Build_SAR_DRN_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Build SAR_DRN Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1rYz_YnKbhnXsvTIpg2IIGvg7PMfSt8FF",
      "authorship_tag": "ABX9TyNI9/HdES3PoWBVRWFMNVcS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/duypham01/SAR_DRN/blob/master/Build_SAR_DRN_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2SXc5Qs5lX5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "#import scipy\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Conv2D, Activation, Input, Dense, Add, Lambda\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.optimizers import SGD, Adam\n",
        "from keras.preprocessing import image\n",
        "from scipy import ndimage\n",
        "import tensorflow as tf\n",
        "import keras as keras\n",
        "from skimage.measure import compare_ssim as ssim\n",
        "from skimage.measure import compare_psnr as psnr\n",
        "from keras.losses import MeanSquaredError"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ur1qzOtrDLpy",
        "colab_type": "code",
        "outputId": "5376810e-ef3f-4e58-a8d7-b87ce32f5180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "X=[]\n",
        "# for file_name in os.listdir(\"./drive/My Drive/UCMerced_LandUse/UCMerced_LandUse/Images\"):\n",
        "#   for file_name_image in os.listdir(\"./drive/My Drive/UCMerced_LandUse/UCMerced_LandUse/Images/\" + file_name):\n",
        "#     img = image.load_img(\"./drive/My Drive/UCMerced_LandUse/UCMerced_LandUse/Images/\" + file_name +\"/\"+ file_name_image, grayscale=True)\n",
        "#     img = image.img_to_array(img)\n",
        "#     img = cv2.resize(img, (256, 256))\n",
        "#     img = img/255\n",
        "#     img= np.round(img, 2)\n",
        "#     X.append(img)\n",
        "\n",
        "for file_name in os.listdir(\"./drive/My Drive/UCMerced_LandUse/UCMerced_LandUse/Images/buildings\"):\n",
        "  img = image.load_img(\"./drive/My Drive/UCMerced_LandUse/UCMerced_LandUse/Images/buildings/\" + file_name, grayscale=True)\n",
        "  img = np.asarray(img)\n",
        "  img = cv2.resize(img, (256, 256))\n",
        "  img = img.astype('float32')\n",
        "  img = img/255.0\n",
        "  X.append(img)\n",
        "#X = np.array(X, dtype=float)\n",
        "print(X)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:104: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "        0.6156863 ],\n",
            "       [0.6       , 0.5921569 , 0.59607846, ..., 0.5372549 , 0.60784316,\n",
            "        0.61960787]], dtype=float32), array([[0.7607843 , 0.7607843 , 0.7607843 , ..., 0.74509805, 0.74509805,\n",
            "        0.7490196 ],\n",
            "       [0.7647059 , 0.7607843 , 0.7607843 , ..., 0.74509805, 0.74509805,\n",
            "        0.7372549 ],\n",
            "       [0.7490196 , 0.7529412 , 0.7529412 , ..., 0.7490196 , 0.7607843 ,\n",
            "        0.7372549 ],\n",
            "       ...,\n",
            "       [0.14117648, 0.14117648, 0.14117648, ..., 0.78039217, 0.7529412 ,\n",
            "        0.7254902 ],\n",
            "       [0.14117648, 0.14117648, 0.14901961, ..., 0.7882353 , 0.74509805,\n",
            "        0.7176471 ],\n",
            "       [0.14117648, 0.14901961, 0.14901961, ..., 0.78431374, 0.7529412 ,\n",
            "        0.72156864]], dtype=float32), array([[0.6117647 , 0.5294118 , 0.36078432, ..., 0.20784314, 0.20784314,\n",
            "        0.21176471],\n",
            "       [0.69803923, 0.54509807, 0.38431373, ..., 0.19607843, 0.19607843,\n",
            "        0.19607843],\n",
            "       [0.7764706 , 0.7019608 , 0.43137255, ..., 0.19607843, 0.19607843,\n",
            "        0.21176471],\n",
            "       ...,\n",
            "       [0.98039216, 0.9882353 , 0.9843137 , ..., 0.7647059 , 0.69411767,\n",
            "        0.67058825],\n",
            "       [0.9843137 , 0.98039216, 0.9843137 , ..., 0.77254903, 0.70980394,\n",
            "        0.6627451 ],\n",
            "       [0.9882353 , 0.9843137 , 0.9843137 , ..., 0.7647059 , 0.7019608 ,\n",
            "        0.63529414]], dtype=float32), array([[0.13333334, 0.12156863, 0.12156863, ..., 0.74509805, 0.76862746,\n",
            "        0.7647059 ],\n",
            "       [0.10196079, 0.10196079, 0.10196079, ..., 0.92156863, 0.8862745 ,\n",
            "        0.84705883],\n",
            "       [0.09411765, 0.10196079, 0.10196079, ..., 0.9529412 , 0.92941177,\n",
            "        0.84705883],\n",
            "       ...,\n",
            "       [0.32156864, 0.32941177, 0.3647059 , ..., 0.8       , 0.7921569 ,\n",
            "        0.7882353 ],\n",
            "       [0.36078432, 0.38039216, 0.38039216, ..., 0.80784315, 0.7921569 ,\n",
            "        0.78039217],\n",
            "       [0.76862746, 0.7647059 , 0.7411765 , ..., 0.8117647 , 0.8       ,\n",
            "        0.79607844]], dtype=float32), array([[0.2901961 , 0.28235295, 0.27450982, ..., 0.44705883, 0.43529412,\n",
            "        0.43529412],\n",
            "       [0.2901961 , 0.28235295, 0.27450982, ..., 0.45490196, 0.44313726,\n",
            "        0.44313726],\n",
            "       [0.27450982, 0.27450982, 0.27450982, ..., 0.45490196, 0.44705883,\n",
            "        0.44705883],\n",
            "       ...,\n",
            "       [0.7921569 , 0.78039217, 0.7921569 , ..., 0.5686275 , 0.4117647 ,\n",
            "        0.3372549 ],\n",
            "       [0.8352941 , 0.8156863 , 0.85882354, ..., 0.4392157 , 0.3019608 ,\n",
            "        0.42745098],\n",
            "       [0.80784315, 0.81960785, 0.84313726, ..., 0.3647059 , 0.30588236,\n",
            "        0.72156864]], dtype=float32), array([[0.52156866, 0.5019608 , 0.47843137, ..., 0.7882353 , 0.6862745 ,\n",
            "        0.74509805],\n",
            "       [0.5529412 , 0.53333336, 0.6392157 , ..., 0.7411765 , 0.7137255 ,\n",
            "        0.7764706 ],\n",
            "       [0.5764706 , 0.5372549 , 0.79607844, ..., 0.7372549 , 0.75686276,\n",
            "        0.78039217],\n",
            "       ...,\n",
            "       [0.74509805, 0.7254902 , 0.7411765 , ..., 0.92156863, 0.92156863,\n",
            "        0.9254902 ],\n",
            "       [0.73333335, 0.7647059 , 0.7490196 , ..., 0.9254902 , 0.9254902 ,\n",
            "        0.9254902 ],\n",
            "       [0.77254903, 0.75686276, 0.7490196 , ..., 0.9254902 , 0.9254902 ,\n",
            "        0.9254902 ]], dtype=float32), array([[0.20784314, 0.20784314, 0.20784314, ..., 0.75686276, 0.73333335,\n",
            "        0.7176471 ],\n",
            "       [0.2       , 0.2       , 0.2       , ..., 0.7254902 , 0.64705884,\n",
            "        0.62352943],\n",
            "       [0.20784314, 0.20784314, 0.20784314, ..., 0.7921569 , 0.76862746,\n",
            "        0.70980394],\n",
            "       ...,\n",
            "       [0.74509805, 0.7764706 , 0.7372549 , ..., 0.6745098 , 0.70980394,\n",
            "        0.7882353 ],\n",
            "       [0.7411765 , 0.72156864, 0.7294118 , ..., 0.6784314 , 0.7607843 ,\n",
            "        0.7058824 ],\n",
            "       [0.7529412 , 0.75686276, 0.7490196 , ..., 0.7372549 , 0.7294118 ,\n",
            "        0.6666667 ]], dtype=float32)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pMbeRDSDscb",
        "colab_type": "code",
        "outputId": "5428c5df-68c5-4fd9-88a9-5a5f440426a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "X = np.array(X)\n",
        "print(X.shape)\n",
        "print(X)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 256, 256)\n",
            "[[[0.8627451  0.8627451  0.8627451  ... 0.37254903 0.3764706  0.3764706 ]\n",
            "  [0.89411765 0.8784314  0.8509804  ... 0.3882353  0.3882353  0.38039216]\n",
            "  [0.85882354 0.81960785 0.76862746 ... 0.4        0.4117647  0.4117647 ]\n",
            "  ...\n",
            "  [0.7137255  0.7137255  0.7294118  ... 0.24705882 0.2627451  0.27058825]\n",
            "  [0.7372549  0.7411765  0.7529412  ... 0.28235295 0.28627452 0.3254902 ]\n",
            "  [0.77254903 0.75686276 0.7607843  ... 0.34117648 0.34117648 0.39215687]]\n",
            "\n",
            " [[0.3529412  0.2784314  0.25882354 ... 0.56078434 0.5921569  0.63529414]\n",
            "  [0.2784314  0.24313726 0.23137255 ... 0.53333336 0.54509807 0.57254905]\n",
            "  [0.21176471 0.22745098 0.21568628 ... 0.5882353  0.5764706  0.58431375]\n",
            "  ...\n",
            "  [0.2784314  0.27450982 0.25490198 ... 0.45490196 0.4509804  0.43529412]\n",
            "  [0.29411766 0.28235295 0.2509804  ... 0.43137255 0.45490196 0.44705883]\n",
            "  [0.2627451  0.26666668 0.24705882 ... 0.41568628 0.43529412 0.43137255]]\n",
            "\n",
            " [[0.43529412 0.41568628 0.44313726 ... 0.5686275  0.59607846 0.5372549 ]\n",
            "  [0.3882353  0.39607844 0.41960785 ... 0.5372549  0.54901963 0.5058824 ]\n",
            "  [0.3647059  0.34509805 0.37254903 ... 0.5372549  0.5372549  0.50980395]\n",
            "  ...\n",
            "  [0.56078434 0.54901963 0.5803922  ... 0.6862745  0.68235296 0.7294118 ]\n",
            "  [0.5803922  0.5686275  0.5803922  ... 0.70980394 0.7176471  0.73333335]\n",
            "  [0.60784316 0.5921569  0.5764706  ... 0.73333335 0.74509805 0.7294118 ]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.2901961  0.28235295 0.27450982 ... 0.44705883 0.43529412 0.43529412]\n",
            "  [0.2901961  0.28235295 0.27450982 ... 0.45490196 0.44313726 0.44313726]\n",
            "  [0.27450982 0.27450982 0.27450982 ... 0.45490196 0.44705883 0.44705883]\n",
            "  ...\n",
            "  [0.7921569  0.78039217 0.7921569  ... 0.5686275  0.4117647  0.3372549 ]\n",
            "  [0.8352941  0.8156863  0.85882354 ... 0.4392157  0.3019608  0.42745098]\n",
            "  [0.80784315 0.81960785 0.84313726 ... 0.3647059  0.30588236 0.72156864]]\n",
            "\n",
            " [[0.52156866 0.5019608  0.47843137 ... 0.7882353  0.6862745  0.74509805]\n",
            "  [0.5529412  0.53333336 0.6392157  ... 0.7411765  0.7137255  0.7764706 ]\n",
            "  [0.5764706  0.5372549  0.79607844 ... 0.7372549  0.75686276 0.78039217]\n",
            "  ...\n",
            "  [0.74509805 0.7254902  0.7411765  ... 0.92156863 0.92156863 0.9254902 ]\n",
            "  [0.73333335 0.7647059  0.7490196  ... 0.9254902  0.9254902  0.9254902 ]\n",
            "  [0.77254903 0.75686276 0.7490196  ... 0.9254902  0.9254902  0.9254902 ]]\n",
            "\n",
            " [[0.20784314 0.20784314 0.20784314 ... 0.75686276 0.73333335 0.7176471 ]\n",
            "  [0.2        0.2        0.2        ... 0.7254902  0.64705884 0.62352943]\n",
            "  [0.20784314 0.20784314 0.20784314 ... 0.7921569  0.76862746 0.70980394]\n",
            "  ...\n",
            "  [0.74509805 0.7764706  0.7372549  ... 0.6745098  0.70980394 0.7882353 ]\n",
            "  [0.7411765  0.72156864 0.7294118  ... 0.6784314  0.7607843  0.7058824 ]\n",
            "  [0.7529412  0.75686276 0.7490196  ... 0.7372549  0.7294118  0.6666667 ]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuGbuqY0MDoT",
        "colab_type": "code",
        "outputId": "93d59ae9-4bd0-426e-e56a-c91f9e59e686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(X.shape)\n",
        "X = X[:80,]\n",
        "X_train = X[:70,]\n",
        "X_test = X[70:,]\n",
        "X_train = X_train.reshape(X_train.shape[0], 256, 256, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 256, 256, 1)\n",
        "print(X_train.shape)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 256, 256)\n",
            "(70, 256, 256, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsrR-9K-yTmb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def speckle(img):\n",
        "    severity = np.random.uniform(0, 0.6)\n",
        "    blur = ndimage.gaussian_filter(np.random.randn(*img.shape) * severity, 1)\n",
        "    img_speck = (img + blur)\n",
        "    img_speck[img_speck > 1] = 1\n",
        "    img_speck[img_speck <= 0] = 0\n",
        "    return img_speck"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsGS0bxexPpw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def reImg(img):\n",
        "    img_new = img\n",
        "    img_new[img_new > 1] = 1\n",
        "    img_new[img_new <= 0] = 0\n",
        "    return img_new"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMewNH1NcFaL",
        "colab_type": "code",
        "outputId": "8785e10a-5f53-4ca6-f44c-89088944fcf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "Y=[]\n",
        "for x in X:\n",
        "  y = speckle(x)\n",
        "  Y.append(y)\n",
        "Y = np.array(Y, dtype=float)\n",
        "print(Y.shape)\n",
        "print(Y)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(80, 256, 256)\n",
            "[[[0.8091051  0.89286392 0.96982562 ... 0.37727987 0.21910375 0.20382489]\n",
            "  [0.9055842  0.96472712 0.954381   ... 0.38112313 0.25915221 0.21840374]\n",
            "  [0.84886256 0.88530395 0.81160836 ... 0.36794028 0.33901255 0.33565349]\n",
            "  ...\n",
            "  [0.72505897 0.70963535 0.82471431 ... 0.2113524  0.13056117 0.15783347]\n",
            "  [0.81393105 0.79014456 0.85419979 ... 0.19985374 0.10920982 0.16035852]\n",
            "  [0.98223712 0.91772205 0.90219541 ... 0.24503812 0.16105118 0.18069092]]\n",
            "\n",
            " [[0.03206237 0.         0.         ... 0.44888713 0.56041553 0.69994953]\n",
            "  [0.05892351 0.02126669 0.01682726 ... 0.51505657 0.61392141 0.70549037]\n",
            "  [0.11145449 0.22560455 0.25195134 ... 0.65874199 0.68202909 0.6519758 ]\n",
            "  ...\n",
            "  [0.16848502 0.26926496 0.34945788 ... 0.4828892  0.52703741 0.45790482]\n",
            "  [0.17230507 0.25213606 0.34549351 ... 0.58842574 0.57921336 0.49739718]\n",
            "  [0.17716119 0.31845027 0.4189755  ... 0.68956604 0.74623501 0.74503775]]\n",
            "\n",
            " [[0.05576256 0.14273181 0.46228362 ... 0.51212241 0.47785269 0.37239515]\n",
            "  [0.         0.01465681 0.31518621 ... 0.49416342 0.42030381 0.37526092]\n",
            "  [0.         0.         0.18121328 ... 0.52702923 0.39966433 0.40348726]\n",
            "  ...\n",
            "  [0.36839683 0.45083538 0.50475843 ... 0.6707359  0.57835497 0.59636909]\n",
            "  [0.20405857 0.31423181 0.39069964 ... 0.45440895 0.4653754  0.67089711]\n",
            "  [0.07468363 0.21720147 0.36099476 ... 0.35599001 0.40954473 0.70379824]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.26862227 0.33567381 0.30550932 ... 0.18722784 0.12359442 0.13816767]\n",
            "  [0.23044805 0.35175642 0.35607794 ... 0.2905165  0.17803341 0.13690549]\n",
            "  [0.1642461  0.30211072 0.29638195 ... 0.28917525 0.15461361 0.05433004]\n",
            "  ...\n",
            "  [0.44594428 0.37619716 0.27761338 ... 0.32588007 0.20242803 0.09930164]\n",
            "  [0.33605808 0.30468701 0.21048032 ... 0.26745893 0.30817697 0.3334034 ]\n",
            "  [0.17839199 0.12307819 0.00751987 ... 0.14914008 0.38653755 0.61142269]]\n",
            "\n",
            " [[0.73850043 0.72287451 0.70412475 ... 0.66514459 0.60693709 0.58939928]\n",
            "  [0.71155721 0.70071309 0.67104545 ... 0.63565557 0.63167824 0.60081874]\n",
            "  [0.69233746 0.68051027 0.67617663 ... 0.67976975 0.67190698 0.69306909]\n",
            "  ...\n",
            "  [0.16456573 0.24058693 0.3284509  ... 0.48216921 0.48127643 0.60288863]\n",
            "  [0.30446804 0.29409344 0.33694652 ... 0.56814602 0.61675575 0.68961043]\n",
            "  [0.31963672 0.25414394 0.26084971 ... 0.65265792 0.74709426 0.74490593]]\n",
            "\n",
            " [[0.16236714 0.24395933 0.41271898 ... 0.35875516 0.57768008 0.57365847]\n",
            "  [0.0877217  0.28623643 0.51212987 ... 0.65909412 0.68971044 0.69116358]\n",
            "  [0.17296455 0.33967932 0.49781722 ... 0.69439335 0.70127562 0.62513662]\n",
            "  ...\n",
            "  [0.72773936 0.72259364 0.78690814 ... 0.56167584 0.38465458 0.46601296]\n",
            "  [0.75182063 0.69962398 0.88671615 ... 0.64060327 0.45680051 0.45541779]\n",
            "  [0.85309845 0.71930044 0.85056038 ... 0.75864474 0.70860301 0.67673933]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNFId0xmUcig",
        "colab_type": "code",
        "outputId": "f344dbff-14cc-4a10-e265-bc0a009f41b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X_train_noise = Y[:70,]\n",
        "X_test_noise = Y[70:,]\n",
        "X_train_noise = X_train_noise.reshape(X_train_noise.shape[0], 256, 256, 1)\n",
        "X_test_noise = X_test_noise.reshape(X_test_noise.shape[0], 256, 256, 1)\n",
        "print(X_train_noise.shape)\n",
        "print(X_test_noise.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(70, 256, 256, 1)\n",
            "(10, 256, 256, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8pIxbqhjzcx",
        "colab_type": "code",
        "outputId": "9b13a493-07a6-429b-fe65-af534ca2e092",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "X_res = X_train_noise - X_train\n",
        "X_res_test = X_test_noise - X_test\n",
        "print(X_res.shape)\n",
        "print(X_res[0])\n",
        "print(X_res_test.shape)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(70, 256, 256, 1)\n",
            "[[[-0.05364   ]\n",
            "  [ 0.03011881]\n",
            "  [ 0.10708052]\n",
            "  ...\n",
            "  [ 0.00473085]\n",
            "  [-0.15736684]\n",
            "  [-0.1726457 ]]\n",
            "\n",
            " [[ 0.01146655]\n",
            "  [ 0.08629574]\n",
            "  [ 0.1034006 ]\n",
            "  ...\n",
            "  [-0.00711217]\n",
            "  [-0.12908309]\n",
            "  [-0.16198843]]\n",
            "\n",
            " [[-0.00996097]\n",
            "  [ 0.0656961 ]\n",
            "  [ 0.0429809 ]\n",
            "  ...\n",
            "  [-0.03205972]\n",
            "  [-0.07275216]\n",
            "  [-0.07611122]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[ 0.01133346]\n",
            "  [-0.00409016]\n",
            "  [ 0.09530252]\n",
            "  ...\n",
            "  [-0.03570642]\n",
            "  [-0.13218394]\n",
            "  [-0.11275478]]\n",
            "\n",
            " [[ 0.07667614]\n",
            "  [ 0.04896807]\n",
            "  [ 0.1012586 ]\n",
            "  ...\n",
            "  [-0.08249921]\n",
            "  [-0.1770647 ]\n",
            "  [-0.16513169]]\n",
            "\n",
            " [[ 0.20968809]\n",
            "  [ 0.16085929]\n",
            "  [ 0.14141108]\n",
            "  ...\n",
            "  [-0.09613836]\n",
            "  [-0.1801253 ]\n",
            "  [-0.21146595]]]\n",
            "(10, 256, 256, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgjNEPUrg3g5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_tensor = Input((256,256,1))\n",
        "L1 = Conv2D(64, (3, 3), dilation_rate=(1, 1), activation='relu', strides=(1, 1), padding='same')(input_tensor)\n",
        "L2 = Conv2D(64, (3, 3), dilation_rate=(2, 2), activation='relu', strides=(1, 1), padding='same')(L1)\n",
        "L3 = Conv2D(64, (3, 3), dilation_rate=(3, 3), activation='relu', strides=(1, 1), padding='same')(L2)\n",
        "addedL1L3 = Add()([L1, L3])\n",
        "L4 = Conv2D(64, (3, 3), dilation_rate=(4, 4), activation='relu', strides=(1, 1), padding='same')(addedL1L3)\n",
        "L5 = Conv2D(64, (3, 3), dilation_rate=(3, 3), activation='relu', strides=(1, 1), padding='same')(L4)\n",
        "L6 = Conv2D(64, (3, 3), dilation_rate=(2, 2), activation='relu', strides=(1, 1), padding='same')(L5)\n",
        "addedL4L6 = Add()([L4, L6])\n",
        "L7 = Conv2D(1, (3, 3), dilation_rate=(1, 1), strides=(1, 1), padding='same')(addedL4L6)\n",
        "#FinalL = Lambda(lambda inputs: inputs[0] - inputs[1])([input_tensor, L7])\n",
        "model = Model(inputs=input_tensor, outputs=L7)\n",
        "optimizer = Adam(beta_1=0.9, beta_2=0.999, epsilon=1e-8)\n",
        "# def mse(imageA, imageB):\n",
        "#   # the 'Mean Squared Error' between the two images is the\n",
        "#   # sum of the squared difference between the two images;\n",
        "#   # NOTE: the two images must have the same dimension\n",
        "#   err = np.sum((imageA[0] - imageB[0]) ** 2)\n",
        "#   err /= float(256.0*256.0)\n",
        "\t\n",
        "# \t# return the MSE, the lower the error, the more \"similar\"\n",
        "# \t# the two images are\n",
        "#   return err\n",
        "model.compile(\n",
        "    optimizer=optimizer,\n",
        "    loss='mean_squared_error'\n",
        ")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhrbqirwpWPz",
        "colab_type": "code",
        "outputId": "c92a66dd-bb97-4c95-c095-99a500f6621a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "H = model.fit(X_train_noise, X_res, validation_data=(X_test_noise, X_res_test),\n",
        "          batch_size=32, epochs=10, verbose=1)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 70 samples, validate on 10 samples\n",
            "Epoch 1/10\n",
            "70/70 [==============================] - 140s 2s/step - loss: 0.0252 - val_loss: 0.0106\n",
            "Epoch 2/10\n",
            "70/70 [==============================] - 129s 2s/step - loss: 0.0115 - val_loss: 0.0116\n",
            "Epoch 3/10\n",
            "70/70 [==============================] - 131s 2s/step - loss: 0.0095 - val_loss: 0.0102\n",
            "Epoch 4/10\n",
            "70/70 [==============================] - 131s 2s/step - loss: 0.0088 - val_loss: 0.0094\n",
            "Epoch 5/10\n",
            "70/70 [==============================] - 131s 2s/step - loss: 0.0080 - val_loss: 0.0090\n",
            "Epoch 6/10\n",
            "70/70 [==============================] - 136s 2s/step - loss: 0.0077 - val_loss: 0.0084\n",
            "Epoch 7/10\n",
            "70/70 [==============================] - 131s 2s/step - loss: 0.0072 - val_loss: 0.0079\n",
            "Epoch 8/10\n",
            "70/70 [==============================] - 129s 2s/step - loss: 0.0068 - val_loss: 0.0073\n",
            "Epoch 9/10\n",
            "70/70 [==============================] - 131s 2s/step - loss: 0.0064 - val_loss: 0.0068\n",
            "Epoch 10/10\n",
            "70/70 [==============================] - 132s 2s/step - loss: 0.0061 - val_loss: 0.0064\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AydQYmcURt0",
        "colab_type": "code",
        "outputId": "27f68b7f-c967-45fc-8ba7-49754ef91baa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "img = image.load_img(\"./drive/My Drive/UCMerced_LandUse/UCMerced_LandUse/Images/buildings/buildings88.tif\", color_mode=\"grayscale\")\n",
        "img = np.asarray(img)\n",
        "plt.imshow(img, cmap='gray', vmin=0, vmax=255)\n",
        "print(img.shape)\n",
        "print(img)\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(256, 256)\n",
            "[[119 121 126 ... 128 125 124]\n",
            " [118 122 128 ... 127 125 131]\n",
            " [119 124 129 ... 127 126 134]\n",
            " ...\n",
            " [106 104  99 ... 152 150 144]\n",
            " [118 117 116 ... 151 151 147]\n",
            " [ 89  81  87 ... 146 147 150]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kvpobsVjMFhUnUpBtq3YgbXhO8GvXrhlvkUgkMDs7i/n5eczOziKfzyObzRps18AeeiZ4Dfquo9Eo+v0+1tbW8KlPfcpcYvV6Hc8//zyuXLmCjY0NHB4eolwuY2dnZ0oLJxIJHBwc4IMPPkC5XMbrr78Oz/NQrVaxv7+Per2On//5n8e5c+fMJUp+olKp4IMPPsB4PMbc3Byee+45pNPpKXitpCKfI8hNyv5zScTTGsdIF6QLo11exOUP2Fxk4poGrskadBw/0/c6j057lmKxiPPnzyOVShkBSZ6BHFYqlTJ+i8qh1+uh0+kYCvA8DxsbG9jc3DQvBeMxeI/kkDxv4ta+ePEiXnrpJUPVfN5isWgu0ydpZ0YwULoeHR2hVCrh937v93D+/Hn0ej3U63Xs7OxgZ2fHpCEndKvVmiIKO52OaUUXmhJu9/v9U+1et6n2cgNkms2maQgAuH37ttmRsVgM2WwWhUIBiUQC+Xwezz77LGZmZmxycBJTK/O56FJcXl42oRGPx/Hnf/7nOD4+NrJVSaVwOIxOp4N3330XOzs7KBaL+PrXv45wOIxr167hX/7lX/Dhhx+iUCjgN37jN+D7PjY3N/Hee+/h4OAAN27cwPr6OlKplEFU1wxQAaHeBvaTvurC5vGPY5Lo/6d5DFxhpd+7xKFLRp62uIO4Fg2eUoHiXo/vi8UiZmdnAUwUSqvVQjQaxfnz55HP59Hr9QxR8t7okuRY0vygF4kRlCQ49Zn5m1arhXq9jmw2ixdffNHm6Hg8RiqVsrXxJO3MCAbf99Hr9XBwcIDFxUW8+uqrKJVKACYDVC6X8bd/+7e2gKiF6QvmcRyQ08grtsfRYEFwVltQNNloNDIPydHRkQ1IJBLBu+++axKcRCc5gfPnz09pLU5U/jGIReM5UqmUPf9gMDBNFIlE0Ol08Dd/8zcAYDYuERSDdZLJJFqtFt566y3cv38fy8vLOH/+PFZXV8HcFRVe7sJwNakuIrcvT4PjehzPqefX94rs3OsHkaAU6i4xqotLSWoVhHruIESpwk6Fis4REn6e502FxPP6NGcYMs3PyY8dHR2ZOcq5xbEnAu31evZ9tVpFu902xOB5Hur1Ovb29szseNx2pgQDJyEDedjhhK2e5z3A1hJm0X9NZvdhE/E0oRDEdAMPutB0wgRJYk4snSyj0QiNRgPNZhObm5s2sSKRCGZmZvDGG2/gypUrBhGJHtQ9Si0QiURQLBYxHA5RqVQMAXECp1IpjMdjvPXWWyY8GYq7tbWFcrmM+fl5tFot3Lt3Dx988AHy+Txef/11hEKhKY3m9sdpWtV9/iDiTn972hi4i1/P+TCBoNdSYRCEKh52D4oQ1QR1UQi/530FuV/1/yDPBhc03ZFqJuRyORP0bFQAwGSM+RlNwn6//wDHwTlHYfG47UwJBk7K4XCI7373uxgOh8hkMjh37hwSiQTG47HZbsCkg0nqUAtqABMb/w+ySXkevgZpmFQqNZVQQy8Iz+k2V6vqubX1+31btP/4j/+Iw8NDIzDz+bxFcZI7oBBMpVL45Cc/id3dXTQaDXS7XXie9wB6qlarU3EL0WgUf//3f4/Lly/jK1/5CjY2NlCtVpHP53Hu3DmbPOxfDcLS1yDvQdAr78M1P/S9jstpC1j7ziUf3esFfeZ+p6YFP3cRhz6jS4C6gsF9XnfhU3i4z+QiEyq/0WiEubk5VCoVlMtluy77gCH55MhIbnP+8545jrz3J2lnSjCQSPze976Hd9991yb5hQsX8G/+zb+x5BAu9OFwiGq1avaw7/tG1vCcpxFKLpnkuriozVOplNndkUjESFAVTu5EfdQgULsrHD08PESxWDTC7+joyLiKeDyOW7duoV6vYzgcIpFIWDAXCSwNItLGiQNM8kzK5TL+8i//0oJ96Dqbm5sz3zuFiZoz2m981XBoNTu0L7Q/tb9USOtn9IC4Tfs2iKx0+1fH1RUIek5XKNBu11R59u3DhId+zj5xzRMufBUEwMQ9DwCLi4uGHhYWFhCJRCx4ibkVodAk5H51dRX1eh2tVsvC5ZlcRVKYZocrJB6nnRnBQFKw3W7j8uXL+OVf/mUkEgnUajWUy2W02234vm/ZjTye7hxC7FarZWGkGnMPBAfD8HPmEmgkHYNQGLXGnHe3nTZBH/asXFhsrVbLcu0Zj8Hkmnq9jsPDQzSbTQvSKpfLhpY4cfk71w3HxcokMMbbf/KTn8Tq6iru3r2LeDxuz0dU1Ol0kE6nzXeuQoLC2BUcvA+NInU1s2tmsD8e5sEIh8PY39/H+vo6PM8zbxCDfJS8pcuu1WpZrInLdbjXDxIeFFK+79uzuJGYLjrV+wWmTSOOC+cZz9Xr9cxk5L3FYjEsLS1hfn4eo9EIzWbTci8ovBKJhAmEc+fOWXaoclNEII/ieNx2ZgQDAAvUefnll/HlL3/ZoG21WsXbb7+Nw8PDKUlOVyNjCMbjSVaahlHH43EsLi5azgDhuHotuJhWVlbgeR4ODw+t87vdrgkG1QRBE+xxG7UjtTZjDCqVCubm5hAOh01QhUIhC3Xl75RxXl5eNgHCOAw+v7pD2RgQ9uGHH5pLslAoIJPJIJVKGYGlYd/FYtE0mRJdc3NzyGQyD5B2LsLQviOqYJ/rd6rhVZNT0BwdHeHWrVs2diTmdCET2dFFuLy8jNnZWftf6y2wKaphvIkKEUU7rgBRYaa/o+3PpglnvFfeL6NTVegoygiHw5iZmcHMzIxdh21tbQ2+71vcAq9P4cMx/5k1JYBJ51JD37hxA7FYDLOzs0aqqOQGYJmQ7DwG8BA2feITn8DMzAyy2ayFLTN8lRKYmnp2dhYXLlxAq9VCq9UyKQ3AkrBYhIVSWWE8WxDpxkmksN5dEKPRCPv7+8hms0in0/B93wqy8DkpGPieQS78Pckpany6wfjHe+V5Go2GZYw2Gg3TrMBkMaTTaQvwCofDJlDH4zEqlcrUQlahAUxnNrr2usaKqCZ2M0XVNOH9Eg2y/9infM/fVqtVQxkMFFtYWMClS5esqM/DOBG9b70nfT4VguqhUEGj/a0EpSscaZ7qefU8rqDiMVovIojz0Od6knamBAPh+o0bN1CpVNDpdHDx4kXE43H0+33Mzc1NDdhgMLAKTsPhEI1GA/V6HYPBAPl8Hl/60pdQKpUwGAxQqVSmosiazSY2NjZQr9cxMzOD5557ztx3xWIR5XIZzWbToirVAzE/Pz8VXk1tzTBtIhmdqGqC6ICrx6PZbKLRaCCXy6HdbmN9fR2hUMhsTAoFAKZBGE1HlOByDlzMjLDkswyHQ7zzzjtYWFhAoVBApVIx4cV0c8LQer1u3A5dxZFIBPV63aLt2Gq1mtUPYKAPBalrUugkDjJ/uKCImqrVKjKZjKXJ83vetwarcT5xbMrlMnZ3d9FqtfDZz34W6XTajtMFrL91x4vvdYG6C9IlGZUz0WsoIuSzBM0LNQWChJF7b9q/KmB/ZgUDB5HFV6jtrl+/jmQyiQsXLiCbzU49YK/XQ7PZtA6nth+PxxYjwGSSbDY7VTGHguZHP/oRCoUCVlZWLHrymWeesdz4ZrNpQVQURABMYDDUlBK/0+mgUqmY7d/v940oVMmuCUw6Qer1OpaWlhAKhbC9vY1GozFluoxGI8zOzuLVV1+1zM5arWYh1Qy6IrpxI+Y4mcgz3LlzB8Vi0cLMWR2KE5IJW6oRWRODnBA9QvF4HIeHh7h27ZrxANlsFp/61KeQz+enhGCQV0D5B14fgKG8fr+P1157DQsLC8hkMsjlcrh79y729/exv79vyoT3x2ftdrsWELa9vY1PfOITyOfzdm195VwMQoGu+ajPoCZTkMnBfmPT6zI+xX3+04Sn+xpExuo6CXIpP6qdKcHAziP7CsCixah5CMM6nQ52dnbwgx/8AK1WC1evXrWFCUyKV3ChE46zziPtauAEwpJ01HiITCZjWpn2oauBj4+PTZP6vo9arWa1GQqFAsbjMXZ3d7G5uWksMXASDqvmxXg8xtHREZaWljA3N4disWhJOTropVLJBJ/v+xbbMR6PzZRoNBpGIKpgG4/HlliztLRkXg4WEWE6MBuj79zAslgsZmgll8uZ/U7BpME4KysrKJVKxp24AiaIm3ChPk24l156yZBjLpfD/fv3sbS0ZMJ2b2/PQoUZZcjaifF4HHNzcygUClOZrToHtWl8gs5R2vC8LxUa7r2rIlA0wzmn6FcT2tzrqaBkXynBqen8el9BZsXjtDMlGBjHoJC43+9bWjEZ+xs3buDDDz/EvXv3sLGxgUajgXa7jWw2a8E+hUJhysPAsGKaE77v4/j4GNVqFc8888wD7DqP0UGgForH48hms1OamMRQt9vFysrKVH6FFoiZnZ01dECug+iIfEckEsHS0hIAmPcBOHGhMjUcOCG1NG6esRD8TjVpr9fDnTt3LFy7VqtZbcxisYhcLmf3xnRd8hXpdNomHPukXq+jVCrB9300Gg0rd8ZGLwdJtm63a4iQAlfT6t1GLgcAVldXceXKFaytraFWq2FjYwP5fB4XLlwwQUzE1u12cfv2bbz55puYmZnBb/3Wb2F2dha9Xg87OzvY29ubSuV2F7fCdTblMShY1D3ruieD+CbORV3EyWQSzWbTxlfnlXp51BRxA8Fcz0MQsniSdmYEg+dNCq/SBanludhZnjfJcPzRj36EW7duWd5APB5HvV43TRWPx1EqlaaIPubNs3DnYDDA1taWTXi1Z1U68/fAiYuUQiuZTGJ5edk6fzQaGXwOhSZRZzs7O0gkElaxN5fLIRKJGFHIxdFut9FoNDAzM4OvfvWrlrG5ublpxxMNMTJUibFOp2MkKYWsRs4RZTFr7/LlyxiPxxY7kclkLIiMwo/uL+b6k7EnWur3+1MutFarhXa7jWKxiEQiYcKF2aCFQsFqbeTzedRqNfMm0TQhZ6QuXY7x888/b+HdsVgMW1tblm24trZmeQp/8id/gvF4Ugy41WpZbY3Lly+jWq1ie3vbFrTLH+l8VIGhaFI1t3pFlHNgn2vAkS5UhfeJRMLMT46hCmCX4OS5g1ylrvdB7/1J2pkQDOy4ZDKJ4+Njk+bslGazid3dXSwtLaHX6yGXyxmDvrq6imw2i3a7ja2tLYxGI0szdaUkJTRLeW9sbCAej1tQkYsalEkHTmr/cXGwNp8SRJxohLILCwsoFotTxUQVMfR6PXS7XdRqNeRyOXzxi1/Eiy++aHzBxYsXrQ9osjCHhNpWXV/8nGXkWF1qNJoUb2Ek3ZUrV6zaD2Pu+VxERxQSdGXqvgskHPf393H37l1ks1l7Jgoa9hn7hGiNQoAmGeP9uRiKxaLFo5AfWlxcRCwWw/r6Om7fvo1QaFIq78KFC1YghwJ/Z2fHkteIVDKZjGXmakq06yIM4j5cEo8LV+M0iNiC3IJB/IHyGJwbzWYTqVTqgesFzUkKHTVD3Wsrqjgt3uK0diYEAwCb9KyTwOpHzE0noRWLxXD37l3UajUAJxJfobVGK2pjB/34xz/G1tYWDg4OsLq6akVc1T7jud33qonVltNjuMCASVn7bDY7BT09z7MkJt4Ti6ZcvnzZ7jWXy6FQKFjtQJYmZ8muaDRqsQvkTNiUD0ilUlazcmtryxab53kol8sWis3meZ7Fg+h9ciFwbFgfgJGSjUbDOBNqt+FwaPxCpVIxwUjvEZEMA5W0HDqD2ubn560836uvvooXXnjB3Mn8Ld3U4XAYn/3sZ5HP5/H9738foVAIn/jEJ4yPIUpzIb8+u/veFRa6WE8j/HRuqjnr8g38LpfLWX0QN9YiiMxUNKzX5jX5WZDAeJx2ZgQDB4j7KrDaEVnb4XCI73//+wiHw1bfjr5/koXUftQWPC8wHcq7tbWFDz74AP1+H4VCYWqhB5FI7GyFZa7tplLcRSrqeuP/apsCsAIyChf7/b7VnqCHhHUeVAApdKUmpLZtt9uWM5FIJAxxMelGn5dar1wu4/r166hWq0in03juuees6KznTfJTjo6OAABLS0vm1aCwTqfTOHfuHIAJx5LP5xGLxTAcDs0EUnJMd2XiJi1ESITuFLSMdWF/8XeskBSPx1EsFuF5HmZmZvArv/IreOmll4wUZu6s2jkAACAASURBVFUrNyDIVQKq1R817i6XoKanmhcqXHd3d6eKFxOBsb/cc7qL2yUWFd26QuwnEQ5nRjCQXKlWq1ORhnQ1lUolHB4eWpYgG1OJqWlisZi53LRpp2kYLRlqheIcUF3IjyJvgrSL2/idDpJG8VHrMveeAT21Ws2EJZOreAy5ABVqRAnsV5KcrNBUKpXMliUi4bG9Xg+3b99GuVw2FyM9CFq4ttls4tKlSwBgcSa1Wg3NZhN37961WAbfn7h06bXg8yvRptcYjUZWTYqm0vb2NrrdrsVM0EPFRdZqtUwg+r5vsSXnz5/HpUuXEAqFcHBwYNGFs7OztnMT/xgVqxGjygsEuRFdM1PHQLkFfs/o0UqlgnfffdcENk1f9uvKyop5etz5q0JLTQ4VBqoE+V1QxOfD2pkRDMBkkTOIhTv4cDISpnqeZ7EEtK17vZ6F8dImVsJSX3VCRqNRq84LBAesEPqpRmFTlxX/Z9PfEEK6ZokKEOYxcFIfHh7inXfeMduc9jA9EoxboN3MNh6PLeDJ933bDSufz6NUKuG1117DaDTC7u4ums2mVY6ix2F/fx/37983T08+n0en08GHH36IdrttZGG1WkWr1cLs7KzVf6hUKsYbUDN/6lOfsloENJs4jp734B4QCr2p2YfDIfb29lCr1WwbOjWp2u02FhcXbUGwhDvNKXdRKbGqvBMFA3kfBpUpcghakO5CdI/R75vNJtbX13F0dGQcSLPZNO6He1MsLS3hwoULtqUiF7eaCRTS7r3pPbgI53HbmREMnGyNRsNqMnJQWXyFi5r+cQ4sbVV6JDKZTCALyw6jNmIU3Wlawf0tMF3lmfenDLP6pt3nY9MB47Gss9hut/HDH/4Q6+vrtkcA7f1wOIzl5WVDATRv1P9NtED4SMFE84KT7Dvf+Q7K5TJeeOEF5HI59Ho9vPvuu9je3sZwOMQzzzxjMQ/r6+vY29uzkHIKkVarhbm5OWP+a7UaXnjhBXz2s5/FaDTC1taWoRk1W5TAU5tYc0Li8fhUboMeRwVC1APANhwi2shms+bZcbU5MAlQC4VCxp+QO+Jx5CJIFNNlSw8M55uiTNes1DkTCk2qi+3t7eH69eu4cOECXn/9dXz44Ye4c+cO6vW6EbKM4L19+zZmZ2fx/PPP4+LFi1OxD4oE1BRyhVfQXHycdmYEAwDTznSNceAZMESiikQbg3MIA33fn9qOzoVX1Npay5+QO0gYANOogAOscNFd4GyKMFzzIUhosAJ0vV7Hj3/8Y2xsbGBubg6hUGjKXJqfnze7XAOEXDRCQlDzTaidw+EwXn/9dYPQ/X4fN27cwM7ODrLZLFZXV/Hcc8+h0+ng6OgId+/enSLFWq2WFdZl4Fi1WsVwOLTK1svLyybctG+USVd0QE+F5g/oonPRV5DgX15eRi6Xs2fUZDpXcxJRucSz9iW5jEQiYclijHDVMHhNf1dTRK+nrnAAeOONN/DSSy/h1q1bxhvRXCRCjEQiqFaruHnzJhYXF6d2MFP+QONAWq2WCQyayE+KFoAzJhgY6358fIxWq2XVcC9duoSVlRW88847trmsm2fOiVkqlaZQAPBgdBt/o/sABmkVFRZBUpeDo3yEklacDLR/tVEDcoJrncjl5WVsbGyYVuNEYRgwawmSbOVkdskuvUfWdQiFJoVf79y5Y5GBzWYTt2/fxurqKl588UUrELO/v4+DgwO0Wq2p6ljdbtc28wEmcRXtdhuzs7NoNpv47ne/i+Xl5ak8FtVsXKwqJBgbodWp2U88VgN9+EyE//T+kJjVTX+BE9eu9j/JPxVCOtaa4EUimK5FABZW7bpeu93uFE/CIDcWgy0Wi3jmmWdwdHSEer2OX/iFX8Dzzz9vXqO//uu/Rrlcxte//nWsrq6i0Wjg+PgYN27cmBpPEuzVatX4CSVhU6mUpc3/zJZ2I2ylm6perxtR9KlPfcoWy49+9CMMBgPLQiQc5C5NDK5hcxe0Rgoy2EiJRwoD1x3k2pPqsnKv4Upo/Q2fVY8jAqAGXVxcxLlz56bccMPh0NKjaXb1ej0sLCzYOdxnCLKJQ6FJAV2Wl79y5Qr29/dRrVaxtLSEg4MDE0Jra2vY39+3aFJdtEtLSxYQBUy8Ki+88AJWVlbQbDbheROvAHNEuEi4qFQ48H83u5LozvUGUMgQsTEAS4ON2BQNKEIheUlCVKMgiYJozgAwE0fTsjkHeBwFNBUc0UWr1TLyl/tcXr9+3QTnxYsX8dxzzyEajaLVauH69etoNBp4+eWX8dprr+Hw8BB/+Id/iLffftuEmN67clAsUkSFQU5F18TjtDMjGCjxotEoVlZWjGCjTet5nm0c22g0LBOSnU5XmJbnDmpqSihBo00XLBeYkolBUFbP4cJWmkhKgKq2pPnE74rFIl588UWMRiO8//77dq9EQ4zR4B8Xg7L8/I0+J983m00cHx9bbsP6+rppwlarZVGZ586dm4LGwEmIMycmM1W5G/fi4iIAGP+zvb1tVYwHg4FVLeZYaFMURaTBY9yQdBUsHB8KFn3vChCOAeszKOIjKuEGQ91u1wLgyEFQIGrJPF6T19P4kfF4bDEU4/EkWvZ73/se/uIv/sI8S9/85jexsbGBl156CblczkK6b926hfF4jHv37uH99983ns2dd4pQ9b40rcD15j2qnRnBQHKsUCjgmWeeMc1Yr9endnuan5+3NGpu1ul5kw1p3B2CVcNzkWoSFAk1TbZ6mEmhgkKP4+TUpgtWz6sCgcdxkVGgLS4uYnZ2Fh988IEx8YlEAufOnTNISE2m2lhjNygM9D7Y+v0+FhcXsbCwgG63i0qlgsXFRczMzNjiicVi5vsH8MCEY7Qiz01eyPM8u19GQmryGBcfTSF6mDSfhHyG67lxBTXND2pF7VMVymxu/EiQ94DnZL/QLGE/b25uIpfLGS9ALkyFBZPXGE+h8QtUEspNvPnmm3jzzTeRz+extraGe/fu4ejoCH/8x39siW6VSiWQV3HnH7kfogglpZ+knRnBQM/CuXPnLCy0VqsZoajQiHYkP+NmHp7n4c6dOwiHw/jkJz85Fc3HRjjHnXuAkxgK4HR/cdAEDep0nWguM63CRxvdphqk5XmTjWLK5bIhJ9YEJF9BV6W6/FxByHvieTudDjY3N62y0bvvvgsAePHFF/Hss8+a+5cQmAuPW7dzQx9qUY3iOzo6QrlcRiaTschK3z/JU6FA5v3t7+/j5s2bZi/znPpeOR66M7nICNlZAo3jqO5Q/a2Oics7UUjpbmRUSBw/eo2azSb29vaMt2HiGv9YNIieM82CZKIc749h8kRyN27cMJQ8Go0skMwd39Nau922MaHAIjK6d+/eQ3+r7UwIBnoeqE1ZS2Bvb8+CWgj/SQDRbUloyr0aW60Wzp8/f+q1OPif+9zn8KUvfckmIIusaH3/INQQ9Lk+hzLb7mR03aKU9iRAuV0ZF4BCR6YM87cap8H/3T5V84HwluiCmaiHh4fIZrOYm5szDRiJRLCzs2Nh2Lx+Op1Go9FAKBSyACFqWfWOcDyZl8CJqhmA4/EkxVyzHDmuJBJJnpE/IJegpkYkEsH8/Lx5DZRs1L5Q0lIzFdXFxzRyxi8o68+0cx7H/uScoUansG632zg8PLRt7znG+/v7U1WoOIf4fIzzIFKoVqsPzD/232kIQlP6fd//2d1whg/NugDsNAbw0C3GwBvCbuZX0I1DEk8RhtuoDZeXl7GysmKfc+ISShIGqwvqNI9F0LPoANL2o3DQSUebmdqZE5xJVLwGtTA1gotiVAhwgRJVASfwWnevOj4+Rq1Ws1Ll+Xze0BcFZK/Xs+twbDgeu7u7GI1GODg4QCgUso1yVUMyN4J9yj5iVSVdoAwu0s16KAy44NgP5J2YXq8kJYAp1EBB67oRVUjr2JAgBKZTpHV8+T9NDeV7mOPBKF6eIxQKYX193c7Payvqohn1/PPPo9ls4tq1a6emo7vzTucCx05dmE/SzoRgACYDUCqVjKihNiiVSohGo7ZNXbvdRqFQMHNDta7ux6A2nTYm+eh+fqrRNZKQi4v3E/Tn2sFsnCT83p2IqtFjsdjU4HmeZ5CVwooLYXd31+6dTLjLJfBa7APeDwA0m01LC+d+lqurq3bNbrc7VQFJXaosNEt42mq1sLm5aVsH1mo1K8yrJhMXvQqzWq1mpiLvT8lI9j0FtT4HXackalutlpWoy+fztkEshbFmU7oRsfRQsd+ICPg/eR8ey/dqYqq5QfREhUahwecgCqM5wfnNuAj2R6fTQb1eN4UVxJm4fcfvNEKS9+0SvY9qZ0IwqAuGGZVEA6w9QJ++lsgeDodoNptmVjAOQDcHZeN7FgrJ5/NTnUUtS9hIIkndccpDANMTnjYqpbRCfh1UHUS+sh6EVnNSW5c5HblczvoAwNS53ed0EYv+PxwOsb29jTt37iCVSuH555/H0tKSEWlahYmh6TQhut0u5ubmsLi4aIlaq6urhtB4b26wjwYNhUIhKz+n9+W206CybhvQbDatengsFkM6nUYul8PMzAzm5uYwOzuL2dlZQx28li4Y3nur1bKYAE2y0mdRYafuYd2olqnr/KOZwBgHjnUymbSs2n6/b2HoVEyMneB5XTNWx1uVgHpl2K9uHM2j2k8lGDzPuwegAWAEYOj7/mc8zysB+I8ALgK4B+A3fd+vnHYONlYDunv3rknu2dlZG6B6vW42K92V3Ltxc3MT6XTatqWfm5sLrK8HTBbcaQEfWodAOYLTeAR1f/E66semOUIYrfas/oa1Aij8fN9HvV43Dc5QaJJ4QbkdrveBpKFqyOFwiPX1dbz55ps4Pj7GYDDAr//6r+NrX/uaZV4eHBxgc3MT1WoVoVDIoiaPj4+NgHzxxRctviGVSln+Bp9di8hQ2ylcD4VCllcBYErgal+fpuVcgeH7vuU2sBALkQVLuxUKBczMzGB+ft6qa3NucRwajQZarZbdNxWWziOahYo2iEgYb8J+cBUJvRU8d6FQwPz8vP1WPWrsa8/zHqj2pcJecyfcue6aKU/S/jUQw8/7vn8k/38DwN/5vv8Hnud946P//+3DTkByaGVlBefPn8dwONm2LZPJWCg06wYwe5JCwfM868hz585hfn4+cO9F/t/tdi3iUWE+cAJTtcYjf6/ncZuaBqFQyCYcYyYUtpKsItFFBMBr0/zQMvh8ZmZHqm8/6J4UrfD+QqFJYBMzH5mpeeXKFdsGL5VK4f79+7ZdHjmFVCqFpaUlpFIp3LlzB/v7+/j85z+PTqeDH//4xxYAxWu6XhouIN53vV5HuVy2Z3X7mPd8WnMFxmkTn7ExNEOVs2DovLoe9dwuj8DnUEGs487YDzVv3eAs3dU6Go3i3LlzKJVKU2HMjNVhNTPGbPR6PZvzRKfM4VBvT1D/PKkZAXw8psSvAnj9o/f/HsA/4BGCgU23gZ+bm7OSaIR57XYbtVrNNCYHmcVAWYchaFJxYHu9nnk6gghEDXjRYBvVxhoQE4Qq2CixuYCVEyAkpkdlb28P6XTauBP60HnvqVTK7H9+5k5inZSEyAqVWdMBOHHRMoCKMRI//OEPcfv2bQuIiUQiyOfzKBaL2Nrawv7+PpaWlmxrQEVYLmOu3zMrUsv2c6Gpve72X9A4ut/pb13ThOPJRqHcaDSwu7trfUPeggQnQ4np9dLCN8AJV8X4Bg2iInLQPU7p6qQnx/NOsjx5TgrV4XBo7k5GWjK6Uk0gLQ/I0HD2OUOyf9L20woGH8C3PM/zAfwfvu//EYAF3/d3P/p+D8BC0A89z/tdAL8LYMrnTe3CxZDJZNDpdKxcOwuj+v4kPLXdbiOdTmNpaemBrcb1FTixrxcWFqYi1U6DX+pGU/ivr0HeAYWnavvpNTiJMpkMbt26hb/7u7/DzMwMXnnlFRSLRVSr1SmffKFQsLgLMtcuKlLuQslX3k+j0UCj0bCF22w28a1vfQvf+973MDc3h5mZGWxtbU3Vw+BuV8Ph0EjGer2OP/uzPzOhTI6k0+lgd3fXNDI1HvkcbiZ0eHgY6BYOWuCPIyzccXZ/63JJKlQpnOgi13FjfAWTqIisSGzSlUqzQRO8OHbkv8bjsUWV8jkZM6JuRVVgFBxEne4z8Y9zdTQaWXUtt6+e1Jz4aQXDf+X7/rbnefMA/rPneTf0S9/3/Y+ExgPtIyHyRwCwsLDg6+TgYAWVJCfMpvZkhuXS0lKgL5+vZI9HoxFmZmamQpTdBU8NzwHl32ntYdqOmjQI7vNaiUTC9rJgABb3y2DQDG1iZaJdwcDrqlDQa3MPUDa6EY+OjqzsmgoY35+4vLjpaq/Xs0rPBwcHhuQqlQri8Tg6nY4VWiVpRk8RoftwOLSIv9PItKDn0j51F76LEtg0cM39Xll8N75CvQisncl+pUBn0V/WH6Up5uYkEBnSlON8IDqggOSxStZSONGkJLqgmavmGQli3U2MhChrlTxJ+6kEg+/72x+9Hnie958AfA7Avud5S77v73qetwTg4DHPNQWdQ6GQhUUfHx/b/grsDObHM3sNOIF3SnZxYShxxKw4IFirANOx7+oy0noLihhcjRUkSFzhoLBzbm7O0pYpCMfjse29GQqFrFCK669XMkw1C++D91ytVqf2wASmSSuXueY5uJAHg4EF7/D3jHZ0NTHHpF6vW/ReKBSyXBZd3CogVOOqB+C0xmu6QkZNPdfc0+/0t64HRa9BRcHjGo2GPTfjahixSUHOyts0W7n1oe/7UzE7GsOgzwNgCg1obA7JZj5fs9nE9vY2Xn31VaysrKBcLmNvbw+VSgXVatVQxOO2n1gweJ6XBhDyfb/x0fuvAvhfAPwFgP8BwB989Pr/PM752Bm0kSiR2aGc0NRE7Ewu+HfeeQc7OzsolUoolUpGKDF2nEEwdGlyYgRpJjZdNC6hxhoEsVjMSq3xWD0u6Pds4XAY7XYbd+/eNZsyEong6OhoqhhrsVicKg1Pc0sZaQooXVQ62fv9PsrlspFXQWZR0D2qicdrkafQsXtY0wVH7kJ/owKWE5334vv+FMmqgkQXFe9Nn/9hgsVVRC6yUx4p6D4pKICTXcja7bYFZpGEJpKIx+NWrRuAcUmc13o9HQvWGWHT5DFFyLqhcTgcxtWrVy0RjzU6ySc9TvtpEMMCgP/0UUdGAPxfvu//v57nvQXgTzzP+58AbAD4zcc9oU5CCgYAtsUboT23ZSchxM48Pj62kmLUTizfrragwi23BX2uQogTh2ZOt9u1ScAsySD73hUY1OTcybtcLiMcDlsMR71eN384zQi9b3diuyHJAKYKlTApiG7ToOd2P/c8z85BroPJbo/Td49DHrIfGMHIxa6LxD2vmgGKLIJcdu7vtLnwWhFmkEDge0WIKkz1N/QUtNttG1t99mQy+cB1tbmCzTWDXCFOgRCJRHB8fIxGo4HV1VXMzMxgcXERV69eDeyT09pPLBh8378L4KWAz48BvPGk53M1neZA0L3Gqs6XLl2y1F92kBI/HBQytgcHB9ZZV69efSCf37Vjgya4OzHC4TCKxaJdixOa6AQ4mcBqC6smDIVCOD4+xuHhobmsXHTheZM4B10wnAQu9NUF0u128f7772MwGKBQKKDX6+Hw8PCBxeCaQHpvXCA6KTUoKYg74T27LWiBA5PFmU6n8ZnPfAa5XA57e3sWv0GXHN9rpGnQwnSfx/2McyvI/NDvgp4jaJ7oaxCXoQtbOQwKBldxuOaYVt3SY11Py3g8CeOmKZNMJm2fCnJ0P9PFYLUjmdzEoBVO6LW1NbzxxhvmviRJNB6PzXOhkBKA7fBTqVRQKpUsaIiLmItAGWPejzs5tNG21IEPkvK8H50cbPfu3TPbk8KN5yMKYBIOBRp5FhUUfKVwKpfL+M53voNarWaE2P7+/tS9AtMaUGE6j+E9sW80QMfV/kFcy2nam98zTmJ1ddVqcJCkZF8ysYmRpZ1Ox4J+yNEoqnNbEDGsSJNj4gq809CeS5A+qrkmjboh1Tzh+XlN9rmeh8fqjlVEJoVCwTZ/ZkIeC+b+F+MYPo7GDmegh+dNkm1IxAEwDiGZTE6RNaPRCG+//TYGgwGuXr2KTCZjvuROp4NsNotSqYRisWjhuPQI0CTRGHnf96di3V2OwX3PyeaSaRw4ZgWyhcNhdLtd3LlzJ9DfTLuV1240Gsjn81PeFNUCDAmnkNja2kK5XLZiNmoCuBP6tAkeZHe7JoerRXXBPEwoeN7J7l1MiqJg57U0O9PNjeEzj8fjqedkZKAbVanXZf+rKcbzKpIIeo7TBMZp/RfUaBbqufR4VxC5KEdNbj7rcDhEPp/H6uoqUqkU7t69a9XWtWju47YzIRgUnlPjs5ZCo9EwVyUTrTSQQ1EBXUDpdNq2hev3+9jZ2bEtzriAOZE87yQnnv53zaHnZHELcbpEFJ/DbUEhtbSrDw8PsbGxYVyCkmm+71vFIyaN8X6o7Vw7m7zLcDi0XbgTiYQJyEeRhLooea+62BWFBY0ff6vkpguPVTPyd0yeY56MZka6CMfzvCn+yfd98+jQHcjn505VGiug96KanPNCj3G9Fvo8jysAg/qKdRIURZ72vMCJ50mPd70Z4/FkawG68rVwjfIbj9vOhGBQyTkej21TEeBkOzRO8pmZGQsAIbRksM/Fixct8ITapN1uY2dnBxsbG7h06ZJFVGYyGczNzdk1mfxUr9dxeHgI4CTlllFnrFWg1Xd1seuk4UTT3AbVSgCwubk5lTOgjVqAYeGEloo8OGHdwiSVSgW7u7vw/ZM6BC6BpotZ7/u05trfp5kgOrFdeKy/A05C0BkApYE8NPVU++u5aILw2RkMxNdSqTQVL8ECrVQIrHDE3/PeXZ7H5R5UMD6JQNA+5BiqsCSCotLQ70OhEObn582r4wpZ9huzbgeDyd6sNFmCguwe1c6EYACmPRIMdfZ93+rt+76PdDqNfD5vgoGFRskhACfxB0wiYgbbe++9h9nZWczNzRnioLnABQ9MJtzGxgZGo8l+khQaTIPlYqcEJ9ylsNCYB9/3LfhFYTjRwNbWFnzfnypDp5qKkx84MRWAB1lq1g5k4x4PDC1nzgA3olG7PMi2DmK9FTkEIST3O5d01ePY6CFiDQcN9mF/usjM8ybRmCTvaP5xkROiE+WxOhb7luiBYcSsGdpoNKZ2v3IRhgozttMEhn4fZBbQqxaEhvRPz+HOD94H8yY8z7PYiUgkgsPDwwdyhp6knRnBAJxMGmro4XCIWq1mg+S6HRUCUspykXLxA5OFw12RuSdFu922MluE8pxQh4eHGI8nRTyTyaRlPwInCIZCgCw9YbyaIPQtU2oTjrO+xN7eHkql0tSCZy1ARihyp2bNkSBK0D5T7VKpVNDv923REX1ptievw3vkguGra2vz3nlN15xyG8fitIg7TvJMJoPZ2dmpwjj8vWpR/ob9x1e6iBmhyFqTSvQRdSSTySnhwQVDtEhFMxwOLXycsQH0ELjCgudRVMjxcPuFn7EKODNCSaZq3+qa8DwPx8fHdg69BtFQPB63tHx6rdLp9NQ9PUk7U4KBg0kGnvs28nPuM6maBXhQWymrT6179epV2yy02+0aEuHEoXZJJBJ49tlnbWdpHahOp4Nr165hb28PX/jCFzAzM2PciJJZ9B4kEgkTQPSwRCIRFAoFdLtdK76qhVKHwyEODg7MRCqVSg94NUjQus8LTNyJzEVgcRcVnm7IrsvXMPmGk1ZjSHi8q8WCmhKx2lT7j8djFItF5PP5KYGk88Fl5lWj8lgKbfYTF5nGurgeKM4P1nVMpVKYnZ2131NgMoeC+TrkLdSFeppL0Z3XnCu5XM7QKE1XXscVOK7g1cA2jken07HQeSpSmlRBAupx2pkQDO7koYZUV6XnebbRJ70UqsEIz10zgaQkySrux6Dp0JyQ7PBwOGxanggkGo3aPombm5s4Pj5GPp+fGkBulKtmTbfbtechMvD9SZzGCy+8gM3NTXNXkkdoNpsWmDU/Pz/VR2pzc6JpLAf7DDgp4a5NJx5/Q20XDoenSD32jWpNwm/a6DRPlDTldfSV96tj7vs+FhYWrJaka0aoEHJdvzQHAEyFazNAjPwFq00RFWofUDhQqGsZOwBTKdG8pu/79swUFiywQvd5ECfCex+Px/jhD3+Ier1uhY/ZN7yO8lZBiIzHcn4NBgMsLCxYFiiVkpLUP5PkIwDreHVVNptNWzS0lz3Pe6BKkkpWLhxyFaHQJFGI2YoUFLroGQtBocJO58ThANEkYc4Cczk4aVlrkgIJwJQZwGvVajWb9MvLy3b/g8FkjwZmJo5GI6se7WpNhdhsvj+J/qxUKvA8L9DVyklGQaSTTjURP9eUY9X0FKQ0S9QDQI3KPnA5EZ4jEomgWCxOwXh3TNlIyummttxkSO+Npp5mNmrUZiaTMZPE933bn4R8FEltzhvXCxQKTTY4It/FRgTBOUsTgf2haIgBdx988IHxW9xljPwD+0nHgn2u8TWc71RS/X5/aqcy8k9BdSMf1s6EYFB4yKIrwCQBhzY8bX0uGDa6x/R/bRxUVuZVBpgMMQUC/ctcUBQe1Mq+72NlZQXFYtHsOV2APFbPr2YCpTgAi+akVkskEiiXywCA8+fPo9fr4ejoyDQnJwLNCDfJi5OOyWVcRGqC8D50Yunkc2G+Nu071Wwk92ZmZuw8JPZojvBV+Q2WMCsUCiYUVROGwyf1E3nP7E9qbQbtcPEzDZrjTnKY96pxDsCJoE4kEsa5AHggfZ/X1XgThehUXNxQWUO6u90u7t69i6Ojo6loXd/3DYHq/WQyGatbybmoikbd5sDJDlkzMzPIZrNW/JUCRgvDPEk7E4IBOLE9mes+Go2MRKMNyE5XeKWSnJ8xIo6dqBqLHU3pS8HDiUTN4roZmcnJUGjf9407IDmp8JQTSiU8CTB9FOxHPwAAIABJREFUDuCknl88Hsfs7CxSqRT29vasP1xCi/fuLm73OV3orH0dBC0VcroxCy66UALOdfPR9KI3QFEGzQ+WKWPRFNVofD7N/2CjsKMAicfjVnvS8zxDHrwPChdqTvYnhYYiApe4JAfFcSCq1f5RLosoiNdV1KDmhcvP6PXoUSI/Qk9Do9GwKmUArMIZg+DIWzHBjYFNSiI/STsTgkFvnAMxGAymfPys/MzoOGXm+RsuPgBTQoGfuQlO7jk01BjA1O7LAKYWPO87kUhY8VmNrwBOyC+t+ah2LLU/753eAxJi7lZoqrG5CHViU/OSTNVYC96v++oudj02yDbV412tyj4IIruoKekezufzZpNrZSPgRDCwb/g/URDHQgUxgCnikcJDx45jrzEK/C2VkfssFB40ZdQ1zvmmiI1zqN/v4+7du8Zpuc+msShuI19FZHV0dGSCi+ilWq3iwoULhnQzmQyOj49RrVaNZB+NRkbecxfwx21nQjAAJ5OMC6HX6xnDCmCqMKYuXrfyL+1JogIOKCeJanJKeOUUOBEJhxU50Oak/cr4A7rKCEepMXlukmO8Vy0fTomvbrVoNGruRk4SRUTASbyGG9Wm6CMoOEcFC3C6AFDPg44PmzLtLs+j52BjfyvSU4LY9T4o8tJnZmQk+4+/Y5+zgI+aOpwXCvHZ1yQeOc+IbjhnqP1dAcO54yIIvhLSs7isChy93qOaosB2u42trS0TWvl83qqek5dKJpNW2XswGGB9fR07OzvmunzcdiYEAxfmeDy2Qh7ULuzMXC5nA6+1GThRgOlkJdf1o2wvcLK5LbU6eQGduMBJElEQXCc3oIjA1eoUMFwEuhAAGAfB+yDSGQwGVtgWmNbytKnZB3wW7QuNkFReQbkEd+Er3+ByDvycjf2svw9CCqf9XnkMddXxfikQ+Z7PRLivrkael96S8Xhsz08zg/U1GSjGY4DJYs9kMrbBDxeeCl0lUtXc0lLvGrFI84H3y+uydodWD9ckMJ17QU0FyvHxMXzft31btV9579FoFOfPn//ZTaKidOeAxuNxrK2t4dq1awBgYbMaZKKdoOfhhKVg4aD6/iQ7cG9vz6rraPARFxjPSbJQoarC0UgkYu47CiVlyF2Es7+/DwAmiIgSeI+0Cz/88ENUKhU899xzU4tJWXtOBBJMDBvv9XoW7enWq6CA0KYCTyMN9Tu+p5AGpqG89ruOA8/LV30WIrtwOGxFU1Wwq+CgQA+HwxaURqGue2vSxc17pILhvFFCkEgvlUoZD8DEOv6exLSiAZorFC40bXl/jG1ot9vI5XJ49tlnrSr2/Pz8FOLhsVR2WmxFXdKnBYmxZsdzzz1nxYJpqvLexuMx5ufnA/dxfVg7E4KBUpqLkQ92/vx5lEolHB4eIp1OTy1c1YYuPOUk1XgFMsfD4RAbGxuYm5vDhQsXpuxNTh49HwWRkpiEoNQIvBbRBZ8JgGkGADYBGe2omofQeTgc4vDw0HZ14iTmca5WVu8HJzKDdVKplGlXzcSjtlTzRQOYNCaBf67QUN6Dr6cRXG6fcCFp2LPL+bApumEmLIVzNBq1xCn2ncaiUHOz/0lsE5XR3FMkxzHkeI7HY3Njui4/XovzQlOhGb+yvLyMWCyGxcVFfO5zn0MoFLK4B4Zjq+eGOR0kEilEglAFBWSxWDQF12q1LAlQCyUH7aPysHYmBANwAsvYucPhpP7h6uqqbWzCoBptHAgXrg6HQ2xtbQGYREzSH18sFvHSSy9Z0JNOdjU7TtOswGTSMVz2ypUrRlxRuFFqEwnoVvJEC6oxVQuTZGKR0YclwGisBZENbWwSYOQwyJUQPnMrPt47hR2j/lyBwj51eQR+p/3lIg33PRdyMpk0lKPn47PwuXhNugy5SDgfiN64cEkkkwPgoue+HJ1OZ0rAULhqWL0KAT6vCgIADxzDcaWwnZ+fRzqdxuHhobkhuQsa3a5c3EQLzNvgVgmMPGUAFcOnmf8SjUZt0xp6UcivhUIhzM3N2Z6kT9LOhGBgp3JgqBlpTtTrdXO9cNK47DGRAbmJUCiEer0Oz/MwMzNjAx6NRrG4uGgLk+fiq7rp1EzRAJVoNIqDgwPs7+9jcXHRrk2+YHd3F3t7e1hbW7PcDGojvX8NQFJB4nme/c6N0WA/sc+4eOLxuC0aamI1Ibi4eD5ORp6Px4dCoQdgJ++Zk5IIQ8fB9VCwqVmiSIxeBzdOgU0XIRGRi1j02hqQpAiLr+pq1GtSsPB/Epf6W96HmjYuqese6/u+eQeYPq/Iif3NmpCsb6qomLVOGYp9+/Zt3L9/H8ViEeFwGNvb2xgMBrbzWjQatVgOz/OMl9NcjMdtZ0IwALAoRN0IJhyebM1WqUx2uFMG341PJ5PPc3mehytXrhi0VoYZOEnI4QDpoGmshAoK/Yzb3JFYpLbo9/t47733cOvWLayvr+ONN96YgpgKm1UTEh0QYZCkUq2tbi7VYuwrno/ncu16lw/gn2vDKjGpi4WRnQzF5bW5wIg0FMLrItPnADBVWEbvj4tQ+4ruV41GVFtc39MtTRiupid3ICPRy8XJgCuel+POnB0VJuwjzjkex3tlv3MfDaLfoPJqRMdELLoW2LfARDHt7u4il8vhM5/5DPL5PP7qr/4KnufZ/q7k1Phbmg9BJtqj2pkQDOwAN0WZ2p57LgAn+0uqTaqZaWyhUAj5fP6B8GglkTgJdELq4PKVDDUXcSQSwcLCAhYWFizAhsRVtVpFtVrF5cuX4XmTrLi5ubkpWxk4yQdwPQPUzPPz89Yvqo2VDFOSTr0ibho2f+t6DlxtHkQ2kj9hi8fjxn1QsFJocNEBkzJy5XIZoVBoKkVa3ZK6ySuvqZmmrmbm+fiMHHdGCHJ+MImInh6Ooe9PQqaJElgKn4hICdhYLIZOp4N4PG6Cgv1O74ba/Xw+ADbfWOo/HJ4kTnF8Oe6uAFSPGs+p7lbfn+x3ybyIaDSKl19+eaqehc4LRZdP2s6EYADwgHTkZyy6cXh4aKQQ8x04UOzwINvvNDeQLoTxeIyDgwM0m02srKw88J3az7we4ftgMMDR0ZHxI7FYDK+88gouX76MbreL7e1tbG1tWYEYhbkUDho1SRKKqcSuYNDn4MDzPBpyS3TkwmqF9O6Eca/jfsY+JnfhHkf3IYlNen2IIIh+uPg2NjawsrKCRCJh+QVqVrkeE+Y6KPlLe57l6wipyUGom5HCQr1frHBFwcFEMZ6D6MgVtGrWcp5yoXN+ArBtAWieqRAcj8eWEMgAvuFwaGHtjN0BJgKZJGMul8Px8TFCoRBWVlam3NQkVNUcDhrrR7UzIRg4IIzwYh1+LjTCVNpcCpOpDdjJlNDUjq5HAZh2pXGgjo+PUavVsLKyYrBOJbtqW14vFouh1WphfX0di4uLVi/i0qVLNuE4afhMKtG1cYJRE5F81OdTLkXjH/R59HONJlTEoILPNS+0f3hO3p/7WZAgGQ4n2aFHR0e2MPkHTLw0DPPd3NzEZz7zGfi+b2y65ilwIRPKt9ttGxdGpXqeZ/U/eU9KpFJ4c+ETBdClyPEgR6Guaz4Pn5FQX2uCqKDlsczbSKfT6Ha7NvbuvNTIWO1HTQzTzxcXF827RmWUzWbtWTnXgBNe5SdBC8AZEQwALCZgf3/fuAZC1GQyaZKck5/wiqz2/v4+Njc3MTs7iwsXLhgkBabdfCpMOEjcIo4luLUoKRuPpzSmACoUCrh8+bJpbd6770+iD5k96W5Iqkktanf3ej2Dxkqu0obUSE/NEeBE1ZBvtoeZDK521qaTXs0wN0qR19V7oYmgZoiaEiRtfd/H9vY2KpWKCXwiqFwuh5mZGSNiG43GVASlEqrU6lwQFATkbBgQBZxEjfK+1aWti1xzLZR3UkRLAaLnHA6H5nkYDAaGBlxoT66K16ZXgeOvkZqe52FhYcHmYL1et02cXaXnkqSuCfk47cwIBkJzmgi+P8mgYwQZc+B1APjqeR4qlQoODg6m8hZ4Xg4GB15ZcdpqS0tLhkoATGkOXUzqJ2dhl1AoZPsDqgCKRCLI5XKmWTi5NMSWg8ZJ3e12kc1mp3gW3gttXT1eJ1o0GrUMVEVD2k7jEvgdPwt6z0Wnx+vzKnJRYcd+o+bmfpihUAh7e3sWqNPr9abs/U6ng8PDQyufPzMzg/F4jGq1asqBC4jn1utq7IZyR9q4ILW+JPuO2lz5JvJS/C3vgc9N86NQKJiblFshsJ90bCkcdHw4vhScdDMTqXQ6HdsKwSVhg5DCaYL/Ye3MCAZqR8/zcHh4iM3NTTQaDRSLRVy8eNEkP1lilwwqFotYWlqy6tDAg/nrCv10ojB6jrYzFz0hHuGtSmB18akQAoJTlNWlxYmk+RAUGJ1OZ0qLcFJrqjWfTYUC31NbawvSFvqZLm4VBO5xav9zzPiez0g+gaSwLkbWHiDCI1rTkGiehyTzYDCwXJR0Oo1er2d2Oe+NEaOj0QitVsvQIjU2IxU114UCiH1KwcKmJC/vhWiVqEdd2Mq5nD9/HhcuXLC5wYXvmnFuv2qfuy5n/rbb7aJcLqNcLmNtbc2QpSvA+Px63SdpZ0YwqE23sbGBO3fuYDweo1KpWHgpvRbAyaRkoEcikcDq6ipisZiZHFyUXFxBWhCACQXVLJTCNB00eIaLkJDXPbdqFxKKo9Foqnz99vY2fvjDH+KVV17B+fPnDen0+32USiW7tmoYTmK+J8kInERuMlNRJ4Uu3tMWNo/V97pQXcGoZCaPdyMB9dhIJIK5uTksLy9jY2PDahi6EZfUshRuXJShUMiCfzRoiVCfGrVWq9lYabAcPQQ0UenV0LR7ZfP5/GpKKNogzwHAIiOJIBjIpCX2tB9dVzL7kAKL84u/IWdCspTHUwmqmatEuY6tfv447cwIBg7yeDwJ7ODiIrlHNyEZZe4Bwe24SFyNRpNUUw1QoceAu/8oimD0H//noJHF9jzvAZcmI9R4fXWHqd2r0NrN1Gy329jd3cWHH35oRTY4iVkZSoUBiUQNTdZFzvvSTMwgLgE4QWd8rxNUJ2wQT6FaKciGVcSiyI33Vy6Xp3az4mILIs3UXNTNjMk9aZ4Cq1aR0KOJ4Hke6vU66vU6lpeXbTzb7fYUStX3Oif1Xjh+LvHIa7FPstksstksrl27ZrVD+RxqCgeZZTrOFH7sL449PR3pdNp4nEajAQBT0bJ6fteEelQ7M4KBEpLJJC4LTsnJ0lXASb1CaiR6MYgsyB+0222rAwBMmy3UyiT2FK6THVZTAJgs0h/84AdYX1/HK6+8YucETqQ+IR4Auw9OpEgkgtXVVXz2s581F5TnecZTRCKRB3ae0gAm9Wywn3zfR61Ws2QilzF3+zqIawhCPvpsGj/hnkM1ogowPu9wOES1WrXcBl6HQlmT4zTOxO1TLkQubApyJV9parLRfajeDPaR53km3PVZFako8eh6elSYcO70ej1UKhXcu3dvagypHE4LT+ZcVtSpdTU43q1WyxQd56Om9eu6UQX5JO1MCAbtWE3o0RZEHLGjVGgAJ4VbeG4XaZD5VaHAqrpKLnGDUDVL+Jt4PI779+/j3LlzKBQKD5BvOlB67xRymUwGL774ok0GADahKQiCPAEasquQnrA4l8uZVtGFGyQI3IX/OOPknoOfqz3uNiIHCnweo5GGuoGrey41CelFUAJQm2vzh0KTClvZbBb7+/uWfq35FLTdOS66QOliVm+YXtcVFqPRCJubm/A8D7VaDcvLy3aczg/tT5fH0WN4XZ6b+SIs/ca5QoWgSFCRbFDU5cPamREM6o1QwXCapHPteIWe6n1Q0k8Hx41kJNJgcg1tUP7R9uPr8vLy1O5VWktBhQs1kxKUGmWnbHyn0zG0o3CfTZlqhZtK/JFj0X5yX10UoZ/xlfevsNRFBS5pqSQY7zfI9BiPJxmLxWLRaiiox4ZzQYOTGMSkUa86P3QBKWcwHo9Rq9XQbDan0Ai5AyaTKSFJIaGVljl/FKGxKf8QCk329eBWcSwYS6KV13CFtr66n+mx9OqQx9C54f5evSU/s+QjtT/LfbGxw5V4C5rI2jjJeLwSaexkEo3qk1eWn5KYJgbr9jPphWiCCS4aEKVZixqerMILwJRdyvDgdDptwiYej5s/nJrNJaw4+Zlsw8SdIJvyYchAF9poNMLW1hYajYa5ChmUpCYCG/t1OJxs1EIIzM9I3vFY2sUMWAMm9TZIMCrbz+NpZlLQ8J6VoHXNIBUOJChpeqRSKRM4DHQqFotIp9PY29ubqvqsaJIBXAAsaUmFHjBJr2dmbzabNVORc4D9EUQYuvOZY87zcy/XxcXFKXNIFZOiGc7jn0nBQJJxMBgYk6uLl/aeyxrzcwAPQNggregyy8rc6yvPORgMjM9QxKHBTCylRYGhpgGRBycvSVANgQZgVaC63a7tI1Eul9FoNLC8vGy7UekzcjEEQVslPl0Bqf+7XAG/GwwGODg4wO7uriEbLgI+byaTmRKUHK/BYIDV1VVEo1GLVaC21sjAfr+Per0+1V8cEwonRQ2lUmmKMNQAII6P8gRqXzMISInE8XgShlyr1cwU6PV6qNfrU4lgo9HI4iaogZm1q+nM6j3I5XJWnFW3D2Dj3DmtBWl4Cv+joyMMBgMrENtoNAwdcE5of+pzP0k7E4IhFArhlVdeQafTQbVaxe7ursHhxcVFzM3NWU46JbnCVIWq7AB2jn6m19PPXO3qwlNFGqplFJmoWcIcAS4gRkJSUGi2ZSQSsQkWiURQqVTw3e9+1yIBGfqaTqdRKBTMG5NOp+3ewuGwadqg5+H9ndZcocEiIuxjmjDcNk8FJAUDS/K1Wi0cHx+bl0UjC100pmOmTD8RB3CSru6aTDp2an6owHPHhsiB9wOclJUDJgKL7k7fnw4oGwwGU6Xf6GWhR4wxKFz0Fy5cQDgcxszMzFQUqy5eCikVzIpulR/g+enxUY6Ma4XJY5qh/JMIBeAxBIPnef8OwK8AOPB9/8WPPisB+I8ALgK4B+A3fd+veJM7+F8B/BKANoD/0ff9dx51jVAohOXlZTQajanKyKlUCp/+9Kdx7tw5Gyjd748TWPdf1G3DdIIot6DElsJudqYKHPc+2dzJCZzYf0QZvJ4mS3Eh6UYu5XIZmUwGKysrqNfrODg4MHi7vb1tA0/+IZvNolQqGZJgEZsg/sBtQce4iIkC2H1+ncC+71u/U4Py/IzIY41OJQjJsuu5VfDqeCivwwWsCIOLjWabJo3xe8J2amgV9BrzQWTH63AeELn4vm/jpS5h7gVCE2NlZQWpVAqFQgHZbHYq94P3xGfn+V1+jMhDU/rH47ERoTRtGQjHIi63bt1CMpnEwsKCKSR93idpj4MY/k8A/xuA/yCffQPA3/m+/wee533jo///LYBfBHDlo7/PA/jfP3p9aONAc78B5sa/9tprWFtbswHhpFDtrRVwdG9Bls3i+XRvRNekCEIMLqJQraZ/j2pqX6sGc8uMhcNh3Lt3z4KA1Kxhngg1BsO/eV5O7M9//vPWNwqtXY3EFoSkRqORBRHpBOb3p/UVx4OaVD05vC6Fvmps9155TZKBQWaDCnC10/V+dXxcQlsXJHkD3gMRiusiBGCbDHEPUs/zptArkV0kEsHu7u4UxA9SOvpeIy21ehYRFwBLCKMJx2djX47HYxwfH1vmZSKRwNzcHM6dO/fAnqWPao8UDL7vv+l53kXn418F8PpH7/89gH/ARDD8KoD/4E9G6J89zyt4nrfk+/7uw67BDmk2m6jX62i1Wnj22Wfxi7/4i8hkMrboWYmGHUkbXxlzhfsuwqCgaDQaVtqbm7hSUgfdm0v6qRmjzbXr3c8V3qqbleerVCrY3d01oaFxGbFYzFK3qf1IjtXrdYxGI3NV8lq6WFz0pJpL74u2/2mFPYKEgz4vkR1zG7ibWKFQQDqdxnA4NITBe3CzDHVhEBGwv10yjp4FXXQUtFQk7g5SPE5tfV2cPI59o+HmRA4Mmdc6EMyPaDabqFQquHr1qnkiXIE8Go1Qr9ftPpQjUsFEhUgTqNVqIZlMThXxoWcrnU5b1CxRH0sQ/pcq7bYgi30PwMJH788B2JTjtj767JGCwfd90/aDwQCLi4uYn5+3hU+p3263jXTRjUrYmYTsdDvqpNWOJNlHZNFoNEwoUXAQZahUB6Y1/6PYf110+h21gavVGazi2shs5DFo16fTaczOztqGI9evX7dK0Zq+HsTeq6DgZxSgXEBBLlMdN/dz2ry0vxX2s0+1ChPdg7ohrfZdKHRS6EXHQnNV+Keh7xwv2uj8vZscpwuPAoPCT4UjeQYuaioeBixp1C6jeLnvKJ9fx7Hb7eLmzZu4c+eObUozGo2wtraGL37xi1PxKuwnztPFxUUzUWjC1Go1y+bl8YwKVoH3uO2nJh993/c9z3uy1C38f9S9W4ikaZrf9/8iz6eIyEPkObOyjn2urenpnult0KiFJXbHGBYjIawbS7bY9YWEMejCsm9kMAJd2DICg2CtNbbAliywkRajZWUPM9qZ1fbM9PT0TFd1d3VVVmZVnk+RccjzIcIXUb8n//FVVHfVWoicF5LKioz44vve93mf5//8n8MrJUnyO5J+R5IKhUJA2MPDQ2UyGU1NTQUTy+J1dHQol8tpYGAg/HncBo6zg5zB0pBe3dfXF00sgHwMBAutjMIgNFStVrW5ualisRgNSJ0k8s3iSuB5EPx5PjUjrdBcqDgglXZ37mcDi/HFndeADKUlHUldXvtBBIEzCPy+WyEF/xv/An1XVlaiPoF+lKwNz5N2IUAGzCvz09XVFTwL8+I9PoHaLvyOlhxl4EJ4CA/j4ogvrTC5L//O8/PzSEEmq7Gnp6eJBIYDOD8/b+qleXp6qlwup9dff11ra2txqjuuU3pumFdC2igzXM16vR5EPa+TJ0Ib+ZcZf1rFsIGLkCTJhKTNp6+vSJqx900/fe2ZUa/Xf1fS70rSzZs36zDCVFAODw83ZaC5RZIujqrv6+vT8PBwlDd7+20s187OTpA23d3doSSAZJ64xKk+Q0NDTSTbo0ePtLq6GpwF3AZWwmPsDgsRTl9oJ7dsPp75DON5ZKH/nob+LjRHR0cqlUrxfo+eECWhiejm5mbTwa+MVkjBn8efg83gRWhpReD5CSjkNA/iUaH0nDCw1v797ial5cfnzSMjJJu5MsBNST8v3+MKAuQA4Xp6eqqNjY1AocViUZOTk9FrgSrQt956K1BBV1eXPv/882fC6DwfaIvGNLjAENDeTp8sTQyJJ0O9yPjTKobfl/RXJf29p//+C3v9byZJ8k/VIB3LX8cvSBcwrVqtRsoqXXP5e9rPBybi4+F7e0t4rPv+/r5KpZLK5bKkizMOCSfC7uPTO4RzH5VIgDcwPTk50draWlgFhBxomiY5HSq7v++jlXZ3C/GiI231eA2llj4sBWX1PIXV6v7SCo/3np6exvkd+Xy+SdnwPR5ude4G6A85m+aP+C53xZgj4LV3Xkor57SL5C6Fr1G69Jlr8HdkD8Xg3MPg4GD0HPV8D65BJG58fDwiVGTAujLDNQPNJUnSlFiHbObz+Xgm3DMiS0RtXma8SLjyn6hBNI4kSbIs6e+ooRD+WZIkf13SY0l/+enb/6UaocqHaoQr/5MXuYm0Yshms+rt7W2yvFLr9mRpEtAtAFbQIxF8plqtRhVmR0dH+IOe5Ued//7+fhBy8Be+4egzgMJyH5T3wx+gwXkW3vu8Dcnzt1Io/j7nC9KfZ06+apN7AtlX8Sbp67RCC1y3Uqlobm5O09PTWlhYaPLl2fhUCILwuBe30rzOBmawSdPKIY0WsO5OyKYjBN4aLs378J1uqGD9QWX8Dc5qZmZGc3NzgTz5Oyd7Y4jgBgg5g159vb3WhHA1FbjpMzl4bmS0Ve3Ki4wXiUr8lef86d9r8d66pL/xp7mRg4OD2HzZbDZ4BOlZxMDCoOW9d4H/nJ2dxQnA9PNDo7PopOzyfrQ/OQNDQ0NNDT+8qQjRg5GRkVAKWCt+yHxjsT0NGCFFu3NqlX/+eTDYf3dftJUbkv58K76A97YiStO/P+81vy+et1gshpvnRBobi+amZ2dnWl1djTVgbdKhxXQEyr/XlT/owQnjdHQgzSX4nDh6cg6FQao8a8Q9VyoVlctlvfrqqxGRgH/wCl+QSKVSUSaTicgSKfQkK/G8uA4UT2HQuC4K0RW8P++/c/Lx39aAF6jVGjnrJGj4SCsIH2miCRcADQ30JP2Y9zKJvhnJ8oPM6+vrC6HzdGk6CwF5CZ968Qo+NsqLrEd+cD38YFxQB387Pj6OdGCUj1v3VnOS3rTPs/Lpv6Xnu9Xf0mim1cBSra+vq1wuhwAXCgUVCoVQANRWMKcoXAQbNOYowje9p38z5+nnw+J6QRjfx2sYBEc1ji7S/Ie7JUmSBL+wtbWls7NGz0c2sPMkbGKeh9d5dpCyW/ta7SLzlKYvFPAh19wT10NOcamet07PG5dCMdTr9YhISIoDWV0ROBT0QeTC3QsmSFKTv89GcrgvKUjJSqXSBLvdEjNYNF84tDSKDSVBfQHKaHNzMywlcWWv7PS0YLecKAeYaT8lmedDqbmwcu+tlIT/3TdBK3TyPOIvPTe8n3VxlMR6dHV16cqVK+rt7Y2mLvjCAwMD8dxenco9sM4oXL6vFY/icyBd9DogzdndCPfB00oPy478gVC9jofuVENDQ3ry5ElTmjvP4G4N8+P3yz1haKSG7C4tLUXjl+PjYxUKhSaj5AV7DOQ/zdG9zLgUikG68L0ymYxGRkae0dKt3AnpWaTgf/OjuZg4VwgshmvmtFVg0oGnHlJ7HlPvbod0cU5kJpOJxCri0B4y9Lp7J70QAGfwXXGg7DxHwGG4++GtlB2v8ww+B8+ba8ZXKY309aVGcdj29rbGxsYCwfFs7e3tQVKmlRQbAFdkJsh6AAAgAElEQVTLI0AOq9Nr6LxPK66AOXIry+dQJlwThU49hSuTSqWi9vZ2lcvlOHvUlUFaTj2zMv3D2N7e1h/8wR9oZmZGd+7cUalU0iuvvBLcmR+ekyZwWU/PX3mZcWkUQ7FY1PHxsTo7O6PwxAk1RwTuQ/F3X3Rep3KOz8L+tooYOKxkgtMZeWnBSZNbaSJMUiAEDpulG3FbW1vkSzij7Rl7rrC4FqFGBJnnqNfrcUisuz0okTQH4gKUVh4+vm7jp5Xx1w3CxxzBx3f6cXXeJt/XfmxsLLI9CQl6qrsrP7ea7pql+YI0wvLnciWaJI2WbRyO40iDuV1dXdX29rauX7/edDxcq7lpNW88J6iQ3IZarRa9KKiy9ef1cG7arXhZpMC4FIoBEoYQIrX5z3vIVqRYWolkMpmmcBz/usVhEHv2EBevY8Vc+aRhJxsbwU7H3Ym4kHnn/RXc5QEFYAEJn/b29mpvby+Ul8NaF2yeEaSULu9FQbj74QrEEQjC3sqV8Hl+3uAefc24VqlU0tnZWbD65XK5qTyZbFfWld9xuSArmS+S3CB2WylwLD9Zr8hWWm7c0Dga6+jo0MjISBPPxLxT1LS6uqrT08Y5ErgRRLLS88dwZcXr3Ove3p76+/vjENve3t5QOLVa7ZkWdlzDFYavxcuMS6UYIG3IRWDS0lqVRePB05livnHImgMaoixc8A4PD7WysqL+/v6mY+TSvQJaKSa+M13N6ffi4cuTk5NoWuv3ynNhIQjfdXV1aWRkJCAsSsqRhT8L1oWcjSRpHNwC/JTUlOrNBnM/1RGGI5K0756G5unfnzfIrvSTwL0TuCsz53E8mkNIrr29cXYHyo34PtyFQ36H3Ax+7+/vb+I2nOvhmbleq+fu6OiI/hIoOdbRUZHLU71eb6qgZE0lRVgyn8+rUCjEsYVENc7PG02NBgYGnpF79gLzle5l+iLj0igGEoQQ4nTySqvNKF0QRFLzxBwfH2t7ezsSpjo7OzU0NKRisRhFPOlQFhEFP0/CvwP+wBUW95VGISgiJ4f4PgTA0Q41926xHOngCvnhvulzMLin2dnZIDu9ZgHFQck6G4XP0zeAe/mqpCqvvvS14N78//4ZSdFwZHBwMBAMz+yZqHy3RwtY/zQXxOdJJkIR86x+IFCSXJyzSTu9V199tam4jnkD2UA6krHo1Y24vRyRiGJgzaSLTFR3f5krNrG7Pm1tbRoZGdHIyIiy2aweP34cJ1pR+If76fvC0bWvQys38avGpVAMEGdSI4OLopk0HG1lmdyCOYGGQPhmZCMDmbl+V1eXXn31VZ2fNxp5YhXo/MOm9/tJcx0OVxFcNhz+Pz5xGr5KF0kp6egJQui9FKXmrDvIznq9URMC6z8/P6/Nzc2A3MwRXAfui/fA4Dv8bMh02DhNZPrcp10MRzTuF6+vr4dwp7kAruExeZ8zBv/38G3atSRXIp/PB1LJZBpZh3t7e/rkk0+a0AUp8/39/err69POzo5OTxtdpvv7+2Pjk6DU1dWlSqWiL774IhBDrVYLtOeckM8V8oh8+BzzHCMjI7p9+7aOjo60vLys119/XfV6I/q1vb2t/f19ZbPZZ/aShztboaQXGZdGMWD5OEQDIeKhWpE00vPTiqlncO1Zr1+EvNKx5O3t7ThlmGt6ai4KKH0AKf+60DvZSUSCXo5YK0J0rqDcv0/7oWmND3rh8w5Xsf7cu99jvV4PgUaIOEE6HdEASsN3MNiIrdws/z2tKNIoaHNzU7VaI3Y/PDwc+St+Hdww1ilNyLayhu72uOyQE9He3h4GqFAo6MGDB/r0008lXVSv4lZWKpU4/Xp8fFzT09MaHBwMLmF4eFhdXV1aXFxUrVaLgrHt7W3V6/VARdwHSV8YHoyH36vPGS3izs/PIzuXuWDNvsqAuoy+zLg0ioHiKYiWtB/XSuMymbzPBefw8DAWNN2zAZgqXUA5TkZiEzozznU9mcaVl3+Oe8EakDVJVILwnCdZ8UwO2bmWW3J6GZyfNyrn+IxvUBJtyBHAXwfCokwkRcr2yMhIJBuhoIHi6S5A+MXuTjH3aSLMqzbTwoor5IQoB6twMBD3zbOzaX2OnXNCTjzK4q8zpyiIrq4ufeMb35AkLSwsRBk/9+Iu3fHxsR4+fKjl5WVls9mYt6GhIY2Pj+vmzZv67LPP1N3drbmnqdAO872gzNceJPno0SOdnp7qypUrkRjF8HoI7gnOCE7MkbBXzLbaPy8yLo1iwDLRFtuF7qs0obsPjFqtFnUX/O5nR2Cxge9eduubOpPJNHX0YcORbIS14j5AFQggcW9QiFfJpTP2mAffECAb0qUlRQen09Pmo88YwFf8ZHgO6aIlvJO2ELS4FHwvh8Ow4dmUmUwmUrddGfAe5s/RlgsqY3BwUGNjY9EKr62tTUNDQ+HWECVyZevrnA478r2sAWtHIpBzViSKnZ83yqY/+OADfetb39La2pqePHmi1dXVSKPn/azx6elptHPb2toK16JQKOj4+FgjIyNRy+Cdsg8PDyPFeXh4OO4vSRrk99LSkvb399Xf36+pqamQBxCGpOhsDU/E/CLzzl94pCrt3r3IuBSKwesK8OMYrdyGtKWSmmP9QM/e3t44Eq27uzvYeM5twKp4G3dgn4cCvQUb94FCAE04GcdnaFeezWajPyVwkpRWJwDdnZAU3+mViSCB9fV1dXV1aWhoSLlcLu6f74R3ACFAgDpHIinuk88yx/39/RFWTLPeIAnP60+TlGxG0B9oaWpqKnJVpqamVCqV4iBfCt5QYAxQYJqwlS6UKd+HEgIBPXz4UFKjLRtpyu5+wgsUCgV985vf1DvvvKNyuazV1VUtLi5qaWlJ29vbIVPuFoDYSqWSSqWSTk9PQ7mxzmm5dX4Bvuns7EwzMzNBeDpPhZLu6+tTZ2dnINvDw8NQFI6UfaT5sJcZl0IxIFy5XC7gktT8YK4EfIOmlQTv7+zsDMg4NjYWJxF52zKHW04cJkkScNF9eAQdv90jAh4b5xpnZ41j2VAcSXIR8vLafxQP12BjS2pyR0ArbEKO3hseHo6FZ5MD9dnU/B1hRbDZRIRHYdnz+by2traaGtu6wA4PD8dhKmxmwrG4IfjAfP/BwYGuXr2q119/PQqrUMI9PT0B451PQNG5gnIeJs3P8Hsmk1G1WtXOzk6UxoNIJycnNT09rf7+fh0cHGhnZ0cLCwvq7+/X6OioxsbGdPv2bb399tv65JNP9MMf/jCehedKF7jxHIODg0Fa+wCduTzDRyRJoqmpqSYUxDq1t7erWCzGOakkWEFK85mDgwNtbW1FUZ+HRl1Jvei4NIpBUsCwNDxMj1YkjQtTrVYLLS49mw3m38kiYHFQEn44iYctEUrQhof00sLgGXkohFZhV7c+7mKgJAgd4haMj49Hsc3g4KCuXr2q9fX1cHEkRTo2w6/JHDK8eMtdJ4QwvVbd3d1x5mZ6uIL1/A0UD/5xd3e3Njc3Q3HCL0DOEnL2JC0s7PNcS56Lz+7u7jYpFKIPxWJRi4uLun79ur797W/r1q1b2traUrFY1NraWnSfyufzKhaLevXVV1UoFFQsFrWzs6Pd3d1wVb0aNkmSKOf39u7MC3Lja+95M7hpPCsGbGdnRzdu3AhZwAWCO0N+Pv/8cxWLRRUKBc3OzmpmZqZpP73MuBSKASHN5/MRH34eacWCDw8PN028X+v4+FilUkl7e3vR/q2np6fpMFzgLSnS+HvcC9aKqkYsuHdqcr+6VZwYK+OEpvMIrty4ByczWVCQAgjhgw8+0Lvvvquuri5tbW3p4cOHWlpaCv6EkJb71Pi0DqN5Ru7FB8x8uVwOAWXQ2NUbqaafh4EiYuOAFgjdQoDCAcHWA7XZeOlsU/8OkAKK/fy8cZLWxsZGEweF4jk9bRyo09bWpmvXrml2dlbXr1/X1atXg8PZ3d1VsVjU1taWrl69qsnJSc3NzUlqJGhtbm5qZ2dHxWJRpVIpOjV9/vnnOjs709zcnMbGxjQ4OKjOzs6mjEWUp2eW4gKTmEZn8KOjI5XLZQ0ODipJGid3b2xstHS5h4eH9fbbb8e8UZPjyW0vOi6FYkCABwcHW4ZufLMeHBzoo48+0ttvv62JiYm4hrsadIX23guHh4dNbdFboREPkdJwlRAnVi8toH7/7oOiNOAEWuVCuAuThnpJclE4hatFn8fp6WldvXpV/f39qtfrunv3ro6Pj6NLsfMkKBj3Qf0esc6Qca6QOCjVU8UJ9TlUbaUUmQ+eRVKchwERi8IEocGD8Ld0gxZ3IVt9D4OND2pq5U7Vao3y7qWlJW1tbcWBPsPDwxodHdXU1FTU2pArQF7EwMCApqamdHbWaE5cKpW0tbWltbU1PXr0SA8ePND8/LwGBgY0ODiokZERzczMaGxsLBL40mvB78gspCY5FJyaTVo0LiNzwolU169f1+joqM7Pz+MQ31YG9uvGpVAMkqItG8y31LohSV9fn15//fWm1m/+HgSKjQVpxDFp0oU1xhLj13q4hyQrD506KekcgSsLlFpnZ6dyuVz0oARxpLP4pAsI6RsNH5ToSFtbW5yZ6K4H908+v7s2ZPfxzNyvK16H/j7nZ2dnUafBBiMzdWBgoKnIzd0TJ7t8I9fr9UgcIizo6IvIEaQmPSNbXduRAbLjqC3dUYs1dPemq6tLb7zxhm7evKlKpaJisajl5WUtLy+rp6cnFBh9Qff395vcA0jOXC6nwcFBzc7O6vj4WGtra1pbW9P6+rq2t7eDxLx7964GBgY0Njam6elpjYyMRPp0NpsNtwAlhAHjJLKBgQFJDa4im802KUsKrvr6+oJkpdvTn0YpSJdEMdTrjaQb+ta5UPl72Ow3b94Mcs//hiCQMuptr/DJ6fmAQO7u7qper6u/v78lGYhfDEGHUiEmjT/L79w/8f/0MyCYaTLVw5nOsrvSqdcbFaP/6B/9I33ve9/TrVu3YhNJFweSeEconsdz/v05UUrpyAOIore3N0J3tVot6hn8Ol8neISiqfnAbevr6wt47h2Mzs/PWypK5or7lNSkzB2N8TdJcSoUCrNcLkevjHw+Hwf87O3taXt7W5ubm1pZWVGxWNT09HTT+RG4fCgGvpP2gAMDA5FFC+xfWVnRysqKNjc39fnnn+vu3bshH319fYEAJycnm7ibk5MTra6uRu4LmZvn5+dNbjQNf2/evBnl3ukU/V9JxACEot24L67DXn9/q/ALm52kpr29vWD04RsIz0mKMuXT09MQWAZFK+67+j35JpNab25JTQQe7omf6Az6cDjJwiMQHR0dTadDP3z4UPPz8/rss890/fr1JleLCAG8CHPllZu8Bsfix7/hioC0crlcpN9ev35df/bP/tnogwmh6mSjD6w0lnd4eDi4BTIuiYSQ9l0ul5uiDVwT5ZxGb87DkETU19enqakpLSwsxBF0165d05tvvqnV1VVtbW1FSLJSqWh4eDgarVy5ckUzMzMql8v6+OOPNTg42CR7KFw/rNj5G1BeT0+PBgcHNTc3FzUWxWJRq6urWlpa0traWhwcvLS0pJ/97GfKZrOamZmJexgYGND+/n4c+ccc0/uBzEmM5ejoaBg+jjh0WXyZcSkUg6SAkK2GCwOQMW3hpAsOYmdnR5VKJZJKsNL8SBdHmN+8eVNtbW3a3d3V8fFxxIk9oYlNghIBRXgtRZp9Jpzkh5DCXXh+RPp5EDT+xvF6JCt95zvfiaQXfNhisRhp15lMpqmqEGuWVhTMuXThg2MJPcwJoZkkiWZmZvT++++rr68v0BRFR5x85AcPE8I8Pz9XLpdTT0+Ptra2Il2dQ2AhHjmpCgUAsqCbl4cH01EWFBRr4d2Xv/Od7+jtt99Wd3e3bty4oWq1qp/97GdxHMDe3l5EIrLZrAqFQhgESvJbIZV0DgEIDblxVEYofm5uLsjx5eVlPX78WBsbGyGz9+7d071799Tf369cLqfd3V1997vfjTNCcSWQJ+YAJbuysqJM5qLuxufqZcalUAz1eqOtF9VjzwtJscE9cpEeKIZqtRoRCZSHQ1NQBFGHbDYbjUBg69N+KgsB7L169WqU27o74+FOF1zckTRZiQsC5E+Si+o/vqter+vGjRv67d/+7bAkp6en+vnPf66f/vSnTYfooIzcojocZ/jzsQ6OzngmSEhy9VGqjggcleDKkNS1v78fJ3WzhpB2nLXg3Adry8aElObINc9udHI6k8kEed3e3jhxuqenRzdu3FChUIiKzv7+fo2NjWltbS0QhdSIeG1ubkb+RqFQCKXtVZLShXuEAvfSf0KOXlXL/SIbQ0NDGh4e1ptvvqm9vT3t7OwEgUnEY2VlRbVao5ZkYWFB9+7di1yM0dFRFQqFaB7Dc5MGn3ZjfyVdCamRw9DT09Okhf1hMpmMdnZ29Mtf/lLvvfdecALSBawkiQafkAIUNiNC7++nSw6Cenx8rLGxsSAuOQugXC7HAo+OjiqTyWhsbCwQRpp0c5cFAo+KRk/UQQkSymRks9kgLcfGGicAknwD+VcqlfThhx8qSZIg3DwJifsiKYfh38n8ubJCoXA/HOxD6M3zQpxnINzI9SBHEdqNjY1w8VAa+Xw+7rtarYYb5fdJwRkK3ROjWEdcIxRMV1eXvvvd72psbCzyY7LZbPRu+MY3vqHZ2Vnt7u5qeXlZS0tLoYCJJBUKheCbMBTwSWnlxDyCLj2pzdcZBOHcBEVk169f1/HxsarVqnZ3d7W+vq7d3d0ocpubmwt364svvtAXX3wRLsv4+HickdIKeT8vcvS8cWkUw+joaFOstZWGI4f+8PAwWFp/b61Wa2rVjR8GiuA97nOxuI4uYMfJQYB/YINx1Hi9Xo9IAdfC0nHCE4Le29vbVPhFJKS9vb2pxJlnoVMy/vn29rauXr0a/QYymUxTy333yf0Q17Qldj7CQ5sOO4HJzA1Kllb6aSTFffu8oiA5Cr5cLjcpwL29vagpIALh3ytdICpyGYhkOOnGfPN+ErDy+bwODg40Pz8f/IZ0wfYPDQ1pcnJS5+fnunfvnh4+fBg5BCBGohHe9s/nDheH66Y3n0dQUBbOTyGPbrQ4SnB0dFTXrl0Lt6xWq2lyclL1ej1cuHK5HD00nzx5IqlBtE5MTGh2djayMH9lXQlJIezpmDujVmu0lf/N3/zNUCBp1pVJ43oQar29vdrd3W2yit6xKI1MJAXchQUmLwIyc3l5ORh0hucPuDJhgGbOzs60sbGhXC6n4eHhpmQXdyXo2NPd3a3Dw0M9ePBAP/rRjzQ7O6uhoSFtbGxE/0EOtqV9uQtluhjJEZOfVuRJXp58kySJhoaGovKVAQrxCI1/HxmOKEI2Pm7G0NBQKAy31t7cxl0Uj+aAaDy93bNDBwYGtLi4qO9///v6wQ9+oLfffjsQEptudHQ0znXAjSiVSuru7tYbb7wR7fQ8AuaZmL7B3Q1jzVGgzgXwXkcRaRfOXUxcUleSkJzDw8OBIg4PD7Wzs6P19XV9/vnnunfvnnK5nGZmZjQ5OamhoaGv24JN41IohiRpNPpMs9BSczNNrJdbOB9pUvLg4ED5fD4m1jcKPAVWx5N7KCYiJNWqRVgaRvIc5EAgBG4tOjo6oqknMX2OG3M/H6Sxtram3t7eINI++ugjffjhh8rlcrp165ZGRka0tbWlk5OTOLvQXTDmjo3Ea9KF1XPSjjnkGRHYer3RV8DnkjVAiTjywGrjWnjtRJIkGhkZ0cbGhjo7O1UulyORzMO0PqfAck8N5xl4D/dMqK9arerWrVvK5/NaXV3V/fv3tbu7q7a2Rrl3d3e33nvvPd26dUsPHz7U1tZWVJTWarVoL9jb29s0h6A+z3vhtXS6ueeuYFQcuTnx7OgHd4P38h5Cy5DfrDd9MEdHRzU3NxdEZbFY1MrKih48ePBM38mvG5dCMXR0dEQRiQtjKwjEYrSy8q7BiYMDu8htYKAMHN5LDaRBFZ6k0NJURnIPWIZ0OI1NwYaBCHMiMJNpdBDCYvnzOIl5fHwc3Mb+/r7GxsZULpe1v78f0JFkHc5pcP+b70qHVCU1RU18zj3s6MLJsYH+Xp9n3k90IQ2zubfT01M9efJE5XI5yF7nliBgndhkXvgO35QeSmS+a7VGrQwu3K//+q/r8PBQT5480Zdffqm1tTVtbGzoD//wDzU/P6+VlZXIEoSIBh0xl+kiKOamVb5AWh69FwOvoyQ8zI0sSnqmcIz58J6UKBIIX5BqV1dXJF0dHR1pb28vsmZfdFwKxcDEu3V1YfbFl9QkuA6RT09Ptbu7G+XCwGGEX2o+CIV4OiNJLhqnegUdVYM+uEb68+6mcLoW1pjFx8+uVCrBL7hbg3APDw9HhWAul9Nf+kt/SZlMJvoxbG9va2FhIRRPGrJLzQjB/481d24jDW2ZL+YFZcm8+IYHKWEZnWDzuUySRiIR9+NcCs/vZzimDYDH83kujwp4r4u9vb2InuRyOb322mtRp7G0tKR79+7piy++CDKUzxUKheijwX06KZ4ujvKBMnMr76jKr8f9IyPORyA/PCuKhCgKbhTrjryDdrk3iq1+JV2J09NT/et//a/17W9/W7Ozs8+EWphEtK7DMP6OQENAHh4ehrC6T02sHr+dTcvCEY0A8iPQ8BpeiORkGb8j0AcHB4EcuEfCiCgxQktofd/EXPP09FSVSkXT09O6c+eOcrmckiRRsVjUv/pX/yo2BWQmioZrpZUXVsmbt2ABHSoztzxXb29vU9QBSFuvXySUeV6E3wMt8w4PDyO5iHoL79bE5xw5uvJHUThZxzOl50y6OATW0d/4+Liq1aq2trbU3d0dhCMy197eHrwP3+FEoXMIzBd9JNyYuXvAD3PtyqG7uztex8X1rFvWwpUwyMERjCsMX3vKA34loxL1el0//vGPtbKyonfeeUfvvvtu9BhI+8fpqIIPLwSCxJLUNMGSAm65a4BAehjUIR9KA4hHNMGtLGEy/GoWiWQeL2mWLuAiv7MxeA8wsFgs6vXXXw8isq2tTTs7O5EKTcivFbJh3qTmMxQgZx0Oozx9IPBeHci/Z2dnkevviiUdpYBfOD091crKikqlksbHx5vCeg6jiehwzyg7ohfSswVarsi8d8HQ0FBEhPb397W7u6uenh5NTk5qcXEx0A2dpfv7+yOCwd/wz30jegQp3VjGw4UoE2SCLFjk0ZvkOMpFmad5HO7Fu3r7e+AfQB2emfoy41IoBqkxAcvLy9ra2tL8/Lzef/993bp1K477llqfY+CDk53IIejp6XkmzZofNqr7aZlMRoODg5IUnX28dbiz/Vh6t7Dn541c/9nZ2UjscWjvMB2iEpLTuRNvCSY1hJ1sOofJuCKUq9MNyWsf0mQkigeF5S6HW2R/vxfxEGk5PDzU6OhoWGOgPJ9jnbBu5JNUq9UgJvmMs/AjIyMaHh7W2dlZhGKJWDHP7j76GaeEku/cuaOBgYFwERYWFqJzVFtbo4Wcu0MoypOTxmnSbMxWcoMryGjFd8BHeF4F2ao0guVaQH2iWIRlff58fngNmeV3J2zdlfE1e5lxKRQDpBULcu/ePT158kTvvvuu3nnnHU1MTDTFY11r8/nT01MtLCxodXU1Epq8DsCZ5Xq9kX/g/iCIAMHI5/MB9ZzIQ0BZeGfza7Wa+vv7NTMzo2KxqMePH4fgeL0EC0w2nws6343F5H6///3v6/Hjx7py5UqcgQCX4qceedMVFx4fHp3he3nd38vccFQ9/n9vb69yuZz6+vpic/nc8Fn+hq/M8XL5fF7T09PBx3Dv2WxW09PT0c0Jdw8FmHY70r06a7XG6U33799XLpdTf3+/Hj16pGq12tTViFRs75bN/XvrNxS0K4V0PkP6/6AgNwaEEzltDY4Fkvv8vNGGDqPjnbwcfXmuh8uLRzuQ+7a2tpAFNw4vOi6FYnCC0X30H/7wh1pcXNSdO3f0xhtvaHR0NCaDDcMDVyoV/ehHP9KjR48CSjKc2EP5YDGwiknSSMahoAqfkKrMdBzduQYWAa3uWh+h82QiFj4dfYFkymazoXSoOdja2opmLHNzc3EuQrpvBJuFgcJy1ECsP82jIHisA4MzFpKkURFJ0hKKA8ufJi+Za9AZrgDly8wBiGR4eDhOVvIUbJCXu5YeHWHzLS4uqlqtan19XYVCIRDKt771rUj2QVGvrq6GwnIf3pu4UmTWigjneZEf5gDFzzyCrvb29jQyMqJCoRCK9Pj4OHIljo6OorbDN7JzJyATR5coNue6QKBeG5MmSb9uXBrFkP4/ry0tLalYLGp+fl5vvvmmbty4oZGRkaYHBVpTAy8phM9LuYFljjScbc7lciqVSlHss729HYJCM1mISDIq3UVIkkS5XC5SahEU+hqQfIVbsL+/33RcHFYKWNnX1xf8CBV26SQfciboy5BGB85QuzBxLbc+rvz4DGQcWYpkPvpzu8V1Vl1SpPienp6Gmzc6OtqUTEVKsJ8p0WoDHhwcBJTn3tlkpVJJCwsLKhaL0Xoul8vpzp07mpiYiA3X3t6ug4MDffnll1HQRTl9T09PlIZLzT1B+R1Lno7a+Jz5M7BBOcKefBAOBNrZ2WlyW7zOwZPMeFYS5Jhvn2t/zd1Oj/q86LgUikF6NpEJKJzJNKoF79+/r+XlZc3Nzem9997TtWvXmvoC0F4LeLy3t6eFhQVVKpVgvx3q+QaTGoRRsVjUwsKCZmdnY3IrlYpu3Liher0em4+6ewqcfEPRzJbjztLPJqlp4aULIg9/njZuKKGhoSG9++67mntamVetVlUsFnVwcKDe3t5wmRwt8GxpRcHz04PRFUN64MOfnzdape3s7DRlY2KBeR5+d2Tm7dkI/5FRCurAUrryZt48OoBlBfGAajiOj/WvVCoqlUoaGRnR+++/r+Hh4SbC98GDB5ECzRzU642eHN55mQ3rVtuVhJ7EY4kAACAASURBVPv7/J52paSGIRgaGtLU1JTGxsY0NDSkg4ODCDu3clnSbu7BwYGKxWJEn+BoUCbu7oL+3M36t64YkiT5nyX9B5I26/X6m09f+28k/bakradv+6/r9fq/fPq3/0rSX5d0Luk/r9frf/h138EE+EZN3UOggk8++UTLy8v6tV/7NX3zm9/UxMSE2tratL29HQfW8pn9/f04TzCfz0cBkofvpIs0193dXT148EBPnjzRyMiIJicnNTExEa21Tk5O4pyCg4OD2BCODoaHhyPhhJJpZ5KxHtJFLgGKQVK0a6tUKqpUKjo6OlJfX5/GxsYi//3BgwdNjW6Bku7eIDAOJ90ieX6Fb4S0zy5JpVJJ8/PzTQqVUHBvb28oyr6+PvX19TWFLiEAURCcCu6JOuk1d0VKSTeRILeEKPrj4+PYNKwleR5/8Ad/oPb2dt25c0fZbFZffPGFvv/972tzc7NJqUsKN8lRim98d/lc+Tk341EeZBHUWavVohMU1b8QsRgFNng6UsT8QTBTr8N3u3JxefJ7fZnxIojhf5H0P0r6x6nX/4d6vf7f+QtJkrwu6T+S9IakSUn/b5Ikt+r1+lf2rnb/1xn+9AMBk4rFon74wx/qwYMH+vVf/3X92q/9mra2tp5JWGJD1Go1ra+vq1QqaWJiQrdu3VImc5EoxEQWi0WdnZ2pWq2Ga3L16lWNjo5qcnJSfX19sQnZtNJFzQCVmJVKRZKiCSekYPr8CiA2Gw1X4vz8vKmJLIUxlNmSfONz9bx8BeY3bcF8YJERZrc49XpdY2Nj6u3tbYp2oPxIM3Zl0dXVFeQk7hbvxyXxA1l9w/F/3l8ulyPfxJPD8KV9ExPhQUnXajUtLCzo937v93Tr1i39mT/zZ3Tv3r3oWeDhWiJS3jyG+XXF6kjC5TIdNXCSmjyH9fV1bWxsROt8SVELQS5IuuIV8vbk5ES3bt2KuXH5Ya848em8w8umQ0svoBjq9fofJUky94LX+y1J/7Rerx9LWkiS5KGkb0n6k6/6UFdXlyYnJwPe0oUG/1Vq9t0Q+qWlJZVKJT148ECLi4uxQe3em6D00dGRnjx5ov39/TjxitBSJpNpYrlrtUYZ8L1796KhB2Guvr6+puuyCKVSSf/8n//zpt4SwEAsC+936OnCWSgU1Nvbq6WlJUmNTXJ2dqb79+/r4OBAU1NTUZ5cq9WCwHLEwHylYblbx1ZhyvTckQw2MTGhWu3iRHJfBycdpYucBW/EmiQXh6vgX9PfU7pQVB6OBtmkCT+UYppkzefz2t7ejp4UnOaNK/rxxx/r/v37khTJQG7d4SWQQUKJriC4Dw+fe5gWBMgG5b24pZIC3SAbIBw+w315cxpI0r6+Pg0PDyufz+v4+Fg/+9nPAn06UvZEKO4zjZK/bvz/4Rj+ZpIk/7GkjyT9rXq9vitpStKH9p7lp689M5Ik+R1JvyMpmmRC1HjPQm8fzsM5c763t6cf/vCHsTkYrfxrPru5ualisRhdcgYHB6NoxqEjglsul/WLX/xCw8PDGh8fj7CZM8Xt7e3a29vT0tKS2toaR5iPjo7GyUR0HEYAnMRC65OQwzVRUkdHR7p7964+/fRTDQ4OqqurS9VqVZKiGxBwlEGoLz0IyVIu7q5DOtKDtWlvb48IBvPO331z8jsb2tlwoj1Yxa6urohWcE/OA7BWuC5YQs8N8PBsX1+fcrlcRDLclYP4pRIVhcCA3OXEMDYsyMAVg5PVHoXIZDLBk/hmB/15WNYjU+k59/uC30Eu6C3R09OjxcXFULauUDE0vjZkz77M+NMqhn8o6b+VVH/6738v6T99mQvU6/XflfS7kjQyMlIvlUpRbtvb2xuwnHg1igICMO3HSc0M9tf5VGdnZ+GvuTZ/em9N7g0CSuOM9fX1aF7ilXDulmxsbKhcLmtqairq6O3Zw+L768TVaVDrxBf3vLu7G3DarRJ+u29GD1XynV1dXcpms8GX+Lw5x+D3dHZ2Fux9K/YdxdgqBOuhRWL29Xq9qWK1Wq2GYnAoDYTmM9JFtaUrfkKbdFyCzCT1fXR0NHotcsyb++CSlMvlmlwC7sGf0Uc6Kuao04vmDg8PtbKyEpEUmgglSRKRMxQQhpF1JMSNW8ZeOD+/6Pvo9+LRCGQLXujfSRFVvV7fsEn5nyT930//uyJpxt46/fS1rxy1Wi3y2rPZbJwV6KEgNg2ZkOTfez8BLAECR2zaLWkaSRwfH0fPPYe/7i8yzs/PY4OgwAYHB6NQJ22FDg4O9Pjx4zgwhCxM7jO9iLg2EHNsKA9BcR9nZxfH05O158KLkkoLuacbOweRzg3gh1Ao/I2H6hxZpBUFrhFzSK9CSU3hxiRJIqpA8hPVlnt7e/Fc7nKhLJkXUAcZp/SUpEX8G2+8oT/+4z+ONnEfffSR9vb2Qi48rO3RMH8echX4myuRNHJgbuv1elTl0lSnXq+Huwj34Bm0rC3PRWSIfAc6drNfCLWyJuksTBoOcZzgi44/lWJIkmSiXq+vPf3vfyjp7tPff1/S/54kyd9Xg3y8KeknL3A9TU9PR879zs6OarWaNjc3Y9HSYRcWCWXhcW1nm+Es2IBoZeliY6BM/H7SbHnakuLmHBwcKJvNRgdkrsm/R0dHEUorFApNJd1Sc6kwVtOrQRnu4vjGJU8CmMz3Pi8FFuUE6kIIXZj4Pnxvh8fMAZvan4NNBZJhQ7EG5IP4iVwglVKppHK5rGq1Guw8sD69Bo5Gzs7OdO/ePVWrVQ0NDUWuANmN169fV0dHh4rFor71rW9pY2Mj5If5IrnKXaU07+L/9/oZ5IW5Q0GwBtVqtSl1vKenJ8hN1sxDjci0h0fdkPhRfm4EcdOYEzeqdMt6mfEi4cp/IukDSSNJkixL+juSPkiS5I4arsSipP/s6cLdS5Lkn0n6TNKZpL9R/5qIBINw187OTggtfltvb+8zG5dF9YxChJPJcn8SQXe3xDMFfdOliTmpmXByFHFycqJisRgt4VsN0MP6+rrOz89VKBTiOVjIJGkkR8HGpwXTBZHP0o9QauZd0p/huTo6OpoOfOE9Tli5Nezo6Ah4Trt4zwUhxdhTcnlejzSgrL0OoRWhl8lkIsfBCTMn89Jk5+rqqh49eqSDgwMtLy9Hb8pCoaDh4WEtLy9rd3dXvb29mpub0+LiYswLVj2Xy0U4EQXoSVx8L8+TlhGPDvkc7O3t6ejoKE64Allub29rd3c3XDk6OoMacE0kRcaoK4R0dIS+kSgfvw7vmZ2dbSmbzxsvEpX4Ky1e/r2veP/flfR3X+Ym6vW6FhYWmmL6mUxG165di+ah7t/6hnEhYUKIZ3t7d2AZvhpKAgIMks3DfygQJxlbDVwhh5SOHPj9+PhYW1tbsUF9M+Nrzs/Pa319valE1620b0LyK7wTEwRden65FmdCuACSn+8Klk3DeZkMD4UxEE6fb9/MkiJNHZjvCog58OFNcFhjRyyguoWFBZ2ensZJUEdHR9ra2tLm5qZ6e3u1uLiojo4OXblyRSsrK3ry5ElYXxQ5KA5ewn13jxawvi57KDTmjflGvigKw5UsFosRhiXq4y4jrhVrQtp7vV6P0LCfxM76OTeTjhaBxl9mXJrMRy8XZmPxmicuSc0ltk44Onzv7u5Wb29vxNEPDw+VzWaDOT44OIgjvVzIPU3YaxW8dNjdFd/4/N8Xxf1Orrm2tqb+/v6I5yNgd+/ejZAgZbVwCGxi3gvHQXjOv4vsybT7Q4KQn4Ttn3N0gaXf3NxUPp9v6rjtOQ48pyMEhNxRQS6Xi6gB+RCemCU1pyA74uM+0+Tm3t6eVldXdefOHf3Fv/gXVSqV9Ed/9Ed68OCB9vb2VCqVVK1W1d7eroWFBX388ccR6uT6HR0dGh4e1tDQUCS/MT98N41hPTqCnHr0Ku2+oWQoduvt7Y0Wcm79nTNB1iBl9/b21N/fH13EOjs7IxTuSsuT5ZgnFFmSJBHFetFxKRTD8yIIZC2m3+OamQmWGgqjq6tLc3NzGhgY0OHhYWSaSYrW4WhsBNVhF+/HD3bCj0VDOSBkfpIT70VRpRENGw9ijUrKJEnihG4YfNANRGO6uIamLYeHhyFY7t+nXQ8v3WbTOkJIz/HAwIAmJydDidWfRl5QLigI53TSSpoBhOfQWLJQKTICnREpch7FCUfpYkPCR3zwwQd6++239fDhQ+VyOU1MTASS8DA3qJH5gV8YHx+PsyRpTks0xhEBn0vLI+4gz+9hdSIrbF5vMej34vCfmpFqtapcLqcrV67o+PhY165dC0ThCpV1b+Vqpl3SFx2XQjFIz/rubPi0z8y/aSIKC1Sv16MxB7kNw8PDyuVy0Ua+p6dHs7OzkclITB++YHJyUh0dHREHHxkZaUIF/ECq8R2SoqGoC0cauvJMdCYeGhqKEmwW+/j4OBrGurAh0OfnjdOMyaRza+Wxf583LAjhMoevDme5/vj4uKamppqIT+9k5REf5gRE40jq4OBAX3zxhdbW1qKn5uzsrF555ZXYlDQDfvjwoTY2NuIZUMReigy30dnZqTfeeEN37tyRJN2/f18TExP6c3/uz+n+/fv66U9/qpWVldiIKDLuF0WFUkjzLGxUD4u2cmeZD3+d/AUPJbO+TjgiD2xsFAiuYT6fjw7guIHb29va3t6OdXIU4y5NJnPRzqBVTstXjUujGHwAYfv7+zU9PR3uQCsf3+FvkjSOJhseHtbi4qKOjo6UzWYjjuvamxoLCEFJwWp3d3dHu3ksJ8oDfxo4n8lkNDo6qg8++EADAwMql8va2NgIyIhrsL+/HyHRtLXd3NwMAooSZ6xQtVqNLEGHnoeHh1paWtLR0ZGuXLkSefP4m+koBRvJlQA8DOiHwbNRBekJWWwULBzf4ZaJjZUkSYTU6EVwdnamvb09bW5u6u7du5HNNzs7q+np6ehLkO4d6a4mfEh3d7fef/99jY6OxklOt2/f1ltvvaXbt2/rN37jN/To0SN9+OGHunv3rkql0jOp6aw3sgRC/SoiWWrmF9x9Arn5+5yfgl/hffAaPKcrWuR/aGhIQ0NDcdjM4eFh5N+AFtIkOQapUqk0GZYXHZdGMTisam9vj5N7YfRdAbT6HBGKQqEQLsPY2Jhu3ryp1dVVPX78OKzQycmJNjY2VK/Xo+aAxZucnIwsxcnJyUh88bg3AyuJZaeR7MjIiM7Pz+OoPBjqBw8exIYqFouhOCi9xkL66dK4ADDbZMN5gtfa2prOzs6ik7O7MFgTrL2Tg4QuvUUdo6OjQ4eHh1pYWNDQ0JBOT0+DoHOUwUi7TWxuws+gN8/JQEmWSiU9fvw4UBOZftQukC1JNacrNojrcrmsgYEBzc7OhiuItd3c3NSXX36p7u7uqIUolUpqa2vT8PBw3A/Vmenaglat3RiOIpgHj1g87xru+nFNT1fv7OyM9neUm0NeQ1w6wvBwKTJNJi6Nbl5mXArF4JbGwyvZbFbz8/NNQueEmisFBJFkn+PjY01MTIT1dfejVqsFOZcOXcEEExvu7u4OJcLwiAM+oVfGMbCyCAAJPK+++qrq9XqcquVt4A4PD8NFwfc+OjqK6kH6Ow4MDGh6elpnZ2fRTn5nZ0eFQiGSdZz0TJIkXA/uhx+vypQUmXu//OUv4/wHEnLISiU85uW/HR0XZ1oyzs/P4+yLdPzfrTJWzftcerh6YGAg0pZdWVQqFf34xz+Oe9ve3g4DQRbp8vKy9vf3YxOSgo0ypwqXKAFcVZqY9Q3v4VNkxxEHMJ5NzrOjpJFzD4N6RIk14ITznZ2dKMdva2sLN9hrStJkc5Ikun79esjey4xLoRikiwlOkkZMfXV1VRsbG88cAecQLT1YACAjKbjwAIw0kcTCTk5O6vr16wH1p6enmyxA+rNA6mw2q3q9HrC/s7MzrA++KwJJDwfyNvh+fNHNzc1QSGwSSpcJrbqFAEEQ76bOP5/Px1mTRFdWVlYilZZzDv0gYZ/nTCYTRCCugEdb3G1BMYyMjOib3/xmpLODgOjE3Iobki4URJrnAN0cHBxoe3tbS0tLQcpCiHLQDihhc3NTuVxOV69e1czMjBYWFrS+vv4MHyVdNNGljsD7e4Ai3JJ7/YSjR/x8LLjLBrLtsusyj9Lhu1wJ8Rn4KZAA7izfhzLhOm78KNf3jmYvMi6FYnArglB4wUuaAWb4RLuWJ3GEwqJ0X8VWXEUmk4lNQjOWQqEQ0QsPY/l30pbs8PBQ6+vreu2113RycqJKpRIKiW5Mr776qh4/fqzj42Pl8/ln7qFSqWh/f19DQ0Pxd39+J1TpiIQFuXbtWvRwqFar8UPRFUVFJHWx2avVaqQhI9yEd/f390PQfN6lZ31woPiNGzcCNZC9yHU8TZpr8ruvDxaVDcfzww9Uq9Vg3amL6OnpCWWxs7MT5znCR8DheKRpdHRUvb29qlQqcWhtOrLD9zovIF204XOyj6gNxCEb1iM+/qzOC7iiAcERLXLl72hVUrh2HmHiXvr7+zUxMREu6MuMS6EYGGnl0Aoa8W8raE/VI92BaAqSLk1Nfx9uSGdnZ0B4DnCly09/f/8zB9ecn58HLIUgpL8fg+pQ4L+jhDRM7e3t1ZtvvqlsNhu8gcPZtra2ODINi4TAw3qjHHZ3d1WtVpsO6sXNQuDgN4rFYnAY+PEQhek5Tq8DI5PJaH9/XysrKxodHY1NQ8YeOftpq5mue/CoiHQBqbGcXkCFsoGnwHrifuRyOQ0NDUWnKDpqUesyPj4eeRV8j1/fLbg/p9+752q4RfewpvMuaZfDX3d3wN/rOTMusy4/yBAhWYhlOn75gdEvMi6VYvAYuLPcPrGtXAgmiaaoEFiUCzufkP4+Bj4rcJ0aAaB5+qwAEp/4XCaT0cDAgHZ3d6OxJ0Qi/EOlUomuR61QS29vbxzEwmBzOvvsm8a5kN7eXg0NDYWwuuCenJxod3c3Wpjv7OxoZ2cnjryDCM3n8xoYGAghb6WY09EOv9f5+XnNzMzEyUe4Lvl8PkK8CLuHcLkW1/O1dmVCaLdcLkfPBf7uiqJSqWh9fT0qLOFq4JGmpqY0MDAQp0t7qDXtJnjrNFwJZM6T2ngGFBRuZNpVYC5R2ChM1ssRi0c5PCwJYgJdoSj9viUFAf6yuQyXQjEQmuJciNPTU+VyuSYYyaK18tUkNcV/T09Pw7L6+RGMVpPEeYtEEZyf8AiAf56MPmoAIAKxQJlMJlwCyCgaxDgs9efc2tp6JlmF72RDITDwCgghgnJ2dhZnU8JtdHZ2Kp/Pa3Z2NrLoqtVqNCaB4CS8RYKP1HwehaR4Tle2rEmpVFKpVIqIUmdnp65duxZVqfAdKFYQlSsJ1tnRBH8D2aXDc2mU6cqHjcfmOT8/jxRllwVPUed7eUbQG7Lm0RXm3OXT19BDu77ZcTXY2EQu0tfh/Sgv1tnT71u5Z86TtDJEXzUuhWJgw3iSCSm47jNJF4vmDHG9Xo/qMhRDmm+QnnVF/F/8YuAmzT5ohpGOYGD5yFsHStL1N0mSppAjXYEgy7xNuGfnURPiVsithLseaavF+xzaMm+kWSPkoKK+vr5oK0aURGokWEGggpogwOga5U10sNR838jIiOr1ehCBp6eN8yK3trZCebpycCXhmZQMh+i8n/XAfZAu/HO3sGnyuL29Pc6Z8DnF0pMh6giW52LzOn/gcshwq5+25l574evrm9yv6+Fp5ADFmZYLv08PKz8vL+N541IoBgQCQc1kGgd/4AagnZMkicNIYMl5nZj8+vp6NMvEFZCaeQm3EggcaagUDaUzERkuLG1tbdE8FBeGw0TSbckhpui5gLVyYpRncUXg9+wLjUJLVzbW6/Um5elCxY9vFreiHj3p7OxUf39/CCvIyatSUSSE0UAv165di0OCyuVyKGksPVEKvzYkJQiP3I40tEdxedYl1/IN5m4WlpP77unpCUKOEKuXePOdnqbs8gIic0uddid4Pa34UeZ8xl3ntBJz94X18XoRV1qt5AG5fB7K/qpxKRTD/v5+hJnQ2pVKpSk0g0VAGZCt5o1RV1ZW4jxCsgM53BZIhSb1smMKkoiGoBiwmL5B+ReL7+E+VwRY5Xq9HuRnmgTlQFMGyibNr3hmG6852mmlTEAtaULX59BZbDZcusIUC4prRdjL/WY2N0lFHR0dKpVKGh0djQIgUqzz+XzkDPA6v0OUlUqlKEumQYxb7VrtoncFiI7rMAeSQgGjGChe6u/v19DQUCCKdKo01jWNLH1duI808vS/uSX34VwNys8jQr6huZZ/Nh2NczThCtKv9SupGMjoorrNOxjxoK1ChW4VC4WCenp6dPv27eh4s7Ozo7a2i2YuQN00tCSCQbhwcHAw+Ik0VHQLTHUjgoHVYcOTToxCYLPh0yPcTrihCKWLoh/nNNxf9ZFm8xEYh5d+HYejbn1g5pnfdPKMw1npAhbX63X19fUpn89HfQLP9cknn2hjYyNa+A8ODmpsbCwUMNaf+T09bRzjtrW1pdXV1SDQCD064UoZt1tl7rOnpyd6eZbL5ZgPDrqFxUf5OJGXnieQmPfAYI1ZO0nP+P2sBcOv50Qk13ClXKs1H6rsKMSJR374v0dv/N5eZlwKxVAoFPTtb39b1Wr1Gb/SJ80hm+eUt7W16fXXX1dfX5+WlpYix/zx48fa2dlp6iPpzT69dVa1WtWnn36qg4ODaGoBAnDLADo4OTlRf39/IJfz8/M4cwLIzgLWao1+DVRIOtxPuwhsaHcZpIssSngYL5SCLCT60NbWqLwkdOpCmyZeHZkAwbkPnsWRAcOtm9Tc4XlwcDAOqMlms9rZ2dHu7q52d3e1tLQU54GcnJxoe3tbGxsbkc1HWvfExITGx8f1yiuvRO7C+vp6nDZGTkZbW1ugDZABSobUYY4ZJI9ibGwsQpgoFtYEZcGaOsnHelC674VkaflMkoujANyNQLmkoxysK7LNNSXF9/ngfemGO2kE5G7Ly4xLoRjy+bzeeOMN/eQnP2ny2aTWDTnTUAri8fj4WIuLi7p27ZquXLmitbW1qAOghBvhR6AymUw0g0mSJFJpSRzChUGR+ILRJAVISNotISgngmjk4sMtQfpv0oXl81BakiQhjG710xl3jg7SFpDh5JcLNj/PszRuEX3DsFnm5+cjTLm1taWFhYVQZN3d3SoUCpqcnNTp6al2d3e1trYWzXLy+bxOTk7i/AzWtr+/Xzdv3tS1a9dUKpW0trYWURTOVyCV3IlNuA82Um9vrwqFQvzf2/y5z58kSSihK1euRIm7hyFxF931ACVyfZQB8+wKn8/zWUlNbm8aPbBe5MU4F+KuoSttN2ovMy6FYqjX63EyDxEFVwIu3P4ZJoJj2gg1ujXgcx6KAtqTGz89Pa2JiYk4d3J0dDRCkYeHh8rn8yoUCtra2tLu7m60cWtra9POzk50LmLhyVBLs8EIllfUOR+QhvetFAYQFEWAEPGdCKZbeFcU7la02vi+yV0gHf76tV0BkeD1y1/+Mnig5eVl7e3tNdWSPHjwQEmSaGhoSCMjI7p582YoEuobsHzMdZIk2tnZ0VtvvRU1BIRMBwYGYg3I2IT8RUkQFRobGwseBAQHMenoh6jU/Px8dAT3Whhv3JNWvL5B00QhvAbfC4+DDCDzrujr9foz3aclRdQr7ZYQMXHD0srwfNW4FIrh7OysqddjPp+PZh1o8vTwSaP/AhEIMvw8VMlwJhuBy+VyKhQKevDggc7PzzU9Pa1isahyuRz8xv7+figLUm9PTk706NGjgPGHh4cql8va3t7WxMRE01kPvb294Z+6n4p18uQWT2SRnvV1URjOwvtcuYCm5yztojhqYTjh6eQYkFdSk2VkHkFe3Mfh4aG2t7fDKlL1+pOf/EQfffSRstms5ubmNDMz01So1dfXp+7u7kg8Ojg4ULlc1v3797WxsaHz83M9evRIe3t7KpfL6uzsVC6Xi2SzXC4XFa4UppXLZZVKJd28eVPj4+MRZj4+Po4oidRQohRXUbMDn+ERAEdWbrycbHT/3609yhzylXVI80coFw/B4jbyfbh6yA3XcU7iV5Z8pFcBR7GdnZ3FSUw+XLD5v6TogEv6L7UBfqIVI02kkTp6fn4exUe0RiPZivRihhM/+KiE9zhrAr82LTStsuI8Hp2uQHS/1JWhcxNsZKm56Qff6dbCGW+fB/51ToOks3r94ug3J+foMAXy6urq0pMnTyLBiRwIlAjcDIVZGxsbWlpaivXr7+/XyMiIxsfHdfXq1djs+Xw+ukDTQIcELngilIekyGaEdB4eHtbU1JRKpVKQxUQshoeH4/O7u7sxn11dXbpy5Yree++9KIxzDsnT1dMK2F0S0IGnWkNaglwd/bGmoAl3SVyGPenOw9HIAp8FTTjaeZFxKRQDef5J0igN9kKUNPR1iwf8GhgYiMXF0jiLnZ5U6cJSUoBDLH5mZkaZTKapK2/ad8Mfdug+MTGhfD4fqdT4ga3Ye4eNXjAjNZTk3bt3dXZ2phs3boQlxafkM2w0VxJpK5EmNtMcg7Pt/N1dL57XyTK3QBQXIZC4UbyPTlbOjdC4hjU5ODgI/mdlZUX3799XR0eHBgYGNDExoWvXrmloaEj/5t/8Gw0ODuqdd96JkOnm5mYkW/H93jEZF3V9fT3mY2NjQ5ubm5qYmNDs7Gy09+vv748QN528BgYGdO3ateAquD7p7rgg7v4hs3t7e3r8+LFyuZzGx8clNROBRKfgqBz9pWG/8wusKbk/GBvWDLmEmHWC+mXGpVAMDs+A724x/aHSG907Lh8cHKinp0dJksT5jq0mxDcO/AS+LHDXu0Y535HmA1xxOfF4cnISCT10+h0fH1dbW1uEXp1thjijKnJvb0/j4+MqFApBlKZzE9x/9A3ozyk9e2qSK0dXGg43/ZquBLgGroIrLBTi2VnjgGAEFQvmqel+bTgXOCbmYnNzbZVTEgAAIABJREFUU5999lko/W9961taX1/Xl19+GaXocBEgOJLS+vr6dOvWLfX09OjevXtRaTg9Pa0kSbS2tqZ79+7p5OQkakyy2Wz0skCJcV3u0zclP562zDzs7+/r/v370cY+TUL6AT5eR5GuQHXkx3oxlx7x8PVLy62/70XHpVAM0kUmG9pPen41pA+Kh0jXpSqSHIS0D51GHT09PWpvb4+wHslJznH4dwP30ezpIidgJpsUa0DprEcAkuQiG5Diqvb2dt2+fVunp6eREJTJNKc3gzJ4nQ3nkH93d1cnJydRTszwSAS/gwr8WRH0dOovawUCQuBQAFhQ0FhHR0ccIORdhJjbzs5OjY2Nqa+vL5rrspYomnK5rEwmo5GREa2vr2ttbU1tbW1aWVmJSkkOywVtQkzSOzObzaparUYzWuo3qtVqU5n26upqNIbx1HXqTvykMOaIH5LnaBrz6NGjJg7h7OwsLLpHMhz2+9w4wnB04OFqz3VJuy/pMPjLjEuhGOr1eoSVCCWltZ7UmkmnTJhsOj/ezBNe0sgB4acPQaVSiZoB6gFaEXOt7p0N4JCOjYsQwXLzGe7L+Qep4bffvHkzBJzN5F2iSd8+Pz+PlGQWHqX485//XEmS6LXXXgthRHE4YpIulBpCyP1Kint2xhyBAwmg4Nra2tTT06OZmRnt7u5qbm5OlUpFi4uLoRicMed6cAGkYLNJvGs016ZNHGXEBwcHGhwc1LVr14IXot9FoVCIDXV6eqqVlRX19PSoUCjo/Pxc5XJZ+XxeV65cCfeBnAtCoMwZB9kQoi6Xy9F0hzb/zD/ng965cyfyXTzknEahrKXPrZdP8xrfhVvG51gHruvckZOULzMuhWKQGoJPRppnHDps5/9MKr7e/Px8E7SilJj3pyE2MJkFB/pxKApVh3zeY/V+Dw7laXHmjUGk5jRn35jpohZntzlqbGNjQ48ePdLm5mYQYK+88opmZ2fD76cOwf3+7e3tQA4QoVgs3ovSQSE4mUayEUShE2e+DigzvgOW//XXXw++ZmlpKZqhsOkdodRqtVgrvtsJWhQJEY/Dw0ONj4+rXq9HFGNubk5vvvmmPv/8cx0fH+vNN9/U1NSUZmZmgmvq7OxUqVSSpDi4BfTHs3R3d8c8s9lwTyqVipaXl7WyshIolbNW4TVIqJIayn92dlb1+sXhvE7oOl/kyEBqbPaVlRUdHx9HzwgMDUouHQ534+KI1N3flxmXQjHw0LDMDs99uFDy96Ojo2iyKkmPHj3SyspKUxQhPSkIOJEELK8XNjnaYNN5vJpBzBuB93wJCEyHj8Bw7iM9uPbR0ZGePHmiTz/9VOvr60F0LS0t6Td/8zejxT1JWGxuP1Hp7OxMg4ODUcRFdiDPdXx8HBtwb28vwr787bPPPlNbW5vGx8fjGbhnEJILfEdHh27cuKHBwcEognvw4EFTLn9aISK8aWTCJiAbNpPJ6NNPP21CC6AKTlri7NPbt2/r1VdfVblc1ieffKLu7m69++67sVH5HjYpnBAK3jMpOTcVTmNoaEj9/f3a3d3V6uqqJGlwcDDC08wRB93gFkkNNNjb29vUkRvDwrqQSVsul/XkyZPIBKUaFuXvhgT5TLvL6ZyTlxmXQjH09PRoenpa5XK5KUMwTYa5z4QykS5yEwgpuQViwhyyM4Ht7e06ODjQ5uamDg8PNTY2FujB/T5Pi8aKeHYbGXoeFkJju4Jx7sFToj1iABext7enSqUSna6pHcCSSxeHr0oXJ2jRmGRsbCxeIxHn+PhYPT096uzs1ODgYBMbDkogrr+7u6u7d+8ql8spm81GJIC5cBTBc3MkWybTqKB8/PixFhcXm3pUIPjcm/MnrrQYHsXgRCfv60DvDiC+Q+3d3V198cUXGh4e1ltvvRUbCzQFF8H3gvjoH8m9VKtVLS4uanl5Wa+99pr+wl/4C5FktbKyouXl5VCAfX198QNfhRwNDAxocHAwTr5OIwVkYWhoSDMzM9FoBl7D+QXWzt3udA4E41c2wSmbzeq3fuu3InQFM0/xDMLBgqcLV1js9GtsUibFN6OksIr45UmS6O7du02tzhlscmCux42xVoQxOTkZt8I3vf+fa6JouFeY9tnZ2ThzgS5ETmKlySrpYuN6nJxN4GFOng2WHeLz/Pw8ul+RZTg8PByIgefi/skX4DuYoyRJomMUHEhfX59eeeUVJUkSRoAsRdrhgWBQPERjODGMcmz+1t7ermq1qpWVFa2trUUX6Y2NDe3v7yuXy2l0dFTShTLicF42JdEsXwPWChSGe7e7u6uPPvpIV65c0ezsrLa2tuLZiThxwjmJUX42Zq12Udx1fHys3t7eQCu+pjMzMzo7O9Pk5GSTy8ocO4eAvLuy9YY7frr6i45LoRiki+gCGWssCgLOsXKfffZZHOtNSIkMRz7jlt6Hh+X8PUw03EI6GYTFwjJ6zJr31uv12Dx+khAp0L55vZgKLe9uEkJLNSJst7euwzUAxpNklQ5lEinhB7RBRIXXuQe4EklxLJ33qZQuMkYd7UjSxsaGyuWyRkdHQ3C9rHx8fFzvvfde9NYkB4HTn+GGKJACGZINSfMXshlLpZJ6eno0NDSkzz//XAcHBxodHdXW1lbkLpBHgKWVLsJ3vhldoToixT3h5Oz9/X398R//cbhQP/rRj/TkyZOo3pydndVv/MZvBOews7OjYrEoqdHdivTtwcFBnZ01zrPkqD1JQaJzpkat1jiDxMnaVtE5/oZ8+6lfToy/6Lg0isGFk39JPsLCFYtFLS4uqlAo6Pbt25IUOfF0QSYHAOEi887Dh9KFZZWaCU1/n1vk9CZgnJ6eamNjI6AjQuZ+HlV2IAWIOldeEFJ+DdBJmu9wYgxIfXR0FF2wiJMjKB4mRXFKzf6+k2AoJ0qTea+z5D4Pnsa9s7MTFmt+fj42EFWOHAjEvOKDk7uwv7+vcrmscrkc68ga0qMD5SBJV65c0eTkpIrForLZrK5evapCoaAf/OAH2tjY0K1btyI3JY0CsbLekxNuwS1yW1tbNJWtVCqR1bq8vBxuKMijq6tLAwMD0Zp/YmIi1gNZpRsWCAPj4WFRz+kAjbjcIhuu1Hj95ORE9+7d09DQkF577TUdHh5GJOtFx6VQDO4v4S4ggAgsRKPUIHvIV5Au0nxd0GDUq9VqNA6lzJfj6rCgbiFwE5zhTYdM0yFGSU2b1VngTCbT1EXKIxJORPrmR/ix3HweuOvcAj66Z0J6xMQzGBlpJpvv5D1fRY76a2mSK0kSTU5ORpdonqm9vT3OC0UpMoj5uyX0rEJ3JTnBGkt8enqqiYmJaN+PSyRJa2trWltb0+joqB48eKBarRYkJe4HmYdOujIn7stzghX8D9mU9O+o1Rr5GCAbJ7fhVeA1zs7O9L3vfU+PHj3S1atXJSnQjx+E49mKHkImSuIEvXMMtVqjLWJfX1+4nF7p+6LjUigGqbn0N02q8FCw7NPT003+IMKZJEl0G2KinKg5Pz/X48ePtbS0pEKhoPb29rBOpVIpLIa3dPNIhG8Az0fv7u4OP86trr/fn5EUXulCoUhqWkQq+nAdUDYIPn4wZBv3hNUgH4P3ejEUbojPrZOK+MLcU6v4u9QcJUIRdXV1aXR0tClkC28xOTmpJEmiZoG/e6TACVypUdrORmOzYn3L5bLGx8fDBS2VShoZGQn0RDHUL37xC21sbOib3/ympqen1dnZqS+//FKSgjsgTwG3jexXCL/Ozk5NTEzo1q1b6urqUj6f1/b2dqw3JKGvPRwMiieTyUSq9N27d3Xz5k3Nzc1Fa31yNHZ2diIcy8FAhKa7urqChIVvcEKbdHJOO+vv748s4JcZl0YxSM2NM9MZkFh3uibxutR80Gh6eKydsGRHR4du3bqlfD7fdG3ckUqlomKxqO3tbe3s7ITf6wRomsNgcVwoIOUg1LjPWq0WmZWe3kpuQZIkTTDSX0tnLOITextyjo4DBdElic1K6Is5BMrz/1bt5phfV3QMXh8ZGYkIBp2X2MzZbFaDg4N68OCBPvzwQ73zzjv6xje+EfOCYKN8nNdBFiA1+/v7I4FJaiCMt99+OwjOg4MDfec734kIxurqqs7OzrS9va319fUgBv/8n//zyuVyEYVigzNGRkbU19cXfSg5eBdXoq2t0doPF+jk5ESLi4v6+OOP9dZbb0W+BWvU1dUVXakkRd4EygkFAGLa2NgIZMSG7+3t1eDgoCSFu1ipVJ4xWGTPVqvViKC8zPhaxZAkyYykfyxpTFJd0u/W6/V/kCTJkKT/Q9KcpEVJf7ler+8mDan5B5L+fUkHkv5avV7/+EVuxn3odFjy7Kxx8nPagvE5qXUVpaTwczOZTBwrj4/PZGL18/m8JicnY9PilnC+4fb2th4+fBjKizMsPByJIvDrYwlb8RxudYCDNJn1jDYE2LtGIdCUeDN/bHiqENPh2q6urngf5CX37rn1ICJ+Z/i1EH6ayO7u7urBgwdNDVYHBwfV19enlZUVra6u6tNPP9Vrr70WfQ6czPTvaDUcJbqiYu66urp09epVHR8f65NPPgmffnFxUcPDw5qenlZ7e7vGxsY0MDCg8fFxnZ+fx9HytVqjZmdmZiZ6f+7v72t8fDwySdfX13V0dBR9OlxOf/rTn2p7e1vvvvuuxsbGwurv7OzoT/7kT7S5uanT01N9+eWXUQuDG0IWJbKItacP5vb2diCznp6eMAKsGYYVI0i9x8uOF0EMZ5L+Vr1e/zhJkgFJP0uS5P+R9Nckfa9er/+9JEn+tqS/Lem/lPRdSTef/nxb0j98+u9zB1bb4/4uIAgeKawIBH/z9/I3z+pjYoCEwNj0d6TzHJIkiVAV6GJgYEDb29uanZ3VjRs3gl2HMPLTs115sVg8I2E4oLtnMUI88jk2piMOj+d7aI3757u8foNn8n8lhTAyZ/zdf9Lz4vONoiZKQOjQayOy2ay6u7ujCnVpaUkbGxu6cuVKbCq/rn/380b67yg65o0UabIlcbFoyUdok3wWV8IQ15lMo9K2Uqno6tWrkf/hIWisdGdnp0ZGRnRwcKD5+XktLS1peHhY4+PjunHjhpaXl/WTn/wkeLP5+Xlls9loETAzMxPRjCRptMgbHBzU1NSU+vr6VCgUtLe3p/v372trayv4GdyV9HklmUwmzgf5t57HUK/X1yStPf29miTJ55KmJP2WpA+evu1/lfQDNRTDb0n6x/XGCn2YJEk+SZKJp9f5yuHx2jScxLIRPmpxn/E+zwyD9EmSJBp2jI2NNSkXqbnazzPWsP4IIFVxAwMDcWoTcBtoz7XY/NyTJ+bgI3qaspc380xYRBhoSU3E1tnZWSQfwU+Qucfg+0BGKBGuCXcBmpIUkRQnIvndQ7Q+YNdh393dwn/Hei4vL+vevXsaGxtrOky21XVbDd8AaTlgLvP5fChnrP/6+roePXqk6elpbW1tRco3c8I90F2KuogkSYKbIlKCFWd+QHqTk5Pa2trS8vKyFhYWtLi4qM8//1yVSqXp+MLt7W0tLCzEmR5SI+NxaWkpFH42m9U777yj8fFxDQ4Oqlgs6uc//7l2dnbi74ODg8rn8+HGgSJI+cedfJnxUhxDkiRzkr4h6ceSxmyzr6vhakgNpeFdVpafvvaVisH5Aice2TSwzt3d3U1Wjc86845fy7UI7ThXkIapafTgcF9SuB4k1aCJaTpCZ2nPwScPwNl2L18GYcAnEH50Np9nSbP8QHwYcRho3A8+i8Jyy849AT+5XquoRJpIdXKVwX3RSr9cLkc6OPdQKBSip+Pc3Jzm5+f12WefaW5uTjdv3mx56GoaQXyVwkhzH2RBghCvXr2qra2tgPHkwXgBmpOzcDacUXL9+vVAAoeHh8pms9rf349Wf5Tp5/N5vf/++zo+PtaDBw+0sLAQirJYLDZFD87OzrSwsKCZmZk4oYz15tyL/f19/fznP9fJyUmgZUjynZ0dVSoVbWxsRN8ODFY+n48Qq3cMe9HxwoohSZJ+Sf+npP+iXq9XfBHq9Xo9SZKXiockSfI7kn5HaiS+uAV3yMrv+/v7kXzkAuzv5fNYXqw3xBsVc+nWWPz49RhsDn4nS25vby8OcCVGnPZ3JUWKN5CTDe0hR5CSJ3SdnTVOj4Y4gy9IJ0vxvZCqXBOGGssvNUdMXAG58nK3ztO+neB15c18g8gODw+1tLQUYbx6va5CoaCxsbG4p7m5OV2/fj2S1lox5q34o5TMPfN/V3g86+DgoIaHh1Wr1TQ1NaV6va6FhQXdvn1b09PTqtfrcRAw3wcRTEcnNhaVu4ODg/H8SXJx4pjUOEH7rbfektSIqOCira6uam3twjZyZAHZn1evXlU2m9XW1pba29t169YtTU5OxuFKjx49Un9/f0TmQDg0KeJ91WpVjx49Ur1e15tvvqnZ2dk4luFlxgsphiRJOtRQCv9bvV7/v56+vIGLkCTJhKTNp6+vSJqxj08/fa1p1Ov135X0u5L06quv1qWLDeLajQ0PrPduRp5OTDjRBZi4L9dAYLC+TtQwPEHINxSvVatV9ff3a3R0NBSJ+7a8HyF114Jrk/nIBuMHXx9Fc3JyovX1de3u7gYsJJGKTNFMppEavLe3F66AuwPcF/6ncw8oLPfn2cx+zygbz4lIcw7UWkB2gVhQxLRQkxTW7OjoKIrB0uvNSJPM/pMmm/m3VqtFFurw8LCuXbumX/ziFzo5OdHQ0JCmpqaaYDcVvWnXxDe+u3zwNX19fRobG9Pi4mK0icP9QH6IPFAIxxzy3fTMIL8DVxYynPmenJzU7Oystre39cUXX6harQb/hcyTdr21taVaraa5uTn2WmRfvuh4kahEIun3JH1er9f/vv3p9yX9VUl/7+m//8Je/5tJkvxTNUjH8tfxC35mIT7f0++O9wDpWkEiJruVz+lJU7VarakgyAXOiTcEgg2LAkAgyGqDcEyeRjn4rFtUh+GQjqSouiJrNUAOCOP+/n6Qjl1dXSHMhMrefPNN5XK5piaj8AjuFvjcMBB6J64YHsJj4/k8JUkSp1mfn5/rlVdeUW9vr0qlkh49ehTl9Lz38PBQX375ZRxQk+YsfNOzIdnsvO//a+9bYyO9zvOewyGH98sMZ4aXIYekuDevdlerhWwJSGBBsGHXhuELbBTujyRojaY/HLQF2h+O88dAfrVoUrRAEcBBAyRF2yBAbVQIUrtJZVuWIEUXa1erFVfaXZFc3jm8DIcc3me+/hg+L5/vcLi7bDbiqJgXWHB2Lt93vnN5z/M+7+X4pLMiCy6ura0tCzyibGxsIJPJ2DUVNWnBG31WnwglCqQi39/fx9WrV5FOp5FOpy14rrGxET09Peb+JrKgy9F3X09OTuLevXtIp9NoamrC4uKizT0G9DU0NNh49/T0WHg5FRDnLM/lYKRwOp0+dp5VkkdBDL8G4DcA3HTOXT947/soK4S/cM59B8AkgH948NlfoeyqvIuyu/IfP+wGDCZhVBcj4RQN6GLjPxX9v7/bacCKfxYEOQN+RyPK/EVCnkM9GoTqGl5MZaQ1D/h9tkmJxUpSKpWsqpTa34py+PtcLocgCNDX12chuPxOsVjE3NycEVrczUlEksRkO9S84SKpRArq9xm4s7q6iuXlZUxNTWF5eRmNjY24ePGilbTTwLX6+nqLCajEJSgvE4lEQnkh/ncpPtIhN9PU1GReCQYn0QVdLBaRz+et5iivexyXomiJz9rU1ITPfe5zOHPmDBoaGvDee+9hcHAQmUwGfX19GBsbM5cpAAwf1I/g2CwvL6OhocGqbsViMSQSCeuzqakpxGIxczHv7+8jmUzi0qVLFg3KuA6a3BcuXMATTzwBoIxstAzBo8ijeCVeAXCcz+hzFb4fAPjuSRrBnIhcLmceBfXJE0lQlJhURKDChUHTgSQY0QJwWFNAPRI8MUohMycq3Uq+bav+fDU/uKtwpyOMVxh+nO28v1+ulP3BBx8Yy87ahMDRmhTMcuSiV9Nhenoa8/PzttPwHowSZRwDg8dYvp0JVf4C0XtT+fGcSe6O2WwWxWIRHR0dOHPmDHZ2dszt2tvbi29+85vo6OgIKVkf8TnnLFeGyluVRiWlyv4FYMpEKyjRbcrcC+bW0KVHhMhFqHND0QXPnBgbG0MikUA6nUYqlcLs7CzefPNNrKysWJ5GPp83xUAUoQVriVjX1tZQX19vxy3W1ZXT1xkmzXiGQqGA0dFRQ2Ks88Df8AhAVpHKZrN4/fXXj11/laRqIh8J9bnTzs7OYmVlBU899ZS5ARmDDxyt1aBl1cgvKAwkvGYUIH/Dxezb0ur71wNqOBCKQjSASBeRhm3rTnOcm02FRVnu3LljkYjxeByXLl2yEmhKtra0tBiRRQ6D39EEHq10RYWTz+cxOTkZgsdUEGS4WUmZ0YBUhLlcDru7u9YnuVwOLS0tyGQydigwI/2Gh4fNHBweHj6CRHxPkMah+Gn1fF0JMfAaHD9WWdK+19OqaK75ZhLJXiIq8kwkhrlIL1y4YEiUSvL+/fuWBXzz5k0jY9vb2+1AXY5HKpXCwsKCVctmvzY1NWFpacmCwPb29qxGQ3d3d6jeI+MtisUistksfvazn2Fubg7PPPMMJiYmMDNzhOZ7oFSFYuDkBWB20v7+Pqanp9HT04Pm5mZblJWgLiGjhtQC4TRpZmByV+CurOm4hKoK85Xoom+ZfncSbmoqkBgFEJqkPlQHjsJmCgd5aWnJJjAAy9vXhQOUkc/6+jp+8pOfWH4A4xLIdNOtyvboIiT85Fjo58qFxGIxfOtb30J3dze2t7cxNzeHmZkZi+EvFg8PguG1R0dH8elPf9o8Ekpw8q8SsHwu/3MfYflmDucElZuSljQhVAGxD/3ALwqJU4ajE12S/I5Go+Y+5Hyg16q1tdXiW2ZnZ0NVrBTZEWG1tbVhZWUF29vbdkrWysqKZe62t7dbCXt6wlhUSM1h5eo2Njbw8ssvY2Zmxvi5k0hVKAbgcBGx09PptFV12tjYOJIGrBOHrjwytFy0ukBZAYeDQu3PnUBteS4G2nM0aUgc8XtqFnBiK8xVe1qVH0UVkE948UQrticSiaCrqwuJRAJdXV0Wqs3dn7YyKzJzZ+NEZlo326hp1BqgxfZwl+QzMJeEynB3dxfvv/8+VldXMTQ0ZPf6+te/jvX1dbz44ou4ffs2ksmkQWo1BdhGPW9SuROiBVUKvotW0Z4qcaJCDVzis3M+8Fo6flQCvJfGgWjSks6ntrY2pNNpOFcucT87O2sH37S2ttoi5ebF4Dpel/U5OT69vb144oknrEYlzan19XXMzs4in8/j8uXLGB4eRktLCyYnJ7GxsYFEImG5EdeuXbMALNYAPXfu3InWY1UoBtpsvvZubm42koiBKrT7dJfmZKZWVeKRsNrPswcOGXv1dOhuQhaY9+np6QkVclElpb/XRa4TnLvOcbaxyurqaqhEHcmuiYkJPPvss0cOOlE7nJOXaEP7SYlbLiLfdOJnmvNfV1dO77558yaGh4eRzWYtNHdqagqf/exncfbsWaTTacshiEQi5o7kvfW+jIRknyuCI8KjsiiVykVj29ra7HqViFG2Vfkpn6jmd7mJ1NfXY2VlBXfu3LEQZMYfKHnN31CZtbS04Mknn8TZs2eNn9rd3cXQ0JC5LIkEAIROvioWy+nbc3NzoaP8GHjF+ZdKpRCPx23Rt7a22jkVAEwh0yvR1NRkmyk9cPQanUSqQjFwsTKMVqEzzQcuUmp9nWh6qAnNEO6Y9BczWEVdhOr24oRSopNBKFx8HACaC/7ErARvGVlI3oNwmr+p1BdBEGB5ednqMHAxr6+v44MPPrDUYbp2NXiKSkFjJJSX0X6j0mKquRK67BNGVLa3t2NtbQ23b9/Gpz71KavYTDOGOyKPut/Y2EBTU5O5yTjZfdJY+0KfRb1PVBQrKyvmxSAK0DgVXouuXSUM+axqJvH+2WwW169fx/j4uGUuZjKZkNJU5UWlG41G8dxzz2F0dNTmZTqdtrnKMy1u3LhhcSYsHByJREKndRO9trW1WW0Gmibsm1wuZ2O5sbGBnZ0dzM7O2mHM9fX1aG9vt1JyjLvhGaAnkapQDCoasQjACmHyTEgfqqv9rmwyz6OkImDqL5ULiSdVKHod1cg6kTWXgaIL3IflPqfgM/z8vS6Q/f19zM3NGQRVVy2zO8+ePWsKlSaKxlAAhyaRJnCp0COgioFhy4ODgzYxORkLhYIx+g0NDbh69SoGBgawvr6Ovr4+BEGAubk5jI+P2zWSyaQ9k+9NUL5I+1/7lKiFiWx+AJYiDCqo2dlZLC0tYWBgAMBhMVRFGbzO4uIibty4gampKeNNgqAcgNTb23vEDa6mUFNTE4aGhpBIJIxjSaVSduZpJBLB8PAwLly4YNWn7t+/j9XVVSQSCczNzdnY7OzsoK2tDZlMBvF4HM6VT8u6ffu2pYZPTU2hVCphamoKa2trhnQHBgZsLSSTSfT29mJgYABjY2MYHx8PFbd9VKkKxcCBVjRAToHvdXZ2hmx44GglZuCQ6ONiJvHY1tZmxVm4YNUVd5w3QXchtdF9u1e/y8XIa2m7dXH4Jghla2sLy8vLBqf9xc6sP1+J6q7Mgi4kp3zlxcAf1rnc3t7G008/jcuXL6OhocGqTLN4yMrKChobG9Hb24toNIr19XWcP3/e+AVyHC0tLfjCF76AQqGAN954wwKfdMxUISh6U1JRF7GPLvjs2o/z8/N4++23La17c3MTn//8562GKHB4ghfvub29jdu3b5tS4K47Pz+P+/fvo6urK4S2VDGQC9vfL5cDYBt5zB0VbVNTEy5fvoyxsTHzSJRKJWSzWWSzWXsGAOjr6zPimN+ZnJw0FLG5uWmKiGumr68P3d3daG9vx7vvvovd3V1LMbh16xbGxsbwwgsvoK+vz192D5SqUAx1FojjAAAgAElEQVQauKKkD3eD1dVVi0/3Rd9jCjSVQ0NDg5VEJyOsSTJ6hgXvx/x3PZKM1XVYzp1wEzgaMcidqBIDXsmE8E0Pfvb5z38e58+fx/z8vB3Emsvl0NDQgN7eXjtajzHyuqiIFFQh8DNG33Eik/grFot46qmn8JWvfMWeJZfL4datWwDKu5Iy6lR2a2traGxstN2MiTwdHR24cuUKhoaG0NraGopPIXrQflKvxIPGWL+ryoIVqTKZDGKxmJGwGjlKM4tniXIXpfehra0Nly5dsixIIiNtpwbeRSIR3L17FzMzM2hpabFj8siFMY2eyIluRqZav/fee/ZsrHzF6Na9vT1MTk5aHgc9D11dXXZdnt7FtcOkrdu3b6Orqwu3bt1CU1OTxTucRKpCMVDo42aYKSWdTiOZTB7rslLeoBLUp63ODgXCdQVJQAHhoB0OGgNNdnZ2LLxWYxOAMAKgf1oJRyIODc/l7/hMlJaWFly6dAlPPvmkMeAsupHNZjExMRFKkFKFwP4gtGQb+MzadlZppn3LSadnKnzmM58J5foD5Qn4yiuvYHFxEdFo1GIWGhoaQqdIDw8PW7q1z/arp0NjCdQbUom/0bHnd7jYo9Eo+vv7EY1G0dnZaV4a7etcLodf/epXaG5uxqVLl5BIJOBcOe384sWLeOKJJ+Ccs+eh2aHKm/3LjYjmKMeciIw1H6anp1FXVxcqppPP53Hu3DlEIhEsLy8bmZnNZtHS0oLNzc1QSnexWDT3MxE1y/9Fo1GsrKxgcXERs7OzmJ6etnJxjLDUAMFHkapQDOrKo4eC2nBtbQ3t7e2hcm5+sBAnVFNTU4jZpluSGZH+otTYB4qeNalmChljZmYqSw2EYwoIWYlUtDKTjxAq9QWA0ORnZh8TdJaWllAoFKxcO3cNhd68F0k3ciq6M2shk2g0agFkXISlUjk/obOz88jC2NzctFPCmR8xNTVlk39tba1i7QznnClonq+gRCeJPSoP36Oiz8hANf4GOKy23dnZGXJ9ArBDdCYmJqwfMpkMRkZG0NLSgsHBQXN7JxIJAOE6HWr2KAolYcoIUvWw5PN5/PSnP8XKyoqVsiO6YTWovb09rKysmFehWCxa/IFuPB0dHUgkEna/rq4udHd3o6mpCXfv3rXTr6h0ObcZtn0SqQrF4Jyz6EFCrFgshkuXLlkNAz1eDQinEOtOQsQBILQrqR8dOCTeVCkoSiGJSLi+u7uLVCpl19aYfG0Dr80Jy3Ztbm7azsL289n9ndAXhd2s35DP53H79m0sLy/jzJkz6O/vP5IBqOQrzQXtQxJ2Ozs7GBgYQCqVOsLz+KRgqVRCPB7HlStXLHCmvb0do6OjuHDhAkqlkoWNa0al/zzchZUL0hJ5VAzqSdH31d4namIwHNtJOz8IypGEN27cwEcffWR8ws2bNxGNRnHp0iUzg4gSCOepDPx2cLw0S1bdn0RujEYdGhrC+fPn0dbWZinejLdwzmFkZMQSpagU4vG4hWwzQ5WmCqN4qfDv379vpejZRp65QZ7tJFIVioGdyMXK/HnGl+fzeYtxP2631eQnTjh2COv9a9AL76sLsr6+3lw/vA8hIs0Avq81I/lXFyKRAhca3VU+M+/3g/+Z2uDOOdsRS6VyKTUWHmHJcEJzPVhGd2CNEqUptb+/j1QqZec98PqazaiwPhqN4uLFi/aMLGzL2gArKysh1x4QNvG0/3ldvz9ow1fqH7ZPYbnGrtBzQnNwf38fk5OT+Oijj8xez2QyRvA9++yzISWgngI/MEr5IuUfqIzpHiaPVV9fj6GhITz55JMYGBiwrFgGSHHekZ9gBaxSqYTnnnsOxWIRi4uLmJyctEQwbjQ8b2NnZwfT09Oh2BaS95VSCB5FqkYxsJhoJBLB5cuXUSqVsLCwYOdC+HZeJcitUE9NgN3dXWSzWWQyGTQ2Ntp5ApXSu3VC8nqFQgFBEJhiUMJSf094SlNCIe/m5qYFmagy8eVBZgZ3QSIZTl5WPs5kMhbDoedlsD0a30C3LRcBIWollydjMYDD8nHd3d2GlphAVSgUbHHu7e3hpZdewtmzZ+3ePT09SCaTRwrAqnJQNMGx1M9V6VNxkbBjJSXyAnwOPjc3ieHhYXzpS19CoVDA2NgYFhcXce7cORQKBSwvL4ciVBUpaP/5Y89+Vq4KKPNmFy5csIAv5xwmJibw8ssvo7+/H1evXjXvEmMvWltbMTAwgL6+PjjnMDQ0hGQyaTUayDlxLjBfpaenx+J3isWiKRkSlyeRqlIM3O046K2trZifn7fQWYY+66Ti5FcbUMUvCktNTrOCKELNDH5PTQlyFKp0fH5C4aVOGE5aKgq+f5L+oUJiYA2VAvkVJkkp+642ORcbn5VIgsRjOp22HANVTn7uyNramsX3q5JhCHepVK55sbm5ibffftt26ZmZGcRiMXzxi1/EhQsX7Hh6Hh7EE7J9l7T2lRK8KjxQSIvrOHd4VihLAjY0NCCVSuGFF16wsy94MAvjF6hYdJyIPFRJECHSxCV6UHc226sBaDQDnXOYmZlBJpOxJCrnnJWT7+npCXlOWPC1s7MTd+/exdzcnPFey8vL6OnpwejoqG2C77zzDq5du4Z0Oo319fXHXz7+4xB2tiqAZDJpUW48eIWLggOjuwknuy4GEj08pIYuG13QhOi+vUozwLlyqDXrGQI4FmKr+HxBa2tryBR5EDLg5/411e6tq6tDZ2enHfnW1dUVCh+mkvRRgN6fioHeBIpObuVKgDKBl8vlABwG/JCAa2hoMOWUy+XQ29uL4eFhdHd3I5fLmYlx69YtixNYWFjA5uYmBgYGMDAwgLNnz1r9Q81sVOWt6LJYLNrJYuxzPisVKWsRXLx4EZlMBolEwghKJiyp2VDpuX3iUzck/tX+IwqgsqBbeWVlBX19fRgaGsLOzg7i8bi1lSenOecsSnJ3dxdjY2PY2dnBmTNnUCqVsLy8jEQigVQqZRwazZbm5mbzqCSTSTuL4hOZREWYrvaQc852gJ6eHqRSqRDH4JN4HFQqBy5iogweTfawBVzJLOFR6YpMfPtXJ4ZPJO7v79tAH/f8/m/1PV5re3sbuVzOiKeBgQHzGKjJQFGvi7ZHvRJEQ4wq1OfyzTQuOipchvwqzI5EIhaxOTo6iitXrlgZt9XVVbz77rtWvYmhy7lcDvPz83jzzTfx7W9/G4lEwkKxqSCUVN7e3kY8HrdkLnUTqilI4pp8y7PPPmvwmiHDCwsLIdNBSWRfMahSVeWgiIwIlbEOFIY+s5wdkSgPAKI3hPOa4fjLy8uYnp5Gb2+vpds3NzfbSVtE2OSMGKAVj8fR399vbflEeiU4qYFDDwVLW83OziKVSoUYar+iEXCoKPSapVLJcuK5y9NG9hOnfFEl09zcbAFFvK4fkFNpAvF5WPfwuHBq3472FQX/5vN5LC0tGZIqlUq4ceOG2aE8EPc4tygnNGEwd/d4PI729vbQ/bnTKU8SBIERZJrgRVOJJgzrAvDcBn6Pvnc9b7KnpwfDw8NWMJW1CWniaCyIQmsmIS0sLCCXyx2x+2kiUrHQpckArO3tbczPz2N9fT10+I4qeEVeyrmo8tDvc0zYPvW88DXzbWguEwFyrNTLtbtbPmv1woULGB4eNn5heHjYzJjt7W1sbGwgnU6jra0N29vbWFxcxNWrV+1sTCZ3nUSqQjEAh5GHVBB83dHRYbt9oVDA+vo6BgcHj/y+kr+c8IzHsgOHx3oxOEhNCZ0EaoezJDeJOLVB9X7kE3yegTbucVJpAevkA8qKYW1tzQrDkv1m5CKrIdHz4CMWvtaiI+R0YrGYHY+m91eXKvszCAILimL/UIFSQSjpSe6DJHJPTw8uX75sYb48cDYIAqtoRMWigVjc+di3PJWM5zxSMSjvwbYzJZlpyLFYDHNzc1hdXbW4FPaJerdU6CrlolYTkpuSjpfGunAuEQWvrq5ae9WlrYoYgPE1qVQK7e3t5jZnnUyGrDc2NiKZTKK7uxvz8/MoFosYHh4OBYt9IgOcAISKXagGHxkZQSKRQHNzM7LZLHK5HNLptHWe8go+b0AoqYFN3MF0Z64E3fl/aloOIiGiD/n9HYRtYAyBBglVQgR6b194HzLRbAMJUQbVEA1VipFQc4J9Q7g+ODhorka9vy4OLhxWatra2rJFRJY8n8+H3HBbW1u4d+8e9vb27AxGnhlKxRyJREy5MKXaR2R67gb7i1GojM/QTFjt962tLas+1dzcbElMin6UaFSFxPfYd3Rd67zTcfR5HBV1lfN3qgx4Tf/UMXJtulk1NjYaSdnZ2WkFXoiOzp07h3g8bt60urrDE9cfVapCMdA2447U0NBgKcXnzp0zmLuxsWETEwgnLinpxP/rAS8K54BwrUb+Xxc7v6+H2eoiq2RGVLLPuWs8yF30MNYdOCyYy12DC0CTpRjg4pdB0wWvWZbJZBLRaBSjo6OhuhP+b7SvWeyGUJnVn+hl0L5glSLa1rwOx2FzcxPr6+uG4LTdusCDoBzYtba2ZgqEfe1nPCoXRORBApoErR78QtGTwqgsdMFyw2L7VEHoLu/PJ38M2Tf8y+fjHGFRWr0uU9i1PRqhy9yZUqlkqe6sA0Gz/BNpShCSchFz1ymVStYB7DwqCcpx/ACVDWvw+WnH+lpdULq46fvmDqjQnJNYr0M4rZOE/uTj0l793UWVky5sjf5kYFFTU5OF7ir5pkqIi0X/RqNRdHd32wEmIyMjR6Czv/NxN93d3cXCwgJWV1ctC5D9rOQlFzMXG59ramoKW1tbSKfTVumI/c9FTt6CsRGM9iS5yihJzg3NBeFzaFh8W1sbYrGYlV0j8UnUsb6+jnw+f8RM9NEg5wcXs/a576Hy//H3NDFUgfGZ9Lpq3uqmRhOSJhaJdZpBVA5a/5MxKieRqlEMrHDL1wAMQrFzWV33OOJHffSEsoVCAdPT02hpabF6DP7OeJxo1CXbQHNCdzc1HzRnAThMoWZCjW/HU+H45gXvxc8JtxmzwPyIlpYWc8uRuVe3Htul/cNEKVb9SSaTRxRBJYXFmoOsI6A7n9rFjMDTfBL+f2VlJRRpGY1G7Rh3FaIQ2sZUhOxrFkLV+6ryI2nM/Bat8E3ij6YYa1DQDUs+gvCb99WFrqn5OmZqrmk/+m50Xfx+P6syUfRKxMbcFkb2ksAkmqPC9xHkSaQqFIOytxwwehMA2KTv6uoKdbhv5/F9XrNQKCCVSllFXV3QQBht6ILltff397GwsGDp1uzkSh1diTB0rhwe/Mtf/hLJZBLPP/+8ZfNVgpraLhKZGmQFwHYDzSWgSUFS0t/R+DzMyORk40lGlULNdXJTSeVyOWSzWSOFyXYzfh+AoTOOgZKGSiaOj4+jvr4+VLqdC5ucBU0TreqkUakcI37Gz/laq22xhgUT7fhMDBKjkmxsbDRCk25EKlNGJ1Lx6jyggiAa0M/8vtSNTREBx0wJTV6bY8bNikoCCBfsZSg00RI5m5NKVSgGsrUkmAibCft0RwKOFkWppBwIezOZjHkxjvMk6A6vioGls+rq6vD000+HBqiS+DZ9qVSuuMS8+8bGRmQyGXR3d1tWXCWyiv/XuA7uDFRM6sFhVh5/x/gEzabkRCKi0eSw4xSd9glt862tLSuaw9OVtra2jmSuKlHou5L39vaQzWZtXLUWJs0xKoVKIeo0rZQ78hUtORASkyQ5VWlRQZCcoxJg/9E+7+rqsnDjiYkJQ0SK4pQTqMQ/VTLVOJf87/sub0WO7ANFEFSSGjtRV1dnbu0HzdnjpCoUA80HwiGgPLkZmabCjjsuwYbCycV8dZ9BBo5qct/vzINcFO5zIfkuq0pKJgjKhUO+8Y1vYHNzE+Pj4/jxj3+MTCaDr371q8hkMlYaX+s3KifBAWfiDT+njczfMCuQOx1TgGlbUpFQKXDy00Pgt7uSKcGQ4iAoV0xisg8ha6Vx4gLVw3i5sLmrKaRmH/M37AOdG0CYO+F9gEPkp0FPvAcT0BhMR+VQKpXrafLwV55mzbwacl1BEIRqU5BD4bEEnBNUvkQvfD5+R1GBj1T9OcrvVVI4vC77WM074LAW6v+LVIVi4GA45+xgVsI74GjosW+bKWqgdmXGH+1DwtXjNKhvQ7LDM5lMaGfXndFXRrqjsV08z3BnZwc/+tGPkM/n8eGHH+LFF1/E0NAQenp6QtejncgwY8J0ntXAVF0GAFEBcOJw8WnJN91pGDVJGK3ngfokmf9s9IDwNGv2kf7l72kKKevO+yuxzDaqmaOeAW3X2bNnEY1GQyHQJBnVDuf/VWHyvnSx8hAe7voaHh6JRBCPxxGPxy2UO5vNhkwI36W5s7NjRwUyBJkKRElGNRd0TnO+qLJjX2rFLH/+axwE78Pv+nP7JFIVimF/fx9LS0u28Ai/nTuM6FNYq4QfEK48zMnEZBifbOQ1jjMjfMWQSCSQyWRsFyDRw4mrnV6JO+D1i8Vy8ZIgKEcwvvXWW7h+/TqamprsHEKSo4VCwdrI2oI0serq6kKxDFQSnLDccTkBfRivOxZt7komlq84yXqzxJzuUpyMGhXoE2xckHzNvmP5tFQqhVgshmKxiIWFBVPsRBdqVqg9TQ5GPUF0fZOv4OKh6aDl7cjd6HF5iUTCxqK1tdUqNdGtqgpB+9Tf2dlmjoefkKYL3OcgFL2q0lflwu8rKtEx5ByuRHI+TKpCMezs7FiGWDQaxcTEBH7+85/jxo0beOqpp3DlyhX09/dbaLO/kwBh3oELuKenJ5QzzwHVsFNOEJ1YwGEdBgbdAIcHzW5tbVnQlYqvEFTondAouf39co1JZsn19fXZLsPJxolJ6E1SjsQbn4mwnNGZvAehtO5AhN7OlUuYTU9Po6ury3Y5TfbidZiMxGPnqCCpHIDDGhUtLS3o6+szGMuzQeh65MKgadDc3IxkMmlBYM45TE5O2vd4fSon3p8Lkc/FayqE5uLQADD2U11dnT0vvV1EGzQ7WHlaRTkb5mKUSiUrBkRly+9RUbW0tBxxbfKvko+USihWF7nOeT6//l+Rx0mlKhRDPp/HSy+9hEwmg9HRUdy+fRurq6tYWFjAxMQEXn/9dfT399vng4ODoTRdv5O3t7exsLCA3t5eAMcfDAOEzRAdFI2aVCje1tYWYruVGPJ3Wn1dKBSwurpaEdZRMfIwWQoz9DjJlWegYlC+gxNYzScf0dArwAUzMzOD9fV1iwzs7Oy08ymZ3RgEgSk19jtjC/iXJCSTgfzfb21tYXFxEdls1vqSfUtEVCgUUF9fb8Vk6aqk61EDjJT0U3ueuzqVA3dsJTPZ5+SL2tra0NjYaL+na5PcFBUsr8kFyjgOEpdaF1PNAiomjilRgJLqnEf+/NCNStGo3of/V9etjypOKlWhGIrF8lHf09PTuH79ekjT7e7u2mfvvPMOWltbDd6fO3cOIyMjdrIvB2ZtbQ2vvfYa6uvr8cwzz+DcuXPmTiOs106kKOIg80yij4OvDPru7q4dQa6l6JU44utcLmc8gf8ZzQyWfwMOSTSaREypVtaZ9SEIm7mbKkxXTkWhLHdOoiCGLU9OTtriJr9BToK7Kb1F7CcqJP5lwpmSblSm6i1gqDIXmxbAYdYh70l3rDL2fAYuMH5XEQOVI5+dkD8IgpDyiEQiVuWLaFGfi21WBMp6mDRZlEhUHoKnS/lokPNFPS+cD6qQfF5MUQb/r691bvmk5aNKVSgGdlCpVDL72odVQHkXX1tbw9raGu7du4dXX33Vzg48f/68nXMwMzODO3fuYHp6Gq+99pqZI8y7oJJQ4Q7KScqJq1Wh1WZzzlnwFCe9KgQ+Fwc3m80+MF6dpbo0qIuTg4uSSomLAECoyrX6xdU29cOGdQHyN1xktKHpsycHoIiEwUZMiyYPovf1TRZek/djWXOiCXqAOOZUciRJmZBFZcR5wmAluj05Vrw3FSgQjkzVhCvd3bUCko80dV5q/3Z0dITcrFSKFPaRjxJ8E4Hv6+cce59Hq7Q+2E7OAZ2rJ5WqUAxAZbiv4nceADvvYWlpCTdv3kRraysGBwfR2Nho8fC5XA6/+MUv8OqrryIejyOTyeD8+fMYGRlBb2+vlTen1ubimZqawuLiIkqlUuigGuBQY5PV1wHWzzkoxWK5bp9mPfqkIJ+F5w3ymblbcfL7HhjlEHTS6w4DhP3m/C3bqMw+Fzvvw2uSt2H/M67CD2bSDE+2jaaChoszuxM4NNv4enNz09K6I5FI6Gg3mgCc/Ho9nwhVclghN/uHuz4VCs+k1O+pQtQdXN3VSoD65iq/o6Xo2UbdENmH7Edd/ERa/H8lBKAoShW0tvkkUhWKQUlA/qX4jKryAT7xtLW1hTt37oTCkhWmLiwsYH5+HtevX0dbWxsGBwdx7do1xONxJJNJS28FDs9OeOONN3D16lWcP3/eCEet7UDFwt3JHwDnyv7upaWlEInlD26xWLTsRGaA+hOFz+JX/K10Tz679qd/VgZ3VeDwlCZOVB5tr5Dcby//6f0VdemzqnlIb0A2mzUikORfPp+3cv/kB5gb4SMRohv673VOKFpiu3yFTSXIz6nsgPBpZOoFoQLw+5FoizESqoAZIaomAhW9IjZVWABCCg04PGTZJxZVAaliUDkpCVkVioGijff9sTrBVcsCCH0PONwNtZP5OyqaXC5nVYV4ClE6nTZz4969e5iZmcHk5CTGxsYsgnJgYACDg4N2FL2f/0DhzgcA8/PzmJmZCWUfapuUfefi5XskIwmZlUDj7zmhiBz8e+j/NchJ4SYVG913XAiVkJxOQiXXyMewjepaJCzWa9C8YNwG20VXodrSzBHQoCJe06/HybbreHMHVXhNM8P3SFEB6e/Uk6Gfc2zIlVBhKQLd3NwMpanz/ppkxuuwLeRO1KRUxabZpf7c0/Hxx+5R5aGKwTk3CODPAPQACAD8MAiC/+Cc+wGAfwoge/DV7wdB8FcHv/ldAN8BUATwz4Mg+OlJGnWcdvPRBMWfcJWIRV846ZeWlqzqzeTkJN555x3LA+DvNzY28P777+PDDz+0ZCge/vH8888jFouZ75uLOpfL4ZVXXsHCwgI2Njas2GilhUvZ29vD6uoqenp64JwL7diaM+FDWU5UTkw1aXTXpGLh/9lPJN58kouLb2dnB/X19bZ4GW6taIamCBcRd0QqDdruProAwkFr5C+oWKmweAJXPp+3ykv19fVIJpNWfZoksE/m8VnVhamoSv8qxNfvaTi5LlD2AQ+KIR/CnB+6RFVRcY7QS6XIUNEMkaiiChXlHNgWn+MBjqK9R5FHQQz7AP5VEAS/cs61A3jbOffXB5/9+yAI/p3X2IsAvg3gSQD9AP7GOXcuCIJjz+HWSQ2EM8qAo7D7QbsuF5//3UoT0kcejMBkiKten5+zEtH+/r4ldbHa7zPPPIORkRHEYjGrYTg1NWV59r4C09fcWXO5HLa2tuxsDU4Sha5EQjqRlFfw3WN8RrWZOXl8NxpfA4cnVX344YdoaCgfdMsj1nW82CblEHTclHPQ94lO9EwHZlXS7GOSVaFQwObmZiiGAQBmZ2ctECkej6O7uxuJRALd3d1obW01V6Tu8j4fxAWvyk3hOPuXY6Q7NoUH/zI8vLOz04rHUEFq4hs9QWyb8gxqvmhko4+MtV3+vKIiOc60eJg8VDEEQTAHYO7g9bpzbgxA+gE/+RqAPw+CYAfAuHPuLoDPAHjt2EbU1yMWi1lhDR0oJV3UrqJoZ6ly8DvCf08nSKWO9++jv+eAdXZ2YmtrC+Pj45icnMStW7eQSCQwOjqKtbU1jI+PW2aoZvSx3T7kA2BnVPb394fKofu2Jq9FJMGdHIDZusohqA2uuyPbocSloq719XXMz89je3sb4+PjdnxbT0+PHXLDXVFtayroSq5EABaxubu7i42NDUxPTyOfz2N1ddXKnWuEoioxFSrB7e1tLC8v4+7du6ivr0drayuSySSGhoaQTqcRj8dtESpMpzeDBCz7yh+bShwFX+u4cVyy2SwKhQI6Ojqsj1iwhnOOiFDNG7qYVYkpX8QxVZcnr+EjZVWGf68cg3NuGMDTAP4WwK8B+B3n3G8CeAtlVLGKstJ4XX42jQqKxDn32wB+GygH5LDuoC5u1bAcOH3PJ2BUfHZY7hv6jb6uZK8BYfeR3jcej9tk2N/fRzabxfz8PCYmJhCNRo0krJRPwEXD15RisYjZ2VmLguTn/C3dgrS7yVhXWtQ+POWioBBy+6aJhvaSEKVJsbOzg3w+j4mJCSusyp06FovZe5p/wahJxmpsbm7aKc50iWrfaD/79jx3bF24vsKnSZbL5TA+Po7W1lbEYjGkUin09vYilUoZidzc3GwH0dA80TH30WclgpmihWoZsKWFfnyitqmpyWJjaDbx/kSEvmmjXhhVvCpq/mh/nkQeWTE459oA/A8A/zIIgrxz7o8A/D7KvMPvA/gDAP/kUa8XBMEPAfwQAGKxWMAoMmpJf8GrncWFUiodpiOr/e5Hifk2mi4Yac9xzx36Lf8yYu7+/fu2kNgmnrzM7xIt+BDW6w8A5QFfWVnB0tIS+vv7rUAqS5yzPaow/X7i/3W30feAw4mvSEGfmSgil8tVNOlKpZIt7vn5+VDhFx4iw0pJ/E4+n7eQbvaJ3pP3UMjOPtFn1D5UBavX4/v09jD0m/USyRP19vZarQUAluWr/aFEKhcqX6s9rwFYGuDlu27VtKJyL5XK4dfK+/imA6/NOcGgKdbo4Bzh94gs/Ln+KPJIisE514CyUvivQRD86GAQFuTzPwbwlwf/nQGgZZwHDt47ViKRCDo6OkJwW3dwnfQ6Ofjg/pFiqmCovdU00Y6qZJ4A4UWgEJbvNzQ0YHFxEcvLy0cQiHMudHakXsPnOvQ+fDZGIbLOAnMIKj2b/pb354T1fflqLukzK4rSHWh7e7tidSVfFBC877AAAAXiSURBVM7ncjmz+xm/UCwWQ7UQ9Fkr7fxUVMpLKGrwRX97nMnB7xEdLCwsoK6uDqOjoxgaGjLzguSuvwuTCNSwZ99sYt/6CkPhfywWQ11dnR0rx2rXPDgGODxwWUlIHSveh5m4fqCaulPZvuP65Dh5FK+EA/CfAYwFQfCH8n7fAf8AAN8A8N7B6xcB/Dfn3B+iTD6eBfDGAxtRX2+nA3Ei8UEJM2lG6ALwbSv+TvmIUqkUynfwzRGNdNMO5HX8DtUFxVOJKZUmrpJKB/12BBr7O3kQlHMTWJuRpdcqFfRUJafsM3cK9invqwiIv1WfOhdIfX19qMDrSaCoKgNVwGo3Hyccl4ftcD7n43tpVBnyryIf3sf31CjvoHOBRVoZ8aneF6IrbbMuYDWHiDQZxetvVPRssA8YCu6be1wLPK1KQ9P5vCdFCSqPghh+DcBvALjpnLt+8N73Afwj59xVlE2JCQD/7KBRt5xzfwHgfZQ9Gt8NHuCRAMo75MzMDBobGw3i+YubykGVxP7+vlXAJdxWGEWYrAe0qJlBRUE0oZl7GpJbCVVEIpEjGX7HLZ5Ku6O+5y+UUqmEtbU1U5hazo1EH5OptKKTRily19XDbTlZlPBSM4IMOok5njZ+UvvUf3bfnHmU6/lmhm4GlfpVx0F3fL9vfaWgHAuV2d7enuWC8N5UcvQkMGbDj+ZU5MBFDxzyRCz13tLSYqd0a3+oCUFvDE0GzcfhNYMgCJln2s/qOfK9KA8T9zCY+HGIcy4LoABg6bTb8giSwCejncAnp621dj5+qdTWoSAIko/y46pQDADgnHsrCIJnTrsdD5NPSjuBT05ba+18/PJ3bevJ8zFrUpOa/H8vNcVQk5rU5IhUk2L44Wk34BHlk9JO4JPT1lo7H7/8ndpaNRxDTWpSk+qRakIMNalJTapETl0xOOf+gXPuA+fcXefc9067Pb445yacczedc9edc28dvBd3zv21c+7Owd/YKbTrT5xzi8659+S9iu1yZfmPB338rnPuWhW09QfOuZmDfr3unPuyfPa7B239wDn3xY+xnYPOuZ855953zt1yzv2Lg/erql8f0M7H16caw/9x/wMQAXAPwBMAogBuALh4mm2q0MYJAAnvvX8L4HsHr78H4N+cQrs+C+AagPce1i4AXwbwvwA4AM8B+NsqaOsPAPzrCt+9eDAPGgGMHMyPyMfUzj4A1w5etwP48KA9VdWvD2jnY+vT00YMnwFwNwiCj4Ig2AXw5yinbVe7fA3Anx68/lMAX/+4GxAEwcsAVry3j2vX1wD8WVCW1wF0Oef6Pp6WHtvW48TS9oMgGAfAtP2/dwmCYC4Igl8dvF4HwBIDVdWvD2jncXLiPj1txZAGMCX/r5iifcoSAPjfzrm3XTlVHAB6gsM8kXmUq1tVgxzXrmrt5985gOB/IuZYVbTVhUsMVG2/eu0EHlOfnrZi+CTIrwdBcA3AlwB81zn3Wf0wKGO1qnPtVGu7RP4IwCiAqygXAvqD023OoTivxIB+Vk39WqGdj61PT1sxnDhF++OWIAhmDv4uAvgxyhBsgZDx4O/i6bUwJMe1q+r6OQiChSAIikEQlAD8MQ6h7am21VUoMYAq7NdK7XycfXraiuFNAGedcyPOuSjKtSJfPOU2mTjnWl25ziWcc60AvoByevmLAH7r4Gu/BeB/nk4Lj8hx7XoRwG8esOjPAVgTaHwq4tniftr+t51zjc65ETxC2v5jbFPFEgOosn49rp2PtU8/Dhb1IQzrl1FmVe8B+L3Tbo/XtidQZnNvALjF9gHoBvB/ANwB8DcA4qfQtv+OMlzcQ9lm/M5x7UKZNf9PB318E8AzVdDW/3LQlncPJm6ffP/3Dtr6AYAvfYzt/HWUzYR3AVw/+PflauvXB7TzsfVpLfKxJjWpyRE5bVOiJjWpSRVKTTHUpCY1OSI1xVCTmtTkiNQUQ01qUpMjUlMMNalJTY5ITTHUpCY1OSI1xVCTmtTkiNQUQ01qUpMj8n8BxKy/0gTRRlIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAlC3h2VvLCY",
        "colab_type": "code",
        "outputId": "2e160ceb-3a60-426b-9471-2ea208692b1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "img = cv2.resize(img, (256, 256))\n",
        "#img = np.asarray(img)\n",
        "img = img.astype('float32')\n",
        "img = img/255.0\n",
        "\n",
        "print(img.shape)\n",
        "print(img)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(256, 256)\n",
            "[[0.46666667 0.4745098  0.49411765 ... 0.5019608  0.49019608 0.4862745 ]\n",
            " [0.4627451  0.47843137 0.5019608  ... 0.49803922 0.49019608 0.5137255 ]\n",
            " [0.46666667 0.4862745  0.5058824  ... 0.49803922 0.49411765 0.5254902 ]\n",
            " ...\n",
            " [0.41568628 0.40784314 0.3882353  ... 0.59607846 0.5882353  0.5647059 ]\n",
            " [0.4627451  0.45882353 0.45490196 ... 0.5921569  0.5921569  0.5764706 ]\n",
            " [0.34901962 0.31764707 0.34117648 ... 0.57254905 0.5764706  0.5882353 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Spyfxd5tvOh2",
        "colab_type": "code",
        "outputId": "7e409b9d-f4a0-49b0-baca-bb7025fc7de7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "img_noise = speckle(img)\n",
        "print(img_noise)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.58626664 0.43203205 0.46569443 ... 0.79608492 0.58946602 0.23795911]\n",
            " [0.43857476 0.49755568 0.60142191 ... 0.53503614 0.39439357 0.28005469]\n",
            " [0.54300666 0.6836097  0.78784722 ... 0.38573409 0.34058848 0.30469564]\n",
            " ...\n",
            " [0.42791453 0.37763039 0.32849429 ... 0.40932608 0.58777136 0.69013601]\n",
            " [0.57283766 0.52529268 0.43519496 ... 0.63149387 0.72993129 0.78755611]\n",
            " [0.51339388 0.34960172 0.16947628 ... 0.76314541 0.79502533 0.86295985]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExiyMo74vRfd",
        "colab_type": "code",
        "outputId": "3c605394-63ce-4a62-9d2c-cc8de160716d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "img_noise_256 = img_noise*255.0\n",
        "img_noise_256 = img_noise_256.astype('int32')\n",
        "print(img_noise_256)\n",
        "plt.imshow(img_noise_256, cmap='gray', vmin=0, vmax=255)\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[149 110 118 ... 203 150  60]\n",
            " [111 126 153 ... 136 100  71]\n",
            " [138 174 200 ...  98  86  77]\n",
            " ...\n",
            " [109  96  83 ... 104 149 175]\n",
            " [146 133 110 ... 161 186 200]\n",
            " [130  89  43 ... 194 202 220]]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "fa7SjbBAa-IA",
        "colab_type": "code",
        "outputId": "6cb657ff-aa5b-4bdf-f08d-ce9bb0301b70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "img_noise_pred = img_noise.reshape(1,256, 256, 1)\n",
        "print(img_noise_pred.shape)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 256, 256, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8bQ9Q_gp4Gp",
        "colab_type": "code",
        "outputId": "bee6a580-2d91-4920-912c-16aa36216fa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "print(model.summary())"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 256, 256, 1)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 256, 256, 64) 640         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 256, 256, 64) 36928       conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 256, 256, 64) 36928       conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 256, 256, 64) 0           conv2d_1[0][0]                   \n",
            "                                                                 conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 256, 256, 64) 36928       add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 256, 256, 64) 36928       conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 256, 256, 64) 36928       conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 256, 256, 64) 0           conv2d_4[0][0]                   \n",
            "                                                                 conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 256, 256, 1)  577         add_2[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 185,857\n",
            "Trainable params: 185,857\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUv-zLFxcxl-",
        "colab_type": "code",
        "outputId": "704837e8-e6df-47a3-8023-d24f58ae5766",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pred = model.predict(img_noise_pred)\n",
        "print(pred.shape)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 256, 256, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcJqiDOl05MU",
        "colab_type": "code",
        "outputId": "889575ad-926f-4180-e533-a618ff2b3f9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "pred = pred.reshape(256,256)\n",
        "print(pred.shape)\n",
        "pred_img = img - pred\n",
        "pred_img = reImg(pred_img)\n",
        "print(pred_img.shape)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(256, 256)\n",
            "(256, 256)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMRa59ZRsegT",
        "colab_type": "code",
        "outputId": "f4b72cd2-5f5e-46b1-86c9-9c2c82224c54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(pred_img)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.46133304 0.46989152 0.49991417 ... 0.4248647  0.42012143 0.4776566 ]\n",
            " [0.4366805  0.43675086 0.46730557 ... 0.42859802 0.42746645 0.5294974 ]\n",
            " [0.41500127 0.3810159  0.390316   ... 0.47242394 0.4661136  0.5543214 ]\n",
            " ...\n",
            " [0.37553477 0.3898221  0.37966046 ... 0.58704615 0.5245501  0.560925  ]\n",
            " [0.41264158 0.4110209  0.42902654 ... 0.53609806 0.49353993 0.5620994 ]\n",
            " [0.32729065 0.2942898  0.34406263 ... 0.53714836 0.5156943  0.55165523]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C44pi_IzyGY7",
        "colab_type": "code",
        "outputId": "bc20c596-7c42-4997-fb9f-fe3119f34d8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "pred_256 = pred_img*255.0\n",
        "pred_256 = pred_256.astype('int32')\n",
        "print(pred_256)\n",
        "plt.imshow(pred_256, cmap='gray', vmin=0, vmax=255)\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[117 119 127 ... 108 107 121]\n",
            " [111 111 119 ... 109 109 135]\n",
            " [105  97  99 ... 120 118 141]\n",
            " ...\n",
            " [ 95  99  96 ... 149 133 143]\n",
            " [105 104 109 ... 136 125 143]\n",
            " [ 83  75  87 ... 136 131 140]]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "fYj5DIueqZzW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "8bd43f07-ba7d-43ac-fdd9-7001b6950298"
      },
      "source": [
        "mse = MeanSquaredError()\n",
        "errPSNRImg_img_noise = psnr(img, img_noise)\n",
        "errPSNRImg_img_pred = psnr(img, pred_img)\n",
        "errSSIMImg_img_noise = ssim(img, img_noise)\n",
        "errSSIMImg_img_pred = ssim(img, pred_img)\n",
        "errMSEImg_img_noise = mse(img, img_noise)\n",
        "errMSEImg_img_pred = mse(img, pred_img)\n",
        "print(errPSNRImg_img_noise)\n",
        "print(errPSNRImg_img_pred)\n",
        "print(errSSIMImg_img_noise)\n",
        "print(errSSIMImg_img_pred)\n",
        "print(errMSEImg_img_noise.numpy())\n",
        "print(errMSEImg_img_pred.numpy())"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16.39564388379037\n",
            "23.844950427219302\n",
            "0.4398233573358169\n",
            "0.7338130932218864\n",
            "0.022931665\n",
            "0.0041257697\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: DEPRECATED: skimage.measure.compare_psnr has been moved to skimage.metrics.peak_signal_noise_ratio. It will be removed from skimage.measure in version 0.18.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/skimage/measure/simple_metrics.py:65: UserWarning: Inputs have mismatched dtype.  Setting data_range based on im_true.\n",
            "  return peak_signal_noise_ratio(im_true, im_test, data_range=data_range)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: DEPRECATED: skimage.measure.compare_psnr has been moved to skimage.metrics.peak_signal_noise_ratio. It will be removed from skimage.measure in version 0.18.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: DEPRECATED: skimage.measure.compare_ssim has been moved to skimage.metrics.structural_similarity. It will be removed from skimage.measure in version 0.18.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/skimage/measure/_structural_similarity.py:17: UserWarning: Inputs have mismatched dtype.  Setting data_range based on im1.dtype.\n",
            "  **kwargs)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: DEPRECATED: skimage.measure.compare_ssim has been moved to skimage.metrics.structural_similarity. It will be removed from skimage.measure in version 0.18.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtDxu-b6-6cB",
        "colab_type": "code",
        "outputId": "a251a870-b7d8-4865-ab58-c81fec1a30cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "from keras.models import load_model\n",
        "model = load_model(\"./drive/My Drive/Colab Notebooks/SAR_DRN.h5\")\n",
        "print(model.summary())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_10\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_10 (InputLayer)           (None, 256, 256, 1)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 256, 256, 64) 640         input_10[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_19 (Add)                    (None, 256, 256, 64) 0           conv2d_64[0][0]                  \n",
            "                                                                 conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 256, 256, 64) 36928       add_19[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_20 (Add)                    (None, 256, 256, 64) 0           conv2d_67[0][0]                  \n",
            "                                                                 conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 256, 256, 1)  577         add_20[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 185,857\n",
            "Trainable params: 185,857\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}