{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_g1g5y7Q0Ofu"
      },
      "source": [
        "##NORMAL VS BACTERIAL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qVyBV2-0T2q"
      },
      "source": [
        "##Getting Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-sw8pr221P4",
        "outputId": "524c01a3-10fa-4777-abf4-6bcb7060e89a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using Colab cache for faster access to the 'pneumonia-multiclass' dataset.\n",
            "Path to dataset files: /kaggle/input/pneumonia-multiclass\n"
          ]
        }
      ],
      "source": [
        "import kagglehub\n",
        "\n",
        "# Download latest version\n",
        "path = kagglehub.dataset_download(\"kmljts/pneumonia-multiclass\")\n",
        "\n",
        "print(\"Path to dataset files:\", path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CVYbjhhx1Bsz"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from PIL import Image\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import *\n",
        "from torchvision import *\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmVe2Vtq1Bsz",
        "outputId": "4c0d19d6-cb45-46ce-b997-1cdfe8e95b6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPcbSMB50a6w"
      },
      "source": [
        "##Train Test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4xUhWta1Bsz",
        "outputId": "6002cb9a-0fdf-44e0-f2fa-78f1bff9ed7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset successfully split into train/test!\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import shutil\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Base and output directories\n",
        "base_dir = \"/content/chest_xray\"\n",
        "output_dir = \"chest_xray_split\"\n",
        "\n",
        "# Split ratios\n",
        "train_ratio = 0.7\n",
        "test_ratio = 0.3\n",
        "\n",
        "\n",
        "for split in ['train', 'test']:\n",
        "    for category in os.listdir(base_dir):\n",
        "        os.makedirs(os.path.join(output_dir, split, category), exist_ok=True)\n",
        "\n",
        "\n",
        "for category in os.listdir(base_dir):\n",
        "    category_path = os.path.join(base_dir, category)\n",
        "    images = os.listdir(category_path)\n",
        "\n",
        "    train_imgs, test_imgs = train_test_split(images, test_size=test_ratio, random_state=42)\n",
        "\n",
        "\n",
        "    for img in train_imgs:\n",
        "        shutil.copy(os.path.join(category_path, img), os.path.join(output_dir, 'train', category, img))\n",
        "\n",
        "\n",
        "    for img in test_imgs:\n",
        "        shutil.copy(os.path.join(category_path, img), os.path.join(output_dir, 'test', category, img))\n",
        "\n",
        "print(\"Dataset successfully split into train/test!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAHjyg_40fi0"
      },
      "source": [
        "##Dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6nRBDF-q1Bs0"
      },
      "outputs": [],
      "source": [
        "class Data(Dataset):\n",
        "  def __init__(self,root_dir,transform=None):\n",
        "    self.root_dir=root_dir\n",
        "    self.transform=transform\n",
        "    self.image_paths=[]\n",
        "    self.labels=[]\n",
        "\n",
        "    for label in [\"NORMAL\",\"PNEUMONIA_BACTERIAL\"]:\n",
        "      class_dir=os.path.join(root_dir,label)\n",
        "      for img_name in os.listdir(class_dir):\n",
        "        self.image_paths.append(os.path.join(class_dir,img_name))\n",
        "        self.labels.append(0 if label==\"NORMAL\" else 1)\n",
        "  def __len__(self):\n",
        "    return len(self.image_paths)\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    image_path=self.image_paths[idx]\n",
        "    image=Image.open(image_path).convert(\"RGB\")\n",
        "    label=self.labels[idx]\n",
        "    if self.transform:\n",
        "      image=self.transform(image)\n",
        "    return image,label\n",
        "transform=transforms.Compose([\n",
        "    transforms.Resize((300,300)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "train_dataset=Data(\"/content/chest_xray_split/train\",transform=transform)\n",
        "test_dataset=Data(\"/content/chest_xray_split/test\",transform=transform)\n",
        "\n",
        "train_loader=DataLoader(train_dataset,batch_size=32,shuffle=True)\n",
        "test_loader=DataLoader(test_dataset,batch_size=32,shuffle=False)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WaDEPWd0iKi"
      },
      "source": [
        "##Download Pretrained Model and change Fully connected layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3pGoyXIF1TJB"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "# Load pretrained EfficientNet-B3\n",
        "model = models.efficientnet_b3(weights='IMAGENET1K_V1')\n",
        "\n",
        "in_features = model.classifier[1].in_features\n",
        "model.classifier[1] = nn.Linear(in_features, 2)\n",
        "model=model.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fs-qm3jT0qcx"
      },
      "source": [
        "##Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1-Z0rVG2P1Q",
        "outputId": "fe05b57e-622e-4d37-a77f-00fa67a46031"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch1/10, loss: 0.22936275601387024\n",
            "epoch2/10, loss: 0.06917072832584381\n",
            "epoch3/10, loss: 0.03319532051682472\n",
            "epoch4/10, loss: 0.019602887332439423\n",
            "epoch5/10, loss: 0.015312849543988705\n",
            "epoch6/10, loss: 0.009472310543060303\n",
            "epoch7/10, loss: 0.005126246251165867\n",
            "epoch8/10, loss: 0.01437431387603283\n",
            "epoch9/10, loss: 0.0060770707204937935\n",
            "epoch10/10, loss: 0.005410835146903992\n",
            "test acc: 0.9816653934300993\n"
          ]
        }
      ],
      "source": [
        "epochs=10\n",
        "for epoch in range(epochs):\n",
        "  model.train()\n",
        "  running_loss=0.0\n",
        "  for images,labels in train_loader:\n",
        "    images, labels = images.to(device), labels.to(device)\n",
        "    optimizer.zero_grad()\n",
        "    outputs=model(images).to(device)\n",
        "\n",
        "    loss=criterion(outputs,labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    running_loss+=loss\n",
        "\n",
        "  print(f\"epoch{epoch+1}/{epochs}, loss: {running_loss/len(train_loader)}\")\n",
        "\n",
        "model.eval()\n",
        "test_labels=[]\n",
        "test_preds=[]\n",
        "with torch.inference_mode():\n",
        "  for images,labels in test_loader:\n",
        "      images, labels = images.to(device), labels.to(device)\n",
        "      outputs=model(images)\n",
        "      _,preds=torch.max(outputs,1)\n",
        "      test_labels.extend(labels.cpu().numpy())\n",
        "      test_preds.extend(preds.cpu().numpy())\n",
        "test_acc=accuracy_score(test_labels,test_preds)\n",
        "print(f\"test acc: {test_acc}\")\n",
        "torch.save(model.state_dict(),\"model_bact.pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xrkl2K-s0t1c"
      },
      "source": [
        "##Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmfP5l-M1nzs",
        "outputId": "265a68d4-04d4-40af-8069-f88e84075f9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.97      0.97       475\n",
            "           1       0.98      0.99      0.99       834\n",
            "\n",
            "    accuracy                           0.98      1309\n",
            "   macro avg       0.98      0.98      0.98      1309\n",
            "weighted avg       0.98      0.98      0.98      1309\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_labels,test_preds))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qEUcNi9l0wdq"
      },
      "source": [
        "##Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "jMaD7FPp1nzt",
        "outputId": "b68243d3-342a-44f3-df9c-f0b03d1d0818"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[459  16]\n",
            " [  8 826]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "class_labels=[\"NORMAL\",\"PNEUMONIA_BACTERIAL\"]\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "cm=confusion_matrix(test_labels,test_preds)\n",
        "\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True,fmt=\"d\", xticklabels=class_labels, yticklabels=class_labels)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdlzyeBt00TD"
      },
      "source": [
        "##ROC Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "pURnE0w31nzu",
        "outputId": "eb41dfe6-5105-4d9b-eb66-c33a3969c05d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "model.eval()\n",
        "test_probs = []\n",
        "with torch.no_grad():\n",
        "  for images, _ in test_loader:\n",
        "    images = images.to(device)\n",
        "    outputs = model(images)\n",
        "    probs = torch.softmax(outputs, dim=1)\n",
        "    test_probs.extend(probs.cpu().numpy())\n",
        "\n",
        "test_probs = np.array(test_probs)\n",
        "\n",
        "n_classes = len(class_labels)\n",
        "\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "for i in range(n_classes):\n",
        "\n",
        "    binary_y_true = (np.array(test_labels) == i).astype(int)\n",
        "\n",
        "    y_score_for_class_i = test_probs[:, i]\n",
        "\n",
        "    fpr[i], tpr[i], _ = roc_curve(binary_y_true, y_score_for_class_i)\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "for i in range(n_classes):\n",
        "    plt.plot(fpr[i], tpr[i], label=f'ROC curve of class {class_labels[i]} (area = {roc_auc[i]:0.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', label='Random classifier')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve - One-vs-Rest')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5v1Ih_8N1SjB"
      },
      "source": [
        "##NORMAL VS VIRAL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7xB5UdIP1U-D"
      },
      "source": [
        "##Getting Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kc8vT8Hj1U-E",
        "outputId": "524c01a3-10fa-4777-abf4-6bcb7060e89a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using Colab cache for faster access to the 'pneumonia-multiclass' dataset.\n",
            "Path to dataset files: /kaggle/input/pneumonia-multiclass\n"
          ]
        }
      ],
      "source": [
        "import kagglehub\n",
        "\n",
        "# Download latest version\n",
        "path = kagglehub.dataset_download(\"kmljts/pneumonia-multiclass\")\n",
        "\n",
        "print(\"Path to dataset files:\", path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v8XR76u-1U-F"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from PIL import Image\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import *\n",
        "from torchvision import *\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBA79PQ51U-G",
        "outputId": "4c0d19d6-cb45-46ce-b997-1cdfe8e95b6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nnj7hTwW1U-G"
      },
      "source": [
        "##Train Test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpcQewvX1U-G",
        "outputId": "6002cb9a-0fdf-44e0-f2fa-78f1bff9ed7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset successfully split into train/test!\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import shutil\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Base and output directories\n",
        "base_dir = \"/content/chest_xray\"\n",
        "output_dir = \"normal_vs_viral\"\n",
        "\n",
        "# Split ratios\n",
        "train_ratio = 0.7\n",
        "test_ratio = 0.3\n",
        "\n",
        "\n",
        "for split in ['train', 'test']:\n",
        "    for category in os.listdir(base_dir):\n",
        "        os.makedirs(os.path.join(output_dir, split, category), exist_ok=True)\n",
        "\n",
        "\n",
        "for category in os.listdir(base_dir):\n",
        "    category_path = os.path.join(base_dir, category)\n",
        "    images = os.listdir(category_path)\n",
        "\n",
        "    train_imgs, test_imgs = train_test_split(images, test_size=test_ratio, random_state=42)\n",
        "\n",
        "\n",
        "    for img in train_imgs:\n",
        "        shutil.copy(os.path.join(category_path, img), os.path.join(output_dir, 'train', category, img))\n",
        "\n",
        "\n",
        "    for img in test_imgs:\n",
        "        shutil.copy(os.path.join(category_path, img), os.path.join(output_dir, 'test', category, img))\n",
        "\n",
        "print(\"Dataset successfully split into train/test!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5vQgghd1U-H"
      },
      "source": [
        "##Dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WPkNkoNX1U-H"
      },
      "outputs": [],
      "source": [
        "class Data(Dataset):\n",
        "  def __init__(self,root_dir,transform=None):\n",
        "    self.root_dir=root_dir\n",
        "    self.transform=transform\n",
        "    self.image_paths=[]\n",
        "    self.labels=[]\n",
        "\n",
        "    for label in [\"NORMAL\",\"PNEUMONIA_VIRAL\"]:\n",
        "      class_dir=os.path.join(root_dir,label)\n",
        "      for img_name in os.listdir(class_dir):\n",
        "        self.image_paths.append(os.path.join(class_dir,img_name))\n",
        "        self.labels.append(0 if label==\"NORMAL\" else 1)\n",
        "  def __len__(self):\n",
        "    return len(self.image_paths)\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    image_path=self.image_paths[idx]\n",
        "    image=Image.open(image_path).convert(\"RGB\")\n",
        "    label=self.labels[idx]\n",
        "    if self.transform:\n",
        "      image=self.transform(image)\n",
        "    return image,label\n",
        "transform=transforms.Compose([\n",
        "    transforms.Resize((300,300)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
        "])\n",
        "\n",
        "train_dataset=Data(\"/content/normal_vs_viral/train\",transform=transform)\n",
        "test_dataset=Data(\"/content/normal_vs_viral/test\",transform=transform)\n",
        "\n",
        "train_loader=DataLoader(train_dataset,batch_size=32,shuffle=True)\n",
        "test_loader=DataLoader(test_dataset,batch_size=32,shuffle=False)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crg9yaq61U-H"
      },
      "source": [
        "##Download Pretrained Model and change Fully connected layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RGH9_PG81U-I"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchvision import models\n",
        "\n",
        "# Load pretrained EfficientNet-B3\n",
        "model = models.efficientnet_b3(weights='IMAGENET1K_V1')\n",
        "\n",
        "in_features = model.classifier[1].in_features\n",
        "model.classifier[1] = nn.Linear(in_features, 2)\n",
        "model=model.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-G04QqqN1U-I"
      },
      "source": [
        "##Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MdMrcCvB2Rx",
        "outputId": "1525df2e-4426-47bc-c7f4-672bcfad8459"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch1/10, loss: 0.29695746302604675\n",
            "epoch2/10, loss: 0.12992669641971588\n",
            "epoch3/10, loss: 0.06569618731737137\n",
            "epoch4/10, loss: 0.051869262009859085\n",
            "epoch5/10, loss: 0.02844330668449402\n",
            "epoch6/10, loss: 0.026652837172150612\n",
            "epoch7/10, loss: 0.026175808161497116\n",
            "epoch8/10, loss: 0.014393065124750137\n",
            "epoch9/10, loss: 0.019540423527359962\n",
            "epoch10/10, loss: 0.01080858800560236\n",
            "test acc: 0.9891657638136512\n"
          ]
        }
      ],
      "source": [
        "epochs=10\n",
        "for epoch in range(epochs):\n",
        "  model.train()\n",
        "  running_loss=0.0\n",
        "  for images,labels in train_loader:\n",
        "    images, labels = images.to(device), labels.to(device)\n",
        "    optimizer.zero_grad()\n",
        "    outputs=model(images).to(device)\n",
        "\n",
        "    loss=criterion(outputs,labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    running_loss+=loss\n",
        "\n",
        "  print(f\"epoch{epoch+1}/{epochs}, loss: {running_loss/len(train_loader)}\")\n",
        "\n",
        "model.eval()\n",
        "test_labels=[]\n",
        "test_preds=[]\n",
        "with torch.inference_mode():\n",
        "  for images,labels in test_loader:\n",
        "      images, labels = images.to(device), labels.to(device)\n",
        "      outputs=model(images)\n",
        "      _,preds=torch.max(outputs,1)\n",
        "      test_labels.extend(labels.cpu().numpy())\n",
        "      test_preds.extend(preds.cpu().numpy())\n",
        "test_acc=accuracy_score(test_labels,test_preds)\n",
        "print(f\"test acc: {test_acc}\")\n",
        "torch.save(model.state_dict(),\"model_viral.pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhcSAoKm1U-J"
      },
      "source": [
        "##Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlrqZNVHB2Ry",
        "outputId": "b51ff4cf-1fd3-4272-8d08-74e76c8db676"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       475\n",
            "           1       0.99      0.99      0.99       448\n",
            "\n",
            "    accuracy                           0.99       923\n",
            "   macro avg       0.99      0.99      0.99       923\n",
            "weighted avg       0.99      0.99      0.99       923\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_labels,test_preds))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_kFBQAR1U-J"
      },
      "source": [
        "##Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "n7GtH-jHB2R0",
        "outputId": "76915eb3-a528-408c-98d8-35da53837195"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[469  6]\n",
            " [ 4 444]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "class_labels=[\"NORMAL\",\"PNEUMONIA_VIRAL\"]\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "cm=confusion_matrix(test_labels,test_preds)\n",
        "\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True,fmt=\"d\", xticklabels=class_labels, yticklabels=class_labels)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUid_7gR1U-K"
      },
      "source": [
        "##ROC Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "YVQzb546B2R1",
        "outputId": "1ec296ed-75ce-4cb2-85ae-c94cd7ee054c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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7Uqa4RrJq165tZMqUybhw4cITn2NctmzZEmvU+RFvb2+jePHixs2bN42bN28ax48fNz744ANDUqzRgXLlyhnu7u5PfayJEycakoy1a9cahmEY5cuXf+Z94lK0aFGjcePGT71NQrfdhI5kxfVz3rRpU5yvZZMmTWJskwnZpl5EYGCgIcl47733nnq7MmXKGFmzZrVd7ty5c5yjkuXLlzcqVqxou/y///3PkGQbEX7k0Z4M/13+X+3atTNy5MgR4z3z6tWrhp2dne2x79y5Y9vz4Hnkz5/faNiwoW0bPnLkiNGxY0fbaGRCn8uaNWvi3OPkcewuCLMxuyDwBD4+PsqePbu8vLzUunVrZciQQWvXrrWNOty+fVvbtm1TmzZtdO/ePQUFBSkoKEi3bt1So0aNdOrUKdtshKtWrVLZsmXj/MbXYrFIklasWKESJUqoePHitnUFBQWpXr16kqTt27c/Maufn58iIyNt3+JJ0q+//qq7d+/Kz89P0r8H6a9atUrNmjWTYRgxHqNRo0YKDg5WQEBAjPV27txZrq6uz3yt7t27J0nKlCnTE2/z6LqQkJAYy1u0aKG8efPaLlepUkVVq1bVhg0bJCXsdX6kR48esSYiePx5REZG6tatWypSpIiyZMkS63knVNeuXWN8Y16rVi1J/04mIEkHDhzQrVu31KNHjxgTLrRv3z7Wt7hxefSaPe31jUuvXr1iXK5Vq5Zu3boV42fw+OsSHBysoKAgeXt76+zZswoODo5x/4IFC9pGRR8Xn3Vs3rxZ9+7d0+DBg2NNHPPod+BpEvr74e3trZIlSz5zvVmyZNHvv/8eY/a853Xz5k399ttv6tatm1566aUY1z3rOd66dUuSnrg9HD9+XNmzZ1f27NlVvHhxjR8/Xs2bN481ffy9e/eeuZ3893cxJCQkwdvWo6zPOk3A82678RXXz7levXry9PTUsmXLbMvu3LmjzZs3294PpRd7z02I+Lw/Prr+v++PUty/x4/eW6R/n4e7u7saNGgQ43lUrFhRGTNmfObz8PPz040bN2LMwrdy5UpZrVbb6+Xq6ionJyft2LFDd+7ceer6nuTXX3+1bcOlS5fWggUL1LVr1xiTsMT3uTwavV2/fr0iIyOfKw+Q1NhdEHiCKVOmqGjRogoODtbs2bP122+/ydnZ2Xb96dOnZRiGhg0bpmHDhsW5jhs3bihv3rw6c+aM3nzzzac+3qlTp3Ts2DFlz579iet6krJly6p48eJatmyZunfvLunfXT08PT1tHxhu3rypu3fv6ocfftAPP/wQr8d40q4t//Xow8O9e/di7Lr0uCd90Hj55Zdj3bZo0aJavny5pIS9zk/L/fDhQ40dO1Zz5szRlStXYkwp/98ykVD//UD96IPyow8jj855VKRIkRi3c3BwiNduNpkzZ5b0f69hYuR6tM7du3drxIgR2rt3r0JDQ2PcPjg4WO7u7rbLT9oe4rOOM2fOSJJt16OESujvR3y33S+//FKdO3eWl5eXKlasqCZNmqhTp04qVKhQgjM++uD7vM9R0hNPdVCgQAHNmDFDVqtVZ86c0eeff66bN2/GKqyZMmV6ZvH57+9i5syZY3xoT0jWZ5XH59124yuun7ODg4PefPNNLV68WOHh4XJ2dtbq1asVGRkZo2S9yHvu/fv3df/+fdtle3v7J67n8ffHp4mrILu4uMRar4eHR4yic+rUKQUHBytHjhxPfR7BwcF6+PChbbmTk5OyZs2q1157Te7u7lq2bJnq168v6d+/H+XKlVPRokUlSc7Ozho3bpwGDhyonDlz6tVXX1XTpk3VqVMn5cqV66nrf6Rq1aoaPXq0oqOjdfToUY0ePVp37tyJ8QVVfJ+Lt7e33nzzTY0aNUqTJk1SnTp11KJFC/n7+8f4Ow2YiZIFPEGVKlVUqVIlSf+OttSsWVP+/v46ceKEMmbMaDs/zaBBg+L8dl+K/aH6aaxWq0qXLq2JEyfGeb2Xl9dT7+/n56fPP/9cQUFBypQpk9auXat27drZRk4e5e3QoUOsY7ce+e/+6vEZxZL+PWbpxx9/1J9//qnatWvHeZs///xTkuI1uvC453md48rdr18/zZkzR/3791e1atXk7u4ui8Witm3bPvFcQ/H1pOm7n/SBOaGKFy8uSTpy5IjKlSsX7/s9K9eZM2dUv359FS9eXBMnTpSXl5ecnJy0YcMGTZo0KdbrEtfrmtB1PK+E/n7Ed9tt06aNatWqpTVr1ujXX3/V+PHjNW7cOK1evVqNGzd+4dzxlS1bNkl64ihBhgwZYhzLWKNGDVWoUEEff/yxvvnmG9vyEiVKKDAwUBcvXoxVsh/57+9i8eLFdejQIV26dOmZ7zOPu3PnTpxfkjwuodvuk0pbdHR0nMuf9HNu27atpk+frl9++UUtWrTQ8uXLVbx4cZUtW9Z2mxd5z/3qq680atQo2+X8+fM/8bQDRYoUkYODg+11j0t4eLhOnDhh+5vzSHxODWC1WpUjRw4tWrQozusflbT33ntP8+bNsy339vbWjh075OzsrBYtWmjNmjX6/vvvdf36de3evVtjxoyJsZ7+/furWbNm+vHHH7Vp0yYNGzZMY8eO1bZt21S+fPknrv8RT09P2zbcqFEjFS9eXE2bNtXkyZM1YMCABD0Xi8WilStXat++fVq3bp02bdqkbt26acKECdq3b99Tj5sEkgslC4gHe3t7jR07VnXr1tV3332nwYMH277pdnR0jPHhJy6FCxfW0aNHn3mbw4cPq379+vHafeq//Pz8NGrUKK1atUo5c+ZUSEiI7QBv6d8/TpkyZVJ0dPQz8yZU06ZNNXbsWM2fPz/OkhUdHa3FixfLw8NDNWrUiHHdqVOnYt3+5MmTthGehLzOT7Ny5Up17txZEyZMsC0LCwvT3bt3Y9zueV77Z3l0YtnTp0+rbt26tuVRUVE6f/78Mw/Gbty4sezt7bVw4cJEnUBg3bp1Cg8P19q1a2N8IE/IblLxXUfhwoUlSUePHn3qlw9Pev1f9PfjaXLnzq133nlH77zzjm7cuKEKFSro888/t5Ws+D7eo231Wb/rcXlURs6dOxev25cpU0YdOnTQ9OnTNWjQINtr37RpUy1ZskTz58/XJ598Eut+ISEh+umnn1S8eHHbz6FZs2ZasmSJFi5cqCFDhsTr8aOionTp0iU1b978qbdL6Lbr4eER63dS+r/R4PiqXbu2cufOrWXLlqlmzZratm2bhg4dGuM2L7JNderUSTVr1rRdflqpz5Ahg+rWratt27bpwoULMU40/cjy5csVHh6upk2bJiiH9O/z2LJli2rUqPHUHB9++KE6dOhgu/z4rql+fn6aN2+etm7dqmPHjskwjBijfo8/1sCBAzVw4ECdOnVK5cqV04QJE7Rw4cKnrj8ur7/+ury9vTVmzBj17NlTGTJkiPdzeeTVV1/Vq6++qs8//1yLFy9W+/bttXTpUr311ltJ8l4OJATHZAHxVKdOHVWpUkVff/21wsLClCNHDtWpU0fTp0/X1atXY93+5s2btn+/+eabOnz4sNasWRPrdo9GFdq0aaMrV65oxowZsW7z8OFD2yx5T1KiRAmVLl1ay5Yt07Jly5Q7d+4Yhcfe3l5vvvmmVq1aFeeHwMfzJlT16tXl4+OjOXPmaP369bGuHzp0qE6ePKkPP/ww1h/OH3/8McYxVfv379fvv/9u+4CbkNf5aezt7WONLH377bexviHPkCGDJMX5Qe95VapUSdmyZdOMGTMUFRVlW75o0aJ4Hd/g5eWlHj166Ndff9W3334b63qr1aoJEybo8uXLCcr16Fvy/+46OWfOnERfR8OGDZUpUyaNHTtWYWFhMa57/L4ZMmSIc/fNF/39iEt0dHSsx8qRI4fy5Mmj8PDwZ2b6r+zZs6t27dqaPXu2Ll68GOO6Z41q5s2bV15eXjpw4EC883/44YeKjIyMMRLTunVrlSxZUl988UWsdVmtVvXu3Vt37tzRiBEjYtyndOnS+vzzz7V3795Yj3Pv3r1YBeXvv/9WWFjYM0+em9Btt3DhwgoODo4x6nP16tU43zufxs7OTq1bt9a6deu0YMECRUVFxSoNL7JNFSpUSD4+Prb//vvl0X998sknMgxDXbp0ibFLnfRvsf7www+VO3du9ezZMwHP8v+eR3R0tD777LNY10VFRdney0qWLBkjc8WKFW238/HxUdasWW1/P6pUqRJjV8zQ0NBYv7eFCxdWpkyZbL8rT1v/k3z00Ue6deuW7WcQ3+dy586dWL9Tj0ZKH+Vxc3OTlLjv5UBCMJIFJMAHH3wgX19fzZ07V7169dKUKVNUs2ZNlS5dWj169FChQoV0/fp17d27V5cvX9bhw4dt91u5cqV8fX3VrVs3VaxYUbdv39batWs1bdo0lS1bVh07dtTy5cvVq1cvbd++XTVq1FB0dLSOHz+u5cuXa9OmTbF2JfkvPz8/DR8+XC4uLurevbvs7GJ+j/LFF19o+/btqlq1qnr06KGSJUvq9u3bCggI0JYtW3T79u3nfm3mz5+v+vXr64033pC/v79q1aql8PBwrV69Wjt27JCfn58++OCDWPcrUqSIatasqd69eys8PFxff/21smXLpg8//NB2m/i+zk/TtGlTLViwQO7u7ipZsqT27t2rLVu22HbTeqRcuXKyt7fXuHHjFBwcLGdnZ9WrV++JxwjEh5OTk0aOHKl+/fqpXr16atOmjc6fP6+5c+eqcOHC8frGdcKECTpz5ozeffddrV69Wk2bNpWHh4cuXryoFStW6Pjx4zFGLuOjYcOGcnJyUrNmzdSzZ0/dv39fM2bMUI4cOeIstC+yjsyZM2vSpEl66623VLlyZfn7+8vDw0OHDx9WaGiobTejihUratmyZRowYIAqV66sjBkzqlmzZony+/Ff9+7dU758+dS6dWuVLVtWGTNm1JYtW/THH3/EGPF8Uqa4fPPNN6pZs6YqVKigt99+WwULFtT58+f1888/KzAw8Kl53njjDa1ZsyZexzpJ/36obdKkiWbOnKlhw4YpW7ZscnJy0sqVK1W/fn3VrFlTXbt2VaVKlXT37l0tXrxYAQEBGjhwYIxtxdHRUatXr5aPj49q166tNm3aqEaNGnJ0dNRff/1lG4V+fAr6zZs3y83NTQ0aNHhmzoRsu23bttVHH32kli1b6t1331VoaKimTp2qokWLJniCGj8/P3377bcaMWKESpcuHetUDEmxTT1J7dq19dVXX2nAgAEqU6aMunTpoty5c+v48eO2Y+02bNgQr4lw/svb21s9e/bU2LFjFRgYqIYNG8rR0VGnTp3SihUrNHnyZLVu3fqp63B0dFSrVq20dOlSPXjwQF999VWM60+ePKn69eurTZs2KlmypBwcHLRmzRpdv349we87j2vcuLFKlSqliRMnqk+fPvF+LvPmzdP333+vli1bqnDhwrp3755mzJihzJkzq0mTJpL+HV0sWbKkli1bpqJFiypr1qwqVarUCx0zCSRIMs9mCKR4TzoZsWH8e5LEwoULG4ULF7ZNd3vmzBmjU6dORq5cuQxHR0cjb968RtOmTY2VK1fGuO+tW7eMvn372k7kmC9fPqNz584xplOPiIgwxo0bZ7zyyiuGs7Oz4eHhYVSsWNEYNWqUERwcbLvdf6dwf+TUqVO2ky7u2rUrzud3/fp1o0+fPoaXl5fh6Oho5MqVy6hfv77xww8/2G7zaGryFStWJOi1u3fvnjFy5EjjlVdeMVxdXY1MmTIZNWrUMObOnRtrCuvHT0Y8YcIEw8vLy3B2djZq1aplHD58ONa64/M6P+1nd+fOHaNr166Gp6enkTFjRqNRo0bG8ePH43wtZ8yYYRQqVMiwt7eP18mI//s6Pekktd98842RP39+w9nZ2ahSpYqxe/duo2LFisZrr70Wj1fXMKKiooyZM2catWrVMtzd3Q1HR0cjf/78RteuXWNMkf1ouuvHT3T9+Ovz+MlD165da5QpU8Z2Ms9x48YZs2fPjnW7J51MNCHreHTb6tWrG66urkbmzJmNKlWqGEuWLLFdf//+fcPf39/IkiVLrJMRx/f3Q/+ZFvpxemxK5/DwcOODDz4wypYta2TKlMnIkCGDUbZs2VgnUn5Spif9nI8ePWq0bNnSyJIli+Hi4mIUK1bMGDZsWJx5HhcQEGBIijWl+JNORmwYhrFjx444p6m+ceOGMWDAAKNIkSKGs7OzkSVLFsPHx8c2bXtc7ty5YwwfPtwoXbq04ebmZri4uBilSpUyhgwZYly9ejXGbatWrWp06NDhmc/pkfhuu4ZhGL/++qtRqlQpw8nJyShWrJixcOHCp56M+EmsVqvh5eVlSDJGjx4d523iu00llt9++8144403DE9PT8PR0dF46aWXjB49ehjnz5+PddtHJyP+r7heC8MwjB9++MGoWLGi7b23dOnSxocffhjjZNVPs3nzZkOSYbFYYpyqxDAMIygoyOjTp49RvHhxI0OGDIa7u7tRtWpVY/ny5fFa99PePx6dyuLx36NnPZeAgACjXbt2xksvvWQ7YXHTpk2NAwcOxFj3nj17jIoVKxpOTk5M545kZzGMRDoyGwAS4Pz58ypYsKDGjx+vQYMGmR3HFFarVdmzZ1erVq3i3GUJ6U/9+vWVJ08eLViwwOwoTxQYGKgKFSooICAgQROxAEB6wjFZAJAMwsLCYh1DMH/+fN2+fVt16tQxJxRSnDFjxmjZsmUJnughOX3xxRdq3bo1BQsAnoJjsgAgGezbt0/vv/++fH19lS1bNgUEBGjWrFkqVaqUfH19zY6HFKJq1aqKiIgwO8ZTLV261OwIAJDiUbIAIBkUKFBAXl5e+uabb3T79m1lzZpVnTp10hdffBHjZJwAACD145gsAAAAAEhEHJMFAAAAAImIkgUAAAAAiSjdHZNltVr1zz//KFOmTPE62SMAAACAtMkwDN27d0958uSRnV3ijT+lu5L1zz//yMvLy+wYAAAAAFKIS5cuKV++fIm2vnRXsjJlyiRJOnfunLJmzWpyGqRlkZGR+vXXX9WwYUM5OjqaHQdpGNsakgvbGpIL2xqSy+3bt1WwYEFbR0gs6a5kPdpFMFOmTMqcObPJaZCWRUZGys3NTZkzZ+YPBJIU2xqSC9sakgvbGpJLZGSkJCX6YURMfAEAAAAAiYiSBQAAAACJiJIFAAAAAImIkgUAAAAAiYiSBQAAAACJiJIFAAAAAImIkgUAAAAAiYiSBQAAAACJiJIFAAAAAImIkgUAAAAAiYiSBQAAAACJiJIFAAAAAImIkgUAAAAAiYiSBQAAAACJiJIFAAAAAImIkgUAAAAAiYiSBQAAAACJiJIFAAAAAImIkgUAAAAAiYiSBQAAAACJiJIFAAAAAImIkgUAAAAAicjUkvXbb7+pWbNmypMnjywWi3788cdn3mfHjh2qUKGCnJ2dVaRIEc2dOzfJcwIAAABAfJlash48eKCyZctqypQp8br9uXPn9Prrr6tu3boKDAxU//799dZbb2nTpk1JnBQAAAAA4sfBzAdv3LixGjduHO/bT5s2TQULFtSECRMkSSVKlNCuXbs0adIkNWrUKKliIr0zDCkyNOH3i4yUfXS4FPFAMhwTPxfwCNtaqmMYhh5GRpsdI8EiIyMVFRmu0PvBcnRkW0PSYVtDcgm9H5Ik6zW1ZCXU3r175ePjE2NZo0aN1L9//yfeJzw8XOHh4bbLISH/vpCRkZGKjIx8/jDP+8E7xipS5x/Z9Cbjkjdkf+Nogu/nKKmpJP2Z2ImAmNjWUh+LJDezQzynNyUp4W+JQIKxrSE5bDvxAn3gKVJVybp27Zpy5swZY1nOnDkVEhKihw8fytXVNdZ9xo4dq1GjRsVavn37drm5PeefOMNQzVOjle3Bqee7/2OcXngNAAAAAJ5H1Xz2SbLeVFWynseQIUM0YMAA2+WQkBB5eXmpbt26ypYtW8JW9mj0KjJUjoEvXrCQevxlzS/fiBEyzA4CIM0oljOT5nauIIvF7CTxFxkVrZ07d8rb21uODknzwQSQ2NaQdAzD0M8bftFrjRrKwcFB9+/clb56OdEfJ1WVrFy5cun69esxll2/fl2ZM2eOcxRLkpydneXs7BxruaOjY/z38TWMf491mPOadO1IjKtC3zsuObrJMKQOs/br+LWE79f5vw/rys2JN5CUrKCjmw4k8JNQZGSkNm36VY0aNWR/ciQptrXUydXRXpbU1LD077bm4Ogs9yxZ2daQpNjWkBTu37+v3r17a+HChRo6dKhGjx6tKGvSvA+nqpJVrVo1bdiwIcayzZs3q1q1aon/YP9/1MowrDJmN5bd9SOxbvKXfUm9Pu6g/t27/hGXBD1Mpfweyubhker+0OLZIi2GnO0lNycHOTqmql81pDJsawAAPN1ff/0lX19fHTt2TPb29sqcOXOSPp6pf43v37+v06dP2y6fO3dOgYGBypo1q1566SUNGTJEV65c0fz58yVJvXr10nfffacPP/xQ3bp107Zt27R8+XL9/PPPiZrLsFplnd1I9pf3y6KYFerx3cYeyvk/10olc2fWil7V4r37R2r8JhMAAABILebPn6/evXsrNDRUefLk0dKlS1WrVq0kfUxTS9aBAwdUt25d2+VHx0517txZc+fO1dWrV3Xx4kXb9QULFtTPP/+s999/X5MnT1a+fPk0c+bMRJu+3TAMhYZH6a2pG7UkeH+M6x6Vq9D/FKv/lipKEwAAAGC+hw8fql+/fpo1a5YkqUGDBlq4cKFy5MiR5I9tasmqU6eODOPJUwnMnTs3zvscOnQo0bMYhqHWU/doyLX+WmJ30ra8YthUvZQruxb2qqMDdrHLE6UKAAAASHnOnz+vxYsXy2KxaNSoUfr4449lb5888yCw8/7/9zAyWscuXlMll/8rWNH5qup/HX3l6uRAkQIAAABSkRIlSmjOnDny9PRU/fr1k/WxKVn6v90EVzj93/m0jEGnZJ8hu9woVwAAAECKFx4erkGDBqlt27aqUaOGJMnPz8+ULOm+ZFmthpp+u0vnrt7QMZcL/y7LWVp2GbIrVZ28BAAAAEinzp49qzZt2ujgwYP66aefdPLkSbm4JGzW78RkZ9ojpwCG8W/B+vtqzHNbWbr9QsECAAAAUoEff/xRFSpU0MGDB5UtWzZNnz7d1IIlpfOS9TAy2lawCmTLYFtusaTrlwUAAABI8SIjIzVw4EC1bNlSwcHBqlatmg4dOqTGjRubHS39lizDMBQaEf3oklZ2L2tqHgAAAADxExISIm9vb02cOFGSNHDgQO3cuVNeXl4mJ/tXuj0mq+u8AB25GSnJ0EqnUcrwzcln3gcAAACA+TJlyqQ8efIoS5Ysmjt3rt544w2zI8WQbkvW4cvBsnN2k6vCVemx82LJ61XJ0c28YAAAAABiiYqKUnh4uDJkyCCLxaJZs2bp9u3bKliwoNnRYkm3uws+8r8P6/zfhUGnpW4bmfQCAAAASEH++ecf1a9fX927d5dhGJIkd3f3FFmwpHQ8kvUvQ1mXPTa06ORGwQIAAABSkC1btsjf3183b95UpkyZdObMGRUpUsTsWE+VrkeyXBUuu+tH/r2QqzS7CQIAAAApRHR0tEaNGqWGDRvq5s2bKlu2rA4ePJjiC5aU7keyHtOV3QQBAACAlOD69evq0KGDtmzZIknq0aOHJk+eLFdXV5OTxQ8l6xEKFgAAAGA6wzDUtGlTHThwQG5ubpo2bZo6duxodqwESde7CwIAAABIWSwWiyZMmKDSpUvrjz/+SHUFS6JkAQAAADDZrVu3tG3bNtvl2rVrKzAwUCVLljQx1fOjZAEAAAAwzb59+1S+fHk1b95cx44dsy23s0u9VSX1JgcAAACQahmGoUmTJqlWrVq6dOmS8uTJo+joaLNjJYp0PPGFITeFmx0CAAAASHfu3r2rbt26ac2aNZIkPz8//fDDD8qcObPJyRJHui1Z852+UB2XM2bHAAAAANKVgwcPytfXV+fOnZOTk5MmTZqk3r17y5KGZvtOtyWrgt1pSf//B+n1KiciBgAAAJLBqlWrdO7cORUsWFArVqxQxYoVzY6U6NJtyXok9L3jcsuSi/NkAQAAAMng008/lYODgwYMGKAsWbKYHSdJMPGFoxsFCwAAAEgif/75p9q3b6+IiAhJkoODgz799NM0W7AkShYAAACAJGAYhmbNmqWqVatq8eLF+vzzz82OlGzS/e6CAAAAABLXgwcP9M4772j+/PmSpMaNG6tfv34mp0o+jGQBAAAASDTHjh1TlSpVNH/+fNnZ2WnMmDFav369PD09zY6WbBjJAgAAAJAo1q9fLz8/P4WGhip37txasmSJvL29zY6V7BjJAgAAAJAoihcvLnt7e9WvX1+HDh1KlwVLYiQLAAAAwAsIDg6Wu7u7JKlIkSLas2ePSpQoIXt7e5OTmYeRLAAAAADPZcWKFcqfP7+2bNliW1aqVKl0XbAkShYAAACABAoPD1e/fv3Upk0bBQcHa9q0aWZHSlEoWQAAAADi7dy5c6pZs6a+++47SdKQIUO0dOlSk1OlLByTBQAAACBe1q5dq86dO+vu3bvKmjWrFixYoCZNmpgdK8WhZAEAAAB4pv379+uNN96QJL366qtatmyZXnrpJZNTpUyULAAAAADPVLlyZXXq1EnZsmXTF198IScnJ7MjpViULAAAAABx2rx5sypWrKisWbPKYrFozpw5srNjWodn4RUCAAAAEENUVJQ++eQTNWzYUJ07d5bVapUkClY8MZIFAAAAwObq1atq166ddu7cKUl66aWXFB0dTcFKAEoWAAAAAEnStm3b1K5dO924cUMZM2bUjBkz1LZtW7NjpTrUUQAAACCdi46O1qeffiofHx/duHFDpUuX1oEDByhYz4mSBQAAAKRz9+/f15w5c2QYhrp37659+/apWLFiZsdKtdhdEAAAAEjn3N3dtWLFCv3111/q3Lmz2XFSPUoWAAAAkM5YrVZ99dVX8vDwUI8ePSRJlSpVUqVKlUxOljZQsgAAAIB05Pbt2+rcubPWr18vZ2dn+fj4qGDBgmbHSlMoWQAAAEA68fvvv6tNmza6ePGinJ2d9d1336lAgQJmx0pzmPgCAAAASOMMw9DkyZNVq1YtXbx4UUWKFNG+ffv01ltvyWKxmB0vzWEkCwAAAEjDDMOQn5+fVqxYIUny9fXVzJkzlTlzZpOTpV2MZAEAAABpmMViUZkyZeTo6Khvv/1Wy5Yto2AlMUayAAAAgDTGMAwFBwcrS5YskqSPP/5YrVq1UsmSJc0Nlk4wkgUAAACkIffu3VP79u3l7e2thw8fSpLs7OwoWMmIkgUAAACkEUeOHFGlSpW0ZMkS/fXXX/rtt9/MjpQuUbIAAACANGDOnDmqWrWqTp48qbx582rnzp1q1KiR2bHSJUoWAAAAkIqFhoaqa9eu6tatmx4+fKjXXntNgYGBqlGjhtnR0i1KFgAAAJCK9enTR3PnzpWdnZ0+//xz/fzzz/L09DQ7VrrG7IIAAABAKvbpp5/qwIED+vbbb1WnTh2z40CMZAEAAACpSlhYmFavXm277OXlpcOHD1OwUhBKFgAAAJBKnD59WtWqVdObb76pn376ybbczo6P9SkJPw0AAAAgFVi1apUqVqyowMBAeXp6KkOGDGZHwhNQsgAAAIAULCIiQv3791fr1q0VEhKimjVrKjAwUD4+PmZHwxNQsgAAAIAU6sKFC6pVq5YmT54sSfrwww+1bds25c2b1+RkeBpmFwQAAABSqN9//1379++Xh4eH5s+fr6ZNm5odCfFAyQIAAABSqDZt2ujKlStq1aqV8ufPb3YcxBO7CwIAAAApxOXLl/Xmm2/q+vXrtmXvv/8+BSuVYSQLAAAASAE2bdqkDh06KCgoSFarVWvWrDE7Ep4TI1kAAACAiaKjozVs2DA1btxYQUFBKleunMaPH292LLwARrIAAAAAk1y7dk3+/v7avn27JKlnz576+uuv5eLiYnIyvAhKFgAAAGCCP//8Uw0bNtT169eVIUMGTZ8+Xe3btzc7FhIBJQsAAAAwQcGCBZUlSxZ5enpq5cqVKl68uNmRkEgoWQAAAEAyuXv3rtzd3WWxWJQpUyb98ssvypEjhzJkyGB2NCQiJr4AAAAAksHu3btVqlQpTZo0ybasYMGCFKw0iJIFAAAAJCHDMPTVV1/J29tbV65c0Zw5cxQZGWl2LCQhShYAAACQRG7fvq033nhDH3zwgaKjo9WuXTvt2bNHjo6OZkdDEuKYLAAAACAJ7N+/X23atNGFCxfk5OSkb775Rm+//bYsFovZ0ZDEKFkAAABAIgsKClK9evX04MEDFSpUSCtWrFCFChXMjoVkQskCAAAAEpmnp6c+++wz7dq1S7Nnz5a7u7vZkZCMOCYLAAAASASBgYE6evSo7XL//v21cuVKClY6RMkCAAAAXoBhGPrhhx/06quv6s0339S9e/ckSRaLheOv0il2FwQAAACe0/3799WrVy8tWrRIkvTyyy8rKirK5FQwGyNZAAAAwHM4evSoKleurEWLFsne3l7jxo3T2rVr5eHhYXY0mIyRLAAAACCB5s2bp969e+vhw4fKkyePli1bppo1a5odCykEI1kAAABAAlitVs2fP18PHz5UgwYNdOjQIQoWYmAkCwAAAEgAOzs7LVq0SAsWLNCAAQNkb29vdiSkMIxkAQAAAM+wdOlSffDBB7bLuXLl0gcffEDBQpwYyQIAAACeICwsTAMGDNDUqVMlST4+PmrUqJHJqZDSUbIAAACAOJw9e1a+vr4KCAiQJA0dOlT169c3ORVSA0oWAAAA8B9r1qxR165dFRwcrGzZsmnBggVq3Lix2bGQSnBMFgAAAPCYkSNHqlWrVgoODla1atV06NAhChYShJIFAAAAPKZChQqyWCwaOHCgdu7cKS8vL7MjIZVhd0EAAACke7dv31bWrFklSc2bN9fRo0dVsmRJk1MhtWIkCwAAAOlWVFSUhgwZomLFiunSpUu25RQsvAhKFgAAANKlf/75R/Xr19cXX3yhoKAgrVmzxuxISCPYXRAAAADpzubNm9W+fXvdvHlTmTJl0qxZs+Tr62t2LKQRjGQBAAAg3YiOjtbIkSPVqFEj3bx5U2XLltXBgwcpWEhUlCwAAACkG998841GjRolwzDUo0cP7d27Vy+//LLZsZDGULIAAACQbvTq1UvVq1fXggUL9MMPP8jV1dXsSEiDOCYLAAAAaZbVatXSpUvl5+cne3t7ubq6ateuXbJYLGZHQxrGSBYAAADSpFu3bqlZs2Zq3769xowZY1tOwUJSYyQLAAAAac7evXvl5+enS5cuycXFRXnz5jU7EtIRRrIAAACQZhiGoUmTJql27dq6dOmSXn75Zf3+++/q1q2b2dGQjjCSBQAAgDTh7t276tq1q3788UdJkp+fn3744QdlzpzZ3GBId9L9SJaro73ZEQAAAJAILly4oF9++UVOTk6aMmWKlixZQsGCKdL9SBYHPgIAAKQNZcuW1dy5c1WkSBFVqlTJ7DhIx9L9SBYAAABSp5CQEHXs2FH79++3LWvbti0FC6ZL9yNZAAAASH0OHz4sX19fnTp1Sr///rv+/vtvOTjw0RYpAyNZAAAASDUMw9CsWbP06quv6tSpU/Ly8tK8efMoWEhRKFkAAABIFR48eKDOnTvrrbfeUlhYmJo0aaJDhw6pWrVqZkcDYqDyAwAAIMW7ceOG6tatq7///lt2dnb6/PPP9eGHH8rOjjEDpDymb5VTpkxRgQIF5OLioqpVq8Y4cDEuX3/9tYoVKyZXV1d5eXnp/fffV1hYWDKlBQAAgBk8PT1VsGBB5c6dW9u3b9fgwYMpWEixTB3JWrZsmQYMGKBp06apatWq+vrrr9WoUSOdOHFCOXLkiHX7xYsXa/DgwZo9e7aqV6+ukydPqkuXLrJYLJo4caIJzwAAAABJ5eHDh4qMjJSbm5vs7Ow0f/58RUVFxfk5EUhJTK3/EydOVI8ePdS1a1eVLFlS06ZNk5ubm2bPnh3n7ffs2aMaNWrI399fBQoUUMOGDdWuXbtnjn4BAAAgdfnnn39Uq1Yt9erVS4ZhSJKyZs1KwUKqYNpIVkREhA4ePKghQ4bYltnZ2cnHx0d79+6N8z7Vq1fXwoULtX//flWpUkVnz57Vhg0b1LFjxyc+Tnh4uMLDw22XQ0JCYlwfGRkpWSJf8NkAsUVGRsb4P5BU2NaQXNjWkFyWLVumgQMH6uHDh7p69aouXryoPHnymB0LaVBSvZ+ZVrKCgoIUHR2tnDlzxlieM2dOHT9+PM77+Pv7KygoSDVr1pRhGIqKilKvXr308ccfP/Fxxo4dq1GjRj3x+k2bflW0vfPzPQkgHjZv3mx2BKQTbGtILmxrSCqRkZGaM2eONmzYIEl65ZVXNHDgQAUGBiowMNDccEiTQkNDk2S9qWp2wR07dmjMmDH6/vvvVbVqVZ0+fVrvvfeePvvsMw0bNizO+wwZMkQDBgywXQ4JCZGXl5ftcqNGDSWnDEmeHelPZGSkNm/erAYNGsjR0dHsOEjD2NaQXNjWkJTOnTsnf39/HTx4UJL05ptvavbs2XJ1dTU5GdKyW7duJcl6TStZnp6esre31/Xr12Msv379unLlyhXnfYYNG6aOHTvqrbfekiSVLl1aDx480Ntvv62hQ4fGOcOMs7OznJ2fPFLl6Ogo8YcCScjR0ZEPI0gWbGtILmxrSGzR0dFq3ry5Tpw4oaxZs2rOnDkyDEOurq5sa0hSSbV9mTbxhZOTkypWrKitW7fallmtVm3duvWJJ5QLDQ2NVaTs7e0lyXZAJAAAAFIXe3t7TZkyRdWrV9ehQ4fUuHFjsyMBL8TU3QUHDBigzp07q1KlSqpSpYq+/vprPXjwQF27dpUkderUSXnz5tXYsWMlSc2aNdPEiRNVvnx52+6Cw4YNU7NmzWxlCwAAACnfpUuXdPLkSdWvX1+SVL9+fdWrV08Wi4XJVZDqmVqy/Pz8dPPmTQ0fPlzXrl1TuXLltHHjRttkGBcvXowxcvXJJ5/IYrHok08+0ZUrV5Q9e3Y1a9ZMn3/+uVlPAQAAAAn0yy+/qGPHjoqMjNTBgwdVpEgRSZLFYjE5GZA4TJ/4om/fvurbt2+c1+3YsSPGZQcHB40YMUIjRoxIhmQAAABITFFRURoxYoTGjBkjSapQoQJ7IyFNMr1kAQAAIO27evWq2rVrp507d0qS3nnnHU2YMEEuLi4mJwMSHyULAAAASWrbtm1q166dbty4oYwZM2rmzJny8/MzOxaQZChZAAAASFLr1q3TjRs3VLp0aa1cuVJFixY1OxKQpChZAAAASFLjxo1T9uzZ9f7773NyYaQLpp0nCwAAAGnT//73P/n5+SkqKkrSv+dH/fjjjylYSDcoWQAAAEgUVqtV48aNU926dbV8+XJNmjTJ7EiAKdhdEAAAAC/s1q1b6ty5s37++WdJUocOHdS7d2+TUwHmoGQBAADghfz+++9q06aNLl68KGdnZ3333Xfq3r07JxdGukXJAgAAwHNbtGiRunbtqsjISBUpUkQrVqxQuXLlzI4FmIpjsgAAAPDcKlSoICcnJ/n6+urgwYMULECMZAEAACCBgoKC5OnpKUkqUaKEAgIC9PLLL7N7IPD/MZIFAACAeDEMQ9OmTVP+/Pm1c+dO2/KiRYtSsIDHULIAAADwTPfu3VP79u3Vu3dvhYaGatGiRWZHAlIsdhcEAADAUx05ckStW7fWyZMnZW9vr3HjxmnAgAFmxwJSLEoWAAAAnmjOnDl65513FBYWpnz58mnZsmWqXr262bGAFI2SBQAAgDht2bJF3bp1kyS99tprWrBggW3CCwBPRskCAABAnOrXr6927dqpdOnS+uijj2Rnx+H8QHxQsgAAAGCzatUq+fj4yN3dXRaLRYsWLWLmQCCB+DoCAAAACgsLU69evdS6dWt169ZNhmFIEgULeA6MZAEAAKRzp0+flq+vrwIDA2WxWFSqVCkZhkHBAp4TJQsAACAdW7Vqlbp166aQkBB5enpq0aJFatiwodmxgFSN3QUBAADSoYiICL333ntq3bq1QkJCVLNmTQUGBlKwgERAyQIAAEiH7t27p9WrV0uSPvzwQ23btk158+Y1ORWQNrC7IAAAQDqULVs2LVu2TLdv31bTpk3NjgOkKZQsAACAdCAyMlJDhw7VK6+8os6dO0uSqlevbnIqIG2iZAEAAKRxly9flp+fn/bs2SM3Nze99tprypkzp9mxgDSLY7IAAADSsE2bNql8+fLas2ePMmfOrAULFlCwgCRGyQIAAEiDoqOjNWzYMDVu3FhBQUEqX768AgIC1KpVK7OjAWkeuwsCAACkMVFRUXrttde0detWSVLv3r01ceJEubi4mJwMSB8YyQIAAEhjHBwcVK1aNWXIkEGLFy/W999/T8ECkhElCwAAIA2wWq26deuW7fLIkSP1559/ql27diamAtInShYAAEAqd/PmTTVp0kSNGjVSeHi4JMne3l6FChUyORmQPlGyAAAAUrHdu3erfPny2rRpk/7++28FBASYHQlI9yhZAAAAqZBhGPrqq6/k7e2tK1euqFixYtq/f7+qVatmdjQg3WN2QQAAgFTm9u3b6tKli9atWydJ8vf31/Tp05UxY0aTkwGQGMkCAABIdXr06KF169bJ2dlZ06ZN08KFCylYQArCSBYAAEAq89VXX+nixYv64YcfVL58ebPjAPgPRrIAAABSuODgYC1fvtx2uWDBgtq/fz8FC0ihGMkCAABIwQ4dOiRfX1+dOXNGmTNn1muvvSZJslgsJicD8CSMZAEAAKRAhmFo+vTpqlatms6cOaP8+fMra9asZscCEA+MZAEAAKQw9+/fV8+ePbV48WJJUtOmTTVv3jxKFpBKMJIFAACQghw9elSVK1fW4sWLZW9vry+//FI//fQTBQtIRRjJAgAASEEOHjyo48ePK2/evFq2bJlq1KhhdiQACUTJAgAASEE6d+6s4OBgtWvXTtmzZzc7DoDnwO6CAAAAJjpx4oSaNGmimzdv2pa9++67FCwgFaNkAQAAmGTJkiWqVKmSfvnlFw0YMMDsOAASCSULAAAgmYWFhal3797y9/fX/fv3VadOHY0fP97sWAASCSULAAAgGZ05c0bVq1fXtGnTJEmffPKJNm/erFy5cpmcDEBiYeILAACAZLJnzx41btxYISEhypYtmxYuXKjXXnvN7FgAEhklCwAAIJm88sor8vT0VKlSpbR06VJ5eXmZHQlAEqBkAQAAJKEbN24oe/bsslgscnd319atW5U3b145OjqaHQ1AEuGYLAAAgCSyfv16FS9eXFOnTrUtK1CgAAULSOMoWQAAAIksKipKgwcPVrNmzXTnzh0tXrxYVqvV7FgAkgklCwAAIBFduXJF9erV07hx4yRJ/fr109atW2Vnx8cuIL3gmCwAAIBEsnnzZrVv3143b95UpkyZNGvWLPn6+podC0Ayo2QBAAAkgkuXLun1119XZGSkypYtqxUrVujll182OxYAE1CyAAAAEoGXl5dGjRql8+fP6+uvv5arq6vZkQCYhJIFAADwnHbu3KkcOXKoRIkSkqTBgwfLYrGYnAqA2TgCEwAAIIGsVqvGjBmjevXqqU2bNgoNDZUkChYASYxkAQAAJEhQUJA6duyojRs3SpIqVKhgciIAKQ0lCwAAIJ727NkjPz8/Xb58WS4uLpoyZYq6du3KCBaAGNhdEAAA4BkMw9CECRPk7e2ty5cv6+WXX9bvv/+ubt26UbAAxELJAgAAeIbo6Gj9+OOPioqKkp+fnw4cOKAyZcqYHQtACsXuggAAAM/g4OCgpUuX6ueff1aPHj0YvQLwVIxkAQAA/IdhGJoyZYo++ugj27K8efPq7bffpmABeCZGsgAAAB4TEhKit956SytWrJAkNW/eXDVq1DA5FYDUhJIFAADw/x0+fFitW7fW6dOn5eDgoPHjx6t69epmxwKQylCyAABAumcYhmbOnKl+/fopPDxcXl5eWr58uV599VWzowFIhTgmCwAApHt9+/bV22+/rfDwcDVp0kSHDh2iYAF4bpQsAACQ7tWpU0f29vb64osvtG7dOmXLls3sSABSMXYXBAAA6dL169eVM2dOSZKvr68qVKigwoULm5wKQFrASBYAAEhXHj58qLfffltlypTRP//8Y1tOwQKQWChZAAAg3Th16pSqVaumGTNm6ObNm9qyZYvZkQCkQZQsAACQLixfvlwVK1bU4cOHlT17dv3666/q1KmT2bEApEGULAAAkKaFh4erX79+8vPz071791S7dm0FBgbKx8fH7GgA0ihKFgAASNO++OILfffdd5KkIUOGaOvWrcqTJ4/JqQCkZcwuCAAA0rRBgwZp69atGjx4sJo0aWJ2HADpACNZAAAgTYmMjNSsWbNktVolSRkyZNDOnTspWACSDSULAACkGZcuXZK3t7feeustTZgwwbbcYrGYmApAekPJAgAAacIvv/yi8uXLa+/evXJ3d9fLL79sdiQA6RQlCwAApGpRUVEaOnSomjRpolu3bqlixYoKCAhQixYtzI4GIJ1i4gsAAJBqXb16Ve3atdPOnTslSX369NGECRPk7OxscjIA6RklCwAApFqXL1/Wnj17lDFjRs2cOVN+fn5mRwIAShYAAEi9KleurPnz56tChQoqWrSo2XEAQBLHZAEAgFTkxo0bat68uQIDA23L2rZtS8ECkKIwkgUAAFKF//3vf2rbtq3++ecfXbx4UYcOHWJqdgApEiNZAAAgRbNarRo3bpzq1q2rf/75RyVKlNCiRYsoWABSLEayAABAinXr1i117txZP//8sySpQ4cOmjp1qjJmzGhyMgB4MkoWAABIkS5evKhatWrp4sWLcnZ21nfffafu3bszggUgxaNkAQCAFClv3rwqVqyYnJ2dtWLFCpUtW9bsSAAQL5QsAACQYgQHB8vJyUmurq6yt7fX4sWL5eTkpMyZM5sdDQDijYkvAABAihAQEKAKFSrovffesy3z9PSkYAFIdRjJAgAApjIMQ9OmTVP//v0VEREhq9Wq27dvK2vWrGZHA4DnwkgWAAAwzb179+Tv76933nlHERERat68uQICAihYAFK1FypZYWFhiZUDAACkM0eOHFGlSpW0dOlSOTg46KuvvtKPP/4oDw8Ps6MBwAtJcMmyWq367LPPlDdvXmXMmFFnz56VJA0bNkyzZs1K9IAAACDtiYiIUJMmTXTy5Enly5dPO3fu1MCBA5meHUCakOCSNXr0aM2dO1dffvmlnJycbMtLlSqlmTNnJmo4AACQNjk5OWn69Olq0qSJDh06pOrVq5sdCQASTYJL1vz58/XDDz+offv2sre3ty0vW7asjh8/nqjhAABA2nHs2DFt3brVdrlJkyZav369PD09TUwFAIkvwSXrypUrKlKkSKzlVqtVkZGRiRIKAACkLYsWLVLlypXl6+ur8+fP25azeyCAtCjBJatkyZL63//+F2v5ypUrVb58+UQJBQAA0oawsDD17NlTHTp00IMHD1S+fHm5urqaHQsAklSCz5M1fPhwde7cWVeuXJHVatXq1at14sQJzZ8/X+vXr0+KjAAAIBU6ffq0fH19FRgYKIvFomHDhmn48OExDjcAgLQowSNZb7zxhtatW6ctW7YoQ4YMGj58uI4dO6Z169apQYMGSZERAACkMitXrlSFChUUGBio7Nmza+PGjRo1ahQFC0C6kOCRLEmqVauWNm/enNhZAABAGrFt2zbdu3dPNWvW1NKlS5U3b16zIwFAsknwSFahQoV069atWMvv3r2rQoUKJUooAACQuk2cOFGTJk3S9u3bKVgA0p0El6zz588rOjo61vLw8HBduXIlUUIBAIDUZe3atfL19bV9RnBxcVH//v3l4PBcO80AQKoW73e+tWvX2v69adMmubu72y5HR0dr69atKlCgQKKGAwAAKVtkZKSGDh2q8ePHS5JmzJihXr16mZwKAMwV75LVokULSf+ez6Jz584xrnN0dFSBAgU0YcKERA0HAABSrsuXL8vPz0979uyRJPXv31/dunUzORUAmC/euwtarVZZrVa99NJLunHjhu2y1WpVeHi4Tpw4oaZNmyY4wJQpU1SgQAG5uLioatWq2r9//1Nvf/fuXfXp00e5c+eWs7OzihYtqg0bNiT4cQEAwPPbtGmTypcvrz179ihz5sxatWqVJk2aJCcnJ7OjAYDpEryj9Llz5xLtwZctW6YBAwZo2rRpqlq1qr7++ms1atRIJ06cUI4cOWLdPiIiQg0aNFCOHDm0cuVK5c2bVxcuXFCWLFkSLRMAAHi6qVOnqn///jIMQ+XLl9eKFStUuHBhs2MBQIrxXEejPnjwQDt37tTFixcVERER47p333033uuZOHGievTooa5du0qSpk2bpp9//lmzZ8/W4MGDY91+9uzZun37tvbs2SNHR0dJ4jgwAACSWc2aNeXi4qIuXbpo4sSJcnFxMTsSAKQoCS5Zhw4dUpMmTRQaGqoHDx4oa9asCgoKkpubm3LkyBHvkhUREaGDBw9qyJAhtmV2dnby8fHR3r1747zP2rVrVa1aNfXp00c//fSTsmfPLn9/f3300UdPPLlheHi4wsPDbZdDQkJiXB8ZGSlZIuOVGUiIyMjIGP8HkgrbGpLD1atX5enpKUkqXry4Dh8+bPuik20PiY33NSSXpNrGElyy3n//fTVr1kzTpk2Tu7u79u3bJ0dHR3Xo0EHvvfdevNcTFBSk6Oho5cyZM8bynDlz6vjx43He5+zZs9q2bZvat2+vDRs26PTp03rnnXcUGRmpESNGxHmfsWPHatSoUU/MsWnTr4q2d453biChOHE3kgvbGpKC1WrVypUrtWLFCo0ePVrFihWzbWt///23yemQ1vG+hqQWGhqaJOtNcMkKDAzU9OnTZWdnJ3t7e4WHh6tQoUL68ssv1blzZ7Vq1Sopckr6940+R44c+uGHH2Rvb6+KFSvqypUrGj9+/BNL1pAhQzRgwADb5ZCQEHl5edkuN2rUUHLKkGSZkX5FRkZq8+bNatCggW33ViApsK0hqdy8eVNdu3bVr7/+artcrFgxtjUkOd7XkFxu3bqVJOtNcMlydHSUnd2/kxLmyJFDFy9eVIkSJeTu7q5Lly7Fez2enp6yt7fX9evXYyy/fv26cuXKFed9cufOLUdHxxi7BpYoUULXrl1TREREnDMaOTs7y9n5ySNVjo6OEr+8SEKOjo78gUCyYFtDYtq1a5fatm2rK1euyNXVVVOnTpW/v782bNjAtoZkw7aGpJZU21e8p3B/pHz58vrjjz8kSd7e3ho+fLgWLVqk/v37q1SpUvFej5OTkypWrKitW7fallmtVm3dulXVqlWL8z41atTQ6dOnZbVabctOnjyp3LlzM2UsAACJwGq1avz48apTp46uXLmi4sWLa//+/bHOkQkAeLIEl6wxY8Yod+7ckqTPP/9cHh4e6t27t27evKnp06cnaF0DBgzQjBkzNG/ePB07dky9e/fWgwcPbLMNdurUKcbEGL1799bt27f13nvv6eTJk/r55581ZswY9enTJ6FPAwAAxOHHH3/Uhx9+qOjoaPn7++uPP/5I0JeoAIDn2F2wUqVKtn/nyJFDGzdufO4H9/Pz082bNzV8+HBdu3ZN5cqV08aNG22TYVy8eNG2a6IkeXl5adOmTXr//fdVpkwZ5c2bV++9954++uij584AAAD+T8uWLdW2bVvVrVtXPXr0kMViMTsSAKQ6z3WerLgEBARo+PDhWr9+fYLu17dvX/Xt2zfO63bs2BFrWbVq1bRv377niQgAAP7DMAzNnTtXrVu3VqZMmWSxWLR48WLKFQC8gATtLrhp0yYNGjRIH3/8sc6ePStJOn78uFq0aKHKlSvHOFYKAACkbMHBwfL19VW3bt309ttvyzAMSaJgAcALivdI1qxZs9SjRw9lzZpVd+7c0cyZMzVx4kT169dPfn5+Onr0qEqUKJGUWQEAQCI5dOiQfH19debMGTk6Oj5x0ikAQMLFeyRr8uTJGjdunIKCgrR8+XIFBQXp+++/15EjRzRt2jQKFgAAqYBhGJo+fbqqVaumM2fOKH/+/Nq1a5feffddRrAAIJHEu2SdOXNGvr6+kqRWrVrJwcFB48ePV758+ZIsHAAASDz3799Xhw4d1KtXL4WHh6tZs2YKCAhQlSpVzI4GAGlKvEvWw4cP5ebmJunffbWdnZ1tU7kDAICU7/79+9q6davs7e01fvx4/fTTT8qaNavZsQAgzUnQ7IIzZ85UxowZJUlRUVGaO3euPD09Y9zm3XffTbx0AAAg0eTKlUvLly+Xvb29atSoYXYcAEiz4l2yXnrpJc2YMcN2OVeuXFqwYEGM21gsFkoWAAApRGhoqPr27auGDRuqbdu2kqTatWubnAoA0r54l6zz588nYQwAAJCYTpw4odatW+vo0aNavXq1XnvtNWXJksXsWACQLiToPFkAACDlW7JkiSpVqqSjR48qZ86cWrNmDQULAJIRJQsAgDQiLCxMvXv3lr+/v+7fv686deooMDBQdevWNTsaAKQrCZr4AgAApExhYWGqWbOmDh48KIvFoqFDh2rkyJGyt7c3OxoApDuULAAA0gAXFxfVrVtXFy5c0MKFC9WoUSOzIwFAusXuggAApFIRERG6efOm7fKYMWN0+PBhChYAmOy5StaZM2f0ySefqF27drpx44Yk6ZdfftFff/2VqOEAAEDcLly4oFq1aumNN95QZGSkJMnR0VF58uQxORkAIMEla+fOnSpdurR+//13rV69Wvfv35ckHT58WCNGjEj0gAAAIKb169erfPny2r9/v44fP67jx4+bHQkA8JgEl6zBgwdr9OjR2rx5s5ycnGzL69Wrp3379iVqOAAA8H+ioqI0ePBgNWvWTHfu3FHlypUVEBCg0qVLmx0NAPCYBJesI0eOqGXLlrGW58iRQ0FBQYkSCgAAxHTlyhXVrVtX48aNkyS9++672rVrlwoUKGBuMABALAkuWVmyZNHVq1djLT906JDy5s2bKKEAAEBMXbp00a5du5QpUyatWLFCkydPjrFHCQAg5UhwyWrbtq0++ugjXbt2TRaLRVarVbt379agQYPUqVOnpMgIAEC6N2XKFNWuXVsBAQFq3bq12XEAAE+R4JI1ZswYFS9eXF5eXrp//75Kliyp2rVrq3r16vrkk0+SIiMAAOnO9evXtXjxYtvlokWLaufOnSpSpIiJqQAA8ZHgkxE7OTlpxowZGjZsmI4ePar79++rfPnyevnll5MiHwAA6c6OHTtsp0nJnTu36tata3YkAEACJLhk7dq1SzVr1tRLL72kl156KSkyAQCQLlmtVn3xxRcaNmyYrFarXnnlFeXOndvsWACABErw7oL16tVTwYIF9fHHH+vvv/9OikwAAKQ7QUFBev311zV06FBZrVZ16dJF+/fvV/Hixc2OBgBIoASXrH/++UcDBw7Uzp07VapUKZUrV07jx4/X5cuXkyIfAABp3p49e1S+fHlt3LhRrq6umj17tubMmSM3NzezowEAnkOCS5anp6f69u2r3bt368yZM/L19dW8efNUoEAB1atXLykyAgCQph09elSXL19WsWLF9Pvvv6tr165mRwIAvIAEH5P1uIIFC2rw4MEqW7ashg0bpp07dyZWLgAA0o0ePXooOjpaHTp0UKZMmcyOAwB4QQkeyXpk9+7deuedd5Q7d275+/urVKlS+vnnnxMzGwAAadIff/yhevXq6c6dO5Iki8Wi3r17U7AAII1IcMkaMmSIChYsqHr16unixYuaPHmyrl27pgULFui1115LiowAAKQJhmHou+++U40aNbR9+3YNHTrU7EgAgCSQ4N0Ff/vtN33wwQdq06aNPD09kyITAABpTkhIiN566y2tWLFCktSyZUuNGTPG5FQAgKSQ4JK1e/fupMgBAECaFRgYKF9fX50+fVoODg766quv9O6778pisZgdDQCQBOJVstauXavGjRvL0dFRa9eufeptmzdvnijBAABICzZu3KgWLVooPDxcL730kpYtW6ZXX33V7FgAgCQUr5LVokULXbt2TTly5FCLFi2eeDuLxaLo6OjEygYAQKpXuXJl5cyZU6VLl9a8efOULVs2syMBAJJYvEqW1WqN898AACC2y5cvK2/evLJYLMqWLZt2796tPHnyyM7uuSf1BQCkIgl+t58/f77Cw8NjLY+IiND8+fMTJRQAAKnV/PnzVaxYMc2ZM8e2LF++fBQsAEhHEvyO37VrVwUHB8dafu/ePc5QDwBItx4+fKi33npLnTt3VmhoqH788UcZhmF2LACACRJcsgzDiHM2pMuXL8vd3T1RQgEAkJqcPHlSr776qmbNmiWLxaKRI0dqzZo1zB4IAOlUvKdwL1++vCwWiywWi+rXry8Hh/+7a3R0tM6dO8fJiAEA6c6yZcv01ltv6f79+8qRI4cWLVokHx8fs2MBAEwU75L1aFbBwMBANWrUSBkzZrRd5+TkpAIFCujNN99M9IAAAKRUJ06ckL+/v6xWq2rXrq0lS5YoT548ZscCAJgs3iVrxIgRkqQCBQrIz89PLi4uSRYKAIDUoFixYhoxYoTCwsL06aefxtjLAwCQfiX4r0Hnzp2TIgcAAKnCjz/+qFKlSqlIkSKSpOHDh5ucCACQ0sSrZGXNmlUnT56Up6enPDw8nnog7+3btxMtHAAAKUVkZKQGDx6siRMnqnz58tqzZw97dQAA4hSvkjVp0iRlypTJ9m9mSwIApCcXL16Un5+f9u3bJ0mqW7cu570CADxRvErW47sIdunSJamyAACQ4mzYsEEdO3bU7du35e7urrlz59omgwIAIC4J/houICBAR44csV3+6aef1KJFC3388ceKiIhI1HAAAJglKipKH3/8sV5//XXdvn1bFStWVEBAAAULAPBMCS5ZPXv21MmTJyVJZ8+elZ+fn9zc3LRixQp9+OGHiR4QAAAzWK1Wbd26VZLUp08f7d69W4UKFTI5FQAgNUhwyTp58qTKlSsnSVqxYoW8vb21ePFizZ07V6tWrUrsfAAAmMLJyUnLly/XsmXL9N1338nZ2dnsSACAVCLBU7gbhiGr1SpJ2rJli5o2bSpJ8vLyUlBQUOKmAwAgmURHR2v06NEKDw/XmDFjJEn58+dX/vz5TU4GAEhtElyyKlWqpNGjR8vHx0c7d+7U1KlTJUnnzp1Tzpw5Ez0gAABJ7fr16+rQoYO2bNkiSWrbtq3KlCljcioAQGqV4N0Fv/76awUEBKhv374aOnSo7WSMK1euVPXq1RM9IAAASem3335T+fLltWXLFrm5uWn+/PkULADAC0nwSFaZMmVizC74yPjx42Vvb58ooQAASGpWq1Vffvmlhg4dKqvVqhIlSmjlypUqWbKk2dEAAKlcgkvWIwcPHtSxY8ckSSVLllSFChUSLRQAAEmtXbt2Wr58uSSpY8eOmjp1qjJkyGByKgBAWpDgknXjxg35+flp586dypIliyTp7t27qlu3rpYuXars2bMndkYAABJds2bNtHbtWn377bfq3r27LBaL2ZEAAGlEgo/J6tevn+7fv6+//vpLt2/f1u3bt3X06FGFhITo3XffTYqMAAC8MMMwdOnSJdvlDh066NSpU3rrrbcoWACARJXgkrVx40Z9//33KlGihG1ZyZIlNWXKFP3yyy+JGg4AgMRw9+5dvfnmm6patapu3LhhW54vXz4TUwEA0qoElyyr1SpHR8dYyx0dHW3nzwIAIKU4ePCgKlSooDVr1igoKEj79u0zOxIAII1LcMmqV6+e3nvvPf3zzz+2ZVeuXNH777+v+vXrJ2o4AACel2EY+v7771W9enWdO3dOBQoU0O7du9W8eXOzowEA0rgEl6zvvvtOISEhKlCggAoXLqzChQurYMGCCgkJ0bfffpsUGQEASJB79+6pXbt26tOnjyIiIvTGG28oICBAlStXNjsaACAdSPDsgl5eXgoICNDWrVttU7iXKFFCPj4+iR4OAIDnMWLECC1btkwODg4aN26c3n//fSa3AAAkmwSVrGXLlmnt2rWKiIhQ/fr11a9fv6TKBQDAcxs5cqQOHz6szz77TNWrVzc7DgAgnYn37oJTp05Vu3btdODAAZ06dUp9+vTRBx98kJTZAACIlwcPHuj777+XYRiSpMyZM2vr1q0ULACAKeJdsr777juNGDFCJ06cUGBgoObNm6fvv/8+KbMBAPBMx44dU9WqVdWnTx+ODQYApAjxLllnz55V586dbZf9/f0VFRWlq1evJkkwAACeZdGiRapcubL++usv5cqVS2XKlDE7EgAA8S9Z4eHhypAhw//d0c5OTk5OevjwYZIEAwDgScLCwtSzZ0916NBBDx48UP369RUYGKg6deqYHQ0AgIRNfDFs2DC5ubnZLkdEROjzzz+Xu7u7bdnEiRMTLx0AAP9x+vRp+fr6KjAwUBaLRcOHD9ewYcNkb29vdjQAACQloGTVrl1bJ06ciLGsevXqOnv2rO0y0+MCAJLa9evXdeTIEWXPnl2LFi1SgwYNzI4EAEAM8S5ZO3bsSMIYAADET40aNbRo0SLVrFlTefPmNTsOAACxxPuYLAAAzHD+/HnVrVtXf/31l22Zn58fBQsAkGJRsgAAKdbatWtVvnx57dixQz179rSdBwsAgJSMkgUASHEiIyP1wQcf6I033tDdu3dVtWpVLVq0iGN/AQCpQoJmFwQAIKldvnxZfn5+2rNnjySpf//+GjdunJycnExOBgBA/FCyAAApxvHjx1WzZk3dunVLmTNn1pw5c9SqVSuzYwEAkCDPtbvg//73P3Xo0EHVqlXTlStXJEkLFizQrl27EjUcACB9KVKkiEqVKqUKFSooICCAggUASJUSXLJWrVqlRo0aydXVVYcOHVJ4eLgkKTg4WGPGjEn0gACAtO369eu2vyUODg5auXKldu/ercKFC5ucDACA55PgkjV69GhNmzZNM2bMkKOjo215jRo1FBAQkKjhAABp2/bt21W2bFkNGjTItszT01MuLi4mpgIA4MUkuGSdOHFCtWvXjrXc3d1dd+/eTYxMAIA0zmq1avTo0fLx8dH169e1c+dOPXjwwOxYAAAkigSXrFy5cun06dOxlu/atUuFChVKlFDJxutVydHN7BQAkK7cvHlTjRs31rBhw2S1WtW9e3ft27dPGTJkMDsaAACJIsGzC/bo0UPvvfeeZs+eLYvFon/++Ud79+7VoEGDNGzYsKTImCRC3tqrzIVKSpxzBQCSza5du9S2bVtduXJFrq6umjp1qjp37mx2LAAAElWCS9bgwYNltVpVv359hYaGqnbt2nJ2dtagQYPUr1+/pMiYJAwHNwoWACSj+/fvq0WLFrp165aKFy+uFStWqFSpUmbHAgAg0SW4ZFksFg0dOlQffPCBTp8+rfv376tkyZLKmDFjUuQDAKQRGTNm1IwZM7Rq1SpNmzaNvxsAgDTruU9G7OTkpJIlSyZmFgBAGrN//37dv39f9erVkyS1bNlSLVu2NDkVAABJK8Elq27durI8ZTe7bdu2vVAgAEDqZxiGvv32Ww0aNEju7u46dOiQ8uXLZ3YsAACSRYJLVrly5WJcjoyMVGBgoI4ePcrBywAABQcHq3v37lq1apUkydvbW5kyZTI5FQAAySfBJWvSpElxLh85cqTu37//woEAAKnXoUOH5OvrqzNnzsjR0VETJkxQ3759n7oHBAAAaU2Cz5P1JB06dNDs2bMTa3UAgFTEMAxNnz5d1apV05kzZ5Q/f37t2rVL/fr1o2ABANKdRCtZe/fulYuLS2KtDgCQilgsFu3bt0/h4eFq1qyZAgICVKVKFbNjAQBgigTvLtiqVasYlw3D0NWrV3XgwIFUdTJiAMCLMwzDNlI1ZcoU1ahRQ927d2f0CgCQriW4ZLm7u8e4bGdnp2LFiunTTz9Vw4YNEy0YACBlmzt3rtavX6/ly5fLzs5Obm5ueuutt8yOBQCA6RJUsqKjo9W1a1eVLl1aHh4eSZUJAJCChYaGqm/fvpozZ44kaenSpfL39zc5FQAAKUeCjsmyt7dXw4YNdffu3SSKAwBIyU6cOKGqVatqzpw5srOz0+jRo9W2bVuzYwEAkKIkeOKLUqVK6ezZs0mRBQCQgi1ZskSVKlXS0aNHlTNnTm3ZskVDhw6VnV2izaEEAECakOC/jKNHj9agQYO0fv16Xb16VSEhITH+AwCkPZ999pn8/f11//591alTR4GBgapbt67ZsQAASJHiXbI+/fRTPXjwQE2aNNHhw4fVvHlz5cuXTx4eHvLw8FCWLFk4TgsA0qjXX39dLi4u+uSTT7RlyxblypXL7EgAAKRY8Z74YtSoUerVq5e2b9+elHkAACnEhQsXlD9/fklShQoVdObMGeXJk8fkVAAApHzxLlmGYUiSvL29kywMAMB8ERER+vDDDzVt2jTt2bNHFSpUkCQKFgAA8ZSgY7I4uSQApG0XLlxQrVq1NHnyZIWHh7P3AgAAzyFB58kqWrToM4vW7du3XygQAMAc69evV6dOnXTnzh15eHho3rx5atasmdmxAABIdRJUskaNGiV3d/ekygIAMEFkZKQ++eQTffnll5KkKlWqaNmyZSpQoIC5wQAASKUSVLLatm2rHDlyJFUWAIAJFi9ebCtY7733nr788ks5OTmZnAoAgNQr3iWL47EAIG3q2LGjNm7cqNatW+vNN980Ow4AAKlevCe+eDS7IAAgdYuOjtY333yj0NBQSZKdnZ2WLFlCwQIAIJHEeyTLarUmZQ4AQDK4fv26/P39tW3bNgUGBmr27NlmRwIAIM1J0DFZAIDUa8eOHWrXrp2uXbumDBkyyMfHx+xIAACkSQk6TxYAIPWxWq0aM2aM6tevr2vXrumVV17RgQMH5O/vb3Y0AADSJEayACANCwoKsk1sIUldunTRlClT5ObmZnIyAADSLkoWAKRhYWFhOnDggFxdXTVlyhR17drV7EgAAKR5lCwASGMMw7CddiNfvnxasWKFsmXLptKlS5ucDACA9CFFHJM1ZcoUFShQQC4uLqpatar2798fr/stXbpUFotFLVq0SNqAAJBK3LlzRy1bttSaNWtsy+rUqUPBAgAgGZlespYtW6YBAwZoxIgRCggIUNmyZdWoUSPduHHjqfc7f/68Bg0apFq1aiVTUgBI2Q4cOKAKFSrop59+Uq9evfTw4UOzIwEAkC6ZXrImTpyoHj16qGvXripZsqSmTZsmNze3p567JTo6Wu3bt9eoUaNUqFChZEwLACmPYRj6+eef5e3trfPnz6tQoUL65Zdf5OrqanY0AADSJVOPyYqIiNDBgwc1ZMgQ2zI7Ozv5+Pho7969T7zfp59+qhw5cqh79+763//+99THCA8PV3h4uO1ySEiIJCkqOlqRkZEv+AyAJ3u0fbGdISkFBwerZ8+eWr16tSSpRYsWmjFjhtzd3dn2kOh4X0NyYVtDckmqbczUkhUUFKTo6GjlzJkzxvKcOXPq+PHjcd5n165dmjVrlgIDA+P1GGPHjtWoUaPiXE9m978SnBlIqM2bN5sdAWlUaGioBg4cqKtXr8re3l5dunRR06ZNtXv3brOjIY3jfQ3JhW0NSS00NDRJ1puqZhe8d++eOnbsqBkzZsjT0zNe9xkyZIgGDBhguxwSEiIvLy/VrFlT+bzyJ1VUQJGRkdq8ebMaNGggR0dHs+Mgjdq1a5fWrVunfv36qU+fPmxrSFK8ryG5sK0hudy6dStJ1mtqyfL09JS9vb2uX78eY/n169eVK1euWLc/c+aMzp8/r2bNmtmWWa1WSZKDg4NOnDihwoULx7iPs7OznJ2dY63Lwd6eX1okC0dHR7Y1JJr79+8rNDRUOXLkkPTvca3Dhw/Xvn372NaQbNjWkFzY1pDUkmr7MnXiCycnJ1WsWFFbt261LbNardq6dauqVasW6/bFixfXkSNHFBgYaPuvefPmqlu3rgIDA+Xl5ZWc8QEgWf3111+qUqWK2rRpo6ioKEn/fpGUNWtWk5MBAIDHmb674IABA9S5c2dVqlRJVapU0ddff60HDx6oa9eukqROnTopb968Gjt2rFxcXFSqVKkY98+SJYskxVoOAGnJ/Pnz1bt3b4WGhio4OFgXLlyINXIPAABSBtNLlp+fn27evKnhw4fr2rVrKleunDZu3GibDOPixYuyszN9pnkAMMXDhw/Vr18/zZo1S5LUoEEDLVy40La7IAAASHlML1mS1LdvX/Xt2zfO63bs2PHU+86dOzfxAwFACnDy5En5+vrqzz//lMVi0ahRo/Txxx/L3t7e7GgAAOApUkTJAgDEZBiGOnXqpD///FM5cuTQ4sWLVb9+fbNjAQCAeGA/PABIgSwWi2bPnq0mTZooMDCQggUAQCpCyQKAFOLs2bNatGiR7XLJkiX1888/K3fu3CamAgAACcXuggCQAqxZs0Zdu3bV/fv3lT9/ftWsWdPsSAAA4DkxkgUAJoqIiNCAAQPUqlUrBQcHq2rVqsqfP7/ZsQAAwAugZAGASS5evChvb29NmjRJkjRo0CDt2LGDE6sDAJDKsbsgAJhgw4YN6tixo27fvq0sWbJo7ty5euONN8yOBQAAEgElCwBMcObMGd2+fVuVKlXS8uXLVbBgQbMjAQCARELJAoBkYhiGLBaLpH9Pwp4hQwa1b99ezs7OJicDAACJiWOyACAZbNmyRbVq1VJISIikf8+D1a1bNwoWAABpECULAJJQdHS0Ro4cqYYNG2r37t0aM2aM2ZEAAEASY3dBAEgi169fV/v27bV161ZJ0ttvv60RI0aYnAoAACQ1ShYAJIHffvtNbdu21dWrV+Xm5qbp06erQ4cOZscCAADJgJIFAIls+fLlateunaxWq0qWLKkVK1aoZMmSZscCAADJhJIFAImsTp06ypUrl+rXr6+pU6cqQ4YMZkcCAADJiJIFAIng7NmzKlSokCQpR44cCggIUI4cOWxTtgMAgPSD2QUB4AUYhqFJkyapWLFiWrhwoW15zpw5KVgAAKRTlCwAeE53795Vq1atNGDAAEVFRWn79u1mRwIAACkAJQsAnsOBAwdUoUIF/fjjj3JyctKUKVM0c+ZMs2MBAIAUgGOyACABDMPQ999/rwEDBigiIkIFCxbUihUrVLFiRbOjAQCAFIKRLABIgICAAPXt21cRERFq0aKFAgICKFgAACAGRrIAIAEqVqyoTz75RB4eHnr//feZ3AIAAMRCyQKApzAMQ3PnzlXdunVVoEABSdJnn31mbigAAJCisbsgADzBgwcP1KVLF3Xr1k1+fn6KiIgwOxIAAEgFGMkCgDgcO3ZMrVu31t9//y07Ozu1aNFCDg68ZQIAgGfjEwMA/MfChQvVs2dPhYaGKleuXFq6dKm8vb3NjgUAAFIJdhcEgP8vLCxMb7/9tjp27KjQ0FDVr19fgYGBFCwAAJAglCwAeMyBAwdksVg0YsQIbdq0STlz5jQ7EgAASGXYXRBAumcYhiwWi1xcXLRixQqdPXtWDRo0MDsWAABIpShZANKt8PBwDRo0SNmyZdPIkSMlSYULF1bhwoXNDQYAAFI1ShaAdOncuXNq06aNDhw4IHt7e3Xs2JFyBQAAEgXHZAFId3766SdVqFBBBw4ckIeHh3766ScKFgAASDSULADpRmRkpAYNGqQWLVro7t27evXVVxUYGKjXX3/d7GgAACANYXdBAOmCYRhq3Lixtm7dKkl6//339cUXX8jJycnkZAAAIK1hJAtAumCxWOTv7y93d3etXr1aEydOpGABAIAkQckCkGZFRUXp/Pnztstdu3bViRMn1LJlS/NCAQCANI+SBSBNunr1qnx8fFS7dm3dvn1b0r+jWZxcGAAAJDVKFoA0Z9u2bSpXrpx27typO3fu6M8//zQ7EgAASEcoWQDSjOjoaH366afy8fHRjRs3VLp0aR04cEB16tQxOxoAAEhHmF0QQJpw48YNdejQQZs3b5Ykde/eXd98843c3NxMTgYAANIbShaANGHo0KHavHmzXF1dNXXqVHXu3NnsSAAAIJ2iZAFIE7788ktduXJFX375pUqVKmV2HAAAkI5xTBaAVOn27dv6+uuvZRiGJMnDw0MbNmygYAEAANMxkgUg1fn999/Vpk0bXbx4Ua6ururZs6fZkQAAAGwYyQKQahiGoW+++Ua1atXSxYsXVaRIEVWpUsXsWAAAADEwkgUgVQgODlb37t21atUqSVLr1q01c+ZMubu7m5wMAAAgJkoWgBTv0KFDat26tc6ePStHR0dNmDBBffv2lcViMTsaAABALJQsACne3bt3df78eeXPn1/Lly9nF0EAAJCiUbIApEiGYdhGqurWraulS5fKx8dHHh4eJicDAAB4Oia+AJDiHDlyRFWrVtXJkydty3x9fSlYAAAgVaBkAUhR5s6dq6pVq+qPP/5Q//79zY4DAACQYJQsAClCaGiounXrpq5du+rhw4dq1KiR5s2bZ3YsAACABKNkATDd8ePHVbVqVc2ZM0d2dnYaPXq0NmzYoOzZs5sdDQAAIMGY+AKAqQ4ePChvb289ePBAOXPm1JIlS1S3bl2zYwEAADw3ShYAU5UpU0ZlypSRs7OzlixZoly5cpkdCQAA4IVQsgAku/Pnzytv3rxydHSUo6Oj1q1bpyxZssje3t7saAAAAC+MY7IAJKtVq1apbNmy+vjjj23LsmXLRsECAABpBiULQLKIiIhQ//791bp1a4WEhOj3339XRESE2bEAAAASHSULQJK7cOGCatWqpcmTJ0uSPvzwQ23dulVOTk4mJwMAAEh8HJMFIEmtX79enTp10p07d+Th4aH58+eradOmZscCAABIMpQsAEnm9u3b8vf3171791SlShUtX75c+fPnNzsWAABAkqJkAUgyWbNm1YwZM7Rnzx6NHz+e3QMBAEC6QMkCkKh+/fVXOTs7y9vbW5Lk5+cnPz8/k1MBAAAkHya+AJAooqOjNXz4cL322mtq27atrl+/bnYkAAAAUzCSBeCFXbt2Tf7+/tq+fbskqUWLFnJ3dzc5FQAAgDkoWQBeyI4dO9SuXTtdu3ZNGTJk0A8//CB/f3+zYwEAAJiG3QUBPBfDMPT555+rfv36unbtml555RUdOHCAggUAANI9ShaA5/bXX3/JarWqS5cu2r9/v4oXL252JAAAANOxuyCABDEMQxaLRRaLRdOnT9cbb7zB7IEAAACPYSQLQLwYhqGvvvpKbdq0kWEYkqRMmTJRsAAAAP6DkSwAz3Tnzh116dJFa9eulST9/PPPatq0qcmpAAAAUiZKFoCn+uOPP9SmTRudP39eTk5O+uabb/T666+bHQsAACDFYndBAHEyDEPffvutatSoofPnz6tQoULau3evevbsKYvFYnY8AACAFIuSBSBO/fv317vvvqvIyEi1atVKAQEBqlChgtmxAAAAUjxKFoA4tWvXTm5ubpo8ebJWrlwpd3d3syMBAACkChyTBUDSv7sHnjp1SkWLFpUkvfrqq7pw4YI8PT1NTgYAAJC6MJIFQPfv31enTp1UtmxZ/fnnn7blFCwAAICEo2QB6dxff/2lypUra+HChYqMjNSBAwfMjgQAAJCqUbKAdGzevHmqXLmyjh8/rjx58mjHjh3q1q2b2bEAAABSNUoWkA6Fhoaqe/fu6tKlix4+fKgGDRro0KFDqlmzptnRAAAAUj1KFpAOzZ49W7Nnz5bFYtGnn36qX375RTly5DA7FgAAQJrA7IJAOtS7d2/t3btX3bt3V7169cyOAwAAkKYwkgWkA2FhYRo7dqzCwsIkSfb29lq0aBEFCwAAIAkwkgWkcWfPnpWvr68CAgJ06dIlff/992ZHAgAASNMYyQLSsDVr1qhChQoKCAhQtmzZ1Lx5c7MjAQAApHmULCANioiI0IABA9SqVSsFBwerevXqOnTokF577TWzowEAAKR5lCwgjbl06ZK8vb01adIkSdKgQYO0Y8cOeXl5mZwMAAAgfeCYLCCNiY6O1okTJ5QlSxbNnTtXb7zxhtmRAAAA0hVKFpAGGIYhi8UiSSpQoIBWr16t/Pnzq2DBgiYnAwAASH/YXRBI5f755x/VrVtXP//8s21ZnTp1KFgAAAAmYSQLSMU2b96s9u3b6+bNm7pw4YIaNmwoR0dHs2MBAACka4xkAalQdHS0Ro4cqUaNGunmzZsqW7asfv31VwoWAABACsBIFpDKXL9+Xe3bt9fWrVslSW+//ba+/vprubq6mpwMAAAAEiULSFVu3ryp8uXL6+rVq3Jzc9P06dPVoUMHs2MBAADgMZQsIBXJnj27mjZtqj179mjFihUqUaKE2ZEAAADwH5QsIIW7deuWrFarsmfPLkmaPHmyrFarMmTIYHIyAAAAxIWJL4AUbO/evSpfvrz8/f0VHR0tSXJ1daVgAQAApGCULCAFMgxDEydOVO3atXXp0iVduHBB169fNzsWAAAA4oGSBaQwd+7cUcuWLTVw4EBFRUXJz89PBw4cUJ48ecyOBgAAgHjgmCwgBTlw4IDatGmjc+fOycnJSZMmTVLv3r1lsVjMjgYAAIB4omQBKYTValWXLl107tw5FSxYUCtWrFDFihXNjgUAAIAEYndBIIWws7PTwoUL1bZtWwUEBFCwAAAAUilKFmCiw4cPa+HChbbL5cqV05IlS5QlSxbzQgEAAOCFsLsgYALDMDRr1iz169dP0dHRKlasmCpXrmx2LAAAACQCShaQzB48eKDevXtrwYIFkqQmTZqoUKFCJqcCAABAYmF3QSAZ/f3336pSpYoWLFgge3t7jR07VuvWrVO2bNnMjgYAAIBEkiJK1pQpU1SgQAG5uLioatWq2r9//xNvO2PGDNWqVUseHh7y8PCQj4/PU28PpBSLFi1S5cqV9ffffyt37tzatm2bBg8eLDu7FPFrCAAAgERi+qe7ZcuWacCAARoxYoQCAgJUtmxZNWrUSDdu3Ijz9jt27FC7du20fft27d27V15eXmrYsKGuXLmSzMmBhPnnn38UGhoqHx8fBQYGqnbt2mZHAgAAQBIwvWRNnDhRPXr0UNeuXVWyZElNmzZNbm5umj17dpy3X7Rokd555x2VK1dOxYsX18yZM2W1WrV169ZkTg48m2EYtn8PHDhQCxcu1MaNG5UjRw4TUwEAACApmTrxRUREhA4ePKghQ4bYltnZ2cnHx0d79+6N1zpCQ0MVGRmprFmzxnl9eHi4wsPDbZdDQkIkSVHR0YqMjHyB9MDTLV26VKNHj1aNGjVsU7K3adNGVqtVVqvV3HBIUx69l/GehqTGtobkwraG5JJU25ipJSsoKEjR0dHKmTNnjOU5c+bU8ePH47WOjz76SHny5JGPj0+c148dO1ajRo2KtXzXrl3K7P5XwkMDzxAZGak5c+Zow4YNkqQBAwaodevWJqdCerB582azIyCdYFtDcmFbQ1ILDQ1NkvWm6incv/jiCy1dulQ7duyQi4tLnLcZMmSIBgwYYLscEhIiLy8v1axZU/m88idXVKQT586dk7+/vw4ePChJevPNNzVlyhS5urqanAxpWWRkpDZv3qwGDRrI0dHR7DhIw9jWkFzY1pBcbt26lSTrNbVkeXp6yt7eXtevX4+x/Pr168qVK9dT7/vVV1/piy++0JYtW1SmTJkn3s7Z2VnOzs6xljvY2/NLi0T1008/qUuXLrp7966yZs2qOXPmyDAMubq6sq0hWTg6OrKtIVmwrSG5sK0hqSXV9mXqxBdOTk6qWLFijEkrHk1iUa1atSfe78svv9Rnn32mjRs3qlKlSskRFXiqH374QS1atNDdu3f16quv6tChQ2rcuLHZsQAAAGAC02cXHDBggGbMmKF58+bp2LFj6t27tx48eKCuXbtKkjp16hRjYoxx48Zp2LBhmj17tgoUKKBr167p2rVrun//vllPAVCzZs2UM2dODRgwQDt37tRLL71kdiQAAACYxPRjsvz8/HTz5k0NHz5c165dU7ly5bRx40bbZBgXL16McbLWqVOnKiIiItZEAiNGjNDIkSOTMzrSuWPHjqlEiRKSpNy5c+vvv/9+4iyXAAAASD9ML1mS1LdvX/Xt2zfO63bs2BHj8vnz55M+EPAUUVFRGjFihMaOHatly5bJ19dXkihYAAAAkJQCdhcEUpOrV6/Kx8dHY8aMkWEYtlkEAQAAgEdSxEgWkBps3bpV/v7+unHjhjJlyqQZM2bIz8/P7FgAAABIYRjJAp4hOjpan376qRo0aKAbN26oTJkyOnDgAAULAAAAcaJkAc+wa9cujRgxQoZh6K233tK+fftUtGhRs2MBAAAghWJ3QeAZvL299fHHH6tYsWLq1KmT2XEAAACQwlGygP+wWq2aPHmyfH19lS9fPknS559/bnIqAAAApBbsLgg85tatW2revLkGDBigdu3aKSoqyuxIAAAASGUYyQL+v3379qlNmza6dOmSnJ2d1alTJ9nb25sdCwAAAKkMI1lI9wzD0Ndff61atWrp0qVLKlKkiPbt26cePXrIYrGYHQ8AAACpDCNZSNdCQkLUpUsXrVmzRpLUunVrzZw5U+7u7iYnAwAAQGrFSBbSNQcHB508eVKOjo769ttvtXz5cgoWAAAAXggjWUh3DMOQJFksFrm5uWnlypW6d++eKleubHIyAAAApAWMZCFduXfvnvz9/fXFF1/YlhUvXpyCBQAAgERDyUK6ceTIEVWqVElLly7VqFGjdPXqVbMjAQAAIA2iZCFdmDNnjqpUqaKTJ08qb9682rp1q3Lnzm12LAAAAKRBlCykaQ8ePFCXLl3UrVs3hYWFqVGjRjp06JBq1KhhdjQAAACkUUx8gTQrOjpa3t7eOnjwoOzs7PTpp59qyJAhsrPjuwUAAAAkHUoW0ix7e3t169ZNV65c0ZIlS1SnTh2zIwEAACAd4Ct9pClhYWE6c+aM7XLv3r31999/U7AAAACQbChZSDNOnz6tatWqqWHDhgoODpb077mwPDw8TE4GAACA9ISShTRh5cqVqlChggIDAxUSEqJTp06ZHQkAAADpFCULqVp4eLjeffdd+fr66t69e6pZs6YCAwNVqVIls6MBAAAgnaJkIdU6f/68atWqpW+//VaS9OGHH2rbtm3KmzevyckAAACQnjG7IFKtIUOG6I8//pCHh4fmz5+vpk2bmh0JAAAAoGQh9fr2228VERGhiRMnKn/+/GbHAQAAACSxuyBSkcuXL+urr76yXfb09NSqVasoWAAAAEhRGMlCqrBp0yZ16NBBQUFBypEjhzp16mR2JAAAACBOjGQhRYuOjtawYcPUuHFjBQUFqXz58qpRo4bZsQAAAIAnYiQLKdbVq1fl7++vHTt2SJJ69eqlSZMmycXFxdxgAAAAwFNQspAi7dixQ23bttX169eVIUMGzZgxQ+3atTM7FgAAAPBMlCykSGFhYbpx44ZKlSqlFStWqHjx4mZHAgAAAOKFkoUUw2q1ys7u38MEX3vtNa1atUqNGjWSm5ubyckAAACA+GPiC6QIu3fvVtmyZXX27FnbspYtW1KwAAAAkOpQsmAqwzD01VdfydvbW0ePHtXQoUPNjgQAAAC8EHYXhGlu376tLl26aN26dZKkdu3aafr06SanAgAAAF4MJQum2L9/v9q0aaMLFy7I2dlZkydP1ttvvy2LxWJ2NAAAAOCFULKQ7Hbs2KGGDRsqMjJShQsX1ooVK1S+fHmzYwEwgWEYioqKUnR0tNlREA+RkZFycHBQWFgYPzMkKbY1JCZHR0fZ29sn62NSspDsqlWrpjJlyqhAgQKaNWuW3N3dzY4EwAQRERG6evWqQkNDzY6CeDIMQ7ly5dKlS5fY8wBJim0NiclisShfvnzKmDFjsj0mJQvJ4tixY3r55Zfl4OAgZ2dnbdmyRe7u7rxxAumU1WrVuXPnZG9vrzx58sjJyYn3g1TAarXq/v37ypgxo+2UG0BSYFtDYjEMQzdv3tTly5f18ssvJ9uIFiULScowDM2YMUPvvvuuBg0apNGjR0uSsmTJYm4wAKaKiIiQ1WqVl5cXp2pIRaxWqyIiIuTi4sIHXyQptjUkpuzZs+v8+fOKjIykZCH1+3/t3Xlcjen/P/DXaV9UtKlIpMWWRPiUIUucxChSVEyRXcPYt8gy1hn72MZQmCgxYRBiNFLJ1omUQiVMWWJKm1Od6/eHX/d3jnNKJaW8n4/H/Zi5r/u67/t9n3PlnPe5rvu68/PzMWXKFAQFBQEA7t69i7KysjofE0sI+XLRlydCCCGfW32MlKBPN/JZ3Lt3D927d0dQUBBkZWWxfv16hIWFUYJFCCGEEEIaPerJIrXuwIEDmDp1KoqKimBgYICQkBB888039R0WIYQQQgghdYJ6skit+ueffzBt2jQUFRVh0KBBEAgElGARQgj5qBMnTsDExASysrL44Ycfqr1/YGAg3e9bz3JycqCrq4uMjIz6DqXR+d///ofjx4/XdxikGijJIrXKwMAAu3fvxsqVK3H27Fno6OjUd0iEEFJrvL29wePxwOPxIC8vjzZt2mD+/PkoLi6WqHv69GnY2dlBTU0NKioq6N69OwIDA6Ue9/jx4+jbty80NDTQpEkTdO7cGStXrsTr168/8xV9OSZPnoyRI0fiyZMnWLVqVX2HUy0ZGRng8XjQ1dXF27dvxbZ16dIFy5cvFyu7d+8e3NzcoKOjA0VFRZiZmWHZsmUSjzNo3bo1195UVFRgYWGB3377TaxOZGQkeDwemjVrJtEOb9y4we0vTbt27aCoqIjs7GyJbX379q12srt69Wo4OTmhdevW1dqvobh37x5cXFy492XLli1V2u/OnTvo3bs3lJSUYGhoiA0bNkjUCQ0NRbt27aCkpAQLCwucPXtWbLufnx8WLlwIkUhUG5dC6gAlWeSTBQcHIyYmhlsfO3Ysli5dSvdfEUIaJQcHB2RlZSEtLQ2bN2/Gnj174O/vL1Zn+/btcHJyQq9evRAXF4c7d+5g9OjRmDJlCubOnStWd8mSJRg1ahS6d++O8PBwJCYmYuPGjUhISMChQ4fq7LqEQmGdnetD+fn5ePHiBfh8PgwMDKCmplZvsXyKt2/f4ueff660zrVr19CzZ08IhUKcOXMGqampWL16NQIDAzFw4ECJ92HlypXIyspCYmIixowZg4kTJyI8PFziuGpqaggLCxMr27dvH1q1aiU1jqtXr6KoqAgjR47EgQMHqnmlkgoLC7Fv3z74+Ph80nHqsx1+TGFhIYyNjbFu3Tro6elVaZ+8vDwMGjQIRkZGuHXrFn766ScsX74cv/76K1cnJiYG7u7u8PHxQXx8PJydneHs7IzExESuzuDBg/H27Vup7z35QrGvTG5uLgPAMjPS6juUBq+oqIhNnTqVAWAtWrRgr169qu+QvihCoZCdOHGCCYXC+g6FNHINsa0VFRWxpKQkVlRUxJWJRCJW8K6kzheRSFTluL28vJiTk5NY2YgRI5iVlRW3npmZyeTl5dns2bMl9t+2bRsDwK5du8YYYywuLo4BYFu2bJF6vjdv3lQYy5MnT9jo0aNZs2bNmIqKCuvWrRt3XGlxzpw5k9nZ2XHrdnZ2bPr06WzmzJlMS0uL9e3bl7m7uzM3Nzex/YRCIdPS0mIHDhxgZWVlLCcnh61evZq1bt2aKSkpsc6dO7PQ0NAK42SMsdevX7OxY8eypk2bMmVlZebg4MBSU1MZY4xdvnyZARBbLl++XOHrMWnSJKarq8sUFRVZx44d2Z9//skYYywgIIBpaGhwdR8+fMiGDRvGdHV1maqqKrO2tmYRERFix9uxYwczMTFhioqKTFdXl7m4uHDbQkNDWadOnZiSkhLT1NRkAwYMYPn5+VLjSk9PZwDYvHnzWJMmTdjz58+5bZaWlszf358x9r6Nd+jQgVlbW7OysjKxYwgEAsbj8di6deu4MiMjI7Z582axepqammzWrFncevnr5+fnx+zt7bnywsJCpqGhwZYuXcqkfd3z9vZmCxcuZOHh4czMzExiu52dHZs5c6bU65UmNDSU6ejoiJWVlpay8ePHc23FzMxMoq2Xt9Uff/yR6evrs9atWzPGGMvIyGDOzs5MQ0ODNWvWjA0bNoylp6dz+12/fp3Z29szLS0tpq6uzvr06cNu3bpV5Xg/lbT3RpqdO3eyZs2asXfv3nFlCxYsYObm5ty6m5sbGzJkiNh+PXv2ZJMnTxYrGzduHBszZsynBf6VkvaZU+7Vq1cMAMvNza3Vc9LEF6RG0tLS4Orqitu3bwMAxo0bBw0NjXqOihDSkBWVlKHDsvN1ft6klXyoKNTs4zAxMRExMTEwMjLiyo4dO4aSkhKJHivg/ZC4xYsX48iRI+jZsyeCgoLQpEkTTJs2TerxK7rHKD8/H3Z2dmjRogVOnToFPT093L59u9pDiconKoqOjgYAPHz4EK6urtxDYAHg/PnzKCwsxPDhwwEAmzZtwvHjx7F7926YmpriypUrGDNmDHR0dGBnZyf1PN7e3njw4AFOnToFdXV1LFiwAI6OjkhKSoKtrS1SUlJgbm6O48ePw9bWFpqamhLHEIlE3K/5v//+O9q2bYukpKQKR03k5+fD0dERq1evhqKiIg4ePIhvv/0WKSkpaNWqFW7evIkZM2bg0KFDsLW1xevXrxEVFQUAyMrKgru7OzZs2IDhw4fj7du3iIqKAmOs0tfT3d0dERERWLlyJX755ReJ7QKBAElJSTh8+LDE4wssLS1hb2+PI0eOYMGCBVKvPywsDG/evIGCgoLE9rFjx+Knn35CZmYmWrVqhePHj6N169bo2rWrRN23b98iNDQUcXFxaNeuHXJzcxEVFYXevXtXen2ViYqKQrdu3SRibtmyJUJDQ6GlpYWYmBhMmjQJ+vr6cHNz4+pdunQJ6urqiIiIAACUlJRg8ODB6NatG/7++28oKCjgxx9/hIODA+7cuQMFBQW8ffsWXl5e2L59Oxhj2LhxIxwdHfHgwYMKe0KDgoIwefLkSq8jPDz8k16HD8XGxqJPnz5i7xmfz8f69evx5s0bNGvWDLGxsZg9e7bYfnw+HydOnBAr69GjB9atW1drsZHPi5IsUm1hYWEYN24ccnNzoaWlhd9//x0ODg71HRYhhNSJ06dPo0mTJigtLcW7d+8gIyMj9oU6NTUVGhoa0NfXl9hXQUEBxsbGSE1NBQA8ePAAxsbGkJeXr1YMhw8fxsuXL3Hjxg0uITExMan2tZiamordH9K2bVuoqqoiLCwMY8eO5c41bNgwqKmpoaioCJs3b8aFCxfQq1cvAICxsTGuXr2KPXv2SE2yypOr6Oho2NraAnj/ZdfQ0BAnTpyAq6srdHV1AQCampoVDsO6ePEirl+/juTkZJiZmXHnroilpSUsLS259VWrViEsLAynTp2Cr68vMjMzoaqqiqFDh0JNTQ1GRkawsrIC8D7JKi0txYgRI7gE2sLC4qOvJ4/Hw7p16/Dtt99i1qxZaNu2rdj28ve9ffv2Uvdv3749rl69Kla2YMEC+Pn54d27dygtLYWmpiYmTJggsa+uri4GDx6MwMBALFu2DPv378f48eOlnic4OBimpqbo2LEjAGD06NHYt2/fJyUXjx8/hoGBgViZvLw8VqxYwa23adMGsbGxOHr0qFiSpaqqit9++41LRH7//XeIRCJs27YNGhoakJGRQUBAAJo2bYrIyEgMGjQI/fv3FzvXr7/+iqZNm+Lvv//G0KFDpcY4bNgw9OzZs9LraNGiRbWu+2Oys7PRpk0bsbLmzZtz25o1a4bs7Gyu7L91PrxXzsDAAE+ePIFIJKJnDDYAlGSRKistLcX8+fOxefNmAICtrS2Cg4NhaGhYz5ERQhoDZXlZJK3k18t5q6Nfv37YtWsXCgoKsHnzZsjJycHFxaVG5/5Yz0hFBAIBrKyspPb4VMeHPQ9ycnJwc3NDUFAQxo4di4KCApw8eRLBwcEA3vd0FRYWgs8Xf5+EQiGXoHwoOTkZcnJyYl9utbS0YG5ujuTk5CrHKhAI0LJlSy7B+pj8/HwsX74cZ86c4ZKmoqIiZGZmAgAGDhwIIyMjGBsbw8HBAQ4ODhg+fDhUVFRgaWmJAQMGwMLCAnw+H4MGDcLIkSPRrFmzj56Xz+fjm2++wdKlS3H48GGpdarzvs+bNw/e3t7IysrCvHnzMG3atAoT6vHjx2PmzJkYM2YMYmNjERoayvXO/df+/fsxZswYbn3MmDGws7PD9u3ba3w/XFFREZSUlCTKd+zYgf379yMzMxNFRUUQCoXo0qWLWB0LCwuxnp6EhAQ8fPhQ4vtFcXExHj16BAB4/vw5/Pz8EBkZiRcvXqCsrAyFhYXc+yuNmppag73fDwCUlZUhEonw7t07KCsr13c45CMoDSZVJisri8ePHwMA5s6di8jISEqwCCG1hsfjQUVBrs6XimZeq4iqqipMTExgaWmJ/fv3Iy4uDvv27eO2m5mZITc3F//884/EvkKhEI8ePeISBTMzM6SlpaGkpKRaMXzsC5aMjIzEF3lp51BVVZUo8/T0xKVLl/DixQucOHECysrK3GiF/Px8AMCff/4JgUDALUlJSTh27Fi1rqG6qvulcu7cuQgLC8OaNWsQFRUFgUAACwsLbmIFNTU13L59G0eOHIG+vj6WLVsGS0tL/Pvvv5CVlUVERATCw8PRoUMHbN++Hebm5khPT6/SudetW4eQkBDEx8eLlZe/7xUll//tpSunra0NExMT9O7dG6GhoZgxYwaSkpKk7j948GAUFRXBx8cH3377LbS0tCTqJCUl4dq1a5g/fz7k5OQgJyeH//3vfygsLOSS6ZrQ1tbGmzdvxMqCg4Mxd+5c+Pj44MKFCxAIBBg3bpzE5BYftsP8/Hx069YNV65cwe3bt7l2lpqaCg8PDwCAl5cXBAIBtm7dipiYGAgEAmhpaVU6cUb58NzKFmlJ6afQ09PD8+fPxcrK18t7bSuq82Gv7uvXr6GqqkoJVgNBSRb5qPIx/jweD/v378fZs2fx008/VXt4CyGENDYyMjJYvHgx/Pz8UFRUBABwcXGBvLw8Nm7cKFF/9+7dKCgogLu7OwDAw8MD+fn52Llzp9Tj//vvv1LLO3fuDIFAUOEU7zo6OsjKyhIrEwgEVbomW1tbGBoaIiQkBEFBQXB1deX+ve/QoQMUFRWRmZkJExMTsaWiH93at2+P0tJSxMXFcWU5OTlISUlBhw4dqhQT8P6anz59yg25+5jo6Gh4e3tj+PDhsLCwgJ6ensTzm+Tk5GBvb48NGzbgzp07yMjIwF9//QXg/Wder169sGLFCsTHx0NBQUFi9r6K9OjRAyNGjMDChQvFyrt06YJ27dph8+bNEvfPJSQk4OLFi1zbkMbQ0BCjRo3CokWLpG6Xk5PDd999h8jIyAqHCu7btw99+vRBQkKCWKI8e/ZssR8LqsvKykoi+SsfIjpt2jRYWVnBxMSE64mqTNeuXfHgwQMuwfzvUn7/d3R0NGbMmAFHR0d07NgRioqKePXqVaXHHTZsmNg1S1usra1r/BpIY2NjgytXroj9yBEREQFzc3OuZ9TGxgaXLl0S2y8iIgI2NjZiZYmJiRX2GJMvDyVZpEKlpaVYtGgRPD09uV9ENTQ0MHjw4HqOjBBCvhyurq6QlZXFjh07AACtWrXChg0bsGXLFixZsgT379/Ho0ePsGnTJsyfPx9z5szhhs717NmTK5s/fz5iY2Px+PFjXLp0Ca6urhVOre3u7g49PT04OzsjOjoaaWlpOH78OGJjYwEA/fv3x82bN3Hw4EE8ePAA/v7+YtNBf4yHhwd2796NiIgIeHp6cuVqamrw9fXFnDlzcODAATx69Ai3b9/G9u3bK4zV1NQUTk5OmDhxIq5evYqEhASMGTMGLVq0gJOTU5VjsrOzQ58+feDi4oKIiAikp6cjPDwc586dq/C8f/zxBwQCARISEuDh4SGW2Jw+fRrbtm2DQCDA48ePcfDgQYhEIpibmyMuLg5r1qzBzZs3kZmZiT/++AMvX76s8F4qaVavXo2//voLKSkpXBmPx8O+ffuQlJQEFxcXXL9+HZmZmQgNDcW3334LGxubjz6baubMmfjzzz9x8+ZNqdtXrVqFly9fSgzpBN73Zh46dAju7u7o1KmT2DJhwgTExcXh3r17XP2XL19KJCEf9riU4/P5uHfvnlhvlqmpKW7evInz588jNTUVS5cuxY0bNyq9PuB9b6q2tjY8PT0RFRWF9PR0REZGYsaMGXj69Cl37EOHDiE5ORlxcXHw9PT8aA+PmpqaRNL24VLZMYRCIfc6CIVCPHv2DAKBAA8fPuTq/PLLLxgwYAC37uHhAQUFBfj4+ODevXsICQnB1q1bxSa6mDlzJs6dO4eNGzfi/v37WL58OW7evAlfX1+x80dFRWHQoEEfff3IF6JW5ypsAGgK96p59uwZ6927Nzed7t9//13fITU4DXFabdIwNcS2Vtl0ul8yaVOjM8bY2rVrmY6OjtgU3ydPnmS9e/dmqqqqTElJiXXr1o3t379f6nFDQkJYnz59mJqaGlNVVWWdO3dmK1eurHQK94yMDObi4sLU1dWZiooKs7a2ZnFxcdz2ZcuWsebNmzMNDQ02a9Ys5uvrKzGFe0VTdCclJTEAzMjISGyK+7KyMvb69Wu2efNmZm5uzuTl5ZmOjg7j8/mVfk6UT+GuoaHBlJWVGZ/P56ZwZ+z91OyoZOr2cjk5OWzcuHFMS0uLKSkpsU6dOrHTp08zxiSncE9PT2f9+vVjysrKzNDQkP3yyy9i1xwVFcXs7OxYs2bNmLKyMuvcuTMLCQnhrp/P5zMdHR2mqKjIzMzM2Pbt2yuMq3wK9/j4eLHySZMmMQDcFO7l7ty5w1xcXJimpiaTl5dnbdu2ZX5+fqygoECsXkXThPP5fDZ48GDG2P9N4V5RWwkLC+OmcD927BiTkZFh2dnZUuu2b9+emx7ezs5OYmp9AGzVqlUVvg49evRgu3fv5taLi4uZt7c309DQYE2bNmVTp05lCxcuZJaWllydiv6mnj17xkaPHs20tbWZoqIiMzY2ZhMnTuSm2b59+zaztrZmSkpKzNTUlIWGhlZ5WvWaKn+fP1z++3fl7+/PjIyMxPZLSEhg33zzDVNUVGQtWrQQm6a/3NGjR5mZmRlTUFBgHTt2ZGfOnBHb/vTpUyYvL8+ePHnyOS6t0auPKdx5jNXwrtsGKi8vDxoaGsjMSIOhUZuP7/AVKv/l8uXLl1BTU8O+ffvg6upa32E1OCUlJTh79iwcHR1paCX5rBpiWysuLkZ6ejratGkj9WZ58mUSiUTIy8uDuro6zW5GJJw5cwbz5s1DYmLiJ7cPamviFixYgDdv3og9xJhUXWWfOTk5OdDW1kZubi7U1dVr7Zw0uyDhlJWVYeXKlVi1ahUYY+jSpQtCQ0NrNC0wIYQQQr4uQ4YMwYMHD/Ds2TOaGKuW6erqSjxLi3zZKMkiHG9vb/z+++8AgEmTJmHLli00gw0hhBBCquxj95SRmpkzZ059h0CqifpfCWfixInQ0NDA77//jj179lCCRQghhBBCSA1QT9ZXTCQSISkpCZ06dQIA9OnTBxkZGWjatGn9BkYIIYQQQkgDRj1ZX6lXr15h6NCh+N///if2UERKsAghhBBCCPk0lGR9hWJiYmBlZYXw8HCUlZVV+OR5QgghhBBCSPVRkvUVYYxh06ZNsLOzw9OnT2FmZoa4uDiMGDGivkMjhBBCCCGk0aB7sr4Sb968wbhx43Dy5EkAwOjRo/Hrr79CTU2tniMjhBBCCCGkcaGerK/E7t27cfLkSSgoKGDnzp04fPgwJViEEEIIIYR8BpRkfSXmzp2LMWPGICYmBlOnTgWPx6vvkAghhBDOiRMnYGJiAllZ2Ro9aykwMJAmbyJfLKFQCBMTE8TExNR3KI3O6NGjsXHjxvoOQwIlWY1UXl4eli1bBqFQCACQl5fHoUOH0K1bt3qOjBBCGi5vb2/weDzweDzIy8ujTZs2mD9/PoqLiyXqnj59GnZ2dlBTU4OKigq6d++OwMBAqcc9fvw4+vbtCw0NDTRp0gSdO3fGypUr8fr16898RV+OyZMnY+TIkXjy5AlWrVpV3+FUS0ZGBtcueDwetLS0MGjQIMTHx3N1+vbtCx6Ph+DgYLF9t2zZgtatW3PrgYGBYscqX5SUlMSOJS0R/TDRXL58OXg8HhwcHCTq/vTTT+DxeOjbt69Y+evXr/HDDz/AyMgICgoKMDAwwPjx45GZmSlWr/xvYd26dWLlJ06cEPshNzIyEjweD//++69EDO3atYOioiKys7MltlXEwsICU6ZMkbrt0KFDUFRUxKtXryTOW75evujo6MDR0RF3796Veiw+nw9ZWVncuHFDYpu3tzecnZ2rHDPwfkRRmzZtYGtrW639GoqsrCx4eHjAzMwMMjIyVf6hJDMzE0OGDIGKigp0dXUxb948lJaWitWJjIxE165doaioCBMTE4l/R/38/LB69Wrk5ubW0tXUDkqyGqGEhARYW1tj1apVWLhwYX2HQwghjYqDgwOysrKQlpaGzZs3Y8+ePfD39xers337djg5OaFXr16Ii4vDnTt3MHr0aEyZMgVz584Vq7tkyRKMGjUK3bt3R3h4OBITE7Fx40YkJCTg0KFDdXZd5T/K1Yf8/Hy8ePECfD4fBgYGDXY4+8WLF5GVlYXz588jPz8fgwcPFksulJSU4Ofnh5KSkkqPo66ujqysLLHl8ePHNYpJX18fly9fxtOnT8XK9+/fj1atWomVvX79Gv/73/9w8eJF7N69Gw8fPkRwcDAePnyI7t27Iy0tTay+kpIS1q9fjzdv3lQ7rqtXr6KoqAgjR47EgQMHqryfj48PgoODUVRUJLEtICAAw4YNg7a2doX7p6SkcO/Ru3fvMGTIEIm2n5mZiZiYGPj6+mL//v1Vv6gKMMbwyy+/wMfH55OOU59/ox/z7t076OjowM/PD5aWllXap6ysjHv9Y2JicODAAQQGBmLZsmVcnfT0dAwZMgT9+vWDQCDADz/8gAkTJuD8+fNcnU6dOqFt27b4/fffa/26Pgn7yuTm5jIALDMjrb5DqXUikYjt3buXKSkpMQDM0NCQxcbG1ndYXy2hUMhOnDjBhEJhfYdCGrmG2NaKiopYUlISKyoq+r9CkYixd/l1v4hEVY7by8uLOTk5iZWNGDGCWVlZceuZmZlMXl6ezZ49W2L/bdu2MQDs2rVrjDHG4uLiGAC2ZcsWqed78+ZNhbE8efKEjR49mjVr1oypqKiwbt26cceVFufMmTOZnZ0dt25nZ8emT5/OZs6cybS0tFjfvn2Zu7s7c3NzE9tPKBQyLS0tduDAAVZWVsZycnLY6tWrWevWrZmSkhLr3LkzCw0NrTBOxhh7/fo1Gzt2LGvatClTVlZmDg4OLDU1lTHG2OXLlxkAseXy5csVvh6TJk1iurq6TFFRkXXs2JH9+eefjDHGAgICmIaGBlf34cOHbNiwYUxXV5epqqoya2trFhERIXa8HTt2MBMTE6aoqMh0dXWZi4sLty00NJR16tSJKSkpMU1NTTZgwACWn58vNa709HQGgMXHx3Nl0dHRDAA7d+4c93qPGzeOaWlpsR07dnD1Nm/ezIyMjLj1D69DGjs7OzZz5kyJ8g/39ff3Z5aWlmzo0KHsxx9/FItNW1ubTZ06VaxNTJkyhamqqrKsrCyx4xYWFrIWLVowBwcHrszLy4sNHTqUtWvXjs2bN48rDwsLY//9eln+/n7Ylr29vdnChQtZeHg4MzMzk7iWsrIy9ubNG1ZWViZW/vLlS6agoMAOHTokVp6WlsZ4PB4LDw+Xel5pcZw6dYoBYAkJCWLHWr58ORs9ejRLTk5mGhoarLCwUGy7tL+vyty4cYPJyMiwvLw8sfL58+czU1NTpqyszNq0acP8/PzE/h0vf//27t3LWrduzXg8HmPs/d+Bj48P09bWZmpqaqxfv35MIBBw+1Wl7X9OFbXPD509e5bJyMiw7OxsrmzXrl1MXV2dvXv3jjH2/jXq2LGj2H6jRo1ifD5frGzFihXsm2++qfBcUj9z/r9Xr14xACw3N/ejMVcHzS7YSBQUFGDq1Kncr56Ojo44ePAgtLS06jkyQgipopJCYI1B3Z938T+AgmqNdk1MTERMTAyMjIy4smPHjqGkpESixwp4PyRu8eLFOHLkCHr27ImgoCA0adIE06ZNk3r8iu4xys/Ph52dHVq0aIFTp05BT08Pt2/fhkgkqlb8Bw4cwNSpUxEdHQ0AePjwIVxdXZGfn48mTZoAAM6fP4/CwkIMHz4cALBp0yYcP34cu3fvhqmpKa5cuYIxY8ZAR0cHdnZ2Us/j7e2NBw8e4NSpU1BXV8eCBQvg6OiIpKQk2NraIiUlBebm5jh+/DhsbW2hqakpcQyRSITBgwfj7du3+P3339G2bVskJSVBVla2wtfI0dERq1evhqKiIg4ePIhvv/0WKSkpaNWqFW7evIkZM2bg0KFDsLW1xevXrxEVFQXg/dAnd3d3bNiwAcOHD8fbt28RFRUFxliVX1tlZWUA4r0P6urqWLJkCVauXAkvLy+oqtas3VXX+PHjMX/+fCxZsgTA+14sT09PsToikQjBwcHw9PSEnp6e2DZlZWVMmzYNfn5+eP36Nff+yMrKYs2aNfDw8MCMGTPQsmXLKsXz9u1bhIaGIi4uDu3atUNubi6ioqLQu3fvj+6rra0NJycn7N+/H2PGjOHKAwMD0bJlSwwaNKhKMeTm5nJDNxUUFLhyxhgCAgKwY8cOtGvXDiYmJjh27BjGjh1bpeNKExUVBTMzM4keWjU1NQQGBsLAwAB3797FxIkToaamhvnz53N1Hj58iOPHj+OPP/7g2rqrqyuUlZURHh4ODQ0N7NmzBwMGDEBqaio0NTU/2vYrinHw4MGVXseePXsk2s2niI2NhYWFBZo3b86V8fl8TJ06Fffu3YOVlRViY2Nhb28vth+fz5cYjtijRw+sXr0a7969g6KiYq3F+CkoyWoEUlJSMGLECO7DZvXq1Zg3bx5kZGg0KCGE1LbTp0+jSZMmKC0txbt37yAjI4NffvmF256amgoNDQ3o6+tL7KugoABjY2OkpqYCAB48eABjY2PIy8tXK4bDhw/j5cuXuHHjBveF18TEpNrXYmpqig0bNnDrbdu2haqqKsLCwrgvlYcPH8awYcOgpqaGoqIibN68GRcuXECvXr0AAMbGxrh69Sr27NkjNckqT66io6O5+1GCgoJgaGiIEydOwNXVFbq6ugAATU1NiS/45S5evIjr168jOTkZZmZm3LkrYmlpKTZsadWqVQgLC8OpU6fg6+uLzMxMqKqqYujQoVBTU4ORkRGsrKwAvE+ySktLMWLECC6BtrCwqNqLCuDff//FqlWr0KRJE/To0UNs27Rp07B161Zs2rQJS5culbp/bm4ul+SW6927N8LDw6scw38NHToUU6ZMwZUrV9CtWzccPXoUV69eFRsK9/LlS/z7779o37691GO0b98ejDE8fPhQ7JqGDx+OLl26wN/fH/v27atSPMHBwTA1NUXHjh0BvJ+4YN++fVVKsoD3QwYHDx6M9PR0tGnTBowxHDhwAF5eXh/97lOeCBYUFAAAhg0bhnbt2nHbL168iMLCQvD5fADAmDFjsG/fvk9Ksh4/fgwDA8kfkPz8/Lj/b926NebOnYvg4GCxJEsoFOLgwYPQ0dEB8H6Y5fXr1/HixQsumfj5559x4sQJHDt2DJMmTfpo25fG2toaAoGg0uv4bzJUG7KzsyWOWb5efp9eRXXy8vJQVFTE/ZhhYGAAoVCI7OxssR+96hMlWY2ArKwsnj59Cn19fQQHB6NPnz71HRIhhFSfvMr7XqX6OG819OvXD7t27UJBQQE2b94MOTk5uLi41OjU1ekZ+S+BQAArKyupPT7V8eFkSHJycnBzc0NQUBDGjh2LgoICnDx5kvvF/+HDh2JfQMsJhUIuQflQcnIy5OTk0LNnT65MS0sL5ubmSE5OrnKsAoEALVu25BKsj8nPz8fy5ctx5swZLmkqKiriJnAYOHAgjIyMYGxsDAcHBzg4OGD48OFQUVGBpaUlBgwYAAsLC/D5fAwaNAgjR45Es2bNKj2nra0tZGRkUFBQAGNjY4SEhEh8QVRUVMTKlSvx/fffY+rUqVKPo6amhtu3b4uVlX+ZrAl5eXmMGTMGAQEBSEtLg5mZGTp37iy1bk3a5Pr169G/f3+pvbfSfNgLNWbMGNjZ2WH79u1Vuh9v4MCBaNmyJQICArBy5UpcunQJmZmZGDdu3Ef3jYqKgoqKCq5du4Y1a9Zg9+7dErGNGjUKcnLvvyK7u7tj3rx5ePToEdq2bVul6/tQUVGR2MQl5UJCQrBt2zY8evQI+fn5KC0thbq6ulgdIyMjLsEC3t93n5+fLzFSqaioCI8ePQLw8bYvjbKyco1+qPlSlP99FBYW1nMk/4eSrAZKJBJxv9aYmJjg5MmT6NChA/drICGENDg8Xo2H7dUlVVVV7svI/v37YWlpiX379nE3tZuZmSE3Nxf//POPxK/XQqEQjx49Qr9+/bi6V69eRUlJSbV6sz72hVtGRkbiy7K0yRakDVfz9PSEnZ0dXrx4gYiICCgrK3Oz0+Xn5wMA/vzzTxgaGort97mH6FQ3yZg7dy4iIiLw888/w8TEBMrKyhg5ciQ3fK88kYmMjMSFCxewbNkyLF++HDdu3EDTpk0RERGBmJgYXLhwAdu3b8eSJUsQFxeHNm3aVHjOkJAQdOjQAVpaWpVOJz9mzBj8/PPP+PHHH8VmFiwnIyNT6RdedXV1qTOp/fvvv9DQ0JC6z/jx49GzZ08kJiZi/PjxEtt1dHTQtGnTChPf5ORk8Hg8qXH16dMHfD4fixYtgre3d4VxA0BSUhKuXbuG69evY8GCBVx5WVkZgoODMXHixEr3B96/Pt7e3jhw4ACWL1+OgIAA9OvXr9KezXJt2rRB06ZNYW5ujhcvXmDUqFG4cuUKgPcTf4SFhaGkpAS7du0Si23//v1YvXr1R48vjba2tsQshrGxsfD09MSKFSvA5/OhoaGB4OBgianIP/wbzc/Ph76+PiIjIyXOU97mPtb2pamP4YJ6enq4fv26WNnz58+5beX/LS/7bx11dXWxfxPKZ2L9b0Ja32g8WQOUmpoKa2trXLp0iSvr27cvJViEEFLHZGRksHjxYvj5+XGznbm4uEBeXl7qc1t2796NgoICuLu7AwA8PDyQn5+PnTt3Sj2+tGmvAaBz584QCAQVTvGuo6ODrKwssbKPDQUqZ2trC0NDQ4SEhCAoKAiurq5cAtihQwcoKioiMzMTJiYmYsuHSVe59u3bo7S0FHFxcVxZTk4OUlJS0KFDhyrFBLy/5qdPn3JDLT8mOjoa3t7eGD58OCwsLKCnp4eMjAyxOnJycrC3t8eGDRtw584dZGRk4K+//gIA8Hg89OrVCytWrEB8fDwUFBQQFhZW6TkNDQ3Rtm3bjz6vS0ZGBmvXrsWuXbskYqoKc3NziZ4uALh9+3aFPX0dO3ZEx44dkZiYCA8PD6kxubm54fDhwxJTqhcVFWHnzp3g8/kV9p6uW7cOf/75J2JjYyuNfd++fejTpw8SEhIgEAi4Zfbs2VUebggA48aNw5MnT/DHH38gLCysRjP3TZ8+HYmJidz7GhQUhJYtW0rEtnHjRgQGBqKsrKza5wAAKysr3L9/X+yHj/J7OZcsWQJra2uYmppWaQbJrl27Ijs7G3JychJ/g+WzKlal7X+ofLhgZcuwYcNqdP0VsbGxwd27d/HixQuuLCIiAurq6ty/DTY2NmLfd8vr2NjYiJUlJiaiZcuWlc4sWdcoyWpgjh49Cmtra8THx2PWrFnVvsmZEEJI7XJ1dYWsrCx27NgBAGjVqhU2bNiALVu2YMmSJbh//z4ePXqETZs2Yf78+ZgzZw43dK5nz55c2fz58xEbG4vHjx/j0qVLcHV1rXBqa3d3d+jp6cHZ2RnR0dFIS0vD8ePHuS+4/fv3x82bN3Hw4EE8ePAA/v7+SExMrPI1eXh4YPfu3YiIiBD75VpNTQ2+vr6YM2cODhw4gEePHuH27dvYvn17hbGamprCyckJEydOxNWrV5GQkIAxY8agRYsWcHJyqnJMdnZ26NOnD1xcXBAREYH09HSEh4fj3LlzFZ73jz/+gEAgQEJCAjw8PMQ+M0+fPo1t27ZBIBDg8ePHOHjwIEQiEczNzREXF4c1a9bg5s2byMzMxB9//IGXL19WeL9STQwZMgQ9e/bEnj17JLYxxpCdnS2xlMc/depUpKamYsaMGbhz5w5SUlKwadMmHDlyBHPmzKnwnH/99ReysrIqTALXrFkDPT09DBw4EOHh4Xjy5AmuXLkCPp+PkpISro1LY2FhAU9PT2zbtq3COiUlJTh06BDc3d3RqVMnsWXChAmIi4vDvXv3Ktz/v9q0aYP+/ftj0qRJUFRUxIgRI6q033+pqKhg4sSJ8Pf3B2MM+/btw8iRIyVi8/HxwatXr8TaWm5urkQS8uTJE6nn6devH/Lz88WuzdTUFJmZmQgODsajR4+wbdu2jybxAGBvbw8bGxs4OzvjwoULyMjIQExMDJYsWYKbN29yx66s7UtTPlywsuVjQznLX4f8/Hy8fPkSAoEASUlJ3PawsDCx+98GDRqEDh06YOzYsUhISMD58+fh5+eH6dOncz3jU6ZMQVpaGubPn4/79+9j586dOHr0KGbNmiV27qioqCpPelJnanWuwgagoU7hXlxczKZPn85Nb9unTx/27Nmz+g6LVKIhTqtNGqaG2NYqm073S1bR1M1r165lOjo6YlN8nzx5kvXu3ZupqqoyJSUl1q1bN7Z//36pxw0JCWF9+vRhampqTFVVlXXu3JmtXLmy0incMzIymIuLC1NXV2cqKirM2tqaxcXFcduXLVvGmjdvzjQ0NNisWbOYr6+vxBTuFU2znJSUxAAwIyMjJvrPFPdlZWXs9evXbPPmzczc3JzJy8szHR0dxufz2d9//11hrOVTuGtoaDBlZWXG5/O5KdwZez8lNSqZur1cTk4ONw26kpIS69SpEzt9+jRjTHL68vT0dNavXz+mrKzMDA0N2S+//CJ2zVFRUczOzo41a9aMKSsrs86dO7OQkBDu+vl8PtPR0WGKiorMzMyMbd++vcK4pE3h/iFpr3dMTAz3OpcLCAiQmNK+fPnv1OrXr19nAwcOZDo6OkxDQ4P17NmThYWFiR2/fArwinw4rT9j76dI//7775mhoSGTl5dnzZs3Z97e3uzx48di9aT9LaSnpzMFBYUKp3A/duyYxJTd/9W+fXs2a9YsxljFU7j/1+HDhxkANm3aNIltVZnCnbH3j1yQk5Nj69atYwDY9evXpZ5r8ODBbPjw4dy1S3t/fHx8KozVzc2NLVy4UKxs3rx5TEtLizVp0oSNGjWKbd68WeoU/B/Ky8tj33//PTMwMGDy8vLM0NCQeXp6sszMTMbYx9v+5yLtNZHWtv8rIyODDR48mCkrKzNtbW02Z84cVlJSIlbn8uXLrEuXLkxBQYEZGxuzgIAAse1FRUVMQ0Oj0scW1ccU7jzGanjXbQOVl5cHDQ0NZGakwdCo4nHVX5L09HS4urri1q1bAIBFixZh5cqV3E2Z5MtUUlKCs2fPwtHRsdozhxFSHQ2xrRUXF3Mzg0m7IZx8mUQiEfLy8qCurk4z2JLPqrG1tTt37mDgwIF49OiRxMyR5NPs2rULYWFhuHDhQoV1KvvMycnJgba2NnJzcyUmHvkU9C39C5eZmQkrKyvk5uZCU1MThw4dgqOjY32HRQghhBBCqqhz585Yv3490tPTq/U4APJx8vLy2L59e32HIYGSrC+coaEhnJyc8ODBAwQHB1f4EDlCCCGEkIbuY7Pclc9w2RB9bOZFUjMTJkyo7xCkoiTrC/TkyROoqqpCU1MTPB4Pu3btgry8fIMZBkQIIYQQUhPls9yJRCLk5+ejSZMmjWK4IPn6UJL1hQkPD8fYsWNhY2ODkydPQkZGBioq1XtQJiGEEEJIQ1Q+y11juyeLfH2o1X4hSktLsXjxYjg6OiInJwdZWVkVPh+FEEIIIYQQ8uWiJOsL8M8//2DAgAFYu3YtAMDX1xfR0dEVPvCPEEIIIYQQ8uWi4YL17NKlS/Dw8MCLFy+gpqaG3377DW5ubvUdFiGEEEIIIaSGKMmqRyUlJZg0aRJevHiBzp07IzQ0FGZmZvUdFiGEEEIIIeQT0HDBeiQvL4/g4GBMnjwZ165dowSLEEIIIYSQRoCSrDp25coVBAUFcevdu3fH7t27oaysXI9REUIIaQh4PB5OnDhR32GIycjIAI/Hg0Ag+OznCgwMRNOmTcXKfv31VxgaGkJGRgZbtmzB8uXL0aVLl88eCyGEVIaSrDoiEomwfv169O/fHz4+PkhISKjvkAghhFSTt7c3eDweeDwe5OXl0aZNG8yfPx/FxcX1HdpXYdSoUUhNTeXW8/Ly4OvriwULFuDZs2eYNGkS5s6di0uXLtVjlIQQQvdk1YmcnBx89913OHv2LADAw8MDbdu2reeoCCGE1ISDgwMCAgJQUlKCW7duwcvLCzweD+vXr6/v0Bo9ZWVlsZEfmZmZKCkpwZAhQ6Cvr8+VN2nS5JPOU1JSAnl5+U86BiHk60Y9WZ/ZtWvXYGVlhbNnz0JJSQl79+7FgQMHPvkDgBBCGquCgoIKlw97jCqrW1RU9NG6NaGoqAg9PT0YGhrC2dkZ9vb2iIiI4Lbn5OTA3d0dLVq0gIqKCiwsLHDkyBGxY/Tt2xczZszA/PnzoampCT09PSxfvlyszoMHD9CnTx8oKSmhQ4cOYucod/fuXfTv3x/KysrQ0tLCpEmTkJ+fz2339vaGs7Mz1qxZg+bNm6Np06ZYuXIlSktLMW/ePGhqaqJly5YICAio9JpFIhE2bNgAMzMzNG/eHK1bt8bq1aul1i0rK4OPjw/atGkDZWVlmJubY+vWrWJ1IiMj0aNHD6iqqqJp06bo1asXHj9+DABISEhAv379oKamBnV1dXTr1g03b94EID5cMDAwEBYWFgAAY2Nj8Hg8ZGRkSB0u+Ntvv6F9+/ZQUlJCu3btsHPnTm5b+XDHkJAQ2NnZQUlJSWxYPyGE1AT1ZH1G27Ztw5w5c1BaWgpTU1OEhobC0tKyvsMihJAvWmU/Qjk6OuLMmTPcuq6uLgoLC6XWtbOzQ2RkJLfeunVrvHr1SqwOY+yTYk1MTERMTAyMjIy4suLiYnTr1g0LFiyAuro6zpw5g7Fjx6Jt27bo0aMHV+/AgQOYPXs24uLiEBsbC29vb/Tq1QsDBw6ESCTCiBEj0Lx5c8TFxSE3Nxc//PCD2LkLCgrA5/NhY2ODGzdu4MWLF5gwYQJ8fX0RGBjI1fvrr7/QsmVLXLlyBdHR0fDx8UFMTAz69OmDuLg4hISEYPLkyRg4cCBatmwp9ToXLVqEvXv3YuPGjejSpQvevn0rNmzvv0QiEVq2bInQ0FBoaWkhJiYGkyZNgr6+Ptzc3FBaWgpnZ2dMnDgRR44cgVAoxPXr18Hj8QAAnp6esLKywq5duyArKwuBQCC1V2nUqFEwNDSEvb09rl+/DkNDQ+jo6EjUCwoKwrJly/DLL7/AysoK8fHxmDhxIlRVVeHl5cXVW7hwITZu3AgrKysoKSlJvTZCCKky9pXJzc1lAFhmRtpnP9fKlSsZAObm5sZyc3M/+/nIl0UoFLITJ04woVBY36GQRq4htrWioiKWlJTEioqKJLYBqHBxdHQUq6uiolJhXTs7O7G62traEnWqy8vLi8nKyjJVVVWmqKjIADAZGRl27NixSvcbMmQImzNnDrduZ2fHvvnmG7E63bt3ZwsWLGCMMXb+/HkmJyfHnj17xm0PDw9nAFhYWBhjjLFff/2VNWvWjOXn53N1zpw5w2RkZFh2djYXr5GRESsrK+PqmJubs969e3PrpaWlTFVVlR05ckRq7Hl5eUxRUZHt3buXlZWVsTdv3ogdLz09nQFg8fHxFV7/9OnTmYuLC2OMsZycHAaARUZGSq2rpqbGAgMDpW4LCAhgGhoa3Hp8fDwDwNLT07kyf39/Zmlpya23bduWHT58WOw4q1atYjY2NmLxb9mypcL4Sd2T1tYIqanKPnNevXrFANT6d3XqyaplZWVlkJWVBQAsWbIEnTp1grOzM/cLHSGEkMr9d7jbh8r/fS334sWLCuvKyIiPiM/IyPikuMr169cPu3btQkFBATZv3gw5OTm4uLhw28vKyrBmzRocPXoUz549g1AoxLt376CioiJ2nM6dO4ut6+vrc9eTnJwMQ0NDGBgYcNttbGzE6icnJ8PS0hKqqqpcWa9evSASiZCSkoLmzZsDADp27Cj2WjRv3hydOnXi1mVlZaGlpVXha5mcnIx3795hwIABVXp9AGDHjh3Yv38/MjMzUVRUBKFQyA3h09TUhLe3N/h8PgYOHAh7e3u4ublx91TNnj0bEyZMwKFDh2Bvbw9XV9ca38dcUFCAR48ewcfHBxMnTuTKS0tLoaGhIVbX2tq6RucghBBp6J6sWsIYw65du2Bra8vdByAjI4Phw4dTgkUIIdWgqqpa4fLhMK7K6n74aAxpdWoan4mJCSwtLbF//37ExcVh37593PaffvoJW7duxYIFC3D58mUIBALw+XwIhUKx43w4BI7H40EkEtUopspIO091zl3dR4wEBwdj7ty58PHxwYULFyAQCDBu3Dix6w8ICEBsbCxsbW0REhICMzMzXLt2DQCwfPly3Lt3D0OGDMFff/2FDh06ICwsrFoxlCtP2Pfu3QuBQMAtiYmJ3PnK1bQ9EEKINJRk1YK3b9/Cw8MD06ZNw/Xr1z96AzEhhJDGQUZGBosXL4afnx/3A1t0dDScnJwwZswYWFpawtjYuML7lyrSvn17PHnyBFlZWVzZh0lB+/btkZCQIDaBR3R0NGRkZGBubv4JVyXO1NQUysrKVZ4WPTo6Gra2tpg2bRqsrKxgYmKCR48eSdSzsrLCokWLEBMTg06dOuHw4cPcNjMzM8yaNQsXLlzAiBEjavy52rx5cxgYGCAtLQ0mJiZiS5s2bWp0TEIIqQpKsj7R3bt3YW1tjeDgYMjJyWHjxo2YOnVqfYdFCCGkjri6ukJWVhY7duwA8D4piYiIQExMDJKTkzF58mQ8f/68Wse0t7eHmZkZvLy8kJCQgKioKCxZskSsjqenJ5SUlODl5YXExERcvnwZ33//PcaOHcsNFawNSkpKWLBgAebPn4+DBw8iPT0d165dE+u9+y9TU1PcvHkT58+fR2pqKpYuXYobN25w29PT07Fo0SLExsbi8ePHuHDhAh48eID27dujqKgIvr6+iIyMxOPHjxEdHY0bN26gffv2NY5/xYoVWLt2LbZt24bU1FTcvXsXAQEB2LRpU42PSQghH0P3ZH2CgIAATJs2DcXFxWjZsiVCQkJga2tb32ERQgipQ3JycvD19cWGDRswdepU+Pn5IS0tDXw+HyoqKpg0aRKcnZ2Rm5tb5WPKyMggLCwMPj4+6NGjB1q3bo1t27bBwcGBq6OiooLz589j5syZ6N69O1RUVODi4vJZkoelS5dCTk4Oy5cvxz///AN9fX1MmTJFat3JkycjPj4eo0aNAo/Hg7u7O6ZNm4bw8HAu7vv37+PAgQPIycmBvr4+pk+fjsmTJ6O0tJR7tuTz58+hra2NESNGYMWKFTWOfcKECVBRUcFPP/2EefPmQVVVFRYWFhKzNRJCSG3iMfaJ89c2MHl5edDQ0EBmRhoMjWo+VGD9+vVYuHAhgPcPpjx06BC0tbVrK0zSCJSUlODs2bNwdHSkh1qSz6ohtrXi4mKkp6ejTZs2NF12AyISiZCXlwd1dXWJiUUIqU3U1khtquwzJycnB9ra2sjNzYW6unqtnZNabQ15eHhAV1cXa9aswZkzZyjBIoQQQgghhACg4YLVEh8fDysrKwCAoaEhHjx4UKsZLyGEEEIIIaTho56sKigqKsLkyZPRtWtXnDp1iiunBIsQQgghhBDyIerJ+ogHDx7A1dUVCQkJ4PF4SElJqe+QCCGEEEIIIV8wSrIqERoaCh8fH7x9+xY6OjoICgrCwIED6zssQghpNL6yuZcIIYTUg/r4rKHhglK8e/cOM2bMgJubG96+fYvevXsjPj6eEixCCKkl5bMgFhYW1nMkhBBCGjuhUAgAkJWVrbNzUk+WFJcuXcL27dsBAAsXLsSqVasgJ0cvFSGE1BZZWVk0bdoUL168APD+2Uk8Hq+eoyIfIxKJIBQKUVxcTNNqk8+K2hqpLSKRCC9fvoSKikqdfp+nzEEKR0dHLFq0CL169cKQIUPqOxxCCGmU9PT0AIBLtMiXjzGGoqIiKCsrU1JMPitqa6Q2ycjIoFWrVnXalijJwvsHea5ZswaTJ0/mPvTXrFlTz1ERQkjjxuPxoK+vD11dXZSUlNR3OKQKSkpKcOXKFfTp06fBPPiaNEzU1khtUlBQqPMe0a8+yXr69ClGjRqFmJgYXLlyBRcvXqRfTAghpA7JysrW6Th5UnOysrIoLS2FkpISffElnxW1NdLQfRGDXHfs2IHWrVtDSUkJPXv2xPXr1yutHxoainbt2kFJSQkWFhY4e/Zsjc577tw5WFlZISYmBhoaGvD19aUEixBCCCGEEPJJ6j3JCgkJwezZs+Hv74/bt2/D0tISfD6/wjH6MTExcHd3h4+PD+Lj4+Hs7AxnZ2ckJiZW67w//fwzHB0d8erVK3Tt2hW3bt3C8OHDa+OSCCGEEEIIIV+xek+yNm3ahIkTJ2LcuHHo0KEDdu/eDRUVFezfv19q/a1bt8LBwQHz5s1D+/btsWrVKnTt2hW//PJLtc67/ZedYIxh6tSpiI6ORtu2bWvjcgghhBBCCCFfuXq9J0soFOLWrVtYtGgRVyYjIwN7e3vExsZK3Sc2NhazZ88WK+Pz+Thx4oTU+u/evcO7d++49dzcXACAkpIStm7dChcXFxQUFKCgoOATr4YQcSUlJSgsLEROTg6NJyefFbU1UleorZG6Qm2N1JXXr18DqP0HFtdrkvXq1SuUlZWhefPmYuXNmzfH/fv3pe6TnZ0ttX52drbU+mvXrsWKFSskyouLizF58mRMnjy5htETQgghhBBCGoOcnBxoaGjU2vEa/eyCixYtEuv5+vfff2FkZITMzMxafSEJ+VBeXh4MDQ3x5MkTqKur13c4pBGjtkbqCrU1UleorZG6kpubi1atWkFTU7NWj1uvSZa2tjZkZWXx/PlzsfLnz59zz6v6kJ6eXrXqKyoqQlFRUaJcQ0OD/mhJnVBXV6e2RuoEtTVSV6itkbpCbY3Uldp+jla9TnyhoKCAbt264dKlS1yZSCTCpUuXYGNjI3UfGxsbsfoAEBERUWF9QgghhBBCCKlL9T5ccPbs2fDy8oK1tTV69OiBLVu2oKCgAOPGjQMAfPfdd2jRogXWrl0LAJg5cybs7OywceNGDBkyBMHBwbh58yZ+/fXX+rwMQgghhBBCCAHwBSRZo0aNwsuXL7Fs2TJkZ2ejS5cuOHfuHDe5RWZmplj3na2tLQ4fPgw/Pz8sXrwYpqamOHHiBDp16lSl8ykqKsLf31/qEEJCahO1NVJXqK2RukJtjdQVamukrnyutsZjtT1fISGEEEIIIYR8xer9YcSEEEIIIYQQ0phQkkUIIYQQQgghtYiSLEIIIYQQQgipRZRkEUIIIYQQQkgtapRJ1o4dO9C6dWsoKSmhZ8+euH79eqX1Q0ND0a5dOygpKcHCwgJnz56to0hJQ1edtrZ371707t0bzZo1Q7NmzWBvb//RtklIuer+u1YuODgYPB4Pzs7OnzdA0mhUt639+++/mD59OvT19aGoqAgzMzP6HCVVUt22tmXLFpibm0NZWRmGhoaYNWsWiouL6yha0lBduXIF3377LQwMDMDj8XDixImP7hMZGYmuXbtCUVERJiYmCAwMrPZ5G12SFRISgtmzZ8Pf3x+3b9+GpaUl+Hw+Xrx4IbV+TEwM3N3d4ePjg/j4eDg7O8PZ2RmJiYl1HDlpaKrb1iIjI+Hu7o7Lly8jNjYWhoaGGDRoEJ49e1bHkZOGprptrVxGRgbmzp2L3r1711GkpKGrblsTCoUYOHAgMjIycOzYMaSkpGDv3r1o0aJFHUdOGprqtrXDhw9j4cKF8Pf3R3JyMvbt24eQkBAsXry4jiMnDU1BQQEsLS2xY8eOKtVPT0/HkCFD0K9fPwgEAvzwww+YMGECzp8/X70Ts0amR48ebPr06dx6WVkZMzAwYGvXrpVa383NjQ0ZMkSsrGfPnmzy5MmfNU7S8FW3rX2otLSUqampsQMHDnyuEEkjUZO2Vlpaymxtbdlvv/3GvLy8mJOTUx1EShq66ra1Xbt2MWNjYyYUCusqRNJIVLetTZ8+nfXv31+sbPbs2axXr16fNU7SuABgYWFhldaZP38+69ixo1jZqFGjGJ/Pr9a5GlVPllAoxK1bt2Bvb8+VycjIwN7eHrGxsVL3iY2NFasPAHw+v8L6hAA1a2sfKiwsRElJCTQ1NT9XmKQRqGlbW7lyJXR1deHj41MXYZJGoCZt7dSpU7CxscH06dPRvHlzdOrUCWvWrEFZWVldhU0aoJq0NVtbW9y6dYsbUpiWloazZ8/C0dGxTmImX4/ayg3kajOo+vbq1SuUlZWhefPmYuXNmzfH/fv3pe6TnZ0ttX52dvZni5M0fDVpax9asGABDAwMJP6QCfmvmrS1q1evYt++fRAIBHUQIWksatLW0tLS8Ndff8HT0xNnz57Fw4cPMW3aNJSUlMDf378uwiYNUE3amoeHB169eoVvvvkGjDGUlpZiypQpNFyQ1LqKcoO8vDwUFRVBWVm5SsdpVD1ZhDQU69atQ3BwMMLCwqCkpFTf4ZBG5O3btxg7diz27t0LbW3t+g6HNHIikQi6urr49ddf0a1bN4waNQpLlizB7t276zs00shERkZizZo12LlzJ27fvo0//vgDZ86cwapVq+o7NEKkalQ9Wdra2pCVlcXz58/Fyp8/fw49PT2p++jp6VWrPiFAzdpauZ9//hnr1q3DxYsX0blz588ZJmkEqtvWHj16hIyMDHz77bdcmUgkAgDIyckhJSUFbdu2/bxBkwapJv+u6evrQ15eHrKyslxZ+/btkZ2dDaFQCAUFhc8aM2mYatLWli5dirFjx2LChAkAAAsLCxQUFGDSpElYsmQJZGSo34DUjopyA3V19Sr3YgGNrCdLQUEB3bp1w6VLl7gykUiES5cuwcbGRuo+NjY2YvUBICIiosL6hAA1a2sAsGHDBqxatQrnzp2DtbV1XYRKGrjqtrV27drh7t27EAgE3DJs2DBuliRDQ8O6DJ80IDX5d61Xr154+PAhl8gDQGpqKvT19SnBIhWqSVsrLCyUSKTKk/v38xkQUjtqLTeo3pwcX77g4GCmqKjIAgMDWVJSEps0aRJr2rQpy87OZowxNnbsWLZw4UKufnR0NJOTk2M///wzS05OZv7+/kxeXp7dvXu3vi6BNBDVbWvr1q1jCgoK7NixYywrK4tb3r59W1+XQBqI6ra1D9HsgqSqqtvWMjMzmZqaGvP19WUpKSns9OnTTFdXl/3444/1dQmkgahuW/P392dqamrsyJEjLC0tjV24cIG1bduWubm51dclkAbi7du3LD4+nsXHxzMAbNOmTSw+Pp49fvyYMcbYwoUL2dixY7n6aWlpTEVFhc2bN48lJyezHTt2MFlZWXbu3LlqnbfRJVmMMbZ9+3bWqlUrpqCgwHr06MGuXbvGbbOzs2NeXl5i9Y8ePcrMzMyYgoIC69ixIztz5kwdR0waquq0NSMjIwZAYvH396/7wEmDU91/1/6LkixSHdVtazExMaxnz55MUVGRGRsbs9WrV7PS0tI6jpo0RNVpayUlJWz58uWsbdu2TElJiRkaGrJp06axN2/e1H3gpEG5fPmy1O9f5e3Ly8uL2dnZSezTpUsXpqCgwIyNjVlAQEC1z8tjjPpYCSGEEEIIIaS2NKp7sgghhBBCCCGkvlGSRQghhBBCCCG1iJIsQgghhBBCCKlFlGQRQgghhBBCSC2iJIsQQgghhBBCahElWYQQQgghhBBSiyjJIoQQQgghhJBaREkWIYQQQgghhNQiSrIIIYTUSGBgIJo2bVrfYdQYj8fDiRMnKq3j7e0NZ2fnOomHEEJI40FJFiGEfMW8vb3B4/EklocPH9Z3aAgMDOTikZGRQcuWLTFu3Di8ePGiVo6flZWFwYMHAwAyMjLA4/EgEAjE6mzduhWBgYG1cr6KLF++nLtOWVlZGBoaYtKkSXj9+nW1jkMJISGEfDnk6jsAQggh9cvBwQEBAQFiZTo6OvUUjTh1dXWkpKRAJBIhISEB48aNwz///IPz589/8rH19PQ+WkdDQ+OTz1MVHTt2xMWLF1FWVobk5GSMHz8eubm5CAkJqZPzE0IIqV3Uk0UIIV85RUVF6OnpiS2ysrLYtGkTLCwsoKqqCkNDQ0ybNg35+fkVHichIQH9+vWDmpoa1NXV0a1bN9y8eZPbfvXqVfTu3RvKysowNDTEjBkzUFBQUGlsPB4Penp6MDAwwODBgzFjxgxcvHgRRUVFEIlEWLlyJVq2bAlFRUV06dIF586d4/YVCoXw9fWFvr4+lJSUYGRkhLVr14odu3y4YJs2bQAAVlZW4PF46Nu3LwDx3qFff/0VBgYGEIlEYjE6OTlh/Pjx3PrJkyfRtWtXKCkpwdjYGCtWrEBpaWml1yknJwc9PT20aNEC9vb2cHV1RUREBLe9rKwMPj4+aNOmDZSVlWFubo6tW7dy25cvX44DBw7g5MmTXK9YZGQkAODJkydwc3ND06ZNoampCScnJ2RkZFQaDyGEkE9DSRYhhBCpZGRksG3bNty7dw8HDhzAX3/9hfnz51dY39PTEy1btsSNGzdw69YtLFy4EPLy8gCAR48ewcHBAS4uLrhz5w5CQkJw9epV+Pr6VismZWVliEQilJaWYuvWrdi4cSN+/vln3LlzB3w+H8OGDcODBw8AANu2bcOpU6dw9OhRpKSkICgoCK1bt5Z63OvXrwMALl68iKysLPzxxx8SdVxdXZGTk4PLly9zZa9fv8a5c+fg6ekJAIiKisJ3332HmTNnIikpCXv27EFgYCBWr15d5WvMyMjA+fPnoaCgwJWJRCK0bNkSoaGhSEpKwrJly7B48WIcPXoUADB37ly4ubnBwcEBWVlZyMrKgq2tLUpKSsDn86GmpoaoqChER0ejSZMmcHBwgFAorHJMhBBCqokRQgj5anl5eTFZWVmmqqrKLSNHjpRaNzQ0lGlpaXHrAQEBTENDg1tXU1NjgYGBUvf18fFhkyZNEiuLiopiMjIyrKioSOo+Hx4/NTWVmZmZMWtra8YYYwYGBmz16tVi+3Tv3p1NmzaNMcbY999/z/r3789EIpHU4wNgYWFhjDHG0tPTGQAWHx8vVsfLy4s5OTlx605OTmz8+PHc+p49e5iBgQErKytjjDE2YMAAtmbNGrFjHDp0iOnr60uNgTHG/P39mYyMDFNVVWVKSkoMAAPANm3aVOE+jDE2ffp05uLiUmGs5ec2NzcXew3evXvHlJWV2fnz5ys9PiGEkJqje7IIIeQr169fP+zatYtbV1VVBfC+V2ft2rW4f/8+8vLyUFpaiuLiYhQWFkJFRUXiOLNnz8aECRNw6NAhbshb27ZtAbwfSnjnzh0EBQVx9RljEIlESE9PR/v27aXGlpubiyZNmkAkEqG4uBjffPMNfvvtN+Tl5eGff/5Br169xOr36tULCQkJAN4P9Rs4cCDMzc3h4OCAoUOHYtCgQZ/0Wnl6emLixInYuXMnFBUVERQUhNGjR0NGRoa7zujoaLGeq7KyskpfNwAwNzfHqVOnUFxcjN9//x0CgQDff/+9WJ0dO3Zg//79yMzMRFFREYRCIbp06VJpvAkJCXj48CHU1NTEyouLi/Ho0aMavAKEEEKqgpIsQgj5yqmqqsLExESsLCMjA0OHDsXUqVOxevVqaGpq4urVq/Dx8YFQKJSaLCxfvhweHh44c+YMwsPD4e/vj+DgYAwfPhz5+fmYPHkyZsyYIbFfq1atKoxNTU0Nt2/fhoyMDPT19aGsrAwAyMvL++h1de3aFenp6QgPD8fFixfh5uYGe3t7HDt27KP7VuTbb78FYwxnzpxB9+7dERUVhc2bN3Pb8/PzsWLFCowYMUJiXyUlpQqPq6CgwL0H69atw5AhQ7BixQqsWrUKABAcHIy5c+di48aNsLGxgZqaGn766SfExcVVGm9+fj66desmltyW+1ImNyGEkMaIkixCCCESbt26BZFIhI0bN3K9NOX3/1TGzMwMZmZmmDVrFtzd3REQEIDhw4eja9euSEpKkkjmPkZGRkbqPurq6jAwMEB0dDTs7Oy48ujoaPTo0UOs3qhRozBq1CiMHDkSDg4OeP36NTQ1NcWOV37/U1lZWaXxKCkpYcSIEQgKCsLDhw9hbm6Orl27ctu7du2KlJSUal/nh/z8/NC/f39MnTqVu05bW1tMmzaNq/NhT5SCgoJE/F27dkVISAh0dXWhrq7+STERQgipOpr4ghBCiAQTExOUlJRg+/btSEtLw6FDh7B79+4K6xcVFcHX1xeRkZF4/PgxoqOjcePGDW4Y4IIFCxATEwNfX18IBAI8ePAAJ0+erPbEF/81b948rF+/HiEhIUhJScHChQshEAgwc+ZMAMCmTZtw5MgR3L9/H6mpqQgNDYWenp7UByjr6upCWVkZ586dw/Pnz5Gbm1vheT09PXHmzBns37+fm/Ci3LJly3Dw4EGsWLEC9+7dQ3JyMoKDg+Hn51eta7OxsUHnzp2xZs0aAICpqSlu3ryJ8+fPIzU1FUuXLsWNGzfE9mndujXu3LmDlJQUvHr1CiUlJfD09IS2tjacnJwQFRWF9PR0REZGYsaMGXj69Gm1YiKEEFJ1lGQRQgiRYGlpiU2bNmH9+vXo1KkTgoKCxKY//5CsrCxycnLw3XffwczMDG5ubhg8eDBWrFgBAOjcuTP+/vtvpKamonfv3rCyssKyZctgYGBQ4xhnzJiB2bNnY86cObCwsMC5c+dw6tQpmJqaAng/1HDDhg2wtrZG9+7dkZGRgbNnz3I9c/8lJyeHbdu2Yc+ePTAwMICTk1OF5+3fvz80NTWRkpICDw8PsW18Ph+nT5/GhQsX0L17d/zvf//D5s2bYWRkVO3rmzVrFn777Tc8efIEkydPxogRIzBq1Cj07NkTOTk5Yr1aADBx4kSYm5vD2toaOjo6iI6OhoqKCq5cuYJWrVphxIgRaN++PXx8fFBcXEw9W4QQ8hnxGGOsvoMghBBCCCGEkMaCerIIIYQQQgghpBZRkkUIIYQQQgghtYiSLEIIIYQQQgipRZRkEUIIIYQQQkgtoiSLEEIIIYQQQmoRJVmEEEIIIYQQUosoySKEEEIIIYSQWkRJFiGEEEIIIYTUIkqyCCGEEEIIIaQWUZJFCCGEEEIIIbWIkixCCCGEEEIIqUX/DwZBo/MqarNrAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "model.eval()\n",
        "test_probs = []\n",
        "with torch.no_grad():\n",
        "  for images, _ in test_loader:\n",
        "    images = images.to(device)\n",
        "    outputs = model(images)\n",
        "    probs = torch.softmax(outputs, dim=1)\n",
        "    test_probs.extend(probs.cpu().numpy())\n",
        "\n",
        "test_probs = np.array(test_probs)\n",
        "\n",
        "n_classes = len(class_labels)\n",
        "\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "for i in range(n_classes):\n",
        "    binary_y_true = (np.array(test_labels) == i).astype(int)\n",
        "    y_score_for_class_i = test_probs[:, i]\n",
        "\n",
        "    fpr[i], tpr[i], _ = roc_curve(binary_y_true, y_score_for_class_i)\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "# Plot all ROC curves\n",
        "plt.figure(figsize=(10, 8))\n",
        "for i in range(n_classes):\n",
        "    plt.plot(fpr[i], tpr[i], label=f'ROC curve of class {class_labels[i]} (area = {roc_auc[i]:0.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', label='Random classifier')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve - One-vs-Rest')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}