{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Targeted HopSkipJumpAttack Using CIFAR10",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ndb796/Deep-Learning-Paper-Review-and-Practice/blob/master/code_practices/Targeted_HopSkipJumpAttack_Using_CIFAR10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNO5NTvp7A51"
      },
      "source": [
        "#### <b>Load Required Libraries</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEj-mAoV6y8p"
      },
      "source": [
        "# load required PyTorch libraries\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQ23hJMr7CGt"
      },
      "source": [
        "use_cuda = True\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odJwpeAn7D4A"
      },
      "source": [
        "#### <b>Image Visualization</b>\n",
        "\n",
        "* Resize the displayed image to be large enough."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb-N4li87DEE"
      },
      "source": [
        "# load an image visualization library\n",
        "import matplotlib.pyplot as plt\n",
        "import PIL\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [12, 8]\n",
        "plt.rcParams['figure.dpi'] = 60\n",
        "plt.rcParams.update({'font.size': 20})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z3JPKfY7Ejb"
      },
      "source": [
        "# show torch.Tensor image\n",
        "def imshow(tensor):\n",
        "    image = tensor.cpu().clone()\n",
        "    image = image.squeeze(0) # [3, H, W]\n",
        "    image = transforms.ToPILImage()(image)\n",
        "    # show an image whose values are between [0, 1]\n",
        "    plt.imshow(image)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cdByU-R37GQ3"
      },
      "source": [
        "#### <b>Load Pre-trained Classification Model</b>\n",
        "\n",
        "* <b>The attack target model</b>: ResNet-18\n",
        "* <b>Dataset</b>: CIFAR-10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53055YYa7FT0",
        "outputId": "2cb42c71-a932-4cdc-be57-0c77ec6a9684"
      },
      "source": [
        "!wget https://postechackr-my.sharepoint.com/:u:/g/personal/dongbinna_postech_ac_kr/EcpGMF03mR9Ko1MM-kMSmloB4ceabuYCvnHaGZPgLNMzrA?download=1 -O cifar10_model.pth"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-12 10:12:35--  https://postechackr-my.sharepoint.com/:u:/g/personal/dongbinna_postech_ac_kr/EcpGMF03mR9Ko1MM-kMSmloB4ceabuYCvnHaGZPgLNMzrA?download=1\n",
            "Resolving postechackr-my.sharepoint.com (postechackr-my.sharepoint.com)... 40.108.156.33\n",
            "Connecting to postechackr-my.sharepoint.com (postechackr-my.sharepoint.com)|40.108.156.33|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /personal/dongbinna_postech_ac_kr/Documents/Research/Pytorch%20Adversarial%20Training%20on%20CIFAR-10/Pre-trained/basic_training?originalPath=aHR0cHM6Ly9wb3N0ZWNoYWNrci1teS5zaGFyZXBvaW50LmNvbS86dTovZy9wZXJzb25hbC9kb25nYmlubmFfcG9zdGVjaF9hY19rci9FY3BHTUYwM21SOUtvMU1NLWtNU21sb0I0Y2VhYnVZQ3ZuSGFHWlBnTE5NenJBP3J0aW1lPXdpMS1YRF9sMkVn [following]\n",
            "--2021-03-12 10:12:37--  https://postechackr-my.sharepoint.com/personal/dongbinna_postech_ac_kr/Documents/Research/Pytorch%20Adversarial%20Training%20on%20CIFAR-10/Pre-trained/basic_training?originalPath=aHR0cHM6Ly9wb3N0ZWNoYWNrci1teS5zaGFyZXBvaW50LmNvbS86dTovZy9wZXJzb25hbC9kb25nYmlubmFfcG9zdGVjaF9hY19rci9FY3BHTUYwM21SOUtvMU1NLWtNU21sb0I0Y2VhYnVZQ3ZuSGFHWlBnTE5NenJBP3J0aW1lPXdpMS1YRF9sMkVn\n",
            "Reusing existing connection to postechackr-my.sharepoint.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 44755840 (43M) [application/octet-stream]\n",
            "Saving to: ‘cifar10_model.pth’\n",
            "\n",
            "cifar10_model.pth   100%[===================>]  42.68M  8.80MB/s    in 7.0s    \n",
            "\n",
            "2021-03-12 10:12:47 (6.14 MB/s) - ‘cifar10_model.pth’ saved [44755840/44755840]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVTe-3Y57HAr"
      },
      "source": [
        "# Define a basic block for the ResNet architecture\n",
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(self, in_planes, planes, stride=1):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(planes)\n",
        "        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(planes)\n",
        "\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_planes != self.expansion * planes:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(self.expansion * planes)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.bn2(self.conv2(out))\n",
        "        out += self.shortcut(x) # residual connection\n",
        "        out = F.relu(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, num_blocks, num_classes=10):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_planes = 64\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n",
        "        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n",
        "        self.linear = nn.Linear(512 * block.expansion, num_classes)\n",
        "\n",
        "    def _make_layer(self, block, planes, num_blocks, stride):\n",
        "        strides = [stride] + [1] * (num_blocks - 1)\n",
        "        layers = []\n",
        "        for stride in strides:\n",
        "            layers.append(block(self.in_planes, planes, stride))\n",
        "            self.in_planes = planes * block.expansion\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = F.relu(self.bn1(self.conv1(x)))\n",
        "        out = self.layer1(out)\n",
        "        out = self.layer2(out)\n",
        "        out = self.layer3(out)\n",
        "        out = self.layer4(out)\n",
        "        out = F.avg_pool2d(out, 4)\n",
        "        out = out.view(out.size(0), -1)\n",
        "        out = self.linear(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "# (The number of layers that contain trainable weights == 18)\n",
        "def ResNet18():\n",
        "    return ResNet(BasicBlock, [2, 2, 2, 2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1NW6vbz7Hxj",
        "outputId": "ca6cb1bf-7381-4606-9f0b-0f637101a558"
      },
      "source": [
        "model = ResNet18()\n",
        "model = model.to(device)\n",
        "model = torch.nn.DataParallel(model)\n",
        "checkpoint = torch.load('./cifar10_model.pth')\n",
        "model.load_state_dict(checkpoint['net'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xBvLIuzy7Jlu"
      },
      "source": [
        "#### <b>Load Test Dataset</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4vahIMZ_7Ig0",
        "outputId": "19edc242-e5b9-4f77-8ff1-35603f032b22"
      },
      "source": [
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
        "test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "dvRvW4SV7LEU",
        "outputId": "7fcf6944-395e-4de9-aed5-2903f3e72b06"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "# Display images in a batch\n",
        "def imshow_batch(input, title):\n",
        "    # torch.Tensor => numpy\n",
        "    input = input.numpy().transpose((1, 2, 0))\n",
        "    # display images\n",
        "    plt.imshow(input)\n",
        "    plt.title(title)\n",
        "    plt.show()\n",
        "\n",
        "# load a batch of validation image\n",
        "iterator = iter(test_dataloader)\n",
        "\n",
        "# visualize a batch of validation image (the first 4 images)\n",
        "inputs, classes = next(iterator)\n",
        "out = torchvision.utils.make_grid(inputs[:4])\n",
        "imshow_batch(out, title='original labels: ' + str([class_names[x] for x in classes[:4]]))\n",
        "\n",
        "print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "for i, label in enumerate(classes[:4]):\n",
        "    print(f'Image #{i + 1}: {class_names[label]} ({label})')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: cat (3)\n",
            "Image #2: dog (5)\n",
            "Image #3: frog (6)\n",
            "Image #4: bird (2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QeGpQKo7MsE"
      },
      "source": [
        "#### <b>Test Phase</b>\n",
        "\n",
        "* Check the test accuracy of the pre-trained classification model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "DmQtvmN47Loz",
        "outputId": "baf860a6-a862-4cd9-aca3-98add9788736"
      },
      "source": [
        "import time\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "model.eval()\n",
        "start_time = time.time()\n",
        "\n",
        "with torch.no_grad():\n",
        "    running_loss = 0.\n",
        "    running_corrects = 0\n",
        "\n",
        "    for i, (inputs, labels) in enumerate(test_dataloader):\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs, 1)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "        if i == 0:\n",
        "            # Display the first 4 images in the first batch.\n",
        "            print('[Prediction Result Examples]')\n",
        "            images = torchvision.utils.make_grid(inputs[:4])\n",
        "            imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[:4]]) +\n",
        "                         '\\npredicted labels: ' + str([int(x) for x in preds[:4]]))\n",
        "            print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "            for j, label in enumerate(labels[:4]):\n",
        "                print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "            print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "            for j, label in enumerate(preds[:4]):\n",
        "                print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "\n",
        "            # Display the second 4 images in the first batch.\n",
        "            images = torchvision.utils.make_grid(inputs[4:8])\n",
        "            imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[4:8]]) +\n",
        "                         '\\npredicted labels: ' + str([int(x) for x in preds[4:8]]))\n",
        "            print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "            for j, label in enumerate(labels[4:8]):\n",
        "                print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "            print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "            for j, label in enumerate(preds[4:8]):\n",
        "                print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "\n",
        "    epoch_loss = running_loss / len(test_dataset)\n",
        "    epoch_acc = running_corrects / len(test_dataset) * 100.\n",
        "    print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, time.time() - start_time, len(test_dataset)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Prediction Result Examples]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: frog (6)\n",
            "Image #1: horse (7)\n",
            "Image #1: automobile (1)\n",
            "Image #1: horse (7)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: frog (6)\n",
            "Image #1: horse (7)\n",
            "Image #1: automobile (1)\n",
            "Image #1: airplane (0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: dog (5)\n",
            "Image #1: horse (7)\n",
            "Image #1: bird (2)\n",
            "Image #1: horse (7)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: dog (5)\n",
            "Image #1: horse (7)\n",
            "Image #1: bird (2)\n",
            "Image #1: horse (7)\n",
            "[Validation] Loss: 0.0033 Accuracy: 95.2800% Time elapsed: 6.8521s (total 10000 images)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_EVasbm7Pao"
      },
      "source": [
        "#### <b>Prepare Adversarial Attack Libraries</b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Rwgci4t7OIz",
        "outputId": "13b2c32c-29fe-467b-c7db-0d912d33412f"
      },
      "source": [
        "!pip install foolbox\n",
        "!pip install advertorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: foolbox in /usr/local/lib/python3.7/dist-packages (3.3.1)\n",
            "Requirement already satisfied: GitPython>=3.0.7 in /usr/local/lib/python3.7/dist-packages (from foolbox) (3.1.14)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from foolbox) (1.4.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from foolbox) (54.0.0)\n",
            "Requirement already satisfied: eagerpy==0.29.0 in /usr/local/lib/python3.7/dist-packages (from foolbox) (0.29.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.1 in /usr/local/lib/python3.7/dist-packages (from foolbox) (3.7.4.3)\n",
            "Requirement already satisfied: requests>=2.24.0 in /usr/local/lib/python3.7/dist-packages (from foolbox) (2.25.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from foolbox) (1.19.5)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=3.0.7->foolbox) (4.0.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (2020.12.5)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (2.10)\n",
            "Requirement already satisfied: smmap<4,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=3.0.7->foolbox) (3.0.5)\n",
            "Requirement already satisfied: advertorch in /usr/local/lib/python3.7/dist-packages (0.2.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-bT17uz7QPU"
      },
      "source": [
        "def get_distance(a, b):\n",
        "    l0 = torch.norm((a - b).view(a.shape[0], -1), p=0, dim=1)\n",
        "    l2 = torch.norm((a - b).view(a.shape[0], -1), p=2, dim=1)\n",
        "    mse = (a - b).view(a.shape[0], -1).pow(2).mean(1)\n",
        "    linf = torch.norm((a - b).view(a.shape[0], -1), p=float('inf'), dim=1)\n",
        "    return l0, l2, mse, linf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNO0lKnzeU2y"
      },
      "source": [
        "#### <b>HopSkipJumpAttack</b>\n",
        "\n",
        "* <b>Foolbox documentation</b>: https://foolbox.readthedocs.io/en/stable/\n",
        "* <b>HopSkipJumpAttack implementation</b>: [HopSkipJumpAttack](https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/hop_skip_jump.py)\n",
        "* <b>init_attack</b>: Attack to use to find starting points.\n",
        "    * The targeted attack does not use this parameter.\n",
        "    * The untargeted attack can use a random uniform noise.\n",
        "* <b>steps</b>: Number of optimization steps.\n",
        "* <b>initial_gradient_eval_steps</b>: Initial number of evaluations for gradient estimation.\n",
        "* <b>max_gradient_eval_steps</b>: Maximum number of evaluations for gradient estimation.\n",
        "* <b>stepsize_search</b>: \"geometric_progression\" or \"grid_search\".\n",
        "* <b>gamma</b>: It's for the binary search threshold.\n",
        "* <b>constraint</b>: Norm to minimize, either \"l2\" or \"linf\".\n",
        "\n",
        "<pre>\n",
        "def __init__(\n",
        "    self,\n",
        "    init_attack: Optional[MinimizationAttack] = None,\n",
        "    steps: int = 64,\n",
        "    initial_gradient_eval_steps: int = 100,\n",
        "    max_gradient_eval_steps: int = 10000,\n",
        "    stepsize_search: Union[\n",
        "        Literal[\"geometric_progression\"], Literal[\"grid_search\"]\n",
        "    ] = \"geometric_progression\",\n",
        "    gamma: float = 1.0,\n",
        "    tensorboard: Union[Literal[False], None, str] = False,\n",
        "    constraint: Union[Literal[\"linf\"], Literal[\"l2\"]] = \"l2\",\n",
        "):\n",
        "</pre>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2QWqjr-Y7R75"
      },
      "source": [
        "#### <b>Adversarial Attack Example 1</b>\n",
        "\n",
        "* <b>Attack method</b>: HopSkipJump Attack\n",
        "* <b>Options</b>: 10 iterations + start from the target (targeted attack)\n",
        "    * Each iteration includes a binary search step, gradient approximation step, and geometric progression step.\n",
        "* <b>Images</b>: 1,000 test images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "wnQGScAe7Q_1",
        "outputId": "23da8c57-7424-4897-9cbc-32bfcc785953"
      },
      "source": [
        "import time\n",
        "import random\n",
        "import foolbox as fb\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "model.eval()\n",
        "start_time = time.time()\n",
        "\n",
        "fmodel = fb.PyTorchModel(model, bounds=(0, 1))\n",
        "attack = fb.attacks.HopSkipJump(init_attack=None, steps=10,\n",
        "                                initial_gradient_eval_steps=100, max_gradient_eval_steps=100)\n",
        "\n",
        "running_loss = 0.\n",
        "running_corrects = 0\n",
        "running_success = 0\n",
        "running_length = 0\n",
        "\n",
        "running_l0 = 0\n",
        "running_l2 = 0\n",
        "running_mse = 0\n",
        "running_linf = 0\n",
        "\n",
        "num_images = 1000\n",
        "\n",
        "for i, (inputs, labels) in enumerate(test_dataloader):\n",
        "    num_images -= labels.shape[0]\n",
        "\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    # generate \"random target instances\" that are correctly classified.\n",
        "    starting_points = torch.zeros(labels.shape[0], 3, 32, 32).to(device)\n",
        "    target_labels = torch.zeros(labels.shape[0]).long().to(device)\n",
        "    for k in range(labels.shape[0]):\n",
        "        starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
        "    outputs = model(starting_points)\n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    while True:\n",
        "        condition = 0\n",
        "        idx = torch.tensor([], dtype=torch.long)\n",
        "        if target_labels.ne(preds).sum() > 0: # to be correctly classified\n",
        "            idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.ne(preds)]], dim=0)\n",
        "            condition += 1\n",
        "        if target_labels.eq(labels).sum() > 0: # to be different from the original labels\n",
        "            idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.eq(labels)]], dim=0)\n",
        "            condition += 1\n",
        "        idx = torch.unique(idx)\n",
        "        if condition == 0:\n",
        "            break\n",
        "        for k in list(idx):\n",
        "            starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
        "        outputs = model(starting_points[idx])\n",
        "        _, preds[idx] = torch.max(outputs, 1)\n",
        "\n",
        "    attack_criterion = fb.criteria.TargetedMisclassification(target_labels)\n",
        "    _, adv_targeted, _ = attack(fmodel, inputs, criterion=attack_criterion, starting_points=starting_points, epsilons=None) # adversarial attack\n",
        "\n",
        "    outputs = model(adv_targeted)\n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    running_loss += loss.item()\n",
        "    running_corrects += torch.sum(preds == labels.data)\n",
        "    running_success += torch.sum(preds == target_labels.data)\n",
        "    running_length += labels.shape[0]\n",
        "\n",
        "    l0, l2, mse, linf = get_distance(adv_targeted, inputs)\n",
        "    running_l0 += l0.sum().item()\n",
        "    running_l2 += l2.sum().item()\n",
        "    running_mse += mse.sum().item()\n",
        "    running_linf += linf.sum().item()\n",
        "\n",
        "    if i == 0:\n",
        "        # Display the first 4 images in the first batch.\n",
        "        print('The dimension of an image tensor:', inputs.shape[1:])\n",
        "        print('[Prediction Result Examples]')\n",
        "        images = torchvision.utils.make_grid(adv_targeted[:4])\n",
        "        imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[:4]]) +\n",
        "                      '\\npredicted labels: ' + str([int(x) for x in preds[:4]]))\n",
        "        print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(labels[:4]):\n",
        "            print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "        print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(preds[:4]):\n",
        "            print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "\n",
        "        # Display the second 4 images in the first batch.\n",
        "        images = torchvision.utils.make_grid(adv_targeted[4:8])\n",
        "        imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[4:8]]) +\n",
        "                      '\\npredicted labels: ' + str([int(x) for x in preds[4:8]]))\n",
        "        print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(labels[4:8]):\n",
        "            print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "        print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(preds[4:8]):\n",
        "            print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "\n",
        "    if i % 10 == 0:\n",
        "        cur_running_loss = running_loss / running_length\n",
        "        running_acc = running_corrects / running_length * 100.\n",
        "        running_asr = running_success / running_length * 100.\n",
        "        print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, running_asr, time.time() - start_time, running_length))\n",
        "\n",
        "    if num_images <= 0:\n",
        "        break\n",
        "\n",
        "epoch_loss = running_loss / running_length\n",
        "epoch_acc = running_corrects / running_length * 100.\n",
        "epoch_asr = running_success / running_length * 100.\n",
        "print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, epoch_asr, time.time() - start_time, running_length))\n",
        "\n",
        "print('[Size of Perturbation]')\n",
        "print('Average L0 distance (the number of changed parameters):', running_l0 / running_length)\n",
        "print('Average L2 distance:', running_l2 / running_length)\n",
        "print('Average MSE:', running_mse / running_length)\n",
        "print('Average Linf distance (the maximum changed values):', running_linf / running_length)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The dimension of an image tensor: torch.Size([3, 32, 32])\n",
            "[Prediction Result Examples]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: automobile (1)\n",
            "Image #1: cat (3)\n",
            "Image #1: deer (4)\n",
            "Image #1: truck (9)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: horse (7)\n",
            "Image #1: dog (5)\n",
            "Image #1: ship (8)\n",
            "Image #1: horse (7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: frog (6)\n",
            "Image #1: bird (2)\n",
            "Image #1: deer (4)\n",
            "Image #1: airplane (0)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: bird (2)\n",
            "Image #1: horse (7)\n",
            "Image #1: ship (8)\n",
            "Image #1: automobile (1)\n",
            "[Step #0] Loss: 0.0182 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 49.2707s (total 64 images)\n",
            "[Step #10] Loss: 0.0172 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 531.2170s (total 704 images)\n",
            "[Validation] Loss: 0.0174 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 771.6314s (total 1024 images)\n",
            "[Size of Perturbation]\n",
            "Average L0 distance (the number of changed parameters): 3065.546875\n",
            "Average L2 distance: 3.326210394501686\n",
            "Average MSE: 0.0040247627039207146\n",
            "Average Linf distance (the maximum changed values): 0.16626308299601078\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukVkR9NNDClh"
      },
      "source": [
        "#### <b>Adversarial Attack Example 2</b>\n",
        "\n",
        "* <b>Attack method</b>: HopSkipJump Attack\n",
        "* <b>Options</b>: 30 iterations + start from the target (targeted attack)\n",
        "    * Each iteration includes a binary search step, gradient approximation step, and geometric progression step.\n",
        "* <b>Images</b>: 1,000 test images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "Gql8U5BdDHMx",
        "outputId": "af5f7780-6df3-4134-ddeb-24d525020dfa"
      },
      "source": [
        "import time\n",
        "import random\n",
        "import foolbox as fb\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "model.eval()\n",
        "start_time = time.time()\n",
        "\n",
        "fmodel = fb.PyTorchModel(model, bounds=(0, 1))\n",
        "attack = fb.attacks.HopSkipJump(init_attack=None, steps=30,\n",
        "                                initial_gradient_eval_steps=100, max_gradient_eval_steps=100)\n",
        "\n",
        "running_loss = 0.\n",
        "running_corrects = 0\n",
        "running_success = 0\n",
        "running_length = 0\n",
        "\n",
        "running_l0 = 0\n",
        "running_l2 = 0\n",
        "running_mse = 0\n",
        "running_linf = 0\n",
        "\n",
        "num_images = 1000\n",
        "\n",
        "for i, (inputs, labels) in enumerate(test_dataloader):\n",
        "    num_images -= labels.shape[0]\n",
        "\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    # generate \"random target instances\" that are correctly classified.\n",
        "    starting_points = torch.zeros(labels.shape[0], 3, 32, 32).to(device)\n",
        "    target_labels = torch.zeros(labels.shape[0]).long().to(device)\n",
        "    for k in range(labels.shape[0]):\n",
        "        starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
        "    outputs = model(starting_points)\n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    while True:\n",
        "        condition = 0\n",
        "        idx = torch.tensor([], dtype=torch.long)\n",
        "        if target_labels.ne(preds).sum() > 0: # to be correctly classified\n",
        "            idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.ne(preds)]], dim=0)\n",
        "            condition += 1\n",
        "        if target_labels.eq(labels).sum() > 0: # to be different from the original labels\n",
        "            idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.eq(labels)]], dim=0)\n",
        "            condition += 1\n",
        "        idx = torch.unique(idx)\n",
        "        if condition == 0:\n",
        "            break\n",
        "        for k in list(idx):\n",
        "            starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
        "        outputs = model(starting_points[idx])\n",
        "        _, preds[idx] = torch.max(outputs, 1)\n",
        "\n",
        "    attack_criterion = fb.criteria.TargetedMisclassification(target_labels)\n",
        "    _, adv_targeted, _ = attack(fmodel, inputs, criterion=attack_criterion, starting_points=starting_points, epsilons=None) # adversarial attack\n",
        "\n",
        "    outputs = model(adv_targeted)\n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    running_loss += loss.item()\n",
        "    running_corrects += torch.sum(preds == labels.data)\n",
        "    running_success += torch.sum(preds == target_labels.data)\n",
        "    running_length += labels.shape[0]\n",
        "\n",
        "    l0, l2, mse, linf = get_distance(adv_targeted, inputs)\n",
        "    running_l0 += l0.sum().item()\n",
        "    running_l2 += l2.sum().item()\n",
        "    running_mse += mse.sum().item()\n",
        "    running_linf += linf.sum().item()\n",
        "\n",
        "    if i == 0:\n",
        "        # Display the first 4 images in the first batch.\n",
        "        print('The dimension of an image tensor:', inputs.shape[1:])\n",
        "        print('[Prediction Result Examples]')\n",
        "        images = torchvision.utils.make_grid(adv_targeted[:4])\n",
        "        imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[:4]]) +\n",
        "                      '\\npredicted labels: ' + str([int(x) for x in preds[:4]]))\n",
        "        print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(labels[:4]):\n",
        "            print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "        print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(preds[:4]):\n",
        "            print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "\n",
        "        # Display the second 4 images in the first batch.\n",
        "        images = torchvision.utils.make_grid(adv_targeted[4:8])\n",
        "        imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[4:8]]) +\n",
        "                      '\\npredicted labels: ' + str([int(x) for x in preds[4:8]]))\n",
        "        print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(labels[4:8]):\n",
        "            print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "        print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(preds[4:8]):\n",
        "            print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "\n",
        "    if i % 10 == 0:\n",
        "        cur_running_loss = running_loss / running_length\n",
        "        running_acc = running_corrects / running_length * 100.\n",
        "        running_asr = running_success / running_length * 100.\n",
        "        print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, running_asr, time.time() - start_time, running_length))\n",
        "\n",
        "    if num_images <= 0:\n",
        "        break\n",
        "\n",
        "epoch_loss = running_loss / running_length\n",
        "epoch_acc = running_corrects / running_length * 100.\n",
        "epoch_asr = running_success / running_length * 100.\n",
        "print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, epoch_asr, time.time() - start_time, running_length))\n",
        "\n",
        "print('[Size of Perturbation]')\n",
        "print('Average L0 distance (the number of changed parameters):', running_l0 / running_length)\n",
        "print('Average L2 distance:', running_l2 / running_length)\n",
        "print('Average MSE:', running_mse / running_length)\n",
        "print('Average Linf distance (the maximum changed values):', running_linf / running_length)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The dimension of an image tensor: torch.Size([3, 32, 32])\n",
            "[Prediction Result Examples]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: deer (4)\n",
            "Image #1: airplane (0)\n",
            "Image #1: airplane (0)\n",
            "Image #1: cat (3)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: ship (8)\n",
            "Image #1: horse (7)\n",
            "Image #1: bird (2)\n",
            "Image #1: automobile (1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/u2kJkvot//dPZyZTfvoPA/gagFbMHYzbMJYNe0NlnE6sA/AAgKcA/BuATnjLAD8UkXc4575W4Zwb4UW4/yG8QK9rAEC8sDHfA3AtvGWFrwLIwAv2+xkAlwD4FXWtT8ELGtsH4HPwQny8yT82Ai8Q8LyIyIUA/gfeMsa98JyBJgCcCeBmAH8JoBueJuT3y9KeYUfZta7zz5+5n70AVgJ4C4DXi8g1zrnHyo7fBK8Mm/0y2QEv8PB/+/ZS8NcASgAeAtCLF5aFPg3gIswu1xn+AZ63+a8D+A68uvl9AFeJyJXlD1cROR9ewOUmeGX5LQAtAN4M4D4R+QXn3O3HyqRfdj+AFzj5u35emwCcAe/tzsfKjl0Lz3Frj3Nu7YJK4cTwjwAa4QWvfjG5wf99spqLiEgMXlDraQA/q3DID+G1n1cC+Pdq0jKMReOcsz/7O6X/AKyFF53eAfhbte9CeBObUQB1Zdtv8o8vAbiuwjVv9vd/BkCwbHsQwBf8fW8q2365v20vgKay7TF4ExQHT0eDCnm4qWxbBN6D2QF4R4V8rVR2t75u2b5G/76HAJyp9p0NT7PzmNr+Yz/tD6jtbyor45sqpXeMMtymtm+ocGwAwJf84y9R+27xtw/BC7Zbfs43/X1/VrY95NdDBl7A3PJrrYA3MeoDED1WXsuufW6F/LbM0QZn1YW//Z4Fltk2//hbl6Gf/IJ/7feovB1e4nRCfnneDG8C97ifzl0AYlVe+yz/Wk/Nsf9Cf/9Di2mT9md/S/FnS37G6cQ4vPh5z+OcewTAVwA0wHugaL7jnPtR+QZ/2el3ARwF8EHnXLHsekUAH4I3KL+z7LRf83//yjk3UnZ8BsBHFnEPN8B7OH/XOfdVvdM5t5gvst4N777/wjn3tLrOTgCfB3CeiJwJeEtkAF4Db0L3WXX8d7BEX0g55/ZV2FaC94YK8N48VeLTzrkedc6H4U2Kf73suNcD2ADgM845yrNz7gg8DU8HgFctMMuzYi662UF/e+G9uVroNV9URKQd3lvTHzrnvrDMyYUA/IX/97sAXg7gywDe6MreIh4nM8vnc2kPZ7Y3VJmOYSwaW/IzTicec5WFqPcA+FUA58F7C1LO9grHb4a3tLMHwJ96HxXNIg3vATrD+f5vpUnHfQCKFbZX4lL/dymW1y7zf8/1v27SbPZ/zwDwNLzyAYD7yieRZdyDJfhCSkSa4U2ErgewHoD+nL5rjlNnla1zbr+IHAKwVkQanHNjeOG+18xx3zM6njMAHGvZ7yvwlkYfEpGvwROL/7zSpNZ52qHdx7jWiebz8Mb79y53Qv6kSfyv8VYAeDWATwB4RESuc851L3ceDONEYBMq43Sif47tR/3fSuLwoxW2zQQz3gTvf9lzkSz798y1Z+XBOVcQEf1GYy5m/mfdu8Djj8XMffzGPMfN3Mec9+BTqawWhYg0AHgYnt5tO4D/ADACoADv3j8AIDrH6cfK1xp4+R/DC/f91nmykzzWTufct0TkDfDeSP46gPf59/AogI845+6Y5/onBSLybnhvPn/Vf0P3ouCcc/Da8ZdE5Fl4S9+fBfCGKi478waq4oceZdvHqkjDMI4Lm1AZpxPtc2yf8UxdaZmg0pdVM8d92zn3lgWmPXNOO4D95TtEJARPEL2Q5bqZB8Fcb2kWw0yeznXOLUQMXH4PlVgKD9/vhTeZ+phTfplE5DJ4E6q5aIf3gcBc+RpXv29yzn33+LMKOOd+AOAH4jmlvATeZOC3AHxfRM7TS6knKTNvT78kIvoNLQB0lX3d2ui/5VtSnHMPisgYPI1YNeyD97Z3vYiEnHMFtX/m7eNzVaZjGIvGNFTG6cT5IlJbYfs2//fxBV5nN7yJzaX+134LYeZLuUpLYlfCE7MvhAf939ct8PjiMa49c62rFnitmfK5UkQqXXPbAq9zLDb6v9+ssG++5cRZ+0VkPYBV8MTgMxOBxd73vDjnUs65u5xzfwDPKWYEC6+jE80D8D6kqPQHeF/MzdjZ5ciA3y/r4L2JPG785cT74X31Wql+Z+rkrmrSMYzjwSZUxulEPYA/L9/guyB4J7y3Ft9eyEX8//V+Bp7bhX8Ukbg+RkQ6Z8TcPrf4v/9bRJrKjovB048slO/B+3LvjSLyyxXSXak2DQNorZRHeJ+NjwH4CxG5uMK1AlIW08zXBt0B7w3S+9Wxb8LSeJju9n+3lW8UkfMwv3j/AyLyvHdv/+OBv4U3jpV/Iv8deG8yfkdErq90IRG5TEQSx0pMRF7hv13UzLzBmy47Nuz7+9owzz0sCeKFHFpQfEPn3Necc++t9OcfMlq27XkBvrwQ3/DmBebpHL+96+0ReEt9AXhuKPR+V/aGbCHMOCP9P+Xp+f7F3g7PQ3ulCbthLCu25GecTtwL4L0icgmAn+MFP1QBAO9ziwt58ZfwwoH8JoAbROQueHqQNnjLClcA+N/wxNxwzv1cRD4D76umnSLyDbzgh2oUCwwv45zLichb4bkv+KqIvA/eG5cYXviKrLzf3gnPd9OPROReeG8YnnDOfc85NywiN8IPcSMidwLYBW+ZcxU88Xazf+0ZfgfeG41PiecQ9Ql4b5V+Ad5k7wZUx3/AE6R/SkSugSf83wRvKe1b8OprLn4OYIcvEB+H9zXguQAeRZn3bedcXkTeAs//1A9E5H54/rSm4d33RfDE8J0omxRV4B/hLYf9HN5EMAfgAng+jnrghT+ZoQvAM/72tfOUASEib4bnHwt4YfnyMnnBQeuQc+4P1Wkz/xmu6o3PPCw2jfcA+DW/vHrgTeZXAHgtvPt6FgDdR5kj14V+tAF45f4WeD7kHheR78Frx2+H97b2NxbZ1w1jaTjRfhvsz/6q/cMLPoBugTfp+A68Scw0vIfwtRXOuQnz+FQCIPCcBN4JTzidgzepug/ARwGsqnD8++E9WLMAjgD4J3hvzrqxAD9UZftWwwvoesBPdxieI8yPquNq4P2P/TC8B58DcEuF8vksvMlLBp6jyt3wPmV/c4W0NwL4BrwHYgreBOv1CykzdZ2bUdkP1ZnwHGUO+Nd/FJ626vl6VMff4m9fD08gvtu/j154Dk3r5ki/DZ4T0Z1+W5jyy+AbAN4FIHSsvAJ4G4D/9M+Z8sttJ4C/AtA6RxvsrpCPY/qhKkt7rj/dbhrhTUDuW4K+M6cfKngT8SKAzQu81hUA/h+8Sfuo3x5H4PWXPwSQqHDOuTgOv1vw/lPxQXhOfNN+ercDuPx42qT92d9S/Ilzi3nTahgnH2Veqr/knLvphGbGMBT+ctZPnXPbluh6b4T3n4bXu3m8vVeRhsBbOrvLOfe25UjDT+f34E2Kz3HO7VqudMrSuxnel7vXOOfuWe70jJcWS6qhEpGoiPyNiBwRL/7YQyLymqVMwzAM4xTkankh1mK1X0teDWDHck2mfM6Gt4y2GP3f8XA1PCe2yzqZEpGd/sT2WG5QDKMqllpDdQu8de1PwXtNfhOA2/14YfctcVqGYRinAh9T9lQ1F3POfaia8xeYxlPwlrCXO51fXO40fP4Z3hLwDN0vUrrGS4glW/LzvyJ6CMCHnXN/52+LwdMcDDjnLl+ShAxDYUt+hmEYxolmKd9Q3QhPwPi5mQ3OuYyIfAHAx0VklXPu0BKmZxgAAOeFslj2/00bhmEYxlws5YTqPADPudmfq87ESns5gIoTqkX6IDEMwzAMwziRPOacu6B8w1KK0jtR2dfOzLYVS5iWYRiGYRjGiWKf3rCUb6jiqBy2IFO2/3l854XzBS81DMMwDMM46VnKCVUalaPEx8r2P49z7jYAtwG25GcYhmEYxqnNUi759cFb9tPMbDuyhGkZhmEYhmGcNCzlhGoHgM0iUqe2X1K23zAMwzAM47RjKSdU34AXmPJ/zWwQkSiAXwPwkLlMMAzDMAzjdGXJNFTOuYdE5DYAnxCRNgB7AfwqvKCh76nm2r/0wW1kxzrayJZgkuzV9bVk57JjZAcjrWT3jrBYv1A/SHb76hjZodKaWXksTpJEDEPZcbLrmviaq+rOITuWO4PshtazyR4bVi/4shycvX+C0w8l+PuAjg4uo6nE02QPTqTIDk5s4fTyZ5F5aOhxss9et5bs3rG9ZHcfVfPpQITM89tfSfa6Fi6Pd1/1KszHL73zKrKTyRqyI4kE2YVMhuxcmsssCJb2hSVItnP8/5FigNtJLMYva2OhMNl1CW6noUKerzfFHkiy9FkHMJrhOo+o/x5JscAbYpw/BPj+IgF25TWe5n4DAC7IeSxp71853hASHmJC4DJsbm4hu0aVSVMt99WA8E0ODXA/+8OP/f2sPJczOcztfKrIbSBYy2VS5zj/mXSJ7GKY7yem20QhR3YaXH6JGMtOJThbhprP8jUyKo1EkOt5Ksv7a8J8DwH17VCpwH3xmWe2k337DznIxdN7uC9vOIOVHl1rG8l+7MmHyGY1LZCb5vt7+xvfQfbVV7yC7FCY23GmxPefT3IZhzPc71qbmjEfQ6lpsgvC422xyO1E8pyHQInrceDQs5xAhsdHF+C+1tK+geya1q18fpHrtOQ4vYB+V6Jkyq7IZRRQbajg2A6psSKb5zpwRS5j54bInhp9guw8VzkAoK5xPdnRGu77JannE0SV+aw8qzIo6ekOt3tR4199nRovF8BSh555N4C/BPAr8CKiPwngDc65e5c4HcMwDMMwjJOGJZ1QOecyAD7s/xmGYRiGYbwkWEoNlWEYhmEYxkuSpV7yWxbCAdYptCZ5LbV5/ZlkS5bXc0sTu8ned/BRsodGB/j6sorsxNhqsgPomJXHQnGYN2R5PXe6yFqPiSKvMWf6+fyS6yE7lGcBTSDDc+HOCJdJIcbrw1P5PWQPHhklO630QYkYawiQ20lmJMKasKxr4vMTvEYfS7D2pNbxenVjA69Xj6dUeS6AkGrN0TCnUSqydiQQ5DwFA6yTCCl9TDDCdklpVZTMAMESXy+vdGpTaRaTNCutS2sta75KXVzHLj9J9vAA10l+hNMLpdmWsNJFJFhztmIl6ziAWVIMTI6zzmuyyHmIh5XmqMAXKBZ5fyLOGqpCiOuwuZ41V4EQawPnJcjijRql5ZhWrokHspzfREzpkVRw+Yw6P6q0N/UR7pf5FLepbFIJjADElF7GcbNAsch9J676WoRlYkhF+QKpoW6ypzKs55lS7ejJR58keyzIATJ2HZ0iO/ucGo/RQLbWnT14L4/P519M0T3QwLcHKO1LrMhlOqvRLoCQGg8DJb4HqL6tO//o0YNk9x94jOzVXdxvhoe5TLNZtluKLyM7XrOJ7GiUy1SUdjGt9KEhpYUMBFS7VtrCUoH3B8H60EKJ29TRXn5+Nddwu4/V8tgFAKOTD5JdW9pMdn3tyzlPSrNaUmNFSWmoIkq/WVIa06Vwh2lvqAzDMAzDMKrEJlSGYRiGYRhVYhMqwzAMwzCMKjklNFStdcoBT5zXQvNp5TdqDfuvwBivtRYOKX1QlHUYa1bzWm2ihnUbbkqtnwPIZXjbxtaVZA875eMoxnPZZBev3/YP8nry6gSvoa9o6uIM1LDvl0yIhQbdGfYdE4yzLqw1xX5MJvOsK4sI10E4qfI7cIDs5jou09bYRrIldJTzO8VilkhwdhnPRzTCGqC80rGlc6wLCAdVHdSxLqCktBm5LJ8vIW6HNaJ0a2MjZEeURqpzLddBbpx1dUWlhRlIcZ1M5JQfqpDyjSOc/zqlY0g0sj+3bJGPz47NdhajdQh1IdZO1NVxX5GI8sOU5jLMT7G20DWyFi81zX01PcZ6npqE8k0zD4U017koCZaS/qFW+efJ51WbUHqeaFiVmdKepHPcrgNR7ldBpdsDgExB6XcmuZ4DIeV/x/Hx0+AyD2TV2HCUfSQ9toM1TAhxu9x6AddxfTvXQWqE8xeq5/3paZWfJq6EWEz5TBrl+wt2cZnGlAO2nLq/oMwu0/kQx9cIFLleAyXen51gzdCRff9DdkOIdWnjo6wzS4+xpjUwxePjxDD7Sqxr4DpIxNrJTtZxmefz/PypiXKZjefZzisHc3osCUZ47EKO62TnI+x77KKLzie7uV61aQDFKW5nAwM8vrkS95X6FqXxVL64CsqHXEnp3KIlpecMaHHe4oqi9nYAACAASURBVLE3VIZhGIZhGFViEyrDMAzDMIwqsQmVYRiGYRhGlZwSGqrJIK+31gfYHhzaT3Y+wuvRrSr2X+cK1jc1jnMx1CZ5TT9Uz8IKCbHOAwDWrOI17OAkr9s31XIsvKEYx3KaHGa/JC7M68lN9exnJNG4gux4nNeXe8ZY0xQIsW6h1XEZNEf4+jtHeI0/lWc72cCarUKItSER8PWTKkZZOsf3Nx1kjUEpwHqmhRAKqlh7ysdRXS3rCmIqjlpRaS1KKrZeJs9r7jkV6266xGUkKlZURPkw2jfEbQA67lsTt7P+SdYbTU5yeutquJ0nWllLODXFx8/y36Pi5GUmuI4AoORYwxRV4a5CYb5GWxO3084VHJNsdIT90XQfZO1IUWk7MkqrIS2L0z2ElPavkOZ2O6H884SUPiec5vSjAdb35JQOI6iGWFfLbaKk4vRFJ2drS/IB1r/odppU7SozrTRIWb6nR556mOzv33EH2QluRoixyyOc0cKx8CYmuK+npnj/0ADX6er1PL4margfxuI8tiRXcT8oqjpKKV1bVFScOacDTs5PqcRpBAMqhmW+n+zBAdadBR2Pv/Wq3U0Ms76yRuk/a1VMSz3+YlxpUCfYLoyoGJE6xmStir2n9ERxpYHNlfj8YJr7bVbFg2xv5HEiEGCdczjE8R8BoE74GTSqAv4N9XK7DalYq4lGFX9WtQNRujAXZDsA01AZhmEYhmGccGxCZRiGYRiGUSU2oTIMwzAMw6iSU0JDNTjOceiyKuZaSwPH3hsbZ81BMsb6oVVr2X/F3sO8/jyVUeu9AdberKiZ7dekLsh6lXyIdVzBEK8Px/OsEcrkWIvRNnY22bUNnIfwEK9RJ1q4KtsjvIY+fIh1Cd2PHCZ701Zefz4vwpqve9N3kd1ZYGFFoVb5MEopf0NZTi+n1twnhNfkwyUVgGwBJJSObHpM6VmUn5JEnHUCJXX+RIp1DqGYWmNXmqOS0vuUlCYqH2bdQlHpNJKNXCY5pc8JltheUctalYAOAxfifpBSmq88WJOlwj+iITbbD1VtlK8ZCan/k4X4ImGVRiHPWpFolNttRLRujft6PMRlGCoqx1HzkNMh2BLcbuPK55OOmZkPcyEH0kq3p/z7IKr8GWVUvLA4pyfF2fHEIinuG8EYa4oKk5yGy3PfeeAJ9mn31G4e7zrW8PVyKk5nTukpW1tYL7quncfTLz/FY8Vwdzen13UR2ZMTfH+v2caxWYMRLqOc8llXqme9UXaI20RT7Wxd2ny4IJdhIctlMjH4OJ+QfprMhjifP53i8Rrq+jVxHptKBX4+1EZZcxQJs+Y2GlTawCD3s2nlE09UHFKtEUNeach0bEPh/CdUv1zZzukHizz25Cb5eQgAcRXjMR/nc9LT7IfvUPdPyd6gnnlBFe+wFGCfeUUVuy/iTENlGIZhGIZxwrEJlWEYhmEYRpXYhMowDMMwDKNKTgkNVXZimOxhFd8rM8G6ji1nsa+bVInXf3PDav1Xxb5qqu0ge0U7x6GLNPCaPQAU+3iNvBRW+pdh1uu0FTkW38gB1hGM93Ieuw/tIvuI8kHUup41XAnlx6l/WMVF6iMT+Vall0mwFsZN8fp0b4TXs8PKP9D4EOffKe1JTK25R/TUXsUnWwjpHJdJLMFr5tE8p5mZ5DylpliXkFE6h2ij0u8o2UFQBYYrZXmNPj3GeqJQVOmDlL+cdvD1OltZR1EocLueDnAbTE+xBiFRyxqB2gT3mwYVdy8Wna3n0RKhQlHlQel3eo8eIbtvgss4GeW+FlS+YpQ7HMQSnF6PikM3H7VhLvOxjIqlF+WxRccwi4HtvNJAJZTOLZfj6+VLrIUMqTrPuNm6tWiE2zECfM2pBJ9z4KknyD58lMeO+jWq4ea4HdxxF2tWWzvWk73t4gvJvucu1g8dOfAY2SUVI3J8+CDZyUZu12dv5vQCOW6HJaXDq89wmygkuI6ntT5oAYiql9ERjoU6Nbqb7LBTsfigYv8J56mk/D6J43YYCXKdO1WGuZCKfxjm8T4eYx9MNXHWveVLKj9Q7VbFUg04pYnNcf4CCc5PUulTcyqWbWpsto87Uc8sl+HxLBbhMpscZV9f/Ye3k71qq/IXqfzuFVXfLpqGyjAMwzAM48RjEyrDMAzDMIwqsQmVYRiGYRhGlZwSGqr6GK/HTiiZQUbFKAsLB6OqibbwCXEWEBXreK21q51jUzUoLUvPc92z8hhUsZLai3yOKP8zhw+qOHJF1gUgwpqqWDPn8cw1nMdQgqvy8cc5TpxTPjouuuoassfHWd9TN6limCm/UcNj7MumvlbF81Kxp6LKSVImzDHeRrK8hl8E60QWwrjwmnuTitUUjnIaYeVHKlTiMpcSN7RCkfUy4SjrCEoqhlpxmnUHdRHWSYQd32NnnPU1m1axLi6rNGLDo6xHisf4/qYc2wf2cR1H2lhnUdfFbTYcm609iQf4HOWmCcUJ1iHklcaqvon1jSWlHenZv4OPT3Ae6tvY91Zbi9IXzUMqq+pQuF2GStwPIwEu86k0n18qcR1mo8qvVYTLNK3iS8aUKC1bUI6yAORLnGZ2jMvkmV0/J/u+3Q+R3bRCxd1UPoEefpj1kFNHWSP61jdfQPYhJX95djvHUq1Vddqwntu1y3C/2tLB+p6G9WvILmaUT6iwCiCp3AIW1GOtFF68hmpihDU/u55kv1PhFN9zZyPfs/av5oLK71OE27GD8n+mfMiVlOYpD7aH1PhZF+Iyh6qTgIqPGwxznYdLnP+88gtYDPLzIBZj/VM4wMcHcqoOCzx2AcDoMJdpXPl7LE1xmbSpR+ahg9wP6tv4GVPfeRlfr6Db0eLbicbeUBmGYRiGYVSJTagMwzAMwzCqxCZUhmEYhmEYVXJKaKhE+bSoC7IeaKCX9TxFpTVpiXBsqKazeY0+n2btzNRwP9kP72c/K2P9nB4AbFlxBtmxIGsl4jleY462cvzB2jpeU59UPpJ29bAmastK1onFJzlW05Vref149Xkcq29Y+UgaUD6Ymlawf6CuiZeTnc+y/5+aEOvUmpK83p0LKuFbnpveaIY1Acm2xfsECbBMAKUop1lfz3XQUMeL8Fmlr+nt53aQVrcgymdQYZL1NIUxrsN4Het9gqqdRrPK98y00jFk2R9bMcVilpDyq9KaZN3dVC3Hyoo4vn40zjqKZGJ2HQSV/7CY+j9ZLMb3EFJSuMNH+J73d7NPotQYl/klF6wmOx5l3UM6zvc8H8GS0gaq2IMTAaUBU77LwnHlvyejtTJ8/BRY1xdW8STzWdZtuJwKqAggF+RrPHqAY/Pdd999ZEdXKK1emO/5wD18/eamtWTf+H4eK1ZGuV384Kc/Izug7mlFB/uVauhkLeBEH49Vl11xBdlx1Y4DqmPHVWzWTIHLMOT4eVF0i4/l9+wz7Itr37Psh6ojzveQBOvAUmFVzyFuN/W1PH6HEzz+Tk5x7MB4iPtyWOlDp5Umabqg/FyVeDwOl7hOlfQPItzPAgFu5zEVlzQzxXVSUnUYEi6PSHh2Ox8a7Sa7Vj0T6pR+M678DIbyPHb0PH0n2WfXsD9JRHlsKcni24lm3jdUIpIUkY+JyI9EZEREnIjcNMexZ/jHTfnHfllEWisdaxiGYRiGcbqwkCW/FgB/DuAMAE/MdZCIrARwL4CNAD4K4O8AvB7AHSIyezpqGIZhGIZxmrCQJb8+AJ3OuaMiciGAh+c47qMAagBc4Jw7CAAish3AHQBuAvC56rNrGIZhGIZx8jHvGyrnXNY5N1s0NJtfBPD9mcmUf+5PADwH4G3Hn0XDMAzDMIyTmyURpYtIF4A2AI9U2L0dwPXVXP/wXhabnb/hXLIDzezUbN++R8let4FFlqPDfPzogR6yB4oc+DKuYsRuaebAxgDQnGERXnqY56C5DN9DUVhEKAkWgTdtZNH6pnYWfQ/sZJH68MAzZAeKLNgbTLNTx2Qzi+BjSgAYcrxKe8GKzWSXRpUgsIWdB0bGWYQ4kmIxsivx+XEVBfdIphuLJaoiLMfj3LynC/zxQSTH9+yUc1ZRou2aCAuWJ4c4IGpSORhsXMVlECsqgbIKoBoOsMPFkQEVTFkFL96wioWlA0Pcpo4cZhF7l3KguGodizRr29T9K9E8ABSmWWgfCKl6VE504/XKEWiRxbwlJZ7tuuJstlexg8L+ARYHP72X2/18SJDzP62CMScKXOc5FVA1pwKtR5SjzlJBBSEvqi8litzmCpGI2j07KPjevezs9NDhfWSvPk99oNLA9bz9e8+RXac+gLnmmrVklw73kv3zHTysH1CC7SnlWLlzzTqyi44//th0Bo8VF7/6fLID6oOVkgr67RzXQRQsSpeS+rAgu3jFydPPckDp8aPc1888i/seHI8tY9PcV5/Zx+1+69a1ZG9R9sQQt/NCmM+PJ1iaXMxwGUxMc1Dyujrul1p/XcxzPxYVDDmryrxU4nYeDHKdFSPKUSknh1J+tuPmiRQL8VOT3K6kQTmIdfyRUX2SU9k3yP1msP8pstu6uAwLyoH38bBUbhNmPuvoq7CvD0CTiJL5G4ZhGIZhnCYslduEmf+GZSvsy5Qd8/x+EXkrgLcuUfqGYRiGYRgnjKWaUM28/6v0FiqmjgEAOOduA3AbAIiI0ycZhmEYhmGcKizVhGpmqa+zwr5OACPOuUpvrxZENMxrpyGlbVm/ehPZu3s52Of2B+8le1ppZcaHeaWypp41BBe08Bp/Q3i2ay0Z5TX2iX5eAx/PsQ4gq/Q0iQCvH4cGWCPVvoqdk/YqLcqhSdZmdDapNe0xzl99A6+Zj/eyLqJ3gMtkxVbWja1qZ8ehfQGeE9eqgNajk6wlKRZ4//gQ64cGcgewWOqEdQ1O6QpSKsBnOs36mKLSwU2O8Dp/Y4TLeNNKFRBVaahqQqwzSMb4/xtR5U2k5LQeh6/nAnz+REppwFSA1c4OLuOVXaytkTinp3UQiVrWugBAWvgmh0e5TFND3G4CUXZI2NnKaVx6/iWcpxhrCXc/y3qhXc9yuz/Qz2U8H7EkjyXZcb5eKsdtJl7L+p8cuFIKIdWG1NiCMLehvHKQODx0mOzndrPeCQD2Huome/8B1jStO5+dCj+7k3VlR/t5PHznL7+S7JED3Nfuu5MDAT+yn8eipHIKnMizTq5ZuM7Hw6ztu+6Xb+TrqbEinVfawohyMpnmsbMQUg5qY9xGXVF55F0A2Ulu131KnwjhdpRIcl9KKCe6dz/Mz4Od33yA7Gtfy7rgmiTfUzrATnzbg1zGo+OqH6jg9AWlVayr4XacUIHjSyHWJ+WLfH0VVx6BgB7LeH9BKX6mlSNqAHBFvmjecV8cn+JnRGGa77G5mcf/OhVg+shhdojb2H4O2fGIirZ8HCyJhso51wtgEMCFFXZfDGBHhe2GYRiGYRinBUsZy++bAN4gIs9/niYirwKwGf7SnmEYhmEYxunIgpb8ROT9ABoAzKzz3OB7RgeAzzjnxgF8HJ7I/G4R+TSAJIAPA3gKwL8vaa4NwzAMwzBOIhaqofpDAOURhd/i/wHArQDGnXOHRORqAJ8E8NcAcgB+AOBD1einAOCqyy8jO1fgF2suxtqRxBivf+/by7qEVJb3R5O8drqyjoMnlw7zGn738JOz8lhQy/QjKfZDNTHCPoGySncgIdY4IcVr8meex7sPFXn9+OgAazGGU+zDKFLLa9hJFVgynOD8FJXfqN493WS31W4luy7BdTKigkOHwNfv6WXdxmSYdRGN4cUHR3aONUXFPC/k5/Ocx2KWyyibYl8y7SqY8sYO1k0ko5zHwQkVvFj7wwly/oLKh5OS1yCgNE2pDLfz3ARrRdrbWH9Um2RtSizEjTSZZM1BKMH9IIjZ2pOC8h+TU76qso6vmQhxHlq7WIs3lmXtxhMPc6DfHbu5nYxnlW+aOuUPaB6KeR6KmhKs/Ujx0IDRw92cP6U3Ckb5/AvP4o4aUnX88HM/JPu+O1gNMT4x2/fX0CD7FApnWQtXXM99K6F83N34xqvJjg3zWPO5f/svsrNhbldHjnC/2NDG+qCLLr2A7IEjrPe57BUc/Pi8M84iO5JnrUxB9ZNClvMTquF+WUxzP4sp32KR3GzfXvPR3rmW7Ccf4b63cw/reS5S2rz21dzut2zivvnAT7aTPTnImqLrX8dy5I3r+Xr5aR67Bvq6yY4qbV9B+eqKgvWfNTHOfyTC54vy2VcUpd/Mq6DhKgi5A3esoXH1vAMQUyLUbJHLfFo9tyXEdizD7aAuznnoPbqT7P4j/Bxfv4l1wcfDgiZUzrm1CzxuF4Brq8mQYRiGYRjGqcZSaqgMwzAMwzBektiEyjAMwzAMo0qWyg/VspKaUL5iQrxWOl3i2xhKs8+MgVHWJUSUk4zoOK/FtoZZl5FVPpxG+pVPEgCjE6wzGMgpzVSG81Sf5Fh9gThrnBqaeP1YR6OSNOsCJrNcJpJnHcOaOKcXq+d4Xm2NrMOYdFxGxQleg8+Ns4agto19c42oNfxgbC3ZSDxLZk2YdRHhGMeeWggFFQetxMv6EO3fRvk5Wb+a07zoXPZvNjnI8RiP9B4kO6j8SLkg12FL03qy47VcBwGluYrEuNYLedY9HDzEbUzA+a9tYE1UDNxGo0nOX1C4/IrF2T6eSmmu96YG1nZ0NXPMx2yI28VzPdwu73/gYbKHxtgfWzbMfVv7v3GL9AmcVro6KfA951Usvq9/5T/JfuTJ+8mezPJY8fa3XUf26i0cm3D7w09zhkI8lmRU6D8AqGvkvtGoQolmc6xre9U2pZnK8z199T9uJ3vvHs5T7UZOoDnOmqmNF1xOdmvnBrIP9HIZXfxy9jUWV2NDXsVHLDquEwny+J0vKf2p8v+WVe8JSjE1ECyAs7eyzmvnkzwWPP0st1sVfhCv7OJ4hhdewNf76Ub2+3fwOdbc7nyEx4L1HexrbGSKx6L0FD+j4q3sQy6oyjiW4HYbDPNYE9JjqdLEqhCWs8aOQoEPSKdYIzuhNGAAkFVxLoNO71d9VWnjlJQaDfXsqyse4L7Wv5f9U67oZL9Ux4O9oTIMwzAMw6gSm1AZhmEYhmFUiU2oDMMwDMMwquSU0FBNBHittD/HsamQ4tvI17WRva5uJdm5o6wDWZ9mHQiGWVuDGt7fuUIrmoCM0pas7lhNdqnIeaytY31LVvn1qE3zAnImx9cvTbGuIK5cfcVKnOeWOhZnNCpfM9Eo6yRE64FqeO49Fub8HUmzPgeO1/jrklwea7rWkp2e5vTijtfcF0IYnKdUhjVT+p7OWM26stdfy9qQsSPsc+jZg6yZckHWgjQov041TaxpautkDVVAx7eaYi1MocDXr6nl620+k/VJRRUfspBjnUJRxZUrhJV/niK360xe1SmA8bTy/dLAepvD/ayReugJ9rfTq2KiZYrc18JK1wWlkXIqJlnALU4fU8NuoRDIcD/cfT/7wXr4vp+QfWiE8xur43737e/eRfab3sjpdbXxWKSkL2iLqLEHwA6lAe15huPC3XTjVWSHouzD6PO3/gvZ+T72WbflfPYjlUpxHa08l/3yXXHWxWR/87/vJjviWLtSCLPmKRfmdh0KcruN5pSWMMxjV0HpR3MxHltdhtu5C6hKXwDrNnAZXvuG15H9w29ynh/bxT6OUsK6tOuvY19cv/pbN5G9+xHWVO17iq93953sS/Gsl3NfbWxkzVQ8zvdc28h2Kcy6ulKJy9hFlO8vp4Rqop4PyolePMDPi4LSh2YPz3ZNOVngMo0luK8XCtzX9TO3JszP/c4W9q21IsnXPzLKZTrcU32EPHtDZRiGYRiGUSU2oTIMwzAMw6gSm1AZhmEYhmFUySmhocoFeP01EOQ19PEJ3p8ACxNqanktdfMW9osy/QRrCgIppS2p4TX/bHS275v6Dta3hNt5Pbevl+NxHR1l31rJBtYwhVQsqPQ+1jWkM6xv0bGYOptYxwDluyXiWAtTKvEa/LjybVPM97G9mTUGhTj7Dzqs4h264FNkh+o5HllHC5efm1C6tgWQmVaxnlQ1bVy9luxXXHk+2Yd7usl+9vGfk52s5f9/SIB1BRGlFXEF7l533fUg2avXsq5s9Rqlu1O+XvJQfqGUv55SntvIUD9rwOo7WEfRLqznCSZYRyE5bvcAEFbxASeyfM8P7mDtyK697F8nXq/0MBHl30ZY9xZUWoxEkrUj0ZLSXM1DZprLbE83a1W+fNtXyT40xTqNkWGu0+Ag6zqiQb7fwz2sJYw2cZlPTnH+ew+ybzEA2Lebx7NXXnsl2bVh1tJ9/7avk73jvr1ktyW5XtsauUzjYfahdPZW1mjt6+khe7Cf87duPfulCgW5DmPCZVhU2sZ4hP1uOeV/KCDKR5/SnxZVLL9wcLYubT5iKjbpprP4njJ5Fsfdfjun+cTTu8memmJt3kWXXET2FTdsI7ulmet0z04eOw718njbvILLtLPr2LH5XJHrLD3NZRRyPL5Dxdkr5bVfKlUHRR58hwa4Xwwc5fwDQCSp9JHKNyNUVw9kOE9KYoWjffyMbWrgsWpNK18/M8Hj5fFgb6gMwzAMwzCqxCZUhmEYhmEYVWITKsMwDMMwjCo5JTRUfcOsAaip5/XhwhhrZwpq/fbIKGtLLrzqFWQ/lWV/FKNKF1IL1nVgRIlbADQ1smZpXz9rIXJKJ1Cv/EK5IK/nTir/NNMqFmAxwLqHoIr71tC5luxOFc9rqpt1Y+kGXuPuU3Hums5gXUWpiTVcR/sf4/Oz7LNJ6xhKOV5DdxOsGegE+2xaCEW1pr5p/Rayr3kF+4KJBtgXyv5n2RdMRAUJCwaVXibEi/rDY+znRLmpwqa1rKurU3qisON2qqQicFqnkOXjx/vZP5uOU1cb7SA7EmS/WSixT6ZAVO0H4KLcFx5/lGMyHh1jn0mNbayNa2phHddkmhu6qDhv8QTnSQrcjrJTs/viMVE+jO6/9wGyQ+D8r2hjX2X5IdYSBrV2Msr9LJflNhEKc5urq1lBdneMtYsAcNV1ryX79dex9u8bX/gi2cNDnMYVV7yc7P29z5B95DDXwUWXnkf2WDdrUR7byb62sqpOSuB7qotznaZLfHwduJ9l4srXl+P/97uw0koqaaGosTTCw/mCkBBfo7mONabnXch1kKxh3dcPfvANsvc+w5rSQwPc7toa9pN9+blcZ1vPfDXZd9/xZbI7unj8zKm+n1BjSTHD/San4nbmRPXLFNdRTQ2PXZM5fh707OWxNK98Ao6Ns786AGiJsY44q+o1l+Jn3HSGKzasfGOFitxOamt5ulOvdLzT2tfWcWBvqAzDMAzDMKrEJlSGYRiGYRhVYhMqwzAMwzCMKjklNFQhlcvJIV5LzU2rmGMh5btmgteTR7vZj0p2gnUOQ2n2GZU6zPHKIvWztSXDB1lnsCfH+paGRl5zrsvxTeWUdqQgfA99U+oelT7ngi1nkx1S2o2BKV6QHnO9ZE/klD5nPd9zSOmFxiYeJ3vccZnGlLRlcoq1M8Fh1p40t5xFdjbH698LYf0q1kxd+5obyG5Qa+j7dj1CtnJng0yatRyJOl6zF+UnpSGqYu8JHx8KHduPVC7LZRzMK+1enrUxReUrrCbA2sF4k9JMhZVvMxU7MKdiAU7llR8YAPuPcDsfSrFW0MW43rIF1iSVwtyuO2pUDDKljwxHuc6yGXX9lCrTeQgpH0VTQ3w/Y473i7LrOtkfUTzBbWTVJtbJubjyuTTO2pT6dq6DK648c1aeu5RPue/cyhqm1CTXe8c61tMcPcTtaF0b6yEHVV/btZ37RVD5Vzvngm1kD09yxykU+J6CytdYLKz8Coa53cdVHeVVPxDhdlxSbSwS4zam9UQLIaDbQZDLIBnhPJ+9dSPZjXXvJnvHUw+T/eDDu8g+sL+b7H2P/BfZm9vZL1Q2x+n37Od2vHIt64Oc0hVnprlMcyVVZmnWOGXTKlas8sUYUGPfeI7rZHySzy+EZvuPKym/dxPjakBWfv0mx/n4TIrTLPAjBpE069ye2sfPwL2HlHD5OLA3VIZhGIZhGFViEyrDMAzDMIwqsQmVYRiGYRhGlZwSGqrJKV4DL4zxGvzkMM8L62t5fbe9gTVPkXHWSNXoNfqg8n2j4oulVRw+AOjt55hdLSt4TX1slP0udWd4vbajjte8g3Wsm0gr/z5F5cNI8pynQwO8Bh5S8QxbmjmmWCjBWpR4nHUYecf701O8vh10XEaBHOvcpIc1VBMZ1ufUx5S2Jctr7gshn2XtRs9e9pG0N8Nx2Yb6eH9miuso3sjtKNrIeQ4meJE+UeA6CangUoGoikmmdQQRLrOA8otSyLNex6n4jBLh7jyW5X6SVTEqk3HuN6NT42QfGpvtl+Xx59i/WC7P1wwpIVpDrYoTV8PXdLwbIS5C5JVOLKz0kdHaxTkZSpdYJzZ5kHUUh3u5H6dG+P5Sh7gdb7yANWAd9dwmevYeIjuS5DppamftYEdhdpzQu771HbLvvHM72Ve+mv1UDQ5zPTrl56lXxQUNR3h83D/E569ey3V6/pmc50iYx6qfPXE32WmlD20oKV2Z0uplVT9WTQT5KNdhJKmup/SboQplOi8qEKgr8D1E41ymAaWXXLmK/Zc1tXAZnX3uxWTf8cOfkP3z7/6I7Cf2cGzAhjCX0eZ1rLEaHec6npzkMovH+RmHIJdyUY3nyVquo9ERft4Md/PzbFT5TZyY4vLKB5Q+CkBIaeuyk8pXlfIDmKxT9Z7mNIbHVd9N8z098hiP9/XtHOP3eLA3VIZhGIZhGFViEyrDMAzDMIwqsQmVYRiGYRhGlZwSGqrMFK/xhyZ5rVSUNqS1jY+PKY3Anr3sO6cmwX5NVLgttDSxb5kDk6zFAYDO1mayA47XuAPKpZCAtRidXazFmBjg2Hor1rBPoWCU9TcDWc5TU5fS16h7FBXHLS8sXmmt4evXNrAG/6BNPQAAIABJREFUS0ZZY9U/wedn+tlvVn2UNVvxSV6zH+vjWFfZ6CJjtAHoOcwxGQ92s0aqoYbT7FrB9xit5XZUp/Q/iVquo8bGNZyBEpdpZkL541Faj3CI/aIUSkqXEWYdR7KeG1FmhOs8V+SGG09wnenuPjHBWplUhs/fc3C2VnDvIY5lV5/gem9u4nbW0KycwaigYtk8+0CaSKlYdgFuB6WM1nZwevMxDU4/0sbakCvaLiD7qYdZb3lggDVWm9ZwG4grH3Ujkxw3DyNcpxefwbqNRx55dFaen9uxj+xmFV8wXOJrPnL/TrLXb72U7IDSz4wMs94mGOV2f+gwa/caGzjmWljpibZqX115FXcUTERpZyLKB186wHYorx5bwu02kON+XDyOsaSk9ItOtcNihtMIhnXgTTajMS7z1V1s3/C668meVlq+R+7lOmpV7aytg/0G1iqfd+MF1iPlp7hOx6f5mQjhOstkWcMbUDo45UIK+bTye5ViDZcE1AkAJrP8zCvklC7XqUJVvhxDMe7LqUnef3CcY51uOvNasl993XvJ/vS/fGlWHufjmG+oROQiEfmsiOwSkZSIHBSRr4vI5grHniEiPxKRKREZEZEvi0hrpesahmEYhmGcTsz3huqPAVwB4DYATwLoAPB+AI+JyKXOuZ0AICIrAdwLYBzARwEkAfwhgHNE5GLn1CcDhmEYhmEYpxHzTag+CeAd5RMiEfkagKcA/AmAd/mbPwqgBsAFzrmD/nHbAdwB4CYAn1vabBuGYRiGYZw8HHNC5Zy7v8K2PSKyC8AZZZt/EcD3ZyZT/nE/EZHnALwNVU6oiqOsq0gXeKUyroL9NSVYn/S00iBkS6xdWVXD+qe2KK/F7utj3zuTk7PjzGVVUUZLvEYdCLBeJVPkNffHd7GGSK8x16jYUbEk30MuzPeQVzHRYnVcJk6FaQsqKcp4hjVQSeV3xKnQgrkezt9gP/sMWVPLGoTz17Mvm948a3Pq4ny9H4H9tFRClA+kzna+qVWdrP2oSShfWmBdhAjXYWaCdQwpYQ1TdprbxbSKIbaygX2TJRpXkJ3PsR4pGFR+WVR8rJSSFCTq+PymRtZp7FW+bAaPcr8oRLhfjSnfNQCwqpPz3NDE7dAVla+sKLc7Ea7Xmkauk4lRpQvL8D3XJ1nbkS/OzuOx2K90dtp3VyrN6T2zh3V4NUq70t7OZZaZ5H6+tmM12Y0NXB4Dfd1kH+hjHQkAbLqQfRZFlM+g1FHWhlxwxsvJHlVxPY/0c71PjbHeJhblflNUzsF2H+HxcEv7Ws5fM5epU5qtdEH3ExVHTsXRq1HClECUFzyCRR7MSkk+v4TF+7QLar2O6osFNT4HVXDVmiiXWTGs/DwVOE/tSoN78aWse3t2F2vxtl1zIdmvuX4r2f2Dd5J99BmOHZjK8tiXV9pC5LnNFMD9WsfdS6l4jrkUl1dS+SJrVmMVALS1skY1qWIyosRppIsqrmeejz86yO0k0cm+ut5ww1vI3rD1ZbPytFgW/ZWfiAiAdgBDvt0FoA3AIxUO3w7gvGoyaBiGYRiGcbJzPF/5vRNAF4A/9+2ZaV9fhWP7ADSJSNQ5R1NyEXkrgLceR/qGYRiGYRgnFYuaUInIVgD/BOABADPfFM68u6v0XjVTdgztd87dBk/sDhE5jtgAhmEYhmEYJwcLnlCJSAeAH8D7ku9G59zMgubMAny0wmkxdcxxUTfFa/CZEPtYCraxPT7AmoL6OvaFk59mfVEwynagmVdCpw/xXHEoM1u3EYlxHuJqzb2o4gNmg7ymPDbJoqTpHNstyjdLoMhF2jjNGqfxcV6TjyRY61LfxR4tXJbzH1a+XYa7e8guqDXxZIrXvxuUD5HQUfaDksruJVsCKu5S/ew4cvOxup31PevXcRlIgX2tZKY5llNXO/sbKxZYu1dQPpOOHOF7mBjnMuto4/xEo1zmJeVPJ+BYT5TK8P8zjvQe4PQmOX5WV5Db8cTUfrLzKX6JnEhymQ+kuI11NHOdAkBTx1qyXYG1FZNpFcOryPdQCHK9ZlPcTiZUPEElj0RG6SRQnB0T7Fgc7GEtycFB5btsN+vkevoPk31lyzqyp5QPvMefYh9QZ1/GfqZWr2ety1e+eBvZ0fhsbUl8hH1hjSsfP7WOx5a8+u9pTze3m8Fe1mnFI3x+QwMXetsq9oF3+DCPrx31PLaUBnl8DOa5H7mQ8mkXV/7eHNdpTZDbWFb5gAonuU2kVJzRSGLxY0lRVNxN9agM6Fh/4PE+X1R6TKWxCojKk7reOReeQ/Z5T7KOrmeEx66JcdYetjbx2NeU4FiCkxM8FjjVrQpaY8ULTCgV+X5CYeV/rp3b1OYVPLZ2dM1u5+kCP8OyY9wXAyqTTWEen/J5zlN7O6fR0MbenmJRvn6hwPrJ42FBEyoRqQfwQwANAK5yzpX38Jma6Zx1ordtRC/3GYZhGIZhnE7MO6ESkRiA7wHYDODVzrmny/c753pFZBDAhRVOvxjAjqXIqGEYhmEYxsnKfJ7SgwC+BuAyAG91zj0wx6HfBPAGEVlVdu6r4E3CbpvjHMMwDMMwjNOC+d5Q/T2AN8J7Q9UkIu8q3+mcu9X/58fhfbF3t4h8Gp6n9A/DcwD679VmMprleZ9TIcpaG/k2OrIc1yii/P3Eang9Oar8CU32s44DcV5PdsOzJWE5pZ8Jt/CacUMTS8w6hNecnyuyliSttCiZGtY4dalCyKv4gvkC6xgGx3l9ukatefc/w3obaeEyr1e+ZOJK09WnfOHElGYrX2L90cjI42R3rOEV4+npdiyWrFr3T6lYdQHhMsmqMj56hONnFQt8vdY2XrNva2LNU0FplBpiKp7WEOtrogFuh6UQxzvcvZd1Z4/tYF9l0Tpu9yPD7COpIaL8sASVPzcVXwtK29JSy9cDgDrlriZXYq1IbbPSCo6wLmFimHVseRV/sDbB/URJEeGySpPF8pp5SQZYx5bMcbsbH+I2EAxyO29ZyXrMkUkVB6/IZdjRwu34md2sRYS6n7ES9yMA6N3NfbdBlVE6eQbZ3YOcRkuC2+2aTdz3g9NciCkVF7RjI5fZeZdwu50+wu2+qDSm2+9l/2dXXHk+2XV1rH3JTnGbKKiYloVpbvdZ4fMjcR4rSyXVaBdAwPE5OvRcSbhhOpUHZcIpLaGS0CIaYx1ZqchlfMW2V5B9x+1fJvvgQfYtdv65fL1VHayDO3S4l/On9Ed5Fauv4Hj8jqnYhStXsbbwnJext6TwNI9lw6N7oEmpeIEF8PhVLHCe0tPcTpXrR5RCfL2pPOd5tPQY2U1d7L/teJhvQjWTwg3+n+ZWAHDOHRKRq+F5Vv9rADl4AvYPmX7KMAzDMIzTnfk8pW9b6IWcc7sAXDvvgYZhGIZhGKcZi/aUbhiGYRiGYTDH4yn9RWdXP+saOiKsU+iqYa1HBKxtCWlXNWo9OB7m4xsaeJ6ZVtqcg6nZOgcEVHw/5TtlRR2vYQeivGZ9ZIzXvLM1yoeG0us0rmAtx96DrFtIhvn6/QfYb8mWDtYX1SZZZzGmtC6l9ew3RbK8vj05rmLxNbOGKq50aNNRLuPBjNL7RBbfNI/08z3GVDytlR0cRy1Rz/qWvh6OUSYqplhHG2vz4iHWCzUn+HqxGGtdJsZYCzOV43ZdynEdyjTnV4XTwugQawTicU4vomKaReNch4Ewa01yWV6d1zHiACCi7jmkYpSNT3KeAlA+gSKsJakJqTxHuM6iAdY9TIyzZgmlxbWT+iK3u7aV7O8n+ST7fGqvZV9fsVbWXNU0cH4vuobjNUYjXIc9Tz5I9pTy9zYxzv0AAFZ1cV9Ho9LeKX1kTvnTySX5+ECedW+ZCGvr1p+zhux3/cobya4P8j1t73uI7GKSx7pdjz1M9t5u1vv88vuuITsRYs3W6BTXWaKW21RC6ZEKykdSTpQmdgGocIkoKY0RlN8pV1AaqZDSbSl/aSWn/FoF+HoBFUd0wyaOCXn0LG5nA4OPkj3Ux+0yr8okUOQbHB3T/uDIRGsHX2/9GtZMnfdy1kw5ld7+XvbnFo3P7rdFJVSbVGU+oeLJTqrYqaEEj01Qfv4mx9j/WkOum+yXXVCVu0wA9obKMAzDMAyjamxCZRiGYRiGUSU2oTIMwzAMw6iSU0JDVd/BflM6V/NaaSHN88LhSdYIJKH0QrW8vt1Wp3zHrHkZ2XuGt5OdaFWOsAAUlI+LqXHWw8TX8hp7tsRr1kmlFSk1sx+rtStYu5Gq5zX2+gyvcUsDl9EqWUt294DS34Q4vxMF1qqsVXHo+o9yfLBgA2umUkFejy5N8/0/u4+1KjVZTi9cZA3XQpiOczsYUbqslUpb11DHWpBUkstkbELFH0zzGr5TPpRySqs3PMKapVjDKrKdEkXlc3y9Fe0qNt80+6l6YDcFLcDhAfbLUiyyPskp3zFafzSR5hsYHmX9EACM5/icSL2KGVnHuqxigOskGmddhHL1hTHlTy0a4Tync6ybCAYX52NoYoLzP6BiVB5R/nmKYaXtS7FeM6/uN5Nnbcpju9jf2qDS0WWneaxKNMyOn5iMsDavNsb3kBfO48Ywa64mpridjasyvv5V7OPoXb/0JrLXbNxC9tQUjxWFyDNk73q8m+wNLaz3+fHdPyV7fSv7CbzkLTyWRII8dqSy/DzIBvn+IwXud8XY4v1QFZXmyYHzUAqohut4v6h3FWo3isppkqgDYsr/WSnKZb5xM5fRM4/yeNl9kMeSUA0fH47z82VC+VPb181tpKtLPY9WbSA7qOKc9g2wnjWg/GxFapTeCUDfEb7Hg4fU+JviMnOqDoJ5tqe0X8K0ilnZyJrYUHh2fMHFYm+oDMMwDMMwqsQmVIZhGIZhGFViEyrDMAzDMIwqOSU0VJvXsn6nLsLrsf0TvP47nlNx67KsnYmMKV82XSrO0VHWXDm1hh8Iz56H1sRYSxFUgpq0WjNvVv5pYkpbEmjidf9sO9vhKOsEVjSzviaV4/RWr2L9zs6HWX8zMs33XBxme7pXrS/H2HdNQw3rGsLKV42Ls5+VaIC1Ipkp1gD0TS/eJ4gOTderYvVFHPshme7gPDQ0cjuL1yqtRo7veXSQ/TRlM9xOOtbxmn2Nin9YUK5tGlp17DwVo7KLdXTriqxb2PUsx8cKxTi9rHD+MplJsgNKY1Xfyf0CAKYLXI+pae5LCeX0LRRVGqi8is+ltCkR5X8sX2CNUUn5qslN8T3Mx3M9rGl65CH2C9W+hceKlZ2byF51AeuZ2qKsIRtTvnEKRdaudFy8leyJPPfTQwPsrwcAkOF2lgixX6m84762d2c32fUdnIfzzzub7F942xvIXrORfQxlVR0la1mbt+EcvqfJPqXPzHGdn9XJ/e7Bnz1FdqiVx5pLXn052TXqPUApxLbElE+o4uKjnxWc0lAVuR0WS8pvlIo5KVozpTRSmRKXkUvx2BIMcnrjk+y763Aft9vpSfYD2D2uYgPGuI10tlxA9uqV/Mzc9dxdZLc0c7sPqefP4BhrrlIp5dMuy/d3VGl4AeDoEX4GHBnk8VuUXrIuyX11dJTbwdEjPFas23oW2ZdfxlrBWHi2fnGx2BsqwzAMwzCMKrEJlWEYhmEYRpXYhMowDMMwDKNKTgkNVV0L6xQOPcOagp7D7PNifQtrPxoaVEy1LOsWjgzz9aJB1r6Ukrz+PT0ye/1345a1ZNcLr8EHppW2pI19xcgU+/zJBFibkp/mNW4dX7AxzHqfUC2vmY/neM27fiWX0XgPa6ZCvGSOg328Rh9s5jKpZTclyOY5/61RvuCoU/G+jvL1Q1EdO2t+UqqMc0rYsL+P9TYhFUdu/SXsb6dGaeXGhlnfkp1mX1qxgCoTFeetVGJdwfg4+1nJKT8pTc28pt/ewdqZjaqIjg6yjqG1nctchd1DIcXH1zawDrBQYh0HAERVmRWmWBsxlWa7VviaOmZZUmnvpjMshMsXuMwiwu04FFc3NQ+XXnM92es7OW5dRvmdOjTAdRya4Da2P8x1uLGJtYwtnUrvpGIdporcryU/u8yf7mGfb6ujyj+Z0ju2NXGa4bgaG6LcTh/6n++T3d72drJrlH5mWulhzjqf/fmkUzyWPfvj+8iur+N+Ma4Cx919+w6yEyoO6pkbuF+4BqXfVL7KJgeVuHIhKK1gSfWFgIrlJ+pJKjku45AaG9JD7O/sSO9estvbWfe2Z9cdZO/dx/ETnfINlkpxGR3s575+9pmc32CQ9Uhb1nK/aKjlsWm4j9v98CiPrX2D3CYHh3jszKdm69oKJaWPLHHfjqsYvsOjrDkdGeS+u2LlGWS/9v9v78zD7KzqO/793WXu7GtmyTKTSUJCFpawJWwqNixSKdYKWIu1uCI+tlpFbWNLa7W26lMXpFWp1KU8yu4jCFVRRERQxIAkIUCICUkmmUwy+3Lnrqd/nHfkfn9zJzM3F3InzO/zPPMk533Pfe95f++55z33Pd/7/W14C5UXt6sgSOF+ZRp7QmUYhmEYhlEkNqEyDMMwDMMoEptQGYZhGIZhFMkxoaEaUT5T23ex3w7G+DSyVcoEpIn319ewrqNfeYBUqDxOfT3sjzGoNAQAUFfGx6wTXg8OK9+RkQTrDLQ+Z3M/eyZVz+M17JZG1pX1NPAauvSp3FPDvD6c0nnumtiDaf8Ar4nv2MNr8Isr+fjtKjeTpFnXsH0bawYGlTYmDV4vb6wtTBsDAM3zOSaJBJ9DNsG+JodGOAbP7OA2Lm9n7UYmy/qh6mrWwcUdHz85zjqLTJj7REUFvz7r+PvNaIpfn1L5DmPKH21Fp8pNVcValf5+pcvTubHqWHeXVv5BAJBR+QZDyqOoXl23ikrWchwcZL2iA2tTwiHWSEH5UPWqsaC8vLD8W6tPY8+k1lbut/ffcg+V93Zxv6+qYn1Sdw/3sZFq1gLOX8LalN7du6jcspR1IG1tHZPaXNvEn6XBOOtXaps5Ztu7+D2qktwvli3kNm15hvt932dvoPIV17yDyq3trDVMjnIMFndybr7wqZwbdcumLVRODXKMw6Pcp+69+Q4q7zqHcwM2ZTk+5a38OWup5ms2E0LgY2hfqazSAobSKVVBjb9Krzg6xJqpWIa1egMvPEflHZsepPKIyseYjnMf2L2XdWSdK8+hcm0T94mdT/Dxj5vHeqUa5aOVHuZr3r2bP9e797I2cCylAhjTnnuACI+vmTSPJd0qR+/wCI9FnR1rqXzp5W+j8poTT6HypGuoEy4eAfaEyjAMwzAMo0hsQmUYhmEYhlEkNqEyDMMwDMMoEptQGYZhGIZhFMkxIUqPp1jsu3TZMipnEiwuK4uyUDU1ygK5AZWkNprm8u4xFqft3PcClSU0eR4aK2NB3cJKFrsODfExm1tYGLrz4U1U7h1k0XpVJQv0kvUsjE8cYhGiy6iYKG+7vXtZ2LpwATtzVkbYWE7KWASpcmFiTAmm22rZVO20ZSwk3RXlmEbjyjw1UljSWwCoqGVhaGKIhaVOCd+7h1h0PfjEM1R+4fldVG5t4H61tJ0NDzvaOHnxuEqQfVAlBI2Vcx/IZrhfDSc5pok0tzemEgmfdByb8Wknz54a/nHFuEoannDcZ4bDk81VU+qzOKoSz6aVOHd4jPvx0Ah3nEgZX/eKcm6zq2XxaqUS2ifik40wD0cmy9ckWs4xfL7rAJV/9cBjVF577pn8/oP8+pE4H/9QiPvx5if2ULkzwUnLO5dMFuvGK/gcqzMcg2d3c+Lcigz3s0Oj27g8zD8UqFCf9R898hCV205iUflVb+XkyckQj33ZJhaBr/2TV1FZG8Y+tG0rlZvXsiB65Xz+IYHKfYzmDr4GFVGOYctxyqV4Bogy7sxGWPSdHOXPJlSS65RK9t614xdUjjjuFx3z+UdBTzz+LJUHe/geNpjg9gx0cXsrqng8f/Vrz6NyfT2/vlz1kZoMG3MOHuLzjShBd3yIjzfYz+Uxx+XsWJ6pR4h/2BWN8Y8NGlv5RzwnnXEylc888zVUPn41i9AjMe6nApUAO6oyXB8B9oTKMAzDMAyjSGxCZRiGYRiGUSQ2oTIMwzAMwyiSw2qoRGQNgH8GcBqANgBjAJ4G8Dnn3D2q7ioAXwBwLoAkgHsBfMg5x5mLj4DECK9tJkd5fdeB11qTGdaaDCiDMBfn14vw8Qf6WV/U06OMP+t4LRcAEOI63QNKU1TJepVFbaxDOOM0Xg8+9PCjVI7WqcSRKdaupPbx+w8nuP78Bo7R4g42dkuDdRdZnaS1gXUNqVE2MDzwAq+RN65lTUFdG6/p1+5jI9NF81nX0ZctPDlySK2J16nEuTXzuA3jw9wP+vazwWF/gvvR8AGOyZ5e1q0tWMT168p1smPWWDU3sXFocoyv6WiWhW/RJMdUJ7lNpPj9s2luX0Mda2/iKTZ7Tag+VVM5uZ+PpfiY0XGl5RvnNvb1svYjm+D3iPIlQ7xPH4/PqbaK9THpbGG6h5jwNSxr5Guy8qQTqXzrD9hUMrKDTSlPrGKTy7jSlPUPcPvSlayVySiD2wfv/s2kNi9YwzqrlNJH7u3imI32c79pbePP+tgwmwZX1bMuLpHk8oHn2Ug5oQwQnePbiEAlvs0os9cO1lh1XHAqlS+89FIqh+o4ZpVKd1eu3q43yfGIK6PmmZDWBrMq4fLWp7gf9Bzk8bAerB3ctZmTGx+/mpNoL23naxQN8Xg4NsgxLCtjE+PVJ3RSefHSE7g9KvF5dTlf4zWrWX8ZGuf7wQM/ZY3v5ud2UTlTxmPJSJLbG6nnsaSphcdiAKhvZK3bkk6+Ry49nu+RixdxzBqVWWk4xv3SKRPiVIjvMaFM8Rqq6UTpiwHUAPgWgH0AKgG8CcDdInK1c+5GABCRRQAeAjAIYCOAagDXAjhRRNY55yZbLhuGYRiGYbxCOOyEyjl3H4D7creJyA0AfgvgQwBuDDZvBFAF4DTn3O6g3mMA7gdwVU49wzAMwzCMVxwFa6iccxkAewDkrmG9CcAPJiZTQb2fAHgOwBXFNtIwDMMwDGM2MyMfKhGpAlABoA7ApQAuBnBrsG8hgBYAj+d56WMA/rjoVoZ5xbBcJVYcifBpdK5kHQQO8Zr6pt/8kl8/ohI5xnme6SJcDg1NXmvtVdqP5gZuY20Ta5D27+RkmPOUp9CypbzGnqnlNf3EIOt/du5j3xNRSWwbWjlhaVUVa7rivdz+gRHWkaUz3L6UShS8YJj1OTFh/dCmR9mHpXsL+1B1rmYtS3lt4QlNoyFex29q5HMMhVQCZuVp1KSuUayCj9ejrvHwCOsqNnezbm60dyeV62t2U3lRO1+Ttjo+5+Z5rJOoqFfnoz69Xb2s4+gf5j4RUz4rI6Oso4iF+Xzrmvj9/TFZWzGm+llaeW/VVHPC5bC6RqEslyvLlO6tnD9HiXGO+dhAYX5lWdVv4yph9nmXXsD1Izz2PPwzHuaUpAzd3exjtXc76/I6W1mr0lDJuo/NKsE2ACw6xPqXiiqOUV8X9zsX4/r1KrF6cg+Pd5kQ61naGnnsaV3M/bRCfQ+PK/+ybC1f0zLlzbVi5Qo+/vGslamoZv2QS/PrI0rfmRTWhzZU8FiZ1kK9GZDNsBYwqfSJ3Upv+ezWJ6ncztZeiFVwjPpUku9fb2IfqL37ledcHXtxdXQspXLrPNYoJYT1Qc88/RSVI8Kfm1QfJ2Ne1MR6ptal7On08008Diw/nb3KTl/JPoRNzawXXbCQ+z0AVCp9ZEMLjz/VtTw+l6n7siivMKdkwE75R4pT/UL4c3UkzNTY8z8AXB38PwvgLgDvD8oTd8L9+kXBtkYRiTnnEnn2G4ZhGIZhHPPMdEL1RQB3AFgAv4QXBv5gOz3xNT/fhGk8pw7tF5HLAVxeSGMNwzAMwzBmIzOaUDnnngEwkZfj2yLyYwD3iMh6ABPPqGN5Xjrx7HnSc2zn3O0AbgcAEXF6v2EYhmEYxrHCkebyuwPA1wCswItLffPz1JsPoK/Y5b7QPF4PrqzgtdSz1nKuqFPX8Xrunu28Pj3u2ANpxxbWGKT7eX05rHQe45i81vr8C6yPWdjAOoHfqxxhYWVzEqrjNpy37nQqb3pyM5X3g2Ow9CRetM+oNfLKaj5+WOnOhsENqorx5WxoU2vqC3lNvFxpBsZVPsSKFL/fuFq+TqhciJFo4evZ2TCvkYdjfIzEKOsi4koA46D2q5xo5fXsh1O/gLUl8Tj7/wwo7UpE+Z4cUN5fe/b1ULlmP2u8Who4yPOUd1e0guuHkiqflvYWiyqtoNLxHRxkDRYADI9xzFJKT+PUMcfjrAVR0g4MZ7hNFVpDpXRu9dWsl8m4wvzKJutpOEb1Ub5mf/b2N1N53RnnUPmu73+Pys/tZ93c8BArIWqXdVB5RL3/kkb2ZAKAaAvrY5zja3DO+edROTHA+sr0oNKAKn+xxlbWs7SsWk3lszecS+VR5ddTVcbXqOwQD/eJKPerSjX2VCld22CKtYrlwvUjKsekKLGMso9DOKkSmc6ArPIyTMU55quOZ41QawP3m+FuzsUno3y857tYozoyzveH9uPYc+nCs9ib68Bezgm57yCPHRXVrKML6RgN8dgzv5l1c1mVD3ckw+e//uINVD77YpZKN7dwn6qq4vho/RMARKKqzSF93bm+zlc76bGM1kyFlH+aGrtCL8FjnSN1Sp+Idp1zrgvAQQCn56m3DsCTebYbhmEYhmG8YjjshEpEJtmZikgUwNvgl/GeDjbfCeASEWnPqbcB/gnW7S9Zaw3DMAzDMGYh0y35fU1EauFd0Lvg089cCWAlgA875yaeG34aXmD+MxH5ErxT+kcAbAbwjZej4YZhGIZhGLOF6SZUtwJ4J4BrADQBGIZ3Sf+Yc+7uiUrOuT0i8hoAnwfw73gxl9+HXwq7hJYG9qOobOUHZ40NvPbaq/Iq7XyBdQ0rl5xJ5Y4OXh9+8pecp2nHHvZMqq6ZvNiT7FswAAAN6UlEQVTaM8pr0r/YpnyWGnkNubaBNUjNao27Jc6X5hSlYQol+f0aVrC+J9nDa+q7fs+6saEBviyxMdaWLF3Pa/gLF7HuIlPF7cumlRdNPx9/+VrOLbVvgDUEmTHWTYzFCs9WNDTOurH+IdZOaNuRmFrHLytjbUa5U/kJq5T/mPJgCk/Knce+KtmQ0p6o/WGVI21siM+nN87eNYlePl5tHeuLtC+LhFQ+rzD3yeoa1k0cOsT5yAAgnmR9TqicNU9ppWtIqFx1EeWRpKR1qFD7M07rwLi+CxWotVP9NBJl/Y4kVb4vdT7HL+bcfR947zVUPmk19/P776NEEyhT2sdaJT5MdU72uNv2u19RuTLCfmQdy9jHqW+Iz6HScT/a8IbXUXn9Wazbamznsamhhi9SJskxS6l+nebmITXGY5tUqc92nNsbcfy5TELFRGmyYkoilSzn11dmJueNmw59FUYzPDY0q/yIkTCfQzTFjeoa43JbO/uRHXcy35MqKtmTrq6Zz6Fh0TIqO5VTs7JGaVLTfL/o3cG+VIOD7Ku1r4u9ymLNPLZcuO4iKi9czD5ZonRyZRH+nIbz5M0Tp/0fOeZOjY8hRNV+PmZY9cusOv4kjVW2cL8yzXSpZ24BcMtMDuSc2wrgomkrGoZhGIZhvMI4UlG6YRiGYRiGEWATKsMwDMMwjCI5Uh+qo8rSRl6v3pPl9eIDvewzlRhhXULfftZ9NNezTqOzhdenY+tZB9GbOEjlwX2TtSUJ5SeTVb5M8WrWEUSSLAaJlLGepUZ5Ai1Zwr4nZSleE+8aVG2KsHalqozbl0zx+zfOZ43UilbWZD17UOX2G2GvVlE517Rn0sFB1nDVtXP99PgAld3Bwrum1ijV1rNeZUhpqspUrr7mFtYtxEdYszQeVzoF7kZIp1kbElJKjGr1fk4dL6Q0XGGVw3JM6fRiUT7eWFz78ygtTTVrpOrq+BplU8o/KMntA4Co8vrKqn7k1DmX6fxayjcqPc7vOTDO/XhcDVGJIdaphTKFmcfE0vz+/eX8/tVKHxTJ8PkOl/NYUhnlXIWXXHYxlVefwJqrH9//IJV3bP09lU9ezv51ANBWy9dtcFhp9ZbweLXmDI7ZqjWsQT3p1PX8+pgauxz3+3SCY5Ll5iAR4Wtep6QoGaVpGk/xBydaxmNjNqNy9zn1uVGaLEzSTPE1FTX2zYRoiM9ZDSVIj/F7ZMBBOTjIn4vqeaxzW3XyGVSuaVW5W9XnJJlQ47VqUCjMureQ8vIa6OF72JAa20bGOEbLTj6Lyh2r1P1HeZml1Oc8orwaw8okSmusAMBluU5Wa6aUL5WL6Fx86vVq7BDRHVO9f2GWdnmxJ1SGYRiGYRhFYhMqwzAMwzCMIrEJlWEYhmEYRpGI9nYoSSMsObJhGIZhGMcOtzvnrsjdYE+oDMMwDMMwisQmVIZhGIZhGEViEyrDMAzDMIwisQmVYRiGYRhGkdiEyjAMwzAMo0hsQmUYhmEYhlEkNqEyDMMwDMMoktmSy28TgB0AFgHYO01d4/BYDIvHYlg8FsPisPgVj8WweCyGU7NMb5gVxp4TiMht2ijLKAyLYfFYDIvHYlgcFr/isRgWj8WwMGzJzzAMwzAMo0hm24Tq9lI34BWAxbB4LIbFYzEsDotf8VgMi8diWACzasnPMAzDMAzjWGS2PaEyDMMwDMM45rAJlWEYhmEYRpGUfEIlIjER+YyI7BORuIj8WkQuKHW7ZiMicoaI3CAiW0VkVER2i8htIrIiT91VIvJDERkRkT4R+V8RaS5Fu2czIvJxEXEisiXPvrNF5GERGRORbhG5XkSqS9HO2YaInCoidwd9a0xEtojI36g6Fr8pEJHlInKLiOwN4vOMiFwnIpWq3pyPoYhUi8gngvGsL/i8XjVF3RmNeyISEpGPishOERkXkadE5C0v+8mUiJnEMIjJVcHnek9wj9kiIv8gIuVTHPedIrItiOF2Efnro3JCs5TZ4EP1TQCXAfgigO0ArgJwn4i81jn3cAnbNRv5GIBz4IWCTwFoA/B+AJtE5Ezn3BYAEJFFAB4CMAhgI4BqANcCOFFE1jnnkqVo/GwjiNNGAKN59q0F8FMA2wB8CN6P5VoAywFcfBSbOesQkQsB3APgCQCfBDAC78myKKeOxW8KRKQdwGPwn88bAPQBOAvAJwCcBuANQT2LoWcegOsA7AbwOwDn5atU4Lj3rwD+DsB/A/gNfMy/IyLOOXfLy3QepWQmMawE8A0AvwLwVQA9eLFfbhCRP3I5omsRuTqodyeAzwN4FYDrRaTSOfeZl+9UZjHOuZL9AVgHwAG4NmdbOYDnATxSyrbNxj8AZwMoU9uWAxgHcHPOtv8CMAagI2fb+UGs31Pq85gtfwBugb9hPQhgi9p3H4B9AGpztr0riOGFpW57CWNWC6AbwF0AQoepZ/GbOjYbgzisUdu/FWxvsBhSXGIA2oL/nx6c/1V56s1o3AOwEEASwA052wR+MrYHQLjU51yKGAIoA3B2ntdeF9Q/P2dbBYBDAH6g6t4M/wWrodTnXIq/Ui/5XQYgA+DGiQ3OuXEANwE4K/gmZwQ45x5x6umSc247gK0AVuVsfhN8R9+dU+8nAJ4DYCZtAETk1fD974N59tUCuAB+kjqUs+vb8IPFXI7hXwBoBfBx51xWRKpEhMYRi9+01Ab/HlDb9wPIAkhaDF/EOZdwznXPoOpMx703AIjCT8Am6jkAX4F/CnjWS9Hu2cRMYuicSzrnHsmz63vBv7n3mNcCaEJODAP+E0AVgNcfaVuPZUo9oToFwHNqwAD843AAWHuU23PMISICf4M7FJQXAmgB8Hie6o/Bx3xOIyJhAF8G8HXn3OY8VU6EXw6nGAaT2Scxt2N4PoAhAAtF5Fn4m/uQiHwlR2dh8Ts8Dwb/3iQia0WkXUTeDOAaANc750ZhMSyIAse9U+CX+bflqQdYbDVtwb+HcrZNxEjH+7fwXwrmZAxLPaGaD/+tTDOxbcFRbMuxypXwj7BvDcrzg3+nimujiMSORsNmMe8FsBjAP06xf7oYzuV+uRz+Rv99AD+CfyrwP/Ax/UZQx+J3GJxzP4TvexfA69B2wy8/f9k597dBNYthYRQy7s0HcCB4KqXrARZbzUfhv0T9X862+QAyzrme3IrBhL8XczSGpRalVwBI5Nk+nrPfmAIRWQn/iPVReP0F8GLMpotrvv2veESkCcC/APikc+7gFNWmi+Fc7pfV8OLVrzrnJn7Vd5eIlAG4WkSug8VvJuyC1+zcCX8Dej2AjSLS7Zy7ARbDQilk3LP7zgwRkY3wT6Xf55wbyNlVAa9Dy8ec7Z+lnlDF4cVymvKc/UYeRKQNwL3wv2i5zDmXCXZNxMzimp9Pwf+q6suHqTNdDOdy/CbO/btq+3cAXA2vPxkLtln88iAifw6vG13hnNsbbL4r0KJ9RkS+C+uDhVLIuGf3nRkQLEN/CsBNzrmvqN1xeBF7PuZs/yz1kt9+vPioNpeJbfuOYluOGUSkDv7xaz2A1znncuM08dh6qrj2Oefm6tOp5QDeA+B6AAtEpFNEOuEHgGhQbsT0MZzL/XLi3LWgeuLRfwMsftPxPgBP5EymJrgb/unfKbAYFkoh495+AG2B/lTXAyy2EO8F+W34L+3vzVNlP4CwiLSo15XBi9XnZAxLPaF6EsCK4BctuazP2W/kEAh/7wGwAsAlzrmnc/c757oAHIT/aaxmHeZ2TBfC9/nrAezM+VsPH8+d8D8R3gIgDRXDYLBYi7kdw98G/y5U2yc0Ewdh8ZuOVgDhPNujwb8RWAwLosBx70n4iesqVc/uOwBEZD38L/seB3CFcy6dp9pEjHS8T4cfY+dkDEs9oboDfmB5z8SGQDj4dgC/ds7tKVXDZiPBr9NuhV9Wudw59+gUVe8EcEmu7YSIbICfNMzl7OFbALwxz99WeGHwG+Efbw8C+AmAt4pITc7r/xJeQzSXY3hb8O871fZ3wU8AHrT4TctzAE6RyRkO3gL/C6mnLIZHxEzHve8DSME/KZyoJ/BPYroA5LMOmBOIyCr4p1K74L+wT7V09wC8dOIatf0a+CX/e1+uNs5mZPIPHY5yA0Rug7+RfQHe0POv4L9RbHDOPVTKts02ROSLAD4A/4TqNr3fOXdzUK8d/tdDAwC+BD8AfwTAXgBnzNUlv6kQkQcBzHPOnZCz7VT4gfVpeL3LIgAfBvCQc+6iUrRztiAiNwF4B3wf/Dm86/LlAP7NObcxqGPxm4LAA+0BeDH6DcG/l8C7n3/dOffuoJ7FMEBE3g8vcVgAf9O+C36MA/yvIwcLGfdE5LPBvhvhndL/FP6HAVc6575zVE7qKDNdDOEn81vhnz5vhJ9c5rIj90u8iLwP/kdRd8D/4vdVAN4G71H36ZfvTGYxpXYWhdevfA5+TXYc3gvkolK3azb+wfvXuKn+VN018J18FEA/vINta6nPYTb+IY9TerD9XAC/hBdY9sDf/GpK3d5S/8EvTf0T/LfYJHzKqA9a/AqK4Tp4J/T9QQyfhb+JRSyGeeO16zBjX2dOvRmNe/CrM38fHDcB//T6ylKfZyljGPxNeX8B8M08x3w3gGeCGD4Pb5QspT7XUv2V/AmVYRiGYRjGsU6pNVSGYRiGYRjHPDahMgzDMAzDKBKbUBmGYRiGYRSJTagMwzAMwzCKxCZUhmEYhmEYRWITKsMwDMMwjCKxCZVhGIZhGEaR2ITKMAzDMAyjSGxCZRiGYRiGUST/D9C+fROxmPSTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: frog (6)\n",
            "Image #1: frog (6)\n",
            "Image #1: frog (6)\n",
            "Image #1: frog (6)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: automobile (1)\n",
            "Image #1: deer (4)\n",
            "Image #1: cat (3)\n",
            "Image #1: airplane (0)\n",
            "[Step #0] Loss: 0.0155 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 140.9289s (total 64 images)\n",
            "[Step #10] Loss: 0.0160 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 1573.8732s (total 704 images)\n",
            "[Validation] Loss: 0.0163 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 2286.0712s (total 1024 images)\n",
            "[Size of Perturbation]\n",
            "Average L0 distance (the number of changed parameters): 3059.7041015625\n",
            "Average L2 distance: 1.7888274788856506\n",
            "Average MSE: 0.0011526371890795417\n",
            "Average Linf distance (the maximum changed values): 0.09304240718483925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37FIjan0DMtx"
      },
      "source": [
        "#### <b>Adversarial Attack Example 3</b>\n",
        "\n",
        "* <b>Attack method</b>: HopSkipJump Attack\n",
        "* <b>Options</b>: 50 iterations + start from the target (targeted attack)\n",
        "    * Each iteration includes a binary search step, gradient approximation step, and geometric progression step.\n",
        "* <b>Images</b>: 1,000 test images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "O4-u3RFDDMUw",
        "outputId": "291b4ab0-fe9d-42dc-a9ea-547550108a54"
      },
      "source": [
        "import time\n",
        "import random\n",
        "import foolbox as fb\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "model.eval()\n",
        "start_time = time.time()\n",
        "\n",
        "fmodel = fb.PyTorchModel(model, bounds=(0, 1))\n",
        "attack = fb.attacks.HopSkipJump(init_attack=None, steps=50,\n",
        "                                initial_gradient_eval_steps=100, max_gradient_eval_steps=100)\n",
        "\n",
        "running_loss = 0.\n",
        "running_corrects = 0\n",
        "running_success = 0\n",
        "running_length = 0\n",
        "\n",
        "running_l0 = 0\n",
        "running_l2 = 0\n",
        "running_mse = 0\n",
        "running_linf = 0\n",
        "\n",
        "num_images = 1000\n",
        "\n",
        "for i, (inputs, labels) in enumerate(test_dataloader):\n",
        "    num_images -= labels.shape[0]\n",
        "\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    # generate \"random target instances\" that are correctly classified.\n",
        "    starting_points = torch.zeros(labels.shape[0], 3, 32, 32).to(device)\n",
        "    target_labels = torch.zeros(labels.shape[0]).long().to(device)\n",
        "    for k in range(labels.shape[0]):\n",
        "        starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
        "    outputs = model(starting_points)\n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    while True:\n",
        "        condition = 0\n",
        "        idx = torch.tensor([], dtype=torch.long)\n",
        "        if target_labels.ne(preds).sum() > 0: # to be correctly classified\n",
        "            idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.ne(preds)]], dim=0)\n",
        "            condition += 1\n",
        "        if target_labels.eq(labels).sum() > 0: # to be different from the original labels\n",
        "            idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.eq(labels)]], dim=0)\n",
        "            condition += 1\n",
        "        idx = torch.unique(idx)\n",
        "        if condition == 0:\n",
        "            break\n",
        "        for k in list(idx):\n",
        "            starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
        "        outputs = model(starting_points[idx])\n",
        "        _, preds[idx] = torch.max(outputs, 1)\n",
        "\n",
        "    attack_criterion = fb.criteria.TargetedMisclassification(target_labels)\n",
        "    _, adv_targeted, _ = attack(fmodel, inputs, criterion=attack_criterion, starting_points=starting_points, epsilons=None) # adversarial attack\n",
        "\n",
        "    outputs = model(adv_targeted)\n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    loss = criterion(outputs, labels)\n",
        "\n",
        "    running_loss += loss.item()\n",
        "    running_corrects += torch.sum(preds == labels.data)\n",
        "    running_success += torch.sum(preds == target_labels.data)\n",
        "    running_length += labels.shape[0]\n",
        "\n",
        "    l0, l2, mse, linf = get_distance(adv_targeted, inputs)\n",
        "    running_l0 += l0.sum().item()\n",
        "    running_l2 += l2.sum().item()\n",
        "    running_mse += mse.sum().item()\n",
        "    running_linf += linf.sum().item()\n",
        "\n",
        "    if i == 0:\n",
        "        # Display the first 4 images in the first batch.\n",
        "        print('The dimension of an image tensor:', inputs.shape[1:])\n",
        "        print('[Prediction Result Examples]')\n",
        "        images = torchvision.utils.make_grid(adv_targeted[:4])\n",
        "        imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[:4]]) +\n",
        "                      '\\npredicted labels: ' + str([int(x) for x in preds[:4]]))\n",
        "        print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(labels[:4]):\n",
        "            print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "        print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(preds[:4]):\n",
        "            print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
        "\n",
        "        # Display the second 4 images in the first batch.\n",
        "        images = torchvision.utils.make_grid(adv_targeted[4:8])\n",
        "        imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[4:8]]) +\n",
        "                      '\\npredicted labels: ' + str([int(x) for x in preds[4:8]]))\n",
        "        print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(labels[4:8]):\n",
        "            print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "        print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
        "        for j, label in enumerate(preds[4:8]):\n",
        "            print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
        "\n",
        "    if i % 10 == 0:\n",
        "        cur_running_loss = running_loss / running_length\n",
        "        running_acc = running_corrects / running_length * 100.\n",
        "        running_asr = running_success / running_length * 100.\n",
        "        print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, running_asr, time.time() - start_time, running_length))\n",
        "\n",
        "    if num_images <= 0:\n",
        "        break\n",
        "\n",
        "epoch_loss = running_loss / running_length\n",
        "epoch_acc = running_corrects / running_length * 100.\n",
        "epoch_asr = running_success / running_length * 100.\n",
        "print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, epoch_asr, time.time() - start_time, running_length))\n",
        "\n",
        "print('[Size of Perturbation]')\n",
        "print('Average L0 distance (the number of changed parameters):', running_l0 / running_length)\n",
        "print('Average L2 distance:', running_l2 / running_length)\n",
        "print('Average MSE:', running_mse / running_length)\n",
        "print('Average Linf distance (the maximum changed values):', running_linf / running_length)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The dimension of an image tensor: torch.Size([3, 32, 32])\n",
            "[Prediction Result Examples]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VOK9yvIT1IzN736or3v74WQB/AmAn2kxAACCEUO1GXkvwq/Hn33V6oMexLr8ef/7J4mQqPvZ2AP8TUbDtt3QhH8dZFf6EynkqcTKAHyIKpvp3ANYhCtR7nZm9MYTQLnDu6xAFsb0OwN8imqAs/iX9FUTBhe9HpMEoIQqq+zEAlwD4BTnWNQB+B9ES3icQhfh4dbxvBlEg4GUxs+cgCqQ8AuD7iMxAewCcgyjQ7QcBbEekCfm/mvJe5PamY70s/v1ifbYB2AjgZwG80sxeEEK4tWn/0xG14WjcJrcDOA3Af6BLS0KIAh83ANwEYA+AQQAvBPBRABehtV0X+WtEbvP/hkgL81JE9b/czC4LIZSa6nEBoiDUI4ja8guIAi+/BsANZvbaEMJRl57itvsaokDWX47LOgLgbETBpj/QtO8WRMatO0IIW1bUChEvQbQcdg2ARjxxPRdRX7s5hPDDVRxrVZjZGgBXIdJ0faYLh3y86vLC+PPrbbZdh8hQ94UAujLpdZwVE0Lwf/7vhP4HYAuAEP/7C9n2HEQTmyMABpq+vzrevwHgZW2O+f54+8cAJJu+TwL4h3jbq5u+f1783TYAI03f5xBNUAIiHQ3alOHqpu8yiG7MAcAb25Rro6S363Gbtg3H9T4E4BzZdi6iG+mt8v0347zfLt+/uqmNr26X31Ha8Er5/tQ2+yYQLRMFAJfItmvj7w8hCrbb/Jt/j7f996bvU/F5KCEKmNt8rPWIJkb7AGSPVtamYz+rTXnHluiDLeci/v57S7TRB+LtH0b0h0CQf/8FYPw4XTfvjvP4RJeOd9zrAqA3PtbcEtvH4u0Hltjecs35P//XrX++5Oc8lZhBFD/vUUKkC/k0IkHua9v85kshBPpLN152+m0A+wH8bgih3nS8OiL9RgDwpqafLS4x/EkIYapp/xKiG9dKuQrRzfnLIRbSSn12r+JYb0ZU7/eFEO6R49yNSMx7vpmdA0RLZIieMjwCQEW9X0J0Q+yYEMJDbb5rIHpCBURPntrx0RDCDvnNuxBNin+pab9XAjgVwMdCCFTmEMJeAH8OYC2AF62wyC0xF0Nr0N89iJ5crfSYi0zEn+9C1KcuB9AP4JmIJrc/BeBzqzzmspiZAXhrnPxElw77eNRlcfl8Ke3h4vftBPiOc1zxJT/nqcStoX2MvO8B+EUA5yN6CtLMzW32PwPR0s6DAN4b3XtaKCK6gS5yQfzZbtJxA4B6m+/b8dz4sxvLa5fGn88ys/e32X5G/Hk2gHsQtQ8A3NA8iWziewCu6LRQZjaK6Kb7CgCnIHrq0MyGJX7a0rYhhIfNbBeALWY2FEKYxmP13rxEvU+PP8/G0d84+zSipdGbzOyzAL4L4AftJrUh0gbdd5RjLcXiH7U1AD8THhNS32Vmr0W03HyFmV0aurv892JEbX9r6IIYPeaJqovjPCnwCZXzVOLAEt/vjz/bicP3t/luMZjx6Ti6DqOv6f+Lx24pQwihZmb6RGMpFv+y3rPC/Y/GYj1+ZZn9FuuxZB1i2rXVqjCzIQA/RqR3uxnAPwOYQnQTHgLwdkSi4nYcrVybEZV/Go/V+/XLFKfvaBtDCF+wyNfonYiegP1aXIdbALw7hPCtZY6/Eqbjz9uCvJUWQiiY2TcA/DKiINvdnIQsitG79XQKeHzqsvgEqu2LHk3fTy+x3XGOGz6hcp5KrFni+0Vn6nbLBO2igy/u98UQws+uMO/F36wB8HDzBjNLIdJ2rGS5bvFGsNRTmtWwWKZnhRDuXMX+y7VjJ7wV0WTqA0F8mczsUkQTqqVYg+gpx1LlmpHPV4cQvnzsRQVCCF8D8DUz60X0csGrAPwGgK+a2fm6lHoMLNZnqQnA4lts+Q7zeRQzm0CkieuWGH2R416XEMKCme0BsMHM1oUQ1MNt8enjA8eah+McK66hcp5KXGBm/W2+vzL+vG2Fx7kP0U3hubZy35zFN+XaLYldhkjMvhJ+FH++fIX7149y7MVjXb7CYy22z2Vm1u6YV67wOEfjtPjz39tsW245sWW7mZ0CYBMiMfjijXy19V6WEMJCCOE7IYR3APgQopcHVnqOjsa3EU3qz1nCMuLc+PORLuS1yFsQvfX5v5dYIj9WHq+6fCf+fFmbbS+XfRznccMnVM5TiUEAf9z8RWxB8CZETy2+uJKDhBBqiN7uWwfgb8ys5S9qM1u3KOaOuTb+/CMzG2naL4foraeV8hVEb+79jJn9n23y3ShfHQYw3q6MAD6JaGL4PjO7uM2xEtYUuy7WBn0L0ROkt8m+r0YX9FOI6gbI5MzMzsfy4v23m9nmpt8kAPwFonHsk037fQmR6eNv2RKhTszsUjPrOVpmZvZT8dNFZfEJXqFp33Ts93XqMnUgYpH9VwCcBHk6Z5F/2ksRnUN9cWIxlMyW1eQnYvSjek/ZYz5p167k2I9jXf42/vwjMxtuOs4WAL8FoAzuD47zuOBLfs5Tie8DeKuZXQLgB3jMhyoB4NdCCLOrONYHATwLkYngVWb2HUS6pglEywrPB/BHiMTcCCH8wMw+hujtwLvN7PN4zIfqCFYYXiaEUDGz1yN6K+ozZvZriJ645PDYW2TN1+23EXk3fd3Mvo/oZnJHCOErIYTDZvY6xCFuzOzbALYieoqwCZF4ezQ+9iK/hUjfck18E7wD0VOl1yK6WV61knochX9GJEi/xsxegEj4fzqipbQvIDpfS/EDALfHAvEZRDfoZyFyx/7zxZ1CCFUz+1lE/lNfM7MbEflpFRDV+yJEgux1aJoUteFvEC0t/QDRRLCCKHzKCxHF5vvXpn03ALg3/n7LMm2g/BaiFwI+Ens33YZoUvsaRE8g3xoiV/1mmgXgq+GFiM7nrSGEW5bZ91jyOO51CSHcaGYfAfAOAHfG11oGUd8ZAfDbquFynMeFJ9q3wf/5v07/4TEPoGsRTTq+hGgSU0B0E35pm99cjWX8aBDFOfsFRJOWKUQ31D2I3tp7D4BNbfZ/G6IbaxnAXkTOzYNo4xd1tDIg+iv/fyFaHqkgehJ1E4D3yH69AP4fRPqs2mI7tGmfjyOavJQQGVXeB+BfALymTd6nAfg8oqcJC4gmWK9cSZvJcd6P9j5U5yAyypyMj38Loqcmj55H2f/a+PtTEAnE74vrsQeRieTAEvlPIDIRvTvuC/NxG3wewM8DSB2trADeAOB/x7+Zj9vtbkRu4ONt2njVPlRN+4wjeiq6Iz7fhxBNhC9us+8wosnJDcdwrXw2Ls+vrWDfv473ffEq83i86nI1ohccFgDMIXoL9FUr+I37UPm/4/LPQminyXWcE4cml+p/CiFc/YQWxnEEi0IE/VcI4couHe9nEP3R8MqwjNt7h/ncCqAaQrjkOObxuNSlKb+rES0HviWEcO3xzs95etFVDZWZZc3sz8xsr0Xxx24ys5d0Mw/HcZwTkCvssViLnb4teQWA24/zZGoQ0XLqavR/x8JxrwsAmNlX44mta6uc40a3NVTXIoqNdg2ix+RXA/jPOF7YDV3Oy3Ec50TgA5Ke7+RgIYR3dvL7FeYxg5W/mdpJPse9LjGfAdBsYHr7Ujs6zrHStSW/+C2imwC8K4Twl/F3OUSag8kQwvO6kpHjCL7k5ziO4zzRdPMJ1esQiQsfdd4NIZTM7B8AfMjMNoUQdnUxP8cBAITojZ628WEcx3Ec5/GgmxOq8wE8EFpfTV+MlfZsAG0nVPHatuM4juM4zonArSGEC5u/6KYofR3ae+0sfre+i3k5juM4juM8UTykX3TzCVUekfeOUmra/iixeeFywUsdx3Ecx3Ge9HRzQlVE+yjxuabtjxJC+ByAzwG+5Oc4juM4zolNN5f89iFa9lMWv9vbxbwcx3Ecx3GeNHRzQnU7gDPMbEC+v6Rpu+M4juM4zlOObk6oPo/ICO5XF78wsyyAtwC4yS0THMdxHMd5qtI1DVUI4SYz+xyAD5vZBIBtAH4RUdDQX+7k2Gefehqly406pZNqQZTmpCVZolUPPI9s1Pl4ELPTmki8UkH2B5C0DOcJ3qda5zzrkkcjJXVqcJ0yiQalCwvy+ywfP5R5/5rUOSlqt1qhROlsgk2SSxXOr1wuUDrd10PpnHgs5zLcPgMj/bxDgo9fWeDy3H/vdizHS67kKEe7dzxM6b4+LsPgGD9MHRoZpvQVV7yS0s958Yv5eBWuw53f/hqlrTZH6XMuv5zS/RtPpnQl1Ch98OB+Sh+e5jafObiT0jC+nNev4Qgn133n21zerfdRes0o1z+f43MKAOmUdJwk97PKoWlK3/hNjigSjPt1scKHq+a4n1Zkh1SG+8lwLkfp+x5+pKXMzZz+ijMpnU1weYYGuE+UZrgfBhkx6w1+D6dRluukxudU659McH2txu0JAMV5boM6+JilAqclC+TyvZTukWuxWOE6Vit8vHqNxyaTazXfn5PtnC4vsDF8ab5K6WyG3ldCvcbHXyhyvw913p6Qc5IM3AAJSR/edQjLsfXuHfyF3HMaxuctK3mUanqP4POazHChsw1O6zlMSD+p1mV8T0ofkez7snzdJmV8DnJPq0r+SeMfFAu8f924z/TlOb9GnX9vcl0AgMk9qi73XW0Tk3tSoc5tUJJ7VF+S+33PYB+l909PUvriS57dUsbl6HbomTcD+CCAX0AURfxORNG/v9/lfBzHcRzHcZ40dHVCFUIoAXhX/M9xHMdxHOdpQTc1VI7jOI7jOE9Lur3kd1wo13jNHbI+XUuzRgB1WZ9t8HpzEN1HEqKxSvH2tGQfyq3z0HqOd0qKBimV5jyC6L5M1uhrsmZelzKYaKZMNFpV1X1JHZMNKY9oSYJxG5RrvB4NOX4osE4CfXxO6rJmXq+ybiORYuGbSGtWxMwMr4E3SnwOEtJNitNkjYbB4Q2UHl+zmdI33nwnH2+OT8qBh1i/k0hwmxTuHaH05E/uonTfAGuYBgZYW5LuYU3T4AYu31rRgG27j8uz56E9lN67Yzul5w9xoIN8b6uGaniC88iKGG/+4JSUmTVJCyXebqKfSTW4n6R6WY+TTYm2MN/O+m5phvu5E2SSfPzqAveJapX7fb3Cfapc5Oskm+MhNS26NtXiJGR7tdqqz6yKrqpRE42QaIpSWdFzVkXnJtdiQ+Usot9M6FgVuA0aFRkPRc/TEK1LQ/Q/QcqXSPPx+vpYq1gvc4HLNR5LGlKc0CpLW5Z/+Mfvcp4J1kOmMzK+Sz9qJI5ep0zg8S4loqZEkts8leBzmpX9Q1p0cODtWdEPpTJ8/LTeU0WY1pDrPMg9NS/axt4k/37fQdZWzkxzGgCC6IoTUoZUmtNB7nlIybWX4TLLLbNFl1ZqvfRWjT+hchzHcRzH6RCfUDmO4ziO43SIT6gcx3Ecx3E65ITQUKl3SyrN68O1wFoWE8+PiphyJGT9up6U9W0x4ajL8WqJVg+NjKn5lRzDVNclphpl9dISDZPoELKik0iKUCAlGqeGcRtURHcRityGhaq0KWeHhlS3pn5C4sOVqvDx5o9w/rkBXu+uyP4rIVsWbUeSy1CUNfdUD3f/iy6+gtK3iibqBzfdQemelHgiic4g1WAN1dzW7ZR+eA/74STFXygh+qKkiF1yGdY4bTmFIz/19PDve4dZ/9Q7Mk7pwh6ODmXzrefg8NxhSs/Pcb+bLbI2wsqs7UCVO1JDtByJKnesao6310TrURf/n+WoFtQHiutYnec2LorfW1r0l3LKUNOxRnQdIl1EpiZayWKbfi/Xar2imik+z5mMtGFRNEbinZXMcb9NG+uB6hW+uKvilwbVnuh4LeVJLPDv1fcqK3rKpGpnRF/EUWKBqtSvJB57K+HundzP6w3JRDSmGdHrFOWelJFnFyqtS4jmCeJzlcryOenPczpdZW1iTcLqNnp4/4Sco0SR6ydWYJguq0+i1E/2r8tYJTI5VFt8ugCTNgpyD9MyW4rHEpHmtfHyEt2Z6IohXlvHgj+hchzHcRzH6RCfUDmO4ziO43SIT6gcx3Ecx3E65ITQUOl6s4mgJ1kVvZD8Pin6Icj6b6Ih2hvxpwh1XpNvE24LkDXoIPED0yZr2uLT0RAtB6riSyJChYRoU0qi3dAl6oaKPVSXIZqthKzhqxakJm0C0aHVpZHK0lfGo+cAACAASURBVOZWEp0Fh1XS1e0VMTY+ROk51WqUuIxnn8+xmiZO4dh6N335W7x9PWuUrMB17NN4ihkxvkqzjmFgnH2pcqKjq4muQmOoNVJcv337dlP6+c9jTVhFtCSVCpf/9r0HKF3Qcwxg6gD387TEAMuJRmq6pv440q9Ed7b51JMo3ShzfsUq63nSWJ3WLoiH03xd/dEkPliR69crPlOWkOtO/HlScp1Vy1z+Sn2B0uVqa8+vVkXzpD5TIv3QMpQrnEetwmXOiy4sLRqmovgA1uvSj6Sf5jKi19HxNCk+WDJ2NSSuXUrG74ZoqNI50eWJDk09k1aCjt91iU9YFw1sXeqQVH8z0RQlkyJCbajXF6fVV6qg+9fVR1B9r1hnl5V+m+7h+hZVkFTgPlQMXD+NBVhWTZVoBU2DCaJNzEi5k9sy95h0gts0I7e8impUpR+ZzhOOAX9C5TiO4ziO0yE+oXIcx3Ecx+kQn1A5juM4juN0yAmhoUrL+ms6L+uvEjepLPNEXaOvajwt8VXROHsNia8Viq3akoJoonokxFhDYvmpDqFa4jLURHuiJ6ok+dXFhyopPikNWZNPSrzCRJACp0SHIPGzEuK7lZQ18bpovsqie+vtEx+UBOdvobWNl2Own32W+gZYrxKyfA5e+IpXUfqmH3FsvZqUISNeW7U816Esyq+U6IPqVe6X2mYlabOM+KbUspxOS6wr9TDaf3gXpTduPpXSfWMSZ2/nQ5Te+tA2KFmJKZYQDVMjK7H2aqwjK9c5Nl5edA9p7VdDrP3oNw1quUoNleiFWrQveT5HedFXqt2cxubLp1VPJLEOF7gTqX9bmqsblQnSJtIvTOuk8QDzEsczrfocLkRd+nEir7EAZTyU+IqjI3zOUw29TvicFWdFD1rj664hOrYQ1KOJ27gmZl8NNf9aAQ0df8S7MEDvGUxN7lkNGV5r4r2l/Sol43VOdGBzBdHs6jmR2H75GS6PhKBEMD5eSjRYJvVJikeU2mjVRKsoYfrQKLZ6g2UkPmKL3ZlcaxJSskUrVxONVUjoPVjuqscS9FHwJ1SO4ziO4zgd4hMqx3Ecx3GcDvEJleM4juM4ToecEBoqE81Ai6ZJPD0S4rGRkDX8hOiL6rLAGySOXbXEGoNMunVNPqV6FondV9A1Y1nzboh+JpmQNeikehKJn4/411RlPVh1ZAnRepgsWFckmFMyp/ogbnO18GhZYhdtTVrjMInvSUq2r4TU0CClJ+96mNIXX3YJl0nW+ffv20fp3mH2iVI/HSvJOevjMperM5QeHBildG1KdBCi8Zqv8/Z+CaAY1MpG2ny31OfMU06n9EKFzb9OvfT5lN670KpzmBhi/5m5mT2UTlYkptkQXxf5GY71V1S/HREV9WW4jAmJUQaJM7ccJ5/D57QlpqaUpyHXacY03hinsxJ3rlHl62p2fo7SSfHWaaRbh+SFIuvOGjJepaBtKGNPgfVAdfG4y6S5TRPqkSceSkXxtUKK69Dfx32kvMDX9qjUedI4pmXgEJgtMd40RpsaD5q0RzrDv19Jj1FfJdXamWj/gmjtgsTyS4kGCeJ/VpHNtYZqmCSOZ168t1TLqHogua7KDU6XZPDIaQxKGZ/18CHJx0uKOVpJ6p+yVr2ShPVEEerNJW0ih1DdbjItbVTnMjZknpA6JvdDxp9QOY7jOI7jdIhPqBzHcRzHcTrEJ1SO4ziO4zgdcmJoqEQ/FBIax0jWt0VPlNB5o+gmGmX1ZJJYUuLjksq2msWkRFdVmJc1+ArrGLLquaFr5mLs0RBNk8l6cV1ESyZ1zIgmqSpx2lKSXzrDbZCEet2Id42sZ6fF6yaf7uHjiYlIKsVtGrD6uEr33n03pRN5rsOlV72M0nfcspXSuX7WkuREB5CV480F1jjVKqJ7K7POoqH9cpZ/j1yLGIRYEK1KuiI6hVnR2ohO7oG9hyk9ILEFN27kOHpnnn0WlCNHDlJ6PHMKpauBy5A4zP2sZ5j3z4kuLSdlClLn+RILbNLp1f1NuGYTa6jqMgSWC6zZyshJWDPC3l0T/WO8v+h1brn1ZkonxaAu08/9vlLi9gOAXEH0OupPJvtXJfZeVjRMlYqOjzJ+Sh1CTXywJD6hJVRzJfpN0fdke7jOE+OsLWz0S/lELFipyvFFC1mtizeZaM4WICKtNjREqFUXTVReyqS6Lo0Hq5rXYdFb1kSUVNTYe9JvLrmQPeVO28D98r4H91L6jq0c57NU4fr0iT5TbqloiC9hTTRmWYklmElK/MeUjKV1NXQDgugNTbR8aen3FWnTrPj0ZeQeqMdXLbXeg44Ff0LlOI7jOI7TIT6hchzHcRzH6RCfUDmO4ziO43TICaGhqskaf1akJuWkxKlrMSaRGD6yBo+UrNlneH03JfPOoMcHUK3zPjWJMwRZg6+LngbG2pF0UryxxO8mI14uQWJDpbNch1JDNABZjrdlYjmUyYo/juoiRNemsQeTGg9RfE9y+SFK94nGq1RbvYZqpsD6k59+xZspXS3zMR/e8SCl+/OsQ5gX/7FKnbc35JyrF1hS+m2/XG5zOdZQHQGTklhUaYmZtlBjP6Ca6BZSZdamPPLg/ZS+9HkXU3rnHj7eBRc/F8p3vvlVSmf7+imdqHK/Wsixbisr8Q3TfdwmlSKnqyXWbJXnWJNVkxiQy/HI3v2UroO1fZvXPYPTmzZxepzrO5ji3ydEn7Pj4UcovaAmSznu97mkDhxABrxPRa7FqsQoSwYeS3IZGWtEYlSTsSGIJ1EQD6O06Hvqou9UT6a+Yb5uknXePp/g66CSlHRFNK5y1ypJbNWEjI3ZHm6PlRCSXMdEg9M18SvLi2dcQsbfhtyD0uKGNd7PbVSW/MfWjFN6SK6jfJ374Wg/X/sXX8jjbW+veIHtmqT03AyP50dkbJ2b11iCvH9PL5/jyUneP6sCUQBV8YHSmJLq2ZZIqYccl6EW9J7J56Qq15H2m2Nh2SOYWZ+ZfcDMvm5mU2YWzOzqJfY9O95vPt73X8xsvN2+juM4juM4TxVWMiUbA/DHAM4GcMdSO5nZRgDfB3AagPcA+EsArwTwLTNbve214ziO4zjOCcJKlvz2AVgXQthvZs8B8OMl9nsPgF4AF4YQdgKAmd0M4FsArgbwic6L6ziO4ziO8+Rj2SdUIYRyCGH/cvsB+DkAX12cTMW/vR7AAwDecOxFdBzHcRzHeXLTFVG6mW0AMAHgJ2023wzgFZ0cP4hAuSZitISYiqnJZa2uJmtiYinGno0yixxDVoSyoTWIYlqCSeYSYmSZE5F2ScxJxeizLuZ0aRGvzhUlGKUYaeZ6WbQ4NrSO0uvG2JBwfJSlbj0ZLb+s2ooIfvc+No677S5eHc6nWBCYTfLxTCP9JkQ5uwLWnLSR0pdexqLrb37/G5Tevo2DJxfFsLA3qwFPuczpHi5zn7zMYHkWlvYMcvDm2RnuZ9bL+5fqIrgWnX55QY3seIdqXd40qIoAfJ77/UCOhavpnta/tzZv2UzpvgHuRxMSNHbH3p2UfmTHLkrXamIKKf2qATbaTOXEfE8MapejssDi2IyId5M9LDrP9fJ1kR3lc9ioynUtgdV7BlhsXJ+W67ahppSt6oiMXnqmJoucFj9aNERAXZPxMtS4n+kLMFbnFw2qcp2ECp/zdWt5rHnmOc+k9ISMRZMH+O/1e++5l9I7d+ygdFoE2aUyC/0f3s5jUbnA18F+cJ9sR62sfV/OkwZ/F4FzssjpjIwl8zUu86C8JDTay/lVjnCddh9i485H5MWABRFkJ3I8Ngz0cRvOH+aXR2bn5CWmHr4OB8b4OunPr6X0TJHrd/AI9/tUptXYc8MEjz/bD3Mg9UJRA07z7zVoeEVMgZNqgStBvyvl1QVab0e3bBMWr5B9bbbtAzBiZqt7HcdxHMdxHOcEoVu2CYt/Nrb7c7HUtM+j283s9QBe36X8HcdxHMdxnjC6NaFafI7e7ilUTvYBAIQQPgfgcwBgZq1raI7jOI7jOCcI3ZpQLS71rWuzbR2AqRDC6sQOzYjmyUQroiaSJVkqTQT+Itfgai/Ms6EhJAhurcBrq+U2rSbeeDAJdpkUjVWqV9aQxQiup4eFExMjE5QeFD3OQN8wpc84m4PQbpHAt7le1kitG+SgsbWGBoTmuXK5zFqUQ1NsDGef5f13P/IAeAdeba6LOWGt1mr8thznP+/FfAyw3mbL6AZKbxvWdX+21lRjzqmC6G9mud9o8OLkEe63J21gk8g9+7dTemxiPaWtl89xYk6CeUqg4CDBmWtijFdq8Dn5yTevp/RFl7+U0u30PKNrTqb0uaecRunK1Cyl1UBwdAMPETu2sRaknOTfzxziczIn3SJdbtViHI0rLrpCjreG0sH4OkrmREuYZe3IwdKUlIeHuaEhPt7aPj5eb5bTjTaasKKYJk7PcZ61lJjqquZKtCIzs9zGGsi9JpFxyzU+h31pboPTTz2f0meefgalT9mwhdJDYvSZPevZlP7p515J6V27WXdXl1uJGjV/Rcxnb7uLg6bfheXRANAN0SOaBB4vJ9g81MTctFSXwLxiDt1T4jYfkvE2JZqoWpXLM1Pjc1qT/OpTfPzSEb6QUhIQO5MW/aUENz5ZDG+LZe50Wx9mnVpCfp9Itxo3b17D/WrdBOvKdk0eoPRsgesYCtwvilW+by8kOM+66BcH0flzna5oqEIIewAcBPCcNpsvBnB7N/JxHMdxHMd5MtLNWH7/DuBVZvbo1NXMXgTgDMRLe47jOI7jOE9FVrTkZ2ZvAzAEYHFN4qrYGR0APhZCmAHwIUQi8++a2UcB9AF4F6InrJ/saqkdx3Ecx3GeRKxUQ/V7AJoNaH42/gcAnwIwE0LYZWZXAPgIgD8FUAHwNQDv7Eg/BcBEz1NLc7Ezst2Sul4tQRHFKyaoR5SsP1dFS5NuE5C1WuI154Fx0U6sZb3OprWscTrplC2U7h9if5yN46xxWjvB2o9zzzuP0qUK631uvpEN7ouy3nw4PUfppMR/TogHSEZ6zvgo1+/1r3sNpb/zzf+i9D3330PpsgQCzhzDewrnXcJajgN7ObDuhrNZ//O2C86k9Pwk6xAOznGb7N3Oep/KAmtZtm1nrYc1uI2HJQB0tcTb58ucn1W5Hw6WpI36uF/vLIiOok+DObPma8/9rC3ZfCm3Xyqw9wwA7K/PUPqSjdwvTz+DtXrT06xbGxatx+ha1o3t28V+O/c+/BClK4fYsyjRv7og2hOjfN3V58QnSoKUj4iGKh1YC7LvEGsHhxOs21i/kX27UhJUtgd8vP5Bzg8A+kf4u117OeDyzPwhSpdFh5UUvUppnL21SlwkhBq3QUICQI/08jlbM8x6mow45NQWuB/XpDy5PC+UDA/w2Jc9mctz9603UXpgmLU2Z5/B53hqtp2bz9FpSHDfuvjkBZPAuqLzrcs9KS1BryuByzxZE52bjDX5vGieRDdck36XEz0lpF8nErI4JbrksvTzviyPXZPT7BG1YzePXUXxUcyIZrdclU4HYNs+Hq9PWs/X5qkbuc0K4q+WKInXl4zP249wgOfivPhOlTqapgBY4YQqhLBlhfttBfDSZXd0HMdxHMd5CtFNDZXjOI7jOM7TEp9QOY7jOI7jdEi3fKiOKyaeHShJzJ6ceBrJ+nHZeG00Ieu7ZdFUJcSPIp1jLUmt0qrvGR1ln6hzn8mapjPOPIvSJmvqDfE9qYsm68GHtlO6Wuc2Oe/cZ/F20d/c/cB9nL+06YTE9tso8bh6e7mr9A7y/v39vL69+WTWjmzcyPqlr/7HVyj9/Ru+T+nZxuq7poYDHNnA+p1CieNVZQPrCtadxLq309OsBUk+9/mUrs2yjqAogQJmJG5cPs/HO+0c1iwdLrOGIC3drPgjjo9Ym2P9zobTzuXybuI+WZrn8u2+kzVU23ayNueyK9mzCQA2b+BjZnsk5pj4iWXXsA5iY429ZtZmOH7XGaLJyq3l3yfuu4XSNs/ajeWYO8RakcNH5NpPchsVMqxl6Q3cRw49zG0WhvkcT2xgbWF5lq/LYol1HLOzrFEDgKF5boPhPm6j0OCOPz3L/TzIeJfSeIMJvnYHRlhj1ZdnTVNOvLgSgetclXiMhRprV3KiN0pIHDoE8eyr8vh+aA/3+/k5Ga8lrunoBu5jKyE5KuOPlEFjNuZkPNfYfynxqCuZeryJ16K0UUVugQUTTyXRE1UltmBIi0deUgZLCU6YSLKH33yRz/HOWR7bpLhIDfDYGpISWzDPfRoAJkXHVZ9iD7p1Et+wV+5Za1q0gRKzt0/8yyTuKCq8/cv/1lLEZfEnVI7jOI7jOB3iEyrHcRzHcZwO8QmV4ziO4zhOh5wQGqq6iU9UUrxnxMOjMMd+O4msaKxkPbkhniKW5vXfisQC7M23+vOsP4m9WE4+nWOcZdO8Zl4RzZTGUqoEruO6Tez9cu5Zz+ACyNS4KrqJ/h72ARkcYF3Bs8/j4w2O8Xp0NstdpVrmNistcFy5A5Mcy+ngIfYE6ennNfQeKV+5IfEVV0Aos+6snGF9SiLNeSQG+RxUxftqKsFtmDfxohnmdDLL+pox0Y7MT7MHU8hzv7rkEtYsDRS4TR++jz2ZehOs4Ro/k2Oo9Vx0MaVnp/h487vYN+vUNax7O2N9a2jOPvFHKyUlvmGR2ySREw830YpMbt9B6TU93C9f8ByOZpWv8TnddtcPW8p4NKzGeqHBNGvCRgYlZtkgjwVze1mfdOiBrZTuO521ggd0iOUugJEkjyWFhVZNWHGBNUMj46wdSWS4Xw/kOZ3N89jTCNzPy0UeX9NyvJ6GaEhLGjtV4r7J2NUwuQ5nRcNa4z5kou8ZyLF+Z8sm7pcP7LyX0tPgsaZ4DGNJb0LqmNQ4mSIaUl2Y7J8UL8RMks9Bsi73ONHxBpFc6ZMQ9bkaSomuTu559ZpoeOWIGSl/RXwCId5kxSrrnbKipZTbHVKNVv+4hrThdFHylPF9UOLB6jnISZueLPFs+0Z5LEseg/eh4k+oHMdxHMdxOsQnVI7jOI7jOB3iEyrHcRzHcZwOOSE0VEE8k+q6dipxgUy9cGRttFhi7Upa4hTVq7zmnxEfqly+tdlCivM4MsnxtbaLfiYnsY1OP5Xjyp15GmuwTjuTtRnjYxzLb6HC+pjSAtfhORdfQOkNG1gv0z/EmqbJfQco/dBDHGPtwD6Oj7Vv9x5OT/Lvj8zzGvvCNOuLqqKLy4i3zkrIVfg3qQa3QbXB573cYK+VRpLX6NNZ1vNUAms/TLxfsoHrJCEncc9ObsPrvvV1Sv/SGl7T7xctzEid9TvJBU4PruXf13OiKehlPdC5lz6Xj9/D/kJze/icAUCjwhqkZJbbtKRxNqu8f79cJwM9rDs7cpivm/XDZ1P68meyF9jB+x5uKePRqIk/W7rOY0lW/H0S4s8znOVzsm6A9UzJCve5IzM8NqUWWA+Uy/L+7WQcaeM8S3ypt+hrcqIBzfSIBkryqIkGFUVuk6rob0z0NQ35fUX0m+ksl6dQ5OtsZkYqVOXrODs2yNtlfJ/cz/5tC318/Hpi9dqYhnhhhYw2Gm+vi+9UI3CbBTGVs4Rqppi6HB9FHluSousdyHGde6TOM3UeGyoVPl5CvBULNTknPXIOJF6ktnBZ7rGZlGjOQhsNlYQfzPTwtZLv5bGkVOF76t7drAkdSfLvK1XOYF7KNNa7er8yxZ9QOY7jOI7jdIhPqBzHcRzHcTrEJ1SO4ziO4zgdckJoqEz8JFKicyhKXKWqiAQs8Ap1vpdFB+UF3r+R5GbJig9KSTUHACb38Tr+9Az7yWwYY++Uc5/FmqaLLryQ0iNr2QeqX9awb/4J+++kxCPpvDM4duDoAP9+XnRn119/PaVvu/U2Su+R9emZGV6/rpVExwYmlWIdhXrjDGQk9tOwHmF5Dt7LsenKgTVSySTnmR7kNfmsxIpK9PA57BHtnEm/yCX475M+ye/MIdbbnPaGN/Hvh3gNv7/EOoXpsugmpE3nkqyLWDD23ylK+Kz1z2ad3lAf95HaDB8PAEKSr6Vyjes8L9dGKHC/mDfenh3lOueSouUrsDakf+1GSq+Vfr4chXnWnS3M8TnXuHazEid04wTnf/55F1H63r0/ofThw+wV1lfn+g1lOP9MplU7WBDPoFSRx6uMmPzIKUG5IFpC0cvURM+SE7+2pPixLRQl/uAc622KJY5HWJHj9cq1ns5K7NU5PkeHG9yG94j3154drE0cPJ11eT19q9djQvSRoSb+anIPSoq3V0NVUaLn0f3V61B9qNKqB5KTXC+w3jHR4PG5JP3aclyfXIN/n5a4olPio5hM8/FCncfGZEM8+PZup/RJW06BUkrxMcbG+DyetZmvvenJbZTedf/tlK7J2DI5zeesNsN+Zfulnx4L/oTKcRzHcRynQ3xC5TiO4ziO0yE+oXIcx3Ecx+mQE0JDpWGTQlAfFNEhiO4hBF4vLkscup4h1o5U67zWujDHmoC+oZGWIq47ieMEXXwJayuefe6zKT0gHhuzRV7zPvQQr2mvX7uB0vv2ss/TIw/wevK2bY9QulJkLcuRg6xTuPVOXn+eX2D9zdgwt9H6k3g9e9MGLl//AHvf1Mvcpodmef36wB72taotsG5iRSzwuv14XmKKHeG0FeS8ZllzNTLB2pG7b2LPo54c6wp6RP9ST7LOoCcn/kDrWE+zIL5ZUxK3bqYiPlhF1lgNzHObZtLchrkFrt9sTuJJis/WjHibAYCJl0tJ/HVqKn0TP7GSGNaMlDjPhLRpSHOZD4PPcc8m9mNbjkqD22j3nr2Urtc5tt/E4OmULjZY2zgyvJbStd18jk18uY6IH10v+Dpbs178fgBkxCevOCdtlpBYfWXePiu6tqT8HZ2WflsUz6Oy6GMSYpYVqtxPDu/Zz+WRsSAt4229weWfl/F330EeC2+961bef5qv4yFwfklwe6yEZIbvEYkgfmIikcpovNk618mM0wnVWKkvlbF20Bp8E+xNcx0z4snUO826sqF+boNpUbkOZ/mcjksfKoDHmnJDxjaJb7swxZrbsSLfb0YbrCcFgPkk++BNyz1ivsB5rBvgMhw4xDEva/18j82uZd1WH/i6qIs28FjwJ1SO4ziO4zgd4hMqx3Ecx3GcDvEJleM4juM4ToecEBqqhKxPVyQMUFI8Nao1Xp+uiO9KUo43kBRfFIkrt/5M1gtdfuUVLWU8+0yOxdfI8pr31nvvpfSBndspXTM+Fc+5+HxKj/az3ubi89m36rBooiZ3szakd4w9OdauZ+3Ji8Yvp/TQBOvEBvv493393GbzouuYPszr2Ydm1KuGPZ6syueshtV7x+x4iHVkIynR1okuYHSE9TIDZ55D6fm9vIY/XGYdgU1zuiqeQlMLOyl9RH6fXsd6nJk8/33TkLhwI2nWBMz1sL7oyI/Zhyt5WGLxJbi+KRGChAnWMDTKrfG20hX2akkl+TxpjLJaD+8/KN5g6pmUaIhWQ7Qbh4+wVmRwVMy1liE3IPqcwzsoXQ2sx9m4UfzTJjmdzLGOo1EVPdBhPl55lvv9QdEPTWxsjSdWbYhPVJ77QVU84IrqodSQeIXiv1YTLV4dEldTggUmRX8zIxqmpPw+neT8ixXWB4WKlD/J4/XUAutFJw9z3NC01K9eZQ1YNtHqG7gcKYndl6+J71SK+30QD6W06M40Hm1GPOsyUudKQ9KiKwtJqVNDvBOP8NiVTrKvVC7HY0Fjjq+rjHiB5XokFqDoL018pwb6uM+MH+RzPFhro1eSIb8mMXXvf+QBSh9Z4Dr2T3EeM+OiMRXZWk+a79ET4x7Lz3Ecx3Ec5wnHJ1SO4ziO4zgd4hMqx3Ecx3GcDjkhNFQN0WVkRW9UEZ+Rmqypj42yf8U5Z5xL6dPOOZXSu3ayh8eaDayhGh7hGEMA8ONb76L01GFe958W7UddlsDzsuZ8x63sCzXUz2veFzzzmZR++ctfQelQ5/XjfJ7Xh2t1XvN+8H4u/77DvIa+/fB2SpcqfPxZWbOfnRONVIPPYV10GEWwriIva/QrYfIge1kVq1IG8bP5cYq1KOnbf0zptaN83i86n3Vyvaexl4odkZhp07xGPzvHurLhEdYs5TJcnoPi79Pbs4XLK95docj1nZmUOHJynVQznG48wuUvTXEfAICQ5mvLRGuS7eM6pUX7d0R8qZID4t0lQopqiq+Lmuh/SjWu83Ik0iykkBBp6BvhLxJp7pcH5lhz1R82U3pokGN2Lsywz9R0mcubGmWtYiLZ+jduXa7lSkViTjbEM+gIa0l6+1i31ijKOTSNlSqeSBJnrjDPdSqLn5uGI6xLv5uf57GnJprXvgE+55UM1z8pY0N9jvttQ3yyTDylVkJabo0mujBLiW5LvMJS0sYhJboy3h1WF/1kg7V6KdUFyznKlrhNR3u5/NMN0anlWNtoBfGTE+1fOq9ejJt4u4wLI1m+7tftFc1ZVfSdAEoJ1vUG0YxWG3ztZBe4X/TXOY9D8zz+7p7i8fRQmdtkqucYYj4KR31CZWYXmdnHzWyrmS2Y2U4z+zczO6PNvmeb2dfNbN7MpszsX8xsvN1xHcdxHMdxnkos94TqDwA8H8DnANwJYC2AtwG41cyeG0K4GwDMbCOA7wOYAfAeAH0Afg/AeWZ2cQihcwtSx3Ecx3GcJynLTag+AuCNzRMiM/ssgLsA/CGAn4+/fg+AXgAXhhB2xvvdDOBbAK4G8InuFttxHMdxHOfJw1EnVCGEG9t896CZbQVwdtPXPwfgq4uTqXi/683sAQBvQIcTqpT4qhQC6xqGhnm99qx1rGtYdxLH8Nm8gddqkynxVBri4x86yNqX7bvZXwgA5udYV9CTFV1CRtbcRUO0exd7q/zghzdR+pGHODZf/9t/h9KZJC/K3//A/ZRueUaY4PXjffs4/0PTrIkqlvgAqnAqzOiau2hlVBsSdUXfrwAAIABJREFU+Ai5DHfFkRxrcVaCxrrrSfIa+pEar7nfsofPY6PO/WjTmXxOM5OsxRvax15fAwMSq2+KdXPJIdb/VGu8ff0gx0NEnsszey+fo0KDdRWDa1knV81xm/auZX1PocznbMNBTt//I/a1AoDyPO9TyfJ5TtS5DfonOG5bDazdKIts4cA0aytmqnwOFjKsXyzMtuq8jkatzm02MsZjQU8vl69Q5frWKqxVGZk4i9LrUuspPX2QY5pVa+wHlMhIXLtKa9y5alU0ohKXUzVI6QTrdaZneX/uxa3XZkLOaUl8qsplboNGjY8fcqx9KdV4PK2JgDSV5H66UGetS0jz/rl+iS0oXl55EShVbPWx/FJ57geNimiiwHlkAm9P5jnPjOiAk1LnimiQ8iL7CoG/6Bdvsi2iqcrzZYOs+FylxXuxLytejHLDULe3Qi/r8pISTzJvfF1uHOXtZR4KAQB7EzxehwzvJPJH9OceovRgL/eT3iy3cSlwv1yQc1IqrF5rp6z6LT+LFIxrAByK0xsATAD4SZvdbwZwfpvvHcdxHMdxnjIcy1t+bwKwAcAfx+nFP3v3tdl3H4ARM8uGEOjPHDN7PYDXH0P+juM4juM4TypWNaEys7MA/E8APwTwT/HXi8/+ym1+Umrah7aHED6HSOwOM1v9O/KO4ziO4zhPElY8oTKztQC+huhNvteFEBYXcRcX0LNtfpaTfY6JpKz/ZgKvb1944XMovW7NFkpv2/kwpe/Yyg/TLM3rx40qr8UG01iArSuladEAJRLyG1ldLYuP0xHxFJIq4/4d2yl93Te+TulLLr6Y0lu3sv7ljrvYZ6o/x+vVGzez9qNSYg1ApSDxvqQJKhJ3bqHI2pO6tFlKvG1yGV6lL6T4HK+EbYdYr7JfdAKpBK/7I8uapSDnfXqKfZxu+8YPKH3yMB8vl5U6SiyqNeey/1klxSd5fg9fJolB1pGNj7D6ZaEuHk3D3GZzGuNNfFsWRGuzp85alwVJA0DSWJxhC9xm2UHuRweT3I+mFrhfjIhpUarIea6VeIs6ZO3amcRqqNe5jfp7WadRWGAN16yUt1bmc6z9fN0Qe5elk9wHekTAVAusR5otcJ8DgIx4EoWEePrItVRc4DZMSEzLuTRr94Z6JYajBFgMDQ2eKnE3Jf+y9Ilh8fbKSizCmWn2F5qeZs1qbkjG0qxoa8SXKoD7RKm4+lh+SdFEJZJ8rac1XqJqlCSW33BK/McynK6Ajx/Ej60umtM1kH4n95uS3I0zSS7vWJ1/3187ROnsAI/H/TIWNfLiqyW6t4GijBPi51YXrSUA5LiIkOENg1XutwnxLqz1cr/sFQ1pUTztWvzVJP9jYUUTKjMbBHAdgCEAl4cQmtW4izOBdS0/jL6b0uU+x3Ecx3GcpxLLTqjMLAfgKwDOAPDiEMI9zdtDCHvM7CCA57T5+cUAbm/zveM4juM4zlOG5ZzSkwA+C+BSAK8PIfxwiV3/HcCrzGxT029fhGgS9rkuldVxHMdxHOdJyXJPqP4KwM8gekI1YmY/37wxhPCp+L8fQvTG3nfN7KOIrE7ehcgA9JOdFrImceDyWYmRNs8ripZgf6DJA7wm3zDxq8hI/LG8rvlzfsVSq19FNi9r7OJ305B4XOUCrwfPVXk9+KSTOb6gerfc+BP2qerrY3FGQ2JPjQ5J+bKyf53ruF88lg6LrqGqPimiU0iaxJ4SHUZGPELWjnE6Nbh675jxNRxfqjfDddzxwFZKp/tZTzM2upbSpSJ7Bs1O8zk75bzLOb886xhmJW7dQpb1NAfET8gW+Pjz+x+k9OaGeHVJ+Xffz/mX81z/bYF9t0Yn2K9t7gjrHkrFVlFBT4Gvjf5BLsMe8f667p57KT07x/18YJB9pc5ex5qps4f42syI1q7xIJ+j5aiWuA0z0kcq4s2VFO1JReSg2x+8j9K9p3P5sjn+m3XtWr4OSyU+51NTrfWZGGCtn0o4Ewn+oiJx42ZFO9cn+syUeNhljX+fkGu5HvjarDdEm1fgfjQ0yucwSD+eXmDdWLEsJko9vH82x+XtS/I5TImvVqGNt9dyJETfmJABLyG+USHN/T5ZYU3RqXnWsJ65Qdqwzn5os4HbrCqecxNl7ieNI6zF25Hn9HiGx+9Th7kfZzKsHdw/zdfp+CD7IJ7ex30kJVrJ8T4eu6ZL3J7T0xwvFwA2i663Z4CvlT6p8+xO0d4FrtNAj/gE9sm1bzweTxVFY3sMLDehenb8eVX8T/kUAIQQdpnZFYic1f8UQAWRgP2drp9yHMdxHOepznJO6Veu9EAhhK0AXtppgRzHcRzHcU40Vu2U7jiO4ziO4zDH4pT+uJNOsxZlVuLm/eTWH1N60xb2ghke5PXphvjrhCDxyCSWVU2W4K3RuiafarCGqCprxskBFT5wsk/WizetYz3PXIHXh+97iL217nyAfac2jbI25VSJZ5jpYR3C7kc4Ttz9D7A2ZGGetR7FMq8/Z8SHa7hPgjXJOWyUOP89dc5//cQ4VsumTaw1qZclBlqN9TDjm0Yp/TOvfw2lb77xR5S+7xZOzw6LlmOc+11/hvVFQ6L9G9WrT9b0k4dYI7D3a9+kdGMb6zQGzzyD0iWxaKrPs67iwHbWRdQO8fFyA63xFKcL4ptU5Ta4YS/rtPb2cBuUhiQ+YYWvm6mHuUzTR7jf1Y2vk4kxiTLGVmQtTM9xjLFKlTVhIfDxi3P7KZ3I8TmaL7GiYe9h8WsTraFl+DpOShy6wlxrbMJiWvrRMPfzWfF9gpyTlMQNPXiQz3Miw55ABRnLkGa9TB38+0KSr90gceE0ll/CuGP2ZtTnT8aKMm9vyPZEnstbg+iTFlbvGy3DGYJoUhNqFCgDekp8pRrimbQ+vZ3SY0OsYaqnWRcmtxMkJQ7ooRq3QXoz99uhNawnGhnkfj99WLy96tzPzz6P++XwEGsj9clMPsnl+eEsuyplRzlWLAA84xyuUzbDbV6Z5evgx49wrhNrOM9Np2+ndDJ/gNIp0QIerKvn3erxJ1SO4ziO4zgd4hMqx3Ecx3GcDvEJleM4juM4ToecEBqqbJoXkAuyPScaAyuzcMFkrTQjsbCQkzV58RypyJp8MimxrQCkJU5QLc1aitICr0lnRaeVT/MadmWOy1Cusu4gLXHi6kfYPwdDI5Q8eJjXj6dnWYe2XfQ0k4d4jbshQrKhPtbXJNOs06jWuI2G+gYpPTfHOozSDJ/V3nxrrKflODjJ6/yHplmXkBrgc3TWhedQOjHE5+DkZ59J6Zt+fCul79zF3i6ViZMo3TvP+VfT4m8GbpOa6DLWjHB5evMS3OoQ+/fkN7FWsP9k1trUIDHPSqJVqfM5SxZbdQ7pmnhpTXIMyp1fE883iauGMtdhTlxVatPc76fHef9EgvvFZJtr8WgslFmXVpeYmkHaYF60i9bg66be4DadmWYdh+W5jQsLfB2la6y1yWRbQ6KqWqdS5WOkJFZfRSVQGf67uS7xCXfs4+smI757eUk3knytWorbKJfkOswWuM0zoi9akHOgsfwa4kOVG+A+kBHfwJ5+0W8WW+MjLktS47vyZhMdWFpi6SHHZdhV5rHk3gN83TxjDfer/j7u92nwdRfk2kxluQ9sGeNzPj4qY6HUZ6bM4/mZp3F9Tl7LfSSIONAg9z+JmZnLig/WSa1xQtf0c6H0WtwnZRwc4Gvr3HO5zD093GaNssRHVFmzrS4uaDv8CZXjOI7jOE6H+ITKcRzHcRynQ3xC5TiO4ziO0yEnhIZq3XqOORbSHNMnl+K10aIsZw82eN7YI/HHavMsOkjImnyvrI+X1BQEQKXM670LQWLXpbmp+3vFjwfiPyPxBk8SX6n5fVzGnQ+Kp1BVPDzmWDdRFN8SS/Ga+Dknn07pbK/4/6xhz47+DK8/33QrezYtFHnNvCTxtYZHh/h4Y+wRtTK4jXtzEjdN/MsyCW6DQ4dZl5DMsC4gN8rnbIdoPU4Xf54DaREqzLLWJK9eNqKxsiofDxIjrSox1iZn2cclV2EdXRBPpFqO65+rc/7prHg8ARiWfrBt/hCl58RTqMc4fcGlZ1E602C/nR998zo+nmgLZ6qsh6keZJ3EctTkOjWJW1ep8zkr1blPNYqcNrmOisb93ETvVKuwNieR5PYcX8djHdDqyzQ1w5qkpPjozRVZL3NkZgfnmWINVFX0kRW9LUi/TMs5Tcn+ddGi1MuiJ5W4dMUKt1kh8DlNZXn/Qp31QtkU9/Oh/BilM4lWb6/lCEluU5PYfnXjMqhPld5Y541jsz4wxWNHT/JOSm+U+0G5wG0ymOU+MDTGY1X/gGiU6hLnU3TB6uFk4l0W6hLzUjSySYknKbdcnHIqt2dfX6v2sSb9olHlVkxn+R54Ctvuweo8Xu47LJ5yEoO3LBqtcrnzKHn+hMpxHMdxHKdDfELlOI7jOI7TIT6hchzHcRzH6ZATQkPVP8j+EzmJKxcavGafSvI8sSg+JwN11oZUE7y2Wi1ITDPRB1XrrbqNcom/S4j/TpDYRpUilymdF/3PkMaC4jVwEx3D3gPsM1WQNfMLn3kBpSfGuU1H+tkTyVKiFUlxm1ZUOyK+Un09rInaN8magaFh3n7a5pMpnbLVd82c+O305biOadUgzbHOICfnZEq0c7UGl+nwJOuHtj/M8RXHRkXDVeHf98g5zgfOrz/L++8V7d/MPGsABvPioVTm+jXqrLMoBz5nvaIhMPFnA4Bigcv4wD4+r4lx1jlsWsPxDZ93xRWUnhYtyV13cMyvAy1eXnyOy4nV/U3YEEOhlPjFNYrcJnMFNXXi67oncBtXKjw2zRZY8xVEXNI/ytfd7gPcpwCgMM/au5JoQTTuXKHAeslq4DZct47zTIheRn37ksbpROAMExXuVw0ZK4plvu4OHRatn8QVTQ9yvysl+Pdiu9VyF8tl+brKZcSXaiVUOZOUjOflqsTOS/D+iYrcMyQG5ZTEjXtkN19Xhw5zP0zL+H/KRm6zDeLJNDnNY8XUFJcnKVo/NKT8xsevqmmT+FBVGtzGlpX4i+IN2djL5YvykLifVe73oaoaUvGVKkhcTLnWyuKB1yhzncud21D5EyrHcRzHcZxO8QmV4ziO4zhOh/iEynEcx3Ecp0NOCA1VWuZ9Yh2DVJrXUgeGWCNQEG3MQlli/EicpKp6JhVZQ2CiKQCAWoHXiPN97DdTlVh+s3PsebQgMcMqkmffAK/BJ0TfomGIkqIj6+3n9ePiLNdxzxR70xyZl1h+DdYMlCoSF058rEYm2AvmpFM4zt3EurWUXjvCcej6BvkcrgQTH5PygsSAFF1EkI5kUsdMhbfn6+IHVOA2vPmb11M6N86agIT4qqzvYx1ZYoDrPNQj8cQO7ae09qnaLvau6cnxOUjluD2Gy1yeVJBYgfXWeFuDfVyn8h72wzkinm6ZC9lPrJbla3E2w/sPbGJ9Y+kw638uuOBSSs/N8fbdj9zfUuZmDFynepFjqhUk7pslWcuSKIl3V4b3T/bzOajWuH0OH2bN2Kycw8IcjwMAUBJ9YpBhO5mWfiz+Zvke0ZjO8NigMrR8RuILJtiHKgnxpeoRXz+x85mf5TaZL3N9euTaR4rbuCaarppkMFPmsasumthe0eCuBI1rmRXNUCZwoyXFvyyRUR0an/cRY61cqsFtWpfxdL7Ex7v3Ye5X89O8fV7G57mSHF+0iPWajOc16TMiXKtKvFv15arLOUtILNq6jNUAUBNvwkZNtHrS5vMNPkcNk5i+4m8W6qzBMvWcS7WWabX4EyrHcRzHcZwO8QmV4ziO4zhOh/iEynEcx3Ecp0N8QuU4juM4jtMhJ4Qofd3Jmyh94AgLpss1Fp9VRYxWKYohmBgoWobFbbm8mJTVeN45OMxiXwBIibIzkWQRYJAy3XDHbkof2MsBn3sGePt6Y8PDIzPcBokG12l6io0+r7/+m5TOpFiAPChBbwfHWUw82M9i4bXDLKAemeAApb39fLwBCbTbM8htnJRgpKisLugtACyI2WhFxKu5FAsjj4gAuE/OUWpezUu5TVNihBnKfA5mD7FguniE+9lcioWq9SSXryFtMiIvKiRyXN5tP+CA1HiQBdq5BovcNcB1Ksv5DQ2xSScAjA2t5y8mWWA80c9tNrSW+8VskJcvqnyeiyKgxiRfV2uGuR+FRquI+2gM90mg8xkWB1eTLHCWJkPQoLgN/n2hxm2Y6pEA13PcBwpVvu5TeXkxAEAuwW1Sk2DGQfp9y7sFYlA7u8BtZmJaWUyKoHqQx7v+PhZ5Z6SfzhS4X1ca3E8HRuVljRy3SVXqk5SxKiT4tjUvRqZ7jnD+CzU+pytBfJNREOPOtGxPimDaEtyvh/q4Tqf0ch3GshyMPpHjsaUsovQ+8PZkme8H9STf83IDfD/KST+rlPSlKT5n/b3y+17p5xLQuigv7BT1/ZZUaz+Xd8tggftlpcBtvFDifqPmpwhiHpoWA28xG52HGhnf3VLG5fAnVI7jOI7jOB3iEyrHcRzHcZwO8QmV4ziO4zhOhxxVQ2VmzwDwfgAXAlgLoADgHgB/EUL4iux7NoC/BnAZgAqArwF4RwiBRQLHwECe9ThZCXYZxIRtfprX1NXo7q6tWyl9/nnnUPq0kzmg62yBTdTmZtjMDwAOS8DmfXvYhLFU5XX8oiwq57K8pnxg3y5Kz8xIEFoJ2Lzx5C2U7uvn9ePBXtY8jY2xtmVsWNPjlE7lJdiz6C5EWoKsasok2HIiwevVyRSfw5IG41wB1cBr4rkkC2DKWdYVbH3wEUr3D7BmqDTD+zfm+RwGMcfLSEDRNFjvU0lwH+kb5n5cTnAbFEXzZSLcaCT4nFbKrNnaO8m6ipyY8W2fZJ1eVQwix/tbzVWTKf5Og37nxPx0uMLai4zoIIYWeHvPNLfhpOglb/n/2zv3IMmrq45/Tr/mubMPNrDLQkAjGIyUkAeYaNQIBGMoY4qHRjSiJBBSqDGQRDeKFROjaBkJoCQYJEGK8LYCCaKSiFQkCeElWR4BEqhdYJdl3zs709Ov4x/317V9zvTsTE+H7R7mfKq6Zn73d7v7/k7f3/3d/t1vf8+DD5rtCZfceDaWDjuzVnVGpMO2n044E+G8k37kyrZPTNVsn8q7sWd0hT3+sjP0LRR95l8YcIlvq87wsF5zyY3xMXGmjS5xeh6XuFbt61WcPmaqbNu8Z6cdb7c7I89G0cZYcCbBVWfQiNULuTzESNHu3+6S2Zc3PG3bOy2b8uxMqW1Tpe50anWn9XOJ2XGf0ZK6bUPD6coqTqM1XLCmv0vG3HnmtIPVmh1b6hX7mRaHre5N8vb1J/fY60tjwplfj9ixrFayz5+astfIQbGfyY4tVms45TS5AINuPBx1httOtsX4dvseu+ruvChZvaeKHWvGJ+3JvGvadOjOaW2cjdlE6YcBS4AvAS8Aw8CpwG0icq6qXgkgIocA9wA7gbXAKHAhcLSIHKeqnSuMgyAIgiAIFgj7nFCp6h3AHa1lInI58ADwYeDKrHgtMAK8QVXXZ/XuA/4LOKulXhAEQRAEwSuOjtdVVLUObABa7/mdCny1OZnK6t0FPAmc0W0jgyAIgiAI+pk5+VCJyAgwBCwFfg14B3BDtm8NcCBwf5un3gf8areNHB2y67+HrbGeTE//0K4oLh2z2pn1G9eb7fIuu/a6ZYdL8Hr/d8327nGrTalV3KI+UHSJFQeGR832CpcAdLnzeSqUbJsHS/b5BefbsWzEaqQGnDfM0lG7XizDTocx7rQbDaeRcnoexHaVWs3GXL1mCufbUrD71ek2fLLN+fhQDTlNUz7vvFV2Wu1FZdLqX779wEO2TVusFqQitp+MFOxnWC/bGA46fc5Kl6Q1P2h1BCWx7S04TZNW7WdYdBqDF53f0KDzlRocsPUr7vtUwX0mU85LDcBVoeG8uCZdAuqJzfbckVGr1cuVnbbEJbrd7Xyq6hteMNsrD+ws8a3XYQwss+ddyfW7IefvlnP9vF6yx192HkoNsQFb7kRYE04PpC4hN4C6zOd5nyw+702R7EFq1SWdLbjk7ur0je71JypWI7Vhk9XriPNgauCSNdt3I+f0Quo9nJwmzKmVaDgfQfcRsbvi/OEa89FQ2RhWpuxrTInzYXKaJRF7TKNTNuZbctYDrpaz43Ee56HkhkPdYzVJpUmrbdy61eqRquP2+tMQ2+91u+sTFdtPS6XXmO3xsh37BkpWEza82WoJx7f4Puf87ABxpm9L3TVVJ+321m12PN7hxp7J0cPMtrPSoliwMd9Vs1rt+TBXY8+/B87N/m8AtwLnZ9vN2c1G/6SsbIWIDKhqZ+rRIAiCIAiCBcJcJ1SXADcDB5OW8PJAcwrbnNa1mzCVW+qY/SJyOnB6J40NgiAIgiDoR+Y0oVLVJ4Anss1rROQ/gdtF5Higed+szfoAzXt40/JDqOpNwE0AIt7oPwiCIAiCYOEw31x+NwOfB45k71Lf6jb1VgPbul3uq7s1/ZUrbW6pTS9aP51JJ3H68dWvNts/tmqV2S4OuTxzY3b9uuh8rpYMT587ypB9zjKnYSrk7P7SErvf54IqFVwuQKc7KDgfJ3VajYIzhqo68Utu2O6vOW8ZJ10hLzaoOfd+RefBJD43n/O2aTgvnJrTeVSrnese8s7rZapm2zjs8guucYvqzz5n9TlDTreWdz4mdadJyi1xugTnwSTOq2zI6d4GBqxuru69a0bdaeRy+y2bdDnWRm2fqkw5LY47+wsT9niHxatXYOs2aysnRXvMjYL9XJ/cbP3URlZZ7YiK7Xebd9rPYHjIxmDU9WN1ee1mI1+yx1hwfm6DOdtHRna7nGp1r5myx1scc3qghjOucqfFsinbR3zONgBxfmR1V6VWcXrFon3P3c6nyesX6z4FWsPpuBr23K/nvQbK9ZOGe32nuys6zZa49qtTXdWcJqtQcTF23l05rwf1+sw5sGOXfc8pd01pOJ+qnOv3BXfNyruxoDFpz4ORmssnO+HyN+6xrzfiNF4rnHbP5y8s563GKlewWsZa1Xktbnc6NJfftjxsn4/zwRoZt+NCobLGbA+Wp2sfC4P2GlnIe69D5w026fwmnZ5z2Qqr6xpdYvcfOGTbvKdqvRfnw3yd0pujzlJVfR54CXhjm3rHAQ/P8z2CIAiCIAgWBPucUInIgW3KisB7Sct4j2XFtwCniMihLfVOIN3BuulH1togCIIgCII+ZLYlv8+LyBjJBf15UvqZM4HXAheoavNe8qdJAvP/FpHPkpzSPwJ8D7j65Wh4EARBEARBvzDbhOoG4GzgPOAAYDfJJf1jqnpbs5KqbhCRXwQ+A/wNe3P5XfCjsEtQ53k06HIz/dRP/oyt7zTuAyUnXPD5spyRSSFn127dJtKY7kOV90m+nDbD+zIVnD6l4FNBqfdUcvudjqKQc/m4nF9O3mk9hpzuoebyJRbderXXVfi8SLg8et76RWv+9bzOwukcvLfOHKgN2PcoFl3QCnZdf8jpHo4ctFo7dTnFpnY7jVTdxqDWsKfT0oNcn6g53yqngzhoidVQVVzz1fW7zZNW9/Cqw50my2nIplynaeSdbm3AxXxg+g3sFRNW59AYtDGpOC3F5h8+b7a3Lrf6xRc32xxiW7bbYxobsDEdXOHOm5o7t2eh6rQvDe/v484jP5aIE56Vhuxn7PU/6vIvem2kb0+xMH1ILhbsMXpNkh/G866NAyN2e3LKakvEJ+J02w137lb94JNzHnXOS6tR9Xojp3Hyn4kbjxtu7HESKSpVp8nKuX7O9PF6NnZX7GuUnQZU3AAn7nMfdPkTa64N487jzWukyhWXf9Gd+ysH7LlexI7fjZzLb+j1nO54Bt2pnqva5ws2r+mY69cyaF9vmbu+Te60WsnBNp/JkpHD7Wu6/IjFnD1ZJ0vew83uz1ed/rFi+8nuqo1Zed6S8r3MlnrmeuD6ubyQqj4KnNx1i4IgCIIgCBYY8xWlB0EQBEEQBBkxoQqCIAiCIOiS7hcN9wM+J5kWrY5j1K3f5t3aa82twXsPpYb3thHvJeP0Prk2GiqnjfAOPjLNu8W12W3Xcl4oYbdLTieQd76qtbpto88H1vAaJecNU3QxrdSdJstpprxnUrXqcp65vHQF9/yCP/55WL2OOh1b1eXSyw+5z6hqvVpKJRuDCeeFNbbcaqAYtN4sTnJFadRqonIT9v1qzueq5rQpY86XpeaOZ9lquz2Qs+83WXZ55Jz4ZrLoBETL7GZ9oo3H009Y3cGWcfcaE/Zzfu4F6yu16etWI1V1eeKWj1mPuaFR+5kMlWzM66OdfSecmLBajpyLccXlXMu7c3/I+1g577CGO+8mnY6j6gyNCkWnP2qjHWx4faHT6ww4qV7Dn1t55yHnxpZh56vn9UCSszq5sjuGvB8/l7ixrj5tNDRbFaexGi/7vHZec+XygvqxqOE0VPMYTOoTtp826l5D6i6dea/3ce/pcvVJw/aLWs35VDnR7JTTsT2bszq4pa7fVtxYU5m07zeywn4GQ0Nu7BuxnWpswGojcwNW+5h3mtvBQdu+SsNqsKple94AbNlq27jbXRMKTle2p2rP3WrDfgbVLbZf7NhsYzpUtP6TNLq/vxR3qIIgCIIgCLokJlRBEARBEARdEhOqIAiCIAiCLhHV3ucljuTIQRAEQRAsIG5S1TNaC+IOVRAEQRAEQZfEhCoIgiAIgqBLYkIVBEEQBEHQJTGhCoIgCIIg6JKYUAVBEARBEHRJTKiCIAiCIAi6JCZUQRAEQRAEXdIvufweBH4AHAI81+O2LHQiht0TMeyeiGF3RPy6J2LYPRHDmXmNL+gLY88mInKjN8oKOiNi2D0Rw+6JGHZHxK97IobdEzHsjFjyC4IgCIIg6JJ+m1Dd1OsGvAKIGHZPxLB7IobdEfHrnohh90QMO6CvlvyCIAiCIAgWIv12hyoIgiAIgmAC8ZyHAAAIqElEQVTBEROqIAiCIAiCLun5hEpEBkTkYhF5QUQmReQ7InJSr9vVj4jIm0TkchF5VET2iMh6EblRRI5sU/coEblTRMZFZJuI/KuIvKoX7e5nROTjIqIisq7NvreIyDdFZEJENonIpSIy2ot29hsi8noRuS3rWxMisk5E/tDVifjNgIgcISLXi8hzWXyeEJGLRGTY1Vv0MRSRURH5RDaebcvO17NmqDuncU9EciLyURF5RkTKIvKIiLznZT+YHjGXGGYxOSs7rzdk15h1IvJnIjI4w+ueLSKPZzF8SkT+YL8cUJ/SDz5UXwROAy4BngLOAu4Qkbep6jd72K5+5GPAz5GEgo8Aq4DzgQdF5GdVdR2AiBwC3APsBNYCo8CFwNEicpyqVnrR+H4ji9NaYE+bfccAXwceBz5M8mO5EDgCeMd+bGbfISJvB24HHgI+CYyTPFkOaakT8ZsBETkUuI90fl4ObAPeDHwCeAPwrqxexDCxErgIWA/8H/BL7Sp1OO79FfAnwD8D3yXF/DoRUVW9/mU6jl4ylxgOA1cD3wY+B2xmb788QUR+WVtE1yJyblbvFuAzwFuBS0VkWFUvfvkOpY9R1Z49gOMABS5sKRsEngbu7WXb+vEBvAUoubIjgDJwbUvZPwETwKtbyk7MYn1Or4+jXx7A9aQL1t3AOrfvDuAFYKyl7H1ZDN/e67b3MGZjwCbgViC3j3oRv5ljszaLw+tc+Zey8uURQxOXAWBV9v8bs+M/q029OY17wBqgAlzeUiakydgGIN/rY+5FDIES8JY2z70oq39iS9kQsAX4qqt7LekL1vJeH3MvHr1e8jsNqANXNgtUtQxcBbw5+yYXZKjqveruLqnqU8CjwFEtxaeSOvr6lnp3AU8CYdIGiMgvkPrfh9rsGwNOIk1Sd7XsuoY0WCzmGP4WcBDwcVVtiMiIiJhxJOI3K2PZ3xdd+UagAVQihntR1SlV3TSHqnMd994FFEkTsGY9Ba4g3QV884+i3f3EXGKoqhVVvbfNrn/L/rZeY94GHEBLDDP+ERgB3jnfti5kej2hOhZ40g0YkG6HAxyzn9uz4BARIV3gtmTba4ADgfvbVL+PFPNFjYjkgcuAL6jq99pUOZq0HG5imE1mH2Zxx/BEYBewRkS+T7q47xKRK1p0FhG/fXN39vcqETlGRA4Vkd8AzgMuVdU9RAw7osNx71jSMv/jbepBxNazKvu7paWsGSMf7wdIXwoWZQx7PaFaTfpW5mmWHbwf27JQOZN0C/uGbHt19nemuK4QkYH90bA+5gPAYcCfz7B/thgu5n55BOlC/xXgP0h3Bf6FFNOrszoRv32gqneS+t5JJB3aetLy82Wq+sdZtYhhZ3Qy7q0GXszuSvl6ELH1fJT0JerfW8pWA3VV3dxaMZvwb2WRxrDXovQhYKpNebllfzADIvJa0i3Wb5H0F7A3ZrPFtd3+VzwicgDwl8AnVfWlGarNFsPF3C9HSeLVz6lq81d9t4pICThXRC4i4jcXniVpdm4hXYDeCawVkU2qejkRw07pZNyL684cEZG1pLvSH1TVHS27hkg6tHYs2v7Z6wnVJEks5xls2R+0QURWAV8j/aLlNFWtZ7uaMYu4tudTpF9VXbaPOrPFcDHHr3nsX3bl1wHnkvQnE1lZxK8NIvKbJN3okar6XFZ8a6ZFu1hEvkz0wU7pZNyL684cyJahPwVcpapXuN2TJBF7OxZt/+z1kt9G9t6qbaVZ9sJ+bMuCQUSWkm6/LgN+RVVb49S8bT1TXLep6mK9O3UEcA5wKXCwiBwuIoeTBoBitr2C2WO4mPtl89i9oLp56385Eb/Z+CDwUMtkqsltpLt/xxIx7JROxr2NwKpMf+rrQcQWSV6Q15C+tH+gTZWNQF5EDnTPK5HE6osyhr2eUD0MHJn9oqWV41v2By1kwt/bgSOBU1T1sdb9qvo88BLpp7Ge41jcMV1D6vOXAs+0PI4nxfMZ0k+E1wE1XAyzweIYFncMH8j+rnHlTc3ES0T8ZuMgIN+mvJj9LRAx7IgOx72HSRPXo1y9uO4AInI86Zd99wNnqGqtTbVmjHy830gaYxdlDHs9obqZNLCc0yzIhIO/B3xHVTf0qmH9SPbrtBtIyyqnq+q3Zqh6C3BKq+2EiJxAmjQs5uzh64B3t3k8ShIGv5t0e3sncBfw2yKypOX5v0PSEC3mGN6Y/T3blb+PNAG4O+I3K08Cx8r0DAfvIf1C6pGI4byY67j3FaBKulPYrCekOzHPA+2sAxYFInIU6a7Us6Qv7DMt3X2DJJ04z5WfR1ry/9rL1cZ+Rqb/0GE/N0DkRtKF7B9Ihp6/S/pGcYKq3tPLtvUbInIJ8EekO1Q3+v2qem1W71DSr4d2AJ8lDcAfAZ4D3rRYl/xmQkTuBlaq6k+3lL2eNLA+RtK7HAJcANyjqif3op39gohcBfw+qQ/+D8l1+XTgr1V1bVYn4jcDmQfaN0hi9Muzv6eQ3M+/oKrvz+pFDDNE5HySxOFg0kX7VtIYB+nXkTs7GfdE5G+zfVeSnNJ/nfTDgDNV9br9clD7mdliSJrMP0q6+7yWNLls5QetX+JF5IOkH0XdTPrF71uB95I86j798h1JH9NrZ1GSfuXvSGuyZZIXyMm9blc/Pkj+NTrTw9V9HamT7wG2kxxsD+r1MfTjgzZO6Vn5zwP/SxJYbiZd/Jb0ur29fpCWpv6C9C22QkoZ9aGIX0cxPI7khL4xi+H3SRexQsSwbbye3cfYd3hLvTmNe6TVmT/NXneKdPf6zF4fZy9jmD1mvL4AX2zzmu8Hnshi+DTJKFl6fay9evT8DlUQBEEQBMFCp9caqiAIgiAIggVPTKiCIAiCIAi6JCZUQRAEQRAEXRITqiAIgiAIgi6JCVUQBEEQBEGXxIQqCIIgCIKgS2JCFQRBEARB0CUxoQqCIAiCIOiSmFAFQRAEQRB0yf8DqH5zq3DLpbwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: automobile (1)\n",
            "Image #1: automobile (1)\n",
            "Image #1: horse (7)\n",
            "Image #1: ship (8)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: frog (6)\n",
            "Image #1: horse (7)\n",
            "Image #1: frog (6)\n",
            "Image #1: airplane (0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: dog (5)\n",
            "Image #1: bird (2)\n",
            "Image #1: cat (3)\n",
            "Image #1: dog (5)\n",
            "Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
            "Image #1: frog (6)\n",
            "Image #1: dog (5)\n",
            "Image #1: ship (8)\n",
            "Image #1: deer (4)\n",
            "[Step #0] Loss: 0.0156 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 241.9456s (total 64 images)\n",
            "[Step #10] Loss: 0.0161 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 2618.0768s (total 704 images)\n",
            "[Validation] Loss: 0.0160 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 3793.4481s (total 1024 images)\n",
            "[Size of Perturbation]\n",
            "Average L0 distance (the number of changed parameters): 3059.6591796875\n",
            "Average L2 distance: 1.319286286830902\n",
            "Average MSE: 0.0006299173182924278\n",
            "Average Linf distance (the maximum changed values): 0.0706325676292181\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}