Face_recognition_in_the_wild.ipynb

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        "<a href=\"https://colab.research.google.com/github/julurisaichandu/face_recognition/blob/main/Face_recognition_in_the_wild.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "Mounted at /content/drive\n"
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            "Archive:  /content/drive/MyDrive/faces.zip\n",
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          ]
        }
      ],
      "source": [
        "!unzip /content/drive/MyDrive/faces.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Rename Celebrity Faces Dataset to CFD and then proceed to run the below cell"
      ],
      "metadata": {
        "id": "W6PRMnBEya8-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path = '/content/Celebrity Faces Dataset'\n"
      ],
      "metadata": {
        "id": "qNxBTg-GaK1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "def load_and_split_dataset(dataset_path, test_size=0.2, val_size=0.25):\n",
        "    images = []\n",
        "    labels = []\n",
        "    label_names = os.listdir(dataset_path)\n",
        "    label_encoder = LabelEncoder()\n",
        "    encoded_labels = label_encoder.fit_transform(label_names)\n",
        "    label_map = dict(zip(label_encoder.classes_, encoded_labels))\n",
        "\n",
        "    for label in label_names:\n",
        "        class_path = os.path.join(dataset_path, label)\n",
        "        for img_name in os.listdir(class_path):\n",
        "            img_path = os.path.join(class_path, img_name)\n",
        "            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
        "            img = cv2.resize(img, (64, 64))\n",
        "            images.append(img.flatten())\n",
        "            labels.append(label_map[label])\n",
        "\n",
        "    images = np.array(images)\n",
        "    labels = np.array(labels)\n",
        "\n",
        "    X_temp, X_test, y_temp, y_test = train_test_split(images, labels, test_size=test_size, stratify=labels, random_state=42)\n",
        "\n",
        "    X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=val_size, stratify=y_temp, random_state=42)  # 0.25 x 0.8 = 0.2\n",
        "\n",
        "    return X_train, X_val, X_test, y_train, y_val, y_test, label_encoder\n",
        "\n",
        "X_train, X_val, X_test, y_train, y_val, y_test, label_encoder = load_and_split_dataset(dataset_path)\n",
        "\n",
        "print(f'Train size: {X_train.shape[0]}, Validation size: {X_val.shape[0]}, Test size: {X_test.shape[0]}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cErCI-KRa0Fu",
        "outputId": "bec70144-56b3-4c80-dc6d-1cbeaa46d39f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train size: 1080, Validation size: 360, Test size: 360\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "\n",
        "svm_classifier = SVC(kernel='linear', C=0.2, random_state=42)\n",
        "\n",
        "svm_classifier.fit(X_train, y_train)\n",
        "\n",
        "y_val_pred = svm_classifier.predict(X_val)\n",
        "\n",
        "val_accuracy = accuracy_score(y_val, y_val_pred)\n",
        "print(f'Validation Accuracy: {val_accuracy}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzpGEwUxbZUV",
        "outputId": "6eaef6e6-659d-44f4-a00f-2b454102168e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 0.25833333333333336\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_pred = svm_classifier.predict(X_test)\n",
        "\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "print(f'Test Accuracy: {test_accuracy}')\n",
        "\n",
        "print(classification_report(y_test, y_test_pred, target_names=label_encoder.classes_))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ts6bMkJHcVFQ",
        "outputId": "017df300-51ab-4b4c-ca2d-0bd1dbc4e983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.19722222222222222\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "    Angelina Jolie       0.27      0.30      0.29        20\n",
            "         Brad Pitt       0.04      0.05      0.04        20\n",
            " Denzel Washington       0.13      0.15      0.14        20\n",
            "      Hugh Jackman       0.12      0.10      0.11        20\n",
            " Jennifer Lawrence       0.05      0.05      0.05        20\n",
            "       Johnny Depp       0.18      0.15      0.16        20\n",
            "      Kate Winslet       0.31      0.45      0.37        20\n",
            " Leonardo DiCaprio       0.16      0.15      0.15        20\n",
            "         Megan Fox       0.00      0.00      0.00        20\n",
            "   Natalie Portman       0.24      0.20      0.22        20\n",
            "     Nicole Kidman       0.20      0.15      0.17        20\n",
            "  Robert Downey Jr       0.11      0.10      0.11        20\n",
            "    Sandra Bullock       0.48      0.62      0.54        40\n",
            "Scarlett Johansson       0.09      0.10      0.09        20\n",
            "        Tom Cruise       0.11      0.10      0.10        20\n",
            "         Tom Hanks       0.19      0.15      0.17        20\n",
            "        Will Smith       0.13      0.10      0.11        20\n",
            "\n",
            "          accuracy                           0.20       360\n",
            "         macro avg       0.16      0.17      0.17       360\n",
            "      weighted avg       0.18      0.20      0.19       360\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CNN based model"
      ],
      "metadata": {
        "id": "W1hFZ7tkfQne"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir 'new_CFD'"
      ],
      "metadata": {
        "id": "AiCVBnWNfUiT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "original_dataset_path = '/content/Celebrity Faces Dataset'\n",
        "target_dataset_path = '/content/new_CFD'\n",
        "\n",
        "for split in ['train', 'val']:\n",
        "    os.makedirs(os.path.join(target_dataset_path, split), exist_ok=True)\n",
        "\n",
        "celebrities = os.listdir(original_dataset_path)\n",
        "for celeb in celebrities:\n",
        "    images = os.listdir(os.path.join(original_dataset_path, celeb))\n",
        "    train_imgs, val_imgs = train_test_split(images, test_size=0.2, random_state=42)\n",
        "\n",
        "    for split, image_list in [('train', train_imgs), ('val', val_imgs)]:\n",
        "        dir_path = os.path.join(target_dataset_path, split, celeb)\n",
        "        os.makedirs(dir_path, exist_ok=True)\n",
        "\n",
        "        for img in image_list:\n",
        "            src = os.path.join(original_dataset_path, celeb, img)\n",
        "            dst = os.path.join(dir_path, img)\n",
        "            shutil.copy(src, dst)\n"
      ],
      "metadata": {
        "id": "ysTxtTWlcwYX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, models, transforms\n",
        "from torch.utils.data import DataLoader\n"
      ],
      "metadata": {
        "id": "fM_RgiQ-fjNl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}\n"
      ],
      "metadata": {
        "id": "CUaWD3Bofnvy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = 'new_CFD'\n",
        "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}\n",
        "dataloaders = {x: DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4) for x in ['train', 'val']}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
        "class_names = image_datasets['train'].classes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgOwqfq-fqU2",
        "outputId": "226e7428-6fd2-4b61-cbdf-3bffe741e457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torchviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVY5NuzwJoys",
        "outputId": "71edf510-8696-4fe3-eebe-49e2f7ba7452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchviz\n",
            "  Downloading torchviz-0.0.2.tar.gz (4.9 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from torchviz) (2.2.1+cu121)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from torchviz) (0.20.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->torchviz) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->torchviz) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->torchviz) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->torchviz) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->torchviz) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->torchviz) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->torchviz)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->torchviz)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->torchviz)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch->torchviz)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch->torchviz)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch->torchviz)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch->torchviz)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch->torchviz)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch->torchviz)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch->torchviz)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch->torchviz)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->torchviz) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->torchviz)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->torchviz) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->torchviz) (1.3.0)\n",
            "Building wheels for collected packages: torchviz\n",
            "  Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchviz: filename=torchviz-0.0.2-py3-none-any.whl size=4132 sha256=744c626fa50d7570e3ac479f3833ccaa3686f877fbad6bcc5138ac95d0fc33da\n",
            "  Stored in directory: /root/.cache/pip/wheels/4c/97/88/a02973217949e0db0c9f4346d154085f4725f99c4f15a87094\n",
            "Successfully built torchviz\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torchviz\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 torchviz-0.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# class SimpleCNN(nn.Module):\n",
        "#     def __init__(self, num_classes=18):\n",
        "#         super(SimpleCNN, self).__init__()\n",
        "#         self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)\n",
        "#         self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)\n",
        "#         self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)\n",
        "#         self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "#         self.dropout = nn.Dropout(0.5)\n",
        "#         self.fc1 = nn.Linear(128 * 28 * 28, 512)\n",
        "#         self.fc2 = nn.Linear(512, num_classes)\n",
        "#         self.relu = nn.ReLU()\n",
        "\n",
        "#     def forward(self, x):\n",
        "#         x = self.pool(self.relu(self.conv1(x)))\n",
        "#         x = self.pool(self.relu(self.conv2(x)))\n",
        "#         x = self.pool(self.relu(self.conv3(x)))\n",
        "#         x = x.view(-1, 128 * 28 * 28)\n",
        "#         x = self.dropout(x)\n",
        "#         x = self.relu(self.fc1(x))\n",
        "#         x = self.fc2(x)\n",
        "#         return x\n",
        "\n",
        "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n"
      ],
      "metadata": {
        "id": "G9fnmZyLfuc9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AdvancedCNN(nn.Module):\n",
        "    def __init__(self, num_classes=18):\n",
        "        super(AdvancedCNN, self).__init__()\n",
        "        self.layer1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(32),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
        "\n",
        "        self.layer2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
        "\n",
        "        self.layer3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
        "\n",
        "        self.layer4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
        "\n",
        "        self.layer5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1),\n",
        "            nn.BatchNorm2d(512),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2))\n",
        "\n",
        "        # Calculate correct input size\n",
        "        # Assume input image size is 224 x 224\n",
        "        size = 224\n",
        "        layers = 5  # Total pooling layers\n",
        "        for _ in range(layers):\n",
        "            size = size // 2  # each max pooling operation halves the dimension\n",
        "        print(f\"Expected size after all layers: {size}x{size}\")\n",
        "\n",
        "        self.fc1 = nn.Linear(512 * size * size, 1024)  # Adjust the size here\n",
        "        self.dropout = nn.Dropout(0.5)\n",
        "        self.fc2 = nn.Linear(1024, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.layer4(x)\n",
        "        x = self.layer5(x)\n",
        "        x = x.view(x.size(0), -1)  # Flatten the output for the dense layer\n",
        "        x = self.dropout(x)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "# Assuming usage of GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = AdvancedCNN().to(device)\n",
        "print(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWBs_RC1Cecq",
        "outputId": "39423890-a6b1-4064-bafe-98d0511b8b48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected size after all layers: 7x7\n",
            "AdvancedCNN(\n",
            "  (layer1): Sequential(\n",
            "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): LeakyReLU(negative_slope=0.01)\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (layer2): Sequential(\n",
            "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): LeakyReLU(negative_slope=0.01)\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (layer3): Sequential(\n",
            "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): LeakyReLU(negative_slope=0.01)\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (layer4): Sequential(\n",
            "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): LeakyReLU(negative_slope=0.01)\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (layer5): Sequential(\n",
            "    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): LeakyReLU(negative_slope=0.01)\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (fc1): Linear(in_features=25088, out_features=1024, bias=True)\n",
            "  (dropout): Dropout(p=0.5, inplace=False)\n",
            "  (fc2): Linear(in_features=1024, out_features=18, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model = SimpleCNN(num_classes=len(class_names)).to(device)\n",
        "model = AdvancedCNN(num_classes=len(class_names)).to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.0001)\n"
      ],
      "metadata": {
        "id": "B0XXf1M5gGnb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19906710-152c-4afc-bba6-f19aab450d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected size after all layers: 7x7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 500\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    for inputs, labels in dataloaders['train']:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "\n",
        "    epoch_loss = running_loss / dataset_sizes['train']\n",
        "\n",
        "    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aA-ZYmdgI0e",
        "outputId": "ea83f7be-9267-4c20-b025-8773768a24b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500, Loss: 2.9114\n",
            "Epoch 2/500, Loss: 2.6150\n",
            "Epoch 3/500, Loss: 2.5083\n",
            "Epoch 4/500, Loss: 2.4882\n",
            "Epoch 5/500, Loss: 2.4384\n",
            "Epoch 6/500, Loss: 2.3787\n",
            "Epoch 7/500, Loss: 2.3224\n",
            "Epoch 8/500, Loss: 2.2834\n",
            "Epoch 9/500, Loss: 2.2568\n",
            "Epoch 10/500, Loss: 2.2120\n",
            "Epoch 11/500, Loss: 2.1901\n",
            "Epoch 12/500, Loss: 2.1517\n",
            "Epoch 13/500, Loss: 2.1220\n",
            "Epoch 14/500, Loss: 2.1089\n",
            "Epoch 15/500, Loss: 2.0581\n",
            "Epoch 16/500, Loss: 2.0432\n",
            "Epoch 17/500, Loss: 2.0548\n",
            "Epoch 18/500, Loss: 1.9887\n",
            "Epoch 19/500, Loss: 2.0280\n",
            "Epoch 20/500, Loss: 1.9413\n",
            "Epoch 21/500, Loss: 1.9225\n",
            "Epoch 22/500, Loss: 1.9175\n",
            "Epoch 23/500, Loss: 1.9256\n",
            "Epoch 24/500, Loss: 1.8818\n",
            "Epoch 25/500, Loss: 1.8775\n",
            "Epoch 26/500, Loss: 1.8375\n",
            "Epoch 27/500, Loss: 1.8690\n",
            "Epoch 28/500, Loss: 1.8521\n",
            "Epoch 29/500, Loss: 1.8613\n",
            "Epoch 30/500, Loss: 1.7947\n",
            "Epoch 31/500, Loss: 1.8353\n",
            "Epoch 32/500, Loss: 1.7658\n",
            "Epoch 33/500, Loss: 1.7425\n",
            "Epoch 34/500, Loss: 1.7096\n",
            "Epoch 35/500, Loss: 1.7381\n",
            "Epoch 36/500, Loss: 1.7650\n",
            "Epoch 37/500, Loss: 1.7398\n",
            "Epoch 38/500, Loss: 1.6705\n",
            "Epoch 39/500, Loss: 1.7023\n",
            "Epoch 40/500, Loss: 1.6932\n",
            "Epoch 41/500, Loss: 1.6598\n",
            "Epoch 42/500, Loss: 1.6818\n",
            "Epoch 43/500, Loss: 1.6609\n",
            "Epoch 44/500, Loss: 1.6231\n",
            "Epoch 45/500, Loss: 1.6124\n",
            "Epoch 46/500, Loss: 1.6079\n",
            "Epoch 47/500, Loss: 1.5713\n",
            "Epoch 48/500, Loss: 1.5607\n",
            "Epoch 49/500, Loss: 1.5585\n",
            "Epoch 50/500, Loss: 1.5515\n",
            "Epoch 51/500, Loss: 1.5384\n",
            "Epoch 52/500, Loss: 1.5228\n",
            "Epoch 53/500, Loss: 1.5866\n",
            "Epoch 54/500, Loss: 1.5498\n",
            "Epoch 55/500, Loss: 1.4759\n",
            "Epoch 56/500, Loss: 1.4777\n",
            "Epoch 57/500, Loss: 1.5101\n",
            "Epoch 58/500, Loss: 1.4756\n",
            "Epoch 59/500, Loss: 1.4636\n",
            "Epoch 60/500, Loss: 1.4491\n",
            "Epoch 61/500, Loss: 1.4709\n",
            "Epoch 62/500, Loss: 1.4657\n",
            "Epoch 63/500, Loss: 1.4653\n",
            "Epoch 64/500, Loss: 1.4655\n",
            "Epoch 65/500, Loss: 1.4059\n",
            "Epoch 66/500, Loss: 1.4124\n",
            "Epoch 67/500, Loss: 1.3976\n",
            "Epoch 68/500, Loss: 1.4359\n",
            "Epoch 69/500, Loss: 1.3492\n",
            "Epoch 70/500, Loss: 1.4202\n",
            "Epoch 71/500, Loss: 1.3804\n",
            "Epoch 72/500, Loss: 1.3817\n",
            "Epoch 73/500, Loss: 1.3130\n",
            "Epoch 74/500, Loss: 1.3010\n",
            "Epoch 75/500, Loss: 1.3328\n",
            "Epoch 76/500, Loss: 1.3453\n",
            "Epoch 77/500, Loss: 1.3097\n",
            "Epoch 78/500, Loss: 1.3204\n",
            "Epoch 79/500, Loss: 1.2714\n",
            "Epoch 80/500, Loss: 1.2945\n",
            "Epoch 81/500, Loss: 1.2778\n",
            "Epoch 82/500, Loss: 1.3162\n",
            "Epoch 83/500, Loss: 1.3138\n",
            "Epoch 84/500, Loss: 1.2795\n",
            "Epoch 85/500, Loss: 1.2798\n",
            "Epoch 86/500, Loss: 1.2810\n",
            "Epoch 87/500, Loss: 1.2437\n",
            "Epoch 88/500, Loss: 1.2514\n",
            "Epoch 89/500, Loss: 1.2737\n",
            "Epoch 90/500, Loss: 1.2004\n",
            "Epoch 91/500, Loss: 1.2226\n",
            "Epoch 92/500, Loss: 1.2059\n",
            "Epoch 93/500, Loss: 1.1497\n",
            "Epoch 94/500, Loss: 1.1762\n",
            "Epoch 95/500, Loss: 1.2070\n",
            "Epoch 96/500, Loss: 1.1808\n",
            "Epoch 97/500, Loss: 1.1415\n",
            "Epoch 98/500, Loss: 1.2014\n",
            "Epoch 99/500, Loss: 1.1505\n",
            "Epoch 100/500, Loss: 1.1714\n",
            "Epoch 101/500, Loss: 1.2040\n",
            "Epoch 102/500, Loss: 1.0850\n",
            "Epoch 103/500, Loss: 1.1213\n",
            "Epoch 104/500, Loss: 1.1578\n",
            "Epoch 105/500, Loss: 1.1707\n",
            "Epoch 106/500, Loss: 1.1507\n",
            "Epoch 107/500, Loss: 1.1440\n",
            "Epoch 108/500, Loss: 1.1092\n",
            "Epoch 109/500, Loss: 1.0698\n",
            "Epoch 110/500, Loss: 1.1132\n",
            "Epoch 111/500, Loss: 1.1607\n",
            "Epoch 112/500, Loss: 1.0885\n",
            "Epoch 113/500, Loss: 1.0649\n",
            "Epoch 114/500, Loss: 1.0940\n",
            "Epoch 115/500, Loss: 1.0377\n",
            "Epoch 116/500, Loss: 1.0300\n",
            "Epoch 117/500, Loss: 1.1011\n",
            "Epoch 118/500, Loss: 1.0722\n",
            "Epoch 119/500, Loss: 1.1062\n",
            "Epoch 120/500, Loss: 1.1019\n",
            "Epoch 121/500, Loss: 1.0572\n",
            "Epoch 122/500, Loss: 1.0463\n",
            "Epoch 123/500, Loss: 1.0281\n",
            "Epoch 124/500, Loss: 0.9939\n",
            "Epoch 125/500, Loss: 1.0327\n",
            "Epoch 126/500, Loss: 1.0254\n",
            "Epoch 127/500, Loss: 1.0157\n",
            "Epoch 128/500, Loss: 1.0174\n",
            "Epoch 129/500, Loss: 0.9724\n",
            "Epoch 130/500, Loss: 0.9614\n",
            "Epoch 131/500, Loss: 0.9631\n",
            "Epoch 132/500, Loss: 0.9840\n",
            "Epoch 133/500, Loss: 0.9765\n",
            "Epoch 134/500, Loss: 0.9999\n",
            "Epoch 135/500, Loss: 0.9243\n",
            "Epoch 136/500, Loss: 1.0047\n",
            "Epoch 137/500, Loss: 0.9227\n",
            "Epoch 138/500, Loss: 1.0197\n",
            "Epoch 139/500, Loss: 1.0328\n",
            "Epoch 140/500, Loss: 0.9983\n",
            "Epoch 141/500, Loss: 0.9519\n",
            "Epoch 142/500, Loss: 0.9601\n",
            "Epoch 143/500, Loss: 1.0073\n",
            "Epoch 144/500, Loss: 1.0166\n",
            "Epoch 145/500, Loss: 0.9074\n",
            "Epoch 146/500, Loss: 0.9240\n",
            "Epoch 147/500, Loss: 0.9217\n",
            "Epoch 148/500, Loss: 0.9803\n",
            "Epoch 149/500, Loss: 0.9841\n",
            "Epoch 150/500, Loss: 0.9657\n",
            "Epoch 151/500, Loss: 0.9700\n",
            "Epoch 152/500, Loss: 0.9640\n",
            "Epoch 153/500, Loss: 0.8982\n",
            "Epoch 154/500, Loss: 0.8992\n",
            "Epoch 155/500, Loss: 0.8981\n",
            "Epoch 156/500, Loss: 0.9520\n",
            "Epoch 157/500, Loss: 0.9430\n",
            "Epoch 158/500, Loss: 0.8978\n",
            "Epoch 159/500, Loss: 0.9104\n",
            "Epoch 160/500, Loss: 0.8750\n",
            "Epoch 161/500, Loss: 0.9322\n",
            "Epoch 162/500, Loss: 0.9571\n",
            "Epoch 163/500, Loss: 0.9277\n",
            "Epoch 164/500, Loss: 0.8573\n",
            "Epoch 165/500, Loss: 0.8661\n",
            "Epoch 166/500, Loss: 0.8715\n",
            "Epoch 167/500, Loss: 0.8813\n",
            "Epoch 168/500, Loss: 0.8066\n",
            "Epoch 169/500, Loss: 0.8535\n",
            "Epoch 170/500, Loss: 0.8518\n",
            "Epoch 171/500, Loss: 0.8796\n",
            "Epoch 172/500, Loss: 0.8882\n",
            "Epoch 173/500, Loss: 0.8121\n",
            "Epoch 174/500, Loss: 0.8648\n",
            "Epoch 175/500, Loss: 0.9019\n",
            "Epoch 176/500, Loss: 0.9179\n",
            "Epoch 177/500, Loss: 0.8664\n",
            "Epoch 178/500, Loss: 0.8571\n",
            "Epoch 179/500, Loss: 0.8790\n",
            "Epoch 180/500, Loss: 0.8712\n",
            "Epoch 181/500, Loss: 0.7878\n",
            "Epoch 182/500, Loss: 0.8259\n",
            "Epoch 183/500, Loss: 0.9105\n",
            "Epoch 184/500, Loss: 0.7976\n",
            "Epoch 185/500, Loss: 0.8265\n",
            "Epoch 186/500, Loss: 0.8281\n",
            "Epoch 187/500, Loss: 0.8294\n",
            "Epoch 188/500, Loss: 0.8066\n",
            "Epoch 189/500, Loss: 0.8557\n",
            "Epoch 190/500, Loss: 0.8680\n",
            "Epoch 191/500, Loss: 0.7961\n",
            "Epoch 192/500, Loss: 0.8233\n",
            "Epoch 193/500, Loss: 0.7980\n",
            "Epoch 194/500, Loss: 0.7532\n",
            "Epoch 195/500, Loss: 0.7943\n",
            "Epoch 196/500, Loss: 0.7621\n",
            "Epoch 197/500, Loss: 0.7607\n",
            "Epoch 198/500, Loss: 0.8024\n",
            "Epoch 199/500, Loss: 0.7318\n",
            "Epoch 200/500, Loss: 0.8000\n",
            "Epoch 201/500, Loss: 0.8287\n",
            "Epoch 202/500, Loss: 0.7939\n",
            "Epoch 203/500, Loss: 0.7960\n",
            "Epoch 204/500, Loss: 0.7860\n",
            "Epoch 205/500, Loss: 0.8128\n",
            "Epoch 206/500, Loss: 0.7798\n",
            "Epoch 207/500, Loss: 0.8009\n",
            "Epoch 208/500, Loss: 0.7558\n",
            "Epoch 209/500, Loss: 0.7843\n",
            "Epoch 210/500, Loss: 0.7051\n",
            "Epoch 211/500, Loss: 0.7364\n",
            "Epoch 212/500, Loss: 0.7963\n",
            "Epoch 213/500, Loss: 0.7257\n",
            "Epoch 214/500, Loss: 0.7483\n",
            "Epoch 215/500, Loss: 0.7685\n",
            "Epoch 216/500, Loss: 0.6847\n",
            "Epoch 217/500, Loss: 0.7444\n",
            "Epoch 218/500, Loss: 0.7584\n",
            "Epoch 219/500, Loss: 0.7385\n",
            "Epoch 220/500, Loss: 0.7442\n",
            "Epoch 221/500, Loss: 0.7302\n",
            "Epoch 222/500, Loss: 0.7516\n",
            "Epoch 223/500, Loss: 0.7319\n",
            "Epoch 224/500, Loss: 0.7013\n",
            "Epoch 225/500, Loss: 0.7114\n",
            "Epoch 226/500, Loss: 0.7483\n",
            "Epoch 227/500, Loss: 0.7478\n",
            "Epoch 228/500, Loss: 0.7551\n",
            "Epoch 229/500, Loss: 0.6816\n",
            "Epoch 230/500, Loss: 0.7415\n",
            "Epoch 231/500, Loss: 0.7074\n",
            "Epoch 232/500, Loss: 0.7460\n",
            "Epoch 233/500, Loss: 0.7099\n",
            "Epoch 234/500, Loss: 0.7125\n",
            "Epoch 235/500, Loss: 0.6850\n",
            "Epoch 236/500, Loss: 0.7239\n",
            "Epoch 237/500, Loss: 0.7187\n",
            "Epoch 238/500, Loss: 0.6989\n",
            "Epoch 239/500, Loss: 0.7113\n",
            "Epoch 240/500, Loss: 0.7371\n",
            "Epoch 241/500, Loss: 0.6871\n",
            "Epoch 242/500, Loss: 0.6984\n",
            "Epoch 243/500, Loss: 0.6945\n",
            "Epoch 244/500, Loss: 0.7406\n",
            "Epoch 245/500, Loss: 0.6839\n",
            "Epoch 246/500, Loss: 0.7215\n",
            "Epoch 247/500, Loss: 0.7701\n",
            "Epoch 248/500, Loss: 0.6850\n",
            "Epoch 249/500, Loss: 0.6834\n",
            "Epoch 250/500, Loss: 0.7270\n",
            "Epoch 251/500, Loss: 0.6747\n",
            "Epoch 252/500, Loss: 0.6990\n",
            "Epoch 253/500, Loss: 0.6528\n",
            "Epoch 254/500, Loss: 0.6761\n",
            "Epoch 255/500, Loss: 0.6619\n",
            "Epoch 256/500, Loss: 0.6585\n",
            "Epoch 257/500, Loss: 0.7133\n",
            "Epoch 258/500, Loss: 0.6404\n",
            "Epoch 259/500, Loss: 0.6900\n",
            "Epoch 260/500, Loss: 0.6363\n",
            "Epoch 261/500, Loss: 0.6863\n",
            "Epoch 262/500, Loss: 0.6866\n",
            "Epoch 263/500, Loss: 0.6939\n",
            "Epoch 264/500, Loss: 0.6094\n",
            "Epoch 265/500, Loss: 0.6611\n",
            "Epoch 266/500, Loss: 0.6565\n",
            "Epoch 267/500, Loss: 0.6626\n",
            "Epoch 268/500, Loss: 0.6393\n",
            "Epoch 269/500, Loss: 0.7178\n",
            "Epoch 270/500, Loss: 0.6405\n",
            "Epoch 271/500, Loss: 0.6398\n",
            "Epoch 272/500, Loss: 0.6014\n",
            "Epoch 273/500, Loss: 0.6770\n",
            "Epoch 274/500, Loss: 0.6783\n",
            "Epoch 275/500, Loss: 0.6167\n",
            "Epoch 276/500, Loss: 0.7001\n",
            "Epoch 277/500, Loss: 0.6443\n",
            "Epoch 278/500, Loss: 0.6422\n",
            "Epoch 279/500, Loss: 0.6882\n",
            "Epoch 280/500, Loss: 0.6490\n",
            "Epoch 281/500, Loss: 0.6294\n",
            "Epoch 282/500, Loss: 0.6571\n",
            "Epoch 283/500, Loss: 0.6503\n",
            "Epoch 284/500, Loss: 0.6727\n",
            "Epoch 285/500, Loss: 0.6617\n",
            "Epoch 286/500, Loss: 0.6347\n",
            "Epoch 287/500, Loss: 0.6135\n",
            "Epoch 288/500, Loss: 0.6436\n",
            "Epoch 289/500, Loss: 0.6273\n",
            "Epoch 290/500, Loss: 0.5789\n",
            "Epoch 291/500, Loss: 0.6476\n",
            "Epoch 292/500, Loss: 0.6285\n",
            "Epoch 293/500, Loss: 0.6421\n",
            "Epoch 294/500, Loss: 0.6266\n",
            "Epoch 295/500, Loss: 0.6091\n",
            "Epoch 296/500, Loss: 0.6200\n",
            "Epoch 297/500, Loss: 0.6150\n",
            "Epoch 298/500, Loss: 0.6641\n",
            "Epoch 299/500, Loss: 0.6122\n",
            "Epoch 300/500, Loss: 0.6114\n",
            "Epoch 301/500, Loss: 0.6240\n",
            "Epoch 302/500, Loss: 0.5998\n",
            "Epoch 303/500, Loss: 0.5984\n",
            "Epoch 304/500, Loss: 0.6729\n",
            "Epoch 305/500, Loss: 0.5913\n",
            "Epoch 306/500, Loss: 0.6256\n",
            "Epoch 307/500, Loss: 0.6065\n",
            "Epoch 308/500, Loss: 0.6144\n",
            "Epoch 309/500, Loss: 0.5952\n",
            "Epoch 310/500, Loss: 0.5678\n",
            "Epoch 311/500, Loss: 0.6131\n",
            "Epoch 312/500, Loss: 0.5891\n",
            "Epoch 313/500, Loss: 0.5373\n",
            "Epoch 314/500, Loss: 0.6144\n",
            "Epoch 315/500, Loss: 0.5778\n",
            "Epoch 316/500, Loss: 0.6124\n",
            "Epoch 317/500, Loss: 0.5931\n",
            "Epoch 318/500, Loss: 0.6395\n",
            "Epoch 319/500, Loss: 0.5726\n",
            "Epoch 320/500, Loss: 0.6162\n",
            "Epoch 321/500, Loss: 0.5961\n",
            "Epoch 322/500, Loss: 0.5819\n",
            "Epoch 323/500, Loss: 0.6240\n",
            "Epoch 324/500, Loss: 0.6011\n",
            "Epoch 325/500, Loss: 0.6104\n",
            "Epoch 326/500, Loss: 0.6028\n",
            "Epoch 327/500, Loss: 0.6008\n",
            "Epoch 328/500, Loss: 0.6093\n",
            "Epoch 329/500, Loss: 0.5449\n",
            "Epoch 330/500, Loss: 0.5580\n",
            "Epoch 331/500, Loss: 0.5388\n",
            "Epoch 332/500, Loss: 0.5549\n",
            "Epoch 333/500, Loss: 0.5825\n",
            "Epoch 334/500, Loss: 0.5798\n",
            "Epoch 335/500, Loss: 0.5529\n",
            "Epoch 336/500, Loss: 0.5381\n",
            "Epoch 337/500, Loss: 0.5815\n",
            "Epoch 338/500, Loss: 0.6151\n",
            "Epoch 339/500, Loss: 0.5272\n",
            "Epoch 340/500, Loss: 0.5448\n",
            "Epoch 341/500, Loss: 0.5471\n",
            "Epoch 342/500, Loss: 0.5650\n",
            "Epoch 343/500, Loss: 0.5832\n",
            "Epoch 344/500, Loss: 0.5889\n",
            "Epoch 345/500, Loss: 0.5732\n",
            "Epoch 346/500, Loss: 0.5613\n",
            "Epoch 347/500, Loss: 0.5200\n",
            "Epoch 348/500, Loss: 0.5943\n",
            "Epoch 349/500, Loss: 0.5614\n",
            "Epoch 350/500, Loss: 0.5448\n",
            "Epoch 351/500, Loss: 0.5614\n",
            "Epoch 352/500, Loss: 0.5058\n",
            "Epoch 353/500, Loss: 0.5327\n",
            "Epoch 354/500, Loss: 0.5918\n",
            "Epoch 355/500, Loss: 0.5255\n",
            "Epoch 356/500, Loss: 0.5654\n",
            "Epoch 357/500, Loss: 0.5435\n",
            "Epoch 358/500, Loss: 0.5099\n",
            "Epoch 359/500, Loss: 0.5636\n",
            "Epoch 360/500, Loss: 0.5689\n",
            "Epoch 361/500, Loss: 0.5330\n",
            "Epoch 362/500, Loss: 0.4855\n",
            "Epoch 363/500, Loss: 0.5675\n",
            "Epoch 364/500, Loss: 0.5264\n",
            "Epoch 365/500, Loss: 0.4999\n",
            "Epoch 366/500, Loss: 0.5482\n",
            "Epoch 367/500, Loss: 0.5665\n",
            "Epoch 368/500, Loss: 0.5367\n",
            "Epoch 369/500, Loss: 0.5062\n",
            "Epoch 370/500, Loss: 0.5465\n",
            "Epoch 371/500, Loss: 0.5669\n",
            "Epoch 372/500, Loss: 0.5365\n",
            "Epoch 373/500, Loss: 0.5484\n",
            "Epoch 374/500, Loss: 0.5265\n",
            "Epoch 375/500, Loss: 0.5164\n",
            "Epoch 376/500, Loss: 0.5089\n",
            "Epoch 377/500, Loss: 0.5177\n",
            "Epoch 378/500, Loss: 0.5660\n",
            "Epoch 379/500, Loss: 0.4899\n",
            "Epoch 380/500, Loss: 0.5449\n",
            "Epoch 381/500, Loss: 0.5169\n",
            "Epoch 382/500, Loss: 0.5014\n",
            "Epoch 383/500, Loss: 0.5192\n",
            "Epoch 384/500, Loss: 0.5295\n",
            "Epoch 385/500, Loss: 0.4983\n",
            "Epoch 386/500, Loss: 0.4668\n",
            "Epoch 387/500, Loss: 0.5138\n",
            "Epoch 388/500, Loss: 0.5596\n",
            "Epoch 389/500, Loss: 0.5297\n",
            "Epoch 390/500, Loss: 0.5346\n",
            "Epoch 391/500, Loss: 0.5390\n",
            "Epoch 392/500, Loss: 0.5138\n",
            "Epoch 393/500, Loss: 0.4673\n",
            "Epoch 394/500, Loss: 0.5403\n",
            "Epoch 395/500, Loss: 0.5122\n",
            "Epoch 396/500, Loss: 0.4668\n",
            "Epoch 397/500, Loss: 0.5310\n",
            "Epoch 398/500, Loss: 0.5052\n",
            "Epoch 399/500, Loss: 0.4679\n",
            "Epoch 400/500, Loss: 0.5589\n",
            "Epoch 401/500, Loss: 0.5777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "def evaluate_model(model, dataloaders, device):\n",
        "    model.eval()\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in dataloaders['val']:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            all_preds.extend(preds.cpu().numpy())\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "    accuracy = accuracy_score(all_labels, all_preds)\n",
        "    report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=False)\n",
        "    return accuracy, report\n"
      ],
      "metadata": {
        "id": "0Y0fgz7bgTKo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, report = evaluate_model(model, dataloaders, device)\n",
        "print(f\"Validation Accuracy: {accuracy:.4f}\")\n",
        "print(report)\n"
      ],
      "metadata": {
        "id": "h2RUrAv-ka7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model, dataloaders, device):\n",
        "    model.eval()\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in dataloaders['val']:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            all_preds.extend(preds.cpu().numpy())\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "    accuracy = accuracy_score(all_labels, all_preds)\n",
        "    report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=False)\n",
        "    return accuracy, report, all_labels, all_preds\n"
      ],
      "metadata": {
        "id": "ZmHCKvRYZuyH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, report, all_labels, all_preds = evaluate_model(model, dataloaders, device)\n",
        "print(f\"Validation Accuracy: {accuracy:.4f}\")\n",
        "print(report)\n",
        "\n",
        "plot_confusion_matrix(all_labels, all_preds, class_names)\n"
      ],
      "metadata": {
        "id": "SmqlSASjkddC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vision Transformer implementation begins here!"
      ],
      "metadata": {
        "id": "nKicMz8cfpWy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install torch torchvision timm matplotlib seaborn\n"
      ],
      "metadata": {
        "id": "GJeO96N8fHYp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0b93ed0-16d3-4cf8-bea5-361c61e48e5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.17.1+cu121)\n",
            "Collecting timm\n",
            "  Downloading timm-0.9.16-py3-none-any.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.4.127)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.25.2)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from timm) (6.0.1)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (from timm) (0.20.3)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from timm) (0.4.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.51.0)\n",
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            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (2.0.3)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (4.66.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2024.2.2)\n",
            "Installing collected packages: timm\n",
            "Successfully installed timm-0.9.16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision import transforms\n",
        "\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "    ]),\n",
        "}\n"
      ],
      "metadata": {
        "id": "aJsQeMd-fUy0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "data_dir = '/content/new_CFD'\n",
        "batch_size = 8\n",
        "\n",
        "datasets = {x: ImageFolder(root=os.path.join(data_dir, x), transform=data_transforms[x])\n",
        "            for x in ['train', 'val']}\n",
        "dataloaders = {x: DataLoader(datasets[x], batch_size=batch_size, shuffle=True, num_workers=4)\n",
        "               for x in ['train', 'val']}\n",
        "dataset_sizes = {x: len(datasets[x]) for x in ['train', 'val']}\n",
        "class_names = datasets['train'].classes\n"
      ],
      "metadata": {
        "id": "snjfhE-kfvRs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(class_names)"
      ],
      "metadata": {
        "id": "hN0o3O18f1uL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6577624e-b475-4c95-9af9-d57024f025e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Angelina Jolie', 'Brad Pitt', 'Denzel Washington', 'Hugh Jackman', 'Jennifer Lawrence', 'Johnny Depp', 'Kate Winslet', 'Leonardo DiCaprio', 'Megan Fox', 'Natalie Portman', 'Nicole Kidman', 'Robert Downey Jr', 'Sandra Bullock', 'Scarlett Johansson', 'Tom Cruise', 'Tom Hanks', 'Will Smith']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import timm\n",
        "import torch.nn as nn\n",
        "checkpoint = \"/content/drive/MyDrive/107_trained_model.pth\"\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = timm.create_model('vit_base_patch16_224', checkpoint_path=checkpoint)\n",
        "num_ftrs = model.head.in_features\n",
        "model.head = nn.Linear(num_ftrs, len(class_names))\n",
        "model = model.to(device)\n"
      ],
      "metadata": {
        "id": "VRZIe1ief3xj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "9d432043-75ac-42ad-e050-6c58074ab0c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "Error(s) in loading state_dict for VisionTransformer:\n\tsize mismatch for head.weight: copying a param with shape torch.Size([17, 768]) from checkpoint, the shape in current model is torch.Size([1000, 768]).\n\tsize mismatch for head.bias: copying a param with shape torch.Size([17]) from checkpoint, the shape in current model is torch.Size([1000]).",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-8f5108a55da0>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/drive/MyDrive/107_trained_model.pth\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda:0\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'vit_base_patch16_224'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpoint_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheckpoint\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      6\u001b[0m \u001b[0mnum_ftrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_ftrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_names\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/timm/models/_factory.py\u001b[0m in \u001b[0;36mcreate_model\u001b[0;34m(model_name, pretrained, pretrained_cfg, pretrained_cfg_overlay, checkpoint_path, scriptable, exportable, no_jit, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcheckpoint_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m         \u001b[0mload_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpoint_path\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    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\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/timm/models/_helpers.py\u001b[0m in \u001b[0;36mload_checkpoint\u001b[0;34m(model, checkpoint_path, use_ema, device, strict, remap, filter_fn)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mfilter_fn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilter_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0mincompatible_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrict\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     85\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mincompatible_keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\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;36mload_state_dict\u001b[0;34m(self, state_dict, strict, assign)\u001b[0m\n\u001b[1;32m   2151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2153\u001b[0;31m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[0m\u001b[1;32m   2154\u001b[0m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[1;32m   2155\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_IncompatibleKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munexpected_keys\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: Error(s) in loading state_dict for VisionTransformer:\n\tsize mismatch for head.weight: copying a param with shape torch.Size([17, 768]) from checkpoint, the shape in current model is torch.Size([1000, 768]).\n\tsize mismatch for head.bias: copying a param with shape torch.Size([17]) from checkpoint, the shape in current model is torch.Size([1000])."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import timm\n",
        "import torch.nn as nn\n",
        "\n",
        "checkpoint = \"/content/drive/MyDrive/107_trained_model.pth\"\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Load the model\n",
        "model = timm.create_model('vit_base_patch16_224', pretrained=True)\n",
        "\n",
        "# Replace the classifier head\n",
        "num_ftrs = model.head.in_features\n",
        "model.head = nn.Linear(num_ftrs, len(class_names))  # Assuming class_names is defined elsewhere\n",
        "\n",
        "# Load the state dict of the saved model\n",
        "checkpoint = torch.load(checkpoint)\n",
        "\n",
        "# Load the state dict into the model\n",
        "model.load_state_dict(checkpoint)\n",
        "\n",
        "# Move the model to the appropriate device\n",
        "model = model.to(device)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178,
          "referenced_widgets": [
            "aa186b352f8d424fbd2f711c11676e20",
            "14c7028f49c6499da0021ef0a03c6612",
            "ee0aaf58a91b49c8b66ab5bfb4e72fc6",
            "dd49c78329a04c76ab44e98708f7b8b7",
            "c6a20490040c4b7eb8377b36c256dbbb",
            "80a5ce1276e448e88ca981747a886110",
            "c6f420475772470984662fc8324b19be",
            "0a9245739d5a412cb2581e15f95fafb6",
            "bd8cfa58d9974728ad350d1068dc49e5",
            "a97acc240a4e43908f55c8f937988e88",
            "a5a8de3565944e1fbe69c239962d037e"
          ]
        },
        "id": "HPI58bn60O3y",
        "outputId": "d5e0ea7c-f69e-4c6e-911f-301a6ba2ee92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa186b352f8d424fbd2f711c11676e20"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n"
      ],
      "metadata": {
        "id": "tgishaaqf7y0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 300\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    for inputs, labels in dataloaders['train']:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item() * inputs.size(0)\n",
        "    print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss / dataset_sizes['train']}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "waBD8TOygCJN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ba05098f-d387-4c39-9ad7-789499456556"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300, Loss: 0.4939724855616482\n",
            "Epoch 2/300, Loss: 0.4440697302007013\n",
            "Epoch 3/300, Loss: 0.4311883173624261\n",
            "Epoch 4/300, Loss: 0.45381955580992833\n",
            "Epoch 5/300, Loss: 0.3971321801499774\n",
            "Epoch 6/300, Loss: 0.41353776419224836\n",
            "Epoch 7/300, Loss: 0.38806008600707476\n",
            "Epoch 8/300, Loss: 0.4687824358128839\n",
            "Epoch 9/300, Loss: 0.4384040545077167\n",
            "Epoch 10/300, Loss: 0.446966510233728\n",
            "Epoch 11/300, Loss: 0.40046712912800203\n",
            "Epoch 12/300, Loss: 0.407041068504461\n",
            "Epoch 13/300, Loss: 0.39644740435243064\n",
            "Epoch 14/300, Loss: 0.4276031068526208\n",
            "Epoch 15/300, Loss: 0.41344603876479796\n",
            "Epoch 16/300, Loss: 0.38315565805985696\n",
            "Epoch 17/300, Loss: 0.4250319731576989\n",
            "Epoch 18/300, Loss: 0.407687908572714\n",
            "Epoch 19/300, Loss: 0.4018062686392417\n",
            "Epoch 20/300, Loss: 0.45240817138821715\n",
            "Epoch 21/300, Loss: 0.39138622756193703\n",
            "Epoch 22/300, Loss: 0.3772307719103992\n",
            "Epoch 23/300, Loss: 0.39565779122317\n",
            "Epoch 24/300, Loss: 0.3517662648784204\n",
            "Epoch 25/300, Loss: 0.38395507965469733\n",
            "Epoch 26/300, Loss: 0.36060034287188\n",
            "Epoch 27/300, Loss: 0.38350111799728537\n",
            "Epoch 28/300, Loss: 0.3866120483648653\n",
            "Epoch 29/300, Loss: 0.3710163171687681\n",
            "Epoch 30/300, Loss: 0.4162810390970359\n",
            "Epoch 31/300, Loss: 0.44003611740966636\n",
            "Epoch 32/300, Loss: 0.36235572938005334\n",
            "Epoch 33/300, Loss: 0.3614878546834613\n",
            "Epoch 34/300, Loss: 0.3676382436210083\n",
            "Epoch 35/300, Loss: 0.3596356267368214\n",
            "Epoch 36/300, Loss: 0.37782400964060797\n",
            "Epoch 37/300, Loss: 0.3754596378861202\n",
            "Epoch 38/300, Loss: 0.3220457471687243\n",
            "Epoch 39/300, Loss: 0.40712686125059716\n",
            "Epoch 40/300, Loss: 0.36079029890372316\n",
            "Epoch 41/300, Loss: 0.33649748413978764\n",
            "Epoch 42/300, Loss: 0.3388902098179743\n",
            "Epoch 43/300, Loss: 0.35373612901635676\n",
            "Epoch 44/300, Loss: 0.43707714153246746\n",
            "Epoch 45/300, Loss: 0.352513431201482\n",
            "Epoch 46/300, Loss: 0.35497363260429765\n",
            "Epoch 47/300, Loss: 0.3163688423752319\n",
            "Epoch 48/300, Loss: 0.34906299003374036\n",
            "Epoch 49/300, Loss: 0.3197028304592499\n",
            "Epoch 50/300, Loss: 0.35086279090707345\n",
            "Epoch 51/300, Loss: 0.3593691466945327\n",
            "Epoch 52/300, Loss: 0.36440440710737476\n",
            "Epoch 53/300, Loss: 0.3533364787736597\n",
            "Epoch 54/300, Loss: 0.3214955873412287\n",
            "Epoch 55/300, Loss: 0.3599999041205996\n",
            "Epoch 56/300, Loss: 0.342712491202595\n",
            "Epoch 57/300, Loss: 0.34226791751118274\n",
            "Epoch 58/300, Loss: 0.34507781837601215\n",
            "Epoch 59/300, Loss: 0.33549078562452145\n",
            "Epoch 60/300, Loss: 0.3262897337444075\n",
            "Epoch 61/300, Loss: 0.31936118700929605\n",
            "Epoch 62/300, Loss: 0.2776980198224515\n",
            "Epoch 63/300, Loss: 0.32421398556842984\n",
            "Epoch 64/300, Loss: 0.3696757306551768\n",
            "Epoch 65/300, Loss: 0.28698696171373334\n",
            "Epoch 66/300, Loss: 0.3344327462345569\n",
            "Epoch 67/300, Loss: 0.3111482531207407\n",
            "Epoch 68/300, Loss: 0.3039117051288486\n",
            "Epoch 69/300, Loss: 0.29606458894623855\n",
            "Epoch 70/300, Loss: 0.339936592076669\n",
            "Epoch 71/300, Loss: 0.3697229364899815\n",
            "Epoch 72/300, Loss: 0.33082901148591193\n",
            "Epoch 73/300, Loss: 0.28016639738214305\n",
            "Epoch 74/300, Loss: 0.31934416451387937\n",
            "Epoch 75/300, Loss: 0.32632497393577875\n",
            "Epoch 76/300, Loss: 0.29915042901929056\n",
            "Epoch 77/300, Loss: 0.2600871552874903\n",
            "Epoch 78/300, Loss: 0.3066768057400218\n",
            "Epoch 79/300, Loss: 0.3052797960013979\n",
            "Epoch 80/300, Loss: 0.2757365652870956\n",
            "Epoch 81/300, Loss: 0.3284033414830143\n",
            "Epoch 82/300, Loss: 0.31700533679557136\n",
            "Epoch 83/300, Loss: 0.330298206779278\n",
            "Epoch 84/300, Loss: 0.2962952713326861\n",
            "Epoch 85/300, Loss: 0.35300448909401894\n",
            "Epoch 86/300, Loss: 0.29356254956219346\n",
            "Epoch 87/300, Loss: 0.2698696390137128\n",
            "Epoch 88/300, Loss: 0.2850170957079778\n",
            "Epoch 89/300, Loss: 0.328959068947006\n",
            "Epoch 90/300, Loss: 0.27375092007763063\n",
            "Epoch 91/300, Loss: 0.3288475379934728\n",
            "Epoch 92/300, Loss: 0.2828982529359766\n",
            "Epoch 93/300, Loss: 0.3045317166530165\n",
            "Epoch 94/300, Loss: 0.26562844740401487\n",
            "Epoch 95/300, Loss: 0.25875221033735823\n",
            "Epoch 96/300, Loss: 0.3237328289836619\n",
            "Epoch 97/300, Loss: 0.2774078893177729\n",
            "Epoch 98/300, Loss: 0.27978800508523516\n",
            "Epoch 99/300, Loss: 0.2734362705111077\n",
            "Epoch 100/300, Loss: 0.2954864433390968\n",
            "Epoch 101/300, Loss: 0.28062259216935165\n",
            "Epoch 102/300, Loss: 0.35134643419749206\n",
            "Epoch 103/300, Loss: 0.3189642055063612\n",
            "Epoch 104/300, Loss: 0.280087283362324\n",
            "Epoch 105/300, Loss: 0.2957583867918907\n",
            "Epoch 106/300, Loss: 0.29234173356671816\n",
            "Epoch 107/300, Loss: 0.27178868408179063\n",
            "Epoch 108/300, Loss: 0.25515965109031336\n",
            "Epoch 109/300, Loss: 0.23361056166597538\n",
            "Epoch 110/300, Loss: 0.25727535454013073\n",
            "Epoch 111/300, Loss: 0.310967454327652\n",
            "Epoch 112/300, Loss: 0.31582390324135\n",
            "Epoch 113/300, Loss: 0.2649967365645959\n",
            "Epoch 114/300, Loss: 0.3004465407879454\n",
            "Epoch 115/300, Loss: 0.31526942926510754\n",
            "Epoch 116/300, Loss: 0.30075297696263686\n",
            "Epoch 117/300, Loss: 0.2778395752163811\n",
            "Epoch 118/300, Loss: 0.236904126555439\n",
            "Epoch 119/300, Loss: 0.23393388005141685\n",
            "Epoch 120/300, Loss: 0.2623474467713903\n",
            "Epoch 121/300, Loss: 0.2028146846397754\n",
            "Epoch 122/300, Loss: 0.284433036393279\n",
            "Epoch 123/300, Loss: 0.28570349123716976\n",
            "Epoch 124/300, Loss: 0.20243922640155587\n",
            "Epoch 125/300, Loss: 0.2986895168849312\n",
            "Epoch 126/300, Loss: 0.22621985719144708\n",
            "Epoch 127/300, Loss: 0.2575675979796668\n",
            "Epoch 128/300, Loss: 0.2632970545350367\n",
            "Epoch 129/300, Loss: 0.24687566719431844\n",
            "Epoch 130/300, Loss: 0.30304438427815006\n",
            "Epoch 131/300, Loss: 0.26288094288384956\n",
            "Epoch 132/300, Loss: 0.25915497374549484\n",
            "Epoch 133/300, Loss: 0.2480736065248493\n",
            "Epoch 134/300, Loss: 0.2676747038210225\n",
            "Epoch 135/300, Loss: 0.24544753389507404\n",
            "Epoch 136/300, Loss: 0.26892510578262674\n",
            "Epoch 137/300, Loss: 0.2524911277699478\n",
            "Epoch 138/300, Loss: 0.23512200538081945\n",
            "Epoch 139/300, Loss: 0.18461897782463993\n",
            "Epoch 140/300, Loss: 0.3008841660100087\n",
            "Epoch 141/300, Loss: 0.22826543745533046\n",
            "Epoch 142/300, Loss: 0.2570368220520322\n",
            "Epoch 143/300, Loss: 0.24919880190023427\n",
            "Epoch 144/300, Loss: 0.2803685035705308\n",
            "Epoch 145/300, Loss: 0.25964637300542864\n",
            "Epoch 146/300, Loss: 0.29081802839258064\n",
            "Epoch 147/300, Loss: 0.26050376829484273\n",
            "Epoch 148/300, Loss: 0.25130985314479204\n",
            "Epoch 149/300, Loss: 0.24647729320147643\n",
            "Epoch 150/300, Loss: 0.24236360550696393\n",
            "Epoch 151/300, Loss: 0.23605764485481712\n",
            "Epoch 152/300, Loss: 0.2234277778942164\n",
            "Epoch 153/300, Loss: 0.2642759629866406\n",
            "Epoch 154/300, Loss: 0.26346285705787725\n",
            "Epoch 155/300, Loss: 0.2377301978206055\n",
            "Epoch 156/300, Loss: 0.22435151213640994\n",
            "Epoch 157/300, Loss: 0.23063227836553576\n",
            "Epoch 158/300, Loss: 0.22691575771176659\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-0109b4e266ef>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mrunning_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss / dataset_sizes['train']}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), '/content/drive/MyDrive/158_trained_model.pth')"
      ],
      "metadata": {
        "id": "nUPv1v4YUKY_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "def evaluate(model, dataloaders, device):\n",
        "    model.eval()\n",
        "    corrects = 0\n",
        "    total = 0\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in dataloaders['val']:\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "\n",
        "            corrects += torch.sum(preds == labels.data)\n",
        "            total += labels.size(0)\n",
        "\n",
        "            all_preds.extend(preds.cpu().numpy())\n",
        "            all_labels.extend(labels.cpu().numpy())\n",
        "\n",
        "    accuracy = corrects.double() / total\n",
        "    print(f'Accuracy: {accuracy:.4f}')\n",
        "\n",
        "    print(classification_report(all_labels, all_preds, target_names=class_names))\n",
        "\n",
        "    return all_labels, all_preds\n"
      ],
      "metadata": {
        "id": "UmPtAikAgGXX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_labels, all_preds = evaluate(model, dataloaders, device)\n"
      ],
      "metadata": {
        "id": "iWCzLqO8ipAL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04000091-0e26-4736-f2fb-f77d1d66a4e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.4417\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "    Angelina Jolie       0.31      0.40      0.35        20\n",
            "         Brad Pitt       0.39      0.45      0.42        20\n",
            " Denzel Washington       0.36      0.40      0.38        20\n",
            "      Hugh Jackman       0.60      0.45      0.51        20\n",
            " Jennifer Lawrence       0.33      0.20      0.25        20\n",
            "       Johnny Depp       0.20      0.15      0.17        20\n",
            "      Kate Winslet       0.36      0.20      0.26        20\n",
            " Leonardo DiCaprio       0.21      0.15      0.18        20\n",
            "         Megan Fox       0.57      0.60      0.59        20\n",
            "   Natalie Portman       0.33      0.35      0.34        20\n",
            "     Nicole Kidman       0.69      0.55      0.61        20\n",
            "  Robert Downey Jr       0.32      0.55      0.41        20\n",
            "    Sandra Bullock       0.33      0.40      0.36        20\n",
            "Scarlett Johansson       0.70      0.97      0.81        40\n",
            "        Tom Cruise       0.40      0.30      0.34        20\n",
            "         Tom Hanks       0.67      0.60      0.63        20\n",
            "        Will Smith       0.29      0.25      0.27        20\n",
            "\n",
            "          accuracy                           0.44       360\n",
            "         macro avg       0.42      0.41      0.40       360\n",
            "      weighted avg       0.43      0.44      0.43       360\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "def plot_confusion_matrix(all_labels, all_preds, class_names):\n",
        "    cm = confusion_matrix(all_labels, all_preds)\n",
        "    plt.figure(figsize=(12, 10))\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
        "    plt.xlabel('Predicted Labels')\n",
        "    plt.ylabel('True Labels')\n",
        "    plt.title('Confusion Matrix')\n",
        "    plt.show()\n",
        "\n",
        "plot_confusion_matrix(all_labels, all_preds, class_names)\n"
      ],
      "metadata": {
        "id": "Qlqw0dzJiru_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "outputId": "b79b6200-0d36-4d65-d95a-e3e777455458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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e+RGhS4IxcsxY7NpzAM7OLhg9cigSExOljpYn5i0+ImUFxMp76eIFdO/VFxsjvsGqdZvw5s0bTBgzDOnpaVJHy5dI7Zueng4nZ2dMnT5L6igFYl8ofqLlrVbdATu/P6l8LVsXLnWkfIn0WQPE6wsi5RWtL5Q4mUw7X1qIxQM1UlJS8O2332L06NHo0KEDwsPDVd7PGZZ/8uRJNGzYEMbGxvDw8MCtW7dU1luwYAEqVKgAMzMzDBs2DNOmTcv1F/1NmzbB1dUVhoaGcHFxwdq1a/PN9d/pAOHh4bC0tMSxY8fg6uoKU1NT+Pr6Ij4+XrlNTEwM2rRpA2tra1hYWKBFixb4/fffC9Uecrkc8+bNQ5UqVWBgYIB69erh6NGjGucEgDNnzqB58+YwMjKCnZ0dxo8fj9TU1ELlKCp/xl5FQ48WaNC4GSrYVEITr9Zw/6gx7ty6LkkeTWyP2IpuPXqhq193ODg6YuacuTA0NMTB/fukjpYn5i0+ImUFxMq78ssN6NDZDzUcnOBU0wUz5y7C48fxuBkbK3W0fInUvp7NvTBm3AT4tGojdZQCsS8UP9Hy6uqWQTkra+XLwrKs1JHyJdJnDRCvL4iUV7S+QNqLxQM1du/eDRcXFzg7O+Ozzz7Dli1b8hzSPmPGDCxbtgwXLlxAmTJlMGTIEOV7O3bswMKFC7F48WJcvHgRVatWxbp161S237FjB2bPno2FCxfixo0bWLRoEWbNmoWIiAiNs6alpSE0NBTbt2/Hzz//jAcPHiAgIED5fnJyMgYOHIgzZ87g3LlzcHJyQvv27ZGcnKzxMVatWoVly5YhNDQUV69eRdu2bdG5c2fcvn1bo+3j4uLg6+uL7t274+rVq/j2229x5swZ+Pv7a5yhKNWs5Y4/LsXg0T9/AQDux/2JW39cQb1GHpLkKUhWZiZuxF5Hk6b/y6ejo4MmTTxw9colCZPljXmLj0hZAfHy/lfK/18nzbV0Cpbo7SsS9oWiJVpeAHj4z1/4tHNrDOrZHouDApHwOL7gjahAovUF0fISFRU+bUGNzZs347PPPgMA+Pr64uXLl4iKioK3t7fKegsXLkSLFi0AANOmTUOHDh3w+vVrGBoaYs2aNRg6dCgGDx4MAJg9ezaOHz+OlJQU5fZz5szBsmXL0K1bNwBA9erVERsbi/Xr12PgwIEaZc3KysJXX30FBwcHAIC/vz/mzZunfL9ly5Yq62/YsAGWlpaIiopCx44dNTpGaGgopk6dij59+gAAFi9ejNOnT2PlypX48ssvC9w+ODgY/fr1w4QJEwAATk5OWL16NVq0aIF169bB0NBQoxxFpUufQUhPS8XEIT2go6MDuVyO3oPHoHmrdiWaQ1NJL5KQnZ0NKysrleVWVla4d++uRKnyx7zFR6SsgHh53yaXy7EyNATu9RrAwdFJ6jh5Erl9RcK+UPREy+tSqw4mzZiPKlXt8TzxKXZsWY+AMYPx1fZ9MDYxkTqe0ETrC6LlpQLwaQsaY/EgH7du3cJvv/2GAwcOAADKlCmD3r17Y/PmzbmKB+7u7sp/29raAgASEhJQtWpV3Lp1C2PGjFFZ/+OPP8apU6cAAKmpqYiLi8PQoUMxfPhw5Tpv3rwp1I0GjY2NlYWDnBwJCQnKr588eYKZM2ciMjISCQkJyM7ORlpaGh48eKDR/l+9eoVHjx7B09NTZbmnpyeuXLmi0T6uXLmCq1evYseOHcplCoUCcrkc9+7dg6ura65tMjIykJGRobIsMyMT+gYGGh1TnbNRP+HMqaMYF7gAdvYOuH/nFiLWLUc5q/Jo8YlmBRUi+rCFhszH3bjbWL/la6mjkMTYF6hR02bKf9dwrAmXWnUwoHs7/HzqGHw7dZMwGRFRyWDxIB+bN2/GmzdvUKlSJeUyhUIBAwMDhIWFqfxir6enp/x3zs0B5XK5RsfJGYGwceNGNG7cWOU9XV1djfO+nSEnx9tTLAYOHIjExESsWrUK1apVg4GBAZo2bYrMzEyNj/G+UlJSMHLkSIwfPz7Xe1WrVs1zm+DgYMydO1dl2cgJ0zDqi+nvnWfHxtXo0nsgPH3a/puhuiOeJsTj4K6tWlk8KGtZFrq6urluxJOYmAhra2uJUuWPeYuPSFkB8fLmCA1ZgOhforBu0zZUqGgjdZx8idq+ImFfKB6i5f0vUzNzVLarhkf//C11FOGJ1hdEy0tUVDhGIw9v3rzBtm3bsGzZMly+fFn5unLlCipVqoRvvvlG4305OzsjJiZGZdnbX1esWBGVKlXC3bt34ejoqPKqXr16kZ1TdHQ0xo8fj/bt26N27dowMDDAs2fPNN7e3NwclSpVQnR0dK791qpVS6N9NGjQALGxsbnO09HREfr6+nluExgYiJcvX6q8hoyZpHFudTJev4ZMR/UjoKOjC4VcOx/VqKevD9datXH+3FnlMrlcjvPnz8K9bn0Jk+WNeYuPSFkB8fIqFAqEhixA1OkTCFu/BZUqV5E6klqita9I2BeKl2h5/ys9LQ3xD/9GOf6y+N5E6wui5aUCSP1UBYGetsCRB3k4dOgQkpKSMHTo0FxTB7p3747Nmzdj1KhRGu1r3LhxGD58OBo2bAgPDw98++23uHr1KmrUqKFcZ+7cuRg/fjwsLCzg6+uLjIwMXLhwAUlJSZg4cWKRnJOTkxO2b9+Ohg0b4tWrV5g8eTKMjIwKtY/Jkydjzpw5cHBwQL169bB161ZcvnxZZRqCOlOnTkWTJk3g7++PYcOGwcTEBLGxsfjpp58QFhaW5zYGBgYw+M8UBf0Xmt/kUZ2PmjTHgZ1bYF3BBlWq1cD9O7dweN8O+LTtXCT7Lw79Bw7GrOlTUbu2G9zquOPr7RFIT09HVz/tHC7JvMVHpKyAWHlDQ+bj+JHDWLwiDMbGJkh89hQAYGJqVuL3ZtGUSO2blpaKv9+aMvfw4T+4dfMGLCwsYGNbSc2WJY99ofiJlHdj2DI09myBCja2eP7sKbZvWgddXV14t9bOeyWJ9FkDxOoLgFh5ResLpL1YPMjD5s2b0bp16zzvOdC9e3csWbIEV69e1Whf/fr1w927dxEQEIDXr1+jV69eGDRoEH777TflOsOGDYOxsTGWLl2KyZMnw8TEBHXq1FHeWLAobN68GSNGjECDBg1gZ2eHRYsWqTyNIS9yuRxlyvyvi4wfPx4vX77EpEmTkJCQgFq1auH777+Hk5NmN45yd3dHVFQUZsyYgebNm0OhUMDBwQG9e/d+r3N7V4P9J+Pb8K+weXUIXr5IQjkra7Tu0A09Phte8MYS8W3XHknPn2Nt2Go8e/YUzi6uWLt+E6y09K8ezFt8RMoKiJV3/55dAICxw1VvWDszaCE6dPaTIlKBRGrf2OvXMWro/9p2xdLFAICOnbsiaEGwVLHyxL5Q/ETK+yzhCULmTEPyqxewsCyL2u71sWL9dliWLSd1tDyJ9FkDxOoLgFh5ResLpL1kiryePUjFqk2bNrCxscH27duljqLWqFGj8M8//+DQoUNSR1Fx+UHRjDwoKS6VzKSOQFQqpGVkSx1BY8YGmt/TRhtkZWt2Hx9tkfVGrB9tROsPIol/8VrqCIVibZb3NE5tpafLGdDFRbTrrpmBuH3ByOP976VWHNJ/XSR1hFw48qCYpaWl4auvvkLbtm2hq6uLb775BidOnMBPP/0kdbR8JScn49KlS9i/fz+mT9fODxMRERERERGVHBYPiplMJsOPP/6IhQsX4vXr13B2dsa+ffvQunVrqaPla/bs2dixYwf8/Pw0vrcDERERERERfbg4bYGEw2kLRJQXTlsoPqINn+W0BcrBaQvFi9MWio9o112hpy14zpA6Qp7SoxdKHSEXcf+XiYiIiIiIiKhEsHhARERERERERGrxngdERERERERUOsn493RNsaWIiIiIiIiISC0WD4iIiIiIiIhILU5bICIiIiIiotJJJpM6gTA48oCIiIiIiIiI1GLxgIiIiIiIiIjU4rQFIiIiIiIiKp34tAWNsaWIiIiIiIiISC0WD4iIiIiIiIhILU5bICIiIiIiotKJ0xY0xpYiIiIiIiIiIrVYPCAiIiIiIiIitThtgYTjUNFE6giFcu3BS6kjFIpLZTOpI2hMT5f1T/ofYwNdqSNoLCtbLnWEQhHts6YnTlcQzsu0LKkjFIqtpaHUET5oaRnZUkf4YOmVkUkdofTQYVtrSqyfBoiIiIiIiIioxLF4QERERERERERqcdoCERERERERlU582oLG2FJEREREREREpBaLB0RERERERESkFqctEBERERERUekk49MWNMWRB0RERERERESkFosHRERERERERKQWpy0QERERERFR6cSnLWiMLUVEREREREREarF4QERERERERERqcdoCERERERERlU582oLGOPKAiIiIiIiIiNRi8YCIiIiIiIiI1OK0BSIiIiIiIiqd+LQFjbGlKBdvb29MmDDhnbcPDw+HpaVlkeUhIiIiIiIiabF4oOUGDRoEmUymfFlZWcHX1xdXr16VLFN4eLgyj46ODqpUqYLBgwcjISEBANC7d2/8+eefyvWDgoJQr169XPuRyWQ4ePBgCaXO2+8XYvCF/2j4tvJCQ3dXRJ46IWmegqSnpWL7V8vx+cDOGNylOeZOHIq4W7FSx8qXaO0LALt27kC7Ni3RqH4d9OvTE9ck/KwVRKSsAPMWFxE/Z4A47QuIlRUQJ+/BvbswqK8ffL0bw9e7MUYP6Ydz0b9IHatAorQvIE7WiC0bMOSzXmjVrCHat2qGqRP98df9e1LHypdoeUX9PkHah8UDAfj6+iI+Ph7x8fE4efIkypQpg44dO6rdJisrq1gzmZubIz4+Hv/88w82btyII0eOoH///gAAIyMjVKhQoViPX1TS09Ph5OyMqdNnSR1FI5tWLcQfl85jdEAQgtfthFuDxgiZPhbPnyVIHS1PorXv0SM/InRJMEaOGYtdew7A2dkFo0cORWJiotTRchEpK8C8xUm0zxkgVvuKlBUQK2/5CjYY6f8FNm7bjY0R36JBw48xPWAc7sXdkTpavkRqX5GyXrp4Ad179cXGiG+wat0mvHnzBhPGDEN6eprU0fIkWl4Rv0+UKJlMO19aiMUDARgYGMDGxgY2NjaoV68epk2bhr///htPnz4FANy/fx8ymQzffvstWrRoAUNDQ+zYsQOJiYno27cvKleuDGNjY9SpUwfffPONyr5TU1MxYMAAmJqawtbWFsuWLdMok0wmg42NDSpVqoR27dph/PjxOHHiBNLT01WmLYSHh2Pu3Lm4cuWKcrRCeHg47O3tAQB+fn6QyWTKr0uaZ3MvjBk3AT6t2khy/MLIzHiNmDOn0WfoOLjUaQCbSnbo/tkIVKxkh5OH90kdL08itS8AbI/Yim49eqGrX3c4ODpi5py5MDQ0xMH92te+ImUFmLc4ifY5A8RqX5GyAmLl9fTyRlNPL9hVrQa7avYYPuZzGBkb4/ofV6SOli+R2lekrCu/3IAOnf1Qw8EJTjVdMHPuIjx+HI+bsdo5ulK0vCJ+nyDtxOKBYFJSUvD111/D0dERVlZWKu9NmzYNn3/+OW7cuIG2bdvi9evX+Oijj3D48GH88ccfGDFiBPr374/ffvtNuc3kyZMRFRWF7777DsePH0dkZCR+//33QucyMjKCXC7HmzdvVJb37t0bkyZNQu3atZWjJ3r37o2YmBgAwNatWxEfH6/8mvKXnZ0NuTwbenr6Ksv19Q1w67r2/qAliqzMTNyIvY4mTT2Uy3R0dNCkiQeuXrkkYbLcRMoKMC+pEql9RcoKiJf3bdnZ2Th5/Ee8Tk+HW516UsfJk0jtK1LWvKQkJwMAzC0sJE6iGdHyEr0rPm1BAIcOHYKpqSmAf0cK2Nra4tChQ9DRUa39TJgwAd26dVNZFhAQoPz3uHHjcOzYMezevRsff/wxUlJSsHnzZnz99ddo1aoVACAiIgJVqlQpVL7bt2/jq6++QsOGDWFmZqbynpGREUxNTVGmTBnY2NioLAcAS0tLleWUPyNjEzi51sHBb7agctXqsLAsh1+jjuP2zWuoaFu4/zPKLelFErKzs3MV5aysrHDv3l2JUuVNpKwA85IqkdpXpKyAeHkBIO7OnxgzpB8yMzNhZGSMBUtXwb6Gg9Sx8iRS+4qU9b/kcjlWhobAvV4DODg6SR2nQKLlpTzwaQsaY/FAAD4+Pli3bh0AICkpCWvXrkW7du3w22+/oVq1asr1GjZsqLJddnY2Fi1ahN27d+Phw4fIzMxERkYGjI2NAQBxcXHIzMxE48aNlduUK1cOzs7OBWZ6+fIlTE1NIZfL8fr1azRr1gybNm0qitNVkZGRgYyMDJVlmdCDgYFBkR9LBKMC5mLjivkY91kH6Ojowt7RGU1bfIL7d25KHY2IiKjQqlarjs079iE1JRmRJ49jUdAMrFkfrrUFBCp+oSHzcTfuNtZv+VrqKBoRLS/R+2DxQAAmJiZwdHRUfr1p0yZYWFhg48aNWLBggcp6b1u6dClWrVqFlStXok6dOjAxMcGECROQmZn53pnMzMzw+++/Q0dHB7a2tsqRBEUtODgYc+fOVVk2bcZsTJ81p1iOp+0qVqqCmUvX4/XrdKSnpaJsOWusCZ6O8jaVpY4mvLKWZaGrq5vrRlKJiYmwtraWKFXeRMoKMC+pEql9RcoKiJcXAPT09FDFrioAwNm1Nm7GXseeXV9j8nTt+z4vUvuKlPVtoSELEP1LFNZt2oYKFbV/ZKpoeYneF8doCCjnEYnp6elq14uOjkaXLl3w2WefoW7duqhRo4bKIxQdHBygp6eH8+fPK5clJSWprJMfHR0dODo6okaNGgUWDvT19ZGdnZ1ruZ6eXp7L3xYYGIiXL1+qvCZNmVZgvg+doaERypazRmryK1y7eA4fNfGSOpLw9PT14VqrNs6fO6tcJpfLcf78WbjXrS9hstxEygowL6kSqX1FygqIlzcvcoUcWUXwR47iIFL7ipQVABQKBUJDFiDq9AmErd+CSpW1ezqmaHmpAFI/VUGgpy1w5IEAMjIy8PjxYwD//nIfFhaGlJQUdOrUSe12Tk5O2Lt3L3799VeULVsWy5cvx5MnT1CrVi0AgKmpKYYOHYrJkyfDysoKFSpUwIwZM3LdS+F92dvb4969e7h8+TKqVKkCMzMzGBgYwN7eHidPnoSnpycMDAxQtmzZXNsaGBjkmqKQnCEvsmxpaan4+8ED5dcPH/6DWzdvwMLCAja2lYrsOEXl6sWzUCgA2ypV8eTRP/hm82rYVrGH1yfq+4JURGvf/gMHY9b0qahd2w1uddzx9fYIpKeno6tft4I3LmEiZQWYtziJ9jkDxGpfkbICYuVdH7YCjT2ao6KNLdLSUnHi6GFcvhiD0DXrpY6WL5HaV6SsoSHzcfzIYSxeEQZjYxMkPvv3iWImpmYwNDSUOF1uouUV8fsEaScWDwRw9OhR2NraAvh3uoCLiwv27NkDb29vtdvNnDkTd+/eRdu2bWFsbIwRI0aga9euePnypXKdpUuXKgsRZmZmmDRpksr7RaF79+7Yv38/fHx88OLFC2zduhWDBg3CsmXLMHHiRGzcuBGVK1fG/fv3i/S4moi9fh2jhg5Ufr1i6WIAQMfOXRG0ILjE8xQkLTUFu7euxfNnCTAxM8fHzVqi58DRKFNGOz/KorWvb7v2SHr+HGvDVuPZs6dwdnHF2vWbYKWFQzxFygowb3ES7XMGiNW+ImUFxMqblPQci4KmI/HZU5iYmsHBsSZC16xHo8YeBW8sEZHaV6Ss+/fsAgCMHT5QZfnMoIXo0NlPikhqiZZXxO8TpJ1kCoVCIXUIosIoypEHJeHmw2SpIxSKS2WzglfSEnq6nHlFYsrKFus6xs8a5XiZliV1hEKxMNaTOsIHLS1D/fRTend6ZbRz2Hp+zAzE/T5h1H6V1BHylP7j51JHyEXc/2UiIiIiIiIiKhEsHhARERERERGRWiweEBERERERUekk9VMViuBpC+vWrYO7uzvMzc1hbm6Opk2b4siRI8r3vb29IZPJVF6jRo0qdFNp513WiIiIiIiIiKhAVapUQUhICJycnKBQKBAREYEuXbrg0qVLqF27NgBg+PDhmDdvnnIbY2PjQh+HxQMiIiIiIiIiQXXqpPrY9oULF2LdunU4d+6csnhgbGwMGxub9zoOpy0QERERERFR6STT0cpXRkYGXr16pfLKyMgo8HSys7Oxa9cupKamomnTpsrlO3bsgLW1Ndzc3BAYGIi0tLRCNxWLB0RERERERERaJDg4GBYWFiqv4ODgfNe/du0aTE1NYWBggFGjRuHAgQOoVasWAODTTz/F119/jdOnTyMwMBDbt2/HZ599VuhMMoVCoXjnMyKSQHKGWM9Hv/kwWeoIheJS2UzqCBrjs+dJVFnZYl3H+FmjHC/TsqSOUCgWxnpSR/igpWVkSx3hg6VXpnA3zJOamYG43yeMOoZJHSFPL/YNzzXSwMDAAAYGBnmun5mZiQcPHuDly5fYu3cvNm3ahKioKGUB4W2nTp1Cq1atcOfOHTg4OGicifc8ICIiIiIiotJJpp2FD3WFgrzo6+vD0dERAPDRRx8hJiYGq1atwvr163Ot27hxYwAodPFAO1uKiIiIiIiIiN6JXC7P9x4Jly9fBgDY2toWap8ceUBEREREREQkqMDAQLRr1w5Vq1ZFcnIydu7cicjISBw7dgxxcXHYuXMn2rdvDysrK1y9ehVffPEFvLy84O7uXqjjsHhAREREREREpZNMrPtL5CUhIQEDBgxAfHw8LCws4O7ujmPHjqFNmzb4+++/ceLECaxcuRKpqamws7ND9+7dMXPmzEIfh8UDIiIiIiIiIkFt3rw53/fs7OwQFRVVJMfhPQ+IiIiIiIiISC2OPCAiIiIiIqLSSUuftqCN2FJEREREREREpBaLB0RERERERESkFqctEBERERERUen0ATxtoaSweEDC0dMVa8CMS2UzqSMUStyTVKkjaMylklhtm5UtlzrCB020awNRDpGuDcYGulJHIC3C/lB8HjxLkzpCoZjZGEsdgUoAf9IiIiIiIiIiIrU48oCIiIiIiIhKJz5tQWNsKSIiIiIiIiJSi8UDIiIiIiIiIlKL0xaIiIiIiIiodOLTFjTGkQdEREREREREpBaLB0RERERERESkFqctEBERERERUakk47QFjXHkARERERERERGpxeIBEREREREREanFaQtERERERERUKnHaguY48oCIiIiIiIiI1GLxgIiIiIiIiIjU4rQFIiIiIiIiKp04a0FjHHlARERERERERGqxeFAI9vb2WLlypdQxVMhkMhw8eDDf9yMjIyGTyfDixYsSy0REREREREQfFkmLB4MGDYJMJoNMJoOenh4qVqyINm3aYMuWLZDL5VJGK7SjR49CJpPh8ePHKsttbW1hb2+vsuz+/fuQyWQ4efJksefy8PBAfHw8LCwsinS/3t7emDBhQpHuUyq7du5AuzYt0ah+HfTr0xPXrl6VOlK+fr8Qgy/8R8O3lRcaursi8tQJqSPlS56djW/D18G/f2d81sET4wd0wb6vN0GhUEgdTS1R+oNIfQEQLy/AvlDcRGlfQKysIvUHkbK+TaT+IFJWgHmLU+LTBCxbMAOfdvJG9zZN4D+oJ27fvC51LK2Q8/uotr20keQjD3x9fREfH4/79+/jyJEj8PHxweeff46OHTvizZs3UsfTWLNmzVCmTBlERkYql924cQPp6elISkrC/fv3lctPnz4NAwMDeHp6FnsufX192NjYaG0HlNrRIz8idEkwRo4Zi117DsDZ2QWjRw5FYmKi1NHylJ6eDidnZ0ydPkvqKAX67tsI/PTDXgzxn4Llm/fg02Hj8P3ubTh68Fupo+VLpP4gUl8AxMvLvlC8RGpfkbICYvUHkbLmEKk/iJQVYN7ilJL8ClP8B0FXtwyCloThy237MGTsRJiamUsdjQQjefHAwMAANjY2qFy5Mho0aIDp06fju+++w5EjRxAeHq5c78WLFxg2bBjKly8Pc3NztGzZEleuXFG+HxQUhHr16mH79u2wt7eHhYUF+vTpg+TkZAD/+2v/f1/e3t7KfZw5cwbNmzeHkZER7OzsMH78eKSmpmp0HqampmjUqJFK8SAyMhLNmjWDp6dnruVNmjSBoaEhjh49imbNmsHS0hJWVlbo2LEj4uLilOtmZmbC398ftra2MDQ0RLVq1RAcHKxy7GfPnsHPzw/GxsZwcnLC999/r3Kst6cthIeHw9LSEseOHYOrqytMTU2VBZwcb968wfjx45WZpk6dioEDB6Jr164A/h0xEhUVhVWrVinbMac4EhUVhY8//hgGBgawtbXFtGnTVIpA3t7eGD9+PKZMmYJy5crBxsYGQUFBGrVxcdgesRXdevRCV7/ucHB0xMw5c2FoaIiD+/dJlkkdz+ZeGDNuAnxatZE6SoH+jL2Khh4t0KBxM1SwqYQmXq3h/lFj3LmlvVVukfqDSH0BEC8v+0LxEql9RcoKiNUfRMqaQ6T+IFJWgHmL096dW2Fd3gYTAueipqsbbGwro0GjprCtbCd1NBKM5MWDvLRs2RJ169bF/v37lct69uyJhIQEHDlyBBcvXkSDBg3QqlUrPH/+XLlOXFwcDh48iEOHDuHQoUOIiopCSEgIAMDOzg7x8fHK16VLl2BlZQUvLy/ltr6+vujevTuuXr2Kb7/9FmfOnIG/v7/GuX18fHD69Gnl16dPn4a3tzdatGihsjwyMhI+Pj4AgNTUVEycOBEXLlzAyZMnoaOjAz8/P+W0jdWrV+P777/H7t27cevWLezYsSPXNIi5c+eiV69euHr1Ktq3b49+/fqptMt/paWlITQ0FNu3b8fPP/+MBw8eICAgQPn+4sWLsWPHDmzduhXR0dF49eqVyn0VVq1ahaZNm2L48OHK9rSzs8PDhw/Rvn17NGrUCFeuXMG6deuwefNmLFiwQOX4ERERMDExwfnz57FkyRLMmzcPP/30k8btXFSyMjNxI/Y6mjT1UC7T0dFBkyYeuHrlUonn+dDUrOWOPy7F4NE/fwEA7sf9iVt/XEG9Rh4FbCkN9gfKwb5QvERqX5GyUvETqT+IlBVg3uL2W3QUHF1qIWT2ZHzWpSU+H9oHx37YX/CGpYTU0xNEmragtY9qdHFxwdX/nzd05swZ/Pbbb0hISICBgQEAIDQ0FAcPHsTevXsxYsQIAIBcLkd4eDjMzMwAAP3798fJkyexcOFC6OrqwsbGBgDw+vVrdO3aFU2bNlX+1Ts4OBj9+vVTzuN3cnLC6tWr0aJFC6xbtw6GhoYFZvbx8cGiRYsQHx8PW1tbREVFYfLkyXjz5g3WrVsHALh79y4ePHigLB50795dZR9btmxB+fLlERsbCzc3Nzx48ABOTk5o1qwZZDIZqlWrluu4gwYNQt++fQEAixYtwurVq/Hbb7/B19c3z5xZWVn46quv4ODgAADw9/fHvHnzlO+vWbMGgYGB8PPzAwCEhYXhxx9/VL5vYWEBfX19GBsbK9sUANauXQs7OzuEhYVBJpPBxcUFjx49wtSpUzF79mzo6Pxbq3J3d8ecOXOU7RwWFoaTJ0+iTZvcf3nIyMhARkaGyjKFroGyH7yPpBdJyM7OhpWVlcpyKysr3Lt39733X9p16TMI6WmpmDikB3R0dCCXy9F78Bg0b9VO6mh5Yn+gHOwLxUuk9hUpKxU/kfqDSFkB5i1uj+Mf4sh3e9C152fo+dlQ3L55HRtWL0EZvTJo5dtZ6ngkEK0ceQAACoVCWXG5cuUKUlJSYGVlBVNTU+Xr3r17KkP87e3tlYUD4N+bFSYkJOTa95AhQ5CcnIydO3cqf6G9cuUKwsPDVfbftm1byOVy3Lt3T6PMHh4e0NfXR2RkJGJjY5Geno4GDRqgYcOGePr0Ke7du4fIyEgYGRmhSZMmAIDbt2+jb9++qFGjBszNzZWjCh48eADg38LA5cuX4ezsjPHjx+P48eO5juvu7q78t4mJCczNzfM87xzGxsbKwsF/2+nly5d48uQJPv74Y+X7urq6+Oijjwo8/xs3bqBp06YqlTJPT0+kpKTgn3/+yTPvf4//X8HBwbCwsFB5LV0cnOe6pF3ORv2EM6eOYlzgAoSs24Exk4NwaM/XiDp+SOpoRERERKWGQi6Hg5MLBowYB4eaLvDt3B2fdPTDke/2Sh2NBKO1Iw9u3LiB6tWrAwBSUlJga2urct+AHJaWlsp/6+npqbwnk8lyPbVhwYIFOHbsGH777TeVQkNKSgpGjhyJ8ePH5zpG1apVNcpsbGyMjz/+GKdPn8bz58/RrFkz6OrqQldXFx4eHjh9+jROnz4NT09P6OvrAwA6deqEatWqYePGjahUqRLkcjnc3NyQmZkJAGjQoAHu3buHI0eO4MSJE+jVqxdat26NvXv/92HX5Lzfltf6JXkH/MLkDQwMxMSJE1WWKXTff9QBAJS1LAtdXd1cN7ZJTEyEtbV1kRyjNNuxcTW69B4IT5+2AICq1R3xNCEeB3dtRYtPOkqcLjf2B8rBvlC8RGpfkbJS8ROpP4iUFWDe4lbWyhp29jVUltlVq45ffy7+J7+JQFunCGgjrRx5cOrUKVy7dk05pL9BgwZ4/PgxypQpA0dHR5VXYT6g+/btw7x587B7926Vv7znHCM2NjbX/h0dHZW/6GvCx8cHkZGRiIyMVLkZo5eXFyIjIxEVFaWcspCYmIhbt25h5syZaNWqFVxdXZGUlJRrn+bm5ujduzc2btyIb7/9Fvv27VN7T4P3YWFhgYoVKyImJka5LDs7G7///rvKevr6+sjOzlZZ5urqirNnz6oUIqKjo2FmZoYqVaq8Ux4DAwOYm5urvIpiygIA6Onrw7VWbZw/d1a5TC6X4/z5s3CvW79IjlGaZbx+DZmO6iVGR0cXCrl2PqqR/YFysC8UL5HaV6SsVPxE6g8iZQWYt7i5utXDwwd/qSx7+M8DVKhoK1EiEpXkIw8yMjLw+PFjZGdn48mTJzh69CiCg4PRsWNHDBgwAADQunVrNG3aFF27dsWSJUtQs2ZNPHr0CIcPH4afnx8aNmxY4HH++OMPDBgwAFOnTkXt2rXx+PFjAP/+ElyuXDlMnToVTZo0gb+/P4YNGwYTExPExsbip59+QlhYmMbn4+Pjg/nz5+Px48cqNyFs0aIFli5diuTkZGXxoGzZsrCyssKGDRtga2uLBw8eYNq0aSr7W758OWxtbVG/fn3o6Ohgz549sLGxURlxUdTGjRuH4OBgODo6wsXFBWvWrEFSUpJKVc7e3h7nz5/H/fv3YWpqinLlymHMmDFYuXIlxo0bB39/f9y6dQtz5szBxIkTldNDtE3/gYMxa/pU1K7tBrc67vh6ewTS09PR1a+b1NHylJaWir//f0oLADx8+A9u3bwBCwsL2NhWkjBZbh81aY4DO7fAuoINqlSrgft3buHwvh3waau9c+tE6g8i9QVAvLzsC8VLpPYVKSsgVn8QKWsOkfqDSFkB5i1OXXp+hiljB2H39s1o5tMGf964jmM/7IN/gDiPSSXtIHnx4OjRo7C1tUWZMmVQtmxZ1K1bF6tXr8bAgQOVv3DKZDL8+OOPmDFjBgYPHoynT5/CxsYGXl5eqFixokbHuXDhAtLS0rBgwQKVu/+3aNECkZGRcHd3R1RUFGbMmIHmzZtDoVDAwcEBvXv3LtT5NG3aFAYGBlAoFCr3CWjcuDGysrKUj3QE/r0r665duzB+/Hi4ubnB2dkZq1evVhmxYGZmhiVLluD27dvQ1dVFo0aN8OOPPxbrL+NTp07F48ePMWDAAOjq6mLEiBFo27YtdHV1lesEBARg4MCBqFWrFtLT03Hv3j3Y29vjxx9/xOTJk1G3bl2UK1cOQ4cOxcyZM4st6/vybdceSc+fY23Yajx79hTOLq5Yu34TrLRwyBkAxF6/jlFDByq/XrF0MQCgY+euCFqgXfeCGOw/Gd+Gf4XNq0Pw8kUSyllZo3WHbujx2XCpo+VLpP4gUl8AxMvLvlC8RGpfkbICYvUHkbLmEKk/iJQVYN7iVNO1NqYvWIZtG9Zg17YNqGhTGcP9J8O7TXupo2kFTlvQnExRkpPdSUhyuRyurq7o1asX5s+fL3UcvH4jdYLCycrO//4T2ijuSarUETTmUsms4JW0iGh9QTR6uto5wikvovUFkdpWRKL1B5Gw75KoHjxLkzpCodS0MZY6wjuz6Ltd6gh5evlNf6kj5CL5yAPSPn/99ReOHz+OFi1aICMjA2FhYbh37x4+/fRTqaMRERERERGRBFg8oFx0dHQQHh6OgIAAKBQKuLm54cSJE3B1dZU6GhERERERUdHhrAWNsXhAudjZ2SE6OlrqGERERERERKQlOBGMiIiIiIiIiNTiyAMiIiIiIiIqlfi0Bc1x5AERERERERERqcXiARERERERERGpxWkLREREREREVCpx2oLmOPKAiIiIiIiIiNRi8YCIiIiIiIiI1OK0BSIiIiIiIiqVOG1Bcxx5QERERERERERqsXhARERERERERGpx2gIRERERERGVSpy2oDmOPCAiIiIiIiIitVg8ICIiIiIiIiK1OG2BiIiIiIiISifOWtAYiwdEpMKlkpnUETR27cFLqSMUSp2qFlJHIHonaRnZUkcoFGMDXakjFIqerjgDQV+mZUkdoVAsjMVpWwDIypZLHYG0hG1ZQ6kjEOUi1hWViIiIiIiIiEocRx4QERERERFRqcSnLWiOIw+IiIiIiIiISC0WD4iIiIiIiIhILU5bICIiIiIiolKJ0xY0x5EHRERERERERKQWiwdEREREREREpBanLRAREREREVGpxGkLmuPIAyIiIiIiIiJSi8UDIiIiIiIiIlKL0xaIiIiIiIiodOKsBY1x5AERERERERERqcXiARERERERERGpxWkLREREREREVCrxaQua48gDIiIiIiIiIlKLxYMS5u3tjQkTJkiaITw8HJaWlpJmICIiIiIiInGU+uLBoEGD0LVr11zLIyMjIZPJ8OLFixLP9F9BQUGoV6+e1DE+WLt27kC7Ni3RqH4d9OvTE9euXpU6Ur5+vxCDL/xHw7eVFxq6uyLy1AmpIxVIpPZNT0vF9q+W4/OBnTG4S3PMnTgUcbdipY6VL5HaFmDe4iLadSFiywYM+awXWjVriPatmmHqRH/8df+e1LHUEqUv5BAl78G9uzCorx98vRvD17sxRg/ph3PRv0gdq0CitK9o1wbmLT4iZZWCTCbTypc2KvXFAyrdjh75EaFLgjFyzFjs2nMAzs4uGD1yKBITE6WOlqf09HQ4OTtj6vRZUkfRiGjtu2nVQvxx6TxGBwQheN1OuDVojJDpY/H8WYLU0XIRrW2Zt/iIdl24dPECuvfqi40R32DVuk148+YNJowZhvT0NKmj5UmkvgCIlbd8BRuM9P8CG7ftxsaIb9Gg4ceYHjAO9+LuSB0tXyK1r2jXBuYtPiJlJe3G4oGG8vrr/8qVK2Fvb6/8+s2bNxg/fjwsLS1hZWWFqVOnYuDAgblGNsjlckyZMgXlypWDjY0NgoKCCpUlJiYGbdq0gbW1NSwsLNCiRQv8/vvvKuu8ePECI0eORMWKFWFoaAg3NzccOnQoz/09ffoUDRs2hJ+fHzIyMpSjLo4dO4b69evDyMgILVu2REJCAo4cOQJXV1eYm5vj008/RVra/37YO3r0KJo1a6Y8/44dOyIuLk75/v379yGTybB//374+PjA2NgYdevWxdmzZwt1/kVpe8RWdOvRC139usPB0REz58yFoaEhDu7fJ1kmdTybe2HMuAnwadVG6igaEal9MzNeI+bMafQZOg4udRrAppIdun82AhUr2eHkYe3LK1LbAsxbnES7Lqz8cgM6dPZDDQcnONV0wcy5i/D4cTxuxmrnKB+R+gIgVl5PL2809fSCXdVqsKtmj+FjPoeRsTGu/3FF6mj5Eqp9Bbs2MG/xESkraTcWD4rQ4sWLsWPHDmzduhXR0dF49eoVDh48mGu9iIgImJiY4Pz581iyZAnmzZuHn376SePjJCcnY+DAgThz5gzOnTsHJycntG/fHsnJyQD+LU60a9cO0dHR+PrrrxEbG4uQkBDo6urm2tfff/+N5s2bw83NDXv37oWBgYHyvaCgIISFheHXX3/F33//jV69emHlypXYuXMnDh8+jOPHj2PNmjXK9VNTUzFx4kRcuHABJ0+ehI6ODvz8/CCXy1WOOWPGDAQEBODy5cuoWbMm+vbtizdv3mh8/kUlKzMTN2Kvo0lTD+UyHR0dNGnigatXLpV4ng+NaO2bnZ0NuTwbenr6Ksv19Q1w67p2/SArWtsyL6mT8v/fu8wtLCROkptofUG0vG/Lzs7GyeM/4nV6Otzq1JM6Tp5Ebl8iyp/U0xNEmrbARzUCOHToEExNTVWWZWdnF3o/a9asQWBgIPz8/AAAYWFh+PHHH3Ot5+7ujjlz5gAAnJycEBYWhpMnT6JNG82qgS1btlT5esOGDbC0tERUVBQ6duyIEydO4LfffsONGzdQs2ZNAECNGjVy7efWrVto06YN/Pz8sHLlylyddMGCBfD09AQADB06FIGBgYiLi1Puq0ePHjh9+jSmTp0KAOjevbvK9lu2bEH58uURGxsLNzc35fKAgAB06NABADB37lzUrl0bd+7cgYuLi0bnX1SSXiQhOzsbVlZWKsutrKxw797dEs3yIRKtfY2MTeDkWgcHv9mCylWrw8KyHH6NOo7bN6+hom0VqeOpEK1tmZfyI5fLsTI0BO71GsDB0UnqOLmI1hdEywsAcXf+xJgh/ZCZmQkjI2MsWLoK9jUcpI6VJxHbl4ioKHHkAQAfHx9cvnxZ5bVp06ZC7ePly5d48uQJPv74Y+UyXV1dfPTRR7nWdXd3V/na1tYWCQmaz6l+8uQJhg8fDicnJ1hYWMDc3BwpKSl48OABAODy5cuoUqWKsnCQl/T0dDRv3hzdunXDqlWr8qxuvZ2zYsWKMDY2VilCVKxYUSX37du30bdvX9SoUQPm5ubKKR05ufLar62tLQDke/4ZGRl49eqVyisjIyPf8yJ6H6MC5gIKBcZ91gGDOjfD8e++RdMWn0BHh5dKouIQGjIfd+NuY35wqNRRSCJVq1XH5h378NXWnejSvRcWBc3A/btxBW9IREQljiMPAJiYmMDR0VFl2T///KPytY6ODhQKhcqyrKysdzqenp6eytcymSzX0H51Bg4ciMTERKxatQrVqlWDgYEBmjZtiszMTACAkZFRgfswMDBA69atcejQIUyePBmVK1dWm1MmkxWYu1OnTqhWrRo2btyISpUqQS6Xw83NTZkrv/0CyPf8g4ODMXfuXJVlM2bNwczZQQWeY0HKWpaFrq5urpscJSYmwtra+r33X9qJ2L4VK1XBzKXr8fp1OtLTUlG2nDXWBE9HeZvcnw8pida2zEt5CQ1ZgOhforBu0zZUqGgjdZw8idYXRMsL/PszQRW7qgAAZ9fauBl7HXt2fY3J0+dInCw3EduXiAqmrVMEtBH/nKah8uXL4/HjxyoFhMuXLyv/bWFhgYoVKyImJka5LDs7O9eNDItCdHQ0xo8fj/bt26N27dowMDDAs2fPlO+7u7vjn3/+wZ9//pnvPnR0dLB9+3Z89NFH8PHxwaNHj94rU2JiIm7duoWZM2eiVatWcHV1RVJS0nvtEwACAwPx8uVLldfkqYHvvV8A0NPXh2ut2jh/7n83bJTL5Th//izc69YvkmOUZiK3r6GhEcqWs0Zq8itcu3gOHzXxkjqSCtHalnnpbQqFAqEhCxB1+gTC1m9BpcraNS3obaL1BdHy5kWukCPrP3900BYfQvsSEb0PjjzQkLe3N54+fYolS5agR48eOHr0KI4cOQJzc3PlOuPGjUNwcDAcHR3h4uKCNWvWICkpqcirWU5OTti+fTsaNmyIV69eYfLkySqjDVq0aAEvLy90794dy5cvh6OjI27evAmZTAZfX1/lerq6utixYwf69u2Lli1bIjIyEjY27/bXn7Jly8LKygobNmyAra0tHjx4gGnTpr33uRoYGKjcxBEAXhfhvRX7DxyMWdOnonZtN7jVccfX2yOQnp6Orn7diu4gRSgtLRV/vzUN5OHDf3Dr5g1YWFjAxraShMnyJlr7Xr14FgoFYFulKp48+gffbF4N2yr28Pqkk9TRchGtbZm3+Ih2XQgNmY/jRw5j8YowGBubIPHZUwCAiakZDA0NJU6Xm0h9ARAr7/qwFWjs0RwVbWyRlpaKE0cP4/LFGISuWS91tHyJ1L6iXRuYt/iIlJW0G4sHGnJ1dcXatWuxaNEizJ8/H927d0dAQAA2bNigXGfq1Kl4/PgxBgwYAF1dXYwYMQJt27bN8ykHhSGXy1GmzP/+qzZv3owRI0agQYMGsLOzw6JFixAQEKCyzb59+xAQEIC+ffsiNTUVjo6OCAkJybXvMmXK4JtvvkHv3r2VBYR3oaOjg127dmH8+PFwc3ODs7MzVq9eDW9v73faX0nxbdceSc+fY23Yajx79hTOLq5Yu34TrLR0+GHs9esYNXSg8usVSxcDADp27oqgBcFSxcqXaO2blpqC3VvX4vmzBJiYmePjZi3Rc+Bolc+fthCtbZm3+Ih2Xdi/ZxcAYOzwgSrLZwYtRIfOflJEUkukvgCIlTcp6TkWBU1H4rOnMDE1g4NjTYSuWY9GjT0K3lgiIrWvaNcG5i0+ImWVBGctaEym+O9Efioycrkcrq6u6NWrF+bPn//O+xk1ahT++ecfHDp0qAjTiasoRx6UhKxsze9noQ30dMWZzXTtwUupIxRKnara9yg6koZo14WsN2L9qGBs8H5Fe8rfy7R3u9+TVCyM9QpeSYuIdm0gymFmIM7Pj/9VadR+qSPk6dFX2jeiSdz/ZS30119/YePGjfjzzz9x7do1jB49Gvfu3cOnn376TvtLTk7Gzz//jP3796N169ZFnJaIiIiIiIhIM9o3FldgOjo6CA8PR0BAABQKBdzc3HDixAm4urq+0/5mz56NHTt2wM/PD6NGjSritERERERERKUbn7agOU5bIOFw2kLx4rSF4sNpC5RDtOsCpy1QDk5bKF6iXRuIcog8baHy6ANSR8jTw3Xadx8gcf+XiYiIiIiIiKhEcNoCERERERERlUqctqA5jjwgIiIiIiIiIrVYPCAiIiIiIiIitThtgYiIiIiIiEolTlvQHEceEBEREREREZFaLB4QERERERERkVosHhAREREREVHpJNPSVyGsW7cO7u7uMDc3h7m5OZo2bYojR44o33/9+jXGjh0LKysrmJqaonv37njy5EnhDgIWD4iIiIiIiIiEVaVKFYSEhODixYu4cOECWrZsiS5duuD69esAgC+++AI//PAD9uzZg6ioKDx69AjdunUr9HFkCoVCUdThiYrT6zdSJyicrGy51BEKRU9XnJritQcvpY5QKHWqWkgdgbSEaNeFrDdi/ahgbKArdYQP1su0LKkjFIqFsZ7UEQpFtGsDUQ4zA3F+fvwvO//vpI6Qp7/DurzX9uXKlcPSpUvRo0cPlC9fHjt37kSPHj0AADdv3oSrqyvOnj2LJk2aaLxPPm2BiIiIiIiISiVtfdpCRkYGMjIyVJYZGBjAwMBA7XbZ2dnYs2cPUlNT0bRpU1y8eBFZWVlo3bq1ch0XFxdUrVq10MUDcUtERERERERERB+g4OBgWFhYqLyCg4PzXf/atWswNTWFgYEBRo0ahQMHDqBWrVp4/Pgx9PX1YWlpqbJ+xYoV8fjx40Jl4sgDIiIiIiIiIi0SGBiIiRMnqixTN+rA2dkZly9fxsuXL7F3714MHDgQUVFRRZqJxQMiIiIiIiIqlbR12oImUxTepq+vD0dHRwDARx99hJiYGKxatQq9e/dGZmYmXrx4oTL64MmTJ7CxsSlUJhYPiIqZaDeXsjbT/CIlNdFuQPgsOaPglbSIcDcaE+imfqLd0C/rTbbUEQolLUOsvHpltPMH17yIdl0Q7XuwaNcGkW6yTFTayOVyZGRk4KOPPoKenh5OnjyJ7t27AwBu3bqFBw8eoGnTpoXaJ4sHRERERERERIIKDAxEu3btULVqVSQnJ2Pnzp2IjIzEsWPHYGFhgaFDh2LixIkoV64czM3NMW7cODRt2rRQN0sEWDwgIiIiIiKiUkpbpy0URkJCAgYMGID4+HhYWFjA3d0dx44dQ5s2bQAAK1asgI6ODrp3746MjAy0bdsWa9euLfRxZAqFQpxxnkQAXr+ROkHhiDZUXaRpC6IRrS+INjyZ0xaKj2jTAEQj0rQF0Yapc9pC8RKtP1DxMRT4T9L2nx+SOkKe7q/qKHWEXPiJJyIiIiIiIiK1BK4REREREREREb27D2HaQknhyAMiIiIiIiIiUovFAyIiIiIiIiJSi9MWiIiIiIiIqHTirAWNceQBEREREREREanF4gERERERERERqcVpC0RERERERFQq8WkLmuPIAyIiIiIiIiJSi8UDIiIiIiIiIlKL0xaIiIiIiIioVOK0Bc1x5AERERERERERqcXiQR68vb0xYcIE5ddpaWno3r07zM3NIZPJ8OLFC8myEREREREREZU0rS8eDBo0CF27di3RY+7fvx/z589Xfh0REYFffvkFv/76K+Lj42FhYVFkx7p//z5kMhkuX75cZPukwtm1cwfatWmJRvXroF+fnrh29arUkTTyzbbNaNXEHV+uWCx1FLVEa1/R8gJi9IXfL8TgC//R8G3lhYburog8dULqSPmK2LIBQz7rhVbNGqJ9q2aYOtEff92/J3WsAonSd0VqX5Gy5hDps5ZDlL57cO8uDOrrB1/vxvD1bozRQ/rhXPQvUsfKF/tC8WPeD4NMpp0vbaT1xQMplCtXDmZmZsqv4+Li4OrqCjc3N9jY2LzTvJjs7GzI5fKijFkkMjMzpY4gqaNHfkTokmCMHDMWu/YcgLOzC0aPHIrExESpo6l1M/YPHDqwBzUca0odRS3R2le0vIA4fSE9PR1Ozs6YOn2W1FEKdOniBXTv1RcbI77BqnWb8ObNG0wYMwzp6WlSR8uXSH1XpPYVKWsOkT5rgFh9t3wFG4z0/wIbt+3Gxohv0aDhx5geMA734u5IHS1P7AvFi3mpNBKqeCCXyxEcHIzq1avDyMgIdevWxd69e5XvR0ZGQiaT4eTJk2jYsCGMjY3h4eGBW7duKdcJCgpCvXr1sH37dtjb28PCwgJ9+vRBcnKycp23py14e3tj2bJl+PnnnyGTyeDt7Q0AyMjIQEBAACpXrgwTExM0btwYkZGRyn2Eh4fD0tIS33//PWrVqgUDAwM8ePCg0OccFxeHLl26oGLFijA1NUWjRo1w4sT/KsdhYWFwc3NTfn3w4EHIZDJ89dVXymWtW7fGzJkzVc5/06ZNqF69OgwNDQEAL168wLBhw1C+fHmYm5ujZcuWuHLlSqHaTS6XY8mSJXB0dISBgQGqVq2KhQsXKt//+++/0atXL1haWqJcuXLo0qUL7t+/X+g2KUrbI7aiW49e6OrXHQ6Ojpg5Zy4MDQ1xcP8+SXOpk56WhkVzAjExMAhmZuZSx1FLtPYVLa9IfcGzuRfGjJsAn1ZtpI5SoJVfbkCHzn6o4eAEp5oumDl3ER4/jsfN2Fipo+VLpL4rUvuKlDWHSJ81QKy+6+nljaaeXrCrWg121ewxfMznMDI2xvU/rhS8sQTYF4oX81JpJFTxIDg4GNu2bcNXX32F69ev44svvsBnn32GqKgolfVmzJiBZcuW4cKFCyhTpgyGDBmi8n5cXBwOHjyIQ4cO4dChQ4iKikJISEiex9y/fz+GDx+Opk2bIj4+Hvv37wcA+Pv74+zZs9i1axeuXr2Knj17wtfXF7dv31Zum5aWhsWLF2PTpk24fv06KlSoUOhzTklJQfv27XHy5ElcunQJvr6+6NSpk7IQ0aJFC8TGxuLp06cAgKioKFhbWysLGVlZWTh79qyy6AEAd+7cwb59+7B//37ldImePXsiISEBR44cwcWLF9GgQQO0atUKz58/17jdAgMDERISglmzZiE2NhY7d+5ExYoVlTnatm0LMzMz/PLLL4iOjoapqSl8fX0lG/2QlZmJG7HX0aSph3KZjo4OmjTxwNUrlyTJpIlVoQvRxLM5Pvq4idRR1BKtfUXLC4jTF0SX8v9FUvMinLJWlETsu2/T9vZ9m0hZRSBy383OzsbJ4z/idXo63OrUkzqO8ETrC8z7YZHJZFr50kbCPKoxIyMDixYtwokTJ9C0aVMAQI0aNXDmzBmsX78eLVq0UK67cOFC5dfTpk1Dhw4d8Pr1a+Vf2eVyOcLDw5VTE/r374+TJ0+q/JU8R7ly5WBsbAx9fX3Y2NgAAB48eICtW7fiwYMHqFSpEgAgICAAR48exdatW7Fo0SIA//7CvHbtWtStW/edz7tu3boq28+fPx8HDhzA999/D39/f7i5uaFcuXKIiopCjx49EBkZiUmTJmHVqlUAgN9++w1ZWVnw8PjfxSIzMxPbtm1D+fLlAQBnzpzBb7/9hoSEBBgYGAAAQkNDcfDgQezduxcjRowosN2Sk5OxatUqhIWFYeDAgQAABwcHNGvWDADw7bffQi6XY9OmTcoPw9atW2FpaYnIyEh88skn79xG7yrpRRKys7NhZWWlstzKygr37t0t8TyaOPXTEdy5dQNrt3wjdZQCida+ouUVqS+ITC6XY2VoCNzrNYCDo5PUcfIkWt99mwjtm0OkrKIQse/G3fkTY4b0Q2ZmJoyMjLFg6SrY13CQOpbwROsLzEullTDFgzt37iAtLQ1t2qgOvcrMzET9+vVVlrm7uyv/bWtrCwBISEhA1apVAQD29vYq9zSwtbVFQkKCxlmuXbuG7Oxs1KypOsc4IyND5UOpr6+vkuVdpKSkICgoCIcPH0Z8fDzevHmD9PR05cgDmUwGLy8vREZGonXr1oiNjcWYMWOwZMkS3Lx5E1FRUWjUqBGMjY2V+6xWrZqycAAAV65cQUpKSq4LSnp6OuLi4pRfq2u3GzduICMjA61atcrzPK5cuYI7d+6obA8Ar1+/VjnGf2VkZCAjI0NlmULXQFnkKE0SnjzGl8sXY8nqDdAvhedP/8O+UHJCQ+bjbtxtrN/ytdRRPkgita9IWan4VK1WHZt37ENqSjIiTx7HoqAZWLM+nAUEIioVhCkepKSkAAAOHz6MypUrq7z3318k9fT0lP/O+Sv32zcrfPv9nHUKczPDlJQU6Orq4uLFi9DV1VV5z9TUVPlvIyOj9x5yEhAQgJ9++gmhoaFwdHSEkZERevTooTLU39vbGxs2bMAvv/yC+vXrw9zcXFlQiIqKUhmVAQAmJia5zsfW1lblng05LC0tlf9W125GRkZqzyMlJQUfffQRduzYkeu9twsZ/xUcHIy5c+eqLJsxaw5mzg5SezxNlLUsC11d3Vw3iklMTIS1tfV777+o/XkzFi+SnmPUoN7KZfLsbFy9fBEH9+7C0Z8v5OqPUhKtfUXKK1pfEFVoyAJE/xKFdZu2oUJFG6nj5Eukvvs2UdoXECurSETsu3p6eqhi9+8fo5xda+Nm7HXs2fU1Jk+fI3EysYnWF5iXSith7nnw9k0HHR0dVV52dnYlmqV+/frIzs5GQkJCriw5UxuKSnR0NAYNGgQ/Pz/UqVMHNjY2uW4ymHPfgz179ijvbeDt7Y0TJ04gOjpa5X4HeWnQoAEeP36MMmXK5DofTS8oTk5OMDIywsmTJ/M9xu3bt1GhQoVcx1D36MvAwEC8fPlS5TV5aqBGmQqip68P11q1cf7cWeUyuVyO8+fPwr1ufTVbSqNBw8bYtGMfNmzbrXw5u9ZGq7YdsGHbbq37ZVG09hUpr2h9QTQKhQKhIQsQdfoEwtZvQaXKVaSOpJZIfRcQq31Fyioi0fpuXuQKObJK+ZOrioJofYF5PyxSP5JRpEc1CjPywMzMDAEBAfjiiy8gl8vRrFkzvHz5EtHR0TA3N1fOsy8JNWvWRL9+/TBgwAAsW7YM9evXx9OnT3Hy5Em4u7ujQ4cOhd7n20+EyFG7dm04OTlh//796NSpE2QyGWbNmpVrlIS7uzvKli2LnTt34tChQwD+LR4EBARAJpPB09NT7bFbt26Npk2bomvXrliyZAlq1qyJR48e4fDhw/Dz80PDhg0LzG9oaIipU6diypQp0NfXh6enJ54+fYrr169j6NCh6NevH5YuXYouXbpg3rx5qFKlCv766y/s378fU6ZMQZUqef9AZmCQe4rC6zcFxtFY/4GDMWv6VNSu7Qa3Ou74ensE0tPT0dWvW9EdpIgYm5iguoPqPFtDQyOYW1jkWq4tRGpfQJy8IvaFtLRU/P3WE2cePvwHt27egIWFBWxsK0mYLLfQkPk4fuQwFq8Ig7GxCRKf/XtDWhNTM+W9c7SNKH0XEKt9RcqaQ6TPGiBW310ftgKNPZqjoo0t0tJSceLoYVy+GIPQNeuljpYn9oXixbxUGml98UAul6NMmX9jzp8/H+XLl0dwcDDu3r0LS0tLNGjQANOnTy/xXFu3bsWCBQswadIkPHz4ENbW1mjSpAk6duz4Tvvr06dPrmV///03li9fjiFDhsDDwwPW1taYOnUqXr16pbKeTCZD8+bNcfjwYeUNCt3d3WFubg5nZ+dc0xT+SyaT4ccff8SMGTMwePBgPH36FDY2NvDy8lI+LUETs2bNQpkyZTB79mw8evQItra2GDVqFADA2NgYP//8M6ZOnYpu3bohOTkZlStXRqtWrWBuLt0j5nzbtUfS8+dYG7Yaz549hbOLK9au3wQrDuEqEqK1r2h5RRJ7/TpGDf1fkXfF0sUAgI6duyJoQbBUsfK0f88uAMDY4apF6ZlBC9Ghs58UkQokUt8VqX1FyppDpM8aIFbfTUp6jkVB05H47ClMTM3g4FgToWvWo1Fjj4I3lgD7QvFiXiqNZAqFQiF1CHV8fX3h6OiIsLAwqaOQlijKkQcl4VlyRsEraRFrM96Ar7iI1hcsjPUKXkmLZL3R6m9nKowNxJpakpaRLXWED5peGS0dn5oHPV1hZrwCAF6mZUkdoVBEuzaI1h+o+Bhq/Z+k8+c89ZjUEfJ0a3FbqSPkorWf+KSkJBw6dEj5FAEiIiIiIiIikobW1oiGDBmCmJgYTJo0CV26dJE6DhEREREREVGppbXFgwMHDkgdgYiIiIiIiD5g2vpkA22ktdMWiIiIiIiIiEg7sHhARERERERERGpp7bQFIiIiIiIiouKko8N5C5riyAMiIiIiIiIiUovFAyIiIiIiIiJSi9MWiIiIiIiIqFTi0xY0x5EHRERERERERKQWiwdEREREREREpBanLRAREREREVGpJOO8BY1x5AERERERERERqcXiARERERERERGpxWkLREREREREVCpx1oLmOPKAiIiIiIiIiNRi8YCIiIiIiIiI1OK0BRJOVrZc6giFYqwv1sdMtPYVibWZgdQRCuVlWpbUEQrF2EBX6ggaS8vIljpCoYjUtgCvY8XpWXKG1BEKxcJYT+oIHzSRPmt6uvybKeWNT1vQHD9FRERERERERKQWiwdEREREREREpJZY46mJiIiIiIiIiginLWiOIw+IiIiIiIiISC0WD4iIiIiIiIhILU5bICIiIiIiolKJsxY0x5EHRERERERERKQWiwdEREREREREpBanLRAREREREVGpxKctaI4jD4iIiIiIiIhILRYPiIiIiIiIiEgtTlsgIiIiIiKiUomzFjTHkQdEREREREREpBaLB0RERERERESkFqctEBERERERUanEpy1ojiMPilFkZCRkMhlevHghdRQiIiIiIiKid8biQSENGjQIXbt2lTrGe8spbMhkMujo6MDCwgL169fHlClTEB8fL3W8EvP7hRh84T8avq280NDdFZGnTkgdKV8RWzZgyGe90KpZQ7Rv1QxTJ/rjr/v3pI6llkjtK1LWHLt27kC7Ni3RqH4d9OvTE9euXpU6Up4O7t2FQX394OvdGL7ejTF6SD+ci/5F6lhqidQfRLw2AOL0X5H6AiBe3rd9s20zWjVxx5crFksdJU+itS3zFj9RrmM5RMtL2ofFg1Lu1q1bePToEWJiYjB16lScOHECbm5uuHbtmtTRSkR6ejqcnJ0xdfosqaMU6NLFC+jeqy82RnyDVes24c2bN5gwZhjS09OkjpYvkdpXpKwAcPTIjwhdEoyRY8Zi154DcHZ2weiRQ5GYmCh1tFzKV7DBSP8vsHHbbmyM+BYNGn6M6QHjcC/ujtTR8iVSfxDx2iBS/xWpLwDi5c1xM/YPHDqwBzUca0odJV+itS3zFi+RrmOAeHlLkkymnS9txOLBe8jIyMD48eNRoUIFGBoaolmzZoiJicm13sWLF9GwYUMYGxvDw8MDt27dUr4XFBSEevXqYfv27bC3t4eFhQX69OmD5ORk5Tre3t4YP348pkyZgnLlysHGxgZBQUHK94cMGYKOHTuqHDMrKwsVKlTA5s2b1Z5DhQoVYGNjg5o1a6JPnz6Ijo5G+fLlMXr0aJX1Nm3aBFdXVxgaGsLFxQVr165Vvnf//n3IZDLs2rULHh4eMDQ0hJubG6KiopTr5Ix0OHz4MNzd3WFoaIgmTZrgjz/+UN/IxcyzuRfGjJsAn1ZtJM2hiZVfbkCHzn6o4eAEp5oumDl3ER4/jsfN2Fipo+VLpPYVKSsAbI/Yim49eqGrX3c4ODpi5py5MDQ0xMH9+6SOlounlzeaenrBrmo12FWzx/Axn8PI2BjX/7gidbR8idQfRLw2CNV/BeoLgHh5ASA9LQ2L5gRiYmAQzMzMpY6TL9HalnmLl0jXMUC8vKSdWDx4D1OmTMG+ffsQERGB33//HY6Ojmjbti2eP3+ust6MGTOwbNkyXLhwAWXKlMGQIUNU3o+Li8PBgwdx6NAhHDp0CFFRUQgJCVFZJyIiAiYmJjh//jyWLFmCefPm4aeffgIADBs2DEePHlWZbnDo0CGkpaWhd+/ehTonIyMjjBo1CtHR0UhISAAA7NixA7Nnz8bChQtx48YNLFq0CLNmzUJERITKtpMnT8akSZNw6dIlNG3aFJ06dcpVzZw8eTKWLVuGmJgYlC9fHp06dUJWVlahMtK/Uv6/wGRuYSFxEippWZmZuBF7HU2aeiiX6ejooEkTD1y9cknCZAXLzs7GyeM/4nV6Otzq1JM6zgdJ268NIvdfKh6rQheiiWdzfPRxE6mjEGlEtOuYaHlJe7F48I5SU1Oxbt06LF26FO3atUOtWrWwceNGGBkZ5fpr/8KFC9GiRQvUqlUL06ZNw6+//orXr18r35fL5QgPD4ebmxuaN2+O/v374+TJkyr7cHd3x5w5c+Dk5IQBAwagYcOGynU8PDzg7OyM7du3K9ffunUrevbsCVNT00Kfm4uLC4B/RxQAwJw5c7Bs2TJ069YN1atXR7du3fDFF19g/fr1Ktv5+/uje/fucHV1xbp162BhYZGrLebMmYM2bdqgTp06iIiIwJMnT3DgwIFCZyzt5HI5VoaGwL1eAzg4Okkdh0pY0oskZGdnw8rKSmW5lZUVnj17JlEq9eLu/Im2Xo3Q2rMBlgXPx4Klq2Bfw0HqWB8cEa4NIvZfKj6nfjqCO7duYNjoz6WOQqQx0a5jouUtaTn3gdO2lzbioxrfUVxcHLKysuDp6alcpqenh48//hg3btxQWdfd3V35b1tbWwBAQkICqlatCgCwt7eHmZmZyjo5f/XPax95rTNs2DBs2LABU6ZMwZMnT3DkyBGcOnXqnc5NoVAA+PeDlJqairi4OAwdOhTDhw9XrvPmzRtY/OevWk2bNlX+u0yZMmjYsGGutnh7nXLlysHZ2TnXOm/LyMhARkaGyrJM6MHAwKDwJ/YBCQ2Zj7txt7F+y9dSRyHSSNVq1bF5xz6kpiQj8uRxLAqagTXrw1lAKGK8NpBIEp48xpfLF2PJ6g3QL+Xf14mIRMDiQQnQ09NT/juniiSXy/N8P2edt9/XZJ0BAwZg2rRpOHv2LH799VdUr14dzZs3f6e8Ob/M29vbIyUlBQCwceNGNG7cWGU9XV3dd9p/YQQHB2Pu3Lkqy6bNmI3ps+YU+7G1VWjIAkT/EoV1m7ahQkUbqeOQBMpaloWurm6uaUGJiYmwtraWKJV6enp6qGL3b8HU2bU2bsZex55dX2Py9NL7WS5qolwbROy/VDz+vBmLF0nPMWrQ/6ZYyrOzcfXyRRzcuwtHf75QIj9rEBWWaNcx0fKS9uK0hXfk4OAAfX19REdHK5dlZWUhJiYGtWrVKvE8VlZW6Nq1K7Zu3Yrw8HAMHjz4nfaTnp6ODRs2wMvLC+XLl0fFihVRqVIl3L17F46Ojiqv6tWrq2x77tw55b/fvHmDixcvwtXVNd91kpKS8Oeff+Za522BgYF4+fKlymvSlGnvdG6iUygUCA1ZgKjTJxC2fgsqVa4idSSSiJ6+Plxr1cb5c2eVy+RyOc6fPwv3uvUlTKY5uUKOrMxMqWN8EES7NnwI/ZeKRoOGjbFpxz5s2LZb+XJ2rY1WbTtgw7bdLByQ1hLtOiZa3pIm9VMVRHraAkcevCMTExOMHj0akydPRrly5VC1alUsWbIEaWlpGDp0qCSZhg0bho4dOyI7OxsDBw7UaJuEhAS8fv0aycnJuHjxIpYsWYJnz55h//79ynXmzp2L8ePHw8LCAr6+vsjIyMCFCxeQlJSEiRMnKtf78ssv4eTkBFdXV6xYsQJJSUm5bg45b948WFlZoWLFipgxYwasra3RtWvXfPMZGBjkmqKQnCHPZ+3CS0tLxd8PHii/fvjwH9y6eQMWFhawsa1UZMcpCqEh83H8yGEsXhEGY2MTJD57CgAwMTWDoaGhxOnyJlL7ipQVAPoPHIxZ06eidm03uNVxx9fbI5Ceno6uft2kjpbL+rAVaOzRHBVtbJGWlooTRw/j8sUYhK5ZX/DGEhGpP4h4bRCp/4rUFwCx8hqbmKC6g+q9OQwNjWBuYZFruTYQqW0B5i1uIl3HAPHyknZi8aCQ5HI5ypT5t9lCQkIgl8vRv39/JCcno2HDhjh27BjKli0rSbbWrVvD1tYWtWvXRqVKml1knZ2dIZPJYGpqiho1auCTTz7BxIkTYWPzvyGvw4YNg7GxMZYuXYrJkyfDxMQEderUwYQJE1T2FRISgpCQEFy+fBmOjo74/vvvcw2FCgkJweeff47bt2+jXr16+OGHH6Cvr//e5/6uYq9fx6ih/yu0rFi6GADQsXNXBC0IlipWnvbv2QUAGDtctTA0M2ghOnT2kyJSgURqX5GyAoBvu/ZIev4ca8NW49mzp3B2ccXa9ZtgpYXDD5OSnmNR0HQkPnsKE1MzODjWROia9WjU2KPgjSUiUn8Q8dogUv8VqS8A4uUViWhty7zFS6TrGCBeXtJOMkXO3fFII76+vnB0dERYWJjUUXJJSUlB5cqVsXXrVnTrVnJVxPv376N69eq4dOkS6tWrl+c6kZGR8PHxQVJSEiwtLd/reEU58qAkZL0R6yOmV0ZLx0l9APR0xZop9jJNrMeoGhuIM8RZtOuCSG0LAFnZYn2fEIlo1wULY72CV6JSQbTvwaIxFPhP0o2Do6SOkKfzgS2kjpALP0UaSkpKwqFDhxAZGYnWrVtLHUeFXC5HQkIC5s+fD0tLS3Tu3FnqSERERERERPQBEbhGVLKGDBmCmJgYTJo0CV26dJE6jooHDx6gevXqqFKlCsLDw5XTKoiIiIiIiIiKAn/L1NCBAwekjpAve3t7SDn7RJPje3t7S5qRiIiIiIjov7T1yQbaiNMWiIiIiIiIiEgtFg+IiIiIiIiISC1OWyAiIiIiIqJSScZ5CxrjyAMiIiIiIiIiUovFAyIiIiIiIiJSi9MWiIiIiIiIqFTirAXNceQBEREREREREanF4gERERERERERqcVpC0RERERERFQq8WkLmuPIAyIiIiIiIiJSi8UDIiIiIiIiIlKL0xaIiIiIiIioVOKsBc1x5AERERERERERqcXiARERERERERGpxWkLREREREREVCrxaQuaY/GAhJP1RiF1hEIxNtCVOsIHKytbLnWEQknLyJY6QqHo6Yo1OO1lWpbUETRmbWYgdQTSIiJ9X2PfLV4PnqVJHaFQqlobSx2BiAAEBwdj//79uHnzJoyMjODh4YHFixfD2dlZuY63tzeioqJUths5ciS++uorjY8j1k+GRERERERERKQUFRWFsWPH4ty5c/jpp5+QlZWFTz75BKmpqSrrDR8+HPHx8crXkiVLCnUcjjwgIiIiIiKiUulDmLZw9OhRla/Dw8NRoUIFXLx4EV5eXsrlxsbGsLGxeefjcOQBERERERERkRbJyMjAq1evVF4ZGRkabfvy5UsAQLly5VSW79ixA9bW1nBzc0NgYCDS0go3VYrFAyIiIiIiIiItEhwcDAsLC5VXcHBwgdvJ5XJMmDABnp6ecHNzUy7/9NNP8fXXX+P06dMIDAzE9u3b8dlnnxUqk0yhUIhzlx4iAM9TxbrpHG+YWHxEu2GiSDdFE1Fa5hupI2iMN50rXrw2FB9+TytevGEiicpQ4MnwLVZESx0hT8fHNMw10sDAwAAGBup/hhg9ejSOHDmCM2fOoEqVKvmud+rUKbRq1Qp37tyBg4ODRpkE/m8mIiIiIiIi+vBoUij4L39/fxw6dAg///yz2sIBADRu3BgAWDwgIiIiIiIiKg0UCgXGjRuHAwcOIDIyEtWrVy9wm8uXLwMAbG1tNT4OiwdERERERERUKn0IT1sYO3Ysdu7cie+++w5mZmZ4/PgxAMDCwgJGRkaIi4vDzp070b59e1hZWeHq1av44osv4OXlBXd3d42Pw+IBERERERERkaDWrVsHAPD29lZZvnXrVgwaNAj6+vo4ceIEVq5cidTUVNjZ2aF79+6YOXNmoY7D4gERERERERGRoAp6BoKdnR2ioqLe+zgsHhAREREREVGp9AHMWigxOlIHICIiIiIiIiLtxuIBEREREREREanFaQtERERERERUKn0IT1soKRx5QERERERERERqsXhQSgUFBaFevXpFtj9vb29MmDChyPZHRERERERE2oPFg3cwaNAgdO3aVWXZ3r17YWhoiGXLlmm0j/DwcFhaWr53lj59+sDX11dl2dGjRyGTyRAUFKSyPCgoCFWrVgUABAQE4OTJk+99/HdVVOf/viK2bMCQz3qhVbOGaN+qGaZO9Mdf9+9JHUutXTt3oF2blmhUvw769emJa1evSh1JLVHy/n4hBl/4j4ZvKy80dHdF5KkTUkdSS6S+K1LWvHyzbTNaNXHHlysWSx1FLVE+azlEySvStUHUz5oofSGHKHkTnyZg2YIZ+LSTN7q3aQL/QT1x++Z1qWOpJUrb5mDeD4NMpp0vbcTiQRHYtGkT+vXrh3Xr1mHSpEklemwfHx9ER0fjzZs3ymWnT5+GnZ0dIiMjVdY9ffo0fHx8AACmpqawsrIqyaha6dLFC+jeqy82RnyDVes24c2bN5gwZhjS09Okjpano0d+ROiSYIwcMxa79hyAs7MLRo8cisTERKmj5UmkvOnp6XBydsbU6bOkjqIRkfquSFn/62bsHzh0YA9qONaUOopaIn3WALHyinRtEPGzJlJfAMTJm5L8ClP8B0FXtwyCloThy237MGTsRJiamUsdLV+itG0O5qXSiMWD97RkyRKMGzcOu3btwuDBg5XLly9fjjp16sDExAR2dnYYM2YMUlJSAACRkZEYPHgwXr58CZlMpjJKICMjAwEBAahcuTJMTEzQuHHjXEWAt/n4+CAlJQUXLlxQLouMjMS0adNw/vx5vH79GgDw+vVrnD9/Xlk8+O+0hZzRFKGhobC1tYWVlRXGjh2LrKws5Tpr166Fk5MTDA0NUbFiRfTo0SPfXOrOQ935l7SVX25Ah85+qOHgBKeaLpg5dxEeP47HzdhYSfIUZHvEVnTr0Qtd/brDwdERM+fMhaGhIQ7u3yd1tDyJlNezuRfGjJsAn1ZtpI6iEZH6rkhZ35aeloZFcwIxMTAIZlr8Azcg1mcNECuvSNcGET9rIvUFQJy8e3duhXV5G0wInIuarm6wsa2MBo2awrayndTR8iVK2+ZgXiqNWDx4D1OnTsX8+fNx6NAh+Pn5qbyno6OD1atX4/r164iIiMCpU6cwZcoUAICHhwdWrlwJc3NzxMfHIz4+HgEBAQAAf39/nD17Frt27cLVq1fRs2dP+Pr64vbt23lmqFmzJipVqoTTp08DAJKTk/H777+jZ8+esLe3x9mzZwEAv/76KzIyMpTFg7ycPn0acXFxOH36NCIiIhAeHo7w8HAAwIULFzB+/HjMmzcPt27dwtGjR+Hl5ZXvvtSdh7rzl1pKcjIAwNzCQuIkuWVlZuJG7HU0aeqhXKajo4MmTTxw9colCZPlTbS8otPmvvtfomRdFboQTTyb46OPm0gdRS3RPmui5RWZtn/WROsLIuX9LToKji61EDJ7Mj7r0hKfD+2DYz/slzpWvkRqW4B5PzQ6MplWvrQRiwfv6MiRI1iyZAm+++47tGrVKtf7EyZMgI+PD+zt7dGyZUssWLAAu3fvBgDo6+vDwsICMpkMNjY2sLGxgampKR48eICtW7diz549aN68ORwcHBAQEIBmzZph69at+Wbx8fFR/lX/l19+Qc2aNVG+fHl4eXmp/LW/evXqqFatWr77KVu2LMLCwuDi4oKOHTuiQ4cOyvsiPHjwACYmJujYsSOqVauG+vXrY/z48Xnup6DzyO/885KRkYFXr16pvDIyMvI9h/chl8uxMjQE7vUawMHRqViO8T6SXiQhOzs713QTKysrPHv2TKJU+RMtr8i0ve++TZSsp346gju3bmDY6M+ljlIg0T5rouUVlQifNdH6gkh5H8c/xJHv9qBSlaqYu3Qt2nXpiQ2rl+Dk0e+ljpYnkdoWYF4qvVg8eEfu7u6wt7fHnDlzlNMR3nbixAm0atUKlStXhpmZGfr374/ExESkpeU/7/DatWvIzs5GzZo1YWpqqnxFRUUhLi4u3+28vb0RHR2NrKwsREZGwtvbGwDQokULleKBulEHAFC7dm3o6uoqv7a1tUVCQgIAoE2bNqhWrRpq1KiB/v37Y8eOHfmey7ueR16Cg4NhYWGh8loZGlKofWgqNGQ+7sbdxvzg0GLZP1FxEanvipA14cljfLl8MQKDQqBvYCB1HKJ3IsJnjYqPQi6Hg5MLBowYB4eaLvDt3B2fdPTDke/2Sh2NiARWRuoAoqpcuTL27t0LHx8f+Pr64siRIzAzMwMA3L9/Hx07dsTo0aOxcOFClCtXDmfOnMHQoUORmZkJY2PjPPeZkpICXV1dXLx4UeWXeAD5/mUe+HfkQWpqKmJiYnD69GlMnjwZwL/FgyFDhuD58+c4f/48Ro4cqfac9PT0VL6WyWSQy+UAADMzM/z++++IjIzE8ePHMXv2bAQFBSEmJibXUxPe9TzyEhgYiIkTJ6osS31T9N02NGQBon+JwrpN21Chok2R778olLUsC11d3Vw3tklMTIS1tbVEqfInWl5RidB3c4iS9c+bsXiR9ByjBvVWLpNnZ+Pq5Ys4uHcXjv58Ide1TUqifdZEyysiUT5rovUFkfKWtbKGnX0NlWV21arj15+le9KWOiK1LcC8HxotnSGglTjy4D1Uq1YNUVFRePz4MXx9fZH8/3MLL168CLlcjmXLlqFJkyaoWbMmHj16pLKtvr4+srOzVZbVr18f2dnZSEhIgKOjo8rLxib/b/4ODg6ws7PD999/j8uXL6NFixYA/i1wVK5cGcuWLUNmZmaBIw8KUqZMGbRu3RpLlizB1atXcf/+fZw6dSrXepqcR17nnxcDAwOYm5urvAyK8C+BCoUCoSELEHX6BMLWb0GlylWKbN9FTU9fH661auP8ubPKZXK5HOfPn4V73foSJsubaHlFI1LfFSkrADRo2BibduzDhm27lS9n19po1bYDNmzbrVWFA0C8z5poeUUi2mdNtL4gUl5Xt3p4+OAvlWUP/3mAChVtJUqknkhtCzAvlV4cefCech6J6OPjg7Zt2+Lo0aNwdHREVlYW1qxZg06dOiE6OhpfffWVynb29vZISUnByZMnUbduXRgbG6NmzZro168fBgwYgGXLlqF+/fp4+vQpTp48CXd3d3To0CHfHD4+Pli7di0cHR1RsWJF5fIWLVpgzZo1yhsrvqtDhw7h7t278PLyQtmyZfHjjz9CLpfD2dk517qanEde55/fiIziFBoyH8ePHMbiFWEwNjZB4rOnAAATUzMYGhqWeJ6C9B84GLOmT0Xt2m5wq+OOr7dHID09HV39ukkdLU8i5U1LS8XfDx4ov3748B/cunkDFhYWsLF9989OcRGp74qUFQCMTUxQ3UF1jrihoRHMLSxyLdcWIn3WALHyinRtEO2zBojVFwBx8nbp+RmmjB2E3ds3o5lPG/x54zqO/bAP/gHa+8hRUdo2B/NSacTiQRGoUqWKSgHh2LFjWL58ORYvXozAwEB4eXkhODgYAwYMUG7j4eGBUaNGoXfv3khMTMScOXMQFBSErVu3YsGCBZg0aRIePnwIa2trNGnSBB07dlSbwcfHB9u2bVPe7yBHixYtsHXrVnz66afvdY6WlpbYv38/goKC8Pr1azg5OeGbb75B7dq181y/oPPI7/xL2v49uwAAY4cPVFk+M2ghOnT2y2sTSfm2a4+k58+xNmw1nj17CmcXV6xdvwlWWjrkTKS8sdevY9TQ//WDFUsXAwA6du6KoAXBUsXKl0h9V6SsohLpswaIlVeka4OInzWR+gIgTt6arrUxfcEybNuwBru2bUBFm8oY7j8Z3m3aSx0tX6K0bQ7m/XDIOG9BYzKFQqGQOgRRYTxPLXi6gzYxNtCuIc4fkqxsudQRCiXrDS+3xSkt843UETRmbcYbMRYnXhuKD7+nFa8Hz/K/sbY2qmpd8qNGSTsZCvwn6bZrz0sdIU/HxjSWOkIuvOcBEREREREREaklcI2IiIiIiIiI6N3pcNaCxjjygIiIiIiIiIjUYvGAiIiIiIiIiNTitAUiIiIiIiIqlfi0Bc1x5AERERERERERqcXiARERERERERGpxWkLREREREREVCpx1oLmOPKAiIiIiIiIiNRi8YCIiIiIiIiI1OK0BSIiIiIiIiqVZOC8BU1x5AERERERERERqcXiARERERERERGpxWkLREREREREVCrpcNaCxjjygIiIiIiIiIjUYvGAiIiIiIiIiNTitAUiIiIiIiIqlWQyzlvQFIsHJBxjA12pI3zQsrLlUkf4YOmV4Ten4mRRRk/qCBp7mZYldYRCsTAWp20BQE9XrIGVegJ9W3uWnCF1hEIRre9WtTaWOkKhpGVkSx3hgyXaz2OG5mJ91ujdiPXdlYiIiIiIiIhKHEceEBERERERUanEWQua48gDIiIiIiIiIlKLxQMiIiIiIiIiUovTFoiIiIiIiKhU0uG8BY1x5AERERERERERqcXiARERERERERGpxWkLREREREREVCpx1oLmOPKAiIiIiIiIiNRi8YCIiIiIiIiI1OK0BSIiIiIiIiqVZJy3oDGOPCAiIiIiIiIitVg8ICIiIiIiIiK1OG2BiIiIiIiISiXOWtAcRx4QERERERERkVosHhSj8PBwWFpaltjx7O3tsXLlyhI7niZkMhkOHjwodQwiIiIiIiJ6D1pVPBg0aBC6du0qdQytEhQUBJlMBplMhjJlysDa2hpeXl5YuXIlMjIyVNaNiYnBiBEjVJZdunQJPXv2RMWKFWFoaAgnJycMHz4cf/75Z4nkj4+PR7t27UrkWO9q184daNemJRrVr4N+fXri2tWrUkdSS6S8v1+IwRf+o+HbygsN3V0ReeqE1JHyJVJWgHmLm0h5D+7dhUF9/eDr3Ri+3o0xekg/nIv+RepYBRLpWiZSVkC8vDm+2bYZrZq448sVi6WOkieRrgs5ROkLEVs2YMhnvdCqWUO0b9UMUyf646/796SOlS/R8or6faKk6MhkWvnSRlpVPBBVZmZmse6/du3aiI+Px4MHD3D69Gn07NkTwcHB8PDwQHJysnK98uXLw9jYWPn1oUOH0KRJE2RkZGDHjh24ceMGvv76a1hYWGDWrFnFmjmnTWxsbGBgYFCsx3ofR4/8iNAlwRg5Zix27TkAZ2cXjB45FImJiVJHy5NoedPT0+Hk7Iyp04u3vxUFkbICzFvcRMpbvoINRvp/gY3bdmNjxLdo0PBjTA8Yh3txd6SOli+RrmUiZQXEy5vjZuwfOHRgD2o41pQ6Sr5Eui4AYvWFSxcvoHuvvtgY8Q1WrduEN2/eYMKYYUhPT5M6Wp5Eyyvi9wnSTkIVD/744w+0a9cOpqamqFixIvr3749nz54p38/IyMD48eNRoUIFGBoaolmzZoiJiVG+HxkZCZlMhpMnT6Jhw4YwNjaGh4cHbt26pVwnLi4OXbp0QcWKFWFqaopGjRrhxAnVyrK9vT3mz5+PAQMGwNzcXPnX/vDwcFStWhXGxsbw8/PL8+K8bt06ODg4QF9fH87Ozti+fXuB512mTBnY2NigUqVKqFOnDsaNG4eoqCj88ccfWLz4f9X5t6ctpKWlYfDgwWjfvj2+//57tG7dGtWrV0fjxo0RGhqK9evXAwCys7MxdOhQVK9eHUZGRnB2dsaqVatUjp8zImTu3LkoX748zM3NMWrUKJWiibe3N/z9/TFhwgRYW1ujbdu2AHJPW7h27RpatmwJIyMjWFlZYcSIEUhJSSmwDYrL9oit6NajF7r6dYeDoyNmzpkLQ0NDHNy/T7JM6oiW17O5F8aMmwCfVm2kjlIgkbICzFvcRMrr6eWNpp5esKtaDXbV7DF8zOcwMjbG9T+uSB0tXyJdy0TKCoiXFwDS09KwaE4gJgYGwczMXOo4+RLpugCI1RdWfrkBHTr7oYaDE5xqumDm3EV4/DgeN2NjpY6WJ9Hyivh9grSTMMWDFy9eoGXLlqhfvz4uXLiAo0eP4smTJ+jVq5dynSlTpmDfvn2IiIjA77//DkdHR7Rt2xbPnz9X2deMGTOwbNkyXLhwAWXKlMGQIUOU76WkpKB9+/Y4efIkLl26BF9fX3Tq1AkPHjxQ2UdoaCjq1q2LS5cuYdasWTh//jyGDh0Kf39/XL58GT4+PliwYIHKNgcOHMDnn3+OSZMm4Y8//sDIkSMxePBgnD59utDt4eLignbt2mH//v15vn/s2DE8e/YMU6ZMyfP9nHsxyOVyVKlSBXv27EFsbCxmz56N6dOnY/fu3Srrnzx5Ejdu3EBkZCS++eYb7N+/H3PnzlVZJyIiAvr6+oiOjsZXX32V65ipqalo27YtypYti5iYGOzZswcnTpyAv79/oc+/KGRlZuJG7HU0aeqhXKajo4MmTTxw9colSTKpI1peIip52dnZOHn8R7xOT4dbnXpSx8mTSNcykbIC4uXNsSp0IZp4NsdHHzeROsoHQ9S+kCPl/0fWmltYSJxEMyLlFeH7REmTaelLGwnzqMawsDDUr18fixYtUi7bsmUL7Ozs8Oeff6Jy5cpYt24dwsPDlXPsN27ciJ9++gmbN2/G5MmTldstXLgQLVq0AABMmzYNHTp0wOvXr2FoaIi6deuibt26ynXnz5+PAwcO4Pvvv1f5Jbdly5aYNGmS8utZs2bB19dX+ct6zZo18euvv+Lo0aPKdUJDQzFo0CCMGTMGADBx4kScO3cOoaGh8PHxKXSbuLi44Pjx43m+d/v2beU66ujp6akUAapXr46zZ89i9+7dKoUZfX19bNmyBcbGxqhduzbmzZuHyZMnY/78+dDR+bcG5eTkhCVLluR7rJ07d+L169fYtm0bTExMAPz7/9qpUycsXrwYFStW1OzEi0jSiyRkZ2fDyspKZbmVlRXu3btbolk0IVpeIio5cXf+xJgh/ZCZmQkjI2MsWLoK9jUcpI6VJ5GuZSJlBcTLCwCnfjqCO7duYO2Wb6SO8kERsS/kkMvlWBkaAvd6DeDg6CR1nAKJklek7xOkvYQZeXDlyhWcPn0apqamylfOL8ZxcXGIi4tDVlYWPD09ldvo6enh448/xo0bN1T25e7urvy3ra0tACAhIQHAvyMPAgIC4OrqCktLS5iamuLGjRu5Rh40bNhQ5esbN26gcePGKsuaNm2aa5238wGAp6dnrnyaUigUkOVzMw2FQqHxfr788kt89NFHKF++PExNTbFhw4Zc51u3bl2V+yk0bdoUKSkp+Pvvv5XLPvroI7XHuXHjBurWrassHAD/nr9cLleZOvK2jIwMvHr1SuX13xtFEhGVdlWrVcfmHfvw1dad6NK9FxYFzcD9u3FSxyJSK+HJY3y5fDECg0Kgr8X3R6KSFRoyH3fjbmN+cKjUUTQiSl5+n6CiIEzxICUlBZ06dcLly5dVXrdv34aXl1eh9qWnp6f8d84v33K5HAAQEBCAAwcOYNGiRfjll19w+fJl1KlTJ9dNEd/+BVgqN27cQPXq1fN8r2bNf284dPPmTbX72LVrFwICAjB06FAcP34cly9fxuDBg9/pJpDF0SbBwcGwsLBQeS1dHFwk+y5rWRa6urq57k2RmJgIa2vrIjlGURItLxGVHD09PVSxqwpn19oY6f8FHJ2csWfX11LHypNI1zKRsgLi5f3zZixeJD3HqEG90cazPtp41seVSxdwYPdOtPGsj+zsbKkjCku0vpAjNGQBon+JwpcbwlGhoo3UcQokUl6Rvk+UtJwn22nbSxsJUzxo0KABrl+/Dnt7ezg6Oqq8TExMlDchjI6OVm6TlZWFmJgY1KpVS+PjREdHY9CgQfDz80OdOnVgY2OD+/fvF7idq6srzp8/r7Ls3LlzudZ5O1/O8QqTL8fNmzdx9OhRdO/ePc/3P/nkE1hbW+c7jeDFixfK43t4eGDMmDGoX78+HB0dEReXuwp55coVpKenK78+d+4cTE1NYWdnp3FmV1dXXLlyBampqcpl0dHR0NHRgbOzc57bBAYG4uXLlyqvyVMDNT6mOnr6+nCtVRvnz51VLpPL5Th//izc69YvkmMUJdHyEpF05Ao5sor5SUDvSqRrmUhZAfHyNmjYGJt27MOGbbuVL2fX2mjVtgM2bNsNXV1dqSMKS7S+oFAoEBqyAFGnTyBs/RZUqlxF6khqiZY3L9r8fYK0l9bd8+Dly5e4fPmyyjIrKyuMHTsWGzduRN++fTFlyhSUK1cOd+7cwa5du7Bp0yaYmJhg9OjRmDx5MsqVK4eqVatiyZIlSEtLw9ChQzU+vpOTE/bv349OnTpBJpNh1qxZylEJ6owfPx6enp4IDQ1Fly5dcOzYMZX7HQDA5MmT0atXL9SvXx+tW7fGDz/8gP379+d6msN/vXnzBo8fP4ZcLkdiYiIiIyOxYMEC1KtXT+VeDm8zMTHBpk2b0LNnT3Tu3Bnjx4+Ho6Mjnj17ht27d+PBgwfYtWsXnJycsG3bNhw7dgzVq1fH9u3bERMTk2tEQ2ZmJoYOHYqZM2fi/v37mDNnDvz9/ZX3O9BEv379MGfOHAwcOBBBQUF4+vQpxo0bh/79++d7vwMDA4Ncj3p8/UbjQxao/8DBmDV9KmrXdoNbHXd8vT0C6enp6OrXregOUoREy5uWloq/35oC8/DhP7h18wYsLCxgY1tJwmS5iZQVYN7iJlLe9WEr0NijOSra2CItLRUnjh7G5YsxCF2zXupo+RLpWiZSVkCsvMYmJqjuoDpH3NDQCOYWFrmWawORrguAWH0hNGQ+jh85jMUrwmBsbILEZ08BACamZjA0NJQ4XW6i5RXx+wRpJ60rHkRGRqJ+fdWK6NChQ7Fp0yZER0dj6tSp+OSTT5CRkYFq1arB19dX+QtsSEgI5HI5+vfvj+TkZDRs2BDHjh1D2bJlNT7+8uXLMWTIEHh4eMDa2hpTp07Fq1evCtyuSZMm2LhxI+bMmYPZs2ejdevWmDlzJubPn69cp2vXrli1ahVCQ0Px+eefo3r16ti6dSu8vb3V7vv69euwtbWFrq4uLCwsUKtWLQQGBmL06NG5frF+W5cuXfDrr78iODgYn376KV69egU7Ozu0bNlS+SSIkSNH4tKlS+jduzdkMhn69u2LMWPG4MiRIyr7atWqFZycnODl5YWMjAz07dsXQUFBBbbL24yNjXHs2DF8/vnnaNSoEYyNjdG9e3csX768UPspSr7t2iPp+XOsDVuNZ8+ewtnFFWvXb4KVlg7pEy1v7PXrGDV0oPLrFUv/fbRox85dEbSgaKafFBWRsgLMW9xEypuU9ByLgqYj8dlTmJiawcGxJkLXrEejxh4FbywRka5lImUFxMsrEpGuC4BYfWH/nl0AgLHDB6osnxm0EB06+0kRSS3R8or4faIk6WjnDAGtJFMU5s56VCoNGjQIL168wMGDB6WOAqBoRx5QblnZBY+0IaL3k5Yh1lxuC2O9gleiUuFZslg3LRat7+rpCjOjGIB41zKRiPbzWEVzsT5rb+u3/bLUEfK0o389qSPkItYVioiIiIiIiIhKnNZNWyAiIiIiIiIqCdr6ZANtxOIBFSg8PFzqCERERERERCShQk9biIiIwOHDh5VfT5kyBZaWlvDw8MBff/1VpOGIiIiIiIiISHqFLh4sWrQIRkZGAICzZ8/iyy+/xJIlS2BtbY0vvviiyAMSERERERERFQeZTDtf2qjQ0xb+/vtvODo6AgAOHjyI7t27Y8SIEfD09CzwkYP/x959R0Vx/W0Af5beuwp2pYkG7BVFbMEu1mjsvWE0ihpsYAUVa+wVNBqj0RhjjwYsaGyx925UbFgiRdry/uHL/lyBZVdZZq88n5w5Jzs7O/Ps5e4g3713hoiIiIiIiIjEo/HIAwsLC8TFxQEA9u/fjyZNmgAATExMkJSUlLfpiIiIiIiIiEhyGo88aNKkCfr164fKlSvjxo0baN68OQDg8uXLKF26dF7nIyIiIiIiItIK3m1BfRqPPFi8eDFq166N58+fY+vWrbC3twcAnDlzBl26dMnzgEREREREREQkLVlGRkaG1CGINPEuTeoEX7bUdLnUEYi+eInJ6VJH0Ii1maHUEUhHvHibLHUEjYjWdw31Nf5eT1KinctEItq/x4pYifVZ+1CPjRekjpCtdd96SR0hC7WmLVy4oH6Dennp3pskIiIiIiIi+pgeZy2oTa3iQaVKlSCTyZDTIIXM52QyGdLTWYEkIiIiIiIi+pKoVTy4e/eutnMQERERERERkY5Sq3hQqlQpbecgIiIiIiIiyle824L6PumqLOvXr4e3tzeKFi2K+/fvAwDmz5+P33//PU/DEREREREREZH0NC4eLF26FCNHjkTz5s3x+vVrxTUObGxsMH/+/LzOR0REREREREQS07h48OOPP2LlypUYP3489PX1FeurVauGixcv5mk4IiIiIiIiIm2R6eiii9S65sGH7t69i8qVK2dZb2xsjISEhDwJRaSKaPe9FU1qWvZ3VdFFhga6emrNnmj37ybtsTYTqy8cvPZM6ggaaVSusNQRvljWZmLdy12k32kAYKif+za6xMxYsMBCYduS7tH4Xy9lypTBuXPnsqzfu3cvPDw88iITEREREREREekQjUcejBw5EkOHDsW7d++QkZGBkydP4ueff0ZoaChWrVqljYxEREREREREeU6Pd1tQm8bFg379+sHU1BQTJkxAYmIivv32WxQtWhQLFixA586dtZGRiIiIiIiIiCSkcfEAALp27YquXbsiMTER8fHxKFyYcwuJiIiIiIiIvlSfVDwAgGfPnuH69esAAJlMhkKFCuVZKCIiIiIiIiJt46wF9Wl8wcS3b9+ie/fuKFq0KOrXr4/69eujaNGi6NatG968eaONjEREREREREQkIY2LB/369cOJEyewa9cuvH79Gq9fv8bOnTtx+vRpDBw4UBsZiYiIiIiIiEhCGk9b2LlzJ/bt24e6desq1vn5+WHlypVo2rRpnoYjIiIiIiIi0hYZ5y2oTeORB/b29rC2ts6y3traGra2tnkSioiIiIiIiIhyFxoaiurVq8PS0hKFCxeGv7+/4vqEmd69e4ehQ4fC3t4eFhYWaN++PZ4+farRcTQuHkyYMAEjR47EkydPFOuePHmC0aNHY+LEiZrujoiIiIiIiIg+0aFDhzB06FD8/fff+PPPP5Gamoqvv/4aCQkJim2+//57/PHHH9iyZQsOHTqEx48fo127dhodR5aRkZGR20aVK1dWGs5x8+ZNJCcno2TJkgCABw8ewNjYGK6urvjnn380CkCkqbfJcqkjfNFS03I9JegMQwOxhpkZ6mtcryXSCQevPZM6gkYaleMtpLUlNV2s38Ei/U4DADNjfakjEH0Sk0++h5/0Bv56WeoI2VreocInv/b58+coXLgwDh06BB8fH7x58waFChXCxo0b0aFDBwDAtWvX4OHhgePHj6NWrVpq7VetH7O/v/8nByciIiIiIiIi9SUnJyM5OVlpnbGxMYyNjXN9beZdEO3s7AAAZ86cQWpqKho3bqzYply5cihZsmTeFw+Cg4PV2hkRERERERERfZ7Q0FBMnjxZaV1wcDBCQkJUvk4ul2PEiBHw9vbGV199BeD9ZQaMjIxgY2OjtG2RIkWULkeQG4EHmBARERERERF9Oj0dvdtCUFAQRo4cqbROnVEHQ4cOxaVLl3D06NE8z6Rx8SA9PR3z5s3D5s2b8eDBA6SkpCg9//LlyzwLR0RERERERFTQqDtF4UMBAQHYuXMnDh8+jOLFiyvWOzo6IiUlBa9fv1YaffD06VM4OjqqvX+Nr941efJkzJ07F9988w3evHmDkSNHol27dtDT08t1CAURERERERER5Z2MjAwEBATgt99+w19//YUyZcooPV+1alUYGhri4MGDinXXr1/HgwcPULt2bbWPo3HxYMOGDVi5ciVGjRoFAwMDdOnSBatWrcKkSZPw999/a7q7AqFXr16QyWQYNGhQlueGDh0KmUyGXr165X+wTxASEgKZTJZlOXDggNTRPsk/p0/h+4DBaNrIB9W8PBD9l26/D5HyRq5ZgT7dOqFR3Wpo3qguxo4MwP17d6WOlSOR2jbTpo0b0KxJQ1Sv7ImunTvi4oULUkdSiXm1S1fz3r58DqtmjEVIP3+MbF8PF08cVjyXnpaGP9Yvxazve+KHb5sgpJ8/Ni6chjcvX0iYOCtdbduciJJXtPOuaL/XAHH6Qibm1S7R8uYXmUw3F00MHToUP/30EzZu3AhLS0s8efIET548QVJSEgDA2toaffv2xciRIxEVFYUzZ86gd+/eqF27ttoXSwQ+oXjw5MkTeHp6AgAsLCwUV3Js2bIldu3apenuCowSJUpg06ZNih8gALx79w4bN25U3PJSFBUqVEBsbKzS4uPjI3WsT5KUlARXd3eMHTdR6ihqESnv2TOn0b5TF6yM/BkLlq5CWloaRgzph6SkRKmjZUuktgWAvXt2I3xWKAYOGYpNW36Du3s5DB7YF3FxcVJHyxbzapcu501JfoeipV3Qrv/IbJ97dOcGvu7QEyNnr0avMdPx7PEDrA77QYKk2dPlts2OSHlFO++K9ntNpL4AMK+2iZaXNLN06VK8efMGvr6+cHJyUiy//PKLYpt58+ahZcuWaN++PXx8fODo6Iht27ZpdByNiwfFixdHbGwsAMDZ2Rn79+8HAJw6dUrjORkFSZUqVVCiRAmlH9C2bdtQsmRJVK5cWWlbuVyO0NBQlClTBqampqhYsSJ+/fVXpW127NgBV1dXmJiYoEGDBoiMjIRMJsPr168BAHFxcejSpQuKFSsGMzMzeHp64ueff1bah6+vL7777juMGTMGdnZ2cHR0VGvqiYGBARwdHZUWIyMjAMDFixfRsGFDmJqawt7eHgMGDEB8fDwAIDo6GkZGRjhy5IhiX7NmzULhwoXx9OlTtdsyL3nX88GQYSPQoFETSY6vKZHyzl+8Ai1at0VZZ1e4upXDhMkz8ORJLK5duSJ1tGyJ1LYAsD5yLdp16AT/tu3h7OKCCcGTYWJigu3btkodLVvMq126nNejSi00/7Y/vGpmLTKbmltgUPA8VPJuiMLFSqK0WwW06/c9Ht6+jlfPpfm98DFdbtvsiJRXtPOuaL/XROoLAPNqm2h5STMZGRnZLh+ObjcxMcHixYvx8uVLJCQkYNu2bRpd7wD4hOJB27ZtFXMlhg0bhokTJ8LV1RU9evRAnz59NN1dgdKnTx+sXbtW8XjNmjXo3bt3lu1CQ0Oxbt06LFu2DJcvX8b333+Pbt264dChQwCAu3fvokOHDvD398f58+cxcOBAjB8/Xmkf7969Q9WqVbFr1y5cunQJAwYMQPfu3XHy5Eml7SIjI2Fubo4TJ05g1qxZmDJlCv78889Pen8JCQnw8/ODra0tTp06hS1btuDAgQMICAgA8L5YMWLECHTv3h1v3rzB2bNnMXHiRKxatQpFihT5pGOSOOLfvgUAWFlbS5xEfKkpKbh65TJq1a6jWKenp4datergwvmzEibLHvNql2h5c/MuIQEymQym5hZSRxGubUXLKzpd/r0mWl9gXu0SLW9+y25Kti4sukjjuy2EhYUp/v+bb75BqVKlcOzYMbi6uqJVq1Z5Gu5L061bNwQFBeH+/fsAgJiYGGzatAnR0dGKbZKTkzFjxgwcOHBAcfGKsmXL4ujRo1i+fDnq16+P5cuXw93dHbNnzwYAuLu749KlS5g+fbpiP8WKFUNgYKDi8bBhw7Bv3z5s3rwZNWrUUKz38vJCcHAwAMDV1RWLFi3CwYMH0aRJzt8CXLx4ERYW//tHXfny5XHy5Els3LgR7969w7p162Bubg4AWLRoEVq1aoWZM2eiSJEimDZtGv78808MGDAAly5dQs+ePdG6detPbVIShFwux/zwMHhVqgJnF1ep4wjv1etXSE9Ph729vdJ6e3t73L17R6JUOWNe7RItryqpKcnY+dNSVK7bGCZm5lLHEa5tRcsrMl3/vSZaX2Be7RItL+kujYsHH6tVqxZq1aqFZ8+eYcaMGRg3blxe5PoiFSpUCC1atEBERAQyMjLQokULODg4KG1z69YtJCYmZvnjPSUlRTG94fr166hevbrS8x8WBID3t9ScMWMGNm/ejEePHiElJQXJyckwMzNT2s7Ly0vpsZOTE549e6byfbi7u2PHjh2Kx5nTVa5evYqKFSsqCgcA4O3tDblcjuvXr6NIkSIwMjLChg0b4OXlhVKlSmHevHkqj5WcnIzk5GSldSkw5BQZwYSHTcWd2zexfM1PUkchIh2VnpaGdXOCkZGRgQ4DRkkdh0gl/l4jooLos4sHmWJjYzFx4kQWD3LRp08fxTD+xYsXZ3k+8/oAu3btQrFixZSe0+QP5tmzZ2PBggWYP38+PD09YW5ujhEjRiAlJUVpO0NDQ6XHMpkMcrlc5b6NjIzg4uKidpaPHTt2DADw8uVLvHz5UqnY8LHQ0FBMnjxZad0P4ydh3MTgTz4+5a/wsGmIOXIIS1etQ+Eims2rouzZ2thCX18/y0WO4uLishQkdQHzapdoebOTnpaGyDmT8PL5EwyZvEAnRh0A4rWtaHlFJcLvNdH6AvNql2h585vG8/gLMLZVPmvatClSUlKQmpoKPz+/LM+XL18exsbGePDgAVxcXJSWEiVKAHj/zf/p06eVXnfq1CmlxzExMWjTpg26deuGihUromzZsrhx44b23hgADw8PnD9/HgkJCUo59PT04O7uDgC4ffs2vv/+e6xcuRI1a9ZEz549VRYrgoKC8ObNG6Vl1BjduQo35SwjIwPhYdNwKOoAFi1fg6LFiksd6YthaGQEj/IVcOLv44p1crkcJ04ch1fFyipeKQ3m1S7R8n4ss3DwIvYhBgfPg7ml7swfF61tRcsrGpF+r4nWF5hXu0TLS7orz0YekHr09fVx9epVxf9/zNLSEoGBgfj+++8hl8tRt25dvHnzBjExMbCyskLPnj0xcOBAzJ07F2PHjkXfvn1x7tw5REREAIDi4hqurq749ddfcezYMdja2mLu3Ll4+vQpypcvr7X31rVrVwQHB6Nnz54ICQnB8+fPMWzYMHTv3h1FihRBeno6unXrBj8/P/Tu3RtNmzaFp6cn5syZg9GjR2e7T2Nj4ywjLt4mqx4ZoYnExAT8++CB4vGjRw9x/dpVWFtbw9GpaJ4dJ6+IlDc8bCr279mFmfMWwczMHHEvngMAzC0sYWJiInG6rERqWwDo3rM3Jo4biwoVvsJXnl74aX0kkpKS4N+2ndTRssW82qXLeZOTEvHiySPF45fPYvHo7k2YWVjBytYeEeET8ejODfQdNxNyuRz/vXr/zZiZhRUMPhodJwVdbtvsiJRXtPOuaL/XROoLAPNqm2h5STexeCABKysrlc9PnToVhQoVQmhoKO7cuQMbGxtUqVJFMSWkTJky+PXXXzFq1CgsWLAAtWvXxvjx4zF48GDFH9oTJkzAnTt34OfnBzMzMwwYMAD+/v548+aN1t6XmZkZ9u3bh+HDh6N69eowMzND+/btMXfuXADA9OnTcf/+fezcuRPA++srrFixAl26dMHXX3+NihUrai1bTq5cvoxBfXsqHs+bPRMA0LK1P0KmheZ7ntyIlHfblk0AgKH9eyqtnxAyHS1at5UikkoitS0ANG3WHK9evsSSRQvx4sVzuJfzwJLlq2Cvo8MPmVe7dDnvv7evY0nwd4rHv0csAgBU920Kv2/64PKpowCAOaOU7z40ZPJCuHwl/Tdiuty22REpr2jnXdF+r4nUFwDm1TbR8uYnXb2zgS6SZWRkZKiz4ciRI1U+//z5c2zcuBHp6el5Eow0M336dCxbtgz//vuv1FG0Li9HHlBWqWlqnRJ0gqGBWCd7Q33OFCMxHbym+kK6uqZRucJSR/hipaaL9TtYpN9pAGBmnHVUKpEITAT+Svq77dekjpCthf7lpI6Qhdo/5rNnc78HqI+Pz2eFIfUtWbIE1atXh729PWJiYjB79mzFhRiJiIiIiIiI8pLaxYOoqCht5iAN3bx5E9OmTcPLly9RsmRJjBo1CkFBQVLHIiIiIiIiEoaeWANZJSXwAJOCbd68eZg3b57UMYiIiIiIiKgA4ARcIiIiIiIiIlKJIw+IiIiIiIioQOK0BfVx5AERERERERERqcTiARERERERERGp9EnFgyNHjqBbt26oXbs2Hj16BABYv349jh49mqfhiIiIiIiIiLRFJpPp5KKLNC4ebN26FX5+fjA1NcXZs2eRnJwMAHjz5g1mzJiR5wGJiIiIiIiISFoaFw+mTZuGZcuWYeXKlTA0NFSs9/b2xj///JOn4YiIiIiIiIhIehrfbeH69evw8fHJst7a2hqvX7/Oi0xEREREREREWse7LahP45EHjo6OuHXrVpb1R48eRdmyZfMkFBERERERERHpDo2LB/3798fw4cNx4sQJyGQyPH78GBs2bEBgYCAGDx6sjYxEREREREREJCGNpy388MMPkMvlaNSoERITE+Hj4wNjY2MEBgZi2LBh2shIRERERERElOd09MYGOknj4oFMJsP48eMxevRo3Lp1C/Hx8ShfvjwsLCy0kY+IiIiIiIiIJKZx8SCTkZERypcvn5dZiIiIiIiIiEgHaVw8aNCgAWQqxnb89ddfnxWIiIiIiIiIKD/ocd6C2jQuHlSqVEnpcWpqKs6dO4dLly6hZ8+eeZWLiIiIiIiIiHSExsWDefPmZbs+JCQE8fHxnx2IKDcv3qZIHUEjTjYmUkfQiKG+1AlIVyQmp0sdQSOGBuJ8c2Cor/HNjiTl4+ogdQSN3H6aIHUEjTgXMZc6gtpE67upaWKdx0Qj0u8JM2P+A4foc+XZb4Bu3bphzZo1ebU7IiIiIiIiIq3S09FFF+VZruPHj8PERKxvWImIiIiIiIgodxpPW2jXrp3S44yMDMTGxuL06dOYOHFingUjIiIiIiIiIt2gcfHA2tpa6bGenh7c3d0xZcoUfP3113kWjIiIiIiIiEibeLMF9WlUPEhPT0fv3r3h6ekJW1tbbWUiIiIiIiIiIh2i0TUP9PX18fXXX+P169daikNEREREREREukbjaQtfffUV7ty5gzJlymgjDxEREREREVG+0OO8BbVpfLeFadOmITAwEDt37kRsbCz+++8/pYWIiIiIiIiIvixqjzyYMmUKRo0ahebNmwMAWrduDdkHVZqMjAzIZDKkp6fnfUoiIiIiIiIikozaxYPJkydj0KBBiIqK0mYeIiIiIiIionzBWQvqU7t4kJGRAQCoX7++1sIQERERERERke7R6JoHMpZliIiIiIiIiAocje624ObmlmsB4eXLl58ViIiIiIiIiCg/6PH7cbVpVDyYPHkyrK2ttZWFiIiIiIiIiHSQRsWDzp07o3DhwtrKQkREREREREQ6SO1rHvB6B+rr1asX/P39FY99fX0xYsQIyfIQERERERFRVnoymU4uukjt4kHm3RZ0Xa9evSCTyRAWFqa0fvv27RoXQPLqj/5t27Zh6tSpn7WP0qVLQyaTQSaTwdzcHFWqVMGWLVs+O1vp0qUxf/78z96PqNavXoqm3hWVln5d2kgdS6VNGzegWZOGqF7ZE107d8TFCxekjqQS82qPKFkj16xAn26d0KhuNTRvVBdjRwbg/r27UsdS6Z/Tp/B9wGA0beSDal4eiP7rgNSRciVKfxCtbQd2aYF2DatkWVYsCJU6Wo5E6QuZRMkr4rmMbatdorRvJtHyku5Ru3ggl8uFmbJgYmKCmTNn4tWrV1JHAQDY2dnB0tLys/czZcoUxMbG4uzZs6hevTq++eYbHDt27JP2lZKS8tl5vhSlyjhj446DimXO0gipI+Vo757dCJ8VioFDhmLTlt/g7l4Ogwf2RVxcnNTRssW82iNS1rNnTqN9py5YGfkzFixdhbS0NIwY0g9JSYlSR8tRUlISXN3dMXbcRKmjqEWk/iBa285a+hNW/7pfsQTPXgoAqFO/icTJsidSXwDEyivauYxtq10itS8gXl7STRrdqlEUjRs3hqOjI0JDc/5WIC4uDl26dEGxYsVgZmYGT09P/Pzzz4rne/XqhUOHDmHBggWKb/zv3buH9PR09O3bF2XKlIGpqSnc3d2xYMEClXk+HsGQnJyMwMBAFCtWDObm5qhZsyaio6NzfV+WlpZwdHSEm5sbFi9eDFNTU/zxxx8AgIsXL6Jhw4YwNTWFvb09BgwYgPj4eKX34+/vj+nTp6No0aJwd3eHr68v7t+/j++//17xHgEgIiICNjY22LlzJ9zd3WFmZoYOHTogMTERkZGRKF26NGxtbfHdd98hPT1dcYz169ejWrVqipzffvstnj17png+OjoaMpkMBw8eRLVq1WBmZoY6derg+vXrub53bdLXN4CdvYNisbaxlTSPKusj16Jdh07wb9sezi4umBA8GSYmJti+bavU0bLFvNojUtb5i1egReu2KOvsCle3cpgweQaePInFtStXpI6WI+96PhgybAQaNNLNPxA/JlJ/EK1trW1sYWvnoFhOHz8Mx6LFUaFiVamjZUukvgCIlVe0cxnbVrtEal9AvLz5SSbTzUUXfZHFA319fcyYMQM//vgjHj58mO027969Q9WqVbFr1y5cunQJAwYMQPfu3XHy5EkAwIIFC1C7dm30798fsbGxiI2NRYkSJSCXy1G8eHFs2bIFV65cwaRJkzBu3Dhs3rxZ7XwBAQE4fvw4Nm3ahAsXLqBjx45o2rQpbt68qfY+DAwMYGhoiJSUFCQkJMDPzw+2trY4deoUtmzZggMHDiAgIEDpNQcPHsT169fx559/YufOndi2bRuKFy+uGNEQGxur2DYxMRELFy7Epk2bsHfvXkRHR6Nt27bYvXs3du/ejfXr12P58uX49ddfFa9JTU3F1KlTcf78eWzfvh337t1Dr169smQfP3485syZg9OnT8PAwAB9+vRR+31rw6OH9/Ft68bo1bE5ZoYE4dmT2NxfJIHUlBRcvXIZtWrXUazT09NDrVp1cOH8WQmTZY95tUekrNmJf/sWAGDFu/fkCdH7g0hSU1Nx+MAeNGzWRievBSVaXxAt78d0+VzGttUu0dpXtLykuzS624JI2rZti0qVKiE4OBirV6/O8nyxYsUQGBioeDxs2DDs27cPmzdvRo0aNWBtbQ0jIyOYmZnB0dFRsZ2+vj4mT56seFymTBkcP34cmzdvRqdOnXLN9eDBA6xduxYPHjxA0aJFAQCBgYHYu3cv1q5dixkzZuS6j5SUFMyZMwdv3rxBw4YNsXHjRrx79w7r1q2Dubk5AGDRokVo1aoVZs6ciSJFigAAzM3NsWrVKhgZGSm9n8yRAh9KTU3F0qVL4ezsDADo0KED1q9fj6dPn8LCwgLly5dHgwYNEBUVhW+++QYAlIoAZcuWxcKFC1G9enXEx8fDwsJC8dz06dNRv359AMAPP/yAFi1a4N27dzAxMcnyXpOTk5GcnPzRugwYGxvn2k7qKFfeE6PGT0XxkqXxMu45NqxZjsAhvbFs/VaY/X9b6opXr18hPT0d9vb2Suvt7e1x9+4diVLljHm1R6SsH5PL5ZgfHgavSlXg7OIqdZwvgsj9QTQnY6KQEP8WDf1aSx0lW6L1BdHyfkjXz2VsW+0SrX1Fy0u664sceZBp5syZiIyMxNWrV7M8l56ejqlTp8LT0xN2dnawsLDAvn378ODBg1z3u3jxYlStWhWFChWChYUFVqxYodbrgPfTC9LT0+Hm5gYLCwvFcujQIdy+fVvla8eOHQsLCwuYmZlh5syZCAsLQ4sWLXD16lVUrFhRUTgAAG9vb8jlcqUpAZ6enkqFA1XMzMwUhQMAKFKkCEqXLq1UBChSpIjStIQzZ86gVatWKFmyJCwtLRUFgo/bxsvLS/H/Tk5OAKC0nw+FhobC2tpaaVm6YLZa70Ed1WvXhU/Dr1HWxQ3VanpjavgixMe/xeG/9uXZMYjof8LDpuLO7ZuYGhoudRQijR3cvR1VatSBnUMhqaOQxHgu0x62LeU3PZluLrroix15AAA+Pj7w8/NDUFBQluHzs2fPxoIFCzB//nx4enrC3NwcI0aMyPVCgps2bUJgYCDmzJmD2rVrw9LSErNnz8aJEyfUyhQfHw99fX2cOXMG+vr6Ss99+Id5dkaPHo1evXrBwsICRYoU0XjIpLkG36QbGhoqPZbJZNmuk8vlAKCYOuHn54cNGzagUKFCePDgAfz8/LK06Yf7yXwPmfv5WFBQEEaOHKm07vFb7d35w8LSCsVKlMLjh/9q7RifytbGFvr6+lkubBMXFwcHBweJUuWMebVHpKwfCg+bhpgjh7B01ToULuKY+wtILaL2B9E8e/IYF/45iTGTdfcPGtH6gmh5M4lwLmPbapdo7StaXtJdX/TIAwAICwvDH3/8gePHjyutj4mJQZs2bdCtWzdUrFgRZcuWxY0bN5S2MTIyUrogYObr6tSpgyFDhqBy5cpwcXHJdcTAhypXroz09HQ8e/YMLi4uSsvHUwc+5uDgoNjuw8KBh4cHzp8/j4SEBKWcenp6cHd3V7nP7N7jp7h27Rri4uIQFhaGevXqoVy5cjmOJtCEsbExrKyslJa8mrKQnaTERMQ++hd2OngiNTQygkf5Cjjx9//6slwux4kTx+FVsbKEybLHvNojUlbg/a1+w8Om4VDUASxavgZFixWXOtIXRbT+IKq/9u6AlY0dqtaqK3WUHInWF0TLK9K5jG2rXaK1r2h5SXd90SMPgPdD9bt27YqFCxcqrXd1dcWvv/6KY8eOwdbWFnPnzsXTp09Rvnx5xTalS5fGiRMncO/ePVhYWMDOzg6urq5Yt24d9u3bhzJlymD9+vU4deoUypQpo1YeNzc3dO3aFT169MCcOXNQuXJlPH/+HAcPHoSXlxdatGih8Xvs2rUrgoOD0bNnT4SEhOD58+cYNmwYunfvrrjeQU5Kly6Nw4cPo3PnzjA2Nv7k6mPJkiVhZGSEH3/8EYMGDcKlS5cwderUT9pXflq5aA5qetdHYUcnvHzxHOtXLYW+vj58GzeTOlq2uvfsjYnjxqJCha/wlacXflofiaSkJPi3bSd1tGwxr/aIlDU8bCr279mFmfMWwczMHHEvngMAzC0ss73WiS5ITEzAvx9MuXr06CGuX7sKa2trODoVlTBZ9kTqD6K1LfD+H9l/7d2BBl+3hL6+bv/TSaS+AIiVV7RzGdtWu0RqX0C8vPlJBh2dI6CDdPs3YB6ZMmUKfvnlF6V1EyZMwJ07d+Dn5wczMzMMGDAA/v7+ePPmjWKbwMBA9OzZE+XLl0dSUhLu3r2LgQMH4uzZs/jmm28gk8nQpUsXDBkyBHv27FE7z9q1azFt2jSMGjUKjx49goODA2rVqoWWLVt+0vszMzPDvn37MHz4cFSvXh1mZmZo37495s6dm+trp0yZgoEDB8LZ2RnJycnIyPi0KQGFChVCREQExo0bh4ULF6JKlSoIDw9H69a6eVGpTC+ePUVY8A94+99rWNvYooJXZcxbvh42tnZSR8tW02bN8erlSyxZtBAvXjyHezkPLFm+CvY6OFICYF5tEinrti2bAABD+/dUWj8hZDpatG4rRaRcXbl8GYP6/i/vvNkzAQAtW/sjZFrOtwGWikj9QbS2BYALZ07gxbMnaNSsjdRRciVSXwDEyivauYxtq10itS8gXl7STbKMT/1rkUgid1+8kzqCRpxsdLNiTpSbxOTPn9KUnwwNxPnmwFBfrFmDqenZX5dGVz14kSR1BI04F9Gtu/t8SUQ7j5kZ6+e+kQ4RqX1Fa1vRmAj8lfSMg+pPQc9P4xo5575RPhP4x0xERERERET06XT1zga6SKyvPoiIiIiIiIgo37F4QEREREREREQqcdoCERERERERFUictqA+jjwgIiIiIiIiIpVYPCAiIiIiIiIilThtgYiIiIiIiAokmYzzFtTFkQdEREREREREpBKLB0RERERERESkEqctEBERERERUYHEuy2ojyMPiIiIiIiIiEglFg+IiIiIiIiISCVOWyAiIiIiIqICiTdbUB9HHhARERERERGRSiweEBEREREREZFKnLZAREREREREBZIe5y2ojSMPiIiIiIiIiEgljjwg4TjZmEgdgeiTvElMlTqCRqzNDKWOQDrCUF+s7xocLI2kjqCRmFsvpI6gNm8XB6kjaMTMWF/qCBrh7wki0mUsHhAREREREVGBpMdZC2oT66sEIiIiIiIiIsp3LB4QERERERERkUqctkBEREREREQFEm+2oD6OPCAiIiIiIiIilVg8ICIiIiIiIiKVOG2BiIiIiIiICiQ9cN6CujjygIiIiIiIiIhUYvGAiIiIiIiIiFTitAUiIiIiIiIqkHi3BfVx5AERERERERERqcTiARERERERERGpxGkLREREREREVCDpcdqC2jjygIiIiIiIiIhUYvGAiIiIiIiIiFRi8UADERERsLGx0dn95aRXr17w9/dXuY2vry9GjBih9SxERERERES6Qk8m08lFF7F4gPd/XMtkMoSFhSmt3759O2Qf/OC++eYb3LhxI7/jqRQSEoJKlSoprTty5AhsbGwwYsQIZGRkYMGCBYiIiJAknwg2bdyAZk0aonplT3Tt3BEXL1yQOpJKzKtdouTd/usm9OrSFk19a6Kpb00M7tMVf8cckTqWSqK0bSbm1S4R8ur65+zm5XNYMm0Mgnq1xpA23jj392Gl588ej8bC4BEY3a0ZhrTxxr93dOvfMJlE6AsfEiWvrvff7IjStpmYl3TF4cOH0apVKxQtWhQymQzbt29Xej7z790Pl6ZNm2p8HBYP/p+JiQlmzpyJV69e5biNqakpChcunI+pNLdr1y74+flh5MiRmD9/PmQyGaytrfNlhIOI9u7ZjfBZoRg4ZCg2bfkN7u7lMHhgX8TFxUkdLVvMq10i5S1U2BEDA77HynWbsTLyF1SpVgPjAofh7u1bUkfLlkhtCzCvtomSV9c/ZynvklC8tAu+GTgqh+ffwcXDC/49BudzMvWJ0hcyiZRX1/vvx0RqW4B5SbckJCSgYsWKWLx4cY7bNG3aFLGxsYrl559/1vg4LB78v8aNG8PR0RGhoaE5bpPdNIM//vgD1atXh4mJCRwcHNC2bVvFc69evUKPHj1ga2sLMzMzNGvWDDdv3lSZ4/fff0eVKlVgYmKCsmXLYvLkyUhLS1PrPWzcuBHt2rXDrFmzMGnSJMX6j6ctJCQkoEePHrCwsICTkxPmzJmTZV+lS5fGtGnTFNuVKlUKO3bswPPnz9GmTRtYWFjAy8sLp0+fVrwmLi4OXbp0QbFixWBmZgZPT88sndLX1xffffcdxowZAzs7Ozg6OiIkJESt96cN6yPXol2HTvBv2x7OLi6YEDwZJiYm2L5tq2SZVGFe7RIpr7ePL2p7+6BEyVIoUao0+g8ZDlMzM1y+dF7qaNkSqW0B5tU2UfLq+uesQtXaaN1tACrVrp/t8zUbNEXzzn1QrmL1fE6mPlH6QiaR8up6//2YSG0LMO+XRCbTzUUTzZo1w7Rp05T+Fv2YsbExHB0dFYutra3GbcXiwf/T19fHjBkz8OOPP+Lhw4dqvWbXrl1o27YtmjdvjrNnz+LgwYOoUaOG4vlevXrh9OnT2LFjB44fP46MjAw0b94cqamp2e7vyJEj6NGjB4YPH44rV65g+fLliIiIwPTp03PNsnjxYvTu3Rtr1qxBQECAym1Hjx6NQ4cO4ffff8f+/fsRHR2Nf/75J8t28+bNg7e3N86ePYsWLVqge/fu6NGjB7p164Z//vkHzs7O6NGjBzIyMgAA7969Q9WqVbFr1y5cunQJAwYMQPfu3XHy5Eml/UZGRsLc3BwnTpzArFmzMGXKFPz555+5vse8lpqSgqtXLqNW7TqKdXp6eqhVqw4unD+b73lyw7zaJVreD6Wnp+Pg/t14l5SErzwrSR0nC9Halnm1S7S8mXT9cyYi0fqCaHk/pOv9V7S2ZV7KD8nJyfjvv/+UluTk5E/eX3R0NAoXLgx3d3cMHjz4k0adGHzy0b9Abdu2RaVKlRAcHIzVq1fnuv306dPRuXNnTJ48WbGuYsWKAICbN29ix44diImJQZ067z+oGzZsQIkSJbB9+3Z07Ngxy/4mT56MH374AT179gQAlC1bFlOnTsWYMWMQHBycY46rV68iICAAq1evRteuXVVmjo+Px+rVq/HTTz+hUaNGAN7/MV+8ePEs2zZv3hwDBw4EAEyaNAlLly5F9erVFdnHjh2L2rVr4+nTp3B0dESxYsUQGBioeP2wYcOwb98+bN68Wamo4uXlpXg/rq6uWLRoEQ4ePIgmTZqozJ7XXr1+hfT0dNjb2yutt7e3x927d/I1izqYV7tEywsAt2/dwJA+XZGSkgJTUzNMm70Apcs6Sx0rC9Halnm1S7S8onzORCRaXxAtLyBO/xWtbZmX8kNoaKjS35kAEBwc/Emjtps2bYp27dqhTJkyuH37NsaNG4dmzZrh+PHj0NfXV3s/LB58ZObMmWjYsKHSH8E5OXfuHPr375/tc1evXoWBgQFq1qypWGdvbw93d3dcvXo129ecP38eMTExSiMN0tPT8e7dOyQmJsLMzCzb1xUvXhw2NjaYPXs2mjVrBicnpxwz3759GykpKUq57Ozs4O7unmVbLy8vxf8XKVIEAODp6Zll3bNnz+Do6Ij09HTMmDEDmzdvxqNHj5CSkoLk5OQsuT/cLwA4OTnh2bNn2eZNTk7OUmHL0DeGsbFxju+RqKAoWaoMVm/YioT4t4g+uB8zQsbjx+UROvkPQyJR8XNGImP/Jcqdrt7ZICgoCCNHjlRa96l/A3Xu3Fnx/56envDy8oKzszOio6MVXyirg9MWPuLj4wM/Pz8EBQXluq2pqWmeHjs+Ph6TJ0/GuXPnFMvFixdx8+ZNmJiY5Pg6S0tLHDhwAObm5mjQoAFiY2PzJI+hoaHi/zPvOpHdOrlcDgCYPXs2FixYgLFjxyIqKgrnzp2Dn58fUlJSctxv5n4y9/Gx0NBQWFtbKy2zZ+Z8XQpN2NrYQl9fP8uQnbi4ODg4OOTJMfIS82qXaHmB95+l4iVKwt2jAgYGfA8XV3ds2fST1LGyEK1tmVe7RMsryudMRKL1BdHyAuL0X9HalnkpPxgbG8PKykppyasvUMuWLQsHBwfcuqXZBVRZPMhGWFgY/vjjDxw/flzldl5eXjh48GC2z3l4eCAtLQ0nTpxQrIuLi8P169dRvnz5bF9TpUoVXL9+HS4uLlkWPT3VPypbW1scOHAAVlZW8PX1xePHj7PdztnZGYaGhkq5Xr16lSe3oIyJiUGbNm3QrVs3VKxYEWXLlv3s/QYFBeHNmzdKy+ixuRd21GFoZASP8hVw4u///ZzlcjlOnDgOr4qV8+QYeYl5tUu0vNmRZ8iR+lGxTheI1rbMq12i5f2Yrn7ORCRaXxAtb3Z0tf+K1rbMS6J7+PAh4uLiVI5Yzw6nLWTD09MTXbt2xcKFC1VuFxwcjEaNGsHZ2RmdO3dGWloadu/ejbFjx8LV1RVt2rRB//79sXz5clhaWuKHH35AsWLF0KZNm2z3N2nSJLRs2RIlS5ZEhw4doKenh/Pnz+PSpUuYNm1arrltbGzw559/ws/PD76+voiOjkbRokWVtrGwsEDfvn0xevRo2Nvbo3Dhwhg/fnyuxQl1uLq64tdff8WxY8dga2uLuXPn4unTpzkWS9RhbJx1isI79W4+oZbuPXtj4rixqFDhK3zl6YWf1kciKSkJ/m3b5d1B8hDzapdIeZcvmoeadeqhiKMTEhMTcGDvLpw7cwrhPy6XOlq2RGpbgHm1TZS8uv45e5eUiOex/7vIc9zTx/j3zg2YW1rBrpAjEt7+h5fPn+DNyxcAgKePHgAArGztYW1rn+0+85sofSGTSHl1vf9+TKS2BZj3S6KjsxY0Eh8frzSK4O7duzh37hzs7OxgZ2eHyZMno3379nB0dMTt27cxZswYuLi4wM/PT6PjsHiQgylTpuCXX35RuY2vry+2bNmCqVOnIiwsDFZWVvDx8VE8v3btWgwfPhwtW7ZESkoKfHx8sHv37izD9jP5+flh586dmDJlCmbOnAlDQ0OUK1cO/fr1Uzu3tbU19u/fj6ZNm6J+/fqIjo7Oss3s2bMRHx+PVq1awdLSEqNGjcKbN2/UPkZOJkyYgDt37sDPzw9mZmYYMGAA/P3982Tf2tK0WXO8evkSSxYtxIsXz+FezgNLlq+CvY4O4WJe7RIp76tXLzEjZBziXjyHuYUlnF3cEP7jclSvWSf3F0tApLYFmFfbRMmr65+zB7euYf6EYYrHW9f8CACo1bAZegyfgAsnj2D9whmK59eEv79YcfPOfdCyS9/8DZsDUfpCJpHy6nr//ZhIbQswL+mW06dPo0GDBorHmddK6NmzJ5YuXYoLFy4gMjISr1+/RtGiRfH1119j6tSpGk+DkGVk3mePSBB5OfKAKD+9Scz+Nq26ytos+0Inka4T7bN26bHuFtk/5u3CPzS0SbS+y98TlMlE4K+k15x6IHWEbPWpXlLqCFkI/GMmIiIiIiIi+nS8CKD62FZEREREREREpBKLB0RERERERESkEqctEBERERERUYEk+xJut5BPOPKAiIiIiIiIiFRi8YCIiIiIiIiIVOK0BSIiIiIiIiqQOGlBfRx5QEREREREREQqsXhARERERERERCpx2gIREREREREVSHq824LaOPKAiIiIiIiIiFRi8YCIiIiIiIiIVOK0BSIiIiIiIiqQOGlBfRx5QEREREREREQqsXhARERERERERCpx2gIREREREREVSLzZgvo48oCIiIiIiIiIVOLIAxJOYnK61BE0YmggVjnTUJ81RW2xNjOUOoJGRPusmRnrSx1BbanpcqkjaES084JIfQEAvF0cpI6gtphbL6SOoJEaZeykjqAR0X5PiHQuE+08RqSLWDwgIiIiIiKiAknGeQtqYwmOiIiIiIiIiFRi8YCIiIiIiIiIVOK0BSIiIiIiIiqQ+G26+thWRERERERERKQSiwdEREREREREpBKnLRAREREREVGBxLstqI8jD4iIiIiIiIhIJRYPiIiIiIiIiEglTlsgIiIiIiKiAomTFtTHkQdEREREREREpBKLB0RERERERESkEqctEBERERERUYHEuy2ojyMPiIiIiIiIiEglFg+IiIiIiIiISKUCWTyIjo6GTCbD69evpY5CREREREREEtHT0UUX6WquHPXq1QsymQwymQyGhoYoU6YMxowZg3fv3kkdDQBw7949yGQynDt3Tq3tMhdLS0tUqFABQ4cOxc2bN/MnbD7r1asX/P39pY6hJHLNCvTp1gmN6lZD80Z1MXZkAO7fuyt1rBz9c/oUvg8YjKaNfFDNywPRfx2QOlKuNm3cgGZNGqJ6ZU907dwRFy9ckDqSSiLlFSWraJ+zTKK0r4jnBYDtq0262rY3L5/DkmljENSrNYa08ca5vw8rPX/2eDQWBo/A6G7NMKSNN/69c0OipNljX9Autq/2iZaXdI9wxQMAaNq0KWJjY3Hnzh3MmzcPy5cvR3BwsNSxkJKSovFrDhw4gNjYWJw/fx4zZszA1atXUbFiRRw8eFALCXXbp7Tf5zp75jTad+qClZE/Y8HSVUhLS8OIIf2QlJSY71nUkZSUBFd3d4wdN1HqKGrZu2c3wmeFYuCQodi05Te4u5fD4IF9ERcXJ3W0bImUV6Sson3OALHaV7TzAsD21SZdbtuUd0koXtoF3wwclcPz7+Di4QX/HoPzOZl62Be0i+2rXaLlJd0kZPHA2NgYjo6OKFGiBPz9/dG4cWP8+eefiueTk5Px3XffoXDhwjAxMUHdunVx6tSpLPuJiYmBl5cXTExMUKtWLVy6dEnp+aNHj6JevXowNTVFiRIl8N133yEhIUHxfOnSpTF16lT06NEDVlZWGDBgAMqUKQMAqFy5MmQyGXx9fVW+F3t7ezg6OqJs2bJo06YNDhw4gJo1a6Jv375IT09XbLd06VI4OzvDyMgI7u7uWL9+veK5wMBAtGzZUvF4/vz5kMlk2Lt3r2Kdi4sLVq1aBeB/IwDCw8Ph5OQEe3t7DB06FKmpqUptGBgYiGLFisHc3Bw1a9ZEdHQ0ACAhIQFWVlb49ddfld7L9u3bYW5ujrdv36p8z5l8fX0REBCAESNGwMHBAX5+fmq9Li/NX7wCLVq3RVlnV7i6lcOEyTPw5Eksrl25ku9Z1OFdzwdDho1Ag0ZNpI6ilvWRa9GuQyf4t20PZxcXTAieDBMTE2zftlXqaNkSKa9IWUX7nAFita9o5wWA7atNuty2FarWRutuA1Cpdv1sn6/ZoCmad+6DchWr53My9bAvaBfbV7tEy5ufPhwNrkuLLhKyePChS5cu4dixYzAyMlKsGzNmDLZu3YrIyEj8888/cHFxgZ+fH16+fKn02tGjR2POnDk4deoUChUqhFatWin+gL59+zaaNm2K9u3b48KFC/jll19w9OhRBAQEKO0jPDwcFStWxNmzZzFx4kScPHkSwP9GFGzbtk2j96Onp4fhw4fj/v37OHPmDADgt99+w/DhwzFq1ChcunQJAwcORO/evREVFQUAqF+/Po4ePaooNhw6dAgODg6KP/YfPXqE27dvKxUyoqKicPv2bURFRSEyMhIRERGIiIhQPB8QEIDjx49j06ZNuHDhAjp27IimTZvi5s2bMDc3R+fOnbF27Vql7GvXrkWHDh1gaWmp9vuNjIyEkZERYmJisGzZMo3aShvi/7/wYWVtLXES8aWmpODqlcuoVbuOYp2enh5q1aqDC+fPSpgseyLlFSlrdnT9cyZ6++o6tq/2sG0pE/uCdonWvqLlJd0lZPFg586dsLCwgImJCTw9PfHs2TOMHj0awPtvxZcuXYrZs2ejWbNmKF++PFauXAlTU1OsXr1aaT/BwcFo0qQJPD09ERkZiadPn+K3334DAISGhqJr164YMWIEXF1dUadOHSxcuBDr1q1Tur5Cw4YNMWrUKDg7O8PZ2RmFChUC8L8RBXZ2dhq/v3LlygF4f10E4H2BolevXhgyZAjc3NwwcuRItGvXDuHh4QCAevXq4e3btzh79iwyMjJw+PBhjBo1SlE8iI6ORrFixeDi4qI4hq2tLRYtWoRy5cqhZcuWaNGihWKqxIMHD7B27Vps2bIF9erVg7OzMwIDA1G3bl1FwaBfv37Yt28fYmNjAQDPnj3D7t270adPH43eq6urK2bNmgV3d3e4u7tr3FZ5SS6XY354GLwqVYGzi6ukWb4Er16/Qnp6Ouzt7ZXW29vb48WLFxKlyplIeUXK+jERPmcit68I2L7aw7alTOwL2iVa+4qWl3SXgdQBPkWDBg2wdOlSJCQkYN68eTAwMED79u0BvB8xkJqaCm9vb8X2hoaGqFGjBq5evaq0n9q1ayv+387ODu7u7optzp8/jwsXLmDDhg2KbTIyMiCXy3H37l14eHgAAKpVq5bn7y8jIwMAFMNVrl69igEDBiht4+3tjQULFgAAbGxsULFiRURHR8PIyAhGRkYYMGAAgoODER8fj0OHDqF+feUhghUqVIC+vr7isZOTEy5evAgAuHjxItLT0+Hm5qb0muTkZMVJp0aNGqhQoQIiIyPxww8/4KeffkKpUqXg4+Oj0XutWrWqyueTk5ORnJysvC7NAMbGxhodRx3hYVNx5/ZNLF/zU57vm4je4+eMiIiIdIluThDQTUIWD8zNzRXfoq9ZswYVK1bE6tWr0bdv3zw7Rnx8PAYOHIjvvvsuy3MlS5ZUypLXMgsYmddPUIevry+io6NhbGyM+vXrw87ODh4eHjh69CgOHTqEUaOUL05kaGio9Fgmk0EulwN4/9719fVx5swZpQIDAFhYWCj+v1+/fli8eDF++OEHrF27Fr1799Z4fk5u7RcaGorJkycrrRsTNBFjx+ftBTLDw6Yh5sghLF21DoWLOObpvgsqWxtb6OvrZ7kQT1xcHBwcHCRKlTOR8oqU9UOifM5EbV9RsH21h21LmdgXtEu09hUtL+kuIactfEhPTw/jxo3DhAkTkJSUpLioYExMjGKb1NRUnDp1CuXLl1d67d9//634/1evXuHGjRuKEQVVqlTBlStX4OLikmX58PoKH8t87sOLHWpCLpdj4cKFKFOmDCpXrgwA8PDwUHo/wPuLPX74fjKve3Dw4EHFtQ18fX3x888/48aNG7leuPFDlStXRnp6Op49e5blvTs6/u8f/N26dcP9+/excOFCXLlyBT179vyk96xKUFAQ3rx5o7SMCPwhz/afkZGB8LBpOBR1AIuWr0HRYsXzbN8FnaGRETzKV8CJv48r1snlcpw4cRxeFStLmCx7IuUVKSsg3udMtPYVDdtXe9i2lIl9QbtEa1/R8pLuEnLkwcc6duyI0aNHY/HixQgMDMTgwYMxevRo2NnZoWTJkpg1axYSExOzjEyYMmUK7O3tUaRIEYwfPx4ODg7w9/cHAIwdOxa1atVCQEAA+vXrB3Nzc1y5cgV//vknFi1alGOWwoULw9TUFHv37kXx4sVhYmICaxUXBYuLi8OTJ0+QmJiIS5cuYf78+Th58iR27dql+NZ/9OjR6NSpEypXrozGjRvjjz/+wLZt23DgwP/uf+vj44O3b99i586dCAsLA/C+eNChQwc4OTllmYKgipubG7p27YoePXpgzpw5qFy5Mp4/f46DBw/Cy8sLLVq0APD+ugnt2rXD6NGj8fXXX6N48bz/g8DY2DjLFIW0hE8rzGQnPGwq9u/ZhZnzFsHMzBxxL54DAMwtLGFiYpJnx8kriYkJ+PfBA8XjR48e4vq1q7C2toajU1EJk2Wve8/emDhuLCpU+ApfeXrhp/WRSEpKgn/bdlJHy5ZIeUXKKtrnDBCrfUU7LwBsX23S5bZ9l5SI57EPFY/jnj7Gv3duwNzSCnaFHJHw9j+8fP4Eb16+n4P99NH7dreytYe1rX22+8xP7AvaxfbVLtHy5icdvbGBTvoiigcGBgYICAjArFmzMHjwYISFhUEul6N79+54+/YtqlWrhn379sHW1lbpdWFhYRg+fDhu3ryJSpUq4Y8//lCMHPDy8sKhQ4cwfvx41KtXDxkZGXB2dsY333yTa5aFCxdiypQpmDRpEurVq6e4cGF2GjduDAAwMzNDqVKl0KBBA6xYsULp4ob+/v5YsGABwsPDMXz4cJQpUwZr165VGk1ga2sLT09PPH36VHHBRR8fH8jl8izXO1DH2rVrMW3aNIwaNQqPHj2Cg4MDatWqpXRLSADo27cvNm7cqNaFEuVyOQwMdKvLbduyCQAwtL/yqIkJIdPRonVbKSKpdOXyZQzq+7+s82bPBAC0bO2PkGmhUsXKUdNmzfHq5UssWbQQL148h3s5DyxZvgr2OjpETqS8ImUV7XMGiNW+op0XALavNuly2z64dQ3zJwxTPN665kcAQK2GzdBj+ARcOHkE6xfOUDy/Jvz9FMXmnfugZZe8m5r6qdgXtIvtq12i5SXdJMvIvDof0SdYv349vv/+ezx+/FjldA4AaNq0KVxcXFSO3FDHyzwceZAfDA3EKmca6gs/m4nySGKyWJ81M2P93DfSEanpcqkjaES08wLbV3tibol1ZfYaZTS/65WUROoLgFifNdHaVjQmuvX9oEZ+v/hE6gjZauOpe9eHEvjHTFJKTExEbGwswsLCMHDgQJWFg1evXiEmJgbR0dEYNGhQPqYkIiIiIiLKmR7vt6A2luDok8yaNQvlypWDo6MjgoKCVG7bp08fDBo0CKNGjUKbNm3yKSERERERERHlFU5bIOFw2oJ2cVgfZeK0Be0RaagvIN55ge2rPZy2oF0i9QVArM+aaG0rGpGnLfxx8anUEbLVyrOI1BGyEPjHTERERERERPTpeLcF9bEER0REREREREQqsXhARERERERERCpx2gIREREREREVSDLebUFtHHlARERERERERCqxeEBEREREREREKnHaAhERERERERVIvNuC+jjygIiIiIiIiIhUYvGAiIiIiIiIiFTitAUiIiIiIiIqkPR4twW1ceQBEREREREREanE4gERERERERERqcRpC0RERERERFQg8W4L6uPIAyIiIiIiIiJSicUDIiIiIiIiIlKJ0xZIOKnpcqkjaMTM2FDqCF8s0fqCob5Y9VozY32pI3yxROsLicnpUkfQSGJKmtQRNOJgaSx1BLV5uzhIHUEj1x6/lTqCRkram0kdQSP8PUFfAk5bUJ9Y/3ohIiIiIiIionzH4gERERERERERqcRpC0RERERERFQgycB5C+riyAMiIiIiIiIiUonFAyIiIiIiIiJSidMWiIiIiIiIqEDS46wFtXHkARERERERERGpxOIBEREREREREanEaQtERERERERUIPFuC+rjyAMiIiIiIiIiUonFAyIiIiIiIiJSidMWiIiIiIiIqECScdaC2jjygIiIiIiIiIhUYvGAiIiIiIiIiFTitAUiIiIiIiIqkHi3BfVx5EE+6tWrF/z9/aWOodLHGX19fTFixIg8239e74+IiIiIiIi0r8AVD54/f47BgwejZMmSMDY2hqOjI/z8/BATEyN1tM9SunRpyGQyyGQy6Ovro2jRoujbty9evXoldTSdtv3XTejVpS2a+tZEU9+aGNynK/6OOSJ1LJU2bdyAZk0aonplT3Tt3BEXL1yQOpJKouT95/QpfB8wGE0b+aCalwei/zogdaRcidK2mZhXu0TJG7lmBfp064RGdauheaO6GDsyAPfv3ZU6llp+XrcajWp5YfG8mVJHUUmUvpBJlLzy9HT8ErEUAd1bo1sLb3zXow22/rQKGRkZUkfLQtTPmSh9IRPzUkFT4IoH7du3x9mzZxEZGYkbN25gx44d8PX1RVxcnNTRkJKS8lmvnzJlCmJjY/HgwQNs2LABhw8fxnfffZdH6b5MhQo7YmDA91i5bjNWRv6CKtVqYFzgMNy9fUvqaNnau2c3wmeFYuCQodi05Te4u5fD4IF9daL/ZkekvElJSXB1d8fYcROljqIWkdoWYF5tEynv2TOn0b5TF6yM/BkLlq5CWloaRgzph6SkRKmjqXTtyiXs/G0Lyrq4SR1FJZH6AiBW3t9/icSff/yKPgFjMHf1Fnzbbxh2bF6Hvdt/kTpaFiJ+zkTqCwDzfkn0ZLq56KICVTx4/fo1jhw5gpkzZ6JBgwYoVaoUatSogaCgILRu3Vqx3dy5c+Hp6Qlzc3OUKFECQ4YMQXx8vOL5iIgI2NjYYN++ffDw8ICFhQWaNm2K2NhYxTbp6ekYOXIkbGxsYG9vjzFjxmSpTPv6+iIgIAAjRoyAg4MD/Pz81Dp+TiwtLeHo6IhixYqhQYMG6NmzJ/755x/F8yEhIahUqZLSa+bPn4/SpUur3YavXr1Cjx49YGtrCzMzMzRr1gw3b95U2iYmJga+vr4wMzODra0t/Pz8chwBsWvXLlhbW2PDhg1qZ8hL3j6+qO3tgxIlS6FEqdLoP2Q4TM3McPnSeUny5GZ95Fq069AJ/m3bw9nFBROCJ8PExATbt22VOlq2RMrrXc8HQ4aNQINGTaSOohaR2hZgXm0TKe/8xSvQonVblHV2hatbOUyYPANPnsTi2pUrUkfLUVJiImYEB2FkUAgsLa2kjqOSSH0BECvvjSsXUK1OfVSpWReFHYuilk9jeFWtiVvXL0sdLQsRP2ci9QWAealgKlDFAwsLC1hYWGD79u1ITk7OcTs9PT0sXLgQly9fRmRkJP766y+MGTNGaZvExESEh4dj/fr1OHz4MB48eIDAwEDF83PmzEFERATWrFmDo0eP4uXLl/jtt9+yHCsyMhJGRkaIiYnBsmXL1D5+bh49eoQ//vgDNWvW1Oh1uenVqxdOnz6NHTt24Pjx48jIyEDz5s2RmpoKADh37hwaNWqE8uXL4/jx4zh69ChatWqF9PT0LPvauHEjunTpgg0bNqBr1655mvNTpKen4+D+3XiXlISvPCtJHSeL1JQUXL1yGbVq11Gs09PTQ61adXDh/FkJk2VPtLwiEa1tmVe7RMv7sfi3bwEAVtbWEifJ2YLw6ajlXQ9Va9SSOopKovUF0fK6lffCpbOn8PjhfQDAvds3cP3SeVSqXieXV0pP1z9novUF5qWCqkDdbcHAwAARERHo378/li1bhipVqqB+/fro3LkzvLy8FNt9eEG/0qVLY9q0aRg0aBCWLFmiWJ+amoply5bB2dkZABAQEIApU6Yonp8/fz6CgoLQrl07AMCyZcuwb9++LJlcXV0xa9YspXXqHD87Y8eOxYQJE5Ceno53796hZs2amDt3bu4No6abN29ix44diImJQZ06708+GzZsQIkSJbB9+3Z07NgRs2bNQrVq1ZSyVqhQIcu+Fi9ejPHjx+OPP/5A/fr1czxmcnJylkJPcrIejI2N8+hdAbdv3cCQPl2RkpICU1MzTJu9AKXLOufZ/vPKq9evkJ6eDnt7e6X19vb2uHv3jkSpciZaXpGI1rbMq12i5f2QXC7H/PAweFWqAmcXV6njZOuvP/fg1vWrWLLmZ6mj5Eq0viBa3jadeyEpMQEj+3SAnp4e5HI5vuk9BPUaNZM6mkoifM5E6wvM+2Xh3RbUV6BGHgDvr3nw+PFj7NixA02bNkV0dDSqVKmCiIgIxTYHDhxAo0aNUKxYMVhaWqJ79+6Ii4tDYuL/5omZmZkpCgcA4OTkhGfPngEA3rx5g9jYWKVv/Q0MDFCtWrUseapWrZplnTrHz87o0aNx7tw5XLhwAQcPHgQAtGjRIttv/T/F1atXYWBgoPS+7O3t4e7ujqtXrwL438gDVX799Vd8//33+PPPP1UWDgAgNDQU1tbWSsvCuXl7oaqSpcpg9YatWLZ2I9q074QZIeNx787tPD0GERFlFR42FXdu38TU0HCpo2Tr2dMnWDx3JoJCwmCUh0VrEtPxQ3/i6F97MSxoGsKWbsCQ0SHYueUnHNq/U+poKun654yIxFHgigcAYGJigiZNmmDixIk4duwYevXqheDgYADAvXv30LJlS3h5eWHr1q04c+YMFi9eDED5goaGhoZK+5TJZJ90tV1zc3Olx+oePzsODg5wcXGBq6srGjZsiPnz5+PYsWOIiooC8H540scZM6cb5BVTU9Nct6lcuTIKFSqENWvW5NpmQUFBePPmjdLy3cixeRUXwPufZfESJeHuUQEDA76Hi6s7tmz6KU+PkRdsbWyhr6+f5cI2cXFxcHBwkChVzkTLKxLR2pZ5tUu0vJnCw6Yh5sghLF4RgcJFHKWOk60b167g9auXGNTrGzTxrowm3pVx/uxp/LZ5I5p4V86z4nxeEa0viJZ3w8qFaPNNT3g38EPJMi7wadICzdt3wfZNa6WOliMRPmeAeH2BeamgKpDFg4+VL18eCQkJAIAzZ85ALpdjzpw5qFWrFtzc3PD48WON9mdtbQ0nJyecOHFCsS4tLQ1nzpzJ9bV5cfxM+vr6AN5fRR4AChUqhCdPnij9wX7u3Dm19+fh4YG0tDSl9xUXF4fr16+jfPnyAAAvLy/FqIecODs7IyoqCr///juGDRumcltjY2NYWVkpLXk5ZSE78gw5Uj/zzhfaYGhkBI/yFXDi7+OKdXK5HCdOHIdXxcoSJsueaHlFIlrbMq92iZY3IyMD4WHTcCjqABYtX4OixYpLHSlHVarVxKoNW7Fi3WbF4u5RAY38WmDFus2K37O6QrS+IFre5HfvINNT/qeznp4+MuS6d6tGkT5ngHh9gXm/LDKZbi6aOHz4MFq1aoWiRYtCJpNh+/btSs9nZGRg0qRJcHJygqmpKRo3bpzlovfqKFDXPIiLi0PHjh3Rp08feHl5wdLSEqdPn8asWbPQpk0bAICLiwtSU1Px448/olWrVkoXMtTE8OHDERYWBldXV5QrVw5z587F69evc33d5xz/7du3iuLAv//+izFjxqBQoUKK6xP4+vri+fPnmDVrFjp06IC9e/diz549sLJS78rRrq6uaNOmDfr374/ly5fD0tISP/zwA4oVK6Zov6CgIHh6emLIkCEYNGgQjIyMEBUVhY4dOypVNt3c3BAVFQVfX18YGBhg/vz5amXIa8sXzUPNOvVQxNEJiYkJOLB3F86dOYXwH5dLkic33Xv2xsRxY1Ghwlf4ytMLP62PRFJSEvzbtpM6WrZEypuYmIB/HzxQPH706CGuX7sKa2trODoVlTBZ9kRqW4B5tU2kvOFhU7F/zy7MnLcIZmbmiHvxHABgbmEJExMTidMpMzM3Rxln5TniJiamsLK2zrJeV4jUFwCx8latVQ+/bVwDh8KOKF6qLO7duo5dWzeggV/r3F+cz0T6nGUSqS8AzEu6JSEhARUrVkSfPn0U19z70KxZs7Bw4UJERkaiTJkymDhxIvz8/HDlyhWNzgkFqnhgYWGBmjVrYt68ebh9+zZSU1NRokQJ9O/fH+PGjQMAVKxYEXPnzsXMmTMRFBQEHx8fhIaGokePHhoda9SoUYiNjUXPnj2hp6eHPn36oG3btnjz5o3K133O8SdNmoRJkyYBeD/KoHr16ti/f7/i4igeHh5YsmQJZsyYgalTp6J9+/YIDAzEihUr1H5fa9euxfDhw9GyZUukpKTAx8cHu3fvVkzjcHNzw/79+zFu3DjUqFEDpqamqFmzJrp06ZJlX+7u7vjrr7/g6+sLfX19zJkzR+0ceeXVq5eYETIOcS+ew9zCEs4ubgj/cTmq19TNKyc3bdYcr16+xJJFC/HixXO4l/PAkuWrYK+jQ85Eynvl8mUM6ttT8Xje7PfX1mjZ2h8h00KlipUjkdoWYF5tEynvti2bAABD+/dUWj8hZDpatG4rRaQvikh9ARArb++A0fglYhlWLwzDm9evYGfvgMYt2qFDt/5SR8tCxM+ZSH0BYF7SLc2aNUOzZtlfvDUjIwPz58/HhAkTFF/4rlu3DkWKFMH27dvRuXNntY8jy/iUifpEEnr6X95ep0HbrM0Mc9+IPklqulzqCBox1OdMMRJTYrJuze3PTWJKmtQRNOJgyYsxasu1x2+ljqCRkvZmUkfQiJmxbk3dIemYCPyVdMzNV1JHyJa3q+0nvU4mk+G3336Dv78/AODOnTtwdnbG2bNnUalSJcV29evXR6VKlbBgwQK19y3wj5mIiIiIiIjoy5PdLeuNjY01vv7bkydPAABFihRRWl+kSBHFc+ri12BEREREREREOiS7W9aHhko7lZYjD4iIiIiIiKhA0tP01gb5JCgoCCNHjlRa9yl3nXN0fH+b1qdPn8LJyUmx/unTp0rTGNTBkQdEREREREREOiSvbllfpkwZODo64uDBg4p1//33H06cOIHatWtrtC+OPCAiIiIiIiISVHx8PG7duqV4fPfuXZw7dw52dnYoWbIkRowYgWnTpsHV1VVxq8aiRYsqLqqoLhYPiIiIiIiIqEDSzUkLmjl9+jQaNGigeJw53aFnz56IiIjAmDFjkJCQgAEDBuD169eoW7cu9u7dCxMTE42Ow1s1knB4q0bKxFs1EuUP3qpRu3irRu3hrRq1i7dqpEwi36rx71uvpY6QrVouNlJHyIL/kiUiIiIiIiIilQSuERERERERERF9hi9h3kI+4cgDIiIiIiIiIlKJxQMiIiIiIiIiUonTFoiIiIiIiKhAknHegto48oCIiIiIiIiIVGLxgIiIiIiIiIhU4rQFIiIiIiIiKpBknLWgNo48ICIiIiIiIiKVWDwgIiIiIiIiIpU4bYGEY21mKHUEjaSmy6WOoBFDfXFqiiJlJRKZmbG+1BE0Ilpe0p5yRS2ljqAR2+oBUkfQyKtTi6SOQET5iMUDIiIiIiIiKpB4yQP18Ws7IiIiIiIiIlKJxQMiIiIiIiIiUonTFoiIiIiIiKhg4rwFtXHkARERERERERGpxOIBEREREREREanEaQtERERERERUIMk4b0FtHHlARERERERERCqxeEBEREREREREKnHaAhERERERERVIMs5aUBtHHhARERERERGRSiweEBEREREREZFKnLZAREREREREBRJnLaiPIw+IiIiIiIiISCUWD4iIiIiIiIhIJRYPPiCTybB9+3apY+TI19cXI0aMkDoGERERERHRl0Gmo4sO0rniwfPnzzF48GCULFkSxsbGcHR0hJ+fH2JiYqSOlkWvXr3g7++vtO7evXuQyWQ4d+6cytequx1p36aNG9CsSUNUr+yJrp074uKFC1JHytE/p0/h+4DBaNrIB9W8PBD91wGpI+VKpPYFxMorUlaAebWNebVHpKwA82qbLubt37EuTv4ShKdHZuPpkdmIjhyFr73LK54vU9wBv8zpjwd/heLpkdn4aWYfFLazlDBx9nSxbVVhXipodK540L59e5w9exaRkZG4ceMGduzYAV9fX8TFxWntmCkpKVrbN+m2vXt2I3xWKAYOGYpNW36Du3s5DB7YV6v97XMkJSXB1d0dY8dNlDqKWkRrX5HyipQVYF5tY17tESkrwLzapqt5Hz19jYk//o46XWfBu+tsRJ+8gS3zBsCjrCPMTIywc8lQZGRkoNmAH9Gw9zwYGepj64KBkOnQDe51tW1zwrxUEOlU8eD169c4cuQIZs6ciQYNGqBUqVKoUaMGgoKC0Lp1a6XtBg4ciCJFisDExARfffUVdu7cCQCIi4tDly5dUKxYMZiZmcHT0xM///yz0nF8fX0REBCAESNGwMHBAX5+ftnm+ffff9GpUyfY2NjAzs4Obdq0wb179wAAISEhiIyMxO+//w6ZTAaZTIbo6GiUKVMGAFC5cmXIZDL4+vqq/f4PHTqEGjVqwNjYGE5OTvjhhx+QlpamtI1cLseYMWNgZ2cHR0dHhISEKD0/d+5ceHp6wtzcHCVKlMCQIUMQHx+veD4iIgI2NjbYt28fPDw8YGFhgaZNmyI2NlaxTXR0NGrUqAFzc3PY2NjA29sb9+/fBwCcP38eDRo0gKWlJaysrFC1alWcPn1a8dqtW7eiQoUKMDY2RunSpTFnzhylfKVLl8aMGTPQp08fWFpaomTJklixYoXabZTX1keuRbsOneDftj2cXVwwIXgyTExMsH3bVskyqeJdzwdDho1Ag0ZNpI6iFtHaV6S8ImUFmFfbmFd7RMoKMK+26Wre3YcvYd/RK7j94DluPXiGkMV/ID4xGTW8yqB2pbIoVdQe/YN/wuVbj3H51mP0m7QeVcqXhG8NN0lzf0hX2zYnzPvlkOnof7pIp4oHFhYWsLCwwPbt25GcnJztNnK5HM2aNUNMTAx++uknXLlyBWFhYdDX1wcAvHv3DlWrVsWuXbtw6dIlDBgwAN27d8fJkyeV9hMZGQkjIyPExMRg2bJlWY6TmpoKPz8/WFpa4siRI4iJiVH8oZ2SkoLAwEB06tRJ8Yd3bGws6tSpozjOgQMHEBsbi23btqn13h89eoTmzZujevXqOH/+PJYuXYrVq1dj2rRpWXKbm5vjxIkTmDVrFqZMmYI///xT8byenh4WLlyIy5cvIzIyEn/99RfGjBmjtI/ExESEh4dj/fr1OHz4MB48eIDAwEAAQFpaGvz9/VG/fn1cuHABx48fx4ABAxSV6a5du6J48eI4deoUzpw5gx9++AGGhoYAgDNnzqBTp07o3LkzLl68iJCQEEycOBERERFKx58zZw6qVauGs2fPYsiQIRg8eDCuX7+uVjvlpdSUFFy9chm1atdRrNPT00OtWnVw4fzZfM/zpRGtfUXKK1JWgHm1jXm1R6SsAPNqmyh59fRk6OhXFeamRjhx4S6MjQyQkZGB5JT/fSH1LjkNcnkG6lRyljDp/4jStpmYlwoqA6kDfMjAwAARERHo378/li1bhipVqqB+/fro3LkzvLy8ALz/o/zkyZO4evUq3NzeV0vLli2r2EexYsUUfwgDwLBhw7Bv3z5s3rwZNWrUUKx3dXXFrFmzcszyyy+/QC6XY9WqVYo/nNeuXQsbGxtER0fj66+/hqmpKZKTk+Ho6Kh4XaFChQAA9vb2Sutzs2TJEpQoUQKLFi2CTCZDuXLl8PjxY4wdOxaTJk2Cnt77Oo+XlxeCg4MV72HRokU4ePAgmjR5/030hxdULF26NKZNm4ZBgwZhyZIlivWpqalYtmwZnJ3f/8IICAjAlClTAAD//fcf3rx5g5YtWyqe9/DwULz2wYMHGD16NMqVK6fIkGnu3Llo1KgRJk58P6Tezc0NV65cwezZs9GrVy/Fds2bN8eQIUMAAGPHjsW8efMQFRUFd3f3LO2SnJycpZCUoW8MY2NjdZs2R69ev0J6ejrs7e2V1tvb2+Pu3Tufvf+CTrT2FSmvSFkB5tU25tUekbICzKttup63gktRREeOgomRAeKTkvHNqJW4ducJXryKR0JSCqYPb4NJi3ZABhmmDW8DAwN9ODpYSR0bgO637ceYlwoqnRp5ALy/5sHjx4+xY8cONG3aFNHR0ahSpYri2+tz586hePHiisLBx9LT0zF16lR4enrCzs4OFhYW2LdvHx48eKC0XdWqVVXmOH/+PG7dugVLS0vFiAg7Ozu8e/cOt2/fzpP3+qGrV6+idu3aSnPPvL29ER8fj4cPHyrWZRZRMjk5OeHZs2eKxwcOHECjRo1QrFgxWFpaonv37oiLi0NiYqJiGzMzM0Vh4ON92NnZoVevXvDz80OrVq2wYMECpSkNI0eORL9+/dC4cWOEhYUptcXVq1fh7e2tlM/b2xs3b95Eenp6tu9BJpPB0dFR6T18KDQ0FNbW1krL7JmhObQiERERUcF0495T1OwcCp8e4Vi55ShWTumOcmUd8eJVPLqOWY3mPl/hRcwcPD0yG9YWpvjnygPIMzKkjk0kOZlMNxddpHPFAwAwMTFBkyZNMHHiRBw7dgy9evVSfNtuamqq8rWzZ8/GggULMHbsWERFReHcuXPw8/PLclFEc3NzlfuJj49H1apVce7cOaXlxo0b+Pbbbz/vDX6GzCkCmWQyGeRyOYD3d3Bo2bIlvLy8sHXrVpw5cwaLFy8GoHxRyOz2kfHBL4+1a9fi+PHjqFOnDn755Re4ubnh77//BvD+Wg+XL19GixYt8Ndff6F8+fL47bff8uw9fCwoKAhv3rxRWkaPDdLoeDmxtbGFvr5+lgvFxMXFwcHBIU+OUZCJ1r4i5RUpK8C82sa82iNSVoB5tU3X86ampePOvy9w9uq/mPTjDly88QhDu/gCAA7+fQ0VWk9GyUZBKN7gB/SduA5FC9vg3sMX0ob+f7reth9jXiqodLJ48LHy5csjISEBwPtvrR8+fIgbN25ku21MTAzatGmDbt26oWLFiihbtmyO26pSpUoV3Lx5E4ULF4aLi4vSYm1tDQAwMjJS+kY9cx2ALOtz4+HhgePHjyv9ER8TEwNLS0sUL15crX2cOXMGcrkcc+bMQa1ateDm5obHjx9rlCNT5cqVERQUhGPHjuGrr77Cxo0bFc+5ubnh+++/x/79+9GuXTusXbtW8R4+vqVmTEwM3NzcFNek0JSxsTGsrKyUlryYsgAAhkZG8ChfASf+Pq5YJ5fLceLEcXhVrJwnxyjIRGtfkfKKlBVgXm1jXu0RKSvAvNomWl49mQzGRsozlONeJ+BNfBLqV3dDYTsL7Dx0UaJ0ykRrW+algkqnigdxcXFo2LAhfvrpJ1y4cAF3797Fli1bMGvWLLRp0wYAUL9+ffj4+KB9+/b4888/cffuXezZswd79+4F8H4O/p9//oljx47h6tWrGDhwIJ4+fapxlq5du8LBwQFt2rTBkSNHcPfuXURHR+O7775TTCMoXbo0Lly4gOvXr+PFixdITU1F4cKFYWpqir179+Lp06d48+aNWscbMmQI/v33XwwbNgzXrl3D77//juDgYIwcOVJxvYPcuLi4IDU1FT/++CPu3LmD9evXZ3sxSFXu3r2LoKAgHD9+HPfv38f+/ftx8+ZNeHh4ICkpCQEBAYiOjsb9+/cRExODU6dOKa6JMGrUKBw8eBBTp07FjRs3EBkZiUWLFildg0LXdO/ZG9t+3Ywd23/Dndu3MW1KCJKSkuDftp3U0bKVmJiA69eu4vq1qwCAR48e4vq1q3gS+2lFIm0TrX1FyitSVoB5tY15tUekrADzapuu5p0yrDW8qzijpJMdKrgUxZRhreFTzRWbdr+/I1b31rVQw7M0yhR3QOfm1bFhVl/8uCEKN+9nP21UCrratjlh3i+HTEcXXaRTF0y0sLBAzZo1MW/ePNy+fRupqakoUaIE+vfvj3Hjxim227p1KwIDA9GlSxckJCTAxcUFYWFhAIAJEybgzp078PPzg5mZGQYMGAB/f3+1/4jPZGZmhsOHD2Ps2LFo164d3r59i2LFiqFRo0awsnp/cZn+/fsjOjoa1apVQ3x8PKKiouDr64uFCxdiypQpmDRpEurVq4fo6Ogs+88cpm9g8P5HUKxYMezevRujR49GxYoVYWdnh759+2LChAlqZ65YsSLmzp2LmTNnIigoCD4+PggNDUWPHj00et/Xrl1DZGQk4uLi4OTkhKFDh2LgwIFIS0tDXFwcevTogadPn8LBwQHt2rXD5MmTAbwfrbF582ZMmjQJU6dOhZOTE6ZMmaJ0sURd07RZc7x6+RJLFi3EixfP4V7OA0uWr4K9jg7hunL5Mgb17al4PG/2TABAy9b+CJmme9eCEK19RcorUlaAebWNebVHpKwA82qbruYtZGeB1VN7wNHBCm/i3+HSzUdoNWQJ/jpxDQDgVrowpgxrDTtrM9x//BKzVu/Dwp/+kjTzx3S1bXPCvFQQyTIyeKUUKfz999+oXbs2nj9/zrlGGnqXlvs2uiQ1PfvrOegqQ32dGpBERERUYNhWD5A6gkZenVokdQTSESY69ZW0Zs4/eCt1hGxVLGkpdYQsBP4xiyktLQ337t3D7NmzUbFiRRYOiIiIiIiIpKKrcwR0EL9izGeXLl2Cl5cXYmNjsW7dOqnjEBEREREREeWKIw/yWaVKlZCYmCh1DCIiIiIiIiK1sXhAREREREREBZKM8xbUxmkLRERERERERKQSiwdEREREREREpBKnLRAREREREVGBJOOsBbVx5AERERERERERqcTiARERERERERGpxGkLREREREREVCBx1oL6OPKAiIiIiIiIiFRi8YCIiIiIiIiIVOK0BSIiIiIiIiqYOG9BbRx5QEREREREREQqsXhARERERERERCpx2gIREREREREVSDLOW1AbRx4QERERERERkUqyjIyMDKlDEGniXZrUCTSTmi6XOoJGUtPEOSWYGetLHeGLlpicLnUEjYjUH0Q7L4jGUJ/fjWjLi7fJUkfQiLWZodQRvmixr95JHUFtTrYmUkfQiGjnMROBx7NffpQgdYRsVShmLnWELAT+MRMRERERERF9OhlnLahNrJIWEREREREREeU7Fg+IiIiIiIiISCVOWyAiIiIiIqICibMW1MeRB0RERERERESkEosHRERERERERKQSpy0QERERERFRwcR5C2rjyAMiIiIiIiIiUonFAyIiIiIiIiJSidMWiIiIiIiIqECScd6C2jjygIiIiIiIiIhUYvGAiIiIiIiIiFTitAUiIiIiIiIqkGSctaA2jjwgIiIiIiIiIpVYPCAiIiIiIiIilVg8oM8mk8mwfft2qWMQERERERFpRKajiy5i8UCLZDKZyiUkJCTfsmzduhW+vr6wtraGhYUFvLy8MGXKFLx8+fKz9x0bG4tmzZrlQUppbNq4Ac2aNET1yp7o2rkjLl64IHWkHP1z+hS+DxiMpo18UM3LA9F/HZA6Uo4i16xAn26d0KhuNTRvVBdjRwbg/r27UsfKlUj9QZSs7AvaJdJ5ARAvLyBOX8gkWt5MP69bjUa1vLB43kypo2RLtL4rWt64588wZ9p4fNvKF+2b1EJAr464ee2y1LFyJFr7AuKeG0h3sHigRbGxsYpl/vz5sLKyUloXGBiYLznGjx+Pb775BtWrV8eePXtw6dIlzJkzB+fPn8f69euzfU1KSora+3d0dISxsXFexc1Xe/fsRvisUAwcMhSbtvwGd/dyGDywL+Li4qSOlq2kpCS4urtj7LiJUkfJ1dkzp9G+UxesjPwZC5auQlpaGkYM6YekpESpo+VIpP4gUlb2Be0S6bwAiJdXpL4AiJc307Url7Dzty0o6+ImdZQcidZ3Rcob//Y/jAnoBX19A4TMWoTF67aiz9CRsLC0kjpajkRqX0DccwPpFhYPtMjR0VGxWFtbQyaTKR4XLlwYc+fORfHixWFsbIxKlSph7969itfeu3cPMpkMmzdvRr169WBqaorq1avjxo0bOHXqFKpVqwYLCws0a9YMz58/zzHDyZMnMWPGDMyZMwezZ89GnTp1ULp0aTRp0gRbt25Fz549AQAhISGoVKkSVq1ahTJlysDExAQAULp0acyfP19pn5UqVVIaNfHhtIWUlBQEBATAyckJJiYmKFWqFEJDQxXbvn79Gv369UOhQoVgZWWFhg0b4vz585/Z0p9ufeRatOvQCf5t28PZxQUTgifDxMQE27dtlSyTKt71fDBk2Ag0aNRE6ii5mr94BVq0bouyzq5wdSuHCZNn4MmTWFy7ckXqaDkSqT+IlJV9QbtEOi8A4uUVqS8A4uUFgKTERMwIDsLIoBBY6vAfi6L1XZHy/rpxLRwKOWJE0GS4eXwFR6diqFK9NpyKlZA6Wo5Eal9AzHNDvpF6foJA8xZYPJDIggULMGfOHISHh+PChQvw8/ND69atcfPmTaXtgoODMWHCBPzzzz8wMDDAt99+izFjxmDBggU4cuQIbt26hUmTJuV4nA0bNsDCwgJDhgzJ9nkbGxvF/9+6dQtbt27Ftm3bcO7cuU96XwsXLsSOHTuwefNmXL9+HRs2bEDp0qUVz3fs2BHPnj3Dnj17cObMGVSpUgWNGjXKk+kTmkpNScHVK5dRq3YdxTo9PT3UqlUHF86fzfc8X7r4t28BAFbW1hInyZ5I/UGkrNlhXyBRiNYXRMubaUH4dNTyroeqNWpJHYUkcjLmEFzKlUfYpNHo1qYhhvftjH1/bJM61hdD1HMD6R4DqQMUVOHh4Rg7diw6d+4MAJg5cyaioqIwf/58LF68WLFdYGAg/Pz8AADDhw9Hly5dcPDgQXh7ewMA+vbti4iIiByPc/PmTZQtWxaGhoa5ZkpJScG6detQqFChT35fDx48gKurK+rWrQuZTIZSpUopnjt69ChOnjyJZ8+eKaY5hIeHY/v27fj1118xYMCATz7up3j1+hXS09Nhb2+vtN7e3h53797J1yxfOrlcjvnhYfCqVAXOLq5Sx8mWSP1BpKwfY18gkYjWF0TLCwB//bkHt65fxZI1P0sdhST0JPYR9vy+Bf4du6Fjt764ee0yViycBQNDAzRq2lrqeMIT8dxAuonFAwn8999/ePz4saIAkMnb2zvLEH4vLy/F/xcpUgQA4OnpqbTu2bNnOR4rIyND7VylSpX6rMIBAPTq1QtNmjSBu7s7mjZtipYtW+Lrr78GAJw/fx7x8fFZTlxJSUm4fft2tvtLTk5GcnKy0roMfWNhr7FQUIWHTcWd2zexfM1PUkchibEvEFGmZ0+fYPHcmZi1cAWM+Hu9QMuQy+HiXh49BgwDADi7lcP9u7ew5/dfWTwgrZPp6hwBHcTigY77cMSATCbLdp1cLs/x9W5ubjh69ChSU1NzHX1gbm6eZZ2enl6WAkRqamqO+6hSpQru3r2LPXv24MCBA+jUqRMaN26MX3/9FfHx8XByckJ0dHSW1304feJDoaGhmDx5stK68RODMWFSiMr3og5bG1vo6+tnuVBMXFwcHBwcPnv/9F542DTEHDmEpavWoXARR6nj5Eik/iBS1g+xL5BoROsLouW9ce0KXr96iUG9vlGsk6en48K5M9j+6ybsPXwa+vr6Eiak/GJr74ASpcsqrStRqgyOHT4oUaIvi2jnBtJdvOaBBKysrFC0aFHExMQorY+JiUH58uXz9Fjffvst4uPjsWTJkmyff/36tcrXFypUCLGxsYrH//33H+7eVX2LNSsrK3zzzTdYuXIlfvnlF2zduhUvX75ElSpV8OTJExgYGMDFxUVpyenEFRQUhDdv3igto8cGqX7TajI0MoJH+Qo48fdxxTq5XI4TJ47Dq2LlPDlGQZaRkYHwsGk4FHUAi5avQdFixaWOpJJI/UGkrAD7AolLtL4gWt4q1Wpi1YatWLFus2Jx96iARn4tsGLdZhYOChCPryrh0YP7SusePXyAwkWcJEr0ZRHt3EC6iyMPJDJ69GgEBwfD2dkZlSpVwtq1a3Hu3Dls2LAhT49Ts2ZNjBkzBqNGjcKjR4/Qtm1bFC1aFLdu3cKyZctQt25dDB8+PMfXN2zYEBEREWjVqhVsbGwwadIklb/M586dCycnJ1SuXBl6enrYsmULHB0dYWNjg8aNG6N27drw9/fHrFmz4ObmhsePH2PXrl1o27YtqlWrlmV/xsZZpyi8S/v09vhY9569MXHcWFSo8BW+8vTCT+sjkZSUBP+27fLuIHkoMTEB/z54oHj86NFDXL92FdbW1nB0KiphsqzCw6Zi/55dmDlvEczMzBH34v1dQcwtLBV389A1IvUHkbKyL2iXSOcFQLy8IvUFQKy8ZubmKOOsfO0TExNTWFlbZ1mvC0TruyLlbdOxG8YM7YXN61ejboMmuHH1Mvb9sRUBgbp7G0SR2hcQ69yQ32SctaA2Fg8k8t133+HNmzcYNWoUnj17hvLly2PHjh1wdc37X5YzZ85E1apVsXjxYixbtgxyuRzOzs7o0KGD4laNOQkKCsLdu3fRsmVLWFtbY+rUqSpHHlhaWmLWrFm4efMm9PX1Ub16dezevRt6eu8HuezevRvjx49H79698fz5czg6OsLHx0dxPYf81rRZc7x6+RJLFi3EixfP4V7OA0uWr4K9jg7hunL5Mgb1/d/PbN7smQCAlq39ETItNKeXSWLblk0AgKH9lfvYhJDpaNG6rRSRciVSfxApK/uCdol0XgDEyytSXwDEyysS0fquSHndPCpg3LQ5WLfiR2xatwJFHIuhf8Bo+DZpLnW0HInUvgDPDZQ3ZBmaXFGPSAfk5ciD/JCanvM1KXRRapo4pwQzYw5p1abE5HSpI2hEpP4g2nlBNIb6nJWpLS/eJue+kQ6xNsv9blP06WJfvZM6gtqcbHVzpFtORDuPmQj8lfStZ0lSR8iWS2FTqSNkIfCPmYiIiIiIiOjTcdaC+sQqaRERERERERFRvmPxgIiIiIiIiIhU4rQFIiIiIiIiKpg4b0FtHHlARERERERERCqxeEBEREREREQkqJCQEMhkMqWlXLlyeX4cTlsgIiIiIiKiAkn2hcxbqFChAg4cOKB4bGCQ93/qs3hAREREREREJDADAwM4Ojpq9RictkBERERERESkQ5KTk/Hff/8pLcnJyTluf/PmTRQtWhRly5ZF165d8eDBgzzPxOIBERERERERFUgymW4uoaGhsLa2VlpCQ0OzfQ81a9ZEREQE9u7di6VLl+Lu3buoV68e3r59m7dtlZGRkZGneyTSsndpUifQTGq6XOoIGklNE+eUYGasL3WEL1picrrUETQiUn8Q7bwgGkN9fjeiLS/e5vytly6yNjOUOsIXLfbVO6kjqM3J1kTqCBoR7TxmIvBk+LsvdLMfF7WUZRlpYGxsDGNj41xf+/r1a5QqVQpz585F37598yyTwD9mIiIiIiIioi+PuoWC7NjY2MDNzQ23bt3K00xilbSIiIiIiIiI8ohMR5fPER8fj9u3b8PJyekz96SMxQMiIiIiIiIiQQUGBuLQoUO4d+8ejh07hrZt20JfXx9dunTJ0+Nw2gIRERERERGRoB4+fIguXbogLi4OhQoVQt26dfH333+jUKFCeXocFg+IiIiIiIioYPrcOQI6YNOmTflyHE5bICIiIiIiIiKVOPKAhCPaLc5EuvUhINbt7ki7DA2+gFK8juJ5gUQl2q0PRbvdnWj/xinpYCZ1BLUdvPZM6ggaaVSusNQRiLJg8YCIiIiIiIgKJNmXMG8hn4hVjiUiIiIiIiKifMfiARERERERERGpxGkLREREREREVCDJOGtBbRx5QEREREREREQqsXhARERERERERCpx2gIREREREREVSJy1oD6OPCAiIiIiIiIilVg8ICIiIiIiIiKVOG2BiIiIiIiICiTebUF9HHlARERERERERCqxeEBEREREREREKnHaAhERERERERVQnLegLo48ICIiIiIiIiKVWDwgIiIiIiIiIpVYPKA8FxISgkqVKkkdg4iIiIiISCWZTDcXXcTigZbJZDKVS0hIiNYz3Lt3DzKZDOfOncvynK+vL0aMGKH1DLrqn9On8H3AYDRt5INqXh6I/uuA1JFyFLlmBfp064RGdauheaO6GDsyAPfv3ZU6Vq42bdyAZk0aonplT3Tt3BEXL1yQOpJKIuUVKatIn7VMorQvzw3aJ1JWQJy8Ip4XALavNulq296+fA6rZoxFSD9/jGxfDxdPHFY8l56Whj/WL8Ws73vih2+bIKSfPzYunIY3L19ImDh7utq+JA4WD7QsNjZWscyfPx9WVlZK6wIDA6WOWKAlJSXB1d0dY8dNlDpKrs6eOY32nbpgZeTPWLB0FdLS0jBiSD8kJSVKHS1He/fsRvisUAwcMhSbtvwGd/dyGDywL+Li4qSOli2R8oqUFRDrswaI1b48N2iXSFkBsfKKdl4A2L7apMttm5L8DkVLu6Bd/5HZPvfozg183aEnRs5ejV5jpuPZ4wdYHfaDBElzpsvtS+Jg8UDLHB0dFYu1tTVkMpniceHChTF37lwUL14cxsbGqFSpEvbu3at4beaIgc2bN6NevXowNTVF9erVcePGDZw6dQrVqlWDhYUFmjVrhufPn+dJ3vXr16NatWqwtLSEo6Mjvv32Wzx79kzxfHR0NGQyGQ4ePIhq1arBzMwMderUwfXr13Pc5+3bt1G2bFkEBAQgIyMD9+/fR6tWrWBrawtzc3NUqFABu3fvzpP8mvKu54Mhw0agQaMmkhxfE/MXr0CL1m1R1tkVrm7lMGHyDDx5EotrV65IHS1H6yPXol2HTvBv2x7OLi6YEDwZJiYm2L5tq9TRsiVSXpGyAmJ91gCx2pfnBu0SKSsgVl7RzgsA21ebdLltParUQvNv+8Orpk+W50zNLTAoeB4qeTdE4WIlUdqtAtr1+x4Pb1/Hq+dPJUibPV1uX6nJdHTRRSweSGjBggWYM2cOwsPDceHCBfj5+aF169a4efOm0nbBwcGYMGEC/vnnHxgYGODbb7/FmDFjsGDBAhw5cgS3bt3CpEmT8iRTamoqpk6divPnz2P79u24d+8eevXqlWW78ePHY86cOTh9+jQMDAzQp0+fbPd34cIF1K1bF99++y0WLVoEmUyGoUOHIjk5GYcPH8bFixcxc+ZMWFhY5En+giT+7VsAgJW1tcRJspeakoKrVy6jVu06inV6enqoVasOLpw/K2Gy7ImUV6SsIhK9fXluyDsiZQXEyysatq/2fGlt+y4hATKZDKbmuvHv2y+tfUk6BlIHKMjCw8MxduxYdO7cGQAwc+ZMREVFYf78+Vi8eLFiu8DAQPj5+QEAhg8fji5duuDgwYPw9vYGAPTt2xcRERG5Hq9OnTrQ01OuFyUlJSld3PDDIkDZsmWxcOFCVK9eHfHx8Up/4E+fPh3169cHAPzwww9o0aIF3r17BxMTE8U2x44dQ8uWLTF+/HiMGjVKsf7Bgwdo3749PD09FcchzcjlcswPD4NXpSpwdnGVOk62Xr1+hfT0dNjb2yutt7e3x927dyRKlTOR8oqUVUQity/PDXlLpKyAeHlFw/bVni+pbVNTkrHzp6WoXLcxTMzMpY4D4MtqX5IWiwcS+e+///D48WNFASCTt7c3zp8/r7TOy8tL8f9FihQBAMUf3pnrPpxakJNffvkFHh4eSuu6du2q9PjMmTMICQnB+fPn8erVK8jlcgDv/+AvX758tpmcnJwAAM+ePUPJkiUV2zdp0gTTp0/PckHG7777DoMHD8b+/fvRuHFjtG/fXml/H0pOTkZycrLSuhQYwtjYONf3+yULD5uKO7dvYvman6SOQkQ6hOcGIiLppKelYd2cYGRkZKDDgFG5v4B0gq7e2UAXcdqCAAwNDRX/L/v/3v3xusw/8lUpUaIEXFxclBZTU1PF8wkJCfDz84OVlRU2bNiAU6dO4bfffgMApKSk5JrpwwyFChVCjRo18PPPP+O///5Tem2/fv1w584ddO/eHRcvXkS1atXw448/Zps5NDQU1tbWSsucWWG5vtcvWXjYNMQcOYTFKyJQuIij1HFyZGtjC319/SwX4omLi4ODg4NEqXImUl6RsopI1PbluSHviZQVEC+vaNi+2vMltG16Whoi50zCy+dPMCh4ns6MOgC+jPYl3cDigUSsrKxQtGhRxMTEKK2PiYlR+oY/P127dg1xcXEICwtDvXr1UK5cObVGNGTH1NQUO3fuhImJCfz8/PD2/+fgZipRogQGDRqEbdu2YdSoUVi5cmW2+wkKCsKbN2+UllFjdOvqtfklIyMD4WHTcCjqABYtX4OixYpLHUklQyMjeJSvgBN/H1esk8vlOHHiOLwqVpYwWfZEyitSVhGJ1r48N2iPSFkB8fKKhu2rPaK3bWbh4EXsQwwOngdzS9265ozo7Uu6g9MWJDR69GgEBwfD2dkZlSpVwtq1a3Hu3Dls2LBBkjwlS5aEkZERfvzxRwwaNAiXLl3C1KlTP3l/5ubm2LVrF5o1a4ZmzZph7969sLCwwIgRI9CsWTO4ubnh1atXiIqKyjKdIpOxsXGWKQpvk3MfZaGuxMQE/PvggeLxo0cPcf3aVVhbW8PRqWieHScvhIdNxf49uzBz3iKYmZkj7sX7O2yYW1gqXWtCl3Tv2RsTx41FhQpf4StPL/y0PhJJSUnwb9tO6mjZEimvSFkBsT5rgFjty3ODdomUFRArr2jnBYDtq0263LbJSYl48eSR4vHLZ7F4dPcmzCysYGVrj4jwiXh05wb6jpsJuVyO/169/4bfzMIKBh+M1pWSLrev1GQ6e28D3cPigYS+++6799+kjxqFZ8+eoXz58tixYwdcXaW5yFWhQoUQERGBcePGYeHChahSpQrCw8PRunXrT96nhYUF9uzZAz8/P7Ro0QK7d+9Geno6hg4diocPH8LKygpNmzbFvHnz8vCdqO/K5csY1Len4vG82TMBAC1b+yNkWqgkmXKybcsmAMDQ/j2V1k8ImY4WrdtKESlXTZs1x6uXL7Fk0UK8ePEc7uU8sGT5Ktjr6BA5kfKKlBUQ67MGiNW+PDdol0hZAbHyinZeANi+2qTLbfvv7etYEvyd4vHvEYsAANV9m8Lvmz64fOooAGDOqN5KrxsyeSFcvtKNb/Z1uX1JHLKMjIwMqUMQaSIvRx7kh9Q0sT5iZsb6UkcgHZGaLtZnzVBfnJl4icnpUkfQCM8LlInnBe1i+2rPwWufNhVXKo3KFZY6gkZMBP5K+smbVKkjZMvRWjdGrXxI4B8zERERERER0WfgrAW1iVMuJCIiIiIiIiJJsHhABXE6bQAAjiJJREFURERERERERCpx2gIREREREREVSJy1oD6OPCAiIiIiIiIilVg8ICIiIiIiIiKVOG2BiIiIiIiICiQZ5y2ojSMPiIiIiIiIiEglFg+IiIiIiIiISCVOWyAiIiIiIqICScb7LaiNIw+IiIiIiIiISCUWD4iIiIiIiIhIJU5bICIiIiIiooKJsxbUxpEHRERERERERKQSiwdEREREREREpBKnLRAREREREVGBxFkL6uPIAyIiIiIiIiJSicUDIiIiIiIiIlKJ0xZIOKlpGVJH0IiZsb7UETRy+2mC1BHUVtLBVOoIXzRDfdaXiUiZaL+DDcX6FczzrhY1KldY6ggaiX39TuoIGinjYCJ1hE8m47wFtfEMRUREREREREQqsXhARERERERERCpx2gIREREREREVSDLeb0FtHHlARERERERERCqxeEBEREREREREKnHaAhERERERERVIvNuC+jjygIiIiIiIiIhUYvGAiIiIiIiIiFRi8YCIiIiIiIiIVGLxgIiIiIiIiIhUYvGAiIiIiIiIiFTi3RaIiIiIiIioQOLdFtTHkQdEREREREREpBKLB0RERERERESkEqctEBERERERUYEkA+ctqIsjDwQSHR0NmUyG169fAwAiIiJgY2OjeD4kJASVKlWSJJtMJsP27dtVbtOrVy/4+/vnSx4iIiIiIiLKOyweSGDZsmWwtLREWlqaYl18fDwMDQ3h6+urtG1mweD27duoU6cOYmNjYW1t/cnH/u2331CrVi1YW1vD0tISFSpUwIgRIz55f5liY2PRrFkzAMC9e/cgk8lw7ty5z96vtkWuWYE+3TqhUd1qaN6oLsaODMD9e3eljqXSpo0b0KxJQ1Sv7ImunTvi4oULUkfK1sAuLdCuYZUsy4oFoVJHy9Y/p0/h+4DBaNrIB9W8PBD91wGpI6kkWl5AnL6bSZS8Ip7HAHHaFxArKyBOXvZd7RMpK8C82rJ+9VI09a6otPTr0kbqWCQgFg8k0KBBA8THx+P06dOKdUeOHIGjoyNOnDiBd+/eKdZHRUWhZMmScHZ2hpGRERwdHSH7xEuCHjx4EN988w3at2+PkydP4syZM5g+fTpSU1M/+z05OjrC2Nj4s/eT386eOY32nbpgZeTPWLB0FdLS0jBiSD8kJSVKHS1be/fsRvisUAwcMhSbtvwGd/dyGDywL+Li4qSOlsWspT9h9a/7FUvw7KUAgDr1m0icLHtJSUlwdXfH2HETpY6iFtHyitR3AbHyinYeA8RqX5GyAmLlZd/VLpGyAsyrbaXKOGPjjoOKZc7SCKkj6QyZTDcXXcTigQTc3d3h5OSE6Ohoxbro6Gi0adMGZcqUwd9//620vkGDBor//3Dagqb++OMPeHt7Y/To0XB3d4ebmxv8/f2xePFixTaZUx/WrFmDkiVLwsLCAkOGDEF6ejpmzZoFR0dHFC5cGNOnT1fa94fTFsqUKQMAqFy5MmQyWZbRFOHh4XBycoK9vT2GDh2aJ8WLTzV/8Qq0aN0WZZ1d4epWDhMmz8CTJ7G4duWKZJlUWR+5Fu06dIJ/2/ZwdnHBhODJMDExwfZtW6WOloW1jS1s7RwUy+njh+FYtDgqVKwqdbRsedfzwZBhI9CgkW4WNz4mWl6R+i4gVl7RzmOAWO0rUlZArLzsu9olUlaAebVNX98AdvYOisXaxlbqSCQgFg8k0qBBA0RFRSkeR0VFwdfXF/Xr11esT0pKwokTJxTFg8/l6OiIy5cv49KlSyq3u337Nvbs2YO9e/fi559/xurVq9GiRQs8fPgQhw4dwsyZMzFhwgScOHEi29efPHkSAHDgwAHExsZi27ZtSu/z9u3biIqKQmRkJCIiIhAREZEn7y8vxL99CwCw+oypIdqSmpKCq1cuo1btOop1enp6qFWrDi6cPythstylpqbi8IE9aNiszSePnCFxidZ3Rcv7MV0+jwFita9IWQHx8n6MfTfviJQVYN788OjhfXzbujF6dWyOmSFBePYkVupIJCAWDyTSoEEDxMTEIC0tDW/fvsXZs2dRv359+Pj4KEYkHD9+HMnJyXlWPBg2bBiqV68OT09PlC5dGp07d8aaNWuQnJystJ1cLseaNWtQvnx5tGrVCg0aNMD169cxf/58uLu7o3fv3nB3d1cqfnyoUKFCAAB7e3s4OjrCzs5O8ZytrS0WLVqEcuXKoWXLlmjRogUOHjyYY+bk5GT8999/SsvHefOKXC7H/PAweFWqAmcXV60c43O8ev0K6enpsLe3V1pvb2+PFy9eSJRKPSdjopAQ/xYN/VpLHYUkIFrfFS3vh3T9PAaI1b4iZQXEy/sh9t28JVJWgHm1rVx5T4waPxXT5i5BQOB4PIl9hMAhvZGYkCB1NJ0g09FFF7F4IBFfX18kJCTg1KlTOHLkCNzc3FCoUCHUr19fcd2D6OholC1bFiVLlsyTY5qbm2PXrl24desWJkyYAAsLC4waNQo1atRAYuL/5heWLl0alpaWisdFihRB+fLloaenp7Tu2bNnGmeoUKEC9PX1FY+dnJxU7ic0NBTW1tZKy/zwMI2Pq47wsKm4c/smpoaGa2X/BdnB3dtRpUYd2DkUkjoK0ReN5zESFfsukfZUr10XPg2/RlkXN1Sr6Y2p4YsQH/8Wh//aJ3U0EgyLBxJxcXFB8eLFERUVhaioKNSvXx8AULRoUZQoUQLHjh1DVFQUGjZsmOfHdnZ2Rr9+/bBq1Sr8888/uHLlCn755RfF84aGhkrby2SybNfJ5XKNj63pfoKCgvDmzRulZUTgDxofNzfhYdMQc+QQFq+IQOEijnm+/7xga2MLfX39LBfiiYuLg4ODg0SpcvfsyWNc+OckGrdoK3UUkohofVe0vJlEOI8BYrWvSFkB8fJmYt/NeyJlBZg3v1lYWqFYiVJ4/PBfqaOQYFg8kFCDBg0QHR2N6OhopYsK+vj4YM+ePTh58mSeTVnISenSpWFmZoaEPBy2ZGRkBABIT0//7H0ZGxvDyspKacnLuzpkZGQgPGwaDkUdwKLla1C0WPE823deMzQygkf5Cjjx93HFOrlcjhMnjsOrYmUJk6n2194dsLKxQ9VadaWOQhIRre+Kllek8xggVvuKlBUQLy/7rvaIlBVg3vyWlJiI2Ef/wk6AQke+kHp+gkDzFgykDlCQNWjQQHG3gcyRBwBQv359BAQEICUlJU+LByEhIUhMTETz5s1RqlQpvH79GgsXLkRqaiqaNMm7K7YXLlwYpqam2Lt3L4oXLw4TExNY6+jFj8LDpmL/nl2YOW8RzMzMEffiOQDA3MISJiYmEqfLqnvP3pg4biwqVPgKX3l64af1kUhKSoJ/23ZSR8uWXC7HX3t3oMHXLaGvr9unm8TEBPz74IHi8aNHD3H92lVYW1vD0amohMmyJ1pe0fquSHlFO48BYrWvSFkBsfKy72qXSFkB5tWmlYvmoKZ3fRR2dMLLF8+xftVS6Ovrw7dxM6mjkWB0+1/zX7gGDRogKSkJ5cqVQ5EiRRTr69evj7dv3ypu6ZhX6tevj8WLF6NHjx54+vQpbG1tUblyZezfvx/u7u55dhwDAwMsXLgQU6ZMwaRJk1CvXj2l21Lqkm1bNgEAhvbvqbR+Qsh0tGite8PsmzZrjlcvX2LJooV48eI53Mt5YMnyVbDX0crxhTMn8OLZEzRq1kbqKLm6cvkyBvX9Xz+YN3smAKBla3+ETAuVKlaORMsrWt8VKa9o5zFArPYVKSsgVl72Xe0SKSvAvNr04tlThAX/gLf/vYa1jS0qeFXGvOXrYWNrl/uLiT4gy8jIyJA6BJEmXiZ8/nSI/GRmrJ/7Rjrk9lNxrrxb0sFU6ghfNEN9zmzTlsRknsdITOy7RPkj9vU7qSNopIyDbo4WUkd8sm7+OWxhrHtzF/gvQyIiIiIiIiJSicUDIiIiIiIiIlKJ1zwgIiIiIiKiAkmme7MDdBZHHhARERERERGRSiweEBEREREREZFKnLZAREREREREBRJnLaiPIw+IiIiIiIiISCUWD4iI/q+9O4+rKf//AP66rdoXSmlaCaFsMQgtlsTYZzCWFtkNjZQy9mxpJrvB2CrrZJ2xL2lR2RLFyFaRrVBChbbz+6Nf9+u6t2SGPufU+/l49Hi4n3OHlzvXufe8z+fz/hBCCCGEEEIqRcsWCCGEEEIIIYTUTrRuocpo5gEhhBBCCCGEEEIqRcUDQgghhBBCCCGEVIqWLRBCCCGEEEIIqZVEtG6hymjmASGEEEIIIYQQQipFxQNCCCGEEEIIIUTg1q1bBzMzM9SpUwfffvstLl269EV/fyoeEEIIIYQQQgiplUQifv58rj///BPe3t6YN28eEhMT0bJlSzg7O+PZs2df7LWi4gEhhBBCCCGEECJgy5cvx9ixY+Hh4YFmzZphw4YNUFVVxdatW7/Yn0HFA0IIIYQQQgghhEfev3+P169fS/y8f/9e5nMLCwtx5coVdO/eXTwmJyeH7t274/z5818uFEcI4d69e8fNmzePe/fuHesoVSKkvELKynGU92sTUl4hZeU4yvu1Ud6vR0hZOY7yfm1CyiukrBwnvLy13bx58zgAEj/z5s2T+dzHjx9zALj4+HiJcV9fX659+/ZfLJOI4zjuy5UiCBGm169fQ0tLC69evYKmpibrOJ8kpLxCygpQ3q9NSHmFlBWgvF8b5f16hJQVoLxfm5DyCikrILy8td379++lZhooKytDWVlZ6rlPnjyBkZER4uPj0bFjR/H4jBkzEB0djYsXL36RTApf5HchhBBCCCGEEELIF1FRoUCWevXqQV5eHllZWRLjWVlZMDAw+GKZqOcBIYQQQgghhBAiUEpKSmjbti0iIiLEY6WlpYiIiJCYifBf0cwDQgghhBBCCCFEwLy9veHm5gZbW1u0b98eK1euRH5+Pjw8PL7Yn0HFA0JQNi1o3rx5VZ4axJqQ8gopK0B5vzYh5RVSVoDyfm2U9+sRUlaA8n5tQsorpKyA8PKSzzN06FA8f/4cc+fORWZmJlq1aoUTJ06gfv36X+zPoIaJhBBCCCGEEEIIqRT1PCCEEEIIIYQQQkilqHhACCGEEEIIIYSQSlHxgBBCCCGEEEIIIZWi4gEhhBBCCCGEEEIqRcUDUqsVFhbi9u3bKC4uZh2lUhYWFsjOzpYaz83NhYWFBYNENUtpaSnu3LmD2NhYxMTESPzwTXFxMc6cOYONGzfizZs3AIAnT54gLy+PcTLZSkpKsG/fPixcuBALFy7Evn37ePvvLSAgAAUFBVLjb9++RUBAAINEVfP8+XPExsYiNjYWz58/Zx2nQpVlu379ejUm+TyFhYV49OgRMjIyJH7Iv1NUVIRu3brh7t27rKNU2e7duys85uvrW41Jaq7c3Fxs3rwZM2fORE5ODgAgMTERjx8/ZpyMEPIh2m2B1EoFBQWYMmUKQkNDAQB37tyBhYUFpkyZAiMjI/j7+zNOKElOTg6ZmZnQ19eXGM/KyoKJiQnev3/PKJlsERERiIiIwLNnz1BaWipxbOvWrYxSyXbhwgUMHz4cDx48wMenQ5FIhJKSEkbJpD148AC9evVCRkYG3r9/L37fenl54f3799iwYQPriBL++ecf9OvXD5mZmWjSpAmAsn9renp6OHz4MFq0aME4oSR5eXk8ffpU6t9ZdnY29PX1efVeAID8/HxMmTIF27dvF2eTl5eHq6sr1qxZA1VVVcYJJRkYGGDLli3o06ePxPhvv/2GOXPm4O3bt4ySyXb37l2MHj0a8fHxEuMcx/Hu3FBOKOdePT09xMfHw9LSknWUKtHW1sbu3bvh4uIiMT5t2jTs2bMHT58+ZZSsYnfv3kVkZKTM98LcuXMZpZItOTkZ3bt3h5aWFu7fv4/bt2/DwsICs2fPRkZGBsLCwlhHFDtx4gTU1dXRuXNnAMC6deuwadMmNGvWDOvWrYOOjg7jhJJKSkoQEhJS4Xnh7NmzjJIRoVJgHYAQFmbOnImkpCRERUWhV69e4vHu3btj/vz5vCke/P333+Jfnzx5ElpaWuLHJSUliIiIgJmZGYNkFVuwYAECAgJga2sLQ0NDiEQi1pEqNWHCBNja2uLo0aO8z+vl5QVbW1skJSWhbt264vGBAwdi7NixDJPJNmbMGDRv3hwJCQniL1QvX76Eu7s7xo0bJ3VRxlr5ReHHkpKSoKuryyBR5by9vREdHY2///4bdnZ2AIDY2FhMnToV06dPx/r16xknlOTt7Y3BgwfDw8MDy5cvR05ODlxdXXH9+nXs2rWLdTwp7u7uUFBQwJEjR3h/bgCEde4dOXIktmzZgsDAQNZRqmTnzp348ccfceTIEfFF45QpU3DgwAFERkYyTidt06ZNmDhxIurVqwcDAwOJ94JIJOJd8cDb2xvu7u4ICgqChoaGeLx3794YPnw4w2TSfH19sWzZMgBlM6amT58Ob29vREZGwtvbG9u2bWOcUJKXlxdCQkLQp08ftGjRgtfnBSIQHCG1kImJCXf+/HmO4zhOXV2dS01N5TiO4+7evctpaGiwjCZBJBJV+KOkpMQ1btyYO3z4MOuYEgwMDLiwsDDWMapMVVWVu3v3LusYVaKrq8vdunWL4zjJ9216ejqnoqLCMppMderU4W7cuCE1fv36da5OnToMEsmmra3N6ejocHJycuJfl/9oampycnJy3KRJk1jHlFK3bl0uMjJSavzs2bNcvXr1qj9QFSQmJnLNmzfnGjVqxOnq6nIuLi7c06dPWceSSVVVlUtJSWEdo8qEdO796aefOE1NTa5t27bcuHHjuGnTpkn88NHOnTs5HR0dLiEhgZs4cSLXoEED7vbt26xjyWRiYsIFBgayjlFlmpqa3L179ziOk/xsu3//PqesrMwymhQ1NTUuPT2d4ziOmzdvHjd48GCO4zjuypUrXP369Rkmk61u3brc0aNHWccgNQjNPCC10vPnz6WmJgNl04D5VJUtn15mbm6Oy5cvo169eowTfVphYSE6derEOkaVffvtt7h37x4aNWrEOsonlZaWypwq/ejRI4m7NXzRuHFjZGVloXnz5hLjz54949XrvXLlSnAch9GjR2PBggUSM3yUlJRgZmaGjh07MkwoW0FBAerXry81rq+vL7N3Ax80atQILVq0wP79+wEAQ4cOhYGBAeNUsjVr1gwvXrxgHaPKhHTuvXHjBtq0aQOgbCnTh/j0Gfyh4cOHIzc3F3Z2dtDT00N0dDSvzmMfevnyJX744QfWMapMWVkZr1+/lhovX+bGJ0pKSuLz65kzZ+Dq6goA0NXVlfl3YE1JSYm371MiTNTzgNRKXbt2xQ8//IApU6ZAQ0MDycnJMDc3x5QpU3D37l2cOHGCdUQJYWFhGDp0KJSVlSXGCwsLsWfPHvGHFx/4+flBXV0dc+bMYR2lSg4ePIjZs2fD19cX1tbWUFRUlDhuY2PDKJm0oUOHQktLC3/88Yf4faunp4f+/fvDxMSEd9Mljx07hhkzZmD+/Pno0KEDgLIeEwEBAQgMDBRP/wUATU1NVjHFoqOjYWdnBwUFYdTVu3Xrhrp16yIsLAx16tQBUNbc0c3NDTk5OThz5gzjhJLi4uIwcuRI6OrqYseOHYiLi4O3tzdcXFywYcMG3q0VPnv2LGbPno0lS5bIPDfw4T37IaGde/nO29tb5vjevXvRpk0bNGzYUDy2fPny6opVJZ6enmjXrh0mTJjAOkqVjBkzBtnZ2QgPD4euri6Sk5MhLy+PAQMGoGvXrli5ciXriGL9+vVDYWEh7OzssHDhQqSnp8PIyAinTp3CTz/9JFUMYy04OBhpaWlYu3YtbwtzRFioeEBqpdjYWLi4uGDkyJEICQnB+PHjcfPmTcTHxyM6Ohpt27ZlHVGCkBq5eXl5ISwsDDY2NrCxsZH6ws23L1lyctKbzohEIl42RXv06BGcnZ3BcRzu3r0LW1tb3L17F/Xq1UNMTIzM2TQsffjaln9pKf/I+fAxn17n1NRUbNu2DampqVi1ahX09fVx/PhxmJiYSM2gYO3GjRtwdnbG+/fv0bJlSwBl/Rnq1KmDkydP8i6vsrIypk2bhoULF4rPC6mpqRg5ciQePnyIR48eMU4oqfz9+/EXbr69Z8sJ7dzLd46OjlV6nkgk4l3TuaVLl2L58uXo06ePzMLX1KlTGSWT7dWrV/j++++RkJCAN2/eoEGDBsjMzETHjh1x7NgxqKmpsY4olpGRgUmTJuHhw4eYOnUqPD09AZQ1zywpKcHq1asZJwQGDRok8fjs2bPQ1dVF8+bNpd4LBw4cqM5opAag4gGptVJTUxEYGIikpCTk5eWhTZs28PPzg7W1NetoUuTk5JCVlSU1fS8pKQmOjo7ibY34oLIvXHz8kvXgwYNKj5uamlZTkqopLi7Gn3/+KfG+HTFiBFRUVFhHkxIdHV3l59rb23/FJFUTHR0NFxcX2NnZISYmBikpKbCwsEBgYCASEhKwb98+1hGlFBQUYOfOnbh16xYAwMrKitfvB1n/n0tLS7F48WLe3TH/1PuXD+/ZDwnh3PvxRY0sCgoKMDAwQI8ePdC3b99qSFXzmJubV3hMJBIhLS2tGtNUXVxcnMRnW/fu3VlH+ixv377lxbnXw8Ojys/l24xFwn9UPCCEx1q3bg2RSISkpCQ0b95cYjp1SUkJ0tPT0atXL4SHhzNMSUjN0LFjR/zwww/w9vaGhoYGkpKSYGFhgUuXLmHQoEG8uzMuVM+fP8ft27cBAE2aNOHdmmby9VTloqa0tBTPnj1DdHQ0fHx8EBAQUA3JKvfq1SuUlJRI7bqSk5MDBQUF3i1hqQlyc3Ohra3NOoaUqVOnypxdkJ+fj++++46Xu28Q8iUJY2EnIV/A69evxR/wn2pqw5cvAgMGDAAAXLt2Dc7OzlBXVxcfK2/kNnjwYEbpPq38Yuubb75hnOTTbt68iYyMDBQWFkqM9+vXj1EiaUuXLkX9+vUxevRoifGtW7fi+fPn8PPzY5SsYi9fvsSWLVuQkpICoKwJnYeHBy+3Pqxoy0B9fX3eNM77+++/4eLiAkVFRYmtXGXh03sXKJsl8dNPP2H79u3iKf/y8vJwdXXFmjVroKqqyjihbAUFBTLPDXzqhyIUn3OX88iRI5g0aRIvigfDhg1D3759MWnSJInx8PBw/P333zh27BijZJ/28VIxPlq2bBnMzMwwdOhQAMCQIUOwf/9+GBgY4NixY+JlWXxw9OhR6OjoYMGCBeKx/Px8iW2/+cTJyQkHDhyQKsS8fv0aAwYM4MWMJCIwDHZ4IIQJOTk5Lisri+O4si0Q5eTkpH7Kx/kmJCSEe/v2LesYVVJSUsItWLBAvMWdnJwcp6WlxQUEBHAlJSWs40lJTU3lbGxsxP/vy7fCLM/OJ6amplxcXJzU+IULFzgzMzMGiSoXHR3NaWpqcsbGxtzAgQO5gQMHciYmJpympiYXHR3NOp4UIyMj8ev74XZhBw4c4CwsLFhGExOJRBLnsYp++Pbe5TiOGzduHGdhYcEdO3aMe/XqFffq1Svu6NGjXMOGDbkJEyawjifl2bNnXJ8+fWR+VvDx9eU4jrt8+TLn6+vLDR06VPxvrvxHaF6+fMmb3Do6OtzNmzelxlNSUjhdXV0GiT4tNDSUa9GiBaesrMwpKytz1tbWvN3K08zMTHzuPXXqFKetrc2dPHmS8/T05Hr06ME4naR79+5xhoaG3IoVKziO47jXr19zHTt25Lp06cLl5eWxDSfDh58ZH8rKyuIUFBQYJCJCRzMPSK1R3jAGgOCmlbm5ubGOUGWzZs3Cli1bEBgYCDs7OwBlDSrnz5+Pd+/eYfHixYwTSvLy8oK5uTkiIiJgbm6OS5cuITs7G9OnT8dvv/3GOp6EzMxMGBoaSo3r6enh6dOnDBJVbvLkyRg6dCjWr18PeXl5AGXLbSZNmoTJkyfj+vXrjBNKGjZsGPz8/LB3716IRCKUlpYiLi4OPj4+vNnRpHz71o9/LQT79+/Hvn374ODgIB7r3bs3VFRUMGTIEKxfv55dOBl+/vln5Obm4uLFi3BwcMDBgweRlZWFRYsWITg4mHU8KeU77zg7O+PUqVPo2bMn7ty5g6ysLAwcOJB1vM+mra3Nm2Zu79+/R3FxsdR4UVER3r59yyBR5ZYvX445c+bgp59+kvgcnjBhAl68eIFp06YxTigpMzMTxsbGAMpmnAwZMgQ9e/aEmZkZvv32W8bpJDVs2BAnTpyAo6Mj5OTksHv3bigrK+Po0aO8auyYnJws/vXNmzeRmZkpflxSUoITJ07AyMiIRTQidKyrF4QQ2XR0dLjnz59zHMdx2tranI6OToU/fGJoaMj99ddfUuOHDh3iGjRowCBR5erWrcslJSVxHMdxmpqa3K1btziO47iIiAiuVatWLKNJadSoEbd9+3ap8bCwMM7c3JxBosrVqVNH/Hp+6NatW1ydOnUYJKrc+/fvuTFjxnAKCgqcSCTiFBUVOTk5OW7kyJFccXEx63gSCgsLOScnJ+7OnTuso1SZioqKzLu3N27c4FRVVRkkqpyBgQF38eJFjuM4TkNDg7t9+zbHcRz3119/cXZ2diyjyWRtbc2tXbuW47j/zZwpLS3lxo4dy82dO5dxOmFzcHDgfvrpJ6nxSZMmcZ07d2aQqHJmZmZcaGio1HhISAgvZ6kZGhqKZx40btyYCw8P5ziu7LNCQ0ODZbQKxcfHc2pqapyTkxNXUFDAOo6UD2dQypqdpqqqym3ZsoV1TCJANPOA1BofVmE/hQ9rWVesWAENDQ3xr/m8XvFDOTk5aNq0qdR406ZNebUrRLmSkhLx61yvXj08efIETZo0gampqbipG1+MHTsWP//8M4qKiuDk5AQAiIiIwIwZMzB9+nTG6aS1adMGKSkpaNKkicR4SkoKr9awllNSUsKmTZswZ84c3LhxA3l5eWjdujUsLS1ZR5OiqKj4Wec0PujYsSPmzZuHsLAw1KlTB0BZd/IFCxagY8eOjNNJy8/PF29/qqOjg+fPn6Nx48awtrZGYmIi43TSUlNT0adPHwBl7+X8/HyIRCJMmzYNTk5OEmu0yedZtGgRunfvjqSkJHTr1g1A2bn38uXLOHXqFON00p4+fYpOnTpJjXfq1ImXs9QGDRqE4cOHw9LSEtnZ2XBxcQEAXL16FY0aNWKc7n/Nqz+mrKyMJ0+eiGd3AODNuSE9PR0cx4mb/n7YmFZJSQn6+vriGYGEfA4qHpBao1WrVhCJROLmQRXhy/7dHy5V+PHHH1FcXMyrKXEVadmyJdauXSvVjXjt2rW8vGBs0aIFkpKSYG5ujm+//RZBQUFQUlLCH3/8AQsLC9bxJPj6+iI7OxuTJk0SN2+rU6cO/Pz8MHPmTMbppE2dOhVeXl64d+8eOnToAAC4cOEC1q1bh8DAQImLXz4U7MqZmJjAxMSEdYxPGjlypHiJkBCsWrUKzs7O+Oabb8TngqSkJNSpUwcnT55knE5akyZNcPv2bZiZmaFly5bYuHEjzMzMsGHDBpnLh1jT0dHBmzdvAABGRka4ceMGrK2tkZubi4KCAsbpJOXn5wvi86ycnZ0dzp8/j6CgIISHh0NFRQU2NjbYsmULL4uLjRo1Qnh4OH755ReJ8T///JOXeVesWAEzMzM8fPgQQUFB4ubQT58+lWpSyUJ582ohKd9mWmjL2wj/0VaNpNZ48OBBlZ9bftJl7fnz53B1dcWZM2dQWlqKdu3aYceOHbyoxFckOjoaffr0gYmJifhu4vnz5/Hw4UMcO3YMXbp0YZxQ0smTJ5Gfn49Bgwbh3r17+O6773Dnzh3UrVsXf/75p/gOP5/k5eUhJSUFKioqsLS0hLKyMutIMsnJyVV6vLyYx7Jg5+3tXeXnLl++/Csm+XxTpkxBWFgYLC0t0bZtW6mLMb7lBcp2Lti5cydu3boFALCyssKIESN4sTf6x3bs2IHi4mK4u7vjypUr6NWrF3JycqCkpISQkBBxZ3i+GD58OGxtbeHt7Y2FCxdizZo16N+/P06fPo02bdrwpn8AAKirq2PIkCEYPXo0OnfuzDpOjbN//34MHToU3bt3F98Vj4uLQ0REBMLDwwXZA4NUnZB35SH8R8UDQnhs9OjROH78OKZOnYo6depg48aNMDQ05H3DxydPnmDdunUSFwiTJk1CgwYNGCermpycHOjo6AhmqQhfCaFg5+joWKXniUQi3m1pVVl2PuU9e/YsunbtCgUFYU92LCgowK1bt2BiYoJ69eqxjiMlJycH7969Q4MGDVBaWoqgoCDEx8fD0tISs2fPho6ODuuIYocOHUJISAiOHTsGMzMzjB49Gq6urrz+jCgpKcGhQ4fE2842b94c/fr14+3U7ytXrmDFihXivFZWVpg+fTpat27NOFkZoV/gFhYW4tmzZ1J39vkwa01OTg6ZmZnQ19evtIjPl5m2RFioeEBqrdTUVKxcuVJi/3kvLy80bNiQcbL/MTY2xubNm+Hs7AwAuHv3LqysrJCfn8/bu80ZGRkwNjaWeeGdkZHBiw9WocrPz0dgYCAiIiJkfmlJS0tjlIyQisnLy+Pp06fi/gEdOnTA/v37qdM3wfPnz7F9+3aEhIQgJSUFzs7OGD16NPr168erYtO9e/fQp08fPHr0SNzD5fbt2zA2NsbRo0d59b1BKIR6gXvnzh14enoiPj5eYpz1LDpCqgsVD0itdPLkSfTr1w+tWrWSmNKXlJSEw4cPo0ePHowTlpGXl8fjx49hYGAgHlNTU8M///wDMzMzdsEq8fGFQrns7Gzo6+vz7oP13bt3WLNmDSIjI2VekPOl+RFQ1vsiOjoao0aNgqGhoVSBxsvLi1Gyim3fvh0bNmxAeno6zp8/D1NTU6xcuRLm5ubo378/63iVev36Nc6ePYumTZvKbALKJw8fPgQA8XZnfPLhRQIAaGhoICkpiXc9RT7GcRz27dtX4bmBT8sAPvTs2TOZefnUV0SWNWvWwNfXF4WFhahXrx4mTJgAf39/qKqqso6G3r17g+M47Ny5U7zlc3Z2NkaOHAk5OTkcPXqUcUJJiYmJUFRUhLW1NQDgr7/+wrZt29CsWTPMnz8fSkpKjBMKl52dHRQUFODv7y/zc5iPvZ0I+ZL4U9YlpBr5+/tj2rRpUo3G/P394efnx5viAQCpKZHy8vKfbPrIUnn1/WN5eXniDut84unpiVOnTuH7779H+/bteb1U4fjx4zh69KhEZ2c+W79+PebOnYuff/4ZixcvFheOtLW1sXLlSt4VD4YMGYKuXbvip59+wtu3b2Fra4v79++D4zjs2bMHgwcPZh1RQnFxMRYsWIDVq1cjLy8PQNla8ilTpmDevHlQVFRknFDYfv75Z2zcuBGOjo6oX78+r88NQNk0dTc3N6SkpEh9RvD1jmhWVhZCQ0MREhKCBw8e4Pvvv4enpycePXqEZcuW4cKFC7zYzSA6OhoXLlwQFw4AoG7duggMDOTl+Xj8+PHw9/eHtbU10tLSMHToUAwaNAh79+5FQUEBVq5cyTqiYF27dg1XrlzhfUH5Q5cvX66wCMrH3jiE36h4QGqllJQUhIeHS42PHj2aVx+qHMehcePGEl9ay7eP+3CaHx+2QCxvPCcSiTBnzhyJu0UlJSW4ePEiWrVqxShdxY4cOYJjx47x8gvgx3R0dCS+vPLdmjVrsGnTJgwYMECiUGdrawsfHx+GyWSLiYnBrFmzAAAHDx4Ex3HIzc1FaGgoFi1axLviwZQpU3DgwAEEBQVJNCedP38+srOzsX79esYJy4hEIolz2MeP+Wr79u04cOAAevfuzTpKlYwePRqNGzfGli1beF/sOHDgALZt24aTJ0+iWbNmmDRpEkaOHAltbW3xczp16gQrKyt2IT+grKws3sniQ3l5eby8i3/nzh3x5+3evXthb2+PXbt2IS4uDsOGDePV9xwACAgIqPT43LlzqynJpzVr1gwvXrxgHaPKlixZgtmzZ6NJkyZS5wU+nyMIf1HxgNRKenp6uHbtmtSWRdeuXZOabs/Stm3bWEeosqtXrwIoK3hcv35d4guVkpISWrZsycsLRiMjI2hoaLCOUSULFy7E3LlzERoayoupvJ+Snp4uszmXsrIy8vPzGSSq3KtXr8TFmRMnTmDw4MFQVVVFnz594OvryzidtF27dmHPnj3iPdGBsqnpxsbG+PHHH3lTPOA4Dt26dROvYS8oKEDfvn2lLrr4tEQIALS0tHi/tOJDaWlp2L9/P6934ynn4eGBYcOGIS4uDu3atZP5nAYNGoiLeax99913GDduHLZs2YL27dsDAC5evIgJEybwspkfx3HiO8xnzpzBd999B6BsWRMfL3wPHjwo8bioqAjp6elQUFBAw4YNeVU8WLZsGWbMmIElS5bA2tpaaoaXpqYmo2SyrVq1Clu3boW7uzvrKKSGoOIBqZXGjh2LcePGIS0tDZ06dQJQ1vNg2bJln7V129fm5ubGOkKVle8A4eHhgVWrVvHuA7QiwcHB8PPzw4YNG3izRWdFgoODkZqaivr168PMzEzqSwvfLr7Mzc1x7do1qdf1xIkTvLmj+CFjY2OcP38eurq6OHHiBPbs2QMAePnyJS+X3CgrK8vsfWJubs6ru6Hz5s2TeMy35SoVmT9/PhYsWICtW7fycivJj3Xr1g1JSUmCKB48ffr0kwVQFRUVqfcOK6tXr4abmxs6duwoPu8WFxejX79+WLVqFeN00mxtbbFo0SJ0794d0dHR4kJieno66tevzzidtPKbDx96/fo13N3debetZPfu3QGU/Xv7EF8bJsrJyQliZiURDmqYSGoljuOwcuVKBAcH48mTJwDK7nL4+vpi6tSpNJWrFnn+/DmGDBmCmJgYqKqqSl2Q82FJSLkFCxZUepwvX7TLbd68GfPnz0dwcDA8PT2xefNmpKamYunSpdi8eTOGDRvGOqKE33//HV5eXlBXV4epqSkSExMhJyeHNWvW4MCBA7zbIjUgIAC3bt3Ctm3bxLuvvH//Hp6enrC0tOTd+0Fo3r59i4EDByIuLk4QxboXL17Azc0N7du3R4sWLaTy8u0OeWpqKrZt24bU1FSsWrUK+vr6OH78OExMTNC8eXPW8WS6e/euxBbEfC3UJCcnY8SIEcjIyIC3t7f4XDBlyhRkZ2dj165djBNWzfXr19G3b1/cv3+fdRSx6OjoSo/b29tXU5KqCQoKwpMnT3i3VIUIFxUPSK1Xvo5RKFPX+W7QoEEyx0UiEerUqYNGjRph+PDh4u2uWOvevTsyMjLg6ekpc52wkGZ/8NHOnTsxf/58pKamAigr0i1YsACenp6Mk8mWkJCAhw8fokePHlBXVwcAHD16FNra2ry7ezNw4EBERERAWVlZ3OE7KSkJhYWFUnfF+LozAJ8NGTIEkZGR+P7772WeG/hWnDl8+DBGjRqF169fSx3j2x3R6OhouLi4wM7ODjExMUhJSYGFhQUCAwORkJCAffv2sY5YI7179w7y8vKCaaYaGxuLvn374uXLl6yjCFZpaSn69OmDO3fuoFmzZlL/7+mzgXwuKh4QQr4od3d3HDp0CNra2mjbti2Asjt0ubm56NmzJ5KSknD//n1ERETw4mJMVVUV58+fF8z2Srm5udi3bx9SU1Ph6+sLXV1dJCYmon79+jAyMmIdr0IFBQXIy8vjVU8RofPw8Kjyc4XUP4Uv1NTUcPLkSXTu3Jl1lCoxMzPDd999hzlz5vByavqHOnbsiB9++AHe3t4SW3deunQJgwYNwqNHj1hH/KwljHzrWP/w4UOIRCJ88803AIBLly5h165daNasGcaNG8c4nbTVq1dLPOY4Dk+fPsX27dvFzR75pqCgABkZGSgsLJQY59uWqD/99BM2b95c4a4x9NlAPhf1PCC1SuvWrT+5JEFBQQEGBgbo0aMHxo8fz6u1w0JgYGCA4cOHY+3ateIdIUpLS+Hl5QUNDQ3s2bMHEyZMgJ+fH2JjYxmnBZo2bYq3b9+yjlElycnJ6N69O7S0tHD//n2MHTsWurq6OHDgADIyMhAWFsY6opQXL17g/v37EIlEMtfn80lJSQlCQkIQEREhc0urs2fPMkomG33p+7qMjY0F07sFALKzszFt2jTeFw6Asunosi4I9fX1edPQT9Y6fFn4uMxx+PDhGDduHEaNGoXMzEz06NEDzZs3x86dO5GZmcmrBoQAsGLFConHcnJy0NPTg5ubG2bOnMkolWzPnz+Hh4cHjh8/LvM4n2b4AEBoaCj279+PPn36sI5CaggqHpBaZcCAAZ98TmlpKZ49e4ZFixYhJSUFv//++9cPVoNs2bIFcXFxEltJysnJYcqUKejUqROWLFmCn376CV26dGGY8n8CAwMxffp0LF68mPedk729veHu7o6goCCJZTa9e/fG8OHDGSaT9s8//2DixImIi4uTGLe3t8f69et5s2zlQ15eXggJCUGfPn3QokULXl4UkOoTHByMGTNmYMOGDbwvfAFlS8YiIyPRsGFD1lE+SVtbG0+fPoW5ubnE+NWrV3kzg4pvPU4+x40bN8S7QoSHh6NFixaIi4vDqVOnMGHCBN4VD9LT01lHqLKff/4Zubm5uHjxIhwcHHDw4EFkZWVh0aJFCA4OZh1Piq6uriDOCUQ4qHhAapXPWaP6448/YsiQIcyKB0KdMllcXIxbt26hcePGEuO3bt0SV+Tr1KnDmwuzXr16ARBG5+TLly9j48aNUuNGRkbIzMxkkEi2zMxM2NvbQ09PD8uXL0fTpk3BcRxu3ryJTZs2oUuXLrhx4wbvljDs2bMH4eHh6N27N+soVbZv3z6Eh4fLnD7Lt4Z+QjNy5EgUFBSgYcOGvG+mCgCNGzfGzJkzERsbK7MQOnXqVEbJpA0bNgx+fn7Yu3cvRCIRSktLERcXBx8fH7i6urKOJ3hFRUXiJqpnzpwRN8ts2rQpnj59yjKalKKiIqioqODatWto0aIF6zifdPbsWfz111+wtbWFnJwcTE1N0aNHD2hqamLp0qW8u8M/f/58zJs3D9u2bRPEFs+E/6h4QEgF2rRpw/Ru7sdTJhMTE1FcXCy+Y3vnzh3Iy8uL+wrwxahRo+Dp6YlffvlFvH/35cuXsWTJEvGXwujoaN500xbS3SVlZWWZzdDu3LkDPT09BolkW7FiBUxNTREXFyexxWGvXr0wceJEdO7cGStWrMDSpUsZppSmpKTE2+7psqxevRqzZs2Cu7s7/vrrL3h4eCA1NRWXL1/G5MmTWceTKSIiosJlIVu3bmWUSrYVK1bwpshZFZs3b4a6ujqio6OlOsKLRCJeFQ+WLFmCyZMnw9jYGCUlJWjWrBlKSkowfPhwzJ49m3U8ABU3/5WFb03nmjdvjg0bNqBPnz44ffo0Fi5cCAB48uQJ6tatyzidJEVFRZiYmPCqUF+Z/Px8ceFbR0cHz58/R+PGjWFtbc3Lgu3q1asFtcUz4T9qmEiIACxfvhxRUVEIDQ2Fjo4OgLK95z08PNClSxdMnz6dccL/KSkpQWBgINauXYusrCwAQP369TFlyhT4+flBXl4eGRkZkJOTEzdzYqmoqKjCztMvXrxAvXr1qjlRxcaMGYPs7GyEh4dDV1cXycnJkJeXx4ABA9C1a1febMXUpk0b+Pv7Y8iQITKP79mzB0FBQbz70hIcHIy0tDSsXbtWEBeNTZs2xbx58/Djjz9KNJ2bO3cucnJysHbtWtYRJSxYsAABAQGwtbWFoaGh1Gt88OBBRskIKxkZGbhx4wby8vLQunVrWFpaso4kJuSGpFFRURg4cCBev34NNzc3cWHul19+wa1bt3hX7NiyZQsOHDiA7du3Q1dXl3WcSrVr1w6LFi2Cs7Mz+vXrB21tbSxduhSrV68WNzPmE6Ft8Uz4j4oHhAiAkZERTp06JXW3/saNG+jZsyeePHnCKFnlyu+S86lvwMcGDx6Mffv2SV3IZGVloVu3brhx4wajZNJevXqF77//HgkJCXjz5g0aNGiAzMxMdOzYEceOHYOamhrriADK1jMnJCRUeBf/3r17sLW1RW5ubvUG+4SBAwciMjISurq6aN68Oe+3tFJVVUVKSgpMTU2hr6+P06dPo2XLlrh79y46dOiA7Oxs1hElGBoaIigoCKNGjWIdpUrk5eXx9OlTqeU12dnZ0NfXF8ydUlI7lZSU4PXr1+IbDgBw//59qKqq8m7JWOvWrXHv3j0UFRXB1NRU6rOMT4XmHTt2oLi4GO7u7rhy5Qp69eqFnJwcKCkpISQkBEOHDmUdkZCvipYtECIAr1+/xvPnz6XGnz9/jjdv3jBIVDV8LhqUy8jIwJgxY7Blyxbx2NOnT+Hk5MSbpRXltLS0cPr0acTFxSEpKQl5eXlo06YNunfvzjqahDdv3lT6/15DQwN5eXnVmKhqtLW1MXDgQNYxqszAwAA5OTkwNTWFiYkJLly4gJYtWyI9PR18vC9QWFiITp06sY5RZRW9hu/fv+fNLjxC7Y0jtJ1NhEheXl6icACAt40/q9LMmi9Gjhwp/nXbtm3x4MED3Lp1CyYmJryaqSjLu3fv8OeffyI/Px89evTg1UwfIhxUPCBEAAYOHAgPDw8EBweLOyhfvHgRvr6+n7UuszpkZWXBx8dH/KXw4y/gfLtbd+zYMXTt2hXe3t5Yvnw5njx5AkdHR7Rs2RJ79uxhHU/sw6ZSdnZ2sLOzYx2pUm/evJHod/Ch169f8/Lilm9Tjz/FyckJf//9N1q3bg0PDw9MmzYN+/btQ0JCAu/OC0DZsptdu3Zhzpw5rKNUqnzPeZFIJO4jUK6kpAQxMTFo2rQpq3gShNobR2g7m5ibm1eaMS0trRrTfJrQPoeFPHVeVVUVbdq0YR1Dire3N4qKirBmzRoAZcXbDh064ObNm1BVVcWMGTNw6tQpQRV0CT9Q8YDUWvn5+YiOjpbZpZxPjaUAYMOGDfDx8cHw4cNRVFQEAFBQUICnpyd+/fVXxukkubu7IyMjA3PmzJG5rplv9PT0cOrUKXTu3BkAcOTIEbRp0wY7d+6U2G6SNSE1leI4Tmq3jY+P8/19IQR//PGH+I7t5MmTUbduXcTHx6Nfv34YP34843TS3r17hz/++ANnzpyBjY2N1LIQvtwZL99znuM4bNiwAfLy8uJjSkpKMDMzw4YNG1jFk/Bhw9fly5dDQ0Ojwt44fCK0nU1+/vlnicdFRUW4evUqTpw4AV9fXzahKiGUz+GXL19ix44dcHNzk5qt9urVK4SFhck8xkJVZ/nw5Tx26tQpLFmyRPx4586dyMjIwN27d2FiYoLRo0dj8eLFOHr0KMOURIio5wGpla5evYrevXujoKAA+fn50NXVxYsXL8RrAfl2F6Fcfn6+uBlPw4YNebPG/UMaGho4d+4cWrVqxTrKZ7lz5w66dOmCHj16YPv27bz8siWUplIfd3qviL29/VdO8mlt2rRBREQEdHR00Lp160r/v/Np3a0QOTo6VnhMJBLxbqq6o6MjDh48CG1tbdZRqkRIvXEaNGiAqKioSouMQrBu3TokJCTwbtaSUD6HFy5ciOTkZOzdu1fm8SFDhqBly5aYNWtWNSeT9vH5KzY2Fm3btoWKiop4jE/nMU1NTSQmJop7D5U31v3jjz8AANeuXUPv3r15dV4gwkAzD0itNG3aNPTt2xcbNmyAlpYWLly4AEVFRYwcORJeXl6s41VITU0NNjY2rGNUytjYmJdT0j+ko6Mj8yKxoKAAhw8fltjKik97ua9duxb37t1DgwYNeN1Uig9Fgarq37+/eD/0/v3787Jo9LG7d+9i7ty52Lhxo8y7dRMnTsSiRYtgYWHBKKFsQtoWtaioCBkZGXj69KlgigdC6o0zffp0rFq1SjA7m1TExcUFM2fO5F3xQAifwwCwf/9+BAcHV3h8/Pjx8PHx4UXx4OPzl4aGBnbt2sW782w5OTk5iffAhQsXJJaMaWtr4+XLlyyiEYGj4gGpla5du4aNGzdCTk4O8vLyeP/+PSwsLBAUFAQ3NzderhdOSEhAeHi4zGUWfOoCv3LlSvj7+2Pjxo28bc7Ely0NP5eQmkoJxYdrbefPn88uyGf49ddfYWxsLHMqr5aWFoyNjfHrr79i/fr1DNLVDIqKinj37h3rGJ9FSL1xYmNjERkZiePHjwtiZ5OK7Nu3j5ezwITwOQwAqamplTbts7S05N3Wh0JhZWWFw4cPw9vbG//88w8yMjIkZk88ePAA9evXZ5iQCBUVD0itpKioKF7Prq+vj4yMDFhZWUFLSwsPHz5knE7anj174OrqCmdnZ5w6dQo9e/bEnTt3kJWVxbvu8EOHDkVBQQEaNmwIVVVVqS+FfLiT7+bmxjrCvyLkplJ8ZmpqCicnJzg5OcHBwQHGxsasI1UqOjoaO3bsqPD4kCFDMHz48GpMVHVCKYICZX0kli1bhs2bN0NBgf9fl4TUG0doO5t8vKSJ4zhkZmbi+fPn+P333xkmk00In8NA2Y4QT548gYmJiczjT5484VXvISGZMWMGhg0bhqNHj+Kff/5B7969YW5uLj5+7NgxcZGRkM/B/09DQr6C1q1b4/Lly7C0tIS9vT3mzp2LFy9eYPv27WjRogXreFKWLFmCFStWYPLkydDQ0MCqVatgbm6O8ePHw9DQkHU8CUK7q3/s2DHIy8vD2dlZYvzUqVMoKSmBi4sLo2TS5s6dC0dHR3Ts2LHCnQzI5/Pw8EBUVBTGjRuHwsJCmJubw9HREU5OTnB0dISBgQHriBIyMjIq3ae9Xr16VAT9Ai5fvoyIiAicOnUK1tbWUsuE+FbsUFVVxe+//45ff/2V971x+DbN/1M+nvUlJycHPT09ODg48GbnjQ8J5XO4devWOHToEDp06CDz+MGDB9G6detqTlUzDBw4EMeOHcORI0fQs2dPTJkyReK4qqoqJk2axCgdETJqmEhqpYSEBLx58waOjo549uwZXF1dER8fD0tLS2zduhUtW7ZkHVGCmpoa/vnnH5iZmaFu3bqIioqCtbU1UlJS4OTkhKdPn7KOKFg2NjYIDAyU6vp94sQJ+Pn5ISkpiVEyaT169MD58+dRXFyMdu3awd7eHg4ODrCzs5No2kT+nffv3yMuLg7R0dGIiorCxYsXUVRUhMaNG8PJyQnr1q1jHREAYGBggF27dsHJyUnm8YiICIwYMQKZmZnVnKxyNjY2GD9+vLgImpSUJFEEXbBgAeuIEjw8PCo9LrQLYD7ZunUrHB0dJe6Ektpn//79GDZsGFasWIGJEyeKdzYpKSnB77//junTp2PXrl34/vvvGScFkpOTJR536tQJ4eHh+OabbyTG+d6XipD/iooHhAjAN998g+PHj8Pa2ho2NjaYOXMmfvzxR5w/fx69evXCq1evmOZ7/fq1eP3169evK30uH7Zc+pCKigpSUlKk1oXev38fzZs3R35+PptgFSguLsbFixcRExOD6OhoxMfH4/3792jXrh1iY2NZx5Owbds2DB06FKqqqqyj/CsvX75EcHAw1qxZg7y8PN5skzlkyBAUFRXh4MGDMo/3798fSkpKFXYwZ4WKoF/eoEGDEBISAk1NzU/2NeDTTAlLS0ukpaXByMgI9vb24kJoeWd4PuM4DpGRkXj79i06deok3haTr969eye1RIhPn8OzZs3C0qVLoaGhIW4+mJaWhry8PPj6+iIwMJBxwjJycnIQiUQyG1GWj4tEIt58ThDytdCyBUIEoGvXrjh9+jSsra3xww8/wMvLC2fPnsXp06fRrVs31vGgo6ODp0+fQl9fH9ra2jK7Z/P1g1VLSwtpaWlSxYN79+7xcrqvgoIC7OzsoKenB11dXWhoaODQoUO4desW62hS/P394eXlhR9++AGenp7o1KkT60iVKiwsxPnz5xEVFSWeeWBkZITvv/+eVztIzJw5Ex07dsT333+PGTNmoEmTJgCAW7duISgoCCdPnkR8fDzjlNJ0dHTEXf+NjIxw48YNWFtbIzc3FwUFBYzTVez58+e4ffs2AKBJkybQ09NjnOh/tLS0xOdbLS0txmmq7u7du3j8+DGioqIQExOD3377TTwDxcHBodKeHtUpNzcXXl5eSExMRIcOHRAcHIzevXuL/33p6+vj1KlTvLvbnJ+fDz8/P4SHhyM7O1vqOJ8+hxcvXoz+/ftj586duHfvHjiOg729PYYPH86rNfnp6emsIxDCCzTzgNRKWVlZ8PHxQUREBJ49eyZVSebTBytQ1tzo3bt3aNCgAUpLSxEUFCReZjF79mzmdz6io6NhZ2cHBQUFREdHV/pcPl2EAWVbQZ0/fx4HDx5Ew4YNAZQVDgYPHox27dph8+bNjBP+zx9//IGoqChER0fj/fv36NKlCxwcHODg4AAbGxvebXlWXFyMw4cPIyQkBMePH4eFhQU8PDzg5ubGqz4CAQEB4mKBqakpunbtKr4b2qBBA9bxZDpy5AhGjx4tdWFQt25dbN68Gf369WOUrGLDhw+Hra0tvL29sXDhQqxZswb9+/fH6dOn0aZNG17dGQfKLsCmTJmCsLAwlJaWAihr8Obq6oo1a9YIakbN06dPedcfp1xBQQHOnTuH3bt3Y+fOneA4DsXFxaxjAQDGjBmDmJgYuLm54fDhw+Lt71auXAk5OTnMmDED6urqOHz4MOuoEiZPnozIyEgsXLgQo0aNwrp16/D48WNs3LgRgYGBGDFiBOuIhBCBouIBqZVcXFyQkZGBn376CYaGhlIXXf3792eUTFpxcTF27doFZ2dn2lbnK3j16hV69eqFhIQE8drFR48eoUuXLjhw4ACv9ngvb9I1ffp0TJo0Cerq6qwjVVlWVhZ27NiB0NBQ3Lp1C7169YKnpyf69u3LvJu2nJwcTExM4O/vjx9++AF169Zlmqeq3r59ixMnTojv1jVu3Bg9e/bk7UUt34ugHxs/fjzOnDmDtWvXws7ODkDZFoNTp05Fjx49eLMVpre3N5YvX17h8adPn8LBwUE8e4IPTp06JZ7hc/XqVVhZWYmXLnTt2pU37wUjIyPs2rUL9vb2ePz4MYyNjXH27Fk4ODgAAC5duoR+/frxrr+IiYkJwsLC4ODgAE1NTSQmJqJRo0bYvn07du/ejWPHjrGOSAgRKCoekFpJQ0MD586dQ6tWrVhHqRJVVVWkpKTA1NSUdZQqyc3NxaVLl/Ds2TPxHbtyrq6ujFJVjOM4nD59GklJSVBRUYGNjQ26du3KOpaUQ4cOISYmBlFRUUhJSUHr1q3FMw86d+7M24vGchcvXsTWrVsRGhoKQ0NDvHz5Ejo6Oti2bZv4yzgLJ0+eRGRkpPhCpnHjxnBwcBDPPuDTNHVSferVq4d9+/ZJvTcjIyMxZMgQPH/+nE2wj+jo6MDHxwezZs2SOlZeONDT0+NVT5QPC6Hjxo3jVZH2QwoKCnj48KF41oaqqiquX78unqWWmZkJIyMj3s1WVFdXx82bN2FiYoJvvvkGBw4cQPv27ZGeng5ra2vk5eWxjkgIESjqeUBqJWNjY5lNb/iqffv2uHbtmiCKB4cPH8aIESOQl5cHTU1NiVkdIpGIl8UDkUiEnj17omfPnqyjVGrAgAHiLcNevXqFc+fOYe/evfjuu+8gJyeHd+/esQ0oQ1ZWFrZv345t27YhLS0NAwYMwJEjR9C9e3fk5+cjICAAbm5uePDgAbOMzs7O4q0637x5g3PnziE6OhpBQUEYMWIEGjVqBEdHR6xdu5ZZxnKrV6/GuHHjUKdOHaxevbrS506dOrWaUlVNRc1URSIRlJWVoaSkVM2JKldQUCBztpe+vj6vejT8/fff6NWrF3R1dTFx4kTxeGZmJhwdHaGrq4sTJ04wTCht+fLliImJQVBQEFatWiWedeDg4IDGjRuzjidWWloq3gEAKFu28vFnGh9ZWFggPT0dJiYmaNq0KcLDw9G+fXscPnyYt4Ua8uW0bt26yu/NxMTEr5yG1DQ084DUSqdOnUJwcDA2btwo1SiPj8LDwzFz5kxMmzYNbdu2lWrkx6dmTY0bN0bv3r2xZMkS3t8JL5efn4/o6GhkZGRIdaXm2wVYdna2eCvBqKgo/PPPP9DR0UGXLl0q7L7PSt++fXHy5Ek0btwYY8aMgaurK3R1dSWe8+zZMxgYGEjNUGGtpKQEly5dwt9//43ff/+dN7stmJubIyEhAXXr1q10mzuRSIS0tLRqTPZp5d3KK/LNN9/A3d0d8+bNY76UBQC6deuGunXrIiwsDHXq1AFQtlTEzc0NOTk5OHPmDOOE/3P06FEMHjwYISEhGDZsmLhwoKmpiTNnzkBDQ4N1xApdv34d0dHROHv2LI4cOQJ9fX08evSIdSwAZe/ZRYsWiZeI+fn5wdfXF/Xq1QNQVmycO3cuL84NH1qxYgXk5eUxdepUnDlzBn379gXHcSgqKsLy5cvh5eXFOiL5ij5n29t58+Z9xSSkJqLiAamVdHR0UFBQgOLiYqiqqkJRUVHieE5ODqNkssn6Is3XrYHU1NRw/fp18ZZLfHf16lX07t0bBQUFyM/Ph66uLl68eAFVVVXo6+vz6gKsfFs7HR0ddO3aVTy1nk/Fow95enpizJgx6NixY4XP4TgOGRkZzGfVlJaWIiEhQbx8IS4uDvn5+fjmm2/g6OgIR0dHuLm5Mc0odGFhYZg1axbc3d3FXdQvXbqE0NBQzJ49G8+fP8dvv/0GX19f/PLLL4zTll3UOjs7o7CwEC1btgQAJCUloU6dOjh58iSaN2/OOKGkXbt2wdPTE+vXr0dQUBDU1NRw5swZ3u7CwHEcrl69iqioKERGRiI2NhZv3ryBtbU1rl69yjoeAMDMzKxKd3D53on/wYMHuHLlCho1asTbzwtCiDBQ8YDUSqGhoZUe59tFwqemdLO+8PrQoEGDMGzYMAwZMoR1lCopnya7YcMGaGlpISkpCYqKihg5ciS8vLw+uXd6dVq3bh3s7e3RokUL1lFqFBcXF8THx+PNmzdo0KABHB0d4eDgAEdHR8EUwYSgW7duGD9+vNS5ITw8HBs3bkRERAS2b9+OxYsX82br0YKCAuzatQspKSkAACsrK4wYMQIqKiqMk8n2+++/Y8qUKWjTpg2vCwd9+/ZFXFwcXr9+jZYtW4oLoV27dqVp9bVQdnY25s6di8jISJm9kvh0Q0dIWQn5Gqh4QAj5z/7++2/xr58/f46AgAB4eHjA2tpaalYH37aQ09bWxsWLF9GkSRNoa2vj/PnzsLKywsWLF+Hm5sabixihioiIEG+J+vGXrK1btzJKJenHH38Uzy6wtLRkHadK8vPzsWzZMhw4cAD379+HSCSCubk5vv/+e/j4+PByyZCKigqSk5OlXuO7d++iZcuWKCgoQHp6Opo3b868p8CFCxdw+PBhFBYWwsnJCS4uLkzzVObj9c03b96EsbGx1FIFPq1t9vX1hb29Pbp06cLbAofQCeHcW6537964d+8ePD09Ub9+fanZHny6oSOErDo6OlXueUDFDvK5qGEiqTVev34NTU1N8a8rU/48vsjOzhZvH/fw4UNs2rQJb9++Rb9+/dClSxfG6SBu4vehgIAAqTG+LbEAAEVFRfGyEH19fWRkZMDKygpaWlp4+PAh43TSHj16hL///ltmf4bKtmtjYcGCBQgICICtra3MLVH5Yvfu3awjfJbCwkLY29vjxo0bcHFxEa9nTklJweLFi3H8+HHExMRIFe5YMzY2xpYtWxAYGCgxvmXLFhgbGwMoO9ex3qZv3759GDp0KFRUVKCoqIjly5dj2bJl8PHxYZqrIh+ff/m01XBFfv31V9YRajShnHvLnTt3DrGxseLlQXwmhKwrV65kHYHUZBwhtYScnByXlZXFcRzHiUQiTk5OTuqnfJwvkpOTOVNTU05OTo5r0qQJd/XqVa5+/fqcuro6p6mpycnLy3MHDx5kHVPQevTowe3cuZPjOI4bM2YM1759e27Hjh2cs7Mz1759e8bpJJ05c4ZTVVXlWrRowSkoKHCtWrXitLW1OS0tLc7R0ZF1PCkGBgZcWFgY6xg1zsqVK7n69etzt27dkjqWkpLC1a9fn1u9ejWDZJX766+/OCUlJc7Gxobz9PTkPD09uZYtW3LKysrc4cOHOY7juN9//52bNm0a05xt2rThxo8fzxUXF3Mcx3FLlizhdHR0mGaqiaKiorjvvvuOa9iwIdewYUOub9++XExMDOtYNYLQzr22trbc+fPnWceoEiFlJeRroGULpNaIjo6GnZ0dFBQUEB0dXelz7e3tqylV5VxcXKCgoAB/f39s374dR44cgbOzMzZt2gQAmDJlCq5cuYILFy4wTlq53Nxc3q5jTUhIwJs3b+Do6Ihnz57B1dUV8fHxsLS0xNatW3l1d6F9+/ZwcXHBggULoKGhgaSkJOjr62PEiBHo1auXxDZtfFC3bl1cunRJvCc6+TLs7e0xZMgQTJ48WebxNWvWYN++fZ88z7GQnp6OjRs34s6dOwCAJk2aYPz48bza9UZdXR3Xrl1Do0aNAJTN9FBTU8Pjx4+hr6/POF3NsGPHDnh4eGDQoEGws7MDAMTFxeHgwYMICQnB8OHDGScUNqGdey9fvgx/f3/MnTsXLVq0kJo1xafZoELIKuSZtoT/qHhACI/Vq1cPZ8+ehY2NDfLy8qCpqYnLly+jbdu2AIBbt26hQ4cOyM3NZRv0A8uWLYOZmRmGDh0KAPjhhx+wf/9+GBoa4tixY7y6GBcaDQ0NXLt2DQ0bNoSOjg5iY2PRvHlzJCUloX///rh//z7riBL8/Pygrq6OOXPmsI5So+jp6SEqKqrCbv83btyAo6Mjnj9/Xs3JagY5OTlkZmZKFArKi3XUQPPLsLKywrhx4zBt2jSJ8eXLl2PTpk3iBpXk3xHauffu3bsYPny4VF8Ojoc7Sgkhq7y8PJ4+fQp9ff0Kt8jlU14iLNTzgNQaycnJVX4uX7YyysnJgYGBAYCyu2FqamoS64F1dHTw5s0bVvFk2rBhA3bu3AkAOH36NM6cOYMTJ04gPDwcvr6+OHXqFOOEwqWmpibuc2BoaIjU1FTxBeSLFy9YRpPp3bt3+OOPP3DmzBnY2NhI3aHhW48GocjNzRX3QJGlbt26ePXqVTUmqrpz585h48aNSEtLw969e2FkZITt27fD3NwcnTt3Zh1PbPPmzVBXVxc/Li4uRkhICOrVqycemzp1KotoNUJaWhr69u0rNd6vXz9ebNNZmXfv3kn1m+HD3Vtvb2/xr0tLSwV17h0xYgQUFRWxa9cumU0I+UQIWc+ePQtdXV3xr/mYkQgXFQ9IrdGqVSuIRCJ8arIN3yqxH5/0+f4hkJmZKW5+duTIEQwZMgQ9e/aEmZkZvv32W8bp/ufjDuWyKCgowMDAAD169MD48eOhpKRUTelk69ChA2JjY2FlZYXevXtj+vTpuH79Og4cOIAOHTowzSZLcnIyWrVqBaDsbviH+Po+Tk1NxbZt25CamopVq1ZBX18fx48fh4mJSYV3+qtbaWkp5OXlKzwuJyfHq3NYuf3792PUqFEYMWIEEhMT8f79ewDAq1evsGTJEhw7doxxwjImJibipWHlDAwMsH37dvFjkUhExYP/wNjYGBEREeKlIeXOnDkj/vzgk4KCAsyYMQPh4eHIzs6WOs6Hf29Xr16VeCykc++NGzdw9epVNGnShHWUTxJCVnt7e6Snp8Pc3BwODg6s45AahooHpNZIT09nHeFfcXd3h7KyMoCyOx4TJkyAmpoaAIi/fPOJjo4OHj58CGNjY5w4cQKLFi0CUDZFjg9fsMrJ2iHiY6WlpXj27BkWLVqElJQU/P77718/WCWWL1+OvLw8AGXdtPPy8vDnn3/C0tKSd3eSACAyMpJ1hM8SHR0NFxcX2NnZISYmBosXL4a+vj6SkpKwZcsW7Nu3j3VEAGX/lrp16wYFBdkf4cXFxdWcqGoWLVqEDRs2wNXVFXv27BGP29nZic8TfMC35T//xrt371CnTh3WMSo0ffp0TJ06FdeuXUOnTp0AlPU8CAkJwapVqxink+br64vIyEisX78eo0aNwrp16/D48WNs3LhRavcQVoR2vv2Qra0tHj58yOsL8nJCydqwYUOYmprC0dERTk5OcHBwwDfffMM6FqkBqOcBITzm4eFRpedt27btKyepup9++glHjhyBpaUlrl69ivv370NdXR179uxBUFAQr/Yar6qYmBgMGTIEmZmZrKPIVFxcjGfPnqFBgwasowhax44d8cMPP8Db21tijfulS5cwaNAgPHr0iHVEAGWFo6qYN2/eV07yeVRVVXHz5k2YmZlJvL5paWlo1qwZ3r17xzqioJWWlmLx4sXYsGEDsrKycOfOHVhYWGDOnDkwMzODp6cn64gSDh48iODgYHF/AysrK/j6+vJyq0kTExOEhYXBwcEBmpqaSExMRKNGjbB9+3bs3r2bN7NmZCk/b/H5wnHv3r2YP38+fH19YW1tLbXMgi9LSQHhZI2KihL/XLx4EYWFhbCwsICTkxMcHR3h6OiI+vXrs45JhIjNJg+EsBcWFsZ16tSJMzQ05O7fv89xHMetWLGCO3ToEONkwlZYWMj9+uuv3NSpU7nExETx+PLly7lNmzYxTPbvvXnzhvn2cZW5du0ar7YYLZeXl8fNnj2b69ixI9ewYUPO3Nxc4odv1NTUuLS0NI7jOE5dXZ1LTU3lOI7j0tPTOWVlZZbRagRzc3Pu9OnTHMdJvr6hoaGclZUVy2g1woIFCzgLCwtux44dnIqKivj13bNnD9ehQwfG6YRNTU2Ne/DgAcdxHGdkZMRdvHiR4ziOS0tL49TU1FhGk6mkpIRbsGABp6mpKd6KWktLiwsICOBKSkpYx5MiEomkfvi4fTbHCStrubdv33IRERHcnDlzuC5dunDKysqcnJwc16xZM9bRiADRsgVSK61fvx5z587Fzz//jMWLF4un02tra2PlypW8vPMhFIqKivDx8ZEa/7irtpCoq6vzclkA340ZMwbR0dEYNWoUDA0NebnW9kPa2tp4+vQpzM3NJcavXr0KIyMjRqlqjrFjx8LLywtbt26FSCTCkydPcP78efj4+AimKzyfhYWF4Y8//kC3bt0wYcIE8XjLli1x69YthsmkcRyHK1eu4P79+xCJRLCwsBD3JeIjCwsLpKenw8TEBE2bNkV4eDjat2+Pw4cP83Ib4lmzZmHLli0IDAwUb4UZGxuL+fPn4927d1i8eDHjhJKEtKxUSFnL1alTB05OTujcuTMcHR1x/PhxbNy4kXfnBSIMVDwgtdKaNWuwadMmDBgwQGK9oq2trcwLX/L5bt68iYyMDKmu1P369WOUiFS348eP4+jRo+Ivr3w3bNgw+Pn5Ye/evRCJRCgtLUVcXBx8fHzg6urKOp7g+fv7o7S0FN26dUNBQQG6du0KZWVl+Pj4YMqUKazjCd7jx4+lGhACZcsZioqKGCSSLTIyEp6ennjw4IG4gbFIJIK5uTm2bt2Krl27Mk4ozcPDA0lJSbC3t4e/vz/69u2LtWvXoqioiJeF5dDQUGzevFni89bGxgZGRkaYNGkS74oHpqamrCNUmZCyFhYW4sKFC4iMjBQvXzA2NkbXrl2xdu1a2Nvbs45IBIiKB6RWSk9PR+vWraXGlZWVkZ+fzyBRzZGWloaBAwfi+vXrErtblN9R4lPTRPJ16ejoiLeLEoIlS5Zg8uTJMDY2RklJCZo1a4aSkhIMHz4cs2fPZh1P8EQiEWbNmgVfX1/cu3cPeXl5aNasmcSWiOTfa9asGc6dOyd1cbNv3z6Zn3cs3Lt3D9999x2+/fZbrFixAk2bNgXHcbh58yZWr16N3r17Izk5GRYWFqyjSvhw5lz37t1x69YtXLlyBY0aNeLNGvcP5eTkoGnTplLjTZs2RU5ODoNEn5aamoqVK1eKe2A0a9YMXl5eaNiwIeNk0oSQ1cnJCRcvXoS5uTns7e0xfvx47Nq1C4aGhqyjEYGj4gGplczNzXHt2jWpL1knTpyAlZUVo1Q1g5eXF8zNzREREQFzc3NcunQJ2dnZmD59On777TfW8QQpOTm50uO3b9+upiSfZ+HChZg7dy5CQ0OhqqrKOs4nKSkpYdOmTZg7dy6uX7+OvLw8tG7dGpaWlqyj1ShKSkpo1qwZ6xhVIoStO8vNnTsXbm5uePz4MUpLS3HgwAHcvn0bYWFhOHLkCOt4AICVK1eiQ4cOiIiIkBhv2rQpBg4ciO7du2PFihVYs2YNo4TSioqK0KtXL2zYsEF8LjA1NeX1HeiWLVti7dq1WL16tcT42rVr0bJlS0apKnby5En069cPrVq1Es9Ui4uLQ/PmzXH48GH06NGDccL/EUrWc+fOwdDQULzTgr29PerWrcs6FqkBaLcFUitt3rwZ8+fPR3BwMDw9PbF582akpqZi6dKl2Lx5M4YNG8Y6omDVq1cPZ8+ehY2NDbS0tHDp0iU0adIEZ8+exfTp06X2ouaDiIgIRERE4NmzZygtLZU4tnXrVkap/kdOTk5iFseHysdFIhHvZnW0bt0aqamp4DgOZmZmUl2p+bbzRkBAAHx8fKQKHW/fvsWvv/6KuXPnMkombKNHj67S8/jwb+1DH2/dmZKSAgsLCwQGBiIhIYE3W3d+6Ny5cwgICEBSUhLy8vLQpk0bzJ07Fz179mQdDQDQokULLF26FH379pV5/PDhw5g5cyZu3LhRzckqp6enh/j4eMEUEqOjo9GnTx+YmJigY8eOAIDz58/j4cOHOHbsGLp06cI4oaTWrVvD2dlZattLf39/nDp1ilefFULJmp+fj3PnziEqKgqRkZG4du0aGjduDHt7e3ExQU9Pj3VMIkBUPCC11s6dOzF//nykpqYCABo0aIAFCxbwbjsrodHR0UFiYiLMzc3RsGFDbN68GY6OjkhNTYW1tTUKCgpYR5SwYMECBAQEwNbWVmZTv4MHDzJK9j8PHjyo0vP4difsU1sK8m0rQXl5eTx9+hT6+voS49nZ2dDX1+ddcQYou0j47bffJKbP+vr68uriQE5ODqampmjdurXMAlg5Pvxb+5BQtu4UEk1NTSQnJ8PMzEzm8fT0dNjY2ODNmzfVG+wTpk2bBmVlZakLRj578uQJ1q1bJ26KZ2VlhUmTJvFyS986derg+vXrUsWZO3fuwMbGhlfbuAop64fevHmD2NhYcf+DpKQkWFpa8q5QR/iPli2QWmvEiBEYMWIECgoKkJeXJ3XBQP6dFi1aICkpCebm5vj2228RFBQEJSUl/PHHH7xbxwoAGzZsQEhICEaNGsU6SoX4VhSoKr4VBz6lfAbHx5KSknjZu2HHjh3w8PDAoEGDMHXqVABl02e7deuGkJAQDB8+nHHCMhMnTsTu3buRnp4ODw8PjBw5kpev58euX7+OXbt2SY3r6+vjxYsXDBIJX15eXqVLmFRVVXlXYAaA4uJibN26FWfOnEHbtm2hpqYmcZyPTRMbNGjAu8aIFdHT08O1a9ekLsivXbvGu+9mQsr6ITU1Nejq6kJXVxc6OjpQUFAQF50J+RxUPCC1nqqqqiDWYwvF7NmzxU0nAwIC8N1336FLly6oW7cu/vzzT8bppBUWFqJTp06sY9RohYWFMpeEmJiYMEokSUdHByKRCCKRCI0bN5YoIJSUlCAvL09i6zu+WLx4MYKCgiSauU2dOhXLly/HwoULeVM8WLduHZYvX44DBw5g69atmDlzJvr06QNPT0/07NmTt9vzCWHrzvL3blXwpVHezZs3kZmZKfMYX4syN27cQJs2bQCU3WH+EJ/ev5/qjwMACgoKMDAw4EUBr3yp2NixYzFu3DikpaWJP4/j4uKwbNkyeHt7M05ZRkhZgbJdVhISEsTLFuLi4pCfnw8jIyM4Ojpi3bp1cHR0ZB2TCBAtWyC1UuvWrWV+4ItEItSpUweNGjWCu7s7nVg/Q2RkJOzs7KCkpCR1LCcn57O+5FYnPz8/qKur0z7zX8GdO3fg6emJ+Ph4iXG+9WgIDQ0Fx3EYPXo0Vq5cCS0tLfExJSUlmJmZidcN84mysjL++ecfqe357t27hxYtWvB2+uyDBw8QEhKCsLAwFBcX459//uHljgs+Pj64ePEi9u7di8aNGyMxMRFZWVlwdXWFq6srL2bWhIaGVvm5bm5uXzFJ1Qi1f4tQVPb6fkgkEqFly5YICwtDixYtqimdtPKlYnp6eli5ciWCg4Px5MkTAGUzJ3x9fTF16lRefHcQUlagbIlQfn4+DAwM4OjoCEdHRzg4OPBqRwgiTFQ8ILXSzJkzsX79elhbW6N9+/YAgMuXLyM5ORnu7u64efMmIiIicODAAfTv359xWmGQk5NDnTp10KFDB/EHVYcOHaCgwL8JTh/eHSgtLUVoaChsbGxgY2Mj1dSPj9NRhcLOzg4KCgrw9/eX2U+Cb12/o6Oj0alTJ6n3AF81atQIvr6+GD9+vMT4hg0bEBwcjLt37zJKVrmHDx9i27ZtCAkJQWFhIW7dusXL4kFhYSEmT56MkJAQlJSUQEFBQbx1Z0hICOTl5VlHFByh9m8Riqq8vqWlpcjKysKvv/6KZ8+e4dy5c9WQTDY5OTlkZmZKTPcv73ehoaHBKpZMQsoKABs3boSjoyMaN27MOgqpYah4QGqlsWPHwsTEROpu86JFi/DgwQNs2rQJ8+bNw9GjR5GQkMAopbA8ePAAZ8+eRXR0NKKiopCRkQFVVVXY2dmJiwnt2rWDnJwc66hVnlEiEolw9uzZr5ymajiOw8OHD6Gvr486deqwjlMlampquHLlisz9xvnu3bt3KCwslBjT1NRklEa29evX4+eff8bo0aMlps+GhIRg1apVUkUFlt6/fy9ethAbG4vvvvsOHh4e6NWrFy/OCZXJyMjAjRs3BLF1Z0lJCQ4dOiRey9y8eXP069ePCh3/wqBBg6r83AMHDnzFJF/HvXv30LJlS/EyQxbk5OSQlZUliK7/QspKyNdExQNSK2lpaeHKlSsyp/u2bdsWr169wq1bt9CuXTvedX0WirS0NERFRSEqKgrR0dF49OgRNDQ0kJubyzqaIJWWlqJOnTr4559/eH3x8qF27dphxYoV6Ny5M+soVVJQUIAZM2YgPDwc2dnZUsf5OJX64MGDCA4OFl8sWllZwdfXl1czpiZNmoQ9e/bA2NgYo0ePxogRI1CvXj3WsWqce/fuoXfv3nj8+DGaNGkCALh9+zaMjY1x9OhRmq78mTw8PMS/5jgOBw8ehJaWFmxtbQEAV65cQW5uLgYNGoRt27axivmvlZSU4MaNG0xngMnJyUFLS+uTU/350K9DSFkJ+Zr4N5+YkGpQp04dxMfHSxUP4uPjxXd1yy/WyL9jYWEBeXl5cSO6Q4cOSd3JJVUnJycHS0tLZGdn87p48Pr1a/Gvly1bhhkzZmDJkiWwtraWWg7Atzv5vr6+iIyMxPr16zFq1CisW7cOjx8/xsaNG3m7RdvAgQMxcOBA1jEqtWHDBpiYmMDCwgLR0dGIjo6W+Tw+3L319vbGwoULoaam9snmZ3xb0jR16lQ0bNgQFy5cEDfDy87OxsiRIzF16lQcPXqUcUJh+bAg4OfnhyFDhmDDhg3iWRwlJSWYNGkS785jVSUvL8+LpWMLFiyQ6DPDZ0LKSsjXQjMPSK20aNEiLFmyBGPHjkW7du0AlPU82Lx5M3755RfMmjULK1aswLFjx3D69GnGaYUjIyND3Nk3KioKL168QKdOndClSxfY29vj22+/ldlQkaWBAwd+snnm8OHDxXfyWDp8+DCCgoKwfv16pk2uKlPesKucrO0P+doUzcTEBGFhYXBwcICmpiYSExPRqFEjbN++Hbt378axY8dYRxQkd3f3KjUR48PdW0dHRxw8eBDa2tqVLm/i05Kmcmpqarhw4QKsra0lxpOSkmBnZ4e8vDxGyYRPT08PsbGxUp8Dt2/fRqdOnWTOVCKfJquPAF8JKSshXxPNPCC10uzZs2Fubo61a9di+/btAIAmTZpg06ZN4u3NJkyYgIkTJ7KMKSgWFhZ4+fIl7Ozs0LVrV4wfPx62tra8bJj4IS0tLRw6dAja2tpo27YtACAxMRG5ubno2bMn/vzzTyxbtgwRERGws7NjmtXV1RUFBQVo2bIllJSUoKKiInGcD9MlIyMjWUf413JycmBhYQGgbFZE+evZuXNn3pwLhLg1X0hICOsIVfbh+1do72VlZWWZy+zy8vJ4V7QVmuLiYty6dUuqeHDr1i2pLWhJ1fFlZ4KqEFJWQr4mfn+rJ+QrGjFiBEaMGFHh8Y8vzEjl3r59C6CsOq+goABFRUVBNOkyMDDA8OHDsXbtWnHjttLSUnh5eUFDQwN79uzBhAkT4Ofnh9jYWKZZV65cyfTPrwp7e3vWEf41CwsLpKenw8TEBE2bNkV4eDjat2+Pw4cPQ1tbm3U8AJLvgezsbCxatAjOzs7irSTPnz+PkydP0tajtdB3332HcePGYcuWLeJdhC5evIgJEyagX79+jNNJcnJywoEDB6T+Xb1+/RoDBgzg3awODw8PeHp6IjU1VeK1DQwMlOiNwBdCeX2FNPlZSFkJ+Zpo2QKp1QoLC/Hs2TOpOwcmJiaMEgnbrVu3xMsWoqOj8e7dO3Tu3BkODg6wt7dH27ZteddZXU9PD3FxcVLbGd25cwedOnXCixcvcP36dXTp0oWaPf4Lubm52LJli0T399GjR/Ny3eiKFSsgLy+PqVOn4syZM+jbty84jkNRURGWL18OLy8v1hElDB48GI6Ojvjpp58kxteuXYszZ87g0KFDbIIJmJA77Ofm5sLNzQ2HDx8W9xcpLi5Gv379EBISwqt/cxVNAX/27BmMjIxQVFTEKJlspaWl+O2337Bq1So8ffoUAGBoaAgvLy9Mnz6dd4Vyob2+hBDhoOIBqZXu3r2L0aNHIz4+XmKcr2uxhSolJUXc/+DUqVMAwLsLcB0dHYSGhkrdmfv777/h5uaGly9f4u7du2jfvj1evnzJKOX/pKamYtu2bUhNTcWqVaugr6+P48ePw8TEBM2bN2cdT0JCQgKcnZ2hoqIivlt3+fJlvH37FqdOnUKbNm0YJ6zcgwcPxLuyWFhYQF1dnXUkCerq6rh27ZrMXWNatWpFa9z/hc+5i8yHHg2y3Lt3T2L3jY/fHywlJycDAFq1aoWzZ8+KGzsCZQ0IT5w4gY0bN+L+/fuMEn5aeVNYPjZKrAmvLyGE32jZAqmV3N3doaCggCNHjsDQ0JDWsn0FWVlZSE5ORnJyMpKSkvD69WsoKyuzjiVl1KhR8PT0xC+//CLRPHPJkiVwdXUFAERHR/Piwjw6OhouLi6ws7NDTEwMFi9eDH19fSQlJWHLli3Yt28f64gSpk2bhn79+mHTpk3i3hfFxcUYM2YMfv75Z8TExDBOWGbFihWYNm2a1LipqSlMTU3x5s0bODs7Iy4ujkG6itWtWxd//fUXpk+fLjH+119/oW7duoxSCRtfCwKfo1GjRrwqGHyoVatW4h14nJycpI6rqKhgzZo1DJJVHR+LBuVqwutLCOE3mnlAaiU1NTVcuXIFTZs2ZR2lxnj27BmioqLEyxbu3LkDRUVFtG/fHo6OjnB0dETHjh15V0AoKSlBYGAg1q5di6ysLABA/fr1MWXKFPj5+UFeXh4ZGRmQk5PDN998wzRrx44d8cMPP8Db2xsaGhpISkqChYUFLl26hEGDBuHRo0dM831MRUUFV69elfp3dvPmTdja2qKgoIBRMkkqKirYuHGjuFj0ofz8fPTs2RPZ2dm4desWg3QVCwkJwZgxY+Di4oJvv/0WQNk67BMnTmDTpk1wd3dnG5BUq8GDB6N9+/bw8/OTGA8KCsLly5exd+9eRsn+58GDB+A4Tnze0tPTEx9TUlKCvr4+75YAAGXFcB8fH0RERODZs2dS69/5MltRqK8vIUQ4aOYBqZWaNWuGFy9esI5RoxgYGEBRURG2trbitdidOnXifeNJeXl5zJo1C7NmzapwOipfemBcv34du3btkhrX19fn5ftZU1MTGRkZUsWDhw8fQkNDg1Eqadu3b8eoUaOgra0tsXwlLy8PvXr1wvPnzxEdHc0woWzu7u6wsrLC6tWrxevvraysEBsbKy4mkP9m3759CA8PR0ZGBgoLCyWOJSYmMkolW0xMDObPny817uLiguDg4OoPJIOpqSmKiorg5uaGunXrwtTUlHWkKnF3d0dGRgbmzJnD69mK5a9nZGQkWrVqJbXbUUlJCWJiYtC1a1cW8QghNQAVD0ittGzZMsyYMQNLliyBtbW1uLlUOT5PS+Sr48ePo3PnzlBTU2Md5V/j+/93bW1tPH36FObm5hLjV69ehZGREaNUFRs6dCg8PT3x22+/oVOnTgCAuLg4+Pr64scff2Sc7n++//575Obm4scff8TRo0fh4OCA/Px8uLi4ICsrC9HR0TA0NGQdU0JRURHGjx+POXPmYOfOnazj1EirV6/GrFmz4O7ujr/++gseHh5ITU3F5cuXMXnyZNbxpFS0JaOioqK4MMoHioqKOHjwIObOncs6SpXFxsbi3LlzaNWqFesoVeLk5ISnT59KNUzMzc2Fo6Mjb2ZKEEKEh4oHpFbq3r07AKBbt24S49Qw8d9zdnZmHeFfMTc3r/QuUlpaWjWmqdywYcPg5+eHvXv3QiQSobS0FHFxcfDx8ZE55Z613377DSKRCK6uriguLgbHcVBSUsLEiRMRGBjIOp6EMWPGICcnB/3798dff/2FuXPn4smTJ4iOjkaDBg1Yx5OiqKiI/fv305aMX9Hvv/+OP/74Az/++CNCQkIwY8YMWFhYYO7cucjJyWEdT4q1tTX+/PNPqYvyPXv2oFmzZoxSyda/f38cOnRIZq8RPjI2NhbUVn3l32U+lp2dLegCPyGEPSoekFopMjKSdQTCEz///LPE46KiIly9ehUnTpyAr68vm1AVWLJkCSZPngxjY2OUlJSgWbNmKCkpwfDhwzF79mzW8aQoKSlh1apVWLp0KVJTUwEADRs2hKqqKuNkss2YMQM5OTno1q0bzMzMEBUVxbzPRWUGDBggqAswocnIyBDPmFFRUcGbN28AlDVZ7dChA9auXcsynpQ5c+Zg0KBBSE1NFTfLi4iIwO7du3nR7+BDlpaWCAgIQFxcHNq2bSt1QTt16lRGyWRbuXIl/P39sXHjRpiZmbGOU6HyrUZFIhHc3d0legyVlJQgOTlZ/J4mhJB/g4oHpFayt7ev8NiNGzeqMQlhzcvLS+b4unXrkJCQUM1ppL1+/Vq8nEJJSQmbNm3C3Llzcf36deTl5aF169awtLRknFJS+RfYyigoKMDAwAA9evRA3759qyFVxT7Oq6ioiHr16km9N8r7CvCF0C7AhMbAwAA5OTkwNTWFiYkJLly4gJYtWyI9PZ2Xd6H79u2LQ4cOYcmSJdi3bx9UVFRgY2ODM2fOVPqZx8KWLVugra2NK1eu4MqVKxLHRCIR7967Q4cORUFBgbj4+fFSR77MRNHS0gJQNvNAQ0NDoueQkpISOnTogLFjx7KKRwipAWi3BUIAvHnzBrt378bmzZtx5coVWrZAkJaWhlatWjFfKywvLy9eu+rk5IQDBw5AW1ubaaZP8fDw+ORzSktL8ezZM0RHR8PHxwcBAQHVkEy2quQF+LeN38e9Lz4kEol4teRGiMaMGQNjY2PMmzcP69atg6+vL+zs7JCQkIBBgwZhy5YtrCOSahIaGlrpcTc3t2pKUjULFiyAj48PLVEghHxxVDwgtVpMTAy2bNmC/fv3o0GDBhg0aBAGDx6Mdu3asY5GGAsKCsLvv/+O+/fvM82hpaWFCxcuwMrKCnJycsjKypLYfkvojhw5gkmTJiEjI4N1FEIklJaWorS0VNyxfs+ePYiPj4elpSXGjx8vszkh+TyFhYVIT09Hw4YNpXYGIP9NcXExoqKikJqaiuHDh0NDQwNPnjyBpqYm1NXVWccjhAgUnalJrZOZmYmQkBBs2bIFr1+/xpAhQ/D+/XscOnSId02lhGL16tVVfi7fpqO2bt1aorEUx3HIzMzE8+fP8fvvvzNMVqZ79+5wdHSElZUVAGDgwIEVXrScPXu2OqN9EZ07d4atrS3rGIRIefToEYyNjcWPhw0bhmHDhoHjODx8+JAXW7jq6urizp07qFevHnR0dCpt/sqXqfUAUFBQgClTpojv6N+5cwcWFhaYMmUKjIyM4O/vzzhhxd69eye1bSffdup58OABevXqhYyMDLx//x49evSAhoYGli1bhvfv32PDhg2sIxJCBIqKB6RW6du3L2JiYtCnTx+sXLkSvXr1gry8PH2Q/kcrVqyo0vP4uJZ1wIABEo/l5OSgp6cHBwcHNG3alE2oD+zYsQOhoaFITU1FdHQ0mjdvztuGg/+GtrY273oJCMmjR4/w999/IyMjQ+qCZvny5YxS1Qzm5uYyt7vLycmBubk5L5a3rVixAhoaGgDKmvoJxcyZM5GUlISoqCj06tVLPN69e3fMnz+fd8WD/Px8+Pn5ITw8HNnZ2VLH+fBe+JCXlxdsbW2RlJSEunXriscHDhxIPQ8IIf8JLVsgtYqCggKmTp2KiRMnSjSZU1RURFJSEs08ILzm6OiIgwcP8r7nAakeERER6NevHywsLHDr1i20aNEC9+/fB8dxaNOmjSBnovBJRcuEHjx4gGbNmiE/P59RMuEzNTXFn3/+iQ4dOkBDQwNJSUmwsLDAvXv30KZNG+a9Zj42efJkREZGYuHChRg1ahTWrVuHx48fY+PGjQgMDMSIESNYR5RQt25dxMfHo0mTJhKv7/3799GsWTMUFBSwjkgIESiaeUBqldjYWGzZsgVt27aFlZUVRo0ahWHDhrGOVSPxfS1rVb+c8mk6Km0xSj40c+ZM+Pj4YMGCBdDQ0MD+/fuhr6+PESNGSNzNJZ/H29sbQNlMqTlz5kjM9CkpKcHFixfRqlUrRulqhufPn0vN6ADK7vBXtvSClcOHDyMsLAwODg7w8PBAly5d0KhRI5iammLnzp28Kx6UlpbKnA3x6NEj8UwVQgj5N/j3jZ6Qr6hDhw7o0KEDVq5ciT///BNbt26Ft7c3SktLcfr0aRgbG9MH638klLWs2tralX5J5TgOIpGI+XRUb29vLFy4EGpqauKLmorQNPXaJSUlBbt37wZQNqvq7du3UFdXR0BAAPr374+JEycyTihMV69eBVB2Drh+/bpEjxElJSW0bNkSPj4+rOJJkZOT++QFt0gkQnFxcTUl+jRbW1scPXoUU6ZMAQBx/s2bN6Njx44so8mUk5MDCwsLAGUF5fL+EZ07d+blv7OePXti5cqV+OOPPwCUvb55eXmYN28eevfuzTgdIUTIqHhAaiU1NTWMHj0ao0ePxu3bt7FlyxYEBgbC398fPXr0wN9//806omAJZS3rh3fxOY5D7969sXnzZhgZGTFMJe3q1asoKioS/7oifLxbR74uNTU1cZ8DQ0NDpKamonnz5gCAFy9esIwmaOXnBg8PD6xatYpXs49kOXjwYIXHzp8/j9WrV6O0tLQaE33akiVL4OLigps3b6K4uBirVq3CzZs3ER8fj+joaNbxpFhYWCA9PR0mJiZo2rQpwsPD0b59exw+fJiXy8iCg4Ph7OyMZs2a4d27dxg+fDju3r2LevXqiQuOhBDyb1DPA0L+X0lJCQ4fPoytW7dS8eA/ENpa1nIfZiVECAYMGIA+ffpg7Nix8PHxwV9//QV3d3ccOHAAOjo6OHPmDOuIhJHbt2/D398fhw8fxogRIxAQEABTU1PWsSSkpqYiMDAQSUlJyMvLQ5s2beDn5wdra2vW0aSsWLEC8vLymDp1Ks6cOYO+ffuC4zgUFRVh+fLl8PLyYh1RSnFxMfbs2YPk5GTx6ztixAioqKiwjkYIETAqHhBCvihVVVXcuHEDFhYWEhfkSUlJ6Nq1K169esU6okxUPCBCk5aWhry8PNjY2CA/Px/Tp09HfHw8LC0tsXz5ct5dLApRQkICwsPDZe5mwcddQp48eYJ58+YhNDQUzs7OWLp0KVq0aME6Vo1z//59JCYmolGjRrCxsWEdhxBCqg0tWyCEfFFCW8sqJPn5+QgMDERERASePXsmNRU5LS2NUTLCwoeFLjU1Ndpy9gvbs2cPXF1d4ezsjFOnTqFnz564c+cOsrKyMHDgQNbxJLx69QpLlizBmjVr0KpVK0RERKBLly6sY1WqpKQEBw8eREpKCgCgWbNm6N+/Py8b7H7MzMwMZmZmrGNI+JwZk/369fuKSQghNRn/z9CEEEER2lrWD/G9b8CYMWMQHR2NUaNGwdDQkPd5SfUoLCyUWUwyMTFhlKhmWLJkCVasWIHJkydDQ0MDq1atgrm5OcaPHw9DQ0PW8cSCgoKwbNkyGBgYYPfu3ejfvz/rSJ/0zz//oF+/fsjMzESTJk0AAMuWLYOenh4OHz7Mm9kS58+fR3Z2Nr777jvxWFhYGObNm4f8/HwMGDAAa9asgbKyMsOUZQYMGFCl5/GhETAhRLho2QIh5IsTwlrWQYMGSTw+fPgwnJycoKamJjHOp6nJ2traOHr0KOzs7FhHITxw584deHp6Ij4+XmKcLzuFCJ2amhr++ecfmJmZoW7duoiKioK1tTVSUlLg5OSEp0+fso4IoGy3BRUVFXTv3h3y8vIVPo9P57KOHTtCT08PoaGh0NHRAQC8fPkS7u7ueP78udR7mhUXFxc4ODjAz88PAHD9+nW0adMG7u7usLKywq+//orx48dj/vz5bIMSQkg1oZkHhJAvrmHDhti0aRPrGJXS0tKSeDxy5EhGSapOR0cHurq6rGMQnvDw8ICCggKOHDlCM1G+Ah0dHbx58wYAYGRkhBs3bsDa2hq5ubkoKChgnO5/XF1dBff//tq1a0hISBAXDoCy13vx4sVo164dw2SSrl27hoULF4of79mzB99++634883Y2Bjz5s3jVfGgqKgIvXr1woYNG2Bpack6DiGkhqHiASHki3JycoK9vT3mzZsnMf7y5UsMHjwYZ8+eZZRM0rZt21hH+GwLFy7E3LlzERoaClVVVdZxCGPXrl3DlStX0LRpU9ZRaqSuXbvi9OnTsLa2xg8//AAvLy+cPXsWp0+fRrdu3VjHEwsJCWEd4bM1btwYWVlZ4q1Fyz179gyNGjVilEray5cvUb9+ffHj6OhouLi4iB+3a9cODx8+ZBGtQoqKikhOTmYdgxBSQ1HxgBDyRUVFReH69eu4evUqdu7cKV4GUFhYyPueB3wXHByM1NRU1K9fH2ZmZlBUVJQ4npiYyCgZYaFZs2Z48eIF6xg11tq1a/Hu3TsAwKxZs6CoqIj4+HgMHjwYs2fPZpxOeD7cpnfp0qWYOnUq5s+fjw4dOgAALly4gICAACxbtoxVRCn169dHeno6jI2NUVhYiMTERCxYsEB8/M2bN1LnYT4YOXIktmzZgsDAQNZRCCE1DBUPCCFf3JkzZzB+/Hh06NABhw8f5l1XaqGqakMsUjssW7YMM2bMwJIlS2BtbS11EaOpqckoWc3w4RIhOTk5+Pv7M0wjfNra2hLLKziOw5AhQ8Rj5S24+vbty5t+Hb1794a/vz+WLVuGQ4cOQVVVVWIXi+TkZDRs2JBhQtmKi4uxdetWnDlzBm3btpXq5bN8+XJGyQghQkcNEwkhX5ScnBwyMzOhpaUFDw8PnD59Gnv37oWVlRUaNGjAmy+FhAidnJwcAOldQqhhIuGjz5l5Zm9v/xWTVN2LFy8waNAgxMbGQl1dHaGhoRLbdHbr1g0dOnTA4sWLGaaU5ujoWOExkUjEm+WDhBDhoeIBIeSLkpeXx9OnT6Gvrw8AWLRoERYtWgQ/Pz8sWrSILmi+ANqajwCfvhjjywWY0MjJyX2yAaFIJEJxcXE1JSKsvXr1Curq6lK7WeTk5EBdXR1KSkqMkhFCSPWi4gEh5Isqn3lQXjwAgP3798PNzQ1v376l4sF/QFvzEfL1/fXXXxUeO3/+PFavXo3S0lJxPwTy7+Tm5mLLli1ISUkBADRv3hyjR4+W2gmHEEIIf1DxgBDyRT148ADGxsbiKdXlbty4gStXrsDNzY1RMuGzs7ODgoIC/P39ZW7N17JlS0bJCCt0AVY9bt++DX9/fxw+fBgjRoxAQEAATE1NWccSrISEBDg7O0NFRQXt27cHAFy+fBlv377FqVOn0KZNG8YJhS8hIQHh4eHIyMhAYWGhxLEDBw4wSkUIEToqHhBCiECoqanR1nxEjC7Avr4nT55g3rx5CA0NhbOzM5YuXYoWLVqwjiV4Xbp0QaNGjbBp0yYoKJT17i4uLsaYMWOQlpaGmJgYxgmFbc+ePXB1dYWzszNOnTqFnj174s6dO8jKysLAgQMFuVUxIYQfqHhACPmi8vPzERgYiIiICJnr8tPS0hglE7527dphxYoV6Ny5M+sohAfoAuzrefXqFZYsWYI1a9agVatWWLZsmUSXffLfqKio4OrVq1KF0Js3b8LW1hYFBQWMktUMNjY2GD9+PCZPngwNDQ0kJSXB3Nwc48ePh6GhocR2k4QQ8jloq0ZCyBc1ZswYREdHY9SoUTKn1pN/j7bmIx9KSEiQKBwAgIKCAmbMmAFbW1uGyYQtKCgIy5Ytg4GBAXbv3o3+/fuzjlTjaGpqIiMjQ6p48PDhQ2hoaDBKVXOkpqaiT58+AAAlJSXk5+dDJBJh2rRpcHJyouIBIeRfo+IBIeSLOn78OI4ePQo7OzvWUWqc7t27AyjbHuxD1DCxdqILsK/D398fKioqaNSoEUJDQxEaGirzebRu/N8bOnQoPD098dtvv6FTp04AgLi4OPj6+uLHH39knE74dHR08ObNGwCAkZERbty4AWtra+Tm5tKsDkLIf0LFA0LIF6WjowNdXV3WMWqkyMhI1hEIj9AF2Nfh6upKM6a+st9++w0ikQiurq7iLS8VFRUxceJEBAYGMk4nfF27dsXp06dhbW2NH374AV5eXjh79ixOnz4tVXwmhJDPQT0PCCFf1I4dO/DXX38hNDQUqqqqrOMQUmMVFhbC19cXGzZskHkBpqyszDghIZUrKChAamoqAKBhw4ZQVVXF27dvoaKiwjiZsOXk5ODdu3do0KABSktLERQUhPj4eFhaWmL27NnQ0dFhHZEQIlBUPCCEfFGtW7dGamoqOI6DmZmZ1Lr8xMRERslqhnPnzmHjxo1IS0vD3r17YWRkhO3bt8Pc3JwaKdZSsi7ACBGa9+/fY926dQgKCkJmZibrOIQQQmSgZQuEkC9qwIABrCPUWPv378eoUaMwYsQIJCYm4v379wD+1xn+2LFjjBMSFp48eYInT56ga9euUFFREffAIIRv3r9/j/nz5+P06dNQUlLCjBkzMGDAAGzbtg2zZs2CvLw8pk2bxjqm4B07dgzy8vJwdnaWGD916hRKSkrg4uLCKBkhROho5gEhhAhE69atMW3aNLi6uoq337KwsMDVq1fh4uJCd+tqmezsbAwZMgSRkZEQiUS4e/cuLCwsMHr0aOjo6CA4OJh1REIk+Pn5YePGjejevTvi4+Px/PlzeHh44MKFC/jll1/www8/QF5ennVMwbOxsUFgYCB69+4tMX7ixAn4+fkhKSmJUTJCiNDJsQ5ACKl5cnNzsXnzZsycORM5OTkAypYrPH78mHEyYbt9+za6du0qNa6lpYXc3NzqD0SYmjZtGhQVFZGRkSGxVGHo0KE4ceIEw2SEyLZ3716EhYVh37594rvgxcXFSEpKwrBhw6hw8IXcvXsXzZo1kxpv2rQp7t27xyARIaSmoGULhJAvKjk5Gd27d4eWlhbu37+PsWPHQldXFwcOHEBGRgbCwsJYRxQsAwMD3Lt3D2ZmZhLjsbGxsLCwYBOKMHPq1CmcPHkS33zzjcS4paUlHjx4wCgVIRV79OgR2rZtCwBo0aIFlJWVMW3aNFpm84VpaWkhLS1N6rPi3r17UFNTYxOKEFIj0MwDQsgX5e3tDXd3d9y9exd16tQRj/fu3RsxMTEMkwnf2LFj4eXlhYsXL0IkEuHJkyfYuXMnfHx8MHHiRNbxSDXLz8+X2RwxJyeHdlogvFRSUgIlJSXxYwUFBairqzNMVDP1798fP//8s7iRKlBWOJg+fTr69evHMBkhROho5gEh5Iu6fPkyNm7cKDVuZGREa/L/I39/f5SWlqJbt24oKChA165doaysDB8fH0yZMoV1PFLNunTpgrCwMCxcuBAAIBKJxNuyOTo6Mk5HiDSO4+Du7i4ubr179w4TJkyQuht+4MABFvFqjKCgIPTq1QtNmzYVz0x69OgRunTpgt9++41xOkKIkFHxgBDyRSkrK+P169dS43fu3IGenh6DRDWHSCTCrFmz4Ovri3v37iEvLw/NmjWjO3e1VFBQELp164aEhAQUFhZixowZ+Oeff5CTk4O4uDjW8QiR4ubmJvF45MiRjJLUbFpaWoiPj8fp06eRlJQEFRUV2NjYyOyZQwghn4N2WyCEfFFjxoxBdnY2wsPDoauri+TkZMjLy2PAgAHo2rUrVq5cyTqi4AwaNOiTz1FQUICBgQF69OiBvn37VkMqwgevXr3C2rVrkZSUhLy8PLRp0waTJ0+GoaEh62iEEEIIqWGoeEAI+aJevXqF77//HgkJCXjz5g0aNGiAzMxMdOzYEceOHaNmTf+Ch4fHJ59TWlqKZ8+eITo6Gj4+PggICKiGZISPHj16hICAAPzxxx+soxBCqtH58+eRnZ2N7777TjwWFhaGefPmIT8/HwMGDMCaNWuoJwoh5F+j4gEh5KuIjY1FcnKy+G5o9+7dWUeqFY4cOYJJkyYhIyODdRTCSFJSEtq0aYOSkhLWUQgh1cjFxQUODg7w8/MDAFy/fh1t2rSBu7s7rKys8Ouvv2L8+PGYP38+26CEEMGingeEkK+ic+fO6Ny5M+sYtU7nzp1ha2vLOgYhhJBqdu3aNXEDVQDYs2cPvv32W2zatAkAYGxsjHnz5lHxgBDyr1HxgBDyxZSWliIkJAQHDhzA/fv3IRKJYG5uju+//x6jRo2ivbyrgba2NnUqJ4SQWujly5eoX7+++HF0dDRcXFzEj9u1a4eHDx+yiEYIqSHkWAcghNQMHMehX79+GDNmDB4/fgxra2s0b94cDx48gLu7OwYOHMg6IiGEEFJj1a9fH+np6QCAwsJCJCYmokOHDuLjb968gaKiIqt4hJAagGYeEEK+iJCQEMTExCAiIkJqj/mzZ89iwIABCAsLg6urK6OEhNQMn9p9Izc3t3qCEEJ4pXfv3vD398eyZctw6NAhqKqqokuXLuLjycnJaNiwIcOEhBCho4aJhJAvomfPnnBycoK/v7/M40uWLEF0dDROnjxZzckIqVmqsvsGAGzbtu0rJyGE8MmLFy8waNAgxMbGQl1dHaGhoRKz/rp164YOHTpg8eLFDFMSQoSMigeEkC/CwMAAJ06cQKtWrWQev3r1KlxcXJCZmVm9wQghhJBa5NWrV1BXV4e8vLzEeE5ODtTV1aGkpMQoGSFE6GjZAiHki8jJyZFo1PSx+vXr4+XLl9WYiBBCCKl9tLS0ZI7r6upWcxJCSE1DDRMJIV9ESUkJFBQqrkfKy8ujuLi4GhMRQgghhBBCvhSaeUAI+SI4joO7uzuUlZVlHn///n01JyKEEEIIIYR8KVQ8IIR8EW5ubp98Du20QAghhBBCiDBRw0RCCCGEEEIIIYRUinoeEEIIIYQQQgghpFJUPCCEEEIIIYQQQkilqHhACCGEEEIIIYSQSlHxgBBCCCGEEEIIIZWi4gEhhBBC4O7ujgEDBogfOzg44Oeff672HFFRURCJRMjNzf1qf8bHf9d/ozpyEkIIIXxCxQNCCCGEp9zd3SESiSASiaCkpIRGjRohICAAxcXFX/3PPnDgABYuXFil51b3hbSZmRlWrlxZLX8WIYQQQsoosA5ACCGEkIr16tUL27Ztw/v373Hs2DFMnjwZioqKmDlzptRzCwsLoaSk9EX+XF1d3S/y+xBCCCGkZqCZB4QQQgiPKSsrw8DAAKamppg4cSK6d++Ov//+G8D/pt8vXrwYDRo0QJMmTQAADx8+xJAhQ6CtrQ1dXV30798f9+/fF/+eJSUl8Pb2hra2NurWrYsZM2aA4ziJP/fjZQvv37+Hn58fjI2NoaysjEaNGmHLli24f/8+HB0dAQA6OjoQiURwd3cHAJSWlmLp0qUwNzeHiooKWrZsiX379kn8OceOHUPjxo2hoqICR0dHiZz/RklJCTw9PcV/ZpMmTbBq1SqZz12wYAH09PSgqamJCRMmoLCwUHysKtk/9ODBA/Tt2xc6OjpQU1ND8+bNcezYsf/0dyGEEEL4hGYeEEIIIQKioqKC7Oxs8eOIiAhoamri9OnTAICioiI4OzujY8eOOHfuHBQUFLBo0SL06tULycnJUFJSQnBwMEJCQrB161ZYWVkhODgYBw8ehJOTU4V/rqurK86fP4/Vq1ejZcuWSE9Px4sXL2BsbIz9+/dj8ODBuH37NjQ1NaGiogIAWLp0KXbs2IENGzbA0tISMTExGDlyJPT09GBvb4+HDx9i0KBBmDx5MsaNG4eEhARMnz79P70+paWl+Oabb7B3717UrVsX8fHxGDduHAwNDTFkyBCJ161OnTqIiorC/fv34eHhgbp162Lx4sVVyv6xyZMno7CwEDExMVBTU8PNmzehrq7+n/4uhBBCCJ9Q8YAQQggRAI7jEBERgZMnT2LKlCnicTU1NWzevFm8XGHHjh0oLS3F5s2bIRKJAADbtm2DtrY2oqKi0LNnT6xcuRIzZ87EoEGDAAAbNmzAyZMnK/yz79y5g/DwcJw+fRrdu3cHAFhYWIiPly9x0NfXh7a2NoCymQpLlizBmTNn0LFjR/F/Exsbi40bN8Le3h7r169Hw4YNERwcDABo0qQJrl+/jmXLlv3r10lRURELFiwQPzY3N8f58+cRHh4uUTxQUlLC1q1boaqqiubNmyMgIAC+vr5YuHAhioqKPpn9YxkZGRg8eDCsra2lXh9CCCGkJqDiASGEEMJjR44cgbq6OoqKilBaWorhw4dj/vz54uPW1tYSfQ6SkpJw7949aGhoSPw+7969Q2pqKl69eoWnT5/i22+/FR9TUFCAra2t1NKFcteuXYO8vLzMi+aK3Lt3DwUFBejRo4fEeGFhIVq3bg0ASElJkcgBQHyx/l+sW7cOW7duRUZGBt6+fYvCwkK0atVK4jktW7aEqqqqxJ+bl5eHhw8fIi8v75PZPzZ16lRMnDgRp06dQvfu3TF48GDY2Nj8578LIYQQwhdUPCCEEEJ4zNHREevXr4eSkhIaNGgABQXJj241NTWJx3l5eWjbti127twp9Xvp6en9qwzlyxA+R15eHgDg6NGjMDIykjimrKz8r3JUxZ49e+Dj44Pg4GB07NgRGhoa+PXXX3Hx4sUq/x7/JvuYMWPg7OyMo0eP4tSpU1i6dCmCg4MlZokQQgghQkbFA0IIIYTH1NTU0KhRoyo/v02bNvjzzz+hr68PTU1Nmc8xNDTExYsX0bVrVwBAcXExrly5gjZt2sh8vrW1NUpLSxEdHS1etvCh8pkPJSUl4rFmzZpBWVkZGRkZFc5YsLKyEjd/LHfhwoVP/yUrERcXh06dOmHSpEnisdTUVKnnJSUl4e3bt+LCyIULF6Curg5jY2Po6up+MrssxsbGmDBhAiZMmICZM2di06ZNVDwghBBSY9BuC4QQQkgNMmLECNSrVw/9+/fHuXPnkJ6ejqioKEydOhWPHj0CAHh5eSEwMBCHDh3CrVu3MGnSJOTm5lb4e5qZmcHNzQ2jR4/GoUOHxL9neHg4AMDU1BQikQhHjhzB8+fPkZeXBw0NDfj4+GDatGkIDQ1FamoqEhMTsWbNGoSGhgIAJkyYgLt378LX1xe3b9/Grl27EBISUqW/5+PHj3Ht2jWJn5cvX8LS0hIJCQk4efIk7ty5gzlz5uDy5ctS/31hYSE8PT1x8+ZNHDt2DPPmzcNPP/0EOTm5KmX/2M8//4yTJ08iPT0diYmJiIyMhJWVVZX+LoQQQogQUPGAEEIIqUFUVVURExMDExMTDBo0CFZWVvD09MS7d+/EMxGmT5+OUaNGwc3NTTy1f+DAgZX+vuvXr8f333+PSZMmoWnTphg7dizy8/MBAEZGRliwYAH8/f1Rv359/PTTTwCAhQsXYs6cOVi6dCmsrKzQq1cvHD16FObm5gAAExMT7N+/H4cOHULLli2xYcMGLFmypEp/z99++w2tW7eW+Dl69CjGjx+PQYMGYejQofj222+RnZ0tMQuhXLdu3WBpaYmuXbti6NCh6Nevn0QviU9l/1hJSQkmT54sfm7jxo3x+++/V+nvQgghhAiBiKuoOxIhhBBCCCGEEEIIaOYBIYQQQgghhBBCPoGKB4QQQgghhBBCCKkUFQ8IIYQQQgghhBBSKSoeEEIIIYQQQgghpFJUPCCEEEIIIYQQQkilqHhACCGEEEIIIYSQSlHxgBBCCCGEEEIIIZWi4gEhhBBCCCGEEEIqRcUDQgghhBBCCCGEVIqKB4QQQgghhBBCCKkUFQ8IIYQQQgghhBBSKSoeEEIIIYQQQgghpFL/Bxshst/QsdAEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#testing"
      ],
      "metadata": {
        "id": "OMkH4k8siuJG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import classification_report\n",
        "from skimage.feature import hog\n",
        "\n",
        "def load_images_and_labels(base_path, image_size=(64, 64)):\n",
        "    images = []\n",
        "    labels = []\n",
        "    label_names = [d for d in os.listdir(base_path) if os.path.isdir(os.path.join(base_path, d))]\n",
        "    label_map = {name: idx for idx, name in enumerate(label_names)}\n",
        "\n",
        "    for label in label_names:\n",
        "        folder_path = os.path.join(base_path, label)\n",
        "        for filename in os.listdir(folder_path):\n",
        "            img_path = os.path.join(folder_path, filename)\n",
        "            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
        "            img = cv2.resize(img, image_size)\n",
        "            img_feature = hog(img, orientations=8, pixels_per_cell=(16, 16),\n",
        "                              cells_per_block=(1, 1), visualize=False, multichannel=False)\n",
        "            images.append(img_feature)\n",
        "            labels.append(label_map[label])\n",
        "\n",
        "    return np.array(images), np.array(labels), label_map\n",
        "\n",
        "# Load data\n",
        "X, y, label_map = load_images_and_labels('/content/Celebrity Faces Dataset')\n",
        "\n",
        "# Split data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.08, random_state=42)\n",
        "\n",
        "# Scale features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Define SVM Classifier with Grid Search\n",
        "param_grid = {\n",
        "    'C': [0.1, 1, 10],\n",
        "    'gamma': ['scale', 'auto'],\n",
        "    'kernel': ['rbf', 'poly']\n",
        "}\n",
        "grid = GridSearchCV(svm.SVC(), param_grid, refit=True, verbose=2, cv=3)\n",
        "grid.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model\n",
        "predictions = grid.predict(X_test)\n",
        "print(classification_report(y_test, predictions, target_names=label_map.keys()))\n",
        "\n",
        "# Display best parameters\n",
        "print(\"Best parameters found:\", grid.best_params_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "br_yZ1-N-eMZ",
        "outputId": "68057cde-82ab-4354-d3bb-5abfeed93849"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-7c8141408769>:22: FutureWarning: `multichannel` is a deprecated argument name for `hog`. It will be removed in version 1.0. Please use `channel_axis` instead.\n",
            "  img_feature = hog(img, orientations=8, pixels_per_cell=(16, 16),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n",
            "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.3s\n",
            "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.3s\n",
            "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time=   0.3s\n",
            "[CV] END ....................C=0.1, gamma=scale, kernel=poly; total time=   0.2s\n",
            "[CV] END ....................C=0.1, gamma=scale, kernel=poly; total time=   0.2s\n",
            "[CV] END ....................C=0.1, gamma=scale, kernel=poly; total time=   0.2s\n",
            "[CV] END ......................C=0.1, gamma=auto, kernel=rbf; total time=   0.3s\n",
            "[CV] END ......................C=0.1, gamma=auto, kernel=rbf; total time=   0.3s\n",
            "[CV] END ......................C=0.1, gamma=auto, kernel=rbf; total time=   0.3s\n",
            "[CV] END .....................C=0.1, gamma=auto, kernel=poly; total time=   0.2s\n",
            "[CV] END .....................C=0.1, gamma=auto, kernel=poly; total time=   0.2s\n",
            "[CV] END .....................C=0.1, gamma=auto, kernel=poly; total time=   0.2s\n",
            "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time=   0.3s\n",
            "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time=   0.3s\n",
            "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time=   0.3s\n",
            "[CV] END ......................C=1, gamma=scale, kernel=poly; total time=   0.2s\n",
            "[CV] END ......................C=1, gamma=scale, kernel=poly; total time=   0.3s\n",
            "[CV] END ......................C=1, gamma=scale, kernel=poly; total time=   0.3s\n",
            "[CV] END ........................C=1, gamma=auto, kernel=rbf; total time=   0.4s\n",
            "[CV] END ........................C=1, gamma=auto, kernel=rbf; total time=   0.4s\n",
            "[CV] END ........................C=1, gamma=auto, kernel=rbf; total time=   0.4s\n",
            "[CV] END .......................C=1, gamma=auto, kernel=poly; total time=   0.3s\n",
            "[CV] END .......................C=1, gamma=auto, kernel=poly; total time=   0.3s\n",
            "[CV] END .......................C=1, gamma=auto, kernel=poly; total time=   0.3s\n",
            "[CV] END ......................C=10, gamma=scale, kernel=rbf; total time=   1.2s\n",
            "[CV] END ......................C=10, gamma=scale, kernel=rbf; total time=   0.5s\n",
            "[CV] END ......................C=10, gamma=scale, kernel=rbf; total time=   0.6s\n",
            "[CV] END .....................C=10, gamma=scale, kernel=poly; total time=   0.4s\n",
            "[CV] END .....................C=10, gamma=scale, kernel=poly; total time=   0.4s\n",
            "[CV] END .....................C=10, gamma=scale, kernel=poly; total time=   0.4s\n",
            "[CV] END .......................C=10, gamma=auto, kernel=rbf; total time=   0.6s\n",
            "[CV] END .......................C=10, gamma=auto, kernel=rbf; total time=   0.3s\n",
            "[CV] END .......................C=10, gamma=auto, kernel=rbf; total time=   0.3s\n",
            "[CV] END ......................C=10, gamma=auto, kernel=poly; total time=   0.2s\n",
            "[CV] END ......................C=10, gamma=auto, kernel=poly; total time=   0.2s\n",
            "[CV] END ......................C=10, gamma=auto, kernel=poly; total time=   0.2s\n",
            "                    precision    recall  f1-score   support\n",
            "\n",
            "  Robert Downey Jr       0.00      0.00      0.00         5\n",
            "         Brad Pitt       0.33      0.25      0.29         8\n",
            "Scarlett Johansson       0.54      0.83      0.65        18\n",
            " Denzel Washington       0.12      0.12      0.12         8\n",
            "     Nicole Kidman       0.33      0.05      0.09        20\n",
            " Jennifer Lawrence       0.00      0.00      0.00         9\n",
            "      Kate Winslet       0.20      0.40      0.27         5\n",
            "         Megan Fox       0.00      0.00      0.00         5\n",
            "         Tom Hanks       0.00      0.00      0.00         6\n",
            "        Will Smith       0.00      0.00      0.00         4\n",
            "    Angelina Jolie       0.17      0.20      0.18         5\n",
            "   Natalie Portman       0.14      0.10      0.12        10\n",
            "        Tom Cruise       0.33      0.22      0.27         9\n",
            "    Sandra Bullock       0.08      0.25      0.12         4\n",
            " Leonardo DiCaprio       0.25      0.14      0.18         7\n",
            "      Hugh Jackman       0.00      0.00      0.00         9\n",
            "       Johnny Depp       0.00      0.00      0.00        12\n",
            "\n",
            "          accuracy                           0.19       144\n",
            "         macro avg       0.15      0.15      0.13       144\n",
            "      weighted avg       0.20      0.19      0.17       144\n",
            "\n",
            "Best parameters found: {'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5BtD3ir4-hw7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}