Variational Auto Encoders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.utils.data\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "import torch.utils.data\n",
    "import torch.backends.cudnn as cudnn\n",
    "from torchvision import datasets, transforms\n",
    "from torchvision.utils import save_image\n",
    "import os\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn.functional as F\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"./data/autoencoder_db/\"\n",
    "\n",
    "\n",
    "loader = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "train_data = datasets.ImageFolder(data_dir + 'train', transform=loader)\n",
    "valid_data = datasets.ImageFolder(data_dir + 'valid', transform=loader)\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
    "validloader = torch.utils.data.DataLoader(valid_data, batch_size=64)\n",
    "\n",
    "unloader = transforms.ToPILImage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Image Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_im(tensor, title):\n",
    "    image = tensor.cpu().clone()\n",
    "    x = image.clamp(0, 255)\n",
    "    x = x.view(x.size(0), 3, 96, 96)\n",
    "    save_image(x, \"./generated_data_vae/ezperiment_13_0.7_mean_mse/image_{}.png\".format(title))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variational Auto Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ngf = 64\n",
    "ndf = 64\n",
    "nc = 3\n",
    "nz = 100\n",
    "\n",
    "class Flatten(nn.Module):\n",
    "    def forward(self, input):\n",
    "        return input.view(input.size(0), -1)\n",
    "\n",
    "class VAE(nn.Module):\n",
    "    def __init__(self, have_cuda):\n",
    "        super(VAE, self).__init__()\n",
    "\n",
    "        self.have_cuda = have_cuda\n",
    "\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Conv2d(3, ndf, 4, 2, 1, bias=False),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 2),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Conv2d(ndf * 2, ndf * 4, 3, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 4),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Conv2d(ndf * 4, 1024, 4, 1, 0, bias=False),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            Flatten()\n",
    "        )\n",
    "\n",
    "        self.decoder = nn.Sequential(\n",
    "            # input is Z, going into a convolution\n",
    "            nn.ConvTranspose2d(1024, ngf * 8, 8, 1, 0, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 8),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 4),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 3, 0, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 2),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(ngf * 2, nc, 4, 2, 1, bias=False),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "        # Change input size in fc1 to your flattened output\n",
    "        self.fc1 = nn.Linear(82944, 512)\n",
    "        \n",
    "        self.fc21 = nn.Linear(512, nz)\n",
    "        self.fc22 = nn.Linear(512, nz)\n",
    "\n",
    "        self.fc3 = nn.Linear(nz, 512)\n",
    "        self.fc4 = nn.Linear(512, 1024)\n",
    "\n",
    "        self.lrelu = nn.LeakyReLU()\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def encode(self, x):\n",
    "        conv = self.encoder(x)\n",
    "        h1 = self.fc1(conv)\n",
    "        return self.fc21(h1), self.fc22(h1)\n",
    "\n",
    "    def decode(self, z):\n",
    "        h3 = self.relu(self.fc3(z))\n",
    "        deconv_input = self.fc4(h3)\n",
    "        deconv_input = deconv_input.view(-1,1024,1,1)\n",
    "        return self.decoder(deconv_input)\n",
    "\n",
    "    def reparametrize(self, mu, logvar):\n",
    "        std = logvar.mul(0.5).exp_()\n",
    "        if self.have_cuda:\n",
    "            eps = torch.cuda.FloatTensor(std.size()).normal_()\n",
    "        else:\n",
    "            eps = torch.FloatTensor(std.size()).normal_()\n",
    "        eps = Variable(eps)\n",
    "        return eps.mul(std).add_(mu)\n",
    "\n",
    "    def forward(self, x):\n",
    "        mu, logvar = self.encode(x)\n",
    "        z = self.reparametrize(mu, logvar)\n",
    "        decoded = self.decode(z)\n",
    "        return decoded, mu, logvar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_function(recon_x, x, mu, logvar):\n",
    "    BCE = F.mse_loss(recon_x, x, reduction='sum')\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "    the_loss = BCE + 0.7*KLD\n",
    "    return the_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validation(model, testloader):\n",
    "    test_loss = 0\n",
    "    for data in testloader:\n",
    "        images, _ = data\n",
    "        images = images.cuda()\n",
    "        decoded, mu, logvar = model.forward(images)\n",
    "        test_loss += loss_function(decoded, images, mu, logvar).item()\n",
    "    return test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = VAE(have_cuda=True)\n",
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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FNfVrlTIyo0OGAhtAVOM7UQW3EKhhpfO8AnZNaZFhtKXZ1Jhv3bYZAPB3f/UlAMAv/NK/\n9rbF02K+N3hUL/KRLxjT0gmwjp8TWrNagmRlK402MyPmZp6mev/FC3L8qEm71KrihsyQGaWLpioA\nFFlN98wTkr5bTursdWtNADHP664w0jNEJqG0hdVPNKR5GUzDWTm3VIPcE3eWyVez1vjXY8jOl82b\nrS1ej8k/X+Wcuh5FC1CVz+hZ5B5FtV4D8wQgrzRDp/4Pl/fKwTy18h5m/jLHuqzMJiSdu88cegEb\nx8SNV7TSZ3G6O0wVBq3LCCIyi5L78uJrfF98WYKyqBo/4DhIRSOARQkMDeIx4FcgAMGGHsbIcR+K\namCHAbC0oZxeV5Ol75VjJwAA//C3X/K2ve+z/wYA0JgmCKIomruxwQTMshpk1HNGTPpLRcc3MsVU\nLsroqakpb4wu2gqkGGJPAAB4+WVhw5kmHfY7Hzb11ioaqFPrYnxMxq5bZ+iYlZFIQTYFC5yTiJMP\n/401SJpzHe48Yb6qcuXP3jRfyf2sP2xwjAb8ggTTtLUY1qSBAeFO0LjdciutOlsM9fUsk846h4H+\nKjx3HvXqzqcHZ383n1rWa5pNKW5bHvVzdOaN7mHuNp2aZ1ZpQJawcZvlx3Hmme/84mt8X3xZgrK4\nDDxwAbeMyphpAFEgu22c7bBiLYSRWsUxmCQD77Ckxs6e6gUARMLG37s4KLXlK5ZJGuj8sEmjPfa3\nnwcA3PsO0bDRpJyrAqPxAx4cV+GrsuyWrY4+6oum2AEnHJprFSSTYg2kqLGfeMLAcRsJJHrooYd5\nO+R4FesciMgKPj4uNdnqE+tx7XkoDqpiqfd5EKGyz5yZLkyU8aZ+f94jp17z21mw2VpL72M4ZuIh\nqPK6+RYu7zFsR1VumxiT59reJtZAJGkxzmC2NiewymINMs025L568aUravcr3K15tHHNqU8nBub5\na85h7HvlzZXb5qMu8HYM1A+uCxbM+v8K4mt8X3xZguL/8H3xZQnK4pr6tSqQnUaotdX7KkWcdWVQ\n+sAdelbqyAcGTEOMGOm0W5sF+aZEkiNDhh5721apv29slTE3NxqT8AfP7gMAnDosTSU27toJAMhX\nTDApSJojJWEMcE0MJ+aiwjJMO5WLElRrTBuXYZBzOvSyNL1oazfY8r1E6DU0STquyIaagbrGnIoY\nlOMoOtA29bXWwasqq6Nxml9eb5dZ1QhVrY6r219te611n0d/zDaJveysGetqR18GQiMp4was6ukB\nAJw+IUHXvj7pz7psjQnoholyDAUUwSj/u0Hb9ZkdaGOjlDpf5Er4/Vky75D61OB8/f28q3bmseP1\nds66j/YcHW/bW6OrfY3viy9LUBadXhu1EoZfOeR91csW0YWKqITGVtHmO3bs8MYk2WJKWUxSnVJ5\ntnKDSWNBGWMIwJk8f8HbdOtuaW7xg6eFlHJsRLatf+BXvDFa3af96ZXeutmiLw5qGovpxSSDi+MM\nPgLAz16QevgcQT7ve9/7vG3a/XRiUjRcU5OkJy34OooF2TYyKBbPNddcA6Ae611hZ98IA4GRyFw+\ngCuDQfg5uy/GFQfb32jAT4N83P8KKcRwVK0So+kcpvaCFb6G9o3gMTs6RMOPjYsFdOHSRW+Igpqa\nm8WCjHiWj9HAtVnhThMOnKf3/BstWpgjCyERtZp+eKdXfoJ6PH7dEdU6uBxPwgLn62t8X3xZgrKo\nGr9UKOD88WNo6TAto3bfysq0JCGpupTZlXPaoop15Jgh1NauH88KRLY0Itua0hbHHFtD/cKHpCnF\nd7//PQDAY0887g257TbhplvWLnOLM2Vnr4w5ttUqkcKnKS0a+8jxY94YZd556CFJHYatmv9xavoW\navpCSXnkjKpRS0P5AJoaDFRXRevvVdMHrUmWPMruha/pVx5Zu/wop15DXVF4iRkL7JRqJJ8g4bgz\n44NmuCPXmGbzknSbQJEfe+ZVb4xaA6GQaPomWmcBS3Ub7od63ofXm7K7olw2d2qfo7412nwH0NSt\nM6/BUM9U5dkxdsn/62BL8jW+L74sQVlUjR9paMHqux6FWzVw3CwZd4tlqcnW8uJw3ALHUPtXM7Jf\nIsT20mGr2YMj/mEI3M/avVgTOPDA4HkAQLpDNjanTFR/+pRsW5OWrMAUgSOJDhNFjtC3rgVE0w71\nir/Zf8K04rp1761yDgJVLOJYT9PrIl3m6h+2Ki2GByTmsXnjtfIFi3WCVs15mFHr+XRWMKRFHKgf\nY2kGD6ai8YArabigFs6Y8zsslAmyWCpQnb3TPFITQE6qyRynlJVrjSTlXmWLpuXzxLj8fV2LFEtp\nG7X77zINJF46KPGUoy9JtmbLZinUaus0z8wzfLSJC5+dRS+IIuNLDhl8g2GTQdFh+sZqvVjQuvmR\nUv0NNE0z7O/0n2DdcQATs1Erbd7bOQuvc7lHtlAqAV/j++LLEhT/h++LL0tQFpdss1pFcWoCUQsU\nk0yQ0pjc13k2EajVjDuQII4/1EDTPifmbzkz7o3RPnLliprGxs6KJsU1OHdGquNaCarZfNNusz/t\nsklSZTVpz3q78QKryHJjQgT57LPSzOD666/3xnSSMkpN7WLJXEcgEq7b5tF0W+aZVvUpvZfd0MOb\nx6xcXcXyJ9SknB38mZf62q37uIzJf3XgiHuFbJgnwbkHd7xnJMcuWVWGSS+oqTdLzHC7nv+GXTcC\nAM6eFlfr+LGTAIAVFsV0z1pWNXqdZHkd1k2PevdY3sVM0fA0lBm4TMSUL0K+z+TNPU/GlQJdKbxJ\nF25lJ/X9Cga0OtBsi0SYFn0dwbk3K77G98WXJSiLm84rFXDpwgm0tpugWmObVKxFYwxYhURDViwG\nnmJJrIByRVJcEVc0bzhiVXqlJN0T1Yq5ktn/8MsCGKqVJWizbfPtsiFhUn7VnAQAm7ra+Y0sv5kJ\nU0mYInz2uRekoUXnMgkiXbvJtOsKMn1XYfSmYuVYwl4LrtnsOEYmJyWotXadzEM1Rblqqw+lbxax\nCSyj4WD9MecJBs1O0Kmmn69izFBG29vmY6+ZBeudUzw2V8eECISqaoOUormO7mVKlsqAG1VkLmeI\nWsO81rXrpa3UihUC7Dr8yivemOkp+VuBUNpII2DxLczMyD0PhpSm20CwY/yJFPg+lomQSsfnWmIm\nhXp5DT5vGu5Ncie8EfE1vi++LEG5qsZ3HGclgC8D6IIoi8+5rvtHjuO0APgqgB4AvQA+6LruxJWO\nFY1FsHbDStgJi1pFVttCRtI1WtoctTtJhrRGnj5xhMAdy8+auHAOAJBqEN+84prClRdfEh/wgYfe\nLV+0iIZwC6YFlwJnNJ5QZfooFDXzOHda2H0mpsQK+NCHPywbLHWopfGq6cPW/mH6larXtI6+WDaa\nLpOV86ZS9HHps5cs7j8tTqlRd5ct60j1UHVWUihgg2xcHcNtmHMZFqo3VPdpiwfVnY+xWqWeOKZ+\nE/32DJuhVmtmjmnyJJYJSAoG5ZmHwkaNaiONPK2AWFwsup17bvHGDF66BAB49dXDAAzoZ9nybm+M\nVwDFe120mrqCDDexkFiXYRYE5ctmTGRWm63ZTTht8dp+Wc9MQUYLKbby5I0SLFAWovErAH7Tdd3r\nANwE4LOO42wC8DsAnnJd9xoAT/F/X3zx5X8BuarGd113AMAA/55xHOcYgOUAHgZwJ4f9JYB9AH77\nigdzAEScejAJGx56HpPCVx2zspcZGS8WWCJKIE9T13pvTHNEgDcIiqY8dciAalrapOVU1ypG36uy\nwjtho/ETcYk1FMiHF0sL/HNq2DTUOHe+FwBw2523mesBUCwbBp0yrydI/9UG58x1+eQbbSYCGC0R\nTybqR1oOozbvrGixjKVOZzPweCx01v0MzMuqM8tH97IB+orUe/l1c5uHDGb2gbQAxS5ZDfHYWTY8\nCYXNNbt8I7SsVi8/EjHgmlxBtG5MC7R4LmU4BoCubgEAaTOTQ4ck3nPi5GlvzIbrxAJcywxAPGI0\nb57xpSJjRlHGlVJhC4hUJPhsTrttiwloVlzEhmlrHMe9Iub2rfXKX9fRHMfpAbADwH4AnVwUdHHo\nuPyevvjiy8+TLPiH70jvon8A8Guu605fbby132ccxznoOM7BkfEF7+aLL778E8qC0nmO44QhP/q/\ncV33G/x6yHGcZa7rDjiOswzA8Hz7uq77OQCfA4Bdm1a51fEhBGMWkD6qgRExoSoe4aCFDSfwJcZA\ni1Pj/lWTzstlxfy/eFGCfAdeOeFte/8jn+TV0iTUuEps7uWrmeZWxPx+/oX93ra114hr0b1cUk0F\nBoGCYXM9yk6jcR0rCec15whqzzcOKlodZFMNTG8ygGmIa4yp6HjmLwNfVp9BpZp2ZkXa6gz1WUgd\nTePV1dN7oJzZ1NGWS3AF3LjhmZFr1qPYYCOXwbRcQcYkG+YSaapFrKb+mEXU2tFqjwemWNGolZUy\nRzlz1ZVz7dpzEwBgdMhUAr7ymgT+Dh58AQBwzbUbvG1bt24HAAQDehx5VnYKVRmiZosdwKta9Sn2\n8QBzj5XnwJTg2Xr5cm7AG3MBrrqXI47KFwAcc133961N3wHwcf79cQDffkMz8MUXXxZdFqLx9wL4\nKIDDjuO8zO/+HYD/DuBrjuN8CsAFAB+46pHCEQSXraxDNrgFCezk8oThsvbeNM8AQkzl6Oo9NCiw\n2qef+KY3Zt8zAqrRDN3bHny3ty25WiC6JRLlBKv6aVbkGaboGsgHqKAfe2W/diPTgPxfefoca/3U\nQI32M7fbWymcOMRKO03tTFvBqJaWNl6qjNF0lp3q8ThcqKojlvbQNN7cwJ2ZhwHw1Otqi5XaAurO\n1Q1zOWT03Pb5Kt5f9rayRQVe4bZiSY7U2WFaoultJ0YHeRpFbZaWH58UaG2EFlRjU70FYM8ySsht\nIS+pv/Yuk867l23ORkeF9ejFF1/0tn3t6N8CAJavkCChNjaxW5rVKnId2izE6+Jb17RD+QkJ4rJ/\nA+QeMMFApdCerzHIWyMLier/BJfPGt7zls7GF198WRRZ3CKdSg2liRwQNL55IEKGlXbRdBWIv9Q3\naHy5lw4eAQAcOSJ++9e/+vcAgMEB055K5eabpR7+3R/+iPfd8KVxnku0Zls3rQlLRTW0iKafYR3+\nkdeEVeed7zJtrnRFrlKLa1GGldkxwB3PbzMaTmGn6q2XuepPW6w0zSnx8dWPrxIiGopcHgxS953H\nLadnUa1hp5bm/7TF2zaPAx+Y5dvrbXStiIY7yx7QNF7V+tpRMAtr5VMxY+XlZkQzh5nWdFjPn8+b\nV7aBrcxUUaqVYN+XEoE2WpOj/n+pbOIqLo+tjMb333+/t216Rp5NX5/wNZw+Je9g2WrxliRcXK0A\nZYFuajTQ9KgHEWY9vs25t4jFOSo+ZNcXX5agLKrGd8JRRDrWo5o3S1z/sKzs3/vKYwCAE2cF9bv/\nwHFvzPHjwopbJrQ1FhLNEIqZRpLZgmjqux98DwCgucPw2RfYunp0WjR/rDQXhAE2wNz3tJTa7tmz\nR8ZYjTW1bDRMy8F1qIUsOG0wqBBXEbv5p/rpZRbwJAjyGRkw/QFiXTI31dfVWe2yAEBp/JTX34aG\nBjTz4c1g9v9AtSb3Qw8ZISuN7fN7jSipzusgtxrGwOzPywOJAnzV7Fqj0UGx2DSbkM8atmLvxaQ6\nHLp4FgDQ0LnKGxMN0RrQ+0EmnZBdks3sSM2VE2vzyXDYxExM5oKFOJY1ECcf4ObrNskXrMeqVMyY\ng4ekh4IWEPX29gIAJiYMm3Q+J+P1vYpYfJHaL0KzPjWvasrWy4zdKJNPoJ5pSa5j4eJrfF98WYLi\n//B98WUJyqKa+sPD4/h//vivcPhIr/fd6fMSPDn4ihAvuo6kVgplYwrVIN9FWLo3VZF9kpZtc/PN\nwqazY5fQdWesyrtgXEyncFzWuUxeAoepWqM35sgrAuJoa5HATDs/7SL1IKuwDDkiTX6LObFMdyAY\nZSAvYLkT1Xoa5Rpt5mK+aO1vdc6FqaorW0w+mg5Ut8LOw7m0/cq00cMBZeSxbparLac0EDh3/dfA\n2xXg94Ych//aMSo9nw4tMPLmVs25+s4LWemGnh4AQCJqTPRqhilOBv6KU/J+tK5Y441RWvFqVVNk\nrOWoM5EZQGTKrUSTOWgx8IQUtGU5JmZ3duClP+EoKMc6x027b6rbX10ObYMGAOMEHmlFYcSuB2DN\ngb5P3qnrgrezSiC1kvHyJRR1whwtAAAgAElEQVRXFF/j++LLEpRF1fgTkzP4++/8uA5OW4MENoqQ\ndFoyJkG5gGuCalVqiZwrwR+Hfe3LVVMTfcfdDwAArr9BKJazJQOKiYRklYyGZUkslCQIMzluNO3h\nVyRAc+vtUnmXapZUjFuxOPdIXV3hih4hT4BrRVi0FViN4J6Q1QZJNYG24pph843x4TFvzJrlq2W/\nWQG0/IyJilUZlPS0uc3VrKmtslJGy/91teG0YgIMFDnz1I3Pic7Ns03TejqzeTW+Wix5UqNb1XUD\nvVIrf+t2Vk3mTHAvGJP9X/uxND8JK224lYP14C40QdQC0LEyj1lsQfzXBtBUZwFnlCdA/tEcoV4j\n79k8eU7FJkWouTWtBwBxVhAGCLZqSBoAkFoKakm6tfr5ACYoCe++avMNK03ra3xffPHlSrKoGj8c\njqNj2SYED09a30k1r+PK6pgrkV3GWpHDhO/WmL4KQvzg1lbjo+/auQUAkGBooFwxa1olJ5o1Gmc6\njWmTo68e9cYEqrJcdneR601Pb2lT4wGyHr5SP1TmJhohwC/t9FWN8NtYTK7xIjX9uTO93pi7br29\n7mSqxVzrfuhxArQ4yhasWF1Xp1bP+1a1imMCs4pBHFpU9Q0gZl20reBU+/EwWutv19ornNjlPGJs\nMDrY1++NaaTWq7DBKKxmG8X+MwCAs0elSOrmm6WJ6sykqQULOQKcCbFISu/VfJrPq5GnmVK2gUSY\nxZxj+f+qfRV6bSgEzRjlYKjy04modWJuqGrofjaJbWw08OQsWZciHoekwnvtefBT06we20/9dS6U\nv8/X+L74sgTF/+H74ssSlMU19SNxrFi1BdGEocXK5MVMK3g2sdInW2SErjYjEJOwVhME3t13G0z1\n9dskzZNlxVYsaGy5QlnsIaVWnhiTdODpE2fM/jtvAAAEiKgqTTLFYnEHeLRaJNDMs7GHYyHFlHhR\nrelywSYWZUCG5til82L29fca8zdNrH4ho3UAsjaHrLTgbDRdzbJbHS9FR5ptoh2rViWi42qPNpqN\nwfpzAYBX4q+PxSYU5WeVJr5WBNouT5XnCNBcTTJY9+LzB70xa5cLMWpIO0+UTAp2/49+CADYvV2C\nnQ0hea69o6aOPsr5poiRDzDNWbNq3z2ac36G9Xk6lhnucQ/wf9tE12uuaUBU+RYsElWHqVOvMQev\nve7XJd9NT0sAM5M1NOFeGs/VtKK6SWZvNfu9zzkzlCvwTX1ffPHlsrKoGh9wUK3GUC2bdIkqoiBx\n7AEGgcpWGVcwJBq6VJIKqQSbbjz0TtMCKxmV1FwxQ6BE1ACAUuzu6uRlRR44KfX8tha9bgNB2MTP\n5LOifZIWqKJqonucD7Hy1socisstVSh3xWoLpX3bs9PynQb1qlbzD+27dOqEtIPyKr1aTDCoOquq\nra6Qnl+phq/SGqha99Ng8utbPzkWvXVI4396CnMGr7KsQnOgwkFVK3WpWlNTXKqEc1NG09Va5CbF\n2Xbs+BNf8bZNDAqTUtd9ErSdPCWNMWqpbdZEZP8gcfgeFbhjW0D6B1Nlmoqci9Gpu43eKRj0rbFj\nsxIhRSz693KlxHMp2aae21gOWkMRZsOX6WnT5qt7mXADFIsKQOK8rOCeBlK9IJ+36Y3xbPsa3xdf\nlqAsqsavVYFC1q1rzuD5h1UB3FS9Bcz4aU6AXHVBWSW3bBU2lDWrTP32k499DQBwx80C2Q1XjTYP\nBQTwMzUiGuLScWFa2bZjizcmzMaa1Wnx7cMBTSua+esqmWHzj0BI/WjLjyf7So6U0TYcVjX+YL9U\npZ0/KxaMwoQBYJh03gcOSO/3TVtEw+1qa/XGuFVt5zSPQ8f5FnJisVQ09WflfaJkOQowf1ejWrb6\nlHp3P6iNIC2dX1ZNr9VsWl5naXwdH6TfenFIrLawxSQU1xr1UYG29p456W1bs0LSvDOnBUrtlsWS\nC1g1/1VW0eXyZOLhdUQsDsKw/q1gJ4/T0VyrYrS82InNmsRnq1WWyq4TtNJ5EbvKEwaoFbBMQU3n\naVrx5ElzrWlyDYYt7kZ7H3u++mmIjOxeXAHfx/fFF18uL4vcNLOMi32X6k7bkmYUW1U9tWi2kLH2\nJAAnKqvurl3XAgCKxVFvxN/99Z8DAO6+SVonh+rC0LK+TVDrTI7Qcth8txlD7r8swSTpVvGpS7Zv\nTO1RyMpxGlpkpa5aGr9MX7CYF42bShjosS7TYwTujA6LpuvuNvxv+t3x48JH0NZ6+XYFCnJynLnr\ndyEv1+PO055JsxAK2dUxNTvUoJqR4CBb41fVp0d9UUzNsQ5AfkTVTGdPSj392Ihpbb7pHXL/v/zn\nvwsA2LPN3IfOZtHmr73yDADgphulHr6vaGDa02x+kidLcTQhFmCzVWsfJpOy17pKrSQLpKNJBb2f\ntqbV+IcWX6nJUAlbY8jj5zE08Rw226+2ZlOWn8FhU8BjinOuwInkaXxmYrwv5hYkLUR8je+LL0tQ\n/B++L74sQVlUU7/qFjBePo212wwNcr5GMEyNtek0k9JupzcmwnRNwpVKvijEDP/T3/srb8zbbhM6\n7YaU7GeDMCZzYlZ998XvAAA23Sm4b6RNym+gX8y1eFLOcWlcTOW2ZcZszLFHXjBOvHeFJl7ZamiR\nl/HpsoBK3CFz/cmIjG+rSlBvfaOwlTdFDKil4bRQj+11xR3ZWyaA53TWGxNqlEqveKtc42TRgFoc\n1vO3BMT8HbnETrLdG81EErLfEDNrMZqIqYLBwTcobHxSXI2C1aNiICzHbumWeUxdkrkmXBN404rI\nRhJiTp6TdNyanDH1Rx9/AgCwlsGxa3fd4G2bCog7tHsz6+8zAnyJvWjuVXur1FWc75V5n+wV1++9\n/+4/eGOmSZteapTnqg6kXfneEBYXsjE/N683cEzqOZIMNjqtYqpPBIwr2sBnHiI/Qpic4COT5lq1\n/r5rtbgzrxw/7G07c16IXTddK89IORmKRQNoSpOItEKKuCJduVTSVDsWsjkEa1ZxyBXE1/i++LIE\nZXHptUHwSdCChjIQ45BdJ+xqiskEioIVWfnCrKo7fFi0R2H8kjfmvW9noI45GSdqKLwrE5IqDDAY\n1d0lq3/fOaOOV64RS0EXWSeovdfN/CuOEjaSzYUrc80iD40FxIogqlfJbmR/Aj3yDCBef73Uoa9e\nZeZ69gXREnfcdzMAYPC8EI2mR01313CyR/4ISWA04poqxQhTa9NjYjlMsVlHyyqTKmKmzyNxCfB5\n2JbL7Ha7FSvepGmniTE2q2Bn27QFb54isenFadHCU1mxuta2Gl2777nvAwDe9eiDAIC+fgPlDnfI\nuCCjg/Gqzt1oQaUyP3NGoNftnWIdjL1mtGnrDTtlHkW5D7GoWGI1CxLuBfz0nZsw1pUGDhuZAq1V\nFbpr3uESG4J4Lc0YOKxZNy1AfoZKkZZQylBv57Kq4fnuKkuRxVakQUYPkKWfFvirWq756TxffPHl\n8rK49NqOI4UUQXPagKYyCJhRN7FYMOw4+YKswCVy5YXYbitiFbtfex0bHQa55FnplsFB0ZrhgIzv\n7pQYQzlj+UMcPnRJ/MW25eLTlS1Np+ANuMq9R8iuBRgxkNn66QCmj/qlPpnP5s0rAQBdnWb9PZuU\n8zdtpE8ZELDPT/d/yxtzZ8/HAABjA2KOlCxWG7coFlN5RnzCkWGZ40bLqR0t0EIhaWGA9eO1vO35\nEpQSlQvJVc3zCJLWupAl8w2topkZk2pzCZEdm5D4w1RWCpEmwuae777zGgBAfAUtBut1DDeLFVTj\nMYNpieusWLXcGzM5LnGABPkNdm7fCgB41dL4zT1sedUk92OarE2OZXUqnLnC1NiQxZWXoxaO0MfO\n0UxyDNIMblmPI581fU1qNoW3WpAyuL2ly9vW19cHAMhOyz1OspisZr18Zb7rNc6nwt9AsWaeS7FQ\n8sBYVxNf4/viyxKURfbxHdQCwTrMgev5WkoypyAK2wejL83Kl2hKVsTuJhP57+oi7JXxAFjlsBfO\nCRims01W7XSa2sxqlrH/qacBAMOTEq29tfk+AECy2UT+Swp48XjXZEUORax4wrSsuIUiy1Fti4F/\njwxLbKFpj/AD5qZNVL6hnQ0gcqIFGjfKdVVf+5k3ZnJKfOHkComC12qGvy1KJXPujGi20RHRMBEb\nVUomI/Xfa47sVHHsgiQWTcUIi80Zbe64ovHTZMUdYtHTxSFTXnz9zWKBnZySa33+oETwu/du9sas\nuesOOfaQXE+4w8r20Cqpsf15LCvnCsXNhfzkuZ8CAHZtEtBWgqAr9zUTKzh6QBh8tjwgnIyTkxL7\niKRMXETLlEv00cemTWAnGJVrDbHoq6YNUizNGrQ5+gCvlDlkgZ4CNZl3kWw7jSkD0z4yJhyU2UkW\nhvGdC1QtaHuRvwv69mVCwlEy56gUS3VMTVcSX+P74ssSFP+H74svS1AWuR7fRbVWm4XprtV9ap1z\n1OqoW4uQYYUxrGJezLWWNhMgKZXFPCvSjHXzJog0xbTfzhv38AASJBy7YAArf/93nwcA3Hz722Q6\nDGaV81baJshgXnRWUbdVzaVkjjUGjIIRqybbY7Vhioi47UrGUIGvWi/04g5BMqDJf8vN13pjfvKc\nmM07H+gBAEwUjTvSFJeblM2KaTkzRZPZqrxTmvEi5Bxac25XTbLkACVXAquBmLkO7WGYZpB2gjTZ\nGcvUT98jqcrXXpEqw2hc9nnHe+8wE5mRmvtEmzzrbMWk0WJhuTfhmJj/oaK4d02dVs+5dhlz3Rb2\ntaMXsHHVCm/M/qPSaXn9Fgn8pVOswrRYi5RNR5uw5Kyqx64OvmN8ri5dONdmPZpVyK/Wdsgx77D3\nyldIr50wiKgIx2XGxc3sTEslZrhmnkeNqWAvnailA2WrQ3G5umC4vq/xffFlCcrianzHQTASru/c\nygCIgmtC83UBdbQdlWidQkFWueYWE9QqVzzkDQBgbMRon0hQVst20jdfOPkiAODl/Sbtc+m8BADX\nrZJUWTrO48wYKvBgSvaPpmS1VgBJtWgsmFhUxhSy1KZl0xKrwm6qjqYhmVIKWWxBy1ZSW2nLrEEJ\njsXXmi6xy5jGO39a5rx66wZvm6ZDtZGH8s9NG/QoEu3KvEMuBI/exQTOStQcM+wvn2hp87ZNTpHt\niPG+8pikv1oilh7JC8T29DGBJT/8sPAjJpPmHDPnxeIKkjsvZ92HpnZJZ05ekkrI9lZJy5XLxkq7\n5ZZb5A9tvZUVy6l5tanyayEA6uV9rPJ77wfluAXzXBzCaUfJxVhyzM+iqUMCyJpddgnKCdpty3jP\nPVYfvg5Rq7y+xtNFA/JlyNLMnS281mF5SJUumb8dj1XOhyifa0XbsFkaP1Apea3Pria+xvfFlyUo\niw7gCYVCqFhwXIXmOh45G9lL7DgA/W2Xab2GpPhEiYTxoQLUGmVqqNOnTLOMOFfe7JSkzV5+8SUA\nQNDC0265RjRtGGSuocYqF0xqJ9kkKZhATVthaQNFM9UAocf5nFgKNYvdNsyiHrUKXEKJc1nDK9B7\nrFeujcUYDXS2UzETB1jfJtDUJ45L2iqQOu5t6+yUQo/GJsZMQnI/hvv7vDGrWwU4lK3U88iFLDXA\nW+01bgjZ3TbYniw/JgCapCvX1ZEwaa2xs68CMEw6W9lf/uCPXvTGbEhLevXceanVb1q5zpyC9er5\nQd6HfmmwuT9res6vameD1RF5Vp1p0c5Ou2Fmuna1PNeDpy5wYhIfgpWCqxFoM9Av1oTNoJumlTg+\nogw8cpOi1k/H5bur1qriZsOW2Zpn7Em/K2QN9LizWdKQLzL1uH6lgJQSFq9frSS/AWUUCvI3UbMs\nSqdarqfmvYL4Gt8XX5agLPiH7zhO0HGcQ47jfJf/r3EcZ7/jOKccx/mq4ziRqx3DF198+fmQ12Pq\n/xsAxwBoRO13AfyB67pfcRznzwB8CsCfXukAruuiUirU9cXT9JeaknGaUiELS+1V87FhgZuXffIl\ngyarMF81Rdrik5apv2qZmOiTI5J26jsn2x5++7u9MTUGus6dkq65Tc2yT7DB0FpHSZ6Yy4oZ73Cu\njms1YCDCamZyqG7OAJBukHGdbXLsAtNHQ4PG1G9xJLDTGdfuqrrFuDVoE3N+6yq5nqd/+n1v00Mf\nEHO3sUXMzVhY5jo9Ykz9SFnM32iJjz8q99PKPEIZx6LsW+haNOHBkqTdggUJRrXE2PvNNd1u+3rF\ntL77VgnAre4Q87WcMeZvio9v2ybyI1hU5jXi1gPbySPwipBTpqLGjK8Sy56mqex0kqbMCnA1MuXX\nwKYlR0hiumr7jd6YMjnVJ8bkevTZA8YNyjEw25KS8wctfm5XG2rQRXAI3Ys45tlni0ofJ35nPjNh\n7gMp2ceH5HnmJuV9CFqurFuWdyUal+/ckrznNes34LruW2vqO46zAsA7AHye/zsA7gbwdQ75SwDv\nnn9vX3zx5edNFqrx/xDAbwFQcHsrgEnX9ShXLgJYPt+OtsSjEWxavxanThtq4Wau1lWmnTJMnzW1\nGCy1FkIpOKdClpF43KR/pqYkFVOY1gCNOW+Fq+OrhyQYtmeXgDmiEaPFcjOyyiYb5ZjFvBwvGTVa\naHpYKuXcqKyqxbJWRhkttqxdAm+FGQl8jY2alT2+XrT5hQuiDdesltV7ZMxoyplDYo387JRoqMCQ\nrPST4ya4h5Wi2WI3SjDs/k9/0Nv0w8f+DADwrvtlHV4hcTwMXTjmjQmXRdtViA1PtYp14ZYMyifM\nNGs8LI98fMxUrIVZEfbSgScBAHevl3kMDxp+hFMnJeCYhkzg//7P/xUAUD1qOBDWl+TYzUGyy1jB\nrDiZbkozcm/SDMYN3GTMEg1Gbr1OmqE0N8g7c+16k96MxuX9amkVQ/XV14TLYfMtd3pjLo7Isx44\nL1bRffe+zds20C/X2t4sxw5qCrRgNG2YlaDlPLUwqzYzBWN5hGiFFGZkTCVvwEozOTIYEVh1nrUl\n61YbIFKN7cUaIgJoCrgMDlZN8DkzPen9jq4mV9X4juO8E8Cw67ov2l/PM3TeBKLjOJ9xHOeg4zgH\nC9bN8sUXX/75ZCEafy+AdzmO83aIo5mGWABNjuOEqPVXAOifb2fXdT8H4HMAsHr1WvfmXdd7PgwA\nZKYUICOrZIp+dNJqg1Qhq4ym7AIR0RTDo0YLFZXimgCemRmjRQtM4y1vp0ZYJ40Y7TZdKVbsnT8v\nTDe33CKMPhnLFwtT01e4+uZpFYRDxvI4NShzusQU1XCfqbwLVeVY56hZNm8RC2BswvC3XXhG0lYN\nrEbrrMkKXxw162rfhFg1RUcW0j2P7PW2rVwj+337B38s266RbcdfPuGNKU7IPV/RLb4sSWYwbqF8\nmpvkmrKjcq2NaYslqFc4A9etEq2capZ7/4PH9nljEuvFANy4SbTxIw9/HABwcPBxb0zTGTlxhyPP\n0w1boBhWzM1MEzLsyrWefMkAeLZulxThiZNiQV0YkHk1NhprcdkysTi2bRErRy3BgePG6hwng9I1\nK+S9CJTMva7m5PxFFt1HNBVdNum4fI4MTwFtFKr97c1xSkXZX+nbMzNT3jalYtdrnByWd6DSYVGz\nK3tThulvNbarRplGgiWvaerV5Koa33Xdf+u67grXdXsAPALgadd1HwXwDID3c9jHAXx7QWf0xRdf\n/tnlzQB4fhvAVxzH+c8ADgH4wtV2CAeD6GpMoTlq/LRihRFhavMw2WDCFbOS1ci1F6XmL5F/rGIV\nSsQS4qde4qp/jlBNANi0RoAe11wjjC/a6rhUNn7WqlWifV995ccAgPPnRCOsXL3eG1MYF22TK4lm\nrDqisTIF41cFHTZ1IJPtawNmHm2N4qfuvfVOAMAN73kIAHDsCbNmDjGy2xWWQo21CfHzmsIGnjxd\nFqtkaEbm39VutnWskL8LJZnrodfkemwShGOvCQjm+oTM4+IlmeO66821Dl9iC3EWALnWM+u/1AsA\nuJ2tzBAT7XNmyNTB33EX+QSvEX+7ze2Rff/SAhsFRIuti4rlUQ6Y+1hwWDBDmHSIFuHzRfPMPvXo\nrwAAWm67ntdIy8ludkGQVKhRip/OPS8xlP4+81x6+8UC2rxLMhCuBdrS2qQyY08FNnoJW5x9UxOi\noTViH/FaYZl7ns/L+6zNMm2LtEwXOMzswiXGgK5ds8wbo5D2sRGZR4KNQkoWE69Tq5o+YFeR1/XD\nd113H4B9/PssgN1XGu+LL778fIqP3PPFlyUoi4rVL+ZzOHfkMIIW6KAjKWaR4u+VgrpkBU8cJgyq\nJLvU3u8hC/ChdFg50g0PjYx52+7duwsAsIJ9yEdGJC1Xcoxp2b1czNZlXQKA+cm+pwAAj37YpFRy\npJHK5iWAl0yxGciYCYrF4mKiO2yEMTp00dt2YFrmdP/b75IvwuIWnO41Ka5lNQlMtRTExE5o5V/F\nXGs7pDqQ8T8kUhaohfGgbbuFVvpSRK51KmLcoscfF+LOzhVS8Zcgxn5i0Ji/pZKYuwkloOw3c4zF\n5bvEGmZwq2K2rr9xhzdmxRZSbLE34viYBDsn+8xz2VITU3YVqcOm8iaQqjyoafYeDEUIksmYkrdq\nlq9vTb5zaeo7cTOmxPvGtvToWC7Pd2TgjDdmhG5h6967eTnGDG8gzdtIVp7x9Jjcz6hFqzU2JLwC\nEVKwRZXa3cL8Kz12PifuYSFrXJYio6tBBgNHBuWdKeZN0FfBVVkGEquJKI9jxgRDjvfbuJr4Gt8X\nX5agLKrGLxeL6D9zAgmLDiZAauQUg3tVpkQKQbNqu4TzFtipNMwVtWZRHE9MMe3D1Fo2Z2iHq9yv\nqHTYXInLNWN5lAl13HDtWgDAs0++AAB49dB+b4wG0eIhVkYx1dfVZFJdMzOyIjtlmfP6HoNr6huQ\nNKbXXJdppKJVB99Vk/k3kEQxBDlH0gomNTF4lVCLJ2Q0foZkkPEmsVRuvEe08Ey3SX1WC3K+Z3/8\nPQDAfe96GACQzRtNN0BC0BaHvetHznnbrt9DxptGmhdsCHHPhx7xxiTWyn2s8nlMsgVW1dJQy1hp\nltJ8IoyVF1SwVoj3iEGsrrSxwArjDOIlhCvAoeVSDZmKNYW4KqVQcoPMK7fPVPm1JuX+tdM6GRgx\n1k0lzw7Jk3IfShlq/pxJx5WmJupnT5xvyGKRcvidNu8o5c37WeL1V3mNeQYAy0VzHXFq+Crr70t5\nDVBblnHJ9ck2ffHFl8vLomr8gOsiWq16jCsAUMioBpCpxJk2stlHHLZmUpc8x9UyFjO8ZWXWlp85\nK+m8gSHjS07SGqhyjPaKj8TMijwyJumv1jbRHlsIPDnw0594Y27bK9pz9co09xHNMDlkVt1ylZDf\nimjDgA1EosWRJYMQEnKcstXCMUlN30n4aoO26YoaeHETjaE4mWPQYKyKaIw92r3eV2IpNKwzoJb7\nH5FYx99+8a8BAN/53lcAAHfc96B1HTKPoyfE8nHT5om0beiRPxwFNMnzbN5gfPwMn2eZTzJMXzlm\nNdRo0fRdWbRoCsYSTJIPfFopwKmx2yOGUr2a1fdInmO+KqmuTMgcpwKx6lro5Ed5X4aHDLBq+0aJ\nh4D3U3kGAGB6ULR5jWApxyEAZ8rsr+29yixkKikwzDF+fCCk7xrZj6yipwo1fj5LhiZ+X7SsAjfO\nlmJBtRzICWGxBZUrRb+Fli+++HJ5WWQGHiDquIhbLae0pXBro/hZyrI7NGEivFUCeILaZosrat5a\nEccmxIc8cFDYdao1s6aN8lilii7N8lm1gB5F+sbRiHzXs0Yi3v1nTbPKkyzwaIiIn6iFEgnrOA4b\nPrhB0fjn+wxnX5qMM16LpjDZVS3OvhobOce8Ul/CQavGqqiV5HoqFTZeDBhoZ5HWg2r8OLVfoGx1\n/2SM4sMfew8A4P/6Qyns+f7jP/SGbL/+JgDAwBAhzDvuMftrU0haLrUEC2pg5jFKTZukxp8qyHUU\nrFbcgZCWv+r1m7iOU3J4jXwufE9mhkxcJuqQdplMSmE2xKxapSQlAn/ytE76Dr3I4xr/eWWnWBFV\nAmdsAE8hI88vVxWrJB5XK8fEKuDKc3ToXytDU9VqulGmL67ZJxtnow09CoxLxdjEo2g1MSkn2dCD\nxoSWdEet31KhUELNZ+DxxRdfLif+D98XX5agLC69ds2FWy4hljLY8sZ2CaatY9pL64nHJoyJrA0c\nokkx7VpaxOx51aqwWnFS0jxDQ2JGr17T420bHBDzcnhI0mndbXL+bM4i0kxKFVyVPfeqdAf27DZM\nLV//mz8HAHSS3WbDBgmSBcMGx372ggA8YmkxW8MWrU13t4w/cYqYdgZ4bMaXBB9JWCvVymrumbk6\npAsPaZA0ZLrlltmEQUmLhkblPi5rspjRWpj+o3vzzoelr9zXvvWMN2Tfs9KXbhXjp1t2bjP7k+hR\n2xMmklLzPmO9Tlkmt2I0bauuzDliuUVJPkfMMBgXsPiomf5zaTY3N4o5Hp4yLk9zo4CltFddjn8U\n6yrE5brDnMehV4TuO50272CQBJjH2WW3ocOwLiXIxzAyxHQk71ld0JZU3Q4rQ4OkwHYtl0MbrKj5\n71hkn1ESCyhVdjzCAHHRql3gOZQSPcM6DSdlnn0uW/C75friiy+Xl0XV+IloEDt7mnHpkmFqSbFj\nbWeaLDAM4PU3mZTKpVEJDKWiopW1g2rS6j9+6qAwzDQEZbVes8KkfYb7BS76wsuSgnnbPVKl1xI3\nPHRBttUK10TFTeVlhQ1GTMDq3vf/AgDgC9+QNNg9y6Tqr6O71RszlZK5tbFeuj1ltFhro2j2yhit\nmREJEDWUTT1/b7OkI6dpgZTjrFlf1eONOdErAbdx7UdvVWg1k8K73CDX4TTzEVtpHw/XSyajTbuk\ntdj9OROw+r3/+QcAgG0fEVBOMWxSn9Eg55uQay0xOJewYKw94wzuMSg1RS7Co80mYHXmE9IWLEnN\nVpk2Vp5iiVLc/0dHROJohWcAACAASURBVBvvnzFBuYdXSjq1JyraL00q7rStz7Sj8ohYfSf3Cy/B\nbXv2eENeHJCAcI2dgfuGTEBXuRwDCTmmJuiKFnhsJdOKja3yHoyz2i4DM9cJNnzpJ713wrI4ltMS\nLKflWgcuyTuwKWjOMT4tgKEcP1exacjQ0IA3puy6fjrPF198ubwsqsZ33RrKxTzaW40PpawlF85I\n0USVfpLdjamZqb5sQdbbGMEYAWt5Gx6WFX3DNZJqcy0usghr9c+Q+WaQHGsdnSan4jDdEovIudLs\nUX5pwFgeybRo4Ztvk9TWd/5R/OC3PXC/uZ687HfdRgEAXZo0UNefsmHCwx8Ujrz9+74LAIg3m5V9\nhD79hjUCi405cq12y6dkm9y/xi5qbqupQpCw1TKUvViBNxaFrp5OEVFs8LHtBgPA+eCHHwUAbN8m\nte7RBtO7XvVFhecNMY5RszR+JErfk/5ulIVE//Jf/WtvzLpbbpNr+7Hcx6btW80pyHijqcNgSu7r\nDqu4pamToCQFArG+H43WXMl08/VvCC9se6cUBsUTFr/BqKTqynltWmE1bGVadZyxkirTZXGr6WUw\nKj8jhSWPELbdRdgyAAyel/fgwEuSTly3wfACJnisCIFqQaboChbIp4Ht4gq0ykbGJF6Vs+jsYrFo\nXeu5K4mv8X3xZQmK/8P3xZclKIveOy8SDiCZNCkI7dyg9NhlVtKlEiaoFmI1npsRk84hBVfEarqR\nZdonlZb98jnjBkSTYhJeYp3z8y9Lz/R33m/O4ZIyepx97JoTEqiJpc0tGiYlcnuHUFS9duQ7Mvfp\nF7wx73rbOwAAz+0nnVV3h7et51o5Vnq5mL0vvSSppb333O6NueN3/7380UBUHmsO2qzUzvRL0hTi\nTD9ryu3ChhCx4DS7K1DsvpEg7x9YOwDiwJNdJiD64MNCC9YQpzltISG1V3yQVYVq4Aes1ykU5Xii\n6lq6Jd3aYqUewXqIJkUyxqx0XjGjk5Uxa3sAAKkhU0fftFWozFFlpVxSmwAaE/l8nwR2978sAbzd\n2wSXPzVjXMFJ9qUvEisfi5hgq7qitQy7IBPxV86ZcxRSdGeIzAwzaFqNmAczxWPX+HyiSVNR+coR\noQO7fou4OlXa6/2DpkowxeYagQgbcrBVcdkK5tVKJdT8brm++OLL5WRxq/McIQkMWPX4oZCsbl0d\nBNCw6ihXsPDrZC9ppmaqkdwyFjBjWhsl+KFkhqm0CfBosVQ5INrzx/sFc79u3RZvzA2bJcU3XZAA\nYP+YaOymNlP/3XtOgjYj08oLIHTMR0+aAEtu6nkAQE+raI3l773L2+akZP5jebEq3vvZDwMAZiZN\nSgbLOe+VcmxUGMyKtZhrzcg2N0NG86j1GKmhqgSzVJS9yAruBdUq4GdIKaOtjriJNAOwFR7bIjbV\n4GCC2HjVfTZgxVWrIi9b06uk9uHPvvBlb8xtDHht3sznYGHkEaM1RkDXEJuXuAkrSBnivKm+SrTI\nIkmjsf/6H74GAGjWtmV8l3rPm5RyW0quNR6QIFv/RfM8Bi/JPXZ4r7qXC113R7uxjjIlSROrsg0k\nRJsPjhtmpmkG4a65Tq61pcMQaR4+IinGtmbR8C5/lkWLTSdPcE+gVv88Q2FjJWULeSwQv+NrfF98\nWYqy6NV54RBQLZuqOu033kif3q0S7lg2YJIKVzttUjldK3GsxT7iNSysb7AJAE5cNFOJrZoGJsUn\n/Nb3TFupFcvFv+rsFL+x9/RBAEA8aM7R1CXHOcdYQzwtcOMZq73U0IhonQfvkEYWPzlgGHxWrJE5\nXRtmXystp+/o8sb84Ad/CAB42+3C/xYkuAMTpoXWOFt/7biTjTSs+5kjdXiIGrPmaX4jYU31pUTD\nVZg6q1jpuAqjAimXGsVKJ4KsNo5nRcjxXBsq62hsQc4cbGDlXMJoqK/9UKoB/+Pem+WLKTNLl3xz\nDlupXZgR4MsqKw2moKQiU1zRFtHqP37KQI/7B+TZLKeG1eq2hiaTUs5Myv4T/ZISHh8yzzPA62hv\nlphPOsmHVrYq52hl9TFWEWYM6+K4aRwzzRjN8lW05Byjc5ctF7j6y68KSKmDbeVSVsNWB5riI1sP\n4wilirGep6ZmUPE593zxxZfLyaJq/GDAQTIRRjhooLaef8nmFkFqrPa0GRMnwGQkx2IIgnOcktF0\nRXLctXaQRXXaAD2UBWaC7bXDDbL6HzpieNO+/h1pj/3wgwIj7V4nEedy1fDQadeiSFKO19wm2qhz\nwqyyK9pEM/zyr34EADA5/Iq37fNf+u8AgJ89J2pn9VaJK8Qsf69tu2QM/ux74pt+9v/8T7JheZs3\n5sD3vgkAePBRgRDDKhJyqb31G13ZSzWjsR3CoiO6le2cqxbE1PFeDY6xmoeaWADjCSwur1oaX5lh\nQjHuR9/6o7/0aW/Mf/qt3wYAnDnNJpHLzX2o8fzKyHz0nMBo73rgnWYeFbLaJsSnP/6SRMf/4Wum\nQcn6NWIhBFnzPjlJIM60iScMnJHIf3FKrKo1K1d62zasFxBOlO/gVEbemYkJE3EPMR6iQDFljBqw\n2p8XCfxJkdfPTrNcu17AWhdP9gIAxskQPdlh3s8U2YHi5KhU9qOhS4bFeWpq0rOOrya+xvfFlyUo\n/g/fF1+WoCyuqR8KobWtCSHLzqkRiFDJEifNjFA4aoJzoZCYkDmai25B++0ZE7erTQJkWuecsaii\n1ewvuLLNJSgk55ig2uM/lZRMgLRUj35IcOu5kgmqTWRInU0QSkeXBAtHLKDFv/230hW2fYOMad9k\n+tF9OHsfAOCFVySIc+KQmLjb77vWG3PjQzLmj778F7LtR9Jddud2wwtwflqCUKlOVgVa5KVJzq3I\ne6x3yDYA1TR3aY7XaKK7rknHhVkzgSCjYVawVNVFWd0HBqoijnke3pG8+J98k15hzPm1m9YBAL73\nxA8AAI+89yFvW8caCbIefeYJmX9Fnsu6jh5vTPa0pNoiBMN8/o/+XwBAe4tJfY4zuNfE4OLpIwLe\nGmTHXwBYzmDapk1i1q/vMSncAN2f8QlJ/1Xo7zW1GwDOOLt/NDKl3D8qJn7Jwtq3dQqQq5IXVyyT\nMa5GA0ljN16zsW6OBQtk5NDTC/LmZzLyG8haYzZuvA6xF17GQsTX+L74sgRlUTV+KOigsSGOXNZo\nUYdgngRJKqMMHNlkiC4DFmFqjbK2d7JaJW3YJAGS4/0SEClUzZrWNyhAikhKwDHDDJ5UgmZljxEI\n8eOXRAuvvUbGNjVbYKOIaFNt0hAJy7Ybrjf01nt2kE0nJ8FCTJi+9Nt3yPm6O+XYwSRBIP0WcKVV\ntMbed7wNAPD3T4k2vDBpatW72PSjuZsWi1WxBoJHNG8VJ9y5Gpj7qLWaTj+dqknnsVsZ3JqkM52Q\n1eBEQUF8dqGQAmaqc8dUlSiVBJ15cx2//O//DwDAH/66VOzte/oxb9vWDRL4/MZX/wYAcN89kt50\nogbyG2FV3e/9NwmatijxZM7cj4kBAeM8fkBgzstJrLlhjQngdbXI80g3yjVmrTmGwnJPks2yrUir\nc6ZgAnehkOx/qU8CbfsPSbOO7tWmOu/atWL5vXRIrL1+i5Ni+0ZJJa9fIam+KbI49XSb93MZg9aj\no2KZXrwg+8fCJuj64Q/8Av7HP5p7eCXxNb4vvixBWVSNX3NdFCsFdHYajrkRarvGmGiNck40zIQF\nWEk3iX80nBW/fWxCVvqeNdu9MTlSPWvTzL6LBoRRYoHJzKQcuxIQLThhabh4RPysvCPAl398Qgpv\nPvDuO70xWe4fIwhFizoy4yalUi3yb/V/I0Z71Aj/TENW7ygLL2A1BgEtnw//i18EAHzz69LgcnjE\npHYe+aikCtGisFpr/aafHbUais6Wmpd2Y7ETHfFAaK4eqIZ0rLHA9K4FQ8otV8Rs0SMFg7X675N2\nrECsgV/7D6L5X/1Hk4Z78rty3Tuuk3Tcnl3kCmDzTQD47pfFp7/42nMAgA3b5H3IWkAgpRr8zMc+\nBAB454OSDmxrMpDuf/zmNwAAzU1ibY0O93vbojGxoKbYLi3VJOm4ggVhHhmUmMux42ItdraQR3J1\njzfm+R9JYxZN5zVYFtRAby8AYOMttwIA7r1dirZa0qaIrMrfxfmTktasktHnF3/5X3hjwvEonMDC\nCvJ9je+LL0tQFrdIJxBAIhVHqWI0RDwp2k4bAUwRfulaBSM1Rp+n2WBg2coeAEDOijQPk8tscEgs\nhemsgdoWKowbBBRaKmqgeZWJzF64IIy9LUHGA6gNn3zqsDdmfY/4nTtukGi0Uxa/8fCLR7wxJw8L\nw0pihoyprmm1FGDUvPe4FKe0dLP11JQFh90tEf5GMsV84pd/Wa5hxmJaIVQYyvuWsXz8OK8poNF4\nfm9RGrkESWn78YBaADULVaK4qrB+Z8A5eiSHun8+7WGdTT70mYcsWK9CrtkYc9udt3ibtu1g4Y5C\nUtl0cv+X/5s3JjMgkfk7bhB/vWeTwJtvvNNq/tFNiKze4g6Jxzz2J3/uDRkZIQNPkRZQxLQbiycl\nc1KoiIY+10vf3Hr3+tmI41rGJZywbDt/xnD37dkp5cCPPirW2v/8/f/hbXvie+KXP3iraPoay6Wj\nFqy3n4w7Q4Pi///qr38WAOqsZ2CBFTrwNb4vvixJ8X/4vviyBGVRTf2qW8NMIYdqwap4S2rfMTFT\nKqx0ckNmakPTEtS7NCrmfOsKwdGPDRuX4QJx0SOkdbaLyWoeplxNfZGRjGnI0dotLkcsK59jxGI/\ne+ZVb8zAGjloQ1xSM5uvESLKYwf2eWO+8CdflM+v/Ef5YtyiWD4pWPI0ASfDgxIMal+9xhszTWBG\nWtNWBCTFGk2NubdeaxzHZjQi7l6psxU4Y8d8tG7emRXkqyvmZnG5+Wa+nmyBumnYYSWrlYR8sJ4d\nJevBsDGH54ZYgCxMi/k9c1GCpSV2Ve7pNkO2bd0FAIj3iOtVyPDYrqmDR4bRvaiY7OeeErO6/6IJ\n4IWC8syTSTGbg1azi7OnCQBqp8mfF1fyhYMHvDFvu10Cj3n2sy/QPfng+97tjVm+ogcAEGCH5g1r\nzTN/njwTId7tlazI7Dtv2IaOvCo1Hx/9qJCgqokf6jA1HNWJUcDvneeLL75cThak8R3HaQLweQBb\nIErgFwGcAPBVAD0AegF80HXdicscQk4WCqG1rQ2TVifcEjXTJLV6jiRihaJJyYxOi4WQapOAVzUk\nK/R03gBfJqZEUxarrEcPmxRZhUEwzd651IK1oEmRhVlRVWLKLsGUX6LBcOZdu34zACBKIE+Jgcgt\nG01asTgtwcAXvy3pm507jYpqbpFVPlch61A7V/2YsYDSbTK+yqq0TEnuSzBi0j8xR+YW8rgHrNQd\nr99D4FCJOpburnpBOYX1MoBnB/f09kd0f6MjVJsHPcuh/nuRWYEmQl1rVpvYgMKytTNwwQQpz12U\nwF2mX4JZCV5P0jFp3gA1dXVSrIIY22zNTJqOvDFaQOHl8hz3Ma3WP2Se/fbNu+V6HLGqCgWjNYME\n5xx+uRcAMMgg2733vM8bMzV2kGPlXI8+Klq5a/01ZgzbtyEq13ru7ClvW5WBz3JR3ucSH93hoyaw\nfNOt0gBkx60yV7AV2czgBW9MQ2tjvWl3BVmoxv8jAD90XXcjgO0AjgH4HQBPua57DYCn+L8vvvjy\nv4BcVeM7jpMGcDuATwCA67olACXHcR4GcCeH/SWAfQB++0rHqlSrGJuaRsBix61wCiGmoZpjkko5\n02t4z/pGBWKbXiYraIatszJls7rlqdU19RewWka5yibLhTzCopZ0wjROyJCVJx2U/bT+uVw0/uLe\nW8WnvPZaSbl98Us/AwB88tO/6I0pjkrc4OVXnwUANMUNCGPd7dJzPpETUM/glHx2bbRSMlllT5XJ\nNpAPrmZR6aqm9ShWq2Vrf/q5ZHxVn69olekUlBWHmj6svn2dktaUX4RjzTObXesPdx79wXpxjSdU\ntT4/aoGVwHkro27SPI+eLQLBznXIvSmShbmlw6TaMCna/9K4WEVNCTlXrMkU6YSbRdOfelF85NFJ\nsSqu22qstMFhecZhcu5VLL7HUTZvVbbdnTtukPlYTUxayMvwsU/Ke6DMxm7BFIol9domyeLcahp6\npJvEgpzJyTW+/IrED+646zZvzG1338q/6p9vuWCVX0Wdt1TjrwUwAuCLjuMcchzn847jJAF0uq47\nAAD87JhvZ8dxPuM4zkHHcQ6OW+QHvvjiyz+fLOSHHwJwA4A/dV13B6Rv4ILNetd1P+e67i7XdXe1\npBNX38EXX3z5J5eFBPcuArjouq6yRn4d8sMfchxnmeu6A47jLAMwfNkjUPL5PF49dgRtzcYUUxbm\neERMoRpr5ocmLbJNVn+FErJfZpLmq9XkocpL0QBeyQoiVStq2mpfPjGHStNm/+aIpGvcDBtrZNiL\nb5WZ6823CG6853ox9bOVewEA+3+0zxvzv/2KIO1wt9Bqf+ULf+Zt+9EB6d/2/k9K77wgsfa5jDEb\n4xqTY6VZ1WuIYUy4Ak30JCm0HKtxgxXNq/u3ZlNfe+SY/M4D1lvBPd7HME1924DUZ4aaowfE3EFs\nROFoBaD8n4cJZOppI5y/02jM3xo7AIcZcIwRt168aFKwkaRQdi/fwerIshxxwiodOP+KYPv/+E++\nBADYtlWCZOGYSYG6jliiL78qqMuwlUreQfx/nsHWUEjM95YWM9eHH/2l+jmz0nPaQlQ6ROE1LBPX\nZXDUuLIJ9oYcnxGXY8duSRPf9d53mAtx+Y7E5CaPDElAs7nL4h7IT6NSe4vINl3XHQTQ5ziOskXc\nA+AogO8A+Di/+ziAb8+zuy+++PJzKAsF8PwrAH/jOE4EwFkAn4QsGl9zHOdTAC4A+MBVTxYOo629\nA+GgOe042UouzcjnubOyks0UjPrYulMwzLFWVumdk1TIVM5oypKm7OY5b5CrrctAV5W1/rGgYeCp\nkl2nMi3qaz0pjx96u+mjnkrLqp+flDTLjpukXvrIa+Zc0zxOOimpx0d+7Xe9bU9966sAgP/yB0Kk\n+Z5PvwcAsPO6dd4Yh73nw6wArHBtzsEE8LRxg3apCletqJyXW+N3mro0Izztrym+MgNGYRv1xH72\ngVhT3eFkAgE9EP/np61G9F7z/DkGFPNWMxWdv6bRQg3mvXBUe9JycFi7EUyu9saUWGvff0ICcD9h\nS7KfvmQeyJk+GRNmld07SKJ6+PBRb8zRQ7JfioSi973tQW/b8KBg8zMZ0cZ37JF3cfutpq5gelTe\n2QjBOSXWiaTbTNB2lAHExIS85+3LzLanHhdQ0TseFPal+z8meP5yn0nVhdtZFZhjLUmDvB9jGZMa\nd6LBWQ/q8rKgH77rui8D2DXPpnvm+c4XX3z5OZfFZeAJhdDW2YFE1KRtOthMYmZatOnpcwKlLFZN\nmiLCZhvHjot/N0AGnamMceZKdO4dBWyErIYarFEvFmV8pURtEjOpobFBqaJ7953im7//fqkOG7z0\nE2/Mb/zvnwEABJNynE1bZczObXu9Mb2XBHiyaZn4XqNWOyY3IKv8oSOiRb74AWGe2X33Zm/ML37g\nE7L/Lqnm6t5MeuiggewGeT0ewtaiGfe0cVlsnwr7y5esdmMlL7VHfgL2dY+Pm6xLlByIwWZqWNtk\n8JC+s1JHVsNGl+pfG3qonk8ErHQc6/jHaqLFmq1UVES1Jb86+bKk44qvmet47JmnAQBn++XZda8X\nKLUbMrX2Tliu6b0fECryo8cFBnv8hIkVXL9TUnSZCYnrnL9gtmWm5V175BGB367k8yiMGDhtgGCx\nmLbuInQ3lzVxqlBEnlmWbcI09gEAv/k7vwUAuOtBsTQKF3rleCnzzMfZ8LVJfXpWNGZz5tlHY5G3\nHMDjiy++/P9IHHeBbXXfClnZ2O3+xs2fQUeTgZiGyrJa56fEb29tEQ2xdtMqb0ylWVbUkzOioZ48\nJRrzTMYcp3eMXGj8LloxqcNkRbR/KMtWXHlZJWcaTD3+GrK6xsgEdO6c1Mz39xl2HYf+aohR3zw1\nbZPF5hJPyHkjhKPmi2ZFnpgSzZbNEdjB1TkSMdeRIh/h8hUCP33gQfGmdlxv2Ho3bZG5rrtmGedl\navWjKTXiyEhErr6ENcdpsvmkWxnjKGq3RwMLrtDfD6XZHjpnoLIhRv9DEWotNTJmDGAlzAwMJnn9\nLIBB0dJIbJgKxlwOPfkdM8cZecavHRZ2nQt9omE7A6a4RZ/Z6XMSuVcrI2/xNRZZFLSKTTsPHxXu\nhPe9z0Bu9+wSBuNPfPyjAIAt123ytn36k58EACzftg0AkDvXCwBIdBnYSr6ZlgsLxZoJ2ko2mgKa\n2oBYDuW83Ltoo2mPBbVuvbiIxl4sNmrvbxmjGLiAhboKwsHuD30aB48cv6ra9zW+L74sQfF/+L74\nsgRlcevxawVMZk+gIWnAD63EUaT4XVurmL2RpMG4jxDqe75PTKn8tJg7FYMFQbAqZmpUU0MBE0Cs\nVsQcKjL9V5oSYMWloT5vzMyUpEXiBHZMT4tpW7RM9STr3tU0V/z2jEV9nSO1swZz1D0AABBUpJTX\nXs8Ki2ZMgTtnzor5+sUviavxF64x59s7xUXZsV0aMPSsNU0qNlwjJu2OG8Q07VlFiuaqcSfUxM+P\ni8kfJ7a9OGWuI5riM2LlWCxmgYSUDovVZNpXL2z1pQcJURGQe3bgh0ITfvaUSVEdfVXSbo1peXb5\ngjn/hT4xycfH5BndepukVbcuv8Eb88ILQojaRjz/zl2SeDp11gTeDr0sfArqnn3qU58CANxw5+3e\nmGPPiTvR09MDALhxt0lg9fXJ+VtIxJlYTVpu670YJR3WylVCp10alfs6csJQq7dzWxS8Z1aHY2iH\nW/IRuF6a07w7AWcW9wHdgoAVYHV86i1ffPHlSrKoGj+RCGL7zhRWdRvwQluDBMPKBQl2VBiMyVhI\nnKFxCX6MjsoqV8rLPlHHqnAi5LfK4El2ypgDVUJio4yhRGlNLO8wwT3VCDWFiBI7G42E5oxxCUJR\nIFAwZFZdl5VyJdIhu1Z6JcjUmoJSajUFFJnUZYD12rGAzDHMY2dzBvhy4Tyr+/qf5zktqyQhmnnl\nCrnHq1aKdm9tMWm04RFJmbY2yzm2bpZ0Ylu7gX+mUnJvlrPJRGubCQ72EkCVIohkcFCOd+6sob4u\n8Dls3yoViccOy7YDVounlkaBSS/vksDl2JgJpD5IEM3KVRIg+9a3/x4AcCJz3BtzO7X2Le+XQN3z\n3xTw6Pe//31vzK7dEri7+z5pyLF283U6QW9MjFwMv/4bkl7t7jGNMJ76tnQm3rdPUoe7bpA0azxh\nAqEr18scJ3slINzU3M7rM++XZwGxs25hwLRdi7VLoNBVK4DvjMW1iYCjoCvCrQlYcyzGHafma3xf\nfPHlCrK4AJ5IDZ0r82juMBoqRP92ZEbADucvCMfZ8KTRgkM5Wd0msuyHHmGNusFHIDMmvvX0JOvZ\nS2b1ixDUE4qrb074Z8Cs+oW8HKxEi0PHhCx2mxrZgjQFqr551Crq0NZflYo2orQYZ2q6SvM7Xa1r\nZtXOVZlG04YYjAc0RIz2qGoPdFoKJUt7lViffeGs+JJtTbL/slajsU++JqCkocFeAMDf/o20qVpu\n1br3sHFkJ+G8n//857xty98u0FIU5J4FnxULpNRo4jKd62X/s71iHbz/ISk4Wd1pGImeenofAGCQ\nbDv33nGHt+2G3QKOyhKiunmzxCzeecd7vTFqgYF8fDk2YH3g7Q94Yx559BH5Iy3W4RR99lzGpCfX\n3CRaHIxxFEaMNr7n3e8CAFw4LACix58Qa2LZMhNX2fv/tfflQZZd5X2/8/al92V6ehbN9Gh2bUga\npJGGRbtAhcDYgC0LlUIgJHaqsImdBGJXUalyubANhqRMXCYmLgIOiwUIs0kQIRYJGCQhENoYDZpV\ns/T08np5+7335I/v991zeqZHaoH8ppW+X1VXd7973r3nnnff+bbf9/uGrgIAFDVow7RmerW71/o+\niTsUSxIHKZQ91qSUgpxkrfVxiHwfn758ium8DJ/BhQhdi6VSbCcaP5FEVqB0FMCzeU2X/et3XoJi\nyfmSIYE2x06IFXDkOfFlpz1wTt3Kbt0AufZYfnngxGQ85shJ2aWVlKd/wIEnurtZ0tmUc89xR055\n216doB7Vpmk2TFDNDwBBW2HB9NXZOKHd9hhwVOyZ5UK6gWtMQK+f8ibi4LTyWtDU7d+dJ8vS5Tyt\nka6SKzHNK33dnKzNIJs9RoEr5hgakvf19cg8dl8l2nVsgytauvXN0rL68x/8MwDA9u2ulffQiFhc\nk1Oy5lPMaoyMOg23/TLRgs/8TCLbn/uc+N/XXHtTPCbSUmry8V396t3uPvrlnmrTJzhW1rOry2la\n6Lqf/gx7MZdmnVyMLKvtGeJz4SVbpsjkO6DH/M9T/eYin70jYp088ICDcqdzMreb7/xXcj8HxapI\nF/3WaGKxRfrsrXL3MTsuWYEc4yrtxQptOI80rcQsLYDsguIpYNdt/yYB8CSSSCKLS/LFTySRFSgd\nNvV77Ife9Uq0ApcKmWNqbnJWglATMxL8mJp1gJHpOZnjbF1+P3eSFW+enZMupPib7/N6xQU0nRqM\nmrQZFOvPOLy1AnVaLTkWssa9UXeByJqm6OxCU98H+Wjtv1FzzTP5lQ9AiShz5CXIeF1q26zpLuRl\nHUIGKUseXbh2x9V+ehkvoNNmHcDIoLg3MwTAFHIuvXnZJRsBAO97nzDHXHSxpK/mKq6SEEYCTbOP\nCffAXXf9U3zoptdJcG/HTtJHD9J188FKSv1DINX4fjGR//A/ONa2Sy+TVNt//M/kaB12Acg6qxzz\nTE/qek6MO1ab4TG5j9mT3rwB9HhpSW3E0WTgz9JnynvVmzmmbDVV1qi6axSKBILx2Qnqso6ZHpdK\n/u6XpbNvhufcR9UzNwAAIABJREFUc4MwMwVz7jyZ9QKsCqckWJkuOIDZLFPARQZH29pUxv9usvo0\nE5ASPdD/vTGRxa7b/nVi6ieSSCKLS0fTeZHNoNYaRKPpLjtdFc02Pi874BHiHI6Ouwqrypz8HSqN\nNHfEoUGnBQeG5DxBWrTvqVkH/5xvESbJXbaLLaxmD7uUTpxCsacx1viaoaQwXE0HKkGeC0TqVpvi\nbh1Zl5a0UWvBxfQabQ94MUfkksKM6zXWb5fcmuVpKRjW2Hd5UNl2WjRSpSJBsd27LgIAXOhVO37z\n68IAVCdlNRi07F7r2G1+9q17AAAf/OM/BQDsVOALgFWrOW6IcGDW8yPnp6j4e16ucdfnxWKIPJx1\nP2mlv3f/NwEAY+dvjI+tV2isrig1XbnHaxfG50FhzmpJBV5jjpBr3q1ErwqPDlwAr02LrcnWbl3d\nTpuj7T4/AMgwHec3H9lz9TUAgA9/+CMAgM1bBBA1smWre+O8fI6WadoZ77y9bJkFWqs+hFvFUMMb\nNpqxrMOPPIvUNtpnciScRRKNn0giK1A6qvGDIIXJqW60jdNQc4Fo38mqaLiD45J2Ojbh2GByBdmt\nh8k+MjIoO2MYTMRj6k0W8ASy24fG7cj5In1xboZVMvDk864mWv0pbqxot7Tflpu/Zt0iwiXbCuTx\nm0TQpzfUCFnjLIZMSu5ba6ht3LrKA/DUCTIyTMOxh3zg1ZibSJmERJv6jEbttqTx1q4RMM6f/Kn0\nUV896DTlIz+U1NrDP5KGIFfsFlbX4w+5BqEf+C/S9HOeFsjG7ZfHx37w0FMAgFtHWRuvtfYtByRS\ncNHX7v0WAODJp+U9b3vbW+Ihv/lWoWn8+le+AgA4csCtw/rzCZulb67xg3zRPTtVAm4UPpsiJLpe\ncxpfYdJqXzWq8lwZL/aSZ7o3UovSYy1ukftP4zHg78CL62R6JFZ0y5vkfr5y7/0AgHftdAVF4DM3\n25KYw4AWTwEAYetqkS6ujflqQ+Zooirn4R7QwNgFoJ/nk0TjJ5LICpSOanyLLAIM49lDjoL/iQNS\npnm8Qi1IcM/oercj9g7IjlzMy65dmT4IAMil3c5uU/SB1D1qe5z72l6LDDOGv30AT6RwWm6YGrFf\n0O6LSqLN1lXqh/vVFCkCfww1TOzXeyfPxRmHM4t0BvOr+RqZgAfkhurVU/GYnj7RehNV0e5Nr8lj\nd5f8/fY7RLNu3iz+ajHrNMHObbK2jz7CVgk1YZl58DsPxmNa8zKnwfOk9PfaN7wtPvZXf/WXAICN\nF0j56kWXXsTJO9DUvZ8TGPDf/N3/AgDc+kYputlzjWsLpW2grr1BCmge+tHe+FDlmBT+9J23UdaD\njVbDjNPU6gurtRYGZ4KmYnAU196Bp1w8osES7JTGbDwAj36e82Qk0qKpfLfLQKAp1tRTz8qcx7ax\nPZfHaGTpv8dQ7B6XeUBBgUwy/1akWR83x9jr10ajZFrKRu4r3GhO4fTWpWeTROMnksgKlOSLn0gi\nK1A6aupXZmZx99fug8m4AI0pSoDtvA1CLpnpFhPIZJ2Z04rEFJtnRVUuK6aYH/AKmZ6xVsyrrHHv\nL1gxxbJWTEsLmvrG4de1G5SahpYY/cgZWciwZVc6TTJEpuFM6MwrrbGPtJeXB8IIGcxrBjQXM6y9\nLziT0LA5epiS+ymQ3aacd8G5akXco+FhmU8x5VJk732PmO23vk4q3Yp9NFsD5w6MbWD9d1PScB/+\n8w8CAFZ7+PHrXyMglEePSdD0qUMn4mOpstTRf+ouCcr9+SsEiPOFT34mHvM//ua/AwC2bZOU1m+8\n7bcBAEOrPBOZblCxW56HfNnd4+S0BC77hsh5UJbnwqZcUC3Q6sQWQS36GXoBLq2rsJG6XjT5C+4Z\nnJyQ56BXCTAz7vOYZQBRaz9MUeb4zM9d7/pjx2WOTx+RNZp9QuoTvssOvQDQZrB5+8VSF3HZHleX\nEBXlObjwEgmymjTTkt6z12ZlaI5sPSkN6Jb8Oo/KglZpzyeJxk8kkRUoHdX4xgDpnEG5y+2o3ewT\nXiTAIuJu1wxd4K7ekr9bBKdo2qbmpTIaLa11Z5NH6+rX04Gmu+S6GfZBD+2xeIw2gkxrxZjCa7x0\nXJs7sFWNog0hfVo9avUcg2n5rEu1pWLmnibnIa8XPT67kCCSAg2WsC1aubvoNQHNy/uLGRm7+5Vb\n4mN33P4GAEBQF405NyF14N3dLphUZvYoQwCQMgrt3LY9HrN5g7T1uud//m8AwPDa9fGxCy+VoN6n\nPy3HPvbxTwAAPvuZT8VjXvNaaUzyx38kTUSH10vKL5idisfEi86qtGMT7tjeh4Vz79U1WfNLqA3T\neaerMrTKQm3Lxeci57VoU84DG2iaVa5Z8a61aqvQaduKrNm816hlYJ2kFY8+K+w63/m+pEJrVWdl\nmaIEZK+6UXgAdr9S1qd7lYOERwQVpQhBDlPu2d1/7CAAR9eeVk5HT3tHmm7mfWijUeXpk4ksTdvL\nuRNJJJEVJ53l3CsXsWvXTqSMu6wWvLTb4r/X6+Iv2bbz33XnKzB9c4INCKsNl3ZpNBdykqVCd42Q\nMMcU4bOpHAtxMq6eXzE0ISlOA6ua39P4kVy/wTSeZo/yJecvpqhRSoTR9nZ7x3juGttTpSyLM9Je\nu69cm++X105NiI/pN8LetFa0d6MuFsttb3ljfCydY9yB91xv0nIaGozH9BAwMlMR//3O26Xp8XzF\nAXAOHRZLYdvWMY516cQ5NmqssEHIpz/9aQDAxrG18Zh/+3tSAFToEp84IEvQbNMBs7poeSlv3DNH\nnAV2z73fBQBM10T7HZ+W+7n+ta6xiLId5zIL06O+mxsxDhBSY2o6rafHA5GxzZlliq/lleP/8N77\nAACPscnmhjGxrt78VtdYM9sv1lBOtW+ez56XXjQhjynoq+RAXxoHqVRkXY3V+I4bk2Na2Wo6s83P\nymt0ms7YJSv9ROMnksgKlOSLn0giK1A6G9yzFjkbwngVaymaYgWm44oM1DQjN6ZGpFyV6bReIqxy\nRQ+jzrSXJZIpE3hBoBxdBaL7Smm5Rs2jrGqHC833Fk39VuTOo54FvZG4Q287dOarosiygf52959i\nSidv2a2Xq1/2qtp6B/g+uiNZS/x22l2jzPu54hJpMnHNqxxJZYs0zjk2xCjmaOo3nf06QOLNNivl\nVrOz7+GGo7eutcTsZFk/vv2NL8TH5llpNtQtDsillwuO//d+/9/FY8ZIthkySBsZcW/yfiUhU6eN\nOtNyafd5Pjct13jkKUldHjwpi75pnXtkN26UisOC1sYrPbVXOae8Cmr/G9Kwz027ysyeDRLAe46E\nmHfd/bX4mKae336ndEoe2S7pOFTd54E+CRq3eR8NUpEVvSq7jPZp1KBey+8GI89RH+m4q3Oy9u3I\nfWY50njFXjK77rbrjnHW2joWtjU+uyQaP5FEVqB0VOPDArYZIu3tyEUGdkoMSuSIkQ/TLuU3n2EK\nhgGS1cMSqGpmnIaoEsRR506c8ZpM9HKXLVPToi07cuhRTgfMyWmZdJOavrGIxq/SAuElMT3jtEdL\ne9Vb2YkDv1KM8yhwh+8uyfz7Sh7ghHwCSpm9ZkTWoTrjdvY2d/vfuvXN8kLW0VrnGNSbf06CcV29\n2qXWaY/5OXm/AqCmK1I7sePaK7z1kDTinm4BtaxZ5+r57/6SaMQsKyHf/Y7fBQCsH3H03JMnhPmn\n3C8as95iWrLfrbllULNFLdU36q5hSvIZz1taCA15VB//masg7O+R6xXIrxDFGHun9ZSWOk65Mj3b\nMzQSj3l67yMAgO/vfRgAcMEFl8bHbrjtDr5PnoMpgnR6vW63k5Py+ZcJxOru4+fhAcziykU1AjJ+\nFC5YMO1UTT7rattZBTmtg1BgG1PcUdN1KEa76ijbX0ASjZ9IIitQOttQI5XBYHkYOc9/L1Izd2nr\nKt32rKdpiWaZoRUwQf+7lnNWQYPpQISizXvy7hpjQzKuB7JLNkjZPGG8xprcgNvaJIPgkILHaNIk\nMCSfYSsvavyevNNiTaYhG9TKocc4kyXUt6ss1+2h31cuepVijBeYSH6nLVl/ImfB7NgiKa2LdkiT\nCbR8wIrELUpZTq5A7WPcekRtbQiirZp4rM8lDXv75ZznbSFwx2uEEVbFQrjyUqHc3rSVmjr0qgw3\nyPjWnKQM2/S/I+M0UpufealPtNmmHRfFx3K0VFpp8W0zPaKhDz7rGlFefinr3bU6j+aaT1eeJiza\nMh5jmf565sD+eMzDP5G2XhdfLJr+yptdehQE6szMisYeWE0Kci+/2sU0oOH1awTrVI49F485eehZ\nvk3msWrYWUfFEunS2bZtrjrD/93zDdXsVVbwkc8v8ioJs6loyY0zE42fSCIrUJak8Y0x7wXwLggf\nzc8BvAPAKIDPAhgA8BMAd1hrW2c9CcWGaWS9JhW9jMb3KtRVARehiwOkCJxpcZstUvNGHvBFG0nY\ngux469Y4yO7l2wU6uSonu/bcKdl19x70ND4tjJBLEqTk3AHcrtvmsUbs/8vrdQ9EETAS22jSf226\nOWoUv6dL/NYSmYEWMKzRL8wTaHLypGjXwNvZb7zuegBATpl3y87iiKbZsGFEtHDzkLSwyg87Hrmh\nIVmP1dRea6mdw/1PufMYXo/reegbX4qPbd4gxTxX/64Af3CSQCivyAZV1rjzc121Vt7T8LjuJqfE\nOlvHdl3n77ggPtY3LHOrVDie9zWf9aPYirPmCmrRVNrzn7mOUVssqBYbbOzbty8ecuWV0vxjy0X0\n7T2eRTVAu7rlPM0aC7S8oidD9/vhR6Rt95H9cu5wzvnf4bzcay8zUbUZx11Q7pLrTU5LXGZkjVg3\nW7Y5sJJmA2oVWevqrJy77LEvlXJ5pF4qAI8xZi2A9wDYZa29EPKc/g6AvwDwEWvtFgDTAN65tEsm\nkkgi51qWaupnABSNMRkAJQDHAVwH4C4e/ySA33jpp5dIIon8S8gLmvrW2ueMMR8CcBhAHcA3ATwC\noGJtjMQ5CmDtWU4RSxEWF6Wa8O2RNP+2aabj0nLKwAMvZHJiyo3Ssu0n0eG8lypbzVTdZEtMzL4Z\nh01f2yNVZ9rb7acVSQldmvpRPCZFk9ASsFGn11JrO9My0J7krIzKEpsdFZypH9FFadE0r7WcSahA\npFYg5mbUoKnq0UDNT9NFycu1+gfl2P5x13v+wt1iAgYDTBlal07MbhDXpD4vfe3y67QfXDwErYyk\nok5WlEBSTO10yTkdawxr9vnG+6YdselNt0k/+igr17I1OU960HsEZgX/3hqQ9ZxlwGug6NyBOHk3\nIRj9VSMuLTl8nsxl9oi4Kq9aL5/dutUuDZfRx4+goGzqTA6EGbpKvZulAu/z/yB1BWM7Xcpuy3XS\nyXeSzTp6I692gn+2SEFumWJrV91z8cCnPgoAKLDscTvrIoY3ufTk8KikSouDTAN69R0gDn+anXyL\nDFqnsl5wb550XJDnI5VTN9GjfQszWKouX4qp3w/gTQDGAKwBUAbw+kWGLhpONMa82xjzsDHm4Zlm\nfbEhiSSSSIdlKcG9GwAcsNaeAgBjzBcBXA2gzxiTodZfB+DYYm+21n4cwMcBYNvgKpvNRwt3CKZ3\nrKZ5GFTKeO2xikx/dbFm/8BhCYK0W14skRZCjimzOlMiAPDkE6LhTxyXKZ4aFy2wc8gFWGpN0dTV\nNtlcGDjs8TqehgRxaNddbctUyHrBIP6dUbLOnLsPQ+2fpuXQIv7SZ/np6hWNONuQ4M0cW2Jdd8O1\n8Zg1hKqCQaG0R2utbbpUs4STXAcvYNU/IMHAnNa2j7BWf9pV4OX6Jd304IPSD377TtdQY4h15qmQ\n1kk/U43zTgtqJDNiUCpNsFXbg7pGxEfnNerpEZvu2SMBt8e/9RMAwLHjog1ffdX58ZiTbJ2Vfugh\nAMDUtAS+5upuPYZGxZr5+v1/DwAo9Ig23nONW8/KuFhMpZJYHNPTXpuuVfJ5KGdBZUIsqS9+wbUU\nu3qrWDqr14pVsnoNmYwy7nOdpXVaPyGp5IFVw/Ex9Mtn1b+GFpOW2IUeGIfpu5hYlJZlEDpTLjTt\nmGnohWQpdsFhALuNMSUjPEbXA3gSwP0AlCT9TgBfXtIVE0kkkXMuS/Hx9xpj7oKk7AIAj0I0+NcA\nfNYY82d87RMvdK5U2qJYjuJe575EBPUot1g66/ak3l7RVgMDon1Xrxb64qlpx5l3fFy01clx2fVn\nPYjr8cPiJ7ar5E/jDtvjURx3UZs36DZOVQlnnXcAHK3DV+rtHsI2217BhYJH4r0+dBo/IPRYOeLU\nRQ29FlotwjfnCdjIU6vffMuN8RiwSGee/emt9ymWWMNdm5RjYY0tpIounZcvyPsHBgkiaXCtvEaj\n1ZMCPskQVnzlax0tdo1rpdo7q00zvXp+sHCmnZL7SDNt26i5tSpr+o9Q18gDdt1wvRQefeljwupT\nJLfc1ITHC0A+vMPsWd9g7KdvyDHfmLxo8wobjL7jDsYnPCh2mQVNzVBr9t2CtqqiQU8dlwKmH31f\nmmWMDLj13LFNavQL9NsNnw/rpaQzfC7atMga815jTo1JaI2+Voo1HGgrrMn8Q58sAEDGo3aXhq1L\ny+ctKY9vrf0AgA+c9vKzAK5YZHgiiSSyzKWjkN1UCij1hAsaWcRNEfi/9pawHidZTzeLbMpsXU3/\nd6DPRcPLBdnl1w6J/zrj7agnTohPPz8lPmFIPrrHDrsYQR+LNvqGxT8rs61S3VOnDfpZdUbs1Zsy\nHu2J+tjafLPlaXOFqLYI/mhqlN8bk6JVUI9EM15ykXDNbbjA8eop/FZbg+cKbh3SBB6VhujTRzzW\ndNo0T4aYzVsUaku/u+zAIE89LSyym69gcwiPGbnGiHae0e+sam4vg4EunotmTZvZmnKvg6qm9Jxa\na1pz/v/W88U3v+Ya0S1bSuI/X/dqx/23dowtvLTltH4Og07j3/N/xBffdoHAgQM+8nMzDlxTZllt\nhoUzBQ8KXmVb670PfFvug1bj7W/7rXiMCcQKUXBQneAleO3PtfVXjoCbtscwNXVK1rPIMRr78But\naNPMtDZlYfzA1/gmlYJZIoIngewmksgKlOSLn0giK1A6a+qnLco9YQx8AZzZmWblXYspurZfdcQG\nGimIKdWiqZ7PO/OzxIBKV7+YUsN9Dqs/SBNWce9NFtbPz7pAU+WQpItah0i8yPRTuuBAJVmCT/LE\nW1u6Ke2WC8LAMN1C873edubvHCOHCuqpsbFG4OU3WxHrCVhvfckuAk0810fN52JJ79FdI2K1Y0r5\nDAgWgtfdtdQtx4paFUj3KDrsQEJ9Q3LffUzdtb3a8izN1W42uUCTn2fuzMepwADqXENM5EJ3l3dU\nO8DKHEtelWKBZvfNr5W0XpEAlrUbPJAQqaqjWXke6i2Cv7x03NHjYobfcas09MizF32Xt5z79ksg\ndOtWcffmJt1zETDw+cunpDnGO26XjrjGOrekSdx8imnJbjI7hZ5aDRTYVQ8XjAWALqaM4/oUBvv8\njzzDZy1N0z5jlVzWkyBcAF56Pkk0fiKJrEDpuMYvdYXIeqk6jcsoRXKLteX1utP4ARtQBATZjAxJ\ngKjhVcVpYMXyljJep9FV/ZJ66c2Lpptj1VSl7GC9M1XREien5Ng4OdmqLdd4IVsUbdXXL+/rIUda\nzgNqRMrdx0Beve1SOg0GbWoEZmiVX+DtvxqU7BqUa1y+m62WPF6+Rku0p3L9+cHSHCsfUwwcNng/\nhWEHde3bLBxzc19kEIrr+9WvOyjGdddfAwBIs1a+5sGjHZSUF1bgTZeD40YpvW/RSdmcaLW2cRop\nq6kpgk6KZc8aII/B2FoBuhx/nNV0WRfcszPTnI9YfuUBsU6eePSJeMzOiyU4qsHJE4cEetw97MBb\nI6PU9Iz31WsuFXzyyEEAwNZNBOkMyj22po+7ueo9atWpNsTw2HBibR4uXBf5k+vH50Ip2lN+4M4u\n/K2a3XognyiKEo2fSCKJnF06nM4zKJQMjPEAPGyCaLmVmZSmLdwYLdgJmNKYbsiuXZ1xgJHuslgB\nfX2i3ZW5FQAa3Mqr8zJ+YlzeHxUcgKe3XzRL9yrRKEME8Bwddxr/FOvHT0zI+8eZ6hnbtNG7S2p8\nTed5AGW1T1qs/ddjPoAnQ7+9h/S2NYJ9ujzASY7Q0mZNfNuM1zIqqzX6yu83rGk1b4+vkCOuzPgF\nmXkrsy7Fdey4aLQt2+T6ZS/W0WRQokWASU5NDq8VmPafD8gkVE6Jpmw3vYIi1XoKJ/YYfNQKSJOr\nrz47zrE4Q6pML5YJXnp6/y/jY5ftvg4AkO8TC6qPy3H0hAN/bd4sQKzjR+Q83UV3H08/Iew8uy6R\nQq/GjMQDCl6jFESM+XD+bVqPfneLDH16BQe1vXtVMI+CvzQ9bDyNr4+RKnT9dqQ9yyGdSi+5jVai\n8RNJZAVK8sVPJJEVKB3vlpvNmthkBxzFswYpIuZAfOot7WluaRIXGMyZbLgKvFyGnWhpT8/PeOkW\nVtOV8mJGrx6W256MHFKtVheztVIRs6vKVFvOq84bOU/cCI2nhKyManpzVTNN7zDw+gQGdF/arERs\ncpC3HAiJhjtwWEzKT/7j5wAAV1/v+qm/4gpp6lAuiXsThC7IWSe6sFmR+89xPb99zz3xmOcOHQQA\nbNTUGNOib3ij41J5ep/QcD30GeFaeetb3xofKyrVV3thIClouDXPELeeJ/JRzfrAby1MpFps4s+5\nzxM0iUP258srIahXeWdYI1CYoWlNNyntUbIp9flYWt8un/PaUUePffSovNbDuoSugruv489JHUDh\nQlmrAgPLmHV9F9VVUdRmSgNvfvyOx4yRiWT9Zhunmfjxe/x/9PuhXX91in5kd6m8W0g0fiKJrEjp\nqMYPwwhzc1Xkck7TFgsEOxDoUmf1VsbDhhfIpDk/S61cIyW3l44LqKGPHZuKr6WiJcpNaiS1IOat\nA7VoC62Q2kLfX/POE5A+WZWWBl8iv485K8wCnq/WckGceab2mtTCbX2/V4euXXr7+iXFdPfdoqnv\ne+B78Zh0kQFEyPxvuuGa+FhPgZ1wxyV4NTYqmuqb994fj/nxDx8AADz4wHfkPKxAHNi8LR5z9Vap\nDajc96j8f4W7xp23vx0A8LrXST/40fWC+e/yOvKqStL0oo1BOh4hp7a8mhWtPPELR519Yt/Tch8M\nMl55Oam0c05TNk5JwK/AysNwRiwGH/w1OsI05jyBYU35nWk6PH43aayrs0wP1p0Fpc1b8tqPvlbh\n/bkxWgOvGlvrT3zjJq6j9/D3p8vzJeIUzKMBwNQig2UeSTovkUQSOYt0OJ2XRrHQi4ZXZ9yiFtb6\naC1aynplxyn6yU3uloFiXAO3++tGGtByCEN/u6UPpSAI+lktD2DR5vYcQyvptgdeGq3FnT2K0yxk\n6/H2T9X+WonX9AAVTV5DX2swZmE9iyEgnLdJXy6dEstnbtat2clDUht+ySuER+71N7kGEBs3sGKN\nVpGtiqb98Xf3xmN2sPFjiz56Rvu5e2lFTQtlC+LPZ1LOAvu/35Ce8T/83g8AADe96RYAwGaPDrrM\nuEGaAYwMAVqFbqfxDxwQnoQT+0TTp6dciu2qHZI+u/AKVn5v2iC/qy7lWGD9ulpi45OijcsFN9eQ\n8Z2AlX95NlytVnyabvXNZezMnPPfFVhW1uYWyiC/SNosWoKLrZr6xYxdTBZ7//ONP2Ps0ocmkkgi\n/79IRzW+tQZRmI0LDgAH00wZAXxMVdg4Yc4VSkSMWrdbtAra6i+5czeporVfg+/jm9gnlz9SBAk1\nvRO0VBvHbbK1Vj46Y0wE1fw8r/UyEPw70CIdrwKnwevV2cK7TZ6e0GsrVcqJRgwI9xkeksYSUcb5\nlM1QtPjFF0qt/MbzxtxCaFEQfemZI+IjP/usK8A5f0x46yps55wpGl7Lg8yyEOmhn0p0f3TUXaOf\nfvbIGvF/b7hR+OsC48y0Y/sPAAAeZ0PKRkM+z8v3uOzE1q1bAQAXrBXQ1NSTrqHHhtXkrWNdf8RM\nhO130fg0ORMstXeDsOIur326YRyhTY2fZtu0oOosqICsR71l0erj4yfjYzlG7PPKjqOEsZ7KPJv2\nts+j1V+Mdl7snGaR9y/Fioiv/6tdPpFEEnk5S/LFTySRFSgdNfWjMML8bBODg45auEyzE6ze6tt3\nEAAwcepwPEbBNUiJmaY4/NDDn2vWLKRpHXp2lgJ/1OS3BNL4lJ8tpcXSijmNH3omleJV9Nwhz2A8\nu0tjee1I3QlnxmtwT432ptJ0eR9DSVN8vLA2DamHDtxSYnXczq1Mv3nkkKgTzMKb2/cLSYv5JI1K\nXd3F6sLDx8QNGF7vahdAks4f/UzM70zOAZkmJySNtnvPLgDAuk1Ck4WUu9eNY2K+j/WKOZ6nC1Ma\ndbRYBf2bPAXVZw7ExyqsJ+hbI1V0qX6Za93rR1c0C4Evw6xoPHrQmeqhrgfr6tt0G/Pes2P42RfZ\nmXd2ygX34lGs9lQXMu31f7Seq9YpOZsbsVQPItH4iSSyAqXDwT1J1zW9VF256R0EQHwF6h4cVEvz\ndWetkjgy9KYfWQ2Uyf+hXwttNMXH9BmvlfdSMiFTaprR0iYXfsAkiMeqVcF5LdLEQIODbS+dp7cU\nWw4wC34DQEQIc2+PaLiQENzqrAvuDRE2unEdIbd+DTZhpyAQ6omfSzORbM5dY/sOAed0r5JAWe0g\ntaJHr31yXOi1jxP6fH7ZkWTquV9/i6TxFDY623DU1+lp0bCP7H0QAPCGd/8+b95jK9LcLbVpxkvD\ntZSGmxp2nhZAzmNdAoE2auV1D4sFMXXqu/GQ/l6ppFzN9l5Rs8FLOgsmo8+KBgLrLrBc1wYg2sWZ\nz1nag8q+mKDacpFE4yeSyAqUjmp8KTtI4dS486Hm56gB2J1wgpqi2fI1tuyyyqlWY8oq8nIiEf1L\ny72svYDYhcV+AAAG8UlEQVTvjBqajm+bKbc+rzmi+vtqjOiZI29vVM6AuH0RNW3kAV+iuB7/TOps\ndfe1UKVNTRF5MYKQzDONmtxriz5q1rNOVrEF1lr2UUfN+b3atqnFhho/f+xhmXvkNG2KVNdKM57W\ndmXdbj32/1hiAzVCmE/OOAaetSzAGV4jqcaQUYtiwaXRmhmBTm/dTOCNrqzHvtSM6G9zXU5OuOdi\ngD70KnIGhBlZ82zGQW2bqvG1SIpLFDTcXCeOSKzo/PUSSzIaC2p71NXkM2jOykTynv8+Pycaf55A\nqDybqyLlvjpL8asXS7/9KvJ8KcIXI4nGTySRFSidL9KZraPWdD5UGAmIRFtQN1rcGtNOe9iU7PIa\n3W/TAgi9vTagnx1Qd0fe1qjaV/3/iBt60HJx/ZCwYIXPBrGv78UBIrvgWGxJRO480Wksu36sQf1+\nZecJCASy1mmYKhljT02IFu9ms4eRVS7iXiZDTB8bbMJjpwWvN8nmjiAM9cKLtsdDRkYkUn7ylDQR\nLRT5GITOKnjscWlWmWEJbjtwWYVrb7xe3tcvx6oQ3z4Fp0V/8pMfAwC29DCDU6E297ju8hn1s0X/\nFLtdW6oW2ZL0I67T4un24dH8zPPkUtSWU5vO2xCPOXxQYhX1WT5nVtuhu7kqo1GrLtq9v68vPlZn\nGfA8Yw6lwTOZjc+mzH3l/HzAm6XI6Zr+1zUgEo2fSCIrUJIvfiKJrEDpqKkfBAEmJiaQybsATZ71\n42oat2luWuv1/abRVGVNtxJShvZMU78NNfm9FBkNo8hoAFDf45nomlpjmkZdhchL1cU11VG04P/A\nM+c1tRbGfcy9wF+kv/l+uheRZzZmWJu/8TwBxfzmb0vl3dg2Ryt94NCT8kdZTWUPijQnaa8f/EDq\n92dIQX3l7sviIalhMWVP7Zda+9KA9rBzaz4xKSCdWlvWuujZlq969Ws5Xn61CIrJpd08Dh4WMM5r\nbia9db+a8R67TUV6xY92S5DQejUcOpOIEdFQm5d4QTlLAE9acfTE3+/c4XgFDhAQ1pgX1yliTUih\n22H+tQKvQUalfq8eIOZyYA5aA4FR5NKrS2xQyznzLUuw1V+qQN5ikmj8RBJZgWLsEgn4X5KLGXMK\nQBXARMcu+tLIEF5+cwZenvNO5vzryQZr7fALDeroFx8AjDEPW2t3dfSiv6a8HOcMvDznncy5M5KY\n+okksgIl+eInksgKlHPxxf/4ObjmrysvxzkDL895J3PugHTcx08kkUTOvSSmfiKJrEDp2BffGPM6\nY8wvjDH7jTHv69R1X6wYY9YbY+43xjxljHnCGPMHfH3AGPMtY8wz/N3/QufqtBhj0saYR40xX+X/\nY8aYvZzz54wxuRc6RyfFGNNnjLnLGPM01/uql8k6v5fPxuPGmM8YYwrLfa1Pl4588Y0Q2X8MwOsB\n7ARwmzFmZyeu/StIAOCPrLU7AOwG8O851/cBuM9auwXAffx/uckfAHjK+/8vAHyEc54G8M5zMquz\ny38DcI+1djuASyBzX9brbIxZC+A9AHZZay8EkAbwO1j+a71QrLX/4j8ArgJwr/f/+wG8vxPXfgnm\n/mUANwL4BYBRvjYK4Bfnem6nzXMd5ItyHYCvQoCkEwAyi30G5/oHQA+AA2CcyXt9ua/zWgBHAAxA\nIO9fBXDzcl7rxX46ZerrYqkc5WvLWowxGwFcCmAvgBFr7XEA4O9VZ3/nOZGPAvhPcPWigwAq1loF\nty+3Nd8E4BSAf6B78vfGmDKW+Tpba58D8CEAhwEcBzAD4BEs77U+Qzr1xV+s3GBZpxOMMV0AvgDg\nD621s+d6Ps8nxpg3ABi31j7iv7zI0OW05hkAlwH4W2vtpRAo97Iy6xcTxhzeBGAMwBoAZYgLe7os\np7U+Qzr1xT8KYL33/zoAxzp07Rctxpgs5Ev/j9baL/Llk8aYUR4fBTB+rua3iOwB8EZjzEEAn4WY\n+x8F0GeM0QrM5bbmRwEctdZqU7+7IBvBcl5nALgBwAFr7SkrJaRfBHA1lvdanyGd+uI/BGALI585\nSDDknzt07RclRkjaPwHgKWvtX3uH/hnAnfz7TojvvyzEWvt+a+06a+1GyNp+21p7O4D7AbyFw5bb\nnE8AOGKM0Rra6wE8iWW8zpTDAHYbY0p8VnTey3atF5UOBkVuAbAPwC8B/Mm5Dm48zzxfBTHTHgPw\nU/7cAvGZ7wPwDH8PnOu5nmX+1wD4Kv/eBODHAPYD+CcA+XM9v9Pm+goAD3Ot7wbQ/3JYZwD/FcDT\nAB4H8CkA+eW+1qf/JMi9RBJZgZIg9xJJZAVK8sVPJJEVKMkXP5FEVqAkX/xEElmBknzxE0lkBUry\nxU8kkRUoyRc/kURWoCRf/EQSWYHy/wDvLaxPc4YqQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2841088c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = unloader(iter(trainloader).next()[0][0])\n",
    "_ = plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/150..  Training Loss: 217964.8813..  valid Loss: 181494.4062.. \n",
      "Epoch: 1/150..  Training Loss: 149520.9062..  valid Loss: 152079.2457.. \n",
      "Epoch: 1/150..  Training Loss: 127783.3823..  valid Loss: 133278.4396.. \n",
      "Epoch: 1/150..  Training Loss: 118445.7948..  valid Loss: 132465.2109.. \n",
      "Epoch: 1/150..  Training Loss: 106301.0911..  valid Loss: 122612.9815.. \n",
      "Epoch: 2/150..  Training Loss: 92870.6986..  valid Loss: 102514.9261.. \n",
      "Epoch: 2/150..  Training Loss: 93519.0344..  valid Loss: 93002.5845.. \n",
      "Epoch: 2/150..  Training Loss: 89578.0714..  valid Loss: 88943.0817.. \n",
      "Epoch: 2/150..  Training Loss: 85593.9083..  valid Loss: 84134.9062.. \n",
      "Epoch: 2/150..  Training Loss: 84172.1687..  valid Loss: 80617.1676.. \n",
      "Epoch: 2/150..  Training Loss: 78109.1703..  valid Loss: 77153.0277.. \n",
      "Epoch: 3/150..  Training Loss: 72755.7490..  valid Loss: 74627.6864.. \n",
      "Epoch: 3/150..  Training Loss: 72491.1031..  valid Loss: 72754.6374.. \n",
      "Epoch: 3/150..  Training Loss: 70649.5964..  valid Loss: 71039.6605.. \n",
      "Epoch: 3/150..  Training Loss: 71422.3646..  valid Loss: 69783.1346.. \n",
      "Epoch: 3/150..  Training Loss: 69675.7529..  valid Loss: 67769.0359.. \n",
      "Epoch: 3/150..  Training Loss: 68328.2276..  valid Loss: 66513.3906.. \n",
      "Epoch: 4/150..  Training Loss: 61371.7987..  valid Loss: 65330.0291.. \n",
      "Epoch: 4/150..  Training Loss: 64959.0383..  valid Loss: 63878.1332.. \n",
      "Epoch: 4/150..  Training Loss: 62394.6323..  valid Loss: 62758.3864.. \n",
      "Epoch: 4/150..  Training Loss: 62809.6906..  valid Loss: 61836.1825.. \n",
      "Epoch: 4/150..  Training Loss: 61192.2438..  valid Loss: 60884.0021.. \n",
      "Epoch: 4/150..  Training Loss: 59702.4909..  valid Loss: 60094.9755.. \n",
      "Epoch: 5/150..  Training Loss: 55695.2173..  valid Loss: 59470.9645.. \n",
      "Epoch: 5/150..  Training Loss: 56619.2951..  valid Loss: 59195.5384.. \n",
      "Epoch: 5/150..  Training Loss: 57613.1880..  valid Loss: 57999.2947.. \n",
      "Epoch: 5/150..  Training Loss: 57168.4734..  valid Loss: 57390.3942.. \n",
      "Epoch: 5/150..  Training Loss: 56822.0891..  valid Loss: 56799.3597.. \n",
      "Epoch: 5/150..  Training Loss: 56472.9477..  valid Loss: 56046.5653.. \n",
      "Epoch: 6/150..  Training Loss: 51641.5592..  valid Loss: 55631.9915.. \n",
      "Epoch: 6/150..  Training Loss: 53041.7320..  valid Loss: 54970.6293.. \n",
      "Epoch: 6/150..  Training Loss: 54604.8654..  valid Loss: 54669.3374.. \n",
      "Epoch: 6/150..  Training Loss: 52932.1932..  valid Loss: 54198.6573.. \n",
      "Epoch: 6/150..  Training Loss: 52743.0924..  valid Loss: 53879.3043.. \n",
      "Epoch: 6/150..  Training Loss: 51594.9742..  valid Loss: 53432.1740.. \n",
      "Epoch: 7/150..  Training Loss: 48586.1767..  valid Loss: 53314.9950.. \n",
      "Epoch: 7/150..  Training Loss: 49730.1737..  valid Loss: 52788.6726.. \n",
      "Epoch: 7/150..  Training Loss: 50542.7214..  valid Loss: 52538.4336.. \n",
      "Epoch: 7/150..  Training Loss: 50549.4195..  valid Loss: 52105.9439.. \n",
      "Epoch: 7/150..  Training Loss: 50429.4529..  valid Loss: 51871.7646.. \n",
      "Epoch: 7/150..  Training Loss: 49953.1250..  valid Loss: 51490.3754.. \n",
      "Epoch: 8/150..  Training Loss: 44271.2526..  valid Loss: 51222.3256.. \n",
      "Epoch: 8/150..  Training Loss: 48453.8716..  valid Loss: 50742.8874.. \n",
      "Epoch: 8/150..  Training Loss: 47656.9135..  valid Loss: 50378.2326.. \n",
      "Epoch: 8/150..  Training Loss: 46718.6188..  valid Loss: 49921.1673.. \n",
      "Epoch: 8/150..  Training Loss: 47298.3667..  valid Loss: 49868.0558.. \n",
      "Epoch: 9/150..  Training Loss: 45172.7538..  valid Loss: 49738.5004.. \n",
      "Epoch: 9/150..  Training Loss: 45056.2888..  valid Loss: 49415.2326.. \n",
      "Epoch: 9/150..  Training Loss: 46239.1883..  valid Loss: 49240.2862.. \n",
      "Epoch: 9/150..  Training Loss: 45275.2281..  valid Loss: 49201.1428.. \n",
      "Epoch: 9/150..  Training Loss: 44678.3911..  valid Loss: 48764.4528.. \n",
      "Epoch: 9/150..  Training Loss: 45259.2289..  valid Loss: 48631.7056.. \n",
      "Epoch: 10/150..  Training Loss: 42259.4207..  valid Loss: 48632.6829.. \n",
      "Epoch: 10/150..  Training Loss: 42860.4896..  valid Loss: 48292.9457.. \n",
      "Epoch: 10/150..  Training Loss: 43303.9484..  valid Loss: 48434.2209.. \n",
      "Epoch: 10/150..  Training Loss: 43318.1534..  valid Loss: 48268.3874.. \n",
      "Epoch: 10/150..  Training Loss: 44122.7070..  valid Loss: 48331.8239.. \n",
      "Epoch: 10/150..  Training Loss: 43745.2544..  valid Loss: 48655.9698.. \n",
      "Epoch: 11/150..  Training Loss: 40235.2248..  valid Loss: 48235.4719.. \n",
      "Epoch: 11/150..  Training Loss: 41209.0456..  valid Loss: 48017.0160.. \n",
      "Epoch: 11/150..  Training Loss: 41806.8070..  valid Loss: 47800.9016.. \n",
      "Epoch: 11/150..  Training Loss: 41493.4214..  valid Loss: 47623.1960.. \n",
      "Epoch: 11/150..  Training Loss: 42544.5534..  valid Loss: 47645.6388.. \n",
      "Epoch: 11/150..  Training Loss: 41907.7953..  valid Loss: 47456.8157.. \n",
      "Epoch: 12/150..  Training Loss: 38839.1297..  valid Loss: 47322.9585.. \n",
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      "Epoch: 149/150..  Training Loss: 9571.7491..  valid Loss: 60896.4535.. \n",
      "Epoch: 149/150..  Training Loss: 9373.2587..  valid Loss: 61802.9023.. \n",
      "Epoch: 149/150..  Training Loss: 9471.2305..  valid Loss: 60591.5163.. \n",
      "Epoch: 149/150..  Training Loss: 9752.7044..  valid Loss: 61267.5337.. \n",
      "Epoch: 149/150..  Training Loss: 9535.1634..  valid Loss: 61959.8903.. \n",
      "Epoch: 150/150..  Training Loss: 8963.6365..  valid Loss: 61411.7905.. \n",
      "Epoch: 150/150..  Training Loss: 9545.3043..  valid Loss: 61591.1307.. \n",
      "Epoch: 150/150..  Training Loss: 9508.8568..  valid Loss: 61720.9666.. \n",
      "Epoch: 150/150..  Training Loss: 9472.0090..  valid Loss: 60844.6942.. \n",
      "Epoch: 150/150..  Training Loss: 9558.4615..  valid Loss: 60495.7472.. \n",
      "Epoch: 150/150..  Training Loss: 9034.1171..  valid Loss: 61683.4006.. \n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "num_epochs = 150\n",
    "\n",
    "running_loss = 0\n",
    "steps = 0\n",
    "print_every = 15\n",
    "decoded = 0\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    for data in trainloader:\n",
    "        steps += 1\n",
    "        img, _ = data\n",
    "        img = img.cuda()\n",
    "        \n",
    "        decoded, mu, logvar = model(img)\n",
    "        loss = loss_function(decoded, img, mu, logvar)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "        if steps % print_every == 0:\n",
    "            model.eval()\n",
    "\n",
    "            with torch.no_grad():\n",
    "                valid_loss = validation(model, validloader)\n",
    "            \n",
    "            model.train()\n",
    "            \n",
    "            print(\"Epoch: {}/{}.. \".format(epoch+1, num_epochs),\n",
    "                  \"Training Loss: {:.4f}.. \".format(running_loss/print_every),\n",
    "                  \"valid Loss: {:.4f}.. \".format(valid_loss/len(validloader)))\n",
    "            running_loss = 0\n",
    "    if epoch % 10 == 0:\n",
    "        save_im(decoded, 'epoch '+str(epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save({'epoch': epoch,\n",
    "            'model_state_dict': model.state_dict(),\n",
    "            'optimizer_state_dict': optimizer.state_dict(),\n",
    "            'train_loss': running_loss/print_every,\n",
    "            'val_loss': valid_loss/len(validloader)\n",
    "            }, \"./generated_data_vae/ezperiment_13_0.7_mean_mse/model_{}_{}.pkl\".format(epoch, steps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = datasets.ImageFolder(data_dir + 'test', transform=loader)\n",
    "testloader = torch.utils.data.DataLoader(valid_data, batch_size=256)\n",
    "\n",
    "img, _ = iter(testloader).next()\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    output = model.forward(img.cuda())\n",
    "    save_im(output[0], 'test_output')"
   ]
  }
 ],
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   "language": "python",
   "name": "pt"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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