DCGAN_Exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Convolutional GANs\n",
    "\n",
    "In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a Deep Convolutional GAN, or DCGAN for short. The DCGAN architecture was first explored in 2016 and has seen impressive results in generating new images; you can read the [original paper, here](https://arxiv.org/pdf/1511.06434.pdf).\n",
    "\n",
    "You'll be training DCGAN on the [Street View House Numbers](http://ufldl.stanford.edu/housenumbers/) (SVHN) dataset. These are color images of house numbers collected from Google street view. SVHN images are in color and much more variable than MNIST. \n",
    "\n",
    "<img src='assets/svhn_dcgan.png' width=80% />\n",
    "\n",
    "So, our goal is to create a DCGAN that can generate new, realistic-looking images of house numbers. We'll go through the following steps to do this:\n",
    "* Load in and pre-process the house numbers dataset\n",
    "* Define discriminator and generator networks\n",
    "* Train these adversarial networks\n",
    "* Visualize the loss over time and some sample, generated images\n",
    "\n",
    "#### Deeper Convolutional Networks\n",
    "\n",
    "Since this dataset is more complex than our MNIST data, we'll need a deeper network to accurately identify patterns in these images and be able to generate new ones. Specifically, we'll use a series of convolutional or transpose convolutional layers in the discriminator and generator. It's also necessary to use batch normalization to get these convolutional networks to train. \n",
    "\n",
    "Besides these changes in network structure, training the discriminator and generator networks should be the same as before. That is, the discriminator will alternate training on real and fake (generated) images, and the generator will aim to trick the discriminator into thinking that its generated images are real!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle as pkl\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data\n",
    "\n",
    "Here you can download the SVHN dataset. It's a dataset built-in to the PyTorch datasets library. We can load in training data, transform it into Tensor datatypes, then create dataloaders to batch our data into a desired size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: data/train_32x32.mat\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "# Tensor transform\n",
    "transform = transforms.ToTensor()\n",
    "\n",
    "# SVHN training datasets\n",
    "svhn_train = datasets.SVHN(root='data/', split='train', download=True, transform=transform)\n",
    "\n",
    "batch_size = 128\n",
    "num_workers = 0\n",
    "\n",
    "# build DataLoaders for SVHN dataset\n",
    "train_loader = torch.utils.data.DataLoader(dataset=svhn_train,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=True,\n",
    "                                          num_workers=num_workers)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Data\n",
    "\n",
    "Here I'm showing a small sample of the images. Each of these is 32x32 with 3 color channels (RGB). These are the real, training images that we'll pass to the discriminator. Notice that each image has _one_ associated, numerical label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AumyJsfDbX/2aO/Zbb701lO/vJ4XZYpa+5Ob8+sHD9GXbwQH6HYxDHmKdQ6jWHtxJajYzs1dvJEXzDZQXaLOcZmw26bynRIy9LY+faa9O47gA7znc+DX7wtuN8fjcjttX8P1OLMyDS8tbN3714+IeX21SWbRYUO2EmDTdruKZEbP/HpVdb4Iy2kUewwvqdc94W3LvONzqha+q+a4xV8c7W6vRQ2fWlxxfj3eo521WJfuakrXik/ZRE52BCSGEEEIIIYQQQgghxIuJXuoKIYQQQgghhBBCCCHEhHhM+4UwfFZfyhRZTticSXVgYzBDuanH3zNT1kFpkDP6x/KS/cLslP3CuJSZidJmMFBfLLh8gTIkqJCpxKosK/IyMkrV0mfXTG6TS44mQ8S5FzIn0rzdfd7fZA3K2WVg+zj+yf0cCUc++MFkmH/pSpKOOksIXPvDh8mE/+ChT8ywz2bkbBNgDwGJcgOT7NYlH6FkgUdAMrXKJyLhqVKCQjkcE/ys23h69xecECqbHUugmtl48iBnaYFr0mayhcPDFBco3WK8oKSohTn/Agk1+LzTsH21TjHo7oOUKI37NzNbzJn4EbEDcSe6+Ie2bueItwXzdjMvA3dyFl6qR+c2EBm8zi5ZQxjvWntn9A/7BcgZl1m7YTuvC8lousINYxtcQzrYbWi/gDrlqimXTGDcbojxtuBEc/FBH+UyplN+DqUZ5Yn9qXy+uEewjnHWUli/RjLQWcMkNWl5yweVSfsoVcxl0oHjHiaqSLSMgUhW1NIOBPF0extjIZRDjUSlZr4hIDivkUhyBVucUmLB5wEfo/Gsmb9fHW0xXHrkccMcJujctEy0mMrOKKYab3/hDAuD4CxlOJaldH480YyTFZ4zUVpVsMJqMT5k/96fM2nYheTkBM9lIfHoOdbRrsYH2JHXj3MpJKA6QBJEJk1z9gtOpu+PzYR53GaG8ZtPij2egNpZsTFB3mZ8Dnj0NySVhRUDx3J1nbZv66l2VCmElMaEpcRPIf+Gq9DuXDxwY5R0LW+/l5Kdvft2slPokIh4Mce8HtXrNv7etfj3AZIDLQMSz6I/OVzRJiQdb2snJUercQ32ab+AhHpmZl9/O1k/vPnG60P5xo0bQ/n6Sy8N5dlEBzh9H9O7koLFDYd3c1jCnbbUGrfeKsUe2jcwDtECrO3G7XXWGCNwHmfm7VTmsFxg0sX57HfNYfS5I1qaK3o7qPL6J1RZJxUKvko+ydi4zUKHuNUZ5yrj9h1uzJKNK0/FwGF5Ihbn3ebWGisXhnqn9uUsRtAfMxlgnhjwvDy/I2khhBBCCCGEEEIIIYR4DtFLXSGEEEIIIYQQQgghhJgQj/1t+qPtF8azfucyq9Kn05FZucP4J8qbQpbWJSRDlIdRPtDXmdyrhk1Dj3fckPpUkCi1bcrIubObVqddQ1X4ZDu3X2B2u02XZAYbyFEoy52s/UIIFo6zULps25AShz6VO0pCM3kdZYn8vJ3yHGZBjWFc1tFBBhJmkFA3+JT+IK2TZ/alDCei3MyQIZb3m22Q6qYNT4L6XqwfvJ2AUwEESN46yn53hvKlvaO2uX97QtKhkJ5bJxulPJOqQDy7beufsw1kY13L9sC10jZdP75Oj/2uYnouH+wn2eL9e0nqxWf6qMIpduzuJFsHSuppP8MY2VJ2grbLulKiGjO7mwq2M05eTvcPbhOm+ntfGCwDzpIVD/Dxy//0mH4llJlWxmcNMnpm4EZ7Wm/a0bJZ1ubjeDulZKh3MlXI6CFVpPSN7WHTZX2Uy1r7aPsFl0Z7QkRL/UMV2X9gHWZbDrSB8S2HMZ1yctpM0Q6lwSCh79L9mqEvmUPKWsMOKqKum8x2xllcoW3SymG5SbGrhm1Ui76EdlB0FXHy/7WPdT6DMK4n2nwDTS7l3s8FpczKLoGyf1acXQHtw3ruDPGjG5fUty2zhMO6pZQt+kz7BdiKFaS2tACpCvZY0cbPLWYWFD6DelrO8Vr3nNgvpLg7bh0WCjYV+Twi//ewPcpdyepnmayjlrCyW0L2bAV7jT6ze+lge8D1trfTuIdzN9aj7xlPu/H1YbGw3rCN+/VKZY6tuonaLwRL0mY3QjtXt5vNfZ29z3g6dz7Pa7SVm+++O5R/60tfThv06Xq/9sr1oXzlUrJGWMy9VU+D+dMG/UCHuHflarJAeO1GKtd16tfeu5OsFVZrxEO2d1iMHP072T3M0SZevoJjsK/P5mJToe/7wb7AWS7AIpDjDY5PZrmMHDtg+2Bc9jEDi908i/0X43taZ+nsF/wYgX1e18PiZSuVt0OaZz32gP59JLfDmwLBwmC55t5pMXYiplZ1g8XeMtLZnBbmmW4cjcl9BwvSTcu+a/w92ablPNhf9wZ2HNs7mI/vpnKD86A1Iud0wXkrcE6X2mTuhmaR1gqpvqtViqt8Hlm/x2GqM3chhBBCCCGEEEIIIYR4IdFLXSGEEEIIIYQQQgghhJgQj2W/QOlH/lnz6PphXDZm5qU7ebb6dDx+rgx5Kj7HZqZfLwcalyv1mQTFJ7SD3BmyHWah6+K4RH42S59K18gCy6+0c7kSpUEHh0kWcrhMkhDaS3SF63TRiRYH+RfldhVke8ysvthK8pwqyza6hLTYZTxkBkLK+yBnfe/m7aH8cJWu9+Vr6d5dvpxsC6qaGUD9oxKgQ6U8uqJNA2QkG0v1cIpm2pVQplqwAzjaxulnUnEOycMi1eOlY7nSe29NKBNoTM+LU1hGyjvHM4HHTA5NicdshrbFe4hnuYE8rEJW9x7yix7xiFlZ791L2d0pFTEzqyGt6PeSHI2ylWpOiUfallljub6dykx7vG3+7zAui3UyEtroFPY7BZ6V0MllfI3jMlWXeRx2CJQPUZZmZtZAYljVvI/jGm9KKWmzQPsAK9kfZQo1l42V9YI8imOBOFH7BbM4WDYxlhglV7intGios2EUXAxsbsjKjH012NcM2Zo7XFdatCy2U3l7N8UOZpbOJcm0DFos5qPrHa6TpLFCLFh1KaYxjrWo3xKysdZ8P82YzWptLVI8ncMSYh5S/aZFtGGsGTgeKa1+lox+3BKFMnxztgfMJA4pKsrueC5ecGD6BPYL2KaHZRUtP3ys4qHHx+lm3srBSpYNznZmqvHG8nTa788u3e4Rl9EO2Nes1uk5X1HOuvSxZNhnNd6vmZk1bAeIPd5yAXYIDewXIuzMCvO+tmDdYOZjGtejHLZrYfHWTnMuZWZWhxNJNJ5N3BenYj4jHTvjjc9On4p0VuL882A/xf4H95PlWA17gs3V1E/1/bhVjJlZh2d+vRmX4W+jL7zx2o10PIyFA+wD3n4n2UPchuXCPJtP0kbv8CD1hW7snqexnyBd19m9O3fNzLcV2gUuFqk/jtupnz41l6jG41Z0tm7j9iZcx9lPFt630JbpMLPO4DPf9Wn8sLeXfDGnZ3pwgQhm4XjOGmw8qATOXZ2NYG6/gOfU2WNi7Au7FWf5tRm3Xlri3dg+LBAPsDy3QFhg/Ml67OzgvU+gDQnOD2Pfyo31cBDOt6LvY9ivub8hDnOcPl882Rx8ujN3IYQQQgghhBBCCCGEeAHRS10hhBBCCCGEEEIIIYSYEI+nxw5JphEe88P2vs2+g6YkFfuitIqfOFMuQ9kIP/Ofz9Ony1ST1Mw6XXuZn8/WmzbqKDuFhJ+ffFOe31Rpv5TONZDB5/YJlBAdIAvnw/0HQ5myhFKW24tOsCSD4DWrkf26bSEJobwjazZ1RUke/kBJX5/kPZtVkm+88+57Q3nnMFkuzLY/MJSvXoV1Bu5p12UZzvn1PH4baSF93LDcst7MSA0ZbzUugbI+k7Yis2RPqQA+3d+5kiQo11+9ZmY+8+NFJ1ocJFs+WzZwdgGpXDf+POfzLWzCbK3cL6TVkI367JxpsZcIQc4MK4a28xLBDWQkvcvyjXsIS4i+Go9zLpOo+12upLsz//Mdj03bElo0TNh+4aTm36i86D7vaUFOjed946w6mGE8lZ39Qna8Zoa2zcyss/S8R2dlAxkTpYO4pXWApUMox9tVP24RYaU4XE+zjzJL9y8WMjc7eyZKSL2a1GZ8nvmsbdLFrWG5gG7QriOz98sof/BDHxrKH3r9jaG8t3d5KOfWUhzb9JStOfl1aoN37t9N5bu3hvKt2yjfR4bxO2nbRfByuwgp3Rp2Ug2zIuMZ2ckyok+JE1mdSyBftDc5y36BO8CDiDZEuwFXdhLh8XVobeDqkVsY0JGH43EGEG5CuTZWcSpd9tXcID80jtc7uSKk4tiomrL9wgj+vpBxq50jSnYW6Acw1nHWcrAzow0erXa8gr9sv+DmT924ZJbWIJzbOOuSguTf1S+X8Hfjsn1aDvEanJqPToQQKpsf9/tzxMwe8XZjHGeyn/bXLFTj1ynG1D9EzBmcxQPaQYu50Ww7jUlqzMc3kBs/OEh2DWZm77yX+pc17tEbb6Z+7uor14byw4PUT5FLmPOsNleG8u2baf+btb/vzGh/iLn9wQa2RHhlsLqb+rwp0fe9HRzbF9SY38x7yOIRO5qG71UyGTkuIWO564Gcx6CNrsPwwWeTlikbvPdZZfZStC/jMZx9GdtsP96H/K4w0W7qZE7oxhH4e2mW2Odd1BlT04Hi+61S34f3Ihh/BljAhKyCnGtz+9hzxVKZjPcffB7q7ER5dnNYZe7CLmS2oe3mk5mHTHfmLoQQQgghhBBCCCGEEC8geqkrhBBCCCGEEEIIIYQQE0IvdYUQQgghhBBCCCGEEGJCvP8mmwUbiFP+YfTOgmcofZlK/mH0imvgn+nL9NGlL5A/Ze4rOs+8cS+XtoV/5rFHzdFJ0Fc1nduOJe/W/BrQU2oFPx/6LuY+vFMkhMoWx/5Pi+3kJdLM4GfkvCaTn1Fss3ZDP1p4i/q12LbS9dvgN4wl/Hjajv4/423Fe7F4D6uuT/dxjfaxoWkQ2nVF71LUyXn+sI1nPtAdfVexr8VO8o69duPqUL505Wj7un4yj5ZnRRzuI/1C098DvKFmdWpXW9ven3F3szOU2xbe17jvm8241xvbD33cGB+4LRtiU3ufSf67Qkzyxj/02i0ZET26nLsTOb8rt9vzmB1NjBPf96I/4TcGhnf6QLM9bVxfktpNR2PazM+YHso12hA95OlDRs80eorxeaHvdKDPaebj1NDGDF5VjDsln7QpESxYfRyLOV6I7H4RLxrco9x+mj7pEf33YpHi0o2Xk1/uGx95bSj//h/6l4byB954dSi/+lpa57XXkgf83qW9dODs2ofC6A62lrbapHHHu++9O5TfefftofzezeRF/4Vf/edD+bNf+JWhfOfOPXeM9SH9G1PFFnN4Qhv8FzNP3mkRs//mPrXj/oJ5uC2FYud1zmNUHAvBP7kwpqXt7rm9fZ8C+stVYbxvyo/kmvA5LPYeN7fHRaHtOrt758ifmnMQxnTGixrzGfoLm5lVYfxvK4yjHzxIeTru3k2epPfupueW488reymusE702r17zz/zh/CI5zyJ/dEMHq07u2luNG/Gg1WE9+02PGSvXLrs1qsj+jbMJw87+HnDQ7E2P56fCiEEq47HAGw3He47fXS7YkzyftTeXxf9PPJCsG1hiuvKFeYqs+00H5lvIa9F431puX2NPAF7ly+l/cKD/uS5Oapfqvcbl1ObvQx/3QVyjTzgnN3MNpUbtA3FFkb5PcY9LfrLqTHkDCj4bleF57TJnk3mKHKxx/lw08t23LuZOU2Yd2K1xnsRjJfXG+8JzW2aLtWDY2k+C8wDUe6E8Eyc4f1enIIV+qxTvvUT4SQ/R2lIwPljKM5Rv1FwTMH3KPT+zt4M0ROeE+Gijy6PMZ4XgN65sRovm5kFxDn2d7vbqR9sZvC+f8K5lL7UFUIIIYQQQgghhBBCiAmhl7pCCCGEEEIIIYQQQggxIR7PfiEmCZf70DqMf5ZMqkyjSNl55aSMpX2lz+tns9lomds2DWT+De0d/Cnz82xaHSwgF+Hn/IeHSb5By4R2My4r4Cf/zcwfu6dMwJXT9rEkB5gY4Vj6P8N94df6Pe0u1pS/Z9JAlGt3XyE/5FqwHGgh0WqhRVxRVo97150hUWzRVpz0ifrjOG6nwNvYQV4SsG13GKKyAAAgAElEQVTV4zo1mRwb13DGz/ghmbv2UpIu7RzbXFQTaj4hJLuIGufPZ5mSrIWzOvCWJX1PyTskP+u0XozpuV5jeUebDtzD1tkypPL2VrJ6mM29pHhvJ92TrVmKL001Hob7DlIRtku0kzaOL+8yHbxvy2hPThnM9jpapUlwctWeVvB0HsOLytjnpLbJ+7Kpxvs6Ptf1nLYvXhrKfoOWNdWc9jPsc1Ix0jKhos4RRdo1dD5I1DPUEfVwcm/sto1eIjcVgoWhb9qep2d4tZ/6+A2ewb5N0uZ5Zo/j5IqIEy+/cm0of9f3/N6h/AM/mCwX/tCP/4GhvNhL+6VssRTG3bjDfJ/Dv1Wo7t5OkjTvXX1jKH/046kc0Fd+5/d++1B+8x99aCj/45/7RXfsd96+OZRbWDHQlmGJuDmrp2kzFS1JE73FTRwt0wMht1/g+JXWUhznkODkg4Vox/FTwX6h6/21L9YXxwscpxckmGx/lPaeZf3AMOb+5Mq/u7Y63wi6rh1k5LQ32NlJsWcBKTrtAvrsmrHf6RAnOD+h/cI92CbsY/k2ZKCXIX1nnbjP+w/uu3qs10uU03KO33b30r7Y/t28APvsO46t0pjp6qUr7tgVJbcY43WYSzR8pkreNBPg5BkLBbussgja/6tylnCYc+LZpsR97SwJEcdXqby1wRjS0j1dLFLbyufgtJRaLDC+wfIO8+vVMt1TjpVmsCvZjrSpSsvb9UN3bFpKzXZgu4RHjDZZUx0YV1Vlu3tH94CxeBuWfZcvJ0uTy5fS87+Yezs7vofg/JW2DGybG9yvjrZ1q7T84QNYxeyne/RwP83LaJmYH6PCeyCOgnjvFou0Tot6d31ah/vkOO60VRL6apx334+/C0j93IR6rBAQa7CYq5xjHJBv73i/Lgce2L7wDsfMz4v9n2i1UbBDLNhJcS4Uab0WMv8EDONmM1omYU5YwxKyf7KLM80IJYQQQgghhBBCCCGEEC8oeqkrhBBCCCGEEEIIIYQQE+LxNChhXPrh7RdstMxP1M28BIDystp9p02pGj/txzH4CX41LtP2x/bvsSnvpxSMn1Q7KQE+5z88TJKBWZ0+tWb27O02yY1y3WR7jqzoLtNknmZ7MkTrjyV+a0goaLmw3Id0C8tj8bt956xgNWSCPVKW9xHXFd+/zyF5r2q25UJG2CzjMLNQzyidhkSREn1KPCLq59opLBf6np/nZ7IpPLaULu1sJZnMLsrNcbuekPuCmbHt05oEz0ZI8kRKZXZ3U+ZbM//crJZJPnjvXpL59D3bDOSouGhVYHxJf3CSSUjAZplsdgGJUIMy40VltFZI580ss7k8/wRatZxStcbx54OZaYOTmkw11nxjKCiSnXyIVkC0sqD0fYbnctElSSIf8Zg9qc6aAXYIkdl8GQzRNtFsrMI9peVCcFmBfZyr8bf5HBJXxJ2WMqEnlAw9a6LFQbZFW5auG38maIMxq/0wqoMFxSsvvzSUv/U7vnkof/onPjWUf88n0vLFdtqXy7SNRlc7qxn2E5lVUeERZlvrbTwW+IzOqU28+aFkufCjn/oR1MJfg3/yj5Idw/Ju6ttvHtzGWugrl9PNKj6GtydjeXy8evRvSo9TuSnIWpnx2UmpmYG5YHUQIUMMmeVKRLxiV8hY5+pasJcplc+yX6CNTOj57HGMhthl0yTGOIz53fyCc4IW1lzunmZWJXyeacEE6wLud7lcjq7PucYClhAc0/DI+ZyO0DYhjsqQszLvu/H+ol2z/8otijD+Y7muKKHGfuNUW05qL1VB+uy1zmg3wT9rFf7N55NjRV5/Ll+v0/06OExzuvki7WcJ64sNbEHaTWb3giEH7cu8ZQPaENwQ6u3UF27N07h/xnELrM5yi6K4oV1UOjbHYBxXLxpvtTQVQhUG+wG+b9mCpckW7Fe2t3Ef5tk5ox20tB7oaEkHS8MNl2N+A6uOFm1lveJ7Ec6h/b1zlj6sHsp9sczY83QWl/7Y4/udIsHwvi9bPpRL7wFzu4pC+Rtxhdz9z+Yzfh5cGoewXHi3GcbHIxy31fnYBv92rwbweHFOV8l+QQghhBBCCCGEEEIIIZ5/9FJXCCGEEEIIIYQQQgghJsRj2S8EC6OSG5fRu1Bu5v577DrQcqEkpxrPVOdkJ1b4/Juykb70Qb7/23mkSwf7+6l8eDiUd7fxfpxZXXG9YvY59eEybb9EmcfjtT1L7nTRObkktKwwXGODPKeKyOabfaBPmXzbJYlHRQl7GLcuWMwhf3fZdp1+Le2Hme1n/lFpFumb+RkytrawB2g6SJpQpjSFx54hLXnbpuXrTG3XQQS3gLRodzuVF5CnhRMZ34TUICGE4Tmn3LDrkW0V7cSclCyLNZSvYnmkJQYzoOJC0R5jjvYTIP3p91KmWCeZzGJNZYx5lBUibsVCbOP9DOOxphzl8r8hrhakJqd2MCFOZDUlqWdJJpRnFWenE5wVBlZBfO42bENpObObO2MQKiaxn7bzDzytEiihpBUJbRa2dxCbmitYHzfVxSPE1N5nGLaAeDZP28y20jF66PzX64n+ThzN4nHMddffjTvGLZn6TA49w3X6wOs3hvJ3/b7vHMrf8V3fOpT3Xk5Sx76n5cK4NJqVatt0v+rG91E+Y3FJqssxD9oTbmNdsH169eVXhvL3f9/3ub/dfPvWUH7ry28P5caNZ1J9V23W7qZEyP5r5i6+k+chjucScspiG9iscOxRsuDyWajT8pKskBYLXVaProJ0ntYsCB91YVxK+WFVkmOGQqdlWf9XGM8/iUT2ouHtFxCLnVQ5lasG9yS3X+jRPzD7OmXS6Js2a2Z+h+0B7ynHyixzzJ21Rd7KlnOpwryKZfZNgVZCaNeVs1igBY3/t7OzcnVEzLbpEofxTVpWsl8IZ475ac1QSG+P57mDHcoabehwmdrE1pLy+rSbjm20yuZSTbL3uHw5jVcu7+4N5c069Q+0Ytyap76T1g0c6sxhH9LnunCck7enSnWcYY5VLx7PsfLCEC09oOewzmTszt87hMLc5bAdjzGbNZYjDq1hucR1GAM7xMA+s84o2YwQb2tWehhotWGj5TN7HD6IpR1MlMF+wZ3LeU4sv2KF/rswRnBbF+yrSmV3P3ILA3evClZABRey4niaFkGBcS6zeuU7LvRxM1or0f6ze7IGNNEZmBBCCCGEEEIIIYQQQryY6KWuEEIIIYQQQgghhBBCTIj3R0tQTI2H8qkvicelH35zp089Y1+nN46lFOVZSug8++4jdlvMxuoy/dJO4gx5b1+QKPXnyBY7LYKF4+vumgQlHszuHTdcy+2JmcYpX1zMk0RmBjnPCtk2uVd379AmAiQeNawecqlZgCSSkpxZQPZyWmrwPPpU7w2zgRrsAKokJ2M2dTMvd+YTvLOdzpty8f5YwjKt5hMhDR5/NigXZrnd8E6bHe4/RBn3ZJXkP5H3mvYpkB5uQXLuLB2QYfPhQ8gnsyy/Tt5EiUfhxvj4l4qxEGNdRtdM/RJL8rzMqqK0/VSIFgd7khDGdTROhuWufX7S5+kb2B75F8jGsJRS/VKcP3UM3K8G8W8LNguBx2N8mY9Lcw0Z71v4uzCjsJlZi7YdV+kZW8xgcVNTtphi0LQIg1UPY0mNAMtnnhLoZWYdcOXyy0P5gx/54FD+xHfDcuFqkooaYw/l65Spon1skDWaUtRgZbkXt4fi1GVAd+2sYInF9sv49OE33nTH/paPfdNQ/vzPf34o39++P5QP99N1a5ppWkuFkJ4ZNwYsZKb3YdjHG0peK5dReVwW7GEGbywtxBVaLFjI5PxOpo6YQYujcJ6xL22CxsfEeYwN7jxwPLfORDsnEPs4SNj53LZz2oulcs3OJdPUsz9zYwCX6Xt8DFUaM5Rs9EoWNEfnMQ5ry3kOz68t2BvR5qpCjKhn3n6hdvYLtKoqjG8mrI0ezqkQS7zkvGCBcub++Q/YS+HerSC153100uWKfRlsHLo83qRj7O4kg6q9vWS/cAirwwXmd5dgfbZYpDlgi3nVbJbWb4KPn7xW7HsX8+2hvIX97i+T/eKUiLEfLB6bfnwes14na8RNm8YnTeuvGWNAPIdl5eFhmmetYaNxeJDu6WqZjs35W3+m/cK4tUr0g/1HUuoj3buXR+/muSRm/zXzlpY9yu6dSsgtOzieHJ/AlsZGVaFM29Y6FO55dm+rwt/O827N1YNDm6I9RPbOCAd342uO87FJlVtHnBN9qSuEEEIIIYQQQgghhBATQi91hRBCCCGEEEIIIYQQYkI8sf1CKclfqZx/Oh8hNaesxmVHLtgy+C+ox7PWFbXmeTK8/tGfYFNSNp8l6QIUTTabjct/YikzopWzSFKOzSzFlCJMiWBm9fHvBxtmvIU8tMb570DyvtwcuH1RxtNAmtW57L6QquIYFOU3uBcdssh7mTuud5V9xg/58e5ukqpQlryPttzdSxYAkXIlZIWlWhaLLWZSBl4DtjtKWCn/3BzLiackOQshDBmNQ8NU7FwHZWfR4O0q1psk+VnCEmO1SuUOqXobZFme4dizOa57na47Y9uyTjKirvX1IIWk5MWf2UqyDtd+EMvaTOLGZ83F5YLEoyRbnAJDdmgsO8+vl2eZL5znyek7RBg8syW7B2f84CwxMtkOJagoz2gBAwlP36d61Miy2kHmHxjycGzaLZiZNSscL0KeuEB/B+nrZj7NPsosScYoM53NmQU7ldeworDMOeDy1SQbffNjbwzlD340WTEYXSrwDNKiKUJC6sYaiD0HBym2felLX3T1+MrvfCVVEX3Dt/2ebxvKr7+R6jRjpnO0ZWbqpXSOmanr2suhX375paH8oQ+lYxzcTzH3rcOvD+Xt7SS7nRLBwtBflKTpTgZfsNka+/coPuV9KlbjMnU3/sTu3eLgj1vKiH6eeFiKY7TKYjA+PSahHQiX4lmY0DimRIxm7fGYlbGbEuZSezhle+BkodRvFtoHLV7i+DFYD2eTQMugbBvul9HA1QkNjzJryriDs/bAHAH75xgtPzYbDu0l2P6r8Dy0oXHrHN86zsgEX7DximgTnEd0cdw6g+NO3+bGLWiy4Y3Vddrv1lZqOQvMbdbLNA/kuLouWIO4GHGG5Q1hTKzcmAuWeptzxOgLSN/1dvjw6BrWc9jOwbarwVxnMUuWE/mDzufQMPfZrNPzvF7CfgGWd0tY3h0cpLnSapXGMaW2lYfDwFjE90v9+Hg7FJY/Lc+HReZpopXsF8bLvLy5mVYoDSQKpj3O3glzKb983OrpDHenc1LaiJ4LWOz6krPmzYWxEfs1tyvZLwghhBBCCCGEEEIIIcRzj17qCiGEEEIIIYQQQgghxIR4YvsFn3U9UfpguI+5/QIz245nK62qwjfb55CzUULiLBb6TJbcj+9rtUwygeVhkglQJkT5/6wZz5LN9+bMmG2WfV1d+IydUtCqK8u5LzIhBJsfS9c74/VL12kPGUb3dpPs5ubtm25fK8heIzKZQsll6w3vPbIlQl4yn1FWm45XzSDdcrcrkyhSDtiyTvjEnm0L8nefyJif9Kc6sd4hEzPUTbIAaXAeXuY2Ip+bkjQkBGtmR+dDWwm2mRrPHxNN5rFmU7JfQMZV2oLMaffg4hGOTauLelxadkqIUdSFFMou/NGeZTzTZ0kWc/TvcYlQMcNrPEtGcrEZq3opkWhZFpTt0+1rXCLs5YaUAnIdyEy5/6Ifh5e8l+SDXFwVJIm+f2Q8QqzILEPYj/J5c8/kArH0KYYUz5JgYciM3YUUL2iz08CK4eAwXacsEbtdupoyZ7/5kQ8M5avXksVAjOkYLsa4QEYpNQ6ABv7lL/7WUP4bf/3/dPX43Oc/j+3TNj/24z8+lD/16U8N5Tc+/OpQbmCp0aNNbNpUb2bzZX9lZra1SP4S119KVgz/X5ssIlbotG+8lq7TpAjhlBTczD9fLcZtXTc+Xs3/7cuIN6X+hbEHjaWk4PO2O5lg242JeY9RX+y4L1hNVAVLCMbJU1YKPL+e+z3PLGM6xBhtfSxX5riiLdgvOCuhrA/wMb5gz1Eocx1vy4T25yzTYB2WZxYvDH5ojcDxMeX8bAflPm5c2n/071TmkNqNBWAz0p8pk73YDPZSYfw58n2I23B0P2b+ftdh/LmjVH/NPgHWd+t1KdahDZmfBzP5e4AVA1wPbN2m+XgX0zH2D5Mtw8P9ZHG3WqX9rGEL0GfXYL2CZQDOo4GdEDfhOlMixt7Wm6NrWEXOXVJbWaHPXmNu1FR+/sk5GC8Or/MKNguHh4cop/0uYblASw3vejhu/WTmLamKtjWP6aH2PNknvB8MsaY4fxx/59Zn79loL+r6icLcxrkIVePjY85HaO/k7OvOsFsp9olFXxoU0S4jx8Hse85yainORxnHZb8ghBBCCCGEEEIIIYQQzz16qSuEEEIIIYQQQgghhBAT4rG1kkM2vPPIeSnx7HP5evp8uZQp1Um2qCPpxmUjLHdO8gbJUJacm3IlrncImTbLlHY30F3OZpDEQ47H86wy+UBdyFjKz7ajO6dpSoZCiDZrjq7tGtd4sZOu30svpYzhc3xuf/++/93h8BCyRmTfXuPa0NKAMpo5LB7myLa92Er3jm2OitKQ/fxBmdHt5b2hPIN0v10necl6Q/kR94sdQ8Yfea9rf9+rBpYSzGRM2RTb9XGjn5KwJFiyOJi55yyVKQHn2VEiaOYl5ZQPbzrIf5xUjGER9idFiXtBFpi1Ga96RJwslClxLa7/BHe1ZLng7Rcee7fT5IzEpc7lopRBGdeydnEfcQQHqTtahozH/1x1E2y8HbANBtdWmLkZO8JjQXlU21KCuDLC9WroIWtIjuZ1nut2igSLx1l2G2Z+RuZdWgetuiQj3N5LskUzs3qWrmcIyDQd0zahGrdoylJ1pzI6jeWdFLe+9Plkv/CbX/htV493vnxrKDfbyRLiVz6fLBBe/cDraR3YS1yDVQS7qPU6jYXmi3QOi8a3gd291J/TDmAFueshpayTbUPxlI3C0VKmUk/FviQTtWwsinLjLBDG9/sk/cCwbeZXw0OUZPiRskmUnTWNy/CM2HhmvQs2a66+08xAT2KMtjm2H6GcmVYnpbmNGzOav+alVsDr3FEKX7AD4T77wpzuFE7OWujnqvH4xvbkk6NjOS0/cjs0J1vFctfWwIQ/Zzq5B24YYuPXOJw1iXH7HG9fPSbMvbM05Bh7/CJ7i7Jx2bSZWTOHrRns7yqkgm+7cfuFO3fuDOWvfu1rQ3m1TPW7f/8BtvXtl9YxtBxyliEuLk849hzfP966DSyQ1rBDYLmp/WuiDu2D14aWlbRfoH3l8jAt39C2EPv3cyv0J9m193Zzj7aUYwwsre/3/+i4euaxnU3bNN/djL3vc+MW3J+qcD+O/s1/lCwX+P5i3HIhuLaBuIFyVRib5P8uGS5UhbLnPHaGeas5h9VRwfrqcZhw1yaEEEIIIYQQQgghhBAvHnqpK4QQQgghhBBCCCGEEBPiCewXHpUN7/E+cTfLMnc7mQYlecyaShnYo7N4t5D5tW0mecO/maWYGRspJaD8ez5Psv3t7STZ3N7aGl0/t0+gfQPlafx8PBYy4E6Jo0y/R9dwg3sU1uk8b99NUlFm3nxwb9/tC6oQ63tc2348u3Jg5ug4/nn/BtYIG1g6UJXYZZ/Cs01R1kxbhkC5EpW0TkoPeT9kRdS8hiz7aF2HsdWcnQfLs2P5wqQEICFJM4pZj3EdacXRdklSZOaz9jrJDiUUhYtDxVqF604JGW0gnBVDrqOHPuVxc3mXrAD6Yr1z2QmeD8pZOnq9lLefEqV7OQrXPSV3GTencBYZtFaoGMNpHURpKOJf6VhZu4mFejQuDhQyoFO6XeofYb+w2SDAmu9TZ7CXcNlpEZ4m22qCDdnEa/TLscL4AOOOao5+pfYZsd+9/fZQ/vmf/4dDeVal9a5duzaUt7cvDeVLV15J+4UNTL+fjv2VX//qUP7VX/rVoby64+9d1SZbiCqmMQntZa5dvzGUF9vJMqFuOA5L+5zNd1J5xn7JW1DsYF/376cM5Z0LWIhDs8ceil4IYvTP0rDcB9OhyOcpl/K6sV6p7Ly5IIMtyAFdNQpPZzjVA41LLV3duTZ227B+6COrGW2isO0pmSbH8/3och+mpymHjrEf5MqUl64gh97QQi5ynOi/xTmPDLnFtWR77bIs5Sc4Wy+XTRzHsjP6qYL02GUyd7YM2JFrpuNS2lzO7Kw+MB6jFR6fo3qydi/peXM2biwXsrqfumYoV25cXdgvnV8qjn/TNZ7NMafleAHXO7/2HFfTCq+GLUOzSMu3dlJftlynq/DO198byg/uPhzKt27dHspdZt0yx353d5LlUA0rBr6gqCYqo7cQhvvEmM4xIa0Y+P4jj9G89xFxZQ1rpeVB2p7zedrLVOw42CYwoOzY5jJbPTcqPssWZmSd89gkvJ/2C1PlpK/ti32MPbJ81jZ8nvjeq/dehSiOz3nqgh1iPo2tQqE/KZbHKfe5qHZ+DQo7kP2CEEIIIYQQQgghhBBCvMDopa4QQgghhBBCCCGEEEJMiPdd83beT+KdFKR6dJm76gsSLZflnjImSOW7zH6hg2yb8jLWnfIS2izs7u6hnKQbW8g0TalJLml2Vg4Ny5BdxnF59JSIvdnm2GqhqtP1W63TPdp/cHMor9dJRhoiZDDmpaO89wa5cxPHP8XnvW6RhfNgP8lD2k26p02gZDCTa+HQlJSynVKWT3lZCJBruibBRk4LAX/sHjYNEWXK/SmxP8leOqkMnDHJ5SgR7Dqe+3j+yjy77mKR2tAOpFvcL9ti8GmFcTxmhx7PYu5iXi53czYSo1XPbBYKMvqS3Kewn1N/w/K+kJGzJNV93vD3K5PyOpsUlJ0WidKZ8eeamaldW/GaptHjmmWZvQvSaD7vJQkzLUpc1mP2lWtvXULZ7iwpFf11o4R3srLWaFVzdK4VLBdomcIuYLFIdgObTZIampm99dWUhfsX8Lv5vdspC/eiSdsHSxd2ucS4A8t3qxS37r5zdyjfv5X2uTz09ajQbi5dTrYJ3/k9nxjK3/6d34R10vGYyThQbI/23q7T8iZrs3dv3xvK77z97lCm3dXudhozXb32kk2SGK3vTlsAUIreI9Z7qV1mBVb4G8e7/r6Q8b7QqRi5Rin1c8Z5pKUcN/eIBT6+oVjoi857PCdRnOiguO+jrY7HubSmWMMKp3XXtWQ/ccY1YxvEc9s5SzfEt0Ib4jWmfL3L5neunQaOwVF28yH69oyPQ/ivUNiPmbeym81TbF3ACo/2C85SYqKU5tclm4rcLsNLzceXc+7bwCKwxpykRsfYYy7UQTvv5ubZGGPD+R6aIPvYl1++jmOke/fwMO3rna+nOeSdu6mPfHD/Puqa2kOOl3aznY7LvKdEFcJgF7lBjOGcZrNOc+LDw2R72GaWXLxOnFNz+HtiLWPmx5qcs/EZrtDOODlqNxx7lO1eSnPb4juogny+tO00e5mnI8Y4jENKY5NQmPv2Z9wrUopVoWB54voSvlPhON2NefJ2wWOM91GF6b/fi3OHGLdu6LNJuG9nnNON1e60dcR5mWaEEkIIIYQQQgghhBBCiBcUvdQVQgghhBBCCCGEEEKICfHE9gv85Lh2GewK62eyBcobvLSdny8z8y5lApBNr9Yor7A+LBewbd9lkqHC5+NbiyTT2NtN0h7aLOwgW+bWFrNCQ6fqPil3h7YF1qOtw2qd7Bs2m3ROfTeeqfai01u0lZ2uewu9xhISDX7z3mTtJiBTag31Yt8xAzUlR0nC0+L6O+kjs0jjk3nKfELMZMU4ndCzzabKbyBNCQ3qDakJLTl4PVrYKvTRy5UCzrVFe15D2rozJauFEaKZdcc6Q55v1Y7Ld2qXjdffq23YoTBzdO/kNQc2ho9BsIGAnKyFnMlJU7J9lbJAh+JPa5SZUjI5nvHb2yrk0sjxsheEnFOTO0nOI6DK5DKlbYJbaYCytppydNzgrigvLtejpkwIfaWTN1OXjRgWnRyy1JbH+9mjv8GyAefH9s9+abKtJtBeAc8abTcQ9Jnl2zbse8w6SAbv3kmWCL/xxbeGcrtK6yz3IblepWO/eu3Vobw3TzHs9ju3hvLhftp/l3VRH/rIh4byH/rMDw/lz/yRHxnKW3tpDMImuN4kS6KK5w2bqLpaYAN/59/+nZSJ/L13UvbxB/eSrJPHu/7SNO0Xoo1nKXbSd45TCuXTfxu3WaFcthSvg41LCWnH4TJEnxqbj8sSmZE6FrI0U3Jdsk1i1vM8xrptUKYVAZf3hb7wohMt2qY7eu7nXXoG/b1O65+VkTs4mzree8yxqnHbg9hh/Bk5lkRM26Tx6npDOx9/7SPbHaT6VQNblxrlKsWSWLB7MDdOggy+9rZss0WKj1s7aU64vUzxkdYgoTzoutBES3GzNHd1NloFSfMpGMNo4+YWc56Ecscypfks41C9v/ZsN2wHATZq27uXhvJLr6R+52rP9dPyr389Wf7cpe0R+ywzw+Z2uExzgNUqlTm+yW3NpkJVVbZzPA9artJJrNv0rPA817Bi4FjPzLc7Z4+Ai9luxudHDWLBbAY7j9n4OGQFa47cfoHjtKrYzl9E44RvLCXbFraLKhvcRM5PrFSm5QJjfT1a5jrsHyuWMw+Dmv1JwcquL9ninWt2UxXKZjbyDuw049Z+j8M0ezYhhBBCCCGEEEIIIYR4QdFLXSGEEEIIIYQQQgghhJgQeqkrhBBCCCGEEEIIIYQQE+KJPXXLvhjjHia5bxe9MUI17oFCb4sNPFfps8LyGuvE3LDspB515tEKfxD6Z27Bw47eufS+nc+TP09d0eOJniPwJ8o8ORr4021vJ0/eFh42q2WF5d5bdTJUZrY4OqcG3rIz+GvNtvaGcr+kAZM3Cuzho9LDo4Q+hj0uUxtTE6cXW32V/3AAACAASURBVN3B88vozYrlER4oZTsfq2jPG+g7BQ87/H7S4Bhcp29RxqG7rN1s4GvGMn3QnP9dPcHfbmK07vgZDniWOz7XzkcJfj6Zj872VnqWZ7j2azxP9JZar9J1jM5rh/Wgxx/WZ8MI5UZD2yfv7wb/LueDS3/dgv8Zd5od2nkxxnFPJL/BRM3DLF2HGMevE3Geqae6DPovjfdRrv+gx53zTxr3lqSvLT0q8/Yb3L0YrxO3L/ldMqY4S3HnsZzdd7ZNrOg8eVFuJuvlHc3CUTzonccf7ikuWoN71GCscPS31K/df5Du98M7yeevQv9Ts486QN+3f3Movo1xTo17tHc5jU0+/LHXXT1+9F/7zFD+5I9/cihfvZH8CQ/b5J3nfMlnaGf06Wy5PJXfe+euO/av/+qXhvLBw+TP225SG9q7kvr8a9eumXhc6EHnOhQs59qMMeO+uWf9jWUXWgsehj38uemJW53xLQk9x30ZfS/GZf0T+s5dBMZ6pJIHKscI9Mc18/6y9L+usV4Dj9tmltbfxBRXGMc5r+LYY7nCs9x7j0DWt0HeCJaZT4I5EXwulcL4Bu2P3t5mvq9fIx/KFuZrnFflc7HpEIexnJs7FPz5S167ZmY1xwzMJUJf7DC+X+amYKoX+uhW6NcazI8XuD9mZgvko+F4Y71O9+vBg9RP3bmbfNnn8FJ++eWXh/IHXvvAUN6/m3yV25W/767No23fv38f9UjPgm+z0yGEYFuLo/cVfcG33OXk6DjuyXzPcc02uEfcAbfnwJqeprNmPD6tS17RZ8R6n9dpfH7jn4X3z2vX1/H58fANIQx9fqlfcrlaCvfteAl2jOuFeTtjcu1ifWobC/Rd6zrN3znO4ViozsY2DZ5f12cgH9ccx5s1zNXD16Xj95xDoTqbx/Uu1wGvJ/aFHCZtex4P3tNM8G2PEEIIIYQQQgghhBBCvLjopa4QQgghhBBCCCGEEEJMiCe2X3DS4ILc08nPQ1niVZLeRH7mv6HlQvpUeo3llANV7jPo9Mn1DNLIoyPzU/1U3t1NssZLl5JccTZLl4yfUHeU/FNiizrlytQasvid7SQjqfA59iHW4TWYEnUd7MpLR5KbytLn85RvHiaFjB0s08WktP3o32mbNuI6Q0ofunSPA+79vNkeXU45a02JkfuM39+8CnXv0OYpEWOTbyiFo4UEZCo1rCZCn9aps2eHD99qldrEAa1I2P4Xs5MTsucVXodccjGfUQqI+0ApYEU7GMj0nKoVEp9qXErhZRm5fILbYL9Vwb4mUM5f0Ms70vI6k4mFUI+sZdZVjN3c1fTbipO4FCxxvArrDMkQtx+/3cVtOzzjHWJ45+x0KOHx7TdSDrmhNUiy/6G8eQMrkQ3WZ6zpuM++eBIWaJMUxm1jWM7b3ZQ4scboXdPHfaGFCdZYZ9dvFhFj0N9VgWMH2CqhW68jpdSIWwtYLuwm+eqbH3tzKH/6j/6oq8cnf/IPD+VL15PVwQrjk9kMfREqUrn+EfVG+eBOGof98i9+wR37137l14by/TsPh3K7YUxMx+Z4a0qE4MdxJ3Ac6ywMcN/zforWYMGV2TfRKqrUB2GfTuaXljeUWze+/bo40YxbxNDuiOMKjn9olcT4xPFWroIvymXjuA1SX7BZmwLDPStIW0vWF3Xtp22c3/D6cb0ZrOIWWyl+lGx0DpdJis77uFmnPqvPxiFss/WsYL9QjY+BYkHO6vbPa5M9c4y5WwvY2sF6i/YLU+bkWeidJYnzgBuKPe2acksuzrvhWce+iXO0KnIsgDKsGGKHcVLH9os5WfBjhNk8/Q3DEjtYpv7o9p1k7/PVr359KO/spH7tox/72FC+8dqraf3ffnso33zgbYJy65kTNrQfwXOx1TyFY+UFgTaO63V6zhlHaPmX9y2053DvWXhtYLPY0d6wHh8D8flvW46R0zNbZfVoFjheQXI/4zm1fBeD9TEndNOvM6zEnPUJ4mNpm7P2dZEZ+nPEF8aEcEY7Ke6z3P2PU5hreAuNx7ODzf/kLEbieebajya/HgWHrLLl0hNaBOlLXSGEEEIIIYQQQgghhJgQeqkrhBBCCCGEEEIIIYQQE+KxtQQnnwS7fNwFCWsslE8zLgF2WTiddJTZb8clsg2y3M+RKXXWZPYLlABgV3NIQryklFk/IS+DTKCF5JWymFzqwc/+mZF2O6RsoJQ3tBtf96lQ1WaL3aPrEHCDuyVW6piFlxni/TXrkTXcKAfqx+WOTc17n8rRWSAg4yDu3QwSwzxjM2WUnZNOj2eb7Vu2WZ4P6g3LhQXbWf6Y4rxZ333IlZhBdHEsVTuV2f4iE8KgUaiLGbidVnMosv2YmW1wJxazJEOkFYM5+Q+kaLTQaMYlkCxTJppna3V2LQXZYxXG5aQRNi7MXutvacFiwDIVSVF2MqH2cSZPJsk93UONS3JK1gNOUsPlheNVhf33Xv/vZJaR8XMDKSwlsrAiWTEmLGlbRGsjPDttdhWqktSqKpSn2YZiTHG5iwXJOdpVj2tfZ33DfI5+Bv3Xpk3bbyMObaFf2ljqFDeQxH70Y68P5e/4jm8byt/7/d8zlH/gU9/v6rFzhTLrFBM5nKHU3llTOal9OofVw1SnX/i5fzaU/97/+w/csX/rN78ylB8+TN5KtBLgU5o7DE2H4Kwq0tJxSxKO+fJM6txPVVGmStlz4Vlzsj0bXcfJV3HoU/VgfZktuoYE09lM4dj1uBUAxzlWjWe8NvPj+Z7zgpIEc6LZxmOMw5zBZw3HOng2OUbNLScO4V3GMUeDuLIFG4L5PMWFDfoBWnusIT9nG6CVwhZsHMzMNhg3uTE45jZsH2usv+gpz8c4hn1kX5bFcj0ej9Z5bqI6zW7KLEbrj2N5iz6IVl/+uaPdjT9pNySkhYIx3qRrWaM9uQz2kdZnyQ6h4biY8/qs/dI6bePmSbTnS+vcuXknrX+ZthGpTrzvW5fSfPrgK++4Y28vUhtuEGdrjMEaXo/ZdO2lTq67G7OiTbhntmEb8Occ4VHIcS7jTYVr1jeMMbBlQfvo3dwFcY/tOhvXO0uaavw8OE/vYeVY6iOLFoW5lH58reeOkzED3x3EgkVAyUbg9L9L5XF8f98VyuNWhXmbcXZqKJ9v+MkxceFc+T7x1Fx+/Lkr2i88oV3mZIfSQgghhBBCCCGEEEII8SKil7pCCCGEEEIIIYQQQggxIZ7AfuGYRydgPzvx3DmyBzpZEuWAVHIZpdlp2xnkjbRSmNW5/UIhazD25ewe+jhaZpZGSh2d1OSUzI1ybkgfFqmO/AK7n001e2tvVXUkK40RGd9xzn1IstMYkOEd2cPNvKzD2S+gKVcVbRbSOhvcI2bqde2Mn8hTxnHGvQuQw8fA9gGJfT/+YFSQ+bAtO1ORPMswnxGc0+F+uoaHB5C2XT5uTxPTjJw8g7WTpSJrPG06KEuHrM/MrMU2c9iv8LLyGd9s+JyNS86rgtzH4y+4k7JieV+S8LvNKdFEPKJE84ys4E65WCrb80VJmlvMoJqvz1hfyMRuBZk1VT9dNy6T9PvEHntfj+jua/rbZpX2tW7HLRcozV2tUj1oBdC5NuTbspPCOul3Pb7ORBtRjHG4nlAIuv6XMYJjivXSx5uD/YOhvLe4jG1Sv+Sk5ZCj715NMulXXnlpKH/ie79jKP8rf+QnhvK3fOKbhnK9lQX4Cm0TJ8LWWwXYyNh4+22XKR7+4j/97FD+W3/7Z4fyL//iF9yh79y9P5TZ18620rhssZPKly4n2e6UCCFk9lxH8DHwVj2pDVCuamZWNRzDjPc17BP4rFFKeK6c0HF8/eOd4Xjj/ZwTUJb6whpxwUkJKa/3eMsF1n18+yfNEP2sCRYGaXAVOL4ZH+tQRpyfsbNmQN9Eq4/FAnYvsE2gbZy3wRu3G+I4eNaUp4/OsmHWjC5/XHLDhfMce2s7nSstXiZrE2TRuuN5RYtxhWFcYc6lAnOpmF979tvs22GbQOk9rQAx3liv0xjD0BbrMC7hp6WDmdnhMm2/2dDCg8EuFZewYljMk6UUB7lbO6kfvXztWtrnzLdZzru5fYXLWffTbCs5J88x2z7tVDimWcCWom58u/HWm3znAqsOxLEIey/O+QPmu5t1uuAHSx6hbJni7Be434r9S2EOMLp0clPkbzjDuIK2AIivVeTYhNYB+Zx43BqQ9gglSww/Jx63/ig561TZHQ1hfERUngdyx+OtprD4lN1l0WaB5+fCrewXhBBCCCGEEEIIIYQQ4rlHL3WFEEIIIYQQQgghhBBiQjy2/cIjs80Wvlzus+28vMflQR7dnp/qN8wM3IxLsRazJCWYo1xX/pS93QMz0qN+HTPBliRylAIwA/CZHhRpe9SD21cLfLY+87KV6RAt2LGsBtYK8y3co5107WeQBsd9bznRd5QT8V7iPjJ7Jq0wcB8bl/55XCYIRZKzWzAzq5gFlcdgxkPIX0PhM/6IdQxtLvSU3rlDWw3JwhqX5+Deg6F8/3YqX9q5fnwwmwzBkoTHy2kSLWLIASRc3cbLoSkTPESG9+UySbcoQ2SsiZGykbRPSrFpt+KlTV625CWJ4zLGClLs6KTwsIOhBB/13mxS++5c1mMz/n6X5+RMBxwtTow4GnPdMsp2cC3z7WLBcqG0L8oFnTSaXZo7Hu5RZJzzsaZDO+g2ePb71M6ZQXof8v/9w1RmRvPYIzsxAl2b2XY0kGJWkE0GxM8Oz8hqk7e76XByZ6qCNCrauHRwa+6zwNeQudKWKSKzd43xwt6VZNHwgVevD+Uf+uE/NJS//wd+/1D++Cc+OpSbbdoLpftrZtZU4/GjRz0aWBV1B2n521/9+lD+0q99aSj/3D/+hbT8S781lG/fv+uOvYKFTYPx13KT4vTWBlY41XQjTlWNDKPRxzPu+3IuBYZEuXDvSjYt0Y0RxoOPs/yhpUvWV0Tn1fPo+1KSXTq7BsbGUl2tbG/mys6b6pHVu5CEEIa4sZgXrOIw3nflzLaD8bvDHIb9EWXWtF+g3J1toilYgbhzyJaXpNysOy1ouE4+vh4oSVuz+8661ONTyDOzs0+Jk1OtC8+am7fgH312zhXmTxxq+rlsWofjh/XqEGWMN/YfDuXVKtnBzWapX6uyuNd3EdukMbkfw9IuCm0W47IOfU6AzcJiG5Y/276v7jFeadfp2OtD1AOTrLzdTRFavLhnE9ZIO7hOs+y9g7PkQXmxSJYXM9zjyPk85zFtuu+HVbrezTJt2+D9jvNPMbP5gvYysH5AAHDz8aK13dMx5VhyFsHS/XWv9Up2a65dZPsq9vklOwTOQ2jRwHcq41TOMiqzQHDlcxlVjZb5rrFgFnI2hbbox1KyXxBCCCGEEEIIIYQQQojnHr3UFUIIIYQQQgghhBBCiAnxxPYLXppayArntvOyTkq8KCPLP5dO++Kn3ZB11fzUGhYNkCg1TvJ21ntsSFU6yj2SRMNl1oM0NRTsE/y1yT4Fd1I6XA9+js3PvKsnzxz7bInWH8uLeX8XuztDudlK2a/rZn8o3+uzzOKQ/cQI+RZkpBEXtsN17WjbAVlG57IxjssEu+zeOWX8rGQZgrbp9otsxZAe1WjXPvFr1mbjeJbW9iDJnR7eTfYL+1eOrjMtRS48IVg9O7FfoHVAul60TDg4QAbdlW8zc8qHunRhl9wGEqvea00eWeY97FzZX2/GkViQ/ReU/RbH1SiusmdZvVCSckobM+ygLHWaFCNZfkvJTUsZ4fN/l6wYAsuwJ2A2Vb/fcdsOtuUqi/MR7WbTprZN6SzbP+0XHu6n5S1k98GlWU317jMBUd0wKzZsBRgPIWGM0dvlTIUQgi2OxwxM0M7HgDG8xrPSZPJ7WvssIUGlxHgGWeEbr98Yyp/+sR8Zyj/4yT84lN/88OupTk6SiP6jycYXbgDGvijVt1ul9vubv/7lofx3fvbvDuXP/fIXhvJXvvb2UL55685QfniY+mwzsx5xtq8eXd+Hh0m2OyWChXH5pZMA8hk6Q1LngxGKGKvQhsCtPm4P48I+1qfUfrPxz2yL57lDzIi0h6JtmZN7F2Im+ynUvM/sXlgXyr3Zd/ptpmnbEUIY2oWTi3K8H87XbkpyaFrELGZJWn1pN421o2sHqT9pZrRfcCm5BzjPMTOrYTdFi4etre3RMq0Y+IzkbSLBdu3vux+uFCxAznk9pwLnUpzXdswQ79qWH9O5uazbF+evsHuCvVcP66gA65z1JvV3tFIojcXM/NhnuUzb03KlhrVHqBiH0n4OYcE2n11CGfaLje+r17BgOzzsUea4CfYLZ74/uNicDAH4nNZ4tnd20nx8d2d3KNMmxSyzh3T2C+mZb7Bf2i/wnq4R61v0CYtDWMVsp2N3vW84W7vpeNs7ODatEW28Lyw9/7QWOMuupRQ9ivs9h43RhQN9lH8vUrBfKFjOHf17PHZ7C4R+tEibOncdS/s8477RMchNgWj/9TRdQ2Bsyvox1zeXtn96i6DpRighhBBCCCGEEEIIIYR4AdFLXSGEEEIIIYQQQgghhJgQj22/MHwtPf4VdCYfwmfa2afzlA1XTnbOzPPI5Fr4zptfKNeUkPAza5fyLv8Mnpk+kX2YdQ/jco++pwSFB0zF2fx8l7iYjfg5kAmFEAa5aXTZKJP0ZbGb5GGvbl8dyttbPqP3232yFTi4l+RioUYmbYOsC3KbDs29QrZMZouNkKnWDWQnWQbQGhlVnSJnjfsF2U5NOTbuaRshMXS/sUDeGLPs1MhyHiNlLmn7h/fSdXr3OJF5u5mONDqEYOFEJgQJuPWUr6fyCjKq9cq3GcrOmcn2oGC/QJl5cPekHi/H8TjXZTJC94z3tODA/UV4ogUMLR54DJ/Nc7xslks0x2MVs22GqdovRBv6Fpc9tBRHcU+qTCLVF6XEzObLrOyw8OByWsA4C45xGXKuPg2wa7GONiHjEuo12jKXu9gGeVzl5HRZnKsQV107T+u0sJTYrP2zNxWCJdsFZk/nOIXPYIO+oV36+Mx7zwzlVy4l2fPrH3hlKP+BP/C9Q/lHfzzZL1x/NWUMt7pgBzXjmMVVw7XfCudx/3bqGz77S78ylP/xP/gnQ/nXfvU3hvKt924P5TsP0rboeuzqK9fcsVtktmZ9131qHy3a9e17ycphcpycaimLcRiPQ7kik/erL/UjhZjEPoT9RinbM+MN+0czb7nQM3M52xOtzmjFUI8fo3TePsO9t1ygFLtsvzBdTtpFKMoycc3O0IT6e0yZe7rQM9gpXNobt1+gXD6i72yacZu5rvf3jhL+7e2S5UIaN88Yu5xdUcFK5AwJM/t6ts26YH3yPNgvkFCYd3Neml++UBgxOmuVflz6TOuMnZ10f2fzdE/deKjHOMT888tn+/AQ/QPiUF2Pz6NpjcC4wLl8g7rOFt5KYH2QrKp6Tt1Qxw7S7OrU+4OJEHBNAu0oUvvY2oadwW66p4vcfqEat1+YLzAnxhyqX9NmDONcbtuluTwtF/a6ZAPRZZd+C3V09gs4J7a1suQdxTC6+IVlLE5yfNwXLt7ThlfOq4Ir23iZNlMYl9bZXWR/UBXq61zLWObcnLZ7fq3RbY+2H/9bKHlYPCETnbkLIYQQQgghhBBCCCHEi4le6gohhBBCCCGEEEIIIcSEeGz7hSr7r5l3NHDZ6eL4Z9P5v0sqWZdB3CjJ8bKfE+jw0PbuG/F0rHhGTSgvLchteWT3+Tc/66Y0rR7PHJsfoytIcb0sxiZJCCnjZsuM712SexmyZdeQuyx2/Ulv76TruX8fUh1IeKoZ5UCpfOXyVZQvD+Vr15ApFVKdAJ3B7uWUGdTM7ANvvDaUl4fpPB7cvDuUD+7dT/uiZJZS+BrHo6SfsrFMbhiNkqi0fMPstPspm/itm0fSFkqVJsGJPPEcMkRne9DltgdJ2rNBmzs8SPJOZvxebKV7UhesYZyclPUoSEVyKJF11jSIQc5yoWC/4I83nkXbzMdo95eC5cJk5YnhHHU/p8LFy3sKtkKM4S0lzbRASO2P7WyDMpefriDsQygto4R6w2OjL0F7CpFWQ5AkIl6GLKM5YyCzD1dO+p3KuZR7MgSz6rjfqQtScY4dXKzO7xf+SRnyjVdeHsp/8Ad/YCh/5id+bCi/+sFX026qcck55YX+FHy7Z9u8dzfZJvyzX/rsUP5//vbfH8q/8Ru/NZQP9hEn0f6u3Eg2C5Ro7uA8zczu4njvvnsrnccq1elgk6Sv77z39VPnMzlK9gtVYQyXZ0d2svOC9Y6ze0FcaMfLfgw+3ofkFghdNz72NTd2fnQGbG8fwHgxHifN/BiGVgzOtmaKmcRHOOmjq0Lf4vpsao+rbDzo5lz4A9rXrB7Pck9LLifVRz3quvDtT7a4Rl9By4UFZNkzjK+5fo95QeTYyB3hjPkk7Rdqzr9guVawQZkSwcKobUcFzTHt5AItArOrWboc/tLQciUt3YKNwd4e5luwweP97SCvz8dn7NsOD1OfsL+fyj4OpeJmk/qpzsbnWMG1ZT++4Vw9Yt7ZxvE2eOr1wUQIIdj82Eaht/GxxNZWuncLlOezzH7Btbtx+0r3noX9H9oQ21MDK5YdjCtcTMrOab6T6rVNO0TGK9dXFG5eaW7knGyyZ4fl6vn/NrIcH0pz86d7ULwdQiiUsQ5uAe9//rxWRTseHpv9zPiYzHe5BfuFrM2U7BdKtltPajP1/LdGIYQQQgghhBBCCCGEeI7QS10hhBBCCCGEEEIIIYSYEE9gv3AsGSp9sl74lDh/e9xgBzU+d2Z2yRjGLRd6lwGcMrK0nJlZ15B0nZVBtfQn97k55WyUZkPmQ8uFth/Ptpfv2GVUd8coyNwmRbCqOZJdbdaHWA5ZfM9P3tP9amzLyGIbsuF5userJSQluP6LrSTLeP3VJH996XqSkW5hn9EZbKT2tHfFy0v3LqX9Lh8kmdBbyyQHWt1P9gsbly2RlhyUlzDTL9YPudyuYBOCZ4Qy6NWDo3KcUNboaOncSs8DpRQzSIQ2Gy8npfx9DVnxek1JGCR7yPZc1czQjCgGuxCfnJyaHR/1Ktz3WUWLBx4j7Ywx1slxGSs6xr9utGzmM1NXTg5ckqBMU2cWLDjLjBPcs+Ucd9K63alTHre2KNmBtC5jfdpLi39s2vHyuuOxMqkXGkIfxqXLK2QYXkNS22I523KkxAgyyVxq22G9Dc6jPn2xjtbPUxRPhdhb7I7sWFrc075L16PdYMyC53TeeHkipYc3Xn5lKH/bt37rUP7hT35yKH/k4x9LG/O+RI6fxm0+nJwsyxAeEQPefuvtoXz71p2h/PFv/fhQ/ug3f8tQfuudZIewwn7efPNDQ5mSydzW6te/+Btp+1/83FDuHyBGob967+Z7NkXi8f/MzCr0D5SDO52ps+3xz4qX4SGAuG1gBdaxv8czj+ffjVfdWKFsV+Tsfdw4n1Lbgh6zGi9Tzs+jtVk/taFtTZv655Il2VQJIdjseEzcwAqHdgHe+mm8zz5FHL93lOfP0uFsgYz3/n5xW7bfVGw6H28ov3aWCzhgQ4snVtv1qVhenJTl/8T4BscoZTufqv0C7aVCMZaMzw+ehL6nlQtjEsYCTTpew7iAudSmTXO62HvLFT7a+/v7Q/nwMM0VOV6mhUczo30dqupk0Khr7e87rTpYrzVizwb9VDXR4U1VVYNFQY9r0zTpmd3a2sJyzoGy8bT3yExFXL8Wyxm7+V7GCvXYRkyqZ6ke+ezVtYN5Ws9ZNrj+j6cw/vyXbDvy9UvRo2TNNkWCccww3jc8LSVzDLa40lCqKsxbOC7NJ3U12lnJiqHUN8RC31q0ic1tMznmcv4L42O6Lj7Z+xp9qSuEEEIIIYQQQgghhBATQi91hRBCCCGEEEIIIYQQYkI8lv1CMHMi1DGcdIsZa0PM1ktll/kU33a3WO7kZe24zYL7lBtyDyeX7fN6MMvl+OfypYyI/HzbSagaltPn3rm01Un0WKeCTCDPbj0VQqisqY+kH0x27CTiyOhbQY4+y6TUFa7zFjJebjbMtIosqLyWkNFYl9pHu0Lb6tPyvk8SHGYiN/NSy3aFbXCCpQyHxjYLpUBEe6+8RtwdO8Rx+UvlFASQJvSn1734xOE+VoXsqXx+ZsjGO8syajMbOC1haLkwn6e2tNhK2aFryL58OlRWFW1vDUlsl9Wj60fL3tLFRvGSRJS5T5xnLv3xqthxqSIlmiH4LMFT4uT83DVgv8QMo8xomvUN7qIXVIix0DnEPpVpW7Ch1Ab1a2EZ4iVqZmto2UKVYhItVhjzelRqhnZNidp8C/Yfs/Hny8zLV112YxdsILvF8aZEXdd2+fIlM/NtH0m7bbOC9cV63BLGzGeUvvHK9aH87d+e7A0+8s0fGco9DwJdVtVQzs9ns2DPlFlf8N/f9JF0vI9++KNYKe33cJWsab72zrupfug3P/pNbwzl+ZzWQf4a/Pw/TZYLHfraX/nnv4pjfG0o3797z54rOG4r9F+nGe+gXexn7ClkSnb2POzvCvYLMZP5lfJ8h5L0kWNcyHY59nWhtCv3U86ColDfqjBunhIhBGuOpcW8TrQOq6txqWhu4+bGmYW+3XWF6NrnGDdZIWt8Saaay0O53nyO8RgtrLxGfrxcwGU7z66BH9OU5kyF8dvEGM7dzREKc9czbCbcvNhZfZXiEOMN5+PLoTxHn8V+oy/M0838uGKDvvAQVnaznTRe2d1J8nxK8GeIPebs7hK5lQDtGGiDxnkC7azmE7XtqEJl29tHFoKMqz6On2G5AFxfw7uJ8YZ/DzQ+3uaNoYXHvE6xo+pTnfJmSZsF9rHlR7sQC56SqVrVvR94m5O0/GmviXvvxb6vHi87+wVnmZjZL7htCpaE56lgwX6hP2NcZYUYy2ln6+Zxsl8QQgghhBBCCCGEEEKI5x691BVCCCGEEEIIIYQQQogJoZe6QgghrM3VfgAAIABJREFUhBBCCCGEEEIIMSEey1PXU/Dnop8XvARbO8MfouDZRO/cTZu8dtYod86Pt+AFRD+p3tej5EXmfC6xPr1bKnh3zOCjO5vBTwqeP/QOMst8PAoeJPTFmqqnrlmwGI6uSYzp2nTwZeK1p9dJyO4p3SYr54mSlrfwUz482B/Kd27fGsqr5cN0vIa/baA9RPhGwRfwqCKpXquD5Cm1f+8BdpXWaeDlwnOIAd5POJ+mgpeQt9i0nt6J8FesvLtaWiV0x/+djpFY7KNtTryKeY68V3hmZov0nC067+vJ67WZwR9rnrbf3t4dyrt7l4ZyM6OnLvbpTMiwnJ6pnb9xzuswjnsR9YW4aoxNjFklL7Tcf8q5ocNHix5D9O18Dn7vc75FvFAlf92sb+Az7vzCIv272B/QY5T3iN6G42X64G422QOP/i40hXtEX9tt+FrWKOMZmaNdOz+y3KvQ9WtpXz6UoG0VnpeLTjOb241XXzczs8U8efbduXVnKB/uJ4+/OE8X4GA/9SVmZvMteP4hFB0sU9/wm7/5L4byzi6uMbwol8vUr3TteP/IMcQKHoRmZjXuy41XXk11R1s7PDgcyg8epPq9/bXkd3v9latDOayT33iD9mDwwTMzu/5S+verNy4P5S9/ZXso79xL1+nw4YFNkWBh8HwLBf/p+hw+qfm/Sz6mCBnW9/RApT/muK9b5rjszoIwBFSFc+K9Z9/ryogFvY3H2XxMXDvvWJyfa/P0aJ3qmDjh/ANdGesU1s//5toNrlmHax7Q59FHc+G8CMvHO6HPWlSprbjcIQWf5JJ/oPfKPaNOBX/Zvhv38Jwyw7kXhvOu3TBeZJ7rdg4fSf/Mp/J8Th/ttP72Tor7i0X6A/usE2/XE155+eWhfPdB8lZfL9Oca92kMdB8kTpVtokGbXmzYe4BzjPzi5Yu0M5uqleNc+W+5vNpjm9CFWxxPC5x41zG+oIXaf5exc9rCjk9+NzZeHxz8QIxvWY+GBy7y/MhsRxLfxHvD66XScXCPDZmeYD8vKfQHviui/0Y8wYVvOVZbqryO7PyuIz9JjYI7qRSkfNGN9cbn+Mf/zEV+/F2LU9dIYQQQgghhBBCCCGEeMHQS10hhBBCCCGEEEIIIYSYEE9hv5CIJUkNpciQ2ptlnxY7RXX6RwvZ6QY2C22fPlHuCpLmUv3OlET3459aOymSFT7/xn4qnmuX1snlSqF/tB4oxEdLZC4+wcxOZDmUaVJGx/sA2Vj0TZSf8XeU1XTjbXC5SpLUr37traFM2WqNz+drKgtwv2jpYGZWo52y3G9SnWq2FdgG9AFtsE7H2JolOeqiStdpfZieAzOzFZ4LSlVo5dD2sCg5bjml5+MiEmNv6+WRJDca5MyQP1EZsQWdc72VSS4oU8WjOYdNCi0XdvaSXHjWsL1SkpTuoZOAzdimfdudo3E1BXlJLNg3lKwbKDWqXFt4/Hs9ndZxNienzkvAuO2vTfmsvZyIVgmQ2xjvI2I95dD9uPSI1g1GKVCTy2sRayDPbxoveT/BxTbYNdAeYjajVBbPRyYJr2G/MOfxCv1832fWEROha1u7e+uumZltbSUZ5nqVzmeD2H7v3t2hHDJ/nL1LsFzCJbv7MFk5/PLn/tlQjugPOP45RN/VF+wX2Pes195+oV2n+s6qcTl0B2kprRju378/lK9c2xvKN29+fCi/9sFXhvLOboqZZmbvfO3mUH5wn9cqtZu9vbTfZqoy+pCuJ68ryzUtu1z5nPYLNWMJrXPStk3D/qFgKUZcbPRUNl5HyhXni/lomfYhJfsFc5JaX7/1JvXj83Y8ltDKasr2Cyd199ZIBGNad/3yPVGfinlLwbqNx2hwT7nbvG2OrRPz+UxBWu3sQwr7KuHaIpefYUHh5nvZvPNxjn1ROXmmy1Lf8WfiLNsOf184b09/WeDZ3r2U7Mr29tL4fHsb9gtbsGjBeGNnO61vZnblarL32bjxL+IYxsKsB9sgx8ud4d0B+jhn6WJmC1g5LDC3mNHmquLzZZMkhDDMnXideP1Kz/xZlpXuWWvH+x22O2e3wzEJPDz8s8yxtq8H/8Y/uVh3QbqHk+s02bjj/XjGl5/35Nz249ZvkX1XwXaHz7Irox3nfZSz9vj/2XvTWMuyLL9r7TPc6U0xDzkPVdVV1dVN2XQjusBgy5bBfEK4EY2M3HxDRpYQGCEkzNRMNgYjhDHIUmMMGGFbatkYQVuAJQtjY7qlxu3u6uqqysrOzMjMyIh48eINdzzD5sN7cdZvnTgnK+JlZb04mesnpXLHfefcM+1h7XP3/79YZlzWY1Vo3wM+BU+8b+m2G6qMnSLtF85XWwbaRTmO4ziO4ziO4ziO4ziO43w+8Ze6juM4juM4juM4juM4juM4A+Lc9gs282C39J3lsiXLDFgvn1TMCKzblFW3DMTIVs0y7Z7lyrRSaC1pNsuosb/N9ccUkcx23p3JOIQeGUN7OfVT3DfpsYQYElGCxHh2rxLI52tKXLqfddqSsFPWvDL2HNgnZXZVyFZ7nkvVc4+ZBDGXVpZm/h5SUY6Cj1nHmbYa15pA2jNG5uhxorKgatOSv0BaZKqKkSJhm7Pzi+fMpngRxDpKsT61vOjLeEkpRo7Mt22JldDOo9B7MJuphGxvTyVgk5lmeC9g8SE1ZUvsz7S8s637ponN8kvZ2XSiz5dy0rLoziBLaZOROdF2BI+3Ktt9jX5vlnVLTcxPfMHW9yHCfpQZ09sWPI9pS2rYWirTRXRnl6/QN/E5sg6xXFWm8eJc7Znw3DPI1MaQPVPuzW0SPmu2IyPdptTbPvcRpI6TkdbZkvUR1khFYS0AhkJZlvLg/j0REUkwRuWwaFlvtE+dzLQtT3I7Rt26dbMp/+Q/+A805Z/4ia/rPujrKblar/UYG8hGKWc0knhUlWLTsrhCPTc9Yo88f3F80pRLjB/jKSSqOTPL61dWla2za9hWLJdaJ4pCbYx4Tn1t8nknCqR0po9ATNDjddCWhiZPkRk8YKyImX4vM7+zzcY+ibYpSy/GygV9g7FZoGwZbSHLuqX9Rsb4MRYUE0ijxfTlw1+LEkJoMm7T/sbMI7i9ybzdjgdZ17Sc9WSwN3JoY8vSZ5lA2Wi37c7ZTvIsGCltny9DT7zXPlTss/Tom1cNlBij2i/U3f04baCeVhJtYhrpns+wL4CziiQp6wfmZBu181kuF1pe2xiBdZD9CuOjgG0uX9FYnZZS7OvWSxx7peX2HJr2CxNYR7APZV2j9dHQ6LLiobw84n6XVc+4IdZCjK9c0oRzsO6Kx+Nx7kLbLmsTwncy/bZkpm8wg2znafT+ode1rn0PUO6TybPfbO7hgLog9jWWbjsoE1K0dovdj9Q8hr4hwA4H3RZX1iaKfq72RNh8E1NNeCJ9cVKPHRLLfE9Zt9sNY2dYxWD+ZKxN5HwMPzpyHMdxHMdxHMdxHMdxHMf5HOEvdR3HcRzHcRzHcRzHcRzHcQbEue0X+mQtfZIQu/7aLp2nhQJlv5QJc//QIyvqPVWUPy6To5Hep93LuY00IFAG2WPFgDXe7TOte5Zjx5pZ759OJvc8E6PI+myFeZKrPD3APsH8vFCrRLNsPd8llqqvBcvWIQ3MIIe3q+91+7InA2g6QhZO1su2DAHylCQaLXNTLCCrrSh/hwaAtgEjSolYB6Qt/YDUrEfaSalkemYdMaRM0XVdy/osGztlEsxSmWUqkUpMUlUrIU9ge5CnlKnic0hIeZsSk4GaVh5anoz0eNOpWjrQVkFEJEO/wDpQ193y9Q1kauvlSj9f6ec1ZP4Z5E/1EzIxytT005oyKyigY+iS3QyDZqhInnGcaG1i+mczxnHs0+1L9GfrgjJ69Gcl+3b0QdQrtdopLRQo/2MdSiB1HqEuJ9wm6ZYupVmPdElEJhNks4bdQ7nRa0JxsGNUkND0xbRZGMMmZTKB5hTXefXGNfNdX/naV5ryN/7hn2rKX/zCG9iK7evT+W3dSK7NH1DGH8qV9jHGWgp1YrM8bMo16nhohZKHB3qQ7dleU55NtnV/9DfDGZlaxCjV2YVwXA41+vqa/Yju+kQ2emOrwbFC723KLOG0IQg6llnLhW4Zbd82ZxfCs8Lx2GcgvkAMw3KSMZYKndtQbi0iMpnodZiYuM9GYqAkSSLb26exwhTxyYj2FWiDvH91bM9hqs6/9d4nI2Gl9QPHCtgVWV8nHrn9xSh3xxuhx06hz7qhL/P5k3SP1bSq6LW4GxLx1CpIRKSClROfu7F6+zh4P3ulxdgcX8sYYzTWOkvbnv2D/aa82GjMWpbWJojjbVngmhiboo5vbaut2Rj2SAXq5gr2C+u5jmttm58RfCR4TYx1TB8Yzv/K5KL5vnXevGp42vbBukaLlz7DHZ5Pz8F7eGJGYyxiut8PmXdFzxqc9r++MjxVXzLA7ibGKKvVaXBvzAY4FhlbSsQEqb3gFJaTdYL3Mz2WP9bigbaSZec2bLvGLqiyYxTHNVN/0C/wnVONOV1pBqOnMYtoP3T99waTpjXm+cbK45xBsa/UdRzHcRzHcRzHcRzHcRzHGRD+UtdxHMdxHMdxHMdxHMdxHGdA/EC0BH2yFiv/a0nNQtJZpnVBFMpCebzzn+vH2i8ASlKzHDKovqyJtF8w1hI41hP6XloucK2/fP/ygIgxynodz8qQzEMuHqH9Dsis/mRmSVpk6JL7ChLlGvuHnjoYkG7XZBaG7Jyys1hbyRCl6plRmjBTNbI/U1qJujWBvDdBO2Dmc0qa2udoEkGz2kEmlJ79oa/uPo/Udd1IqGjDMqa0HPYL1LW2s2NT4plMVbpFTQ2fb7GmRKs7bSelirOZyidTnGuStNs76jVk+IWRYqhUbA059BJyshU+p0SV0s12VvGqpx+JsVvmEp+QVg6HTjnU00hAz/H9HD8oK2TZZDQ10lnWJ0HZ2ofQMoQ2IZQcZcjizDaSoa+R1NwE/dhkuLf1ZgQ7mpx1m5YzpjxMeWIIIqP89NqLDcdlfY7MwJ3gvla17Z+PTo6b8r0HD5ryrds3mzLjmclI+6SyZF3pyV7fl6W+naGZsn+cY0KLK8Q2JcLBMeoc5Wx5vqPHRn1aQuIqIrJcFCijH5trFvT5sd6nujW+DonH96c3I3Jr6z6sbBQxsbH26q6DthvrllLHnn7oyezm3fuzrhl7jqQ7fk96pZXSuY2ItY2KPVZufTH7kEiS0MR+lHqzf6fFhc0S3prD4FlWNePX7njFPhccomdcNM5oRrFv6415wr1p5M8fg37M0UR66nPdazky0MkUsO2g577ymbb/1PcPk8wddQhWTpcuq6XOCy/casonh9q/F5jDrB8eNOWq1X43sFxg5M3xax/nt9lovL0949iJ8WgJG7OlnlNV0itKpEJbqHvm4JWxixh+vfmkhI+pU4+xzau7XzHuH+c5jz5nym4V/zMfxIx9H9NvDcnW8FmIMUpxZo3C52naKGMTxoytex0Rt/D9Rd+YY98j6vf0Df0ZxsoY8V4osTv0PimOm5hT81rL3jEN389tQv/f+uwXop2oft/jdeErdR3HcRzHcRzHcRzHcRzHcQaEv9R1HMdxHMdxHMdxHMdxHMcZEOFZZCghhPsi8s6ndzrOM/BqjPH6RZ/E0+D15rnB64xzHrzeOOfB641zHrzeOOfB641zHrzeOOfB643zrHidcc7DU9ebZ3qp6ziO4ziO4ziO4ziO4ziO41wsbr/gOI7jOI7jOI7jOI7jOI4zIPylruM4juM4juM4juM4juM4zoDwl7qO4ziO4ziO4ziO4ziO4zgD4nP3UjeE8MUQwiqE8D9c9Lk4w8HrjfO0hBBOWv9VIYT/4qLPy3m+8XrjnAevN855CCGMQwg/H0J4J4RwHEL4lRDC77vo83KeX7zOOOfB641zXkIIfziE8MshhHUI4b+96PNxhkEI4bUQwv8aQjgIIdwNIfypEEJ20ef1afO5e6krIv+liPzSRZ+EMzi83jhPRYxx+/F/InJTRJYi8pcu+LSc5xyvN8558HrjnJNMRN4TkX9URPZE5N8Ukb8YQnjtAs/Jeb7xOuOcB683znn5QET+fRH5by76RJxB8adF5J6I3BaRr8tp3/MvXugZ/RD4XL3UDSH8jIg8EpH/86LPxRkOXm+cT8BPy+nA8n9d9Ik4g8LrjXMevN44T0WMcR5j/HdijL8VY6xjjP+LiLwtIn//RZ+b83zidcY5D15vnPMSY/yFGONfFpH9iz4XZ1C8LiJ/Mca4ijHeFZFfFJEfveBz+tT53LzUDSHsisjPicgfuehzcYaD1xvnE/KzIvLfxRjjRZ+IMyi83jjnweuNcy5CCDdF5Esi8usXfS7OMPA645wHrzeO43zK/Oci8jMhhFkI4UUR+X1y+mL3M83n5qWuiPx7IvLzMcb3LvpEnEHh9cY5FyGEV+RU8vHnLvpcnOHg9cY5D15vnPMSQshF5M+LyJ+LMX7ros/Hef7xOuOcB683juP8EPgbcroy90hE7ojIL4vIX77QM/oh8Ll4qRtC+LqI/B4R+c8u+lyc4eD1xvmE/EER+Zsxxrcv+kScQeH1xjkPXm+cZyaEkIjIfy8iGxH5wxd8Os4A8DrjnAevN47jfNqc9TN/TUR+QUS2ROSaiFwWkT9+kef1w+AznwnujN8pIq+JyLshBBGRbRFJQwhfjTH+9gs8L+f55neK1xvn/PxBEfljF30SzuDweuOcB683zjMRTgObn5fTBHv/RIyxuOBTcp5zvM4458HrjeM4PySuiMjLIvKnYoxrEVmHEP6snCbc+9cu9Mw+ZT4vL3X/jIj8T/j3vyqnL+v+0IWcjTMUvN445yKE8A0ReVE8C73zDHi9cc6D1xvnnPxXIvIVEfk9McblRZ+MMwi8zjjnweuN88yEEDI5fVeVyumiqomIlDHG8mLPzHleiTE+CCG8LSJ/KITwn8jpgryfFZG/e7Fn9unzubBfiDEuYox3H/8nIicisoox3r/oc3OeX7zeOJ+AnxWRX4gxHl/0iTiDwuuNcx683jjPRAjhVRH5F0Tk6yJyN4RwcvbfH7jgU3OeU7zOOOfB643zCfijIrIUkX9dRP65s/IfvdAzcobAPyUi/7iI3BeR74pIKSL/8oWe0Q+B4EmSHcdxHMdxHMdxHMdxHMdxhsPnYqWu4ziO4ziO4ziO4ziO4zjOZwV/qes4juM4juM4juM4juM4jjMg/KWu4ziO4ziO4ziO4ziO4zjOgPCXuo7jOI7jOI7jOI7jOI7jOAMie5aNL1+5El948aWOv/QlWwtaCsH8hQnaqkrL63XZlFcrLVdl1ZTrqsb3dp9FXUd8rhslrfOw/6ylE2yUJCk+xvVh8xjxPTip0HqFnuCD0HMh0XyBFj/84K0HMcbr3Sf8fDHK8zgZT0SkdT18LomW0yxDORWS4d9ZqtuFhHWt+zyiua9dZyESWv/qI7Qf5uPvrfXZ9yYh7P/aZ9489rS9dnsTEdnffygnJyfPePSLYTYdx72dLRGxzyQket+TpKd/ad0S3qMKz4d9RG+bAzxE6Km7/Lz9PTyeLeOcYvc2RVF2bs9zSjLUyfYl4HvThG1I9xnl2p4ylN/78P5g+prd3d14/fqNs39198/9Dar9h+568OkkF+2vN5825+kQnqaP/d73hjNGTafTuLOze/YvM5o3JbbN2FNu72PGHO4jPZ+jzff1N/xDVz/fdey+P/SNx2I+ZQzSE/M80en2tJ2eUyLHR4eDqTf5aBwnk5mI9D8jiX3PqP9umDGvJyQJvS33aeJxfNquQ71fy7ZQd5ajrfDd5/SUHU5f/befKwePjgZTb6bTadxt+ptuvv9T7PojxpGefqUsu2OJvnI08UZ/nJ0gNmPczngjSXvmPH3jH7YpCz2P9Xptjl1uSvxL90/M3E23SFP9/HixGEy92dnZilevXnni87LS+XFR4tlV3bGliH1efC6c41ZRPy/xvSF2x6B6Fvb5Jqy1rfMIfBcgHP961pz1Dlnff3xmXyUiUuLdg4np+9oRjn1yOJy4eDQaxdnZOEV6p6i49UnrOYTQPc6ZeZCZp6Ff6Ju/9X7OZ9K/BjH0DAqtt07dO59rmt49zvWNf4+L9/YfytHxMObgu7uX4o0bt0REJOfcMOt+B/ax85aeOJVz3Ap9Fb+Jc9/laqmfl4Vujx3MOFaxR7LPNB/pNY3HeVMejbTMMaOs9HhFoeXNpvtzvms4Pcee2KjvvQLu2dHD46fua57ppe4LL74kf+Gv/M8df0GnWekDSJKRltPc7MEB4vBIH9Tbbz1syt/+jftNef/BcVNezE+acp6yUukTWKxwoyu8uBhNzHmMUnTqqZ5HHbQypPm0KY/HO/guvaYswUtnBBwBg0Y2sp3SZKL3Z5SNdX/sU0UEK6nes5/7t37/OzIQJuOJ/OSPf11EWoEFAonJVJ/L7pW9pnwJZRGRa9c1oLly5XJTzid4Fll3tbbBrH7Ol8N50O8JkmJ722GN8hm20zq4Wq10HzRwQ898LwQ29Nj9+eleTams2d4weKZPdrz/0X/4J7rP5zlkb2dL/vnf/4+JiH2e09mks5xi8G//NlPg2R2fzJvyYqntNNa6f1VrW474MvY1OerueNzdjouSEw3bJy1RTxZz7XdWGCBOFlq+e+9APz/W/m801nPauaz9VHt8rfBj2eWt7aZ8aVvr8asv3WjK125p2/qX/t3/ejB9zfXrN+SP/cd/UkTsRIKDeYKgxAbv7ZcbDDj4oyLrB+CL+t5Ise/lnNanOtp6Y4OlxPxFz51RTfeh+0iT7kCifV4k6QlL+fLgp//pf3Iw9WZnZ1d++p/5AyIikqLfrzBNXa83TZmB5qa0/XxVbVCuUdbvKjEAFQWOsdI+KUsYSHcH1UnCsc4+FPPDduypy+jr+l4QBzMW6TZZ1l9na/wIbybVPS+IyP/+i391MPVmMpnJT/zE7xYRkRRxRJJoHBFrfV6x70WniCQMjVK+IOO4Hjq3CaYv6O4AOCk2P47nNl5KzO/o3fV3tV5oeYOJVqH1t0ZsIgHnlPK67bmaHykRz+S4ByN8nmGHv/QL/9tg6s3uzq78zE//syLS//I2hu66whcgZ580JT5j9lHrlfZJ+w90jrVe6nNcLhCTLDXGWC71+XJyP8rtgovZTOOPy5c1bt/e3mrKW9sae4zGmPPgOZo5AtrR/XtHTfnt775rjn3/fZ0rCibfW2O9H1OERzuXNHb867/0S4OpN1evXpF/49/+V0TENqkHjw6b8r0DfXarQ22P66XWARGR8a7e/609fS6TqcZ+JyuNbR/sa11JC60rnNceo87mmPNPEbekGztWhFLP6wTjSDbW2JTxuaB/4wvAGDm+cnzWe7DASyERkYNDvY4c7ysmmZ77BmMZF6D9jb/ypwdTb2aTmfyOn/xHRKS9+K37RZp54YX3FCKtF3y53qcx2vNkSxsb+4WtrVnn55zLTaaYT3Fuldt3SFnP4q/ExLP4gYc/OfS8YGNEwt4ttN9+88Uh5nkVX+ptOBaebv9Hfu4/laFw48Yt+ZN/4s+IiMjN2/o+8eoN7R+ykd6lkvPmVmjHhXp897LEws2DA+1f2EN88NFHTflb3/lWU/7wow+bcoH3jouFfs/xkb43FBFhDXrhhWtN+Y03X2jKL7+i1zqd6YUcHN5ryu9/+H5Tfu/OBzgn3WY+1/MQEdngvSDfkzKOyxFDjtEf/bX/8f946r7G7Rccx3Ecx3Ecx3Ecx3Ecx3EGxDOt1H0a+IKevwKZnxVFpMTqlhO80X548KgpP3iA8j399XGD1QDTMST4QX812GB1bhX1/XxV2vNY1nrsq9fwy+W2/nK0XGClH375jFF/YQz4nrrQXwci3sjv7Oov1iIiWzP9dTRLsQoCKwjnS/11ehOt5GhIhLPfDwLrQY+MNDVyDbsaIDWrl7A6F79wsMyVY3XPStgEv20YGQh+q2tLHRPUNS6MqfGLcrHpWRGF1TZcNRgS2nawnrZ+JcS5963UNSuCzu7tpyMb/3SIItIscMIvWXXQZ7tcsy7x11Z7nWVBaZp+PsdK2CVWD/CehqDlyUh/Odua6K/MownqAlfUZC35RVpiO6wAxi/QpZFfa//CX6Kx8EuqwJVvaDetX0qjWRHVs1oOdTptKSuGRHJWz20bQNugzAfXXNU9K+tFpIpsyz1tE0WusuxbtWtterrlrk/sj3rOftKsuCi7V1+Y+sHVXt2XcHYuUKxg9b+V+fIgMkiqupaTo9Oxlv32qtA2OJ/rKn+uwC1LK/Gq0H9wdaMp474WXPlNBUnC9shy1ll+cqWulk1VY12O9twf02dtw1W7SU/9ExEpsWKFf0t6ZJlPqlGGQZpksrd7SUTsysPRWOPHDH0p2+AT6y1Nm+pejWZWJyV9K3V7pH2he5UvyyJ2dVMt3fV3iZWci4XGu/OltpESbadE31pjHQ5XrIjY8TO2/qKwrg14XcrjuOwpjBa48im2VsUnPX3AaqnbPfjoQVP+6ENd+UQbAz7fAs9uidW8iZFo2L5jjHi8WOoKyPLKJd0f9XqSX23KKWKrDfpGjjNjKCNnU6u4zHOuGEMsh5WVx8dLbKPnNzSys/60wMrb4kjb3fzhflM+gQqsXNo+en6g/dXdBPVgS9vqHKsQC6w8HKEfOznGivAl5mQLxMWUQbdcACY7HPR0uzGVs0W3fQhtGSPUtZUZ16gascfe4J4ssWI4UoJd4tjz4dabZ8H0tq2b1uNa9wMzDTPfw/GrbV+JcNSszjUrdbFNj8qtb6VuYsLuj7GX4mpxlOsuW74Bxcfr1Vre+u7bp2WoHyJW5+5dVgshrto1cwURSTHPKjDmLE/wTgtWLwu0s/fv6OrXb37rrab8zge6eNXEP+hr0lZcenlXlfYpBtUpFPzbW/qebjzTc1qu4RCA8Spybhm56taucMerC6lCd/wn8fyCAAAgAElEQVSVcaP6fLHNgCMix3Ecx3Ecx3Ecx3Ecx3Gczx/+UtdxHMdxHMdxHMdxHMdxHGdA/MDtFygHpOIithJNVVi+zoRCTCB0eKTWA8cw8c8yrGHPKOfrlqZtkCSoLed/8ZWbTfkb3/iRpnz7ZTVRfvdtNeH/f/72bzbl995VGdMLt3UZ+td/6sv6Pbc1YUDS0kQnQeUvWVA5NxP2rMpFZ/nP/1kZFI/lB33qA5OAhYnLso8xR6flQuguR2goKGGrTPIX2iyknZ+3tR+FycSr9Wt+ovV0DcNuyt+zsX7vCFkXk74EIq3EJ5RmVrFb2mqtT5Kz/YYjca2qKA+PTiUfE8g6BPe0giyqEMp9rTyR5uOUxR+vICeF5HqDjmuD5EhS6bOd5CoNu3JJj729BflZy8FgU1Bir9KMHIkmSj5PY9eA66blRoTMnzKilqya9S+a1La4t7Qt+ZhMs8OBCZogyRFa9lC208rKjP56QykxpOXchrq0ZKoSHn4vpYMFJE3s15KkPU50ZwwfIynfEmMD27mRztOKhgmr8v5nTXljgKQq6UtsM9B6U1eVHB+fysjnJ9q2mXRhA4sWyqx68n2dbmfyN/L+6+ehpoy+Ows50/CyZVurjv4ESjXbv8k0353NPjIZlUnI1V3fn7Anor0E7ltN6yGMzW27mKGQZalcvnoqI58iCcw2EkKNR4jzjH2CjUXZ7tlPGFuXnMl5uxNB9g7zoftZ8zm0D1jDNmG9YXJPvab8GP0p6soKSYk2SKzES2NyFZFWdvqeZNE2SnrGrJDPITaDOD43W9Fyq2XPA4uoEslYHx1oQrSDfU20ul4gGSOTtGAc4BwrRf2gXUYrD6wdE2B5VW107Nwweznk/Dnahe1L9NhTJGyazcZCmHR6ARn9poakPuq9yYOVxg6FWNeyORuT5kiIFmHZN0OMe4K4pWglSlvPkUwM4/YGtglz3LPlQufjCWJZqZAsK9f57gTjEW0w6tr2e6sjWDGu1MplFOYoI/Ekv5cJq5iQ0ySz7U/MOIVtxxTvElbwaavXiIHq4cyh2jyOC/usFXifaA9VVjauYB+f4FmaGKPne+08BH0/rAep3M9MwlBbbzI8O5NMlBZPtG+gNR6/COfKK025fXsOXqGvK/m9tNLCu4QzW8aPixWfN8qylPv3z8YQ2JEElNeYH+xdVmuD2cza47BubDge8N3JkY4N772nSdC+d2YBISJy766+i5vDToduclMmNG9NwmuMjzWSh0YkKI+c8yCAT1Axs1zHD753tOO3bTcZ7Bgy864S+8C+s/0e42kZ5gzMcRzHcRzHcRzHcRzHcRznc4q/1HUcx3Ecx3Ecx3Ecx3EcxxkQn8B+oVuGUFOSl+SdZRG73JnLsU8WlD6qZKsoKXnVZcyUSUhk9ktdxrxAJtZsprI4EZFXXlWbhS99+XZTvnQF8h587y/9vzjvtcpDrl15uSl//e/7QlO+9QLOtbSZM+sKcoJal6tTllfDlmETdXn70GikH3guoUc+mMICgcvUT/8NCTD2Sox9A7MRUpLT/XmQbvmbkfy1ZDcnkOgeHapEd36idWK10s8zyEOmW8iciCNmI94PymX77ReMjDqhLIm/1zy2X5DBUJa17O+fysuyRO81FfIlb0sOSwqxsoWtCaSwubbrBaQfvHd72yqdn8PW4c57d5tyDfnN0fHlpjyC1DZp9ZGTEWVq2q7HE9RxSmHxCJmxPkKiESMyDFM227oHkTYDknWWf3D5ay+OEIJkZzKZstI6cXR42JTnh9pGaW0w2bKSob3LKiWk1QtupXEbODxReeL+/iMcWyWF7Ef4vGaQbseW1Iv90wj1l/YwzDS/i/q7u6fXENFHlsw6D/lYW+LGc6RdBC0sWG3CQKtQXdeymJ/JWufaby8hazVjF/bNUhvb8B6maPPZCDYwrfv8GFqrMA7gWBmtTwL27tf2MRsxbXrsPpRAQpaNc6XdQw2Z2mqFLMYiskk11mHbocUL6zW/a0gkaSqTndP2tgXLhW1kWZ5MtF+hHPAJ+wXGLei7TEbuhLFRt50Cqevu2EbMeNCy6omwiCn1uZYLbf9SoA6l3ZZVwfhd4ft7ym1ojcOTN9YlQ+1weuhvwbQtsf1Nibbz2EJGRGT/gUpVH+4/0P3xXLa2tZ5OpxqXMgl3UWis8uihjqPVwj68CmPKYqHnkUFmzTiYx84YN6XdfV2eI1v51NonzDB2V6izmzXnjXrf8om1bxgMMUr1eKxHrJjAfiLneIw5z9Hczj+XGGsiderYbFPq58sj3f4Suq7bsB4cX9c+cAqJ8kT0fk9F65OISHWi9eNDxEoHx7AMwXMsEK/UpV4fXLEkRo4zSmhZzaxwHRNMLqaw0Yvo37ZuXmnK3/11+UzRZ8tQtexeEoxN/BvnojVtCIzlAjtv2oRJTzl2lp/cv6ec8B1Bt2XWkzPls3Kf54/Y6zBxYdpTbmrhgPwXRK2BTo50znTvLsaSrDumzVt+WrTUKGHplMJy8niuY8uHH9xpygf7+015gnHixdu39HiwMNwb61gwSdtWL9q/BFrh4R3hBrEsHO6MNUKGMThFzMOxtYp2zWyO2C01ITj6M9gWMXZ7FnylruM4juM4juM4juM4juM4zoDwl7qO4ziO4ziO4ziO4ziO4zgD4hPYL/TRvWS4LZIyGbOle+09FM6SMtMhylTFF7BiCIESJS23pUuXL6l0ejxSuUixUcn3OIPUBxkNq4JWAMhsB4lhWcFCotBspacnCXsK4bJrbJPq58nHZCl/nolRpKoe2y9AMsSsfybrd3dZRCTBUvcUcoqU2eN77BfM9/ZlDWf2wR7rBhGRY2RqfAhJ2nKp0t31WrcZjSlBheXCGJKQmhJZyi9t6+E1zdeUVFGOSYn96THaMprnmTQNcmnrtH2YOkC9u5H1anFd2iy/I2QMNzLfUp/PZKw6i90x+otC7+mlLUhf0Sfs7lI6qPtWVUueiP5pvVE5y3Ktz2U0QZZ7ZpYtKf9H/UnQ74TufvH0xLplQex6++RXQ+PxuZ+cqNTmre98tyl/9zfeasqUzr/+5qvme7701R9pyvkE2ZfRjuawYfmNX/+WHgMZW0djlRtevXa1KVPiPpt2y09FrNUBz3f/vmY3P3yk5RduqY3QV7765aa8tavSSErLKkh+ksQe21h6mPoMWVu/Sm0wnI5Rp9dU07qG9ibsPtHWYrBjA+2hJhN99ltbkKZCLpxAVlwj6zTrR0BcRLlfNFL0p7NfyJhp3kgSu2WLlPlX6D8p+35wX6XeIiI1sqNX9Mwxdkq8pvNl+r1wQpDkLG7MZtqGxztqpzKe0NaFz67VWjDOhazHWooSR0pLUQWhjpViqfeVdjSUym5q7cNOzxf9DWTdGWLcsNQD1qiEhakfkKgimGfG9CRaeWRtGhn2TylrRJ804HGqaa9PpbLUjVrDg6zXsK871jnGMcolpKbbM62bV66oPc/untr25CNY+8D6rmK5sm22hIR1hXJAPZ/OtD8sNrC2gRVAYFyH+jCCdcl2K7v6pSuwcsDu65X+ow56vK1du/9QCEkik+3T55dCijzF/GS20Hu5PNA486Rll5EFvQertT7vkwOdw5SwPbi5p8/up37s9aZ8/apaGL7zSGOu7334kX7nXO/93kjrmYjIj732ZlP+wtc1Bvvwww+a8p13tfz+Byr/3oddY10go3zgfBy2G635f46xdAwLK861L93SNnLl5etN+W/9dRkkxo4C439ipgUcm+3+tNjkHKdAuaxohUkrBhyDc5IeOTrtqNYbW3+Lsvs9gbWOwrOnVQDiIVqPcDxhnxTR74mI1Oj7Kvh+sFyj/HjOEAc0XqVZJpcuXxIRkQ1ih2PY102najl3+ZK2kzZVjbaV0lpSP5/DfuHRQ7VciIgpXnpB5zY3XtR+h+PB1kjL9UL7MhGRO9/7XlM+OtI509GB2uWtTi415fG02wp0Op6hrP1iind666IV06Je0oaIljAI/42FybMwzLeEjuM4juM4juM4juM4juM4n1P8pa7jOI7jOI7jOI7jOI7jOM6A+IHbL8Sect6W0TMTHJbFc4k8pZ9RKBOENAgZueu6W7pFucHVq1eE3Lp1U88j6ZbG2Yy+en4ryFmqKu0uM4NdS6ZJGWRNaRzkChUlevFTcMv4IfFYssEMlJTCjJBmkNmy07a0lbfDyNDxvZDbmwSWxn1Al8bTWqGGTLCALHm9sfKLBeRlxycLlFV+VEO+sZvpcn0js8SSfGaSTCFRKFvSjxVkQjzHEvW/rJC1uji9CW07gOeZPAty6+ppnTDtnZ0K+hDWq6KymZHZd6yR2TLM9H7voLy7jWy3Y/2u7anKPZi5nHUmGOuPVnvF88UjlP0D1BlmwkS9LGEDwQSyPEJipOL22BmkHBmtPQLagelvh0mMsZF+HT5SSc13v/3tpvyd76gEZzZRKeDOnko4RUReX2tdyWGfwj6F9emjD1Ru+F3YPfyO3/W7mvKP/thXm/Iop7QHMsCRrb8psqgul3q8g/sqQ7z3wV3dAbKf1199pSnPIAM3DgPITh4/Rg7N6056xuY40JpT11HmZ/d2tdLGudnovZwgqy5tGWjFICKSQNqXY58Zsr1v70KeDwsePgub0Zk+KbRr4Oet3+gh62IMxPGVssUoPDYk/3i+tFzYYExsWz9wbDbZoe1W2EaGSRBpulq05zBCfwGp3QZy5rIlJ2UMQ3leTusxSvVYxr2kDPYIcQrlgBXsLlaFyilFRHaD1s3dHNLCMfqrUY980MhX9TsT1M1MmDnaWkIV2KlCmXEf5b/MrP5ZxkimW3MpxnXLFbJ4b7QfK41VQnfW+RGsPSZTytR1PNrZVguZzcrGpXP0BwXL6DNW2IdtgbYuOc4joH4wY/hsS+uliMjlq5DMwjJkvdH+N4oeb7qNtOZDIqgtYY0+IsOcJ6MkPofNSsu2o4BdweGB1omjE93/1VdUUv3bvvZSU/7qj6j9wslaz+Pgjkqa776l/cpirnXx0dj2N5e3NAa7+YbaU736xRebchzpdRzCGvFwhdgldM+7a7QeZpcXEamWel75TO/B6z9yS8/jC1rejM6Xkf55oMueybzzoMUTN412P75nYbvtLVe0X6BfFA6B7zfvPGAbU7fk7MbKgFadmJsxZpsy/kV7oV0DB61qwzm0tfSrMSdnmVYBLMsA7RfyLJObN0/fjz08VCufk7m23/lcx5uq5LzSjlEbvKdjvLBc67uTxVLnwau1HmMMe7KXb+v7ui//qNrjXdrbbcoZqtg+50Ui8ghztAd31dLl5JHaSGwWeEcA67BxrvVnd0vHm91dfac4m6ptxObEjo856uU41zEK0wWJnJuXdl7xtPhKXcdxHMdxHMdxHMdxHMdxnAHhL3Udx3Ecx3Ecx3Ecx3Ecx3EGhL/UdRzHcRzHcRzHcRzHcRzHGRCfwKS121vGeOrB2yK2rK+qir4s9A/t/pyWUPRrqWv4lsDHI8Afro7qkXH5KrxNReTqNfWISlL4QNXwSYHHywheaXwlXuFiS94D+MbmQb2DRKzXFE3DQkqvUD3e0Yn1lBkS4ay+0MMvz3BvUi0nof+3BnrSGG81WvXAv402Qjx2gL8JDSYr+AWt1+qjM19YT51j+MoczdVv5uhEyzzEJMLbC5fHS03hkZWiDlTR+kCVuNh1oXV7BY++Db12i9Ptq3o4/nMhBBk3PqbwgIRXFv11c9zIcWrrTyl6L2cT9WLb2dZnMsr0y3L4kJWob7Nt9cHJ0K43S22XsYb/b239k4w/3BaOvdLrK423Nvyk0B8FeMMluDeh1mtLWp66CfqXLEF9ilqvq6LPn3w4BBFJz9r5Bu33zp07TXm1UE+2CTwjY+ua8xwe14lxNezcZ462Tz/DGmPRGN6845z+y6hDUX2qREQinvF6pb5T7995pyk/uHevKV/Zu6zXgD6WfQ3rYgYPw6LlHVbRtxxerDHQ13L41LGW9ZmHcjaCJztumrGsjT3+cyISRvAencA3fjpCWe8l7OTNc6kq9ivcRvcd4Znw87O99G89T4m+75XJT4Dxw/Qxui995pgf4PRcMNYyRwC6HraRIXnNtXn8+NnHRtzXFcaHObwli7X1XMsw5k8mWimmomX6IlaBcbN+1wK+2/uH8Kyjh2lkfNvqb+CznmCsGKfwWUbfOMK5juAHXq1Yf3XfHKZ3dWn73ID7tok8D92mQl2u6qHWmyBNzXmaS0Afw7mTiEgBr8nVij6Q9HTk9vrs6TtPv+Zd0XnSFjxPZ1v6+fxEx1ERkQXGP7teCDEbzpV+5ZzrjTi+8lmX2nZGI3r+ily+pN6vM8RWZU2ffL0f1jN4QMQgSXHa3iLuR8z02hIMSFe2NQ9E9qLeIxGRRxpKyN1332/K2yP1i/zyG+qd+yNfvNGUK8TYR4hN5wt4hmvXI1uc+8KrVETk0X09kSX6qK0d3efWF9RLs8Q8Kaz0vN95R/1847h7Dlis7Ry6KrTvu3Vd46YXv6I+untX9fN3PjiUodL2OT390ASF+APjO9tBscvlzLQyOV26/XVN7iBsHxEjMBZmvFCW9tnV7ZdKZ3DuzNxNJkahf7DQ1JTxF2KgqnVsE5txPmVeRGg56X5f9jyT5plcunbafyxLPf8j9PsVJuF8d1eULQ/igH6hwtyI889a+4UEOYU4Z9pCrHFpS9/dXd3VPksK3Xc1su11Ntbxa4q53yTVMSODh3weELOPkPcJ86rrV7VfPLimx0tL295S1LkJ5gUcd+nDPElsfPi0+Epdx3Ecx3Ecx3Ecx3Ecx3GcAeEvdR3HcRzHcRzHcRzHcRzHcQbEJ7Bf6IbyMC6Or1urz6l0NVJfaByN/cIGy7zT7uXuCWXXWLo8mek2t17QJdsiItMp5c4qxUgSLCWHLLGqdbn4plS5UYXl5UlOmSpk9JWV/GSQfEdYRBSQ0qUjXRb+/nsqIR4SQdT6gBIIyiRC0i3XinVb+tEtuaAVg5GnsT6aSqjHqyH9pJRtudL6cHwMLZGIHBzpMvtHR0dNebFQW4YcEqB1qc/RWHsE/RxVRQKkjkmrmYZcN1xCYk7ZZWUcG07PY0gK1yCJhGTSlB8zhlR8HLm9kmAbEZFMaLuhWxboX1JaI6BejiHzox3COIeUYqw3e71m/bEyM6NdxglnmUo8iorSI7QDyr2lu4/l97ccKCRgf8o9hNYe6IdDy/JjKERRKTfloMeH2l4p+2Zfu95YGXIBa5MEaqIKcivKjNiH7UEOmlBCirFEaOVjJGe2oVLKfTJXqSKvI+CBTyFLos0MbQU2pV5b3ScfE2uFY8uowLFbtjcoosYhtG7qswWIaGtPyhrZvmAXRBsCPFO6oQTEEdaSg20e5YrjRGtsTDj24XxxfkmP5DL09K0cZ1nOxPa5I/ShNS1A6M7FbYY0OIEgItnZReW4fynuN8fl4wPtk5ZzK6+jvC+5rPdsgn4i1vp5BRue1VqPd3is/dj+Iz3efMX4APFqy+6F9Wgrx3nMaD+k55RjG/ZDFfoSSlRzxjZt6xLhPpBpVojXGB8OtN58EtqXHND2soBnYfoJSFKXel8ficautFMpql09AMa1yWyGso2Js1zrb7FC3Ya9WTD9RHfMz5id80n2gWkrwElT2oFoPeXQFDCny5LuecRzTwxSx9NrLSItuWBrN9V7vLutz5G2UyIi1Ur/PcU9e+VVtR64fetqU2acuqo4rnVbLhr7xUAdvTkNqTawlOE8joPFRL/s8g211Lh1W8/vo/c1Njos9NrGM5VZh1ZsdfmGxkq3X1Wriu3rsGmEnVJRDrW/CZJ0rOEz9lK09kGs0h6bjatAd6hj3u8wni0hL6edAssZxodgYhg7J+mLGcw7qJoxFyxK2CcZF0y8c+L41TpW25Ki69jBTOhb/x8AaZrJ9qVT65GRsXGCDc6alqW6b3v2mPBdD9p/BiuVjNac6M8LzHNWS32/Mj/S9r4z1fY6xvsS2nqKiOzu6rxse0v7xhGsGEYJrK9S/XwLx0AYJquVXu3xI71PSet932bJ94uInTENTEdaQbZG55uD+0pdx3Ecx3Ecx3Ecx3Ecx3GcAeEvdR3HcRzHcRzHcRzHcRzHcQbED9x+gYvSqZzPEyvP4zJ1ynsoz+cbZyOvR1bXCC1HaqQbKr946faVpvzmm5pFU0QkpLokuiqxPBrLtleQz1E6n0/0mlJI5KBukqP3Dpry8QHSjYpIBhuJmzdVBrK9q8u89+/rcu5vf+uuDJMgyVlmbmYNTCHbY2b1FPWhLW1NIWlgneJ2RpYRu+WiNgu31qEFJEqHhypdPDjUZf8iIo8OVbZ2eKLlxxnURUQmkMIxOzVl/DVlvGm3vKyO9h4kPRlLo5FB98hAhkIITTZ3XhalObnJYApams4cfQrVYYmg/plbjH9Asp5Qli2UhqKOoV4lrT4vp20G+ogNrGWqmlI03cY41NA6xJw2JeHm0BIpG8MNpWxvgmulRHZIxBhlddYGHzx40Hx+AqlOXUPag0zgm8LaZRSQ/yZ4GEuMB5SHvfzqS0352g0dZ15+4ZWmnOJ+F2vKJ7XitG89k/4e4zpoFSMYrxL0q/O1jmm7tcqNeN7rjfZ5dcvuJknQXwfaCnGroUoSlSDSCINTSv6M7I5lSqNs/1zDWqFCvFBA3rxeUpLI72WdwJGNNJL6VfYxtuLwW4PpQ2EvA6soWjEkHI9piYXr7j9aq35QkogeK4GOMw5JlwhCiDI663czhrRoRwU6+xPEEcfHtr+hjc/WTNtqrJmdG20Q8lB0Y7JcQg5I6wfENjXsF9La2i/MkG16sw07kDGyQqN+ZOh7aI2wQswTkfHe2G61hJpWnotzRAxjJMNtX7ehE7rHadOWW7uwPY8YU9P2hDEggoY16mbEH6ZbagsW0f4nU5Wj5mPdRkQky7V+BNQPxq8RY4ixYuDz7esLGNfZAahlQ4UiyiltO6ph1psq1nJyFqfMYRc1wlxjChuXCjfg3l07h3n/HUiFMed68Y2tprx9U5/phs+RFj7Y18QuqMsl7X9aMUaJ2GqD7QrMj+kKtbWr53frdf2u72B+vP+R2m3RNiaktr+59oK+G3jhFbVfmG1Bao17mKUt74ghkXSs4Uu655KcN4dWeGctBjhXZ3vumaNKd59m3htxHDVNvj2pMWfVWTbH6HkvEKTverptYNrb0YMiBLYLeYL29zzPRBGpzh7pCcbie7CbLPCe4hj2TkVhK802+iRJtA1NYEG6CzuES7tariqd86zmeowHd+/ruW5gPYgKdPBA37+JiDy49wj/0rFoMtE+ZTLSMW46mqGs11Di2c6w72ym7/EmU9vflmX3hN7UP3wv7W6eBV+p6ziO4ziO4ziO4ziO4ziOMyD8pa7jOI7jOI7jOI7jOI7jOM6AOLf9gs1KGjs/N5mVW8vOY48s3uzSuz6/exk9M0qHXD9/8QWVwt6+rRILEZE0hW4N8pIS8hxmoT8+UckKM9Vz3fRqrdt/85vvNuW/8zf/tjn2m29oltHf/Xt/sinnE13y/av/37eb8q/88m/KcOmS6HZLERJkVmbWxNO/9ckj+K2Uz+rnzAC8gfSZ9hoLZChcrLS8WluJYk35UabnmJSQ6kDTn7IMyUIGfQk/53UmrcziCZrtOFNJwDpF5k5sn6Wny/iTJ7K0P98095UaHNpSdCtgpCptZuMATwTaf4wgJRT0HbQ6sAlMIUOmN4KRQ7NO2u41JCqnoE1HCRk9HRsis5IHyFcD60a3VLydrZWy2MpcFKXRlEMPU1Jf15UsFqe2KfsP9pvPi4229/FY20w0EnArzTNSGMjUI/qIMbJGv/nmF7A3MpJnzBqv5Rqy5xJy6JYXiLH0mJ9gH8j2C1gS3YXtxJUPP2zKoxky2U/1elaQTbWfOjOzsjGMYMtg3U6GIy8zBH3exhmKmZRNNmTdpmplZa4gVVtjDJmnzKIN6ybK92ihgmpAa50S8rIydsdhp9fBsUiPN0GdDUEl1JOczxTjLKXO6PbY59JaQkSkgoyefWXszSg9zHoTRMfwLGVsotuwf1+i75jPbUwRkVK5pHyxx1aJsniOWSWCng3GwjWeSUTsmpZWUszYiOdOssD6q50Eh8Ul+tyKmZ9p3RDsWF3AF4t9mrlutIv6M2a/0NciWM5ym1V7BOs3Wk3liHVyWCtF1I8aY16F8YS2FiaOwXOv21JiWqDRTgH1t2I8zjmkCeBpqwYJf8X4xva5xnqsR1HOuxur82UWfx6oz86d/SfbxAafHx3p2H7/A9vfnBzodrNr+ox3dmGRMWJfAusz1MjUPDttz2Xsbr+MtUVEigL74LnUKeZYa73AHP3s7jWN4a/fUCuFjz7S2K+GN82k5SVw87rKpa9fUcn3CHHanDZc6TDjYgnhSU+20z90lhO0wbaNXOA8lXNf9D0pPrdlzoOxL8sJrRj7LVcYu/PaUmMHgmOjf0oTxue0A+Lz1c+r2JqDB9OR4aT6bSva5/m8EyXK5ixmOFqqddM+rezQ7xwcqeXJfKltSURkCuumJOj8awRLwuvXbzTlj2Cf8tE9tVB45219n/bgI7Vf2NvZ02PBMoF2TiIiiyO1REhTxL4T7Qdm052mTBusCYbdE1jW0cKP/deaczqx8RcHQsZ6CeLjPJ7vfc2w3vI4juM4juM4juM4juM4juN8zvGXuo7jOI7jOI7jOI7jOI7jOAPi3PYLlNERq3ahnMeuRafkokLmOpNJnkvhucLdvIuGHCjqcuctyEtv3lRZxnhil9FHyJopV0zN0nsu4YZkBTYJH91Vucc3f+07Tfntd+405UcHumxdROTlV76h37Wl5/itb/1WU/6NX/2u7n+XmfuGxeOsl5Ri1nW3BIpyj1HezrBL6VmPFIZ1BbrEzUZlP/O5Lp9fLFSWtFxREo3za6kmppDuM3srVNoygdx5OlXJwQRSOOFEhEAAACAASURBVGYfHzHjOO5Bndq2ZmQklMmYbMmQ3j6WEg1I+hGCSHKWSpeZlCPkOAXaJVVR7ayjvGpKxfKUUkLYuMBOoUJ/lAqlNZQI4rmZJNNWZlbjfIuSUjjYL1C2iLprq2K3zNf0lx9jv1D32C8YebkMU55YVZWcHJ/Kde7dVeuB2rRRZm7n3radLRcq1UmQpZVZ2Sk3zEaQtQufI+T4S0iSax17KNkPrc5mib7qwT2VHG2QUZ517cO7OuYw2/PR8cOmfP361aa8u6tyo+lMxzQRETQ9KSpI7DfMCN1vlzMkHjcFkzEd/Q3rEMeeuiUnLZEadwnZWgWZO6152F+lTB/OvgRSrA1iqYLWBq16Qzuf0VjHHPYTec62ANkt6nUNedhmrXVgDUk9PxcRKSFJix9jC/OYrkzRgyAESfPT+57g2TE7djQWObDwacnrylh3lqu6+/4ZKwv2/ag3yRhts9BySbuhqjVWsHtDXc4FEkoeA3LZGjHIeg3rhwXGOPQpo5ZGldda1d11kzFAX30aAo+vwlzBU0gu21FcBpuF2bb235OZxs7zpdrGVbBKiYh1xrnGtCnthmglgpi9bsXfpYnt0cdgTBhBw5qawRf9Keo146zKBEE2PmHcFIyMnDYBem/b2dmHQogiozObDNaDHOPJGjYJDx/o8330wPbRSaXPYmui85MsIOt6yfuPZ4pnZ5wRcb9pSxSMl5CNs0w/xqzwqEMZHz3m+Vmu21+/fkmvJ9frWSx03j27rJnqRUSu7iH2SWFRUqDPpuXQkNfBPb37gnleoTU4J8ZWEH005uafqIz5MedrSes82u+UurbL0PcYWwZaIOIajP0C5muh9a6iNlZ1PKmPt18Yzgz89FLWZ5dN64AC9918jj61rlrPqsIYADs6Gen+2zuIV2GBcHSi85zf/NZbTXmz0Nh6Z6btemfnclMeT+x8ZoSx8oVb15tymurYl2U6bo7HWsauUhca+9L2tYRVy3Jj+9sjzOPKFYIsWGQluJ9pp3/H92fAPZTjOI7jOI7jOI7jOI7jOM7nD3+p6ziO4ziO4ziO4ziO4ziOMyDOb79AKTI/h5SCsqzYkhVtmPES6sWEmRVzHAPStjWy4iY4j9FY9716TbPhXb+lUtP5SiW1IiK7W7osvMQyaC59Ho8hcQ96snmuy8gfPjhqyn/r//47TbmKeryvfe1Nc+xXX7ul+x9qtr/vfFuXmM+PTpryTn7ux3WhRNHMtRH3LysoRca9Rx2YTqz9wnSsy+Rp09CWZjymQPbmBZbrn5zofaUVw2pFWaJ+z3hkMw4nzGiPDI6LpZ4H680M1zGGTJuZi0eUgUBiSDmviM02X6EtxKJbthayMxuDgSnOkjM5PPua1GRVpueCFtPWb1W0K0ikW7ppsy9TRgvtFZU51tQBf9D6E9rSUtT9IqqkJM+6ZbsJMtNnCew+6m75v0kw3LLaMJL+CsfDuSc5bRlkkNR1JScnp33x0bH2qbTIoNyKWbSPj+zY8N677zflK9dU2mey/Br5Op8jZTT6nZSQGvsZyrYSe/NXkLkv55DRlirv2dvT7K17V9TKZwLblwoZ7zfoe5drrYtpO7M6stZSUktLItq+DK2PIcnZfadMj5ZMJQIVIxltOZXUsNJg/tsS2XDZQ5l7xm4IElcq5AvUmw0kz7SpERHJRrr/dlAZ2oyyRcoscVa0mqhWWs8WqH+LYy2Xa5vpN5q6TbmtiRabUjogayASgkjy2H4BslSbIZx9D+KG9GMk5NS897WpBLE2xhC22dmWPvcCfcwGlbYu7HiZof7nzBIOebKx94kck2GPBDnmaq3Hy1BV6lbYxlpQo27WkJSz8QzYfaGhPVI/JjHxRr+VHfurrV0dB7YvaebuVaF9/ErDXakQC+xcRgZwjCcZLDyKinJbKy89gdUMt9tGHdyl3H0KGW7K69OP2QystY+V8FMebe4nYx20vbqVFX0opCHI7ug0FqzR39c5LX90++UJ59ktG7cUligp742WqwLxIesgHgxtZCpj80XrMoxT7SCBfREs8mj7M0lgjQAbqBrldAprNfS5CcK6nW07n9zd1brJMYgS84TWD/Vw7aW6gvrYU2YMyncvIiIJ4pLMlDGXhf3FaMS5L6XtsCQc83Otf9b+yrbZSKvOnhgj6bEGS3vKpo7DMqBO7HMPppPqtr0bciwsIlKUldx/eDqXOjnRTsXMfBPeR8Q5Cfp2EalrWlxoWy4xh5kv8D4I/XaCtp+jDw8Z51j6/XN0gPsH+l5ORKRCH5jDkuzWib4jPEGMu8b7oxSWrnz3RAsS3h3uKyJyeKId0fJYz7FGbCSwbEjOaYHoK3Udx3Ecx3Ecx3Ecx3Ecx3EGhL/UdRzHcRzHcRzHcRzHcRzHGRDPqOePKjs0EtFuiTIVdbGV8ZIy9w2k9yPI1Clho9yTcq8Vsi5Tgvb66682ZWb3Pjp6ZM4jSzVr3iinjISZ51W6FCATOoJlQproeRdLXWZ945Z+/tUff90ce3tPr4PLxG/eUvnstW3N5HfvzkFT/qu/KIMhxgjpL+SslVa/yth26H0ZT+wy/tmWytAnWD4vkD4WlBljOftiocvqDw+P8TkyGXLFPOxDQiur+wjWCjFhZnFKBSAHypGF16jcmam3W68RWnK7UEIGDblSZBZFyBHys0yUQxO4PpZD877YWwR5TKSHS+u3KkjFjOUC+p2IL46UivF7KFXG99Sm+8Oxgq0zazwrWmpQUlLjyyjnpyUJsxAHkwW6R4IoNll0iv1RRSVH/5cMVGVWlpXs75/2k7RbuXRJ7Xhu3dasp8dz7Xfnc2u/cO/ug6Yc8ftnX4bxONJxglm3TT3o0QvTPiYJtv4uV9o/LdaQuELec/vmzab80muv6Hf1ZPnNjQUF+pOWxK2ATUNOawa2nYRtwY7zg+JsDKIlRzTxDPpX3rOWFLjiPyn/pTSd/Qcle6g3GTKPsw4VuMclxs00secR0T9aO4BuyRxliBxLCvRbK4yVJ0c6hlallZrxJhiJp7m3KA9udDojwCYjpf2C3st81C0zndrQRjLEESnjgh7pMvuYESTyW1FjpD30JbQYmjNj80afqYjICLZi+YhSWNiBlLSpomSVYy2ks6getH5IM/vcOe5E9Il13WPxMnCJq0grAX23kld63KFExNov0K7symUd8xhLLBY6ztV4MDuwbtja0TnTCHV2BYui9cparnDsSDA/zCmnxhiS5JRGY2xBO2LMZaTNrTHShnysj8zIrluU9TDtF5Isla0zm4wq0XhjCaMfxoTjmd7XsT5eERFZFxpLrOGDstxof7NL6wGML2YMMidIuwta+yCWbVlfJD3WIpRgS87YG7YTsDFbRr2eDazOYqX7Tnas/UK2o/dnlek11fBcyCI7pYGOU9Idepqu1E52tNiaTwXGqrQWQtvmuDGCzcIY/dOkp8w5v7FfaNUb9jcsM57lvKkvxk76rJ8+5lHzXho3K1Me9uC0WW/knbfeFRGR+5gLlej3ab2WJ7RfsK8Wiw3GBvQ1J7Dw+vCDu015fqyx5dZM45k3XtP3ensznYftXVJ7vCO853nv3TvmPA4e6Xzv/v69pvzOHa27127q2Hf1ln5vGKEDjZxX6b60M9y0LMnWsDFbLWHRhvtZGW8ku//T4it1HcdxHMdxHMdxHMdxHMdxBoS/1HUcx3Ecx3Ecx3Ecx3EcxxkQz2i/AHmg0QZBAgiZIJfjP86i95jvfe+tpnw8RwbL0NKInFFudCny7rbKitKU56HLrm/cuNaUmbX7vTua0VxEZDJRqep0qtliKQMJkIhFZJ4vNir9YCbzLUhyv/jlN5vyy194wRx7vKPfezlRicL1S3pO40yXmB8/1Ov7D/64DIcYm8zhFZbrp1W3BCrtkQyLWClYDv0isyiuYclxgoyDx8cnTXkOCVqB7IMBEsgsZWZPmxWeErESng0ZPs+NfB6yJCrKzJdS04FPW/YLrM/UzHDpv8lkfvZdQ1KDhBAauSezgvPaU5tDHsWWXQXuK2VgwWR+p8RV962lRw9pbDP0U8oOk2j1tcuF1suIbKAZ6zH6lMDslwnkZPRSCN3nIW0pduiWuFEia2XSw5SZlUUp9+7ti4i15vnSG19syq++9nJT/rVf+9WmvIa1gYjIcqnPYrOCpJEKPlYPSpppN4TM0omwzkFKTalxZZ8d5bJLZHalzOzSno5dV6+ofQ9tiyjtn05V0pSPIPtu2cxEYzPSba1grZWG+juxWgRVvRYSeNhGytfajLcA94YOUuxX2A+l7JMoYYMunTYQKZ5PlresivCMt2ZbnZ8zA3UOK4aqwD1AkXEY5WWxNZKZIcrIa7vH/KHKoUWChDPpeGTbYXZttK8J7v1m2RqnYDFgFOiIcRnvGksoWPhExEx1qs/XuISsYemSt2KsCSTbGJvYR1F+zf6K0ugRjr2mvB62MXVrnEkRwyTWvw1bod8cqm2HRNHrYOzSHYcYWvEgJcpTWH2ES2rdxv5+udR5BK2mmHV+uqWDHDPILxc6Ji7mLXloYD2HHciOjk2jLZ3PpBPUTVT4gn1myXoG247U3py8HZ8/3p//QB9TVOUT2w6Bsq7k4eZUmryB3YDk2m5K1qcRYs6ZlkVEVidaD0rIgfcXaiu4V+nzGuNmVsbGrDum5vwpNQ+iNeuhNwv6FY5/K8y7x6iPJfqPx/dFRGQZYBs10rqcTW092aSoX6J9Yg7riJGwLchniqdxsnni8xC6ihLQJhPcvzTjOyFYgBm7Bi2PUOb3PxkhdMcVtLPiuMFxqoSVQxLYF3TbTtHqTKRtP6RlWi6kvE9PnPHzz2a9kfe+946IiNy7r/YLa9jBpddxvYwJWlYZBZ7PBn3NAd4L3vtQ7RCWsPa6sqN90Cs3rzblF2+p5dyVazrnWeHYd97/0JzH/Xv3m/K3v/2dpvzw0X5Tfuf9d5vypesaN4+39b1cNoE1J6wu+Q6mLlsxMe1ksB3rblnCbqQ4X20Z6gzMcRzHcRzHcRzHcRzHcRznc4m/1HUcx3Ecx3Ecx3Ecx3EcxxkQz2y/0MCVxMgOSWVQgWXQRbEWwiX2OeTvJyeUBkVsDwkEM2xCrnTtmsp8rt1Q6RHlRgcHKi053R9ZiiHhWa/1fEtkAC9KLU8gU4sQB1y7ebspv/CCLtmOAXIjEXnvji43TyEveRnLyne2VTY12WqlSx4IUdR+IUTaAkAPaCTskG6ktoomRh6s260hj5jPVR5wdEj7Ba0HmxXk7DinMSQhE9S50cQ+u7Ji3YZ9COpmhnPPUkqckfWXslpqe2qjpTfHtpL57nLJrL/1+my/4Yg/YtRrMJIn2AhQoppQ9lBbWWCNtmnuN61i+Hmmx1hFrUslJPi0mSmRpZJy99iqu+sCGXXHkBtNkFWcmcSxr5HtB34vpB+R29hnXeGaasihAqwBAoeDeP6h4SIpy0oe7p9KeujucvtF7ZNfffWlpvzW977dlJcLK7GaIdvpeKQynDToPaOkpkq5P7I4Q15jnqkZQ/ttDlaQOy2Q2ZX95Ayy1kmrr2rOG8+dGYn5PaHlJRB6NMDsS0wm4F7rguebKLBE6LGsMBYDkBpmLU1m5D009hzYnTYw6CfMeGf6j+7MzRw4J2Ob2ZuWC7vbGhttb+nnE8RhGS178D3MLL2h/QLK7XpDVX2kLI/b4F89TkzPPyE0Y0owQbGWRxj7p4gpNrm9aMaZ3d8kVo+Kdpsn6MdRHdn3rCHFXsGuYdlq4wkG1hRyWWY6NxJc6W4LE9iBlMiGbrKTZ/bYdGlIII1OjJ0Q5PkDimksQT42vXp7a/axLXseZiBn3zAa49mlmEfAUoM2MOa50w5ho8dbwEJqvbRy/hHq1Paujp2XYAc0RibzgFibcv71Rr+3QLlCjD8d2zEuJJT9476m3fHxphyo/UJVyoOHp/PGmGjb3russuQMsTAtqDYtq5MSVh2HK50n/fpb32vKtMh49bbaB+aokAEx0A6238HYsv+hypuTltXO9LUbWt7R+lHCDqGClLkS/d57D3Sbh4+0vBbGu3qsdGTHSMG5jwQxET4vUf8lGepAZcfkht4uiPOQtkkg/kb7BY4J6NeTnPYL3VYMtFlk2YTIrdjSxgyMR2nZoluUsTsuo60L9+U7q7KwfV2FOV+E/QIjs4wWXc33D2i8irVUZ5Z0NeyaUsTH0xEsVvAujjaCItb2ZIXvmh9pv7M4VHtMQSz04k21Un3llReb8uUrGtMy/hwj7n3lNWt5+tHdj5rytetq4/qd7+g88MO7atlw+V0dx269qP3U7uRSU65pmYB+I9St9zYYcipaMWA+n4Tud4rPgq/UdRzHcRzHcRzHcRzHcRzHGRD+UtdxHMdxHMdxHMdxHMdxHGdAnFtjG03GVi2mWHJeQSY0Glm5zJtvvt6UH+zrsutf+3sq/aDsL4Eu6/DoEb5JM4C+8rJaHezCtuCd9z9oyouFzXDODMI19ZEoHx2rZQMtF3b2dJk39+W1LhZ6DX/3V94xx377Lc2+N4Gk7B/6hh7jS1/WY2Sz7gyvzzsxakbKlFm/sWSetgoJZMKUGIqIlJDhlKUu8T850ed6+EgzJx4eanmx0GX/zD44hiRnNtXl9jMs48/G1vpisdbjhTWuA/LDjFk/Ye2RQabGckgoUdRjtZfh18ycaOwsdBtKY6vyVDrSn9X9+aOsatk/PL3HRtYDeeEYks7pmFmz7XVSjkflssmSWlHeBRkJssAzYTKzJ68p5Rtl+NxKdspa/70zg1x+rCe1WlL2xEzYlLvSJmGM7bWO1ZWV+Zh/87tSZvGEDH9AKiFSlpUcPDjtr2nZc+PWraY8nsLKB/vGliyQ8qkJ6lqG+1RDhhUw3tHqgJIr1i2TeRjPpyisfchiqX0N7Rcu7aqsdXtH+y0em1ZFlLUl1vtBryHY/pb1jhYgNsHwQCsLibGx1GEMY9M7d0vO2/csgf4d3bPEPisHPou0exuWrT0R6mhrjJqhnk8hi51wLMK5Vsj2vFp2W34w8/EGErv2+GirBOSQtBkx0s9h1qEgobH+SWBDlqI8xnVOmdG9FdtYux3UL1OJaJWCdot6k6M+TiB3zY0vQ3ccdnrwpLuc8XNK/mEBgr5nhj6zgly+wvicJva5Z7AJotOWaQtPk7J9UPAieuxuWG7FccaCi7J4diu0VuA4b/obhdnK12tYm8HCrK0O3ZpqvLyzs9OURzOMnYiPQs8YWUCee7JQeW650v4mlho/ibQk2yYbebfdWxWHEwuTNIhsT06vKUFfMsWcYMGYFSFoUdnpfon7FBN9xvcPdM70zW+/15TX+ijkDVgm5HiOWxPOedb4XPe9iVhMROSVL6s1VjqD5UoK25+1jlkPYLPw9ncf6ucfaN1crSGjhw1knlj7BVou5DXsYjDP5Fi/u6uS7c8cxvOnO+5p/5tjBy0X0rR7Hkw7nwz2KznKtCoMjJcrO17WOHb1FDZhbP9FwTK317pSwXKhLG1MbuwXatryoT9Fn/t4jBxSrDydTuTHfvQLIiLyzjvaTh490jY3zmiFpPcrSssCEcEebaZWJ9qpFOjft2faTm9fV2uZWzfU9mCyg/gCgxFcnyTN7TuzJFUrhwrz8eVK+7z7D9QWdY15/tFc+5cR7O54bXUB68a5vQfFinUZdZf797zreBZ8pa7jOI7jOI7jOI7jOI7jOM6A8Je6juM4juM4juM4juM4juM4A+Lc9guhRwNlVL+U9rWkrZQDzbDUmkv7U0jZa+xfQHZ/5ZLu++YbLzflslSJxvvvv9uUF8jyKWKXgnNpPKVtO7u61PprX/tKU06CZsm7cwcykPua6fPol4+a8vHcZlw93FdbhynUA9cv6/43X9LsfZe3h/kOPkkSGZ3JP5kofIwsvLNtvcfjmS71r1syqeVKpcgFZO/3cM/v3tXl84eHev9jTRmNSpcvX9Ll/VtbKhujlULRSs9dQzZQbfg3ZhzWcg5NwGSs1zoaU0amz3e90fq7XNtMkiUkAWxvG5zjAjLZ9ep0+6oajuSs2JTy3p1TmxWeN7udHJK7Wzc0E+bVK1ZilUA2auT2lPb0SPPqmjIsrQ8l+iMjI8YBjhfHYkD6y60J6wZ1prR7gIVGTbkPpbmQZYce2fjphvgH5dDM/IrygOoKqetaTo5P+4idHZXK3bipcsEsYX1iRtyWbceoO5M47xOleWkKKxWOJT3SMMJNNq1Mu0tI4XmG7DMpd51NIEuiVBfHYAZ1yuOS1rnScuFprmOo1DE2GdF5DygjNGVKnp+QTENOZT6mTDp0fWxk5oyZIiTrlN2OYKUwa2WE356qBdV0RMsFHBBjRolM6cu5jrPLZfeYa5pOu24wuzQzkbPvMVm0hyNLNESRx8rlvIb1QKBdi96zBW5a3rLIoQyPFi+MQ2gblZg6RCmqbj2GpVO9RnwAGf24lRE+Qz0qUZfLyLgDGb9TvY5tZLmeY4yLUy3XiIXWpbVDY3b5JKPlgo5/9UDHJkvEfKPbMiHYjkE/f8K2AzZNxsOD1jmdX2WsXIw1CuTJC8hOjw4Z09jz2NpR+4W9KxiPoL3nudMSiuPUHLH1/r37go30GkpbB6ZjPQYcJSRNKb+ltQfb1HAISZDxWV9eFXqhy6XOEYoNbQQZ09k5DGPpgBi5wDjwAHOpxb7OpQ4e6nzrC1/UeffV29eb8o//5Feb8vpY2/nOtrXOuPySxu6zXX2O1UbP6dFd3f/9O1on3n1bs9k/vKfnV6z0Wsfol2sbWhkLN/qJ0IpoNNZ6Pcq2Zag8MUa3Pwvdtjshte2c86ne+MiUs54yLBpQzk3bxLiW2DbLvsteWrdVD98lWFsGzOUwty5hn1eXbTsBtCV+r7Ey6rBBG5D9wmiUy+uvvygiIlWlY0Bda/+cZexTtA9KMtvX5JhL1UH/drJS61XaFV6fqc3C5cuXmzJt5mKmz6QsEJtgXKIllojICPYfl6/q9166qsd7eKTv8lYbPac54uBt2FsK6nQCu4e29QPbmnmngXcMwvNNz/d6dphvCR3HcRzHcRzHcRzHcRzHcT6n+Etdx3Ecx3Ecx3Ecx3Ecx3GcAeEvdR3HcRzHcRzHcRzHcRzHcQbEuT11CX0J6XMS4YFyMj8x+9y9q74V2Uh9MjL4ZNJrIsJ/aTLSbW7eVD/UV9+43ZRDqv4e9Nfd2bJ+PrSnivTShP/Q7pZ66rzyinrwPDrQbR7cVz+fFbw39h8dNOXF2nra5EE97zbwaPnwffUrmp/oue+Ww/Q0DEH9T1N4q4zh3TaeqJdSPtZ7nI6sLwkfWAlfraKA5zL8VUp8LjXqpjU7RIk+ZPDsaflt0p6qhBdejDgG/FFq402ndblEvWZ938DTrCxb3nsVPGPoXYS2k+Ie5mdeSSEZTv2JEqWqTuu+8T2F79PxAv7Y97WdbO1oPyAiMprA2xvPMfD5moPDpxb3LMDjJqCOJfAOX8OLa3Fi/bsz4xeOetJTB4xfq/GPUj+fJIX316jHX1dE1rQujOoTFKREuccLdEDEGKU88/3c3VN/txn6/QjPWvpeG/9ZMZbwMhrT71rvf8B9ztJuL1tjM9973ijXtr1vNjqW0ctrNtVrms10LBnBy8l4P8fuM6HPYdLya0x6/hY+Y78HhxC0L8V9osetcaW2D8x+Wez2wmTfFXvamolB6KmL78QQKnmmz33c8vCawD8spVdvgfpU0GdV+6v5kcYzy4X6qZU18gKYPsnWbHrk1jh3k4mBfo8yHK85EqJIUp3WmxTejSnigATxQYK2nbT89dii2H/3eVmb+ljz+aLOMo0Dxiz6k6Z5y6sQMUVEbgnjqQv/yQTPeozxcoTPM3xPhcMlrb5OsF3I6BmMtlP2e6t+lojG9JR1ILY31CK2s/koQmeZY02FOHO9gpfgCf0U9Vu2trXvERHZu6w+ulPkxQg9QwWvr0Juk81Sj72Ct3eEn/cktX1dhbod4ClvvM9xb7KBeupKHaRan8XzUedMEWNFgjaUw9c6a/uW0x89pUcoYoaVPpeHJ/r58eqDprz/UH2WJzj2DP7au/CsLPdsvYnwrC2Xus+d9x405Q9/S98ffPSRfn74QPPSJOjeprnemxH6X16PiEiFeVbA25AUxsyc+62Wdv9h0TGOGE9dfG7Sc9gGHIzfLnxje8pP57vbXeZJZS1v34q+86E7No19sZyJs56t/OS/MWYx1uGxY3vL5580DbK3e9ooZlv6TCYzeJNn7He03ApFZTSGnyzywxxhDr8p0QcxP0muYwl9lU2eCWwzQjnNbJ4JxlIBeQU4t+E7SLLGGFOYfBe6TYKYO5/YXAX0pq7ac4bH26D/bM/hn5bP1szMcRzHcRzHcRzHcRzHcRznM46/1HUcx3Ecx3Ecx3Ecx3EcxxkQn8B+gdIo/bRP8HNycizk177595ry1asvNOU0wXJpSNiqSiU5U0jLX3tN992DrGM8021++2/70aY8Gtkl0Ttbug/lPREywylkJLdu7Dbl+7dU8v3Wd99vysWhLusOWKafteTvm7Vut8HS7vlKv3e9WWGPH4hbxg+dEIKMxqfnnsE6Y4TnmEECSKl50pZ10rrASIsplcA2kJfWld5jWmRQJh9Q51JIvAr6LYjIYqkS1iUlOZAEZBkl2zg2pG0J5AS81mKt228Ke+wSdSqBVGACiXmGpf+PpSJZNhzJWZYFuXH1tH7weaaQ1j08Rps5VHnWamMlUlsRfQrqTEp9vbF4gOQHz5CS+op9BerkZq3HrtdWbDODXDHFnzaUkEa2A61/Vh5CSZJgG0o3bLtJKeWILKP+oS72KH4HwWNJ6aU97avH6F9KXPMoG2G/1hex3kGOx/scMOKlkdLXPvk02q50P69NSyI4P4F3Bo6xu6vXR/sFDry0XMmMnIcyMXzaevChu1mc6s67DjhUQpA0O2tvuCEB15lQkhm6yyJtiR2tB2h5Af21aQAAIABJREFUQVmwbl1GffYVxpxg5PjaL+Ton0aZ/Y0+x/nmfI7srxBfLBCjLSiL2+h4Rdlh0iOTFLH2C1b2iG0YLcYhCRNJkDSeWUvhQQZzoeai+0nYEPs2oq0F5OuRYxPl09gTMaaxAmrJWgMHFdShCrJstn+GtRzXEvansBiqjK1Iy4KC9ZR9LmxyTN/Ttm8YPN2yTFoHGDuq1t9sVxR7yrSHwfNFvVlizDk6OMT2us3WrsrmRUT2LqmN3mQGWyN6gNACjR9v9HvLtcbWG8TZ1Rq2DCMrqy1WsJSZ6t9qVELGN6OspQ8eCkHkcXMNqPs12yPGgXys84AssfF/jTk1509TtLUcz6vGfO0YFj7Jhzpm7aEBT1+BHeLuZd23JYle3tV5cPXgblO+855aPDy6r8c72cAOpNJjT2HnR2ugfKP3poCV1em/YWVGq4qR3ivGTbGin83AeIag3tgQtt5bJBgvEtNH91gx9MznbQxFm69uH4gnrcG65y4J2nzss0YwsZh+Z0XLhARWoGn/OGVienNveE6nO/RZKT2PhCCSnwWOY3h+5bjGNGN8jPsQ7DgWYfNXi7a5NazwIuNJ+jegr04xXxNsX9FGEPPmqrb3m++DqopxPqzKcDw+dcbjFWNavv/k881a4zRfN7Besoy6mNTnqyu+UtdxHMdxHMdxHMdxHMdxHGdA+Etdx3Ecx3Ecx3Ecx3Ecx3GcAfED0fNzSXkFSUiGpfnXrl41++xua6ZUSvqsHIuZWSEvy3Sp9c2b15ry3i4y4M10qfSXvvS6nmtsvceGFIzLoMewCRhjOXaZIqsrlpgvT1T+TeniChYL020rVxpBTvBopdstS9XMGRnIQF/BhxBkdHY/U9zXDJkCKUflwv2yZXvATKQbZFGkFUMJGVkBmXyBpfcZMnpT2kcbjCB671elld08PFSp6nyp30WVoARYeMy1Do0gBa965Dw1Mliv13YbZmFkytbZDFIrHOPxkn7e7+edNEllb+s0Yy7bU0D7m070Hh0/0jZTtRSMVv7bLadmFvgamb3ZXWwgOaNsmdK1cqXP5rEs9zG7O7ADgJStnKO903IBz7YoKCnqhlnP26rUminHIyQs0ITURnY+HJmQIcZGIvqlL2q/P53odT5c0NJGr5O2KCIiBdrZCJJBZuE9PjnS7SHty5C9lXKePOuWiVWotEtk/BYROXqkY8tkpGPI9RvX8V16TiUkiX1WAHy+1najtUdgnZBOBltXQBDN6mxcJhLeJ1og9A/GoUcq3WvZQDcUJiHnJrRSwBg6m4w7yyIiE0jYJuj7a2SaX6KOr9ewfYIE2thA4B7QJonjr4hIjQuhbD/2WAYMlRCCZGcyzdRYKaGuGLcbXH9L0lkj8omhOxu9SPe9rHH/C0gaqyXGNdho0H4hH1spej7SfzO7uXlaPf0HB8yImD2a8ZVWDObQLWsBWmGFjk9FkjjkvqdrJO+26rE2LnY/8y/jpdMTKdC9ghJ+tPMl7MmOj3SM43gwmdp6Mx5DwooqUSMet+eKzytahmifVC5Ql1Gvy9ZYXUJGzzJj+BHOL+/JcP68EyRK+ljmHBifQDK8Ql9g5tYtSy48pGyE9sU5icn4ruPDCLLrl17ROOuNl1/Vc8Uc5P4jtfB4/zf3zXksH8E6Y47njTlaXOr5lWM9do5QNkCCXcDbI0U/NEe8JiJSogNKaFuD62PW+tQ6WAyLZ+kqTRDU2rHXk+tT5glrsB77Bg64oXv7GLu3D8aapr8vrc24BQs2xou0XxhgjBxjbGwg4QomWeA7qW7bjPYYxZ4/y3WfKWzjjo5gR0qrTLTLCnPiEeIUKWkRBsvE0salBcaWakP7Ly32PasSlqym7vXYVbU8Olr1l+8kOA7S1vF88fFAXxM6juM4juM4juM4juM4juN8PvGXuo7jOI7jOI7jOI7jOI7jOAPiB6JBoayDEkXK9nb39sw+r7+pko1791WacXiwwFbdS9m3d2ZN+fIVtXHIISGpK6b9RRbzxEqGovkb/4Bl21zOjYyhC8iSTo70GmKJTPVYLr48Usm+iMj2nlpSjKZ6TVCqSVkgu3Ww8srBEFQeYeWs0lmORmLYsidgVnkuTzfZX3EMelZAM1FAoryAfcIaGXZLyNnna5uR/gBSgRVkjaMx5IeJ7jOe4pxyWEiU1A91ZwYurNJMioJL97U8muj+WztqxTCbnR4jy4ajHQqSSJqc1vc0w72vkF030TaTJdoW69LKFjJYD1AybMQRyNwZcIwStgd1gT4lpSwNcnxIxmg/IyIyHvHfzCTenVHeZAOFr0e1hhyl5Llim7ZtCTN9msybbF9sTzJI0iyVS2dZuC8hGzf7gQTtIEOm7HTUGhuE1hTIFo+suNOptrMcEkhmVi0oFaX9DPop2i8sFtZ+YbPRZ7QFC58xsj1TFh/to9dtnuKZtjdJjGUD5WjdXzZAlZmInMqp8jRvyvz8MX1K77astU/ebNRYPEbCDM3YBvL8NNftKSOe/P/tnVlzHdeVpXdOdwAuQJDgoLEkWR5VLtdDV3W0I/z3u5/sCEeUO1QO2dY8cBAHEMAdcuoHALm/ncykKFrVZpbW93R4kTdvDufss08y19oL73N7C8wlZraPvp3jPq5RUX6H+W6NfrfderupeayISTifqonzY425trYRGVnoLBO2Yri6sUGW6s3g+EVlaP+VilCde9iKoYVlC3+OEr66QnVpxB7OCZSfL+ex3yzmtIuhvJLjn3kY8m7MvSVsozZol9hP2ZNp5rQDQJegu0dK+Wcx3fdSrqS+Y3ZKtEYas5C63BG24+ccU7SOwfb4Ai3uNhvmx573HK4851rM4/KRfZD3nvZZVYV4iGMqz2EDA3skWngwFlc9We1m499ZbGl9xGMcls9OidYSa9ormyBavPh5tjg3pK+h8rtZb4nbcjuPGQlyzTu3rnftd9+707WXh/75o51bRX3xly+79t0vvuraeeo5k5lZk7gE+wx2GyvkaXPkwlWKmJZAdo2ca81UtvbrxLWemdkOgWWOHLtsYP2Axdiaa4BJkQwnMEg4gvElc5hevAmWLWxjPf5C7YZtzF8NbAv4w/1AOZKnJSP2EOmIZRbz2izjmMI2Vbx2DSd1nBMtCMJas0sRphN3mqqxk4cX42CL+Gw1LBByH7tzWtSl8VlDjrG1v+dzyM1bN7r2vbtuy/LwyeOu/c39+/7da/7dG5k/+2NOkCTD632z8PggzDMVnu/wOxnyH/YNPktpwva0F43rSdq4xESf4wPXuRlZyH0H082IhBBCCCGEEEIIIYQQ4keIHuoKIYQQQgghhBBCCCHEhPg77BeGK7kleJWYErw0iz91fAOvXd93W4LNlhXmvU0l8i9+8X7XfvNNl4FYgqrfrH4ZDjU+x2YF0AaS/ApVP+coscntk5SvTeP17dy3n+MV7J4q3EpUoW7wen9i/v3KL4G1zTQrtpq5nDCFTCijDBrtAtcs70miqWHNK0hP91zSc3jNX8s3vIq/gySMEuokVI73m0QZ0q6K8lJWB9+GSryQYFeUw0OWyH2FKtd+HA0sA+oySjZoC0E5y3zp/YaVIff2LyQSab8a46tMYnal4Igy9TFbFFb5jQONI57VTfuyIt+IUkdKefyebFA582SNfoLjW0GqePH1EXku2g2rX9aU1KIyL8+1pvQV+2niufGnc8hfqU7kGJxQTwlkWWbXblzIAZcrjwMlpHn5wu/LDFKgXa87nEF2ukOsXiwhD6O8BxetrGhrAQlzAzkY5LWMO4+fRJueCsd+/brPm6sDPz/GsKQZltpGKKum5CwZ2yzIPV9I+jYpEssv0yFKxaqGscC3pnQwec5oSZPhWJ1gv1WQX/k4p83CPmw+grXOvkveijweR7n1/rtF/Dh76hLZp+hrG8jRakh1a8w3CSXdBa5NL96wH7EydYOYxn5tY7H4Fac1s+oynla4NjXsdaoU1xKxt5/O8QrUqGxf0yInQT4UJHy+Das0r2GjUePzkB8sXUJpZraYeZ+aMV+jZRBiAefIHfrNGhL+LXKvBqfQpuPvlbBCebCUoSVENtV444zFzFBBPOQhcXvac4ztN/xGGzOiK+CMYGvYsmwx9xWISdG6yez81OPKZu397uSxS2lpp8B+w0rktH7IMuTTCD1nPYuib/Eb+QJWfdfcrijL/QTrsrcYmwiJeTyta1+DBFu7wnOaBJJoWkKZmVWIxbSQS5GDXkcO9bP3f9q1jw48Znz40Sdd+6v73gca5Dqz7NCPo3ftd1hrZ1j7ZpRwY37gc4UWfbDGHFQzdsBycVPGPvv4qdsmLm4c+W9jbos2atONN0NTbDtyarT/aXoycM7hbHMNFtq4X1XYph5sc5+Mdf01zejybcR+gXkIt4k2kMOWUMF6zGLf5nqTP50Nrren03/qurGnjy7i7PbMx03aInco3OJub+7tWR6tQmnHsLfw2HH9yG1Zi8K3efToYdf++BPYPcyw9kre6NrLhefE4ZFRE6/3BvPGk4c+Z+zw+RxzznwfuTZycK6Vd9Ebys9nHq9BUfCZE/4Q5mau7WW/IIQQQgghhBBCCCGEEP/t0UNdIYQQQgghhBBCCCGEmBB/h55/+N13VlbPIFdOi/gq8izHq8ypv46dQn5RlrBfgE74nfde69qHR5CatF5lNchiWeH1mTea+Ro9Xo+G5IJSjiC7xNVbHfn5/fKXv+zaP4NVxMPH/kq5mdn/+d8fdu2nj0+6ds6qppQoldOUKJr56+Y5pHN8lX6GqvB8TT0vYhVFyoznC7/m+62/+t+i3y32XH5VhUq6qCwMNdAGVgpZ5hVQm141x9MzWHXAOqPAu/8Fqkrz8xznmkIy20D/FirT9pRilFGxEjL7Sp7x2j5b1f1VJ0m8snWG6rVlQ5kpqoIHaWi/OvSwVQzlPJQCc4zT9uAclXm/+tLH8rdPXbqxWnmfnN92OZeZWQIZWDxC2ncwfkICFuQ/kA6FWIFNep2GVjEWKmlTDgkJVDtNeWKaZrbYv5D6VeZjYI3YeXLq45qy9LY3xs9gsXJy6jL1GvKjFJLrBWRFeeHz25byQsrSU9qHeDw5OfV5zMwsxVi+8xrmvmsuXYpyd357WH42TowRUbI2bDUR1W7TiTEkscTSy3QooTwZw6AN8Was5Hy8X5Qx8vu0UKFUn3Y8rJ67WHp7ueft2QI5Sy+bq2BBVe28f9HWijZTLecfarEpe4R9AO90X2nI6JGFHAt/aNvhzydEa63tLu/fDvcxR1wItgwIMXUMN0E+TEuOOngPjElC2c/8Xp/vfG4qsZ8FcpO9vZ79wny4inUW7hHHiN/fLXKssy1tIPy3C8gb855Ms0D8yEIP8+9H6ew0481FxLk49mC7gfZotH6mCvxIVW3kgw3ngXBdRyrFs6I34hntNU6e9Oap1Pva+ZnL8M9OfO4sgqQecQH5Vx3yYG/Tmi8t+tXova+NqK/DeTA/nhSJ+aqd97qA1SHTSSx4k941y2EVt4cYdR15zDuvv+n7xTj/z//4uGs/PvF8qt3SkgfzJY+j6a3pKJFG/9ic+z3d26Fvok/MD3z9f3DDrSIeQU7dYM2wjS56du/eo659/TU/73nmx7vFSKzTl5NE/+NprQscY24SI6G07U3ObUNrhmGbBlr9lLDzqEKbVkyc75g9MHfo2y+gr/FzbjSSC4+38W3mvj2boPAvxLEYb569oFNKj+uqsZOHF2N7d+73ZJb62ma19DUI7ReyXjJK164M13UJK7wbsJY7OfG55e43d7t2zvwJ9jPXYBNDyyjmvWZmj++75cKDB/d8OzxrpKXZ0TVfwx/u+7Mkzo81bDpr9O+kt4bm2i/LYA+TDH8nGXnG+l3oTV0hhBBCCCGEEEIIIYSYEHqoK4QQQgghhBBCCCGEEBPie9svXCk+Wkg/ggwMr8SziuFs5q9mm5k1Fat1++vObQ0ZPi0aINmibPr0dIdtXFKW5zwmP4609xp9w4qgrJSI396V/p2vH/jr2yfnLjF6851bXft//K9fdO2f//ydrn33vlcANTP7BtKPzz72V8HLjf/G0aGfk2XTlH4kidsu0E6hmA23s1Epcax2TPuGJvH7lcCqY2+FfgeZBCWyUIrY+Zm/hj+buyVGNvf9m5mtITNKZ141fLHn92t1CHuIQ5cHsHp5CjlrBYliUFCV/fuO30al9jRlZchnq9tPTRqdXMYSHncb2tR0UM4Q99MEySq+Hyp4j9gvQBbPissnsFw4P/X7fx1ysLwvd6fckBYIlDdT6YxxwAq8De45rUZ43v1rEH4PciNWqa0qykCmqYduW7Py0mrh40+/7D5vPvURtYPU5sHDb7v22bnLCM3MHj3x8f/F11937T1I4Rdzv/60dJnPvR8Q2iQUue/n23OP+euzeBxzyKGPUC12Bnl+E+TJiAPPqS7v2yCGJH1Z6vD3GWui9cO0YswVbdt2di41xymtmxBumqDS648VVuEekSracDst/Hrn6B8z9IF54TLRWeHzTZr17hXyL1qrjFWaDmEoeCtgv/gNKt/zngx+LBaxanBGmfU0u82F/cKl3UGJMVgjJ27QHyr0gbKO8jz+rRkv7e3NkWOq8d1N6fMX95nCAmrey21mOSrQU5KP40vD3OnfLWFns8N8wmOaY/6a9ey1ZjNIFJHDcy5kd3whR5lXkrbLOXinx2S9UV7c6xtBrcyLw/v13fBeB9sNzA9PIYute1p25ju7nedElKTukE8F67CG+Zfvs0mG+8BszhLnsXI67dtIVflvp8k08xtLUkuKCxlwnvn5BLXz2nOdFrlOvojnvEA/2sMS4/bB9a59uPR1y+NHkC7f97Ur58scN6+ClR1z3CSLliu0Vqyx8skg588Zu3a+zZ0Dt6P6p/fe7dof/uVvXfvuUz/WzS7Gm/tfuDXIz973uNdksLOBjVdiMWZPhQvzhWf7fBIiw3De0/Ss3JoRy7ZoL+V9s8a4Y+7B3KgOdkPDY7NvCcfjYv/i16Mbzdh+XyDf69/2YLlAmwb8NufqrjmdRGe73drHf/2rmZnlGKNvvPV6175905970QKhSPrP2fy+05b19q3jrv3uO2937c3G49ann33Wtf/6l4+69pNH97v20YH/doX4t8V+zMzmiD3bja/h9w89p779uh/Ta7dudu1rsLvLkcOwW5WITSdPPF6aRSuIGa5nleEBFD5v25d73jfZlEgIIYQQQgghhBBCCCF+jOihrhBCCCGEEEIIIYQQQkyI722/MAQrPKd4DZ5V7vovnbd85b2CHBi2DFnq1eZKSNP//OHnXXu78VeaV3uQDCX+GvTqwE/zvZ+6XMPM7MYtf227hGTAaj+OR0/8Fe7f/+HDrv3JJ1907Q8++GnXfv1NfyW9Nsj5Ic03M3vt9Ttd+/jYq+zdPPbzznM/jyT9QW7X/3eSJOlkl7RfyPgK+0gF3770IwkWCpAyQyITq4/TksB/j79NSUgG6RblHk1PH3p6hEq8M1QmX/rr/YewXziEPGB/5Z/ztDe419Z6Xyy3UUKQogJrmtNmARJ9yjwvJZj96qGvPpf2C5R302YB5biTxO9B20aJVbQeoOx5xMqB8iJ4c1ApegS5RoL+dm3lsvtFr8JyFqq949iDe83wNpRGNzzWlBYUPIWetBf7qlgVG9cqYXuq/9+XJJ01wL37D7qPzzZPuvY+qpvuULn07DxW8370+GHX/vah27icwXKB/YNz1NENl/AcHKAqbArLBISU9drH+ClsPszMihkq1cNOJkH8o/xszHIhysGetWe5+Dx+Jx0pJR7V+cP7mhJta1ZWF2MmQ3xtcZ3qZzR4V999jqRuREId5rVkWNbOCu3zmffZBawYZugbae/mlZB7sXI87UfKyvt/VXublgFBtt8iP4tnFH67YA/hfA6rigxWE6N2A684rbVWXtovUELaBPsFpwk2P71q3hW1nxiTLexRQsrOfbGquF/7zRbSPu4TueQ879kvYC5NQwX7MX8O5O+QDNISBt3GMkyk+SzmtHlByyHaJnGux/w8HTXrM1ydUbCEaofHXRvG0/N2yrgyIkk2Xsth6xzmpcyVy3OPEesdTcLiYZWVz2GznHMCra3qoY+DjQxz/INDXyMtV1HCf3DgaybaFdEmqA15zzRl9E3T2NmlPVOKi5bBqqepMH4XyOlmMd4kJ6z47tf86A6uJez/rmWe837wa7cVbFuOYVzvGr/N+5v3LJ5mmAewqwXm4TliT435a46K9NnSj69G3Ko23j6pYp/94gu32Hp4zw/ytZmv15Yzz+lPt27lMDW6tc/YVMsgHbbpe7kxLjHG0JZh7PMRO6qRNmPSMzYQzfBc2ozY2RlyjwTnRCuWaHMz3O5/EmfCkRw7Gdj4FWc+L+y9n7xhZmZF4TnC7dd8bXPnjlu1HF3ztUk6iye6rZhzIt/FhX3vvXe7NuecAvPHvbtug7ddu/3pN6duo1Ij5+lbV2YtrR/cWuHmdT+P60duk3oNa7fVHGv+hc8xe2U1uM3hfrScfTJzO4YG673wzKlG7O474b0g01yBCSGEEEIIIYQQQgghxI8UPdQVQgghhBBCCCGEEEKICfG99fxDcidK4oOKHlKMZ1+dxyvYlGxiXyiaaLutb/OXj+517Xvf+GvXOaTIDSQaRzdcQtKXoO8f+uvOBWUgjR/7g7v+G3/6o1ffe/zIJb2/+ed/8fPBeZ+jEt/6LF68HSpxsor96tAlJRaq/r7k+9ivAFcqClbYzUZku2OVjp9HqPwdFMOQi2a0fvDPK9h/5EEaiM9n8dovFt5vqErk58vlAu0l2pCOUfqBIbLLIUHp3fYm8df9W5To3O68z2823ueL84sd9OWerzpXkkMeNiujNmFbWgfEysi0Eqhha1E3fr0odaTUs0B/Xe37Nb1+7JIsy32bowPIL/a8bWbWskI8zil8PCK5bCnRhGalgQVFlUByZlF2Eip0hlDsU0Cesep5vIZTYbXat9/97rdmZpYv/dy2Ja0VIEmGPOfeV3fDvvYxZo+vu+QIyiB78K1XYH104nKg2dJj+HLf+0qBG1+jUvzJqX/37CzaL6xgucAYFiSkwy4J4Q+jUttkOPb2t0uDPPe79zs1rqRzQbI7Iv9rwnV9zk7D4KZ9BWyBMMkV6FyLwueJJdrzwsdpzgmyimOe1enX6FOn7Gvr8669QcX6ktJoHCumJWtaH0dNiWTNzEqcKxTUVtMyAO2+dcRUaFuz5tJyoAnWA8NWBcE+qu6dM/6dQiaYtZDktcO5LO0X4Lpha0gRM8T6Fu20F+uZv1rt30+Q6CRjhZnxVc5NVsB2Ay5k2TxeA8ZWystpj9Tw82mq6C+4PPUxy6Vxa4n+H/jv4QsStgjy5op/6Vqsdj6jtB9zZ1PFfDJKq7FX9Ik8G87HC1iYLZA3z+fIuW65RHa+F/ssLWlWsGJYLHwO53m3U+04bWtJdXEPkmJ4nuK5JewPdVxMbXZ+j28c+3V6623PdW7c9GtujcuS53Pmo7zBtIphDGMe0VvQIP+gA0Dd+tqZ4yLlggty5W9P0X/nnnPNUvTZIlogPlr7vPi3r91OcXHNbRqXKz/2eRpzsynRWXSFYEBfFq6Bxi21kmDXNZZ4DrfH7eWQF/MZDT+v45ilfQv/FrYb3pWFxwXsj8GmZhzG5mbkEmTh2jxnZ68o8/nMfvqzty7aCx9PR0fXuvaNY48JyyUsENN4r8qaVhQNvuPjcbHwdU6BpHEJu7u7tz0ePT1xO4Nyg1hRDedeZmac7oLlKfZ746afH63z5nM888EYmCHmrTDfHK3cwsXM7Omh77fAM4mEDzqRR2cN5+YXR2/qCiGEEEIIIYQQQgghxITQQ10hhBBCCCGEEEIIIYSYEN/bfsFhVWe+4jxcTT1UOjWzFtK9NKvRbgfblLY9eeyvXa9R9Y4SnAqvY283/tr0Zt2TJaNdQEa92fgr4mdnLtM+P0NF38b3GyRvO//u3XvfdO3PP/djNTP7/R/cyoHS+X969+2uvTr019vreqrP4FtrL+8fZZYF5Vc5rRHYjl00RSXEZqQKZ7BvSLLBzynnS9LhPpehEjPbZmazOY4Dw4j2C6zCS3lZESqW+z5RsNW2M8gmi3jfaRFBCdyu9D5/VSHXzDpJSV++8spzKQeqguzZr8UOsoUZ7k/Ri2r1SDVUqmPShrJH2DXg0qe47jXlxajgvbeC7CuP9gs7yMma2o+9GpGLsFrziNI+9l3Ip9KeDDPIfpvhflCif1SjJXJfbWazwt56+7aZmZWUirYu6cxx47/65DP/bi/WzDFmj667VIdjvFdP2lvp8NwXJar++Rkk8f25cglJD2NKGyxDfHse05jNQmgHKVpPakeZkA3L7v672C9cyXOD+g9XM8e8VDbDcw/3Y2YxwAcrIFr++Od7qKp7sICFx8z7b0H5I3662kW51vbc7RROn3ru8fTE2+cj9gucW9MUUmeeDmSw7MtmZslYZeogo2fsmqa1VGI+LtJQ7Xp4HIxVBTeDPNYsqOjjGMS9YB9sGG98kw0sOGY5JbXDtjtm8U2PpuEcNPz9rIA9BPpyglyK7RRzNeX4F/uFRJ6V1XGurGxfT1VGbx6nY6H54MU0+L1+zxqdqUNaMawrZn9k3r2/53nMwYGvQbbnmFNrz2cufsPbOfpEglg3C/Zk3u8oc10duGx1Aauy1TU/prSXE9NOjdYRY2uE5EV93V4xkiTp8pJ84dev4fmc+lpyVmGd3cRxXiXIJVLIoGFDeOCKYVtvEcdRwT7JMAYLjzfBJoxWM3U8jgSWMtb4vaOd2A7PCJYF5sIS1iCtWyNUsNVqOTX17vsOOe+Xn3/etY+P/PPjY4yLWNB+OrStj/WQ3wwvLLJ2ONd79t8/UL43YpPA6N53DwzT5Ui+EffFuQXwFFraOGCe6eU3vG7hcuC6sad19igTWlYVs9zuvHFhS7DT2LXCAAAT1klEQVSAFV2BuJMWnIs97rR92wPkfTnykH1YFFa4xvsHPsbfesutUI6OfA44P3dLvWqL+0O7q6bXPxEXaNOzgm3ifIlnmEi3aqzZy5bxxc+tyPwL+7CsMDO7cc3n0X08pEgRow1t2S8IIYQQQgghhBBCCCHEjwA91BVCCCGEEEIIIYQQQogJoYe6QgghhBBCCCGEEEIIMSH+Dk9d4s+GW3hYpPA5avoeYzA7aVr3qthV7pOx2br3WwXLDHqp1A1Pwdsl/D0qel7AJ7FPA5/fJnimuNfc8bH7PZ2eubfI/fsP/Ld3frB7e+7b8eXX/zf83tMz99v9+a/e6dof/Oa9rr1Y+vHW1TT9w9rWPfcy+BbP4EVawM8yg+8RvXbNzNKcvoZ+PdKavsz0LoQ/Cnz7Svij0NsuhQ8UvQ6LnofXYk7fX//bEh4x9AwLnsE4jhyf1zP/7dkc3ryL2Gcb+MKUCfosPGnOz91fqrz0Wqyr6Av0auM+zPQCprVRA/+ZfXgc7837fj6IT/D5brEzehPX8J/bwTunQvt8A68y9mP48QTDy4s9D7fpC0gTKBxTC/9K+kHTgzClb1H/v+twrknjfZ8+vPT/bEf8/F512raxqr6I/VXlnn+8NpYg1sADvmnj+NhsfAzttr6vgwP3SroGz3NeM47fFveavt6bjc8rT5888WPq9Zubx9e79tE1n3+KOeY7+EvTyz4Y5tLnij649ALuTdNJRm+1YU/NFPG6/YEs1v4RuFccvNfoRU2fyIpxtz8vD48dXifOLewrhyt4Sy69ny3x2xluUlP6cey2nvOYmZ2duU/z+drb2y3mhtK/08CD2jBHJYgxSYqYSY/gWQw4jFfG+I2+Fq7aZPtNYtZe3psa465iLPWPW1zLJovxpsb1pw8pxxr9ddnvStSAWMNTs4TPcl36gTxGvHn4OHq/He67n90sY2yA3yX83ujFbsyFVp4LMcevEZfp+WtmlufDHvEJ2vRibiY6T31/OEB6vudj3rsj/rz0ew41J+B1vL/0PnCw7/fxEfpDW8TcvECM2tv33Hd/5fuiH/0++sfhoc9rS/g3plgvsH5A/3WklNcnBBZ66v4XeIH+A7jq8w1ibAoP/wLjNKx3k+jRGLzw8f0S+coGa86S1rlYV6Vot4wXwcM4VK/pnQ++j/tCb/AG+61DXEBtitqfHVjizw6KuedZ654PdGn+/XsP/fr87TNfzx/dutm1X6fJ8IRobShP6fvU4h+I42mvFsuoL3yY6L47JrFPcM3OPInjuk56xw/PZfabeBjBeNf3xXMIfrxYW6NGDf1UL/Y78swA59Hmz9bzmdK6Kk0T2zu8iONz+OgydJYN8kesQbI8PlpsWO8Fz3ror7uFR25T+1gs3PrbDlKfG/b24KeNWMH1T57GWJMhn1/uwZsbh1u1Hi82tbdZ96pBvOSyMWcc7tVoWRR+DfO595+sQH7NdVyvz70oelNXCCGEEEIIIYQQQgghJoQe6gohhBBCCCGEEEIIIcSE+IHsF0BLeRje0+49Pq4hgyhLygHPu/Zm5xKKWeEy14TSD76e3/o+f/L+61373/7t51373fffDMfB1/7NKKn0167ffe921/73//nrrv0f//HXrv3F55907XvfvN+13//l2137t7/7l/Db//yvP+naNw5dYnvjtr+mnWb+2vr9eyc2RZLELLuUIlAmlIQ2JfKU20fJUANpUAkJbBWkSOiDOaWjKbaHjDRoULgf71tZHmUTS7z6P0c/n8OWYQ6JLWUZGWUnyYgsDr+V95Ri6P622/FaQSqF61FfyvinJP1o27aTjvL+bCHXLGFDcX3Px8yiZ5WR2rCFQkVLAtyfGn0jCXIwfteP43DhFisF+lvbRKlXQksXagSfsWm4oIGsp6X0EM0k/INSw7hPqt+oyKdCk7YnfRn+lGgvZVahF9TD1gMZpIBJE0+6hNxmB8l6YkddewFZEvtENqOUDWMUP0HLngf373fttHfvDg9dHg1lZZBoh2NveebJYDPYL4T73pP2jsjlOF6CVH+kL7/qJIlZfiknziEjriHXSkYHRTznhtI8fJ7S9iPlPOF9aG/P7/XewuVh89zblJBRd1aVca7cbj2HqUbsInjk7AcW7jXuL2R1KV0++v0GNgNV6r9NW4YggZzs6wWJpZf2C22NOaSipJP2BPhq0es3sGNIIEvk9edcyJxnfQYrl8dutVGG+dJz669hNZYk0Qbi9s3jrn0ddi+cV8+3/v3ztf8255192NQ0kGluNi6z3FWxz1KOOYetUQ59ZBJC+VQnqsRPZCwv4xjEmH928zFbgWG7tmC3g9+gpH4fcejakd+jm+d+rylVNjNbLl0nuzpYoO2S2QI2LTNYMSwW0NjinrbIuZj39vMbC9dn2KqjH6OmSNu6DV+LMTyrcF0zv945LHyyWRxre3PPTxd7Pr7q1O/FpkTuYbTegf0Kd9vAci7BOhb5Sdvrl5TVNwnXa94/ctzHCnn/duMxzWpvryBv3q18+74F3QF+o8X66cuv3Z5m+ee7/nt3pmmBaDa8/gsrX8xTSVgfR5uVkD8EKwZslAznTSHXHGmP2S+E/OTig8HfG17N960Y0J8wjmo8b9gh/3+u/QJyXq7zm+JZ+wXmhq88aWLplX0LrCR4CvWI/WPbW0uF5QniyBY5SY31U01bB6zZ+byuCBYPzJHw3AUxyMxsBksEWkokOI9qh74BO4SaOdNYf+WzHetR02YB/SdYmODz3vz6okx/lhNCCCGEEEIIIYQQQogfEXqoK4QQQgghhBBCCCGEEBPih7dfCBUKx58Z13i1eIcKveu1v3ZNuWKRu6SkXFMG7ftsIBl46+07XfvXv3E7hOMbsXpljlfBM7T3F95ezPw17Z/93NuffPJN1/7000+79h9+/6euPUMV2DfecRsHM7O3oWpYFn5+CeQNX3zhstz//NNXNk2SToIRpBjYgrKEIHMvo4SdMuooAzG0RyrewsoiSE2we1ojzPF6fzmPr/FnlKTi1f88dznPDPLB+QzbZMPyNygcbA4JcEVpWu/YS2ifSkhKKHO92mZK9gt109rjzYXk/WznEghWy85xwVaoZJn1wg7lHlTsBBkJ5V0Vr5Pfw/WWfcZ/+3DpEtUZKpW3PfVE27Ba53DftZRSXciWcx8HCZVAFTpNTQlKlExlOPE8Q39qh8dTX904HdrugqY8N5xPzbGPeLSE3N0sVvOmrQAloYRynhnatFXheGWcm8/9nrASuFmUtVJ+NFZsOMTYF5Cccpu+xC0dkRkF2TMtFyZqv2BJYsll9feSMjBWtg0StHH7lBml4hmvGb4SOyQ+Z2yHdB7jv8GOqq3nS2enbldlZrbbwH4BcZPyV8qhU3ogYO7LMlQQxpiqYVeVzPoSTVRTxxROF5SU6eeUZfSX59ri+jHvCPkF+kNe9GL0DPe19fu1hT0Zphcr0TnLklYb3t7t3Iphs6FNgt+U+aO4DGjR/0vYzty64RZhm51/f13CpqbydllRqu99JcaU+NszXJMZ8u58xu0g126ipHyKxJwYFh5Bpj5msWA2NlmPyaErxJ4Mv5FDmjqb+ZhfrVyC//obvoZJeonWYsncF/0clhq83ZxqWkrwG7ZphcXxEmXMLdeavDy0z7GROWtCJEli2dUagxXfYc+1XNCKbDwvznKsHSAv//ahj/nzrVv+JUg804yy5OF7RPuFHPenX9N9i3tZsj9iwwKxdYuK9A0ml3Tj57N9gvi09h1VbYw3S1ae31GC7Z9//NHnXfv828c2TTwvjpYEYQt83g62n/n3yDBKRmwTomw9+852iHq93JR5K+fbMfsFC5/DogXnw7VBaPdy/hZ9nhYv9JGijdqVLH9CS3AzS6y9zP1qWqzBDzK6AmL89K1/MP80iADnWFPTkiwYyPERDp6d5LROaZFvlnx+FKNNU2K8w04qxVq7DDYzOI98OHfLRuee8NPW8vkMrRXQl1rapFX9SPli6E1dIYQQQgghhBBCCCGEmBB6qCuEEEIIIYQQQgghhBAT4oexXxh7n59S4jS+i0z5epDbozolZX+0YuAr7qxOx9eYNxv//O69h117u4uvNOd45TuDPL9qXUZWVf5O9UNIUx4/8sp9243v549//FvXPt/5Mf37bz8Iv33zFqwgapdOnj7y6sAf/smlHx/92e0eJsflpaUUOch5+d76iBWDmVkS5A38gzfDb4RjaIc270k8IBuh5K+NFggNyzlSUhJk7rBc4Ov6VJ3y8CBroJQtSZZGKOGsYbOwK1DlNUgQmsv9TEfiWje1PV5fjK81JMYJxuut6z5+9vb8GrXPyBMp04G0D4FrC5lGtUWfg2TifO2SrtnMJYnLnNYp7HE9STJCYNXwHvrnNfp+AwluwsqbLeIiKq5bA+uHvvcDrlueeF+mXA5KxSgpmhhXpxFlY5BYQe7OfvPmW2/29gNrhvke/oD4gvFOy5R5DukhpOwp+uLx8Y2u/asPftW1t+jvF8f1Vtde9KxYuv0yliJGvpj9wnCl4uf9LWW4pqq1X6F4IqRpYotL+w3aQTWNj/kd5nJKhFnx9gJWWYa0CveesvEK3z9/6nM/pVjVxvsEo0q1cynqac9+4fTM/72DPJ/WV/He8V5TfonzxhcqSNOqXnVoulOU6bCtS4LxmT0TsydC25pdzsGhiDFl34y9sOaYYY43M2vMY0ZWoA8aKkTzC7iurGw/3/dN9g4Qe2Yj8vgiSiUbWDPsas93Nzs/vu0WuS8sGrZbWj/4fir0ZeZhWRLnyBw5f5GzjXkKc2E1LT3rd9ImI/8Ybl5sZvzbsPXOqLSafRZjnrFgNvc+dJivunaax3tXQM7KmMF4k9C+hUGi5nH7x1z3Mdft27WMWVgko5n+dPvNVditYRFQIxbkuV/vAhGDOYmZWZP4nPLgvtu0nD/19Ws283yjhGy63NB+iHkBLBqS4X5WNjFHWHM9j+32cb/20G92OG5aShzPPPDVa+83i/zQj6PsrSdpxYWFGa1I1qce6z5/4tdpagzPsMNWCs+1X8CG0eFl2HIhG2mnI7YMoc399+0XmHcFKxdvN+HQYdGAc4p2L1x/Yc3QvwbsNy3b+A5/7+rYJzZf1Zf3lHaBtBdLMU5oB9W00RYpg+8O73uJ53c55o8ccT9YdoTngHyGyGd3ft23vby0Rr7c5v6dAsuqhvMg5ri04RyD8xnpY/17nXEOpw0XLWQRY+vm5frKNFdgQgghhBBCCCGEEEII8SNFD3WFEEIIIYQQQgghhBBiQvww9gt8HR+SvCijia8SL5delZOVv1crl/cUhUsdKlTVLSBB5avZC0hkP/vMbQuePL3XtQ/3IaM1s+UCWjW8tn2+cRkkK5Y/fOySxscn/ip3kft+t5XLzj7681dd+8GjKI987Y7Lbw22E2dPXO7xzZf+nbPTXkXBiZCY94VQaT20R+SXvTfQk+iz0DUpBKO1B6WP3FXal6d338Vr8SPVGM1ipWu+ch8rXQ/LQyht5WkXsF8wVBNPEx8rZmYpqj6yiCJlw+WO9gvV5femI3Gt68ZOn16MgxzX5frKx+shLC1ayEPK3t2ipUHLNiugVrQ3CBphb6KTrULcwG/jHmR9uVsGuRxkFhVuYg2rl6RBnGtgL4FjpRTVknq4bb3xFbbD9aCTw0SrQ18U+b06p2EpDGU+x8de0f39998Lu6JNw+HhQdcO8QWXKYfEiDJTBh7asFyH/cK1a96mtMvMbIXfZkX4svZ5JpQVb797nI/aKvQkbkEiF/oQmtnwuU6KJOnuzXrtcnLaGzDW8rpQqmlmluL+tQklf5DUY9yVqNr9FBWEdxuP8+e08MB3a1T/Xp953mBmdg7ZKO0XGPdCBXu0E0jbGE4pQ9zBymazQV80C3YvbbBDGh6T6Qv02VeVKylnqG4cdKmQnyJ2zGfRfoHzSFpgXxnsWPB9SlmLpW+/WPnnB9d93pjvvA8V6E97e/E4Fgv/G9wQrKy8D20xLsrSj6/cebtGThskvLhOSW+uZiXpjFZWaNeUQU413tgLhOmxnLh30gnHc3AoGLYhSLPh/bb1cE48Qz/NIJ3vr0aaMG9xnMOWoR7O32NyPuztk4b1Qv8og3bcnrPhtGmRstUeCzanfu927SNvb5507ePDaON2fuox+8ljX++erX3dneWep1KOTkvCkCtC0txA+lzXPq/RYsWsN86RziJNsxz7zRM/7mDzgfn19rE/R9ihXz8+OQm/vUv9O3BQtG3pc+cS/X/R9mK2eAGGLVCSkfaYUcSrRLSwoaWf0zTPPq+ZkvtCa7DRoiUmcpOUtnZcx9bRfoFzTgFLF9pr7pBHcPuUC+9glcFjhcUVLPWSWbwHFZ7l1fiNJOWamPlFNtjm8x/e57C0buNv55x3YWGRI09qkIO/bEqsN3WFEEIIIYQQQgghhBBiQuihrhBCCCGEEEIIIYQQQkyIpF/Z8LkbJ8l9M/v0v+5wxPfgnbZtb/2jD+JFUL95ZVCfES+D+o14GdRvxMugfiNeBvUb8TKo34iXQf1GfF/UZ8TL8ML95ns91BVCCCGEEEIIIYQQQgjxj0X2C0IIIYQQQgghhBBCCDEh9FBXCCGEEEIIIYQQQgghJoQe6gohhBBCCCGEEEIIIcSE0ENdIYQQQgghhBBCCCGEmBB6qCuEEEIIIYQQQgghhBATQg91hRBCCCGEEEIIIYQQYkLooa4QQgghhBBCCCGEEEJMCD3UFUIIIYQQQgghhBBCiAmhh7pCCCGEEEIIIYQQQggxIf4fB4+8hSt6uNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2314e4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of training images\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "plot_size=20\n",
    "for idx in np.arange(plot_size):\n",
    "    ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.transpose(images[idx], (1, 2, 0)))\n",
    "    # print out the correct label for each image\n",
    "    # .item() gets the value contained in a Tensor\n",
    "    ax.set_title(str(labels[idx].item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing: scaling from -1 to 1\n",
    "\n",
    "We need to do a bit of pre-processing; we know that the output of our `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(0.6510)\n",
      "Max:  tensor(1.)\n"
     ]
    }
   ],
   "source": [
    "# current range\n",
    "img = images[0]\n",
    "\n",
    "print('Min: ', img.min())\n",
    "print('Max: ', img.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper scale function\n",
    "def scale(x, feature_range=(-1, 1)):\n",
    "    ''' Scale takes in an image x and returns that image, scaled\n",
    "       with a feature_range of pixel values from -1 to 1. \n",
    "       This function assumes that the input x is already scaled from 0-1.'''\n",
    "    # assume x is scaled to (0, 1)\n",
    "    # scale to feature_range and return scaled x\n",
    "    m, M = feature_range\n",
    "    return (M-m)*x+m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled min:  tensor(0.3020)\n",
      "Scaled max:  tensor(1.)\n"
     ]
    }
   ],
   "source": [
    "# scaled range\n",
    "scaled_img = scale(img)\n",
    "\n",
    "print('Scaled min: ', scaled_img.min())\n",
    "print('Scaled max: ', scaled_img.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Define the Model\n",
    "\n",
    "A GAN is comprised of two adversarial networks, a discriminator and a generator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator\n",
    "\n",
    "Here you'll build the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. \n",
    "* The inputs to the discriminator are 32x32x3 tensor images\n",
    "* You'll want a few convolutional, hidden layers\n",
    "* Then a fully connected layer for the output; as before, we want a sigmoid output, but we'll add that in the loss function, [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss), later\n",
    "\n",
    "<img src='assets/conv_discriminator.png' width=80%/>\n",
    "\n",
    "For the depths of the convolutional layers I suggest starting with 32 filters in the first layer, then double that depth as you add layers (to 64, 128, etc.). Note that in the DCGAN paper, they did all the downsampling using only strided convolutional layers with no maxpooling layers.\n",
    "\n",
    "You'll also want to use batch normalization with [nn.BatchNorm2d](https://pytorch.org/docs/stable/nn.html#batchnorm2d) on each layer **except** the first convolutional layer and final, linear output layer. \n",
    "\n",
    "#### Helper `conv` function \n",
    "\n",
    "In general, each layer should look something like convolution > batch norm > leaky ReLU, and so we'll define a function to put these layers together. This function will create a sequential series of a convolutional + an optional batch norm layer. We'll create these using PyTorch's [Sequential container](https://pytorch.org/docs/stable/nn.html#sequential), which takes in a list of layers and creates layers according to the order that they are passed in to the Sequential constructor.\n",
    "\n",
    "Note: It is also suggested that you use a **kernel_size of 4** and a **stride of 2** for strided convolutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# helper conv function\n",
    "def conv(in_channels, out_channels, kernel_size=4, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels, out_channels, \n",
    "                           kernel_size, stride, padding, bias=False)\n",
    "    \n",
    "    # append conv layer\n",
    "    layers.append(conv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        # append batchnorm layer\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "     \n",
    "    # using Sequential container\n",
    "    return nn.Sequential(*layers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_side = 32\n",
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim=32):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.conv_dim = conv_dim\n",
    "        # complete init function\n",
    "        self.conv1 = conv(3,conv_dim,4,2,1,False)\n",
    "        self.conv2 = conv(conv_dim,2*conv_dim,4,2,1,True)\n",
    "        self.conv3 = conv(2*conv_dim,4*conv_dim,4,2,1,True)\n",
    "        \n",
    "        self.fc1 = nn.Linear(4*conv_dim*image_side*image_side//(8*8),1)\n",
    "        \n",
    "        self.lrelu = nn.LeakyReLU(0.2)\n",
    "    def forward(self, x):\n",
    "        # complete forward function\n",
    "        x = self.lrelu(self.conv1(x))\n",
    "        x = self.lrelu(self.conv2(x))\n",
    "        x = self.lrelu(self.conv3(x))\n",
    "        \n",
    "        x = x.view(-1, 4*self.conv_dim*image_side*image_side//(8*8))\n",
    "        \n",
    "        x = self.fc1(x)\n",
    "        return x\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "Next, you'll build the generator network. The input will be our noise vector `z`, as before. And, the output will be a $tanh$ output, but this time with size 32x32 which is the size of our SVHN images.\n",
    "\n",
    "<img src='assets/conv_generator.png' width=80% />\n",
    "\n",
    "What's new here is we'll use transpose convolutional layers to create our new images. \n",
    "* The first layer is a fully connected layer which is reshaped into a deep and narrow layer, something like 4x4x512. \n",
    "* Then, we use batch normalization and a leaky ReLU activation. \n",
    "* Next is a series of [transpose convolutional layers](https://pytorch.org/docs/stable/nn.html#convtranspose2d), where you typically halve the depth and double the width and height of the previous layer. \n",
    "* And, we'll apply batch normalization and ReLU to all but the last of these hidden layers. Where we will just apply a `tanh` activation.\n",
    "\n",
    "#### Helper `deconv` function\n",
    "\n",
    "For each of these layers, the general scheme is transpose convolution > batch norm > ReLU, and so we'll define a function to put these layers together. This function will create a sequential series of a transpose convolutional + an optional batch norm layer. We'll create these using PyTorch's Sequential container, which takes in a list of layers and creates layers according to the order that they are passed in to the Sequential constructor.\n",
    "\n",
    "Note: It is also suggested that you use a **kernel_size of 4** and a **stride of 2** for transpose convolutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper deconv function\n",
    "def deconv(in_channels, out_channels, kernel_size=4, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a transposed-convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    ## TODO: Complete this function\n",
    "    ## create a sequence of transpose + optional batch norm layers\n",
    "    layers = []\n",
    "        ## a kernel of 2 and a stride of 2 will \n",
    "        #increase the spatial dims by 2\n",
    "    deconv_layer = nn.ConvTranspose2d(in_channels, out_channels, \n",
    "                    kernel_size, stride, padding, bias=False)\n",
    "    \n",
    "    # append conv layer\n",
    "    layers.append(deconv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        # append batchnorm layer\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "     \n",
    "    # using Sequential container\n",
    "    return nn.Sequential(*layers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self, z_size, conv_dim=32):\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        self.z_size = z_size\n",
    "        # complete init function\n",
    "        self.fc1 = nn.Linear(z_size,4*conv_dim*image_side*image_side//(8*8))\n",
    "\n",
    "        self.deconv1 = deconv(4*conv_dim,2*conv_dim,4,2,1,True)\n",
    "        self.deconv2 = deconv(2*conv_dim,conv_dim,4,2,1,True)\n",
    "        self.deconv3 = deconv(conv_dim,3,4,2,1,False)\n",
    "        \n",
    "        \n",
    "        self.relu = nn.ReLU(0.2)\n",
    "        self.tanh = nn.Tanh()\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        # complete forward function\n",
    "        x = self.fc1(x)\n",
    "        x = x.view(-1,4*conv_dim,image_side//8,image_side//8)\n",
    "        \n",
    "        x = self.relu(self.deconv1(x))\n",
    "        x = self.relu(self.deconv2(x))\n",
    "        x = self.tanh(self.deconv3(x))\n",
    "        \n",
    "        return x\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Build complete network\n",
    "\n",
    "Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (fc1): Linear(in_features=2048, out_features=1, bias=True)\n",
      "  (lrelu): LeakyReLU(negative_slope=0.2)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc1): Linear(in_features=100, out_features=2048, bias=True)\n",
      "  (deconv1): Sequential(\n",
      "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (deconv2): Sequential(\n",
      "    (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (deconv3): Sequential(\n",
      "    (0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (relu): ReLU(inplace)\n",
      "  (tanh): Tanh()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# define hyperparams\n",
    "conv_dim = 32\n",
    "z_size = 100\n",
    "\n",
    "# define discriminator and generator\n",
    "D = Discriminator(conv_dim)\n",
    "G = Generator(z_size=z_size, conv_dim=conv_dim)\n",
    "\n",
    "print(D)\n",
    "print()\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training on GPU\n",
    "\n",
    "Check if you can train on GPU. If you can, set this as a variable and move your models to GPU. \n",
    "> Later, we'll also move any inputs our models and loss functions see (real_images, z, and ground truth labels) to GPU as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU available for training. Models moved to GPU\n"
     ]
    }
   ],
   "source": [
    "train_on_gpu = torch.cuda.is_available()\n",
    "\n",
    "if train_on_gpu:\n",
    "    # move models to GPU\n",
    "    G.cuda()\n",
    "    D.cuda()\n",
    "    print('GPU available for training. Models moved to GPU')\n",
    "else:\n",
    "    print('Training on CPU.')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Discriminator and Generator Losses\n",
    "\n",
    "Now we need to calculate the losses. And this will be exactly the same as before.\n",
    "\n",
    "### Discriminator Losses\n",
    "\n",
    "> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. \n",
    "* Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
    "\n",
    "The losses will by binary cross entropy loss with logits, which we can get with [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss). This combines a `sigmoid` activation function **and** and binary cross entropy loss in one function.\n",
    "\n",
    "For the real images, we want `D(real_images) = 1`. That is, we want the discriminator to classify the the real images with a label = 1, indicating that these are real. The discriminator loss for the fake data is similar. We want `D(fake_images) = 0`, where the fake images are the _generator output_, `fake_images = G(z)`. \n",
    "\n",
    "### Generator Loss\n",
    "\n",
    "The generator loss will look similar only with flipped labels. The generator's goal is to get `D(fake_images) = 1`. In this case, the labels are **flipped** to represent that the generator is trying to fool the discriminator into thinking that the images it generates (fakes) are real!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out, smooth=False):\n",
    "    batch_size = D_out.size(0)\n",
    "    # label smoothing\n",
    "    if smooth:\n",
    "        # smooth, real labels = 0.9\n",
    "        labels = torch.ones(batch_size)*0.9\n",
    "    else:\n",
    "        labels = torch.ones(batch_size) # real labels = 1\n",
    "    # move labels to GPU if available     \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    # binary cross entropy with logits loss\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.zeros(batch_size) # fake labels = 0\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "Not much new here, but notice how I am using a small learning rate and custom parameters for the Adam optimizers, This is based on some research into DCGAN model convergence.\n",
    "\n",
    "### Hyperparameters\n",
    "\n",
    "GANs are very sensitive to hyperparameters. A lot of experimentation goes into finding the best hyperparameters such that the generator and discriminator don't overpower each other. Try out your own hyperparameters or read [the DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf) to see what worked for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# params\n",
    "lr = 0.0002\n",
    "beta1=0.5\n",
    "beta2=0.999\n",
    "\n",
    "# Create optimizers for the discriminator and generator\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Training\n",
    "\n",
    "Training will involve alternating between training the discriminator and the generator. We'll use our functions `real_loss` and `fake_loss` to help us calculate the discriminator losses in all of the following cases.\n",
    "\n",
    "### Discriminator training\n",
    "1. Compute the discriminator loss on real, training images        \n",
    "2. Generate fake images\n",
    "3. Compute the discriminator loss on fake, generated images     \n",
    "4. Add up real and fake loss\n",
    "5. Perform backpropagation + an optimization step to update the discriminator's weights\n",
    "\n",
    "### Generator training\n",
    "1. Generate fake images\n",
    "2. Compute the discriminator loss on fake images, using **flipped** labels!\n",
    "3. Perform backpropagation + an optimization step to update the generator's weights\n",
    "\n",
    "#### Saving Samples\n",
    "\n",
    "As we train, we'll also print out some loss statistics and save some generated \"fake\" samples.\n",
    "\n",
    "**Evaluation mode**\n",
    "\n",
    "Notice that, when we call our generator to create the samples to display, we set our model to evaluation mode: `G.eval()`. That's so the batch normalization layers will use the population statistics rather than the batch statistics (as they do during training), *and* so dropout layers will operate in eval() mode; not turning off any nodes for generating samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   30] | d_loss: 1.4112 | g_loss: 0.7346\n",
      "Epoch [    1/   30] | d_loss: 0.7896 | g_loss: 1.3830\n",
      "Epoch [    2/   30] | d_loss: 0.8232 | g_loss: 1.6701\n",
      "Epoch [    2/   30] | d_loss: 0.8678 | g_loss: 1.5893\n",
      "Epoch [    3/   30] | d_loss: 1.0643 | g_loss: 2.8926\n",
      "Epoch [    3/   30] | d_loss: 0.7198 | g_loss: 1.6067\n",
      "Epoch [    4/   30] | d_loss: 1.0324 | g_loss: 1.5617\n",
      "Epoch [    4/   30] | d_loss: 0.7109 | g_loss: 2.2401\n",
      "Epoch [    5/   30] | d_loss: 0.6008 | g_loss: 1.6101\n",
      "Epoch [    5/   30] | d_loss: 0.5689 | g_loss: 1.4037\n",
      "Epoch [    6/   30] | d_loss: 0.9964 | g_loss: 2.7648\n",
      "Epoch [    6/   30] | d_loss: 0.6398 | g_loss: 1.7437\n",
      "Epoch [    7/   30] | d_loss: 0.3851 | g_loss: 2.5553\n",
      "Epoch [    7/   30] | d_loss: 0.6005 | g_loss: 1.6595\n",
      "Epoch [    8/   30] | d_loss: 0.5800 | g_loss: 1.8815\n",
      "Epoch [    8/   30] | d_loss: 0.5425 | g_loss: 1.2453\n",
      "Epoch [    9/   30] | d_loss: 0.5269 | g_loss: 1.7007\n",
      "Epoch [    9/   30] | d_loss: 0.3492 | g_loss: 1.8140\n",
      "Epoch [   10/   30] | d_loss: 0.3011 | g_loss: 2.3025\n",
      "Epoch [   10/   30] | d_loss: 0.6324 | g_loss: 1.0446\n",
      "Epoch [   11/   30] | d_loss: 0.4667 | g_loss: 1.6869\n",
      "Epoch [   11/   30] | d_loss: 0.5188 | g_loss: 2.1302\n",
      "Epoch [   12/   30] | d_loss: 0.4215 | g_loss: 1.8503\n",
      "Epoch [   12/   30] | d_loss: 0.3691 | g_loss: 2.8226\n",
      "Epoch [   13/   30] | d_loss: 0.8442 | g_loss: 3.5584\n",
      "Epoch [   13/   30] | d_loss: 0.3630 | g_loss: 2.4916\n",
      "Epoch [   14/   30] | d_loss: 0.3437 | g_loss: 2.8049\n",
      "Epoch [   14/   30] | d_loss: 0.3237 | g_loss: 2.3782\n",
      "Epoch [   15/   30] | d_loss: 0.5378 | g_loss: 1.6775\n",
      "Epoch [   15/   30] | d_loss: 0.4444 | g_loss: 3.1901\n",
      "Epoch [   16/   30] | d_loss: 0.3180 | g_loss: 2.7029\n",
      "Epoch [   16/   30] | d_loss: 0.3551 | g_loss: 2.1532\n",
      "Epoch [   17/   30] | d_loss: 0.3549 | g_loss: 1.7052\n",
      "Epoch [   17/   30] | d_loss: 0.3988 | g_loss: 3.4727\n",
      "Epoch [   18/   30] | d_loss: 1.6333 | g_loss: 4.4692\n",
      "Epoch [   18/   30] | d_loss: 0.2856 | g_loss: 2.4030\n",
      "Epoch [   19/   30] | d_loss: 0.6557 | g_loss: 2.8408\n",
      "Epoch [   19/   30] | d_loss: 0.2376 | g_loss: 2.2023\n",
      "Epoch [   20/   30] | d_loss: 0.3483 | g_loss: 2.2740\n",
      "Epoch [   20/   30] | d_loss: 0.3455 | g_loss: 3.0730\n",
      "Epoch [   21/   30] | d_loss: 0.5019 | g_loss: 2.1753\n",
      "Epoch [   21/   30] | d_loss: 0.6124 | g_loss: 4.0180\n",
      "Epoch [   22/   30] | d_loss: 0.4135 | g_loss: 2.5489\n",
      "Epoch [   22/   30] | d_loss: 0.4907 | g_loss: 1.3448\n",
      "Epoch [   23/   30] | d_loss: 0.3153 | g_loss: 2.9148\n",
      "Epoch [   23/   30] | d_loss: 0.3209 | g_loss: 2.9492\n",
      "Epoch [   24/   30] | d_loss: 0.7439 | g_loss: 4.5678\n",
      "Epoch [   24/   30] | d_loss: 0.4595 | g_loss: 2.4023\n",
      "Epoch [   25/   30] | d_loss: 0.2759 | g_loss: 2.8763\n",
      "Epoch [   25/   30] | d_loss: 0.2157 | g_loss: 2.1007\n",
      "Epoch [   26/   30] | d_loss: 0.3457 | g_loss: 3.0013\n",
      "Epoch [   26/   30] | d_loss: 0.1653 | g_loss: 3.1263\n",
      "Epoch [   27/   30] | d_loss: 0.7742 | g_loss: 3.7799\n",
      "Epoch [   27/   30] | d_loss: 0.2085 | g_loss: 2.8339\n",
      "Epoch [   28/   30] | d_loss: 0.6667 | g_loss: 5.9424\n",
      "Epoch [   28/   30] | d_loss: 0.4099 | g_loss: 2.9516\n",
      "Epoch [   29/   30] | d_loss: 0.2940 | g_loss: 2.4691\n",
      "Epoch [   29/   30] | d_loss: 0.4111 | g_loss: 2.3838\n",
      "Epoch [   30/   30] | d_loss: 0.4110 | g_loss: 3.5996\n",
      "Epoch [   30/   30] | d_loss: 0.1821 | g_loss: 3.0356\n"
     ]
    }
   ],
   "source": [
    "import pickle as pkl\n",
    "\n",
    "# training hyperparams\n",
    "num_epochs = 30\n",
    "\n",
    "# keep track of loss and generated, \"fake\" samples\n",
    "samples = []\n",
    "losses = []\n",
    "\n",
    "print_every = 300\n",
    "\n",
    "# Get some fixed data for sampling. These are images that are held\n",
    "# constant throughout training, and allow us to inspect the model's performance\n",
    "sample_size=16\n",
    "fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "fixed_z = torch.from_numpy(fixed_z).float()\n",
    "\n",
    "# train the network\n",
    "for epoch in range(num_epochs):\n",
    "    \n",
    "    for batch_i, (real_images, _) in enumerate(train_loader):\n",
    "                \n",
    "        batch_size = real_images.size(0)\n",
    "        \n",
    "        # important rescaling step\n",
    "        real_images = scale(real_images)\n",
    "        \n",
    "        # ============================================\n",
    "        #            TRAIN THE DISCRIMINATOR\n",
    "        # ============================================\n",
    "        \n",
    "        d_optimizer.zero_grad()\n",
    "        \n",
    "        # 1. Train with real images\n",
    "\n",
    "        # Compute the discriminator losses on real images \n",
    "        if train_on_gpu:\n",
    "            real_images = real_images.cuda()\n",
    "        \n",
    "        D_real = D(real_images)\n",
    "        d_real_loss = real_loss(D_real)\n",
    "        \n",
    "        # 2. Train with fake images\n",
    "        \n",
    "        # Generate fake images\n",
    "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "        z = torch.from_numpy(z).float()\n",
    "        # move x to GPU, if available\n",
    "        if train_on_gpu:\n",
    "            z = z.cuda()\n",
    "        fake_images = G(z)\n",
    "        \n",
    "        # Compute the discriminator losses on fake images            \n",
    "        D_fake = D(fake_images)\n",
    "        d_fake_loss = fake_loss(D_fake)\n",
    "        \n",
    "        # add up loss and perform backprop\n",
    "        d_loss = d_real_loss + d_fake_loss\n",
    "        d_loss.backward()\n",
    "        d_optimizer.step()\n",
    "        \n",
    "        \n",
    "        # =========================================\n",
    "        #            TRAIN THE GENERATOR\n",
    "        # =========================================\n",
    "        g_optimizer.zero_grad()\n",
    "        \n",
    "        # 1. Train with fake images and flipped labels\n",
    "        \n",
    "        # Generate fake images\n",
    "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "        z = torch.from_numpy(z).float()\n",
    "        if train_on_gpu:\n",
    "            z = z.cuda()\n",
    "        fake_images = G(z)\n",
    "        \n",
    "        # Compute the discriminator losses on fake images \n",
    "        # using flipped labels!\n",
    "        D_fake = D(fake_images)\n",
    "        g_loss = real_loss(D_fake) # use real loss to flip labels\n",
    "        \n",
    "        # perform backprop\n",
    "        g_loss.backward()\n",
    "        g_optimizer.step()\n",
    "\n",
    "        # Print some loss stats\n",
    "        if batch_i % print_every == 0:\n",
    "            # append discriminator loss and generator loss\n",
    "            losses.append((d_loss.item(), g_loss.item()))\n",
    "            # print discriminator and generator loss\n",
    "            print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
    "                    epoch+1, num_epochs, d_loss.item(), g_loss.item()))\n",
    "\n",
    "    \n",
    "    ## AFTER EACH EPOCH##    \n",
    "    # generate and save sample, fake images\n",
    "    G.eval() # for generating samples\n",
    "    if train_on_gpu:\n",
    "        fixed_z = fixed_z.cuda()\n",
    "    samples_z = G(fixed_z)\n",
    "    samples.append(samples_z)\n",
    "    G.train() # back to training mode\n",
    "\n",
    "\n",
    "# Save training generator samples\n",
    "with open('train_samples.pkl', 'wb') as f:\n",
    "    pkl.dump(samples, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training loss\n",
    "\n",
    "Here we'll plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff1e02de160>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1e032b748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n",
    "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Generator samples from training\n",
    "\n",
    "Here we can view samples of images from the generator. We'll look at the images we saved during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function for viewing a list of passed in sample images\n",
    "def view_samples(epoch, samples):\n",
    "    fig, axes = plt.subplots(figsize=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        img = img.detach().cpu().numpy()\n",
    "        img = np.transpose(img, (1, 2, 0))\n",
    "        img = ((img +1)*255 / (2)).astype(np.uint8) # rescale to pixel range (0-255)\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)\n",
    "        im = ax.imshow(img.reshape((32,32,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1e02c9748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}