14.2 Icon Autoencoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.layers import Reshape, Concatenate, Flatten, Lambda\n",
    "from keras.models import Model\n",
    "from keras.losses import binary_crossentropy, kullback_leibler_divergence\n",
    "from keras.optimizers import Adam\n",
    "from keras.preprocessing.image import load_img, img_to_array, ImageDataGenerator\n",
    "from keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import glob\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from io import BytesIO\n",
    "import PIL\n",
    "from IPython.display import clear_output, Image, display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3937, 32, 32, 1), (695, 32, 32, 1))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_icons(train_size=0.85):\n",
    "    icon_index = json.load(open('icons/index.json'))\n",
    "    x = []\n",
    "    img_rows, img_cols = 32, 32\n",
    "    for icon in icon_index:\n",
    "        if icon['name'].endswith('_filled'):\n",
    "            continue\n",
    "        img_path = 'icons/png32/%s.png' % icon['name']\n",
    "        img = load_img(img_path, grayscale=True, target_size=(img_rows, img_cols))\n",
    "        img = img_to_array(img)\n",
    "        x.append(img)\n",
    "    x = np.asarray(x) / 255\n",
    "    x_train, x_val = train_test_split(x, train_size=train_size)\n",
    "    return x_train, x_val\n",
    "\n",
    "x_train, x_test = load_icons()\n",
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3937, 32, 32, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_3 (InputLayer)             (None, 32, 32, 1)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)               (None, 32, 32, 4)     40                                           \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)               (None, 32, 32, 4)     20                                           \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)     (None, 32, 32, 8)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling2D)  (None, 16, 16, 8)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)               (None, 16, 16, 8)     584                                          \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)               (None, 16, 16, 8)     264                                          \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_11 (Concatenate)     (None, 16, 16, 16)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling2D)  (None, 8, 8, 16)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)               (None, 8, 8, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)               (None, 8, 8, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_12 (Concatenate)     (None, 8, 8, 32)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling2D)  (None, 4, 4, 32)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)               (None, 4, 4, 32)      9248                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)               (None, 4, 4, 32)      4128                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_13 (Concatenate)     (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling2D)  (None, 2, 2, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)               (None, 2, 2, 64)      36928                                        \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)               (None, 2, 2, 64)      16448                                        \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_14 (Concatenate)     (None, 2, 2, 128)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling2D)  (None, 1, 1, 128)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "dense_3 (Dense)                  (None, 1, 1, 128)     16512                                        \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)               (None, 1, 1, 128)     147584                                       \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_10 (UpSampling2D)  (None, 2, 2, 128)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)               (None, 2, 2, 64)      73792                                        \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_11 (UpSampling2D)  (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)               (None, 4, 4, 32)      18464                                        \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_12 (UpSampling2D)  (None, 8, 8, 32)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)               (None, 8, 8, 16)      4624                                         \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_13 (UpSampling2D)  (None, 16, 16, 16)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)               (None, 16, 16, 8)     1160                                         \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_14 (UpSampling2D)  (None, 32, 32, 8)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)               (None, 32, 32, 1)     73                                           \n",
      "====================================================================================================\n",
      "Total params: 333,229\n",
      "Trainable params: 333,229\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_autoencoder():\n",
    "    input_img = Input(shape=(32, 32, 1))\n",
    "\n",
    "    channels = 4\n",
    "    x = input_img\n",
    "    for i in range(5):\n",
    "        left = Conv2D(channels, (3, 3), activation='relu', padding='same')(x)\n",
    "        right = Conv2D(channels, (2, 2), activation='relu', padding='same')(x)\n",
    "        conc = Concatenate()([left, right])\n",
    "        x = MaxPooling2D((2, 2), padding='same')(conc)\n",
    "        channels *= 2\n",
    "\n",
    "    x = Dense(channels)(x)\n",
    "\n",
    "    for i in range(5):\n",
    "        x = Conv2D(channels, (3, 3), activation='relu', padding='same')(x)\n",
    "        x = UpSampling2D((2, 2))(x)\n",
    "        channels //= 2\n",
    "    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "    autoencoder = Model(input_img, decoded)\n",
    "    autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "    return autoencoder\n",
    "\n",
    "autoencoder = create_autoencoder()\n",
    "autoencoder.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3937 samples, validate on 695 samples\n",
      "Epoch 1/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.6032 - val_loss: 0.5559\n",
      "Epoch 2/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.5370 - val_loss: 0.5651\n",
      "Epoch 3/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.5161 - val_loss: 0.5061\n",
      "Epoch 4/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.5079 - val_loss: 0.5111\n",
      "Epoch 5/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.5057 - val_loss: 0.5036\n",
      "Epoch 6/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.5006 - val_loss: 0.5105\n",
      "Epoch 7/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.5011 - val_loss: 0.4999\n",
      "Epoch 8/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4975 - val_loss: 0.5003\n",
      "Epoch 9/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4983 - val_loss: 0.4979\n",
      "Epoch 10/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4982 - val_loss: 0.5027\n",
      "Epoch 11/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4978 - val_loss: 0.5007\n",
      "Epoch 12/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4946 - val_loss: 0.4978\n",
      "Epoch 13/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4958 - val_loss: 0.4983\n",
      "Epoch 14/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4942 - val_loss: 0.4912\n",
      "Epoch 15/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4929 - val_loss: 0.4951\n",
      "Epoch 16/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4899 - val_loss: 0.4916\n",
      "Epoch 17/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4843 - val_loss: 0.4887\n",
      "Epoch 18/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4820 - val_loss: 0.4932\n",
      "Epoch 19/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4793 - val_loss: 0.4796\n",
      "Epoch 20/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4777 - val_loss: 0.4920\n",
      "Epoch 21/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4759 - val_loss: 0.4754\n",
      "Epoch 22/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4737 - val_loss: 0.4726\n",
      "Epoch 23/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4716 - val_loss: 0.4767\n",
      "Epoch 24/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4676 - val_loss: 0.4683\n",
      "Epoch 25/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4640 - val_loss: 0.4663\n",
      "Epoch 26/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4615 - val_loss: 0.4625\n",
      "Epoch 27/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4610 - val_loss: 0.4626\n",
      "Epoch 28/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4569 - val_loss: 0.4662\n",
      "Epoch 29/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4543 - val_loss: 0.4607\n",
      "Epoch 30/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4518 - val_loss: 0.4565\n",
      "Epoch 31/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4519 - val_loss: 0.4578\n",
      "Epoch 32/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4476 - val_loss: 0.4475\n",
      "Epoch 33/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4486 - val_loss: 0.4528\n",
      "Epoch 34/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4444 - val_loss: 0.4407\n",
      "Epoch 35/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4431 - val_loss: 0.4543\n",
      "Epoch 36/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4426 - val_loss: 0.4384\n",
      "Epoch 37/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4409 - val_loss: 0.4417\n",
      "Epoch 38/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4389 - val_loss: 0.4422\n",
      "Epoch 39/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4384 - val_loss: 0.4429\n",
      "Epoch 40/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4359 - val_loss: 0.4399\n",
      "Epoch 41/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4366 - val_loss: 0.4369\n",
      "Epoch 42/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4351 - val_loss: 0.4434\n",
      "Epoch 43/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4320 - val_loss: 0.4539\n",
      "Epoch 44/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4321 - val_loss: 0.4345\n",
      "Epoch 45/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4305 - val_loss: 0.4312\n",
      "Epoch 46/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4291 - val_loss: 0.4585\n",
      "Epoch 47/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4278 - val_loss: 0.4338\n",
      "Epoch 48/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4266 - val_loss: 0.4242\n",
      "Epoch 49/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4255 - val_loss: 0.4247\n",
      "Epoch 50/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4239 - val_loss: 0.4290\n",
      "Epoch 51/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4239 - val_loss: 0.4226\n",
      "Epoch 52/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4215 - val_loss: 0.4299\n",
      "Epoch 53/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4208 - val_loss: 0.4227\n",
      "Epoch 54/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4193 - val_loss: 0.4190\n",
      "Epoch 55/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4184 - val_loss: 0.4211\n",
      "Epoch 56/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4182 - val_loss: 0.4221\n",
      "Epoch 57/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4166 - val_loss: 0.4153\n",
      "Epoch 58/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4159 - val_loss: 0.4165\n",
      "Epoch 59/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4145 - val_loss: 0.4191\n",
      "Epoch 60/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4135 - val_loss: 0.4197\n",
      "Epoch 61/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4118 - val_loss: 0.4209\n",
      "Epoch 62/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4123 - val_loss: 0.4154\n",
      "Epoch 63/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4115 - val_loss: 0.4119\n",
      "Epoch 64/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4104 - val_loss: 0.4140\n",
      "Epoch 65/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4096 - val_loss: 0.4111\n",
      "Epoch 66/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4085 - val_loss: 0.4083\n",
      "Epoch 67/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4083 - val_loss: 0.4128\n",
      "Epoch 68/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4080 - val_loss: 0.4095\n",
      "Epoch 69/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4055 - val_loss: 0.4094\n",
      "Epoch 70/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4063 - val_loss: 0.4055\n",
      "Epoch 71/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4043 - val_loss: 0.4060\n",
      "Epoch 72/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4033 - val_loss: 0.4066\n",
      "Epoch 73/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4031 - val_loss: 0.4068\n",
      "Epoch 74/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4020 - val_loss: 0.4043\n",
      "Epoch 75/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4017 - val_loss: 0.4039\n",
      "Epoch 76/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.4000 - val_loss: 0.4053\n",
      "Epoch 77/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3993 - val_loss: 0.3989\n",
      "Epoch 78/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3988 - val_loss: 0.4031\n",
      "Epoch 79/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3982 - val_loss: 0.4011\n",
      "Epoch 80/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3972 - val_loss: 0.4021\n",
      "Epoch 81/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3961 - val_loss: 0.4011\n",
      "Epoch 82/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3959 - val_loss: 0.4069\n",
      "Epoch 83/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3955 - val_loss: 0.4020\n",
      "Epoch 84/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3941 - val_loss: 0.3953\n",
      "Epoch 85/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3949 - val_loss: 0.3985\n",
      "Epoch 86/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3937/3937 [==============================] - 0s - loss: 0.3927 - val_loss: 0.3978\n",
      "Epoch 87/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3916 - val_loss: 0.3927\n",
      "Epoch 88/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3926 - val_loss: 0.4009\n",
      "Epoch 89/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3911 - val_loss: 0.3929\n",
      "Epoch 90/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3904 - val_loss: 0.3900\n",
      "Epoch 91/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3895 - val_loss: 0.3906\n",
      "Epoch 92/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3892 - val_loss: 0.3959\n",
      "Epoch 93/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3891 - val_loss: 0.3940\n",
      "Epoch 94/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3889 - val_loss: 0.3901\n",
      "Epoch 95/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3871 - val_loss: 0.3960\n",
      "Epoch 96/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3866 - val_loss: 0.3922\n",
      "Epoch 97/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3863 - val_loss: 0.3903\n",
      "Epoch 98/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3861 - val_loss: 0.3916\n",
      "Epoch 99/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3862 - val_loss: 0.3889\n",
      "Epoch 100/100\n",
      "3937/3937 [==============================] - 0s - loss: 0.3844 - val_loss: 0.3892\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff3880cf320>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import TensorBoard\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=100,\n",
    "                batch_size=128,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test),\n",
    "                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 32, 32, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = 25\n",
    "idx = np.random.randint(x_test.shape[0], size=cols)\n",
    "sample = x_test[idx]\n",
    "decoded_imgs = autoencoder.predict(sample)\n",
    "decoded_imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAABMCAIAAACgQw6mAADttUlEQVR4nOy9d1yN//s4fp/ZqdPe\neydNq1I0KRFFVHbISKLMXrKSPYqMjIzIzohEkVRERRSFiPbQnud09vn+cf26f+d1Tp1OeL/fnz9c\nf3jkPvf9nNfzel77wuzcuRP5C3/hL/yFv/AX/sJf+At/4X8N2P/1AP7CX/gLf+Ev/IW/8Bf+wl9A\nkL+M6V/4C3/hL/yFv/AX/sJf+D8CfxnTv/AX/sJf+At/4S/8hb/wfwLwvP+JiIgQfIPL5WIwGA6H\ng8X+LhcbGRkpvC9eYLPZOByus7Nz165dR44cQRBk/fr1O3bskJGRgZ/+VF9cLpfL5WKx2C9fvrx6\n9Qp9Pn78eBMTEw6Hg8FgMBjMkPricrksFgt9iMPhsFgsi8Xicrm8H+LxeARBeN9EEASDweDxeA6H\nw2azed+EMQxpDfmAxWJhsVjR9/GX+8rLy2OxWPb29qJ/8st9cblcDoeD4gONRmtpaWlpaens7GQy\nmUwmk8FgdHR0FBUVXb58WV5ePjU19fr16/32xWazYYOGtErC4Xf2iw84HA6Hw0H60GbQvlAkhPOS\nnp4+e/bsnp4eGxublJQUWVnZfg81POzo6PD09MzPzyeTyYmJiW5ubuihA1QcaF4wQmi2p6fn5cuX\nr1+/Li8v//nzZ0NDQ2NjY1tbG4Ig8vLyKioqampqqqqq+vr648aNc3BwkJSU5Guh33nBH4Kn6ZcB\nTpxgXxYWFoGBgS0tLX+kF15QVFSMi4v7+PEj+uTXcIPNZmOxWOHUCeBP4aEoF4GQvnjJ6ZcvX16/\nfp2amvrhw4fm5ubOzk4EQRQVFZWVlQ0MDDw8PBwdHU1NTfm++g/NSxQQsS8g3RgMBofDibI1v9xX\nb28vi8WSkpJCEKS4uPjo0aPx8fEIgmzdunXHjh0YDIZAICB9BwqDwcCFAkPivUB/eQ0Fj+ofmReC\nILCAwlvmcDhcLlcIJ9DvvYzBYC5evBgUFMRgMEQZ8JIlS44dOwakSchu8va1bNmykydPZmdn5+Xl\nycvLX7x40cvL6xc4KPgkOTk5ICCgra1t7Nixzs7Oq1evPn/+PN+8/nPwvzpf/d9wAED3MRhMW1ub\nvLy8iCvb2dkpLi5OJBJ/Z4hwC7a3t7u7u//48ePixYtEIjEkJCQnJyctLU1OTk4U3lQUAEzFYDCX\nL18ODg6m0+l4PJ7NZnM4HBKJFBsbu2jRIvQ10ZtFiQIvDMRPCL6J/FHeCDhv9Pbt7Ox88+ZNenp6\nVVVVRESEqanpH5E6kL6DVFZW5uHhwWKx3r17Z2Rk9Kca7xdgXjgcrqam5vLly/fv3+/q6mKxWDQa\njclkwg3B5XKZTObw4cPl5eV//Pjh6ek5d+5cwXb4aNx/dNgiAowK6ZsmL0q0tbXJyckJx0kUCeHf\nW7duaWtrHz161N3dvampSUlJSci3TU1N+fn5T548Wbdu3a1btzw8PPrFUj5AF+3Dhw/Hjh3Lzs5u\nbGwUExOzsrLS1ta2sbFRU1NTV1fHYDB1dXUNDQ0/f/6sr69PS0s7evSoioqKk5NTaGjoiBEjEBHW\nf6DT9Adh5cqVDg4OgYGBwBj9kTZBBo6Li1u2bFlISMjvtIPKYx0dHR8/fnz16lVtbW17e3tHRweJ\nRNq7dy/I1QMtI4vFwuPxCQkJV69ehVMzUF9AIQ0MDKKjo9XV1X+Z9qKDyc/P37t3b0FBQUdHh6Oj\n4/z58xUVFTU0NNhsdm1tbXNzc0lJyaZNmyQkJExNTQ8dOmRtbY0MnQj/DkBfx48ff/DgwaJFi/z9\n/UWUgmB/0TX/42OGYdDp9O3bt9+/f5/D4QwfPnzs2LFRUVHq6upXrlxpbm5ev3796NGjZ86c2dPT\ng8PhxMXF4VveI/P79A1mivzpOfIidldXV15eXmZmZn19fUVFBZfL1dfXV1dXd3FxsbW1lZaWRoYi\nm8GUGxoagoODV65c6eHhIeRcw6Tq6+uXLl1qb28fEBAA50WUKSxatKigoMDX13f16tV3794NDQ2d\nMGECmUwGThoZjJVHBQkMBtPT0xMaGurk5DRr1qysrKzTp0+/efPG0dFRlGEM2gXsYHZ29r59+1pa\nWmxtbXfu3KmoqIhu6JAwhMvlwl4UFBRERkY2NTVxuVwlJaUdO3aMHTsW9lR0PBlwoVE94tatW+Pi\n4pKTk+3s7ETZGx8fn7CwMF4Vy1ABPmxra3Nzc2ttbS0qKtLS0kIQxNHR0cnJyc3N7enTp/Ly8r/P\nm8Icm5ubN2/efO3atcjIyM2bN6O/7t+/f8WKFdnZ2QcOHFBUVESECky8bQIrf+vWrfb2dgwGw+Vy\np02bZmlpefv27bKyMiwWC/2SyeSAgAAEQS5evEihUAARORyOkZGRr6/vx48fU1JS4HM5ObnZs2fL\ny8v/gooIVglGfufOnQcPHiQlJeFwODwe39bWtnr1aqSP2P0+wLwaGxvb29sRBGlsbDQ0NPxTjffb\nHQaDYTKZe/bs2b9/v5aWlrOzs6mpqZKSkrq6urKyMolEkpWVZbPZKioqT58+nTJlir6+flNTU7/t\nYDCY3Nzcp0+fcjicuXPnDh8+/L95EQoOSfAkV1VV5eXlvX37Ni8v7927d/v371+7dq2QC6a5ufna\ntWtUKpXD4eDx+IKCAkNDw0mTJmEwmGPHjunq6vY7QXhYWVmJwWAmTZpkaGhYUFBw4MABULeLi4sv\nWLCgX6YWRvLz589//vknISFh9OjRixYt8vLyGjly5KDzLSoqSk5OfvDgwciRI/39/Q8ePKiqqipk\nakwm8+bNmzU1NXBABm1/IIDPtbS05syZI8h5Nzc3h4SEODs7/3L7A4GYmNi9e/d++XNUHnv+/Hlc\nXFxSUhKXyx02bJiMjIycnByDwUhNTXV3dxfOmMK6gYwaEBDAZDL7fRP66u3tvXnz5sePH9PS0rS0\ntH6B9sInjY2NISEhiYmJjo6Op06dmjx5MolE6vd9Go2WnJx88uRJGxuboKCgQ4cOSUpK/il9hHCA\nRYuIiNi9e7erq+uiRYvExcV9fX0H/RDlZq5fvy4rK7tw4UIxMbE/zrdhsdjg4ODbt29v2rQJj8e/\nfPly3759gYGBR48ehR189erVsWPHvn37Fhsbi8FgPDw8tm3bpqqqeufOnXfv3tFoND8/PwcHh98c\nBgaDiYuLo9FoISEhf2qOsPI4HO7NmzenTp26d+8eg8GwsLCQlZVVU1PDYrH19fWfP38+evQokUic\nOXPmqlWrbGxskKHw2e3t7TQabdu2bcIlcxQiIyO/fv2KDOWWzMjIuHbt2rx58xAEMTAwmDp16ufP\nn0ePHi0iX4tiOIvF+vz5c3d3d1hY2NixY+fNm+fs7Dx//vzfZ0zRtWpubvbz8zMxMRk/fvytW7ea\nmpru3Lkz1NbQ2womGB8fn56evnDhQi6Xe+vWrbi4ODs7O+hRdCmi/5UC5hePx2/ZsmX//v0Igjg7\nO6elpbm4uAxKGhITE8lkMvJvY4HogFrwXV1dQbenrKzMZDIxGIyWltbLly/t7e3d3NyeP38uok1f\nCIDcQKFQUlNT4ar4/PlzQkKCgYEBg8HQ0NAgEolpaWlgFRVuNUCBxWIRCIRbt26tWrVKWVkZhCRd\nXV1LS8v79+8/efIEh8PBRioqKgKxO336dEtLC2wYm812d3f39fUtKSk5cuQI6MmAlwoKChKi1Rho\ndjgcrre3986dO/v27auurrawsIiJiXF1dQ0NDW1pabG3t+e1g/8mAHsXFxdnbm6OIEhcXJy9vf1/\niLcDSkShUPz8/B4/fnzkyJGAgAAZGRm+1968eaOpqYkgiIKCQkpKyvbt2ydPnsz7AuqpsmrVqitX\nrigpKdHp9KioqMjIyI0bNyL/XT0NCsB2MJnMhoaGwsLCx48fZ2RktLe3s9lsGRkZPz8/TU3Nw4cP\nr1mzRsjebdmy5fz580pKSmw2m0AgdHZ2Dh8+HFAoMTFROJWE10AQLS8vj4mJYTKZOByuubn506dP\nvLYkANiOr1+/Tpgwgc1m371718vLC+2CV/XLO0ekD2dGjhw5cuTILVu2JCcnr1q16tmzZ8+fPzc2\nNha8b2BId+/e9ff3V1JSAiT/HcBisc3NzXg8fu7cuYJONWAthU7RwfNyw6KoQATH39vb+8tIhWLs\nkiVLrl+/bm1tHR8fP2bMGG1tbdCNFRcXu7u7g5Zx0IFhsVgLC4sLFy4M2um9e/fq6+udnZ2fPXum\np6c3JNoLL2dkZPj5+YmLi6enp7u6uqIto+gBAChBIpH8/Pz8/Pxu3LixePHib9++paamEgiEXzAf\nDwkA5d68ebNr165z584tW7Zs3Lhxd+/eHZQxhSl8+fJl0qRJbW1tDAYjMTHx/v374uLif5Zvq62t\nvXfv3tGjR5cuXYogSHh4eGVlJciZvb29RCLRzc1t5cqVb9++3bhxo5KS0v79+xMTEyUkJLq6utTU\n1IBEJyUlgcrwF1YS7OxfvnwJDAxEEMTV1XX48OG/f48AkjQ3N2/atOnmzZsmJiYxMTFOTk7a2tq8\nQiOTyayurs7Ozj5x4oSjo+OcOXMOHz4MVE6UAcA929PTA7otIdMHFgik+iFNxMDA4MuXL3D1Dxs2\njEajVVVV2djYNDc3r1ixQkZG5siRI9A7h8MBmoPrAwqFsmHDhvr6+tOnT2toaFRVVdFotGHDhqFr\nbmBgMKTBCAKLxSorK4P9KigoaG5uTklJMTY2FhcXP3HiRGlpKWAymUzW1tYW3hTKkuJwuJ6enrdv\n32ZlZSUmJpqZmQFJKS4uTkpK0tbWdnZ2tra2Bo8IUdjTARlTPB6/cePG2NjYuXPnpqSkTJs2zcPD\nIyMjY9y4ccL1po8ePRo3bpy+vv4vID3gVktLi5ubG51OT05OptFoHA4H8JLNZmtoaLx8+dLFxcXZ\n2Tk9PV1RUfF37ErwoZ6e3pQpU+Lj41tbW6dMmcJgMHx9fcXFxa9evdrT0+Pt7a2kpARvij6j1tZW\nVVXVhoYG3ofXrl3r9+XS0lLBh/PmzQORC0EQNTW11tbWoc4Ohvr48eOwsLDS0tKFCxcmJiZaWFgg\nCNLU1JSenp6QkAA3HPrJ77hGwUbk5+dfuXLl+fPnCIJMmDAhODh47Nixf1zPAWqD7u7uiRMnNjQ0\nfPz4EeZ148aNw4cPv3jx4siRI1paWu7u7mPHjk1OTvb09IR72sfHJzQ0tLKykrc1Go02bdq0t2/f\nXr161dvbG0GQAwcObNq06cOHD+fPnwfc+6/xpmBdamtr27t3b3Z2dlFREQaDsba29vLycnR0tLe3\nB+X9x48fx40b9/z5cyGmiTdv3gQGBp45cwb+O2vWrM7OTmCw0tPTYUEGgrdv39rY2ODx+M7OTg8P\nj7t378LzlStX5ubm9vsJg8EAZWpGRoaCggLC49M8qJc2uM/i8fiZM2c6OTlNnDhxwYIFr169EqQz\nQDS/ffumrKzc2NgopE3RQUVFpaysDBFQivA6wAi/nFBJXpTuwEn3lxW94Drs4+OTnp5+586d6dOn\nw3MmkxkfHz9r1qze3l7wrhaxNfB7GYiqwyI0NjayWKykpKTIyEgnJ6fnz58bGhqKeK6B0j59+tTd\n3X3u3Llnz56VkpLitQkMhBtAjubOnWtjY+Pg4ODk5PTgwQNQdP2HxEXYFCaTuWTJEldX12XLlr14\n8eLdu3eiOF0AAuzbt49AILS2tpaXl5uZmd25c8ff3190K7AoIwQnJRMTEzabTafTJSQkdHV1EQQB\nbh6DwRCJRAwGc+bMGX9/fwRBli9fvnTpUgKBsH37dmBrZs2atXHjRg8Pj18bA6hL1q1bN3bsWARB\n1q1b9+TJk98UEWGJ8vLyfHx8SCRSQkKCn58f76zBQRaHwxEIBAMDAwMDg4CAgMTExC1btowaNerO\nnTu2trYirjNcH4B7Qi50uAqRoVsUra2t37x5g8ViORyOnJycpqbmlStXampqbt++XVNT09PTU1BQ\noK6ujnqdIQgiJiZGIBAkJSVbWlrq6+slJSU9PT0XLFiQmZmpqakpJyeHykvC6fagE8dgME1NTeC6\njQLsI4CJiQn8MWHChIyMjEFbw+FwVCoV/Jt//Phhamo6ffr0WbNmAf2JjIy8e/funTt3du3aZWBg\nsGTJknXr1klISCCDHWH+XUEt+OHh4dHR0cnJyUuWLKFSqdevX587d+748eNfvHgBYsRALb569Qo0\nfEPdTiBzXV1dU6ZMaWpqevXqVWlp6dq1a7FYLJPJBIUlh8PR1NR8/vw5MJFdXV04HI43SEj0vkDf\nFhUVpaCgkJWV5ejoSKPRtLS0iETigQMHPDw8qFSqo6PjixcvVFVVo6KiKBQKFosVsS8cDsdisUAe\nQj1LgBbzArzM95D3ZbSRITF2MLuenp7Vq1d7enqOHj26srIyPj7ewsICiFpsbKycnNyUKVOQPt0D\nbDqBQBj01gQ5j+8hqsVZt26ds7Ozi4sLCA/r1q0DTkuwQc6/o7tEB0BRNps9Z86c8vLy7OxsCwsL\neFhaWkomk6urqyMiIh48eJCenq6mpmZgYPDs2bOampr6+noikQg8E+90AgMDc3JyXr9+7e3tDQu+\nefPmV69e3b1799KlS0BfhAyGd4t/eV7QDgCXyz1z5szRo0ddXFyys7OpVGpubu6RI0fs7OwoFArc\nTObm5rq6uomJiULaJBKJvb29HA6HQqFwOBwrK6uMjAxpaWmggBwOB5CBD+ChlJQUkUiUlpbOyMgY\nM2YM2gioZPg6Agb06dOnJSUlN27cUFBQgAOLx+NFFOSwWCxwzEwmU0FB4caNGyUlJU+fPoWQQcH3\nCQQCqm8QnILoAJ+DRln47iAIcvz4cQsLCw0NDRkZGQsLCw8PjxUrVly6dOnbt28o/4rGz/0OcPp8\no3mfABLC0T537lxycvLz58+nT58OMRz5+fkmJibbt2+nUqnq6uo4HK62thYRjQiDL7JwgE1XVFR8\n9uyZlpaWnZ3dt2/fRKG9HA4Hi8Xm5+e7u7sHBwdfv35dSkoKZTLgIKPHh+/sAOvAZrMNDAwyMjIa\nGxs1NDQmT56cnp4O1OaPuwnBaPfv319aWnru3Lm2trYZM2bMmDFjzpw5g/YFL5SXl9va2pJIJFNT\nU2Vl5ZqaGmToV+FAAEx8W1ubhITEvXv3cDichIREe3t7UFCQlpbW2rVrOzo6kL6FTUlJaW9vZ7FY\nJBLp2rVrly5dMjAwoNPpXC53woQJra2tMNlfuKaxWOzDhw+fPn0aFxcXFxf39OnThw8fin45CgKH\nw8Hj8bdv37azs3NwcCgsLPTz8+O7EPF4PBwx3mvUz8+vsLDQwcHBzs7u9u3bwjmT/xqMHj36/fv3\n6MLa29unpKTEx8cXFhZ6eXl5eno2NjaamJjY29tPmTJl/vz5CxYs8PDwcHBwsLCwqKurmzx58owZ\nMz59+hQfH//o0aPx48dDO1wu9/3796NHj/7N4QFZTklJqays/PHjR3l5eXl5OfrH9+/fKysrfX19\nKRSKkEbgWudwOCdPntTU1Dx06JC3t3dJSUleXt7FixenTp0KuOrh4XHhwoW8vLySkhJvb+9Dhw5p\namqePHkS/XzAQfb7NCws7PDhwzdu3HBzc7t58ybQjosXLzIYDHd399TUVGdnZ84A6sPTp0/DH0Pi\npTgcDg6Ha21tdXFx6e3tffPmjZycHBi43717d+rUqQsXLqCkWUtLKy8vz8nJyd7ePjMzU0FBYaDB\n9AvAAZeUlPj4+JSXl+/evTsoKEhaWtrW1tbBwcHPz4/BYNy/f3/s2LHZ2dldXV1nzpwJDw8/f/78\n3bt3zczMRNETsFgsOp0OQ0IHht4uvCMR/BYV5lAHZDqdLroRH6TG3NzcxYsXd3R03Lt3D3Qq0Bce\nj6fT6RcvXpw3bx6ZTAYqA2zls2fPEhISlJSUVq9eraenNxDSwNzBuYLbFx7HZrPFxMTu3LmTm5tb\nVFQEb8bExIwcOfLevXs+Pj50Oh3IJdJnCRUlmEYQUMHJ398/Kyvr7du3+vr6NBqtpqbG0NDwn3/+\nWbFixcuXL/F4vISExJgxY7Zs2TJjxoyysrLk5GQrKyssFsvHV4Hm+MWLF2ZmZhD6BtsEWv+XL18G\nBgYOJNgB1vHJ6L8QEMCrmQOIjY09dOjQpk2bKioqEhISnj9/Xlpa+vXrV3V19eLiYnDLW7BgQWxs\nbE9PD3CZgviPcvCwQcHBwerq6q2trSNHjkRt+v1iIJvNHjZsWGpqalFRkby8/MyZM9FG+KyuaEcI\ngnz69ElMTAwUOWgSiSEBLAKbzTYxMRETE/v06dO0adP6vTh5Dei/adVF90v4wBAEUVZWdnFx0dDQ\nIBAIDQ0NDQ0NFRUVly5dIhKJo0aNmjx58qZNmwC7hkSOeIH77xQTfINEBc7jx48HBQXZ2tr29vaK\ni4s/ePDA19fXy8srKipKVVWVSqWSSKSqqqpB5zVUoFKpOBwuLS1txowZ9vb2GRkZFhYWg6qpaDTa\nnDlzPDw84EJC+mgIEFLeyaLTRB+CSgZQorCw8M6dO8nJyZMmTYqMjNyxY8efnR2sfElJSURExIkT\nJ3R1de3t7ZWVlc+dOyd6R0CueY/eHxweFostLCycPHlyS0sLBBdOnz792bNnZWVlixcvPnHiRGVl\n5f379319fSUlJVevXj158uSXL19SKJRnz57p6uqOGDECzBcdHR1EIhGGN6QRcvscjtevXz979mxL\nS0sEQWbPnr1+/XpXV9dfc6hFRZc5c+asWbPm+PHjSN8txvca0ochaBeQlODGjRtKSkpz5szR1taG\nOJv/bdyqqakpmO9Bk21ubq6goFBcXBwYGFhXV2dmZmZiYnLs2LF+vy0uLpaXl//27duSJUvOnDmj\npKQExkAsFltZWUmj0UxNTQsKCn5zhBwOR1tbW0dHZ6AXpKSkBjK5oFdwc3NzYGDgs2fPtmzZsm7d\nOjExMd7XeLdPSkrKzMzs8OHDe/bsOXr06JYtW54/f3727FnwxeoXCf+190ARMjMzz549e/v2bR8f\nH86/HSyuXr2KwWBCQkLevn07EBb6+/uvWbPG2tpadAMu6lc6ZcqU9vb2V69eaWhocLlcdXV1JpP5\n5cuXT58+If/m8NTV1TMyMsaPHz9lypT09HTR/U2B+uTm5oLTw6tXr1gsVkRExLFjx/T09GxsbMrL\ny7FYLHiEYLHY0NDQzZs3L126dPr06WPGjMnKyrK1tRWC+jCG6dOng9GEd334qLAoAJ+fOXMGvDYH\n/Rx4ggcPHsyYMWPatGkXLlxQVlZGDfQsFguDwaSmpra0tIB/EtKnK42Pjw8ICLCxscnJybl+/fqr\nV6/09fX77SIvL09BQcHIyIj3ISDJunXrAgICRowYQafTEQQZMWJEQEDAunXrfHx8+LAWQZDKysq6\nujpUHBQRYIIHDx68cuVKVlaWqakpl8t99uzZnDlzPn/+fPr06RkzZigrK7NYrNmzZ5ubm0tKSnK5\nXE9PTx0dnc+fP4MliLfBS5cujR8/3sHBgcPh8A7y0KFDnz59unjxIjLA7Y7i2+PHj5OTk79//15f\nX08ikaytrefMmePi4iLiZQa4RKPR0tLSaDQakUgsLS39+fPnvXv3Dhw40N3draqqqqGh4eDgUFRU\n5ObmRiKR1q9fr6amtmLFim3btr1+/drNzW2glslkMmi8EAQhEAiQYgJgUMFgwoQJEyZM4HsfYkv7\nfZ9IJHZ1deXm5trZ2YFD6lCvB1R5mZub29XV9ZuZPf44zJkzZ86cOXwPu7u7s7Kybty4ERMTExsb\nGxkZuWTJEmCvf8F9Bc7pt2/fMjMzHz58+OPHj+rqak1NzUmTJv3zzz/gLV1VVdXQ0DBnzhwOhyMu\nLn79+vX58+eHh4fv27cPQRAOhyMhIYHD4cCP6I/rFBkMhri4eHJyspeX19ixY3NyckaPHi1kslgs\ndu/evY2NjXl5eegTpI8Oczic27dvQ0Kxzs5OFxcXX19fc3NzvpsF3pSWlg4ICAgICDh79uzKlSuN\njY1nz579yxMBbQtoAdCHHA5n6dKl48aNW716dVhYWEFBQWFhobS0tOi8DrcvkhLGD1r5QVXyogOH\nw+np6YmJibGwsLhy5UpUVFRnZ+fVq1fnz59va2u7ZMmSqqoqbW3tmTNnmpmZQQDi58+fq6qqqFTq\nhg0b9u/fz+Fwnj9/rqurC2aHIWEpXBanT5+uq6vLysoC7IqOjjYyMjp9+vT69es5Q8xfAcv18+fP\nGTNmzJw58/jx48D3CIo6/a4/6BEwGMzx48cbGhpmzJhRWFiooqLyH/L0EBGMjIxkZWXfv38PjOmo\nUaNaWlrodDpwnMbGxhcvXmxsbATbHW/KyI6OjvLy8qlTp+bl5YEJt6WlZdSoUdDs+/fvZWVljYyM\nfp8xRRAEEimCANDW1vb58+fx48djMBgGg0EikYQsIOD29+/fvby8vnz5EhYWNmrUqNTUVDqdjqI9\n77eoBZjL5YqJiY0aNQpiGb9+/ZqcnDyQy2w/yh5LS8u3b98OGzaMwWCgdwOqV0tISCgpKRHieHfl\nyhUPDw9ra2sRaSIagz9hwoSenp43b96oqalB16qqqiUlJWPHjlVTU+NwOFlZWV+/fg0KCgJhVFtb\n+82bN+B79Pz5c1Hi9OGFt2/furq6+vj4XLt27eLFiyEhIerq6ufPn7e3tzcwMECl+R8/fuTk5Bw4\ncODcuXPHjx/PycmZP3/+hAkTsrOzhbDd6BqCNMm7ROHh4S9fvhRiGoafHBwcIOAM/Rx1NhVOHGFI\n9+7d8/X1DQkJAZkMFT25fcFbt27dAp0ZUBnAxe3btwcHB588eZJCoVhZWe3du5cvHgIaB0cxdXV1\nExMTNIADWv7582dPTw+kIkM5vMjIyHv37g0bNkxVVRXVH3C5XHFx8bKysurq6idPnkyaNElEGxCI\nSa9evdq8efOpU6ecnJxAh9TV1WVmZlZXV3fgwIGmpqaYmJiYmJipU6dmZWVdunQJ/Hg2bdpEo9EE\nNaZv375dvHgx4HZBQcHWrVv9/f3fvHlz/PjxvXv3WllZ9XsnwbWak5MTGhoK5hVra+vx48ezWKz0\n9PS4uLjQ0NCYmJhB7UrQeG9v7/Tp09PT01HcUFdX19bWnjt37siRI42MjNTU1JKSkuLi4rZt21Za\nWnr06FETE5OQkBAnJ6ezZ89OmjSp37MmLy8P8XwgkMCpQf2rBl1tuLlRn2O4LUpLS0GCFwSwxfv6\n+t6/f9/Kygrp8xFEeE6BIMFCeCgX8LKQaaXfy+l/C7A1nL5kLnB2pKSkPD09PT096+rqoqOjV6xY\ncfr06atXr5qamg7JsxAw8Pv371u2bHny5AmLxXJzc/Px8dHR0amtrT1z5kxiYuKLFy+MjY0bGxt7\nenr09PSwWGxeXt7ChQv/+eefffv2ofZxyMOgqqqK/FHfaCwWq6SkBMcHj8dnZGSsWLFi+vTpt2/f\ntrW1Hegm+/nz55kzZyIiIlRUVFCaCWifmJgYERHx/fv3UaNGGRsba2pqXrp06fDhw9HR0UFBQXzn\nDi4gcBoJDAzMy8sLCwsT0UuS0+dNhF6cvMpa1IsAh8NFR0e/efOmoaEhJSXl8OHDEILzyy7yQOjA\nBs3reCBiYDIfwAqMHj1aWlqazWaD3Lhz587g4OCQkJAzZ840NzdzOJyWlhYdHR0KhWJsbOzp6Xnz\n5k0HB4cXL15kZGSsX7/e19d35MiRysrKkJ7F29tbdNs3vFlXV7dt27YtW7ZAei8EQTQ0NLZs2bJt\n27bZs2fDTS26RArkKCwsjEwmX758GdXG8fULnkIIgkyaNEkQMWBgly9ftrS0DAsLS0hIGBJ/DGYK\n5Ff3RRD09fXJZPL79+9nzJiBIIiGhoaUlNTbt2/V1NQ6OzuNjIyamppaWlpUVFQQBOno6MBgMBC2\n297eXlNTo6Oj09LSMmzYsPz8fCkpKQ0NDRje+/fvyWTyQAqjoQJozeE4MxiMhIQEUEPAk7a2tn5x\nnsFg9PT0iImJ/fjxo66uTlFR8dixY4cOHRpS12JiYoqKilVVVcXFxaqqqnQ6XVJSku9e/hfdhF1R\nUFDo1ziO3jED3UwAgkopIYBype7u7p2dndnZ2YDZBAKBxWJpaGiQyeSIiAgPDw8sFhsVFTVs2DCk\nLzaLw+Goqak9f/7cycnJ3d39yZMnwnlTwPjOzk4fHx8nJ6dr165t3LgxOjp6//79vFmiUPo+bNiw\nYcOGBQQEHDhwYOnSpZ8/f7527ZqHh4ePj8/Hjx+lpaX7pcVwG92+ffv+/fvXrl3jfUdWVlZVVXVQ\nxlRWVpZ3zBgMZv78+TNmzPD19RVi0AfCmpGRMWvWrLCwsIMHD6KuOeg7GAyms7MzNTX1zJkzwKPA\nWtHp9Lq6Ok9PTwRByGTyiBEj+o3HQhCESCTKysrW19crKSkZGhqiSILBYNTU1EJCQjQ1NQsKCr58\n+cLlck1NTa2srFJSUo4fP87rRobFYr9//15dXU0mk0XXinH78nD5+vr6+fnB1QXHGwLFEhISwOon\nISERGhqalJQ0c+ZM+Ba8rZubmwkEAu/yIghSW1uroaGBwWCam5tnz55NoVBWrVolKysLSoh+txim\ncPPmzblz506bNu3GjRuAlgB79+5NSUnx9vaWkpLavXu3kBkBGjCZzJkzZ7579+7r169KSkoTJ07E\nYDCPHz8GsgVApVJDQ0Pnz5+vrq4eHBysra399evXqqqqefPmbdiwobOzU0ZGRhCpQKOmqqo6UCag\nIQGcyqqqqoGwl0qlSktLW1tbW1tb//PPP6tXrwYNH18j6B2ADgld4dra2pMnTx48eHDGjBnd3d1U\nKvU3x/xnoV/PAbhKORyOhobGkSNHVq5c6e/vP3bs2Dt37ri7u4t+SWMwGBqN5uXlRaFQDhw4sGzZ\nMl4qumHDBkdHx3nz5r179w4YFCKRyGAw5s2bN3369AMHDkBHsLxlZWWtra2CgvHvALQcGxuro6OD\niiumpqaghkhJSRk3bly/tPfRo0dUKnXp0qUowwFDvXDhwrJly2bPnn3//n1jY2P0/f379wcHBw8f\nPlwwAwz4wgL+REVFaWlpCQ/OQP7t8gFLAUIaDof78OHDy5cvDQwMpkyZAm1++/btn3/+OXHiBJvN\n9vT03Lhx4+zZs38ncJNIJH79+vXixYtKSkpAXVEAqW+oVgVYQ3CiWLt2LYvF0tHRSUhIWLduHYVC\nUVRU7O3tHTdu3JEjR4KDgz9+/AhBqLNmzbK0tNTR0dm/f//Hjx9Hjx5948aNtWvXzpo1q7KyctDI\na97ecThcRESEvLw8JKpDtcJr164F8eP8+fOie5qieqIrV65A3oB+ZTnQPhw8eBBBENBi8C0aeKJL\nSEjs37/fz88PNdiKMgY+rXm/BH+oZgc8Hq+hofHt2zcYp6GhoY6OTn5+vqWl5c+fP6WlpdXU1AoK\nChQVFVeuXHn//n0EQWbMmHHmzJmSkhIZGRklJaWGhgYTE5P79+/r6uoaGhpCs9++fdPQ0Ph9cR2w\nffny5UFBQajde8yYMSdOnIC/KyoqHjx44O7uLvhtXFzcmjVrCATCoUOHmpubkb7wAPi1X7kCESiQ\nAZ79CIJArAuTyTxx4gRkrvz/11Cwb7T1fm+ggfpGQXT3fziZHR0dEydO7OzsfPXqFW/2Zjwer66u\nnpmZaWZm9ubNmwsXLpSWloIDK7wA3tba2tqvXr2yt7efOHFiZmbmQPVs0O7Cw8O7u7tTUlKOHj0K\n0V2enp7cvtywGJ5APNTfa/PmzWZmZl5eXhoaGg8fPlRSUtqyZUtsbGy/NAv1t4M8hbyI/s8//4iy\nLHytYTCYe/fuQXLTgRYWNr6iosLDw2PJkiUHDx4ULPIE36alpbFYrGHDhqGKYQRBxMXF3d3dlyxZ\n8ujRo9LS0sTERDSUGwXwEHV2dn779m1gYKCkpKRgwrOXL18aGRm1traCVh8ijS5evHjr1i2+N6dP\nn66goBAbGwuKW1FIP2DdsmXL8Hj8mTNnuFxuQUHBkiVL7t69e+TIEVtb2+HDh7PZ7EmTJkFrEyZM\nePv2LeTXAEeImpoaEokEUe28AKskKSkpKytrb2+/c+dOMpkMqb765UoxGMyXL18WLFgAAgDy75Q3\nGAxm2rRpR44cCQ8PR/0l+p0OTHzJkiVPnz4tLCwcNmzYhAkTmpub37x5A+olpI8dhJydu3fvLioq\nun//fkpKytatW69fv7569eo1a9Y8fPhwwYIFgqe1o6PDw8Pj1KlTg66t6LBq1SrUJssHTCZTXFw8\nKSnp2LFjcEWNGzduzpw59vb2CgoKJBIJrmFenGSz2TQarbW1NScn5+bNm69fv2axWDExMaGhocBP\n/8GR/4cACC5QSy6XO2zYsFevXoWEhHh6ep4/f170iGwul0sgEBQVFY2MjIKCghAEycrKioyM9PHx\n8fb2VldXv3r1qrm5+fPnz83MzEgkUllZ2YsXL1pbW8+ePcvhiZjE4XBRUVHKysoODg4inixRAIvF\n6unpnT9/HpWNgTRpaWn9+PEjMTFx3Lhx/XrH3rt3z8XFBc3aDTTz48ePy5Yt271797Zt25B/6yw3\nbdp09uzZ/Pz8fp1hUH/EY8eOaWlpPX36lFd+E1xSOL+1tbVgtsbj8crKygiCREZGHj58GM6gu7v7\nnTt3cDjc0qVLbWxsVq5cOWbMGDc3N7B6/w5nLycnd+7cuRs3btDp9OXLl8vLy9++fXvSpEn79u2T\nlJQcaqYXpG+h3N3d//nnHyaTKSYmxmaz5eXlL1++DC/U1tZevXp19erV9+/ff/PmjYuLi5KSUlhY\nGIFAePz4cW9vr6urKxCKw4cPp6SknDlzBjxABgXYnaKiovj4+MTERN6csmw2W1JS8tixY35+fqtX\nrx45cqSIk4K1jYmJsbS0nDVrFkdoYibhKgxQVAELHhMTc+3aNRE3DovFlpeXnzt3TkJCYtmyZWpq\nany8KRigh8qbOjo6njx5EkqaJSUllZSUgJ7r58+fWCzW0dHx9u3baWlpjx8/PnfuHJFIXL58eWho\nKB6Pt7e3b29vp1KpBgYGRkZGJSUlwcHBkBooMzOTj3v7NYCbVFlZeffu3SCnIX2BHxgMBmRsTU3N\nfitjeXt7jxgxoqCgYN26da6urnCxIgjS2NiIw+EUFRUxGMzbt2/j4uLA38DKymrFihWQSaClpYXN\nZquoqMAul5SUrF+//siRI1ZWVoJq4H7wQLhD9FDdpQcCwN3W1tZJkyZ1d3dnZ2erq6ujjkdYLLao\nqCgpKcnQ0PDAgQObN2+GMl86OjpMJhPVJcDL6urq2dnZLi4uEydOfPr0Kah7+boDGl1eXn727Nn7\n9+9/+/Zt/fr1sbGxnp6e0CDfkUDvGwRBmEymp6dnbGxscHDwlClTLl++PGPGjA0bNujr6w9kwBIT\nE4O6FH8EpKWlBX00eaeGIAibzZ4/f76JiQm46gumfAJ0vH37NpVKtbKyWrJkyaFDh1AuLSEhwd/f\n39ramsPhBAcHL126VHAN4V4xNDQEUpKQkDBv3jzQxomJiR04cCA8PHzBggXBwcG2trYIguTl5cXG\nxjo6OoJOmk6nA5t1/fr15OTkjx8/AispomcwFos9fvx4UlJSXl6enJwcgiC1tbVMJrO7u/vcuXMf\nP358+fLlwYMHp06dmpyc/OjRIwRBQKM5d+7cT58+ff/+va2tDY/H80Xlk0ik3t5eBEHExcUXLVq0\nbdu22NhYSUlJXjRDxwBOkDgc7tixYzo6OgcPHhS0AYGQs3z58j179ly6dKlf9EBlnpCQkKtXr+bk\n5FhaWnp7excVFeXn56upqUEvsGVUKjUyMjIwMFBNTW3ZsmWjRo2aOnVqZmbmpUuXdu7caW1tfe/e\nvQULFgjeBBAoA5mDhBgxQP02aIVPaKSnp2egKweLxQIrGRoaumTJkitXrjx8+HD58uVUKlVfX1+h\nD4B4NTc3t/ZBeXm5hISEg4NDZGTkwoUL4eAMVcuLhvGhk0JHBcgD2Avnul8WalCAXQNuBqUP6P7y\nuk7GxsZqamouWrRIVVUVdDyDdoeKzR4eHi9fvnRwcJCWlpaVlY2IiNi0aVNQUFBUVNSoUaPu3r07\nYcIEMFnW19dDnnA6nU4gEIDpP3Xq1NWrVx89ekQikX6TrwLA9AV+lZeX9/uCiYmJEMz59OnTsmXL\nuP8uGHH8+HEzM7Nt27axeSpPQiOpqan19fVeXl5IH8ERNLAWFxffuHFDVVX1x48fAzGmsE11dXVh\nYWE3btwANCCRSFu3bm1vbz9y5EhUVNSGDRvev3/v6urq6+urrq7+5s0b4AM+fvz4/v17PB4v/OAM\nCj09PU5OTo8ePXr06NHs2bNVVVW9vb1v3rxZXV29dOnSvLy86dOnQ374IYGNjU13d/enT59Gjx4N\nOI+66Ghqam7evFlaWjo4OHjjxo2HDx/u6enhcrm7du3S1dVNSkpCjRhYLFZFRQXqA4sIXC43ODjY\n3t7e29sbJVAIgoCdytvb297ePjg4OCcnR8QGgbg9evTo0KFD2MGC+gdlDeHWCw4ODgsLo1KpYCUb\ndABFRUUeHh6ysrKdnZ1xcXFv375VUVHhZayJRCKk3RiSb0BQUNCTJ09GjhxJJpMNDQ337Nnj6elJ\npVIJBEJdXZ2zs/PKlSsJBMLVq1chKxaJRFq4cCGTydy3b9/Hjx9HjBgBufN27959+/bty5cvUygU\nOzs78GMUcRj9ApPJBG19bGyskAD/JUuWFBcXCz7X0NDQ0NCwsbGJiorKz883NzcvKytbvXp1eno6\ngUBYuHAhiChWVlZjxozB4XDv3r2zsbEJDQ3t6em5cuUKk8l0c3M7efKkkZFRfn6+urr6qlWr+uVt\n/mdeXGBVd3d3B32JpqYmpy97BeDoxo0bMzMzVVVVMRiMkpJSZ2fnx48fq6urwfSAcoRAcbS0tLKy\nsuzt7d3d3TMyMgQTrQNZPHLkyLBhwzw9PV1cXOzt7VetWiWKPgOPx7NYrFWrVt24cSMoKCgzM3PY\nsGFHjhyBIFMhngN8T378+AEOJQPhFvwkKyvL5xEsHBdR76jc3FyI7xG8COGd+vr6zMzM8ePHb9q0\nafny5cnJyadPnzYwMGCz2SQSadeuXWvWrLGxsUEzBQr2BRFUFhYWy5Yt2759u5+fn5iYGBaLTU9P\nDw8Pv3Tp0pw5c2JiYpYsWcLhcAICAs6fP+/q6rp48WJQQnA4HPBnXbZsmSjxvOjgwfUQHDfHjh37\n6dOnYcOGzZw509HRsbi4GDhjHA4XFhaWmJgICc4QBOnq6jp79qyZmZmnpycej//06ZOsrCxfj2pq\namiYSEBAwJ49e8LCwk6dOsVLhuCCh4sfPi8oKIAqhYJKKSBhJBLJ29v70aNH06ZN45sOeknv2rXr\nxIkTDx48sLOzCwwMvH//PqicGQwGHAEWi0UkEm/cuFFbW7tt27bi4mIItOJyuS4uLqdOnaqoqFi9\nevW8efO6urr6FYSwWCxvVdKB1jYzMxPNfiwEgPsR/g6CIEwmE67G4ODg+vr6jx8/lpWV1dfXQxnS\nt2/fIgiira2tpqY2cuRIdXV1IyMjS0tLdXV19PNf4AZgF/q9PNANEvxjSICaU3iBz4KE2tPDw8Or\nqqo8PT0/fPggShUxkLEnT57s6Oi4cePG/Pz80aNHJyUlNTQ0PH78ODQ09OnTp8rKysXFxWw2e9Om\nTT4+Pnp6eqtWrUL6XLqrqqp27tx56dKlEydO/HLudOTfdjfUCMBLDdAX4Pyi7hn9Ql1dnY6ODh/7\nnpmZGRAQwOGJs0b/yMjIGD58OAQ1Cq42XBDTp0/X1NScM2fOt2/fxo0b1+8UgJ0NCAj48uVLXl5e\nb28vFovNyMg4dOiQlJRUYGDghg0bZs+efejQoTNnzgQGBnZ0dJw8eTIzM/PixYtaWlpbtmy5ePGi\noP5sSNDb2ztt2jQymTxjxgw8Hh8VFTV//vxJkybNnDkTCsScPHnyzJkzc+fOFXGz4B09PT09Pb0n\nT56MHj2aw1NxB+mzgAUFBR07doxGo3358mX48OHnz59vaWmRkZGBY9Xa2tre3n7jxo3c3NwdO3aI\nMhG4UCBM7e3bt7ALHR0dkO/lwYMHMjIyGAwmOjra2tr6zp07vr6+ImocXr58SaVSJ06ciPy22wl8\nPnHiRCqV+vLly34t0YKwc+dOFRWVV69e9fb26uvrx8XF7dixg9dbQF5e/ubNm/7+/pMnTxZxUhgM\nRllZ+fnz5xUVFdLS0oqKiqBzFRcXd3FxiYuLi46OlpKSolAoEB2FIIiOjg40PnXq1OXLl0NMMIfD\n2bp168aNG1taWrq6uvT09AYqkyYigDOMpqZmS0uLgoKCENyWkJDo9ydUMldVVa2oqEAQZNWqVZWV\nlU+ePGlrawsKCmpvb7969ers2bMBJ5lMZmJi4oIFC+Tk5BISEuTl5VetWrVq1ar09PSKigpVVVVu\nX/IKPvz/nzGm4Ffa1dUFMfjolqP6DDU1NV1d3cePH1+8eBG8a+Pi4q5fvx4YGHjgwAGItEB5Uzab\nramp+erVKxcXF1dX1ydPnvB1B+/cuXNn/fr1lZWVWVlZkCdcFH9ndNUOHz5sZ2dXWVm5ZMmSI0eO\nHDt2bCBqAumi+B6uWrUKPLiFw6RJk/jGLyRdFFC0xsbGyMjIPXv2DOSqD/dKVlZWW1tbZWWloqJi\nbW1tSEgIbxJjBEHExcV37NixefNmISgLVGn37t23bt2Kioratm0b+GXOmzdv0aJFZmZmlZWVgYGB\nRCIxMjIyISHh06dPT58+XbVqVVFREZlMjoqKam9v3717t3CHEL6RczicZcuWjR8/PjQ0FATKgoKC\n9PT0SZMmmZmZcTgcf39/GJifn9/w4cNzcnLU1NS0tLSkpKSio6MRBIGcKWvXruVrX11dvb6+Hukz\nSMXExMyfP3/q1KlTp05F6/3AON+9e/fs2TMo6fHu3Ttvb++BxGg4usOHD0fz0vP+BCbFkydPRkRE\nJCQkeHl57dixIyEhIS8vDxIdo0Yr2Mc9e/YEBARoamqCTQQ8T5ycnKSlpR8/flxVVWVgYCBEoS58\nbTEYDJVK3bx58549e1xdXUXclH6BQCB0dXW1tbXJy8uj5UzU1dXV1dX5qm0NNBi4VgkEQltbW1dX\nlyjsKaBHY2Pj/v37Q0JCPn369PXrV1VV1bS0NEhHsGHDhmPHjgUEBIwZMwbMT2ZmZseOHQsPD4fo\nXVGmBq99//795cuXZWVljY2N6urqhoaGnp6e8vLyqIUaXka1s8ePHy8qKpo3b97r169hT0UhNRcu\nXBg1atT8+fPBZ1pNTW3p0qXe3t7BwcE3b940MDDAYDCzZs2SlpaeOXMmmUz+/v37w4cP09PTX7x4\noamp+ejRIw8Pj9/ZRyxPziZ0wOgfvKK4KGokFoslqCag0Wji4uJ8/C40RaVSwR6CIEh7e3tqaqqD\ngwN4gcPVgMVia2trt2zZIi8vX1dXJ9gjt88P7cCBA+np6QUFBWPGjLlw4YKvr6+jo2NQUBCEhbHZ\n7JkzZ+JwOD8/v8TExHfv3qmqqvr4+Jw5c8bZ2Xn48OEjR448d+4cJCz7tcVkMBhUKpXL5dJoNBaL\nJSsry2QyoUpzZGTkunXrZs+eHR0dPXfuXNEdkTkcjoyMjJGRUWZmZnh4OB8Co6lXbG1tT548efLk\nydTU1MmTJ4MrBZVKXb58eW5uLtiaDh48CG7Qwjvl9qWIWrt27bJly6ysrIAw0un0z58/IwgCjlss\nFsvKymrZsmVr166dNm2aiKmjIAs1KJv+CGOqra1tYWGRnZ0tnDFF162iosLFxUVCQkJCQmLRokX3\n7t3bunUrb5k3LpeLx+ODg4Ozs7MhDknEa4tEIqHJ6kGljcfjPT09//nnn9u3b69evXr//v2RkZGx\nsbFEInH37t1sNnvFihXy8vKFhYXg9QdcoJiYGOgpkd+oKwEf0un01atXEwgEOTk5KSmpzZs3A9fL\nexLBqpmamopqCngB5o7D4VRUVMCEsnnz5jFjxkDkhoWFRUVFxdSpU5G+PAMEAmH+/PmysrJ6enqQ\n1f/t27fv3r1DEKS8vFxFRWUgVvt/xpj+888/FRUVnz59As05Hy/F5XJ37tzp4+Pj4uIyduzYlJQU\nLBY7atSo27dvh4WFlZeX37x5EzKxwYegb4C6UGZmZmhqFQB4raioiMFgODs73717V1dXF5gAEbcZ\nXhs7dqyuru7du3ednZ0PHDhQVFQ0ZsyYfg0QUlJSampqfA8TExMFuVVBEGQy1NTUpKSkhIxt27Zt\nioqKkM2+32MDDy9fvuzl5aWnp2dvb79p06YzZ86AsyaNRuvs7MRgMElJSUFBQSNGjJgyZcpAhhVg\n8VVVVYGbCQoKamxsbGxs3LBhQ1RUFOypnp4egiCBgYFmZmZgMnN2dq6qqlJRUdm3bx8Ubh5Seq+D\nBw8WFxd///6dw+FQqVRPT8+Wlpbw8PDS0tJLly5dvnzZysrqyJEjly9fplKpAQEB4eHh1tbWRUVF\nHz9+NDIyArfU9vb2BQsWCK4taExhXvPmzXv79u20adNevXqFFjlraWlZuXJlUlKSrq6urKwsBoNx\ndHQULuVjMBhVVdWWlhbBn/B4/OXLl9esWXPs2LGFCxceOXJk9+7d/v7+FAolJSWF1w0fi8XCLQLe\npampqeCiwGQyJSUlHR0d4+Liqquro6Ojwdts0MVElxTpYwRxOFxhYeHbt28fPHjg5uYGaZ5QeU/E\nBgFIJBKLxdqwYUN8fDwo0vgSJQpygbw/wXyh0w0bNkBi8EE7hTbb2tpOnz4Ne3f//v2dO3e+e/eu\ntLR0/fr1tbW1UNzP2Nj42LFjBAJBXl7+1KlTgYGBojOmAGfPnr169aqcnByBQKDT6R0dHStWrJg2\nbdq+ffuMjY1R5SLSx7ERiURgJa9fvx4QEDAowgM7ZWho+PDhwxkzZhgbGwcGBurr6xcUFLx48SI2\nNnbq1KmLFi06derUrFmz6HT61KlT582bd/v2bUVFRSgr6u3tTSQSf1lXCuvZ1tZGIBBAAd/d3U0k\nEsXExDo7O0kkEpFIxGKxLS0tUHxBlAZxOFx7ezvfQz09vbKyMj6+FgS2sWPHxsfHNzc3Kyoqnjx5\ncseOHdu3b9+1axfv8urp6YEVUlDwAzNlV1fXtGnTqqqqvL29IX/ImzdvxowZQ6FQRo0aBfmbcDgc\nZJvKzMy8e/cumUz28fHx9/cPDAxksViVlZXbtm2bP38+REz+WggU1GLAYDBiYmKHDh0yNjYGYUlJ\nSQlU2h4eHqDzFh1gc8eNG3fp0iXIYczHrMA4d+7cKS8vn5WV5eXl9eDBgylTpmAwmFOnTl2/fj0m\nJqa9vf3ChQtz584VhWjAwTx06FBnZ+euXbtQRhaDwYC5nFdo2bVr140bNw4dOhQRESGKJ0ldXZ28\nvDyBQPhNCzU6VDjg/UosvIAOjEQidXV1Qe+zZ88+efLkjx8/eINZW1paAgMD379/v2LFisePH4s4\nTmD1UKTF9ZWicHNzCwsLgwrh9fX1ly9fhrsSfjp16tTu3bvl5eUh0QqaDgVt5zd5dyaTWV5e/ubN\nm56eHhUVlc2bN3/+/LmhoYEvoAiHwxkaGjo5OfXbCJwFFRUVSOIJ9yB8bmpqampqCvuO5gLicrnA\nqsI7srKy8Mn379/NzMwQnsSLvPA/Y0zj4+NPnz6toqIiaLkDFsHAwODJkyeqqqpdXV0wMQRB1qxZ\n4+jo6OTk5OnpCWWIUSoMLm4qKir79+8PCgravn072iDs69evX7FYrLm5eXh4uIuLCzIU+QMVKVxc\nXB4/fhwUFITFYr9+/TpmzBg+TIX9CAgIgNrKvDeEoOZgUEDNW8CYCuYcxmKxdXV1169fj46OHiiq\nEabZ3t6ekZFx+/Ztb29va2vrVatWJSUlmZubi4uLe3t7w2hXrly5ffv2T58+oZGqA42Ky+Vu2LAh\nLi7u+PHjI0eOZLPZ5ubmixYtCgwM1NPTA/5bT08vMDDw8uXLISEhbDb769evt27dUlZW3rBhg4hK\nCEDZsrKybdu27du3D8QyW1vb5OTkS5cuEQiE7u5uFovl7+9///79DRs2IAhCIBDKy8vpdHp4eDge\nj9fT08Pj8R8/fgwLC9u7dy9E1/F2oaamBs40MCkOhxMdHV1dXT19+vT8/Hx9ff2fP386OTlxOJwH\nDx64uLiQyWTezwfCHw6H097ezmcch/169OgRrHNISAiXy83MzCSRSFevXk1ISOi3qV27dqmqqi5f\nvtzW1haypYB31+LFi6dOnWprazt79uwh8SLom0AOIHdEWlpad3e3EPlnUACm9u7duxBMMGzYMOiI\n178e/ZfbF6yD9OkkoJFv374tX768sLAQh8OJHvyEx+PB24fL5UpISMycObOxsTE2NjY6OjozMxOP\nx5eXl+fm5uL6QElJaUjBrTBm8HVBo5jLy8vz8vJiYmJGjx4N+lremxiImK6uLiRCnj17NplMHvRW\nA1Wrs7NzQUFBdHT0oUOHWlpazM3Nra2tZWVlFyxY0NraumbNmtevX2tpaWlra69YscLHx2fcuHGg\nBYTVRp3/eAc/KABfmJWVFRISgsfjfX19t2zZsmfPHrg+ly1bFhgY6Orqunbt2kePHk2ZMuX48eOi\ntKyrq/vjxw+U0sKJdnNzi42NPXnyJKYv5ALpO4CzZs3avXv38uXL79+/D+lfnJ2dP3z4ICsri+YD\nl5GROX/+/LFjx1JTU/m6g/3du3dva2trRUUFKuSfPXsWQRA/P7/z589LSEigdkYCgeDi4pKXlycr\nK0uj0UaMGIEgCB6P19HRuXDhQkpKysaNG0+ePIn8UiHDlJQU0P6KiYlt2rQJXZAXL17o6upisVgy\nmTzU1BNwpiZOnHjgwIEvX75AVABf7gIEQfT09I4ePdrb27t69erp06ffvXtXXl7+wIEDhw8fhgx3\nJ06cqKmp0dLSEn7K4Baoqqrau3fv/v37wf0dJSC8dwRsn5qaWmRkZHh4+OLFi7W0tAalS6CtQPrQ\nb0hLwQeob4m8vDxUlxAFSCQSKGW4XO6IESP09fWvXbsWGRmJTo3JZKqpqV2/fl1XV/fMmTMrV64U\n3aDPJzBwOJxhw4Z5eXlt2LBh6tSply5d8vPzKygo4HK5I0eO9PT07OrqOnXq1PLly6H2DWpDFp0f\n5fb5wSM8mU8gEprD4UhKSp49exbUOqA4e/z4sYgtowD4pqGhkZ2djfDUnUZ4KkvzLUK/77S0tIAa\nuF9/yP9bmQIB0AqcW7ZsUVZWTkhIQB0ROBzOiBEjcnJyIEXU9evXNTU1B+UvUbUK3Fvl5eUODg58\nBrhBAZZPW1s7MzNTQkICVAvI0AnWHwTo+tKlSyQSae7cuf16ZSF9jCmk04LykvPnz3d2dj58+PCX\nL1+ePHlSUlIyc+ZMDAZTUFDAYrEgo+8vK11+H3jxmMvl+vv7u7i4WFpaqqio1NTUbNmyxcPDw9TU\nlMlkor4sM2bMgNIMRCJRXFycSCSiuaKqq6s9PDwmTZoUFhYmSChNTEzOnj1Lp9PFxMTQa/LWrVu2\ntra+vr45OTkbN27s7OwsKSmBQDGwUGD+HfjCB7ARaWlpY8aM4X2OxWJpNFpAQIC1tfWuXbsAzx8+\nfDjoglRXV0NRO8jcDqcdVFba2tqQ9F5E4sXhcMAEw2Awuru729raLly4EBERERsbu3fvXk9PTwkJ\nCSgaqaGhMajXKS9QqVRlZeW0tLSFCxcaGxsvXrx49uzZtra2fPm5APhG29HRkZeXd+vWrUuXLlla\nWubk5EyePHlIdzaLxZo5cyadTofw8M7OThqNxmazW1patLW16+vrnz9/rqenB2cWSr8OasTkA3Fx\nceBKgW7o6+vr6+vPmzfv2LFja9eu/fbtGzid8yIYh8PZtGnTxYsXY2Njw8LCBlVQYfrcWA0NDU+f\nPo1W0UMhNDS0s7MzIiLCxsZGQ0NDMD31L9/uQEwqKioqKyuvXLkC4iuDwbh69Wp4ePj379/ZbPbB\ngwfj4+Pj4+NHjBgh4urZ2Ng8f/58165dvA8XL168b9++w4cPb968GSXC2L5kedeuXXNzc1u+fPm5\nc+fWrFlz5cqVlJSUxMREyFQFbDeHwykuLhZMdVRZWZmTkwMZDO7cuUOhUAgEAqYv7s3Nze3u3buo\nFurUqVMeHh5GRkZkMrmmpgZCYeAogTTl5OQUGxurpKS0ZMkSwdxngsBrIuBwOLyxxmgUl5iYGFp3\n7RcAGreysmKz2UVFRQNVTocbU1xc/MKFCxISEhBMJiUlBcU4OBwOi8Vas2ZNXFwcH40SBCwWu3nz\nZm1t7dDQUDQOZKA3ORxOaGjo6dOnN2/efOPGjaEesf8+yMnJAZ0BM5Szs/P9+/cjIiJQVRQOh+vs\n7NTR0YmIiAgODnZ0dASl4K/dj1wuNyYmxszMbOPGjbGxsR4eHry5eB8+fNjT0xMaGor8qmMDpj8/\neJQmFBQUWFtbBwUFycrKQj4ZiGcQvSl0YNra2hwOp62tDXW8Qf6dBLDfAaDNtrW1cTgcIS4c/zPG\ndMmSJeHh4V5eXnxBcEjfNEJCQi5cuJCWlgYeqPAQg8EwGAxzc/PXr1/PmTMHwvC1tbWB0BAIhMbG\nxvDw8CVLlgj2SKPR4I/u7m6wQQyJp4SXJSQkuru7+RrkBRaLRSAQLl68eObMmdLSUt6prVu3Lisr\nCzdwgWn4ydnZ+ejRo/AEPp84ceLKlSshcR3f+ywWKyEhYdGiRZDJsl+0gJFDlQXg45lMpoaGRkxM\nDIIgRkZGs2fPxvUVMRIXFx800QwabtXU1BQSEgKpIkpKShYtWrRjx46QkBAwT1RUVJw9e3bXrl0l\nJSU4HM7Y2NjR0RESaG/ZskWIVIAWw8Dj8adPn87LyysvLwdfsfLy8osXL3Z3d1++fBlqTsTFxSUl\nJRGJxDVr1owfP97KyopGo+Xn50My8Pfv37u5uVlYWNy5cwfbVySCty8TExMSiVRUVITWsgN57tat\nWxYWFhs3bkxMTExOTlZUVGQwGILZGwRXBj5/8eJFcnJyYmIiuGGhvxKJxA0bNkRGRp4+fTooKIjN\nZu/atevdu3c0Gq2trY1v+7BYLA6H27Bhw8yZMydOnBgbG7tw4ULU0+7Zs2dKSkrJyclpaWngmC9k\nYOgAEATJyckJDw/v7e2VkJCg0Wj6+vohISFdXV3Hjx+/du0alUplsVh+fn4xMTHgGoVetMLPC7Dd\nlpaW7969u3HjxtGjR2/evKmoqAhuM+BsqqampqmpicFgampqGhoa6uvr6+vr8/PzKysrW1paTExM\nEhIS5s6di8fjoRrCoDPi7f3kyZN5eXmFhYWwbvBva2urgYEBFovNz88fN25cS0sLKNXk5OSwA2cU\n7heAyABhhb/hIISGhurp6U2fPl1PT2/Dhg3oMYTjqaamNnXq1Pv372/atEnI2UcQhM1mQ7U5pG+n\noLIr6hQB679o0aK4uDgsFtvZ2QnCNm7gdJgcDkdJSUl08ZtIJBIIhBkzZujq6mZnZ0tKSoqLi9+9\ne1ddXZ1Op8fHx69bt27KlCkUCgUjtNQ1Cl5eXgEBAeXl5VDlGPRGOjo6UVFRISEhEyZMsLGx4eNN\nx48fn5SUNHnyZBqNBrFcoFLi9rmbw5QfPny4cuVKvu42btx49+5dNTW1wsJC8Dvn/ZVvxyUlJQ8f\nPgxxyrC8uH8nMyGTydra2jt37iwuLhbMjscH8DnwfPCEN4yMdwt+h13D9NVzhnLcy5Yt63frMX3V\nfTEYzP79+x89eiQlJUUgELy8vAoKCiwsLF6+fLlu3TpbW1shQfSAyZDK7fHjx9CgEMYUUAKPx0P4\nHYTwC+fhdHR0wMUfztTvmKrRO6utrU1IsU0+kJOTgyAeGOSsWbMSExM/ffpkYWHBYDB40XLHjh3P\nnz+fP38+JEL6hdGCFUVFReXgwYNgSIHoOiwWC3Gu79+/t7OzU1JSAiohYheoaxYGg2lubo6IiMjL\ny1NUVNy6dStYhr29vZcvX25gYGBvbz979uxTp07V1NSAiAJi25BmAe/r6+t3dnY2NTXJy8ujCyUK\nsNlsIpHY1NTU2dkJklu/A/ifacUOHjyop6fn4OBQV1eH5UkVQafTMzIyJk2aFBcXl5aW5u7uDoql\n7u7u+vp6bF/lHmNj48TExG/fvr1//x64VTBqOzg46OnpgTSAAsxcSUkJRGFNTU3IDTukLYGXm5ub\nNTU1QZ6G6PV+G+nu7gbPRV7Q09MbMWKEpaXliAEAfkKdTlBoaGhAuWE+KCsr+/btG9Qn7JdewHHt\n7e0tKipydHSE/6K+E48fP66urobkz1C1aNKkSeCmNtA6AKH5+fPngQMHtmzZoqCgoKOjo6KiEh0d\nvXHjRj09PXNz8/Xr10PyVz09PahioKKioqOjo6CgsGXLlgMHDkA6t4F6uXz58ufPn/F4fFVV1aZN\nm3bu3Kmnp+fr6/vy5cvq6mpgLKAw6aNHjwIDA9PS0pKTk1+/fi0tLX348OHk5GRVVVUikfjs2TMH\nBwcbG5uUlBQwpApulomJCZTlQHjKMYMnCYTnW1hYwLqJcoaxWCyUhHV1dV2wYAE4SPD+isFgwsLC\ndu/evWrVqkOHDuFwuObm5kePHn39+tXOzs7U1NTMzMzc3Nzc3NzMzMzMzIxMJi9evLiuru7cuXPv\n3r07ePAgejE8fvx4xowZixYtCgkJodFoopAGGMDixYuPHDkC801LS8vNzZWVld2zZ09VVZWdnV1b\nW9u8efMOHz5MJpNhnWHBUY2ykPbhYsbhcAsXLnz//v379++DgoKkpKRycnLOnTsHPI2hoSFkNV+3\nbt25c+dycnKkpKSCgoLg/YULF6KNDDodFNhsdk9Pz8iRI7W1teGkQNwJgiCVlZWamprq6uqVlZW2\ntra1tbUMBgOikiG+WPRegMlgs9lMJhNOAWoR9vLyio2N3bhx46dPn+Ad9BMulzt79uyPHz+Wl5cL\nX8CIiAg1NTUVFRUVFRU1NTU1NTUlJSVFRUVlZWU1NTVVVVV4qKurW1dXl5eXB7VqNTU10a8EQU1N\nLSIiQvQ5crlccMFUUFBgMBhdXV2urq5v376tr6/v7u4GJYe/v7+enl5vb68o2tmpU6cqKyufP38e\nZWRhfdasWTN9+nQfH5+Ojg5eXgcuaXd391evXj179szS0nLChAmXLl2Sk5MDkQDkjcjISAqFIqiA\n+PHjx7Jly+rr6yFpIvPfQKfTef/b3t7e29sLidKAfrJYLN4X2traqqqqli5d+uPHD+HTBJkzISHh\n9evXUK5ZiFHlN41RMFRXV9fnz58LxygQhCQlJW1sbJSVlQsKCqqrq1++fCkmJmZpaZmRkeHg4ABJ\ncwUBRC8mkxkaGjpp0iRw7hp05EDVp0yZMmnSpNDQUCaTibpI9gsaGhptbW1MJvM3vScBMBgM7BrY\niIUAevXIycnRaDTUm8jV1RWHw6Wnp3N5ABSBGAzm8uXLpaWlW7du5fOWGdIIuVyuh4cHHo/Pzc0F\nyRPILAaDKSwshEgyILm8lEQIwPDgaISEhCQlJTk6OkJRbmD609PTjx8/Pm3aNG9vb1Bja2lpAc/6\nC8sOOGBnZwcVDZhMJpFIxIkMRCKRyWTu379fQkLCzs4OGeA4/M80pvLy8s+ePZs4ceL48eMhXRSU\nIT179mxoaKi1tXVeXh6k6oW9vHLlyrZt24yMjIhEYm1trYmJybt376AUJIfDERMTq62ttbe3V1RU\nfPbsGZ83Jy9j2tjYaG5uXlJSggwxawy8XFJSYm5u3tjYKJwxxePxgjFMISEhQ1yk/w/ExMQGugOu\nXbtmYGAAyfDYbLZg2j+44798+fLjx4/p06fjcLiOjo6GhgZQQsfFxVlYWIAPcm1tbV5eXmRkpHBB\nDWTi7du3y8nJbdy4kcPhkMnkU6dOTZo0adKkSe/fv4+Jibl06RKHw4mIiFi7du3ly5evX7/+9OlT\nsDhv3Ljx3Llz27dvP3fuHFughgfA4sWLJ0yY8PTp08DAQF1d3e3bt//8+XPu3LmQP5XNZs+YMYNA\nIDAYjJUrV7q7u+fn50PqbCaT6e3tDY2AgXX58uVnz55FtYyCfcnLy2tra2dnZ69evRqdNby/aNGi\nyMhIbW1tCQmJQYkyqLUePXp05MiRgoKCZcuWxcTE9Eu82Gz2+vXryWTyypUrGQzGqVOn5OTkLly4\nMGXKFMHcUgiCWFtbz5kz59WrV8ePHw8JCZk+fbqpqWlHR0dNTc2KFSuGDRt29uzZ3t5eETOJwGma\nO3fu8OHDFy1atHTp0idPnsjKykpISGzZsiU9PR0twtHT0xMfH3/79u3q6moFBYVJkyZt3rwZvLr7\nbRmLxQIvyO6rnWZiYgJxqWw2m0qlUigUKpXa09ODIIikpKSEhASZTIbC7tACty8wH5oS/f4mkUgj\nR45EEERWVhZiP1VVVcFDUVJSUk1NTV9fX0dHZ9iwYQYGBgQCwdjYOC4urqqqCiyhIt4xcC54y5wA\nqwrliFatWnXx4sUdO3bcvXsXHTkcRnD2z8rKMjAwENLX69evXVxcoqKiBjoXvDCokAAHf+PGja9f\nvxZldijAjlRVVenr63/58sXS0rK2tjY1NRUUzzU1NXv37n369KmIV5qUlNScOXOuXLmyefNmNFIH\n2Jf4+HgTE5PAwMBbt27xEi6InBs3blxhYSEkbJ89e/b69esNDQ3Brnrx4kVItYZm20GBSCSCIWuo\nsk2/AKPiLc3dLwBxuHv3LlAMCEL9zxXUhanZ2Ng0Nja+fv16oJpbAEDKgP8uKytDEERKSgqekMnk\nkJAQb29vyPrUby9Xr14tKir69OkTiu287wh2iunzID927JiZmdn169cXLVokZHhOTk5RUVHV1dVw\nNH5TY4rBYKqrq4uLi4VUDQBGU1xcHI6PrKxsb28vpIlgs9kEAmHmzJm3bt1av349MItosTE6na6n\np3fu3LmFCxdOnjzZ2dn5Fwz6oPVQU1PT0dHJzc318/PD8IQ3TZgw4ciRI7q6unPmzFm0aJGJiYko\niuTq6moKhYIgCAaDSUlJCQ4O3rp1a1lZmbW1dXZ29ty5c/X19Z88eTJ27NgdO3agVkHhRlEhAOwy\nmUw+cuTInDlzkpOT+Ty+UNsygiBwI/CNv6enp6Oj4+bNm7yutHzwP2NMORyOjIwMlEp3dnbOzMwE\nX+lp06adPHkS4kxRJw8EQZKTk/X09KytrSkUyrhx4378+LFw4cLt27cDD1pTU+Pi4qKgoPDkyRPB\n8oywLqNGjWIwGPn5+fPnz/f29u7p6YFAFlEOA4yEQqFkZmYmJSXl5+czGAzwxRzo8184Y+CHznv5\nDdpUV1dXeXn5mjVrgoKC4EpG+px00a+4XO7r16/FxcXHjRsHiFtdXY22cPXqVcD+R48eKSkpWVtb\nC7nzgJ0tLi4+f/785cuXIWkIFouFQimLFy9+9uxZcHAwZLvIy8tbtmzZ1atX9+/f7+bmhibY3717\nN+j5LCws+sVLbW3trKwsT0/PV69eJScnY7HYW7duZWVlpaambt68+eLFi+PHjz948ODLly9ZLNb8\n+fOhCGRBQcGnT59MTU0bGxuXL1/+8OFDtNgsV2islZ+f3549e1pbW9FKvKDC0dPTi4uLg1IxwpkA\nWPDHjx97e3svWLBgz549jo6O/X6F6YvQDAwMlJCQ8Pf37+7uPnjwIJlM9vT03LFjR2RkJJTcgPMv\nJiZ26dIlc3PzmJiYtWvXXr16ddGiRQUFBZ8/f4byg1euXLG2th5SXB3oFUaNGnXw4EEPDw8pKany\n8nJ9ff2ioiJbW1vgSjMzM1euXNnU1OTg4ODi4lJeXg5lA4lEItTEEmxWXFycSqXeuXPHz88PtFCg\niAJpXkpKaqDIKvB/QDcIj8cnJiZSqVRxcfFB5wJfQUAJgiAjRowADdDSpUuh7BbUFkIQBMwCwCMW\nFRWhXQvxFeYFuB56enouX7789evXadOmTZo0CX0O8vP69esXLVoEiMR7kZPJZFtb27y8vKVLlwrp\ny9DQsLy8XEjW618ALBY7pOLaXC63ubl5xowZkH3z6dOnFhYWfn5+ly9fZrPZW7ZsCQkJ+fjxo4h2\nfGhw06ZN58+fj4yMjI6ORrNQcblcOTm5O3fuODg42Nvbr1mzhiMQOqaqqnrnzp2bN2/GxMTY2Nho\namqSyeTKyko6nb5hw4ZVq1YJ6pM0NTUhCcCfNQ0LcTCFYXd1dW3cuHH58uWQGfTX1GmiAJDQrq4u\n4L1mzZqVlpY2YsSIgcJegctvbGwsLS21s7Nzc3MbP348Foslk8ksFuvkyZOCbspIH8Hs6OjYsGFD\naGjo8OHDBZkwLpfb1dUlOFnAjeHDh4eGhm7YsGH69OkyMjID7YWDg4OEhERGRoZwmU0UgM8zMjKg\nWsdArwGj+enTJ4gXVFdXRxlTaMHX1/fixYsQGdbV1dXU1AQ8FuDkggULUlJSFixY8OnTJyHzEjJI\nsAghCPLt2zd4CDc+k8ncsWPHlClTbt++fe/evYMHDzo7O2/atAmyvwnpZcmSJVB7FuDw4cOHDx+G\nv0NCQubOnQtsa35+vqmpaXd3N/CRv6O2h2+vX79ubm7u7e3Nq/AGlMPj8Xl5eVwu187ODhz6eV8g\nEAj37t27cePG7NmzBxrGH2BMOf8uhCrir8C2KygoZGRkuLi4ODk55eTkqKur6+vrP3z40MbGBkow\nw7nq7u5+9uzZrVu3Zs2aJdhFfX29k5OTjIxMRkZGvyVJoS81NTWoP3vq1CkikQi2xSElLYLqYQ4O\nDqtWrTIxMVFTUxtIZqLT6XBoeaG0tBRcCPoFKSkpS0vLflvr6uoaKM/UgQMH9PT0Tpw4cf78eRcX\nl/Xr17u4uACFAqkI7t27d+/Onj373LlzK1as8Pf33759O+gAsFgs0FwMBnPlyhUHBwcZGZmB1gQO\n1ffv39euXevl5eXv74/w+DVv3rx5/PjxAQEBjx494i1J+uLFCyATqArZ39//7t27a9euRUuS8qGH\no6PjjRs3UlNTly1b5uLiwuFwfvz4Afwii8VasmRJQkICcJxYLNbAwGDBggXR0dEKCgqmpqYgI2Kx\nWMj3JBw5AaZPnx4SElJYWDhx4kSUOMLduXz5cvivcCSBu/bo0aOTJk26cuUKMtihgIksXLhQQkLC\n19eXSqWeOHFCTU0tICCgra3t+PHjSJ8vDofDMTMzO3jw4Lp167y8vK5evWpkZHT16lUsFkun001N\nTR8/fgw3jejpopA+m3tycrKtre2JEye2bt06a9as0aNHp6SkMJnM/Pz8CRMmzJs37+jRo8rKyl+/\nfjUwMKBSqVFRUbt379bX1x/IrQ1BEH9//97e3kWLFqHPge8caCm4/w4gxWKxly9fDgwMRIYo2nH6\nwjIA53n/QDvC9KUFEDKegQaJwWAaGho8PDzq6+uhCOSiRYtgp5A+zej48eMVFRUfP368cOFCVGMH\n36qpqQ2awsbJyenevXs/f/5UUlL6I9qj5ubmoqKifh3uBQHWf/To0YsXL5aWlj5y5IiqqqqXl5ea\nmpqjo+OOHTtkZWX9/f3FxcUTExMPHz4MGqZBiScQ+ZiYmIULF/r6+tra2sJXgLH29vbR0dEhISE2\nNjZjx47lbRBQFIPBzJkzx8/Pr7y8PCcnp7W11dLSMiAgQFlZGcQevu5MTEwePHgAsYy/zx3CKauq\nqupXpwgAaFZWVlZdXQ0WpF/WRQ0KsD40Gs3Ly+vz588PHz4MCgpydHTMzs4GiwG6JoCQKKofOHBg\nwYIFDAbj0qVLEhIStbW1Dx48iIqKolAo9+7dy8jI4OsIsHffvn1sNjs8PLxfIYREIoEptl9bDYfD\nCQ8Pj4+P37dv36FDh/pFFQ6HIyEhMXXq1NjY2GXLlglH+EGPA0wWsqqBjUvwBS6Xq6Wl5ePj4+rq\nCnJyb2+vsrJyT08PGsRjZWU1bNiw0aNHMxiMnp4eLS2tmTNnorZ+LpcbFRUFoXVQH1H0vQYaBcqU\nioqKkydP8i4L+NdZW1tbW1tHRER8+PAhIiLCz8+vuLgY/LMH4uEoFIqvr+/hw4chaBjhoXVwy79+\n/VpSUhJyF4qujBM+i5KSkkePHiUlJYHAzweBgYFlZWUkEqmhoUGwvDlM09vbG+zP/U5tcMYUM0Bw\nFgpCWG/hum6gTbKyshkZGe7u7uPHj8/OztbW1jY2Nn7z5s24ceNmzpyZlJSEx+P3798vLy/v5OQE\nfuWAJRBmVF1d7eTkpKioCBZJ4bRy9erVK1asOH78+Pr168PDw2fPno2WfxQyQTDo1NfXh4eHb968\nmcVi3blzJy4urt+XYcvNzMwgKpwXA8LDw+/fvz9QLxYWFm/fvuXLSwx/zJw5E6ztgvgkISGxbt26\ndevW3bt37+zZs25ubiNHjly8ePGyZcsABVksVk9Pz5cvX5qbm8+dO3f48OGNGzfytgB7FBcXl5+f\nzxc8y7cIOBwuKyvL29u7o6NjxIgRPj4+6P6CeS4kJKSsrKygoODLly9cLtfU1NTKyurVq1eQz4jb\nF7uAxWKrqqo+fPhgZWWVkpLi7OzMx1SNHj36yZMnEydOTElJ+fr1q7GxsZGREQ6Hc3d3B+rg7+/v\n5OQEzpHg8gsaSnB79fHxOXv2rJycnIiVpdTV1W1sbM6ePevq6sq7wpi+YN5BjfhYLDYlJSU7Ozs3\nNxds+sKTw4OIzGKxZs2aBRnRu7q6Ll++PHz48KlTp3Z1dZ0/fx5a4HK5dDo9NDT01q1bc+bMefPm\nzYEDB1auXGlpaenk5NTb21tZWblmzRpkiBEVcGm9efPm/fv3paWlhw4dSkpKgqyQ+fn5gYGBs2bN\nunbtGoIgHR0dw4cPB6+bXbt2SUtL79q1CzLY8QGFQlFQUFi1atXixYsTEhK2b9/u7OyMcpwooIZ+\nwRaysrJ27979/Pnz7du3nzp1CgR9EQHFQ5T7RP9Ap8z3MjJEzdbWrVu7u7s/fPigqqp6+/ZtPz+/\nqVOnuru7o5Z3bW1tWVnZr1+/8rYMV5eqqiqqph0IJkyY0NXV9fLlS19f31+wEvICfP7y5cuurq4J\nEyaI8gl0Z25ufuHCBXjC5XIXL14Mf0dGRsITX19f1HNalNBy4C8XLFhw+/ZtHx+fjx8/ysvLo3p0\ncGtJTU0NDAx89+4dhid7FDokOMWGhoaGhobwfPTo0a9fvxa0LCEI4u3tvXfv3g8fPgjmURoqwCA/\nfPhQUlJy6dIl4S8zGAxwasIOHBv0mwD8bk9Pz+TJk8vLy1+9emVkZGRqajpt2jQvL6/4+HgXFxdY\nVXgf1FTArjk7O797927kyJHR0dFRUVFubm7gO7t582YNDQ0+xhQm/v3792PHjp04cQJicXhXEvZF\nRkYG0irzPkT/ZrPZSkpKBw8eXLNmzYoVKwwMDPrVuSIIsnbtWhsbm7t37/r6+gqh2P2WbkcBPrx9\n+/bHjx/Pnz+P9He0AbvExMQSEhK8vb1bWlqwWGxpaemNGzegcRi2oqLi6NGjU1NT9+zZA5cOaHOx\nfbGzmpqaSkpKcMxFxzGYPpVK9fb2zs/Pz8jIAD+ily9fbtq0CYPBDBs2bOTIkSNHjrS0tFRQUBg3\nblxcXJyRkVFHR0e/00EB8gkIifcCISE6Ovp36uvyTeTSpUuamppubm6ofQ/pYxKCgoJu37599uxZ\nqJkM0gKKQjARNzc3TU3NS5cuRUVF9UvrRCowKEQZw+FwSkpKuDxZCfkm8O3bt9bWVsFfAYA2ycvL\ngwl+woQJEDNkbGycnp6elZXl7+//7NmzgwcPhoeHQ5VtPB4PGY8JBEJDQ8OECRPAJUBeXl64qw2X\ny505c6ampubatWt37typqKgIMc44HE4IhUXN4gsXLlRUVNy5c+fatWs1NTVBihLkFOFc+fr6wr3O\n+8L169c7B4bc3FzQKfLxRgiCXLt2DS6Dfo01sDszZ8588uQJVEXat2+fqqpqWFjYhw8f8Hh8fn5+\na2vrjx8/rl+/vnHjxu7u7lmzZmH6AGa3atWqf/75B02TOdBqMBiMjo4OdXV1RUXFhoaGpqamnz9/\nNjQ0NDY2FhYWTps2rba21srKauHChf7+/lZWVrW1tdOmTSssLGxsbGxoaPj582dTU1NDQ4OioqK2\ntjaFQumX3ECpvXXr1pmamk6ePLm1tXX+/PmxsbHm5ubJycnLly9ftWoVFNReunQpHO8vX744Ozvv\n37//9OnTiYmJcnJyIrp5AVcRGhp6586dwsJCzL/9zXEDxzsDAMPNZDLXr18P2ZG4PFk5hQPwplOm\nTHn+/PmdO3dmzpzZ3t6+cOHChISEWbNmZWdnV1VVYTAYMTExMTGxu3fvfvjwITo6+p9//hk9enRu\nbi5InAQCAZQlovMxoEQpKSn5/PnzzJkzi4qKNm3a9Pr166ioKHl5+bVr17a1tZ06dQrV/YDHOofD\nYTKZGzdu1NbWBjZFsFksFrtr167MzEwqleru7q6vr79x48YHDx4UFxdXV1eDDQEaRBCkq6sLHMIe\nPHiwceNGfX19d3d3KpWamZm5a9cu7BBD5v8L8PDhwxUrVqiqqvb09Pj6+hoZGaWnpyN96wkyDIFA\nAKsIr6YWQRAVFRUIRBgIuFyumpqal5fX7t27kb7Qk18bJ/rt7t27QeUpelNAT9BsDKg8yfuEzWYP\ndWs4HM7FixcRBIGk7mizQN/Onj37+fPnkydP9svVQRke0LtDki9TU9Pa2lpBtTeXy4Wog/379/+O\nTggFDAazf/9+a2vr0aNHD7SG0JGioqKUlNTnz59/Z+OEAGAXhULx8vL6+vVrRkaGkZERpNQ4efJk\nTU0N1MCbP38+qBh6enogbBFYVRaLpaKisnr16vv370Oo5evXr0+cOAGljPj6go1et26diYkJVI7t\nl6Gk0+mPHj0C+xgicL/D+Q0ICDAxMVm3bh2mP/cwEFqsra0XLlwYHh5OpVJRMzffawiC/PPPP+Ah\n1q/mFYfDUanU8PDwhQsXCpFJYLPExcXnzZsXEhKyevXq4OBgOp2OXkOYPhd8FxeXNWvWrFq1itf5\nFaZQXV3d0dFhbm6OiKx6hFump6fH09Pz/fv3UAIaQZCYmBhHR0cFBYXRo0d/+fLl1KlTPj4+Ghoa\nW7du5XA4eXl5SkpKQkJZ0DFjMBjQQDEYDAaDkZWVBVGeFAqFxWLV1taeOHGipaWFM1hmlUEBbjdw\n2Zo/fz6JRILbAfLVkEgkLpebl5cXHh7+8uVL+OP169dcLpdEIgHnBpFSJBJp/vz5d+7coVKpvHW2\nUBB2g8JaGBgYLFy4sN8hYjAYJpM5f/58S0vLK1euYHgcjzgcDpFIBHX3/fv3wSDbby8ob5qdne3m\n5mZjY/Pq1Sttbe3Ro0dDkPjNmzfnzZsHTkiAbeDpWF1dPX78eDU1tfT0dCEGaHQubDYb4sh8fHzm\nzp1bUFCgr6/v6el5/fp18AfnYzS5fbEO3d3d8+bNy83NLS8vf/LkyeXLl+/cuSMhITGQeQKLxX78\n+LGkpGTevHm8bYqLi4viNie4yODMYWlp2a95ArypYE0sLCwuXbpUU1Nz+/btmJiY2NjYmTNndnV1\niYuL37t3z9XVtaKiwsvLq7GxMT4+nrdqiJaW1tixY4UYEGGmkyZNys3NVVBQgMhTPtDS0oqIiLhw\n4QKQKjExsYiICElJSdSZhhcqKyvr6uqgKDDfMo4cOVJaWjovLy8lJcXGxsbBweH169fLly+PiYlZ\nt24dvPPjx49Hjx5duHBBXFz86NGju3fvHjVq1Nu3b4cNG8bpK20vygoDAfX19Y2Ojt68efOTJ0/g\nahGF4sB9icfjd+7cWVFRAbGcg3oO8ALwpi4uLhkZGT4+PklJSSQSSVFR8eHDh48ePYLKMXAW3Nzc\nQkJCduzY4eXldeHCBVdX14kTJ6anp2OxWEtLS2QojClQgbq6ulOnToGdF/QNGzZs8PDwcHFxsbe3\nB0dSoHcwTiwWCz7QUEcRVPh8AIfI2dk5Nzc3NzcX0k9GR0crKiqSSCRxcXH4F4PBUKlUGo0G3l0t\nLS2GhoZz586dNm0aGAf7tdL+z8HQ0DA/Px9BEElJydra2oaGBvDPg8sMEInFYkG+Wz4cYDKZ4Kk2\nEADdCA8Pt7KyunjxYkBAgGDlEREBzVhXXFwcHx8/pMXks4+hSIU+/AU9Luq1lZaWZmFhsXfvXihH\njhr09fX1w8PDIyIifHx81NXVBTkh3jOFwWBMTEzOnTtHpVIFC1jgcLjdu3dPnjw5KSkJvN9+bQ3h\nw6SkpPv376elpWEGjo+Ggeno6Ojr69+6dcvNzU2U2DUAEbkEmBeFQpk8eXJZWVlubq6hoSGdTieR\nSCUlJf7+/gYGBnV1daWlpaqqqsAFqqmp6enpubi4TJ48efjw4XDvzJ8//+jRo6tXr3ZycgL/fkSA\n9sK+pKenp6SkpKengyudoACAwWB+/vwJwZoVFRW6urp8NBMOBR6PBwVteno6rIzg6nG53EOHDo0a\nNWrRokW3b99GBAyt8De4dCMCGIg6TS1atIhCoRw6dGhQ6g3XJTovCoXS29uL8HgVe3l5QWA799+5\n2GC/bt68yWAwIMpWRN0HDodrbW2dNGlSU1PTq1evhg0b1tnZuXLlyps3b/LaMNvb26uqqvbt23fj\nxo29e/d+/fpVWVl5UMYUj8c3NDTg8Xj0OFy/fj00NFRTUxNM+S0tLcA4/r46H9Y2KysLNO5AWHi5\nPiwWq6qq+uDBg9mzZ/v5+e3evVtZWRmOOa9wwuFwli5dum/fvqysrH6daIUtK2yStbV1vzVpgA0V\nExM7duzYlClTuFzu1atX4Sfwdbh58+bcuXM3bdrk7Ows3O0G5CQZGZnU1FQXF5eJEydmZ2erq6vb\n2to+f/78y5cvwBmjsjsOh6uvr584caKcnFxqaipEOw2qUYdeZs2atWbNGg8Pj2/fvr17927ChAlj\nxoyJioqCFMSCkJycDFrGd+/eUSgUDw+PNWvWzJo1a6AegSo9ePDg8OHDfIzpoDghiHzw+cqVKzdt\n2gQB6QN9iPqVcjgcLS2t9evXr1+/PjExMS4uLiMjw9TU1NXVNT093d/fX0FB4c2bN4IBrf0OQBBs\nbW0RBAF/Z1TtAWE6R48e9fX1DQkJgQIqHz58uHjxItAaOp2O60uxAR/q6ur2OwYEQWRkZOTk5AoL\nC8XFxR89euTo6DhlypTk5OSgoCBjY+OioiJzc/PJkyfj8XgjI6NVq1a9fv169+7da9euRQaobyYE\nMH35C0+fPm1ra7tnz55t27ahMcKcvgQ3gqQZJAE8Hp+QkLBnz54rV65AAvCh3txA+iGTEZfLBUZw\n9+7dsbGxhw4devfuHeRQjImJgXj24ODgp0+ffv78mUwmR0ZGGhsb89bmFQXgTaglDTsCMiuTyTQx\nMVFQUAAdG6TWKyoqYrPZ0dHRt27dAo2+sbFxZ2enYLPKysogtQPraWdnZ2dnt3fv3pqamh8/fvzs\ng/r6ei6Xq6GhodoHBgYGWlpa0Aho5np7e5lMJoSd/V8AWKV9+/ZNmjRpwYIFtra2p0+f1tHRWbBg\nASAPMKb19fWNjY0otwrfwh81NTXCqxUAdRozZsz27duXLl2qq6s7YcKEX+Cr4JPnz58vXbp0+/bt\nUFDjP+TyKDrAzQR+AkuXLnV2dnZ0dOTwBBpu2rQpKirq3r17oIAQbAE0tVwul0gk6urqtra2CjKm\nsIbu7u7BwcFz5sx59+6dubn5L69hSUnJnDlzgoODoaC8EFscuIMvWrRo+/btO3fuFL2iuoj7Asq2\nadOmff/+PSsrC7hSMTGxHz9+uLm5jRkzZtasWeHh4VJSUqamppmZma9evXr+/Hlubm58fPyWLVtk\nZGTs7e0dHBy0tLTa29tbW1vPnj0L+iDBAcBOhYaGent7u7q69jsRQGlJSUmo6gLOmoJ3Bwgkrq6u\n3t7eoaGhxcXF/TbF4XBUVVXv378/bty4kJAQqCgmaNNHfWd5H6KvhYSE3Lt37/Xr16qqqoMuPlyX\ncLfq6upKSUkVFRVBKDMKgmVB4KSz2exTp06tWLEClFmD7jIscmNjo7u7e1dXV3Z2NoSZzps3r7u7\nOzs7Gw4CcMlycnJycnLa2to/f/5EEKSoqEhFRUVMTExQNuAbampqakhIiJGREUzKwsLi+fPnGRkZ\nsJInTpwQbvcWHYASnjhxYvr06QNFVUZFRXl4eMTHxyckJNTX1z958qRf9l1fX3/69OmQ8nZoGlNY\n0+Li4mPHjgEq870AmOfs7PzkyRMnJycikXjx4kUcDqegoJCenj537tzNmzfv379fFCYdzoOCgkJO\nTs7EiRNtbW1fv36tqalpZWVlZWWFMjRAIGpqasaPH6+iopKRkSEtLS36rQwtxMTE1NbWmpqapqen\nV1VVrVixwtfXV1NT08/Pz97eXltbG4fDVVRU5OTkJCYm1tbWzp07Ny4uLi8vz83Nbfr06TExMcIR\nBelLFwUIh/BwNoL2DoRH4ECnielLIQE/CUkXhQJqQkLjYHA4nJ+fn5+f38OHDxcvXgwmyGnTpl24\ncIFMJgOniH4IKkasCBHKqMmS9yFcCTNnzrSzs1u7dm1mZiaCIGvXrrWzs5s5cyaoz/la5giNEkA1\nwdra2i9fvnRxcXF0dHz+/PmUKVOmTJmCIAiLxdq5c+fevXsdHR0LCwsNDQ25fQnPhY9fEACNrays\nzp07FxAQoKSkFBgYKOhdyntfYrFYUHbu378/MjJyx44dCxYsGCpPzDcAWHzIUYJmEkXjKzs7Oysq\nKvLz88XExFgsFplM7u3tLSwsFHRlFh04PBFasOAcDkdKSqq5uRml3aNGjQoLCzt27Ji9vf21a9d0\ndXVramr4FP+AnFOnToUUrYcOHUL6aorg8XgtLS2U7xQCYKUFEX/37t1YLBYKEfeL+Zi+nP+/qVhF\nPxe+gMA8ubi4pKamRkREJCcnz5kzJzIyUkpKilfylJGRWbhwobOzM8Jzg8Kv9vb2aNIMIb2w2exd\nu3bV19dPnjz5xYsXtra2/d7HQuZCIBDy8vImT568dOnSXbt2/TJO/nEAg3JAQMC9e/dWrlxZVFQE\nFdKBJktKSi5YsCAuLm7NmjX9Dhh1VqZQKFACrd81gdaOHj1aW1tra2ubkZEBMVUIjxdyv8Pj9nnA\nIwhCIBDy8/MnTpw4ZcqUo0ePDkrtgVKtWrXq4MGDMTExUVFRougguFxuS0uLKEWAe3p6pk6d+unT\np9evXw8bNgy40tLS0nHjxpmbmz948GDfvn3S0tInTpyYPHmyp6fn5MmTwRLV0dFRUVFRUFCQlpZ2\n+PBh+PDChQtCWG0MBnPmzJkvX748ePAAjegQfIfD4cjLy0OgJ6oP69crgMPhHDx4cNiwYWfOnAkO\nDhbsEajf2LFjb9686efn19zcHBcXB2wf2h3St33cfzsNgj1zxYoVN2/eTExMRIukDLqk6CzExcU9\nPT1PnDgREBDAN3LBCwuHw92/f7++vn7FihWicDVw+iAMhkwmQ9mXc+fOhYSETJw48fLlywoKCqjp\nAOlzCy4rKwOZvLq62srKCtoRIlzR6XQtLa0XL15cuHABdYRFFwqPx5ubm1dUVPxBD5PKykpDQ8Oq\nqiqOQIwvl8tVU1O7cuXK5cuXuVzu4cOHVVVVKysr+d4BSZ7NZldWVvbbxeCmfDk5OSjg2e87EGXm\n6Oj46tWriRMn0mg0CQmJ+Pj4pKSkDRs27N+/X3REAXFEWlo6NTXVzc3NxcUlMzMTzW+K9G1zbW3t\nhAkTFBQUUlNTh8SVIjxU6caNGytXrnRycoqIiIDsg7GxsWlpaSdPnkTzLBoaGs6YMSM4OFhXV3fX\nrl2RkZGLFy8+c+YMepEL6QjUaXyRHwMZeQeisChjBCU3BuoL1d7xNs5kMlEu09PT88WLFxcvXpww\nYQJc9giCQJQVWP14W+NLNSUIA5EzOLdHjx61tbUFxjQrKysvLw/wrx/v5oGxora2tqKiYtWqVRgM\npre3V1NTMzMz08PDw87OLi0tbfjw4RkZGevXr6+uro6NjV2xYgXSJzr/8h0Mg1yyZElTU9PKlSur\nq6v37t2LIEhFRUV7e7u2traioiLvgJuamh48eHD69OkvX77Ex8cvXLhQuE1AlAGgf3M4nOHDh/v4\n+Hh5eRkZGenr60OyfUtLy3nz5qE3WW9v79evX4EN6hdgUyBVuChjgKPk7OwMzhjAEULxukWLFq1e\nvfrr16/6+voQy8/XEZvNVldXP3bs2KJFi7q6uqKiolBtFq93vCAAuSQSidBdT0/Pxo0bz549e/ny\nZXV19YFON5PJBGr+OxFCSN+y43C4QZcIaL2rq6urqyvv4HmpgaSkJFRTQ3jmC13MmzeP7/lA4+Fy\nuXFxcS0tLY6OjseOHYPsV6JYJ6Gj06dPh4aGenh4xMXFcYVmSeP9lslkguFy0Jd5AU0KJuL7MBhI\nUXTt2rUlS5bA/kIjS5YsuXjx4tu3b8FHkNd+isFgPn/+fPXq1fb29vT09B8/ftjY2PTL0sEqEQgE\n4HLGjRt36tQpSPKA9JXi7HdsqDsmgiBnz55dtWrV1KlTb968iXLPwqcGxsPt27evX7/+n3/+QfOF\nCfkEg8G8efMGHMSFg6enZ11dXU5OzrBhwxgMhpiYWFlZ2cSJE01MTB4+fIjD4aqqqshksru7u5+f\nX1BQUGlpKdwdsrKyo0aNGjVqFGQXgbLs1tbWQhCjubkZBP5+nbX4gDeFgpDXjIyM9u7du3PnTr6C\nIygAF+Hr65ubm+vj4zNq1Kh9+/b5+fmhL6CmJLib0IVNTEzcsmULjUbLzc21tbUVMdqVt1kEQTZs\n2DBmzJgTJ06sWbOGyWTCTSq4d0ABYmJi3N3djYyMBmVs4EL8/v27q6urjIzMixcvJCQklixZcunS\npYiIiJ07dyIC9j0gaE1NTdbW1kwms6OjY6CgZ17o7u6eOHFifHz8QC98+PBh5MiRf0RAhZFER0fP\nnTt3IIMnL6BW9H5BWlr6xo0bSH8TFLaLsO4qKiqenp5C9gDYpnHjxj1+/Hjy5MkMBuPGjRvh4eH7\n9u1Dq96LCMCbKioqZmVlTZgwwcHBIScnR0NDA3V0g9pO8vLyz58/H9SvtF/A9IXmxcfH29nZbdq0\n6eTJkzt27Ni0adO2bdtQH0EQBeh0emJi4q5du5hM5tmzZ4EHEoVOKSgo/Pz5E6qtcrnc48ePz5s3\nb/78+U+ePMH11XpWVFSE9GMTJkyACEG44N3d3a9du3b9+vWQkBCgL01NTQoKCkJmBCUck5KSjh07\ndvToUTs7O2A3y8vLg4KCxo8fHxYWFh0djSBIW1tbbGxsTk7O6dOn9fX14bXc3Nx169aB+UbEPO2C\nAPq2sWPH+vv7r169GovFQnrRX9BirlixQkxMbPbs2QiCgHJOS0uruLgYeNPp06enpKTY29s/ePAA\nfJu4IscbCQEgjv/884+KikpQUNCNGzdoNBqIsKhyV1lZmUQiVVdXczic3t7eGTNmJCYmGhoaDqpT\nER3Qdk6dOhUQEFBQUPDu3buHDx9evXoVrJlEItHc3Hz8+PFAQ42NjQdqisViSUtLEwgEEU2Z8Nre\nvXtv3bq1b9++yMhITl+ki6mpKRiGkpKSXrx4kZ6e/urVK95v4eT6+/sTCITg4OCbN29u3rzZx8fH\n0NBQeHJyFL5//37nzp0DBw5gsdjr169DlIwg5sD6GBkZNTU1DZRRdUiAxWKbm5vRaj18fcGNKFhk\nEjVu8LU2EHFA30e1UP2+hukLS79z586uXbtCQ0MvX74cFxdnaWkphOzATyUlJcuWLXv//v3mzZt3\n7NghivwML5BIJNGRhBfgExKJJCLyg25MV1d36dKlhw4dWrJkCa6v5CMGg7G0tDQyMnr06BFwTnyz\nq6mpSUhIUFJSCggIiI+PNzIyApNUvzPicrkkEik5OXnXrl1r1649c+bM7t277ezsFBQUhFxkra2t\nubm527dvLy0tjYiIQDOSijI7mJq/v//+/fvDwsLi4+OBdPSrbgR61dDQkJCQAEUKhV9kxcXFnp6e\nw4cPBx3N169fx48fb25unpaWBuEmPT09kF7mwIEDJiYmBw8e3LFjByqRwvJisVhQvwnnqDZv3kyl\nUh0cHKDw5u+TNVgBBweHvXv3bt26daCMsMBF2NraFhYWbtq0CVZyzZo1Tk5O2travLWgmUxmdXV1\ndnb2iRMnvnz5MmfOnMOHD0PqgKHeAnBnjR49+siRIyEhIQYGBlC5Hgg+795xOBwCgfDx48fc3Ny0\ntLRB5TEgFGg9v5ycnPLycgcHh/LyckjDAqjLt+mw2vX19RoaGs3NzZ2dnVCgZFDPBOH+67Kysnx6\nq18GGImHh8fnz5+7uroGapMvgLjfd8AJBPBBcIKDm/LfvXsXFhaWmZkp3EkUYjiysrK8vLxWrFix\nd+9e4EiGuhxww8nIyEDkk4ODQ3Z2NpgCa2pqnJycFBQUnj59+mtcKQBKL1asWOHt7R0dHb1nz57Q\n0NARI0ZoaWmBbqyxsbG2tvbDhw9KSkrLli3bsGEDJBdEBjurcDaAqWpvb4e+IIJvxowZZmZm2L4k\ni2QyGYT+oKAgqD0NPBDckVDYE9OXiRoaFHS7wWKxVVVVERER7969+/z5s46Ozrhx48aOHWtoaNjU\n1JSdna2srPzixYvz58+PGjWKSCS+efOmqalJUVHRxMTEwcFBTU3tx48fubm5enp6CxYsMDU1HTNm\nTGRkJLhLDnVhYV7Lly8Hp+TTp08PSaGCgpmZ2datW8lkMpPJvHnzJrDvw4cP37ZtW1xc3OXLl8eP\nHw+Jt0BE/iNHDvh7DoezePFiKyur+Ph4ZWXl4cOHy8rK1tbWNjY2UiiUtrY2cHvX1dW1sbGB/DV/\n3FoK05GSknJxcXFxcYGHFAqltLQUKtB+/fr18ePHHz9+9PHxAbTsl3IpKyvfu3ePSqWi+e0GBZTF\nh8xQGzZsQPooCwaDuXfv3oIFC9avX+/q6srHmCJ9VH7u3Lmurq7R0dFHjhyJiIgYM2bMyJEjR40a\nBe6kUF0Tg8HU19dDSoefP38WFhYWFRW9e/dORkZm5cqVcNYGkmdgeD4+PiwWq6ampt+7X3TA8KQ2\nRATOF5fLFRcXB0/iX+6CD6ApNJt3v0OC13bt2gV+focOHbp69aoQJIefoqKivn79mp+fz+ctJxw4\nHE5xcfHSpUvBxjKkucBtXVxcDMnaRAEgCJBQrLCwcNSoUUDEIDJVTU2tuLgY+XcWHhiVu7t7bW0t\ngiB5eXlbt249e/YsMrDXPsrf79ixY+rUqREREZ6enpqamubm5kpKSoLT5HA4zc3NJSUltbW1U6dO\nPX/+/JgxY5Ch1EMH0i0lJXXs2DEfHx9LS0s0TLNfqK6unjRp0vDhw5cvXz6o7u3cuXMzZ850cHAI\nCAgoLS2dMGGCqanpw4cPSSQSuMNiMJiOjg4cDqenp3fo0KHQ0FBHR0dBW4oobiGFhYVUKlX0DR0S\nvHz5cu7cuQP9CgRESUnp0qVLq1atOnXq1Nq1axkMhoWFhaysrLy8PBaLbWlp6ejoKC4uJhKJM2fO\nPHv2LFQ9/GVHasC9devWffv2zcvLa8uWLWFhYQO5g0dERBgaGrq6uopiH8NgMPPnzzc3N3/x4sX9\n+/cXLlw4atSoT58+6ejoCL8yOjs7VVRUWltb29raRIn953K5SUlJUNxLMDIMFFu80vXvA4fD0dDQ\nGLTuq4hN9YuQgwc/WVlZgb1AeAdwqdvZ2X39+lVeXp47xPBkvn4hTj89Pd3d3X3kyJFRUVFEIjEk\nJMTQ0DAtLU1OTu43uQFMX9otJSWlAwcObN++/cOHD48ePaquri4rK+Nyufr6+iNGjDh16tSIESMg\nIaiIPULL8vLygjWIBzJkhIaGCj60tLSEaGvBxvmAyWQ2NTU5OzvfuXNHR0enoKDg/PnzpaWl6urq\nN2/edHd37+7uPnfu3LNnz4BrXL58uZSU1JMnT65evfrt2zcTE5NDhw5ZWVlVVVWdPHnyx48fIlp+\nBQF2XEVFRVZWFhKU/BoOgGNlYWHh/Pnz29radHR0WCxWdnb2kSNHoLW8vDwrK6vTp0+DcuVPaSuR\nPs2Hubk5KJiFA3hW/Yd8+FCeHiZIJpPHjBkD9yWCIBwOp7OzU0JCQog+cu/eveHh4VCnR3Tujcvl\n6ujomJiY7Nq16+rVq97e3mZmZrW1tcnJya9evQoJCTl48OBAQgu2L38hnKkXL15cv3792bNnT548\nYfQBmrGB2Ac4HG7s2LHbt293dHQU8awRCIR+U4X8WZCXlz9+/DhULPtTOAZSRFxcnLy8vPDXoDpX\naGhoRETEvXv3hAuKWCy2t7d3x44dUN8OmBXhI4EXbGxsvn379unTp1+7t/B4PKRSQUTTroH2bsSI\nEaqqqnfv3uVlTCGJTFNTU79NgRLr/fv3Li4u8+fPnzBhgnBeBHAe4slSUlLKy8vv3r2bn5//9etX\nQbcoHA6npaUVEhIya9YsCOlgC9R2HhTQ4NpTp06tX78eeBFBBWFra+vr168vXbqkq6t7+/ZtSUnJ\nQbHL29t79+7dgYGBOTk5kCgqNTWVt06yn5/fzJkz09LS3N3doTSXi4uLv7+/i4uLkZGRnZ0dtq96\nxaCzyM7O7unp+eM0DfZCXFz82LFjQl4DrQ2Hw7GxsbGxsTl+/HheXl5mZmZ9fT14Serr61tZWe3d\nu9fW1lZaWhrp26nfcekBmn/69OmRI0fu3Lnz5MmTCxcuHDNmDNQwh3dYLNbly5dTU1PhGhXx0mlp\nabGzswsLC4uKigoLCztw4AAiAnEzNja+e/fu9evXR48era6uPmhfHh4eL1++/PTp00D+fng8fvLk\nyYI10n8ZsH8ul99AGze4JgCPx4tY8BCGi3KlQxvgvwF4Uzk5ufT09F27doFj8vr163fs2PE7ulLB\nXoDekcnkcePGjRs3rt/XfoH/QH1i0I7AUszHH4D6hO9KQFV3vHjWr14QNtXQ0PDx48foQ3t7e3t7\ne97XJCQktm3bhtZmBPD29kbLygMYGxufOHGCr/EhAXxiZGSUlpbGZDJB9fsL7YAH4cOHD9XU1B48\neADtdHd3FxUVFRYWvn//Picn5927dy9fvvz9NNr9zgKwgtduy+0LLEP6zDRYnlzW/wng4+lhDKjO\nA4vFoqVKBgIrKytItPlr8Pnz57179x46dAjU+a6urmlpaaCGEXK6ec8UGqnW1NTU3t7e1tbW3t4O\nBUgUFRXl5OTk5eXl5OR4o+9FP2u/4BM5EGD6S9WOIMj58+dXrFiRlJT0R3rhBUVFxfPnz3/8+FHI\nkCBb8/r1642MjHp6eoRIF/ATVLWFOAlRyC9aqwJKuP0+iKJXBs0imUxeuHAhL2uO68tGB/HI/fpI\n4HA4iLI9e/asKFcMpq/8Lw6H09fX37Rpk4gT+c0oxqCgoFGjRh06dGjNmjU0Gk3wtZEjR4aFhYWF\nhYmLi4sSg8Hlcrdt22ZpablkyRJjY+OUlBRerpTL5UIl5I8fP06ePJnNZsfFxY0fPz4uLi4hIWHU\nqFF5eXlEIlHES1lI6eD/DqC7hsFgpKWlJ02ahCaK4gPh4bND6hFOUGBg4IwZM06cOHHnzh20qBsK\nVlZWSUlJDg4OQjx0UYCjeuzYMX9/fzExMfB4RgbT7MIe7dmzZ86cOfLy8uCgOejGCSmLIwh/SsD+\nTef+QQEDTrh/4S/8hb/wF/7CX/gLf+Ev/G/hP8v2/oW/8Bf+wl/4C3/hL/yFvyAi/GVM/8Jf+At/\n4S/8hb/wF/7C/wn4y5j+hb/wF/7CX/gLf+Ev/IX/E/Avd/V169ZBUjreHHgi5sMTAuC9y2azIWk5\nAGSJEw4cDodOp9PpdEjTg8PhZGRkpKSkIJJX+Le8HsGD9gWRRgwGg81m0+l0NERUTEwMTQKHDLwI\nvH1t375dyJuiAF/QH19TQ5oXX7OC//Kmc0JjGzF9Rar27Nnza339Agx1XhAJxGazaTQalUrt7u7u\n6elpb29nMBhdXV2ANgiCQAI8cXFxmT7gcrk3b95E2wkPD4egjV/esn4DNX55XoLAu1m8/8X0AdIX\nU8J7vlavXg0pQiDHLZ1Oh6p0UNeRRCKRSCQcDofD4QgEAm/tFgaDwWKx0E+gBLOEhATE0ePxeCji\ntW/fPrSvLVu2oENisVhoR5i+fMC8gPRFj6Grh74MQUiQUxOwEV77/TUUHXj7UlRUbGxshEyuaMZN\nwBY0zBkijUgkEpprGWIyMH05UCF3vYyMjJiYGJPJ7OrqIhKJNBpNWVm5vr7+1+bFiwxooB6nr8gc\n0hdtiR5nIWeZy+Wy2WxAFUgFgKIW4AbaII6naLjoa7h161Y+qoIOGzrt6enp7e3t6OhgMpltbW1A\newkEAolEkpSUVFBQIJPJ4uLiMADBLB9/CjfQgcEBgU2Hn2D3GQwGb0CMkL7Q44/+8Qt3KO+8Nm3a\nBBuBBmLCCGHjGAwGlUql0Wjt7e10Op1CofAOHtYNkBYOvqSkpLS0tJiYGKwzg8G4cOGCKPPiBRTr\nEJ5aMKJMUHC/0K+ExDLy7shARb8QgRWOjIxE/960aRNkEGexWBQKpaenh0Kh9Pb2IggCCfUAzchk\nsrS0NNBGYDNEDK7inde4ceOAc4BFhn9JJBIej4d/0cr1vPiMhreyWCw2m81kMhkMBoVCgWQmkBIe\ngt4SExP51nBIgHYE3A6dTu/u7oZsU8AFAZUjEAh0Op03rnrbtm3/0Zgn3jX8F2MqISGB4SlmxRcO\n9ps395CmxGazu7u7a2trc3JyOjo6qqurIWGblJSUubn5iBEjjIyMBBM7i4LifAA71NPT09jY+P37\n987OztraWgqFoqCgoKOjY2pqKi8vTyKRRGGFBceA8rtUKpXL5RKJRMBL3qXgXW1I206lUoGpAuQQ\nExOTkJD4zWSK6NUFxxvICtA1eALcAAwPGbhIlejd8a3GfwJgUlQqtbW1tbq6uqWlpampiclkQiQs\nTBOCxEkkkqysrKqqqqGhIV/WjF9OgyrkKyEB1EMF9B6CvYP9QlME9EvgACQkJACXWlpaampqamtr\nu7q6uru7gf+AHLpiYmJwUQFDKSYmBumcoBcqlQpESkFBQUlJSUZGRk5OjkgkqqmpCa4hl8tlMBjd\n3d0VFRWfP3+m0WhQM4JGo0EqUOB9oWXoC9CbRCJRqVQKhQKzgFtBW1vbwMBAVVUVKNJAiwNEHAgr\nBoMhEoloXwgPJ8R7JJl9APG8sIZwugU7qq+vb2lpMTExkZaWxmAw3d3dMFmUScVisXBkoAQdpi8J\nHdKHAzQaDS4YMTExAoEA/8Xj8d+/f/+19Eyw9cCUAIajebhoNBrcpkQiERYW5WYGWkMul9va2pqT\nk9PQ0PDlyxcikdja2griAWwQWklYW1tbSUnJ2tpaRUWl34z6A9FelGyi7BTgSUtLS319fXt7e319\nPTwRFxenUqkoF4jFYslksqGhobKysqqqqpSUlLy8PIxK+IHt91dRziOsJ8gSDAYDiCQ8J5PJIubH\n4WUcUbEBbvp+cUyU0fIVPYFmGQxGb29vZ2dne3t7TU1NV1dXS0sLYDiaH57L5QJhRxk7KSkpSUlJ\ndXV1dXV1KSmpX6N+KP8EDBPgm4i5IAadO28vvb29XV1dr169qqysLCkpYTKZZDJZV1fXzMzM2NhY\nV1cXJFjeTwZqEyodgvBTUFDw7ds3OIwEAqGtrQ1SWYmLi0tISMjIyACTamBgoK6uLi8vP9AqDYTz\nMjIycOl3dXUhfbKBmJiYuLi4goKCpKTkQMUs0APCZDJ7enq6uroaGhoaGxvb2tp6e3vl5OS0tLQG\nyqXPN0LhCA8qv+7u7vr6+o6OjoaGhubmZjQNKpFIlJeXV1FR4ctFKHoybOFjEwX4eR0OhwMKD6Cb\nkBMH/ZeXxg10ong5Wl6NjoiD43A4FAqlubkZUsFXVFTIysoCfYSU2o2Nje/fv/fw8LCyspKVlRWO\nMYP2RafTOzs7P3z48OPHj9raWjabXVdXR6VSZWRkKisra2trtbS0dHR0VFVVpaWlYRiDNs7lctva\n2n78+FFZWdnZ2dnT01NUVATMn5qamqKiIrACWCyWyWRSqVRQTsBl2dHRAfwEjUaTkZERFxdnMpka\nGhqmpqYTJ078hdQYqGxNo9F6e3t7e3vhfMIW0+l0eCgvLy8uLi4nJycjIwOKsV8mWChRRnHmPyFm\nAaL29vbW1NTU1dU1NTWB6gVYDRCOQfsCRBxOoJiYGF/ddrhv0GravDzNoHQT/uB97c/yo6gswWQy\nQRfS29tLoVBoNBpI4RISEoqKikBS+VqgUqnV1dW5ublAInt7e9EUMwwGo7Ozs7u7G5gASCWDIAjK\nmELKITqdDiRSWlpaTk4OTBZUKtXPzw8K5fECi8X6/PlzZmZmcXExi8UyMDCAHmk0Gso6A9Zx+gDY\nKWAT4e/e3l5IKZWamiorK+vp6Tl16tR+iTiFQqmpqXn//n1DQwOdTi8oKIBdUFZWlpOTI5PJkN2Q\nzWZTKBTgwkE/1NHRQaFQOjs7KRSKoqKimJgYBoMxNDS0sbGBIiu8UFBQoKioqKKioqurKykpCboo\nPgIISA63BTCjvIwpHGrAMfhJVVWVyWRWVFQMtXwinCzQMnZ3dwOJAJ0KUIzOzk7YUDU1NSkpKTKZ\nrKSkRCKRyGTyQAeQwWBcvXo1IyPDwMCgvb1dRUVFQkICCB2BQKBSqT09PVQqlUqlQsKvtLQ0Gxsb\nLy8vBQUFvkuLl+bzPedwODQaDYTGtra22trampoawEloREJCQk5OTlZWFugGl8uF2dHp9B8/fpSW\nlkIjo0eP1tHR0dfXl5GREUJSBLkT4acSegQrAbDIXV1dcP3R6XRgX3R0dAZKus7XFKrr6unpQY8w\n6NTl5ORE5N6E3K3ADvb29v78+bO5ubmxsbG9vb25uRlq3MB6ojVckD4ZBj1oFAoFQZD6+noDAwNd\nXV3Q6g06HsE5Asq1trZ2dnYqKipKS0uDFvbXSD3fBjGZzJ8/f0KRJC6XW11dTSaTgXMCLjw9PR2L\nxY4cOdLc3Hzq1KlycnLoURqIN0UFxbq6us+fPzc1NZHJZAKBALeeuLg4SKd0Or25ubmurg6uDAsL\nixEjRkhLSw/pqBIIhO7u7tbW1p6eHji2IP2KiYn19vZC9RBQyvLahUAi6u3tbW9vh7UFVUJHR0dL\nSwv8TafT1dTUhrqefD+BorStrQ34q64+AArJZDI7OztBuaOurj5oR+ilj/BUzELRD9Wmo9yjcOMz\nCv9abkyfLa+np6ezsxMQHVqUkJCAzQOkR/pSJaODQ3gEKQ6HAwIiaHSGxJV2dXW9fv3658+fjY2N\nBgYGGhoa6urqQKRoNFpbWxsogSorK4lEoqWlJS+REiIw9dsXjUYrLy8vKyv79OkTl8sVExOTlJTE\n4XC9vb1QmqWurg7IqK6uLshPEhISg3KHdDr9+vXr2dnZTU1NRCLRxMSkt7cXGOufP392dnaiqw3J\nTVGxT1JSkslkSkhIANcI29nU1FRaWvrmzZtRo0YpKSmJODt0jqD6AhsiUF4MBgM5w4EWg2AHp4JM\nJuvo6MjLy8vIyIjOBPNyUWC47OnpYbPZkpKSoOgdSCP1y4Dedl1dXT9//mxra0MQRFpaWkZGBmr0\nQUJB9JxQqdT29nYWi1VfX89HiLu7u7u7u9va2sBUDZwKYK+4uDjwuMKp7Z9iRnmnhq4kaMIoFApw\nHuC00N3dDXZhEolkYGCgoKCgqanJx8C1t7cnJye/efNmxIgRCgoKJBJJR0cHXRlQCNFoNBCKgI8H\nBSecNZTvp9FoYPdnsVitra319fWvX7/W0dHhGzaDwcjJyQFdqbq6uoODA6hnMBgMaDFBQoCXgb2D\nmcIRYLFYwAbBzfHu3buysrInT57Y29srKiry9cVmsx88ePDgwYPv37+LiYnZ2dnBTgFHCONHaRRU\niwA+Q0pKCnh6MTExOp2OwWB6e3urqqrevn379u3bqKgoQdVUb2+vjIyMoqIi5HJGR87LhIGylvcr\n3q3kJdnAIoDyAx2bcED5eGAsQI3d0tICW4NOlkajdXZ2gpDZ0dFBJpNxOJyGhoasrOzw4cMF5RYA\nFov19etXBoPh5uaGx+PV1dWhjC1QHmDrwXjFYrEqKyspFEp7e3thYaGOjo62tjYI2LwTFzzjNBqt\noaGhurr658+fDAajra0NcrKC+lNOTk5CQkJKSopAIKC1FYD60Wg04L+h2E9jY2N5eXl5eXlFRYWt\nrS0I0qKQFOGXNHpPw+XS0dHR1NQELD4ejwe6AVc1JN4X3hHsLzAW7e3tcM2jFrNRo0YBbzromIW0\nD/vb1NT0+fPnlpYWCoUCOnI5OTkgWQiPrQC9jsFQi+I8nU6vra0Fw8Wg6ZAFh8Fisbq6usC62NjY\nCGjJ5XIVFBR+wdTGu0EcDufLly+pqalFRUXa2tpsNnvs2LF2dnba2tpgCoNT0NjYWFVVRaPRnj59\n2tLSoq+v7+zsjN5Z/eIhajrA4/FQAkZVVRWHw0lJSXG5XBkZGfgEVJWA82CuAXXGkO4vMI+gShnw\nhgLWFtU0g7IPmgWaDIoAVH8EmiwZGRlwvQAvF1AhC05N8IkQtAf6393dDZYcEGKVlZXBhEWj0Vpb\nW5lMZkdHx0DCGHpJoWIScDhwrTAYDG6f5h7uU+DL4V/wkESdtQYaZD8aUxqN1tjYCHREXFwclBxi\nYmIyMjIgOsCqAccJ76O6AdB8cDgceXl5KSkpZWVl0FWIIkiB80dZWdm7d+9kZWWVlJR0dHRwOJyk\npCSIoVBUBvQ9tbW1L168qK6udnd3hyKi6JIJ3xWk74TTaLSfP3++fv26oaGByWQqKSkRCAQFBQU5\nOTlUXAZ5pays7MePHyUlJePHjzc1NZWWlhbOtLHZ7NraWjExsWHDhhkaGk6cOJFCocAnoHNFTeow\nVLizwSzI5XJJJBIorgBR3rx58/Tp08bGxqamJtEZU1QpRaVSQROM1k0G2RqIMrYPKBRKU1MTlUpt\na2szNTUFXmTQXqAR1CsRDLgUCqWuro7FYqmrqwMiqqqq/o4iVrBTuKfb2tqAK4WTgPTVZUDZBVhY\nEKs4HE5HRwfYR3hba21t/f79e0VFhYSEBDA3sOwIgigqKiooKIANEbzc/iBvLTgpbp+9vqenh06n\ng+4c9KOgxQRWFVRNwMYBqQIzK59MDzisrKxsYWGhrKxMpVKhph/6K5xZpM/Yyu3zi0AvMxgMrKeU\nlBSFQmloaKivr//+/TvoXfjGz2QywbCgr69vbGwMGkHUCxNew/T5mCJ9FxLK53H6/F709fVxOBwo\nY0CvKdjX58+fGQyGpqampaXlnDlz6HQ6kUgEPQTSh/ygNOXwJDAnkUh0Oh3YBSqVCibj9PT0zMzM\nsrIyCoXCh/MqKipkMhk8Cnhd6PiIjBBrLPoTnDJYag6HA7pk4SgBSA68IPwBIh+CID09PagjGtod\n0Hqw29Lp9IaGhrKyMiUlJVVV1X61YjBCCQkJZWXlYcOGwRXFZrOBRsGv0tLS6AGXlpbu6uqqq6v7\n/v37x48ftbS0XFxc4D4TMgsKhVJZWdna2qqoqAgmI9gO8PbG9wFo7jF9PpSwg1DhT0tLi06nw5Rr\namry8vJqa2ttbGxGjx49kLZvUFkRPWsglgNlACd18KQC5KFSqR0dHe3t7aD+F94g6qXw9evX7u5u\nYHegtdbW1vb2dgkJCXNzcz51Lx+eCG8fFF21tbVVVVXgoAycJRaLlZSUxGKxqE2P90zBngI6wcUN\nHvnV1dUKCgoDCS39AhAHCoVSXl7e0tLS2dkJpVBbWlpYLBaodYZEJHl3qre3t6io6P79+1wuV09P\nz93dXVJSUktLC2gyBoNBL01Y6p8/f75//765ufnhw4efPn1ydna2s7MTRbUJlnp5eXlw/cRisSB2\ngvQIVQ8A88EyA6y/6JcXqDPg9gH+EpXDu7u78Xg86v+AOs7RaDRQsQP/DScOtpJAIICsCIgKDkWi\nryrfc06flbi9vR1ICmo0gxsTBGYQIMEHlw9gUnAHdXR0tLa2tra2/vz5E+gqegUDsqF6B6B+YmJi\nioqKoG2UkZHho6u8wL+LqMa7p6cHqiejLA4I0DgcDuaDOo11dnaCEg6Px3d1dcHkdXR0wD9DRBGK\nwWDU1dUVFhZWVlbi8XhtbW15eXl1dXVw3EY5OW6fMUtOTq6srAyDwXz79g0QC5VEB6VKgB8/fvz4\n9OnTly9fCASCpKSknJwcl8uVlpYGdyI4xtLS0uCDUltb29HRAVE1I0eOlJWVFcJqs9lsGRkZY2Nj\nVVVVBQUFY2Nj2HVsX8AEqmDG8DizI31KaFBfcfpq/CgqKrLZ7Ly8PNEpCLfPogSWONg70OuA1AJE\nFqZJo9EkJSWBQMN9JiEhIehf0m8vQKcAR0ECa29vp/VBR0cHuCIAuZSUlOT1YRVxLv32C3xwU1NT\nfX09jUaDdUMlJUxf6SBYczQ8BTh+vsNWUVFRXFzc3d2toqKCx+Pb29tBb4qybsD1gqsxGn4hZPy8\nuzmkSUFfYEui0+kQyEWlUmEr4fIGWZNIJMLdiSAIqp/mQ/uuri4qlSonJwdsK51OB0MbStlhrVAL\nC6xqb28vGmSAvgyMVG9vr5iYWGlpKWy04PilpaW1tLSUlJSUlJRQNUy//j8o/iP/9pqApVNQUJCQ\nkOjq6mptbR0ICeXl5V1cXBQVFQ0NDUGVJehoy7sgfH2hukwulwusVXl5uaAqC+57WVlZIKC8rQH1\nQ080r6SKIgDvGIAJg4Xl9FUO63dqAMCFgIIAqnQyGAwSicThcOBahX1BGSw2m00ikdh9gTs4HA6k\nd+C9+iWJMAZFRUVVVVXQHsEKoPiAIAiIytAmUAklJaXm5ubv37+z2eyKigpUVzfQRJqbmz99+iQl\nJaWlpQWXPerOju2rRIr0iZF8K4aSQeDCu7u7iURiV1dXRUUFDodTVlbW1tYWsoYDASoCgVqoqamp\npaUF6CSNRoMhgaKLy+WCJNDQ0NDQ0CC8WZQuwQEhkUiwTSDTwqYDKRbc+n6VfHwAUmhdXV1paWlz\nczODwZCTkxMTEwMGCzX1oEiO6TMQczgcYExRDz0MBgMiaGdnp6AGTsgEUUNKe3s7iAqgqEK9dAa9\nfAeCrq6u06dPZ2ZmiomJeXp6mpqampmZAUfIOwCERx2opKSkra1dV1eXlZXV1tZ2//59VVVVKBPY\n73rC+pBIJDjO4AAAHCfo/uFCBMYU2Ay4K3mtPXwLgvR3l4mLi0tJSQHLzv13lCpgCFADsNEBMvT0\n9LS0tMBBBsyH4wlO1dAmiM0EAgFwEp3UUJcazhocQEBy9KwBHwm+tqAf6bcFNpsN5KWurq6xsRHk\nMaA/4MsL/wJKAJ+NMpPAy9JoNC0tLXBY73cW/YgXsChgfpWQkEDpLGrZBOd3lC+BjQQFAGgiKRQK\nWA36jaMU7A6shJ8+faqsrJSVlVVTU9PQ0IBTBL+iMjRwdRgMRkFBAY/HNzc3f/36tbS0dMyYMaam\npqK4ywCzX1tbC8ebRCIpKyvLy8vr6OjQaDQymYwyi0wmU1ZWFrTcEhISbW1tDQ0NT548wWAwY8aM\nAQm13y7AHDlixAg9PT2Eh9DzYgDCozdCV5jX7QH9V0xMTEFBAcxMg84O4bH4gHYQi8WCyx2KGaBl\nQaVq4BoJBAKsMIRBGBoaCi9Mh5K53t5eMH7BOQQjhZiYGNysCIJA111dXXJycuD6DTzWQKKSKLMD\nh0uQ+dCVxPTF2PFypQiCACZzuVwJCYmOjg7UlQIA4uqUlZXNzMzk5OQ6OztR0zZUogfHo9bWVjSM\nHfCf1xIBmAnyW0dHR0dHB4vF0tPTE9EvjcvlghTb0tICDD0Oh0P9k2B3UMSAJAMgbYMNpb6+vre3\nl68v0DiiynheTQaKgShKwHNYWEADhCfwHA4yZDYQExNrbm4WvMxYLFZjYyN4a2H+HUCJ7SuciPm3\nKQMl67xcGoqTUlJSX7586e7uFvSpYrPZ1dXVFhYWOjo6UlJS2L4Sssi/WV7efwFQ5hUV/xAEAb6h\nvLy8p6cHqm/zTgrTF27MN3ggfShzz/cT71y4PAUMMX06CZANBkIJ4CYbGxu/fPkCyjxxcXHwqcDh\ncKiqFb19uX3ehNAXsJgQ2tLQ0DAQ3YCj1NbWhhJ5dL/Qv9l9ETzAycGVo6SkhMViGxoaiouLi4uL\nHR0dTUxM0JHwHepv377R6XRjY2PwgwJ7Gupdh+rmeXEG+bdmHd04SUlJTU1NaWnpnz9/FhcXv3r1\nCqz/ogPMhUKhgJ8x2B9Qj1Jsn3s6SjdAEmAwGE1NTc3NzcIbh7sP9BfoFQnNkslkRUVFJpPZ2toK\nkayCpE84bwr3YGdnZ3V1dUdHB5gugdFHVc4Ynlq+KMbCbvIef2CG8Hg8xD+IeK2gwwBPPwqFwulL\nuAEXCiwUsDsiEnb01LS3t9+4caOmpmbKlCmGhoZjxowBfxtMn6KBFzdQHgCHw4HvlrKycnl5eXp6\n+oULF9atW6eiojJQj0BLQbfH4Ulngd7F6H9R7hCW/f+19yY9kh3X2XDEvTdryrmycqi5qjl2k02a\noymJMjwJsgzQBgyYMDcW7J0Be+FxbXjhP+CFdvbSELQxIMsLi7RJgZJlmS2JzaaaPXfXXFlDVmXW\nnJk33sWD8+DkvTezi/T7fXgXFQsiWZ1540bEiTM+5xy4Lc8JEUY8BCo1r08oPcZxj4IgwB9BbLgI\nTLmjOgvjzUnUAj8/z9722/CuZJswLGDFFQ2+ASFurT04OABGLv6Eo6Ojhw8fPnjwAIZcJpNBJ1v6\n5mHiOnHQYu0Qc/C53Lx5E8pJrVZL3NUoxpQaJ8NwjI3iLyBxTMy9pu6Ib56enjKnZ7Biim3a3Nz8\nxS9+sby8XCgUnnrqKSyS2hsVKfwEz4QDzPO8Vqu1sbHxgx/84PDw8PXXXx/s58MBr62t/fznP79z\n5w6iV9VqtVgsTkxMwK3Nb2JRYKbDw8P1ev3hw4enp6c3btyYmJi4dOnS6Ohoom7abrdv3LgBXZk5\nwloZ1YFUDPqxqO5oIWqMaTabcSqJrw5a4MnJycrKyv7+vnMul8tRNTHimeNjMQsxNEdHR2trawBI\nzczM9JsFdsjOzg6sn6WlJTjtoCVADw7DEGEsXLNUKrW6uprP58vlciaTAdLj8yZy4lCAN4DiBQ8E\nWAl2DxvOiw1fAjgaskYiM6J0SCaTgfDQ/bs5Y6vVYvA6DEMALsHguOHGGPCaBw8e3Lx5s91uP/XU\nU7/5m785eDkg74ODgwcPHty9exdJDGEYTkxMkO/rSJAR3opIAnMs4l4xwCi70nqeogubo4P1+C2o\nDlwPlxqWOgLEQ0ND8MnBwo7DPKy1q6urlUplampKm2EULZr+9aDia9Qdh24KEzdx3z7++OPx8fGX\nXnqJoDotuvD+XUlC4t817+LlCoIAEdJ4jIz3ggNvi6WFAhvV99T03nTeMgyoCBHEbZwktra2Pvvs\ns9u3bz969AjElslkTk9P6YABA8EuscAWrCaW9MJJIZTRhwCNcw44TtwdLsqIUQHvjpMKHhCo8LPC\nIkKG6J//+Z+XSiU+U0+BHA6UFhkbGxsZGdH8nJiiiPrO/aRqhauNVJVyuWytvX///ufy9gEOcXx8\nfP/+/d3d3ZOTE14xvjZoA4cFrRTQmkajMTIyoj1V8ecj1gE0LdzwWB2fBnAkdKlEpKnr9QjqAX9K\no9Go1+vOOWC6MpkMTsSKwQNyJffjfuJkEWaFE87zPKhB5wE68x1QlGpzcxO5Mk7w06HAxljY65zP\nNMZsb29/61vfev/997/yla+89dZbhUIBPhHug1Yc9bqwsbCZx8bGTk9Pv/3tb//93//93/3d3wEo\nEp8LrGxoaIgaC+4vIsCaxxqpDQdLButCvlREgsQnGh4eTqfTiGyEylENh6jnebjLkLxGwXapE+M5\n2AGIFSBQwzBENEnPbpXHYcA+U1QBWMJbrzNA4FFCfQ94OiMPAZ3X6/Wf/vSnwBbiVgKt6wmYhOXJ\noHGRYYKlb21tNRqN3d1daAuJ8ecEjykDGcYY6L84JFiB+Ffof9xE7Hun00GMGKmdCMkNAJ2ALdbr\n9Vu3bu3s7BQKhUKhAMQMBKcVXAF5pc6+CoIAyTpBEGxsbHz729/O5XKXL18egC7H03AqzrlsNou8\n5nQ6jTC3DifhHYwxwCAjdAJ0zvr6+uTkZD8UJsrW4IF0F/GZOCrcZL4V+BdWp91jHKlUqlAo9FuX\nEeKmKxEqDiK/iLPobxrxh2nmBQf52NjY9vb27u5uJ6mcDcmrXq+juhasZ/AImiuIU+BSGWO6ks1Q\nr9fv37/v+z7Qt4CdDY5p6qkhXVBEAxU0TJ+CD54Az8G5KB6w+fqbuFGVSgUkHX8UXKRYi5Oca+qU\n2FuqX6g2ks1m7969++6779ZqtQHLYXhheXn51q1ba2trnU4HSNBQBdlJQvEXA+oAyW2RLzCt3onf\nrqsKNEJnJXSVm0MnN8UbVTGjGGWc8vE1bfHT3UvNhnasVdFG8geeI7lzqMqdRuZitSm/txoRfojd\niBAwjZa4tYw/Jl5nraXxmVgLcISAVcF7ip90VeY+6F9rohSHiVIEjlgEjgGrQCoklQ8nNaHxfSig\nUHb1BzjU4QIZkCSEPUFMFg/nwCnQsAS1OynxASulWq3evXv35s2bd+/eBWorcZbR0dF4fCmUIsTc\nVY0OxAFRH9VKKkYQBDs7O8vLy4kzxg8RmQnI1MHBQbRFzAluC7hxGIbEAg5OWsJeIXsPgpnEhoPA\nkcEEAmSwn/YWJwwcAa4zHovcD2wpDc7I/tNACnsj7PrLKPdxzj2EEYs4FfcnFFwKRA+qjJ/faWqM\nAaBuYmLipZdegkTAZaFWaiQaYBSXoLmCv4yNjb344ov//d///d577/3bv/3bO++8E59Ia7TaICF3\ninAqBtbhNAXEv1Ao+I/LmYEoHB0dHR4ehv0T2UkncXPNXiKGBD9jLqjI2Wy2UChsbm5GHvjYTcb9\nhZm6t7cHv2HkjKxCG9J/EX8UwqRhGAKni+AMADARUoRY1NBE6BilUmlra2t/f1/nh0RGAsYUPBoY\n00AVBYRrHbPSi+PERWGtbbfbSGYHUh5B2wEE2u12NzY2Hjx4sLKyMjIyUi6XkfvveR6YIKR+s9mk\nVwwZvlrAoILg8PDw5ubmBx98MDY2tri4mHjnQXMokdDtdpFfBRc9uI/prbrnCb4WNlmhUGi1WoiS\nI+cGJkJ8gYAxEFoUCjTWiWcLxgpIFiOQmo6+JAGY3qofp6en/fRgXifg93d3d/f397vdLnaSZh/v\ngLY+NcArlUqhsKVzDhQTnwv0DS91vV6H3MIl9DwP0WS8NvDU3MOjoyPY6PhAnNk5/aZcIzLxoTqz\nFFGoQBFGcV4KhlDQM2dnZxGIAlLdCeUxqvw7FUTwSiZnhFJSzYiLBV8DIAlxds/z3n///QG4NCwH\nOWdIssabYyfJ3HHXqChoq8xaixkTfR4HBwf1et3zPJSPRkIrlFFwKKj4RkKWVhIZuQOISKCIL3JX\nAS/DCSYuKowNeCPwZFR+wDfBTyJ+XN4+SoXEiZxyFmpFCsLbOYe5EELCT4iu1ooOjxhnEdc8AJXm\nlcF5GUn8h++qLfWVrfKkwgmBC2VUlwcjChkiiYmso9lsfvrpp2tra5GdxzMjVgpW5AnGms+k0I0Y\npZHhSSokBTP5FZMKPAk1GmMQT0RRNiTsn5yc3Lt378MPP3zllVcS805QDQOyg9I3lAK0qKvPPA8j\n98JKZQxWSsHTcBeMMel0Gnen39JIKnhgs9lEOB6zMEnOKZ8cXSGewCHAx6w4kwbPBfUFi8V6rQQr\nYFrgIgC0k3pcidnEhwN9Doerr4rxJT5K3wv9RzJMmiLneQGQBJwCCFXBHWjF6IUnDC6eAWp3fNy/\nf39qaqparSJZlroRSA5vqPlGZKX4XwAbfvmXf/n999//+OOP/+AP/iBxY8E3hoeHWZOY7++JxzQU\nH2dHii7jEsFQHJIxYIFUonK5HILmkZ0MFT5B/4pfcGLSg3GFEhzP5/O1Wu0Xv/jFOfeWg4sCmwU+\nLWKuULO01kJewLUceXmQAV6mWCzCKiD3tgrkxqtE3YMYuVwuBxhAmBQ4MomhfPpmUX8En8nQI0px\nKCAJCAmwYBTPg2Drt1PQzx48eHBwcFAulwEtDQWrB5U8lBxA0CUXFkojFkyHCOze3t7Nmzf/4R/+\n4U/+5E+efvrpxLPZ39//8MMPHzx4MDU1hV/BE+mJC9pTWDfqhXAP0Pder9eXlpYmJyer1WqiGY29\nRgFzWgwYnudRToTiUtIWmNc7rMD7rLWJgEUnle1QaxB6g+/7hUIhlUohmcYY05aGLpSyPDVtYFiB\nnB4fH8cV01BKiS0tLS0vL7daLZgixpjj42NfAP7YNyyTOBvP89LpdBAEKFC1v79//fr10dHRxcVF\n6Pf96MTITYZ2hSJzAFxH3lzLNv1bfLDWomRERMBAZsC6BQ1TzTW9GRiY0fTCSzyJHlBIjI2NTU1N\nHR0dvffeex999FGlUokvx4nrFwm2kPGQVWdnZzg4rxfaGEo9PKNYmzEGpiAEoZ6FHoVMJoMzAnuF\ngMR/GbiEJg2NCnsCldTzPCD0MUs6nUYGVb/UV9eb9cWVQgvpSMEaXjS8D2ah4kLiQUQscSKQE+IS\ntteh25UKHrTFya/IOrX2ZowZkRGZhf5d0lUosAQSBhg3vM5G9Cpcc9x9q/CsJMVEqRmG4dbW1vLy\n8vXr151zzz33HO5+t7cqMPcWvJpKAN4/kEpnjNhQ2MeHtRYB7lBFMMl4oXca8XwE0vIKcXlg+5rN\n5q1bt37yk58ghzI+Rb1eB7Kc70AtkO9AhUDLFBoD3HOqesi4cs7duHFjgLkeSlmDo6Oj9fV1gBbi\nvhJPhSZ81ToBCgGNnwFZ+U5g/fAIoL4PZSVds+Cf7XZ7dXW13W4jnzIxPhYZoQSpgD7Hr7qC+9IX\nKqLccJBijVgjICdcyQFT6wWCX62trVlrYXRB/jqBlyDsibs8WPpzdLvd3d3dl1566ZlnnpmdndXy\nVPM9yC/eYi6TqbQ4r+eee+7SpUvvvvvuX/zFX8QZr5FQPqIEBwcHDGUA3kZxH0pMw0q9JxAkILkw\nRAeUBwaJptPpTCYDB4peFMnbShIqRQylJzVX0kwmk0GdoniVkscOPISVUJ3AZuL6vRGnAPRp9LyI\nvPzx8TFiDlggg3J8Jh+rn+8JoBF/zOfzBCckvnNUwEAgFQoFiBNY4bjMmiw8CfBpVcCpiM9jgSaH\nh4c/+MEP/v3f//2FF14ALp4VKJ34FEHuzWYTShICxDhO+g/w/ZGRkZdfftla+5Of/ORf/uVf/uzP\n/ix+NoeHh/fu3bt27ZoxBhWR4NOF5p2SwmN6K5z4g6G44DZaa+v1+vr6+s7ODn4bmQuhK5Qf0mLJ\nCR4I+Ws0X6ykUDAMR7MDx4ymEXEWjBuL3KPd3d3t7W16LnHHfKkMhbuh3S1WIeUxyPR9349TDIh7\na2sLObl0dSCBHd+nvA8F8YkuF1gaHAmZTAbx63a7vb29jU4/g9EXXcl22t3dXV5eXltbazabxACQ\nL/MmdwVUrlmzMQZqemTVqCrPdCJqRVr5wF2lYuokxqENNgZtPc/L5/PT09P9jgy2OPqZ3blzBx2S\noIl6CvhFTRf+JCfWp9bDEEBJNN+R0gcFDtokLiZQRJCyRBCSSsG2cPQwh8DHUW8LdkiiBy4IAigf\ntjc6H0p+K/fQqFAR/hioZmNOebCMOKQjA+gL6F5a/pEdMeRtenk9NcWIboqjj2vbgaoggY3qSNVh\nDAowBJf4TCeuSujNcWboktxUnU7nxo0bcOwVCoWJiQnCS3xJmdc0bwUviz/C/MCMdKpF8poxyLo9\ngd9ocaLdGGzFxIFAB9Ju8M4//elPh4aG+uVkoAUrNSEt7EGN2EZQCPkeAx3cLr4GdIsBJUoIl0I2\n4fLyMtQvlP414ibXBEMeQjVRK6m8j/1mNJKvRvAfyYz7hrXDIGw0GtZaOH2gngbSTqzfilDJFRdQ\nZ2jZ3mHk0hkVQTK9tS+sYJRhAA9wqOvNPz093d7ehrsUJSBBXfRhdwUHiYpIQ+drRrOysvLuu+/+\n8R//cbVapYeCr82FgCsSFOGpNAyjqLdUKn3jG9+4cePG+++///bbb8en8yRYF0qDD6DntQtTOyZ8\naQ5HPgknAhL2+znR8RPYJ57nHR8fg666gkcHkeudB3fikVH0kH+Wy+WZmZnp6enBkJL4CCV7DzXU\nqZiCe2idpytVlg8PD3d2dhgtiTwNQG3cUL0zViEByMydeFL5GecYBEE+nwf/T9RNE0L5wG7CPoCg\nJXpJX1d+XwtLqFy+74OrDqBLFEx2zqE9GqhcO8+pkcDli7PUGeUdKY6FHcnn85cuXXrvvfe+//3v\n/97v/Z6eC2fcaDTW19exI+iJArObTiAtV4zyOWkLA8Huo6OjjY2N5eVl4GgjS4NRC6riTXOq2xhK\nk6D2FqV4ILnqvJaaRiGG41RydHR0584dWEJQBOEv0YQeCraPv+JNjszlS/IEtRMOYhiA0cb7oFQT\ndWg+ikzZqNgcrUPMgqiQ53lEu0cGNw3ALHQcRU49mAvlK/U2o5p662GUEhnx6+B92Co2vnC+iVNu\nObIw22t0ehJURbGYiK5DFg8EOmCynudRylqJYEZ+ZZSYibyJp0p+6F95Ai0AecNE4Ut6Khk/cef5\nNX2+oNVE4JEnuDcE0J3qwA4jBLEhrBdnxAQyE0uKAsUi+B6fy1oLAANsek3kqFxzcHBA0AinY3TV\niV/WqtIiIPv4ojicJHJ1ZIClwmxgmosxhsX8IPM8MZC0EE1kjGdnZw8ePPA8D/k9CJMxiMl3cL06\ntxHaZi1rT/zcYAVxb7reSZCHvrxYF2onoRg+M6uoaZEfjo2NTU5OIraWOEVXMj+M0D9cLF1JxQBV\ngOcH0iSM0ALP8zrSKZHvrMVeZLpOp7O9vW2MQVgGIHi4duBlpIpglIOfu6oj7NxJqviJC8QgJcA1\nAE//sPSjN0rdx8U8OzuDE3dqago2JHoiJD4cwSIixIZ6i5XyaxEW0RV8JD/rAW8C5NGAdRkhCbgh\n0EoerwrDtSu1IJxzUN+htaC+jZcEddNPfvjwYbfbnZ+fj7R00e8PHkI6tErd1wQA5vnss89aa7/3\nve8lKqZWsvJBb+RUet/0Zy2+jTEIcyEIgESoRHMFC4eCUS6X0eaXWiA/WOUbwstzIk+h+Jxz6XR6\ncnJycnLy85ahwMG1Wq2trS00fTQxq5gylDoD7nWlUqlWq5FiFKHA2FAnn64crVFoe9KIoai3lzET\n5FedSzE1Es8FDqArQDrYQEZQmKQ2K5WGcBjoXYSXHkyU6+vrjx49Ojo6gm2BC2ytRUcTLB5RmFar\n5YlljxAzrA1UTnXinR0ZGSmVSk888cS9e/fi2D4wrPX1dd/3C4UCSqYDv8WkLtOrqIHu8Xf+F04a\nFEmmHygyVxiGKJbGR/GCoWjt5uYmtG3oQ/AmGpHHEXZjelXJyKL29/fRIACqOeILCB5BAPDUaRRS\nrYkIOdgecCTHEyYg9RuNBjTgQDJgYAP5ArHg86nnUSzRC2ilGTcga9jGyNKoaiBZDd3e9/b2zs7O\nwBFgSBCd5iTRyu8dtDc6nc7R0dHQ0FAkhwxnylB+KBkDTmIu1D+60rbUqEJCRpK9uIF47NjYGOpf\nRhYFGgACHQn1TgL0uN4M1kcus4tZ80aicom0ASwp4t2+oCywXixTuzqMgOu5aiMwDCjKPOUgCJDy\nZWIjkIw3MB0wRFQT63Q66B7UlaRg0A/Ud2rJVrl8aFjHJ3LOIVJmJCuC3pqtrS0YjQCEQT/DlyFN\nA0kX1bppV8qmRCbC37W5eHZ25vs+Ho4BTsX8fZAZuSLuY0e6j9pe0zcyIPastfC1ALnFLlwgRStA\nAvzEKtsM/JNaFNB+CFPE5+LPh6TbmVWOk263y2ak5OSa6rgcpEBF6rzqgZawZA5YI7gTfIeIEWOL\nUtJM2IgC50kwIbJjVsKO+o9hGB4fH9+9exdqHCIh2AorWG2qUJTBFKhUpKj6BEGAh2h+Hh+8NQDN\nw7NjxHvNYDfZo+/7QGQ2m00UBDXGwBWdKDGhmEJdYNofmXnE6UB20VXJjnD0cGPhIoFaMNhj6iQK\niubVQRDQxQsLvyOJa4xT+VKknTvQ7+GdTuf999+HCzYS9nFiMcI/B1QuyQPaQnzHrLWlUunSpUs7\nOzuJV8wIXSE825VqaGFvihXpzRMMOqwjFKk9Pj7e3t4+ODjo1xrXSkwAOgaKrPuSAEfGqxVTTxyo\nTmU3YgdGRkbQ6iUxPDtgUGTv7u5ubm7quIQRlUZ7HEBRiCuihOL09PSNGzfiTwaFo5yiVa4K8jc6\noXjxjeK6RkQY+GfiSSUrpp70tobeAA8ZXj3CI6xyAxgRKsyGGbBrGxsbJycn6LjFcDDMa23HeMon\nSoeWr1Am+oyHhoaq1Wqn0/nhD3+o5wIfhI+qI60BuF+aKcdvkVXDqKASurQ/8cQTcdkJ5qsdS2QW\n7XabUe+uwOB4frCl+BdOCqRUnIMgbgLPazab5U7S50H1TjMmrtooCBe1N2ABQwHP6SXgX1EEyghl\nhwpXanqtMe6bSxq+1Ac9OjqKt4Kkz2Z7e3tjY2N1dRV1BnANqLJQ7aZXj3TiVI0baLfNZhOVqiKH\nBVuoq8pOOWXVGZHEVmWk4bfa4R25gYgkAmkd2cODgwOQIsr+OQEL6hM36mbpzYz8xcR8jRzUsLkz\nmlPgBJGKF0rQPJRCeqaXuRjlZ4X+mqiYasuHBOwLMtKPVeeIeB+d0ntwTbp9UteNMUgcjP8rj4yS\nEgMKJbxfoSqHbCSfOpIKwPMyvTxBswJeK0+FFD1BpHkqv1XTiX5CZC6o70EQFItFwN+ZlMaQmekl\nDF/6yWF1nsCFIQjRp3SA5uGc09XRjbBK+JvR/RKvwX0DFIRhdxoz8MfE16VN9wgtcd88VUCUPira\nA9pW5Np9349rioA53rx50/f9YrEIK5Q+Yy2/sBz9VkacLHwlMnbsz2C4EZgVdSY6MrxeSAA3LZVK\nwW4EeHR0dJT2dvz5CLACNwVGoTGRetu1Z7QrWEnyNBfzjUHs9luXEQ8ZsJU8YrIRfs0qsxyHiMRB\n1P7spwkcHx/fvHmz2+3q/DbSOSinIwU+sQQYxqEg3/TZGcUkl5aWEsmeegKOldTbkbJCRkWorDJm\njFDp2NgYGtmvra2hJ0u/raNuCnNdv6qmRooP7THtKoRrJpOpVCqJlaoSBwkAtZC3trY6UgeTXEhr\nU7z7KNKCplBHR0fz8/OJOF2t3Ya98AMrKjUFRNhbgcQTBFcodTORqBpnHX0VU4bbqJvqi+16VRwn\n5gWMufP0lv3ss882NjbYdJuKlFZPjch+I3gvLJLFqjqqSosV/7kmAh4VyroiEIyLDbWbDuD420Zo\n3SgzzgjYYn9/Pw75xz/B1UGy42+pK4RSjQWnGFG2PAWW6HQ6kB+RiZrNJuoIlkqlQqFAlgHSQQKZ\nVkS0u4j7oH1F4IBQPSMaT0cKiKJkKY1XEkZkA+M6llMuVSfpAp7ntVqteGFzgLUbjcbdu3fREtA5\nB4QJ6dD0ukwiUEVsF8KFrVYLNgk6oem52EUa6dWkPSqpobSSIj2Q+Ll1Wsg5yV7f398vFApaMYUD\nGA3cdnZ2ut0usHoQYGEfv7jeQ9Or+oeC5Y3TPMNe1NepTkE5DsMQWksoOfhOamlBxnSkhBBvPf4e\nn4tv6FRczIr3FwqTUxEfXDoYZvDIIvxNsQe0A6yFxN1ACDIiGqkBs96qlba0OGUocF1VssoIHiZe\n1cX0qqRG+BsGtV4qH+SZPCarHG+B9M6gdpK4qJOTk/HxcSRWwg0JJgxUgFHBEww8FvoKW7nwTeA1\nHMyH6fTlrUQXXFi8KEwDoYjMIaDnGV6njT2gw2qEWsj9nCDsIbmpxFsFgaVg074iKwZAZKK9vb3r\n169/8sknly5dmpqaQkuIturayvtFjmR6VV4NEMLOk3gGOKtwcRDNs73GGBVTX4rWESgJxo46yvV6\nHRubKDdhIRwcHMC93ZVWtE5p6kZsGzJYwuEgd5yyakLBzVPOJo5QUk6bzSZq40C+ezFkhS9AKQxI\nc1iYtIrjz9/a2rp9+7ZOtOXLh1KMry19mPE+9JhStdJvgt8CW9yPdViJKsD6RX1ZCOL4XoF3ETtB\npXZ9fb1arT733HP9cDJW7EZg/7TdqxkyadipcoT6pIIgQKLV4CQ5iiT6oTqdzsbGxr17946Pj9Pp\nNHMYrHKv4PRpupBgisViOp2+cuVKBGLnlMMIfJWBI0111OXIUkiljId0BZrP1PbI6KvvUxhrZcIm\nIXu4KdQ8+K79Hm6MQXAEIXWQciigItKcVkGssrN9GRQJnqA34F6GhhFfDoQWQtVMYtDcqt87a4Kw\nEommJRf/clx+6/1xysIwvYxbSx39W7qC9ABJMVLJGkPamaRfQ3+wyvDlDhsRG4GUCePwpIQKM0Wo\npkSeHHnJfkqMJ4Uk4ksLJRCMNipQXyjRuT+u1zqKD/4d51WtVp988slIKJ+iQrN4Eh5VRqcKgEf2\nxFOe2vibRFZthBShCGo7asBP9KYZJWITf6J/6CVFqDUpRo6JRlFcikd+Hp9Om46cIsK29JEl/tEo\nlc70IR4jYj5O55HBv+urHd+Qx7J7/m9XZdzHV6H/Enl4fOHx6Twpdu0phEbkfOPkQZZIlVTzz1Kp\nhAzxxAVaCd7Re+RUpdKuSgrRUoC/tdLAAm6hxCkiFzZyhfWIX+34Genr6ceaFEBkplKpYrGIprha\nf+UtixxKZDlWoSMwgiDI5XIRgzb+evSL215rkHzDqVRXI95u+OQQRZmdnXVJXmeGE/nmoULdaLYQ\nJ7DH7uQAn5+TJqvA1EF9pF+mq6od06sdCtaoKwWSgUwNkgpHonoPpDb/1caiNBHWGhH6kTEkDbcT\nr5iVPp/Q1VwS+jbxV7bX3oB/4bHwXCPy1IgQj78wl2xjzNY5BwgliwIlnpFRLJ2gC4TsGJ6N3AJy\nV6u8FdCdAMjO5/OTk5PxoIRV8QTt+CAN6HXpn+iddOLTgceNef16or4e00A6nqEqELOO+Huye80s\njMBKBthhGIVCIZfLjY6OUkeEEcON04p25N20huoJFAnXHnmLlUplaWlJ/wrUD1wjThplArUMiAuw\nOOfiSSAzQ/tIOFLSD9Aq5Y9GCbxBuPDa5gsFVhy5G06V9Y5M5HkeNpAFXBhvcoKm0neAN8TELgDJ\nC5E71JTVc8F0m5ycRIsB+p9CyawKBaZjVHxf+4ypAXviCLECRY/sIT2LSAK1UimN7InckNtFlKdR\npSK73S5AcmdnZ8ChT09Px3GfrCPBWxcqhIBm93p2skhNk1YMZTDxiBKM9aLEhjHm8PAQsQVmfoS9\n2VRGXavEu8CzMzFGzFtM2jYqeg4LnrAZT8rNEr/h9+K9+IEba3qH53kou8b3BD2f9bbmCnvTASOK\njlEyAPCsxPiptXZ8fFzD1/h87CQcBnQS8OHdpAxQK56J+EQj0u6cGhs8haGKjcZpI/KBJ6iZb9zI\nMVKJibtnjMFakIRE1QTvQLuOmUl8Ds4UCELUZIjPxbWjODZ2zElsFw2NgTHFDXXiTY8jwp1zxWJR\n6xZ60JWLfYBmr+0fLUF4XvrhiVeA4k1/GazViKfKSBFD09v/2Sqb1vVW6tFP0xQF8Gi/bcTBwd+h\ngRwRrmt71ThGAn2peZmovRkhg7AXc0WzQfP8MIYxpQobeSYO1PO8dDqd2FPQqULLy8vLbelv1263\nh6TfGP2XXem1RkaKZYKKPM9DI9bIFJlM5oknnlhfX0+pEiv6moAa2UjSk5x62+t84cBulEqlSqXS\nD3rheV4mk5mfn19fXyd1gWMP0E3J6nFY6XS6WCwOTpDXkteJK9FKzMTINdc6gGYXoKKhoaFMJgPF\nNJFp8KRAq0dHR/DBI7uaJqsvaQZcIOfCZfF9P5fLocokFAlMGlkRPkBd1gTAh/OVtObDPeRu0GO6\nvb19eHgY3/Nka8mKmdjtdnd2dsIwRKyTs5LVaq2rI6Voj46O9vb28vl8v5tmjJmdnb127drW1tbe\n3h5rvtDFwoPxpdAxNQ8OzQLwhru7u41Go1qtPv/88xHFFN8kt0XZoJQ0iNdcyfbqpiQUrteIRohk\no8QQj3NO96LlWsAuIQYwL0UR8Cgj0pBdW1oAmCYcXhAguEOFg7Bgci76sPW7WcmOJ7PDOQJYlslk\npqenIwmAePlsNsukFiMZTppHWGVZcle5A1Zp6oCKsjepnuvs7KzRaKytrTUaDRKb31sDyKqiM7YX\nwthVzYXDMET1rtnZ2YWFBUD39FzZbHZ1dRV5SJpajDJG/d5K1FqYaTcJV9fpdFDQ6tlnn7116xbn\nAmMFsAxNYuB7jjALF7NMjDJV+Xf8BUFwE1NMPUkQ1HI3kAY2KVWjsasKInp9vFlW6QSgsQgpgoBR\nKk+73+JL0LoIicGpEYYhUL/5fL6fUhWGIVzpETMdC2TJM6sakKakfw8Xa0XG7+3tUWPWQ6eY8Nbw\n9PWGkw06FYzrKpBrnGXHF4W3gtXKoC2WQ45hpG0s5RaiAayc5SnoGEB+A1xixhj0X+iqxqrgKkjb\nYrsHhko0d7LWnp2dra6ushJTfDCxj0smpXWlX65W44y6d/pkteFnxdEbsWkxC0tlWYE6RHZbiyR9\nl63ymBpR3dDZLpPJDG68h02jiqCf5imPqVNFuPlfnLXGWUYGFFMnGra+XJFt8VSKguZapECv17c6\nPDxcKBTiDbRCqXFRr9fr9Tp6cqLkDqlO8wo+U9st0GvR8GVhYSFer3p0dHRqaurRo0dbW1t05fCM\nyKiR/YO3hZQEzadUCQUrIqxerx8cHHz1q1/tt5l4cqFQaDQaKNVHI9MoIiSd2F4Fxgn29Dz1sKwC\n85DNplQ5SL1kQi314cI+j+uI+qTCMES2H/pyoUQr5HIgBXasUuW1VwjPB4wEQOf4yUaGpkO9UqsM\nS21z2l5XDr7vSXQIlS7n5+cjtDEIuovbUiqVYC11k3JXtURhAAjQGfSo6Lehi4uLV69eXVpaevTo\nEWCpo6Oj8OXoGrBGvKeu19mL7aPxdHJysre39/Dhw6Wlpaeffnp2dlbPZSWYCy/37u7u3t6eUc1w\nabVoWvRUuShfEEJWCvogjSYxUQ5GHtmHPjPky+OCofyHLlUYSMUoTRbg/v0QM5C10POQtUDzgFHU\nrmRZdVThFSdNePmX09PTVqt1cnIyNjYW7x2Aly8UCsgPpdRnGgHJLq48RbhzKC0x0WANl0HPtb+/\nv7y8jNIKOk2BviLOogUV8VUAhEFjg0oKd2k+n49nieZyOSCPyZ68WF0ecgpKOIbVtAlhxRZErlU6\nna5Wq1oxteILROtX1kezUnXF9fo2yAKs0kr52QpkJ5E2nHOg9si6YI14ypnN7/MFKGA0TzHiNgh7\nq4/pgZvIlmbcNMa+tRJgJBqgIew45VarxTzcxFlYRrfbm1gDMcAIjFHYPi3CjZI33W4XDoY4F9Yx\nbiO8mO5Y/CvSqsBJsNXAQ+t7F0jBPz4k7sEyioytOHHhyfB9P5/PE/pM8LonkBu6r6hX4ZtQOuMK\ntx6+wopp8YyngYqIGoRSxWQO51y73V5ZWUmn0/3qtANvjftupNCBE/dzRK3hOfJrRgFmtAUIOomU\ncMLOYKsjgtDE+BLvsvboeMq7CfoHuB+so98eghOCm2FzbO+IfFm/m+d5KPNSKBT6+XEODg6azSYs\nFh4Tv+kpR1SiYqqvsFGqLXyfkYqwrrfGxcrKCsqwwPZwqpCcvuAkJCONSDzPg6q0t7e3trY2NDQ0\nPz8fmSsIgi996UvXrl3b2NiYnp42UgmE/8qLkMvlOp1OSqoDEUgW2d52u/2jH/3o008//Zu/+Zt+\nh2WkPUcul0OZ3sPDQ0DqPQkcdSW3z4qD06mih9AXcV4DZjFKrBNb6ElDFvpfrWAzjIr8nJ6e4mjQ\nNT2+Ug40wkXZUSS9IH0Z1UgCKeBgjIFYhJEJNRRKFxxDgeqm2W85TtACHSmtbRQz96RRoknSoPQ3\nrWSVIXp5Jo2s9VyPwZgCuhsKEtlTbkV8wD+F0p+AYgYF5AZYgRMTE3Nzc3t7e6gHkVI1p6hs4eCB\nw6MF7Et2LaUmZmw0Go8ePQqC4NVXX40k04AOisUiqlmxMkXEVRM/A7J4DcQGa0ilUvl8PlEx7Ur9\nHb83Cc6XzFnKYyseU5Avi/tQiFrBxCR68nFLh6QtoelFnFgJpmshbZU/IPJABAKOj48rlQrs1wg9\nkEG0Wq1Wq6XZruZQVOyMIlCtbevlswK/ngtWTbfbBWAIH4wIDE88LvFZqC+CirLZbDabnZ6eRnJu\nYgucfD6/vLwc8fCRtCJKqv4CPmhJ5kTnbjabzrmFhYWpqanIdJRDnU5nf3+fJe4T903TJLUffoEa\npBW9NnJeeDK3C6ePizMyMkKfOu51JO9b8zXNrazE6E1seJJbzW/yXKC0WcFaBNKSwJOECU3zTlCP\nwAbEJ8J7MqjHE+cqUJsCRgiLXiGmjLuvuTx2I7GKSJzAQELkqp1OBzeaGqRTqDWwqZQqhz5g4GjG\nx8ePj4/z+Tzbc0Ae+6q5MfUDCraUtDz1VBFQPDNSHjI+KfLzKJmCIEBxD3hTarVaGIYQjWxHAji7\nL3lLeJ9+elsmk7EKf8ZdAnuBl5Fau1GUT+4XOXcjvkyU7tdsCpUjEfOBDpSS4mumF52ipS/fzfTe\nO3w+OzsbHh4eEBrGAG2ADELVJYv8yvV6Lk2vamJVQYD4gKphlPWo5/V6HcmRobmKiQkIYMD0XLCm\n0E59e3sbaBBcEKSH2t6YmBOrz4oSRiOQVZnW1tZwR2ZmZiKbtrCwUCwW46mHViltnufpJFEan5oZ\nYlHHx8cff/xxOp3+yle+MuCwwBay2SxrVtBNqBUpGn5Mq9AumHhFxfjQVE07xFdpCZpbcut8aWOB\nsnEDAKbGGHhJV1ZWWMPOl6YAmtGRGLBwQM8Zjtf88LErghdMmz1cAodWRjW5RmbBPgAZGNE3Bqn8\nnkQegVFFkI6xZrxTKMWx4CakI8E5t7u7CxxG4pqz2ezVq1fhHN3b29vf369Wq+Ctvu8jkQ2+Up2z\nCZKCh9VTkVwkNuZyucnJyddeey0SAQSVl0qlK1eufPbZZ6Fg6bRdbpQodcoFzcOw0oYHnrl0Og0f\ne9xsAocikXnKU4WwO+wzo8jFl8r2iSlZCKNENA9jzPDwMLRt+AnYh8Oq2LfpxZsapUVZ5RSkopPJ\nZGq1GvJD43uIioA4ZcAwmC8Mxu2r/gtcCPUPI04y6J2ZTAbmWmQuUBT0+HQ6jYCmZrVk9wzUOqm3\nBYkO1AEsB06RSIcLCwsAicPBH0j5Vc3W9SaYXvHA3bOCYmSuwJUrV6rVamQPtRIThiH6h8EkoOrp\nxVw71LpojxlVninRfwmoCewcq6x/nDLo0BMPgYnx0LjAw3dg+qPTdGRp2WwWzhVPOSbx8mfSzqoj\nRf7xr1RYjXL80DnXrzomj9uLedGowOkKUHgI6VN/34j7BM3u43uIgk28Kdg3mM3I5w2klDIWnsim\nNTPRhBQZQ0NDs7OzJycnExMTxWIRcRUIGxgSTgEG+PIYsFE9qbffbrdxdwb3+3XSb5l5k77vj42N\nQch1Oh1EsaCG0lkFosJOopfHgNz/fD6PYG6tVqO6hkMHE4BXIp427quC2Xw3I/QJ/1ZEGwb/73Q6\nMEQDKayLEcSqC3ETNF0Zxf+hkAFzP8A9ZpXhZ60lr6MixYl8wSOBd4GzBUkNTSLHpG0bLR18VaqZ\nS6DeyeUQexNKoj2c/WCYei4g8RAQ39vb63Q6lC+BpPzyOjDQQV2c1E6hBlG+trb2s5/9LG69PPnk\nk8Vi8caNG8888wzMeH3d+HyUwvXEG+fL0EfpnEPyfrlcjjDe+HnB7jJSlSKTyYCinHMwloxSFnUk\nE7IJFX4GHBkHzXj6hhjKN73GEukHsg8fIrVH4qPdbgOdyL+kpJ6xlsL4Oxy9hUIBomGAI7bfvkF+\nkS/xVoYKE2glMVEzPW0dWbFU8QKJqTWPUUxhKHek/BA+0Fhx4trFW4aCxUZ0DCGe+fn5RN00lUrV\narWXXnppd3d3fX39zp07BwcHs7OzWLNRKATqOlYc+/gL5gXMYGVlZWVl5fLly88888zExEScF3ue\nl81mn3766Var9ejRo42NDRhAXSl1qW879qgjFdSAqoT2sLm5CVAsDngoqRukJ4GPdm8jO/wd6+p0\nOuAIDDvCo8NKW/oUcT3iBeeAtchkMjs7OyhcBWHJOKzfC3IiDRljSP1OfMMo3FAoFCqVSmJhZBgM\nUH+BQrtz5w6/Bmkd0f9CiRd3pRIHE2KKxeL8/HypVApiFQCg/TOUGXFp6JPiACXgJxMTE4VCAbuh\neYpJGqjGDIw/OBShS+S2PAi9LiOXjTcQPGJ/f//o6CibzT733HPxUmK0v6FlAraFAj1W6v5EdkPH\nlbSWY8Uw7STVfKGI0iFaI5ca5iWMCnZMMMYwIAtfNZ0TZKNQFlESPLKuarW6u7t7dHTUlqK5ZGSe\n1L9E8SzsMONxBAVyb/EaqIAWPzJrLeJoXZXMZAUmgfuFozRSrAq7jYhE5KZj2xPBrL5C6VlxCHWl\npCVMJqe8+L5qNsE9DGRooo0bmZgunU6js2U+n+e8tMSMMopsrzZvBU/J5wPK0i8niQN+R3pAfWnF\nTosO5jQNKiv9Tj3pg4qQaBx2jPHqq69+97vfXVlZWVhYoI+ffBV3J85F9a4alXWH+MDx8fHW1tbZ\n2VmlUkGfJ4yxsTHE6CCq8Z6J9gCvmJaXkQEjExWpc7ncAMczj4mf+9EtmRivJ2TQ4GKiJLNQSuUT\nAEaxy10ixRLHRd3IOQfn0f7+Pjzx5XI5gp0Fvn95eRkHDd+5U44hTXv83BVYLX0rVFmq1er8/Dwq\nQ8UV0+Hh4enp6Q8//ND3/bfffrtWq3mSNMJNA6eiQq8HvoMln5ycXL9+/ezs7Dd+4zcGu7eNpEzA\nQ2QVjs4Yowv80ZNKDgl+iDS7xFusB+gcVRG5HKsA09pjin2GlGS3F5gQtNPiAwG6k5MT5HODc1px\nWFA2YTq0faHZNpgzJA5P0I9IIsd0XSkx69TguvTPrdJqjDgmWq0W6nbpbz4eJAFDGTcTMBfoJbTz\noGd0VcVHzLe/v7+zs1Or1ahcRsbw8PDc3FypVMpms0CirK2tTU1NIbpEH4DWa3k/cWadTge+0na7\n/eSTT77wwgvFYjFxLlyziYmJXC53cnKyubkZBMH4+DiOivdcc38oviAsdJQ5ODhAG1VEdvrlyiEz\nDm5Fz/Ow43g49g3oCFhsvgpi+kl9zz3Pg9/i8PAwMhEkcT6f39raQp1Otmo0Uh7BV1VgfakfCar1\nFcDl9PQUlXVBtYmanJUyWJBYxWIxl8uhsQRqplql1jvl1cOggpVOp2u1WrlcXlxcRIZcZCLkqBE1\nEXEVaHFlFdIFzXLQJAOFnakNDLiBmUzm0qVLN2/efPDgwebmJjg1C1mjTB1NTxKhVgRZ/u3o6Aha\ny+zsbD8DySh3MmKjzrmtrS0wVuILWWiClzziCKE5wX+KzMLYBbGJFIq+dKqEBQisM9QppqbClQLK\nZISEWkviFfN9H2RweHioJ+X3nXO4C4ECiBtjaD848QgC0d6P7xO7CUS1VuCYHtHtdpG9R8MPLDXe\ngdNKPDqu3Ov6Azg10GpK0oBMDJUehiE9Llq51/aGU67BCGHkcrnNzc1ms4neB041KYh8Wc/LY9XE\ngHBbPzQw32Rvb0/7uaFDk9hgPFNnNeK9o+ceWcNoiZw4xS/90i+tr68fHh5+9tln4LddSciFwmQF\nqs5tCaWAA8mbfn2Y9I1G4969e0EQzM/Pa8UUIPh0Oo1KcwhW2l5TVquG+jj0ZyrQ6DvYbrcfq98b\npW6SY0TmcioCo29u2NuDKj7oUmpLWVDsG7m3jVmhTgwA/Jawb9jAW1tbqDMwNTUV8Zju7e3dvXv3\n7t27MzMzpVIJPNAoTYIrIv07gZcY0du0CpvL5WZnZ7vdLnxPkaV5nvfOO+88evTogw8+2N/f/6M/\n+qNKpQLVh8/X9B85NZ7XycnJj370o29961uLi4tvvfXW4JPCCIKA1Bgo6Cq7SWma5wWB3De9JBQf\n+hSgw2kKtOIzIiOlOg65TGh4R7VOTZxOd+qG6WiUhAITAEtE6gjQ0l9MK8V7wtgGnrVQKPhSQAnb\nwsCOUx5u03vL+BdfUOzg5z2nM/g9rGSLj4+P4/dGql5ZhbLnMXgKqH54eLi8vDw9PR1I+8r4CIIA\nRaNGRkbu3Lnzs5/97ODgAJ6SoLd0Jb7vJDZnpXNSu93OZrO1Wu3SpUuD41aAcNVqNShhKHGCKBJ3\nkIoprzSle1dybIvF4pUrV2ZmZvohaPFlRseMCnD4Etknnfkqtkive+Q4U6lUu92O32rcEwDnAfqM\n/Cv5IJ2Omr6dRIGJDE6lUnA3+v1BLTga1N7yfX93dzeVSu3s7LRarVCacxqF5tYMFPVWx8fHK5UK\n1cf4RFaZsNwckyTL+UeQKGKg7GJ8nruXSqXgW0UfS+QkQZkIVdaLVRXQeO2pAYAwkP4PWHYcONtv\nGycnJ/H9vb29RqPhSTEd6DehSlFi2E7fNdwd5FDr54Ml6dA/lScj5hD+CcJGqyNGdMf4HmqsVWTA\nfCUmUvtO+GSyY08VmIxwyVC6BvSDLeINsdWRmwuVEcdBk8wqcEKcsGn4xXW4+DI9Sf+KsF39DsQX\n0d1ieo0HJ4l68TdBkQrygcjZRYYWjfoLUNkBjuzndeavyuUyg/X6ovG8jPAo8karYsQIjsG2SZyi\nWq1evXrVCnQ4k8loSjBKYvGDFe+Rk2IsRiWNEdswPz8/Pz9/7do1zgVRlcvlms1mO9brjm8el5Sm\nV9Fx4siEtl2pVAZvI8/CE2xMqMCI+uhDlbfAAaGeGKTi4H2JbFd8jf1ezCi+Ojo6Wi6XFxYW5ubm\nItVXIOaCIAC0lCUguBxSiCbRiO9APzCQ9pWpVCrRMTw7O/uHf/iH3//+9+HjKJfLpPnIchL3B1t6\ndHR0/fr14eHhb37zmxEka7/9HBoaQm3a7e1tK/CACNOLXCsSBhwQgzm8UT5sq+IenELfX9Mrr6mV\nnpycHB8fA2KbqJvSdxO5UKHERfEooP7y+fxgxOp59g1p3wih071FevAludYosjG9fNKIrs/gUnyc\nK60MZR1OTk6QfQnXK3FF+tozSIdANprcsKdqvykAriqXyxMTE+vr681m01OoKQQcee3xX5h99Xrd\n933Up4zL5sQ9vXz58pe//OWNjY379++fnZ1NTk6iVl9EhOhrf3x83Gw2l5eX4Tqdmpoql8vQShMX\nlU6n9/f3P/vss8XFRd2H2hO8UURZ1GcWfyZeIAzDeCE9bHU+nx8fH4d46Ep3b6vQ9PSdaKbGO+Z5\nXle6PqTT6UQXZuJmWsmcgFK4vr6O9iSeJCF5ku8FFfP09LRUKqH2dblcRv3axA30BNaD//VVLN6q\nQbaO7wNRx1KC57x7vu9XKpWXXnopk8ncu3fv9u3b0G7hOdaBbL1vUCxgtyCnO51Ow/c/AM8aPz5E\nbwFIPzw83NnZOT09BaPsqmx6DEQqPNUHEn9HimXk1AAxDCX2p20eYwzwNlaSTMkNuedaMfUVeA6P\nTbxoqMzcaDRCVQdNS+Jut0tfCIYVr1uEKTvn4DTtRx64X2yXwGtF9ZoGs/Zr8pv6psN+Bs+JTISG\nyVoe+74PB0ZHhunN1PE8D94sT1AWZL4UFSwJGacHAKNh42nOEHk3iqi4BLUK75hOpycmJgaTIlzL\ndOhqNggFRXsHrAKuWHGhjY6Ouj5AZ2NMOp1+4403zs7OdnZ2VlZWAF4EnjuXy8EmJFIc+0naMMpF\nZ4yBQ2Fvb29jY+PnP/85q+hrwhgbG6vVavAswg3jq1yTRIVD76f+VycBBDh6B29jKHVRsGkg9chP\naM2S99IjEPcY6RFIbjXlCImNlZucGLE6gg9/Gwpbolpku93O5XK1Wm12dnZ+fh5QZj0XuBA6w3kS\ngiBtpHo7Yxnl7KA2qcPT+ABrAS4DPRev/5e//OXx8fF/+qd/evfddzc2Ni5fvowv0xbSRguPzBiD\n8ODe3t4///M//9d//dfc3NyVK1ciV6PfoDIKSZRSrfIib6iPD0zp7OwMMY0Bz8f+sx4FtTQOfjNR\nEceq2+323t4ebmiiuIx4BgNVaQcEiRtKYNtjlenEtfMvuGKwZo+Pj9G/EDSPACAkI5RDChGeINkI\nYt1bW1vdbhcJmpGlPV4FsZKDhn0BW8FrGaVddXvr3UBXANEcHBwk5hbogSkqlcre3t7S0tLx8TFI\nk2AIJ7AB7Hiz2VxfX/c879KlS0899VS/sGlkIUEQVCqVr371q7dv33733XdXV1fh2uxKLhutUswF\nk2V9fX19fX1paSkIgmq1evny5dnZ2cQ0XozR0dG5uTngnFD33vbmTcdvGv83QjphGAKh8uyzz05O\nTibuWzabLZfLBwcHQPjBB4yz577ZXmctVRBKWWSKlEqlUqk0ID80vp8AJARBADRMo9EAwzXGIAkJ\nCDZUyEcoAVXTBkQToABB4LVjlef5AW8Oz9no6CjywOj7eez76+kKhcKlS5c6nc4Pf/jDMAwbjUYm\nk5mamnLO4VU9cZXRlQKhwsqvMKsGuz0SB52I8P+xZu3h4SEMMEyKG06Efihh0LGxMWjDkXmhxRJz\nDHZA/mV745smpvpQuuD7XSkNi2pi8Y4gxphyuTw3N4fP0AxoOZDd06omd3axzjGo4VKpVKanp/sp\npgsLCzAJ0DgEO8NrFarSVGFvUZuI2YAcvnQ6/eyzz8Zxacjub0tvbvBfNo6PIFy5mW3pRaKRA9SK\nQEhAHO3s7ERmBCGFkijmxap6mSSBYZRqRaV8dHR0fn6+XC4PIEjP81599dXNzc0hafKsH2hi9TSM\n0lyxsUEQXL58uVAoRHRE/bZApUNTXFtbq9frW1tbYJI8ESJx8SvqcDhZ7Mbx8bG1Fojw6enpr33t\na/HkwlQqVSgUACZB5TKicTS160iXZsuknK60dJ6enl5cXDwPS7SSG0d2GrEuePv0TzwptTFAy2GL\n3a5UcKNXiA4zTfCgH3RYAKIUTXeHh4fHx8cXFxdrtVqlUgErjgT9oFLncjlyGz42Yu1rdYrSzZPM\nNj6tI8XgBvBG3/cvX7785ptv3r179z/+4z+WlpZ+5Vd+ZXp6GmenzQZuFAy8Vqu1tbW1tLTUarX+\n6q/+6vXXX9fAg8EDerYnxct4NHouTwFeeXxAqw/QN/jNUBUwobqs+aF2VBm5bnDH0gI8PDxstVoA\nBXm9ybgYoDrnHDA5kAtG+k1AJUWx0uB82U4DvmCtzWQyMzMzQMMfHh4iQ87zPLTM7Uo9e6O8G1qE\nGSESdDgPggBpkZ9bMTWqbgJEIDz8uPwgfUT5cRu1bMD1htGQGPWOrLlUKj311FOe5yE/CYVCUTga\nthEqWezv76+urp6cnLz22mtXrlzBLTrntgZBgF5bZ2dnDx8+XF5exkTj4+OlUgm5L9BKASo9PT19\n9OjRw4cP19fXi8UiPLuoq99vImttOp2+ceNGtVqdmpqam5uDwhGGITNnyfFpFNLyYCTdObe7u/vJ\nJ5988sknKOoZn8iqqnKoG4cyFiRB7T214mUMBUTlnEOx962trf39/c/r57eSxovnw2GJGr9glwjc\nI5ECCDB8ebDp5nkeNSrtqkEMxVoLpRCBNryAtmHO+fJ6FXA8Ly4uHh4ebm9v371711q7v78/MzMD\ndRMGK+YFBHBnZwccYWJiYnJyslQqfQGtlOvFORaLRYCBoAJiLhRMBpcB04FmfHBw0O12AZuOnxp8\nMMQI0u+rma9TQ8s5F/OIQEXe2Ni4cePGAFdBs9ms1+vHx8ezs7PwNepkPj2cAss6aT0F5ePu3bvL\ny8sDygQ654aGhq5du5ZOpzc2Nq5evTo0NIQSXZTxFGAkM2TdgQ/6Utz0/v373/3ud2H3x88OCTSw\nsuA9In6UO8n35/+GCjKoHbS+ZGQjkBpH5mA0Go16vY4XhpSlGUnZZpK4HDcT4XV431utluuDTsNx\n3759m1gOE0PODUgicVIrHnIlpeqBRAaeOTY2tri4WCqVtra2fvzjH+MS1et1z/NyuRwr5eGtYBLA\nLEc22MHBAWo0drvd559//o033lhYWIjLFChAIyMjSMKDZ9pKRS2SYj/FlKPdbm9vb6+urvZrpxnf\nCshjX7oN86JpmjeKYJCq0pXcx37ZY8YY6IgoogcPEStbAw7kqzpl+Cf074Dz6PT0FIClarV66dKl\nubk5wNgixbYwiNvG+6DCjBVPORU1pzxQRhUipKpglc3p9VYtjJwX7+lbb73VarW+973v3b59+zvf\n+c7zzz//4osvInOAdMg87O3t7U8//fSjjz7qdDovvvji22+/ffXq1c/lDuSgx5QZIHyOhmx2pXEd\n9DyvF5IXoQfozcAoG+XzZtTLV+X/NGE4CYVB2LFqckcq1UR4rxPMN94ZggM2ydHREe4CgCj+wCRg\nbsuA/zVyv3Aoo6OjYPhwEMAlBDZLdL4VCPKJGqSNk5OTarWKoulfRDE1qhInsiI6nU42m4WcRiUp\n8FnicnA/WeK0X1QuMlAxDm6GDz/8EKul5QHZfHx8DH/qlStXrl69GgmyPHYWCMtSqfTGG2/MzMz8\nz//8T71e39zcRH1adPCDuob65MaYdrsN/OL09PQrr7wyPz9PcFviGBoaeuaZZ+7du2eMWV9fh+ME\nqXNIcqToohuMwzk3OTnpS3GWzc3NBw8eeJ731FNPxVO8jYINQZEyxiCfN5AePxHF1KkoJ6yLZrOJ\n/7bbbaYtP/akIi+AgHixWJyensbT8A5sJkG/yGPvhhFmp9OJnDLKqVXgbavVaq1WQxcuHfD6XAPs\nplQqPfnkk77vo6ZYq9VqNpvw9YJBQJDAMK3VagB1IFnqc21afFiVEA3DD5LVSo8c8Caj0Dm4GqiN\nkgh4Oj09XVlZuXXrFtQCeMXIL6gX0q/DZeK20rYBWW5tbd26dQvetUSPKVK5O53OJ598kk6n5+bm\nEI4YivWJ8SSfF8pHV4oHQQt58OBBu92emZmpVCr9Duvq1avoKHjr1q0gCIrFYqPRODk5ASPuqjIr\nVHnBss/Ozubm5qwk4d2+fXtjY6NWqy0uLsY30DnXaDRWV1c1Sp77ZpSWrwFexI9SkOMzyBjiCpZG\n4tKCIDg+Pr59+3a5XC4WiwjMMa1BP5MvY8Xg7EhH1uXl5VarVavVcrlcouDEh06ns7u7i+4+2WzW\nORcBNfGt9G8ppyFvNjc3gThPPCwO7ADiYJ1O5+HDh1DI1tbWWq0W/KnE77LaF0om4+xgtI+PjyPJ\nNdFusRJ8QI8GKxXloJmlpFJPSrXx5H/pqEa20N7eHu5XqVQ6p2KKqj2geeg6YCy8X9RmoGhS5zBJ\ngGYOcE5jDJLMCInGecE8gBwxqt8Y7ilqYVYqlYmJiXw+D5ShBupEFB1KB7A+9E924qwFq+fN0rSH\nD9AQ8DUnnqn9/f3t7e0wDBP5Bt/B9/1CofDmm282m83//M//3N/fBw0vLCwAA4ADQtH+R48e/fjH\nPz4+Pv7Sl770W7/1Wzpkek7+z8tI//qQFJ/n5dLQIGCiQPOBatcXHyQGONEg97lXqIOJIwtV4pq+\nWfDWU7uF8eb3lgbTc0HFAlVDr8XpVyqVmZkZ8IHzpD08dtNIA8451DleWFiADk3a7qpeuPgMNwqy\nq3d3d/EaEKxTU1MLCwvaGMA4r2KqX8sKjAl7Bx8PmrbBaYcjRMgPOe9xBFKE93H4vo9swfn5+evX\nr29ubhpjkOEEP1wul3vmmWeef/75+fl5JtB93lVAmAHntLKysrS0dP/+/Xv37iGMiNXhFkGsPvvs\ns+VyeWpqamZm5rF4o5GRkW9+85u7u7thGO7u7kKlZiwG5ixb8lhr8Rcjluju7i6ud7lcHhoaGh8f\nf+ONN6DR9ltLPp+fnJxsNBrA3bJwmi85KHRcGRWcRcVN1CJttVpwan4xp6MRDRgiHGBtT8pgaaXk\nPA+HGwCVDRDLxgccNxIFkPLFhKfU5ywUHBlW8CozMzMoT1uv13FquPbsgwBVG5AppP3+L1VSPahz\n+FLaAs7mfD6PmDWkDnwtEA+o/mtiV6lWq/3qr/7qv/7rv/7jP/4jTgS1IOBDApOC4ktBFUqvICdJ\n5SkpjhsEwc7OjnNueXkZCTrxDcxms4uLiw8fPrx27dp3vvMdSEpUnGH5XiwNCNcwDAFlA6dG1pS1\nFknQ4KeJu+T7/u/+7u++/PLL9Xr95s2b29vbm5ubyA6Ec6XZbLJMBCQ6LEzIlWazCbUeSS2XL1/+\n+te/Pj8/H6ecarXa7XbBSZ1z4PWelGX1pZkkVRCjQh9G6XCegvlDd0SCTnwPQYH379+/fv36jRs3\nnNQGQZg7VPBc6nA4UOweaMMYA9n5wgsvlMvlAfQGZvvBBx/89V//NXYGdpEnAVlELUnksBwYBBsd\nHT09PW00Gi+//PJv//ZvD5iIA4ra5cuX5+bmTk5OkFNcr9dXV1eNwpKenJwg+HNwcJBKpXK53MLC\nAkp5MIaW+HycOJDH6Ki5vr7OHtRg3awO5sQvyP/iyGiEDA8PT01NJSr3kQHnLtSIk5OT1dVVX9rT\nsIcIFR3QPDwsnuednJwAv9FP0clms5OTk4eHh2tra9h/GJm4LyAA+ozQSQSXNJvNAi6fzWaBFn2s\na6BYLE5NTcG2WVlZQe0OynRwEl/a14FHsZwimBJmgRsSpkWn00F9ngjGVJ8a1z43N/f7v//709PT\nt2/f/uijj3ALECwdGxs7ODgAuAv84Z133nnzzTd1Q6nz838cR7fbBQPpSL8bNJfxpBoJXG8sijw0\nNITagpOTk/1sCepk0GIBGYeDZmRkBBpwKHXltIMAP4Gwo/+bqIyuKg0WWQiKzoI7QeIXi8VsNjsz\nM1MulyPtXvsNF/P+JhIktXYy88SfaOs9FFA+vfhWstsBw4tf58+nmBqRmp4UtzfGhGGYzWaRqKXd\nzngVxL+MXP7ILsTVU7gH5ubmJiYmrl69+ujRo/X19Xq9joT66enp2dnZ6enpeGsi/dh+N7xn5UGQ\nzWbh2nnllVeazSYMu93dXUgIHDO0OgA1wBMfe8bWWuQhGmNmZmZcUidG/bamN4qkn2OMefnll8FP\n43EQGpqjo6OTk5O4sSsrK05aSpLciU3UZg3QFwicgYhnZmbIsr+Ahke3OoJZpODP+xzf93O5HEkI\noSjP81C0NZ/Pg97gJQWBeb0Ysi8wrMASsBu0pLFpKem+A6lG4On/ZsbEwbA+7hcgpPSEdaT4CASA\n53kI5MVfY2ho6LnnnkMrlHa7XSqVYIjjXAiMAbPgQiDkwBAJ+4MoOjo6mpiYqFarwH7F3xxR8q99\n7WuFQuHRo0ebm5ue5yHv1RhzenrabrdRCzmQ4iyAxDjngH/KZDKo25DNZl944YXEEAHGyMjIk08+\n+cQTT7z22mtdKRLiSdJP/BI5FSzjXuEDtOdERadUKjUaDRhI8FugXBFrcML2Bm/1pUhwWzozcVIr\n8S+Euvb29uCFjc/o+/7k5OSv//qvZ7PZ+/fvr6+vQ7+xUoQklL7YnuSledIGBUAamFWItQHm1JfU\njEmlUn/6p3+ayWTW1tZWVlaYO2itPZO+ymQRRpXagIgdHx8Hb3z99dfPU1BJLxOZT9lsFvXddnd3\nt7e3EWmBVg3aJk4GxVvOA5LzJZ0FSZkjIyNQAYFFRpYC/tcILMRJwSCYGeDe2MNarXYeZ7AxxvO8\nUqk0PDyMfkIMxcLjaCV51whJwIuGJlWlUqlf4RpjTBAElUoFwhQB/UB6IqKeNMxL7Ce0YdAkVxSo\nwnl69+KCEk6BQqEAHw3uIKQeQtiA86HeHFTPs7Oz7e1t1A9Cr2AoUvAjwF+Yy+VQyWvwHmJnJiYm\nfud3fge0t76+fv/+/Tt37iwtLZXLZd/3Eae6cuXK+Ph4P7TPeYa1Fj5FYpepC4KfIDYNGRRKlQ/w\n//Hx8dnZWU9VctUD33cqHRwOchoSnU7HV32/tD4QSsNnSjQoHuAAcQef7/uZTAaKLE4f5450EdD/\n+QOJ51Gc9Aaac8t3PDmXy4WquI1+VGTqL3iu+m14PPTPaSAjNaF4Gc4BuwChtbi4WK1Wd3Z2tre3\nQQ24wIOd0p9rc8nfwWenp6fhooO1hOP0ZPCP+gn9zCYrwLLzv0ziGMCwKPl8qZyA8kOwqmkkdFRp\nMQxPEN9Iwy8UCsjTLJVKWnP9Yi/8hfVRPXDfgiBglVBk8iIahZQy1tSMM9wvPHDceHI2m+1KPUU+\n35das/+3Zkx8B35wzmGNKSm/7HrDxJ7CUOqHeJ63uLg4MzPz9a9/HSnn0GzAfIlPZ2VEMD7AWLvd\nLpAn+N9SqVQoFFKpVKVSYVgq8c1TqVS1Wv3GN74BHVr7A2gIwU0LlZcXypdCLfwLRWm/ufCv/UpK\nnX8MUDvQExUdzjY3N7e2tqDQcy1Q0SD+ISMpV0xviWKksrKe3dnZWdzrbCTeXavVsIf6TKF/w5sI\n/w33EIZZIA0LCGgbjOTGKJfLf/mXf8kEI03VHamR56S6EzE2YHqkTEz6ubadi02n0+VyGQBu7G0g\nzZmLxSKEK/sUnCc64QTpCF22Wq3SF4UpsIfQFwmTgEzBfwG+AuAea6Rzod8IggBqOrxrbWkjQmgm\n9hBHg5PCjYaGx4BYomPFWptOp2dmZsbHx+G8hHhlAAfgAbwzH55SFXwTTWgnLvzIQiYnJyuVyuXL\nl9uq0SODJ35vjRRcbchNpCscHBzASwrjkxEeOAvPRxrGSD70pUuXFhcXf+3Xfi3srQ7x2J8/Vg2w\ngvbOZrMLCwuASRBcBCxEoHLhQQZEhiD/iSJYP5mNpoeHh9Ez7EzawVtVCMX21rjAMWGHh6Uzn5HG\n4zgpluzkgDOlVqshyYEVWnB92Pnv/yNRdf5BgonbRU4KLPR8/2//9m//f33Bi3ExLsbFuBgX42Jc\njItxMZLG/zV43MW4GBfjYlyMi3ExLsbFuBj/m3GhmF6Mi3ExLsbFuBgX42JcjP8nxoViejEuxsW4\nGBfjYlyMi3Ex/p8Y/wfW1O5zgO5jHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def decode_img(tile):\n",
    "    tile = tile.reshape(tile.shape[:-1])\n",
    "    tile = np.clip(tile * 255, 0, 255)\n",
    "    return PIL.Image.fromarray(tile)\n",
    "    \n",
    "\n",
    "overview = PIL.Image.new('RGB', (cols * 36 + 4, 64 + 12), (128, 128, 128))\n",
    "for idx in range(cols):\n",
    "    overview.paste(decode_img(sample[idx]), (idx * 36 + 4, 4))\n",
    "    overview.paste(decode_img(decoded_imgs[idx]), (idx * 36 + 4, 40))\n",
    "f = BytesIO()\n",
    "overview.save(f, 'png')\n",
    "display(Image(data=f.getvalue()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def augment(icons):\n",
    "    aug_icons = []\n",
    "    for icon in icons:\n",
    "        for flip in range(4):\n",
    "            for rotation in range(4):\n",
    "                aug_icons.append(icon)\n",
    "                icon = np.rot90(icon)\n",
    "            icon = np.fliplr(icon)\n",
    "    return np.asarray(aug_icons)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train_aug = augment(x_train)\n",
    "x_test_aug = augment(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62992, 32, 32, 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_aug.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 62992 samples, validate on 11120 samples\n",
      "Epoch 1/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.5095 - val_loss: 0.5130\n",
      "Epoch 2/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.4824 - val_loss: 0.4658\n",
      "Epoch 3/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.4529 - val_loss: 0.4454\n",
      "Epoch 4/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.4302 - val_loss: 0.4283\n",
      "Epoch 5/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.4135 - val_loss: 0.4249\n",
      "Epoch 6/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.4013 - val_loss: 0.3980\n",
      "Epoch 7/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3920 - val_loss: 0.3942\n",
      "Epoch 8/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3856 - val_loss: 0.3889\n",
      "Epoch 9/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3807 - val_loss: 0.3866\n",
      "Epoch 10/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3768 - val_loss: 0.3767\n",
      "Epoch 11/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3731 - val_loss: 0.3801\n",
      "Epoch 12/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3703 - val_loss: 0.3732\n",
      "Epoch 13/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3675 - val_loss: 0.3769\n",
      "Epoch 14/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3648 - val_loss: 0.3736\n",
      "Epoch 15/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3623 - val_loss: 0.3727\n",
      "Epoch 16/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3603 - val_loss: 0.3647\n",
      "Epoch 17/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3581 - val_loss: 0.3718\n",
      "Epoch 18/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3563 - val_loss: 0.3644\n",
      "Epoch 19/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3541 - val_loss: 0.3583\n",
      "Epoch 20/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3524 - val_loss: 0.3588\n",
      "Epoch 21/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3503 - val_loss: 0.3608\n",
      "Epoch 22/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3488 - val_loss: 0.3565\n",
      "Epoch 23/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3470 - val_loss: 0.3581\n",
      "Epoch 24/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3452 - val_loss: 0.3514\n",
      "Epoch 25/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3436 - val_loss: 0.3554\n",
      "Epoch 26/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3418 - val_loss: 0.3492\n",
      "Epoch 27/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3407 - val_loss: 0.3476\n",
      "Epoch 28/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3392 - val_loss: 0.3473\n",
      "Epoch 29/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3381 - val_loss: 0.3507\n",
      "Epoch 30/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3365 - val_loss: 0.3495\n",
      "Epoch 31/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3356 - val_loss: 0.3427\n",
      "Epoch 32/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3342 - val_loss: 0.3429\n",
      "Epoch 33/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3331 - val_loss: 0.3437\n",
      "Epoch 34/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3319 - val_loss: 0.3422\n",
      "Epoch 35/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3309 - val_loss: 0.3404\n",
      "Epoch 36/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3298 - val_loss: 0.3489\n",
      "Epoch 37/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3289 - val_loss: 0.3393\n",
      "Epoch 38/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3279 - val_loss: 0.3417\n",
      "Epoch 39/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3269 - val_loss: 0.3386\n",
      "Epoch 40/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3264 - val_loss: 0.3340\n",
      "Epoch 41/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3255 - val_loss: 0.3313\n",
      "Epoch 42/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3247 - val_loss: 0.3328\n",
      "Epoch 43/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3240 - val_loss: 0.3362\n",
      "Epoch 44/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3231 - val_loss: 0.3339\n",
      "Epoch 45/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3227 - val_loss: 0.3370\n",
      "Epoch 46/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3220 - val_loss: 0.3380\n",
      "Epoch 47/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3213 - val_loss: 0.3384\n",
      "Epoch 48/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3207 - val_loss: 0.3339\n",
      "Epoch 49/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3201 - val_loss: 0.3330\n",
      "Epoch 50/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3196 - val_loss: 0.3336\n",
      "Epoch 51/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3191 - val_loss: 0.3359\n",
      "Epoch 52/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3186 - val_loss: 0.3278\n",
      "Epoch 53/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3180 - val_loss: 0.3300\n",
      "Epoch 54/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3176 - val_loss: 0.3311\n",
      "Epoch 55/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3173 - val_loss: 0.3254\n",
      "Epoch 56/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3167 - val_loss: 0.3304\n",
      "Epoch 57/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3162 - val_loss: 0.3270\n",
      "Epoch 58/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3159 - val_loss: 0.3271\n",
      "Epoch 59/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3156 - val_loss: 0.3250\n",
      "Epoch 60/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3152 - val_loss: 0.3285\n",
      "Epoch 61/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3144 - val_loss: 0.3277\n",
      "Epoch 62/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3142 - val_loss: 0.3294\n",
      "Epoch 63/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3137 - val_loss: 0.3253\n",
      "Epoch 64/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3134 - val_loss: 0.3273\n",
      "Epoch 65/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3131 - val_loss: 0.3290\n",
      "Epoch 66/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3126 - val_loss: 0.3323\n",
      "Epoch 67/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3123 - val_loss: 0.3275\n",
      "Epoch 68/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3120 - val_loss: 0.3304\n",
      "Epoch 69/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3117 - val_loss: 0.3237\n",
      "Epoch 70/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3114 - val_loss: 0.3257\n",
      "Epoch 71/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3110 - val_loss: 0.3208\n",
      "Epoch 72/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3105 - val_loss: 0.3224\n",
      "Epoch 73/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3103 - val_loss: 0.3233\n",
      "Epoch 74/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3099 - val_loss: 0.3211\n",
      "Epoch 75/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3096 - val_loss: 0.3212\n",
      "Epoch 76/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3093 - val_loss: 0.3314\n",
      "Epoch 77/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3089 - val_loss: 0.3224\n",
      "Epoch 78/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3088 - val_loss: 0.3211\n",
      "Epoch 79/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3083 - val_loss: 0.3197\n",
      "Epoch 80/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3081 - val_loss: 0.3214\n",
      "Epoch 81/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3078 - val_loss: 0.3175\n",
      "Epoch 82/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3074 - val_loss: 0.3233\n",
      "Epoch 83/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3072 - val_loss: 0.3240\n",
      "Epoch 84/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3066 - val_loss: 0.3160\n",
      "Epoch 85/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62992/62992 [==============================] - 5s - loss: 0.3065 - val_loss: 0.3229\n",
      "Epoch 86/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3062 - val_loss: 0.3185\n",
      "Epoch 87/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3058 - val_loss: 0.3178\n",
      "Epoch 88/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3057 - val_loss: 0.3188\n",
      "Epoch 89/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3053 - val_loss: 0.3189\n",
      "Epoch 90/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3051 - val_loss: 0.3209\n",
      "Epoch 91/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3049 - val_loss: 0.3228\n",
      "Epoch 92/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3044 - val_loss: 0.3144\n",
      "Epoch 93/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3042 - val_loss: 0.3253\n",
      "Epoch 94/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3038 - val_loss: 0.3133\n",
      "Epoch 95/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3036 - val_loss: 0.3142\n",
      "Epoch 96/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3034 - val_loss: 0.3189\n",
      "Epoch 97/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3030 - val_loss: 0.3186\n",
      "Epoch 98/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3028 - val_loss: 0.3157\n",
      "Epoch 99/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3025 - val_loss: 0.3201\n",
      "Epoch 100/100\n",
      "62992/62992 [==============================] - 5s - loss: 0.3023 - val_loss: 0.3163\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff368751828>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import TensorBoard\n",
    "autoencoder = create_autoencoder()\n",
    "autoencoder.fit(x_train_aug, x_train_aug,\n",
    "                epochs=100,\n",
    "                batch_size=128,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test_aug, x_test_aug),\n",
    "                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 32, 32, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = 25\n",
    "idx = np.random.randint(x_test.shape[0], size=cols)\n",
    "sample = x_test[idx]\n",
    "decoded_imgs = autoencoder.predict(sample)\n",
    "decoded_imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAABUCAIAAAD1ScR1AADjHUlEQVR4nOz9dVxU6d8/jp8zBcMA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\ngg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def decode_img(tile, factor=1.0):\n",
    "    tile = tile.reshape(tile.shape[:-1])\n",
    "    tile = np.clip(tile * 255, 0, 255)\n",
    "    return PIL.Image.fromarray(tile)\n",
    "    \n",
    "\n",
    "overview = PIL.Image.new('RGB', (cols * 32, 64 + 20), (128, 128, 128))\n",
    "for idx in range(cols):\n",
    "    overview.paste(decode_img(sample[idx]), (idx * 32, 5))\n",
    "    overview.paste(decode_img(decoded_imgs[idx]), (idx * 32, 42))\n",
    "f = BytesIO()\n",
    "overview.save(f, 'png')\n",
    "display(Image(data=f.getvalue()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 250\n",
    "latent_space_depth = 128\n",
    "\n",
    "def sample_z(args):\n",
    "    z_mean, z_log_var = args\n",
    "    eps = K.random_normal(shape=(batch_size, latent_space_depth), mean=0., stddev=1.)\n",
    "    return z_mean + K.exp(z_log_var / 2) * eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_55 (InputLayer)            (None, 32, 32, 1)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_465 (Conv2D)              (None, 32, 32, 4)     40                                           \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_466 (Conv2D)              (None, 32, 32, 4)     20                                           \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_155 (Concatenate)    (None, 32, 32, 8)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_155 (MaxPooling2D) (None, 16, 16, 8)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_467 (Conv2D)              (None, 16, 16, 8)     584                                          \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_468 (Conv2D)              (None, 16, 16, 8)     264                                          \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_156 (Concatenate)    (None, 16, 16, 16)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_156 (MaxPooling2D) (None, 8, 8, 16)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_469 (Conv2D)              (None, 8, 8, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_470 (Conv2D)              (None, 8, 8, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_157 (Concatenate)    (None, 8, 8, 32)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_157 (MaxPooling2D) (None, 4, 4, 32)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_471 (Conv2D)              (None, 4, 4, 32)      9248                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_472 (Conv2D)              (None, 4, 4, 32)      4128                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_158 (Concatenate)    (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_158 (MaxPooling2D) (None, 2, 2, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_473 (Conv2D)              (None, 2, 2, 64)      36928                                        \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_474 (Conv2D)              (None, 2, 2, 64)      16448                                        \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_159 (Concatenate)    (None, 2, 2, 128)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_159 (MaxPooling2D) (None, 1, 1, 128)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "dense_80 (Dense)                 (None, 1, 1, 128)     16512                                        \n",
      "____________________________________________________________________________________________________\n",
      "flatten_25 (Flatten)             (None, 128)           0                                            \n",
      "____________________________________________________________________________________________________\n",
      "dense_81 (Dense)                 (None, 128)           16512                                        \n",
      "____________________________________________________________________________________________________\n",
      "dense_82 (Dense)                 (None, 128)           16512                                        \n",
      "____________________________________________________________________________________________________\n",
      "lambda_24 (Lambda)               (None, 128)           0                                            \n",
      "____________________________________________________________________________________________________\n",
      "reshape_46 (Reshape)             (None, 1, 1, 128)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_475 (Conv2D)              (None, 1, 1, 128)     147584                                       \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_131 (UpSampling2D) (None, 2, 2, 128)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_476 (Conv2D)              (None, 2, 2, 64)      73792                                        \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_132 (UpSampling2D) (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_477 (Conv2D)              (None, 4, 4, 32)      18464                                        \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_133 (UpSampling2D) (None, 8, 8, 32)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_478 (Conv2D)              (None, 8, 8, 16)      4624                                         \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_134 (UpSampling2D) (None, 16, 16, 16)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_479 (Conv2D)              (None, 16, 16, 8)     1160                                         \n",
      "____________________________________________________________________________________________________\n",
      "up_sampling2d_135 (UpSampling2D) (None, 32, 32, 8)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_480 (Conv2D)              (None, 32, 32, 1)     73                                           \n",
      "====================================================================================================\n",
      "Total params: 366,253\n",
      "Trainable params: 366,253\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def VariationalAutoEncoder(num_pixels):\n",
    "    \n",
    "    input_img = Input(shape=(32, 32, 1))\n",
    "\n",
    "    channels = 4\n",
    "    x = input_img\n",
    "    for i in range(5):\n",
    "        left = Conv2D(channels, (3, 3), activation='relu', padding='same')(x)\n",
    "        right = Conv2D(channels, (2, 2), activation='relu', padding='same')(x)\n",
    "        conc = Concatenate()([left, right])\n",
    "        x = MaxPooling2D((2, 2), padding='same')(conc)\n",
    "        channels *= 2\n",
    "\n",
    "    x = Dense(channels)(x)\n",
    "    encoder_hidden = Flatten()(x)\n",
    "\n",
    "    z_mean = Dense(latent_space_depth, activation='linear')(encoder_hidden)\n",
    "    z_log_var = Dense(latent_space_depth, activation='linear')(encoder_hidden)\n",
    "    \n",
    "    def KL_loss(y_true, y_pred):\n",
    "        return 0.0001 * K.sum(K.exp(z_log_var) + K.square(z_mean) - 1 - z_log_var, axis=1)\n",
    "\n",
    "    def reconstruction_loss(y_true, y_pred):\n",
    "        y_true = K.batch_flatten(y_true)\n",
    "        y_pred = K.batch_flatten(y_pred)\n",
    "        return binary_crossentropy(y_true, y_pred)\n",
    "\n",
    "    def total_loss(y_true, y_pred):\n",
    "        return reconstruction_loss(y_true, y_pred) + KL_loss(y_true, y_pred)\n",
    "\n",
    "    z = Lambda(sample_z, output_shape=(latent_space_depth, ))([z_mean, z_log_var])\n",
    "    decoder_in = Input(shape=(latent_space_depth,))\n",
    "\n",
    "    d_x = Reshape((1, 1, latent_space_depth))(decoder_in)\n",
    "    e_x = Reshape((1, 1, latent_space_depth))(z)\n",
    "    for i in range(5):\n",
    "        conv = Conv2D(channels, (3, 3), activation='relu', padding='same')\n",
    "        upsampling = UpSampling2D((2, 2))\n",
    "        d_x = conv(d_x)\n",
    "        d_x = upsampling(d_x)\n",
    "        e_x = conv(e_x)\n",
    "        e_x = upsampling(e_x)\n",
    "        channels //= 2\n",
    "\n",
    "    final_conv = Conv2D(1, (3, 3), activation='sigmoid', padding='same')\n",
    "    auto_decoded = final_conv(e_x)\n",
    "    decoder_out = final_conv(d_x)\n",
    "    \n",
    "    decoder = Model(decoder_in, decoder_out)    \n",
    "    \n",
    "    auto_encoder = Model(input_img, auto_decoded)\n",
    "\n",
    "    auto_encoder.compile(optimizer=Adam(lr=0.001), \n",
    "                         loss=total_loss,\n",
    "                         metrics=[KL_loss, reconstruction_loss])\n",
    "    \n",
    "    return auto_encoder, decoder\n",
    "\n",
    "var_auto_encoder, decoder = VariationalAutoEncoder(32)\n",
    "var_auto_encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_56 (InputLayer)        (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "reshape_45 (Reshape)         (None, 1, 1, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_475 (Conv2D)          (None, 1, 1, 128)         147584    \n",
      "_________________________________________________________________\n",
      "up_sampling2d_131 (UpSamplin (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_476 (Conv2D)          (None, 2, 2, 64)          73792     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_132 (UpSamplin (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_477 (Conv2D)          (None, 4, 4, 32)          18464     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_133 (UpSamplin (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_478 (Conv2D)          (None, 8, 8, 16)          4624      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_134 (UpSamplin (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_479 (Conv2D)          (None, 16, 16, 8)         1160      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_135 (UpSamplin (None, 32, 32, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_480 (Conv2D)          (None, 32, 32, 1)         73        \n",
      "=================================================================\n",
      "Total params: 245,697\n",
      "Trainable params: 245,697\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3750, 32, 32, 1), (500, 32, 32, 1))"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def truncate_to_batch(x):\n",
    "    l = x.shape[0]\n",
    "    return x[:l - l % batch_size, :, :, :]\n",
    "\n",
    "x_train_trunc = truncate_to_batch(x_train)\n",
    "x_test_trunc = truncate_to_batch(x_test)\n",
    "x_train_trunc.shape, x_test_trunc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3750 samples, validate on 500 samples\n",
      "Epoch 1/100\n",
      "3750/3750 [==============================] - 1s - loss: 0.5771 - KL_loss: 2.7625e-04 - reconstruction_loss: 0.5768 - val_loss: 0.5455 - val_KL_loss: 4.2084e-05 - val_reconstruction_loss: 0.5454\n",
      "Epoch 2/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.5274 - KL_loss: 3.1445e-05 - reconstruction_loss: 0.5273 - val_loss: 0.5249 - val_KL_loss: 2.5529e-05 - val_reconstruction_loss: 0.5249\n",
      "Epoch 3/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.5129 - KL_loss: 2.1664e-05 - reconstruction_loss: 0.5129 - val_loss: 0.5129 - val_KL_loss: 1.8901e-05 - val_reconstruction_loss: 0.5129\n",
      "Epoch 4/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.5041 - KL_loss: 2.0849e-05 - reconstruction_loss: 0.5041 - val_loss: 0.5084 - val_KL_loss: 3.5252e-05 - val_reconstruction_loss: 0.5084\n",
      "Epoch 5/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4979 - KL_loss: 2.6181e-04 - reconstruction_loss: 0.4976 - val_loss: 0.4984 - val_KL_loss: 0.0014 - val_reconstruction_loss: 0.4970\n",
      "Epoch 6/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4905 - KL_loss: 9.2406e-04 - reconstruction_loss: 0.4895 - val_loss: 0.4928 - val_KL_loss: 0.0012 - val_reconstruction_loss: 0.4916\n",
      "Epoch 7/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4873 - KL_loss: 0.0010 - reconstruction_loss: 0.4863 - val_loss: 0.4909 - val_KL_loss: 0.0015 - val_reconstruction_loss: 0.4893\n",
      "Epoch 8/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4859 - KL_loss: 0.0011 - reconstruction_loss: 0.4848 - val_loss: 0.4899 - val_KL_loss: 0.0014 - val_reconstruction_loss: 0.4885\n",
      "Epoch 9/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4852 - KL_loss: 0.0012 - reconstruction_loss: 0.4840 - val_loss: 0.4899 - val_KL_loss: 0.0015 - val_reconstruction_loss: 0.4885\n",
      "Epoch 10/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4844 - KL_loss: 0.0012 - reconstruction_loss: 0.4832 - val_loss: 0.4883 - val_KL_loss: 0.0014 - val_reconstruction_loss: 0.4869\n",
      "Epoch 11/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4837 - KL_loss: 0.0013 - reconstruction_loss: 0.4824 - val_loss: 0.4875 - val_KL_loss: 0.0015 - val_reconstruction_loss: 0.4860\n",
      "Epoch 12/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4823 - KL_loss: 0.0013 - reconstruction_loss: 0.4810 - val_loss: 0.4854 - val_KL_loss: 0.0017 - val_reconstruction_loss: 0.4837\n",
      "Epoch 13/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4785 - KL_loss: 0.0020 - reconstruction_loss: 0.4765 - val_loss: 0.4819 - val_KL_loss: 0.0023 - val_reconstruction_loss: 0.4796\n",
      "Epoch 14/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4750 - KL_loss: 0.0020 - reconstruction_loss: 0.4730 - val_loss: 0.4777 - val_KL_loss: 0.0021 - val_reconstruction_loss: 0.4755\n",
      "Epoch 15/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4724 - KL_loss: 0.0023 - reconstruction_loss: 0.4701 - val_loss: 0.4761 - val_KL_loss: 0.0027 - val_reconstruction_loss: 0.4734\n",
      "Epoch 16/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4700 - KL_loss: 0.0025 - reconstruction_loss: 0.4675 - val_loss: 0.4731 - val_KL_loss: 0.0027 - val_reconstruction_loss: 0.4704\n",
      "Epoch 17/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4653 - KL_loss: 0.0029 - reconstruction_loss: 0.4624 - val_loss: 0.4673 - val_KL_loss: 0.0033 - val_reconstruction_loss: 0.4640\n",
      "Epoch 18/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4609 - KL_loss: 0.0032 - reconstruction_loss: 0.4577 - val_loss: 0.4637 - val_KL_loss: 0.0034 - val_reconstruction_loss: 0.4603\n",
      "Epoch 19/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4573 - KL_loss: 0.0035 - reconstruction_loss: 0.4539 - val_loss: 0.4593 - val_KL_loss: 0.0035 - val_reconstruction_loss: 0.4557\n",
      "Epoch 20/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4540 - KL_loss: 0.0038 - reconstruction_loss: 0.4502 - val_loss: 0.4552 - val_KL_loss: 0.0039 - val_reconstruction_loss: 0.4513\n",
      "Epoch 21/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4500 - KL_loss: 0.0041 - reconstruction_loss: 0.4459 - val_loss: 0.4510 - val_KL_loss: 0.0045 - val_reconstruction_loss: 0.4465\n",
      "Epoch 22/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4470 - KL_loss: 0.0044 - reconstruction_loss: 0.4426 - val_loss: 0.4489 - val_KL_loss: 0.0047 - val_reconstruction_loss: 0.4442\n",
      "Epoch 23/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4460 - KL_loss: 0.0046 - reconstruction_loss: 0.4414 - val_loss: 0.4457 - val_KL_loss: 0.0045 - val_reconstruction_loss: 0.4413\n",
      "Epoch 24/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4420 - KL_loss: 0.0046 - reconstruction_loss: 0.4373 - val_loss: 0.4440 - val_KL_loss: 0.0050 - val_reconstruction_loss: 0.4390\n",
      "Epoch 25/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4398 - KL_loss: 0.0049 - reconstruction_loss: 0.4349 - val_loss: 0.4420 - val_KL_loss: 0.0052 - val_reconstruction_loss: 0.4368\n",
      "Epoch 26/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4376 - KL_loss: 0.0051 - reconstruction_loss: 0.4325 - val_loss: 0.4422 - val_KL_loss: 0.0052 - val_reconstruction_loss: 0.4370\n",
      "Epoch 27/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4366 - KL_loss: 0.0051 - reconstruction_loss: 0.4315 - val_loss: 0.4386 - val_KL_loss: 0.0054 - val_reconstruction_loss: 0.4332\n",
      "Epoch 28/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4344 - KL_loss: 0.0052 - reconstruction_loss: 0.4292 - val_loss: 0.4367 - val_KL_loss: 0.0054 - val_reconstruction_loss: 0.4313\n",
      "Epoch 29/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4344 - KL_loss: 0.0052 - reconstruction_loss: 0.4292 - val_loss: 0.4360 - val_KL_loss: 0.0052 - val_reconstruction_loss: 0.4309\n",
      "Epoch 30/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4316 - KL_loss: 0.0052 - reconstruction_loss: 0.4264 - val_loss: 0.4341 - val_KL_loss: 0.0054 - val_reconstruction_loss: 0.4287\n",
      "Epoch 31/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4297 - KL_loss: 0.0054 - reconstruction_loss: 0.4242 - val_loss: 0.4317 - val_KL_loss: 0.0055 - val_reconstruction_loss: 0.4262\n",
      "Epoch 32/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4275 - KL_loss: 0.0053 - reconstruction_loss: 0.4222 - val_loss: 0.4306 - val_KL_loss: 0.0056 - val_reconstruction_loss: 0.4250\n",
      "Epoch 33/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4262 - KL_loss: 0.0055 - reconstruction_loss: 0.4207 - val_loss: 0.4288 - val_KL_loss: 0.0056 - val_reconstruction_loss: 0.4233\n",
      "Epoch 34/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4247 - KL_loss: 0.0054 - reconstruction_loss: 0.4193 - val_loss: 0.4280 - val_KL_loss: 0.0056 - val_reconstruction_loss: 0.4224\n",
      "Epoch 35/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4236 - KL_loss: 0.0055 - reconstruction_loss: 0.4181 - val_loss: 0.4264 - val_KL_loss: 0.0054 - val_reconstruction_loss: 0.4210\n",
      "Epoch 36/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4219 - KL_loss: 0.0056 - reconstruction_loss: 0.4163 - val_loss: 0.4251 - val_KL_loss: 0.0059 - val_reconstruction_loss: 0.4192\n",
      "Epoch 37/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4206 - KL_loss: 0.0058 - reconstruction_loss: 0.4149 - val_loss: 0.4236 - val_KL_loss: 0.0057 - val_reconstruction_loss: 0.4178\n",
      "Epoch 38/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4214 - KL_loss: 0.0058 - reconstruction_loss: 0.4157 - val_loss: 0.4237 - val_KL_loss: 0.0059 - val_reconstruction_loss: 0.4178\n",
      "Epoch 39/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4198 - KL_loss: 0.0057 - reconstruction_loss: 0.4141 - val_loss: 0.4234 - val_KL_loss: 0.0058 - val_reconstruction_loss: 0.4176\n",
      "Epoch 40/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4173 - KL_loss: 0.0057 - reconstruction_loss: 0.4116 - val_loss: 0.4209 - val_KL_loss: 0.0061 - val_reconstruction_loss: 0.4148\n",
      "Epoch 41/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3750/3750 [==============================] - 0s - loss: 0.4155 - KL_loss: 0.0058 - reconstruction_loss: 0.4097 - val_loss: 0.4187 - val_KL_loss: 0.0058 - val_reconstruction_loss: 0.4128\n",
      "Epoch 42/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4145 - KL_loss: 0.0059 - reconstruction_loss: 0.4086 - val_loss: 0.4180 - val_KL_loss: 0.0061 - val_reconstruction_loss: 0.4119\n",
      "Epoch 43/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4140 - KL_loss: 0.0060 - reconstruction_loss: 0.4081 - val_loss: 0.4179 - val_KL_loss: 0.0061 - val_reconstruction_loss: 0.4118\n",
      "Epoch 44/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4127 - KL_loss: 0.0059 - reconstruction_loss: 0.4067 - val_loss: 0.4157 - val_KL_loss: 0.0062 - val_reconstruction_loss: 0.4096\n",
      "Epoch 45/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4113 - KL_loss: 0.0060 - reconstruction_loss: 0.4053 - val_loss: 0.4153 - val_KL_loss: 0.0063 - val_reconstruction_loss: 0.4091\n",
      "Epoch 46/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4108 - KL_loss: 0.0060 - reconstruction_loss: 0.4048 - val_loss: 0.4147 - val_KL_loss: 0.0064 - val_reconstruction_loss: 0.4083\n",
      "Epoch 47/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4097 - KL_loss: 0.0063 - reconstruction_loss: 0.4034 - val_loss: 0.4132 - val_KL_loss: 0.0064 - val_reconstruction_loss: 0.4068\n",
      "Epoch 48/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4077 - KL_loss: 0.0063 - reconstruction_loss: 0.4014 - val_loss: 0.4118 - val_KL_loss: 0.0065 - val_reconstruction_loss: 0.4054\n",
      "Epoch 49/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4063 - KL_loss: 0.0065 - reconstruction_loss: 0.3998 - val_loss: 0.4107 - val_KL_loss: 0.0065 - val_reconstruction_loss: 0.4041\n",
      "Epoch 50/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4051 - KL_loss: 0.0065 - reconstruction_loss: 0.3986 - val_loss: 0.4095 - val_KL_loss: 0.0067 - val_reconstruction_loss: 0.4027\n",
      "Epoch 51/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4036 - KL_loss: 0.0066 - reconstruction_loss: 0.3970 - val_loss: 0.4089 - val_KL_loss: 0.0068 - val_reconstruction_loss: 0.4021\n",
      "Epoch 52/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4021 - KL_loss: 0.0068 - reconstruction_loss: 0.3953 - val_loss: 0.4073 - val_KL_loss: 0.0068 - val_reconstruction_loss: 0.4005\n",
      "Epoch 53/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4005 - KL_loss: 0.0067 - reconstruction_loss: 0.3939 - val_loss: 0.4057 - val_KL_loss: 0.0070 - val_reconstruction_loss: 0.3986\n",
      "Epoch 54/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3995 - KL_loss: 0.0069 - reconstruction_loss: 0.3926 - val_loss: 0.4050 - val_KL_loss: 0.0070 - val_reconstruction_loss: 0.3979\n",
      "Epoch 55/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.4007 - KL_loss: 0.0068 - reconstruction_loss: 0.3938 - val_loss: 0.4045 - val_KL_loss: 0.0069 - val_reconstruction_loss: 0.3976\n",
      "Epoch 56/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3993 - KL_loss: 0.0070 - reconstruction_loss: 0.3923 - val_loss: 0.4043 - val_KL_loss: 0.0074 - val_reconstruction_loss: 0.3969\n",
      "Epoch 57/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3970 - KL_loss: 0.0071 - reconstruction_loss: 0.3899 - val_loss: 0.4030 - val_KL_loss: 0.0072 - val_reconstruction_loss: 0.3958\n",
      "Epoch 58/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3960 - KL_loss: 0.0072 - reconstruction_loss: 0.3888 - val_loss: 0.4032 - val_KL_loss: 0.0074 - val_reconstruction_loss: 0.3958\n",
      "Epoch 59/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3952 - KL_loss: 0.0072 - reconstruction_loss: 0.3880 - val_loss: 0.4007 - val_KL_loss: 0.0075 - val_reconstruction_loss: 0.3932\n",
      "Epoch 60/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3936 - KL_loss: 0.0073 - reconstruction_loss: 0.3863 - val_loss: 0.3984 - val_KL_loss: 0.0074 - val_reconstruction_loss: 0.3910\n",
      "Epoch 61/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3923 - KL_loss: 0.0073 - reconstruction_loss: 0.3850 - val_loss: 0.3991 - val_KL_loss: 0.0075 - val_reconstruction_loss: 0.3915\n",
      "Epoch 62/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3916 - KL_loss: 0.0074 - reconstruction_loss: 0.3842 - val_loss: 0.3987 - val_KL_loss: 0.0075 - val_reconstruction_loss: 0.3912\n",
      "Epoch 63/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3914 - KL_loss: 0.0074 - reconstruction_loss: 0.3840 - val_loss: 0.3982 - val_KL_loss: 0.0076 - val_reconstruction_loss: 0.3906\n",
      "Epoch 64/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3908 - KL_loss: 0.0074 - reconstruction_loss: 0.3835 - val_loss: 0.3978 - val_KL_loss: 0.0076 - val_reconstruction_loss: 0.3902\n",
      "Epoch 65/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3896 - KL_loss: 0.0076 - reconstruction_loss: 0.3820 - val_loss: 0.3954 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3878\n",
      "Epoch 66/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3891 - KL_loss: 0.0076 - reconstruction_loss: 0.3815 - val_loss: 0.3984 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3906\n",
      "Epoch 67/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3894 - KL_loss: 0.0076 - reconstruction_loss: 0.3818 - val_loss: 0.3976 - val_KL_loss: 0.0076 - val_reconstruction_loss: 0.3900\n",
      "Epoch 68/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3889 - KL_loss: 0.0075 - reconstruction_loss: 0.3813 - val_loss: 0.3959 - val_KL_loss: 0.0076 - val_reconstruction_loss: 0.3883\n",
      "Epoch 69/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3872 - KL_loss: 0.0076 - reconstruction_loss: 0.3796 - val_loss: 0.3950 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3872\n",
      "Epoch 70/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3868 - KL_loss: 0.0076 - reconstruction_loss: 0.3791 - val_loss: 0.3939 - val_KL_loss: 0.0076 - val_reconstruction_loss: 0.3863\n",
      "Epoch 71/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3862 - KL_loss: 0.0077 - reconstruction_loss: 0.3785 - val_loss: 0.3936 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3858\n",
      "Epoch 72/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3849 - KL_loss: 0.0077 - reconstruction_loss: 0.3772 - val_loss: 0.3925 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3847\n",
      "Epoch 73/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3849 - KL_loss: 0.0077 - reconstruction_loss: 0.3772 - val_loss: 0.3927 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3850\n",
      "Epoch 74/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3839 - KL_loss: 0.0078 - reconstruction_loss: 0.3761 - val_loss: 0.3926 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3847\n",
      "Epoch 75/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3832 - KL_loss: 0.0077 - reconstruction_loss: 0.3754 - val_loss: 0.3921 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3844\n",
      "Epoch 76/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3822 - KL_loss: 0.0078 - reconstruction_loss: 0.3744 - val_loss: 0.3914 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3835\n",
      "Epoch 77/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3814 - KL_loss: 0.0078 - reconstruction_loss: 0.3736 - val_loss: 0.3910 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3833\n",
      "Epoch 78/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3812 - KL_loss: 0.0077 - reconstruction_loss: 0.3735 - val_loss: 0.3903 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3825\n",
      "Epoch 79/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3804 - KL_loss: 0.0079 - reconstruction_loss: 0.3725 - val_loss: 0.3898 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3820\n",
      "Epoch 80/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3812 - KL_loss: 0.0077 - reconstruction_loss: 0.3735 - val_loss: 0.3904 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3826\n",
      "Epoch 81/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3819 - KL_loss: 0.0077 - reconstruction_loss: 0.3742 - val_loss: 0.3902 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3822\n",
      "Epoch 82/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3750/3750 [==============================] - 0s - loss: 0.3792 - KL_loss: 0.0079 - reconstruction_loss: 0.3713 - val_loss: 0.3894 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3816\n",
      "Epoch 83/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3790 - KL_loss: 0.0078 - reconstruction_loss: 0.3712 - val_loss: 0.3900 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3821\n",
      "Epoch 84/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3787 - KL_loss: 0.0079 - reconstruction_loss: 0.3708 - val_loss: 0.3884 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3806\n",
      "Epoch 85/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3774 - KL_loss: 0.0078 - reconstruction_loss: 0.3696 - val_loss: 0.3886 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3807\n",
      "Epoch 86/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3770 - KL_loss: 0.0079 - reconstruction_loss: 0.3691 - val_loss: 0.3871 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3792\n",
      "Epoch 87/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3786 - KL_loss: 0.0078 - reconstruction_loss: 0.3708 - val_loss: 0.3896 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3819\n",
      "Epoch 88/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3783 - KL_loss: 0.0077 - reconstruction_loss: 0.3706 - val_loss: 0.3886 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3808\n",
      "Epoch 89/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3766 - KL_loss: 0.0079 - reconstruction_loss: 0.3687 - val_loss: 0.3871 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3791\n",
      "Epoch 90/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3758 - KL_loss: 0.0079 - reconstruction_loss: 0.3680 - val_loss: 0.3872 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3794\n",
      "Epoch 91/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3752 - KL_loss: 0.0078 - reconstruction_loss: 0.3674 - val_loss: 0.3857 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3777\n",
      "Epoch 92/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3743 - KL_loss: 0.0079 - reconstruction_loss: 0.3664 - val_loss: 0.3865 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3786\n",
      "Epoch 93/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3743 - KL_loss: 0.0079 - reconstruction_loss: 0.3664 - val_loss: 0.3869 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3789\n",
      "Epoch 94/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3746 - KL_loss: 0.0079 - reconstruction_loss: 0.3667 - val_loss: 0.3867 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3788\n",
      "Epoch 95/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3734 - KL_loss: 0.0079 - reconstruction_loss: 0.3655 - val_loss: 0.3867 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3788\n",
      "Epoch 96/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3739 - KL_loss: 0.0079 - reconstruction_loss: 0.3660 - val_loss: 0.3869 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3791\n",
      "Epoch 97/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3734 - KL_loss: 0.0079 - reconstruction_loss: 0.3655 - val_loss: 0.3860 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3781\n",
      "Epoch 98/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3716 - KL_loss: 0.0079 - reconstruction_loss: 0.3637 - val_loss: 0.3841 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3760\n",
      "Epoch 99/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3712 - KL_loss: 0.0079 - reconstruction_loss: 0.3633 - val_loss: 0.3847 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3768\n",
      "Epoch 100/100\n",
      "3750/3750 [==============================] - 0s - loss: 0.3708 - KL_loss: 0.0079 - reconstruction_loss: 0.3628 - val_loss: 0.3841 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff0443a2048>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_auto_encoder.fit(x_train_trunc, x_train_trunc, verbose=1, \n",
    "                 batch_size=batch_size, epochs=100,\n",
    "                 validation_data=(x_test_trunc, x_test_trunc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAAAAABWESUoAAACn0lEQVR4nCXOSZIcRRAF0O9DZORQ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\nlp5mEfr45ad5XbIWxmlRIcIPxJIx7vv/YQO8kfbZIN0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=32x32 at 0x7FF043E4EBA8>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_number = np.asarray([[np.random.normal() \n",
    "                            for _ in range(latent_space_depth)]])\n",
    "img_width, img_height = 32, 32\n",
    "def decode_img(a):\n",
    "    a = np.clip(a * 256, 0, 255).astype('uint8')\n",
    "    return PIL.Image.fromarray(a)\n",
    "\n",
    "decode_img(decoder.predict(random_number).reshape(img_width, img_height))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAFwCAIAAAAue7doAAEAAElEQVR4nOz9WYxsV3rfia49RERm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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=368x368 at 0x7FF043E4EF98>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cells = 10\n",
    "\n",
    "overview = PIL.Image.new('RGB', \n",
    "                         (num_cells * (img_width + 4) + 8, \n",
    "                          num_cells * (img_height + 4) + 8), \n",
    "                         (140, 128, 128))\n",
    "for x in range(num_cells):\n",
    "    for y in range(num_cells):\n",
    "        vec = np.asarray([[np.random.normal() * 1.4 \n",
    "                            for _ in range(latent_space_depth)]])\n",
    "        decoded = decoder.predict(vec)\n",
    "        img = decode_img(decoded.reshape(img_width, img_height))\n",
    "        overview.paste(img, (x * (img_width + 4) + 6, y * (img_height + 4) + 6))\n",
    "overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 62750 samples, validate on 11000 samples\n",
      "Epoch 1/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3844 - KL_loss: 0.0075 - reconstruction_loss: 0.3769 - val_loss: 0.3844 - val_KL_loss: 0.0075 - val_reconstruction_loss: 0.3769\n",
      "Epoch 2/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3778 - KL_loss: 0.0076 - reconstruction_loss: 0.3702 - val_loss: 0.3795 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3719\n",
      "Epoch 3/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3731 - KL_loss: 0.0076 - reconstruction_loss: 0.3655 - val_loss: 0.3757 - val_KL_loss: 0.0077 - val_reconstruction_loss: 0.3680\n",
      "Epoch 4/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3694 - KL_loss: 0.0077 - reconstruction_loss: 0.3617 - val_loss: 0.3722 - val_KL_loss: 0.0078 - val_reconstruction_loss: 0.3645\n",
      "Epoch 5/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3664 - KL_loss: 0.0078 - reconstruction_loss: 0.3586 - val_loss: 0.3703 - val_KL_loss: 0.0079 - val_reconstruction_loss: 0.3624\n",
      "Epoch 6/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3643 - KL_loss: 0.0079 - reconstruction_loss: 0.3564 - val_loss: 0.3682 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3602\n",
      "Epoch 7/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3619 - KL_loss: 0.0081 - reconstruction_loss: 0.3537 - val_loss: 0.3681 - val_KL_loss: 0.0081 - val_reconstruction_loss: 0.3600\n",
      "Epoch 8/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3603 - KL_loss: 0.0082 - reconstruction_loss: 0.3521 - val_loss: 0.3650 - val_KL_loss: 0.0082 - val_reconstruction_loss: 0.3568\n",
      "Epoch 9/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3584 - KL_loss: 0.0082 - reconstruction_loss: 0.3502 - val_loss: 0.3641 - val_KL_loss: 0.0082 - val_reconstruction_loss: 0.3559\n",
      "Epoch 10/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3571 - KL_loss: 0.0082 - reconstruction_loss: 0.3488 - val_loss: 0.3623 - val_KL_loss: 0.0083 - val_reconstruction_loss: 0.3540\n",
      "Epoch 11/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3556 - KL_loss: 0.0082 - reconstruction_loss: 0.3474 - val_loss: 0.3622 - val_KL_loss: 0.0081 - val_reconstruction_loss: 0.3540\n",
      "Epoch 12/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3543 - KL_loss: 0.0082 - reconstruction_loss: 0.3462 - val_loss: 0.3603 - val_KL_loss: 0.0081 - val_reconstruction_loss: 0.3522\n",
      "Epoch 13/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3532 - KL_loss: 0.0081 - reconstruction_loss: 0.3451 - val_loss: 0.3601 - val_KL_loss: 0.0080 - val_reconstruction_loss: 0.3521\n",
      "Epoch 14/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3524 - KL_loss: 0.0081 - reconstruction_loss: 0.3443 - val_loss: 0.3586 - val_KL_loss: 0.0082 - val_reconstruction_loss: 0.3504\n",
      "Epoch 15/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3513 - KL_loss: 0.0082 - reconstruction_loss: 0.3431 - val_loss: 0.3580 - val_KL_loss: 0.0083 - val_reconstruction_loss: 0.3497\n",
      "Epoch 16/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3502 - KL_loss: 0.0083 - reconstruction_loss: 0.3419 - val_loss: 0.3570 - val_KL_loss: 0.0084 - val_reconstruction_loss: 0.3487\n",
      "Epoch 17/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3498 - KL_loss: 0.0084 - reconstruction_loss: 0.3414 - val_loss: 0.3576 - val_KL_loss: 0.0085 - val_reconstruction_loss: 0.3491\n",
      "Epoch 18/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3486 - KL_loss: 0.0085 - reconstruction_loss: 0.3402 - val_loss: 0.3557 - val_KL_loss: 0.0084 - val_reconstruction_loss: 0.3473\n",
      "Epoch 19/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3483 - KL_loss: 0.0085 - reconstruction_loss: 0.3398 - val_loss: 0.3558 - val_KL_loss: 0.0087 - val_reconstruction_loss: 0.3471\n",
      "Epoch 20/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3473 - KL_loss: 0.0086 - reconstruction_loss: 0.3388 - val_loss: 0.3550 - val_KL_loss: 0.0086 - val_reconstruction_loss: 0.3464\n",
      "Epoch 21/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3465 - KL_loss: 0.0086 - reconstruction_loss: 0.3379 - val_loss: 0.3546 - val_KL_loss: 0.0087 - val_reconstruction_loss: 0.3459\n",
      "Epoch 22/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3460 - KL_loss: 0.0087 - reconstruction_loss: 0.3373 - val_loss: 0.3555 - val_KL_loss: 0.0087 - val_reconstruction_loss: 0.3468\n",
      "Epoch 23/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3452 - KL_loss: 0.0087 - reconstruction_loss: 0.3365 - val_loss: 0.3529 - val_KL_loss: 0.0087 - val_reconstruction_loss: 0.3442\n",
      "Epoch 24/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3446 - KL_loss: 0.0088 - reconstruction_loss: 0.3358 - val_loss: 0.3522 - val_KL_loss: 0.0089 - val_reconstruction_loss: 0.3434\n",
      "Epoch 25/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3436 - KL_loss: 0.0089 - reconstruction_loss: 0.3347 - val_loss: 0.3533 - val_KL_loss: 0.0090 - val_reconstruction_loss: 0.3443\n",
      "Epoch 26/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3430 - KL_loss: 0.0091 - reconstruction_loss: 0.3339 - val_loss: 0.3522 - val_KL_loss: 0.0092 - val_reconstruction_loss: 0.3431\n",
      "Epoch 27/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3424 - KL_loss: 0.0092 - reconstruction_loss: 0.3332 - val_loss: 0.3509 - val_KL_loss: 0.0092 - val_reconstruction_loss: 0.3417\n",
      "Epoch 28/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3415 - KL_loss: 0.0093 - reconstruction_loss: 0.3322 - val_loss: 0.3507 - val_KL_loss: 0.0093 - val_reconstruction_loss: 0.3414\n",
      "Epoch 29/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3412 - KL_loss: 0.0093 - reconstruction_loss: 0.3319 - val_loss: 0.3499 - val_KL_loss: 0.0092 - val_reconstruction_loss: 0.3407\n",
      "Epoch 30/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3404 - KL_loss: 0.0094 - reconstruction_loss: 0.3310 - val_loss: 0.3495 - val_KL_loss: 0.0094 - val_reconstruction_loss: 0.3401\n",
      "Epoch 31/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3400 - KL_loss: 0.0094 - reconstruction_loss: 0.3305 - val_loss: 0.3499 - val_KL_loss: 0.0094 - val_reconstruction_loss: 0.3404\n",
      "Epoch 32/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3396 - KL_loss: 0.0095 - reconstruction_loss: 0.3301 - val_loss: 0.3493 - val_KL_loss: 0.0095 - val_reconstruction_loss: 0.3399\n",
      "Epoch 33/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3390 - KL_loss: 0.0095 - reconstruction_loss: 0.3295 - val_loss: 0.3477 - val_KL_loss: 0.0096 - val_reconstruction_loss: 0.3380\n",
      "Epoch 34/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3382 - KL_loss: 0.0095 - reconstruction_loss: 0.3287 - val_loss: 0.3479 - val_KL_loss: 0.0093 - val_reconstruction_loss: 0.3386\n",
      "Epoch 35/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3380 - KL_loss: 0.0095 - reconstruction_loss: 0.3286 - val_loss: 0.3480 - val_KL_loss: 0.0095 - val_reconstruction_loss: 0.3385\n",
      "Epoch 36/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3374 - KL_loss: 0.0095 - reconstruction_loss: 0.3279 - val_loss: 0.3466 - val_KL_loss: 0.0095 - val_reconstruction_loss: 0.3371\n",
      "Epoch 37/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3369 - KL_loss: 0.0095 - reconstruction_loss: 0.3274 - val_loss: 0.3477 - val_KL_loss: 0.0095 - val_reconstruction_loss: 0.3382\n",
      "Epoch 38/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3366 - KL_loss: 0.0095 - reconstruction_loss: 0.3270 - val_loss: 0.3466 - val_KL_loss: 0.0097 - val_reconstruction_loss: 0.3368\n",
      "Epoch 39/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3362 - KL_loss: 0.0096 - reconstruction_loss: 0.3266 - val_loss: 0.3484 - val_KL_loss: 0.0096 - val_reconstruction_loss: 0.3388\n",
      "Epoch 40/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3359 - KL_loss: 0.0096 - reconstruction_loss: 0.3263 - val_loss: 0.3459 - val_KL_loss: 0.0098 - val_reconstruction_loss: 0.3362\n",
      "Epoch 41/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62750/62750 [==============================] - 4s - loss: 0.3355 - KL_loss: 0.0097 - reconstruction_loss: 0.3258 - val_loss: 0.3452 - val_KL_loss: 0.0097 - val_reconstruction_loss: 0.3354\n",
      "Epoch 42/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3352 - KL_loss: 0.0097 - reconstruction_loss: 0.3255 - val_loss: 0.3454 - val_KL_loss: 0.0098 - val_reconstruction_loss: 0.3355\n",
      "Epoch 43/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3347 - KL_loss: 0.0098 - reconstruction_loss: 0.3249 - val_loss: 0.3445 - val_KL_loss: 0.0097 - val_reconstruction_loss: 0.3348\n",
      "Epoch 44/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3344 - KL_loss: 0.0098 - reconstruction_loss: 0.3246 - val_loss: 0.3455 - val_KL_loss: 0.0099 - val_reconstruction_loss: 0.3357\n",
      "Epoch 45/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3343 - KL_loss: 0.0098 - reconstruction_loss: 0.3245 - val_loss: 0.3448 - val_KL_loss: 0.0099 - val_reconstruction_loss: 0.3349\n",
      "Epoch 46/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3338 - KL_loss: 0.0099 - reconstruction_loss: 0.3240 - val_loss: 0.3441 - val_KL_loss: 0.0098 - val_reconstruction_loss: 0.3342\n",
      "Epoch 47/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3334 - KL_loss: 0.0099 - reconstruction_loss: 0.3235 - val_loss: 0.3441 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3341\n",
      "Epoch 48/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3335 - KL_loss: 0.0099 - reconstruction_loss: 0.3236 - val_loss: 0.3439 - val_KL_loss: 0.0099 - val_reconstruction_loss: 0.3340\n",
      "Epoch 49/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3331 - KL_loss: 0.0099 - reconstruction_loss: 0.3231 - val_loss: 0.3436 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3335\n",
      "Epoch 50/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3326 - KL_loss: 0.0100 - reconstruction_loss: 0.3227 - val_loss: 0.3445 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3345\n",
      "Epoch 51/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3326 - KL_loss: 0.0100 - reconstruction_loss: 0.3226 - val_loss: 0.3431 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3330\n",
      "Epoch 52/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3324 - KL_loss: 0.0100 - reconstruction_loss: 0.3224 - val_loss: 0.3431 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3330\n",
      "Epoch 53/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3319 - KL_loss: 0.0100 - reconstruction_loss: 0.3219 - val_loss: 0.3431 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3331\n",
      "Epoch 54/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3315 - KL_loss: 0.0100 - reconstruction_loss: 0.3215 - val_loss: 0.3431 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3331\n",
      "Epoch 55/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3313 - KL_loss: 0.0100 - reconstruction_loss: 0.3213 - val_loss: 0.3423 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3322\n",
      "Epoch 56/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3308 - KL_loss: 0.0100 - reconstruction_loss: 0.3208 - val_loss: 0.3422 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3321\n",
      "Epoch 57/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3309 - KL_loss: 0.0100 - reconstruction_loss: 0.3209 - val_loss: 0.3418 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3318\n",
      "Epoch 58/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3305 - KL_loss: 0.0100 - reconstruction_loss: 0.3205 - val_loss: 0.3416 - val_KL_loss: 0.0099 - val_reconstruction_loss: 0.3316\n",
      "Epoch 59/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3306 - KL_loss: 0.0100 - reconstruction_loss: 0.3206 - val_loss: 0.3414 - val_KL_loss: 0.0099 - val_reconstruction_loss: 0.3314\n",
      "Epoch 60/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3301 - KL_loss: 0.0100 - reconstruction_loss: 0.3201 - val_loss: 0.3426 - val_KL_loss: 0.0099 - val_reconstruction_loss: 0.3328\n",
      "Epoch 61/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3301 - KL_loss: 0.0100 - reconstruction_loss: 0.3201 - val_loss: 0.3414 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3313\n",
      "Epoch 62/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3297 - KL_loss: 0.0101 - reconstruction_loss: 0.3197 - val_loss: 0.3421 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3321\n",
      "Epoch 63/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3297 - KL_loss: 0.0101 - reconstruction_loss: 0.3197 - val_loss: 0.3408 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3308\n",
      "Epoch 64/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3295 - KL_loss: 0.0101 - reconstruction_loss: 0.3194 - val_loss: 0.3417 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3317\n",
      "Epoch 65/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3295 - KL_loss: 0.0101 - reconstruction_loss: 0.3195 - val_loss: 0.3413 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3313\n",
      "Epoch 66/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3290 - KL_loss: 0.0101 - reconstruction_loss: 0.3189 - val_loss: 0.3417 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3316\n",
      "Epoch 67/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3292 - KL_loss: 0.0101 - reconstruction_loss: 0.3191 - val_loss: 0.3403 - val_KL_loss: 0.0102 - val_reconstruction_loss: 0.3301\n",
      "Epoch 68/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3287 - KL_loss: 0.0101 - reconstruction_loss: 0.3186 - val_loss: 0.3404 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3303\n",
      "Epoch 69/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3285 - KL_loss: 0.0101 - reconstruction_loss: 0.3184 - val_loss: 0.3406 - val_KL_loss: 0.0100 - val_reconstruction_loss: 0.3306\n",
      "Epoch 70/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3286 - KL_loss: 0.0101 - reconstruction_loss: 0.3185 - val_loss: 0.3405 - val_KL_loss: 0.0102 - val_reconstruction_loss: 0.3304\n",
      "Epoch 71/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3281 - KL_loss: 0.0101 - reconstruction_loss: 0.3180 - val_loss: 0.3404 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3303\n",
      "Epoch 72/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3285 - KL_loss: 0.0101 - reconstruction_loss: 0.3183 - val_loss: 0.3407 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3306\n",
      "Epoch 73/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3281 - KL_loss: 0.0101 - reconstruction_loss: 0.3179 - val_loss: 0.3396 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3295\n",
      "Epoch 74/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3280 - KL_loss: 0.0101 - reconstruction_loss: 0.3178 - val_loss: 0.3397 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3296\n",
      "Epoch 75/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3275 - KL_loss: 0.0102 - reconstruction_loss: 0.3174 - val_loss: 0.3405 - val_KL_loss: 0.0102 - val_reconstruction_loss: 0.3303\n",
      "Epoch 76/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3278 - KL_loss: 0.0102 - reconstruction_loss: 0.3176 - val_loss: 0.3397 - val_KL_loss: 0.0102 - val_reconstruction_loss: 0.3296\n",
      "Epoch 77/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3277 - KL_loss: 0.0102 - reconstruction_loss: 0.3175 - val_loss: 0.3402 - val_KL_loss: 0.0103 - val_reconstruction_loss: 0.3299\n",
      "Epoch 78/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3272 - KL_loss: 0.0102 - reconstruction_loss: 0.3170 - val_loss: 0.3392 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3290\n",
      "Epoch 79/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3271 - KL_loss: 0.0102 - reconstruction_loss: 0.3169 - val_loss: 0.3397 - val_KL_loss: 0.0103 - val_reconstruction_loss: 0.3294\n",
      "Epoch 80/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3268 - KL_loss: 0.0102 - reconstruction_loss: 0.3166 - val_loss: 0.3396 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3292\n",
      "Epoch 81/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62750/62750 [==============================] - 4s - loss: 0.3268 - KL_loss: 0.0103 - reconstruction_loss: 0.3166 - val_loss: 0.3392 - val_KL_loss: 0.0103 - val_reconstruction_loss: 0.3289\n",
      "Epoch 82/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3268 - KL_loss: 0.0103 - reconstruction_loss: 0.3165 - val_loss: 0.3390 - val_KL_loss: 0.0101 - val_reconstruction_loss: 0.3289\n",
      "Epoch 83/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3265 - KL_loss: 0.0103 - reconstruction_loss: 0.3162 - val_loss: 0.3389 - val_KL_loss: 0.0103 - val_reconstruction_loss: 0.3287\n",
      "Epoch 84/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3265 - KL_loss: 0.0103 - reconstruction_loss: 0.3162 - val_loss: 0.3392 - val_KL_loss: 0.0105 - val_reconstruction_loss: 0.3288\n",
      "Epoch 85/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3264 - KL_loss: 0.0103 - reconstruction_loss: 0.3160 - val_loss: 0.3392 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3288\n",
      "Epoch 86/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3260 - KL_loss: 0.0104 - reconstruction_loss: 0.3157 - val_loss: 0.3419 - val_KL_loss: 0.0105 - val_reconstruction_loss: 0.3314\n",
      "Epoch 87/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3262 - KL_loss: 0.0104 - reconstruction_loss: 0.3158 - val_loss: 0.3387 - val_KL_loss: 0.0103 - val_reconstruction_loss: 0.3284\n",
      "Epoch 88/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3257 - KL_loss: 0.0104 - reconstruction_loss: 0.3153 - val_loss: 0.3389 - val_KL_loss: 0.0105 - val_reconstruction_loss: 0.3285\n",
      "Epoch 89/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3258 - KL_loss: 0.0104 - reconstruction_loss: 0.3154 - val_loss: 0.3385 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3281\n",
      "Epoch 90/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3257 - KL_loss: 0.0104 - reconstruction_loss: 0.3152 - val_loss: 0.3386 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3281\n",
      "Epoch 91/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3255 - KL_loss: 0.0104 - reconstruction_loss: 0.3150 - val_loss: 0.3387 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3283\n",
      "Epoch 92/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3255 - KL_loss: 0.0104 - reconstruction_loss: 0.3150 - val_loss: 0.3391 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3287\n",
      "Epoch 93/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3253 - KL_loss: 0.0105 - reconstruction_loss: 0.3149 - val_loss: 0.3392 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3287\n",
      "Epoch 94/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3251 - KL_loss: 0.0105 - reconstruction_loss: 0.3146 - val_loss: 0.3380 - val_KL_loss: 0.0103 - val_reconstruction_loss: 0.3276\n",
      "Epoch 95/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3252 - KL_loss: 0.0105 - reconstruction_loss: 0.3147 - val_loss: 0.3377 - val_KL_loss: 0.0105 - val_reconstruction_loss: 0.3272\n",
      "Epoch 96/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3248 - KL_loss: 0.0105 - reconstruction_loss: 0.3143 - val_loss: 0.3385 - val_KL_loss: 0.0106 - val_reconstruction_loss: 0.3280\n",
      "Epoch 97/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3248 - KL_loss: 0.0105 - reconstruction_loss: 0.3143 - val_loss: 0.3374 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3270\n",
      "Epoch 98/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3244 - KL_loss: 0.0105 - reconstruction_loss: 0.3139 - val_loss: 0.3374 - val_KL_loss: 0.0105 - val_reconstruction_loss: 0.3269\n",
      "Epoch 99/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3246 - KL_loss: 0.0106 - reconstruction_loss: 0.3141 - val_loss: 0.3374 - val_KL_loss: 0.0104 - val_reconstruction_loss: 0.3270\n",
      "Epoch 100/100\n",
      "62750/62750 [==============================] - 4s - loss: 0.3244 - KL_loss: 0.0106 - reconstruction_loss: 0.3138 - val_loss: 0.3380 - val_KL_loss: 0.0106 - val_reconstruction_loss: 0.3274\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ff043e4e8d0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def truncate_to_batch(x):\n",
    "    l = x.shape[0]\n",
    "    return x[:l - l % batch_size, :, :, :]\n",
    "\n",
    "x_train_trunc = truncate_to_batch(x_train_aug)\n",
    "x_test_trunc = truncate_to_batch(x_test_aug)\n",
    "x_train_trunc.shape, x_test_trunc.shape\n",
    "\n",
    "var_auto_encoder.fit(x_train_trunc, x_train_trunc, verbose=1, \n",
    "                 batch_size=batch_size, epochs=100,\n",
    "                 validation_data=(x_test_trunc, x_test_trunc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAFwCAIAAAAue7doAAEAAElEQVR4nOy9aZhc1XUuvE+dmud5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\nNl638f8ApmlUyQQ1plwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=368x368 at 0x7FF043E600F0>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cells = 10\n",
    "\n",
    "overview = PIL.Image.new('RGB', \n",
    "                         (num_cells * (img_width + 4) + 8, \n",
    "                          num_cells * (img_height + 4) + 8), \n",
    "                         (140, 128, 128))\n",
    "for x in range(num_cells):\n",
    "    for y in range(num_cells):\n",
    "        vec = np.asarray([[np.random.normal() * 1.2 \n",
    "                            for _ in range(latent_space_depth)]])\n",
    "        decoded = decoder.predict(vec)\n",
    "        img = decode_img(decoded.reshape(img_width, img_height))\n",
    "        overview.paste(img, (x * (img_width + 4) + 6, y * (img_height + 4) + 6))\n",
    "overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAFwCAIAAAAue7doAAEAAElEQVR4nOz9aZBk6VUfjD/35r7v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\nRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=368x368 at 0x7FF043E63F28>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cells = 10\n",
    "\n",
    "overview = PIL.Image.new('RGB', \n",
    "                         (num_cells * (img_width + 4) + 8, \n",
    "                          num_cells * (img_height + 4) + 8), \n",
    "                         (140, 128, 128))\n",
    "for x in range(num_cells):\n",
    "    for y in range(num_cells):\n",
    "        vec = np.asarray([[ - (i % 2) * (x - 4.5) / 3 + ((i + 1) % 2) * (y - 4.5) / 3\n",
    "                            for i in range(latent_space_depth)]])\n",
    "        decoded = decoder.predict(vec)\n",
    "        img = decode_img(decoded.reshape(img_width, img_height))\n",
    "        overview.paste(img, (x * (img_width + 4) + 6, y * (img_height + 4) + 6))\n",
    "overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 128)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec = np.asarray([[np.random.normal() \n",
    "                    for _ in range(latent_space_depth)]])\n",
    "vec.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.2,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,\n",
       "         0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.54127268,  0.75542285,  0.02418055,  0.12812252, -0.45038135,\n",
       "        -0.12498562, -0.50406062,  1.7153789 , -0.36611748, -1.68523352,\n",
       "        -1.4068772 , -0.62122251, -0.20882113,  0.86702964,  0.56838321,\n",
       "         0.61278478, -0.56450945, -0.6132218 ,  0.62080908, -0.45884909,\n",
       "        -0.81596489,  1.90906008,  0.47090321, -0.70723417, -0.08987115,\n",
       "        -0.98996504,  1.5055198 ,  0.33933324, -0.00727612,  0.53081118,\n",
       "        -0.92274769,  0.61856344,  0.63260797,  0.15246067,  0.6993734 ,\n",
       "        -0.81163242, -0.8842845 ,  0.882867  ,  1.17233759, -0.87674041,\n",
       "        -0.63333666,  0.54598216, -0.68672041,  0.92107567,  0.24558123,\n",
       "        -0.81178472, -1.54235875,  0.19795614, -0.72898594,  0.60573802,\n",
       "        -2.44154986,  0.1288156 , -0.83384018,  0.99805866,  0.12594563,\n",
       "         0.19831904,  1.35020792, -0.82157632,  0.79485373,  0.05986338,\n",
       "         0.05885011, -0.70727683,  0.1848128 ,  1.28740186, -0.67821694,\n",
       "        -0.49225507,  0.29651263, -1.28020207, -0.70930825,  0.8809047 ,\n",
       "         0.08697025, -0.34838283, -0.2025712 , -0.25570614, -0.89226412,\n",
       "         1.35551396, -0.48711742, -0.77591912,  0.46266573,  0.24299313,\n",
       "         1.19124487, -1.34561794,  1.11215833,  0.82253167, -0.77368409,\n",
       "         0.34910987,  0.14495987, -0.93083334, -1.85279519,  0.94960277,\n",
       "         0.64864293, -0.32589747,  0.10332585,  2.13881787,  0.17412005,\n",
       "         0.45042838,  0.62243691,  0.23059319,  1.5312478 ,  0.72376044,\n",
       "         1.02391736,  0.74497012,  1.03035196, -0.43284613, -2.06800808,\n",
       "        -1.76586909,  0.35805356,  0.63645777,  1.36920846,  0.1793834 ,\n",
       "         0.3787182 ,  0.14024296,  0.48471698, -0.24865715, -0.82991791,\n",
       "        -0.48684372, -0.31630593, -0.29120097, -0.32853159,  0.62634298,\n",
       "         0.76709016,  0.47991485,  1.04398778, -0.09921053, -0.77286324,\n",
       "        -0.15991888,  0.68730866, -1.27763871]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray([[np.random.normal() \n",
    "                    for _ in range(latent_space_depth)]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}