hnn_attack_defense_all_digit.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ericyoc/adversarial-defense-hnn/blob/main/hnn_attack_defense_all_digit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b23530d-c160-4139-8b3e-52b9bf469164",
      "metadata": {
        "id": "7b23530d-c160-4139-8b3e-52b9bf469164"
      },
      "source": [
        "Classical-Quantum or Hybrid Nerual Network that combines Quantum Neural Network (QNN) and Convolutional Neural Network (CNN) Model Using either MNIST or EMNIST Datasets with Protection Using Adverarial Training Against Compounded Attacks"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QCsgCc7B6OTd",
      "metadata": {
        "id": "QCsgCc7B6OTd"
      },
      "source": [
        "Make sure to set runtime to GPU (e.g., Google Colab T4 GPU) instead of CPU"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "OB7Knnyr6R4e",
      "metadata": {
        "id": "OB7Knnyr6R4e"
      },
      "source": [
        "Assumes that datasets are on Google Drive and that Google Drive will be mounted with default directory structure"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8352c686-f25b-4c25-9a0a-44bc7555c846",
      "metadata": {
        "id": "8352c686-f25b-4c25-9a0a-44bc7555c846"
      },
      "source": [
        "ATTENTION: MUST INSTALL THE MINIMUM PYTHON MODULE VERSIONS FOR PYTHON CODE TO WORK CORRECTLY"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "955c572f-5f61-4d61-b126-8f2c3571d366",
      "metadata": {
        "id": "955c572f-5f61-4d61-b126-8f2c3571d366"
      },
      "source": [
        "Python version: 3.8.18\n",
        "torch: 2.2.1\n",
        "torchvision: 0.17.1\n",
        "torchattacks: 3.5.1\n",
        "numpy: 1.23.5\n",
        "tabulate: 0.9.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "e53e8627-4a37-4ed0-8c7c-4bc8cf4e54a6",
      "metadata": {
        "id": "e53e8627-4a37-4ed0-8c7c-4bc8cf4e54a6"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "# Ignore all warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "07ZDiyWILm2P",
      "metadata": {
        "id": "07ZDiyWILm2P"
      },
      "outputs": [],
      "source": [
        "#!pip install torchattacks\n",
        "import torchattacks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "ArbA2OMn0i6m",
      "metadata": {
        "id": "ArbA2OMn0i6m"
      },
      "outputs": [],
      "source": [
        "#Pick a compounded adversarial attack from the following: fgsm_cw_attack, fgsm_pgd_attack, cw_pgd_attack, pgd_bim_attack, fgsm_bim_attack, cw_bim_attack\n",
        "# fgsm_deepfool_attack, pgd_deepfool_attack, cw_deepfool_attack, bim_deepfool_attack\n",
        "global compounded_attack_name\n",
        "\n",
        "compounded_attack_name = \"fgsm_cw_attack\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "EwsUoqDp1dyj",
      "metadata": {
        "id": "EwsUoqDp1dyj"
      },
      "outputs": [],
      "source": [
        "# Set the dataset_name\n",
        "global dataset_name\n",
        "dataset_name = 'MNIST'  # 'MNIST' or 'EMNIST' or 'SVHN' or 'USPS' or 'Semeion'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "fCVtnLGaMsZJ",
      "metadata": {
        "id": "fCVtnLGaMsZJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17adaac2-c5d9-4af2-9d03-89af2ac469a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Access the mounted drive\n",
        "drive_path = '/content/drive/My Drive/'\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "global device\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "metadata": {
        "id": "jRsRjgCao2Xx"
      },
      "id": "jRsRjgCao2Xx",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "ekL_geF31MQ0",
      "metadata": {
        "id": "ekL_geF31MQ0"
      },
      "outputs": [],
      "source": [
        "global num_epochs\n",
        "num_epochs = 10"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install cirq\n",
        "import cirq\n",
        "#!pip install cirq-google\n",
        "#import cirq_google"
      ],
      "metadata": {
        "id": "jL3yJCGEglVh"
      },
      "id": "jL3yJCGEglVh",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "1b7274e6-5dd7-48cb-b4bf-59d121320669",
      "metadata": {
        "id": "1b7274e6-5dd7-48cb-b4bf-59d121320669"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import pkg_resources\n",
        "import importlib\n",
        "\n",
        "def get_package_versions(packages):\n",
        "    versions = {}\n",
        "    for package in packages:\n",
        "        try:\n",
        "            module = importlib.import_module(package)\n",
        "            if hasattr(module, '__version__'):\n",
        "                versions[package] = module.__version__\n",
        "            elif package == 'cirq':\n",
        "                versions[package] = cirq.__version__\n",
        "            else:\n",
        "                versions[package] = 'Not Found'\n",
        "        except ImportError:\n",
        "            versions[package] = 'Not Installed'\n",
        "    return versions\n",
        "\n",
        "# Specify the list of packages you want to check\n",
        "packages_to_check = [\"torch\", \"torchvision\", \"torchattacks\", \"torchvision\", \"numpy\", \"tabulate\", \"cirq\",\"cirq_google\"]\n",
        "\n",
        "# Call the function to get package versions\n",
        "versions = get_package_versions(packages_to_check)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "88bb6b46-5683-474e-8162-03b61b15b364",
      "metadata": {
        "id": "88bb6b46-5683-474e-8162-03b61b15b364",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f42bfde-1ee1-4831-ba20-7c8b40eb575b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python version: 3.10.12\n",
            "torch: 2.2.1+cu121\n",
            "torchvision: 0.17.1+cu121\n",
            "torchattacks: 3.5.1\n",
            "numpy: 1.25.2\n",
            "tabulate: 0.9.0\n",
            "cirq: 1.3.0\n",
            "cirq_google: 1.3.0\n"
          ]
        }
      ],
      "source": [
        "# Get the Python version\n",
        "python_version = sys.version.split()[0]\n",
        "\n",
        "# Call the function to get package versions\n",
        "versions = get_package_versions(packages_to_check)\n",
        "\n",
        "# Print the Python version\n",
        "print(f\"Python version: {python_version}\")\n",
        "\n",
        "# Print the package versions\n",
        "for package_name, version in versions.items():\n",
        "    print(f\"{package_name}: {version}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "e4bc0e60-8668-4269-ad82-68be0fd52ef2",
      "metadata": {
        "id": "e4bc0e60-8668-4269-ad82-68be0fd52ef2"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data import DataLoader"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "theta is the rotation angle applied to even-indexed qubits (0, 2, 4, ...) using the cirq.ry(theta) gate.\n",
        "\n",
        "phi is the rotation angle applied to odd-indexed qubits (1, 3, 5, ...) using the cirq.ry(phi) gate.\n",
        "\n",
        "These rotation gates introduce controlled dynamics into the quantum circuit, and by varying the values of theta and phi, we can modify the evolution of the quantum state and potentially increase the expressive power of the circuit.\n",
        "\n",
        "During training, theta and phi are initialized as PyTorch tensors with requires_grad=True, allowing their values to be optimized via backpropagation"
      ],
      "metadata": {
        "id": "9LQuCTNr4tKs"
      },
      "id": "9LQuCTNr4tKs"
    },
    {
      "cell_type": "code",
      "source": [
        " # Initialize trainable parameters for the quantum circuit\n",
        "global theta\n",
        "global phi\n",
        "theta = torch.randn(1, requires_grad=True, device=device)\n",
        "phi = torch.randn(1, requires_grad=True, device=device)"
      ],
      "metadata": {
        "id": "6QUQXr7Pv0d1"
      },
      "id": "6QUQXr7Pv0d1",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The create_quantum_circuit function has two types of quantum gates being used: rotation gates and entangling gates.\n",
        "\n",
        "Rotation Gates:\n",
        "The code uses the cirq.ry gate, which represents a rotation around the Y-axis of the Bloch sphere.\n",
        "The cirq.ry(theta)(qubit) applies a rotation by an angle theta around the Y-axis to the specified qubit.\n",
        "In the code, there are two fixed rotation angles used: theta and phi.\n",
        "The rotation gates are applied to each qubit in an alternating manner based on the qubit index.\n",
        "If the qubit index is even (i % 2 == 0), a rotation by angle theta is applied using cirq.ry(theta)(qubit).\n",
        "If the qubit index is odd (i % 2 != 0), a rotation by angle phi is applied using cirq.ry(phi)(qubit).\n",
        "Rotation gates introduce single-qubit operations that can manipulate the state of individual qubits.\n",
        "\n",
        "Entangling Gates:\n",
        "The code uses the cirq.CNOT gate, which represents the controlled-NOT operation.\n",
        "The cirq.CNOT(control_qubit, target_qubit) applies a CNOT gate with control_qubit as the control and target_qubit as the target.\n",
        "In the code, CNOT gates are applied between pairs of adjacent qubits.\n",
        "The loop for i in range(num_qubits - 1) iterates over the qubits, skipping the last one.\n",
        "\n",
        "For each pair of adjacent qubits qubits[i] and qubits[i+1], a CNOT gate is appended to the circuit using circuit.append(cirq.CNOT(qubits[i], qubits[i+1])).\n",
        "CNOT gates create entanglement between qubits, allowing for multi-qubit operations and introducing correlations between the states of different qubits.\n",
        "The purpose of using these quantum gates in the circuit is to introduce quantum operations that can manipulate the state of the qubits. The rotation gates (cirq.ry) allow for single-qubit transformations, while the entangling gates (cirq.CNOT) create entanglement and enable multi-qubit operations.\n",
        "\n",
        "By applying a combination of rotation gates and entangling gates, the quantum circuit can perform quantum computations and potentially capture complex patterns and correlations in the data. The specific choice of gates and their arrangement depends on the desired quantum algorithm and the problem being solved."
      ],
      "metadata": {
        "id": "UFsZnMB9544K"
      },
      "id": "UFsZnMB9544K"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note: The code uses fixed rotation angles (theta and phi) for simplicity, but in practice, these angles can be parameterized and learnable during the training process of the hybrid model."
      ],
      "metadata": {
        "id": "Kxid6Ibu6Vhj"
      },
      "id": "Kxid6Ibu6Vhj"
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "def create_quantum_circuit(theta, phi, output_dim):\n",
        "\n",
        "    # Check if output_dim is valid\n",
        "    if output_dim <= 0:\n",
        "        print(f\"Output dimension {output_dim} is invalid. Returning an empty circuit.\")\n",
        "        return cirq.Circuit()\n",
        "\n",
        "    # Number of qubits required for output_dim classes\n",
        "    num_qubits = int(math.ceil(math.log2(output_dim)))\n",
        "\n",
        "    # Create qubits\n",
        "    qubits = cirq.LineQubit.range(num_qubits)\n",
        "\n",
        "    # Initialize quantum circuit\n",
        "    circuit = cirq.Circuit()\n",
        "\n",
        "    # Apply fixed rotations to each qubit\n",
        "    theta = 0.5\n",
        "    phi = 0.3\n",
        "    for i, qubit in enumerate(qubits):\n",
        "        if i % 2 == 0:\n",
        "            circuit.append(cirq.ry(theta)(qubit))\n",
        "        else:\n",
        "            circuit.append(cirq.ry(phi)(qubit))\n",
        "\n",
        "    # Apply entangling gates (e.g., CNOT gates)\n",
        "    for i in range(num_qubits - 1):\n",
        "        circuit.append(cirq.CNOT(qubits[i], qubits[i+1]))\n",
        "\n",
        "    return circuit"
      ],
      "metadata": {
        "id": "SCSwkooGiP6O"
      },
      "id": "SCSwkooGiP6O",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def simulate_circuit(circuit, device):\n",
        "\n",
        "    if device == \"gpu\":\n",
        "        # Create a GPU-accelerated simulator\n",
        "        simulator = cirq.TensorflowSimulator(tensor_env=tf.distribute.cluster_resolver.TPUClusterResolver())\n",
        "    else:\n",
        "        # Create a CPU simulator\n",
        "        simulator = cirq.Simulator()\n",
        "\n",
        "    result = simulator.simulate(circuit)\n",
        "    final_state_vector = result.final_state_vector\n",
        "\n",
        "    return final_state_vector\n"
      ],
      "metadata": {
        "id": "3T2zciL4k1-j"
      },
      "id": "3T2zciL4k1-j",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the quantum circuit\n",
        "quantum_circuit = create_quantum_circuit(theta, phi, 10)\n",
        "\n",
        "# Print the circuit\n",
        "print(quantum_circuit)"
      ],
      "metadata": {
        "id": "wFeF8--GiqGx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90e4cd39-c6d8-47d5-9122-1c00e4d17b05"
      },
      "id": "wFeF8--GiqGx",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0: ───Ry(0.159π)───@───────────\n",
            "                   │\n",
            "1: ───Ry(0.095π)───X───@───────\n",
            "                       │\n",
            "2: ───Ry(0.159π)───────X───@───\n",
            "                           │\n",
            "3: ───Ry(0.095π)───────────X───\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Simulate the circuit on CPU\n",
        "cpu_state_vector = simulate_circuit(quantum_circuit, device=\"cpu\")\n",
        "print(\"CPU state vector:\", cpu_state_vector)"
      ],
      "metadata": {
        "id": "PhGdq1xglFqF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca79e5eb-3d73-4dbd-ed9f-85aefdd0bf12"
      },
      "id": "PhGdq1xglFqF",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU state vector: [0.9178265 +0.j 0.1387159 +0.j 0.03541998+0.j 0.23435956+0.j\n",
            " 0.03541998+0.j 0.00535321+0.j 0.02096485+0.j 0.13871588+0.j\n",
            " 0.03541998+0.j 0.00535321+0.j 0.0013669 +0.j 0.00904421+0.j\n",
            " 0.05984182+0.j 0.00904421+0.j 0.03541998+0.j 0.23435956+0.j]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Simulate the circuit on GPU (if available)\n",
        "try:\n",
        "    gpu_state_vector = simulate_circuit(quantum_circuit, device=\"gpu\")\n",
        "    print(\"GPU state vector:\", gpu_state_vector)\n",
        "except Exception as e:\n",
        "    print(\"GPU acceleration is not available on this system.\")\n",
        "    print(e)"
      ],
      "metadata": {
        "id": "-Y1BuLs9qrgS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4ec58bd-483d-4f60-cd4b-1ecfc5716738"
      },
      "id": "-Y1BuLs9qrgS",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU acceleration is not available on this system.\n",
            "module 'cirq' has no attribute 'TensorflowSimulator'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def hybrid_forward(input_data, classical_model, theta, phi, device, output_dim=10):\n",
        "    # Move input data to the specified device\n",
        "    input_data = input_data.to(device)\n",
        "\n",
        "    # Pass input through classical model\n",
        "    classical_output = classical_model(input_data)\n",
        "\n",
        "    # Construct quantum circuit based on classical output\n",
        "    quantum_circuit = create_quantum_circuit(theta, phi, output_dim)\n",
        "\n",
        "    # Simulate quantum circuit and extract results\n",
        "    simulator = cirq.Simulator()\n",
        "    result = simulator.simulate(quantum_circuit)\n",
        "    quantum_output_amplitudes = result.final_state_vector\n",
        "\n",
        "    # Get the batch size from the classical output\n",
        "    batch_size = classical_output.size(0)\n",
        "\n",
        "    # Reshape classical output to match quantum output dimensions\n",
        "    classical_output = classical_output.view(batch_size, -1)\n",
        "\n",
        "    # Compute the squared amplitudes (probabilities) of the quantum output\n",
        "    num_qubits = int(math.ceil(math.log2(output_dim)))\n",
        "    expected_quantum_output_size = 2 ** num_qubits\n",
        "    quantum_output_probabilities = np.square(np.abs(quantum_output_amplitudes))\n",
        "    quantum_output_probabilities = quantum_output_probabilities.reshape(expected_quantum_output_size)[:output_dim]\n",
        "\n",
        "    # Repeat the quantum output probabilities for each batch element\n",
        "    quantum_output_probabilities = np.tile(quantum_output_probabilities, (batch_size, 1))\n",
        "\n",
        "    # Convert quantum output probabilities to PyTorch tensor\n",
        "    quantum_output_probabilities = torch.from_numpy(quantum_output_probabilities).to(device)\n",
        "\n",
        "    # Combine classical and quantum outputs using a linear combination\n",
        "    alpha = 0.5  # Weight for the classical output\n",
        "    beta = 0.5  # Weight for the quantum output\n",
        "    hybrid_output = alpha * classical_output + beta * quantum_output_probabilities\n",
        "\n",
        "    # Apply log_softmax to the hybrid output\n",
        "    hybrid_output = F.log_softmax(hybrid_output, dim=1)\n",
        "\n",
        "    return hybrid_output"
      ],
      "metadata": {
        "id": "LqWsHyqciUiR"
      },
      "id": "LqWsHyqciUiR",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "CNN architecture is designed to recognize digits 0 to 9 in the MNIST type dataset.\n",
        "\n",
        "Input:\n",
        "The MNIST dataset consists of grayscale images of handwritten digits.\n",
        "Each image has a size of 28x28 pixels.\n",
        "The input to the CNN is expected to be a tensor of shape (batch_size, 1, 28, 28), where 1 represents the single color channel (grayscale).\n",
        "\n",
        "Convolutional Layers:\n",
        "The first convolutional layer (self.conv1) has 1 input channel (grayscale) and 4 output feature maps. It uses a kernel size of 5x5 to learn local patterns in the input image.\n",
        "\n",
        "The second convolutional layer (self.conv2) has 4 input feature maps (output from conv1) and 10 output feature maps. It also uses a kernel size of 5x5 to learn higher-level features.\n",
        "\n",
        "Max pooling is applied after each convolutional layer to downsample the feature maps and reduce spatial dimensions.\n",
        "\n",
        "Fully Connected Layers:\n",
        "After the convolutional layers, the feature maps are flattened into a 1D vector.\n",
        "The flattened vector is passed through two fully connected layers (self.fc1 and self.fc2) to learn non-linear combinations of the extracted features.\n",
        "The output of the last fully connected layer (self.fc2) has 10 units, corresponding to the 10 digit classes (0 to 9).\n",
        "\n",
        "Output:\n",
        "The output of the CNN is a tensor of shape (batch_size, 10), representing the predicted probabilities for each digit class.\n",
        "The log_softmax activation function is applied to the output to obtain normalized log probabilities.\n",
        "\n",
        "During training, the CNN learns to map the input images to their corresponding digit labels by adjusting the weights of the convolutional and fully connected layers. The network learns to extract relevant features and patterns from the images that are discriminative for digit recognition.\n",
        "\n",
        "At inference time, given an input image of a handwritten digit, the trained CNN will predict the probability distribution over the 10 digit classes. The class with the highest probability is typically considered as the predicted digit."
      ],
      "metadata": {
        "id": "V5iYNLK44Tk7"
      },
      "id": "V5iYNLK44Tk7"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "b1e15569-ff43-4b6a-a18e-455147688571",
      "metadata": {
        "id": "b1e15569-ff43-4b6a-a18e-455147688571"
      },
      "outputs": [],
      "source": [
        "# Define model\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        # First convolutional layer\n",
        "        # Input: 1 channel (grayscale image)\n",
        "        # Output: 4 feature maps\n",
        "        # Kernel size: 5x5\n",
        "        self.conv1 = nn.Conv2d(1, 4, 5)\n",
        "\n",
        "        # Second convolutional layer\n",
        "        # Input: 4 feature maps\n",
        "        # Output: 10 feature maps\n",
        "        # Kernel size: 5x5\n",
        "        self.conv2 = nn.Conv2d(4, 10, 5)\n",
        "\n",
        "        # First fully connected layer\n",
        "        # Input: 160 features (flattened output from conv2)\n",
        "        # Output: 80 features\n",
        "        self.fc1 = nn.Linear(160, 80)\n",
        "\n",
        "        # Second fully connected layer\n",
        "        # Input: 80 features\n",
        "        # Output: 10 features (corresponding to 10 classes)\n",
        "        self.fc2 = nn.Linear(80, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Apply first convolutional layer and ReLU activation\n",
        "        x = F.relu(self.conv1(x))\n",
        "\n",
        "        # Apply max pooling with kernel size 2x2\n",
        "        x = F.max_pool2d(x, 2)\n",
        "\n",
        "        # Apply second convolutional layer and ReLU activation\n",
        "        x = F.relu(self.conv2(x))\n",
        "\n",
        "        # Apply max pooling with kernel size 2x2\n",
        "        x = F.max_pool2d(x, 2)\n",
        "\n",
        "        # Flatten the output from the convolutional layers\n",
        "        x = x.view(-1, 160)\n",
        "\n",
        "        # Apply first fully connected layer and ReLU activation\n",
        "        x = F.relu(self.fc1(x))\n",
        "\n",
        "        # Apply second fully connected layer\n",
        "        x = self.fc2(x)\n",
        "\n",
        "        # Apply log softmax activation to get class probabilities\n",
        "        return F.log_softmax(x, dim=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "6c2d9762-e9f9-4b70-9888-983828929995",
      "metadata": {
        "id": "6c2d9762-e9f9-4b70-9888-983828929995",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46ca7589-90b8-4dbc-fa1f-e4eac4fba2a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Architecture:\n",
            "CNN(\n",
            "  (conv1): Conv2d(1, 4, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (conv2): Conv2d(4, 10, kernel_size=(5, 5), stride=(1, 1))\n",
            "  (fc1): Linear(in_features=160, out_features=80, bias=True)\n",
            "  (fc2): Linear(in_features=80, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "classical_model = CNN().to(device)\n",
        "\n",
        "print(\"Model Architecture:\")\n",
        "print(classical_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Hybrid Neural Network (HNN) combines a classical model (e.g., CNN) with a quantum circuit. The purpose of the HNN is to enhance the performance of the classical model by leveraging the capabilities of quantum computing.\n",
        "\n",
        "The HNN supports the CNN in recognizing MNIST digits 0-9 as explained below:\n",
        "\n",
        "Integration with the Classical Model:\n",
        "The HNN takes the classical model (CNN) as an input parameter (classical_model) during initialization.\n",
        "The classical model is stored as an attribute of the HNN using self.classical_model = classical_model.\n",
        "This allows the HNN to access and utilize the features and predictions of the CNN.\n",
        "\n",
        "Quantum Circuit Parameters:\n",
        "The HNN initializes trainable parameters (self.theta and self.phi) for the quantum circuit.\n",
        "These parameters represent the angles of rotation gates in the quantum circuit and are learned during the training process.\n",
        "The quantum circuit introduces additional non-linearity and expressiveness to the model, potentially enhancing its ability to capture complex patterns in the data.\n",
        "\n",
        "Forward Pass:\n",
        "In the forward method of the HNN, the input data (input_data) is passed to the hybrid_forward function along with the classical model, quantum circuit parameters, device, and output dimension.\n",
        "The hybrid_forward function is responsible for integrating the classical model with the quantum circuit.\n",
        "It takes the features extracted by the CNN and uses them as input to the quantum circuit.\n",
        "The quantum circuit applies quantum operations based on the learned parameters (self.theta and self.phi) to transform the input features.\n",
        "The output of the quantum circuit is then processed and combined with the classical model's predictions to produce the final output.\n",
        "\n",
        "Hybrid Learning:\n",
        "During the training process, both the classical model (CNN) and the quantum circuit parameters (self.theta and self.phi) are optimized jointly.\n",
        "The gradients are propagated through the hybrid model, allowing the CNN and the quantum circuit to learn and adapt together.\n",
        "The quantum circuit can potentially learn to extract additional features or patterns that complement the CNN's capabilities.\n",
        "\n",
        "Output Dimension:\n",
        "The HNN's output dimension (output_dim) is set to 10, corresponding to the 10 digit classes (0 to 9) in the MNIST dataset.\n",
        "The hybrid model's final output represents the predicted probabilities for each digit class.\n",
        "\n",
        "By combining the CNN with a quantum circuit, the HNN aims to leverage the strengths of both classical and quantum computing. The quantum circuit can potentially capture additional patterns and correlations in the data that the CNN alone might miss. The hybrid approach allows for the exploration of quantum-enhanced feature extraction and classification."
      ],
      "metadata": {
        "id": "wL2nF2Y747F1"
      },
      "id": "wL2nF2Y747F1"
    },
    {
      "cell_type": "code",
      "source": [
        "class HNN(nn.Module):\n",
        "    def __init__(self, classical_model, device, output_dim=10):\n",
        "        super(HNN, self).__init__()\n",
        "\n",
        "        # Store the classical model as an attribute\n",
        "        self.classical_model = classical_model\n",
        "\n",
        "        # Store the output dimension as an attribute\n",
        "        self.output_dim = output_dim\n",
        "\n",
        "        # Initialize trainable parameters for the quantum circuit\n",
        "        self.theta = nn.Parameter(torch.randn(1))\n",
        "        self.phi = nn.Parameter(torch.randn(1))\n",
        "\n",
        "        # Store the device as an attribute\n",
        "        self.device = device\n",
        "\n",
        "        # Move the model to the specified device if it's not None\n",
        "        if device is not None:\n",
        "            self.to(device)\n",
        "\n",
        "    def forward(self, input_data):\n",
        "        # Call the hybrid_forward function with the input data, classical model,\n",
        "        # quantum circuit parameters (theta and phi), device, and output dimension\n",
        "        return hybrid_forward(input_data, self.classical_model, self.theta, self.phi, self.device, self.output_dim)"
      ],
      "metadata": {
        "id": "_0nbvwq-6Qjd"
      },
      "id": "_0nbvwq-6Qjd",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an instance of the hybrid model\n",
        "hybrid_model = HNN(classical_model, device=device, output_dim=10).to(device)"
      ],
      "metadata": {
        "id": "pHocHFOw6xSi"
      },
      "id": "pHocHFOw6xSi",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the hybrid model architecture\n",
        "print(\"Hybrid Model Architecture:\")\n",
        "print(hybrid_model)"
      ],
      "metadata": {
        "id": "i9gnPUtS7FEs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "579bc8a9-ff66-4f49-9ef9-76485392602d"
      },
      "id": "i9gnPUtS7FEs",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hybrid Model Architecture:\n",
            "HNN(\n",
            "  (classical_model): CNN(\n",
            "    (conv1): Conv2d(1, 4, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (conv2): Conv2d(4, 10, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (fc1): Linear(in_features=160, out_features=80, bias=True)\n",
            "    (fc2): Linear(in_features=80, out_features=10, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "d6ea1172-9b8b-48bf-ae82-de28b755a161",
      "metadata": {
        "id": "d6ea1172-9b8b-48bf-ae82-de28b755a161"
      },
      "outputs": [],
      "source": [
        "optimizer = optim.Adam(classical_model.parameters())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "d87f07a6-d229-446f-a951-3536f0e8604a",
      "metadata": {
        "id": "d87f07a6-d229-446f-a951-3536f0e8604a"
      },
      "outputs": [],
      "source": [
        "loss_fn = nn.NLLLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "H09bz5fwDFj7",
      "metadata": {
        "id": "H09bz5fwDFj7"
      },
      "outputs": [],
      "source": [
        "import gzip\n",
        "import numpy as np\n",
        "from torchvision import transforms\n",
        "\n",
        "def load_emnist_digits(data_dir, train=True, transform=None, filtered=range(10)):\n",
        "    if train:\n",
        "        image_file = 'emnist-digits-train-images-idx3-ubyte.gz'\n",
        "        label_file = 'emnist-digits-train-labels-idx1-ubyte.gz'\n",
        "    else:\n",
        "        image_file = 'emnist-digits-test-images-idx3-ubyte.gz'\n",
        "        label_file = 'emnist-digits-test-labels-idx1-ubyte.gz'\n",
        "\n",
        "    image_path = os.path.join(data_dir, image_file)\n",
        "    label_path = os.path.join(data_dir, label_file)\n",
        "\n",
        "    with gzip.open(image_path, 'rb') as f:\n",
        "        images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n",
        "    with gzip.open(label_path, 'rb') as f:\n",
        "        labels = np.frombuffer(f.read(), np.uint8, offset=8)\n",
        "\n",
        "    dataset = EMNISTDataset(images, labels, transform)\n",
        "\n",
        "    if filtered is not None:\n",
        "        digits_to_keep = list(filtered)  # Convert the range object to a list\n",
        "        mask = np.isin(dataset.labels, digits_to_keep)\n",
        "        dataset.images = dataset.images[mask]\n",
        "        dataset.labels = dataset.labels[mask]\n",
        "\n",
        "    return dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "Wvn2OTedITEr",
      "metadata": {
        "id": "Wvn2OTedITEr"
      },
      "outputs": [],
      "source": [
        "def filtered_dataset_EMNIST(train_set, test_set, filtered_digits=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]):\n",
        "    if filtered_digits is not None:\n",
        "        train_mask = np.isin(train_set.labels, filtered_digits)\n",
        "        test_mask = np.isin(test_set.labels, filtered_digits)\n",
        "\n",
        "        train_images = train_set.images[train_mask]\n",
        "        train_labels = train_set.labels[train_mask]\n",
        "        test_images = test_set.images[test_mask]\n",
        "        test_labels = test_set.labels[test_mask]\n",
        "\n",
        "        filtered_train_set = EMNISTDataset(train_images, train_labels, transform=train_set.transform)\n",
        "        filtered_test_set = EMNISTDataset(test_images, test_labels, transform=test_set.transform)\n",
        "    else:\n",
        "        filtered_train_set = train_set\n",
        "        filtered_test_set = test_set\n",
        "\n",
        "    return filtered_train_set, filtered_test_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "r2Vbxm040dSn",
      "metadata": {
        "id": "r2Vbxm040dSn"
      },
      "outputs": [],
      "source": [
        "import gzip\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "class EMNISTDigits(Dataset):\n",
        "    def __init__(self, root, train=True, transform=None, target_transform=None):\n",
        "        self.root = root\n",
        "        self.train = train\n",
        "        self.transform = transform\n",
        "        self.target_transform = target_transform\n",
        "\n",
        "        prefix = 'train' if self.train else 'test'\n",
        "        image_file = f'{self.root}/emnist-digits-{prefix}-images-idx3-ubyte.gz'\n",
        "        label_file = f'{self.root}/emnist-digits-{prefix}-labels-idx1-ubyte.gz'\n",
        "\n",
        "        with gzip.open(image_file, 'rb') as f:\n",
        "            self.images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n",
        "\n",
        "        with gzip.open(label_file, 'rb') as f:\n",
        "            self.labels = np.frombuffer(f.read(), np.uint8, offset=8)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        image = self.images[index]\n",
        "        label = self.labels[index]\n",
        "\n",
        "        if self.transform is not None:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        if self.target_transform is not None:\n",
        "            label = self.target_transform(label)\n",
        "\n",
        "        return image, label\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "BUkp5z9P1Egk",
      "metadata": {
        "id": "BUkp5z9P1Egk"
      },
      "outputs": [],
      "source": [
        "#!ls \"/content/drive/My Drive/data/EMNIST/raw/\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The MNIST (Modified National Institute of Standards and Technology) dataset is a large collection of handwritten digits commonly used for training and testing machine learning models, particularly in the field of computer vision and image classification.\n",
        "\n",
        "The MNIST dataset contains a total of 70,000 images of handwritten digits. These images are divided into two subsets:\n",
        "\n",
        "Training set: The training set consists of 60,000 images. This set is used to train the machine learning models, allowing them to learn patterns and features from the labeled examples.\n",
        "Test set: The test set contains the remaining 10,000 images. This set is used to evaluate the performance and accuracy of the trained models on unseen data.\n",
        "Each image in the MNIST dataset is a grayscale image with a size of 28x28 pixels. The digits in the images are centered and normalized to fit within the 28x28 pixel frame."
      ],
      "metadata": {
        "id": "y8g-al5C1aO5"
      },
      "id": "y8g-al5C1aO5"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The EMNIST (Extended MNIST) dataset is an extension of the original MNIST dataset, providing additional handwritten character classes beyond just digits. The EMNIST dataset consists of several subsets, including EMNIST Balanced, EMNIST Letters, EMNIST Digits, EMNIST MNIST, and EMNIST By_Merge.\n",
        "\n",
        "Specifically, the EMNIST \"Digits\" subset contains handwritten digit images, similar to the original MNIST dataset. The number of images in the EMNIST \"Digits\" subset is as follows:\n",
        "\n",
        "Training set: The training set of EMNIST \"Digits\" contains 240,000 images.\n",
        "Test set: The test set of EMNIST \"Digits\" contains 40,000 images.\n",
        "Therefore, the total number of images in the EMNIST \"Digits\" subset is 280,000 (240,000 training images + 40,000 test images).\n",
        "\n",
        "The images in EMNIST \"Digits\" have the same format as the original MNIST dataset, with each image being a grayscale image of size 28x28 pixels, centered and normalized."
      ],
      "metadata": {
        "id": "HgiVyr1W1mBJ"
      },
      "id": "HgiVyr1W1mBJ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The SVHN (Street View House Numbers) dataset is a real-world image dataset for developing machine learning and object recognition algorithms with a focus on recognizing digits and numbers in natural scene images. The dataset was obtained from house numbers in Google Street View images.\n",
        "\n",
        "The SVHN dataset consists of two formats:\n",
        "\n",
        "Original Images:\n",
        "Training set: 73,257 images\n",
        "Test set: 26,032 images\n",
        "Extra training set: 531,131 images\n",
        "The original images are in color and of varying sizes, containing one or more digits in each image.\n",
        "MNIST-like 32x32 images centered around a single character:\n",
        "Training set: 73,257 images\n",
        "Test set: 26,032 images\n",
        "Extra training set: 531,131 images\n",
        "These images are preprocessed to be similar to the MNIST dataset format, with each image cropped to 32x32 pixels and centered around a single digit.\n",
        "In total, the SVHN dataset contains 630,420 images (73,257 + 26,032 + 531,131) in both the original format and the MNIST-like format."
      ],
      "metadata": {
        "id": "UqDUEfkp1uGm"
      },
      "id": "UqDUEfkp1uGm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The USPS (United States Postal Service) dataset is a collection of handwritten digit images scanned from envelopes by the U.S. Postal Service. It is commonly used as a benchmark dataset for digit recognition tasks in machine learning and computer vision.\n",
        "\n",
        "The USPS dataset contains a total of 9,298 images. These images are divided into two subsets:\n",
        "\n",
        "Training set: The training set consists of 7,291 images.\n",
        "Test set: The test set contains the remaining 2,007 images.\n",
        "Each image in the USPS dataset is a grayscale image with a size of 16x16 pixels. The digits in the images are centered and normalized to fit within the 16x16 pixel frame.\n",
        "\n",
        "Compared to the MNIST dataset, the USPS dataset has a smaller number of images and a lower resolution. However, it provides a different set of handwritten digit samples and can be used as an additional benchmark or for testing the generalization ability of digit recognition models trained on other datasets like MNIST."
      ],
      "metadata": {
        "id": "PeJ5tHJU2El2"
      },
      "id": "PeJ5tHJU2El2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Semeion dataset, also known as the Semeion Handwritten Digit dataset, is a collection of handwritten digit images created by the Semeion Research Center of Sciences of Communication in Rome, Italy. It is used for benchmarking and evaluating handwritten digit recognition models.\n",
        "\n",
        "The Semeion dataset contains a total of 1,593 handwritten digit images. Each image is a grayscale image with a size of 16x16 pixels, similar to the USPS dataset.\n",
        "\n",
        "The dataset is structured as follows:\n",
        "\n",
        "The dataset consists of 1,593 rows, where each row represents a single handwritten digit image.\n",
        "Each row contains 256 columns representing the pixel values of the 16x16 image, followed by an additional 10 columns representing the one-hot encoded label of the digit (0-9).\n",
        "Unlike some other well-known datasets like MNIST or USPS, the Semeion dataset does not have a predefined split into training and test sets. Researchers often use their own split ratios or cross-validation techniques when working with this dataset."
      ],
      "metadata": {
        "id": "sJoaGX062Ou0"
      },
      "id": "sJoaGX062Ou0"
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "31916be4-b247-445e-bdb4-f7f651135af8",
      "metadata": {
        "id": "31916be4-b247-445e-bdb4-f7f651135af8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c483138-06b4-404a-aec5-a0bb7f168916"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/data/MNIST/\n"
          ]
        }
      ],
      "source": [
        "import torchvision.datasets as datasets\n",
        "import torchvision.transforms as transforms\n",
        "import os\n",
        "from PIL import Image\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "if dataset_name == 'MNIST':\n",
        "    # Load a dataset from Google Drive\n",
        "    data_dir = drive_path + 'data/MNIST/'\n",
        "    print(data_dir)\n",
        "    train_set = datasets.MNIST(data_dir, train=True, download=False, transform=transforms.ToTensor())\n",
        "    test_set = datasets.MNIST(data_dir, train=False, download=False, transform=transforms.ToTensor())\n",
        "elif dataset_name == 'EMNIST':\n",
        "    # Load a dataset from Google Drive\n",
        "    data_dir = drive_path + 'data/EMNIST/raw/'\n",
        "    train_set = EMNISTDigits(data_dir, train=True, transform=ToTensor())\n",
        "    test_set = EMNISTDigits(data_dir, train=False, transform=ToTensor())\n",
        "    print(data_dir)\n",
        "elif dataset_name == 'SVHN':\n",
        "    data_dir = '/content/drive/data/'\n",
        "    train_set = datasets.SVHN(data_dir, split='train', download=True, transform=data_transform)\n",
        "    test_set = datasets.SVHN(data_dir, split='test', download=True, transform=data_transform)\n",
        "elif dataset_name == 'USPS':\n",
        "    data_dir = '/content/drive/data/'\n",
        "    train_set = datasets.USPS(data_dir, train=True, download=True, transform=data_transform)\n",
        "    test_set = datasets.USPS(data_dir, train=False, download=True, transform=data_transform)\n",
        "elif dataset_name == 'Semeion':\n",
        "    data_dir = '/content/drive/data/'\n",
        "    from torch.utils.data import DataLoader, Dataset, random_split\n",
        "    import numpy as np\n",
        "    from torchvision import transforms\n",
        "\n",
        "    class SemeionDataset(Dataset):\n",
        "        def __init__(self, file_path, transform=None):\n",
        "            data = np.loadtxt(file_path)\n",
        "            self.images = data[:, :256].reshape(-1, 16, 16).astype(np.float32)\n",
        "            self.labels = data[:, 256:].astype(np.int64)\n",
        "            self.transform = transform\n",
        "\n",
        "        def __len__(self):\n",
        "            return len(self.images)\n",
        "\n",
        "        def __getitem__(self, idx):\n",
        "            image = self.images[idx]\n",
        "            label = self.labels[idx].argmax()  # Convert one-hot encoded labels to integers\n",
        "            if self.transform:\n",
        "                image = self.transform(image)\n",
        "            return image, label\n",
        "\n",
        "    # Define the data transformation\n",
        "    data_transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Resize((28, 28)),  # Resize the images to 28x28 pixels\n",
        "        transforms.Normalize((0.1307,), (0.3081,))  # Normalize the images\n",
        "    ])\n",
        "\n",
        "    # Load the Semeion dataset\n",
        "    dataset = SemeionDataset(data_dir, transform=data_transform)\n",
        "\n",
        "    # Split the dataset into training and test sets\n",
        "    train_size = int(0.8 * len(dataset))\n",
        "    test_size = len(dataset) - train_size\n",
        "    train_set, test_set = random_split(dataset, [train_size, test_size])\n",
        "\n",
        "else:\n",
        "    raise ValueError(\"Invalid dataset name.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "db1e3f95-e99c-4dae-84ec-a2257034b356",
      "metadata": {
        "id": "db1e3f95-e99c-4dae-84ec-a2257034b356"
      },
      "outputs": [],
      "source": [
        "\n",
        "from torch.utils.data import Subset\n",
        "import numpy as np\n",
        "\n",
        "def filtered_dataset(train_set, test_set, filtered=0, dataset_name=None):\n",
        "    if filtered == 0:  # digits 0 and 1\n",
        "        if dataset_name == 'SVHN' or dataset_name == 'EMNIST':\n",
        "            train_indexes_0 = torch.where(torch.tensor(train_set.labels) == 0)[0]\n",
        "            train_indexes_1 = torch.where(torch.tensor(train_set.labels) == 1)[0]\n",
        "            test_indexes_0 = torch.where(torch.tensor(test_set.labels) == 0)[0]\n",
        "            test_indexes_1 = torch.where(torch.tensor(test_set.labels) == 1)[0]\n",
        "        elif dataset_name == 'USPS':\n",
        "            train_indexes_0 = torch.where(torch.tensor(train_set.targets) == 0)[0]\n",
        "            train_indexes_1 = torch.where(torch.tensor(train_set.targets) == 1)[0]\n",
        "            test_indexes_0 = torch.where(torch.tensor(test_set.targets) == 0)[0]\n",
        "            test_indexes_1 = torch.where(torch.tensor(test_set.targets) == 1)[0]\n",
        "        else:\n",
        "            try:\n",
        "                train_indexes_0 = torch.where(train_set.targets == 0)[0]\n",
        "                train_indexes_1 = torch.where(train_set.targets == 1)[0]\n",
        "                test_indexes_0 = torch.where(test_set.targets == 0)[0]\n",
        "                test_indexes_1 = torch.where(test_set.targets == 1)[0]\n",
        "            except AttributeError:\n",
        "                train_indexes_0 = torch.where(torch.tensor(train_set.labels) == 0)[0]\n",
        "                train_indexes_1 = torch.where(torch.tensor(train_set.labels) == 1)[0]\n",
        "                test_indexes_0 = torch.where(torch.tensor(test_set.labels) == 0)[0]\n",
        "                test_indexes_1 = torch.where(torch.tensor(test_set.labels) == 1)[0]\n",
        "\n",
        "        num_to_sample_train = min(len(train_indexes_0), len(train_indexes_1))\n",
        "        num_to_sample_test = min(len(test_indexes_0), len(test_indexes_1))\n",
        "\n",
        "        sampled_train_indexes_0 = train_indexes_0[torch.randperm(len(train_indexes_0))[:num_to_sample_train]]\n",
        "        sampled_train_indexes_1 = train_indexes_1[torch.randperm(len(train_indexes_1))[:num_to_sample_train]]\n",
        "        sampled_test_indexes_0 = test_indexes_0[torch.randperm(len(test_indexes_0))[:num_to_sample_test]]\n",
        "        sampled_test_indexes_1 = test_indexes_1[torch.randperm(len(test_indexes_1))[:num_to_sample_test]]\n",
        "\n",
        "        selected_train_indexes = torch.cat([sampled_train_indexes_0, sampled_train_indexes_1])\n",
        "        selected_test_indexes = torch.cat([sampled_test_indexes_0, sampled_test_indexes_1])\n",
        "\n",
        "    elif filtered == 1:  # digits 0 to 9\n",
        "        train_indexes = []\n",
        "        test_indexes = []\n",
        "        for i in range(10):\n",
        "            if dataset_name == 'SVHN' or dataset_name == 'EMNIST':\n",
        "                train_idx = torch.where(torch.tensor(train_set.labels) == i)[0]\n",
        "                test_idx = torch.where(torch.tensor(test_set.labels) == i)[0]\n",
        "            elif dataset_name == 'USPS':\n",
        "                train_idx = torch.where(torch.tensor(train_set.targets) == i)[0]\n",
        "                test_idx = torch.where(torch.tensor(test_set.targets) == i)[0]\n",
        "            elif dataset_name == 'Semeion':\n",
        "                train_idx = torch.tensor([dataset.labels[idx].argmax() for idx in train_set.indices])\n",
        "                test_idx = torch.tensor([dataset.labels[idx].argmax() for idx in test_set.indices])\n",
        "            else:\n",
        "                try:\n",
        "                    train_idx = torch.where(train_set.targets == i)[0]\n",
        "                    test_idx = torch.where(test_set.targets == i)[0]\n",
        "                except AttributeError:\n",
        "                    train_idx = torch.where(torch.tensor(train_set.labels) == i)[0]\n",
        "                    test_idx = torch.where(torch.tensor(test_set.labels) == i)[0]\n",
        "\n",
        "            train_indexes.append(train_idx)\n",
        "            test_indexes.append(test_idx)\n",
        "\n",
        "        min_num_samples_train = min(len(idx) for idx in train_indexes)\n",
        "        min_num_samples_test = min(len(idx) for idx in test_indexes)\n",
        "\n",
        "        sampled_train_indexes = []\n",
        "        sampled_test_indexes = []\n",
        "        for train_idx, test_idx in zip(train_indexes, test_indexes):\n",
        "            sampled_train_indexes.append(train_idx[torch.randperm(len(train_idx))[:min_num_samples_train]])\n",
        "            sampled_test_indexes.append(test_idx[torch.randperm(len(test_idx))[:min_num_samples_test]])\n",
        "\n",
        "        selected_train_indexes = torch.cat(sampled_train_indexes)\n",
        "        selected_test_indexes = torch.cat(sampled_test_indexes)\n",
        "\n",
        "    else:\n",
        "        raise ValueError(\"Invalid value for 'filtered'. Choose either 0 or 1.\")\n",
        "\n",
        "    filtered_train_set = torch.utils.data.Subset(train_set, selected_train_indexes)\n",
        "    filtered_test_set = torch.utils.data.Subset(test_set, selected_test_indexes)\n",
        "\n",
        "    return filtered_train_set, filtered_test_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "1fcd9364-6c99-476d-9e15-ee310e2d1dce",
      "metadata": {
        "id": "1fcd9364-6c99-476d-9e15-ee310e2d1dce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "616885fc-a7c6-45ff-90cc-22d2ac81baba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MNIST\n",
            "Number of images in the filtered training set: 54210\n",
            "Number of images in the filtered test set: 8920\n"
          ]
        }
      ],
      "source": [
        "global filtered_train_set, filtered_test_set\n",
        "print(dataset_name)\n",
        "\n",
        "filtered_train_set, filtered_test_set = filtered_dataset(train_set, test_set, filtered=1)\n",
        "# Print the number of images in the filtered training set\n",
        "print(\"Number of images in the filtered training set:\", len(filtered_train_set))\n",
        "\n",
        "# Print the number of images in the filtered test set\n",
        "print(\"Number of images in the filtered test set:\", len(filtered_test_set))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "d49fa0ce-279f-486b-9946-5aa2da6cc28b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d49fa0ce-279f-486b-9946-5aa2da6cc28b",
        "outputId": "ab18e10f-bd62-4960-91df-38c4da8d286a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MNIST\n",
            "Number of batches in the filtered training set: 848\n",
            "Number of batches in the filtered test set: 140\n",
            "Number of images in the filtered training set: 54210\n",
            "Number of images in the filtered test set: 8920\n"
          ]
        }
      ],
      "source": [
        "global filtered_train_loader, filtered_test_loader\n",
        "print(dataset_name)\n",
        "\n",
        "filtered_train_loader = DataLoader(filtered_train_set, batch_size=64, shuffle=True)\n",
        "filtered_test_loader = DataLoader(filtered_test_set, batch_size=64)\n",
        "\n",
        "# Print the number of batches in the data loaders\n",
        "print(\"Number of batches in the filtered training set:\", len(filtered_train_loader))\n",
        "print(\"Number of batches in the filtered test set:\", len(filtered_test_loader))\n",
        "\n",
        "# Print the exact number of images in the filtered sets\n",
        "print(\"Number of images in the filtered training set:\", len(filtered_train_set))\n",
        "print(\"Number of images in the filtered test set:\", len(filtered_test_set))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "7d774a42-1370-42b3-abbe-3dbe61c54d75",
      "metadata": {
        "id": "7d774a42-1370-42b3-abbe-3dbe61c54d75"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def print_images(images, labels, preds, n):\n",
        "\n",
        "    fig = plt.figure(figsize=(6, 6))\n",
        "    for i in range(n):\n",
        "        ax = fig.add_subplot(2, 3, i+1)\n",
        "        img = images[i].squeeze()\n",
        "        ax.imshow(img, cmap='gray')\n",
        "        pred = preds[i].argmax()\n",
        "        ax.set_title(f\"{labels[i].item()} -> {pred.item()}\")\n",
        "        ax.axis('off')\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "97f6690b-ad8c-4e0d-b6ca-6a0cb80df1b0",
      "metadata": {
        "id": "97f6690b-ad8c-4e0d-b6ca-6a0cb80df1b0"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "def save_clean_model(model, save_dir):\n",
        "\n",
        "    if not os.path.exists(save_dir):\n",
        "        os.makedirs(save_dir)\n",
        "\n",
        "    model_path = f\"{save_dir}/clean_model.pth\"\n",
        "\n",
        "    torch.save(model.state_dict(), model_path)\n",
        "    #print(f\"Saved clean_model state dict to {model_path}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "0vbZvTM3rqeZ",
      "metadata": {
        "id": "0vbZvTM3rqeZ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_accuracy_loss(train_accuracies, train_losses):\n",
        "    epochs = range(1, len(train_accuracies) + 1)\n",
        "\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(epochs, train_accuracies, 'b', label='Training Accuracy')\n",
        "    plt.title('Training Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(epochs, train_losses, 'b', label='Training Loss')\n",
        "    plt.title('Training Loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "006d4aa3-14bf-4ee8-9d9a-eaeed40a3933",
      "metadata": {
        "id": "006d4aa3-14bf-4ee8-9d9a-eaeed40a3933"
      },
      "outputs": [],
      "source": [
        "# Train model\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "def train_model(model, train_loader, loss_fn, optimizer, num_epochs, device, batch_size=128, learning_rate=0.001, weight_decay=0.0001):\n",
        "  # Model folder\n",
        "  save_dir = 'clean_models'\n",
        "\n",
        "  train_accuracies = []\n",
        "  train_losses = []\n",
        "\n",
        "  model.to(device)  # Move the model to the specified device\n",
        "\n",
        "  # Initialize trainable parameters for the quantum circuit\n",
        "  theta = torch.randn(1, requires_grad=True, device=device)\n",
        "  phi = torch.randn(1, requires_grad=True, device=device)\n",
        "\n",
        "  # Adjust learning rate and weight decay\n",
        "  for param_group in optimizer.param_groups:\n",
        "    param_group['lr'] = learning_rate\n",
        "    param_group['weight_decay'] = weight_decay\n",
        "\n",
        "  for epoch in range(num_epochs):\n",
        "    print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
        "\n",
        "    # Training\n",
        "    model.train()\n",
        "    train_loss = 0.0\n",
        "    train_correct = 0\n",
        "    train_total = 0\n",
        "\n",
        "    for i, (imgs, labels) in enumerate(train_loader):\n",
        "      # Forward pass through hybrid model using model's forward function\n",
        "      hybrid_output = model(imgs)  # Call forward pass on the model with training data\n",
        "\n",
        "      loss = loss_fn(hybrid_output, labels.to(device))\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      train_loss += loss.item()\n",
        "      _, predicted = torch.max(hybrid_output.data, 1)\n",
        "      train_total += labels.size(0)\n",
        "      train_correct += (predicted == labels.to(device)).sum().item()\n",
        "\n",
        "      if i == 0:\n",
        "        print_images(imgs.cpu(), labels.cpu(), hybrid_output.cpu(), n=3)  # Move data back to CPU for printing\n",
        "\n",
        "    train_loss /= len(train_loader)\n",
        "    train_acc = train_correct / train_total\n",
        "    print(f\"Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.4f}\")\n",
        "    train_accuracies.append(train_acc)\n",
        "    train_losses.append(train_loss)\n",
        "\n",
        "  # Plot accuracy and loss curves\n",
        "  plot_accuracy_loss(train_accuracies, train_losses)\n",
        "\n",
        "  save_clean_model(model, save_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "84139728-03c9-4c31-bed5-37d8bf23160b",
      "metadata": {
        "id": "84139728-03c9-4c31-bed5-37d8bf23160b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5067642f-7073-433e-d5e4-dd737675672d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.4322, Train Accuracy: 0.8676\n",
            "Epoch 2/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.1330, Train Accuracy: 0.9590\n",
            "Epoch 3/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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CFDIAAApQyAAAKEAhAwCgAIUMAIACFDIAAApQyAAAKEAhAwCgAIUMAIACFDIAAApQyAAAKEAhAwCgAIUMAIACFDIAAArEOY7jRHoQAADEOj4hAwCgAIUMAIACFDIAAApQyAAAKEAhAwCgAIUMAIACFDIAAApQyAAAKEAhAwCgwH8BGL1wYDF5FjQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0970, Train Accuracy: 0.9699\n",
            "Epoch 4/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0782, Train Accuracy: 0.9763\n",
            "Epoch 5/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0658, Train Accuracy: 0.9790\n",
            "Epoch 6/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0579, Train Accuracy: 0.9821\n",
            "Epoch 7/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0502, Train Accuracy: 0.9840\n",
            "Epoch 8/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0455, Train Accuracy: 0.9860\n",
            "Epoch 9/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0416, Train Accuracy: 0.9870\n",
            "Epoch 10/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0381, Train Accuracy: 0.9882\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import time\n",
        "# Start the timer\n",
        "start_time = time.time()\n",
        "train_model(hybrid_model, filtered_train_loader, loss_fn, optimizer, num_epochs,device)\n",
        "# End the timer\n",
        "end_time = time.time()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "bbd069ac-c142-49bd-8967-57ccce7f81c4",
      "metadata": {
        "id": "bbd069ac-c142-49bd-8967-57ccce7f81c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc917d39-bcd6-491b-d6bb-10d8b904cfaa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training duration: 113.35 seconds\n",
            "Training duration: 1.89 minutes\n",
            "Training duration: 0.03 hours\n"
          ]
        }
      ],
      "source": [
        "# Calculate the training duration in seconds\n",
        "training_duration_seconds = abs(end_time - start_time)\n",
        "\n",
        "# Calculate the training duration in minutes\n",
        "training_duration_minutes = training_duration_seconds / 60\n",
        "\n",
        "# Calculate the training duration in hours\n",
        "training_duration_hours = training_duration_minutes / 60\n",
        "\n",
        "# Print the training duration in different units\n",
        "print(f\"Training duration: {training_duration_seconds:.2f} seconds\")\n",
        "print(f\"Training duration: {training_duration_minutes:.2f} minutes\")\n",
        "print(f\"Training duration: {training_duration_hours:.2f} hours\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "f989b4a1-7c0f-4e9c-9e08-93cbae908922",
      "metadata": {
        "id": "f989b4a1-7c0f-4e9c-9e08-93cbae908922"
      },
      "outputs": [],
      "source": [
        "def load_clean_model(model, save_dir):\n",
        "    # Model folder\n",
        "    model_path = f\"{save_dir}/clean_model.pth\"\n",
        "\n",
        "    if not os.path.exists(model_path):\n",
        "        raise FileNotFoundError(f\"Model file not found in {model_path}\")\n",
        "\n",
        "    model.load_state_dict(torch.load(model_path))\n",
        "    model.eval()\n",
        "\n",
        "    print(f\"Loaded clean_model state dict from {model_path}\")\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_clean_elements = len(filtered_train_set)\n",
        "print(f\"Number of clean images: {num_clean_elements}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMBln0mEkk5U",
        "outputId": "c2e09cb7-3c70-4575-ae50-2b32749bf1bd"
      },
      "id": "CMBln0mEkk5U",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clean images: 54210\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "11oH9oQGw7Vp",
      "metadata": {
        "id": "11oH9oQGw7Vp"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_test_accuracy_loss(test_accuracy, test_loss):\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.bar(1, test_accuracy, color='r', label='Testing Accuracy')\n",
        "    plt.title('Testing Accuracy')\n",
        "    plt.xlabel('Evaluation')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.bar(1, test_loss, color='r', label='Testing Loss')\n",
        "    plt.title('Testing Loss')\n",
        "    plt.xlabel('Evaluation')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "ea9b8cfc-5d52-420d-a066-075752637960",
      "metadata": {
        "id": "ea9b8cfc-5d52-420d-a066-075752637960"
      },
      "outputs": [],
      "source": [
        "def evaluate(model, data_loader, loss_fn, device):\n",
        "    test_loss = 0.0\n",
        "    test_accuracy = 0.0\n",
        "    num_misclassified = 0\n",
        "    model.eval()\n",
        "    misclassified_images = []\n",
        "    misclassified_labels = []\n",
        "    misclassified_preds = []\n",
        "\n",
        "    images, labels = next(iter(data_loader))\n",
        "    images, labels = images.to(device), labels.to(device)  # Move data to the specified device\n",
        "    num_clean_test_elements = len(images)\n",
        "    print(f\"Number of clean test images: {num_clean_test_elements}\")\n",
        "\n",
        "    # Create dataset\n",
        "    test_ds = TensorDataset(images, labels)\n",
        "    # Create loader\n",
        "    test_loader = DataLoader(test_ds, batch_size=64)\n",
        "    dataset_size = len(test_loader.dataset)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (x, y) in enumerate(test_loader):\n",
        "            x, y = x.to(device), y.to(device)  # Move data to the specified device\n",
        "            logits = model(x)\n",
        "            batch_loss = loss_fn(logits, y)\n",
        "            test_loss += batch_loss.item()\n",
        "            preds = torch.argmax(logits, dim=1)\n",
        "            test_accuracy += torch.sum(preds == y).item() / len(y)\n",
        "            incorrect_inds = torch.where(preds != y)[0]\n",
        "            num_misclassified += len(incorrect_inds)\n",
        "            if len(incorrect_inds) > 0:\n",
        "                misclassified_images.append(x[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_labels.append(y[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_preds.append(preds[incorrect_inds].cpu())  # Move data back to CPU\n",
        "\n",
        "    num_misclassified = min(num_misclassified, dataset_size)\n",
        "    print(f\"Number of misclassified images: {num_misclassified} out of {dataset_size}\")\n",
        "    test_loss /= len(test_loader)\n",
        "    test_accuracy /= len(test_loader)\n",
        "    print(f\"Clean Test loss: {test_loss:.4f}, Accuracy: {test_accuracy:.2%}\")\n",
        "\n",
        "    # Plot the test accuracy and loss\n",
        "    #plot_test_accuracy_loss(test_accuracy, test_loss)\n",
        "\n",
        "    # Handle empty lists\n",
        "    if len(misclassified_images) == 0:\n",
        "        misclassified_images.append(torch.tensor([]))\n",
        "    if len(misclassified_labels) == 0:\n",
        "        misclassified_labels.append(torch.tensor([]))\n",
        "    if len(misclassified_preds) == 0:\n",
        "        misclassified_preds.append(torch.tensor([]))\n",
        "\n",
        "    return test_loss, test_accuracy, [torch.cat(misclassified_images)], [torch.cat(misclassified_labels)], [torch.cat(misclassified_preds)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "3bc286ff-8d98-49a9-bb75-d2530073f446",
      "metadata": {
        "id": "3bc286ff-8d98-49a9-bb75-d2530073f446",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5de7ea2b-c841-4f2f-dfef-c1d5d65002c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded clean_model state dict from clean_models/clean_model.pth\n"
          ]
        }
      ],
      "source": [
        "# Model folder\n",
        "save_dir = 'clean_models'\n",
        "\n",
        "# Loading\n",
        "clean_model = load_clean_model(hybrid_model, save_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "e8853f9a-36e6-4a5a-bea7-b2bee29d3f2c",
      "metadata": {
        "id": "e8853f9a-36e6-4a5a-bea7-b2bee29d3f2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49ed3b54-db51-445f-c4db-072e6b68d7eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clean test images: 64\n",
            "Number of misclassified images: 0 out of 64\n",
            "Clean Test loss: 0.0010, Accuracy: 100.00%\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import TensorDataset\n",
        "# Evaluation\n",
        "test_loss, test_acc, misclassified_images, misclassified_labels, misclassified_preds = evaluate(clean_model, filtered_test_loader, loss_fn, device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "3d5e1d14-2ef0-48ae-831d-6fa01bfab343",
      "metadata": {
        "id": "3d5e1d14-2ef0-48ae-831d-6fa01bfab343"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "def analyze_for_misclassifications(misclassified_images, misclassified_labels, misclassified_preds):\n",
        "\n",
        "    MAX_IMAGES = 60\n",
        "\n",
        "    if len(misclassified_images) > 0:\n",
        "        misclassified_images = misclassified_images[0]\n",
        "        misclassified_labels = misclassified_labels[0]\n",
        "        misclassified_preds = misclassified_preds[0]\n",
        "\n",
        "        print(f\"Number misclassified: {len(misclassified_images)}\")\n",
        "\n",
        "        # Truncate more than MAX_IMAGES\n",
        "        images = misclassified_images[:MAX_IMAGES]\n",
        "        labels = misclassified_labels[:MAX_IMAGES]\n",
        "        preds = misclassified_preds[:MAX_IMAGES]\n",
        "\n",
        "\n",
        "        num_images = len(images)\n",
        "\n",
        "        fig = plt.figure(figsize=(6, num_images))\n",
        "\n",
        "        rows = math.ceil(num_images / 2)\n",
        "        cols = 2 if num_images > 16 else min(num_images, 2)\n",
        "\n",
        "        for i, (img, label, pred) in enumerate(zip(images, labels, preds)):\n",
        "\n",
        "            # Add subplot\n",
        "            ax = fig.add_subplot(rows, cols, i+1)\n",
        "\n",
        "            # Add empty dim if needed\n",
        "            if img.ndim == 2:\n",
        "                img = img[:,None,:,]\n",
        "\n",
        "            # Permute to HWC format for imshow\n",
        "            img = img.permute(1, 2, 0)\n",
        "\n",
        "            ax.imshow(img)\n",
        "            ax.set_title(f\"{label} -> {pred}\")\n",
        "            ax.axis(\"off\")\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    else:\n",
        "        print(\"No misclassified examples\")\n",
        "\n",
        "    return misclassified_images, misclassified_labels, misclassified_preds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "b9fd9757-996f-45c1-a3d4-73516de83f01",
      "metadata": {
        "id": "b9fd9757-996f-45c1-a3d4-73516de83f01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a4c07a8d-cdcb-4df3-aae8-f7fa688a1873"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number misclassified: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x0 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "clean_misclassified_images, clean_misclassified_labels, clean_misclassified_preds = analyze_for_misclassifications(misclassified_images, misclassified_labels, misclassified_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "3cbab15a-728a-4fc2-ab4b-f6e54188cc25",
      "metadata": {
        "id": "3cbab15a-728a-4fc2-ab4b-f6e54188cc25"
      },
      "outputs": [],
      "source": [
        "model = clean_model\n",
        "# Define compounded attack\n",
        "def compounded_attack(choose_attack_option):\n",
        "    if choose_attack_option == \"fgsm_cw_attack\":\n",
        "        attack1 = torchattacks.FGSM(model, eps=0.3)\n",
        "        attack2 = torchattacks.CW(model, c=0.1, kappa=0.0, steps=1000)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"fgsm_pgd_attack\":\n",
        "        attack1 = torchattacks.FGSM(model, eps=0.3)\n",
        "        attack2 = torchattacks.PGD(model, eps=0.3, alpha=0.01, steps=5)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"cw_pgd_attack\":\n",
        "        attack1 = torchattacks.CW(model, c=0.1, kappa=0.0, steps=1000)\n",
        "        attack2 = torchattacks.PGD(model, eps=0.3, alpha=0.01, steps=5)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"pgd_bim_attack\":\n",
        "        attack1 = torchattacks.PGD(model, eps=0.3, alpha=0.01, steps=5)\n",
        "        attack2 = torchattacks.BIM(model, eps=8/255, alpha=2/255, steps=10)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"fgsm_bim_attack\":\n",
        "        attack1 = torchattacks.FGSM(model, eps=0.3)\n",
        "        attack2 = torchattacks.BIM(model, eps=8/255, alpha=2/255, steps=10)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"cw_bim_attack\":\n",
        "        attack1 = torchattacks.CW(model, c=0.1, kappa=0.0, steps=1000)\n",
        "        attack2 = torchattacks.BIM(model, eps=8/255, alpha=2/255, steps=10)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"pgd_deepfool_attack\":\n",
        "        attack1 = torchattacks.PGD(model, eps=0.3, alpha=0.01, steps=5)\n",
        "        attack2 = torchattacks.DeepFool(model, steps=50, overshoot=0.02)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"fgsm_deepfool_attack\":\n",
        "        attack1 = torchattacks.FGSM(model, eps=0.3)\n",
        "        attack2 = torchattacks.DeepFool(model, steps=50, overshoot=0.02)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"cw_deepfool_attack\":\n",
        "        attack1 = torchattacks.CW(model, c=0.1, kappa=0.0, steps=1000)\n",
        "        attack2 = torchattacks.DeepFool(model, steps=50, overshoot=0.02)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    elif choose_attack_option == \"bim_deepfool_attack\":\n",
        "        attack1 = torchattacks.BIM(model, eps=8/255, alpha=2/255, steps=10)\n",
        "        attack2 = torchattacks.DeepFool(model, steps=50, overshoot=0.02)\n",
        "        attack = torchattacks.MultiAttack([attack1, attack2])\n",
        "    else:\n",
        "        print(\"You did not chose any of the possible options or an you made an invalid option\")\n",
        "    return attack"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "11559bea-21a9-4806-b46c-b35e5706ac10",
      "metadata": {
        "id": "11559bea-21a9-4806-b46c-b35e5706ac10"
      },
      "outputs": [],
      "source": [
        "attack = compounded_attack(compounded_attack_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "25bb76b8-c3ca-45b5-a5a5-804961d548d7",
      "metadata": {
        "id": "25bb76b8-c3ca-45b5-a5a5-804961d548d7"
      },
      "outputs": [],
      "source": [
        "import tqdm\n",
        "\n",
        "def perform_adv_attack(model, attack, data_loader):\n",
        "    adv_data = []\n",
        "    adv_labels = []\n",
        "    device = next(model.parameters()).device\n",
        "\n",
        "    total_batches = len(data_loader)\n",
        "    progress_bar = tqdm.tqdm(total=total_batches, unit='batch', desc='Adversarial Attack')\n",
        "\n",
        "    for x, y in data_loader:\n",
        "        x, y = x.to(device), y.to(device)\n",
        "\n",
        "        # Convert labels to long data type\n",
        "        y = y.long()\n",
        "\n",
        "        x_adv = attack(x, y)\n",
        "        adv_data.append(x_adv.detach())\n",
        "        adv_labels.append(y.detach())\n",
        "\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    progress_bar.close()\n",
        "\n",
        "    adv_data = torch.cat(adv_data)\n",
        "    adv_labels = torch.cat(adv_labels)\n",
        "\n",
        "    # Move data back to CPU if necessary\n",
        "    if device != torch.device('cpu'):\n",
        "        adv_data = adv_data.to('cpu')\n",
        "        adv_labels = adv_labels.to('cpu')\n",
        "\n",
        "    # Create dataset and loader\n",
        "    adv_dataset = TensorDataset(adv_data, adv_labels)\n",
        "    adv_loader = DataLoader(adv_dataset, batch_size=64, shuffle=False)\n",
        "\n",
        "    return adv_loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "66c00ee0-efbe-4ea7-ab6b-ae46821c9c1f",
      "metadata": {
        "id": "66c00ee0-efbe-4ea7-ab6b-ae46821c9c1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9aa8f00d-2379-4001-f8bb-68ed1f0edc27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Adversarial Attack: 100%|██████████| 140/140 [00:58<00:00,  2.38batch/s]\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import TensorDataset\n",
        "# Adversarial attack\n",
        "adv_attack_loader = perform_adv_attack(clean_model, attack, filtered_test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_adv_attack(model, data_loader, loss_fn, device):\n",
        "    attack_test_loss = 0\n",
        "    attack_test_accuracy = 0\n",
        "    num_misclassified = 0\n",
        "    model.eval()\n",
        "    misclassified_images = []\n",
        "    misclassified_labels = []\n",
        "    misclassified_preds = []\n",
        "\n",
        "    images, labels = next(iter(data_loader))\n",
        "    images, labels = images.to(device), labels.to(device)  # Move data to the specified device\n",
        "    num_clean_test_elements = len(images)\n",
        "    print(f\"Number of clean test images: {num_clean_test_elements}\")\n",
        "\n",
        "    # Create dataset\n",
        "    test_ds = TensorDataset(images, labels)\n",
        "    # Create loader\n",
        "    test_loader = DataLoader(test_ds, batch_size=64)\n",
        "    dataset_size = len(test_loader.dataset)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (x, y) in enumerate(test_loader):\n",
        "            x, y = x.to(device), y.to(device)  # Move data to the specified device\n",
        "            logits = model(x)\n",
        "            batch_loss = loss_fn(logits, y)\n",
        "            attack_test_loss += batch_loss.item()\n",
        "            preds = torch.argmax(logits, dim=1)\n",
        "            attack_test_accuracy += torch.sum(preds == y).item() / len(y)\n",
        "            incorrect_inds = torch.where(preds != y)[0]\n",
        "            num_misclassified += len(incorrect_inds)\n",
        "            if len(incorrect_inds) > 0:\n",
        "                misclassified_images.append(x[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_labels.append(y[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_preds.append(preds[incorrect_inds].cpu())  # Move data back to CPU\n",
        "\n",
        "    num_misclassified = min(num_misclassified, dataset_size)\n",
        "    print(f\"Number of misclassified images: {num_misclassified} out of {dataset_size}\")\n",
        "    attack_test_loss /= len(test_loader)\n",
        "    attack_test_accuracy /= len(test_loader)\n",
        "    print(f\"Attack Test loss: {attack_test_loss:.4f}, Accuracy: {attack_test_accuracy:.2%}\")\n",
        "\n",
        "    # Handle empty lists\n",
        "    if len(misclassified_images) == 0:\n",
        "        misclassified_images.append(torch.tensor([]))\n",
        "    if len(misclassified_labels) == 0:\n",
        "        misclassified_labels.append(torch.tensor([]))\n",
        "    if len(misclassified_preds) == 0:\n",
        "        misclassified_preds.append(torch.tensor([]))\n",
        "\n",
        "    return attack_test_loss, attack_test_accuracy, [torch.cat(misclassified_images)], [torch.cat(misclassified_labels)], [torch.cat(misclassified_preds)]"
      ],
      "metadata": {
        "id": "XSFrI87gm4fT"
      },
      "id": "XSFrI87gm4fT",
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "50c206b9-3580-40c9-afc1-865bc3f72676",
      "metadata": {
        "id": "50c206b9-3580-40c9-afc1-865bc3f72676",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf5b424b-696d-4248-cfec-bb21f1ad7515"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clean test images: 64\n",
            "Number of misclassified images: 45 out of 64\n",
            "Attack Test loss: 2.8268, Accuracy: 29.69%\n",
            "\n",
            "A few key ways to evaluate whether an adversarial attack on a machine learning model was successful:\n",
            "\n",
            "Drop in Model Accuracy: 20-40% drop in accuracy indicates the attack was effective.\n",
            "\n",
            "High Loss Value: A sharp spike in loss is another sign of a working attack.\n",
            "\n",
            "Misclassified Examplese: Images wrongly labeled indicates a successful targeted attack\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Evaluate attack\n",
        "clean_attack_test_loss, clean_attack_adv_test_accuracy, misclassified_images, misclassified_labels, misclassified_preds = evaluate_adv_attack(clean_model, adv_attack_loader, loss_fn, device)\n",
        "\n",
        "print(\"\\nA few key ways to evaluate whether an adversarial attack on a machine learning model was successful:\\n\")\n",
        "print(\"Drop in Model Accuracy: 20-40% drop in accuracy indicates the attack was effective.\\n\")\n",
        "print(\"High Loss Value: A sharp spike in loss is another sign of a working attack.\\n\")\n",
        "print(\"Misclassified Examplese: Images wrongly labeled indicates a successful targeted attack\\n\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "e0c41cbf-0201-4188-b62e-2d771d104738",
      "metadata": {
        "id": "e0c41cbf-0201-4188-b62e-2d771d104738",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d5b1bced-af6e-4b68-8f00-d177e49590cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number misclassified: 45\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x4500 with 45 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "adv_misclassified_images, adv_misclassified_labels, adv_misclassified_preds = analyze_for_misclassifications(misclassified_images, misclassified_labels, misclassified_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "065a9a4b-f936-4dde-b9c1-1b483974fd60",
      "metadata": {
        "id": "065a9a4b-f936-4dde-b9c1-1b483974fd60"
      },
      "outputs": [],
      "source": [
        "# Generate adversarial dataset\n",
        "import torch\n",
        "import tqdm\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "def generate_adversarial_dataset(model, attack, data_loader, device, num_samples):\n",
        "    adv_data = []\n",
        "    adv_labels = []\n",
        "    device = next(model.parameters()).device\n",
        "\n",
        "    num_generated = 0\n",
        "    progress_bar = tqdm.tqdm(total=num_samples, unit='sample', desc='Adversarial Attack')\n",
        "\n",
        "    for x, y in data_loader:\n",
        "        x, y = x.to(device), y.to(device)\n",
        "        y = y.long()\n",
        "\n",
        "        x_adv = attack(x, y)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            orig_preds = model(x).argmax(1)\n",
        "            adv_preds = model(x_adv).argmax(1)\n",
        "\n",
        "        # Filter samples where original prediction is different from adversarial prediction\n",
        "        mask = orig_preds != adv_preds\n",
        "        x_adv_filtered = x_adv[mask]\n",
        "        y_filtered = y[mask]\n",
        "\n",
        "        # Append only the required number of samples\n",
        "        num_remaining = num_samples - num_generated\n",
        "        if num_remaining > 0:\n",
        "            if num_remaining >= len(x_adv_filtered):\n",
        "                adv_data.append(x_adv_filtered.detach())\n",
        "                adv_labels.append(y_filtered.detach())\n",
        "                num_generated += len(x_adv_filtered)\n",
        "                progress_bar.update(len(x_adv_filtered))\n",
        "            else:\n",
        "                adv_data.append(x_adv_filtered[:num_remaining].detach())\n",
        "                adv_labels.append(y_filtered[:num_remaining].detach())\n",
        "                num_generated = num_samples\n",
        "                progress_bar.update(num_remaining)\n",
        "                break\n",
        "\n",
        "        if num_generated >= num_samples:\n",
        "            break\n",
        "\n",
        "    progress_bar.close()\n",
        "\n",
        "    if len(adv_data) > 0:\n",
        "        adv_data = torch.cat(adv_data)\n",
        "        adv_labels = torch.cat(adv_labels)\n",
        "\n",
        "        if device != torch.device('cpu'):\n",
        "            adv_data = adv_data.to('cpu')\n",
        "            adv_labels = adv_labels.to('cpu')\n",
        "\n",
        "        adv_dataset = TensorDataset(adv_data, adv_labels)\n",
        "\n",
        "        if num_generated < num_samples:\n",
        "            print(f\"Warning: Could not generate {num_samples} unique adversarial examples.\")\n",
        "            print(f\"Returning {num_generated} unique adversarial examples.\")\n",
        "    else:\n",
        "        adv_dataset = None\n",
        "        print(\"Warning: No unique adversarial examples found.\")\n",
        "\n",
        "    return adv_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "QN2lLX_vnc9d",
      "metadata": {
        "id": "QN2lLX_vnc9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "441cf995-681c-4ef8-90cf-a94b4d1bff1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MNIST\n",
            "Number of batches in the filtered test set: 140\n",
            "Number of images in the filtered test set: 8984\n"
          ]
        }
      ],
      "source": [
        "print(dataset_name)\n",
        "\n",
        "filtered_test_loader = DataLoader(filtered_test_set, batch_size=64)\n",
        "\n",
        "# Print the number of batches in the data loaders\n",
        "print(\"Number of batches in the filtered test set:\", len(filtered_test_loader))\n",
        "\n",
        "# Calculate the size of the filtered test set\n",
        "test_set_size = len(filtered_test_loader) * filtered_test_loader.batch_size\n",
        "if len(filtered_test_set) % filtered_test_loader.batch_size != 0:\n",
        "    test_set_size += len(filtered_test_set) % filtered_test_loader.batch_size\n",
        "\n",
        "# Print the sizes of the filtered sets\n",
        "print(\"Number of images in the filtered test set:\", test_set_size)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of adversarial images used for training as a defense against adversarial attacks on a model follow general guidelines and considerations:\n",
        "\n",
        "Balanced dataset: It is generally recommended to have a balanced dataset, where the number of adversarial examples is similar to the number of clean examples. This helps the model learn to distinguish between clean and adversarial examples effectively.\n",
        "\n",
        "Percentage of adversarial examples: A common practice is to include a certain percentage of adversarial examples in the training set. The percentage can range from 10% to 50% of the total training examples, depending on the specific requirements and the sensitivity of the application.\n",
        "\n",
        "Iterative training: Instead of training with a fixed number of adversarial examples, an iterative approach can be employed. Start with a smaller percentage of adversarial examples and gradually increase the proportion over multiple training iterations. This allows the model to adapt and improve its robustness incrementally.\n",
        "\n",
        "Diversity of adversarial examples: It is important to generate adversarial examples using different attack methods and parameters to expose the model to a wide range of adversarial perturbations. This helps the model learn to defend against various types of attacks.\n",
        "\n",
        "Validation and testing: It is crucial to evaluate the model's performance on a separate validation set containing both clean and adversarial examples. This helps assess the model's robustness and generalization ability. The test set should also include a mix of clean and adversarial examples to measure the model's performance in real-world scenarios.\n",
        "\n",
        "Computational resources: Generating and training with a large number of adversarial examples can be computationally expensive. Consider the available computational resources and time constraints when determining the number of adversarial examples to include in the training process."
      ],
      "metadata": {
        "id": "joYwXloRI_l0"
      },
      "id": "joYwXloRI_l0"
    },
    {
      "cell_type": "code",
      "source": [
        "global num_of_adv_examples\n",
        "num_of_adv_examples = int(0.5 * len(filtered_train_set))\n",
        "print(\"Number of adversarial examples created:\", num_of_adv_examples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URq89jR-I7hH",
        "outputId": "60550d28-4a82-4653-bbf8-df92ad1504b6"
      },
      "id": "URq89jR-I7hH",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of adversarial examples created: 27105\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adv_dataset = generate_adversarial_dataset(model, attack, filtered_train_loader, device, num_of_adv_examples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kshgrJLyDbr-",
        "outputId": "0401b684-0eca-4f34-c5e3-2354e2e4cd78"
      },
      "id": "kshgrJLyDbr-",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Adversarial Attack: 100%|██████████| 27105/27105 [04:42<00:00, 95.90sample/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "4c97dc22-32d7-451b-8a41-9bc190b40854",
      "metadata": {
        "id": "4c97dc22-32d7-451b-8a41-9bc190b40854"
      },
      "outputs": [],
      "source": [
        "# Print adversarial images\n",
        "def plot_adversarial_images(adv_dataset, model, max_images=60):\n",
        "    adv_loader = DataLoader(adv_dataset, batch_size=len(adv_dataset))\n",
        "    images, labels = next(iter(adv_loader))\n",
        "\n",
        "    # Move the input tensors to the same device as the model\n",
        "    device = next(model.parameters()).device\n",
        "    images = images.to(device)\n",
        "\n",
        "    preds = model(images)\n",
        "\n",
        "    fig = plt.figure(figsize=(6, 6))\n",
        "    for i in range(6):\n",
        "        ax = fig.add_subplot(2, 3, i+1)\n",
        "        img = images[i].cpu().squeeze()  # Move the image back to CPU for visualization\n",
        "        ax.imshow(img, cmap='gray')\n",
        "        orig = labels[i].item()\n",
        "        pred = preds[i].argmax().item()\n",
        "        ax.set_title(f\"{orig}->{pred}\")\n",
        "        ax.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_adversarial_images(adv_dataset, model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "sIXBupxFLR1Z",
        "outputId": "8f4a5300-6d24-4da4-8106-a52a4b03b4f4"
      },
      "id": "sIXBupxFLR1Z",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "897f815c-f091-46c1-a30d-0413c365eb70",
      "metadata": {
        "id": "897f815c-f091-46c1-a30d-0413c365eb70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61605ac7-df87-4bf7-defc-38c583e8cb48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of adversarial images: 27105\n",
            "Number of batches (with batch size 128): 211\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "# Adversarial training\n",
        "# Create adversarial dataset\n",
        "adv_loader = DataLoader(adv_dataset, batch_size=len(adv_dataset))\n",
        "adv_images, adv_labels = next(iter(adv_loader))\n",
        "\n",
        "adv_ds = TensorDataset(adv_images, adv_labels)\n",
        "adv_batch_size = 128\n",
        "num_adv_batches = len(adv_ds) // adv_batch_size\n",
        "num_adv_images = len(adv_ds)\n",
        "\n",
        "print(f\"Number of adversarial images: {num_adv_images}\")\n",
        "print(f\"Number of batches (with batch size {adv_batch_size}): {num_adv_batches}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "ce797f73-d153-4f46-a98b-5ff1a4eb2fb2",
      "metadata": {
        "id": "ce797f73-d153-4f46-a98b-5ff1a4eb2fb2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab192ab3-009e-415d-ac46-a6b28cea278c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clean images in training set: 54210\n",
            "Number of batches (with batch size 128): 423\n"
          ]
        }
      ],
      "source": [
        "# Filtered training set\n",
        "filtered_train_set, filtered_test_set = filtered_dataset(train_set, test_set, filtered=1)\n",
        "clean_loader = DataLoader(filtered_train_set, batch_size=len(filtered_train_set))\n",
        "clean_images, clean_labels = next(iter(clean_loader))\n",
        "clean_ds = TensorDataset(clean_images, clean_labels)\n",
        "clean_batch_size = 128\n",
        "num_clean_batches = len(clean_ds) // clean_batch_size\n",
        "num_clean_images = len(clean_ds)\n",
        "\n",
        "# Clean images\n",
        "# Print the number of images in the filtered training set\n",
        "print(\"Number of clean images in training set:\", num_clean_images)\n",
        "print(f\"Number of batches (with batch size {clean_batch_size}): {num_clean_batches}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine into one dataset\n",
        "combined_tensors = (torch.cat((adv_images, clean_images)), torch.cat((adv_labels, clean_labels)))\n",
        "combined_tensor_dataset = TensorDataset(*combined_tensors)\n",
        "\n",
        "combined_batch_size = 128\n",
        "num_combined_batches = len(combined_tensor_dataset) // combined_batch_size\n",
        "num_combined_images = len(combined_tensor_dataset)\n",
        "\n",
        "# Create combined data loader\n",
        "combineloader = DataLoader(combined_tensor_dataset, batch_size=128,\n",
        "                                 shuffle=True, num_workers=4)"
      ],
      "metadata": {
        "id": "Ex2DnIp0dTeq"
      },
      "id": "Ex2DnIp0dTeq",
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "4b31cf9f-cc7e-4141-bf4e-68a66e966182",
      "metadata": {
        "id": "4b31cf9f-cc7e-4141-bf4e-68a66e966182",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4554bd87-6a06-4ea4-d3f9-79716da8f54f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of combined images: 81315\n",
            "Number of batches in combined loader: 635\n"
          ]
        }
      ],
      "source": [
        "# Combined images\n",
        "print(f\"Number of combined images: {num_combined_images}\")\n",
        "print(f\"Number of batches in combined loader: {num_combined_batches}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "06331625-3cb7-47f1-bd95-b25240b2eb4f",
      "metadata": {
        "id": "06331625-3cb7-47f1-bd95-b25240b2eb4f"
      },
      "outputs": [],
      "source": [
        "def save_retrained_model(model, save_dir):\n",
        "\n",
        "    if not os.path.exists(save_dir):\n",
        "        os.makedirs(save_dir)\n",
        "\n",
        "    model_path = f\"{save_dir}/retrained_model.pth\"\n",
        "\n",
        "    torch.save(model.state_dict(), model_path)\n",
        "    #print(f\"Saved retrained_model state dict to {model_path}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "cb914981-f164-42f4-a7cb-17e77311d06b",
      "metadata": {
        "id": "cb914981-f164-42f4-a7cb-17e77311d06b"
      },
      "outputs": [],
      "source": [
        "def adversarial_retraining(model, combined_loader, optimizer, loss_fn, num_epochs, device,\n",
        "                           batch_size=128, learning_rate=0.001, weight_decay=0.0001,\n",
        "                           print_images_per_epoch=1):\n",
        "\n",
        "  # Move the model to the specified device\n",
        "  model.to(device)\n",
        "\n",
        "  # Initialize trainable parameters (if needed, adjust based on your quantum circuit)\n",
        "  theta = torch.randn(1, requires_grad=True, device=device)\n",
        "  phi = torch.randn(1, requires_grad=True, device=device)\n",
        "\n",
        "  # Adjust learning rate and weight decay\n",
        "  for param_group in optimizer.param_groups:\n",
        "    param_group['lr'] = learning_rate\n",
        "    param_group['weight_decay'] = weight_decay\n",
        "\n",
        "  train_losses = []\n",
        "  train_accuracies = []\n",
        "\n",
        "  # Training loop\n",
        "  for epoch in range(num_epochs):\n",
        "    print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
        "\n",
        "    model.train()\n",
        "    train_loss = 0.0\n",
        "    train_correct = 0\n",
        "    train_total = 0\n",
        "\n",
        "    # Track modified data\n",
        "    retrained_imgs = []\n",
        "    retrained_labels = []\n",
        "\n",
        "    # Iterate through all batches in the data loader\n",
        "    for i, (imgs, labels) in enumerate(combined_loader):\n",
        "      # Forward pass through hybrid model\n",
        "      hybrid_output = model(imgs)\n",
        "\n",
        "      # Calculate loss, backpropagate, and update parameters\n",
        "      loss = loss_fn(hybrid_output, labels.to(device))\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      train_loss += loss.item()\n",
        "      _, predicted = torch.max(hybrid_output.data, 1)\n",
        "      train_total += labels.size(0)\n",
        "      train_correct += (predicted == labels.to(device)).sum().item()\n",
        "\n",
        "      # Print images for the first \"print_images_per_epoch\" iterations\n",
        "      if i < print_images_per_epoch:\n",
        "        print_images(imgs.cpu(), labels.cpu(), hybrid_output.cpu(), n=3)\n",
        "\n",
        "      # Here, you might modify \"imgs\" or \"labels\" based on your training logic\n",
        "      # to represent the \"retrained\" version (example: add noise)\n",
        "      retrained_imgs.append(imgs + 0.1 * torch.randn(imgs.size()))  # Example: Add noise\n",
        "      retrained_labels.append(labels)\n",
        "\n",
        "    # Calculate and print epoch statistics\n",
        "    train_loss /= len(combined_loader)\n",
        "    train_acc = train_correct / train_total\n",
        "    print(f\"Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.4f}\")\n",
        "\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracies.append(train_acc)\n",
        "\n",
        "  # Plot accuracy and loss curves (if needed)\n",
        "  plot_accuracy_loss(train_accuracies, train_losses)\n",
        "\n",
        "  # Save the retrained model (optional)\n",
        "  save_retrained_model(model, save_dir)\n",
        "\n",
        "  # Create a new dataset from collected data\n",
        "  retrained_dataset = TensorDataset(torch.cat(retrained_imgs), torch.cat(retrained_labels))\n",
        "\n",
        "  return retrained_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "502cbe2b-7413-40d1-8b68-9006ceb8159d",
      "metadata": {
        "id": "502cbe2b-7413-40d1-8b68-9006ceb8159d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "444564af-a362-4ad4-baa2-25b82cb7d0c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.4175, Train Accuracy: 0.8654\n",
            "Epoch 2/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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wMlZOTo7KmpubVdajRw+VubwwSCQ8++yzKrN9phgsPkMOj/PPP19lq1atUlnbtm0DjtesWaPOueKKK/ybWAyK9/vRtnPX559/rjLb4h5PPPGEyubOnauypqYmle3fv19lJ5xwQsCx7fthux9HjBihsjFjxqjMpqGhQWVFRUUqW7lypafzwo3PkAEAiBEUZAAAHEBBBgDAARRkAAAcEJGFQYJla/S69NJLVfbcc8+pbP78+SqzNXV51bJJLFoNYu+9917Q133mmWcCju+//351TiiNevHeROO3/v37q+yDDz5QWcsGLhGR559/PuD40Ucf9W9icYL7MTb16tVLZbYFls444wyVef0Zv2jRIpU98MADAcc1NTWexvKKpi4AAGIEBRkAAAdQkAEAcAAFGQAAB/je1BXu1btsgh0/FN9++63K+vbtqzJbE5ZtFTHbzk6219p4XZXslltuCTiORtOCX2KtiWbAgAEqs72/nTt3VtnYsWM9vRaBuB/j26uvvqqyQYMGqax79+6exps9e3bAsa3pNRQ0dQEAECMoyAAAOICCDACAAyjIAAA4IGordXlt2Ip1XhvOUlJSVDZv3jyVZWRkqCwpKUllBw8eVNm0adMCjmnqCg/btni2lYEGDhyosvfff19lU6ZMUVl9fX0wUzuucD8ef1puAykismDBApXZVjesqqoKOLatBBYKmroAAIgRFGQAABxAQQYAwAEUZAAAHJAciYscLw1cNravvWVzlYjI1KlTVbZlyxaV2baftAllq0l4Z1uBa/ny5SrLzMxU2YYNG1R2/fXXq4wGLsCburo6lb322msqszV1de3aNSxzOhY8IQMA4AAKMgAADqAgAwDgAAoyAAAOiEhTl5/bL4ay1WK4m8vy8/NVZluN6dRTT1XZyy+/HJY5+cHv9yFW9ezZU2WPPfaYyrw2cM2YMUNle/fuDXJ2gN0FF1ygMtv9GA9s25fOmjUrCjMJDk/IAAA4gIIMAIADKMgAADiAggwAgAOitv1iNNiakx5++GGV2VZf8uL1119X2Y033hjUWOGQlZUVcMz2i4dn2w7TtjXi8OHDVVZbW6uyoUOHqmzTpk3BTM2qR48eKuvdu7fKQnmPxo0bp7LTTz896PG2bdsWcGxrimxqagp6/Hi6H0NRUlKistmzZ6ts7dq1EZiNfwoKClRm+xns1eOPPx5wPHPmzKDHsmH7RQAAYgQFGQAAB1CQAQBwAAUZAAAHHFdNXbamHK+SkpICjg8ePBj0WCNHjgz6tV6tX79eZU8++WTAsd+rbcVTE80JJ5ygst27d3t67dKlS1X2wgsvqMzWdGVrzho1atRRr9m+fXuVdezYUWWReI927NihsjVr1qjsiSeeCDiuqqrydR7xdD+GorCwUGVnnnmmym666SaVVVZWhmVOR9K9e3eV3XXXXSqzbVnr9T23NfO2/G/0zz//9DSWVzR1AQAQIyjIAAA4gIIMAIADKMgAADjAmaauSGzxt3z58qBfm5gY+LvLX3/95dtYhxtv9OjRKmtsbFSZbcWnlg1cofD6PsRTE00oTV02DQ0NKmvbtm3Q43nR3Nyssnnz5nl67c6dO1W2YMECT6/dv3+/ymyrl4VbPN2PoejWrZvKPv30U5V16NBBZWVlZSorLy9X2W+//RZwPHjwYHWO1/fjoosuUll6errKbN9z29f1zDPPqMz2ddXV1XmaX7Bo6gIAIEZQkAEAcAAFGQAAB1CQAQBwQHK0JxBJS5YsUdnEiROjMBNt3bp1KrvllltU5nXLRFuTnBd+N9LFKtu2f998843Kzj//fE/j2Rq4tm/frrJly5Z5Gu+ll14KOLY1jdl4PQ/xw7Zy2rBhw1Rm25LxyiuvVJltK9GWbA1XoTTZFRUVqezjjz9WmW01xlCaMSONJ2QAABxAQQYAwAEUZAAAHODMwiDRkp+frzLbghxeFgb517/+pTLb546R4OdnyF4XbWEhBriE+/HYpKSkqMy2i9jkyZNVlpGRcdTxd+3apbI33njD09xsrw1lcaZoYGEQAABiBAUZAAAHUJABAHAABRkAAAc409TltXEolPNswr0QRrDNVSKxt0gHTTRwCfcjXEJTFwAAMYKCDACAAyjIAAA4gIIMAIADnGnqilcuN3V5bZDziiYauIT7ES6hqQsAgBhBQQYAwAEUZAAAHEBBBgDAAcnRnkAk+d3EFKxQrunn1xBrK4EBQDzjCRkAAAdQkAEAcAAFGQAAB1CQAQBwgOeVugAAQPjwhAwAgAMoyAAAOICCDACAAyjIAAA4gIIMAIADKMgAADiAggwAgAMoyAAAOICCDACAA/4LpYeLxAOIZIQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.2040, Train Accuracy: 0.9345\n",
            "Epoch 3/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.1557, Train Accuracy: 0.9504\n",
            "Epoch 4/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.1289, Train Accuracy: 0.9587\n",
            "Epoch 5/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.1122, Train Accuracy: 0.9642\n",
            "Epoch 6/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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AUICGDACAAjRkAAAUoCEDAKAADRkAAAVoyAAAKEBDBgBAARoyAAAK0JABAFCAhgwAgAI0ZAAAFIix1tpIFwEAwKWOO2QAABSgIQMAoAANGQAABWjIAAAoQEMGAEABGjIAAArQkAEAUICGDACAAjRkAAAU+A+4HkVKc+RZOgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0972, Train Accuracy: 0.9693\n",
            "Epoch 7/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0881, Train Accuracy: 0.9717\n",
            "Epoch 8/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0793, Train Accuracy: 0.9750\n",
            "Epoch 9/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0724, Train Accuracy: 0.9770\n",
            "Epoch 10/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0657, Train Accuracy: 0.9790\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Start the timer\n",
        "start_time_retrain = time.time()\n",
        "adv_trained_dataset = adversarial_retraining(clean_model, combineloader, optimizer, loss_fn, num_epochs, device)\n",
        "# End the timer\n",
        "end_time_retrain = time.time()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "5e16b45d-9255-4638-ae33-55d4df03b1d2",
      "metadata": {
        "id": "5e16b45d-9255-4638-ae33-55d4df03b1d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dd640b5-7a4a-468d-f767-324e6c9fe46f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training duration: 71.67 seconds\n",
            "Training duration: 1.19 minutes\n",
            "Training duration: 0.02 hours\n"
          ]
        }
      ],
      "source": [
        "# Print the training duration in different units\n",
        "\n",
        "# Calculate the training duration\n",
        "retraining_duration_seconds = abs(end_time_retrain - start_time_retrain)\n",
        "\n",
        "# Calculate the training duration in minutes\n",
        "retraining_duration_minutes = retraining_duration_seconds / 60\n",
        "\n",
        "# Calculate the training duration in hours\n",
        "retraining_duration_hours = retraining_duration_minutes / 60\n",
        "\n",
        "print(f\"Training duration: {retraining_duration_seconds:.2f} seconds\")\n",
        "print(f\"Training duration: {retraining_duration_minutes:.2f} minutes\")\n",
        "print(f\"Training duration: {retraining_duration_hours:.2f} hours\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_clean_elements = 0\n",
        "num_adv_elements = 0\n",
        "\n",
        "for clean_images, clean_targets in clean_loader:\n",
        "    num_clean_elements = len(clean_images)\n",
        "    print(f\"Number of clean images: {num_clean_elements}\")\n",
        "\n",
        "for adv_images, adv_targets in adv_loader:\n",
        "    num_adv_elements = len(adv_images)\n",
        "    print(f\"Number of adversarial images: {num_adv_elements}\")\n",
        "\n",
        "num_combined_elements = num_clean_elements + num_adv_elements\n",
        "print(f\"Number of combined images: {num_combined_elements}\")\n",
        "\n",
        "adv_trained_images_tensor, adv_trained_labels_tensor = adv_trained_dataset.tensors\n",
        "num_adv_trained_elements = len(adv_trained_images_tensor)\n",
        "print(f\"Number of adversarially trained images: {num_adv_trained_elements}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHNL0oJBp76Y",
        "outputId": "17828ac0-24d9-4975-a958-46a6ca30309b"
      },
      "id": "AHNL0oJBp76Y",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clean images: 54210\n",
            "Number of adversarial images: 27105\n",
            "Number of combined images: 81315\n",
            "Number of adversarially trained images: 81315\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "b56c7aa2-e278-4187-9e9b-e753496e8987",
      "metadata": {
        "id": "b56c7aa2-e278-4187-9e9b-e753496e8987"
      },
      "outputs": [],
      "source": [
        "def load_retrained_model(model, save_dir):\n",
        "    # Model folder\n",
        "    model_path = f\"{save_dir}/retrained_model.pth\"\n",
        "\n",
        "    if not os.path.exists(model_path):\n",
        "        raise FileNotFoundError(f\"Model file not found in {model_path}\")\n",
        "\n",
        "    model.load_state_dict(torch.load(model_path))\n",
        "    model.eval()\n",
        "\n",
        "    print(f\"Loaded retrained_model state dict from {model_path}\")\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "a8ed695e-4ec9-4d96-b130-8ae51030b87a",
      "metadata": {
        "id": "a8ed695e-4ec9-4d96-b130-8ae51030b87a"
      },
      "outputs": [],
      "source": [
        "def evaluate_after_adv_retraining(model, data_loader, loss_fn, device):\n",
        "    after_adv_test_loss = 0\n",
        "    after_adv_accuracy = 0\n",
        "    num_misclassified = 0\n",
        "    model.eval()\n",
        "    misclassified_images = []\n",
        "    misclassified_labels = []\n",
        "    misclassified_preds = []\n",
        "\n",
        "    # Create dataset\n",
        "    dataset_size = len(data_loader.dataset)\n",
        "    print(f\"Number of adv. retrained test images: {dataset_size}\")\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (x, y) in enumerate(data_loader):\n",
        "            x, y = x.to(device), y.to(device)  # Move input data to the specified device\n",
        "            logits = model(x)\n",
        "            batch_loss = loss_fn(logits, y)\n",
        "            after_adv_test_loss += batch_loss.item()\n",
        "            preds = torch.argmax(logits, dim=1)\n",
        "            after_adv_accuracy += torch.sum(preds == y).item() / len(y)\n",
        "            incorrect_inds = torch.where(preds != y)[0]\n",
        "            num_misclassified += len(incorrect_inds)\n",
        "\n",
        "            # Filter indices to current batch size\n",
        "            incorrect_inds = incorrect_inds[incorrect_inds < len(x)]\n",
        "\n",
        "            if len(incorrect_inds) > 0:\n",
        "                misclassified_images.append(x[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_labels.append(y[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_preds.append(preds[incorrect_inds].cpu())  # Move data back to CPU\n",
        "\n",
        "    num_misclassified = min(num_misclassified, dataset_size)\n",
        "    print(f\"Number of misclassified images: {num_misclassified} out of {dataset_size}\")\n",
        "\n",
        "    after_adv_test_loss /= len(data_loader)\n",
        "    after_adv_accuracy /= len(data_loader)\n",
        "    print(f\"Adversarial Retrained Test loss: {after_adv_test_loss:.4f}, Accuracy: {after_adv_accuracy:.2%}\")\n",
        "\n",
        "    # Handle empty lists\n",
        "    if len(misclassified_images) == 0:\n",
        "        misclassified_images.append(torch.tensor([]))\n",
        "    if len(misclassified_labels) == 0:\n",
        "        misclassified_labels.append(torch.tensor([]))\n",
        "    if len(misclassified_preds) == 0:\n",
        "        misclassified_preds.append(torch.tensor([]))\n",
        "\n",
        "    return after_adv_test_loss, after_adv_accuracy, [torch.cat(misclassified_images)], [torch.cat(misclassified_labels)], [torch.cat(misclassified_preds)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "3739430e-455c-4eba-8ec3-7fb2a0f197dc",
      "metadata": {
        "id": "3739430e-455c-4eba-8ec3-7fb2a0f197dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47a9bcb1-c746-45c9-c436-c79a17422238"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded retrained_model state dict from retrained_models/retrained_model.pth\n"
          ]
        }
      ],
      "source": [
        "# Model folder\n",
        "save_dir = 'retrained_models'\n",
        "\n",
        "# Loading\n",
        "defense_model = load_retrained_model(model, save_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "e6f1eb5b-1a84-4c95-ad31-724aaa0ff558",
      "metadata": {
        "id": "e6f1eb5b-1a84-4c95-ad31-724aaa0ff558",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49a22fa2-9825-47f5-b595-a4d90e881097"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of adv. retrained test images: 8920\n",
            "Number of misclassified images: 127 out of 8920\n",
            "Adversarial Retrained Test loss: 0.0639, Accuracy: 98.56%\n"
          ]
        }
      ],
      "source": [
        "# Create dataset and loader\n",
        "adv_retrain_loader = DataLoader(adv_trained_dataset, batch_size=124, shuffle=False)\n",
        "adv_retrain_test_loss, adv_retrain_test_acc, adv_retrain_misclassified_images, adv_retrain_misclassified_labels, adv_retrain_misclassified_preds = evaluate_after_adv_retraining(defense_model, filtered_test_loader, loss_fn, device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "d89bfa58-0661-425b-86ca-006829db4d64",
      "metadata": {
        "id": "d89bfa58-0661-425b-86ca-006829db4d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e5845e56-1753-4722-9673-512710442f38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number misclassified: 127\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x6000 with 60 Axes>"
            ],
            "image/png": 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WLVoUpZ0B9sT69f7pp59K7969pVOnTjJmzBipW7euLF68WN5++225/fbbZdKkSdHeYmxwcM45t2rVKici7qmnnvo+KykpcR06dHC9evUKe928vDx37NixSGxRNXv2bCcibs6cOVV2DiDeWLjeb7rpJle7dm23d+/eE/I+ffq41NTUiJwjHvDjxOPmz58viYmJcvPNN3+f1alTR8aMGSMrV66Ubdu2hbXuk08+Ka1atZJ77rlH8vLyIrXd782dO1fq1asnV111VcTXBuKVhev94MGDUqdOHWnUqNEJeVpamqSkpFRq7XhCiR23Zs0aycjIkNTU1BPynj17iojI2rVrw1p3woQJctlll8nzzz8vXbp0kfPPP1/+/Oc/y+HDhyu7Zdm9e7dkZ2fL4MGDpV69epVeD6gpLFzvffv2lYMHD8rYsWMlLy9Ptm7dKtOmTZO33npL7r///rD2F5eifSsYKzIzM93FF1/sydetW+dExE2bNq1S6x84cMBNnz7dZWVlORFx9evXd2PGjHEfffRR2Gs+99xzTkTcu+++W6m9ATWNhevd7/e78ePHu1q1ajkRcSLiEhMT3fPPP1+pvcUb7sSOKykpkeTkZE9ep06d7z9eGampqXLzzTdLTk6OrF+/XsaNGyfvvPOO9O7dWzIzM+VPf/pTyGvOnTtXTjnlFLnkkksqtTegprFwvScmJkqHDh2kf//+8tJLL8m8efNk4MCBMmHCBPnrX/9aqf3FlWi3aKyozHdmhw4dcjt37vz+165du4I656ZNm1yvXr2ciLizzjorpP1u3rzZiYgbP358SJ8HwMb1/sQTT7iWLVu6Q4cOnZD37dvXpaenu7KysqDOG++4EzsuLS1Ndu7c6cm/y9LT0wN+7tNPPy1paWnf/zr33HMDHltaWipz586VSy65RDp16iRr166VkSNHytSpU0Pa79y5c0VEZMSIESF9HgAb1/vUqVPl4osvlvr165+QDxo0SHbs2CFbtmw56Ro1QVK0NxArunfvLkuXLpWDBw+e8Je9q1at+v7jgVx//fUn/LsSbXLo448/lpkzZ8qrr74qBw4ckLPPPlumTJkiw4cP90wfBWPu3LnSoUMHOe+880L+XKCms3C9FxYWqv+ouaysTERE/H5/UOvEvWjfCsaKnJwcz78bKS0tdR07dnRZWVlhrzt//nyXmZnpRMQ1atTI3XrrrW716tWV2uvq1audiLgHHnigUusANZWF671r166uSZMmbs+ePd9nfr/f9ejRwzVo0KBK//2pJdyJHZeVlSVDhw6V+++/X3bt2iUdO3aUl156SbZs2SIzZswIe91FixZJs2bN5OWXX5YhQ4ZE5N93zJkzR0T4USIQLgvX+29/+1sZOXKkZGVlyc033ywpKSny6quvyqeffiqPPvooj5r7TrRbNJaUlJS4e+65x7Vs2dIlJye7c88917333nuVWvPw4cMR2t3/V15e7lq1auXOOeeciK4L1DQWrvf33nvPXXTRRa5Zs2audu3a7swzz6z0+H+84dmJAACzmE4EAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYFbQT+y4JGFoVe4DCFp2xRvR3kJc41pHrAjmWudODABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgVlK0NxAtiY0aqnlFx9Zq/uXNKWr+0WUT1fy/d1zmyc5J3aoee1ujbWr+3P62av7sikvVvMtjBWru36qvDwA/VHplTzXfd7peFy0+KVXzxGVrvKFzYe8rEO7EAABmUWIAALMoMQCAWZQYAMAsSgwAYJbPueDGRS5JGFrVe6kyvuRkT7ZjXnv12C6nFKr5nHZLQjpnos/7/UG5qwhpjVDtryhR8yHj7vRkyYtyq3QvVSm74o1obyGuWb7W4RVo2nBHn0Q1/+d1T6l588S6IZ33nKfHe7KWEz8KaY1grnXuxAAAZlFiAACzKDEAgFmUGADALEoMAGBWjXh2YmJ6S0/2YJdF6rHtau1R82f3n6HmXxxqpeYr/5npyfzNytRjz+6kP1OxV5Ov1PzXjTeqebPEemqeeJcycan/9gEYkNisqZpvGdfZk7138/+qx9b1+dT8qT0XqPnrH5+r5ult96p52kDv1zWnP2q2UrgTAwCYRYkBAMyixAAAZlFiAACzasRgR/kO78si7/nHdeqxGZ12qPnmwmZq3vl3+9S8/ZaVQe5OpDhA/kHLjmqe/7Z3UEVE5MXWK9T81c6verJrBtypHpu82O7jqIB48/WTvdR84pCZan5ZSrYne+2w/nLdSU9co+aNZ+lfuzJE/9rg+0lXNd84orkn6yjb1WMrgzsxAIBZlBgAwCxKDABgFiUGADCLEgMAmFUjphM3/uFsT5bebpd6bP7GdDVvskZ/gZw7cCj8jZ2Ev0B/Qednezrpn9Baj5smpHjDu3brBy8OYmMAImrjH7PU/O9X6i+obJOkv6Cy47tjPVmXh3eqxzbeFvwE9Y9xn/xLzdPa6b+nSONODABgFiUGADCLEgMAmEWJAQDMosQAAGaZnE70X9xDzb+6Tu/kp/vO9WQvXD9YPTYj5+OQ9lIe0tGRUbziFDUf01R/md2MNh96smYph9Vjq27WEqg5Sq/sqeYd/3u9mv+tzXQ1L3f6FOJmf4man/EH70t9/du+VY+tavXmr6qW83AnBgAwixIDAJhFiQEAzKLEAABmUWIAALNMTifW3hPgXci16qnxCxntlfTzyG2omrWbs03N/9HuDDUvb/1PT1bhfBHdExDvEk/Rp4ILhnjfwD7nvmfUYxcf1t+C3P6NcfpJk5wa//bihWruKzmqrxPHuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWSanEw92bqjmPTpuVvN4ex7gpptOVfN2b5bpn3BFFW4GqCHqvKnnuR3+6MlyjtZRj11ygf769c6la9X8li/0Ker3i85Uc//2HWoez7gTAwCYRYkBAMyixAAAZlFiAACzTA521DpSoeZf7mmu5unifVFcPNo9Vn9RnmZNfls1z5DdkdoOENMSmzVV81aLStV86qnvqXnHhbd4stPv1AcyKkoPqHnJVfpLNK+o+5Ga//qv56h5B1mp5vGMOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYZXI6MXlRrpqnL6rmjcSYkiO1gz42/X2T/+mBH+fzvuz16GU/UQ8d/HS2mt/WSH983dUbr1Tz0+9e58kqSvUJR1+Sft0l3LZLzYd9fYmad57yrZr71TS+cScGADCLEgMAmEWJAQDMosQAAGZRYgAAs0yOqBXc0VvND3bRXwqZMVafZox1Sae2UvOyBk7N669JUfPXzj3FkzVct189tjzIvQFRlZCoxnt+5X0G4Qv3T1KPPaOWvnTGwvF6Pu7j4PYmIr7kZDXPn95VzXPOmKzmlzz7GzVvuVV/pmJNxJ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzDI5nVjWQM87vBZfs3V9Fuer+YImC9V8i/+Imo8f+CtPVrFuQ/gbA6IsqV1rNf/4wT96sm/8x9RjL3xEn/zLmB7a25ET6tb1ZDvHdFeP3XjJFDXv9PcJej6RKcST4U4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZpmcTkzZpT87sCLR+1ZXERH9KWuxw39xDzW/JlV/5puIdxpKROTy2fq0VbvPQ5u2AmKFr5b+tvJTXytU82+UCd3rHtCvi2YvR+a6+Pq3Z3mydWP0KcTz1lyn5p2uXx2RvdRE3IkBAMyixAAAZlFiAACzKDEAgFmUGADArJieTkzM7KzmtYr16cRaSz6tyu1UmuvlnWISEfnmMn0C6+3D+ltgn1v9UzXv+N9MISK+FN70EzVf1Eqf/hu1ZaAnaxTiFGKgaeGDvz6k5m92fdaTdVx4h3psxq2x/TXKIu7EAABmUWIAALMoMQCAWZQYAMCsmB7sKF/3pZo3XFfNGwnDgZHnebJXH3taPfaI0x+MNWb9KDXvfPsWNY+vV4ICImPHv63mm/0lal50Y1NPVjqwk3rssVv3qvnCrpPV/MPSFmo+7td3erKMt1apxyLyuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWTE9nWjZz+5Z4claJOqPl1pzTP/PUPJ+czUv378p/I0BhpyX8pWat03Sr6Vx77zrya6oe1g9dme59wWaIiL9Pr1JzU/9tT4RWfcrJhGjiTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmBXT04kJ3U7XP/D1djWuOKS/tK4qBXrR5aPNX/Jkj+85Uz12WbcUNW8pH4W/MSAO3HPDLWre+smNav5Uq/c82aV5w9Rjy6a0VPOWf/1Yzf1qimjjTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxfR04oZxqWp++lT9+ETn1Fx7Q3RiZmf12KMt6qv5jj7Jav7RGP1tzXnHvFn2vX3UY2tLrpoDNV3iB6vVfEdv/W3o1zcd7MmSdn+jHpskeg5buBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWTE9nZhxq/4Ms33X91LzPzw4Xc1zS07zZBVOn3qq5StX8zsa629Tfq6oi5rPfPFyT9byPZ6FCEREhX6dlu/eXc0bQbRxJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzYno6MZCjjXxqfmEd/d2rfVM2e7KcUn26aWrhT9V8wE0XqnnSBv35ay33M4kIAFWNOzEAgFmUGADALEoMAGAWJQYAMMvkYEeLyfrQxOWTz4nA6gfV1Cefqbk+HgIAqA7ciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALN8zjkX7U0AABAO7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJHXf48GF58MEH5bLLLpMmTZqIz+eTWbNmRXtbJ8jNzZXx48dLZmam1KtXT9q0aSPXXHON5OfnR3trgCkWrvfvrF69WgYNGiRNmjSRunXrSteuXWXy5MnR3lbMSIr2BmLFnj175OGHH5Y2bdrIWWedJR988EG0t+Txhz/8QVasWCFDhw6Vbt26SUFBgUyZMkXOOeccycnJka5du0Z7i4AJFq53EZH3339fBg4cKGeffbY88MADUr9+fdm8ebN8++230d5azKDEjktLS5OdO3dKy5Yt5ZNPPpFzzz03Iutu3rxZ0tLSpG7dupVe69e//rXMnTtXateu/X127bXXyplnnilPPvmkvPLKK5U+B1ATWLjeDx48KNdff71cccUVMn/+fElI4AdnGv5UjktOTpaWLVtGfN3Zs2dLWlqajBs3TnJzcyu1Vu/evU8oMBGRTp06SWZmpuTl5VVqbaAmsXC9z507VwoLC+Wxxx6ThIQEKS4uloqKigjtNH5QYlVs+PDhMmLECJk3b5707NlTunXrJpMmTZK9e/dGZH3nnBQWFkqzZs0ish6A8EXyel+yZImkpqbK9u3bpXPnzlK/fn1JTU2VW265RUpLS6tg9zZRYlUsIyNDpk6dKjt37pQ5c+ZI8+bN5a677pJWrVrJddddJ9nZ2ZX67mrOnDmyfft2ufbaayO4awDhiOT1vnHjRvH7/XLVVVdJ//795c0335Qbb7xRpk2bJjfccEMV/04McfDIzc11IuJmzpxZJetv2bLFPfTQQ65du3ZORFzbtm3d448/HvI6eXl5LjU11fXq1cv5/f4q2CkQ/2L1em/fvr0TETdu3LgT8rFjxzoRcfn5+VWyX2u4E4uAkpISKSgoOOHXj2nbtq08+OCDkpOTIwMHDpStW7fKH/7wh5DOWVBQIFdccYU0bNhQ5s+fL4mJiZX5LQAIUnVd7ykpKSIiMmzYsBPy4cOHi4jIypUrw/wdxBdKLALmzZsnaWlpJ/wKxO/3y4IFC+Tqq6+W1q1by+LFi2Xw4MEyd+7coM934MABGTBggBQVFcl7770n6enpkfhtAAhCdV3v313XLVq0OCFv3ry5iIjs37+/Er+L+MGIfQT0799fsrOzf/SY9evXy8yZM2X27NlSWFgoGRkZ8sgjj8jo0aM9/yf9MaWlpTJw4EDJz8+XJUuWSJcuXSq7fQAhqK7rvUePHpKdnf39YMd3duzYISIip5xySvi/iXgS7Z9nxqJI/ox86dKlLisry4mIS0lJcaNGjXLLli0Lay2/3+8GDRrkkpKS3KJFiyq9NwCxe72vXr3aiYgbPnz4CfmwYcNcUlKS2759e6X3Gw+4E/sPU6ZMkaKiou+/01m4cOH3/zJ+woQJ0rBhw5DXXLZsmZSVlcnUqVNl+PDhYa3xnbvvvlsWLFggAwcOlH379nn+cfPIkSPDXhuoaWL9ej/77LPlxhtvlD//+c/i9/vloosukg8++EDeeOMNuf/++/lrhON8zjkX7U3Einbt2snWrVvVj3399dfSrl27kNcsLi6WevXqVXJn/1/fvn1l2bJlAT/Of0ogeLF+vYuIlJWVyeOPPy4zZ86UHTt2SNu2beW2226TO++8M2LnsI4SAwCYxXQiAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALOCfmLHJQlDq3IfQNCyK96I9hbiGtc6YkUw1zp3YgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMCvp9YgCA4CQ2aqjmR+fr+d+7LFDz0xbepOYZY3PD21gc4k4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZjGdCAARtueqLmq+/IzJap7129vVvO1uf8T2FK+4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZTCdWo43PZan5V7+YruadZ9yi5u0e/Fg/QUV5WPsCEFlu6N6Qjj/a0KfmjV7mGYknw50YAMAsSgwAYBYlBgAwixIDAJhFiQEAzGI6sRpddO56NS9z+lThv26couZXvHujmvtWfhbexgCErexnPTzZ0u5/DHB0opqWp0RwQzUMd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwi+nEarT3aL2Qjs9YqD87scs336o574AFqo4vSf9yWXGv9zmJtXz6FGIg6f/7UVh7AndiAADDKDEAgFmUGADALEoMAGAWJQYAMIvpxGrkv7m+mvedNFTNu/zvLn2d7TsiticAwfFf2E3N/5apv5ld86cD7SO1HRzHnRgAwCxKDABgFiUGADCLEgMAmMVgRzUq/3KTmje8o6P+CUdLq3A3AELx9cDalV7jT38cqObNhcdOhYs7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJjFdGIMCDS1CCB2zBw8rdJrtFy2T80rKr1yzcWdGADALEoMAGAWJQYAMIsSAwCYRYkBAMxiOrEaJbVtreb+rduqeScAAin+RZaad6m1IsBneJ+p+GFpHfXIhMNH1JzpxPBxJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzmE6sRvum6W+G3fNFLzVvf+/KqtwOAEWz27eoeYOE4N/s/Mhm/Q3OyVv0tRE+7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFtOJ1ahurTI19zf0V/NOAATSrv5eNU8I4Xv+gpw0NW8rW8LZUqUk1Kun5geuPFPNG8zLqcrtRBx3YgAAsygxAIBZlBgAwCxKDABgFoMd1ejYH/W/7G13pFzNE7p3UfOKtesjtiegpko85RQ1b1dno5pXBHh15V8ON/dkHV74Rj02GiNcpRfqX0eKhx1Q8wbzqnI3kcedGADALEoMAGAWJQYAMIsSAwCYRYkBAMxiOrGKVFx0tic71kD/niE1Z6uau9KjEd0TgH8r6dFOzW9p9G5I63x11Dud6P92ezhbqhJ7utZS83i5g4mX3wcAoAaixAAAZlFiAACzKDEAgFmUGADALKYTq8jRxt6JoJJTfOqx/p0FVb0dAD+ws3fN+PKXdOE+Na9w+tcja7gTAwCYRYkBAMyixAAAZlFiAACzKDEAgFk1YzwnCnacn+jJGm50UdgJAE3KWfujvQVEAHdiAACzKDEAgFmUGADALEoMAGAWJQYAMIvpxEqquND7BmcRkeT2Bz1Z/X/Wq+rtAPiBhHr6ddcrfUtE1l/w7ZmerKFsisjaODnuxAAAZlFiAACzKDEAgFmUGADALEoMAGAW04mVtLN3ipqvPe9Pnuxns26p6u0A+AF39Kia5xW10j8hPbT1i//R3JMxnVh9uBMDAJhFiQEAzKLEAABmUWIAALMY7KikOnv1F112eWW8J0sN8BfGdSK5IQAnSGzhHbwQEZnUaV6gz1DTMleu5sn7edltNHEnBgAwixIDAJhFiQEAzKLEAABmUWIAALOYTgzSweHnhXR8+/tWerLExo3VY/WZJwCR4N++Q81/+dloNc/5yWw1Lyz3q3nTGd5r3YJa7zSK9hYigjsxAIBZlBgAwCxKDABgFiUGADCLEgMAmMV0YpBq/bJQzXs00SefNnrfiSnl+/dHcksAKqHZU/pTSz98Sc/n7v5pgJUOR2hHVSNlbiM1b7jwMzWvqMK9VAXuxAAAZlFiAACzKDEAgFmUGADALEoMAGAW04k/kNS2tZpvX99CzffvTFPzdPkoYnsCEHkJH65V8//tcGaAz4jtKcRAGszLUXNrU4iBcCcGADCLEgMAmEWJAQDMosQAAGZRYgAAs5hO/AH/1m1q3qqL/kfV7ry9av7V5ixPVu/NVeFvDADgwZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzGI6MUgp/b9Wc/19zyL1hElEAKhq3IkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCY5XPOuWhvAgCAcHAnBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSuy43NxcGT9+vGRmZkq9evWkTZs2cs0110h+fn60t/a9w4cPy4MPPiiXXXaZNGnSRHw+n8yaNSva2wLMsXC9f2f16tUyaNAgadKkidStW1e6du0qkydPjva2YkZStDcQK/7whz/IihUrZOjQodKtWzcpKCiQKVOmyDnnnCM5OTnStWvXaG9R9uzZIw8//LC0adNGzjrrLPnggw+ivSXAJAvXu4jI+++/LwMHDpSzzz5bHnjgAalfv75s3rxZvv3222hvLXY4OOecW7FihTt69OgJWX5+vktOTnYjRowIe91Nmza54uLiym7POedcaWmp27lzp3POudzcXCcibubMmRFZG6hJLFzvBw4ccC1atHBXX321Ky8vj8ia8YgfJx7Xu3dvqV279glZp06dJDMzU/Ly8sJed/bs2ZKWlibjxo2T3NzcSu0xOTlZWrZsWak1ANi43ufOnSuFhYXy2GOPSUJCghQXF0tFRUWl1oxHlNiPcM5JYWGhNGvWLOw1hg8fLiNGjJB58+ZJz549pVu3bjJp0iTZu3dvBHcKoLJi7XpfsmSJpKamyvbt26Vz585Sv359SU1NlVtuuUVKS0vD3mO8ocR+xJw5c2T79u1y7bXXhr1GRkaGTJ06VXbu3Clz5syR5s2by1133SWtWrWS6667TrKzs/nuCogBsXa9b9y4Ufx+v1x11VXSv39/efPNN+XGG2+UadOmyQ033BD2HuNOtH+eGavy8vJcamqq69Wrl/P7/RFde8uWLe6hhx5y7dq1cyLi2rZt6x5//PGQ1uDvxIDIicXrvX379k5E3Lhx407Ix44d60TE5efnR3SfVlFiip07d7r27du71q1bu+3bt5/0+CNHjridO3ee8CsYBQUFbuDAgU5EXMOGDUPaIyUGREasXu+ZmZlORNyyZctOyJctW+ZExL300ktBnTfe8ePEHzhw4IAMGDBAioqK5L333pP09PSTfs68efMkLS3thF+B+P1+WbBggVx99dXSunVrWbx4sQwePFjmzp0byd8GgCDE8vX+3V5atGhxQt68eXMREdm/f/9J16gJ+Hdi/6G0tFQGDhwo+fn5smTJEunSpUtQn9e/f3/Jzs7+0WPWr18vM2fOlNmzZ0thYaFkZGTII488IqNHj/b8nxRA1Yv1671Hjx6SnZ39/WDHd3bs2CEiIqecckpQ68S9aN8Kxgq/3+8GDRrkkpKS3KJFiyK27tKlS11WVpYTEZeSkuJGjRrl+fFAOPhxIhA+C9f76tWrnYi44cOHn5APGzbMJSUlBfWjz5qAO7Hj7r77blmwYIEMHDhQ9u3bJ6+88soJHx85cmRY6y5btkzKyspk6tSpMnz4cGnYsGGl9jllyhQpKir6/ruxhQsXfv+v9ydMmFDp9YGawML1fvbZZ8uNN94of/7zn8Xv98tFF10kH3zwgbzxxhty//33B/Wjz5rA55xz0d5ELOjbt68sW7Ys4MfD/WMqLi6WevXqhbstj3bt2snWrVvVj3399dfSrl27iJ0LiFdWrveysjJ5/PHHZebMmbJjxw5p27at3HbbbXLnnXdG7BzWUWIAALOYTgQAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADAr6MdOXZIwtCr3AQQtu+KNaG8hrnGtI1YEc61zJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADArKDfJ4bql1Cnjppv+v3Zan5mr01qXjrU58nKC3eFvzEA3/vmjTM9Wd75s9Vj+/5rsJonX7olgjuqWbgTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlMJ8awr+8/R803jJwS0jpnTx/hydIGM50IREJFhXf6t8yV68c677GoHO7EAABmUWIAALMoMQCAWZQYAMAsSgwAYBbTiTGg/Kf6FOKSG/43wGfUDWn93HNf9mSDz7pePbbis7yQ1gaAaOJODABgFiUGADCLEgMAmEWJAQDMosQAAGYxnRgDtlyerOZpiaFNIQaSJIne0Mcz3ADYx50YAMAsSgwAYBYlBgAwixIDAJhFiQEAzGI6MQZcdOEXVbr+a4dP8WQJhfvUYyuqdCcAEFnciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphNjQG5Ba/0DAeJAEn369yQPv36NJ2u3c2VoiwNADOJODABgFiUGADCLEgMAmEWJAQDMYrAjBrR4po6a3zUxS80npq1S8zJXruYNN4W3LwCIddyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCymE2NAWar+n6FTyq6Q1sl4Z5yev8QjpoDKSuySoeZnpu+o5p3gP3EnBgAwixIDAJhFiQEAzKLEAABmUWIAALOYTqxOPp8ab71Cz29t9LWaD/v6EjXvfPvnau6C2BqAH7ft8mZqvrr9q54s4+1b1GOb5SaqeYro1zpOjjsxAIBZlBgAwCxKDABgFiUGADCLEgMAmMV0YjXa9rtear7pqilqvvaYX80P39hIzd3RvWHtC0BkdZ5RrObu03XVvJP4x50YAMAsSgwAYBYlBgAwixIDAJhFiQEAzGI6sYr4ftLVk00bMzWkNR7cepWal+dvDmtPAE5uz1h9injtXfoU8dnPTPBkaZ9+FNE9ITDuxAAAZlFiAACzKDEAgFmUGADALAY7Kimx42lq/tNZKz3Z+ckV6rEZS8eoecfr9ZdcAqg6L/x2kppXiP5CS0QXd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwi+nEIPlq1Vbz377/FzXXJhGv/epS9djOd32r5uUV5UHuDgBqJu7EAABmUWIAALMoMQCAWZQYAMAsSgwAYFaNmE5Matvak+3qd6p6bP/bP1Tz6xsvVfMOSSlqPmpLP09WfFmJemxFcbGah8qXpP/n/Paenp6s7ewt6rH+7Tsishcg1iVmdFDzOj7vc0+Pf0bQaweaZpbunYNe48c0n7xVzT/8uIsne2PQ5JDW3nAsTc1XHOyk5lc0Xqvmnx1p68kaJh1Rj539+BXBbU7BnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMiqvpRP/FPdT89396wZOdm+wLcXV9CjGQ/Fmne7KmxYGmniJj41M/0fdyzRRPduV7I/VFmE5EDVHyR7+aZwSaLAzgaNZhT7a1gX4tfn7zcyGtHcjHR/WvXznNvW+aH5ZzU0hrp6yqp+Yt/+8jNb/zyRvUvO17Rz3ZkZb6n+3e7qF+Pf437sQAAGZRYgAAsygxAIBZlBgAwCxKDABgls3pxPO6qfETM6apec/kWp6s3HnfvPxj9lbozz1smqBPLV52m/cZjKvfaKIeW150QM0DPQtx5wTvsxBFRJb8/H/VvPv/3evJWq37RD0WiDcHh5+n5ve0nRuR9f91wUxveIF+7O93d1fzBS9fGNI56+7Sv36d9kpOSOtEQvvfBj913SBQ/lqAD3i/dHlwJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzTE4nfnNZfTXvUVt/82ook4jPFbVX83duu1jNd07wPh9MROTz82Z7sk6TfqUee8Z92/W1r9b3svoe77MQRUSmFmWqeeu/Fniy8rJj6rFAvNnbVX8u36B6+yOy/tf+Uk92492/Vo9NXbtLzdM26c8lxMlxJwYAMIsSAwCYRYkBAMyixAAAZsX0YEdiZmc1/8eN+uOVROoGvXZ+mfcvY0VE3vqvS9U85YOP1bxNrv4CuUc/6urJNv7sT+qxQ1/vr+YrO0xW86POqfmiX+qPrnEb/6XmQLzZd0MvT5aUcSikNQZ+OUjNj5TpL3SseLG5J6s3f5V6bHlIO0EwuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWTE9nVhRW99e88TgpxBFRH6xaYAnOzZYf+xSyn59CjGQiuJiNV85+mxP9vAs/fE3b3T4m5rvr9D32O/p36h5y094dA1qtpxH/+jJKkSf5j1zxWg1P23MFjVPORRoyvHrIHaGqsKdGADALEoMAGAWJQYAMIsSAwCYRYkBAMyK6enExH0H1Ty7JEXNGyUcUfNjv/B7svL9kXkhXiBuzTpP9nHvRuqxl519o5onHNOftNbyY6YQAc1Zz433hj0PqMd2uGO3mvsDTiEiFnEnBgAwixIDAJhFiQEAzKLEAABmUWIAALNiejrRv3Wbmk/seEaIK+2t/GYiINBzFhM+XFu9GwHiVKsng5/c9c4swyLuxAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALJ9zzkV7EwAAhIM7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWLH5ebmyvjx4yUzM1Pq1asnbdq0kWuuuUby8/OjvbUTHD16VO677z5JT0+XlJQUycrKkuzs7GhvCzDFwvU+evRo8fl8AX9t37492luMCbwU87ghQ4bIihUrZOjQodKtWzcpKCiQKVOmyOHDhyUnJ0e6du0a7S2KiMiwYcNk/vz5cuedd0qnTp1k1qxZkpubK0uXLpULLrgg2tsDTLBwva9cuVI2b958Quack3Hjxkm7du1k3bp1UdpZjHFwzjm3YsUKd/To0ROy/Px8l5yc7EaMGBH2ups2bXLFxcWV3Z5zzrlVq1Y5EXFPPfXU91lJSYnr0KGD69WrV0TOAdQEFq53zfLly52IuMcee6zKzmENP048rnfv3lK7du0Tsk6dOklmZqbk5eWFve7s2bMlLS1Nxo0bJ7m5uZXa4/z58yUxMVFuvvnm77M6derImDFjZOXKlbJt27ZKrQ/UFBaud83cuXPF5/PJ8OHDI762VZTYj3DOSWFhoTRr1izsNYYPHy4jRoyQefPmSc+ePaVbt24yadIk2bt3b8hrrVmzRjIyMiQ1NfWEvGfPniIisnbt2rD3CdR0sXa9/1BZWZm8/vrr0rt3b2nXrl2l14sXlNiPmDNnjmzfvl2uvfbasNfIyMiQqVOnys6dO2XOnDnSvHlzueuuu6RVq1Zy3XXXSXZ2tlRUVAS11s6dOyUtLc2Tf5ft2LEj7H0CNV2sXe8/9Le//U327t0rI0aMCHt/cSnaP8+MVXl5eS41NdX16tXL+f3+iK69ZcsW99BDD7l27do5EXFt27Z1jz/++Ek/r3379m7AgAGefPPmzU5E3MSJEyO6T6CmiMXr/YeGDRvmatWq5fbs2RPR/VnHdKKioKBAzj//fCkrK5OcnBxJT0//0eNLSkrkwIEDJ2QtW7Y86XkKCwvlpptukoULF0rDhg2lqKjoR4/v2rWrtGjRQv7+97+fkK9fv14yMzNl2rRpMnbs2JOeF8C/xer1/p8OHz4sLVq0kIsvvlgWLlwY9OfVBEnR3kCsOXDggAwYMECKiopk+fLlJ/0/tIjIvHnz5IYbbjghC/S9gd/vl3fffVdmzpwpixYtEuecDB48WG666aaTnictLU39tyE7d+4UEQlqrwD+LZav9//017/+VY4cOcKPEhWU2H8oLS2VgQMHSn5+vixZskS6dOkS1Of179//pP/geP369TJz5kyZPXu2FBYWSkZGhjzyyCMyevRoadGiRVDn6d69uyxdulQOHjx4wnDHqlWrvv84gODE+vX+n+bMmSP169eXQYMGhfy5cS+aP8uMJX6/3w0aNMglJSW5RYsWRWzdpUuXuqysLCciLiUlxY0aNcotW7YsrLVycnI8/06stLTUdezY0WVlZUVqy0Dcs3C9f2fXrl0uKSnJjRo1KkK7jC/ciR139913y4IFC2TgwIGyb98+eeWVV074+MiRI8Nad9myZVJWViZTp06V4cOHS8OGDcPeY1ZWlgwdOlTuv/9+2bVrl3Ts2FFeeukl2bJli8yYMSPsdYGaxsL1/p158+aJ3+/nR4kBMNhxXN++fWXZsmUBPx7uH1NxcbHUq1cv3G15lJaWygMPPCCvvPKK7N+/X7p16yaPPPKI9O/fP2LnAOKdletdRKRXr17y1VdfyY4dOyQxMTGia8cDSgwAYBb/2BkAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWUE/seOShKFVuQ8gaNkVb0R7C3GNax2xIphrnTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmJUU7Q3Eq2/v7+3J1o5/Tj3206P6Gr8/q6+aVxw6FO62AMSBpLat1fzrZxp6si96vRzS2hfdfoua13tzVUjrVBfuxAAAZlFiAACzKDEAgFmUGADALEoMAGAW04lBSmjQQM2/fDRTzd8aNNGTVQT4nqFHsp4feuMUNU8dUUvNy/fuU3MANn31v73U/Lmf/1nN+6Uc8WQVAdb+S3ETNW+46ls19wdYJ9q4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZTCf+QELdumq++cXT1DzvQv15iJH4/uDvZ85T88znblLzDsOZTgQqKymtpZp/M7K9mhefWerJxp/zgXpsufhC2svtjSereUIIX18e3dNNzRdP7KPmjb9dGfTasYA7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJjFdOIPJLTQn1f4xYUzKr32z8aPV/NdP9G/l/h8tD6ZdGNXfXpoRct2au4vKDz55oA4dXTAuWr+v1OnqnnrRP1V680SU4I+Z0KAKcQKcUGv8d1KoTj9H7/yZJ1/rT8LsfFuW1OIgXAnBgAwixIDAJhFiQEAzKLEAABmUWIAALOYTvyBPRekR2SdyftP92QNPshXj6238JCaX9h9uJov7z5XzV8b0U/N055hOhE1Q+nAnp7s2clT1GO71U4MsErwU4ih+mdpbTXvU+dYRNZv84r391S+e3dE1o5V3IkBAMyixAAAZlFiAACzKDEAgFk1drAjsVFDNe/7a/1RLKG8hE5EZNpa7wvnOu5fE9Ia+4rqh7SXT3+tv6Bz8JtXezL/lm9C2gtgwaGbDniywAMckXHp+p97w6f1x9elbDuo5jfd0ljNv/y5/misQLYM8T7uKuNvIS1hDndiAACzKDEAgFmUGADALEoMAGAWJQYAMKvGTiduvqeLmr/dXH9ETYVUqPkze7uq+en37PBk/iD39p3Od3vXEBF5Ntv7SCsRkTubrFfzsjTv5JOP6UTEoZY37vFkZ90yQT229JTykNZu/5cyNU9e/rknc/6t6rGBztj5dw30PPUmNf/yZy+q+T8unejJxre6Rj3Wv13/+mINd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwq8ZOJw4YkBuRdV6drb+IMr3go0qv7S/QX2b54hfnq/mdF+nTifv/q8STNbky/H0Bsap8z15P1vqRyl+LP8ZFYI3ii89Q88/7TQ7wGbXU9Odrf+XJmm/fEO62TOBODABgFiUGADCLEgMAmEWJAQDMosQAAGbV2OnEwY1WR2Sd1K36MxWrUvO36+gfuEiPPzx7jicbJOdGcEcAKmN/hv6lONmnTyEGcuRf+hui4xl3YgAAsygxAIBZlBgAwCxKDABgFiUGADCrxk4nPrblCjV/9/S/qvmqo/qUUIONh9Q8Es9TAxBfEs/opOa33fB2SOs8uqebmnects2ThfpGeWu4EwMAmEWJAQDMosQAAGZRYgAAs2rsYEdCP+9fgIqIXHz1rWped6f3xZIiIrLm80htqdIS+J4EiAmJp5yi5t8+oX/Jvamh/vVIxKemr3zRU807bltz0r3FG77qAQDMosQAAGZRYgAAsygxAIBZlBgAwKwaO50YSN2/rIr2FsJWIdX/gk4AXgcvaq/mfVqtVfOKAA+qO1xxVM2dP/j7D1+S/mU+oV1rNS/f9HXQa8cC7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFtOJBh1qG9r3HhesGeHJmkh+pLYDmFT2sx5qPuD/Pqj02r9u/LyaB5pCDKSWT7/Wb/nJB/on/Etbo1w9dMZM/fmL6U8znQgAQLWgxAAAZlFiAACzKDEAgFmUGADALKYTfyChQQM1//o3XdW8IqNYzRv+rZ4nazJzZUh7SWrZQs1HjsoOaZ093zby7iWkFYAYk5CoxhXnd/Nkm6+trR676MqJat6xVnL4+zou0Rdgf06fFAwk2VdLze9sHPx0caBnqr5UGtJWYhZ3YgAAsygxAIBZlBgAwCxKDABgFiUGADCL6cQf2DdYn0L8bMzkkNbZ3dv7RtZN/52qHpvg06eH/vebAWp+Z5P1Ie2lyWp9Ugqwas/bHdQ855w/hbBK5acQAyl3+jW9qUx/U/Pzey6qsr3sOVpfzZtP+ajKzlmduBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWUwn/kDfX+vPN0wIse+1ecCeyfrDyuom6M9269XpnQCrh7aXlu9v92T+kFYAomP/6F5qvvzsSQE+I/hJ3DNevU3N/+fK+Wo+rEFh0Gtv9peo+U33/FrN681fFfTaodtfhWtHH3diAACzKDEAgFmUGADALEoMAGAWgx0/8GjzT9U80IvlzlgyVl/I+TzRPy/W/zK6VqAX6AU4Z6g23dTKk3V8wanH+rdui8g5gVCUXNVTzV/9/VNqXsuXouaXrv+5J9u3yPv/fxGRZ2+bpebnJu9ScxH9nEfcMU82+t671WMbvvu5mkfmSq+ZuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWUwn/sDvCn+i5o+2+FjNHz/vL2p+dX1twim0l/Bt9XunnkREZuw7X80fbp6r5p+P9r7Q877L9Mf55PUIcnNABCWW6vN5DRK8U74/5vK0f3myO3/zVoi70acQH99zppp/cJ/3emzwXo56LFOIkcedGADALEoMAGAWJQYAMIsSAwCYRYkBAMxiOvEHFiw+T80fHa1PJ+pTiKF5cNfZar5kkj6F2Gzhl2p++uO3qvnyARM92eBGq9Vj86SbmgNVqe76AjVfXpKm5oPq6S96vLNxftDnPOrK1LzHipvVvM1z+vf8tT/Up4JRPbgTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlMJ/5Ah2cDTP41vU3Nn/jpG2q+rayJJ5v6YT/12DN+q5+zcdFKNS9XU5GMsfvUfGzr6zzZl3eeqh7bQfRnvgFVqbx5QzV/tVB/4/Og9n9T83+W1vZkv/rgBvXYztOOqnm7XP3ty4hN3IkBAMyixAAAZlFiAACzKDEAgFmUGADALJ9zzgVz4CUJQ6t6L0BQsiv0iVBEBtc6YkUw1zp3YgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmOVzzrlobwIAgHBwJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJXZcbm6ujB8/XjIzM6VevXrSpk0bueaaayQ/Pz/aW/veunXrZOjQodK+fXupW7euNGvWTPr06SMLFy6M9tYAUyxc76NHjxafzxfw1/bt26O9xZjAm52PGzJkiKxYsUKGDh0q3bp1k4KCApkyZYocPnxYcnJypGvXrtHeorz77rsyefJk6dWrl6Snp8uRI0fkzTfflOXLl8v06dPl5ptvjvYWARMsXO8rV66UzZs3n5A552TcuHHSrl07WbduXZR2FmMcnHPOrVixwh09evSELD8/3yUnJ7sRI0aEve6mTZtccXFxZbcXkN/vd2eddZbr3LlzlZ0DiDdWr/fly5c7EXGPPfZYlZ3DGn6ceFzv3r2ldu3aJ2SdOnWSzMxMycvLC3vd2bNnS1pamowbN05yc3Mru02PxMREad26tRQVFUV8bSBeWb3e586dKz6fT4YPHx7xta2ixH6Ec04KCwulWbNmYa8xfPhwGTFihMybN0969uwp3bp1k0mTJsnevXvDXrO4uFj27NkjmzdvlokTJ8rixYulX79+Ya8HIHav9++UlZXJ66+/Lr1795Z27dpVer24Ee1bwVg2e/ZsJyJuxowZlV6rpKTEzZkzx/Xr18/5fD6XnJzsrr32Wvf++++78vLykNYaO3asExEnIi4hIcENGTLE7du3r9J7BGqyWL3ev7Nw4UInIm7q1KmV3l88YbAjgA0bNkhWVpZkZmbK8uXLJTExMWJrb926VWbNmiWzZs2SLVu2SNu2bWXs2LFy//33B723b7/9Vnbs2CGvv/661K5dW55//nlp0aJFxPYI1CSxfL1/Z/jw4TJ//nzZuXOnNG3aNGL7s44SUxQUFMj5558vZWVlkpOTI+np6T96fElJiRw4cOCErGXLlic9T2Fhodx0002ycOFCadiwYdh/r3XppZdKUVGRrFq1Snw+X1hrADWVhev98OHD0qJFC7n44ov5JzU/FN0bwdhTVFTkunfv7po0aeLWrVsX1OfMnDnz+x/vffcrkLKyMvf222+7wYMHu1q1armkpCQ3ePBgt2jRorD3PH36dCcibsOGDWGvAdREVq73737U+eqrr4b0eTVBUrTKMxaVlpbKwIEDJT8/X5YsWSJdunQJ6vP69+8v2dnZP3rM+vXrZebMmTJ79mwpLCyUjIwMeeSRR2T06NGV/jFgSUmJiIjnu0MAgVm63ufMmSP169eXQYMGhfy5cS/aLRor/H6/GzRokEtKSqrUXdEPLV261GVlZTkRcSkpKW7UqFFu2bJlYa1VWFjoyY4dO+bOOeccl5KS4g4dOlTZ7QI1goXr/Tu7du1ySUlJbtSoURHaZXzhTuy4u+++WxYsWCADBw6Uffv2ySuvvHLCx0eOHBnWusuWLZOysjKZOnWqDB8+XBo2bBj2HseOHSsHDx6UPn36SKtWraSgoEDmzJkjGzZskGeeeUbq168f9tpATWLhev/OvHnzxO/3y4gRIyq9VjxisOO4vn37yrJlywJ+PNw/puLiYqlXr1642zrBa6+9JjNmzJAvvvhC9u7dKw0aNJAePXrIhAkT+DEDEAIL1/t3evXqJV999ZXs2LEjolOT8YISAwCYxRM7AABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJgV9BM7LkkYWpX7AIKWXfFGtLcQ17jWESuCuda5EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmJUU7Q0AqDmSWqWreXlaEzVPOFiiH79pi36CivJwtgXDuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWTV2OvHogHPVvHTC/pDWyek+PxLbUd2+Q9/j5w90V/PkxblVthcgEprOP6zmp9fbrOZ/WnO+mtf/LEvN0579KLyNwSzuxAAAZlFiAACzKDEAgFmUGADALEoMAGCWyenExM4d1TzvnsZqPuWnsz3ZFXXXRnJLVWJyuj5tuOi5L/TjO55eldsBKq1J7SNq/pum69X81dQear7srilqfl7Tuz1Zu/9aGeTuYBF3YgAAsygxAIBZlBgAwCxKDABgVkwPduS/qD926esrXqyyc563dkhIx9d5Th8mCeURUAfe1QdVAj3S6oq6pWo+OegzAtGx5PWeap5w+8dqvrbnKwFWqqOmH4x6ypP9z8/6q8d+e3MbNXcbvtJPeWYnNdZe3Fmerz9GC5HHnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMiunpxLZv6fl5rUKbICz65BTv2v+jP4qmoWwKae1I6Nl8a0jHB3pZpkhZ5TcDVKG0Ffpjpxb9qr6aX1FXf4lmIM0T63qyaacuV4+d8dqpav7UmkvVfMNFf1bzOYeae7IHl12tHtvyg0Q1T301R82rUumV+qTorh56LbT5fWy+cJQ7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJjlc865YA68JGFoVe8l7gV6mee7S/VnJAZy+p9uUfNAE5fxJrvijWhvIa5F41r3nXummu97SH9O6PLuc9U8SfTpv1jRaf6tap6+XP8y/NIzz6j5tL0Xqvklqf/yZB1q7VePbZDgU/Pzlk5Q807Xr1bzqhTMtc6dGADALEoMAGAWJQYAMIsSAwCYRYkBAMyK6Wcnxpu8e/S3QIeq3vaILAPEDJf7hZo3vkI/vt+Q8Wre8g7vG5WHtdDfGt261l4171G76iYcXar+fNM6e/XpxDFfjlTzsj+1VPNzH/W+lbpd0tEgd/f/dXihIqTjo407MQCAWZQYAMAsSgwAYBYlBgAwixIDAJjFdGI1mvLT2RFZp8UHu9W8PCKrA7Gv3vxVan5IeQzpC9JeX+S8wWr81c/rqXmvC9epeVbDrz3Z/71zpXpsoC+4O247pOZtkvRpxr9PnBpgJa9En36vUu5sTSEGwp0YAMAsSgwAYBYlBgAwixIDAJhVYwc7jg44V823/lw/vkUr/cVyobii7tpKryEi8tWIU9S8/Rz9+PIvN0XkvEBcyflcjdvn6Ifvaaw/Nm5R7TO8axSG9oLaQ9eep6/97MshraMJNMBxxvLRat5+1Xo1D+rtyVHAnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMiqvpxPwX9YlD7XFPkZoUjIYNv3pe/8Cv9Pi8tUM8WdEn+oRj2/8JbaoKqCnK9wc/oZzUsoX+gZQ6alza2KfmFQFmAlce1V/c+ZPaxzxZnv7kKmk3WT+nK/OuEcu4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZJqcTb9+0Qc1DmTg8bdFNap68U/8jufTyT9R8cnpu0OcM5PQ/3aLmR9P8ah7oOY453ZU3AgbKu+t7uf1yfcJz04i2as5zGVHT+XpkerKBsz9Qj72p4baQ1t5fUarmN751t5rP+8VkTzbm2TvVY1t89FFIe4lV3IkBAMyixAAAZlFiAACzKDEAgFmUGADArJieTtwztpeah/rcw9t3eCfuMm7SpwoDPX8xElOIPX6vTyG2nR6Z5xVe3tn7jEQRkbx7vG+k/fqKF9VjA/0+z5uoTyc2uaujmjO1iJpi89BUTxbqFGIgF039jZpXpJereffalf+SfjTAhPKxBvrzGhvMC/Aq7GrCnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMiunpxBYf7FbzRb/R3456RV39OWPqxN2OQGdde/KN/Qdt8lFEf9Zgsy+r9q3JgSYCM5THRJ72ov7syEBTi4Gey3jeRH0ikqlFxJtdt/ZW85UjnlLSlKrdjP7CZ/mgtJYn6339avXYj4/o099z/+dpNW/g0086et4F+maqCXdiAACzKDEAgFmUGADALEoMAGAWJQYAMCumpxMDTbI9NWGU/gnPzVbjQFOLmvPW6tN2RZ+couZt/yfQxGFsT+EFenbkok2hTX4Gmlo87R59+lGblARiycFh56n5gTP05xU2Tqi6ScTPx09R8xu+6avmDRK81+nUVvqzDcsf1t/svLy0gZr//tYb1by26G+9ry7ciQEAzKLEAABmUWIAALMoMQCAWT7nXIAHmJzokoShVb0XxIBALyL99MHnQ1pn0RF9QGRyx9ND3tMPZVe8Uek1EFhNv9bz//wTNX/7Yn3IIrNW7Srby9Si09R88juXq3l53QpPljHrcEjn3DRMH+zocE/1v/wymGudOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYFdOPnUL1azY9wGO0HgxtnYAvKA1xP0B1O+Ppg2re8VL9e36/eB9HdajimHrswH9dr+Yl77ZQ8/S/71Hz9uuDf8FuUOPn/6HDpyF+QpRxJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzmE5EUHr8/hY1f/g3M9V8/FL9xaUZor+ME4gV5evz1fyn99+h5sXpPk/W6g/6CydTZXNIuf4aTvwn7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFm92hjm82blqca0jVvBmZwBAXKPEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWT7nnIv2JgAACAd3YgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosSOy83NlfHjx0tmZqbUq1dP2rRpI9dcc43k5+dHe2snOHr0qNx3332Snp4uKSkpkpWVJdnZ2dHeFmCKhet93bp1MnToUGnfvr3UrVtXmjVrJn369JGFCxdGe2sxhZdiHjdkyBBZsWKFDB06VLp16yYFBQUyZcoUOXz4sOTk5EjXrl2jvUURERk2bJjMnz9f7rzzTunUqZPMmjVLcnNzZenSpXLBBRdEe3uACRau93fffVcmT54svXr1kvT0dDly5Ii8+eabsnz5cpk+fbrcfPPN0d5ibHBwzjm3YsUKd/To0ROy/Px8l5yc7EaMGBH2ups2bXLFxcWV3Z5zzrlVq1Y5EXFPPfXU91lJSYnr0KGD69WrV0TOAdQEFq53jd/vd2eddZbr3LlzlZ3DGn6ceFzv3r2ldu3aJ2SdOnWSzMxMycvLC3vd2bNnS1pamowbN05yc3Mrtcf58+dLYmLiCd+B1alTR8aMGSMrV66Ubdu2VWp9oKawcL1rEhMTpXXr1lJUVBTxta2ixH6Ec04KCwulWbNmYa8xfPhwGTFihMybN0969uwp3bp1k0mTJsnevXtDXmvNmjWSkZEhqampJ+Q9e/YUEZG1a9eGvU+gpou16/07xcXFsmfPHtm8ebNMnDhRFi9eLP369Qt7vbgT7VvBWDZ79mwnIm7GjBmVXqukpMTNmTPH9evXz/l8PpecnOyuvfZa9/7777vy8vKg1sjMzHQXX3yxJ1+3bp0TETdt2rRK7xOoqWLtev/O2LFjnYg4EXEJCQluyJAhbt++fZXeY7ygxALIy8tzqamprlevXs7v90d07S1btriHHnrItWvXzomIa9u2rXv88cdP+nnt27d3AwYM8OSbN292IuImTpwY0X0CNUUsXu//ubfs7Gz30ksvuSuuuMJdffXVrqCgIKJ7tIzpREVBQYGcf/75UlZWJjk5OZKenv6jx5eUlMiBAwdOyFq2bHnS8xQWFspNN90kCxculIYNG57059xdu3aVFi1ayN///vcT8vXr10tmZqZMmzZNxo4de9LzAvi3WL3eA7n00kulqKhIVq1aJT6fL6w14gl/J/YDBw4ckAEDBkhRUZG89957J/0/tIjIvHnzJC0t7YRfgfj9flmwYIFcffXV0rp1a1m8eLEMHjxY5s6de9LzpKWlyc6dOz35d1kwewXwb7F8vQcyZMgQyc3Njal/0xZNSdHeQCwpLS2VgQMHSn5+vixZskS6dOkS1Of179//pP/geP369TJz5kyZPXu2FBYWSkZGhjzyyCMyevRoadGiRVDn6d69uyxdulQOHjx4wnDHqlWrvv84gODE+vUeSElJiYiI526wxor2zzNjhd/vd4MGDXJJSUlu0aJFEVt36dKlLisry4mIS0lJcaNGjXLLli0La62cnBzPvxMrLS11HTt2dFlZWZHaMhD3LFzvhYWFnuzYsWPunHPOcSkpKe7QoUOV3W5c4E7suLvvvlsWLFggAwcOlH379skrr7xywsdHjhwZ1rrLli2TsrIymTp1qgwfPlwaNmwY9h6zsrJk6NChcv/998uuXbukY8eO8tJLL8mWLVtkxowZYa8L1DQWrvexY8fKwYMHpU+fPtKqVSspKCiQOXPmyIYNG+SZZ56R+vXrh712PGGw47i+ffvKsmXLAn483D+m4uJiqVevXrjb8igtLZUHHnhAXnnlFdm/f79069ZNHnnkEenfv3/EzgHEOwvX+2uvvSYzZsyQL774Qvbu3SsNGjSQHj16yIQJE2TQoEEROUc8oMQAAGYxnQgAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADArKCf2HFJwtCq3AcQtOyKN6K9hbjGtY5YEcy1zp0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzEqK9gas8CUnq/nOW3qoecVFRZ7si6y5kdxSpZz9+K1q3mL6J2ruyo5V5XYAICzciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsmjudmJCoxgW3Z6n5XePmq/moBiuDPmW5C/rQKvfJ/VPUvFPPX6n56XdtUfPyffv1E7gY+s2iRgs0WbzvunP0/NJSNW/bYq+aX9HyX55s6mcXqcfWyk/R1373kJq73C/UHP/GnRgAwCxKDABgFiUGADCLEgMAmEWJAQDM8jkX3BjZJQlDq3ov1crXI1PNFy2YXc07sW1g9/5qXr57d5WdM7vijSpbG7av9W/v7+3JfOceUI/9LCt2rvVd5UfU/IrHf6Pmp0wLfirasmCude7EAABmUWIAALMoMQCAWZQYAMAsSgwAYFaNeHair1ZtT/bV0NQo7CT+5D3RVs0zflV104lAIGdeucGTzWm3RD12Z4CJwIvm36PmqZv07/nTXvE+OzGQreO7qvnnt+nPMv3wvyep+ZA3r1DzqpwKjlXciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsGjGd+M19P/FkeaP0aaCqtOKo/j3DjX8Zp+ZX9v1EzX/fYrma1/fpb7CtSpsGvKDml4v+1lyguvVcfZ2aN3m6npp3XJYT0vrlIRzb/KfbQ1r7V1v1Z5NWFOnPg6yJuBMDAJhFiQEAzKLEAABmUWIAALPiarAjsUVzNf/tyNer7Jx7K0rUvPe8uz1Z5yk71GM7bNH/IjkvwDkvvf7Xaj7yvnfV/NZGXwdYCYgvBwcnerJmezfrB1eEMpIRmu33eV/OKSLy8Rn/F+Az9C/Fq5ecoeZtyz4KY1fxiTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmBVX04llGa3UfESD96rsnI8W9lXzDvd4Jw79ETpno5dXqvmfGusvyrv13up/xBYQDVF5KWSCdyKy4+X6RGTdBO8LekVEFhTXVfMOL2xR80h9LYkH3IkBAMyixAAAZlFiAACzKDEAgFmUGADArLiaTvxqbLR3AKCmabo81ZPNbrdYPXZ/eama/+a1W9S83XZ9Ehn/xp0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIqr6cSE7XWq/Zy5u9uoeaoEeJssAJO++R/9bc3PnfqUkqaox2atGKfmpz3AFGK4uBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWXE1nZgxfaf+gZFVd87U2kfVvGRwz6DXqP+hPslYvmdvWHuqTmfnjlDzNMmr5p0AkZHYtImav3Xj02reIck7iZijf1mQtlO4b4g0/kQBAGZRYgAAsygxAIBZlBgAwCyTgx0VF52t5p2e/aKadyLy7ukL9A/8Mfg1Ht3TVc2/KdH/gnn17G5qfvAMf/AnjZBWD+p5RfVuA4iY0rNPU/PPjrZS84xa3gGske/rj5fK+PDj8DcGFXdiAACzKDEAgFmUGADALEoMAGAWJQYAMCumpxMrLtSnEP/rzy+p+YV1qn86LxL+u9m/QvuE3/2zajbyI7p/rD+7K/2LL6t5J0BkJHbuqOZ/eHGqmp9Zu5aaP7u/kyfr8uh29VibX6FiG3diAACzKDEAgFmUGADALEoMAGAWJQYAMCumpxM3jdCngaxOIVrWuG5JtLcARFZSohon+FxIy0zN7evJMr79NJwdIQzciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsmJhO3DW+t5rP7/9/AT4jJrZdoyzt+qaaX3bhGDVPWLamKrcDVFr5Ov25n1+VNVPzzFoHq3I7CBN3YgAAsygxAIBZlBgAwCxKDABgFiUGADArJsb81vxOf5NquYuJ7eFHbP55spp3WlbNGwFQI3EnBgAwixIDAJhFiQEAzKLEAABmUWIAALMY/0OlNP3MF+0tABF1d85QNR948Qw1f7T3Xz3Zy9I6klvCj+BODABgFiUGADCLEgMAmEWJAQDMYrCjhur60S/V/Fip/n+J1FUpat585sqI7QmIBaf/tlDNJ/2to5rf0XiTJ/vv6T9Xj80Ymxv+xqDiTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxcR0Yo+HblHzjx/8YzXvJDreONxUze9f/gs17zytVM0Tt+8J+pxtCtfrH6goD3oNIB75t+9Q8+dW9FPzO670Tie+03+yeuwtg+9Q85S/fhzk7vBD3IkBAMyixAAAZlFiAACzKDEAgFmUGADArJiYTmz2Yo6a9zp2m5r/7I4Vav5I87WR2lLQ/l6SrOZjs28Ieo3Tpx1S84zPPlFzF2Adf9BnBBCq0+/ZoOYvXuh9AeZNDbepx86c9KyaD+50r5q3fnFdkLv7/zbd28W7l0Hvq8f+/Zfnqbn7NLRzRht3YgAAsygxAIBZlBgAwCxKDABgFiUGADDL55wLNOx2gksShlb1XoLmS9KHKhNPaabmeb9t58mS9+v93faJT0PbTIX+x+fKjoW2DoKWXfFGtLcQ12LpWjeh55meaP+D+vNNV3afV9W78ei3Tn/LdN07a6t5+fr8qtxOSIK51rkTAwCYRYkBAMyixAAAZlFiAACzKDEAgFkx8ezEUDm//pRA/84CNe90h56ra4e1IwA11sdfeKJmw1LVQ88drj8PdtUDU9Q8QXxq/lxRezVPr7Xfk6XcU0c9tny9/ixIa7gTAwCYRYkBAMyixAAAZlFiAACzKDEAgFkmpxMBIJaVHzyo5qdMW6nmV07rEaEzN1Ky+JhCDIQ7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALN8zjkX7U0AABAO7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJHZebmyvjx4+XzMxMqVevnrRp00auueYayc/Pj/bWTrBx40a57rrr5NRTT5W6devK6aefLg8//LAcOXIk2lsDzLBwva9bt06GDh0q7du3l7p160qzZs2kT58+snDhwmhvLabwUszjhgwZIitWrJChQ4dKt27dpKCgQKZMmSKHDx+WnJwc6dq1a7S3KNu2bZNu3bpJw4YNZdy4cdKkSRNZuXKlzJo1SwYNGiRvv/12tLcImGDhen/33Xdl8uTJ0qtXL0lPT5cjR47Im2++KcuXL5fp06fLzTffHO0txgYH55xzK1ascEePHj0hy8/Pd8nJyW7EiBFhr7tp0yZXXFxc2e0555x77LHHnIi4f/3rXyfk119/vRMRt2/fvoicB4h3Fq53jd/vd2eddZbr3LlzlZ3DGn6ceFzv3r2ldu3aJ2SdOnWSzMxMycvLC3vd2bNnS1pamowbN05yc3MrtceDBw+KiEiLFi1OyNPS0iQhIcGzfwA6C9e7JjExUVq3bi1FRUURX9sqSuxHOOeksLBQmjVrFvYaw4cPlxEjRsi8efOkZ8+e0q1bN5k0aZLs3bs35LX69u0rIiJjxoyRtWvXyrZt22TevHny/PPPy+233y716tULe59ATRdr1/t3iouLZc+ePbJ582aZOHGiLF68WPr16xf2enEn2reCsWz27NlORNyMGTMqvVZJSYmbM2eO69evn/P5fC45Odlde+217v3333fl5eVBr/PII4+4lJQUJyLf//qv//qvSu8PqOli8Xp3zrmxY8d+f60nJCS4IUOG8FcH/4ESCyAvL8+lpqa6Xr16Ob/fH9G1t2zZ4h566CHXrl07JyKubdu27vHHHw/qc2fPnu369+/vXnjhBffmm2+6G2+80fl8Pvfcc89FdI9ATRKr1/t3e8vOznYvvfSSu+KKK9zVV1/tCgoKIrpHy5hOVBQUFMj5558vZWVlkpOTI+np6T96fElJiRw4cOCErGXLlic9T2Fhodx0002ycOFCadiw4Ul/zv3aa6/JjTfeKPn5+XLqqad+n99www3y+uuvyzfffCNNmzY96XkB/FusXu+BXHrppVJUVCSrVq0Sn88X1hpxJdotGmuKiopc9+7dXZMmTdy6deuC+pyZM2ee8OO9H/tjLSsrc2+//bYbPHiwq1WrlktKSnKDBw92ixYtOul5LrzwQte7d29P/tZbbzkRcdnZ2UHtF8D/F8vXeyDTp093IuI2bNgQ9hrxJCla5RmLSktLZeDAgZKfny9LliyRLl26BPV5/fv3l+zs7B89Zv369TJz5kyZPXu2FBYWSkZGhjzyyCMyevRoz7RhIIWFhdK4cWNPXlZWJiIifr8/qHUAxP71HkhJSYmIiOdusMaKdovGCr/f7wYNGuSSkpIq9V3SDy1dutRlZWU5EXEpKSlu1KhRbtmyZWGtdeWVV7ratWu7L7/88oR88ODBLiEhwW3fvj0SWwbinoXrvbCw0JMdO3bMnXPOOS4lJcUdOnSostuNC9yJHXf33XfLggULZODAgbJv3z555ZVXTvj4yJEjw1p32bJlUlZWJlOnTpXhw4dLw4YNw97jb37zG1m8eLFceOGFMn78eGnatKm88847snjxYvnVr3510p/lA/j/LFzvY8eOlYMHD0qfPn2kVatWUlBQIHPmzJENGzbIM888I/Xr1w977XjCYMdxffv2lWXLlgX8eLh/TMXFxRH991sff/yxPPTQQ7JmzRrZu3evnHbaafLLX/5S7r33XklK4nsSIBgWrvfXXntNZsyYIV988YXs3btXGjRoID169JAJEybIoEGDInKOeECJAQDM4okdAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZgX9iIdLEoZW5T6AoGVXvBHtLcQ1rnXEimCude7EAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYFZStDdgRkKiGhfckaXm/3PLK56sXHzqsb997zo1T92onzNtxlo1rygpUXNxTs8BwDjuxAAAZlFiAACzKDEAgFmUGADALEoMAGAW04k/4EvS/0g2P3aumm8YOaXS5/zFL54P7RN+q8dn/PMGNe90/35P5t/yTWjnBIAYxJ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzGI68QfcOWeo+Scjng3wGclVt5kQ5fWZqebdJ470ZOm/0J/LKBXlkdwSAFQp7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFtOJP5BUWKTmj+7qreZPtvhUzR/ec6YnO+BPUY8d2GiNmmclF6t5iq+2mgeytqf3LdOXnT9GPTZhxef6IkwtAqrdt/TyZIdbV+05L++fq+a/b7Hck+0r16/dMTfdqea13v8k7H1FA3diAACzKDEAgFmUGADALEoMAGAWJQYAMMvnnHPBHHhJwtCq3ktMSzq1lZp/c11bNW/9doEnK9/4VUjn9P1DP+e8jPlqXt8X/HMcf7frHDX/vE8DNa84dCjotatadsUb0d5CXDN9rff0TgV/c7n+/+myjCNqnn1+aG9rb5HonRZO9sX+4Pc3fv33P67tBdW8k8CCuda5EwMAmEWJAQDMosQAAGZRYgAAs2L/bx9jhP/b7Wqe/rSeR+IhTQm/0P/i9S8r9WGSUQ28wySBPN58tZoPqn+5msfSYAdqjs1zzlbz+b2nqXnDhA89WZukuiGeNdTjbTolUf/y77+4h5on/UN/xF60cScGADCLEgMAmEWJAQDMosQAAGZRYgAAs5hODFJCA/3RNdL+VDWu+CzPkyU2bqwe6z+jjZof+B99IjCUKcRAvjhWpubOz8svUXV2/lp/ueyD47wvbhURubKe/vLHJKkV4AyBcvxQoJfrlt23T82T/lGVuwkfd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwq8ZOJybUq6fmRYO8L9UTEekwYYOa35s+U83HfznMk3VtslM99rkAa1Slny+/Rc077dafqQiEYtsD+hTiF+NCe+GkSGLlNxMFI7b8TM3X726h5g9nLlTzgXUPRmxPwVra9U01v1z0F+lGG3diAACzKDEAgFmUGADALEoMAGAWJQYAMKtGTCcm1PW+qfXLP56uHrvxkqkhrq4/fyzQhE9V+vSY/tzDYR/e7Mk6TuMZiag69c/bHe0tRNzdBT3VfMPNnT1ZwuZv1WNPTd6r5h+8q389Glj34yB3Fzp/gPfP95h8h5q3ko+qbC+VwZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzKoR04m+01p7so2XvBiFnYTmnNwRal5/Tqqa1/u2VM07rlwTsT0Bwfj47DfUvNyFts6bxfrb0B96Wb82Wly03ZP5n2+pHptSeDSkvSQe0q8v9/k6T+ZrqT8j8auxHdR8YctQnykZvBJ3TM2z/vhrNT/1D7E5hRgId2IAALMoMQCAWZQYAMAsSgwAYFaNGOzY0a9pla396J6uaj4n7yeerOWrdUJau9U/N6p5+f68kNYBrPqfV/UBjraPBhg+eNQb1ZatEdlLRYC87FLvte6/d5d67L/OqLoBjkDOfOd2Nc94wtYARyDciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsGjGdWK8g0FxR8Db7S9R82d291Py0JZ9W+pyRem1l4hmdPNm+HvrEZuP5a9W8olR/5A6gef1wQzX/Rb39an7emuvUvN3j+nUU4tOrIsJ/cQ81n/biJE/WISmlqrcTtDo74vvLPHdiAACzKDEAgFmUGADALEoMAGAWJQYAMCuuxlaSWp+q5n/8g3d6SKRWSGtfu3aMmjePwBRiVcucs8mTPdlinnrsoL9fruYVOwsiuifEt98tGKbmvxg2Vc0bP1FXzd3R0F5cGQl7btYnjq8ev1TNozGJeLBCnxb+7c5+nqxuQTRmOasPd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwK66mE4vOa6XmZ9YOfhJx9qGWap42ZreaR+r5hpFQcUF3Nb+xiTYRFtpbpoFQdJp9QM0v/ORWNU/9eLWaR2Ourv8tK9T8/qbrq3kngT2++3w139LT+4zXZrKyqrcTVdyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCyT04kJdfXnrLW+c2Ol15705cVqnp58UM0L7uhd6XMGcurrX6m5P8BzDIsy9Ge4HXWJnuzFA63VY11p9T+rDvGn4rM8NW/wmX58fD/dL3wdF41V8zMmFgX4jMp/DbSGOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYZXI68eAVZ6r5O+2er/Taf+r2spof+qf+rMEL6/grfc5Alt+u/+d5elt/NU8q2aXmN35xvSdreV+Femz5/po33QT8p38dTNc/0Hxtte5DROTnPfQ3xy/6faaat3vK+7UxcZv+dcFfUBj+xmIId2IAALMoMQCAWZQYAMAsSgwAYJbJwY4dl+mvokz0Bd/J5U4fbOheW/8jSfQFGISowuflBBoaubDTopDW6fjezZ7slLxPwtoTEO/8Y/THt9077ydq/r8tq+5aCrR2wHMq78p8YFd39dAFX+sDcm1uCfAC4EJ9QCTauBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWSanEwMJNHEY62tXtVp1y6K9BcCM8k1fq/mGoe3U/NbXkj3ZY2lL1GMbJ+iTj1XpkQCPywqUn373bWre/l6mEwEAiChKDABgFiUGADCLEgMAmEWJAQDMiqvpxECu2niFJ1uX11o9tvaeRDXv8OyGkM65f0BnT9Z48ZfqsV9POEPNTzl/p5ov7fpmSHu5qvPnnmxd48bqseX794e0NlBT+L/aouZbenqzvvf9Rj12+fin1Tw1QX/pblUqccfUvM17eh6ruBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWT7nXFDvJr4kYWhV7yVoviR9qDJQXnFMeXZghf526Fiy/be91bxh3wI1X9HtLTUfMGCYJ6v4PMC0ZXD/d4iq7Io3or2FuBZL13q8Kbq+l5ofbOdT858OXK3mz6V/VOm9HKwoVfPrWutfd6IhmGudOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYZXI6ETUb04lVi2s9diSecoqab/jvDmr+6c+fDXrtfRX62+pvbXtB0GtUNaYTAQBxjRIDAJhFiQEAzKLEAABmUWIAALNqxJudAcCi8t271bzTHXp+3R2x89zD6sKdGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFk+55yL9iYAAAgHd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEjsvNzZXx48dLZmam1KtXT9q0aSPXXHON5OfnR3tr31u3bp0MHTpU2rdvL3Xr1pVmzZpJnz59ZOHChdHeGmCKhet99OjR4vP5Av7avn17tLcYE3gp5nFDhgyRFStWyNChQ6Vbt25SUFAgU6ZMkcOHD0tOTo507do12luUd999VyZPniy9evWS9PR0OXLkiLz55puyfPlymT59utx8883R3iJggoXrfeXKlbJ58+YTMuecjBs3Ttq1ayfr1q2L0s5ijINzzrkVK1a4o0ePnpDl5+e75ORkN2LEiLDX3bRpkysuLq7s9gLy+/3urLPOcp07d66ycwDxxur1vnz5cici7rHHHquyc1jDjxOP6927t9SuXfuErFOnTpKZmSl5eXlhrzt79mxJS0uTcePGSW5ubmW36ZGYmCitW7eWoqKiiK8NxCur1/vcuXPF5/PJ8OHDI762VZTYj3DOSWFhoTRr1izsNYYPHy4jRoyQefPmSc+ePaVbt24yadIk2bt3b9hrFhcXy549e2Tz5s0yceJEWbx4sfTr1y/s9QDE7vX+nbKyMnn99deld+/e0q5du0qvFzeifSsYy2bPnu1ExM2YMaPSa5WUlLg5c+a4fv36OZ/P55KTk921117r3n//fVdeXh7SWmPHjnUi4kTEJSQkuCFDhrh9+/ZVeo9ATRar1/t3Fi5c6ETETZ06tdL7iycMdgSwYcMGycrKkszMTFm+fLkkJiZGbO2tW7fKrFmzZNasWbJlyxZp27atjB07Vu6///6g9/btt9/Kjh075PXXX5fatWvL888/Ly1atIjYHoGaJJav9+8MHz5c5s+fLzt37pSmTZtGbH/WUWKKgoICOf/886WsrExycnIkPT39R48vKSmRAwcOnJC1bNnypOcpLCyUm266SRYuXCgNGzYM+++1Lr30UikqKpJVq1aJz+cLaw2gprJwvR8+fFhatGghF198Mf+k5oeieyMYe4qKilz37t1dkyZN3Lp164L6nJkzZ37/473vfgVSVlbm3n77bTd48GBXq1Ytl5SU5AYPHuwWLVoU9p6nT5/uRMRt2LAh7DWAmsjK9f7djzpfffXVkD6vJkiKVnnGotLSUhk4cKDk5+fLkiVLpEuXLkF9Xv/+/SU7O/tHj1m/fr3MnDlTZs+eLYWFhZKRkSGPPPKIjB49utI/BiwpKRER8Xx3CCAwS9f7nDlzpH79+jJo0KCQPzfuRbtFY4Xf73eDBg1ySUlJlbor+qGlS5e6rKwsJyIuJSXFjRo1yi1btiystQoLCz3ZsWPH3DnnnONSUlLcoUOHKrtdoEawcL1/Z9euXS4pKcmNGjUqQruML9yJHXf33XfLggULZODAgbJv3z555ZVXTvj4yJEjw1p32bJlUlZWJlOnTpXhw4dLw4YNw97j2LFj5eDBg9KnTx9p1aqVFBQUyJw5c2TDhg3yzDPPSP369cNeG6hJLFzv35k3b574/X4ZMWJEpdeKRwx2HNe3b19ZtmxZwI+H+8dUXFws9erVC3dbJ3jttddkxowZ8sUXX8jevXulQYMG0qNHD5kwYQI/ZgBCYOF6/06vXr3kq6++kh07dkR0ajJeUGIAALN4YgcAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAs4J+YsclCUOrch9A0LIr3oj2FuIa1zpiRTDXOndiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMCsp2huINUeuzlLz0kaV7/usW1ereXpykZr/ZeLFat7s9c/VvKK4OKx9Adb4kvQvXYmtW3myr0d4MxGRNhdvVfOFnReoeWF5iZr/bvvlnmz5Z6erxzbI1/edPvljNXd+v5rj37gTAwCYRYkBAMyixAAAZlFiAACzKDEAgFk+55wL5sBLEoZW9V6q1f5f9lLzFx76PzXvnpxchbsJTc81+n8L37xmat7o5ZVVuZ1ql13xRrS3ENcsXOsJDRqo+alLKjzZlFM/iMw5A3zPXyHec271H1OPrevTv9z2m/0bNe/4wrdq7t+6Tc3jTTDXOndiAACzKDEAgFmUGADALEoMAGAWJQYAMKvGPjvxaCOfms/ep08tdk/Tn3sYDR+frU/s7OmmPztxZN44T+Zyv4jonoDqVHHokJpv+IP32aefPfOBeuxZtfW1b9hyqZpvndQ5qL2JiKTs1qcTt1yuTzmvGz1ZzZ+88iw1//jqDDX3f7Xl5JuLM9yJAQDMosQAAGZRYgAAsygxAIBZlBgAwKwaO53YctJHap73cmM1v+DVn6v5h93eitieKqtZYj01//LmOp4sI7eqdwNUv7pvrfJkt5xyu3psWX19Qjl98idqXr8sJ/yNHdfW10PNd1xzVM1/10yfIr52lv62an+f8PZlGXdiAACzKDEAgFmUGADALEoMAGAWJQYAMKvGTicGUr5/v5qnjtL/qLq9OEzNR3f0Tkld2UCfNMqopU8VRsqjfbwTlC9L6yo9JxArmk0P7c3mQb3qPkx7u+rPTkxPCu3N8Wc13K7mH0mAB0LGMe7EAABmUWIAALMoMQCAWZQYAMAsBjuCVL57t5qnDdbzv0mqJ3tt0Wj12EAvuQRg06Frz1Pz3PueC/AZ+v1ELV+imif6KsLZVlziTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmMZ1YRQ5d551Ompk5McDRKVW6l8+P8IgpoDrtvMSv5hUS2lRhWYBnYJU77j++w58EAMAsSgwAYBYlBgAwixIDAJhFiQEAzGI6sZJ8tfSX0O3/RbEny6xdtVOIgay59SxP5pPPorAToGY4q9O2Kl3/7wWd1TxFvq7S88Yi7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFtOJQQo0hfjlVO/kn4jI1+e/WGV7mXGgpZ4/NFjNG2340pOVR3JDQA3m65Hpyf633QsBjta/jgSy5pj+rMUGvzyi5voTG+Mbd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwi+nEIH1z70/U/OsrplbzTkTyS/XpxAbzctScSUSg6pTX9U4ctk0KbQoxkOFvTVDzDgX6tV4TcScGADCLEgMAmEWJAQDMosQAAGYx2AEAQfAl6V8uD91/yJMlROj+YNOwaWpedp0+rnX+2us8WaPH6qrHJj22W82/XNtGzU9/+hs133+Bfnyjf2z2ZOW79XNWBndiAACzKDEAgFmUGADALEoMAGAWJQYAMKvGTicmnHWGmhdc0FjNP7nl/wKsFJnHywCIbYev6qHmy86a4sn0V1mGrszpeUWAMyzvPtcbvhHaORMy9HubdYP1V27uKq+v5n/YMsCTJY87TT22fNPXQe7OizsxAIBZlBgAwCxKDABgFiUGADCLEgMAmFVjpxM3jmyk5ptGBHrJZexMIS5+pbeap8lH1bwTwK4dv9Gvow5Xep/5JyLyTkf9a0OkJhE1Mw7ozyV8d/eZQa/xRX5rNU/aq3/5b75aH4k8cop+z+PXH80oqVu9fzK1GnifM1lZ3IkBAMyixAAAZlFiAACzKDEAgFmUGADArBoxnVh0fS9P9o9rnwpwtP4csKq0ueywml89+V41T5/0sZoHeMwaAMW1o/6h5r9p+kWAz6i67/l/Nn68mqd+sl3N/du+DXrtDCkIa08/FImvjFXxNYo7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJgVV9OJB0acp+azHn7Gk7VJqv4pxEA+PdpKzQ+fVq7m+f+nv2E2kFr7vd+rtHtgZUhrAFbteydDzW9rPCvAZ9Sq9Dn/cri5mj8xbZiat3x7lZr7K/SvAfg37sQAAGZRYgAAsygxAIBZlBgAwCxKDABglsnpxIQGDdT8wOBiNT+jdoBXj8aIa+of0POfT4/I+nMONfVkLz+gv+0VsMCXpH/p+urhcz3ZurOnBFglOaRzVgR4h/OVG37uyfYdSVGPbTmRt69HGndiAACzKDEAgFmUGADALEoMAGCWycGO4ovPUPO881+o5p3YkFl7hyc7cvUQ9di6f9EffwPEksTW+qPa2mZ5XxYZaCAjVCtL9UGQhH7bPFmziJwRweBODABgFiUGADCLEgMAmEWJAQDMosQAAGaZnE6s/9HXat5zzVA1//jsN6pyO9VuWpE+mfXqfVcEvUb9rfqjriIzxwVUrbL0xmqeIHuq7Jxj545V83bCC2ajiTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmGVyOrF89241372zrf4JZ1fhZiLg/SO11PyONdepednWemreYWFO0OdkChGWbRxVW803nP6XSq89ef/pat7uAaYQYxF3YgAAsygxAIBZlBgAwCxKDABgFiUGADDL5HRiIJ3+XKbmPZff4smOXl2kHvtZz1cjuaWgPHr3DWre5u2Pq3kngA1N1iTqHxgY/BqBphA/uKJLgM/wvsEZ0cedGADALEoMAGAWJQYAMIsSAwCYRYkBAMzyOedcMAdekqC/NRmobtkV8fWm7ljDtY5YEcy1zp0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABm+ZxzLtqbAAAgHNyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJHbdu3ToZOnSotG/fXurWrSvNmjWTPn36yMKFC6O9te998MEH4vP51F85OTnR3h5gBtd7/EiK9gZixdatW+XQoUPyy1/+UtLT0+XIkSPy5ptvyqBBg2T69Oly8803R3uL37v99tvl3HPPPSHr2LFjlHYD2MP1Hj98zjkX7U3EqvLycunRo4eUlpbKhg0bwlpj8+bNkpaWJnXr1q30fj744AP56U9/Km+88YYMGTKk0usB+Deud5v4ceKPSExMlNatW0tRUVHYa8yePVvS0tJk3LhxkpubG7G9HTp0SPx+f8TWA2o6rnebKLEfKC4ulj179sjmzZtl4sSJsnjxYunXr1/Y6w0fPlxGjBgh8+bNk549e0q3bt1k0qRJsnfv3rDXvOGGGyQ1NVXq1KkjP/3pT+WTTz4Jey2gJuN6jwMOJxg7dqwTESciLiEhwQ0ZMsTt27ev0uuWlJS4OXPmuH79+jmfz+eSk5Pdtdde695//31XXl4e1BorVqxwv/jFL9yMGTPc22+/7Z544gnXtGlTV6dOHbd69epK7xGoabje7ePvxH5gw4YN8u2338qOHTvk9ddfl9q1a8vzzz8vLVq0iNg5tm7dKrNmzZJZs2bJli1bpG3btjJ27Fi5//77Q15r06ZN0q1bN+nTp4+89957EdsjUBNwvdtHiZ3EpZdeKkVFRbJq1Srx+XzqMSUlJXLgwIETspYtW5507cLCQrnppptk4cKF0rBhw7B/Fj9s2DB566235MiRI5KYmBjWGgC43i3i78ROYsiQIZKbmyv5+fkBj5k3b56kpaWd8CsQv98vCxYskKuvvlpat24tixcvlsGDB8vcuXPD3mPr1q3l2LFjUlxcHPYaALjeLeLfiZ1ESUmJiIjnO6//1L9/f8nOzv7RddavXy8zZ86U2bNnS2FhoWRkZMgjjzwio0ePrvSPLr766iupU6eO1K9fv1LrADUd17tB0f0rudhRWFjoyY4dO+bOOeccl5KS4g4dOhTWukuXLnVZWVlORFxKSoobNWqUW7ZsWVhr7dq1y5OtXbvW1apVyw0aNCisNYGaiOs9fnAndtzYsWPl4MGD0qdPH2nVqpUUFBTInDlzZMOGDfLMM8+E/V3PsmXLpKysTKZOnSrDhw+Xhg0bhr3Ha6+9VlJSUqR3797SvHlzWb9+vbzwwgtSt25defLJJ8NeF6hpuN7jSLRbNFa8+uqr7mc/+5lr0aKFS0pKco0bN3Y/+9nP3Ntvv12pdQ8fPhyhHTo3adIk17NnT9ekSROXlJTk0tLS3MiRI93GjRsjdg6gJuB6jx9MJwIAzGI6EQBgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZQT+x45KEoVW5DyBo2RVvRHsLcY1rHbEimGudOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGBWUrQ3AKDm+OoPvfQPOD1usl7PG728MjIbgnnciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphODdPTyc9W8uEXwf4T1Cv1qnvxublh7AqzZMPKPal4RYDzxiDum5nseKY/YnqpCoLuDAS/9Rj/+mC+k9Vv9dJua75t3qidr9kJ8T3JyJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzmE78geJfZKn5f/9hlpr3SzkS9NpXb7xSzcveDXoJoEap66ut5m1i/CtXgujThl+MmVKl5/3NWO/Xr7wXqvSUUcedGADALEoMAGAWJQYAMIsSAwCYRYkBAMyK8RmfyEg8o5Mny5yzST32jmbPqHmLxJRK76Njg91qvvqqniGts/Na/Xlyb/aeFvKefmjwB7epeafRn1Z6baDblPFq3viigoisf0bjQk829dR/RmRtxCbuxAAAZlFiAACzKDEAgFmUGADALJODHftH91Lz3b31l05e2v1fnuzxFp8EWL3yAxyBPNVylf6BqQHykNWq/AopZRHYB6A79YmP9A88EZn11w9VHhv3f9U/2PHX4kZq/mphaENcn+a3U/PEffq13uHNYiX9PKRzWsOdGADALEoMAGAWJQYAMIsSAwCYRYkBAMyq1unExGZN1dyfcaqa952Wo+ZXpT6r5hm19BfoAagZDp+aWGVrH3X65O5Zb9zpyTJmHVCPrfgsL6RzZsiekI6vibgTAwCYRYkBAMyixAAAZlFiAACzKDEAgFnVOp24/U/N1fyTc/8c4kpMIVZWYXmJmqf+rV417wQIXWLTJmo+/MbsSq/9+bFyNb/tgTvVvOMr3inqikrvAsHiTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmVet04mc9X1XzMledu4CISIME/Rlz+7vo/zH0WTAgOvb3z1Dzmxu9o6TJIa19SuIxNT/880MB8kxPlrCioXps2sQAb3Gv0CcicXLciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsap1ObL/kRv0DMTSdeEHGJjWf0WZpNe+katX16c+fvPSitWr+ddfT1bziXxsitSUgaKlz9be+n3v23Z4sb/iUkNZOS0xR8zVZLwe9RkKWT80799C/BlaU6fcTnZ/Vn3Hq275LX+eAd4LSlenTlvGCOzEAgFmUGADALEoMAGAWJQYAMKtaBzs6Xb+6Ok/3oza+dI6an5u6tZp3EtieAC+uDPUBNfV83u9V6ifoj+KZlL5CzR+doz9yJ+esWiHuBqg6GbP2ebLM8vHqseOu/JuaT2i8MaJ7+k95F80I7RN+FtrhPT8Z4cmaPxHgJcI5n4e2eIziTgwAYBYlBgAwixIDAJhFiQEAzKLEAABm+ZxzQT306ZKEoVW9l2rVfY2eP9r80yo753mrh6l5RYX+vUT62P1q7i8oDOm8Wx7p5cn+dWNoj+IJ5MpWPSKyTiiyK96o9nPWJPF2rUfK/tHe60hEZM+5FZ7soh7r1WP/1HpZRPcUjERlOllEpOca/b/zgdXN1LzdAysjtqdgBXOtcycGADCLEgMAmEWJAQDMosQAAGZRYgAAs6r12Ymx5MMnzlPzrCb6BFIkNP9Trpo7v1/N9RRANDSepU/nNZ7lzQrq1FGPHVhPfxjihoc6qXlG5rdqvrDzAjXXVDj9aasrur+m5nvO1J/Z2u/o/2PvzuOjKs/+j1+TjSQsEYJAgIAiBBRFBQFFjSIIUjVCDaKABa0QbHFprW2tT+tTqVutC2pBaCkoIJtQARUKWqAWASOKCxjCIiggQcBAIAnZ7t8fv9ZHONfIzGRC5pp83q+Xf/TLzT231pNvDrk855dqnv6HdwM+S03gTgwAYBYlBgAwixIDAJhFiQEAzKLEAABm1dnpxAZz1+p5DX5mQA+pBGBeVWmp/gt+8g53rVNzX8OGan5D82x9+3apnuzuP+tTiNcmH1LzprFJav7Rnc+r+XV/OPXPT/0u7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgVp2dTgSASFdVVKT/gp+8stPpnmzl4U7q2muT9YlIa7gTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlMJxoUm3GWmmfM2qnmz6Y+paT1gvrMqYfTg1oP1KQ99/dS82tu8b59+fVt56pr2wz+JKxnqo7S63uoeUWiT819P/5azf929jOe7Mw4/S3TwZpZlBaWfcKNOzEAgFmUGADALEoMAGAWJQYAMIvBjgjmb4AjfcYeNX+yhb/HyAQ3xKH56+M3qHlj8f4gHQiXuNat1Py3o2aq+aD6Bz3Z7U1Wq2tv/tkv1Dx1Y1mAp/v/vujn/TJ60SX56toqpw9qzGzrHcgQEUmJCXYoo/pDHF9Vlqj53B9e6ed36H+vpwp3YgAAsygxAIBZlBgAwCxKDABgFiUGADCrzk4nVl1+oZoPmLhKzQ9W1FfzRS9fruYjb18a2sG+o2mcfpZbGhZUe+9jrlzNz195p5p3fH2zmldW+ySAf/uubqPmVyXpE7radF77eH069/1fPB/qsU4q3her5uXO3xUTnkdD7VcmC18t6qyu3VbqfYGmiMjaZy5S85RNa0M/WA3iTgwAYBYlBgAwixIDAJhFiQEAzKLEAABm+ZxzLpCFV8cMrumz1Jiya7p7sp89rz97bUByUU0f55T7TYF32mjBv3qqa9vfG5kTSN+1vGpebR8hqlm41luubajmOc1WerJu1X90aNCCnU7097zCT8qaqvm9r96m5qmfeL+cp8yM/Gvan0Cude7EAABmUWIAALMoMQCAWZQYAMAsSgwAYFadeHZi0s5CT7b6SIa6dkDy+ho7x5GqY2q+vSK4/xuyV/xEzdss0L8nSd5x2JO1/9TuxBKw52J9ivjhjkM8Wfe5n6lrh532XlCfeeMHo9S88v3TgtpH02SzPrVY/1X9be3teKP6t7gTAwCYRYkBAMyixAAAZlFiAACzKDEAgFl14tmJmrgWzdV863g9DwffZv3t0G1/x6RRMHh2Ys2KtmsddvHsRABAVKPEAABmUWIAALMoMQCAWZQYAMCsOvHsRE3F3gI1P2OIngMAIg93YgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGb5nHOutg8BAEAouBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYl9h8bN26UwYMHS7t27SQ5OVmaNm0qmZmZsnjx4to+2nG2bNkiN998s7Ru3VqSk5OlU6dO8vDDD0txcXFtHw0ww8L1vnLlSvH5fOpfa9eure3jRYy42j5ApNi5c6cUFRXJiBEjpGXLllJcXCzz58+XrKwsmTRpkowePbq2jyhffvml9OjRQ1JSUmTs2LHSpEkTWbNmjTz00EOyfv16WbhwYW0fETDBwvX+X3fffbd07979uKx9+/a1dJrIw5udv0dlZaV069ZNSktLJS8vL6Q9tm3bJmlpaZKcnFzt8zz66KPy4IMPyqeffiqdO3f+Nh8xYoS8/PLLcvDgQWncuHG1PweoiyLtel+5cqX07t1b5s2bJ9nZ2dXeL1rxx4nfIzY2VtLT06WwsDDkPaZPny5paWkyZswYyc3NrdZ5Dh8+LCIizZs3Py5PS0uTmJgYSUhIqNb+QF0Wadf7dxUVFUlFRUXY9osqDsc5cuSI+/rrr93WrVvd008/7WJjY93QoUND3m/z5s3uzjvvdKeddpoTEXfeeee5Z5991u3fvz/ovZYsWeJExGVlZbkPP/zQffHFF2727NmuUaNG7t577w35jEBdFcnX+4oVK5yIuAYNGjgRcbGxse7KK690ubm5IZ8vGlFiJ8jJyXEi4kTExcTEuOzsbHfw4MFq71tSUuJmzpzp+vTp43w+n6tXr54bMmSIW7ZsmausrAx4n3HjxrmkpKRvzygi7sEHH6z2+YC6KJKv99WrV7sbb7zRTZkyxS1cuNA99thjLjU11SUmJroPPvig2meMFvxM7AR5eXmya9cu2bNnj8ydO1cSEhJk4sSJnj/Cq46dO3fKtGnTZNq0abJjxw5p27at5OTkyAMPPHDS3ztjxgyZMWOG3HjjjZKamipvvPGGTJ06VZ577jkZO3Zs2M4I1AWRfr2faOvWrdKlSxfJzMyUpUuXhu2MllFiJ9GvXz8pLCyUdevWic/nU9eUlJTIoUOHjstatGhx0r0LCgpk1KhRsnjxYklJSTnpn8XPnj1bbr/9dsnPz5fWrVt/m992220yd+5c+eKLLyQ1NfXkf1MAVJF0vftzyy23yIIFC6S4uFhiY2ND2iOaMNhxEtnZ2ZKbmyv5+fl+18yZM0fS0tKO+8ufiooKWbRokQwaNEjS09NlyZIlMnDgQHnllVdOepYJEybIhRdeeFyBiYhkZWVJcXGxfPjhh4H/jQHwiKTr3Z/09HQpKyuTo0ePhrxHNOG/EzuJkpISERHPd17f1b9/f1m+fPn37rNp0yaZOnWqTJ8+XQoKCiQjI0PGjRsnI0eODPiPLgoKCtQR+vLychERppeAaoqk692f7du3S2JiojRo0KBa+0SN2v2RXOQoKCjwZGVlZa5r164uKSnJFRUVhbTvihUrXM+ePZ2IuKSkJHfrrbe6VatWhbTXdddd5xISEtzmzZuPywcOHOhiYmLc7t27Q9oXqGssXO/79u3zZBs2bHDx8fEuKysrpD2jEXdi/5GTkyOHDx+WzMxMadWqlezdu1dmzpwpeXl58tRTT4X8Xc+qVaukvLxcJkyYIEOHDpWUlJSQz3j//ffLkiVL5PLLL5exY8dKamqqvP7667JkyRK54447pGXLliHvDdQlFq73IUOGSFJSkvTq1UuaNWsmmzZtksmTJ0tycrI8/vjjIe8bdWq7RSPFrFmzXN++fV3z5s1dXFyca9y4sevbt69buHBhtfY9cuRImE74/61bt84NGDDAtWjRwsXHx7uMjAz3yCOPuPLy8rB+DhDNLFzv48ePdz169HBNmjRxcXFxLi0tzQ0fPtxt2bIlbJ8RDZhOBACYxXQiAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMCfmLH1TGDa/IcQMCWV82r7SNENa51RIpArnXuxAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGBWXG0f4FQouaGHJ9vVx6eujS3Ve33e4GfV/Ifv3BnwOU57t56aN/2oOOA9RETituxS88r9B4LaB6jLYpKT1Xzr/56v5r++/u8B7/1e0ZlqfmGDL9R8ypNZat5k6pqAP7Ou4k4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZtWJ6cQGm/Z7st898W917S0Nd/vZRe/7z/pMCvgcMX30PaqkKuA9RET+/E1HNf/b9GvUvNUT7wa1PxBN/E0hbvutPoW4ZfhENT9SVarmRVUVnuyshH3q2iuT9Gt900/fV/PNL8WquVRV6nkdxJ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzPI551wgC6+OGVzTZzmlYhs3VnNfg/pB7bPnhjb6/iXef6wHLi1X17505V/VvEXsUTVv6NP/L2sSqz+b8aXDbT3Za9mXq2srN25W80iyvGpebR8hqkXbtV623Pvvv4jIis4Lg9rnwj/8RM2bTfBO/1Zc1U1d+/aMKUF95uVjc9Q8ecG6oPaxKpBrnTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmFUnnp2oqfzmG/0X/OV+NHtBf8uyJtXPYNIjcoGax5x/tpofeNT7rDYRkXcueEXNb2v0pSd7/M7T1LUdxqoxYELxD3t6srkdn1LXjvqyv5qvna8/U7Hli4FPBO69WJ8UDlb8Yf1ax//hTgwAYBYlBgAwixIDAJhFiQEAzKLEAABm1dnpxEgSc8E5an7h1E/V/NbGa/3sFB/4Z5bw/QuiT8Ih7zRfWlwDde2Kd85T87P+WP03oef+5Fk/v5Kgpn8uTFfzxA8/V3Pe6/x/+EoGADCLEgMAmEWJAQDMosQAAGYx2FFDtj15iSdr1PGgunZdt+lB7h7cI23+XHiWJ8uYXKCu5QfGsKxe7hZP1v6VMerajs/vVnN3Woqaf5HTWc0n5EzwZMkx+gDH8B1XqvnuhzuoecKBXDXH/+FODABgFiUGADCLEgMAmEWJAQDMosQAAGYxnVhDfjLgH57srsbeySkRkSqpCmrvn+25XM2XrbxQzbVJxMot24P6TMCCysOHPVmHlwvVteWtmqh5wkv6iyg/zfBOIQYr98s2an7GUqYQQ8WdGADALEoMAGAWJQYAMIsSAwCYRYkBAMxiOrGGTHnlGk/W846t6tqO8WVq3tDP89fend5Vzc96Tn+ZH89DRLSJ7dBOzUvOSvVk8/7yrLo23qd/D58SkxTyuU7mtZ4vqnnCTn1C+Qdrf6Lmvjz9RZ9nPLrek7ljxwI8nU3ciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsn3POBbLw6pjBNX2WOmvPL3qp+Yp7nlTzTeX11fzxa25U88r8baEdLEItr5pX20eIapF0rcfU1/9d/8OnK9S8Wz19ojcYrx3VJ/8eeexWNS++1vu8xqbT9HMn7Tmq5re+slTNhzU8oOb+9Pz1nZ4sde0+da2FrwuBXOvciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphMjWOPV+ptnXzrD+9ZoEZFz5t2l5u3vXRu2M0UCphNrViRd65W99eeEvjXzbwHvMbuosZr/75yb1bzdrP36WT7T38weDr44/TG2X43toeaz7/2TmmfEJ3qzFT9W13a4baOau3L9Wa61gelEAEBUo8QAAGZRYgAAsygxAIBZdfalmHHtzlDzz/5XH6ZI+Nz7A1MRkdSee9W8wTXbQzrXd330j05qHpOzXM0bnVlY7c8EIknsqo/UvO+w29X8wNne6zRtvv4y2rYFa9S8Nl4i6yoq1LzFs/qLbn/+lv7333hSgSfbdtVUde1P3r1YzXcMb6/mlZv1f461jTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmFVnpxO3Pd5QzT+7dJKaX/3pTWoe+0xTP59Q/enEuFI9r5IqNV970XQ1z5Lu1T4LUCuq9FnB2BUfqHkz5V2ZtTFtWNOqPs1T88IRZ3rDf+l7TGilP45u7uIUNZ/a5Wz9LKV+vlCdItyJAQDMosQAAGZRYgAAsygxAIBZlBgAwKw6MZ341X29PNknlz6vrtXn/kQa3lGu5hVf5oZ6rJMqbuHvNABQMw5WNFDzAN+ffMpxJwYAMIsSAwCYRYkBAMyixAAAZlFiAACz6sR0YmW9wNdevmGomqce2B2m0wRuwOUfnvLPBFC3TdpyuZo3O6Y/r7G2cScGADCLEgMAmEWJAQDMosQAAGZRYgAAs6JqOrG830Vq3qH/toD3SPxLYzWvKt4S0pm+Ky6thZq7Bslqfkvqq0Ht3yP3R2reQj4Lah8AsII7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJgVVdOJe3smqPk/2r/pyQ5Vlapr445UhuUsMRec48neePMVdW3mJ4PUvF1csZoX+DlivddOC+hsAGyLO6ONmm/6xenV3vvQrhQ1b1btnWsGd2IAALMoMQCAWZQYAMAsSgwAYFZUDXa0fuuomm/98TFPtqVc/wFo0uYCNa+qX1/Nt005S83/cckET1buktS1y8+dq+Yi+ts8L5h0j5q3mfaun30Am2ISE9W8dHFzNU+6V1lfsF9dW7n/QMjnOlVim+vjFJt+pT/C7vOsyQHvffGGbDXv9Odv1Dw8I2/hx50YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIqq6UTfmo/UfOD0+zzZp7e/oK494x39RZSlLlbNz09Y6ec0+mRhMDot+qmaZyw7Uu29ARNi9O+z7znjbX35a1WebEbBJerawl9doOa+1RsCOtr32feTXmre8u/b1fzoBelqfsXj+sTxm6cvC+1g35E6ukTNK3btrvbepxJ3YgAAsygxAIBZlBgAwCxKDABgFiUGADArqqYT/WmkDwSpOsbrU4jhcNvOPmq+fqn3BZoiIp3+tEHNq4r1l2UC0cbfv+uTB16r5o++/rInm9tOn2QsnlOm5lMOddA/M/9SNX/7or94smaxG9S13zyg//0kx8SreT2fnvsz/0gjT/aXoVnqWrd7Y1B7RyruxAAAZlFiAACzKDEAgFmUGADALEoMAGCWzznnAll4dczgmj7LKVV6XQ81/+Wz3ukmEZGPituq+bsH26n5jqVnerJWfp6DhuAsr5pX20eIapav9bL+F3myxg/uVNcuaL+8po/jkfnJIDW/ve1qNf+6oqGav7j8ajXvOPFrT1aZvy3A00WeQK517sQAAGZRYgAAsygxAIBZlBgAwCxKDABgVp2dToRdTCfWLK51RAqmEwEAUY0SAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFk+55yr7UMAABAK7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJ/cfGjRtl8ODB0q5dO0lOTpamTZtKZmamLF68uLaP9q2VK1eKz+dT/1q7dm1tHw8ww8L1PnLkSL/Xu8/nk927d9f2ESNCXG0fIFLs3LlTioqKZMSIEdKyZUspLi6W+fPnS1ZWlkyaNElGjx5d20f81t133y3du3c/Lmvfvn0tnQawx8L1npOTI3379j0uc87JmDFj5IwzzpBWrVrV0skiC292/h6VlZXSrVs3KS0tlby8vJD22LZtm6SlpUlycnK1z7Ny5Urp3bu3zJs3T7Kzs6u9H4D/E2nXu+bf//63XH755fLII4/Ib37zmxr5DGv448TvERsbK+np6VJYWBjyHtOnT5e0tDQZM2aM5Obmhu1sRUVFUlFREbb9gLoukq/3/3rllVfE5/PJ0KFDw763WQ7HOXLkiPv666/d1q1b3dNPP+1iY2Pd0KFDQ95v8+bN7s4773SnnXaaExF33nnnuWeffdbt378/6L1WrFjhRMQ1aNDAiYiLjY11V155pcvNzQ35fEBdFsnX+4nKyspcamqqu/TSS6u9VzShxE6Qk5PjRMSJiIuJiXHZ2dnu4MGD1d63pKTEzZw50/Xp08f5fD5Xr149N2TIELds2TJXWVkZ0B6rV692N954o5syZYpbuHChe+yxx1xqaqpLTEx0H3zwQbXPCNQ1kXy9n2jx4sVORNyECROqfb5ows/ETpCXlye7du2SPXv2yNy5cyUhIUEmTpwozZs3D9tn7Ny5U6ZNmybTpk2THTt2SNu2bSUnJ0ceeOCBoPfaunWrdOnSRTIzM2Xp0qVhOyNQF1i63ocOHSqvvvqqfPXVV5Kamhq281lHiZ1Ev379pLCwUNatWyc+n09dU1JSIocOHToua9GixUn3LigokFGjRsnixYslJSUl5D+Lv+WWW2TBggVSXFwssbGxIe0BIHKv9yNHjkjz5s3lqquuiqj/DCASMNhxEtnZ2ZKbmyv5+fl+18yZM0fS0tKO+8ufiooKWbRokQwaNEjS09NlyZIlMnDgQHnllVdCPmN6erqUlZXJ0aNHQ94DQORe76+99poUFxfLsGHDgvp9dQH/ndhJlJSUiIh4vvP6rv79+8vy5cu/d59NmzbJ1KlTZfr06VJQUCAZGRkybtw4GTlyZLX/6GL79u2SmJgoDRo0qNY+QF0Xqdf7zJkzpUGDBpKVlRX07416tfsjuchRUFDgycrKylzXrl1dUlKSKyoqCmnfFStWuJ49ezoRcUlJSe7WW291q1atCmmvffv2ebINGza4+Ph4l5WVFdKeQF1k4Xr/r3379rm4uDh36623VmufaMWd2H/k5OTI4cOHJTMzU1q1aiV79+6VmTNnSl5enjz11FMh3+WsWrVKysvLZcKECTJ06FBJSUkJ+YxDhgyRpKQk6dWrlzRr1kw2bdokkydPluTkZHn88cdD3heoayxc7/81Z84cqaio4I8S/WCw4z9mz54tU6ZMkU8++UQOHDggDRs2lG7dusldd91VrVv4o0ePSv369cNyxueee05mzpwpW7dulcOHD8vpp58uffr0kYceeojHTgFBsHC9/9cll1wi27dvlz179jC4paDEAABmMZ0IADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgVsCPnbo6ZnBNngMI2PKqebV9hKjGtY5IEci1zp0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzIqr7QOEIrZxYzWv7NBazbf8JCHgvc+aWqXmMas+DHgPAMCpwZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIro6UR/U4ibH+qo5nmD/1ztzzzWt1zNe71/m5qn3/mNmld8tbfaZwEAfD/uxAAAZlFiAACzKDEAgFmUGADALEoMAGBWRE8n5o3LUPPNg6o/hehPPV+8mq/vPkPN//Vv/bmMv/3NKE/WcM7a0A8GAPDgTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmRfR0Yv0vYoNaXyX6W5k7LRvj3Tuvnr6Hn38is0c9reaZifr6RX96ypP1vPg+dW3HhzapeeXhw/rmAAJWdk13NZ/zl2fVPDUmSc1jffr3/JVO/7oTDvsqi9W83wu/DHgPX6Wepz31bihHijjciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsiJ5OTB+wI6j1vT4YpuYZt62v9lnu+vhuNX/6uRfUvEuCd2wx7yb9mY/dzhyu5i2HHFNzd0zPgbqi6rIL1HzfL7zXxtPn/U1d2zhGHy2uEqfnzs+YXw1qGqtPSn5wz/MB71Hu59zntrtLzTv+Wp+WrioqCvgzTyXuxAAAZlFiAACzKDEAgFmUGADArIge7Hiz45tqXq7/3FVSH9V/CBoOiYvfU/P7Kn+q5mf97jNP9mL6KnWtvxduXjRHH/hIG7xNzV15mZoD0WbWLH1IKsXPsEYwPi7TByFu/eu91d67pr344wme7BL9CXuyeaB3rYjIta/8WM19qzeEeqwaxZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIro6cSbtvdR85lnLlPzuMOlal6TD4up92aumm+vvMiTbXzxbXVt5wT9/4b3/UwtXnz7WDVvOmmNmgORbveveqn5yrFPqrm/KcSt5d7HTmWvH6WubTtmn5o7P49XSi+N/JdI/jjlTk+2aZj+aLxowZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIro6cT3t56h/8KZ1d97z/36NNT5g/QXwm16+exqf+YP/+WdHBIR2dz3L0Htc/gsPW8a7IGACNH/prVqHswUoojIsCfu82StJ+pTu6f+FZfhE5fWQs0fGfhKwHtsLKtQ85jScjX388jaWsedGADALEoMAGAWJQYAMIsSAwCYRYkBAMyK6OnEhh/6eSXp1Xq8/6Imat4k+TxP9q+7/6SubRDj5zN/qz/3sDY8PnCmmj+2ZZiaN3ttqyer/PrrsJ4JqI5/Tr1Yzc9t3lPNm3ymz8qd/krdeH7o1p/qI9qD6r8R8B6DVv5EzTPWrw/pTLWFOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYFdHTia1mblbz7lV3qXnLRZ+puWvjfc7Y11X6dFMDA7WeVf8bPf9f/Q2uBb8t8WTXP/5LdW3ajE/VvPLw4QBPBwSv2QuR/9bk2nA0W5/OXPkj/Y3XIkme5Ce7MtWVncbmqXlVQCeLHAa+ZAMAoKPEAABmUWIAALMoMQCAWRE92FG5/4CaN39e/yGw35fcfeMdhPjRg96X54mIHPlhkZq3Sjmk5q93WujvUyNG81jvD3vfe/B5de3Td3ZS87dH6y8R9a35KPSDARARka9e01+6+0KXSWreVLmmRUT2V3qHuDZM6qKubXI0Oh7RxZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIro6cSalDJjrZ9cX++L0/9R3ZA6IODPrEpvpv+C0x+BFbMruBdXfvZ4upqv6jPek6X5mW76eRP9UTTrnjhDzYt+e6Gax6z6UM2BuqDgbn2a93B7fYb6jQueUfP28X5e0uvHVX/zPk6uzd+i+5Fe3IkBAMyixAAAZlFiAACzKDEAgFmUGADALJ9zfkbjTnB1zOCaPsspFde6lZof7dJSzeu9mVuTx6lRx37Q3ZMNeGKlutbfdKI/N269Vv/MK/YGtU8wllfNq7G9EX3Xem2oeKuNmi89++9h2b/jW6PUPOMO70ttXXlZWD6zNgRyrXMnBgAwixIDAJhFiQEAzKLEAABmUWIAALPqxLMTC390iSf72YOz1bV9k3ep+Q9++ws1bzwt8t+Oqk1WrlqnP2cxbbX3LdgiIrc0LFDzP50xX81H3KK/ObvRLP2ZlUA0KTjcUM1Xl8ar+UX1itW8nk9f70r0L92WJxFDxZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzKoT04llDX2ezN8UYkpMopq/88hzat6/4CdqXm9JZD9rsfLAQTWfNvYGNc+eNlHNz4zT/3l1uHuTmhfMCuBwgHGtfrhRzR+TLmp+55atan5t8qGwnSlacScGADCLEgMAmEWJAQDMosQAAGZRYgAAs+rEdGKzP7/ryS5N05+F+OltL6h5jL++j7JvA76+oJ6ax/q8E57f5528DmqeIeuDPhMQLb74XS8175ukP1N0a3mVmp/90A41rwzpVLZF2ZdgAEBdQokBAMyixAAAZlFiAACz6sRgh+asZ/PV/NbeV6v59DOWq/mYp19V89+PvM6TtX1c/yGtW68/oiZcvnjI+8PkUYOXqmuHpzyp5jGiP14KqOsq+nTzZEU/P6yuvb3tP9Tc38svv6zQr7vKgn0Bni76cScGADCLEgMAmEWJAQDMosQAAGZRYgAAs+rsdGLl/gNqfvi6xmo+f21TNb/Kz8s1b+w1zZOVL9QfClNeww+LSfYF86in4KYQf723u5qf8z9fqXlFULsDkaP0+h5qXnBriSf75ILZQe09/4j+9WXKj/WX1MbIhqD2j2bciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsOjud6E/lN9+o+dSObdX8sXtvUfO2g7Z7srGt3lbX9k4qDfB0NW/s7svU/O1VF6h5xz/vVvOK3V+E60jAKVX8w55q/uxTz6t5l4RYT3aoSr+mf75rgJrv/UU7NY9ZvUHN8X+4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZPuecC2Th1TGDa/osUS/uTH3CsfK0Bmq+Oae+mjd/R//eY39X/XMbbfV5smZr9TfP+jZ/ruZVxcX65rVgedW82j5CVIu2a91Xr56al1x9vpo/+OxUNQ9mirjj/J+oeYe71wW8BwK71rkTAwCYRYkBAMyixAAAZlFiAACzKDEAgFk8O/EUqvh8Z1DrM8YEt3/KzMDX+htJDWhUFYhQB+64xJO1G5mvrl185sSwfObcI808WfvZkfM81GjHnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMYjoRgDmFP/JOIYqIzPvtk56sdVxSUHt/4+etzJfN/IWat59+0JP5Nn4U1GcidNyJAQDMosQAAGZRYgAAsygxAIBZDHYAMKfxJ/pLXfvNud+TbRr2grr2NwUXqfnbky9W8zNfXKPmlWqKU4U7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJjFdCIAc9yHG9W83Yfe7LpfdvO3i5qeLvoUIiITd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwy+ec00d0AACIcNyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEvuPjRs3yuDBg6Vdu3aSnJwsTZs2lczMTFm8eHFtH+1bK1euFJ/Pp/61du3a2j4eYAbXe/SIq+0DRIqdO3dKUVGRjBgxQlq2bCnFxcUyf/58ycrKkkmTJsno0aNr+4jfuvvuu6V79+7HZe3bt6+l0wD2cL1HD97s/D0qKyulW7duUlpaKnl5eSHtsW3bNklLS5Pk5ORqn2flypXSu3dvmTdvnmRnZ1d7PwD/h+vdJv448XvExsZKenq6FBYWhrzH9OnTJS0tTcaMGSO5ublhO1tRUZFUVFSEbT+gruN6t4kSO8HRo0dl//79sm3bNnnmmWdkyZIl0qdPn5D3Gzp0qAwbNkzmzJkjPXr0kC5dusj48ePlwIEDIe952223SaNGjSQxMVF69+4t77//fsh7AXUZ13sUcDhOTk6OExEnIi4mJsZlZ2e7gwcPVnvfkpISN3PmTNenTx/n8/lcvXr13JAhQ9yyZctcZWVlQHusXr3a3XjjjW7KlClu4cKF7rHHHnOpqakuMTHRffDBB9U+I1DXcL3bx8/ETpCXlye7du2SPXv2yNy5cyUhIUEmTpwozZs3D9tn7Ny5U6ZNmybTpk2THTt2SNu2bSUnJ0ceeOCBoPfaunWrdOnSRTIzM2Xp0qVhOyNQF3C920eJnUS/fv2ksLBQ1q1bJz6fT11TUlIihw4dOi5r0aLFSfcuKCiQUaNGyeLFiyUlJSXkP4u/5ZZbZMGCBVJcXCyxsbEh7QGA690ifiZ2EtnZ2ZKbmyv5+fl+18yZM0fS0tKO+8ufiooKWbRokQwaNEjS09NlyZIlMnDgQHnllVdCPmN6erqUlZXJ0aNHQ94DANe7Rfx3YidRUlIiIuL5zuu7+vfvL8uXL//efTZt2iRTp06V6dOnS0FBgWRkZMi4ceNk5MiR1f6ji+3bt0tiYqI0aNCgWvsAdR3Xu0G1+yO5yFFQUODJysrKXNeuXV1SUpIrKioKad8VK1a4nj17OhFxSUlJ7tZbb3WrVq0Kaa99+/Z5sg0bNrj4+HiXlZUV0p5AXcT1Hj24E/uPnJwcOXz4sGRmZkqrVq1k7969MnPmTMnLy5Onnnoq5O96Vq1aJeXl5TJhwgQZOnSopKSkhHzGIUOGSFJSkvTq1UuaNWsmmzZtksmTJ0tycrI8/vjjIe8L1DVc71Gktls0UsyaNcv17dvXNW/e3MXFxbnGjRu7vn37uoULF1Zr3yNHjoTphM6NHz/e9ejRwzVp0sTFxcW5tLQ0N3z4cLdly5awfQZQF3C9Rw+mEwEAZjGdCAAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMCsgJ/YcXXM4Jo8BxCw5VXzavsIUY1rHZEikGudOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYFVfbB4g0cW3T1dwVl+p5q9PVvDIp3pN9PihZX9vimJo3fbuemp/+zldqXrF9h5oDQLTiTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmMZ14gi/HN1Dzke03qnl2o7+reatY7yRilbjgDtNHj98u0accn2l/dnD7A4Bx3IkBAMyixAAAZlFiAACzKDEAgFmUGADALKYTT/Bw58Vqfm3yIT+/Iyngvb+p0p+/mBKToOYxfr7H6JNUrOZ/f08/y44eJQGcDohOsc2bqXl5Rqug9tnXTb++rhnxbtBn8ux9rKGa77q/fVD7JGzZ48kq9haEdCYruBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWUwnnuD5MUPU/PS//EXNe9QL/HmIPf9xj5qnfKRPJ+b++vmA9xYRua/5W2p+18V3esO1Hwe1N1AbyvpfpOY7h1YFvMe5Z3gn9kREFrefEtKZTqnZq4Jafn3+dd6wd5jOEqG4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZdWI68csHe3myM+bu1Re/vV6NH7lmsJpXNqkf8DnOztui5p/f0zngPb7PHXnD1bxB3k5PVhmWTwR0n8/uoubrLn0xqH3q+d7zk9eJL11Be7n9PE82TC6thZOcOtyJAQDMosQAAGZRYgAAsygxAIBZdeKno+mPeF9a52+wIa5tuppvG66/WK/sjGNqPrbrSk92T+Ot6tpYn/5omUrnU3N/dm3Wz9ipeaw3LPT3kk+g+qb2mKbmjWIST+1BToHVx7z3ApfWC/yxWKge7sQAAGZRYgAAsygxAIBZlBgAwCxKDABgVp2YTtQU/7Cnmt/1+Gw1H1T/YFD7x4h3srBK9BdoVrnwPARqy40T1Xyt8p68x764Vj9Ljv4YrcrN+mQloPnfH92u5ndNnavm1yYfqbGzPHHgbDV/adFVQe3jy9DP2Kn5Pk92afslQe0dLlfmjvZkrWRjLZzk1OFODABgFiUGADCLEgMAmEWJAQDMosQAAGbV2enEoyML1fyG+vv9/I7g+v6Yq/BkH5YF94+7Q3yJmjcO8vlzPep5pyL/3uF1de2wKf3U/HDvBDV35WVBnQV1Q8y/N6j5iz30qeAXY2vu++mvbu6o5g3K9Wlhd60+ifzGBVPUvFlscmgHq4ZOM3+q5u1f8T4TNdqf4sidGADALEoMAGAWJQYAMIsSAwCYRYkBAMzyOef0EZ0TXB0zuKbPEhG+GXGJmlfpw3l+JRR5/7E2nL02qD2ODeiu5kda61OOpan6m6BvH77Uk93VeEtQZ7nyZ/o0VIO5wf09hcPyqnmn/DPrEgvXetUVF6p5yh++9GQvn/mmuraeL/qGs/+w/1xPtm7oeepat22nmleVlob1TNURyLXOnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMYjqxDohtmurJ8p5uq67d3Ocvat7xrVFq3mHEB6EfLERMJ9YsC9d6m3X6G8hfbP3OKT5JZIn1ee9LKp3+9MSOs3+i5mfdd+onjv1hOhEAENUoMQCAWZQYAMAsSgwAYFb0PXcFHpX7D3iys3+rv8gvpo/+6Kotff+q5j+QrqEfDIhALx7Sh54W7NEfdVUbpma8ouatgnhB5wM/eE3N50/JVPPKTfkB730qcScGADCLEgMAmEWJAQDMosQAAGZRYgAAs5hOxHGqJKCnkAG1asvD56j5+T8+w5PVW9pIXRvn592PjT85rK//cGNAZzsVXvywl5qPa7Yh4D1GNtqj5s9e21TNWzKdCABAeFFiAACzKDEAgFmUGADALEoMAGDWKZ1OzJ/YQ83jG+tjQmfe/HFNHqdOO3B5q9o+AhCyem/kqnmrN6q/t4X53H///mL9F/684ZSeIxJwJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzTul0Ytuz9qn5zi/0Z3UhPCqv9L59+ar73lXXflZeruY3rPipmmfI+tAPBpwCR7N7qnnKmi/VvGK3/kzBuuLi7I/U/IsnT/FBAsSdGADALEoMAGAWJQYAMIsSAwCYRYkBAMw6pdOJb3deoObn/WvsqTxG1IpLa6HmN01805P9qNFude1tO69R84zbmEJE5Nt7j/eNx6vvf1pd2//ToWreQL8EIsqR2w9Ve49jrkLN33vlfDVvIfpEc23jTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmndLpxNFfZqr5J3e+oObXLv2Rmrv3Pw3bmSyKueAcNe8zY62aj2zkfRZc7jF97+3PdVLzhqLvDUSSw+d4n/2Z5EtQ1/683VtqPlnahfVMgfhm5CVqfqiDvn76ec+peawv3pNVuip17W8L9LdDtxgfmVOI/nAnBgAwixIDAJhFiQEAzKLEAABmndLBju0P6UMD9/xBfxHjtS+9o+Zz/0d/Lkzy39eFdrBaFlO/vprvuF9//Ms/bv+jmreKTVbzKnGebNhr+ksu289mgAN1w+lxh9W8ZGAPNU/cp09DxX6w2ZMdvaaLurbrbz9Q83ua6m+cbBOnX9MisWrqb4hDs+wL/etxS9kU8B6RgDsxAIBZlBgAwCxKDABgFiUGADCLEgMAmOVzznlH1xRXxwyusUN8M0J/5Mo7j+qPVimu0qcZf/rFtWq+7r2OAZ8lttSn5m2Xlqr59oH1At67/pn6i+ymnv+SmndJ0CeQ/NlVUaLm/Wfe78na/1GfQKosrP7L9mra8qp5tX2EqFaT13pNq7iqmyeb9ZL+dSQ1JknNY3369/ZLi/Vr/fdbrvNk/+5SO/+OlrgyT3bZEz9X17aat03NK/YWhPVM1RHItc6dGADALEoMAGAWJQYAMIsSAwCYRYkBAMyKiOlEf74eo08t3nDnKjX/TdNP1DxGvBOH2vMEwykcn/n8N/ob8Z7P7a3mncbr04lVG2w9C+1kmE6sWZanEzX5L+rPQhzfZ4aaX5t8pCaPExYXvDdczY/s9z6HNWNUbk0fp8YwnQgAiGqUGADALEoMAGAWJQYAMIsSAwCYFdHTif7Enpai5nm/P1v/DY29zxOr4eFEUYYTJeHzRHVp2yX6NFTs1t1qXrn/QMjHigZMJ9asSLrWa1JFH+9zFkVELvjjh6f4JCJflzVQ86/ub6fmcR/kq3lVcXHYzhQJmE4EAEQ1SgwAYBYlBgAwixIDAJhFiQEAzIqr7QOEwt/bhzvcs/YUn6RmVdb2AYAoFvf2ejX/VB9arGGH1TRGNqh5VQ2exBruxAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMMvnnHO1fQgAAELBnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlNh/bNy4UQYPHizt2rWT5ORkadq0qWRmZsrixYtr+2jH2bJli9x8883SunVrSU5Olk6dOsnDDz8sxcXFtX00wAwL13tubq6MHTtWOnfuLPXr15c2bdrITTfdJPn5+bV9tIgSV9sHiBQ7d+6UoqIiGTFihLRs2VKKi4tl/vz5kpWVJZMmTZLRo0fX9hHlyy+/lB49ekhKSoqMHTtWmjRpImvWrJGHHnpI1q9fLwsXLqztIwImWLjen3jiCVm9erUMHjxYunTpInv37pUXXnhBunbtKmvXrpVzzz23to8YEXzOOVfbh4hUlZWV0q1bNyktLZW8vLyQ9ti2bZukpaVJcnJytc/z6KOPyoMPPiiffvqpdO7c+dt8xIgR8vLLL8vBgwelcePG1f4coC6KtOv93XfflYsuukgSEhK+zbZs2SLnnXeeZGdny4wZM6r9GdGAP078HrGxsZKeni6FhYUh7zF9+nRJS0uTMWPGSG5ubrXOc/jwYRERad68+XF5WlqaxMTEHPcvO4DgRNr13qtXL8813aFDB+ncubN89tln1do7mlBiJzh69Kjs379ftm3bJs8884wsWbJE+vTpE/J+Q4cOlWHDhsmcOXOkR48e0qVLFxk/frwcOHAg6L2uvPJKERH58Y9/LBs2bJAvv/xS5syZIxMnTpS7775b6tevH/I5gbookq93jXNOCgoKpGnTpmHZLyo4HCcnJ8eJiBMRFxMT47Kzs93BgwervW9JSYmbOXOm69Onj/P5fK5evXpuyJAhbtmyZa6ysjLgfcaNG+eSkpK+PaOIuAcffLDa5wPqoki/3k80ffp0JyJuypQp1T5jtOBnYifIy8uTXbt2yZ49e2Tu3LmSkJAgEydO9PwRXnXs3LlTpk2bJtOmTZMdO3ZI27ZtJScnRx544IGT/t4ZM2bIjBkz5MYbb5TU1FR54403ZOrUqfLcc8/J2LFjw3ZGoC6I9Ov9xLP27NlTOnfuLO+8847ExsaG7YyWUWIn0a9fPyksLJR169aJz+dT15SUlMihQ4eOy1q0aHHSvQsKCmTUqFGyePFiSUlJOemfxc+ePVtuv/12yc/Pl9atW3+b33bbbTJ37lz54osvJDU19eR/UwBUkXS9f9fevXvl0ksvlfLyclm7dq20bNky4N8b9Wr3RjDyTZo0yYmIy8vL87tm6tSpx/3x3vf9Yy0vL3cLFy50AwcOdPHx8S4uLs4NHDjQvfHGGyc9y+WXX+569erlyRcsWOBExC1fvjywvykAqki63v+rsLDQXXDBBa5JkyZu48aNQf391AX8d2InUVJSIiLi+c7ru/r37y/Lly//3n02bdokU6dOlenTp0tBQYFkZGTIuHHjZOTIkQH/0UVBQYE6Ql9eXi4iIhUVFQHtA0AXSde7iEhpaalcf/31kp+fL2+99Zacc845Af/eOqO2WzRSFBQUeLKysjLXtWtXl5SU5IqKikLad8WKFa5nz55ORFxSUpK79dZb3apVq0La67rrrnMJCQlu8+bNx+UDBw50MTExbvfu3SHtC9Q1Fq73iooKl5WV5eLi4oK6c6truBP7j5ycHDl8+LBkZmZKq1atZO/evTJz5kzJy8uTp556Sho0aBDSvqtWrZLy8nKZMGGCDB06VFJSUkI+4/333y9LliyRyy+/XMaOHSupqany+uuvy5IlS+SOO+7gz8mBAFm43u+77z5ZtGiRXH/99XLw4EHPf9w8fPjwkPeOKrXdopFi1qxZrm/fvq558+YuLi7ONW7c2PXt29ctXLiwWvseOXIkTCf8/9atW+cGDBjgWrRo4eLj411GRoZ75JFHXHl5eVg/B4hmFq73K664wvOzNwng53B1DdOJAACzeGIHAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMCfmLH1TGDa/IcQMCWV82r7SNENa51RIpArnXuxAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGBWXG0fAABiTz9dzQ8MaB/UPvuuKPdkTZodDmqPS9M+V/M3N5+r5meNr/CG730S1GcidNyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCymE6up6vIL1Xz3lUme7Fhqlbo2b/Cf1Xz0l1eqecH1iWpe+fXXag5Euh/9+301v7nh8lN8Ev+ea5mr5keuKPVk5715l7o2Y7S+B0LHnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMYjrxBIeHXqzmd/z2NTXvXO+van5hPX0SUeNv5eT0lWp+/sQRap6ezXQibNpbkeLnV745pecQEVlaXE/N1x09S81/2sQ7cXha86Kwngn+cScGADCLEgMAmEWJAQDMosQAAGZRYgAAs+rsdKK/KcQ//WGCml9Ur1LNY/x8HxD4bGLwurbcpeYHT9MnvCoLD9XgaYDqW/6D89R84qgBap78lU/Nm79X/anA2C/3qXnVYX3vth+c6cn+0XWKunbE+Xfoe3/0WYCnw4m4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZ0TWd6NMnlvKndPNkC696Vl3bMT42nCeqEVPa6m+77X2N/jbZhrPX1uRxgGqr2Pmlmp/xP3rujwvHWfzkOx65RM1HNnpXSeura7f8Un8re4fR+vqqo0f9nAb/xZ0YAMAsSgwAYBYlBgAwixIDAJgVVYMd2gCHiEhe/4lKeuoHOK7+9CY1/3JXqprnXzMpqP1/9LvFav732acHtQ8Ar/JG1X+Y3NbeU9U8681r1Lziev3rVOXhw9U+S7TgTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmRcR0Ymznjmr+zfmN1Tzxtq/UfGGHF/x9QijHCsjfjzZR81/9c4gnO/uXeerajHMa6pvrA0t+ZTfMV/MX777BkzV/TntUDgB/Oj28Xc3bxeR4su2DgpssXtRhqZo//W47NV82Qn8Ellu/MajPjQbciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsiJhO3N1Xf3bg2794Us0bxiT42an6U4hvleiTgg+Ov13Nf3PXTDU/u+Mub7iwgbr2tpb6Mw+Dte6Y/s+x5Vv7PVllWD4RqDsqv/5azTuM9V5f19zbQ9/k3A5qXP5kkZq/fc4iNb/iVX3S+dcjvZOSMas+1M8SJbgTAwCYRYkBAMyixAAAZlFiAACzKDEAgFkRMZ3YYrz+HL+FY85S8+GNvqyxs7SKPaTmg+5Yqef1D6r5DRkLw3Ca4L7H8Hf2Ix1O82RJm0I5DwAP57xReZm+9kP92YYJ1yWq+ZWLBqr5ynNfU/O+L/zbk/3zvPr6WaIEd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwKyKmE/3548f91PxHl00Nap9H95/nyaat76WufeKyeWr+m6afqHm8T39eY7l3YClowe7dOUH/nqQsR5mg9OnPdkt67b2AzgYgfKpKS9U8+Wf11HzZwng179/gU0+2qv3N6trKrZ8HeLrIxp0YAMAsSgwAYBYlBgAwixIDAJjlc055Zori6pjBNX0Wj9hGjdTcndkqqH1iDhz2ZBW7duufuaKlmv/dz2OkYvx8H1AlVQGezr+a3HtreYWa3z/gR2pe+dmWan9muCyv0odvEB61ca0jOHt+qQ+mfXLvBE92wWM/Udc2f15/3F8kCeRa504MAGAWJQYAMIsSAwCYRYkBAMyixAAAZkX0Y6cqD3unCkVE5CM/uR/BzPLtPNg4qL2D8VZJQzX/sixVzV8ad72a+/zMkybcvlfNn+0wx5N1TkhQ17qEiP5XAoCInL7Bz0s3FYe6HlPz5uE6TC3jTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmMYp2grY/+VrNLxx1j5pfdK33JXQiIu+/ca4nO2PBfnVt5aZ8NW8ka9Xcr9l6POhvP/Vk+f0nqWu33Xyamp/5UXBHARAZ/po5Tc3/KN6XBVvEnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMYjoxUD49LrhEf45junjfmloZzvMEIXVNvCeLvyZWXbvxRy+oeebH3glHEZFGs4KcoARwSr1ZeL6fX6n+G+IjAXdiAACzKDEAgFmUGADALEoMAGAWJQYAMIvpxBNUtTxdzT8cM17NO6fcpeZn/SJypvZS/7LGkz19Tzt17U8bb1bzbzrp3+80Cv1YwCkRd2ZbNa/4fOcpPkn4fHWx/mZ2zYL3u6l5huSG6zi1ijsxAIBZlBgAwCxKDABgFiUGADCLEgMAmMV0YjU9mjVLzSe8fZMnS16jv8G5svBQWM90ol0P9PJkMb43g9rj3sEL1fzvD+nTnEBtOHZtd0/24oRn1LV3bBmq5hXPtlDz5H/qb3GvKi4O8HTB83XrrOYLbv+Tn9+R7Ekab4juL/PciQEAzKLEAABmUWIAALMoMQCAWdH9E79TYFD9g2p+w18neLLz/nWHuvbMWz4Ky1ni0lur+U03r/RkPz1tm7o2Ol6Th7oq5pj33+Az4xuoa98+Z5G+yWQ9XnTUOzQhIvJZaStPNmnFVera+l/oL6M92kZ/Ze7VF3+s5mcn6GdZf6zMk6Ut3aOurVBTe7gTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlMJ54gdr/+CKgnD5yn5v/TNE/Ny50325j5N/1Dd+txvE+fZCp3+iSTyHo/eTB7+1vv7zOByBH/zw2e7Aebf6CufbNjcI9ey6qvP14qq/4WT/arbG8WTkeqStX8poX3erL2n0fOC3prAndiAACzKDEAgFmUGADALEoMAGAWJQYAMIvpxBNUfLlLzdf84Cw1f3qp91llIiI/bby52mfxNylYFYYnHAa790tfXKLmSfJ5tc8ChE2Vd4rW9yP9e/VH3+io5nc30Z9l2iAmMfRzhdnl60eqeft7o3sSUcOdGADALEoMAGAWJQYAMIsSAwCYRYkBAMxiOjFAFbv0Bxy+ddNFar55WnNP9svmy9W1rePqhX6wMHv1SAs1971wup/fwXQiIpu/ieNVXZLUfGWvHDXf162+msdcfSDgs2S20t+o/mza+2ree+MNat7iQX3/uvhmdu7EAABmUWIAALMoMQCAWZQYAMAsSgwAYJbPOefnKXrHuzpmcE2fJerFrmip5n/PWKjmMX6+xwjHsxMvnHiPmp8xSX8jbeXXX1f7M8NledW82j5CVONaR6QI5FrnTgwAYBYlBgAwixIDAJhFiQEAzKLEAABm8ezEU6iy9x41z5Lup/gkIunyrpp734sLAJGLOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzfM45V9uHAAAgFNyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEvuPjRs3yuDBg6Vdu3aSnJwsTZs2lczMTFm8eHFtH+0469evl2uuuUYaNWokDRs2lH79+smGDRtq+1iAKRau99zcXBk7dqx07txZ6tevL23atJGbbrpJ8vPza/toESWutg8QKXbu3ClFRUUyYsQIadmypRQXF8v8+fMlKytLJk2aJKNHj67tI8oHH3wgl112maSnp8tDDz0kVVVVMmHCBLniiivkvffek44dO9b2EQETLFzvTzzxhKxevVoGDx4sXbp0kb1798oLL7wgXbt2lbVr18q5555b20eMCLzZ+XtUVlZKt27dpLS0VPLy8kLaY9u2bZKWlibJycnVPs+1114ra9askS1btkhqaqqIiHz11VeSkZEh/fr1k/nz51f7M4C6KtKu93fffVcuuugiSUhI+DbbsmWLnHfeeZKdnS0zZsyo9mdEA/448XvExsZKenq6FBYWhrzH9OnTJS0tTcaMGSO5ubnVOs8777wjffv2/bbARETS0tLkiiuukNdff12OHDlSrf2BuizSrvdevXodV2AiIh06dJDOnTvLZ599Vq29owkldoKjR4/K/v37Zdu2bfLMM8/IkiVLpE+fPiHvN3ToUBk2bJjMmTNHevToIV26dJHx48fLgQMHgt7r2LFjkpSU5MmTk5OlrKxMPv3005DPCdRFkXy9a5xzUlBQIE2bNg3LflHB4Tg5OTlORJyIuJiYGJedne0OHjxY7X1LSkrczJkzXZ8+fZzP53P16tVzQ4YMccuWLXOVlZUB7XHeeee5jIwMV1FR8W127Ngx16ZNGyci7tVXX632OYG6JJKvd8306dOdiLgpU6ZU+4zRghI7wWeffeaWL1/uXnrpJXfttde6QYMGub1794b1M3bs2OH+93//151xxhlORFzbtm3do48+etLfN3HiRCcibsSIEW7jxo3uk08+cUOGDHHx8fFORNz06dPDek4g2kXy9a6dtVGjRu6SSy457hvZuo7BjpPo16+fFBYWyrp168Tn86lrSkpK5NChQ8dlLVq0OOneBQUFMmrUKFm8eLGkpKQE9GfxDz74oDz55JNSXl4uIiIXXXSR9O/fXx555BH5+9//LgMHDjzpHgB0kXa9/9fevXvl0ksvlfLyclm7dq20bNky4N8b9Wq7RSPdpEmTnIi4vLw8v2umTp367R9J/Pcvf8rLy93ChQvdwIEDXXx8vIuLi3MDBw50b7zxRsBnOnjwoHvnnXfcxx9/7Jxz7oEHHnAi4jZu3Bj43xgAj0i83gsLC90FF1zgmjRpwjWu4L8TO4mSkhIREc93Xt/Vv39/Wb58+ffus2nTJpk6dapMnz5dCgoKJCMjQ8aNGycjR46U5s2bB3Wmxo0by2WXXfbt/37rrbekdevW0qlTp6D2AXC8SLveS0tL5frrr5f8/Hx566235Jxzzgn499YZtd2ikaKgoMCTlZWVua5du7qkpCRXVFQU0r4rVqxwPXv2dCLikpKS3K233upWrVpV3eN+a/bs2U5E3J/+9Kew7QlEOwvXe0VFhcvKynJxcXFB3bnVNdyJ/UdOTo4cPnxYMjMzpVWrVrJ3716ZOXOm5OXlyVNPPSUNGjQIad9Vq1ZJeXm5TJgwQYYOHSopKSkhn/Ff//qXPPzww9KvXz9JTU2VtWvXytSpU+Waa66Re+65J+R9gbrGwvV+3333yaJFi+T666+XgwcPev7j5uHDh4e8dzRhsOM/Zs+eLVOmTJFPPvlEDhw4IA0bNpRu3brJXXfdJVlZWSHve/ToUalfv35Yzrht2zb5yU9+Ih988IEUFRXJmWeeKSNGjJCf//znnv8oEoB/Fq73K6+8UlatWuX31/nS/f9RYgAAs3hiBwDALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzAn5ix9Uxg2vyHEDAllfNq+0jRDWudUSKQK517sQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgVlxtHwBA3RGTnKzmvqTEoPapOnxEzV15WdBngm3ciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphMDVHFVNzUvOT1ezfdf6PNk2f1Xq2vHNdsQ8rm+a19lsZpfNv8Xnqz9z9aG5TOBYHRZrf87+nCzVUHtc8PmgWp+7KmWnqz+B1+qayu+2hvUZyIycScGADCLEgMAmEWJAQDMosQAAGZRYgAAs3zOORfIwqtjBtf0WU6p0ut6qHnSz3er+YwOc9W8cUySmldJQP9Yv9cXFSVqvrdSf/5cj3qBf+Z1rfRpSwuWV82r7SNEtZq81l/fvV7Nq6Sqxj7zH8Upav70vcPUPHGPPkEpn25RY57XWHMCuda5EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZdeLZiXvu7+XJ/n3PU+raZF+Cn130N8/ml5eq+b3bbvJke19vo65tlqtPQ8Ud1veuStbP+LtXpqm5NrVYeOsl6trTpq9RcyAcOv3zDjXfdNVkNT9YeUzN/3xQ//f3ltPe82T9kw+pa/tPnqDm/lyzKVvN965u5cna/O+7Qe2N0HEnBgAwixIDAJhFiQEAzKLEAABmUWIAALPqxHRicUvvc9n8TSE++02Gms/4S381b714j/6h23d4ohayS1/rh7+nycW1aK7mX1c28vM7vNNZTVd/pa6sCOBcQKg63L5RzS8dcbeaFzf3viFdRCT9EX367x+3/9yTTfvd0+ra9vHBfflbes6r+i+c442uuVSfZKx6Tr92G3yiv2W6YscXAZ2tLuNODABgFiUGADCLEgMAmEWJAQDMqrMvxTwyuKeaN5i37hSfxL+4Vi3VfMePzlDzj8e+EPDeV+WMUfPE172P7Yk0vBSzZkXbtV512QVqXnaaPtz1zR1Faj6yvf614aeNN3uyGD/3B/5e/nn/V95H44mIfPzQBWpe741cNY82vBQTABDVKDEAgFmUGADALEoMAGAWJQYAMKtOPHZKU3+X/sLJ/Bd7qPnIS/6t5r0bblLzKuf9/mB07nB1bXmh/sLNYRfrL6h87fTF+meqqS5xv/73D0SbmH9vUHP9qhNJe13P305Xni8lIkvaXeHJdl2ZpK792c2vqfmTafpjtAomrlDzH435mZrXe7NuTC1+F3diAACzKDEAgFmUGADALEoMAGAWJQYAMKvOTidmPJen5m+01KeE/IkR/aV9VVLpyTZePjVMe+t2VZSo+XUv/tKTtV4b3N8nUNdVfKm/1DZGydus0vd47dXL9V/w877NEY12qvmxlFg1r6dvE9W4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZdXY6cfs19dV87rvN1PymBvtq8jhhsbOikZq3foxJRCASHLywsZpfUz/fz++oi/OGweFODABgFiUGADCLEgMAmEWJAQDMosQAAGbV2enEygMH1fylkdep+ZPd9WlGf4rO8D7h8IzXy9W1Bd31CaTUjRVqftMfl6j5Dxt8puZHbrrYkzWYu1ZdC6D6YhvrU4ivP/aUmjeM0b8G/LtUf/90Sn6RmrsAzhZtuBMDAJhFiQEAzKLEAABmUWIAALPq7GCHP741H6l58zXB7dM8iLWt/hnc3n9tc72aj35wh5rf+Ltlnuwfc/VHVAHQxSTqQxaHbrjAk737zIvq2nKn7+FPrJ9RjfyRDfTfMLKnJ5p27SR16SX1vC/uFRHJWJqj5uf8j/5S0Iqv9upnOUW4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZTCeeQjFdOql51cd5Qe3T9JOSoNbf1XiLJ/uHdAtqDyDaxDRsqOenp6p55V/1x8at6Pi8Jyt3+v1BlXgfR/d9Lkk8puaf/fCFoPbRz6LLu2ai/gvX6HFWq+7VPkt1cCcGADCLEgMAmEWJAQDMosQAAGZRYgAAs5hOPIWCnUL0J37/ETXfWKa/RLNzAv83o+4qGdhDzVvf753aFRGZesa8an/mh2X67N/Nq8aoeWyC/hzD5NxkNT/aSt8/o9sXnmz3ojPUtXfc8Yaajz5tq5pHKu7EAABmUWIAALMoMQCAWZQYAMAsSgwAYBZjayeI7dxRzX2FRWpesXtPTR5HVfmZPlX1ZlEXNT8v9TNPFtv+TH3vrZ+HfjAgzGLPydB/oUx/juFnPz/dk83y8yzAC+sF9xxDfx7ff74nW3OH/mzSDrnrw/KZ/mgzji1E/xr12rXec4swnQgAwClDiQEAzKLEAABmUWIAALMoMQCAWXViOjE2tYkn2zXCz1uWE/Q90p/dGc4jVYu/N9I2jd+n5lXiPFllU30PsTWYBGN8cfqXnC1/ukjNn7n2ZTU/WNlAzW9puDu0g33Hb/b2VPNVE/W86UveiUNX/km1zxEucemt1fynbd8Kap+L379VzZtJeJ4JGyruxAAAZlFiAACzKDEAgFmUGADALEoMAGBWnZhObLDQ58lubLhSXbv2slQ1ryotDeeRqqXigvZqflujlYFvsvbj8BwGCIIvKUnNHxkwR837Jx/ys5O/3GvdsXg1//kjd6r56XM/VfPUojVq7p39rT0x55/tyTr8TX/Wqv9/trrmf9D/Odb23z93YgAAsygxAIBZlBgAwCxKDABgVlQNdhwZrD8W5s0zvS/Fu/i3P1XXNvHzw9tIUtg+sbaPAISkqkh/ueyjk25R8yd771fzg183UvP2L3lfCxn3TYm6NvVj/VoPz6sya5a/R0ltueU0Tzbp9JV+dqmnpp1fuVvN23+YG8DJTj3uxAAAZlFiAACzKDEAgFmUGADALEoMAGBWVE0n+hPr83b12nF/VtdeeNpYNU97+t2wnikQ7tIL1HzR759U8xhJVvP2i8Z4sgx5L+RzAeHm9/p6Wo+bBrG3hWlDf4p/qE9cn3n/Z2q+oM1zSqpPIV6+Yaiat/+N9yWfIiKuokLNaxt3YgAAsygxAIBZlBgAwCxKDABgFiUGADArqqYTG8xbp+bZd/f1ZLPa/UNdu/bnz6r5xZkj1LzeotPUvNnyLz1ZZbMUde2BLvpz4Prf8281bxqrv1jwL4fS1bzTz7wvwLQ8sQVEm7i0Fmq+62r9lZO7NmWoeSc/uebspw6reWV5WcB7RALuxAAAZlFiAACzKDEAgFmUGADALEoMAGBWVE0n+lM80DuL1/Gp0eraD/u+oObru89Q85juPjV/+zfe55Wdl/CNutbftGGM6Hsfrjqm5i9OvEHNm5ee+uc+AghcxVd71TzjTj0PB+87sG3iTgwAYBYlBgAwixIDAJhFiQEAzKLEAABm1YnpxMoDBz1Zh5HeTETkuh/eq+adfvWpmr/Y+h01751UqqT6FKI/b5fob2T9xUQ/b59+nilEAHULd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwq05MJwYjeYH+dugvFujrfyBda/A0ujRhChEARLgTAwAYRokBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMMvnnHO1fQgAAELBnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlNh/bNy4UQYPHizt2rWT5ORkadq0qWRmZsrixYtr+2jfWrlypfh8PvWvtWvX1vbxADMsXO+5ubkyduxY6dy5s9SvX1/atGkjN910k+Tn59f20SJKXG0fIFLs3LlTioqKZMSIEdKyZUspLi6W+fPnS1ZWlkyaNElGjx5d20f81t133y3du3c/Lmvfvn0tnQawx8L1/sQTT8jq1atl8ODB0qVLF9m7d6+88MIL0rVrV1m7dq2ce+65tX3EiOBzzrnaPkSkqqyslG7duklpaank5eWFtMe2bdskLS1NkpOTq32elStXSu/evWXevHmSnZ1d7f0A/J9Iu97fffddueiiiyQhIeHbbMuWLXLeeedJdna2zJgxo9qfEQ3448TvERsbK+np6VJYWBjyHtOnT5e0tDQZM2aM5Obmhu1sRUVFUlFREbb9gLou0q73Xr16HVdgIiIdOnSQzp07y2effVatvaMJJXaCo0ePyv79+2Xbtm3yzDPPyJIlS6RPnz4h7zd06FAZNmyYzJkzR3r06CFdunSR8ePHy4EDB0Le87bbbpNGjRpJYmKi9O7dW95///2Q9wLqMgvX+3c556SgoECaNm0alv2igsNxcnJynIg4EXExMTEuOzvbHTx4sNr7lpSUuJkzZ7o+ffo4n8/n6tWr54YMGeKWLVvmKisrA9pj9erV7sYbb3RTpkxxCxcudI899phLTU11iYmJ7oMPPqj2GYG6JpKvd8306dOdiLgpU6ZU+4zRgp+JnSAvL0927dole/bskblz50pCQoJMnDhRmjdvHrbP2Llzp0ybNk2mTZsmO3bskLZt20pOTo488MADQe+1detW6dKli2RmZsrSpUvDdkagLrB0vefl5UnPnj2lc+fO8s4770hsbGzYzmgZJXYS/fr1k8LCQlm3bp34fD51TUlJiRw6dOi4rEWLFifdu6CgQEaNGiWLFy+WlJSUkP8s/pZbbpEFCxZIcXEx/2ID1RCp1/vevXvl0ksvlfLyclm7dq20bNky4N8b7fiZ2ElkZ2dLbm7u9/63GXPmzJG0tLTj/vKnoqJCFi1aJIMGDZL09HRZsmSJDBw4UF555ZWQz5ieni5lZWVy9OjRkPcAEJnX+6FDh2TAgAFSWFgoS5cupcBOwH8ndhIlJSUiIp7vvL6rf//+snz58u/dZ9OmTTJ16lSZPn26FBQUSEZGhowbN05GjhxZ7T+62L59uyQmJkqDBg2qtQ9Q10Xa9V5aWirXX3+95Ofny1tvvSXnnHNOwL+3zqjdH8lFjoKCAk9WVlbmunbt6pKSklxRUVFI+65YscL17NnTiYhLSkpyt956q1u1alVIe+3bt8+TbdiwwcXHx7usrKyQ9gTqIgvXe0VFhcvKynJxcXHujTfeCGmPuoA7sf/IycmRw4cPS2ZmprRq1Ur27t0rM2fOlLy8PHnqqadCvstZtWqVlJeXy4QJE2To0KGSkpIS8hmHDBkiSUlJ0qtXL2nWrJls2rRJJk+eLMnJyfL444+HvC9Q11i43u+77z5ZtGiRXH/99XLw4EHPf9w8fPjwkPeOKrXdopFi1qxZrm/fvq558+YuLi7ONW7c2PXt29ctXLiwWvseOXIkTCd0bvz48a5Hjx6uSZMmLi4uzqWlpbnhw4e7LVu2hO0zgLrAwvV+xRVXfDv+r/2F/4/pRACAWUwnAgDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADAr4Cd2XB0zuCbPAQRsedW82j5CVONaR6QI5FrnTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxdX2AQDg2IDual7Uxs+XKOdnI18114pI8xX71Lwyf5ufjVCbuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWUwn1pBj1+rTVpov+8Sq+ZrBT6n5sPyb1bzgjXQ1b/2PA56scuPmAE8HhM+hN9ur+a87zFLza5MPqXm5q1TzeJ/3WgpmrYjIPXdcqub/XHKJmrf93Ro1x6nBnRgAwCxKDABgFiUGADCLEgMAmEWJAQDM8jnn/D1Z7DhXxwyu6bPUGF+9ep4s9vSm6lp3tFjN8/+no5r/bMAbaj4mZacnq/L7ELfwiPHzMLj2C8d4soyfvFejZ6lJy6vm1fYRolpNXuv+phMrq/Tvp5tcl3/Kz/KbDm+qeVZ9/WuDNv149R3ea05EpN6S3ABPB5HArnXuxAAAZlFiAACzKDEAgFmUGADALEoMAGBWnXh24ucveycLP71sqrp20dHGap5V/60gP9XPa2ODMPrLK9V8cvrKau8N1IaUH2yt7SN8y99ZnrlmmJoPmPKimmvTiT9/bqa+91363glLmVoMFXdiAACzKDEAgFmUGADALEoMAGAWJQYAMCuqphPzp1yk5lsvm+zJqvzskVX/GzWfelh/a/Jfn7hBzRvnHfWGVfqzE/NzvM92FBG5qnOemvvz2IFz1Dx9WVDbAHWav0lBf89D1CYR/T1n8Z5B+mdmLA3sbPDiTgwAYBYlBgAwixIDAJhFiQEAzIqqwY74r+OrvYe/Rz0V3Hq6mjfesibgvbc+c7Gav3rVc2reJSFWzaccaqPma7Iy1Dxph90XYAKRwt8LLcdtvs6TDbhwlrp247V/VvNB0iP0g9Vx3IkBAMyixAAAZlFiAACzKDEAgFmUGADArKiaTjzz1/qkYOyPlK52+oOnVuV3UPP4P1SoeeYZ9dX8L+mrPVml+0BdO6FQ/8ybXhug5u1/tlbNRb7wkwOoKbEx3q8l8T59shjhx50YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIqq6UR/rsgZ7cmefu4Fde3mPn8Jy2eeufBObza/Ul2buH67mrf/xt8UIoBT7diA7mr+6w7e5ySWO/1a95cjdNyJAQDMosQAAGZRYgAAsygxAIBZlBgAwKw6MZ2YuNj7ZuNxv7heXTuv/Zth+cy4lDJPVtIsUV0b/803YflMAIGL7dhezQuu0N/i/t5D+luZq8R5smUl+jNV/zT2VjVPEP2t0Tg57sQAAGZRYgAAsygxAIBZlBgAwCxKDABgVp2YTtQcu65YzXsOG6vmcdfvV/PHOi1Q87wr/ub9zEz97dCP/rKbmi/982VqfvrsT9W8qqhIzQF4tZvxpZq/mvaKmleJ/rZm7XmIlY77g1OFf9IAALMoMQCAWZQYAMAsSgwAYJbPOed9Zori6pjBNX0Wk+LOaKPm+65q5cmm/e5pdW1GfEJQn3n5R0PU3M3SH5dz2vQ1Qe0f6ZZXzavtI0S1unKt/2PPBjX39+LKSzfcrObO+TzZ2gtnq2vv2tNLzZ9v+a6aa4+0EhG576uL1XzrUO/Xo8r8bepaCwK51rkTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlMJ0aAI4N7qnnOuPlqPqzhPjWP9enfk3SYfqcna/cruxOLTCfWrLpyrR8YdYn+C36+Iqb+NfBrxt/ezVbp1+6+K5qpeY/RH6r5+Jar1fyePZd6sn8uuVBd2/Z3kf81gOlEAEBUo8QAAGZRYgAAsygxAIBZlBgAwCymEyNYXHprNd82Kl3N5/9IfzZj2zjvs926vfQzde0Zv31PP0yV/jy52sB0Ys3iWrdr58Peqch+P3hfXbt4/QVq3vY1fe96S3JDPFXomE4EAEQ1SgwAYBYlBgAwixIDAJhFiQEAzGI6MYocu7a7mi+f/GLAewzscb2aV+zeE9KZagLTiTWLaz26xGacpeZnzNyt5tec9rGaP3PXMDVPWFpzU4tMJwIAoholBgAwixIDAJhFiQEAzKLEAABmxdX2AUREfPEJah7ToL6aV37zTU0ex6zEt/SpopcPt/JkP2qkTyZt//EZat7m4ciZTkTdsX+0/obk5iv1NyRX5m+ryeOY5O+fyabf6dPMz//1XTV/OF2vi9TQjhU23IkBAMyixAAAZlFiAACzKDEAgFmUGADArIiYTvzqpxep+W2j3lTzlyb8QM2b/Vmfqqkr3LFjal7q4gPeo6peQI/SBE6JtQ+9oOb3jLpUzT/4ur0nS3qusbq2Jp/5F0m+eaODmv8mY5aaV4n+NaDH6A/VfNtfQjtXuHAnBgAwixIDAJhFiQEAzKLEAABmRcRgR6tZW9U8dpT+A8b3f6P/sPeaH96g5mXPpnmyxMXvBXg6O3wXdlbzq5K9P3mNkUR1beLXvrCeCaiOyz8aouaVVfr332svnO3JqqboX0diRP933d9gQzDrg9374t+PVfNLRn2g5uNbrgniLPoe/s6yrER/3F9uQRs1byL5an6qcCcGADCLEgMAmEWJAQDMosQAAGZRYgAAsyJiOrGyQH/B3ZLL2un5An0Kb27GHDX/6NkGnuznrXLUtS1e1SdtKvcfUPPaEFNfnx6q/1yBmrePr+fJvqkqUde2nrNdzSsCPBsQTik/0CeX/ek56qfe0M+T1Nb8Xp9yLneVah7viw14fTBrRfw/Xsvf+iqp/lkyFt+p5me8pv8DaxKhj+niTgwAYBYlBgAwixIDAJhFiQEAzKLEAABm+ZxzAb0F8eqYwTV9lmorva6Hmt/9tPd5aln1v1HX/qs0Qc0f/N0oNU+ZuTbA0/kX1+4MNd91fUs1H3j7KjX/XdNP1Fx7RlrGP/TpzIzb31fzSLK8al5tHyGqWbjWI92xAd3V/OhPD6m5z6d/GXZOfwZjk+tq93mFp0og1zp3YgAAsygxAIBZlBgAwCxKDABgFiUGADArqqYT/Ylt3NiTfXNNR3Xt/4ybpuZXJBaqeY+1d6h5yf5kT9a505fq2qfPfFXNz4zT377szxMH9GdKvvnolZ4s5bUN6tqq0tKgPrM2MJ1Ysyxf64guTCcCAKIaJQYAMIsSAwCYRYkBAMyixAAAZkXEm51rWuU33uckNpqlP/Nwwsor1fzJC9PVvJmfz4y/7ytP9vcOr/tZrU8hdn3vVn31ohT9LCt2q3nDHd6/1yo/JwEAS7gTAwCYRYkBAMyixAAAZlFiAACzKDEAgFl1YjoxGBVf7VXzen5yv970RtdJt6C2aCmbglpfEdRqALCPOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzfM45V9uHAAAgFNyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEvuPjRs3yuDBg6Vdu3aSnJwsTZs2lczMTFm8eHFtH+04W7ZskZtvvllat24tycnJ0qlTJ3n44YeluLi4to8GmGHhel+5cqX4fD71r7Vr19b28SJGXG0fIFLs3LlTioqKZMSIEdKyZUspLi6W+fPnS1ZWlkyaNElGjx5d20eUL7/8Unr06CEpKSkyduxYadKkiaxZs0YeeughWb9+vSxcuLC2jwiYYOF6/6+7775bunfvflzWvn37WjpN5OHNzt+jsrJSunXrJqWlpZKXlxfSHtu2bZO0tDRJTk6u9nkeffRRefDBB+XTTz+Vzp07f5uPGDFCXn75ZTl48KA0bty42p8D1EWRdr2vXLlSevfuLfPmzZPs7Oxq7xet+OPE7xEbGyvp6elSWFgY8h7Tp0+XtLQ0GTNmjOTm5lbrPIcPHxYRkebNmx+Xp6WlSUxMjCQkJFRrf6Aui7Tr/buKioqkoqIibPtFFYfjHDlyxH399ddu69at7umnn3axsbFu6NChIe+3efNmd+edd7rTTjvNiYg777zz3LPPPuv2798f9F5LlixxIuKysrLchx9+6L744gs3e/Zs16hRI3fvvfeGfEagrork633FihVORFyDBg2ciLjY2Fh35ZVXutzc3JDPF40osRPk5OQ4EXEi4mJiYlx2drY7ePBgtfctKSlxM2fOdH369HE+n8/Vq1fPDRkyxC1btsxVVlYGvM+4ceNcUlLSt2cUEffggw9W+3xAXRTJ1/vq1avdjTfe6KZMmeIWLlzoHnvsMZeamuoSExPdBx98UO0zRgt+JnaCvLw82bVrl+zZs0fmzp0rCQkJMnHiRM8f4VXHzp07Zdq0aTJt2jTZsWOHtG3bVnJycuSBBx446e+dMWOGzJgxQ2688UZJTU2VN954Q6ZOnSrPPfecjB07NmxnBOqCSL/eT7R161bp0qWLZGZmytKlS8N2RssosZPo16+fFBYWyrp168Tn86lrSkpK5NChQ8dlLVq0OOneBQUFMmrUKFm8eLGkpKSc9M/iZ8+eLbfffrvk5+dL69atv81vu+02mTt3rnzxxReSmpp68r8pAKpIut79ueWWW2TBggVSXFwssbGxIe0RTRjsOIns7GzJzc2V/Px8v2vmzJkjaWlpx/3lT0VFhSxatEgGDRok6enpsmTJEhk4cKC88sorJz3LhAkT5MILLzyuwEREsrKypLi4WD788MPA/8YAeETS9e5Penq6lJWVydGjR0PeI5rw34mdRElJiYiI5zuv7+rfv78sX778e/fZtGmTTJ06VaZPny4FBQWSkZEh48aNk5EjRwb8RxcFBQXqCH15ebmICNNLQDVF0vXuz/bt2yUxMVEaNGhQrX2iRu3+SC5yFBQUeLKysjLXtWtXl5SU5IqKikLad8WKFa5nz55ORFxSUpK79dZb3apVq0La67rrrnMJCQlu8+bNx+UDBw50MTExbvfu3SHtC9Q1Fq73ffv2ebINGza4+Ph4l5WVFdKe0Yg7sf/IycmRw4cPS2ZmprRq1Ur27t0rM2fOlLy8PHnqqadC/q5n1apVUl5eLhMmTJChQ4dKSkpKyGe8//77ZcmSJXL55ZfL2LFjJTU1VV5//XVZsmSJ3HHHHdKyZcuQ9wbqEgvX+5AhQyQpKUl69eolzZo1k02bNsnkyZMlOTlZHn/88ZD3jTq13aKRYtasWa5v376uefPmLi4uzjVu3Nj17dvXLVy4sFr7HjlyJEwn/P/WrVvnBgwY4Fq0aOHi4+NdRkaGe+SRR1x5eXlYPweIZhau9/Hjx7sePXq4Jk2auLi4OJeWluaGDx/utmzZErbPiAZMJwIAzGI6EQBgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZAT+x4+qYwTV5DiBgy6vm1fYRohrXOiJFINc6d2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwK662DwAAFsRccI6a73gw1pM9ccF8de3E7BvUvOqjz0I/WB3HnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMYjoxQLt/3UvNfT0L1fyTnq8EvPdvCrqo+foL+R4DiBTn/k2fIFzS4kNPVumq1LX//NsWNf+sW+jnquv4KgkAMIsSAwCYRYkBAMyixAAAZlFiAACz6ux0orvkfDX/1YwZap6ZuF7NY8Sn5pUu8LP8vpl3uklE5LJbx6r5adPXBL45gLCY/3FXNX+8uf61QbPwowvUPEPeD+VIEO7EAACGUWIAALMoMQCAWZQYAMAsSgwAYFadmE7UJhF/M2O6uvbyxAo/u+hTiOHgb8Lxyp/rU4gb9KMDqEG+g/EBr32nVP/SmjG5LFzHwX9wJwYAMIsSAwCYRYkBAMyixAAAZlFiAACz6sR04ohpr3sy/1OI4XHFJ9me7HftF6tr+yQdU/OHTn9Pza8ccY+aN36JZyoC1RV7Toaa//G6wN/Wftu/blPzjLWBP2cRgeFODABgFiUGADCLEgMAmEWJAQDMiqrBjr339lLz7AbjlTQ2qL2vvOtONW+0oUDNG+7Z48le/uel6to+bf+p5vV8+v89VQlqDCAM9vdIVfOB9QsD3sN3VL92C+7Wv0a1XKp/HanM3xbwZ9ZV3IkBAMyixAAAZlFiAACzKDEAgFmUGADALJvTiT3OU+NHx/5NzeOCmETs8Hd9CrHjmx+peUVpqf6ZZ7b1ZD1TNgR8DhGRvHL9cVSNdpYHtQ8Ar9iO7dV8wM/+Ve29twyaGNT6h273vrhXROTtP+oTzY1eWRv0maIVd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwy+R04uc/96n5NUnFAe+xuLiRmnd6dp+aV/qZQtz1gP4stD/+2DspGcz5RES2lJ+u5ok7C9W8Mqjdgbptc05TNV/c9JNTfBKR35+uTz/f98Q6NR8Q83M1T5lR96YWuRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWSanE/9xyQQ/v5Ic8B7jx96s5km+g2qe8m/9ba/T2vxRzZvFBn4Wf65PPqz/wutL1fhXs29V8zN+u6baZwGsiu3QTs3/nDU1LPt/UuZ9lumPH7tXXXvatjI1P+ORzWo+OV1/juOx07j/+C/+SQAAzKLEAABmUWIAALMoMQCAWZQYAMAsk9OJwVpa4p0UTN6sPyNxb79War74zFf97F79KcRg+ZtabDJMf5vs49Nv9GSV+dvCeiYgUh26sJman59wQM3Xl9VT8zsfu1vNUz/xPhO16ZrgJoI/bXmJ/guP69OJFwzXn++454WgPjYqcCcGADCLEgMAmEWJAQDMosQAAGaZHOwYMO2Xav7zm15T81n3/sCTJex4X9/cpw921KTHDpyj5puK0tT87rS31PzSelVqnjfW+/K/Dncz2IG6ocFc/UWRQw/dq+bxRyrUvOnqmnt8W6Md+kt3/RnQ5GM1nyJnhuM4pnAnBgAwixIDAJhFiQEAzKLEAABmUWIAALNMTie2fehdNX9txsVqnrh7kyfT5/hEkvbrv/JVpffRMiIiaX5efplf7p02GvL8L9S16a9sV/OKr/aq+S2Tx6j51msnqfn6Qc94sv7r71PXNn6JF2iibkj4h58J5VpQ1EZ/1BVOjjsxAIBZlBgAwCxKDABgFiUGADCLEgMAmGVyOtGfyi36lF8w6s9fp+bDS3+m5juz9H3OnOedckx7S5+q1J/U5t/Zv8xX8wtOH67mG3rM8GQX36VPZm1+KcjDAKi2+rftCWr9r5fcouYdRH9OZDTjTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmRdV0Yk1K3Ks/O/Hs+79U88rDh2vsLJWFh9S89W/1N0HLP7xRTtN/qUt/2TpbzSt27Q7obAD8i7lAf4v7b86cE9Q+KVu4//gv/kkAAMyixAAAZlFiAACzKDEAgFmUGADALKYTTxDbNFXNb5zxtpo/un6AmmfcudWTVRUVhX6wMOsUr79JdvvtbdW8zcNMJwLVlTemgZpfmViu5s8XtlPzFisPqrm/N9ZHM+7EAABmUWIAALMoMQCAWZQYAMAsSgwAYBbTiSco+GGGmo9stFzPe09R844v/tiTdRjjnVgUCd/Uou9rfWLpoa/P92S/P/2jsHwmAK+Kq7qp+cS++qvTS1yZms96XJ9+Pu3TNaEdLApxJwYAMIsSAwCYRYkBAMyixAAAZjHYcYJm736j5stLktT86qQSNd98pXfg44bF16prjz57tprXX75RzauOHlVzd3oTNf/96UvVHED1+S4615P9avLL6to+ScfU/GdfXa7mp01ngONkuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWUwnnqDq0zw1n3/gIjW/rJX+sswkX4InW9jhDf1D/6zHN2zRpxm37NYfjVW/fqm+EVBHxCQmerL9Qy9U1zad96ma+3sMXNUV+j4lDxzyZP6mEKcXtVDzLbefpeYi+tcj/B/uxAAAZlFiAACzKDEAgFmUGADALEoMAGAW04kB+qKn/rzCX+ZeqebPt3y32p/pd5qxQ7W3lipxah5TXv29gdqy+yddPdmH972grv3LL9LV/Il/6VPBL/WbrOaX1qvyZP5ecjn5oR+qecOP16o5To47MQCAWZQYAMAsSgwAYBYlBgAwixIDAJjlc87pY2onuDpmcE2fxaSYczup+ZYHvG+C/uzKv+p7iC+sZwrE6mP69y+PtLvg1B4kBMur5tX2EaJatF3ro/O3q/nA+oVh2b/Tqts9WYdH9De+V27cHJbPrCsCuda5EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZPDuxmvy9CfqsYd6s+113qWuLeuqTTPm9p4R8ru/aUVHsyX5/xz3q2jhZH5bPBCLF5Ix2eh6m/dvJBk9WGaa9cXLciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphNPoebP6297bv68vv4H4n1LbbgwhQggGnAnBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYJbPOedq+xAAAISCOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKLH/2LhxowwePFjatWsnycnJ0rRpU8nMzJTFixfX9tG+tXLlSvH5fOpfa9eure3jAWZwvUePuNo+QKTYuXOnFBUVyYgRI6Rly5ZSXFws8+fPl6ysLJk0aZKMHj26to/4rbvvvlu6d+9+XNa+fftaOg1gD9d79PA551xtHyJSVVZWSrdu3aS0tFTy8vJC2mPbtm2SlpYmycnJ1T7PypUrpXfv3jJv3jzJzs6u9n4A/g/Xu038ceL3iI2NlfT0dCksLAx5j+nTp0taWpqMGTNGcnNzw3a2oqIiqaioCNt+QF3H9W4TJXaCo0ePyv79+2Xbtm3yzDPPyJIlS6RPnz4h7zd06FAZNmyYzJkzR3r06CFdunSR8ePHy4EDB0Le87bbbpNGjRpJYmKi9O7dW95///2Q9wLqMq73KOBwnJycHCciTkRcTEyMy87OdgcPHqz2viUlJW7mzJmuT58+zufzuXr16rkhQ4a4ZcuWucrKyoD2WL16tbvxxhvdlClT3MKFC91jjz3mUlNTXWJiovvggw+qfUagruF6t4+fiZ0gLy9Pdu3aJXv27JG5c+dKQkKCTJw4UZo3bx62z9i5c6dMmzZNpk2bJjt27JC2bdtKTk6OPPDAA0HvtXXrVunSpYtkZmbK0qVLw3ZGoC7gerePEjuJfv36SWFhoaxbt058Pp+6pqSkRA4dOnRc1qJFi5PuXVBQIKNGjZLFixdLSkpKyH8Wf8stt8iCBQukuLhYYmNjQ9oDANe7R38gbwAAuVBJREFURfxM7CSys7MlNzdX8vPz/a6ZM2eOpKWlHfeXPxUVFbJo0SIZNGiQpKeny5IlS2TgwIHyyiuvhHzG9PR0KSsrk6NHj4a8BwCud4v478ROoqSkRETE853Xd/Xv31+WL1/+vfts2rRJpk6dKtOnT5eCggLJyMiQcePGyciRI6v9Rxfbt2+XxMREadCgQbX2Aeo6rneDavdHcpGjoKDAk5WVlbmuXbu6pKQkV1RUFNK+K1ascD179nQi4pKSktytt97qVq1aFdJe+/bt82QbNmxw8fHxLisrK6Q9gbqI6z16cCf2Hzk5OXL48GHJzMyUVq1ayd69e2XmzJmSl5cnTz31VMjf9axatUrKy8tlwoQJMnToUElJSQn5jEOGDJGkpCTp1auXNGvWTDZt2iSTJ0+W5ORkefzxx0PeF6hruN6jSG23aKSYNWuW69u3r2vevLmLi4tzjRs3dn379nULFy6s1r5HjhwJ0wmdGz9+vOvRo4dr0qSJi4uLc2lpaW748OFuy5YtYfsMoC7geo8eTCcCAMxiOhEAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWQE/sePqmME1eQ4gYMur5tX2EaIa1zoiRSDXOndiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMCuutg8AALXBXXqBnj98QM3f7PSamsf7Yj1Zu2U/Vtd2GLk+oLMhcNyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCymEwPl86lxXIvmal7ZMtWTbR5VP6xHClRMg3JP1i5tv7p265Y0Ne846aiauw83hn4w4BTwN4U4dtpcNW8WW6TmF/3pLn39dV96stlXvKiu/cXAn6p50mvvqTlOjjsxAIBZlBgAwCxKDABgFiUGADCLEgMAmFUnphNLr+/hyb4aeiyoPWJiq9R846UvhXSmiNVJjwed/QM1L+tXT83dseD++QI1ZcvIeDX3N4X4y5/dqeYtFr6r5uWfdPNk5/v5slByR6GaJ72mr8fJcScGADCLEgMAmEWJAQDMosQAAGZRYgAAs6JqOvHzRy9R8xm3POfJuiV438YK//7Q9jU1/3XbYWpemb+tBk8DVN/LBy5V86SFwT3HMGHVJ57surwfhnQmBI87MQCAWZQYAMAsSgwAYBYlBgAwixIDAJgVVdOJp3ctUHMmEavvhqV3q3lGPm+kRWT7/Nq/qPlFD+nPSEyVNUHt78rLPFnRMf2Zou9eOEvNb8i4Sc2Z8j057sQAAGZRYgAAsygxAIBZlBgAwKyoGuzwZ2Wp96V4Y1/KUddWJTg1b73C+8NbEZFlL+s/NI50d+3ppea5Ey5U87Pnb1TzyrCdCKgZ5e7U/1ta5Xx6LvrLdRE67sQAAGZRYgAAsygxAIBZlBgAwCxKDABgVlRNJ552xzE1f9o3wJO1+fLdoPaOSUwM6UzV8eKhtmr+VO7Vat7piSP6RpXKRNRX+9SlTQ7rj9xhChHQxTZq5Ml6NPtCXbukuLGa+46WhPVMdQl3YgAAsygxAIBZlBgAwCxKDABgFiUGADArqqYTK3btrrG9q47pk48XPTpWzd//zQvV/swRjbao+Z+OeqctRUTcdn0iqqq0tNpnAax6aJ/+PNCwSWvmiZ5qqb/88rIPh6l5k935YT1SXcKdGADALEoMAGAWJQYAMIsSAwCYRYkBAMyKqunEGuX0Nz6nzdTfeHzLLd7nG05ou0hd2zgmSc2TfAlqvvWGF9X8nu6XqPknv+/hyRJff09dC0SbWe/3VPPUMO2//5LTPVmMn/uDUWf9W80f+8u1ah5zNFbNOz2jT2JX7PxSzaMZd2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwi+nEaqosPKTmhy7zZn3uuV9dO27sNDW/NtnPm5r9GN9SfyvzL39f7sk+qOiurk1YmhvUZwKRLvHLeDVPOhCe95WfvmKXJ7tnz6VB7THggk/UfGDj9Wp+2g3Fan7zYu+zXDv+Wt+7qljfwxruxAAAZlFiAACzKDEAgFmUGADALJ9zfp6ndIKrYwbX9FnqrLgz26r5WfP2qPkzaeuC2j/W5/1epePUO9W1ZzyoD4dEkuVV82r7CFGNaz1yxJ6ToeabH6iv5puumuzJ+t6lv7g3eUFwX0dqQyDXOndiAACzKDEAgFmUGADALEoMAGAWJQYAMIvHTkWAis93qvm2G9PVfOOqMjXvHK+/RLPSVXmyW69foa5958FENQdw6lVuylfzjo93VPNVlyR7sl39vde/iEjGgtDPFUm4EwMAmEWJAQDMosQAAGZRYgAAsygxAIBZTCdGsIqdX6p5UZU+hQigbqjcuFnNXy+8wJPFNfS+FDeacCcGADCLEgMAmEWJAQDMosQAAGZRYgAAs5hOjGD5k7qr+UX1cv38jtiaOwyAiOHr1lnNh6dO82Qbm6XV8GlqF3diAACzKDEAgFmUGADALEoMAGAWJQYAMKtOTCf6lDcexzSor651x46peVVxcbXPEXtOhpoXPKGv39p1kr+dgvrcKnGebMGE3ura02VNUHsDVvmb8HPrN57ikwSvtJn3Dc4iIudrj1V97HQ/u3wRtvPUJu7EAABmUWIAALMoMQCAWZQYAMAsSgwAYFZUTSfGdu6o5uXPeScL3+i0UF3b4a079HzEB2rui9P/EVZc3sWTHf1Vobr2vfNeVfNgaVOIIiIZS3O82YtMIaJu2PbUxWretecWNT90WU2e5tSr92Whmlee2mPUGO7EAABmUWIAALMoMQCAWZQYAMCsqBrsyMs5Tc23dJoV8B6NmxxR84qruql55a8PqPnbnf8a8GcGK/eYPsBxy9tj1DxjlL+XaALRr7KRPsIw88xlan7piLFq3uSV9WruystCO1gAYju2V/OhT7+h5o/vP98bHvgmnEeKONyJAQDMosQAAGZRYgAAsygxAIBZlBgAwCyT04mx7c9U8+nXTaz23u91na3/wvRqbx20d0r1/3v+cMdINc9YwRQicKKEAv06qpIqNX/n0efU/Lof/VDNd69I92St/3lUXet79yM19/c1be+T+gtwr0zWH5k17MlfeLImB6L7EXPciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsk9OJrkGSml9c7xQfJExG7LxKzb/67VlqHrdCf4YbAK92j+oTgWc3vEvNZ2c9r+avd1qg5jGdvPcCn48qVdc+t0+/1jNT/qnmg+ofVPMu7+rPSW3zt+ieRNRwJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzfM45/TXBJ7g6ZnBNnyVgcW29zyoTERmx/F9qfmP9U/9m07zyY57shvk/U9d2+N0nal51VH/+Wl23vGpebR8hqkXStV4b/H19ybu3lZoPufJdT/ZQs+AmiDuvHK3mTZYlqnnqgk/VvKqoKKjPjXSBXOvciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsk9OJ/vgu7KzmB/9Q5snWXDBHXfvQ1+er+cx3L1Hz2GL9+4CMx71vXq3cf0Bdi+AwnVizLFzrqBuYTgQARDVKDABgFiUGADCLEgMAmEWJAQDMMvlmZ3/chxvVvPG13uwH0jWovTPkvaDWVwa1GgAQCu7EAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzPI551xtHwIAgFBwJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALErsPzZu3CiDBw+Wdu3aSXJysjRt2lQyMzNl8eLFtX20b+Xm5srYsWOlc+fOUr9+fWnTpo3cdNNNkp+fX9tHA0yxcL2vXLlSfD6f+tfatWtr+3gRI662DxApdu7cKUVFRTJixAhp2bKlFBcXy/z58yUrK0smTZoko0ePru0jyhNPPCGrV6+WwYMHS5cuXWTv3r3ywgsvSNeuXWXt2rVy7rnn1vYRARMsXO//dffdd0v37t2Py9q3b19Lp4k8vNn5e1RWVkq3bt2ktLRU8vLyQtpj27ZtkpaWJsnJydU+z7vvvisXXXSRJCQkfJtt2bJFzjvvPMnOzpYZM2ZU+zOAuirSrveVK1dK7969Zd68eZKdnV3t/aIVf5z4PWJjYyU9PV0KCwtD3mP69OmSlpYmY8aMkdzc3Gqdp1evXscVmIhIhw4dpHPnzvLZZ59Va2+grou06/27ioqKpKKiImz7RRNK7ARHjx6V/fv3y7Zt2+SZZ56RJUuWSJ8+fULeb+jQoTJs2DCZM2eO9OjRQ7p06SLjx4+XAwcOhOW8zjkpKCiQpk2bhmU/oC6xcL3fdttt0qhRI0lMTJTevXvL+++/H/JeUcnhODk5OU5EnIi4mJgYl52d7Q4ePFjtfUtKStzMmTNdnz59nM/nc/Xq1XNDhgxxy5Ytc5WVlSHvO336dCcibsqUKdU+I1DXRPL1vnr1anfjjTe6KVOmuIULF7rHHnvMpaamusTERPfBBx9U+4zRgp+JnSAvL0927dole/bskblz50pCQoJMnDhRmjdvHrbP2Llzp0ybNk2mTZsmO3bskLZt20pOTo488MADQZ+1Z8+e0rlzZ3nnnXckNjY2bGcE6gJL17uIyNatW6VLly6SmZkpS5cuDdsZLaPETqJfv35SWFgo69atE5/Pp64pKSmRQ4cOHZe1aNHipHsXFBTIqFGjZPHixZKSkhLUn8Xv3btXLr30UikvL5e1a9dKy5YtA/69AHSRer1/1y233CILFiyQ4uJivnEVfiZ2UtnZ2ZKbm/u9/y3WnDlzJC0t7bi//KmoqJBFixbJoEGDJD09XZYsWSIDBw6UV155JeAzHTp0SAYMGCCFhYWydOlSCgwIk0i83k+Unp4uZWVlcvTo0ZD3iCb8d2InUVJSIiLi+c7ru/r37y/Lly//3n02bdokU6dOlenTp0tBQYFkZGTIuHHjZOTIkUH90UVpaalcf/31kp+fL2+99Zacc845Af9eAN8v0q53zfbt2yUxMVEaNGhQrX2iRu3+SC5yFBQUeLKysjLXtWtXl5SU5IqKikLad8WKFa5nz55ORFxSUpK79dZb3apVq0Laq6KiwmVlZbm4uDj3xhtvhLQHABvX+759+zzZhg0bXHx8vMvKygppz2jEndh/5OTkyOHDhyUzM1NatWole/fulZkzZ0peXp489dRTIX/Xs2rVKikvL5cJEybI0KFDJSUlJeQz3nfffbJo0SK5/vrr5eDBg57/uHn48OEh7w3UJRau9yFDhkhSUpL06tVLmjVrJps2bZLJkydLcnKyPP744yHvG3Vqu0UjxaxZs1zfvn1d8+bNXVxcnGvcuLHr27evW7hwYbX2PXLkSJhO6NwVV1zx7Tiw9heAwFi43sePH+969OjhmjRp4uLi4lxaWpobPny427JlS9g+IxownQgAMIvpRACAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmBfzEjqtjBtfkOYCALa+aV9tHiGpc64gUgVzr3IkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADArLjaPkBtqezdVc3fmvk3NV9bWqnm9+UPVvOjb7bwZC0mvKeudRUVag4A+H7ciQEAzKLEAABmUWIAALMoMQCAWZQYAMCsOjudmFBwRM3v+0qfWhyR+q6azzj7ZTVvc16yJ+tz3Q/VtUm/SFTzqo/z1Byo6+LObKvmLWfv92R/SV+tri13+sSxP+dNGavm9b7xBbxH69d2q3nF5zuDOgv+D3diAACzKDEAgFmUGADALEoMAGAWJQYAMKvOTidWbspX80+76evvl4vVPK5VSzXfNM6bb+0/WV1726Qr1fzrPt4JRxGRquJiNQfqisMXeJ9NKiIyr9U8T1buYtW1wU4nfnD7+IDXxvv0z/zDyC5qPnfBFWp+5qsH9A/Ys0+NK7/55uSHizLciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsn3POBbLw6hj9DcYI3PZXLlDzvCv0t0mf/7z+rLZWj+vPcawrlld5J9AQPhau9djOHdU8//bG1d77oov1yeW/tV0a8B7+phODnYj0p9+nN6v5sTnNPVmTqWvC8pm1IZBrnTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmFVnn51YGxr/I0n/Bf2xaVJ8dmnNHQYwrHLjZjU/677q733gigvV/MOp+pfLCxMqqv+hQVp27mw171b0Y0/WZGpNn6Z2cScGADCLEgMAmEWJAQDMosQAAGYx2HEKNZ7+nprfcac+2fHrHvpjbuZLs7CdCairPn/8EjW/ZcC/1Lw2Bjj86fKv0Wreemb8KT5J7eNODABgFiUGADCLEgMAmEWJAQDMosQAAGYxnXgqVekvxPuquJGaX9NSfznfa50vV3N/j+IB6orS63p4slWTJ6try936IHfXX3SpOX/NCDUv2Z+s5hlj9Mllf86SDUGtj2bciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphMjwBcHG6t5q1h9kqm4jT7NWG9j2I4EmLRvRIknK3f6VLC/PFjacwzPGrohLHvj5LgTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlMJ0aAZo2O1PYRAITo40zvsxnPe+ZudW3Hv36j5jz3NHTciQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphMjQGJcuZq/U6r/31PvHx/U5HEAsyq3NvCGvU79OfJu+rOa/+aKi9T84641eZroxp0YAMAsSgwAYBYlBgAwixIDAJjFYMcpFJOYqOY/a7NMX++rqsnjAFHnzAfWeLKLCu7SFzs9bjhgr5ovO3d2qMf61qjUf6v58JG/UPPG07x/Pzged2IAALMoMQCAWZQYAMAsSgwAYBYlBgAwi+nEU6iqtFTN71wzXM3zr5qi5iVZ3dQ86bX3QjsYEMVaPPtucL9hvB4Pkh5qvud+73OtPvnZBHXtmXH6hPLqR15Q8x4N9MnKZi8E+fcUxbgTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlMJ0aARuuS9F+4So+/HKA/9C3jtfCcB0DgWj7pnRRce2elurZLgp77M/W+Z9T8NvmZmtfFqUXuxAAAZlFiAACzKDEAgFmUGADALEoMAGAW04kRoMVLn6j54nsaqfkZ7fbV5HEAVNNvb7tDzUdOXqTmg+p/peYZ8T41X/3rZ/V9XtCf7xjNuBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWUwnRoCqoiI1n7uvu5rntPmXmk+VtmE7E4DQxaz6UM3/dudANR/08sQaPE10404MAGAWJQYAMIsSAwCYRYkBAMyixAAAZjGdCADfcfC2S9T8yA+OBLyHz6e/fb1R8qGQzgT/uBMDAJhFiQEAzKLEAABmUWIAALPq7GBHbMZZat53gf64mFk7LlLz/8fencdHVd3/H/9MFkLCvoawCLKEHQUUBBRUFneECiIIdQGBVlBbsWqrpdVSsWIVFxAU0QIqIiqiokLLomyiggshrIKyhR0CCZBMzu+Pb8tPvJ+RmWRC5jPzej4e/NE3J+ceLDfvnORw733pH6v5Exuu8GSVr90Q5Or+z7d70tS8Z9U1Ic0DwCu+eWM1X/a354o8d6IvXs3znD/Qaop8zVjFTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxezpxIJt29X8ozsuUfMWT21R8xvKHNTz1m94sib/uFMd2+Dhr9S8T/01ar7iqH6yUiQ/QA7g59xW/XNAy6W3qvlXHacU+ZqBTyeG5ouTpcIyTzRgJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzYvZ0ojtxQs19y79W86zb09W807gb1XzpeW96si8G/FMdO/Wapmo+sqJ+IrLh+8PUPF1WqTkAr4Jjx9S8+mvJar7hQv1Fl+mJvrCt6efeOlpTzafdcZ2ax4n+7Ndoxk4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZsXs6cRQ+TP0tzJX7Kn/J7z8quGe7Idf6c9N63++fqpwRnZ1Na+5gK89gOKS/O7nav7Hu3ur+VuN3i22tfzl855q3uhT/XmrsYjPhgAAsygxAIBZlBgAwCxKDABgFiUGADCL04lF5PL1tymXnus94ZQ+V5/jywBfS3wptdW8rKwMbnEAwsZ3e6Kaj3z9ck/2Qp3F6thAb2QeOf63at7kX+vUPDzvh44O7MQAAGZRYgAAsygxAIBZlBgAwCxKDABgFqcTASAI+d9vU/PtF3mza6VtSHPXkGVqzinEM2MnBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYJbPOedKehEAABQGOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFi/7V27Vrp27ev1K9fX1JSUqRq1arSuXNnmTt3bkkv7ZRFixaJz+dTf61YsaKklweYYeF+X7VqlYwYMUKaN28uZcqUkXPOOUduvPFG2bBhQ0kvLaIklPQCIsW2bdskOztbbrnlFqlZs6bk5OTI7NmzpWfPnjJp0iQZOnRoSS/xlLvuuksuvPDC07KGDRuW0GoAeyzc748//rgsXbpU+vbtK61atZLdu3fLc889J23atJEVK1ZIixYtSnqJEYE3O/8Cv98vbdu2lePHj0tmZmah5ti8ebOkpaVJSkpKkdezaNEiueyyy2TWrFnSp0+fIs8H4P+LtPt92bJlcsEFF0ipUqVOZRs3bpSWLVtKnz59ZPr06UW+RjTg24m/ID4+XurUqSOHDh0q9BzTpk2TtLQ0GT58uKxatSpsa8vOzpb8/PywzQfEuki73zt27HhagYmINGrUSJo3by7r1q0r0tzRhBL7mWPHjsm+fftk8+bN8tRTT8m8efOka9euhZ5vwIABcvPNN8vMmTOlXbt20qpVKxk/frzs37+/0HPedtttUr58eSldurRcdtll8sUXXxR6LiCWWbjff8o5J1lZWVK1atWwzBcVHE4zbNgwJyJORFxcXJzr06ePO3DgQJHnzc3NdTNmzHBdu3Z1Pp/PJSUluX79+rlPPvnE+f3+oOZYunSpu+GGG9yUKVPcnDlz3GOPPeaqVKniSpcu7b766qsirxGINZF8v2umTZvmRMRNmTKlyGuMFvxM7GcyMzNl+/btsnPnTnnzzTelVKlSMnHiRElNTQ3bNbZt2yavvPKKvPLKK7J161apW7euDBs2TB588MGQ59q0aZO0atVKOnfuLB999FHY1gjEAkv3e2ZmprRv316aN28un376qcTHx4dtjZZRYmfQo0cPOXTokKxcuVJ8Pp86Jjc3Vw4fPnxaVqNGjTPOnZWVJXfccYfMnTtXKlSoUOjvxffv31/efvttycnJ4S82UASRer/v3r1bOnXqJHl5ebJixQqpWbNm0B8b7fiZ2Bn06dNHVq1a9Yv/NmPmzJmSlpZ22q9A8vPz5b333pPevXtLnTp1ZN68edKrVy957bXXCr3GOnXqyMmTJ+XYsWOFngNAZN7vhw8flquuukoOHTokH330EQX2M/w7sTPIzc0VEfF85fVTV1xxhcyfP/8X58nIyJCpU6fKtGnTJCsrS9LT0+XRRx+VW2+9tcjfutiyZYuULl1aypYtW6R5gFgXaff78ePH5brrrpMNGzbIggULpFmzZkF/bMwo2R/JRY6srCxPdvLkSdemTRuXnJzssrOzCzXvwoULXfv27Z2IuOTkZDdo0CC3ePHiQs21Z88eT7ZmzRqXmJjoevbsWag5gVhk4X7Pz893PXv2dAkJCe6DDz4o1ByxgJ3Yfw0bNkyOHDkinTt3llq1asnu3btlxowZkpmZKU8++WShdzmLFy+WvLw8mTBhggwYMEAqVKhQ6DX269dPkpOTpWPHjlK9enXJyMiQyZMnS0pKiowdO7bQ8wKxxsL9fu+998p7770n1113nRw4cMDzj5sHDhxY6LmjSkm3aKR4/fXXXbdu3VxqaqpLSEhwlSpVct26dXNz5swp0rxHjx4N0wqdGz9+vGvXrp2rXLmyS0hIcGlpaW7gwIFu48aNYbsGEAss3O9dunQ5dfxf+4X/w+lEAIBZnE4EAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwK+jHTnWP61uc6wCCNr9gVkkvIapxryNSBHOvsxMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmJZT0AsJp2yMd1PyTW5/wZOcklFXH5jl/SNc8b/ktal73kXxP5vthlzrWf+hwSNcEUHSuw3lqfqhxippXemW5mu++p6Mnq/XBbnWsf+OWIFeHYLETAwCYRYkBAMyixAAAZlFiAACzKDEAgFkmTye6Tuer+RuDnlbz1PgkTxboFGKBFIS0ltUdpuq/Mc8b3ba1hzp0f6eQLgkgBDdnblfzq8ssVfMUX6Ka91p/h5pfc8tnnuy73jXVsf4uaowiYCcGADCLEgMAmEWJAQDMosQAAGZRYgAAs0yeTkzcukfN+7/yOzVvdLn3eWVx4tSxNVP05xg+VfPTIFcX2DPnvK/ml426T1/LuGVFviYQ60b/51dq3umaf6r55c/co+Z/fOX1oK+5OKuhmvf4+qSaH8kvrebvLm6n5g1/vyLotUQ7dmIAALMoMQCAWZQYAMAsSgwAYBYlBgAwy+ec04/p/Uz3uL7FvZaIEFeunJr/+NuWav7lXeODnzvA1wzvHauk5i9febma52/ZGvQ1o9H8glklvYSoFiv3ekLdOmru35ml5vE1qusT5Xvf4u4qV1CH/vrtT9S8b9n9av74/qZqvrhVsr6WKBPMvc5ODABgFiUGADCLEgMAmEWJAQDMMvnYqXCIr6j/4HXdY43V/J0rnwowU9G/Dri2jP5D3QeGpql5/Qe2FvmaQKzL3/ZjaON/1F+uqdn2m3PVPNABjm9P5qn5S0suVfNGsjLotUQ7dmIAALMoMQCAWZQYAMAsSgwAYBYlBgAwKyZOJx6++SJP1mWU/lK5OdUnBpil6P+pEn3xap4X4MFfGYOeU/MW/hFq3uCf69Xcv//AmRcH4BfFN6qv5hvvSPVkS/uPCzCL/riotSdrqnmNz3xBrS2WsRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWVF1OjGhhveUkIjIM3971pO1LqX3d4EUhHVNP/XPA43U/M5K+qnCQL659Rk1f/56/bmPH7coH9L8QCyIb9xQzfd1qKbmXe7STzTPTX1LSUN7aeX5SfpzGf2lOJ14JuzEAABmUWIAALMoMQCAWZQYAMAsSgwAYFZUnU48dEk9Na+XcFJJS4c09+oTet/3//dwNa853/ucxIpLvlfHNvtsh5pflnw0yNX9n0CnHBc0H+jJ/GtDOxEJWBWf3kDNL3xznZpfVvZtNa+bcCTAFVI8SaA3Nf9j55Vqvv93tdW84ufLA1yz+PgSS6n5kRvaqHm5N/RTm2cLOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYFVWnE8vOWqnmg378jSfb1amsOrbGihw1jz98XM3Tv1sV5OpE8gPkz1xznZq/N11/ntpTNT8N+poiIpv7V/Zk9R4KaQrArB976s9UnVt1lpqvOanfqYkBHmM462gVT/bevvPVsYeGVNUnWfetnpeAuGT95PbQv+inNl9/Q38r9dnCTgwAYBYlBgAwixIDAJhFiQEAzPI551wwA7vH9S3uteBnNj7TXs3X3fBcSPOsPJHoycYM+LU+eMU3Ic1dEuYX6D+QR3hE272eUL+emt+/4F0175QU2otxL/n9bz1Zmd0n1LFxi1eHNHck2f7Hjmp+sqJeIfX/UPRHZgVzr7MTAwCYRYkBAMyixAAAZlFiAACzKDEAgFlR9dipaFNjqf6cm7gbQvvao7PyFJm7LtQfu5Vasu+3A8Iuf8tWNX/o90P18aVDu78q/mezJ/Pv3RvSHJHEl6DXwokq+qnN9TdNUPOr/6C/RDPc2IkBAMyixAAAZlFiAACzKDEAgFmUGADALE4nRrBKq3ar+XvHKqn5tWX2q3leUE/HBGJL8rufh2Uef1hmiRw/3tdOzTf1108htvr8ZjVPk3VhW9MvYScGADCLEgMAmEWJAQDMosQAAGZRYgAAszidGMECPfNt3fGaah7odCIQ6RJq11Jzf1plNXdfZugTFUTbWcHiE1+1iprfd8tban604Liap/29ZGuEnRgAwCxKDABgFiUGADCLEgMAmEWJAQDM4nRijDrSWD/FlXqW1wGIiFR/64iav1Rnrpq3mDxCzevNPuDJ3Pot6liXdzLI1dkW16qJmh98PE/NB5XTn9naetVtap72+beFW1iYsBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZxODFJ888Zqvvdx/bXJyS9V9GZhepNsOMRXPlHSSwBOWfFRSzVv1fIcNf9m6LNqnjU415Nd89Ud6tjUx0upecK+bDUPxJfjfaZg/o6dIc0RSEIt/TmpJ+vr54j3tUr2ZCNGvK2OHVj+RzVvumSImtd7zqfmJY2dGADALEoMAGAWJQYAMIsSAwCYxcGOIJWe6H2cjYjIsgYf6R/wvDdq0uVOdWiTZ3apeV7NSmrerewUNY8L8DVJoi/ek5VZnqKOBUrCOX9ZFtL4FjP1RyCdPFjaG5YqUMd+NPNpNc/Rz2pJvQT9nvkgp6wn+/3n/fRJQvT4hbPVvFeZQ0HPkS/6I+aa/WeomjcctDrouSMBOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYxenEIK37dyM1L2jwYdBzZN6oHFkUkYIb9dNTzx/UH3XVOkkfr6cihwu8L/8rfTDQaCDy1ev3TZHn6H/V79S89O4cNd/Ws4Kan6jvfezU/EufUcemxeuPutLuUZHAJyVnHa2j5n99rb8nS9D/ONLwidBOhEYqdmIAALMoMQCAWZQYAMAsSgwAYBYlBgAwy+ecC3D+5XTd4/oW91oiWqCX0/X7t/6iy37lvM9DDPRsw4KA5wrDo+n84Z6s0a1fFus1i9P8glklvYSoFuv3enHa+5sOap766UE1L/gusziXE/GCudfZiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsnp0YpPwdO9X8L5/coOaX93rSk6XGJ4d1TT/3zMEmat704SxPll+sKwGgqTZxuZrzJNPCYycGADCLEgMAmEWJAQDMosQAAGZRYgAAszidWESN7lqp5sOeuMmTrRtTXR27ruukkK4Z6BTiomuaqXn+jz+GND8AWMFODABgFiUGADCLEgMAmEWJAQDMosQAAGZxOrGY5P+43ZM1+rU3ExHpKReG6aqcQgQQW9iJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmOVzzrmSXgQAAIXBTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZTYf61du1b69u0r9evXl5SUFKlatap07txZ5s6dW9JLO2XVqlUyYsQIad68uZQpU0bOOeccufHGG2XDhg0lvTTAFAv3u4jIxo0b5aabbpLatWtLSkqKNGnSRB555BHJyckp6aVFjISSXkCk2LZtm2RnZ8stt9wiNWvWlJycHJk9e7b07NlTJk2aJEOHDi3pJcrjjz8uS5culb59+0qrVq1k9+7d8txzz0mbNm1kxYoV0qJFi5JeImCChfv9xx9/lHbt2kmFChVkxIgRUrlyZVm+fLmMHj1avvzyS5kzZ05JLzEi8GbnX+D3+6Vt27Zy/PhxyczMLNQcmzdvlrS0NElJSSnyepYtWyYXXHCBlCpV6lS2ceNGadmypfTp00emT59e5GsAsSrS7ve///3v8qc//Um+++47ad68+an8lltukX/9619y4MABqVSpUpGvYx3fTvwF8fHxUqdOHTl06FCh55g2bZqkpaXJ8OHDZdWqVUVaT8eOHU8rMBGRRo0aSfPmzWXdunVFmhuIdZF2vx85ckRERFJTU0/L09LSJC4uzvO5IFZRYj9z7Ngx2bdvn2zevFmeeuopmTdvnnTt2rXQ8w0YMEBuvvlmmTlzprRr105atWol48ePl/3794dlvc45ycrKkqpVq4ZlPiCWRPL9fumll4qIyODBg2XNmjXy448/ysyZM2XixIly1113SZkyZQq9zqjicJphw4Y5EXEi4uLi4lyfPn3cgQMHijxvbm6umzFjhuvatavz+XwuKSnJ9evXz33yySfO7/cXet5p06Y5EXFTpkwp8hqBWBPp9/ujjz7qkpOTT61RRNyf/vSnIq8vmvAzsZ/JzMyU7du3y86dO+XNN9+UUqVKycSJEz1b+qLYtm2bvPLKK/LKK6/I1q1bpW7dujJs2DB58MEHQ15r+/btpXnz5vLpp59KfHx82NYIxIJIv9+nT58u06dPlxtuuEGqVKkiH3zwgUydOlWeeeYZGTFiRNjWaBkldgY9evSQQ4cOycqVK8Xn86ljcnNz5fDhw6dlNWrUOOPcWVlZcscdd8jcuXOlQoUKIX0vfvfu3dKpUyfJy8uTFStWSM2aNYP+WAC6SLrf33jjDbn99ttlw4YNUrt27VP5bbfdJm+++ab88MMPUqVKlTP/oaJdyW4EI9+kSZOciLjMzMyAY6ZOnXradv+X/rPm5eW5OXPmuF69ernExESXkJDgevXq5T744IOg13To0CF3/vnnu8qVK7u1a9eG9OcBEFgk3e+XXHKJ69ixoyd/++23nYi4+fPnB/eHinL8O7EzyM3NFRHxfOX1U1dccYXMnz//F+fJyMiQqVOnyrRp0yQrK0vS09Pl0UcflVtvvTWkb10cP35crrvuOtmwYYMsWLBAmjVrFvTHAvhlkXS/Z2VlqUfo8/LyREQkPz8/qHmiXkm3aKTIysryZCdPnnRt2rRxycnJLjs7u1DzLly40LVv396JiEtOTnaDBg1yixcvLtRc+fn5rmfPni4hISGknRuA01m436+99lpXqlQpt379+tPyXr16ubi4OLdjx45CzRtt2In917Bhw+TIkSPSuXNnqVWrluzevVtmzJghmZmZ8uSTT0rZsmULNe/ixYslLy9PJkyYIAMGDJAKFSoUeo333nuvvPfee3LdddfJgQMHPP+4eeDAgYWeG4glFu73++67T+bNmyeXXHKJjBgxQqpUqSLvv/++zJs3T4YMGcLPwf+npFs0Urz++uuuW7duLjU11SUkJLhKlSq5bt26uTlz5hRp3qNHj4Zphc516dLF8714CeL78gBOZ+F+d865lStXuquuusrVqFHDJSYmuvT0dDdmzBiXl5cX1utYxulEAIBZPLEDAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFlBP7Gje1zf4lwHELT5BbNKeglRjXsdkSKYe52dGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMCuhpBdgXUL9empeUD6l2K7p27FXzf179RxA8OLLl1fzLfe2UPO1Q54Lfm6fvm+4P+t8Nf/6Qv1TtMvPD/qa0Y6dGADALEoMAGAWJQYAMIsSAwCYRYkBAMzidOLP+JKS1Hzj2NZqPrnni2reufTJsK3p5/6+r6Wavz31UjWvNWOjJ+MkI2Kez6fGW16qq+bfdnpWzQtCuGSB86v536p/qebXxXfUJ+J04insxAAAZlFiAACzKDEAgFmUGADALEoMAGBW7J5OjItX4/UT9JN/G658vjhXE5I/Vv1Wz+/T8x7X/MqTJfxNP20Zt3h14RcGGLJ3Trqaf9v2lbO7kF+w99Y2al510vKzvJLIxU4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZsXs6cS4UolqvuHKSWd5JcXvk2Zve7INr+rPdrzj/t+pebmZK8K6JuBsOXBbBzX/rM34AB+hn1zenp+r5lcsu9OTpc4qrY69Z+zrat6zzEE1P9xQjaWqHsckdmIAALMoMQCAWZQYAMAsSgwAYFbMHuyw7IcAP2D+d47+GJ3byv/oydITS6ljZ/xjnJrf+Z/eas7LNRFJjgy4yJM9+qeX1bGJPv0Ax9AfL1XzXSP0l2We+8XXniw+vYE6tkzcCTUPpGJmSMNjEjsxAIBZlBgAwCxKDABgFiUGADCLEgMAmBWzpxMLTuinhNqNGanmS//4tJoHOuGU47yPdcpzBerYCz+8R81r/lufu8JHGWqe27Gxmt825QU119ROSFbzzD/XV/NGIzmdiLNv2yP6o6RmDPI+SqpVKf0+CmTFR/qLcc/5YlnQc8RNzlHzrsl6Hki57XkhjY9F7MQAAGZRYgAAsygxAIBZlBgAwCxKDABgls8554IZ2D2ub3GvJaJl3+R9JpuIyMEm+tcB507f5cn8m74P7aJx+qkqX+smat5r2iI1H1zhh9CuG4Jra7UttrkDmV8w66xfM5ZE0r3+w587qvmXQ59W80CnhTU3f99DzY90Parm8dWrqfn6x72vqPym82R1bCjrEwn8Is6rX/6Dmtcd87knc/n5IV0zkgRzr7MTAwCYRYkBAMyixAAAZlFiAACzKDEAgFkx++zEUFX8co+al9taVs39Vct5w6qtQrrmj131ub/+7bMhzRMOq0/qz30EilPFDllqHuiU3z6/9zTf+rzy6tijV+vPJXQBnquaX6uymq/rMkVJQzuFGEigZ5l+M1T/HNC8ye2e7Nz+3jdPRxN2YgAAsygxAIBZlBgAwCxKDABgFiUGADCL04k/E1e6tJ6/qD/DbE6j2HiO361T7lbzOhL8226BUCVM9j6XUESk993Xqvnu6fU8WZWXlgeY/UghV3Vms4/q696dX0HNJ6+7WM2HNv1Mze+suFnN37zI+8zGP3Qapo71LV2j5tawEwMAmEWJAQDMosQAAGZRYgAAs3gp5s8knFtXzd/97O2zvJKSkf7BcDVvfKf+6BqXd7I4l6PipZjFK1bu9VDFV9EfO7X/6saerOoC/QW4+bt2h3RNX2IpNd858gI1//Je7+OoOq7ur46tdss+NffvPxDk6oofL8UEAEQ1SgwAYBYlBgAwixIDAJhFiQEAzOKxUz/j375LzbuvvUHN5zefXZzLCYslx70nnIbPGqqObfKX1WpeUAKnEIFIEujUXsVp3sda5YfpmoFO/9aZ9YOa//u3KZ5sWevX1bFXtNY/ByQuiJzTicFgJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzzurpxN9s3KTmf574azVP++fZf+FioNNAh+fUVPM361ZX8xvL7gnbmopq68lqnqzyWn1swfHjxbwaAEWV/+N2Nf/bA7d6sq7jJ6hjf7itQM0bLCj0skoEOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYdVZPJ16XckTNr1HeRioi8sFvKqj5n1/QTzPWfnld4RYWhNSXvlTz6VO9b3UVEZnua+LJXL7+RLW45NL6RatXVeM5C9/Uxwfw6/I7PFnOAx+qYz9a0FTNQ30jLRDpfAn6p7/to9qpea2xZ/+0dKiS9wb/jNOqlbKLcSVnDzsxAIBZlBgAwCxKDABgFiUGADCLEgMAmHVWTyfG+/TOLHB+Nb8m5bCe/14/zSi/L9SygnLz9z3UfMdR/QSlZteeimrevYl+qvK5WqGdQgzF8Ipb1PzD6h31D+B0IqJMoNOJq0fqn1+athqs5g0GrAnXklAI7MQAAGZRYgAAsygxAIBZlBgAwCxKDABg1lk9nZi+RH/m4dpLpp7NZRTKjHM/KeklhNWGAG+w9uUG/+w1IBp9n6+/3fytDpPU/J5rRniypA9WhXVNwUrccciTzT6qP4P16GL9rfQVZFM4l1Ts2IkBAMyixAAAZlFiAACzKDEAgFln9WBH/VvWq3n727w/GBUR6T5suZr/rbr+gkro1uXlebJhD/5OHVt+w4riXg4QEQqO6wc4rph/t5pvuEo/2DH22Rc82f1xv1HHlp77eZCrK5y8WhU92Q1l96ljJ395oljXcrawEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZZ/V0ojuhn4ap9oJ+CvHbN/QXTl5xwVA133ZLgZonbCvtySq12auOvePcz9T81+V3qPmIHRer+df7aqm55tra36l5Spz+CKg3f2ij5rkf64+RSVt6xJOV/4JTiICm/mtOzZdcVkrNO5f23qd/H+89sSgiMvDaYWre5O5v1dz59c9p+we2VfMLf7tazaMZOzEAgFmUGADALEoMAGAWJQYAMIsSAwCY5XPO6UdxfqZ7XN/iXktESKitnyosqFJezX3bdqq5/9DhoK8Zn6qfKvT5fGqevzsr6Lmj0fyCWSW9hKgWK/d6qI7d0F7NFz4zochz/31fSzXPc/FqPrramqDn/vyE/nnkb78aqOYFazKCnru4BXOvsxMDAJhFiQEAzKLEAABmUWIAALMoMQCAWWf12YkW5G/Xn5EogfIw8GftKba5AYRH+a/1+7Tp4sGe7KX2/1LHdirtfcu6iMgfq+rPTgzV6pPeZy3+aeRv1bFJa1aF5ZoljZ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzOJ0IgAEwb/pezVvMMCb3fngcHVs7W4/qPn7TeaEtJa+m65W88N/r+PJkj6OjlOIgbATAwCYRYkBAMyixAAAZlFiAACzKDEAgFmcTgSAMKv92DL9Nx7T42ulbYhX0N/uXipAHs3YiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJjlc865kl4EAACFwU4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALErsv9auXSt9+/aV+vXrS0pKilStWlU6d+4sc+fOLemlnbJo0SLx+XzqrxUrVpT08gAzLNzvq1atkhEjRkjz5s2lTJkycs4558iNN94oGzZsKOmlRZSEkl5ApNi2bZtkZ2fLLbfcIjVr1pScnByZPXu29OzZUyZNmiRDhw4t6SWectddd8mFF154WtawYcMSWg1gj4X7/fHHH5elS5dK3759pVWrVrJ792557rnnpE2bNrJixQpp0aJFSS8xIvicc66kFxGp/H6/tG3bVo4fPy6ZmZmFmmPz5s2SlpYmKSkpRV7PokWL5LLLLpNZs2ZJnz59ijwfgP8v0u73ZcuWyQUXXCClSpU6lW3cuFFatmwpffr0kenTpxf5GtGAbyf+gvj4eKlTp44cOnSo0HNMmzZN0tLSZPjw4bJq1aqwrS07O1vy8/PDNh8Q6yLtfu/YseNpBSYi0qhRI2nevLmsW7euSHNHE0rsZ44dOyb79u2TzZs3y1NPPSXz5s2Trl27Fnq+AQMGyM033ywzZ86Udu3aSatWrWT8+PGyf//+Qs952223Sfny5aV06dJy2WWXyRdffFHouYBYZuF+/ynnnGRlZUnVqlXDMl9UcDjNsGHDnIg4EXFxcXGuT58+7sCBA0WeNzc3182YMcN17drV+Xw+l5SU5Pr16+c++eQT5/f7g5pj6dKl7oYbbnBTpkxxc+bMcY899pirUqWKK126tPvqq6+KvEYg1kTy/a6ZNm2aExE3ZcqUIq8xWvAzsZ/JzMyU7du3y86dO+XNN9+UUqVKycSJEyU1NTVs19i2bZu88sor8sorr8jWrVulbt26MmzYMHnwwQdDnmvTpk3SqlUr6dy5s3z00UdhWyMQCyzd75mZmdK+fXtp3ry5fPrppxIfHx+2NVpGiZ1Bjx495NChQ7Jy5Urx+XzqmNzcXDl8+PBpWY0aNc44d1ZWltxxxx0yd+5cqVChQqG/F9+/f395++23JScnh7/YQBFE6v2+e/du6dSpk+Tl5cmKFSukZs2aQX9stONnYmfQp08fWbVq1S/+24yZM2dKWlraab8Cyc/Pl/fee0969+4tderUkXnz5kmvXr3ktddeK/Qa69SpIydPnpRjx44Veg4AkXm/Hz58WK666io5dOiQfPTRRxTYz/DvxM4gNzdXRMTzlddPXXHFFTJ//vxfnCcjI0OmTp0q06ZNk6ysLElPT5dHH31Ubr311iJ/62LLli1SunRpKVu2bJHmAWJdpN3vx48fl+uuu042bNggCxYskGbNmgX9sTGjZH8kFzmysrI82cmTJ12bNm1ccnKyy87OLtS8CxcudO3bt3ci4pKTk92gQYPc4sWLCzXXnj17PNmaNWtcYmKi69mzZ6HmBGKRhfs9Pz/f9ezZ0yUkJLgPPvigUHPEAnZi/zVs2DA5cuSIdO7cWWrVqiW7d++WGTNmSGZmpjz55JOF3uUsXrxY8vLyZMKECTJgwACpUKFCodfYr18/SU5Olo4dO0r16tUlIyNDJk+eLCkpKTJ27NhCzwvEGgv3+7333ivvvfeeXHfddXLgwAHPP24eOHBgoeeOKiXdopHi9ddfd926dXOpqakuISHBVapUyXXr1s3NmTOnSPMePXo0TCt0bvz48a5du3aucuXKLiEhwaWlpbmBAwe6jRs3hu0aQCywcL936dLl1PF/7Rf+D6cTAQBmcToRAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFlBP7Gje1zf4lwHELT5BbNKeglRjXsdkSKYe52dGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMxKKOkFnA1x5zfzZPM+fE0de/X6q9X85CM11Dx+4VeFXxgAoEjYiQEAzKLEAABmUWIAALMoMQCAWZQYAMCsmDidqPG7AjWfm/6+mu95NUfNh1zSX83zt/1YuIUBAILGTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxezpxEXHE9X8L6OG6OOfm6jm3T/8Vs3nNa9YqHUBiF4JdWqr+fH0VDVP2pWt5v6MDWFbk3XsxAAAZlFiAACzKDEAgFmUGADALEoMAGBWTJxO9O3a58nunjxMHVtrzko1b9zpt2q+vv8ENX//Uu8px/hFvAUaiDrtWnqiXX/MV4eObvaBmvcsc1DNPz/hU/OB/xmq5s3+ssuT5W/foY6NFuzEAABmUWIAALMoMQCAWZQYAMAsSgwAYFZMnE70Z+3xZLUe92a/pNFf1+q/ob/YWdxD3hORsiReH1zgD2ktACLHjsvKebKvL3xOHVsgTs3jRD+F2C5JH//91S+p+bMd6nqyV/95tTq2ypTlam4NOzEAgFmUGADALEoMAGAWJQYAMCsmDnaERUGBGu/w56j5J03f9WTXJl+iT33sWKGXBSDyBDrAUSD655FA+4lA4/P06WVoxU2e7PrRT6hjb3D3qXnll20d+GAnBgAwixIDAJhFiQEAzKLEAABmUWIAALM4nRikQCcIf505UM0XtXjXk8WV9z6e5pfmBhD5ai3M9mSTb6mnjh1aYauaf5BTQc3bJO3WrxmfEmA13n1JoLG/vvdDNX//5UoB5o5M7MQAAGZRYgAAsygxAIBZlBgAwCxKDABgFqcTi2jff2qqub+595ln1//nG3XsysP11XzzkapqnnKn/rWHf+MWNQcQvP2DO6j58ar6iys1H1x0rpq/9Ovr1PzQ+SfVfHGPp9U8tGcz6p8vtOcsioi8LxeqeaRiJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzOJ1YRLXGLlPz5m1uCXqOd9pNUvM6tfWvMfYuyFfz3/a8Q80Lvl4X9FqAWHGk/0VqvvyR59Q8TvTTidpJwbi7Ao1dGOLcySGN1/YlgcZ+eSI69jDR8acAAMQkSgwAYBYlBgAwixIDAJhFiQEAzOJ0YjGpe+O3QY+9Rzqqee717dT81Wf+qeZtX/1OzVdfVcuT5e/S3xgLxIq91x1Xc/35gyKBvuYP5XmF4Zk7tPGBTiE+NFg/zRwvXwW4ZmRiJwYAMIsSAwCYRYkBAMyixAAAZnGwI4Ilz/lczf/24JVqPrnOEjXv3tx7QCSRgx2IcY3v36fmfafrL658p+GHAWYK/lFPgfYN4RrfePYIT9borpXqWGsHOAJhJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzOJ1o0MLPm+u/EeB0os/7zj4g5uX/uF3Pu+jjr7hsiJpv6Z3oydLS96pj/9NyZoDVhOexU/XfyQswPnqxEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZnE4EUCTb/up9qWt+oxx1bKDnFQY6KRhJ4hfqzxpsuqm2J9vR6xx1bFzL4n124tYh3qPIDRYGmDpKsBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZxOBFAk3w551pMFeubf+/+uouZfHqun5u+9cXGh11VY9a/eouYtyu9U87ZlvM8s7VnmoDo21Gchhjo+FvFfAgBgFiUGADCLEgMAmEWJAQDMosQAAGZxOtGiCrH39lZErqaLB3uydV2mqGN7lTmk5j3LrFbzR+9ao+YFor+uXHumYChjwzU+XM9CDDR+9J7Wat74/j2eLD/AzNGCnRgAwCxKDABgFiUGADCLEgMAmEWJAQDM4nSiQX9p/15JLwE4Jf3RY56s3bKR6tjPH/Q+Z1GkeJ8pWNzPK9THhzb30B8vV/MV81qqef0p29Q8f/sONY9m7MQAAGZRYgAAsygxAIBZlBgAwCwOdkSwPSM6qvnVZZ5Q835brlfzpC83eTJ/4ZcFnMa/bqMnq65kIiLXPtdWzfcP7qDmx6sGehxT0fW86TM1n/VJJzVPOhD8Wkrv0x9dVWXK8gAfka2m58gyNY/2R0mFgp0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIqJ04nxjep7svzq5UOaY0eXlJDG59b0nv+77IK1Ic3xRq1xav7VSX3th/9YR83jDukvHAQiReBTe8Xny8f1r+Hry9lfCwqPnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMsnk6MS5ejTc+daGa/7uX95TfOQn6acODBblq/utNfdV8y94qav6HFv/xZNeXXa+ODaTr6sFqXuUfyWoe9xmnEAHEFnZiAACzKDEAgFmUGADALEoMAGAWJQYAMMvk6cS8y89X87nXP6Xm3d+8z5NVDvAYw2rzt6m5f8dONa8rev6OVAsq+yXVJLTTjAAQa9iJAQDMosQAAGZRYgAAsygxAIBZlBgAwCyTpxMTF3yp5r+v10HNG4Twptb8Qq0IAFAS2IkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGCWzznnSnoRAAAUBjsxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYv+1du1a6du3r9SvX19SUlKkatWq0rlzZ5k7d25JL+2URYsWic/nU3+tWLGipJcHmGHhfhcR+fLLL+XKK6+U8uXLS7ly5aRHjx6yZs2akl5WREko6QVEim3btkl2drbccsstUrNmTcnJyZHZs2dLz549ZdKkSTJ06NCSXuIpd911l1x44YWnZQ0bNiyh1QD2WLjfv/rqK7n44oulTp06Mnr0aCkoKJAJEyZIly5d5PPPP5fGjRuX9BIjAm92/gV+v1/atm0rx48fl8zMzELNsXnzZklLS5OUlJQir2fRokVy2WWXyaxZs6RPnz5Fng/A/xdp9/s111wjy5cvl40bN0qVKlVERGTXrl2Snp4uPXr0kNmzZxf5GtGAbyf+gvj4eKlTp44cOnSo0HNMmzZN0tLSZPjw4bJq1aqwrS07O1vy8/PDNh8Q6yLtfv/000+lW7dupwpMRCQtLU26dOki77//vhw9erRI80cLSuxnjh07Jvv27ZPNmzfLU089JfPmzZOuXbsWer4BAwbIzTffLDNnzpR27dpJq1atZPz48bJ///5Cz3nbbbdJ+fLlpXTp0nLZZZfJF198Uei5gFgWyff7iRMnJDk52ZOnpKTIyZMn5bvvviv0OqOKw2mGDRvmRMSJiIuLi3N9+vRxBw4cKPK8ubm5bsaMGa5r167O5/O5pKQk169fP/fJJ584v98f1BxLly51N9xwg5syZYqbM2eOe+yxx1yVKlVc6dKl3VdffVXkNQKxJpLv95YtW7r09HSXn59/Kjtx4oQ755xznIi4t956q8jrjAaU2M+sW7fOzZ8/37366qvummuucb1793a7d+8O6zW2bt3q/vKXv7h69eo5EXF169Z1f//73ws118aNG11ycrK74oorwrpGIBZE8v0+ceJEJyLulltucWvXrnXffvut69evn0tMTHQi4qZNmxbWdVrFwY4z6NGjhxw6dEhWrlwpPp9PHZObmyuHDx8+LatRo8YZ587KypI77rhD5s6dKxUqVCj09+L79+8vb7/9tuTk5Eh8fHyh5gAQeff7n/70J3niiSckLy9PREQuuOACueKKK2TMmDHyzjvvSK9evc44R7TjZ2Jn0KdPH1m1apVs2LAh4JiZM2dKWlraab8Cyc/Pl/fee0969+4tderUkXnz5kmvXr3ktddeK/Qa69SpIydPnpRjx44Veg4AkXe/jxkzRrKysuTTTz+Vb775RlatWiUFBQUiIpKenh7aHy5K8e/EziA3N1dExPOV109dccUVMn/+/F+cJyMjQ6ZOnSrTpk2TrKwsSU9Pl0cffVRuvfVWSU1NLdIat2zZIqVLl5ayZcsWaR4g1kXi/V6pUiW5+OKLT/3vBQsWSO3ataVJkyYhzRO1Svr7mZEiKyvLk508edK1adPGJScnu+zs7ELNu3DhQte+fXsnIi45OdkNGjTILV68uFBz7dmzx5OtWbPGJSYmup49exZqTiAWWbjfNW+88YYTETdu3LiwzWkdO7H/GjZsmBw5ckQ6d+4stWrVkt27d8uMGTMkMzNTnnzyyULvchYvXix5eXkyYcIEGTBggFSoUKHQa+zXr58kJydLx44dpXr16pKRkSGTJ0+WlJQUGTt2bKHnBWKNhft9yZIl8sgjj0iPHj2kSpUqsmLFCpk6dapceeWVcvfddxd63qhT0i0aKV5//XXXrVs3l5qa6hISElylSpVct27d3Jw5c4o079GjR8O0QufGjx/v2rVr5ypXruwSEhJcWlqaGzhwoNu4cWPYrgHEAgv3+6ZNm1yPHj1c1apVXVJSkmvSpIl77LHH3IkTJ8J2jWjA6UQAgFmcTgQAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgVtBP7Oge17c41wEEbX7BrJJeQlTjXkekCOZeZycGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMSijpBRRGfPnyap75t6bFds0Hus9V86EVdhbbNScfrqnmk56+Xs2rTlpebGsBgEjETgwAYBYlBgAwixIDAJhFiQEAzKLEAABmmTydmNW/uZpvvOH5s7wSEb8rvrkHl9+u5lN77dE/4MV4PS/wh2lFQPRIqF1LzVvM3aHmDUrr992cy1t5si131FfHHk/LV/MOrTaq+WvnLlTzxlN+o+b1Ho69E8rsxAAAZlFiAACzKDEAgFmUGADALEoMAGCWydOJB1sVlPQSSlSPmplqvqqaftrKnxXgNCMQZeKbNvJk635XUR275upn1DzFVyqka972xY8hjQ9FXoDTz4/fNE3NJz7csNjWEqnYiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsk6cTG//hOzW/son+xuMH6n0Y9NwtSx1R8ypxyUHPUdze/0F/dmTVrA1neSVAydj01EVq/l7vpzxZemKg04ahnUKMJIuONAnwO/qzGaMZOzEAgFmUGADALEoMAGAWJQYAMMvkwY6CY8fUPK6rnv9DWnoyX1KSOtZ9WE3NP2zyXpCrK373pc9X87/+ub+a133sC0/m8k6GdU1AcTh8s36A453eT6t54EMc0WX+7HZqXluWneWVlDx2YgAAsygxAIBZlBgAwCxKDABgFiUGADDL5OnEcNgxsq2ar2ny3FleSej6lt2v58P0tTcud6cna/jHL9WxnFpESdBeZikiMmWM9zFSIuE5hdjui5vVvOo4/RFzBaP1++6jpu8UeS2hym18/KxfM1KxEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZMXs6sc6H+9R888hcNW+QUPSXYq464dT8wiRfkef+JesHPO/JWh0coY6t/ffYe/YaSl7Bpm1qft/3N6j5nPS5Qc/9xP5mal6j/w/6WnJy1HznYf1ltCXhm64T1Pzvq73PVFwzoLE61r9uY1jXVFLYiQEAzKLEAABmUWIAALMoMQCAWZQYAMCsmD2d6M/YoOa//tO9an7FfZ+q+Wd7G6j5kRm1PFnltUfVsQeal1Xzw/qhIvns5nFqXiUu+BOUx5vqpzCBkhDomZ1bltRT8xON8tT8yf1tPNnn15yrji3I2RHc4iJQki9Rzf9afbUnazK0ozq24e84nQgAQImixAAAZlFiAACzKDEAgFmUGADALJ9zTn+g3890j+tb3GtBkDY/0UHNtWckBjI3p7yaT77I++w1ERH//gNBz13c5hfMKuklRDUL93peN/3N7IkL9DeWh8OGqfo1p3aeGvQct78/VM2TDur7CX8p/dPz1Jv0e71dknf8vJxy6tjnb9afSymff6vnJSCYe52dGADALEoMAGAWJQYAMIsSAwCYFbOPnbKsxooCNd/TT3+ZX/X4FE92XcoRdeyLpUoVfmHAWVKcBzgCSb9Nv+Zj0iroORrJirCsZcw/LlNz/2zvIY73m8xRx778xB41P3FFaTUvOH48yNWdXezEAABmUWIAALMoMQCAWZQYAMAsSgwAYBanEw0qM3ulmn88pr6aDyq3uziXA+As8x86rOYFjyifA17T55jV8EM179l4oP4BX68LZmlnHTsxAIBZlBgAwCxKDABgFiUGADCLEgMAmMXpRJxm02/PVfN6D3PCEUDkYScGADCLEgMAmEWJAQDMosQAAGZRYgAAs6LqdOLxa9up+eVjPgt6jjdmX6rm5zyyrDBLMqfeB8dKegkACmlvq+Sgx96w6Rr9N9Z/H6bVnB3sxAAAZlFiAACzKDEAgFmUGADALEoMAGCWydOJcSkpal7t/i1q/lDV74Keu9yNx9X840fKBz1HcTsy4CI1v77MPwN8ROmg507YtkfN84OeAUBxi69aRc2vH7I46Dkyl+nPST33+PJCramksBMDAJhFiQEAzKLEAABmUWIAALMoMQCAWSZPJ7qTJ9V884Fz9A+oH/zczUrvUPMFTduquX/dxuAnD5Osjk7Ny8cFfwqxzaqb1Txt79n/8wDQBTqJvf5p/XPdnKqfBD137X/rn0etYScGADCLEgMAmEWJAQDMosQAAGbZPNiRrz8E6Uh28C+EC6R7cq6af/3mGjWfPa6bmld6teiPbtkytoOar+4V6PFSSUHPXbC0kpoH+m8L4Oxr/Kl++OK9Gi8FPccfsy5Q8+SN0fGIOXZiAACzKDEAgFmUGADALEoMAGAWJQYAMMvk6cRAGv/1sJpPaO19+dtvK34f0tyjKq9X8+FjvlHzr0cX/aRkm1Ir1DzZF/wpRBGRCYe8f/46U/U/jz+kmQGEwndhSzXfcq9Pzd+rMSXATPr+451jlT1ZRp+66tj8bVsDzG0LOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYFVWnE/0bt6j5B7d29mS/fTe004mBlA1wUrBTUkEYZi8VhjlEPrjlEk/m9n0XlrkBeO0drj/3dPaDT6h57YRAp5n1fcbBguNq/rdJ3pfdpm1ZFmDu6MBODABgFiUGADCLEgMAmEWJAQDMosQAAGZF1enEQNyXGZ7sum791LHXzNafVzi8wrawrqkoXjisPwvtpeevU/PqX60szuUAUaXgktZqfrBJaTU/VtP73MP3b/+HOjbwKURdjtPf7Nz9n/epedpT0X0SUcNODABgFiUGADCLEgMAmEWJAQDMosQAAGbFxOlEKfC+r9i/bqM6dO55aWr+zJ+vVfOM259X86E/ep/XuGKHfqowd3s5NQ+k6fjdal49yp+RBpxJ3PnNPNn2HhXVscNvnavml6ZMUPP0xFCeZVr0N7uLiHT5x71qXuMZ7vX/YScGADCLEgMAmEWJAQDMosQAAGZRYgAAs2LjdGIIXJ7+rLJ6Dy9X86sfbhNgpqOepLasLeyyTpMfllmA6LO7Y0VP9tXdz4Y4S3jeqK5pNWmkmtefvFnNU7P0zzv4/9iJAQDMosQAAGZRYgAAsygxAIBZHOwAEDWqT/A+junaCW1LYCW6c0R/XBSHtQqPnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADDL55xzJb0IAAAKg50YAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMosf9au3at9O3bV+rXry8pKSlStWpV6dy5s8ydO7ekl3aajRs3yk033SS1a9eWlJQUadKkiTzyyCOSk5NT0ksDzLBwvy9atEh8Pp/6a8WKFSW9vIiRUNILiBTbtm2T7OxsueWWW6RmzZqSk5Mjs2fPlp49e8qkSZNk6NChJb1E+fHHH6Vdu3ZSoUIFGTFihFSuXFmWL18uo0ePli+//FLmzJlT0ksETLBwv//PXXfdJRdeeOFpWcOGDUtoNZGHNzv/Ar/fL23btpXjx49LZmZmoebYvHmzpKWlSUpKSpHX8/e//13+9Kc/yXfffSfNmzc/ld9yyy3yr3/9Sw4cOCCVKlUq8nWAWBRp9/uiRYvksssuk1mzZkmfPn2KPF+04tuJvyA+Pl7q1Kkjhw4dKvQc06ZNk7S0NBk+fLisWrWqSOs5cuSIiIikpqaelqelpUlcXJyUKlWqSPMDsSzS7vefys7Olvz8/LDNF1UcTnP06FG3d+9et2nTJvfPf/7TxcfHuwEDBhR6vvXr17vf/OY3rmLFik5EXMuWLd3TTz/t9u3bF/Jc8+bNcyLievbs6VavXu1++OEH98Ybb7jy5cu7e+65p9BrBGJVJN/vCxcudCLiypYt60TExcfHu0svvdStWrWq0OuLRpTYzwwbNsyJiBMRFxcX5/r06eMOHDhQ5Hlzc3PdjBkzXNeuXZ3P53NJSUmuX79+7pNPPnF+vz/oeR599FGXnJx8ao0i4v70pz8VeX1ALIrk+33p0qXuhhtucFOmTHFz5sxxjz32mKtSpYorXbq0++qrr4q8xmjBz8R+JjMzU7Zv3y47d+6UN998U0qVKiUTJ070fAuvKLZt2yavvPKKvPLKK7J161apW7euDBs2TB588MEzfuz06dNl+vTpcsMNN0iVKlXkgw8+kKlTp8ozzzwjI0aMCNsagVgQ6ff7z23atElatWolnTt3lo8++ihsa7SMEjuDHj16yKFDh2TlypXi8/nUMbm5uXL48OHTsho1apxx7qysLLnjjjtk7ty5UqFChTN+L/6NN96Q22+/XTZs2CC1a9c+ld92223y5ptvyg8//CBVqlQ58x8KgCqS7vdA+vfvL2+//bbk5ORIfHx8oeaIJhzsOIM+ffrIqlWrZMOGDQHHzJw5U9LS0k77FUh+fr6899570rt3b6lTp47MmzdPevXqJa+99toZ1zJhwgRp3br1aQUmItKzZ0/JycmR1atXB/8HA+ARSfd7IHXq1JGTJ0/KsWPHCj1HNOHfiZ1Bbm6uiIjnK6+fuuKKK2T+/Pm/OE9GRoZMnTpVpk2bJllZWZKeni6PPvqo3HrrrUF/6yIrK0s9Qp+XlyciwukloIgi6X4PZMuWLVK6dGkpW7ZskeaJGiX7I7nIkZWV5clOnjzp2rRp45KTk112dnah5l24cKFr3769ExGXnJzsBg0a5BYvXlyoua699lpXqlQpt379+tPyXr16ubi4OLdjx45CzQvEGgv3+549ezzZmjVrXGJiouvZs2eh5oxG7MT+a9iwYXLkyBHp3Lmz1KpVS3bv3i0zZsyQzMxMefLJJwv9Vc/ixYslLy9PJkyYIAMGDJAKFSoUeo333XefzJs3Ty655BIZMWKEVKlSRd5//32ZN2+eDBkyRGrWrFnouYFYYuF+79evnyQnJ0vHjh2levXqkpGRIZMnT5aUlBQZO3ZsoeeNOiXdopHi9ddfd926dXOpqakuISHBVapUyXXr1s3NmTOnSPMePXo0TCv8PytXrnRXXXWVq1GjhktMTHTp6eluzJgxLi8vL6zXAaKZhft9/Pjxrl27dq5y5couISHBpaWluYEDB7qNGzeG7RrRgNOJAACzOJ0IADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgVtCPneoe17c41wEEbX7BrJJeQlTjXkekCOZeZycGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMSijpBQBArMjt1U7Nd/TJU/PMy15S80RfvJrnOb8n256fq469buIf1LzW2GVqHqnYiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsTieeTe1aqvHme/STRoEkbExR8/xGOWr+u/P/7ckKnP71y8wf26r5wpaz1LzXRdfra/lxu5oDVm1/sKOa56Sf8IbOp45957Jn1Tw9UR+f54Jb2/8f7z2dePVU/RTiOcZOIQbCTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmcTqxmGz5RwdP9u+bnlDHpsUnq3mBFKh5XBf9a4+A45WvVQKNHV5xS0hzx0/PV/P8LmoMnHUJdWqreXbbmmp+y2PvqfmlKf9Q83MSvPevdkrw/+inEItTlW/1tQT672LtZDE7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJjF6cRics+173uyQKcQ4wKcWHrhUEM1711urZpf9eVQNT+2vZwnW/+rCerYwG+MVWM5cV81/Tdkd4AcKLqDt3pP/4qIZJ/jvZfaXpmhjp11znMhXrVUiOMjwyfj9ec1Dtl2hZofuiNdzf0ZG8K2pnBiJwYAMIsSAwCYRYkBAMyixAAAZnGwI0j7B+s/SO4xYqmaD62w1ZMFenRToK8lXvvhAjX/4M+d1bzmqm/VfOOz7YNeS6ADHM8faqD/xuf6NYFQbJ3ZSs3PrbZfzcfVm6jmHZK8j1gK/Aio2PZS3Y/VvFuru9S8nH4+psSxEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZMXs6Mb5iBTU/0UZ/1NPKR55X8wLRj/Npj5K69Nt+6tiyfy6j5uUDnPwLcIAwoI2/8p7kWnVC//pl0Fsj1Lz+H5aHeFXEss3jLlLzdf31+0jky7BcN9Bj04pTpF/ztm1d1XzNe83UvNYbywq1ppLCTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxcTpxC3/8D738K5rPlTHDq24QM0LAvR9oGcQtnvsbk+W9q/v1LH+I1vUPJBd7zZVc+f0l2sWKCe/Bi4foo5twClEhEGgU4gl8RzDWLmmiEjH1f09WbXbDqpja+21dQoxEHZiAACzKDEAgFmUGADALEoMAGAWJQYAMCuqTice+6i+mme0fM6Tac82FAl8CjHLn6vmvf9yn5pXf9l78ifQeaWEOrXVfMPIOmqeeWFoz3FMnzfMkzX7yy51bL6aAqGZekT/u1sQ4ARtqF4c31PNq684FPQct7z5kZr3LJNVmCWdVZ1H3anm1dZ434Tt37u3uJdTotiJAQDMosQAAGZRYgAAsygxAIBZlBgAwKyoOp24sOUsNdefb6j3d+dvblTzCn9OUfPKq0J41mC7lmoc/4R+eqj+fUfVfFUf/RTi7x/UTyylz1zhyTiFiOI0u2n1Yp2/quj3nf4kU92xgqTwLKYELBmnn1DutOYmT1b11mrq2Gg5tchODABgFiUGADCLEgMAmEWJAQDMiqqDHW1XDVTza+uu9WSfPNdJHVtliv4DY/0oReBHRq37Qy1Ptv5XE9Sxkw/XU/O2b2xV89E33arm5T73HuAAoHt+fG817//Q+LO8kvBZdN4MTzZ0Tg917L7j3s9RIiJ5f6uh5gn/8b5cNxKwEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZPudcoIN3p+ke17e41xLRcq9vp+YX/0U/EfjX6qs9mf74K5HJhxqq+bw++jX96zaqeayYX6A/XgzhEev3eqjyurX1ZMdqllLHHrrmmJqfW837Mstf8lGTD/S1uECv3g1e09dGqHmD+0J4xF6YBHOvsxMDAJhFiQEAzKLEAABmUWIAALMoMQCAWVH17MRQxDdLV/O0l3eq+eQ6L6h5QYCnKsaJT001T/3nSjVv4juo5gAiR+IC7zMFKwYYW/Ffeh7UEfGfaPbQb9X8/KvWebKX6n4c0tzf9NefHdn7Pv20dEljJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzYvZ04s6uVdX8nTreN6OKiBQE6PtAz0PUvj4INDbQG58/v0Y74Sjy+I9Xq7n/5ng1z9++Q80B2HTOB4fV/OvWNb1h3WJeTAljJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzYvZ0YurKbDXP8p9Q80Btf9WXQ9XcrajoyY41OqmOHdVRf7bZ0Apb1fydhh+q+YSPz1Xz95tXUnMAZ1d8anU13zpUf7t7IF8Pf1bNw/Fm5x7f3aTmZWRLkecuDuzEAABmUWIAALMoMQCAWZQYAMAsSgwAYFbMnk6Uz79V46E3/CakaWqu0ucJxQfl9VOFL/36OjX//EH9ZNLQipvU/NXBd3myKlOWB7k6AOFSULOamn8x7OkQZ9KfkxoOBa/oJyiF04kAAIQXJQYAMIsSAwCYRYkBAMyK3YMdAbgwHNQIlf/IETWv/twyNY97UH9ZZqCvSY5XDTQeiC5HBlxUbHN3vm+Fmj+eusaTBX7805cB8tAOaiT6gh9/waqBal6j1zo1Lyf6nzNSsRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZxOjGD7B3dQ84IAJ5wKpKA4lwNEjO0PdlTzNSO8j2QLx4sif0me854ULO5rnjdxpP4bzhvVm7xRHVq8Kzx72IkBAMyixAAAZlFiAACzKDEAgFmUGADALE4nnkUJdWqr+eEXS6n58pbPqXmWP1fNL3v9PjWv/7j+DEaguOz5rX56cMTIt8My/6Up/wjwO8lhmT8U2/O99+PHxxqrY5ccTFfzg7+tEdI163wd/D0dLacQA2EnBgAwixIDAJhFiQEAzKLEAABmUWIAALNsnk5s11KNk57Yq+b+m5W3oPr0tx1vG3BOoZf1UyeqeB9iNrbXDHVszzIH1TzQsxB7/yXAKcSXlwe5OqB4ff4n7zMMRcL5TEH9RG8o2iwbrOYnjiSFNE+ZTd611Bob6PSgfq8HznEm7MQAAGZRYgAAsygxAIBZlBgAwCxKDABglsnTiW0nfa3mf0/9Rs3zVnpPRCX6lBOLEvj0VJzopxknHDpXzZ/54GpP9pe116pjR6+oqOZ1Z2xT88rbOYWI2HbejLvVvOoa76lgn/K2YxGR+h9mqLn/yJFCrwtnHzsxAIBZlBgAwCxKDABgFiUGADCLEgMAmGXydOKsTzqp+V8HrlZz7RmEDRcOCema9V7STyeW+nKTmtc/UvQThPlFngEoGdfWalus89eXot9f0f7G41jBTgwAYBYlBgAwixIDAJhFiQEAzDJ5sKP+/foPda+9P/gfJjcQ/RBIqPjhMACUHHZiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALJ9zzpX0IgAAKAx2YgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosT+a+3atdK3b1+pX7++pKSkSNWqVaVz584yd+7ckl7aKYsWLRKfz6f+WrFiRUkvDzDDwv1+6623BrzffT6f7Nixo6SXGBESSnoBkWLbtm2SnZ0tt9xyi9SsWVNycnJk9uzZ0rNnT5k0aZIMHTq0pJd4yl133SUXXnjhaVnDhg1LaDWAPRbu92HDhkm3bt1Oy5xzMnz4cKlXr57UqlWrhFYWWXiz8y/w+/3Stm1bOX78uGRmZhZqjs2bN0taWpqkpKQUeT2LFi2Syy67TGbNmiV9+vQp8nwA/r9Iu981n332mVxyySUyZswY+eMf/1gs17CGbyf+gvj4eKlTp44cOnSo0HNMmzZN0tLSZPjw4bJq1aqwrS07O1vy8/PDNh8Q6yL5fv+f1157TXw+nwwYMCDsc5vlcJqjR4+6vXv3uk2bNrl//vOfLj4+3g0YMKDQ861fv9795je/cRUrVnQi4lq2bOmefvppt2/fvpDnWrhwoRMRV7ZsWSciLj4+3l166aVu1apVhV4fEMsi+X7/uZMnT7oqVaq4Tp06FXmuaEKJ/cywYcOciDgRcXFxca5Pnz7uwIEDRZ43NzfXzZgxw3Xt2tX5fD6XlJTk+vXr5z755BPn9/uDmmPp0qXuhhtucFOmTHFz5sxxjz32mKtSpYorXbq0++qrr4q8RiDWRPL9/nNz5851IuImTJhQ5PVFE34m9jOZmZmyfft22blzp7z55ptSqlQpmThxoqSmpobtGtu2bZNXXnlFXnnlFdm6davUrVtXhg0bJg8++GDIc23atElatWolnTt3lo8++ihsawRigaX7fcCAAfLWW2/Jrl27pEqVKmFbn3WU2Bn06NFDDh06JCtXrhSfz6eOyc3NlcOHD5+W1ahR44xzZ2VlyR133CFz586VChUqFPp78f3795e3335bcnJyJD4+vlBzAIjc+/3o0aOSmpoql19+eUT9M4BIwMGOM+jTp4+sWrVKNmzYEHDMzJkzJS0t7bRfgeTn58t7770nvXv3ljp16si8efOkV69e8tprrxV6jXXq1JGTJ0/KsWPHCj0HgMi93999913JycmRm2++OaSPiwX8O7EzyM3NFRHxfOX1U1dccYXMnz//F+fJyMiQqVOnyrRp0yQrK0vS09Pl0UcflVtvvbXI37rYsmWLlC5dWsqWLVukeYBYF6n3+4wZM6Rs2bLSs2fPkD826pXsj+QiR1ZWlic7efKka9OmjUtOTnbZ2dmFmnfhwoWuffv2TkRccnKyGzRokFu8eHGh5tqzZ48nW7NmjUtMTHQ9e/Ys1JxALLJwv//Pnj17XEJCghs0aFCR5olW7MT+a9iwYXLkyBHp3Lmz1KpVS3bv3i0zZsyQzMxMefLJJwu9y1m8eLHk5eXJhAkTZMCAAVKhQoVCr7Ffv36SnJwsHTt2lOrVq0tGRoZMnjxZUlJSZOzYsYWeF4g1Fu73/5k5c6bk5+fzrcQAONjxX2+88YZMmTJFvv32W9m/f7+UK1dO2rZtKyNHjizSFv7YsWNSpkyZsKzxmWeekRkzZsimTZvkyJEjUq1aNenatauMHj2ax04BIbBwv/9Phw4dZMuWLbJz504ObikoMQCAWZxOBACYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGBW0E/s6B7XtzjXAQRtfsGskl5CVONeR6QI5l5nJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzEkp6AZFm01MXqfnmfi8Uee5L7hym5invrCzy3AAQi9iJAQDMosQAAGZRYgAAsygxAIBZlBgAwKyYPZ1YnKcQA/n0+Ulq3qDzcDVv+LsVxbYWAIgG7MQAAGZRYgAAsygxAIBZlBgAwCxKDABgVkycTszp3d6TFecpxFAFWsslS3jWIgD8EnZiAACzKDEAgFmUGADALEoMAGAWJQYAMCsmTicGemZhpAu07iveOf/sLgQIk42vtlHzhF1Jan7uA8uLczmIAuzEAABmUWIAALMoMQCAWZQYAMAsSgwAYFZMnE4sTpfcWfTnG368c01I10xdXl7NszocCWke4Gzb2O0lNc8Xv5qfV/92Na/xamk1T/pgVeEWBrPYiQEAzKLEAABmUWIAALMoMQCAWRzsCFKDmcPVvOE7K4KeI9CBjFD9q+4SNb+kNy/RRGSL9+lfNxc4p+bfdnpFzZdfEK/mrz58caHWFW7bj1VU863L6qj5xd2/Dct1t/6psSdL+PeXYZk7UrETAwCYRYkBAMyixAAAZlFiAACzKDEAgFmcTgxSzSX66alANj11kSf7uO4L4VqOamdnn5o3fKdYLwsEze8KwjJPhyT9MVUd6iwOy/zFpknxTj/4b959Sda/i/eaJY2dGADALEoMAGAWJQYAMIsSAwCYRYkBAMzidGKQeP4gUHQP7zlfzf9affXZXUiU+vQb77MT0yW6XxTKTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxcTpxF9v6+zJAr0dWRv7f46EdM2Gv1Pe+NwvpClCtrmf/mzGS5Z43/jMaUuUhDU3NlTzS8/voOb7++ao+SPnv1fktVybslfNE336W6OLU5Y/V80vn3Gfmlf7Sn8GZdOP13ky/SmT0YOdGADALEoMAGAWJQYAMIsSAwCYRYkBAMyKidOJ3/+jqSdr0LmZOlY9VRgmgU4+BjopGS7n/sF7YimLtz2jBPg3blHzsoHyWfo8U+TcIq+l5dZdat4w8eyfTvxPTj01LwjwGbrsLP10cbSfRNSwEwMAmEWJAQDMosQAAGZRYgAAs3zOORfMwO5xfYt7LQhSTu/2aq4d4BDRD44EOmSS1SG0x2uVhPkFAX7aj7CIlXv96a3L1LxhYtJZXklgBaI/XmrlicSg58gp0P88f370djWv+sEGNffv2x/0NcMlmHudnRgAwCxKDABgFiUGADCLEgMAmEWJAQDM4nRiFAl0avHT5ycFPccld3pfoCkSWS/R5HRi8YqVe/1I/4vUvO4I/XTe8BqL1Lxd0nFPVhIv1gyXVstuVfNzB29Tc/+R4jvRzOlEAEBUo8QAAGZRYgAAsygxAIBZlBgAwKyYeClmrAh0grBB5+GebHO/F9SxOzv71LwhL9FElCn/uv4C3IOv6+Mfk1Zq/v1jHTyZPzmoQ9+F1rrtJjV/vf7HRZ77m46vqHnXWX3UPPmKkn3eKjsxAIBZlBgAwCxKDABgFiUGADCLEgMAmBWzpxMDPWcwkEh6diCAyHHug8uLbW5fkv5W5q+nNtU/oH6xLUX+3eItNb9W2hbfRYPATgwAYBYlBgAwixIDAJhFiQEAzKLEAABmxezpxEDPCAz0TMEr3jm/GFcTHpue0t9UG+jPFMrYK353fmGWBNgTp7+VOb5aFX38iRPBzx3gtGFO27r6+Hv2qnFG8ynBXzNEJ1yemrf8eISap8sXxbaWYLATAwCYRYkBAMyixAAAZlFiAACzKDEAgFkxezoxVB/vXKPmDWZ635osItLwd963xgZ6XmOoz2VMXV5ezT+uG/wpRCDW/fDnjmp+oppfzdf/aoKaP3uwkSeL8xWoY++suDnI1ZWc82bdo+bpyue0SMBODABgFiUGADCLEgMAmEWJAQDM4mBHEQV8pFM/LVyjj30+TIsJg4AHVSQyf6gLFNbbt49T84aJ+qOhAhlZaWM4lhMWG/JOerLRP16njj16T6qap6/7Rs31oyolj50YAMAsSgwAYBYlBgAwixIDAJhFiQEAzOJ0Yoz69bbOaq49LguIRld/dI+ar7zmKTWvFFe6yNfMcd7TgyIiOQX6o64C6Z85UM3zXqrhycq+Geie3qemLqSVlDx2YgAAsygxAIBZlBgAwCxKDABgFiUGADDL55wL6jBK97i+xb2WiBDoxZU7O/vUPOCzEyOIdhIxq8ORElhJeMwvmFXSS4hqsXKvB5KQ5j3hJyKS8fdaRZ479d+Jal5hOqeCNcHc6+zEAABmUWIAALMoMQCAWZQYAMAsSgwAYBanE4vJpqcu8mTFfZIx4FuZo+x5iJxOLF7c64gUnE4EAEQ1SgwAYBYlBgAwixIDAJhFiQEAzOLNzsVEOxF4xe/OL95rSnSdQgSAM2EnBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWT7nnCvpRQAAUBjsxAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAIYM2aM+Hw+adGiRUkv5TTz58+Xiy++WFJSUqRSpUrSp08f2bp1a0kvCzCN+90u3uys2L59uzRu3Fh8Pp/Uq1dPvvvuu5JekoiIvP/++3L99ddLmzZtZNCgQXLkyBEZP368JCUlyerVq6VatWolvUTAHO532ygxxU033SR79+4Vv98v+/btK9Jf6szMTGnQoIEkJiYWeV3NmzeXkydPytq1a6VUqVIiIvL1119LmzZt5J577pEnn3yyyNcAYg33u218O/FnlixZIm+99ZY8/fTTYZlv7NixUqtWLRk1apSsW7eu0PMcOHBAMjIypHfv3qf+QouInHfeedK0aVN54403wrFcIKZwv9tHif2E3++XkSNHypAhQ6Rly5ZhmXPkyJFy5ZVXysSJE6VZs2bSqVMnefnll+Xo0aMhzXPixAkREUlOTvb8XkpKiuzcuVN2794dljUDsYD7PTpQYj/xwgsvyLZt2+TRRx8N25xt27aVf/3rX7Jr1y6ZNGmS+P1+GTx4sKSlpcmQIUNk+fLlQc2TmpoqFStWlKVLl56W79+/XzIyMkREZMeOHWFbNxDtuN+jhINzzrl9+/a5ypUru3Hjxp3KunTp4po3bx72a2VkZLhRo0a51NRUJyKuWbNm7sUXXzzjx91///1ORNwDDzzgNmzY4L744gt3+eWXu8TERCci7tNPPw37WoFoxP0ePSix/xo+fLhr2LChO3HixKks2L/U2dnZbteuXad+7dmzJ6hrbtq0yXXo0MGJiDvvvPPOOP7EiRNu8ODBLi4uzomIExHXo0cPN3z4cCcibvXq1UFdF4h13O/RgxJzzm3YsMHFxcW5Z555xn3//fenfrVv396lp6e777//3u3fvz/gx48ePfrUXzIRcXXr1g04Njc3182YMcN169bN+Xw+l5yc7AYOHOiWLl0a9Hp3797tlixZ4tavX++cc65///4uLi7OZWdnBz0HEKu436MLR+xFZNGiRXLZZZf94pi777474AmmLVu2yJYtW0797+TkZOnUqdNpYz7//HOZOnWqvP7663L48GFp3bq1DBkyRAYMGCAVK1Ys9Nr9fr/UqVNH6tWrJ8uWLSv0PECs4H6PLgklvYBI0KJFC3nnnXc8+UMPPSTZ2dkyfvx4adCgQcCPr1+/vtSvX1/9vdmzZ8vo0aNl7dq1UrFiRbn55ptlyJAh0rp167Csfdy4cbJr1y559tlnwzIfEO2436MLO7FfcOmllxb5Hz/efvvtsmXLFhk8eLD06dNHPTIbrOnTp8vs2bOlc+fOUrZsWVmwYIG8+eabMmTIEHnxxRcLPS8A7ner2IkVs2effVbKlCkTlrnS09PlwIED8uijj0pubq40btxYXnjhBRk6dGhY5gdQNNzvZx87MQCAWfxjZwCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmBf2PnbvH9S3OdQBBm18wq6SXENW41xEpgrnX2YkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMxKKOkFWLf1bx3U/NIeazzZt0+ep44tN3NFOJcEoAjizm+m5pdPX6nmNRMPebI2ST+qY/+x+wo13/H7+mruW/a1muP/YycGADCLEgMAmEWJAQDMosQAAGZRYgAAszidGKSjfdur+UeDnlDzmglJnixn3L/Vsdcfv0fNk+d8HtziAIQs4dy6av7Yuy+reePE+BBmT1TTyXUWqfnFDVuoecVlIVwyRrETAwCYRYkBAMyixAAAZlFiAACzKDEAgFmcTvyZ+CqV1fzuMW+ouXYKMZCycfrYnZfoX0s0mBP01AACSKhbR82bzf5BzUM7hYiSxk4MAGAWJQYAMIsSAwCYRYkBAMyK2YMd/kvbqHmjJ9aq+fVl9hXbWhbdOE7N+y+9V81T3tFfzgfEvDjvoYw9zyerQ99ODc9j3dadLPBk5yZ6MxGRFF8pNd/bXh9f8V+FX1esYCcGADCLEgMAmEWJAQDMosQAAGZRYgAAs2L2dOLu9qXV/IOan4Vl/vePVfFkPcscVMdWi9cfR3Wknv74m5TCLwuIat+PaefJvm39TFjm1u5pEZGXe1zqydY9UlUdu77ri2r+4pUvqfmTjXuruX/9JjWPRezEAABmUWIAALMoMQCAWZQYAMAsSgwAYFZMnE4suKS1J5v5mycDjA7tP8klawaoedX7fJ6s5/zXQ5q78Q3r1fzwUyFNA0Sdg7d2UPOvBmk3R3hecvnAOzeref2tyz1Z0uba+iRd9fji0sfV/P6Lq6l5FU4nnsJODABgFiUGADCLEgMAmEWJAQDMosQAAGZF1enEhBqpau57dLcna5gYnj/6sc/000OVMpYVee5uVdap+WypXuS5AcsuGam/3TzRF56TiJr3+uknmid27eLJ7qr4cliuebhrrppXmRKW6aMCOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYFVWnE080qaXmHzSaVGzXLCil57t+31FJvwxp7vRS3lOVIiL5Cy5Q8+/X1lTztKXerOybK0JaCxBJqpfKPuvXrJ+YqOZPpIVyEjm0fcM95/9bzSf9/jo1L7e9wJPt7a2fcGww5oSaF3yXGeTqIgM7MQCAWZQYAMAsSgwAYBYlBgAwK6oOdpSENUPHhzA6tK8ZOpTWf/D6YdPZ+gc01ePnuzb2ZP9e3kwdm//j9qDWBqD4DamwRc/vDeXzjq7p3hFq3uiuIk99VrETAwCYRYkBAMyixAAAZlFiAACzKDEAgFlRdTox4bNv1LzZzJGeLKPfs8W9nIhxZ6X1nmxefe+L/ERE4jidCAPm/K2rmr/QXf97HQpfovfRTSIi55/7o5qv3lDXk3VqtkkdO7Wu/hipkjDt2olq/tfXb1Nz3/Kvi3M5hcZODABgFiUGADCLEgMAmEWJAQDMosQAAGZF1elEl5+v5g1/730BZM/fX6hP0q6lGm/5VVk1f7Gv/sLNOglHPFmDRH2OPKcvpUD0U1JtVtyq5ic3llfzclu9WbXFy/WLAgaUm6m/1LXczOK75rEAebrs9WTe5P8k7oxX80CfAwJ551jlAPN4P6XXS9RX0y5Jv+iuTmXUvGaEfspgJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzoup0Ylh8/q0a1/9cH/7YA62Cnvr9HV+qeaBTiK8e8T6TTUSk9g1rg74mgMhx2drr1fyjZm+FNM8rF5yn5v4j3lPR8Y3050luGFZdzdM/0E8z+oNc29nGTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmcToRAM6SrT9U03+jmR53+bq/mlc4+n3Q1/Rv3KLmDUbpeaSeQgyEnRgAwCxKDABgFiUGADCLEgMAmEWJAQDM4nQiAJwllT9P1H/jSj0+dqKUmldw+vNWYxE7MQCAWZQYAMAsSgwAYBYlBgAwi4MdAHCWVF9+MKTxX7R7Vc17l+uq5tpLMaMdOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWz04sLu1aKuGXZ30ZAKLP979voebn/GXZWV5JyWMnBgAwixIDAJhFiQEAzKLEAABmUWIAALM4nVhMtvyqbEkvAUCUyi/tSnoJEYOdGADALEoMAGAWJQYAMIsSAwCYRYkBAMzidGIxaTQ2wxsOCm2OVkk/qvnbF3ZVc7fq29AuAODs+n6HGv9u5yVq/lTNT9XcVzcnbEuyjp0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzOJ0YjHxHzrsyS65/0517CW/X6nmjZKz1JxTiIBNBdnZar7uUF39A2rq8eR209T8MWlVmGWZxk4MAGAWJQYAMIsSAwCYRYkBAMziYMdZVGH6CjX/Zro+/hupXoyrARApSnXfpuY95cKzvBJ72IkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzfM45V9KLAACgMNiJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgtgzJgx4vP5pEWLFiW9lFMuvfRS8fl86q/ExMSSXh5gViTe7/+zYMECufzyy6VChQpSrlw5adu2rcycObOklxUxeCmmYvv27dK4cWPx+XxSr149+e6770p6SSIiMn/+fMnKyjotO3bsmAwfPlyuvvpq+eCDD0poZYBdkXq/i4hMnTpVBg8eLN27d5eePXtKfHy8rF+/XmrVqiWjRo0q6eVFhISSXkAkGjVqlFx00UXi9/tl3759RZorMzNTGjRoEJadUvfu3T3Z9OnTRUTk5ptvLvL8QCyK1Pt969atcuedd8rIkSNl/PjxRZ4vWvHtxJ9ZsmSJvPXWW/L000+HZb6xY8ee+qpp3bp1YZnzp1577TUpU6aMXH/99WGfG4h2kXy/v/DCC+L3++WRRx4REZGjR48K3zjzosR+wu/3y8iRI2XIkCHSsmXLsMw5cuRIufLKK2XixInSrFkz6dSpk7z88sty9OjRIs+9d+9emT9/vvTq1UvKlCkThtUCsSPS7/cFCxZIkyZN5MMPP5TatWtLuXLlpEqVKvLwww9LQUFBWNYbFRxOee6551yFChXcnj17nHPOdenSxTVv3jwscx8+fNhNmjTJtW/f3omIK1u2rBs8eLBbtmxZoed89tlnnYi4Dz/8MCxrBGJJpN/v5cuXd5UqVXJJSUnu4Ycfdm+99ZYbMGCAExH3wAMPhGWd0YAS+699+/a5ypUru3Hjxp3KwvmX+qcyMjLcqFGjXGpqqhMR16xZM/fiiy+GPE+HDh1ctWrVXF5eXtjXCEQzC/d7XFycExE3duzY0/Irr7zSJScnuyNHjoR9rRZRYv81fPhw17BhQ3fixIlTWbB/qbOzs92uXbtO/frfV3ZnsmnTJtehQwcnIu68884Lab2bN292IuJGjBgR0scBsHG/lylTxomI27Zt22n5q6++6kTELV68OKjrRjt+JiYiGzdulMmTJ8tdd90lO3fulK1bt8rWrVvl+PHjkpeXJ1u3bpUDBw4E/Phx48ZJWlraqV8XXnhhwLHHjx+X1157Tbp37y6NGjWSNWvWyMCBA2XChAkhrfm1114TEU4lAqGycr/XrFlTRERSU1NPy6tXry4iIgcPHgzmjxv9SrpFI8HChQudiPzir7vvvjvgx2/evNnNnz//1K/PPvvMM2blypVu+PDhrkKFCk5EXOvWrd3zzz/vDh48WKg1N23a1DVo0KBQHwvEMiv3+0033eRExG3evPm0fMqUKU5E3NKlS4OeK5rxj51FZN++ffLZZ5958oceekiys7Nl/Pjx0qBBg0KdYJo9e7aMHj1a1q5dKxUrVpQBAwbIkCFDpHXr1oVe7+rVq6VNmzby8MMPnzp+CyA4Vu73d999V3r37i1//OMfZcyYMSIiUlBQIF26dJGMjAzZuXOnJCUlhTxvtKHEfsGll14q+/btK9K/4L/99ttly5YtMnjwYOnTp48kJycXeV2jRo2SJ598UjIzM6Vx48ZFng9A5N3vzjnp3r27/Oc//5E77rhDzjvvPHn33Xdl/vz5MmnSJBk6dGih544mlNgvCMdf6mPHjoX133AVFBTIOeecI6mpqfLll1+GbV4g1kXi/X706FF56KGHZObMmXLgwAFp3Lix3H///fws/CcoMQCAWZxOBACYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGBWQrADu8f1Lc51AEGbXzCrpJcQ1bjXESmCudfZiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwK+j3iQEAvOKrVPZkbf69Vx3buWymmo/79QA19y37uvALixHsxAAAZlFiAACzKDEAgFmUGADALEoMAGAWpxMBoAjWjWnoyeZUm6+OLZACNX/pH9vVPLtHij5PTk6Qq4t+7MQAAGZRYgAAsygxAIBZlBgAwCxKDABgFqcTz6Kc3u3V/D/PTVDzC8aNVPPa7+5Q8/zvtxVuYQDOaPuDHdX8qrZfFXnuL7bUVfPGBRlFnjvasRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZxOjAB5zq/mX4x6Vs3/dmsrNd98rJqaHxxQ3pPlb/0hyNUBEBG57FdfqvmTNT9TUn1/sDC3rJo3GXtUzf3Hjwe1tljGTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmcTrxLCq34ZCat5p5l5qvu+l5Nb+/ymr9AlX0uE/KwDMtDcB/7R/cQc3/UHmymscpe4FEX7w69v2D56u5P2NDcIuDBzsxAIBZlBgAwCxKDABgFiUGADCLEgMAmMXpxLPIv3a9mje4N8AH3FR8awGgK7szX80vLq0/x7BAyfb59bGLZrdV81qyLKi1wYudGADALEoMAGAWJQYAMIsSAwCYVeSDHT++1ULNzxnrzdwX3xX1cgBQrA42TizyHL/d1lPNa43lAEe4sRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWUU+nZjRcbqad6491JMlf1HUq8WWQC/WA1B0hwbpL7+c+/t/BPiIpKDn3j2ugZony/6g50Bw2IkBAMyixAAAZlFiAACzKDEAgFmUGADArCKfTsxzfjUve/d2T+Z/t6hXiy2B/tsGygEE7+DVOWqeFp8c0jxN3rzTkzV8d0Wh1lRUrtP5nmxH5xR1bJ1x+nFxl3cynEsqduzEAABmUWIAALMoMQCAWZQYAMAsSgwAYFaRTycG8vd673iyG/55tzq28eNb1NyftSesawIQe3b+oaOar+38rJoXSEFI8zeeuNeTFff54d336H+mR+98xZNdkXJYHdvW6Z+Prb19mp0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzCry6cTru/dX8/qvbvNk3974jDp24AXXqPnep9qreco7K4NcXWSJT62u5j++UDXAR3xZfIsBYsSYO14JyzwXPKWf5qv9w1dhmT8U1a/7Uc0DnUTUHDs3L1zLKVHsxAAAZlFiAACzKDEAgFmUGADArCIf7PBnbFDzpa8qj0V54FN17PT6H+iT60+FkQtuvl3Nzxmrj3dffKf/xlmW3fFcNV95YYA/qMQX32KAKHRoUAdP1iV5eYDRpUKau+p3+kGIguPHQ5onFJtntFbzdU1e0tcSwtwfX/m0mvd8ZJSal2p1SM1Pfl1Jzauvzvdkye9+HtTaQsFODABgFiUGADCLEgMAmEWJAQDMosQAAGb5nHMumIHd4/oW2yI2TLpQzUtX1k/9dK67Sc2fq/VZ0Ndstnhw0GNFRFJWpaj58cr6f77vbn8upPk1iT79dGKeC+2Ve9qjwQKdKrVgfsGskl5CVCvOez1c4ps3VvN733vLk3UufVIdGyc+NS+QoD4l/qK/7j1fzUdXW1PkuUWKd+3hmnthbmlP9mTD5iHNEcy9zk4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZhX52YnhkD5sVUjjt59bV827tvpN0HP0Gq1f8/wyP6j5jV32qPnhAv0E5dqT3v+0leP1Z699mqv/eeIDPAnt4mT9hXiV40J7FhxgVc455dX84tLe+zHw8wT1r+ELQnoCoS7QKcRwzP1/im/tgeaefLiemq89VkvNt/WppqT6566iYCcGADCLEgMAmEWJAQDMosQAAGZRYgAAsyLidGKo8r/fpubJAXLNN3MC5FJHzUc/qT9PLvGw/nVAtW+8bzU9XFf/z11j/DJ9MQG8v+w8NZ98zichzQNYta1v0Z8RWJyy/Llq3vsb/a30gcxu9bKap8Unh7ymn2s2c6San/OJ/mzWlG93qHn+jp0BrhD+k4gadmIAALMoMQCAWZQYAMAsSgwAYBYlBgAwy+TpxJLQ4N4VRZ6j6OeJgNhyaFAHNV/c7YkAH5EU9Ny3beuq5qv+3VTNG0zTn5+q8eXrJ/wqbwntjeprNlZX87SU7KDneOlwfTVv/NhmNffv3avm3vPWkYGdGADALEoMAGAWJQYAMIsSAwCYRYkBAMzidKJBa19rpubdr62s5hV26aeNgEjX/d7P1DyUZwf+kK8/x3DXQw3UvN5/lqu5ft4wPOLL62+qLhN3Qs3jxBdgJu++ZML069SRtfeG9szWSMVODABgFiUGADCLEgMAmEWJAQDM4mCHQWkL96v5nAdnqXnL343wZOe+fVgdW7Amo/ALA8JsdLU1al4gBUHP0e2j36l5+n9WFWZJxeJId/1RVy1LzVPzAimt5pMP1/Nk9V7dqo6N1MdIhYqdGADALEoMAGAWJQYAMIsSAwCYRYkBAMzidGIUyXP6g3G+un28J+uw/x51bI01YVwQUERTj9RR81vKbwt6jvpvBn+SsaSUmb1SzbvWv0/NS3fZp+b7N3sfPddoR9Ff6BvJ2IkBAMyixAAAZlFiAACzKDEAgFmUGADALE4nxqhyV+3Wf8N7kBEoMe9e30HNH7v3GjUv832iJ6u9+HN1rCv8ss6amk8EeHHlE3qsvxY3urETAwCYRYkBAMyixAAAZlFiAACzKDEAgFmcTjSoYMP3at583p1qvvaq5z3ZJy3eUMf2lnaFXxgQZv4Nm9U8fZieayycQkThsRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZxONMjlnVTzhtPz1bzDd/cEPXcNCfCsNgCIQOzEAABmUWIAALMoMQCAWZQYAMAsSgwAYBanE6NI3OLVal5j8VleCACcJezEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzPI551xJLwIAgMJgJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEosgDFjxojP55MWLVqU9FJOM3/+fLn44oslJSVFKlWqJH369JGtW7eW9LIA07jf7eKlmIrt27dL48aNxefzSb169eS7774r6SWJiMj7778v119/vbRp00YGDRokR44ckfHjx0tSUpKsXr1aqlWrVtJLBMzhfreNElPcdNNNsnfvXvH7/bJv374i/aXOzMyUBg0aSGJiYpHX1bx5czl58qSsXbtWSpUqJSIiX3/9tbRp00buueceefLJJ4t8DSDWcL/bxrcTf2bJkiXy1ltvydNPPx2W+caOHSu1atWSUaNGybp16wo9z4EDByQjI0N69+596i+0iMh5550nTZs2lTfeeCMcywViCve7fZTYT/j9fhk5cqQMGTJEWrZsGZY5R44cKVdeeaVMnDhRmjVrJp06dZKXX35Zjh49GtI8J06cEBGR5ORkz++lpKTIzp07Zffu3WFZMxALuN+jAyX2Ey+88IJs27ZNHn300bDN2bZtW/nXv/4lu3btkkmTJonf75fBgwdLWlqaDBkyRJYvXx7UPKmpqVKxYkVZunTpafn+/fslIyNDRER27NgRtnUD0Y77PUo4OOec27dvn6tcubIbN27cqaxLly6uefPmYb9WRkaGGzVqlEtNTXUi4po1a+ZefPHFM37c/fff70TEPfDAA27Dhg3uiy++cJdffrlLTEx0IuI+/fTTsK8ViEbc79GDEvuv4cOHu4YNG7oTJ06cyoL9S52dne127dp16teePXuCuuamTZtchw4dnIi4884774zjT5w44QYPHuzi4uKciDgRcT169HDDhw93IuJWr14d1HWBWMf9Hj0oMefchg0bXFxcnHvmmWfc999/f+pX+/btXXp6uvv+++/d/v37A3786NGjT/0lExFXt27dgGNzc3PdjBkzXLdu3ZzP53PJyclu4MCBbunSpUGvd/fu3W7JkiVu/fr1zjnn+vfv7+Li4lx2dnbQcwCxivs9unDEXkQWLVokl1122S+OufvuuwOeYNqyZYts2bLl1P9OTk6WTp06nTbm888/l6lTp8rrr78uhw8fltatW8uQIUNkwIABUrFixUKv3e/3S506daRevXqybNmyQs8DxAru9+iSUNILiAQtWrSQd955x5M/9NBDkp2dLePHj5cGDRoE/Pj69etL/fr11d+bPXu2jB49WtauXSsVK1aUm2++WYYMGSKtW7cOy9rHjRsnu3btkmeffTYs8wHRjvs9urAT+wWXXnppkf/x4+233y5btmyRwYMHS58+fdQjs8GaPn26zJ49Wzp37ixly5aVBQsWyJtvvilDhgyRF198sdDzAuB+t4qdWDF79tlnpUyZMmGZKz09XQ4cOCCPPvqo5ObmSuPGjeWFF16QoUOHhmV+AEXD/X72sRMDAJjFP3YGAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwK+gndnSP61uc6wCCNr9gVkkvIapxryNSBHOvsxMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmJZT0AkpKXJkyat5p+X41/2DspWru8+vzl5u5wpPtGdFRHXv4ghNq3ujWL/XJAQAiwk4MAGAYJQYAMIsSAwCYRYkBAMyixAAAZsXu6cRyZdV8aKVP1DznvlJq/vnvLwj6mjcNm6/mdUvtU/NXy7VQ84Ls7KCvCaB47bnTe+p49Z8mqGOXHNfnePTXt6p53GdrCrmq2MFODABgFiUGADCLEgMAmEWJAQDMosQAAGbF7OnEnTc0UPMqcclqPuvjTmpef+FyNT9880WebFRl/cTS5vxcNX+1wVVqLmsy9BzAWZe2YI8nW3pvgTq2U5I+x6aB+unn9M8KvayYwU4MAGAWJQYAMIsSAwCYRYkBAMyixAAAZsXs6cT4Ey6k8Q3HfKfm+hkkkYQQ5m+QoJ+I3HthBTWvsiboqQEUM//6TZ5s3pHz1LHtqq0p5tXEHnZiAACzKDEAgFmUGADALEoMAGBWzB7sOHqOns8+Vkn/jby8kOYv89ZKbzg+pCkkZa8/tA8AUGzyurVV818/N9eTXVvme3VsnOiHuJ7r9i89T2mj5gU5OWoei9iJAQDMosQAAGZRYgAAsygxAIBZlBgAwKyYPZ14Ii1fzd/eq59Acu5Yka/57KH6aj6y4hY1397Vp+aN3i3yUgAEcOLqC9X8yeefV/NWpeKVtHRI1+yRrH9+ufcB/fFVdf+sv4w3FrETAwCYRYkBAMyixAAAZlFiAACzKDEAgFkxcToxrlUTT1b/3Cx1bNVS+imhQ/6iP8fwxcxOaj7yIv10oq/SySJfE4h1vsRSar7+mfPVfPE1/1TztHj9uYdNZt7pyerPOaGO/fC1l9Q8kPQu+jMY9dljEzsxAIBZlBgAwCxKDABgFiUGADCLEgMAmBUTpxOPNKnoyZY0fU0de/E3fdW8vF8/QRiXkqJfNL2eJ1rVfqo+NsD/DdWrHAkwHkCwtt97gZpv6PlsgI/QTyE2XTREzRuOWuXJfIn6PR0n+vNQC8QFWAvOhJ0YAMAsSgwAYBYlBgAwixIDAJhFiQEAzIqJ04k7uwf/3MPpzV5V8yc/76bmNZMOqPmNFeZ7siSffuopkFnN9bUMlotDmgeIZX8b/K+QxvfI+JWaN7x1rZq7Au3zi/6pNdRTiGu/qqevRXaHNE80YycGADCLEgMAmEWJAQDMosQAAGbFxMGOc2d5f5j6QNu26tixqV+q+bM1l4V41dAOcWhmHG5d5DmAWFcxLkfNAz0CalrjGWrebcZv1DxpWTlPVvutberY6zdcp+Zz0ueqeaUMfY34/9iJAQDMosQAAGZRYgAAsygxAIBZlBgAwCyfcy6o56B0j9NfFmmV63CemifsP6rmO69I1eeJ1+ev/d5OT5ZxX3V17PfXT1bzrhk91bz0YP3/svxtP+qLiTLzC2aV9BKiWrTd6/Gp+n237q/11PzVHvr92CEp+MfXnXB5ap7o0z9hxAXYT7x3rJKa/+NvN6t5xX8tD2J1dgRzr7MTAwCYRYkBAMyixAAAZlFiAACzKDEAgFkx8exEjW/512oe6PxR6obNIc2fr2R136umjj18Xa6af9L0XTW/6Pmb1LzytcGsDIgt/qw9ap4+XM//1uEWNd/8W/1rfue8zzd8sZP+QtvOpU+qeagqfXtEX0tYZreFnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMitnTiSUhad4qNW+7RH9j7PouL6v5tXW+U/MVKRXVvCBHf7MtAK9AJ5cbhvBYwn/Ena/mbywvreYTai9R88ee0J+RWGV1dD0jsSjYiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsTidGgArlQjs9+Nm+BmoelxMbb3YGIp0vUf/U+vvU+Wr+Q36Bmld/e72aB/+O6ejHTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmcToxAhxdU0X/jTZndx0AwuPwr1qrecPEZWr+7MFGau7ffyBsa4pW7MQAAGZRYgAAsygxAIBZlBgAwCwOdkSAamv0R86sOuHU/PmGb6j50KvuUfNAL+MEUDz298xV8zjxqfmzn3VT83T5PGxrilbsxAAAZlFiAACzKDEAgFmUGADALEoMAGAWpxMjwOEG8Wp+YZJ+kkkkWU2TRu3Sh88rxKIAFNqNTb9S8wLRTxwnb+dTcWGxEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZHImJALX+fVj/jbtDm+e9Ju+o+XWd7vBkvqVrQpscgKrgEu8LMB+oOlEd+8T+89T83Klb1Ty/0KuKHezEAABmUWIAALMoMQCAWZQYAMAsSgwAYBanEyOAb+1mNb/4m75q/lmrWWq+8kSimifuPOjJOPUEhIdL8D7jNMmn34szX+6q5jV2LAvrmmIJOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYxenECFBw/LiaH38/Vc37l+uu5j9mV1Tz8t/rpx8BFN3eu3M92dAfL1XHpj3/hZrr73tGMNiJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxOJ0aw6s/rz1M7/Lw+vrzsL8bVANAsueBlT3bVqN+pY8vlrSju5cQcdmIAALMoMQCAWZQYAMAsSgwAYBYHOwCgCG6s3cGTlRMOcJwt7MQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZPuecK+lFAABQGOzEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQUwZswY8fl80qJFi5Jeymnmz58vF198saSkpEilSpWkT58+snXr1pJeFmBapN7vIiILFiyQyy+/XCpUqCDlypWTtm3bysyZM0t6WRGDl2Iqtm/fLo0bNxafzyf16tWT7777rqSXJCIi77//vlx//fXSpk0bGTRokBw5ckTGjx8vSUlJsnr1aqlWrVpJLxEwJ1LvdxGRqVOnyuDBg6V79+7Ss2dPiY+Pl/Xr10utWrVk1KhRJb28iECJKW666SbZu3ev+P1+2bdvX5H+UmdmZkqDBg0kMTGxyOtq3ry5nDx5UtauXSulSpUSEZGvv/5a2rRpI/fcc488+eSTRb4GEGsi9X7funWrNGvWTO644w4ZP358keeLVnw78WeWLFkib731ljz99NNhmW/s2LGnvmpat25doec5cOCAZGRkSO/evU8VmIjIeeedJ02bNpU33ngjHMsFYkqk3u8iIi+88IL4/X555JFHRETk6NGjwp7DixL7Cb/fLyNHjpQhQ4ZIy5YtwzLnyJEj5corr5SJEydKs2bNpFOnTvLyyy/L0aNHQ5rnxIkTIiKSnJzs+b2UlBTZuXOn7N69OyxrBmJBJN/vIv/3s7AmTZrIhx9+KLVr15Zy5cpJlSpV5OGHH5aCgoKwrDcqOJzy3HPPuQoVKrg9e/Y455zr0qWLa968eVjmPnz4sJs0aZJr3769ExFXtmxZN3jwYLds2bKgPt7v97uKFSu6rl27npbv27fPlSlTxomI++KLL8KyViAWRPL97pxz5cuXd5UqVXJJSUnu4Ycfdm+99ZYbMGCAExH3wAMPhGWd0YAS+699+/a5ypUru3Hjxp3KwvmX+qcyMjLcqFGjXGpqqhMR16xZM/fiiy+e8ePuv//+U3+BN2zY4L744gt3+eWXu8TERCci7tNPPw37WoFoZOF+j4uLcyLixo4de1p+5ZVXuuTkZHfkyJGwr9UiSuy/hg8f7ho2bOhOnDhxKgv2L3V2drbbtWvXqV//+8ruTDZt2uQ6dOjgRMSdd955Zxx/4sQJN3jw4FN/uUXE9ejRww0fPtyJiFu9enVQ1wVinYX7/X/fYdm2bdtp+auvvupExC1evDio60Y7fiYmIhs3bpTJkyfLXXfdJTt37pStW7fK1q1b5fjx45KXlydbt26VAwcOBPz4cePGSVpa2qlfF154YcCxx48fl9dee026d+8ujRo1kjVr1sjAgQNlwoQJZ1xnqVKl5KWXXpKdO3fKkiVLZP369fLxxx/L4cOHJS4uTho2bFioPz8QS6zc7zVr1hQRkdTU1NPy6tWri4jIwYMHg/njRr+SbtFIsHDhwlM7m0C/7r777oAfv3nzZjd//vxTvz777DPPmJUrV7rhw4e7ChUqOBFxrVu3ds8//7w7ePBgkdaen5/v0tLSXIcOHYo0DxArrNzvN910kxMRt3nz5tPyKVOmOBFxS5cuDXquaJZwdiszMrVo0ULeeecdT/7QQw9Jdna2jB8/Xho0aBDw4+vXry/169dXf2/27NkyevRoWbt2rVSsWFFuvvlmGTJkiLRu3Tosax83bpzs2rVLnn322bDMB0Q7K/d7v3795I033pApU6bImDFjRESkoKBApk6dKpUrV5a2bduGPGc04h87/4JLL720yP/48fbbb5ctW7bI4MGDpU+fPuoR+WBNnz5dZs+eLZ07d5ayZcvKggUL5M0335QhQ4bIiy++WOh5AUTe/e7c/2vv3sOjqu88jn9nAsEkFAIaCNcEEhIIyCVWlIuKWizi9gEttlxkVRJouKmtUNgVZbfUliqsIqJQ6rWgoLDiUrU21Cp3KQgslyDGAC13SBACxIRMzv7RLY/2fMfOJDPJfCfv1/PwRz+cnPn1aU4+/MqX33Fk4MCB8sEHH8jYsWOlR48esmrVKsnPz5dFixbJuHHjqn3vaEKJfYNQfFNfuHBBEhISQrKeLVu2yNSpU2XXrl1SVlYmmZmZMn78eBk3bpx4PJ6QfAZQX0Xa8y7yt3/gPGPGDFm+fLmUlJRIZmamTJs2TUaNGhWyz7COEgMAmMV0IgDALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzAj52aqD37nCuAwhYftWbdb2EqMazjkgRyLPOTgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWQG/TwwAEB4xV12p5r7lcWp+9sW2at506eaQrckKdmIAALMoMQCAWZQYAMAsSgwAYBYlBgAwi+lEAKgtHo8aH8zLVPNdnZ9V8/QBP1LzpkurtyzL2IkBAMyixAAAZlFiAACzKDEAgFmUGADALKYTAaCWxHTppOa7xutTiOVOpZq3e0efcqyP2IkBAMyixAAAZlFiAACzKDEAgFmUGADALKYTw6R4bB9X1j/vz+q1c5O3qPmeSxVqfu8TP1HzFs9tDHB1AMKpQYcUNb84rzyo+1z73ENq3nYVz/rfsRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWfViOjHmyuau7NNHM9RrnRhHzdNW6JOC3o+2q3lJ9ypX5m8K0Z+uDWPVvMuoAjUvfi6o2wMIk5MDWqv55qwFau5vEjllxQk191VvWVGJnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMiqrpxJikJDUfunaPK8tpsiaoe3e+MFHNO34U1G1CIjG2TM2La3kdAERimjVzZbc8sEm9ttLPXOHoefp5qMn7OSPxn2EnBgAwixIDAJhFiQEAzKLEAABmRdVgx8kh6Wqe0+T9Gt+75Z/dx0h9k+T1Hnd4V42XISIiiQ0u+vkd/kwC1LYj93VxZatbPKtee96pVPPkpxjgqC5+6gEAzKLEAABmUWIAALMoMQCAWZQYAMAsk9OJMZ06qvl/Tn+pxvc+V/WlmseWBvcauiaFpTVeiz/fT9yq5tukd9g+E6jvztzbR80fGb/UfW2VfjRc77f046U6ycfVX1g9x04MAGAWJQYAMIsSAwCYRYkBAMyixAAAZpmcTrzUuqmaD4rzd6Zg4D4ud7/gTkQk9qNdau7U+BMBRJKYrplqnjzmgJp/P+GMK5t56nr12k4PhHcKseqGXq7s5Lfj1Gtb/3qnfo8LF0K6pnBjJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzTE4nhtPAOP3Ms3c36G92/n1hdzW/VNYwZGsCUHsKHv6Wmhemv67muyouubKPJ12jXuuVHdVeVyAKx7j3JYW36W+ZTs/MU/OMvC0hXVO4sRMDAJhFiQEAzKLEAABmUWIAALMoMQCAWSanE2M/P6nmGR/kqPn+W16o8Wc+1Uo/88xfDiCy+XtD/Jpbn/bzFfFq+sPXHnJlqes3VW9RATo6pa+a7xg4V0kbqddmZhxRc8cbo3+oo09oi1O3J8iyEwMAmEWJAQDMosQAAGZRYgAAsyJisMPbM0vNq3bsVfPKw/pfSHa6/4Sad586yZWVtfKp16Z0Oa7mP07NV/PsRvqQSasY/S+BAUSGVr/Vn93UBvqzm/mhPjiW9oh7iKNBhxT12or2zdW84d7Dan5obCc1/9l9S9S8sUcf4tAsSFuu5rM3fVfN/1jQWc07P6y/LNRXXBLwWmqCnRgAwCxKDABgFiUGADCLEgMAmEWJAQDMiojpRH9TiMFyKivVvO0vN9b43gskQ/+N64ep8bHp7hflbb92aY3XAeAbKEcmnR15rXrpW22f8XMT/cdih+TTat56s/slmve3eFu9tnPDC2q++cskNb8j/n01DwV/U5gL265T8+LWf1DzET0fUPMGf2Q6EQCAb0SJAQDMosQAAGZRYgAAsygxAIBZETGdaNrm/1Xj+Devd4f6kJRfMR79zxgxUrcvoQMiVdUN3V3Zhl895+fq4H78/b6zPnEYjIeP36Tmc5O31PjeIiLnqr50Zb3XTVCvbd9Cnx48uratmqesPqvmDbZvC3B14cFODABgFiUGADCLEgMAmEWJAQDMosQAAGYxnRgm5zrU/M8HPqdKz8VT43sDlnnj9XP/bn/uTzW+964K97mnIiI/WP6Qmqe/op+pqHEO6W+lT5/3IzUvHLxIzbUpRBGRb6/4ifveP94c4Or+pr38Rc0jdSaanRgAwCxKDABgFiUGADCLEgMAmEWJAQDMYjoxTNq/p5wzNr721wFEoyN5PdV8cuL6gO/Rb+cP1PzKH+mTfx3+uknNfQF/ooj3iivU3N8Uoj99XnpYzdMf09cYzdiJAQDMosQAAGZRYgAAsygxAIBZlBgAwCymEwFErCPT+6r5uolz1DzG4z5TsdvmUeq17XOOqnnlmTMBri54B6dn+/mdjWqaXxan5h1m71Rz/bTV6MZODABgFiUGADCLEgMAmEWJAQDMYrDDoCs8+kE3MVddqea+08XhXA5QYxWDrlXz23+gH6PUxKsf31RQcdGVpTzwhXptOAc4/Gna+2RQ1z/+0/vUPP7ixyFYTXRgJwYAMIsSAwCYRYkBAMyixAAAZlFiAACzmE40qLlXn070pbfRv4DpRES4I/eVq/maltv0633uKUQRkftnTHFlTQ9vrv7CQqzJzxPU/Hu7B6h5/PmtYVxNdGAnBgAwixIDAJhFiQEAzKLEAABmUWIAALOYTgwT71n39NSOikr12p6xwf3PcNHR89M99MmnpK3u+zuV+lqAuuD5XP/eLe+vf5/e/chUNU9cop+1GCk8G/WXWerzxggEOzEAgFmUGADALEoMAGAWJQYAMIsSAwCY5XEcx8+s29cN9N4d7rVEvcytDdX8qVbhfUvr7Xff78r8TUlZkF/1Zl0vIarxrCNSBPKssxMDAJhFiQEAzKLEAABmUWIAALMoMQCAWZydWIve3d9VzYOdTix39PPkvr15jJq337bPlQU0kgoAEY6dGADALEoMAGAWJQYAMIsSAwCYRYkBAMxiOrEWZfzc/bZnEZER7Qeq+b+3eVfN71o7Xs073fuJmjOJCCBasRMDAJhFiQEAzKLEAABmUWIAALMY7KhFvr371fxsf/36aXKdmncSfYADAOobdmIAALMoMQCAWZQYAMAsSgwAYBYlBgAwixIDAJhFiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsj+M4vDMRAGASOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDEAgFmUGADALEoMAGAWJQYAMIsSAwCYRYkBAMyixAAAZlFiAACzKDE/Hn/8cfF4PNKtW7e6XsplAwYMEI/Ho/5q2LBhXS8PMCsSn3cRkfz8fOnfv7/Ex8dLs2bNZNiwYXLw4MG6XlZE4c3OisOHD0tmZqZ4PB5JTU2V3bt31/WSRORv39AnTpz4WnbhwgXJy8uTwYMHyzvvvFNHKwPsitTn/Xe/+50MGTJEsrOzZfTo0XLu3DmZN2+eNGrUSLZv3y5JSUl1vcSIQIkphg8fLqdOnRKfzyenT5+u0Tf1vn37JC0tLWw7pSVLlsjo0aNl6dKlMnLkyLB8BhDNIvV579q1q1RUVMiePXskNjZWRER27twp2dnZ8tBDD8ncuXNr/BnRgP878R+sXbtWVqxYIU8//XRI7jd79mxp06aNTJkyRQoKCkJyz6967bXXJCEhQYYMGRLyewPRLlKf95KSEtm7d6/ceeedlwtMRKRHjx7SpUsXWbZsWSiWGxUosa/w+XwyefJkyc3Nlauvvjok95w8ebIMGjRInn/+ecnKypJ+/frJiy++KOfPn6/xvU+dOiX5+fkydOhQSUhICMFqgfojkp/38vJyERGJi4tz/V58fLwcPXpUjh8/HpI1W0eJfcXChQvl0KFDMmvWrJDd85prrpFXX31Vjh07JosWLRKfzyc5OTnSqlUryc3NlU2bNlX73suXL5fKykoZNWpUyNYL1BeR/Ly3bNlSEhMTZcOGDV/Li4uLZe/evSIicuTIkZCt2zQHjuM4zunTp53mzZs7c+bMuZzddNNNTteuXUP+WXv37nWmTJnitGzZ0hERJysry1m8eHHQ9+nTp4+TlJTkXLp0KeRrBKKZhed92rRpjog406dPd/bv3+9s3brVueWWW5yGDRs6IuKsW7cu5Gu1iBL7f3l5eU56erpTXl5+OQv0m7q0tNQ5duzY5V8nT54M6DMLCwudPn36OCLi9OjRI6j1fv75546IOJMmTQrq6wDYeN7Ly8udnJwcx+v1OiLiiIhz2223OXl5eY6IONu3bw/oc6MdJeY4zv79+x2v1+s888wzzoEDBy7/uu6665yMjAznwIEDTnFxsd+vnzlz5uVvMhFxUlJS/F5bVlbmLF261PnOd77jeDweJy4uzrnnnnucDRs2BLXmWbNmOSLibNq0KaivA+o7a8/78ePHnbVr1zqffvqp4ziOM2LECMfr9TqlpaUB3yOaMWIvIh9++KHcfPPN33jNgw8+6HeCqaioSIqKii7/57i4OOnXr9/XrtmyZYu89NJL8vrrr8vZs2elV69ekpubKyNHjpTExMSg15yVlSUVFRVSWFgY9NcC9ZnF5/3vfD6ftGvXTlJTU2Xjxo3Vvk80aVDXC4gE3bp1k7feesuVz5gxQ0pLS2XevHmSlpbm9+s7duwoHTt2VH9v5cqVMnPmTNmzZ48kJibKqFGjJDc3V3r16lXt9W7fvl0KCgrk0UcfrfY9gPrK2vP+VXPmzJFjx47J/PnzQ3K/aMBO7BsMGDCgxv/4ccyYMVJUVCQ5OTkybNgwdWQ2WFOmTJG5c+fKvn37JDMzs8b3AxB5z/uSJUtk5cqVcuONN0rjxo1lzZo18sYbb0hubq4sXry42veNNuzEwmz+/Pkh/TdcVVVVsmzZMsnOzqbAgAgTyuc9IyNDSkpKZNasWVJWViaZmZmycOFCGTduXEjuHy3YiQEAzOIfOwMAzKLEAABmUWIAALMoMQCAWZQYAMAsSgwAYBYlBgAwK+B/7DzQe3c41wEELL/qzbpeQlTjWUekCORZZycGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCrQV0vAAAsiLnqSjUveDLVlV2Tfki99tQTHdU87r1P1NyprAxscfUYOzEAgFmUGADALEoMAGAWJQYAMIsSAwCYxXRiPVB++7Wu7C8jfOq1aYsdNfeu2x7SNQGRKiYpSc0LZnVQ88LbFgZ+cz+XZj85Sc2Tn94Y+L1FRHpf7Yq+6NxYvbRJUZmae9fvCO4z6xg7MQCAWZQYAMAsSgwAYBYlBgAwixIDAJhlcjrR06iRmjvl5bW8ksji7d5Zzf99/suubMLH96jXxh4uUXNOcEO08SYkqHmTVfrkbmFqEFOIQXo47w01X/p0WzWvuqGXmj/5qnuNV8c2VK/9S+VFNZ/Q5Tb9My/q19c1dmIAALMoMQCAWZQYAMAsSgwAYFZED3Z4r7hCzQdvO6bmLz07WM2Tnt8UsjVFBG+MGh98VP8L3F8U3eHK0kbpx0gxwIFo4+9llolvV6n5b1PX1PgzT/r0IYjXz3VX82e33qzmmT31o6F+8eoiNfc3xKFp3yBezQ9M76HmKY9F5s9RdmIAALMoMQCAWZQYAMAsSgwAYBYlBgAwK6KnEz0d26v5hET9RXELm3nCuZyIEdOsqZrv7vuKmvefOsGVNZKDoVwSELEO/6aFmq9OXRqS+y88m+LKfrPge+q1LRboP7s63Krfu/crO9W8Z2z4fnRXpNo6vo+dGADALEoMAGAWJQYAMIsSAwCYRYkBAMyK6OnE0s7N6noJIdUgpZ2a7/+lfrZbhwX6ff7aVz/z7EzVajVvvuGIK+OMRFjmaaD/6Ir/wP0z46MOL/i5i342qz9zSjLVfO33uriy5JM71GuPT+qr5v899Qk193e+YSiUORVq3uL92LB9ZjiwEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZET2dOPzn76r5svNJap6y+DM194VsRTVTMEtf97YbnlXzEf81Tr9R3y/UeOie0WqecKjon64NsOTYxN5q/kma9iwFN4W4+mITNdemEEVEytLdz3XO+xvUa4c3Xu/nU8M3haid7Sgi8uaUQWre9N3NYVtLOLATAwCYRYkBAMyixAAAZlFiAACzKDEAgFkRPZ34WVlLNd87qauae07pb0GtC6XDr3dln96qH4bY/ddT1Lz9Vv0tsI9326/mD+bfo+YZwnQiostjE5eE7d4VToyalzzfUM3zr3Y/13Geujl/8KTvoit75Yl/Ua9t9u6mcC+nVrATAwCYRYkBAMyixAAAZlFiAACzInqwY/8P9ZdIegojZ4Aj5ir9hZZ5/7HClQ0quFO9tv3P9L9gvXjXdWp+R/wnaj5XP6ULQBC+n3BGz7u/6ecrIuclkj8scB891+zl6Bjg8IedGADALEoMAGAWJQYAMIsSAwCYRYkBAMyK6OnE4j7Jap5YeKCWV+LfpzM6qfmob+W7smvTl6nXFn3WXM3bxPh7OZ1+/M2Q2WvU/I0J2X7u41ayQ39x56Xm+qtFU9NOqPmPU93//UVEFnTKCHgtgD/Tt96l5kNverGWVxJZYn/ZTEkj5+dlOLATAwCYRYkBAMyixAAAZlFiAACzKDEAgFkRPZ2YNXG3mh9bpp9V5lyqCOdyVH1671PzM1Vlrmzkzhz12vMXrlDzfX4mrVZfbKLmrxTqZy0GIy7rCz33c/3RLa3VfOEDd/j5is+CXhPwjzrNPKfmL/+P+/vxviZHQ/KZqy4kqvm/fTLUlWUkn1KvfbvTOyFZS/rqPDXPXO8+V9UJySdGLnZiAACzKDEAgFmUGADALEoMAGAWJQYAMCuipxN/0+4jNR948zg1b/iHreFcjurET1PV/N6jo1xZiyJ9krF1ckv95tv0+MlH7lHz5Df8nbVY+/STFoHQ8H1WpOYrr3Ofzbkyrrt6bdHENDWPKfOoecrze9Q8zec+m/CmTaGZiPQ3iZz1K/3M0srKypB8riXsxAAAZlFiAACzKDEAgFmUGADALEoMAGBWRE8nxnj0jj1wp55n/CGcq9F51+9Q82BmhE7e3lHN37nYWM2bvKdPSVUF8ZlANPKdU85U1DIRSXnsZHD39pOfnNDXlf2kmT5Z7c95p1zNZ7zwr2re5sDGoO4fzdiJAQDMosQAAGZRYgAAsygxAIBZlBgAwKyInk685OjzQFdtianllYRX1V3Fav7oniFqnlxaEM7lAAjCmIk1f1tzQYX+tvo2s5lC/GfYiQEAzKLEAABmUWIAALMoMQCAWRE92HHrhPFq3vztTbW8ktCISUpS8+Ed9LdfLnnhu+FcDoAgFI/to+YTEhfU+N73vj5JzVPF5s+62sRODABgFiUGADCLEgMAmEWJAQDMosQAAGZF9HRi3Ntb6noJIVWZ3lrNJybqx9b8cX4zNXdCtiIAgfrydv3lmhp/L/T1Ofqra5vv5qmuLnZiAACzKDEAgFmUGADALEoMAGAWJQYAMCuipxOjTYOCg2re6+UH1Ty1knPTgEhxY7vPA77W3xSiP6/NnqPmE/aOVfOqnbwY9+/YiQEAzKLEAABmUWIAALMoMQCAWZQYAMAsphNrke+Ls2qeOoMpRKA+a98gXs1P9ElU86SdYVyMMezEAABmUWIAALMoMQCAWZQYAMAsSgwAYBbTiQAQgA2vZav5+Yf/5MoaexoFde9zVV+qeePjvqDuUx+xEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZTCcCQACSn9qo5j2zHnBlhYMXBXXv7Pf0t7tnrNoS1H3qI3ZiAACzKDEAgFmUGADALEoMAGAWgx0AUAMZY//sygaLfkSV33uI+x4IDDsxAIBZlBgAwCxKDABgFiUGADCLEgMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABglsdxHKeuFwEAQHWwEwMAmEWJAQDMosQAAGZRYgAAsygxAIBZlBgAwCxKDABgFiUGADCLEgMAmPV/if0sFxcVtmIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "retrained_misclassified_images, retrained_misclassified_labels, retrained_misclassified_preds = analyze_for_misclassifications(adv_retrain_misclassified_images, adv_retrain_misclassified_labels, adv_retrain_misclassified_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "45ecfee8-6779-470e-b649-5e97623ba464",
      "metadata": {
        "id": "45ecfee8-6779-470e-b649-5e97623ba464"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import tqdm\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "def perform_adv_attack_after_retraining(model, attack, data_loader, device):\n",
        "    adv_data = []\n",
        "    adv_labels = []\n",
        "    device = next(model.parameters()).device\n",
        "\n",
        "    total_batches = len(data_loader)\n",
        "    progress_bar = tqdm.tqdm(total=total_batches, unit='batch', desc='Adversarial Attack')\n",
        "\n",
        "    for x, y in data_loader:\n",
        "        x, y = x.to(device), y.to(device)\n",
        "\n",
        "        # Convert labels to long data type\n",
        "        y = y.long()\n",
        "\n",
        "        x_adv = attack(x, y)\n",
        "        adv_data.append(x_adv.detach())\n",
        "        adv_labels.append(y.detach())\n",
        "\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    progress_bar.close()\n",
        "\n",
        "    adv_data = torch.cat(adv_data)\n",
        "    adv_labels = torch.cat(adv_labels)\n",
        "\n",
        "    # Move data back to CPU if necessary\n",
        "    if device != torch.device('cpu'):\n",
        "        adv_data = adv_data.to('cpu')\n",
        "        adv_labels = adv_labels.to('cpu')\n",
        "\n",
        "    # Create dataset and loader\n",
        "    adv_dataset = TensorDataset(adv_data, adv_labels)\n",
        "    adv_loader = DataLoader(adv_dataset, batch_size=64, shuffle=False)\n",
        "\n",
        "    return adv_dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset_name)\n",
        "\n",
        "filtered_test_loader = DataLoader(filtered_test_set, batch_size=64)\n",
        "\n",
        "# Print the number of batches in the data loaders\n",
        "print(\"Number of batches in the filtered test set:\", len(filtered_test_loader))\n",
        "\n",
        "# Calculate the size of the filtered test set\n",
        "test_set_size = len(filtered_test_loader) * filtered_test_loader.batch_size\n",
        "if len(filtered_test_set) % filtered_test_loader.batch_size != 0:\n",
        "    test_set_size += len(filtered_test_set) % filtered_test_loader.batch_size\n",
        "\n",
        "# Print the sizes of the filtered sets\n",
        "print(\"Number of images in the filtered test set:\", test_set_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axVFHEgrq4gN",
        "outputId": "c1970a44-1647-4328-b917-f39b7c0a977a"
      },
      "id": "axVFHEgrq4gN",
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MNIST\n",
            "Number of batches in the filtered test set: 140\n",
            "Number of images in the filtered test set: 8984\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "39ff4209-d0f0-408f-82e7-bb8fc6f1529e",
      "metadata": {
        "id": "39ff4209-d0f0-408f-82e7-bb8fc6f1529e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edd5b721-50d7-48d7-946b-4625668b0dde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Adversarial Attack: 100%|██████████| 140/140 [01:28<00:00,  1.59batch/s]\n"
          ]
        }
      ],
      "source": [
        "# Adversarial attack on retrained model\n",
        "adv_retrain_loader = perform_adv_attack(model, attack, filtered_test_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "998e2d3b-78ef-495d-bb29-c775be0f5588",
      "metadata": {
        "id": "998e2d3b-78ef-495d-bb29-c775be0f5588"
      },
      "outputs": [],
      "source": [
        "def evaluate_adv_attack_after_retraining(model, data_loader, loss_fn, device):\n",
        "    atack_after_retrained_test_loss = 0\n",
        "    atack_after_retrained_accuracy = 0\n",
        "    num_misclassified = 0\n",
        "    model.eval()\n",
        "    misclassified_images = []\n",
        "    misclassified_labels = []\n",
        "    misclassified_preds = []\n",
        "\n",
        "    images, labels = next(iter(data_loader))\n",
        "    images, labels = images.to(device), labels.to(device)  # Move data to the specified device\n",
        "    num_clean_test_elements = len(images)\n",
        "    print(f\"Number of clean test images: {num_clean_test_elements}\")\n",
        "\n",
        "    # Create dataset\n",
        "    test_ds = TensorDataset(images, labels)\n",
        "    # Create loader\n",
        "    test_loader = DataLoader(test_ds, batch_size=64)\n",
        "    dataset_size = len(test_loader.dataset)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (x, y) in enumerate(test_loader):\n",
        "            x, y = x.to(device), y.to(device)  # Move data to the specified device\n",
        "            logits = model(x)\n",
        "            batch_loss = loss_fn(logits, y)\n",
        "            atack_after_retrained_test_loss += batch_loss.item()\n",
        "            preds = torch.argmax(logits, dim=1)\n",
        "            atack_after_retrained_accuracy += torch.sum(preds == y).item() / len(y)\n",
        "            incorrect_inds = torch.where(preds != y)[0]\n",
        "            num_misclassified += len(incorrect_inds)\n",
        "\n",
        "            # Filter indices to current batch size\n",
        "            incorrect_inds = incorrect_inds[incorrect_inds < len(x)]\n",
        "\n",
        "            if len(incorrect_inds) > 0:\n",
        "                misclassified_images.append(x[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_labels.append(y[incorrect_inds].cpu())  # Move data back to CPU\n",
        "                misclassified_preds.append(preds[incorrect_inds].cpu())  # Move data back to CPU\n",
        "\n",
        "    num_misclassified = min(num_misclassified, dataset_size)\n",
        "    print(f\"Number of misclassified images: {num_misclassified} out of {dataset_size}\")\n",
        "    atack_after_retrained_test_loss /= len(test_loader)\n",
        "    atack_after_retrained_accuracy /= len(test_loader)\n",
        "    print(f\"Adv. Attack Against Retrained Test loss: {atack_after_retrained_test_loss:.4f}, Accuracy: {atack_after_retrained_accuracy:.2%}\")\n",
        "\n",
        "    # Handle empty lists\n",
        "    if len(misclassified_images) == 0:\n",
        "        misclassified_images.append(torch.tensor([]))\n",
        "    if len(misclassified_labels) == 0:\n",
        "        misclassified_labels.append(torch.tensor([]))\n",
        "    if len(misclassified_preds) == 0:\n",
        "        misclassified_preds.append(torch.tensor([]))\n",
        "\n",
        "    return atack_after_retrained_test_loss, atack_after_retrained_accuracy, [torch.cat(misclassified_images)], [torch.cat(misclassified_labels)], [torch.cat(misclassified_preds)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "a37544a3-e216-44e9-8cda-152bda7c539e",
      "metadata": {
        "id": "a37544a3-e216-44e9-8cda-152bda7c539e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c159f430-631c-4a09-fe66-5e3505d5da2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clean test images: 64\n",
            "Number of misclassified images: 0 out of 64\n",
            "Adv. Attack Against Retrained Test loss: 0.0005, Accuracy: 100.00%\n",
            "\n",
            "A few key ways to evaluate whether an adversarial attack on a machine learning model was successful:\n",
            "\n",
            "Drop in Model Accuracy: 20-40% drop in accuracy indicates the attack was effective.\n",
            "\n",
            "High Loss Value: A sharp spike in loss is another sign of a working attack.\n",
            "\n",
            "Misclassified Examplese: Images wrongly labeled indicates a successful targeted attack\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Evaluate attack after retraining\n",
        "retrained_test_loss, retrained_accuracy, misclassified_images, misclassified_labels, misclassified_preds = evaluate_adv_attack_after_retraining(defense_model, filtered_test_loader, loss_fn, device)\n",
        "print(\"\\nA few key ways to evaluate whether an adversarial attack on a machine learning model was successful:\\n\")\n",
        "print(\"Drop in Model Accuracy: 20-40% drop in accuracy indicates the attack was effective.\\n\")\n",
        "print(\"High Loss Value: A sharp spike in loss is another sign of a working attack.\\n\")\n",
        "print(\"Misclassified Examplese: Images wrongly labeled indicates a successful targeted attack\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "5ad629c3-7174-4fc6-82ec-51f470bd8d5e",
      "metadata": {
        "id": "5ad629c3-7174-4fc6-82ec-51f470bd8d5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "98e8253c-0c5d-4458-c415-aaceb940dfe0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number misclassified: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x0 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "retrained_misclassified_images, retrained_misclassified_labels, retrained_misclassified_preds = analyze_for_misclassifications(misclassified_images, misclassified_labels, misclassified_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "d10babee-9534-4187-b9d7-4babdd96b8df",
      "metadata": {
        "id": "d10babee-9534-4187-b9d7-4babdd96b8df"
      },
      "outputs": [],
      "source": [
        "def precision(true_positives, false_positives):\n",
        "    if true_positives + false_positives == 0:\n",
        "        return 0.0\n",
        "    return true_positives / (true_positives + false_positives)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "3e20ecbb-accf-4238-8f7b-5e96678c518f",
      "metadata": {
        "id": "3e20ecbb-accf-4238-8f7b-5e96678c518f"
      },
      "outputs": [],
      "source": [
        "def recall(true_positives, false_negatives):\n",
        "    if true_positives + false_negatives == 0:\n",
        "        return 0.0\n",
        "    return true_positives / (true_positives + false_negatives)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "89773386-b77a-46c8-ad7d-3ae9ba0995cd",
      "metadata": {
        "id": "89773386-b77a-46c8-ad7d-3ae9ba0995cd"
      },
      "outputs": [],
      "source": [
        "def f1_score(precision, recall):\n",
        "    if precision + recall == 0:\n",
        "        return 0.0\n",
        "    return 2 * (precision * recall) / (precision + recall)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "54a1cc1a-f492-40ff-b85b-d43d101f654d",
      "metadata": {
        "id": "54a1cc1a-f492-40ff-b85b-d43d101f654d"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "def auc_score(true_labels, predicted_probabilities):\n",
        "    if len(set(true_labels)) == 2:\n",
        "        return roc_auc_score(true_labels, predicted_probabilities)\n",
        "    else:\n",
        "        return \"ROC AUC score is not defined for a single class.\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "369a998a-4447-46f7-a83e-e00e289e55ec",
      "metadata": {
        "id": "369a998a-4447-46f7-a83e-e00e289e55ec"
      },
      "outputs": [],
      "source": [
        "def print_summary_images(images, labels, preds, stage, attack_name, dataset_name, num_clean_elements, num_adv_elements, num_combined_elements, num_epoch, num_test_images):\n",
        "    # Check for no images\n",
        "    if len(images) == 0:\n",
        "        print(f\"\\n---------------------------------------------------------\")\n",
        "        print(f\"No misclassified images for stage: {stage}\")\n",
        "        print(f\"Attack: {attack_name}\")\n",
        "        print(f\"Dataset: {dataset_name}\")\n",
        "        print(f\"Training Epochs: {num_epoch}\")\n",
        "        if stage == \"Clean\":\n",
        "            print(f\"Trained Clean Images: {num_clean_elements}\")\n",
        "            accuracy = test_acc\n",
        "        elif stage == \"No Defense Attack\":\n",
        "            print(f\"Adversarial Training Images: {num_adv_elements}\")\n",
        "            accuracy = clean_attack_adv_test_accuracy\n",
        "        elif stage == \"w/ Defense Attack\":\n",
        "            print(f\"Retrained Clean and Adversarial Images: {num_combined_elements}\")\n",
        "            accuracy = retrained_accuracy\n",
        "        print(f\"Test Images: {num_test_images}\")\n",
        "        print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "        true_positives = int(accuracy * num_test_images)\n",
        "        false_positives = 0\n",
        "        false_negatives = num_test_images - true_positives\n",
        "\n",
        "        precision_value = precision(true_positives, false_positives)\n",
        "        recall_value = recall(true_positives, false_negatives)\n",
        "        f1_score_value = f1_score(precision_value, recall_value)\n",
        "\n",
        "        # Create the true labels and predicted probabilities based on the variables\n",
        "        true_labels = [1] * true_positives + [0] * false_negatives\n",
        "        predicted_probabilities = [1] * true_positives + [0] * false_negatives\n",
        "        auc = auc_score(true_labels, predicted_probabilities)\n",
        "\n",
        "        print(f\"Precision: {precision_value:.2f}\")\n",
        "        print(f\"Recall: {recall_value:.2f}\")\n",
        "        print(f\"F1-score: {f1_score_value:.2f}\")\n",
        "        if isinstance(auc, float):\n",
        "            print(f\"ROC AUC Score: {auc:.2f}\")\n",
        "        else:\n",
        "            print(auc)\n",
        "        print(f\"---------------------------------------------------------\\n\")\n",
        "    else:\n",
        "        # Count the number of misclassifications for each situation\n",
        "        misclassifications = {}\n",
        "        for label, pred in zip(labels, preds):\n",
        "            if label != pred:\n",
        "                key = f\"{label} -> {pred}\"\n",
        "                misclassifications[key] = misclassifications.get(key, 0) + 1\n",
        "\n",
        "        # Plot images\n",
        "        num_images = len(images)\n",
        "        print(f\"\\n---------------------------------------------------------\")\n",
        "        print(f\"Number of misclassified images for {stage}: {num_images}\")\n",
        "        print(f\"Attack: {attack_name}\")\n",
        "        print(f\"Dataset: {dataset_name}\")\n",
        "        print(f\"Training Epochs: {num_epoch}\")\n",
        "        if stage == \"Clean\":\n",
        "            print(f\"Trained Clean Images: {num_clean_elements}\")\n",
        "            accuracy = test_acc\n",
        "        elif stage == \"No Defense Attack\":\n",
        "            print(f\"Adversarial Training Images: {num_adv_elements}\")\n",
        "            accuracy = clean_attack_adv_test_accuracy\n",
        "        elif stage == \"w/ Defense Attack\":\n",
        "            print(f\"Retrained Clean and Adversarial Images: {num_combined_elements}\")\n",
        "            accuracy = retrained_accuracy\n",
        "        print(f\"Test Images: {num_test_images}\")\n",
        "        print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "        true_positives = int(accuracy * num_test_images)\n",
        "        false_positives = num_images\n",
        "        false_negatives = num_test_images - true_positives - false_positives\n",
        "\n",
        "        precision_value = precision(true_positives, false_positives)\n",
        "        recall_value = recall(true_positives, false_negatives)\n",
        "        f1_score_value = f1_score(precision_value, recall_value)\n",
        "\n",
        "        # Create the true labels and predicted probabilities based on the variables\n",
        "        true_labels = [1] * true_positives + [0] * false_negatives\n",
        "        predicted_probabilities = [1] * true_positives + [0] * false_negatives\n",
        "        auc = auc_score(true_labels, predicted_probabilities)\n",
        "\n",
        "        print(f\"Precision: {precision_value:.2f}\")\n",
        "        print(f\"Recall: {recall_value:.2f}\")\n",
        "        print(f\"F1-score: {f1_score_value:.2f}\")\n",
        "        if isinstance(auc, float):\n",
        "            print(f\"ROC AUC Score: {auc:.2f}\")\n",
        "        else:\n",
        "            print(auc)\n",
        "        print(f\"---------------------------------------------------------\\n\")\n",
        "\n",
        "        fig = plt.figure(figsize=(6, num_images))\n",
        "        rows = math.ceil(num_images / 2)\n",
        "        cols = 2 if num_images > 16 else min(num_images, 2)\n",
        "        for i, (img, label, pred) in enumerate(zip(images, labels, preds)):\n",
        "            # Add subplot\n",
        "            ax = fig.add_subplot(rows, cols, i+1)\n",
        "            # Add empty dim if needed\n",
        "            if img.ndim == 2:\n",
        "                img = img[:,None,:,]\n",
        "            # Permute to HWC format for imshow\n",
        "            img = img.permute(1, 2, 0)\n",
        "            ax.imshow(img)\n",
        "            ax.set_title(f\"{label} -> {pred}\")\n",
        "            ax.axis(\"off\")\n",
        "\n",
        "        # Print the number of misclassifications for each situation\n",
        "        print(\"Misclassifications:\")\n",
        "        for situation, count in misclassifications.items():\n",
        "            print(f\"{situation}: {count}\")\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "496d47bd-2cdf-443e-9958-f43f3b8fc818",
      "metadata": {
        "id": "496d47bd-2cdf-443e-9958-f43f3b8fc818",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72c552b9-0074-4cf9-f15c-5a5915a02936"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clean images in training set: 54210\n",
            "Number of clean batches: 423\n",
            "Number of adversarial images: 27105\n",
            "Number of adversarial batches: 211\n",
            "Number of combined images: 81315\n",
            "Number of combined batches: 635\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of clean images in training set:\", num_clean_images)\n",
        "print(f\"Number of clean batches: {num_clean_batches}\")\n",
        "\n",
        "print(f\"Number of adversarial images: {num_adv_images}\")\n",
        "print(f\"Number of adversarial batches: {num_adv_batches}\")\n",
        "\n",
        "print(f\"Number of combined images: {num_combined_images}\")\n",
        "print(f\"Number of combined batches: {num_combined_batches}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "acc5b456-0852-401a-ac21-5dde331211f4",
      "metadata": {
        "id": "acc5b456-0852-401a-ac21-5dde331211f4"
      },
      "outputs": [],
      "source": [
        "import tabulate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "fa6be136-af9e-4d9b-9ac6-746c9b181f7d",
      "metadata": {
        "id": "fa6be136-af9e-4d9b-9ac6-746c9b181f7d"
      },
      "outputs": [],
      "source": [
        "def summarize_performance(clean_loss, clean_acc, adv_loss, adv_acc, retrain_loss, retrain_acc,\n",
        "                          clean_misclassified_images, adv_misclassified_images, retrained_misclassified_images,\n",
        "                          clean_misclassified_labels, adv_misclassified_labels, retrained_misclassified_labels,\n",
        "                          clean_misclassified_preds,adv_misclassified_preds, retrained_misclassified_preds):\n",
        "\n",
        "    table_data = [[\"Metric\", \"Clean\", \"No Defense Attack\", \"w/ Defense Attack\"],\n",
        "                  [\"Loss\", f'{clean_loss:.3g}', f'{adv_loss:.3g}', f'{retrain_loss:.3g}'],\n",
        "                  [\"Accuracy\", f'{clean_acc:.0%}', f'{adv_acc:.0%}', f'{retrain_acc:.0%}']]\n",
        "\n",
        "    print(tabulate.tabulate(table_data, headers=\"firstrow\", tablefmt=\"grid\"))\n",
        "\n",
        "    print(\"\\nExample Misclassifications:\")\n",
        "    print_summary_images(clean_misclassified_images, clean_misclassified_labels, clean_misclassified_preds, \"Clean\", compounded_attack_name, dataset_name, num_clean_elements, num_adv_elements, num_combined_elements, num_epochs, len(filtered_test_loader))\n",
        "    print_summary_images(adv_misclassified_images, adv_misclassified_labels, adv_misclassified_preds, \"No Defense Attack\",  compounded_attack_name, dataset_name, num_clean_elements, num_adv_elements, num_combined_elements, num_epochs, len(filtered_test_loader))\n",
        "    print_summary_images(retrained_misclassified_images, retrained_misclassified_labels, retrained_misclassified_preds, \"w/ Defense Attack\",  compounded_attack_name, dataset_name, num_clean_elements, num_adv_elements, num_combined_elements, num_epochs, len(filtered_test_loader))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "5e465011-93ef-4447-84b8-838bf0f291fb",
      "metadata": {
        "id": "5e465011-93ef-4447-84b8-838bf0f291fb",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e97dd773-0e77-4942-ed6e-987d7d4cbaf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+---------+---------------------+---------------------+\n",
            "| Metric   | Clean   | No Defense Attack   | w/ Defense Attack   |\n",
            "+==========+=========+=====================+=====================+\n",
            "| Loss     | 0.00102 | 2.83                | 0.000529            |\n",
            "+----------+---------+---------------------+---------------------+\n",
            "| Accuracy | 100%    | 30%                 | 100%                |\n",
            "+----------+---------+---------------------+---------------------+\n",
            "\n",
            "Example Misclassifications:\n",
            "\n",
            "---------------------------------------------------------\n",
            "No misclassified images for stage: Clean\n",
            "Attack: fgsm_cw_attack\n",
            "Dataset: MNIST\n",
            "Training Epochs: 10\n",
            "Trained Clean Images: 54210\n",
            "Test Images: 140\n",
            "Accuracy: 1.00\n",
            "Precision: 1.00\n",
            "Recall: 1.00\n",
            "F1-score: 1.00\n",
            "ROC AUC score is not defined for a single class.\n",
            "---------------------------------------------------------\n",
            "\n",
            "\n",
            "---------------------------------------------------------\n",
            "Number of misclassified images for No Defense Attack: 45\n",
            "Attack: fgsm_cw_attack\n",
            "Dataset: MNIST\n",
            "Training Epochs: 10\n",
            "Adversarial Training Images: 27105\n",
            "Test Images: 140\n",
            "Accuracy: 0.30\n",
            "Precision: 0.48\n",
            "Recall: 0.43\n",
            "F1-score: 0.45\n",
            "ROC AUC Score: 1.00\n",
            "---------------------------------------------------------\n",
            "\n",
            "Misclassifications:\n",
            "0 -> 6: 20\n",
            "0 -> 8: 6\n",
            "0 -> 2: 9\n",
            "0 -> 5: 5\n",
            "0 -> 9: 2\n",
            "0 -> 7: 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x4500 with 45 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "---------------------------------------------------------\n",
            "No misclassified images for stage: w/ Defense Attack\n",
            "Attack: fgsm_cw_attack\n",
            "Dataset: MNIST\n",
            "Training Epochs: 10\n",
            "Retrained Clean and Adversarial Images: 81315\n",
            "Test Images: 140\n",
            "Accuracy: 1.00\n",
            "Precision: 1.00\n",
            "Recall: 1.00\n",
            "F1-score: 1.00\n",
            "ROC AUC score is not defined for a single class.\n",
            "---------------------------------------------------------\n",
            "\n"
          ]
        }
      ],
      "source": [
        "summarize_performance(test_loss, test_acc, clean_attack_test_loss, clean_attack_adv_test_accuracy, retrained_test_loss, retrained_accuracy,\n",
        "                          clean_misclassified_images, adv_misclassified_images, retrained_misclassified_images,\n",
        "                          clean_misclassified_labels, adv_misclassified_labels, retrained_misclassified_labels,\n",
        "                          clean_misclassified_preds,adv_misclassified_preds, retrained_misclassified_preds)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}