[1]DAI_ASSIGNMENT_2_Part_B_FGSM_.ipynb

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      "name": "DAI_ASSIGNMENT_2_Part_B_FGSM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "GPCdYYoLRqPc"
      },
      "source": [
        "You may use any external libraries or GitHub codes. However, the evaluation will test your\n",
        "knowledge of the algorithm and the choice of hyperparameters. Do cite the libraries/codes.\n",
        "\n",
        "AIM: ​Understand and Perform different types of Adversarial Attacks, Detection and Mitigation.\n",
        "Dataset: D-10 Dataset(​CLICK​ for Dataset) - 10 Class Animal Dataset\n",
        "Reference Paper: ​Adversarial Attacks and Defenses in Deep Learning\n",
        "\n",
        "b. Perform both ​targeted and ​untargeted attacks(on testing set) for any ​3 attacks ​among the following.​ [ 30 Marks]\n",
        "\n",
        "i. L-BFGS algorithm\n",
        "Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, et al. Intriguing properties of neural networks. 2013. arXiv:1312.6199.\n",
        "\n",
        "ii. Fast gradient sign method\n",
        "Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. 2014. arXiv:1412.6572.\n",
        "\n",
        "iii. BIM and PGD\n",
        "Kurakin A, Goodfellow I, Bengio S. Adversarial examples in the physical world. 2016. arXiv:1607.02533.\n",
        "\n",
        "iv. Momentum iterative attack\n",
        "Dong Y, Liao F, Pang T, Su H, Zhu J, Hu X, et al. Boosting adversarial attacks with momentum. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition; 2018 Jun 18–23; Salt Lake City, UT, USA; 2018. p. 9185–193.\n",
        "  \n",
        "v. Distributionally adversarial attack\n",
        "Zheng T, Chen C, Ren K. Distributionally adversarial attack. 2018. arXiv:1808.05537.\n",
        "\n",
        "vi. Carlini and Wagner attack\n",
        "Carlini N, Wagner D. Towards evaluating the robustness of neural networks. In: Proceedings of the 2017 IEEE Symposium on Security and Privacy; 2017 May 22–26; San Jose, CA, USA; 2017. p. 39–57.\n",
        "\n",
        "vii. Jacobian-based saliency map approach\n",
        "Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A. The limitations of deep learning in adversarial settings. In: Proceedings of the 2016 IEEE European Symposium on Security and Privacy; 2016 Mar 21–24; Saarbrucken, Germany; 2016. p. 372–87."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXejwgqHQuyH",
        "outputId": "8e8feb39-54e9-4f57-c393-4e58fca334b6"
      },
      "source": [
        "# FGSM - https://www.tensorflow.org/tutorials/generative/adversarial_fgsm\n",
        "# colab ref - https://colab.research.google.com/github/shreehari117/ML-2/blob/master/Sureshbabu_Shree_Hari_aml.ipynb\n",
        "# https://colab.research.google.com/github/andantillon/cleverhans/blob/master/tutorials/future/tf2/notebook_tutorials/mnist_fgsm_tutorial.ipynb\n",
        "\n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive') \n",
        "import os\n",
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2'\n",
        "!ls\n",
        "%cd '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images'\n",
        "!ls\n",
        "!find . -maxdepth 2 -type d\n",
        "!find . -maxdepth 1 -mindepth 1 -type d -exec sh -c 'echo \"{} : $(find \"{}\" -type f | wc -l)\" file\\(s\\)' \\;\n",
        "\n",
        "# Install bleeding edge version of cleverhans\n",
        "# !pip install cleverhans==2.1.0\n",
        "!pip install cleverhans\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd \n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import os\n",
        "import sys\n",
        "import time\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from builtins import range, input\n",
        "\n",
        "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten, AveragePooling2D, Dropout, Conv2D, MaxPool2D, BatchNormalization, GlobalAveragePooling2D\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "from tensorflow.keras.applications import InceptionV3\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from sklearn.metrics import confusion_matrix, roc_curve\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (8, 8)\n",
        "mpl.rcParams['axes.grid'] = False\n",
        "import cv2\n",
        "from glob import glob\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "#Install Cleverhans (version Cleverhans 2.1.0 is most compatable with Python 2.x)\n",
        "\n",
        "import cleverhans\n",
        "\n",
        "print(\"\\nTensorflow Version: \" + tf.__version__)\n",
        "print(\"Cleverhans Version: \" + cleverhans.__version__)\n",
        "print(\"GPU Available: \", tf.test.is_gpu_available())\n",
        "# from cleverhans.utils_keras import KerasModelWrapper\n",
        "# from cleverhans.attacks import FastGradientMethod, MadryEtAl, CarliniWagnerL2\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2\n",
            "Classes.py  Images  processed_images\n",
            "/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images\n",
            "cleverhans  final_model.h5  test  train\n",
            ".\n",
            "./train\n",
            "./train/cane\n",
            "./train/cavallo\n",
            "./train/elefante\n",
            "./train/farfalla\n",
            "./train/gallina\n",
            "./train/gatto\n",
            "./train/mucca\n",
            "./train/pecora\n",
            "./train/ragno\n",
            "./train/scoiattolo\n",
            "./test\n",
            "./test/cane\n",
            "./test/cavallo\n",
            "./test/elefante\n",
            "./test/farfalla\n",
            "./test/gallina\n",
            "./test/gatto\n",
            "./test/mucca\n",
            "./test/pecora\n",
            "./test/ragno\n",
            "./test/scoiattolo\n",
            "./cleverhans\n",
            "./cleverhans/.git\n",
            "./cleverhans/.github\n",
            "./cleverhans/assets\n",
            "./cleverhans/cleverhans\n",
            "./cleverhans/cleverhans_tutorials\n",
            "./cleverhans/defenses\n",
            "./cleverhans/docs\n",
            "./cleverhans/docsource\n",
            "./cleverhans/examples\n",
            "./cleverhans/scripts\n",
            "./cleverhans/tests_tf\n",
            "./cleverhans/tutorials\n",
            "./cleverhans/cleverhans.egg-info\n",
            "./train : 15702 file(s)\n",
            "./test : 10477 file(s)\n",
            "./cleverhans : 424 file(s)\n",
            "Collecting cleverhans\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ac/a0/f0b4386b719f343c4ed3e13cd7792a7a7a4674566ca9b2b34a09b7424220/cleverhans-3.0.1-py3-none-any.whl (198kB)\n",
            "\u001b[K     |████████████████████████████████| 204kB 5.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow-probability in /usr/local/lib/python3.6/dist-packages (from cleverhans) (0.12.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from cleverhans) (3.2.2)\n",
            "Collecting pycodestyle\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/10/5b/88879fb861ab79aef45c7e199cae3ef7af487b5603dcb363517a50602dd7/pycodestyle-2.6.0-py2.py3-none-any.whl (41kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 5.0MB/s \n",
            "\u001b[?25hCollecting mnist~=0.2\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/c4/5db3bfe009f8d71f1d532bbadbd0ec203764bba3a469e4703a889db8e5e0/mnist-0.2.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from cleverhans) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from cleverhans) (1.19.5)\n",
            "Collecting nose\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/d8/dd071918c040f50fa1cf80da16423af51ff8ce4a0f2399b7bf8de45ac3d9/nose-1.3.7-py3-none-any.whl (154kB)\n",
            "\u001b[K     |████████████████████████████████| 163kB 14.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability->cleverhans) (4.4.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability->cleverhans) (1.15.0)\n",
            "Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability->cleverhans) (1.3.0)\n",
            "Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability->cleverhans) (0.3.3)\n",
            "Requirement already satisfied: dm-tree in /usr/local/lib/python3.6/dist-packages (from tensorflow-probability->cleverhans) (0.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->cleverhans) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->cleverhans) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->cleverhans) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->cleverhans) (0.10.0)\n",
            "Installing collected packages: pycodestyle, mnist, nose, cleverhans\n",
            "Successfully installed cleverhans-3.0.1 mnist-0.2.2 nose-1.3.7 pycodestyle-2.6.0\n",
            "\n",
            "Tensorflow Version: 2.4.0\n",
            "Cleverhans Version: 3.0.1-a10e65bb507c677e6b244d2441ff37ca\n",
            "WARNING:tensorflow:From <ipython-input-1-fb2247320e08>:64: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n",
            "GPU Available:  False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOWdGFe6SWQV",
        "outputId": "e29a8c77-4d70-45bc-dc4a-ab050b7c926b"
      },
      "source": [
        "model = tf.keras.models.load_model('final_model.h5')\n",
        "model.summary()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 1, 1, 2048)   0           conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 2048)         0           average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 256)          524544      flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 10)           2570        dense[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 24,114,826\n",
            "Trainable params: 24,061,706\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OciMFG4SzcA"
      },
      "source": [
        "pretrained_model = model\n",
        "pretrained_model.trainable = False\n",
        "prediction_classes = [\n",
        "           'cane',#0\n",
        "           \t 'elefante',#1\n",
        "                'gallina',#2\n",
        "                  'mucca', #3\n",
        "                    'ragno',#4\n",
        "                     'cavallo',#5\n",
        "                      'farfalla',#6\n",
        "           'gatto', #7\n",
        "              'pecora',#8\n",
        "                'scoiattolo'#9\n",
        "]\n",
        "\n",
        "def preprocess(image):\n",
        "  image = tf.cast(image, tf.float32)\n",
        "  image = tf.image.resize(image, (224, 224))\n",
        "  image = image[None, ...]\n",
        "  return image\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQTQTk_OZEbd"
      },
      "source": [
        "# image_path = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images/test/cane/OIP-xjKAgPIdyArSOeG9W66qfgHaFH.jpeg'\n",
        "image_path = '/content/gdrive/My Drive/DAI_ASSIGNMENT_2/processed_images/test/cane/OIP-zsY8Glt0yKJU0hZwjmYeGgHaGc.jpeg'\n",
        "image_raw = tf.io.read_file(image_path)\n",
        "image = tf.image.decode_image(image_raw)\n",
        "\n",
        "image = preprocess(image)\n",
        "image_probs = pretrained_model.predict(image)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rfahGVZwaDV1",
        "outputId": "e9e58371-f490-4343-8c0b-15062548a5f4"
      },
      "source": [
        "image_probs"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.48516655, 0.32246217, 0.00342547, 0.02062883, 0.01478333,\n",
              "        0.01049882, 0.10858054, 0.01651611, 0.00155433, 0.01638386]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9mz9n77aY4d"
      },
      "source": [
        "Y_pred = [[i.argmax()]for i in image_probs]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tvg2Ayo5cE_r",
        "outputId": "cf3f541c-8aec-4f88-a147-8dd35517d4d6"
      },
      "source": [
        "Y_pred[0][0]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "_5EZW_ZAcGDo",
        "outputId": "4d242043-233c-484b-ff71-354ba2e7b7d6"
      },
      "source": [
        "# original image looks like\n",
        "plt.figure()\n",
        "plt.imshow(image[0]*0.01+0.01) # To change [-1, 1] to [0,1]\n",
        "image_class, class_confidence = prediction_classes[Y_pred[0][0]] , image_probs[0][Y_pred[0][0]]\n",
        "plt.title('{} : {:.2f}% Confidence'.format(image_class, class_confidence*100))\n",
        "plt.show()\n",
        "\n",
        "# image_probs[0][0]\n",
        "# image_probs[0][Y_pred[0][0]]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iG1kktN7SOMFC8JRoII5chKA72tAiepUPlrdKCXwA7w2x4cSr+M34D97szR2FG67JBB05lyZc6MgFO8lpCgpB39zCC15w2tx3EPbdcMOBK7Y2amsdmX1XTCxl3YmxU5/0UTt2vau3b/7EyAsvveTvLL7PNz39a7w7+xp1eMaxf6x238yxibHK3NxKxxU9I4VdQ9d1HCRLelfcQI3amY2pTK6jL26g0kUsvUlbKegaKu3IBBtrMysEQ7kiufyZRlAnqxqVRHQ7N6+7f43Gwsrc2tDa0EiWgo52lMIOulaObGyMzFOMHZ9AlBpLldoyKbihLoKOUkdjZSkqSLIUpDQW1qZWVjKZSibIbFQ6CgMjpZ30qZCeci/NfGVlYW5ibpJwltKeQ086UBjIVAo1uknlZTizNLdnT2FfUFmZueeejsyeHaOwI/RLI7sO7Wq3Y1fHxNiReyZmgsKZuT1zN11RekTX1iht3I6OxlZjrVA68KhgH3c1JmqLdK1dcUttBIWOUbr/Uw0edUPrqmdGBgYad+QG1tYWpnpKQal0TUiKukhPME8GJLr3NeY27srVMj3RAMVarWgGYJoCr9evmcvyliiBpmm2qRLth0SV9FebpnljR5xzCTKN2spaSJBJdEjXOnJVCge6+roGGqWNhSI0OgHWGmt5gnkO7VpYGOlba+wrLJ3ZuK+xMpJ7/967PPK+K37iky/50fEHfO3om+0WV10N34JPmPhpTHUs5DY2xoaGSh2dtHWaFMs3VoKgUptaKXTsGlyy0xGkia56neCtQ5l9TKwtbNJD2lorUlydp00fbC0sBHOFIs2gSCPHsWuNjdrSOi3FnhJ92zRKVy1YWVta6pgkyK2N/dsQJsawW1u1Wl+VlmEEqEoZ+iJoOjZL8ffYGOzZ17NjYeHIqY6Oqx418JSuHXERz9HX2ArJu+vKksqIm6SxdupEV3DVVZmJ2olKJbdQWxt5VPAsXtJ4Wa6yY6Cxp6NwzTVdBwn/eDI9h09irHCmkjt2DEaGOmprt5Vy0rwIlmZWZnr6OjoyfVGhH9u6Z22lk6BjTkQwMW7goKNj5LpHde26MAjvkSsMjJ2a6ShtLR17US6zZ5hCnq2+PRE7OnCx0Wc4trYydWbfR+VyuY6QnmdlnryPIE8e3YPkLcMEmqb5e/h7n+W7TU0sLOzYGtrRSY9kZmtmZmlpaORQLejpyj3tCTTmFueampG+kaBWW8mtHerK7NjaGqG0FhSedcNTzzzjH7z6Ad//4v/kV3zpv+tdnSfl4UvtyNRuyUzsuuvMsWCtsCFlLqJdPLO0MLCysjKzSFs402iSZ1IpBLlhsoONGGu3gGCM/tqIvsUSQkK/c7XKPG3e0r5D9NIn1xo1KjMTc3ONxo61RpPsRyf5IWvb9G9hoVQmhVakc+fqFPM2aQM0WFnYWOqlWLSQqxVWZo4TeDsz1dWzY0ejNjH2qtt6+gb2DHREZTPTWKXZbGzMdQUjPR0jhZiBWZt6zR11dWYvu6EIfXMnaqsU9uQGHhO9g561qWBjR99QN+U+MrWp3AHJNY/A2lqp57o9pU+Zm9qxo6uvtME9QWMkZjGmFmpbRYIxGWvct3FkbalQ6NoX5NZOdZIb32Zg4nodiSUbM9E7HKbYf21lYuSKWu2eE129dA1zlZVGrjAUknLOTAWzhBMUKVyeG9lXuCG4ioXCPUzSs3xz+YIBg6+XYGlhlkClrco11wx1zzHpY+ME7BR6dpX6Mlt9Q1laNtGCFCmtlDlzZKirJ26R3ERHN2npXRKO+6se+1o/89ir/uFH/p5v/KJf672dm8rwrNwX4xWFIyMfcttLbuoqjXDfxtQs3eSxhbGxtZWuLAFp0UUtlTLdhC8sRPCvReMXKjM9RdpwE2yTNWrVQmNra2oqCAkM7auM3XdPLtc1MNK3ME9eUZ1i7gge1ibWlubOLCzPodbCPYVgaKRQmjlzz32VoGfHxNLKSl/pqqu6BpbG7rnr1Jm1lVyur6/QSaHE3MKpKnkbmZnGq8J5tiO3dh+FoUMxEXyqo07PbqT0qGcM/KO7P+LLrv5y/U7fKIUoc8cyjVgz9a80xkrXcOo1H1fZetxNEcA7s3uevciTB/MoHpM7k7tt456VYGNmX+kCdZ+hNnfXqWMHDhx6VATrVskz2LWxFDf2daVe+uxIxInmGjONRnAqaAS5xg+rzS1NjPTNTSxFzy6TW1gqEqS6Ujuwp6fjVZ+wo2/fo4JgbSvT1bdnha5p8jDzBA1Gr2brzYsx3yZKoGPgwCzBZSz1LORi3v5ATy2Ym+skhDnoadyzMNaolcltjg9gT98VjZXMIkE5mVoh2IguaY6JSq70iK/wlGu/4An/6BM/ZfrE0vvKJwz08IjGxFijMEpO1X21EzNjp5bJhmfmZsmt3wpWcoVKLdOXWSoSWBitRCV6ARu79hFUNmamZs7UCn0H6T0LHYVHPKKrr7I2c8uZM0vL8w3YlSU7OTdPqc4giLBcZZsseuudVGohKYvW+pd6rrpuYpYyLNH65PZlcitzd9w1NpYJRkay5KtE3kZIVuyGfsIaaku1hTx1QAsJQ4n4SUfHQMc+6d5J/Isrhu6+fN987xV7nV8o6KI2SKFIVPpjS1PdlOPvKtKSX6XryazT84rIxqGQQNat21aOTZyZuK+U6yXHubYWAbdM18ZIo69KynupMk/3L0vjddK829AqKl9Wlk4sLHTUekpl8oQqM20eoLaV6+jpqlN4N7Rn6JCEL62TMpmaG1rrOrQrs6uxTmtraavjFZWVhUkKYELCKx4sbxMlkOvZ17GwtrLC1Fops2Oga8eBAkeyFPlENDyms0qdFI9VpIdHmUCWVYqfC8FSbYppwsFLS8HAUG7XEw59yVNP+Pjt50yu3fJ13ffr6aA0NdTRJCcxusW1RbIKWyFRUEqF3NbYkSCi0UORLNRYJ0vQoulVshJBzAY0+glai9snLlamcpmBHY09tdccuWtpadeeHTtyuWnKOnTlMo1NsiSZ3MBQkWLvRqZIOMzSMmUCKjuuKu0rRRJNJcPKTGVtbZnczz17rrmmSfYtk1lZCHJdXYPkfl810ThOWHXHhYu8p6vWtRA3TT8dW4mbu0RP4Snjk8rPbj7lavOLlWEnvbclL01VVoLGqVsytZWpZdpyfYO0AlYJ6CwsLWXuq+XuuWXsriqlQ2Oqc6F2bGFiY6WvZ6SjTPH81qmVpbm5jY1cT6224yRhJ2eCyM8I6Zqz821W6thN17xKartwz6mJlYFDuw5stBmfqFIzhxqLdJ49MxOVucZesvRZUlaFlbkTY5XVef4l4gQtf+PT5W2iBBpVutEdW5WNXKnUUxgIdvV07GvETPbM0kxlY5AeUVyO/WTV4kLKzjdRhxTVzeQaTaLVjAz0E5CyxtjX5O+VPzr2wp0XfPBw7Ys673ElP/Corzb3oo2N0hW50p5g7a6ZmUym1DWyK5ObGltYpLRnN7n3raVr87sblalKlUKMqM46CZOOP7uCrcE5gYdtQh96eq65prSvsjB3pqfj0HU9h1bumjpR6MgMRJZjppLrKgWZqYVZsqY0ugk7KZRueNRKZW6mUZmYyGR6enKZnj7Jixkai4s72tHoevdFBGGd7u/SBZmq6yKn3bLs6nR/Vun3gqbw0uyWX7yzUIa99LkRCrWFSUrhUuvqJv/i1MrCroPz+722sEjA7dzG3NrUmSsGrttXyvQNdA0tLd1PHqaUxyEzNrYylqdQbZU8kExmm8K+rUl6fjV6STEO5Rr3zZRqhbXG3MZCZALMrS0N7eidg+RzJ06UJsoUvtVqI7HhdTSC0+TZRoJQnkLfrUqha2hk5Tjlo1ry1qfL20YJrNXn8XxX344dQyMtxTXTN3SoUVk5tUkofKRvtmj5PtYqExER72iSTa2SalgaKHVNbFxTJLx7K09Wqwkz78+fsf9o4aduPW/a2/iK0S9ys3wmod6vKg21m7N0jH4ClUo9OzoGenpmzvQMUqponjyHZUraNYIqnbvrgjXYUkLLSz/7LshEMabcsWtkT+kK6Rr27BrI9D2CXaWNQxtBoyCl99Y6Cn09VQoKFha2liIn4Z5CzzWP6dnXVenrqW2dOT1/Ss4V1dAFD2Jf3KC3RUJTk8KNVqG1tNxWMbeW/wyn6f82XOois9k07ty+b3t1SXktfX6OqYVjR47kuOGqvkOlzC0vu++eHT2FUm1jbuausYmVrWCTrHVfz1VXdOUqwVpuYmWjtOvA0IGNVUKsoj3eNZTJzNwXZIaGOsljjLyJjdpGYytTalOqS3VSBDPB3Noimb+QrHYESisszGyMBSc2XpMbGNh1Rc/QroWxpZME1ka+Scy25PpGadV3zbyqUOmc81c+Xd4mSiBomWWljn37hm5oZCqnGmORLhNdq2666c15YkzKk1In5lae3O/GRiVYqix0xPzsVROvOUuBR5eUD9/glkzfe8JTwqOVj9+558df/XFfe+P9rg2uGhhyHi/SVRgZ6bnqguc/SJSUnjYLsHLX3LGtpYF18lnKhDa3Fm6l5RxETsBaxC+IFjRWL/Q84jG74oYbirngSulQqStu0IVMV9cVjanaTGNpoKNjR0fXylpXJ3kfkUsYl9ijBm4KSrUTtbXSyDUjlWVSXq01v0iEOvcMxi4YiPsiaeXABSWalpMQN/799JlMBO2eEPllhe1s5fTuWlXPk7c31nhZ5b6llVxmmGxv0JdrDHScmZiYijn0wq4dM7WptXWzMm6muiE3DRMLc5ROTM3lNmKqc8fTOnZVjnQUuoKtuUppbGxprZDJbOTmeknhZOqkWCs9PUWy/bXcxEJmo7SxNLW1MjJSixTjbQpkgq7ajrWNY/d1xMqIgYF9+8Y2jh277jDdzVEKQ6K3HL25SQp/Nj5TufDbRAlUgrHMTFxIjUZubW6ZHLdMY6Cnb5gedldtYWsqiGThWESy1PLuahtV8jFymX5y3foq13QSjj2xsbU1UMrkrgpuY9e7ww35dX7yYz/jR2+f+CWPfoPd/jWlA1LaceggobGPi4t+7KLgo6stbpmlJRbDhixtm9b6B63lo2UhLlxgHJc9hIKk/aMS6Ioodoy1naPTLfjZJrqCUk9PLUtJylzHNVd1bVVW9hONNnMzjTXRmIjkpGuCPYVbNlbpWW1dkKHaAqC2S3ibl44cwKgIFul6LuesO+k9URXHfP670DdZvmxn1WjOcuPNqb1uLYSFyplZgsK6Sn2lSPtdoWPPnq1YDra0NTSwZ1djoBJw5LSJieWz0Jg60HUok2mSTY3BZK2x1dOXW9smjkBcNadaCtGxM3fct+NUIxjZtbVNhm2TYNu+mLSO63GutkqIfcwlbcQsR5kQnlowsG8o07NV2aosnOnKbW3Nrd11ImgME1rWJCQhqCwstdTsNvn8IHmbKIGNwtSuLAEdW1sTM/OE824SWrsR66H6ooMfMe/MJoFuFOeLKdJ5K/etjXVSlj4u6BPDBCt29VLo0LLyWoDqRcGTns6edPBFwXN3n/NTd/+pZ65+uSeGX5rswmNJ+7aocFvc01Jy22xFDF0GyZnrGyYMOhYHvb4CMVJ5Iz9/KNjTZgjieJcfWUjXmbkoKIr05Iuqvw4OZHaT6onzCSLtaNdVI5mFI71zhdZWOHbldmTnAF5k83VcEwlaxxozmVE61iqj2gUPot3g0eu6qILspPnupXN1RKW2n14P/tXHf0hvsnS487Rbk9fcHFayMFDrK4w0ptYpvs9Uuu4n4xGBza3crus6dtLGruzblYdanm9tbQz17bri0LscKi0tLZwlYPU2+imcWyvTetmaylV27SoUztxz5NjATM/I9twLzBLsWtnTVSa2R0wdV1YiSamWC3rmJnKNMq3ujh09fTdcMzeztLa2cepeCrJ6FjaJFrZJubWNUqGbVFRtpU5hwpvJ20QJMEgTDwYa+blbVSarsrJO9NJKaaZRW1ubGesq7KMwcPFFQpGB19E1tbC2tONAV1905a+LiZ3Wak3FuPSe6LquRTf7ir3spq+43vexu5/00t2Psh/c2HtCllW6roipyZjljYs9RnVxLDi061mSo3Zh3duYv+cC8e6Jm2bu9eW7rVLZpmscuCj+2RU3U3kE66MAACAASURBVJ3OvZ/eP740DhelqW3M3dKTC5E21FbEtcdyXE/XN3LxNQAj0dM4FTnprQK6XA3ZTfNqw6xxmm8uKovL3wRWpHt+6EKRzjz/4Z+2Ohm7+UUHJrfvq68tySKAHGswlnKVbsIbGoO0boZ2dfVcNUrzjazOzNLC1spNN/X1DA3seET8DpWBnqWevpk7jt1TKu05SEZkT8DEqb7SgQMdHUtLUwuxNDjWipKJLMBKZakwMzM3NUvp1KBQKlLOqatvbWpioquja5R2QUwarxOJrqcxNjNP2ZiRHT2lHQNjJ5qkgmIKdp0gxrnp2x8YjGzpPFmCGNV0VNY6evqKlFxb6wiqRGndnjtJVBYJ+2431hwLa2e25goxzx8530tt2OF84bcL+FRrLVuKZlAaZE/6Bdevef7oOa+cfcTx8rZHrjzpSrmrsBS57VMXMfzGhUVsyznnomKYudiIe6JymIqP47roAcxchAitB9DWnLebfioCcG2c3WIQsRotXkdb2dhL/7cFQ61CiC7pViU7R+XbHHeL2LfKtU5jx4xAcCBPIKlzLKItOspdhC1tjX7pIuS5HOa0x5p0/wpHk+fdu/uSg/5Qr+H47l3b+rYyYRWsDF0xdE1USjGuzywcuqZwLd33Dk4FS4VD+3K9ROnuu6Kj7QcxSesiKuOVjeMEvEV670DP8Bxw3rdvx56OjgNLcxtZCgPPzHR1RV7FQiWWgt93amVl365DhxpbhcbCRFAla92SjqPyyFIqshITyX09MyvbpPzi/HKxtiMzNDJ01capibXKxtI8gcIPlreJEmgsLPR0xXKheCuylKqKaaauRpFQ2CGGapW+l6wc2dokRDSGExHQOlOb6unp6svsiQtj7SJk2Igbf+PCEkcSR1wcp6QlU2TXPXvt67w8+5Dnb33YdBEUT/9ShWOhfslhEV3ACw+g3RBtA44WLLviwnVvXfpaXMzX0+9jEqfhYnG2QFzrTu8k8PNUZavj0TRGL31m6CI3P7r0e1tM0rrubYOKmcZxih97ac6rdN72fnRdNB/Zc7HZ27HbNGFw0WQjc1Hq2qYE19oeBFHacGdH5bYPfOSHLGdTT11/N7OlQa+wae5pvw5insC6WF7bKvSYloxNQh5HT+22iY+Zueeqp3U9rnvu1VxPc31VzGiENP5Ax07CnbJUHjTXpNVZawzs2IhNSwZ2XEmbdKsyTbUERbLlVUIoIpuldMWeG65bWyRi/No2EZOKRFfaqi1TOXRPT6UyNtY1sLaytdXRJGLUUmZrY2XfNZmR2l0LU6U8editofh0edsogbWlLBXstK8V51ZkmzQlcdENcCXd2Pvm7qh0E2LNRX18pesJffsukPcWMGvBuLXYjn6lRdXjYmxTWK3bfg9DeRh6fPhV3Kz9qzv3fPLOLb3BXH9wamCYcudDF6SXQlQqjXaRXyDlrVWuXcTPLahYpPMvXGyk1jLPXGxwSREQN34L2LVeTuvhXP58O37LXYhMwewco3gjGFmle9R6FW0pcxtKtXhIy8vopzm0Cqw9X+uSthmFlhNArIFs9Azdd8eHn/uwsMoMQ0+3yuz2d81nx8qyts5eNnamr6tnqZEnLsYoKfprondSaayceNnHfdwXqz1xHi52L821f+l+9LFjYGPXbrLxKxsrjTq5/5GAdeTIvkN9fft2kuVfWdu66lAmtzTTKFy1r29o4kxX29MhcgPzBNtGMtNaaSPXsbYxNVWKdSLHTgQTLR040rRZWbnvTGbjqqsaKxNjWys9Iz09g9eBsa+Xt4kSaHvTLMXinOiOFsl6Ra8gpNdbFPoIW5mlgb6OPXExRqpmjKJHnHfQuSta9Yrzara2mUe7Obri5m03ThvDHmjrt7mhCIceH3yl8NgdH739SevpzLPv2fNKc8tu03M9e1Rbm3DhlrcKoLX+rWZuF19r3Zfpva371hWLhaL121rbO/+avCrdg1LLQmzc0aRkkvNuR61CW6Vzt/F4xD0ivboQjETr3nFB4Bml/0/T/Rq4aKfWbvzupXOciZu/9SJm6dpaBV27UG7TBIDGesQxHnHTJ+694ORs6rHuvn42sN/dkxc9s3vH7J1ah/uq0FhbO01h1I4rhnIdXWXqotSW9O7YVcjdd8eOgaE9kTM5Ec4JRbGqkYnI4YjKMbYoi+Voi1T9WItMyzuOUuTf1VZ3ZoJDHNgXuf0xtRm5/xtbC+tE0GorSnOxwmLbNM5M1YEde2IB8NLARiNY2GhsdA30DfXOi5A2Fk3MVmzCSjCxtE48mSaxad48Sfg2UQKNhdgBJ7Zt2HG511tkO/WS29e20IoxXIzwDoVzl7RlnbUKY+ICpW9j1SBa4EieibKjdWvb1lzh3J0fu6C2nmKrCH03y0e4Xnn57DmvHb+m6NfmvVxoZg7Ddbk9UVkdiRvjejp3LCK68EbiPbi45jZFGFmCse3IbS97wdrWewwMkmIJyYVti5trW2ununbE5iixY07ceEsX2EJbIHO5Ddk2Lf7W0rdeULvBL7v0LUnIpXs+Eb9cqL3XZTpHEBVpGxoda7GIyBaNpVhzpZe94qee/xmLcW00PHB176YroyuWNubztdLAyCMig+/MfacKmTPzlIBd4VTHrr3ECd2z713eZW7iJZ+wby9xIvp23NBJpLSVswSuRXDy1LFVKpBquyEREf1K35mZMrnvMYsf+xk2aQRyB3ZT2NDo6yTocWFjJZalFzY6Th2bNDOLeiPkK8IiwcKZsbmuviJ5BDHFm5lbi1UGtULPUEesEJmIdLTc0EjndbP/dHmbKIHY6nNm6op95etQ5coFYt1z4UpHTR3OU2Kt1b3MPGs38GVEvW3O0RYStVTW3fRzC8cq25RubMG50zTmWATG+jqh4/H+Fb3Os37q+Eec3Ct0HnnUK83Wpjlxs39FvMUtd6BtBdXm1XfFLjEtBtB2C4oe0YXyqo3dMzZWadxx21WlgYHclXRPNiTuWZ6oUUHbxONMLTakzM7Zh21mYaVW2aYYvbCRJSrTBXDYIvZtlqKl+7bSKrUW+Lyf7unepfsd8Zba2tZtkem+K+g4deLIieCG5+8+70Mffk44o+jvOtx5zN6V64r1qfn2nmyd2ek/JdhX+oSpjSDWOWysUzXe1o59AzfFcu+FQUor33JsY6mvp2OstrbrIJmFUydOxCYuuZmZWm1ubteuXTuKRA+K9OLKngNtp6JCTzAyUKX03EYpsjRiI9uuUltaHK1/9GZO3anuOV1OBEHVC9Z5rCaMTdxidWypb20rTySzRQIIhwauhF09IVWKTlURzta3J1O/NZhACOEJfJ/obzf43qZp/vsQwn8tfrdbi459Z+ot8BmlTPFZ7zw+a5Hinti3tBAXX9tBhQsXOjZ54FjjlugG7oub7KIXX5Q2bo3ps0g/LlxU9w1FF3vlIjcerXfjVorh2uzCXG7fleK69+1/g+fWP+OVl489duMxn5ofOZv9c89cfUzXEy5i9dYbuRy/t0UxbQXd5RChxQ04cGBtmwpkThKtuiXiRMQ/S3p/aWadrHmtSl7WnoFdrNWOSRTmWm1qZmOtb65roNQTHIo97doUaqto22akbR+7rbbZ5gWgeOIiC9JmJXrJ5SZzYGtr7JZPedlLJga6fvpjH/ex517z1PIm14aKbEeRD223E93ujtOjY/uPL4UQ18quw2QPI+13ZZlQ962eIHIJ2oYzXXm6Pz09aysnjlVWSqWllY3YgK0nUyq15dix1nEo2uZM6dAjCciT7m8kqcWEa2OptlIlKtraRi22zRvoKWWJyrZNhV9reSgVeUdTZVb1Wl50hBDEdm0Lpb6VtQiAdtKdjDT7jlJla2prsl0r89xeiESjhbFNwl4eJJ+PJ7DFH2ya5kPpG1w/GEL4h+nYdzdN899+9kMF113XKBPC3YhobYZHxJ5ysWrsIs8ci4La2oKL7qpt3NsCYW3BTpsvb8lE0JGfp89ay9sIdlJkNzr/7EWHvYWRWKjSOJFZKAxcL9/DtcKPvvwvvfjKz3ry5jWvTW5pTmfevf8+XQdp3m0WQprvK2K24MSF1WzTd7RZjJ6evr61SbI6HZnr4uY7xanaVuyOG9OUlcbC3NzCwsq+rcJIYWNpokog7CbV3sU+ONFqMXZVLvekWGDVYjFbUb9fDl/WokdzDTfTmV+xdM84AWORsNNJG+xx+560dsd9R8bGtnXt1cmRl189cnZ35Wi+dLu7cPreSn9YuT9e6l7vajaFrVMdt1C54oqppXuOU+S8lduoTU3dNzNJQF5wp77t3urIE/3H9FMbmKmZSqWva2HlxImtyp69ZPG7yaXumJkqlYapSrI1WI2p3jkYu1CbCSk/sDaxMtPRM7fW09FLIWljK5dHTyQr7fQiW3G8PDOejfX6fcP+UBNqs2puaWO5Xqs2pzp5T9kbGhRD87CyrqfK0FEHQhbXdawh2DUxdfZWpAjT1y/fSr9PQgjPiSb7c5BMz764QXfEDTEX0zyxBv31G7pt+tBu7gs6ajjPGUdLFIGnjmDoIoxoLe5QLPOMaPbaqbmFkR2FXXEDtmN3xWaWdaJhbhNWscCRXMe17k1f//j7ffz4p51N7zjcecRktfGROy97Zv+LHHQPxA3Uov1tRV2bjWizIW0o0xJuamUCmE6dWljbd9WhjcaRjZcTl31jaanUMzRKGfg64fxNUgZjgwQSTU2NnWk0enrnnYBPklu8tPKkK4JH0j1di4r3ngsl29ZLTESv5CBdy0Bpx45X0pwi3ebMxNIJXjVx25lJBMFC4dbJ2PR0Y7vuOLm79GJ1170vXXjsvVcc9oLtYKnoVU5PjuS7c91ioApMLJ05EenhSMXDZ8isBbGp6N3qyPH01NP9pwwMrc8Z/R2xnKu2bmLHo2Go9FP+YceOublj94xS56rcTJuODWqx41RQGZs6tWtf7F8QV0pPqRHETo2VpdiRudSxY9cm1NYh4TBNsF5vdDpdRShsq8ZsPqPJFUVXHRqreq2TCs9rjZPVxJXuvkHYJcuTz9axTEyF6VvNEwghPI2vwo/i6/F7Qwjfih8XvYWTB3zm/HsHnnzyhrigzlzksVtLOBW/LXoiWlIucvxtvzouNlWbmpq7yKtzAVC1bLhWodCmq2YWzixUClfOiTfRU4jhwiix07KERbT5+PsICmtXe4dG17/CT9/6gNdeu+Xm1S9VVyOfPD3zzMHUfpkJqcVVVEpt+rKbrqcFMtt5t52QYuvKWmzNeeLYoRetLN13O9UB1kqx59AyeUSFUlffViTAnBqrEtC3Tq4vbUY/uqfRckwUOh53KtMXm3LEr/ZYmuimQqpYIVimeb8ggn6ZoCv3Jfoe0XUiKAU9lSOn7njBh7zmtrWtPddVi9rqbGM13qrXOU3HS6/c9eGP/Kwv/UVf7vDpp83qu7bVmVdfvKVzo+vKzUfdc2Rma5Uq8jKsmoW6yqyK3FDbnJQ8lPKi1JbZxEYsA5GpH5GArElt20KhktvKLGxNLIzNbNRiDcZSN/X4ufgqlbWzhDn0LJSGOvaMlBq1HUONLHEH54kDEJuMHm+PzRZz3e5AyEuD4Z5Bd6gIfYvNxHy5VBZ9h4M93XwYlVZWC4Ge0rwoZCF+IcrSVmw1v7U2TxmI1vv9dPm8lUAIYYT/Df950zTjEMJfwB8Td9Yfw58Wv5P+dXL5ewfe//4vaWKPnWnKZrb59VO8pLIwcSchtR0dPQO7iTjUgmktyh5hrmAtGAiuY23jSGWrPOcTRBS+1gimZhapWmGtsjU8d9u4IKJ0XGzaWuxXnydv4szWXbWefvGM9z3yPr37n/DS3U8Ylo8b7vZ99PQTvmjviivd98jOsxJn7Z108Q08F6DdBagWqaS7rtpz3dDQba+YOkkZgdyZttNwbmtqXE/0m6Gr+TVrc8fGVlYqu5r0eyRhtf15NkqlKv2sZBql2Esg0lJiX5wi0VU2IkN/rjZViO3D1zYOvAuPu2AVnpG21No9x+6LDVD2TDdjJ8cr9enS+nhqs1hoyl23T079yEc+5Oq/fMR7Z0/r7FZ2R42TWwtlvXb10SuqcJr4pDNnpkITq0Amy0bdC9ZFBAQPHLiRP2qzW1vLTFXJVNTJG8jVhsKaajm17BeKbpkUzMTKwtLKUM/a0tiJgZGeY20P6Fi5GseKzILjBN5e14aAtamN2OGpwdzc3eq+O+M7prOF3d0rdnZ29TpdWejaho61jLJr2N/X7Qz1w1BlY7KYUGbKvGNY7Dp03dTUC/deM1tNjIYDWSd4fPiYm+HyNwC8Xj4vJRBC6IgK4G82TfOD0DTNnUvH/xL+7mc3WqyCupDWdY8bIhiLXwvRSxThmHRZOjM2N7BjoFSZ2aSiib61IhFaChu5VYpvW3BuauwoOYxR+krr1Putq/12nYsGGMFOijxPk70eJAu0sjZVmys9qt95t2ev7erkP+GTd5+3nezaPRz5yO1X3Lw29fTgF+k6deERtFmEFgwt1V4zNhObrewnaCd+EcZA6baXrM2N9NVNYV0tVJuxsp8Lm8r4/pE7s5ctrp7pDLqW24WizExz5u6bOjsvdJmYCzKHDnQMBSsbPWOVHWsLZcofxNZf49R0oyvILVJ93tqR15w4s7JUuuPQl4mp0djZpk6YRVesyZuaWVVd13auCVcaH/QJs9MTm3mtn+2Z9WsvHL9m+bG5fr/25KMj919b2ykLfV037Jg7VVu7577IMT2g19fJd02M5SrXPOKxcCjL4/cMjFVObZxZWKv1DezYs1scOhzmdvKBYOt+fcdxc6yTUYUIhG5tHLmrdCJPlLahYSo/3nGavghlYyPo2DHSKK285NiRSSqNDwobW6tmbV1tVSHolH2jcMU6qyysBLm8NzQoO8psYLpZkGd6eW6YlUYhtnA9CHtGHnHPR925e093UFostzaTjYPhNjFnHiyfT3Yg4K/guaZp/syl1x9NeAF8Cz7yc4/WuuZt+W3LMY/0zszSUPzuucgIi8WSG7GU8rYjN1xT2bE01dEkUGYrWModCB5NbnWbUozjjVM6Zdd1AztOHZmauKP9kqpYYnLBPdjBrv+PvDePsSy77/s+59z9vn2rpau6epuemZ6NM8MRKXJGEvdQtGhLlkDZTowAVpAABvJXgFgIAgdwgMTIH1GAIAGE/BM7hmE7UiiZsmLK3BdxESmSs3Gme3qmt+ra3/7uu/vJH+eefjXiGpGUm/QBGtX16tV9972653d/y3exiKsW3jqawRhXAaesntPCtTZ4oOeD82VevHkNJc4x6J/naHKAsp7ngvcoHsZRyEBthxhI7ZgjhsyxWVBDs81LcrQMdZuQgII5mkMhKIqCdBGT2h5Nu0bDCzi+u8etZcTGzhZh2MCSghEnDNUhMRGB0L5E83SBbTs4MsDDRqH170cYCUyNHoxJ2WBQkWsXLJkTk7LGAM3RyHBpcILW5uvem3po3wOfHVpEHLKHj0uHPpa3ztL1uTV5keHBGEe4bG3t8OSjP887nnyGi2fPYFkpR3s3ubs3ZP9oRtGpkeMRsFYpO9XwqKOEwlEOvlWnKRpMiAjwq9S7wTnqHHDIkAlztWCUzYiTHFsuKRyLB+1NNsVFXOFxzGtMptc5mB2y1d/ECkIOmOAwZVyOQJWUuSJwfTaES4BiTsKEJR1cGnRxKwZizpzbDBkzR9vq+Xj4WFW+assFlihx7T6+6FeZVU6LEE90yK2MkpJpcoJyBY7VxHW16nasZroMEHvsHb2Oipac2dzEsiySJMFViojvqMjvrR8lE3gW+LvAC0KIb1aP/TfA3xZCPInOf24A/8UPPpSqqrqougeaJp6ZS9toXWCBEYnUxo4pMVqGSvvdtKoRWUpJjFHJX12EXVZowBFaFsujxKZRyW2U1Wgnr+76GphpoZgQkxHQR7CGoo1WCOojKv6+nsUa8I0eZzpyncvNx5EPwJe/cYt+eJGN/iP86dc/ifdIg53mxQpUdBdd/kSMuMmEEaqaVQdoMckuDTwcWvQIeJhNEiYco0U1LWwhyZVOR3t0aLZqWJcE8+UC3/WoWTWEcFhWkmaygp7mFAgpqvbenFlVqtRpoDUIc0JCYuZELNhhE4dtYMo+15mjkAxwEWxwgQENZkzQlKqA5xdf4/XrN1CJ4uzWBmfObODSok6DNgNSIXjt9nW++qVvUmQWv/zBD/PMlV/i5558J9uDDcpljGsrLp49y42bLzCJT5COyyLxcX2PGU0icqxyQMuy9bgvTrgxfxW/brEZXKREMmbMGhv06WP8FzzpsCxTouWCcTlk3BhSr4VMojHfev1rXLv9CrIuOd+6gBU47B5fR9kFwgbX90jzhKTI8IIYKWIsMjxqeLQJaRKRccQBQ6bk+JR00WL6qmoNKoRsELrrFHnCMoYTe0FNWGyzxoA2DdGoxrwJ45pLLlKWsynpIiVzPPKiYDg+4Zp6kcliSgOL5HiElBaLxRw/U4i6geN/5/pRpgNf4M3Wsmb9kF4Dp5eWBclJmVNU93rtluPTqJpwxrhBA0OWJMxJUTgEtNG2YG08QmaMiIhpVRlFyRQNBe2gASyacQYBCTrtkkQIFvhINmjQo42kV00bQiDD4xA9EIkR9zr6B6woxKacGWH0AaHAlnMeCC8Svv0SH/vkF7Acj2eefpavvvESw8sxj4cXcYGUmBljXCRbbHDMBEWBjcIiqe5oes4MbVzaGBWauhjgOBHjLvhSYguFLWzONNeYhlPmKqFUMXWhsfGO0N0Mn4AZEaVUKKEDQbqMEVIiXHCFjUeDBi327mk7LqvApVGVfXYQPAIotNyF4I1yyGS5YO/6Vf717/0xX/38t8lmirPnuvzyX383v/Ghv0lvcJExY4bFCVFmc27rMR440+ehnWcI6VKkgv2DIYvJmF6vRbdeZ+vCZWTLIrNSUAPqdDjDgDvqdUQ2okgXWCrFSTOm4ykWdcKgRlQm3El3yXxFlz4+dUJR0LZKlA9RrgVal9GMb+x+jRdfeZFXr79MSsoDjz2ETROFQuUNlvGCsBngSx/lxxR5wRyJh6yQLi2iqjO/zz431U1mSULg71RFgxZ9zdEag6GosVNfZx5GTIs5UZrTskM2rA51XMoKAJSR4sgSleZk8yVlnJMlBdPxhBvXrzGfz7hw4QKlUnz9q5/Hti0azSaNy5LN843vufvuG8SgVtOvk1VjG0XBnDEOEm0eYVCDDiVTFsSMmRGRUSJZEHNcoQOnTJgxqtJUeQ+cqXXvDRBpB5gSskvGFB+nQlZp7SE9ImywUsPdQEtlZ6ysxwzF1mgCNNE1fYpuhKVQmVRI0WfLeZpfe9+H+Pw3v8DV165y9vwDvHr1NWZnp7yjcwFXPkkHm5I9LAJ67DNnRo5ES0hPKbDQQh6H2Fhc4iJaNLRBjzmH0sUVet4fkyBFgWfbeuyFxwYDtAmmTg8l2rNPloJCKGwpkZ6DUgZmVOBSo0aHNRa0CKpQd0TEIQKbPhdQtJioEa8kt7n6yi2uvXIdF5fXnr/Oa185xBnXScYxN4czfv/oixy9YfGRj/wmb3nkERqqhiNSBm2FLJrMxinLbMpC5thCUhY5o9EQRxa49RwZ2CxzxWuvTTl/oYVfX6OtXAI1Yra4iW3P2Gi06YiAYRqxoGCcL7i9v0fSybjQukRadYFCakhXUuvErKs1+qrP1fk1VJnTafXwGwH91jqphJA6W2sPsnu8BzlIanSlg+MaXV8XhxoTMqbkSOBQTbm7P0IJm7k3pWiHOALqaLcGFwtHaOJxIH0assGCOQ7auRFgUo45jA6QhSS0XVSm8HIL1/VRRcnxeMFyf0KzHtKWAQf7hwxvHCNlQVKfcabWRfQ2v+feu0+CgFbn8Rng3wOkKHqssYLtrjjxxsByzATN4vaYsGCMNi8pSapurtbrX2e7uqMbkQ1tDAFNWpyhSaNq+E1YWT2dljCXaOTcVnVuU1byXuZYZkRpZK6Mx/wxNgu03v4BG/ZFPvj0s3zmpS+wf/cWG90zxNMFn09f5i3rO/TlQ1isAyMcEtpVQ05UKEJZ1eq6fPAIeQSdgSTUhZYP0whIi4QRMwrq+GwwoCY28NkCUgLcao5SMGGKpQRlWeJKh7pokguNPl8wRXMXLNZZQ3IFXfLcYEGCRZcxGTeLb/HCC8e8+sJtTvaP6dVqrPW2+Or15zl5PaYd1ilzQZxm3H7pmD/a/zRHd1KeevvjCD9mvoggDxHZguV4D1KHeJ7iWQ6bZwa4roXnCJQbU+96pCXMFwcMD6E/aDMYNGm367RDjyCIqJNzU+aM0pRROWOWLoimCTejXQoh6TTbuHi0RJNOVdq1RRsbm5rnsbWxSbfXI1UFjvSYxEsawTqXrAvMHcHR4pCwOWDLXqdFizFjjjlkWgGQAmzaNKnTphP28IIGA+dBbHxKlTAvR8gkI7QclmXCdDalFtZZr2uxk/HihFExp9PcoSZC2naDmluj5fRYxDPuntwlzwtsy8ItPbY6Z3ju2WfxPJdXsldZf98aQgocy+Gxxx8l6F0B/ofvuvvukyCgEEyA86wgqnfRSDpDOTWw3wRJihbMVmjvPh/NQtQAzYQFMRELctq0GGCINgaNZ6MRibVqc2lWn2CAJuPMWJFlDO+gh77z32DFRde23SvdvBRDYdb/5sAUce//txGUNCS879FH+crtq1x97SUeefgJhicRnz35Ns9deQvr1mMIbmLUfD3usJLe0h1jLX/WZRWoAOo4+NV7neChlY8QBu+g9Rkhr+DDmo4scRF2yRFDPHy2xToChyEjQgIe4MEKgFwAXQR3UbToqwFfy17hC5/4IndvS+azBsnEIhAeVy5foZjGpCcLnLhAlDFurojjAl/apJOEP/3Ul3n+Wy/ityxc36Me9AmcDg4hUnnMZ0vIcnb3W6yv99k8s4bfhHQ6I0sEZWpxeGtEfJKx98oenY7NhUsNehfXECzx8jbLQHCvQAAAIABJREFUyQGj+S4FBXlUcPHSOUgFIQ1qBDgVF0DfjR1OpntE8wlb6+sIz+X2wSGj6YLbb9ylKB2e6G3Sba4xWy5QwuUM56jT4YA/50iN8LBByEq/Qrst9ZtrdBmwLbao0WShTngl3+Pw9i3GRY4j4WQ8pN5p4J5PcCwXkiWh7OHQRRKTqxgLCylaqDglOo7I05zBYMDlC1fov/WXcOpPgGjw1kGBFmvVHTHdw7/vNQbNLDxgJbVlcOrN6ueaoJJymxl36dGiSYM9RsSoSnE2YF6RghzqeNhssoZ7j99uJg4hGvWmmXXiTYy5DO6p5Rh0YQvuZSWnlYGMHmGDleSWkQ8zmH8DE55X7+0EgUcgL/CunR3K4uO8+uq3+PA7fp3n77zAJ77yIh9++zZN6+cR3EEwAi4DL1fHfhodiAx12qAmXVZiki202cppQREDr9aIDHGPJt2gyRqXhWCrEm3VNmc11uigVZR7VYam+RiKMama8K3Zq/w/v/d5XntpiCvP4bsDQtmk5SxRi4jXX36ZfDrm7KCFI2yiZQKFntkkoiSPE7KFA9IjXeakswWBb1MPBLYscDwL2w1YxhF7+/sgFH1Zh7iAtEQWHpNpxrw8JI9jbjsxR7fqnOxvsLYeEpWKYukwycY4joVb+nSbA6LlnNH8GOo91hngobn7CyYkKsKxJUHg44V1ilJSlkcMJ1Oufvsa6oqN5XucX7/AU/JJgkoVeZ1N7qgbHB0d4ggL2cqZliPGR0Mc16G70aJkxpwRipRtu0bYbTMdDhFFzuDMJr3BgNCtM0nn1L0Ga8E5BA3SeMHiJMaWLn6voN0+z1vf9iRwkRUEXrfNESDE6pEfZt0nQQBWs/sFKyGKlBVBZkLJDWLeQGERsgP0CbjDgjFad9ajT50Y7YrrUBBUKkSrqYC5cxrpcBMYSnR33q5ezzASTXBS1bn0WG1o4+picAQRq2AjWfUTTGYQAbfRd9NzINZ4z4W/zifLf8W/+Pjv8psf/C3qTod//vv/hg//+tvZkgOEeABjZ62bkHfRf9x1jIrSSuxjiA5EA/QkxOj6w8qzXktzlUQIYrTb4RoBmwTVfKDgDhpyo6XQBSmKFiitQHzEAR//7Gf4oz/6HK9fn7DZeYiNrfME/iY1u47PHgevv8a3/uyLJJNDzm1vIYXg+DhivkjJMihyi9LW/kV2Jbxq42IpiSpy8qIgCAPCWo35bMrR7IhYxEyXIbad40ubmgzIFjGTw2MajRClYm5cj7n67Q6XHjrL5sUBzc11wn6NCzvnNcC5zLld3GI2GyNL8BraOu04PiJbxnRlg15rQKYUjuOwfWYDv1ln//iY67du8/LL3ySohfzCE+/B8S5hgu86P09fXOfVO6/QaTSYpRKnEIh5Qb3dpEuNghGJiojyKdlwSkPa7Gyfpxf2sCquSi7m+PY+0grwxHngEoH/Vra3VuQy8R17+4fb7N9r3UdBwFzQhlJrHjPpelEBL7pAH1HpAro00EBQwyZ0qBOy5JCICZpM20Xr2RsdgQRNTBqzwiRoQwu9yc0d/3Z1HiYbMBTkLithEPO7ETqAmYZig9Ufx+gVjqv/G52BFCE6vPfSh+l2/pg//OT/wTOPv5t3v+cdfO5Tn+HSw3Xetv0oQjwLPIieTFwFzrEiUDnV+VC9hgl2xvXHsCYNf18rM+lMwBh/dKteiG68WhVDUEOo7gJnUeqEz139MnkeMJ3Y/PlnD5ju1mnba9Q4h0+bhttAZClpNGM6PWK4d0g8T8jTJZZlIa0C29FNSISilBZKSCzp41guttSiHEppX8QonpHmCapUeIG++CeTMTVHsb454OygC8mcG+khyfIQYdsks4SXj4YcHC94GzV+870fwGWBhUNJxkyOcdsed9xbLJIxt45vEiURxyeHeJlF69wjbPT7RHFCYUEoAkRDUm806K71mSdLhJQ8bF+pei/GR1r7KQdIrEWGIwpqro+sN+m4AQGCOgE1BuTWlLvOLfpejzB8GIQGFUEHmy26sqmPWV0+b970P9qG/27rPgkCJmU1Ahac+pqhN46P4DKaoLKHvuAbaBx+hgHo6M07xaescN3BqeNoS2j9XGOXZeiwRgTD9CHMHdds6gV6ExsB0Dn6zmyEPTvVcw392FA3JSv2YItVP6FA9yXmCNHh6d4HefxDW/zRFz9P4G3zK+/5IC+8+g3+5Otf5QNvfRQhTGr/aHXcGXoTGwIUrMxIDMknZTXBMICfsPostfSXqsoCjXHQgUBwoTruIQb89NLos/yjf/g7XD7/Fh698i52Bs8wCJ/l6DBhOYshcfCkICsiltECR0katZBoOuLk5IBa4GM5NmG9hvItpHJIcfG9Gr7l40oX25JYAkqVoiQky4g8K2m3O6wNBgjLhmhOWyrW6wGXz3ZoBg3OtDK+9rWvEec2rmsz3U85Hh7jBnv8yqzEakh2y9sgClSZExQOi+mMpIixXD2ervk1XMcCZeGUNqEjmWZzMhnj2pYe/dU9VF3gKAeHHVYjYVDscXT3FucHm5zb3mFNdOiKpqnKuecPIfo44p2c67ZPXZfHGK0MYZ7+V7juoyBgNOjM5td4/RVf3dCAjdyVi74DmtFco/pdLSgi7zXzwODW9c9baNpriN5IRgbLmGaYfkSzer7JDIx/4JJVej6vzsu8vuE88BfO2UA2zUY2mYEpESJgC4cP8GvPXuAzL/7ffOIzf8jTj74bsbbDa9PPcanZQojN6s5jyFamN2F6FxYrbYEhq9JnxIp1mXGaWFUyqc7O9At0A3EVyHQmUfdsRkcZX9+7Tj7eYmu9R6exxUbTZirGFOWcIl6SJnNsS9AOBqyvbTKbDonjJbZt4bgutudS92tYwmNZODhOiC1dLMvBsqucr9A9GilzHF8S1G2CukdZlhBLFsMTrs0PEfEdLu10sZ0lvZ5gPM1IlUunPWA4D7h2fcRHP/Zx3vmBbZCKet1nGk25uxxzcnRCq1sj9H3sXBJ6LnW7jkwthtMT3JpHmiyxhWTN7iOQJByyJOZIzWiIT9NR769u02/wMl+i7jo81H8IV9h0aFaN5iZv9qX4i/N6B52l/vtb90kQgBWAx6Dt5uja1q++P2Q14jP4f3ORGhxBVv3OCrG3quU7rIQxTf/BiI0YJmLAm1N64+gzZ7X5TfRvoNNys1mOWTUUzYYyGYYRtjTv5VV0VmGMN4xHwRzY5l2PfZjPFX/A8y98nve9+/1M8oQv3fwmD+0c0ZOPI+4dy/QbjC6Bgkp6Sx8rqM5XsaJcy+rz0HLnVkWBXVlrR9VnbZqeGXCHeuDx2CNd/t0fD5kdfYELOwWd5l0GvR08z+Z4eJPbd4fEyxMe2u7T7m3RaLSo+U0tfG55xHFJFOfIAEoJaZaBzFGUSAuUJSnJycscVE5Q92g2WzQbdfIiIltkNCyb8Tzi2s2XuHsDJk+cY3trjbNbZ7G9BflI0lo7xyRrcWP3LsLyaNR67O3tki4THE/SDpu4Z7ZxfBiNjxgeHtCo1+lsbZIuFAeHx7QHHYqyxHV8PBpYKDo0adMgiqa8Kl7i6aCNLXwiFjzIgzwyeK6aNkXV9bbFX/lt/S+x7pMgINHWUw764j1Cb5BN9Af5DXTqDPqCv8gqI+DU48bR5qT63nT1Dfbf1MWGmHT6Tm2adwYDYO6YKasswWweY8C5yUq09JjVxjfQ4fqpY8nq8avATVbOvKYEGqPLnLvAOr/4ll/n20f/lH/2B/81/9EH/w4Xtx7h2o056kLBQFxAB6BXgeusGql3q8+pz6o0MAGtREtrz6pzMtLnRkLcYiXEarIU4xWoGMjL/IPf/vvcuvm/8fyfH2Ef/glv3GniWB7d7hqj0ZBer43vgCwDDm9dI7AsXMdjMhUox2KWpJSOR7PeYDFfMJ1PaTsuCF9Tmm2XUhZkSkvLOp6H64XIQlLMEtQyIRbQ2OjxQOMJZod3+fa3hywmAXk2ot5ps3X+IexmF3ee4LbqPPfcO1BFQRrvo5IEt2Xj25LN9llabpOFs8deFpBHCf20DfUuXjKGSGBZ0BaXsFkHUjqcBZp0609V19IlQJuN/zSv+yQIGNOP02KIAfpiXkAlD6E39IAV2cZsUFNCmDuaeVtGYtyMAGusjD7MRjXCnmZKcLoMOS2vbVxzvFOPnzb9MP/MuZlMwwijGv0DG7352qzUkY1mn0kdNZrvyuBXCX5F8E//yf/OX/vQf8Vbd36Nu+kxfc84E2XVncfgAMx40jARO+hAZcZIhqBkMiCjGGR8D8eYnoLiFtp52UcHuC0e2/57/I//0yH/y+/8S/Z3U268PmH/uOBofEC8hFIc40h4NZ/TEh6t9gAFLJKEvFDMs5xaK0BY2qqlKDOUSBBWXj2WUkhBoSSWZWG7HlJaiAJkrnBKSafXZDqfcLA/Jl+UNPwGe3cTZpMprYGCcExNWEhb0e45fP3LX2dta53z2+dpdiyGJ7sMdw84+8jDSC7RCASNS0bLUTedO6Zc/55r/Qc94adq3SdBoEDfxYAKsLMaFy5YjeZOu8WYzXtaZ9BgDJqshEVgJfVt5vYmqJimo6nnjdaf6UMYgwwjo60Vc/RzzeubfoYRMjVNOiMQYkxDjXRY59RzDX4gZmVBXqs+ixnwEOf8v89/+Z+/g49/9UV2hx/liUefZVSOaUmBJEDRQxBUDb7TIh8m2O2z6lWYvoCRVy9RVZ9D3MuEtBfhkpQA7cgHHUo1piy/wDsefjsP/u5THNxd8q1vvsL167cJ3T55bmEJj0994hMMdyeE4RqtdpveoM80WbLIFNPhhDTLiOOUJEmQUuI6LpZwQengLUqQQuHaHq4VIkqHMi0pCwsLwXA45qmnn+A//o3f5NqLL/PJf/snTKcLCmzGUUJxZ58gTWkOmjTaXbKlg5pZlJGP3+rw4JlHse0WiO/n0/sf1rpPgkCOTuFXSj5vlgs/g75w77KysDab2IB8zDjR3MklevMmrOr0Kau7f1i9pkKjq4zzUMiqB3E6jTfefxNW6sbOqa9G4jxnlUobGzDFSjm5OPVcoyVoGnILVrW7lusWdGiJ9/Dk2xSf/fSLJC+nbG2c56HBA7REB4GH5jS4vHk6MKre35hVlmImF6YBm1aNwQITQEsOgQkBmwgcYoZ4eNxR1zk8Oubx9Wfoiefob13m0a28+ptoPMF8cZvXb7zMGycRjU4Htxbg+D7t3oCgFIyXCWmeM55OiOMU3w+phU0cx6cQFqKwkCgcy8JRLjIXJGlKsswReUbN9ul1+/S7PWqNBpbrIRyXwk6od7tkomQ4W6KKE1qqpFP6xJNdxjfH3HrlBo2mx4MPn+PhR65gu+vYjoOU1l+cwf0Ht+6TIGBSYslKBtxB11xGMnyGrpknrGTHTRptygHjpWckQoy2/iYrXL+FTvvMMU3qTnVsk4Ec8GaPPGNSoem++sI3PzttQmrKCNNEPG1usmSVnahTzzPApePqq5lK5MBrCFIeFE8yePcmH/vGp7l6a0wQOlyqX6HGNnrEdwG9IRMUJyhSJBGKBSuFohpahZnqdcYsWeJWak1a5+Y2BQkNnkRUj0KNxeI1ClEiq76HwkKpCct0F8uaATWidMgsGeP4LrVWA6SFsF2anR5xrnDcfRaLBfl8geV4tJodAr+OJTzKUlSwC63D55Q2LBVxmlAuE2whqTcsOu0uRwdHXHv1Grdfv0VeCtbO7uAEAcP5nHg+o8xKnEThL1PspKARSOI4ZXH3mNHtPW6+dJ1z589z/sGHcBtdpBPiuA6Oc59sh7/idZ+8axvooJij2KtQbC3gAfRGM40+cwfVTjCrkZfZTCY7MDW9luaEQ8S9TW/ScEP9NSM0kykY67Hi1DFNaWDEQJesShYjiJKyaiqakWIO9zYh1WMm6JjsQLFy/DFjPpMV7APaRUZwm46wef9TT3D1+JCj4U1Cp8uOU8eR2iRTNwtjtCj5HYLqcykr1Tko8PHQ7jxLtKdOVkGeIko1Y6piJtmYbXlM3b6IEJeADucaI97IX8NGy4ordZO78z/gq6+8wMXzj5GnDru3h4zHhxTKYZFmNKQkaLaxS0V0dEyS5WRpgWMJWs0WnWYHSziUFSBMlApbWbjKwVUOMlfkqf5nOza+GxIvUzxXUAsb9NfX8Wt1Wp0+aaHlwqy8wHJtHMtHZSWhAC9dQF6gygSV2yyk5HaiODoYM88Ejf4Wl688xM65dSxLlwlpllOUJbZtY0tR4e9/NtePQ2PwBqsiPVdKPSOE6AL/EjiPBrp/5LuJja6Wh1YWvkXKGAvt5aIFRrusbKzM5jEvZ2bmhrG3ctk1d9oChWSIIIY3/TNWXKYfYUqJmJWtVq163ABzDPzYAI98Vv6F361JCavNbR43qTmsAoSZ25v+geEDJCimlETVe2ixKdZo9Xp86cYLvH7rVdJeyGZjk151F1O4pGww5jbLSm9WEbMsExJyBrKF5hrOSFkwVzmLImNe3CViTpLF7O0NWYRf58qgJHA2EKIgoMUjnacpKIGvkDPkiy98hskk4cFLV1jECUeHe+R5ymi6gPQuyvHIS4iznL2jQ+aLCIXAcXxqtQau61GWoCQgBFIIHGFjK4lVgChKKBQoKMsSIS1arQ4Qk+WKM2d3KFDs3tmnEIqwFtAoUvKyROYp5bLAcy2abkYQOiilSOIp8eGEIhpTDuvEVoByfQ6PT/DrIWHgkKYph8Mp8zgh8D0ubA1ohKbX87O3flyZwLuVUsenvv9t4JNKqX8shPjt6vt/8L1/3czyJ3gYCKWhyxpZbt0nULiU2FictsIyUwXTIR9RMkJRYNGviDRGcjliJY9tanxjnmFciyenjmdGaMZ23JzrWvVcU8MbdJ05V5NtmOBiPPhMpmKyCJN5dFhlE4aDsCDhhIwEDwdtCBIQyg7vPC/5s9073Nm7Srwocdc2saWN7eiu/w4fAAoiXmWaf4sinVDYJbnrslSQqIwoS0hym9lsQhwtCWwH23Loh+dwpc0omeE5IZI583xKVqSkYoHtSHzqvPXhtyFoUJLRCRtsDzbo1NqMGDGLU7792htkRU6cZsznSyzHxxIOjhMQL3MsK8b1bRA5CknoeviORbqMiRcpWZwgEeRFwcnxDNtWBL5Ft1On3mwSpxnD4ZAomzOPFti2RSu0iZYRKklouSH90GGtKRj0azSCBlkakxYlueVy7e4h0yjHCkqChiTJFkSJxcnJmCBwOTo65KVvv8hv/d2P8JbHH/nel+9P+fpJlQN/A3hX9f9/AnyG7xsE9Gxa3OMMmDl2p/reiHFqQ8clC5r3avLFqd/Rm0hxQs6IlBk+W9hvYiaaWt2UA9pVRv9+jOKQkgXWKW9C/bsGf28MT7TnnX5Nk10YbwSDJNxEYxqW6Bm9KVMWp45jyhrTU4juvU5JwpgRe9zlMR5Hj0wPEaQEssPjWy43gjk3b7xIkStaXpdWXeEHEs8usYVDyFtYxA7x4uu4gWSSL0EURNmU4xPNvrOkRcPv0Gt18R2XLE/xbIdO0MISDXIWjKYJ4+kJfiDYXtskFFs0un2UusRx/EnCzjlqIuHxK69z69UvMxlFRPMjlklCWUJZShA2rqs9eFFQFooszchzPZERRQaZjUpyijglXswos4K0LImSBXcPJGWeUdsP8TyHrEzIshTsHNsusaRF3bHpNW1C16PlWPSDmLaf0wtCug0bC4csLxnHMSN/SZymqMVdTu4oRifHKHuA59fZv3OHT336U+zevc1v/uoHv8+l/tO/fhxBQAF/IoRQwO9WUuLrp8RG9/kug9Xv9B1YoqqLX4tcmbr8iFXn2wQJ7RFYEJMywsbDwaYkoqwai5qfFlZEI9MxN6d7ejOfxhwkpJwQk9HEqgg0Btttxocz9GY1JYMWHl0dV29ybfNlbD5MdmCmA+b9LUmZV2fRqZCABSnjSnoKfDr0cLDZQeFQcIJNHYFNW7ZYb+UcNu6ymL/O8viIIym4dPksfh1UKSmKlEH9EdJYcevgywQh5EXEyeiIaDaj3WzR63WpeSGD2gaBXedgeIt0mSL8EEQHhzo1mVPYLqFTI2QLI9UmxBkGwduBM4Trdf7efzLg2gv/kM998c+Jshzb8ymznGxZ4Lo2tu0ihQWlpMy0umThZAghUGWJKm0cCVJkpGVCFEfkqgBVMItOUEVJ6DWwXZuCBGmVhDWoe9DttLi40aNlS+x8SSAVXd+hE0LTSgnR7gfYEISCvO2i8pj9xT5Ho32mRYuIAY7b5Natm7xx7WWUylHFaVDaz976cQSB55RSu0KINeDfCSFeOf1DpZSqAgR/4fFTvgMPKXM3LCmw7nXYzYze+AsU2LjUaFAimKh9jtmlKwb0sTVVk0VFSm0i7kGEHVa1/JSEMYqCnBiBqjzmHAQWJ8xQ2DRPNRdXbL2QVafdBI9lda4tVhmFQ1lxAiRDTqsP5ZVFmEWtGsFpj5guYSWmalVGIlCjS6NSpQNJwpgUQbMKKAJB23LZ2q6jJoLhzWOU9CApkJ7P3vEReZmy1h2w1n6AVrPFcPoS+ydXWU5TyF1qbpt2uI5n1XDFOSRd6l5AJuNKY7ELSPrtbfptI9hqEIjms9WkJiGgUXuYx688xdefv06SjQjCGlZWoEhwHBerGsuVpSLLcgQlhSxwbIFSORQOnnSwPIFdcyGPyOIEyxEUIiGKl5RlqD0hpP5EszTFDhwubLZ5x2OX8JMFs71bWGVO4Fr4lsTOcmSk9DRQCHzpsm47LH2HeLlkPJ2yjOYcp3MK4ZMnCY8/fJ7xZIh9Wgn/Z3D9yEFAKbVbfT0UQnwUeBtwYKTHhRCb6IL5+x2FVQce9F1XC4JqvcGIsvqZTR1Jg5gTDstDjtQJvt1Ad8HtSnGwxLnXhTdz+7A6dkHEkpKcGWMEJVsorArimyKp00LLkWn+vbqnN6CNJ717pYKh655mPxaoSvFgBRs2pYdNRkZBQoCLpEGIhWSCuMdAtPCRBDRx2ECrCLVQnDBVC6JYkTspWXEEUuE4NvWaoFVrcrn7MC9cfZ7nn/8G57cf4+bNE46ODpD2N3n6qSs8/OBDDLND+k3BRvcx8jSn0WjQqjVxrEZFePFo1M5U78VMQ/7/LIHr1Nnc3mb/6IhyGSOEhet5uG6ALR0ENqqEIi8pycjtFKQkLy0KYWO5NTrNBmudBp4vWOzOSVWOtF2KvCDJFFgZtlNi2wpLwnq3wc56m/ODGt6yYDIXZMuCpBCUS0m2zCnsAtuWWkQ1n+EVio6w6dsOc9dlvoRhGeP4NlvrA8KazzJu0mzWf/Db/ileP6r5SA2QlRdhDfgA8I+Afw38p8A/rr7+4fc/kkK7uGr3WMP0U7xCSkRBTkaJQ4BNi5KlNsoq55QFCFsTg0osElIKMqxKetzCrzICM47TrnuaTispiFBYFEQkZNRo02QNhY9iXDnFLFGkxGTMGXP2nuX2kBVQyEwuYnJ0F9ylxSo46EBlV8i9osouLNaps46u93UT0iNDB5ku9+i+qkc0O+DL33wJJV9hf7SH5SnOnztLr9ei3g9w3W12ti4yHb7BV77yVXZvzZhMZpy/uE4a66nJzubjrLAM32u5P8Rf/ztXnue89sKLZLkiaDRRto1w9Ka3sbBtB4mEUlCWglIplMxBlFi2xFbasjR0Ss5utFhfW6e1azOc3CWbRTi2g7IdLCEQIse1CvoNj52NNo+dX6frlhTjfTwH+k2fJI85SkritGSRLlFFTmDpwOm5DrZl4xXgFYowL6kLSUBKnExJForFNOf9H/owOzvn/lKfx0/L+lEzgXXgo9UM1Qb+uVLq3woh/gz4V0KI30KzZT7y/Q9TEjGp1GzqQI7imJR9YhYk5IBNqWxiRmSiIGZJaAV4wsHFIauMxBJStB+b9np3CLAoKJlWuDgHnw6iUpgrKm+WnBEz5nS5gINW0Ek4JGVJXllrTdSCBQk9keOxJGKMwsUix6WOUxGCSupV1V8jr3IZsIgzjzi1yHKXtJiBSrW6rwjI8gSltIBGHC/JllNIc/J4znRxTFG47B3f5Atf/lPG0xH7o31anYB3vec5Lr7vl6mxhSUlG73LBE+tEU0+yZdufYmdsw/zn/3W38K5t6+d7/YH+LGsLE345Kc+zrwoSRKF69SRlo1QFghZ5WgSSwqE0JqFQoKwJJYlaLgWG57Nhg9nWwUPn6+xVutxcttGFgm2byO9FOw5tippe/DAmZDHLvW5st2iLgucLEGETazWAH9e0HQiZJmRLhcMZ3NsVVCrhYSiSZzEzPIcqyyoW5KOLIlcycFiSnQ8Yf3sGR688gj15g8kE/xUrx8pCCilXgfe8l0ePwHe+8MepyRnypR25VKjOCLjBhFDHAIKpUiLjGUZMVdTMsvCFha28PFtC4VizpQlUaUOoA05A8LKm31EWqXkPXrU6SOr5qBVqRE5ODRo4LCGwEEx0yo3JIzVCXOWeDTxqJMi0JZbOQKFjSDERihFkiviZcYyyphNXmU4GjNbRBSZIC4kSVKSJClZllEUBVIKLMvSarsoLCmZTuckk2NqZQZZzuHJmNFoShzPEQLqjQbPXXkbDzxwiace/3ke3fiFqnbXq9Vq8Uvv+iVgTLyMcNwlbyZW/fiXUoppMuXq0R0cp4EqbXyvwXy+ABRBEFIICQqEDVJU7VEbLAm2BZ2mz4VejS3P5ky94HxLsebXubPTwi7HSN8jszOEG9P2HM7UXR7eCnj8YpNO28fKS7BrUGtDkiP8CC+OKFRM7mSkIiYWgC3IRM48iykQ1Ooh0vXAK1gcz4mtgtyRPPeL7+bs+Us/sc/sfln3BWIwp6gstgQZcyKGJESkpAzoIFXJotoAJS6jcQwSgsClHnooqaq+fJMCyZQxOSldAnISImakVVDwCXGY4OBi36v7dQlSY4DGAERozcImc3XC3fgQaXtsOedwaOPTR5t5u8zSmNuHR5wc3GI6n7NwrKZEAAAgAElEQVSIYqJZxmwYMzqaMh0viBcpSZKjlIDSoiwLirKgyBVSKhzXZTFfIkSJ5/lkWUmxHCOTJY5USGmT5w6XLl3gPe97D/3BZc6dO8dgsEZQa7MiLa1Wq9Xjve/9VZbLIa+/9grRIuGxt/z8T+5vWOR87iufZTyf0W6FuK5Ps9kmjjOiKML3dYBTCqSUlVKSAqGQpcKVFo3Ap9No0As91roN6o0aDd/hwbNbWLYEN6B0fPwwpF9zWa/ZnGs36fXqVUlY4TDyGBBQ87FiD9fK8YuQJC9IioLClpSOjSVCLGlj2x6ZSHHzHN91aDVqXHjsUR5/63OEje73f+M/A+u+CAIliiU5U5bY5JUxRolPAHhkZUQU54R+DVfWSKYpru/jBW0CGhQkJJT4OFgVzDYp5xyUEYWaYVs5jvSw0R7xGsOX0qhgyGlFSnJoIChRHDNnvzIx83GtDg25jrYe61PS5ihZcvXaId++eo3duwecHA5ZzmPSJCOPc/JlAYXEdXxEKUijlFKkFGVBmi7J8wIhLJQqWSxylMpQKkMICII6rcAlCBpcurjNL777w7huh7NnN3jkkUdA/nAM9lq9S63eJU1vIsT3tqH6cayiKHj++ZdwXY/ZTE9A/CBASkGaJGRZhuu5WFIiLQspBKoKAqLIUVlBluSkiSKzS4pckc4TLJXTDmqc6XQpXZfeYINBr0PbdwlkRigLUDpjIq9Yn6kAy0GELq69gZNGON6c0vYgilGlIlOCwnIQ0iHDIkUSlyWdtQGuUjz09C+wvvPAT/Qzu1/WfREEtFEGjCprcm0u5uDTJFYlkzwmTkosWSJVichcfL9FYDVRhc0iiljGMwLPpVYLqcs1LFFnnB4yjSJqvsAOA5A2KUVlmiEoGbEkIWZOgzo9JAnXmKhdDtI9ut6AvtjmQXebFItMNTheZuy+8TovX7/BK8+/zp2ru2RxhioUKi9JkwRVlFhKIEtFImKSOGE2n5GVWjUnzVKUKvBciR84nDlzlocevMD22S3OnDlDrbZD6PsEXsDaWp+LDzyKNoD+y60zW9/Z2Nrfv05RLOj3L+B539ui6odZRVHwxa99EcfxcNyAo4NDlLKQUiKlRVEWGtRDDcu2sKSFkAKUQloC21KQlUTTmIm7YJymDFHUkFhSUcYpoRAooWiqkp4qaANClYgshjQHz9U1hrRApNWkViLsQI8SEwiwUH5OmmZkhaIsBCW6SZkLG7/R4mA4xW136W5dQDg/u1Dh0+u+CAIKwThaImVGxwspVElapuSMSZYjhrM5VuExmyfMJxHLCWTznJPDQ6J4znQ5ISsTzpxZ48KlizSCOjVhkcmYjCmz4yGRPWLQsUkdQRRN2G6eQcmChAKFi1tNDCZc06wDK0RQx+MyLjukHDDJS77xja/z/37s8xzuTfFKn+ObC7IkwbEtQj/At0PyfMl8PGY2GZGlCWEQ0Ok22bl4gZ2dbS5cukC7fwnH8XFci263S7/XpdVu02o1kbLxEyesSJmjVIoQ5Q9+8g9YRVnw6S98Fs/1KDJIkhwhFAKBH/h4noZ8C3QpYNmWfn9KYTsSWyryJGKazRkqSTMLmQgLrxjiu2CpksAWCAlqMiJNY7JWiOsBogBPgGWD4+iveYFKJmSpQOETTWNm04hclSBsEiGYLOcs0iog5AosiwSLm7t7PHnlGQY7l3/kz+WnZd0XQaAoCw4Phtgyw+oLiiJhOV+wmC9RqYVVeKx126RJyfH+ESQwyUomswnH4yMWyYIg9GmHLnm6YEGKlBLPsllvbnPjJOZo/4RQdBGh5OBwSt3usVnrsC52EEi0U2xGgx62OEvD1oadcAVBA1Kfj37s/+RPPvkVDnbn5LFAxBOsTBLaHpaEeDplkcUEnsXZ9TaX3/kkT/zcs2zuXCYMA5rtOq1mg1a7hee3EfLf38ff6ewAOZb1o93tyrLkU3/6WWazBZlXIJAEQcBiocuPWhgShiGlKhFCIIWsWKLaKEOViiwvKRYJosg4UYKOlJwA6WxG3VfUPIWQpc4a/Bo1z8Eu04rHJfTmtyV4FggLlUZMTk44mUakqcf+4YzxeEZeFAjHI8sLojhhHmekWU5eKKRtk2LxwFPP8M4P/Apu+KNlRz9N674IAnmeMz9aMJ2cQCrY6HcplhHZvKRd65KmiixSJMucvdu7TA61hPX2ubP0u1vs7u+CUIS2YnZyQF5mtDsd6vUagdNiu/sA5dIjHUtK5dH1N5kcJ+wEDXzrElqiS7MAJS0EfZxKQkzRYJy9wf/1z36f3/uDf4Pr9CgjC3KBLBI8q2AZTWn3uzz+zNt49heeY237AmHg0W416fTX8cP7D2zi/JhS3VKVfOnLX0WUFkcHJ9TrTcbjGcfHJ+R5Qa1Wx3VdprMpSmlLLKX0AFUpRVEWCFViuy4qE0wWKXtMEOmStcCizAR5kmGLAs+RdDsdwkvnkSpHHO6C62uVIAUoBY6FkpKbu3t85RvXSEuP128c4/suy2VGo92qyjJJnEGj2aiwjzY/9/5f4Z1/42/R29j6sXw2Py3rvggCZaEo5iXLUcIknNJxm9i5SzJMcXwXS9hks4xuq8fbn3yG1176czxPcOHsOl7gUw8sLEdybnuDTJVkuaLpurjCZjlJ6YYbDC6fJaCGY3dB2JTFOjVpXIc1PFijQ41cmL5YDxcv8z//zv/KKy/fwcp8jg+HNOodkuUC284pVczf/jsf4QMf+lWarSa9Xg/XD7/3m71P1xc+9wkee+Jp2u0fvhuulOLW7i2Wyxjb9kiSHMfJEEJiWTZpllGqklotJEkT8iKnKAosy0Ip3UsoVYFlg2U75IVikma4i5jAFoQ2+LlAqhRXplgKQs9CSoEYjclnMyxPIuYFzCYov0ZWjnn5jdt88Suv8vL1PaQfMJ6ndByP3aMZfSVRlk2uHGzPQ6UlW2fP8u4P/waP/uKHaHQHP8FP+f5c90UQoISW2ybo2bR8n5ZTx6/1YFLgFh55JihiOHP2Mr2dd/KWi3coiq/pzr/ncunseWzLxXE95tmSjJyW18OhQdHoYcnzWCJACouVAanB/n+f0yoL/rv/9r/ntWsHhF6H/4+9Nw22bbvq+35jzrnW2s1pbn9f36lDpcbq6FwWoiyEjcvgQAxlf3DF4ASTij8kxDYmcWHH5INJU4Syy9iAMIHQplwmMigmtjGxMAg5kpD0kATS629/7+l3s9aazciHOdc+5973sJ707n3voXtG1Xnn3H12s85+e4455hj/pjaGsYtMasPl7cu8/d1fyfd93/dx9uwZTpw8/R99rld7vOWt72S69sVVLIryyU89zvrGBstFhzEVu7v7pKRMJhN639N3HRsbm8SYWLZL+r6nGTWZsuV7ggYqZ1ERohiStSwEluJoXYW3kGzmCYhVnnjmKTZ29jB+QSPK2Urog6fTgIzmbC06Ll64BtWEBx55gHrzDE89e5WTJ8/SU1ONxpiq5oGHX8Njr38db3zHV3P6sbdw4tQZmundcwQ4Gq+KJFC7ije/5k1MJzVVlZjUNRMZc8+bzrJ9fc5eXDKfB+IiMD4/Ztw8gKonJRCpEVGQrEdY2wnKGCv3Zvy/cRxCe198JE386D/5YT7/6cuMRqfwS8VZy8MPnuPTn/49/u7f+et8w5/8Ru6595U1jrhdsXni5Be+05FQVT728Y8yXyyo3Ijt5T7WOpbLOSJKM2qo5hXLdsna2hp9P2G5XBBCoIp50hFCoEs9vXPZMNRYTGXR2sFkiowcMklUNUxMYr02LNuAzOc0BvoU0BvbqBHmoWf99AjbrHPf/WuceWTKvE9c3F2w/sAb+Nbv+bvEGDPLSYS6bmiahtFkiq1HX+Cv/fKOV0USmNQTXn/uLRiT6cIi+4gaTk4eYOMBId17lpRGOJdxAwAib8Hao5efqcFWBgmyQXLsiw9V5R//6D/kg7/6G6yP72Vv74C2W2Bd5Pd+70n+/Ld/G//pt3074/EfvbL/dsZHPvI71JMp29s7XL9+o3T+K2Lo0JQQkxuBKSnGWKx1uQ8QM7RLFWJK+L6jxxGxWFPh64bOWTpjiUawlaMejxmPHHa9oUljfNeyWM6IPnH29BnOPnw/kiwfe/z3ubQ1Y/O+R5gHYWe/5Ru+5Vs4+8Ajr/Tb9aqNV0USEFmnsm/hUEKsBakRTN7I3UDrhcMFLnyhcv6lxK/96m+wVp/mYG9G6hISExcvP8X73vtufuBv/493dQJQVZ555hnEOA72D+i6nr7PbsxGDG2IBN9jxFDXFW27zBVA5UhJs50YYK0gauhih/ZKUEUlMiExFc84GaZGWXPKpAqoGFrfs3fQIqpM6oaOwBMXL/L7/+7jLPxJ3vOn/jhv/d7/AbE2K1IkZXQX/796MfGqSAJ5MQ9Ov/CFKax3bvGrKv/0x99Pij1dP2fz1ITdp6/Q9wu++qvfxQ//yI+xtnZ3nh2Pxm/+1m+z7D0xKXVdIyLs7e0hYui6zIsAwdqarsvSbtY6UuoJIWSkoEAthnHvCJ2SYkerke1ugektLlimzjA1wkgT6y4xsVNi7zl97710KE9cuECs1vn2H/h76Gu/mbqpceNX3zTm1RyvkiQAX2rpfifitz78bzmYX+PEiZPMFjvM2ys8+NCjvP+f/iyTyfGuArA3b9k5WDIaNaQU8b7l4GAXVcVIg2oGgSFQ1yOsFbzvC2+iy/qPRnDBstFbvE/0KaImsAg91/ueCsepZsTpytFaIY5rzpwc0U3h3LnTfOhTn+H+N7+Dt/6V/42qbhB75xiSX87x6ll5r5L42Z/5SZ584nM89tjDPPXU56icsLOzz3/93/yXxwmgxJ6PfP7pp3DOEWIkpoSxlqpy2MILSFHxZToQQshJQfNEIaVIjIEQPMF7UghU1jAd52YdKG3XcbBYMJvP8T4wqsZsTjc5ubbOvadOsHPlKmdOnuHt3/0PqEbT4wTwEuI4CdwSv/Irv0KMib29Peq65vqN66ytneS97/3WV/rSXjXx5JNP8dyF5wjB03UdwYcs0NGMcM6RUsrnfhFEDCFG+r7LsuHlKJdSxIdAiIGAogaSFZIxGOcwxhB7T9d61EPlGibjNQiR7a1rLOcL3vGnvglTNV/WngAvR3zJxwEReQPZW2CIx4AfIHNx/wuyQijAf6eqH/ySr/BljJ/+qX/EcrHAOsv29jZr6+t8/vPP8mv/8pezXdVxAHD69CkW8zkpJebzOW3XArk3EGLEh5YYldEkT3LadkHf94iAc46YDEQhaUTEYSphkXr6LiBWqStHZcc4IjFm3caGhipZkveYBK957SOYd71llVSO40uPLzkJqOrvA28DEBFL1tT+58B3Aj+sqv/LbbnClzF+7ud+jr5L7O3v8eAD9/O5zz+NEccb33znePh/FOOhM6e49777uLG1RQyBxWJJjAljDaaYiIizWJsT5zAeFCPYaIipyo7EJBKJNnq6GAkpYlTzKBEhRYNohWOEsw3qlfHahMuzZ9jo50zc3UPyuZNxuxqD7wWeUNVn/qiWZqpK8J4YAVW2trY42N/nJ37iHx+Xm7fElf05+3v7nD9/nv29fdq2xRSpoJQSo9EIEUfnl/iQtR3H4zFiBO87QnSEaEka6X2g7XqCGHCGZIRl8IS+w6gnxIqqmjBqNrCmYT5bUtcN43PnQc7c9WaityNuV0/gLwA/f+Tff01EPikiPykiXxwU7RWKn/mpf8Jkus6ybXFVxd7ePvfd/yDv+9Pf9kpf2qsmVJWkyi/+4i8wmU64evUqbbuk6zpiTKtkKcZgbaYLpxjLuBBiyD875xiPx4xGDVXlMCY3DI3LIh+d9/QhkqLBVmPWTpxmsnYKqUfM2siDb3or5qHXHR8FblPcDi/CGvgW4PvLTT8K/CCZ1/WDwP8KfNcLPO6I+chDL/UyXnL81m/9DtPpBkZuYKxlc3OT17z2K46rgBIpJf75r/1rPvzhDzNfzFkulsznc7quLYQhi7X5uw+JSKSqKrz3dN2SlFImDKWAdZZxPcY6QbCkKPhlh1ElGSEp1M2IWgJuPKGarhGqEYu4ZDrZgPMPgj/+/3K74nZUAt8EfExVrwKo6lVVjaqagB8n+xA8L1T1x1T1Xar6rrNnX1nmlqpy4blLaDAZ5971dF3Ha1/z5S8y+YVCVQkh8Isf+BU+8pHfQURoly2z2SyrInuPGKH3Pctl1vazzq52aTGyEhJpmoaqqhCR0iSsGDcjRq7CxkjoWlIIWGOoqopka7ok7C49e52ntw3u1FlYOw1n7z8+CtymuB09gb/IkaPAYDpS/vmtwOO34TXuaPhuhu8DM50zm83Y29tjbW3KI4888kpf2isaKSW6fsEP/8g/YvtgTlTFB8/Vq1fZ3dulaRqm0zWccxzMDpjP5tR1TVNPscZhjMteASlhrSJGEMlJRUSonFCrJdgKlxKxVxKCWCEgLIJnt12yM1+y20dOn9rAbJ4k6Qayng1SjtPAS4/bYT7yPuCvHrn5fxKRt5GPA0/f8rtXZfz7D32Eqqq5dOkSo4llY2Od8+fP8cCDz7NQvCti2P0/+9nP8ou/9Eu4ZkTX91mnfzZjuVyiSQk+0EuPD56u7eh9j4jgTMRUmtkdIkVrUFcrNt8mRYIsUkWl8dmELhnAZlXiPgV25kueuHIVqy3zxXm2b+wzurjHu6Znqe/jOAvchnipvgNzsj/30dv+0ku6olcg9vf22NjY5MqVq8QYaUYjELh27Qu4p32ZRUopqzzNZnzoQx/i2WefZXNjg0XnGTUNxhp67xmNRsxmMxaLBaqKasrSXTK4DVuaWhGr9EVMxNl8NEiasg2YZMCQhIiNqbgPCUFBk+T7uIq59zx19RI7Wxd47sJzPLB5D8ZMqc89yju/Ro9zwG2IVxF34JWLvdkBrY+M19eJcYlIZHd/n9/95Kf4jr/wSl/dnY9M5/Vs7+7y6U//Hk898TS7+7t0fUf0kUXvUSPUTYNzLjf2JmNSSuzv73FwkK3YM1pQqesJG+ubuVfQd8QYqCrBuQoRxTqyBVxI0Hti6MnGKxY1QkggasDURBVmPrBsO3w8QDmB9pH9cPzRvV1x/E4C1xZz9lNExmMkQLdccPLUKT72qU+vfIa/HCMpdF3Hla1tfu9Tn+XG1g0O5vsQhenmaVjM2N3ao4+JvvOkFEHBWcdkPEEQ5vM5MUb6Pou7Jk2EEFmbjnHWkZLHOUuMYUUhrrUqgqM1nXr2fM+MhLGK2EwmTyGRIoS6ppWGmIS6Osl+fZpr13epzz563Bi8TXGcBIB58PTWYCcj5ttzqvGEarzJxUuXefrZZ3n0VTDCvJ0RYmS5bNlvez7xqc/w7HPP4XufjwMxl/TaBfoo2NEYq5FQNAGss9R1nS3UrGGj3aDrWg5mSowRTUrlDFcuPMv5e+/l7W9/GzEGnrtwgflsvpoQOGdZzOds7e4z63tSZbO6UGWzJ4QmUIOYBq0aWu/Yj1Out46d4HjkTW8/Ht/epjhOAsCpzRPURokK1tacOn2aJ598itOnT/OX/vO/yr/51X9BU315vFXzxZyLly7xiU//AU89e4m+96gmnKuYTCaIGHz0BB/wPqAACDFlwM/Q6Ksqx9iNiDESfMBax3wxZzGfs7u9zdrGJmfOn6eqKqo6G8Xv7+/hnGMymeL7nr29Xbq+x1YVRhOuqqiqrCURQkAqwdoK6yqa8QQxDnGO0XTM2Qc2j5PAbYovj0/2S4x7z53GGgg+srm5SVXV7Ozu8KY3vYlLly7xHz71OH/iHW97pS/zJYX3nt3dPT7+8Y/yu596nHmbsPWIZjSi6zpms1lG81UO33v6vifEkPm/xSg1akKTFhpwzHP+8ZiNjQ2cs1RVlc/1a8pyueB3P/5xvO95xzvfyXve8/XcuHGD3/j1X+fG1SsYa9nb2qaZjLDWEEKEknIAjBEM2bcxhMB0MmFtMuXUmQ3CsnvF3scvxzhOAkBlEyZ2bE4n9B7atuXtb38HIUYee+wxfuZn/g8evuc8586eI6bIYrEgpYQxhtFohLWWGCPL5ZKubUmqGGNwzuVmWUz0fYdBqFx1CKlNubMOmV1X1zXWWlJZaEo25wDFWItzrnD2X/wOGGNif++Ap59+ht/9xCe48NwFVBwhJW7sXqNpaprRCN97Qgg0o4YUE957fPCkmIjREwrk11pLCIEQQnFVNown49wwtC77QLYti7lSOctnP/MZ5rM53/3d383b3vY29vf2+N2Pf4zZfIGpXO4lOJONSFY5QBExGGtIKaGqTCZTJpMJ3vc88OD54yrgNsZxEgDObK4xsonKSjbE9IkHH7w/jwinlvF4jV/+l/+ar/3ar2W5WHLl6hVSTFRVxZmzZ2jqhvl8xuXLV9je2SbFRF3XjMdjxuMxfd8z292jUsPaaMpoNFp10mMMGGOYTNc4cWKDUTPCh5ihtqrEqIgkRqMxk8ka6xtTRqMj2PyUinBnxuxryrJdquB9z8HBgs9+5gk++clPYgxMJyfZn8/wITKdZMu3ne0dnHOsra3RLpfFFCTRdxkb4PuWxSK7QjejEQpYazAmA05HKx2ByHxeEXvPxsYGpMhsNufyxQu8/yd+nPvuux8EmmbEtWvXcvlfV6SU+xHW2ZV4bEqJuqoxRqjrmskkv4/LvT2+63v/W5w7/ujerjh+J4E3vOb1rI9qFu0SJ1O8ES48d4mNjQ1UDdPpBu2i5zf+7YdIMWap6vE4w40vXMZ7nwE0mvX2m7rG+8RyuYfvr2OsZVTVpJBoF3vAXtn5G5zLxp2LNnB9ayebdBqLLSScvMMaDg5aIP/eWkvdZDGNrm1ZLLPlV+UcIcSyG3fs7Oywt79XqgnLctGS0hJjHSI9bdchRgrHPzKbzQAKBVgJMRQIdUvXdQX0k8VEXXNYAQ33N8ZiTL62kCLGWTZOVGzduMFTTz3NtevXib0nxEhV19R1g7FC1pRk5UxkTE5wA/NwbTrFWsuNnau8861v5s/82W9e0ZSP46XHcRIAzpx5gBOTEd3yAIPBiskEGNewNlnHd4Htrd3c5KoqUoS+yyOvtm0JPmQ7LTGkoHRVFtP03pcmWkW39DRSYzDkBRYJyeNcwhiLc2BE8ofbGFCDxZJSVucRlVWTLin0/bKQc7Ltd0qHLL7e98xmM/b3D/B9T1XXaDKIqYkRvO+yFVfIRw5jcwXR9m3G+ZcFFnwgacI6x2QyJoSI9wXyWzliTPR9v3ofqyr3CKy1IMWSPPS4uioQ5B7fe2KKWOeQEGhsxdraOhsba8QY2T84QBBC8GxsrDObzUlJubG1xcMPP8Tf+Jt/g6apX+6PyJd1HCeBEvefPcnFK9cxtWE8aqjrmtFojCAsF8tVwyx/wId+mSLmKAcrl+Z935NS1idwLi8WTQkvPSImP64s+GHB2LLLWufyz0Dq/aobLyYbeRpTzsoxv1YoVN2BsmuOUHgrZ4gBgu8K3qH8V1PZdUE1z+Pz2TuhKqukEkIoVQQ5EUksfoL57zc2X0vwgbyD5x5J0kSd8sLvvcdVFXHgC9Q1pmAGet+TUsA54dTpk5w9e45Tp04zm824fv06VV1T1T2LxZxr167zQz/4g7zxDW85Vnm6zXGcBEqcP7PJyAhpNKIebzKdThHJ7Lj5fEFVVflDb0zm1acECnVd41xWzRnosoP7boyR3vvcMUez8AaaobNILp+tkGKG0rqSFIyxWBJx0eUGWTHxyJp9pSGmoKSheb9CNGlSFF0Zf4aYuf6mQHU1GFQjqoXaqwkRLTRfBYr2n/dHNAIyBVihYP5zIjHGYsQUFeHDZmjlKmKIVFWiGTUguaoQkUwiguI/EPHBs39wQHjmWfb392mahq7tSCnSd7lyWFtbYzKZ8lXv+Eqq417AbY/jd7SEE+XMqRP0oxNU05NMxmO89/R9KGaaWs7nWU1Xi2Fp13liYnU+DiE36XKXP62mCNYK1uVpQNJYpLktYpQQIkYs1gnWWKytEE3Q+mzAYskIOzEgWn62IINwp2EoSHzIKr6xJKmYstLv0FvQBEnyzh/L9R0mgUiMUv7ufnXtQxJYVSSSCT4pRrz3tF1XGqWuJC2DO4KrsMbinSeEXNlY53ClAkITXbdksZhz4/qNImGeE+ZiMafvezY2Nnj3u/94USI+jtsdx0kAiPvPMT/Y5dzZ0yybM6hbQ0SIUTHGYc3gnJM/nCr5XA+QVAg+C2KqpjL2y2U2qvgQEYnUtUElouqLBHcqJTarndqkIs6RHMQIbb/aPfPCF1zlGDUNSLZdUwCVsvgzoCeRyrWk4gScENNgrCOkCMatqpk09DJSwpceQIqRmIoiUFBUI0iirqqciEqEkHfyASmY359chTjnEKQ0Hi3WWZZLJcWcnIyxBTloGE8aJpPcaBWBxXKJpgw/Hk/G7O7u8k3f8D5Go7vbM/BOxXESAHav/AEH+3usbdzDeO0EC29o2zbvfhbqppxpDaAx77SlVG7qERrz+dmKwRpDIkFKgKCxLFYnxODxcUkMnqQDA07KUcFgkyWVL2IixQ6j2ZE3C3ha1ERqHKJK1Lz4UAjJo0lxrsIIRE0E7/Ehl+qVWhK5MjAiJJVVpSJyyCDMpp0gCJqyd4BqxFpB3WD/xk1HImsMYvPur0lLTyPl6QbZbbgyjlosvfdoSnnCYC1WhJSEylbFID4xqTOL0xhhc22d+XzOV7z2dcdHgTsUx+8qcH1rG59gMp5gRo7aCSEJdAFjlcYaum44fOcFbqzmUl0DaDFD1YSoycuknM0tEVFFg5JsIEUlRkoSyJz6lBJeI5UDKlPO4wGjUIsDM9QdjmUfibTUpkFdfg4iRAzGWqIaQgqECAlLslXuV+DwasDWRLH46Fedfeek6AemFTjnMEHknV8VQgAkYY3N1YMKqgbnRplSnARNkVoVRyKQhUMThoQQmgZvXTYrdZaqahAVUg8xgDVCiD2VVoDSVA02CKBibrEAACAASURBVK9/8FHq6thc5E7FcRIALl3fIjlHRPBxAVQgnpg6kra5OSZ+df+hOW0UTAy5vAXKIRw0JwCDUA3n776HKrfWFQEt3wtyMMaENUpSiCERo9I0E7AVxuWjgCaIMTDrAk1lseT5fIqpNPhAU1gt5lyOV4hYggiShsZmpg+nglbUcr1ZBCT/LsaMNzDWoDoYhkCK+X7WGoQB3GOIMVcRGhKkgJOEFYOKoBi8JnLvQsCCcY7KNaQk9OQphLN2hRS0Vjl94hQG4dH7HsbZ44/qnYoX9c6KyE8Cfxa4pqpvLredIpuPPEJWEPoOVd2R3E7+EeDPAAvgL6vqx27/pd++eOKZC0gzYul7AgsiDV23pO9bfOgRZIXKOxqqBhtuJhsriqaUUXXGgmqG3ab8+GgK2041NxiTrKDDw9hx9TN5EYHJlYDo6t8hRUKfjgh4liaeUDT8cssw4wtycpGUsphn2emNmGILlsv63NQDo4oazddosoU4Uo4kpkwrTNYShBokk5BCiMSkeA/GZv0AY4QkCU0REcWYfH3WWCpbk6xgqx5CIGnmCzRNQ11XbG6cRgTOnLvnGBx0B+PFptefAv4h8NNHbvtbwL9R1b8vIn+r/Pv7yMKjrytfX01WH/7q23XBdyKeu3CJ6XRMTLnZFQmr8ZkmBdGin38YAphkkJhueTYpZ1vF9/2KfYeVMj3I3n0AaswRDoGuJL2Hcnzo0OfFxmoUeNTie9WYU8Vq7tw757DG5gU4jC3TkUQ1JIHC/x+OAEMScdisEKS59IdcIQzjyoyNyAtZXKkSYkJMgNIDCGIxUoERgkaSCCo5OYoxGFthSpKo6oq2z56FMQQwAkboY8Qaw8bps5jjJHDH4kUlAVX9dyLyyC03/zng68vP/zvwG+Qk8OeAn9aMRvmwiJy4RXz0VRXd7DKz2YLxaI0uRHSkJImr30upkYP3tzxSMEkg6i23lvGgKsvlMkNkq4rKVBlFmAKx4ASstaXcHiYLOemkAuZZcWQ0755KwojFWEtKYXV/IU8YBjBTSglX/Pwg8wA0u4HmeUK5ZGMMJMWnzOAzJiMTUc09D81TCVOS1dAwXFUcpQIyNqsJ22jRKiHGomIIaiEqSYWIIZJKRSOoEbSMTgdSVPCJtu/og2eSIvN2gbWWs/fcu+IUHMftj5fyzp4/srCvAIMq5/3Ac0fud6HcdlMSeLX4Dnzw//4gPhoCjtliSW09STL5JsZIjAkkz9+PhgBGBXmBY0LUvLAxEUMiaU/Xe/oUCKQVmGdYWJSdeWjO6ZGdefjKAB1WaMABljyc6/OIL1cFsdBvsy3Y8NjD11Dy+dscGRVmBGMBQqnBloSxSlZGDv/yw8sut5Ty3lUYDFJVGdrcRULoQAXU4KOiKVIBNmaasDEGIeKMopXgeyXEHmSED4EuRc7dd+8xYegOxm15Z1VVRUS/8D1vesyPAT8G8K53veuLeuztjJ//hf+TU6dO4aoGH5aktgWb6Ir3QAyxNOVuvsQBhnvrhauC77s8PmwGQ86W5bIjmbIDknfhYfHlSuBwl9ZSive9x7qES27FGFzxA/p+pflvjUWMHGr6F1TfcHwY8AspxSLxld1/b/1rhsRk1a4qDBngzPZwSrCCHBc8AsBgNipicHaCD8JieUDbR4wBJwafEhoDBojWoMmgEUgLKvFUlaAjiw9kXIUm+i6wtjFBjHAcdyZeShK4OpT5InIvMEjzXgQePHK/B8ptr8o4efI8o/GYZ565zEOPvp7LN3aJJp9PU4ws2yW+97mBFocdWkrJD6EPh424Av7JYzWh7/vVTuusReoqJwEZyEKuJIO4QtINqj1VrOn6CIFyvjerPgEyWHrlKiSIWcGW09CjEFZkoKGEr6oKazOopyqCoYNw6CAvNtwvcx4yGChponZZ68D3PhOjrKGyjiCCKZoHqhFrLEEjneZ+Q9d3aApMaofEQOx6vHgqo0gFlVGsCVjrCSGxiAtSCIzqDdCe6ahmc5RRmsdxZ+KlJIEPAP8Z8PfL9//ryO1/TUR+gdwQ3Hu19gM+8ju/ydlz5znYnzMZn+TZZy9jRw1RWQljhhDwwWPEFFhtQkzevY2ChCMknyMa+8OOuZq5q5K8QoHLJpMbgsNObowU3EBOItZBrfl8P+yCg8rOEFISQyy3HS3tjTEZX3Ckx2BdbvjlBmUeT5qCFhwIRBy5hsPjSn4esYd/Uz7CDJWLHhEEYVVFVM5Su2InrgkjinPgNNGkjjXnOLM5xQL7e579gzmt6RCTEH8AJjBZO4M1x5qidzJe7Ijw58lNwDMicgH4O+TF/0si8leAZ4DvKHf/IHk8+HnyiPA7b/M137b4zX//YerRlKoXzpw+z2c/9zmapiLEXIp7H1aQ2EAozL2MqEux2GiH/kgSyJXA4Rn+MAlEVUIwYF2m0Zbfi8B4PHnetRljqBuH9z4vuEL4GZLAMAEYVIAGJd/8+rlyyB3+PJE4KgISYyjPlw6FSVIsSchgbW72DRVEKBiADKWOhBhQtVjLkeQxRB6RVmqwlUXriqgBS8LgcS5iUqBKykY15v5TY2ojbJsFDR3Jw0ISkyoxXWvYODVdHUWO487Ei50O/MU/5FfvfYH7KvBfvZSLejni//3NX2dvts/Vy9vUzZS9/QWT6Qa95qNA32eefgxxtVOGGIghlsUrWBQX+9Xse1hkZjWeO+z6RwSVmjLIX43n/rAy14jJWH+f6cKormjDWoRFhgQTQpb/MiY/t7OsGooxZTnwvJDya2UhkEQ08abXzEecVDgAgMmGIQRdgYdCyCKkaoexZlr1MKC8RMqL3kpEjRIkYtVjaKlIiPbUKCca4eyaozaWCSPWa6i05er2LqILToxOcc/JCdUXIad2HF983JUt10999qN8+CO/w333Pczly7vEJFy+eo2Tp06yXHaEkCG1g9jmSlzTZyGOvOtmSHBm1x212zKrHsGqG6+KikGqDJmNZVEaY5Hqheffqnnhee/L7q83YReyYIkUMtKhpBiqROLqCBHicM2Fioysrj+mcqwRs8IiaNJcFcQMRwbFl7/bJoMv/AJFV5OGw+5o/sGQMg44BjQscdrRSEDE4zRQO2VjWnF2o+HM2ohxnQhr6yzaMRIWLObbdGHBtFbOb46x5rgSuJNxVyaBn3z/+3ngwcfoes8bXv8mnn32IpVTui438kKIpfver6YDQyk+kGxUE5aAoAUJN/QEZHWePnocUCNYU+N90RiwhvF4kkdfL7DRqWbyTtu2eO9Xu3Q5oh8mhtLtH+DAq6ol5OPLMEocNAmH587lPmhKYM1NlczgRbg6gnhfQEx2lZicOihqSLeGkFAfiP2S0M6w2uOahsbU2JRYq4X7Tm1y/5kTPHDuBGdPnCD0c67v7LO7dY3N2uArx0P3nOaxB++j+jKRe3+1xl337v70z/0Ub33b17C/N+O3f/ujfN2738Pu3h6T8TpXrl5ienJSFnkG4AzjtRg9qh0xZXLPat5OWgmNZIitYC2oyiEAqJBrMD2hV9q2xVpLIxata0xSnIAYRVIh34gSuwBdz6AE5FRWu736iNdczjuEaAUIpQLpV7JkWUAkNzQz3VkAIaWsYDQ0MAfgj6oSQwQVos8egzHlaYBVIYTMjbAhYTAkUawCRlARqiT0qkRa+rAg+AVOIuNk2awNo2TYHBkeOTHmoQ3Lg5uOc+fWWM4sftkztiNq02BVOTtpePOD5xgdJ4E7GnfVu3v9YMkijHnuuYukFDl330N84tOfZbyxjg+RtRMn6P18NQcHpe+XhOgRk0jSgQlEciIgxoziG0RBrSt6fbn01nTI2ddkSV0W+KhFqJ3F4hG/xMQKV6jG3dITxGCcQ4PQOLBRibHPIKIYCX1P8BkMlFV+E13bEwhkKtPQqMuIvFzJJGYzX9SDDF1XUzcNlasJ0TJfzCEJlcsCoNZWaBJCBA2eGNsMczaGkTE4akIfSzLMtydN9ElQE8AFjI1Mm5pNVU5a4VxlODtZ55H7Nnnw/Cbn1x0n6h4Je8hin3TQ4phgZUroDmi6JdPNCXLcGLyjcdckAVXlX3zggzz+qc8wnUyo6hrnPN6X+boGonpiPMTiH00GSgDNoiCoz/8mZQIQmXCTNEEqI7M0cAFSkfyKGAyVa7D20KiDpJl2nBKSO2yZcOMD81l7WMJTkHki+NKQk8IxTikhGoGIcrTZl0jpkCSkmogp4/tDMKsJgojN8gcqBSWZjzXDCd9IZvWtqpAY6WNfWgCmmAcaTFEySuTqQYiMa8fJquKeScW9k4rzU+G+9YZTdWLaKK4GYovpO6zCeDThxMZJlkbR5RK0R8KT4L4C5JhOfCfirkkCn3vuKv/hYx9jsVhw/p6zBcgiYPJOHVMoXXa/GomlogkAA4ovz8aH76SCy0chHmLphwdk1R8ycAeDlbz4m+LuexOOIB0yAm+WKjuc2w+EoEG9KIN7cpMvJS2A5KNxSEwalIJSjOWo0heOQSjCnRbBgUasjSvikKaIkDUQtQiSxhhJIcujOVfsxknleZUUFY3g1LJRW+49ucEjJ8bcN7WcncDJScLqHA2KyLToMSSq2rG2PuH02ZNsxzmhX5KmY9jdhrMR3HESuBNx1ySBD3zgAyyXSyaT8WqnjzEQSxPt6Fcsbjt50eTFiQxn+7T6IqXSNxjEwHPbzlqLeR7WXbC4I14C9sh1xBUdOMbB889m5d4jCcL7sNrRB2VgHRJTSllW7Hl/+eHvVyPOMmnwBbVoXUXlxnksKIqLjmgFEXf4/JJIaWAlZoCSs3kMakRBE6rF/ASDCZaGxEY94r5Tm7z23pOcH0U23Byn+yz2t+h3tpicNsjmBrZyNI1jPFKmk4arYUnXGXQyhuMR4R2NuyYJbO9ss76+Rtf1XLp8ic2NjcwPaLuy63p6n7+GJDAAcIYmYEqRm+m+ESkiG5EjrLpinjFo7OXvBtEBJnw4Ohz+fWsSqCq74tAPtw/3ubWCYBjV3VoJKAXlGG96fL7+DEIyxlFVDVq7IoSqeOmxxmUl5BSRFPHRA3b1+pTG53DUGJCHmeRU4cyIJgrrVcP5zSkPnzvBKTfH+Bmpa2mZ0y4X9AdjmkkDpkFF8ETmywV7ewfYM1MMHYzWB9WW47gDcdckgcX8IEtja2DcjFks9pnPZ3R9vxqlqfZHEkCm166OA+UDPszYh1J/6AM4OZQiP7QFE4wWJmASbCnnu65j8B0Y8ASH0F0tiSDjFG5d+EMVkFKPMVmGK6aAkgVFjyaBIVEdJoGMDEzl/ikKRvKoMV/LiAhE54na5x6FlOf3PeAOSUYrf4Mjegfl+FEZSyWOkQms1ZYz44ZT6xWNCMw8NImTmxWhs1gN4CO9V5a9Z29/xuUrV/Ch48GHHqQWsr7AcdyxuGuSgPd5UY3HY06dOsnlK1fo+lwJaIHQplUpDnmMpiv2YEyDCu/N983Y/EhB7wM2d9RVVwy8bN2liIsrsM6KSsyhyOeQALLpp2exmJXryWO9DAjKqL2cCAIxlgolDROBAcevqz7GcFxJmsv4lGJWPk6CihKTKYKiw/iwJCWTirx6UR8irbAEw/WvwFEm4w4MmdNAMhiUykTGDpqqEAAaASrqakLtgckYTNZOvH7QcXX7gBt7c0bTNTbPnMYiMB7DMWDojsVdkQSSKuvr6/i4S0qRre1tlsslvs8+ez6EFXnH2sEoRMv832Tbsb4jho5wZHpgC8FmVSIDcKi8A0dIRJorgMrJSvhj1bQrO/XqelNCNSJFVnzoO9xaaaQE3oOuqMEmL/ojpCFdSaAXzYEQiDFgXSYviVGi9rRdwsWQNQZjRUwVTnJvwwZDgiNVCpB6mrrBVa4kOUuSLCUWCz4iieKMzbBfl/KOPm4gVhAdjNfQtRMkqbmytcXjT17n89cWLFQYj9Z48uJVHtmbsXbPw9wlH9VXJO6Kd3br4IC2XyKSP5zdfEkMHdkNN6ApFHefI6Kag+4fR0eFLyYOF70kKTp+CRUgpVUVsJLshpvO/odfkRD7bFSShmNA7kc8H2GYd/Cjo0lWPx8KieR7SlEiCkhKJGMxJq0mADHVxORJyROTIBFi8qtKZRXJ4Fy1kl8bJhNZB6GH0JOahHGKmHzUEg05a/Ue1IKpSfsdF3eu8PjTN/j9i7s8dxBRYGf3Bg/ff5bQ9qg2eRx6HHck7ookcOXqZbpuyaB7ktTjQ0cIPbEstFy8WpKalbLPsKheoOX+Hw89pPSqDLrDQNkh813KMaOw84Y4bAJ2KGUqIWUakB/Jzay9UgkM6sUMpKXyfSgDCirQGClHmr4AnbLCkEiBR4ea4DuCsxloxKB3MOgkDC864Bfy9ECMUvxWCKKIzRyGznfMDnbotyLNJEHnoY1Qj0lac+XaAY9f2OPJGwtudHCgDdYafLfPfgcHV7dZ9xF7PB28Y3FXJIG2nyEun5sFwVUQU0dILVE7ElkXL6VB/VdXTb9hV3+xkXdDhSOehBmWq6ARIzcDkW7tB6wMQIgYd6jllwYEoiZSeL7KERx26tMKpDQkAQbyYj67G9DChhzqHWMqBuFSFzp8sFjnMuORbGpy08fFVPm4VHgROXFmzEAyCRWl10Qfe5Z9S9/WNEbR1kML1FMWveHpS5f4g4vb3OhrfLNBlIpl1zHZOM0sGa7szDifEscyo3cu7ooaK7sD21UZnDX4fPk5KwilFMH4gqoboL6HY8HDkFu+5534pq78MPIren+DDsEf9tW2LV3X0XVdZi6umIqlYpDhdZ4PLlqBjI5MFzKOoVzRMFFIWkREiniozZJkg3z4YDIaY1gRp4L3BN+vJiI3xVFU5DCmLNWHD4F537IILW3sM6jQOvCRuOzpOiWmhoOF8PSVfS5szzgIlt40LIJhZxGZJ8OnnnyGeRgqnOO4U/EFK4E/xHPgfwa+GeiBJ4DvVNXdokj8GeD3y8M/rKrfcweu+4uKru1wxhGJtN2S2WJO9AUIFBVRzdZ+MngAKBn/O6j4ZKZe9iHLx4Z8yi4lsaRyLqakg6Fkt5ioWWcTMs3XHHbxwRR+fn9I9uFwtIeR/LwFsLRq9t36B2q5Xg77B8LQEIygme2X0KLmneHH2e0490JM8VdM2meeQ/TEaLG5o7GyVTt8OSl8AYeVoQ+SEZTBKJ0PHCz3uGGEq9uG3Q1L00DoAm0yjJJj/2DJ1kGHN1NSNaXrhbbPBq9tSDx19QqnH3w9VXPsQXgn48UcB36K53sO/Cvg+1U1iMgPAd9PlhsHeEJV33Zbr/Ilhk2GST2h218SFp7F3gJJivFgYpYNl3J0znkNEI8zASmyWMELairEOiARC9ZejJLUk8SDCRgbEI1IUqqkVFojWlx6DNmHQPJizOw+QAPOVhiTz9fBt6iAqxsGfa9hvKfKSt4MPVx8qoGER5IgFDMRhZTy5COrDmd+Qu4V5OvPWSdlvICYDP+FrB2oHusqjK3AVGQuwqBunGHBIgVOHXpiUkxlqJyjMTXaw/5sxo0ty/7pMRtak7yi1jGbtVy5vs1+L4Rmk+AmpLZH/BKnPSkGFt5Q3ftWTDV+mT8xd1d8wSSgL+A5oKr/z5F/fhj487f3sm5vOFNR4+gWPalPmChozHh/EzLtV6IgVrOZqEkZQy8x693FUkZTZbpvQcwlzY49UQ29iSSTEAKiHTaBxgaTLDaVQ3mdgTfIIOGdz/lG4krRN2hEkidoIqaqaPzn149FOUSPlN4ryHD5WY5qIKsUF0ApOz0Z4iuJVJqJ+X6ZICWSHYCyN2KWAzOpwjZTrHE5iUhBBZqEGC0SY0KMWZC0MiOaugLXUC0bYjvHByUZS4/FJ0Fqx7XdfT5/4SI7bWDuKvYXPYuDfVJ7ADGx8B3jtY1j56GXIW5HY/C7yHZkQzwqIh8H9oG/raofeqEHvZy+AylGurbDF/Xf2lXMu3nG0aeMB3DOZUPP2BdCC+SxWzkcSP6PqGJUcUnzjZoXhpCQFNHkIUUYvAdUkVQWT0qkVDiJBXZrjOBMKlgYm0E/ObOwXC4LRdkiZnA8zmQgZagAbkETPv99XpX9CAUDUZLCLSrxR6cUedcPJDXZ2TgWsxIMUpKYEnP1QY2x2etwgEtrEFIUFEcyDR0182SY+4iawDM7uzx5bYsDxnQxsbO/x87ODiEGjBgWiwXvfve7ma6t3aFPxXEM8ZKSgIj890AAfrbcdBl4SFW3ROSdwC+LyJtUdf/Wx+rL6DtwFKE3yHYfheMOyDcn9sh4kOLwO1Boy06qitF8TCBZiJk3b4zioTTkBGcclRqcCpVoxigQs+V4Kif4Qaq8tkXlp1yLtRhVuiIyKuUMn9v7R9SKBkajplXCuek9Ls+ZUrETKwKpg6GK3ILCEw7bnZkNmJubQT1JdDVOzFVMoO/z6zmTBVhSUsRbqBqcmlw5uTGeht0eQuzZ2pnTSsvnr1zn8qJFT54iIiyWi5XYCi4nqW/8xm/ixOaJO/a5OI4cX3ISEJG/TG4YvreIi6KqHdCVnz8qIk8Arwf+v5d+qV96xDiU21mcs+/7I14Bh8i+gQcgZWcNyRBUi/O4QzRiNOGKroCqosFTW5u9Bk1NSCnLeYcMbzFo+UrURokmHzmGMl4RgqlX12CdzQu2EpIhC5UMpKNbOAYrr0Q9RCYeDYVCHYZkD52K8qTgBaTBZNXBzBOHIquGRrQk0Hw6UYyJ5RgQGdVFOzElVAQXawSLkRGqib2l8vS1A0Y2srU7Y7trubCzz0E1om7GdCEfWaqqWsGQz5w5zTve+TVMpseVwJ2OLykJiMifBv4m8B5VXRy5/SywrapRRB4jm5I+eVuu9CXEUcHOgbl30+I/MqsnJXKz/xCAk8oemRd1LNWCR1P+GskIV9eoMwQF30fUCJU6mpS/rBjSOPfiQgqEmIhJCYWlF0KZF1hbEpajtk3B/2efwUHkUwZ04NFjwIAJOBqqh8XBi6i1hIIBOpJUYkwYE0GyM3KIWqoim98TI7mfYKRgIZTo899HEJZRubK7oGvnjBvBa8+12Zwtr7C2wcIHZvMOYw2j8Zi+GJM+9prXMBodTwVejngxI8IX8hz4fqAB/lVZTMMo8OuAvycinlzxfo+qbt+ha3/REUKgbduVgrAxZoX/h8NEANlfMInBlF06iRAlk2esUZJPWPUQevAdtVFq47NDz2SMjzW9ibhxTWNrRjiqaDCqeOdRUUJyzJctISidj4SUd9W8YqXYiCuuclR1ld2JY6KWmvlsjnMWYyzJp7KjF9hxqXhg4B8Uoo+RlYrQAPPllkoovxE8D42IUHoNISeZJAVU5UrTLoHkJGFN5l/4EPB9IPrALES0bdnbaxlPLGZk2O1g2UxQN2J/tqSf90xHUyaTMb7vESO84fWvZzKd3uFPxnHAi5sOvJDnwPv/kPv+M+CfvdSLut0xgG+stSswjvd+lQQO5cET1ghGQSXvdApEIgZLVQmxz8cB0R5ix6mNhma9YrQ5Zbp5itmyY3++ZFRPmNQjRuKwXkmxZxliJtgky+Kgw+Iw5LN3FLCuAjX0PtH7Dmk1n4lVqes6qxGlRNOMMghpnoghZhFQa6kqpfd+xXA0RZuvcBAzDLngDOxgg1aUhgftgRBZJSNjzEphKESPlpFi7pFk4ZN8vGpxrsK5OmsxxkAIPVKqHA3QJZgvA0Tw1hFMw2LZM1u2iE+sTQyTyYS2bXHO8ZVf9VVsrK+/zJ+UuzPuCtjw0PwbjUbZUSeEW3bMgR2niMsjL68un3nVEGM+HjRVTTKW2sJkXDOeCvffe5Iz99zL5OQ5pF7j6tY+V7d2EByjqqYWi60iGhRdCj4pIXb4dk7VTBASMXiWweNDIqQs2tn1HUqf1ZDGEzY21okpcvLUqXw0WHpGTYOMTEEl5oZfCHnU55wrHAY9NChNBjGKSZkzYK3FOnsI/9UiXLpqMhYk4QofUKoGAUxENRCjL9VEaWqS9RQRT5LsKqxJCUlwERo3wjYV+13L7nJOjMoIlwFMxnJi8wTWWh599HWMXsCZ6Thuf9wVSeDg4IC9vT1ijDjnVjbXgwHnQI9VzW6/UStiyt1+r4akgnVNts9G2GwqHj5zkvvPjXnjGx7j9Pl78DLm8tacdunZ35mxXHYogq0N06ammlTUdWT3YIfFoqNxhsoaDuZzBEPsehbLFtsG6rpGDIzGjq3tbfouA5j6vuex1zxGUzfUdYW1jrZdsru3l3seRYvAlJ19aIg6d+hqTAQ1BmcdrnKHfgQFj2DNIR05xLAyYX1eU0FiFmeNPSJ2NYpUEmoFqSLRZ8HRFD1jI1RVTTNeI1WGOJ/Rt11mNVb5+hA4dfo0qkozWivah8dxp+OuSAKz2YytrS2A1U65XC6Bm8UxAPqlJ2FAHVEgiQFrqOqKlGZYgfOnT/LH3vgIr3noBA+cP4VXw6XtA7avXGX36jbL/RnLRUese8ykZzydUjcNa2dO0rhI3y6J6xOm66dIxtCqIemCfrHIRxexGBOxrmI8GoPA1vYWfa9U9XOMRiNGoxGbm+M8VjOWajJdLdrlcklKkWY0KhUB5Wyf3YaSppwEnFthEIaJwQDO0aTgoY99SQIDnLr4k0FhOAZUs9R5hjRnCLVUCVwkSgSTsJMJo7UxKkLseybW0jtH5yO2cBmMsUwnk1VyPo6XJ+6KJDDgArz3xZfP0nUdcOjZN3z4TcHb52aaxZi6lLu5JD6xNuF1jz3Mm974Ws5MlBsXn+bi9R0ubi145vIBNw48XXTEAMuuJ7ULpF9CmvDwqXv4Y2/9E1y8fIXf/MhHkarhvnvPs7Po6XtDC/goROMI3rO9vcc995yl9z2TyZT3ve8bCb7j2vXr7O/tMRqNMgkpRYIPLJdLRuMRTdOsUIRDrAxQySAla22xJy/mqEfGj3ZFLsp8AtVUaNhHjgRoQQwKxQIRYwdCUsDaRKoUnGKsY7I2pm4aZgf7dO2curZsVjULPMZacC2EowAAIABJREFUbKlYbp3eHMedj7siCezu7q4AQ0f5+1mq69DqW1UZWQtiETE44zJnKClxcUDlPA/cc4o3ve4xHr3/HtqDyzxz8TmeePYy+60wn0MIuZz2fSR5j3qht4m+Vk6fPMFbv/4/4R1rZziIP8QnPvEJqrphuXXAou+IKWsPZOFOS4rZEXh2sOA9X/cn+evf+/3EEFksFyyXu8QUmS/mXL50me2dbR5//HE+8+nPsGyXVJXL+okpUVXVTbZo2SXJYqzFFTdlFUOIPtuwK0g5JqwOAiKIZsWkwa9IimmqFYt12ejEWEu0PcaSbcgraMQyHjcQIov9fRbzA9bWRtjaMW0aonWIMVRVRdt11FUzaLwdx8sQd0USmM/nNE1D13X/f3tvHmXZVd/3fvaZhzsPNXX1qG611FKL1jwgCRsZgWSDGBwgcWL88AvPBhKIk7VCHrwsv7fitV6yYmfFXu+xQhJi4gwEbJ4hYCCGYDAyIEtCY7d6UKun6pqHO595vz/O7VYhdauru6rrdnWfz1pV99x9z93123Xu/Z09/Pbvi6Zpy+YAXg2wSWP0NZbiBE3T0z30QQ9btLFkjBo1qMiALbbF5pKKpXic7iwxFST07Dw+Ib4fEAYhQT9RSRRFmIaJ0CWRjNhy25up7rgFzczxyGPv5aWjR5ltdFnqejR9n3aQoFsmnU6PoWoVGYWIWOL3erSbTTaPb0vtTtf6SPryYr6fKii3mk1ePnqYL3/pj3jyr58kCgM0TcPSdTRVo9vrIvuTolJGBF6EphtYhkXYC4g9D12C1BIwUln0QCap4mKsYCra2UGBrqjEKHQDiaFEKBIMoWAKBSk1iH0sXafR9bjuuq38jXf+Et/8719nRvqgJvTiEBGmUYiWZRLGIT2/h/QUSnmTM0lLMi4/V70TSJII3/fO5vU70ws40wM40/XUNI1EGAQShKIjoxA78iipIUUlQI3mqRgJu+vbqNsx0xOHee7wy5zoRkjdJohVpJ2gqyDCCCNQUJx07F6uFnFch1xtC5qZrn1vu+Ut1Lb8KT/+1nfwEmi0PBQnh2I5BEtdOkGA1/WJHIPheo0nfvzX/PSpv+LW2+9L78NCTe/AChh6GlRTKdcYHR3n+p03cPToYf7sG3/C448/ThyGyDgm5zhoQmGpuUSYBChxjFQ1VMvFFhYEMT3PQ3MtemFAIMAtFQgCSXOpg6oY/eFHgtS1fkCVJFEluibQhELc12VsNboUTZP77r6PT3/6/0SGLV545kccO/ESChArEMqEsNehZGpYmoMfhkQhOCacI4daxmXiqncCBw++xMzM7NmsPWeCgsIwPCsCsny50HQcDDQ0GWLqCY6WUDcVNN2gWlDYvWucUsll4vRh5ufn6HUjFN0kjpN0XGsaJIqGF/RwbItcLkdzaYk7776L8c1bOfPh1qwcH/mNj/LCocO8dPgY+WKOyugWZhebVMZH2FQr0zHOCHtIFhbm+Xsf/yg//NEzb9hewzAY37yV4ZExbtxzM+98136+/rU/5nvf+5+0Wy2GhocZGqkTyxAZBPjNNn67SxKH5N0c+VyehXYDPwmojA3hAQvtBoZuoWoaCREk6Z5/wzQw+pOLqpZ26RNA13Rs28ExND796X/MdbtuYOL4S+TyNpquIMI43bAsBHGYZj9WVQXP8wn9BErLdzFkXG6ueidw5MgRer00snm51BekvYAz8ephGKaJPIIQw9ZwHBsrDLGjGFUk1MolxiomrmPRbbeI44TRap3p3gJ+DEHPI+r4CBRMw0IxLDShogkNQ7cZv/l+8kM/u1tybOvN/Ovf/wM++Q8+xaGjx4AQ29ApVCpMnHwFOw5QSGi1PPa96Saee/7Qitut6zr1+gilUoUbbtzL//p3P8rjj3+Pb3z965ycOEUQeSgCLBQMXUeJNTq9HpGMiQTomkbBzWEj6HUDDKOAqqh4no/neSiKgmVaGMaZ+QaWyaDbFIsl7nnTLWzfsRegP/EYY+oaGj6JjNNNVo5FIZ9DSkm71QKp4ZyRbM9YF676//T8/EJf8qsfFtwPFrIsC13XMQzj1WAhGeL3lsibCq5t4Kg6VuCjC8H42DB7dmzCMW2mpybodjrYmo4jBYkXIjs98AJU3cJWDdySSxRF2IbNwx/+TW7/+XemyTmWIRSdTeN7+Oxn/wMnp47z2//sn7H/wBHq1RK9pTxm5OE6NqbVJIzSSGwZtxHqyjfV6LpBtVqnUqkytmk7j/ziL/PCC0/xF9//LvtfeJHm/AJ6DIQJxBJVKhQcC8OxKOUKdMKASrGIZhRQFA3D7KEbqeOMoxjfD9B1rZ+uTIFE4nshIon5h7/1mbNtNnSdaqGAraq0kSAjhGqCoaUCp0GILlSkUDBNM1siXEeueifQ63X7S4LtdMdbf1nNdd2z8wNngmp0VPTAQ098dAmODmXdYsgxuPmG3ey9fgvN5hSddgcVgQgDZKdD0gkRPQ9TKuQMG1PTUYVKoVziLe96N3e87e3o54l+E0KlXh+iUqnyhX/zh3zxq3/M5/7dv6PdXEznBXpdRkdHcd0c//T/+BQolxZFJ4SC7bjYjsP99z/MnXe+hSgKaS1Oc2T/ixzZf4DTk1O88NyzHDj0EjfedAObR4Y5fPwY+AFOzkTRNBRA68dVdDtdemGAKnRMTQdBP1FqxNbxrRSXbQO2DJOhUhlbU9D7ux910kCksNsjkQo5p0yn5TFcH8o2D60jV70TCIJ092C73U5n602TOI7J5/NnYwfOOAFD6BRtAz0JiHsRhgm1ap4ttSJDlRKdbounnnwC32swumkI0wvRowg1jBB+CIqKKiV52yVKFLZft4ub7vw5dKdyQTtVVaVcKvPhD/4qe/fcwJGDB1HiiO3btvKmfbeCcPpd79XeIdPIPV1Pty8XCyVGx6/jvod+8exuyjiO6c69wuc//2/oLc5SdMsI0n2DmiJIFBVVVdDzeQq5HIqahh13ez16nQ4K8NHf/NjP/FXT0KkUc5iqwCAmkWAgsSyDSDfp+QkiTui2WuzZs4dSKcsjsF5c9U5gfn6OXq+HaZoUi0VqtdpZLb1Op3M2nDidMIzImwpFS0cJexBEOHqZ0eEKCjFP/vgJnnryGXbvHkfKmM7SEluHhjjNInHgY+UKlGo1dNNGWA477v458ltuuKg1b9M0ufe2+7h73z0A/eAb9bIFzwhFQVOM16l+F4tFNGC8WmOyE6EJldAPSYIIDYGKim7oNBpL1Gp15ufnacwtUKvVmZmd4+7b7/6Z+gxdp1oqkrd1XF0BmZC3NQrFHNONHkW3RK+XBj2NbKpj2eZlaW/G67nqnYCiKFiWheO4OI5DqVRCSslCX4rM87z+pGCCrsREcRs/FuSEpFQqsnVsiC2jo0xPHOXEieMkSUQ+Z2PoOkhJr9Nh8/AItuMiDQunVGKx47Pn7rdy+8PveV32npWgqq8qEg8KoaiMVCvMuwaxZtJV9HQHoZbuK1AVhTAI8LoeSRxjGQZDtSGGhofYsnX76+1XBJquYCsqeVPDUGMKlo5rmvRcjcVOj247oFgoYJpWFjG4jlz1sy9Spoq7lpWG0wohCMOQRqNBu90+6wTSJcQAJfJJOk1MmbCpXmK4WsQQCSePv0JzfpotQ2XK+TxBEOC6DnnXYHbuNIaukM+7eIHPbffdy6Pvew+KurF97ObREXqLDYqWi6nq6Y+iYygahqKhSQXXtAi7Hr1Wh8gLmJ2c5lf+1t96XV2CNCTb0hQqhRzD5SL1Yo6CbVJ0bKQf4vc89u3bR71eW//GXsNc0AkIIT4vhJgRQrywrOy3hRATQohn+j+PLnvtnwghjgghDgoh3n65DF8py0U9PM+j0WgwOztLt9sl6CcePascrEiIeziawqbhMiO1MnHQ48TRI8xMnUZFsG3bNmzbZmF+nl63wxlF41q9Rixj8sUSO/Y9gFK4vMlT14PhSoWpkxNEXi/NOtTXQ0iSmCiK+wla0mVDSIcyjuvy8Nseen1lIh3WOLpBtZBnpFxiqFKg4OpYmkredpBS8u73/Q12XLdzfRt6jXOpugMA/0pK+S+XFwgh9gAfBG4CxoDvCCGul1LGDIDlX/AkScNrkySh0WicjRg8s2lFSokWg99qUNqyiV1bxxiuVvDm55g8foSF2WmGKjmGahXQEqIopOf5hJHCjl1bCJUEzTJ42wc+zNidj1wVse/FQoHY8wh8DzRIEtlPKJrKpIVhcFbzMN3ZaLNj53Xn7sprGmrOwTU1YiWdmMznHBKzQDOKWGzFqFJAP+goY/244H9bSvkDYKUpwh4Dviil9KWUrwBHgLtWYd+qOHzoeXq9Nrqeznqd6Q10u92zIcQ/k1wkism7DuPjo2zeNIZrGTQWF5icOImmSKq1ClEc0Gq1UFSVXC5HoVwgkiGxiLn30fczduc7r57xrJRUyzkMXUfVVDRNPZt/QPQzE2ma2h9KxczNzVIpl89dlwBV07A1DUsomApYhk7ezVErV1CFQilfwLHsq8KBbiRW43I/LoR4rj9cOHPlNwEnl51zql/2OoQQHxFCPCmEeHJ2dnYVZpwbKSVf/vJ/48QrR9IgIN+j0+3RbHu0OgFhrBAnkCQBJB4iaKFGDXbv2MzObWPUqyVCv8Xs3GkWFhqMjo0wNDxKs+UzNd0g8FVQXTqBzqnpFlv2vY0b3vGRq8cBAN3GItVKmbypkdcTHCPBMQSGKdC0VMC0F/qAwHEcbNtm7969564silE8H01RUGWMLhNsmeDEPlU9wU663HLjDoaHs/mA9eZSncBngeuAfaRaA797sRVIKT8npbxDSnlHvV6/RDPOT685zVgpz+ZaETXpIRMP1XLpGQ5L2DSERisM8LtL5GQTs3uSm0dd9myps2U4j6l4PP3k9zlx8iBmXqLYBrPNDvnhrSx1DPygzMGTMYemDR74wD/mvl/9zJq3YdBMTh2n1ZpGDZZwojnMYAFb6eGoEpSQME7oeDG6k6MXhZyYnOCx973n3JWZJUJ7G635FsKLcH2fXHeJrarPeDjNdjfkFx66i7FNI+vbyIxLWyKUUk6fORZC/Fvg6/2nE8DmZaeO98vWnZnJSU6fOk7kt9AI0hRexKmIqCaIooRaPg9LS6hBk7oZs6lismWswkilyMH9+5k8NUUQSmr1HKVqlfHxnRx48RWkmmNipo1ZHuHvfeafY+/eM4gmXnZOnjyGqirkbYWe9DHjmEDGJNJEhICqECcJS60Whq4wPDz8BrVJdF2nXCqhe210GWAlAr3bRmsvoXkLDG3ZjHu+4UTGZeOSegJCiNFlT98DnFk5+BrwQSGEKYTYTqo78MTqTLx4pJScOv4KfruJTCKSJAIRIaSHSHroROC16C3NsTQzgaOoXL9lC7u2be9nvPV54omnmZ7uYBga45s2EYYRp09PsthsMLe0gOpafOjvf/KqdQCQZiPStXSDkKaqqVSbUM6maNc0DdMwzm4cuu+++96oNjRNx3Xds0lOzkzKCiCOPOJYB4x1al3GGS5Vd+DnhBD7SPNLHAP+NwAp5YtCiC8B+0nlyT42iJWBsDXJ6WMHUWSE0v/ya1LBjyVaHOMoIaYJotOg7Bjs3bmDbZUctUIJXbd45pnneeWVGfK6ythojZH6CItLbU6cOEF9dAuNU9M8/L4PULrzHEthVxEzMzMUigWKxRLtCDphiCYSVFSESHUbLMsiCALCIODhhx9+g9oikJ10F2cUofedgKqomJaF61qoarYqMAjWVHegf/7vAL+zGqNWQxxHfONrX+VHP/g+jqEhIw9FemgoKHGMGsU4aowRJ+iq5MbNO9g8NIQZe1Rqo9x0zx185cvfoJS32T5SZvuWLWlevjhBtyxaXkBty072vv/jg2riuuF7HoV8AdM08IVE1RKU6FXlo16vh2m7hFGEKuCtb33rG9SmI0SeJE4Io5C8pfezCafBXPWhOjk3SzE+CK4613vy5Wd56Zkf0ZidRktihPRB9hDCQ4vbmEkPK+lBZ5GiAVuH6xAE3PSmN3HTr/0O3PheUAzuuuNWbr75JurVOguTs5w+PUMQRBw9eYo7HnjzoJu5LoyOjqLrOo1GgzCMiMKIIAjx/TSnQKfTTvMPC8GOHdsvWJ+ma5imgaqqOLZNmqxUeVUEJosPGAgbO671NXQ6Lb799a/ywjNPk1cNSrkcnXCGHhGJTDASgUwitCRGtVSGKgVqlTwjtTo3/tpHzibWHNtUY9f1N+LKGG9pgYX5BToNUOMEqZnc8sg7B93UdSEMw7NLnnEc9bM1+4ShwPODs+HWjuty7733XrC+NHzbJjZMDNNASlCVVJF5aXEJz+uQZjTOnMF6ctU4gSDw+PM/+yI/efzPKeXzjFfr2KaKrWrEpk0viIkJU9Vg36darVMqFdl+8z7u/zu/hXJmG52UXL/7eiamJikrKscOvEgUgmubGNUyflIC57rBNnadqNVrvDIzST5fJE5UkqQLCJR+1mbLslhcWKBQKPDQQxeeH/F9nzAKcXMucRyhWyae5yEUge24KIoHBECWS2A9uSpcbhQFHNj/BH/9V99Fk5Jtm8aoFfMoUUxet7ClSkHTUH2fzmKDnGNSqZS4/YG3cN+vfDLVAFzGzutvIJ/PMze7yNJ8C5GkefknJ+f40G/+LwNq5fpzRp3J6/Vot1p9/caEMAzpdjp4nketVqPVanHnHbddsD5N1TDNdIuwlJIw6udyEAqObaHrFTIHsP5cBU4g4fiJg3zjv3+ZgwcOUMkXKLs5LEWgk+AqBoqXsDQ9h5pItm8aZvPYCHc++BDv/PVPoRuv37deyBcJ/YTGwhJez0OgoWsWpeoIo/e+dwBtHAyqqmIYBoZpptoEUqaZhlO547O6hfoK8wHquo7Tz7AkpSQKIyQSvb/MuFwDImP92NDDgUQmzM2f5lvf+ho//MEPqDsmo0PD6Iog7nkE7TYaAukn5I08KAaGm2ds982869d+C9Oyz1nvUqPF7OwCUZDqBgB0ewk///5H1rN5A0fTNFzXJecWaAVLBGFIt+MRxHq6vGc6TE9Pc9u+fSurT9ewHZs2qe5jlET0ohjL1HEcB0PPYgQGwYZ2AtOzp/nil77At7/5NYSMyefyOLZN1GlDENBeXCKKEvKmy3C5yMmJWW667X7e84nP4OaL5603CELa7Ta6qpPPF1AVHS+W3PKuD6xj6waPYRhnhUaTRBIGAT3Pw0+SvjiIpNlo8M53vWtF9WmaliZ2TSRCpHqHsi+7njMMND0Vgk8TmWWsFxvWCSwuLfHtb32b//Gtb+N3OtRyFtVinqDdIlyYo6gqGIqgF4aUakMUKlV23fkWHvzbH8MtvHH+uqGhESzLRHY66IaBbdpsrRWhtHWdWndloOsm7VYbUNJdgIaGZZtEsY4XR3h+D8PUePTRd6yoPkVVUA2FmAghEhSRYOoaedthut0hiRqkE4Pn7qFlXB42rBM4evQoP/zLH1JxC6gGbK8WGdYUotkJnCig7DqUSg56wWbLHW9m887d7HrzI1i58/cAzlAd24br2HTneihEaJbDzjfdug6turLQVIvIC2mKNoEQKJbAQMOKVXpCIQoiZDukXFxZCnQ1Z6EN54hFF6EmKESo6GihTyERWCKVV8t0R9aXDesEkjim1+6iRRGj+Tw1TUFtLKC0G2yqlhgt52l4HuW9t3Hrez5MsT7OSj5dAlDzRSxdIdFDNBGjaIIte26+7G260pARaEInTGI8ERLEPkEUEqKh2w45U+fU1OSK61PzLvp4DbQQlDh1sEjU0KesOdiaSzYUWH82rBOwLIvheg1aMTVHQfEbhN0mw6UcxUIO27HpIdn9wC9SrI9xUbcXXeAaAs3S0dFQ8w7VG2+8bG25Ugm8HhoSRdcwSROMhkFIIECqkjCMKBcv3LM6i1DRdBvTMpB+hygMCEmzNAkNFCUPIpscXG82rBMwDYMt42NobY3WycN0Zk8z5GpU61Vy+Twz8wsM3/UL5Eeu56LvLgbkTIFp6ViKRmSZUMxflnZcyYgoQk1idAwsxURHQCRRhABFobXUZPPYxcyTqKiqg67rJJ4kCMNUCl5TSVRIULgqVq03GBvYCWgUXZNeM0BLAgrFHMOlVKCj1esQmzrVW+7DLA9dfOU6OJog0sDRtVQ2q7kEF3HTuxqQkQ9hiI6NiYIlDGwlRBEWoaIT9GL27b39YmtFUVQ01cCwHGzDRrN1klimkusZ686GdQICiDotlk6foqopjNfq5AxQVcFss811Dz5GccuuS6tck1hKRCAjdCVBxAEszf9supRrgE5rARl0MZIculRwFZOCKfGFS0+xMBSdt9x/MdupFVTVxrZctDjG0nV0BH7oESQxCclla0vG+dmwfS+ZxATdNnFnCSXsQNBFJUbXNapjo9T33IlRuMR8dapElSGakqDIEJUA/PbaNmADsDA9iZA+lgAjTHAUQd4wcHUDRzWwVJ1777mYPLIaupbDttx0e3YiEUJNk74oEkXJlgUGwYZ1AooAnRhTSXDVBEdE6Iqk2W5S2/sg5a03XHrlMqHXbKMkEbHfxRQJ9FprZ/wGoTE/i6ND3lJxVUHeVMjrCo4KliowhKBavwjNQL9FsnQapEIYJnS7qfDLzPQM3U4XIULSYKGM9WQlmYU+D/wSMCOlvLlf9t+A3f1TSsCSlHKfEGIbcAA42H/tx1LK31hrowHCwCf2u4xVSmwvqFQsQUDEYruHVh5Gd1chaNnz8QMfJY4gjrAKCnMnj3Ot5cF1LRUtCCnndDRTRyogtBgRqIR+gkp4UbeRbmOB6ZcP4gcxmmLQ6vQQPQ+v08E2cihEpFuJs2XC9eSSxEeklGfjZ4UQvws0lp3/spRyZcHkq6DXbdNrLbCrXKTuSEqWZLbZoFotUS6vUtH2xBS5cpWSI2nMzaIIycmjh6gRcC3lwMtbOp3pFgVbQ7MUgkgnAHoyIVpaJPAuYogUtZk+9lMe/8vHKUc+I7aGadhYQlIomuiOg9qbgbADeqZIvJ6sSnxEpBkn3g/81zW264LIJEFXoWinCsLC75F4PUq7H6S4/dKj+yRw6NkXaXsxUmj4fkDYbVErGDD307VrwBVPg9Dr4HVCcoZCzkhw7ISiq+GYEHstdOmvuLbQ63L6yEssNdrs2LEDx3HJ54vUKzVGhoao5Aqox49CY/EytinjXKx2deABYFpKeXhZ2XYhxE+BJvAZKeVfnuuNQoiPAB8B2LLl4nX7XNdldHgYx+hhJDGR10MVEqc8jGqtbk2/0Qs4PTuHlA3UKAKZUHQsmDkCtWFg26rq3xDM70cXPpYKli5QNJWyFKiOTk8NcZ1ZhuorXzNV7TK3vP3vsuu+D1B0PE7+zz+i85ffR7dMDEAoGkqzCZ53+dqUcU5W6wT+Jj/bC5gEtkgp54UQtwN/KoS4SUrZfO0bpZSfAz4HcMcdd1z0ArFr21RLeZJWE9c0URKfvGuvQbJKSaU6xFEvZNHvULdNvDCk1VzAeOUwlgRucoG1F0y5olicZsvICPOLC6iEWJpO3hQYmo2f6BRyBqVw5bP5iqqTr46Rr46BjBjefh+L+58n9toomoJmKqlsscyWCdebS3YCQggNeC9wNlpESukDfv/4KSHEy8D1wJOrtPN1qIpEVxKiwCdSIyzdJJ8vYruFVdYssGMf0JBuGVEoMLvUoDe1QGifohxJtPk53FvvhfzAZBYvP8UCw/UavVd0LE0hUSKswMNVLBRTJ9cNsIJL/MIKDbc2TDI+ApNH0YI2QrPB6oGSJRZZb1bTE/gF4CUp5akzBUKIOrAgpYyFEDtIxUeOrtLGc5LEIWriY2gqQZBmp7HyVTR7ZTva3oiR++9E+eYf09Msum6NI69M0518mZulSXGpiWFY6AcOY217kR1vv0rTjQmJpkClXETPWYigCUmM6YfovsBpdBgvj11y9X7oE6gRTkmHZgBGD/QOqbSRJNtKuH5ccGKwLz7yI2C3EOKUEOLX+y99kNdPCD4IPCeEeAb4Y+A3pJQrVTS+KFLlGknOdXDzDoqqgG4iVpjq6nwIIVCGHmCm1WaqExDaReyhTcx0ujz/8nGefvElTs7Mc2Jiiv0vPgv+ZWne4FlcYqRS48bdN6CEIVHgYxsWShQRNJtYQnLzjRdOM34+NNNAyzvgOohcDhQNvADiFqluTcZ6caniI0gpf+0cZX8C/MnqzbowAtA0FddwsIMEr9lMg07XIne90Nl9zwP89Mmn+cEzz2M6Nlv37mN+fh5husx6MQYJYbvDV778Jd77ty9LKMRg2fEm1GMvQLdJa/Y4qg16YpDEMUIICvk8e/ZcekCWli9QGt8GiwZhECMiiSpcEDMIOqThJxnrwcaNGFQVLNPEMHQURUFVVBShslZNeuvH/4Bf+egnKWzeydCum3h5rklTmIzs3stUL+apw8eIDJty9SqdIGxOwcwsjIySG9uEAHrdHkiolMuMj4+zfRXbq0V1hKA8RiBNYqtM4lagPAQvHULOza9dOzIuyIbdQKQqCo5lYiUSzdBRCzkwNFiDnWhCCBTdZOy+v8nHbrmNXrPJDz7xCVzbpae75DZvZ9ddb+bRd30Aq3b96htzJXLyJEgFPA+hJOi6RqwKSEDJ5bhuZxllhQlGz4kYYqFdYu7Ay+iLMwzXqpR32Mg0+eDatSPjgmxYJ6AIganr6EmEkAaYOogYkrWJPU/joFSU/B7cfMIf/OF/wnSGEcIk/bcpCCHOKvRcdTSb4Fhw5ADIJmrBQM07yCYgJfXxcTBXk3NRgGows9BAmZqk4phIQkS9CvYcqap9lmtwPdiwTkDX0hz2stUiEQI156Z7T+Jw7f6IEAhASgU7t4uf/b5fpV9+ACTBwQPo7RlEJCHnkET+qwOtJGFuYoL6Kh3gyK0P0Tz+PM3v/CGmrUBrDvQYHMAwQd8D9HUhxNlfGWvMhnUCSRIRhV1kHEC3mwbkvHmsAAANBElEQVSaaBYka9+VvGrv9uclRs850I6g1wZLpxf1MAJB7OtYJRWvtfrIPiEsaiPbSAoF/IVZktMaatgEM4Hw28B3YGgcmj6MvgO0YUDANXc9Li8b1gnEYYjfaaNqCpIIFhcgV16z4cA1jTyNGCpAUkVqCWgBrVOT6H6ICB0sRaFaXZvZe83MkZgurUaTQi9ENQxQBZgqdHvIUy/DqWnE00/BHffB2PsAI3MEa8iGXR3QdQ03Z4HjIBwXZIIMesgwGLRpGxyJ/KtvIGcmYXaG+PjLRNOnabfb+H5wVkY8itbC2UqOTkxxdL5H4NaQZgkZaNDyYWYOfvB9kv/yJVhYQtaG4MABeOKPIA5SCaMsHdmasGGdgO3YlCoVZBxB4oMiSaKIJNOzWyUtfvrkT/A7baQJas4gISZJYgxDx7JSwdBut7sGfytiruNxtKUyT5kwycNCAMdm4dQcLAYcODjL0sFTyHaMLNSRt94Kpx4nidupQ5KZI1gtG3Q4IJEyIfEDaDZR/QCSBEX07w4yAbFh/duAafHS4ZeQNlxfsciNjWFKD9f3sFUHQxhg6Oj6hWu6MAaqW6Nt15gIbeqBja4kWKEHrSYURxjfo+EOb0UIi6Dho790GEyXA1/6PXa+85cwcrdl04WrZGN+U2QHv3OEhcnThD0PLBM0FUGCaB2A9vFBW7iBGWP3bfdz890Pkt99AzgmMoFyvoJpGKiqAF2jXFntbs0UwynQikyeP9ngxWmfU0GORpCj59mEosqpnslU6OIVN7MYuxw7eIK4WObY1Cz/+p/+X8gspmDVbMyeQBwhW4so7RaakoCqgqWAUMERoMZkm1AuFcHtH/6X6eH8N4m/8v+iqiZOfgTR8oi8kOjUIbTr10aRaffu63miPsozh0+w98Z9BCNbeXFygjB0mDs0y8FDDbSTh6ifCphpLOElMR/esZdHP/z3eVRTsx7fGrAxnQCSJPKJgy4UbSgWQM1Du0sycRzK+xFjJYRWTh3DZbTjVa5Ch1N9iNj5L4jpKRQ1D3GMZulgapBbm/9rfXwX737/h9gyuo04iviLZ48wcWoCIQSNVov5lkJFtxmpbuVd7/hldt91G2qunq0OrCEb0wkkCUmQ4EUS3wsRsUCtD4Heonv8NN7j38HZu4C57edR3HGEslaO4MyM9PK7T9R/fjUmxzQwN4/jT53EUOI0kKdQgPoI5NdmOACwY9/t7Nh3O3RbzJ86QeT7lCtVjKE6aHb2hb/MbEwnEMUoQUCkKEyeOElxYZaaeSvCtPG8mG67hb//KOpsROmWt2FWNyPWrNvokYa0QXr3X5MZsiuXXXswTx4BqYIaQcGCqgtqQpo/xly7v+XkqV5/09rVl7EiNuaAStexh8eobdpCpBo05xdJpueQiy3mJqboLPZwFZtDzx7g0FOP43del93sEhG86gCuEXK5dOK14qY/lgQ9gcSD5OCF359xxbOSpCKbhRDfE0LsF0K8KIT4RL+8IoT4cyHE4f5juV8uhBC/L4Q4IoR4Tghx25pbrZnIQg2RL1IcHkVxXJZaHfBjOs0uiwttop6kmq/x9JM/Zf4Nt6YmZEEnb0Q/xbqlQM5MM/8kPmgxNA+/8VszNgQr6QlEwD+UUu4B7gE+JoTYA3wK+K6Uchfw3f5zgEdI04rtIs0m/Nk1tzqO6SwscfzULIteRDMWLEYgilUKw5uJhcnU1AKmkeP2u+6lWCqSxg9EBF6H0O+ejXzLsthcgPz1kC+mG7PUJN2pqcj0eGZi0NZlrAEr0R2YlFI+3T9ukSoMbQIeA77QP+0LwLv7x48B/1Gm/BgoCSFG19TqKCHpRbSChFOLLZJckfr2nVAbpb7rJoxChZNzTQ69cop8bQjDBBn7kPh0W/P43aVle9YNrsqZ/TVjF5gOBGG60mJZoKkgQ5if5md1ZzI2Ihc1MdiXGbsV+AkwLKWc7L80BQz3jzcBJ5e97VS/bHJZ2ep0B3QLc2QXtetmifwedkEjXx8mlgJf1QmsHAuNNlPHTpE8/Sz5oVHKdR2h5yjVL17j4JqnOgQuECWwOA/tBpg5SEIIngHjLYO2MGMVrNgJCCFypPkDPymlbC7fXiullEKIixpYr0p3QDWxh3ez64EhhoarLB5/lqnFJZAdJpYWONVts4QkzrlYpSpmvopirFwoI+M1GDaEDkQB+DF0ezDkph2oQ4/DeBVKu7nqV0quUlbkBIQQOqkD+M9Syq/0i6eFEKNSysl+d3+mXz4BbF729vF+2dpjl8nvvJdYFywuTqBoZdzcdraPhuzQHGy3xPY9e8kVsqSVq6IXw/QSRBH4SZqzQRFphuBXDoH/JdjxDjCKaQ9BW03GoYz1ZiWqxAL498ABKeXvLXvpa8CHgP+7//jVZeUfF0J8EbgbaCwbNqw5ilunsvstVGiDWuKaW8JbD9oBLHRBSDBt0o+NTHM66gosTcOxP4MwgfwwjD0KxV2DtjpjhaykJ/Bm4O8Az/f1BAD+d9Iv/5f6OgTHSYVJAf4MeBQ4AnSBy6/OoRaA1SoPZZyPuB2gCAehA+UcRB0IY5A+RDHoBnJ+jt4L+/FUB7EnRNn5CMWtWeDPRmAlugM/5PzT5w+d43wJfGyVdmVcQbRaEUU3B5YGtotsJMhXXkHoKn6njVVWoFIBtUBvao6e/zxVMUJimigjOwdtfsYF2JhhwxnryvGpRfaUYjSrhvBjaIeE8x6dToepqSk2bZcUnWHskZ2I2nX4UQEn8Fl88QmqhgWV8UE3IeMNyJxAxgWZ73rsn3iZm/RbUMdGoVhB3X49yulJgpaHXh4GxULYOvbIOEZSYOKFo5xotNikKGy/561gDw26GRnnIXMCGRdkz9vewdJf/CnCzINVQNgKqmozd3yK40tdRqSOY+bBNMAt480HfP+HP+LY4gLu0SPsnV/kbb/8m4NuRsZ5yJxAxgUZ2ftWSrUSysmXmZ6axwt8gk6H/ZMLPHN8Gs85zK5QoTI8hNWKac51KAwPc8t126CQZ2R0bQNGM9aWzAlkXBitgLX5bihuJfzxtzj48kkWFhYQTomxvbdh1ofQamNUt12HOjyCi0v9wSKaoSM1DTuXrdxcyWROIGOF2FDYwtDd78XZ8zbCMETty8BrmoZlmtiOA4aRBhGd2X2YccWTOYGMi0DFLA5jFocvfGrGhmFjJhXJyMhYMzInkJFxjZM5gYyMa5zMCWRkXONkTiAj4xoncwIZGdc4mRPIyLjGyZxARsY1TuYEMjKucTInkJFxjSNeFeEYoBFCzAIdYG7QtqyCGhvbftj4bdjo9sPlbcNWKWX9tYVXhBMAEEI8KaW8Y9B2XCob3X7Y+G3Y6PbDYNqQDQcyMq5xMieQkXGNcyU5gc8N2oBVstHth43fho1uPwygDVfMnEBGRsZguJJ6AhkZGQMgcwIZGdc4A3cCQoh3CCEOCiGOCCE+NWh7VooQ4pgQ4nkhxDNCiCf7ZRUhxJ8LIQ73H8uDtnM5QojPCyFmhBAvLCs7p80i5ff71+U5IcRtg7P8rK3nsv+3hRAT/evwjBDi0WWv/ZO+/QeFEG8fjNWvIoTYLIT4nhBivxDiRSHEJ/rlg70GUsqB/QAq8DKwgzQz5bPAnkHadBG2HwNqryn7F8Cn+sefAv75oO18jX0PArcBL1zIZlI9yW+SStDdA/zkCrX/t4F/dI5z9/Q/Tyawvf85Uwds/yhwW/84Dxzq2znQazDonsBdwBEp5VEpZQB8EXhswDathseAL/SPvwC8e4C2vA4p5Q+AhdcUn8/mx4D/KFN+DJT6EvQD4zz2n4/HgC9KKX0p5SukArl3XTbjVoCUclJK+XT/uAUcADYx4GswaCewCTi57PmpftlGQAL/QwjxlBDiI/2yYfmqDPsUsBHS8p7P5o10bT7e7y5/ftkQ7Iq2XwixDbgV+AkDvgaDdgIbmfullLcBjwAfE0I8uPxFmfbnNtT660a0GfgscB2wD5gEfnew5lwYIUQO+BPgk1LK5vLXBnENBu0EJoDNy56P98uueKSUE/3HGeD/I+1qTp/prvUfZwZn4Yo5n80b4tpIKaellLGUMgH+La92+a9I+4UQOqkD+M9Syq/0iwd6DQbtBP4a2CWE2C6EMIAPAl8bsE0XRAjhCiHyZ46Bh4EXSG3/UP+0DwFfHYyFF8X5bP4a8Kv9Gep7gMayLusVw2vGyO8hvQ6Q2v9BIYQphNgO7AKeWG/7liOEEMC/Bw5IKX9v2UuDvQaDnC1dNgN6iHT29tODtmeFNu8gnXl+FnjxjN1AFfgucBj4DlAZtK2vsfu/knaZQ9Lx5a+fz2bSGen/p39dngfuuELt/6O+fc/1vzSjy87/dN/+g8AjV4D995N29Z8Dnun/PDroa5CFDWdkXOMMejiQkZExYDInkJFxjZM5gYyMa5zMCWRkXONkTiAj4xoncwIZGdc4mRPIyLjG+f8BaFEfRJAZIVgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h47KBARwk44g"
      },
      "source": [
        "# FGSM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3pGMvoViCAY"
      },
      "source": [
        "loss_object = tf.keras.losses.CategoricalCrossentropy()\n",
        "\n",
        "def create_adversarial_pattern(input_image, input_label):\n",
        "  with tf.GradientTape() as tape:\n",
        "    tape.watch(input_image)\n",
        "    prediction = pretrained_model(input_image)\n",
        "    loss = loss_object(input_label, prediction)\n",
        "\n",
        "  # Get the gradients of the loss w.r.t to the input image.\n",
        "  gradient = tape.gradient(loss, input_image)\n",
        "  # Get the sign of the gradients to create the perturbation\n",
        "  signed_grad = tf.sign(gradient)\n",
        "  return signed_grad\n",
        "\n",
        "# Get the input label of the image.\n",
        "cane_dog = 0\n",
        "label = tf.one_hot(cane_dog, image_probs.shape[-1])\n",
        "label = tf.reshape(label, (1, image_probs.shape[-1]))\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4C8v0dOieQq",
        "outputId": "bf16bbc9-82ea-4e6d-9ddd-7d8368888824"
      },
      "source": [
        "label"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 10), dtype=float32, numpy=array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "1012olUBjFWW",
        "outputId": "a426304c-5af3-4959-875d-91144afa285a"
      },
      "source": [
        "\n",
        "perturbations = create_adversarial_pattern(image, label)\n",
        "plt.imshow(perturbations[0]*0.5+0.5); "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JlK6pcWQjThW",
        "outputId": "5dd753c9-d8ce-4117-dcef-93d4cc73e549"
      },
      "source": [
        "def display_images(image, description):\n",
        "  probabs = pretrained_model.predict(image)\n",
        "  # print (probabs)\n",
        "  label = [[i.argmax()]for i in probabs]\n",
        "  image_class = label\n",
        "  # print(label)\n",
        "  class_confidence = probabs[0][label[0][0]]\n",
        "  # print(class_confidence)\n",
        "  image_class, class_confidence = image_class, class_confidence \n",
        "  plt.figure()\n",
        "  plt.imshow(image[0]*0.58+0.58)\n",
        "  plt.title('{} \\n {} : {:.2f}% Confidence'.format(description,\n",
        "                                                   prediction_classes[image_class[0][0]], class_confidence*100))\n",
        "  plt.show()\n",
        "\n",
        "epsilons = [0, 25, 50, 75]\n",
        "descriptions = [('Epsilon = {:0.3f}'.format(eps) if eps else 'Input')\n",
        "                for eps in epsilons]\n",
        "\n",
        "for i, eps in enumerate(epsilons):\n",
        "  adv_x = image + eps*perturbations\n",
        "  adv_x = tf.clip_by_value(adv_x, -1, 1)\n",
        "  display_images(adv_x, descriptions[i])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFoCiA8IjvVi"
      },
      "source": [
        "# for ref: CW Attack from scratch - https://nbviewer.jupyter.org/github/Harry24k/CW-pytorch/blob/master/CW.ipynb"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hk_vXJfIlX_v"
      },
      "source": [
        "# here I am following : colab ref - https://colab.research.google.com/github/shreehari117/ML-2/blob/master/Sureshbabu_Shree_Hari_aml.ipynb\n",
        "# https://colab.research.google.com/github/shreehari117/ML-2/blob/master/Sureshbabu_Shree_Hari_aml.ipynb\n",
        "# https://colab.research.google.com/github/andantillon/cleverhans/blob/master/tutorials/future/tf2/notebook_tutorials/mnist_fgsm_tutorial.ipynb\n",
        "# https://harvard-iacs.github.io/2019-CS109B/lecture/lab21/AdversarialNN/"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSZHs9ISnA37"
      },
      "source": [
        "# wrap = KerasModelWrapper(pretrained_model)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15u5CkmWnLxs"
      },
      "source": [
        "# from tensorflow.python.keras import backend as K\n",
        "# sess = K.get_session()"
      ],
      "execution_count": 16,
      "outputs": []
    }
  ]
}