Test_script.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example script for using texturegan model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from argparser import parse_arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dummy command\n",
    "command = '--display_port 7770 --load 0 --load_D -1 --load_epoch 105 --gpu 2 --model texturegan --feature_weight 1e2 --pixel_weight_ab 1e3 --global_pixel_weight_l 1e3 --local_pixel_weight_l 0 --style_weight 0 --discriminator_weight 1e3 --discriminator_local_weight 1e6  --learning_rate 1e-4 --learning_rate_D 1e-4 --batch_size 36 --save_every 50 --num_epoch 100000 --save_dir /home/psangkloy3/skip_leather_re/ --load_dir /home/psangkloy3/skip_leather_re/ --data_path ../../training_handbags_pretrain/ --learning_rate_D_local  1e-4 --local_texture_size 50 --patch_size_min 20 --patch_size_max 40 --num_input_texture_patch 1 --visualize_every 5 --num_local_texture_patch 1'\n",
    "args = parse_arguments(command.split())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from main import get_transforms\n",
    "from dataloader import imfol\n",
    "from dataloader.imfol import ImageFolder, make_dataset\n",
    "import torch\n",
    "from torch.utils.data.sampler import SequentialSampler\n",
    "from torch.utils.data import DataLoader\n",
    "import math\n",
    "from torch.autograd import Variable\n",
    "from utils.visualize import vis_patch, vis_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load from validation folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "args.batch_size = 1\n",
    "args.image_size =152\n",
    "args.resize_max = 256\n",
    "args.resize_min = 256\n",
    "args.data_path = '/home/psangkloy3/training_handbags_pretrain/' #change to your data path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "transform = get_transforms(args)\n",
    "val = make_dataset(args.data_path, 'val')\n",
    "valDset = ImageFolder('val', args.data_path, transform)\n",
    "val_display_size = 1\n",
    "valLoader = DataLoader(dataset=valDset, batch_size=val_display_size,shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_network(model, save_path):\n",
    "        \n",
    "    model_state = torch.load(save_path)\n",
    "    \n",
    "    if \"state_dict\" in model_state:\n",
    "        model.load_state_dict(model_state[\"state_dict\"])\n",
    "    else:\n",
    "        model.load_state_dict(model_state)\n",
    "\n",
    "        model_state = {\n",
    "            'state_dict': model.cpu().state_dict(),\n",
    "            'epoch': epoch,\n",
    "            'iteration': iteration,\n",
    "            'model': args.model,\n",
    "            'color_space': args.color_space,\n",
    "            'batch_size': args.batch_size,\n",
    "            'dataset': dataset,\n",
    "            'image_size': args.image_size\n",
    "        }\n",
    "    \n",
    "    model.cuda()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextureGAN (\n",
       "  (model): Sequential (\n",
       "    (main_model): MainModel (\n",
       "      (main_model): Sequential (\n",
       "        (conv_1): Conv2d(5, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (batch_1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
       "        (norm_1): ReLU (inplace)\n",
       "        (res_block_1): ResidualBlock (\n",
       "          (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (conv_2): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        (batch_2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "        (norm_2): ReLU (inplace)\n",
       "        (res_block_2): ResidualBlock (\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (conv_3): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        (batch_3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "        (norm_3): ReLU (inplace)\n",
       "        (res_block_3): ResidualBlock (\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (conv_4): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "        (batch_4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "        (norm_4): ReLU (inplace)\n",
       "        (res_block_4): ResidualBlock (\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (res_block_5): ResidualBlock (\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (res_block_6): ResidualBlock (\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (res_block_7): ResidualBlock (\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (res_block_8): ResidualBlock (\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (upsampl_1): UpsamplingBlock (\n",
       "          (biup_block): Sequential (\n",
       "            (conv_1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (upsample_2): Upsample(scale_factor=2, mode=bilinear)\n",
       "          )\n",
       "        )\n",
       "        (batch_5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "        (norm_5): ReLU (inplace)\n",
       "        (res_block_9): ResidualBlock (\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (res_block_10): ResidualBlock (\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (upsampl_2): UpsamplingBlock (\n",
       "          (biup_block): Sequential (\n",
       "            (conv_1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (upsample_2): Upsample(scale_factor=2, mode=bilinear)\n",
       "          )\n",
       "        )\n",
       "        (batch_6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "        (norm_6): ReLU (inplace)\n",
       "        (res_block_11): ResidualBlock (\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (res_block_12): ResidualBlock (\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (upsampl_3): UpsamplingBlock (\n",
       "          (biup_block): Sequential (\n",
       "            (conv_1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "            (upsample_2): Upsample(scale_factor=2, mode=bilinear)\n",
       "          )\n",
       "        )\n",
       "        (batch_7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
       "        (norm_7): ReLU (inplace)\n",
       "        (res_block_13): ResidualBlock (\n",
       "          (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
       "          (relu): ReLU (inplace)\n",
       "          (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
       "        )\n",
       "        (batch_8): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
       "      )\n",
       "    )\n",
       "    (conv_6): Conv2d(37, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (res_block_14): ResidualBlock (\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU (inplace)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "    )\n",
       "    (res_block_15): ResidualBlock (\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "      (relu): ReLU (inplace)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
       "    )\n",
       "    (conv_7): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (batch_9): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from models import texturegan,discriminator\n",
    "\n",
    "#change to your location\n",
    "model_location = '/home/psangkloy3/texturegan_master/texturegan/best_models/textureD_final_allloss_handbag_3300.pth'\n",
    "\n",
    "netG = texturegan.TextureGAN(5, 3, 32)\n",
    "load_network(netG, model_location)\n",
    "\n",
    "netG.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = valLoader.__iter__().__next__()\n",
    "from utils import transforms as custom_transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_input(val_loader,xcenter,ycenter,patch_size,num_patch):\n",
    "    img, skg, seg, eroded_seg, txt = val_loader\n",
    "    img = custom_transforms.normalize_lab(img)\n",
    "    skg = custom_transforms.normalize_lab(skg)\n",
    "    txt = custom_transforms.normalize_lab(txt)\n",
    "    seg = custom_transforms.normalize_seg(seg)\n",
    "    eroded_seg = custom_transforms.normalize_seg(eroded_seg)\n",
    "\n",
    "    bs, w, h = seg.size()\n",
    "\n",
    "    seg = seg.view(bs, 1, w, h)\n",
    "    seg = torch.cat((seg, seg, seg), 1)\n",
    "\n",
    "    eroded_seg = eroded_seg.view(bs, 1, w, h)\n",
    "    eroded_seg = torch.cat((eroded_seg, eroded_seg, eroded_seg), 1)\n",
    "\n",
    "    temp = torch.ones(seg.size()) * (1 - seg).float()\n",
    "    temp[:, 1, :, :] = 0  # torch.ones(seg[:,1,:,:].size())*(1-seg[:,1,:,:]).float()\n",
    "    temp[:, 2, :, :] = 0  # torch.ones(seg[:,2,:,:].size())*(1-seg[:,2,:,:]).float()\n",
    "\n",
    "    txt = txt.float() * seg.float() + temp\n",
    "\n",
    "    patchsize = args.local_texture_size\n",
    "    batch_size = bs\n",
    "    if xcenter < 0 or ycenter < 0:\n",
    "        inp, texture_loc = gen_input_rand(txt, skg, eroded_seg[:, 0, :, :] * 100,\n",
    "                                              patch_size, patch_size,\n",
    "                                              num_patch)\n",
    "    else:\n",
    "        inp, texture_loc = gen_input_exact(txt, skg, eroded_seg[:, 0, :, :] * 100,xcenter,ycenter,patch_size,1)\n",
    "        \n",
    "    return inp,texture_loc \n",
    "def get_inputv(inp):\n",
    "    input_stack = torch.FloatTensor().cuda()\n",
    "    input_stack.resize_as_(inp.float()).copy_(inp)\n",
    "    inputv = Variable(input_stack)\n",
    "    return inputv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from train import gen_input, rand_between, gen_input_rand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 151.5, 151.5, -0.5)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XpcacEFp5wtA0TSqVClvb23IitGXR7nTQdB1VCDRVJUvTYnhLepPO9x1DSQg3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\nKFCggMH/D1h2C1O3S4b4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9bccc30250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m06mLzXgmjPkqSOHzU0HWhPC+8Jpr0TTBSMdHR8xmM3Stz94XUrK5ucnW5ja9\nTg9da+q6xhqDVIo4jl2FpHYH6fnYhgjEy6oyXSWeV0D2opZknTTgPA1NJacoJPQDlJRYYzDGYMDZ\nP5QibrfYGG7i+V5jX4E0S5lOJ0ynE3RdNXYEc25c/CSMjO/XAPyZE8Jl3Yfv4JTPw8VgpMZ+cHx0\nxGKxAGudfUAKwiBga2uL4XBIHCeUhevYhAWEIEoS2l0Xf6B8H/vECT/gBD+rjmwROONoFMfESYvA\nD/A8DyUkwlrqqkZrA03rtjhJ2NzeJIhCpKcQUlKWFfP5nOl45NKhtW48Dh9uiB87PnNCWEE0u1pR\nFEynU46PjsiztNGbLVHg0+t22dzcpNPtIpVkPl9QNf0SQZAkLVchKYlRnte8uyI4S3RyUoLnhyg/\nQBuXzWiMI4yyyKmq0gUdWksUxQy3toiTBN8PzlShLMuYTCaUWXYexnxlxsXPD58xIbxssrxIcnid\n1xvgdAc1hiLLmE0mTKcurkAIgTGGMAzp93tsbGwQhgFlWTIZj6mqEk9JfN8jSRKX3eh5SCmRZ81R\nP6R00Pw8XaxSIX2fMIkJoggjBXgeRliyPCdNU4rMFYEx1hIlMdvb2/T6PcIoRDSelLJ0NRLypuLS\nu8HnQy4fASFc9YNY1Yd7wUJuDUWeMZtNSZdLt1M2lZbarRZbm1sM+gPCMKKua6bjEVVZoKRCSkmS\nJLRaLURDBuJ0EX7QYT+5c7tUaIkfhfhR6MhACoq6Zr5cslwsyLKUqizRxhBEEYPhkF6/TxAG1NrF\nZBRZxsnxMcv5nLqsTi0lZ6d0P69Kh/gQc+cym8zV2Rk+AkK4SqwiEVzAhazHsixYzueuUKp2xjIl\nYdDvc/P6dfr9AXEUYa1mOj6hLgs8JcBa4jih1WqfLw0hVtZgrpREBT4qCimtYZFnjGcz5vM56WJJ\nnmXUWuP5Ae1u9yyRK8sydFWzmC84PjxgPplQlUUzVntOAis67lXFZ0YIqw8LaF2TLhZMJ04VcGTg\njIn9fo/h1hatxqVotHY7aeGIw1oII2eoQ6lz3/wqcOGZ18G4F9YZSaOQ3qBPbTXLLCMvCibjMely\nQaBUE4jlEcYJN2/eYmM4bIrOGhaLOfv7Bxzu7zvj68VzvbBHw6eCqx/TZ0II71PUe4OHdOoiw7pi\nqlXJcjFjMhmh6xJrajCGMAjodLr0+n2iKEJgqYuSMs8pyxKtDVIqF8TTarmGLPCk++99LIpX3u7z\n61CeR7sD5y1+AAAgAElEQVTTYvv6NfwwoDaavCiYzabkaXoWmSikwg9CtnZ22Bi67tW+p6iritl8\nyvHxMcvFHGucp+EZMjhzRcLbL6RVUDtfdP1v93w/E0J4X3iLh2Et1lgwmqosmc9mTMcjjKkwdYWu\nSsLAp9Vy9gHf9zFVRZkuqcuCqqqcrh2GJK22kxCEbDjAcrkJ/J4muhC4dGyJHwV0BwNu3blDr99H\nKkle5CyWS/KiAJyXwQLScw1rh5tDBn1XI0F5HrWuGU/GpPM5VCUYffYd+8lJCZcZw5vbFNaEsEqw\nBl1WrpTYfE6apUgpCYOAOI5dbcQ4wVMexrqyanmeY4xBa41A0Gq1ieLEqROnevSHqrR8KUj8IKQ3\n2CBOWni+DwKquqKuK4Q1nIVVSXHe6KXddmngnqLVSlCexFhNVZU80RX6k8O7fZafASG8l9A83j5E\n9jShqWY2mzKbTV11JCHxfJ8wDOl0usTNrmitpSyLphW8E5OVknQ7HaLIxR84laHZ9V/LN/+O3KpP\nHP/8d+m5kmpREhNGEb4foJSrjnQakIW1WAt+FNEbbLC9vY1U6uxoabokTRdN7MJlPSpv8sw+QOr4\ne8SHrLj5jj77vvAuJoZFm5rpeMxsOqXICwQCT3lN/cQOcZKche+WlSOEuq4Bg+f7dLpdojBECvVU\nh+dXLebXvccvO94pCV3mMM4+IJXnchbihDhxWZx+4COwCGubYjEGKz16gw1u3b5DEIbU2iVDHR7s\nMx6NKPIMA04lafCsoPCigir2Eq8PiXd/DR+JhPChH8R7gNsGMdoVEbW6phVF+EqilMQPAjrdDnES\nE4QBcbcLQlJkKWWRY7RGCvB9D/lE/YNmcT6xKlaLYIUQKKUwQN20n/N9nzAI8Hwf5ftIIRrPBC6M\neXOLjcEGUkrGkwl7jx9z788/2b1/jyrPzusjPMMGV5H09OnC+9AX8NnjgivOaE1dlizmM4yuaSUR\np56HMPDp9bq0kgTf9/GkJE9TptMpZVGCBU8pPOV6IbpjN/+cRSm+q0XwBsd+uh29oCkRL9DaUJYl\naZoyn06xfo62Fl3XBFHIcjalLEukUgRBiO8FpGnK8dERB/v7fPFtShBGCKkunOpVJLgmCFgTwlvg\ndXfZF084e1r/oCop8pTFYoYxmlYSYYym1pow9Ol1e87DIBU2z5menHBydERVls0u6zWSgSMYcZpl\n2CQ9nV/Hu5AQLh7/4nsvWWjCRSy6LA1cvIFSGGOZzeYc7O+z++efWD/ASgkYAuUxnUzYe7jLIk0J\nw4iNwYDpfMZysWAyOqFMM0yrRvqy0VxWSyJ6M7wfwloTwkrAUpcly8Wc6XhMnuX4vkfc71GWBVlR\noDwP3/fwpKQuC6bjCY92d9l/fEBRlFR1Td2UT6uqilrXeH5wYTHYd8cFb4pTcV6KJgnL1Vpst9qU\nZcW9e/cpy4ogabG5vcW1a9tM85zffv2VH/7rB+qyJEtTyrJw1GM0VVk4FcpolDotPX+RElbpBqwe\n1oTwDJ5m4hcZzZ73cXtho7RP/v/Cd621GOtKnQkrwAqM1iznC46PjijyDCFcIxYBaG0adUBSZEuy\n5Zzd+/d59PABy8WcdpJQVhWe51FXVRPqbFfD3XjxEl6yyVlryYvCVX7KMvIiRwhBEIZciwKi0CNJ\nYmxVkKVLDg72oemIreua0PewRrNcLsjzFK01vpCuerMUWCkuXM6TXo4XXNElBneWsfXUz1VSP56+\nxpfjIyCED31zL8bEv+iBi+bdi0Ys88Tks2cRcrJJaHS9Ga0x6EpTNe3Ojw4P3I5nXV7/abaisJYy\nzzg82GMyHvP7b7/yeO8hdV2yudEnKwr8wKfIM6yunR1ByOc4A941STytJlw4n7jw/tk8be6PsRRF\nwWKxYDydopQgTiLiOOTm9WvcvHGdjeEGvoQ4ijDWkKcZdVW5FvLtGGs0i9mMdDGnKkuC0/gMIRBW\nnj+PJ2wqL5pfzxvHi+Ib7IXjftz4CAhhVXA6eV7suqutxViDsAZPivPQ4eb71trzOgDK/S3Lc6Yn\nI8ZHD9m9/weHx/sIZamLmtksJ/B9jNEs0gX//l//znKxZDw+YTYZUxU1vucRRj7GampTc3x8SJY1\nvQoaz8WTNoRVgT1bX1JKwjAgjmLiOCYMQ5SEjcGAr7/8kju3b7O5uUmUxCgG9Ad9et02oe+hqxph\nLZ4nsboiW845fPyITt/1u/SixJ2tqUd5/gxfd6P5PLwTK0YIH+pmX9y5Tnd5+2TCXCMhCHHh802f\ngPOpIs78367/okU2KsLZS4AVAmsFGJjPlzzee8i9337g6OAhy8WEOAqQUlObCmNACAvCMpmOWSwX\nLPMlCOuKCQmDNTUSlzC0XC7IUpc2HCZNncHTDMAPvos9bdhsSBJQyqPdbtPr9ej3Oi7pKQyo6wop\nhbOf+D4iiuh2O2wMBszkFCkEke/jSYGxlny55Me//x0vTEg6PQZRAhaM1UjhuWKup5fzQpfkReLg\nOffsFVLjqnHva2CFCOGqyeA1n8rZ6ncWeqyBpjzfmfAvBEIp5xVoJowVbiIaezFATqKNRmBAXiAE\nBMYKmr8gNEymM+7f/51ff/2RPJ0SRYqWVEjPgmfBapQnEUJRVBV+6NFWLaSOyOdLdF6hGxVDYinL\nkqIoHCE4Q8Ur7Anvy+Pw9N9tEx7hPiOEoNPtMtgYMDpuo6RASZiMT1gs5vSyAV4QoDyPTqfD5nBI\nnqYEnseg18VXiuVyyWy+4B8//UyrO2D7+i16WzsoITHGYoV5tvv1E6RwUQoQTS1K6+YBTcSksLg3\nmsoLF1WfM6+OeOp+fzxSxQoRwgrAnu73xvVVbNQDp/O7bsrS85zoaS2gUUI0RCAoy5Jaa7ASJQSe\nFEgL1mpn6ZYSyfm88X1YTA/57Zf/ZDY5JI4lvV5CHCtMBQKJ1hblK6SnoAKlPbTWUJTI2lBZoK7w\nm+pIeV67Uu3K48lQ5VXctsRZ8RYrBP1Bn06nQ1XVSF9htaaqKo6PjpFK0c8zru3sMOj12Nra5Oef\nfz4LyOp1Omc2HKsNB48e8fsvv3D7y6/w2113nmcClV5gcBMCcDYHlydSU9clvpJNKTsBQjXS4GnM\nR0O6q+bJeU2sCQGeJPDT3dycxtCD1QapJMvlnD/+x48oNEpAVRVoa6m0Jisr/vL9v7B17SagXDt2\nC6LhGIF10XNCoISlqmt+/fln9h/8Riuw6MTD9yzC1AhriCOf2PeYz1OktAS+IggTagN1pbGeR2Sh\nVgrKEisFWij8liuhppQCY7FNp2jguR6PD4az4ClHCEIpknaLIAzJ85y6hFYcooRovAoHTKYzet2u\nK5bSNG4psgxrLGEU4vs+1goq7cqzHxzsow0I5TXP0lx4vu7BiOZarNHNwq/QWmO1ccJBXVMUOVm+\nxGhNnCT0h0P8uI2SzlV6TjIvYoNV9D48H58hITzPAn7qInS6vbVOtHequwsayouMo71dfv63fyWw\nFZ6y5EVOpWvysmKe5XSSiCQIMFbRandQQYRAIE4nIhZrNFJahC7585cfmBw+Yquf4JkYrSsU7pyB\n5+OHHlVRgRCEShK2YmptKcsaoxRGSGrPo8pS8rpCeT793iadThul1LmYe3Go7xWXNN4JAUo2JeDA\nWkNda6w1BIHnqkdlGRbBd99+SxCGDDY22NraYjIauS5Pno8KFRZIs5yyyJlNJi7PowmAotZo7Ra8\nrk7byLtXVeaUeU6WpdRl4eoz1hpd1+RZymw+Y5GmDIZbfPPXf6LrJyhfXtAynjI6PuVFea378QHx\nGRLCyyCdno8ET2LrGqzGCwN+/I9/497P/84wEQRIFDWEiqKylBqqbsDDH/5f7v34P8krwf/6v/3v\n3Pn6r+6wlSMEISS2rhAYlIJ24qOTAGVrlhKUH5F0Oo6UtKW2hm6ng9EaISD2PbQn8KUHysf6PpkU\nLOdTxrMZXtTi+t0ecStxHZukOvc0rKocK87Vsul0SpHlDPo9MJpet0sSxy42oSjx/BApJVtbW/jK\n4+DggN9/+43peMJ0mRKGIUjX4k5nBXWRUU+OMN0OKkqgrsgXC9Klq9eI1Zi6osgzsnRJupyf5ZGY\nukaXJVZr8jxjNp9xcDLh2s079Lpdot4GQZycj0HwHCPlcwfMKpPCJ0wIz9MPn3oQpzuEbboHN9Jf\n01oRYVymnacUQmg8KjotD6oSW9c4+1RTMFRr6iqlqpaUac0v/+Nf2bv/O0L6fPv9v7Ax3MIKgZTN\n5Kkc9Qg0EojCEGMtdaVBSAQS5Xl02wllkZMXBbbWCKFQAowA1bRs86MAu5BoAX5jeHN2DnNuR/hg\nXoYXiMunxsTGiGu1ZjI6afpTWgb9Hv1uB4lr7up7PpvbW653Q9Sis9Pi23/6Z2pj+eP3PzBYoqRF\nr9ul2+0yHk9RAn754b9YZhm9jS1sXTGdjJlNJ6TLBUJYBJq6qshTRxTpckG/2yEOfKxnqEyFoSIU\nhjpfkqdzqiJzVayshqcbRl00Lp7e92cMuqurQnzChHCKV9z0C2KjMQZtBLpxMkgLuq4YHYyoyiVR\nJFHKUJY12lZ4wsMqsLWhLnOUFxIKS24K9n77O/beb0gvpNfrIIRBIM6KllRVQV1lzuBoDWEQkuUF\naZoRBAFSuh6GcRwisC4cudZITyKFpDYVUnn4YUiUtPCDBSgPL/CdAfLUQv5E3MSLYyjePZ6/M1pr\nXIWjuiRdLknTJbrW+L7Ly0izDGMM/W6XO7dv4wUBVvkErTa3v/mWoq4xQpKlKRuDAcONAXWeszEe\nky5Tjg8PXITnYoa1ltl0ymQyYjod43uKwFcoJdBlgS5ybJUTeR3asQ9WkaGxtaQKFL5y9h9MjS5T\ndJkgG+OiEPKCUfGCXUFcHPeKSGkvuZzPgBBehNMHpnAmP0NdaSorqa0EK4iCgOXsmP/7//o/kdWc\ndmDQVUlVulj5sJEOCqOZLBdEQQlGYKucVhLixwrpKx7++P+x/+t/IISkqmuMM2JjqgwlNEbXKE9S\n64r5fEGv18P3PYyuKUtXgt3zPMq6xleudkCe5YCrSZi02oTRDCN94jhB+UGjLlzciVYz091anPHT\nWoLAx/d9amM4Ho1J04ykFbO9vcP1Gze4+8UXKC+g1jWhEvS2r/HXIODmrduURQGmSeiqS6R2z2o+\nn5IXBeOjPSzCSVvLKceHe7RbMd1Oi6AV0+0lKNmmqkriJjCqrg1FXZKWOVlR4gU+YRSgpCWfnBBI\nQdzuggpAes1c4gIZnI5yRYjgEvj8COGC8cdisXXpRHchUMrHapdfoHx49Mcv3PvHf2LzOVJUaG0R\nQlPWmqoqyIuCOI4wwlIbzXg2Jc8KlouMHeWxEUa0wxbKlugsI8tztNYo38MLAyd6WoMS4qzbc1Xk\nGB0DIRbLMk0J/IAwCjH5af6Dq5tQWYOwgqqusQi0MYxGY4o850w6uFDafaXmZXNNQjoXnpQB12/c\nZPLVN+j61NMS0ut1GW4OsQh2Hz7i1t0viIVsekP6JN0NgrCFqXNsXVMXOYvRMfPxiOV8RlXmZHlG\nmi1ZLJaETdGVTiui223RbsUoJdGmRmtLVVYURY7RhlrX5HlOWdUYIajqisPDff7jP/4nw709Wt0+\nUavLcPs6G9vX6A6GmCZYST6RO3H629uoCO/H9vD5EcITsGBrLAZhJRIPaQV5mvLnwz/47b/+jcMH\nv9JJfPCgrgwIp3OWRUldVyjP+aalp8iXFeP5jNHJBBVE+EFIEoegJboqqbPUdVgKfTzZos5TQCCD\nAKMrjK7QdcF8NsPzPMIwIk1zaEniKGrqHFiM0RhdIawCC7WuXbXmumY0nlDkxUrbER3OA5IQrn/j\ntZu3sLqm3W652geeR7uVIJUgzVIe7T3m+s3bSAHoGoTL8/CDACstKIk0NXVVcjw65vjwAKXAGOc6\nnE7H9HsdWklMt5vQboX4vqQqS4q6pm68ClVdoWvtGs4adyOlcp8bT2dMZnNa7fv4YUwQtfjLt3/j\nu+//hXanixUSIZWLO7FPjnX1wsefxWdMCE3wie85l2AN1bLEojh49Ij/47//dwKz4Na1HqIXOT+3\np6jQ1LpwC7wuKYvAVTBqJRRVBdMZ4/kc9h9TaYM2GgX4SuB5gsl0jPIU7bqLRSClR1260mnWaHRd\nsbu7S20MrXaXstKIvMQinX/8TD/VLvFJKJQnsdZSVTVZ2pRUOyu80gz36Qi9lcG5fWNz6xobvT5/\n++6vLjysrimzlEePdlkuU8q8QEmBZw0UKSiJKUqqLHV9HY2hyFNmkxOOj/Z5vP8IpSRxHOD7kjjx\nCUKFH7jCrtrU5Ius6RBVobV5gkNPk6adPUdR1xXz2Zx6PGO+/JOyqvGDiOV0TitpcePmLYJW58nw\n9ifGuPr4vAjBPvkf0YSlgtuhPK/EommF8PXNDWZHS6rFmNJLsDqm9n2UdItXG316BKTyXA3AMMIP\nIpQXMJ3MmS8WHI9GBJ5HEke02wlB3KIsC0ajMd1uDz8KUErh+YpWu0WnKDgeT5jP54xHI4IoRgiJ\nsRD4HmVVu9bnQlAUOXWtKYsKKcBT0uVQnLpJTse8kvPxVAS25wViTO3SvsMAggiDRCUFwfEhnrCg\nS2bjY8JAoXyP+WLO8cEho+MTfOUKqJRFxvHxIYcH+6Tpgs2tDVqtgDDwKEuJtTWL5RxjNLp2UYhV\nVTtJwNqzalPO+WEbVRKENWhrQLhkrLqqWC5StJnz26+/8tU3f0GXBbQ7Lou1GeWTt/9tH8a79058\n+oTwMt+wPY1Fd5ZjaXLm0zHL0UNubLbwipB0OWE+q4iNIWm1CKLQqeZKIZSrGCylQnkBseefVTqu\nygcslxmT6ZQwCLEIwjhGKA+pXBUkqRTKU86irhTtpvX5ZL6gLGsODg8YDjfRtaGqNSJJ0EZTVyXC\nNGXYs4wyL6hrt1u12y183wUlnQXkrAwjPO0CbkjhVJoRAiuddWe5nKONxWpNupiyXEyYz0Y8uPc7\ni/mYMAqZz6bcv3efh48e0Wu3iUIfMCyWC8pyiedDHHv4gQBpQBiKoqAsSqq6akjgXBIQQjROpyY/\nRbvcB20dMVhcRGTgR4wmroxbmmXs7e1xfHxMXZWNd9fZec6GvCq3/xL49AnhhWiMbUpBXUKRoucj\ndn/8d3756T/Q1Zx2O8AYn72DYzaUott3XZM0Fo1FeQo3fXG7jNbNwvRptROKomS+mFMFrjNzqxUD\nljAM6fV7+J7n6iMGPkIq4lbLNSvxPH797Q/2Hj1CWEun0yOOW01cgcQaDXVJWRRkyyWj0RgjfPqb\nbW7dukkraTWOhUZNOPONr+bMFFJipUR5ArTEVBn79/5BOh2jrOHBH7/x8NFDjkcnnIwOabVatFoJ\nvqe4d/8+9+894Mb1HXa2Nun12iSJTxz38DxJFPmUZe4SvqraVZcqGxeulK5sm3RFaYWzCDYRztZl\nmmLQxmC0QUpJv9djuLHFo8cHlFVJnmdMZlMWyyV1XTs3KufkIq6cEd6tYfEzJoRTSFfLcH7CeH+X\n44MHLOcnxK2YpDUg7sTYICRJWoRRTG0tQimUdMbEuipdf4QiR0lJEAT0+xvEcYudnQWjkxGz6Yyy\nKpnNZ2wONwijCGsdidAkTPme3xRDEXQ7HYYbA+azOYcH+8wmU9qdLkm7TRjFLk+hLl2Is+/TbSdY\nL2H72jX+8re/0R1sOOPWcwlgFXSIc4HaGO3chY0rdjYZsfvbL/zb//OvzE6OSaIAYTV1VeB7knYS\n0mlHRFGA1jWhr2i3IpQAgUYJix/6VBpqU5OmS+paU1U1VVlRVa7UnGmyT2niQ4QQWOPULa0NWrsM\nVleGzVKVJZPZlDhusam26XS7bG9t02q1SIvKGSDteZSSfWlA0tvg3aoNH5AQVmFiuggkXRWU2ZzZ\n7IQsnwMVQdjB8z28IGAoJL4fEPqBcytZMLWhKnOKPKMsC6q6JI5joigijJwR0vM86rp2sfNa43ku\np18pzxkbpcRa4YyATdt2pRSe8uh1OmwOBxwdHnOqG+d5ShDFRGGIAgLfx/cUSdKivbHDrdt32b52\ng6jVxkp5Fiz3+nh6sl3Fc3o6UtElF9mmXLpAUGU5k+NjHt77k/1HD5mNT2jFEdd2Nul0WoSxT6/b\nIUliPM9jNi/xPY9WklDXNVmWEUU+cRximkxJi1vgtT4nBa1PIzhPbRj6iUS2qm6kAgseTVOcuqYo\nm/BpJbl16ybtdofJeML9R3tNUOIT2SOveeteElH7HvEZSwguZBZTonVOWeVUdYHyBHHifNPGGAyQ\ntNv4yqUXV5ULYEozV/a7KHKEsMRJSKfTIQgCwJLnOVmaUtc1nW4H3/OJogglPbRxeQ1hFGOtoShK\nJ8qGPlEYUtc1cRRy4/o1ep0uZVWRLlN2Hz6C6ZQ4jkmiiFbSQsQJURhz8/Zd7n71NX4UI7wmj+F1\n78cr//YmxGCf/d02ipausdplgOJ5LCZjpkcHLKYT2nGIsl0CX7G9vUmSxNR1hec7EV9rQ13V7j6G\nEdPZFGM1xhq63bZr66brpoqzI4S61lS1dk1xnaUQgwHtsljAGRK1cRKCBbT5/9l7syc57ivf7/Nb\nM7OquhsAwUUazZ25HjscYz/c8J/vF0f4xX5x3LgTMXO1jBZS3EBi66Wqcvltfjgnsxsccq6kESVC\nUEaAANHopbIyT57zPd+lyM9sDVZl6C5E/vEf/5FxnPn0t5/x+u4oaVrbOfpzMkO/yVf4/b7/O1wQ\nACzYgAsd1gduziOn3Cg2shRDjIEuQFsWQZlTYRpnjqczd8cjx9MRaOx3A0+ePOHq8hLnHLc3twJI\nYTjsD9TacN7T9wO1VEG4S+Hm5pZSZN0YgqO1LO5HzhOHnrjbcXn1mGkcubm+5nw+8fzFS55//RVD\n7Hj69H1CCKDRbT5E3XL4bxkX/tzd2MOLtIqAKC0Y0yjLwvn1kZdffsbLr55xur3mMPQ8uhgY+o4Y\n/UbxPp0mvPM454EmW5Yl0cUea2FeFlJKOC+dVi4CyKaUyUV9KZywOGtrmzy9tnXb0RSQRcFF+clL\nAx+CuDZZy6MnT3jcHDTLLz/+LTFGaq20Vdzw5z7df+Dxl18QHvLKgW8T2Tjr6PYHHn/4I7rDpZiS\n2MDp+iuO119Dg3mZGE8jr65vOZ7OzMtMBQ4Xey4fXfH48RO8Po1qkdAVgK6L2hFI3kBKiXkSzCEt\nM9aixcBjTMNaQ1zxBB+w0WP1ifjkyWPO55F5nGit0XeRR5cX9MOeZZ65u7nh/SVhuybknXZv8vLH\nIcf8rmq+7/g83Sa0VjRZqfDq+XNuXj7n5tVzrl++5Prlc0qa6LvAbujZ7fuNdFVKEUDQZJz11CqU\n7q7vabUyLxPncWK329E5izGQcmFeEktK27+3TiPmzf2GoRa1xzIyxlTtFIxua2R74CgNpnnhPE7s\nhwNXF5cSNuu8muPo5/wPz8nvcg7/9KPDX35B+HePBjlhrGV/+Zj/7b/8H5iwA38AIv/0//yffP3l\n5zgqx9s7nj9/zhfPnrOkjA+eq0ePeKLBoxeXV5zujqQl0/cd0zhSaxPHZGPIuXCeZsbzyPHujtu7\nO4xp7Pc9V+FAKZlaxeVoDStZpoUQDM04YjdwefWY92eZm20z/M2PPuInP/4RDcftzTWfffIx73/0\nn7jsdtgQoTaM9W9y6tc167/h2f+xqLHf8TXUD4JaoGZhGtbMr3/xM37zi59y/fI5hiagoDc4a7Bq\nGp2y8j5qUzA2kfJEqYX9fqAfeu7ujpzGE+fxrCYqIv1eUpJVYxJ6t/MB54IoVK3dHOaqka8PMM+j\n5FyUim0Oa9Upu8AyZ25vj3z88W95//ET9v2wAcJFPRwESfi+RU3fD5X53SgI33nuLIRImRO1NFn/\nGQMkoDJOZ26PJ3rXJF34dGSeZ26PR/b7A//z++/z3pP3GPqB02kU45LapL+0Hm8lqHWe1HzjPPLp\nZ18wL4m+77m42HF5ecHF1QFjJJfR+yAA2bSwpMLVo0eYZii5MI6zriavuNrv2fU9yzIyT4nb88J5\nznzyy5/zkwZPPvyx6PWb5d7q68EF+ifBdL/RmdWqBaFw+/oFv/nFz/jZP/8TXz/7HFMzfRcJfUff\ndSLuapXT+YRxYkRSamWcZk6nM+M0g2l89OGHPH3vKV98+QW3t9fcHQvTMuM7r2thsby/BxINzjrd\nyt7bpK3meQYotYkqVc+PrCAVgKyV8Xzm88+ecXm44McffEBOC16TqDdS2BvZmm/P8W4UBPj2omAA\nPMYHTOvkwjNOM0Ub87xwc3dmMkmzBDV09eKSq8ePee/pB+wOF1jrSeNMrpVSKnNKxCCEpVYFYHz5\n4gXPvnrO9c0du/1O0PKLPf3QY13AOVl95VIpc2IaJ0HFY2SeE7fHI+M0cXF54MnjK64Oe1rJLNNE\nLjPLNHIeE7/++X+nVrkwP/ibv8VGsAS20eGNhsDwRlbCf+j4rs5A/tNqUeORxPH6NZ9/8jG/+Om/\n8OLrL1nGE7s+igelFQJQbUUaiVrx3inXo3AeJ27ujpxOI/v9gHOe/X7g8uLAbjdwfWOZNKMiEmWF\nWOWGds5izfo9zGa42krVaaZt2EJtkpux8bp0DdFapeTM119/zfOvnnN3fYPxni5EvPf3r3m1rfud\nAb4/Zjfxh3+Nd6QgrGq/9o33R56Uznc4b2g1iz16M4Bjnip3d2dO+UT0ja4fuLwSufGTp0+5vHqM\ntZFcCkXW6aQsbLgYe4yxwq2/ueGzzz7nF//6ax7riPHBB+9jg4BbuVQwntwqJSXysgjrrVTOtze8\nur7h1c0dw34vken7C3FzrkWptAbTMqe7I9evb0lJxE77/YHd1WNMb6BZuQGa1SKgv7/hS/5ds+vv\nUzTe7EKabnNazuRlZplHvvj0Y379rz/ns99+TEkTuz6yH3piF7HOkNJCKUIca0DW9n1eEnfHMze3\nJxJnNbMAACAASURBVE6nMxcXB4wRlefQ9+x3A7ELzPOCjzNYGdVqEaDP+ftkbKMBOFXtsnWg2QpS\nA5y1GGu2h71ZN1OtsSwzN9e3XL++5h/+4X+i6zpi7MQVi3ts4t+emz9mx/DH7z7ekYLw8FhbhRVs\nrKDofmuFVho0i3USApLmRG8Nt7d3VCqP3/8RT957ym5/4O54hnqnqrhMU/bi1dUVfdexnE+8eP6c\nX/36Yz7//AvmZeHpB+/z3tOnGOeozW7JTEnNPJdpwtRKHz3ee169esWrV7ek2vjb//Qel5eXWOeE\nJXc+k6YJ38TZqSwzr1+fKLWxLJLx+L/87/+F93/8E2zoWePUMZY3HJT+aNfpt4C3tdByIi8Td69f\n8dlvP+aTX/2Cr774FGcKFxd70WBU+dmK8gcEU5En9bwsTMvCPCWmaeH2eGRZEjlXUspM55FxHAHo\nul4A2yTYUC0SL2+MxTuPfeCU3BTXkO3A+tdmOzXWWaxTNqORMJnpvHB7e8tu6FmmmfE8cT6fySkR\ngowp5vde+f5wjnesIHzL1b/lMIhRSasID91W5mnmdHeihkJpVVSNuwPDbk8IHePpzDLPIkXWxqIz\nHV0MLNPI9fU1L168YFlmht2O3e7ARx99xNWjRzgfMc7SdPMwzhO1ZIy1BC/ZD8fjiU8/+5KUKleP\nHrEbdvT9gHGO+bywLIlWKj54aYWNoQ+Ou+vXfJL+lSUl+mFH13U8+uBHiMz4Owgwb4wPvy9g9Y1C\n0OS8thUzaIXXL77mi99+zMe//ldefvUly3Ri33f0neA2KWeWlEg5iSfBIq8v5aTcgbLxN5x10BZe\nvHjFYdfTxyAkpAbeOUIQTcm67WlaEJzO+bVVLCIEK0W2Ca21N1++dhHGGKx+rjFGClWpXF1c4I3j\nxt2IviQnDKJ5+MPUjj8MrOEdKwjtzfOuF+5qzV1qgWZwRjIXU1oYx5GWK4dDx+FwKSzDXEltIc9J\nZvgs+3SswZpGzQO3N9e8evWSm7s7hmHHo0eP2e8veP+DDxn2e1qD2AWmaeI8jozzRAye/W4gGMPd\nzQ3PvnrBp5894+rqih/tdgQv1mrGGHJKtNZwzhF9xPtAHyP+0vPl81e8+PoZx/OJx4+fcDhccLi4\nxHU7rNXuYKUxfqvX4jdXs99yKv9NXXlYXCTXopVMK4mSFr787GN++fN/4dnnn9JKovOO/a4H5AY1\nxnCeJqZpppTCPEnq9TxP6sbssM7SxYh3nmAdz58/J3rLftcz9B3WGJy1dNq6gyhT9a3BWfm7NWOj\nNMF8ahXLdZkgm24gVoxB+QkN5ZDI63zy6IqL/Z4YvNCkS5ZOY8Ui1lP0A9WPfNfxjhWE9XjQJVgH\nVea+VEX7blygNcewP/Dk6XtEk+g6i7OG4+0NN69e0WrFG7E9kxk3sT/s5QaYR6ZpppbGxeUVPgRi\njAzdQN93WGBZZvwQqHnhdHfH4eqCLkacgbu7O7549hWff/ElpTYuLi55/PgJYFjmBZOEIBu8w+ur\nubp6hLWRL7/6mr6LpFy5efWKX/38p3R9z/5w4Onf/j27EOSpbS1iq/bNYvAfOa33MzaAsYbz8cTn\nv/oFH//yZzz/8lPycuIw9PSdJFvXVlmWxPE8Mk4zy7JQigiPLvY7Hl1e4Jwjl8w4Snv+/ntP+Ye/\n+zv+v//6X7m5veXZV8/5z3//t/RDz7DsWJYF55xImVtjbsv28+WUaSlrohZU7WhiDEIlL1W6AWu0\no5BV56KuVCFE+m7g6upS3bFOVN0uSZei+4oGzXw7kvBDPt7RgvDg2ByXLdZFsIHmArYF9hcXvKcF\nwVnRwucKYx1JKbHUJtLl4FiyOCiNp8ztsmi6s6MfOpkpjWFOM/MkTLu0TFy/nLm5veV4d0fXd1gM\nqVWWlLHWc3F5xdXVY54+fUoIQfwAl5FUFtJ0wrRKdJ6r3QHvO/q+Z9cPHI8jhkYfPS++fsYvf/5T\nLi6vwHs+cJ5+f8EaR2ceouG/05TQ/u0fN/dqGRNaE7HS8eYVX376MT/95//Gi6+/oKSRzhu8MxhE\nM1BbI5VMqZWcM9ZaDvu9BKUg/gTD0JNSoubMq1evgCe891hcmW/v7ri+uSaXH+F9YBgGdY2Tm7Hr\nOsDI07vWbWRqVURqUSnp3jkMjdyarjyFQwIyVmT1TDDGELzHeykalSoYtPm20/dN6vIfQlD6HshJ\n/86P8Y4VhG/BEOoqV/UCbllHaYbz8Q7nDE+ePCKSaC2zUk5C8EyjI82CDWAMpclaq+TEqNZoXT/Q\n9d0mYFqWxHg+08UgtuPXt9zc3HA6jwxDv40BrSFt/v6wpSE3C+fzieP5jtP5SJrPGGDfD/j3Lfud\nw3vL1cWB29sjZyeF5+buls8++ZhhGNhfXLDb7Yn7Pfe5hHLR//vkum/ZPmwGLLqf19GrrqNCTbx8\n9jmf/OvP+M2vfoZHgFKJnDPkkklZqMSlyoqv5ELfd7z/9Cnj+cw8TZScGWIkOMt4DppdOUErHPYD\nd8dbbu/uWJbEsBfhV1NqeGtNui7rNps5QOTWrRGCmLp679SgpYJrGILgCkapy62Sy72dmhjWyIiZ\nVi2GtVpszDaNfcfJ/EEf71hB+JbDiesyrUGptGq4fv2a//f//r8YX33GMERMLrQWJFfBWZy3DENH\nqxXrvFzcpdDFQHCG9957LIYm1hK7jhCiEGtywTtPDJ7gHNMsdmfH4x2HiwMlZ927i7vv0PfE2OG9\no5TM3fGWly9fcHN7TUkT3juW/QV96HA2sN9f8OjyArA4H/jFr34NxpLmiU9+/Us++OBDrq6uZIzZ\nXWB9hJzB2W+ZdX+Hi3jtDOqailuVG3HmePuaLz79mK+ffYpthd0QGboojL5aKbmw5EW7J0fwQTIr\nMex3e6ZRurA0z+T9DmOlrY/BM40jz549Y5pGzuczUxIQcn/YEWNgWTxZFYveB3ZRTG3meWFJC6U1\nvHd4zWpcC8XaKZRayCUzJ/VVfPBxEHp5zplcC8YaWpNiYDan67enAHzzeMcKwre3XwbhyDor1d3W\nzHj7ijqf6WzDOktWaaugzUJCKbngQ9hGDqdfwzur3YR89aHrNz1CTpngPX0XeaIqPGmXC5hEjB37\niwP7/W7rDoyBtCykZeZ4jFgDp3nEJ4e3jvPpTLpIoCBj10X6vqPrOvbDQAhiXf7Fbz+m3+3ohh0f\n/OTv2V0+xrigZ+Gbqcjryflm29q+5fcGVbY00+mO29cv+frL3/Lq+Zfk+czFrmPoAjEKWWteEi0J\n9mJcU5qyhNjWWsVyXtemRslK1hohFlnDNE+8en1NLkXEXdZIJmMuOO+IIUh9LwVjHSGGzQzFTDDN\nsxSbJFsd7x191xFjlCKbksqfk7IURZBWa8VbOb/N6HYC1E0J3Vb8e9ff9zQC/BGPd6wgfPPQixmL\nQUI4bGt4Gp2tZCreNKwztAq5SeUXpB4y4nHgnMM5JxkKreGCl+QfxPi06zu8C9QK8zzLZqDreBLF\nT9Fax/XtLcZa+r7j8vKCfrcjBrmxrTWULmJaJZeFnGeW+cTqAVjbPe8g7oLu3Q0XhwNPri7wznF3\nPPHq+VcY7xh2O7wy67qLR/enQXtdSTde/+Kb48T6j3nA4yjUsjCdjly/+Irnzz7ny89+w+nmJY7C\nsB/wzirRx2p+oygVHWCNzO9VFY1rknZKSTMSzFYYvHOknLg7Hmk09nsZ2QyGWgseKQgr67M1cE5m\nfmE8ivHtchrJRZ7whg439ITgRKSkhK97bkJTXkTBOOGHrCNDqY3mBKAUNWX7dxqEH3YxgHe+IDxo\n71T6Si20lKlJ9sreWaj6lNL3M+dMThKiEoOg005nYJHZVnyIyoTLsoq3bJbhBnSGjVw9fiJ27dfX\nzPMM+j1LSpwWcWPa7Qb6LrK/vCD0gYvLPSFaasr0oefq4hHncQJu+fCDD3h5fcu0zPR9px4MhnE8\nsywzr59/xc//+Z+IMeKC58f7Pa06vamszMIPsxwe4gcPwEPZJqy/Cmk68dVnv+GL3/6G5199wXi8\noZUFbyGG+/1/y2VTLtaSCSFgDZSSMYhK05hKTouYycZAqUVLNgxDL4rPRTIxLgdJgd7vd3hNefbK\nGXBWNAprwcZYGoMyE+V1ee8ZetFOOGeBIgxQHQ2MsRSRjmq3IPhEykU8LHLGGrfRne9bhLdzbHi3\nC8J2rd+3v2tUeEqZYB2x89SsdmetyWrKWpqzdF23dQjr7LjiBtZ6rDN0HRgdF1orG3FFnILEOm1/\nuCC3xul0lLUi8iS0im+IPiJjjWXYHfBBEpHPdydalhbbGo8PjrvTka6PPHlyJe31MlFzBgPRW1pd\neP3yKz775Jf0u4G+H7h47wN8N8jP11Amo56fh+BhE6S+1oJpkk1Z88LN65c8/+ozPv/Nr3j98ivG\n4y2GgndCsjKrpFB39CIRF0qxM8L7KKXoElRwiFKysDeXRd4LvWEP+x3WWFqrEro6DBoFH9XHUCP5\nipilWOeUUyDqRleLYgHyftdamJeZXJLY4jnLkpJImVk5Svq5TkaDVISoVqp0Od6JzFxi3Vbqu372\nf7gufBed/Ps53rGC8G3z8Pp3bXv4FZ3tvTVY5zEmYIoIdIy1WOcIymQLMQhzzoqq0FgIXY9xQqQJ\nIVBzpeRESgtdjKw3ebEFFyTKvO97Ss6ybmsSMBtjJxdfqdS2CCoePUM48PT9D7mLt8ynM7ZJBmTw\ngePpxOGwp+sjrVVevHzJlGaiFyCzASXPvPzqC/pe5uafWMvF46f40ElBWMlK7RsXdC1QCq2qoWya\nOd285ovPfsPnv/01L7/6nJpmrKn44AjBiGiLdr+aa+JmXEpRLofBW9Ej0KRQzPNEyUkCcayV98KL\nL8R+t5Oo+9q4vLxgGIYNx1mWlQdQVb4sN3+/29GvhUlHLGE+qn2bNQL0Bo9vQT43l+3qsEa8Fa1z\nojytFbHgVxVkWwuGly6Lb3ZX33X9/fCOd6wgfNdxPzC21igNqjHMKTEtll3n8a6SSlOPA6c3vGjl\njbUEJ7TgWgFjqUhse9dFljaSFxH4+K4DYFaufdPC4qwleE/yTsdWeSLXnDdwUlKF5GteXD5mN+xY\nzmdO1zfEcO/heHGxx3nHssy0Vrg7nkhpwTQE0Bx6puXEl59+zDwvlNr423/4X3n/Rz95EH2tT3Vr\n7t2blYrsgHE68fL5M375s3/h2RefcHvzAtcy+6Fj6Ds9rdpqs05kldwqS06ULFkSwYth7VjEjyCp\n0cs0jpScIUYsEmHXrDIVvYwGfR8VqLTcHs+czidyThx2A/MyczqfmZbERx9+QNdFwGpBEqrxNM/U\nWgTTiQf5utoVVQV7nXNbl+Cc0yIpmMX2cAiBGKS4GvsNcPYtO96xgvBNhPwBfrDiY6oK9CFCmyi1\nsqS6EVUEhGobFbaq5Jmi4qQl03UdQz9QFWgUPr2swUqtdF1H6DqmaVbjTukyghcuxDzPYrk2DMTe\nyRNJ6bboEzY4gw0B2w+0fdYnmSX4SE4ztTmurvZYB7tdx/ksa7xW5Gv10UPLXL/4it/8609FR7Ak\nHr33lH63x3kvI0JptFRY5pnpfGY6n0jLzM31S158/SVffvYx5+M1tiWGPuK9rOZWLwI5tU3PurTo\nyyyMREMV/QZSO6J3WGCeRryzsq1Rg1oDdIo3GGPk5tXzn5JyPEahOrcKKSemWZySJtVF7IJsVGoT\nEtTqQRHj2mEsYNDuRRyYm233JK4H2ErWrQMgOgnvcVsux//o+vvhHu9YQfjG8caOyAAWY4RhaNZ5\nkCakFOX/i1AGBd7Y5tUGkpMwzmAEA/De0RQsM+be7yBi6Pue0ziRs6y2Qt/JXtwY5nnGOs+w3+Nj\nh62iwQdpqbMBlHS3biYWJTXFECgl0SgMfcTZA130BO8Yx5FlTpQiHo45V47HW7787W+Yl0RaMn/z\nn/6Ox+89ZTioCrFk0jxy/foVN69fc3tzw7JM3F6/4vr1C87HG5yp9NHRBYd1sqFYJRMr0aoBtQmg\nuKRFOhYM0zSKRgM0v1Lel6HvMCDYi1qZBe8kSkPzLGptao8mpCPRPHjmJSsRCWLs1BBlhRHb5mpk\nnZDM+qGjlsqSFulmSlOu0ZskI7P9T9uKu0R7uM0te/OyfMMk5e0BGt/RgvBglqvqpGOceK4aizUS\nmeZrE6AIMc9c6a/BC5lm3U+3VbqbE+fzibvjkf0w0MdIodFFjzMdNNHnT/OC9bJ3F8Q90/cBY8Eg\nT6oK+K7jUeiw1lFt3YhLcjOJcUjQcaWlJG491uA136HVSowOiKRFUHu5JDtySuScSGniPE9My8zp\neMf1y2d89OMf8eFHH3Fx2FNK5vb2ho8//g0vvv6a090t1sAyT6Q0Ebxh2PX0XaDUrBZlYl2+Iu9r\nl1BrIaWFWjI1i2rwNme6rme/O3B5sRfsJEjrHrxnHCcMaOZBIyiIa43FRSteEk0s6bvHHaVUrm9v\nybXgQ+TRo0uuHl3Sd52+f9JthOCpCjB678ktkYvwQZwVANPJG0LLSoW2ZoOdtkAWa8WuPwiIfH/L\na8v5FhSBh8efsSD8OU7UQ1DnTTCRVsX6DKi5cHd7S8+Zy25HFy2pFjFObUV47VkQ6XUFmdLC+XTH\neLqjVJjHkbwbxFpLtxN9FxnbrApH4RFUNeVc5gmvT3u54WaOd3fCdPQBEHZdbbpbt47W6v0TUseY\naRKD0s4HQrBiOW5g0GATa4z4DFRL10d8jNyezizzmZdffy7RdTdfc7r+Sp2EC+M08frVC+5ub5nG\nEWug6yP7ix3GNEHfW90ciaTLsoJHGMEkVo9BMTCVnT4YjscTNzdHnL8lRlkBHnYDMUYu9gND12GM\noeZCXjLWK26jQF8Inr6PNKDre9nMWJimCAYuLy/YDT3OWwFtlaMB8gQ39l4WjTFYW5R8ZgU4bGsH\n2FQNufYZbF2HV4dnq0zFb/adb9PxjnYI33HUjGmNmmZON9dUN7PsDd518lTThOaUMyBc+NakM1im\nkTSNzOOZlCvLdKaVS0LfYxoY0/DBscwwpYVxHgkKYlmDODAHL0Bb8KRpZp5G5mmkhSLRBUFuBqcr\nMMmISKRxpOs6nHcSN+8MQXf/1oC3huYtXfAaRFLwXkDRWiHMlpQX8rJwPd6SxhvGu5cyWwNVZ3hK\nwiEtf99Z+tgrHiu3h0Siyalc/07+dN+CO2e31avFMk0zd8czKTf6IbIfBsb9jvceP2K/29ENkWle\n1DSl4ryl9r1QyJvDB0+vxabrO5z3XF4chHJuDUPf4YMQn6BKsdpGACsAsbV4/dm8l3XjRktpq7uS\nUbNVoziS/KOmxW79+H0BeNtKgRzvcEG4v2A3E9JaMC3T0onz3SuqWxj3Blqi6qqwlsQ0nqm1Mgzy\nJLPGbCu5mhbGkxQGQ+Pi4sB4OqnuwWJqxbWGB2paZFvpHTUn5iLrvL7ryKUIhTYvLE3GlXquHA57\ndrsd641WamVZZrouCP2XJmSZWQpHHwPeQE4z1jSsldccQ2BOidN4orVCH8W56fZu4ub6OTfXz7m8\nvFAPAcOHH7zP/jCQkufFy5FlHpmjk0LkVNhjmubnyt20zuqNijWiHei7KB4C3mGr8A/O48jtcaIf\nO47xzPXNLSUXPngKj64uaU38LedlwVjD/rDH6BrQ+YAf/AZclpIYhsjB9xq2UyhFiFD3mKDeyM5q\ntyHsSesdNMe8LLJ2bIIrWWs1kyHR6kqhlq8jbEXpPKx5O4vAw+MdLgjrY0JNMOpCKxOff/JLfvbf\n/xu7WFjGI18+O7HfHxh2O5z3TPOEPA0lWjwtlWWeeH39iiWNooosmePdHefTSS4SK1BFaY05L0xp\nIuUFp5r8nOVidSq0MaaR06Lt/4eEKL6NKctoUErGO0srkgQtKLww62qrnMdEyllaWWukS/CWZZnF\nrRhD10ex+zJG4u11FVdSoFUZQ6K37IYB7wNJV5jOOS4u99LGr4oNs/L51UxVY9PXG9A5cXNqyHYg\nBk8KnlakaxAdwY7T+czxOHLkzKPDJS9fXfPi1TWPHl3R9z390FNaxVrDeBa157Db0w29SJGrqBK9\nM7qNaEq8dNgqluwSqyehrBZ5/W31VdTXYRCwOOWC8+Lz6LwkdedUSFl0GFVBxZyyjCO1Pri+HnJc\n3p5C8Q4XBHiTlluhJsbbl5xefcGjneXVtHB3e2IaTzx69Ijdfk/VeDBjDY1CpWBspe89hr2IJ2mk\nPHI83TIvIykvesMhvgk1U1vGoiSXVrG2YXC6/s/kPJPSBBScrr4yhVIW5rlRrBH7tpyxtM2LMGeZ\n+efF0oWAQVaUpciN5KxsOoyuBve7ntYqKSfmqZH7jtYq3ie8M3hvCcFp0ImMIvv9oOKfSkVoxath\n7YrRGN2ArE/NdaFjrSHGSBsa1nguLk5yw7q4bQ2mceY0ztsK7+rqEcMwMOwGcklM88SyLIxn0SnE\nLkqSkzH65F7xCulPNoDzgUhpZaQKSUpi320z2wp65R6sL8Z52STkLErNogQyg1LZc95yHWjtwVbj\ndykGv+u/+f7Xlu94QWjbbwahze6i5ckhMl91zGfD+TRzc3OHd43oDf3Qi8uOKTQr8/5+v+ODDy6p\nKXO8PfLs6xdClBmvubl7uV0wtQjKbZ0g5rUVSknUUvDB4E1UL8CZWhdqTczTCWvlAktpxmRLNpaa\n1fBDbcrneWZeEjRETah/X2tPUPn0oCGpr2/uNmJQFwLNOOlKsqPXmXu9cZZlorSC9wEfnDAQVV+Q\ncqZSKa3gmtmQeKO6jRWck9Vh227ELka62LMf9qRcGfojtZotlHWaFs7jzG7Y8fjRpfhR7vdcPbok\n54XbW8M8zZzPI84Hur7fPA+tuWcQisFqJav+oKgRixClZLTCCA/EupVSLh+31hGsl7yGpvRl7zGp\nqgjrHmDMWTsE6ialfmPr+BYd73hBgI1Gp2utWjMtJ1ytkAtlWahJflEWXPMycxrHbug4HHb0XSA4\nMK1y2Ef6IfLpF18L7blmvAXrDcVIaEizUK0j50at4rHgvcN5K6w8K+0urZLTTCsB66xIjFumYkCt\nxa2WspxlNeqUA1EMnE9ncloIisw/jh19P3BVIal6b5rzlmy8Wo91XScKT1B6higInfebL6G1ssNv\nILO888oDMBvQuAq9Wqk46zECMMjO3gd2u4GrRxdgLMtS+Jt+4HC4YL87YK1lv9/x0UcfcHkpmQve\nWUyzxCDy8WmSn/l0Om2rP2ctKa+jQWHJiTkpKFkrzju6vufSOlVCOoLiQGZ9MjwQr68cCnQNWnwl\n2SyOzJtFuxSehyPD21gM4AdVEP40LdG3ikV0vl1FN61WPIbOejoXyCbTcqEsCbc3WCwGh22WXTdw\ncdFjjXDxO9U2nKeZlBumVblxmiEbVBEnFNppWolNsquXvEGzrRVD8CxzYgnyFKvlwROoiNgJIyq+\nUlRlqdRZA9rmFxZNPrq6vCIOssbLWXCClBOp6CfYFaiTPXw1bbMh9yop3tpha/DGaptutSOwmzfA\nqgdI6jfYB6ejFjil+IrvgH4t7+mHuLH9Sqk8enzJo6vLzfgE5WiklJRIlGkqjNrWgg8EVGsQ7DgJ\nz6LWxm6/oxsGDrFTDZJRsVRTwRZKdEIpygIWGmsxwVBy3bofr1Zq8oZUVj9FeQ8Nb44Cb0eF+AEV\nhD/18ZB+Jjz5NTK8lYbHs48DV7sDNINphrxUTHNYG2g4lrlgjacPO6wVAVMrEELhyeMr5qXIDG/l\nCV2spTa3ff9aRC2X81oUDNY5SmnyBB0cacmM40IshlrMvc9fbRjbhExl5QZalkTbXIB0M2ENJWWm\neaG2hg9RfAlMR8mZaVlwIeDmgJvT9rOVhljPe1FvOs1OqNvNJ+Itp3Lpe/txNjqxhKTc8xC8taBr\nvmUpvHx9zc3tHSlVYuhoQIyRJ08eEbzncNjR9ZHdTorYPE+Sun08SaTb+cxeRUXreWnqkVlKJa2K\nyXlhnISGHvqewXli18mDQCXNtVSMFmI0l9N55VVYiX+rRrwVrUWxFaE952p0XHhgMKvn5I1r7a/i\nprfgMOjSuSifYOF0mri5Hbm+GZmnytPH77PfH4hdT1W02gZ4fLggz4272wlnG/M8Mc0T8zLT93u8\nb9wdJyAz9D0XB5EkLylxOp/JxWB9x0V/oI8BQ2OaZ2418vzR1X7b5NfW2O/2wh+wRkeKRitNrdkS\nCyLYEUBMnpRen/Z9F1UmDakJMJZLVjMQQ4gRFyLn86TS33sD1qr/b1cNgQKGRunbAt4VBdQtpTbS\nIspN7xx97AnKLmwViuIfXex4/MgDhuC7TaIs+/4KtZGyFCljNWKt3oeuGCNd3bo9WDsU0Jnf2DcK\nlbg5Gx2hzD1LQjsKMcmRG9kq6akqk9U6sXj31qquRFK7uy6Qz4uwSEvWNWt7ozC8TccPrCB8Szv/\nJznapoITH7+sTzaDs4HDcMlut8c4z3E8M6dCZ71e6JFWVUOfARzORryL+rSa5ElZG96JV6JziWnO\n1CY7fh8iseuouahtl8VYj3NBLNZyFncmJ5oJpz4DIThMMOQlEYOkHE9ZKMzrRY1BjFK6jhglsnxc\n9AKuRZmPRlyFjOPELN/Dug1oyyVjmxVPwxjWM6Y6hfUXrFkI1NWToGKc+BQ6KxHsxcjTW/b8jcuL\nA10nCcpNsQnnBCSdpklMY9aMBGtlNagjxroKdN7z8Om7rjvfbNrlY+sTvyr1uClbtNYqBUTNUldK\n85rJYCw446SYKGPRO7FeO58XkWrPM7VkrA/fsWz8YXcH8IMrCH/K45tvThPn3SzkoOgDu37AOo/z\nkdYsOVWWpW4JQtFH+q7HOsM5F7rY05ueRhEwrWacruOauvoKNoCkC2F0U6A2YlZArr7rwRiWRVpe\nMfto5HzcAMguRq4u9+x6GQGWZcFPkviUlYTjjQB9+92OD99/D6tzu7TP5f5iN059DZ2KuIwiD7f3\n/QAAIABJREFU70338VnWhV0gqDIwlyybjgcFYeVQrPh7rZXcZEXnzD2LUezHKqkUngw9l5eXxNAJ\nnblVjG20VliW9a5eKcZS4Iw1WC8sxdh1DEMvgCNsWIJ5UA3WP1rlZBj1Q8CYB+vI+mD00THB+w10\nXkNxvBPgshbw1uOijBUpiaP2RU7iK7HGua2r2LekU3iHCwK8WRRk/qz6RK61MPQ9oY/UVrm+u5M1\nm4H9Yc/lxR5njQqGHEMfCM5hbKO0qoEjlaHvFM025CLRZDSx+WpV7LrMakVmhBcvjEBZVaacsNYR\nNao+lUxN6vDsPb0qBEspknp8lqRngxSdy8OBJ1dXXO73HM/i8mysbDNqLTIetIaxHh/FUZoGxq0p\nzHVb1aF5CxiDQzQaYiF2n6SMruhCCGSNYJuXWdyIfJDuJooxqkmFaZ5wJ4s9yKcb06BCrfLap2Um\n14JFbd62JvJeRxBCuL/hmoww1pjNx9Gpm/K6CVnvz6ZfR76fMCqzBrJsr2fDGSrVrLwRPcPGYJol\nuEArVUJbSlVg9RsF4C3ZQ77jBUEPRZvXuc9agwsB70XUdJwXilpuD11gN3R0IchFuzRontgFeQI5\nK646ztEFjzE9pay0WlE3SiZC1cKjgSR2XclZkTCr9+A8L3R9r45BVjtj4Ris0eaSZ+jVUSlubLt1\nPdepmatkHVZlT0q2QKMqS6+J21P02Fow9t5dHYRFKWtZ4SSsWZKidVit1ZT4qedQ0HZROJbY4fWG\ncN7rRkM2MMZKsVzfCmPYinJT0tVqicbmTcBGtHJOzsM9HV2KkpjOiAy9CwEfIsF5ESmorVur7cGn\nSadj9dxU7aAkFHY9H02hEoPB6c8iwqllnrUQ2DefNVsd+C7W4g+HzfjXggA83DjIfj0QI0LlnSdS\nES/E2AUuLg50XcDZJt1BKxhTCd7QnKEauwFUq9+i2KxXldeKIYiAekUu+lY140NBNW2Na6mklAgh\nYo0UCu8sUOmCkIlqKaDOTV6LgrMWa8RwxCkIlpekak02T8LV9ccoaGZMpe8DKQtJ6P7GE7MScUrO\nxBBFGKUdwzwtLCULG9Kpo9T6jNTPqdoBNV2temvxwdB3Pd65N2TkIAYntQkIKIEo4nGwfk9ZhyoX\ngLZl2Na63ZJghbMQ1exlGAacF5Voq+a+Q0CYm8IvMZKDocnT5gER4X4YEvNVi5V7X0eQnBMNQ1M6\ntNFr6m06/loQ1kO3DQYBtYbomRaDUdOMOSeaheglabnVArXgoxenH80GyLOg3tYajJOVmLS0RSzU\nU6LBJrZB/5xLFl2BKguDF6u0aZoRL4aCcx3B682uI8Q6/xpdvznn6EIkBkfwlpwT17e3zPNM8IE+\ndiwlbYYv1kLXiUGrc42LXce0wPk8IhkqcoksywzI3Nx6BS03L2T0JhPWYhN5J6uQqFTJvqytEEKU\nxKYGwYWtk5CsRXk9q/lI0QIqrEm7zf2yiXDiIxnctv9vrSBGtmtnI6NDDF6KhhXlp2xCinZIshFw\nVmjaMVi8i+RiSSVjEf/G9aEgr1UdFVul5NWdGfFo5L6Qvm3FAP5aEPS47+9WkMz7gC9uk7TGENjt\nena7Hu9E8dbFoOlKAeM0kagmXYVlZIPgNay0CPDnvaDtJcscjyjmliR5kMu8bGnEXey4uJCg0fM4\nEWKgi8JarG11gt6aZLwXNmHwnl3fcXHYkVOi5Mz5PNLFsrENYxAqckNWirRMrTJ+RGdonWwkSmkb\n9bjV1ZV6tRWTa95Zac8LapTa7lt27x0pSbufS97Qvnvn43Wev8csal0Liq49tSDIhkUwAGvF9bqL\nUZyUNg5CfaBdKKrLcJst5HqfBg3TwQgeUGqVEcMZmnNCF29FfBiygMTW23vyWLmnYhvlfeS0aOe1\ndhL8UCaB3/n4a0F4OOw1BRabtI9rO22Moe8DF4c9u91A8BbvDF0ImxV7NWBbowLLkjiPIzlLspBR\ncVFKq3+g/UaHkJnnmZwz0yRJxdFHueD7nuvbO87jKM7ERp9wZRXmFFUAenXvCWoa2nF1eUlaFsbz\nmbv5jnEaCUG4/8GLUWwzjnkZpUA1A0ZyIdwg1OC5JslF0JveGDbnKMEq7qnXOReJaaBSm45fXkFO\nI3mOpVbJhLASmtJMpRmrRUGf2Kbp6KSErlL0vUDxCyVse6fgqn6th2vQVb9QixqpOPVRbFhTNXaN\nbcUoa0dD8DJwGIRZmooY4JTaMCGKk5I1GlRrNhC11iprx1rvMSmj19ZbVBT+WhC2w9xzlLQobJl/\nNNbwdGvNlguIos4UodCu7axzwpMvpYLJtKQhJSsBxqJgHnqDSZT4siwyIjRwh6AMwQDGstvteHR5\nifMy/+ecBZhsjTInSpkpSgQyrK9BdAZdF2kXO8ZxEtpva7jswTYqhdZE2dh5T+xW89K1lbbkELen\ntrVO5dprrJ0QgKx2A3IzQlIfSmPlZ19hfWNQZSCEEBWkkxHEqUErqDLTuy0lqdWq2xZPa/Iac8rb\nv5dDCrqAgoaWRU9hjNdk6I5SNc15mUFNc6viBl3wdJ2XYlvFKKXWog+EKK+jGWII5DQLqmDkHNRS\nhYy1Ga/KNfU2cA8eHn8tCCvA21bjTJlv0V13iI6+D1QqaZlZlpkYxW2ooXl+NLBNfPsxW4pTzkku\nFi0uoiEQM40l5+2iozXV1AuwVoq0r0Pfyw9nerouMvSdjgsN7wrTNMps32TseCi/ddYwTpPgG95t\ndmiTGo0Eg9yABjoficEQo4KQiDeAxMx5OsQLctU+yFwfiDECaPFgi5UD+f71wWhhrRXtBbJBaLZp\nUpIwH50TTAJt9YWTYYgxbIa1zlkMXgFLFSMhHdnqE1HVym3t7EqtTPPCGvsWnAFTmJeFmjPOe4bd\nwKDGLc5ZJn0fDE2t6uT1FQWBrRXHbVlxsgGnyzzRcsLUKgG6b+HxZywIP5RVi1KAt/WWKNeMNfgg\nT9f9ruc8jizLxHk80w8d3Qow0dQ6UG4EK/iYOPXkgvdCeBIQzoj/YskqPKobaabqnG71co8aMbbk\nTMDRmlnxbblBrBcX5SUJyLmCetrOWyOhps6Km7MLnq515FrFa8BZjPHE6OiDVzBN3pXCeoNbAUaV\nZLOo38IayioZFXYj7qyvReqp2TqVkvNmryYbl6ybGS9Arp6zZg3OQtY7zSBFIWiIirMGy/33oXpy\nEc7Hur5F7eVWsHNd9dZa2R/2+JWzYAy1FFxr9CEwdFHl4nU7lzQFUZsCnMrwhJWS3LbxKedF4uuX\nJNwS92231ndd7z+E+0COv3YI26GocV1Te4y2mb20+LUwLjPn05ndbkff90T1+WfdiTsF1tAnnn7e\nFgVmHdOcSbOkONUsiUTBOTJQlY0XY2S/Gxj6jnyulDKTFnFQij4SQyR0UYrLkoT8hKEgugMB4e6B\nNIxo+W2r2MXRDNtTc+hlxm+tUTPg9HbT/bvgFGJFL2CgoRQJrZ1nAUnDtg6VdZwxopVAO4dWpQNC\nV7JLWsTQRIuxaRWD4hMGrGm0milpIZfKfrfTTYF8bFUl1uJklZvyFqoijEWzrQLTvMi2wEp2pXMe\nYxx9L9mczlop5htoinYfK/gJthopkqxgqaGUJGBpUXv5ZWE8H8nLLIVmDdV+y453sCB820xnNoqq\nxWwofW6JKUMz4GMgos5FuYgMtncbRVbEMo2cE+M8Cm8hBDUyrZuibpomJgUQrTUEnfGDjgnFOXa7\nnYwL2x68qaahkJdCiZLnuAaPruvSde8va8xELpmUV6dmNfbQleWiYbarS3KtUE2FB4GvpVYWlTGv\n7f+6PVip2G39ta3kuM+A1Y1EaY2c76nBaxDKNE4SbRejdljrs7Jtq0QRMQqb0ysIek8kKhu5SByh\nVH5dG7WsxaximuRHrpiIrGMjLQu34XwW/8vYRYwTNaax9kGB0J2BMrVW0VStMka1Wsh5ZhyPpDR/\nw0rt7TrewYKwHt8sDEZbbdke7IeB4ywOPrnULbIrlSJOxykJhddbVQWKVn+aZ8ZpklVcFyU8dEni\nxaefl1WdZ2PAe0cfo3LlZXtweTgQvFPdv3oUrjd6FXLRbjdoW6sjwrrP191/LYIxVN2fr2AmQBcD\nULeUpdbkySyuRgXjAOcoTUC5eVnun5z6PYwWkqaS6IJsT2SIQtiPCNi2ZE05UtIXyKbifD6z3+02\nMRZNy9panJUUJY7XARsCKwtw9YZYmZT3Ia+VatpWdKzmbXrnccYSrHojhsBiDWkpTFk8Kq2zOGM0\nzW5de9atMDcNuRWxloC7KS3QCjkvjOOZtMyUmr/jxvqhjMnffbzDBQHWlhWDkkwkZSkGz67vmdIo\nT+ENORbGyzLPTNOZWva4bsA6MSiZpkki05ZE34vceNte1EZKstKr6+wMOGM1ALZtK7ZdL6Eix+NJ\nvAuVH2+MVW2BAJZW9+mtFqrmFhojHQ5Ubm5u71dgSGfQdx27LuKHDkMj5wURN8kaMiexm6cINoIW\ngJIfiKG0JW+1USjb0znGuD2lrbIlK6JlSLlsH+u7ntbgdDqzLFm1HlGLnqgva5GbxzuvJCVxcXHW\nKXmp6L9FdAetsaTEUvL2fbCW3bCjFMEw+m4g+IixEH0gOM9iDHmlSLdGzVVNXSreeVrN5CYWaULX\nXnMY1ve10KiUkpiXkXkeyXmhewtDWuAvqiCsJ//3WfNo76nVf5knpnGUJ6vu3r13VDytWVyVbqDU\nwt3phH/tuSgZHyRhaJlnMAiaHfzWVopVu9vm7XGcdKfuVCCVJRNgfUKVIjfjqndYn3bWYMO6m8/E\n4DBDTymyXwdBv1NJmmPwgF1H49IcsF2UovRwPbbOPUbIP7kIIWf1YZAnf9EI93bfKSCbBOfMA+PZ\ntdO4lxXXKupIUw0he/zB46wnJUH5BXewKmuGGDOt3bFsMW1As/TdTrc2RnCNXDcSVzMCeqZSdAwS\nSXcfO0pxuo69B2ZByF3y6etrN9oR3Z+77apaQWNriNETYyRGjw+WkhqlJMbzkfPpjjRPun6wf2S2\n4ve/xvwLKQjmG3/+fU+agmhVWsB5lvViQwxGSls9xqTdPI9ignJ9c0szhq6LGt4ijMYY/HYzAGoq\nCn3umPys+YVV48lFM5HLvUW5qC4rLQs9el3juQ25FsNV7wxdH0iLCnFqhVLvDVC1s8CwbR68Kh2L\nqvusrgdNA4e0y6s0ua3CnpWwpVsMw2o+Ik/i1ZsAI6181fXn2s4be/91amsEH+i6fhvF5HuKvNg6\nT/AdrVlSkq4kl4ZzC2lXdYNgMUb0BqXeg31iCrtsI00McHm4pKoxiuF+RdoaiP2cGso2KKXdOz3V\n+/Wl1c3F2jB57+iHjmHpxBSnVOkQxzOn4y3LNMr7poK1+8vsh98x/IUUhD/0UGYQDuN6ocE6S62Z\n83imOZnr29yUjWeJJlCBcZ6ZppkwTjjvudgfcJrN2Grl7ngi54xXTYJ34okoN2Chliw+fyWTs+U0\nTlwe9lzt92KHNi1M40SIkegF6HLBU5UCfTyd6LtI30WGoWdJi2ww1PIdUIWkdgi1yK7d+231V7Qo\nBeEfq09A3ViQuZT7YqAZivfaBTl1XYyi9HSWaQs4gRgizhm8EzDQGhUZWjFaGYZen8yGJS3Y2dF1\nVteumpCEeESUZWZZCikVhkGSlmszmqykM733GOu0Q6nS1RhD13Ws0nLjpLsrDXJphBCJXWE6LsxL\npjajPhZtwynQAmBMR9YCZaxh2HXkvGOaZqZxpuRCWhbubm4YzyfNDOV7mBq+3y7hL7Qg/A4nTYEr\nQZBm6nzH6XiDs43LwyAJyirQKU1WWxUnVubFYRbDvCT6JUEVa3HvDLVm5ml+o9uwxtJ3DW+FDZej\nZzyjI4YFI4GtFjHaEPKdWKwvs3AGcA5yUkBzETt3vVhDDLjgNgehZU4kJTqVUnDW0O96dn2nFOOs\niL/ddvIrVtDMfau/AnVrApNZtykNcXf2nhClOyi1cjqPgNCVRSRmCaHRlUDStObgPcELqakBaUn3\nZqtqOiMybxWGec+8iJ7gPE3sDwe8f7CtUDakxarK08jNHh1D37PfD7oiFQcmWVvI6256DeSUN63G\nqiUx1hK7oOChXEsy+mg8vbH0Q8fjx1fM08Q4LtRSOR3vmMezjgzfdeGt1+gP73jLC8Lve1Lbg98f\n/CqJPN7x8vkzlvlM3zlqSeDEZjzreqri2dudCJa82H0vKWn0uOoJ1lgwK8DbPM/CdiuVLsata2yt\nsSwJjCWGIhmEzlHy/dxvjWFKCYrFelntlZKhCfBp3X3oqXOBEJzYk8/SXZzPEyBP6P0gngrrnG+V\n4iwPaXvPutQ1p6z9VhWhAHxGQTWMgH3ir2g0dk5m7zWvsoHKkwO1VvoSt4KwmpbEGMgpi/3Ykoix\nU0cizUpUu/ZWG4sW2rp2LYizs7x+RzNNdQaW0mSrMPQ9fddTSmVeFpJiIyjHZMVISr3Pc5RsiKws\nzU4YqbVusnTbqqw0m2xpdruBvu/ISTY+4/nEMk86k3xfW4Xvr0t4iwvCH3qi7+m9ClFDKaTTkc8+\n/YRyfMnjwZGrwzVZo6WUSCWJmMGIE1EIYYtmlyfEGe8GghMNfhcDaXHMwDSO5JSZY8Q7x7zMyp5r\ngOQWDF2vzD10WyDinjLOYJtQqZ2sKCXDQaPLcsJlWYn2fcQ7TxxHDDBOEz54OmX61VYpBflaCuKh\nOIbgBnnDPYCNCi1MyKbuw5bgvHg0WMs4L0zLwpyEPiwiJKFXR+827wVh9ak5CvIarWoJWpXsxpXW\nbTVizmjnMi+JWht9120syVUsFjQ8hiwdmFOHa/G0iGK1FgMhC826LIvgIPrkV33SRrm21lKWus37\nTbdQ1hm8GqJgGtOShGuhGNIyS5czjqN0fhuAYu7nq//wtfv9H29xQfgjHE3+s8wT490NtSSc05Rl\nFS+tuX8i4ZV0pK7vGXY7rq4usEYNPHJini3ZWX3KS+HoQhA0vFTSkihW9QRzEp1A13E4HMTRSGd7\nr74GMXbETtSMsjbriZ2XLUETN6WSC8u84K0YrgKaYixbDekmIjHGrZuptUpIq25CVsrz6rO4eiMK\nrUBBu1V3hHgNNgxLLsIxgM0MxmoxcEbMV7xztBZ0/GjE2FGq2L1ZZ7ef6Q1loBFhk0kiUe77qKtJ\np4YxYRNXeR1BnLWCi5RMFwO73Y7gPPMksnLnBX/JSmV2zm2bneA9rYqPxZIWVmLUPE/EGLeuyqjg\nSjZNlZSELBZiYBgGSjXCslwzGlZi2VtSDOCtLAi/6wn9rjXkw3ZLngp5mTWtuarHQYfRnIaikmVB\nuyvLkul7w9D3PH78iJwWFfKIYObhzOmUzNRa3ph+EtpaSJq85KzeuLDJmb3mDPi1S1Az0xgDfQzq\nz2g26+9lXrZ8hBCcqiy1ZTdiJ+68k0SjKvM41uBa0YtdtxTUjYyzwgpoe27MGovuALFaX7JwNOTm\nlPGlqE/AeoqNkpFWIxfn1AQmJbxRrEGxj+CF+Vmb3MDOi94jdlE8CVLiPJ5lGxOjBqUEHV2csDOT\ndAIxiM3bPM9s3pBFV6d1DVUxChoKv6OtKkc1ihX/SSk867H6OMgqVt6X/7+9N12S40iaBNXcPSIy\nq0Dw6O5vvxnZFRmZ93+ykekmgarMOPyw/aFmHlFFgARIAiiAaSIgwKq8ItLd3A411RQTphOgzQRz\n8sbOxjvX662G8CftU+Vg/roNrWTUbUEU4G6a8Or+Dtd1Qd4y1nXriDeIWKXZqus/fo91XTqgKeeM\nam0qf3VSeXt+LgdtQVgvnepGDq31Xnk4oA5zzgiVtGPRmIPHlNDGAbVWXFZqKSzrhtPpBDGq8+gU\n6AMX9DAOUFEoWGRri6UigYhLsg9ZmgALpWFhcwi2+QaUgo7gJH9CtMlFkqO25m3KCkU0rUjiDkha\nkm22Y8LpNGJq5HA4nQaEAJSayeCUE/KyslC6bXi8PAICTAN5Iqdh6FyT48jHeFdFFJ3cpJnUmvMy\nBrtmdnuqPafBfQTTtoaqAIp1MjzVMYyFdyKY2hGUJYhEqs4ksx2eBwd/+Rr+6+sIX4FD+BRmBR8B\n+9BaUcuGZJqKVY0518Za87qhGogmSuAfQ8O1GgGjCAsSoBYyc9TX6cNNdlx5Yk7jgHwiWk8BzBuZ\nds7TiLvzyajDMkpj2FpyJs9fztAokIGUbWNKKJYW+LDRtm3GmaDY8gaREbUKtrxzOYhMUKjJyzeI\nkLDVUyjCmNTC6mhND6FsmgokBVbRq/awOwRBaTZvYIzN2U7YEEfEIQFBumoy2YiI3WjasM0bJfSG\nERJiRwXWspPH8KTWXviUICg54/J4Be5PhBbb99taQSkEHQWLPmI0uLUqgjBaa0Zd1w6F1FwyoeSN\nOpeHAIHFV3vv0AljtKeWyzLjerlgvl4Q714jIB2e+TKjgqO9UIfwCdFdvfq7O4VSMvK2YBwiWtvw\neL0iV0Os2aINRsCptqCjzTGUHJDB0ygMPNWdvkskELADBQqjhCQBOiScTyOFYUrFsixodWBkEEgf\nXmrjJl9IaiI6QIuFvKJAomMahwHDkLBsG5F6eUMavPqfbVy4QdUQkzEgmgxbaGLAHjVuBu3QDE8R\negfCagBVK/NksMYQQ/BZKN6fPi25TxyWWnn/IsFLonSmpbAQ15qnLDB0pnEbOFo0RmAASuApPy8L\nhjSglmIakgWqBaqVsyX2WQCS2yah9JzafaAkHWwIid+lj0kXI3N14pbgYCkvQlp6xXKKPCmMamOd\naJ4vuF4ecfdjBnRAHyL51dp+eQ7iCzqEz3Uz3hNWaSOaTBR5m7HOV0xjwnK94PLLjK1ksu5YXz2E\naMVFji2L0kkQ4WiMPiBzUKlE+kVRagi2hiYkAKWcGkk+SuaCXlb2sGvJ2LYV//zHTwCIutvWFTmT\nfxGNQjJbA0Il/mCwEe15WTDPBZs2DI36B60WoHEaDyd2J/iRdW+NguO7rQKhhR7ROBJRHSoM0pG1\nRhp5pz+PHRrN12tqLdM+bGUpT+BAEEeUB2gDto2FUSDgdDrhPFHD8TITQl5KtbrJhJwLFpOAf3h4\nIO35lonIVMVWVpxOg4HAAlzvcllJUltNYHfdFrYuh4FqVUGgQTrgayvFuBYFWjiyndLQodqQXZiG\niGfToNBiE6kZy3zF9fKAVjZAp68Coej2QiOEz2F2FJYNy+MD3vzyH5RlxuPjBQ+XtyiNnHkxDvj+\nu9d9sTZU1FowLyskCy4XnuAppD4ROU4Ry0oyDWfm8UlKRytO44DzmHpkkEulA5KAyzwjxYi78wnr\nekIrJBPZ8ootrxxhPo0cyx6IhDydJ6x5w+U6Q0SNTIRU7WocDbVRyNYhyRKAAG8LekqRDPW3YxOy\nE8d0klWOFAeJgEGPjxyL6LMEfA1KzTPt+fH7HxjBaECthkuICa/u7jAOA9Z1w+Xhiv/8/AuGccS/\n/vkPQIHHesW2ke1JADQbpKL+IguZ65oBVQyv7hCCUbIHUrqjsHs0DSNkMkUFQyw2G57qxC+WMgEw\n8V0yRcU4wCXeWlM044gcRhZHa2GNgq3oGWq09y8xEniffcMOQd/zb/S0QVuFzlfk6wXrMluXWZFr\nw5Lt5IoDxoltOxFSiqvy1NMqhiUIBvZJCCExx7QaQsmuh9AMCESCEQhwOo2dvHReM6oq1rwBohgT\n24uvv3tFrcTaMM8zrsuKpooffnzNKT9hRDqOA86nEbUW5stB0ODSZMEWaoYU0w1Q73IIVMN+h8RB\nnKwBZCse1ro7jSCON9hl01rbF33v7zTK3hF4tELbYC1Jpg/jkFhHaA0lZywKq4OsmOeFGz5RbWlZ\nVuNidGgx6xeirPaX2pC1QbVAAjBNQ+ctgBr6ssLqQPaztjMnOyzFJyl3TAUM1Wh4BPvsMdGJFJsw\nDcFVogNq2bBcH6kGrr/ScHrHuvyjDuN9nbQ/bt+wQ/gtU0KDS0W9XFBtGCWmiGEckYYRbcsd4HK+\nO/O004aE0GGzQEBMg3Uf6ECawqYZxd7GCESsL02GX+bVr+9fYTTkXhoSHq8zLssCn7MfY8Q/f/qR\nIKN5wS8Pb/Hvn39hKD0RdKOiSCPHcc+nkeV1iwAcaEOWo2oDWGIFsX0Re2dDhV2QqgUq5GR0XUlX\nTubEJgt0IQISFALWV2rVQ3TMacRSiESEDVDBaONSCMAwYLVBsW3dkNLQR52PXAdOtc60DCbKoqYc\nxQiotYqqFTkzTXj93Svcne/69WsFtFZUKLQFG7SCOQWrmciukaFig2DGYsX7pFY3YF2jxmBFymZp\nmHVqWsF8eUTd1kOh87D2XnDE8Pd0CE2BVtHKhuX6gG25otVMYVJDsU3W5z6fJtzfGfw1b4A29Da7\n+jSk4PHxihAihjFh2zZD+ZGteRgGQMmmtFlHQULAdZ673sNWMqaRofq8zCxUpoR///IzTuOIIILz\necJ0HVHqFQ+PD2ioyPWM893JaNMaqcyszVmr/d3YPfFimM8keGczpIBpnDCOEx7nmXqKlbDgagQo\nhGQnUtCn1KXkfPgJ4MbxUxfYNRG3jTJtMQScTE4thYAWlJRmIHQZ6l8NuxUp7jqMwTYsoxHKw5VW\nOqvTuq2M2qC4vztTVDdRzj2bapWKYsv74FeMA9S6BNUcAwCHJ3C+NUR+Xms1OBtSEOkYklr4szQk\nnJqilYyHt78grwu0OjM09hd/wU7hG3YIz/u0+qSthhCgQbCtM7RuSFFYaDQEHEVAqOMYY7Aw0gZ+\nqvEF2mMVijVnrNsKKMNfJ1CNKTxRJnYMglhFv4LFzbu7E+KQEJYNTSvWdcO8LFiWBUNKfaz6dBqx\n5Q2P84xqNOwxResk8ARVO0EdhKSqNvcgNorMPywsOn7AkXqlcymEyE/tG9EFWQh4ctr6pyAvbws6\n6rEaLXoAyVzGRGfgzEhH7gQIcRgh4CmQqLIDMsSAZi3NLW8o267y5FJxosSKMFoJtvGJwBb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VbVrIYBqHEMHh2q7A+U48+f3jVW5LG36ZQITUqix+6cOA0KQ+3F/nl4T0kr17QBDYT62tQjnW/G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\nbnazbjeHcLOb3azb/w+KFAHnpWrvOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9bcc7a7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_space = 'lab'\n",
    "\n",
    "data = valLoader.__iter__().__next__()\n",
    "\n",
    "img, skg, seg, eroded_seg, txt = data\n",
    "\n",
    "img = custom_transforms.normalize_lab(img)\n",
    "skg = custom_transforms.normalize_lab(skg)\n",
    "txt = custom_transforms.normalize_lab(txt)\n",
    "seg = custom_transforms.normalize_seg(seg)\n",
    "eroded_seg = custom_transforms.normalize_seg(eroded_seg)\n",
    "inp,texture_loc = get_input(data,-1,-1,30,1)\n",
    "\n",
    "seg = seg!=0\n",
    "\n",
    "model = netG\n",
    "\n",
    "inpv = get_inputv(inp.cuda())\n",
    "output = model(inpv.cuda())\n",
    "\n",
    "out_img = vis_image(custom_transforms.denormalize_lab(output.data.double().cpu()),\n",
    "                                    color_space)\n",
    "inp_img = vis_patch(custom_transforms.denormalize_lab(txt.cpu()),\n",
    "                            custom_transforms.denormalize_lab(skg.cpu()),\n",
    "                            texture_loc,\n",
    "                            color_space)\n",
    "tar_img = vis_image(custom_transforms.denormalize_lab(img.cpu()),\n",
    "                        color_space)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(np.transpose(inp_img[0],(1, 2, 0)))\n",
    "plt.axis('off')\n",
    "#plt.figure()  \n",
    "plt.figure()\n",
    "plt.imshow(np.transpose(out_img[0],(1, 2, 0)))\n",
    "plt.axis('off')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}