/H6upqSXjf3d0ty9AEUQIjk5OTJXeSeltymEXis3HifSLq/kuNjF8uHRuAEnEYxWcMJpDh/7rmyN4F2ycPETPBClG0h1q9LZlXRc/KKWtj9r8rTRpq/s7+ez2+VduJwMP76gBFqF3tkCDTWB6lnNomjdqclcU2MdfCt0VLEekZ9whUtkL0CpMqVC9+zc7OFkOlhLSZmZkCYAh8qBB3d3djbW0tNjY2aqFdAkUpH7VRORnqv7wv8dmX1TIPiICE/SZAojF3/rosubHi9WHG1NugnUqbm5uxvr5e8k8EuqSUBUp0wGPWT9bhkU93YthX7xN56UaRTkbGd1EGfKT8qS88CuP9oXw3RcUyckfDdUDbfHSQkv3NLda9Xi/29vaGOub8KD2Q9Ym614EJPxmgdN1LB1YbIba3t2Nzc7PMQ9WnZSudsUOA4u1kXb7sQ4Ciea7ytZykiKH0Sq/XKzpAbeQuNfZT17UNnO2hXm4CJ87rbDxc/w8LNi+XjgVA4STMwEkmzMMqSvfyeS+BCLcey4BkRpvr0USbHFwqx8yTypCpgxz9zXZmYTcCMC+XCrtJYKmcva/MYo+IWjsyxdE2Dl7nMGDG74+oe3Y0AjJwMu4KpaoPuo/jqp06iqpwCzrHXqBE3g/7zHLFSxlajonapJwKKSvJlOcoSPk5kPEQtc8dnz8ZuKBRp5GnfDfJDZV20/izPoEjeYdbW1uxublZIkwaj07nIHqpcDydkQxAuVwQNJMfut5UlgNxr0O/sZ0uu6qTYJ9/04Dq/8xI0Hv1uX4UYMnkQNez372OtrHUnMtA0DCUyWlbtJzXBUKY4+LLNPz2eSEZVE6ZIqiKoAiY6ORjRjBc17IugR/aA/KR5/Iob01RNoE8RVZ2d3djcnIytre3awCN37706fbGZULt9Wgyx0T9yeb6tQYnEccEoEQcPrAmIp9QTZQx1yehC5AM0dTUVCwtLRXBlALKJjyVprc7Q58ykHye11zRZsZek6nJEBE0eTvcc854xonOdjtAYRk+gdrKz64PC1Bo+Mk/ndQqkvHXBFYkheBR46TJrdCqkv9mZ2fLqZk8oZJbA5WPwv7z7/Hx8bJM0e/3Y3NzMyKiRGEWFhZicXGxJN3u7OyUNjOkrjNaSFweoBL2ZQMaPpeZzDDreqaoMhmlLLcZNUVOtre3Y2NjI/r9fqysrJR+q355mPJcPceAskDKlmAE7iibVXVwZD4NmIMwyrLzodPplIiqQCjBhJZvBJZdvgma6FBwB4nazoRb1e0y3KTvqCczoJIZNd6XzXXJinafXcnuDkZB2R+vk7KlcWMCuQNN/5t9UT0CAXQyuH19eXk55ufnS2SVY+i6jqChiRw0aYlHSbJzc3Ml0XZ3dzd6vd6how18p6H4oOg4z3HyHafkpcuN94F6RM+MAAooU6CO/P16RJ6/4YDCFTQHRwPkiUcRh5eRWK+DEk54X07w9vg1tt+9Kgpk1i8qYYaRXVEdBRacf+xv27JZZsx0TxM1jeVRv/EaFZSDpab+0aPg0odAh+dwUHk2KXQfZ28jt5rS8Lr36uDAPaIm+RomFHspysb70DaOw5QlHmiJh1Eglc852AZ4m/TDUX1wHcByMvnw+jKlznkccTgh1o1l9t1Ut/fLgWH2d6Yjs3FvkmPWk9XJOXM5hsvnjF9rekZtyXSkt9c/kiUCIS276Bk5pYrccecN6SiA0jZPvJ16TlEVRX4ZFWbu1DB6jvzMdFTbXMlA3lNFxwKg+MRyj+go5vKj+z1yQSUoL03LOTx+2df/Ig4rFbaJ4Vj31quqOnRSKYnlepIT7+HR7exTk0IiYFL9TQLt/eJ7h1wR0GjzmgM0Rjuaxi2LhrGcDBz4rqtOp1PCs/QC6OG5l72zsxNPPvlkbQzl1VRVFefPny+Huc3OzpYyOBaUDUVp5P3v7e3VzvfY3NyM8fHxEpXp9Xpx4cKFmmxERPHI5Wn5KbdsB3cB6L7BYHAoETqTERIVvss2+5gZMTfQGemE262trVhbWyv80FZPhdG5fdXnnrc3M6L8LTP0TFJm7oHmVbZ9Oeuz5qjuFyjhnCDvNGeZh6SyGQVzR4l1ZctPTUBZ35l+0HN89Ye3NXuG8qclyWEiKJnjQr3MCAqjKtI7ilZRRgkGMrlTP/ThOOnDpVeNjZZ1Z2dnS/IpnQmW7ZTpQ10nmGP/Of5KuFdUhRE0L9fLyngs3U1ny2WP5LlW3Bn0VNCxAChOwyI/kSumLHRI5CpAInAiA0dFQY8383QoEEySdA+cCJ6GxstknT4R2gCK/+3gyZOnmpQXy8gUkCtV8dWfHcYrOup3gjneTy9Vij2bVBoHeUQsWwaz0zlYOtOE7nQuHg+tZxm5ci9G5XHnj5ZplIynd+Eo4W5sbCw2NjbKO0s05jzThZE5KRl6U1q6Uj4MD45rMrBtPM8Avo9n0/gdpcTUByUB6x0hNEL61lhkuwvYzqP+9+dcpqncM2DQRCw7WwqSzGb6wkGA5lHTPZLtbBnDx6cpetY0Ng4wCOCzOvgcZTOrL9ML2T3UkfzoOvnaNAZZH6njKV8EWd6fbrdbO03cd3A2AeKmOeBAwfvJ/6UD5Jx0Oge7kLi5wXnmB0W67WvKYcrkxfW567drDVSOFUAZVqlG5EmlGWgRwxm+owC5AmCSXqZE3NNQCF8C50LoibYR+VkXuuYKmu3IAAnJl5246ybjs5dBYKWkMgIv9jczBl7m5Qo3+UgFmk0c7ujhi830LYDCbbb6eMIpvTgejy0AykiNymJWvo5qv3DhQmxsbBTQwvcBKerR7R68kIseD9tJg0bFpLeoqs/ihecpUYbcG3TZY79cTtqozUgqKXZra6sAFI4pz3Rokym2JTNamfwM005GUzxU77zhPCQYoSzxOYINzcUMxJD3HkFxo8+52QYe24y4e8cExbzWBvYyagOVmV7nkhHnMPvgz7IPbW0gbx186cM5xq3NGR/928ee9fPvzNmkHhUPVFa3263xw3UVl0adl9mHzzeRns9sTtNS0tWkYwVQIvJJ4BPOmcnn9Du9IwmhEh/pDRCkyKAp9KyyaSDd0+KJfL5P3tvOaxnA8roo3A4qPLTov/l9GbjJvBiGozVRdAw5ee85EyzfQ99Nfc+IvxM8uPLQ3zzbRn/Pzc3VomMRUVsuUb+lHDTuUgJjY2PlLaVKXhsMBjEzM1NObuQR9ooIKLrx5JNPxubmZiwtLcXJkydL2FjladlHa888BZZHZnMnksCQgJMiPVS6elcMx4p8dYXDCJTqIkj1Mc6oSQkKSG1sbESv1ytJstqWzndiDXMYm8qkjGRzqAnQe4hb/VYuAEGIy2KWe+BlKxKXOR2ql23i74yeSSb9aH8CXI6POyVse8Y7N5gEvuyzL2X63GmiDAx4GZJRnfmhHXSZ16/nnG9HAVrqDc8tIfCRHLCNzq82sJbxwscs01/UsZq3mS3RNSXME9B4OdKBfhZP1ka3BWy354NdSzp2AIXkE24Y46/rPlEimpNeXeiHmegEL2409d00gZrAicrLJp8LUlZHBkKyvjeBFdbvZWR9ccOQ8SmjjM9tSknPZO2WwSNAEchQJCSinofg9WYGRWBBHxkhKld5s1IOqptrz+In2yZvm9/cpqrnaIBUr55Xv+j569NmRLKxdHnMZM/HaxgSf5zvbryy9jQpVrYzI3+mrY62dnB+N5Xh5TQt2zTxt+mjsZc8+DJjNn/aqI2Xw1CTXFzJ/e4YNS37ZG1vkx3+znwUX75XGwiMM33neiGrJ6L5PXH6jXbC9Q+X8/S3j/lR1Absmn7Prrfp9GtBxw6gNBlbV1qcwC4kEkRRVVXF0FAIlTCrNXBFWGQ4WB4VEH9TkhXbnw14ZjQyoWkS9AxIsHze40LZ9PFoAstlVIBRimFBYhOJ74o+eMgyU1JZ/7ilWoecqfytra3Y2dkp23xptAnaRHqtPNeg+/1+9Hq9WrRka2ur1g7xS2MufmrLsxJ4q+pi8m1VVXHhwoXo9Xq1sL6e03Z3Xtva2irvAlJeiyIonc7BiwkVCWCOAfnVpPDUbl1jzo7z3ceRRiCTAy3xKAKnevi6ADce9DTdA1QdnkhOeW4CDFzO8frYB/eYs2vOW/VJ7VX0gfdKRtg2LkWqX1pu8DwEyRqNqtrXNEf8/2GBShOQ8zEa5nmWQdK8YgRFYFZ9VH6Gl8Ox4t/ika7xvVoaEyZEuy7P2uyyTT5mzkEboHH9RR2StSOLQLeVyXmiOjxRN3vedf9R+VhXk44dQGmiLPrRhHozoXLDR4VBJaUB4+8RUZSGKy8pmEwwCbYyzz0DLZnHpfu972338P8MmFCYPWeG31IWNGJt7fS+Z9f5rLc3azfJ+5Dt7NHOGkZVaNw4AfWcv2NHO1C47EIjq2e1PCBgQIOl8gSilCyqF3GxLVVVlePeJyYmaktHKpsRHSXm6gRcyp/LRMZ3yjeNOwECyxh2PElqMyMoGXjIQIDLK+vK5nAmRy4XWV3UFW5osg/LlacbceAUKaJHUOjAyQ0ZdQyfJVBtWmbKqE0PZGPLPnk5/lub8SVl/PL7Jdf85vKwj2s2hg5WMsCbgdK2sfX6yGsH9E0y5X3V8778nkXM23RkG2W60u2m909tauL5taZjA1CyycMBcsXkz/I3Kg5RlqDG9W+f1DJaGdCJOLwWSMGUcVI5+vZogQs+++z1sl+ZMnDeZbwhD8gTlstrmbHIyJF726R3o+P9IB/FY0ZLCExcVhy4ScE3TX4qBwEE5a3IexVAobFVxEXRJe62Yf83NzfLb/1+v5wOy3VlgZyJiYno9/uxsbER4+Pj5f08yjthvgyNqtrJyIeAGZObm5SfzzEfs6Zx9PFsUo5qB+tqqpdj4WVldVKhZv2iDsh+z4Bwk5zwGue8fpesSTY0vgQs1B36ZhmMjCjXiTziORmaBwTBPsd9Xkbk5+t4/11Psa9NOqBprjfJjtrK/IlsGXBYIh+lt+k4ZhGyJkDB/ri8tOk13u+yxf7S2fEtwT4WR7VZz6kcn/e6x8eNjkg2xm36/mrSsQIoTYbXf29SYByUDMRExCEB9p09EgYt/6hcTSQZBJVHL13Pclspk7K8nexPmzDo9wzQZP11vqlOleNLAJwcfDkYBbYtr8FBWgayeK8rIv5GsKh2KuxLJcwzRHwic1tu03kCAgaKjOmV7AIpov39/QIuFP3QkdidTqecLkvvT8mvSgyVAiFYUvLxYDAoZ65UVRVra2sxNjZWOw6ez3JLclVVNdDEsqmoXMk1yYgDvEwhX4rSEl89CTOTjQxEen1sp0clMpDsu3McpGRAR/cO62VLXrlMI8CgvvOdXnrGvyX7WipiPyKiGB+VzXv4oaH3Mtw5cL3k/OU18fMoOgpUcLwY5fQk2Taet5WrZTIa8TZw0gRQsv6o3a6HMx6rf9SvdGLo1DD1wImyoTawftolBykOjlg+eesRSl9tuJZ0bABKpmgyo+KedxOwaTLg3W63hOUVUudZKHxFthSLvCAaXzfKNKhcy/OJQQGnAmlbb8w8EeeV97NJwTO65Ms2mdJ3ofb6LkeI25RQ5m27EnZDqu+MD85fKveIesi607mYv7K3t1fLRxGg1YvtqHw8gsJytTtH97HP7k0J5Gr3kHJNWBflLVOSVERtCqZtHCmXV0pUzln92f38bhrTpue8DvGKjgj/HtaYtoG67F5f9hkMBrUzb2igJTcq0yOX3CqvZ7kckhnDbO429UnXMqfJnzuKsnua5Ijyyk8214ctk+Xq7yZdRd2b6demsv07my90pLy9rnckG1nUirZCsqRnWW/Th/Wxz86Ltr5eazoWAEUTl4lmGpCmQWBYjB5zpkwiDpTF5ORkLC4uxuTkZNxwww2xvLxcXhQl8MKkw4j8RWC6xuO7lVSpN2ZOTEzE4uJiOWWUb7EVuaA0oejsd59YFH4HKFQArghoXH2iOfLmUlVmFFlnk3KhhycFrJwXJaSyDwQXfE7t8bwJ95A0Tq4I9c3ThBXBuOmmm+LUqVMxPj4eJ06cqEWYqqoq79hRbomX633N8lfkbWspICJiZWUlIuKQB9TpdEpCt5JwB4NBSUDVzh6OFw0WQbZ78AwJE2RJ5i/FkyWx76LM8ZD8efTNx3UYpUmQwDe/zs7Olqgoz71o6lvWDsqTgwCvV/fs7+/XthHrUC69F0bbTLX8x51iAig6El1bwZWjxJ1pOo+nCQTrunSaAwRd87lMeRmWMh3nJB6MjY3VXjXBhHz35t2hUjkEWFlEm7aEY6rnPMLCPmS62XWd969JTslL2TrZG9e7XC7mUiCjyjoLiXaQMpH1NdOlPm4ZsLpWdGwACgdPQuc7cTJBda+hSZGqDr7KfXp6uvb2ym63e+jY+4gDo6z7IqJmcNwbFmDZ398vO3ykGNSWrH2OxrM++e9enk808q7J66ZSywyI3+O/6X/W10ZsD8dFyiKb8F4Xn80AivNFE5gTOtv+qkPUlpeXI+LgxXBVVdWOhZYx4DZkL1ftI0ARcfnPQ7/ed7WNr1j3ueHGiHzzv503TXz232igh6FMfrL6fQ47DTO3vW80THwDrPJ9mnjA695eXte4+Xzk/1T0vnSj37XEqP991wWBAo0Q54DLXDYO3o8mw0odm/W7jThGR8kJZdbn5VFy7H3K2p7pKj7HSDJ/4zMuo8O0JZtnTbKjsfdnCCK4hMjUBG9r9mEb+J3xj31u0gHXgo4NQJG344o8oi4InMAaLIZPPZ9EXur09HT53HDDDWVL5+zsbCmHSNxD9hEHp4Dqd7Un4qLBmZmZiampqRLaj4jY2NiIiIher1eWkXRwF19c5YLfphQyhezfPtEyYMPns0lEBU907cqAaLzJIHIsswmkcfOthgIvmsweveHf+o1LIGqbPM7M0/UQa7fbjc3NzRJNYX7H/Px8eX5/fz96vV6JpqgdqovkSb3aVsmdBqrb+aVntX06GzeBJfY5G1s+m8keeTSMkWlTZjQGNBwytlSGDmRc6bMvPEgqO9BM80rzXYmrGsvsGX249TcD7Rk1/c72yktmDgkTn8UfPx7dxyei7olLbpVvpbrIX0aZqRfYzqYorTsCTUtjmT7K+MH7BbY8h6LT6RSg7uBFfSZv9a3x8zFkYrkvu7NPRy37DQNMHFTqm1G4DMA6QPHymFemcjwiquvMvXPi8hD7kP1/FCC9GnQsAIqMBQWWipYDSg/TAYqfISAwMD4+HvPz8zE3Nxezs7Nx4403xtTUVAEUmcFV+RRw1u2TZWxsLObm5qKqLp74qZ0YKysrNUM0OTkZCwsL5SVRPEMlIvd2MpKgt4ET3sf/M/6710HQIIXuisKBgZ7zcXPib24AuFQhhcOj4n1y+w6fiKidqMrj4RUF45kI3l7xYXNzM1ZXV2v16yWC4hmX9iQDg8HBjp2jjIzkQrlQBA16hm1T0qwMLvk3GAwKeCGQawrPUyG78tdzGn/KGdt2FPmcoofP8eR93N7u9dDIEKDQERE/6TBMTk4WgMJTa90wueESn9qoySkQz3y5lfImgMJcNy5rZxERGqaIqBlh8Y5giE6UO0BuSDNQSt44kGka72H4RODkIIUARUbZI000/sMAFL/myz8sq6kvTQ4X7+e4MXGazoC+Xe84Hzl3mEyrtmagkXPB+0Gd3tS3pwKUkI4FQHEl5kwVZVnkHCS+Q0dCoSUceVNKfvT8liY0q3s4sP5x4SLAcW9NCspPIG2b+M4H1ZMpr4y3+o3KJwM4TZ+sPB+r7L6jlJWel7H25D/dq/HMPAsRgYTGmHKl5Fe2l0rXZa6qqlokh89VVVVe2Ee58ege+U2Phl6RDAqNk4+TACR55787z/2+DKRmRiMba/3tRngYZZYBNPKMc5Dtyeahe42e/MprXKalB33UJyPnZfab/s5klMsYAt+Ksuq8He0S851bWkLUjig9y5OM+Z3xluRG0eXF9dzlkMsN25Ndd0B6VP1N8pt93Ihn453pAl0/qv0Zj73sJp7weZcVjbn0lg6eZDsE1Jr62jQmWT+aeHyt6VgAlMHgYpIXTwSNOBzBIALOBM0ToZSkKg9K4d6ZmZnibfgr7TOF2mSUaQD1e0TUFOji4mJE1L1a5TnIiCovxpVd00T0dg5LbojUB3pQDBlzsnqfFelw76ztb7ah0+nUlCyVMLeldrvdEgXTsoiW2tim6enpWFpaiomJiVhaWipjrMjc+fPnY2trK9bW1sqprpncsL/9fr8Ao/Hx8VLm1tZWrK6uFpDS6Rxs7RXRYEq+XVa2t7drY65kaoVouQTAaAfBLP/26KLq4b1Nc4hLYiRdc9mkd3hU2J+KlzzPvLksFK7/BUaYyK7ICMEIl3gEVrVM16ZLvO1+LQOH/jyBp+a8QIgMDt+ntLe3V14yqZOPs6UezQ19c+5QDzlQZjul77Kdchn4dCBNoNxEmQxl8qBvtr0pF0Xty4wv5Vdjq3uYs8Gx9uc8muZg+ihdS1nNQI9H5UWKChGUSFZ6vV7s7u7GyspK7UykbrdbEr61hV3pEVVV1eYGx8MdjCZb4G2/1kDlWACUiDg0iBkKZj4EBZXon/cLjCiELuWv+zh4R30iDt6cG3F4cnFi+jKC2ugTsSliQPI+8nrmJbUZCn9m2Gezj0e6LhUo6RnxIeIAxPlEca85Mx5azpucnIzZ2dmYnZ0tBl8AaGxsLLa3t2s5J6yD/aBh9skrRc+Qa0Q9H4DtZp8zpSAeZBFC8ixTGhlo5f9tnlsGfIelJjl1appLLntZOZmypxzw41ET3pMt17T12edI0z1N16gXNLYC1oqcyCHj+Tr9fr/szMhkSyA3AyiZI5ERl9q8f9nfl1K2y2HT7218bJIVl32/1vS7R06a2tVkvJt05aWQ+sL6XZfSUdve3i75Zr1er8iHdnnJtqmt2TKc6zP97f3M2kq+XGs6NgBFjKNykdeTrRs7kzlQ2qEzNjYWs7OzZfeNby+kgDD0KmUij5Tom+u6BBmZ56I+yKsTutUBcNzZQwF2IfN+6n7/jULpESGSKwFFApRPocmgt89ubW3VwBSPkm8ygk1KgUCTE5+7ELicw/ayvxoH5iKobevr67G1tVXzqiYnJ+PkyZOFrzs7O7G2tlZOc2W0KCJienq6gJy5ubmyXCjQo5NkKUfqr+SM4EO8k3KRTBAoq+2uXDKPVGBG/MrkIQMRnCd6RjzypUr/+1INNseODojawpwCb79vvdQYSnb4nhXXDSpPW8D1rMbGE2xdp6g+8izrE4E6d+5Jl/AEYoGR9fX18p4nyd/6+nqZb1ri4TZTygjros7JHDa2l/rVQXf2TZ1GvebzJJMB/90NcxNlvxFo+nJe27Kdj1OTnmIkLzPsnIMeKc/aTACodmTLalzWW19fL3KgyJpyF/UeLo3D2NhYbGxs1PSQ8nY4NyT3itI4n2kz9DdlqSkqerXpWAEUKQ2e9aBrWWjPv8XYubm5WFhYKMZAE9NfIS6iMtnY2CjCozfGUjEqlKb1YCZicquowm96mdvS0lIBTVKWrpipaLJ2tgmM+h8RKXBoQstqr0LPKysrBbHLY1N4WvzNjlQe1rtwANLpdAqv1X83zJnXpvsE+niGzvr6eu25qampuOOOO8qL+LrdbmxtbZU+q10ymp3OxdNg5+bmYmpqKhYXF2syuLe3V0Cmr1lT1rgFWbwSaJ2YmIj5+fkYG7t4aixD/xEHiXXsu4NrjnkTn9gu/u1lCKC48W0b26MUWGbwHMBrnN1jdoAgkMElnianRe2mwt3Z2Sll8P1J7gy0ARNdZ8SUoJ1REjk7+l+5BKurq9Hv92NtbS3OnTsXOzs7sbq6WgvjM6rq+TMRUVv+kfFhXh2XLL3t5HlTFJTjxfs0P4ZZ5nHdQ9DQJjcOJujQMGLWBEzaohRN9fkc5m/82yPlmX5lRFjETR/SB5ubmyVScv78+djZ2SkvEu31enHhwoWSe8LymFep9AG1h469vxSxycmgTJAfTxVIORYARYqjqqqi3LNogIiTkB+eZeLREmZSEzHKOMrD2d7eLgZZuyLo+UrYBGiIrCnkno3PnAIZe7ZFdTRN4GEEhQjfjYrK5fo218V7vV5B9LrOyIDa27RGnLX1qDbTe5WB5GTOAJw+BIO63ycjFcrq6moBYgqfakz5JmspA8kQc1vYFvGL9bm3SwMg/vEsDuXWyLP3Z1QOw/jskxQS62S/j/J0HbQ4n/V3k6Jq8pibyMtle7O+Nxke5lBwm7+3U/OT81EOA/O+mEg7zJIA28rzcDSvdHowIyj6KDqpZRoCmyxqSOMjGeKYiwg424A957V/fJwob95vJwe9WftUNu/P2knQ6FENgkqPamRyrN/b5NcdrSz6wb5n84sOjt+nsZb+2d/fL1FegVVFUKR/1R7KpQPQ/f39krep+6lfZPM8csn2sq+yjwSDI4ASUdCgwu1UuJroNNzd7kHiJAdvfn6+JMTOzMxExIEQMpGIXo/eeaKQml7OJvRKL0aDL8XEgfcQtLYvC5jIO6JXx/AivRT1v83b8N8zr4fomAheSXkrKyuxtbVVkrKUfCoApWe1FCUFq+/Mm2oyKhn5Eonq19KaeK9ydU33CWTJwPMUSvFAoFBnmhAUaSLPzMyUk1kFShcWFmJhYSGWl5fjuc99bkxPT5dlMHk6BJXsO3dscJlQiZva/irgwwiGjFqv1ytgmSBMfJNh1smoPHJfY8OkVBFlo+llmFTAnlzooET9bwMpTYBT75bRd6fTKX8z0c9BGOce543qIgBXhEzjw/ctKelZyfMELZmxE3lio+pkUiz7LF2iJZ6tra1YX1+PjY2Nms7RuDQBBDlgNJ6sy42n2kRd4bwk2Pfx0rfkSO1ri6D4nM5koQn0sp3S14wMZU4Ty8ocBdel+mT98ygU2yRnlmCa5GBL4GF/fz/W1tYK+FDU7Ny5c7G2tlbGX+0YDAa15SzK6MzMTFRVVcpVBGZsbKwsQyvPMuLiMrX30R1j6kPZI+nD7OTzq03HAqBIIUUcXnf3SUqGOZN5YiRDmarD19mpZGh4mYNC1M4JQoWiweX6LtuuuiiEapOoDYyQXIGRD5lH5N6PlKWiRgIomiTMBdFH7eRLrdrGxcenrS/6dqBFvvD+Jk9fYyvlQ2MRETUlxKUCrttSucvL1lKPlud2d3cLqPD+cTydGCli8qbq5+Fa3KmRlUmeuKfJcW8CDcMA3zbAcanU5L26LLVFfngv5yZ3fEVETYa1VKnnBYAEUAaDQcl10fWI+tkfepbfbQCFBp9zXpFaXx5tWo71uUGHhkZWfda36x4aYpZ11Pg26ZI2cOLOid97lNPl/eUcc9BEECHeOEDR3yyLYMb1HB1gdxQ5rg7oVA/HgLtyFKEWMN3e3o7V1dVYW1uryTLL8uUaAWoHjPpbzzMK0rRMQ96QX9RJowiKkUJRnLx8twg9gIh6kpM8YW0lJhIWg7lspImzu7tbhGdlZaUWFWBoUVEFbgFdW1uLnZ2dWjhN3l/E4UH3LZLqAz2KTKCqqmoVFJbhuTCc1Mpx0FZbJmVxQvBAKy57MOqkydHkRbBdTcRJ5f3gNY2xxlS7sfhOE43NwsJCRERtd4RCoIpuyTPT+O/s7NR2d4l3TK7VtuW1tbXo9XoREWWpSPdmr0vo9Xqxvr4enU6neELi0c7OTpw/f74WAaQh0FgKVEkmKVN8VrlNjJxoPlFJO6+prH2uaZzcYA9LTL5158Db1CQ35IX0wdbWVmxubta8VD5LIyEjpLq2trbK8hoPVNQ1vZPLk0lZLv8nGOHfvE/zjjLJSJEAL+eUZJT6RSdQewSF0TpGVSTPTPh3wxzRfvZJ5gg5KeKhqLGey5yprFz9TV2uyKDkeHd3N3q9XpEHBxd8yas8fy6Jae654da8YWqAJ7CrTuUWMYLnxl4AVEn4mufaLqyoyf7+fi3XstM5WH7UKgDtxuzsbMzNzUVEHFqaZ/3qm/ioCA35nrWb/Z+enq69s+pa0rEAKI7sJQQOUBzZ6llNchkvV95ZaEuKQ2EyZdKrXIX+Iw4MWafTKdtWhYh1Iq2UgTxgJckqoUnKMKKeb6L6msCJo3td53fEQT4NDY1QvNY8dc6CErCkNKWYFSpkXoY8UYUUPVrDMbxcagInHGMa5cHg4KVr8pJnZmbKKa+dTqcWNtc1yYMMuQyFlrQkYxwP7eKZm5srkaaIKEt2eobn62jnWMRFsCQFI4VBz8oVLUPMVLAuy+5p6h4pJMm3iPKlOp3XvgSTeZeko8Y88/xVX1Y2jacbLylkAWuBbOUCedu9DVK2ciqmpqZKBGV/fz8mJyeLYZeMyGliJKQJVFNPeSSIUUtuEfa2EaBorHk8gowmAYoiOJJ37iKSYdWYu97wpRH2y/t31HjToWFdjIQ7+ZwnYNPHAQqXgfm8L8NzvjBiqd+YXC8dLf5nmykIUFyv8D5uIxcoOX/+fJw/f76WgyI5EygbGxuLhYWF2qsZOM9nZ2eLftPz6+vrRado95d0BjebZFEUEkGenD/y/1rSsQAomkgeAvWEriz0SrRIb4OH9JDJmjxCrDMzM7G3txdzc3MlR6WqLh78tbCwUASp3+/XBFfGe2FhoeQSaFfG+vp6rK+vF8UqYyhloPf/yIi5J0yAFXGgrLKoBZduJPxcptLSDXMamBmegSJ9uyeYgYgmQzPsuHu54oOP/WAwKNEP1kPFwzLkmWgM6Tmp3ZqM2vVF2aNhZSjVdxMQyAioEkhIEUq+mCDJ9wMRQLhB4bZ0lw+t0zs/I5ojWtk8yp7NgMmljDHBVOa58eMy79EIyTWXSDW/vX/eNo6llHYGmhhu5xg0Abij+KC2aguxHCLfJah+yCDQc5Y3m0V1BoNBDUSpLIIc3edREjp6dAydh9kYZTKhPhCQONjkc01AW+OvOaek0Z2dndjY2Kg5Ft5O9Zl62gGKR0vkgIyPj8fs7GyJHEgHqD3M8fLcO84TjjE3XKiv4jXlW8BSIGN8fLwk8WeOCp1tzX0BbfJB4IVliGc+/8gn8msEUCJqgiiDzvVdkQ+QBlOCxePs/d0mVHSKdiwuLpZlDXojVVXFwsJC3HTTTVFVVXz5y18uB+ZsbW0VRDs5ORm33HJL3HbbbTE7Oxs333xzTE1Nxec+97n4/Oc/H5ubm/HlL3+59s6WhYWFmJ2dLQaXBo5oWgqJRORO4dak0DLVxsZGSYSVly7lTsVBQOdeLNc2XUlTkWnSkTKvLCOWp375Di7Jh7zcxcXFmJiYiNnZ2YiIePLJJ2Ntba2mYMU/7cCpqqoWVpcsKdJx0003xZkzZ8qyjeRKnquMydjYWEkgE+/ohZ04cSI6nU7htcDP5ORk3HjjjTEzM1OWJ/QyQp5XQG9HMqOlK9XB7HwBewHULPTsvHbjTODb5u02lelgx+93JUeDlBknjQ/D6ZqXPF8k4iBZnrJI5U9Aprkm/ZDlqskT7Xa7ZbxpyJsMbVN0QEC01+vF6upq7OzslGVVzTG1SbLFpEjJqCK5Kpc5cjSAMuSa3zSijKiwvZy/BGcEMh6JyMZawMDntMrI5MINIsvnRgXly2knHk+bpo5oklsaeoE9JaozWX1hYaFcn5+fL9EYOUzZErob+16vV46q0PZxyRIdJc9BksxzXsiJGh8fj4WFhdjd3S3ODpf9ZCe41KnIfkSUXYuScScCZEZQPIn+WtCxACgadKLTpknvRK+W3q0fwsRypZTkke/t7ZUwn4ROykLRFEZC3MOZn5+P2dnZWFhYiKmpqZJUub+/XwychIP5HBH1JEcqa05WTgTnhYCcJrHOXNAx2jK42a4jTnAqNS5VeSQlo6xdwwi2K32f7H4fQ7haoxcYlYfN+yUb8iDdKGoi+m4B8cc9j2x8fNzcILvxbYqUZc+rD9xhIuDt4+dGNON125g0za1hqOk+H8smzy1rhwwCAQoNHseS5WbRgibe0piyzYz6EDQ5jwhOHMirbOYkMEJMR0EGQvkHXGJSQi/rIM+Zb5LxlvLpuSdN4zXMWLY9Sw8/u8+fySJ60kE8NFJLzYo4kNfU7x4F4hKpdDL5KRugCFZVVSWKElGPUut/1u3jwW3lvoWcMudAOssNUpkTExPR7/cLiKB88lv9kjx5PqYDapcVd16uNR0bgKKB9RCufhdpEIX4BBB0xLmMDZdPRAQrnU6n9iJB5TSoLpWtZRwBHiFRLdOcOXMmbr311loex/Lyctxyyy2xtbUVk5OTJc9Fx6yLHLFKqGQ8Fdpn9rj4IQW+trZWtgs/+eST5QhttbMJ7CmqQm9Rf3P9koLKCTcMEBnWIMrD9ciMvsUXgQl5I51OJxYXF2N2dra8H0cHX21ubtbC5jLyKmNi4uI7e2QAZEBUv6If+ghwKrdodna2JOcqL0Xf8vQlq/v7+3HhwoXaAXKdTidOnTpVZJGGimBCCXNTU1Nx8uTJmJ+fLwaPeUeSCX3ce82iVR7ap6FvWgbiuGT3ZTJA8EQl6JE7GgB5oepjt9st0Shfn3cwK/mkvDLRVoneioZymVDypegZ1+LpMLBP5C3/VkSOOTPKo+GOQ3ntigC7ofD5pG8CIC5PuuEUsHd9IH3C8dVvAjTsn+psch7dKXCHQeWyfwJl4rfmy+bmZlRVVbZlKzKsZa0meaM8izzCq3vk1PR6vbJEsrm5GVNTU7Gzs1OLWnofXOZ40OfGxkZtWU8HMSpCJ8dRaQNKktX4Md9IDrHkaHJyMk6dOlXshmwEo4EaW0WBdJQGl8UYSZY+lAP0VERORMcGoLiyjcgP85GAUri5rKM1xsyT9b+13qiBoVATmc/MzBRwImHVhFpaWopTp07VIh5zc3Nx8uTJsstje3s7zp07V0PvBFlSVNnEjai/clxt0wRT0qtOJGSynPpK5eo81330sggIpLilODIAeaWk8eIaKsefSo1LQBFRy/vgzg6deyLAurCwUNvtNTk5GfPz82WC8/hwKVkZDMnH5uZmaa/kjbuy+OJAyrFygFSu+Dk/P59GEKTc6VVPTk7GwsJCLC4uxvb2djkCe2xsrAAr1utKusnT5zWOqXtcHgUZhpoATlNEg+vxUuoywswVEO81T1yu1Xe+w0Sy4SdzOkDR0kpVVSWiyja70+SghN6xll6VnO55R4yScOcEgV/GU/bVQWkG8gkGNcfbnJds7MjbjAhAZew5T7Ox55yWg6Z75WD1er3Y3NwskeGIqC1rZO3wNjYBKskBE+rlqEQc5HVIR4gv3N3S6VyM2hF4CIQyGV5n3gioKCeQO7NUhmwMx1oyJJmZmZmJhYWFAmQITiKibEuWnHGDRgZemcPpgO5a0rEAKBH5Tg5d1+Qj46SouJbc5m1E5Ic6+SSVslJbJAwSVu2Q0ODu7+/H6upq7RklySqDfnx8vCTQCrxonVP5DAJWajMVhCtIgR5NWgm7AJ7AkvNxMBiUejudTi3r3xNnFdGQsu52u7VdQk7DGi0fYw856p62/8kjKlspu7m5uUNhTq3bMk9JL5KUghfw0NKdjIbKUChYgGV3dzdmZmZid3e3lMdx0jbjLPIQUT/Vk/kfHm0gMJOM6H4ZNEYKMiPl/Pc8o4yuFIDS+DdFWXwOcr5n0TNPhueODYL/qqpK1FKOhXI2/CBGefqSf+b4cKlYxoGGnomTep5bf7m0Q2fAI0eKvtFoaM6pneKT+sWdLYoyaD6zbQRQrF8fz9+gfvPlr6MiZXTUWIbrNPWRThr1m+4joNdvkinmKUqX6RrbxPIY1dRvBEtyRBW905gTDHsujsZW4y2ZUwRFcqSIzGBwMRdsY2OjpqeZtL+8vBydTqfodfGSfOLHwaD0VVVVNfDnCb6MoOjTZIuvBV0SQHnggQfi137t1+Kv//qvY2ZmJv7BP/gH8Z//83+Or/7qry739Pv9eMtb3hLve9/7Ynt7O175ylfGz/3cz8Xp06cvu5FUmCKGUKV4Gf5WroeMjKNafesay2b5PnG5tKG2afmHGd48Dv/s2bMxGBwcHS9BpactNDs3Nxc33XRTLfchi5a48HFy07O4cOFC2cIm8CQDynVOTZbFxcW47bbbYmxsLNbW1kq0YX19vXgQ8hI83M0luDbQMIxwu4fgHinHMbuuDxWT2nrDDTfE4uJizVPTGQMKpet8k8nJyeKtC4xWVVV2ZwlEagmx2+3G/Px8AUFLS0tFiSrBUWBICdLkmcZSvJYh86Q/KSONQ9OpmmqTksybkmUzYOLj5mN5pSSlrnZnSwoR9SVMGWOFozVeiqBoPsnzlGwqeVmAJiLK+054/oSS3TV3ZNzluSq0L89TxlHGRXNC/eHbh30rquoheGCUVHItj1tjpyRZRXA0ntIv6+vrJcdMeRl0HsjPLCqssdFc9WUh/a6xoYw0RXYYDRHoHx8fP6S3VQaTMnl+lZfJ80jUToJCzS+NocYg4mD3o05U5dK/eO85HASatAXUh5qL5IOWcuSU6BwULQnL2VEi/Ze+9KU4f/58DWRps8epU6fijjvuiE6nE+fOnSu7MyU/XHqXftM1Rsbm5+eLXtLxGB49o6OmOaTIn2TmWtIlAZQPf/jDcc8998Q3fMM3xN7eXvzoj/5ovOIVr4hPfepT5ZCYN7/5zfHbv/3b8f73vz+Wlpbi3nvvjde+9rXxR3/0R1fUUPeqec0nFsNS2VrtUZRFKLK/eb/qpceqtsljIUDRxJyYuHhMvP6WsWHkh56xt03XXNFw3ZnnKohHarOMEvmmXBl5Glrv1b0ygJqoTBZrAx8+bsOOxVH3OyCSF6MJSS9IHoNPYCkVGjzyXmVqvAge3Stk2bqHO3qYfCvFwN0VGmt+c6x0jdET7vChHDiI4xjp9yYjRc82G4tsTg07z5xXJLaLYyiZIxAlH3x5gMZe9xCg0EAyEug6g44KE8Qzb9Pr0LhlYFrlcVwkZ1n9yrWRIZJxY/RWxlhLVnJKCII5bzOwz/GmDDTJDO9vGmvOo6M+4hv56c+Lr2NjY4fkmjz2eS5+8pu/Z1FIfov3jLBw3DjXVYdky3mmD5cl5VAob455bwJqPJRNr9Sg4+r88uVHyir1VxbJUvu4OzDb6XOt6JIAyu/8zu/U/n/ve98bN910Uzz88MPxLd/yLbG6uhrvfve745d/+ZfjZS97WUREvOc974kXvOAF8dGPfjS+6Zu+6bIa6QIWkYOTsbGDbXdLS0uxvLxcBrzJA3TjJ6NCAdfASVHwTcVUXlJ2vJfr5vqW1yZUyzwCRWG4xZh99ygQjY0+8gTlSSlao2Qr8lG8EYI/depU3HbbbTE5ORmrq6slUfDxxx8/lDfhuSxN79/hGLLdTQrNlSaNlCaPQpLdbrckNSrKMD4+Xt4wrInb6/VK9Edel8ZQuULasqutx9yiyaSzbrcby8vLsbi4WLaFT0xMlOQ3RkUU+dIYCwRNTEzEyZMnS5L0448/XpKmCYLIJ4bnxYfFxcU4ceJEicIxGU6RMX0TtNJTohzJ+LrBolFqM0T+e1s4mGF8l2fKEJendPIvFSbztGjUqKgVfdHfEVFC7f5iPsmR55pEHCQ460yK2dnZmv6hgZKcenRKfWGd4r2fa8Ln6LVS1mVcJHeKnmZjRd4S7BJQEfhRB6q9mc5sApsingNE0EBZVXs1R+fm5mJ2drYWQdEzmldqO5fKtKStyFmn0ykRzfX19VhdXa05LbOzsyXSqSRkRRy1BEUHgbKhcdcxAjriQEuGOiaA2361JVhzTZH+ubm5OHPmTImQnTx5Mnq9Xpw7dy46nU6cPn06FhcXY3l5OU6fPl3mr6LhdHAYFabjTIdUzpoS/f3VDxFRjm7QeCgXk4dyXku6ohyU1dXViIg4efJkREQ8/PDDsbu7G3fffXe55/nPf37ccccd8dBDD6UAhadvRkSsra2ldTV5WiRfMtGunbYISuYJMMzOsimgvr1RHxl/gRyuL7tyUjkiV7AZ+nWAoj4QsAlESeDoaTmap8HvdC6e33LixInCt5mZmTJBSDRsWgPNEvHI4wydt1Hb72wzQ9jikaIgahPD5wIKilwoajQzM1MM/t7eXpw/f74oEilwKTCek8DMdjf8NFTciaW8Fy0hXbhwoZb46v3UkgGPbld7eICUG3sad46P53W4/Egx00u80jHL7s3mJkFwRBwy6BFRA/BN0RP3thWBkNHlvHT+6B6WR+NOmVd7PapFQE3eEqg4oOc2UdXJ85/UTkVIGP0ReNIyVRbuzwAjc3MYLXSAxKgw5aYpCsJxFnDT//7xCDEjjZ5HKIMr2dA3dd/29nZZeqWDt7u7GxsbGwVkCAzxfBk5W4zKeGRCvNe4C4DJkGs5UHImvspB0XwWQJmbm4v5+fk4ceJE0U26V/bxxIkTZafe4uJiWc5rilwT0NFxFi/EQ+kw6UhG29UG3036VICTiCsAKIPBIH74h384vvmbvzle+MIXRkTE2bNnY3JyMpaXl2v3nj59Os6ePZuW88ADD8Tb3/72I+trCtFJ0GSU5ufny9YvD9W1eXM0mplSJjhxxeOKluBGAqKJIOWv9Uqh7fn5+VpSLMO89F7dkKh/DBdTEaoNqkOeh54TYpbRVoKsFKOOCudab0T9/RWe90B+ZrxlP9ooC4lmz4pP8jK73W6sr6/XEkfHx8fLwXp6XhEJjYfkhYZDE9qVOPlOXlOBEohKOdFjVN6K3oSs7X7a3SV+ylhwPVplKalZSk9ASDxhYp4nyPr4eFTLjU3T39mzbnAyhSblJ5BIA8g2ciwyQyh550GKMiBSupyfKmtjY6OAeJ1CKkCv8eDSbNPYu/PBZQcR56eDIfFCOkLLfoqSyEjr7KWdnZ3akQH+Ik/xX0ZeRoe88XEkGCPIGgwGh8A39ah47787cQ6JH5n8SO8xYT1b7lZZbKPkXudN6fUWckoEQKQDVd7i4mIsLi4WPjBCpXnb6Ry86do3YKg9TCSlrlcEVhENbTBQNIKGXyBTtoG2hLtylNtEWyLHhuCCDhKdSI1Bp9MpbSOQoq5vciKG0eFXSpcNUO655574y7/8y/jDP/zDK2rA/fffH/fdd1/5f21tLW6//fbaPZo8VO5M3JLwzs3NxYkTJ4pgMw9Bz/mkoqJzAKRrnPhZVIUAxtvHNut+nXKqxLrx8fFYXl4uSUuM+lAJe1RCioZJYa4wVe/k5GScOHGieAryEjY2NiIiyimJJ06cKAp2Y2OjePaaXKrDk4U1Tg5QHJwMC1K8v03eH2Vjb2+veJbkrzykM2fORKfTKcte4hXbGRG1xFot08jboHfBewhQmAjo7+ORPAwGg5ieno4TJ07EYDCI+fn52N7eji9+8YvxhS98oSxniNcaExlxrllriUfGQQrLEzF9eYjU5BE13cfrBM2ZZ9wUwZTR3d3dLYCOYEQfAiuCYgJKAWqBCxoG1s+EydXV1dr2TtbLvCBd19zU3JN3zFwvRkCpDzR2PKRLfRUw0XLD7Oxs9Hq9svVchunmm2+O2267Lba2tuKzn/1srK2txeOPPx6rq6tFlqkTtdzQ6XQK8OJ9dPoYueBSldpHUMMyGGXj2LnMyIAzQifeUGdwN4vOF/LjIbgkxeVKnUulpFc5rHTO1tbWSsRfgEbAT5Ep7q5UXbpP7dAYExxoTgrIMCdEyfWuKyhDY2NjJfIlWdOOnYiDiEhVVeXFiAIyiqS6s0weS65Vl2ybcloI6qkXPTjQBEKvBV0WQLn33nvjt37rt+IjH/lI3HbbbeX6mTNnYmdnJ1ZWVmpRlMcffzzOnDmTlqXw0VHkhpqCTcRPw9n0TFMEhcqXoTANJhWwvBISvSspUt1LsKLrXG5oS3Tkt1PWtyzy4JNbE1wKl8s/8si0PCWFwnVXRoYynhL0NXnQw5B47qFHr4fA0yMNMgAEkxmQyvjOejLeRkQtNOqes5fnIJmggspSYEJKw5ct5CEqciJlw2UANxhZNCOLnjSBS38mA6QeSWkjKnqXE5XtgCeLnMjz0xhQ1rlcpXsFUPT2aUVOCIDUBgJjn0c+V51f2b3ZR7/xHkWXaBhlSLrdizvFBoNBrK2tFW+d+SGMvLiTpnnsYM+XyrxtNFRuZF3OndwRbNIJ5EmWS8TfI6J2JogbT+5uEy94xLsAigCQR8BUj/Qyd22RXy6XTWOsNuse2in2SbZHY+N8o35xcOTl6JOBUkajuITmY8OoPKPkTwVdEkCpqire+MY3xq//+q/Hhz70oXje855X+/2lL31pTExMxAc/+MF43eteFxERjzzySDz66KNx1113XVFDyWhGC2R8hLiptJ3EXCpWehCucLQGK6+VXpN7Dg4k/PAejwbQq1ConssoTcawiS+uMDiJNME0AaXodGIlvSQp7cFgUN4Loi2WEmgZVIVKNRZS8uRlZnzYzqY+8VsehcpzflM2uB232+2WM0jUtm63W/pEJaM8Ep+kmTHU/+KFlgkUqeBheO5F09jJe5+YmCgJdhFRlnx0uihBj+o9depUebupeLK6uhq9Xi/W1tbKs/TiZMh8KS6LgLhBaJLBo8K/TU6BFLSWLHSGjHJMHOg4ABdPdPoqI1Qa18Hg4ICsjY2N6PV65X7mLYk/Y2MXT3mem5uLTucgsZJv9VYE0pcMmOSuCK6A4vj4eA3sa87pADaNMXm5tLQUZ86cqXn/J0+ejFOnTpW8qM3NzbKcrW2sbrykU3i+C89U0TjwvBjqDY1btkTkS1htczoDaVzy1D1qi3jDA+rUZo11t3uQ+E85IfCX8yDZUH6I+OPtYNIzZTXi8LlEAi2M1LuT4s6zylAOmiK/0hWehKy2cw6IT7J9itAogqL2MiFeTif5pHsVfdne3q5FCNkmkZZi28b6atIlAZR77rknfvmXfzl+8zd/MxYWFkpeydLSUtk584Y3vCHuu+++OHnyZCwuLsYb3/jGuOuuuy57B48TBVBCkiVUEa3quTZl2eSliZqS4prKzCak6qJXwixrIl/2lahYZfh9HhXIPDTyjJ4V16/5QjGGuAnkNMHkvbBOtpEIne1t4rH3KSvLQQP7T8UgJU6PY3NzMzqdTm2dl0lfXDbIeE3lTKCrPBbuysgiKfTmI6LseuLL4BYWFso4dDoHoXkBI7VTu9TEFyXQ6kWQzDmRPHJ5KQMA2bgcpYSy8fC/m4iKW4pcBoUyLY/fwSg9O40zPW9FSRTVXVlZqRlygngZQZ2PI53CMefyK3cRubfPfAAZF4+SEgQw6iPSmRgK809OTpZl2ImJiVheXo6ZmZlYW1srr3CQftK4qB8aD4960omQwfPIiWRH3xqbzOsfRlY8MqD54MDY+elARnxXhITyTYdJYIy5Lb5jif3QNY0VZY5zisCfvMp0MaNPlDvm1kgHe9K06oioH6eg8iS309PTZceSiDuFpD8I3imDnucjvhDkyfnjct+1pksCKO9617siIuJbv/Vba9ff8573xPd+7/dGRMQ73/nO6Ha78brXva52UNuVED0cRkF0TQhQip5MbvIEMyPuk1PCQEXi3ruvl3PyZv3QhxM08xQjDhsSCk0TGGD5rhTVXiVOUjEwlEgPRH2WcIrEYwc4jKC4EqYH0jbW+s4mOeWAXlJElARZRo3UX0UTCEq0xU4ypHbycCsaTIIOJRDv7+8Xo8Wj0wVUXIkx2qX+aWcB8ye05VG5JCpL/VPmP8E63+nkO3Y0ZlJYGQhxI9YEsEkcozbKfpeHrw+jRZlDwf+5nCUPr9/vH5pLkkvxTPeKB51OpyyfTE1NxY033hinTp0qSj8iyjH0nN9yKHhAHmWUfytqIkOgpRt561w6VVnKzxEo0o609fX1kkezs7NTDh7c3d2N2dnZQ7udxEsHI5QH1ZfpQz1LWRIYzOSGesTlhA5lRJRllW63W+Re97phd30u/sspFcikE6n5xL4S0PpyHmVVfeG8zyLw5AF55Esq4g9Bd6arlD+T2QoCFNVNPrjd0j0+hzNHkc+yfvGRQIn9OmreXyld8hLPUTQ9PR0PPvhgPPjgg5fdKCdOLB5TrGvyPGdnZw8pCyrczDt0wXcvQ31Wwhg9Ug1URPMhZR6uyz6OvgkOHKC4UBD9U9iYTS4lqnYqedL77f2RZ85+cLLQiBOgUMFIKfOeYcab3z428mykBPQtYCGF2+12y3kz3W43er1eOX9EW9AXFxdroJaHXDH0LgWiura2top3rvK1xKNTeTWxmRwbceDhccKvrKzUIoDctsiyJN8LCwuxsLBQ222iCIp7eowAsF8ZkPbtnG3j4vLdBsozEgjTO0eYO5OBExFBpF7nIEUqeVAuAU+PnZ+fTwHK1NRULC8vx/T0dNx6661x+vTpErmIiLJMpOUzGZhOp3NolwkND8EHE2yVGC8Pl5FIheyVAO1AVbKpZarp6ek4c+ZMLbdGy1mKqjH50Q2f2uoAxQ0Ux4OAhcBD8ulyozHT3GfuR1VVZfnVZciBCXksp4CHHurMK+pR3xpNkEXHzeWM4JIRUUbvvL101sQDX+JRHYwMcXmfZVL/qXw9n0VZsghpE0DxKJg+JN3P6ItHcK4rgPJ0UWa43WBl+7OP8v78nmxyNoEahuszJcw6M/Ta1k/e49GSNvL2u2F3UNTULi9DCXsRBwBFBrOpfU1tdoQ/LGXPUC4iDpbhOCbsIyMHzgsZRkVA2jz5praR5y53lCm2mzux9LwUo8hBtvoiT5kRG4aJPdqX7bLwMTxqzrTNpez6UWNNOfNweVOZ4gk/GnOCYxleOi2ZjOjwLJ4iTEPL5RqBCV8WbFLWPpcoI2rfYDAo4EXlCRhERAEImROU/Z2BOtVLnebLU03jzjnLvtCTZmQiI8kfy5DR5pbetv45P5t0HA2wl9MEUDTfGG10x8/7Tb42yb230W1FFs3I+slxyHRuG9/bnOYmEtjxMbkUe3Q16FgAFIWvlR+hxEK+PIkJgxQYFyD+78KTGRY3UJoIbFtE/aAgPeukMnw9NTNcXm8TCGqawPQ0FCqWRy6+sV00XOKLlLE8PpLO69Cbc12hOfhxI8j7mojPu9eiNlPhaGsuIyhKeOTY0POSh6uE4H6/X3uzMOWE9Qu4MYdCIet+v19yDzjO9EAEIBjh8SgUlyc0FuKDEmKV9Li9vV1ODlaEjHIjr5uerMtgRNTmjytS9fso8vnjoMhJ3rTyBuRRq053PBQhIejTs4oWiGd6n5KSLcVDGlct54yPj8fS0lLt9fYiebqLi4s1gMIDrMhHX+6kJy3ZUIREZZBXOzs7cf78+RgfHy9nJel58kIAlVvs9T8THdVPzk2CLN/5yLnlek2yL1mgAcuWeKqqKu9vIZ+UWzMYXEzIp+z4dnOvn2PMD3Wi60+XUfaVib+UDUYgFbnlHOD89zmjfg4GgxK9ZFTHeext8082h3yOZp+MMkeJS1/S95ozaqf0JXXztaRjAVAi6gcdiSk0ElkExdGp/nbK0KqXQXIUTRTepsDpJbAtPtiXMuht/fEoE0OkXA/NnmXZ3MJGwVZeR5v3yP7o+WEReDb5SDT25CuBiCtY3UtAKoPQ7/fLwVdU4pkSyUAtees8oZLRh8sZUloEI6xTIXo9K1DCqA8TalmelCTziI7y2Ib10PyeJmDeRAQhTV48nQLer5wh5jgJWCrsr119OimYxAgCd+WoPMqc6sx2gAzrGbu374BDy6/ScxpfGUYu1Yo0rr7F3A2Uyyn74LkjmT7y/ngEwaOWTh7Rk+5WOzJQ0WZkGY3yKFaTHm2zAX6/yibI5zXq/raoX9ZG1udtbdOjrj/Jay8rK7uJqGsoI2wvy6Ieu9Z0LAAKlXlEFMUg5SPlwvAeIxtiuk9uJyoKPZsBI93rk/EoVOkKl54IB9y3lWV10SN3RSPF596wgxUqmjaB5sT39+80GbXMIKu+o7LAMyDCBDXWm01weYvayqczI6TspdSVs6HkQ0WEdFhSRP1wJEVelAOyuLhY2xWgyAXfGizQoLYJjNCwME+B0Q3xTYdH8fAwGiL9f/78+djY2KgZV0VSdFCb+qT5wHVxymiT1zkseTnZnFD0R4mMHFsqfoEGlqmtteSJ5wxp/BWl4Lzy+cKDwCRbBLU+N3WfJ78yQsnnmcfhXqjK0W+6pnntERGR5FyebafTqW1rJk/cYHLMfbxEBP4ZWPBxIl9I9MA1RxhNogySZ/TqjwLNbrTb5C7TL1za0bf6onlDUKIINA9qo+yyzeyr5jsBIcE12+SAqs2ZcEDt+tCX5Bi9UXSLp17zcDx3uC7Hkb5cOjYAhV4sAYrOIeCaIQ2aiIzWPSQXDhkSGoGjgEfmQWX3OFhgm+jhtOVAUDjpZWcgxT04/saDh5rABhWdlGQ2SbIP+9HkCfhYZ9cc5Lj34DxWcvDc3FwsLS3VzstQf9Q2ARSdMKt34nQ6nVreQbd7cGiWXipIBaE6lCS7u7tb3otCJSjSPTJYPEyOCmF1dbW8ml1Jj9oxpHsVJucSl4AJd3YQFCsBk6CpSbb970uhpnmjxE4BFAfTiorKEaFxnZmZOXQyKvtAQMmIBfvCqCLPAXHZYlKzL5M4QJFiF0ARaKAeERhxgMJxp87hwYmKWKq9TH4X+NaZMlxOyOYlkz99iYP9Zls4DzVmjIBkSzwqV9G+fr8f4+Pj5SV0BI50DqlrmiLTTTrHdYPLdhZtcB1JsMK6CMp86Y2/ZyBWZ0+xfQ5QpE+cjz4/3Ab4R/dpLqgMd+QFGh2gaJcZnWTyrAkAXk06FgAl4nAegxv4JuSeCSIHR9+ZQc0mZlNZXp/+jzhskFk211b17cAjUwxOTej6cijjo/fHf28T1AxYtSmco/73vlEJUT6y8C/HQF4tIx0cD3nDvj7v407ZUxvGx8dribckGiHuPNFHXpbq5d9+r3Iv6AGqz5JdeqLOQ92n68OCcB/TyyUfG9bjgJpLPTIO9LR9eYOGn86JyPNCXEYoSyyDv2d5EHQ6aCDoPPjSKIGV2qDlRQIrz8Xy6K74RqCl+t1BYF3UUdJNNLSMfF6OYWIdMoSUS80X8cAj1+xfJm+uIzO9kcms86fJUfP6m+5xe3BUuXyO1zL9pvskU1n//fmj7J8vEXoOTpNNe6roWAEUTRopFR0LrrVZN+oR9axtX1oYGxsrxkBEj4xhMAcqHrZlO/2beQ+qQ96shEGKjUpVk1iGLCI30CL+RkPlApm1LSLfzuZ88DHJUDuNC3lDA0FU75QBTipV/c8+8Dcea53tvNBzinYowZA7aSYnJ+PUqVPlNFHxSeOgQ6DGx8dLBE/j1el0yvkqZ8+ejdXV1VqelD6DwaBEPJSMRs9sYWGhJEkyZ2gwGMSFCxfKdtLV1dXY398vLw4Uf6R0fAmFhpn5GwzpZ3Ll16hUM+V6FFiWcaf3T1CiOaFDqMTrTqe+/KKPcjbU76wtGnsC0ImJidqL2WRE9TejGxznqroY9dLLGvWtushTgim9MddPHFZd+makhX1k/Zy7BGPUaew/5ypzbfxbwFX6iLx2Y+pzMDNi4p2ilRpL3aszXPjiQyXVNkWvM4Mrucrk0suQ/tFcFO8yAJH1h8BD/KHzQDBNuyV+ZvPC9Z73kdcyUO/6UfezXZojiqpqY4CirWqHooDZmD9VdCwASoagNSGZa5E9p2/3DHxScgLzegZMsgnpAsR2Z6BJgy1Fw/yYJg+h6bfMO5Cib+KlgwrWIfK+HlUW628SZp9QTb9fisHLxoc8aHpGu2joQTCfR9tOeQ4JJ7vOO3AFIaUuxSeAwPwFKS8ZUuY78RyFiANDrmcHg0E5LZZJvJoTHNds7rR92uQhG6umsW27LxvrzMP0cLsviUYc3gmhyIQfLJi1lbuwfCcVx9UjCW7IGTkR//Uc+8GIHiN7HrH16AsTnfVhOF7y6ksL7HcGUDL9qHq8z21A0/Vd0zgrCiMj6DtkIqLMFc6zJv3D+pvAUZPRdp6wjUdREy9oW5pAfpP+O6p/+s7mtN/n9sD1I/U6dRlBL/mRAdKnio4FQIk4UERcz9Nar697EnTQK3RhrKqDY8pdyUXUt7q5x6b7MkCjNuhbf3tyqXYN6DcqIa//KHKhYgKa595kngd5klETGFM72TfV5QqAHoUbgay+rI/c6kfPpQlwZuXzkC8uj8hr4MnE8nY7nYsRifX19aiqgx0VOjej0zmIevR6vVhfX68tG83OzsYtt9xSjJl4tLKyUtqspSbxRwfLiXf61jwYGxuL+fn5uO2228rzAkQCXlQubUaKvHLl1kTDgJG2cZCXvLe3V9up4i/QzJ6l8XdAljkW5IF4qvw1XdecZrJtRP0UUh6Y1+l0yjtQOKbiW7Y8IRlT/VVVlUiKxlDPO8BRGa6LCG7dALIPbnwzAOeAykGKG7qM1xmpXDpnelt3VV18w7vApfqxtbUVY2NjsbW1VQNi1JGsm3k0Lnuu+ylDWZvFU77ZPAPvBKquwzO+NIGg7L5hiX1gOQQe/tnZ2Yler1c2BUhXaG6Qj231Xko7L4eOBUBx70KeFM8fyJ6JOLzlNOIADMhjolC1oc0MjLgn4oCCv/kSE/fuq01SkGq7JoWvj7NsURNA8YlII85rWZubxsLL0gTOQqRqUxZ2HoY4aWmo+btvrWxTBHxxmi9/6LwYfQQgZZz0wjm+Ul3LD+PjB6eV6nwKtWtmZiZuvvnmkrQZEbXzY+j5RxwkPaouHY+uJQYZnNnZ2Thx4kRUVRUXLlyIXq/XCFAynnIsrtRLagInGYmfWrLQuGpOZ9FRNy4E3RFRi17ous9HgUrt3NHOCr5AkLs2KHN89YFAlF4gqLaqXv9wDgig6OwX6TV6tBEHJ676VmYZT4FRd6LIKy4ZZnOzabwyoELj3GSEm0CKL4lUVVXmh5bYxAfpZC1/aledzhJxxzOiflJtk+PjgMV1iMqhcWciscuv7teYsG3OI2+H89ttnH4/ah6yLOdFBkwYgdvc3KztOqyqKrUXTXU+FXQsAIqIk85DpZn3R/IJlKFsv7cNoGSf7LesDIaUqWiboi/yEp0P7LMLDXNQ6EFQYTuvsnIcjLTxVW2jMmKbm565FOIzBJ0eGaBn6ZEtjr97yQSJUowCMxo7PSdFJkUq4MP8BS3PCFg4/6mMIqIGYhXGz6JgWoKiDNBjotyxfB+HDGQ3jbMoAyJObgyaQBJzrJgAmpWrMfWoSbakod+bylSd+taYse0+NjTanhCbGZ0smkiQKYO7v3/wPid661rq8x0iEQfyysPJHKRz3BhVzAxzRpczT9sAqd9DkKGxFPDT/cpbcbBAMMC/Wa7rTddv/D8zyBnocjvDSH3T/UfNhTY+Z45Wpv+PAohuAwRSXO5pM7IxuxydfSV0rABKxMF6PF+ixWTQiMPRAEfHnBD0Xtwjy5Z4jgIkvuwjb1a/RUTtraRqv8K7mYDp/0zZiaiApEAHg0Et+ZaRDlGbAcnawP8l6FIenc5B7oWDI5Ke8baQfAI67zWZqGQ4Bnr5mF4hzrFkAqUUvJYK9/f3S8LsuXPnSpRDdej9LEog297ejgsXLkREFCCjE16rqipv+da7ZpxvzC2JOFj2EBBSIqPkU/dPTFx88y6XSfr9fslNYfJlxtOIg+PymxRcm0y0eYEcd/e+SWq7coEUEfA2uPEVr7jcK95wDqs8zkmWp7rJB0YsHFRKvnW+iqInkncCE/ekKX9MqpYO63Tqb66WHDJSLJl3w845pT7zxYsi9S8bD9dpRwHXzOBmIJjlk4+SP8q1th5r84KW/jQ20peKLgq8ixe+FNWUPzFMBIDztClS7mCUPNzf368lV2eRKBHHSfM7A8hZ+2mXMnvEeahrihwrgsJDHRXd43JcE7+G4eOV0rEDKBF1A+1GUIPCAXLgQgGjFy7ipPdB1+/Zh/fSC6Jh8j40IWHe19T/NsHx6BJ5Mkwdwxgp77vXp3ZkdXvovY18DDOF6JM4C3Nm5ZJXUnoy7lojJy+ZpOnglctGDPnKY+W5HQQdzlPdI8WRJbBF1A2h99UjNW1Gw8dyWGKZl6OsXKEThLDMJgCle6QHBBQ496WYqWz1vHjMeU6wwWdd2XMJKpPDJs+ZURXJG3eaSb4ow25s6AmrLkYPKO8OUJz/Tfxt4ndGw459plO8Hs1D/Sb9nH0o+7zWBqoul9yGRERt/mb88vnK74yv2TXXbc67rG5vg9dB/eJHK/jSqTuJWTuuJR0LgOLGzie7Pk0DRiYTSSqkmhmJrJwmI0sDQeNGYKKEWIXm/RA1RoEcMDkQo1FtUiAexaiqqiRgzszMlMmV8altEuj3DATQUPj5NPSSNSkuxRiqDRnwIz/Udr7FdXd3t+SWEGzwSHgdha43A+/v78cXv/jFUoY82eXl5fLuFr6noqoOclu2trZiZWWl5DYolLq+vh6dTqcWKdNLGDOPVnzSwVtVdfFUWZLq05qytgr6Mo/PAw+VXy61gWUHQE7cnUTgpvtdtuXhaS5pTJSfQ1nL1t6Ze6byfO47QOC3orbKY9GyjAwj20nHx/tGgEL+aYynp6fLKceSq/39/ZK8zT7QOCtqojdgK5LoRjoD82ofozD+6gSCSJXVpLMyWWgi6Usemic9yXFV+8gr/d8kw6rXo3out9Jp+paeYL8Y6Whyavm/A1rXuepD2y7UrG8OkmiP+DvnucZPuXE6ME/6Qs9kKRNPFRjJ6FgAlIjDitBBSmZcIw6j0mFRNRVTBkr40aTVkcH7+/slmZLt0w4FhaKb1q6ziEGTN6Y2uXfoERS1LyJqxquN321KhYrNlS+9WbWLk2SY+puI4fosCqF2axwERObm5sqx50wYFE+mp6djbm4uBoNBOaX1iSeeKDtpBHA6nYu7L5S3oEk/GAzKqaj9fr+cTSJ5kTyIZ1VVxdLSUpw8ebIYVQcRykGggRDIE18JUHRsfBaN4LhRlo8K4x5FbeCEQDaTJSluAgifq36/xo5Jqkpk1jOee0Q5pSEiiGbURPLqx8TrTBoBI4IZN9qeM6QTg8lvLiVpeVH5KJ1Op7zcUUs2XNL2nBWBkcHgILla8u8RBpcFRpIo09zBQll2/ZktpVwKcby0hKMlcIIxtVX9jogyRxw4uQy5rWiyJ3RefTnG9X7E4feAsU7JLAEP57dHRb2MJh3s9iGbYz7PNYbSJdQXbQCFbXk66FgAFE1mT0jLvCwSB6gtVJY95+Vnil0fGQUNugyvPD1tRV1YWEi3/kbUjyD2sBoVJ795f/aMR1okxDJ2UsS63yd4JvDkm08KGRxGb8ivo8arbTyOApquCHgmBb0GKfAmfukY+7GxsXJIm8a20+nE6upqbG1txdzcXMzMzNSiZoqg6KWDBC9cshFvJiYmSk4LD0kir9l+GhJ6YVpa4nhkSixTNLrOxMm2ueFe6FHjdlR5mdJvqpf18xrD/a6saeA4H9oASsSB0ex0OsWh8Ddgu8dKI8nxoYGgsZfc8D7JhSJDvgylaKwAkkc2HJjyID6PMvs35ypBQwZOfAxo3C9nfot/zBlidETyJjkVr7x9dFochFCWPEJAwNGm9zlHIw5vVaZuzsCQt8f1T/abA8Km//1bsuPARB9f4mkaG4L6p5qOBUAZGxsr4Xl/b4N7XSIKHAXdt7pmz3GgKfgRB4qLuQZ6xb0MSafTKSfcnjx5Mk6dOhUTExMxPz8f4+PjBblyksizz9Cw7lH7PWfCFQhBh0KmBCba+06FK/LJm7WHEREaRoVl6eHJ+8tyIy6XvJ80QFVVlbNMlGy3tbUVEVH6q0gWy5JiWVhYiOXl5XLK7PT0dFy4cCHOnz8fW1tb0ev1otvtxvLyciwsLMRgMKgtXYm/SpRVdEP1jI+Px9LSUtmSfPr06djf348nnniigBHKmkiRHpXDF8jxOxvHjGcR9TMcHPzqGRLLpGI+ajyb7snAvsqmcvf6HaCrHzTSlEmCKc0HzQ3mFlHuNWby6iOiJMVqbARa5cGrTIIBzQ/NeYJNyoaIwFrOz8rKSm3JRcBWkTy1RdE28YYnwDrgcICl/7MICkGQj4XkJ4uuuOzw240pQXxmCLvdbpw+fToiohwBoP5qXJl3k+kGyo3XTXmkXNLOiCcOALnMJ+DLzQkEwZTrNn5lDkUW8cme84j+3t7BSdmbm5vR6/VqW4zFN+cPy8yc+6cCsBwLgELGeYisCaDonkwIMgY7MPHnJcRZ2NiNr5SDDKGO0+ZOEU52b1sTkvbfHU37veQd++NLM1KsGUpmef43l3ZYVxblcm9zGMo8iOz5bKI40BKAoDL1ujR22vWjl79RCckobW9vFw+XbxgmUKHnIqIHTC9ZLxJj5EkfGTz1y8cwy+lxGW7zgly+MsoUVBPRKxyWjjJuTWW5UcnmvIOvtggKvc9sLkqOeSYKQY7aJHmhTNCL1a6r7e3tWjsVrVEZlAVfqlLdahfPyJFe9H6xDxwj13X8fxgQ6mVcDrkO9nYSlGfgllGVYduQ6XpvR2YHMt2kdmYAOwMWbXLtbfSIT1a3t9PtVJZLdKljdaltv1I6FgAl4rCh3d3djdXV1bKNVB4xvSoaYU3mLP+DSJqDzMHkK93dMEhhzM7OltDr8vJyeSOk3qUiRcZDvnRAlMoXWMhAlCtYGrC28y/UX7W13+9Hp3NwkiZflKY2kC8ZeFNSprZQcy1dv1dVVTy/bL21yVj6xGHd5IsjfT6vbcaMmikKoSPiGa5V7kinc7DleGlpqfY2XG0hFo+VUKtERHrykkW+bDDiYq7LbbfdFsvLy3HjjTfG8vJyMVqzs7MlSqPoi8rim4gpBwyLa+zoRZGPTcsymSHOflcZPi7Z/cOWzTEjD/1ZPS8DoT67kXZwpPYy8qgIChPV/X0zbK/GQFvKNYccxCo/hfJKb1b5QufOnYvt7e04d+5cOZlYOmpubq7oEJ2Torm9tbUVFy5ciJ2dnVheXq7NbTlEjIZSH+h/zXHvI8eC0QwaukwfeHQmm5Nt407KnDIuqyipU5FZPSMgJh0qG+DnxzhQOwqcZE4OIyXkP4FqtryjMiRrHAeNDZfgMn74bxmgk6wpaqe3FGsJWTykPlF5tDusv+nb/74WdKwACgda4ar9/f1YXl6urTvqHl9SoHHPohQOVARElFcgY8SoicqVYdMrxE+cOFF2Gvgr2gUIdnZ2SiKclEF2foijVjc0DsaavEfdy8nN80vogZAvGWkpQ8aZbdP4MJrAMn08MzoK2bMuPqOJq2gJT/iksRfQFGnJTsth3W63nBDKpRvJQ1VVtaWdTPFprClzMzMzcfLkybjppptiaWmpvCRtfn6+dq+WA1yOHaCoPx5JbAq5DwNCHAhmiqltXIapL6NsLDMlT553OgcvAXVQ7t8C0L5lnLxkOQ6c+v1+rK+vF6C6t7cXs7OzZelW4+PLA3R21tfX49y5c7G1tRWPPfZYrKyslLnb7XZjaWkpZmZmYn5+Pk6cOFEDCNyRQ8fM+8P5Jnmi59+m/zIvvMlpYP+a7rsUagJMGmudKaXTf7kcwSVOLukTsLQ5P96vDNQ4eNE3QYnLnddDWWOCOMFKxoumdvJvgkvxgzknWl6UviAocYdvGHDyVNCxACgUTk1GGff9/f1YXV0tKFqKQkDCGc8Jqv9dsXNCePhcSoCekq5p6ykPj2ta8qDAdrsHp5WOjV08kVTC40hcBosenydOZp4A6/blKSk1TmROTCohhqeFztUPgp8mr/hKSArH++XrxhFRlku4DZXJyXx2b2+vhNq73YMjtTc3N4tXpjevCsDwFEYClAwcqh3ayrywsFDe36L28gj8bvfgTbeSe+UsSaZ9iU7XmhRdZoyarrV5SpmCcs+U93k9fo8vYxD4i6++xKW/GRkleU6AK2LvoxtrzjPmEa2trcW5c+diZ2envGtJEdKpqak4ceJEkROfO5o36+vr8dhjj0W/34/z58/HxsZGDSRxh4WidcpxEwjVcuDu7m7tXB6BI+oe57frkmwpwP/OdIs7ccMsGahOyobLi//P9mquMddPfW0CKL7URbAmyoBXBggofwSF3LGYLaerPnfisn5Lbh3gOFDi/fpu2olFYMK+cynRbWLWNh+fpwKsHAuAovDm1tZW2fIpgRQKXVlZKedTaDCrqipnB2RGOzPoDN8qwYgKToaEQqQQL/NM9M1lj8y4Krqyvr4ea2trtXJZhrYe0vBK+D0jO1vi0cRkBGB8fLwACrUxU2zOm+3t7djY2Ii1tbVSjpSjXuzlIXNH/PzOqMmbcnDnCpbKdWxsrIABKhYpFSblra+v184E2NvbK8mJelfI2NjBe0G2t7drZ6awbVxK1Pfy8nKcPn06ZmZmSvRE/ImIsmNoZmamLDdVVVXz2iOi1j6PTGX5KA4sXN79Osc/i3pdCh2l4Oj1qw0EfhwrtY0ATXPDI5QEWm3X3POVTIkXWp48d+5c9Hq9ePzxx+PRRx8t4GJ7ezump6dL0uatt95akjedrwKZa2tr8cUvfjG2t7cLCJ2amioRNAFlLvsqwZHnoPCUWfbBwYXawD4TnNPTdq+bS9iUqcyJI5AcZl5T/7bdK91L/TM2NlYiz3TwpK/ZBoKJbE5kACEDJ+Qh57d0dPY6CvKOz3JZhd8ENWp7G0ghUGTyteyXbCaXeORQqi0RUeMPwRRlJ+PbCKAk5EzU5JfS0nUNLkPfTZEFlssJLmJ5jIgQRVNAs/XHrB8qOzMenU6nllzJ/f76n+Hktonl/XZ0TwXkAur3UxG4IdQ9EvYMBLLvl0s+fk0eT5M3xHsYcVEEiCfCbm9v1/ojBcO+ZcsplAUZUskJFY3zmzJGz0ztzRIEm8ZZ5AqGPDyKx8NSkwfcRvRAvY9jY2OHwGbEwVo9Pc1MHtiPywFW4rGiazobxx0B/d7tdmNra6sG8jge3ILOELscGzk5XJL0BFsfc3277DgQa9NB/LTJU+ZcZPI/rNNxufKVtZF8kSOW8amtjU0RijY+RRwsG3tdTW1hxIkJv1ndmZ1yGW/iQ1MEbFi6nHlzLehYAJRut1tO7VQEQODDvSlN8sXFxZIDQu8zIleeKlcKp6ouRl8WFhbKPRQCen9c187WI50oNFIqMzMzNRAggVKyJwGK+qT++C4SekZaClO+BBWrfq+qqpY8rMnF9iqi9OUvfznW19dLcl+mAGn4M0XBcjNjqg951BZRyXJcVN/m5mbpnw5/Yt/liRGojI1dPH323Llz0e/3Y3FxsbRjfn4+BoOLCWg8IG0wGJSlvenp6VhcXDy0ZCbveHV1tYwdPXeBUsliRNSMlZbW2FffDipvtk25kUf81pj4/U2Kiko9G0P/22l6ejqWlpaKoZZ8M6+J3p0nuQsMZEqdvw0DVr3tWuLTqcCrq6uxu7sbS0tLsbi4GDfccENUVVUiip1OJzY2NmJzc7MWtZEMM+KlPut9PoqgkE9bW1uxvr5eO4hPybPcLcQ2q07pQY9ocP5TR2gu0HgyMsj54cbQl3eyOd0mQ07UGyqHEV3vA7ff01lUnbQLtBlsU5uj47rS3xUl+Zyfn4+qqmovO9SSnQDq3t5erK6uFlna2NiI/f39mJ2dLYcNTk9Pt/KPTjd1H3foyMmSjiKYV+SY4CmLemW64ukALccCoHQ6nbKsw8kixst4axDkkfgzbSFIH+Squrh0QeAgwMABzwCJR1pImTJVm2dmZoon75Of4UxXtgzFOpLmtkYqKn0zByWLnvA+TarV1dWyNMQxYp9Yl3tnGS8ul5oMDwGe/ufR5OwTw+mTk5MlHN/r9cqyohJmtYQoRUSeR0RRhjqHheu+Wk7q9/sRUT/vg8uAGndGCOitS/4IaJrC+j4+mTxm493mfWfh32GAipP4qtwyKU15pYqiaMeBrqmNGsumvjTJxFEkQy8lr7Mj5EgotN/tdmNtba0kusqxURk+J2W0JEsnT56MhYWFmJycLAf2aa7Sy2b+gOQlA6Gue+hwsP8ZUPFrWfTB+dgUWRh2XjcZwSZj6QBIBlp9ltyIb342CtvpZbdFmRzM0YHg81qmdUdBMqxll7W1taJLNde4lHkUv5wfbuO4nZ3n7HhqQgbcmxzBpyuaciwASsSBstG22IjDeSTKzWCYVALTBBzoiQmBC5Ts7+/HxsZGahiIkpl4SWOictkW75MrA5KEjQBFnjqXoSSgXDuW97WxsVHCyjSE6p88Au4G8B0Nei8I35Y7GAwOheY1JhFXlr/g3hgNNX/ztXHdxzpZjrwrJvlqvZa5HfpbSaydzsGuGuWGKEFbYCXi4C3V8oi73W5JcBwMBrGxsVFks+n8C46r2k9PkHxwHtCLpnyJxJujxsKByTDREAct/snu13xlVCTiYDssx4TzlF4g//a+CSSoTJavtvJbPJJs65027tQIqKjt7sSINMccoAjcKKInwyRArTksfaSo3fz8fDkaQP1gRJlgtgl0EMy6Yct+49zKQAlpWADY9HwmRz6mTfWKD9KDdFYjDqLnTeDE9VYmG4xOSA+QlINYVVUtL1E6dG9vLy5cuFC2qjOiz1cnNKUIiCccXwJZAiimNlDvi0dt43g90bEBKCIdGy+qqoPwmc4dkWBEHAhVRNRCya6shbh5kuPq6mr0er3o9/slIVRnnShJ1Y/gl4Ary5xr7JnyzKIf6pe8JwEkhfcEJviitcFgUHImtre348knnyx73rk2Tq9bHuL+/n4xElKWVEjr6+tx9uzZ6Pf7sbKyEv1+v0xGjgP5yUlwueCEhpe/USG54SAAUx+kCKQEuEtCXozKYdKfdv8o6qGwP0PiERdfNNjtduOGG26IxcXFmtFSufLEldAn+ZHczczMlJCwlvH06nkZKhlOtVPKhspIRsoNYwYYLmVcMqDRVA6BYhtI4UsX+V4bArTB4OCkXsm9wDrHPANfjB4QlDBC4/IpZa62ybmhLI+NjcXi4mJJWGV9Isop5ZYy6omp2jGkfBUtbc/Pz8fY2FjMz8/H/Px8MYJcdmC5GcjIch8yo0YnR3xyyoybz9nsGX+2jcRLvzerl86W+KBxF680X2k7KDOsz781xgImSlzn2GoZbmJiItbX14uNmJ6eLjK8t7cX586di3PnzpW2acwU8actyeYNAWgWLaEsqX0CatJjHCfnZ5sT8nTQsQEoztSIPKs4W2bJlmAyZOpLMlQwUp582RzvYU4By6MybfMIaEzaPBUaJF2XIuKW1GxbWVa/o3H1QUBGk0tJgh4ZYT+yPl0OOm9SgEeV1+bp0zBRQUuuyI/s/BHxQ4pGv0mumiJT2RIMc6j0UYSEyzs0iFK0VL7OD49mtVEbSGm7dlQdV6rgfOwz7z/icC5CU90sg95nVpeDDYFwvU+LY53pGN/KnAEUtTNrF3nKnXt8fYRHhwVyCY4YGaFeaTJMGa/dUJOfR41d2+9eRnZ/m/w0tUftjzgsG+Kvvv1aU9vYHrcjXGr0KB2XlXWPH6Yo+aKD6/apbc65zfCx9TnB8i5XL+vZpxLAHAuAQuOrqIUrBIIG/a93Z+jMCU5wZ7KiJzQq8nAmJydrXoxyRbTmrDVqvVmV68xc91O9meHlVj8Kc8TBbgcZNd5P4RePtNbJJQg3uCLtQJBnwe3Ba2trJUnwySefLO3hcdzqg9qWhYKz0HfTJKEh0nNc1mB99KZZFj0GlbGzsxMXLlwo7ZRiF7CUF67TQgeDQQnL0muRB8ItrvKEuEODic7ivYBJr9crsqP+CaRQucgo6b0/vV6veIm+XZDGUNcz3lIeRZkybLo2DGVOREa+ru8KlmNHYO7b9tUfbllm3Vy24dIQo6UuizpscXZ2NnZ2duLkyZPllF/JFd9n1e12i55R4ivHhB6v5IrJ7fp7bGys6C31h1tZfTlsZ2ennKVCL1m6QHUpKkM9Q4Oe5S04T7h00BYtGVY2msCAX3fD6o6qfuMytoAADz90Z41zgQbdZUeR1Lm5ueh0OiXqJ75p7kq3KArmc6jTuRhR57vZJiYmyonVTBXgGDsIy5bjsoiY+u/L9hx7jx4eNXYcn8sFOZdCxwKgaIJzHZFghAMrksLiFj7e56hU5dDj1UQYG7v4Zlu9kXhmZqbsK+eEV708m0GDyglBxeoKuWn9N/PQtexAhSfDJ0XC5CuV56FvTTQZPZ5psrGxUZJFq+pgbZW80980rm0ghM80Xc9AnI9Xk6chful/9VHJqZmHpfGRgtH9HA/xXcsN4pWUCRMae71eVFVVA3NqO7eZajz4rTbS8EputAspUyxNQCLznC41cnIUZZGLYRQeZT0DshH1484ly+KBxsyVZ0Rdzlke9UiTN9/tdouzoZc7rq2t1SIiVPQRBzkIcoo4D7kThAnrXhYjZora+FknlHnJJPUhjVQWwXMwSP3DCJPz5CiAcBQdBU6GoUwn6H/NKX1zmYq/+ZI7dYB4KKIDoNeTEPD4Kc4CSmwjgYKWfvTWdC0lum3SWDfpwGzsGDmjk8O+Zby7lIjIpY75ldKxACgRBwaJBpAKnB6GBl0Ji0TRbYbUJzBzPeTRUNCUvCZDFnFgDOXV6I24rmg80sC+6Br/doGS0dIyBY2gRxW4tMD+RRw+DEgRmb29gzdfKp9Gnyaw4BOH9wxL2fOuMDiJdY11STGzrep/Zrz5PJd9nF8cJz2zt7cXGxsbRTbpEet3ekUEm1SQzKdRO+bn58v7gRR52djYKFEy9Y8G3nnYNA4Ezpmnx/b5M5c6lplXLEWubbr06n2Ou6cv/ggUKl/DHQKOHZ0DAReNta47kFNZymvTR9EwOUrUHxp7yaba7hEK5gnokC1GJTg/tXTAc3Gkh1SHdEGnc/BSPfHUwYk+WVs4Xg5IeI87UN6+YeUjkzP+RplTvewDf6cM6D7NK4/OUcbV9ky+OV+npqYKnyVD1MEZcHMbpZOt/SWycmizdADKtPrpUWOXWZ/X5F+my7L7szG5lPl/NehYAhRN2Igo505ooDXos7Oz5cVbPP014vAOEwcnEji+S0fbC6UYGMZVaJ/H7/d6vdjf368t++ibLzcUaZIwtOdtpACpHVzm4b1S5K4IibBZJz0yKTedbuseu+qgx0fe+iTlM8MgdxopPpdNUv3tip1tltyobiZVsu9NStjXjlVmVR28ZJDgxtsrPvPFg95fgVz1vd/vFxlT+7a2tsoZCu4BugJ2GScffQyagIo/q+/LASkZ6YwPzR8u9WSKlaDMx5c70KjAySsaG+3moqfJtlKJLywsRFVVRafs7e3F2tpa7ewQRnkyQKa+KcrJ48e5fM2yuEyhvjEiqiUr9Vc6gCH/bBmAS8mer9YEJN2xyYwwHadLIQdFvK7yRQQe0sPeBskLl850zgj1lsrn+62aAIrm/eTkZOztHZxg7oBQ+sLBm8Ctlu8UIZPdoj1wZ5DfEfXNAE2OIdtNnSD+VdXB0rk7gM5/Hyv+fakO6KXSsQEoIh8kfvyah0MjmhO1aJDcsGSAoakt9Gz0rCa1PCtNHraN4cgmI07hYxvduNCoynOj8mG42N/u7B6K2tTEx6b/m65l/RqGjrrfjcJR3pkbcPLTvWl62M7npjLcq6M8et0ZwJABYblcGvDnM/DRRKrDl54yXrV5t01gRvc08YllS/b8lN1M6fJ/GWbxtqqqsvTI8ikXVPZU2BxfAl3nqwNAL0MAJeKwE6T5KGNGMMpyCZrUB42Vv2tHyw2MDqvu7JPlLWSefwawMn42ycXl0FHRAL/ukQC20e/l/ZQvOr2cC7yH8pDJoepkFEb3EBQrIZZ6gO2ino04HA2KODzPsz66jaDzlrX9eqdjBVCo/BXVUGRCYTOhUn24BucCGHEwMeTJCFlSaToKjYgaMGAymw7rkVAoUY1RFbVDQu0GzHcDcJLqHnpOUlIRByfi6v0tUkTknc5SIKBSHWon3+2gPopfXKM+ihwMOLjK7s8MP3/nt3hC5czfHVhl98gT07kn/sI3tZWTW7ulNO5suxId2Vd6Lh5pyWh3dzdWV1drYFvGSePEiJ8r8yZwoPGjcSOfmsCx838YgNlm8DqdTpkvg8GgbKlWgrJ4IBDtSymKQmjbOEGK5n5TG7kc694ujTbHq6oubiXVuTZa+iTYc0PnhlZ1yqtX5IjbjVU++aRdPFxiVjRWfeUSsohzmEf1Sx8RLGXj4/LP64wyugFsI48KuLxkwJQAnUtiKoPAwMvTWPPEbNVPR4KOA+erQCPnLTcoRBw4fK4HVLZyGP19PXpvmY4dIJ89DYB9UhuyZTr2TTKj+3xprI2G+f2pADnHAqC4gRbzpbj4RsksgiJydMzrBB9ZdKINgVKo9DdRrRJX+aprritLCfNNyV4/20FPL+sHBZfLOQRABCjuOfqzXMJxxXEUtSmuYZ5vM5Jt9zeBHB8/93TcwEREkSfKCb1lz29SDhTlj6F1KniCJO8jz3XgshCjcgTDLKPN0/WoQVOE8CjyOtvuy4g7aSYnJ0t/M6/Q5ciXeGRA1J+mRG56zJzz4oUDFEY1ZBi5W4LtpPNAAx5xYHBYriercu5y3soIy9hIFrRbRb+7zvCoiZ95wigKedUEVOmoZTqxbaxJTeCEZXn9nL/knbfRy3Te0ilUn5zXEVHjDcfSI0+q25duHaBoBynbKvDEMWuKiJMymXGgmNmNJvDn7T1q7J5KOhYAZWxsrBxQRAAij0GDL2+C2w0zD5pEtOuRCwc4TeVQabrQTU5OxsmTJ4uXuL9/8I4PKiOtBbOeJq9Aylj1cRcPDxDzCI0SfZU8zPKkwHRqrEAK72Gbmgxbptxc+Nv6l/XXwZnznQDRf8uAiY85Qa4bLZXhW9uV7yT5o7Gcmpoq7+IR6Yjr/f39ckib2igQy7XhiAMjzOVA9lVbw/28G/JZcpiBZ1e6TfxtGxcZK4/cHEWSOXl+epZjreidvkmuyHl/t3vw7ipvE8PpOipA4y5vlgZdc1PzbHNzszZvqTfcIydf+K4hvhdLv6tdyo1hRJces3bs6F46EXREut1uLYKiiJ9vbyZIIq/47YDYvzNAeRS548cyM1nx/z2K4QaderKqqpIfwvGiw+FLLJwTHi3i/bJBckp83COi6AjqFwJfHneQPU85I0hknhFlyXmsMnysMz43lePfmX28FnQsAIpO1VRykoelFUlxgHIUOIk4EGY35hFxJMjxyaVdCREHgjs3N3fohYM67nhnZydWV1eLEvSTQRnV0N8u1BR0FzxGliYmJmJxcbG8J0bJXjzqXS+X0vH+jBJ42dlEco82uz8ra1hqUmg+Tk2K0pVoFnXjffqbES6dGjs1NVU7Q4YRqrm5uTh58mQxIFVVlXGm1y3a3d2NlZWVQ/VKCdLDY5I2tzo3eZVZAiBznWgAPUk784qbxjHzwDJQSeIyg3t+VJaUfwdGbCMBnfqh8VO5nF87Ozs1YKqx4JZgLcVwfqht4r0DFNYr/kt+/JA+ya9AwMzMTAGsOrVY85FLcwIlGnMCFI2j2iw+M5kzAygcKwedal8TEHA91UaZfqAcZdfYPo01x416m9HJNoCib+k56gaOD8GZzwUluM7MzBSnJHNsdY3J4JubmxERNee0aR5ERNm0QX77mTbZ+LE/mSPjfG7T7ezP5UZeL4WOBUAhYuPbKvUbl3QoIE0M9rL1nS2pUOlkA8dnpRwo6NrZo3siomYcPRTt4WeiZVfQbD8FizwTv6QgeT4Ly5Y3yZ0Jwyga5y2VT5uB8sme/d5Wp/eZn8wToOHQh8BEk9iNIQ14VVUFlEjRENioThkX5k3Ia3bwRE+abXQ+6DffrdIE+tqAoxt2HxMfv6OIY5z93VQGIyhcMlHdTYbBwaN7t4yCuWFVFEP1c90+Impt4W4XLnuqXOcR63Ed4kaCHjudI4Ju5tWwDrVd7WU0R/2VjPLgQN/K3QQomnRlE/BnP4ZxCi+Vsvp8uSXi4EWSag/lz+dz5uxojBygiLKlGAf1vObtZeTEl3gdjOiaZFT1MkpIPeV9dhDJiKCDLFIGcPz3azHGTXRFAOUd73hH3H///fGmN70pfuZnfiYiIvr9frzlLW+J973vfbG9vR2vfOUr4+d+7ufi9OnTl10P0e7MzExty1hEPUlJCWUUnDZmZmBEH2bI++Tj3xROKUBFJra2tuLcuXPR7XZLXkLEQYSFwsO1bioPTkSGeiWwVJoESvK4tc9ep+GyLCUbrq6uljM25Dm28S/rv4e4aUA4kYb1tDLF74aBssHdDVyy8Gek8JX70Ol0iqJXfSQZqE6nExsbG6VtKkNKQ/1aW1uLc+fOHeKFR8RcaXCJkUZW5Wj7vMtldm+TEWkiBwNuZIehTPG1gRSdgrq5uRkbGxvl8EMqX7VNkQNdc8Cua3QCaKwZpZDsyzkYGxsrY09QomXYjY2NMt9UjpJUacgkB3IGqDeY1KhyNeaM9qgcvlJD81wHCEZE7d1c4plH8hhBUZIsxzgzwpl+oyPjhpWGuNPpHErUvVTKnBrXFVV1cBiaolo8S0T3iJfqq3giIMO5R11bVQfHDVCv6D1snl7gOWIaf9H+/sWNBxoP5SSyX5IlT6BXVL6qqpLKoIiNeMPxosypP37ekEfCyHt9UyYJgPz3aw1ULhugfPzjH4+f//mfjxe/+MW1629+85vjt3/7t+P9739/LC0txb333huvfe1r44/+6I+uuLERByfERtQBiqPZS2EeGa9vDvQwhloCQUHV0g1PemS+g/riIWg9K0GlwKm/Utj6n+Fd8oLLXppIXA7QpFAExVG2G5o2oea3/nZ0T4Ef1jv3e48aB46HTyqVxQgKoyesS/cTsLgBj6jnirhXozKoxKg0PWrgCp91aXwJZJxPTTzMDE8T+JAsuaFooyzqctSzBOb0Kt04utzoOnlND9XnEYl9zyKFagcjKMrl0NzleHCutIExGnieceJlZfJKMOqAlMtZrre8D0wsVpuaxphzKPtk919No+Xjz+sO8rlM5fOfZXhkVOPgzqB+E2+lK3yeUp8wKub6U+VJjpRbJGI7PVqupUYCrk6nU3II+bx45u0SIOcW9kuZ29m4PFV0WQBlY2MjXv/618d//+//Pf7jf/yP5frq6mq8+93vjl/+5V+Ol73sZRER8Z73vCde8IIXxEc/+tH4pm/6pstqpLyH/f39ghw5ITWBPf+kjdyQutJuUt5t91Gp+PH6EQfb/jj5snwTN27uyURETVnScyIvGFVitIbv6VhbW4vd3d3Y3NwsyD6LiGR8aProWVcQrtiOGiM+lxlkTmhNRAcEBHl+iBQVMI2HvOulpaXaCY97e3uHtopL4bAsGgmNhyJjPtY+7ox+ZTxww6sluSaA4iFjjp0blCal6WM1jHJz56HNCCpHSqDbZceBo/rFcYs4AIqSCQF5AjtGGhg+Vxl8r5V0DqMMGmc/4jwi0rHe2tqKiPqbizXPHAQMBgdvvZXDwIgqE38lOypX8sm+Egx5ImZE1Nrv4+PLUR5BySJuNNpXg9p0jztXXE71Zwlq6BRkW299iUeyKP5RZiMO7+BUdI1AdjAYlOhgthmCy3nU7xpD9YnLe5T1Jt0qB1lRJkXRmub25YKWa0mXBVDuueeeeM1rXhN33313DaA8/PDDsbu7G3fffXe59vznPz/uuOOOeOihh1KAopCXaG1t7dA9g8FBohonGXdWyABRMERtXoK+M5CRgRgKldcjwaUSoTejHQBUev58Vp+uDwYHR2vTk/YwNpWwn1Iooe33+yUhVspYhtZ3M4mHWTvJC/5OQERq8/R5j4OarC30Mtzg8lUADL07P2noZDz0egIuqXS7F18kKIDSFB5XOxhuprHI+kXFKHkWX11ZutHwE0B9bJpCwPz42LQprDY5yMaxjdgWzV3x2qMjTeXRc9U4EyDqmpev+6lPBDj39/eLUm9aiuQJtR61JXDIdswIvPB+fZjI6gCFukn8UJkEY3LUKKfeVz3fJjdZlILzheOo7yYwOiwN68SoXcqtyV76yHZlDg1Bo+YmZYkOT6fTKbsbs2iMnu10OrUX/2kOawy5O1Ljz7NseM6Nn8dEgMJl5azfkn2ecK6lIp/XRwGT7Ldh9PjVoEsGKO973/viE5/4RHz84x8/9NvZs2fL21dJp0+fjrNnz6blPfDAA/H2t7+9tU56ABycJu/9qAnSdG9TOW3AoQ3lU3llu0RcsDJFLEHzLY/MU3Hj7MLnXrqMsBC1lGgT79hOTtgMmGR0JdET51cGmPg/2+WKmMtgNPZsk46jZjJxxEHESm+slSERT/Qsk1j5PI1Rtpwk2RCgFPDi2Dn/vI+sLwNz2bPDzIOjyMEWyxiGxDsuRfqyGped+Jx+1z1+H5U7Q/ARdU9UbecOHgIDr5PfGlvmhfl85TXuKKFMECAx+VXywvu5PKH+q9+UNR9b8SQDFa6XMrkT+Vg06bSrRT73HRhwfNUv/7utja5vXL816TCff5neiYhyJIAihWwXDwTlLj0dKqcxUHuy6CrbRL5waadpLJv4m0Vf9Tu/ryVdEkD5whe+EG9605viAx/4QElUvVK6//7747777iv/r62txe233167RyEyIUFFXHSS7FE7eJroKOCRKSF6nU3hTBrTwWBQkt2I+hVqk/BwHZVhRv0mEEGPmeCEkSO2XfXJK9zf34+1tbXY2NiIfr8fq6urhzwr74eDA8+FGIa/fr1JuAmkeI1bAdUvN8LkA9eMIw4iR2NjY2WLroxQxEF4nu/LkbHhycQzMzNRVVVsbm5Gv98vCXNjYxdPi/R3fvR6vRK21xjqf0Z3lHQ5Ozsbs7Ozsb+/X7aZqv0uk75bIDNIDvIyMKuy2gBm07Xst2EBjtok3ili1ev1otPpRL/frxkeB8csQ0ZbY+nt0HxijpbzVeOqCOPGxkZUVVXGNyKK7iE/CW7oJdORUA6Iz2G13ee8G58mL5eOkMBZZox0j+oQKPSIDB0ftsXr0/1sq6JKV2K4/HnyIIsGMrdDbedSrutotjmTI/JK/MpAABOsOZecJxEXAYjkW8uZkhcCSkW8qX/YNueL800kWdJuQq0+MNLOvme8Jp8zO/dUgZRLAigPP/xwPPHEE/F1X/d15dr+/n585CMfiZ/92Z+N3/3d342dnZ1YWVmpRVEef/zxOHPmTFqmXojWRppUHmqMaPfEmyb0UeSGIAMyTeUJlHBwmRcRcbDbwI2Oey4MrzIEeZQ3rbL0zbVXroNyT757DBm5l5DVyUl+ucKbTUY3tFkdbpD8eRoVN+4Eejz+P6IOfEQaBx18NzZ28TBBARh6MAwV85qDDQIhB9zZGLDPl0M+jlTIGVjlc36N/19Oe8RjOhwsU21yxehjm0VQ6FVqfkYc3uLtXidD8SrLo5GMVgiA0lgyf2QYgELd1jZ/KJtcUs76pfuz+ZJFm5ynbW3wMTnqmayMJmPbBIx9PnBMMhnOdBRlNZtDXGrMAI3ud4AScbA7iP3hickCKNw+Th3Aee8AzXVhRuQHk8/b6FoDjculSwIoL3/5y+Mv/uIvate+7/u+L57//OfHW9/61rj99ttjYmIiPvjBD8brXve6iIh45JFH4tFHH4277rrrshupSa2lCZ0rIo+JRwhnwp4JakSeFMi/M0+T5boSpaLX4Vm6RuWhchmqpYdFT0ffEmYmaHqbXdGpvk6nU3s/z/r6emxsbNQ8UzeWKtMVvQOvTEGRFw7A/L42YpuagIgrH/VJ93Ny+ngqpFpVVQnB6o21LOPUqVNx6tSpcqDa7u5uSYSbnp6O5eXlchDe7OxsDVxOTU1Fr9crY7K7uxtPPPFEiRJEXAz7Li0tlcTqiYmJ4o1qDH1Zk3lXjNA5eMuiUVS42Vi2AcBLoQzo+O/eFilpvo/GZZ7jrvweJo0PBoPabjpX6DpS32VB4ELLnzqUrd/vR6fTKRHcqjqIlqheAg/nuep2Y5T9lkVQ1H9FZng/c2si6q8C8TFTvexz09zMIicRh6N0ztus703U5sQcdd3nPqPM4osvQ7F/EQdvsNZyCstlBMUTZzM943rSZYsOpua27hEol+zzN7bXd+GQ3xw3LRP2er2yvZnPHQVwyIfLdX6uFl0SQFlYWIgXvvCFtWtzc3Nx6tSpcv0Nb3hD3HfffXHy5MlYXFyMN77xjXHXXXdd9g6eiIMQv0J529vbJQlMuQQR7Qr1KEZT8PlM9uFvGbmAOkDRPYyk6JoARVVVxehICTkw0d8Ocvib/la4b2dnJ3q9XjnFsA14UdmQrx4idJDCMjXJs/XPJg+JbWkjKiAa5CYj4UqZilwKaX5+PpaXlwvPBoNBLC0txU033RRbW1slSVa5IpJ/HW8/OztbFJHGhWdibG9vx/r6em2pYHJyMubn52vLpoywqDwHwGo/jzwnn8l398TEryawl9FRgIP3uRwcda9IStpBcpPhE3gXiGuKArmxJ8ijjAoEKHmfALfX68X6+npt6UYAUQDCDZTGiZGONl3iW9U1fgIuWhpWu5SvJLCkvykvIoI6nwv6nbz2sTnKsRjW8Wgj1yVsd5OOUZsFAqQrPUop0nWNhxxcX/4gKCNAkZxI97pTmAF/Ltdr15HO7OKSOSMyzttMH/v80FL+3t5eAdmuF+gEZ/PzUnTCtaarfpLsO9/5zuh2u/G6172udlDblRANjwbYd0RI6LLlE1fGTXQpytQNiJ53A+v5Kg5G2FauI7PPAiveXypP/sYPD3bTQT0EdE1KzP+nwuVk8j41laX+kYfZ2qYrFPciXKlnCtbHiuOXrQ+zPBmabrdbdvBMTk4WBeOAQEuauqaDoOSRed0M92tcOd5UbjpETkeys/9NIMOjepQvKiTnXwZELwXgN43jsKCG56HQ4/N+OeBy4J7pBMoQnQDOL84XOULadad7CED0tuqpqalDh2ax3Tz/yK/5coEMjC8jac7qbcoyQpRHGTmCIRrNbNmHY+67dNo+mdEfZqx9Ljf97tdcF/B/Gne2g3qU8pLpN58/GYDUeLn8sF0ECW5vWL7aS/DKMSHIHQbwEXxn4NLLoN5tmu/Z/7r/qQQuVwxQPvShD9X+n56ejgcffDAefPDBKy26RvJIhQp5bkJE/dAhR8Ju3DJq+43PSyg9f4G/8Xd5uxFR250hNM6XBfK0SSJ2tkHCz0OYFNokaJGS1/ay3d3d8k6PqqonZnn/3eiRr1pD1QRTXZlRULk+0fQ7t+KSfE3Xx8KNFvnvHgXHjSBWQJYnPkZclKPNzc2Ynp6OW265pSz3KGyqM2V0UnCv14svf/nLERFx4403xtLSUszPz5eTk91g8lwCRWEUDRS40Wdubu5QDhINjmSEilW/ZaBE7aARy6JlPk5ZuJkymT3nMtVEGhvtKNPyipZ4aNDJB8m3kgodnNKB8PaqXwKidA6kX9bW1uLJJ5+seciS+fHx8VhYWIjJyclYXFyMpaWlmsHJQDL5ze3u5GtmZOStS9bW19fLyc/MZWG5aofAsPSCjDYPuqSe0X0OVvxb/SEv24BHRk33Z3oiA9X6KK+D51/RUXAd5jLt88ABinir8fe5xARU8YcAm84n26sIii+vi8/SAZzzXjd5pT5rjnA5ijqAfc14fynA46kAKsfiXTzZQDQtGdDDygaW5IrUGc7BzZSdfssUtU8IlS9BJ+CIOHh1uvro4bw2T8A9HCokJcU2eaYZH5w/+pvoP5tYTWUcxee2MWmjS7k3oh4OdSDFezSx9YZSrv2zPvFXeQmbm5slydUjYQSkVBystwnc6UPZZn8yvlApt93jvMmMgfObfGirg2Vn1CQHboSyPmdKOhtftjNrF4GcxomKXoAo4uCttNrNNT09XXKWFGnzCJbPN+YZsG90LLwfyrOR/KgdEfXzOvhxY9Y2JgQfGV+vJR3VtqOIxp9yf6nlqKxM9rJ6VL7P86qqai8JdYAi/UBAe1T9bfotmxd+PetnVkYbNen3a03HAqBwwHSYlqIGriyZXChvwT0L3n+UgvT/PcTsnoXuZ5jVoxSMdtBz8SiQ/qbnyNMP/U2WVKo6VEhr6VkbOXHaiMs6foAQJ6LzsImGUYJtxjciP4fBvQT/XQBN/OD2UfJe4zwzMxPz8/Nlu29VHZwcu7m5GVtbW7G1tRUrKytlHJR8fOutt0ZElO2qm5ubxfvlqw8UAVFEhy+ko+EUQPGD2WTcdH+m1JwvPHeB46h7mwwdyyOfOPYsT2X58yIlGTPJVOvzvV6v7IZRLgjHnXkU5EN2PpD4yLnFZzkXNzY2otfrxe7ubg2MKAn61KlT5awnJTVrfiinQM+RJ3y5pPSR2rm9vV3khuOpPiqvjOOj6KXq0TVfRs2MHkGl6mOkhfLk8uMgmX3MDPswlBnZDGD57wJ80vFadiNJDnwbvcu8vilXrrc1ztlLV7ms63o+02W8Rh3OCD3L1zXKu8aOoNqdW+9vU/TkeqVjAVAi6mH8tle0a3JFHGRp0wPNFDf/bppkDmwiDi+7kHzpIPNW1V4X5CaFQABCpeLl6eh6voU1awuVNPvj9yp8TOXry0+ZUsx4nKH8JsqMYtYPrzcL70ccgBCe5Cl++6Suqqq8lIsvWeMpn1oOUOJsxEVAMj8/XzxfvRxuY2MjnnzyyTKeDlBkHDReUpZqt8bJtw02KbDMA9RY+qGBTbx2j9R573VkZTU5AeKBog4CKtxBJSCsxFCOmXjCssVLAiTvJ41QtsSql6op+bbb7cbCwkLMzMzEqVOn4tZbby0AZXJysoxbt9st281nZmZiZmam1jad38SlGDkYOqtCY6h+qL1NY0hDzXKpU/isA046FwRHGbhhOUeN67DUZLh9vJruFb+Y06H+Eyg70GAdbrTJXwLZTufghFjpBd2n3/QyWO4mI189Qicnm46qrw54JNA/fCZzFrK/jxMdC4BCIeFamz48fZJI1JU3BdInuOphnfpWvRGHk5hIFAKuoXP9kR5MBhBccTR5Q+qjBF1LDdpWxm3Ll8pr/+bOisyrHkbRZH8fNWmawGRbXc4vJ4ZppTho1MTTXq8XZ8+eLTk8MjDr6+slV8ITjpmXo4MFFWnRKaNUoGyHTrCNiEPjT0VHOW6KWmV8c6Wb8S3jbdO1pnHKxrhprNlHGlmdjcSlsqxfbmiYeJwdPEeedzoHkQm2QWOgfKNO5+Ib1GWc6LEKTOhvtVVRXgJGRX05fzlvdQSArlEWt7a2SuRNh9f50lGTY0UZcwfII7mUK5aVyceVGLxLedbliUsq3kfqVBp2BytZH3yJjBFE/u9n9Xgkj6Tr5LvmcEQ0HkXRNFZ0tBl19+gvI/fuaHi5V0JPBeg5FgAlov7yN+5IUZhYgkKFrsHf3t4uykXhOREVOJVMRH1iOmChcDpokNdHAyIv0TPt/bwCKtom5a5+Kpqi00o3NjbKNkivPyOfOGyrew7uMbhR93Bpm6FrQvlOmcFtCl+yP6689K3+8CAwAsmIg9e39/v9+NSnPlW8orGxsdjZ2Skn70q++LwS4AaDQaysrERExIULF8oSEOsUMNJzU1NTMTs7W/O8+C4WHu7mZ56o/sxASVlxWYfynYWW20BFBkaoYF35Ny0J6BrBGqMQ+/sHLwbllnjvr+qgLPOdXJ7Y6JFQXx6bm5uLubm5Wv95DLnGWy/844FujI7xGYEhT4wXyOWLCXnAm7aLasnJ++gGNBsrXfe8J3r1zE/jWUw+/8hnGkWvaxhqc1DawGwWbXZDnC1bNzl75GF2UBr1Bd8Mn+2cYtv5rOtYzZUMbPC8FDpQ+k3l8ABAT5RXPdwhyHEk75qcwGHGr2msriYdC4Dik1CDynC3AwhXslImTQKaUeY5ZIYgWy9kGeyH94XfXHLI+q/73BOk0vCM8qxvfj0DQKw3m4RtYMBJbSQYJD+ansmoSbG1GVXvpwOa7LfBYFCMkMCxb4PNPFmNj5aFsrfeej+z8c76lbU141NWXhOAYXlZnU3U1p9h73WPjkq97VyIpr5wHtAzbQK4utfrJkhjn6RDIuoAhTusBILocUdEzQDxhYQ8d4U6jbk3us9lrI2nGZDP+H65BuZKnvVyLuW3YdueOUrD9tltictRm26KOPowO69D3+4keySLfaCOb2p3UxupN4bl/6WAl6tJxwKgaF03ImqTem1tLXZ3d2NmZiZmZ2cP5RTIc6HXE3Hx5E5tTVtYWKi9+dfJPQ/3eoWsJVDykjqdTs070/o6vVhFdAaDg3wFRT+oZAg8VC69eiXEqr1NkRNXuCIHW9maq6/v8n1CbkAyJcC1WdUxPT19pIF1BdPWL04+zz1QKF40GAzKuPmzGldGMQiKuZbM/ut7Z2cnzp07F1V18G4Xjz5FRFnOGB8fL9uWmVfEej2ZmhFCX85g/zMlmylqKtcmBTeMkmpSypdqUJV0WlVVLfmTyp+yyrKkHzwE71EBLvfqHiWB677BYFCWWDY3N8sJwCsrK7U28Zuhd46XxtfBCPnDZHkuuxAoay6RX6ojImoRPpanelynRRxe+hSPmoCay5CebQNGR1EGJrxer9/nSDbOLl90+Lw8dyL5IZ8FGP0N3LQ9dDpdJtimjJdsm2RHKwf6dgeJ9i9zvPVbE/Bw4NI2348Ca1eLjgVA6XQubs8i2Njf3y9HUBMIcP1Ng+5hM4VqFVZnuC7i8OTIDC6Fk0eNc22aRkHGiFsMqdgiLioJXxdXu3V/Bj64ZNF0T6Y02jxthjx9Tz7zf9RuF3RGcPQtgEK+DSvgTZ5PkwHM2iPZETBhZIPtZZKbnvNEY9/tQKOuxMeqOljqE08JAJgkSmPF/roB9DC380JtcNB0FB8zXh+lpLK6m+ZN09hl93DO7O/v1wwA62rzLumoSJalFzQGzjM6HpyjBBRyctzARRw4M1qSU9ItEyOltziOTFrOIp80dF4n/+eyQNOOjgwMUm96+W2gI2tDm2wNI3tNzzWV5fMk4vA5SnSsMkDg/fWPg2DpQAIdzhfKGZfYfO76slOTDiOwYRQ3azP/dz55HUfN7WFByrWkYwFQSET529vb0el0aiFQXzrhUdR6jksWfEGbclNo4IXK6a0q2kJg4BNfXhmTqTxnRQqRzzlSz5QylXW3e3GXgZL7BOL4Jlz3nJu8Y5UtAKHICXMeqPx8AmeKjDwQEBRQy7YFenubZIA8cePoEzLzKhz8sV4RjYZ7tc6zTqdT+K7IFuUnU5JcV2Z0h4CKYNC9QHpp4nOT90RyZUV+0SPLQtWZ59RW3rAGyZ+VvGjNX2NBPjmJJ+RzJkdMjOZ8Vf8JGjRH6SBlkRGOqwCtEmAdMHiOGcE6o2HSBVkiJr1wRm8JZGm0yR8HI7yW5b+5py+e+Fj7WDZda5MNn5/kQwYesg/rccDA8j09QFHVqjqI2nkfVJ4DFLWTPMvq4tjwXtcvXBLkUp92ZrqdIQj3fol3lwMOn246NgCFilnCpmOfldSohNksl0NCqm8NqLaH8a3K9GKkEPU/k6S4F97RsMAOE6rkmVMBcafC+Ph4bUeADBRRuBII6WWeOnUqIqJ449o1IuPH6EcTEZzwpEO1zxPqaAjVL48C8B7xemxsrLx3hqH0Yajp3gykENiJsgRf8ZtgU2X6NS7BqDzmGWxvb0ev16uVJ5DnSkr/82RUV+LukfN3RkgcAOp5V9giBzoOUNxoZ7xnUrrzOTNaGTkQdaOpJVc5D7zP2+P/Z+PPugjAePLq7u5uibBp3LTdWVuQec6Qwu3su64rwbVpmVgksN7tdsuZKwQrvjSodtIoU168LtZJI8oy6WBoDmdLTREHesufcRnN6ChwkgEZ/u6gvAmgZM9SnjXn1M+9vb2i2xn58LY7AFCbJSviCceKc552QvwkyNXv0qVK2t/b24vNzc2S+6S6fEcRAXAG2tqoCdxdio6+2nQsAIoGjt4rr2lyuAIi8qanIeHm1llft9S3X2tK3DtKKWSDTKDi65Z+PfMO6GEIKExNTZXclqxfWdtUhu8wcv5noeFMqWTeGZeLmA1/lPAPi/wz7zBrq7fTP9mYe1lOR01iL48A2pdvmvrsipdlZUooa0/W1yaD4IZumD77vd7WpvZkRIDIrZ0+x7PnjpLLrJ00Gh6xctnP2szf2L4sP8iNH3f90NhkRpj99jrdSfJ6m2T7Uony08SPpudIbXPb68ioyQA3gZ+me6RjuZRC0OAyxW/amGxpSXV51MRBSwZk2A7mFTXxoA2QXApQcX61zd9rTccCoOzv75c38IopBCS9Xi+mpqbKNl55/4peuAerwZJBZ3g34mAwM4XILYy67oohImrbHNtC7jJW9JIYgm5StkqaYl1TU1Nx6tSpmJmZifX19ZJnoSiBHx6Xed+MDHFiMCHUI0HcxeAhZYERve1XycKKVrUZKE7wzADw3og45CH5hPcxYGRIvGbEi+PhJ7iyHnliWr6SwWEfFEGSt+VJjArjEiSyDLVDbaBcMXeKQNfb6R//3fnLSJmX12Qk24y4k4Ml5lowF2R2drZ4kDxB2ueEg21vRwb2xVdFDRQN4Q7Bzc3NEsXSuExPT9eWhjUnB4NBTE1NHRoHAnK1jUZQ9Xu0RO1WWxkhUzs9WqRv8SEDr5yv1DFZ7gqjLio/S8o/Cog2zV21xe/1+8Qv9cvPJHEeuA5tAuSMPG1tbZUde4pI831P7Gen06kt9UtvZvrex4b2yPVKVVVlOaff79fOtlL/Xb/obzqYzG3SXHaeNo0Xy8zuk9xca5ByLACKJoR2Q2jCSMFrSx6XP+ip6zX2LgjuqThiz8J8HHS2L1vrdS8oIwoYIzy8xnC+1ynFLOOq5ZypqalyLDt5QqKg04OjUEspMMeHfdLEzsLY4pd2TUmpa0lN5R4l5F5X2z3kjxtAHzN5TfqNkQ0vy3NPMoNOhaklPtZP/ne73ZKr4EnHPva+bs3fs7ayjCbK+OjGnMuLJDcqfP5SFZbLCxWtjJDkRztzMnDiHl+2zKu5lQEbyrHLpMaJoMOjf5nCFmDVtwyd5IDJtNoR57yknBEocr4dtXTrYJJtdoDi5fn46Jp7/5l+OYoywHCUDLn8N82BJuOZXSdA5KsmBJSbIiPUcdK74kMTz71O8pzjwMM2qSMcPPDvDJA1gYxMN2R6so1/1xqcRBwTgNLtXtz+R48zou4lywjLCM7OzhblwC15ruQ1IA4wSC4IomygHJx4mDarw+ujQdL7Oxgl6XQ6te2o9HRlHOfn56Pb7cbq6mrZ7cSJ4waOSkeT1TPPMyOQATMCHh37rReryeBoQjdRNlnalK2DB1d0apPupXconusaFW6n06m9C4b1u0fu41xVVclFGBsbqx3opXHzc2uyj+eFEJRmvMoUkhuVDBiQN5QJKiX97cnF6pfTUQqvjSTPU1NThVc8Jp56gG1xoEUesH/iodbt1VcBMwEV5Z3wwDyPHCh3S3/rMzMzU37T7xpDycbu7m70+/3U8CvfxJeY1G4CCuc157fnPrAO8iY740f1agycl+TnMJRFDJrK82/pQr4J3DcfkLJrfp180RjTKdva2jo0v5km4DmGWQTFAYUDFLdPyiPkycKMmGYJ8d4+8ct5rTHw+cu2NtEwIOZq0rEAKGNjY+WcEx05HlFPWpJSmZmZiampqbKzxRVyFh5WWfRgVW+TAfIJFXE4zOaAgN/+t7wPBwvuQemdJX4+R0TUXla2tLQU09PTsb29Haurq7U62U5f3nKgkuXG+LNeNhNi5+bmyqvp5+fna1GlJs/PFXET/3ivX8siDgIoiiplnpbAHutxL5FGTaHdprCuEispD5RFjzxRWbrxiMgBCnnVpJAzI8Bx0zfbQQDGezl2TaCxqR3DkngdESUCqrA7AYqPfxYhcbDnil5jqX654vcXdEbU9YjGVgBcoFwyr+UqLgEI+CgyvLm5WQMjzIPp9/s1o9kEUDzCyjmr+zwhUx/fPaJrHFdub/WxziJbGbmezcATx5/AR0BA0VgBlKYIc5v88X7yQ7bF9Tx1ICPvzGP0JVnW5baEfOCcU1v6/X7RU+I5T7l1gELdrHkjMOe6JdMVDnLIR+cby7vWdGwAig71EmqNOPCw+FZaDSiznemxuqLKwr4iN8oycr4rRuVTYBSO1rOiowyulCU9rOwad/KI2FcJ5/T0dMzNzRW+eUSHRtOVhOfsKFQtxa4lNTdmXLflBGb5DgxEiv5wzdT7mBlZ3peNDQGVJr6Uip5pGiuOry9/ce3Z80fUBg/tc6yzduqj53garS/rkQeSNzdMrItlZAqKRp/Kz8PcGb98LPhRzhHHUduyOa40zFz6ksxo7is6qmcdVOh60w4ajpcbQ8oxQYg+Ln+abzKY9KppkFzu2Sb1hYaMy3vKi1D9ki/mxLEulUu92ARGyCcHL2wnwU0GRtV3vkpEv09OTsbs7Gy6lOnzlTxxmauqqvauJs87cxmjbuP1NhA77LNss8YqA+beF++zOwWaG4xmsT6NRcTBHKUd425KRtXVRuoyByb+0W/81t+XuqR3OXQsAMrU1FTcfPPNhyIGBCxSCBcuXCjfmWHT33y+iZoGxpW0/uZ9mUedle3kE8AnDsGWC4kAjCaxwqA33njjobY3ASTnEfvlSJ0Tze9zQNfpdMrL0nRP5gGfOHEiXvCCFzQawGFRezY2GeBoG6OsPFeW2WQmb2hcs75k/dF9NMCZ7DZRm2zxO3vGDc9RZWbPZgraTw2uqirOnz8fGxsbh9roMs9E0IWFhUMGtK2NbXN0GCXc1CYvm0aIwEfnVujMJpZLwKjnPXcpImJmZuZQG9yoNfXfx8afzeZ7xrem35yXnU4n5ufna7+Nj4/HTTfdFCdPnjwEFpvqahqHiIPcOS7fH6UXjuLNMHolc5pcH7bVPYwuo57Pltia5DWzdYo4KofL68vmadNvTf0ZdlnvculYABSFT4ch7Wx5NlCbwRNwGR8fH5p315qalnRIPI9mRM9s2t7erp0hMgxlr6O4nonRyKPoUgDzcaJOp1NeVXItaBhwMaxjc6nlDDOuTyfRDhxHurbwZ0QjGtGIRjSiEY3oMmgEUEY0ohGNaEQjGtF1R9dd3EchtEsN/Y5oRCMa0YhGNKKnj7jD9mpQp7paJV0l+uIXvxi33377092MEY1oRCMa0YhGdBn0hS98IW677bYrLue6AyiDwSAeeeSR+Jqv+Zr4whe+EIuLi093k54yWltbi9tvv/1Z1e9nY58jnp39HvX52dHniGdnv5+NfY6o93thYSHW19fjlltuuSo7fK67JZ5utxu33nprREQsLi4+qwZa9Gzs97OxzxHPzn6P+vzsoWdjv5+NfY446PfS0tJVK3OUJDuiEY1oRCMa0YiuOxoBlBGNaEQjGtGIRnTd0XUJUKampuJtb3vbs+7Armdjv5+NfY54dvZ71OdnDz0b+/1s7HPEte33dZckO6IRjWhEIxrRiEZ0XUZQRjSiEY1oRCMa0bObRgBlRCMa0YhGNKIRXXc0AigjGtGIRjSiEY3ouqMRQBnRiEY0ohGNaETXHY0AyohGNKIRjWhEI7ru6LoEKA8++GA897nPjenp6bjzzjvjT/7kT57uJl01euCBB+IbvuEbYmFhIW666ab4ju/4jnjkkUdq93zrt35rdDqd2ucHf/AHn6YWXx36D//hPxzq0/Of//zye7/fj3vuuSdOnToV8/Pz8brXvS4ef/zxp7HFV07Pfe5zD/W50+nEPffcExHPjHH+yEc+Ev/4H//juOWWW6LT6cRv/MZv1H6vqip+/Md/PG6++eaYmZmJu+++Oz796U/X7jl//ny8/vWvj8XFxVheXo43vOENsbGx8RT24tKprd+7u7vx1re+NV70ohfF3Nxc3HLLLfHd3/3d8aUvfalWRiYf73jHO57ingxPR431937v9x7qz6te9araPc+0sY6IdI53Op34qZ/6qXLPcRrrYWzUMPr60Ucfjde85jUxOzsbN910U/zIj/xI7O3tXVJbrjuA8qu/+qtx3333xdve9rb4xCc+ES95yUvila98ZTzxxBNPd9OuCn34wx+Oe+65Jz760Y/GBz7wgdjd3Y1XvOIV0ev1avd9//d/fzz22GPl85M/+ZNPU4uvHv29v/f3an36wz/8w/Lbm9/85vhf/+t/xfvf//748Ic/HF/60pfita997dPY2iunj3/847X+fuADH4iIiH/6T/9puee4j3Ov14uXvOQl8eCDD6a//+RP/mT8l//yX+K//bf/Fh/72Mdibm4uXvnKV0a/3y/3vP71r4+/+qu/ig984APxW7/1W/GRj3wkfuAHfuCp6sJlUVu/Nzc34xOf+ET82I/9WHziE5+IX/u1X4tHHnkkvu3bvu3QvT/xEz9RG/83vvGNT0XzL4uOGuuIiFe96lW1/vzKr/xK7fdn2lhHRK2/jz32WPzCL/xCdDqdeN3rXle777iM9TA26ih9vb+/H695zWtiZ2cn/viP/zh+8Rd/Md773vfGj//4j19aY6rrjL7xG7+xuueee8r/+/v71S233FI98MADT2Orrh098cQTVURUH/7wh8u1f/SP/lH1pje96elr1DWgt73tbdVLXvKS9LeVlZVqYmKiev/731+u/b//9/+qiKgeeuihp6iF157e9KY3VV/5lV9ZDQaDqqqeeeMcEdWv//qvl/8Hg0F15syZ6qd+6qfKtZWVlWpqaqr6lV/5laqqqupTn/pUFRHVxz/+8XLP//k//6fqdDrV3/7t3z5lbb8S8n5n9Cd/8idVRFSf//zny7XnPOc51Tvf+c5r27hrRFmfv+d7vqf69m//9sZnni1j/e3f/u3Vy172stq14zzWbqOG0df/+3//76rb7VZnz54t97zrXe+qFhcXq+3t7aHrvq4iKDs7O/Hwww/H3XffXa51u924++6746GHHnoaW3btaHV1NSIiTp48Wbv+P/7H/4gbbrghXvjCF8b9998fm5ubT0fzrip9+tOfjltuuSW+4iu+Il7/+tfHo48+GhERDz/8cOzu7tbG/fnPf37ccccdz5hx39nZiV/6pV+Kf/kv/2V0Op1y/Zk4zqLPfe5zcfbs2dq4Li0txZ133lnG9aGHHorl5eX4+q//+nLP3XffHd1uNz72sY895W2+VrS6uhqdTieWl5dr19/xjnfEqVOn4mu/9mvjp37qpy45BH690Yc+9KG46aab4qu/+qvjh37oh+LcuXPlt2fDWD/++OPx27/92/GGN7zh0G/HdazdRg2jrx966KF40YteFKdPny73vPKVr4y1tbX4q7/6q6Hrvq7eZvzkk0/G/v5+rVMREadPn46//uu/fppade1oMBjED//wD8c3f/M3xwtf+MJy/V/8i38Rz3nOc+KWW26JP//zP4+3vvWt8cgjj8Sv/dqvPY2tvTK68847473vfW989Vd/dTz22GPx9re/Pf7hP/yH8Zd/+Zdx9uzZmJycPKS8T58+HWfPnn16GnyV6Td+4zdiZWUlvvd7v7dceyaOM0ljl81n/Xb27Nm46aabar+Pj4/HyZMnnzFj3+/3461vfWt813d9V+0tt//23/7b+Lqv+7o4efJk/PEf/3Hcf//98dhjj8VP//RPP42tvXx61ateFa997Wvjec97Xnz2s5+NH/3RH41Xv/rV8dBDD8XY2NizYqx/8Rd/MRYWFg4tTx/Xsc5s1DD6+uzZs+m812/D0nUFUJ5tdM8998Rf/uVf1nIxIqK2JvuiF70obr755nj5y18en/3sZ+Mrv/Irn+pmXhV69atfXf5+8YtfHHfeeWc85znPif/5P/9nzMzMPI0te2ro3e9+d7z61a+OW265pVx7Jo7ziOq0u7sb/+yf/bOoqire9a531X677777yt8vfvGLY3JyMv71v/7X8cADDxzL97n883/+z8vfL3rRi+LFL35xfOVXfmV86EMfipe//OVPY8ueOvqFX/iFeP3rXx/T09O168d1rJts1FNF19USzw033BBjY2OHsoEff/zxOHPmzNPUqmtD9957b/zWb/1W/MEf/EHcdtttrffeeeedERHxmc985qlo2lNCy8vL8Xf/7t+Nz3zmM3HmzJnY2dmJlZWV2j3PlHH//Oc/H7/3e78X/+pf/avW+55p46yxa5vPZ86cOZQAv7e3F+fPnz/2Yy9w8vnPfz4+8IEP1KInGd15552xt7cXf/M3f/PUNPAa01d8xVfEDTfcUOT5mTzWERH/9//+33jkkUeOnOcRx2Osm2zUMPr6zJkz6bzXb8PSdQVQJicn46UvfWl88IMfLNcGg0F88IMfjLvuuutpbNnVo6qq4t57741f//Vfj9///d+P5z3veUc+88lPfjIiIm6++eZr3LqnjjY2NuKzn/1s3HzzzfHSl740JiYmauP+yCOPxKOPPvqMGPf3vOc9cdNNN8VrXvOa1vueaeP8vOc9L86cOVMb17W1tfjYxz5WxvWuu+6KlZWVePjhh8s9v//7vx+DwaAAtuNIAief/vSn4/d+7/fi1KlTRz7zyU9+Mrrd7qFlkONKX/ziF+PcuXNFnp+pYy1697vfHS996UvjJS95yZH3Xs9jfZSNGkZf33XXXfEXf/EXNUAqkP41X/M1l9SY64re9773VVNTU9V73/ve6lOf+lT1Az/wA9Xy8nItG/g40w/90A9VS0tL1Yc+9KHqscceK5/Nzc2qqqrqM5/5TPUTP/ET1Z/+6Z9Wn/vc56rf/M3frL7iK76i+pZv+ZanueVXRm95y1uqD33oQ9XnPve56o/+6I+qu+++u7rhhhuqJ554oqqqqvrBH/zB6o477qh+//d/v/rTP/3T6q677qruuuuup7nVV077+/vVHXfcUb31rW+tXX+mjPP6+nr1Z3/2Z9Wf/dmfVRFR/fRP/3T1Z3/2Z2W3yjve8Y5qeXm5+s3f/M3qz//8z6tv//Zvr573vOdVW1tbpYxXvepV1dd+7ddWH/vYx6o//MM/rL7qq76q+q7v+q6nq0tDUVu/d3Z2qm/7tm+rbrvttuqTn/xkbZ5rB8Mf//EfV+985zurT37yk9VnP/vZ6pd+6ZeqG2+8sfru7/7up7lnzdTW5/X19erf/bt/Vz300EPV5z73uer3fu/3qq/7uq+rvuqrvqrq9/uljGfaWItWV1er2dnZ6l3veteh54/bWB9lo6rqaH29t7dXvfCFL6xe8YpXVJ/85Cer3/md36luvPHG6v7777+ktlx3AKWqquq//tf/Wt1xxx3V5ORk9Y3f+I3VRz/60ae7SVeNIiL9vOc976mqqqoeffTR6lu+5VuqkydPVlNTU9Xf+Tt/p/qRH/mRanV19elt+BXSd37nd1Y333xzNTk5Wd16663Vd37nd1af+cxnyu9bW1vVv/k3/6Y6ceJENTs7W/2Tf/JPqscee+xpbPHVod/93d+tIqJ65JFHatefKeP8B3/wB6k8f8/3fE9VVRe3Gv/Yj/1Ydfr06Wpqaqp6+ctffogX586dq77ru76rmp+frxYXF6vv+77vq9bX15+G3gxPbf3+3Oc+1zjP/+AP/qCqqqp6+OGHqzvvvLNaWlqqpqenqxe84AXVf/pP/6lmzK83auvz5uZm9YpXvKK68cYbq4mJieo5z3lO9f3f//2HHMtn2liLfv7nf76amZmpVlZWDj1/3Mb6KBtVVcPp67/5m7+pXv3qV1czMzPVDTfcUL3lLW+pdnd3L6ktnf+/QSMa0YhGNKIRjWhE1w1dVzkoIxrRiEY0ohGNaEQRI4AyohGNaEQjGtGIrkMaAZQRjWhEIxrRiEZ03dEIoIxoRCMa0YhGNKLrjkYAZUQjGtGIRjSiEV13NAIoIxrRiEY0ohGN6LqjEUAZ0YhGNKIRjWhE1x2NAMqIRjSiEY1oRCO67mgEUEY0ohGNaEQjGtF1RyOAMqIRjWhEIxrRiK47GgGUEY1oRCMa0YhGdN3R/weUqE4mNWgyjwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Happy   Sad Surprise  Fear\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()  # __init__의 __에 주의\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 7)  # 클래스 수를 7로 수정\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "net = Net()\n",
        "\n"
      ],
      "metadata": {
        "id": "Cr8GYRC11VL9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "# 손실 함수 설정 (CrossEntropyLoss)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# 옵티마이저 설정 (SGD)\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n"
      ],
      "metadata": {
        "id": "8cPp4-u_1c_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# GPU 사용 가능 여부 확인\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# 네트워크를 GPU로 이동\n",
        "net.to(device)\n",
        "\n",
        "# 클래스 수 (Emotion Detection 데이터셋의 경우 7개)\n",
        "num_classes = 7\n",
        "\n",
        "# 손실 함수 설정 (CrossEntropyLoss)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# 옵티마이저 설정 (SGD)\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "for epoch in range(2):\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        inputs, labels = data\n",
        "\n",
        "        # 데이터와 레이블을 GPU로 이동\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        if i % 2000 == 1999:\n",
        "            print('[%d, %5d] loss: %.3f' %\n",
        "                  (epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Finished Training')\n",
        "\n",
        "# 모델 저장 경로\n",
        "PATH = './emotion_detection_net.pth'\n",
        "# 모델의 state_dict 저장\n",
        "torch.save(net.state_dict(), PATH)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "S47S4vpk1gBp",
        "outputId": "2e877f89-a00a-4483-b4ed-7a546e775505"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-c4a1e3c8a4d0>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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---> 25\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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     26\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-28-4fe531fbb9ff>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\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     17\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: shape '[-1, 400]' is invalid for input of size 5184"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "HAhXRnaU8H-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataiter = iter(testloader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))\n",
        "\n",
        "net = Net()\n",
        "net.load_state_dict(torch.load(PATH))\n",
        "outputs = net(images)\n",
        "_, predicted = torch.max(outputs, 1)\n",
        "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
        "                              for j in range(4)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "_PlFEbnW2OV-",
        "outputId": "61a07b06-ef37-4909-9fc9-63337d4980b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GroundTruth:    cat  ship  ship plane\n",
            "Predicted:    cat  ship truck  ship\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
        "    100 * correct / total))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "ZSpiip6D2bpu",
        "outputId": "9ac3b3e8-35f7-4b20-9c04-103d6bda787b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-5ef0030d31c6>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtestloader\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      5\u001b[0m         \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'testloader' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ocPxH_79XOM",
        "outputId": "3a41449c-62e4-48c8-b8e6-d2a99865cd22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Google Drive에서 Colab으로 압축 파일 복사\n",
        "source_path = './drive/MyDrive/archive.zip'\n",
        "destination_path = './'\n",
        "\n",
        "shutil.copy(source_path, destination_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "lJeEUtdQ9v0v",
        "outputId": "72851a74-e190-40d7-e924-fde68dfdb7b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'./archive.zip'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "\n",
        "# 압축 파일 경로\n",
        "zip_file_path = './archive.zip'\n",
        "# 압축을 해제할 디렉토리\n",
        "extract_path = './'\n",
        "\n",
        "# 압축 해제\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(extract_path)\n"
      ],
      "metadata": {
        "id": "LmX1c3Wf-Mqj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.datasets import ImageFolder\n",
        "\n",
        "# 데이터셋 경로 설정\n",
        "data_dir = './archive/train'\n",
        "\n",
        "# 데이터 전처리 설정\n",
        "transform = transforms.Compose([transforms.ToTensor()])\n",
        "\n",
        "# Emotion Detection 데이터셋 로드\n",
        "dataset = ImageFolder(root=data_dir, transform=transform)\n",
        "\n",
        "# 데이터셋에서 이미지 크기 확인\n",
        "sample_img, _ = dataset[0]  # 데이터셋의 첫 번째 이미지\n",
        "image_size = sample_img.shape  # 이미지 크기 확인\n",
        "\n",
        "print(\"이미지 크기:\", image_size)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEJwa-kZBfD1",
        "outputId": "97155802-5aa5-4155-acc5-d04f9f7979cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "이미지 크기: torch.Size([3, 48, 48])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn.functional as F\n",
        "\n",
        "# 새로운 이미지 크기 (Emotion Detection 데이터셋의 크기에 맞춰 조정)\n",
        "# 데이터셋 이미지 크기\n",
        "image_size = (3, 48, 48)\n",
        "\n",
        "# 데이터 전처리 설정\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize(image_size[1:]),  # 이미지 크기 조정\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "])\n",
        "\n",
        "# Emotion Detection 데이터셋 로드\n",
        "trainset = torchvision.datasets.ImageFolder(root='./archive/train', transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        # 수정된 입력 크기 (48x48 이미지에 대한 계산)\n",
        "        self.fc1 = nn.Linear(16 * 10 * 10, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 7)  # 클래스 수를 7로 수정\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 10 * 10)  # 수정된 입력 크기\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "# 클래스 수 (Emotion Detection 데이터셋의 경우 7개)\n",
        "num_classes = 7\n",
        "\n",
        "# 손실 함수 설정 (CrossEntropyLoss)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# 모델 정의\n",
        "net = Net()\n",
        "\n",
        "# GPU 사용 가능 여부 확인\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# 네트워크를 GPU로 이동\n",
        "net.to(device)\n",
        "\n",
        "\n",
        "# 옵티마이저 설정 (SGD)\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "for epoch in range(2):\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        inputs, labels = data\n",
        "\n",
        "        # 데이터와 레이블을 GPU로 이동\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        if i % 2000 == 1999:\n",
        "            print('[%d, %5d] loss: %.3f' %\n",
        "                  (epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Finished Training')\n",
        "\n",
        "# 모델 저장 경로\n",
        "PATH = './emotion_detection_net.pth'\n",
        "# 모델의 state_dict 저장\n",
        "torch.save(net.state_dict(), PATH)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "kJj_AAZZCAHp",
        "outputId": "7d57cef5-68af-491c-9afc-f2294cac138e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-fa0c1826f829>\u001b[0m in \u001b[0;36m<cell line: 63>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\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---> 72\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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     73\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-14-fa0c1826f829>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 수정된 입력 크기\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: shape '[-1, 1600]' is invalid for input of size 5184"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 첫 번째 배치 로드\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# 배치 크기 확인\n",
        "batch_size = images.size(0)\n",
        "\n",
        "print(\"배치 크기:\", batch_size)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EeX4qUV0C01e",
        "outputId": "de0973a4-28ab-4c46-e59e-91f91e323c6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "배치 크기: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import pairwise_distances_argmin_min\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 가상의 사용자 데이터 생성\n",
        "user_data = np.array([[1, 2, 3, 4],\n",
        "                     [3, 4, 5, 6],\n",
        "                     [2, 3, 4, 5],\n",
        "                     [5, 6, 7, 8],\n",
        "                     [1, 1, 2, 2],\n",
        "                     [7, 8, 9, 10]])\n",
        "\n",
        "user_sub_data = np.array([[4, 2, 3, 4],\n",
        "                     [5, 4, 5, 6],\n",
        "                     [9, 3, 4, 5],\n",
        "                     [5, 6, 7, 8],\n",
        "                     [1, 1, 3, 2],\n",
        "                     [7, 8, 1, 10]])\n",
        "\n",
        "user_class = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]\n",
        "\n",
        "# 클러스터링 수행 (K=3)\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(user_data)\n",
        "labels = kmeans.labels_\n",
        "\n",
        "for i, user_label in enumerate(labels):\n",
        "    user_class[i][0] = user_label + 1\n",
        "\n",
        "# 클러스터링 수행 (K=3)\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(user_sub_data)\n",
        "labels = kmeans.labels_\n",
        "\n",
        "for i, user_label in enumerate(labels):\n",
        "    user_class[i][1] = user_label + 1\n",
        "\n",
        "# 시각화\n",
        "colors = ['r', 'g', 'b', 'c', 'm', 'y']\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "for i in range(6):\n",
        "    plt.scatter(user_data[i][0], user_data[i][1], c=colors[user_class[i][0] - 1], label=f'User {i + 1} (Data)')\n",
        "    plt.scatter(user_sub_data[i][0], user_sub_data[i][1], c=colors[user_class[i][1] - 1], label=f'User {i + 1} (Sub Data)')\n",
        "\n",
        "plt.xlabel('feature')\n",
        "plt.ylabel('position')\n",
        "plt.legend(loc='upper right', bbox_to_anchor=(1.25, 1.0))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "i9pJhivZyr6C",
        "outputId": "a979fbb0-52e9-4940-e79d-8b9adbd2533e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# 각 카테고리에 대한 키워드 목록 생성\n",
        "data_keywords = {\n",
        "'Category': [ \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\", \"Data\",\n",
        "\"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\", \"Web\",\n",
        "\"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\", \"System\",\n",
        "\"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\", \"Design\",\n",
        "\"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\", \"Plan\",\n",
        "\"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\", \"Security\"],\n",
        "'Skills': [\"data analysis\", \"data mining\", \"data modeling\", \"data visualization\", \"machine learning\", \"big data\", \"statistical analysis\", \"data preprocessing\", \"data cleansing\", \"business intelligence\",\n",
        "\"data science\", \"data engineering\", \"data warehousing\", \"ETL\", \"data governance\", \"data integration\", \"predictive analytics\", \"time series analysis\", \"data exploration\", \"data storytelling\",\n",
        "\"data interpretation\", \"feature engineering\", \"data-driven decision making\", \"data quality\", \"data pipeline\", \"data cleaning\", \"data transformation\", \"data wrangling\", \"data analytics\",\n",
        "\"data insight\", \"data architecture\", \"data strategy\", \"data lifecycle\", \"data validation\", \"data architecture\", \"data mining techniques\", \"big data technologies\", \"data modeling tools\", \"data visualization tools\",\n",
        "\"machine learning algorithms\", \"deep learning\", \"neural networks\", \"natural language processing\", \"computer vision\", \"data preprocessing tools\", \"data analysis tools\", \"data visualization tools\", \"data governance frameworks\",\n",
        "\"data integration tools\", \"predictive analytics software\", \"front-end development\", \"back-end development\", \"web design\", \"user experience design\", \"web standards\", \"web accessibility\", \"frameworks\", \"version control\", \"web security\", \"UI/UX design\",\n",
        "\"performance optimization\", \"web service deployment\", \"responsive design\", \"cross-browser compatibility\", \"HTML\", \"CSS\", \"JavaScript\", \"React\", \"Angular\", \"Vue.js\",\n",
        "\"Node.js\", \"Python\", \"Django\", \"Flask\", \"REST API\", \"UI/UX design tools\", \"responsive web design\", \"web development tools\", \"web frameworks\", \"version control systems\", \"web security best practices\",\n",
        "\"web performance optimization\", \"responsive web design principles\", \"front-end libraries\", \"back-end frameworks\", \"web deployment strategies\", \"web application architecture\", \"responsive web design tools\", \"web development best practices\", \"node\", \"front\", \"back\", \"css\", \"redis\", \"server\", \"route\", \"dns\", \"dhcp\", \"ip\", \"subnetmask\", \"system architecture\", \"network management\", \"operating systems\", \"server management\", \"performance optimization\", \"virtualization\", \"container technology\", \"database management\", \"cloud computing\", \"problem-solving\",\n",
        "\"system design\", \"network administration\", \"Linux\", \"Windows Server\", \"system administration\", \"performance tuning\", \"virtualization technologies\", \"container orchestration\", \"database administration\", \"cloud platforms\",\n",
        "\"problem-solving skills\", \"system analysis\", \"network configuration\", \"operating system maintenance\", \"server deployment\", \"system architecture design\", \"network monitoring\", \"cloud service deployment\", \"troubleshooting\", \"system documentation\",\n",
        "\"network security\", \"server security\", \"database security\", \"cloud security\", \"troubleshooting methodologies\", \"system architecture tools\", \"network management tools\", \"operating system administration\", \"server management tools\", \"virtualization tools\",\n",
        "\"containerization tools\", \"database management tools\", \"cloud computing platforms\", \"troubleshooting tools\", \"system documentation tools\", \"network security practices\", \"server security practices\", \"database security practices\", \"cloud security practices\", \"pwntool\", \"creativity\", \"graphic design\", \"user interface design\", \"user experience design\", \"design tools\", \"communication skills\", \"user-centered design\", \"design trends\", \"color theory\", \"layout design\",\n",
        "\"design system\", \"web design\", \"mobile design\", \"visual design\", \"illustration\", \"typography\", \"prototyping\", \"wireframing\", \"design thinking\", \"motion graphics\",\n",
        "\"icon design\", \"branding\", \"logo design\", \"poster design\", \"print design\", \"digital design\", \"sketching\", \"storyboarding\", \"design collaboration\", \"design software\",\n",
        "\"creative problem solving\", \"conceptual thinking\", \"visual storytelling\", \"empathy in design\", \"user testing\", \"usability\", \"interaction design\", \"information architecture\", \"design research\", \"design critique\",\n",
        "\"user feedback\", \"design principles\", \"design tools and software\", \"design thinking process\", \"visual design tools\", \"prototyping tools\", \"wireframing tools\", \"design collaboration tools\", \"design software tools\", \"figma\", \"strategic planning\", \"project management\", \"business understanding\", \"user-centered design\", \"decision-making skills\", \"project planning\", \"scheduling\", \"domain knowledge\", \"team collaboration\", \"communication skills\",\n",
        "\"project coordination\", \"resource management\", \"project delivery\", \"scope definition\", \"stakeholder engagement\", \"risk management\", \"business analysis\", \"requirement gathering\", \"business strategy\", \"project leadership\",\n",
        "\"problem-solving\", \"goal setting\", \"project metrics\", \"KPIs\", \"milestone tracking\", \"project management methodologies\", \"business process improvement\", \"business process modeling\", \"project management software\", \"agile methodology\",\n",
        "\"Scrum\", \"Lean\", \"Kanban\", \"PRINCE2\", \"PMBOK\", \"Agile frameworks\", \"business process improvement tools\", \"business process modeling tools\", \"project management software tools\", \"agile methodology tools\", \"Scrum tools\",\n",
        "\"Lean tools\", \"Kanban tools\", \"PRINCE2 tools\", \"PMBOK tools\", \"Agile frameworks tools\", \"idea\", \"insight\", \"stamina\", \"energy\", \"security expertise\", \"hacking prevention\", \"network security\", \"encryption technologies\", \"vulnerability scanning\", \"cyber threat awareness\", \"security policy development\", \"risk management\", \"regulatory compliance\", \"security certification\",\n",
        "\"ethical hacking\", \"penetration testing\", \"intrusion detection\", \"firewall management\", \"access control\", \"security audits\", \"incident response\", \"security assessment\", \"compliance auditing\", \"security awareness\",\n",
        "\"security best practices\", \"security risk assessment\", \"security analysis\", \"security tools\", \"security standards\", \"security protocols\", \"cybersecurity\", \"information security\", \"data security\", \"network security tools\",\n",
        "\"vulnerability scanning tools\", \"firewall management tools\", \"security audits tools\", \"incident response tools\", \"security assessment tools\", \"compliance auditing tools\", \"security awareness tools\", \"security risk assessment tools\", \"security analysis tools\",\n",
        "\"security tools and software\", \"security standards tools\", \"security protocols tools\", \"cybersecurity tools\", \"information security tools\", \"data security tools\", \"ctf\", \"kernel\", \"rev\", \"pwn\", \"web\"]\n",
        "}\n",
        "\n",
        "category_data = data_keywords['Category']\n",
        "skills_data = data_keywords['Skills']\n",
        "\n",
        "# 데이터프레임 생성\n",
        "df = pd.DataFrame({'Category': category_data, 'Skills': skills_data})\n",
        "\n",
        "# 데이터프레임을 CSV 파일로 저장\n",
        "df.to_csv('./it_job_skills_extended.csv', index=False)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUDeyw5yHlbM",
        "outputId": "32690ff4-ba41-4065-81b0-7cb136970ba0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data visualization\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import joblib\n",
        "\n",
        "# 사용자가 지정한 모델 저장 경로 및 파일 이름\n",
        "model_path = input(\"./\")\n",
        "\n",
        "# 모델 저장\n",
        "joblib.dump(classifier, model_path)\n",
        "\n",
        "print(\"모델이 저장되었습니다:\", model_path)\n",
        "\n",
        "# CSV 파일에서 데이터 불러오기\n",
        "df = pd.read_csv('it_job_skills_extended.csv')\n",
        "\n",
        "# Category 열을 타겟 변수로, Skills 열을 텍스트 데이터로 사용\n",
        "X = df['Skills']\n",
        "y = df['Category']\n",
        "\n",
        "# 텍스트 데이터를 TF-IDF 벡터로 변환\n",
        "tfidf_vectorizer = TfidfVectorizer(max_features=1000, stop_words='english')\n",
        "X_tfidf = tfidf_vectorizer.fit_transform(X)\n",
        "\n",
        "# 학습 및 테스트 데이터로 분할\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# 다중 분류를 위한 Naive Bayes 모델 초기화 및 학습\n",
        "classifier = MultinomialNB()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# 모델 예측\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# 정확도 및 분류 보고서 출력\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
        "print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQIAyh76Ip5M",
        "outputId": "5e8211ba-2109-4816-a6a8-79cbd4f5af26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "./model2.pkl\n",
            "모델이 저장되었습니다: model2.pkl\n",
            "Accuracy: 60.00%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Data       0.88      0.70      0.78        10\n",
            "      Design       1.00      0.25      0.40         8\n",
            "        Plan       0.90      0.69      0.78        13\n",
            "    Security       1.00      0.43      0.60         7\n",
            "      System       0.30      0.90      0.45        10\n",
            "         Web       0.86      0.50      0.63        12\n",
            "\n",
            "    accuracy                           0.60        60\n",
            "   macro avg       0.82      0.58      0.61        60\n",
            "weighted avg       0.81      0.60      0.62        60\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import seaborn as sns\n",
        "\n",
        "# CSV 파일에서 데이터 불러오기\n",
        "df = pd.read_csv('it_job_skills_extended.csv')\n",
        "\n",
        "# Category 열을 타겟 변수로, Skills 열을 텍스트 데이터로 사용\n",
        "X = df['Skills']\n",
        "y = df['Category']\n",
        "\n",
        "# 텍스트 데이터를 TF-IDF 벡터로 변환\n",
        "tfidf_vectorizer = TfidfVectorizer(max_features=1000, stop_words='english')\n",
        "X_tfidf = tfidf_vectorizer.fit_transform(X)\n",
        "\n",
        "# 학습 및 테스트 데이터로 분할\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# 다중 분류를 위한 Naive Bayes 모델 초기화 및 학습\n",
        "classifier = MultinomialNB()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# 모델 예측\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# 정확도 및 분류 보고서 출력\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred, output_dict=True)\n",
        "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
        "print(report)\n",
        "\n",
        "# 분류 보고서 시각화\n",
        "report_df = pd.DataFrame(report).transpose()\n",
        "\n",
        "# 정밀도, 재현율, F1 점수 시각화\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.heatmap(report_df[['precision', 'recall', 'f1-score']], annot=True, cmap='coolwarm', fmt='.2f')\n",
        "plt.title('Classification Report')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "JaksduvVcUJb",
        "outputId": "57c6b2bd-0bdf-4422-e166-3f800581dd1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 60.00%\n",
            "{'Data': {'precision': 0.875, 'recall': 0.7, 'f1-score': 0.7777777777777777, 'support': 10}, 'Design': {'precision': 1.0, 'recall': 0.25, 'f1-score': 0.4, 'support': 8}, 'Plan': {'precision': 0.9, 'recall': 0.6923076923076923, 'f1-score': 0.7826086956521738, 'support': 13}, 'Security': {'precision': 1.0, 'recall': 0.42857142857142855, 'f1-score': 0.6, 'support': 7}, 'System': {'precision': 0.3, 'recall': 0.9, 'f1-score': 0.45000000000000007, 'support': 10}, 'Web': {'precision': 0.8571428571428571, 'recall': 0.5, 'f1-score': 0.631578947368421, 'support': 12}, 'accuracy': 0.6, 'macro avg': {'precision': 0.8220238095238095, 'recall': 0.5784798534798534, 'f1-score': 0.6069942367997289, 'support': 60}, 'weighted avg': {'precision': 0.8122619047619047, 'recall': 0.6, 'f1-score': 0.6238439698279515, 'support': 60}}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 불러오기\n",
        "loaded_classifier = joblib.load(\"model2.pkl\")\n",
        "\n",
        "# 불러온 모델을 사용하여 예측\n",
        "new_text = [\"javascript develop\",\"java\"]  # 실제 사용하고자 하는 텍스트 데이터\n",
        "new_text_tfidf = tfidf_vectorizer.transform(new_text)  # TF-IDF 벡터로 변환\n",
        "predicted_category = loaded_classifier.predict(new_text_tfidf)  # 분류 예측\n",
        "\n",
        "print(\"예측된 카테고리:\", predicted_category[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_n8-OD9kflIj",
        "outputId": "cad64cda-9e43-489a-e238-3493bf847b3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "예측된 카테고리: Web\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t"
      ],
      "metadata": {
        "id": "ZRvPmaTAy1T1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 모델 로드\n",
        "loaded_classifier = joblib.load(\"model2.pkl\")\n",
        "\n",
        "category_length = int(input(\"Category length: \"))\n",
        "user_skills = []\n",
        "user_skill_donate = [0, 0, 0, 0, 0, 0]\n",
        "category_names = [\"Data\", \"Design\", \"Plan\", \"Security\", \"System\", \"Web\"]\n",
        "\n",
        "# 카테고리 이름을 인덱스로 매핑\n",
        "category_index = {category: i for i, category in enumerate(category_names)}\n",
        "\n",
        "# 색상 지정\n",
        "colors = ['skyblue', 'lightcoral', 'lightgreen', 'lightblue', 'plum', 'gold']\n",
        "\n",
        "for i in range(category_length):\n",
        "    skill = input(\"Write your skill in English: \")\n",
        "    user_skills.append(skill)\n",
        "\n",
        "new_text_tfidf = tfidf_vectorizer.transform(user_skills)  # TF-IDF 벡터로 변환\n",
        "\n",
        "predicted_categories = loaded_classifier.predict(new_text_tfidf)  # 분류 예측\n",
        "\n",
        "for category in predicted_categories:\n",
        "    user_skill_donate[category_index[category]] += 1\n",
        "\n",
        "percentages = [user_skill_donate[i] / category_length * 100 for i in range(6)]  # percentages 리스트 정의\n",
        "\n",
        "# 그래프 그리기\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(category_names, percentages, color=colors)  # percentages 리스트를 사용\n",
        "\n",
        "plt.xlabel('Categories')\n",
        "plt.ylabel('Percentage (%)')\n",
        "plt.title('User Skills Distribution by Category')\n",
        "\n",
        "# 색상을 카테고리에 따라 지정\n",
        "for bar, color in zip(bars, colors):\n",
        "    bar.set_color(color)\n",
        "\n",
        "plt.ylim(0, 100)  # y-축 범위 설정\n",
        "plt.xticks(rotation=45)  # x-축 레이블 회전\n",
        "plt.show()\n",
        "\n",
        "plt.figure()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IVljI55SgRD5",
        "outputId": "418cce1b-01ee-4c9f-bdbf-2eb3a51a3a5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category length: 30\n",
            "Write your skill in English: data analysis\n",
            "Write your skill in English: datamining\n",
            "Write your skill in English: machine learning\n",
            "Write your skill in English: big data\n",
            "Write your skill in English: ip\n",
            "Write your skill in English: route\n",
            "Write your skill in English: dns\n",
            "Write your skill in English: css\n",
            "Write your skill in English: back\n",
            "Write your skill in English: front\n",
            "Write your skill in English: idea\n",
            "Write your skill in English: subnetmask\n",
            "Write your skill in English: web security'\n",
            "Write your skill in English: version control\n",
            "Write your skill in English: frameworks\n",
            "Write your skill in English: figma\n",
            "Write your skill in English: user feedback\n",
            "Write your skill in English: Lean\n",
            "Write your skill in English: Scrum\n",
            "Write your skill in English: Kanban\n",
            "Write your skill in English: PMBOK\n",
            "Write your skill in English: business process improvement tools\n",
            "Write your skill in English: cyber threat awareness\n",
            "Write your skill in English: data cleaning\n",
            "Write your skill in English: statistical analysis\n",
            "Write your skill in English: data preprocessing\n",
            "Write your skill in English: rev\n",
            "Write your skill in English: pwn\n",
            "Write your skill in English: web\n",
            "Write your skill in English: design software tools\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}