StyleGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1GHD1I4tdbSfBqnHJtXF46R-OGugA4kpZ",
      "authorship_tag": "ABX9TyPBhfT2a9UnJh1iN3g2xBKt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Hrishabh17/StyleGAN/blob/main/StyleGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Components of StyleGAN:\n",
        "### 1. ProGAN\n",
        "### 2. z-Mapping (to unentagle the latent space z into w)\n",
        "### 3. AdaIN\n",
        "### 4. Upsampling + Convolution with factors\n",
        "### 5. (Styling + Stochastic) Variation\n",
        "### 6. Truncation Trick"
      ],
      "metadata": {
        "id": "INAr-bbApkOv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ATulodD4k5wM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ba3d743-dfbe-4d3c-ed33-b7e501f2ab0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorflow_addons in /usr/local/lib/python3.7/dist-packages (0.18.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (21.3)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow_addons) (3.0.9)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (4.5.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from gdown) (3.8.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from gdown) (4.6.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gdown) (4.64.1)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.7/dist-packages (from gdown) (2.23.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (3.0.4)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.7.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow_addons\n",
        "import tensorflow as tf\n",
        "import tensorflow_addons as tfa\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from functools import partial\n",
        "!pip install --upgrade --no-cache-dir gdown\n",
        "import gdown"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IElFsF7MrlbV",
        "outputId": "cea1ce11-e6fb-46da-fe6d-75a555bc05fb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "celeb_dataset_url = \"https://drive.google.com/uc?id=17w_hePC1xO2eX83gAAF8ptKC59mTPQiL\" \n",
        "gdown.download(celeb_dataset_url, 'celeba_hq.zip', quiet=False)\n",
        "!unzip celeba_hq.zip -d ./celeba_hq"
      ],
      "metadata": {
        "id": "2EeFhP7VprB3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a87745e6-856b-42f7-cf7f-ecefd0b77da2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=17w_hePC1xO2eX83gAAF8ptKC59mTPQiL\n",
            "To: /content/celeba_hq.zip\n",
            "100%|██████████| 2.74G/2.74G [00:24<00:00, 112MB/s] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_sizes = {2: 16, 3: 16, 4: 16, 5: 16, 6: 16, 7: 8, 8: 4, 9: 2, 10: 1}\n",
        "\n",
        "train_step_ratio = {k: batch_sizes[2]/v for k,v in batch_sizes.items()}\n",
        "print(train_step_ratio)\n",
        "\n",
        "\n",
        "ds_train = tf.keras.preprocessing.image_dataset_from_directory('celeba_hq', label_mode=None, image_size = (64,64), batch_size=32).map(lambda x:x/255.0)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1EEceNh6zcw",
        "outputId": "0712f130-1649-4027-a4b9-d2f81b0f7bad"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0, 6: 1.0, 7: 2.0, 8: 4.0, 9: 8.0, 10: 16.0}\n",
            "Found 30000 files belonging to 1 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Helper Functions"
      ],
      "metadata": {
        "id": "EqaUYhBoFEIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def resize_image(image_new_dim, image):\n",
        "  image = tf.image.resize(image, size=(image_new_dim, image_new_dim), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n",
        "  image = tf.cast(image, tf.float32) / 127.5 - 1.0\n",
        "  return image\n",
        "\n",
        "def data_loader(image_dim):\n",
        "  batch_size = batch_sizes[int(np.log2(image_dim))]\n",
        "    \n",
        "  dl = ds_train.map(partial(resize_image, image_dim), num_parallel_calls=tf.data.AUTOTUNE).unbatch()\n",
        "  dl = dl.shuffle(200).batch(batch_size, drop_remainder=True).prefetch(1).repeat()\n",
        "  return dl"
      ],
      "metadata": {
        "id": "BM8ChwyoIEAj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def PixelNorm(x, epsilon = 1e-8):\n",
        "  return x / tf.math.sqrt(tf.reduce_mean(x**2, axis = -1, keepdims=True) + epsilon)"
      ],
      "metadata": {
        "id": "lMoGbry94Hug"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fade_in(a, b, alpha):\n",
        "  return (1-alpha) * a + alpha * b"
      ],
      "metadata": {
        "id": "R37G0cfVHTsz"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def WLoss(y_true, y_pred):\n",
        "  return -tf.reduce_mean(y_true * y_pred)"
      ],
      "metadata": {
        "id": "W0wbrv4bHcG2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def minibatch_std(input_tensor, epsilon=1e-8):\n",
        "    n, h, w, c = tf.shape(input_tensor)\n",
        "    group_size = tf.minimum(4, n)\n",
        "    x = tf.reshape(input_tensor, [group_size, -1, h, w, c])\n",
        "    group_mean, group_var = tf.nn.moments(x, axes=(0), keepdims=False)\n",
        "    group_std = tf.sqrt(group_var + epsilon)\n",
        "    avg_std = tf.reduce_mean(group_std, axis=[1, 2, 3], keepdims=True)\n",
        "    x = tf.tile(avg_std, [group_size, h, w, 1])\n",
        "    return tf.concat([input_tensor, x], axis=-1)"
      ],
      "metadata": {
        "id": "glq1gtj15XIT"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Custom Dense Layer"
      ],
      "metadata": {
        "id": "DJ4b-w-Fvf_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDense(tf.keras.layers.Layer):\n",
        "  def __init__(self, units, gain=2, learning_rate_multiplier=1, **kwargs):\n",
        "    super(CustomDense, self).__init__(**kwargs)\n",
        "    self.units = units\n",
        "    self.gain = gain\n",
        "    self.learning_rate_multiplier = learning_rate_multiplier\n",
        "\n",
        "  def build(self, input_shape):\n",
        "    self.in_channels = input_shape[-1]\n",
        "    self.initializer = tf.keras.initializers.RandomNormal(\n",
        "        mean = 0.0, stddev = 1.0/self.learning_rate_multiplier\n",
        "    )\n",
        "    self.w = self.add_weight(\n",
        "        shape = [self.in_channels, self.units],\n",
        "        initializer = self.initializer,\n",
        "        trainable = True,\n",
        "        name = 'kernel'\n",
        "    )\n",
        "\n",
        "    self.b = self.add_weight(\n",
        "        shape = (self.units, ),\n",
        "        initializer = \"zeros\",\n",
        "        trainable = True,\n",
        "        name = 'bias'\n",
        "    )\n",
        "\n",
        "    self.scale = tf.sqrt(self.gain / self.in_channels)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    output = tf.add(tf.matmul(inputs, self.scale * self.w), self.b)\n",
        "    return output * self.learning_rate_multiplier"
      ],
      "metadata": {
        "id": "aGtZ8a1yvevE"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Custom Conv2D layer"
      ],
      "metadata": {
        "id": "39-TdfnxFTcg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomConv2D(tf.keras.layers.Layer):\n",
        "  def __init__(self, out_channels, kernel=3, gain=2, **kwargs):\n",
        "    super(CustomConv2D, self).__init__(**kwargs)\n",
        "    self.out_channels = out_channels\n",
        "    self.gain = gain\n",
        "    self.kernel = kernel\n",
        "    self.pad = kernel !=1\n",
        "\n",
        "  def build(self, input_shape):\n",
        "    self.in_channels = input_shape[-1]\n",
        "    self.initializer = tf.keras.initializers.RandomNormal(mean = 0.0, stddev = 1.0)\n",
        "    self.w = self.add_weight(\n",
        "        shape=[self.kernel, self.kernel, self.in_channels, self.out_channels],\n",
        "        initializer = self.initializer,\n",
        "        trainable = True,\n",
        "        name = 'kernel'\n",
        "    )\n",
        "\n",
        "    self.b = self.add_weight(\n",
        "        shape=(self.out_channels, ),\n",
        "        initializer = 'zeros',\n",
        "        trainable = True,\n",
        "        name = 'bias'\n",
        "    )\n",
        "\n",
        "    self.scale = tf.sqrt(self.gain / (self.kernel * self.kernel * self.in_channels))\n",
        "\n",
        "  def call(self, inputs):\n",
        "    if(self.pad):\n",
        "      x = tf.pad(inputs, [[0,0], [1,1], [1,1], [0,0]], mode='REFLECT')\n",
        "    else:\n",
        "      x = inputs\n",
        "    output = (tf.nn.conv2d(x, filters=self.scale * self.w, strides=1, padding='VALID') + self.b)\n",
        "    return output"
      ],
      "metadata": {
        "id": "gW9DALYvP3dn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mapping Network z -> w"
      ],
      "metadata": {
        "id": "YduJfR4Bq9gJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def MappingNetwork(num_stages, z_dim):\n",
        "  \n",
        "  z = tf.keras.Input(shape=(z_dim))\n",
        "  w = PixelNorm(z)\n",
        "  for i in range(8):\n",
        "    w = CustomDense(512, learning_rate_multiplier = 0.01)(w)\n",
        "    w = tf.keras.layers.LeakyReLU(0.2)(w)\n",
        "  \n",
        "  w = tf.tile(tf.expand_dims(w, 1), (1, num_stages, 1))\n",
        "  return tf.keras.Model(z, w, name='MappingNetwork')"
      ],
      "metadata": {
        "id": "a5WEK_UeDsLF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mappingModel = MappingNetwork(2, 512)\n",
        "mappingModel.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtHK9gc4rEdZ",
        "outputId": "376b1dc3-7b1c-4cb9-fa8d-c8b21e0ca3df"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"MappingNetwork\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 512)]        0           []                               \n",
            "                                                                                                  \n",
            " tf.math.pow (TFOpLambda)       (None, 512)          0           ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " tf.math.reduce_mean (TFOpLambd  (None, 1)           0           ['tf.math.pow[0][0]']            \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " tf.__operators__.add (TFOpLamb  (None, 1)           0           ['tf.math.reduce_mean[0][0]']    \n",
            " da)                                                                                              \n",
            "                                                                                                  \n",
            " tf.math.sqrt (TFOpLambda)      (None, 1)            0           ['tf.__operators__.add[0][0]']   \n",
            "                                                                                                  \n",
            " tf.math.truediv (TFOpLambda)   (None, 512)          0           ['input_1[0][0]',                \n",
            "                                                                  'tf.math.sqrt[0][0]']           \n",
            "                                                                                                  \n",
            " custom_dense (CustomDense)     (None, 512)          262656      ['tf.math.truediv[0][0]']        \n",
            "                                                                                                  \n",
            " leaky_re_lu (LeakyReLU)        (None, 512)          0           ['custom_dense[0][0]']           \n",
            "                                                                                                  \n",
            " custom_dense_1 (CustomDense)   (None, 512)          262656      ['leaky_re_lu[0][0]']            \n",
            "                                                                                                  \n",
            " leaky_re_lu_1 (LeakyReLU)      (None, 512)          0           ['custom_dense_1[0][0]']         \n",
            "                                                                                                  \n",
            " custom_dense_2 (CustomDense)   (None, 512)          262656      ['leaky_re_lu_1[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_2 (LeakyReLU)      (None, 512)          0           ['custom_dense_2[0][0]']         \n",
            "                                                                                                  \n",
            " custom_dense_3 (CustomDense)   (None, 512)          262656      ['leaky_re_lu_2[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_3 (LeakyReLU)      (None, 512)          0           ['custom_dense_3[0][0]']         \n",
            "                                                                                                  \n",
            " custom_dense_4 (CustomDense)   (None, 512)          262656      ['leaky_re_lu_3[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_4 (LeakyReLU)      (None, 512)          0           ['custom_dense_4[0][0]']         \n",
            "                                                                                                  \n",
            " custom_dense_5 (CustomDense)   (None, 512)          262656      ['leaky_re_lu_4[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_5 (LeakyReLU)      (None, 512)          0           ['custom_dense_5[0][0]']         \n",
            "                                                                                                  \n",
            " custom_dense_6 (CustomDense)   (None, 512)          262656      ['leaky_re_lu_5[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_6 (LeakyReLU)      (None, 512)          0           ['custom_dense_6[0][0]']         \n",
            "                                                                                                  \n",
            " custom_dense_7 (CustomDense)   (None, 512)          262656      ['leaky_re_lu_6[0][0]']          \n",
            "                                                                                                  \n",
            " leaky_re_lu_7 (LeakyReLU)      (None, 512)          0           ['custom_dense_7[0][0]']         \n",
            "                                                                                                  \n",
            " tf.expand_dims (TFOpLambda)    (None, 1, 512)       0           ['leaky_re_lu_7[0][0]']          \n",
            "                                                                                                  \n",
            " tf.tile (TFOpLambda)           (None, 2, 512)       0           ['tf.expand_dims[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,101,248\n",
            "Trainable params: 2,101,248\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adaptive Instance Normalization"
      ],
      "metadata": {
        "id": "EbGLKN_cFMDw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class AdaIN(tf.keras.layers.Layer):\n",
        "  def __init__(self, gain=1, **kwargs):\n",
        "    super(AdaIN, self).__init__(*kwargs)\n",
        "    self.gain = gain\n",
        "\n",
        "  def build(self, input_shapes):\n",
        "    x_shape = input_shapes[0]\n",
        "    w_shape = input_shapes[1]\n",
        "\n",
        "    self.x_channels = x_shape[-1]\n",
        "    self.w_channels = w_shape[-1]\n",
        "\n",
        "    self.scale_factor = CustomDense(self.x_channels, gain=1)\n",
        "    self.bias_factor = CustomDense(self.x_channels, gain=1)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    x, w = inputs\n",
        "    sf = tf.reshape(self.scale_factor(w), [-1, 1, 1, self.x_channels])\n",
        "    bf = tf.reshape(self.bias_factor(w), [-1, 1, 1, self.x_channels])\n",
        "\n",
        "    return sf * x + bf"
      ],
      "metadata": {
        "id": "CsU8NYSLFckC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Add Stochastic Variation through noise"
      ],
      "metadata": {
        "id": "WDINRbk_FoiD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class AddNoise(tf.keras.layers.Layer):\n",
        "  def build(self, input_shapes):\n",
        "    n, h, w, c = input_shapes[0]\n",
        "    self.initializer = tf.keras.initializers.RandomNormal(mean=0.0, stddev=1.0)\n",
        "    self.wghts = self.add_weight(\n",
        "        shape=[1,1,1,c],\n",
        "        initializer = self.initializer,\n",
        "        trainable = True,\n",
        "        name = 'kernel'\n",
        "    )\n",
        "\n",
        "  def call(self, inputs):\n",
        "    x, noise = inputs\n",
        "    return self.wghts * noise + x"
      ],
      "metadata": {
        "id": "DK0DzB0nFsBg"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generator Model"
      ],
      "metadata": {
        "id": "MjTzjuXzIoOh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Generator:\n",
        "  def __init__(self, start_resolution_in_log2, target_resolution_in_log2):\n",
        "    self.start_res_log2 = start_resolution_in_log2\n",
        "    self.target_res_log2 = target_resolution_in_log2\n",
        "    self.num_stages = self.target_res_log2 - self.start_res_log2 + 1\n",
        "\n",
        "    self.gen_blocks = []\n",
        "    self.toRGB = []\n",
        "    self.noise_inputs = []\n",
        "\n",
        "    self.filters_num = {\n",
        "        0:512,\n",
        "        1:512,\n",
        "        2:512,\n",
        "        3:512,\n",
        "        4:512,\n",
        "        5:512,\n",
        "        6:256,\n",
        "        7:128,\n",
        "        8:64,\n",
        "        9:32, \n",
        "        10:16\n",
        "    }\n",
        "\n",
        "    self.start_resolution = 2 ** self.start_res_log2\n",
        "\n",
        "    self.input_shape = [self.start_resolution, self.start_resolution, self.filters_num[self.start_res_log2]]\n",
        "    self.gen_input = tf.keras.Input(shape=self.input_shape, name='generator_input')\n",
        "\n",
        "    for i in range(self.start_res_log2, self.target_res_log2 + 1):\n",
        "      filter_num = self.filters_num[i]\n",
        "      resolution = 2**i\n",
        "      self.noise_inputs.append(\n",
        "          tf.keras.Input(shape=(resolution, resolution, 1), name=f\"noise_{resolution}X{resolution}\")\n",
        "      )\n",
        "\n",
        "      to_RGB = tf.keras.Sequential([\n",
        "          tf.keras.Input(shape = (resolution, resolution, filter_num)),\n",
        "          CustomConv2D(out_channels=3, kernel=1, gain = 1)\n",
        "      ], name=f\"to_rgb_{resolution}X{resolution}\")\n",
        "      self.toRGB.append(to_RGB)\n",
        "\n",
        "      is_base = i == self.start_res_log2\n",
        "      if is_base:\n",
        "        input_shape = (resolution , resolution, self.filters_num[i-1])\n",
        "      else:\n",
        "        input_shape = (2**(i-1), 2**(i-1), self.filters_num[i-1])\n",
        "      g_block = self.build_block(\n",
        "          filter_num, resolution=resolution, input_shape = input_shape, is_base = is_base\n",
        "      )\n",
        "      self.gen_blocks.append(g_block)\n",
        "\n",
        "  def build_block(self, filter, resolution, input_shape, is_base):\n",
        "    input_to_the_block = tf.keras.Input(shape = input_shape, name=f'input_to_block_gen_res_{resolution}X{resolution}')\n",
        "    noise = tf.keras.Input(shape = (resolution, resolution, 1), name = f'noise_to_gen_res_{resolution}X{resolution}')\n",
        "    w = tf.keras.Input(shape = 512)\n",
        "    x = input_to_the_block\n",
        "\n",
        "    if not is_base:\n",
        "      x = tf.keras.layers.UpSampling2D((2,2))(x)\n",
        "      x = CustomConv2D(out_channels=filter, kernel=3, gain=2)(x)\n",
        "    x = AddNoise()([x, noise])\n",
        "    x = tf.keras.layers.LeakyReLU(0.2)(x)\n",
        "    x = tfa.layers.InstanceNormalization()(x)\n",
        "    x = AdaIN()([x, w])\n",
        "\n",
        "    x = CustomConv2D(out_channels=filter, kernel=3, gain=2)(x)\n",
        "    x = AddNoise()([x, noise])\n",
        "    x  = tf.keras.layers.LeakyReLU(0.2)(x)\n",
        "    x = tfa.layers.InstanceNormalization()(x)\n",
        "    x = AdaIN()([x, w])\n",
        "    \n",
        "    return tf.keras.Model([input_to_the_block, noise, w], x, name = f\"gen_block_res_{resolution}X{resolution}\")\n",
        "\n",
        "  def grow(self, resolution_log2):\n",
        "    resolution = 2 ** resolution_log2\n",
        "    num_stages = resolution_log2 - self.start_res_log2 + 1\n",
        "    w = tf.keras.Input(shape=(self.num_stages, 512), name='w')\n",
        "\n",
        "    alpha = tf.keras.Input(shape = (1), name='alpha')\n",
        "    x = self.gen_blocks[0]([self.gen_input, self.noise_inputs[0], w[:,0]])\n",
        "\n",
        "    if(num_stages == 1): #base block\n",
        "      rgb = self.toRGB[0](x)\n",
        "    else:   #any block after base block\n",
        "      for i in range(1, num_stages - 1):\n",
        "        x = self.gen_blocks[i]([x, self.noise_inputs[i],  w[:, i]])\n",
        "      \n",
        "      old_rgb = self.toRGB[num_stages - 2](x)\n",
        "      old_rgb = tf.keras.layers.UpSampling2D((2,2))(old_rgb)\n",
        "\n",
        "      i = num_stages - 1\n",
        "\n",
        "      x = self.gen_blocks[i]([x, self.noise_inputs[i], w[:, i]])\n",
        "\n",
        "      new_rgb = self.toRGB[i](x)\n",
        "      rgb = fade_in(new_rgb, old_rgb, alpha[0])\n",
        "\n",
        "    return tf.keras.Model(\n",
        "        [self.gen_input, w, self.noise_inputs, alpha],\n",
        "        rgb,\n",
        "        name = f'generator_{resolution}X{resolution}'\n",
        "    )\n",
        "\n",
        "  def test(self):\n",
        "    return self.gen_blocks, self.toRGB"
      ],
      "metadata": {
        "id": "Yx9eCXm-GnZg"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Descriminator Model"
      ],
      "metadata": {
        "id": "tAUdT0hyH3vY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Descriminator:\n",
        "  def __init__(self, start_resolution_in_log2, target_resolution_in_log2):\n",
        "    self.start_res_log2 = start_resolution_in_log2\n",
        "    self.target_res_log2 = target_resolution_in_log2\n",
        "    self.num_stages = self.target_res_log2 - self.start_res_log2 + 1\n",
        "\n",
        "    self.filters_num = {\n",
        "        0:512,\n",
        "        1:512,\n",
        "        2:512,\n",
        "        3:512,\n",
        "        4:512,\n",
        "        5:512,\n",
        "        6:256,\n",
        "        7:128,\n",
        "        8:64,\n",
        "        9:32, \n",
        "        10:16\n",
        "    }\n",
        "\n",
        "    self.disc_blocks = []\n",
        "    self.fromRGB = []\n",
        "\n",
        "    for i in range(self.start_res_log2, self.target_res_log2 + 1):\n",
        "      resolution = 2**i\n",
        "      filter_num = self.filters_num[i]\n",
        "\n",
        "      fromRGB = tf.keras.Sequential([\n",
        "          tf.keras.Input(shape = (resolution, resolution, 3), name = f\"from_rgb_input_{resolution}X{resolution}\"),\n",
        "          CustomConv2D(filter_num, kernel=1, gain=2),\n",
        "          tf.keras.layers.LeakyReLU(0.2)\n",
        "      ], name=f\"from_rgb_{resolution}X{resolution}\")\n",
        "\n",
        "      self.fromRGB.append(fromRGB)\n",
        "\n",
        "      input_shape = [resolution, resolution, filter_num]\n",
        "\n",
        "      if len(self.disc_blocks) == 0:\n",
        "        self.disc_blocks.append(\n",
        "            self.build_base(filter_num, resolution)\n",
        "        )\n",
        "      else:\n",
        "        self.disc_blocks.append(\n",
        "            self.build_block(filter_num, self.filters_num[i-1], resolution)\n",
        "        )\n",
        "  \n",
        "  def build_base(self, filter_num, resolution):\n",
        "    input_tensor = tf.keras.Input(shape=(resolution, resolution, filter_num), name = f\"disc_res_{resolution}X{resolution}\")\n",
        "    x = minibatch_std(input_tensor)\n",
        "    x = input_tensor\n",
        "    x = CustomConv2D(filter_num, kernel=3, gain=2)(x)\n",
        "    x = tf.keras.layers.LeakyReLU(0.2)(x)\n",
        "    x = tf.keras.layers.Flatten()(x)\n",
        "    x = CustomDense(filter_num)(x)\n",
        "    x = tf.keras.layers.LeakyReLU(0.2)(x)\n",
        "    x = CustomDense(1)(x)\n",
        "    return tf.keras.Model(input_tensor, x, name = f'disc_base_res{resolution}X{resolution}')\n",
        "\n",
        "  def build_block(self, filter_num_1, filter_num_2, resolution):\n",
        "    input_tensor = tf.keras.Input(shape=(resolution, resolution, filter_num_1), name = f\"disc_res_{resolution}X{resolution}\")\n",
        "    x = minibatch_std(input_tensor)\n",
        "    x = input_tensor\n",
        "    x = CustomConv2D(filter_num_1, kernel=3, gain=2)(x)\n",
        "    x = tf.keras.layers.LeakyReLU(0.2)(x)\n",
        "    x = CustomConv2D(filter_num_2, kernel=3, gain=2)(x)\n",
        "    x = tf.keras.layers.LeakyReLU(0.2)(x)\n",
        "    x = tf.keras.layers.AveragePooling2D()(x)\n",
        "\n",
        "    return tf.keras.Model(input_tensor, x, name=f\"disc_block_res_{resolution}X{resolution}\")\n",
        "\n",
        "  def grow(self, resolution_log2):\n",
        "    resolution = 2 ** resolution_log2\n",
        "    num_stages = resolution_log2 - self.start_res_log2\n",
        "    input_image = tf.keras.Input(shape=(resolution, resolution, 3), name = f\"disc_image_input_res_{resolution}X{resolution}\")\n",
        "\n",
        "    alpha = tf.keras.Input(shape = (1), name='alpha')\n",
        "\n",
        "    x = self.fromRGB[num_stages](input_image)\n",
        "    x = self.disc_blocks[num_stages](x)\n",
        "\n",
        "    if(num_stages > 0):\n",
        "      num_stages -= 1\n",
        "      downsampled_image = tf.keras.layers.AveragePooling2D((2,2))(input_image)\n",
        "      y = self.fromRGB[num_stages](downsampled_image)\n",
        "      x = fade_in(x, y, alpha[0])\n",
        "\n",
        "      for i in range(num_stages, -1, -1):\n",
        "        x = self.disc_blocks[i](x)\n",
        "    return tf.keras.Model([input_image, alpha], x, name = f\"disc_res_{resolution}X{resolution}\")\n",
        "\n",
        "  def test(self):\n",
        "    return self.disc_blocks, self.fromRGB"
      ],
      "metadata": {
        "id": "wvdOtWdahD6P"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "gen = Generator(2, 4)\n",
        "\n",
        "# gen.grow(4)\n",
        "g_blocks, rgb_blocks = gen.test()\n",
        "\n"
      ],
      "metadata": {
        "id": "kmLa9P1qpZpx"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g_blocks[1].summary()\n",
        "tf.keras.utils.plot_model(g_blocks[1], show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ASx1iLgR6Ld5",
        "outputId": "3d68240a-f538-4cec-ac7d-4e24db13e42d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"gen_block_res_8X8\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_to_block_gen_res_8X8 (In  [(None, 4, 4, 512)]  0          []                               \n",
            " putLayer)                                                                                        \n",
            "                                                                                                  \n",
            " up_sampling2d (UpSampling2D)   (None, 8, 8, 512)    0           ['input_to_block_gen_res_8X8[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            " custom_conv2d_3 (CustomConv2D)  (None, 8, 8, 512)   2359808     ['up_sampling2d[0][0]']          \n",
            "                                                                                                  \n",
            " noise_to_gen_res_8X8 (InputLay  [(None, 8, 8, 1)]   0           []                               \n",
            " er)                                                                                              \n",
            "                                                                                                  \n",
            " add_noise_2 (AddNoise)         (None, 8, 8, 512)    512         ['custom_conv2d_3[0][0]',        \n",
            "                                                                  'noise_to_gen_res_8X8[0][0]']   \n",
            "                                                                                                  \n",
            " leaky_re_lu_10 (LeakyReLU)     (None, 8, 8, 512)    0           ['add_noise_2[0][0]']            \n",
            "                                                                                                  \n",
            " instance_normalization_2 (Inst  (None, 8, 8, 512)   1024        ['leaky_re_lu_10[0][0]']         \n",
            " anceNormalization)                                                                               \n",
            "                                                                                                  \n",
            " input_5 (InputLayer)           [(None, 512)]        0           []                               \n",
            "                                                                                                  \n",
            " ada_in_2 (AdaIN)               (None, 8, 8, 512)    525312      ['instance_normalization_2[0][0]'\n",
            "                                                                 , 'input_5[0][0]']               \n",
            "                                                                                                  \n",
            " custom_conv2d_4 (CustomConv2D)  (None, 8, 8, 512)   2359808     ['ada_in_2[0][0]']               \n",
            "                                                                                                  \n",
            " add_noise_3 (AddNoise)         (None, 8, 8, 512)    512         ['custom_conv2d_4[0][0]',        \n",
            "                                                                  'noise_to_gen_res_8X8[0][0]']   \n",
            "                                                                                                  \n",
            " leaky_re_lu_11 (LeakyReLU)     (None, 8, 8, 512)    0           ['add_noise_3[0][0]']            \n",
            "                                                                                                  \n",
            " instance_normalization_3 (Inst  (None, 8, 8, 512)   1024        ['leaky_re_lu_11[0][0]']         \n",
            " anceNormalization)                                                                               \n",
            "                                                                                                  \n",
            " ada_in_3 (AdaIN)               (None, 8, 8, 512)    525312      ['instance_normalization_3[0][0]'\n",
            "                                                                 , 'input_5[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 5,773,312\n",
            "Trainable params: 5,773,312\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(g_blocks[1], show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sp0o4ITO69pG",
        "outputId": "53dd98bd-0503-4158-a5b1-13e7585517b2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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6vtVS1+727dtx9OhRGBkZqRITdW2vXLkSkiTh+++/N1h7jamtrUVtba3GSZ8nbdiwAYmJiTh27Fi9a/ZJda+L1Y0JfWBSgYiIiJ4LGRkZEEKgX79+qjITE5MWewReUz179oSlpWW9/5ieOnUKVVVV6NOnT4v08+9//xu1tbXw9/dvdiweHh6ws7PDV1999dR+hwwZAgcHB2zZsgVnzpxp9D3fp8nJycHVq1dx+/ZtVFdX4/r164iPj4etra2qTqdOnSCXy5GVlaVTHy3laXEUFhaqntro0KEDPv30U/j7+2v1JEfdl7ZffvlFrby0tBT37t1TLf1Y5+rVq5g3bx6SkpJUy42amZnhr3/9K0xMTDBlyhTVr8+6tN+etZV7gT45OjpCkiQUFxdrtd+KFSvQrVs3nDt3Tq28pe9bLXXtJiQk1EtK1D2BEhkZCSFEvVc2WrM9APVeGwOA06dPQwiB/v37a9WWEALz589HdnY20tLSGnxy5El1Y8DJyUmrvrTBpAIRERE9k2pra3H//n08fvwY58+fR0REBNzd3TFx4kRVHR8fH9y7dw9paWmorq7G7du3ce3atXpt2dnZobCwEHl5eSgtLdXqy8eT+xobG2P27NnYs2cPEhMTUVJSguzsbEybNg0uLi4ICwvT6XirqqpQXFyMx48f4+zZswgPD0fnzp3VjvdJcrlco1jMzMywaNEiHD9+HOHh4bhx4wZqa2tRWlra6BfjwMBATJw4EStXrsSZM2e0Pp7p06fD3d0dZWVlTcY/adIk7Ny5E/Hx8SgpKUFNTQ0KCgrqfTnWp6fFUVhYiKlTp+LixYuoqqrCuXPncO3aNbUvtU/j6emJIUOGYOvWrTh+/DgqKiqQn5+vOkcffPCBqu6jR48wbtw4xMbG1nssuk+fPli4cCFu3LihNh+GNu23N/q+Fxw8eLDNLSlpbm4OLy8vFBQUaLVf3WsQda8Y/ba8Je9bmly7oaGhcHJywtmzZ7VquzGGau/GjRvYtWsXHjx4gOrqapw8eRJTpkyBu7s7pk2bplWfFy5cwGeffYatW7dCJpOpvToiSVKDTz/VjQE/Pz+t+tKKVmtIEBEREelA2yUlN2zYIJydnQUAYW5uLgIDA8WmTZuEubm5ACBeeOEFcfXqVbFlyxahVCoFANG5c2fV8olhYWFCJpMJV1dXYWJiIpRKpXjnnXfE1atX1fq5e/euGDJkiJDL5cLT01P8+c9/FnPnzhUAhI+Pj2rJubNnz4rOnTsLhUIhBgwYoNWyjw3tW1tbK9asWSNeeOEFIZPJhK2trQgKChKXLl3SuN3fSkhIEEOGDBGOjo7CxMRE2Nvbi3fffVdcu3ZNVefzzz8XTk5OAoCwsLAQo0ePFkIIrWLZuHGj8PPzE3K5XMjlcvHSSy+JTZs2idTUVGFraysACA8PD1FUVCRKSkpEp06dBABhaWkp/va3v2l1TMeOHRP29vYCgOpPJpOJ7t27i9TUVFW9R48eifnz5wt3d3dhYmIiOnToIMaMGSNycnK0GjOaWL16tVAoFAKA6NSpk9ixY4dGceTl5YmAgABha2srjI2NRceOHUVkZKR4/PixVp/JnTt3REREhPDx8RFmZmbC0tJSvPbaa+Kf//ynqs7GjRuFg4OD6lzs2rVLrY1Zs2apfa6enp7iyJEjGrevDbTAkpLt4V5w4MABYWVlJaKjo5t1rEK07JKS4eHhQiaTiYcPH6rK9uzZI7y9vQUA4eDgIKZPn95gm3Pnzq23pKQm9wptzk1T14wQQgQFBQkAYunSpVp9Ho0tAWmo9mbPni28vb2FhYWFMDExEW5ubuJPf/qTKCwsVKt38uRJ8dprrwkXFxfV9ens7CwCAgLE119/LYQQIjs7W+2e+OTfmjVr6vU/YsQI4erqKmpra9XKW3JJSUmIJ6bVJSIiImphycnJGDduXL3Z/PVl6tSpSElJwd27d1ulP2p58fHxyM3Nxfr161VlVVVVWLBgAeLj43H//n29viNMzSdJEpKSkhASEmKwGNrbvSA4OBgAkJKSovE+U6dOxb59++o9lXDlyhV0794dCQkJmDBhQovG2Rpqa2sxePBgTJw4UbUs6rPcnj7cvXsXbm5uiI6OrjdBa0REBBITEzWaK6dOY+OTrz8QERHRM0mbCcqobbl58ybCw8PrPXJvamoKd3d3VFdXP1Pvv5N+PQ/3goqKChw+fBi5ubmqifl8fHwQFRWFqKioJl8jaotqamqQlpaG0tJShIaGPvPt6cvy5cvRu3dvhIeHA/h1TobCwkJkZmbiypUrLdYPkwpEREREWrh48WK991gb+mvOfzRbo4/Wps0xKRQKyGQybN++Hbdu3UJ1dTUKCwuxbds2LF26FKGhoVAqla0eV0t7Fs8zGca9e/cwbNgwdOnSRe1X84ULFyI4OBihoaFaT9poSBkZGUhNTcXBgwdhbm7+zLenD+vWrUNWVhYOHDigmqw1PT0drq6uGDhwIPbv399iffH1ByIiItK71nz9YdGiRfj8889RVVUFDw8PrFmzBmPHjtV7v9SyvvnmG0RFReG7775DeXk5LC0t0aNHD4wfPx4ffvghTExMDB0iPYWhX39oj/cCXV5/0MRXX32FY8eOYdWqVS3aLrVN6enpuHDhAubNm1dv0s3maGx8MqlAREREetfacyoQkeEZOqnQHukrqUDUEjinAhERERERERG1KCYViIiIiIiIiEgnTCoQERERERERkU6YVCAiIiIiIiIinTCpQEREREREREQ64Vo8RERE1GokSTJ0CETUisaNG4dx48YZOox2h/dKaqsaWpaVSQUiIiJqNUlJSYYOgYhaybhx4xAREYH+/fsbOpR2Y/369QCAmTNnGjgSovrqxueTmFQgIiKiVsP16omeH+PGjUP//v153WshJSUFAO+V1DbVjc8ncU4FIiIiIiIiItIJkwpEREREREREpBMmFYiIiIiIiIhIJ0wqEBEREREREZFOmFQgIiIiIiIiIp0wqUBERETUDn377bfo3r07jIyMIEkSnJycEB0dbeiw1KSmpsLLywuSJEGSJDg7O2PChAmGDovomTJ16lTVNSZJUoPX2JEjR7Bw4cJ61+R7771Xr+4bb7wBKysrGBsbo0ePHjh79mxrHEaLqaysRLdu3bB48eI20V50dLTa+an769mzZ4P1a2trsX79egQEBDS4PSoqCr6+vlAqlTAzM4OPjw/mzZuHsrIyVZ29e/di9erVqKmpUds3LS1NLQYHBwedjulJTCoQERERtUP9+vXDf/7zH7zxxhsAgEuXLrXYf6JbypgxY/DTTz/B29sb1tbWuHnzJhITEw0dFtEzx87ODgcPHsSlS5ewfft2tW3Lli1DXFwcFi1apHZN2tvbIzExEfv371er/9VXXyElJQUjR45ETk4O/P39W/NQmi0yMhKXLl1qs+01JTc3F7/73e8wa9YsPHz4sME6x44dw/Tp05GXl4c7d+4gJiYGsbGxCA4OVtUJDAyEXC7H0KFD8eDBA1X5qFGjUFBQgOPHj2P48OEtFjeTCkRERETUIioqKhr9dY2otbXGeGwrY16hUGDYsGHo0qULzMzMVOWrVq3Crl27kJycDCsrK7V94uLiYGRkhLCwMBQXF7d2yHpx4sQJ/Pjjj22uvR07dkAIofb3ZLs//PADFixYgGnTpqF3796NtmVpaYmwsDDY2dnBysoKISEhCAoKwqFDh5Cfn6+qN2PGDPTq1QvDhw/H48ePAQCSJMHV1RUDBw7ECy+80OzjqsOkAhERERG1iO3bt6OoqMjQYRABaJ3x2JbH/JUrV7BkyRJ88sknkMvl9bYHBAQgIiICN27cwJw5cwwQYcuqqKjA3LlzERsb2ybbe5pevXohNTUV48ePV0sMPWnfvn0wNjZWK6t7jeHJpxuWL1+OrKwsvR8DkwpEREREz5D4+HhYWFjA3Nwc6enpeOutt6BUKuHm5oadO3eq6sXFxUEul8PR0RFTp06Fi4sL5HI5AgICcOrUKVW98PBwmJqawtnZWVX28ccfw8LCApIk4c6dOwCAiIgIzJ49G1evXoUkSfDx8dEp/m+++Qa+vr6wtraGXC6Hn58fDh8+DACYMmWK6l1gb29vnDt3DgAwadIkmJubw9raGnv37gUA1NTUYOnSpXB3d4dCocCLL76IpKQkAMBnn30Gc3NzWFlZoaioCLNnz4arq2urPeJMDRNCYN26dejevTvMzMxga2uLd955BxcvXlTVac54bK0xf+jQISiVSqxcuVKvn9fTxMXFQQiBwMDARutER0ejS5cu2LZtG44cOdJke5qcH03vP0DT16guIiMj8fHHH6NDhw46t6HP9vTpxo0bUCgU8PT0VCu3tbXFoEGDEBsbCyGE/gIQRERERHqWlJQk+N8O/XjzzTcFAHH//n1VWWRkpAAgjh49KoqLi0VRUZEYOHCgsLCwEFVVVap6YWFhwsLCQly4cEFUVlaKnJwc0bdvX2FlZSWuX7+uqjd+/Hjh5OSk1u+aNWsEAHH79m1V2ZgxY4S3t3e9GL29vYW1tbVGx5OSkiKWL18u7t27J+7evSv69esn7O3t1fowNjYWN27cUNvvD3/4g9i7d6/q33PmzBFmZmZi9+7d4v79+2LRokXCyMhInD59Wu0zmjFjhtiwYYMYPXq0+M9//qNRjKQZACIpKUnj+kuXLhWmpqZix44d4sGDB+L8+fPC399fODg4iJs3b6rqNWc8tsaY37dvn7CyshJRUVEaH3udsWPHirFjx2q1T1hYmHB1da1X7uXlJXx9fRvcx9vbW/z8889CCCFOnDghjIyMhIeHhygrKxNCCHHw4EExatQotX00PT+a3n+edo1qIzMzUwQGBgohhLh9+7YAICIjI7VuRx/trVixQri5uQkbGxshk8mEh4eHGDVqlPjuu+8a3efVV18VvXr10qj98vJyYWVlJcLDwxvcvnDhQgFAnDt3Tq18xowZavdWTTQ2PvmkAhEREdEzKiAgAEqlEh06dEBoaCjKy8tx/fp1tTomJiaqXx59fX0RHx+P0tJSJCQkGCTmsWPHYtmyZbC1tYWdnR0CAwNx9+5d3L59GwAwbdo01NTUqMVXUlKC06dPqyYeq6ysRHx8PIKCgjBmzBjY2Nhg8eLFkMlk9Y5r1apVmD59OlJTU9GtW7fWO1BSU1FRgXXr1mH06NGYMGECrK2t4efnh82bN+POnTvYsmVLi/Wl7zE/YsQIlJSUYMmSJS3Sni7Ky8vx888/w9vb+6l1+/fvj5kzZyIvLw8LFixosI4u56ep+4821+jTVFRUICIiAvHx8Vrt11rtvf/++9i7dy/y8/NRVlaGnTt34vr16xg0aBBycnKa3X5MTAxcXFwaXf2nbu6E7OzsZvfVGCYViIiIiJ4DpqamAIDq6uom67388sswNzdXe6TZkGQyGQColkZ7/fXX0aVLF/zv//6v6nHeXbt2ITQ0VPWe8aVLl/Dw4UO1JdsUCgWcnZ3bzHGRupycHJSVleHll19WK+/bty9MTU3VXk9oaW1tzLeEoqIiCCFgbm6uUf3o6Gh07doVmzZtQmZmZr3tzT0/T95/WvIaXbRoET788EO4urpqtV9rtdepUye89NJLsLS0hKmpKfr164eEhARUVFRg06ZNzWp7z549SE5OxuHDh+tNxFmnbgzcunWrWX01hUkFIiIiIlJjZmamejKgte3fvx+DBw9Ghw4dYGZmhnnz5qltlyQJU6dOxU8//YSjR48CAP72t7/hgw8+UNUpLy8HACxevFhtTfZr1641ukwbGVbdsneWlpb1ttnY2KC0tFSv/RtyzOtDZWUlADQ54d9vyeVyJCQkQJIkTJ48GRUVFWrbW/r8tNQ1mpmZiezsbEyZMkWr/lurvcb4+fnB2NgYly9f1rmNXbt2YdWqVcjIyICHh0ej9RQKBYD/jgl9YFKBiIiIiFSqq6vx4MEDuLm5tUp/x48fx/r16wEA169fR1BQEJydnXHq1BOT7FEAACAASURBVCkUFxdj9erV9faZOHEi5HI5tm3bhkuXLkGpVKJz586q7XUTq61fv77eMm4nT55sleMi7djY2ABAg19O9T0eW3vMt4a6L5J1T/hoon///pg1axZyc3OxYsUKtW0tfX5a6hrdvn07jh49CiMjI1Vioq7tlStXQpIkfP/99wZrrzG1tbWora3VOOnzpA0bNiAxMRHHjh1Dx44dm6xbVVUF4L9jQh+YVCAiIiIilYyMDAgh0K9fP1WZiYnJU1+b0NWZM2dgYWEB4Nd3fqurq/HRRx/By8sLcrkckiTV28fW1hbjxo1DWloa1q5diz/96U9q2zt16gS5XI6srCy9xEwtr2fPnrC0tKz3he3UqVOoqqpCnz59VGUtPR5be8y3BkdHR0iShOLiYq32W7FiBbp166ZaWaWONudHEy11jSYkJNRLStQ9cRIZGQkhRL1XNlqzPQB4880365WdPn0aQgj0799fq7aEEJg/fz6ys7ORlpbW4JMjT6obA05OTlr1pQ0mFYiIiIieY7W1tbh//z4eP36M8+fPIyIiAu7u7pg4caKqjo+PD+7du4e0tDRUV1fj9u3buHbtWr227OzsUFhYiLy8PJSWljb5pay6uhq3bt1CRkaGKqng7u4OADhy5AgqKyuRm5vb6Lva06ZNw6NHj7Bv3z6MHDlSbZtcLsekSZOwc+dOxMfHo6SkBDU1NSgoKMAvv/yi7UdErUAul2P27NnYs2cPEhMTUVJSguzsbEybNg0uLi4ICwtT1W3ueNT3mD948KDBl5Q0NzeHl5cXCgoKtNqv7jWIuvlJfluu6fnRtJ+nXaOhoaFwcnLC2bNntWq7MYZq78aNG9i1axcePHiA6upqnDx5ElOmTIG7uzumTZumVZ8XLlzAZ599hq1bt0Imk6m9OiJJEtauXVtvn7ox4Ofnp1Vf2mBSgYiIiKgdOnXqFHr27Il//etfAIDu3bsjJiYG8fHxqtcJXnzxRfz000/YunUrZs+eDQAYNmwYcnNzVe1UVlbCz88PCoUCAwcORJcuXfDvf/9b7bHcjz76CEOGDMG7776Lrl27YsWKFapHafv374/8/HwAv37Rd3R0hK+vL4YPH47t27fDx8cHV69eRXFxsdp/fk1NTeHs7Iy9e/eqJhLz8/PD/PnzsWnTJri4uCAyMhKDBw8GAAwYMEDVDwC8+uqreOmllzBp0iSYmJjU+3xiY2Mxc+ZMrF69Gvb29nBxcUFERATu37+Pzz77DOvWrQMAdOnSBYmJiS1yTqh5li1bhpiYGERFRcHBwQGDBg2Ch4eHWuIJ0H083rt3D4B+x3xdH23BiBEjkJOTozY/wj//+U/VNdm3b1/8+c9/rrdfv379MGvWrHrlmpwfbe4/TV2jwK+P7RcVFSE9Pb1FPg9DtTds2DAsXrwYbm5uMDc3R0hICF577TV8++23sLe3V9X79ttvMWDAAHTs2BGnTp3CDz/8ABcXF7z22ms4fvw4AKgmp9XG6dOn4erqihdffFHrfTUlCV0iIyIiItJCcnIyxo0bp9N/iEh/pk6dipSUFNy9e9fQoehkxIgR2LhxIzw9PQ0dCjVAkiQkJSUhJCTE0KGotPUxHxwcDABISUnReJ+pU6di37599Z5KuHLlCrp3746EhARMmDChReNsDbW1tRg8eDAmTpyIyZMnP/Pt6cPdu3fh5uaG6OhoVWKnTkREBBITE3Hnzh2N22tsfPJJBSIiIqLnmDYTuRnab1+nOH/+PORyORMKpLX2NOY1VVFRgcOHDyM3N1c1MZ+Pjw+ioqIQFRWFsrIyA0eonZqaGqSlpaG0tBShoaHPfHv6snz5cvTu3Rvh4eEAfn3SobCwEJmZmbhy5UqL9cOkAhERERG1C/Pnz0dubi4uX76MSZMm1Zuhnuh5de/ePQwbNgxdunRR+9V84cKFCA4ORmhoqNaTNhpSRkYGUlNTcfDgQdXrUc9ye/qwbt06ZGVl4cCBA5DJZACA9PR0uLq6YuDAgdi/f3+L9cWkAhEREdFzaNGiRUhISEBxcTE8PT2xe/duQ4f0VObm5ujWrRt+//vfY/ny5fD19TV0SNSOtMcxr4nNmzerrVbw5BwhK1euRHh4OD799FMDRai9oUOH4u9//zucnZ2fi/ZaWnp6Oh49eoSMjAzY2tqqyt955x21saLNqw9N4ZwKREREpHecU4Ho+dMW51Ro63SZU4GotXBOBSIiIiIiIiJqUUwqEBEREREREZFOmFQgIiIiIiIiIp0wqUBEREREREREOjExdABERET0/EhOTjZ0CETUik6ePGnoENqVgoICALxXUttUUFAANze3euVc/YGIiIj0rm71ByIiImq/xo4dW2/1ByYViIiIiIio2biEJNHziXMqEBEREREREZFOmFQgIiIiIiIiIp0wqUBEREREREREOmFSgYiIiIiIiIh0wqQCEREREREREemESQUiIiIiIiIi0gmTCkRERERERESkEyYViIiIiIiIiEgnTCoQERERERERkU6YVCAiIiIiIiIinTCpQEREREREREQ6YVKBiIiIiIiIiHTCpAIRERERERER6YRJBSIiIiIiIiLSCZMKRERERERERKQTJhWIiIiIiIiISCdMKhARERERERGRTphUICIiIiIiIiKdMKlARERERERERDphUoGIiIiIiIiIdMKkAhERERERERHphEkFIiIiIiIiItIJkwpEREREREREpBMmFYiIiIiIiIhIJ0wqEBEREREREZFOmFQgIiIiIiIiIp0wqUBEREREREREOmFSgYiIiIiIiIh0wqQCEREREREREemESQUiIiIiIiIi0gmTCkRERERERESkEyYViIiIiIiIiEgnTCoQERERERERkU4kIYQwdBBERERERNR+hIWF4dKlS2plZ8+ehaenJ2xtbVVlxsbG+Otf/wo3N7fWDpGIWomJoQMgIiIiIqL2xcnJCVu2bKlXfv78ebV/e3l5MaFA9Izj6w9ERERERKSVP/zhD0+tY2pqiokTJ+o/GCIyKL7+QEREREREWuvZsycuXLiApr5OXLp0CV26dGnFqIiotfFJBSIiIiIi0tof//hHGBsbN7hNkiT06tWLCQWi5wCTCkREREREpLV3330XNTU1DW4zNjbG+++/38oREZEh8PUHIiIiIiLSSUBAAE6dOoXa2lq1ckmSkJ+fD1dXVwNFRkSthU8qEBERERGRTt577z1IkqRWZmRkhAEDBjChQPScYFKBiIiIiIh0EhwcXK9MkiT88Y9/NEA0RGQITCoQEREREZFOHBwcMHToULUJGyVJQlBQkAGjIqLWxKQCERERERHpbMKECaplJY2NjfHmm2/C3t7ewFERUWthUoGIiIiIiHQ2evRomJqaAgCEEJgwYYKBIyKi1sSkAhERERER6czCwgJvv/02AMDU1BQjR440cERE1JqYVCAiIiIiomYZP348ACAoKAgWFhYGjoaIWpMk6l6AIiIiIqIWExwcjN27dxs6DCIi0iN+nQZMDB0AERER0bOqX79+mDlzpqHDoHZo/fr1ANCuxk9iYiJCQ0NhYmKYrxgnT55EbGwskpKSDNI/PV/qxhsxqUBERESkN25ubggJCTF0GNQOpaSkAEC7Gj+BgYGQy+UGjSE2NrZdfWbUvjGp8CvOqUBERERERM1m6IQCERkGkwpEREREREREpBMmFYiIiIiIiIhIJ0wqEBEREREREZFOmFQgIiIiIiIiIp0wqUBEREREbdbatWvh6OgISZKwefNmVfmBAwdgbW2NL7/8Um99R0VFwdfXF0qlEmZmZvDx8cG8efNQVlbW5H5TpkyBlZUVJElCVlaW3uLTRGt8Ts+KI0eOYOHChUhNTYWXlxckSYIkSXjvvffq1X3jjTdgZWUFY2Nj9OjRA2fPnjVAxLqrrKxEt27dsHjx4jbRXnR0tOrz/u1fz549G6xfW1uL9evXIyAgoMHtmly7e/fuxerVq1FTU6NTzPRfTCoQERERUZs1Z84cnDhxol65EELvfR87dgzTp09HXl4e7ty5g5iYGMTGxiI4OLjJ/bZt24atW7fqPT5NtMbn9CxYtmwZ4uLisGjRIowZMwY//fQTvL29YW9vj8TEROzfv1+t/ldffYWUlBSMHDkSOTk58Pf3N1DkuomMjMSlS5fabHtNyc3Nxe9+9zvMmjULDx8+bLCOJtdu3RKoQ4cOxYMHD1ol9mcVkwpERERE1O6MGDECxcXFGDlypN76sLS0RFhYGOzs7GBlZYWQkBAEBQXh0KFDyM/P11u/Lak1PidNVVRUNPrLsiGtWrUKu3btQnJyMqysrNS2xcXFwcjICGFhYSguLjZQhC3rxIkT+PHHH9tcezt27IAQQu3vyXZ/+OEHLFiwANOmTUPv3r0bbUvTa3fGjBno1asXhg8fjsePHzf7GJ5XTCoQERER0XNPCIGUlBRs2bJFVbZv3z4YGxur1XNwcACARn8hrSNJUssH2c5t374dRUVFhg5DzZUrV7BkyRJ88sknkMvl9bYHBAQgIiICN27cwJw5cwwQYcuqqKjA3LlzERsb2ybbe5pevXohNTUV48ePh5mZWaP1tLl2ly9fjqysrFY7hmcRkwpEREREbUB4eDhMTU3h7OysKvv4449hYWEBSZJw584dAL/+ciqXy+Ho6IipU6fCxcUFcrkcAQEBOHXqlE59f/3113jllVdgbm4OpVIJPz8/lJSUAAC++eYb+Pr6wtraGnK5HH5+fjh8+DAAIDY2FhYWFjAyMkKfPn3g5OQEmUwGCwsL+Pv7Y+DAgejUqRPkcjlsbGwwb948VZ/NOY7MzEy4u7tDkiRs3LgRABAfHw8LCwuYm5sjPT0db731FpRKJdzc3LBz5061/WtqahATE4OuXbtCoVDAwcEBnp6eiImJQUhISJN937hxAwqFAp6enqoyIQTWrFmDrl27wszMDNbW1pg7d67mJ0BPmvM5aXp+NB23ERERmD17Nq5evQpJkuDj4wMAOHToEJRKJVauXNkaH0k9cXFxEEIgMDCw0TrR0dHo0qULtm3bhiNHjjTZnhAC69atQ/fu3WFmZgZbW1u88847uHjxoqqOtmN16dKlcHd3h0KhwIsvvoikpCSdjzcyMhIff/wxOnTooHMb+mxPnxq6dgHA1tYWgwYNQmxsLF8X0pUgIiIiohY3duxYMXbsWK32GT9+vHByclIrW7NmjQAgbt++rSoLCwsTFhYW4sKFC6KyslLk5OSIvn37CisrK3H9+nWt+iwrKxNKpVKsXr1aVFRUiJs3b4rRo0er+ktJSRHLly8X9+7dE3fv3hX9+vUT9vb2qv2XLVsmAIhTp06J8vJycefOHTFs2DABQOzfv1/cvn1blJeXi/DwcAFAZGVlaX0cubm5AoD4y1/+oirLz88XAMSGDRtUZZGRkQKAOHr0qCguLhZFRUVi4MCBwsLCQlRVVanqrVy5UhgbG4v09HTx8OFDcebMGeHk5CQGDx7c5GdVXl4urKysRHh4uFp5ZGSkkCRJfP755+L+/fvi4cOHYtOmTQKAOHfunFbno44u46chzfmcND0/mo7bMWPGCG9vb7V6+/btE1ZWViIqKqrZx5qUlCS0/Xrj5eUlfH19G9zm7e0tfv75ZyGEECdOnBBGRkbCw8NDlJWVCSGEOHjwoBg1apTaPkuXLhWmpqZix44d4sGDB+L8+fPC399fODg4iJs3b6rqaXoO5syZI8zMzMTu3bvF/fv3xaJFi4SRkZE4ffq0VscphBCZmZkiMDBQCCHE7du3BQARGRmpdTv6aG/FihXCzc1N2NjYCJlMJjw8PMSoUaPEd9991+g+r776qujVq5dG7Td27dZZuHCh1terLuPtWcUnFYiIiIjaIRMTE9Wvob6+voiPj0dpaSkSEhK0aicvLw8lJSXo0aMH5HI5nJyckJqaqnpUeOzYsVi2bBlsbW1hZ2eHwMBA3L17F7dv31Zrx9fXF+bm5rC3t8e7774LAHB3d4eDgwPMzc0xYcIEAFD7xbYlj+O3AgICoFQq0aFDB4SGhqK8vBzXr19XbU9LS0OfPn0QGBgIhUIBf39/jBo1CsePH0dVVVWj7cbExMDFxQXR0dGqsoqKCqxfvx6///3vMWvWLNjY2EChUMDOzk7n+FvL0z4nQD/n57dGjBiBkpISLFmypEXa00Z5eTl+/vlneHt7P7Vu//79MXPmTOTl5WHBggUN1qmoqMC6deswevRoTJgwAdbW1vDz88PmzZtx584dtVdr6jR1DiorKxEfH4+goCCMGTMGNjY2WLx4MWQymdaff0VFBSIiIhAfH6/Vfq3V3vvvv4+9e/ciPz8fZWVl2LlzJ65fv45BgwYhJyen2e03dO3+1gsvvAAAyM7ObnZfzyMmFYiIiIieAS+//DLMzc3rfWl/Gi8vLzg6OmLChAlYvnw58vLymqwvk8kAoMll2ExNTQFAbeKzuv2qq6ubbF/X43haLL/tt7Kyst5jzjU1NZDJZPXew66zZ88eJCcn4/Dhw2qT+V25cgUPHz7E0KFDWyReQ2noc2pIS58fQyoqKoIQAubm5hrVj46ORteuXbFp0yZkZmbW256Tk4OysjK8/PLLauV9+/aFqanpU1/refIcXLp0CQ8fPlRbVlGhUMDZ2Vnrz3/RokX48MMP4erqqtV+rdVep06d8NJLL8HS0hKmpqbo168fEhISUFFRgU2bNjWr7cau3d+qGwO3bt1qVl/PKyYViIiIiJ4RZmZm9Z4geBqFQoFjx45hwIABWLlyJby8vBAaGoqKigoAwP79+zF48GB06NABZmZmavMi6Isux6GN4cOH48yZM0hPT0dFRQW+//57pKWl4e23324wqbBr1y6sWrUKGRkZ8PDwUNtWUFAAAO3infKWou/z01oqKysBoMkJ/35LLpcjISEBkiRh8uTJqmukTt2yhJaWlvX2tbGxQWlpqVbxlZeXAwAWL14MSZJUf9euXXvqRKG/lZmZiezsbEyZMkWr/lurvcb4+fnB2NgYly9f1rmNpq7d31IoFAD+OyZIO0wqEBERET0Dqqur8eDBA7i5uWm9b48ePfDll1+isLAQ8+fPR1JSEtauXYvr168jKCgIzs7OOHXqFIqLi7F69Wo9RP9fzTkOTS1fvhyvv/46Jk6cCKVSidGjRyMkJARbt26tV3fDhg1ITEzEsWPH0LFjx3rb61YMePTokd7ibUta4/y0lrovkk09dfOk/v37Y9asWcjNzcWKFSvUttnY2ABAg8kDXT6zukTV+vXr6y21ePLkSY3b2b59O44ePQojIyNVYqKu7ZUrV0KSJHz//fcGa68xtbW1qK2t1Tjp86SnXbu/VffaU92YIO0wqUBERETURpiYmDz18fPGZGRkQAiBfv36abVfYWEhLly4AODXLzGffvop/P39ceHCBWRnZ6O6uhofffQRvLy8IJfL9b5Uoq7HoY2cnBxcvXoVt2/fRnV1Na5fv474+HjY2tqq6gghMH/+fGRnZyMtLa3BX58BoGfPnjAyMsLXX3+tt3jbkobOT3PGrSE5OjpCkiQUFxdrtd+KFSvQrVs3nDt3Tq28Z8+esLS0rPeF+tSpU6iqqkKfPn206qdu5ZSsrCyt9ntSQkJCvaRE3ZMmkZGREELUe2WjNdsDgDfffLNe2enTpyGEQP/+/bVqS9Nr97fqxoCTk5NWfdGvmFQgIiIiaiN8fHxw7949pKWlobq6Grdv38a1a9carFtbW4v79+/j8ePHOH/+PCIiIuDu7o6JEydq1WdhYSGmTp2KixcvoqqqCufOncO1a9fQr18/uLu7AwCOHDmCyspK5Obm6rxsZWNa6ji0MX36dLi7u6OsrKzROhcuXMBnn32GrVu3QiaTqT1+LkkS1q5dC+DXRMyYMWOwe/dubN++HSUlJTh//nyDk/K1R5qcH03HrZ2dHQoLC5GXl4fS0lJUV1fj4MGDBltS0tzcHF5eXqpXWDRV9xrEk6/KyOVyzJ49G3v27EFiYiJKSkqQnZ2NadOmwcXFBWFhYVr3M2nSJOzcuRPx8fEoKSlBTU0NCgoK8MsvvwAAQkND4eTkhLNnz2rVdmMM1d6NGzewa9cuPHjwANXV1Th58iSmTJkCd3d3TJs2Tas+Nb12f6tuDPj5+WnVF/3/WnOpCSIiIqLnhS5LAt69e1cMGTJEyOVy4enpKf785z+LuXPnCgDCx8dHtYxfWFiYkMlkwtXVVZiYmAilUineeecdcfXqVa3jzMvLEwEBAcLW1lYYGxuLjh07isjISPH48WMhhBDz588XdnZ2wsbGRgQHB4uNGzcKAMLb21vMnj1bmJubCwDCw8NDfPPNN2LVqlXC2tpaABBOTk7i73//u9i1a5dwcnISAIStra3YuXOnxsfx+eefq/a1sLAQo0ePFhs2bBDOzs4CgDA3NxeBgYFi06ZNqlheeOEFcfXqVbFlyxahVCoFANG5c2dx+fJlIYQQx44dE/b29gKA6k8mk4nu3buL1NRUIYQQ2dnZatuf/FuzZo0qxtLSUjFlyhRhb28vLC0txYABA8TSpUsFAOHm5iZ++OEHrc9LSywp2dzPSdNxpum4PXv2rOjcubNQKBRiwIAB4ubNm+LAgQPCyspKREdHN+tYhdBtib/w8HAhk8nEw4cPVWV79uwR3t7eAoBwcHAQ06dPb3DfuXPn1ltSsra2VqxZs0a88MILQiaTCVtbWxEUFCQuXbqkqqPNOXj06JGYP3++cHd3FyYmJqJDhw5izJgxIicnRwghRFBQkAAgli5dqtVxN7YEpKHamz17tvD29hYWFhbCxMREuLm5iT/96U+isLBQrd7JkyfFa6+9JlxcXFTXorOzswgICBBff/21EEK7a7fOiBEjhKurq6itrdX4mLmk5H9JQjwx9S0RERERNVtwcDAAICUlpcXbnjp1KlJSUnD37t0Wb7s1Geo44uPjkZubi/Xr16vKqqqqsGDBAsTHx+P+/fsGf7dan+NHU+1tnCUnJ2PcuHH1VvZoypUrV9C9e3ckJCSolj1tT2prazF48GBMnDgRkydPfubb04e7d+/Czc0N0dHRmD17tsb76TLenlV8/YGIiIioHdJmcrm2rLWP4+bNmwgPD8cHH3ygVm5qagp3d3dUV1e3y/kB9OVZGWeN8fHxQVRUFKKiopp8HaYtqqmpQVpaGkpLSxEaGvrMt6cvy5cvR+/evREeHm7oUNotJhWIiIiInjEXL16s9x5xQ39t+T/6+qJQKCCTybB9+3bcunUL1dXVKCwsxLZt27B06VKEhoZCqVQaOkxqRQsXLkRwcDBCQ0O1nrTRkDIyMpCamoqDBw/C3Nz8mW9PH9atW4esrCwcOHAAMpnM0OG0W0wqEBEREbUjixYtQkJCAoqLi+Hp6Yndu3fXq9OtW7d6s7M39Ldr1y4DHMGvNDkOfbC2tsZXX32FH3/8EV26dIFCoYCvry8SEhKwatUq/PWvf22VONo6Q50fQ1m5ciXCw8Px6aefGjoUjQ0dOhR///vf4ezs/Fy019LS09Px6NEjZGRkqK38QtrjnApEREREetAW3omn9ovjR3t8x51aE8fbf/FJBSIiIiIiIiLSCZMKRERERERERKQTJhWIiIiIiIiISCdMKhARERERERGRTkwMHQARERHRs6qgoADJycmGDoPaoYKCAgDg+NHCyZMnAfAzo9ZRN96Iqz8QERER6UVwcPAzvwwfEdHzjl+n+aQCERERkd6MHTuWSwKSTrikpPa4xB+1prrxRpxTgYiIiIiIiIh0xKQCEREREREREemESQUiIiIiIiIi0gmTCkRERERERESkEyYViIiIiIiIiEgnTCoQERERERERkU6YVCAiIiJqA/7nf/4HHTt2hCRJMDIyQpcuXXDkyBG1Om+//TaUSiWMjIzQrVs3/L//9/+07ueHH35AaGgoPD09YWZmBgcHB/Tq1QvR0dEtdSgtYu3atXB0dIQkSdi8ebOq/MCBA7C2tsaXX36pt76joqLg6+sLpVIJMzMz+Pj4YN68eSgrK1PVSU1NhZeXFyRJUvszNTWFo6MjBg8ejDVr1uD+/ft6i5Na1pEjR7Bw4cJ65/a9996rV/eNN96AlZUVjI2N0aNHD5w9e9YAEWvnH//4B/r27QsrKyt07twZkyZNws2bN9tEe9HR0fWuJUmS0LNnzwbr19bWYv369QgICGhwuybX8N69e7F69WrU1NToFDP9F5MKRERERG3AjBkzUFhYCAB45ZVXcPnyZfz+979Xq7Nv3z7s3bsXr7/+Oi5evIjXXntNqz6ys7MREBAAZ2dn/Pvf/0ZxcTFOnDiBYcOGISMjo6UOpUXMmTMHJ06cqFcuhNB738eOHcP06dORl5eHO3fuICYmBrGxsQgODlbVGTNmDH766Sd4e3vD2toaQgjU1taiqKgIycnJ8PT0xPz589GjRw98//33eo+ZmmfZsmWIi4vDokWL1M6tvb09EhMTsX//frX6X331FVJSUjBy5Ejk5OTA39/fQJFrJikpCePHj0dwcDAKCgqQnp6O48eP46233sLjx48N3p42cnNz8bvf/Q6zZs3Cw4cPG6yjyTUcGBgIuVyOoUOH4sGDB3qN+VnHpAIRERHRc2Lt2rWwsbFBbGwsPDw8IJfL0aVLF6xYsQIKhcLQ4WlkxIgRKC4uxsiRI/XWh6WlJcLCwmBnZwcrKyuEhIQgKCgIhw4dQn5+fqP7SZIEGxsbDB48GAkJCUhOTsatW7dUMbc3FRUVjf4S3J76eJpVq1Zh165dSE5OhpWVldq2uLg4GBkZISwsrF2ewzpffPEFOnbsiLlz58La2hq9e/fGrFmzkJWVhVOnThm8PQDYsWMHhBBqfz/++KNanR9++AELFizAtGnT0Lt370bb0vQanjFjBnr16oXhw4frPRnyLGNSgYiIiOg5cffuXRQXF+PevXtq5aamb/53DwAAIABJREFUpnp9naAtE0IgJSUFW7ZsUZXt27cPxsbGavUcHBwAoNFfRhsyduxYTJw4EUVFRWqvcLQX27dvR1FRUbvvoylXrlzBkiVL8Mknn0Aul9fbHhAQgIiICNy4cQNz5swxQIQtIz8/Hy4uLpAkSVXWqVMnAMC1a9cM3p6mevXqhdTUVIwfPx5mZmaN1tPmGl6+fDmysrIQGxvb8gE/J5hUICIiImrH4uLiIJfL4ejoiKlTp8LFxQVyuRwBAQH1fjHs27cvysvL8frrrz91PoZvvvkGvr6+sLa2hlwuh5+fHw4fPgwAiI2NhYWFBYyMjNCnTx84OTlBJpPBwsIC/v7+GDhwIDp16gS5XA4bGxvMmzdPp3iflJmZCXd3d0iShI0bNwIA4uPjYWFhAXNzc6Snp+Ott96CUqmEm5sbdu7cqbZ/TU0NYmJi0LVrVygUCjg4OMDT0xMxMTEICQlpsu8bN25AoVDA09OzyXpPmjhxIgDg4MGDWu2nCyEE1q1bh+7du8PMzAy2trZ45513cPHiRVWd8PBwmJqawtnZWVX28ccfw8LCApIk4c6dOwCAiIgIzJ49G1evXoUkSfDx8dH43DWnDwA4dOgQlEolVq5cqdfPC/h1PAohEBgY+P+xd+9xUdX5/8BfAwwMAwwXL6AgctMUs7wnqGvalpabiopS2W/VTHS30MIkM40QU8JVy0ut2tpFU7yFrmmWGpnrJUtNxVXJvJOKCHK/CO/fH36ZdeQ2M8wwA76ejwd/eM7nfM77zPl8znvm4znnU22Z+Ph4tG3bFitXrqz0npP76XMODG2zs2bNgq+vLxwdHfHII48gKSnJ4OMMCAioNHhT8f6DgIAAi9dXH6rrw+7u7ujbty8WLVpUL49XNUpCRERERCY3YsQIGTFihMHbAZDHHnus2vXff/+9PPHEEzrLIiMjxcnJSU6dOiVFRUWSmpoq3bt3FxcXF7l06ZK2XEFBgXTr1k0ACAAJDg6WhIQEyczMrLSfDRs2SGxsrNy6dUsyMzOlZ8+e0qRJE+36d955RwDIoUOHJD8/X27evCkDBw4UAPL1119LRkaG5OfnS1RUlACQY8eOGRxvWlqaAJCPPvpIu+zy5csCQBYvXqxdNmPGDAEgu3fvltu3b8uNGzekT58+4uTkJCUlJdpyc+bMEVtbW9myZYsUFBTIL7/8Ip6envL444/XeE7y8/PFxcVFoqKiKq0LDAwUV1fXarfNyckRANKqVasa93E/Y9rPrFmzxN7eXr744gvJzs6W48ePS5cuXaRp06Zy7do1bbkXXnhBPD09dbZNTEwUAJKRkaFdNnz4cAkMDNQpp++5q8s+tm3bJi4uLhIXF2fQ8SclJYmhP28CAgIkODi4ynWBgYFy/vx5ERHZv3+/2NjYiJ+fn+Tl5YmIyI4dO2TIkCE62+h7DvRts1OnThUHBwfZuHGjZGVlyVtvvSU2NjZy+PBhg44zJSVFlEqlfPjhh5KTkyMnT56U9u3by4ABAwyqx1z1zZ49W3x8fMTNzU2USqX4+fnJkCFD5Keffqp2m8cee0weffRRveqvqQ+LiEyfPl0AyNGjR/WO2Zj21ljxTgUiIiKiRsDOzk77v6PBwcFYtmwZcnNzsWrVKm0ZR0dH7N+/Hx988AHatWuHU6dOISYmBu3bt8cPP/ygU9+IESPwzjvvwN3dHR4eHhg8eDAyMzORkZGhUy44OBhqtRpNmjTBc889BwDw9fVF06ZNoVarMXr0aADQ+Z9afeM1VGhoKDQaDZo1a4aIiAjk5+fj0qVL2vXJycno2rUrBg8eDEdHR3Tp0gVDhgzB3r17UVJSUm297733Hlq0aGHUDBkuLi5QKBTIzc016pj0VVhYiAULFmDYsGEYPXo0XF1d0bFjR3z88ce4efOmzuMddWWOc3evQYMGIScnBzNnzjRJfdXJz8/H+fPnERgYWGvZkJAQvPbaa7hw4QLefPPNKssYcw5qarNFRUVYtmwZwsLCMHz4cLi5ueHtt9+GUqk0+LPu27cvYmJiEBUVBY1Gg4cffhi5ublYuXKlQfWYq76//vWv2Lp1Ky5fvoy8vDysXbsWly5dQt++fZGammpUnfeqrQ+3adMGwN2X2ZLhOKhARERE1Ah169YNarW60o95pVKJqKgo/Pe//8XBgwcxdOhQ3LhxA+Hh4TVOf6hUKgGgxunX7O3tAUDnhWcV25WWlhoVr7EqYrl3v0VFRZVuby4rK4NSqaz0/HWFzZs3Y/369di5c2ell/jpIz8/HyICjUZj8LaGSE1NRV5eHrp166azvHv37rC3tzf65Xn6MPW5qy83btyAiECtVutVPj4+Hg899BCWLl2Kffv2VVpf13Nwf5s9c+YMCgoKdKZVdHR0hJeXl8Gf9YwZM7B8+XLs3r0beXl5+P333xEaGoqQkJAaXz5aX/W1atUKnTt3hrOzM+zt7dGzZ0+sWrUKhYWFWLp0qcH13UufPlzRBq5fv16nfT2oOKhAREREZGXKy8urXVfxI1gfDg4Ole4suNdjjz2Gr776CpMmTUJGRga+//577bqvv/4ajz/+OJo1awYHBwed9yKYS23x1tUzzzyDX375BVu2bEFhYSF+/vlnJCcn4y9/+UuVgwrr1q3DvHnzkJKSAj8/P6P2efbsWQBAu3bt6hJ6rSqmxHN2dq60zs3Nzex3Spj73JlDUVERANT4wr97qVQqrFq1CgqFAuPGjUNhYaHOelOfg/z8fADA22+/DYVCof27ePGiQS8M/eOPP5CQkIAJEyagf//+cHJygr+/P1asWIH09HQkJiYaFJep66tOx44dYWtrq+1DxtC3D1fMflPRJsgwHFQgIiIisiIeHh5IT0+vdv358+e1b1mvSWlpKbKzs+Hj46NdNnz48CqnTXvxxRcB/O+t6JcuXUJYWBi8vLxw6NAh3L59GwkJCYYeikGqitfUYmNj0b9/f4wZMwYajQbDhg3DyJEjsWLFikplFy9ejNWrV2PPnj1o2bKl0fv85ptvAABPP/200XXow83NDQCq/OFq7s+1Ps6dOVT8kKzp7pv7hYSE4PXXX0daWhpmz56ts87U56BZs2YAgIULF1aaavHAgQN615OWloaysrJK7Vij0cDDw8PgxwtMXV91ysvLUV5ervegz/0M6cMVjz81lKl1rQ0HFYiIiIisSP/+/XH16lXs37+/0joRwaefforHHnus1npSUlIgIujZs6d2WXFxMU6dOlWp7JkzZwAAjzzyCIC7zxWXlpbib3/7GwICAqBSqXSmjjOHquI1tdTUVJw7dw4ZGRkoLS3FpUuXsGzZMri7u2vLiAhiYmJw4sQJJCcnV/m/zvq6du0aFi5cCB8fH4wbN84Uh1Cthx9+GM7Ozvj55591lh86dAglJSXo2rWrdpmdnV2tj6MYoqpzZ+p9mEPz5s2hUChw+/Ztg7abPXs22rVrh6NHj+osN+Qc6KNiBpVjx44ZtN39KgYz/vjjD53lubm5uHXrll6DlOasDwAGDBhQadnhw4chIggJCTGoLmP6cEUb8PT0NGhfdBcHFYiIiIisSHx8PNzc3BAeHo6vvvoK+fn5KC4uxq+//ornn38ed+7c0d5ZcK/y8nJkZWXhzp07OH78OKZMmQJfX1/tlIYVwsLCsH79emRnZ+P27dvYsmUL3nzzTQwZMkQ7qODr6wsA2LVrF4qKipCWlmbyZ/L1jdeUXnnlFfj6+iIvL6/aMqdOncL777+PFStWQKlU6tx2rlAoMH/+/ErbiAjy8vJQXl4OEUFGRgaSkpLQq1cv2NraIjk52ezvVFCpVIiOjsbmzZuxevVq5OTk4MSJE5g0aRJatGiByMhIbdmgoCDcunULycnJKC0tRUZGBi5evFipzoq7Zi5cuIDc3FztIIE+564u+9ixY0e9TCmpVqsREBCAK1euGLRdxWMQ9z8yY8g50Hc/Y8eOxdq1a7Fs2TLk5OSgrKwMV65c0f6gj4iIgKenJ44cOVJtPf7+/ujXrx9WrFiBvXv3orCwEJcvX9bG89JLL2nLWqI+4O50j+vWrUN2djZKS0tx4MABjB8/Hr6+vpg0aVLtH9Y9jOnDFW2gY8eOBu2L/k/9TzhBRERE1PgZO6WkiMj58+fl5ZdfFn9/f7G3txdHR0cJDg6WWbNmaaezu1dkZKQolUrx9vYWOzs70Wg0MnToUDl37pxOuW+//VZGjRolgYGB4uDgIPb29vLQQw9JbGysFBUV6ZSNiYkRDw8PcXNzk/DwcFmyZIkAkMDAQImOjha1Wi0AxM/PT3788UeZN2+euLq6CgDx9PSUNWvWyLp168TT01MAiLu7u6xdu1bveP/xj39ot3VycpJhw4bJ4sWLxcvLSwCIWq2WwYMHy9KlS7WxtGnTRs6dOyfLly8XjUYjAKR169Zy9uxZERHZs2ePNGnSRDulJgBRKpXSvn172bRpk4iInDhxQmf9/X+JiYkiIrJ161Z55JFHRK1Wi729vdjY2AgAUSgU4ubmJj169JC4uLgqp+vUhzHtp7y8XBITE6VNmzaiVCrF3d1dwsLC5MyZMzrlMjMzpV+/fqJSqcTf319effVVeeONNwSABAUFaaeGPHLkiLRu3VocHR2ld+/ecu3aNb3bWl32sX37dnFxcZH4+HiDjt+YKf6ioqJEqVRKQUGBdtnmzZslMDBQAEjTpk3llVdeqXLbN954o9KUkvqcA0PabHFxscTExIivr6/Y2dlJs2bNZPjw4ZKamioiImFhYQJAZs2aVeNx3rx5U6ZMmSJBQUHi4OAgzs7O0qtXL/nqq690ylmqvujoaAkMDBQnJyexs7MTHx8fefnllyU9PV2n3IEDB6RXr17SokULbZ/08vKS0NBQ+eGHH0RE/z58r0GDBom3t7eUl5fXGOe9OKXk/yhE7nsFLhERERHVWXh4OABgw4YNZt/XxIkTsWHDBmRmZpp9X6ZgqXiXLVuGtLQ0LFy4ULuspKQEb775JpYtW4asrCyreaa6PtuPIay5ra1fvx6jRo2qNMNHTX777Te0b98eq1at0k5/2pCUl5fj8ccfx5gxY0zyiI2112cOmZmZ8PHxQXx8PKKjo/Xezpj21ljx8QciIiKiRsCQl81Zg/qO99q1a4iKitK5NRu4O42fr68vSktLrf4dANaiobW1mgQFBSEuLg5xcXE1PhZjjcrKypCcnIzc3FxEREQ0+vrMJTY2Fp06dUJUVJSlQ2mwOKhARERERI2eo6MjlEolPvnkE1y/fh2lpaVIT0/HypUrMWvWLERERJj9vQdknaZPn47w8HBEREQY/NJGS0pJScGmTZuwY8cOqNXqRl+fOSxYsADHjh3D9u3b9Z6qlyrjoAIRERFRA/bWW29h1apVuH37Nvz9/bFx40ZLh1QjS8Xr6uqKb7/9FidPnkTbtm3h6OiI4OBgrFq1CvPmzcNnn31WL3E0ZA2trRlizpw5iIqKwty5cy0dit6eeOIJrFmzBl5eXg9Efaa2ZcsWFBcXIyUlRWcGGDIc36lAREREZAbW+kw8NQxsP4bjM+5Un9je/od3KhARERERERGRUTioQERERERERERG4aACERERERERERmFgwpEREREREREZBQ7SwdARERE1FgdPHhQ+8I9IkMcPHgQANh+DHDlyhUA/MyoflS0N+LsD0RERERmsWDBAhw4cMDSYRDVmx07dqBz585WO4UgkTlwhhYOKhARERERkQkoFAokJSVh5MiRlg6FiOoR36lAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERkFA4qEBEREREREZFR7CwdABERERERNSzZ2dkQkUrL8/PzkZWVpbPM2dkZSqWyvkIjonqmkKquBkRERERERNXo378/vv/++1rL2dra4urVq/D09KyHqIjIEvj4AxERERERGeS5556DQqGosYyNjQ3+9Kc/cUCBqJHjoAIRERERERlkxIgRsLOr+UlqhUKB//f//l89RURElsJBBSIiIiIiMoi7uzueeuop2NraVlvGxsYGYWFh9RgVEVkCBxWIiIiIiMhgo0ePRnl5eZXr7OzsMGjQILi6utZzVERU3zioQEREREREBhs8eDAcHByqXFdWVobRo0fXc0REZAkcVCAiIiIiIoOp1WqEhYVVOV2ko6MjnnnmGQtERUT1jYMKRERERERklOeffx6lpaU6y5RKJUaMGAFHR0cLRUVE9YmDCkREREREZJQBAwZUem9CaWkpnn/+eQtFRET1jYMKRERERERkFKVSiYiICNjb22uXubm54YknnrBgVERUnzioQERERERERnvuuedQUlIC4O4gw+jRo2FnZ2fhqIiovihERCwdBBERERERNUzl5eVo2bIlrl+/DgDYt28fevXqZeGoiKi+8E4FIiIiIiIymo2NDV588UUAQIsWLRAaGmrhiIioPvG+JDLYgQMHcPnyZUuHQUREVK2RI0eapd4rV65g//79ZqmbqCFr2rQpAOCxxx7Dhg0bLBwNkfVp1aoVQkJCLB2GWfDxBzJYeHg4Nm7caOkwiIiIqmWurzfr16/HqFGjzFI3ERE1XiNGjGi0A268U4GM0pg7BVFdhIeHAwD7hwEqfqRxjJtMob5+9LO9kjVRKBRISkoy2x06+tq4cSNGjBhh0Rj0xXxN9amivTVWfKcCERERERHVWUMZUCAi0+KgAhEREREREREZhYMKRERERERERGQUDioQERERERERkVE4qEBERERERERERuGgAhEREREREREZhYMK1CBt374drq6u+Pe//23pUKiefPnll+jevTtcXFzQunVrjB07FteuXdMpExoaCqVSCYVCAXt7e3Tp0kVb5tNPP4WPjw8UCgWaNm2KZcuWGVx/fWIbr9nEiROhUCi0f6NHj65UZteuXZg+fTo2bdqEgIAAbdkXX3yxUtmnnnoKLi4usLW1RYcOHXDkyJH6OIw6MXWbNUcfKC8vx8KFCxEaGlpp3datW5GQkICysjKd5cnJyTrntmnTpnWKgagmcXFxCA4OhkajgYODA4KCgjBt2jTk5eXVuN348ePh4uIChUKBY8eOGbzfhIQEtGvXDo6OjnByckK7du0wc+ZM5OTkGHsoJsHcoz/mGMvVFx8fr5MnKv4efvjhKsvXlIsA/a4D1eUs+j9CZKARI0bIiBEjLBrDtm3bRKPRyNatWy0aB9WPdevWCQBJSEiQ7OxsOXr0qAQEBEinTp2ktLRUp+yuXbtEoVBIUFCQ5Ofn66z78ssvpUePHlJSUmJ0/bUxVf94kNp4UlKSGJqOIiMjxcPDQ3bs2CFnzpyRoqIinfWzZs2SZ599VnJycrTLAgMDpUmTJgJAtm3bVqnOHTt2yJAhQ4w7iHpmyjZrjvpERM6ePSu9evUSAPLoo49WWWbRokXSt29fycrK0i4rLy+XK1euyN69e+WZZ56RJk2aGLRfY9qTNdVP9atv376ydOlSyczMlJycHElKShKlUikDBw6sddu1a9cKADl69KjB+x00aJDMnz9fbty4Ibm5ubJ+/XpRKpXy5JNPGnMYAkCSkpKM2vZeD1LuqUu+Zo6xbH2zZ88WAJX+OnToUKmsPrlI3+tAVTlLX9bw+8mcmBXJYI29U9SkoKBAQkJCLB3GA6dfv37SsmVLKS8v1y5bsmSJAJB9+/ZVKj9p0iQBIK+++qp22ZkzZ6RVq1Zy/vz5Otdfk8bYP8zd7o0dVPD29q5y3dy5c6Vt27ZSWFioszwwMFDWrFkjNjY24u3tLdnZ2TrrG9IXPlO2WXPUd+zYMRk2bJisXr1aOnXqVO0XORGRqKgoCQkJqfKL5eTJkzmooCfmJ+MMGjRI7ty5o7Ns5MiRAkAuXbpU47Z1GVQICwurdI0KDw8XAJKenm5wfaYaVLAm5m7TxuZr5hjL55jZs2fLF198UWs5fXORIdeBmnJWTRrj98N78fEHIgN88sknuHHjRr3vV0SwYcMGLF++vN73bQ0uX76MFi1aQKFQaJe1atUKAHDx4sVK5RMTExEQEIAlS5YgJSUFhYWFCA8Px+LFi+Hn51fn+h80lmr3xvjtt98wc+ZMvPvuu1CpVJXWh4aGYsqUKbh69SqmTp1qgQhNw9Rt1tT1Pfroo9i0aRNeeOEFODg41Fg2NjYWx44dw6JFiwzeD/1PQ+qn1mTbtm2wtbXVWVbxyE1BQUGN297bXwy1efPmStcob29vAKj10YsHhTW2aeYY68gx+tI3FxlyHWDOqhoHFcjsli1bBicnJ6jVamzZsgVPP/00NBoNfHx8sHbtWp2yIoIFCxagffv2cHBwgLu7O4YOHYrTp09ry+zbtw++vr5QKBRYsmSJdvkPP/yAHj16QK1WQ6PRoGPHjtpnE8vKyjBr1iz4+vrC0dERjzzyCJKSkgw6jilTpiA6Ohrnzp2DQqFAUFCQ3jEboqysDO+99x4eeughODo6omnTpvD398d7772HkSNH6pSr7pgM+cz18f7770OtVsPFxQU3btxAdHQ0vL29cebMmVo/25rOi74CAgIqfbGoeA4vICCgUnknJyesWrUKADBu3DhMmDABTzzxBIYMGWKS+s2tqjau7zn98MMPoVKp0Lx5c0ycOBEtWrSASqVCaGgoDh06pC0XFRUFe3t7eHl5aZf9/e9/h5OTExQKBW7evAmg+nb/zTffQKPRYM6cOfXxkejtww8/hIhg8ODB1ZaJj49H27ZtsXLlSuzatavG+vTp34b0N1NciwDTt1lL9gF3d3f07dsXixYtgoiYdV+W9MUXX6Bbt25QqVRwcnKCn58fZs+erXdfBKq/ntYlPy1atAhOTk6wsbFB165d4enpCaVSCScnJ3Tp0gV9+vRBq1atoFKp4ObmhmnTppn8M9A3Vn37Wvv27aFQKLTHVPGjYNq0aXB1dYVKpcKnn35abZxXr16Fo6Mj/P39tctEBImJiXjooYfg4OAAV1dXvPHGG0Z/FlVJS0uDm5sbWrdubdJ69cXcUzvmmIaXY4xV1XUAeHBylsEsc4MENWTG3L4zY8YMASC7d++W27dvy40bN6RPnz7i5OSk83z7rFmzxN7eXr744gvJzs6W48ePS5cuXaRp06Zy7do1bbnLly8LAFm8eLGIiOTl5YlGo5GEhAQpLCyUa9euybBhwyQjI0NERKZOnSoODg6yceNGycrKkrfeektsbGzk8OHDBh3H8OHDJTAwUGeZvjHra86cOWJraytbtmyRgoIC+eWXX8TT01Mef/xxnXK1HZO+n7m+KuqbPHmyLF68WIYNGyb//e9/a4yjtvOir5SUFFEqlfLhhx9KTk6OnDx5Utq3by8DBgyocbvJkycLAPH396/xmI2tvyqmur3t/jYuov85jYyMFCcnJzl16pQUFRVJamqqdO/eXVxcXHRu43vhhRfE09NTZ7+JiYkCQOccVdXut23bJi4uLhIXF1fnYzXl4w8BAQESHBxc5TaBgYHax1/2798vNjY24ufnJ3l5eSJS9a2p+vZvfc+Nqa5Fpmyz5qjvXo899liNjz+IiEyfPr3K28gby+MPCxcuFAAyd+5cyczMlFu3bsk///lPeeGFF0REv75Y2/W0LvnpnXfeEQBy6NAhyc/Pl5s3b8rAgQMFgHz99deSkZEh+fn5EhUVJQDk2LFjBn9utX0Gpuxrd+7cET8/P/H19a10S/Nrr70mCxcurDbO/Px8cXFxkaioKJ3lM2bMEIVCIf/4xz8kKytLCgoKZOnSpUY//lChpKRErly5IosXLxYHBwe9bumuCkz0+MODlHuMydfMMdaRY2bPni0+Pj7i5uYmSqVS/Pz8ZMiQIfLTTz9Vu40+uahCddeBCtXlrJo09scfOKhABqvLoMK9z59VJOPffvtNRO4+O+fs7CwRERE62/70008CQCeB3J/0Tp48We2LcQoLC0WtVuvUW1BQIA4ODvK3v/3NoOO4P8EZErO+unfvLj169NBZNmHCBLGxsZHi4mIR0e+Y9PnMDVFVfbXFUdN5MdTbb7+t8zIeHx8fuXz5co3b/PrrrwJAFAqF7N692+T1V6U+BhVqO6eRkZHi6uqqU9/hw4cFgLz77rvaZXX5YmdKphpUyMvLE4VCIc8++2yV29z7hU9EJDo6WgDIK6+8IiKVv/AZ0r/1OTemvBaJmK7Nmqu+Cvp8kfvXv/4lAOTzzz/XWd4YBhVKSkrEzc1N+vXrp7P8zp07smjRIhHRry/Wdj2tS36qGFTIzc3VLvvss88EgJw4caLStuvWrdP7+EVq/wxM3ddE/jeIsX79eu2y/Px88fX1ldu3b1cb64wZM6Rt27Y6L+ArKCgQtVpd6SWKdXmnQgVPT08BIE2aNJEPPvjAqEF/kfoZVGhsucfQfM0cYz055tKlS3LkyBHJzc2V4uJiOXDggHTu3FkcHR3l5MmTVW5jyKBCVdeBe1WXs2rS2AcV+PgDWYy9vT0AoLS0FACQmpqKvLw8dOvWTadc9+7dYW9vr3P73P0CAgLQvHlzjB49GrGxsbhw4YJ23ZkzZ1BQUKAzzYyj1Z0IAAAgAElEQVSjoyO8vLyMfkShQl1irk5RUVGl26nKysqgVCq1z3sZe0z3f+Z1VVscNZ0XQ8yYMQPLly/H7t27kZeXh99//x2hoaEICQnB5cuXq9ymuLgYY8eOxcyZM2FjY4Nx48YhNzfXZPVbC33Pabdu3aBWq+vc5q3ZjRs3ICJQq9V6lY+Pj8dDDz2EpUuXYt++fZXW17V/339uTHktMnWbtXQfqDhn169fN/u+6tvx48eRnZ2NAQMG6Cy3tbXF5MmT9a7H0OupqdrvnTt3tMuUSiUAw3NIbZ+BqfsacHfKR1dXV53nnlevXo2hQ4dCo9FUWc/mzZuxfv167Ny5Ey4uLtrlv/32GwoKCvDEE0/od8AGuHz5Mm7cuIEvv/wSn332GTp37mx17xGoyoOYe5hjrCfHtGrVCp07d4azszPs7e3Rs2dPrFq1CoWFhVi6dKnB9d2ruuvAvRpzzjIWBxXIamRnZwMAnJ2dK61zc3Or9gchcPeiuWfPHvTu3Rtz5sxBQEAAIiIiUFhYiPz8fADA22+/rTOX7cWLF2t9CZM5Y67OM888g19++QVbtmxBYWEhfv75ZyQnJ+Mvf/mLdlDBnMdkiNriqOm86OuPP/5AQkICJkyYgP79+8PJyQn+/v5YsWIF0tPTkZiYWOV2r732Gvr27Yu4uDhMnToVFy9eRHR0tMnqb4gcHByQkZFh6TDMpqioCABqfTFgBZVKhVWrVkGhUGDcuHGV2qWp+7ep+q2p26w19AFHR0cA/zuHjUnFO2Tc3NzqVI+h11Nz5Cdj1fYZmCNWZ2dnTJgwAfv378dPP/0EAPjoo48QFRVVZfl169Zh3rx5SElJqfRC3ytXrgAAmjVrZnActVEqlWjWrBmeeuoprFu3DqmpqXjvvfdMvh9Laiy5hznGunNMx44dYWtri7NnzxpdR03XgXs15pxlLA4qkNWo+LJR1UU0OzsbPj4+NW7foUMH/Pvf/0Z6ejpiYmKQlJSE+fPna78ELFy4EHL3kR/t34EDBywac1ViY2PRv39/jBkzBhqNBsOGDcPIkSOxYsUKbRlzHpMh9ImjuvOir7S0NJSVlaFly5Y6yzUaDTw8PJCamlppm/Xr1+OXX35BQkICAODdd99Fhw4dsGLFCnzzzTd1rr8hKi0tNbpNNhQVSb6srEzvbUJCQvD6668jLS1N+8K4Cqbu36bqt6Zus9bQB0pKSgD87xw2JhWf670vXDSWIddTc+QnY9X2GZgr1qioKCiVSixcuBB79+5Fq1atEBgYWKnc4sWLsXr1auzZs6dSPwCgfct/cXGxUXHoKygoCLa2to0m7wCNK/cwx1h3jikvL0d5ebnegz73q+06cK/GnLOMxUEFshoPP/wwnJ2d8fPPP+ssP3ToEEpKStC1a9dqt01PT8epU6cA3L2ozp07F126dMGpU6e0b60+duyYVcVcndTUVJw7dw4ZGRkoLS3FpUuXsGzZMri7u2vLmPOYDFFbHDWdF31VJNU//vhDZ3lubi5u3bqlnZKowrlz5zBt2jQkJSVpb9V1cHDAZ599Bjs7O4wfP177vwPG1N9QpaSkQETQs2dP7TI7OzuTPQpjDZo3bw6FQoHbt28btN3s2bPRrl07HD16VGe5qfu3qfqtqdusNfSBinPm6elp9n3VNz8/P3h4eODbb7+ttow+fdHQ66k58pOxavsMzBWrj48PRo4ciY0bN2LmzJmYMmWKznoRQUxMDE6cOIHk5OQq/8e4Ij4bGxv88MMPRsVxv8zMTDz//POVllf8+GoseQdoXLmHOcZ6csz9j1IBwOHDhyEiCAkJMagufa8D92rMOctYHFQgq6FSqRAdHY3Nmzdj9erVyMnJwYkTJzBp0iS0aNECkZGR1W6bnp6OiRMn4vTp0ygpKcHRo0dx8eJF9OzZEyqVCmPHjsXatWuxbNky5OTkoKysDFeuXKl0gauNh4cH0tPTceHCBeTm5sLW1tbomKvzyiuvwNfXt8Z5qk15THVRWxw1nRd9+fv7o1+/flixYgX27t2LwsJCXL58WfvZvvTSS9qyxcXFGDVqFBYtWlTptrWuXbti+vTpuHr1qs5zzIbU35CUl5cjKysLd+7cwfHjxzFlyhT4+vpizJgx2jJBQUG4desWkpOTUVpaioyMjCrni76/3ZeWlmLHjh1WN6WkWq1GQECA9lZlfVXconr/HNV1uSZVt5/a+m1ERAQ8PT1x5MiRausxpM1aoj5jVJyzjh07mrRea+Dg4IC33noLe/fuRVRUFK5evYry8nLk5uZqBwT06Yu1XU/rIz+Z6zMwdV+7V3R0NO7cuYOsrCz0799fZ92pU6fw/vvvY8WKFVAqlTq3jCsUCu1dIM2aNcPw4cOxceNGfPLJJ8jJycHx48exfPlyo2JycnLCt99+iz179iAnJwelpaU4evQo/vrXv8LJyQmvv/660cdraY059zDHWE+OuXr1KtatW4fs7GyUlpbiwIEDGD9+PHx9fTFp0qTaP6x76HsduFdjzllGq5/3QVJjYujbS5cuXSpqtVoASJs2beTcuXOyfPly0Wg0AkBat24tZ8+eFRGR8vJySUxMlDZt2ohSqRR3d3cJCwuTM2fOaOtbvHixeHl5CQBRq9UyePBguXDhgoSGhoq7u7vY2tpKy5YtZcaMGdqppIqLiyUmJkZ8fX3Fzs5OmjVrJsOHD5fU1FSDjv3IkSPSunVrcXR0lN69e8u1a9f0itkQe/bskSZNmui8IVepVEr79u1l06ZN2nI1HZMhn7k+EhISxNHRUQBIq1atdKa8qimO2s6Lvm7evClTpkyRoKAgcXBwEGdnZ+nVq5d89dVX2jJLliyRpk2bCgDx8/Or9Hby119/Xedz9ff3l127duldv75M8Xbfqtq4Iec0MjJSlEqleHt7i52dnWg0Ghk6dKicO3dOZz+ZmZnSr18/UalU4u/vL6+++qq88cYbAkCCgoK0U4BV1e63b98uLi4uEh8fX6djFTHtlJJRUVGiVCqloKBAu2zz5s0SGBgoAKRp06baN3Hf74033qg03Zc+/duQc1PbtSgsLEwAyKxZs2o8fn3brKXqO3DggPTq1UtatGih7XNeXl4SGhoqP/zwQ6XygwYNEm9vbykvL9dZ3hhmf6iwZMkS6dixo6hUKlGpVNK5c2dZunSpiOjXF2u7nhqbnxYtWqRtv35+fvLjjz/KvHnzxNXVVQCIp6enrFmzRtatW6edqcDd3V3Wrl1r0s/A1H3tXv369ZOVK1dWWn7ixAmdXHv/X2JiorZsbm6ujB8/Xpo0aSLOzs7Su3dvmTVrlvYt9r/++qtBn8XgwYPF399fnJ2dxcHBQQIDAyUiIkJntg1DwASzPzxouceYfM0cYx05Jjo6WgIDA8XJyUns7OzEx8dHXn75ZUlPT9cpp08uMuQ6UKG6nFWTxj77AwcVyGCNvVNY2tKlS2XKlCk6y4qLi+W1114TBwcHnURG1sca+kdkZKR4eHhYNAZDmHJQIS0tTezs7Iye693SysrKpE+fPvLJJ588EPWJ3P2yqVKpZP78+ZXWNaZBBSJzMsWgQl01tNxjTL5mjmlY9ZlDTTmrJtbw/dCc+PgDkRW5du0aoqKiKt1yb29vD19fX5SWljbI5xCp/hnyIqmGqrCwEDt37kRaWpr2pUlBQUGIi4tDXFxcjY8QWaOysjIkJycjNzcXERERjb6+CrGxsejUqZP2rfwigvT0dOzbtw+//fabyfZDRObX2HMPc0zDqc9c7s9ZdBcHFeiBdvr06UrPTlX1V5eLmyH7cHR0hFKpxCeffILr16+jtLQU6enpWLlyJWbNmoWIiIhq59e2xmO3xn1T43Hr1i0MHDgQbdu2xbhx47TLp0+fjvDwcERERBj8Qi1LSklJwaZNm7Bjxw6950FvyPUBwIIFC3Ds2DFs375d+2LVLVu2wNvbG3369MHXX39tkv2Q6fE6roufx4ODOaZh1GcOVeUsukshImLpIKhhCQ8PBwBs2LDBwpE0Tj/++CPi4uLw008/IT8/H87OzujQoQNeeOEFTJgwAXZ2dpYOkWpg6f7x1ltv4R//+AdKSkrg5+eHxMREjBgxwiKx6Gv9+vUYNWoUTJ2OKl6ENm/ePJPWS6axZcsWnDp1CtOmTav0ArO6MFd7qq/6iYyhUCiQlJSEkSNHWmT/DTH31DVfM8c8WOqasyz9/dDc+OuEyMr06dMH3333naXDoAbqvffew3vvvWfpMKzCU089haeeesrSYVA1hgwZgiFDhlg6DCIygQcx9zDHPFiYs2rGxx+IiIiIiIiIyCgcVCAiIiIiIiIio3BQgYiIiIiIiIiMwkEFIiIiIiIiIjIKX9RIRjl48KD2LaZE9D8HDx4EAPYPA1y5cgUAPzMyjYr2ZG5sr2RtFi5c2GjfLG8OzNdUnw4ePIiePXtaOgyz4Z0KRERERERERGQU3qlARunZsydHw4mq0NjnITaH9evXY9SoUfzMyCQq2pO5sb2SNVEoFHjttdcwcuRIS4fSYDBfU31q7HfE8E4FIiIiIiIiIjIKBxWIiIiIiIiIyCgcVCAiIiIiIiIio3BQgYiIiIiIiIiMwkEFIiIiIiIiIjIKBxWIqnHw4EG0b98eNjY2UCgU8PT0RHx8vKXD0rFp0yYEBARAoVBAoVDAy8sLo0ePtnRYRGY3ceJEbbtXKBRVtvtdu3Zh+vTplfrJiy++WKnsU089BRcXF9ja2qJDhw44cuRIfRxGnXz55Zfo3r07XFxc0Lp1a4wdOxbXrl2zmvoAoLy8HAsXLkRoaGildVu3bkVCQgLKysp0licnJ+uc26ZNm9YphobkzJkzePXVV9GhQwe4uLjAzs4Orq6uaNu2LQYNGoQDBw5YOkSTagjHGxcXh+DgYGg0Gjg4OCAoKAjTpk1DXl6etsz915iKP3t7ezRv3hyPP/44EhMTkZWVZcEjIVNjjrFcffHx8ZX6m0KhwMMPP1xl+ZpyEaBfP68uZ9H/ESIDjRgxQkaMGGHpMOrNgAEDBIBkZWVZOpRqBQYGiqurq6XDIHnw+ocpJCUliaHpKDIyUjw8PGTHjh1y5swZKSoq0lk/a9YsefbZZyUnJ0e7LDAwUJo0aSIAZNu2bZXq3LFjhwwZMsS4g6hn69atEwCSkJAg2dnZcvToUQkICJBOnTpJaWmpxesTETl79qz06tVLAMijjz5aZZlFixZJ3759da6v5eXlcuXKFdm7d68888wz0qRJE4P2a0x7sob6V65cKUqlUv70pz/JN998I1lZWVJUVCTnzp2TdevWSWhoqPzzn/80+X4tpaEcb9++fWXp0qWSmZkpOTk5kpSUJEqlUgYOHFip7L25uLy8XLKysuT777+XMWPGiEKhkBYtWsjhw4fNEicASUpKMkvdjVVd8jVzjGXrmz17tgCo9NehQ4dKZfXJRfr286pylr4a+/dDDiqQwRp7p7ifNQ0qFBQUSEhISKXlHFSwHtbQP6prJ9a6D2MHFby9vatcN3fuXGnbtq0UFhbqLA8MDJQ1a9aIjY2NeHt7S3Z2ts76hvSFr1+/ftKyZUspLy/XLluyZIkAkH379lm8vmPHjsmwYcNk9erV0qlTp2q/yImIREVFSUhISJVfLCdPnvxADCocOHBAbG1tpX///tV+wf7mm29k8eLFJt1vfVwrqmKp4zXGoEGD5M6dOzrLRo4cKQDk0qVLOstrysUbNmwQGxsbad68eaVrjylYelChoeUdEePzNXOM5XPM7Nmz5Ysvvqi1nL65yJB+XlPOqok1fD80Jz7+QNSAfPLJJ7hx44alwyArVx/txFrb4m+//YaZM2fi3XffhUqlqrQ+NDQUU6ZMwdWrVzF16lQLRGgaly9fRosWLaBQKLTLWrVqBQC4ePGixet79NFHsWnTJrzwwgtwcHCosWxsbCyOHTuGRYsWGbyfxiI+Ph5lZWWYO3cu7OzsqiwzYMAAvPLKKybdr6X6saWO1xjbtm2Dra2tzrKKR3IKCgr0rmfEiBEYM2YMbty4gY8//tikMVqDByXvMMdYR47Rl765yJB+zpxVNQ4qEBlo2bJlcHJyglqtxpYtW/D0009Do9HAx8cHa9eu1Zb78MMPoVKp0Lx5c0ycOBEtWrSASqVCaGgoDh06pC0XFRUFe3t7eHl5aZf9/e9/h5OTExQKBW7evAkAmDJlCqKjo3Hu3DkoFAoEBQUZFf+PP/6I4OBguLq6QqVSoWPHjti5cycAYPz48drn0gIDA3H06FEAwNixY6FWq+Hq6oqtW7cCAMrKyjBr1iz4+vrC0dERjzzyCJKSkgAA77//PtRqNVxcXHDjxg1ER0fD29sbZ86cMSrmxk5EsGDBArRv3x4ODg5wd3fH0KFDcfr0aW2ZurST+mqL33zzDTQaDebMmWPWz6smH374IUQEgwcPrrZMfHw82rZti5UrV2LXrl011qfPudH3mgDU3G8MERAQUOnLdcWzqQEBARavzxDu7u7o27cvFi1aBBEx676sUUlJCXbv3o0mTZqgR48eem2jb18FgB9++AE9evSAWq2GRqNBx44dkZOTU20/1qfNL1q0CE5OTrCxsUHXrl3h6ekJpVIJJycndOnSBX369EGrVq2gUqng5uaGadOm1el49Y1L377Yvn17KBQKbfwVPxqmTZumzY2ffvpptbFcvXoVjo6O8Pf31zt+ABgzZgwAYMeOHQZtZw7MO8Zhjml4OcZY1fXzBz1nVctSt0hQw9XYb9+5X1WPP8yYMUMAyO7du+X27dty48YN6dOnjzg5OUlJSYm2XGRkpDg5OcmpU6ekqKhIUlNTpXv37uLi4qJzO9ULL7wgnp6eOvtNTEwUAJKRkaFdNnz4cAkMDKwUoyGPP2zYsEFiY2Pl1q1bkpmZKT179tS5vXj48OFia2srV69e1dnu+eefl61bt2r/PXXqVHFwcJCNGzdKVlaWvPXWW2JjY6N9XrTiM5o8ebIsXrxYhg0bJv/973/1irEhM6Z/zJo1S+zt7eWLL76Q7OxsOX78uHTp0kWaNm0q165d05arSzupj7a4bds2cXFxkbi4OIOO35SPPwQEBEhwcHCV2wQGBsr58+dFRGT//v1iY2Mjfn5+kpeXJyJV35qq77nR95pQW7/RV0pKiiiVSvnwww8lJydHTp48Ke3bt5cBAwYYVI+56rvXY489VuPjDyIi06dPFwBy9OhRneUPwuMPZ8+eFQDSs2dPg7bTp6/m5eWJRqORhIQEKSwslGvXrsmwYcO066vqx/q2+XfeeUcAyKFDhyQ/P19u3rwpAwcOFADy9ddfS0ZGhuTn50tUVJQAkGPHjtXpeE3ZF+/cuSN+fn7i6+tb6Zbn1157TRYuXFhtHPn5+eLi4iJRUVGV1tWWi3NycgSAtGrVyqBj1wcMfPzhQc87Isbla+YY68gxs2fPFh8fH3FzcxOlUil+fn4yZMgQ+emnn6rdRp9cVKGmfi5Sfc6qSWP//cQ7FYjqIDQ0FBqNBs2aNUNERATy8/Nx6dIlnTJ2dnbaEejg4GAsW7YMubm5WLVqlUViHjFiBN555x24u7vDw8MDgwcPRmZmJjIyMgAAkyZNQllZmU58OTk5OHz4MJ555hkAQFFREZYtW4awsDAMHz4cbm5uePvtt6FUKisd17x58/DKK69g06ZNaNeuXf0daANRWFiIBQsWYNiwYRg9ejRcXV3RsWNHfPzxx7h58yaWL19usn2Zuy0OGjQIOTk5mDlzpknqM1R+fj7Onz+PwMDAWsuGhITgtddew4ULF/Dmm29WWcaYc1PTNcGQflObvn37IiYmBlFRUdBoNHj44YeRm5uLlStXGlSPueozVJs2bQAAJ06cqJf9WZOcnBwAgLOzs8nrvnDhAnJyctChQweoVCp4enpi06ZN1c6oYUybDw4OhlqtRpMmTfDcc88BAHx9fdG0aVOo1WrtzCwV//NqzPGaui/a2tpi8uTJuHTpEjZv3qzdpqCgAJs2bcK4ceOqjeW9995DixYtjJoNysXFBQqFArm5uQZva0rMO8ZhjrGeHPPXv/4VW7duxeXLl5GXl4e1a9fi0qVL6Nu3L1JTU42q81619fMHOWdVh4MKRCZib28PACgtLa2xXLdu3aBWq3VubbMkpVIJANopcvr374+2bdviX//6l/a2rnXr1iEiIkL7vNmZM2dQUFCgM3WPo6MjvLy8rOa4GorU1FTk5eWhW7duOsu7d+8Oe3t7ndtETc3a2mJd3bhxAyICtVqtV/n4+Hg89NBDWLp0Kfbt21dpfV3Pzf3XBFP2mxkzZmD58uXYvXs38vLy8PvvvyM0NBQhISG4fPmyQXWZoz5DVZyz69evm31f1qbix7Uhz+frKyAgAM2bN8fo0aMRGxuLCxcu1FjeVG3+zp072mUVOaaiHxhzvKbui8Ddx/1cXV11notevXo1hg4dCo1GU2U9mzdvxvr167Fz5064uLjoHX+F/Px8iEi19dcX5h3jMMdYT45p1aoVOnfuDGdnZ9jb26Nnz55YtWoVCgsLsXTpUoPru5c+/fxBzlnV4aACkQU4ODho7wyob19//TUef/xxNGvWDA4ODjrPugKAQqHAxIkT8fvvv2P37t0AgM8//xwvvfSStkx+fj4A4O2339aZH/jixYtm+WLcmGVnZwOo+n/t3NzczP4/WpZsi6ZWVFQEALW+GLCCSqXCqlWroFAoMG7cOBQWFuqsN/W5MVW/+eOPP5CQkIAJEyagf//+cHJygr+/P1asWIH09HQkJiYaFJep6zOGo6MjgP+dwweJn58fVCoVzp49a/K6HR0dsWfPHvTu3Rtz5sxBQEAAIiIiKrX1CvVxPTLmeM0Rl7OzMyZMmID9+/fjp59+AgB89NFHiIqKqrL8unXrMG/ePKSkpMDPz8/g/QHQHrOl79pj3jEOc4x155iOHTvC1ta2TtdSffv5g5yzqsNBBaJ6VlpaiuzsbPj4+NTL/vbu3YuFCxcCAC5duoSwsDB4eXnh0KFDuH37NhISEiptM2bMGKhUKqxcuRJnzpyBRqNB69atteubNWsGAFi4cCHk7tS02r8DBw7Uy3E1Fm5ubgBQ5ZcHc7eT+m6L5laR5CvuutFHSEgIXn/9daSlpWH27Nk660x9bkzVb9LS0lBWVoaWLVvqLNdoNPDw8DD41k9T12eMkpISAP87hw8SBwcHDBgwADdv3sR//vOfasvdunUL48ePN7j+Dh064N///jfS09MRExODpKQkzJ8/v8qy9XE9MuZ4zRVXVFQUlEolFi5ciL1796JVq1ZV3tq+ePFirF69Gnv27KnUTwzxzTffAACefvppo+swBeYd4zDHWHeOKS8vR3l5ud6DPvczpJ8/yDmrOhxUIKpnKSkpEBH07NlTu8zOzq7WxyaM9csvv8DJyQnA3We/SktL8be//Q0BAQFQqVQ60/tUcHd3x6hRo5CcnIz58+fj5Zdf1llf8VbvY8eOmSXmB8nDDz8MZ2dn/PzzzzrLDx06hJKSEnTt2lW7zNTtpL7bork1b94cCoUCt2/fNmi72bNno127dtrZTioYcm70Yap+U/FF848//tBZnpubi1u3bmmn6bJUfcaoOGeenp5m35c1io2NhYODA15//fVq7yI4efKkzvSL+vTV9PR0nDp1CsDdHxxz585Fly5dtMvuZ+o2Xx1Dj9dccfn4+GDkyJHYuHEjZs6ciSlTpuisFxHExMTgxIkTSE5OrtN7L65du4aFCxfCx8enxnc21AfmHeMwx1hPjhkwYEClZYcPH4aIICQkxKC6jOnnD3rOqgoHFYjMrLy8HFlZWbhz5w6OHz+OKVOmwNfXVzu1FAAEBQXh1q1bSE5ORmlpKTIyMqqct9fDwwPp6em4cOECcnNza0zCpaWluH79OlJSUrSDCr6+vgCAXbt2oaioCGlpadU+szdp0iQUFxdj27ZtePbZZ3XWqVQqjB07FmvXrsWyZcuQk5ODsrIyXLlypVLSoJqpVCpER0dj8+bNWL16NXJycnDixAlMmjQJLVq0QGRkpLZsXduJudvijh07LDqlpFqtRkBAAK5cuWLQdhW3qN4/R7Uh50bf/dTWbyIiIuDp6YkjR45UW4+/vz/69euHFStWYO/evSgsLMTly5e18dz7qJIl6jNGxTnr2LGjSettKDp16oQ1a9bg5MmT6NOnD7Zv347bt2+jtLQU58+fx4oVK/DSSy9p308A6NdX09PTMXHiRJw+fRolJSU4evQoLl68qP1Bd38/trW1NWmbN9Xxmrov3is6Ohp37txBVlYW+vfvr7Pu1KlTeP/997FixQoolUqdW8oVCkWVd3yICPLy8lBeXg4RQUZGBpKSktCrVy/Y2toiOTnZ4u9UYN4xDnOM9eSYq1evYt26dcjOzkZpaSkOHDiA8ePHw9fXF5MmTar9w7qHMf38Qc9ZVaq/iSaosWjsU6JUOHjwoHTo0EFsbGwEgHh5ecmcOXNk6dKlolarBYC0adNGzp07J8uXLxeNRiMApHXr1nL27FkRuTudklKpFG9vb7GzsxONRiNDhw6Vc+fO6ewrMzNT+vXrJyqVSvz9/eXVV1+VN954QwBIUFCQduqlI0eOSOvWrcXR0VF69+4tH330kQQGBgqAGv82b96s3VdMTIx4eHiIm5ubhIeHy5IlSwSABAYG6kzxJCLSuXNnmT59epWfT3FxscTExIivr6/Y2dlJs2bNZPjw4ZKamioJCQni6OionTrriy++MOWpsWrG9I/y8nJJTEyUNm3aiFKpFHd3dwkLC5MzZ87olDO2nVy7ds3sbfHatWuyfft2cXFxkfj4eIOO35RTSkZFRYlSqZSCggLtss2bN2v7SdOmTeWVV16pss433nij0nRf+pwbQ64JNfUbEZGwsDABILNmzarx+G/evClTpkyRoKAgcXBwEGdnZ+nVq5d89dVXOuUsVd+BAwekVz4hYW8AACAASURBVK9e0qJFC+11yMvLS0JDQ+WHH36oVH7QoEHi7e0t5eXlOssfhCkl73Xp0iWZOnWqdOzYUZydncXW1lbc3Nykc+fO8tJLL8l//vMfbVl9+uqFCxckNDRU3N3dxdbWVlq2bCkzZszQTqNYVT/Wp80vWrRI2+b9/Pzkxx9/lHnz5omrq6sAEE9PT1mzZo2sW7dOPD09BYC4u7vL2rVrjT5eU/fFe/Xr109WrlxZafmJEydqzK2JiYkiIrJ161Z55JFHRK1Wi729vfZ7g0KhEDc3N+nRo4fExcVJZmamkS2jdjBwSskHPe+IGJevmWOsI8dER0dLYGCgODk5iZ2dnfj4+MjLL78s6enpOuX0yUX69vN7VZezatLYfz9xUIEM1tg7hSlFRkaKh4eHpcMw2jPPPCO///67pcNoUKy1f1hzWzTloEJaWprY2dk12IGssrIy6dOnj3zyyScPRH0id79sqlQqmT9/fqV1D9qgApGxDB1UqA/WnHdEjMvXzDENqz5zqCln1cRavx+aCh9/IDIzQ17oY2n3Pk5x/PhxqFQq+Pv7WzAiMqWG1Bb1UVhYiJ07dyItLU370qSgoCDExcUhLi4OeXl5Fo7QMGVlZUhOTkZubi4iIiIafX0VYmNj0alTJ+1b90UE6enp2LdvH3777TeT7YeI6l9jyzvMMQ2nPnO5P2fRXRxUICKtmJgYpKWl4ezZsxg7dmylNxUTWZNbt25h4MCBaNu2rc6Lz6ZPn47w8HBEREQY/EItS0pJScGmTZuwY8cOvedBb8j1AcCCBQtw7NgxbN++Xfv8/JYtW+Dt7Y0+ffrg66+/Nsl+iIhMhTmmYdRnDlXlLLqLgwpEZvLWW29h1apVuH37Nvz9/bFx40ZLh1QrtVqNdu3a4c9//jNiY2MRHBxs6ZDIBBpiW6zNxx9/rDNd1urVq3XWz5kzB1FRUZg7d66FIjTcE088gTVr1sDLy+uBqG/Lli0oLi5GSkoK3N3dtcuHDh2qc25v3rxpkv0RUf1pjHnnXswx1l+fqVWXs+guhYiIpYOghiU8PBwAsGHDBgtHQmR92D8Mt379eowaNQpMR2QK5m5PbK9kjRQKBZKSkjBy5EhLh9JgMF9TfWrs7Y13KhARERERERGRUTioQERERERERERG4aACERERERERERmFgwpEREREREREZBQOKhARERERERGRUewsHQA1TBs3boRCobB0GERWi/3DcPzMqCFheyVrM2rUKIwaNcrSYTQ47MtUX0aMGGHpEMyGU0qSwQ4cOIDLly9bOgyiRmPUqFGYMmUKQkJCLB1Kg1JYWIi1a9di3759KCoqQteuXfHEE0/gkUcegY0Nb8R70Jlrar0rV65g//79Zqmb6F6nT5/GkiVLcOfOHcyePRvNmjWzdEhEVAetWrVqtN/1OKhARGRhnF+8boqLi7F161YsX74cu3fvRosWLfDiiy8iMjIS/v7+lg6PiMggZWVlmD9/PmbOnIn+/fvj008/hZeXl6XDIiKqFv8rh4iIGjQHBweEh4fju+++w5kzZ/Dyyy/jyy+/RFBQEJ588kl8/vnnKCwstHSYRES1unTpEvr164fY2FgkJiZix44dHFAgIqvHQQUiImo02rRpg9jYWJw/fx47d+6Eu7s7XnrpJXh7eyMyMhLHjx+3dIhERFXavHkzOnfujJs3b+LgwYOYPHkyn/cnogaBgwpERNTo2Nra4s9//jPWr1+PixcvIiYmBrt27cKjjz6Kbt26Yfny5cjPz7d0mEREKCwsxOTJkzF8+HAMGjQIP//8Mx599FFLh0VEpDcOKhARUaPWsmVLxMTEIC0tDd999x2Cg4MxZcoUtGzZEpGRkdi3b5+lQySiB9TJkyfRo0cPfPnll9iyZQs+//xzqNVqS4dFRGQQDioQEdEDwcbGBn/+85/x+eef4+rVq0hMTMTBgwfRp08fdOjQAQkJCcjMzLR0mET0ABARfPDBB+jWrRuaNWuGY8eOYfDgwZYOi4jIKBxUICKiB467uzsmTJiAX3/9FT///DN69+6N+Ph4+Pj4YOTIkdi1axc4ORIRmcONGzfwl7/8BVOnTsWbb76J7777Dt7e3pYOi4jIaBxUICKiB1rXrl3xz3/+E1evXsUHH3yA9PR0PPnkk2jXrh0SEhJw/fp1S4dIRI3Ed999h06dOuHUqVNISUlBbGwsbG1tLR0WEVGdcFCBiIgIgEajwYQJE7Bv3z6kpqYiLCwMiYmJ8Pb2xpNPPokNGzbgzp07lg6TiBqg4uJivPnmmxg4cCB69+6No0ePolevXpYOi4jIJDioQEREdJ/g4GDMmzcPV69exdq1awEAo0aNgp+fH958802cP3/ewhESUUNx+vRphISEYNmyZfj000+xfv16uLm5WTosIiKT4aACERFRNRwcHBAeHo7vvvsOp0+fxujRo/Hpp58iKChIe/dCSUmJpcMkIiv1+eefo1u3brCzs8ORI0fw4osvWjokIiKT46ACERGRHtq2bau9e2Hnzp1wd3fH888/Dy8vL0RGRuLEiROWDpGIrMTt27fx3HPPYcyYMXjppZewb98+BAUFWTosIiKz4KACERGRAWxt/z97dx5WVbn+j/+9gQ2bDWwEJxBEGdQccMQ+gJqZqZnhjHqO9slKRcvUnHAoc0ATsZzST6Idz1FTETVnc8ij5jeOaUgipjnEoGg4AwIy3b8/+rGPW0D3ZlqA79d1cXldaz3rXvda61nIvvdaz2OO119/HVu3bkVCQgKCg4Nx+PBhtGzZEj4+PggPD8ejR4+UTpOIFPKf//wHbdq0wb///W8cOHAAy5Ytg6WlpdJpERGVGxYViIiISqhevXoIDg7GlStXcPjwYXh4eOCjjz5CvXr1EBQUhF9++UXpFImoguTm5mL27Nno2LEjWrZsibi4OPTo0UPptIiIyh2LCkRERKVkZmamf3rh1q1bCAsLQ1RUFHx8fNC8eXOEhobi3r17SqdJROUkMTERXbp0QWhoKL744gt89913qFmzptJpERFVCBYViIiIypCDgwNGjRqFc+fO4cyZM+jYsSPmzZsHFxcXDBo0CEeOHIGIKJ0mEZWR7du3o3Xr1rh37x5OnTqF8ePHQ6VSKZ0WEVGFYVGBiIionLRr1w6rV69GcnIyli1bhmvXrqFbt2546aWXEBoaipSUFKVTJKISyszMxPjx4zFw4EC89dZbOH36NFq2bKl0WkREFY5FBSIionKm0+kwatQonDlzBufPn0e/fv0QFhaG+vXrIyAgAJGRkcjNzVU6TSIy0i+//ILWrVtj06ZN2L17N9avXw+tVqt0WkREimBRgYiIqAI1b94cCxcuxPXr17Fx40ZkZWVh8ODBaNiwIaZNm4b4+HilUySiYogIli1bBn9/f7i6uiImJgYBAQFKp0VEpCgWFYiIiBSg0WgQGBiIw4cP4+LFixg2bBjWrVsHT09PdOvWDZGRkcjJyVE6TSL6/6WkpKBXr16YPHkypk+fjsOHD8PFxUXptIiIFMeiAhERkcIaN26MhQsXIjk5GQcPHoSDgwP+/ve/o379+hg/fjzOnz+vdIpEL7RDhw6hVatW+O2333DixAnMnj0bZmb8M5qICGBRgYiIqNIwNzfXT00ZHx+Pjz/+GHv27IG3tzd8fHwQHh6OR48eKZ0m0Qvj8ePHmDZtGnr27Ilu3bohNjYWfn5+SqdFRFSpsKhARERUCbm4uCA4OBhXrlzB4cOH4eHhgbFjx8LFxQVBQUGIjo5WOkWiau3ixYvw9fXF//3f/+Ff//oX1q9fD1tbW6XTIiKqdFhUICIiqsTMzMz0Ty/8+eefWLRoEaKiotCuXTs0b94coaGhuHfvntJpElUr69evh4+PDywtLREdHY1hw4YpnRIRUaXFogIREVEV4eDggFGjRuHcuXM4c+YMOnbsiHnz5sHFxQWDBg3CkSNHICJKp0lUZT18+BBDhgzB8OHD8f777+PkyZPw9PRUOi0iokqNRQUiIqIqqF27dli9ejVu3LiBZcuW4erVq+jWrRuaNm2K0NBQpKSkKJ0iUZVy7NgxtGjRAj/99BP+/e9/Y9myZVCr1UqnRURU6bGoQEREVIXZ29tj1KhR+OWXX3D+/Hn07dsXixYtQv369REQEIDIyEjk5eUpnSZRpZWbm4vZs2fj9ddfh4+PD86ePYvOnTsrnRYRUZXBogIREVE10bx5cyxcuBA3btzAxo0bkZWVhcGDB6NBgwaYNm0a4uPjlU6RqFJJSEjAq6++itDQUHzxxRf47rvvULNmTaXTIiKqUlhUICIiqmY0Gg0CAwNx+PBh/Pbbbxg2bBjWrVsHT09PdOvWDZGRkcjJyVE6TSJFbdu2DW3atMGDBw/w888/Y/z48UqnRERUJbGoQEREVI01adIECxcuRFJSEnbu3AkHBwf87W9/g5ubG8aPH4/z588rnSJRhUpLS0NQUBAGDRqEwMBA/Pzzz/D29lY6LSKiKotFBSIioheApaUlAgICsHXrViQkJGDChAnYvXs3vL294ePjg/DwcGRkZCidJlG5OnPmDNq1a4fvvvsOu3fvxurVq6HVapVOi4ioSmNRgYiI6AXj4uKC4OBgXL16FYcPH4aHhwfGjh2LevXqISgoCGfPnlU6RaIyJSJYtmwZOnToADc3N8TExOCtt95SOi0iomqBRQUiIqIXlJmZGV5//XVs3boViYmJmDNnDn766Se0bdsWPj4+WLZsGe7du6d0mkSl8ueff+LNN9/E5MmTMX36dBw6dAj16tVTOi0iomqDRQUiIiKCk5MTxo8fj9jYWP0j4jNnzoSLiwsGDRqEI0eOQESUTpPIJAcPHkTr1q1x6dIl/Pjjj5g9ezbMzPjnLxFRWeJvVSIiIjLQrl07rF69Gjdu3MCyZctw5coVdOvWDU2bNkVoaChu376tdIr0gsvPz3/m+qysLIwfPx49e/ZEt27dcO7cOfj6+lZQdkRELxYWFYiIiKhI9vb2GDVqFKKjo3HmzBn06NEDixYtgqurKwYNGoQ9e/YgLy9P6TTpBZOSkoLXX38dmZmZRa7/7bff4Ovri3/+85/YsGED1q9fD1tb2wrOkojoxcGiAhERET1Xu3btsGzZMty4cQMbN27E/fv30adPHzRs2BDTpk1DQkKC0inSC0BE8M477+Df//43Jk2aVGj9+vXr4ePjAysrK0RHR2Po0KEKZElE9GJhUYGIiIiMptFoEBgYiMOHD+O3337D0KFDsW7dOnh4eKBbt26IjIxETk6OSTFTUlKK/daZ6EmrV6/GwYMHAQBff/01du3aBQB48OABBg8ejPfeew8fffQRTp48CU9PTyVTJSJ6YbCoQERERCXSpEkTLFy4EElJSdiyZQs0Gg3+9re/wc3NDdOmTcOVK1eMirNkyRK88sorSElJKeeMqSq7evUqJk6cqB8wVKVS4e2330ZERARatGiBqKgoHD16FAsXLoRarVY4WyKiF4dKOJQzEZGiVCoVIiIiMGjQIKVTISq169ev49tvv8XXX3+N+Ph4tGvXDqNGjcKwYcOg1WoLtc/NzYWzszPu3r0LFxcXHDp0CE2bNlUgc6rMcnNz4evri3Pnzhk8CaNWq+Hq6orWrVtj7dq1cHR0VDBLIqIXE59UICIiojLj6uqK4OBgXL16FYcPH4aHhwfGjh2LevXqISgoCGfPnjVov3fvXty5cwciglu3bsHHxwcHDhxQKHuqrEJCQhATE1Po1ZqcnBwkJCTg5ZdfZkGBiEghLCoQERFRmTMzM8Prr7+OrVu3IjExEXPmzMH/+3//D23btoWPjw/Cw8ORlpaG1atXw8LCAsBf30ZnZWUhICAAX3/9tcJHQJXFL7/8gpCQkGJnGsnPz8cnn3yC//znPxWcGRERAXz9gYhIcXz9gV4UIoJjx45h7dq12LFjB8zNzZGRkYHi/hQZN24clixZAjMzfgfyonr06BG8vb2RlJSE3NzcYtuZm5vDxcUF58+fh52dXQVmSERE/F+aiIiIKoRKpUKXLl3w7bffIjk5Ga+++irMzc2Lbf/VV19h4MCBnBniBTZp0qTnFhSAvwpWiYmJ+PDDDysoMyIiKmChdAJERET04qlRowZiY2Of+WExPz8fe/fuRadOnbB//37UqVOnAjMkpR08eBDh4eHFPsmiVquRm5sLCwsLdOjQAW+88Qb69etXwVkSERGLCkRERFThjhw5gsTExOe2y8nJwblz5+Dj44ODBw9yZogXxJ07dzB06FCoVCqDooJarUZOTg4cHBzw1ltvISAgAG+88QZfeSAiUhCLCkRERFThwsPDYW5uXuzge0/KycnBzZs34evri927d6Nz584VkCEp6b333sPdu3dhZmYGMzMziAhatmyJAQMGoFevXmjTpg1UKpXSaRIREVhUICKiYgQGBiqdAlVTjx8/xr59+5Cfn19sm4IPjAX/5ufnIzU1FV26dIGPjw8aNGhQIblSxYuPj8eZM2dgbm6OunXrol69enBycoJGo8G5c+dw7tw5pVMkqhT8/PwwceJEpdMgYlGBiIiKtm3bNvj6+sLV1VXpVKgcKHl9LS0t8dZbbyE3NxcigpycHP2/+fn5yM3N1f+bl5dnsCwnJwd37tyBTqeDg4NDheZdMGWhr69vhe73RZKbm4tHjx6hU6dOqF27Nmf+ICoGp1ClyoRFBSIiKtbHH3/MqS6rKZVKxetrooKndyIjIxXOhIhedHyakCoTln+JiIiIiIiIqERYVCAiIiIiIiKiEmFRgYiIiIiIiIhKhEUFIiIiIiIiIioRFhWIiIiIiIiIqERYVCAiIkWMGDECdnZ2UKlUiImJKXW7irJ//37Y29tjz549FbrfuXPnolmzZtDpdLCysoKXlxemTp2K9PT0Cs3jaUqdj6pi9OjRUKlU+p9hw4YVanPkyBFMnz4d27dvh4eHh77t22+/Xaht9+7dYWdnB3NzczRv3hzR0dEVcRilsmnTJrRv3x52dnZo0KAB3n33Xdy6datSxAsJCTG4PgU/LVq0KLJ9fn4+lixZAn9//yLXG3Of7t69G6GhocjLyytRzk9j/1E2HvDsflHc9d65c6dBn6tVq1apciBSEosKRESkiLVr12LNmjVl1q6iiIgi+z169CjGjh2L+Ph43LlzBwsWLMDSpUsVn1ZMqfNRlTg6OuLAgQO4dOkSvvnmG4N1n332GZYvX44ZM2ZgwIABuHbtGjw9PVGzZk1s3LgR+/btM2h/6NAhREZGIiAgAHFxcWjbtm1FHorJIiIiMHToUAQGBuL69evYtWsXTpw4gZ49eyI3N1fxeKa4fPkyXnnlFUycOBEZGRlFtjHmPu3duzc0Gg26du2KBw8elCon9h9l4wHP7xfFXe8+ffrg+vXrOHHiBN58880S7ZuosmBRgYiIyAS9evXCw4cPERAQUKH7tbW1RVBQEBwdHWFnZ4dBgwahX79++P7775GUlFShuTxJqfNRlMzMzGK/QVaStbU13njjDTRu3BhWVlb65QsXLsSWLVuwdetW2NnZGWyzfPlymJmZISgoCA8fPqzolMvM6tWrUa9ePUyZMgX29vZo3bo1Jk6ciJiYGJw6dUrxeACwYcMGiIjBz/nz5w3a/Prrr5g2bRrGjBmD1q1bFxvL2Pt0/PjxaNWqFd58880Sf5hl/1G+/xjbL4q63iqVCi4uLujUqRMaNWpk8r6JKhMWFYiISDEqlapM21Vne/fuhbm5ucGygsdli/vW9EXzzTffICUlRek0jHLlyhV8+umnmDNnDjQaTaH1/v7+mDBhAm7cuIHJkycrkGHZSEpKgrOzs8E9XL9+fQBAQkKC4vGM1apVK2zfvh1Dhw41KAw9zZT7dPbs2YiJicHSpUtNzof9p3L0H2P7BVC6601U2bGoQEREZeLHH39Es2bNYG9vD41GA29vbxw8eFC/XkQQFhaGJk2awMrKCvb29pgyZUqhOMa2M8aqVatgY2MDrVaLXbt2oWfPntDpdHB1dcXmzZsL7ffLL79E06ZNYWVlBQcHB/Tt2xcXL17Utzl58iTc3NygUqnw1Vdf6ZcfP34cL7/8MrRaLXQ6Hby9vZGamgoAyMvLw6xZs+Dm5gZra2u0bNkSERERJTqep924cQPW1tZwd3cvk3imKup8GHvOly9fDo1Ggzp16mD06NFwdnaGRqOBv7+/wTeG48aNg6WlJZycnPTLPvzwQ9jY2EClUuHOnTsAgAkTJmDSpEm4evUqVCoVvLy8AADff/89dDod5s+fXxGnxGjLly+HiKB3797FtgkJCUHjxo2xdu1aHDly5JnxjOm/ptwPZdVvPTw8ChV6Ct5f9/DwUDxeRSjuPnVwcEDnzp2xdOlSk18jYv+pev2nNNebqNITIiJSFACJiIhQOo1CTM0rMjJSZs+eLffu3ZO7d++Kr6+v1KxZU79+5syZolKp5IsvvpD79+9LRkaGrFy5UgDI2bNnTW5nrJkzZwoA+eGHH+Thw4eSkpIinTp1EhsbG8nOzta3mzVrllhaWsqGDRvkwYMHcu7cOWnbtq3UqlVLbt26pW+XlJQkAGTFihUiIpKeni46nU5CQ0MlMzNTbt26Jf3795fbt2+LiMjkyZPFyspKtm3bJvfv35cZM2aImZmZnD592uRjedKjR4/Ezs5Oxo0bV6Lty6rfPX0+RIw/50FBQWJjYyMXLlyQrKwsiYuLk/bt24udnZ0kJibq2w0dOlTq1q1rsN+wsDABoD/PIiIDBgwQT09Pg3Z79+4VOzs7mTt3bqmPdeDAgTJw4ECTtgkKChIXF5dCyz08PKRZs2ZFbuPp6Sl//PGHiIj89NNPYmZmJg0bNpT09HQRETlw4ID06dPHYBtj+6+x16as+u2xY8dErVbL8uXLJTU1Vc6fPy9NmzaVHj16mBSnvOLNmzdPXF1dpUaNGqJWq6Vhw4bSp08f+fnnn4vd5n/+53+kVatWRsV/3n06ffr0Ev1uY/+pHP3nScb0i+Ku9/jx4w3+vzRGSX4fEZUXFhWIiBRWXYoKT1uwYIEAkJSUFMnIyBCtVivdunUzaLN582aDP7CMbWeKgj+CMzMz9csKihRXrlzR79fW1laGDBlisO3PP/8sAAw+kD79Ifr8+fMCQPbu3Vto35mZmaLVag3iZmRkiJWVlXzwwQcmH8vTx9W4cWNJTU0t0fYVUVR41jkX+esDt729vUG806dPCwCZM2eOfllpigplqayKCunp6aJSqSQgIKDIbZ78UCgiMmnSJAEgY8eOFZHCHwpN6b/GXJuy7reffPKJAND/uLq6SlJSkslxyiNeYmKiREdHS1pamjx+/FiioqKkTZs2Ym1tLefPny9yG1OKCs+7T//xj38IAFm/fr3RObP/VJ7+8yRj+kVx15tFBarq+PoDERGVC7VaDeCvx2CvXLmCjIwMdO3a9ZnbGNuutCwtLQEAOTk5AIC4uDikp6fDx8fHoF379u1haWn5zAG8PDw8UKdOHQwbNgyzZ89GfHy8ft2lS5eQkZFhMD2dtbU1nJycDB4rNtWOHTuwdetWHDx4sNAAbZXV0+e8OD4+PtBqtaU6P5VdSkoKRARardao9iEhIWjSpAlWrlyJkydPFlpfmv4LFL42ZdlvZ86cifDwcPzwww9IT0/HtWvX4O/vDz8/vxINMFrW8erXr482bdrA1tYWlpaW8PX1xbp165CZmYmVK1eaHO9JxtynBX3gzz//NDou+0/l6T+mKsn1JqoKWFQgIqIysW/fPrz66quoXbs2rKysMHXqVP2669evAwBq1679zBjGtitrBdN82draFlpXo0YNpKWlFbuttbU1jh49io4dO2L+/Pnw8PDAkCFDkJmZiUePHgEAPvnkE4P5yBMSEko8uOKWLVuwcOFCHDt2DA0bNixRjMrOysoKt2/fVjqNcpOVlQUAzx3YrYBGo8G6deugUqnw3nvvITMz02B9afpvUcqq3968eROhoaEYNWoUXnvtNdjY2MDd3R1r1qxBcnIywsLCTMqrrOMVx9vbG+bm5vj9999LHMPY+9Ta2hrAf/uEMdh/Knf/eZaSXG+iqoBFBSIiKrXExET069cPTk5OOHXqFB4+fIjQ0FD9+oLRyR8/fvzMOMa2K2s1atQAgCL/eH7w4AFcXV2fuX3z5s2xZ88eJCcnIzg4GBEREVi8eLG+OLJkyZJCU9ZFRUWZnOeKFSuwceNGHD16FPXq1TN5+6ogJyfHqHNelRV8sMjLyzN6Gz8/P0ycOBGXL1/GvHnzDNaVtv8+raz67eXLl5GXl1eor+p0Ojg6OiIuLs6kvMo6XnHy8/ORn59v9If2p5lyn2ZnZwP4b58wBvtP5e4/z1KS601UFbCoQEREpRYbG4ucnBx88MEH8PDwgEajMZiyq0WLFjAzM8Px48efGcfYdmWtRYsWsLW1xZkzZwyWnzp1CtnZ2WjXrl2x2yYnJ+PChQsA/vpj+vPPP0fbtm1x4cIF1K9fHxqNBjExMaXKT0QQHByM2NhY7Ny5s8hvFKuLY8eOQUTg6+urX2ZhYfHc1yaqkjp16kClUuHhw4cmbTdv3jy89NJLOHv2rMHy0vTfopRVvy34MHrz5k2D5Wlpabh3755+Kj+l4gFAjx49Ci07ffo0RAR+fn4mxSrJfVrQB+rWrWv0fth/Kk//MVVJrjdRVcCiAhERlZqbmxsA4MiRI8jKysLly5cN3sOtXbs2BgwYgG3btuGbb75Bamoqzp07h/DwcIM4xrYraxqNBpMmTcKOHTuwceNGpKamIjY2FmPGjIGzszOCgoKK3TY5ORmjR4/GxYsXkZ2djbNnzyIhIQG+vr7QaDR49913sXnzZqxatQqpqanIy8vD9evXC/1h+ywXLlzAokWLsGbNGqjVaoNHilUqFRYvXlwWp0ER+fn5uH//PnJzc3Hu3DlMmDABbm5uGD58uL6N6fSj4QAAIABJREFUl5cX7t27h507dyInJwe3b98uck55R0dHJCcnIz4+HmlpacjJycGBAwcq3ZSSWq0WHh4e+td9jFXwGLu5uXmh5SXtv8Xt53n9dsiQIahbty6io6OLjePu7o4uXbpgzZo1OHHiBDIzM5GUlKTP5/3339e3VSIe8Nd0j1u2bMGDBw+Qk5ODqKgojBgxAm5ubhgzZszzT9YTSnKfFvQBb29vo/Nm/6k8/cdUT19vomqjokeGJCIiQ6gmsz8EBweLo6Oj1KhRQwIDA+Wrr74SAOLp6SmJiYmSlpYmI0aMkJo1a4qtra107NhRZs2apR99+9dffxURMbqdMVauXClarVYASKNGjeTq1asSHh4uOp1OAEiDBg3k999/FxGR/Px8CQsLk0aNGolarRYHBwfp16+fXLp0SR9vxYoV4uTkJABEq9VK7969JT4+Xvz9/cXBwUHMzc2lXr16MnPmTMnNzRURkcePH0twcLC4ubmJhYWF1K5dWwYMGCBxcXFGH0dsbKzBaOVP/4SFhRkdq0BZ9Luizocp5zwoKEjUarW4uLiIhYWF6HQ66du3r1y9etVgP3fv3pUuXbqIRqMRd3d3+eijj2TKlCkCQLy8vPTTT0ZHR0uDBg3E2tpaOnbsKLdu3ZL9+/eLnZ2dhISElOpYRcp2Sslx48aJWq2WjIwM/bIdO3aIp6enAJBatWrpR+t/2pQpUwpNCWhM/zXl2jyv3/br108AyKxZs555/Hfu3JEJEyaIl5eXWFlZia2trXTo0EG+++47g3ZKxZs0aZJ4enqKjY2NWFhYiKurq4wcOVKSk5MN2kVFRUmHDh3E2dlZf985OTmJv7+/HD9+XERKdp/26tVLXFxcJD8/36S82X8qR/8xpl886enrXYCzP1BVx6ICEZHCqktRgaqWynB9g4KCxNHRUdEcTFGWRYXLly+LhYWFbNiwoazSq1B5eXnSqVMn+eabb16IeOXhzp07otFoZPHixfplxubN/lO14okUfb0LsKhAVR1ffyAiIiLFmDLYXFWVmZmJgwcP4vLly/qB2ry8vDB37lzMnTsX6enpCmdomry8POzcuRNpaWkYMmRItY9XXmbPno3WrVtj3LhxAEzLm/2n6sQr8PT1FhEkJyfj5MmTuHLlSpnth0gJLCoQEVGVcvHixULvKhf1U5k/TADV5zjo+e7du4c33ngDjRs3xnvvvadfPn36dAQGBmLIkCEmD7qnpGPHjmH79u04cOAAtFpttY9XHr788kvExMRg//79UKvVAEzPm/2nasQDir7eu3btgouLCzp16oR9+/aVyX6IlKISEVE6CSKiF5lKpUJERAQGDRqkdCoGKmteVDaUvr4zZszAF198gezsbDRs2BBhYWEYOHCgIrkYKzAwEAAQGRlZpnEPHTqEo0ePYuHChWUalyqnXbt24cKFC5g6dWqhQRNLgv2ncivr612gvH4fEZWEhdIJEBER0YtnwYIFWLBggdJpVArdu3dH9+7dlU6DKkifPn3Qp0+fMovH/lO5lfX1JqqM+PoDEREREREREZUIiwpEREREREREVCIsKhARERERERFRibCoQEREREREREQlwqICEREREREREZUIp5QkIlKY0lP7FUelUimdAhERERVj4MCBnFKSKgVOKUlERMWaMGEC/Pz8lE6DysHgwYN5fU20ZMkSAMDHH3+scCZE9KIr+H1EVBmwqEBERMXy8/OrdE9QUNkYPHgwr6+JCr4R5DkjIqXxCQWqTDimAhERERERERGVCIsKRERERERERFQiLCoQERERERERUYmwqEBEREREREREJcKiAhERERERERGVCIsKRESkqE2bNkGlUsHf39+k7UaMGAE7OzuoVCrExMSY1G779u3w8PCASqWCSqXC22+/XWi77t27w87ODubm5mjevDmio6NNym///v2wt7fHnj17TNqO6EVx5MgRTJ8+vULuRyVs2rQJ7du3h52dHRo0aIB3330Xt27dqhTxQkJC9Of7yZ8WLVoU2T4/Px9Lliwp9vf03Llz0axZM+h0OlhZWcHLywtTp05Fenq6vs3u3bsRGhqKvLy8EuVMRJUXiwpERKSoTZs2wdPTE1FRUbhy5YrR261duxZr1qwpUbsBAwbg2rVr8PT0RM2aNbFx40bs27fPoM2hQ4cQGRmJgIAAxMXFoW3btkbnBgAiYlJ7ohfJZ599huXLl2PGjBkVcj9WtIiICAwdOhSBgYG4fv06du3ahRMnTqBnz57Izc1VPJ4pLl++jFdeeQUTJ05ERkZGkW2OHj2KsWPHIj4+Hnfu3MGCBQuwdOlSBAYG6tv07t0bGo0GXbt2xYMHD8o1ZyKqWCwqEBGRYu7evYsLFy5gzpw5AID169dXeA7Lly+HmZkZgoKC8PDhwzKL26tXLzx8+BABAQFlFrM6yczMNPnplMq4DzLdwoULsWXLFmzduhV2dnYG68rrfqxoq1evRr169TBlyhTY29ujdevWmDhxImJiYnDq1CnF4wHAhg0bICIGP+fPnzdo8+uvv2LatGkYM2YMWrduXWwsW1tbBAUFwdHREXZ2dhg0aBD69euH77//HklJSfp248ePR6tWrfDmm2+WezGEiCoOiwpERKSYrVu3olevXvpvsAr+yDWWSqUqdTt/f39MmDABN27cwOTJk43eN5XON998g5SUlCq/DzLNlStX8Omnn2LOnDnQaDSF1leX+zEpKQnOzs4Gv3vq168PAEhISFA8nrFatWqF7du3Y+jQobCysiq23d69e2Fubm6wrFatWgBQ6OmG2bNnIyYmBkuXLi37hIlIESwqEBGRYjZt2oT+/fvDzs4O3bt3R3x8PH788cci24oIwsLC0KRJE1hZWcHe3h5TpkwpcbsnhYSEoHHjxli7di2OHDnyzLYigi+//BJNmzaFlZUVHBwc0LdvX1y8eFHf5uTJk3Bzc4NKpcJXX32lX378+HG8/PLL0Gq10Ol08Pb2RmpqKgAgLy8Ps2bNgpubG6ytrdGyZUtEREQ8M5eKZMxxjxs3DpaWlnByctIv+/DDD2FjYwOVSoU7d+4AACZMmIBJkybh6tWrUKlU8PLywvLly6HRaFCnTh2MHj0azs7O0Gg08Pf3N/gmtjT7AIDvv/8eOp0O8+fPL9fzRUVbvnw5RAS9e/cutk1Z34+rVq2CjY0NtFotdu3ahZ49e0Kn08HV1RWbN282iFdW96GHh0ehglbB+AceHh6Kx6sIN27cgLW1Ndzd3Q2WOzg4oHPnzli6dClfEyOqLoSIiBQFQCIiIpROo5DyzishIUFq164tubm5IiKyYcMGASDvv/9+ke1nzpwpKpVKvvjiC7l//75kZGTIypUrBYCcPXvW5HYiIp6envLHH3+IiMhPP/0kZmZm0rBhQ0lPTxcRkQMHDkifPn0Mtpk1a5ZYWlrKhg0b5MGDB3Lu3Dlp27at1KpVS27duqVvl5SUJABkxYoVIiKSnp4uOp1OQkNDJTMzU27duiX9+/eX27dvi4jI5MmTxcrKSrZt2yb379+XGTNmiJmZmZw+fboUZ7l4pl5fY4976NChUrduXYNtw8LCBID+WEVEBgwYIJ6engbtgoKCxMbGRi5cuCBZWVkSFxcn7du3Fzs7O0lMTCyTfezdu1fs7Oxk7ty5Rh97gYEDB8rAgQNN3o7+y8PDQ5o1a1bkuvK8H2fOnCkA5IcffpCHDx9KSkqKdOrUSWxsbCQ7O1vfrqzuw2PHjolarZbly5dLamqqnD9/Xpo2bSo9evQwKU55xZs3b564urpKjRo1RK1WS8OGDaVPnz7y888/F7vN//zP/0irVq2Miv/o0SOxs7OTcePGFbl++vTpRf5OJuPx9xFVJnxSgYiIFLFp0ya89dZb+kdme/fuDSsrK0RGRiIzM9OgbWZmJpYsWYLXX38dEydORI0aNWBtbQ1HR8cStSuKn58fPv74Y8THx2PatGlFtsnMzMSXX36J/v37Y9iwYbC3t4e3tze+/vpr3LlzB+Hh4cXGj4+PR2pqKpo3bw6NRoO6deti+/btqFWrFrKysrBq1Sr069cPAwYMQI0aNfDJJ59ArVZj3bp1z829vJXmuE1lYWGh/9a5WbNmWLVqFdLS0srsPPTq1Qupqan49NNPyyQeGe/Ro0f4448/4Onp+dy25XU/+vv7Q6fToXbt2hgyZAgePXqExMREACjT+7Bz584IDg7GuHHjoNPp0KJFC6SlpWHt2rUmxSmveO+88w52796NpKQkpKenY/PmzUhMTETnzp0RFxdXophPWrBgAZydnRESElLk+kaNGgEAYmNjS70vIlIeiwpERKSIglcfCuh0OnTv3h2pqanYtWuXQdsrV64gIyMDXbt2fWZMY9sVJyQkBE2aNMHKlStx8uTJQuvj4uKQnp4OHx8fg+Xt27eHpaXlMwdM8/DwQJ06dTBs2DDMnj0b8fHx+nWXLl1CRkaGwXRu1tbWcHJyMniMWymlOe7S8vHxgVarrRTngUonJSUFIgKtVmtU+/K8HwHA0tISAJCTkwOgbO/DmTNnIjw8HD/88APS09Nx7do1+Pv7w8/Pz2DgQqXi1a9fH23atIGtrS0sLS3h6+uLdevWITMzEytXrjQ53pN27NiBrVu34uDBg4UG4ixQ0Af+/PPPUu2LiCoHFhWIiKjCnT9/HrGxsQgICDCYI33Pnj0ACs8Ccf36dQBA7dq1nxnX2HbF0Wg0WLduHVQqFd57771CT0wUTINma2tbaNsaNWogLS2t2NjW1tY4evQoOnbsiPnz58PDwwNDhgxBZmYmHj16BAD45JNPDM5HQkJCsVO4VaTSHHdZsLKywu3bt8t1H1T+srKyAOCZA/49qTzvx6KU1X148+ZNhIaGYtSoUXjttddgY2MDd3d3rFmzBsnJyQgLCzMpr7KOVxxvb2+Ym5vj999/L3GMLVu2YOHChTh27BgaNmxYbDtra2sA/+0TRFS1sahAREQV7ttvv8Xf/va3QtOZ3bt3D9bW1jh06JB+EDIA+lHiHz9+/My4xrZ7Fj8/P0ycOBGXL1/GvHnzDNbVqFEDAIr8sPLgwQO4uro+M3bz5s2xZ88eJCcnIzg4GBEREVi8eLG+CLJkyZJC5yQqKqrEx1JWSnvcpZGTk1Pu+6CKUfBBMi8vz+htyvN+fFpZ3YeXL19GXl4e6tWrZ7Bcp9PB0dHR5NcLyjpecfLz85Gfn2900edpK1aswMaNG3H06NFCuT4tOzsbwH/7BBFVbSwqEBFRhRIRbNmyBR9++GGhdQ4ODggMDEReXh42bdqkX96iRQuYmZnh+PHjz4xtbLvnmTdvHl566SWcPXu2UHxbW1ucOXPGYPmpU6eQnZ2Ndu3aFRszOTkZFy5cAPDXh5fPP/8cbdu2xYULF1C/fn1oNBrExMSUKu/yYspxW1hY6B8nLwvHjh2DiMDX17fc9kEVo06dOlCpVHj48KFJ25XH/ViUsroPC4oZN2/eNFielpaGe/fu6aeCVCoeAPTo0aPQstOnT0NE4OfnZ1IsEUFwcDBiY2Oxc+fOIp8ceVpBH6hbt65J+yKiyolFBSIiqlA//fQTdDodOnToUOT6MWPGADB8BaJ27doYMGAAtm3bhm+++Qapqak4d+5coYHYjG33PAWPXT8977pGo8GkSZOwY8cObNy4EampqYiNjcWYMWPg7OyMoKCgYmMmJydj9OjRuHjxIrKzs3H27FkkJCTA19cXGo0G7777LjZv3oxVq1YhNTUVeXl5uH79eqEPEkow5bi9vLxw79497Ny5Ezk5Obh9+zYSEhIKxXR0dERycjLi4+ORlpamLxLk5+fj/v37yM3Nxblz5zBhwgS4ublh+PDhZbKPAwcOcEpJhWi1Wnh4eOhfUzJWedyPxe3neffhkCFDULduXURHRxcbx93dHV26dMGaNWtw4sQJZGZmIikpSZ/P+++/r2+rRDzgr+ket2zZggcPHiAnJwdRUVEYMWIE3Nzc9L+DjXXhwgUsWrQIa9asgVqtNnh1RKVSYfHixYW2KegD3t7eJu2LiCqpCp9vgoiIDOAFmlLy/fffFxsbG7GwsJBWrVpJdHS0wfp58+aJs7OzABAA4uLiIitXrhQRkbS0NBkxYoTUrFlTbG1tpWPHjjJr1iwBIK6urvLrr78a3W7Hjh3i6ekpAKRWrVoyduzYIvOdMmVKoSns8vPzJSwsTBo1aiRqtVocHBykX79+cunSJX2bFStWiJOTkwAQrVYrvXv3lvj4ePH39xcHBwcxNzeXevXqycyZM/VTaj5+/FiCg4PFzc1NLCwspHbt2jJgwACJi4srs/P/JFOvrzHHLSJy9+5d6dKli2g0GnF3d5ePPvpIpkyZIgDEy8tLPzVkdHS0NGjQQKytraVjx45y69YtCQoKErVaLS4uLmJhYSE6nU769u0rV69eLbN97N+/X+zs7CQkJMTkc8Yp3Epv3LhxolarJSMjQ7+svO/HlStXilarFQDSqFEjuXr1qoSHh4tOpxMA0qBBA/n9999F5Pn3Yb9+/QSAzJo165nHeefOHZkwYYJ4eXmJlZWV2NraSocOHeS7774zaKdUvEmTJomnp6f+97Grq6uMHDlSkpOTDdpFRUVJhw4dDH4vOzk5ib+/vxw/flxERGJjY/XrivoJCwsrtP9evXqJi4uL5OfnPzNPKh5/H1FlohIRqagCBhERFaZSqRAREYFBgwYpnYqBypoXlY3KeH1Hjx6NyMhI3L17V+lUihQYGAgAiIyMVDiTquvKlSto2rQp1q1bh2HDhimdjsny8/Px6quvYvjw4XjvvfeqfbzycPfuXbi6uiIkJASTJk1SOp0qi7+PqDLh6w9ERERUaZgyiB9VPV5eXpg7dy7mzp2L9PR0pdMxSV5eHnbu3Im0tDQMGTKk2scrL7Nnz0br1q0xbtw4pVMhojLCogIRERERVZjp06cjMDAQQ4YMMXnQRiUdO3YM27dvx4EDB6DVaqt9vPLw5ZdfIiYmBvv374darVY6HSIqIywqEBERkeJmzJiBdevW4eHDh3B3d8e2bduUTonK0fz58zFu3Dh8/vnnSqditK5du+Lbb7+Fk5PTCxGvrO3atQuPHz/GsWPH4ODgoHQ6RFSGLJROgIiIiGjBggVYsGCB0mlQBerevTu6d++udBpUQfr06YM+ffoonQYRlQM+qUBEREREREREJcKiAhERERERERGVCIsKRERERERERFQiLCoQERERERERUYlwoEYiIipWVFSU0ilQOeL1Nc3169cBAFu3blU4EyJ60V2/fh2urq5Kp0EEAFCJiCidBBHRi0ylUiEiIgKDBg1SOhUDKpVK6RSIiIioGAMHDkRkZKTSaRDxSQUiIioaa85E1YOnpyeCgoIwdepUpVMhIqJqiGMqEBEREVVjaWlpsLW1VToNIiKqplhUICIiIqrG0tPTWVQgIqJyw6ICERERUTWVl5eHzMxM2NnZKZ0KERFVUywqEBEREVVTaWlpAMAnFYiIqNywqEBERERUTaWnpwNgUYGIiMoPiwpERERE1VRBUYGvPxARUXlhUYGIiIiomuLrD0REVN5YVCAiIiKqpvj6AxERlTcWFYiIiIiqKb7+QERE5Y1FBSIiIqJqKi0tDRYWFrCyslI6FSIiqqZYVCAiIiKqptLT0/mUAhERlSsWFYiIiIiqqfT0dI6nQERE5YpFBSIiIqJqKi0tjU8qEBFRuWJRgYiIiKiaevToEZ9UICKicsWiAhEREVE1lZaWxqICERGVKxYViIiIiKopDtRIRETljUUFIiIiomqKAzUSEVF5Y1GBiIiIqJri6w9ERFTeWFQgIiIiqqb4+gMREZU3FhWIiIiIqqn09HTY2NgonQYREVVjLCoQERERVVNpaWl8UoGIiMoViwpERERE1RQHaiQiovLGogIRERFRNcUxFYiIqLyxqEBERERUDWVnZyM7O5tPKhARUbliUYGIiIioGkpLSwMAFhWIiKhcsahAREREVA2lp6cDAF9/ICKicsWiAhEREVE1VFBU4JMKRERUnlhUICIiIqqG+PoDERFVBBYViIiIiKohvv5AREQVgUUFIiIiomqooKhgY2OjcCZERFSdsahAREREVA2lpaVBo9FArVYrnQoREVVjLCoQERERVUPp6ekcT4GIiModiwpERERE1VB6ejrHUyAionJnoXQCRERERFQ6P/zwAwIDA6HVamFrawtbW1ukpaUhKysLw4cPh42NDezs7KDT6dCiRQv07t1b6ZSJiKiaYFGBiIiIqIrz9/dHRkYG7t+/X2jdxo0bYWb218OpOTk5CA8Pr+j0iIioGuPrD0RERERVnLW1Nbp27Qpzc/NC6/Ly8pCTk4OcnBxYW1tjyJAhCmRIRETVFYsKRERERNXA815pUKvVGD58OMdZICKiMsWiAhEREVE1EBAQgPz8/GLX5+TkYOTIkRWYERERvQhYVCAiIiKqBurVq4dmzZoVuc7MzAzt27dHmzZtKjgrIiKq7lhUICIiIqom+vfvD7VaXeS6sWPHVnA2RET0ImBRgYiIiKia6NWrF3Jycgot12q1GDhwoAIZERFRdceiAhEREVE10b59e9SsWdNgmaWlJUaOHAmtVqtQVkREVJ2xqEBERERUTZiZmSEgIMDgFYjs7GyMGDFCwayIiKg6Y1GBiIiIqBp56623kJubCwAwNzdHhw4dih3AkYiIqLRYVCAiIiKqRnr06AELCwsAgIhwgEYiIipXLCoQERERVSO2trbo2LEjAMDe3h79+/dXOCMiIqrOWFQgIiIiqmb69OkDABg1ahQsLS0VzoaIiKozlYiI0kkQEb3IVCoVIiIiMGjQIKVTqbYCAwOxbds2pdMgIqJyMHDgQERGRiqdBtELy0LpBIiIiCqCr68vPv74Y6XToCpm8ODBmDBhAvz8/JROxWT/+te/8M4771T4fpcsWQIAvN+oQhT0NyJSDosKRET0QnB1deXTIGSywYMHw8/Pr0r2nd69e0Oj0VT4fgu+Ma6K54yqHj6hQKQ8jqlAREREVA0pUVAgIqIXD4sKRERERERERFQiLCoQERERERERUYmwqEBEREREREREJcKiAhERERERERGVCIsKRERET1m8eDHq1KkDlUqFr7/+ukxifv7557C3t4dKpUJMTEyZxKwMyuNcPUt+fj6WLFkCf3//YtucPHkSHTp0gFarhbOzM4KDg/H48eNyz+1Z9u/fD3t7e+zZs0fRPKqCI0eOYPr06di+fTs8PDygUqmgUqnw9ttvF2rbvXt32NnZwdzcHM2bN0d0dLQCGZtm06ZNaN++Pezs7NCgQQO8++67uHXrVqWIFxISoj/fT/60aNGiyPbPux/nzp2LZs2aQafTwcrKCl5eXpg6dSrS09P1bXbv3o3Q0FDk5eWVKGciUh6LCkRERE+ZPHkyfvrppzKNOX36dKxevbpMY1YG5XGuinP58mW88sormDhxIjIyMopsExcXh+7du6Nr1664ffs2duzYgX/84x8YM2ZMheRYHBFRdP9VxWeffYbly5djxowZGDBgAK5duwZPT0/UrFkTGzduxL59+wzaHzp0CJGRkQgICEBcXBzatm2rUObGiYiIwNChQxEYGIjr169j165dOHHiBHr27Inc3FzF45nCmPvx6NGjGDt2LOLj43Hnzh0sWLAAS5cuRWBgoL5NwdSnXbt2xYMHD8o1ZyIqHywqEBERUaX366+/Ytq0aRgzZgxat25dbLt58+bByckJc+bMgY2NDfz8/BAcHIx//vOfuHjxYgVmbKhXr154+PAhAgICFMuhQGZm5jOf9FDKwoULsWXLFmzduhV2dnYG65YvXw4zMzMEBQXh4cOHCmVYeqtXr0a9evUwZcoU2Nvbo3Xr1pg4cSJiYmJw6tQpxeMBwIYNGyAiBj/nz583aGPs/Whra4ugoCA4OjrCzs4OgwYNQr9+/fD9998jKSlJ3278+PFo1aoV3nzzzXIvhhBR2WNRgYiIiCq9Vq1aYfv27Rg6dCisrKyKbJObm4t9+/ahc+fOUKlU+uU9e/aEiGDXrl0VlW6l9s033yAlJUXpNAxcuXIFn376KebMmQONRlNovb+/PyZMmIAbN25g8uTJCmRYNpKSkuDs7GzQP+vXrw8ASEhIUDyesYy5HwFg7969MDc3N1hWq1YtACj0dMPs2bMRExODpUuXln3CRFSuWFQgIiIyUl5eHmbNmgU3NzdYW1ujZcuWiIiI0K//8ccf0axZM9jb20Oj0cDb2xsHDx4sNt6ff/6Jhg0bwsLCAm+88QYAYMSIEfr3mD09PXH27FkAwLvvvgutVgt7e3vs3r3b6JwXLVoErVYLOzs7pKSkYNKkSXBxccGlS5eeezwlNW7cOFhaWsLJyUm/7MMPP4SNjQ1UKhXu3LlT6n0U5dq1a0hPT4ebm5vBck9PTwDAuXPnymW/z3Py5Em4ublBpVLhq6++AgCsWrUKNjY20Gq12LVrF3r27AmdTgdXV1ds3rxZv+3y5cuh0WhQp04djB49Gs7OztBoNPD39zf4JtrYcz5hwgRMmjQJV69ehUqlgpeXFwDg+++/h06nw/z58yvilBSyfPlyiAh69+5dbJuQkBA0btwYa9euxZEjR54ZT0Tw5ZdfomnTprCysoKDgwP69u1r8LSKsdcAeP69bywPD49CBZ2C8Q88PDwUj1cRbty4AWtra7i7uxssd3BwQOfOnbF06VK+LkRUxbCoQEREZKRp06Zh0aJFWLJkCW7evImAgAD8/e9/x5kzZwD8VSQYPHgw4uPjkZycDFtbWwwdOrTYeI6OjvDx8cGOHTvw/fffAwDWrl2LAQMGwNzcHD/++CPatGkDAFi3bh369euHjRs3PvOD19OmTp2KiRMnIj09HQsWLIC7uzt8fX0hIs89npJavnw5Bg0aZLBs5cqVmDNnTqniPk/Bh6mnH53D749JAAAgAElEQVTXaDSwtrbGn3/+Wa77L07Hjh0LjTvxwQcf4OOPP0ZmZibs7OwQERGBq1evwsPDAyNHjkROTg6Av4oFw4cPR0ZGBsaPH4/4+HhER0cjNzcX3bp10z9Cbuw5X7p0KQICAuDp6QkRwZUrVwBAP0hefn5+uZyD59m3bx+aNGkCrVZbbBtra2v885//hJmZGUaOHIlHjx4V23b27NmYPn06Zs6ciZSUFJw4cQJJSUno1KmTvh8Yew2A59/7xpoxYwZu3bqFFStWIC0tDXFxcVi6dCl69OgBX19fk2KVRzzgr/FfHBwcYGlpCXd3d/Tt2xenT58uUaynZWRk4OjRoxg5ciQsLS0LrW/Tpg1u3LiBX3/9tUz2R0QVg0UFIiIiI2RlZWHVqlXo168fBgwYgBo1auCTTz6BWq3GunXrAAADBw7EZ599BgcHBzg6OqJ37964e/cubt++XShebm4u3nnnHYwYMaJQkWDMmDHIy8vTxwWA1NRUnD59Gm+++WaJj2HhwoUYO3Ystm/fjoYNGz73eKqaghkenn7cGgDUajUyMzMrOiWj+Pv7Q6fToXbt2hgyZAgePXqExMREgzYWFhb6b92bNWuGVatWIS0trcyuVa9evZCamopPP/20TOKZ4tGjR/jjjz/0T5Q8i5+fHz7++GPEx8dj2rRpRbbJzMzEl19+if79+2PYsGGwt7eHt7c3vv76a9y5cwfh4eGFtnnWNTDm3jdW586dERwcjHHjxkGn06FFixZIS0vD2rVrTYpTXvHeeecd7N69G0lJSUhPT8fmzZuRmJiIzp07Iy4urkQxn7RgwQI4OzsjJCSkyPWNGjUCAMTGxpZ6X0RUcVhUICIiMsKlS5eQkZFhMLWatbU1nJycih0AUK1WA0ChqdLy8vLw97//HXXq1NG/9vCk1157DY0bN8Y//vEP/WPAW7ZswZAhQ4r8wFxRx1PZFbyLX9RAb9nZ2bC2tq7olExW8O3tk9+SF8XHxwdarbbKXqsnpaSkQESe+ZTCk0JCQtCkSROsXLkSJ0+eLLQ+Li4O6enp8PHxMVjevn17WFpaPncAw6evQVneKzNnzkR4eDh++OEHpKen49q1a/D394efn5/BwIVKxatfvz7atGkDW1tbWFpawtfXF+vWrUNmZiZWrlxpcrwn7dixA1u3bsXBgwcLPU1UoKAPKPVUERGVDIsKRERERih41PqTTz4xmL89ISFBP+DYvn378Oqrr6J27dqwsrLC1KlTi4w1duxYXL58GV9//TUuXLhQaL1KpcLo0aNx7do1/PDDDwCA9evX4/3336/Q46lqCsYTSE1NNViekZGBrKwsODs7K5FWubGysiryKZiqJisrCwCeOeDfkzQaDdatWweVSoX33nuv0BMoBdMS2traFtq2Ro0aSEtLMym/srpXbt68idDQUIwaNQqvvfYabGxs4O7ujjVr1iA5ORlhYWEm5VXW8Yrj7e0Nc3Nz/P777yWOsWXLFixcuBDHjh1Dw4YNi21XUPgr6BNEVDWwqEBERGSE2rVrAwCWLFlSaLq1qKgoJCYmol+/fnBycsKpU6fw8OFDhIaGFhlr0KBBOHz4MGrUqIH//d//LfKb9eHDh0Oj0WDt2rW4dOkSdDodGjRoUGHHUxW5u7vDzs6u0Kj3BeMGtGzZUom0ykVOTg4ePHgAV1dXpVMptYIPkk8/0fMsfn5+mDhxIi5fvox58+YZrKtRowYAFFk8KMk5K6t75fLly8jLy0O9evUMlut0Ojg6Opr8ekFZxytOfn4+8vPzjS76PG3FihXYuHEjjh49WijXp2VnZwNAlXiqiIj+i0UFIiIiI9SvXx8ajQYxMTFFro+NjUVOTg4++OADeHh4QKPRGEzz9qQuXbqgVq1aCA8Pxy+//FLk+8UODg4YPHgwdu7cicWLF2PkyJEVejylZWFh8dxH+Mtjn2+++SZOnDhhMODggQMHoFKpTBrgsrI7duwYRMRgMD4lznlZqFOnDlQqFR4+fGjSdvPmzcNLL72knyGlQIsWLWBra1toEMVTp04hOzsb7dq1M2k/ZXWvFBQzbt68abA8LS0N9+7d008FqVQ8AOjRo0ehZadPn4aIwM/Pz6RYIoLg4GDExsZi586dRT458rSCPlC3bl2T9kVEymJRgYiIyAgajQbvvvsuNm/ejFWrViE1NRV5eXm4fv06bt68qZ/G8MiRI8jKysLly5ef++527969MXz4cMyfPx+//PJLofVjxozB48ePsXfvXgQEBFTo8ZSWl5cX7t27h507dyInJwe3b98u9ARBefj000/x559/4rPPPsOjR48QFRWFsLAwDB8+HE2aNCn3/ZeX/Px83L9/H7m5uTh37hwmTJgANzc3DB8+XN/G2HPu6OiI5ORkxMfHIy0tDTk5OThw4IBiU0pqtVp4eHjg+vXrJm1X8BrE0+OMaDQaTJo0CTt27MDGjRuRmpqK2NhYjBkzBs7OzggKCjJ5P8+7V4YMGYK6desiOjq62Dju7u7o0qUL1qxZgxMnTiAzMxNJSUn6fJ58vUmJeMBf0z1u2bIFDx48QE5ODqKiojBixAi4ublhzJgxzz9ZT7hw4QIWLVqENWvWQK1WG7w6olKpsHjx4kLbFPQBb29vk/ZFRAoTIiJSFACJiIhQOo1qbeDAgTJw4ECj23/xxRdSt25dASA2NjbSv39/ERF5/PixBAcHi5ubm1hYWEjt2rVlwIABEhcXJyIiwcHB4ujoKDVq1JDAwED56quvBIB4enrK0qVLxcHBQQBIw4YNJSUlRVJTU6V+/foCQGxtbWX9+vWFcmnTpo1Mnz69xMceGhoq1tbWAkDq168vGzZs0K973vGU5lzdvXtXunTpIhqNRtzd3eWjjz6SKVOmCADx8vKSxMREk44jKipKOnToIM7OzgJAAIiTk5P4+/vL8ePHDdoeP35cXn75ZbGyshJnZ2eZMmWKZGVlmbS/AmVxf65YsUKcnJwEgGi1Wundu7esXLlStFqtAJBGjRrJ1atXJTw8XHQ6nQCQBg0ayO+//y4iIkFBQaJWq8XFxUUsLCxEp9NJ37595erVqwb7MfacR0dHS4MGDcTa2lo6duwot27dkv3794udnZ2EhISU6lhFTL/fRETGjRsnarVaMjIy9Mt27Nghnp6eAkBq1aolY8eOLXLbKVOmSJ8+fQyW5efnS1hYmDRq1EjU6v+PvTsPr+na/wf+PhlPTuYYkkhEJkpwDeWWqKuqD8olgpBf6b3oEHo1VKixaoip8eBrukrb3G/pJdRYQ/XipqqGakkFFzEPoQiRkUTy+f3Rb851Mp5znHP2SfJ+PU/+sPc6a33OXnuvta2z91r24unpKZGRkXL+/HltGkPqoKprJTIyUgDI9OnTK/2e9+/fl7Fjx0poaKg4OjqKi4uLdOrUSbZu3aqTTqn84uLiJCQkRJydncXOzk78/f3lnXfekfT0dJ10+lyPqamp2n3l/SUkJJQpv3fv3uLn5yfFxcWVxvksY843IjItlcj/TStNRESKUKlUSEpKKrPGPJlOVFQUAGDTpk0KR2K43r17Y/ny5QgKClI6lFrJGq7PkSNHYtOmTcjIyFAsBkMYc71dvHgRzZo1Q2JiIoYOHWqu0MymuLgYr7zyCoYNG4YRI0bU+PzMISMjA/7+/oiPj0dcXJzen6vO7TtRTcHXH4iIiKzIs+/Enzp1Cmq1mgMKZNAkhtVRaGgoZs2ahVmzZiEnJ0fpcAxSVFSEbdu2ITs7G9HR0TU+P3OZMWMGWrdujdjYWKVDISIDcVCBiIjIikycOBFpaWm4cOEChg8fXmZmewA4d+5cmfeTy/sz9j8Q5s7fUmVQ9TJ58mRERUUhOjra4EkblZScnIzNmzdjz5490Gg0NT4/c1i0aBFSUlKwe/du2NvbKx0OERnITukAiIiI6L80Gg2aNm0KPz8/rFixAmFhYWXSNG3aFOZ8e9Hc+VuqjJpgypQpSExMREFBAYKCgpCQkICBAwcqHZbZzJkzB9999x3mzZuH+fPnKx2OXrp164Zu3brVmvxMbfv27Xjy5AmSk5PLTLpJRNUDBxWIiIisSHx8fLlLTFLtNHfuXMydO1fpMCyqe/fu6N69u9JhkIVEREQgIiJC6TCI6Dnw9QciIiIiIiIiMgoHFYiIiIiIiIjIKBxUICIiIiIiIiKjcFCBiIiIiIiIiIzCiRqJiKhWuHnzJjZu3Kh0GFQNHTlyROkQqpWbN28CAK83soibN2/C399f6TCIajWVcD0nIiJFqVQqJCUlYdCgQUqHUmNFRUXh66+/VjoMIiIyg4EDB2LTpk1Kh0FUa/FJBSIiqhV400nG4KCf4aKiogCA1xtZRMn5RkTK4ZwKRERERERERGQUDioQERERERERkVE4qEBERERERERERuGgAhEREREREREZhYMKRERERERERGQUDioQERERERERkVE4qEBERPScNm/ejODgYKhUKqhUKvj4+GDo0KFmK2/evHlwd3eHSqVCSkrKc+dXOv6SP7VajaCgIIwYMQJXrlx5rjyrOib/8z//gwYNGkClUsHGxgZNmjTBvn37dNL8+c9/hpubG2xsbNC0aVP8+OOPRn1fsm779u3D5MmTy5xDb775Zpm03bt3h6urK2xtbdG8eXOcOHFCgYgN889//hPt27eHq6srGjVqhOHDh+POnTtWkV98fHyZdkClUqFFixblpi8uLsbixYsRHh5e7v5Zs2YhLCwMbm5ucHR0RGhoKD788EPk5ORo0+zYsQMLFixAUVGRUTETkRUQIiJSFABJSkpSOowabeDAgTJw4ECzlxMSEiLu7u5mL0dEZP369QJATp48abI8n42/qKhIfvvtN/nyyy9Fo9FI/fr15f79+8+Vpz4AyEsvvVTh/n//+9/SrVs3g+MwFq9Pwz3P9TZ9+nTp06ePZGVlabeFhIRInTp1BIDs3LmzzGf27NkjERERRsdrSRs2bBAAsmDBAsnMzJSTJ09KcHCwtG7dWgoLCxXPb/bs2QKgzF/z5s3LpL1w4YJ06tRJAEirVq3Kza9Lly6yYsUKycjIkKysLElKShJ7e3vp2bOnTrolS5ZIly5d5OHDhwbHbKn2nYgqxicViIiIqAwbGxvUr18fb775JkaPHo27d++WeXKAzC8/P7/CX4GrUxn6mD9/PjZs2ICNGzfC1dVVZ9/SpUthY2ODmJgYPHr0SKEIn9+nn36KBg0aYMKECXB3d0fr1q0xbtw4pKSk4NixY4rnBwBr166FiOj8nT59WifNr7/+ikmTJmHUqFFo3bp1hXm5uLggJiYGXl5ecHV1xaBBgxAZGYlvv/0WN27c0KYbM2YMWrVqhV69euHp06dGxU1EyuGgAhEREVUqNDQUAJ7rEW0yzueff467d+9W+zKqcvHiRXz00UeYOXMm1Gp1mf3h4eEYO3Ysbt26hfHjxysQoWncuHEDvr6+UKlU2m0NGzYEAFy7dk3x/PTVqlUrbN68GUOGDIGjo2OF6Xbu3AlbW1udbXXr1gUA5OXl6WyfMWMGUlJSsGTJEtMHTERmxUEFIiIiBRQVFWH69OkICAiAk5MT/vCHPyApKUm7/4cffkBYWBjc3d2hVqvRsmVL7N27t8L8fvvtNwQGBsLOzg49e/YEALz99tvad6JDQkJw8uRJAMDw4cOh0Wjg7u6OHTt2VBlrWloagN//I2HId6iNRASLFi1Cs2bN4OjoCE9PT/Tr1w/nzp3TpomNjYWDgwN8fHy02/72t7/B2dkZKpUK9+/fBwCMHTsWcXFxuHTpElQqFUJDQ7F06VKo1WrUr18fI0eOhK+vL9RqNcLDw3V+mX6eMgDg22+/hZubG+bMmWPW41Vi6dKlEBH07du3wjTx8fFo0qQJPvvssyqfmtGnHlauXAlnZ2doNBps374dr7/+Otzc3ODv74/169fr5Geqcz04OLjMAE7JYF1wcLDi+VnCrVu34OTkhKCgIJ3tnp6e6NKlC5YsWQIRUSg6IjKKcm9eEBGRCN/ZtgRrnFNh/Pjx4ujoKF9//bU8fPhQpkyZIjY2NnL8+HEREdm0aZPMmDFDHjx4IBkZGdKhQwepU6eO9vOl51QoKCiQAQMGyPbt23XKGTBggNja2sqtW7d0tr/xxhuyY8eOSuN/+PCh/OMf/xCNRiO9e/c2+DsYekxEqv+cCtOnTxcHBwdZu3atZGZmyqlTp6Rt27ZSt25duXPnjjbdkCFDxNvbW+ezCQkJAkDu3bun3TZgwAAJCQnRSRcTEyPOzs5y9uxZefz4sZw5c0bat28vrq6ucv36dZOUsXPnTnF1dZVZs2bp/d1LGHO9BQcHS1hYWLn7QkJC5MqVKyIicvjwYbGxsZHAwEDJyckRkfLnVNC3HqZOnSoAZP/+/fLo0SO5e/eudO7cWZydnaWgoECbTp9zXR/Jyclib28vS5culaysLDl9+rQ0a9ZMevToYVA+5spv9uzZ4u/vLx4eHmJvby+BgYESEREhP/30U4WfeemllyqcU6G03NxccXV1ldjY2HL3T5482eC5YjinApHy+KQCERGRhT1+/BgrV65EZGQkBgwYAA8PD0ybNg329vZITEwEAAwcOBAff/wxPD094eXlhb59+yIjIwP37t0rk9/Tp0/x17/+FW+//XaZX3pHjRqFoqIibb4AkJWVhePHj6NXr15l8nr06JH26QZPT08MHz4cU6ZMwdatWw3+DrVNfn4+Fi1ahP79+2Po0KFwd3dHy5YtsWrVKty/fx+rV682WVl2dnbaX+HDwsKwcuVKZGdnm+zY9+7dG1lZWfjoo49Mkl9lcnNzceXKFYSEhFSZtmPHjvjggw9w9epVTJo0qdw0xtRDeHg43NzcUK9ePURHRyM3NxfXr18HYNpzvUuXLpg4cSJiY2Ph5uaGFi1aIDs7G5999plB+Zgrv7/+9a/YsWMHbty4gZycHKxfvx7Xr19Hly5dcObMGaPyfNbcuXPh6+uL+Pj4cvc3btwYAJCamvrcZRGR5XBQgYiIyMLOnz+PvLw8nWXanJyc4OPjo/N49rPs7e0BoMyya0VFRXjjjTdQv3597WsPz3r11VfRpEkTfPHFF9pHijds2IDo6Ogy7zoDgLu7u3ZytgkTJkBE4O7uri3/eb5DTXfmzBnk5OSgXbt2Otvbt28PBwcHoyfO00e7du2g0Wiq5bG/e/cuRAQajUav9PHx8XjhhRewYsUKHDp0qMz+560HBwcHAEBhYSEA057rU6dOxerVq7F//37k5OTg8uXLCA8PR8eOHXUmLlQqv4YNG6JNmzZwcXGBg4MDOnTogMTEROTn52PFihUG5/esLVu2YOPGjdi7d2+ZiThLlJwDv/3223OVRUSWxUEFIiIiC8vNzQUATJs2TWct+GvXrmknL9u1axdeeeUV1KtXD46Ojvjwww/LzWv06NFIS0vDqlWrcPbs2TL7VSoVRo4cicuXL2P//v0AgC+//BJvvfVWlXF+9NFH8PHxwZQpU8r8B0Wf72Cs4uLiCvcVFRWVGeCwFpmZmQB+n/G+NA8PD2RnZ5u1fEdHx3KfZLF2jx8/BoBKJ/x7llqtRmJiIlQqFUaMGIH8/Hyd/aauB1Od67dv38aCBQvw7rvv4tVXX4WzszOCgoKwZs0apKenIyEhwaC4TJ1fRVq2bAlbW1tcuHDB6Dw2bNiA+fPnIzk5GYGBgRWmc3JyAvDfc4KIqgcOKhAREVnAwYMHsXjxYgBAvXr1AACLFy8us3TbkSNHcP36dURGRsLHxwfHjh3Do0ePsGDBgnLzHTRoEP71r3/Bw8MDf/nLX8pdjm3YsGFQq9X47LPPcP78ebi5uaFRo0ZVxuzq6or58+cjOzsb7733ns6+qr6DocekhJeXF9LT0yv8zJUrV7Sz21sbDw8PACj3P62ZmZnw9/c3W9mFhYVmL8NcSv4jWfopnMp07NgR48aNQ1paGmbPnq2zz9T1YIpzHfh9wtOioiI0aNBAZ7ubmxu8vLwMfr3A1PlVpLi4GMXFxXoP+pS2bNkyrFu3DgcOHCgTa2kFBQUA/ntOEFH1wEEFIiIiC/jll1/g7OwM4PdHjNVqNVJSUspNm5qaisLCQrz33nsIDg6GWq3WWTLuWV27dkXdunWxevVq/PLLL+W+q+zp6YnBgwdj27ZtWLhwId555x294/7LX/6Cl156CTt37sTGjRu126v6Dvp49piUePXVV3Hr1i0cPny4THoRwT/+8Q+89NJLRpdpTi1atICLiwt+/vlnne3Hjh1DQUEBXnzxRe02Ozs77eP1ppCcnAwRQYcOHcxWhrnUr18fKpUKjx49Muhzs2fPRtOmTbWrmpQwpB70YYpzHYB2MOP27ds627Ozs/HgwQODB8tMnR8A9OjRo8y248ePQ0TQsWNHg/ISEUycOBGpqanYtm1buU+OlFZyDnh7extUFhEpi4MKREREZlRYWIjffvsNycnJ2v9Aq9VqDB8+HOvXr8fKlSuRlZWFoqIi3Lx5E7dv30ZAQAAAYN++fXj8+DHS0tKqfA+8b9++GDZsGObMmYNffvmlzP5Ro0bhyZMn2LlzJ/r06aN3/CqVCkuXLoVKpUJsbCwePnyo13cw9JiUiI+Ph4eHB6KiorB161bk5ubiyZMn+PXXX/HGG2/g6dOnePPNN/WO35LUajXi4uKwZcsWrFu3DllZWUhNTcWoUaPg6+uLmJgYbdrQ0FA8ePAA27ZtQ2FhIe7du4dr166VybPkyY2rV68iOztbO0hQXFyMhw8f4unTpzh16hTGjh2LgIAADBs2zCRl7Nmzx2JLSmo0GgQHB+PmzZsGfa7kNYjSc4MYUg/6llPVuR4dHQ1vb2+cOHGiwnyCgoLQtWtXrFmzBgcPHkR+fj5u3LihjefZV5KUyA/4fbnHDRs2IDMzE4WFhThy5AjefvttBAQEYNSoUVUfrGecPXsWn3zyCdasWQN7e3udV0dUKhUWLlxY5jMl50DLli0NKouIFGbRtSaIiKgMcElJszP3kmNbtmyRkJAQAVDp35YtW7SfefLkiUycOFECAgLEzs5O6tWrJwMGDJAzZ86IiMjEiRPFy8tLPDw8JCoqSpYvXy4AJCQkRJYsWSKenp4CQAIDA+Xu3buSlZUlDRs2FADi4uIiX375ZZk427RpI5MnTy6z/ccff5QmTZpo42zQoIGMHDlSJ82wYcMEgHh4eMi8efOq/A7GHJMSV65ckXfeeUeCgoLEwcFBnJycJCwsTKZPn65dRtBSDL0+i4uLJSEhQRo3biz29vbi6ekpkZGRcv78eZ10GRkZ0rVrV1Gr1RIUFCTvv/++TJgwQQBIaGiodmnIEydOSKNGjcTJyUlefvlluXPnjsTExIi9vb34+fmJnZ2duLm5Sb9+/eTSpUsmK2P37t3i6uoq8fHxBh8zY6632NhYsbe3l7y8PO22Z8+hunXryujRo8v97IQJE8osKalPPaxYsUI0Go0AkMaNG8ulS5dk9erV4ubmJgCkUaNGcuHCBRGp+nqNjIwUADJ9+vRKv+f9+/dl7NixEhoaKo6OjuLi4iKdOnWSrVu36qRTKr+4uDgJCQkRZ2dnsbOzE39/f3nnnXckPT1dJ92RI0ekU6dO4uvrq72WfXx8JDw8XL7//nsREUlNTa302k9ISChTfu/evcXPz0+Ki4srjfNZXFKSSHkqkf+bCpqIiBShUqmQlJSEQYMGKR1KjRUVFQUA2LRpk8KRKKt3795Yvnw5goKClA6l2rDG63PkyJHYtGkTMjIylA6lXMZcbxcvXkSzZs2QmJiIoUOHmis0sykuLsYrr7yCYcOGYcSIETU+P3PIyMiAv78/4uPjERcXp/fn2L4TKY+vPxAREdVQz75Pf+rUKajVag4o1BCGTGpYHYSGhmLWrFmYNWsWcnJylA7HIEVFRdi2bRuys7MRHR1d4/MzlxkzZqB169aIjY1VOhQiMhAHFYiIiGqoiRMnIi0tDRcuXMDw4cPLzJJPZE0mT56MqKgoREdHGzxpo5KSk5OxefNm7NmzBxqNpsbnZw6LFi1CSkoKdu/ebbVLxhJRxeyUDoCIiIjMQ6PRoGnTpvDz88OKFSsQFhamdEj0nKZMmYLExEQUFBQgKCgICQkJGDhwoNJhmcycOXPw3XffYd68eZg/f77S4eilW7du6NatW63Jz9S2b9+OJ0+eIDk5ucykm0RUPXBQgYiIqIaKj48vd4lJqr7mzp2LuXPnKh2GWXXv3h3du3dXOgyykIiICERERCgdBhE9B77+QERERERERERG4aACERERERERERmFgwpEREREREREZBQOKhARERERERGRUThRIxER1QpHjx5FVFSU0mFQNbR48WJs2rRJ6TCqjaNHjwIArzeyiKNHj6JDhw5Kh0FUq3FQgYiIaryOHTsqHQJVU9VtucbDhw+jXr16aNy4sWIx8D94ZEkdOnRgG0+kMA4qEBFRjTdu3DilQyCyiBYtWiAiIgIzZ85UOhQiIqolOKcCERERUQ2Rl5cHJycnpcMgIqJahIMKRERERDVEfn4+NBqN0mEQEVEtwkEFIiIiohoiPz+fTyoQEZFFcVCBiIiIqIbg6w9ERGRpHFQgIiIiqgGKiopQWFjIQQUiIrIoDioQERER1QD5+fkAwDkViIjIojioQERERFQD5OXlAQCfVCAiIovioAIRERFRDVDypAIHFYiIyJI4qEBERERUA3BQgYiIlMBBBSIiIqIagHMqEBGREjioQERERFQDcE4FIiJSAgcViIiIiGoAvv5ARERK4KACERERUQ3AQQUiIlICBxWIiIiIaoD8/HyoVCqo1WqlQyEiolqEgwpERERENUBeXh7UajVUKpXSoRARUS3CQQUiIiKiGiA/P5+vPhARkcVxUIGIiIioBsjPz+dykkREZHEcVCAiIiKqAfikAhERKYGDCkREREQ1AAcViAE3iHcAACAASURBVIhICRxUICIiIqoB8vLyOKhAREQWx0EFIiIiohqAcyoQEZESOKhAREREVAPw9QciIlICBxWIiIiIagAOKhARkRI4qEBERERUA3BOBSIiUgIHFYiIiIhqAM6pQERESuCgAhEREVENwNcfiIhICRxUICIiIqoBOKhARERK4KACERERUQ3AORWIiEgJHFQgIiIiqgH4pAIRESmBgwpERERENQAnaiQiIiVwUIGIiIioBuCTCkREpAQOKhARERHVABxUICIiJdgpHQARERERGeaXX34BALi7u8PW1hYajQZFRUUcVCAiIovjoAIRERFRNfPhhx/iwIEDZbZHRkbCzs4OarUaarUa7u7uOHbsGOrUqaNAlEREVBvw9QciIiKiaqZv376wsSn/Nu7p06fIyclBRkYGQkNDOaBARERmxUEFIiIiomqmX79+EJEq08XExFggGiIiqs04qEBERERUzTRq1AhNmzatNI2npyf+/Oc/WygiIiKqrTioQERERFQNRUVFwd7evtx99vb2ePfddyvcT0REZCocVCAiIiKqhiIiIlBYWFjuvqdPn2LEiBEWjoiIiGojDioQERERVUNt27ZFgwYNymy3tbVFeHg4GjdurEBURERU23BQgYiIiKia6t+/PxwcHHS2iQhGjhypUERERFTbcFCBiIiIqJqKiIhAQUGBzjYnJyf0799foYiIiKi24aACERERUTXVpUsXuLq6av9tb2+P4cOHQ6PRKBgVERHVJhxUICIiIqqm7O3t8ec//1m7ykNhYSEnaCQiIovioAIRERFRNdavXz88ffoUKpUKrVq1Qps2bZQOiYiIahEOKhARERFVY7169YKdnR1EBKNGjVI6HCIiqmXsSm+4efMmDh8+rEQsREQWMWjQIKVDqDGOHDmCGzduKB0GUa3XtGlTnDt3Dmq1Ghs3blQ6HKJaqWHDhujYsaPSYRBZnEpE5NkNGzduxODBg5WKh4jI7Eo1e4pTqVRISkqqloMdUVFR+Prrr5UOg4iISHEDBw7Epk2blA6DyOLKPKlQwtpuuqmsqKgoAGDjZYCSQTOe37UTB03NgzdRROZX1eDjrVu3cO3aNYSHh1s4MuvF+ySypJLzjag2qnBQgYiIiIiqBz8/P/j5+SkdBhER1UKcqJGIiIiIiIiIjMJBBSIiIiIiIiIyCgcViIiIiIiIiMgoHFQgIiIiIiIiIqNwUIGIiIiIiIiIjKLIoMLu3bvh7u6Ob775RoniTSY+Ph4qlarMX4sWLZQOzSA1pT7MZeTIkTr1O3To0DJp9u3bh8mTJ2Pz5s0IDg7Wpn3zzTfLpO3evTtcXV1ha2uL5s2b48SJE5b4GkYz9DwvLi7G4sWLK1zWbNasWQgLC4ObmxscHR0RGhqKDz/8EDk5Odo0O3bswIIFC1BUVKTz2W3btunEULduXdN9UbIYtjk118KFC1G/fn2oVCqsWrVKu91SdW5N55Y+bZ0SrOkYWbua3LcDwD//+U+0b98erq6uaNSoEYYPH447d+5YRX7WdO9BRFVTZFBBRJQolirA+qial5cX9uzZg/Pnz+Pzzz/X2ffxxx9j6dKlmDJlCgYMGIDLly8jJCQEderUwbp167Br1y6d9N999x02bdqEPn364MyZM2jbtq0lv4pZpaWl4U9/+hPGjRuHvLy8ctMcOHAAo0ePxtWrV3H//n3MnTsXS5Ys0VnfuW/fvlCr1ejWrRsyMzO12yMiInDz5k0cPHgQvXr1Mvv3IfNgm1NzjR8/HocPHy6z3VJ1bk3nlj5tnRKs6RhZs5retyclJWHIkCGIiorCzZs3sX37dhw8eBCvv/46nj59qnh+hjD3vQcRVU2RQYXevXvj0aNH6NOnjxLF68jPz69wVFMfa9euhYjo/J0+fdqEEZpfTaoPc3FyckLPnj3RpEkTODo6arfPnz8fGzZswMaNG+Hq6qrzmaVLl8LGxgYxMTF49OiRpUM2KX3O819//RWTJk3CqFGj0Lp16wrzcnFxQUxMDLy8vODq6opBgwYhMjIS3377LW7cuKFNN2bMGLRq1Qq9evXS3pCoVCr4+fmhc+fOaNy4sXm+LJmdudoca20/yDx1Xl59W1N/pm9bZ2nWdIys9ZqtDX37p59+igYNGmDChAlwd3dH69atMW7cOKSkpODYsWOK5wdYz70HEVWt1s+p8Pnnn+Pu3btKh0H/pzrVx8WLF/HRRx9h5syZUKvVZfaHh4dj7NixuHXrFsaPH69AhJbVqlUrbN68GUOGDNEZeClt586dsLW11dlW8hpD6V8YZsyYgZSUFCxZssT0AVONU53aD3p+1l7fhrR1tZU11mFt6dtv3LgBX19fqFQq7baGDRsCAK5du6Z4fvrivQeRdbD4oMKhQ4cQEBAAlUqF5cuXAwBWrlwJZ2dnaDQabN++Ha+//jrc3Nzg7++P9evXaz+7dOlSqNVq1K9fHyNHjoSvry/UajXCw8N1RkFjY2Ph4OAAHx8f7ba//e1vcHZ2hkqlwv379wEAY8eORVxcHC5dugSVSoXQ0FALHQXrUR3q49tvv4WbmxvmzJljiUOit6VLl0JE0Ldv3wrTxMfHo0mTJvjss8+wb9++SvMTESxatAjNmjWDo6MjPD090a9fP5w7d06bRt+6AYCioiJMnz4dAQEBcHJywh/+8AckJSU935c2k1u3bsHJyQlBQUE62z09PdGlSxcsWbKEj+zWEM/T5gDA999/jz/+8Y/QaDRwc3NDy5YtkZWVVWH78cMPPyAsLAzu7u5Qq9Vo2bIl9u7da3C5wO+/mrVr1w5qtRrOzs4IDAzE7NmzAZjmejMkHn3ai08++QQajQaurq64e/cu4uLi4Ofnh1GjRsHZ2Rk2NjZ48cUX4e3tDXt7ezg7O6Nt27bo3LkzGjZsCLVaDQ8PD3z44Yc6ZVd2TMtTXp1fvHix3PelVSoV/vWvf1VZTnn1XV45+h4rQ88FY1XU1lkK+/yq1Za+PTg4uMyATsn8B8HBwYrnZwm89yAyISklKSlJytlsUjdu3BAAsmzZMu22qVOnCgDZv3+/PHr0SO7evSudO3cWZ2dnKSgo0KaLiYkRZ2dnOXv2rDx+/FjOnDkj7du3F1dXV7l+/bo23ZAhQ8Tb21un3ISEBAEg9+7d024bMGCAhISEGPU9Zs+eLf7+/uLh4SH29vYSGBgoERER8tNPPxmVn6EGDhwoAwcOfO58rL0+du7cKa6urjJr1qzn/q7GnN8xMTHi5+dXZntwcLCEhYWV+5mQkBC5cuWKiIgcPnxYbGxsJDAwUHJyckREZM+ePRIREaHzmenTp4uDg4OsXbtWMjMz5dSpU9K2bVupW7eu3LlzR5tO37oZP368ODo6ytdffy0PHz6UKVOmiI2NjRw/ftyg72/Mef7SSy9Jq1at9Mo/NzdXXF1dJTY2ttz9kydPFgBy8uRJne1jxoyROnXq6P9FxDLtmzEASFJSktJhGMWYdsjYNicnJ0fc3NxkwYIFkp+fL3fu3JH+/ftr25Dy2o9NmzbJjBkz5MGDB5KRkSEdOnTQOW/0vZ4WL14sAGTevHmSkZEhDx48kE8//VSGDBkiIqa73vSNx9D2YsyYMbJs2TLp37+//Oc//5GPP/5YAMixY8ckNzdX7t+/Lz179hQAsmvXLrl3757k5uZKbGysAJCUlBS9j2laWpoAkL///e8V1nlaWppMmjRJcnNzRUTk9u3b4unpKeHh4VJUVKRXOeXVd3nnlqnbVmNV1dZVxVTtRG3q841pn2pL356cnCz29vaydOlSycrKktOnT0uzZs2kR48eBuVjrvys9d6jMqa6Lyeqjqzu9Yfw8HC4ubmhXr16iI6ORm5uLq5fv66Txs7OTjviGxYWhpUrVyI7OxuJiYkWjfWvf/0rduzYgRs3biAnJwfr16/H9evX0aVLF5w5c8aisZiLNdRH7969kZWVhY8++sgk+ZlCbm4urly5gpCQkCrTduzYER988AGuXr2KSZMmlZsmPz8fixYtQv/+/TF06FC4u7ujZcuWWLVqFe7fv4/Vq1eX+UxldfP48WOsXLkSkZGRGDBgADw8PDBt2jTY29sbXC/mPs/nzp0LX19fxMfHl7u/ZO6E1NTU5y6LrF9l5/XVq1eRlZWF5s2bQ61Ww9vbG5s3b650FZCBAwfi448/hqenJ7y8vNC3b19kZGTg3r17epdbWFiImTNnomvXrpg0aRK8vLzg6emJt956C+3btzfp9aZPPMa0F/Pnz8fo0aOxefNmNG3aVLs9LCwMGo0GderUwf/7f/8PABAQEIC6detCo9FoV7t59ldVfY9pZUJDQzFv3jxoNBoAwHvvvYf8/Hx88cUXsLGxMVk5pm5bn0dVbZ01qO19fm3q27t06YKJEyciNjYWbm5uaNGiBbKzs/HZZ58ZlI+58uO9B1H1YnWDCs9ycHAA8PsNXWXatWsHjUajc9NjCQ0bNkSbNm3g4uICBwcHdOjQAYmJicjPz8eKFSssGoslWHt9WNLdu3chItob4qrEx8fjhRdewIoVK3Do0KEy+8+cOYOcnBy0a9dOZ3v79u3h4OBQ5SRHpevm/PnzyMvL01l6ycnJCT4+PgbXiznP8y1btmDjxo3Yu3dvmcmwSpQc499+++25yqLqp/R5HRwcjPr162Po0KGYMWMGrl69anCe9vb2AFDpkmGlyz116hQyMzPRo0cPnXS2trYYM2aMSa83feJ53vaiqnKenZys5HhV1u7rc0wrs3HjRmzduhUzZ87ECy+8YNJyTN22Gkufts7a1MY+vzb17VOnTsXq1auxf/9+5OTk4PLlywgPD0fHjh2NmkjU1Pnx3oOoerHqQQVDODo6GvTrhbm0bNkStra2uHDhgtKhKMpa6sNcHj9+DACVTgr0LLVajcTERKhUKowYMQL5+fk6+0uWLnJxcSnzWQ8PD2RnZxsUX25uLgBg2rRpOu8qX7t2zSQThJniPN+wYQPmz5+P5ORkBAYGVpjOyckJwH+POdVeTk5OOHDgAF5++WXMmTMHwcHBiI6OLnM9PWvXrl145ZVXUK9ePTg6OpaZH0AfWVlZAH6/Fstj7uutNFO3F4YyxTEtkZGRgffffx/t27dHXFycyctR+lgB+rd11VlN6fNrS99++/ZtLFiwAO+++y5effVVODs7IygoCGvWrEF6ejoSEhIMisvU+VWE9x5E1qtGDCoUFhYiMzMT/v7+SoeC4uJiFBcX690h1UTWVB/mUtLZGPKLWceOHTFu3DikpaVpJ3crUfKflfJuMIw5lvXq1QMALF68uMxyTEeOHDEor/I873m+bNkyrFu3DgcOHECDBg0qTVtQUADgv8ecarfmzZvjm2++QXp6OiZOnIikpCQsXLiw3LTXr19HZGQkfHx8cOzYMTx69AgLFiwwuMySc7RkgrnSzH29lWbq9sIQpjqmJcaMGYPMzEwkJibqzMxuqnKUPFaAYW1ddVWT+vza0renpaWhqKiozDnp5uYGLy8vg18vMHV+FeG9B5H1qhGDCsnJyRARdOjQQbvNzs7uuR9XrErpR2EB4Pjx4xARdOzY0axlWzOl6sOS6tevD5VKZfAa1bNnz0bTpk1x8uRJne0tWrSAi4sLfv75Z53tx44dQ0FBAV588UWDyimZuT0lJcWgz5XHlOe5iGDixIlITU3Ftm3byv31prSSY+zt7W1QWVTzpKen4+zZswB+v7meN28e2rZtq91WWmpqKgoLC/Hee+8hODgYarVaZ7kzfQUGBsLLywvfffdduftNeb3pw9TthSFMdUyB359E+Oqrr/DRRx+hefPm2u0TJkwwWTlKHStj2rrqqib1+bWlby8ZzLh9+7bO9uzsbDx48EC7FKRS+QG89yCqbqrloEJxcTEePnyIp0+f4tSpUxg7diwCAgIwbNgwbZrQ0FA8ePAA27ZtQ2FhIe7du1fuOrleXl5IT0/H1atXkZ2dbVAneOvWLWzYsAGZmZkoLCzEkSNH8PbbbyMgIACjRo0yxVetFsxdH3v27LG6JSU1Gg2Cg4Nx8+ZNgz5X8qhk6bWS1Wo14uLisGXLFqxbtw5ZWVlITU3FqFGj4Ovri5iYGIPLGT58ONavX4+VK1ciKysLRUVFuHnzprbTj46Ohre3N06cOFFpXqY8z8+ePYtPPvkEa9asgb29fZml5Mr7xbnkGLds2dKgsqjmSU9Px8iRI3Hu3DkUFBTg5MmTuHbtmvY/M6XbD19fXwDAvn378PjxY6SlpRk134CjoyOmTJmCgwcPIjY2Frdu3UJxcTGys7Nx9uxZva43UzJ1e2GIgIAAAM9/TLOysjBy5Ei0bt1aO8nd48eP8fPPPyMlJUWvcvTpv5U6Vsa0ddVFTe7za0vfHhQUhK5du2LNmjU4ePAg8vPzcePGDW08b731ljatEvkBvPcgqnZKLwdh7iXXli1bJj4+PgJANBqN9O3bV1asWCEajUYASOPGjeXSpUuyevVqcXNzEwDSqFEjuXDhgoj8vpyRvb29+Pn5iZ2dnbi5uUm/fv3k0qVLOuVkZGRI165dRa1WS1BQkLz//vsyYcIEASChoaHapY9OnDghjRo1EicnJ3n55Zd1lvepSlxcnISEhIizs7PY2dmJv7+/vPPOO5Kenm66A1YJUyxdUx3qY/fu3eLq6irx8fHP9V1FTLukZGxsrNjb20teXp5225YtWyQkJEQASN26dWX06NHl5jlhwoQyy04VFxdLQkKCNG7cWOzt7cXT01MiIyPl/Pnz2jSG1M2TJ09k4sSJEhAQIHZ2dlKvXj0ZMGCAnDlzRkREIiMjBYBMnz690u+v73l+5MgR6dSpk/j6+goAASA+Pj4SHh4u33//vYiIpKamaveV95eQkFCm/N69e4ufn58UFxfrbOeSktbB0Hboedqcq1evSnh4uHh6eoqtra00aNBApk6dKk+fPhWR8tuPiRMnipeXl3h4eEhUVJQsX75cAEhISIhMmjRJ7+tJRGT58uXSsmVLUavVolarpU2bNrJixQoRqfp604ch17c+7cWCBQvEyclJAEjDhg1l7dq1IiKyZMkSbTmBgYHyww8/yPz588Xd3V0AiLe3t3z11VeyYcMG8fb2FgDi6ekp69evFxGp9JiOHTtW+xlnZ2fp379/uXW+cOHCCtuBXr16VVnO9evXy9T3tGnTypSj77Ey5Njrw5i2riqmaCdqW59vzH1Sbenb79+/L2PHjpXQ0FBxdHQUFxcX6dSpk2zdulUnnVL5Weu9R2W4pCTVZhYfVHheMTEx4uXlpXQYVsEaGq/qVh+mHFRIS0sTOzs77Y16dVNUVCSdO3eWzz//XOlQKnT//n1Rq9WycOHCMvs4qGAdrKEdIqoNrKGdqG59vjHtE/v26pWfOVR271EZ9odUm1XL1x+MXbaKzKM21Ed+fj727t2LtLQ07eQ9oaGhmDVrFmbNmoWcnByFIzRMUVERtm3bhuzsbERHRysdToVmzJiB1q1bIzY2FsDv70Wmp6fj0KFDuHjxosLRERHVPjW9z2ffXn3yM5fS9x5EVLVqOahgLufOnSvznlV5f9bcEJJ5PHjwAD179kSTJk0wYsQI7fbJkycjKioK0dHRBk/spKTk5GRs3rwZe/bs0Xs9bktbtGgRUlJSsHv3bu369Nu3b4efnx86d+6MXbt2KRwhUdXYr9QcrMvag3179cjPHMq79yCiqtkpHYAhpkyZgsTERBQUFCAoKAgJCQkYOHCgyfJv2rQpRMRk+dV05q4Pa7Fq1SqsWrWqwv1z5szBd999h3nz5mH+/PkWjMx43bp1Q7du3ZQOo0Lbt2/HkydPkJycrDPxVb9+/XiNUrXCfqXmqO11WVv6/BLs260/P1Or6N6DiKpWrQYV5s6di7lz5yodBv0f1sd/de/eHd27d1c6jBojIiICERERSodBRET/pzb2+ezbaxfeexAZj68/EBEREREREZFROKhAREREREREREbhoAIRERERERERGYWDCkRERERERERklAonaoyKirJkHGSEo0ePAmBdGeLmzZsAeMxqq5L6J9M6evQorykiC1i8eDE2bdqkdBjVBu+TyJKOHj2KDh06KB0GkSL4pAIRERERERERGaXCJxU4Em79SkbeWVf627hxIwYPHsxjVkuV1D+ZVocOHXhNEZmZSqXCBx98gEGDBikdSrXB+ySyJD4RQ7UZn1QgIiIiIiIiIqNwUIGIiIiIiIiIjMJBBSIiIiIiIiIyCgcViIiIiIiIiMgoHFQgIiIiIiIiIqM896DC0aNH0axZM9jY2EClUsHb2xvx8fGmiM1kNm/ejODgYKhUKqhUKvj4+GDo0KFKh0XVxMiRI7XnjkqlKvfc2bdvHyZPnlzmXHvzzTfLpO3evTtcXV1ha2uL5s2b48SJE5b4GkaLj4/X+f4lfy1atCg3fXFxMRYvXozw8PBy98+aNQthYWFwc3ODo6MjQkND8eGHHyInJ0ebZseOHViwYAGKiop0Prtt2zadGOrWrWu6L0pWqTr0MeZQE9uShQsXon79+lCpVFi1apV2++7du+Hu7o5vvvnGrOVbqhyqOWpy3w4A//znP9G+fXu4urqiUaNGGD58OO7cuWMV+VnTvQcRVe25BxU6dOiA//znP+jevTsA4Pz585g2bdpzB2ZKAwYMwOXLlxESEgJ3d3fcuXMH69atUzosqka8vLywZ88enD9/Hp9//rnOvo8//hhLly7FlClTdM61OnXqYN26ddi1a5dO+u+++w6bNm1Cnz59cObMGbRt29aSX8Ws0tLS8Kc//Qnjxo1DXl5euWkOHDiA0aNH4+rVq7h//z7mzp2LJUuW6CzF1LdvX6jVanTr1g2ZmZna7REREbh58yYOHjyIXr16mf37kPKqQx9jDjWxLRk/fjwOHz5cZruIWKR8S5VDNUNN79uTkpIwZMgQREVF4ebNm9i+fTsOHjyI119/HU+fPlU8P0OY+96DiKpWI19/yM/Pr3CkkkzLEsfaGurTyckJPXv2RJMmTeDo6KjdPn/+fGzYsAEbN26Eq6urzmeWLl0KGxsbxMTE4NGjR5YO2aTWrl0LEdH5O336tE6aX3/9FZMmTcKoUaPQunXrCvNycXFBTEwMvLy84OrqikGDBiEyMhLffvstbty4oU03ZswYtGrVCr169dLekKhUKvj5+aFz585o3Lixeb4s1SrW0L5UpSa1JeXp3bs3Hj16hD59+pgsz/Lq1Rzl1Ca1pb8Hakff/umnn6JBgwaYMGEC3N3d0bp1a4wbNw4pKSk4duyY4vkB1nPvQURVq5GDCp9//jnu3r2rdBi1giWOtbXW58WLF/HRRx9h5syZUKvVZfaHh4dj7NixuHXrFsaPH69AhJbVqlUrbN68GUOGDNEZeClt586dsLW11dlW8hpD6V8YZsyYgZSUFCxZssT0ARPBetuXZ9W2tsQUqkO9Vje1pb+vLX37jRs34OvrC5VKpd3WsGFDAMC1a9cUz09fvPcgsg5mG1RYuXIlnJ2dodFosH37drz++utwc3ODv78/1q9fr023dOlSqNVq1K9fHyNHjoSvry/UajXCw8N1RjZjY2Ph4OAAHx8f7ba//e1vcHZ2hkqlwv379wEAY8eORVxcHC5dugSVSoXQ0FCj4v/hhx8QFhYGd3d3qNVqtGzZEnv37gUAvP3229p3u0JCQnDy5EkAwPDhw6HRaODu7o4dO3YAAIqKijB9+nQEBATAyckJf/jDH5CUlAQA+OSTT6DRaODq6oq7d+8iLi4Ofn5+OH/+vFEx60NEsGjRIjRr1gyOjo7w9PREv379cO7cOW2a5znWlqrPb7/9Fm5ubpgzZ47ZjlVVli5dChFB3759K0wTHx+PJk2a4LPPPsO+ffsqzU+futH3ugIqP/esza1bt+Dk5ISgoCCd7Z6enujSpQuWLFnCR5dJhyHXwvfff48//vGP0Gg0cHNzQ8uWLZGVlVVh+1JZ+29IucDvv7S1a9cOarUazs7OCAwMxOzZswEYdo2aui2pqP8ZNWoUnJ2dYWNjgxdffBHe3t6wt7eHs7Mz2rZti86dO6Nhw4ZQq9Xw8PDAhx9+qFN2ZceuPIcOHUJAQABUKhWWL18O4Pf/1JX3LrVKpcK//vWvKsspr17LK0ffY2VonVsL9vfGqS19e3BwcJkBnJL5D4KDgxXPzxJ470FkQlJKUlKSlLO5Sj169BAA8vDhQ+22qVOnCgDZv3+/PHr0SO7evSudO3cWZ2dnKSgo0KaLiYkRZ2dnOXv2rDx+/FjOnDkj7du3F1dXV7l+/bo23ZAhQ8Tb21un3ISEBAEg9+7d024bMGCAhISElIkxJCRE3N3d9fo+mzZtkhkzZsiDBw8kIyNDOnToIHXq1NEpw9bWVm7duqXzuTfeeEN27Nih/ff48ePF0dFRvv76a3n48KFMmTJFbGxs5Pjx4zrHaMyYMbJs2TLp37+//Oc//9ErxoEDB8rAgQP1Slti+vTp4uDgIGvXrpXMzEw5deqUtG3bVurWrSt37tzRpnueY22J+ty5c6e4urrKrFmzDPr+xpzfMTEx4ufnV2Z7cHCwhIWFlfuZkJAQuXLlioiIHD58WGxsbCQwMFBycnJERGTPnj0SERGh8xl960bf66qqc09fs2fPFn9/f/Hw8BB7e3sJDAyUiIgI+emnnyr8zEsvvSStWrXSK//c3FxxdXWV2NjYcvdPnjxZAMjJkyd1to8ZM0bnmtSHse2buQGQpKQkpcMwijHtkDGM7WNycnLEzc1NFixYIPn5+XLnzh3p37+/to0pr32pqv3X9xpcvHixAJB58+ZJRkaGPHjwQD799FMZMmSIiOh3jVqiLSnd/3z88ccCQI4dOya5ubly//596dmzpwCQXbt2yb179yQ3N1diY2MFgKSkpOh97NLS0gSA/P3vf9duu3HjhgCQZcuWadNMmjRJcnNzRUTkWZq2uwAAIABJREFU9u3b4unpKeHh4VJUVKRXOeXVa+lyjDlWVdW5ORnaTtT2/l7EuPaptvTtycnJYm9vL0uXLpWsrCw5ffq0NGvWTHr06GFQPubKz1rvPSpjqf6QyBpZZFAhPz9fu23FihUCQC5evKjdFhMTU+Y/+8ePHxcAMnPmTO02Sw4qlDZ37lwBIHfv3hURkX379gkAiY+P16Z59OiRNG7cWJ4+fSoiIvn5+aLRaCQ6OlqbJi8vTxwdHeW9994TkfKPkb4Mbbzy8vLExcVFJx4RkZ9++kkA6HTYz3uTYYn6NIapBhVycnJEpVJJnz59yv3MszceIiJxcXECQEaPHi0iZW88DKkbfa4rfc49fV2/fl1OnDgh2dnZ8uTJEzly5Ii0adNGnJyc5PTp0+V+xpCOferUqdKkSRPJysoqd/8XX3whAOTLL7/U2c5BBetgDYMKlV0Lp0+fFgCyc+fOcvPVp30p3f7rU25BQYF4eHhI165ddfJ6+vSpLFmyRO9r1NJtiYhoBxWys7O12/73f/9XAEhqamqZPDds2KD3sdNnUKG0yMhIUavVcu7cOb3L0WdQwdTtrrkZ0k6wv/+doe1TberbRUSmTZsmALR//v7+cuPGDYPzMUd+1nrvURkOKlBtZvE5FRwcHAAAhYWFlaZr164dNBqNzuNhSrK3twcA7TIzr776Kpo0aYIvvvhC+2jUhg0bEB0drX1n6/z588jLy9NZ/sbJyQk+Pj6KfK8zZ84gJycH7dq109nevn17ODg4GD2Rjj6srT6f1927dyEi0Gg0eqWPj4/HCy+8gBUrVuDQoUNl9j9v3ZS+rkx57jVs2BBt2rSBi4sLHBwc0KFDByQmJiI/Px8rVqwwKK/StmzZgo0bN2Lv3r1lJsMqUXKMf/vtt+cqi2qH0tdCcHAw6tevj6FDh2LGjBm4evWqwXmWbv/1KffUqVPIzMxEjx49dNLZ2tpizJgxRl+j5m5Lqvp+z05cVnJcKuvP9Tl2ldm4cSO2bt2KmTNn4oUXXjBpOaZud60J+3vj1Ka+ferUqVi9ejX279+PnJwcXL58GeHh4ejYsaPOxIVK5cd7D6LqxaonanR0dMS9e/cUKXvXrl145ZVXUK9ePTg6OpZ5b1SlUmHkyJG4fPky9u/fDwD48ssv8dZbb2nT5ObmAgCmTZum807otWvXKlzyxpxKlsdxcXEps8/DwwPZ2dlmLV/J+jS1x48fA0ClkwI9S61WIzExESqVCiNGjEB+fr7OflPXjbnPvZYtW8LW1hYXLlwwOo8NGzZg/vz5SE5ORmBgYIXpnJycAPz3mBMZwsnJCQcOHMDLL7+MOXPmIDg4GNHR0WWuwWdV1f7rIysrC8Dv1295jL1GLd2WGMoUx65ERkYG3n//fbRv3x5xcXEmL0fpY2VOSn+36trf15a+/fbt21iwYAHeffddvPrqq3B2dkZQUBDWrFmD9PR0JCQkGBSXqfOrCO89iKyX1Q4qFBYWIjMzE/7+/hYp7+DBg1i8eDEA4Pr164iMjISPjw+OHTuGR48eYcGCBWU+M2zYMKjVanz22Wc4f/483Nzc0KhRI+3+evXqAQAWL15cZkmcI0eOWOR7Pavk5ra8Tszcx9rS9WluJZ2NIb+KdezYEePGjUNaWpp2orYSpq4bc597xcXFKC4u1vvGq7Rly5Zh3bp1OHDgABo0aFBp2oKCAgD/PeZEhmrevDm++eYbpKenY+LEiUhKSsLChQvLTatv+1+VkvO6ZBK60p7nGrVkW2IIUx27EmPGjEFmZiYSExN1Zm03VTlKHitzY39vnNrSt6elpaGoqKhM/+vm5gYvLy+cOXPGoLhMnV9FeO9BZL2sdlAhOTkZIoIOHTpot9nZ2ZntMcNffvkFzs7OAIDU1FQUFhbivffeQ3BwMNRqtc4SOSU8PT0xePBgbNu2DQsXLsQ777yjs79khuyUlBSzxGyoFi1awMXFBT///LPO9mPHjqGgoAAvvviidpupj7Wl69Pc6tevD5VKZfAa1bNnz0bTpk21K4aUMKRu9GHKc6/049sAcPz4cYgIOnbsaFBeIoKJEyciNTUV27ZtK/fXm9JKjrG3t7dBZREBQHp6Os6ePQvg9xvyefPmoW3bttptpenb/lclMDAQXl5e+O6778rd/7zXqKXaEkOY6tgBvz+J8NVXX+Gjjz5C8+bNtdsnTJhgsnKUPFbmxv7eOLWlby8ZzLh9+7bO9uzsbDx48EC7FKRS+QG89yCqbqxmUKG4uBgPHz7E06dPcerUKYwdOxYBAQEYNmyYNk1oaCgePHiAbdu2obCwEPfu3St37VsvLy+kp6fj6tWryM7OrrQjKywsxG+//Ybk5GTtoEJAQAAAYN++fXj8+DHS0tIqfO9t1KhRePLkCXbu3Ik+ffro7FOr1Rg+fDjWr1+PlStXIisrC0VFRbh582aZhtcS1Go14uLisGXLFqxbtw5ZWVlITU3FqFGj4Ovri5iYGG3a5z3W5q7PPXv2KLqkpEajQXBwMG7evGnQ50oelSy9VrIhdaNvOVWde9HR0fD29saJEycqzevWrVvYsGEDMjMzUVhYiCNHjuDtt99GQEAARo0aZVBcZ8+exSeffII1a9bA3t6+zHJx5f16XHKMW7ZsaVBZRMDvgwojR47EuXPnUFBQgJMnT+LatWva//CUbl98fX0B6Nf+V8bR0RFTpkzBwYMHERsbi1u3bqG4uBjZ2dk4e/bsc/cPlmpLDGFI31mZrKwsjBw5Eq1bt8akSZMA/P4I8s8//4yUlBS9ytHnPkDJY2Vu7O+NU1v69qCgIHTt2hVr1qzBwYMHkZ+fjxs3bmjjefZVXiXyA3jvQVTtlJ650dDZ0Y8ePSrNmzcXGxsbASA+Pj4yZ84cWbFihWg0GgEgjRs3lkuXLsnq1avFzc1NAEijRo3kwoULIvL77MH29vbi5+cndnZ24ubmJv369ZNLly7plJWRkSFdu3YVtVotQUFB8v7778uECRMEgISGhmqXLzpx4oQ0atRInJyc5OWXX5a///3vEhISojMjbXl/W7Zs0ZY1ceJE8fLyEg8PD4mKipLly5cLAAkJCdFZJklEpE2bNjJ58uRyj8+TJ09k4sSJEhAQIHZ2dlKvXj0ZMGCAnDlzRhYsWCBOTk4CQBo2bChr167V+7iLGDfLbHFxsSQkJEjjxo3F3t5ePD09JTIyUs6fP6+TzthjfefOHbPX5507d2T37t3i6uqqs/qGPky5pGRsbKzY29tLXl6edtuWLVu051rdunW1M0KXNmHChDLLTulTN4ZcV5WdeyK/z6gOQKZPn17p94+Li5OQkBBxdnYWOzs78ff3l3feeUfS09N10h05ckQ6deokvr6+2mvKx8dHwsPD5fvvvxcRkdTU1EqvwYSEhDLl9+7dW/z8/KS4uFhnO1d/sA7mnu36efuYq1evSnh4uHh6eoqtra00aNBApk6dql2lp7z2pbL2f9KkSXpfgyIiy5cvl5YtW4parRa1Wi1t2rSRFStWiEjl16i525KK+p8lS5Zov19gYKD88MMPMn/+fHF3dxcA4u3tLV999ZVs2LBBvL29BYB4enrK+vXrRaTyvnPs2LHazzg7O0v//v1l2bJl4uPjIwBEo9FI3759ZeHChRW2Eb169aqynJJZ45+t12nTppUpR99jZUi7a06GthO1vb8XMa59qi19+/3792Xs2LESGhoqjo6O4uLiIp06dZKtW7fqpFMqP2u996gMV3+g2sxkS0o+j5iYGPHy8rJomabUq1cvuXz5ssXLtdbGy5rr05SDCmlpaWJnZ2fwYJC1KCoqks6dO8vnn3+udCgVun//vqjValm4cGGZfRxUsA7W2g4R1TTW2E5Yc38vYlz7xL69euVnDpXde1SG/SHVZlbz+oOxy00p4dnHKE+dOgW1Wo2goCAFI7I+1ak+9ZGfn4+9e/ciLS1NO3lPaGgoZs2ahVmzZiEnJ0fhCA1TVFSEbdu2ITs7G9HR0UqHU6EZM2agdevWiI2NBfD7e5Hp6ek4dOgQLl68qHB0RERU0/p79u3VJz9zKX3vQURVs5pBhepk4sSJSEtLw4ULFzB8+PAys/1SzfPgwQP07NkTTZo0wYgRI7TbJ0+ejKioKERHRxs8sZOSkpOTsXnzZuzZs0fv9bgtbdGiRUhJScHu3bu1a9Bv374dfn5+6Ny5M3bt2qVwhEREVBOxb68e+ZlDefceRFQ1xQcVpkyZgsTERDx69AhBQUH4+uuvlQ6pShqNBk2bNsVrr72GGTNmICwsTOmQrEZ1rM+qrFq1SmfZpnXr1unsnzNnDmJjYzFv3jyFIjRct27d8NVXX8HHx0fpUMq1fft2PHnyBMnJyfD09NRu79evn05dVLRcHxERmVdN7O+fxb7d+vMztYruPYioaioRkWc3bNy4EYMHD0apzWSFoqKiAACbNm1SOJLqg+d37Wat9a9SqZCUlIRBgwYpHYrB2A4RWUZ1bieUwvaJLInnG9Vmij+pQERERERERETVEwcViIiIiIiIiMgoHFQgIiIiIiIiIqNwUIGIiIiIiIiIjMJBBSIiIiIiIiIyil1FO1QqlSXjoOfAujIcjxmR6Xz99de8pogsYPDgwRg8eLDSYVQ7bJ/IUgYOHKh0CESKKDOoEB4ejqSkJCVioVroyJEjWLJkCc+5Wubq1av497//jbNnz+LGjRsAgIYNGyIsLAzNmjVDs2bN4O7urnCUpI9x48Zpl9EiIsv44IMP0KlTJ/4HhsjKNGzYUOkQiBShEmtbsJ1qlY0bN2Lw4MHgaVh7ZWdn49ixY9i3bx8OHTqEn376CYWFhQgODkanTp3w8ssvo0ePHmjUqJHSoZoN158nIn0VFxdDo9FgzZo1ePPNN5UOh4iIqOLXH4iILMHV1RWvvfYaXnvtNQBATk4Ojh49ikOHDuHHH39EbGwsnjx5Al9fX7z88st47bXX0KlTJzRv3lzhyImILO/WrVt48uQJgoODlQ6FiIgIAAcViMjKuLi46Awy5OXl4cSJE/jxxx+xb98+jBkzBo8fP9YOMpQ8zdC2bVu+N0tENd7ly5eB/8/enYc1deX/A3/fQCBhCYuigIgSUKtoxQUVBK116eKgUkWcsZ2xrUptO7iNgvtGrZRW+Wp1pi7V79RRwWW0Vlv7UL9qnVLHPgwW9VdFEEUBcWdVlpzfH33ImAJKIHADvF/Pwx+9uTn3fW9OUvPJOecCLCoQEZHZYFGBiMyajY0NgoKCEBQUhKioKFRUVODcuXP66RJLly5FQUEB2rdvD39/f/1ohj59+kCh4A1uiKhlyczMhFqthqurq9xRiIiIALCoQETNjKWlJfr164d+/foZFBmqpkusWbMG0dHRcHFxwcCBA1lkIKIW5erVq9BqtRyZRUREZoNFBSJq1p4sMsycOROVlZX45Zdf9NMl4uLiEB0dDXt7ewwcOFC/JsOAAQNgZWUld3wiIqNkZmZy6gMREZkVFhWIqEWxsLCAr68vfH19MX36dAC//iM8KSkJSUlJ+OSTTxAdHQ1bW1sEBATo12QIDg6GtbW1zOmJiJ4uMzMTAwYMkDsGERGRHscCE1GLp9VqMX36dCQmJiI/Px8ZGRlYu3Yt3NzcsGPHDowcORLOzs4ICgpCdHQ0kpKSUFpaKndsIqJqMjMz4eXlJXcMIiIiPY5UIKJWp6rI8ORIhqo1GRITExEbGwtLS0v07t1bP11iyJAhcHBwkDk5EbVmJSUlyM/P5/QHIiIyKywqEFGrp9VqodVq8cc//hEAkJOTo1+T4fDhw/joo4+gUCjg5+enny4xYsQIODk5yZyciFqTjIwMCCFYVCAiIrPCogIR0W+4u7sjLCwMYWFhAIC8vDx8//33+tEMGzZsgEKhQLdu3fQFhmHDhqFt27YyJyeiluzq1auQJInTH4iIyKywqEBE9Ayurq4GRYb8/HycOXNGP5ph69at0Ol00Gq1+ukSw4YNQ8eOHWVOTkQtSWZmJlxdXWFjYyN3FCIiIj0WFYiIjNSuXTuEhIQgJCQEAFBYWIgzZ84gKSkJp0+fxvbt21FeXg6tVqufLjFq1Ch07txZ3uBE1KxdvXqVUx+IiMjssKhARNRA9vb2GDFiBEaMGAEAKCoqwo8//qifLhEZGYnHjx/Dzc1NP11i8ODB8PX1lTk5ETUnvPMDERGZIxYViIhMzM7OzqDIUFJSgpSUFP10iZkzZ+LRo0f6IgPw65cFIQQkSZIzOhGZsczMTEyYMEHuGERERAYkIYSQOwS1XomJiQgPDwe7IbUmFRUVOHfunH66xFdffQUAaN++Pfz9/fWjGfr06QOFQiFzWiIyB0II2NnZYePGjZgyZYrccYiIiPRYVCBZsahABEiShA8//BBqtVo/muH+/fvQaDQYMGCAfrrEwIEDoVQq5Y5LRDLIyclBhw4dcPLkSQwZMkTuOERERHr8CYyIyAxotVrMnDkTiYmJuH37Ns6fP4+4uDg4OTkhLi4OwcHBcHZ2xsiRIxEbG4vTp0+jrKxM7thE1EQyMzMBgAs1EhGR2eGaCkREZsbCwgK+vr7w9fXF9OnTAfz6haJqusSnn36K6Oho2NraIiAgQH+HiaCgIKhUKpnTE1FjyMzMhLW1Ndzd3eWOQkREZIBFBSKiZkCr1WL69OkGRYaqu0vs2LEDK1asgFqtRt++fQ3uMKFWq2VOTkSmUHXnB66zQkRE5oZFBSKiZkir1UKr1eKPf/wjgF/nW1etx7B3717ExsbC0tISvXv31hcYgoOD4ejoKHNyIqqPq1evcuoDERGZJRYViIhaAHd3d4SFhSEsLAyAYZHh8OHD+Oijj6BQKODn56efLjF8+HA4OzvLnJyI6iIzMxN+fn5yxyAiIqqGRQUiohbot0WGvLw8fP/99/opExs2bIBCoUC3bt300yWGDRuGtm3bypyciGqSmZmJ0NBQuWMQERFVw6ICEVEr4OrqalBkuH37Nn788Uf9aIatW7dCp9NBq9Xqp0sMGzYMHTt2lDk5ET169Ai5ubmc/kBERGaJRQUiolbIxcUFISEhCAkJAQAUFhbizJkz+jtM7NixA2VlZdBqtfrpEqNGjULnzp3lDU7UCmVmZkIIwaICERGZJRYViIgI9vb2GDFiBEaMGAEAKC4uRnJysn66RGRkJB4/fgw3NzeDu0v4+vrKnJyo5bt69SoAwMvLS+YkRERE1bGoQERE1dja2hoUGUpKSpCSkqKfLjFz5kw8evRIX2SoGs3Qt29fSJIkc3qiliUzMxPt2rWDvb293FGIiIiqYVGBiIieycbGBkFBQQgKCkJUVBQqKipw7tw5/XSJZcuW4eHDh2jfvj38/f31oxn69OkDhUIhd3yiZo23kyQiInPGogIRERnN0tIS/fr1Q79+/QyKDFXTJWJjYxEdHQ2NRoMBAwbop0sMHDgQSqVS7vhEzUpmZianPhARkdniz0dERNRgVUWGmTNnIjExEbdv38b58+cRFxcHJycnxMXFITg4GM7Ozhg5ciSWL1+OpKQklJWVyR2dyOxlZmZypAIREZktjlQgIiKTs7CwgK+vL3x9fTF9+nQAv34xqpou8fnnn2PFihWwtbWFn5+ffrpEUFAQVCqVzOmJzMvVq1c5UoGIiMwWiwpERNQktFotpk+fblBkqJousWfPHsTGxkKpVOL555/XLxI5ePBgqNVqmZMTyefWrVsoKiriSAUiIjJbLCoQEZEstFottFot/vjHPwIAcnJy9HeXOHz4MGJjY2FpaYnevXvrCwzBwcFwdHSUOTlR08nMzAQAFhWIiMhssahARERmwd3dHWFhYQgLCwMA5Obm4vTp0zh9+jSSkpLw0UcfQaFQwM/PT38Ly+HDh8PZ2Vnm5ESmcfHiRYwYMQKenp547rnnoNVqkZeXB0tLS1hYWMgdj4iIqEaSEELIHYJar8TERISHh4PdkFozSZKQkJCAiRMnyh3FrN26dQunTp3ST5lISUmBJEl47rnn9GsyDBs2DG3btpU7KlG96HQ62NnZobS0FAqFAhYWFqisrIROpwMAWFlZwcPDA926dUNISAhmzJghc2IiIiKOVCAiomaiffv2BiMZbt++jR9//FE/ZWLr1q3Q6XTQarX66RIvvPACPD09ZU5OVDcKhQJ9+/bFv/71L+h0On0xoUpZWRkyMzORmZmJt99+W6aUREREhlhUICKiZsnFxQUhISEICQkBABQWFuLMmTP6O0zs2LEDZWVl0Gq1+ukSI0eO5Cr6ZNYCAwNx9uzZWm+3amFhgS5duiA0NLSJkxEREdWMRQUiImoR7O3t9XeNAIDi4mIkJyfrp0tERkbi8ePHcHNz00+XGDx4MHx9fWVOTvRf/v7+KC8vr/VxnU6HNWvWQKFQNGEqIiKi2rGoQERELZKtra1BkaGkpAQpKSn66RKzZs1CaWkpXF1dERwcrB/N0LdvX0iSJHN6aq38/f1rXWfIwsICPXr0wJgxY5o4FRERUe1YVCAiolbBxsYGQUFBCAoKQlRUFCoqKnDu3Dn9dIlly5bh4cOHaNeuHQYMGKAfzdCnT58G/yr86aefon///hg0aJCJzoZaqs6dO8PJyQn379+v9lhlZSVWr17NohcREZkVjp0jIqJWydLSEv369UNUVBQOHz6Mu3fv4qeffsLChQuhVqsRGxuL/v37w9HRESNHjkRsbCxOnz791KHptdm0aRMGDx6MOXPmoKSkpBHOhloSf3//aoUDCwsLPP/88xg9erRMqYiIiGrGogIRERF+/dLWr18/zJw5E4mJibh9+zbOnz+Pjz/+GE5OTvj4448RHBwMZ2dnjBw5EsuXL0dSUhIeP3781Hbv37+PX375BTqdDhs2bMBzzz2H48ePN9FZUXMUEBAApVJpsK2yshIffvghRykQEZHZYVGBiIioBhYWFvD19cX06dP1RYaMjAx88skncHNzw+eff46RI0fC2dkZQUFBiI6ORlJSEh49emTQzvfff6+fI19RUYGcnBwMHz4cr7/+Ou7duyfHqZGZ8/f3N7j7g4WFBfz8/PDKK6/ImIqIiKhmLCoQERHVkVarxfTp0/H3v/8d169fR0ZGBv7617/C19cXCQkJGDlyJDQaDfr374/o6GgcPnwYSUlJsLKy0rdRWVkJAEhMTETXrl1x4MABuU6HzNSAAQMM/ruyshJr1qzhKAUiIjJLkqhtiWGiJpCYmIjw8PBaV7omag0kSUJCQgImTpwodxRqoIyMDJw6dQonT57EyZMnkZWVBRsbm1rXUVAoFNDpdBg/fjz++te/wsXFpYkTk7lyc3NDXl4eLCws0KdPH5w9e1buSERERDXiSAUiIiIT8fb2xptvvokdO3bg6tWruHjxYrXpEE/S6XQAgEOHDsHb2xubN29uqqhk5gICAiBJkn4tBSIiInPFogIREVEjuXbtmr5w8DQVFRUoKirCO++8g5dffhnZ2dlNkI7M2cCBAyGEwMCBAzFixAi54xAREdXKUu4ARERkftauXYvk5GS5YzR758+f109xeJaqaWDHjh2DVqvF888/D61W29gRyUzl5+cDAFQqFcLCwmROQ9RyzZkzBwEBAXLHIGrWOFKBiIiqSU5Oxo8//ih3jGYvPz/foKAgSRIUCkWNC+5ZWv5a53dxcUGHDh1QUlKC+/fvN1nW5mrfvn24ceOG3DFMztnZGW3btuU6G0SNaN++fRwZRmQCHKlAREQ1GjRoEPbu3St3jGarpKQE7dq1Q7t27eDh4QF3d3d07NgR7du3h5ubG9zc3ODq6gp3d3e0a9cO//znPxEeHq7/hZrqRpIkzJ49u0UudJqRkQFvb2+5YxC1WLyjCpFpsKhARETUCFQqFYqKiuSOQc0YCwpERNQccPoDERFRI1Ao+L9YIiIiavn4Lx4iIiIiIiIiqhcWFYiIiIiIiIioXlhUICIiIiIiIqJ6YVGBiIhkMXXqVNjb20OSJKSmppq8/aNHj8LBwQGHDx82edtVVq5ciR49ekCj0cDa2ho+Pj6YP3++rAs0NsV5N2fvvPMOJEnS/73++uvV9klKSsKCBQuwf/9+aLVa/b5vvPFGtX1HjRoFe3t7WFhYwNfXFykpKU1xGg2ya9cu+Pv7w97eHp06dcKbb76JvLw8s2gvJibG4PWp+uvZs2eN++t0Oqxbtw6BgYE1Pl6X9+iXX36J2NhYVFZW1ivzb7Xk/mNOr8/BgwcNMrRt29Z0J0pERmFRgYiIZLF161Zs2bKl0doXQjRa21WOHz+O999/H1lZWbhz5w5Wr16N+Ph4hIWFNfqxa9MU593cOTs74+uvv8alS5ewbds2g8eWLVuG9evXY+HChRg/fjwyMzPh7e2NNm3aYOfOnThy5IjB/t9++y327t2LkJAQXLhwAX379m3KUzFaQkICJk+ejLCwMNy4cQOHDh3CqVOn8Morr6CiokL29oyRnp6OIUOGYM6cOSgpKalxn7q8R8eMGQOVSoXhw4fjwYMHDcrU0vuPMRr79Rk7dixu3LiBU6dO4dVXX2308yGi2rGoQERELdLo0aPx8OFDhISENNox7OzsEBERAWdnZ9jb22PixIkIDQ3FN998g+zs7EY77tM0xXnXVWlpaa2/UMpJrVbj5ZdfRteuXWFtba3fvmbNGuzZsweJiYmwt7c3eM769euhUCgQERGBhw8fNnVkk/nss8/g7u6OefPmwcHBAX5+fpgzZw5SU1Nx5swZ2dsDgC+++AJCCIO/8+fPG+xz7tw5REdHY8aMGfDz86u1rbq+R2fOnInevXvj1VdfrXcxpDX0H8B8Xh9JktChQwcEBwejS5cujXOyRFQnLCoQEZFsJEmSO0KDfPXVV7CwsDDYVjUEt7Zf5lqTbdu2IT8/X+4YdXLlyhUsWbIEK1asgEqlqvZ4YGAgZs2ahZs3b+Ivf/mLDAlNIzs7G25ubgbvvY4dOwIArl3lInDXAAAgAElEQVS7Jnt7ddW7d2/s378fkydPNigM/ZYx79Hly5cjNTUV8fHxRudpLf2nrszt9SGixsWiAhERmcT333+PHj16wMHBASqVCr169cKxY8f0jwshEBcXh27dusHa2hoODg6YN2+e0e3UxenTp+Hp6QlJkvDpp58CADZt2gRbW1vY2Njg0KFDeOWVV6DRaODh4YHdu3c37OSfcPPmTajVanh5eZmszbpqyHmvX78eKpUK7dq1wzvvvAM3NzeoVCoEBgYa/OIcGRkJKysruLq66re99957sLW1hSRJuHPnDgBg1qxZmDt3LjIyMiBJEnx8fAAA33zzDTQaDT744IOmuCR1tn79egghMGbMmFr3iYmJQdeuXbF161YkJSU9tT0hBNauXYvu3bvD2toaTk5OGDduHH755Rf9Psb0ycrKSixduhSenp5Qq9V4/vnnkZCQYPR5arXaaoWeqvUPtFqt7O01hdreo05OThg6dCji4+ONnkbUWvpPU2iM14eIGheLCkREZBK3bt1CeHg4srKykJOTAzs7O0yePFn/+JIlSxAVFYWIiAjcunULeXl5iI6ONrqduggKCsIPP/xgsO3dd9/F7NmzUVpaCnt7eyQkJCAjIwNarRbTpk1DeXl5/U78CSUlJTh+/DimTZsGKyurBrdnrIacd2RkJKZMmYKSkhLMnDkTWVlZSElJQUVFBUaOHKkfirx+/XpMnDjR4BgbN27EihUrDLbFx8cjJCQE3t7eEELgypUrAKBfbE2n0zXKNaivI0eOoFu3brCxsal1H7VajR07dkChUGDatGkoLi6udd/ly5djwYIFWLRoEfLz83Hq1ClkZ2cjODgYt27dAmBcn4yOjsZHH32EdevWITc3FyEhIfjDH/6An376yajzXLhwIfLy8rBhwwYUFhbiwoULiI+Px0svvYRBgwYZ1VZjtAcACxYsgJOTE6ysrODl5YVx48bh7Nmz9Wrrt571Hu3Tpw9u3ryJc+fOGdVua+k/QPN8fYiocbGoQEREJjFhwgQsW7YMTk5OcHZ2xpgxY3D37l3cvn0bpaWlWLduHUaMGIE5c+bA0dERarUazs7ORrVjKoGBgdBoNHBxccGkSZNQXFyM69evN7jd1atXw83NDTExMSZIaXp1OW9LS0v9r6M9evTApk2bUFhYiO3bt5skw+jRo1FQUIAlS5aYpD1TKC4uxtWrV+Ht7f3MfQMCAjB79mxkZWXVWBQDfl1LYu3atXjttdfw+uuvw8HBAb169cLf/vY33LlzB5s3b672nKe9No8ePcKmTZsQGhqK8ePHw9HREYsXL4ZSqTT6dRk6dCiioqIQGRkJjUaDnj17orCwEFu3bjWqncZq709/+hO+/PJLZGdno6ioCLt378b169cxdOhQXLhwoV5tPulZ79GquflpaWl1brM19Z/m+PoQUeNjUYGIiBqFUqkE8Osv01euXEFJSQmGDx/eoHYaQ9WvYQ0dqXDgwAEkJibi2LFj1RZpM0d1Pe/+/fvDxsbGYNh1S5Ofnw8hxFN/ZX5STEwMunXrho0bN+L06dPVHr9w4QKKiorQv39/g+3+/v6wsrJ65gKGv31tLl26hJKSEoPb9qnVari6uhr9uixatAibN2/Gd999h6KiImRmZiIwMBABAQH1WlzU1O117NgRffr0gZ2dHaysrDBo0CBs374dpaWl2Lhxo9HtPaku79GqPlA1GqAuWlP/aY6vDxE1PhYViIjIJI4cOYIXXngBLi4usLa2xvz58/WP3bhxAwDg4uLSoHbM1Z49e7BmzRqcOHECnTt3ljuOyVlbW5t0pIi5efToEQA8dUG5J6lUKmzfvh2SJOGtt95CaWmpweNVt72zs7Or9lxHR0cUFhYala9qmPzixYshSZL+79q1a0YtCJqbm4vY2FhMnz4dL774ImxtbeHl5YUtW7YgJycHcXFxRuUydXu16dWrFywsLHD58uV6t1HX96harQbw3z5RF62l/9TG3F8fImp8LCoQEVGDXb9+HaGhoXB1dcWZM2fw8OFDxMbG6h+vWg398ePHDWrHHG3YsAE7d+7E8ePH4e7uLncckysvL8eDBw/g4eEhd5RGU/VFxZjRMAEBAZgzZw7S09OxatUqg8ccHR0BoMYvf/W5llXFuHXr1lW7lV9ycnKd20lPT0dlZWW1fqrRaODs7Gz08HVTt1cbnU4HnU5X5y/tv2XMe7SsrAzAf/tEXbSW/lMbc399iKjxsahAREQNlpaWhvLycrz77rvQarVQqVQGt5jr2bMnFAoFTp482aB2zIkQAlFRUUhLS8PBgwdr/FWxJThx4gSEEAaL7llaWppkYUtz0a5dO0iShIcPHxr1vFWrVuG5557Df/7zH4PtPXv2hJ2dXbVF8M6cOYOysjL069fPqON07NgRKpUKqampRj3vt6q+jObm5hpsLywsxL179/S3gpSrPQB46aWXqm07e/YshBAICAgwqq36vEer+kD79u3rfJzW0n+A5vn6EFHjY1GBiIgazNPTEwCQlJSER48eIT093WDer4uLC8aPH499+/Zh27ZtKCgowM8//1xtwbFntWNOLl68iI8++ghbtmyBUqk0GFYsSRI+/vhjuSPWi06nw/3791FRUYGff/4Zs2bNgqenJ6ZMmaLfx8fHB/fu3cPBgwdRXl6O27dv49q1a9XacnZ2Rk5ODrKyslBYWIjy8nJ8/fXXZndLSRsbG2i1Wv00nbqqGsZuYWFRbfvcuXNx4MAB7Ny5EwUFBUhLS8OMGTPg5uaGiIgIo4/z5ptvYvfu3di0aRMKCgpQWVmJGzdu6L/QT5o0Ce3bt0dKSkqt7Xh5eWHYsGHYsmULTp06hdLSUmRnZ+vzvP322/p95WgP+PV2gnv27MGDBw9QXl6O5ORkTJ06FZ6enpgxY8azL9YT6vMereoDvXr1qnPu1tJ/APN7fYjITAgiGSUkJAh2Q2rtAIiEhAS5YxiYMGGCmDBhglHPiYqKEs7OzsLR0VGEhYWJTz/9VAAQ3t7e4vr166KwsFBMnTpVtGnTRtjZ2YmgoCCxdOlSAUB4eHiIc+fO1amdutiwYYNwdXUVAISNjY0YM2aM2Lhxo7CxsREARJcuXURGRobYvHmz0Gg0AoDo1KmTuHz5cp3PNy0tTQCo9S8uLs6o62eKz8OGnndERIRQKpWiQ4cOwtLSUmg0GjFu3DiRkZFhcJy7d++KYcOGCZVKJby8vMSf//xnMW/ePAFA+Pj46F+nlJQU0alTJ6FWq0VQUJDIy8sTR48eFfb29iImJqZB51rF2PdPRESE6NChQ7XtkZGRQqlUipKSEv22AwcOCG9vbwFAtG3bVrz//vs1tjlv3jwxduxYg206nU7ExcWJLl26CKVSKZycnERoaKi4dOmSfh9jXpvHjx+LqKgo4enpKSwtLYWLi4sYP368uHDhghBCiNDQUAFALF269Knnf+fOHTFr1izh4+MjrK2thZ2dnRg8eLD45z//abCfXO3NnTtXeHt7C1tbW2FpaSk8PDzEtGnTRE5OjsF+ycnJYvDgwcLNzU3/nnN1dRWBgYHi5MmTQoj6vUdHjx4tOnToIHQ6nVG5W0v/MbfXp8rMmTNFmzZtnpq9Jub4/1+i5ojf5khWLCoQmec/aupTVKCGMYfPw4iICOHs7CxrBmOZqqiQnp4uLC0txRdffGHKeE2msrJSBAcHi23btrWK9hrDnTt3hEqlEh9//LF+W11zs/80vppenyosKhDJi9MfiIiISK+xbt1pTkpLS3Hs2DGkp6frF37z8fHBypUrsXLlShQVFcmc0DiVlZU4ePAgCgsLMWnSpBbfXmNZvnw5/Pz8EBkZCcC43Ow/je+3r48QAjk5OTh9+jSuXLkiczqi1o1FBSIiahZ++eWXanNua/pryD+Km+IYJL979+7h5ZdfRteuXfHWW2/pty9YsABhYWGYNGmS0YvuyenEiRPYv38/vv76a9jY2LT49hrD2rVrkZqaiqNHj0KpVAIwPjf7T+Op6fU5dOgQOnTogODgYBw5ckTmhEStmySEEHKHoNYrMTER4eHhYDek1kySJCQkJGDixIlyR9ELCwsDAOzdu1fmJK2H3J+HCxcuxCeffIKysjJ07twZcXFxmDBhgixZjNEY759vv/0Wx48fx5o1a0zWJpmvQ4cO4eLFi5g/f361RRPrg/3HtEz9+jzJHP//S9QcWcodgIiIiOS3evVqrF69Wu4YZmHUqFEYNWqU3DGoiYwdOxZjx441WXvsP6Zl6teHiEyP0x+IiIiIiIiIqF5YVCAiIiIiIiKiemFRgYiIiIiIiIjqhUUFIiIiIiIiIqoXFhWIiIiIiIiIqF549wciIqrRvn37IEmS3DFaHV5z44WHhyM8PFzuGERERK0SiwpERDKTJAlCCLljVDNo0CDMnj1b7hitRnJyMuLj45GQkCB3lGYlPDwcs2bNQkBAgNxRiKiZYTGSyDRYVCAikpmFhQUqKyvljlGNh4cHJk6cKHeMViU+Pp7X3Ejh4eEICAjgdSMio7GoQGQaXFOBiEhm5lpUICIiIiJ6FhYViIhkxqICERERETVXLCoQEclMoVBAp9PJHYOIiIiIyGgsKhARyYwjFYiIiIiouWJRgYhIZiwqEBEREVFzxaICEZHMWnNRYdeuXZAkCYGBgUY9b+rUqbC3t4ckSUhNTa3Tc3788Ud0794dCoUCkiShffv2iImJwf79+6HVaiFJEiRJwhtvvFHtuaNGjYK9vT0sLCzg6+uLlJQUo/ISySkpKQkLFixosX19165d8Pf3h729PTp16oQ333wTeXl5ZtFeTEyM/no/+dezZ88a99fpdFi3bl2tn4krV65Ejx49oNFoYG1tDR8fH8yfPx9FRUX6fb788kvExsa22v+vEFHTY1GBiEhmrXlNhV27dsHb2xvJycm4cuVKnZ+3detWbNmyxahjDRo0CP/v//0/jBo1CgBw6dIlLF68GOPHj0dmZia8vb3Rpk0b7Ny5E0eOHDF47rfffou9e/ciJCQEFy5cQN++fY06NpFcli1bhvXr12PhwoUtsq8nJCRg8uTJCAsLw40bN3Do0CGcOnUKr7zyCioqKmRvzxjp6ekYMmQI5syZg5KSkhr3OX78ON5//31kZWXhzp07WL16NeLj4xEWFqbfZ8yYMVCpVBg+fDgePHjQqJmJiAAWFYiIZNdaRyrcvXsXFy9exIoVKwAAf//732VOBKxfvx4KhQIRERF4+PCh3HGaXGlpqdGjRszxGPSrNWvWYM+ePUhMTIS9vb3BYy2lr3/22Wdwd3fHvHnz4ODgAD8/P8yZMwepqak4c+aM7O0BwBdffAEhhMHf+fPnDfY5d+4coqOjMWPGDPj5+dXalp2dHSIiIuDs7Ax7e3tMnDgRoaGh+Oabb5Cdna3fb+bMmejduzdeffXVRi+GEBGxqEBEJLPWWlRITEzE6NGj9b+qVf3Du64kSTJ5psDAQMyaNQs3b97EX/7yF5O3b+62bduG/Pz8Zn8MAq5cuYIlS5ZgxYoVUKlU1R5vKX09Ozsbbm5uBp8HHTt2BABcu3ZN9vbqqnfv3ti/fz8mT54Ma2vrWvf76quvYGFhYbCtbdu2AFBtdMPy5cuRmpqK+Ph40wcmInoCiwpERDJTKpWt8pekXbt24bXXXoO9vT1GjRqFrKwsfP/99zXuK4RAXFwcunXrBmtrazg4OGDevHnV9vv+++/Ro0cPODg4QKVSoVevXjh27JhRuWJiYtC1a1ds3boVSUlJ9Tq3piKEwNq1a9G9e3dYW1vDyckJ48aNwy+//KLfJzIyElZWVnB1ddVve++992BrawtJknDnzh0AwKxZszB37lxkZGRAkiT4+Phg/fr1UKlUaNeuHd555x24ublBpVIhMDDQ4FfbhhwDAL755htoNBp88MEHjXq9WpP169dDCIExY8bUuo8xfb0ufW3Tpk2wtbWFjY0NDh06hFdeeQUajQYeHh7YvXu3QXuVlZVYunQpPD09oVar8fzzzyMhIcHo89RqtdWKVFXrH2i1Wtnbawo3b96EWq2Gl5eXwXYnJycMHToU8fHxRhVsiYiMxaICEZHMVCoVSktL5Y7RpK5fv45Lly5hyJAhAKCfD1zbFIglS5YgKioKERERuHXrFvLy8hAdHV1tv1u3biE8PBxZWVnIycmBnZ0dJk+ebFQ2tVqNHTt2QKFQYNq0aSguLjby7JrO8uXLsWDBAixatAj5+fk4deoUsrOzERwcjFu3bgH49cvlxIkTDZ63ceNG/bSTKvHx8QgJCYG3tzeEELhy5QoiIyMxZcoUlJSUYObMmcjKykJKSgoqKiowcuRI/XDrhhwDgH6kTmtdW6QxHDlyBN26dYONjU2t+xjT1+vS1959913Mnj0bpaWlsLe3R0JCAjIyMqDVajFt2jSUl5fr24uOjsZHH32EdevWITc3FyEhIfjDH/6An376yajzXLhwIfLy8rBhwwYUFhbiwoULiI+Px0svvYRBgwYZ1VZjtAcACxYsgJOTE6ysrODl5YVx48bh7Nmz9Wrrt0pKSnD8+HFMmzYNVlZW1R7v06cPbt68iXPnzpnkeERENWFRgYhIZmq1utUVFXbt2oXf/e53+mG8Y8aMgbW1Nfbu3VvtWpSWlmLdunUYMWIE5syZA0dHR6jVajg7O1drd8KECVi2bBmcnJzg7OyMMWPG4O7du7h9+7ZR+QICAjB79mxkZWXVWLwwB6WlpVi7di1ee+01vP7663BwcECvXr3wt7/9DXfu3MHmzZtNdixLS0v9L9Q9evTApk2bUFhYiO3bt5uk/dGjR6OgoABLliwxSXutXXFxMa5evQpvb+9n7luXvl6fvhYYGAiNRgMXFxdMmjQJxcXFuH79OgDg0aNH2LRpE0JDQzF+/Hg4Ojpi8eLFUCqVRvepoUOHIioqCpGRkdBoNOjZsycKCwuxdetWo9pprPb+9Kc/4csvv0R2djaKioqwe/duXL9+HUOHDsWFCxfq1eaTVq9eDTc3N8TExNT4eJcuXQAAaWlpDT4WEVFtWFQgIpJZay0qvPbaa/r/1mg0GDVqFAoKCnDo0CGDfa9cuYKSkhIMHz7c6OMolUoAqNeaFTExMejWrRs2btyI06dPG/38xnbhwgUUFRWhf//+Btv9/f1hZWVV70Xl6qJ///6wsbExGPpO5iM/Px9CiKeOUnjSs/p6Q/ta1S/oVSMVLl26hJKSEoPbKqrVari6uhrdpxYtWoTNmzfju+++Q1FRETIzMxEYGIiAgACDhQvlaq9jx47o06cP7OzsYGVlhUGDBmH79u0oLS3Fxo0bjW7vSQcOHEBiYiKOHTtWbSHOKlV9oGo0CRFRY2BRgYhIZjY2Nq2qqHD+/HmkpaUhJCTE4L7thw8fBlB9CsSNGzcAAC4uLs9s+8iRI3jhhRfg4uICa2trzJ8/v945VSoVtm/fDkmS8NZbb5nda1R1qzg7O7tqjzk6OqKwsLBRj29tbW30CBBqGo8ePQKApy7496Rn9XVT97WqaRaLFy82+Ay4du1arbdSrElubi5iY2Mxffp0vPjii7C1tYWXlxe2bNmCnJwcxMXFGZXL1O3VplevXrCwsMDly5fr3caePXuwZs0anDhxAp07d651P7VaDeC/fYKIqDGwqEBEJLPWNlLhH//4B37/+99Xu8XavXv3oFar8e233+oXRgOgX7n+8ePHT233+vXrCA0NhaurK86cOYOHDx8iNja2QVkDAgIwZ84cpKenY9WqVQ1qy9QcHR0BoMYvdA8ePICHh0ejHbu8vLzRj0H1V/VF0pgROk/r66bua1UFwnXr1lX7HEhOTq5zO+np6aisrIS7u7vBdo1GA2dnZ6OnF5i6vdrodDrodLo6F31+a8OGDdi5cyeOHz9eLetvlZWVAfhvnyAiagwsKhARyaw1FRWEENizZw/ee++9ao85OTkhLCwMlZWV2LVrl357z549oVAocPLkyae2nZaWhvLycrz77rvQarVQqVQmue3kqlWr8Nxzz+E///lPg9sypZ49e8LOzq7awnZnzpxBWVkZ+vXrp99maWlpsEheQ504cQJCCIOF60x9DKq/du3aQZIkPHz40Kjn1dbXjelrddGxY0eoVCqkpqYa9bzfqipm5ObmGmwvLCzEvXv39LeClKs9AHjppZeqbTt79iyEEAgICDCqLSEEoqKikJaWhoMHD9Y4cuS3qvpA+/btjToWEZExWFQgIpKZjY2NUUN+m7MffvgBGo0GgwcPrvHxGTNmADCcAuHi4oLx48dj37592LZtGwoKCvDzzz9XWxzO09MTAJCUlIRHjx4hPT3dJOsKVA0N/+294eWmUqkwd+5cHDhwADt37kRBQQHS0tIwY8YMuLm5ISIiQr+vj48P7t27h4MHD6K8vBy3b9/GtWvXqrXp7OyMnJwcZGVlobCwUF8k0Ol0uH//PioqKvDzzz9j1qxZ8PT0xJQpU0xyjK+//pq3lDQhGxsbaLVa/dShuqqtrxvT1+p6nDfffBO7d+/Gpk2bUFBQgMrKSty4cUP/hX7SpElo3749UlJSam3Hy8sLw4YNw5YtW3Dq1CmUlpYiOztbn+ftt9/W7ytHe8Cvt3vcs2cPHjx4gPLyciQnJ2Pq1Knw9PTUf97V1cWLF/HRRx9hy5YtUCqVBlNHJEnCxx9/XO05VX2gV69eRh2LiMgogkhGCQkJgt2QWrv33ntPDB06VO4YBiZMmCAmTJhg0jbffvttYWtrKywtLUXv3r1FSkqKweOrVq0Sbm5uAoAAIDp06CA2btwohBCisLBQTJ06VbRp00bY2dmJoKAgsXTpUgFAeHh4iHPnzgkhhIiKihLOzs7C0dFRhIWFiU8//VQAEN7e3mLv3r3C19dXKBQKAUC4urqKDz74QBw4cEB4e3sLAKJt27bi/fffrzH/vHnzxNixY016TZ5Un89DnU4n4uLiRJcuXYRSqRROTk4iNDRUXLp0yWC/u3fvimHDhgmVSiW8vLzEn//8ZzFv3jwBQPj4+Ijr168LIYRISUkRnTp1Emq1WgQFBYm8vDwREREhlEql6NChg7C0tBQajUaMGzdOZGRkmOwYR48eFfb29iImJsbo6wZAJCQkGP28li4yMlIolUpRUlKi39aQvl6XvrZx40ZhY2MjAIguXbqIjIwMsXnzZqHRaAQA0alTJ3H58mUhhBCPHz8WUVFRwtPTU1haWgoXFxcxfvx4ceHCBSGEEKGhoQKAWLp06VPP886dO2LWrFnCx8dHWFtbCzs7OzF48GDxz3/+02A/udqbO3eu8Pb21n/2eXh4iGnTpomcnByD/ZKTk8XgwYMNPgNdXV1FYGCgOHnypBBCiLS0NP1jNf3FxcVVO/7o0aNFhw4dhE6ne2rO1oqfH0SmIQkhRBPVL4iqSUxMRHh4ONgNqTWbN28eTp061air9RsrLCwMALB3716Zk7Qe5vp5+M4772Dv3r24e/eu3FFqJEkSEhISMHHiRLmjmJUrV66ge/fu2L59O15//XW54xhNp9PhhRdewJQpU/DWW2+1+PYaw927d+Hh4YGYmBjMnTtX7jhmiZ8fRKbB6Q9ERDJrbXd/oOanPrfkJHn5+Phg5cqVWLlyJYqKiuSOY5TKykocPHgQhYWFmDRpUotvr7EsX74cfn5+iIyMlDsKEbVwLCoQEcnMzs6u0W//R0Stz4IFCxAWFoZJkyYZvWijnE6cOIH9+/fj66+/ho2NTYtvrzGsXbsWqampOHr0KJRKpdxxiKiFY1GBiEhmTk5OuHfvntwxiKpZuHAhtm/fjocPH8LLywv79u2TOxIZ6YMPPkBkZCQ+/PBDuaPU2fDhw/GPf/wDrq6uraI9Uzt06BAeP36MEydOwMnJSe44RNQKWModgIiotXNyckJhYSEqKipgacmPZTIfq1evxurVq+WOQQ00atQojBo1Su4Y1ETGjh2LsWPHyh2DiFoRjlQgIpKZk5MThBDNangyERERERHAogIRkeyqhqfev39f5iRERERERMZhUYGISGYsKhARERFRc8WiAhGRzFhUICIiIqLmiiuCERHJTKPRwMLCwuyKCjdu3EBiYqLcMVqN5ORkAOA1r4eqa0dERERNj0UFIiKZSZIEBwcHsysq/PjjjwgPD5c7RqvDa268+Ph4xMfHyx2DiIioVWJRgYjIDDg5OZlVUWHv3r1yRyBq8f72t79h4cKFuHfvntxRiIiI6o1rKhARmYH27dvj1q1bcscgoiaUl5cHNzc3uWMQERE1CIsKRERmwN3dHTk5OXLHIKImlJubC1dXV7ljEBERNQiLCkREZoBFBaLWJzc3lyMViIio2WNRgYjIDLCoQNT6cPoDERG1BCwqEBGZgaqighBC7ihE1EQ4/YGIiFoCFhWIiMyAu7s7Hj9+zFXgiVoJIQRu3brFkQpERNTssahARGQG3N3dAQA3b96UOQkRNYU7d+6gvLycIxWIiKjZY1GBiMgMdOjQAQC4rgJRK5GXlwcAHKlARETNHosKRERmQKPRwM7OjkUFolYiNzcXAIsKRETU/LGoQERkJjw9PZGVlSV3DCJqArm5uVCpVHB0dJQ7ChERUYOwqEBEZCZ8fHxw5coVuWMQURPgnR+IiKilYFGBiMhMdOnShUUFolYiLy+PUx+IiKhFYFGBiMhMeHt7Iz09Xe4YRNQEcnNzWVQgIqIWgUUFIiIz0aVLFzx48AB3796VOwoRNbK8vDxOfyAiohaBRQUiIjPh4+MDABytQNQKcKQCERG1FCwqEBGZCU9PT6hUKq6rQNQKsKhAREQtBYsKRERmQqFQwMvLi0UFohauuLgYRUVFnP5AREQtAosKRERmpGvXrrh06ZLcMYioEeXm5gIARyoQEVGLwKICEZEZ6dWrF86dOyd3DCJqRCwqEBFRS8KiAhGRGenduzcuX76MkpISuaMQUa3RHeYAACAASURBVCPJzc2FQqGAi4uL3FGIiIgajEUFIiIz4ufnh8rKSqSlpckdhYgaSV5eHlxcXGBpaSl3FCIiogZjUYGIyIx4e3tDo9EgNTVV7ihE1Eh45wciImpJWFQgIjIjkiTh+eef57oKRC1YXl4eiwpERNRisKhARGRm/Pz8OFKBqAXLzc3l7SSJiKjFYFGBiMjM9O7dGz///DN0Op3cUYioEXD6AxERtSQsKhARmRk/Pz8UFxfj8uXLckchokaQl5fHkQpERNRisKhARGRmevfuDRsbG/zrX/+SOwoRmVhFRQXu3LnDkQpERNRisKhARGRmlEolBgwYwKICUQt069Yt6HQ6FhWIiKjFYFGBiMgMBQUFsahA1ALl5uYCAKc/EBFRi8GiAhGRGRo8eDAuX76MvLw8uaMQkQlVvadZVCAiopaCRQUiIjMUGBgICwsLJCcnyx2FiEwoNzcXGo0Gtra2ckchIiIyCRYViIjMkEajQc+ePTkFgqiF4e0kiYiopWFRgYjITAUFBeH06dNyxyCiesrPz0d+fr7BNt5OkoiIWhpLuQMQEVHNgoODsXnzZhQUFECj0cgdh4iMtH//frz77ruwtLSEs7Mz3N3dUVhYCLVajeXLl8PV1RXu7u5o164dunTpgjZt2sgdmYiIyGgsKhARmakRI0agsrISx48fx7hx4+SOQ0RGGjp0KACgoqLCYNSCUqnEmjVrUFlZiYqKCgDAl19+iZCQENmyEhER1RenPxARmak2bdqgf//+OHbsmNxRiKgeunfvDkdHx2rby8vL8fjxY1RUVECSJHTp0gW/+93vZEhIRETUcCwqEBGZsZdffhnffPON3DGIqB4kScILL7wACwuLp+6zaNEiSJLUhMmIiIhMh0UFIiIz9tJLLyErKwuXLl2SOwoR1cOwYcOeWjBo27Ytfv/73zdhIiIiItNiUYGIyIwNHDgQzs7OHK1A1EwNHTpUv27Cb1laWmL+/PmwsrJq4lRERESmw6ICEZEZs7CwwPDhw7muAlEz1atXr1rv3mJlZYWpU6c2cSIiIiLTYlGBiMjMvfzyyzhx4gRKS0vljkJERlIoFBgyZEi1dRWUSiVmzpwJBwcHmZIRERGZBosKRERmbvTo0SgrK+NoBaJm6oUXXqhxXYU///nPMqQhIiIyLRYViIjMXPv27TF48GDs379f7ihEVA+/XVdBqVRiypQpcHNzkzEVERGRabCoQETUDLz22ms4fPgwysrK5I5CREbq06cPbG1t9f9dUVGB2bNny5iIiIjIdFhUICJqBsaPH4+CggJ89913ckchIiNZWFhg8ODBUCgUUCqVGDNmDLp37y53LCIiIpNgUYGIqBnw8PCAv78/p0AQNVMvvvgiAKC8vBzR0dEypyEiIjIdFhWIiJqJ8ePH4+DBg7Xe856IzNeQIUOg0+kQGBiIQYMGyR2HiIjIZCQhhJA7BLVeiYmJCA8PB7sh0bNdvXoVWq0W3333nf5XT3NQ9T4mIiIyxoQJE7B37165YxBRA1nKHYCIiOrGy8sLffr0wZ49e8yqqFAlISFB7ghEeuHh4Zg1axYCAgLkjqK3fft2TJkypcbbS5qDdevWAQAXkaQmUdXfiKj5Y1GBiKgZmTx5MlatWoX/+Z//gVqtljuOgYkTJ8odgUgvPDwcAQEBZtUvx4wZA5VKJXeMWlX9YmxO14xaLo5QIGo5uKYCEVEz8oc//AFFRUU4evSo3FGIyEjmXFAgIiKqLxYViIiaETc3NwwfPhxffPGF3FGIiIiIiFhUICJqbt544w0cPXoUd+7ckTsKEREREbVyLCoQETUzoaGhsLa2RmJiotxRiIiIiKiVY1GBiKiZsbW1RWhoKKdAEBEREZHsWFQgImqG3njjDfz444+4fPmy3FGohVm5ciV69OgBjUYDa2tr+Pj4YP78+SgqKnrq86ZOnQp7e3tIkoTU1NQG53j06BGee+45LF68uMFtNcTRo0fh4OCAw4cPy5qjOUhKSsKCBQuwf/9+aLVaSJIESZLwxhtvVNt31KhRsLe3h4WFBXx9fZGSkiJDYuPs2rUL/v7+sLe3R6dOnfDmm28iLy/PLNqLiYnRX+8n/3r27Fnj/jqdDuvWrUNgYGCNj9flc+DLL79EbGwsKisr65WZiFoOFhWIiJqh4cOHo1OnTti2bZvcUaiFOX78ON5//31kZWXhzp07WL16NeLj4xEWFvbU523duhVbtmwxWY5Fixbh0qVLJmuvvoQQckdoFpYtW4b169dj4cKFGD9+PDIzM+Ht7Y02bdpg586dOHLkiMH+3377Lfbu3YuQkBBcuHABffv2lSl53SQkJGDy5MkICwvDjRs3cOjQIZw6dQqvvPIKKioqZG/PGOnp6RgyZAjmzJmDkpKSGvepy+dA1S1Shw8fjgcPHjRqZiIybywqEBE1QwqFAm+++SY+//xzPH78WO44Zqm0tLTWX+GodnZ2doiIiICzszPs7e0xceJEhIaG4ptvvkF2dnaTZPjhhx9w/vz5JjnWs4wePRoPHz5ESEiI3FHMtk+vWbMGe/bsQWJiIuzt7Q0eW79+PRQKBSIiIvDw4UOZEjbcZ599Bnd3d8ybNw8ODg7w8/PDnDlzkJqaijNnzsjeHgB88cUXEEIY/P32fXTu3DlER0djxowZ8PPzq7Wtun4OzJw5E71798arr77a6MUQIjJfLCoQETVTb7/9Nu7fv48vv/xS7ihmadu2bcjPz5c7RrPz1VdfwcLCwmBb27ZtAaDWXzWrSJLU4OOXlpZi3rx5iI+Pb3BbLY059ukrV65gyZIlWLFiBVQqVbXHAwMDMWvWLNy8eRN/+ctfZEhoGtnZ2XBzczPo4x07dgQAXLt2Tfb26qp3797Yv38/Jk+eDGtr61r3M+ZzYPny5UhNTeV7lqgVY1GBiKiZ8vDwwMsvv2zSIedN6YsvvkD//v2hUqlga2uLzp07Y9WqVYiMjISVlRVcXV31+7733nuwtbWFJEkGt9I8efIkBgwYABsbG2g0GvTq1QsFBQWYNWsW5s6di4yMDEiSBB8fHwC/DmVfu3YtunfvDmtrazg5OWHcuHH45Zdf9G3Gx8fD1tYWCoUC/fr1Q/v27aFUKmFra4u+ffsiODgYHTt2hEqlgqOjI+bPn2/ya1DXrJs2bYKtrS1sbGxw6NAhvPLKK9BoNPDw8MDu3bv1+3Xv3h2SJOnPqepLwfz58+Hg4ACVSoUdO3bUmvPmzZtQq9Xw8vLSbxNCIC4uDt26dYO1tTUcHBwwb968el+LKosWLcJ7770HFxeXBrfVUKdPn4anpyckScKnn34KoO7XfP369VCpVGjXrh3eeecduLm5QaVSITAw0OCX6Lr299r69DfffAONRoMPPvigKS5JNevXr4cQAmPGjKl1n5iYGHTt2hVbt25FUlLSU9szZb8HgMrKSixduhSenp5Qq9V4/vnnkZCQYPR5arXaagWdqvUPtFqt7O01hZo+BwDAyckJQ4cORXx8PKcLEbVWgkhGCQkJgt2QqP4OHjwoJEkS6enpsmWoz/t43bp1AoD48MMPxd27d8W9e/fEZ599JiZPniyEEGLy5Mmiffv2Bs+Ji4sTAMTt27eFEEIUFRUJjUYjYmNjRWlpqcjLyxOvvfaa/vHx48cLb29vgzaWLl0qrKysxBdffCEePHggfv75Z9G3b1/Rtm1bkZeXp99v2bJlAoA4c+aMKC4uFnfu3BEvv/yyACCOHDkibt++LYqLi0VkZKQAIFJTU42+bs+6BnXNumjRIgFAfPfdd+Lhw4ciPz9fBAcHC1tbW1FWViaEEKKiokJ07txZeHp6ioqKCoMcs2fPFuvWras1Z3FxsbC3txeRkZEG2xctWiQkSRKffPKJuH//vigpKREbN24UAMR//vMfo6+HEEKcPn1ajBkzRgghxO3btwUAsWjRonq1BUAkJCTU67lPys7OFgDEhg0b9Nvqcs2FECIiIkLY2tqKixcvikePHokLFy4If39/YW9vL65fv67fry79XYia+/RXX30l7O3txcqVKxt8rhMmTBATJkww6jlarVb06NGjxse8vb3F1atXhRBC/PDDD0KhUIjOnTuLoqIiIYQQX3/9tRg7dqzBc0zZ74UQ4i9/+YuwtrYW+/btE/fv3xcLFy4UCoVCnD171qjzPHHihFAqlWL9+vWioKBAnD9/XnTv3l289NJLRrXTWO2tWrVKeHh4CEdHR6FUKkXnzp3F2LFjxb///e9anzNw4EDRu3fvOrVf2+dAlQULFhj93q9PfyMi88RvcyQrFhWIGqa8vFx06NBBLFy4ULYMxr6Py8rKhKOjoxg2bJjB9oqKChEfHy+EqNuXrPPnzwsA4quvvqrxOL/9AlZSUiLs7OzEpEmTDPb797//LQAYfCmrKioUFhbqt/3v//6vACDS0tKqPXfPnj11Pn8hnn0NjMla9eWqtLRUv63qy/2VK1f026qKGImJifptxcXFwtPTUzx8+LDWrIsWLRJdu3YVBQUF+m0lJSXCxsZGjBw50mDf3bt317uoUFJSIvr37y9u3LghhGgeRYVnXfOIiAjh4OBg0N7Zs2cFALFixQr9toYUFUzJ2C95RUVFQpIkERISUuPjTxYVhBBi7ty5AoB4//33hRDViwqm7velpaXCxsbGoL2SkhJhbW0t3n333TqfZ5XFixcLAPo/Dw8PkZ2dbXQ7jdHe9evXRUpKiigsLBSPHz8WycnJok+fPkKtVovz58/X+Bxjigo1fQ486fPPPxcAxN///vc6Z2ZRgajl4PQHIqJmzNLSElOmTMHnn3+O8vJyuePUyc8//4wHDx7gpZdeMthuYWGBmTNn1rkdrVaLdu3a4fXXX8fy5cuRlZX11P0vXLiAoqIi9O/f32C7v78/rKysnrk4mpWVFQAYLEamVCoBwOhr/6xrYKqsT+aaOnUqHBwcDOY979y5E+PGjYNGo6mxnQMHDiAxMRHHjh0zWIDvypUrKCkpwfDhw+t2wnWwcOFCTJ8+HR06dDBZm02ppmtek/79+8PGxsZgOH9zlZ+fDyEEbGxs6rR/TEwMunXrho0bN+L06dPVHjd1v7906RJKSkoMbquoVqvh6upq9PVftGgRNm/ejO+++w5FRUXIzMxEYGAgAgIC6rWAqanb69ixI/r06QM7OztYWVlh0KBB2L59O0pLS7Fx40aj23tSbZ8DT6rqA7du3WrQsYioeWJRgYiomZs6dSry8/ObzYKNBQUFAABHR8cGtaNWq3H8+HEEBQXhgw8+gFarxaRJk1BaWlrj/lW3PLOzs6v2mKOjIwoLCxuUxxjPugaNkdXOzg7Tp0/HDz/8gH//+98AgL/+9a+IjIyscf89e/ZgzZo1OHHiBDp37mzw2I0bNwDAZOsenD59GmlpaZg6dapJ2jN31tbWuH37ttwxGuzRo0cA8NQF/56kUqmwfft2SJKEt956q9p71dT9vri4GACwePFiSJKk/7t27dozFx19Um5uLmJjYzF9+nS8+OKLsLW1hZeXF7Zs2YKcnBzExcUZlcvU7dWmV69esLCwwOXLl+vdxtM+B56kVqsB/LdPEFHrwqICEVEz17lzZ7z66qsN/jWqqbi7uwOAwYKL9eXr64vDhw8jJycHUVFRSEhIwMcff1zjvlVf4Gv6YvLgwQN4eHg0OE9dPesaNFbWyMhIKJVKrFu3DqdOnULHjh3h7e1dbb8NGzZg586dOH78uD7rk6pW+TfV7Uy3bduG7777DgqFQv/Fr6pg8cEHH0CSJPz0008mOZbcysvLm7y/NZaqL5KVlZV1fk5AQADmzJmD9PR0/aKkVUzd76v60Lp166rdajE5ObnO7aSnp6OysrLae0Gj0cDZ2RkXLlwwKpep26uNTqeDTqerc9Hnt571OfCksrIyAP/tE0TUurCoQETUArz33nv4v//7P6Slpckd5Zk6d+4MZ2dnfPvtt7XuY2lp+cxh5Dk5Obh48SKAX788fPjhh+jbt69+22/17NkTdnZ21b6cnjlzBmVlZejXr5+RZ1J/z7oGjZXVw8MDEydOxL59+7BkyRLMmjXL4HEh/j97dx5WVbn2D/y7gQ2bGZxwQGRynk1LUDP1fTXzOE+co/5yKjQTTVRyyIOKhlGiGNZxeO04g0NqJlbqUbPILCERc0wEQ8KJGWW6f3/0sl+3gO2NGxbD93Nd+5+1nnWve631LHTfe63nEQQEBCAuLg4HDhwo9Rfj4vxMTExw6tSpcuXxtC1btpT40lf8S/6iRYsgIiUeia+uTp48CRFB9+7dtcv06e9VUYMGDaBSqZCenm7QdsuXL0erVq0QExOjs9zY/b54lpbY2FiDtntacTHjzp07OsszMzPx4MED7VSQSsUDUOJVKgA4d+4cRAReXl4GxdL378CTivuAk5OTQfsiopqBRQUiohpgwIABaNGiBT799FOlU/lLFhYWWLhwIU6fPg0/Pz/8/vvvKCoqQmZmprYg4OnpiQcPHuDAgQPIz8/H3bt3S8zdnpycjGnTpuHy5cvIy8tDTEwMbt26pf2yVqdOHSQnJyMhIQGZmZkwNTWFv78/9u/fj+3btyMjIwNxcXGYPn06GjVqBF9f3ypzDjQaTYXl6u/vj4KCAjx8+BB9+/bVWXfp0iV88MEH2LhxI9Rqtc4j4yqVSvsUSP369TFy5Ejs3bsXmzdvRkZGBi5cuIANGzY813mpiYqKivDw4UMUFBTgwoULmD17NlxcXDBx4kRtG336O1CyT+fn5yMqKkqxKSWtrKzg7u6ufR1GX8WvQZiampZYbsx+r9FoMGnSJOzatQvr169HRkYGCgsLcfv2be0Xeh8fHzg5OeH8+fNlxnFzc0OfPn2wceNGnD59Grm5uUhKStLmM2XKFG1bJeIBf073uHv3bqSlpSE/Px/R0dGYOnUqXFxcMH369L8+WU/Q9+/Ak4r7QPv27Q3aFxHVEEqMDklUjLM/EBnP6tWrxcbGRtLS0ip1v+W9jz/++GNp3769aDQa0Wg00rlzZwkPDxcRkfv370ufPn1Eo9GIm5ubzJw5U+bNmycAxNPTUxITEyUhIUG8vb3F0dFRTE1NpXHjxrJo0SLtlInnz5+XZs2aiaWlpfTs2VNSUlKkqKhIQkJCpHnz5qJWq8XR0VGGDx8uV65c0ea1Zs0asbKyEgDi6uoq3377rQQHB4u9vb0AECcnJ9mxY4fs3r1bnJycBIA4OjrKrl27jHoO9Mk1PDxcm2vz5s3lxo0bsmHDBrGzsxMA0qxZM7l69WqJ/fbp00c2bdpUYnlcXJzOaPRPf0JCQrRtMzMzZerUqVK3bl2xsbGRnj17ypIlS7Sj2P/yyy8Gn48nVYXZH9atWycNGzYUAGJlZSVDhgwx6Jz7+vqKWq2WJk2aiJmZmdjZ2cmwYcPkxo0bOvvRp7+LlN6njxw5Ira2thIUFPRcxypSvtH4/fz8RK1WS05OjnbZ/v37xcPDQwBIvXr1tLM9PG3evHklppQ0dr9//PixBAQEiIuLi5iZmUn9+vVl5MiREh8fLyIiw4cPFwCyZMmSZx7nvXv3ZPbs2eLp6SkWFhZiY2MjPXr0kM8//1ynnVLx/P39xcPDQ6ytrcXMzEycnZ3ljTfekOTkZJ120dHR0qNHD2nUqJH2vm7YsKF4e3vLqVOnRMSwvwPFBg0aJE2aNJGioqJn5vkkzv5AVHOoREQqtGpB9AyRkZEYO3Ys2A2Jnl/xO8crV64sc/C9isD7mKoilUqFiIgIjBkzRrEcpk2bhj179uD+/fuK5WCI0aNHAwD27Nmj9zbXr19H69atsWXLFowfP76iUqswRUVFeOWVVzBx4kRMnjy5xserCPfv34ezszOCgoLg7++v93bl6W9EVDXx9QciohrCwcEB48ePx8cff4yioiKl0yEiGDaIYXXk6emJZcuWYdmyZcjKylI6HYMUFhbiwIEDyMzMhI+PT42PV1ECAwPRqVOnSi1mE1HVwqICEVENMnPmTFy/fh3Hjh1TOpVa5/LlyyXePS7tU5W/HBgTz0ftsWDBAowePRo+Pj4GD9qopJMnT2Lfvn2IioqClZVVjY9XEVavXo3Y2FgcOXIEarVa6XSISCFmSidARETG07ZtW/Tq1Qvr1q1D//79lU6nVmnVqhVfAXlCbT8fCxcuxJYtW5CXlwc3NzeEhIRg1KhRSqdVYVasWIGvv/4a77//PoKDg5VORy/9+vVDv379ak08Yzt48CAeP36MkydPlhh0k4hqFxYViIhqmNmzZ2PkyJG4fPkyWrVqpXQ6RLXSypUrsXLlSqXTqFT9+/dnMbMWGTp0KIYOHap0GkRUBfD1ByKiGmbo0KFwd3fHunXrlE6FiIiIiGo4FhWIiGoYExMTzJw5E5999lm1GXWeiIiIiKonFhWIiGqgyZMnw9zcHBs3blQ6FSIiIiKqwVhUICKqgWxtbTF16lSsW7cOeXl5SqdDRERERDUUB2okIqqhZs2ahbVr12LPnj0YN25che8vMjKywvdBZIjo6GilU6hWbt++DYD3MlWO27dvw9nZWek0iMgIVFKb53sixUVGRmLs2LG1etoxooo0duxYXL9+HT///HOF7aP4PiYiIjLEqFGjsGfPHqXTIKLnxCcViIhqsLlz5+LFF1/E6dOn8fLLL1fovlgcpKpEpVIhIiICY8aMUTqVamP06NEAwC95VCmK+xsRVX8cU4GIqAbr1q0bvLy8EBoaqnQqRERERFQDsahARFTDvfPOOzh06BCuX7+udCpEREREVMOwqEBEVMONGDECrq6uCAsLUzoVIiIiIqphWFQgIqrhTE1NMWPGDPzP//wPHjx4oHQ6RERERFSDsKhARFQLvPHGGzAzM8PmzZuVToWIiIiIahAWFYiIagFbW1tMmjQJa9euRX5+vtLpEBEREVENwaICEVEtMWvWLKSkpGDfvn1Kp6K3K1euYObMmWjbti1sbW1hZmYGe3t7tGjRAoMGDUJ0dLTSKRpVdTjeZcuWoU2bNrCzs4OFhQU8PT0xf/58ZGVladvs27cP7u7uUKlUOh9zc3M0aNAAr7zyCkJCQvDw4UMFj4SM7dixY1iwYEGJ6z9hwoQSbfv37w9bW1uYmpqibdu2OH/+vAIZG2bnzp3o1q0bbG1t0axZM0yaNAkpKSlVIl5QUFCJ+02lUqFdu3alti8qKkJoaCi8vb1LXa/PfX7o0CGsWrUKhYWF5cqZiGoOFhWIiGoJV1dXDBs2DB999JHSqehl8+bNaN++PS5cuIDVq1cjKSkJ2dnZiImJwfLly5GWloa4uDil0zSa6nK8J06cwNtvv42EhATcu3cPK1euxJo1a3TmnB85ciR+++03eHh4wN7eHiKCoqIipKamIjIyEm5ubggICEDbtm3x008/KXg0ZCz//Oc/ERYWhoULF+pc/7p162L79u348ssvddp//fXX2LNnDwYPHoz4+Hh06dJFocz1ExERgXHjxmH06NG4ffs2Dh48iNOnT2PgwIEoKChQPJ4hrl27hpdffhlz5sxBTk5OqW30uc+HDBkCjUaDfv36IS0trUJzJqIqTogUFBERIeyGRJXnzJkzAkC+++47o8WsiPs4OjpaTE1NpW/fvpKfn19qm6NHj8q6deuMut+cnBzx8vIyakx9KHW85TFo0CApKCjQWTZmzBgBIImJiTrLPTw8xN7evtQ4e/bsERMTE2nQoIGkpaUZPU8AEhERYfS4+qqMvmTsfYwaNUpGjRpl8Hbvv/++tGjRQnJzc3WWe3h4yI4dO8TExESaNGlS4jpHRUXJ0KFDnyvnytKnTx9p3LixFBUVaZd9/PHHAkDOnDmjeLzly5fLtm3b/rJdbGysjBgxQrZv3y6dOnWSjh07ltrOkPvcz89PvLy8yvzbVZby9jciqnr4pAIRUS3So0cPvPjii1i9erXSqTxTUFAQCgsL8f7778PMzKzUNgMGDMDbb79t1P1u3rwZqampRo2pD6WOtzwOHz4MU1NTnWX16tUDgDJ/9SzNqFGjMHHiRKSmpuLTTz81ao5VQWX0JaX665OuX7+O9957D0uXLoVGoymx3tvbG7Nnz8bvv/+OuXPnKpChcSQlJaFRo0ZQqVTaZU2bNgUA3Lp1S/F4+urYsSP27duHcePGwcLCosx2htzngYGBiI2NxZo1a4yfMBFVCywqEBHVMv7+/jhw4ABu3LihdCqlysvLw/Hjx1G3bl28+OKLem3j5+cHc3NzNGzYULtsxowZsLa2hkqlwr1797TLT506hRdffBFWVlaws7ND+/btkZGRgdmzZ8Pf3x83btyASqWCp6cnAEBEsHr1arRu3RoWFhZwdHTEsGHDcPnyZW3MNWvWwNraGiYmJnjhhRfg5OQEtVoNa2trdOnSBb169ULTpk2h0Wjg4OCA+fPnP9fx6pvX+vXrYW1tDSsrKxw8eBADBw6EnZ0dnJ2dsWvXLm271q1bQ6VSafMv/tIwf/582NvbQ6PR4LPPPiszl99//x2WlpZwc3PTO38AmDhxIgAgKirKoO0qgj7nU99+VlpfCgsLg0ajQYMGDTBt2jQ0atQIGo0G3t7eOHv2rFH2AQBHjx6FnZ0dVqxYUaHnq1hYWBhEBEOGDCmzTVBQEFq0aIFNmzbh2LFjz4xnzH4NAIWFhViyZAlcXFxgaWmJDh06ICIiwuDjdHd3L1HAKR7/wN3dXfF4laGs+9zR0RG9e/fGmjVrICIKZUdEilLyMQkivv5AVPkKCgrE3d1dZs6caZR4xr6Pr169KgCke/fuBm03btw4cXJy0lkWEhIiAOTu3bsiIpKVlSV2dnayatUqyc3NlZSUFBkxYoR2/ciRI8XDw0MnxpIlS8Tc3Fy2bdsmaWlpcuHCBenSpYvUq1dPUlJStO3++c9/CgA5e/asZGdny7179+TVV18VAPLll1/K3bt3JTs7W/z8/ASAxMbGiOwXqAAAIABJREFUPtfx6pvXokWLBIAcP35c0tPTJTU1VXr16iXW1taSl5cnIn/2CVdXV3FxcSnxyPM777wjoaGhZeaRnZ0ttra24ufnV2Lds15/EBHJyMgQANK0aVODjl0fMPD1B33Ppz79TKT0vuTr6yvW1tZy6dIlefTokcTHx0u3bt3E1tZW55Hy59nH4cOHxdbWVpYtW6b3sRcrz+Po7u7u0qZNm1LXeXh4yM2bN0VE5PvvvxcTExNxdXWVrKwsESn99Qdj9msRkblz54qFhYXs3btXHj58KAsXLhQTExM5d+6cQcd58uRJUavVEhYWJhkZGXLx4kVp3bq1DBgwwKA4FRVv+fLl4uzsLA4ODqJWq8XV1VWGDh0qP/74Y5nbvPTSS2W+/vC0Z93nIiILFiwQABITE6N3znz9gajm4JMKRES1jKmpKWbOnInNmzfj/v37SqdTQkZGBgDAxsbG6LETEhKQkZGBtm3bQqPRwMnJCfv27dM+1vu03NxcrF69GiNGjMD48eNhb2+P9u3b49NPP8W9e/ewYcOGEtu0adMGVlZWqFu3Lv7+978DAFxcXFCvXj1YWVlh/PjxAKD95bU8x1uevLy9vWFnZ4f69evDx8cH2dnZSExMBPBnn5g1axYSExOxf/9+7TY5OTnYt28fJk+eXGYuK1euRKNGjRAUFKR3/sVsbW2hUqmQmZlp8LbGVJ7zWV5mZmbaX+HbtGmD9evXIzMzE1u2bDFK/EGDBiEjIwPvvfeeUeI9S3Z2Nm7evAkPD4+/bOvl5YV33nkHCQkJePfdd0ttY+x+/ejRI6xfvx7Dhw/HyJEj4eDggMWLF0OtVht8vnv37o2AgAD4+fnBzs4O7dq1Q2ZmJjZt2mRQnIqK9/rrr+PQoUNISkpCVlYWdu3ahcTERPTu3Rvx8fHlivmkv7rPmzdvDgBVYjBZIqp8LCoQEdVCU6ZMgbm5OTZu3Kh0KiUUf7k25P18fbm7u6NBgwYYP348AgMDkZCQ8Mz28fHxyMrKQteuXXWWd+vWDebm5jqPrZfG3NwcAHRGc1er1QCA/Px8AOU7XmPlVZwDAEydOhX29vY670Vv374dw4YNg52dXalx9u/fj8jISHz11VewtbXVO/9i2dnZEJEy41eW5z2fz6Nr166wsrLSeby/ukhNTYWIwMrKSq/2QUFBaNmyJcLDw3HmzJkS643dr69cuYKcnBydaRUtLS3RsGFDg8/3okWLsGHDBhw/fhxZWVn47bff4O3tDS8vLyQlJRkUqyLiNW3aFJ07d4aNjQ3Mzc3RvXt3bNmyBbm5uQgPDzc43pP0uc+L+8Aff/zxXPsiouqJRQUiolrI1tYWb7zxBtatW4e8vDyl09Hh6uoKjUaDq1evGj22paUlTpw4gZ49e2LFihVwd3eHj48PcnNzS21fPE1aaU8RODg4GOUX9vIcb0XkZWNjgzfffBPff/89fvzxRwDAJ598Aj8/v1Lb7969G8HBwTh58iRcXV0N3h8A7TG3atWqXNsbS2Vc52exsLDA3bt3K3QfFeHRo0cA8MwB/56k0WiwZcsWqFQqTJ48ucR9Z+zrkJ2dDQBYvHgxVCqV9nPr1i2Dinh37tzBqlWr8Oabb6Jv376wtraGm5sbNm7ciOTkZISEhBiUl7HjlaV9+/YwNTV9rr+l+t7nlpaWAP6vTxBR7cKiAhFRLTVr1izcvXsXu3fvVjoVHRYWFhgwYADu3buH7777rsx2Dx48wNSpUw2O37ZtW3zxxRdITk5GQEAAIiIi8OGHH5ba1sHBAQBK/TKTlpYGZ2dng/f/tPIcb0Xl5efnB7VajdDQUJw+fRpNmzYt9dH2devWYfv27Thx4gQaN25crn0Bfw4qCAADBw4sdwxjqIzrXJb8/PwK30dFKf4iWVhYqPc2Xl5emDNnDq5du4bly5frrDP2dahfvz4AIDQ0FCKi84mOjtY7zrVr11BYWFiir9vZ2aFOnToGv15g7HhlKSoqQlFRkd5Fn6cZcp8XF6eL+wQR1S4sKhAR1VJNmjTB6NGj8eGHH1a5EbsDAwNhYWGBOXPmlPkUwcWLF3WmXzQzM9N5nL80ycnJuHTpEoA/v3C8//776NKli3bZ09q1awcbGxv89NNPOsvPnj2LvLw8vPDCC4YcVpkMPd6KysvZ2RljxozB3r178d5772H27Nk660UEAQEBiIuLw4EDB55r3IuUlBSEhobC2dn5mWM2VAZDzqc+/cwQJ0+ehIige/fuFbaPitKgQQOoVCqkp6cbtN3y5cvRqlUrxMTE6Cw3dr8unnElNjbWoO2eVlzMuHPnjs7yzMxMPHjwQDsVpFLxgD+nnH3auXPnICLw8vIyKFZ57vPiPuDk5GTQvoioZmBRgYioFps/fz7i4uJw/PhxpVPR0alTJ+zYsQMXL15Er169cOTIEaSnpyM/Px83b97Exo0bMWXKFO34BADg6emJBw8e4MCBA8jPz8fdu3dLzPeenJyMadOm4fLly8jLy0NMTAxu3bql/UJXp04dJCcnIyEhAZmZmTA1NYW/vz/279+P7du3IyMjA3FxcZg+fToaNWoEX19fRY5Xo9FUWF7+/v4oKCjAw4cP0bdvX511ly5dwgcffICNGzdCrVbrPFKuUqlKfeJDRJCVlYWioiKICO7evYuIiAj06NEDpqamOHDggOJjKhhyPvXpZ0DJvlRcJCgqKsLDhw9RUFCACxcuYPbs2XBxcdFOr/m8+4iKiqq0KSWtrKzg7u6O27dvG7Rd8WsQpqamJZYbs19rNBpMmjQJu3btwvr165GRkYHCwkLcvn1b+4Xex8cHTk5OOH/+fJlx3Nzc0KdPH2zcuBGnT59Gbm4ukpKStPlMmTJF21aJeMCf0z3u3r0baWlpyM/PR3R0NKZOnQoXFxdMnz79r0/WE8pznxf3gfbt2xu0LyKqIRSZc4Lof3FKSSLlvfLKK/Lqq6+We/uKvI8TExNl7ty50r59e7GxsRFTU1NxcHCQzp07y5QpU+S7777Ttr1//7706dNHNBqNuLm5ycyZM2XevHkCQDw9PSUxMVESEhLE29tbHB0dxdTUVBo3biyLFi3STqN4/vx5adasmVhaWkrPnj0lJSVFioqKJCQkRJo3by5qtVocHR1l+PDhcuXKFe2+16xZI1ZWVgJAXF1d5dtvv5Xg4GCxt7cXAOLk5CQ7duyQ3bt3i5OTkwAQR0dH2bVrV7mPV5+8wsPDtXk1b95cbty4IRs2bBA7OzsBIM2aNZOrV6+WOO99+vSRTZs2lVgeFxcnAMr8hISEiIjIoUOHpEOHDmJlZSXm5uZiYmIiAESlUomDg4O8+OKLsmzZMrl//345e8Zfg4FTSupzPkX062cipfclX19fUavV0qRJEzEzMxM7OzsZNmyY3Lhxw2j7OHLkiNja2kpQUJDB56w8U/z5+fmJWq2WnJwc7bL9+/eLh4eHAJB69erJ22+/Xeq28+bNKzGlpLH79ePHjyUgIEBcXFzEzMxM6tevLyNHjpT4+HgRERk+fLgAkCVLljzzOO/duyezZ88WT09PsbCwEBsbG+nRo4d8/vnnOu2Uiufv7y8eHh5ibW0tZmZm4uzsLG+88YYkJyfrtIuOjpYePXpIo0aNtPdtw4YNxdvbW06dOiUi+t/nTxo0aJA0adJEioqKnpnnkzilJFHNoRKpYs+8Uq0SGRmJsWPHVrlHr4lqk8OHD2Pw4MH45Zdf0KFDB4O3531MVZFKpUJERATGjBmjdCpa06ZNw549e6rkVK4AMHr0aADAnj179N7m+vXraN26NbZs2aKdLrU6KSoqwiuvvIKJEyca5TWcqh6vIty/fx/Ozs4ICgqCv7+/3tuVp78RUdXE1x+IiGq5QYMGoXXr1li7dq3SqRDVeIYMalgdeHp6YtmyZVi2bBmysrKUTscghYWFOHDgADIzM+Hj41Pj41WUwMBAdOrUqcyZYoio5mNRgYiollOpVJg9ezZ27NhRYuAwIqK/smDBAowePRo+Pj4GD9qopJMnT2Lfvn2IioqClZVVjY9XEVavXo3Y2FgcOXJEZ4wbIqpdWFQgIiK8/vrrcHBwwPr165VOhahGWrhwIbZs2YL09HS4ublh7969SqdkVCtWrICfnx/ef/99pVPRW79+/bBjxw40bNiwVsQztoMHD+Lx48c4efIkHB0dlU6HiBTEogIREcHCwgLTpk3D+vXrkZ2drXQ6RDXOypUr8fjxY4gIbt68iVGjRimdktH1798fwcHBSqdBlWTo0KFYsGBBiVk8iKj2YVGBiIgAADNmzEBubi62bt2qdCpEREREVE2wqEBERACA+vXrY/z48QgNDUVRUZHS6RARERFRNcCiAhERafn7++PGjRs4fPiw0qkQERERUTXAogIREWm1bNkSAwcOxEcffaR0KkRERERUDZgpnQAREVUt/v7+6Nu3L86ePYuXXnpJ7+1Gjx5dgVkRAY8fPwbw58Ci+ggNDcWePXsqMqUa5YcffgDAe5kqxw8//IDu3bsrnQYRGQGfVCAiIh19+vRBly5dEBoaqlf7pk2b1siR7KlqSUlJwTfffIO4uDi92o8aNQrOzs4VnFXN0r17d37Jo0rTvXt3eHl5KZ0GERkBn1QgIqIS5s+fj3HjxuHq1ato0aLFM9t6eXnx12CqMLm5uVi6dCn279+PkSNH4tNPP0WdOnWUTouIiIj+F59UICKiEkaNGgUPDw988MEHSqdCtdi5c+fQuXNnfPrpp/jss88QGRnJggIREVEVw6ICERGVYGpqioCAAGzduhW3bt1SOh2qZQoKCrBq1Sr07NkTzs7OiIuLw4QJE5ROi4iIiErBogIREZVqwoQJaNy4MVavXq10KlSL3Lx5E3379kVgYCCWLVuGr7/+Gk2bNlU6LSIiIioDiwpERFQqtVoNf39/bNiwAXfu3FE6HaoFtm7dig4dOuDhw4f44YcfEBAQABMT/leFiIioKuO/1EREVKY33ngDjo6OCAsLUzoVqsFSU1MxbNgwTJw4EZMnT8ZPP/2Ejh07Kp0WERER6YFFBSIiKpNGo4Gfnx/Wr1+Phw8fKp0O1UBHjx5Fp06dEBsbi//85z9Yu3YtLCwslE6LiIiI9MSiAhERPdNbb70FU1NThIeHK50K1SA5OTmYNWsWBg4ciJ49eyImJga9e/dWOi0iIiIyEIsKRET0THZ2dpgxYwbWrl2L7OxspdOhGuDs2bPo1KkTtm7dih07diAyMhKOjo5Kp0VERETlwKICERH9pVmzZuHx48dYu3at0qlQNVY8VWSvXr3g6uqKixcv4h//+IfSaREREdFzYFGBiIj+Ur169TBnzhwEBwcjNTVV6XSoGrp8+TK8vLywdOlShISE4KuvvkKTJk2UTouIiIieE4sKRESkl7lz58La2hrBwcFKp0LViIhgw4YN6Nq1K1QqFWJiYjBr1iyoVCqlUyMiIiIjYFGBiIj0YmNjg0WLFiE8PBy//fab0ulQNfDHH39gyJAhmDFjBt5++22cOXMGLVu2VDotIiIiMiIWFYiISG++vr5wcXFBYGCg0qlQFbdv3z60bdsW8fHx+M9//oPg4GCYm5srnRYREREZGYsKRESkN7VajeXLl2PHjh2IiYlROh2qgjIyMuDr64tRo0bhtddew4ULF9CzZ0+l0yIiIqIKwqICEREZZOzYsejWrRsWLVqkdCpUxURHR6NLly44cOAADh48iK1bt8LGxkbptIiIiKgCsahAREQGUalUCA4ORlRUFI4fP650OlQF5OfnIzAwEL169ULz5s0RGxuLIUOGKJ0WERERVQIWFYiIyGCvvPIK+vfvjwULFkBElE6HFHTp0iW89NJLCAkJwUcffYSoqCg0atRI6bSIiIiokrCoQERE5RIcHIyff/4ZkZGRSqdCCiieKrJbt24wNzdHbGwsZs2apXRaREREVMlYVCAionLp3LkzJkyYgPnz5yMnJ0fpdKgSJSUloV+/fpgxYwbmzZuH7777Ds2bN1c6LSIiIlIAiwpERFRuq1atQnp6OoKDg5VOhSrJnj170KlTJ9y5cwc//PADAgMDYWpqqnRaREREpBAWFYiIqNycnJywZMkSfPDBB7h27ZrS6VAFSk9Px4QJEzB27FiMGjUKP/30E1544QWl0yIiIiKFsahARETPxc/PDy1atMC8efOUToUqyPHjx9GuXTt88803+OKLL/Cvf/0L1tbWSqdFREREVQCLCkRE9FzMzMwQGhqKgwcPIioqSul0yIgePXqEd999F/3798dLL72E+Ph4DBo0SOm0iIiIqAphUYGIiJ5bv379MHLkSPj5+eHx48dKp0NGcPHiRXTv3h2ffPIJPvnkE+zduxd169ZVOi0iIiKqYlhUICIiowgNDcWdO3cQFhamdCr0HIqKirB27Vq88MILsLa2xvnz5/Hmm28qnRYRERFVUSwqEBGRUTRt2hTz58/H8uXLkZycrHQ6VA63bt1Cnz59MG/ePCxYsACnT5+Gh4eH0mkRERFRFcaiAhERGc38+fNRr149zJ8/X+lUyEDFU0Xev38fZ8+e5VSRREREpBcWFYiIyGg0Gg3WrFmDnTt34tixY0qnQ3q4e/cuRowYgbFjx+L//b//h59//hmdO3dWOi0iIiKqJsyUToCIiGqWIUOGYPjw4XjjjTdw8eJFTj1YhX399deYNGkSzMzMcPz4cfTp00fplIiIiKia4ZMKRERkdOHh4UhLS0NQUJDSqVApcnNzMWvWLLz66qvo0aMHYmJiWFAgIiKicmFRgYiIjK5hw4ZYuXIlPvzwQ8TExCidDj3h3Llz6Ny5M/79739j69atiIyMRJ06dZROi4iIiKopFhWIiKhC+Pr6onv37vD19UVhYaHS6dR6BQUFWLVqFXr06IGmTZvi4sWLGD9+vNJpERERUTXHogIREVUIExMTbNq0CRcuXMDHH39cYn1ubi4SEhIqP7Fa6ObNm+jTpw8CAwOxfPlyfPXVV3B2dlY6LSIiIqoBWFQgIqIK07JlSwQEBGDhwoW4efOmdvmPP/6Ijh07IiwsTMHsaoetW7eiQ4cOSE9Px9mzZxEQEAATE/7zT0RERMbB/1UQEVGFWrhwIVxdXTFjxgzk5eVh8eLF8Pb2xvXr13HkyBGl06u2Tpw4gezs7DLXp6amYujQoZg0aRImT56Mn376CR06dKjEDImIiKg24JSSRERUoSwsLPDJJ5/glVdeQatWrZCYmKgdY+Hq1atISUlBw4YNFc6yeklKSsKIESPg4+ODTz/9tMT6qKgoTJkyBebm5vjPf/6Dl19+WYEsiYiIqDbgkwpERFShCgoKEB0dDVNTUyQlJekM2qhSqXDixAkFs6t+CgsL8fe//x1ZWVnYsGEDvvzyS+26zMxM+Pr64rXXXkPPnj0RGxvLggIRERFVKD6pQEREFebXX3/FuHHjcOHChVJngDA1NcWxY8fwj3/8Q4HsqqeVK1ciOjoaRUVFUKlUmDBhAn799VfcvHkTEyZMQHp6Oj7//HMMGzZM6VSJiIioFuCTCkREVCHCwsLQqVMnxMXFlTmlZH5+PqKioio5s+rr3LlzWLp0KYqKigAAIoKsrCz069cPPXv2RMuWLXHhwgUWFIiIiKjSsKhAREQV4oUXXkDdunWhUqme2S4lJQVXr16tpKyqr/T0dIwYMaLE+czPz8elS5cwbtw4fPHFFxyfgoiIiCoViwpERFQhevTogV9//RWDBw9+ZmHBzMwMx44dq8TMqqdp06bhjz/+QEFBQYl1IoLIyEhcv35dgcyIiIioNmNRgYiIKoy9vT327t2L0NBQmJmZwcys5FA+IoJvvvlGgeyqj88++wy7d+9Gfn5+mW0KCwsxfvz4Ml81ISIiIqoILCoQEVGFUqlUmDVrFn744Qc0adIEarVaZ31hYSGOHTvGL8NluHbtGt56662/bJefn48ff/wRH3zwQSVkRURERPQnFhWIiKhSvPDCC7hw4QJGjBhRYl1WVhbOnz+vQFZVW15eHsaMGVPqKw9PMjc3BwCo1WqcOnUKubm5lZEeEREREYsKRERUeezs7LB79278+9//hoWFhfZ1CLVazXEVSrF48WLExcWVeO3B1NRUe+6aNGmCiRMnIjIyEvfu3cPRo0dhaWmpRLpERERUC6lERJROgmqvyMhIjB07FuyGRLVP8VMLiYmJyM/Px8svv4xTp04pnVaVcezYMfTv31/791GtViM/Px8ajQZ9+/bF3/72NwwYMADu7u4KZ0pERES1GYsKpKiqWlS4ffs2vv/+e6XTIKrxHj16hE2bNuHbb7+FmZkZtmzZon2UvzbLyMiAv78/MjIyAADOzs7o2rUrOnbsiBYtWpQ64CVRbePt7Q1nZ2el0yAiqvVYVCBFVdWiQnFeREREVDVFRERgzJgxSqdBRFTr8acOomeoasUOMo6qWsyq6lQqVYX+J/7XX3/Fzz//jPHjx1dI/OqiqKgIAGBiwmGPiMqiUqmUToGIiP4XiwpERFQltG7dGq1bt1Y6DcWxmEBERETVCf/nQkRERERERETlwqICEREREREREZULiwpEREREREREVC4sKhARERERERFRubCoQERERERERETlwqICkUKmTp0KW1tbqFQqxMbGPne7ynLkyBHY29vjiy++qNT9rlq1Cq1atYKlpSWsra3RqlUrvPfee8jIyKjUPJ6k1LmoLqZNmwaVSqX9lDZV5LFjx7BgwQLs27cP7u7u2rYTJkwo0bZ///6wtbWFqakp2rZti/Pnz1fGYTyXnTt3olu3brC1tUWzZs0wadIkpKSkVIl4QUFBOten+NOuXbtS2xcVFSE0NBTe3t6lrl+2bBnatGkDOzs7WFhYwNPTE/Pnz0dWVpa2zaFDh7Bq1SoUFhaWK+ensf8oGw94dr8o63ofOHBAp8/Vq1fvuXIgIiKFCZGCIiIipCp2w8rKa9euXQJAYmJijNKuMhw+fFjs7Ozk0KFDlbrfQYMGyYcffiipqamSmZkpkZGRolar5b//+78NjmWs66vUuVAKAImIiNC7va+vr9SpU0eioqLkypUr8ujRI531S5YskcGDB0tGRoZ2mYeHh9StW1cAyOHDh0vEjIqKkqFDh5b/ICrR7t27BYCsWrVK0tLSJCYmRtzd3aVTp06Sn5+veLzly5cLgBKftm3blmh79epV6dGjhwCQjh07lhqvd+/eEh4eLvfv35eMjAyJiIgQtVotr776qk67NWvWSO/eveXhw4cG5/wk9h9l44no1y9Ku95FRUVy+/ZtOX36tLz22mtSt25dg/dt6N8jIiKqOHxSgYgMMmjQIKSnp2Pw4MGVul9zc3PMmDED9evXh42NDUaPHo1hw4bhm2++wZ07dyo1l2JKnYvS5ObmlvkLspIsLS3x6quvokWLFrCwsNAuDw4Oxu7duxEZGQlbW1udbcLCwmBiYgJfX1+kp6dXdspG869//QuNGzfGvHnzYG9vj06dOmHOnDmIjY3F2bNnFY8HANu2bYOI6HwuXryo0+aXX37Bu+++i+nTp6NTp05lxrKxsYGvry/q1KkDW1tbjBkzBsOHD8fRo0eRlJSkbTdr1ix07NgRr732GgoKCsqVN/uP8v1H335R2vVWqVRo0qQJevXqhebNmxu8byIiqlpYVCBSkEqlMmq7mmz//v3QaDQ6y5o0aQIAOo9X11abN29Gamqq0mno5fr163jvvfewdOnSEtcUALy9vTF79mz8/vvvmDt3rgIZGkdSUhIaNWqkc/82bdoUAHDr1i3F4+mrY8eO2LdvH8aNG6dTGHra4cOHYWpqqrOs+LH2nJwcneWBgYGIjY3FmjVrDM6H/adq9B99+wXwfNebiIiqPhYViIzk22+/RZs2bWBvbw+NRoP27dvjq6++0q4XEYSEhKBly5awsLCAvb095s2bVyKOvu30sX79elhbW8PKygoHDx7EwIEDYWdnB2dnZ+zatavEflevXo3WrVvDwsICjo6OGDZsGC5fvqxtc+bMGbi4uEClUuHjjz/WLj916hRefPFFWFlZwc7ODu3bt9eOdVBYWIglS5bAxcUFlpaW6NChAyIiIsp1PE+7du0aHBwc0KxZM6PEM0Rp50Lf8x0WFgaNRoMGDRpg2rRpaNSoETQaDby9vXV+MfTz84O5uTkaNmyoXTZjxgxYW1tDpVLh3r17AIDZs2fD398fN27cgEqlgqenJwDg6NGjsLOzw4oVKyrjlOgtLCwMIoIhQ4aU2SYoKAgtWrTApk2bcOzYsWfG06fvGnIvGKvPuru7lyj0FL+/7u7urni8yvD777/D0tISbm5uOssdHR3Ru3dvrFmzBiJiUEz2n+rXf57nehMRUTWgyEsXRP+rJo2psGfPHgkMDJQHDx7I/fv3pXv37jrviS5atEhUKpV89NFH8vDhQ8nJyZHw8PASYyXo205fixYtEgBy/PhxSU9Pl9TUVOnVq5dYW1tLXl6ett2SJUvE3Nxctm3bJmlpaXLhwgXp0qWL1KtXT1JSUrTtkpKSBICsW7dORESysrLEzs5OVq1aJbm5uZKSkiIjRoyQu3fviojI3LlzxcLCQvbu3SsPHz6UhQsXiomJiZw7d87gYxERycvLk9u3b8u6devEwsJCtm3bZnAMY/W7p8+FiP7n29fXV6ytreXSpUvy6NEjiY+Pl27duomtra0kJiZq240bN06cnJx09hsSEiIAtOdYRGTkyJHi4eGh0+7w4cNia2sry5Yte+5jFSnfmApNmjQpsdzd3V3atGlT6jYeHh5y8+ZNERH5/vvvxcTERFxdXSUrK0tESn8nXt++q++1MVafPXnypKjVagkLC5OMjAy5ePGitG7dWgYMGGBQnIqKt3z5cnF2dhYHBwdRq9Xi6uoqQ4cOlR9//LHMbV566aUy351/WnZ2ttja2oqfn1+p6xcsWFCuv2vsP1Wj/zxJn34th1UlAAAgAElEQVRR1vWeNWsWx1QgIqrmqt63OapValJR4WkrV64UAJKamio5OTliZWVVYlDBpwdg1LedIYr/I5ybm6tdVlykuH79una/NjY24uPjo7Ptjz/+KAB0vpQ+/UX64sWLZQ6KlpubK1ZWVjpxc3JyxMLCQt566y2Dj0VExMnJSQBI3bp1Ze3atTr/mddXZRQVnnW+Rf78wm1vb68T79y5cwJAli5dql32PEUFYzNGUSErK0tUKpUMHjy41G2e/FIoIuLv7y8A5O233xaRkl8KDem7+lwbY/fZxYsX6wyC6OzsLElJSQbHqYh4iYmJcv78ecnMzJTHjx9LdHS0dO7cWSwtLeXixYulbmNIUWHRokXSokULnYEUn/Q///M/AkC2bt2qd87sP1Wn/zxJn35R1vVmUYGIqPrj6w9EFUStVgP481HY69evIycnB/369XvmNvq2e17m5uYAgPz8fABAfHw8srKy0LVrV5123bp1g7m5+TMH8XJ3d0eDBg0wfvx4BAYGIiEhQbvuypUryMnJ0ZmiztLSEg0bNtR5tNgQSUlJSE1Nxc6dO/Hvf/8bnTt3rvJjCTx9vsvStWtXWFlZlfvcVAepqakQEVhZWenVPigoCC1btkR4eDjOnDlTYv3z9F2g5LUxZp9dtGgRNmzYgOPHjyMrKwu//fYbvL294eXlpTNwoVLxmjZtis6dO8PGxgbm5ubo3r07tmzZgtzcXISHhxsc70n79+9HZGQkvvrqqxIDKRYr7gN//PGH3nHZf6pO/zFUea43ERFVDywqEBnJl19+iVdeeQX169eHhYUF5s+fr113+/ZtAED9+vWfGUPfdsaWlpYG4M/R25/m4OCAzMzMMre1tLTEiRMn0LNnT6xYsQLu7u7w8fFBbm4usrOzAQCLFy/WmZP81q1bJQZu05darUb9+vXRv39/7N69G/Hx8Vi5cmW5YlVFFhYWuHv3rtJpVJhHjx4BwF8O7FZMo9Fgy5YtUKlUmDx5MnJzc3XWP0/fLY2x+uydO3ewatUqvPnmm+jbty+sra3h5uaGjRs3Ijk5GSEhIQblZex4ZWnfvj1MTU1x9erVcsfYvXs3goODcfLkSbi6upbZztLSEsD/9Ql9sP9U7f7zLOW53kREVD2wqEBkBImJiRg+fDgaNmyIs2fPIj09HatWrdKuLx6h/PHjx8+Mo287Y3NwcACAUv8DnZaWBmdn52du37ZtW3zxxRdITk5GQEAAIiIi8OGHH2qLI6GhoSWmrYuOjn7uvD09PWFqaor4+PjnjlUV5Ofn63W+q7PiLxaFhYV6b+Pl5YU5c+bg2rVrWL58uc665+27TzNWn7127RoKCwvRuHFjneV2dnaoU6eOwX3W2PHKUlRUhKKiIr2/tD9t3bp12L59O06cOFEi16fl5eUB+L8+oQ/2n6rdf56lPNebiIiqBxYViIwgLi4O+fn5eOutt+Du7g6NRqMzbVe7du1gYmKCU6dOPTOOvu2MrV27drCxscFPP/2ks/zs2bPIy8vDCy+8UOa2ycnJuHTpEoA//0P9/vvvo0uXLrh06RKaNm0KjUaD2NjY58rv/v37+Mc//lFiefF/lIunRavuTp48CRFB9+7dtcvMzMz+8rWJ6qRBgwZQqVRIT083aLvly5ejVatWiImJ0Vn+PH23NMbqs8VfRu/cuaOzPDMzEw8ePDC4zxo7HgAMGDCgxLJz585BRODl5WVQLBFBQEAA4uLicODAgVJ/+X9acR9wcnLSez/sP1Wn/xiqPNebiIiqBxYViIzAxcUFAHDs2DE8evQI165d03kXt379+hg5ciT27t2LzZs3IyMjAxcuXMCGDRt04ujbztg0Gg38/f2xf/9+bN++HRkZGYiLi8P06dPRqFEj+Pr6lrltcnIypk2bhsuXLyMvLw8xMTG4desWunfvDo1Gg0mTJmHXrl1Yv349MjIyUFhYiNu3b5f4z+2zWFtb4+uvv8aJEyeQkZGB/Px8xMTE4PXXX4e1tTXmzJljjNNQ6YqKivDw4UMUFBTgwoULmD17NlxcXDBx4kRtG09PTzx48AAHDhxAfn4+7t69W+qc8nXq1EFycjISEhKQmZmJ/Px8REVFVbkpJa2srODu7q591UdfxY+xm5qallhe3r5b1n7+qs/6+PjAyckJ58+fLzOOm5sb+vTpg40bN+L06dPIzc1FUlKSNp8pU6Zo2yoRD/hzusfdu3cjLS0N+fn5iI6OxtSpU+Hi4oLp06f/9cl6wqVLl/DBBx9g48aNUKvVOo/+q1QqfPjhhyW2Ke4D7du31ztv9p+q038M9fT1JiKiGqTSh4YkekJNmv0hICBA6tSpIw4ODjJ69Gj5+OOPBYB4eHhIYmKiZGZmytSpU6Vu3bpiY2MjPXv2lCVLlmhH4P7ll19ERPRup4/w8HCxsrISANK8eXO5ceOGbNiwQezs7ASANGvWTK5evSoiIkVFRRISEiLNmzcXtVotjo6OMnz4cLly5Yo23rp166Rhw4YCQKysrGTIkCGSkJAg3t7e4ujoKKamptK4cWNZtGiRFBQUiIjI48ePJSAgQFxcXMTMzEzq168vI0eOlPj4eIPO75AhQ8TNzU1sbGzEwsJCPDw8xMfHR+Li4gyKI2KcflfauTDkfPv6+oparZYmTZqImZmZ2NnZybBhw+TGjRs6+7l//7706dNHNBqNuLm5ycyZM2XevHkCQDw9PbXTT54/f16aNWsmlpaW0rNnT0lJSZEjR46Ira2tBAUFPdexFoORppT08/MTtVotOTk52mX79+8XDw8PASD16tXTjtb/tHnz5pWYElCfvmvItfmrPjt8+HABIEuWLHnm8d+7d09mz54tnp6eYmFhITY2NtKjRw/5/PPPddopFc/f3188PDzE2tpazMzMxNnZWd544w1JTk7WaRcdHS09evSQRo0aaWcNaNiwoXh7e8upU6dERCQuLk5nVoGnPyEhISX2P2jQIGnSpIkUFRUZlDf7T9XoP/r0iyc9fb2LcfYHIqLqr+p9m6NapSYVFaj6qArX19fXV+rUqaNoDoYyVlHh2rVrYmZmJtu2bTNmepWmsLBQevXqJZs3b64V8SrCvXv3RKPRyIcffqhdpm/e7D/VK55I6de7GIsKRETVH19/ICJSiCGDzVVXubm5+Oqrr3Dt2jXtQG2enp5YtmwZli1bhqysLIUzNExhYSEOHDiAzMxM+Pj41Ph4FSUwMBCdOnWCn58fAMPyZv+pPvGKPX29RQTJyck4c+YMrl+/brT9EBGRMlhUIKpmLl++XOJ95dI+VfkLBVBzjoOe7cGDB3j11VfRokULTJ48Wbt8wYIFGD16NHx8fAwedE9JJ0+exL59+xAVFQUrK6saH68irF69GrGxsThy5AjUajUAw/Nm/6ke8YDSr/fBgwfRpEkT9OrVC19++aVR9kNERMpRiYgonQTVXpGRkRg7diyqWjesqnmRcSh9fRcuXIiPPvoIeXl5cHV1RUhICEaNGqVILoZQqVSIiIjAmDFjjBazeADO4OBgo8WkquvgwYO4dOkS5s+fX2LQxPJg/6najH29n1QRf4+IiKh8zJROgIiotlm5ciVWrlypdBpVQv/+/dG/f3+l06BKMnToUAwdOtRo8dh/qjZjX28iIqqa+PoDEREREREREZULiwpEREREREREVC4sKhARERERERFRubCoQERERERERETlwoEaiZ5h9OjRSqdAFeD27dsAeH3LIzQ0FHv27FE6DSIiIiKqIvikAhERERERERGVC59UIHoG/iJbM0VGRmLs2LG8vgZSqVR45513OC88ESlOpVIpnQIREf0vPqlAREREREREROXCogIRERERERERlQuLCkRERERERERULiwqEBEREREREVG5sKhAREREREREROXCogKRwnbu3AmVSgVvb2+Dtps6dSpsbW2hUqkQGxtrULt9+/bB3d0dKpUKKpUKEyZMKLFd//79YWtrC1NTU7Rt2xbnz583KL8jR47A3t4eX3zxhUHbEdUGx44dw4IFCyrlXlTCzp070a1bN9ja2qJZs2aYNGkSUlJSqkS8oKAg7fl+8tOuXbtS2xcVFSE0NLTMv9HLli1DmzZtYGdnBwsLC3h6emL+/PnIysrStjl06BBWrVqFwsLCcuVMRERUlbGoQKSwnTt3wsPDA9HR0bh+/bre223atAkbN24sV7uRI0fit99+g4eHB+rWrYvt27fjyy+/1Gnz9ddfY8+ePRg8eDDi4+PRpUsXvXMDABExqD1RbfHPf/4TYWFhWLhwYaXci5UtIiIC48aNw+jRo3H79m0cPHgQp0+fxsCBA1FQUKB4PENcu3YNL7/8MubMmYOcnJxS25w4cQJvv/02EhIScO/ePaxcuRJr1qzB6NGjtW2GDBkCjUaDfv36IS0trUJzJiIiqmwsKhAp6P79+7h06RKWLl0KANi6dWul5xAWFgYTExP4+voiPT3daHEHDRqE9PR0DB482Ggxa5Lc3FyDn06pivsgwwQHB2P37t2IjIyEra2tzrqKuhcr27/+9S80btwY8+bNg729PTp16oQ5c+YgNjYWZ8+eVTweAGzbtg0iovO5ePGiTptffvkF7777LqZPn45OnTqVGcvGxga+vr6oU6cObG1tMWbMGAwfPhxHjx5FUlKStt2sWbPQsWNHvPbaaxVeDCEiIqpMLCoQKSgyMhKDBg3S/opV/B9dfalUqudu5+3tjdmzZ+P333/H3Llz9d43PZ/NmzcjNTW12u+D9Hf9+nW89957WLp0KTQaTYn1NeVeTEpKQqNGjXT+7jRt2hQAcOvWLcXj6atjx47Yt28fxo0bBwsLizLbHT58GKampjrL6tWrBwAlnm4IDAxEbGws1qxZY/yEiYiIFMKiApGCdu7ciREjRsDW1hb9+/dHQkICvv3221LbighCQkLQsmVLWFhYwN7eHvPmzSt3uycFBQWhRYsW2LRpE44dO/bMtiKC1atXo3Xr1rCwsICjoyOGDRuGy5cva9ucOXMGLi4uUKlU+Pjjj7XLT506hRdffBFWVlaws7ND+/btkZGRAQAoLCzEkiVL4OLiAktLS3To0AERERHPzKUy6XPcfn5+MDc3R8OGDbXLZsyYAWtra6hUKty7dw8AMHv2bPj7++PGjRtQqVTw9PREWFgYNBoNGjRogGnTpqFRo0bQaDTw9vbW+TX2efYBAEePHoWdnR1WrFhRoeeLSgoLC4OIYMiQIWW2Mfa9uH79elhbW8PKygoHDx7EwIEDYWdnB2dnZ+zatUsnnrHuQXd39xLFrOLxD9zd3RWPVxl+//13WFpaws3NTWe5o6MjevfujTVr1vAVMSIiqjmESEERERFSFbthZeR169YtqV+/vhQUFIiIyLZt2wSATJkypdT2ixYtEpVKJR999JE8fPhQcnJyJDw8XABITEyMwe1ERDw8POTmzZsiIvL999+LiYmJuLq6SlZWloiIREVFydChQ3W2WbJkiZibm8u2bdskLS1NLly4IF26dJF69epJSkqKtl1SUpIAkHXr1omISFZWltjZ2cmqVaskNzdXUlJSZMSIEXL37l0REZk7d65YWFjI3r175eHDh7Jw4UIxMTGRc+fOPcdZLl15rq++xz1u3DhxcnLS2TYkJEQAaI9VRGTkyJHi4eGh087X11esra3l0qVL8ujRI4mPj5du3bqJra2tJCYmGmUfhw8fFltbW1m2bJlBxy8iAkAiIiIM3o7+5O7uLm3atCl1XUXei4sWLRIAcvz4cUlPT5fU1FTp1auXWFtbS15enradse7BkydPilqtlrCwMMnIyJCLFy9K69atZcCAAQbFqah4y5cvF2dnZ3FwcBC1Wi2urq4ydOhQ+fHHH8vc5qWXXpKOHTvqFT87O1tsbW3Fz8+v1PULFiwo9e8xGYZ/j4iIqg4+qUCkkJ07d+Jvf/ub9rHZIUOGwMLCAnv27EFubq5O29zcXISGhuK//uu/MGfOHDg4OMDS0hJ16tQpV7vSeHl54Z133kFCQgLefffdUtvk5uZi9erVGDFiBMaPHw97e3u0b98en376Ke7du4cNGzaUGT8hIQEZGRlo27YtNBoNnJycsG/fPtSrVw+PHj3C+vXrMXz4cIwcORIODg5YvHgx1Go1tmzZ8pe5V7TnOW5DmZmZaX95btOmDdavX4/MzEyjnYdBgwYhIyMD7733nlHikX6ys7Nx8+ZNeHh4/GXbiroXvb29YWdnh/r168PHxwfZ2dlITEwEAKPeg71790ZAQAD8/PxgZ2eHdu3aITMzE5s2bTIoTkXFe/3113Ho0CEkJSUhKysLu3btQmJiInr37o34+PhyxXzSypUr0ahRIwQFBZW6vnnz5gCAuLi4594XERFRVcCiApFCil99KGZnZ4f+/fsjIyMDBw8e1Gl7/fp15OTkoF+/fs+MqW+7sgQFBaFly5YIDw/HmTNnSqyPj49HVlYWunbtqrO8W7duMDc3f+agae7u7mjQoAHGjx+PwMBAJCQkaNdduXIFOTk5OlO6WVpaomHDhjqPcivleY77eXXt2hVWVlZV4jxQ+aWmpkJEYGVlpVf7irwXAcDc3BwAkJ+fD8C49+CiRYuwYcMGHD9+HFlZWfjtt9/g7e0NLy8vnYELlYrXtGlTdO7cGTY2NjA3N0f37t2xZcsW5ObmIjw83OB4T9q/fz8iIyPx1VdflRiIs1hxH/jjjz+ea19ERERVBYsKRAq4ePEi4uLiMHjwYJ150r/44gsAJWeBuH37NgCgfv36z4yrb7uyaDQabNmyBSqVCpMnTy7xxETxVGg2NjYltnVwcEBmZmaZsS0tLXHixAn07NkTK1asgLu7O3x8fJCbm4vs7GwAwOLFi3XOx61bt8qcxq0yPc9xG4OFhQXu3r1bofugivXo0SMAeOaAf0+qyHuxNMa6B+/cuYNVq1bhzTffRN++fWFtbQ03Nzds3LgRycnJCAkJMSgvY8crS/v27WFqaoqrV6+WO8bu3bsRHByMkydPwtXVtcx2lpaWAP6vTxAREVV3LCoQKWDHjh34+9//XmJKswcPHsDS0hJff/21diAyANqR4h8/fvzMuPq2exYvLy/M+f/s3XtYVOX6P/73AAPDDDOAqYAiyEHzmFq6U8xt5vXRyjygAl5pOzUNLSOS1NQyUzylW9kYfkpts3f5SfCUmqmV9iVr6zbLUNSd4hmPKCoHQTnM/fujH7MdB5SBGdYA79d18UdrPet+7nUam3vW86zJk5GZmYm5c+earfPy8gKACr+w3Lp1C/7+/g+M3b59e3z11Ve4dOkSpk2bhtTUVCxZssRUBFm2bJnFMdm3b1+198VWarrfNVFSUmL3Psj+yr9IlpWVVXkbe96L97PVPZiZmYmysjI0a9bMbLnBYECjRo2sHl5g63iVMRqNMBqNVS763G/58uVYs2YNvv/+e4tc71dcXAzgv9cEERFRXceiAlEtExGkpKTg9ddft1jn7e2NiIgIlJWV4YsvvjAt79ChA5ycnPDDDz88MHZV2z3M3Llz0aZNG/z2228W8T08PPDLL7+YLd+/fz+Ki4vxxBNPVBrz0qVLOHbsGIA/vsAsWLAAjz/+OI4dO4YWLVpAo9EgPT29RnnbizX77eLiYnqk3BbS0tIgIujevbvd+iD7a9q0KVQqFXJzc63azh73YkVsdQ+WFzMuX75stjw/Px83btwwvQpSqXgA0L9/f4tlBw4cgIigR48eVsUSEUybNg0ZGRnYvHlzhU+O3K/8GvDx8bGqLyIiIkfFogJRLdu7dy8MBgN69uxZ4fqJEycCMB8C0aRJEwwbNgwbNmzAp59+iry8PBw+fNhiMraqtnuY8kev73/3ukajQVxcHDZt2oQ1a9YgLy8PGRkZmDhxIvz8/BAdHV1pzEuXLmHChAn4/fffUVxcjN9++w3nzp1D9+7dodFoMGbMGKxduxYrVqxAXl4eysrKcOHCBYsvE0qwZr9DQ0Nx48YNbN68GSUlJbh27RrOnTtnEbNRo0a4dOkSzp49i/z8fFORwGg04ubNmygtLcXhw4cRGxuLgIAAjB492iZ97Nixg6+UVIBWq0VwcLBpiFJV2eNerKyfh92DI0aMgI+PDw4ePFhpnKCgIPTp0werVq3Cnj17UFRUhKysLFM+r7zyiqmtEvGAP173mJKSglu3bqGkpAT79u3DuHHjEBAQYPr8rapjx47hww8/xKpVq6BWq82GjqhUKixZssRim/JroGPHjlb1RURE5LBq/X0TRPdoaK+UfOWVV0Sn04mLi4t06tRJDh48aLZ+7ty54ufnJwAEgDRv3lySkpJERCQ/P1/GjRsnjzzyiHh4eMhTTz0ls2bNEgDi7+8vhw4dqnK7TZs2SUhIiACQxo0by6RJkyrMd8qUKRavsTMajbJ48WJp1aqVqNVq8fb2lvDwcDl+/LipzfLly8XX11cAiFarlUGDBsnZs2clLCxMvL29xdnZWZo1ayYzZ840vVLz7t27Mm3aNAkICBAXFxdp0qSJDBs2TI4ePWqz41+uOue3KvstIpKTkyN9+vQRjUYjQUFB8sYbb8iUKVMEgISGhppeDXnw4EEJDAwUd3d3eeqpp+TKlSsSHR0tarVamjdvLi4uLmIwGGTIkCFy6tQpm/Wxfft20ev1Eh8fb/VxA1/hViMxMTGiVqulsLDQtMze92JSUpJotVoBIK1atZJTp07JypUrxWAwCAAJDAyUEydOiMjD78Hw8HABILNmzXrgfl6/fl1iY2MlNDRU3NzcxMPDQ3r27ClffvmlWTul4sXFxUlISIjps9jf31/Gjx8vly5dMmu3b98+6dmzp9lnsq+vr4SFhckPP/wgIiIZGRmmdRX9LV682KL/AQMGSPPmzcVoND4wT3owfh4RETkOlYhI7ZQviCytW7cOUVFRcLTL0FHzIttw1PM7YcIErF+/Hjk5OUqnUiGVSoXU1FRERkYqnUqddPLkSbRt2xbJyckYNWqU0ulYzWg04umnn8bo0aMxduzYeh/PHnJycuDv74/4+HjExcUpnU6dxs8jIiLHweEPREQOxJqJ/KhuCQ0NxZw5czBnzhwUFBQonY5VysrKsHnzZuTn52PEiBH1Pp69zJ49G507d0ZMTIzSqRAREdkMiwpERES1ZPr06YiIiMCIESOsnrRRSWlpadi4cSN27NgBrVZb7+PZw9KlS5Geno7t27dDrVYrnQ4REZHNsKhAROQAZsyYgeTkZOTm5iIoKAgbNmxQOiWyk3nz5iEmJgYLFixQOpUq69u3L/7v//4Pvr6+DSKerW3ZsgV3795FWloavL29lU6HiIjIplyUToCIiID58+dj/vz5SqdBtaRfv37o16+f0mlQLRk8eDAGDx6sdBpERER2wScViIiIiIiIiKhaWFQgIiIiIiIiomphUYGIiIiIiIiIqoVFBSIiIiIiIiKqFhYViIiIiIiIiKha+PYHogdQqVRKp0B2xPNrvaioKERFRSmdBhERERE5CBYViCoQFhaG1NRUpdMgoho6d+4cpk6dioSEBPj5+SmdDhHZUFhYmNIpEBERWFQgqpC/vz8iIyOVToOIauhf//oXACAyMpJFBSIiIiI74JwKRERUb+Xn5wMAPDw8FM6EiIiIqH5iUYGIiOqtgoICqFQq6HQ6pVMhIiIiqpdYVCAionqroKAAOp0OTk78546IiIjIHvh/WUREVG/l5+dz6AMRERGRHbGoQERE9VZBQQGLCkRERER2xKICERHVWwUFBdDr9UqnQURERFRvsahARET1Fp9UICIiIrIvFhWIiKjeYlGBiIiIyL5YVCAionorPz+fwx+IiIiI7IhFBSIiqrf4pAIRERGRfbGoQERE9RaLCkRERET2xaICERHVWxz+QERERGRfLCoQEVG9VVBQAJ1Op3QaRERERPUWiwpERFRvFRQU8EkFIiIiIjtiUYGIiOqt/Px8zqlAREREZEcsKhARUb1kNBpRWFjIogIRERGRHbGoQERE9dLt27chIhz+QERERGRHLCoQEVG9VFBQAAB8UoGIiIjIjlhUICKieik/Px8AiwpERERE9sSiAhER1UvlTypw+AMRERGR/bCoQERE9RKHPxARERHZH4sKRERUL3H4AxEREZH9sahARET1UkFBAZydneHu7q50KkRERET1FosKRERUL+Xn58PDwwMqlUrpVIiIiIjqLRYViIioXiooKODQByIiIiI7Y1GBiIjqJRYViIiIiOyPRQUiIqqXCgoK+DpJIiIiIjtjUYGIiOolPqlAREREZH8sKhARUb1UPlEjEREREdkPiwpERFQvcfgDERERkf2xqEBERPUShz8QERER2R+LCkREVC9x+AMRERGR/bkonQAREVFNrVy5EkeOHIFWq4W3tzd0Oh2ysrLQvHlzfPvtt9DpdNDpdDAYDPDx8YFOp1M6ZSIiIqJ6gUUFIiKq8+7evYvly5fD1dUVKpUKIoKysjJs2LABGzZsMGv766+/4vHHH1coUyIiIqL6hcMfiIioznvhhRcAAMXFxbh79y6Ki4tRVlZm1kalUqFz584sKBARERHZEIsKRERU5wUFBaFVq1YPbOPk5IQ33nijljIiIiIiahhYVCAionph6NChUKvVla7XaDSIioqqxYyIiIiI6j8WFYiIqF4YMGAASkpKKlynVqsxduxYTtBIREREZGMsKhARUb0QFhYGg8FQ4bqSkhK8+uqrtZwRERERUf3HogIREdULzs7OeOGFFyyGQDg5OeHJJ59Ehw4dFMqMiIiIqP5iUYGIiOqNF154AaWlpRbLJ02apEA2RERERPUfiwpERFRvPPvss3ByMv+nTa/XY/jw4QplRERERFS/sahARET1hre3N7p37w6VSgXgjwkax40bB41Go3BmRERERPUTiwpERFSvDB48GM7OzgCA0tJSTtBIREREZEcsKhARUb1SPsCMBKIAACAASURBVK+CSqVCr1690Lp1a6VTIiIiIqq3WFQgIqJ6pW3btggICICI4PXXX1c6HSIiIqJ6zUXpBIjI/vbt24elS5cqnQZRrXF1dYWbmxtSU1Oxfv16pdMhspsePXpg8uTJSqdBREQNGJ9UIGoAsrKysGHDBqXToDpmw4YNuHDhgtJpVIufnx+CgoIs3gRhb//+97/x73//u1b7pIbr3//+N/bt26d0GkRE1MDxSQWiBoS/2JI1VCoV3nrrLURGRiqditXu3r2Lq1evIiAgoFb7jYiIAMB7jWpH+fVGRESkJD6pQERE9Y6bm1utFxSIiIiIGiIWFYiIiIiIiIioWlhUICIiIiIiIqJqYVGBiIiIiIiIiKqFRQUiIiIiIiIiqhYWFYjIwpIlS9C0aVOoVCp8/PHHNom5YMECeHp6QqVSIT093SYxHYE9jtWDGI1GLFu2DGFhYTVqU5u2b98OT09PfPXVV0qn4vB27dqF6dOnY+PGjQgODoZKpYJKpcJLL71k0bZfv37Q6/VwdnZG+/btcfDgQQUyts4XX3yBbt26Qa/XIzAwEGPGjMGVK1ccIl58fLzpeN/716FDhwrbP+w+mzNnDtq1aweDwQA3NzeEhoZi6tSpKCgoMLXZunUrFi1ahLKysmrlTERE5AhYVCAiC2+//Tb27t1r05jTp0/HJ598YtOYjsAex6oymZmZ+POf/4zJkyejsLCw2m1qm4gonUKd8P777yMxMREzZszAsGHDcPr0aYSEhOCRRx7BmjVr8PXXX5u1//bbb7F+/XoMHDgQR48exeOPP65Q5lWTmpqKkSNHIiIiAhcuXMCWLVuwZ88ePPfccygtLVU8njWqcp99//33mDRpEs6ePYvr169j/vz5SEhIMHsN5KBBg6DRaNC3b1/cunXLrjkTERHZC4sKRER1wKFDh/DOO+9g4sSJ6Ny5c7XbKGHAgAHIzc3FwIEDlU4FRUVFDvMEx70WLlyIlJQUrFu3Dnq93mxdYmIinJycEB0djdzcXIUyrLlPPvkEzZo1w5QpU+Dp6YnOnTtj8uTJSE9Px/79+xWPBwCff/45RMTs78iRI2ZtqnqfeXh4IDo6Go0aNYJer0dkZCTCw8Oxc+dOZGVlmdq9+eab6NSpE55//nm7F0OIiIjsgUUFIqI6oFOnTti4cSNGjhwJNze3ardp6D799FNkZ2crnYaZkydP4r333sMHH3wAjUZjsT4sLAyxsbG4ePEi3n77bQUytI2srCz4+flBpVKZlrVo0QIAcO7cOcXjVVVV77Nt27bB2dnZbFnjxo0BwOLphtmzZyM9PR0JCQm2T5iIiMjOWFQgoiorKyvDrFmzEBAQAHd3dzz22GNITU01rf/xxx/Rrl07eHp6QqPRoGPHjvjmm28qjXf16lW0bNkSLi4uePbZZwEA48aNM41lDgkJwW+//QYAGDNmDLRaLTw9PbF169Yq5/zhhx9Cq9VCr9cjOzsbcXFxaN68OY4fP/7Q/amumJgYuLq6wtfX17Ts9ddfh06ng0qlwvXr12vcR13x008/ISAgACqVCh999BEAYMWKFdDpdNBqtdiyZQuee+45GAwG+Pv7Y+3ataZtExMTodFo0LRpU0yYMAF+fn7QaDQICwsz+yW6qsc7NjYWcXFxOHXqFFQqFUJDQwEAO3fuhMFgwLx582rjkFhITEyEiGDQoEGVtomPj0fr1q2xevVq7Nq164HxRARLly5F27Zt4ebmBm9vbwwZMgS///67qU1VzwHw8Pu+qoKDgy0KOuXzHwQHByserzZcvHgR7u7uCAoKMlvu7e2N3r17IyEhgcOFiIiozmFRgYiq7J133sGHH36IZcuW4fLlyxg4cCBefPFF/PLLLwD+KBJERUXh7NmzuHTpEjw8PDBy5MhK4zVq1Ahdu3bFpk2bsHPnTgDA6tWrMWzYMDg7O+PHH39Ely5dAADJyckIDw/HmjVrHvjl635Tp07F5MmTUVBQgPnz5yMoKAjdu3eHiDx0f6orMTERkZGRZsuSkpLwwQcf1ChuXfTUU09ZzDnx2muv4a233kJRURH0ej1SU1Nx6tQpBAcHY/z48SgpKQHwR7Fg9OjRKCwsxJtvvomzZ8/i4MGDKC0txf/8z/+YHiGv6vFOSEjAwIEDERISAhHByZMnAcA0SZ7RaLTLMXiYr7/+Go8++ii0Wm2lbdzd3fGPf/wDTk5OGD9+PG7fvl1p29mzZ2P69OmYOXMmsrOzsWfPHmRlZaFXr164evUqgKqfA+Dh931VzZgxA1euXMHy5cuRn5+Po0ePIiEhAf3790f37t2timWPeMAfc794e3vD1dUVQUFBGDJkCA4cOFCtWPcrLCzE999/j/Hjx8PV1dVifZcuXXDx4kUcOnTIJv0RERHVFhYViKhK7ty5gxUrViA8PBzDhg2Dl5cX3n33XajVaiQnJwMAhg8fjvfffx/e3t5o1KgRBg0ahJycHFy7ds0iXmlpKV5++WWMGzfOokgwceJElJWVmeICQF5eHg4cOIDnn3++2vuwcOFCTJo0CRs3bkTLli0fuj9kf2FhYTAYDGjSpAlGjBiB27dv4/z582ZtXFxcTL+6t2vXDitWrEB+fr7NztOAAQOQl5eH9957zybxrHH79m2cOXMGISEhD23bo0cPvPXWWzh79izeeeedCtsUFRVh6dKlGDp0KEaNGgVPT0907NgRH3/8Ma5fv46VK1dabPOgc1CV+76qevfujWnTpiEmJgYGgwEdOnRAfn4+Vq9ebVUce8V7+eWXsXXrVmRlZaGgoABr167F+fPn0bt3bxw9erRaMe81f/58+Pn5IT4+vsL1rVq1AgBkZGTUuC8iIqLaxKICEVXJ8ePHUVhYaPZ6NXd3d/j6+po9Vn0vtVoNABavSysrK8OLL76Ipk2bmoY93OuZZ55B69at8fe//930KHBKSgpGjBhhMUa5NveH7Kv819t7fyWvSNeuXaHVauvFecrOzoaIPPAphXvFx8fj0UcfRVJSEn766SeL9UePHkVBQQG6du1qtrxbt25wdXV96ASG958DW94nM2fOxMqVK7F7924UFBTg9OnTCAsLQ48ePcwmLlQqXosWLdClSxd4eHjA1dUV3bt3R3JyMoqKipCUlGR1vHtt2rQJ69atwzfffGMxEWe58mug/GkSIiKiuoJFBSKqkvLHrd99912zd7ifO3fONOnY119/jaeffhpNmjSBm5sbpk6dWmGsSZMmITMzEx9//DGOHTtmsV6lUmHChAk4ffo0du/eDQD47LPP8Morr9Tq/pDjcnNzq/AJmLrmzp07AFDliTU1Gg2Sk5OhUqkwduxYFBUVma0vfy2hh4eHxbZeXl7Iz8+3Kj9b3SeXL1/GokWL8Oqrr+KZZ56BTqdDUFAQVq1ahUuXLmHx4sVW5WXreJXp2LEjnJ2dceLEiWrHSElJwcKFC5GWloaWLVtW2s7d3R3Af68JIiKiuoJFBSKqkiZNmgAAli1bZvHKtX379uH8+fMIDw+Hr68v9u/fj9zcXCxatKjCWJGRkfjuu+/g5eWFv/zlLxW+Rm306NHQaDRYvXo1jh8/DoPBgMDAwFrbH3JcJSUluHXrFvz9/ZVOpcbKv0je/zTPg/To0QOTJ09GZmYm5s6da7bOy8sLACosHlTnmNnqPsnMzERZWRmaNWtmttxgMKBRo0ZWDy+wdbzKGI1GGI3Gar9NZfny5VizZg2+//57i1zvV1xcDOC/1wQREVFdwaICEVVJixYtoNFokJ6eXuH6jIwMlJSU4LXXXkNwcDA0Go3Zq97u1adPHzRu3BgrV67Er7/+WuEYY29vb0RFRWHz5s1YsmQJxo8fX6v7U1MuLi4PfYyfqictLQ0iYjYZX1093k2bNoVKpUJubq5V282dOxdt2rQxvR2lXIcOHeDh4WExieL+/ftRXFyMJ554wqp+bHWflBczLl++bLY8Pz8fN27cML0KUql4ANC/f3+LZQcOHICIoEePHlbFEhFMmzYNGRkZ2Lx5c4VPjtyv/Brw8fGxqi8iIiKlsahARFWi0WgwZswYrF27FitWrEBeXh7Kyspw4cIFXL58GQEBAQCAXbt24c6dO8jMzHzo+O1BgwZh9OjRmDdvHn799VeL9RMnTsTdu3exbds2DBw4sFb3p6ZCQ0Nx48YNbN68GSUlJbh27RrOnTtng8wbHqPRiJs3b6K0tBSHDx9GbGwsAgICMHr0aFObqh7vRo0a4dKlSzh79izy8/NRUlKCHTt2KPZKSa1Wi+DgYFy4cMGq7cqHQdw/x4hGo0FcXBw2bdqENWvWIC8vDxkZGZg4cSL8/PwQHR1tdT8Pu09GjBgBHx8fHDx4sNI4QUFB6NOnD1atWoU9e/agqKgIWVlZpnzuHdqkRDzgj9c9pqSk4NatWygpKcG+ffswbtw4BAQEYOLEiQ8/WPc4duwYPvzwQ6xatQpqtdps6IhKpcKSJUsstim/Bjp27GhVX0RERIoTIgWlpqYKL0P7s/Y4//WvfxUfHx8BIDqdToYOHSoiInfv3pVp06ZJQECAuLi4SJMmTWTYsGFy9OhRERGZNm2aNGrUSLy8vCQiIkI++ugjASAhISGSkJAg3t7eAkBatmwp2dnZkpeXJy1atBAA4uHhIZ999plFLl26dJHp06dXe98XLVok7u7uAkBatGghn3/+uWndw/anJscqJydH+vTpIxqNRoKCguSNN96QKVOmCAAJDQ2V8+fPW7Uf+/btk549e4qfn58AEADi6+srYWFh8sMPP1S5jTUASGpqqtXb3Wv58uXi6+srAESr1cqgQYMkKSlJtFqtAJBWrVrJqVOnZOXKlWIwGASABAYGyokTJ0REJDo6WtRqtTRv3lxcXFzEYDDIkCFD5NSpU2b9VPV4Hzx4UAIDA8Xd3V2eeuopuXLlimzfvl30er3Ex8fXaF9FRIYPHy7Dhw+3apuYmBhRq9VSWFhoWrZp0yYJCQkRANK4cWOZNGlShdtOmTJFBg8ebLbMaDTK4sWLpVWrVqJWq8Xb21vCw8Pl+PHjpjbWnIOH3Sfh4eECQGbNmvXA/bx+/brExsZKaGiouLm5iYeHh/Ts2VO+/PJLs3ZKxYuLi5OQkBDR6XTi4uIi/v7+Mn78eLl06ZJZu6rcZxkZGaZ1Ff0tXrzYov8BAwZI8+bNxWg0PjDPe1XneiMiIrI1lcj/P7U6kQLWrVuHqKgo8DK0r7p8nAcMGICPPvoIQUFBSqfS4KhUKqSmpiIyMlKxHCZMmID169cjJydHsRysERERAQBYv359lbc5efIk2rZti+TkZIwaNcpeqdmN0WjE008/jdGjR2Ps2LH1Pp495OTkwN/fH/Hx8YiLi6vydtW53oiIiGyNwx+IyKHcOy7+8OHD0Gg0LCg0cNZMYlgXhYaGYs6cOZgzZw4KCgqUTscqZWVl2Lx5M/Lz8zFixIh6H89eZs+ejc6dOyMmJkbpVIiIiKzGogIROZRp06YhMzMTJ06cwJgxYyxmtweA33//3WKMckV/1f0SYe/4tdUH1R3Tp09HREQERowYYfWkjUpKS0vDxo0bsWPHDmi12nofzx6WLl2K9PR0bN++HWq1Wul0iIiIrOaidAJERPfSarVo06YNmjdvjqSkJLRr186iTZs2bew6lMPe8Wurj7puxowZSE5ORnFxMYKCgrB48WIMHz5c6bTsZt68efj222+xYMECLFy4UOl0qqRv377o27dvg4lna1u2bMHdu3eRlpZmMekmERFRXcGiAhE5lPj4+ApfMUkNz/z58zF//nyl06hV/fr1Q79+/ZROg2rJ4MGDMXjwYKXTICIiqhEOfyAiIiIiIiKiamFRgYiIiIiIiIiqhUUFIiIiIiIiIqoWFhWIiIiIiIiIqFpYVCAiIiIiIiKiauHbH4gaEJVKpXQKVMdERUUhKipK6TTqHN5rVFvq82tWiYiobmBRgagBSU1NVToFqkOioqIQGxuLHj16KJ1KnbFs2TIAwFtvvaVwJtQQlF9vRERESmJRgRTFX/NqV2RkpNIpUB0SFRWFHj168Lqxwvr16wHwXqPaUX69ERERKYlzKpCiyosKIqJwJkRERERERGQtFhVIUSwqEBERERER1V0sKpBDYFGBiIiIiIio7mFRgRTFORWIiIiIiIjqLhYVSFEc/kBERERERFR3sahADoFFhbpt48aNCA4Ohkqlgkqlgq+vL0aNGmW3/hYsWABPT0+oVCqkp6fXON79+Zf/aTQaBAUFYezYsThz5kyNYj7smPztb39Ds2bNoFKp4OTkhNatW2PXrl1mbV544QUYDAY4OTmhTZs2+Ne//lWt/SXHtWvXLkyfPt3i+nnppZcs2vbr1w96vR7Ozs5o3749Dh48qEDG1vniiy/QrVs36PV6BAYGYsyYMbhy5YpDxIuPj7f4DFCpVOjQoUOF7Y1GI5YtW4awsLAK18+ZMwft2rWDwWCAm5sbQkNDMXXqVBQUFJjabN26FYsWLUJZWVm1ciYiInIIQqSgL7/8UgBIcXGx0qnUa6mpqVIbt3tISIh4enravR8RkbVr1woA+e2332wW8978y8rK5OrVq/LZZ5+JVquVpk2byvXr12sUsyoAyJNPPlnp+v/3//6f9O3b1+o8qgOApKam1kpf9cXw4cNl+PDh1dp21qxZMnDgQMnLyzMtCwkJkUceeUQAyLZt2yy22bFjhwwePLja+damlJQUASCLFi2SW7duyW+//SbBwcHSuXNnKSkpUTze3LlzBYDFX/v27S3anjhxQnr27CkApFOnThXG6927tyQlJUlOTo7k5eVJamqqqNVqefbZZ83aJSQkSO/eveXmzZtW51yT642IiMhW+KQCKYrDH8hROTk5oWnTpnjppZcwadIkZGdnWzw5QPZVVFRU6a/AdamPqli4cCFSUlKwbt066PV6s3WJiYlwcnJCdHQ0cnNzFcqw5j755BM0a9YMU6ZMgaenJzp37ozJkycjPT0d+/fvVzweAHz++ecQEbO/I0eOmLU5dOgQ3nnnHUycOBGdO3euNJaHhweio6PRqFEj6PV6REZGIjw8HDt37kRWVpap3ZtvvolOnTrh+eefR2lpabXyJiIiUhKLCqQoFhWoLggNDQWAGj2mTdb79NNPkZ2dXef7eJiTJ0/ivffewwcffACNRmOxPiwsDLGxsbh48SLefvttBTK0jaysLPj5+ZlN0NuiRQsAwLlz5xSPV1WdOnXCxo0bMXLkSLi5uVXabtu2bXB2djZb1rhxYwBAYWGh2fLZs2cjPT0dCQkJtk+YiIjIzlhUIIfAokLDU1ZWhlmzZiEgIADu7u547LHHkJqaalr/448/ol27dvD09IRGo0HHjh3xzTffVBrv6tWraNmyJVxcXPDss88CAMaNG2caFx0SEoLffvsNADBmzBhotVp4enpi69atD801MzMTwB9fJqzZh4ZGRLB06VK0bdsWbm5u8Pb2xpAhQ/D777+b2sTExMDV1RW+vr6mZa+//jp0Oh1UKhWuX78OAIiNjUVcXBxOnToFlUqF0NBQJCYmQqPRoGnTppgwYQL8/Pyg0WgQFhZm9st0TfoAgJ07d8JgMGDevHl2PV7lEhMTISIYNGhQpW3i4+PRunVrrF69+qFPzFTlPKxYsQI6nQ5arRZbtmzBc889B4PBAH9/f6xdu9Ysnq2u8+DgYIsCTnmhLjg4WPF4teHixYtwd3dHUFCQ2XJvb2/07t0bCQkJ/PeQiIjqHoWGXRCJiMjWrVsFgBQVFSmdSr3miHMqvP322+Lm5iYbNmyQmzdvyowZM8TJyUkOHDggIiLr16+X2bNny40bNyQnJ0e6d+8ujzzyiGn7++dUKC4ulmHDhsmWLVvM+hk2bJg4OzvLxYsXzZa/+OKLsnXr1gfmf/PmTfnHP/4hWq1WBgwYYPU+WHtMROr2nAqzZs0SV1dX+fzzz+XWrVty+PBhefzxx6Vx48Zy5coVU7uRI0eKj4+P2baLFy8WAHLt2jXTsmHDhklISIhZu+joaNHpdHLs2DG5c+eOHD16VLp16yZ6vV7Onz9vkz62bdsmer1e5syZU+V9L1edMe7BwcHSrl27CteFhITImTNnRERk79694uTkJC1btpSCggIRqXhOhaqeh5kzZwoA2b17t+Tm5kp2drb06tVLdDqd2Tw3VbnOqyItLU3UarUkJiZKXl6eHDlyRNq2bSv9+/e3Ko694s2dO1f8/f3Fy8tL1Gq1tGzZUgYPHiw///xzpds8+eSTlc6pcL/bt2+LXq+XmJiYCtdPnz7d6nliOKcCERE5Aj6pQIri8IeG6c6dO1ixYgXCw8MxbNgweHl54d1334VarUZycjIAYPjw4Xj//ffh7e2NRo0aYdCgQcjJycG1a9cs4pWWluLll1/GuHHjLH7tnThxIsrKykxxASAvLw8HDhzA888/bxErNzfX9HSDt7c3xowZgxkzZuDLL7+0eh8akqKiIixduhRDhw7FqFGj4OnpiY4dO+Ljjz/G9evXsXLlSpv15eLiYvoVvl27dlixYgXy8/NtdtwHDBiAvLw8vPfeezaJ9yC3b9/GmTNnEBIS8tC2PXr0wFtvvYWzZ8/inXfeqbBNdc5DWFgYDAYDmjRpghEjRuD27ds4f/48ANte571798a0adMQExMDg8GADh06ID8/H6tXr7Yqjr3ivfzyy9i6dSuysrJQUFCAtWvX4vz58+jduzeOHj1arZj3mj9/Pvz8/BAfH1/h+latWgEAMjIyatwXERFRbWJRgRwCiwoNy/Hjx1FYWGj2qjZ3d3f4+vqaPaJ9L7VaDQAWr14rKyvDiy++iKZNm5qGPdzrmWeeQevWrfH3v//ddJ2lpKRgxIgRFuOdAcDT09M0QduUKVMgIvD09DT1X5N9qM+OHj2KgoICdO3a1Wx5t27d4OrqWu2J86qia9eu0Gq1dfK4Z2dnQ0Sg1Wqr1D4+Ph6PPvookpKS8NNPP1msr+l5cHV1BQCUlJQAsO11PnPmTKxcuRK7d+9GQUEBTp8+jbCwMPTo0cNs4kKl4rVo0QJdunSBh4cHXF1d0b17dyQnJ6OoqAhJSUlWx7vXpk2bsG7dOnzzzTcWE3GWK78Grl69WqO+iIiIahuLCqSoeyfYoobj9u3bAIB3333X7H3w586dM01g9vXXX+Ppp59GkyZN4ObmhqlTp1YYa9KkScjMzMTHH3+MY8eOWaxXqVSYMGECTp8+jd27dwMAPvvsM7zyyisPzfO9996Dr68vZsyYYfElpSr7UF1Go7HSdWVlZRYFDkdw69YtAH/MeH8/Ly8v5Ofn27V/Nze3Cp9icXR37twBgAdO+HcvjUaD5ORkqFQqjB07FkVFRWbrbX0ebHWdX758GYsWLcKrr76KZ555BjqdDkFBQVi1ahUuXbqExYsXW5WXreNVpmPHjnB2dsaJEyeqHSMlJQULFy5EWloaWrZsWWk7d3d3AP+9JoiIiOoKFhVIURz+0HDs2bMHy5YtAwA0adIEALBs2TKL17ft27cP58+fR3h4OHx9fbF//37k5uZi0aJFFcaNjIzEd999By8vL/zlL3+p8JVso0ePhkajwerVq3H8+HEYDAYEBgY+NGe9Xo+FCxciPz8fr732mtm6h+2DtcekXKNGjXDp0qVKtzlz5oxphntH4uXlBQAVfmm9desW/P397dZ3SUmJ3fuwl/Ivkvc/gfMgPXr0wOTJk5GZmYm5c+earbP1ebDFdQ78MdlpWVkZmjVrZrbcYDCgUaNGVg8vsHW8yhiNRhiNxioXfe63fPlyrFmzBt9//71FrvcrLi4G8N9rgoiIqK5gUYEUxaJCw/Hrr79Cp9MB+OMxY41Gg/T09ArbZmRkoKSkBK+99hqCg4Oh0WgqfaqlT58+aNy4MVauXIlff/21wvHK3t7eiIqKwubNm7FkyRKMHz++ynn/5S9/wZNPPolt27Zh3bp1puUP24equPeYlHvmmWdw8eJF7N2716K9iOAf//gHnnzyyWr3aS8dOnSAh4cHfvnlF7Pl+/fvR3FxMZ544gnTMhcXF9Pj9baQlpYGEUH37t3t1oe9NG3aFCqVCrm5uVZtN3fuXLRp08b0RpNy1pyHqrDFdQ7AVMy4fPmy2fL8/HzcuHHD6kKZreMBQP/+/S2WHThwACKCHj16WBVLRDBt2jRkZGRg8+bNFT45cr/ya8DHx8eqvoiIiJTGogI5BBYV6q+SkhJcvXoVaWlppi/QGo0GY8aMwdq1a7FixQrk5eWhrKwMFy5cwOXLlxEQEAAA2LVrF+7cuYPMzMyHjgUfNGgQRo8ejXnz5uHXX3+1WD9x4kTcvXsX27Ztw8CBA6ucv0qlQmJiIlQqFWJiYnDz5s0q7YO1x6RcfHw8vLy8EBERgS+//BK3b9/G3bt3cejQIbz44osoLS3FSy+9VOX8a4tGo0FcXBw2bdqENWvWIC8vDxkZGZg4cSL8/PwQHR1tahsaGoobN25g8+bNKCkpwbVr13Du3DmLmOVPbZw9exb5+fmmIoHRaMTNmzdRWlqKw4cPIzY2FgEBARg9erRN+tixY0etvVJSq9UiODgYFy5csGq78mEQ988LYs15qGo/D7vOR4wYAR8fHxw8eLDSOEFBQejTpw9WrVqFPXv2oKioCFlZWaZ87h2OpEQ84I/XPaakpODWrVsoKSnBvn37MG7cOAQEBGDixIkPP1j3OHbsGD788EOsWrUKarXabOiISqXCkiVLLLYpvwY6duxoVV9ERESKq63XTBBVZMeOHQJA8vLylE6lXrP3KyU3bdokISEhAuCBf5s2bTJtc/fuXZk2bZoEBASIi4uLNGnSRIYNGyZHjx4VEZFp06ZJo0aNxMvLSyIiIuSjjz4SABISEiIJCQni7e0tAKRly5aSnZ0teXl50qJFCwEgHh4e8tlnn1nk2aVLF5k+fbrF8n/961/SunVrWs6TdwAAIABJREFUU57NmjWTCRMmmLUZPXq0ABAvLy9ZsGDBQ/ehOsek3JkzZ2T8+PESFBQkrq6u4u7uLu3atZNZs2aZXiVYG2DlKyWNRqMsXrxYWrVqJWq1Wry9vSU8PFyOHz9u1i4nJ0f69OkjGo1GgoKC5I033pApU6YIAAkNDTW9GvLgwYMSGBgo7u7u8tRTT8mVK1ckOjpa1Gq1NG/eXFxcXMRgMMiQIUPk1KlTNutj+/btotfrJT4+3upjVp1X/MXExIharZbCwkLTsnuvn8aNG8ukSZMq3HbKlCkWr5SsynlISkoSrVYrAKRVq1Zy6tQpWblypRgMBgEggYGBcuLECRF5+L0aHh4uAGTWrFkP3M/r169LbGyshIaGipubm3h4eEjPnj3lyy+/NGunVLy4uDgJCQkRnU4nLi4u4u/vL+PHj5dLly6Ztdu3b5/07NlT/Pz8TPexr6+vhIWFyQ8//CAiIhkZGQ+87xcvXmzR/4ABA6R58+ZiNBofmOe9+EpJIiJyBCoR/kRMyvnmm2/w7LPPIjc3FwaDQel06q1169YhKiqqwT8RMmDAAHz00UcICgpSOpU6QaVSITU1FZGRkUqnYjJhwgSsX78eOTk5SqdSoYiICADA+vXrq7zNyZMn0bZtWyQnJ2PUqFH2Ss1ujEYjnn76aYwePRpjx46t9/HsIScnB/7+/oiPj0dcXFyVt6vO9UZERGRrHP5ADqGhf9kl+7h3TP3hw4eh0WhYUKgHrJnUsC4IDQ3FnDlzMGfOHBQUFCidjlXKysqwefNm5OfnY8SIEfU+nr3Mnj0bnTt3RkxMjNKpEBERWY1FBVJU+XjgB71Cj6i6pk2bhszMTJw4cQJjxoyxmCmfyFFMnz4dERERGDFihNWTNiopLS0NGzduxI4dO6DVaut9PHtYunQp0tPTsX37dod8XSwREdHDuCidADVs5UWFil4DSFRTWq0Wbdq0QfPmzZGUlIR27dopnRLVwIwZM5CcnIzi4mIEBQVh8eLFGD58uNJp2cy8efPw7bffYsGCBVi4cKHS6VRJ37590bdv3wYTz9a2bNmCu3fvIi0tzWLSTSIiorqCRQVSlIvLH5cgiwpkD/Hx8RW+YpLqpvnz52P+/PlKp2FX/fr1Q79+/ZROg2rJ4MGDMXjwYKXTICIiqhEOfyBFsahARERERERUd7GoQIpiUYGIiIiIiKjuYlGBFMWiAhERERERUd3FogIpikUFIiIiIiKiuosTNZKiWFSoXevWrVM6Bapj9u3bp3QKdcqFCxcA8F6j2nHhwgX4+/srnQYRETVwLCqQolhUqF1RUVFKp0B1TEJCAhISEpROo87hvUa1pT69VpWIiOomlYiI0klQw3XmzBkEBwfj559/Rrdu3ZROh4jqsN9//x1t27bFoUOH8NhjjymdDhEREVGDwDkVSFGurq4AgOLiYoUzIaK6rqioCADg7u6ucCZEREREDQeLCqQojUYDALhz547CmRBRXceiAhEREVHtY1GBFFX+P/8sKhBRTZUXFbRarcKZEBERETUcLCqQosqfVCj/MkBEVF2FhYUA+KQCERERUW1iUYEU5eTkBFdXVxYViKjGioqKoFKpTMVKIiIiIrI/FhVIce7u7hz+QEQ1VlRUBI1GA5VKpXQqRERERA0GiwqkOHd3dz6pQEQ1VlRUxKEPRERERLWMRQVSnEajYVGBiGqssLCQRQUiIiKiWsaiAimOwx+IyBaKior45gciIiKiWsaiAilOq9WaZm0nIqouDn8gIiIiqn0sKpDi9Ho98vLylE6DiOo4FhWIiIiIah+LCqQ4T09P5ObmKp0GEdVxLCoQERER1T4WFUhxnp6efFKBiGqssLCQcyoQERER1TIWFUhxfFKBiGyBTyoQERER1T4WFUhxLCoQkS2wqEBERERU+1hUIMWxqEBEtsCiAhEREVHtY1GBFMeiAhHZAudUICIiIqp9LCqQ4jw9PZGfnw+j0ah0KkRUh/FJBSIiIqLax6ICKc7b2xtGo5FPKxBRjbCoQERERFT7WFQgxfn4+AAArl69qnAmRFSXsahAREREVPtYVCDF+fr6AgCuXLmicCZEVJcVFhayqEBERERUy1hUIMU1btwYLi4uLCoQUY0UFRVxokYiIiKiWsaiAinOyckJTZo0YVGBiGrkzp07fFKBiIiIqJaxqEAOwcfHh3MqEFG1FRcXo7S0lEUFIiIiolrGogI5BF9fXz6pQETVVlRUBAAsKhARERHVMhYVyCH4+vrySQUiqrbyogLnVCAiIiKqXSwqkEPw8fHB5cuXlU6DiOqowsJCAHxSgYiIiKi2sahADiEwMBBnzpxROg0iqqM4/IGIiIhIGSwqkEMICQlBbm4ubty4oXQqRFQHsahAREREpAwWFcghBAcHAwBOnTqlcCZEVBdxTgUiIiIiZbCoQA4hMDAQzs7OOH36tNKpEFEdxDkViIiIiJTBogI5BLVajRYtWrCoQETVwuEPRERERMpgUYEcRkhICIsKRFQtRUVFcHZ2hqurq9KpEBERETUoLkonQFQuODgYJ0+eVDoNInJw//nPf/D8889DRKDX66HRaHDnzh14eHggMjISer0eLi4u8PLyQvPmzRETE6N0ykRERET1FosK5DBCQkKwc+dOpdMgIgfXtm1bFBcX49KlSxbr1q9fDxeXP/5pKy0txaxZs2o7PSIiIqIGhcMfyGG0b98eFy5cwM2bN5VOhYgcXERERKVDHUpLS1FaWgqVSoWxY8fWcmZEREREDQuLCuQwOnfuDBFBRkaG0qkQkYMbPHgwiouLK13v7OyMZ555BoGBgbWYFREREVHDw6ICOQx/f380btwYhw4dUjoVInJwf/7zn+Hp6VnpeqPRiOjo6FrMiIiIiKhhYlGBHErHjh1ZVCCih3J2dsbAgQOhVqsrXG8wGDBo0KBazoqIiIio4WFRgRxKp06dWFQgoioZMmQISktLLZar1WqMGzcObm5uCmRFRERE1LCwqEAOpVOnTjhy5EiFXxSIiO7Vv39/05se7lVSUsIJGomIiIhqCYsK5FA6deqEO3fuIDMzU+lUiMjBeXh4oG/fvnB2djYtc3JywpNPPol27dopmBkRERFRw8GiAjmUdu3awdXVFb/++qvSqRBRHTB06FCLZRMmTFAgEyIiIqKGiUUFcihubm7o2rUrfvrpJ6VTIaI6YPDgwTAajab/1mg0iIiIUDAjIiIiooaFRQVyOL169cKPP/6odBpEVAc0bdoUTzzxBFQqFdRqNV566SXodDql0yIiIiJqMFhUIIfTq1cv/Oc//0F2drbSqRBRHTB8+HCoVCqUlJRg3LhxSqdDRERE1KCwqEAO56mnnoKTkxP27t2rdCpEVAcMHToURqMRbdu2RdeuXZVOh4iIiKhBUYmIKJ0E0f06d+6Mvn374q9//avSqTyQSqVSOgUiIiKHkJqaisjISKXTICKiWmb5gm8iB9CrVy/s2bNH6TSqJDY2Fj169FA6DaJ6a9++fUhISEBqamqlbdavX4/nn3+e8yncIyoqip9PVGuioqKUToGIiBTCJxXIIa1fvx4vvvgibty4Ab1er3Q6lVKpVPxlhsjO1q1bh6ioKDzon6s7d+5Ao9HUYlaOj59PVJt4vRERNVycU4EcUu/evWE0GrF7926lUyGiOoAFBSIiIiJlsKhADqlp06b405/+hG3btimdChEREREREVWCRQVyWC+88AK++uorGI1GpVMhIiIiIiKiCrCoQA5r4MCByM7Oxi+//KJ0KkRERERERFQBFhXIYT322GNo2bIlh0AQERERERE5KBYVyKENGDAAX331ldJp2Mz27dvh6elZr/aJ/rBkyRI0bdoUKpUKH3/8sWl5bZ1zR7q2Fi1ahDZt2sDd3R06nQ5t2rTBe++9h7y8PEXzcqRj5Oh27dqF6dOnY+PGjQgODoZKpYJKpcJLL71k0bZfv37Q6/VwdnZG+/btcfDgQQUyts4XX3yBbt26Qa/XIzAwEGPGjMGVK1ccIl58fLzpeN/716FDhwrbG41GLFu2DGFhYRWunzNnDtq1aweDwQA3NzeEhoZi6tSpKCgoMLXZunUrFi1ahLKysmrlTEREDRuLCuTQBg4ciPT0dJw7d07pVGyCb3Ctv95++23s3bvXYnltnXNHurZ+/PFHjB8/HufPn8fVq1cxd+5cLFq0CMOHD1c0L0c6Ro7s/fffR2JiImbMmIFhw4bh9OnTCAkJwSOPPII1a9bg66+/Nmv/7bffYv369Rg4cCCOHj2Kxx9/XKHMqyY1NRUjR45EREQELly4gC1btmDPnj147rnnUFpaqng8a2RmZuLPf/4zJk+ejMLCwgrbfP/995g0aRLOnj2L69evY/78+UhISEBERISpzaBBg6DRaNC3b1/cunXLrjkTEVH9w6ICObSnn34aHh4e9WYIxIABA5Cbm4uBAwfaNG5RUVGlv1KRsuxxzis63/a6tqrD1dUVr7/+Opo0aQIPDw9ERERgyJAh+O6773D58mXF8nKkY+So9+zChQuRkpKCdevWQa/Xm61LTEyEk5MToqOjkZubq1CGNffJJ5+gWbNmmDJlCjw9PdG5c2dMnjwZ6enp2L9/v+LxAODzzz+HiJj9HTlyxKzNoUOH8M4772DixIno3LlzpbE8PDwQHR2NRo0aQa/XIzIyEuHh4di5cyeysrJM7d5880106tQJzz//vN2LIUREVL+wqEAOzc3NDYMGDcIXX3yhdCoO7dNPP0V2drbSaVAtcfTzvWnTJmg0GrNlzZs3BwCzR64bMkc8hydPnsR7772HDz74wOL8AUBYWBhiY2Nx8eJFvP322wpkaBtZWVnw8/ODSqUyLWvRogUAVOupOFvHq6pOnTph48aNGDlyJNzc3Cptt23bNjg7O5sta9y4MQBYPN0we/ZspKenIyEhwfYJExFRvcWiAjm8UaNGYe/evcjMzFQ6lRr56aefEBAQAJVKhY8++ggAsGLFCuh0Omi1WmzZsgXPPfccDAYD/P39sXbtWrPtf/jhB/zpT3+CVquFwWBAx44dkZeXh9jYWMTFxeHUqVNQqVQIDQ0F8Mcj6O3atYOnpyc0Gg06duyIb775xup+gT9+NevatSs0Gg10Oh1atmyJuXPnAgDKysowa9YsBAQEwN3dHY899hhSU1OtOjbW5CMiWLp0Kdq2bQs3Nzd4e3tjyJAh+P33301tPvzwQ2i1Wuj1emRnZyMuLg7NmzfHxIkTodPp4OTkhCeeeAI+Pj5Qq9XQ6XR4/PHH0atXL7Ro0QIajQZeXl6YOnWqWd8POqYVqeicnzx5ssLx0iqVCt99991D+6nofFfUT1WPlbXXQnVlZmbCy8sLgYGBNotpjZrcf4mJidBoNGjatCkmTJgAPz8/aDQahIWFmf0SHRMTA1dXV/j6+pqWvf7669DpdFCpVLh+/TqAis8hAOzcuRMGgwHz5s2rjUNiITExESKCQYMGVdomPj4erVu3xurVq7Fr164HxrP19WeLzxoACA4OtijolM9/EBwcrHi82nDx4kW4u7sjKCjIbLm3tzd69+6NhIQEDhciIqKqEyIHV1JSIr6+vvLBBx8onYoFAJKamlrl9llZWQJAli9fblo2c+ZMASC7d++W3Nxcyc7Oll69eolOp5Pi4mIRESkoKBCDwSCLFi2SoqIiuXLligwdOlSuXbsmIiLDhg2TkJAQs77Wr18vs2fPlhs3bkhOTo50795dHnnkEav6FRFZtmyZAJAFCxZITk6O3LhxQz755BMZOXKkiIi8/fbb4ubmJhs2bJCbN2/KjBkzxMnJSQ4cOGDVsaxqPrNmzRJXV1f5/PPP5datW3L48GF5/PHHpXHjxnLlyhWLeG+++aYsX75chg4dKv/5z3/k/fffFwCyf/9+uX37tly/fl2effZZASBff/21XLt2TW7fvi0xMTECQNLT06t8TDMzMwWA/O///m+l5zwzM1PeeecduX37toiIXL58Wby9vSUsLEzKysqq1E9F57uia8vaY/WwY2+t4uJiuXDhgixfvlzc3Nzk888/r1ac1NRUscU/V9W9/0REoqOjRafTybFjx+TOnTty9OhR6datm+j1ejl//ryp3ciRI8XHx8es38WLFwsA0/0qUvE53LZtm+j1epkzZ06N91XE+s+n4OBgadeuXYXrQkJC5MyZMyIisnfvXnFycpKWLVtKQUGBiIjs2LFDBg8ebLaNra8/W33WpKWliVqtlsTERMnLy5MjR45I27ZtpX///lbFsVe8uXPnir+/v3h5eYlarZaWLVvK4MGD5eeff650myeffFI6depUpfi3b98WvV4vMTExFa6fPn26AJDffvvNqrytvd6IiKj+YFGB6oTY2FgJDQ0Vo9GodCpmbFlUKCoqMi1LSkoSAHLy5EkRETly5IgAkG3btlUYt6IvKPebP3++AJDs7Owq91tcXCxeXl7Sp08fs1ilpaWSkJAgRUVFotVqZcSIEaZ1hYWF4ubmJq+99lpVDolJVfIpLCwUDw8Ps/5ERH7++WcBYPZlrKJ4ImIqKuTn55uW/fOf/xQAkpGRYREzJSWl0pzvP6ZVKSrcLzw8XDQajfz+++9V7qcqRYWaHqv7j311+Pj4CAB55JFH5G9/+1u1CxS1UVR42L5HR0eLp6enWbwDBw4IALOCZ02KCrZmzedTQUGBqFQqGThwYIXr7y0qiIjExcUJAJk0aZKIWBYVbH392fKzRkTk3XffFQCmP39/f8nKyrI6jj3inT9/Xg4ePCj5+fly9+5d2bdvn3Tp0kXc3d3lyJEjFW5jTVFh5syZ0rp1a8nLy6tw/d///ncBIJ999plVebOoQETUcHH4A9UJo0aNwsmTJ/Hzzz8rnUqtcHV1BQCUlJQA+OMR2qZNm2LUqFGYPXs2zp49a3VMtVoNAA98Zdj9/R4+fBi3bt1C//79zdo5OzvjzTffxPHjx1FYWGj2qjN3d3f4+vqaPeJcXffnc/ToURQUFKBr165m7bp16wZXV9dqT4pW3s+9k5OVH6/yvitSlWP6IOvWrcOXX36JDz74AI8++qhN+6npsbr/2FdHVlYWsrOz8cUXX+Cf//wnunTp4nDzCFSkqvvetWtXaLVam1zrSsvOzoaIQKvVVql9fHw8Hn30USQlJeGnn36yWG/r68+WnzUzZ87EypUrsXv3bhQUFOD06dMICwtDjx49zCYuVCpeixYt0KVLF3h4eMDV1RXdu3dHcnIyioqKkJSUZHW8e23atAnr1q3DN998YzERZ7nya+Dq1as16ouIiBoOFhWoTnjiiSfQvn17rFmzRulUFOHu7o7vv/8eTz31FObNm4fg4GCMGDECRUVFlW7z9ddf4+mnn0aTJk3g5uZmMT9AVeTl5QEAvLy8Klx/+/ZtAMC7775rNjfAuXPnKn29WU2Uv+rMw8PDYp2Xlxfy8/Nt3ue9bHFMy+Xk5OCNN95At27dEBcXZ/N+lD5WwB/FkCZNmqBfv35ISUnB0aNHMX/+fLv3W5vc3Nxw7do1pdOosTt37gDAAyf8u5dGo0FycjJUKhXGjh1r8Vlk6+vPVp81ly9fxqJFi/Dqq6/imWeegU6nQ1BQEFatWoVLly5h8eLFVuVl63iV6dixI5ydnXHixIlqx0hJScHChQuRlpaGli1bVtrO3d0dwH+vCSIioodhUYHqjBdffBFr165FcXGx0qkoon379vjqq69w6dIlTJs2DampqViyZEmFbc+fP4/w8HD4+vpi//79yM3NxaJFi6zus1mzZgBgmmDufk2aNAEALPv/2Lvz8JrO9X/8751xZx6KJETIYJaazyGoKqXqiDHk0+hVlAZtQ02JsYZQEQdfxGkNJy2KRGnUrOrE0KrhkIooDYoQSkImCTLcvz/8so8tQXayd1aG9+u69j9rPft+7rXWs1ey773WepYuLTL92fHjx3Xu71UKixvFfSFJS0uDq6ur3vsspK99WmjcuHFIS0tDZGSk1pPZ9dWPkvuqOF5eXjA2NkZCQkK59mtIubm5iuxLQyj8IqnL1TAdOnTAhAkTkJiYqHlwayF9jz99nWsSExORn5+vObcVsrW1haOjo87jU9/xXqSgoAAFBQUlLvo8b8WKFdi4cSMOHTpUJNfnFf6NLRwTREREr8KiAlUaAQEBePDgAXbt2qV0KuUuOTkZFy5cAPD0n+svvvgCrVu31ix7Xnx8PHJzczF27Fh4eHhArVZrTXdWUvXr14ejoyMOHDhQ7PrCmRLi4uJ0jl0azZs3h7W1NU6fPq21/MSJE3jy5AnatGljsL71tU+Bp1cifPvtt5g5cyaaNWumWT558mS99aPUvkpNTcV7771XZHnhl6/CqfaqgtjYWIgI2rdvr1lmYmJSpltGlFKrVi2oVCqkp6fr9L558+ahcePGOHv2rNZyfY8/fZ1rCosZt2/f1lqemZmJ+/fv6zw+9R0PQJHbzQDg1KlTEBF06NBBp1giguDgYMTHxyMmJqbYK0eeVzgGnJycdOqLiIiqLxYVqNKoV68eevXqVeZ7Siuj5ORkjB49GhcvXsSTJ09w9uxZXL9+XfNlxtHREcnJybh27RoyMzPh4uICADh48CAePXqExMTEUj1vwNzcHNOmTcORI0cQFBSEW7duoaCgAJmZmbhw4QLUajWGDx+OzZs3Y9WqVcjIyEB+fj5u3rxZ5J9sfVCr1Zg4cSK2b9+OjRs3IiMjA/Hx8RgzZgxcXFwQGBio9z4Lubm5ASj7Ps3IyMDo0aPRsmVLhISEAHh6mfHp06cRFxdXon6eP97FfYlVal9ZWVnhwIEDOHToEDIyMpCbm4uzZ8/igw8+gJWVFSZMmGCQfstDQUEBHjx4gLy8PJw7dw7jx4+Hm5sbhg0bpmnj5eWF+/fvIyYmBrm5ubh37x6uX79eJFZxx3Dv3r2KTSlpaWkJDw8P3Lx5U6f3Fd4G8ezVNoXL9Tn+SnKu8ff3h5OTE86cOfPCOO7u7ujatSvWrFmDI0eOICcnB0lJSZp8PvzwQ01bJeIBT6d73LJlC9LS0pCbm4vjx49j5MiRcHNzw5gxY169s55x4cIFLFq0CGvWrIGpqWmRaWyLu9qtcAx4e3vr1BcREVVjyjwfkqh09uzZIwDk3LlzSqciIro97XrFihXi7OwsAMTS0lJ8fX0lIiJCLC0tBYA0aNBArly5IqtXrxZbW1sBIPXq1ZM//vhDrl27Jj4+PuLg4CDGxsZSu3ZtmT59uuTl5YmIyJkzZ6RevXpiYWEhnTp1kjt37khwcLA4OjqKvb29+Pn5ycqVKwWAeHp6SkhISIn6LbRy5Urx9vYWtVotarVaWrVqJRERESIi8vjxYwkODhY3NzcxMTGRmjVrysCBAyUhIaHE+7Gk+0FEpKCgQMLDw6VBgwZiamoqDg4O0r9/f7l06ZImXlhYmFhYWAgAqVu3rmYqw2XLlmn6qV+/vhw9elQWLlwodnZ2AkCcnJzk22+/lS1btmhmLnBwcJDNmzeLiLx0n44fP17zHisrKxkwYECxx3zx4sVaT4l/9vXuu+++sp/CJ8M/e7xnzJhRpJ+S7itd9n1J+fr6iru7u1hbW4u5ubl4enqKv7+/1uwautDH7A9l+fyJPJ39wdTUVOrUqSMmJiZia2sr/fr1kytXrmj1k5qaKl27dhW1Wi3u7u7y6aefyuTJkwWAeHl5aaafLO4zu2fPHrGxsZHQ0NAybWshXc5PIiJBQUFiamoq2dnZmmXbt28XT09PASA1atTQzPbwvMmTJxeZUlLf4+9V55r+/fsLAJk1a9ZLtzMlJUUzo5C5ublYW1tLx44d5fvvv9dqp1S8iRMniqenp1hZWYmJiYm4urrKqFGjJDk5Wavd8ePHpWPHjuLi4qI5hzg7O4uPj48cPnxYRETi4+NfeL4BIOHh4UX67927t9SpU0fn2ZZ0HW9ERFR1qERE9F6pIDIQEUHjxo3RvXv3CnHFgkqlQlRUFAYPHqx0KkRVVnR0NIYMGQIl/1yNHj0aW7duRWpqqmI56ErX89Ply5fRpEkTREZGYujQoQbOTv8KCgrw5ptvYtiwYRgxYkSVj2cIqampcHV1RWhoaJEHyL4K/x4SEVVfvP2BKhWVSoXRo0dj/fr1Ot/7S0RUFqWdOrSy8PLywty5czF37lxkZWUpnY5O8vPzERMTg8zMTPj7+1f5eIYye/ZstGzZEkFBQUqnQkRElQiLClTpDB8+HCKCb775RulU6CUuXrxY5P7d4l4V+R9seorHsvqYOnUq/Pz84O/vX6kKt7Gxsdi2bRv27t0LS0vLKh/PEJYsWYK4uDjs2bMHpqamSqdDRESViInSCRDpyt7eHkOHDsXKlSvxySefwMiItbGKqHHjxoperk76U92P5bRp0xAZGYknT57A3d0d4eHhGDRokNJpGcz8+fNx4MABfPHFF1i4cKHS6ZRIt27d0K1bt2oTT9927NiBx48fIzY2tshDN4mIiF6F38aoUvr0009x+fJlHDx4UOlUiKiKW7BgAR4/fgwRwZ9//lmlCwqFevToUWkKClR2ffv2xdSpU1lQICKiUmFRgSqlZs2aoUuXLli6dKnSqRAREREREVVbLCpQpTVlyhTs27cP//3vf5VOhYiIiIiIqFpiUYEqrV69eqFNmza8RJeIiIiIiEghLCpQpRYSEoLt27cjISFB6VSIiIiIiIiqHc7+QJXagAED0KRJE4SHh+Prr79WJIfjx48r0i9RdVH4GYuOjlY4k8qH5yciIiIyNJVU53nCqEpYv349Ro4ciUuXLsHd3b1c+1apVOXaHxERUUUVFRWFwYMHK50GERGVMxYVqNLLy8tDw4YN0atXL0RERJRr3yqViv9EERlYdHQ0hgwZAv650g3PT1SeON6IiKovPlOBKj0TExNMnjwZ69atQ3JystLpEBERERERVRssKlCVMHz4cDiY5ID9AAAgAElEQVQ6OuKf//yn0qkQERERERFVGywqUJWgVqsxdepUrFq1CklJSUqnQ0REREREVC2wqEBVxujRo1G7dm3MnTtX6VSIiIiIiIiqBRYVqMowNTXF559/jsjISPz+++9Kp0NERERERFTlsahAVcrQoUPRvHlzzJo1S+lUiIiIiIiIqjwWFahKMTIywty5c7Ft2zb8+uuvSqdTZr/++iuaNGkCIyMjqFQqODk5ITQ0VOm0DG7btm3w8PCASqWCSqXC+++/X6RNjx49YGNjA2NjYzRr1gxnzpxRINOSW7x4MWrVqgWVSoUvv/xSs3zPnj2ws7PDzp07Ddp/efVDVcPBgwcxderUKvlZBIBNmzahXbt2sLGxQb169TB8+HDcuXOnQsQLDQ3V7O9nX82bNy+2fUFBAZYuXQofH59i18+dOxdNmzaFra0tzM3N4eXlhSlTpiArK0vT5ocffkBYWBjy8/NLlTMREVVvLCpQlePr6wsfHx+EhIQonUqZtW/fHr///jt69OgBALh06RJmzJihcFaGN3DgQFy9ehWenp547bXXsHHjRuzevVurzYEDB7B161b06dMHCQkJaN26tULZlsykSZPwyy+/FFkuIuXSf3n1Q5Xf559/juXLl2PatGlV8rMYFRWFgIAA+Pn54ebNm9ixYweOHDmCXr16IS8vT/F4ukhMTMQbb7yBCRMmIDs7u9g2hw4dwieffIJr164hJSUFCxYswLJly+Dn56dp4+vrC7VajW7duiEtLc2gORMRUdXDogJVSQsXLsThw4dx8OBBpVOp0HJycl7461ZFsXz5chgZGSEwMBDp6elKp6N3vXv3Rnp6Ovr06aO3mMUdV0P0U52Ux2elInweFy5ciC1btiA6Oho2NjZa66rKZ/Grr75C7dq1MXnyZNjZ2aFly5aYMGEC4uLicOLECcXjAcCGDRsgIlqv8+fPa7X57bffEBISgjFjxqBly5YvjGVtbY3AwEA4OjrCxsYGgwcPRv/+/bFv3z6t2ZLGjRuHFi1a4N133zV4MYSIiKoWFhWoSurUqRN69eqFqVOn8hfal1i3bh3u3r2rdBov5ePjg/Hjx+PWrVuYNGmS0ulUCpXhuFY25bFPlT5uly9fxsyZMzFnzhyo1eoi66vKZzEpKQkuLi5QqVSaZXXr1gUAXL9+XfF4JdWiRQts27YNAQEBMDc3f2G7Xbt2wdjYWGtZjRo1AKDI1Q2zZ89GXFwcli1bpv+EiYioymJRgaqsBQsW4MyZM4iKilI6Fb1btWoVrKysYGlpiR07dqBXr16wtbWFq6srNm/erNX28OHD+Nvf/gZLS0vY2trC29sbGRkZGD9+PCZOnIgrV65ApVLBy8sLAHD06FE0bdoUdnZ2UKvV8Pb2xv79+3XuF3j6a1vbtm2hVqthZWWF+vXrY968eQCA/Px8zJo1C25ubrCwsMDrr7/+wmMVGhqKhg0bYu3ata+8+kREsGTJEjRp0gTm5uZwcHBAv379cPHiRU2bRYsWwdLSEjY2Nrh79y4mTpyIOnXqYMyYMbCysoKRkRHatGkDJycnmJqawsrKCq1bt0bnzp1Rt25dqNVq2NvbY8qUKVp9v2zfFefYsWNwc3ODSqXCypUrATz9Ylfc/dQqlQo//vjjK/sp7rgW109J95Wux7yiKMm2BQUFwczMDM7OzpplH3/8MaysrKBSqZCSkgKg+H26fPlyqNVq1KpVC6NHj4aLiwvUajV8fHy0fp0uSx8AsG/fPtja2mL+/PkG3V/A0ysRRAS+vr4vbKPvz6Iu40uXc8bLeHh4FCneFD7/wMPDQ/F45eHWrVuwsLCAu7u71nIHBwd06dIFy5YtY0GeiIhKToiqsOHDh4urq6tkZWUZJD4AiYqKMkjsZ/Xs2VMAyIMHDzTLpk+fLgDkp59+kvT0dLl796507txZrKys5MmTJyIikpWVJba2thIWFiY5OTly584dGTBggNy7d09ERAYOHCienp5afW3dulVmz54t9+/fl9TUVGnfvr289tprOvUrIrJ06VIBIF988YWkpqbK/fv35auvvpKAgAAREZk0aZKYm5vLd999Jw8ePJBp06aJkZGRnDp1ShPD09NT/vzzTxER+eWXX8TIyEjq16+vOZ579+6Vvn37auU/a9YsMTMzkw0bNkhaWpqcO3dOWrduLTVq1JA7d+4U2Y5x48bJihUrZMCAAfL777/L559/LgDkxIkT8vDhQ0lJSZF33nlHAMju3bvl3r178vDhQwkKChIAEhcXV+J9l5iYKADkX//6l2ZZUlKSAJAVK1Zo2oSEhMjDhw9FROT27dvi4OAgPj4+kp+fX6J+ijuuz/dTmn31qmNuKFFRUaLrn6uSbltAQIA4OTlpvTc8PFwAaD4nIsXv08DAQLGyspILFy7Io0ePJCEhQdq1ayc2NjZy48YNvfSxa9cusbGxkblz5+q0/SK6n588PDykadOmxa4rj8/iq8ZXSc4ZJREbGyumpqayfPlyycjIkPPnz0uTJk2kZ8+eOsUxVLx58+aJq6ur2Nvbi6mpqdSvX1/69u0rJ0+efOF7/v73v0uLFi1KFP/hw4diY2MjQUFBxa6fOnWqAJCzZ8/qlHd5/T0kIqKKh0UFqtLu3LkjdnZ2MmfOHIPErwhFhZycHM2yiIgIASCXL18WEZHz588LANm1a1excYv7EvO8BQsWCAC5e/duift98uSJ2NvbS9euXbVi5eXlybJlyyQnJ0csLS3F399fsy47O1vMzc1l7NixmmXPfpEREZk4caIAkE8++UREin6Ryc7OFmtra624IiInT54UAFpfzIrbDhHRFBUyMzM1y7755hsBIPHx8UVibtmypcT7riRFhef1799f1Gq1XLx4scT9lKSoUNZ99fwxNyRdiwq6bFtZiwp2dnZay06dOiUAtM45ZemjLHQ5P2VlZYlKpZI+ffoUu768P4vPj6+SnjNKasaMGQJA83J1dZWkpCSd4xgi3o0bN+TMmTOSmZkpjx8/luPHj0urVq3EwsJCzp8/X+x7dCkqTJ8+XRo2bCgZGRnFrv/3v/8tAGT9+vU65c2iAhFR9cXbH6hKc3JywtSpU7Fw4UKD3ttaUZiZmQEAcnNzATy99LZWrVoYOnQoZs+ejWvXrukc09TUFABeOtXY8/2eO3cOaWlp6Nmzp1Y7Y2NjjBs3DpcuXUJ2drbWFGkWFhZwdnbWujT6eaGhoWjUqBEiIiJw7NixIusTEhKQlZWFtm3bai1v164dzMzMSv3QtMLte/bhZYX7pXCbi1OSffcy0dHR+P777zFnzhw0atRIr/2UdV89f8wrEkONg5Jo27YtLC0tXzqOK6K7d+9CRGBpaVmi9ob+LD4/vkp7zijO9OnTsXr1avz000/IysrC1atX4ePjgw4dOmg9uFCpeHXr1kWrVq1gbW0NMzMztG/fHpGRkcjJyUFERITO8Z61fft2REdHY//+/UUexFmocAz89ddfZeqLiIiqDxYVqMr77LPP4OrqimnTpimdSrmzsLDAoUOH0KlTJ8yfPx8eHh7w9/dHTk7OC9+ze/duvPnmm6hZsybMzc2LPDegJDIyMgAA9vb2xa5/+PAhAGDGjBlazwy4fv36C6dFAwC1Wo3IyEioVCqMGDGiyHYUToVmbW1d5L329vbIzMzUeVt0oY99Vyg1NRWffvop2rVrh4kTJ+q9H6X3lSEpvW3m5ua4d++eQfvQt0ePHgHASx/496zy/iyW9pzxvNu3byMsLAwfffQR3nrrLVhZWcHd3R1r1qxBcnIywsPDdcpL3/FexNvbG8bGxvjjjz9KHWPLli1YuHAhYmNjUb9+/Re2s7CwAPC/MUFERPQqLCpQlWdmZoZFixZh8+bNOHr0qNLplLtmzZph586dSE5ORnBwMKKiorB48eJi2964cQP9+/eHs7MzTpw4gfT0dISFhencZ+3atQFA8xC659WsWRMAsHTp0iLTph0/fvylsTt06IAJEyYgMTFR89DHQoVFjOK+sKSlpcHV1VXnbSkpfe27QuPGjUNaWhoiIyO1ntyur36U3FeGpuS25ebmVsr9V/hFUperXcrzs1iWc8azEhMTkZ+frzlHFbK1tYWjoyMSEhJ0ykvf8V6koKAABQUFJS76PG/FihXYuHEjDh06VCTX5z158gTA/8YEERHRq7CoQNVCv3798Pbbb2P8+PEoKChQOp1yk5ycjAsXLgB4+k/5F198gdatW2uWPS8+Ph65ubkYO3YsPDw8oFartaZJK6n69evD0dERBw4cKHZ94QwKcXFxOscGgHnz5qFx48Y4e/as1vLmzZvD2toap0+f1lp+4sQJPHnyBG3atClVfyWhr30HPL0S4dtvv8XMmTPRrFkzzfLJkyfrrR8l95Wh6bJtJiYmer2FIzY2FiKC9u3bG6wPQ6hVqxZUKhXS09N1el95fRbLes4oVFjMuH37ttbyzMxM3L9/XzMVpFLxABS5bQwATp06BRFBhw4ddIolIggODkZ8fDxiYmKKvXLkeYVjwMnJSae+iIio+mJRgaqNpUuX4ty5c1i/fr3SqZSb5ORkjB49GhcvXsSTJ09w9uxZXL9+XfOFx9HREcnJybh27RoyMzPh4uICADh48CAePXqExMTEUt1/bm5ujmnTpuHIkSMICgrCrVu3UFBQgMzMTFy4cAFqtRrDhw/H5s2bsWrVKmRkZCA/Px83b94s8s95cQovvX5+7nW1Wo2JEydi+/bt2LhxIzIyMhAfH48xY8bAxcUFgYGBOm9LSbm5uQEo+77LyMjA6NGj0bJlS4SEhAB4ehny6dOnERcXV6J+nj+uxX2hVXJfGZou2+bl5YX79+8jJiYGubm5uHfvXrHPX3nRPi0oKMCDBw+Ql5eHc+fOYfz48XBzc8OwYcP00sfevXvLZUpJS0tLeHh44ObNmzq9r7w+iyU5Z/j7+8PJyQlnzpx5YRx3d3d07doVa9aswZEjR5CTk4OkpCRNPh9++KGmrRLxgKfTPW7ZsgVpaWnIzc3F8ePHMXLkSLi5uWHMmDGv3lnPuHDhAhYtWoQ1a9bA1NS0yDS1xV21VjgGvL29deqLiIiqMWWeD0mkjDFjxoiTk5Okp6frJR4M/LTrX3/9VZo1ayZGRkYCQJydnWX+/PkSEREhlpaWAkAaNGggV65ckdWrV4utra0AkHr16skff/wh165dEx8fH3FwcBBjY2OpXbu2TJ8+XfLy8kRE5MyZM1KvXj2xsLCQTp06yZ07dyQ4OFgcHR3F3t5e/Pz8ZOXKlQJAPD09JSQkpET9Flq5cqV4e3uLWq0WtVotrVq1koiICBERefz4sQQHB4ubm5uYmJhIzZo1ZeDAgZKQkCDbt28XT09PASA1atTQPGH+eZMnTy4yjV1BQYGEh4dLgwYNxNTUVBwcHKR///5y6dIlTZuwsDCxsLAQAFK3bl3ZsGGDiIgsW7ZMs33169eXo0ePysKFC8XOzk4AiJOTk3z77beyZcsWcXJyEgDi4OAgmzdvFhF56b4bP3685j1WVlYyYMAAWbFihTg7OwsAsbS0FF9fX1m8eLHWU+Sffb377ruv7KfwyfHPHtcZM2YU6aek+6qkY82QSjOlZEm2TUQkNTVVunbtKmq1Wtzd3eXTTz+VyZMnCwDx8vLSTA1Z3GclMDBQTE1NpU6dOmJiYiK2trbSr18/uXLlit762LNnj9jY2EhoaKjO+03X81NQUJCYmppKdna2ZpmhP4u6jK+XnTNEns6UAkBmzZr10u1MSUmR8ePHi5eXl5ibm4u1tbV07NhRvv/+e612SsWbOHGieHp6ipWVlZiYmIirq6uMGjVKkpOTtdodP35cOnbsKC4uLppzhLOzs/j4+Mjhw4dFRCQ+Pv6F5xMAEh4eXqT/3r17S506daSgoOCleT7P0H8PiYio4lKJiBikWkFUAaWkpKBhw4b46KOPsHDhwjLHU6lUiIqKwuDBg/WQHREVJzo6GkOGDEFF+3M1evRobN26FampqUqnUixdz0+XL19GkyZNEBkZiaFDhxo4O/0rKCjAm2++iWHDhmHEiBFVPp4hpKamwtXVFaGhoUUeEPsq/HtIRFR98fYHqlZq1KiBefPmYcmSJXp7gBYRVV+lnS60IvLy8sLcuXMxd+5cZGVlKZ2OTvLz8xETE4PMzEz4+/tX+XiGMnv2bLRs2RJBQUFKp0JERJUIiwpU7YwZMwatW7fG6NGjK9wvn0RESpo6dSr8/Pzg7++v80MblRQbG4tt27Zh7969sLS0rPLxDGHJkiWIi4vDnj17YGpqqnQ6RERUibCoQNWOkZERIiIicPz48Wr10EYi0p9p06YhMjIS6enpcHd3x3fffad0Snozf/58BAUF4YsvvlA6lRLr1q0bvv32Wzg7O1eLePq2Y8cOPH78GLGxsXBwcFA6HSIiqmRMlE6ASAlt2rTBmDFjMHHiRPTu3Rs1atRQOiUiqkQWLFiABQsWKJ2GwfTo0QM9evRQOg0qJ3379kXfvn2VToOIiCopXqlA1db8+fOhVqsxbdo0pVMhIiIiIiKqlFhUoGrL1tYWixcvxrp16/DLL78onQ4REREREVGlw6ICVWv+/v7o2bMnRo8ejdzcXKXTISIiIiIiqlRYVKBqb/ny5UhMTMSKFSuUToWIiIiIiKhS4YMaqdrz8vJCcHAwZs6ciYEDB6JevXo6vX/p0qXYunWrgbIjUl52djbUajWMjJSpQ9+8eRMA4Ofnp0j/lRnPT0RERGRoKhERpZMgUtqjR4/w+uuvo2nTpoiJiSnx+/glh6q6O3fu4OTJk2jUqBEaNWqkdDpEVIFNmDABHTp0UDoNIiIqZywqEP3/fv75Z7zxxhv45ptvMHToUKXTIVKUiGDRokWYNm0a/u///g+rV6+GpaWl0mkRERERUQXDogLRM4KCgrBhwwbEx8fD1dVV6XSIFJGRkYFhw4Zh586dCA0NRXBwsNIpEREREVEFxaIC0TOys7PRsmVLNGzYELt27VI6HaJy99tvv2HgwIF49OgRtm7dykuZiYiIiOilOPsD0TMsLS3x9ddfY9++ffj666+VToeoXG3atAkdO3ZEnTp1cPr0aRYUiIiIiOiVWFQgeo6Pjw8+/fRTfPbZZ5qnzhNVZXl5eQgJCUFAQAACAgJw8OBBODs7K50WEREREVUCvP2BqBjZ2dlo3bo1nJ2d8dNPP8HY2FjplIgMIjk5GX5+fjh37hz+/e9/c0YTIiIiItIJr1QgKoalpSW2bt2KkydPYs6cOUqnQ2QQR48eRdu2bZGSkoJff/2VBQUiIiIi0hmLCkQv4O3tjaVLl2L+/Pk4cOCA0ukQ6dXq1avRrVs3tGvXDidPnkSzZs2UTomIiIiIKiHe/kD0Cu+//z4OHDiAs2fPonbt2kqnQ1QmWVlZGDlyJL777jvMnz8fU6ZMgUqlUjotIiIiIqqkWFQgeoWsrCy0a9cOTk5OfL4CVWqJiYkYMGAA7ty5g82bN6N79+5Kp0RERERElRxvfyB6BWtra2zevBknTpzAjBkzlE6HqFR27tyJv/3tbzA3N8fp06dZUCAiIiIivWBRgagEWrZsiX/9618ICwvDpk2blE6HqMTy8/Mxe/Zs9OvXD4MHD8Yvv/yCevXqKZ0WEREREVURJkonQFRZDBs2DPHx8fjwww/h6emJv//970qnRPRSKSkpeO+993D06FF89dVXGDlypNIpEREREVEVw2cqEOmgoKAAffv2xX//+1+cPHkSrq6uSqdEVKwzZ85g4MCByM/Px7Zt29CuXTulUyIiIiKiKoi3PxDpwMjICJs2bYKjoyP69u2L7OxspVMiKmL9+vXo1KkTmjRpgri4OBYUiIiIiMhgWFQg0pGNjQ1iYmJw7do1fPjhh+DFPlRRPH78GB999BGGDRuGoKAg7Nq1C46OjkqnRURERERVGG9/ICqlQ4cOoVevXggKCkJ4eLjS6VA1l5SUhEGDBuHixYv4+uuv0b9/f6VTIiIiIqJqgFcqEJXSW2+9hW+++QZLlizBkiVLlE6HqrH//Oc/aNu2LR4/fowzZ86woEBERERE5YZFBaIy8Pf3x4oVKzBp0iSsX79e6XSomhERhIWF4e2330b37t3x888/w9PTU+m0iIiIiKga4ZSSRGU0duxYXL16FSNHjoSTkxN69uypdEpUDWRkZGD48OH44YcfMH/+fAQHByudEhERERFVQ3ymApEeiAiGDh2KnTt34j//+Q/atGmjdEpUhV28eBEDBgxAeno6tm7dCh8fH6VTIiIiIqJqirc/EOmBSqVCZGQk2rdvj3feeQfx8fFKp0RV1ObNm9G2bVvUqFEDp0+fZkGBiIiIiBTFogKRnpiZmSEmJgbe3t7o1q0bLly4oHRKVIXk5eUhJCQE7733HgICAvDTTz/BxcVF6bSIiIiIqJrj7Q9EepadnY1evXrh0qVLiI2NRePGjZVOiSq5u3fvwt/fH6dOncLatWsxZMgQpVMiIiIiIgLAogKRQWRkZODtt9/G7du3cfjwYbi7uyudElVSx44dw+DBg2FtbY3t27ejefPmSqdERERERKTB2x+IDMDW1hb79u3Da6+9hu7duyMpKUnplKgSWr16Nd566y20bdsWJ0+eZEGBiIiIiCocFhWIDMTBwQE//vgjrKys8MYbb+Dq1atKp0SVRE5ODoYPH46xY8di2rRpiImJgb29vdJpEREREREVwdsfiAzswYMH6NWrF65fv46DBw+iWbNmSqdEFdjly5cxYMAAJCcnY9OmTejRo4fSKRERERERvRCvVCAyMAcHB+zfvx8eHh546623cO7cOaVTogpq165daNeuHUxNTXH69GkWFIiIiIiowmNRgagc2NnZ4cCBA3j99dfx5ptv4sSJE0qnRBWIiCAsLAx9+/ZFnz59cOzYMdSvX1/ptIiIiIiIXom3PxCVo5ycHAwYMADHjx/Hzp070blzZ6VTIoWlpqYiICAAsbGxWLFiBUaNGqV0SkREREREJcYrFYjKkYWFBWJiYtC9e3f06NED33//vdIpkYLOnj2Ldu3a4cKFCzhy5AgLCkRERERU6bCoQFTOzM3NERUVheHDh8PPzw9ffvml0imRAtavX4+OHTuifv36OH36NP72t78pnRIRERERkc5YVCBSgLGxMVatWoX58+dj7NixCAkJUTolKiePHz/GuHHjMGzYMAQFBeHHH39ErVq1lE6LiIiIiKhUTJROgKg6Cw4OhqOjI8aMGYO0tDRERETA2NhY6bSolEQEKpXqhetv3ryJQYMG4ffff8d3332HAQMGlGN2RERERET6xysViBQ2atQobNu2DevXr0e/fv2QlZVVbLv169fjwoUL5ZwdlVReXh569+6NW7duFbs+NjYWbdu2RXp6On799VcWFIiIiIioSmBRgagC6Nu3L/7zn//g1KlT8PHxwY0bN7TW//e//8WoUaMwYsQIFBQUKJQlvczixYuxd+9eDBw4ELm5uZrlIoL/9//+H95++2289dZbOH36NJo0aaJgpkRERERE+sOiAlEF8fe//x2nT5+GsbEx2rdvj9OnTwMAUlJS4Ovri/z8fJw6dQpfffWVwpnS8xITE/H5558DAE6fPo0JEyYAADIzM+Hn54dJkyYhNDQUmzZtgpWVlZKpEhERERHplUpEROkkiOh/MjMz8X//93+IjY3Fhg0b8K9//QuxsbGaX78tLS1x6dIluLq6KpwpAU+vROjatSt++eUXrSsU5s+fj2+++QaZmZmIjo5Gp06dFMySiIiIiMgwWFQgqoDy8vLw6aefIjIyErm5uVq3PJiamqJ79+7Ys2ePghlSoTVr1iAwMBDPn0pNTU3Rpk0bbN++HS4uLgplR0RERERkWLz9gagCMjExwTvvvIPHjx8XeYZCbm4u9u7di++//16h7KjQnTt3MHHixCIFBeDpFQxJSUkwMzNTIDMiIiIiovLBogJRBfTHH38gICDghdMTqlQqfPTRR0hLSyvnzOhZY8aMwaNHj4pdl5eXh7t37+L999/nwzWJiIiIqMpiUYGogsnKysI//vEPPHnypNhfwIGnv4Knp6cjJCSknLOjQt9//z1iYmK0nqPwvNzcXOzfvx8LFiwox8yIiIiIiMoPn6lAVMEMGjQI27ZtK1FblUqFw4cPo3PnzgbOip6Vnp6OBg0aIDU19aVXIRgZPa3bGhsb4+zZs2jWrFl5pUhEREREVC54pQJRBbNo0SIsWLAATZs2BYCX3pNvZGSE4cOH4/Hjx+WVHgGYPHkyHjx4UGxBQaVSwdjYGCqVCm3btsWSJUtw48YNFhSIiIiIqErilQpEFdiff/6J6OhorFmzBleuXIG5uXmRAoKxsTGmT5+OOXPmKJRl9XLkyBG8+eabWremqFQqmJiYIDc3F6+//jpGjBgBPz8/1K5dW8FMiYiIiIgMj0UFokoiLi4OUVFR2LBhA27dugUzMzM8efIEwNPZIuLi4vhruIE9evQIzZo1w59//gngaUEnPz8frVq1wtChQ+Hn5wdXV1eFsyQiIiIiKj8sKrzE8ePHkZSUpHQaRFpEBFeuXMHPP/+Mn3/+Genp6QAAT09PzJ8//4UzRlDZbdmyRTOVp5ubGzp37owOHTqgZs2aCmdGVDY+Pj4siBEREVGpsKjwEn5+fvjuu++UToOIiMigoqKiMHjwYKXTICIiokrIROkEKrpBgwZh69atSqdRbURHR2PIkCEvnEqRiqdSqbBp0ya4u7ujffv2SqdTJWVlZcHa2lrpNIj0jlc3ERERUVlw9geiKsLY2JgFBQNiQYGIiIiIqCgWFYiIiIiIiIioVFhUICIiIiIiIqJSYVGBiIiIiIiIiEqFRQUiIiIiIiIiKhUWFYiIiIiIiIioVFhUUNjIkSNhY2MDlUqFuLg4vcffs2cP7OzssHPnTr3HLhQWFobGjRvDwgu01ScAACAASURBVMICVlZWaNy4MWbOnImMjAyD9fkq5bHdldno0aOhUqk0r6FDhxZpc/DgQUydOhXbtm2Dh4eHpu37779fpG2PHj1gY2MDY2NjNGvWDGfOnCmPzSiTTZs2oV27drCxsUG9evUwfPhw3Llzp0LECw0N1To+ha/mzZsX276goABLly6Fj49Psevnzp2Lpk2bwtbWFubm5vDy8sKUKVOQlZWlafPDDz8gLCwM+fn5pcr5eVV5/FSk4xMTE6OVQ40aNfS3oUREREQlwKKCwtauXYs1a9YYLL6IGCx2oaNHj2LUqFG4ceMG/vrrL8ybNw9hYWEYNGiQwft+kfLY7srO0dERe/fuxaVLl7Bu3TqtdZ9//jmWL1+OadOmYeDAgbh69So8PT3x2muvYePGjdi9e7dW+wMHDmDr1q3o06cPEhIS0Lp16/LcFJ1FRUUhICAAfn5+uHnzJnbs2IEjR46gV69eyMvLUzyeLhITE/HGG29gwoQJyM7OLrbNoUOH8Mknn+DatWtISUnBggULsGzZMvj5+Wna+Pr6Qq1Wo1u3bkhLSytTTlV9/OjC0Menb9++uHnzJo4cOYJ3333X4NtDRERE9DwWFaq43r17Iz09HX369DFYH2ZmZvj4449Rs2ZNWFtbw8/PD/369cOPP/6I27dvG6zflymP7S6pnJycF/5CqSQLCwu88847aNiwIczNzTXLFy5ciC1btiA6Oho2NjZa71m+fDmMjIwQGBiI9PT08k5Zb7766ivUrl0bkydPhp2dHVq2bIkJEyYgLi4OJ06cUDweAGzYsAEiovU6f/68VpvffvsNISEhGDNmDFq2bPnCWNbW1ggMDISjoyNsbGwwePBg9O/fH/v27UNSUpKm3bhx49CiRQu8++67pS6GVIfxA1Sc46NSqVCnTh107twZDRo0MMzGEhEREb0EiwoVgEqlUjqFMtm+fTvUarXWsjp16gCA1uW71dW6detw9+5dpdMokcuXL2PmzJmYM2dOkWMKAD4+Phg/fjxu3bqFSZMmKZChfiQlJcHFxUXrs1e3bl0AwPXr1xWPV1ItWrTAtm3bEBAQoFUYet6uXbtgbGystazwMvnnfz2fPXs24uLisGzZMp3zqS7jp6Qq2vEhIiIiMgQWFfTs6NGjaNq0Kezs7KBWq+Ht7Y39+/dr1osIwsPD0ahRI5ibm8POzg6TJ0/WOU5JHDt2DG5ublCpVFi5ciUAYNWqVbCysoKlpSV27NiBXr16wdbWFq6urti8eXPZNv4ZiYmJsLe3R7169fQWs6TKst3Lly+HWq1GrVq1MHr0aLi4uECtVsPHx0frF+egoCCYmZnB2dlZs+zjjz+GlZUVVCoVUlJSAADjx4/HxIkTceXKFahUKnh5eQEA9u3bB1tbW8yfP788dkmJLV++HCICX1/fF7YJDQ1Fw4YNsXbtWhw8ePCl8UQES5YsQZMmTWBubg4HBwf069cPFy9e1LTRZUzm5+dj1qxZcHNzg4WFBV5//XVERUXpvJ0eHh5FCj2Fzz/w8PBQPF55uHXrFiwsLODu7q613MHBAV26dMGyZct0vo2ouoyf8mCI40NERERkEEIvNGjQIBk0aJBO79m6davMnj1b7t+/L6mpqdK+fXt57bXXNOunT58uKpVK/vnPf8qDBw8kOztbIiIiBICcPXu2xHFKKikpSQDIihUrtHIAID/99JOkp6fL3bt3pXPnzmJlZSVPnjzRuY9CT548kZs3b8qKFSvE3NxcNmzYoHOMqKgo0cewLMt2BwYGipWVlVy4cEEePXokCQkJ0q5dO7GxsZEbN25o2gUEBIiTk5NWv+Hh4QJA7t27p1k2cOBA8fT01Gq3a9cusbGxkblz55Z5W0VEAEhUVFSJ2wcGBkqdOnWKLPfw8JCmTZsW+x5PT0/5888/RUTkl19+ESMjI6lfv75kZWWJiMjevXulb9++Wu+ZNWuWmJmZyYYNGyQtLU3OnTsnrVu3lho1asidO3c07Up6bCZNmiTm5uby3XffyYMHD2TatGliZGQkp06dKvG2i4jExsaKqampLF++XDIyMuT8+fPSpEkT6dmzp05xDBVv3rx54urqKvb29mJqair169eXvn37ysmTJ1/4nr///e/SokWLEsV/+PCh2NjYSFBQULHrp06dWuScVBLVZfxU1OMzbty4Uv2d0PX8QURERPQsXqmgZ4MGDcLnn38OBwcHODo6wtfXF6mpqbh37x5ycnKwdOlSdO/eHRMmTIC9vT0sLCzg6OioUxx98fHxga2tLWrWrAl/f388fPgQN27cKHW8unXrwtXVFbNnz8aiRYswZMgQveWqTyXZbhMTE82vo02bNsWqVauQmZmJyMhIveTQu3dvZGRkYObMmXqJpw8PHz7En3/+CU9Pz1e27dChAz777DNcu3YNISEhxbbJycnBkiVLMGDAAAwdOhR2dnbw9vbGl19+iZSUFKxevbrIe152bB49eoRVq1ahf//+GDhwIOzt7TFjxgyYmprqfFy6dOmC4OBgBAUFwdbWFs2bN0dmZibWrl2rUxxDxfvggw/www8/ICkpCVlZWdi8eTNu3LiBLl26ICEhoVQxn7VgwQK4uLggNDS02PWF9+bHx8eXOGZ1Gj+V8fgQERERGQqLCgZmamoK4Ollt5cvX0Z2dja6detWpjiGYGZmBgDIzc0tdYykpCTcvXsXmzZtwjfffINWrVpV+GcJlHS727ZtC0tLS63Lrquau3fvQkRgaWlZovahoaFo1KgRIiIicOzYsSLrExISkJWVhbZt22otb9euHczMzF75AMPnj82lS5eQnZ2tNW2fhYUFnJ2ddT4u06dPx+rVq/HTTz8hKysLV69ehY+PDzp06KD1YDyl4tWtWxetWrWCtbU1zMzM0L59e0RGRiInJwcRERE6x3vW9u3bER0djf379xd5kGKhwjHw119/lThudRo/lfH4EBERERkKiwp6tnv3brz55puoWbMmzM3NMWXKFM26mzdvAgBq1qxZpjgVlampKWrWrIkePXpgy5YtSEhIwIIFC5ROS2/Mzc31eqVIRfPo0SMAeOkD5Z6lVqsRGRkJlUqFESNGICcnR2t94bR31tbWRd5rb2+PzMxMnfJ7+PAhAGDGjBlQqVSa1/Xr1184VV9xbt++jbCwMHz00Ud46623YGVlBXd3d6xZswbJyckIDw/XKS99x3sRb29vGBsb448//ih1jC1btmDhwoWIjY1F/fr1X9jOwsICwP/GRElUl/HzIhX9+BAREREZCosKenTjxg30798fzs7OOHHiBNLT0xEWFqZZX/g09MePH5cpTmXg5eUFY2NjvVwKXBHk5uYiLS0Nrq6uSqdiMIVfVHS5GqZDhw6YMGECEhMTMW/ePK119vb2AFDsl7/S7MvCYtzSpUuLTOV3/PjxEsdJTExEfn4+ateurbXc1tYWjo6OOo9Zfcd7kYKCAhQUFJT4S/vzVqxYgY0bN+LQoUNFcn3ekydPAPxvTJREdRk/L1LRjw8RERGRobCooEfx8fHIzc3F2LFj4eHhAbVarTXFXPPmzWFkZITDhw+XKU5Fkpqaivfee6/I8sIvWoXT6lV2sbGxEBG0b99es8zExKRMt4tUNLVq1YJKpUJ6erpO75s3bx4aN26Ms2fPai1v3rw5rK2tcfr0aa3lJ06cwJMnT9CmTRud+qlbty7UajXi4uJ0et/zCr+M3r59W2t5ZmYm7t+/r/OY1Xc8AOjZs2eRZadOnYKIoEOHDjrFEhEEBwcjPj4eMTExxf7y/7zCMeDk5FTifqrL+AEq5/EhIiIiMhQWFfTIzc0NAHDw4EE8evQIiYmJWvf91qxZEwMHDsR3332HdevWISMjA+fOnSvywLFXxalIrKyscODAARw6dAgZGRnIzc3F2bNn8cEHH8DKygoTJkxQOsVSKSgowIMHD5CXl4dz585h/PjxcHNzw7BhwzRtvLy8cP/+fcTExCA3Nxf37t3D9evXi8RydHREcnIyrl27hszMTOTm5mLv3r0VbkpJS0tLeHh4aG7TKanCy9iNjY2LLJ84cSK2b9+OjRs3IiMjA/Hx8RgzZgxcXFwQGBiocz/Dhw/H5s2bsWrVKmRkZCA/Px83b97UfKH39/eHk5MTzpw588I47u7u6Nq1K9asWYMjR44gJycHSUlJmnw+/PBDTVsl4gFPpxPcsmUL0tLSkJubi+PHj2PkyJFwc3PDmDFjXr2znnHhwgUsWrQIa9asgampqdal/yqVCosXLy7ynsIx4O3tXeK8q8v4ASre8SEiIiJSlAIzTlQapZlSMjg4WBwdHcXe3l78/Pxk5cqVAkA8PT3lxo0bkpmZKSNHjpTXXntNrK2tpVOnTjJr1iwBIK6urvLbb7+VKE5JrFixQpydnQWAWFpaiq+vr0RERIilpaUAkAYNGsiVK1dk9erVYmtrKwCkXr168scff+i0zb6+vuLu7i7W1tZibm4unp6e4u/vL/Hx8TrFEdHPlJJl3e7AwEAxNTWVOnXqiImJidja2kq/fv3kypUrWv2kpqZK165dRa1Wi7u7u3z66acyefJkASBeXl6a43TmzBmpV6+eWFhYSKdOneTOnTuyZ88esbGxkdDQ0DJtayHoaUrJoKAgMTU1lezsbM2y7du3i6enpwCQGjVqyCeffFJszMmTJxeZErCgoEDCw8OlQYMGYmpqKg4ODtK/f3+5dOmSpo0ux+bx48cSHBwsbm5uYmJiIjVr1pSBAwdKQkKCiIj0799fAMisWbNeuv0pKSkyfvx48fLyEnNzc7G2tpaOHTvK999/r9VOqXgTJ04UT09PsbKyEhMTE3F1dZVRo0ZJcnKyVrvjx49Lx44dxcXFRQAIAHF2dhYfHx85fPiwiIjEx8dr1hX3Cg8PL9J/7969pU6dOlJQUKBT3tVl/FS041OIU0oSERGRElhUeInSFBWobPRRVCirwMBAcXR0VDQHXemrqJCYmCgmJiayYcMGfaZXbvLz86Vz586ybt26ahHPEFJSUkStVsvixYs1y0qaN8eP4RV3fAqxqEBERERK4O0PRMUw1NSdFUlOTg7279+PxMREzYPfvLy8MHfuXMydOxdZWVkKZ6ib/Px8xMTEIDMzE/7+/lU+nqHMnj0bLVu2RFBQEADd8ub4Mbznj4+IIDk5GceOHcPly5cVzo6IiIiqIxYVKpmLFy8Wuee2uFdZ/ikujz5Ieffv38c777yDhg0bYsSIEZrlU6dOhZ+fH/z9/XV+6J6SYmNjsW3bNuzduxeWlpZVPp4hLFmyBHFxcdizZw9MTU0B6J43x4/hFHd8duzYgTp16qBz587YvXu3whkSERFRdaQSEVE6iYrKz88PALB161aFM6k+oqOjMWTIECg1LKdNm4Z//vOfePLkCerXr4/w8HAMGjRIkVx0oVKpEBUVhcGDB+stZuEDOBcuXKi3mFRx7dixAxcuXMCUKVOKPDSxNDh+9Evfx+dZhjh/EBERUfXBosJLsKhQ/pQuKlRW/FJARKXF8wcRERGVBW9/ICIiIiIiIqJSYVGBiIiIiIiIiEqFRQUiIiIiIiIiKhUWFYiIiIiIiIioVEyUTqCi+/XXXzUPbCTDu3nzJgBwn5fC0qVL+VBRIiIiIiIqV7xSgYiIiIiIiIhKhVcqvEL79u356285KpxSkvtcNyqVCp999hmnhCMinalUKqVTICIiokqMVyoQERERERERUamwqEBEREREREREpcKiAhERERERERGVCosKRERERERERFQqLCoQERERERERUamwqFABbNq0CSqVCj4+Pjq9b+TIkbCxsYFKpUJcXFyJ3vPrr7+iSZMmMDIygkqlgpOTE0JDQ7Ft2zZ4eHhApVJBpVLh/fffL/LeHj16wMbGBsbGxmjWrBnOnDmjU75ESjp48CCmTp1aZcf6pk2b0K5dO9jY2KBevXoYPnw47ty5UyHihYaGavb3s6/mzZsX276goABLly594Tlx7ty5aNq0KWxtbWFubg4vLy9MmTIFWVlZmjY//PADwsLCkJ+fX6qciYiIiKhkWFSoADZt2gRPT08cP34cly9fLvH71q5dizVr1ujUV/v27fH777+jR48eAIBLly5hxowZGDhwIK5evQpPT0+89tpr2LhxI3bv3q313gMHDmDr1q3o06cPEhIS0Lp1a536JlLK559/juXLl2PatGlVcqxHRUUhICAAfn5+uHnzJnbs2IEjR46gV69eyMvLUzyeLhITE/HGG29gwoQJyM7OLrbNoUOH8Mknn+DatWtISUnBggULsGzZMvj5+Wna+Pr6Qq1Wo1u3bkhLSzNozkRERETVGYsKCktNTcWFCxcwZ84cAMD69esVzghYvnw5jIyMEBgYiPT0dKXTKXc5OTk6XzVSEfugpxYuXIgtW7YgOjoaNjY2Wuuqylj/6quvULt2bUyePBl2dnZo2bIlJkyYgLi4OJw4cULxeACwYcMGiIjW6/z581ptfvvtN4SEhGDMmDFo2bLlC2NZW1sjMDAQjo6OsLGxweDBg9G/f3/s27cPSUlJmnbjxo1DixYt8O677xq8GEJERERUXbGooLDo6Gj07t1b86ta4T/eJaVSqfSek4+PD8aPH49bt25h0qRJeo9f0a1btw53796t9H0QcPnyZcycORNz5syBWq0usr6qjPWkpCS4uLhonQ/q1q0LALh+/bri8UqqRYsW2LZtGwICAmBubv7Cdrt27YKxsbHWsho1agBAkasbZs+ejbi4OCxbtkz/CRMRERERiwpK27RpEwYMGAAbGxv06NED165dw9GjR4ttKyIIDw9Ho0aNYG5uDjs7O0yePLlIu6NHj6Jp06aws7ODWq2Gt7c39u/fr1NeoaGhaNiwIdauXYuDBw+WatvKi4hgyZIlaNKkCczNzeHg4IB+/frh4sWLmjZBQUEwMzODs7OzZtnHH38MKysrqFQqpKSkAADGjx+PiRMn4sqVK1CpVPDy8sLy5cuhVqtRq1YtjB49Gi4uLlCr1fDx8dH61bYsfQDAvn37YGtri/nz5xt0f1Uny5cvh4jA19f3hW10GeslGWurVq2ClZUVLC0tsWPHDvTq1Qu2trZwdXXF5s2bteLl5+dj1qxZcHNzg4WFBV5//XVERUXpvJ0eHh5FilSFzz/w8PBQPF55uHXrFiwsLODu7q613MHBAV26dMGyZct0KtgSERERUcmwqKCgGzdu4NKlS3jjjTcAQHM/8ItugZg5cyaCg4MRGBiIv/76C3fu3EFISEiRdn/99ReGDBmCa9euITk5GdbW1ggICNApNwsLC3z99dcwMjLCqFGj8PDhQx23rvzMnj0bU6dOxfTp03H37l0cOXIESUlJ6Ny5M/766y8AT79cDh48WOt9ERERmttOCi1btgx9+vSBp6cnRASXL19GUFAQhg0bhuzsbIwbNw7Xrl3DmTNnkJeXh7fffltzuXVZ+gCgeaBcQUGB/nZONbd79240atQIlpaWL2yjy1gvyVgbO3YsPvvsM+Tk5MDGxgZRUVG4cuUKPDw8MGrUKOTm5mrihYSEYNGiRVi6dClu376NPn364L333sPp06d12s5p06bhzp07WLFiBTIzM5GQkIBly5ahZ8+eaN++vU6xDBEPAKZOnQoHBweYmZnB3d0d/fr1w6lTp0oV63nZ2dk4dOgQRo0aBTMzsyLrW7VqhVu3buG3337TS39ERERE9D8sKiho06ZN+Mc//qG5jNfX1xfm5ubYunUrcnJytNrm5ORg6dKl6N69OyZMmAB7e3tYWFjA0dGxSNxBgwbh888/h4ODAxwdHeHr64vU1FTcu3dPp/w6dOiAzz77DNeuXSu2eFER5OTkYMmSJRgwYACGDh0KOzs7eHt748svv0RKSgpWr16tt75MTEw0v1A3bdoUq1atQmZmJiIjI/USv3fv3sjIyMDMmTP1Eq+6e/jwIf788094enq+sm1JxnppxpqPjw9sbW1Rs2ZN+Pv74+HDh7hx4wYA4NGjR1i1ahX69++PgQMHwt7eHjNmzICpqanOY6pLly4IDg5GUFAQbG1t0bx5c2RmZmLt2rU6xTFUvA8++AA//PADkpKSkJWVhc2bN+PGjRvo0qULEhISShXzWQsWLICLiwtCQ0OLXd+gQQMAQHx8fJn7IiIiIiJtLCooqPDWh0K2trbo0aMHMjIysGPHDq22ly9fRnZ2Nrp166ZzP6ampgBQqqnVQkND0ahRI0RERODYsWM6v9/QEhISkJWVhbZt22otb9euHczMzEr9ULmSaNu2LSwtLbUufaeK4+7duxCRl16l8KxXjfWyjrXCX9ALr1S4dOkSsrOztaZVtLCwgLOzs85javr06Vi9ejV++uknZGVl4erVq/Dx8UGHDh20HlyoVLy6deuiVatWsLa2hpmZGdq3b4/IyEjk5OQgIiJC53jP2r59O6Kjo7F///4iD+IsVDgGCq8mISIiIiL9YVFBIefPn0d8fDz69OmjNW/7zp07ARS9BeLmzZsAgJo1a74y9u7du/Hmm2+iZs2aMDc3x5QpU0qdp1qtRmRkJFQqFUaMGFHkCgqlFU4VZ21tXWSdvb09MjMzDdq/ubm5zleAUPl49OgRALz0gX/PetVY1/dYK7zNYsaMGVrngOvXr79wKsXi3L59G2FhYfjoo4/w1ltvwcrKCu7u7lizZg2Sk5MRHh6uU176jvci3t7eMDY2xh9//FHqGFu2bMHChQsRGxuL+vXrv7CdhYUFgP+NCSIiIiLSHxYVFPLtt9/i/2vvbkKibLs4gP9HZmg0G9Qyx5xG1BGKjIJWfiySwBYtTHTSoIWlYfYhkojUQsKvKKMWZYsk3FhpY2YbhYjQFg0SmDkUlAmlYUFp6kSl08x5Fy/O84xfOeP43KX/H7i5P851vDlcMIf7vq6DBw/O2mJtdHQUgYGBePjwoXthNADulesnJycXjDs4OIiMjAzo9Xp0d3djfHwcFy5cWFKuiYmJOH36NPr7+1FZWbmkWP4WEhICAHP+oBsbG4PBYFi2sR0Ox7KPQb6b/iHpzRs6C9W6v2ttukF45cqVWfOA1WpddJz+/n44nU5s2rTJ47hOp0NYWJjXnxf4O958XC4XXC7Xops+M129ehWNjY14/PjxrFxnmpqaAvBPTRARERGR/7CpoAARQVNTE06cODHrXGhoKMxmM5xOJ27fvu0+npCQgICAAHR1dS0Y22azweFw4Pjx44iNjYVWq/XLtpOVlZXYsmULnj9/vuRY/pSQkIDg4OBZC9t1d3djamoKu3btch9Tq9Uei+QtVWdnJ0TEY+E6f49Bvtu4cSNUKhXGx8e9um++Wvem1hZj8+bN0Gq16O3t9eq+maabGR8/fvQ4brfbMTo66t4KUql4ALB3795Zx549ewYRQWJiolexRARlZWWw2Wxoa2ub882RmaZrICIiwquxiIiIiOj32FRQwNOnT6HT6ZCcnDzn+cLCQgCen0CEh4cjMzMTLS0tuHnzJiYmJtDX1zdrcTij0QgAePToEX7+/In+/n6/rCsw/Wr4zL3hlabValFSUoLW1lY0NjZiYmICNpsNhYWFiIyMREFBgftak8mE0dFRtLW1weFw4PPnz3j//v2smGFhYRgeHsa7d+9gt9vdTQKXy4WvX7/i169f6OvrQ3FxMYxGI3Jzc/0yRkdHB7eU9KOgoCDExsa6Px1arPlq3ZtaW+w4hw8fxp07d3D9+nVMTEzA6XTiw4cP7h/0OTk5iIiIQE9Pz7xxYmJikJqaivr6ejx58gQ/fvzA0NCQO5+8vDz3tUrEA/6/3WNTUxPGxsbgcDhgtVqRn58Po9Honu8W69WrV7h48SLq6+uh0Wg8Ph1RqVS4dOnSrHuma2D79u1ejUVEREREiyA0r6ysLMnKyvJrzLy8PFm7dq2o1WrZsWOH9PT0eJyvrKyUyMhIASAAJCoqSurq6kRExG63S35+vqxfv16Cg4MlJSVFysvLBYAYDAZ58eKFiIiUlZVJWFiYhISEiNlslmvXrgkAiYuLE4vFItu2bZOAgAABIHq9Xqqrq6W1tVXi4uIEgGzYsEFOnjw5Z/6lpaWSnp7u12fyb83NzeJtWbpcLqmtrZX4+HjRaDQSGhoqGRkZ8vr1a4/rRkZGJDU1VbRarcTExMipU6ektLRUAIjJZJLBwUEREenp6ZHo6GgJDAyUlJQU+fTpkxQUFIhGo5GoqChRq9Wi0+lk//79MjAw4Lcx2tvbZd26dVJVVeX1cwMgzc3NXt+30hUVFYlGo5Hv37+7jy2l1hdTa3V1dRIUFCQAJD4+XgYGBuTGjRui0+kEgERHR8ubN29ERGRyclLKysrEaDSKWq2W8PBwyczMlJcvX4qISEZGhgCQ8vLyBf/PL1++SHFxsZhMJlmzZo0EBwdLcnKy3L9/3+M6peKVlJRIXFyce+4zGAxy9OhRGR4e9rjOarVKcnKyxxyo1+slKSlJurq6RETEZrO5z831V1tbO2v8ffv2SVRUlLhcrgXzXK04fxAREdFSqERE/qsGxt/GbDYDACwWi8KZrB53795FdnY2/rSyPHbsGCwWC0ZGRpROZU4qlQrNzc04cOCA0qn8Ud6+fYutW7eioaEBhw4dUjodr7lcLuzevRu5ubk4cuTIio+3HEZGRmAwGFBVVYWSkhKl0/kjcf4gIiKipeDnD0SL5MuWnKQsk8mEiooKVFRU4Nu3b0qn4xWn04m2tjbY7Xbk5OSs+HjL5dy5c9i5cyeKioqUToWIiIhoRWJTgYhWtDNnzsBsNiMnJ8frRRuV1NnZiXv37qGjowNBQUErPt5yuHz5Mnp7e9He3g6NRqN0OkREREQrEpsKRL9x9uxZNDQ0YHx8HDExMWhpaVE6JfJSdXU1ioqKcP78eaVTWbQ9e/bg1q1b0Ov1qyKevz148ACTk5Po7OxEaGio0ukQERERrVhqnblQSAAAAOhJREFUpRMg+tPV1NSgpqZG6TRoidLS0pCWlqZ0GvQfSU9PR3p6utJpEBEREa14fFOBiIiIiIiIiHzCpgIRERERERER+YRNBSIiIiIiIiLyCZsKREREREREROQTNhWIiIiIiIiIyCfc/eE3WlpaoFKplE5j1eEz9152djays7OVToOIiIiIiFYRlYiI0kn8qaxWK4aGhpROg4iIaFklJSXBYDAonQYRERH9hdhUICIiIiIiIiKfcE0FIiIiIiIiIvIJmwpERERERERE5BM2FYiIiIiIiIjIJ2oAFqWTICIiIiIiIqK/z/8AC2sU8z2DRHwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "disc = Descriminator(2, 4)\n",
        "disc_blocks, fromRGB_blocks = disc.test()"
      ],
      "metadata": {
        "id": "XAr9n03l_iNZ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(disc_blocks[0], show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "L91FgXF8IeMF",
        "outputId": "635f575f-6fbc-4562-cb3b-53a29c734dfd"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(fromRGB_blocks[0], show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "EUAJeJNZIvi0",
        "outputId": "14916730-3abb-47fa-97d9-215ed8c64f6a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(disc_blocks[1], show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "gN9IwxWlI3NH",
        "outputId": "52a6d698-0981-4f9e-d4e1-d074e3f898c0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building StyleGAN with custom training loops"
      ],
      "metadata": {
        "id": "e9o9znE4NFXQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class styleGAN(tf.keras.Model):\n",
        "  def __init__(self, z_dim=512, start_res=4, target_res=64, **kwargs):\n",
        "    super(styleGAN, self).__init__(**kwargs)\n",
        "    self.z_dim = z_dim\n",
        "    self.start_res_log2 = int(np.log2(start_res))\n",
        "    self.target_res_log2 = int(np.log2(target_res))\n",
        "    self.current_res_log2 = self.target_res_log2\n",
        "    self.num_stages = self.target_res_log2 - self.start_res_log2 + 1\n",
        "\n",
        "    self.alpha = tf.Variable(1.0, dtype='float32', trainable=False, name='alpha')\n",
        "\n",
        "    self.mappingModel = MappingNetwork(self.num_stages, self.z_dim)\n",
        "    self.disc_builder = Descriminator(self.start_res_log2, self.target_res_log2)\n",
        "    self.gen_builder = Generator(self.start_res_log2, self.target_res_log2)\n",
        "    self.gen_input = self.gen_builder.input_shape\n",
        "\n",
        "    self.training_phase = None\n",
        "    self.train_step_counter = tf.Variable(0.0, dtype='float32', trainable=False, name='train_step_counter')\n",
        "\n",
        "    # self.loss_weights = {'gradient_penalty': 10, 'drift': 0.001}\n",
        "    self.loss_weights = {'gradient_penalty': 10}\n",
        "\n",
        "  def grow(self, resolution):\n",
        "    tf.keras.backend.clear_session()\n",
        "    res_log2 = int(np.log2(resolution))\n",
        "    self.disc_grow = self.disc_builder.grow(res_log2)\n",
        "    self.gen_grow = self.gen_builder.grow(res_log2)\n",
        "    self.current_res_log2 = res_log2\n",
        "    print(f\"Model_Grow_res_{resolution}X{resolution}\")\n",
        "\n",
        "  def compile(self, steps_per_epoch, training_phase, resolution, disc_optim, gen_optim, *args, **kwargs):\n",
        "    self.loss_weights = kwargs.pop(\"loss_weights\", self.loss_weights)\n",
        "    self.steps_per_epoch = steps_per_epoch\n",
        "    self.training_phase = training_phase\n",
        "    self.resolution = resolution\n",
        "    self.train_step_counter.assign(0)\n",
        "\n",
        "    if resolution != 2**self.current_res_log2:\n",
        "      self.grow(resolution)\n",
        "      self.disc_optim = disc_optim\n",
        "      self.gen_optim = gen_optim\n",
        "\n",
        "    self.disc_metrics = tf.keras.metrics.Mean(name='disc_metrics')\n",
        "    self.gen_metrics = tf.keras.metrics.Mean(name='gen_metrics')\n",
        "    super(styleGAN, self).compile(*args, **kwargs)\n",
        "\n",
        "  @property\n",
        "  def metrics(self):\n",
        "    return [self.gen_metrics, self.disc_metrics]\n",
        "\n",
        "  def generate_noise(self, batch_size):\n",
        "    noise = [tf.random.normal((batch_size, 2 ** res, 2 ** res, 1))\n",
        "            for res in range(self.start_res_log2, self.target_res_log2 + 1)\n",
        "    ]\n",
        "    return noise\n",
        "\n",
        "  def gradient_loss(self, grad):\n",
        "    loss = tf.square(grad)\n",
        "    loss = tf.reduce_sum(loss, axis = tf.range(1, tf.size(tf.shape(loss))))\n",
        "    loss = tf.sqrt(loss)\n",
        "    loss = tf.reduce_mean(tf.square(loss - 1))\n",
        "    return loss\n",
        "\n",
        "  def train_step(self, real_images):\n",
        "    self.train_step_counter.assign_add(1)\n",
        "\n",
        "    if(self.training_phase == 'Transition'):\n",
        "      self.alpha.assign(\n",
        "          tf.cast(self.train_step_counter / self.steps_per_epoch, tf.float32)\n",
        "      )\n",
        "    \n",
        "    elif(self.training_phase == 'Stable'):\n",
        "      self.alpha.assign(1.0)\n",
        "    \n",
        "    else:\n",
        "      raise NotImplementedError\n",
        "    \n",
        "    alpha = tf.expand_dims(self.alpha, axis = 0)\n",
        "    batch_size = tf.shape(real_images)[0]\n",
        "    real_labels = tf.ones(batch_size)\n",
        "    fake_labels = -tf.ones(batch_size)\n",
        "\n",
        "    constant_input = tf.ones((tuple([batch_size] + list(self.gen_input))))        #(batch_size x 4 x 4 x 512) in our case\n",
        "    z = tf.random.normal(shape = (batch_size, self.z_dim))                      #(batch_size x 512)\n",
        "    noise = self.generate_noise(batch_size)\n",
        "\n",
        "    with tf.GradientTape() as gen_tape:\n",
        "      w = self.mappingModel(z)\n",
        "      fake_images = self.gen_grow([constant_input, w, noise, alpha])\n",
        "      disc_pred_for_fake = self.disc_grow([fake_images, alpha])\n",
        "\n",
        "      gen_loss = WLoss(real_labels, disc_pred_for_fake)\n",
        "\n",
        "      trainable_weights = (\n",
        "          self.mappingModel.trainable_weights + self.gen_grow.trainable_weights\n",
        "      )\n",
        "\n",
        "      grads = gen_tape.gradient(gen_loss, trainable_weights)\n",
        "      self.gen_optim.apply_gradients(\n",
        "          zip(grads, trainable_weights)\n",
        "      )\n",
        "\n",
        "    with tf.GradientTape() as disc_tape, tf.GradientTape() as gradient_tape:\n",
        "      disc_pred_for_fake = self.disc_grow([fake_images, alpha])\n",
        "      disc_pred_for_real = self.disc_grow([real_images, alpha])\n",
        "\n",
        "      disc_loss_for_fake_pred = WLoss(fake_labels, disc_pred_for_fake)\n",
        "      disc_loss_for_real_pred = WLoss(real_labels, disc_pred_for_real)\n",
        "\n",
        "      epsilon = tf.random.uniform(shape = (batch_size, 1, 1, 1))\n",
        "      interpolations = epsilon * real_images + (1 - epsilon) * fake_images\n",
        "      disc_pred_for_fake_grads = self.disc_grow([interpolations, alpha])\n",
        "      gradient_tape.watch(interpolations)\n",
        "\n",
        "      gradients_fake = gradient_tape.gradient(disc_pred_for_fake_grads, [interpolations])\n",
        "\n",
        "      gradient_penalty = self.loss_weights[\"gradient_penalty\"] * self.gradient_loss(gradients_fake)\n",
        "\n",
        "      disc_loss = disc_pred_for_fake + disc_pred_for_real + gradient_penalty\n",
        "\n",
        "      disc_grads = disc_tape.gradient(disc_loss, self.disc_grow.trainable_weights)\n",
        "      self.disc_optim.apply_gradients(\n",
        "          zip(disc_grads, self.disc_grow.trainable_weights)\n",
        "      )\n",
        "\n",
        "      self.disc_metrics.update_state(disc_loss)\n",
        "      self.gen_metrics.update_state(gen_loss)\n",
        "\n",
        "      return {\n",
        "          'gen_loss': self.gen_metrics.result(),\n",
        "          'disc_loss': self.disc_metrics.result()\n",
        "      }\n",
        "\n",
        "\n",
        "  def call(self, inputs:dict()):\n",
        "    w = inputs.get('style_code', None)\n",
        "    z = inputs.get('z', None)\n",
        "    batch_size = inputs.get('batch_size', 1)\n",
        "    noise = inputs.get('noise', None)\n",
        "    alpha = tf.expand_dims(inputs.get('alpha', None), axis = 0)\n",
        "\n",
        "    if w is None:\n",
        "      if z is None:\n",
        "        z = tf.random.normal((batch_size, self.z_dim))\n",
        "      w = self.mappingModel(z)\n",
        "\n",
        "    constant_input = tf.ones(shape=(tuple([batch_size] + list(self.gen_input))))\n",
        "    images = self.gen_grow([constant_input, w, noise, alpha])\n",
        "    images = np.clip((0.5 * images + 0.5)*255, 9, 255).astype('uint8')\n",
        "\n",
        "    return images"
      ],
      "metadata": {
        "id": "Yuu_AHBuJD4e"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_the_StyleGAN(start_resolution, target_resolution, steps_per_epoch, display_images):\n",
        "  style_gan_model = styleGAN(z_dim = 512, start_res = start_resolution, target_res = target_resolution)\n",
        "\n",
        "  start_res_log2 = int(np.log2(start_resolution))\n",
        "  target_res_log2 = int(np.log2(target_resolution))\n",
        "\n",
        "  optimizer_config = {\n",
        "      \"learning_rate\": 1e-5,\n",
        "      \"beta_1\": 0.0,\n",
        "      \"beta_2\": 0.99,\n",
        "      \"epsilon\": 1e-8\n",
        "  }\n",
        "\n",
        "  val_batch_size = 16\n",
        "  val_z = tf.random.normal(shape=(val_batch_size, style_gan_model.z_dim))\n",
        "  val_noise = style_gan_model.generate_noise(val_batch_size)\n",
        "\n",
        "  for res_log2 in range(start_res_log2, target_res_log2 + 1):\n",
        "    res = 2 ** res_log2\n",
        "\n",
        "    for train_phase in ['Transition', 'Stable']:\n",
        "      if res == start_resolution and train_phase == 'Transition':\n",
        "        continue\n",
        "      \n",
        "      train_data_loader = data_loader(res)\n",
        "\n",
        "      steps = int(train_step_ratio[res_log2] * steps_per_epoch)\n",
        "\n",
        "      style_gan_model.compile(\n",
        "          steps_per_epoch = steps,\n",
        "          training_phase = train_phase,\n",
        "          resolution = res,\n",
        "          disc_optim = tf.keras.optimizers.Adam(**optimizer_config),\n",
        "          gen_optim = tf.keras.optimizers.Adam(**optimizer_config),\n",
        "          run_eagerly = False,\n",
        "      )\n",
        "\n",
        "      prefix = f\"res_{res}X{res}_{style_gan_model.training_phase}\"\n",
        "\n",
        "      style_gan_model.fit(train_data_loader, epochs = 1, steps_per_epoch = steps)"
      ],
      "metadata": {
        "id": "IUbPOqfuNLkU"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_the_StyleGAN(start_resolution = 4, target_resolution = 8, steps_per_epoch = 1, display_images = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbSDWPSWsATU",
        "outputId": "6042ce3e-10e6-447a-f9d6-b061c8e8f0dd"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model_Grow_res_4X4\n",
            "1/1 [==============================] - 6s 6s/step - gen_loss: -0.3247 - disc_loss: 297.8477\n",
            "Model_Grow_res_8X8\n",
            "1/1 [==============================] - 12s 12s/step - gen_loss: 0.1430 - disc_loss: 10.5548\n",
            "1/1 [==============================] - 9s 9s/step - gen_loss: nan - disc_loss: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CH8j9v_nsuh3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}