Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Oct 12 10:29:37 2018       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 396.44                 Driver Version: 396.44                    |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  GeForce GTX 108...  On   | 00000000:04:00.0 Off |                  N/A |\n",
      "| 23%   29C    P0    64W / 250W |      0MiB / 11178MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  GeForce GTX 108...  On   | 00000000:06:00.0 Off |                  N/A |\n",
      "| 23%   29C    P8    17W / 250W |      0MiB / 11178MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   2  Tesla P40           On   | 00000000:07:00.0 Off |                    0 |\n",
      "| N/A   25C    P8     9W / 250W |      0MiB / 22919MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   3  GeForce GTX 108...  On   | 00000000:08:00.0 Off |                  N/A |\n",
      "| 23%   30C    P8    16W / 250W |      0MiB / 11178MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   4  GeForce GTX 108...  On   | 00000000:0C:00.0 Off |                  N/A |\n",
      "| 23%   31C    P8    16W / 250W |      0MiB / 11178MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   5  GeForce GTX 108...  On   | 00000000:0D:00.0 Off |                  N/A |\n",
      "| 23%   33C    P5    20W / 250W |  10631MiB / 11178MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   6  GeForce GTX 108...  On   | 00000000:0E:00.0 Off |                  N/A |\n",
      "| 23%   34C    P5    20W / 250W |  10631MiB / 11178MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   7  GeForce GTX 108...  On   | 00000000:0F:00.0 Off |                  N/A |\n",
      "| 23%   34C    P5    21W / 250W |  10631MiB / 11178MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "|    5     13653      C   ...eanyu/.conda/envs/tf18_keras/bin/python 10621MiB |\n",
      "|    6     13653      C   ...eanyu/.conda/envs/tf18_keras/bin/python 10621MiB |\n",
      "|    7     13653      C   ...eanyu/.conda/envs/tf18_keras/bin/python 10621MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=0\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
    "%env CUDA_VISIBLE_DEVICES=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jimmy15923/.conda/envs/tf18_keras/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape:\n",
      "(733, 512, 512, 3)\n",
      "(184, 512, 512, 3)\n",
      "(733, 512, 512, 1)\n",
      "(184, 512, 512, 1)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img (InputLayer)                (None, 512, 512, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_290 (Conv2D)             (None, 512, 512, 16) 448         img[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 512, 512, 16) 64          conv2d_290[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 512, 512, 16) 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_291 (Conv2D)             (None, 512, 512, 16) 2320        activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 512, 512, 16) 64          conv2d_291[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 512, 512, 16) 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_60 (MaxPooling2D) (None, 256, 256, 16) 0           activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 256, 256, 16) 0           max_pooling2d_60[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_292 (Conv2D)             (None, 256, 256, 32) 4640        dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 256, 256, 32) 128         conv2d_292[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 256, 256, 32) 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_293 (Conv2D)             (None, 256, 256, 32) 9248        activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 256, 256, 32) 128         conv2d_293[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 256, 256, 32) 0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_61 (MaxPooling2D) (None, 128, 128, 32) 0           activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 128, 128, 32) 0           max_pooling2d_61[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_294 (Conv2D)             (None, 128, 128, 64) 18496       dropout_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 128, 128, 64) 256         conv2d_294[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 128, 128, 64) 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_295 (Conv2D)             (None, 128, 128, 64) 36928       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 128, 128, 64) 256         conv2d_295[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 128, 128, 64) 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_62 (MaxPooling2D) (None, 64, 64, 64)   0           activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_10 (Dropout)            (None, 64, 64, 64)   0           max_pooling2d_62[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_296 (Conv2D)             (None, 64, 64, 128)  73856       dropout_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 64, 64, 128)  512         conv2d_296[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 64, 64, 128)  0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_297 (Conv2D)             (None, 64, 64, 128)  147584      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 64, 64, 128)  512         conv2d_297[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 64, 64, 128)  0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_63 (MaxPooling2D) (None, 32, 32, 128)  0           activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_11 (Dropout)            (None, 32, 32, 128)  0           max_pooling2d_63[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_298 (Conv2D)             (None, 32, 32, 256)  295168      dropout_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 32, 32, 256)  1024        conv2d_298[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 32, 32, 256)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_299 (Conv2D)             (None, 32, 32, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 32, 32, 256)  1024        conv2d_299[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 32, 32, 256)  0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_60 (Conv2DTran (None, 64, 64, 128)  295040      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_60 (Concatenate)    (None, 64, 64, 256)  0           conv2d_transpose_60[0][0]        \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_12 (Dropout)            (None, 64, 64, 256)  0           concatenate_60[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_300 (Conv2D)             (None, 64, 64, 128)  295040      dropout_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 64, 64, 128)  512         conv2d_300[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 64, 64, 128)  0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_301 (Conv2D)             (None, 64, 64, 128)  147584      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 64, 64, 128)  512         conv2d_301[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 64, 64, 128)  0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_61 (Conv2DTran (None, 128, 128, 64) 73792       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_61 (Concatenate)    (None, 128, 128, 128 0           conv2d_transpose_61[0][0]        \n",
      "                                                                 activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_13 (Dropout)            (None, 128, 128, 128 0           concatenate_61[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_302 (Conv2D)             (None, 128, 128, 64) 73792       dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 128, 128, 64) 256         conv2d_302[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 128, 128, 64) 0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_303 (Conv2D)             (None, 128, 128, 64) 36928       activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 128, 128, 64) 256         conv2d_303[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 128, 128, 64) 0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_62 (Conv2DTran (None, 256, 256, 32) 18464       activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_62 (Concatenate)    (None, 256, 256, 64) 0           conv2d_transpose_62[0][0]        \n",
      "                                                                 activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_14 (Dropout)            (None, 256, 256, 64) 0           concatenate_62[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_304 (Conv2D)             (None, 256, 256, 32) 18464       dropout_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 256, 256, 32) 128         conv2d_304[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 256, 256, 32) 0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_305 (Conv2D)             (None, 256, 256, 32) 9248        activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 256, 256, 32) 128         conv2d_305[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 256, 256, 32) 0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_63 (Conv2DTran (None, 512, 512, 16) 4624        activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_63 (Concatenate)    (None, 512, 512, 32) 0           conv2d_transpose_63[0][0]        \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_15 (Dropout)            (None, 512, 512, 32) 0           concatenate_63[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_306 (Conv2D)             (None, 512, 512, 16) 4624        dropout_15[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 512, 512, 16) 64          conv2d_306[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 512, 512, 16) 0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_307 (Conv2D)             (None, 512, 512, 16) 2320        activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 512, 512, 16) 64          conv2d_307[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 512, 512, 16) 0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_308 (Conv2D)             (None, 512, 512, 1)  17          activation_35[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 2,164,593\n",
      "Trainable params: 2,161,649\n",
      "Non-trainable params: 2,944\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import pandas \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import shutil\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import tensorflow as tf\n",
    "import sys\n",
    "# sys.path.append('keras-deeplab-v3-plus/')\n",
    "# from deeplab_v3_plus.model import *\n",
    "\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "import skimage\n",
    "from skimage import io, img_as_bool\n",
    "from skimage.transform import resize\n",
    "\n",
    "# config\n",
    "crop_size = 512\n",
    "batch_size = 8\n",
    "\n",
    "def conv2d_block(input_tensor, n_filters, kernel_size=3, batchnorm=True):\n",
    "    # first layer\n",
    "    x = tf.keras.layers.Conv2D(filters=n_filters, kernel_size=(kernel_size, kernel_size),\n",
    "                               kernel_initializer=\"he_normal\", padding=\"same\")(input_tensor)\n",
    "    if batchnorm:\n",
    "        x = tf.keras.layers.BatchNormalization()(x)\n",
    "    x = tf.keras.layers.Activation(\"relu\")(x)\n",
    "    # second layer\n",
    "    x = tf.keras.layers.Conv2D(filters=n_filters, kernel_size=(kernel_size, kernel_size), kernel_initializer=\"he_normal\",\n",
    "               padding=\"same\")(x)\n",
    "    if batchnorm:\n",
    "        x = tf.keras.layers.BatchNormalization()(x)\n",
    "    x = tf.keras.layers.Activation(\"relu\")(x)\n",
    "    return x\n",
    "\n",
    "def get_unet(input_img, n_filters=16, dropout=0.5, batchnorm=True):\n",
    "    # contracting path\n",
    "    c1 = conv2d_block(input_img, n_filters=n_filters*1, kernel_size=3, batchnorm=batchnorm)\n",
    "    p1 = tf.keras.layers.MaxPooling2D((2, 2)) (c1)\n",
    "    p1 = tf.keras.layers.Dropout(dropout*0.5)(p1)\n",
    "\n",
    "    c2 = conv2d_block(p1, n_filters=n_filters*2, kernel_size=3, batchnorm=batchnorm)\n",
    "    p2 = tf.keras.layers.MaxPooling2D((2, 2)) (c2)\n",
    "    p2 = tf.keras.layers.Dropout(dropout)(p2)\n",
    "\n",
    "    c3 = conv2d_block(p2, n_filters=n_filters*4, kernel_size=3, batchnorm=batchnorm)\n",
    "    p3 = tf.keras.layers.MaxPooling2D((2, 2)) (c3)\n",
    "    p3 = tf.keras.layers.Dropout(dropout)(p3)\n",
    "\n",
    "    c4 = conv2d_block(p3, n_filters=n_filters*8, kernel_size=3, batchnorm=batchnorm)\n",
    "    p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2)) (c4)\n",
    "    p4 = tf.keras.layers.Dropout(dropout)(p4)\n",
    "    \n",
    "    c5 = conv2d_block(p4, n_filters=n_filters*16, kernel_size=3, batchnorm=batchnorm)\n",
    "    \n",
    "    # expansive path\n",
    "    u6 = tf.keras.layers.Conv2DTranspose(n_filters*8, (3, 3), strides=(2, 2), padding='same') (c5)\n",
    "    u6 = tf.keras.layers.concatenate([u6, c4])\n",
    "    u6 = tf.keras.layers.Dropout(dropout)(u6)\n",
    "    c6 = conv2d_block(u6, n_filters=n_filters*8, kernel_size=3, batchnorm=batchnorm)\n",
    "\n",
    "    u7 = tf.keras.layers.Conv2DTranspose(n_filters*4, (3, 3), strides=(2, 2), padding='same') (c6)\n",
    "    u7 = tf.keras.layers.concatenate([u7, c3])\n",
    "    u7 = tf.keras.layers.Dropout(dropout)(u7)\n",
    "    c7 = conv2d_block(u7, n_filters=n_filters*4, kernel_size=3, batchnorm=batchnorm)\n",
    "\n",
    "    u8 = tf.keras.layers.Conv2DTranspose(n_filters*2, (3, 3), strides=(2, 2), padding='same') (c7)\n",
    "    u8 = tf.keras.layers.concatenate([u8, c2])\n",
    "    u8 = tf.keras.layers.Dropout(dropout)(u8)\n",
    "    c8 = conv2d_block(u8, n_filters=n_filters*2, kernel_size=3, batchnorm=batchnorm)\n",
    "\n",
    "    u9 = tf.keras.layers.Conv2DTranspose(n_filters*1, (3, 3), strides=(2, 2), padding='same') (c8)\n",
    "    u9 = tf.keras.layers.concatenate([u9, c1], axis=3)\n",
    "    u9 = tf.keras.layers.Dropout(dropout)(u9)\n",
    "    c9 = conv2d_block(u9, n_filters=n_filters*1, kernel_size=3, batchnorm=batchnorm)\n",
    "    \n",
    "    outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid') (c9)\n",
    "    model = tf.keras.models.Model(inputs=[input_img], outputs=[outputs])\n",
    "    return model\n",
    "\n",
    "def image_pad(image):\n",
    "    h, w, _ = image.shape\n",
    "    # Height\n",
    "    if h % 64 > 0:\n",
    "        max_h = h - (h % 64) + 64\n",
    "        top_pad = (max_h - h) // 2\n",
    "        bottom_pad = max_h - h - top_pad\n",
    "    else:\n",
    "        top_pad = bottom_pad = 0\n",
    "    # Width\n",
    "    if w % 64 > 0:\n",
    "        max_w = w - (w % 64) + 64\n",
    "        left_pad = (max_w - w) // 2\n",
    "        right_pad = max_w - w - left_pad\n",
    "    else:\n",
    "        left_pad = right_pad = 0\n",
    "    padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]\n",
    "    image = np.pad(image, padding, mode='constant', constant_values=0)\n",
    "    window = (top_pad, left_pad, h + top_pad, w + left_pad)\n",
    "    return image\n",
    "\n",
    "def read_data_and_split(split_seed, train_ratio, is_normalize=True, is_resize=crop_size):\n",
    "    \"\"\"read data into np array, normalize it and train test split\n",
    "    \n",
    "    split_seed: set seed for same train test split\n",
    "    train_ratio: ratio of training set. range from 0 to 1\n",
    "    is_normalize: True for normalizr to -1 to 1\n",
    "    \n",
    "    return: x_train, x_test, y_train, y_test\n",
    "    \"\"\"\n",
    "    \n",
    "    idx = next(os.walk('dataset/'))[1]\n",
    "    # remove two file with different size between image & mask\n",
    "    idx.remove(\"S2016-30816_9_0\")\n",
    "    idx.remove(\"S2016-30816_9_1\")\n",
    "    \n",
    "    # set seed\n",
    "    random.seed(split_seed)\n",
    "    random.shuffle(idx)\n",
    "    \n",
    "    train_idx, test_idx = idx[:int(len(idx)*train_ratio)], idx[int(len(idx)*train_ratio):]\n",
    "\n",
    "    x_train = np.array([skimage.io.imread('dataset/{}/image/{}_slide.jpg'.format(x, x))\\\n",
    "                    for x in train_idx], dtype=\"float32\")\n",
    "    x_test = np.array([skimage.io.imread('dataset/{}/image/{}_slide.jpg'.format(x, x))\\\n",
    "                       for x in test_idx], dtype=\"float32\")\n",
    "    \n",
    "    if is_normalize:\n",
    "        x_train = (x_train / 127.5) - 1\n",
    "        x_test = (x_test / 127.5) - 1\n",
    "        \n",
    "    y_train = np.array([skimage.io.imread('dataset/{}/mask/{}_mask.jpg'.format(x, x))\\\n",
    "                    for x in train_idx])\n",
    "    \n",
    "    y_test = np.array([skimage.io.imread('dataset/{}/mask/{}_mask.jpg'.format(x, x))\\\n",
    "                        for x in test_idx])\n",
    "    \n",
    "    y_train = np.expand_dims(y_train, 3)\n",
    "    y_test = np.expand_dims(y_test, 3)\n",
    "    \n",
    "    def arr_resize(array):\n",
    "        return np.array([resize(x, (crop_size, crop_size)) for x in array])\n",
    "    \n",
    "    def arr_pad(array):\n",
    "        return np.array([image_pad(x) for x in array])\n",
    "\n",
    "    if is_resize:\n",
    "        if is_resize <= 1000:\n",
    "            x_train, x_test, y_train, y_test = arr_resize(x_train), arr_resize(x_test), arr_resize(y_train), arr_resize(y_test)\n",
    "        else:       \n",
    "            x_train, x_test, y_train, y_test = arr_pad(x_train), arr_pad(x_test), arr_pad(y_train), arr_pad(y_test)\n",
    "    \n",
    "    return x_train, x_test, y_train, y_test\n",
    "\n",
    "def data_gen(x_train, y_train, bz, augmentation=None):\n",
    "    i = 0\n",
    "    from sklearn.utils import shuffle\n",
    "    while True:\n",
    "#         if i == len(y_train) // bz:\n",
    "#             i = 0\n",
    "#             x_train, y_train = shuffle(x_train, y_train)\n",
    "            \n",
    "#         x_, y_ = x_train[i*bz:(i+1)*bz], y_train[i*bz:(i+1)*bz]\n",
    "        img_idx = np.random.choice(range(len(y_train)), bz, replace=False)\n",
    "        i += 1\n",
    "        yield x_train[img_idx], y_train[img_idx]\n",
    "        \n",
    "def IOU_cal(y_true, y_pred):\n",
    "    iou_res = []\n",
    "    for i in range(len(y_true)):\n",
    "        y_true_flat = y_true[i].ravel()\n",
    "        y_pred_flat = (y_pred[i][...,0].ravel() > 0.5) * 1\n",
    "        intersection = np.sum(y_true_flat * y_pred_flat)\n",
    "        union = np.sum((y_true_flat+y_pred_flat) - (y_true_flat*y_pred_flat))\n",
    "        iou = intersection / union\n",
    "        iou_res.append(iou)\n",
    "    return iou_res     \n",
    "\n",
    "\n",
    "x_train, x_test, y_train, y_test = read_data_and_split(split_seed=7, train_ratio=0.8, is_resize=crop_size)\n",
    "\n",
    "print(\"Data shape:\")\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "def dice_metric(y_true, y_pred, smooth = 1.):   \n",
    "    y_true_f = tf.keras.backend.flatten(y_true)\n",
    "    y_pred_f = tf.keras.backend.flatten(y_pred)\n",
    "    intersection = tf.keras.backend.sum(y_true_f * y_pred_f)    \n",
    "    return (2.*intersection + smooth)  / (tf.keras.backend.sum(y_true_f) + tf.keras.backend.sum(y_pred_f) + smooth)\n",
    "\n",
    "def dice_loss(y_true, y_pred):\n",
    "    return 1 - dice_metric(y_true, y_pred)\n",
    "\n",
    "input_img = tf.keras.layers.Input((crop_size, crop_size, 3), name='img')\n",
    "model = get_unet(input_img, n_filters=16, dropout=0.05, batchnorm=True)\n",
    "\n",
    "model.compile(optimizer=\"Adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
    "\n",
    "early = tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=12, verbose=1)\n",
    "check = tf.keras.callbacks.ModelCheckpoint(monitor=\"val_loss\",\n",
    "                                        filepath=\"test_resize.h5\",\n",
    "                                        verbose=1, save_best_only=True)\n",
    "reduce = tf.keras.callbacks.ReduceLROnPlateau(patience=3)\n",
    "\n",
    "t = time.time()\n",
    "history = model.fit(x_train, y_train, batch_size=batch_size,\n",
    "                    epochs=10, \n",
    "                    validation_data=(x_test, y_test),\n",
    "                    callbacks=[early, check, reduce]\n",
    "                   )\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.savefig(\"learning_curve.png\", dpi=500)\n",
    "\n",
    "model = tf.keras.models.load_model(\"test_resize.h5\", custom_objects = {'mean_iou': mean_iou,\n",
    "                                                                       'dice_metric': dice_metric})\n",
    "## inference\n",
    "_, x_test, _, y_test = read_data_and_split(split_seed=7, train_ratio=0.8, is_resize=crop_size)\n",
    "\n",
    "y_pred = model.predict(x_test, batch_size=batch_size)\n",
    "\n",
    "res_iou = IOU_cal(y_test, y_pred)\n",
    "print(\"TESTING IOU: \",np.mean(res_iou))\n",
    "print(\"-\"*10)\n",
    "print(\"Elapse time:\", time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 733 samples, validate on 184 samples\n",
      "Epoch 1/10\n",
      "733/733 [==============================] - 48s 65ms/step - loss: 0.3538 - acc: 0.9115 - val_loss: 0.1936 - val_acc: 0.9616\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.19363, saving model to test_resize.h5\n",
      "Epoch 2/10\n",
      "733/733 [==============================] - 33s 46ms/step - loss: 0.1987 - acc: 0.9670 - val_loss: 0.2004 - val_acc: 0.9692\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.19363\n",
      "Epoch 3/10\n",
      "733/733 [==============================] - 33s 45ms/step - loss: 0.1539 - acc: 0.9672 - val_loss: 0.1434 - val_acc: 0.9692\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.19363 to 0.14337, saving model to test_resize.h5\n",
      "Epoch 4/10\n",
      "733/733 [==============================] - 32s 44ms/step - loss: 0.1291 - acc: 0.9673 - val_loss: 0.1202 - val_acc: 0.9692\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.14337 to 0.12018, saving model to test_resize.h5\n",
      "Epoch 5/10\n",
      "733/733 [==============================] - 32s 43ms/step - loss: 0.1177 - acc: 0.9672 - val_loss: 0.1931 - val_acc: 0.9692\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.12018\n",
      "Epoch 6/10\n",
      "733/733 [==============================] - 32s 44ms/step - loss: 0.1049 - acc: 0.9673 - val_loss: 0.1464 - val_acc: 0.9691\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.12018\n",
      "Epoch 7/10\n",
      "733/733 [==============================] - 32s 44ms/step - loss: 0.0956 - acc: 0.9677 - val_loss: 0.4883 - val_acc: 0.7069\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.12018\n",
      "Epoch 8/10\n",
      "733/733 [==============================] - 30s 41ms/step - loss: 0.0841 - acc: 0.9703 - val_loss: 0.1016 - val_acc: 0.9587\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.12018 to 0.10157, saving model to test_resize.h5\n",
      "Epoch 9/10\n",
      "733/733 [==============================] - 31s 42ms/step - loss: 0.0798 - acc: 0.9713 - val_loss: 0.0734 - val_acc: 0.9730\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.10157 to 0.07336, saving model to test_resize.h5\n",
      "Epoch 10/10\n",
      "733/733 [==============================] - 30s 41ms/step - loss: 0.0786 - acc: 0.9717 - val_loss: 0.0797 - val_acc: 0.9705\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.07336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jimmy15923/.conda/envs/tf18_keras/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TESTING IOU:  0.20045580943079236\n",
      "----------\n",
      "Elapse time: 494.84544372558594\n"
     ]
    }
   ],
   "source": [
    "t = time.time()\n",
    "model.fit(x_train, y_train, batch_size=batch_size,\n",
    "                    epochs=10, \n",
    "                    validation_data=(x_test, y_test),\n",
    "                    callbacks=[early, check, reduce]\n",
    "                   )\n",
    "\n",
    "model = tf.keras.models.load_model(\"test_resize.h5\", custom_objects = {'mean_iou': mean_iou,\n",
    "                                                                       'dice_metric': dice_metric})\n",
    "## inference\n",
    "_, x_test, _, y_test = read_data_and_split(split_seed=7, train_ratio=0.8, is_resize=crop_size)\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "res_iou = IOU_cal(y_test, y_pred)\n",
    "print(\"TESTING IOU: \",np.mean(res_iou))\n",
    "print(\"-\"*10)\n",
    "print(\"Elapse time:\", time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test, batch_size=batch_size)\n",
    "\n",
    "res_iou = IOU_cal(y_test, y_pred)\n",
    "print(\"TESTING IOU: \",np.mean(res_iou))\n",
    "print(\"-\"*10)\n",
    "print(\"Elapse time:\", time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_pad(image):\n",
    "    h, w, _ = image.shape\n",
    "    # Height\n",
    "    if h % 64 > 0:\n",
    "        max_h = h - (h % 64) + 64\n",
    "        top_pad = (max_h - h) // 2\n",
    "        bottom_pad = max_h - h - top_pad\n",
    "    else:\n",
    "        top_pad = bottom_pad = 0\n",
    "    # Width\n",
    "    if w % 64 > 0:\n",
    "        max_w = w - (w % 64) + 64\n",
    "        left_pad = (max_w - w) // 2\n",
    "        right_pad = max_w - w - left_pad\n",
    "    else:\n",
    "        left_pad = right_pad = 0\n",
    "    padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]\n",
    "    image = np.pad(image, padding, mode='constant', constant_values=0)\n",
    "    window = (top_pad, left_pad, h + top_pad, w + left_pad)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_test = image_pad(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f004eb2bcf8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(536, 536, 3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jimmy15923/.conda/envs/tf18_keras/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TESTING IOU:  0.14717587759955159\n",
      "----------\n",
      "Elapse time: 520.3622889518738\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model(\"test_resize.h5\", custom_objects = {'dice_loss': dice_loss,\n",
    "                                                                       'dice_metric': dice_metric})\n",
    "## inference\n",
    "_, x_test, _, y_test = read_data_and_split(split_seed=7, train_ratio=0.8, is_resize=1000)\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "res_iou = IOU_cal(y_test, y_pred)\n",
    "print(\"TESTING IOU: \",np.mean(res_iou))\n",
    "print(\"-\"*10)\n",
    "print(\"Elapse time:\", time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0104563048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XN8+xojd7iCUeiEHO2Onz6UkAZnc9mF3yJ1bafZh68g9muW7n3f1wAHbxiSUjdXl+ej8bMh7yriCNeLI5wT4usAcrVHmadoZiZT51daVYJgt8XPQA7xWeJHOXPvhzdfZxSlBSiCbvA/fzNTyYJqyJBLaIf0eCnEcPgplIX7pgiAwlmwjjDFF84AO5xFL5IJOByy9QAERTyp+c4DKJgB3TOu2nKC8Vc5EZF3iNlpYlf+Qdw9a6KzI8uXv+Eukk0bZrLxrLGihsrCJep4Tep+fgNGAy5hs6o3myG0majWTJq9jITA4QSgxR7N7xjb3cJ56qv+qgC/rISCGl7DxhKw5iIBOj0aTkMxGMtRrJAd+5lGXEkfPyDYoHn3G1skW7pI6utAcgFgVTaYlfnh6zhzfHu8GEJOKJz05hRvsZ7GIwCYTT9D6dvtu6YVCvg5qlKcWmahSW5yDSM0CMgjzikFCLCgpENKFJI6YSIwyQcGDvHpZNWk9ffNkv+oUUDAHoTx8cMcKRroRxkpqiLK4HXGNJ3n58e/ZikcYM1NAnl1qmsZTTfMFSQ4QN6mQi5xNJfG2IJch8AsvoSzHqXCUbOTUkkIwNdrzjHp/3XMedNuzIuYC4ToRGVRkTt6+n76PJ+G7xJAe4QAVLgbIjD7ENr0TP1haLyEomuUiZhTZz0GAsejjVG5F84DYLGE9Eu0u4kMFjw5n44Yk2pjhjjXPX0bRqbkVb5EyhN7ttFgF17Pp2jSIM8MYOXXQAaD0ggALVOoxpR5qtO3ZDxzA5ZSYjnu5hy+tUpl2I/Fs2/N/uSvyv0EykK+yRTsOhvTuOM7qz++gl8sNjUKeGBCp4RDiJUz/hcrotxR43MYkdgirQCxEPfoxrXrQ9jpAZSzBPCMCay4Szwn0vuiP1cOrjQ3l2E9LqEt4fT+BVRAjVfKMD7uhjzKgb+/h88xELzxygm2NnhoaMxNjQgAaZAhU1BYu8d/Cw4SzDCeQBxwllBtu4QQQlXLr0hoJ9H5g2bTI+gXac2RtL64VufH0eTVn3J1wTLpPMJ3rjzjMieYvyh2cJZujwcNET0vfcZihbGYon0+kJ0/vgoWLJy7sP2Zt3nUmSbjhqWVNZ/pksFFRQjAx7pBpiLJW1KLQ16DTQl5LodGJjwhg4dwPbQq+TWvaC2QOWYqcHb0L+ZBvh/Mk40nmDPjas5C1d0cQYuIuc37TW8nvNDrJRMAI9pjksJbW6geKiSgZP70Hy1zjKPsTylIfMCdiAan4N1XIR+iVKfEu/x7FdtVtL+x5tuHoyDCl1xBCKAW2YsSGEExum4IkxrbceQy87jjwdOdWJhShqlfR+PAc9dNjKEJxa+1IY9QUZcmrJRRsZMhzRwQpjmpCbiplXdIxZNEcDGwyARprxklsE0B9j8iiiiiDus19/HZUVMVQjo52KCxfVxPy2aiqHNwbTCRE5KBFTSh5KsimmI4XM5XvIzRa8eUAiqtQzEj8akJNGLNdwxgE1BmGKCQ7UIUYX0EaDIWzADy1eUYM/qihQss7wMJvK1rOCidziFpo0oUoV1rijRwdKiEADDUJ4xUcauL/5I+XrthOHPkriyUaL8zzmU1IGbZ3tiAkOo9WUgfhhylKHDcxI+4VCIH7ZXUrN9DFtLqJJw4iXwbHoX8ginUg6b1+A36r2//tcif8VKqgmpf4R21/PQpVG/FcOx8G/OynE08XCj6QLCZQO1Aba0iN2CNvphRKYQ9BfafjM86A5LnTAgR4e0xnKAAbFTePm8U8s7TyTFqMtuT19N3ERu/lFZygaiOjXdyWO3u5oDtWly5kRXNt3jennZ6LQVEElt476t+aU389jSItegMB4F29etjnFI6LRpBRNOnN2dDAKwyYOB/9J6PBnXF11iFW6Y7jRbT5hwiM66fQigkYWEUEYSrrjgiNSAKoB9ZpykIq4zAT88eQd6Qw8OYS3Obdps6I30URgpCuhsPwbWWgyyro9NtateM4zZLVpqBppIHW24suZ56THvKEOKL31iN/2D2XZ+kOsuLOBtPRqqn/I0QtisMWbNMrI6X6NQAIoRc4UWvCs5gRyRIzEhiIqSUx7zPyizZzkAcdPhmFfZUsSAtco5uG9K+gYmGI9ygMLexPm04EHrGBXxhbmnxzGAx4zl1DKUUeDJh4dOsviwI1k8ZFHa0byKfIDjWIx6n6GmHW3BqAZVqhggigqHYVIile3vogQ40pLjFGirychgUomF23lF5x5hxIJjXRy6YG9WIsONCObJLKpx9K0LTtYiKKiFBujtgQTx8imo4yyt8N5Y392sx+JpT1S9AjmBde5TBcMyKWWP+jIdDRRwZw3VPGSesQ0UI8+OkiwQcpbwpHgClTR2sEAGeXkcgU1dHhFDQAvUfIW6Fv2C4Yo+MhbetIXZ7xpQWuW8YaZ7OIOn5i3+E8c+f4lUV1Rj4aJHWe5w2HC6WM5jpi7MdQ1fncHzB2bk/+ulObYkJR2k6EMJ2TkAeKUMgwcDUkuKMNBbk9DCiTxkIBRfkRXJP4tm/yPEAYrqRXd6YSAIX6T3GlhpI3/oc70oh2duziz/9IBNBzSSH5zgTyakYMeW/HBa8wg1qlPI/lKErVuEsK4g76xFZ/8VKky0mcb06nKO00b8lhKF0K5xgEiuC87gSt9af9gOiUO+gAMYQIvom9ShpyK2GperXnOtKAx/Bl8g0WfVnKaVRQlJ/GyOgUTtLBEIO/Uc8TWEobOHcOKN6tp8tFla+1vTJuzlHSKMUFMiewVQ350twF80eNXRvDO7U9ejz9Op2OjOFgfhnXrEVhrd0AfFxzpit/k+ShuJXCMaZibmiI1ciCeFO7kvELIyePUqB10a9MPj1ad8JMJqCFiFzdpII9HObcJm3CaFr3hwtI/mRc+l6IfjTWUWB4RjoRiljybRRMy9jU7iSESpjEUHRppiwWOtCadd7ycf5ejU/7gKCfYlXCCvYRghCq/84I+z6bwdvOflP3iihgZs/iNd8At4As5vO5xjrb4ML/jeoyK84h8l4AlAaSSSOmn28jj8inNV0NsaMTJnoc50mU1bVu1oNHKkCyhknPPLyNCj2doEMQdblemcp/vw1XNsCOAZghY8CIxEomZGmrYcpF7mGCEb/8+dOzWnnJS6L9gCKOlgQBIimt51Wk3UVtSeZF3iQ5mxsxgLLtFs8lGQh1dKaSOakxZTRglV2SYAG8pwQIJ+yliIPmEMB07qRa6mBOa9pZSEwdCsUaBHgBb8WDSX7UuYhj9SCaOu4SSTCY6uP51NBoQ73ZEFRXG4wUbQ/FfMYdB+NEfHybmTcXM3AR11XJSrqZj5uvKH52COMknrOhIdxtX2vl6oVCT01RXz5vTT9m3bAvzPwzjOskkvChmQttRf8sm/yNcCX2RupB4IoHpgfb44kgyqVjbTmb9+ePo+epQTT1p37IRScWo10gxk+rRw60l+/qs4O3D6zhhjP3DjVRX5uI1qjOzmEwxuVznPutZwwEOc0ZrA/drHuCJHd1WTcN9e2daMYizfXvR6sFsji+5gs98HySl9Xy9mc7HzQ/RcNKi54gAOu5oxx+aS1GVZzMp7BCx997z9dg9RMpGfOaMZ9ORgVRRxerBF0i1qaPVOykR6eF0Pb6MxEsveXh5B3YY00AV5phQjDr65q4MXDeZjwc+EJ5wmWn4c4gTVFBEOPVk0QjAcWairqrPHmUMK9tMpdpAwTBve07uvEpc02t0sGT0mF8oVyoQqzfQtbsn0eXlPFq8HxWq8Dw3C3u7Vlz17YGAHhUUAzqUUUs82VghoT+DkCMmhrtcp4ZK5LRABRuaWNJxO2m+Jsz4PZCQlocwkWiTVZFGfkohnTWbUSOPw75XZzS0RHwOi2Ox8gBawAOn/fRNCSIXcAAm0oVyEtHBDSc8eMEzbLFnOY/QphlXuy4m9/UXzEysaVCIqayooEFRjR1aHOcrtdiQhoxs4tjcaipNMXexQZdpvGCOdBxTJo/g6bFrBHEQgBvMQdrOA3VvUxw1jRE3aXNkzw42c53zHruYELuEtvSgMx4cYD9HVVbR6OKIV309z9MukU4KZ8ijO2ZkUsiuljtY8eUk8STTC1X86YKuQUeCyndjiBNzcKe9dyeCI3dxg3y6Y89hu5msywhDGxts0SedFB7wlM6IaEMHUoinP0MYxRkAhmFOBgUkAha0ZO3obXy+tA8tEthONmY40EfcgrONtzjHHBLJxNfMnz8KT/CcSu4tuoV1fxPMtPSIvxjByX37GUFbXnKT/r7b8X899N92Jf4jhEEiEglXev5Kh/VBRI07j1FOASe4yhiPCfitWIblJH2+vE0jVVeOobYe7vZmACQ/LUOSKSdk0XbqKr8xdvkG3HYGAqkcH30SUY0S6ZM03td9pTPW2DbzRKOdFfWqDXS6MgaA1Y5ryUuNZkjH7qj0dyP1czwdXPz4fcdWivlEA3n0Ioit7GYp7XDX7IClty2amXV8zHhDB6+J6Mpr2Jq0hTb4ETh8MRlCDlYtLNkfcpSglkP5kCenIeIlHi07YuLbimffniHYWjPq7EgA1IFBQCkQQG8yKeMMH5mJL65YUuPWGhv/tlw5epoyHX3GmWiTlKZgC4/wpznm5FFFHU4WLai0M+Tsu/38arCO1PK7HNq6A5l/a9Z06YScYjKppDtdSKWSIr4SgDf51DBGaxqZNUkcJpSXyPiVvtRSRl+HIRQMssGsWoVfT2xjZ/NAsmoKsbVwojGnnLjC97Q2bEXdCm+qi+rQ3fUBOQWEcod3FJHI90CWbcxjGQdxBTYwg0aa2MhJ7FlEIukscG6NrdKQalkBs4rOMoDu+Np5UJWRTCl1lGuZ8UfNU7RQ48jgWShufeAbcVQjRoQxR7lCsmcwO7695QZP+JWWtHLsikqfFpREfSI2q5iunbvx7sp5Ouq1JrYymeYSa0oaUsltZk/3Zk60fjMRMOUGA5Ei5jVhKPFnJxfIdtyD0f6p1BnI2d15Po4UU0w5zoP2MOT2QG4GnGHIvVGMwYKL5NMZaIsYB0Yh6+yFLFnO1NljcN/swgTUaMSOLnjyhdeIkdJAHj3oyw0iuEwFbrgSTwKPpzziQ/AudvAWa8xpiyPniSMQY04QhQv2dMMdGyx5TzK3ecEYXJGiSi86c5EHSLDhOuHwNz5X/kcIg5lILAShSYs+KzFO0CYh8wF5FPGUj5gC5x6W8+DXCAZH9CMtPpK85CoMzE1QN5NgWNNEgayGikpVvNWbE+Q3GIFSthy/z7XfzqJM+UQW6SxZdpGQ33/Hw70NEXFvWMu5/0sZTDHBEnO6YIgmxuzkGoaIKUMKVNOfaXyhnGxuYUQTv7KEIHYTYDCVBUP7MenUSOwBH9y4SDztAFXg+o/0p6OKHCVqQAWaXEjMJd64FsetBazcvZoHPGAi1nwjHwXq3KOGfrhgjioLO66gQlTFvpJMgpIlXOcaR6hmk/ZyVD3KsRzSkqibkfRY6od+jSHWbnY062BGIM6svXaV1E4mzLD0YTXeBHKJzfjjiDXBhDCAcTh7+hP8bSVe2JOJnHCyAQkxlDMcD363n0W+lRoPpOVsfrqGzeN2Mszag7dH7vBO9o7TRBM37TolshLydGqJfhCFXl4YmyilFphNC47y9a9n3Rotjmqs4lvtaa5SwPx2x5DOt0ZSICbp1jOmh4fijx1z9bxR1VRHRaeJ4qR8jFFgp2lNzSgPUqJjyIkuZR37yViUztQ9q5Hq+fKg8hfO9rzJpCdDOG+wnQnlG8i6lIbd6C40kU5Z5+fsDt/LFm5xfPAZZtyaAtgR4jKByJRU9iovMJyhjMCUFLKYSEtUFw3gwvsnHA3fiCVwjhXc4T7GmJCKgBOumLXzocq4kMToWHqunIv4zld2P7lOMLcYp32EC9Vz6EF3Vnccx+eIR3ziKWqIuUoBPQEFurghpZhirgNy4BhujAt+zv5d+zj29REZfGI13hQjpQWtSPZywO5bLmJ7MUHJ57FFmym0YiOXGYgTIgpIpZr/Eivw/6/BxzoUyDDArEiB1x/jSKWS/gxnIcPxohNH+wTSc1sHOtGMhC2JfHxTjlRiiaOtKwPdR9EQUYSprSrxOjJ67jvEEWKwmWGJl9gaV9U2fCUb1axSOkuoQIyRAAAgAElEQVTMKY2LwQMJl9jEXIYzk0Gk7C/j5by3eODEQV7yHk1Ws5zNevM44n0RgFhucXHvapphRT2gigZre+zl6qqNrD41kqmMwBAwpZrpwNHpjxEhYhkG2AIJKAmZ9Q0zWmKMM5YuBrjHCuS2aOQNMZQBd8ihCCU2qBMA3CeR08SxO2Iv9u3cmTd/IvXksG1cCI3k0lidyrigQYwdFkBDdA48K2PTlJUcnrWXvHVFeOLEneHLqJp9iVr0aL92JIFYo6CYWoqxw5oX3OfKt6uM1wtCExk26NENE4oppy9iLElmdfpmnrzZRfzTExQ/yaXbkiEklybhO6g7kzot4Q8WEHXqBYor8fRp2YkhK6eQjxxjvjcwCYV4oYXbj/qOooZjtXtQ4MR9agj4OB7jKgWl9SWYGrjzaflxlrbsi3W1Gnn5n1FJKiCSGPIRUSFuJC34Np6uztSTx3rG0hCbRjhhzJ7QiRkWCzA2rebriOtkl99lJmNZ9esuXh67yLG5fyIbpYIK+QzAH09LO75H61viurA7Y//8BYAIIimnAk2U3CeJ0Xt8GW/iQyOq5AJ7uEIDthzgKU40x8nMhcSGDAz0amlRIOD+NoeKoRooxWAMlFVvZLPFIVJpoEdEIM3VXblIKecpoA6IAzb7b+EixZznuygAPCaeJ0F7GOjvTwafAPDzns6kWYsYv34eE8xMsFGtx2HSIIZrT2VD518o4TJGgBPuVGPMAiaygg6Mp+3fssn/CGEwRJfrZHIiaj1xrRvxGTuPLOAi18jgMw4Y8yriLefuPuVQyErO7gwlNzsOHWyY2XMzsgoHlE0mnF93heMLTgHmrGElWfE5yA2suEMhF5++JLLhDcd4TSSFvCWUQ1zjGLcZH+TNxYOb8HHsAujyknNodbDnUuVX9L01cWUcGZTis9CbLLKZZbqIuWzBw8mBDcvH4k1bVFBiSwcsTQfS+Y8QYlZ5cnlzNtP6BmMFXF0RwZI/F1JDEV9IYSY+zPOzoqqlCftWbmE1g5HwvVFUYYwN9gCc63MHCQVceR9O6R+32MhHauwd2ed6k0zSkQBf6hNYcn4NzXrYYCd1xCRaieVmU1oNmc1mInh0+zKX/ziL4yAXtNAimkxyyCOBQmbQk8B+Y/B5Gkg9IrSpojudCcQCAwzwYgj9mYAedjSjhGM9B2KbV0OTqRG1thbcfveWHGL5lev0Yw/nFq7H6mkRvbvvxBcbxqLFRxqIpoYUvs+wm0svmuFD4ID5eP64T6M6PU5cucrAu8PQTIklvSgF/WE9KEJGFVoc5xXfSCC28jMVyJAZa9DSsAVuaHPy3WvqqcZc6yNdOnQi4MIEvmRm4jQsEClNNE83or44Hd/erUnuaoihYwfMSaTTEX9uH3xEzKvNxBbmoOdkydeoeJ5dfMoLDJCigRQXVjIT61u9yUHJn8zDChPyyKAHbZFRzJnCw/gEeGPq2wuvdxvJneLO8bmLsG+sYQlDcEYTeX4YGbwB1LhTF/1X21+HB6/mP+SpNIvckiZcgRbAKrpyBaivgnpJFVHT3qGHPrYrWlDroaQw8iOV2gJSSwO6GHpSVb2FqeELOAR0BD7zlRhKucQVyslCQcbfM0pBEP63/7xMPYVgxglLcBSGgrCY1kLNpSKhckO0EISbMAKJ4AnCGUYLQohMAIRZuuOErSOXCjUXioQRTBOO9NsphGx/LETHNgr7mS08NT8taKMunGO20J8uwlomCBvoK9wR7RTASJiLixDCaAEQQpgi/M4YoTX6AiAYMkAABCs8ha54CaAmuDBQKP/wPe+l5muFm/33C0vwFkaiJbiA0B1N4crYQ8LTjwnCp6gkYQ2+QtKAF8IWugrHmSa8f5gq3Dz1Uahc9UH4Mj5MWEsrIZg+Qki3A8LjDQ8EHRAmYiQsxFpo9/01JtxgkcCP/6boC1uYLXRDJACCMdbCOPoI1RFVwq2Lr4Xw+8lCQkK8cHLLLaEiuECYz3ghb1eGkDI3UVhCZ2E9AcLnrU+Fyju5wgAQhoKwAxdhE/bCGOyFqktxwgm6CvvoJLz45ZLwafJlIbdPqJDceqewA4kwGi/hIAOFHXQTAGEstkJsaIpwwWG74AbCcAyElbQX+qEhjMBZOGu0RMi6+E2Ia75f6IGG0A0dwQ814RSz/7qnxdgJTyy3Cg4gfPM/KFzddEMoCs4VXi14Iew2WSb8QjthOd2FY8wUzqgtFzYzSnDHS9jIOGE+/YSI7meFnnQSAOFp/zvC1zfJwuvVT4XQ5W+FmivFQsTjDwIgNENXCKKDUBj0Vngf/lXIjkgTXk++KgzF6q+ylB5PEmI+fhOe33srbJi996/9B3EWLnmfEBxA2MEIYSNdhbmMEZqhJSSNeypsxEnYh5fwG72Fj57HhI34Cyog2IMwCTOhIxIBEIKZIExlhHBt+FmhbMDVv9J3AWGn7RxhPQOExrdVQmF4klCdWieM+HF8IVaCbF+ccOa3y4J80RchrNV6IXrefUHjx/FPLnuF1UwRvEBoDcI02ghD0RPm4yy4g5DhckUYiaNw8Htb//jv2uR/RI8hsyie/iMWo0APBSbIiGLf6HmUDzQicMqfGKHHMAyI4jlyw1K+HYwiuSocR5feOI8z5SqnmHN/OftXjaHeYx5BHOVFQQh3WMIXnnN24RnE1KOKKhVCKjsYhQYK0n7MmDxOMGboMRM/RuCBGd8AiDgaxguiMaE7XXp3Y1j70ZzqeINXBS9wtvAgnUi60ZJ3QW+4GBGP0/LeaEtU0dWR0MapC2F3LjLGZxH+s8cQ2qcdkdNGMWZ7ANqNGnzmC7/xkInP5xPx6C1vJ4fRGzcuU8xHIJA+uPoFMBA7AAJw5gMlVGDDSlpSQg4XeMiuebtR+/U1Ef1mkHEymcC1g5kwZRIHCMVyiR0HDu3E3aEbYuSEr9lMQ0MFvRiGC5o0oUQHA9phw+TR7TBHijlu6MxujWpQN5o0xVhMG4jbymCmbl6KpqUDMWRhijZfycRJRRMDbx1+oR1yyrnCF+TAVZJYVnqK3+9cRGtHb3riT0+ccUcbLz3bv+p9NxmYjLViCYu4//Iyrul15GRHI87Jp69bK4zRwBV33pFDmSIbO+wYJepJN73OOKJGxLMHtEEdgB53B9BQVovzSCss21ViObInFvrGFLzOJIsq9vOetvsHUX4/AZlQiM7cDmRTzizGM4iZbF+3HzS0aaNjytQloyi+m8HbeVEUoENm5E6cgWNcJYlPjG41nG1GK4i/cJdaxOQhwoHO7P62gfdk0wSkAw6YEEEDAK95SxqpvL4WQ+CdE8ynKwA+wNSDGxiyfgGiykai9ZVoO6rjDgRixV5yqYrPoUuViAV7ltOtzRhEyd+n25sA+xP3UCuR4I0tXTDChGIWSlazesJF4oAc0wrWs5CW08b9LZv8jxAGLTQ4cnUJPrijAB4D2SShI69H3kcTPdrxjXK+UkRwv0Curz7DkQOPubtlPXsHhWH613IPfmznC81x5DWVbOUGDdiQmRTJ0NFTcDPx4ylvUJKBOobc5gMAV+8Wo9XJnxhecpVYOlHH/YGh5N99RdTZrxTziLy36bRz7s+0iKHkoWTt+ZNEAPd4x7L9A6gqKUZFDSzkAlE1WQzYvoR3XKXX28EEHz1NFvX8SRr3KeHN5S3oIhDP9xUDejS2oCy7lloc2Ke/hjOehzlDNG8KE5kunUiw9zWC+cCxkwfQw5iedGESPdjGOjQ+ZrMt8RiLeYGLfwACcIcXXOh7AIB9nKTDtEEYY4GcaoJnHSKSGHKRk0AGM4L2MGv3UdzpxV0y+YVTlN98w/PO03lgKmPmfB9k974gr2ykU+BQLDDGCAEpUP74M+nSSmxpTTGQSh3d6I4r9tgjZbjIjeyCEnxb96cePeJR4UrlIbpiQs8fBq1p5MZc9pBAORY5YoylBkilqujoaaKNBhYYMc1/Mu+JIoRLNApvKNFtwkvcn0SimbhkNy1wZxR9GDnIn5oGbewdnPj0/hkJz79RXpBF0qtCJLiSQymOX7Wpfl+LSmUe+7ZeBQq4zTGiCr6h/bCeQN/pFN9IYfOIDfgcbE0xGsRQyEzNtaQBIch4HxPCmtL1nGAviaTxhFgWsYtSjMhHFTPMAdCjBX2xJpK9DNMMpAEpe9nNdd6jjoyRfJ9lqJ6q4NimvkwJMKJNgyFDMWAT8PbHehCqPp5E6YrpbtSBFaeHMfxhP1oiYhbd0UKFKw3HaIM/tWghxQb/hhVYnG9D7gcZSZmlHGAFnl3+z1iaf4f/CGFQIGKY71zyyKUtOvRHgxpSSTj8iiavJgZEB7NUfS/OuPGaKHKrzuNY3kCX7oOpKopjoV1PnPkFexyxwomOtOQFkTwmjkps2S+qQWFggHlxBcaAATW8JZ1VmhtxBz49yGH4u3GEoWQsxoykBf3CxtIUnoqVriW72+wlsyaVB0lveTLhJcNpzfW6C0RuDeMOcJkKjBwcSDcoQV5SgfUVOWtHdiQfEYsYxzZCuY6cqdiyjN5M4gUXgQfM4zAj6Bw5iq7PRmFCHSEVt5n87ReWEkRc4lcEA1U67PLhzvkXlFuVMNyyPSG8RoMCRGQASubSnzUMZvMAd5K7vWQtA7n54Cy+tCT39Bs81/bjPnK+0Q7d4hLWzjyJPa0ww4XH+58RvHgl8Ix5N68R+zqJ4ieN9L23C0lSMtu/ZmIj0cTO3Zv2m7oSTwzFNJACrDkxmrFDxqFt50RrdJmAFiN7zKYXxuSiYPu57eTMPc7jqGtMHjKfl5Sxjxy00aAHfnQFEle/owN+gMDZx5uo+ZIPOjKSyzIww55knpORmM1FEsgin+Htx/JHdQYrG/+gc4upnNm1iD8YjTH6pFHOh3vlNGvnTCllaBc2UquiTpVpPa8Sr7KV2cy+OY7NC3fQslcHfr/wglwNd/a224oPNjgudkGHXEKX7+BdbSaHGIo6VdhhCYOac9NpLRNpyzJukg10xo85rr+zu91hltGRNjSnO71YjBfv7Fai79SNBzQiqNSRIH+LKUrWi4JYjQ9nKKKXynpMsWHgQivMUccOuNJzD33ohTGwnYE8d9hJfVEGtiseE1N6jzb4sJK+RO3/QsdhY7FFHz8gmxgyyKKU9L/sSrdCDbUsGT7M4eG01X/LJv+JhVr+NiZWtsTJiulqMJjQ8tXU0YiAJc1nj6buWSrazctIHWKL9sVCLGhOHO95sv4UbaZ2gQY9vCMGMpMl9NLURSmvROTakeYJ6tyglmBuwd0QwlDBEzmutOY18WxTX8dA+QK80CHoQGuarlSzbqQ1eliT9OOxjC7dxIf8MYiN9cgim2recP+mKfs4DIDUzJbpdEaXcoTYMkzcTdg+oDcJyNCimC4M5D3JLGMYPfEkQ1pLev1ZAPYxn195jSGprMSXx7wmhBtk/5gFt521qKKDVcFgtvm1ZjK9eCVtYs6l37B7/REHbzcKz3/k651bxFFFAUqGMZFzzw+y5MgRDOZYE3cqlrpyBVCGA18xo45CylDRUBJHGa0oI51HmGBHPV5IU2T8Nm87J3NuEV30Hvt2DoS00MEpLYlShwBW+W+g5OVutNAmiRIeUcPcYbPZOHc+aoeMKSGNbU8no4UYT7TpiAN3OcMgs6XkW2pwkiBUieMVidzjEYlAIAsZxyTUqMSH3uQXFKLubk6TuRa6DqpUpZWgUvCFq+ItXGu8hszAirsL+lNsNJXJ8zfxjhdYAJ0026IpdyZt42YcGE3zFDV0A3pSb1VDbl0RYnVr1nCULQxAQj12TOJm7E0kNGfLvsW8Hn0Xcs7hihH52BDJOaYxiwlW3YnIfcO3Jx/xMbbFtNlwfLOqcKEBLWqpTM6jgBqUWKCHGuc4z2xG8zUjA4lbC6CQ7Fn/g7r/jA6q7NpH318SEkINJKH3Kr333jsoKqIoKFhAQEDFhg3FgqIoIIgKggoqRaT3KiAg0jsIAUJJgEAgCSQksPYHGe8+59P5P2fsMfbz3mOsj2vca617XnPMNct11RF8vc8jajgdHFdYuLXtvlDmZoilf8a7jt3us8FuXa/M1VZ+w9SUINrRU0fMevkXTwx4T7cfc4vLui5/ZB0bEo+p+lhLGxe8roCcCmlho30qilbARYlr7urUvrTyLsqjOVL+I0z+V0QMl86fMW/vCLnqlFNQLYdkKSLan23fsGjIB77p8IQTEtRUw0GH3EKEcJtmTLF09lgPquqqpYrfTHLBZrfi4j337puaq6yn8paV/kmSmyJkt9shlZS3IH2l3Er5etY/hqqtfK9Kdsq0yF7HXPeVbuqq7J+/DirRooxUB3DX56lf2jnzvA6aunI8zYPK2e2ID3s9ak/VVyTLsMNl61FVlI/qD/Nw056Sw6+r06GWZi994Z+x/yCXHfZKleKizVrKqbximsntYaW9rrnqirso004XDbVaTEYh7z3wtHxPtnWzQrjYofcp1+sBy530sxOWOeuaVJlXklHQkwMeEd/oqoVfL7BJhDdsESZESkKyz/tOk6iAUw66IU4NNW2YsMgbj77rRbWcvS9a7LgPLEX3aXu1WLVIo+lDFM7fQ0F3hcgSj6VW0aywZ3u+5zn9NVJWeaU9pa2cilmHbYkLJMxeK1+ponY67HvxNiMKCbIb70dkOC1Z/KY9bq6Okz0hTerta2JEuSNTQuZ+dXXWctXD8o8u75+S10xc95kVA5arX+Ixi28u03fUfEkOirfSk48/49rVWw6fOCoiCBHy1wWDig7ylqVetUZD18WLcNIW8U0HevXcUEeHzlW20pNK5y9gZtXP1W3XUErxMCWbNNe1diMFSpXULDyn7mKUVlQ+99l+51cdoitJk6aeot7UxxlnnHZZofM31VVB9R2XpUtz0S2TbNKx0zt6rB1u4p+/a66ncbm+tNhuKfhVvNmSpEoTgdnWmuOunt/3EP1mF4W6ttIv/SOVC9/nTulsVklSTmVbLZKFO+54RSlZGw6rJ1SWu+qJlPH/xbb4/3v9VzQ4hYSEBKs8a7jvtFDZCVclSVVQmgFaOa+KzlVb63joMcVk6ama7p9PN+PlVxy1U0kl3ae0n6wyq+kcc7Z+IEl2jRT3isNaaugXP1ljqIV+c1lhmfa4I9Tsr84qObS4s9/tdrRRHjVPh1k+d5NVP43ztcMKKmb+xt9df/0P322f54Ehb1s3+SdxxcupeW6rbh0fFbnqqrHekemOwmL96oqi8puhjcgybYUWyVRi3FNOrNmsWmSMA0cv6zCzB6iIx1WWV6z+U3/29KAKsgvVQA3zHNZbT5n1GyjXqrw/v1yraK6qRiQ/pYwWFh35XPj1wC+NXvONY/poroI7AjdVrvmgNvue8bgHvT39ab8M/8va1Bl6aSpLvAQ33O8hv5nmy3tG84SCAjc1V9pVt5xx1uS/Eq0OifNj/bp6v/GD1i1qmd/zVxnpK2x1Hpe9tuO0jL/iXH5hjg/NcEtOI/X2iu8deX6vJ76uop2uUpQUZ7Hp/uV2XKGJxSJttU9bpXQu96jlJ191VIg57++xNfSkYlfyWPblRFFKqFeiihzt67lU7IzzP96Qq3sOD3/1FGI0VVYvxxRvMtKZXVzI2GuXy5qWqKTto52tHrfSi8veM6frdKfNFGgi2XZpathinxcrdtdl8MNuR6T7Z8cBbbu3drp0dtEHLrpeNL8TA79Rpk1zf207qHa7ejLO3/HzgtUaFirlauJJl2STKN4PloITD6038bdxHqk0UPOjD4AN3nHdVQVFaeJfNoIfPOaaU4pFtVKsTgNzN8z1pTl6K62vDk67aahZGmvhww6d5M9d1t+rdrnSLcYzTzWTda2QF/rUkilNhl5WmmeSTk7ZpJ3+1poiHmlCrXD3f1eDU0G5ZcjvCU0tc8RDmvncSGvxjY3ibLPs0MdeVFcNIa4JZCVeU120r0ce9bVjJlvlGj7a+rF80W1UUNwFJRyYsUc+cWpjv5Mmzjgl1jk9dBYjl55Di8uFTs/WMX/sHKnXcqpdvJQzUs0qOcNIZSR+tU/zHu0ttsPkyZ/57KcpRjWvrFt4S4dWHRHepJA+hujracnyK6u0Adqbb6MDcb9Z/eduQehNu1OOyNGggKw5h8Wi8T0+gQOOaHjwB1eqpZrT7W/NdDDCdn1CB4pzSdXs2fw67mP1M3O4dn23YHycvXsWCr2VX8i1fO7vM8y7esotTB4FkMuafYutrDTNe8P7SqtWz6hvBot0Vv1WrTTr/oHTynnep7KrBkqKVFIppZWT5bYi8kmU3fk7aUpm5VRHSfXaNnakfF63s4fpWvFpWS67KpsyoXlEdSil4ZTn5FPGHtccc8IXUS9YFLdJuNzyilZRqlsi3Ic22C1dugMOSxLquuMntyqkhSyBwe/UcvDLA35f/afwel0N8bUjpbPbd2KddmP6+fTMDM0ea+b3l1dY/O63pi36znL1rP5zuSIRl5V0zR13fBg/VZdxrzgqRKGuBSXnTBapki/MEKGoTKdUUE6FsJLyJkYIyxPqvi71xOfNFHrwnIJ5ijs+dq0NpxO8+/1ARUKzi6xY2MwF82x1zezEeXK44vHHRrpPgHzmdf9Whd/amGSFyq/VBlHyW26ZMxJ94nPTCnzkVU940i9qVXjDH9cXO/rXRkkWaaOh0moh3b/1B1oIVzG2gZDbmRLT9ouac0aTzs2sfOs7HfxLQ9BGiBqilFRDiOJ22+oYrmFyxen/ESb/KxxDTuESpPvCVosfW++E7ZpMfNErqtmNKfYg2Vg7HBVona+nFb/NF+6001PmgIc84xSaqG/A3g9t9JdXTDK4f1tRosUixCUT+3dVTnsJbigmn0qyOfTrDR9kf8VTzwzSp19HbT5uY/5Ls9QskkO8/A7/ftyWB2KMKTLCynd/t/b9hTb+stjYzBW+s0PBKwX0N1FIRFUrndBHUR+Y6ztJ/nRDSXdMaDzEoIoPS1x42Je3PjdKHf1VUBw/L74iNDRUzLVA/6WtTbUQVM5b1GOVH7F5ywm/2miRlR79Zait3e46X+y2u3mySTl13pJf9hhsptsKyHLJefFuuCq4e1qOujFS8t6QlP+6cccSebymRks6me93YfLJ45qv9fSdJ210VJwrQsSKc8m4WbstGjra9chsTrnP9R93qXot1DPf9DQlYrNwBbVF3/oxYtckOVcvp0H1hmkpXKQMD4x7Sb+VzwuT7pZsChasqb7G0rAeb9ttgcuy8NjzXxttkZ/9oShqamThldHmHR7tqcLljFLXrM3vaV6xowndJvtlw1QXbicLqZShQO1CQivltdot3WOfNzXlS1EVmmijjh5aGJ/raWddM+6RRQavfcWue/yHobK0U02/iOrCsqc5fmSbYtmyi5TpyiUqZ2sm58NllMisZLJ5lrom4tBZ2S5cNfijF7zX7xHr7PKejeb80tF+5+ztNE30I/dZPeaQtJ1pzhc5Z828Hc6sTdC18EA9Kj0gv9aSLv+t4L1JzM9OfCZcEQvSpuvkRevt8ImF3rVDmlwONZvnH9tt/HmGrJqlXMwXpuXznUQh/4PV7PWHfEp51Vyreu2RwyVznbbAIVmiZJfPe8cn/UeY/K9wDJckCxHveZ2c2n5KWe18Muxp76/cJXnFNROi3rPGGTGKifScQckrfXFysRH2anHzVceHbff+5o9t+nClhhOfk3E40S89ZhmumEISvDf+Z5vwkl0uu2SheQ47J0JZ52TX9dG8vswY51ivCWb1/Vgntb01vrcOO54xyRLv3hnnZJWxutw/QIH3Kmj+WneE2mynnK6YcPwtMOD2S2C9vYZp7EXVlXXHPnsUd8vuSYutvbzLapcts1vV/O2sR3LiTeOr1BAUKW6PyzqparQyPk/+yo9H5gtEaqipcCWFNIhQ8k6qkLNXFLiRx82/s5SJikGadoVr6u83H9kuQqz6y16RUDNG5YQMB/Jed6j9KDVzREqY9G/3/D7nVY/p7kxYCU/aLI/CColVQEk3hUtNitNq6stirgYqYsXmbRKjc9pVONO2g2uVq9hXyfCnxSrq+6HDFDsSiL9xTpRM12X6+LnuFpR7XznFJdnh+KXNhq755n+Iav55d5ODf120d8YOB3Zss+PHv2zZc8n4d9Z6cP00j6nkEDYv/VCvkG46aOqp6W0936mrjR8vkFsODes1UTJ/URkhl8X9PV7os4Uc0960E1+qXzi/Mf3eVDDtrAr+8c/c8Q63+tx7MR38EfK+Gkoo0ayGIi/XENOqrjpPdJZ0PkvRyPvE9F2l5BP/JqGvNQz1tvyeUsF+f8j49bCrS/Za9+Pr1vVf7sd2X+vfbYJf7VN/ZR8piYH5Y9+wftNyYSXzqVYyu1xR4VolPOeHoz+aaYVmjZ/3u4Pyyic1trjx1tsul0V+cbXHH56Pft850ao2beurLT/6TZoU69WsVseF5A2Sr2czb9Fe3R56VEmVHZMO5s6b5R2LnZcpvywD9VJWsiLO/keY/K9wDOUrVTPV78Lt0bBADs3adPBay+fl6FTG0RUbxOTLa0j9cULVMXHe24Z3G+rclmN2dVunJm7P32/H1r9VrVBfbKlC/vn1gHqL29gnUsscT0qPvqGJvOCAawZorLY8wpx2VJrmmsiBo1e22/bTWi1Vdld9l6SCrV7xl+1mL1yKKwY+86ghrdqboo3usrvroMe1MFB5r2vmTzfNs020PPKIkem21U66W62sDk8+4hE5FFBQ3LUrJr/4rQ0zZmr+0GhncrD11UNyiXLBDc0UksdN2+xUT37D3xjpUPJ5l2PDnQuS5Tp8zagZIzyZPNxEwyxPWHbvi+bxQHhLf89eLmcI8dkDMdlzK758qLQixUTEB/6YeMAPdb+WfDfS2DsTJTinjjBbxElyUrxTdg//zOkcYW7luOXxbV+4EjdRkJaqxPXSDruq4PFtTmSecUSKcbZp9WRlZ44fVklJ+xww2m61Phjuglv2i3dFhpkzpxmopUPKBykAACAASURBVEaY8d44S16c75f+z6u6e6eVv2/xT44QiQ/lExMdYbGj0vvutlWiN4PfRVdrooVAxNCiOjXtbPZvP1v68xJH9lxALmXavavrx+09I0F7JRxM2GFfwkkVhnTT/9tv1b3/IUmFIyTXy2lPcNkIs5SvV0ZG+Tue+nKUdx9uJ33kcivvf9eIYIa+aKiSInkznXRdPYXtc0S+/KXlPnpNjCIuzFol/lqciyfjPKOGM71X6VKtpic+qqtmu7bunDnurYYrfFL/FT9631EXjVLRnetndW/1mJoxXUz69Q2T+82SIZu5Tnty8WO+vvqORAmyx6Yb/d6rFnjMLWWlb/tHllse/bmr/UlJ2jUu43eJSov1jAZOOybeXU+oqZdOkoV6pvSPlrryH2Hyv8IxhN28Y+O4824q7IG/Hrf7xH6rd2/CBSMmTvDumZniEzIdcdD3UxebtXSDEc1e9tbS/j70iq0Xtoq6EmbcI919P2qchIRrWinhpJN+ufW90ZMn6DBomufl87jGSgx+31JHfe+MojjnvIrKat3wEdX6d7LbBiss9qQmHlPdcnON7fupZxMuyYnvR30iI18R092QpIm6RpjtD2dcdd1ZeeX1sWYuOOkz8TIlI0TbX7u7GZ5NETmVU8phq5VvUlXjp9pr/u5jnqtayJH1G9113XS3XRWlWs7GhuXqqKESpn08VvY5l5TbFqHI3cKyxea11SH3qSoQJ5s7ntVDMw94PPMjBYJi0ldfkroo3m9PTHDmreWCK2miPy3v8A+/qSVG6rWzXi35rYUv/qSJZvJJtV2q6vI647T7np0nW+ZtIRl5dNPTqzXb2nn/a97XwRkJunQeIpfb6gtXQllVipUSuKOhAl5TSa4Ne62S6IV876mkkhwHr2j1eD9F0MQJ5bbOlybF7d7ddevXw1+1nhOaks/G6X94v8sPXvxpmDg3Lbdf/2kvOHCvk3DVNwsM6DVCs3z19RjR2N3ZJzVL3qGKwj50ADlss1fJNXGOHz9h03N9FN9zVVCikJWrJvvZNBkoGeRWqmRlWc7b4aQ+PtCiVkcPKu84Ju+d6VS9W+o2G+L0vZLf77snWHpttX5Pv6BYoaK27tqnxZGhPiz4thfmjBTRKb9ClXpbs/W84gUqSHXRWz43ziS91DbWZX0Of6JJ1TLmLfhAjtgcUrOdd2HHceS00XVjdLH/x1UyLmSza+Y+P1vrrFAvTBzmOY/ro4h8n+9VWx79VJZfpDh/yS+XEYUH+8k+K5xRQXafnx5igpf/I0z+V1QlokIigol1vpS7XH635u0X26apJes/UlRRF+Sz1wIdtPPu6u8NH/GSFkV7ynZkn6nnx+uijYLhxdQa3MPlCetF96jhSmqm99c/Lkyog7JsW3rHreUb5e9UVY8eVe2QpLrcCknVXxezLJdPjLcrjRPaqqi1U1+w0i0hzquhiBd7TncsJMmpnBGq1Clm3Buz/JqxEnFaqKyEdDkV1M9DjjpsgsMO3uuqhF7KKhrSWUaQTbs3u3j4w46aKqK5ux4aP0/SpfOKPdPG6REL7Vm6TAHhnveb1tqroIj7pDvjhIKaWWu79kqbbbn6mru/+SP+2LzCQmucluz78LcNyBzjecPklKFFi/a++ONLHdSW6LiB08bb/8pCp9PuGnd7pdUNhjixL1Fkxj5nBj8gfspomW4IkV1dGerPXyqiaJhTuc4LEyXXZ8fcnHvI3xlr/eqIv93wkvoiVDLWT/Z/tkWNkc00RXOlhCsnPLSgU3d/VVoz94U39ciSMap0ivC2jqLz1nancGDu8fE+fX+J5Y+WF1bxZX85KlMF31kqAitqTRX6bEk5yhS0p8tn7koV75ax1vnKaEONNdaDEsTb6oQ2CmqXt7eDNxIVE6fysy8b8V0PFdzwJwaoaoNDaoq2zFVTPlhoxVuT/W6NvfMvuFghm2x7Tjr97XET/vxGITRVXKYIF8VrN26UOx1LeL1GVxWVcMoND8ljrs166uuEIsb51DDNxcotp6KuuO6U86rJ0jK6n2euTtW8zTAvf9HextWrdGrVQtF8BTxWoa2FDhinsXqNert9jpznLxkZbLTDn8rgca3dcNKvzmqhmLuKi7JTq5zj9OpeRZM5ncWKUEkZeYQKlDbWiv9dVYkw+Rza/bMeZRrbKofX1n+lrvre8ptsRQv7ecwSRVQ1qMOjWlVpasjaYRITb1npsm1O25d5SpEmdaRK8tTih+UpW9A6d62W5QI+69bLG1M+lLDllB2SPC2fB8UorqwTolRW2B+SzD06zIKp78mQw1LnpMspm0jLLh/x27pl0nftc3XibkNjK/hFd4Nkl80Rs8VZ6LrmXrXJBYVd0iJvHzXxpCZql3pM+OBqVjnhsw8/01A9W120wRXx0/50dfImSRuP6b50oJs2eN5voKICNtjna3v96YhW7XvbaKd8CjkszQ9WeXDzAO0K1tXeYyBHZoIPtdfCLb3bdXLoj/nCnJXihBAJfn/mJX/dTvfJ7V8UdEf/v57yaMaLrpZpbfiUB9R4caiVTkl0xDzxkp740uEXp8pxs6yomzkd+Gmay51KSHbKo6p6R4xv7TTWTzqroNjNfxNqOUWJVlJOkYavmORPbLLdW5m/mvzc677Ze8a3Vll2Y4bjx7dKld2cd17QZv9td2WJclM1lw1R39decyMmXZGc+V3LvGvQ3PFUrqGrmn7S32qzUUEONwRi7ZSgY65HZatQULQsoywx97s3DbxvqOaNxzmPeQ4ZM/ewUZ1nC0FGkwIenzpTKXzycD87an7ouafa+uHPOWb7U4I/7bLGP44KVUJ4ztzypEXqpYIn8j1o9sgpvrBfMXnM9pOqUr2shVtCHBViu2UqijJIexV11vLqC5Z8t8Sn77Z0N/WWzu07y54zxt5FN611wAi1ZFfInegKOp4bYWVwxAhhXlfDIDVcdcxWZz2muYvSLLBDbiUIye7PxasNyD5Md11sdsxu6cZa8R9h8r/CMeQOCbdKokWfvi7SGfud8aedyKNE+UKyhYV7w3cOuSxi6zl11DEzK87THlRVaTMc823vB/XzvZ2YP+0BP90TFvlAAyfs8/Xi0V749FvF0UQjXSf9IlDVEr9IEOabPt84oaB8coiToKXcKkqTTawG33TQpO79yjzaVeG8MWac/8oya5XXQW2V9JHLZUc1F66EMCclyHvjqFGFPvd0vcFqP9dI/67N7d3yiyGN73fAv2O3ZQSqtmmmZIPmis09pTnCFQRtNZIouwRJbgm3S7p9h7aBx158wRt6eU8P33lazktpmkWXMi3XKKHVSytXo7f3bbRs7WJXpWiputoV62nU7gl12vQ2N+0L7xV40vCYhk65LUpV38fNN6ZoP9WfbWb96I2OIlJeE9JXmrhjhqWNe7mat7Qa5ds5sWipF96aq2G/l4TL737lNJZLohPCqsR4RA1RcsktSqRLToZeM6zsKw7LUkiKYWfHKz3/tAXrL9slxVpbVFfGOSGWPDzNGSEaqWenSybb6a5TqvftIyJbuMjQRNuLX1ajQVXX6pZ31W0PRPbyjspyCxcl0EZPt9MIT84UFfWvnOEYWxWvXlqDj7tbvvewz7efcrZubsNWdHYQWUPXSL+VYDFet9YNtzygrr2WGylSUQXFKqKbDkK6VXC9bm4HP1hjt5PWJH/onwvxCrgulxT7MNwUEcKki/GPo3a547Dr4p0R75xIkSo9W9a7Hb9xt1ioJPEOTtnmkZEjRMvvvDuuu2zj8u/BYfstlMtY+4UrpKU6dmGCzfLdU4VMVlqxtBMm3LqgfssHzXLRPmz4T0eu/Zc4hvjgiv3+McJfepQr58cCQ32QewBSvPLHMMVHNXTVea3lcu7iQZ+/9aKZA0b61QJHbLGw5HCxStnWZrUF3nRBmJrFS5nmUVelqOa6tfP2eSpnHedw0UUfv9DE5M++VkgjLXTU8bEeIoWJkd8NN5SRzWlkyVL0Qkl7Nv5h9bsLVd/fR0517RdnswMyFDRu3FY90VZxf9oqTrqldnsj8WXr/55g/diVYhbudqDCdbXee1QHWTqiktp+/ep12c4lCe/TRJQQ590FH7QZZqEZUpzzolpKKe3dC6+opY1xXwyXIUGoqyrJr4MxXr660DNpH8l9JY+60TkMCu3pM1vVUlyEsp4//q2Za1c7eSlejOqGXX7NoKQJFkeM81WeJ+RyWfyFfVZVecrR1EsqaGyuJHOddd4Nu+xUZspyV37oomWZrn744GuVq1RR9oXRaomwTZrdSM1+XbtcjWRKdsgG1yUplZ7l91PjlMKfrmohROkPWvqwzWjb3bRCds0HjhapmGHGi3fQD1aIvTdZuh/fj/xK3M0MMdmK2dh0rK9+eNykXYNts1bl9FC5nLXBTnntNm7AM7I3vW7ByTi3m7bXW7QBGDv/JVm5Q0QF6RRMUfhGiBBlpGP04TE+frG7p3TWBP2efskuyUbpZYl016XY7bBNtqlyOpddrb5y37LLuoX39fT9i8Qt2GnWpkTr0BfXMdsGFQRmF33Rx+Evi1XIGVkyhVuc+xWN8WDDDva9v0yZkGq+mDzPJ41ba6+hZY4qo45I/9IYRinmmgQDdDDTAb0t1VddLyqki1re1168Y+4WyGuJ3/RfPc9OJ72Wf5JuBuql/H+Eyf+KHEONSrWDwseKWmO5Bfo7Jq9M8aKEetV8GXhGJdMc9YDsXio4yoxLS7UJ7ebg3d+10laiTKddlOmU81JEymWqPeBddSS6LTxPQ5NSpmurvCquKKaE85IdkGzZkj26dS+vnGjpYsxywtOqGtnrRYk3i5my7GWhmllkuilGyivZFj/oqJM4d12SLrebUiT7XaJQN1UW6aYk4SI9fOGYWqfTBOmlPN6mtF0ue1lVb09Z6v7BZXTRws3QwuLurlBPXaVGv+Kb0fPstshjKrukkGjXTRKHOIP0VlcZG63QVEUlmvTx0bEtaiR9q4DcUt1wWx1TbNZAA4VlFy6P6sK06tzf1yvm2+eyn7JVsyfrkufM1kNuuYWpoo49zvrNSdBBqK+nHHB25kppN5KtOPqxwuoJXNer5Su+2TRSPqVsFufrCYsd/XSjUefHuSLFo2p7c9w068f8rGy1CAfq3WfoxKfcRG6UE62FVh4qXU2VpzpLvhMIWb7flqsnFQnPp2K39g4c2yY6V3ZRfWrJ2n/ewnfGeN9eRdFNI1fsUExhz+hqhoP2Ouz179d7dcCbjoi3qOVgD2waqjl+/GGvxHqRQm8n2f/5EV2D6vI3q+/N54f72CRLjTDbLHNdkR+HYucpdKWXcmggm1yKqF34YeXqlzV1yYvKKGqhs172iPvff8Hd7uV8Uru9u3KbaodnlbBdvHFGup4nu6iYaJdPn1Bu8WDzenTyhQsK45cBS7T+vrs8svnIY8gmvyhLbBejprv+MdU6cLrlUqU3ddNRQXcV1EAJP9ilrLIeU87zFimng9sSLP5opqdGvWHfv7+n/7tyDLeuZPipTQ9zSv/kR5u9YYJ3LJBNsvcMAL85ChJlUyZ3MYHcLlfOZ3CZlxy2zC2ZOpVoLVYutTX3oLaaq6uuTlpXG46zahaJtCHvROWFiVbKebdFKKKzDOu7d7FuUqIIlc1yAkx3yKFr/+iz7GFvFhos2Vy/GCRMirajRirsCaOsk6ahTbYh0hlJmrvlI71V0sBZYerKI+NGqltz/nZs2zG77oV+gSxb5i/0kG6G1xkq19250gSOyue9z8eo5byn1RGUbm6237026jtkF6uuqeYo3KOhr8evVmnUc679Od3zSSmSpVjsoiPStNIIxIh302apljtkjf4rHtSgclHT3nvHb1lfiJVkmLYqa+Bn133tlAICA7TVWoRe2liUccrivz6R61qMh/MNlD93GzdwcNNuu90y2h6HJLt06KyEC9cclCIBqS6b+Up/FfOVtz04q85TdSSuP+HW0nMuz0/SVRn5LbHy9Dzvj25s0JgmLrctJOjfQPH+TZ3MPClf7Spup91xbM91lUt0EHGvMWjLhMO+tV0egfJyedhuFcXa6IZOA+r5YfzrdoyapfGH7dwWbh3unEgRnZFbueyF5TyVpeLshkKfD5Xmls2fxyvuorIedAdXMOrKvwNz5eRSRT3NtaVMlPoj7zfm/tlOOSsOmxxX9HYeGbuPG7NijcN26KyY0gqJRyefmZCyXafToy2xTYVcEYZs2K2iliqo5sfv39DW/V7zlA+s94IZIuxQSLRjDkgQb2Gnle5sz9Jl03OgowetcdBOKbZHv6Wf4p4o0wCpUhy1ZtI0qXeOqfQvEex/tP4rIoaquSsEbdMa6/r4QJ1mN/OpfubbYLBGwuXzjO/uNYb+ux7RQXlZPrIe9NPUBQe00METbZ8yaN3DKmqhjvuEKeRrP+ouQoJjxs06YOATD0l3WVu1LHLU4xpKcsghJUy3Sia+0EjYlI9lDZ7tJdPAMG1lc8LA6p/Zc2CZ8xKVVkmia/bbb+is+Upl5vdK/xZ66uBPu2wTJ9xZ732yRbvXXjev51h7fl/nfe/qgSyR8sjppki/uOCN3K9okztGep6cFpxY7Kyb3u75iTG/j7DOLlv7/mbxTz9IVdEzrzxg+LgPHXHcT7VGOrP3DwP9IhyvqezlgR+p9E1PO4xx+4k2Js8a7JrbtjqiilzKy1Tcg172q4LorYorrnk1+3DrM3Yo0KGTS6vH2Os+pRxyQIIGCjktu4fz9TEn+XNbZIpBaw/LX7aC70/9ZEajd63YPtVZ/yitiKbud0u6ySZ43AgFe1fXcHBrtVqWdca/DNlPlvrA22fe0kmsUtKNleqKGIuGTVChQVUpN1JlJt4UeuamkTMH6yKPBY77cmucs6v+1rlAPUfz33Yn6ZJ65asK7xrtn4HbxRSMkX9MBdGqOvbmHP0+rKYoEuXUXBe1lDfXEikOKaGb7y01Wg/tfhwva9I6+3ZutcxK1TUUrbg7ijprr3H3EsQnOiy0Y/VW7xrnpH+VnJf9cF6/J2tLctWjunvW72BH2Agnqpf2xN4RtndZ6a3lr3mr5+duxR3x6d4ZNthtrDYSRCipvZe87FtPOCFUdld8YLkPwgcp36inv+/GaZZCoYcrKp8t1pJRX1ohXktVXHBJs1HPuRxJwsezbbs1Tx6F/OT4/66I4UZaiua56/px9hhveRbH9NdOgtt2WaubEgrKLadwpdU3fd73ishu+3O/Ov3FYfWEe0gXtVV140qyYZXf17RXZ60G9vSkt4TLb6WDYsRatfEPoz6cLsP1e86hremOC9VKsste1tpQFZ2XV5fXd/vh3qEO1dctKQoobtyBYf62wwVJllpjv7+lOGPWE885GbdXpAiXwrK5JM5VLENW4l015Hc4OKj9quf8M+Wg2jo6Kl2yFMdccPq57fKkrna0QUG3o+94pHpvbTWwd+8azzbtbe+CfQqOaCq0RFGRTpg87mX53bTn09VO791p370SaW1U1szumtnMGbnAPHO9NauFkZpqq4DHxWosSkU9TLdKK+U8qqnayol0zfyMyc7YLX7PX6644YrtbihpMRZKc95ZHyePtUqm/Jj1wlLd3x6gy7RhJn6z1I1SeWSKtMV13XM85ox9ZlroGFZZ5ts5E2Wk3nHm3vm3VkaZ7Lk0EOshXe2RqpVGMiWZ/+tix3cdFFs+RnSBKDN+nKWkSJeFG6mXtU07a9G8pr1TvxXy1RpJG/6y9sxa67b+7Y1vOnptTEuNdBMhyrJVf8lU1nQUVsYt5x1zUh4ZjuJzS+VR3jeuqdqvvD07LyoRXcMDequqkjoV6ki3QxFRxuhroiE2/r3fKUm+M8rn2tmK8k8W88Doby1eddV+F3VUAXx552ur935mnPtFDCyh/W+fyVMph7Crqd7xoE8N8p2t8ggT2KODHq5Kkd0Nue7pjKzIPOWZzf0Mal3J3P37RJSMUmBUDfPNU1FeJd2RV7pWRUtZt+KoWsMe0kFftTz6H2HyvyNiKFwpeDf7G3qffcq6itOdzxXmyJ6fHFNJJXdctsx34hVWT4K/nd10QcmWRY3QTikFNH72RXO/e9yzsaPsCbukzncPWjXpd3fzxrr422yt1ZbipEXO2CdShkg9JMshyQf3zLOdMvKKc3/MGElJ3zogRLjAQVfscMswlV1yTH2dDOz/qfozqgkTIRDmQS31DKsn8q8h4iIzfVO1pd/F+VoD86S65B/Ley12oGzgzrYr5vwxU30RPrZFOeHKi3XMaR/UesObe781a8QSU/MeVPb3BK17NHe66VVJx++ILRqpSYHKDs4/otnkTp7xmAV2mFphuNB/rlgXfOIBDwh9pJNTcUnid27RrG09hR7tKe3Zrxz8oY9e+co4n+u23QO+s/bsWC310n7QSFOmPq6wCsoq4ogExcLLyD+6nWD9RQPXjVRZhMfrvqrvi4/bdORvS8Zvk+NWmq5vPCRPixipubPJfuG6i1sP2f/LFomXT6srVoI45531ratuow/OoZ1qBn+/yOYBrS2VQ/83xrpzf3Vpk1Y7MXuzSwr70BemaqP1d+MkVc6U53SorEUXTJzXTyeFXFLfwypJK5kifs0wVeZcklIykFg2TFhEbjsafSqbjeq++o7rl5PFhIWLDMnv5G+XXL16QklpVvpHYSnC5ZUkQxP53QjL71aX5trWrmrv/EP2H14t1Gl7HJWpnN8cMKLE29rnL25m4kkPd2rj5slE/bc8aZj+JpqhnVLuuiNaikz5ZMmyRZrrksGhwpMdTUg0xDgJbiHMMzppoYJkl0Qr4iuLVFLfNgdMVlFPS6TI9FCOAeb8/qYzRTLkCY1y4coJtVq3AvsaztF1Rx9VIz+0Kv11Y73kqtW2umSrS/+7dCXKF6sUzFmw2MynZ/jx0EY3nDIu9/Nmps51yCnvaSZTCXvMF6GRPKL8aIuBarjiok4x94tOumadZUqL9dSGH924mVee1HDfT5nqwqa54pxUWRWrBAY3ekna9rFevide+q6ajruoqCjZRGqonvGW2uLy/zzjcBUVV0acfT6ZuseTg4r8j2ZED+U1EWHQ9wtkVSiod/PymmhljAXKKqaoBN094pQ9Kihqhw1Ka+53x8S4oZXK0t1wRYKxDX/x/o5xKmjsjENenDRGvspFbP1lI2Up1bSqHLdipM0/aPf0TbZYbsQ742xa8IeEg+N9NGavNX9vMXLRU+qqZUiD5x37a5XHLdAOXTXRZOXH8hQspWOdBvZ+t0efZ4vrpKFJtjt1751+8YiwZzqrnlVd5Zn/2lKEWC/nekzIrevK3NfIsSO7VOvbTsGCIVKuZcp27KoC1zOlpYc6eumq+NBMHUuVlr5vk4POuOW8DFetk2o3rq665JWOZZyUprAnPV6huibD2ziXI91vxy84+8lqbasXVPS9nvLFRrh+OknKrF0Or15njeVC8HbFr9To18rn0983dN5b9h+LFxVbXKnYvK6dSZI++m9f7n9Ph/rPKxZb1vYVvwmkCpHdUpckOuhd/dwSKdM1d11WNqye5IH3KfdETUVPMqXvRGN9q7l8CsoQpaHXXx/lTEiGy3lzadS2iG4NBjlsk2sdV3p71UsWOOwCPjTI26a6s/CukAf+DdDbK2SwZ/S8N3pdTzN/2wIe00I2edUSoVDF1u5GRjgXlq7quXRF29RXf04bCd/9rdyzA5zYME9qaLrs0dm90vkjba5UtyH9fcuV1q/YIHdyX1X+2Hnlokoq0L6GZvO7/R87hrDRo0f//4/o/4fW8JEvjO7euoPqnet4tnxXVYpW1qRefR/vWqy+CqrKId51n4/f4oNV3/nDOqSpKqfSKgrPV1jV2hXsOLvIeWmSEvKr2qSkn2f+oMqBSD+lLHRIhp4ayBSmca1aGhRqb/LZeXrK7rrzCqnrqHQXHPKPOE95RmxYfUWCgtqJVl5+u2xWU2PJSw9Kc95WKfpoKru7aqpgxqJRNs7Y7K2/ljmzfpfoa9ltcVh3VaQ4J1KWGzIUkNsOe+USK0YhsbKLFMiQavX59VLlM8EiMXKpF1bD6R0XJaQnue/+phQMlT3bHfMm/Gpc0izX3dS5XnuN2rf25/JvHNqwybFjB7z/9Bxb98xz6Px2pyT5sNg7qkfUlhESLTUuRGydvJ4dNNyt9BTDf5uiq3qKyTJAN82VUTSqqh+3rVGpaGUlK9VWPKa81b8tU6BOEdlSw23+a6G/hGsQFy0845qSEQXdib9lzKG54q4eVSojXL70BDcS71jiis45qhNZTefo5rJSzzntmv2zpsvmhoo6+sZv6lyNJu6S0k1Lq1qjrMolC6hcsLBcZ9Ll+TPJuve+knRylUCs+mr6x2FLkpYbv36K8ORDEjcnqPjSQFcnbJa447Rhb3Z1MHGbeiq4ceGq8HPRpmct0VFXsW4ab6WrMo2uNNiOvDm8du0T5ZSUGWwVuTOXOq1rWjtltuo3o4XcSrbEabVEKiPGhbOZmg1rJF+tojJzXDdk2Asyv1qpeu5qJt24bHzniWYf/8l6f5tf5UuPTmos8Z69t1ZeojuSXdZceev9K1GfS0G3ZaokVm6popOy1OtU15Uapbyy5mPlDh2V4rh6FR5VvWoz1ZoXcCMsXUZEiKh/Mn23f5JW7tOq0DMOXVzjsftq6TCir/N5L7h+7ox5Z9ddHD169Lf/R6D8f5s6/l7EEvxw/49B0ahuwZFth4Ldu48EY3t+GBAePKFeML3tF8Hc7J8GSytPCmY3mBP01TjoKCYYpHzwlibBRL2DFc1+Circo9SeoX8Qvzo12L5sfzBJ33/pxYdsCIYrHXykYXC4+5xgWc1ZQUnVg8kaBAOFB301CkKFBn3VCM73XhM8J0cwQr6gDcHrage7vBGs90LwTqVRwWtKBd0UDSLlDhA8pXLQQZ2gizLBSIWCK4cSgjXDfg4qCP+/KcJ1DYapGvRRPHhfxeADNYMqBE3FBOPdH/SSL3hX7paelQAAIABJREFUu+BjnYJh2gfPGxoM0yKYalBA6aC2ksGpw3eD7RP3BferHZQTHVAxiFIw2D9kQXB686Wg9r33//+8QglqEcwN7x/sOXI6iCX4VIcAQfwbfwcvah9UIBil+P/c84zGwboGa4JvWs8IEMx94lBwYUNK8GbHj4O66gcPqhp822tGML3hhGBlpyXBnCJjg4EeCKq7L8ghfzDt6W+CnVN3BnkUD3rlHBZQMqjqgQDB2q4/BqteWx08olYwWe+grvCgFME0HYNc9/ZfMGRecG7N6WD30v3B1xFjgo89HhS9R8PeRGjwjZFBf/2DCQYHdUQGCPIQjL931gg6EKzwWlCJ4HGhwVe6BEM0Cj40NHhY1WB54Q+DaZGfBNMqfREseWRacPSXk0Hn0n2Da3+eD97tuyBAMFzHYMeEzcGvXy0PNrb6NUAwRONgar1fAooEZ9ZtDU6tTw6u7j8f7F6++X/23lL5lSAbwbzOC4JPDQmOFhwXPKV0MET+oCbBTH2C1zUOeosJusgWFCaoJVtwtd+e4O08zwZfeyB4W9fgK48EozQJEPzQ/ecAwSMqBghOTP07OHHoTHBy44ng6qaE4GHNAwQtRAVl/V/U/Wd01cW/Nv6/NiUNkhAIIaH33nvvoIBSFBBQUUHEBthQUYqKYkUFFAVFBGmCdAVBeu+9994DJIQWkuzfA7jPff7/dX6/c773Og/OPU8+e8+eNWv2njXXnnnPdV3v8sE39Qh+qFdwR4XJwToqBLur9C/Zx/+P8HyEG+Xj7e71q0tp+6VfT1Xtyya+nZ3hnJEKD3pU6jtbrNi4SRETtQ90kzWYaolJdkiSJl39q4mG666tiSLk9X7LdoZP/t1ON31tuGbfN1FfhM/0FJi/11RfmDfwHws+bm+de5rZbWHZb0XXKGvh31uEyKxE/hftPzNabTX8aJa+2Z/U+8nHDRk0TDbV9VbVCH/aKqCWa35zVoxUmeYmOjDyPYcfBIzuivCBjfprbqRpJou1SrjHlJUok31WK+SefS6bYacy8qjqku4F+ll1+k8vyKfqoDaKlq1l6qcT9fm4h+YD+5jRZaIr89a5cCRd1KrzJtT8zvZNy/Q1S2c57XVVMXmUVdab99aacT1VLaFWOKaR7P7+tK/uPT9UbFyshEwlNMgYrpkCYjTQb9NrqsXVQy2dJ5Vj0v+aqXjPFmssfn9A2z2DtFHeadclS5fspskHVql8NMHuLxaZU3uQc8dveqTGNzI9ccfeEQU1/6u7Rn/V1EoZJZpU8ua9aj5a860s+Tt4/UwJuWxzb/wOC39Yp9qTbV1PPeCYAzLJ7S15RYjT21fgRz087xnX3XLJZsUlG6KQCyJFSLTeagcQkKG6SE2VckyyJ0IaSrsX5fk7fTjAriHr3KtwxfCFr7px446ktEOgiuI+6jfAqxKcVhQscUrz+Et+/eYnqTcz+XbMG8pvqaTT8vvBvRfEWb//tJUz1qvXqaWBtb53fONOp5yQW5inPWeahfpWHiBhx0qpMmnoljV2Op3phN7LvnapxjDHAzl0DL4jtxAfaiJail61v7dqwxw/i1HixeouDN7nq61r5P/rgh3O+VxNBRV3Tm5HJfpg4NciP77PpP3XRNf/Q2IMeQPxwfMuGlz+I90G97Ro0ial6hfw59uDxIr37MgeCvdtIJMy/lTTWsfEiUOyba6aYKsN5aZatfcbb9skM8YG3nCtaJjNR3/UyVM6GulwYIR/gkvlkEmjNz61d89G2SIiTJgzyHiH7KgwypEcmaSv3qXRxIEKds+vLkrIa2Sr+XosrOax6MHOJC10Bpex1Q2dlfaeOZ7XSKx01wUdtdZesa4I85Wm+rpvAjtcc090fc/OOyc0r9LIjSPHXdi208I9O4W4ZrbdGinvR3+6iEG66frsywo2ipf9ueK++3ie+onpsiTmtmzZEiVuR8l845p/UueIwTJBRx2R6I7n1XMRk601XAcJFcu6vmuV8Xa5IMmjwuVTQsBpiUIkuS6Tu7apZIvzKrgmVV7HZfbPa+M1+raztkqaZ/W/zd3xyTtNG/KL4jXKSLt11T9zx8gpj9LK6fhqDzm+awCu/HHSxVx3lGvSBkf8XuRzVy8lKvlYM81+eww3wZLIrwTvXRQmk9t5wp05uVAe0dpYrIZC0pzUq+hoBUPD/bF/hV9N8ItXrLLaFdncs14hXBUhzS1zEI/nFFFdM6Fl6rl06ZSQmHS/ndhp1voptqxc5HauaOkZd8VlyiVHwRi5wmN823mIzGdO2e+gKmp6x2SFsX3uGTvyX1c8PFW3ai/7vNMX8j2fV3haHr2btvdc1nZCOuXxwZQJHopto9qVq1ZbomW2Nt68OcIT2nh34HvKflzcQWmqyuazsP7qjOrtaJEUK5r39Wj1VxTb8gjoKa/XOox09uQ5BdvWcW38Zq+eXOCres9qtrbjv81FP+10ydlAibLlLDxzxJgTH1jzwIK+vlBr3P3vCz4GAoFf8AguBYPB8g/qcuJ3FMYJdA4Gg9cCgUAAI9Da/WxrzwaDwW3/2SCKBBKCD6lljGX6163j9TcWyNuxsPuu+0E7Ko+R1udhAz/93h9T3jKp5nc22KSbFjbZYKAZRsd+5IMrk2Vy1AsSVNdKfEIpY88vcMRpZaT7wVEJaCLK41n6qFe7nm+ynjCkXDMR35XSQ5S3aowVaFLQJ1/UFSm3mmpKcs8iqwXEyeaGGa7qqZlxtvikzIe27Z9vpqUGedxQMz0iSiU1rHFNRzVUjaqoXvIrBuvmmtO22q6qdLUk+NYxP3+2xuB3H1ZcFmmilVfaaJuVFypOuIc8rE6Z2q43qGF16kpNNxay+OQ6N29d8KmfdFFROaWluaeX2dqrq7gaDtqovTiLbHbaedlRVj7VGgxx8+Zd3Rq0MG3EOxKle7nps5KfrGvtR2OtP3lPk0dye6hfB9szLojces3i94ZoOGmSTk+11fClxjrlq6FwpsK2fDLHLzf3+6zLO1ZN+0uth+uLyROmRHiMET++JbcaUtyTxRGN/hpq4+vj5chdzp618w29TznyXaZhXs343/bme3pu8uy4l21xxd8txgnZd1LRivn1XfikZzTS3R+2jlip9zs/aNqkocbZ88samUfdX2p509NySJRDupXW6qqXepXrO3L5iGVnFxlsmeICZv5+wtrD64TmyVC3cXVXLl2VMzWnHGduSrySZvnufdo/X1/9um2dtk9PfT1WtoR5+yYYYwvYt3WH1Nt3pOZJkSU91ppqk026+ZfMClrvb800Ulikd4v0tPT4eEWVt9NRm+wSp4RC8nvbaK8rKb5ALSV+fFmRvNn91eM7ja4XM+D42+qo6pz9mmrrpis6vPim6HpV/TVyjny7k426M8scG8w31BFbrXdXokiv5iyv39XBJnvXXtdss973dv23AkNDpGDivwOGL3A1GAx+FggE3kVMMBh8JxAItEafB8BQCyOCwWCt/7e+/1fJGYgMTtBbW8O1U8jsuccVapdFLQ+LcVcO1fTsVdnZfAH3Mme2etA8l2021nEdPKdafDmZL8w0wB5r203Sf+5jcrotRpzfXHLN50r4QD/1HHRJunNqCjHTLWtcd3bRQbuenebs+YN2CigvzFR/ueuCkorY77hwWa10TytFLHTcvg7L/Tb7d+9UeFx6pYBhk/rZLtqSMSttLbhZ7n1hKr3Z3qAyfdzYv86HZuvsBdON1Uw+S53VRFa13dMx51Chzz/q4y9qSRcmQT4j7VNLnHJySnFOQxWdVkKxsDyi7pzVxW9e1NqPFnhVHrtdNMIbvjXWESm6aaXS969bNn+v9n8fFHitq8Rv55hjhyI9n9N3XHfbB6wSfiuLw+GZbP7se2Mt0lkjC2Q2atEn4j5fZMOylTbb7c3f/3Zh6DeOhWZWu+GzjqxY7NCxy7Im3RSSM1qpOvldWHrIjTsnxRQtKWtodgf373BawCNNuli+/FutZv8iesEy6ZWr2PT6NB+mLnXtAW19dfxnVly4ZZCP3Kox1OubB9klWkEF/e6Sb3VUenwXwX0XrP/ye5UmvS8uLVlwf5hBn8/y+OPN1K9SUNWBLXDbaHXdE+1tG/RW1ykH7XHEwxrJ4bxssb0MuPKJjd8sk1Djqhxp9eVsHGuQJ6WXr+qjPb0lTdpi4jX69Kkus4DZ3nGsE6kzzlpuo0/3T1XsQEkn40+a3W+1QZteMs4jIlTU1W/yyKK7suoq7Y4Ud2SRWVZ1y5dztWYuYRtOe2NfPx90nKDUZ40t//UP1V9o58b6RK8+UctbHrPaPtsdUEkZqQKqqaTNiPes+3O57JmCmi/qB770pGyivOwHOdRT3Fq15NQx5CnXs5d0++pm3Uz4772uDAQChfHnvwOGg2gcDAbPBwKBBKwIBoOlAoHAmAevp/7/t/v/6j97IHvwC31ULVrNhGOTZbjn6/YDZZ9TRxd1XBTr1bIt5KqX06ply+RLKCfP+uu2pR8TK1aSFAOMsyr7F95PeduAwGD7g9sdtkJC4X5KnkzT5J3nZd5yRpMlj9rjhiFKuSfCDelG2eW18Of17vWco9P3uXs51dn0RU7ZbpXTMosQ6Y7FDwROSQMPmf3xSJfsNc4mB91UTZhk2Z2XqLJwRZT0zrM/Oh1+xLTZ2/x9YZpPNfCy3zVQSA4Z4oSpoaR7wuy0TTaXfTgnUbv2oRrFDNS8Zk3fLOqimJoOOqGU8ipG13I3a8DTV+7bydXFOuQR4UvtTbZIY/Em2uu7rqPlerGuIqm5/DN+NXHxbv+x1C83TsmadNbHD3c06lCS3479om/1hmqczS2uc3XlWjRzcula/3wzwj4ndOn7rr3ztzpxfINxthmXf5DeZ4ZKezB/n5Uf4909vcEveiozvK0Lf1ywef18NfO2kJEz1sYLq700ZYi0pTv0/XyiBaZK3HjO1gt7LfruiOH/vGRL45/tjE6SY9Mlx88fMtcSD2V/y8CUIYqo5eFCTQ0b+rzR3d/xj52e7/2tv8d8oPuTb9ufkKpGuRL6P/euEoHKygZ3qNuso4VLN+le41Gvbh5irT1e005ZMU4674gwDUo2M+/QVLmUNc447TykprwuipTVYYOG/azFe521lFmUirLEpcp1Kd0Ja5xU13jjNVHdHSE6qCLVAWli3HVAAfWcs981l1XX0gAjfKuva867IMnz7wxX+fMK+ory0p+H7Zk739odW/Tu3V1i6VxyrbshR/6SPun2sPU26mGgz3yoh85KPVJfqaa1Xd943KVLiaIKZfb+rzMttVgZlfSSLl4WRxV20FpzXXbjX9BK/J8Cw/VgMJjj331+LRgMxgQCgT/xWTAYXPOgfineCQaDW/6DPl/ACxAhUG3vEyv99vvPbuerKO3s+fto533PaauQXBqXbumrA7O1UkRMwVh3Th1UNqqyV5M/ssUleWX1pedkCJEjNNaWu0d8aJKuMhnRepWr2dJ1nvGwV3RTVkUVX37YhYgkOVNTPT3yEe/qZZSZZvyyW9kedbzsSX94R1VlnbNPIY298eIww38c5Ly9YsTJJZvL8tjslFaKCLVFqoLm26SDKINcVlCMU64pIMqnOd4SKFfYiLUvaayM047JL58Xen8tf/PyXu6UIF6UHTJ7SITXnDUy9mt9r7yBMNwxsvDnGjWpqdL4JgZo6bZjFjuqj8ckOWOmjfrpaq9DGpXoZF9cioxghNnr1tjnmE3dRrgSe8PlYwec/vO0JhOetfrbAzJOZhV7d7cVNzcoH15OjdtxqvvA4cUXnTy5yoReH2ga+oRv7y6WUzHLTTDGUHfsEZO5qISEog4khOuz+Sl/HtykzPBTLpQv6/2+nXSKbqPOb13cvcuUTl8YZRo4uTtJ4pVkgeQrku9ktbrHJ17r+oaPfh6mUYUu3t89Qhc1TbBIXqHOu+15bVSoVM1PO1eZboz5L/0uZ+cycv21W74nGztz6RI5CyhTI1ZJ7Pv9qu+eeEsOmdyVLDq2rGCpUJ+vPeiGRMftUFxeXeSw303lhEt11x3ZfG2hnsrIJlqSMOlC5FXUL5Zb23qgfxbMl124q9K8YbIfE77x4vnXQSPlrbTHrwZYYYlhLT+zf8sOV+/e0unmIK96VCmZHXDUbbuNXXbHzp3/iKxZ17yvv9JpWF/9S5V1yjVF5bXfOV/k/VSYKDkLlfDn+kUent5NziI53AwkW7H2ok1zVvjqrfc1aRNptFeMMF5t7SW5bYzZLvwLwPDffSsR+A/q/kPkCQaDYzEWsgUCwRW/TxMtqye+aScia7SQZed9O+qI14y3bew+YWtOKH4gh4IhkYqEJfjAB95K/tlV9NZaaRHC3HMnS6zUQrFebviI9J8vuuwfCxd86qrsXvWM5MoR/tgxw8HRp+wOvempnk94L+INK2/NUk02PXrU1lUefb2jpCx+dUA1fNlrsK9+HOK6s24ISpEov0y6JTRW+ep55euW8fPyv8TI7iH1ZHVXVQUlSHLKNYWU9tT1wayln+fUVMABn+OOaWOeVu/IR/p3/8ugiW0EsEmIPho+AAV6queGFMknDto04zpy+tRiY3USopBr8njPTJO87Gej3ZbLgcOfKXk4l4tymfzVr+7evWdPoasqFKzkWN5SWm5d6XZIVr23v4NLpnhZjchyCuco7O3TS8Ddq6mCO0r4zV717m7ykTL2RiU4kFxd1VYVfLrwd2/Xf8TPK0cpeKayZzRXfP8t5xoUkjRktihnrU3aLmNRcU+80NRyW8HOd7d6o8LjZlqiGJYtXqfKr0PM6f2TTGJs3j3Ph1qbaK0cYg14vJfsjYoplJrL6IXTLTLGm5p49IcnrCu4TMrTLZxx2fB+P/n5wGhfetlBMw18op9iwl1z0F5pfr4y3ZArD+miuA0OWlTzK9HJKT49MFE9heRw2DUF3HJDe6UsdUQ1pc20AUwr8IUJp3+X9ckKAunXFdkVFHN+jxkesu78T0pIUNzD2shrpT2uS5RPBT0W91FDgpZtXuIvDsrQMVtDReObSD66RIWmYd7UwyVDhUlW4IW35RCpiw5mWaG6WBvO/aVhse7KrW/pER09Gp9JUrZ06anhisSFC2taWDCYIhwjfS9BvHqP9/LSzBZiZHbhgTv2f6X8n2olLj44QnjwvPSg/gwK/Lt2+XHuP+ssu2xmGKewJN+/9oHwwpmtvLvZU48+o7wagqVyOH94q7eHv2BE6lj/HJqkugY6yudiuaVKuC2bm0KFSMtE1qoxpm9bq2CghaBC8otXVKjc4dk9+0pfb02fpEBMvAJ3b7o1b6eK43oo88g7amZprY7sLthmZ68t8qnqulzeqTDKH7Nm2GOj6867KUpRUcbbYWfEOU2n9BHapJhE5ayWaIeVVtuuk4Cm4rymqd0PdAzN8beVVj9XWh1NnXBXoljrl451Iy5clIJKCpcgzOefTLOu5ES7u672RvGn1BYh1W6FUrOp5n6S0g0uihfihJ0e1dQU46Rgo0SFRXqjzR9+tcmEwVP8PXeGEvGxxjVsrdGQA6ac/9yJCetNbzBdN+2ddFK9vKV9cHq2ZRY7vGa35l0e8ez3L5r1wVrZZDhuo+rJBzRTSGJcUP/RYx07cdLDoa0UddUES5ybe8D126mijl2SRW4nZdLn+xfsSj1tzwPl6sxN8yTaZrLmxnhV6MUSvhs1R8rVU87a7Qt/aOcTb77wjLde6qvAk+UtGzDYzaJZHT+2VR/PaJvvcVB3QFN72003e/giZw8c1tEz+hutrDo+85vw0DgB2V2RBMZbpFh0hkIOGLlpgBYH+ptv7QMNYym3nVVJvPzqixUjEn09b1SBkWLKhFux46RMR6+6u2iGLeenmG2Hv1yU6pphXrHQeMWr1tfHQ14z1jXZ/O2woEwiq+Q2o+1vhvV527ngVXuP7hCimFLiPe8XlyS7KYteD9UzqOwg550V6q6SSklx3O6jM11ecMzXq9+TOTqr9HsZsmZkVqJyMa0fbWLOvPti+QoKaq6hjMJhvuw6xe1/ART4Pz9KfInEfxd8zBkMBt8OBAJt8Kr/HXwcGQwG/9M0u+XCCgeH3W3uJ78YHTPC6msbnXPc2w/kog3QzpPafdFPg7drekW8nhVGGLN7qGuu66izseZ4yKPyRBWTrXVFeapHOjr2oIcO/e/032M86o54TaMq6Jv8rtoaqZi9hJSUeYIay+aWVFnklOJ789zGF7HDJGeLtOjkZNtsVF4RZ53XQkmLJaudo42CeeLNOzhUCQU1ierpt+TJzjonn4K22aG8WI9o74wjzjjlvGvaa+tK93L6Tux//zcQ8Jh82nX7zrgp7W1R1Ctq2mSdOwqLkm613Soqp4CsLkpVTllJDskt1UXZDbFMdkXcclwGuqogu9zWuC5or1l/bRHIHmVJo85KtnpB+CvNlYkP0av6AHP8arxuQtz2pNm2rjhs5+h5zs88KnfeZPlrtjVz5jBPfzTW/I/mWpO23CvKS8wZ6tLVfT594BXQS21dJ/xgwjNvuS6rd6s/Y9KWOQb+2t+JGze07/Ow17r9pmuDki7vTzV5ZB/f2mh00fe0qt7YjU3HLcq4oP+pEchkatfP1Xu5vr0F00wtVM5Tattus5zdJ+g18SmHSv9sxYE1DtjlB9s0U0y8YnKJFS/Bydy51b+WKph23JPGS8UbqvvOFgUQhbrKqq2pp3334DsUlV2qVzv/ocz0p3XVWKe6ZRVvUFxEhfzmDx1pwcHxsqG8kq4KqK2SPy0QL7tU+Yy0FVkNUM5ceeyzyJoPF5g+e75uXZ+UFHXXrZSgeRMWGL/na/21kF1xa82x2P2QXHtF1ZRXNF61QVCMHqrp/spzEl6pIz3tFu6TFDNy3LPvh/Ne+qy/LHapp7iAIwZP3Gzl4L/kux6j4/V+/33qykAgMBXrUSoQCJwJBAI98RlaBAKBw2jx4D0swDH33bR+wsv/lUGcuHvSo1U6qKKlqde+c9BOh/8dJaOsOG+Z7MaRZKe23jTHBc12P6mICoop7Xe/ySOnacZ4Lrmv/tMGu7N4v/TIu//WR0mFZLilcflGtiZflktJ6yzUolwL013ynXkSctbw1NQfXRalrIoy5LT5ympLTi7ztQ0CClkjUTkJMoTrpI6k62vsODjGI55xS8CXye9KdFFmAbsd8mvFadrleU1/P+n7xFeyySog3D5zPfb0E/82vjBRLrljw4VTqon1rOb+tMN5KTLc9q21tkp20R1xAork76KvX9DQTdUNsUxpMUZ6RqsHZJzyymmmrikl3hEqXrarsRIPblR15meKPFvNnQVnpU7cqrVYYzwrVrxoxcDVOfu8MX2y99N/UKDXK87OXK+IOBcG/2Zx2hpfVr9v2Lrx6iGZXXdo2l0/RA32VPuPPPZMexNk6KG2tVumWGeDpFq57Px0rtcjXnFlymCrP53pq5GfaRHdAQSvBOxbt1XFE70VTrxtiCcN1cugqUPsXbtPgcSsItD8l8lqLVlp88Qxqmlu0dl9cjRt4Wvb9NLD8wXekRBZR9m8dYW769vLH+uYNtglG3ylncqona2RIFJE++ONxeI8rFjtSla+t97iVpOVU0krjb04va7mHrbFWdGPVJEUmuT4mB2KRTWyFNOx3CHPlnvTFXc9r4djEo20VQ/NzKk2Thk1tRPl0KQDMtfKJWfhOAc2r1cwbzZV6hXW4cWWbo5JFlDYSgu1FqmTvHIjxlmV1JUmIChNtAhV5ZEle7JDW7dITb3vmJ0lSxa50rIb+/UUaWJcwmxHJCL2Yoost8O1GdTjv7IU/638p8AQDAa7BoPBhGAwmDUYDOYPBoPjgsFgYjAYbBYMBks8eF590DYYDAZfCQaDxYLBYIX/KOj4H5V7GLL9Df2eGSivKIvtcckFj4jwnlzC5BCLqmOb25PnmlT17Zemda1uTklUXV2hEuVDTeHWWeW3xbPc2LrGIxr7VFdPSHDAaY32PGWSb/xhh5XIqFFFrDi7XNVkzpsmdx2kYpZqfrHLWlf1tdB7344Hm12zQ5ITUnSeOstpp+QTK0RZTxlruENCZZdHpOwCYqXruesJaRm3tVbGS7+/oJp6rgqxRbxxLV71Z6URsgjYKkm8nDrFlZUozRJjVVdLUfEO2Cov2qso1l1Z5Ndz4H3z12O2+8MGdRRRX3lzTHfTGXUkeN80eWT45PCHdjqr/dOdRJ0vbsvQFeaO2abl6BcMGvmj0eba645bMux1DVz4a6vrDnpdGfkGb5HLJYVqtxYfiPRGoKyTW6bYKM40i1yR16xPB0tN3upGyDXXnVTWJWvNcUeY5wr24Bwh9Uu5EnlDhzZf2Xpqs81mKf1CW9nlcitHiNLDulk7K8mAm58pkxDvlFPCVHLr0G27+k7Qa/pmL/UoLVOdgj77aZJaooSVKKnzsjdMfWi+SoEcyufPJeTGGXnP7XbeIgVkBVOckyZNFTVlvZmiuziLe05w+PQh0ZH57N2wUY6Q24q9U12Tpf2tLR4mqwzFXJHFNdkrFpOjdGmXc19W98PH3M+fFuciUsvmUr9HH9dyJaijjaJy+cVSh7d+K1WGT83Q4KkO0m/eFFUwp1avPOFGzjRXwq4p06KMSYcX+sJPljnhthxKyq2NQq7K5W9TnJWsiUxG6iwS+Uq08vTTH4uMjJQlSxbp6elOzDguMjVZbllUfGDjlojn+7dTJG9Bx64d/K9iAv6HMB+jAoFgNeSRw3vxnzpwYZPp5pkpUWUM9pyJxjuDCYdOirwcpmC9PE5WnCr4QhVb/l5tw5+/OG69chq7KL8WsitWu73PNvQQKVx5hW23WrRcCinubavVVUYF0cKkKaOMp7742q7tG2VaddRPZ3/V2aMO2O64nYpoKkSUIX5VVoaqKthrr6uSRQtVShu77LLCEV0VcFsBpx1UQ4TzzouTx2ln9fS8ziYIPqBLrxuyTN0Pm6K0cg7oorIIN7y8Zqkn6pdxA9fddgY50d0A7/vDzJKbAAAgAElEQVQUXHz7kjxfxJlUf7qn1nSWXX4pzmirgAgplrlmZPa3nE+5YqRfPaODj6yQ4ZobP13wTq9XjDZbcy97vVJlBXeeUsFHTs29YPOI+UYv+8lxqZYOmuWNoS96pFJTe3af0qtzVzOnfSFEESFCPDHnJecv3FTqr+NODSrvtwbD3L577IEb83KDGow2dPXLXmwxRuTxGy4eWSxWjK/9robieuskvEJ2uQsUUrFeKbvLFHTqsQGOuG2HAwooaeSgobY3z6TijSzOhocr2SyPGWVHybhxzczTK1xxWlNhBtoNlnjVdltdtctdJXz+4XxZhxTwsUd0e/k1H49ub7wUNTHrl1UOZsmuSGhO50rck+P2LfcyomzpMcKZw9PVqTTQ7mxp9mVJ80KLKgq3LOenWo+o12yATZuX6D9+qMUnVyletKAbZxNF3ojx+ruNxbttvvR/E0+9poF+236UnjlUwt3szkdcMLDzDyb9NNKFEbuMnT5A5xdftfDH7/S3RHMJXtDKSku10kWOiCi7bx2WvWs9a48f9/SztVXKU9GC75brvPQ5K2qN1njjy27N3mdyh0F6PTCT6SHKeakWuvN/l1FLHvGKixYixZIL0yyxSLqsooX4zBA3pBiad5RKMil2NdbFsEQZ+zIM3/WxWzMOkC2rl3p+pZ5uBvUcq7ys/rFdlQ0PW+ictmr501IbpTrpvGm2SEeq/SrLZostCsjng7f7mzvjD5Ve6aiQBDXeaiNelElOGeBXj3XsqrnCCoiyyCbZRMsuxkUFjDTL4w8s3CuqZo51gnIJk8ejuomQKiBgmY0me8onunpeSVdCGKotDnjUw2oo4pRbMiZvMs9t1wSUl1cIDuJ9X/lfl0nPffE8omxZcz8W015rDTRwU1aZ3NNVDbdTbhrmV9VUUF8Rr2nv7+eXmf/WMGXd8op2Kgsxbudc5x/8uxZsF+/3Zb3kUFNzZWxockpVUcJ2LvFpu5c1mva87fJ7x08yOefp9k8rU66AEYdmiDsVdOzun7LKL8xlbdT35NuPqaCYH/8ZJa5aecT72u8qe0qKeDHZsslWrJSWCwZp+f4bCua7aoFdOnTvKEF2pRUSNrS6Oo2K+/T7Cdo2e86THnV63wIrT/8gynpFHfH7A1Cor4S1FmiigazyKRHXwrdDnjXBa47aLP1uQJiKJnteSw2cSEpVKDqzMe+9p9KurMIiwu3atNLjHZ9yVorvd0731rrPRGYkWj97qxdrdZRfgrlLv9OiVUvrg4cUalxYSuFbbjeNt2HPRqVE2CNdTnTJ00dH9dWt3d7N4xnyXcmnXc1XXFhzQ7uSzWSpFyr/9BoC9vjyx8EKiPP+AwF2uKBcIp1zzqlbZxWNKaBcrhiPNK/g8tFLZnfoo/PS58CBzfeP3w91KKvdrp8VeJCwuLvXxQv7l9bk/whgyBQWrpJHXZGmury+yvahBCEmeM4Wi11yQr5m9dXVQd/axZRLz2njuc2uuGn3ym/c3XFB1EvF5Xv7MRPH/2CVdQqrK4BKnvKPNe7hAKZ2366FyuAlXay0UVEl5Q/Jo5Ichqf9KuS9fM7a6vsxf/jHFhdxA3n+qKP7y99plvc1T0Z+6ZiblrhmhyNghYkel80NJ/2su1Ki7HfXr3ZrqIkI8epp4YpTLjvlqKPmzpxrrv1qq+Ca8yY6ItZNy39YpIWyjrtlpXPqi9VfE695Ubg4HdRQyG2vaatWhZp6GmSSsQJi3BTnkIDMKtnklicNsNwV3fzslE0O/PyJY0n73XTHAbsl2maW+WaZZvX0+5dIn5X/WDVX5I0IFfxph6e793S7ysPKzX7MXelm+QG3te7ztgyXrZ/9twrzhxjW5S2vdvhGZVF6PfmBz999Tcqa9cqINO/94VrHRJhmq9eKD9FWquICQurHC1wIyCLNIevdTbun5rPtVQvP79lWvf1lvrlNF3k57C3PPtZTqCOaqiqT25Jc9qiHVNLQvIZTTPeYP7p9paLiztkpPrqpa4EbrtrvtplqesrX49p6tf1ge+xxXYhiOUpLjeD94QOsLXZPesZdNWpXtC3lmp8km2eFvvmeUTgkUvnsBc2xxwmUlsf1excVzJVTeDCTbOnxClzPIfLIXcVkyI51T27y0/wv/GGNsA0Hbf9qmfBm4ZaYqf6LDcyf8/O/rYPpUsUqLGfeak46KFSkNTZIcsctmZyVKjx/YV99943cqTksH7/DTCt8q4PaIh3KWCo7igtxbMA/fjz8o+eV09iHUuX7l9bk/4ijRCCQKThBD7vskWyjK3hDd5uckU+C1TYpq4YEOTxmtHpYsuycu6FUrJdXTQ080qaJtp2f0fOZ6uolvKB2hZr+WPylD54aaNKfi319faRnPaKo0s5bLl2kk/bLJOh7l/RTWRkBN0WbYoWPvetiOKm3U0z0nTryCFHcdInCRIpXXqeCtTh1SaLDVlijV9EnnSycU41jt905cco/TvvSAtBFYZukOeaMGioq7boLzuk3YrmU/QfUmH/HqLMTfGuL+vJ5SHExinvVOK9pLqskUQoaZL5ntFc4S0kfpn3hF0/43E6hCtvlhPtm67ygl59sVEYNw9t18uPcZ7XxuBf8iFALKn/ndNhpUzYs10p12RzVx2yHFx6zbvhax1f+ocay8do0KIUkX2kiNCSvCm+3lGXGPtMO7lFRlIbfDzT3tS8Eoqk/tLW7ty8oXOYh37V6Ub/lI5yJuWde5ddNss6ojqPc/GOuBl3ecuOJSInjdsnTs4SrJ0ItfX2CcBtECFP+ld5Kti1vxEPvWeW29uKU7PK4a9NWSsud287L8416wIf4VlGHBb3bdpS/571pt8LquqPG8mVGNMkjh+Juyy3ERk/uPW15rRfEp+y3wEVPZ3nJT2m/yeW6Rc5ateWMXeHXZYuIFnEy4IfG7R3G60o4W6G0iGs3TD4z0i6porH4id+l96kkMT6oyPEMp+OiRGRccytbqJDwJPFrowzs0s5oBz2ruLMqeUw1Weyyx34j7ART9bDKLsmyeKb6C1puuR8onKiDvwU0U0S5/nWEZ41TLCbGjebxzNoqYejDWsviJR0UbNXVsoWTHbLZ63pqY4jisnmq7hfKXMjqxLEkj+n/f9dRIl5OO5w2wkZZtbQKI0x03kbHrbfNYaNN0dd9x959iGia19Vbt+WVU4S13v5rrrzPtJWuitbBiv5ePMUTsV0EChdQpFRD4xLGu+eYqzaLkGGrVXLJJU26hmiisRS5vWmFwdHf+8skWW7vN8c4p/GNi6qo6XktvaSZ81bKkz+br40SFV1Y775fKRoerXBKFgdPXLXBUcVk0kEOrVWQJotjziiCai6IFap7yBuO9HvF3R8X+7vcPaGSdVLeGmcdx3JLdVbRYkettUu4WKTa7ZaP0z5GqgVuqaKstnKaop3H3Y/yb7FJZ2XkM9fyuVP88Ptms0vtQ5gpZX60av8W6XHxwhRVrGo1lyU78891B+esdXTJLFvuHbG0QW/xrlga/okm3V+St93DIkpUlaVGXdWadZSjT3dJyzZrlJ5dq1c7iKnV2s1btd3Lmctrw7/yT5/Z7o05Jl6cR9UV2b2mVs+/a8G0KQ69OdWpnddNfeIXpz4Yb4l13rPZFJftznLTtbyRAiFxWigmul0TBeLzinbOjcuzRLogLxrjjjw662LEvI9kUkET5e0VbW3z950RpWS2epIcUaPBh5Ke/1mmlLUSinVXVIT9aYs8qo5QAdFYUH+gueWekfu3A06NnCfWIQkOuma32ByRMpdIMHbkTgO8qCg+/L2LlLf/kuXiXfvz3xWRkeROpuxO9FtsVIGeJnQZZYbTIES6dMle8p4zWYu5I9JQz/jNe7a6Lq9awhUyfssC4zquF6mmMRY5ao+QmOwiAwXYf8Wod4bJmH1Kn6HPeUdRTUXhru+O/K1t665KyWe6aV70ooVumrtugMshGS7853Si/5/yP2THEAiWkVtNtcyS6Ib1eqkoxWlRShhjky9UsNhuWVFYuJZqy6Sm0LzhTp6/pkR4OeNu/SGv2wqLcFtmtbJWs+/eSaMcc0UWTZww1QlD1VBWIQv8I1oulxyTgqe8oqPvlROhlhhdtDPTMuWUUkkBf/vHLZFuilBbfrFiTPOXrtE97SoQ6tnMeXy+a6Xlwd32OqmyCE8qLl41xwW1qF7Nui0LlY2u6YOkSTbaL7+cRuXs5M71DF0zfsEVT+misjPGWOMwntVS40BNuaIj9bs+wzFbbPGJcxJFCtPEMFBccwOKvSglLckvJ78175fJBMJN+2yekwf3+PCHPq5XDZecfE6xiCJm1pugl291VcNDMlQbPsyXb35jwCf9/fn+byaZh6xmPTTYwctXfLdtrj8td2rkDu36Puax6EeFJW1XMK6OpsPflPvp+9r/0XH9FS1dSJbNKfbc3myWPRoopnn9pmav+d1Imy1vPkaTJff1FQO94pLrer/5nCKlE2xfu0TCi230rvOR1cGJjlYfZ+iWnnaihrLG2qeeshqK81B0K78lfaqEisqVbKll7S5GJc2ybe6P6qqvSNXS8n/4uJiInK6t2S5z4zJGN+qtiExO22u5q6LlkdMhpVTzg63yy+8AesrvsejWziad1LRYY0OPfiivovLLZZf9ImXRRGUxH3QT6FxYXFK4ZW1G6XV1mDi0F1BGR7ftlCGfCm26iStfWsiKqw5snOykZPnE2+egqqpIkmSu6/5yP2v5ix7VWmlpUvxqnntKeLfvMJ1H1nUR76uiisqimtf0/pKhXvWwfY56bMS3BvWr4g5WoaUKEpQywR//d+0YcoXk11gDIZa6+NafHlNZjMse9bAxDxiDia6KkUcT1eTUwL3Ix31jqp/PfeFMMFXmWyfUl0M1JZVr9L4KD73gk3tzxGYtLUWKC1aoF3OfVjHBZitt0D8w0C035ZJfDiHW+N2XquihgaOu2mOrHBL0Ndcch51xVEBWm11yzC1L7NAm8JRjSYkG7Zlq5YHTambOo5+HkaakBurFPWyGAwb7wmtbvhFdIpfLd/Z6RS2Twl6zrMonribddDl3gmxKyKuzDsqYaoeHNdZCbu2UsjA4377ra0175SMTy35ut6UO1k1wzU2DtfRn2UmSrHPi6C9On1zuo1eGOlgi1ZVKqZ4Z21rzfg0sP3vKvVPXpaTHGNHxe1vslEsJt0SZZZP0G+R2w6/vt3TcCI2q9nXDDT9v2e3gtqMCMntBN2tS15n442yFkpLJU9u9tBDlnm7p82FTzByxWNncxdyOijX/9iphMiuijtpFWrh4MEk9db0d+rq1S+7v/gZ5xkRbjTXZ4OFvqdqrnFxt6zgTF251cKJf47/y+5Y5crqvFumguqJC1HFLARF+TBouSqjFVkk+tF+FiSU93rSFp3/9SauvB9Ewn0y5UuUJu+fXzNtlTr+m75C+On4wQKjydruHxj4yVFGZtUQhyTijgjiHkv5xV1CPo4NNcMSnFssdW10RZaQKdcopJz6Y4a+yT+hbp43hV0fpr6ESsmmspH0O2CNKKVXddd3BE3vtuLDTHTfccsbNB4zIysWrmmGq3xr2sDHmK6N1Us4Z21zwvh+Ei5HhkmEjB2koSjGZ5REqX4WaTp45rYQQv/hFpKNO9hvp79+Pin6QxWq7FLmV/pfW5P8IYMidI7sIAZnkNG/WLK1UUdTjssiphRLmlhunlMYaqiirKi646sKNJZqqpJUm4m1T6rOX/G66HW64tHKM24um2mW3K/cu+kE3n3hRpxEdlRIrRmaPaGhYcJBUoZLdkEc+yW45J9ktVyS4bY6Ncrugs/KaKC2rKvIprpOSsslskkO6B39zJGcWeaS4nHrF+bRk/UxRQGGXnfH2pXHKyKG1Nr5p9KGJhzdIu5vDi773+p0frd2/m+wB4RcPmhzzkLdEueSKl72kcZMX9an+uYjYwsY8PlpJpUz6/mXb943wnGX6r+tvlHHKirVr369CFTPPNXec0Pf79jY2GG9l98n2hmZh20mhlwLWbdsneeo+zWo1kU+ay3aKxhUZchXJZLu/VdJSy1ojrd82yqAWM3yS+LlDzrkiRhlFbHlrsNDjV5V+qoObKRmuVyrpkrO2vjfH9H4DZW58z5nQm2rLI65ELRP8alfxrC5mSpG3ZQOn7h4XJUZmLQ01VUGhhlb6VG9POYHkcgGlN98npxUokEmc7O7IpZYirglVSzZZZBIuXXE5POQxTdQ3OTxC5+yvSrxzT+1bkdaumCp2S9CrdSuYMewXA+M7y/Lufgs+/NXMD37SUJyvPKKY0yobZK4MWfFsxKvw/1B3X9FBVd0X6H9JSCO0JLQEEnrovUhvAtKkC4hiATsCFhS7IIoFRcSGAorSFKQjXaQJCkjvPbRAaAktPbkPMr5777gP9//dl+t/vZ8zxjl7zzn2WnuuuQTJb5JE6U4Z33/mf/Zrzccbe8UqE212yFkRCqispw1uyhGqvKKCRMmnrhBXPaSPwDIlJP19TdhvSfYn/qhRoZYKiVBWczUV1+3Ye1bL9fGG0b66ttgz5gqUYKr5DuKCK85gpd9MfmuXynLco40S7ZtYcGiKok4Zpb0NzvrG997rN1LmnSvxTEnO3al1/U/jX2Htlpae4RPzPKKqPSc2uSBPIdEm+Bi3rN4/2EutPja6aVed3u/shNtihdnvgmzxAkS6/vcW9+mk6ZyPfdM33qQ77z5qtnWuae5hx2f+5onKr9tzeKK9DquonAxB2ultk82yZeilrY3O3On/o7USujXs48C2mRbZJkFNlZTTdkAvNWbV1d0b9l69bXavMS4uWC9DoMe1kC1IsCAL7ZSiqGWWy7d+kQ3+UEi4dDFuy5CbfltQeroAueJiK3vy2pdqq2NUtfauJJ8VkBbiscsvajmvmFaauCVduKI+1MlIU11T0kkHBSturOYm2yifksqoYZffVdtXW+E/zol9t4eUnEA10+KU79Jdabm6qWaFB82x3crH1jhao5Cxfx6wovGX/vprjXgtVdpywIyQj6zL3GV4w9pK1Yp1ZOo9Ej7soqJ6jtlh2rmJgoWZY45P3v9Ni1fvtuuJ6er43u5Ki710dITXVz/rZR39tGqhLU66ooZfCndU/umXhNxbQdVmlXyqjeUtxssXGuRmZmElFJMvt5CYwPLK5dbVQiPHnRKumCoq2O2kq/Lb7qKDDnkmLckFGTaOXCEw4BEn89b5yWbtxQv5dYWxv34oS6AIpZx03VHBTitquW3m95yq14LBpnVaLHLzfpM8o0uH7q6uOqJWlZEa/NTUgmlzlb5QWmrVHDu2r1YhK1C/p360cPcyTWRZUuQpZVNuWG6hYyLMtt+z7b9w9+qeDvTa6OR3y81KXW5soeEGX3/FY4VHO5W6XpgijjgJbtojQgLY7GctUEcn3boPdSsi02Oz3jb7vbHqqOsTv6o9frMcyT4Yc96GN99SXgFNNHfRdXmyPaC7NAfN9v/ql/R/i38FMRQpEyVx70kX8ZNZ0gUgBre01UOoq3LKRttf5KZ6wt2loRsOG6qvnVa55Kyf5y5RWkV1+7b0f02iTktWCgFSfbHyDa8GjRSjjaO2yHRLORUcd0y8iuqpZJmlYtX9z/P7rFf6WjsXHNVMBTtt9bfbpsxa6Nt2szVeU8Mhm1xaW1mxvGj7HTfRbKHKyXDSEK9oUqCYIzfbOXVnhsVZN0XL55JEB+T53mqZTkpq8rxu+web7AOft3jTgG/GKqiwM/jLJXk26x/6iJtxBVQ7dtAB4c47prKBDjprq3m+7fOzr34ZY4N9YHSf8Xb9vVW1u+8VfDrdjVIRXqw9UM3ABF/vnOaGP0x3RNef1qjWtbfgEjne8rlYcdIV1+JmfoucstYBjS7E6tT3Mev8jNtei++mfGZzC24dFi7P/n6HjH/1PQ/EDrVvU7ovCo30y7Lv7HbZ+OZfKVe8sFdL1fPK55PEOa1AdIiSdarYMmE1cpQUKysjh43HpAfV9HyVQbL+Pmeer4TLb4d1QkT5QbKKd3ohCos21S+KC9DVZXCPGBfzvvBIqy/Frs8vTDlrLVFEvMqq2OGUWJFu+tPLuhuotfw1insxe5qxS57znB7yueTwnl2SZLjn0D2ydmbbn3XamlXTtKj8jHoNSoL1Dy739u4Jpv+cbna/sa66rqgKshzRTmMbV39tQds5fttzVMHQAjprIze+qBP7eDx1hJ887pJdmqMMvkCsI3f2Hu1UsMsKqYuCLLbP091fduX3371/fac8PCFMaUVsfPMzW/ytrNqGWuHyezcNycg1+p3+Cilnu2Sn78y0+J/EvyKVyM1MV1w9a131sEeQafPINchnrdU2SdL+TITnRo7wqf0+8rN80mU4KlCk910w31wHJeKUChpqhtujE91Q1BJ8Za5ZLqqZ84IrLtvuIIq5Jtxrligk1nabRanvT7vM0t9bKgsS6MCxNU67bLHjCrrPXCesFmH//p3O2CfZHt+mTHbUAaFu+6HhCOt6/oim1tnt+ZuLdAioao9fTSo30y5/6qKTdyq8bJLfBQvRX18zp3xhYGhhI3Q2e+tGAQ4LdVUbBZ1AYfF2Z2zUtHxVq9zQWA9lWr2tsBIS3HRZQVV/aeOtFz40sf8Ymx5Z4uNfPlG3xT0WvTJX3vzLbu65oE3HWp7d+aR8YpSLfNK7bWcJyAhTpUd114JzLX50njf18JR05d102XHdJSjZsr3CL0X7wXoTQ4Y4f+6YOnNHu29oO+9qYPWGpSaZ7rOfx0nNn+zpYU8aY6E+9R9R75029iz5y/TPX1IrMMQ1kU6UDLWs/zCZc2eDKU54YutLps7cKnb2z7q9N8hhGzWUoAByZYuVozBKydS6cFtRznhHRyOMsHz0P/WomwLcEz/MlvXbBCnstHOilFFBZYWEOi7ceH/oZrDjjgkQKnb5TfekxTrinKF+sNF2713YrJL2YEjdOqocjPFiq7fc1TMW1BXm8ox52mjpx35veMh7NgkSX723byU455LtVikdVMBbvw2yJXmmJH/Zsu9NH3ocbPOr5XYooIz8Ufc4PHytb6fOdXVLpokfbJI/ppnOjSY4aKm3dJO1aIGOaz6xacptj2rjW+kWyrLcZssdc1KecfqY8npHX79zjwP2WSz1vyIF/iW3EtVq1MzrUukpHy981rgiP3sppZ9aehlRvo2kiFMS957xlTngSX1MsUjOnfxpuL6aqqrT3Ned2b1TuYrx5jzytM222eqc16PeNfnqWZVsdtMFAa4IFqmK6s655oxzklwx0MNuOCBONReddkmyy/YrIcGoOwz+rDZeGzVN7Kgy4A1vUjLQc+8OFvdYXckTD/lwWC/tx8/R6oUYB78+b86QJ+QvUFnbp/s4fC1V3QEVbJ7wt+kLv/N49YGW7F/pJ0uQ6kO9pbikqJLaNOnm97RTuldsrv8vrbXRQnnFrLfRNwMXGji9mQbqS3fQ6E1XDG9eQrRy5tuthlAtdHdKtmsua1ell8IvlxdbO0agAgLScmQeyPXZE2+Kludzm3Up/Jw3y5czJ2eNm1cL6VmzsVat2wkfWUwOvig0RMD1ayqoar6tOkU2d/jaMZtkqCvE8407mLJ7kfS0KwZs+kDy96f8OrWHIXd96uW/ZqoovxwR6g992rGFqz1YtbXsHjV9/cwgBQVLE2+0H8wJfFnusIZOLtssMzfPnmMTVFZOSwluK6Xeg/3MmvG2YCU0erSHjd+vlm2NTCFOOSsUVQsM1KRnDQumf66kBC9ba7RW0lwQo7pUud6y2EANNFXFJtvNdMA/ovMAX3lUckCOUXnfIVVBpT2kmOIKSXZRNWVtcko7rVwTZoTPFMSvd0124fwp7515TyGVbfRPf8KRgA8l5I30c8lJfrvwvUzXxMl2XIbvlu8T1ilSeTW9V+5B+U7lqPxAZesX7VeqbYKyFYpYOX6So5arLNJIF/QRYIE8D5R6VsHrSa7c+EOQLDPvGL92UthOuRrqrK5z9jnimHR7XP//zajl/1ME5fLC0L4OL5xnZ8pqcMFVK0+sMNNyjxqmqaYCFNROJQWUV/m5t1WvW07N2JoKtS/AfaPB7H4LzLbRQVecxuirb9jSZJlmWybZg+cUUUyUQ44qrYI99njLCy66qIKWrktxzSW5CjiMmop7WbyqqnvUZxLmfG14vqf0Hv68wNRkhduUkl42Q5oyGg97xgPh7ZQucs72YSvUfLqjx3TV6eWWsovlyP9HkrP5QoRUy/BuxdcE3wr3cMh1BXdm2CfdzoBAwx78VNPp9Y3YkunVkWNs/WSWUgJFSReCW9LtmLlKx6qjFStXTNeYqk7mO6OgmiIUFGufmu6SI9DH5huooTWH5no173Onb9wSlS9MRm6WfJWDvfbdOEGbr7l/81mNDvT28UPJ8j//kls1hmi//H5dlkeZaJDjReoISTkiPby08zXyW7JtiTbXqikkU6hzilXs7JU/P/Xy9FleHvig5hHpynau566FH5i9/7qo4AaCsrY7ppzcz3+y2iITz+zw4bl2SgsQrKCoiDLconKXarb2aajKhO+lyFNbdxVCGtqbuUOFpnd7c8ZHvvvkZ0tffFuN3ne7uOyEmIvRFpkvC8XUEXFzqZJvf6jxtpt+PbTY155QxFkFtHHBLaccFiTCT7Yqq7wEleV33m3ZyHC0bLDOAzrZHNXN1e35zfjpW2sc00Y5JcVJE+mk3f6WomXRhpZmfWFG6mda/vXPKWCMRxzzk2yBarrLt3lfekZbZy5s1VQ79USbZLZJ81Z5uFOCj0JeVapuRSFXbliT963lMy7pYoCSRSjWqpyEvY3dWH1KgGQRCinguqHquC9fLWcLZjt5o4ySqgu1XDGVfGgDaC3T8juDiG86ekf69j+Lf0UqkROQJ7VcniV+10yoD2vOl2yd2LAqyG+61Tbb7KJgg0yV45YnJgyQULmcs+e3e6/0u2D1O1s061hJnJI+0Mub2oh8tfsAACAASURBVHmv4Lt2JVw2477FdkZMlqOAGy6prrZaaishynUXnXXCPPMdclq0GOfd1lgbSY46a53DVoFzh4959oNXtPykspzYi3KrX5FcINDM4S/57t0hIirlM2fQ+/6cuES2JIHCVIgoLbxajNi+tUSGxmrUoZ7cyreFtw+SGB2ooGhbLFWvVxtjp38JWgbUNPPDiSJKxMsVopAIAfJ5y0gncrfJf/CUfBdSzQ5P4vJ1ecLtlKSvgeKE2GcFqKeRSBH6DO6mXGJZR3/fJ/RmsIB8efa+OlVqZIDhB563Ydg+iS9P03nodHElS4N+NUZIdkrtqiVNttKRtJMitx0zxuPWW6K4HC2VVrNYIbFuyx8Yb74/RCWFSz50WHKxWy7f/EtkVrT+PT4yfkhPnde97vmyb8twWfv+fZRXS4SKHr31uQtLDvJ5I483L6tYpX4etVyaWy5kpqhwV2/7Lp8XpagzyckKz3vImuemmXZxjPBqCdoUe0QpxdV3wxDXBH68U9nRfaxxy9O+dUies67LdUuXuAHe1c9IwxUTL055w/W27LHZ1sz406Ov9PL42E5CO4WJfS/Sm3MH+Xr8KFVdFamyOS7opJd+Ax+2+/ISKalzFL5jQHO3NlaZJl1J9RVTT4gY3F+9uyDbPeo9tbyghIu+6f2SYZ50T+3SiiUecynkosf6j1UlvLXhPmNwnIwaISpO6KNHzSdU1NHx2j8q7V673bA6cbzyFTq7b/j3SlSsbbZUH9qg0B1cTbfIZcccdkUrrf4rTP4rUok6VWvmrf12jciCJQTWDVBSZTt+3uODfkNVCC7mrq4tjVuwWqArXn34aTfmrtPq9su6am3opLFqNo8RW6McOPH5YeWHVva+F7WPqW5i0jMiBNgqzWu1PzZ+9wiFcRTV3KutplJN1l03P9vrtt3q6GCmNUYZ4JSrcqR5yFzwvf4KirPTGVECXJDiB6utH7tB1dc6er7sM9qeynVeiufMkSbFcE/r+taDivaKl5fvuny5QYID85GXR0qGGycyBCQFSz16xrmFm/185ZRldpukr5qht3yZsdBRh+1FcdynrCVOOYJTCw67umKn6Llnjbs63lTnPaCIIJVcsE1JtcSpZItLfrXFa+p4dvV0e4oFib6c7qt2o3xvnlaqaaGpd02xdtR2bUc1kLvkosB7S3heMz9KMTq8hfiPejs9brk6JVrYvW2rBWbpp43HTQPx7vJS5Z6GHn7FrG7TZG07JjXpomEmgwVxryhRo57sarm2jP9e2ej6yrdqoHr3uuIe6uuKbVb+dtyBuz8z2iSD5FdamCQXZSgsw23pIjzf+VP5R7Z3dtx6HywdZsrSHVZ1He2YdbLUlWWHJ5+apPqkDkZ7WLDTooSgoKf84lVPqBpe0rmCEZYkzzB86GvqPlpbXl4+GcG5iqSGuZZ7TVrhcCH5spS8VtSwFq9rElrEnIxThpW6x6XMRA/17m3x5J88lzNOCn7QxzEndNLaH/aKEe5W/kquhaVJvbrO5w7op6epFuioimhXVJSgRpG7FC0Wp83R5x2besirg/sbt2WxjPB8ssJTJb2+1u21Z1R+4m5vfvCAbno4aptkJ9XW3Kd+t2rkWu+vW2zpXz8pK8MfzuogTmVR9jponcz/XQKnvByeafOAEyfOS1DS+nfnie0Xqmbb7g5mnZB3OtFxO8y1Tr0fGtl6+6rPPWKpde55qinn8nOnMar20OrmL97uVZ9okDTIbem+liYeUW3qixStlDj3qWJQRF1lnTDPCQNN0T60jRDFHPaXZ/Xym1nS3HBFirvkV0qUpX5y0W7nXXbIdQ3VN9dob77W3f3qmX5qkj9tcNo+LSSY1fJXmx3W/p1m8h1MEZIeKjg8T3b2LfnTAuUUDBVRp4iclnlCe5Z0LvOCUtKR7LhMOzOu2ipTcRWko5gIF6XeqXqw+7GvxRYs6/WrI4QLkIbLakt2TbQ4+eVpGNrQcwlPaKOysbb5pf1EKffPt7jdRAnyeUAdLTWS4oivgp8UXynYvl4/m/3DbFW0dESuSGUkpR0RciufwtEVNN/W02EBqurhmiTt3O37ptN1UcrOw0tA/tO5kpIOal28jifDnkVBkUVLWr/6d5ePJIqLqepyapCG83qZl3TeFds8a4ASh5P0rVZFd1WUUsJ2wbaIN1GKb2S66Lpfl30uIDPNt0sf1tRlv3TtLNMtcYoJ9KcglxyatNGbXnDLCSfdVLxnP39I9plHJNrpdFqeS+lXPd19pMpdEmTky5aSnkpWtkt5ybJy0uVlZ4u6EmFsi+fUU0RgiWxNVTHj3GYvX5rmUFyIj3M++09p75SD2mlsu2siVHdatroNajqdL0u6mzoi1QKVxFrhkILKuylTVMp1FRMDnem00NUF+z3/2Fh/fTZX2MYkQTcyFXmioXvmDJL3QIxnO72hrBJuu2WHFOcV0VYLiWu3GPFSH5NHf6J3/vu0ECVXpqXCrJP5X2HyX0EMWUF5pmxfbNQHP6ouRrmwcJU1d+7ATqmyHchK1Du0rRPPLdfRw17ygYc/mKCnuh73uKb33O3a35sUVMAN2arFF5K47LzvG89U704rdIaqTk2Y59vqk3TUS4gbFtx6X6Aku7HHTb9nzBEpVqA8STJlCpAhQgmlLagzy3wDtdXUIqvss9Z6231lhfOxQbobJli4y1J86ZD3bNYxfxtfbXjd2Ede9E3T2c58sdkbDV4QcTFUXlCg6yG58gLy5AVliSgS7HbNHE+t/FhpEUg2zrs226KRfsJV87Q2wuXacac9ugqOXplp54w1KmthjXPKi7DQeguc8Z0z5tjrmYyJBh95022ZhupjtLn6HxwjXrhESWqpa4xpRn4827as006M323p+s2W/rLOml++VTWqrWOWqSLG5ldek3ws3ROV3zbRWJ/7TKyyvqrzlE2bZ3l//gdqhNVye/pBQQcvSnHT/OTFWgZGWukVuwrmWJ59RPCWG8qfz7Ql6zDKiDl63bt6+7T5YLOfedCgA8949Jsv5KhhljM23bnqhUoqqxnQ1IGen8hWS4qqXrDPa+b53WUhQtTQU7aLlllhoY0quyF8wUZZD7Q33DQR8nvDl/Zf/0udh2oJK5FfaE4BJSNKK3CzgIzcQDH5YlXLraRsq/I2+FNY8fw69u8j1xXL/SBThKav17R6/l73iddXbamuWmq+do/fp9uEx9Xv2Elu1RDrk6cpo6bBHtdPd4+IkoAwSbqF9rTUJrMyvzd2+SDnls4VGRYsMeOMr4e+okRwCVVjq5g8bJqyewv6ZflMT/rOHOekYIIZFljh5o5Ef78/x8Hv/pByO0iM2ta4qKxzPop47r/C5L8jlahRJ++jj37waJcuagnUNe5Jz555Qwc1PPXUO6ITiju/4oIxq0aJ19AK/zgqPaKfOkp5zninJh22M/9hPR/qpm9ILaPXzDX9sdnGHhllomet9oXO+lppoWLqiJJshVN2Y7Cu2gr0pcU2o5hAp/+4KmDhRTXH1fGkho7YIVioZzpOsmnFSkftEOii+uWGKNQxxpCvXxSnuHriXZYm1DXnpHjlwcn+mLHUh04o4awjzuOKcfd8pttbfd0qdktUSD63ks4KLBxj7qszJC/a5wur9dLL8Kh4b1xdbqNDigtx2yU3ueNWRSH5xarhoOOKSxGkoyBJrgsQKFOEYA10dNAlhYT5w1/uVtIqWVZZZagH1BLsWnApL2dNwWUZf15RqXFrWW7ZMGOtzZ9+rV7vDq69Nk5iwTgzbmRp0Km8ZpWq2P/lX17Ked+hJ1fK619K2zYjJFkh7Zd9ZvT53HUXlQgra3/6Qcud9kRMbzeKZ0k9clrtKs0s3jnFC12ftW3pZlnyNBvYV3zvur4b/JZtV35yU5b90hQWJL8cixNmW3rkV12r9DT60DD362Oqmapp7mULwfvauupvV+Wa6ga4X4goxVQWL1YZK2VYJFWytbrpZtbSb6QWSJeVzuCOD7v/7sFW/Pa1M0pqKtzzHXvYHnTFxl9X+dRCX3lJMUmuCpQRXESdiXf74ulXbHNQFXTQWLYId/d/XNigOm4XvubX+78VciLV4K/ec2bePvt/W6qIm36xVssizzqSst1uy8FH4zaZP/cXO7dOUVOMbGU8V3aAsafGWOikJP/4Vd6ruGoaOStRE52UiIr1/dUpZtinp0EW+E5fQ80xmf/CqOVfQQyxRUrlje7/kdaVOnltRCMdGg/3xJ/D3KerkuI0v7ehuBLlfT5lstkWqquDnRZYkG+cLdk/2Idl9jk4/jdVX7gbnNh4xM2MYAGbEk0Z9bb7tDLDUi/GDbEj6ZQN2TNcdMkvbjrfarW31/cQqp4vbBSnsBECrJfix3eT/fTGSCctU0MTxWMb+O38EgN0UsMo4dj08BLjfhjiab1sl+yGPCVkSbRTgrr2u6Wh9s7Zba3ifjXuP98+0n1WO6528+6eaNPd1RPXfDlzlGXWi1TJM9p6zzcorpWW9lkuQ5AXJDgiySmB/rzTwddOoAty1VLfLH/rJ0GYcL855axURCsuVKggH3R5XOGiRXX94RkveNqnZskTrrPqvnn2He9+8bg1jjsrxpZhs4Ump5jy0xvufvhVJTt0llXurAOLVwv5+6YNq0+5apsPx34hJSnF12t/8cYXo5RrU1opBYwr/5p+40dIfHuG4bu/1KjJs97c8qgndPJ6hb46Hx9iv9vqKqKoFKVFyCdPpMIuy4cM8co47ZJE5xRRVAstBTsvVn1XJLrtlgBh9tusgHx6GWaVeSq64aJMZ50RIkhFDR2V6pKDjqlnkwNe8aL3vfP/2Jd13OWircqq57qTBmniTZvclmqrMSbbYvIdqfFTanv/uZmmTnjeY1UfVeTgAB3xWsQrCn3UU3K1YGVnnleqVE0PjC6jkWrOS5IrVwXlHZNkZL3xJuyYpntAba/nfeeYSMmO6SFac2WFi7XCUknyPKSLyxKVVFKUCL/Zo7E2chXyk03mvL9Z9Ksh3i/xicAy6eo3qK/dVx3/d9UYwkLD5Q/PJ2FEtKKiJIdcVUsl65zQrFRrVcLL27dttxb1WyJPaw1BUNsqYrRVyD8qtJYv9PVti0/008GfLWaaM3yy4OaVfGaL5t4xyQ5tzgx2Jfu8bIVFK+xWjd8MWt/FEbcM6fK85siT6jkpmmhi1oyp9ssSqIZc8QJiCmsd08uRO2a1Afjqh9ESnVUmprZpNvvd347JUEIVpx1TTQG3HbDaVk0F6HtH3AIfmmuHHb7f9LYmY+p4YubLamutmEaCFfGVrSiIVHFKW/HQVqWVVFxreaopJF5PTRXBGdFqaOSCRIM11DHkQS209WDwEESop5Mm6jjjkgd+/VTazUsgWpwfO82ystFkec6L+6KZd96eJ79YtRWzc+IUqw6e0r3XaIt+mG7Bm08onpLPPQ89Ku6Frib7WrgC1oxb4J7Pn5V5IlhKdqJlT80z8aH5CjSpa8O0GdZWuO1P17y55VGzGs7xreVqHn/UdbfVV1Y5+QUgQJYeBghQ1EiP6KqzV8q9INE5hVTxhyTRQ3sY8ON8+UvHihKvvApqtR+oQaFHdSg+xFu+0bvM/cIGjNDlk+nemrjX8MHLpckV5iLK2mibPLf+QwrjPKeTmuqoKEEpEcJ0M0BzpQQp5kW/un3Hgv4NJwx/dZRXPOUejbXp8LLICTWU1snEg/tBAw1MvfWBmrfLONHmQ8sWrXVj9BRttRGjipWu2S3bSoecdEFaXIQIV+QUDDHU45KdNEQr7bT3p4taR7TV1X0ecK8QQdrqKVSCvPw1tM/3sAAUiCnhN9s1frWlMFXcO/gu9T+4W3i/kv8VJv8dxBAZLn5oFevG/eqcI3o931+Coi5JNe/ccicTT9u6e68//97hg2KvO2gvCrkkzPMmGpC/j0BccsWWs1mGT3nPAKON/GCYE4c2oTGY6TXD1bTSNF9N2qiPh3Xb10GaNtZj+tE9fllxVJ4WCqN7lUcsPfS1BoJlSpNkp9/+/sqapEl+t8GvemkmxN+266uDWxUjfdx5gkfV1Ekdl9ySI8Bgc31ovS6aOOacOSb7scTnpnpVM9Xdp4+yqoFztvnMj7rrKdlZ1yT63qMm662Zkrr8OMhF6V7zg5/9YZXDCrimgtIOu+SGcoooLVyYnMxEQRHZ+r7QD1HO+tsiy0wI7O2P4fN8PO+SmkoraI/Plo9waevvWkd3ABs37bTXSSU08Y0lqh0I0Xp+V6fkUyKyvL8e+c071R70Sac+9hcb64Ni/WVeO6GNAtqmJfup/UtmTpopbdYG12euF7VwuZHzh7rkhO2zEv0ZvgC8qqwZtcea/fcG4YEV3KWjYhFdVVoxVNuF410vVNCfTpt48j2HZVlgr466qpgWbtdDn5p49gsT/aCrr+1Z/YX461cdSP5Dr/hHXKgV7sdZk7zxYndZ23INmnqvu9R01CUNtFBLtCk6eEgTcNIVxxXUTDMNFTAsoIEBYhWQp5emDpSa7f47cvk4510sdF3LhQ/7eMZkRw9udezTvdb60wazPK2SQ4552ii7Xhov1HUfXpplWel8TorxqPneM1hf7bTQWgMtrFs0S1HJUquGS+hU3uzKb7s7uLaiCsjnpivZKVbb63lLBBfpaY7FLtrj/tvv6Z/9mSQB7k96FTwX3MX0ewfJe6CMuMhiihb6z+C4/1H8K1KJUuHF81Yt3qpHhxcUtt99tQZplBFu1OEtAt008sHhMjKvSDyWJKZCrGLBRf0xe5N9eRcMf/wJKyYvUdAxI+6Mmr+06YLizUsa7XFP7f9UwcsZGrca7H21pdmpt8UeV9lHfad5b84L8uQ44JTlkv3geXXrNLBs11J/mS1OaXGqOO2ixioqrZIZfnZNiitSbfPPxN8H1NG08/0G1egi/KMapnhBojVqa+Qrq/V1DyJN8a2y2suwy8vd3rVs8VqEet9n/skar/+f/0Uz5+w3zdOC8uUKyY400C9CcNMOvfT1bMM+Ptw2xg5nVFFGa7EuuypYrubqOeySHg/0dz0o0talR22++rNmyrol0uu+EaeU+fXetWHHYm9YqZcWLsuw0jLvFXhew949dPjhXoT4zIPOFgm2KuWQfmjTfpDPVn/qJ+sVxsy6U3Td+RhoLki6HOHY+H9Z64/Chrtn63PKrsz0yEt1xIv3yss/+mT6l8aM/tAfIz4X/nY3V3Ye8/6MB03ss9SSX1523BnXBMmWX9XwR32VtkEtqRq46Js7dqtPiDGq6dc2bN0oqkysZce3G9Cht7KVEpT5spayuBsNNTTLNsUUVVBJQYLFu+sf8ElTUiFlKzRx7/EnPGaIWX5222WlFPZ++DBfpP0qz3Frfz5k0pB3HL2c5f4PHxJVJVzi5ZuqhMcZOWC0BaZ7Wj2FpPnQQc9o74S9drjgbWNluSbXWTvs81iRB01I+ULf+Ps1bNfCkRMXdFn3uPwYqYVsJax0QIJUh5Twj5d4mGy3VRAnRQlPN+rjra0vq6iO3n0ec6trlOi6sXIDAwWhTs0K/7tSiZz0DKnZx6zeOsEj7vfbnoWOnbtkgwOCZZg0Y7y9Ww8LCAuTncvhxCOuBV73k5maTK5rjB8sssDrMf8YlgQcC3Rt41lf2OjkH2d88sAIcVXLWuI7Z2pU11x1Jx0WmlBMsos6Nh9q4B0ByMM+NWHXZGelaaS2LLmuS3XWYUv96ZwriqnstFQpSLgzla+YEA0r1nZy+xHdvGSxc6LFKFqstOf1VqNhfSdjAmx3W7jzeqhmw+KZ4uXY5w/ddDW9yie+azhTXfXFGOCak0gxxyZrs9e4Kk05kT7SG+HOOmfrtnVW2m244dIkSnNLrOYmOieuVCUtQ2tZN3OqnB/XuX11oVwnPOd7KRJxS6giBu14VbZIaS6ZaZGeFdv7POhtudGFnDt4TG1FvRn9spm+kZLyp90WS2h+n5SCBf1kvREetGr+JZ/tfMy7GnlIFffqqKJ4AzTQXAmVFJaAVelfOBN60+Zpk12U5qZgn330hOtJv9v29a+u5M+0763p1s0YY9yjK3z3y5MC5IhFKw100Mlnae/5KuEhj6mrjIKelOA5HbUOHOz21TS7srdq3LmPDJtFF46w6MuPXJp91mfdptqhsDq9P5BfZ5tcVlZlddV1d/kqwmUpKMwLZoioG2hMyY/Vq93VbZf1N9D8bnMVTKjh2YL3qKWeIf0aibqcptfAu+R0zBJQNlNM8wJu1L6pTef66qopXaj61QZ4RnPVRaqlkic0McRrnjNOxehO4lS1IeW8hc7IPJ1pwy9rZabe8IYhpnpDnIoax3aVrLgbioh3W2utzXLSHGfEucvXFjgaHGzBsrOSoqr4KylZfJ0I2REE5xKY+99h8l9xYggNCMlb+vHfKvUOUK5cTZDx3B6PTxjiRzu00tyoz0aIal5cVE6U5O3Jrm+6qd2sHvaM+l31UXV84F6nnfObfQ7LdGTcPsdiTqpQKsGZ5Rd8/tFoi+zFJT9Ev23WldGmNvhRVNcmJowaYKPzmirthMNCRVrgpFF6yhDmtNOuSzTFWQ1EyueaJw0RJsVqqwQL1URjaQXKOnZzttnOOY/fvedk3RArd37ktfbT1F7dRb9891mavdUtiT7UX5QyFtpsoy3GeFaJYmVsvLTIl9a7XxNtNPS+lb5u+YbArUmGpn+qi/byRPvUj0jxmg6OyVRMCWUEqF2yhTEX3vFgkUFCU1Kdt0Oig24JEKSaye/ONviNzhKEC1NGLkJkqXLv8zovaaGTFnoVbim6YJSdKec0vLetX2cvUFEpZZvEiU4JsS3ggGsHzroWkeDT71/UuG8BLcXo0u8jjZo2M3P4MO27DfLp4k/0TOihQeF4X20biTBNOg8UdH87swb2cMkFhVUSJcNeieop6NFKX3j06OM6q6aIaPv9Jk8ZIzp/KvV0sj+OHbW72G2fTv/Qpkc+d+rUXLucddJlXdSWI0C7Jz71+7fDZEvXVEeLLXZQoipKSXbJdJmGaKGKKAHyZIkWIViqAEF964qOKO7h73u5+OoFr70/Qlyh4kreKKhYqYJ6n33FUL2QLEO2A8K179JJn/fuFZiXKzA3T3KR2/JfLGpC0xF+td746i+7sn+1K27Lr443TPS0DuLkOCfVNQUcE6z6nalWv1lkQP5eijev6PtVHygsWA13+d0xAeKN3zrFldunXT5+y5+T5qlbu4WoU5keXNPdq950vzFg66d/m7F/pqeGDFGt7v+yE0OmLB1G1HIqraDPv/vBc+7x2IR+7rn/Yf00UlS4SzmB8h1Mk7gvUcbNLAf+2i5Hjshmhd1btqM4dznltCaagoSXaig7OM2U3tMVuxJhkbVOjD9s610/OX5ltVsYvv0h/Ua1cH+5ER7UUnq+qkqLUUCSZvJ73wI3XfX8wI9EKiUGua6JwHhfmmSmAy4JFahIeE2nb35vtnOS5fOxcvp7Xf+andRQXe3VXcCNkudtWbXExtmHBOQr5arTQl1wXbZs5/W/9LwvrTNQQ+0kOOm2p5V2fMNif6Uv8ZBq6imodNvm/lm+bJdcMMcaX5opXSn3XHjSrIm/S05Jkhl2S8VyXXzripku+9EGbd9orH6JPiI1tNpau/wsXKovl/zjQrHcfmNSv3fy7DY/3Zzv49lfi64QZas5YgZUFFLumgHPPuqqAGm3Nvlx7C9i1ZPrls0/P2388Cqqt24m7JkmEhRWsH1VaTGl5QkQIt7SZV9pERopzW0lFFVdlEsuWouP3bDt6GpbZRpll91+0znyEU+v3Oz3ZT/5Yd9Ix9N/VuXMXya0rieuVjlXhGiphnJKO+CwSqLcalFCvtC2iLHIfH/L8isqilVMAfeJFC3cTTdlu650QLAQtzUb1sa+OX/4ded2hOn5ySRTzTPq+nhD8mZIqpBPV219bo7WI76WIN4mq7396wsSTsW6FZbrUKkApVNKqN80XpWynV11UfD+w2qpIUek9HKVFFZWARU0bdDLJtvNss5Wq31vlXfMMuLlNVbe3imqRGmtxn6qqPPCpBr1zTTTzBLVKNrV+ADF60Vq2rKPmHoFFfy8sT34ye8GaaGzppo/39ORKafEX4v/rzD5ryCGUAES0Ox2Ca0atjLBSleddfeANu7RRi0xTs7aJLpMWfvPHFG6YgkV6pTxrAFqt2+u3OlK1luppnitReimlAdVUy2jr5+ufmHZ9z/bUOJ75V+I8uhfwxXSzE3VVVTfBRdE9W/kpgh3ZV+X5JpYTUQLc7cgZWV6ZHp/K/0lCdEKuV9Hg93lSQOUUslcp/2dNkWWIOeRLdvv/vGXyP9jDYF3VJnfFP7BsrObJU8+p0ZIqHbjW8orXNyzTcd41dsuOYNIUBMv+MX7Jtsk01H5hCgiRKip1qqwdolnNAPbZWunqVcMdsRRVLBwwUZFZDqVHqffyVf/869rC1XKGTEXt3vFcqtdUliMU87oHVXUrObzkN9Fgda6YtHzy71d/X5PvvSAZzctVa54mPDyCea8PNlFJ5VV31O7HrHaDpGquq2+kgnDRPZra+mTo7R76kGxjYrZGJckXIb9Djnlov59W3l+wCxrhPvK38LFiBQKHjEDtBVo3J/XlNvwkcMPj5NkpylSnZTtlB2SnPHw4pcMfupHoZpa6KxCKjhpu+8GthKecVhN8WqoqYF/im9XJAtWyL0a6xvS2S3psoTLFxBppX1KlaqsrlxZu7aqopbymTONjnjS8hdXuDJqnmot4l2XhxDDPm4t12nv6me4zmJ6RIi9FaH18fyS0wLseXWH7FO/e1p3ZWo3U69RWyFC/XDyPSUluIQDR49IQ4H/rFCAmc1/Mvqj13xtpU7Te7o3tYygMgPMtUCJuHA7d+yy4+gxcamRctKvSszeIn+jGtJuXrLp6H5Jkl12zMCYh2U67bB1vu7xyX+FyX8FMRRVyDn0aFBGtcth9l/Is0+0a/MOqTuoo7d9rWR8GZTtIwAAIABJREFUnBMl0jTv0VhW6Uy1X2qp/0uPuOScybm7hAtyXCHnNPB4hee010tDEe4Xo0juSeMuvqWvPkZ6xhpb7ZLkoBTvBk4x5v1OBk4Y64ZCXq063lI7BIoRKswG6/3mjD141wNuuO53ez3nL6/7wyevzrbto7/8LsXkO0O/h6jt5xd2iBEFVpvgbZV1yLtsQctvVB55l+3VcpSpXlNOx2g1vmqnylN13T3sfasNsdAroqr0N0R9W1rM81RCT/VKtpQl13ZJfndA8VJtPFiql10N19lpj0SpPjBVMUH+bPKRYb8/KaRAgveNAq9HvyIYpWXqpbn1/jJOWdMNlCrPh45YcXW8vZsmeld/D+muYXQbX8382Y1r6UY99Ya1zR+2ecx+eZHRYgLLqqK6Bm1qqS7Qk6rJcl0Rtz345AOqZxdxPHGvsKGtSUvVMaSocso6IZ9TSqok2vLAW0467Y1qnwsp3tqujzY5u+qy653nWl9/lrVynfoz0fFWX9tzYZVtyMQuFzxksLZ62euK+ElVJJYs6J2Ax/0p0Xa3tFDLX/brYoYXrRAl1LuqidHQNRGuK+j9zB+86w8vWS4jN9lfkpw4es40GRZYY2yfD8xw1JRbK23+ZIxjF66JrlHa1L+/lbLogmqiDP7mF+9a654uPXTU3msNXlK4WZy7mseqGldZEUHK5a/qauJ5n20d5oB5nhYh2Cp3B0eQ+k/Py00VNdBFgmYmbnpWEft1UNZVmTp82NTmxEkeDp1gauc3hf8f1N13dFdVty/8T0hIQgg9AULvvXekVymKKOiDCiKCiNhQLIi9I6LYQMWG2EHpVXrvTXrvoYQACT0krPcPuM85977vPfc5Y5wx3nPnGHvs31hr7zV3m/O35ppzfufMbDLH7tWxdhcrt59UrldLpetVN7/fTNlWX/DZnK8U6fuie48/4vrX53VMespz6UP+UzL532KNoXBEbMgj0b0SzbfRtytOq3hLgld00KXRP4zYfdzDLQsp/VZjVzKvycweXM6WW965J9ieodIXXcx8cJZOY5sgr6XFXrLg6BQ1dfaAt6z3pvKe9bGnnbFZ85g2CuQqZfbpX71ouj88bIeFeucabMb5dY7aqnSeJg6lrVVEDtU6PWrL0d1O/P2TtbbY60YS1v6BS70yuqtNcprS+VPlZnQF9ymge7EHzT/6te3SPWyAT8zQWRuvGqu3ajrX6a7RhEHmffmzug/Vc20/xxfsVelsommzN9qfvE2SC/JULq/LT88oUfcGsOdXbpPddR86aJvtYIzHPDxkmOeH3S/BdV1eGOLkxcJ2f/60y/lqunb2b3EKWC9T7xqtLf17kpdM1V85y+1VVB5/SXO/hq444Dxa6mSJv8y+CTv+iAfcd2cbZ2JjnDu839DlXxvXbpRxc9/0SuPX9FjZUSVl3SqPn23wcZexFszbrmn/5hLLFjPxw8kOHfzaCMfAUHXtim5mZ8ZMR+yWjvbo6nZVa7f0+3VGbx6MPEgzSClbHPSYB6TJeSMq02LXHPKgV3T0llLYvfS8dd9NN/f7JWrI4U4fuUcZJWTXRGtz7ZRbTln2y62Qv6XIqZxmUkTFdpOzdoZNx9PtPLjSg+WaiMiq6fsDn3r4tgdtTNuu2zMviC+e7vKl7JIu5fdIhz6W2OKr+95zbMM6hc7kseXUYQWVVSl3pLxlo+1uUYaFh0zY/IYamiknSrzsltspoURLTx5+T6yarths1K0/mDfncWnOK62kFQ5pqqm+DQZZl/+ApodyemLHKNeddU9cV09dGq2+uuaMHed87uB0KarFRzixIELeA0lufb+wxws8pVOrfvL/UeFfXmMQQvj/fatTsnaoJyYsiP4kvK9qSCAM1Dc0I9yvdFgY/XVAQNi05kDYvXJ9+GXcz2Ht+m3hyObD4e8v/w5fN/8ktNM6IOSWFLqrG15zd0i/e2PoLV/ooEL4S/8wSP7Q1y2hsqTwtFqhhaZhgObhNlHhe73D9YXp4cDEI+FF3cM7moe5eYeHXgqFeZW/Clc/PxzWP70wfJX0Urhb69BcbMh+87rKESYaEIaoHEboFS4PWBvG9fspzGj1fUCYOGRNQOimRHhD0xB587z9c/eFpYtXhO0r1oWN27aE9T/OCrW1DNU0Dd8bEL5zfygpKhyenxzWzN8Wzs3dEBYtXhy6uC9QO9TSIeRXKAzXLJwZtiV86aHwwyNTwvluf4b0ibtDXcJLbg/D9Qmf6hJ6ahnuuMm7JOFbrcIIPcKY7IPC0wqEbhLCO+r983kP1j0M1jm87KGbbflDTpVCU20CwtISEwPCT0mDwyc6h3HuCUOVDLcS3lM+VFM0pPy6MSwbsSykdPk+tBEdHlIozNUgvKhBGKhiaC9PeEC5MEjB0FFkSCL00Dj8ceuUsLjDlNCEsLvI96GTfOExNUJ7wo6mX4UHxIUVdUeFX5NeCh9r8M9rnlH584DwZsyj4drj68OSgp+Gh9QN72sahmkS7tEwDNMuNFU+IDRyR0DopHh4R7Owwhvh1Zv3hzD//jlh+cMzwviG74U9v+4LvdsPCKf+2h127UkJZ37dFxC66BsKKxQGaBYiRIYGbg0Ia15cGlbc/lNASCRMNzSs7vN72DJpXegsIUx0V1j24urQTPEQRfi07hfhKTXC424PO1p+F7bO2B6Ozz4YtiWMCHdICAcGrg4fZx8cEL4zMjzprnD0o23hl9ELA8Lu1ZtC9+I9Qyzh8vLzoaeXQicVwwSvBaz7V2Xyv8WMoUREQhjiH5Itt0VbU/2bPTTRIBkS9fASaO5Ok2d87mq1TMkXrwvZLsl9Nbsz6yP8/uLXvj453o+l33HpwjkHUpY45pjLchhrrlc9juP22aawWmrL8okNOqspwVk/W6ik7PIr741pixzo9Z3552Yrr76lNmlRuZ5a99/p+J/b7dk4T86alaRu/laK3Dba6aqrKqtgj90uK6CYsp6f9YMDXy3QvPEd8r7QQEfJ2rnF37IcV0qmC2abobGmJq6f4GrUEblDTWdX7ffSgD5+s8ojSvjKYf1bPO/1X56R62q8Yz9uNmvheg1797Dhx7k2LnjPdXUsM96j2Qfo881QqblShdgYRZIvWbr7pA3DH1NBLZ/5TRsVnXLdPHs84R6zzfZJj9+t/O0TC+01UapMl6S7qp3sqioptzYW2OK2creI2HtQQlQ1fTNfN7jySEXzB4mbzpt18RvpjsuQqaHihryzxII8B4x/vJcuvYa7+uMmx61RUnFT/amcomLlkFeiDGedEuVta1VAS6Xc/8C75o0b7rh0sS4pq7AE9a2tnaVvj/t9/kJ7D9b/UMzaIwaZoLBTjrtiOb6Kfdzq3NkknLrojO0u2Kqdu/Q1GyeRExf/+a39Q0dPSlCiYzfbDu+Rp1dTl0es0HzME7bnP+zPtz4zZPAb9hdKF3fhjEOFIl1dt13k+qL+GPmFLoNvVyZXWcNf/1yKa9r1vV29fMH8EV8YYamvvaha2ZKWNEzW7vGuHrjlFk3UVitbNUOvL3bQbl89/KkqFZOULtDQ6ZitIsolubI6TeKsoMzMVroob3jPr1T6qTVy4DI4PeasmDKxlDjqSEakxIy8Tl284Oz6FN8NGipBtOGm/d/llTgu1WzL1VVKKfN8pJm/8o70rnvNccAXNwOXiLPEJE07dzSm01i5LgeRVzJdluVC/Uy9v/mH5RNmyNUwys5ryV72u/UuGJj7di+rba45ol3xs52m2mCBdbbYZ5qlljriSe+45JpY+81+5kMVPr5TTgnySJLTKdN2fGLny2Ot3fizVJkaDuvvtsWL3b7wN6W1Nzz/COdk6aC0WLEOWuO+jpXVfLKdc/WuO75ogxwS/e1vBRxX0w7tlQHp9ipb9y6vNX3d/p/+VnZAKx0q38gM7RX/iPLyObJ4gsdaD5V17JpLVeK0H9FC9rKnVGxZ0Le2OmijPa6odC1e/d5dpJ1Nd73oNTtrXzPp12mGSrfcdpFqOOGQGupa82OqBWYoJYeWv3WUb8iTKiusrstqyOFpzRSQYLO9mhYvIUGUhL053FLrNq07NPFokee0qlnS0uVL9Lr4ml+ccWvu4bLJJ7fSBr9UWsafO2Vz3ns/9vOmTxVQ1lqXVFdVIQVUVFGyi0qoIcVBr2mulgQFZChSuTRKyCuX/dKcU9QwX0vduNgfH0/y6Xu7tFs7SLaBbfVUWhfNncKvUa8YfmWU8acWGO5bJUWqr4WH/YCTDq9YK332EUMa32/blBmSFFXYNfOtkC0hv9jzeRx6f57WpweLuiva9S3nlI6uaHinh4Q951w/c0XclUh169axbs0KR+0Wuz7TsUmbdM5b1nJ/qrArzY8jHtcs5lbzEj6w0J+e2DdAj/4DFM1WQEE5jbXS6etbHLTbg25xT/f7FSxY1Mbf5kirV0rhmLx2/rnK9KU3gHfGjVnufLYsxJrb4DPh7VSfeVLf/uXkaptDrgrVZQ9prl2+LupKpJxNrxrx83ibb8K+/av030IxlJbkUe38ZbOc8khz0aJzv4mW7qRckm4CclZU3+S4N1RS3mvbXjPl4d/F58stT7YIxa9FiS2WW7ZyUQq+XFeLUZ2MeWS8WLEapD+pT5ePrLTHKdGe1lRJkW7J8bj2ShvkbtcVc693ZankI1d8sGe4Xx4caYrt1ljhpM06e8hVsfIo5pLTzj2+2B9tRlg9eqrm93QVW7eoMsraKLuG8juO0riemdf3bZqb/8V4323ap0PjYQoro4B450zwhtpKK+iSlUa2f8u4EY/ggkk7Dpo1cK5qY3qZ9+YG8x2Qe9dFeZvlUyK+mBLZEmwZvsqySYuMfvg9qUpopq5RluoS2d2M9StFXT0l39pLmvZrJVKcPBpZ4G8/uuKqtfr3queXoZs1LfK0++V3YeIqaSoqqILKLop03F+OW4jJR/Yo67xx/tBs04PmLlzoSPI37v7tLtdvBl/lleSL9Kl6NPtIigNiVDRl4QsqqCPLZYMM8JUfFUcpVaxwyBbJOtd5wlXR8strmp3GO22yZPOn/On2H17SvOTdGivllK2uilHTRbeXbuXpFzuY1OZtSW3LaNTzCdulyiu/ezPfUlEBo4o/bmyB4Woobb9Mv3f+0u75a2zIuOzRDnWMWfmzi/uzSXBMsMhxRzzzY3PbDs8z/uyvnnKHDir64Im25s76zGsmatKjhhwhu3zXg5MrjrmvVyddC9zn60Vf2r55lUL3V7B4ywZHSmY554oCbaqJblRJe01lyO5EzFU9G3XTKsf9BmvhG8dBpBh54s+5kBoh/9IU5UdeFn09ztpFG2w5P1JFPN//FuPGfaGM8tqt6Wfoy33ssstd2hquh36qib9UTPqVZH+nbFW73i2SJ+90yoX/lEz+t1AMBDvsklui2ZaIFq+YfMorIIcrdliNRIUUM+fSUrdoCJZsWOiWkm3kPlnKtcjrMiM4mZJKZpQC9RPcdu+t8t90Uw2bOkp/ecVJM9IygysP0e/yYFMdcMpObeT0vkd81mG0NroqJo85vnXePjNMk4G9Fthtpd12Oe6UX/YNcDRzmkqpSfLXLWl6Uppu744wxm7nRGmvkEaqi8nIki5JwwnHHDp3SKe3ezskRlFxsivmmqui5dBTWyP/HClDnFtkKCjVktE/W/bEt759/2u5lPSD392jooSOiXLVKWrr9KkqnS2ifaUOcgiqyKaB+j7Ketb40R/LfirRwWPnJbZK0LfRXXo/e7dHVVVbkifs00oBhyJT7Li411mxWn/+JOYoJV0V7Y2w1xm0kd9oY31oo7J2eFAhpy6uUUVZl3HcSuWc9aLCajkt88x5V+V2yUlVlFZAkjaaueSo+3S02nx7bHen2+QWbd6GT2U6JVqE/M5Z2Giy9+M+sXDVJ4om5JdYt5yDYn3hkL2uilPH7o2b1JCgUNvGtlxJs/CnQSrhijN6Kqe/e/Q+MsCDqc/bZq+ZZjudlSYte5ySi0556ugsS79cZ9WobSY/u8lGlSSq6EH32mWf8gk11e//kCbi/OycVXYbX/5lE/pPcWxe8EGdt63pO1fzAd31S33ag40es1OGK6ezKXYkRr1bW7jVvSbOfEJM7khrq8d5qMkzstIzPPOPwfq9+IyDzkpyA3X6qnV2rzsp4dbavsqxys7rf0uJyvDsujelyOXbfC9rHttX5QblLBs7R0UdLHfE5+aYZZPn/ebBFu9LapioUpsa2parIpvimkx4TFG5/lMS+X9cY4iIiCiOcSiM6xgTQvgkIiIiP35HKRzEPSGEsxERERH4BJ1wCQ+GEP7DahclI/KEB1VQQink0c+3nnO7Mgo4KMMYv3o69hOvXnnS6/pbZ7+Saht1M325hLyGD/pG69vaOJN5jryRsuXM9NeIOc7/lO5s1nGp0uXyh70qm261T8o8647efaycs8ayFR9JlF2CEkqVv9Vtex6VNvmiIV07qaeaR40SgRJorrQnWnyiQJHCBv/a0aPVhyh+322KF4pzIvWCRd9N1XvHi/oqLs1xOWSqpbC+qw5Y1OkFlac/LXXlTsUKV/DO/S3tEWOBveBlt5vttHVWek8LBZRz2CWH7NFefrvFuSJOQWc8ZzYiDHafD/2svgamvvWH9155Wtv8jXQ585zWaltio2VDt7je+7qciy+6VDWfYiP3mfjHz57yq4/VViS+taIZOWTGRsj2WHOb33vELFFm3MSJeso9nuzY3cuz+rlDe5ttUUiiAnI4L9VVCaJcdSP8a6t9rktW0tyb8KNvqqmMUiIkWme5ttqb6S8Z0sXLJYcIcfIprLSW5doaufdFR5035ab9v+/tuQ7lyuGXp57zjZUaKK48qkpwSWkDHu/vXME4aa/+bqdtzspvicl6V/7QWzsm6luli4jtG0yzXWnBGeeMd9ietSulR2dKyV1UtunbtY4pbHq1a7bd8p5itmo8aryS1arr3iKXuzr86NSizV648p6nkh7RonYdw2e+amBUDymZlySLVLJYEdmPnrXTKnXltU68SuJ96Du9tXP3jHFC9Hk5F6U6+e40f4a/FBSvnmpG+VorjdRUwrmW9bQY0ctpl52PuSwi8qq4kaf9+PVIr1rtXjX17jmULVsd3nLR5usbFFLUq0bop61aGssllynGqlT/Oe+u7WOAB3xp3H8dHkNEREQSkkIIGyIiInJhPbriQZwJIQyLiIgYgnwhhBciIiI64YmbiqEhPgkhNPyPeBSPiA9dRbnLXdY46Vs7lHDR7ZrLpYhcEUUdC8HThjj6xRbLxuzUc+PdMuVAJDenSfU198yt/S04s175M/llvxzp6eShZtf8XIfNT5kc8b6yDfPad/a0rrtu+HXHGuSwGK9637NqyK24mvEtFL9wWh3DlVbCgZsp1reJV1ch+RVwQIph4xY7VvCS6HNXLBy52sLVv/jeYg+jnHoWOaGQTDOd8Lnqaq6aZcfsbcqfSJXStZyyiYWMr/uqCgktPHv6IacxzAeW26qRTAesUdztfrJONWV943vPaqioSxLVd0aa3eJFOqax7I7LaYIiikalaHZLC/OXLHHZXlskmz3+L4Pu6aCAix55f4aDw/9UPfW8380UpPvYOQ1RVV7pIvzhrKJyOXYT6OQd99vpL8UU1UIdl7LnUfDp1hLqlbRp0ARTk8d4PNsA31z/2Vh7Xcd9OikhJzbIpYQLTogSoaDyyheuK/XECcF1R+200zJFldJYC++bpbLgsmKyO6hP7cdsfbGRZl8d8uX8d3zusNLoory59tgyLl3PB3J799V1/nxzgFyCvS4opbhU13Xe/LVsH/1l8g+vWHcT4enVqo/Z1629X98cIt1mk2ddtrjTUOfDMUtMUEEnka7Io4j2Iz9y/9M9tK/dVp12t4rbc9wtk25EsvZQ1526OBuRISmkS5XTQ4b5ve96//i2Lv5tiXBJkc9NT37JrZWGObpzjmUO+dpGo2Jf0vP2Vr6YMNRq+8TK5utvVll77qSizfJJH39W9Vtq+LFbe/2s8KLqbsnVydUCBXU/ONimPKPVShtoUJNhwtpdPsn4Xh/V1NZEiixLJXtUPf/w5n/d4mMI4fj/+McPIZzHDjcAhO7ADzcP+8ENZeFm+7hwg1Yh703l8r+lYy46Itgk2hAzjdBLrFM6l+wkiLY+rBFcd5euzm3YoMfGu2Xicc1VujkNq6Sa4nLaOmeiCWunGbXvV08nDzX/zllyZIvTXj1jwu8+WzXar7ve01U7v/VYZrnzslwCuURLlNNbF97wninggDQfecgX7pDgmiPO2O+cEpIMeKCTdzo8ZEWfb0xe/YirN8fJVMwyO9yvmzlOOIXTKiq/P68vX39C2aJtJT8zSTifouPCd+Svnt0DOkvFdvO0LF3KmZw55FNKyezx/hFVR5UiDVHQJRV8IM1pV112yWrznHTGentctt39goTMdE8uGeCKFFXF+b74c2reU8U2V012XbsXGhmdOtkoK11U1g7FwWqcliZTgpEe1ta9xnb90awOc6y0RQlFHZMp6vfXfX9tpLMTVtkzcoZRycNMcVLup9rgmHy4Q0PdCrX1vSle/mqd+RYqKUEhxU23TFaV/KpULofraqmoooqyRPjWGkslG+O4dGvldVF6h0ZKT0vz5omdut892midtVBJCWUcwoF5G9VQya5ZcyyU7oSLtorxi9WSnZL/970u9r9VrcoPqaewulr5c9uvTrz5gttE66KSzzs20vTX5xxy0CjBU2ZIU8RG6417abgORTr75u/l2g6v48lJb2iilRUPrlJJok322Bi22SO77fZIHrtFjWfyGNHpdz+X+9mY7CO9oLk/k0e7Qw8bd44312SV5FdVLq2b1vH5hNEmWuOnLtNlk+XStn1OrJtLdC6ff/iQc1uPmuSyvio54aqSBZN8fvD5G/KTkSp5wnH9P+gmvWVOL0a/6XtbrbPPamn2WGu95f8nUf9/Cf6/vLlhNhx2Iz/43P/Sd/bmfjqa/rv2+aj3H40bL8c/fcaLu/wZhmsfRmgapnsu7K4zMXwd/WqIVyUQFdY8Ni8c6n4orK+5PrysY2ijSHjN84Go8Jj7wqd63RwrOiDc6dbQTLXwg8cDQlUx4TVVwpRsw//Jc1bxd8PIxJdDeZVCfxVCAUI+ecIET4enonuHQ6+uCp9oGRBqyhYeVTL0lRD6iA9faBU+0DwMUDW8rVnYEP1J+Ey3ME7/MNaT4Q5lAkLBm7wQ3lU6DFMxVCfs/WFHGK1b6C0hNBIZHpAnjNUrvFv1jfCTx8NQD4c9P2wOcx7/M7xf7+1QUqWAcL7XxvCujuF5vcKSOuPCYFXDY6qGyR4JT2j8T14d5AhdNAxFVAwI9+n7zz6EFsqHgtqGibVWhMfcE5YVGR4m3fpjeDD+8ZBPm/CQimGM/qGrIgGhBuFFwto7fgipC66Fd/UI76kaGhHGVX0ztJMrZA44HIaoE7rJE3IRRtb8LCA8r3T4u+D7YUn9UWHro/PD4zqF5e2/CS+oF95QPYzQOOQl5CIMUj08qlaYdMf3Ia/aoa6eAaGSe8PvugWEwoSn1AyFCH0lhM4Khk+LDQyHBy4Jr+kY2koMzygdahAWdv017FiaEr6P6hcQKhAGqRV6SAwjtQ3vahAeEBnWuS8c7bEy9PGPMKXzr+HikENhZsxrYYQuYVaxz8O221aGadFvh+e0CL8ZGH71yj+f5e4H/grHVhwK+w7sDVu2rAwIuVQJd+kS4hUMCAlyhD8NDAMUC++pFr71cDj02spwZNjaUJsQR7hTRPgu7uGQsvxw2LZif+hAuF+jMLPD2PCljgGhmmLh3ZtxOxef3RJebTwonNl7KuzbfzQQHxAGahJ+KPxueMPT/+Ma/+vjGCIiIuKxGO+EECZGREScCyHk/Xf9Z0MI+SIiImbgvRDCspvt8/F8CGH9/zJef/SHIvkK1y1/Nr+ltuumtiJifWKlTcV+MfPoRFWTqqje4jbtf+vtrexPWHjtD2Md9qS8iok0TbLVThvlealWGGy1jz1nrD8kOyZSbiW110ikldYZoJ61JjsqwxQpRrUfp+zVK55ePN1rcY0cuvS3GNfNsdhzWosr00qj/f1BZ1Fu18xma+xwUaJEGVJEyKOOGs66aLODKmlstBnui3pRp8RYR49PlaqWSb4ViStoie4eMdE0BeUzMOohL2W+rYc2upjqugzv62mT/To0/Ycqd7dxYe9e+y6ctOr7Tb7xhd4GKifNNRneNEFp9FbDZdm8b9M/n/dj7jTGQanfL5a7T27PeUAOl+VBfmdtEqO8KAtN0a/YR8oUT1Jx5b2e185wczVTSmelxEo0yASD5fRKz99tX3LQ14df89UHB7373B2ilJBlp+m22Oii76Of1StjBEjAa5pJcUSK0y6rqrHGpvtRG3UdccB4eyx8YL6Pxv1ql+OeSmjqo9OLbLHHlC7vyPNKIxfemGXl9Lnmm62OSt6y0fV3Ux0vEeXKF3Ocjom0Y8E4003RVFWD3EBUqo5xg39WqGMtWYcvKnilqGmvjnXx9BklRDgqTYqcUp12whHfWqyCRM/m7uXT9HkmN//QzCVjdOrRV1y1RJNmTXFx+W5bHJPNJcWr/8OdT7VVplhJPTu9qm+pjqbvf9VkZ5x10MTKI/2y4xN36+Eewxwp9bsDVdPl7lVJZI1CTv+2Q6837/CmewQJVlugdeF7XT9xROvRL5k0cJAI+wyw1Ssa6fnwu774eo6Pve/Y2n2OpaUqXaSk/JcSRNaLBPfo6L7q3XTd0g/+a+MYIiIisuNP/BxCmHiz+eT/MBFu7k/dbD/KzbnpDSrGzbjaf0chhDEhhHohhHq5CuTxyUdfeEt9K2zUxHX3a+/do2/KroasrILW/znfZVnuvTZQbmcVtldOF+yzW3f19VTRVGNUkM8IzeSxwkD1POi60drpYL96imqhhnn+UlxDV6U4cNciJcvmdfr8MUmCS1HRLjglQT6AuZjFAAAgAElEQVQ1lTXcVmP3T9fn5i0VFmOzvdLkkSlWDin+VsZkaQ667rK9nlJFtpsW1y+Zo+2LyWOE/T7wrQZiZcfjBQZ45eUV9tqnuFpKq2J25hLxcolTzFQ9QVFVFZCg24hmTpY+6NT+bUpVreSFrwZqrZWijrsk1RbT3SZSV5WkyRB5s8hYVzXBZlsNkN3zffqaFvORprmbetMEr1hsnU1OmCGXOOVU9vbRIXKG7J7TSpRIFRW31EFDLDLVKlBVGz/+NM6iw78oqqpdKSvExJSU5LRsUlRVSRuJ8rduZ2mfOb7zmAHaGm2p3Q6KlV2MLGcdlSS3C5LttcfU2JfMnTRP3SKlVZfHzNMzLTJHdcG5PcecOrNfxvkLcjkv3mXllPGO5i4dP6tLzzx+Wf6rKn3ruVq7vGo6uOCyJ8X4WFn9VDXzw3HeafusMROOOFK1iO6nX/KFXZbZo1LDNs47qLbyKilouNu0U9fO9HlqRzZWbskQ93z0tqm/jbXy5W816tHR8+b60VItNPL2luf16/eMZzr0MeP6KHftv01T1fTV2Fu53zJtxzSj+n1jqfV+d5/pB/9QPHuSXKULyn72rBPXsnlIPdXi6vrLdOdc8fOJ14w1yaTnPrDBFN/Y6j4FLXfGxK+nuMUeJAmZ2eUpVFRilUL/VArvetV4s/y2ZayZLX75V0T9n/R/VAw3vQzfYkcI4aN/1zUVvW/+7s1No/xG+wMRN6gR0kIIx/8jHjHZYhVOT5DrZjJVT6vltd+t7vSCr6TnyuvstWP6aaCYGLEKOi+nCAkyMddkFeR3m2YyXNRQPZdtds5BMQoZa5qjrrjfUGVd1k1jiYpoprU8Crvtiy7iKnSwwFYppaLEKI98mupqti2+NFWSlvKhjIYiZXiu6Evqq2GfCLVvVlleZYOcSlkoXZTzumupoXu9fXC80wprpZ14BbSVx6rU33329kMWW+VNM8WLR6b88lhsseXWGB811AkLlRdsWpqpyPUainZqLlerss6UzPTisKH2iPOexSa5bLosl5yzS6q8snlRHa1EqypSRXEyZGier5ELLUr6IP0Fj6ikjQw5REpDmn3W26Gfvt5a9bL22Rr5wyK7HPGUznprp4niuqhsp2N22OW8VOtts2L4R55Jam61I0Y7IMJ+I275UNqhPbK3S3DLnMHeNs/iH8/oc/88M1Uw0xZr7ZdNpFb3veqBfj/YVyOPUee3ejt5nrMSpYkFixyQt3QeuaoVszdEC677pOFPMpyV5piCn5UTg+cnf2rHnvnaff6A7vc8bp3zhj2zQ7dar9hor9yumWeW2/dmKdciwp95P/Sh1k6L8Njqj50SrLPBwPnfadK2p3pyOeqCYh0b6qyOK+ULKqKIPVY5/tkSpHow96eWiDbDy8o46i+z9dTKYJ0kKqmhEkoVL61kuTs9+M0QpYrVdVwOhQvXNm3DHBdiI12KjLPxq+nyqy79Upo0hx1xUJLGBuihc+8B8mhnHX5zygK7RTRItNIRHHdqwz4lM2NNKHFjdtZCFUO96VatFBYrfcve/5Oo/89y/y94JZq6gc61xQ13JQx1Y61qvBtevMO4O4Rw5qYi+Rwd3HBX9gkhrPuPeNSsWicMyNaFrXsst8ha2e12SFEVHPuny6yXrrkru3CNPZdXm2OTZ3Q3xTiNdPCXpWY66CGV1dHAJH/Jr5osK33jgkqKqOa0P2Sogvv18JLfPO0xOczwrYNOYmnpT7xz4AezbfCLx31vvUOOeVojZRRwqy987h4XHNei9zOWLVwv7vBlK3wtQQFbHRCHaTfvrQh66WaKfRLk1Nxht2jjB39LEOeAlcqqYIcd2qvtvAyVVHNEiqEWgMqq2GG7zpoau+4vW6/uVj9nIeekO7Xssh8fH+eg9VZa7RG9jTVTS3EiXJBTlNkO2Ye+Otgv3euqanGzMlQD9HW7BtnaWHV9oQwZnjJLFO6VTc9npzq66G8RhzOtS8/QpnINeQ5tlJwZI6Sfcto1Oy2XD+nO+/ImYvWE8p/YfmSzvFGFNWlT0sm0aF8smqOY8+oX62DS0c0OOGObiWb0nyzrfLLkQymurThntQtOSfGXyYj2WekhGt7V0oAPO7i3eH/PHfncsyqIEKukJFxwWBAtzSinHBg21+7ICz55rq+6ynvw5y/sGT/TH1NeFiOHslpZ7YDM+CaaXzghLnsx3a+9pZH67pLkvhEfq/LsHS5LMTfqMXsyMzzsLePdpWjxFqYemeh9i0EF7HbDTPrSIGl5S9iQ54Bbm7Qw4JfnRAh+bPmKZxf9YoP5IiTpW+UJ32wfClbc/qWkt7pI/mWDZcN/8oLf3KOtJeY5cfMbelQpe2WaKzsO+EgTZRJ7ajSmp9F93zb1zCTdFPSKZeBbfYwx1UapmutklW1G5H/KgDPP/NcVtb25VhDxv+lu8/9xfMBj/wrzfzvnmhb/aGLV+SxlDi0y3yGUsbH2V+7d+JP5lilbrIUjKUdNuTpFH3Wky26eX+UQa441aujs1ececXHGFm9vH+BP55HidfFm+0gPz6iuuPvEyCdeeSc1U9UCq9yjmZPOyq+d9HwxXqr7vmKzv5OcQcuMOqKUVit7JT9cex/8YZGXdBEeK61L1YIqPt/EXbKppBAi/SoLN/ICk/G+Py0vNsHYo6PUUNQMy024WeuwCS5JVkSEc1IcdEqaq66KcK+q6ipnhxT51ZBpmYXDxrj94U42nD/jcpE4hZpkM9InyPKO3JJyJnr8YkcN5bXGZhddVUV++2yUKF1nLUSUS1BrL5twjzIaF+1o0bF9ckh1wUU1VdFCMZctVLV5TV+OGKyeZpY75euNXxvcvKuce3MolH5Vx+GPaZZxrw2vz5CZucl47VyRV1S5BAX37JBhj5+mTPKxHWCUdlYc/U5ZdeQUqaonXDtyQcGSBeUPkb60yE/2KyKngXppVaS+TqPbmh9LyodFbT6y1tTSH5l4erex57/U1BaNlXJahBj7rV2a7NKVFLt+/cuAgaNUjIyVrchF115p5rHnt0rpOcWyA7+5KMvGC+OVFOP6tRs4ENWVU/Kzu52scsKUx7/SqFEdpXvGmlD8WzFHqjnco7vFv33uxM3iNzXl82aVF0XuOmx91jLXROt77hn5znHXDy+I+6WQGEcVDJE22CJeVVdE+Gb7UHd61TGLXdibIuvoaUuGj1VFRTXVNdBdKssw1lGH7NdCJ4Uk22CdShpKlUP2iFTD7+zuvR5DWHJWQnKazpqZYZckyZoqqq+uyhVvq/WRex2LzfGfEcn/HpGP1yIjhDvKaVCnrmGS3a2+Co4YunGQPz96xu8D31X46nUPXH1du9w9XBTvNndKUkNtjfVwlyaK2rx0vtx9qnrAy6JRXaa14nXwjHM46IjT9souxdOW2WmbRk5rcFt7z6mine2e2vC87TPn6nXfA0pVq+0loyTe3V32eiXMdtE0rzvvlNWOuPzdFlfWHldTSa+3+sMG+dQw0CPqqivejw9O8FLs4xqI9sfRYeqrZbN4FXTURC3Nxeiohe+l2SibrY7LUtQl5+URr7J4z5rpgFQbHLcCuVZc9FWPbyzu/b2v73xWtshIBxfdsNSOK+bwxXly16tsq80SXBGjvDoyDVbeLissN0/KpStuV929IjSMesiYY5+657UejkmVJdrjqigiSkEVJebIa6EjXvKNi5IlqeZqSk4nkyd7y7eeeb61SW9MsCtzmzJKyyxeRqPn75LjdKrjLjkszVb7dVFCeTmlOSDDRkdMsNUE63ym66yeVnz5s7SKuVVveqs2mkiTZo6jij9Twf4SkUrGXfJWs6dV00TG2ZOe+XOowrLrppUEUdqqbqsIkdmuOV0mn6xjCfaMnmHkZ5/avma3xIzrLkdetuDAZ562wI+Wi5XulHh1lTDt7l+1f+Y2NbMq69fmFhu+Hq58z1ZOoG6/W92vmn8831wppTVRyOSY5w0v/IK520eL++w2T3033SrrFMFQ7VXZe9rImj31d6sLiw/pIoePo7v6vmhPgwqMUCU6y0/dXlS8clmnP9spIb6i/Y64u3RvLQ3UrvZrarnsd48567Bol6Q6qqtCCiimfkIhXz7aX4vfWjmYHGmLoLEyPlLLaoV96G9DLDXnyEIQkXrwPyWT/y2yK6uXqhYeL3a3WkmtvPNHR9NuxgM0Eq2oW3w3crgpC5J9OW2BDkp4w7M6SxKPasoqp6zieWtade4vVTTRxWceU8Y9umhkqH5KiJPlskjRcjnn9M18h6vKiJDqomZ6GuxNh9ClyhCXts8zbtHv5s3cYsNny9SMidbx2QEOL07119wPnLPJDNt1VFbTyB52ZB1Wo2JFK3f9onPDgaLzR9v+12pns05KFON+f3pJR7FKe8Vo0EcvJ+wzywrQWgELpErEM+qqqYbEXA18ef4LqY6prJrcsjnriOH+zWZMnnzCB11HKmyxCNmUur+XgpVKiXpnlUv5g2nJSzzYsrcpix6zyyW1Cg4Vdeq4I75XXVcXRah42x3+mP6OrQ4qqIKB2e608vpcb1rtSy8r0bKqToue01pnd8jhuoVSXVJLZd1NVVlhOZzQTUElIrtZmJWmT+s+mi1o9z+965poIMlxxxUT75pcvnVcLYk2STEjaqhKdVuYv36m9IiCTlzboHj2RHeNedqqSevE7Iuyb9tkh0R5dewbuj1YRiv1JChigY2qqqW0OKFCRXf8/JTpj3yuaoES1sz9XqMvvzdhQHPvOmbf4h2+bPGocup4xEe2rdkqMyNTzaa1XPx2i/n1L9j66CLFjxSSdniCcrGdbL0ySbQ0LboNtujcXrcPeVSTdom2LE/zdpOqrsjpsF0SRLgt37Ounk1xyXE1Iup4O7ynitp61HnE9qux9mzbqGh0LkXLFVH8YIY9l3a6JlO6bPa65oOb1daWlP3O1H3jnVPeU8WbmHpkmpf87C2dXJChuIpqtO6k/4JP7TTHYLe7Ko+KEo213MuJj9qecsAsUyyz+f+u7MpTh/YrnlBUw1eaK3pTKUAXddUW74sX35ezaaJzNplmryzEiNJeI3Hi5JPfmnPrVdXQDjv9UOA5H1mli9c10M1SVx2T4rII12VprqMzdoh23hVpdkrR3ZvaqWphuV98/egT8qroZPwFldtUUeLeypp918eyzcs1mFtbljKa1njaDmxwyOtZ79jmuIg2ZfVbusaq1b8bPOt927MyVVfeHhd8pqfSskm2w0CdvFXyAzvt+qdSGKCkAq5rr4BaSHHcRcnGnH/Wt/5WVTZnJEuTXQGF9NdAD61Bka6FjfS+CzJdkd3Un3/SpG4zq+KTXXu4jqdGfCC1ZJzs2giIOLVQlVLl1VHbagsVlG7F9K/coaFKCqivvMsRXHFFL6WdMllsmexaK+fJyOIui1VAdY11sdoOQ1QyPf+jXtHKIem2Zk2UU5o7FwzTTi2F/t273ofTTmqshAh5ZcnQW4xNbhS/adK+u2dXf+By5nFlrqU67YKPry03bfgkGbnTZbTIcrxkHi3yVpMZG2s/ZlinrOIecqezVtphiqYj+pg++BuHNoyTuSnVVkslv/KLHKI0RpFLEf5R7G61alY3vdx0sVlRYrJnd3VOhhPV8oi7ksPry0cadPhV55xw6kq6eBdkibHp1HGJZyLkP37etmXnHTue4WNHnZFNHoV11NPSs8utt8NBERqH91yTV07RLu0/r9e2p7zpE1FJeTTq0MjMSxOsttACixy2WJLd3veCH6KG+W3fV+Ll8I3PPHDkfrHSvOBOKU74yDyf+9krC3rrpxSIV0kV+Z1z0HprfZPyhVzOuXgzPftfpf8WiiFX7oJyv9XIz32G+RLdZPeLlq66KEm8UkUbqlklSdEccdb6FXQw2FGXTfKXODkssFt7b2gd0dzF1OPu0cIpGdaZbpeT8ingJ4cscUJeF1VRToZUfzvsys2w3xwYufdBHZ+o7g8/O7Mph+TZy9z7QFXJlTMV61xaB3295m3bLt+ob5xbPi/o5xfzzB79li86Pu+S4joqJ8lpm0uc1v2NQaqKttAiuyXrfsvdSh26JAXDtfCDzjcrU8fK75x8qKaBDfbpYIAdCV/bIcUie2ywzzTLfW+naDUN0fafz3GPU+KSqjrqbz906u3WshXF31NW/sjz6qRlE/dCH831lmyr1QfXyDf1I5VcNtN8M610TJx6WiqgmEtZZ3TVzpsRAyQqoX7Z+nJZ5JGsNwwxzA2H2GU9IvuLL3+7WWe2uaao6pqJcFWa+XJbafpviwzTWFcRrladaqgyiinqjls/0y/3Y17J8Zx7S79jlAbuQY+ZdUwyT5pDGtzRwlw7HLBFtcG3qDaoqcr31ddzyhAnm/Lnu+O96xE5xdviLzssdcppFyTKueO8yUu+cUKEnJcvi1DS1ZRpopxVWTH1OnZyrOgxkzZPE1tgr4KHk+w+u92aGX+KX5Lq3JqjGmpuwttz9arxihgnHHbYXuv/H+ruO7irumsb/Sc9oYQESIDQe++9CEoTBKQrilhQKRZQUbGAgg1FLIgooqJiQ1BURHrvvfdeEkogkBAS0nP+wNvnec7zzpz3Puc579z3mvnNb2bPnj37O3tfa19rfddalx6tOnh751j97m/kiTaF1e8Xo6t2ZjvoRxd87rCeKqusiI5Fbgr4HJdsvW3eSH7NK+EP3jx2+rBvP/xEUeW00cznjnm00BCzbXPvLfVk5hzXRGv11DDBHQbprabGmgfUN6LJq/betdWonpPFxA223VGJ0V9KLnxBvXqlhIv1lC56qOWyfXb+k2rX/xKhRGRAcP603m+quCzCn6kjHUKSYF1VU9WtQitVkVghV8NyNez6+nthQpwW6UVT9ROnl1bS3HDBNZVUcFiQSm64Z+DLCn7fRp5IS5u/6rHNL7tDATlSJLhqvNEmeleQAMvkOocQPKm6Qip62xnBrntxyJPaDW2rUHZRMeGhShwqIWn0St+c/sYJB9US4SmbxOFRrbR7aLir68+qklHC8DNDffT7eode2efC7nkqC3OsTGObU5PVSilosg+kSFJeqGEa2ee0OCVVF229FXJVs8sREz1qts8VFeHVsE90zHzDZse1UUE/1X3pvL32+HzBGo/e0dYwZVUWIC2yuYGrP3Ns7TqlQwvI3Zjvp28e9UCVsRKvnTQt8biGLnveUsEYINotumujnJVWiNXR3V53pw4yLDfScH9a6U7tRbRt6NSaVcrXaGDmoTf11FtCTFmFLm203k471FBKmMbivOJbcJ9Y0/rOsubSIR1LNLDn8AGpydfsPbNaaWW1u6OP6Que8oo9ntJBiChDpr9OwyBXgrME5xQQfybVwr7TdYkr4Odz8018coa2U1qpKFCOPN9HfeRw2nm/Z39rfM8v3PV7Fz3Us8MpP7omBHFKmz/jTycHr7DHj0baanr0S4ZcfUthHByyRZnpzaT32GnmH9/73kIT9HPaCQuskideqMJuqzjcgydvJqXbKKaj6sb9xQIf0VCkXJkStNDdFRfstFtP3SWFVfNI5vN66qV1waKeT5uhoxYmlnpGo/N3gSDh3tHZDZlOOyBOKTdky0OePB/Y7Wkv+cBUfWPu8sulz92CoXXfdvLIbp1b9HFt3Qm/537qY6f4ny5w+v/fgvXrM9yC1P0mu1lJtUqOwgLtslrY2WuiNlxUP6WUrRJ8a7sclzUVp5ACronytV2ChVpuq312WW+/Zb/Nk+ca4pXrWMfICo+Ld028ZBmYY4FbdLJLGefcjH8b46DD8pzXT6L6Qm2avkP9o0XlBV0XlRlmzStfKXW6qxcsUtGDVktzt5uzF6Kd0mTEHSrd3cKSM9+7W2XRmcXs3j1bRYk+NFd+/H61a1e0yp/aq6qGEGOMcN5FpbXypZ0qKqeth8x0xB5scs2f2O2GlzPfEem4dsJFuOC0c+5QzRjt9d53s702CMdcVjmwgjENa7j84i8uZOY6MH+5EDnWH5vvUuJVc63Vp+3jyiutm4ouu+qAZaaYLkCgXCme1Uc/rS3GSJ/KlesHf6jbsYmNTth3aI1MyVZYLOTSDonOCFbJFuv8brnG0S3siJsDvpNo2aLFDq7f5a45rczeM4kzCX7yuzUWWLdghbMS5WCS5QoUrGr/1pMyk3KFBgWLyCUqJVieXG+f+8kH63eL7FLKE+UeV0iYYgqbmvyVP7P/dE68538fopbqEoW7JloquminmUZSViS6LsFxB9TVTIGrlzR3m4566zT9Vv01sOiPqa4G5XhQX1+b4w2Lfe+sPfIdc82xkyu9o4V2irgkSb26gzzrDo1wwWHd9ZYrznDfKKKC826IuK2GMZlf+u2Rxb6b9LIeHfsZZrBOKvztFF7zoTs1Equ4TOk6qK6wKDkyhKOa0gaqL88BvZQ04fsxZj22RBKKncnTpmEbtXu1EF42WpASivyTiAwaN27c/2s4/0/ZrA+/HZf04zbxLsp0UiH5aimgnOpSHHUo74CgnKvSjhZxJP9nt6niF8fEy7fGCSccU1iODupYIcMi2/VWW/1izXxxfb7p3hVQKUvzh7q5vV4fV1Yft9dJuyXa5rhGIu12TS/lDdLGL47KEKK6JDtdttE+b/88xV09uhrR4SnlkyL9br271FRLrpJKC3fSDzJtlKpZaGnRdatpPmq4yGbNrH1gkuG/TZLxWE+Dhrxh0ZcDnD+b5pU3J9qw4gt1xTopx3Vn1Aq/y9ycpQZqLMlF8x01XJzSymimtnKibLFTLppprJBCiomV7rBwN6xcesALwZ1dz0sQoabLGSsRZHf2TjVO1FeoSJjgS4W8ZY7PbParF7U8/ahvvajtbT3khtex9vJh+Vp53yK3F26mbIkKKl7LNMN6V1BCpOIu2LNyg1iF3dvsPusTFsiQKVOQRloKVsBRiYZoZUTGx2qlVrLQek0FWZa9Vrv8ogoo6ZRDOjYf4kZCkmIq+dJCKbI96UF9tXQm/7qF2/bKk6Na3RJyk667uO6YOVt+tF9xh2bMVHVwSyFlI51Y+rtMWa7Is8Bx6xCOBRIskuC8FHue/1NEeH2vnHyTPQGqd6nro2MLXHdGDRF6dx6m1vEMKa6JlK5Dz/4Ca8aKPXhWoBBNdHDDLv21VFwppcU5JVcN6dZKcTpxqe8dch4N1ZDlmgdjH7chba7ygpQULu9UuB8tN2vHEm2rttby256eG/igPk8/4Ll73vXuz2/pr7WZZloj0Xz7FHLDBVc00cwF8aqpIla44sXq6DFwgJJlA239fpNvLyS4MzPHvvhrsqLCpZyJ17pRVwVPxVsv/vy4ceOm/+9g8l8ilIgJKJx/2XXhGKWBdOe8Vu9Lb+55VBGl5Upy0lVrBWqtiIvOaqSOKurbKF6K625VWr4zIpSw1RZT/6pG/MXryo3sIurOCl7q9YGU1HhLzDRYcQEui1LSecniZVjzn+5phBgf/ZUQe1ic6b+esfPPjZp8cYtXtJcqQU0tTLXS1IaTHd253ixHLDbPHx6S3rOB4nXKycgNc/jtd/0m20/Pvir59opWdHrZMbt0uHOoGh06mjhysN9s00ht7RTUqER/t198ThXF9VFRCTHOuSpFuC+sdPalk87+vMiJkyeVLV/eymM/yZHurCtylFBXpBtOaRb1oKHJX6qtgPYKmmejYuK0U1tJlRyzVJTu1lliZIPxVu5a4JQjqqqtTZ/O2sy9y6UN5xzfeUnKvJWmLH5KmEKCFRUnVXNdRURWtPHad2ICuruWv9gVBT1xz4tK9bnV1P59rbfNfJlq4UHdVWvQxpxdnykl1SSX1RXutAwRirgoxc4Hd2v5dX09BHi84Zu270w2wxeebfm2nzaulydf/ZDCZGd415du01+4C+pVamfEC0+YNOQBrdS30m+WOqK5EloXH6JUs+p6LbjPm+5234eTHL980dw3XpEhVLExHfVOqGXXV7NkC1TUVXEqWGmzALWdtM9WFxx0WAVl9FZfnhpuUVR07UJ+2r/So9W7W3Z4tuviHXXRTx+dNXxEWzttM8Zgi/3hugwtVXdWkLphfSVFhdp0cbeZ/kSiKc0+1DyqtIuNIzUPLyM4uYBZ86d7+Juxzl4+a8Gdr6qijLNOWmadcmI0KfuEeUV2unJolxFfTdRtUDujtHFZiJqhVWRkpXji/Y+cP75L3ald/ufmMfyfsGpBZfI75dV1d7X+jh4557otjtrjmlQZMlRRSJpyPrLNi+5WukCkJ9I/F4ER6iooXIZzrspxRLqOAl3Q2BKH9VVTIY20e+NWCWdzVavTTHrWNXNG3eaqSyoro6KmUqU5Jd4EO3UXrpIyKqhkvaOKiTbdf8ya6aCMumLVU8tX1nlIH8/5WpIrPnaPyza6qrCnHxpvV7kcBeZc0vnA457XUsuX3rT/rfFyJTgvVNuhrzr77Q4p6Xu8ZaEWivv28d/9uGid5ceXu0NpSy3XTS1nJfrDblN6/+DCrwvlS3FRAbWjalmePE2MSN/ZL0K4J/TQsFFPd+/4whF7kaS8psZE9nTl2mYxKhnsC+9F91N/YA/PffyunTb7xAu2mqOIWH07PC6oRRkBRXMtGfWyY/Y4JsNGeZ7RBGekC1BOFQ+9MtX2M3u9/vUgD+jlW9u1Eo40WcoKc055BcXN+9mRN38UtnmnZyxQUpAich12M/n7lLaKyvdAvYHWSPbunk1qiva1r0ws/r7nLz+jtdpOuyBeEmKQigxLXpojKzvbteoxOtZpKOzyNVNfm6jCqVjvJK6y2xrjiw/QZcZzChRm7V1zLbi0zhFF5DrhjQJ95KSfsVumLY666qy9LmqgnUyZDtqqnYraq+g9q/V2r/qIVs9ZF/V+4lZLPp7ghLVWyncAfbXXyjn1Co72ZNrT3nePoT4VKdJrhZ8TVSzcC6d+li3CLqsUV81lR1wcuUOJyY1A5sjdmk9u7+eyE2w8+6eGIbdYlr1PxdByema9BpLXpzn621JN3+0lccvnHjkAACAASURBVPgxZ8P3K1I/TsgN0g6mO3jhlL6zH/j3cgwBAQH5sfhQV/daqKxQ7xtmhyQXXXV7o25+2fGUFHmmNZzpyZ2TJDuulsKiJYhwp3Hmqauak464S2EFROmhm4U2KSvEOUW9ZzEqydy4y8edRsi7nqGGIybZoZIYeZIlyFZRkE4eMU+GZFe0cdBVVZXRxpNeNkxbtcW6KtjH/jRSb1VFWSjdV774e12fhdyv8epnVNoQ4GjnaMHXrljQ5jOsEy/VZ045/PBKz335nkNClLHXLSpo0WmAd5f+on3T21XKzlAyupL2K2/Gns+ppYKazovW7ek+SnSoIDw82gcd+4p3zI9/9bKN1sEZq+WpIkplmx1RUVlXnXO/VqJjmxqY+LjaYo0e9I6yD7ax+acF7mjfyX0DnpHonNGRHaWXrKjQLaed/HKLIDc75L6y6u81fl1oog6Na3pqdW/3bdroaM/PNLu9mYyyUVI+WGtb+nyh0qW74bh0Z9TwZsgdjhWixRNd5Cw9bM+mfcqEFxNVLMjFq9eNTV+qtjgl1dZ37MPGvP64MV3uVf7hujJLRWnYpol/qII/p4MVkr3iTiddUcx1V2RrfuftwnqXkd+ihIvjdxo/a7R0hfy8bbaQjBBzH5xn1LHnzKozwZV92yQqIk2GONGeNhUcXbLP6dOnHNuUpH//3vIrXZV0Md9dt4x0wkGnP5qn6Ijm3mrynYnb7nJ48BJbIpNc+2mHeedfswU99Tao6d1mb33Reef09pBfrdZIRQECXZGra40O9l9JEFWjvOJlwixbfMG5pFQFvW+fALHqiZatskYm+c5Hd72v0sjOjoybq+3Vpl7aNtVM83VX1Yg6ryuYfVRIu9o+3/6HR+58xJpXn3VOMR+Y/++VfIwUqqVmPrXQ7ciQpUHbfib4Xh01pe44oavBOujl852faK+iS66ZLkGdOh/IkOWboAmGaeAtt8lAqjCrbdLd7Q6L8J7FOmpt9bDJVq7cptsfT3vHLC/b4TgyXbJXthtIl2ubhepJN98f1ikq2g2//tUnFiDCRgn2Oa2WKOmybLXDKRu1VUbJf6wr+7pvWw2z7tkpql4rpHBACfFWWWSvQm4IVVhquYIefm2wBMd00AI5FixdaoWFdm79zbJdR6yJzffV6zcZSy2VbPeLYi7b/sEHUlOzfTPkNcM3zTNu4GequNm80nXYUNGa+ckhn/lTCyHaqKG0NCdtdSBxtuUmueSGZ78dL3vVebc+cotr5dJ0vrOV9upr/Pz9nj3ymlJluuj9xZuKlq1n7OxpTo1b+veze/L6RGfWnXRdjpY5JdQY2EGhflVF9qzqRMZF+Qq4JMM21/y6OlNbYZZnz7bu6iLNX2/viU3D9Xh9kMvFwn2csNyO9FQHHbbESYft8MPrz3m7x2Nq5JZycdtFFdLDvfHoJDHa+NRTcqV5I6ynJMmi7bLcSSPNdPHgZaMfmiTkYr5Gw27TTTN3KO3gO4dNGPCjUceec4tqtuzb6muXfeyCd/0gXFm/D//QtTNnnbuUoVCHZi5sWC4wIsSQap2MHTZanHypjjiamKWiIrK3zZPshgkzXlCvRSl9fnnBsMrjvKSLD/2q8dYBDruumyEuu6i1+grKFCBFQzVtObTH8cRLGt93qxK162tUPdrL3450f7cPxKpip30iVTJozoO2zt2p7fB+Asvkqlm0vmM3Thg56SX9VNNUFa/se13rw2M1m97HF9u/sm7HWmJrGzlp3D+FyX8JxlAtrHx+ZtZFsQrop5MXzMbNqTB10ECsNBGG1nzF6oMrDfWdSirY9MEak5/uZIHDiqtsqeO+9oDBvvmr2yvAKF3VU94DPnWflhoqqfvwIXKGVheQme/4H9tNe+MuUYKckqsgwtBVF+nyPWuxTV3Wmb3oCTEu2iDTTUH6m1ZVhJe0tV2SOE1ts0NzmUqKMtsZfzoBbhVt8frDwlrHgqEa2StN59oP6vtUdydPZcj5cr+1F2ZqXuphA84PVF0HQxv2U3tkOWVbhok5UE9M71gd1ZThpL2yvaqzJ9b87IPnRmr38mPKHci2ctUyLQZ1M3xgN0skqOBmT8YdejngoksCzLVBKbzjbhddomwFD52d4divJ6WWui7weoYkaSolVZZVLll+SLjkb/dYOvkzPeZOtvfheT69OsN6h73oSQfNMPXrxfZ+uVfxttVs+HWNUwc2e/Ktl517abSwUU+bPWWWwlnr1Y9+QpGrmdp53XPqKa2Yq2KVii7vvleGOZV13sFNOxRLC3b9aorE9HQl0iMlndwnW4bSfW8T2aSJtEJZlj35jCaaCguNtjxrtQhBKissrXhDz1x+12txj3lo8XCL+vzo+tErcgJLeS5vFHjB3Qq4ZKdzKuhlnV98OHaSgtVLqxhW2sD+Dd33yXuEFFS7XEXxC3epNqiNK7uvOH38tEYN6nuwf1t5rlonw13KKaKJ4sFFNHqli1mvDFZaZZVVlOya9kFtPJr7umghPo75wMpLP0kVaIbjzop3L+4pOVHtXhUdrJ4n4nola8aO1UQZKT0aq1WuphlTpzsl3meLP7Ft7DyXtpzU/KXBtvyxXaWQgqbv+NK4H37V5N5GDq7e7FihJMVEyg7K17BBhX+vUKJEQGR+L7HiVPOjhYI1tN9Or2hhhk3u0N7rk7+SGpRo1xM/Wud7l103/LHvPf/JYFukiZEpGlNrTLP60HpJgqy2VrQUt2nlVfPAMPVNem+5QqMqWvv0H5pXKarc4/VcQGcRigtTQnH5ooWjUdmHhBcNUa5tOe9OeUeSHIv+S5qSFz3omp0qqiZJtgl+s9REnTwvAn2VVEqM9s+OF9o5RofOt4hRWDPhIpQ0qsNzSg5q6NLpsxJfnW2i7yyadc2unD0UzVC9bBPF6hbymEect8fLZXqYHZ9qoonCMNnt4tRQ6ZU7hfUoJ+1ahuQNuxTu10bwrKMSr2d6870e6qmjkpae8rniiltnmBreAA8KVFBd+cLlCTFu5s+CaodKv5rhZNQexUPLOzvziKw55yw4PdmA79e4/Oc2N35Y6n4fgOUP/eB8+0hdI+uSRN/B5VWp8KAnZz8rPD9PfFS0VdX7OmmP6sIEKW+TJPMk+EBfd7Tub87++arc3sHVYxc1LlnLxRMX7Dg43QEl3aqoul16KRpbSdrRFS5s3GO1A6qJU6doW2evfGeNTFPt96GmvrFVP9U0MUiCvRLtUd2tDghSoUI1A6YPU7tzKb07PW3C0rHg1M6T8jKS7VmVqtmlIHM//VKZXl2Viy6i2MDKZBeSXixZduYNhTMi7Bm43obTc0222zAV9H/nc+NHv+2d7RNc/WSZ1O8O6Zn5JvjdUC/7zEGxciV6TRuXHZAq2leOa+7mlvesv96ry2+fEtGykA8Gj3O6RIbPN3yhCC78cN3j99ZxyClVVXVNlnc+/V2Z5pXsDjwnvVSeqlfIygyUnxPkuhwFsqnesua/VyhRTJRirgtzTlsVfPzMNDlbc41bucFKn5puhRdeGCvzYqISxUuLUU+gpjIW7fddxd8EqePkI2stWHbB+4eettZKxW1xm6vKClddEd+VnWeIe1VX2tujnvN841/c8sGtthcs6tcaN3dwAoS4JFmefGcl2OKQC2f3yD59xv7AJJP7jNBMhv7K6YsWbla1pUp0j/tkSlRADvjGfFXwjKKqi9FRKTMm9VG4Wr7ZLWa5JNV+WaJcsnH5XMVzQ6SFx6kY0lJ/g5QcUNmw+75xdV2+j7q9hKJiJLvqpLfjd6tar6Keyuinga/ttchlp1/7StjFHOcqRGpbu7Xo01fs3bJF82aNVFLBKvu862tVVBPomju8Ief+rWKxTp5Mu8W6pp5iSt5fUkzjog5uPCMsOFpeSo7ekwboeXq4RKUtf3i8LTn7FI8qgJtqzce+2WTXoDHu7VndpcMpiutoSp+XVbsR5nJepnr7EnV3u7ZKuKa00q4JlOBx9VxyWkRGoA51OqhdvaK4kgUcP75KmXLZjiruJ/MFuebCkaNqzbzTsf7NLRKshZYC5Nh6ZaPFzikqBTxlq7IoLEb7p3qa4U+pqvnQVq/aaOCpkd45+aG5k74VueUm/9v02HbrB3ztUsn6er3YVujZQCduHPDSjwM0+aSzFd8u1frWDnKv54uOKC40vKiepwc74rIMZ9VUxMzRnbx37ygbPpotpUCoWr06/PVuRVpkkTmVJqguUUeB5lpnrSu+chzcLVQrrT0Y8BbIP7XftAHPGDB6hGbXIzVWWjvM2TjftG+2WzThkK+OHDZuyXQ36mU6eHmnaik52q3NlpCdKTUkTGZeYUHBEQJCQ/4pTP4/tl3/n7CsyDAVr99NmSCPjx8o4XyYr5o+IkwZG8zwmA5+ujFT0OsnlJZh6HszFVmarPOiViLwYeCLHviin1lfXAQfqSBIST9Ls89psXYKP5tqkzWmu6Knyp4p/qCJ6Prg3db3f83wQ40ESxCtkroqmGyDdVIF2eW5AgMcmL5f8PuPKjq3jNKK2SBMd6XlqmWvY+qrboUQwTLV0dagQvcoez3ZLHscd0U/p5RT0pd9Jxs94UPFOlNJigAR1lki++FsVVvf5VToMRey01TQwB6fOvV9OePPfCQK5URIkeVVdbXf84quZR5yR/OK9v/yukRzrFbB4e7PyBGswzfva96nsRw0HnGvLFlyUUK4D/U2wTtqKO985XTfvL3UvhdmCXTSaXskC9FcBwVdt2PsSPPGbjas6ngZbuihi1ulKl+mlOwDgeJT8zypjO46uZ53xjFJBqgoY/dmn/4wzYYGqTrUaijhzGUrly13wkEj5m1w/In5Tp9Z7oxEv9njHqX9uf0Xjcu0cOLkPnWatbQ/P8ui43Sq1daoxk+5fuyU0wcOaqWcrAXr9C5f3czTM1VXXJhE1YXZ89c29TBFxajrtnvv9/PR1XpXecOpY/u8VqKD6zHF3Ni3S5vtbQTc20CpXmetOPaeWZ809iH2ft7FgmZTZTQpKHVOrtOKeEBjJ6btV1ol41s+ZUjPIfr+/qixOqsQ0NaB/HMe2PeLpT1etOvENpmnkxS/q44bLUuK+ol7BDvvmgpjHvHbiGA9rs9w2EH3Ke6aYIVccE4Fk6y3/+nxvn6fIdMm+tVqPb7r4dyeq2b1nuzFX0e6s1Rtp1tdl301S2j6EeHFygjNQZFCUvLzpZYPEXkjW2BQlpygfOE5eQID/zkO8C8RSlQIiM1/UkUzbLFq9SYJieWcH/urY4dWWmGJT19e68k3WyLdWUzp+4Ps7Hyd5g30e6PP5ISEmrN5nCVOe053o8wXgrv0UlS0DIfUUt9kC7TRWrptimigW8FWLhbLdezMBqst0U176Y4Jly1fiHjFhUlVSzX5yoiTK1WiFU6IUlsjAWbZq4/2UuyRKtxa+3TQWBGZcmSbZZUIWSJluEUbvTbNsO6t5RbMmybSETHKuE1DKywSp5PY4KoKlizsXPxh15wX4qxDislRVFFBsmT6yApPqaK4+sb4wR8GmWmpOX+N9qgnwMMa2u2Svuo4Kc9Vl0WJRAE3JEmXJVkhi6yx67OLlr87Tcj4LlY8O8mO83NsdZMFzC860db8RG9f/cIxyW5VR7bC7hJrmxO2yXfQPrGKaa6CsjJEinHVQeGi3f/1LIkHDqrWo61L93xsQ/wCsREtXL0RZrKP5CMWwz3qvJOC0KZNXzsPHdKwQ2XzfsqUe2uMd1Y9aM+w+TIupdm1d6+4LuUU2EPSDQ5t361GXoR9ecuQZqGjhhvueetcste6egut27PEVrPEijNyzFc2vvGJH0wTjBZqu/uRtwUMKi08NU/57jfZ9rGlW5S4WNYL9w2TIFVBRYQ6JExDv5pntOG2yfOD95z4Yq+PH3nNVPNkyjTbY5r+8paKfaOM0990c8zxlPLD77A1+aJVP37ig9cX6j62hXQnzd6SYf1DT6j140DJIREyroX7rvkDura+S/XHO4guE05uvhsRoa6HpyucnyE7r6C8vDx5eXnC8/4D+BkZGQILhssTLC8oTGherpycHLX/3UKJbNxzzwx7lmU5FXrF8f17fHVoovvmTjbIcK3e7Gxs229UcruXm/wk4cwlOzeuMLX1JLcO7GXS5qe018d9emparrsn9feee33jN92a9VNVObFBdRxzRqwwBVW3yiGH0zYrU7GkoXcOd2/kY6Ji6+leY6R8OfYLMMMam5yzxTpHLXfFBRGydVHZGZs95Av1FDLUBFkKS3JCbeEO2iTZBU000ERNtyohG+cdk70jT9enmkpxyGU3JMpXonsfi10T6IDSJcpbEH/YaclGWyo8eKCiSqosSVkV5EtEphR5xrg5x2+DNWqK0E8ZNdVSS3VnXfVimVetsdCnFhtruz/sdswB5yUJUkxBZ4z1oPQiueLGdRGTlWnku2N1LfyYVa2nW1L/M7OvTFbxapg3Kr5gpOcclWK9jRY5YoO9mqukufoSJfmu/1TFVfRkz1E6amjCs9/YN2Su3ROniYkv7If4T2S5Zu+NZe4c+oDGarlLA+VRSlE/OuBXJ1wvmmHO5UNW7slWt0mAPau26qm3MZ//avAvvxly5A3nftsvukpV/Te/ZULOGp/mbdfMHb531BY8MPoJXdUC6Qe2iXDYhx4y/LvPhDcPt9pMBdBLY0W1EFmokC+e/thX321zadMNm8f94VzGDacqFzbN70Z/8pbv/eorB4U7prfy9lpoo48UwoqvD3jfHG95Rmst3eUTOWsP+Gn4V/p8/IJzWGqb5Z+u0/vHQeKC2uk4toHG8qySKbZZgAK9b5GZH6HM5UBRMQVNWzlHiyndZFYMlFgo19WoQPnB6QpmZ8kIDJOeni43N1dQUNDfWMrPzxcWHv7/GZP/EoyhSsHy+RNDnjUk5WlHthx3KijND43H6z3zAZkv7NXh3Ase194Z5/zhkOkGSHVWoQatHdl1xC1B7T2SO8Klt9PEvdDKeYeM1sE7FhjtbiWdd1SuRoVvdSI1zVv+NK/l61ZvnKhKq0e06tjUzNd6K1JqmDbVq5m36hU/OyT+r/ubYoAERzTRwHGJf3VjBlohwSbxamkpW4bDLnjHbaIVNNhkU3RzXoamws22V6YUpV13+8F9OsdHatqptB8XnpBZIsqZRi+Y4ivrFDDfC3Zb7xVrXJeFDFPVsVWybuHdHc1YLUWQuhq64Lw/LROivIaiXZGqqsJiVTDHbzqq7iuH7UE5BdQV4KBCTrjoac3VVEu4LIn2avn9T4LfW2xfmRuqt2+gaOnylg6f5vzl6UI11u7JUTpM6eO10i+LS9jkukJiAqsrXjzKrMRZCkqwXYxOColWVFCBpjanv62EUsJVUEqgt61RVUHd3OKuwc+a+93nWldq4odDb5ojWQJe7/mysb9/hyTzh34qdk2yM+WDzT29yiODHlD6cKgl34wUrLRjUpQMvE2DF7s7v+eUq398o+Mtw7y19glZInRQSlFl7RSmmTzh992iaotG3nviYTUU00ZD21zUvudgr26b6XLCBSULBpvwzNuK3BYjN+Wikb1f9/aE111onWvOkO9VrVpVWqnKHp/e1pfuUSgk1r7sG3q27mDr+j+ddU7tEc+qFVFIwNnzQgpESDhwwsENW0WIcso1reLqW3duiSpKC5RlkT/MXLVfQtgV+Jv65+bmCgwMlJub+zdegoNvZgAyAv8jRAjMyPkvmAoODpYnWFZQiMD8bNevX9f4tsb/XoxBNstTPjdYZdWaVZD73T4FKsepHRgjr0GMbyPGuk0R7VXUXVHHbLbFepd3bVEyorISnVo48eJxFV4oaLxWOotVXLw71VFcLU/booh6QlLT//qGHFUktorqWqh5PkdEdJSODV7QrEq0LRvXa1LzMc97QnllPKK/FhqL1dYj9ptkt8px3XSuMcgNRcxsOlsdUR7RzO1qWGO9LDc8oKtTckUIE1qghR+c8YsUxxW2rua9FsefsPGPFL/++qNXGhW31HT7ZbshRUBEpGfM960hPvWQ3S1mCorqqbRGnsmY5v7+02VKtdAvVtts7N1/Kq6Nd+xyVrzN9iBRtJq2C9BbY6PE6CL0r2Ev2eqIVU9dh2230E8uOSf+6W+8teN1lfNjbX91ocX97xNYONigqVslC9NhSh+jddC9Qw2tX35Hvsq25x1WpnRpb9/2qk713nBGtNdss0KCEelv+l6uksJFOu2UzToo4hHD7XLN1zPeIStZ+KkQH0r242NblVdUqUXxnol+RD0tXD2cLDk2SIWdSWod3GTbS6N89c3jqhsgWJDfbfdS3tvWv3m/+X9O12rZFLkN8yRqY47TAnQW6Kouo7qqt3Wc89G5IrflYJ9Crwy3o3Bhi/zuzt8fEJzwvYlrPzJmzRS/fzBJaHC45Fq5Ok56zMSxU/RsW9/aQ58qeTHdHUGB5rT4SKcN7yq+7F4v7Bgn7Lmq6g/v4dYqXeR+9LP0X3aZ9cOfyrRqILBctD4Th7nvt6cNfKyn3ILpwoQJEC56YCvvrN7ifOEkOTk5/+33n50C/j4enEdgTh5ZOf8NUv84JzsrG4T/kyziX4IxFA4IyH9CWxGK+dUSu6TJ2ZznVPoBmVtTJO9OsOL71wQprLqS9kux3AqEKa+pSNnilNKlUgc/nfhGhGTF1TO073hDf3nWDIs95QF7JKjuugbqGWqGOHV0Eu1b58Sp5BVV1enQ0oW0NJePxRty+RsketMI6+22wDJttNUvsIn6lWsZefQ3e8w1UHvf/zW49WU9xYlyVopfbTZQbaXEWu2wK85KkW6Mhyy1Q57D7n9uuqW/bfXt0QnqivBesYkmJH2pmuqKlWlpY/xZxRqFK30qy6NX3jXj0Z988vl97vOgNPsECbbAWmOGr3H600/97EdN1NBYdetsV04nraq29MfRpVbapJauepetq3JIhO3tomUv2eVIwiIVFHZGvA1OqirGNZkainJJGwclW+J3kfgg+HFlhrZzeupPdvjdh3L0QCv1xcvVVF+Tfa+B+tpVaCH91Hn5QTn+yN2oo7rqKGqOhb61XxXFHXBZZ40tcVN25COtFK/SzKJjJ4U5pqJIfVbNcvSntX7+dJQmKtppq7JauTt2gMWJb6mhuZVyTDXPkbUXbYjfJW/QGvNy9hsacKt9+Uv0X/qxYxUyNToW466udYy69UXRY7qZ2fF1Y8eP8cKrHbTVSfc5z0ioU8iXNUf4zXrLt/8uKbC8vJwAjZqWVkeAy/JVFKC2ii4Itd8hP07ZI65gpnNnUiQfSpK/8Kp+KaOlSbEg6n1vJ7/kmSdmqjukgzMhybI2nzXswW5i5Zu786DksEwhmRkC0vMFBQX9N2cQGBgoJyfnb4aQl5cnMCCQ/Hz58uXm5f3NJP5heYJlBYYI+2tDolqTav9ejCGuWGW97xulQdV20qR5TwvvtX9YROECsgrkKT+0sxGD5sgV6upfE/DKusUb1d7x6ezfNAzp7EO/udSskAGTPlFCfcNnzfTCLz2Uk6arVjIlWWGZVBHypSAH+zVw0DOh94q33z2dO7ieluLG/mNCYoqooATyzbXKfW4x2v3u1EB0XoC0yyf88dKHRhisqXQtFTDGMAsssd9edVSUoaCCwqVI95DOxujsDX0tka20UEdd8fi7fYQeXamqYA9p57OkT0yxS5xYoYGlTPGeynWaqrL0WVAyvKxtslUTrbCSXrXW93ctVDsuUqU3h+ioqi41nrZXpKoq2+ArG46u9qH1EhUX56TjZ9eYd2KOC9t2WZiwwQNvfKHSG29qFvmAO4q8KEOuAEm+cdw7vvGY5rq5WSQVn7Pb8al/Cr6jk+ty9MZWfGm3gtIV94cGzgmz0clTk+VZZGPufl17TDBDsksRuQZEdNdTGY9rBzLtcar0VFM97PZSPRW/tbpSCqovTqePPxRR4JqKD7WSq6DlNrldT+EypZQJckpJtcYM1T+mtTQMu6WtupkFlHyhj14Du5qTP0W+i8Z3H6ZyapTzK/crppGoif290bW/t8e/reSrNd0SOdRuqxyLLaRwZrAG3fs44rJlu3cKybmqWMBeqQeSzduSYOqM1eJ0VUxN8x1yEqefHG/4sxPUqN5QjSKxvk0ZpqVi6muufO+yxj/3mYYDa4kKS1L9WpYaNWOt3LXFrP07pOWnCUy4Tlqg/Px8GRkZ/0ucBAcH/60UFRQUJDCfoIBAuf8LxvCfLTs7W3Z29j+FyX8JxlA0IDD/j++2aX1bQynB+aJKBBmknO6aesMv1v102bxv5/hl/lN+k+lHz3vXRLeLdUKmUZPnia1X0de3PWuttbLkCpNoGd7XzWpb1TXCaQt008xoSzyrv7eNl4Df2rwj9skOInYluLrtCrkFtV9xl8895HV/Gtv8cxUD0xQrE+bLOUtkuuLW0o0knDupXLkS+ox43NejRikdVFu33JfUEKGr3rbZoJW6MmSrJFqMcg5Y7I2/FKJK4AFFfeSKDNTEM5r43DZb8GXlMdpNHeJqmSQFcosIOhAh4FqaFUPnGu55s4wzwDiPqihGaWHSjf56hYO/LhN+LNeU/e8LlWibUzKVVURJy2zRQRnZzmuqooIaiBRss0Sn5CokVDtxauMnM62UL+mv5zRQWfku+8ENk8q+YtPZt5RQQH29NKzeQkhmrpwa0b5ZNE9toWoPu8/3054yzSHPK+iOoAd9mbtcCalqaqt4QGOb8teJkiy4QCej0l+W8/FBzz3RyqTf4n3Rq59zgoSGh4kpV1m3YYPd/Uxjo3Q03wYD9fKHX5TXVFM1nZXropUKBnX1SO671nX5QkJilrt3PObXgo9JzyuuVKNi4m5roXzPGiY37We3I26v94DoPUfNtV7fd6fKbx6jZ9sWeqnq892rJGenyQjNFJbLxvf/sDuzkG2znzdt73mligSILheljEBVlDP5/e8UuaWqnBsp8oODpAVmygrOVyAn++8kYVB2noB8biothx50igAAIABJREFU3Pz/T5KOuMkI8F8Zwv9tyzEgIODva+Tn5/+dj8jLyxMsUG5AkNygcDl5maBu69r/XowhWLDL943waulaFoz7TeaE08Z0+tJpl/3cbL0n745T980Bbg+/Vy0VBUg2PeBV5yRqIErRM2FywwI89erz3mzypsllX3Xgr2vHO+ppdbxhjKsK2Gip0w5YaLd0zB192rrDx2TGX/BH8jaVG1WQkntz1Nt+m5yR6NHNgx3ceFD8hct6dLhNlutCAmIcyQ8x4PTrwke1NMQv3sn9WhN1tRTg3ibd1KryqN8RoZTaGvrSEjfkm6aPvCfPuV8TF+QYLUolNx3FbNt0MRDUiG5MVgCbg13+ao8a95SyYuj7gm0FgUEFddPYkLs+N/aZHxVUQL0HozT8vZ8Kt9/qE5uFCRAiyl5nVBUjUnlTvt5s1APfat/rRa/5WZnyd4pVwiAxarmopSJOOKaZ1lq7qZ3QFU1VlKwg2H52kwGFntTdnUooKLpuFedDA/RddJ9sq6w1R+E21bVX1sM6KiDSkdw12j/0nDhFfeNHD+U/a6LfVFXLtPR3PW+4cU88LF2coF4FTbFQqOs2Zqxw25Eow59pZM1LhyRK8vljm3TzuXBhztjpBR9abK5F9rl72hNOHYgXcXdt/ScMVQ6H0pIUuHFRaLcGQs/niWlaRPMGd/p+2Q6FX2roB+vNd0ZIADGRN5UXR/adKqZ+abkZWYJy8gTk5Kn7VB+Dh/TwzNOfuZh32ZEb++zakmTVb8dN27lAWIdIN4LjpUWlygi7JiwjW9Sp//q1Dgi4Cd5/OIN/hA3/2Hr8h1P437GsrKz/4lBCQkIEBQX9l3AkMDDwn65j+JdwDAEK6GW91xzSOCFcgRfLm3Jitrl2GrmlrVAllN2X5I4lY9yrlAGmu1Qg0kbkKSZ8xUmbe37ls/Gjjd02wrtnx/1dufW+I0bab7TW5lvmospeMUykS94yWp93+nj11Q+ErrqsTen2jh49o3R4ISsbzXZ7gYe01FiEIPWbNTRi7ceqdqykW4MODsRvVz2qpsIaK6IeMgwO6aefugLVsXjbbIMKlHfaH1Id8LlFskQZXmK4ZZaqNCXOTNvUc5swpXRRWBU81+INL45/1nAM3tZblTvLavpwXd99+JGyasi0wYmbY7p8nTvJ1/dM1XR2RxHvl/WOzZoIVgDV3i/hcZVEijbA/0XdeUdHUa9x/zNlZ0t6TwgQegtVihTpTRALiIKIHQVF8YINvYjYRcECIiIoICig0lEB6b1I750QICG9bp3y/rE7m01E7/U973uO93fOkmXqb2bnad/n+zzTldF0ZTcbKCGDPfN/IeP0JWIHNwPgd/0EM1jEocSGzOAo89nOo13HsYXTfNjgKzxAIRBLDI/yGN2JoSsxtPnXEGaxk3nMRN14iFb1GiEC4Uik0wt7gpe4lm15oeHt5CKylGO0FmrQvtEIWlGHAiAeC8c4wBvcR3PCuLNGD5JpB8Cwzu/yzLA3qUdNlrOSp3mAj94bxtfsQPqiLlsaL8ROS1rSndq0YC7XOIrA/FVz8XpK+WHOanZMW8q/GUQD4lGxkdY9hbgn06lJKhftpRTho3Gj1izhCvlAengs0eUGs+6aTe14/0vaiwoK/G67DoqgQbJKw8HtcP92mHinjMUoQ6/hwi36QDWIcNuIcIo43DoRikRksgOL5c+Zh6qq4nQ6b7ouVFlUVRqapgXDC4/HgyiK+Hw+vB5PxTr+7yKCf0QoYREEYxaP8DkbmEI94h97hbiO1bElJVN6+Dj3TezBcw3fA0Viy7FXmYvOQ7SnmFyclCMBP47fyycfPMJWtjAyZTK1k6JZfHgBKznFBfJJBqYlTiI3pxitegxcPYuHCGq3ScfepDHit7upNa4tqz/5DouRjUF1CskhLrI7S0oW0S+yDwdLXGznRz6wjuCqp4zUurGkNq7NwWN5dLm/MUqEhuOMSIvv/C3Tn6ITe/DSjxp4KMZHNDE4ETFomXonZYVXEJ15lJFLRFoaHVc8S6bkISy7nPCkmixv8RoXWUo1OrOOLHZzmI/pjRHdjOp6Ap+VrGUX+9g18Cvk04WcPnWcC1yjMY25QAEyR4knkQw0prKOZfIYMlUnLzGH+sTRh26MnzsTr8NNPgaHFqxhypovkRDYzVHsROKiBLCTTDg9KKIjgwkjjgl8R0NS+Oyrb1j51Hh8XKET3Uj/dhxJjRuQse0cH73wCB2fHMnVH1aRXhzDp2zgaZpw6/BnObjwB1oOfYLdi1ewnFW05FZqo6OKycTquXzNUeogcRaNaVuXs2DgJAZ9PpmnhzUkHzfv8CSr2MA6LjGJDpQB3uSWzMteQBFlLGz6Lnsvn8Fedg0FO5mc4UPOcWTd70Qn2ilyCyR5bJQlW9n47GoSNxyl9qu3s/fsFaJevJ1HO9TDDWxas4HExMRgGBBqSU3ZMQwj6AEgSYiAxVs5FNCyCogKj6A8QqGovJTi/AJSU1OD+xuGgc/nI8IehtfrDYYIoeRkURSxWCx4vf7GrmboEApMqqofb7BaJZAVXLoIgt9z+DsEp3+EYhAEwViQ9iUPZYxiMoMp4zCTlpzgtSGD2cRhDDIpoBEXOc1jNKIhAus5xXDrRLp378qctd+SxY+8GfsxDxeMYUH7pezpVEaXzj3Yds8MBvMWo2jC2L4TyNh0CjUlmnevrEDGzvtRw9hRvI3ONGM7R/GSTR0acJhj2LDjpSV1YpKo0zmRF1e9i4Nonmv9ELY6aZTmnee2ZnW5fOISshCNL7YEa7M6nJl7nNKLh5jNDk7g4OfaL7Hz0gLq1+tErc4d+XzuF9SgGl0RELFwiSPUimlO4uJniUqOJi4/lwvyNapnx7Nr7hZO/LoeCyd4g0yGcAe3EUc8EjmEc5FC0kngBNepQyoD67dn7bmtbGAN4ejUAAQSGdt3CudyM3nl4DwyyaM5qfzErzxIDcKpzix24yCWzjRnXUi/ha2OD+ju/Ig1b80netYxdlxbSywl7MKKiMgTr75E1okbxGWr7N/3A3nsYx9uooAioBzY8+F1Zrz8MrfEtSCqhpWjyVbkK25mn3ye6tiYix9s6wy0pwHDHM/RyvkcADu6L0DtWp0i4yxXj5cwf+lLNCMWHyXUoSPJqMTXGEDsHW24djGLh9c/ggX4jNtZhUErBJphYT+H2Ukm/568goZ3NKfYUkBqSTQFpSW82ONxOlNGr5QheBISSXmhN4880oRLwIHTlykqKqqw+KFAnyjg1TUsFos/ri/1gKphdTjwin65MhVDpD0Mp9dNuaBhuL2IIdGCKIpBQFF1e6tkFyq+67oexBVUVfUDkAGOgxk6yLIcWO/G6VVRwmMwBP+c/+eYjwISD2WMYlnaNF7hJ2rTnNeHPEgRsOZf2zkL/PiE/wUcM7/ZQwy3sA143PMWRYkiZzlHPApvFUzkGZ5j3J5naJcdzfZV64ga3pbhyFzgDGfWbWSpbwb5V/azk+0Uk8vm4vls5iAdGEs0R7gv/n6G8Rn3xAzBoJgPeJezhb9TesDfJuzFvvdzS+ckhMhj1GoczradJ9i1K5f1RSdxpFWn4ek82o1rQ3on/1uOn6YeEeWl3D7nU1R3CdPmPsYAmvApMym6pQ2xD3ek49hnaTH1cVb2fZFPWjzGz0N+JCqiMZ4mETTs34o27fqRFjuKjxlNcwSqkUgxCt9zkM9YiBURCZ1FLOGWc/fzFDNoRxNq0ZYM6qATz4B1/Wl/8GEOcgwnYbRseSe1qc77zV+jPen0oz8v0oOmVDyxa+7dRlfneA7Hv8mtHVryS34eFynnG47w74c+JwYrpd8dIS2mOmpKNH17jmUv1biVLkiIbAZGMwrx5WrsYiGL81/m0Llz/Lx2DZ+dXMQztudIg0Apmv/lqFmcpcB5gpk8DcD2zV8xe9JQmrjT2Ll0M224mytAdZIRKeMkN+j40J3sXreWVlIUrVFY2e9zmvcfylrW8RVrqRbdgVIieNj2NAlX7ci5OmqxhNNmxVEjiTzy2YjB9qyzUCRgtcXw1tNLWLfxAN4yJ1ZBQtZB8Gmg6sGP7tOCQi0IArqmce7gMUpy86k6il3llAkaogEWQQoKt2npzeNYLJYggGj+NT+iKAY9CVN5aJoW9CAArly5Qn5+PrquY7fb0TSN/xvj/4/wGGyCbHShCR+2GMSvR67RkxbsYi57cNIu7W6mZkyhCbG80HsaH//2Ci898jbe+Us4Qxl7uYLecgDq4V94jB5YiGAXedQmjml8yvCYF9lQOJW7w5+BsoPo5ALVSU+5C2v96tzYcYjD+hQstCCCRpylhG/Zx4yIF1hT+gNL2c8YuhJGMe9wknr0pgupDO5+J/t37eDe53tT5MzD49axGFD/9g6UFRSSGJnE4g/nIZ/NR46I4vfsQ0xnBTtf2MzhqVMopxSBaHrMfI7oBrEoUhQXY100y03hwNZtVBtSk9TiWI4fO0t1LZYTa3ci6DX48uh+XurbGREXtevEU5pbyKlVx1HPX+NFZgXQB7gHOw7q8z1H6cktFHKQg8D9wLj4N9iRd449HCSOMGYFOARjqYGLGBKpQwYXiSSCcxRTB4FoWvAeC2lDTx7gHPPROcpVbqcWGlmoJBJFe1bwI6unbqVmvJ01j0zkPBdZxkUepyaNaEKTCaPY/9tVxu19gx1vzyOhURKP3XdLoOG6fwwHBOpwOy2AOErROcbvqBSjkksjmjGNvWQCd5DASxO+xdayOhaHFe+0rWT8fgVnnhuZ61wnixVs4pU75qNcK6f8agGxnarx88qlxBPGyBWTKT1YRvu37sbLRX5oPIXmbw3kbGMNzVJK9XwFXdfRNA1dNxB1E8wTMEQBVfC3chdEEa/TiVpQgi0qEmuYIwAy+t142RDwyKB4DVR0RLmCxqz5VEBA01RExAB4KKJpOoIgoes6kiT52YyBkEVVVQRBCCoUw9CRJBFV1fD5vNhsCroo49YlFJuEqqr/e1mJcKy4ySF90uO4OMIifuAo54FCwovD2PrERnSiqFMmM2zAGLqMvo+Z5PIx29jKZaZ/8RZfHT9CIWF8zhIikOk2ahBxVKOwcBVbMWjVpz1dur/FcUop5QZzs75mxLZ3Gai/j0hjRHQOkkEEVl6jBa7SC9wgAisQhU4iDn7lecYn9KRvixaU2a4Ra5EpcBYQJiaSUGyh5GIBl3adY/jIN3j5gfcwDhWSW36Q/OzN9IvrB8D+qfPoIPeijAgms4qNT3+Nb7VOjW71SL2sc2zCLLo07YBQGkOmYlC/eXMKs/JQtSjOZ2TSRAnn4uyVrJ+3if1rz/D7rgtEtW6GlFiPUfTnedLpTQwrcJFAHAAbA0oB/K8n/yFvB41ad6cBHYNKYS5DiMZHONcYdt9QRsfcSwlOnm/0NLu5wHusBaA5CYj0oylOBhBPFi7S6IaMheq4mcJTtMzz4G4Yyd0LpjNszCfMjn2JRNIYyRr0yZuI3XuEFErRLl8jL9HC7FfXBp+FXkTTli7Uoye30pgL5DKTb1jCUQ6Swdc4eZW9DCKNdsB3a3dT7e66WOuKFFfXiTQSGJr3NpZH6tD312ksx8nMF36mabcmnHNlUOOBNsxbuYTR495mA+u5lF9ARv4hdu75jZObz9Fg4f0U1HISW+4iqUTBMEQMQwQkRFFGFGVARNcFcGmIRW6smowiKCiSTHxadRSHDcMQ8eOEEiChCiKyLqFJEohy8LiGISIYErJowWqxY7FYEUUZVTUwDH/YYOIbpmdgKgpz+BWEgWFoKIpERERY0Av5u9mI4DH/CR5DrCAYXyRNJH7wXdT99hCPlj5JU+pRmyS69hjFC5sm04do9nOBaCJpRhPqJNTlaqHGVvUTPl94hOKm0Ug/XWLqO49Sho9sbrDogSNMXDSepWylPTKr8fPQxxFNKt1Zxz4SuJNe2MhnOWkMoJhiGiGzkJ+QSOMwJ3iPsWzhB97gGkOoRQ86cZQyejYaTFL/KK7uOEXqZTvTcj4mimSuUo+zbKM/hUynlPo4+LHBLL48+wtfsohBdOLlFi/T78hTjKMtuZyiDIn2tR5Hy85Dd5+glb0bmuTkgphH5znPc+2Uh6X7vuKhiU9x6bX1nNm4GZVE1nGAr1+YSZOpHYFGHHxtLie+msPLeV+TDsSSwg9ksX/1dRKi7JT9doalbz/AQS4RRTWevGcSJ/Yf4o6Hh7Dw/RkMuKU33BLPijmbKaaUCEQaibU5op/CRza3UhO9Zg3Ou0tJztHJYD0eGpGOzFWuc50cysihPmmkNhxAUsMk7MVhuPI8XD5xnFF8yXexr7K74H2a0pFWI54j6fFb6N+xISeB97gLB7G0S+/E/BMvMmn0Cs7O2oblX33RPl7KV/rvzJ7yIcc6qGh1ktDPlRDhFXAoYUT9lMM708aRQiq9xw9i18yV9PnXCArvTsRhSMTJ0eRcyyK6WiJXL18hOSWBcqsOAoQ7vegCOC0geqGwsJDExEQEV2VugRywpdnZ2STFxiOqGrrDiiaCJGhBHKBqRwNZlvF6/fiBGUKYuIWMGAQSTU9AEARkWa4UCpj/N49hbu8PK/zUaVVVsdls6LqOiohusSOI/jk1vrXh/5bHIABxhTojZ3zJ/aXTmBD7FV9wngns5LlNDzH9kS+5wRmiSWUwd7OB5XydO507GnThafqx7IN51CyN4pziwUoK33GN4Yxh5aKhfMlqnJTwnPURnCtdvEtb/vXSMvK4wecMYlT9hnhrJ1CTztzHDOaxBid2mtERkPmoxjQWsZR0GgJQF4kINJaxnkGnH+bo8gNc37eTGTmzySEXByIjiaI3HVkYePXdOZxsPbsYIwCyxZJK+yN3U4hKz+oDmcYFvuEs714ezwNfjqVZv3tY79rIVVsxd6x+A6G5gD6wjLFvjkaJjaH91MFIOLBj46V2z+ELK2f/gG1s6PAxik0n/dlR7JhwkOdHruTF1+ezdvQWPr5zBHnfLiW6RiSXiWMl8C3XMa7ZqXtN5OjULaQLaew/f4F35gzCwnlWMo83+IZ19a9T7dVhzGETnzKPE1eWMCfnU2QsZOKkB6mkkMYudnGGPOZSQg4648+8T86qsyjnC7lyYid5lJIC9H1vNJ8DmZRydu0+yowSzuFnVi5hFZnMI/HJDlzBxZMzRmFUt/PblFdYW9tJs749KWqRRERYPbqm9OX8lB3c1usOVry6khrTBvMpezjPau744Bnq3DmUHm/2wV5s91vjSDvYJErxkpIYjlfSCC8BwwMe0YZPsBHusQUxg0rPaBUyUUREBJrDgjPWjipCAGv803jeBAjNrIGiKGiaFvyEFk2ZCsPtdlfax1QG5j7l5eUIglDpGIqiAAS/O53Om9Zb/EeZ/Cd4DFGCYLQkmcTUFzh8bTvnOQJkcBdNec1xDzanzkNM58jKYvYM/ZynXGN4M2kW994YyUHeZSk/8y57ea3FJ/ROSqL7+iGAv1/koeUe1EgBe0+FvNGn2DFjFMPZShn+15VbaYm9ayrJW3fwMP63Jd1LGEspD85v28gf+GnWvxhGT1bgpIClpNCONylnKM0Y2uMWZm/6hDoIrOE6dfE3ZFW5ixpsx8JYDEHjPltNfnWdo2akj9s/mUjUEzXYFPUiYWM6k3fOw+nF/+YMWdw65B2aq3Zi72qMJzGGwg2nUfo4iKtdG7XMQ7zTwgWLi+NPf41y0EPN/unYfrlC20CbNoAGWOlAQ9wcZwhDiOl+Cys2L+ccOaSgMFTojgeVgcYs7oBgB+iPOs7k2qHdvOb6lmgi+eaXdXjiRFKsMZzZdI0+47ozlAQ2kst4+pPDDURKcFGADQuzyKYZiTTCSgmZhJFKMmG4yATs1KQ+q8lkI9fph8KUe+cSeVttxo0dTTNOsI909nOIdBzUJ4Ghgz5h7bI56OSzjr18/d52th9az+g3JhDbNJ6Nn69CS4wgVrHT+J6mgMG6j9ZxS58mfPHsFHp070K1Xi1wh2kYmqcCvHOrQcYhEHTZ/ULkX261WtE8lclJKv6ahFAX3RROGRG8KoX5+UQlJQAEBVJVVRwOBz6fD8Mwgm6+IAh4vd5KAGRohiF0f13XsVqtQEWGIjgPWQ8qEK/XS4Q9Cqs9jDJVxK2XIOjQ9H8tXRkrCEZXJAa1+YTzZzJYV3qcvaxjCLAdGEx7BNxcJ4tHeIKlrOed+z5j5Y/LWMenDOJBMts1ZvDDvSjbvo+DJ0pIa16X7d+/SCJxjOEQAJ9xP498Op3x/2pKPgI/kgNEcjftGffBuzjHT2MJS5FJ5dXBU5FL7Yxd9zDLyALgUSLJJYKfuRYye4m7GMq7/36asN3X+X3TPFazn0JyWRPYohfxGBRwHJ0bwDhu4yLVWcFiVt65jPtXj8RDLl2xM7/ZQqYce44G9CAtuSllpWWcKj9NCjVo2L4ZaeNa4K1npcBqEJ2psOD21/iQpQBM6PIFYx7oh6uRk+lvLmLKlne4sbiYpKFRfERTyshFoR35/IaU0I97V0zEYgmncNkeOnTvyeWMC/jSEyk3yrBbDNwuH7KggAphgohLVpHDLWi6H1E3AqCcVKgSniUSU2Bh3KsPcl+z4bTo1oG3po9hJacYTX+sOImhiAiakcsBFDK5InfjqVdfY8Lbg5k+ZiETp/0LmSP0GvIxdV+6G2+czKkZ6zg7/TseXPMZxMhk+4pIi4jF7QSbNQyvWkS5pKE6PURb7EiylfwyJynVa5CVk0WYLKJIPjQvYEiVypRDFYOZAbBYLAiCJeiWC1pl+VDxx/dV6csmOCjqUJybT3isnz1p1iiYacTy8nIiIyMrEZKcTicRERHIshxUTqIoBkMG8xzgVzRWqzWoMIL1E5IWTGH6fCqKaMUQJDRLGF7K/jcVQ5wgGu0w+GDaNq6fO4ijJI7d80/xM6vYwXFepD4/U8Ap8ulMOP3ozrgXljBr6j2UUoydBjSt34H6PVrxy6zpDB0zkcLfj/DWrqG0oC5daMl6dvNwr09ZtWEl+RyjJo0QqMs4PuQ22vJ4mydJjrPxy7qXaEQ7Gkgd2KCt5ALnmY2/ZdyNu5cxdOUgNgfm/QnPU04ki5nPw/Z+eFwXSCCcOawjERe/AKkILAt/gxFlk7ARzn7KWMvrrGU3bmL5kp+4h95YOcwabgT9lM9JJ5wu7GEHg4d+QM6u0yT6JM5lnaFO3w60f7IbmgTeI3m0nfQYEZzlJC5Gk87DD7zJrYsGUw2YeP8URv3wIm/SmqtIPDDoYbT7W9IgKYl4ZxkXHODQJLKiFKLLXEgIxJfDDbsbUZdRdAFBF3HJ4FY0VFHFoVeOQC2IOC02ynSIdgt4HBK6RcJS4CHj98u4XS46dm5G7GUvZzLOc27Zr2w5PY/J3+zkeud6LKk/EBseUilnFMf4LvI+0qc+id4kBtmIpFAowZ0URuMrXsoNlSKHQDgKLlEnwq2hGTqyJCOgYgKDhi5T6hDQLAY23YeiS/h8FalYk0cQWotgDsOosNiGT6sUVujiH+sWTEvvwUA2/PdDCrj85r6apuHz+YIWv6rX4PF4gh6AiS+YSsFUWjcb5jlEJfBbWCx43B6sst2fVZUdqKITQTdIb///UDEIgmADtuHvqi4DPxmG8YYgCGZD21jgIPCQYRheQRCswLf43w+bDwwxDOPyX50jSbAa5ydt4XA3mfLJRSz59Q1+YDcn+37HJ+s+5SpnOEMSXRlKXU7wBcs4D/x61wqy1hwl/ZY6HPp9G6P4ilmMYgfFDELhPEfZziG60I16te7mnstjWclUsljNXDK4QQwWvESRzFP4CG8ygORsFz0KJgIwinacZh97gLex0fKLndT88jwNjw5h94M/kta7NR8/2pVu1scp9WRTlxi68QFOIBaJgkA7+mhgANX4letcARqhMIwHqE88jpp1GHJlNHPSZvPvjPfIR8TCBQqBjXVm8/nFJxlpfYGTnkMYlHOacGazkZdpQB5FLCKHYbTmKif4OupdphSvpkm9NtgaxJKfc5a72nZne2YE/VpGcqFTdRITSrAWyXhEhWKbh1JDIK1Axm2XEAoLcCdEkCsJJJYp+GQVt8WDLhpEecAihYGhYASb5/uHTwRZ9WDXdAxJxWu1IogWJMmgGC8eTccepeIUk4gqLiVaNnAWFfLDlfOMatGDnUohsXkGiTVsFJY4qSbU4oTnKikOO4IvjDjVi0+HX+f9yC0PDaS6YsXi9FAku3ErdhRRQjMMPIYA6Ai6Fwk3cWWRlIaDbKgoAni1v65CNIem+UMDRVEQ9cpK42aKAfyW2ycJiKqOYBhBQNFUDKWlpURGRgbBRFMJhDIXfT4fdrs9CCia4YOiKEFvwOPxVCJASVKAExF4zb3X68VmsyFoEqKs4NRldNn9/0UxCECYYRhlgiBYgB3A88A4YJlhGIsFQfgSOGIYxkxBEJ4BmhuGMUoQhKHAQMMwhvzVOVrWSjfuyGiOm50kU48NbGE9BgebL2Dc0bG8xlOUcoky3KznF3zEsgE3cx0vMMI5gcWRH1CnUxtq/9qLOQxkO8d5mHtYz0+0pAVJWLmMwOMsJhULXUlmePq77D6xjcucYAG7mcTdHGEnvXiYrsm1WZT9Ie0ZSNMHhuDeuIeJOZN5u/FC9p/6mHMcY1DP1xi08RMucp4eOFj7wVUeHP8g7dD5nXUcA04CfQM3zPQEXiKVs1zjBrAHOPT+bmLaxlCyJYtJ73RnGfBm/Gjumj6ez6ct4+vdzwMwNFC9dydd+YA9LOcMT1ATFZHlXKYE6IDCiI4f8NmuCXSgLSMWTiKsfhI+VUUSJTRdQ1aNoCWSQyi0mqZh1SosYyjQBmBYJHw+HxaLpdKDrWkagiWkYEeuTOENFSK1CtRt0XxBEM4r+62dpmmoHi8x9ghyMjKxJsagCNabPZeBLkX+4fP5COQHgzG/eV63243dbg+GDh6PB4fDEbz2PxxbM4LbmvfBVA6hpc+2VBFZAAAgAElEQVR6oAeCqqpBT0CWZTweT5CUFHoMEz8IraoEgsxJXdeDSiFImgqEKaqq4jO0ILgIIBlC8P4akhYsnjK9D0QFjyBjGD5EQaTJ38hK/K1QQhAEB/7n/GngZyDZMAxVEIQOwCTDMPoKgrAu8H23IAgykA0kGH9xohY1GhtPXK2Ljyy+4xxhaLTCQQYq1WjLQ45BfOucgBcrrWhKg9Z3cPuByQygKXNGvM2oOY9zkTMcRaUd0I4GxBJPKVnkk0ciHpKpSyTt8VkjUdslU7NOLcKvOzm/4QACEQybOpHIcWHMVl6jx61NeHf7cI4BOvAk7QmnIcOZD/ipuytf3se3M5aztXw2KeRhpwGbiWR83FC25H/AF4FuxTUADbgeuNZaQC/CeJSx3MY7DOAuJr70Bv0+eoDt93/G1cYJXDx0kZ9WLWY/GyimhI1pUykqyGZb6R7iucZZ8nDRkGIO8E2j+dxx+iGOAvcRyyub9hFhV0DwUqR4iQmU9rvdbgzDQA4Iq6ZpSEZl5F3WK1BxUxCCrDtbxQP5B/dbFivFwmasbP7kiqLg9XrRFbmSorBRURTklSuObbi8hIkWCrNyCK+ehGnoQy13WVkZdrs9KJA3E/JQq27OK6ikAtuHKrlgqfN/KG4MVRpmSBB6r0Iteii4GTr/0CyHuV2o0glVSEGlZFOCHocsy6DqQaWkCb7gNZupUFUXKdfA6vUhShINujT5rxXDf9U+XhAECTgA1ANmABeAIsMwzF/jKpAa+J4KZAYuVhUEoRiIg4CkVBzzKeApgDAcHEThOjkcCqT42uJEpAnD0kZwJWMr1ykilUSyyYADx/h5xCKObNjOnt+2s5kTvEY7jrKPfUBv4rgr4l6ulJ4nrnozjl9djgMBD6WUetyMGPcip/YcwnNW5eEBY1ix+inWvP0RtaiP4pXZfvIUY6pPpeVV/xuL5tQayN4ruTykt2Q/OtEUIF1xIpRfoAstOc429nCW32bcYMuylfTe2IcYNrGBbDoAhdg5gYvqRAHFlFHOJY7wKgOJ5SIzP3qMfM6y+ofJXCCXQ5zABhQDiTRifngu7739GD0S3mJUv7b0ZAixlijO+yysLz/ORSD/y0sU2ou5oV4jzJpAxtUsUuKrge6PbS2S5C/R1QxEQcDQQVCrpLACpJnQph6hMbJJqgktE/ZvVKHzZUQMn4am6kGkPzfrBg6HAyMgp6awaCGKwXRWDMNAVUQkTSc8OR4nKtbAYxoq6OHh4X6L7At4Kobfzf9Po2razjy/6QEAiIL0h/1Ch7ltqPWvOr/g/QgI+18pBXOZx+NBkiRcLhd2uz14LtNLM0MT0yNCC2FBhhRvmudzu71gsbFh9a8MvHfgf745ofP5mx5DNLAcmAjMNQyjXmB5DeAXwzCaCYJwAuhrGMbVwLoLQDvDMP5IIA8MSRANPVAeOpx6uJDYxxn6kUwHerGKhdwAUqjD4zzIGbaQGNadwhQLvSxp/HpqG+OYQz1iGEZbFrOe93iQuD730LRjGxIm1eYx0pDwcYMCZozcyG9zVqFpLq5xmRwyqEM6KdTH3kCj/Oxxeg98hq3L13GGTcRj4W32UQ+/u1QPeIzGPPevH+j/aTumv7yJ6JrhnN5xmH2LNyB1a8mtmT72XFjItxzlHHAb4AX2AUOoi8wFqnE/tzwyEDFDpXNUGq7Sa2zYtJ1DZPA7N8jhDO+kvcPDGc/zGnfxXuBtWjN4jNKaDgZ99AxiPTtOnxPDMFAUBbfLhURFTly5XMjF65k0atUCt+ZFFiuEO9SlNTn3Zm/AqmCXLot/UAyhglBBtPHvF+oxWK1WPB5/sxATgBMEAcEiBc9V6XyB4wYtcug8dB2LIAVjbnNUbXRizu9mmYfQbf8TM9BUksHqyiAF2V/QLCkVbr/Pp2JRLMiBYihze9OChxZBmXMOvf9WqzWYujTX6WKFt2HuV1RURHx8fCUylChWeHhBopRkxWWI2DX/vvU7//cYw9964YxhGEWCIGwB2gPRgiDIAa+hOhXe8lX8HvTVQCgRBQHK4Z8MHYN36MeddGMwb3IOf236XkrogpOWNGU1x4kllXgEIqlHzWF3kja7LZe7LGPVqTkkA/eRTLvbh3F97X7KKKDbvV3oP7IOr3AHD3cYzoHdS7nOVdbO+oVERNr0GYy1bgKyIZC9+yClpW7EYak0aP40B0Z/j+PxrvQ83IzpB8ewpfUPfHhgLL89Np0pc99iPofpVHKRPtyGklVMZHgy/RY/RHUkXqtzG7fe3QfxM5nuDpmich/tqtXg290/MhIVe0p7mo/vzgdLNvHe/E/Q8TFGGkqhdp7aJHGKZRTj41Me4amM53mHvlwJAfz6Lv03edXK0KLCKb+WhzXCjoGB7lWRdAEh4G5aRBFfQjj1qrfEpWlohnDTH9x0f80Y/8/GX60LFdLQbUVRxOVyBd310OIfQawQikqW1zxGFeEHwAAtUNEYul8oESj0usw5BAUtBBeodNibkJqAYCFSVYsvSRK6UYFpeL1erPku7AnReESj0nWa5zWP7/P5KlGdTcVx+fJl4uPjURSl0lxMINQcSUlJwSKs/2b4DA2L/PfeRPUfnS9BEBICngKCINiBXsApYDMwOLDZIxB4FTSsCvyfwPpNf4UvAERhp2Ozx/mE+bxPdzpQhzfoSRROHmIZnRjMc4xiNdvRcHGc35k4+yF+/moPp3zXaIOdHoTzG6eYsXYynz6xmzaR/fEKXh7icVLq9WHK7h+YyQq2coRwjtCwRz3OcYJtR1dw+dhe5h+ZR+bFU5SsPYw7IponsxaR/PtZPJ0b8uZrZ8k+sINO1GbM3EF4KWarMJWvvpmIAaQvuJ24ibUpaL6cLnQnes9lxo99CWdYBBaHFaus8O3uLeyhiBJi2Zp1kJKVx2m9y4ODeMoQ+F7byXhm02n4/fSNfpZz5DOIz3maB5jAOj6f/R3JxLHnUjnFNdyEWUEqLaG2NRLD40PWAa+KoGqIhoEECAFswCcYYBjIQkV8HfqTmA+ZSb6pWvFXtcmoOUIVxc2ECioEwiwRNo9lHjtUYMzjCIFiJUHVwadXCm1EUQgqG/P45vVUWE/xD/iCed2hWEKoe26yB0OHZIZfgXugKEqlVu6aVqEYZFnm+LnTqPgBRtMLMkOwUMxAlmV/FsPnC/Ze8Pl8QS5DqJcTeq+CnZ8D+5n3UxRFPIHmLOZ1mfMURRHBpqBKN/99/mz8N5ToFGCzIAhH8ff9/M0wjDXAK8A4QRDO48cQvg5s/zUQF1g+Dhj/n04QQRSHjn1HLPWJoRO7ucibbGTdxVIKN16gF5M4w0neoB9HpBtc4gbRnMb2xRZav3gvDRlGMn2Y3G8+CinM++1TtpYc4OzyVfRGYq/vMG8NfJ1cwqmBnZGsweeK4MX1b3Jwp0jk5VKSCKOGGEG6Xp+5vd/mCgdJeaUTSuEZvKd+57dUH9PI4IGIyYxqNZmpxlS6kI4BPEp9GgJfWvcz8rsPaeZpwrievcgqKSQ7TGbehUVoTfpziHC24WIxS1i7aRFjeYO9/EIGB3l2/DBufJLLEwufp1Yjv3VoTiOaPNWfVZ8v4mKbQjb9vpOw/CvYDYG88zn4PDrXBRcWRH+aTPXH3LpPR/fpGKq/vbjh8SEafup5qDtrurfmgyQIBqAT6l2bD60FAUkzEDUdSfd3M5IDOIX/owc+IKj4PRdDQFV1/NwC44+diFQdQfen9qySxQ/6aToY+BWZxUJpWSk517OxCBKoOoZWEd+HXovX6/2DcJvhjakkQgVWEAQURQkKbSgAabruoR6JqYBC43xd0zBUDTQDCZGm3TrgU/z3SxdAF8AQ/aQoXQBNMEASQBbRRRAVGclqwZAESl3l2MLsCBYJXTD86y0yoAUKpHyAiijqASjI/13X/ctNpRUaHgavXTIQLX9PMfzHUMIwjKNAq5ssvwiBHlyVl7uB+/7OJK6STWatKD69/CttiWNt2kxaP96FYzmnyHeVUXDcTf6hM9SJrc3VC9fJm9kM5VQW3x9eRcfYpwhvWYvns9uz/Nfv2MMmJhZ24nsW8FDXV2n462jGJH3BtT07aUVNGlCLCFaxZfcaotB5l1eIvPY41Qhnhf4Lc/dNZBmv8a3yAVcu2ojucAvNnm7Bc7W/4BhfcqH0FN8depfbuYfP+YlGNOAE53iWB2iupGDZVsZTF77kq45j2b7xJyyZhfSqPYGfTi6gFS5+YjkAh/GQSmPq04mfDnxGxrkTLPzoF0SSCWvWmgUNl3D7PR3It7pQI30YZj7dMNCAhPh40PzlvqIgBpl0gcfC/28o6h34KoQsLy0trfS+gT9z7ARBQMBEByF42D/xEvznEZAEAUMOjan/CPdrqoZq+B9st9tdMZ+AsIeHh1cqJDLPaAp7aGqyatv10MKlqqGCruuUl5cHsxqmlXa73cFl5vLQe2MqEI/Hg81mu2kD19CJCgFhFaWApxE4T1VPKSoqqmLuQuBuC4EbXqU9m2HoIddfeZ6VtgtubwQ9rP92/COKqADGzZnB3IXr8AAzMl5gyxtT0GNE6tStxnnxHJ5bbVxPLOPo3tOcPXWGz/iY9eyiQLhBvdvTmZk9iwQi6U8DFpV+Txca89T4EWQBr+57hvZZzzHv65+RxXYcH3gAA5021CAKmMs8rMSygEyygDl8iiqEMfr1ETjP5OEgiZmXFjPhlskkJ9ThDh7lJMU8l/gWj/cdx2+v76fPg/cQVwYZC/eym+0cPnyOBaxnJ07OXtqKzmXq0SJ4vas4zKT2z7KFOWSGn8GIk1DrZPH+969R7/40Wo/pQmZkBp7qKmq4FcGjVvqEWl4T2KuKdlcFrUJHaCMPqGgbZubCqw5zu6opNvMBNcMQ0z33n6MyGl/VVQ9l/xmGgc1mq5T6MwU+JiYmGJebHIFQdD6UWxGaRQlVCKbnYw4TUzHnbnohxcXFQUsbigmEFiKZ1GnTtQ+9ntDrhz9iL+bcqoZyNyulNkOwqryHYBu5m4zQexB6nxwOx023/7Pxj6BEN0luaEyr/yr53arhemc5n7KYZOKZeeJXXDqUW3008tblxLf7OfLhQorZTAtuJat5LVIXdCfNGc/vr2+nYMN+ksUkCvQjeIgnkWKaNO7NnFOvo9CccKrhpYz0xCa0bdaZYxuXsYENlFGCAzudGUZJl0SKtuVzmbXYSEDAxQNDnqSwkUKOJlBrczmLd65nJj9x6JVdHCg6y+A77+PHed+T1CqJvh27cnrzKXw+D23e70otmjK9/khOlJ7iUsMa1I5oyJI19/NM63fYcWAlD741ltod0vEmWxAsIqVFpSgWOw5rGLJXxWt4sZW7cBtCJTe3Eqc+oN/9caXOX/2kYiD3LYoiq1atYvDgwbjd7mBGocKFrkDhoSI+/7NhCkvo86RhoBEKxPkqcSRCrXhVTMCMke12e9CNhxDgL4RnYS6v1D49cE7ze2j8bZ6vIoTyK43Q7so+ny/IOPTXT1R4Dua2wXtqKjOx4v+hwKcp3KFK0sQI/vR3CuAHul6R3TBHKFW66jBBSl3XUXURr2gB0a8QG7f+Hyu7VhWdAnLp6anPWBZxhCIkzrP4tvdJzIwlOs+C5BP44sPRrGA2ufg4j06Tfh25OPMkW4d+TsIZNwPmDee2PeO45YmRREREEtukK7+d2kUMTcilhF4tbyUhOZX0bs3IDS+iGIWDZLKSYhoJT9DqzjZI3jAy+ZVrXGXUk5MpQOTiuas0DmtN29q1yb9yiXy2cBf1aTW5IyNmPcoFXzFlOXls/ffHtOuewort22lw1kXuz1lMbNUL9dwNwmvVoM9t6fywZjyDop6k88D2vPLdx6Tc3Qp3ouLn9/sEIh2RRFgURJcLw+1CcvlwGxUPZZAR5w0oBUWhKEIkTFZwWUU0u4waLqPbZXRRR1REkEEXdXRRR5MEUGR8gsGAAQOCLzcxQa3gbyKAnlWIK6+IYs3vkfwVZ9/r9eJ2u4MIvq7riAYoAfxDVG/uuYSOUGGCilAglBRluuGmgJpAntPprCiQCmmJZl6bCd6Fnss8linoqqoGPZKg8pFF3KoXj+bDZ2io6P40qywGP4JFwuXzBJmN/vOqmDiAYfjwymrwo6OiKCKgYhg+VEW/6cctevHK/ndUemVQFRGP5Od5qIqIVyb40W0yuk3GLer+jwxuq387XZTRxb+VgPxneAxNGzczNn28gl9+Wsdj34xmKaP4icU0oQ77Oc5QhhCX0JB9uQt5l/P0pQfFrGfJcwcZOP1RepKCQia2e4Yzovt97Lp2lMghDajliuXiq8uoHlETa4eGFB04TmyzWrgyr1KelELuvvN4D16kX/HrAGy1TaSr+y3eZTj7+Y0V3GDXyO3IWZcozSygvHY4aWn1adS/PXf37kMfS3O+8E2nIy0BKw/TlU1spgCDaPK4yi28cv/rZAmHiLy9IQ16NEPLV8nMzUZKCMMXZcdaVlxxIzQd2fBTcvGq6AGufCh/INStFCQJwWohTBPJVzQkQUTR/JZL1AzUcn9c6fV6g5ZECJQUa5qGVKVyUNfVoHXVRQmxoBxdESm3CITLSqUGIVWHxWKhqKgIh8MRtOpVny3DUpmqXGldQEhD26xX9UJCFYdpgc11Ho+nEjYQOsxrqsp9ACo1VDVHqHcg25Tg/foz/oMgCOTk5BAXF1ehnBQqDV2uUKqy6rfIpiIWwv4471CvypyzqSD/m65MOqAKMrpgQQy0l0tvVfd/y2PIPp2Nd38R/cfcy7VNF9htL6M2t1GTWDR81KU+Wbln2clpHqQfTvL59s2N/PDVInZxlITPhnEQjb7P9mfQhBG0GNWBGoJCUUwZkbP6kvxmByIGJHJZvEK5VMxvjkLuW/oVPTc/iPv5dI61+4o1DGWe+wwAbd59hAln9zOdx2jVJ524t3rTZN6j3Pl0f1745GkKS3N44eWJWDtZsSOwn6OM2z+f1nteZcj4D3nx2CpG/LwNF+WsvP4btd/sSmS7BG6U36BMyEZJlbBY3MQXlaDoYDUEPMWlKFqgHNjjCwptqBtrPsSiKGIoFoQwK7pN5kakv/9g00yRbMPlt5Yh1issLIy4uLhKrraJWlctyBFFEZvNhlWSISYMOdxBhMVaqYDHtMah1lzTNCIiIioJVdUYPRSTCB0mUSc0rx+qXEJ5CKF/Q2Ppqi9tNXECk+xjKpnQwiXzuGboYA5TAZjYQ1XgsWplJUC1atVwOBzBEMArgiqLfhq4Upm5qar+dKPVaiUsLOwvZcNMX3o8nkr3oupLaoCgt1aVsv7fKpPQ8Y/wGByCzbg05gwrxJ8ZNvYuTruttNkaR+enJGoTzgju5UuWIKAzoc6HXHOUUXtCP6YMHcaDz79C8Yi61FdqIQn5FGkxRJTcQFdqUFySQ4JDJcchka8INMtxkH29kJglF5ny0yYe7NmBWpEGl5ons/39RaR6o9C5wBryeOrqbG47UJ1L6mWEumEkOxWORhXSxpmKQ0+kxPBRHpMLFwVcqVbcag6FecUomW6q14zGEheNJNs4GeEj2etFK/Vhl+y4JQvlig+LrhLh09B9fkFDEPxdgHwqggEWTcAtqkiiiGBaNMOPcgMYkoiKgUfXEMrdtPBE8MKARjyx+SA4rH4egE/FbrVx8uRJGjRogNPpRJGUoBBYqVw3IIoCNwKWzyIreAzNf35VRxAFDMP/kHk9nqDCMgwjOCdBENBCwTiMQIwtYxg6qlBhBc2HvCp7MbQvQigwGspYDAUGQzEG81k2Y/pQXKGqB1NVsZgKL3S90+kkMtBX4WbkLjPbIAUbu1ZgFV5ZqzRPk8GpahqiN9At2mLxh3KOgDeH/12UCGDogblKIgYCgmHgUt0okgU0w58lEkQsij8tbAgSHs1/bhEDQRdQBQVVFBHwezvpLf57j+EfoRgShAjjUZpwe6vhPHBoDBe2XOdaaiFSeV1qhItYbngg0s5tbe/mxfgm1Hp8EMKtTg7dsYm+Kx7jTEQOZ+Zup/+nz6JcuMKVHSdpWqcnil7K/lqlOEQJr+zDLkOEFk1uvps4i4MoycrVjCs0jEwmd/91er7emZn3L2DTttUMXv4CFlHDIisIFgeKYudInzeotWoEstWO4pTxyhWxrN1qpbCwkMjIyD9obLM4SZIkVAEkA6yiP372WbSQtwbJyLofzQcQ8GEAPhlkb2WN75NFLLKdmvFx7P51Ew4DWoWlkNcskXKXnzmqaRqUuVEUhZiYGHJycgKNPPwIv1KlOEkVCfL1bQFfONj8Q6iw9qJaGfDzCf6MgsfjwRYSy+oY6GLFfmYoYgJ8oVwEURSD1ZuCIFBYWBjMRvxVMVTocnOECmOoAqigbRNMY97M+odaWovdWikjEYp1lLnKsUc7Kvarchzzu6r607FWn4FHMjA8qr+2w7T0Fr8y0DSNcntlryrBsFDu82L3gSF7MAyZIkHCK8nYjDB0Wz6aIWHxyiSJNlwlxUiJEeTYfVDsIPvYKWISaiBpBh36NPvfCiVEFKawjzVZJ4ngViK71SPcGUmecYhJXUayac4KXNmlrPx5AWlP9KBVWjta39GTGiNbYqkTSaJoZ8eyjZDvQ/ZZyLxeyAcDR+O7VMCHrQdyePR8Wrji8OkG5ZoLe6wPp72QTCETTzUfu3IPc0XI4oVbX+OMzWBP9kZSwsIhzIbXImBXFJx5xViefpa0TTHsnbsHzeHAooOk6lh0wOUjzhGJRTUQ3SqSV0P26shevZIwuGUDXRJwl5WTde4SXq8HRbH4KxJdEpJkQVM0fOEaOgq6ZKOeFktMTAyRkZHB8uEYVSQso4iNE+bQsG1z7E3r4kxJIrvQVYnPb7qioWk4p9PpDwUCwJUJZomiiNVqrUS/DUXvq4KPplEJbVIaui50HzOtarIsQ/+a24cyEWNjY/8yE2KGJ6FpW3OuJhBaXl5e6VrM9GXocUNZgqHHMdeXlJTcFJsQBIGwsLDgtd/M8zHvtSRJXD51howz57DpIigSPhk0RURTRAyvD0uACRkhOVBUEW+RE5tuodwlomLHq1qIq94I2ZLMwQ9X0np9Ka+2bEXmsF+InnGRV1o8zaRmwxnf6RGa1m/Pd9XH80b6cO4cOoBPeg7nQJ/Zf1Mm/wEjrkYSAJHZTu5H5W5SmdxiJIs+2kSPtx8l4ZF2XEjIozy+nIj4SN5/4jFqEYNaJJGXU0zZmau0bpeOavOgOrz0fvB2hu0Zz/l+KouX72Xlrnk82q4/kW4bIm4kTUIUFWR7GJFxCdRp0hShTji9J91Fs4wS2hHGNcmFpBs4dBHjRhHVRDsrXn2Swwe34nUXozvL0AJvFBJ1sCCgu73oHh+SDqrz/1B31tFRXd/b/9w7FncPkgSX4C7BtTgUpxQpUEHaooXixVoKX5wWh+LurkWCS0gCQSJEiHtG7p37/jGZCKW/9/dd633Xavdas2Amo+ecu88+ez/Psw0oRgnFWKx3oFarsTVYMvRqext8qgahUblj1NtiluyZ2WcaHhnOeOW5YJ+hoGh0uKjt8ajny549e4qo07Isk1aQi12ADw37dibqxXPkAonQY1dIuhZRBJnVaDRF5+qSFQEnJyeLw1CpSt3+23Oo6n94TcnyXMmSXcmyYMmL1hrufyxp+eFn/t3nWU2r1Vp2aJ3uo3yCj1VH/u59S8KgrUewDwlb1sjnwwjFagkJCbgHlMWvUhCZpgJMihmTLBeNu+Rsi9FgxFNli3+kyImJv2N/O4ddny9jRsPeeO1IZWGzIWwsM4bPajWke7vh7JzyG4PsJjDj4a9cXX+HkY7NyUPHfp6RQxorOc4N3gMyFanHU5L+dkw/Op7/hKNE7eB6yk+1v8fX3ZEstYE2IT1o36sF2eQyrtVw3Gra0WBAf3K9cnk5dS+ZNYJoHFSHX7/4ioEDxvPUNpqO3/ZEUECtgJRTgOKsQVFUaN7n4Sm4oQ/L5lX0K7KbKJQLqoxOpyvaVTxd3Zk4cSLffPMNIwZ2JW5XFu2GNuAGUczquY4ZP3zKy4N/4jisNrGxsZQtWxaj3oBskooW36uwcPz9/bGzs0MliBQYDWhcHFA0KmSVGpUhDYPWmdwjL4g36nGrXYbWvRtwbeVdOk1pTn28mOg3gio1g/AMqU56Cz8cNHoWDPuSGfvXk6cW8DLlEdiwIQ+O3qVsuXI8j4rCuZwT2W/i8XXy5vL+swz4rB+ZQTKJeQJ+yBSYddiawCCZMGpFREVBrYjkZWRhW5iws17EkghqyYzZaJFIg+Jw13oetkYgUDpX8KEpioJaJSKrQGtrg86kUGAylnrPktGIdR2aTCays7NxcHAocmpWs5YUS+IYFJVQJLrydySvkkCfklHBh2YFS5UUSynJqgSQJD2KVoVZJSKICqJkgwodZk02RlQYJRUqewVJm4VTsg/z5q2lZfkKNJ7aBhujGwUGGZ80CVtXB+KNemJ2XGTP+llE8YYp7ffT92J/uqJjTp35KFUqo02VSHz0jAvpN6jWvD9Rr1OwSXrELXKxw8z8jjNJfx7O7cRQ8s1xNJu3gLjVLxHr2zLu3GB2113FiUe3Gd23J+0ODfx35Rhq1KypHFlxEFsXGzIdZYjTc2HMURyiDejI5C4PWMct3t94T3rMY+5kvqdZoxboY9LQCi5ka2UcyqsssgAmidgXrylXLRB0tiipeUj+tnhITqwZ+RN9R/bFrq5X0WI0mUxoHO0ti85kwkUS0eSKGKNyuB6fQ8POVdBrY3FURGS1U1GtXkTAqLfUvJ2dnTl+8DDDhg0jMzPTggFQzIhaS9ZfLRQQbXagxi09eXtfMjR9Pt8unkb9pLIEftGE/7gsRMi8Sdt2EzkVfp5BqyZy88lV2nfuwStNGt72Jmxzy5Lm+gLDCajgVpaManreX35FmTLepBjz8VWreX7mBq9jEnDoWo9OrVuTkPGa/PJOaAxm8vUF2Lq7YJuhx5hXgFRgQOf2fhEAACAASURBVOdvaUhTlKiTzH/rGIAiWvD/xjFIkoRWpwWNiI1JQZdlIMdFW/Q6a1beYDCUYhMqilLkbD8EEn1YugMQNCoiIyOpU6dOEQL0Y/Bnq2MomZQsaSXRkdaKDRQ7hJKOQdCpUTRqREWgQJBRNDIiBlR6FYY4A346H2LCnvPuQATT7+0miWju9l7LkiOLqN1qCN9em1H0uUNpwi7ucI6BNG8zgIiweL5KmclbsmhBIHM6r+Bp2A16zJ5EzzGduE44x1lADywl9qrACPoycsQoPLd25UalXcRnPMffpy6PY1+BkI+YJXGPW2zj+r/PMezZcwBFq0bOzufpztuErj5BHDdwoT5/FDZZvbPxEl4tA8jJzUXraIcsC2Cy4eGD+9RvWANBsTDy7AQ1BeiRtDbkpmTgolawtXXAZONMPgI6Kbc4Y42F7FKMXjMjaTXkIeIpJSKhQZL9MJts0AkpxfVs2Qzm4tq7q7sbmZmZAEhaiyKCo84W2WDi6eM4EkZ8j3ur3jRdMBYXFxU3f9nNox3HcKEi81lvkXrrfoxlJxYwtuZgdoStZtnBa2SW05J+7RaKqw0+IZVoWbkSucCtBwnUru/HtwxmBbv545ttuOgl8lKd6X/0Uw7bzcBz3VCc6mrQmhT0JgOCrQ6twYxolIiLjqFK9WpFZbCCggIUrRqNrBTqHJYuhX0s2fchVLekWRyvGbPKgqmwLZDJ0ShF/IK/27U/ZlZyVMmeCVbT2Or+FiJsnauPoTJLOouSZtWLsOYhrI6kOH8igVaFJAqIioBkq6ASbPA3uvPmShjpVx9z+dhBbIlFpDHN8aYlK/mBSXx+cjRGPx1ylkxym+3EIRFrZyQzP4/uLUOYf+NHrhFT6vtMphP1Kzdj0Ms5RY+1pw3nL14m4tQTXl45gVrrzdq7O8nCxG3u0IGK3OYVTalAF9qRhIFun9UjZMfEf5djqF6lunL0wEn0Zolq+QGYsvKp3rU8Szv/SICbL8dOHyGiLCzZv57YBUepP64rkb6RuBUEYNaoMJsktIWbQEnorNXzSwIglKgSGM1Fk20wGNDIf4XmqtVq9IVroWhx6KXiBS2IoJiRRBlZY/kcWzkbs60z7ySRykkaTOm27Bz8Jae5RYdJSxn8eXPcdj8jbdlj2rMNO4y8IJmeDKIO4XQfO4WII285kLyPIY/2EGC0IeWzP1D/3hkvZ3f+6P0by9/8zPlncTTT++PQUCRr+wsOLztN0+FNsY2M4WJ6OlWyUqjg7s9lV3dqTgpA8zQHdYKI2tedvArZ6EUtOpPliKMRFBKfp1DZvRq5jvFFZT+jCBglC50bkKTSMOOS9iEE2GoqlQgopWDMVmyAra1tUR4EiinQJXsqWEP6kspMJSsL1my/1WRN8WuNRiOiurgMKZjMqJSS0nO6UsKqkqQvKpF+rFJhNZMig609JhFEcwGqLEdS72VwZfV6asaa8BbdSdMYmGM4y0NeIV8yEObyHEF5j4OxDie//IPZT+aTSTYAGZPDiDz2jF1R2zjPOaIAHbWpRDxhpNKE6hzccpuyI51RgBG40i9kKqrrTizjR5rQk3xiWcklzj7M5eH+G/ywxNIOMXnfS+YM+JH+9UNo82AyUPDvqkqYlWKP/9oti/hAiStPo2g2bzCuE5oz9vwaDs7bxOpq/Tm0eytVQypSLb4uWkQMej2iUryrWM2aZZZlGUWWMUvFNyjOHH+IlrO+j8lkKnq+LMmYZbOlHCWKIIooWjUGBy2SnS2Czh6DjY4EZ1d0givC0rus6dyf2Km/EcUlnpDCJz/2IH/bW/YuW08VFpJNPG9Q4wccYw/uNObYsQf4+PoT4hBCfVmH661UonNA2ZZGvdqVCH1zlHLaENq8y6V5Q3e6UpWcHBOOzib+nLUKqUlN0iL1DLyynYKeDen4TQCqSBNZoSlkPUkh8vJjbiw7j6fkbOm4rEC+bKIm9szsUQW1VmsRcy3MqP9dQu5DkNDHTBAECgoKSmkIWFWKrOStD5OP1pJlSUBRyTn98PM+1ImwVimszuFjyVHrYydOnChFoS6FfiwBdzaLhRyIwvuyvT1mlYyIHrMgonuWy/OVO3gQe5L31Rzw7FyOjqsnsH39EerhwoxPeyC4eqDS1iGomS8e73K5f/xd0ff59peuNI0axNpCpzCV0Rh4Qlk6E1ZvB3cIp8xIZ9aX2Uz4zHBOIfPd9Y38wGReouGpgxkTlanJcJxGHeGHJV24PyGcmFkxdBswiCFN2vNWk87uLif5b+wfETHUrF5TOXHoNDkGPSaVhAYRQTEjmgXszBJpOlsiVl7g3ZG76NIK+Hznz2R6GTE6G8HBBkGSCzsGF5t1BypaTCXqxsgldjwFtErpXbAoKvhgUUli8WI0iQp5ZhlPW3eccwQinz7DrV4d3vx6iqv79jLcsQO3ci7Qa9ZksvtUQ1h3ij823eYE23gA7Alew6Bn39AODfVpjxkjDejCA65QfkxX2o5sQ1yTZQxgP5loAB1VMDBv80ka1/PnycRQmujcOXzrD4LXjMVm6WWSOtSihVKO8PZmtNWD8M9VeLPpErrXWmJj3uFS3xUXF2c0zTyx8dQgJ+aQ2dgdzzwdHveySG7ggF6vLw6vjZLlBoBYlA/4MHKw5mq0Wm0p+LIgWKKGkuf0kmIp1gQflGYOWisYRqPRUqX4QGTkY85IEAQkdbGgiVarRa21OAuj0YgOFRpRVWKeS/8Glaq40mBUU+o3WiNPlUpFtqxgYxSQswuwl514OusEOg8B96mtcfJ2IEyXQ90wgWfLHuN58xnO5BHasCWP4yO4mbCG5W1W8T43lex7cRSQy2O7KD7rMZY5e2fQidbUHjacLjtbcnPIFY7EP2V86758P3caFe2dWJK3vuj3Jo99SsDGWuQDyX/k4jXEgZ4E8sfL5+yrPJpqoyfQbFNvtrf/luCuLXh8OpKRF0f+u44SNavXVPbvOoQkCshiIUtQtKgri4CTHvIFPelmI7W8K5BhoxB/7RHVqlWhQJAxFRgwmks7BuuuIcsyOkVEKHHxGynpGBREU+nw0eoYSrL0ACSbYgixhIQ5W6bMK5mL363H96ehTP1mKd+1GUpgcC7+VVvy/m4YNnNaollzj/cbs5iTO4shvyynoY8z1KtExIDfaSrUZNrTbYjEsZ2nSFckbrm8Ja7uLIJ2zeDiyj14P1BIV94Scn4Lpzq2xs++L18Oa8ul/DcIQypxr9PPNGzXFVtkDIuaE+Bgj3MEnNmwjs4dPmXQtLEMrDmc6oMCsW/oS3ZSJqaYdNStamCb9BYbnQ7B1x8FczGQp1ARquRRQhAEEhIS8Pf3LxonqwmCpcuSld5riQAEBIH/0TF8eASx5jWsTkZRFERtaXyEFZ/xoRkL1461XGtrb8GHFBQUoBPUFOTkFkGQhUL11GLwk74ogtGrS0cj1iOPSqXiyqULuCUGMGjpFxgKO5Ktoj9ubm68S4+lx/ixvHU1s3L+ryysMBgXe4EqT8dhSRNGsokhtCrfHNfJn2AKe4RT1+ocmrIJnUMg1aqVp3q2B6ue7Ke8vQtN3Brjf7MDXnjya/VpDA1fCGQWfa+29OQyxzhVfTWnw49T0b46XSpWpWBAFdJNOYiJEjl3MwmZ1Z8lfUawhIP/LsdQo1pN5eCuQxavX3h9fiwxBBSFuWAprYFl4lSKRZHowYMH1KhRo+jpsiyjEyzJQCugplTYqABK8ULVi8U5Co1syYIbBAWVToNZrSDmpWHn4k+ejZnoGVfJuRBFpn0Ww77pxyUXB/Kn/0Gls1+jn3Ec3yVfkjJ+BY3v/kgfmvDr9o3klbEBgx0RM/bCmwLm58zmKSDvyuV67G00n9ji/dAO/1cidj/VYVXHFbw8f5g13ABgI0OoM6AH2Klxq+tL5J3nBBgdMUUmENepMj2X9wIk9rVfS9OJIRzuvglHTHh8G0LtelWJjUwksHMQSW+SsY/IJMwuH488E3ce3KPTsnHYGcyoJAWTWijVlVkwlEAgSsUXsyRJaLHs1Hl5eajtbIrHvfCYVvKiL+kMSqk6S8WNVQSNqhQmoOTrFUDR/hX5qFKp0Agy2YIZIwL2si06JR/BRkAyg9kskv0iDqWyLzYS2Jlk9FoJRVEBWpxzFTKcBOyEAnJUlqhIp7JFJ4HGDFqVCrMAr5PjcHQTyH+tJeVdLu6d/XDQ66ie50PUwn0cSr7H4BHjmfr1QCaXH0pG0js2Ga4zbeYK5vw0iUHUwm9QawRZIuZJMiNfTGdPjbV0r9Gc/vt7kUI0WjzZ9N09fvt1HhpM+GmduG9MYRcHGO76PWcytlKDGhyecxwXVzPdJ9XlJLH0xR0XHGk1YjZBPjaUq9SeciO9aMlwwtlOGvy7cgyiWIxzV5v//qYqlCeTJQm5cNGW5KZnZWVRtWrVUnkDqxOwPi87O7uo5FjSPhQIVavVlvO2SoVKp0EWBewEHWZ7F4R8DdoMJ7KO3EGlz6TejvFc+ukAbifDqNStLs4JOt5eeY5p82NO3b3ENMbx0+EdnEqKZnOfHdToWpZWcjA7ctaRBxw7eZtjxpus37YL7Slg7n1G2f3KD96j4Fk6+3gCwAoGYP60Bd6BToQ/jiTvVRaVXMrw+ukLIj2zqdIhkGcHn3Pt88PU/KE550fu5i059Jk1HidPT6KuPUDISOX1o1dkv4hFDq7J/Y0HCLDXUm9uL8QCCVWuRFhEZNE4WLP6wF+ESayhviSCrBKwdXYsImaVzB9Y7WOCrSXnqWTzG/hrtcP63h+LFmRZxmhBFmOnV9CHvSVJZSbdKJCPGpNGjb2TK7bocJZ1ZLkUoBEccRBcsBHskJ0VZJsC3CV7nLHHwaxDMuXgahJIlY0YnP24+scDnNMKiLmmpSA1mwa1PWh0Nof8bluZ06wrKv8gEu5HEHPmCf2WzMIpxJN8QwqzBmyh0U8tGEcPmu6ciGsfb1y/qE+ZMa2Y6jQdO8lM2f11uEw0VanNS1J4Gx6GO55U8PSmZp0a7CpsuBwiVGBvl3Xs/PFnXOf5oJ7kzliH6XjjxyHS2Ew0PlvDaVS/K0NHjgPsyCSecf4TPjruf2f/HUn7/5NZuQFms7lIdMRslksJWqnVaiSzbHEOH9EEKNkX0FrPhsKQtkT04eDgUDoULRExWHYhy3vn51v4Bs7ursiFjxUYDGgVW86PW0+ZJ9nk9q5K8MgulMvQ8OaTWrRv14GIijJPx2zCEVfu7j7LmOFzmXVyF1dWXOHrG2MBGEsLpoTN4DhJPLmfR+sG9qipTQpP6PyDnpHsY+LMNujIZwKb0RROU+/pk8iS35OfJbP30XFcc10p627HiJdzGN1hA5qn72hiKktie1eML01kpmQS7OLEkSP7qTesCwlXogkIaUR2RiAHFi/jvjKPsadOki6ZKfMwmVyPLKJuP6Zq/06AsSghKEkSGrEEPVkpnhNFUTCri3d0sUT+5u+Uo61WKpEoirx//x53d/e/5BuK5l+SUGs1GEvMf0los6RYAEo2ohqvMmUxSirWfbseLy8d8VIaE4d8RYSczdINvzF+3DTun4li6PpOvLgXjZAi4VnZn1n9FtL3u+64SfZUtq3EFSmeKg7eJGy7Qeiei1Rt1Jrnz14zPW4Ma6tvRAiPIJ0UThJPj2dv8KQsMdGvCSlfhXO7TiPiQd99wWxwX01y7UQaNnQl+62ZyrG+zP2+Fb1oQ8/sJYW/RkMK73AEKv/Yku/Dj/G92p54ez2f0ZlO5aqSavuOi3/Gk3Q9kwPDf+Pw5eM02TKcRR2uU7Zcc/SVFRrO74pebeLa5YOs7zKV8loPYuLD/nLN/E/2j3AMCKCIguUalc2YzcWJIGsvEzNS4f/NRa3IEAtBMZKZDw8dagREQUAqJPKAJSpRVKJF86Aw461SqdDLEqJKRBCKdyk7OztMahGj0YROFFFEMzce3yf4tTN+b/RkmOOo3WMojq9SiU8yYdujFhu+n0yLU6sxJ6fg6upNga87U7Z/xrz91+nSP4iwdsfIeviWpxnhOKvL8+XSzdRuEMJuZjGYhXxHZepu+oFbpwbR7Egv+uEKwDg8KN9xEsmuRhxOprP1xj2mdZqEKkbP++eRHBy8DIfvOhLwZwZx7+OpbvJn67hNFPCehm0H0PnwlzxP74/sX5m1W3eykv0AdKEanZ3d2TJ1Dp3GDcMhWyZQ5U2ePg1HR3tMJj0OanvUaoECRQIUzIhoJcs53Gw2Y6fRodWoyZeMqCSQRQuLUJHNXDtznjZt21JgZyGHKQKoRLFIqLAkEEmPETc/DxRAMCsIKKhUoFZb0YoSogiSUY8gWhiNsiRjEkEnFtKzTX4sWbyU+vXK06ZeU1RKOfrM+ww3rQ+Breqw7GRx8s78EBq39iM8PB79kwx4pycrJp7ey78i4JmGsTOGconQouc3A7woj1r4hE7d1Zxb1w0fX2dGhp8iiyg68glLnt7kCFsoE6ZjYVg7dHhSqU4XeLyKt2lRLL28lcfXh5Hy820WRs2no3oMzbq1h6PrAKhPIGbesTtkOxHLLjMi1pvaE5th4xPAmyvPUefb0ad2B3Iq5OFQ2w2HdgG0cCnPbwN/YiZ72e5QCznHBtdm1fHAgy/oStVAD6o41+D561gKezn9r+wfcZQAi2NAFDCLArKgIBcq5SqC5Wam8N+SuwUCYrFMaSkTzQqKJKNGQBEsegWoRAS1CkUQkGQZlVqNIgiWpiEqC7y2JF5fVBTMKhE9MrmSTOfmrTi/9GfeZb3B4O6Lr7cn1yYuI3PxObwVOwYum4AhLgEHjR32DSvxW/hExp+5TuX+AayyWcwjQyx3Mk6ynPMMlmaicrcHHrCBDcxmMD06r6Tu6NqsOTIEgHTKAtDGfjCdmtch4cwrnkXk0L9Jc6Lj3mEQctEGulJr1OfkPsjhys97cPZ25d39OFwwU6lWMAl6Iwp5fLtsBH59azHj/FxuXAmjnmN7zhDBmBbtadOrN6nejphi0jG6OuDq6YKgiNjZOaJoRCSVyjJuYuH4iRZxWI0Zou4+QjbLmItUWi3zqFaradGkGSoEi1pyIZZEUImlnlNkWhFZpSCrFES1gKAWQbSoLCsimAUzqAARBEFBMhqQTAa0AshqMOlEvhn0FXMmf0ezVs0xl3dDMMfiVNYevUMWA+t3BGDXNItTXL95EcP6D8Fm0C7SfrvKueXb0GaaGNqtOn1m1OESoUxmHF2oTD2qcgs4Sgw1T3Uh3UPHJwQz49IIsohiuv8iJv0ynnVbVgPgjyMyQRgR6fG4C1pULH7yHy433cy+MaO4HHULe1rxurIzx49beqf3xpc5wZOY0HIF8YHl6H6sN9UGtebO+iN8MqMW9Xz8MVR3INopl+qnuxGamYpbez/ObD1Bh7FteLYphVPht/g+9DuaUJNUEogggTVvL/Hz45mMXPnfHSX+EY6hZKLRCrD5sFb+ocZfSWbdx6wUQ1AozQo0qwtxCJjRm6USJStL2GrdyVSGLFCZ0Dk4YZOv493OMIJwJqB1C2qtGcmazuMoX74lcYMacWTIFCLq+FLtTQGVg6oTFvGcGX88JnfNHs54/kBSJ1du/zmemDohvCIaGxxp8HkVFtCL66TSuVxLZt05zQF6sbuwBe5lnnKVYWTNbknszZf0ujqKp2V0mOvIfBs+g0qN6rH3VRTdO02nXf/auOk9SL0TQ3mTG9nqdCqOCSGwQQMOVJrNjxfWkdxEINHNlpZtalIjJ5N1Q9bQtE9/dE8TyN9+mcT7r1EqupF+L4nke/HkvsnCrBHIVueXvohVIpjNOIta9IlpaDR/zScoioLoZIdBI2AvfTyRbFW2/hBQZNaoMGtEZI2ISVSQVBTdzFrL37DRoHG0QyPak4uIURb56dAsHB00JO0O48/mi/BM8yek6ids7LCGhYt+IOFhHFVlBzZ4zCFxyw28+lTknezK0Ss76Dd7Mlk52ZhR8ZQ8wI4p82Zxhpdko8YfOD3jMmlnM/lj/jg2sprXVAAgNz4U51tetBjYiKM1t7C03kRCmrej26xR3Dr5gKNHr3J+wnK63e5Ph+ZTkXhFOEncDt/NXvN81MDWrXcxueSTfPM1fbe3YRTtydyzicotQohFwq19TTyem8l69IbomS9xMzqwo+JKWgRV5vSi3TwYvZAYruIODHMO5GmzFcwbOop6eDFsz1Zia/xl+P9H+0ccJRRKJ6ZK1ret9iHl9/9mRqPxL48V1aYL16Eiy4iKRUzV+tmpqam4urpamIk2KrIVE+L7fF7suUyfDWO5XmcVfp/VRl3elcqUJT0/j64h9dGfq0vlLHt2njnGg7uX+HHfCQ58tpBOEwaRV1fGfckfjN3yhPEjR/Kk0VG63+2HGQdGnlhPwOfVyGzryKgDrixmDav5msP8jicBeHw/jD9XH6FcvU8J7XcRO8c8UtOTWVX3F148CsNRLIuLdJ0NPnPQVgvizw3HeF2uBjOlHVyOH0vLxdWIH3mOJDcvLo1fweA139CGtnTs2oEqQ5tgjjeQt+0xiqJCsQPHV+/R55pQyyoUo4DkpUFtA2iLx09nVtDrIFsQqNKpBUaj4S9jLUkSikpAFFToJJm/zkZp512ESrUmfS0PWvIWHxCZrM/X6HQYM2TUOg22GXpctr9i8O7unOUtv9nPIOzsfbKI4EB6BM3GeRLx9ipVqESarTsND81haZWvsWvpxtyFV3DrZc+B7sfZVG0h1yJukkcMdeZUZRK9qUMFfDyGE7vtBksXd2dJ9yN4xUTiEWTPm6O7iOQlJw8vI5anXH8fSi0k3hJLg+lfIFdx4Xalb1nAdQASb54lnUS6ew5gc8oyJtAfPTfpOSKYsTZfE2l+xdYySwh7F84FnjD3i2VwGeIPveZwwWlqpnmRkp7FiKTZABypMo9eU/qRVBDP0aQT7D/4O59tWMRr70z8/AJYMbEjEUtPkefysRn4e/tHOAaUYoHPIpESK6lFDfZGMGDGpBHRyfylfwCUXjSKoqAVLQKdKo0GRShd95asyTIRzLIZDMUwW1dPC7FIwoxkcEOtM3N780lubj/K9Trr8P08GNfgekQsPk+DVl05eO0IASeuUuCnwktvJOCxhoYPf2Ztvf/QvH5tQvftZPjM2Zh6jcZtpEU+3replgd97rFj1Qn0f75mf9plvswOJiHvDQ+B9sQzynM6fovaYlstGN89t2h3vC1lqE0b7GlWdwBfPprE7c+fYPt0DkNrtWbo0x+5rNlOqgJlxRRS98QTMMiCN8hdVBfttQT23DpNtYnB7D2wHXNmCufPPyT4lQupzi6ort8hs2Ygd2PeU8FRh84sYozQc277cb6dO4kEHz1Xzp2jRZNmSI62FrSpYgZbrQWSXji2VtwDhaAyUQGDaMFFWPswFrEYtcWIV1u05NqIiE4OGJNTyTObUds4oMs142SrJU0WyHSRKPs+i3wve0IX3cfubjw+g6vgXac6cYdC2bd7PeHE4ARk5WWSvP0uuxmLR2B5CmLe4qcKIEJbnnkF04idewGbBnWJXHQbt/UWbMOnJ3oAlq7M2xvvwy39CTNXTKNzt1o4pCajwYEKBHHR4SG+rSozcXUfdtRZSLtcM/ZurowbPJ2HkeFEbjxLwKDa2JT3JGXldVbwZ9HaG8IGamFH7wpV6DXgNp4pKQwJmYtQUU3G6TCcLtvzNNuW5ewAYMegcgjArYJLrIo+jm2qliUNOrKQbvxJNHYqD+J3JnA+KZw+zGWgd29kRwM+7mWR3uRwb9YBXj+M42VhZet/a/8IHEPNGsHK/j1HSgmUWh1DgUpBJ4PWaGkiKgnFCStryF+SkGPVJrRXaTCYTBZdRJUlsVnEkPuQhluCZmur0vDq1SsCAwNJ1RmoqHflxYA/cHtppOyST3j44i37t25DRTqBVCELPXu5xN01F3D+pibrA1YRlxxKVP4DenT+jBovynDu7UVmFE50XSpy4cs93A27wdO7j2hg25CXmmy+SpnFSCpxkCg+oTrfXNxH8/bBnGl5GJs6ZbF7cJvY2BSis0TCcxJZ8Pt37Bz3I42/6kbb1cM5svAW5ZPzqF3gSLffR5PNK4auPkjzqvWp06EWVXBgZchc5F5uXP3uO1YShREI7bUJe8mM06dNSch4h7NiS4LWjDZOooHJBW2rMsT65JKrmBAKTOg0GvJkU9EcQTGepHBwSzEuS8mpqYQihKTVMRTNiSjgIam4seswmWdeoo9/T0DVAJpvmkpY+ntqqAK4vf80tjW8uTT/GouSl/H8YRYr6w1kcJU2qDOMnEy+xlIusNdjPgNTVwAZbFN/hbtowln05o7+DZ8v/Y4K05qR8ViPuo5IHUBLMHd5BkDBiURu7LpKxr5w9nOHQ1wo+v5jQr5nysIp3Fq7gbvRsUweMZXAcVXxBCYzinFX1/HQLRoHs5qLA36iU8hELv2+FDOJfD9tLbduXeHmjfMM6zuFsO4Z1PMIwKtbMLuarWTorUkcc1rLluzlpOHNQ1K5PG0fNrcfkBgg0WXHbPY1+54YD3emHv+CA16LqBtSg+sHD5BFGRp6e6ELcuHc02s4eIgM/+5b7P0qMerTNnSnLv4jO9J8S+9/F8DJ6hisYBmgCP2W+DoG57I+FgCLBJJcrL5T8uIu6SwAVLJiqa9rRYQSGgJWs+YylFLRihljdi4eHh4YDAbsJEceb75Mr9+HMYXmDJrzH/rPa8orTPR1msBoc1Wk3EQc6pTnjV0cjrHwSchoom2e8NrVi4qPb/KmbRN8ykpM+GwUy7yHkPneRBrRJJHE2K6TuJ8cRRIpuKvKMTd0HKHAmq5LmHx6JgOZQjjP+KxWRUbYtWDTndN4NfKEjCgq9R9PWkoaDYa3497t27hmF5B7+w3nLhyg+w+LqP9pI964ZnCy/zOm3+3OnS+Poa0ukXwhh87HP8eXsmxYsI32KjUPGtviFpNPqspEkHtlynUrz2m/xXydMIPzv6OHnQAAIABJREFUD19zIfMejeyqY2MWUZuLkaPWMdXIxWNodQzWuSilz1gY1FmJU7KumLZdvkIgtw6cwsPGAYdGNSkATCaR3HQDzklJZJ99SdXOXag90JcERD7/ZDafBrSnSxkfDv++DRtbZxxzcpkVu48JQ7+iavnWiOHx5N68jezqTH5SAWGqJD7dMAE3yZbKg6vwZlYmDRdWpCb+hIz6itgnKbRyENGmOjAlbCo+2HEcC6/hys0oGm16yoUXb8nMTaHDuEFsnreBne+Pcv/rSzy9comW4RMI/+EKBxYt5xEPOEoig2nE6JABeGYG8io2gdDMpzzkFqsfneNUyBTEHD0+FVox6PUSFBK53nMb3kG1WbpiK1mYGTOpC3fPP8MQ/gKFJOLRseXnDVSaEsT5pbc4N205n3w5AyU2loj4MJxcHakT0ITLYQ+JyEziWtQBBqpb0e3wDFx7eP07HYPZbC4KRa1RwC9fT+WbFQuQ7HXYmEA0FjuGkorFJRl3UJiTEC2Jxg85D9bXyrJMXl5ekTaDSqXCXmVZtFqtlqej9tGtWkcenT5LvO171G8LCK1WhVErRtGlc18M3OVC4BKSXdK4FPaMwd3a0u3Izyy78gc726xk4qpv8ChvT5Szgn+Pa2zKfkA8OzGgogatCFLXo/rIfsRG3CHsxk1q4MFQ1hG67zW3B6yk9/c9KL+8A7canuDivfWMXv4bft/X4zM68uuEKXisshxNkhbcZefP6+nS9nNqHm2FL/DpzPlMce5H2anVWUYf2o4fy7o9J9CmvkFLNOFEEYOJfW/j8Y9LJ8pexCbRhFFwZ88nc4E8VrOXuAevEUzvyLTxRTDJhZTs0olgnSIUcRTMxuJjmVVP4cPcjnX+SjoGDBJuOjuyVDK+soFcnR36LFsWt+zB6HnTqS9XI+Xma8I/9cfWvQyxd2Zy+w8dzYJ8qR8UzOtdp4hzk2m/fC7pHvEEHxe5eugwbhkFXFMEnnOToaMmE+4ssufXGYQRTUbhWjja7hQBC5zxTtHwU8+mdC/zO8ontRiwsSFXT17At0wQ+dMjCDrbjcdznzFr7mzq2bnRVuuP0dePF7HPEfJSqTd1NJV6VKR6iwp0xJ0/eE9lPNg2eQ+//bIUFZ5sZg9Vga/ab8Pp4iM6fteHfc/j6dqmFVunz2IJO6lEG85NWUbKo8u4detNpUlBhPU+wutbD3D08abnk/HEXIplSLvmrBj4H6ru7UPs/ChSol7y686fCaIKak0F5pim8vC7S+iS9ZhdTASv6fXvcwx7/zgEgEYRio4DKpUKs1EqJtxgKV0V9RTQqFAXSquhFkuFrZIoFfHx1Tal24q/l/XUz3BneKuKvEbPoYhYDKmZ5Lvo0AlmDDb2RFaaT7zynq/GTudyaCh3Hl/nKtFcvX6F9AyFMj3deNjsAJNu/c51zpN2LYW0O+84vPg09b+sRqVODbBxsOfExoPodrzlP4aTPCCMCjShDSrGNJ7CxdCz/MAGDvou50riZoYMmI1rVSdqzRvCJIcxNOsYhHuGC4uvXGL2nm8YMXw5kcbt3G65n6Y3+gMqjg7ahHT9NvPif+MZUI4Ant8N51RsKLF7wsm+mcrCpDnsGvwnQ3e3oANtSCaMvtXG0W9qf1RZmUTcjGTpgS/oEjyX6Gcn6BrSG+F6Mq3vLiTl7Ru8Trzl3K79OPh5Y/J1ov7sHiT72aFWzLjmwTsHAXsFdCYjisHa4MWMRqVBFASLc9aqkW0U7HIkZMxIWhVqwYaErDQwZmIQvGimtSPFTcA72ZbOXbqwd+1e7BIdcFtYgevr/kTX0gZ3tTfTgsfznwWTiGpgh9udFOz+c5vM2q64//wpxoQsUk4/Rjwejpwn8i4jDvthjagYI3Ds+gV+4Cjx8x+zbtfPlBfKEjK4F6/MKYTOO8o6NtOHb3B2MZJdXs30RfO5PXA6zrItqqAGnMlNppZrLq56LY8jntGnVV/i9fG4DCyPSzs/TOdV1J1c+rrb1WAGd++nsIpN/MgkPFycsPE2USmwGs6uLsg6E3KGlqPHfmcpxykPxALPd7wg7+xjEuLe8LiMLW0jdWS+uIHPyjFUJoD6YwK4OuslcoaJh+fOoqRn0yokhMvht2g0ticZ5ZyY8elAznCHdvjTljrM5NT/2jH8I5KPJUNPQRBLKQOrdBoL0lEQUEpIhatUKsyiiAgYCgpAFktJgZU851pBNda25uVkB5KcjXz282b8G9dB1hsocNbiJ2nJeZvGwz7fEkc2uTzn0KEAIlPvUIBAJ/9qtAopx1zPBQzDmZQ30dTHj5WzH3DlXRj9prVhI6PRdW3J84x3XGy9jtF2zVhluMEDwkg7pMe9rw37qxyhfmgvrjY9Abc3EJ54FR+0vIiJolyON11oR6XGXtT1C+L+2fOc4TeGqrsxZXJHEn/3wD+4PAcbnyawckVOjpmDgJpezMfIVXafW0uaLplOvg25d/IC0wx7GCx8z9DdLZjFp/ipK+Cjqo+dhwdxE3eQby4gNDeTQXxNYIOGjGzTlmfCK7JFO44MnM3zN6/o1WcICX5eVFcc8Ml1pnLPBjx4nkDM20hyopKp7FmeVG8t+c4i6gIZRAFBUCOZzYiygqi2pCaNWhE3RN47qvDJgSxb8PJwx1HxZlrIBGzu/45nrolp3cYzpnU/suPe8OWS5TzfdA/7xm5IV7L4fsJA8tTevCqvpfqtLK7OuckAFqG+BM/EUcScu4Hbw/cIlcvx5+XbtOjTiopdu/P22j0quw9Fc+QK9w4c4zN1LfLr1GLUnBnc5ErRWoxmMwfXviDdNYUpXesSEjyOIK9KjLv0La11n9G6XVsyMhORAgT0dWRa9upHdGImV5ZdYvyuiXgDrWmIBx64Ah02TuFO/VaMoCadfF1Jqu1M3Zb1uHr9Kq4GT+oH1Gfotk+ww4AtFgxS5h/vuXv7Eu+uRtNrxQgCL1wlWkyi+dkZRCVkkfrNfloxjOXrFjO+02iy30QTJDjw8uhV8knm1/Xz+c+rg9QB1AQwp/1qVl5c9F9dk/+IiKFG9ZrKvt2HMRqN2IilCTKyqjiHoMgWsY0iUo3acuY1Gy2lsZKOwVhYIBOs4CahOFlpktVoZBn3LD1JKiNlVc5E2Uk8W7gFcf9TXhLAYtZzf8h+XLvWo9OQitSiC9Nur+ebpgGUpTyHieGuagbpoaOpZIxnaLMf+LnSJM5HTaN+83EYIg2sSltHIqkEUpFZ27Yz+/NO9KMPucShowJG8qjXsyfxsdH0rVyXsBeRdHk8i2zSuFR/Meoke67F3+FaEzNrhy7i9oqdFLxW8RWz+KPOjzg9TuUJycziEBGbXvIu+xnOFcujvpDFmNVj6OjQn0W5i9nNbAr8BCqu6c/jb5fhEWMgsFwDbqtjaeobQKaNI2su/cRpTIykMeO+GkDC1mv4jeiMe6A9T44cRchyR+/rRFCHpqjeJLLt/hEmrpiO4KwmJi8PQauirCSgl4sFb0RZQS1bINOKRoWXTof6XTZPKmmplQTvXAvQyA5I71x5cfIOYzZ052DN2eiODSdu5T2erL5Cjwtj0ZUrw7Iqo9h0dAVR1aJxTHDA57wNtovrcMT+B5blneXqqXNE/noWe0UgPSOVJEcz9Ua1Y9P1w8zdPI97ex5h76RApDuXwy/huvkyv7EHe2pzmue44M35kHk80T+hrsmblFrBlKtYjho/1gLgcf21hBPH+wepBA2uQ8MZPYkzm2hcO4gnnx1F8lahOZRJeEIY1Ua15dzDa9Rf1pd+Leuza+Ru0l7G0+jH7jy+9oSqKXaYNAW8C0ugvLkiPf7shlWhIfVtPrEpr0lZeoVOh77lSodLRIUfQ127LBXsKuN9LZ5zvil4PL3PbWS6eDSmeoe6xKZItLrYk71jLnF96xzWmf6kHdCCrznPCY7/chLPybX+XSQqAQEtInZqbalEoEV5x1KpUJstzThEGy2SSBELUxIt8ueKrYBRLaEXjRhUpiJhDUUlFLckLzyCGO3VKMY8XnqIuGjsCWnSl0a1qpC4P4F8tPSY05veVEMdHMDF8UtJBSav+pEqOVomDjhCcxpystcpMqYH46wxYRPjQ1ta0jaqHxd5zbObr7FtVJbmBPMaI7cQaP15QxrRnobOlUkmh7YBdTGSjW+QhsbNKnAv/DnNH39JNmmsLrcJBwcXfNpUosP4cVy6s5dHa88R9Doft2pqDpZbRY1IJ7qznlkcIpBG5AbmUKZpdWx9NcjmbO7zGpvcVKI/uc9g5lP1qx7c6NOZrBgDDmSRG2BPS6/qvIs3suzSFrLIJ2HtWX64vhAHe3fiC2JxVCuEbbpG2bQyPHl+jby7Cg/3XsS2jzf91O3ZOHozJgN46wTczCbyVZa5QG2J+tQ6BZOtgFGjRdEauSsmsfJKJAHZuYR76phcrxcZeXmEHznB+0svSQGqzhqO1pRHy8p1WchGnNKd6FMlkG8njCapkgrzGxU2O+MwCLms2HSFgP0D2H3+GMe+X4O9ox2vfHVkSNCux3DWDp9Mw80v6UgN4gdt5+onS1n02yzK3s7HtUtd6jMAR0ReDtjIzmbTyPVyoZxjExLMOgxZUby4H86ar3cQ/9M9IjIT8ZSq4OXhy+kDz/ELLkvj2kF8SWviQtPZtOEydq2qcdwnmx1rl+H5XODzlu0IpglNFXea9Q3h9MJQBi4agLNBS8N1/el9fRIj/xxIVXwBWNZ+OoKciX5nAp0OTeD5N0dpc6E1Qv8atK5Sm94HR3GLaKrlGUkuV4U1nKCCfxA4aXh3+TwAA39rR7bpHpsYxki/JdREx2c0YdXuS//VNfmPcAxWs4qrfNgm/EP7kGH3Mbbdx8wqLWabLVJgpxCorcCDC7E8uHiPk1PPk0ouX3OMpvM68rX/QCKmj+BB+hliT2WhrpzOlZ+P8mLfabo3HUzW9VOUHdiWBq988B9UmTqOnjgDvtTjNUlsP3OMrdwGIOzgBcbQAG8EsrJekUcKeS4K/bp/yfAVi0h4lknCs7f8p+MeALbFLiehdSC/79pH09UhPJv6hMiIc0iiDm+TIzdjL3FZ/4Rnvzzj5NKHJJKM4BfAr9P+4MbIQ1xfe4wNfIq7qioBpxqQNj+R5IMP2UIG9vjxglz8VQ7cvHORHv1HcJXb3CQZdZoB2WxHzPt4WrRui6F7M6JfhOKYnMtXDYZhU/CORY/WkOZelx2hOzn18iZvGi3DLDljixem1PyiuRFFEZMiYlArYCNhwoTPaQ1BaTKi2QVFzOCLu6dodM8Wp1d5JFdNJfRMOHk+JhzybLlw5x4AVQdU5MvgH5C7e5Jko6Jg0TOOZsThsqgFLar5EehWhn2f/0r/yHlUPNqXvL1h2GQL2E92ZzEXUK0fyU9bDqHDQON5w5j53RgUN4myc3ux8MYkvu0/HarWA1stbQ/2odOl5ZSv7ktl10D6HBvE4bXjSNx7n0b9BtHhyQiGpC7Ay1SsifB18ChabBiCMSeTUzX1vIy+i3eVpuzNXsZGl5ks7/cDEXnvePDnfYQbofSgA4E7hqADKgJN+ISLJPL0z0i+WDiaZ2NOcmH1UhK6naDGmu68vPCCiukKz/ff4WLrX/h01EBynGzoUasBobU2ce3pY0Zv/JmvzfuKvtOCxltYyx1iEp5zk1s8J52LD9f8L69Ci/0zjhLVaip7tu4tpeFfsvWYdbHJKJjMpaW+ijAM6r+iIj9sBGJ9bm5uLg0b1mVplcVMezGD5UxgLqvYOWI7FV39ibjwDGNWMi999IxdPwnDPiPGtFiebL5Df2by0nE2yat6EjZiGWV9a5Ph54TxwWuCZ/UEp1w6Te1GYxqz6to1KrWy6BMYR6YwY0sXGtKVFl+H8OpdHB5SGe4+fIApMYWxLGeL6yQWZ+whFX9W1+7K0CcLudLjOPLxwxR4VuRBbg5PC3bwgkR+O3kfVS0trk+8Wb/yN36YOZrFbcfTgeocZB8NqvXnq4gFHGQ5/fgegSJSJBHM4yJvMIl21PP0R1Xdl3J2zkTqk7lyaT9fLFlMlpBDvWkduVtrPTWaBbHgSQRLbk/i6rI/WTt1EhO2rcEmOpmkE/cIKleTGA8ddiHOOFf1Qm0GnUqDUTag0wpwI5f9i08xM3EB0tEIXts4Irq/IvGbRyQ2qMXMtd8RGXaPt7aJmHPSMQkO6I7GcXHTFYK/6IhTWw+yDAbutxtNi5q9sf91FJXKerKs/ngqN6zIczGPvVcWEjryJAee3aJplh3n/WTGrh5PpJDE/Z7rcNe40/uXcSR7m7m+4Dj9j4/k12q/8l3Ed4AGsGAzTlSdj3+t6uSEZXM+JYvfUmaxb945tswZxi7eUgkf4D1f0w1TSA8czEmMWPAdmWIiPq0qEj3+PnNWd6Zd3ekENaxARlYmWTcSGZcwn1wEoKDUGn2x8QkaTyNXt91kxfFfGEIDpnMUgPxjjwldeIFX96L4gt84MGgpQfYVCd99FS+dFm+fcmyNCOM//M4UfPmMkfj6BHI/6TXveE8kMTQY9QWbN69h/7YduH4e9P/+KCEIgkoQhEeCIJwsvB8oCEKoIAhRgiDsEwRBW/i4rvD+q8K/B/zf3xxEGy2yyiLnVZKPL4oWSbHY2NiivohF/AlRArUZqbCyXvIGheIdGg0alQ0mWcAoqslHpFJ+MADj94/k2Lc7UYhnx+IjNM0pw75fl/Hps2+5FHue7nu+xzndg4XLvqH95nbM5gC7bT4n6sxYXEacJLpiGZTOwZDjwgu/TM7eOsuqqQsQacMZQjl/7REA7fFh2pZ+vCcJkRM8yDXgeO4a2ad2kKP6P9SdZVRVa7v3f4vu7kZKKREJCRvEwMDE7u4WuzuwuwNbVOwAERFRBCQEREC6uxew3g+49/M855z3nL3H+354zjXGGmuued8zxljzuueV/38NzXIC/IAaBVX2Dl7CEaPRiMXnEK6wgfpHD8k1lEfetwubG/bgS0eCh2SjXCGDWoEKboP9qH9Tycg+zihRRTgvSaEKh33jCOUoI1nOSY+TWCKON5DCMr4qFuNm0INlbc8wc7eiXiRDnye7yHvzjd7mA/lw7DlZq1/hgibJoiKS7v1i2Uhfro66T87OCKY7r6VSNgspdx1E0ydw9cE1FHIrsdSRQV9Cg5bEViImvkVcXp7Pa25wMOgWUn3E+BUTSwlC5EOLeL3kKzoCLcYc74u4ooCIk/d/e3wiOjwrI/dhBN4uruS8yKF6YRjWs95iq+tNeouAptc/WNlpCK/riyl5l0FC6AU2HLvLD4lG7HWsia0RsDRsDXJ28jCrDsmf4rSlZPN+9E0OOy9AJS+PT51DWfZ9GXOHbeB1VAS394QTMT2QkykQePsKrwVfUSnJZQ9OBG2azh2UAPhBIdAZO5xYGT6T8UunUXI3lrU9BxNteojKqDQEGGGd8ov6z2nkmBlTlh9BLY0sYQrmYtMw//18Ru1+g7hQyLPhmykoSOBK7FsstTUxZyQA8UMPkvq5Eo3RfQEYvmU5Jh0d0WuSQdzQjNiWX7jRzFGWYUgnRhDCpsJd1KuoII8mC7utRu78XV5ef8n53Mi/qurtOvY35i4Gvv/T7z3AIZFIZAFUANN/758OVIhEInPg0O95f0n+b9aLmJgY+vr6/+34/00kJSVpaG6nRpNoa0fjKZX6SmpmCVnxccjf/M535BnWqQ9Rag2MXryKvDNlXCYGyRclyHvLc54XPNc7wXHLReiO6kGX8/HYsonpq6fj4aFCUdomdtw8S8DQmQxClYjD5wn7ksT8jW50xZDlzKWUn+wYexH1XtNw6eRAWmMDRTYDWJy7g4X1+5nNRgYpayFZI8/E7ACyLNT5WPudVFqx7NsThTZZXo78SCPyWDwyolVFERUZFcqJRIpojjAXG7SpRJG77+NQqWqiNwuxxYyJMyehjgGL5TcQIyeFmpQhTrnTOSLwI/RVHD5h4/HGBdduXuQJf2HU35K6ZZ3YeP0GUh6O9CxZSv7jGNRamphYuQa1pbIom5ojLS2Pj50aYw7MQ3GpHZka6lxefZxD08dy8ftasgV1NJQ3sWLsOAYsGEapqI3CxGJG7emLsUIHPhV+Zx1+xAW/p+tMX6rzK6m/k8q+U6/p+3UjX1MySYl8iYqSNHfL48g1EdGlvyPVqmJ0UHdkgb0fVcZw510CdkqGNKVmISwrxrGLGectDnDFYh+OkfZMZS+fdKXpWT+PjgauKLXKUeWczZfHuWzYvJLqN2m0JuUTFZzLUzaiv2oQ66YtIJZP6N0KYNbXK7SSxxA0KTuRwg/i6Msmak/Xc/HsK66fuIo7g2lRV+NJRTaX+MqtkR5cib1Dyc5LLKbd/6/hKeltF0gHso58J+9TLjG7wnhKOYMnTKMqKB5zm+7MN+zMcOxoUtFj1vfluM514MfzTF4suMjbI1dplVZErqSVlB8JfJOuZvLrqaifWYS6vCYO9nPxGO5Jz6E2aPo4M4S7bDu9gXn9RvxVNQT+oishEAgMgMvADmAZMBgoAXREIlGLQCBwAzaLRCIfgUDw4vf2R4FAIAEUApqi/+ZCNta2onu3H9HS0oKMmMSfEGz/keZMJCbgD/iWdljxln9kLP5DZeM/LxVtElK0NDYjJRQhKyFFSlw+qitfkt4oxviqjWggybuIZNKG3sfQVg19aTWKffQwljdg5uI9OEipo1LzgWET1pGs14jSk1RS5RugtorVyccooJaovrfo9mYMAFWxlSh3USGQkXTR7kl00TPEUaDriDFYaWpx4tRGFNBmFVGMwpEZup5UF0ShsXoy2XJNCPM0yD6zia2Esl6wle7acvgUrqAvingrbMR8qg3Tjy4g+eZbNvnPoT9WZJLKU2KY6LMfQUkc4l+LSTeyomudCiFld9BAjgwyuMUPAIYwhn5k84gWjr8I437ARl5/fcvRqCAaf0qwa8ZcPOWNqC+tQIg8g4b1RdGrEyI7PcozfuE61ROQZTt+jIrbgtTPKh6N2MfwQ+t4LpbMzMXTSH4ezaj+vUihlJSvuchKiHPFPoB9BBG86inMdOCtzUJGWzrTqCZEqG7ElAfzOLkpl95bjFjCCDwtNbGzckFsqAkaVhpc23mIPsuXkrgngjGvRpMWnIrQXprWYzHUPc9Dw1SHVnn4FBuJIko4+njzIzMVG3lDIiOTKO+kSq9p3SjUqUO3RYakWxG4GDmjt7ErYcufoqZUye3AJ+ypSGIkupzZfpuQbzfoNMmTjqJGzg5+RL/VfljtceDF0jfIyylBYhLPX91hwsg5hD+NobNpJ/Z/HkNfQQA16uLsK93Ot7XfmH9lE8utemJYLqJb7E5unH3N/lVHWWviSVZrPXIVVbj5eBF99RZaTQ3Yvt6FUKmN/T572Vaxkxey28hvKEZSTAp7R1s6XfImWa4NlRRlJg90ZJXtCrYkHscLA4zpSBxh7Il8RKNAjsSlr+gdNfb/uysRCKziH1a6OlApEon+wOnKpb33hN/fOQC/x6t+z/8XEQgEswQCwReBQPClorKC+vr6f4Fh+2c4tv9XEba2YzApSsogrK7jzqRxiO/1wuf1bD44PyHhUi4aMlDfKEBLRZ1bHx+S11pOvFkRK+eMRK4mngGTZ3JCqoDCExH8cjWmsqQVQVkDl50W0w8L3r+5xVL6A6DcRYUBWDMgcB5Xi0KQRZUOdr1QLGvg26nzjJ6xglFvD+FCT+QQEFIQRb2DGzQr4zPKAqOutTShxEvVPaiKGlAaOQh1RhBgtZo1tWvo4G1N9NHXfLz7ig4oUEQZ6qgyEm9E2T9R+FqFlrMPW7O3c7IsGH1FZ27zDXfFFZylHUWqDw3c5DsLN82jTiUXdZEaC6bMQTK3Cp/x5txtSGdh6XnkjXSZELQc0SQrcg0lKXv/neLPNZzpEMAc93Xs4gZVD/JZOGIjQmkt5DqrY2lvwBQ5H0JXnOXIlGvY4IWFowGPtlxg0dDpXFx3Cz1/XbpHl/C0+RYl8lI01cgS/yKZ8Yzh8dGN3PY4QA0FXEsLZ/Dj2Yyc4cv5tfvp4dGHmvJianLKAJCTlKWpMJ+cX8mI1E3wejKdGnEJrI1MUTYxJuVjFKrfK9h9+zyva8q5lfAKRUcl+vReRe7aN6j9bOPCnYesc9mA/Bht0gPL6Ld4CHlhTxnXZwpT1+sgEfGJVwFPuej/nDjVCpbvX8P5jtcpDo/k7cNiPB9OIdVEg/fZkVhO9GDY5zXoyg/A+thATB31yb5SR+Kju9zLfUDgm22cjv2CBBaEPnzOnq0DcYudwthv87Dt1hUFQ1m+NjXhxXEqDMtxc/HjcMUBovteoUC6ER0xaZQV2zAY78yXnRGonEtgy8Bh9JAYy8DEuYzAAl/HwfQYZIOBZhcq6lv5suYRo6Im/C2d+R/D+QKBwBcoFolEMQKBoNcfu/+LqaK/MPaPHSLRGeAMtFsMkpKS7alKMcG/lDELfwcQBQIREgIQCNra02G0tF9ITKy9Qeo/nL+lWYhUmwAkJWhCgJi4AGFbM8LyJjY9ukzOhQo0JreT6ZbbtrDXyQsLepIYXkPfXj5IDzBHsUSFc4cX4NFtKCorenDTzhM7dFlmOoCKC3m4nPTDZm4/jjKFRmrJ4iPWiJFMG2NwZcaS3bznJb74ssLBgIirh8mmmDHTNmCqoEE0l4kGdrIIO89OaPaVI/5dGUkvEnAysUTKUIvl71dz55UhZ3wH8SrkPr6Yo6cmj84QTXYxEkvXQTz+dB8v1KhWkqdfiSHZg+zo+2QUAKuHTeFx8Fd+UsDimtmMoDfXDfegnfORFvfJNNRJofyxjZLkfHQ7dmD9yNHcPRuOla4BW9dcRTu5kiYFWRrMxbGKFWfB+sXEIST20zdchFlo+kthqGlECE+ZOSOYpG/FdPIwRaG+GeMSGdQMpQhcOgkT6Q0UKJYzdd1SAldfoLBBnGch71jGZBQra5CSk8HZWpXErEbKS2NwH+dPuVNCuDYLAAAgAElEQVQNcw4vAtr4tvMzMt7KlDS3ENw7EIG5BqmzPpB08QlKvSyob5TjyftwOmNKm3wdRT/rMXM3oF5Njye3wjjC7fZXVBV4WaWwrPdoUGnDaFUvfBRaUBWD2rci8pp+YD1kIEl7HmK2rg+Hz1VRW16E/tXPFEoVcT5mHQAhKZHkP/zBozN3SZ2YRpVMLtIVAsR0tXkf9g5ZrRY2TjjDBAcPDk9ayoCJg7FOtMVL0Zesmh9MVPehPLyI+BxpQpwPEvT5OWIWirzctBNVWtDBjIVWk8knh2qakJSSxqEN3rTlU1+VRMs6GahPYSJh/KSCV+NWcOPGaly2jqTZsYn05UEolhhyyGs9EhTiyRSCOf8/qfuf8lcsBg9giEAgyAJuAn1otyBUfrsKAAZA/u/tXGiHHvo9rgyU/7dXEAj+bKz5fdyfbsQfiEqNjY2UlZW1j/EnOti/HPPHXIDysmqalRRoagM1sToUJJopjy7mW8BbAoecoSoljpStzznS5RBvRx3CBHmayMDIUJ3P9vX8el1Ovz6eCKiiftswOtjp8pOfLF+5k/qvBezmKlZz+/H5cAwllLKUuzyiimyU2MJwkhY4sghbbrMZbwxRF7Rg5zGKBBNDFOpE6NhL8kxiAydVj6JAEabLnDD0dcRnbl/KgyuQz4rl3PuH7HDcRmlVFWJhyXQTOKKHKfM9h3JcciN9LQdR+imaYahxgXJyqvP57FTL8CcTeDL8HQCRwe85wR12KC/nguJe7hHK0pzzPCaL0fsX8D4sheVLDhHQdJzaoDfU4M7Gmbt48CCctacWEtGWzoPBl7jYaSWXjp7i5KkbHPRYyhRXNyTqipl4YzxfnoYQmRRH92GduLtkPVKvC7GUtEB3cVfExunSab4Pkn4GpH/M5WpwJAk15YjLyfI16AGGluaYLxvGodjXVEuZsaj0JNmu9kybvxSjXHXGM4+8sGzqh4NGkwJnPVazr/k2j5N3UqZbg7S8BsWbXqGfLcNAWSHDveah81rAkKwAmr00Ud5kyQ0SeWC3h/XMZDTjSSIP43QhvR8spZv3KvRT9Hm16Al+C+cx9NkMwh3XY9G7J2q5VQSa9aFw7U3k8ttIL8lhq95CfqRkcmXvC/SGamPyqxphUQ69Fvdh4+apFMv/4sHBK0ywnsX5r7upaRZSZ2yE/dXBJJOIm+Mgxlj2pbmsAFF1C6HxH5BKlSOLXHx39qLRWJfOvXzYNe0gW2cuJXbHCa502MiWZ3uQ6+1DMr+Q1RiKX/0ulG28SKeChYqz6DrDBvOUAD6mZTKw/2TK2lq5RzZrgw6yjzd/a1GAv7AwiESiAJFIZCASiUwAf+CtSCQaD4TC7/ApTAYe/t5+9Ps3v8ff/nfxBWg3Mf4saPqt4AJBO4vxPzMityu9CIGYGALBv976H8cKhUJaWlowlFcHoQjFVkmyv9dR+aqQjIPhbEray0Gu4zC+D5U2skipiiGdmUYnrJjHU0q6qOE+qjveS93wkvdgTfQzfL1VGcAkABSkq6jI+4kptjwPeAQxmXTBAiWs8MWMWiqZ9OwEVl+qqSOX7zzF0NUWXR1Ten+YA2rizPD2JYhlCOXFuFRxgc4K1pTGVFJ7tYj5dMSdBDbxmhCS0OlnydzCAPbWXqJEVIQr9ixx2kyqMIlJafs5zVNKBaoMRBr3Lr3Y+SqUKpoouP+e9czGackorDBjSCcHUmvaIeiLScNFsTtlCmIs9ZvGfc6wmzkk0EwIQYTyjNnnp6DnqcZhHmNor8g5njPt/CayHCRwnOHMIrmhrPYZxIctV9F11UasqYWU2FoGG3vx8uhdFgkD0XYyQ6pFljcb7vEi4BJrQ9ayfO5abBR0ubVoEyMmzaVUU5oXZy8wadYInkc+p5Ia9h7chC3idKkz5TrHODVkDcqN8mwbtRkx6uiAMeWAioY0NFRSbqhIm4Y0EkYqNKeVYJw1AdBBwUaXjtb9mKWuR3NWGW42FpRQhiMO2BlYAooU1oagN0odS0NtQoMus7inN93senD51nVubbqKdGcXZBVNiblzA381JSbcGcu3ywkErlrN5aGhXEx8xbmDs/BHjwevEpH52YJmkzTzhncnfPoT9HU02PprPU+6BbEEd+zedWdg2lL2coqjHEFIAT7V6/iABGffFzF66xSaWyQZcWEo92/lURRbzueMZAJ3XSBSmEo5jSyL2sw8xiBSk6Jw7RN2393IL/kGjpqPQcrYlq7I0O/QPHLJpN6wllluh3izMfl/UvV/kf+XAqfVwDKBQJBOewzhjyXpPKD+e/8yYM3/dKI/W6X/A6pwXV0dvxFakZOXR1NL6z/7JL+lsqaJqpJangQ9Jisxk8kTZtHUJqIlvRKb7/KU7A/HztiBtSNuAlAkVY1AR4U5bxdj2cOZNCM7ZrIC+f6daGsTMZyerNk3EwkXKUajwcQew9mCP2oO+mTlpxBy4jblP77z88pNmqlHmxoe8pP1OHB9/WFMor7ThDKXyaRNW4XXr6IA2D1xBeMV/FCU0OdR1TvaaKOqtgmLUebcnbiRIfQjHmX0cGAC/fEZ6AHAMNlxZFLALUIJ+LIPf1dvUkjCFU9URUKMFE0Rn+tCYusjACQoQMPaHPHqakZiz6+MPIJI4OWC1/gxGCUdNepLG3m19zj3WIEEuXTq3IX+dOWA6T5+7nrBZQIIlJjP0Ok9SPxaTn5zKUatssTkJCPbCEPwoJ+RE0q9O6BfL8+0VfO4/iuYD/n5WCFLi7IcRRJSTAiax8Qxswl/H8nxgv3M8ZjI8/cXaYz/iYagDbuLSzH2d+fTbzCRgx7d2TZ6B4fyvwHwozoGzVxV8gvSSCGRKVp9uNT5MFI9jZHWlMFTToeGhmoEXXQwRQeQJ/3dPS6fiKaNn7hZd6JSXhlFay0OzJjB/o5uiCkV8Mb1Fh5Y8co/BJNF9jTLN7KUZUgu9qJjhQRjAtaRkyaGy70JuFzdRcbNQYjaDBmxawinOg4n4+Fe+lBOID9x6DON8tIUqoM/o10k4tfHYjSKWnmy/whvDU7yLuo4498e5kdqPo+epgJwzHsvp8IvE3/tIfN7u1Fy4jwPAq+j3CIi2P0a/X3VWXZ3Dw7qfTh89wwd/Iy59iWY+MRYOlCNrX9vUmZaE50ax4muszicdpTTh+dx5flDRva3R4d45OQh8eMeDmwd9D+p4b/I32qiEolEYdBOPS0SiTIAl/9iTiMw6m/dBf/gL/gDAv5P6PK2P/uwEbYJkZBq77L854IdAF1JqFNVYvjs2ZgbKlK+5TNBrqtprBHgtWgmCgNtyarNpr+mDfutplA9ypxPk6/RGw2uhL/gGPfIj07haMRX1owdx0jsaVExAVqZihdh4ZdQ6OnGj/e5BOReImDeJaJnhHOeQ2y7/42K4XcYiDv6veaikFaMJGAqaEXfYhCSNgYkvXpGPwyofJvPvNo9QBu6uHDLaBKp2R/ZwlSmcJoJjCKYB9zEgzASaFFp5GrvY9CoRszHIl7ymcNiO3n76SsnmY8xCrTqqFCtIKRDqzigTx9kqEeJvclXmZTsiBSVvCvOY/WkDRSFf2SgUg+2/HiCTp+HzLAbgcl8J5RT9VEz78neIAG5EWnUBRTQLdAHkVtnnvysZoyjGpPxQBm4zAees4Shr44jMhZQmtzGnGF9GcdQ7K4OwW/iSKCB+z3X4n5yNScYgvRsAzSA6D5nyUwto625M9LHB1Gh24hUaRH1bwo41GkWD1t6snD6PHqsGUQM8Tw4H03i9GvMOXyI2aiiefUFUyd2YVRQLZGjdiBTr4axvy+Zn5/QW6kbVysukJYUT8WNZAY2l+PYZwfub5e1PyTt1Ax44oZpijGPWMK5yWtxGaHCw9nP0Mlqo16/hYbnkZi5eXLlwiXi6j+zwmE+2l1lCBx7CMUqKwCsF/Rn/YKbWGFG5srztEq3sGvRdQa72vHuTSLeXfoxOOQY47Dgcu1nVlY+5rTpao5WnOGZ/Vaqf9byKbWCw7vf0PT0KSsXzCUuLpV+J+by4dALhJ8zePj1G9HEc3ixKb79nKHNmvAjIXibd0F2wRwinkYRNf800jjiYDeUlvcmeDboI9/fHDkc+RD9mKAuR+jtuAAnPS2ehsz6y/r4b1ES/Yf78AeHwR9ZiX92L8TExBD/zT34x6e1tZXy8nLExMTIkxNHTloR/XxxwtW3En7+AT42viwniOzoz6jJS+I4vT+JbTn4dPdnWQcLVr3fQigiYolDGik+qTTh+V6atYziLt94M+4I45jKHUEt46LuseHdajzHDQbg6eznSHzMpxQVMn/lUEo5T4nEeqg39VU1tCGi1kOdI+uX0Bz5A42GenrTkabmQs6zkNMswJZSnmV/RRtLNnGRlThxjTvU0kI0uaTynUZlcUrS85n4cRzPuMdOlW242NiznjOYoUgucXiOG8nV9Bco6egAhRhjwV5ukE8hXSRMSaEMx15+dDXrQP23dNKq4zBHnnUz5mI1vw+yCjbkXRRSEXAN6YgMpnOCSB05ap2UibsQxphxlgxkNJf5wKoHD+nDCH7JiFOgKKBSWE/6qzQ8mclGjuHq5glU4083PBs1SVx2gyO0WzGlgJ6RAWZyaqi1ytKo2YpVcTON4jLEBYUhNGlk5enZFDUVEEM8t/c+oYe5Ohs4wrTxo1DHgAhhKPeuhWFSWkBdahKJZuro7euEmbQaNmcGUm8jhniLBGn3k6j/rIT7ID8yvmUQlRrD9P5LAVjedRFbAhdyQm8LLXXGxB4pJ/bzJ/JLClFSV6XpXTo3Th7EsrKVQPvxLFo3lnoZETO7jycwYzEVa8OICH7G5R0nKOA9BnpG3Ir8REO5BA9D3iIuLk5W/kf6ONpipC5k3ZctGIpEXK8IYpjDNs7lvyPqaTTeAw1JfBqGFfr8ehODhYo2McP3Un47BLH8EkatmUluVCJyozUp/qGNtrs8VeG5zNnoT2pSHopvitjHe7JoYV7CCrSmS7Fl7VR8GU/JqydcdtmHnkcrC7v3Rbni7zVS/1ssDH+8+f+rUMQ/A30IxAT/yd3Q1NREXFwcpatlBBoPZo9rd5RVVRDWttAvajEAko56iDnp8/n6G8IO7iP6VgiPX9dzlanswI2v/CQi4RN1Z1o49WAD4dzheoezlJPJDS5yJi6Yed36M5g+WLq2U4xribVxIukG90ihq4sNz1xv8CGwkO87L9BFXxWVTrqIe3Sk5MwLGjJL2cgR/Meu41ZGNVH8IpGvjKAHTkrWfKfd/9u64yE3pl7m5918BCiyxG8PpWKSpOQkMhB/tjMTv8keFCrUIY0UklKyxFCE2kELdi++y7xJAYymN83kIY8OUIwC8hgZjeLEu2DebdpGCClc4imPeIKatRt7Fh7AZoIDkytXkVidyzclGUzpwZzC7dzz2oMw6DsgwWee8eljNmWmmRx4ewcZPWWqp9wkbc4Trh1fQh6JKGNLy/P2PsEh5oMQocCMiiNYM4jI+CIA9IzU+J6eTFx5CdplQopkZZHLFUNOVgGleb34UpmJoFzITvOl9Ojak4U9FzIWf/I2X8GRo/jZuvMiKITTYzYwl0dsDZvG9knHMJ7kgyoWLD62DbPOJtTm15Nbm8ShB0dpFYmQLhdnbgdfFNGhcVATHZYMRmqYB4Yvk8n6lc4A17E06Rih0iROfUUz7hY90dRWIa4yAh01E77fSSL762sA0j7k0pYhz+fqeA5veMzlL/dx93VhzMhuDBvTHSdHS+T9PZH/mk6LsirJLxJoDhdyvONSWtuqGWbti9LlJE5JbeYKlzDx6E5xeRUfX4dS7GOE27oZtA4wRmeEFbm5jcgFK7NpYl/KhsdgbKqHn8tkfM29EJrVc3PgTerJYwDOfLweRcdN0wjhOqe8D2FGNZYavsw9PIfSEpW/pZP/Fr0S/4zg9Eeh0h+QYH9YCyKRiFYxkBaJAS3US0GDsAmNFhXWevhzcf9VXm88g52yEXGpCVi6O2EbOYX82ELKKgsRC8ngy6sMSkqKSS6oZpKOEZIV9QQ3Pcc/9g7q6RKYjLJmLB4E8ZxtzKQECdIVi2itSWeqwQD8c/fgQjeSyKCOYl6Mv07k9Yd0RBPjpVM5eWgvlkihIKmJg1CEUe8enAtdiwYOOM8bwP2YDAI/bWECDti5Tadrah1e5f8IwTwYth+/4BUAhJpuJUOjiSufQ3lHCpMYwvSBXnRzdkF6iyUWKNAZNWZYb6R/8gxOOp6mRrmNc6FzGc9QFDFF1UYbZ9fu2F7wZKfSGkZo6FLY0kqFqTJF3zOYXbyDlGfpiDRrqE4ow3uqF1mfSsgU1GKS2UCCkZD8/d+QCq1nZPlsIhITacmp4vzMRxjklrHMcyC5EW+RGtiVdU/PIIUicRRwqP8M0Jbk9uVgAtZsQmysA2I1hWzw7MUgejOBK2xZcxZfOV1+lBbQoUETq2F2ZNgIUCtpouJTOQ9vx7MpfB7PJjxGw8QI5+2deau3FvuHc5EvqebttCQGFY6n4lU8v2SF5G2LZ9CLSSzS3IJKQz2TpngjrmtAroMeu4fP5XnTHaCZvOhcqhTysbdexjEnX1K+xNDX3JDY9BIc9axIqS1CXk6cnm7ulIg3IWgrwOP+bm7YLsbarANVBk20CSWQEsnQll2JtBp8EtZTGy1Odxs1BGLNFJXUkixspFgsj8P7FyLobYEbGmz8EcV6C38UUaGNOGabLyEmPYmDBNEc2kzX3urcCniHSmYOoc/DGV95gB0me/me9Q4fVFFVcyej/DsNFLCauxyX28KF+kt4ajoz6cFitOp0GeXjiAAlBspM5X7jFmazmcEH/Ph8NolhKeP+d7Vd/yF/pBv/yEoAf2YZAMRpoUWiDqFEOyuVTb0aBUc/cer2WTJS8pAz0uFL+lcMbU2QGuZE5r6fSMU2E3P2JaU5FfRcNJr3BbfpK1VNXmEpr5oyGbtjLQqNYjwetZ7trCaI50xmMOFk032IM5tdPZnNCEauXcDXKTfZtvUy0EbQxnd0ndYdGbRRnjEIieg4/NCkEUmWCg9R3c+N2aF72UUKAlJRd+9M4KctAFwjjvCPYTSXVyGJKX2kR/D0cwIt7TEpXLBCQVkNnQo5pkm5cVFnA1e4hHKlgGyxdGLDfuBIJzZ1PsTq5FMAtGo1MNTVHlcGIk1XnvMJ9/nTuRf9BXX0ka4uIjUjHonmVuRLGzEXKCCLET+kqpAWV8RFxpxL+FMblcsklwGoj7HG8nYF4+5NZWn5C2bhj5RAjc4K+ljnlnCIc6yP2IbakTloHR7MxohzrAvZxYUnJ9Ha3AMrzy7YU8m63bsRVZRj7mnGBqcAiiSaeT3xHK3hCSzeuJXFR2aSkFtGi1oLzdXVlEnC+2fRqEiIc8DuJI+vHSc57CmL8KHuylgaW1uwG2hDfqMQEFIrJkmlgRCb/lpcM7jFCEt9/HrpMOvYSuatW073QUrM0ulLK8283xJMqXYLGiWm/DxyHX3DTrzhF7GVzQzwHkp4/ht8n65g4u11pGTFomysi5K2AbGTg/Ezd6BCpEhLrRH5CY3cOv8KXQ1zNA0tsEOdflYaNLRWUC7WjM/C6cip1ePlPx6T3l1wpAcXDwbjXCHBavshvHj5kHshyaSUJDCxZw8A1h/ZzZPId3TeNQiLm0PpfnQM04efJC8/l7GmPUjiF77lASziKAqGzgC46pvx4MIz5oSup0ZRDBUlSQbTiQCGsKFxCxY4kEsZL08+ZljKuL+li/8WCE7wj9qF1n9CcIZ/WA6/Z4FICnmhPCqN0vzc+RBtfVV+GKtQGPaCltoastuqcBjtwP7AK4xvNEVgLcnQxRMoMFVmh5MNo+lGq4QEk5oP8mVtIsEx9+j8ogoZNMghkjH0wB87vko1ICMpTe7rOEZwhXvfNBm4eAr9uyhyzX4HsnJCPvadwqQ++9h4bjGBa4MY8mEWR53P8qLaDFFWKb3RQYFuONAZwy5WnBt5h5S7B5j1OozxXh24TSJCMjm+6ypNamI4fF/BagagKzCiKruYLj5diUy/x/vCBMYwmMKYBJwOLCVw1CIkaWNw/AqyyOQky3GxNCczPgmhljNrits5B8Rlm7mY+Jgr3QNRT00hrTiV+sJ8NCX0KCv6xTK36Qzu2xWAo6yjESnkQ2LZ676ZC5Hb+BIdxeURx5C994F4wCJWioAJE/FGi2A6UNarNxl+CujmVNMmaqFJvRlFZTkkReo8336A0R3nUJhyF7tehlzufoJqT2O6VFojHVXBlnlb2RqphiGKrHq2m+7bglFpladRqMzCJ0s52HEZPn7eaPpvJ6g1k5W7ZiOfkMWTxJc8HnKbTo9GkxbyEcO+FiTHVpJblsOP3CiUOuhT1VTPLu/tJL26ghfT0NYXwi949OIHc8f4ot5TAjs6kcB3QIdDPnvpd30aqyyGkdeQxL2NoSwfPpHn32PR/VVO1odoqrz16fvqHm1E4c90bnIe6etiGLh1pvFTCi4uRiAmSWVxGQc3bMF9qCZ9V/YEGvl++wLnEp5TPfsKU5evQLefPPt6nWKEWg+ktXXIPJBNfl4+B1ZcRUgBWe/qKFURkTCxF8fH3sa4jxZdzaewpl6ZZO1Q3J0GsABLJLrJUefYiqhFBqXoGj5Fv8PZYBAzcvcD4IwtM4L2cHPsQbowhNjfsZ6/pI//Nq7ErYf/iVAEQEziH70SQsRRrhbn1KZduHTrRkN0NS5duvJg20Z0tPshPcqJkcd6s9h9D5N0OiOzTR+jX1p8flzBp8wnWD4vwV1Rn6CaN2TTgIRuR5QLYhk19AAdHzpzmEXkUMx0v4lkmQsYsG8g3ziIPcsoiWxF012c20b7cRnYmx/Xw/CuWf7n/cawgXcKDXRRtaDsghcjvdtZilbRizoS8d54GsfwOioyMuicvZkA3NA08qNIO43lZ06QXd3I1J7dCL50mzoVbeLM8xhTYcKS7r2YN3E1O6/uQ1rNgrrycLRRpAp5rhKLHsPJ5z5PBtzjyrNAHOiMvHw1xeOtCDlzkdnqIxGrEmLQIqAACTpKVJPVooDG4I5kdsjD4pIkz2qzsLZVZfTuORSJi6N88C1vn8cjThLuYUeIkMmgQ64q0i+zUVVXI05PiuaFx9Hp5kVR1B0qUEJaXpdeayfQWizGmsMLGH5pC5Gv47FtlaWLvxly5saUx+Xza/xjTgpaKROdY+3zWHL6L0Fn42z6uzqQUSqP12Qdgu2PEl5TwpDVC6l4+QoDc30iv4RRmBCHrowGP3Lk2M1hkkNS+RlfidzdKH6lpCCxpidmMUJUfraQN1wXm45WJBy8RLOWB8uebWHsiBFsu7eMZ0NDqX70EoO1vcgqS8d5ki/i+7+TP1kK60ZHDi0+SWFhKhPdvShKDMcoZB7vAwLpot2LmtI8tDT1EFaIaBBKM+D9eKIGPiSjsJIzX2+ycOhQ9F11aVIxJ37pLqbtm0fkEClMV+ejs8yZab1n0EN1JGV5IXQzsULWxZK2sibi33wlk0LGp+zFpeMIIJH19GPhzT0kmjWi36hGoN9BQktDGIAZi59eQmTcimJdM5ouNvwcGcnpuwHs5R0lH4rQ9Giv6vXClv2+K9kQcoHHvPvfhfn4V0VcJKJFuo2ZW1fQJgblpmWELDyNlsACTZtSGp3aU51tka+QCx+HcomAeQO7oYwGYrQhbzKLDlkLWEgvBm9ewvXN51DDicCHVwEQqCgxyKILcd9j8fEcykOWYM8yYoJ/cL/3egKYiLK2Ji9PneNzezsIaxlBHhlkUENHW0tKEkpxTW9mDI6444wcBVQqzcJEQpu0gjjSDKXxytZDH01UOkvgv2cll5Ztx83ai4Oam0h7EkX3SaMoF5Pl1Pyz3OUbYVcP852vyJTnsQ0njNBnLhf5si+FMRvHItNgReyz20xQ7EdizS/klTTpZOxCvVQ9Tr36cf7eMe4Qy2vS8W6Zjv9Cbxx7WxM9/BS1hNKAJgHx6YgP+I5J1/6Yx8zBEnBQHEFIL0emXwpBx1SThLltVCnLYlSmTfkgX2qffOYyNeQRz237k7zefhqRlRZ77p+k1kyeTk4WiNXW0qSqinSRHFJpdej17Mm7dyM4M/8iorFXMcCIjK0naPv8ALdBSoQ6BVOX94PDBduQO6+BZXMVFb9KKY35hUuHfmRmZLObXQDIammQ8/gmrtcmoPmzD4LQYpJqItAb14t+63pywGc3PjadsL3Sj/N+Fxg7fjjb7i2jLPQDVeJ5mNS04qrXjTfHQmhMzEUuwYxp64fzg3YglgDLaUi79kdLoIDf3g3kBf+gRxdLCmuksNnbrl935DZx/elpjvKUh4FPsfK1R1pCgTUmfTi0cQ8/fCyw3/OSY1/DmP1chvMBB1i3IZA1CwLI/hpLc3wVYgW1VBDHwq0bkXpTBSTyXv8S1XmpfD/8EQtHcy5fOc8AZVUMDQazYPNU6vUbKGttJCVfgVP6awj7cp49H66ysU6F0R69GIQxK8ftpl5NiRnHtrJo02oeb3n3l3Xt38NisLUX3b3dbub8UQb9J1eE2D8wGASI0YIYLe0kR2iL1DhnOYX+o8fRskUbp2oHprtOY9Wz5ciJi/G433rmf3mGuJMYvRAQ9jv/ESq5DcPkZby1GMIs2iGvTmkew95JBVUZGSKT0jHVkqdPxMJ/uU9fXNm+/hLB2wOYv3Q7gw65EU0NJhiT9CieSws2YK3gSGZyKIo0kUQefelMd45zoGsA+2IOUUgr2zFH2nwiEzfPY/fx7XRqsWD25y340Zv5u6ZirWFKgYWIlD77qWurRG6YK4uCj3PZYSvRcWvoLz+TXXUvCeE93+KqONJjBqbV5Vi4rmb0Jx/eDL/GqbQ4ShJz8UKCzpIOlApzWMtpimkg62k11gPVqKGFm5dC8NG34v24Mwwp2cdR5hBFKssiz1OaXMnmGcOImBSM+BVHEiPy0Eor5sGT52Tde8QxMgk5fJtei6dyREvQUqkAACAASURBVGMKva0MyVMpwmT/KGiqo168HoU2Sb7/UGDISEvs6ct3viMkn2HzdtH7xHV0/BbiPMiGn0IFvOc6MJ7hrJ+ykCo5WdTcDFGvhpXzxzJ28Aa8H/uyXeswp4tfc//pegyyJQkOfYp3XwduLD+Gt2V/OoxzIFNThPektQSNn0FrTiG7w7/TR1kaRVNxTLW8kfvyBS0fPZqaNIl5Eo7AUgXX4X1R76RB1owwFDz0qfnyk+7XZpAwfzsVpyYis/Aq9ic38O1BCGnX06ktr0HXUg23LT1Ib65Gx8YRyQYBh1fv5ETkIWIXnKestxXGSY0Um6ugXyjkx+Vn1CmoIW6mRkOZkOy3yWg3SHCH56ybv4kOy9247XkA00pV7tSn4mfriHpOC+JV9UjpqGO70pX63gZki+chLiuGQbEix7yOI9dDl6Hr59EkV8Y6pw2sxJaFHOXlozBOTtiD34vptMkpYNvZ5n9n8BHaUZf+IIP5z9L2O/DYgkpTCzqWimjZd0VxniufXN/w7fYLZvwM4GvgC3Sabcl460/h43aGoTBEvBwRRNS0K+h+G8ksi07M4g176MYOz/PY2CtT8OwODx7cZHraGpIjUv68auzAQILcTmCJLdF7LuGBMccOrad/x5WMQp/JGPCkOAnXvnZsTt5GDQrk0koe8IhoACbunkUhjXx3vsbgfadwC5pMaXMxgR/3c73iJS/nH+QB17Hr3pHLM6ehOvkLeW3qGOt50E3elpeHLpCd8wFDBnJniCEnXz3k24s0ZBrL+FXdTAHyjP7Uj8Ke15DprseMCRNJ5A2acoYMFi6ninQuOm+j4Gw2iwb2JO9TJl9fxaCwIY0T3iOxWjEKX9N5eJ6ehg4SXBi1kqIfhUzFCfErju3/zYuPxIZ8ptO9FnbxkZTEb2j1lMIfZTQqGnj1IZTMJ5GUtInRWiWi6lsryzr7MW6kJQCrxF24SjeK7pbT/8Qvul+7Sq9ZXanrpkfw3EA+2Rylp70xJXmvaa1KJeTwY9Tn6zN9xkwKZRvoIT+GoccHsmH7Uj6sCiP06m3sJAwxn+XNjZoSwgre8e5mMo9mPKH4znZkc+owalXACU0c+vRi4uE1hLzchvP5uZwJDqGpQY7CplI8h3WjoU2G5JOPsN8/gvzKHOxcbPkU8oKYqKUIV1xD7/52cg7cRvZDDl/LY7BY7Y7ZMS+EusbIZOlh66RFns85pk2byuf4b9RPG0DiiPXIjHPFRlyKlqMxeK6ag9Pu4dgv6Yf451/4depGFMls2XiKhiGO3J0eRDcFM2rqTzDOy58+/cxwWOGK2Consvo0UtNdk6qWCrTO5xPvF85cz0GMvDsHC4tOdOihwDgnK2TJJXdHBavxo/5aK71ereDJ1jpsO/f7W3r4b7UwtBOVCmhra0VCSoCUjBChSJJmMQEtYvW0tjbQ2cwM5TyZds5JQFrbkNiVIfQdY4TMbC86VOsweJ0/P4x+sLDWnhVbAqi/U0zczve8vHeOkxcecrbTRkS0L5zryEVZt5WG0mI0LYZgJbDlqe1pCmhv62372Manz8kIC+vx6N2VJqGIOZxj+qNzHEzZyMmIZNZffoidgjx6Uz35STWHOEcK0tgC71Di9Y1SdniPRXi5kpWfF2MwyBQdBXFkLGT4+TGXHUFbaZWSIWX9R+Z4TsTJqBuOv47wlS/k5Kcy4/p4siLSaSr7QMkAJwKDpmDorYaTjydZ77L4TARJlNCX6aQ4llGjrMjTNSe53HcX5vUCzs09iQhTJDOK2TxzPPsiH1OaUsc4byfUcvKYarWSh7tvs2bdVNocpegmZ09E3mtaD0cgoaxG9udyEr9kIRjTEdlljki8a28vP+m1gZ0Oi3CkIwrWumznMs2oUjvuJi0HvxEyfiMHnJazleUoI43hRBNyaebDyKVU00izNqTryJA+6S6+KgZ8TEnErNUYw6H9qM1tYtmXOcQ+SMHv3Go0lCXIrvtG1MHrlJ+5h5uPLpa+bnhe929/DmjAtvNQ5LvXMfzSYCI2PSanoJh9H75yaMkw2r7/pC0omkGDlqNkoojzgB7UVGcwxH8QotoaXmw7Sfq7BBbOGUBzQQ7lciVoLnNl0nMZBsyfQ8Txk1QZyxEXW8OoGbPoMrcHkhpifNn9guKtj7lrdwbnxzOp92zCOteEL8tPMDnqJhlPP3BkzGpUhnemyKKWVlUhCrFlJBR/AQkJ0jEhtTIHXcMm4sMekpP2Ca+ZNxEpZvI2uYjspnqqYjPpNLcfWclFfLkcjnSrOiuTFtOn3xJaVGTwPT4YQ+DRxnimarnT32MFQ1fPw+C2JcZieux5tosdLPhbuvhvtTCIRCIEImkE4m1oaakiKdCjTbKZDo3KdIzX5sSEQGp+tmHUWx/dRmXih71GpvAXbfYiNFeN48ywGaigSMLpH4hnSSCZoI2MegdeXn/C1zOvcTYdinFHX8YNXsBkVDnrvJXkC4+JuHOBjkoGvPgRygjRZi7mf+MKn6h8UMTggX2YsW4HZg7mJIRGo0AF52de5P76bdSigGyrBKvW7yQyKJxf6yJYzii2dz1KM+FkUsz2CydJXbMBd7/VvFFIZ3XcIwpbiikozsCp+2TM3AyQHx3BgEPDKL/+mZUjt9BsqMUyrWF4YIAa4jjgxch702jAgrnT2hWh9kENCR+jsRplSX9UccOJDqSjre1JhZkUvdUMaInI4haP8Js7hiyKSSsrZdvds9i6G3Bp8jjm4EIo73kqCMNjrB3TZowk414WJi/nMFfCB/vGPHSrhIT02cBNp9GUrIikh6cTGo+SmYgz/Zb7MD3xGa5bJ7E34QxlKLOYS/SPX4P9wwmIzfXlQV0GyzhEZFI6mgs9GRS9H8sHc+nzdS0Kcjp4dLGlLa8JWWc1TAxkQK6GKwvOcvLdcZJP5dHFryP3n4aj1kWSy/5HmPkxgNfZKaQmFFAUksOzDgv57n6IU+YjaXgWTM9R43g87jQK/ftid9qXG5xBIrAbY1ICePY2Ef8n/sh1UaDDTwNMtdXQmOhA1TJnKslAYGTBHb4i0dkJ2bWdsapV5WrwdaLef0IhX4X7J14wW7SNHyphUNfEgaEX6JJZRJdRloxMmMVOv0nE9DhEq1ormXHRXO+2CfXrNXTaNoz8caooqqhyc9Jx8tNqqNTV4EnmK8YO7MKAAC8SnDexXnwwVu59OXo2EDcTR5oz0lHV1YDjbv+Hurf8ymrx/rUvbm66u7sbBFEMDMTC7u7uQN3itru7ULELExVFEBQVRBBEuqS7u+O88DvOGec8Yzzn7Gc8Z4z9m3/BejGvueZac34+E4nobm4s2IswpoucNhGKgPjgKGwGGFCVVMvOXntoF29FytIE7cGWTLl1iMbMSnR7y1JJCJu2eP4jFv9VheHPmFIMEZFOGhtr0TfWw1Bcl9vbz5H0IJThtg7ktZXzK+U7eYJq1M2l6BqozZBV07loNY35UXeZOXQSA+97YTPOGhE3AQu9F1BVVomnnRPJOfHsS1vIxtcLyaON2Se28D41GkvkSDMUMvP8ciqjGznx8CAdNLNt4kzOzT6F74blhLanokYZLitGoaaniYuKOYZYITNIhtMFp3BRMiX+SyYbuYxyWymmKHDiZTjDF5njmK+J1l4L9GxkEFEq5+DQ66ipK1Id9uf/RmBHAwD3c57Q76k7/WdPYNlUD2qp5Crf2eq5kaV40kw1tzb+cfttE8sj8fJXMrfGMoIxHOI89piw8a/b6CvoUFqdz/i2/UjYuyJZI8ARc/r8vZgOVS2esR1l7MhAmoUBzxh85wB6I0ZhjBZTj0xAFWkGbl3EYx7T6/zfRDV8ozeuKKfX8lLGh7IxdvwV7I+xuyG6pbUM2TWSbYqbgTpCFvvxISyGkJRfjFYxIi31Az+jS5AXNqLSKUGHjAgyFmo0yHcR/vgjt+fcwmqSM8quDtiZ2dLQ2IqYlAEv+YWotQzP9gQzcvQAZCp6MDLQ45TDPqb3GsD04aPpyC5Df+QIfveSJ99QjSrauOm2ASVsEBugg4xuJy0pf0xed0vsYchoFw7rH+S25AFe/rpOj7UyiqIyyL4rYLbOKCbunsdxjf3ojFZHrFONivAs0pMLUZKQQ0a8HV1dFWKm3cdt0AR+vv5JTUE0sekJZMsLuKiynE3vX9C9bxfj3NxYoTKWPrMHI3fECaexHnT1dJMem4CroSXRR69x8u5xln3Zg/qNwVzWn0NZkyy6nd4Ui1QzafI8QqN+4r5gHKmyTZicqaQttIgJBqOIiI6m13U3FrECf75RcuolpX4RONoP5MvfvlR7GrHCaBrHnxzB2mw0GmhTd68L/eMb/xGL/57CICoCoiIUyTXSLN6OsLGLCU7T+LbtHfPmbKPKXg2BqRpCmhBtkcJIxZSEuDQmT5zDKmdbJiSeRjKvgNNfLpD5OpbExATaJCURaa1EKFNDw2QrFGllCXoMw4PiFUNoaenm43FvJKhFONkEM1c7BgwaiMEIRfbK7yWRMu5eOInHgp1cCHzJaWKot9Kk81Yi3xNzEJINwCrjNSTee8NdjrPR9AAJSVGMwIGhC1ayQXo+rQhpVaikracJs1JbHlRepyioHbuhupxnEgqFicgCvfizU5A2oJHHFx+jJ+/Fe6J4GnIWN/RxxJnEwjBan3agXG3GjTvbMC5VwXD8Hy1bAaIUEI91qSJbuYosMP/uKi6Pv8I1LnOtJhDjwWqkydVwkKdc4iPFza2YumoyY3w/JsqPIIANHB8wkbqH35jy+Cmlso3cIYvxXKBeqh5jeQmE70roMJSiUCBBfGsdpwSbeVd7DwtUcZpng7GGDnJ1qrw/cJitARdBtoauch2kv0oiWtcIjd04lDchoaqEl6E2lRmZfDn/jPzCGtz6DGX5rLWALKriMrg7mDGBgTT+qkaiLR8dqS50jLRx9HYipiSRFtUmjCfOZPqHZdic3oqWXi+e8Bnr3np0i9ojZS3G+aFH0NJvRMHZCpW2Wk63nuQ1H0koSCHzVx7tJxP4WvQDsXcpWDlLotZHGbUWKY7sPMh4a0c6E3JQ6uzC2FoRYS9lLA2dSLmTit8EH+yWbcPDdzJ9VoynSbSTUyv68XjeGd4KqtDtY4Ows4kquRakm+WQL+1AkFPDdT4TalpGbl0twVorWOwwk+r/jOotrc0wtLNhwrdVZL5LInz+PtZd2sHLtw+Rcu5i8PH5VF8q4fK6xcRtu0l+vS4xUYl8DQzDNnU3Vsb1OAVNZqn7OfKJ5sMaX+7MWcUt+v4jHP89U4nnb+ju7sasTkCKajddSook9j+M+7SRvCjIxaRFHEcXE5rHKSLsEUe8TBLR/G7s5zhz0mkbfe6NRLJTkpaSFoy1zJjqMJNAnnPD9jqLk5YA8MjwAim57exjE/Mt1vI0/TwnGIrtZz9Kz71h6tPzSJPHU6NHjM6ZQNGdau57X0KiPJs0anBbPJzVN1bSgzQZ468REpbD/Ia/mY01dw6FkCsn4Nv1z9z+5cNPfhMT1ITRSBnAiNcbnjL1zCKCI6/h3u8PyA/cH1D+OZUN7MeZQTzafYCHe6+w9PpBQpf4sBp/6ujkMCMxxhg5dEigiL+4RIj7Cz59vko7bSw6f5ZHe46z7vpplJWVuLRhDzI/69jBVd5Hh3Df1Y8MctieeZEhZrasYxAHCSc/LBH9oXZcZBQK6KEroolAtICOyI0oiktTFl5D0973xFdH4YQFKm4miEqL0pCWjtrtZbT/SEezupsbxx7ym2r8+YAE0Aa81diLXNAklDqksHU1xRU3ovnGCPpSgyHRPOKpYDlS4y0xNrBCOTaPl9GfENFVRWP4GHTUtcm68BwJaRl0R5nTkFVEfnEhyvr6xCYWMrCkjpfcYeXb79iMtgCUgBrWsB0/btGQVchUU3OGqo5nrJc7MRWZOHeocyjkDttX7eH7918oDdaiNq8aizIFFA01efPkOv3j96JW1c3L5ZeYbD2UL5FByFZK4NV6ArAg4eZN5DSseTJ6C5oqInSqaTBothcdw5RpfRJLnWonMgN7U7L/Gf1XzOBrXgpmXTLUiNWgUypF/K0IBpiO4u8vG5k3by3ikkZ0pWaR+CUI9SkDuf30HLtVFlJZVUjnEAOMzk6ivVkciXPxlPdSI/rsEwQF8Whs2Mib88FYdxWw/rMv2pN2sbCylu/EIZD14tSW4Qibob1Chn5+iym8HI3WSqX/mnsMQqGQGANxCnw/IPe9gklntxEU/ZkpFeJ8EFRQ7emBFPV0dHXTo9RFh1I9OWk5lLTl0qSsRnFoIgalSswY2Rc32xlManL670XBz3YPpUl3qQTibyVxbsEqNjGE+e5beZWfQH5hLs64ckx0Im2duXh7HOX52ceoiQsoJx0TuujtZc+BG0sR7TucVJlmzBuaOMRMfHjI1OpoJvhMBOAg4zFWGIhMyJ+O4sTW7cw8toBWEmlRlWUDwzCgje7Pb9nAPQDuBj7l0fHjtNDC3SU+WFm54ZraRA2VyKHIdC4BQryZjSpW2OuYUIgpWvpaiGRWMGTAIGpqSgl9EcLqn/tRQUgVnShKG5BHBrXIEXongD1MQhkpAERkxXhnvhOpjHKKaAMnHUTLmzBqU+DX4Tf4vTjD9H5rGTdgDv5n3+NSaIiLsgBaVDg0bA9TnVx4+DOQoTZjGSaiyHLJqYhMdsPEQBvfWe4sq5zAWS8fNkj4cKbtEI7osCkphBG2cqxhAOZbpqCSX0tmShZSRpoM6z0fSXNdYtpjkDGQRstDmpLCBqrKKtCQlEHS1poyoSR7S7Zzjw0YMg0JCXGyw36Tfv0jox8sZdJYLypex9HyMoejk05S3FRNsWQr4sbq5KZWMX/5GtLSfqGnoIyUiAYl3+N5VJCGRbI2+kZaNHzIQ6SmFRM5ExKSChAILUGlhXfrA9AzU0ZOUwsDN0US+lyg0UyJYl1JhMNNybnzCCl7M7RcTbnmNI8pH/1ofJ+G0cdUataORv5XGyU/SjA0MiJKNZEFp70xdnegWUKWB33u4tF3MLae4xjiNJzoK88IrIpn6al1tNNG9rM43DW1qE7qQLQgHbmVK6k/c5c7+POzpIbs381YVsZzi2gqcxp5t+McvXd7cVDhMM4jVHg04RKF+f/37OP/r4dAIEDiSx7KUlp0aGjy6egl+icKeJkWASaiaIj3QI+QHhEhTeKdPNx8h2pBEWIi0rR7f0DZv5TnO69hbOLFqpuzKepq5t5mf8In3+Jtkh+KahMYNG87skWN9EUJKyx42RPLg6Wv2Bp1gvvmfWkVacRgxhCGGbuSUR/H9O3zKEYRRfrQ8hNqaaQ6KgUt1d447l2ND3+uR/VZPYwzbntZzygSiWfcvnlcODmfW6NuMnr5IEYZOFH/tZz+jRqkUUY3TuzjLSWvywl8F4i8VDcG4v2wQ4+PPGBs6lps7CbzgwjyKOb9qFhAAiGpnGA00Q8fkUgcofk/SDv3CINyGYqDC6n6WgrAJcM/rv21Hao8IJIYsrmx/zxlRCCCBAuQora0GPMFo/lBDdYWzsiK9iCwtuT12TCOvXiMJipo7+1HhaU4BlYmzCzw4XniNxS3GXHg0krGn5jEkszTaLpqUJYUjnxXC9WHnvB1wXkSsWOI51iMZazwmmnBAIZy91MgZTdD6I0z8xVHUfwzia/tVRjYWVBf3kpjcjnPNpxG/nY+tZ/KEDWwQUnPGmNjYzISkukBmrpbgWoi8aOGfEw9emE8tBff33/ig8p+KollxSB37uy/TEx0InXVVQiFPejKKtC5agipxb+R1VahKD6RioQiSgriGNlrCHpz+1I7yJi8Z1lIfS7id3wOI1NWMDV3NZpDjdDqZUCTaDPVbTVMxwsZIwXyJ9njoWlD1K6riKnqoOdqjV//Y3h7X0HjWS6pZdV0jnRELziBG8dfcvbjNSIjw8FIGu2hQ6nL6SZp/RPkJCVJ6epBbbkdYYf9SCnMYvHpo9RptJO8OgDp44nktjQh/iaH8hFeGN+OIs/KkpqEKgyLWgnY408/JMiOrCV45mW8Huxgnpo3Rhpq2Dv0ZurLeQSH/vpnLP7/yPX/5xABOlra6OropL2PJnZuRiDZiL6aFTnFhbiMGMaoxdNoFIKgUxLNChl2WCxCz0aX2jsZPLjxkbiQL1QYidJvxQjMbEzZ0duHn/kp9Egrk/HsO1NEpjF43ww8h/Xn3A53lhGAqYsdVkuH86rlOimLU5FubmN35wVePn+FzvsoZhj248nlB5wlkCVc4dj+Obwhgok+Ywg9dxOp3bok+aQQ/CmS6UZb2fHtCiqUo2qwAhuVHvYRx9mIR0gJZInLa0F+gDo3e20hiFRGeA4ig2p6DOpRsLNDd6gGBL9mFmd5ByQnxzHvr15MYxotdOPkIcl3+U3ookQRefiRiPvCbTwhHWMUCPsWQN2jNyhnx+OMBtNzjwPwe91TpjEeY6TZOnMVHdiyHD/6ia7h7byXSA3XJo9KOkWbuBUTSIuwg9CnR/jMZya4LUXmegEqAe2IxxdzR3Q+tn1duHXmLZKSUkRffE2g2SrkB3ugfnkl4ic9sQv1wjF4EnvfbGHMxLkcr/Fn5i1vZu2fh1BRjdiTNzhlv4iGQQoU1jdjUCZKZ0sH84OuUNKqzrrWGxyI3c3i01MwlVZFuqmD12+SaB/aG1EVLV69/gyIYyXjgMuzVSTuDcSQDmbdOY2shBQvXz/HQNcWt+XDMJ7igISSHF1yUihJqNB1MIovr5/z7t4jflSHYKeqgb1WPwqb6nEcYYf/lXPYKnQiGOXEiL7WfD8WTJZ/POIyINfUgrq5GQ1f65jvOZZUK2U83ncjWl1Hv2OL0J5hzlm3zczuZY9ob3UML4zg0M07aGmZczPcly3B63C16MeMN7uwduuDn8MSfl77RIupIoP7jyEi5jNNVKN4fDKeLw5S2FHKU4u39CjqkOsuxs/4b7TNMkfsezZO+9chpVjPSftNXHTZxFxbTzw3bEGxnzzxxmIkfUnmTsUx+tvpob3DgYTb73AzN/1/Q/D/Ef+KwoAI9EiJ0SYKIgjITv2NVLcErQXVSGqroeCkQ5WwhaauNkR7Otm8aC3v+MTUBXMx0DZgo/ZggsoDGDx2JBKyKmx7tZ3L3GHmdh8cI0qYfO0IoobaKAhlOTzPk1FMIIwdqGyezPe3kTQ+bOf0jWV8LSzlgeF1NDoFVOT/5mVIII1NbQBcYzuStPIxPpmeznzec4t7Pq8xGKeFQF6Uz1zm64Qv2OLEhbztFPtm0B8T3sUG0FnSwW+e0A8rYmhnM15EhzzHjOG0dYFVgRoA87jBabNLJD4vo01MjCsP7uOPPwNQ4of3fnSkhVihixGivOA1SZ8SWYwTDQipQ5ZuJFCmm1F4AaXMZDHjv8zn2ERvDuuuY/SqIZwmlB24ktD1kW2153j/LoJN8/+mpLUKVUSITU5nsP1yxjEVucZO/B/v5nPAI2QRxbRLSEvkdxRL2vBftIXc5x/QQRO7+ZpMXTmYMUPH8eauAD/vBJzGuLBh/k6u7jhDwM8oJswcz4k5x5iMCQajHNCUEKeroJHYX3HElifxk68sCV+Df04Nl0yuIooB4n00EJQ3sTlzLeWGlYj2USenpQNoR3ewLjJGhkzevZZu2kn0Wkx+XStHxu/i2eu3pJbmICEiSZuEJPU5Faj0syayLgd77FFCDD3UuJ/+EaGFPM1tZcQcjGSnwnwyqppIOHSft4XJ6CR0smzaPtT1VQnJzUcyS4kNf89AV1mbjuBIotszKHGRJ629nM7sNkRbRdHzdCfw0XV+LY1lbm8rBIIm5oe/ABV5DLqlODdmK/Frn+A+eQ4Kypp8u3qQ16/O4eVpTF1SGcoDDLGYoEnA1vckNUQw7fV8pAZZMO78EjIf3cSjlzk5cWkscZvFHO/VzL7mTVxzDoEP3xHg+5WJO0fQf+BCdDBG0KlG6NEQNCskeXrv1j9C8l9RGLoB8R4Bct2itGWWodouSXF2IT2SUjg69qKqs5JOsR6kEaFDspodbw+T/qWQk6uOUiorSdS2nWzceZ625nZ+B8TQSCMA6oevY7F5OspLFbAxtubdjivYYoKcQT/yySUz8jvbHm0g8MwjBiq5UynThpyIGPl5aTwkFgk5BWzc7djGaN6zh++UUC9ST0xQJOtZjrOXLsUiWajJmvA1sQCnl6bUI+DRmUDCP4dxefIDNM0kebztjxbDGAWGjprLSQJYwX0yCebYkZe4umlznrkAjDvmhYxFG4JWCXwD/xwqVUccPcSJaq2gVEcRSRRRBwpzApk6agw9Ihq06BmygWu85SMlxDOaqdjzx3n76otTXC/0Jek/6+WtCBmKO49Zi5uDGwdvX0ZRyQxzSugjZ8bDhLdMcRxESXo24xnEeCVnaqmgFTFKgCG2XvQX7UsFoqzkOVsYzLcZoZx1PUfK+dlMECsiycOX0xPGM/LgCLoEnaTtesfApDZaxaVIfvuF8IhE1G316dW/H2bGdkTc+cGz8Hv0zy3hmXMtt5O/klCRRkleF1MYxYARA9G2UaJDNpeBzMRSdzDCPHECNvtyQmU/SlOm09jUw/2AIyTXJyApgNSP3zHSM0b0dzVFlLDu5kZ+U0EpPcg7DWTlQW/eZH9i9vWtjI5Yxuu6jwglJfEcP4VJJTJcf/GeMJ4jN9SZFeu90RqjxJxJWyguSMXlxGJ+vnlLu5EEBs0ikNGGraoxCZE/sZNXRSYlEmtjRQpp49uTWL6e/0Ba5g/yyMNoyEASn71Av7kLJSxYEXkZTk/F91UgObeysGEQf+2ayfDeRmwy2MTUGaOoT67B3MCeoLAX7Lm/ltigCGpDv1Nw6RvW7RLMODMf2976aHVL42NwAG1MeJT0AFN5A54dCSDvf2PU/r/Gv2IqYWPr0OP3IYjCk88xG+JK0+0UennMQnK5ODeFi3BK3I10SxWiKhp0NgnoLG1m08xVvC97Dzao8gAAIABJREFUz0FG8dfnp+S25pC1KQZpdR16pjYiZqZLZ5UIX6YfRFHeiPf1WQTwmlNGe1GU74ZfvxHt44xxlQhXsz4xf+1aWgqryX3xFl0pbWy9XPF/6ksBihiaupGd9Y57BJIYXckd18WUIeQqz/j1JpeVmw4SmXGNFxxlHyGExdzj7raPOGS38zW3irdc5Bdl3GEK2is2cuKKDxrEcZESfCfcxylVgcr0SDYQx/vQu8jbiaCsrgrAWjxxFxmEZE8SMh5u/IoV8Ln2Fk2IEUwUWa8KODFuKVcIInHbc/yPrqM3Cyilkd18pIRf+GnNZVHJXa4ozOdjXTVTxPSRl5FCpLaZRIRkUk0ZJZzZvYXW9ma+HX6MP095SxdCINBwHZZjxhGc9YyJUzfht3gn3qG+rJg1F8uyOiYdP0H4twhWPl9P9ss8NCcYUHi+AN21eizHmhlbDhJ23I+BGKHn6UxuSxG1rirYaTsw1XsOaWSRW95E9KQbHP+6jguhKZTatzDqQj3j965h4endeB96RV7FPaIPRBJ79isrK7aynHmMUtBBz9II37QkSupyaeMjoTTwYsh+TGSdKHn9GplTC7GqVmDHAR9qiGHpogvUVFajYilOn7VTOb/qCI6xbWgPsKAktQ67YQPJ+PwFoboW5bX5nP32kVC+kHfuO831qUQmV6BT2cXgcYOJse6izxsFTp1ejbWGK3kdWSwNvY6kk5DKkCpyLNJp96vD8K04Vxuu0n/+LFQf1qLrqk54QjFmbjqkhabQlFiHhJIA16UmyLg58eb0PWTMtGjMqWXwvBFEXntP808B6u1ZfOhsxFFgzspvBxH0EVCb1cGK0as5uWYb4Vfu01qcx+xru/j8PYwnJ3Lw5R3u2PEZv/9qWolutL+3YN1qQf3dDBLic5FcLs5b84sEdaag3ipLj5Qytd2tdAlaEBeIc/fcTRbgQi4/WeM+lS/DzyE/xIDM3Hf0tRrB4WGjeDd9ET6E8rs+lhV2s1jCYib1d6Gf0IAeZU0GVEkQmPWaU54zabeWJfbFblppRbV/HyKCfqCPHnY6JmRkReIzaD37+1+n+3kiHlhjjSEn5vnS/KuByIxrAORKSmFFO63JpVgMcGFx7kJSiGCI+0ayfNP5KCtNY+ZLji7bQjnGAOx7eY7e6WNoslUinSAOHjhJSXYPC6UPsYZBjMOFsr5CWpGnNiab1NoQvORncnbMYW4Nfsyt4zdxRIAjFkgamXCcYsQs9fAcNwYBRQBoO7rzTOEmdXU/GG/lgf3UsXQLhBjYmxNHNTa9+rLUZTTVVt2Ue+nhnHaSKwVFfP+dw8fISIyfzqKtuh3n1sGELTuMlaE1xfnZLAs4zvLo5xzxOQjxNQR4vkdzggHBVyKJL4vlNBtxxBBRkWbc+wygXrSbsu5GpFW1sLcfQvLB55ghxlUW8GLBfaZ+XUc00GgqgVO5AskFHXiK6GHXbxh5FfcAZVz/7k+d0p+lpavcIc1CEtnBLkjXJfGKV7yngcJr2VQVaRPxOoYY63ZK8nMIO+CLG3YcGH2Flqo8xizuh/V0Ly7rzWSj/izEVLXIK+5hR+IxXl27QUt9KW0F1bgOmU8ob4A6ktPvIz/MhsSXAWhZaFDSXE3z6KsMPj2bv3hFez95dlU/p/DKB3w8fCjSq0GyTZacE6H8aAphxq2dFIs1024gxdlrJ3E21Kc7IoVDiWdZwj7m1uwhWlmO4sRWeos642Y0gOdhX/mY+IuBq2Yw+8pkXDzGk0oes96txW+DN0mv8yj8nMujDF8urNuMbGMzLpG7+ZCTSeXlKlIIQYwavvL0HxH5rygMQoGAG/POMeXCal48ekSHojL5p4sx1hFyePgaiq5G8n7HfSLWXUU5v4fQkw9YN30C3jLD2TLrEg7OnuQhx1v/e0zbvYK5g4fxiirEpx5iNn0pJRPvxDnsmriSuHsfOBd7hX79B3Mr6yQrJ6+nxU0RBQNF3OlPHLF8iY8FEWkMDA1wMjRkiJY5SbFRyPzKJv7IY9KpYz0nWT13AW477NjRy48Q1rCxdTMSFBP+MJwR+8yYzhTu8wxLVVnWLXPmxIsz+IaGM8l3InvmHCQ/MB8L5DmtfQUtA0OW4knLx1iS1z4jtzmIcPKR1ZRB4tsXZJBDVlWRX4iwpN6bz6nRxFamM8i+HwposE1/CjHl6bTSTVluFWkhn9jBCAA0HQfzve4WjgwlIzWNV0FhqKio0VVYyYVjh3BVlkY9uZhi/0JErtZxy3I7VTdS6bqaiPzNUrLWR/DmwSt6fZqOw7FtiHdK47twDzKtnWQoFDJM0xj17BYWhozgIodofZOIUXkPreShhCTicvIIzQ3poA0VS226hTK4L5hAc00t0nQz0seHDW+XYYMCv5QOIVcjxNrGlKiYJNrEJJCUreX0upsE2B8gd3UkwrI6JrOOyUzAtF0Fi6O9cJH9H/boV5fuoim7nqXsZXbIUQzbVLjJXVTGOZLjLsWQJdP49TkXVWclKpBF9KILxjZOjPm6gnWSG5m+eAV1mnIcSLpF4ueLxMx9yetZL+mrM5kueWk2LlyNnIEuAjkV6ttaiSQXAMPZjvi/j+D5VX9WnJtEq4IoSrESmOkZEttbQMKnOCZggnypOAOHzsLk8XCcYzbzeNF5fDSW0u77G6NPTfju2si+j+eY+fcuCqhlwvKJjF03lxrxcsa93877L2HkUUpfvdG8PPsa20VmnFbawObv1zD4NIvsFy8I33qamU3eqGLFIsx5NfbiP2LyX1EYRDoE7K0/TwoZyLjaEVAcQ1xAENIz3WlIz6P3lbEoyRqh+bqIi8NX4/pdlJX0QknekMMPjqBQUceG+aYMvb6dcfO9EBe6cXSkH557WnGdtZr7lNCbNei/cOEItdj1XUfV2184eW5BXluRqkEGRIweTYPQjOkaE7EW6WFhw3bK82poTSihsiSJhsZy2hu7aKGDAehwhhV4e3pQHNLIkikOvDeU4jSarP8awvsvP3nq8pBaYvDAi6bnXxlquAofz774k8dIpa3Y3BuMvpc+LajxV3Eg/QOnYoYki0UGYFkhwnrJvqyhP1Glb9lHEe2zHZHY3J9lGkP5qH+S6KpakmWrMbfXZiY3uZjvR69x9rjQlwWt+4lpacXIuj8ftQ8x/PBo3hHPCB6zB1/kqqupq2mkSEmez/tP0pBWQI68NJKfv/Pj7gGWIk3drSgkXyVS+TSR4IgP5CFNjG8CezcfZlthMoHEIlUpibiEGB67p5FiKcHbicFI6lSS9CYVG9+J9BvpRRpV1Heqsf3uK4w0delub6a7Dpxwo3lsLxJpQv/QH/WlOC1crXnFW8dthF8OR7u7kr/aAzC0scJKzhw5MQG5lcXcqotgmacXzoggLWGEPIrYHpvDybkbKLicRxHiDLnrTFjmF1qKuth5eQV2uNN3sSNB+44Tse8J+YPV6VgexaZDi/Efv5gHD/3xn/uKxK5I5M7aYrhzHSF8w/PrbrzueuPqqEbR3E5+HXmNQFaHii5ZDFZZYlBxloaAWBI230dNR52Ca5H4EENnqxKLLFbye3UIpo9nU30rHfltqeRGFqByqRe6R0z5eTaS1BlXqKpoJb/sN3OXHSWwq5jrfCGAL7RTQWzMW2q70wh9E8TtpYG0Y0Ji8l0kHvegqS2C1udyEk7H4pG8GTH5DiYYD2L1jnuIyNnj73iGl/gx4NQG3rx584+Y/FcsODU2NdEXQ9IE9uyPvkHSjjdkNGYgpdXKy7xcAMIfPebQ21MYffyF4HcjqnpGGJ2dTDtwMz+azybhhI/djgYGrP64GW3xLkQVZTDb0InbiEhc549jfd8znI3awLIyG3K7SnGb3Y/acnmyPPay0NKHDw1FDLLqQ1hCBj699qLdpMzP9CwO8YXTzOIrP5mj7EZKVz0OdYrI0ZsLnvMxQw9javiODSGrd/C2+QFtP/LRoBehJDAea2Rz8+hCE2/G8qAmlP3yR4iuj2QIpnSQzoWdydT5hpBVlo2ytQPWr/ujZXeIN2RRii0P7ifgf9+XXYzjO3lUi9rhmgWfve8DIIcEPxJrkaIIaGGkpT1StorEf4UjjOQmsfy5IthNDhmYdqvRUw/dCNH26ouaghQOx/74D+6WNcEwNxszRKmgGSus6aAbMVkBD3iGCwMZxxhaPmaQ87WMh2eCuc8LstNauMgpzuGDMkLictLoizXSjhLs1TWlw0yRgoo2fnxN4QM3mFWkhylSOLGGe1zgJ+0sVXfhe/kTlBxO0NCiyVrWIjQWx/jnb8p0tdCozGaDwIGjIT6EEUvPlkfUT6nl5Nnz5KcnkXZ3Fvue3eN3VykdR5I5emMjywyXY7KqP6/2XWfs2AU00EPHvUjyPfvw+14ibz/FMVjeFrUfaWw5sA93te0I5eoZzEo+EYA02TR66SOIrqcx5DcNJnD061W6vtQgqibNQDR4ePcdys1a5D/N5ILXBmrEFEiuD0Ju5HzOuO2mit/Y3t9Ni4MUu85/QvXqR+z62mNWI8L79ACuXnyFzGpZZoZswQZdpmNG/3snKJQrQQU1bo8/Rm5DBrdGL0Q8VwQJq0YyP+XhdWkWNW5CxBpraErVIYd6IIajDakQ34gb07iyaSsu/DOX6H9Fx9BaVIcVA6jtfslAhb9Yf9Cb3lN747vwEtVU8VR2FaNPziXGRYaKD/H87quC3riBPNE/xBGZlXzv44vchURyaeMhoSAdS0TiG7oK5OjnOpbCwBygkhFbxgE65OWkIEU79/5+RF17I42UkliWj4qGBjWtDchUN1Ial8SP9CLWcJYQjUtEkMoTgokW7UDa1IIimW6qqEUbZSauXEQpXZy5eJetwX8s63WwRJRaRmOMvZEVH4lixoptbHq5gAI+0FZfgTUCBo90pZZ8lE2kqGjKw8HagRefggkKSOAxMYRTjjh5yNIMSFNEM5aYMtrKgeHyekg3xHOL+SxiNpfm7sKKXoAo/dNWEvbsG3OLV9EulCCbRDwRJ8z4HP1GLSezpZOCigwsDE2IvP+UkpYS0qNTSE/JYOmbjXSZKdOlLI8ckM8X5GknfM59ntrsZR5aOJnq05lSza0z+5mxfRsfb2djbSDOGeFeKpTbiLybQWt6EflOZnT8HUR0YTL5PaKoqRlytOUgIIaN5QBSaeMeF/57LlgsHYsEfVFtEPA2O4KhA5zIyv5MZEsHAwPG01wnQWJ3EglkAhD+LBAJpHiV7svxZZPwjnlCk5gYpoHVhN+4zgKTxdjvmUhedgFqogpI2GriNHwAvRaNoiA4ljYJEQzFDOl2VUQxq5pWByksB0iT/zafT1xmPx6sVVtAp6CL7k/pGMqr8iEtg/PX/ehQbmGbxmHOzPSl1E2I//g9OLgaYnrCi9or7zkvtw3FSkkimt4zt9cyOkxliD32nYdXl6OuoEpH1Ge2pl+jDT3KDIuRRIeHHGcxTiw4tAPR3t3IFQupPF1LZXkWC6ZMoZUqWnMbyErJx3bWUCqG1dEq2U7Pq1qGTPLAkAF4sRRoBJz4RjTjJCYz1mruP2LyXzGVMBTR6MmjnKOuu9kWvZe8C1l8OnaVQV5DyZFrxGb5cMQrOgk/H4Db8mHUJVRza83ffCIf3y03KAiKo7lVhmHrh1Lm0oqwWASjSc687OXLkbiDRPGT4AEReO7rxQ+fz5yNWsGYfnuZETmPrFWBhF7yx2HNAvwunGeErgeTC1czn8VU0oPvpfMUlteze48HIxjPxv/Yit0zO4G2iBhNTU0oGuky6us8op7lYjvZkCdjbmBbKcn7qFe0CTWw1dYjIz+ejfx5u+fsSOTIwdkUYcg89/E4qWtg9nQMh5lLXxUrxBS7kXIw4OjzrdhJLkVugSuyGkXYO7jwctJJbvKOcSzDlDJ0KCUQKe4TzyXl6VgrGlDd3kiXsIOeTnluFCZiRgLetx/wZf562lBgCXd5tCiKGX5/hDXPmYnV9rVUdZWSnVqIzjBT7MxsEI9qoPtnHpmZcRSk/SBGTQLxVjEMrU0x+F5EA5As2oWIuhH3SsKYK+6E++LeyHmoIWnpgIWtOr9O/CL0pC/OF9YwaLIVoZOeU5CeQHlLI4nZgfgceMiJu0fY/eAU9Y21rBk0hvFj9uAWl0SmsQbaOnLozXMkM6eLiDXnMFYQZ9Chbfj4nGe/32rEDTRY474WRykZ1t8+wpIxS3hNANOxRQILxMwUWTx1PMGHnmJiZID58YnY1GtzYN06RnksZFDAKN6I/o3lWHtMX07jZ/EP5DJUyfAOQ7qxkYdpVzkfE8Hv3DxajmbQ68dUynLL+WvxCcrCm7i4ZBzF6xQwCVfGZKU5P3+nI5ksQsg4XyCTIlTZ8fAIAYsOkNwiwk7OEDz5Ig+eveYWQfx6lIisuhZBz97iYW3Nk9XbGP3sMJo6KqSuCiCuupqtuQcIexZEd0w9QacesnHfNpr7KdMhL034pVDc1YyojsgnMCKfIx030UeRfLJofFzK8OnjMaAac4zZy6P/WlqJelrZu34Xc1bsYbL/ZH5/TsdyuCeN7hK0DzdDNrCLcX/N5nb4ZSLXPUAtTxyX/lMZpCZD+HF/AnlFIOmMWTONN0Tw3GgLWgiYFTeFGfRiDmN593Ulj4fWMQQLdtz0xWqhJ5OwIcxNBD0FW+pvN+E4wghye9jNEvZyHYArPs6s37IEeYEn2uLGbGw1IB8ZZD1tSbmUjJGUCAOL5lH9rIFGORkApAsyqFPqi4GoPGXiUpjZOtMqIQuZAUAjRgft2Cy7i0HCCqI+P6MGKRaiTjnxtFbVIV8lpP53FhdcD3I3+inRV35x7M4xmiJrKaIGX8v95KW9w0XBlZC6nxhgTNWeFxx4/gC3hGquEoup0JqGzkTe8xA7FqDVKeS9WB2zOv7sNszw60vWj2peuiymlDZ0jGvIXHqXFLpQ/JTPD+k4FE30kGxopruggziNbsa9OYJOUjf10WU0pAfRVpvNVEsbLJJ9ADD2mEP9Jltk241RfJFP1JYIfnlfZ9LpadSbNpP4oQXfYd7oOpsSklxNIx5Y/e1I8o94NLsVuPLgMTvPhJARFMHXNhGUUopQP7mayp46+larcZBgous8cOwuQtROSLVCDg3vqxBvjmfei0fsHDEXmf8oXtMQx5hXbHmWQj/7kUxFhIFXlqBT2EKYbxDajcpoThsAAaAlJsOHr0kAdLXlknq5juwfIfQ2GI331kcU6FfQtj0L7x+biH8bTWRQJk9Cw2gkj/KFfTCJtmPZylEkfM9FtF2G1KMPeUswHsxgFz6Uv9NCpKWDAG7SElHHp7kvuUUQoXseIapSx4nlIXzP/IJO32aG5M7CKE+L4DUhuDtbor3eBg/hNJQKmyguzWTFYW/kEwV8Of8ct4nD2OC7gPd9TjP4ezCdX+5zb+AVro9ZzPA3K+g1vT+n7JZwNfEWAZT9Iyb/FZ8SOuY6bFm3FR0rEU7uXklXazF5YmWIuJpgfaeCH3FB+Mb5I9OmjKGGJeVdrYhWVKHfIIc/txnHGKSAN/gDRQhyEvjw1xvKQkqYMnU1A7fsw8tsJQsH/4Vm6BHUrZ0BZ56TTOXco0R9qGB48VS0m4yQkxTSz86WqYxmiYIPJjJVPL56Bv/usyxr9SaUUqaM2syTS4tpJBZR9XIAlCfLYTp8GEcn+6KoaYfnp3UUiLRhPtiFzLhohq2cz3jWAWCLGFqNtWjVdjPGYQJWaLCQISxBDRlSqSINZW0V/KIDSOE3g6WcqLj3g/jLgYzHGtMuMax1xiDp4IyT+UK0lWzZuWchvRPCeE0ySnTQ2fkbLf7Y7iujQFZOLvvuXCCFKpYymyUMoKOkjHnLdzN61lbqUsQZ+/I6rohR196BREUpzrIKtItUYLywL1uf3qR/b2OaH0fzNfIdja31SAnUySqoAyAtsJRhW4ei/6CJmhPhrPx7Fj21zdzmGSL2OlTnVbFs2FiEjibk5MbQm0yiuMmvu6k0/epGvLcMh65uxGODCVaK3ZgYSuM83gmhSifWojpsmruUSKq4H3wYtd5GHPHbRXu9PjZCCW74f+LJ2MMUIEYRPdxmPftVl3D83W+an+WxC2f+Gu+NjZIRqVcjqCpJQMZWi578bHzHXqJQrYUVlfvI/5LG+9tCxj72wF5gyuq884R1hNGWXkNs7DcuvntGm5MoV1ecpJEfQA+SHUbc8Y3gFb8Qke4mNzgO1XJxnpHAKnxoT6xgx7D19EOcnLcZrO2/hMrsbGbobsZqxDA6bQy4nHkOVb7TXF6EwbUhKA/SY8aPRdRs0UG8uRz9BiVebIvGSWkgCuXS3L11FC1RcV5HviT31G/Cc5KBt0gPVCLxfhLD36ygKKyctJ9RlBsK8Q8Kw1zG6h8x+a/oGKpqm5A2UeSq5xUUFhui09cRo7ouig9+IlkoYMDfo7hsNZvlYzdRk9/I6/SPrHdYwNnQfZhgQaOGFCfE9tBRmEYxmeiuXkxxXAZfgr7SR683aU8eYrl1HBm6nQzJF6UqOR2IxVdqH26mVtTX5DDP+QoCvzG8M9+G9tzJ3Dq2iKS2OjQbCqFDnSuLophvMYThovIUfMrAkEH8xQO2580khaP4kcbMg/u5t2MLrXbDaKAAr2mzufvgJeNQ4sD+wwRwiO+napAuKuLTyZPIa9mjWNNOEtXoWLtjk7IagINso6O4A2t5W7TqlTATk6JbXp1ysUTWcpJTjWewH9AHxyduaGHBFcNVDJ9ylqxrT9jFec5L7sC0SxQ5Yznk08N4wDsMAtUxnzCFXDIQxRyvaUtpz81D/epIWjaUoS7TzsfCX/RHhydtHczlEns+GZPWHsiEHAElhX+s7ioNVDDJE6OhtYkpXOey1XEOdu4k5PkLcr+VMmDCUtJvvmCV3VpKHkTTjQ41Cp18vP+Nc/GviXIcjtGQUdgf8WYhFZj30fmfcuGGhw8VP0vRd/fE8Vo/ssZX8HDCQRxRY/3PFKjMpUxMBZ02CQZPciDE/RwZn6+wk8dMoS8Z5HOPL0RUPubO0XLSP4UzauI0Otb1prqxBdOhQ5AXryD51VfyIyOwzq9GwdGI79uiqJSSIHjPc14bX0RNUY25uhoYN3TzYVUQsx8u4EN3KdO1FnNcZDI+AV8wtFKjYHo0NnJSFH5spKOmmKfr91L1HwdxaVT41fmL3vOG0f6undejVvKTaq6zl9ywDCYO+pu0kivMwYwbWdm8fx5Mp1EOLbUVlBXXYfgffcNhNmCq1MWk2A+k8Y1V9OZJ/jeGmtiTcuE+erVqgCztNLJpzl46rrUxb2hfulEhBQWCuxI5+WMf/laX/o+Z/Fd0DLXl1aRseMDkNc40RGWS/zOZuOw0WmX08JzuSvqVaHrLuPGlNpuGwnKGM4CIXzmM05iEhqwVvXeOY6CBNA4TR7Lr3CsCL5+gTbQSoUQ9kUmP0Ho4j3ZPbdwaVYi8FsPdE9vJXhxEWWsJb2SqWZT1lUuxgfiZT2Pew9PkPUhEZpQ8CqrtaOvY8v7ycxaJauKdvg56wNXVjt47VgNSTN3ijcrxMawdM5SKpy8YY+SEdF4yt3Uu8+3BUwr4gZSlKfYT/9y8rAmu4VfIT2Spo7msitLKCppIRSUli1C248d8RpmZYqwkh2KDAClaiaoPRkm2h1mzvTDHnE0lGzjx5BRTRXfwaMNV8nNDaI9JItj0z7ZktZQMUR2/SetqxothyKJFH30r2rqauEAxV/DFQk0fBWUrjjCKgkc3+P75IjlrDpENuBo7sYQ+mAiqWWQzFlML2PhyA4MxpunqNUTTWtBQ0iRn40eEfTVZu3MuE1d5IKYlxcNDy3CjnbjEHwQ1RXFw+zFE8iqYNW8JWh9jGenpjfHM2ej3EUenTBYY9J8s+PPs8aFZ6DlJYDRJEwDF+hxWcgZv3qIhKSSwtgt1CVkKwkr4PPAmKZ9/kooNYxlPGA38ph0jLPg06RS2zp709T9C/cb+uHTbErXpJp2CYpIexTFYwRoZHUNMOtWpCk7GZYYovVy2c1RTk6LSUmzjpjHHzAHTu0IEmaKkWVaglaqNGJ0s9puAvLUV3hPv8yougn6HzMjRjyXk+ju8LdfTipCBpjMZazsFA6EdOuiRvO8hJ6ghllCiwopAkEGbnB51wNnwjwT6nqH5QhhS0v3pFsjQ0fWn29PHgGRCiaopw4AB5NPOD+q4QBgeJzaj59aXN+2pTPtv7Z13VBXX+r+foffeQTpKVUQFRVQES2zYUDEaezf2XqOxd2PX2GPvvWNXQBEBkar03nuH+f3ByfeXe5N7b7JubiRr8aw1a/Z+zz4zH5iz37Pfmf3u06nhF73bmumwfuIwjrbayXkesefeSpZumkVuZdof6pONwjEYaWgiO64lZS2NmLhzBl8N9MbTzBXTtqp8fJ/A/X2HkXc2oaSqkoxPn9jAddLl6tFS18DRyYpCoYS66R4UF+ZwfeZalOu1KKqqx0bOBMcWDujJSaP3qJAfpp1Gy1ifAQNm8urWK1y99fEs1SKGGzhixVpSGT5jHSF1N5EBSpDhzoNnyGQWk1rXEDJ8SorgyLPjGIYkc50hpFy8T6r/B9KC8rA2MMA3YSFTim8wOm0qD3mBLd7ci77HN0c9AVh0rwNWdfVko0qz9vZQnoKRcV+qqCOeOFLJILA8BNHDlNPiJ16QzRn8KTRXo0JPh3305xtGcjzkGLPWdSLuh+vM4BZPwoLY+ek79jMAk4IYzJDBSaYZD/lIIM8IeBKDkYYmAMpoYWqmj/kIMzrhxKPMi5jvXoQ0quwmjK/iv6UN3UmrjCDsYzC5Ni256Lkcc1RxXLyIfEUVOhWsRW5Ic/p0a0WOsTYLvNYj+gcyydSLWLJxcx7E6PF+aHfVR8XZlAv7jnN9zipsHvYnyrC8QYePOtDwWwebjfYC1jjpuZPZ1pJCg1IujD3Mt6NGARB8/xUlNXUoJ1Rz99Rr2s9yRakQXpHOHhZwk+s43KjtAAAgAElEQVTMpQMT6cnmyctoNb03J/cc5sTQ2TztvIaEYw9p6erOtcsfOBH7gLi6GvQ+51GuooZn1Vai/JXYomWKdncvzNvb0p0hPDsfjEXVIvZVHcC2ogMRt59w3X0hKW2UqQ2JZ6igxRliKVBXQb3GBJ6kcSH6OFdJ4Ob5FZyPOIhYq4U3Iq0DZhFHw5oINgaaxCeoEBq7DCdsyEyJJ2jzc7KrrDHoo0DIm3SSD93mltUptMjnFFlgb0nvZYNxo5iLvGZz3+WEPLvBkzsBgCy6L57hw3DeJH7kDPdZGnaD6F0R6FrrIa2Wjb2M0x/qk43CMSiaaJAtW8enT5lEBBSRlp/H2xv3UCiXoTwolVbtB9Dz1ihsTG1o4fsVa1znoVudx6LYNXQ9PAK7HvZUpJShZGNMmw1+ON/ww3CMDbTTxrxtZ/Rfg5GRHTVttZGpLGHDtXGkZ1XS89EqvCKG8elICoVkEnw2lEsn11FHPPnhn1DWKEBnnAkeh8dgoGHGMdWFaEsp0L6TD6K7FjahP9Bu52Te3bvLy5x4CkydMaId4wdtw4UBDGj1LX1ttdClDqjGnZZ4M5KjH68hg0Dy68f05iipaa9xYxdmym14RRotV45k9M3xnOAsTrRmXpczuKxqz8sbKWSYG+Gu24yBLovxWDyaCeIOxjOdd2IePvTG6evJJFLBc2Jxix7HTYeZRMwPpcapFVnX0wgacYqvGY3KQnsAOrIZ77QXzG3VmalcpReObGm5HLNmUtSgg+OQ4ZTHPkAzsYLLFNNsozulndVoJ+tJrnI6IQY1zG3nxY6u49FGhbDkQEyULekS2h3Nca1BRY3jHmvwuFCPFsZUX82iMKBhaX5H+nLT+yFX3PaSk36Rd6P2oduuHqN+rdF7XUbysY+EoERiTCoyxtJUFUNRbh75G1/RnE58+BDJBe4D0I1BdO/TisekkWNaQaWlArVVZRhSy9qpC4jIjKfqczoOmkZMsZvIhpf7yXsczqKgedzeFErHYe44yhjwuSoWqbx0llo5U6ctUpT0mssvw9G2U6BNYjUmE62RypQi3e88Uz+s4GX0YWpkygmbfBFdUZk+8+YR/jyMYmV5etjP40brFdyjiOc+t9AAwmM/UpNTyOvu5/m4IZSrlw7wZsJF7NHGVL6A954nyZ9zmtcno+n/eTnHxz6lOjiR1t27MGCdNSuMTpN/MpEWGtU4pOiSVZBOCA/ZSyYyVNJDVhctbGnWXAdFH1tm9puEGOCIvNMf6+qNwjHUCwKyNbIYmGijrirP+wev0cySpvDFR9KTcwhJaPi2LkzPJT4ygvNvVlFnrsBXHXwpkFdkfs/lxIYH08q9NVpOVqhbmWBh6MTs7cv4GJlA3KtoXu45xSBLE4JunmU8I1lEw3oF2bs+YD2+GWOO7MNvwlwO3zmHtkw3iouzkJfXRqtGgbg7sRhrehBSkkVOjRyf8jORrlVmkrMWTwZ48okoQgllxsEpFFNO0Ad/cihkYNi37I6+RYVyS0bQHU+saYOAt9CCWrLRk1LlAbORow0ygE/ZOiKQ5VVkEqBPkMcLysnjdVYwExlCZcx7vkmci4llG7xl1ZgjTKMXo5mzYiE5tOEZjyjMyiGXMnr0n8cMVlFRV0rykauMe9Wb4uJiBFVIJY1aoDnGjNNbiP7HUobQlkPKS0iiEpMCWXJS8vnIR9INBTTCpSjPLKGEGkAOYycVjgWcR7dSk9avdPDGinXXtjD7px9QRhspNXX6CjNYPGsXLdxNkMrI4A6B6E72QW6gPuPXDwR0ieAW/fy7MydoB8tXf8e9gAsoWatglqvOpulLiKWeWEKoqMlBuUCLZT02UfGqnDSUyCKIbNIB6EE3+qq0IynpJY50YMQyPx7cT+MwV/Adv4HUiM8IdTIUFxahqqrC11GjiSWDMj1lllrPoOJhAGtc9lFr24LsD6nkhCWRWlbChLAlRKbmICctz1i6kdamDiW3tgTMu8S8+tMMNVpAjUwltQnNuPfsGSF5ieSolCCvJU95qQz6kRV8IJeF+NH5hgc7BnxPQoECBl1sGWDSAbXuSjw/+J7kyjSUVfRxdGrOi1dPyfkQyTcT51JHIkuO7WLqyLl0H+DM4v4/UKBfT251LG5CG84duMNKDjKIcUAcNSgTXRPLkcFrmX12PkJNAecvXaGV3wC0sPlDfbJROAapehF5FVWqiut4vHYPJQ/TKZarJyktHTmVenoYSPN84mPcLBwpiUzhNHGMObOQNtPGohKdwZYug+lp3Bu9MXY8X3QZjaN5WHS3ZIZsF05f2UPquQyePryIzWAPui5YTjiqaAFlgSKqMx0YiinvMt/wYObXdJjpxoiYZUjLNKdQvoDo40nopkixJuEoepSS6yJNCxVt1q4exUtERiBNBg5IocSPbrPphCVb7s4lhaecYzKu2DOx7DtyCMYMLZrpaFAixmOAIklyShQZ2zGRbcwy2IQPQ9CR8cBFSotN0ot4+vJHnMb0RIhOogWqyJRm0JvWPHsXiZecOtniU+5yg1FrZhOj8JYjK85BfCqrzuxBqiKP3ayiLDkPlQI5jAETYxNuH7jIaEw5yxRiScNaTpGXPb5HlToMDHTZxhFyUzLJJwUFjPm0ay1hNQ8RKz8TNfpHltGP8pAqVGOKWO42lTezd2DtPZRyNLH7xoJYcuiZsZ1hE0fgVabLfPqSigpbCOTulQhen0mkn8kMhnZewDzdeazyPMQM+1Es/W4Z/e+uQHtCZ1Z27EM00jziOm/eh1Mio0re53SGuHrz4JU/+9iBKirooMRW2+0M8xiFs4Mc6nL9eMA5Rs3bwoopVnwcEES7I91IMy5Ew9KAEkU1Hjx8wna1mRy0XErHuV6U1pZhpGhLYlY4h58fg6hMVJU00RaN8V/3gLR39aRufcHUlVOwXz8DN/tBWCpZEEcm43Z0pzC2GIdemhhgTFFtJUY+tqhLC1R+rqErhQQSwnx+5NDg7zHePhDb6hrcTYag62fFgLadMFKwJItadD1s8bq9hLSaQqZynlK5IvLiYtj8YT7Tb8ykuiCNr6psiHx/huAPSSSdCiKw4iH7vU+znW3c7/+U/iijiBWKYztQo5lDXkYG2g6yjKYlY9V7/7E++XsaCYKQKAjCB0EQQgVBCJbYtARBeCgIQpxkrymxC4Ig7BIE4ZMgCOGCILj8p+OLUgK1gjSl/nGY9u6Cl4cn4rtkTOya49KlC4K0QMyFi9w7cxhZFUgKK6GduyU61XUErL5EUXQx6QUNz2kVzTU5d/cBV/vcQFE0xAkj1rOG705cQjqukENb9rGIDeQDyu0FDHHkVOAn4s4nUDrGEL1SaZRq65BWr+XyiIMM3DKcxNJsVrUYg6FXO6ztrUktqGPlxCMAtMaQnzjGKe7hFNSPtWePkyffkP2niEgWafzAOJqjSCbhpBVno6rTidFcxvXCCJppVWGNNsrlNSSjy43g0eTEVfGo7iS2fdxQbW6EmYYB2vpW6DQzpBtd2Fq7kpiyaDrY92GazCSOzFvBQtt2lJ8Ip2/CUtTz5Cj7nMdxrX3Il1eQLpRQgEBZWjm+2PGBBIZzAADrOmOkpfQpUdKj8vMnzrMEgUKGmHekm4whrtgzhyj0Z/qy6sRR9FUduef/nCsjdmGIGvKqVlRWZTF34tccclnNE7Ixw4rTh3aglqpECOUc4TxzWy2l5VBLEp4HcTN1N8nPX9BHVQfdpy9ZFrmFF7py2Gbq8MhtE7GYkI0+q/ufYlrraUSNuUT07TAePL3HIy5xoM8xdjqtwb5zRwo0pRn/cj/fBz0kNeQtS5mP3IsqBjKW6Pd3CBxyF9VaJQqjKkl/Hc6kgUOYW7yTfvGLCd4TRKGSHu43uzK2azcmKbZBClVqK6uRkc6muU9LMtJK8L+8n4oxztjaWJDHK6oS49jvtYxaoxoMSpSof1RPDCHMOrCIzNxi3t+P5cmwlcgBE3Ah6F4ijjN9ULyUzLaxxzncYxZHL/nzjiwO3w3Fn1IM2pgwi3Z4uU/n2s67yM7TJT1XgapMBZTCNTgw6CBP7x3FZGo/TN5Us0t8yfKxP3HF/wjd6cSr67uJIRAXLze0LOqJF7ORKdSnPT60pYAJ04b++Y5BQldRFJ1/MXNqMeAviqIN4C+pA/QCbCTbJGD/fzqwWFpNZN/1qOeBnZE16R+SyEvK4MihExS9iwNTLXq/2ki7Of1oP2cQd9fs5Nm9UGLPXeP220ssC9hI/sNAXlscRkdDkWE9hqGmmIn3zO6Ek4Ffu3HsObieM6sOM8h3Kvc9T2GCCy60YxKGXHwVybTwWSTr5ZOnLEOhlBTltfKMHvoN49qOpf+hQdyNPYpOp46URGdRWlDGyh93ArDAdSwPrbczi/kABCdGUJRWxhOvS+ziDIcJ4gCneE89fbtMoEu9I365y0kJKkPlbhT+H+6gR0s+aRkjIKAhyPP1rf584z4MHXcnKmXVcHBqTmRWLFUVldRSjxKWOLt6o26hjQEV5B27hW5oBjnJaXgzgqgZG9H6XEpFYTq1svJ0GdSd677XGbN2Isd5TxYVKCINQGhGJjfq75BbHotRf1c0LfSxb9uWT8mxGNaKmGi1oPZROaWW2pznIhdLziJNNvaGzejY2h0VX10SQt5R+DkeQaymm5wFqx26sdPXD30DJVrQjN3jj/Ldhv6U3w2njbIBc5hCPlVklxUhytUy224dzfOkGOPhjmm1Gbko8oozfH99NcG8RC4wm6iLW6nSVmX2nHtYV4F5WS15Yel4a1oAAfh69CJYS2QF67nz5gLN0MfI3oyXt/158CqYt6/9sTayYOPVBod+y3wVxY7y1OnV4N9sI2JsOsEVN1FGg5T6HGS66pCelkVdWBzTly9APqcCD1oS6PeYTVn7GLlmCNLqmhzffhHLbq25RQjV3jJot9SlpZ05VnadcB/gx22eU5EbiqpWLYrjnOnUwpSio1eRzqwGZDCulWJx++EcXXcEKQzp+dobB/dmFBTGoFepwdJv1mLra8cQz68J4CVDhwwl530Or4lh+7EtSFHCMJzpZtWNgVNX8tFGgTyhFtVkNapvZaBCLDltQHqU8W91vX/JfxNK9AdOSMongAG/sJ8UGwgENARBMPx3B6rOKYeZg9Af5k7ixgs4tnJDsZ09a5asQElfn5ae7RHkisiuzUdZUMDZrAW6KXL4HJxPuLYrDwglnnwiU6OwG9+V5jsdmHfpBcO2r+fEkzv0c/Rm5uuNGGvao9arLaOfXuVbTPjR5zvMzUeimZBN4OUg7NJAraqW+rspLHIayKYZ14gMjsPEXJkpQ5ezffUNpINKsFVVwJSG+x6L3mzBemovVowdjA6KmOvKEbLuMbcer+EVpcShQSpWaOBGdVwRRrWrKHwSxoceY3m6/yMG/foyds0MtOqfM36YKeqthjCQnkSnZLF02QG8F/jSys2Zr+w80Bbl+JFzlJNDQq0Sd24fxUPPgsH5FyhxcUFLQYl12PPO1oiTRFKqVE6SlSLPr18l9epTIvnAPu7Tql0fqrDnkclG1rOe/QTR5tBCLNztuZqwl9Tg5xSraCDIqjE7/ygXFEM5MXsri/FlkdJARqm240JGGL3fzyPrbhpy5SIq1cpUySoz0qcvKmUiL19n0CduFvGIzDgyjvRX9UyN34KGjDFmZLHHphvzsjbyrDoBDZlQbtdfYLCeH09qg7Agg7X44Ioc5/gOV+829H1yDkeTalx2nKPbo7H8EH+Vp0UZ6MkpccRlDp0G2NA1P4qF+DAKM2SHafCuVIX55VtRbelAR2MjbqW+YS+3iPruNeMTL2EUp8/Ap3PxTvmJ6DcxGNCSfNkcuo33RWV5Xxy+ssU1O4cMZztS3ZZwoOco3l3Yz7k3eWTqKHBl4Q1ygt6wwH08qTG5lPlr4WBoyfWehzHUV8fzmg/XiaWFdSucnNrir7OY4NvXMd3wDcly2ahhyOTvOtN1hCsd6MgcNlNZUYOopINejR1Tj67ATceBg6ZbKe9Yy94tz7nptYLjldGYI8t7QuhBN9R0NNjyeSnOY3sx7+BgKn/KxaSrJTMODeXs1UfIvpPnfo/rf6hz/17HIAIPBEF4JwjCJIlNXxTFDADJXk9iNwbJDI8GUiW2f0AQhEmCIAQLghBcKl2Jj7sjIVf9Ke1oSlJCFM2tjCgZro7Ngg7snr2a0ytO0XXIEHQczXn24DGCpS6vLyRx6/5Gcg4GY1CmSHBtCh8SMjlkcYlQfmLrkyN82nmZrY8aHomlx6XRdrwMfuZtCFVTpFaqhh2Jh+i9py/yzZSorrNCLr+WfTO3UoA5R9nEGs+ZPOh0FtPOetzePo1qWTkmx1+ms/NYbLBhu+dkDsybzuWCAnKpILGdFs9unuEAYfzATE5rj2MZbVms1o0P6YWETb1E/P5ATFVcySSJt6+LKd4XjUWyOuXXUtlKJ9wVXVmfshMp5NmlMpyMw8H0j5pIYN5H4sgitP1FpocsYEbHudSmZ1BEBGJMOrad2vOccDKiH7Oz/dfUlcaiH5+IqpwZRgqy7GYGFcC0bROp5yNppmUc5XsAHIy1yA2KYAC9+cg7NGXreF2TxYHcEJzyBbx69sdCxoJ23q7El9QwHmWOtdyKfF4OtmbNCQl4hUZJGekR8cgmgVHDUpnc5yRD6I51RxOWdjvE0E1t0NcxI626kGN2u1k0bzNLPhzFm57IZudwiyhMkcZC2pEhuu7oeFugs9STt0N/YlXYUQ6jBMBPPCWSlwSkvsVK04HMswkYtB1LFwdHtD3aEHkvgF0vDgLQSVGOYmM5/GaN4eOzWE6/+UAWrzAZ3TAbcCPmJBKKvVELxtccQraHIVSL+OGHe8gWvvZtQUffGXxOqiFTuYJ0pSjKA3IZpurADzxg8p4hVNeWs2v8EuRQpdi6Dt3eLUgGCl5WEKlWwpYJhzF20cHHuxeBAU+Y32MgD6cexKiHKxtnTKPj5CEoYkpGXALVlFBWmIlNpi1vIm5RkxFGTW4RgUuOg5YFNzlHJnr0phMR5CJ62JFJGTl3YuiAM4Uxn6k/Xc/q/rOZN3AoBjJq9NnT73d29QZ+r2PoKIqiCw1hwnRBEDr/m7bCb9h+lakliuIhURTbiqLY1sjYhC1OS8l7moDymHbkGlaRmZVAenUhaUrZ9Jzfn5nTZzCtgw/xkQmM9BrP6YWn8V5sR5VGFQllCZg7D2X+pTGsmbWDzMS7HFxxnLB5R7C7PoEfU1YDcJPHAOxK3IVRhTqxwid2x5+h8EUO+TJF6GZLsdVzO8HE84AT2OFAdU4S376YxuvjjwmZu5p6K1UiCaJDn36c8l1DN3UjfOW8+Zj0lGubL9FLy4xNJuO4oLMGdSlZDPMyMEUgtPgJChRiX6NMwYWHlKals57LHMhbikqFJpt5ysyqDSSQjFOFAm60ZpCGHcdKLxJYXM1Vw4WUYcEEVvI28BqXWk1HIS0Lf3IBMLKzoSi5gIWcp4gqlgZuZjm3yFEwoHf5AqQNTZjNbgAW7V3D0annUS004AOvOdj6O3LUSoi+8pg60uhgPJSKGg2WcwTlklJiQmNIf/EeS5tm3Ll5FRkyML64HDk9GWqkK9F3NkdGqorS8npuRT9FFKsIzXtI5dlSltjOYNvKc+yZtwGlRw/RpQdpCqZUm6ryOTeV9IyPvJ5/n6mtB1NKPudaLsYAFdQ0FWnboTVySgqs8x5D83FDAW2+1mxOH0Zyb0ggd3jP0JG9UVZQoDwzm89aUTj27MqDNxlse3yFSzcOsZrhiMp6bH5zB1kXIxy6dGDn3TlEjnlA9J0X7GUmaQTREjtELS3ABPUOjuTsf8uw5h58wyCm05GIN1H0jx7N5CcbUS1WJnT9Yy5cPE9PBvC+vIR5veZgSx45F97Q5vBgVi7sx4+D76DpoUi7eE1mLxqEjKkGBSXw5MYbKtJL+Xb/D2h0VCGCNDYcXMGbR3eplJJHWijFsNKKtTPHY4cuE3+aT/aNHEbWrmds/kIAeuKCNBnYOPVk57XzPDwTwMbHrwgghYgrJ7kybzm6Fo50x4WKnmpk6lf8zq4u6cR/NLtSEIRVNOR0TgQ8RVHMkIQKT0VRbCEIwkFJ+aykfczP7f7VMZ1NncRt0zZg2dOePJVSZOpVUZZRg/piykTQqIeC6mqMSuVJkion69ZbPj6J5OsDU0l/nkLI7lu0dlWjbvFkNJOiOb/gBWskiT2XdXaRmBtJjoU+yQlxnOHM/53XFTc2BOwkeWcc2g7SmPZwo137ttRQyEyGM9u2L6ejrzD/9EkURzQkSAX63WTqOT/G+m1g9ExfUrY+4Osru2lPETnyLZhT14cyqWzyqjOppZA6KpHDjHrqkaWUekpJQKSVbXf2xcYxycmR9MIMtiYd4gh9KdJUILo8C+MaHWrqpchSUsPOuzmKN29RqW+NUk4NZS2aURQfi1ZVHs+oxkXdi5FFEzlguIUpGasAgYX0o428EZVVlUSSS+f5Xpj4DkZQLECt2BDzTipsoT9nVAs49Ogwjsr6pDmuIpQM6lCniHAeU8zUiVNJDVHGJCED128Hc+H7dZioKJFkosO46LXo400x/lxmPPnUUY4qcXzEwWYoY+KmELchEpsl9hxkPuX6VczJ2kPy/WJMe6pyznQdBWIWerLGRMUHoiPVHJ1mZpxJusAo5284F3obaZpxmh9wxJSFHtOptjHi5LEjdJH24VPdMxYum4S2bkdMZ2vwvMst6oo+MSP0ABEUAtn8yCS2EYSr2QgG9Ndg0K4JBPQ7S/7N27ylmvbokWVsgLmJBhWmyuiv70rEgqt4Zdjz/PMtbHOrqVo5nOhrN2mzfzJK2gKRa14RefocORiy9O56ii2LyL5Uy+Vlu3hEFOemrWLCPj9Onw7Ge8RUulLMUIdRxKsV49bHg/7L99GaMNZdvsS5wdPYSjjHNp3H3lsHQd4QZaVo1gwMppVsFp2NXHjxLoa09HTqTJzYnDqFLUproDwdhw62nAsIRgF1DnEdA5R57LGfly8vMfD8AkIeZrPosC9XwwPJkxZp62D85635KAiCsiAIqj+XgR5ABHADGC1pNhr4OYi5AYySPJ1oDxT9O6cAUFZSgoGdEdXKoCaooSQtIIoliIKAAiKVUiKKCrLkadegqClgMaIzA08ux/itEnl7A2jRwYzmY7+lZbkMES3qUf5YwcOrOfjhxZ7cCOQFBWZ07sYZ3v7DefcfOI2Zkjpjz49CsacpVQafONhjDeebbaODpTWvok/gsXY2q1dMwgJnAJz6efDt7O14z/BD3d2I+JwsXr1+TgDy7Hl2nE57B7Cl+iee84ZmGKGDMfnUUK+jhha1uA6cRSj1JGVms2ZBLxSN5dmQdAYlbEigkJYjezKg17co10eiIavLsvKVnLm5EwVrY6KywimoT6M+tpSxVdsx8/OjCyaMKprRcK2kBKCM0biRRhgzqrahTRkp3EdZz5IKRRUKCrOJvfiM9XQml2qOhpyg7kQpZxzd2cwOLFt6UYUyUpjS3dWbHj8eZsS7sVw1SefM9yt5RwDWNf0orhDZO+0MmhSxgfEYevekQs8YeaRQRY8Bg3tz9qvr3F9yiCMKo7DzbY2xgwteqFOoKjJCfyxlddm4WdiTGP8BM103dESIly/FWs6GPB1lznGFDu3s2NlpB4sXbMbp+86UB2TznKdE1R2hTFaOWlM9Rs5uGCZ/qr5PTH4e29xm0x5PzipNwWx4O668OsncXaOxMGyIdktjPmHWojUqQFFrB9oZmbMtaC9qbXsiVSkQeu0sL0KvoJUrcIBAtCrkcdo7BiUlfVKEcvqdHkM2SiTLmPI4+QrqSdbEH7yGi7sr73jBk3eVvEWLQSP6MEi1OZO/XUWZXBXnAl4QoJDB2nbOeDTvRsnqp8Siw1cWnrj4qKGsqIi+0gfKZBxZPW4Epe/8Sb8ZTkR6NhvYy+jVfdjDKOTKy5EjD/fJAznJTwz7diKQwn37KUx7uQ7nPcPw939Jz8OuzLMfhVhUTUVqzn/q6v/A7wkl9IGXgiCEAW+A26Io3gM2At0FQYgDukvqAHeAeOAT8CMw7T+dQF5dGWUrfWp+oaauro7a2tp/aFcpgo1oRtjcnwj0+Z7M+BRqauu4GxGAjpcGzu1a0bXAClsnQ/IOXKM91jzhENfEezx/8AyI44nLOTJfpvP69EeOHzuOXJ0M3fCiLS1YZr0VT6eOvEsJx87HkyRM0WptTnJ8FKPxIMD3EV+N6EB7y/bcn7KdHSwk60UUqu7KnOi0mpf7L7Nh8mhcsKMNHblNJAbtPQikHEU1IzIo5s3VEzzBHykpKRLjEshLSmaB7jjsMSOeIjQ1jXG81p48rHlfEwJAcyyQrlKhg/M3aJi54Fu3jkMDzlKRm0csn3muu5U7zX/Aq06B40xGHzm+oiXfMZFclDHDG8+F3RndehyyZSp8u2sECmgy7fYJMkbuZfY+F+LJ4CvGMDv8ezZyhHJK0BXbUkUkQ/Bm6dlNFFHGdXIxq+rDxP1LmL7vayaYDsRQ3oig4FBMTQzRNTYhkg/s2riUg/fmI0UFvY6v58TDYHJrkvHuMQ6UahioaYhtC0fCI+JRRRsl+SreirFYtbGksLqcuY/mcHbSI5r3M2Ho9wPo6OeGlKgI5QIxJwrohiNHHm0k81EY3/eYwalOp+i+ZinVNrpUlMSzX88L534+9Dg7EfVSA/z6L6P1Eh9AmuyMUp7HvMXFwR0FQw3mvl1O91beGLWXIupBGa7NBrG16h5yGlZs+uEm/uefo6unSq2Qh/57kTV8g7mKFanS8bTq0BHNHrpsSt6LZUt9rnY7SX3QY3Yzit6YYlxSTPzRa+i1tERRqZL+3l2Rz6rC28mV++H+gDLDRg1DWk6BuhrIKzbA16wX42f3ZMCQhcSo5jBkeF+meC7kwNRV1FCMsmBAp36D2Ly8YR3HuIdB7FDYyZPIDzzlEaHRLMAAAAPtSURBVPbWzYiIaggxHVq7k1pTgJG56e/o6v+fRrFQiyAIJUDMl9bxO9EBSWDfuPm76IS/j9a/i074ba1moijq/p43N4q0ayDm98Y+XxpBEIL/Dlr/Ljrh76P176IT/nutjWJKdBNNNNG4aHIMTTTRxK9oLI7h0JcW8Af4u2j9u+iEv4/Wv4tO+C+1Noqbj0000UTjorGMGJpooolGxBd3DIIgfCUIQowkTXvxf37H/1TLUUEQsgVBiPiF7U9LL/+TtTYTBOGJIAhRgiB8FARhVmPUKwiCgiAIbwRBCJPoXC2xWwiCECTReV4QBDmJXV5S/yR53fyv0PkLvdKCILwXBOFWI9f5P10KAVEUv9gGSAOfAUtADggD7L+gns6ACxDxC9tmYLGkvBjYJCn3Bu7SkBvSHgj6i7UaAi6SsioQC9g3Nr2S86lIyrJAkOT8FwA/if0AMFVSngYckJT9gPN/8f91LnAGuCWpN1adiYDOP9n+tGv/l/0h/+KP6wDc/0V9CbDkC2sy/yfHEAMYSsqGNMy5ADgIDP+tdl9I93UaZqA2Wr2AEhACuNEw+Ubmnz8HwH2gg6QsI2kn/EX6TGhYW8QLuCXpSI1Op+Scv+UY/rRr/6VDid+Vov2F+a/Sy/8KJMPY1jR8Gzc6vZLheSiQDTykYZRYKIriz3Pef6nl/3RKXi8CtP8KncBOYCFQL6lrN1Kd8D9YCuGXfOmZj78rRbuR0ii0C4KgAlwGZouiWCwIvyWroelv2P4SvaIo1gHOgiBoAFeB3/pZpJ+1fBGdgiD0BbJFUXwnCILn79Dypa9/R1EU0wVB0AMeCoIQ/W/a/mGtX3rEkAo0+0XdBCRL/zYesn5egUqyz5bYv7h2QRBkaXAKp0VRvCIxN1q9oigWAk9piHM1BEH4+Yvpl1r+T6fkdXUg/y+Q1xHwEQQhEThHQzixsxHqBEAUxXTJPpsGZ+vKn3jtv7RjeAvYSO78ytFwE+fGF9b0z/xp6eV/JkLD0OAIECWK4vbGqlcQBF3JSAFBEBSBbkAU8ATw/Rc6f9bvCzwWJYHx/xJRFJeIomgiiqI5DZ/Dx6IojmhsOuGvWQrhi9wo+6cbJr1puKP+GVj2hbWcBTKAGhq87Hga4kZ/IE6y15K0FYC9Et0fgLZ/sVYPGoaD4UCoZOvd2PQCLYH3Ep0RwEqJ3ZKGNP5PwEVAXmJXkNQ/SV63/AKfA0/+/1OJRqdToilMsn38ud/8mde+aeZjE0008Su+dCjRRBNNNEKaHEMTTTTxK5ocQxNNNPErmhxDE0008SuaHEMTTTTxK5ocQxNNNPErmhxDE0008SuaHEMTTTTxK/4f44D5d5DO3isAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0104534a90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YWo5/G3Btaf8y8Ndl+aPAl8vytcCtHf7v+gngW8Cmst6rdT4DvPmgtpb97jv2g7zBD3cRcOe09RuAG7pc05KDguEJYGFZXkjjmguAfwGum6lfl+q+A7isl+sF5gG/BC6kcfHNwMF/B8CdwEVleaD0iw7VN0zj2SKXAJvKP6Seq7Mcc6ZgaNnvvttTiaZu0e6yY7q9vBPKMPZcGv837rl6y/D8IRo32t1FY5T4cmbun6GWA3WW7buBUzpRJ3AT8Clgsqyf0qN1QhsehTBdt698bOoW7R7VE7VHxAnAd4GPZ+bvyj0tM3adoa0j9WbmBHBORMyncXfu2YeopSt1RsSVwM7MfCAi3t1ELd3+/bf8UQjTdXvEMBtu0e7Z28sjYpBGKHwzM79Xmnu23sx8GbiXxjx3fkRM/Y9pei0H6izbTwJe6kB5FwPvj4hngG/TmE7c1IN1Au1/FEK3g+F+YGk58ztE4yTOxi7XdLCevL08GkOD9cC2zPxCr9YbEaeWkQIRcRzw58A24B7g6jeoc6r+q4G7s0yM2ykzb8jM4cxcQuPv8O7M/FCv1QkdehRCJ08+vcFJlBU0zqg/BXy6y7XcArwAjNNI2dU05o1bgO3lfUHpG8AXS92PAss6XOu7aAwHHwEeKq8VvVYv8KfAg6XOx4B/KO1nAvfRuD3/O8Cc0j63rI+W7Wd24e/g3bz2rUTP1Vlqeri8Hp/6d9PK371XPkqqdHsqIakHGQySKgaDpIrBIKliMEiqGAySKgaDpIrBIKnyf4fjfwusDAbqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(y_test[4][:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = read_data_and_split(split_seed=7, train_ratio=0.8, is_resize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_gen(x_train, y_train, bz, augmentation=None):\n",
    "    i = 0\n",
    "    from sklearn.utils import shuffle\n",
    "    while True:\n",
    "        print(i)\n",
    "        if i == len(y_train) // bz:\n",
    "            i = 0\n",
    "            x_train, y_train = shuffle(x_train, y_train)\n",
    "            \n",
    "        x_, y_ = x_train[i*bz:(i+1)*bz], y_train[i*bz:(i+1)*bz]\n",
    "#         img_idx = np.random.choice(range(len(y_train)), bz, replace=False)\n",
    "        i += 1\n",
    "        yield x_, y_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = next(os.walk('dataset/'))[1]\n",
    "# remove two file with different size between image & mask\n",
    "idx.remove(\"S2016-30816_9_0\")\n",
    "idx.remove(\"S2016-30816_9_1\")\n",
    "\n",
    "# set seed\n",
    "random.seed(7)\n",
    "train_ratio = 0.8\n",
    "random.shuffle(idx)\n",
    "\n",
    "train_idx, test_idx = idx[:int(len(idx)*train_ratio)], idx[int(len(idx)*train_ratio):]\n",
    "y_test = np.array([skimage.io.imread('dataset/{}/mask/{}_mask.jpg'.format(x, x))\\\n",
    "                    for x in test_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data_gen(x_train, y_train, 12)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "55\n",
      "56\n",
      "57\n",
      "58\n",
      "59\n",
      "60\n",
      "61\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-5b1648c2b5e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-10-ec32610a46c8>\u001b[0m in \u001b[0;36mdata_gen\u001b[0;34m(x_train, y_train, bz, augmentation)\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mbz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m             \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36mshuffle\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m    341\u001b[0m     \"\"\"\n\u001b[1;32m    342\u001b[0m     \u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'replace'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 343\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;31m# convert sparse matrices to CSR for row-based indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtocsr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m     \u001b[0mresampled_arrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msafe_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    270\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresampled_arrays\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;31m# syntactic sugar for the unit argument case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;31m# convert sparse matrices to CSR for row-based indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtocsr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m     \u001b[0mresampled_arrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msafe_indexing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    270\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresampled_arrays\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;31m# syntactic sugar for the unit argument case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36msafe_indexing\u001b[0;34m(X, indices)\u001b[0m\n\u001b[1;32m    158\u001b[0m                                    indices.dtype.kind == 'i'):\n\u001b[1;32m    159\u001b[0m             \u001b[0;31m# This is often substantially faster than X[indices]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    _ = next(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184, 1000, 1000)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f79d5004e80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (733,1000) into shape (733,512,512)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-f41a1b7da184>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0my_train_inv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0my_train_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrop_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrop_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0my_train_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0my_train_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train_inv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (733,1000) into shape (733,512,512)"
     ]
    }
   ],
   "source": [
    "y_train_inv = np.where(y_train, 0, 1)\n",
    "y_train_ = np.zeros(shape=(len(y_train), crop_size, crop_size, 2))\n",
    "y_train_[:,:,:,0] = y_train\n",
    "y_train_[:,:,:,1] = y_train_inv\n",
    "\n",
    "y_test_inv = np.where(y_test, 0, 1)\n",
    "y_test_ = np.zeros(shape=(len(y_test), crop_size, crop_size, 2))\n",
    "y_test_[:,:,:,0] = y_test\n",
    "y_test_[:,:,:,1] = y_test_inv\n",
    "\n",
    "print(\"Data shape:\")\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184, 1000)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid dimensions for image data",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-7976b6a944f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/tf18_keras/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3203\u001b[0m                         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3204\u001b[0m                         \u001b[0mimlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3205\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   3206\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3207\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/tf18_keras/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1853\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1855\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m~/.conda/envs/tf18_keras/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5485\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/tf18_keras/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    651\u001b[0m         if not (self._A.ndim == 2\n\u001b[1;32m    652\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[0;32m--> 653\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid dimensions for image data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid dimensions for image data"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arr_resize(array):\n",
    "    return np.array([resize(x, (crop_size, crop_size)) for x in array])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jimmy15923/.conda/envs/tf18_keras/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    }
   ],
   "source": [
    "y_train_test = arr_resize(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.14.0'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skimage.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def IOU_cal(y_true, y_pred):\n",
    "    iou_res = []\n",
    "    for i in range(len(y_true)):\n",
    "        y_true_flat = y_true[i].ravel()\n",
    "        y_pred_flat = (y_pred[i][...,0].ravel() > 0.5) * 1\n",
    "        intersection = np.sum(y_true_flat * y_pred_flat)\n",
    "        union = np.sum((y_true_flat+y_pred_flat) - (y_true_flat*y_pred_flat))\n",
    "        iou = intersection / union\n",
    "        iou_res.append(iou)\n",
    "    return iou_res  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jimmy15923/.local/lib/python3.6/site-packages/ipykernel/__main__.py:8: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TESTING IOU:  nan\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "res_iou = IOU_cal(y_test, y_pred)\n",
    "print(\"TESTING IOU: \",np.mean(res_iou))\n",
    "print(\"-\"*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.models.load_model()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tf18_keras]",
   "language": "python",
   "name": "conda-env-tf18_keras-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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