dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "\n",
    "### Get the Data\n",
    "\n",
    "We'll be using the [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) to train our adversarial networks.\n",
    "\n",
    "This dataset is more complex than the number datasets (like MNIST or SVHN) So we should prepare to define deeper networks and train them for a longer time to get good results. GPU is required for training.\n",
    "\n",
    "### Pre-processed Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.\n",
    "\n",
    "<img src='assets/processed_face_data.png' width=60% />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "# !unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'processed_celeba_small/'\n",
    "\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import problem_unittests as tests\n",
    "#import helper\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the CelebA Data\n",
    "\n",
    "The [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations, you'll only need the images. Note that these are color images with [3 color channels (RGB)](https://en.wikipedia.org/wiki/Channel_(digital_image)#RGB_Images) each.\n",
    "\n",
    "### Pre-process and Load the Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This *pre-processed* dataset is a smaller subset of the very large CelebA data.\n",
    "\n",
    "> There are a few other steps that you'll need to **transform** this data and create a **DataLoader**.\n",
    "\n",
    "#### ImageFolder\n",
    "\n",
    "To create a dataset given a directory of images, it's recommended that you use PyTorch's [ImageFolder](https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder) wrapper, with a root directory `processed_celeba_small/` and data transformation passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataloader(batch_size, image_size, data_dir='processed_celeba_small/'):\n",
    "    \"\"\"\n",
    "    Batch the neural network data using DataLoader\n",
    "    :param batch_size: The size of each batch; the number of images in a batch\n",
    "    :param img_size: The square size of the image data (x, y)\n",
    "    :param data_dir: Directory where image data is located\n",
    "    :return: DataLoader with batched data\n",
    "    \"\"\"\n",
    "    \n",
    "    # Implement function and return a dataloader\n",
    "    transform = transforms.Compose([transforms.Resize(image_size),transforms.ToTensor()])\n",
    "    \n",
    "    dataset = datasets.ImageFolder(data_dir, transform)\n",
    "    \n",
    "    dataloader = torch.utils.data.DataLoader(dataset, batch_size = batch_size, shuffle=True)\n",
    "    \n",
    "    return dataloader\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a DataLoader\n",
    "\n",
    "#### Exercise: Create a DataLoader `celeba_train_loader` with appropriate hyperparameters.\n",
    "\n",
    "Call the above function and create a dataloader to view images. \n",
    "* We can decide on any reasonable `batch_size` parameter\n",
    "* Our `image_size` **must be** `32`. Resizing the data to a smaller size will make for faster training, while still creating convincing images of faces!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 32\n",
    "img_size = (32,32)\n",
    "\n",
    "\n",
    "# Call your function and get a dataloader\n",
    "celeba_train_loader = get_dataloader(batch_size, img_size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, We can view some images! We should seen square images of somewhat-centered faces.\n",
    "\n",
    "We'll need to convert the Tensor images into a NumPy type and transpose the dimensions to correctly display an image, suggested `imshow` code is below, but it may not be perfect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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euPh/yrZq2GSf8vQJ6kWHDxo+BLj9BjYZrlc205S0YzSU/KTVVa4X1zX+zs8UA44oz2lejTzK9/zSXIDS2Dk2/mw0dA/1psrNpnIdytnySj6HeIL1ASGwIsWi/IrTvg1J+Wy+yJNC5IfdtuLiBTcofqHy4QvJEpptbRbd60iSNYZxB92jui2Nk7fuyxHz7FT50AXGyZBGSIhCy3RrfRnqImePiragJKqGdYgSGMb+sxPl5zWfcxHSB2wm3Y7VP426ygPIkGYe5HTo/xgxt1dfdG4AedQTuMiMxjQLUL0ijL9eT2MiRk4yhSTxEJtcn51BcoTgzm0MKoYFiB00sjk71XnWiUk+db9IF2P0eEttOO5DzlbHcxZG0Rxr2M3d3bL8+BON/wDH37mF50uk3J0GJG9YyPxQ7ZunKu/0F32w28emyFgXwkpcxvYLWM+4ufkptrt49lw5zXiI9QD1mmXIaSE5jiPVkc+Xzle7zor5vVZ1VC6jgAzt/cmn2sz7n/2L75TlDz7U5tDzKY1gdMoAOXwjxvhEG84xXyeQJEXM//Fc4CCzD4t3ATQf4kb8XmXDfxqS8J0D6kszi0tM9CaQPz4+0Lr4nT+VVKqFtfNrX363LPNZJHsJM0xj2hgMBoPBYDAYDAaDwWAwXEPYSxuDwWAwGAwGg8FgMBgMhmuIV5RHyYEDjNwK7ZO7ON+8fassb2yKgkkXC6/iHAHqL2iRY2zTfAba/JPHomh1u6K2bu3oWnFBp59PQb2baqdn7iheh+Rn3qA7BOjmkAXMU1Aop9xRnzvFYwd50LzYThW3G3Db4oLevM7dwEejkfvB3/zQOefc0wPtqD8ElW0wEo32bCzqZAdUti1QoDfpXb8pWu/Rqc55fA5JBnZ6n4LCfQEZ2pjU48LBKgEfDd3mwoB0N/1O6jfdc7LK8egrSOy2IJXqw41hdPSsLH/3n//jsvwPQG2+cVdSqWydVMVLsX7ZhwdqYQSKba8p7mkH5TY4qTGkOxXJW8GRnGL7+AHkaey7GnZX9+nSAQnPcK7xMie1EhTTaUJpJQMVKabchV6/5/hbtz4DNyEXNTZHvCN9NK6pzi3MmxbmX6+tsdcEbTwClZt0/9lcNzMFrTNJVi8HdLLotot5gaYZTXTNALTjGmIfEWAu1kD7TSFhSEDlpyTKw/HjCd0wNBYumw90rVovvHKMVpx+MK480vMpiarQfSl9UbmGeNXF3Nptqr/ubmr920M5inStGaQSS2BJcgdwWzk8h0xiDqoymnACh0WOKY41fhai2xdjebbKVu5zuNpA6jnRyivV51iFHK2LObHbE5W+16Q0Vo01mkKGhnufgbZNGU4AeQDbLUOHLevWacABCbKbEHF7hlg5napvfYzREZye6ELXg5x5ZwPyj7nyKOZCmxh/j59IzpFAInZ2DuuNNSLPMzct8rsQi2+tHqz+A6wtCQZuDW4tNcwhH/z48UgyiGQGVxNK4pGoRGjTNiRah4WcpdFU7pRirl6MlIPV4HozGKh8dqi8ZNK/obrUdR3enxcjpsQav++8JpnJZ8/k4HdywXWa8s7lvV7N/GR+xrFK50q6ztYwL+m0FzU0hm/c0nMJJakXY8W/AO6FKZxSt9uaa1957U5Z/p03X3POOXd6pnZyhzrHpy/kWDWH9MkPdP0htlh4+IlkJgnG2SyB820l50EZOVKC2F1xkFQtXe5fzbf71M/ccbxo09mm4lINzwhcCzmee3Bs2uwpntRQ842W+vr2tsbw7oZicLsBaSNkKO0W3NuQ97eKPDaE9H88gnsc81lK/yFPGyDup03d37Cl6+9lmpcXeN46GWjeTy9xwuWcoDvc+l2jFtKwpQxtMFA7/OVf/6As//gXkmnOp8hdIV+qBXB+a1LOr/Ic68ME+cTQR56BgTtnPoG2WoYDOhDTDY7PE3RArGMcsF4N5FycKnPk7JPKWq+DPn2ief+nf/pXZfnWnuLsDuR/L6NtM6aNwWAwGAwGg8FgMBgMBsM1hL20MRgMBoPBYDAYDAaDwWC4hngleZTnRD2qekXAbQg7JN+791pZrsE5gLslp3NR/1K4QZ0eiVb76UfvleWTwydlOYfbSLgrulE8E813GCzobkPsBn988kL1aopiF7cosRL1sY0dyKdd3V+aaMf+NmjRR0dHK8tz7OrOFiSdtuKqkb0MbfzVMJ7O3M8+WlAvL0aQF3iQLEGa1AhEU+tFooYmA7WnD9lGE1Kp7o6o1JugKU4hg5iC7zaakAan654XtO0zSGlIqad7BliNFYpzC/1D9yjSY0lx2+roPjYhm4tgE/H+T0URDCAF+ff/452y3AFV+W8Hq11cLsdq57c4VPtvoC1ubWuObDbV7x1IyFr4vQHXkqBwNslBSeRYm0M2FYWgOYMyOIYM8QzuJedwWDga6DyPnstNYky5U0AJF6jW+WodVH5l8oxF+9N0KAJNc7OjdtjsaQz322rXXmM1lZPyCEpS53QDAtWdjkGkAbMPN/sL2nKrKbrzZTTtRgeOMnDJ+Nl7vyzLJ6fqwxRyhgZo7geHitednqjVdITLR5DnQMtWYX5f0rfrwFI2zFjE4ca4nkFikVbkVDoeYdftgmL9zh05w7x7R7TaN25sl+UNxOCaRxkSHBT8Zb0kbXsBCcTHz0Tr/fCJZLRHOOZsovY/Bq17AvfBhM4YKHNpozrq6vy9fg1y51wRBwLIMDCdKhLQG1uKg3e2ROVvw0mKToZJohNx/s3oGBZA2hhp/LNNEsTLpYtIG05PrRbyLEfJBNZ6rHN7e1qrZpBNffrJQ90HZJYh4uDWpsbiIXKqGOtuBsr78ZFkPr7/ysr8l0Tm8nyxRmCZr0hovNlqx1Lfg8QXcmfmYSPkkcOxLhA2ILfw1b71SPcfYvx7uJYXLP6239K5X3yqnHcj1vk8xFG6dj4+0rYBg9PHZbndU17swVlsBjejKFXg2e0qvr75QPHl2RFyaqgsy/jxEq4nL41c6w/PmmKdZlzxsJbTDW+eQWLb1XrlQ3I8HWj92UV+u498dQuSqK++8aAsv3VPMqs7xTwaQY50a0N99cNfqD8PLnTNJ+cqPz+R09DwQu1N56kZLQA9Sp+Qx7HRkFNRIuLhe/1VyaPy0HdZIVtK2lqT5ljnmTc65JwRHCbnqQZcXFddH9zUmnd/R/F4pw+5OGSRAZ47m03VpwH59XLEZZCtxhGlSXQXxbMr4mut4qoK+TmedcMA91TTOM0w4g9PNAaYunjoL24/EBc5k7/O/syd8wtJ7hnG58NPJcccIA7GPqVPOs023Oq2umr7Jh2VMS4mc8oWV0vGZjh+CnfiQeHISsdGOrZxS5S4IomCIyTW+ia2IvDQn2dDyfnyU9X3dATJ41Dn/+hjvbv44MOHZXlrW+OY69RlMKaNwWAwGAwGg8FgMBgMBsM1hL20MRgMBoPBYDAYDAaDwWC4hnhljqroQSTtila00Rdldhe7IpMq7CWiiY3OJR/67IP3y/KnH39QlpOpKITbfdFQ9+5IntRt6ncfu1bPCmpX4onC1YIbwCndqB4+LMuPmx+W5f42pFeg/Pfg1NDtoAwnqyQRbevDD3XOCXYkn4Fyy93A83RR9tYozUiS1D07XrSn75OCrXqCDe26oNp3wHfrg1J4b18SoP1dtc8UdMAt0LbBcHMTyKNiSC4iuAcdny4kLidwnKDcIALtkS0VQT4RgRpMqvgFHDOewjFgOhH1bYKdxDPwFKfYPfz7f/mXZfnu618uy9/++/+mu3Lkr/rulVxL7EQPal4P8pu9LfXLPlwAephHYD+6JqjFFRpqa0EzDuFGQv0KqaF0bOP4T9Dmg7H6bjxVnz49h/PcRDHlyYno/3PQX0m79nH+ilTxpRxuXg25y122jKPoh2ZDjbnR1RzdaOv3LtztWiEd0NifulYAGqgHl5pKGe/wvYrDE6jimwvK9+bGJo6F0x7mbWdDtM+tG5Kb+rjm//1H/7wsH7yQlC2jexScHiagJPc2JEsJUccUVFniKvrQucJ5aDkHsfZUeOr+r/7F8ndv1a+uA0r2V2+pHX/3q2+V5bfuKu5uYV2sQWYT0qkR89uvFXIuNMntidr23i2tuW/dkTzqx7+QtO2Dp4qXcpFx7gIuZDPE9xxzNEEcrTD+X8oBY3HMFXVnJVeJsRj2MC/3+pI+9CEPrjOekqYe6m8z5EsZ5xxing+XtOp81fFhsaZ5XOcqdG+41Phq7xwyuZs3lUPt70sq9f6GJCEf/lJ9PsZ6GaE9PEiIhlina76OocwxAv18/VjcawJZRZgrjvqok8t1jOcgzx5RnqLyDJT7KFJOQ4l7lkEG72E9wZym80itGM8XB5IceBeSJ25CKuLHyi0bgerLrQI+Qx4zOld+G8BBCSoeFzjEC6zNd3d1rW2sQyNIIUvHs7Wqo3KXF0GBORzlLdxigVI7xoTK1gtYN/xE9b8JB6K9pu731qby2DffuF+Wt3e07jW5tUIh86kjln29/aWyvI+2fPpU8fQJHKZ+8N5HZTk51Rx6AldVuvcx/FUkwRXZlDrmsq0X8uRqZMOZ89y4GB+zyepgPc70HNTd1RoWYX1AmukiuEft9hWjtjtws23CtYhyVcTUCPlCADlTVqyXUaR5EECqmlJu5iN2YJzWmLvS+S/l2qD7rjcQmxHrKcUc4299yAG7NckZlxL48JHiyBdHXkrFDp4rlhw8UzmFZLfZUYy7va1nhdtb2DYDzwd1rnOY0wnajZJ7yvkzlGfol4tCws1tOEJISVuIg8y1cUjFJZAurDS7PjxTvuo/1No2R54zxlIwHKm+x5jfCRIguiJfBmPaGAwGg8FgMBgMBoPBYDBcQ9hLG4PBYDAYDAaDwWAwGAyGa4hXlkctSUOkMZO22O2IBtgB1dJPsOv+mahVH733k7J8fiRaVz0QfXTjlmjguzuiinfauhapyCnofllBVYqwo387w07/oI/TaeOz55+W5R/9QNKX7RvaPf7Nd75Rlvfu3S3LlOLcunW7LM9A0Xwfu8mPx2qbZAZXJrdKivbFkDvtvF+v0MBBWcMu7qQd3gQlnLvof+V1SR9mU9H+PKfjJzP15xw7/0d1URz9UFS5FJS4XuFItrcpyVKWUQaCd4+k/aJvKauZp+qHC1CPSRt//EJOFxfYHT2gIwXmwGQoutsv3xed/Mvf/La7Oiyv/zLjI19Z5m7oAeQTNVACG6B116CniMFbrcGRgW4jAf7WL2RmISirAeaKz3JA6j1cIzCHSGdsQaLgfB1zEy4vpwP9fjpUOcf56ZrkMtKGr8A9Kl/IFZ2ruhw14KhA54KIagvKSDDOs4xyM8EH3dhH/0T42yhiH2KHfbhHNZd9SLcr/F0cq9yAc0oj0rj5xlffKcv/6s/+qiwfnkiSw/uYMnbM4VQQw+1lCkeyKeI/HbHW6XDyK1jGgop8BbJKmgLQJSGDk0+EebkDt7uvvXavLL99V+XbkLa04KrlBfpbL1e7RGgvv+gnHzZVXUjSuqD+bm7o3DfglLT3/sdl+bs/lvT36UDnOcMg9Kd01YAjESjBjPuU3VEiuWT5p+tbFp3znPMKaRvjeoS1pQ15MGNihJnG+6ITnQfXQTqMUULnVZyMGEMpm0LsLOJoRtsxXpMSA1Dqe33FxDjW721IYr/8FcnwhucaC48+e1qWUzj5hUglp7DHqkNm8OJU5zkbyIlknchz57LCvawGR8c6Yth8BhdKxPv5VLkAHVFmyMnqDeU0PvrdY15AZyPIb+bIL5IL5RfnTxduT6cfKxfuwvmoUVOOFASSHIQtSt5UxxFcWA8g8/Id5BbIkSm/a0Gu0IV0YHdDOfPjpzp/WubV65uMnnPOK3JT5gdcjkNIViil9hEU5nBmStAO9zZ1L2/sbZXl+yzf0ZYIfTxz+JBWVCSMxXQNsB1CBHliHX93c1syxK/BGeerr8tt9/WfaCz88ffkUvrzR5JWDTHPUg/yPMf1BfmXAzhGc7rarg9JnrsXxVYYMEtyTciXopbaaDxVH3UDrTnNttazDtx7e81wZbkDN80ATnZ8vmDf5IivWeEy5DvKxrF4Q1foR5pPboL1ADK0FtetiswVOR760cOYShDXT2HZNoH6+2LyedekLH6B22oAACAASURBVFuf3C1zuZsV68jBC0mAZiPFgA24K72+r7n1YF+xCiaoLgqYf+r3gG8juBZGzO3p5EQtkY5PC4fb9BKH4RryshDrOJ+FPMRqOoklyJGiEG7KE2wLMpTk8fk5t2FQHw4RozNI/vyXCKPGtDEYDAaDwWAwGAwGg8FguIawlzYGg8FgMBgMBoPBYDAYDNcQryaP8hy2TwZ9CdSjGLQz7oQ8hkvUez8SJf7iSHTbDjhU3W3t0t7r76AsqiJ3788gf8kcdrcvdmb2MjgphKRNiRrYAIV2G1KgTz4RlemDn/2oLD96+FlZfvu3vlWW33hb1OLtbdV3PNZ5RgNJfR6Dxnx4oGOmBf04W6NNhuc8FxZU6QZkKlsdtWW7JgrYrS21w7uvSQb12r2bZTkHvYu7pTcaoo85UDBjyD980BdzutCAzr3c9Z7U8woFMKccgrvoQ/6Fe4VJisshQ9joiNp3cCKq5tGFaJA5aO4ReP0eaPRPn0nmN5pILnZ1eFXZx+rjuTM6iYcB53qFcggKP3bP99AuGfoxLeJEBlqj53HLdkiiyJUEZ5AuJT6o7QHGQyNULOjBtawJecjFCLRFKo3otkAJ0hU41eQuL+n5dVA2m3XNiWjFPHCuKrFJUo09TAWXke4JHirVhDjE5aB2JxXJmA46cwsZIAm4vR5kqnBOmcGBbY7yzpZi4jtvy2Hj2Z9IhjqFIwHp+1PI407gkjKHIyElpjM4Sc1nq12lvjA8z7mijpQKB4HiX20OR7MR6ko3GpzyBtr07g25MG71RRvvwKUw7oqe69cUswMf6x71dUs5I6jKlAnWWjpfraY5RLlLvaF4maP2//L7Py3L50OuxRiP+F7EuJNVBuTqSXc1IjfP5UWdKtIkyt0wcSpumChzjlbWbV/9H9AxikZ+OH9eoWevlonNi7GTQwJHyc5kinUZ+U890hgKcPzpsWTru9uSirzxuqTQxyeSAR+cyOEozXkt0N8hSQgwj5MUeqI1Is9zN5st7imFDHo+lzQrhJQXJpju+PAJTgS5A9azlNoEuH7kkLiHkCdOIcUfJnA7PHyo8mcLaWFtpn9vQfrfijUXMw/Oi5HqQvfUPTi4DjLFyLOR+m4MtymHfqnM9Vjn6UOi4mE9zpfjep2T0vOcX8yFlNZy7BPIoxpwLJwNlT+Pcb9bkMHvdxSXb+9qLrz+4FZZ7m0ohkZNjOGKLEvlcJkxhRrXY8gqmOeEHrciUPmNWHG+0fxKWd7ZUtv/07/4cVn+659Lnno8gVQqV325TlPmEeaMxVdjw5fmmTspHJBqmO/NvsbzObYV2N6FI19b0hquA32si51Yd0epfkXmjWc6F6LvIInK8WzgivWNzzE+11BImbIZB73a1s91rz7aFjVxPYwBnxZDmH9zbBHgjiQnPR9Bfowx/vxgEb/nyXrznKS4TTp3Rljz9vrqqwf76p/NFnL1S9ymQ59rFzkkyA8qai/KpyErQwBa1i3HsXSDCilr5XNkJfdA/oscLUAO00L/3N3VehlGGsc/fwSp1Jly4IuJpJuMcV7063k0xrQxGAwGg8FgMBgMBoPBYLiGsJc2BoPBYDAYDAaDwWAwGAzXEK/sHrUkIVX2baYrB+jhKShjR4dyYzp6/qgsd5qirLXgJNQHxbMOR6qgISpWhndOVYIf3H6Kmgb5ajo2b4SyjRA7Q+9sirY4HKruh0/eL8v/5y9V/sbv/J2y/MZbb5flVlsU1v19OWLldDYCbfXps4V0bL3kRc85r6B1wQmoWVf77MOJ5O0Hciv5MhyjorkoXQkcXSjtIFWcNMWMO7N7pCqL5roBGuRkstwVHZRxUADpkkGKP91FSFWPKLPCa8suaLAdOCd8/EwUxNO56Ld857nRhQsW5Hk+aOPrxvIuMu/zvzn3K2w/zhVQBQNSdQPdfwvObw1QeLkzPs/vQ8bjh3CpwWTzi75J56CDVuYi5iilAnRDoGTpkt34HeJRiDIMjCpuPglm2OUGQ8s2W+9sXFI4G5BuNeDoQokppVspZIBsHx5TNZXBDvV0jOJO/rhYwD6E9G0piUtGkv2NQC8OMYfrlHJMNW/iDdX9zbfkmPGnf/G9sjwcSypA6VPTKb6cnoIyDDea4VA01Nk4QflqXDKcc84r2tEHZd4HJT5IdT9ePsQfqkiq8K0drRW7oOq3IOf0Y62FPtarOmTDHuMuKOTLS/m4ZiVeYj43QCcOYRtGqcA330XchSRt9AO5JF7McN+Q9AWcfwHHeEU39bnj1xtZ81ImUHFxYuxJdMWsIvWiPAr0+ZzyTZ4zXXk83S5y5E6eo1QKMS9fjjnmXMIc5w4gCR/D9SmI1YdHp6Js796QJP02nDE/fvy4LJOan2SKAeMpHTbUz7t7kkXW2pD5rBFZlrnp0ikIw6cOd8E41Hw6PZQkjMaB3a5yoAx9Nx6Byo6YFjHWRZDBz3T+4VBysjly4MZ0MS96bclGej21fw10e4rKapWxAKeWluq+OYXrDCSaLy5E288hAWy21EcNuOPUa7SBw1rrrT+/8TzP1YptFlI4AeY+xzPWM6xz55BHhZBzbG+vjqcP7sndtQtnohiOXWGgsZrTscZj3FrUzUfMgmGtq0H6nWAuJjXI1BB/KUf72n1tS9DHlgMtjNHv/o2ePw5Odd9TSkEcJeEQ61yRq2KeS0HoNSkxUu4wQxxt9/RMVKNsN4c7cU9zJM605jNf5IDwgtXupA7rW0AZUvG3OcRMIWVVwer4ns5wTa6plEvT/RPuyHVsKZLCKamDXGKKR/UU9+2lcqFbSrO5fn1RZHnmZsW6fXKs64Yh+wSOcz21ZZOy0hniJuYNnUyR0rocD2Yh86iKrJoxietoIRsmJwXP/9mcElc+FKhYlWmr6KEf6E7cjHXQ/rbu6cVQcfkYEtoU7oDc0uBlLDGNaWMwGAwGg8FgMBgMBoPBcA1hL20MBoPBYDAYDAaDwWAwGK4hfgN51OcpxKQJBqDD5qCPHR3KUSeFw4iP3fA9OFDUQPGOa6CBg5rJjZ9JYfUovynKeYV6Vb2j8hxkJoEyeGN/vyyfnYmORhLaJ08/Kst/9q++U5Y/+FC//xu//e2yfOemKI+jC9HGOy1RaM8bC5ov5QxfFLnL3bzgoSWUTOCYnQ1J0x7cUT23e6KYxqBxpaBgppAwTeDWEqFPanX1JymenaboqXXQCqeFFCMEpdHDdZKZKKYJduKegc4+SURHq8M9Yjql24t6dHsD1OcnooGfnKl/li4Viz8GDRX0SMry/nWClMS88jvcviCJ6kLKR0lG4GGXfKoiK44r3spjssJBoOL8hdoElETxHJUd5kmJxPVxfBT4K8uUCFV2h3cEZQ8O5eWO9GukEnvOLZmfYQ3zoyJjIf1cdaa70wyiCB/0Wjr51eCWEIEmXIM8og6ZEx1T6pTYFJfN4KKQY57TvSOBq00A7msMivnrd++X5Vs7ezoP5vfZiWi5rabo4WcDzcunz+VCOIR0q+IeNb0iqaLnOa+ITV5l7K92ImOcoRsbHcRu7Eoe0W4rLvroU7rt+RW3NUiPKPVBLPKK62bg8NOlgezgAPRwH1LlDONxB25HfzeU80kQ6/o9uJ2cQWby+EjSsRdTHT/xNB4zVC5bikTW2p1eOcfpYhGhXSPkHnSxc8nq5KIilUI7pwxckIlVpCbU7XqMVaCWF0UPMmcG3BydGEKe4TD/YqzFHrU3OKYD2ciNfc3Rg4ODsjzmPENuAAWP68PhqAXns7Uiz11SSMyDkGuS+u4ca/j5ucbe9qbmnJeKyj48l2zMhzw6oDsY4usczoQJ5sXsRO3lhnCKai3apb8tGVqrIxlIxdEQUlEHl7wMjkH1WG3ba2tMjVHd84n+9hxS1Gmiew1SjQ0a0uSUJRdzdK3ropO7HJ1J3SXuoQnk1hO4EW1gyG+01D5vURIF2U5FYoE5V3WTw5rG+Fvcf4LYm0NCUrFx4rNTJW7DVRW/1+Gwentb+fIffOtrZTlEfv2d70mS+gRj11VknPo1rVRuffB9r5Qlttpat5GiuB7cdTtYW1ilbI7cFbElZPvjeY3udVxfQ8rIGb8r5cX/pZel7fnqtqJENoS0nM+lVOXM6TKI7SUy5qiQFE1HijunZxrj52fq33x5gTW6DbvcK6VKzJ9puBVSMViRL3FuaY6G0ernTof3BcklY5LbaXh8HsS8zPOlC+TqZwtK5rxLXCC5jlK2lVFynCiGJnA7DRA7WrjuBrZAiLjVB2V2geb6ZbgeT5QGg8FgMBgMBoPBYDAYDIYK7KWNwWAwGAwGg8FgMBgMBsM1xCvLo0pqH9VRlF5QHjMWdWs2kQQoBg2cTh8bPVFvI8ijanCpyCg34QbP3AEd1F7t9g5qIOlOoNvllfOpTHr67o4otA1Q9bcfyyXg6QvR+T/64Jdl+fmBaLnf+m05TN3YlTtVCAefZn3RBr6/RnlU7lxS8L1GoMiOJ6DXgtbZpFsIpEcgvrkgXL0Te9DBDuxwOglboPtjN/yKDQAkF3G6OM9OAPpkKjpaOtP4mEMGNYUr1xj83sl8tbuFDyrz1oboyTu7GscfncOlBvKrBOOlBueBWqhx/LeDivhJv3JAY6rwdzpsVVyIQElkX4d0e/Ip/8DxpBwWEy8FtdlPQEOcUQYFQM7jsY6UCoCiWYMLUqOhsRGGpHDTBWv1TvGXteW64DnPBQXFMwwpj0IZ9+VX2hLthv5J0G/TnDEPu+fD4QJK1YoLWYt0U2gc4oIXO4J8YATq7gwc4OmFZEpTSqjgitDblvzyrTfkJPXeBx+U5TEkV0dHmn/PXijmHp+DMnwhuv8MLneUSq0bpTyK/VUZPpwr6F/IZlqQqm33RYNvg08eIUaGpPZiDPMuK3RlUOiX7giUMFcdFjFeaMwB55OsrsHT6ytehqjLP/jGu2X5wW2t7w8/e16Wv/+enCX/5hO52hxCQjVBjrGUZa3Xx00yiIorHtqkBskCqfneJY56wSXfxBhuKPf2IAdjalbJbaqJV1Fv0MQZnxlH6lqHolgy2HZX/RbVJIueQPrbg9PTjRs3y/KTx5K8n2DOTXGDU3DLG7hueDWKDJfneZmDxsgbS0cp59zJqSTuGaQRUYD7P1euNjzSmNyMKU/Uded0DcKc9saQWQ8l4+60tsry7t5CrrO5JfecOub8HPLvnLKBhB6uWPMghWshnwwxYzY6utZoLkewWQ75F2L5UOH+V2SZi2ula3SscbnkT5RBUXbiYcxPkcdmM605vY7keG++JunZFtyjAp85z69UoizSbRPxlzKLZTyFnKsGeTAlELyMX5nbOveMrpe4Zoz6biMJ/8Z9beFweKy18Phnag/O6WrwvJrJGASB6xROtP1N5cTNGHn2tuJPDXkeUnF3cvaiLNMRbIZ5RoVTXkmTVue9HmXzFUfSRdzNmP9U/g5xmUknJcRcDyA5jSBjTRD381R9lFHyOFQjHD+XtPLpc8WjwUSxIS3ma75GeZTv+6WT2uaGcpLHEdoPz1bTsW54jvFfQ+5dr0MmhFhFV+GEz+UY/3QHDhDb6GTpSsdo1YVdTLc8us76WDsp90wvcYRMOUhzBEg8d8aQXDbRBqFjPOAY/fXP+sa0MRgMBoPBYDAYDAaDwWC4hrCXNgaDwWAwGAwGg8FgMBgM1xC/gXtUQfGpKArg+DABVR5ljztDT0VbOj46LctvvP7VslyP4VhDGlrF7Wk1DaxCj1v5dzgbaJZhKLqVj52pKUvo9Xr4Xe+8bu1L4vTiRPTECXYAf/ZUdOL/55/8k7L85htvlOUG9BBVl501ouCK0WmpQkMFpdAH1aviXIB2owSGDk+1gG3L3btp+wOKP+hpFabqfEGho6NYChpsRW7jrXYbyPl+EpT3HNvEz+EA0e2IQru1IZptHRTOScTzo7qoQpW2d01QkUfBhQE0R8pcUkwY7xKHAx/U5QBlSkHSQrqWppdQnivubeivjFTWig0DihyD2LG9L0ldq4WYcipZJkUJHq47x7hatxTDuYUco1k4Q8QVlxrOodUytQxBjmHiYqg5epqrTIkF3TDoDNaGlKzTVHmrp7mwWdTXx9/5kCR6iIlDUH1Pn4iC//hUcsPX3lblH7wmqdTDzySZmWPteHZ0jGOe6FqQIQyGlGVBRpleRS+6xdgrJKU+6Os+xmfCvsPv6F7XgJylBTlL6KkvArgLRCjTHcfhPH60eh1LC6quD6kapY8e9Suo+wRShMEADghz3FNd8RIKBXcX992EE0UjoERF5R89FA38MzjyTAodX7bGWek5uU1UHLfIq77EXatSDbqYYB6HmBc+nZwQfTzQvb0QrppYcyruUMW8mEKSTte3EBYfrOIUlPo5JL6bO3JyGY7gOoMYvbN3qyzfuK3+yQ+U2yS4bgDnmxru6fxUcqV1wvNy5xeU9DEkUZTqX5wr52y3tT7MJzpmgLw0hPa53lH+RxniHHLRKWIRJaKUJN2+q5yvWbhHRQgGKdbfBDnkHBKX6QTrEypDFS12Iqjkb16qsdaAqxfXd8ppzkcaM5XczF9/TM1djlxB8buGWMVYPoVkxsNiuI/xvH9Tbe8gQ80Czi2UIe1wdENDjpQxj10+FmGt8iGNyNFBeUVigTLzMuTOOeZ0Ruc3HN+G9KLfoiQc55khB0dEyLyrWRezPHfTYhxPU42f++iLNhxp61jzjp4qtpyfaT5tthBf8azk16EVg0tPhueOFF1aMXWFFDGtLWJUHlAGSdk+Jck4H3NFrh/IXUOuB3A8q8WKHbVM5QbiQY54cAIp+GgC56nl2Fxjd3pOa8oGFvM65fyXuFVSKhjC9TJEUIpCPk/omBliLp9Tczg1RshtQjyLLZ8FUjjqZbTuylYWK66OPurFU2eU5+HZMcMzDR1uuXTniJXM35nAB9mv7zxj2hgMBoPBYDAYDAaDwWAwXEPYSxuDwWAwGAwGg8FgMBgMhmuIV5JHec4r5USkSJLqM7gQxXSEsgMN9/xEDiAncAOZg8JEuU4OKjXZypRc+R53eFZ5ucNzTqEUnXHoxhGyOeDIgrrHNVB8c9Fvb9yUq8KjJ6L2DQ91f6RaH5+K5v/XP/heWd7flYPBVuEgQnrYerCoRwg5EB1/aLLjgfaVkqYGalpAV41LdvhP4NySpZKmOOw2TrlN7OGcBVVuzj4G222G/kkr9cXxGEM10Co9UG4noA+HQ9WrC8eIHmh+Scg20zl7PTlDeCGp8FeF35wPWdlVHXNuDhriFFTFGWjDc7idpD7oqaA5kma4dGojbdgjJZnneIld5ekM5DA2SdHc3BSd/da+nN8eP9Nu/O063Iz66rsnz+VONCrG6Ro35nee51y92IWf0hjSMYNLXKtmdEYbw0EA/eZBPkNJDh1TxjPFaNJu67HqczaSPOqikDy1GoqDPuKth/IIMoEL2I/MEsmazgc65s4DSQa299QPz7henCvmPj1A/8DBihIVvyLRpOfd+uB5ntyjfPULnZs8uEvkGNs5Yypi1NGxHG4+eiLnjbttjec6qP0NyFA8xCXKjQLwudOivoEHGjhkbodPJWd7+KEcEA+eqe8G6JcAsWMTkpONPmR0mLs9yEYfbOl4/0taR6k+GH5MV6lF3ZNM4/6LIy/HMV25YvCbY6yXlBfkFakB5yvWSMotaDFGh79KdUAtr8imKJUoZM6QAGagZntYJLOp5l8y1n2cYpzt3Vbb+5AqjLB2b2xJcnLngdzeuBb6p/pbukcNLtRfCdtjjcjz3M3TxTo+GSieTSH3SudqizjUfDo/1hhLxzrm5r4kYRHm2fmR1pCjY82L8VDSqt3t22W5jbUlgFRsOX6GuOZ8pBxpjvoOZ4ypOoZz16dLDaXuTpiOlX9yrfUz1WuMvGowVNyt7FZwFRL+PC8dmzidmhiTPu5mhHZrYIFuwJn00VP1Fe9lE+O529M5O231cx1zKsw1hulOExR5Dp8gKOVI4Z6YUfqR6y+mGcqYH8M55KmQfCSoC111a5SqB8yjVIfgMveaNSIIQtcpnmF29+USFWN9COAoN0esePSZ1p8A8bKJ+deE/C2Gw69fg1ynDpkpzlNxSqVrbdF2HrZioGTQ41YBlTgOqQyfe+bIo7EtSIZcJGhACl151tS1Wg3kLpAMzueKB0sXwXzN/ekV47+N7QUaWBf7bTkDN5EX1mrIwyBBSzDOJ1PKoDQ+z891X5Oxnsu4fUirrvO0Wrzu4hg+Nk+RI0+mOt+Yz5F45qQkuIFnvjjgGFq9XQH3X+mgPeox8vSKkRv+I6XodjWMaWMwGAwGg8FgMBgMBoPBcA1hL20MBoPBYDAYDAaDwWAwGK4hfgP3qGUB1G9wGM/PREk9fC5HgRooQ2dwVxoNRRn66OFD/T7V773+RlnudEVnjSvUN+y8D4pRWuzSPZuCVnohWvfZucrnZ6K1Pn78SOcGfWkfEovBQM4AfbhKbW+JCnh4ArnYjNQncbfGoDc/PRCNMyso0slLUKZeFp7nle40AXf+h6whZN/ib1PuDE4ZVE20OdLKdFfOJaAjzid0teFRqCeqFhb0xDkdAyZwj0pAWYW2KwHFnJYKNboe0WkBdNogUr0a2O28BXrmENe6cWO/LH/jt35LdY+vRpLxhUCND+5/DmeEc4ztiizAI/UakhvIBoMGZQEcQV7xv5Aeok/HF6J7T+Zw/sD84FxIZuh3jOXtjuiaEai4O5BhdJu6p51tUP7vifJ/jtiwpFRma1Qq+r7n6vVFWzTp+AAKKN2yKuMfsSTB3I3oqED6LijAFQkhboi02gl2yT9CTE+LYzpw94svcdvhnE+w034dVFkffeuD6rvZl1PBi3PF0KrMERRjxKyY9HCM3TC6OqnikmLt0V0CbREhtmRzjdtZqnaeQh786InkFslYEpbHR0/L8je++c2y/OZbXy7LcSiqeEWuQ/lvQb8/H0gm8cM/+9Oy/OyTj8tyd0Pz5t6b98tyvycp7xgy58fvSU714cfv6Xi0wUZdc7Rd0/lva6l3wzvq658fKTYcLcf++pZF5zvnlorAGJ+yeqA3b/ZUzxokKLnHMtzyEOdmczpEQPqELgnA5/YZ2zBGKPdbXtW/RD6eoHxBxxHIhk4ulPMcw20ph9zp3p7WtuY7mpfb+/o9xVo4+Dmo7efqt4sR3ORQXifSLHWD4WK+zAaIG8gXmnCaCSArGg11/HZHOZyPdfHxM43thx9/WJYPDz5BHXT/wzPNi1asczbgsBX5izGWQoZ2CrnV2YVywg+faF4enercIWTe/b6u0+0rL6Uj1Wj4oiwndc1jf6o5Ohzo/CncfwLICJaOdNlqFe9vjOXjgse8DZOFkpnZfHU+efxC8tn3M/1e5xxCLpji950dSdnefVOy3fs39spypQ+Luk3RNsxVuPXDCRzFPoXE9+AIjklwOJtABpdC5titM09Ajoo4220pzh4N1Z9etjoHXCdqtcjdub1witraVdwIEFsaDcXUzz7VM9cTOO3e3FObXwwV60Z4dJjnyJ8g2/ecfveR33A7jZzbKMwXJ83gvMkdDri+T9CnA8i8T19oTb041jwb4rkzQ9xpxJhPkNzQZZe5680dze8hnpO5xqwNnmTKMWJ8E9KkLvLtGM5dHtZIus8dnyifeYotVC4GOmY8hrMxtkHYaCvv3NnUdXe3Nb7azUV8j5Crn8NR9ADupQen6rfzgf4AobiSQ3Z6uuZ2R+2x29NcbEA/GkN6usnnS7pJwvE4fwk3PmPaGAwGg8FgMBgMBoPBYDBcQ9hLG4PBYDAYDAaDwWAwGAyGa4hXlEflzpVSIboewNFlJvre4EI03K1Q1K0JdnJuNkCTxg7g8zlojkeimE3gSNLviSZWxw7cdDmaTsbFOURD/Oijj3TuU9EQc9D5U9AHe11R0+oN1bHTE4Wu2dHve6BWPnokmiupfRFon6RUT8DLOnyxqBvdq74oPM+5Wrh0AAPtGhTJdosyIVGJ55C6TPC+bwq652yivj2Ho8sYdG9yaSPKGiA9onVIGC9+TyHZOAftOKHEg5YjoLvGoLnHdI+CTG0CGjRdeCLIo+pwGoobutY777xTlh+8JonNF3F2+vV4+XNTqkHFUg5pDSm8F5BHcT6doN1Jhdzc1By5tS+69U5X7d4qmp3OD3SPo5TwGHTTKcZ/A7KcDcgVuEv7ENT7JmRbcaTrbnbUj6/f2dXxLdA7QbX2CserCq34CyLPnZsVThLjOV1+dAzpuKS/Tmak+kK+FtDhBk5fcC6gSxHlTHSVSjAXxvjbafGnk4b+vY15G8MVYQJnkTEkkXVPf9sFZXQC+nAn0rpwc0vxNITrSoD7Gw0k7aC8M4Arl197ZTXwS8JzfnFNn1xzuhSiXeqQ7OWQHcQt1e/NdyV3+tINUsg/Lct//M/+aVkew53r61+XbMq1QMNt6Px5IR09/UyyDpYf7EoGvHfnjuoIt5V6By5RTa3FbTiWxB9prJ0div4/hoNKXNcY2EL8vg854F7ns7L8+GRxr+sU2Pi+57oFTb2GfmvSRQLyugD0+YokCTnEIXKL03PFU0o+SDnvQO7Xi0HrR/vU8HteBHIa+DC2jykJh/TpdCy5xQDSkjn+tt7WNb/1ja+U5W04tty4LVelu025JJ0eSs4TIjb94gON3e///AN3FcjSxA0vFvKEHLlixVXIwb0NbRGgHeuIoxnyjhqkHTubWjdqCNqTqej/dUhoLuAY2m+Kzt8tmpouUS8ONd6fH2penpwpF6b8bgPy/FYDeRRi/QS52dkJ3KO6kB1hjKeQJfCctTHWpGT9+U3unFsuh5TJ1yBToOteNqPTl+LQ3/uWZOq3797V8Vjnnh1rjn4Ex6JffPiwLJ/BVSz+u98qyzcQC/Ol5A5OQ8zbTyAx/s6f/2VZ/ukHuk4D68LWhu5jo62yV+cYUX2H0ApFnvqqAykgc3w6gOX51Xy797zc1WuLScWxV7NZKgAAIABJREFUv7Unl7o58pjHn0oSxZjG/GYAmc2f/0gxZLuvmHMTDrz7W9Lb7kLa0qLZqY9YXjh15XAkO8fz6jOsYY+ePC/LjO9dPC/ewrYJd/Z132eQTb14qmfE2UDxqNVUrrODLTe++sb9sszc7EmRP/v++vRunkMeiXx42a/OOdeAVCpFx73A3Hp+qPL5OVwE6Z6JxStC/9PdLICEPh/BNXhAKX4RwyoSOD5/6jnDz9TPdcgva5ggno98mc+3eFafDzWH+h3FhV5Hzyu9pgbdaApnQ0jUWw2tC5fBmDYGg8FgMBgMBoPBYDAYDNcQ9tLGYDAYDAaDwWAwGAwGg+Ea4pX54nkhj6LcgmSsWoRdvEHTIoWY9PwP4VLhQP3dhWNBwF3dN0V3a8SQPpA2DIrWoHBN+OSRrvPZY9F0ez2db3NblHBKtUYjUdboqoNNx12Ae90FJa9VB6UaLlQR3VwgF6IQajQqHGvWKMnwnFdSv2i+QSeWdkcULTDU3TFcXD4DNXBwJsraCHTr5xV6uOhgNdxvN1S/bYPi29tEeXshj/DhBnXwQtd/caZ6HaF/xpD7hKApc7fzDmi/Hul0OSjUoLXFLdHdgrEa5yao4nSpybIr2NF9id+QBelh7IUod0CJ34D0IYJkIUlFkRyAljx8DukDnDrO2mqL128tXAB2N0RfDUF3JfW/wcEJpyQ6IkWQLrRamq/NOu4PlMdmQ/243dfxN3c0X6cpnK9Ay1xKQNdJBs/y3A0ni/GRwi3rKNf9ViRLmIwJfm9BGublcG6BxDREDG1A4lenu1JA5xsNrimkTWeFzOIk1HzeaSte1OuaH4eQTdIZaR/04T0E0RB1ubmlWHwOV4EcUoW9TY2jeVNj14dUIaCbFqRS68ZSxuZBmlWdnxiTcD2sZ2oLL1AbPTuWxGIEhx8Hyv1QTeEePpTzxu0drZ17tyCji+jIsPjjDub/t7729bJ88Fxy4h/96P2yPIkeluUXoPw30Xf7PY2HfkO07s1d3beP69ZqkMhOVd5KdIM3QW3/MFzI6E699c3G0A/cVrHuzUGHjzmWILHh164MfXJAZ4wXkvuN4J5Gh8NmjWNS/d+AhVUKerYXsg7FeRIdO56tXn+PByqPE8o+IY2hJAhyi2ePJdV5+kTlOw8kOWk1FYu3eoqtTx/L7ewUkpzncPZZK/LMpYXMKEQcjTHeWiFcd9CnXax5nabiWAYaPCWn3Z7mmfN1fDPReXqQzTdrKlOSEha/R5CBZHTAq2sO7XdFvZ8iWaQ88uBIa0m7ozmXIdaen+oYz9O6WMPa6eXICVuQX43VBtNCdpB46/v+m+fOzQvZVYS5Qpn2BE6TOdxDKY2pI95/8JGcvp5gjp6j3aJIbXXntsb2LTgZTiDbHsRow3DR5xEcc6aQJ56earxHke7pjS9JSj9LsWCklJ/Awa6v/t+Go9B772nLh+FYczfGcwkdk8KKew0fA0duXUjmM3f4bLEube5oPedYffiRpEHHLxSj+l04mmHOHeFZY4zH1ydneh54HzKr3bba7tvvvl6W37ir+jQj5j2Lug0h5/kUsrlnR1rzBoi7g4na9nQk6dPJherbwnYCHcTLnV1JSwch8ujxOY5XfHnrtXtleYg8+eSHP3POVdfWL4o8dy4pZJKTicZ7A1tDxDW4R2NevsDWBzPI/9uIsyGSpDlzG0hb55hHHrZzQLHiqpYV82uOuZhC7u/jOiHyaA8Ocw1spdCBuxd/9wKNrQnW7iHWi0ZX91eHq+4YDtqnz7VGbm1Jin4ZjGljMBgMBoPBYDAYDAaDwXAN8UpMm9x5Lveiz/3uc5M3vBmv4wtaHuBLKL5+Pz/QW9EjvGn+vS296X7+UF93PvqlGDN1bCy5uam34fxifHayeHP5kx//uPztHN7w3Q1sJhbobeYZNlv73vd/4XSQ3kTfu62/bYKl0O3oq0QTG4HV8IY9wJtHbjbKTaS4MdO64DnnooLxk6H7yT4J6rruIMXGyM+12dfhM5VPBjr++Ynerh5M9IY0TcCuAaOi6ekryQEYTW9hHN26vdjAK8WX1Rnesj58obfTLwZ643oxVt1HYHc1wBzZQr/tbGgMtdrow1jlGjZOrUd481zpN9UtuYKN+pxbMNfC4othZTMvsJhyfurHxloh5yg2Ect9NCq+xs7wltrD2+v+ht6YHxzqK+rT5/pKMR/o/PduLdhITWy410P7nJ1jM1kwemI06NGhvqjkicZXhrK/ia/7YEZxs2LOyxSMFI9fnXJ+dSw2787X15+1MHR3txdflEAEc3mmr+Vpprbn14opyinqORhhczTsLDoD42s4U/zrYbO7Br5AR2A01bCJ7qxgl83xdXNzT/P5dKjfnx5pPqt3nEvwNW/nVP+yh69S+3dUr7v39WXpH//xvyzLb731Zlm+fVubgvr4Su1jLfDc1bDePOecX3RHhnnmc0NkHF+pU09fFFOM4Z+8r00WX39LX2Pv3Ff50+/9VVkePdSX1jvbWpf6YIwGWI/H08VcS8BGIEv2DCypHbRzApbcd//3/0PX/0zs1S2wS9+4q6+Iv/31b5TlTWwunU51rfmJ8oFWTbH87oaO77UXX62Dwfo26Pc95xoFHbiOL2v1GjcIV5zgTpkzbLKfgB3U7WmtaGKOzrDJ/Wyq4w9PFP+mE2wcvqv2rGMD22g51jA/Qf50I1AxBgPEc8wDmhHEITcoR6wZqX8mp/pCSNYXw+YAxhEJ2Fgj/G0yv5q5mDvP5UWuUcOGw+0a1oGa1h8fv/c3NW8S9OnpmfKLo3PlN798qBzo0VPlqDvb6qMvP3igOuwoj0AK5GoFOzFGbhGjnz3E1Fmu2PH0VPX60Q9/ouOnOv7NB4qL2ztaFxPk2uMjrd3cwziNkFNjHoCE7LKCaUNDgy+M3Dm/yAt8bPyc4L4mYHS3sZb3+2LNvvepWBy/eKS8ob27p2PeV9yqY934t3/vd8tyDBYvNxof4ut+11+0rReoAUfTCxyrONvfFMvj6LHG0Pd+9l5Zfg72+M6+6rsJ1vdv3RG7ew8s9UNstEpmQoZcIvd1r1F4Rd/us9xlxdi9qIGBcfywLF9coB872Mwem+anYFp/9lRj/s6XtFl/56ba9L0ffL8sz87EcKL5RK8HVlVXzz6pV8Q0X/1MBniETeHHF4p/u2++XZa//yPNxX/x3T8py00wOXb7uuYfYqztbys2xXWuPZqvoVN9dnY1vzc2FoyN4NH6WIx5nrlktui7ZAoGH1jrXAcmQ60VrHMAxuoZFBtnIHYNRnzm0Ljl3zZrutYYZgAB3gV0+ot4Sqbd+TkMAk5UfnKMvBj5cpprbjeQN7XxvNRv6/zbfeTREXIqvC/wkeTXG3z1wncBoE9fAmPaGAwGg8FgMBgMBoPBYDBcQ9hLG4PBYDAYDAaDwWAwGAyGa4hXkkd5njZapHc6JT0hpCdRQNmPaEg1eLzf2hclLsNmkvf2RXfbgqxhfCY6YYzNDyOU53NRqJ49WVAkP/5Q9PFOj5usiuK0iQ2E6/BL/+ADlcfY/ZFtwPsO49Wb3rZAKTubYpNQyFi4F5lX1M1zoGV/Qfiec3FBG+MGz7226hmBYj0fiBI3PpPs5eJENMXjc2wOBYr9/i42+8JGfT4kV0mi8weRztOBrOXNBwtJwAhj6PhIdWljE2W/rXGw3YEEBn2SQnqTYtPc02PRhEPQXJs91ktj9BzXffFUtH5SBD1stLxOeM5zYUHnTbFBbc6tcjmWOFYh4fAgG8tmKtebogT3u6LehpAONLGJ99kpKI+QH85BP1/KbALMlRbkOZ0mN01UXaJY59jEpoDtThPHqF6cZ1BiVmSZrYbOOQXN2UUYM6QWlxsRr3PzU+d2Wou+oHTSq+zaznGra4+hDxyDet3BeLuYQBIXq614B3XE6BB004ibyOOco2KT1s2uxsfXv/ylsnwGSeLzwc/LcgNSoY2+6tLCPG82EZdj9cN+T7KFN98RDfkOpDeclx43VAWdPZ3/eurpb4yiuSp9h3LO9sSfhZC23MDm69/cVTy+9UD0+AevvVWWT5+I/t/2sNEetFiDGejKifpsXGxy72NpiTFXN7e1yeo2JFnzDa3Xv/Xld3W+FjZXhrz2Njb0vP3gvuqL+Tc8wcaNoC57iNNbmNN7zcV4fN9fX38GuXOdYvGlfLONvCKHvMBljKeU2yo+nWIdGEAGVYeWKMbG3bMZ1iXEv1Nu+Iw+2ijiclhT20eoVwwqf7eFzc0xPhqQlmxB7trCRu29DZ1nd09jsUb1LTYajyDRbaC+U+Rl8/WF0SryvJQkN9AXbW5UDxlSraF51sT6M61MY6x5dZ3n7Tc1F7YgPeLasgM54d7ezbJMCd68kJHHyJ16LdU9Q4dlkep79742Vr2xJZnExbFy5PGFNrlsNnmvuo+DA0mHGheQEEPCkSQw4Ej1e1Z+912flH/xnLFY35ifJwk36EdswMamX7qjuDXCnOsjD9/s6/gTjPlGTe3/xuv3y3IdcuUGNgX3ua1B0Q4hpIcpJHY+1oIIOca9u5L+/vB9bf0QQKr14I03yvKTT35Zlj97rg1yv35PayHvO6ckmIYSWI/S2tV8u0+zzA3Hi7rMIXW9GKB++Wp5fq2rvjg/kyTx9fvaqPVbv/d3ynIdm5/PnmpdzC/0nFLH2nU0llzrJrahWKr/E/RXg1si1FXHr76pfrn9tqS/3B8/OZFUaQObgjewzcAOpF3dtu4bu0u4FM+L8xnqhlynXmR26+zNLE3ceHha1EfrULulMezBPiekjAvr4pxbZSB3jbERv+frnBwXNRq7wLAjQEPToKReX8z1BnJLb6Bjs0DzMoDKq4fjueUENxfnpss5Nh/2kVVTOlbDVhQe5Fp9yBlplpK/xPYLxrQxGAwGg8FgMBgMBoPBYLiGsJc2BoPBYDAYDAaDwWAwGAzXEK8kj1qgoO+ABuWBYp9wh/IcrjZ0g/F1zK1d0UBT+J7PzkUr60GqdKMvetyNG6JCkh41HIhO9/zZgio3n4D6vym61c6W6KsdUIUvhjrHFqjCz8ei2+WkUJEGBbpdDzuT91E+HoouRmeXFLTF8uc1mkh5nnPLjaujuqh4u6BrNUDXqoN+F8KVJAJVdWsDbi2hKGY56Ka0e5pCksSd2fuboB4/EPW3UVDvSUe9v6e+H56LtvcC7lV0SPCbqhcFIslcfTIcYytzykbwarMFql4PdW+jLSuUuCtyrMmdc1m64txU1pC+jjKlLzVIgCLMV0plerEoxF1IC8/omjLT8ayV52v8+IWDSqUukRqX8yODhM6Hq9HWpq5fh9VFHOuYBqRwpCTOMc9IhQ9BVz6fiMY7hatUtkZZlJCX0kef7l6gY9KKrIZ7qYHS3KzTmUbt7cGNpkYXu5raOU8h94QZD+VaCQZSPl/0ea8pmUQEGuyX3xJl+BcfiXavkeLczrboxjduKO7Q3aQJOvuzM93H/Qf3y3IHx/j8/EB9EGJ0mq4xkAK551xWrIcB6a0c6KD5c9H1UfHdLcXXb7+rdpyNJR/yDyTh/A+//ffKcgfuLilcS3ysxwEcGeKCYp2Bhh921S+3fcXX9PSkLDcQF//gt0QJn78pCVUe6DobG5JtMHa6qda/ilsanKSyiSjsHVDnbxf045q/vv70POfiIhZ121orAl4DUlJS4ClvThBjOpC+7cJRMgohw4HkcTRW7KFrHId2App8UqypnS1IaSAl78KBJkAds4pjmMblHhy9fvcPf78sv/YlOSBtQWIQIS57OOf+/o2yfHSumT+aqOytM6mpwHNBkbME4L5HkDtHAZxz6BKDch+5znZf7TKbc05rJk/nGs8eXKt6kCExTk7Gmsfzot/DFDEaTqadpv6ut6n5FEC6twMJx+mZ6j4ZK06nkK31+5LWTJ9IQnUO17i4xa0AVEb65rLlerPW5TF3XiFD8tGWnFt0b9uDrOF2X+WwpX6jDPXkQnHlW+8ozu5h3O5AbrOFeOAQn8aQgedFbE2xiDK272xLAtOH3O7FGery9a+U5YeP5UYWjnSdez3Nv69ANrvb0vge4P4470N4GEaog+9fzbd7zw9cELeKayAnQ26ZwnmoHkLijHV7f1cy/AZc1Q4+lvx6/56eC//g93+nLGeQB+fwsIymaqNkpLiUF/KXGtfFXTz3oB9TX7H2/LOHZfn2rnLU/j/8+2U5QK61gTF7aweudVz/Eq0HI2xdkGPxqdUouVm0H7cfWAeWjqkx1u8mHTCxhlXcaD3EX/ztVl/1S0M6QCKeTuFeOIYMOtM52w04EkNWttw2odvT/B+OdL6LM53Pz9DekPC3sF1Is+ImiTlUV7nbwPMHJFQp3pMkmIsxpGCBx/769euiMW0MBoPBYDAYDAaDwWAwGK4h7KWNwWAwGAwGg8FgMBgMBsM1xCvLo5YuNCTxBKCjJ3AImEEy4Xmi9dKxJvJEc7x351ZZvn9LVMVaQ3SzDbiWdFHO4ZowHor6NhkuqPXcvT2BS8P5qVyItkFTiyFxubUvmlULzledpihODUhlKO3ogFq5Ccrlk0PRY4dwf6lQpQpq9jqpxLUgcLcLiYsPycR2X/TtOqQ+HUicbmCn/bfvadf7PBdNMJmr/seJaIezlDRI3c8G6tCBA0kcQ75SyGAyyDToAPbGm6Jv74zU93Q0mmN8eJAVzSai7Y3h+jAGE5c08BZcbfp11fEGqOU16qmuSh6V55hrq+U0lDDyGMqjNlqiAdZyzYtOQ+3SghQnwv0Msav/BHPdB7WUfbZ0dWMVa3Dg2tgE9R7tnKHv6pC51SCDCiCzYvNnkE1WzIM8/G1NtNynn36qe0Isy6+kGz2XFvN9XulCuBkgHlBCFaBvfdD6w1AV3WprfA6Gmgu8GY7tGPM+h9zh7ESxql84svXhhDAaaM7FkM+8eUdU4kM44HRAoQ0R/xn6ZpDeffLok7LchEsUxzTddvyKg5NbXV4zlvI5CLMqjlEs+/hWEoI23IFjzN1drYU9SILjOtxOEK/yKSTBT+SUWMMaRU3P0uXBRwyL4LbThHtUlMMpAhLmzrbug04KXqI4koP+no01Bqcz1Xc2guQAa/cUEqoY4+RGIaOMAq3dXxS557kkLuYipFjsKx+Bi/Iozssm+gQpQeU8OaQdVEI2aoib1ZGk47F25kWb3L4jV6IM62kCOv6orXkzwRrpIRZsQSq+f1Pn3NmWDCPDmj5N6Wqp/u/f0PGj9+SIc4QYQAn5WpHnLp0tZAVepvEcoP3Z5jW0bYy1rQ76fxRrjUw9uqZgDYEkIQiU5znE0elU45USo2UzJjh3EKjuSQopYaJyDXKSHczdHtbO0UTx4vxcMsdd9DV11IMznb/fg+QrhuNPonvN0kU7rVMd5XmeiwrZXh3OaJ7H9Q/SI6wJ9VTjs4l1/bfflcOhxzUV568h/jaRZ7QgIbygGx/XmSKnzCCZo+tYiHW2B6kk48Um8t+3b6l/ppDtNxB/tzq6j+lIYyvJIFuvyCxV3xYcIf0rylE9zy/lUWPE+AROcwFki3VI2GZwG3r3NUnY9iFbnE0huoZjYQypTID2On8hV7VggvnK54Ei54tytC3yq15XYySEVGc6hfS4q37f6ysWQuXoanRwxVYT8wTSMeQ0zIE9xLIYY3O72MYjDNfHxfCc58IiLgUcz3RUYu5N9ehQ9V86+jnn3BzJ7gSyr/lM7dDA+kenqhqkxSGkVRt4HtzeXIyRJhwDR5B9juGK18aaO8UzTIo5VIdjXKsO2RS2TYkhCQ8xXhJuUQF5aoLx5TFPfIlAakwbg8FgMBgMBoPBYDAYDIZrCHtpYzAYDAaDwWAwGAwGg8FwDfHq7lEFL9jDjuNVBjqoPnCrSB1cPOagksFt6MED0eC2d0XPjuEe1W6SLokdpkGnn4BiPVvyUMEAHILO//zps7J846YkWf1N0RPbN1WX23uSwWRzUIWx0zfYbpX69iHnakSk65ITpYrmHinS60E9jNybewvK3vFEdPW4hl2/4R7lgtVU/h5oao1QlDGvo7662cNu8KCNOrRbiL7w4S6RchwtpQc4ByVILUhsKJm7C4epKdzDMtDwJnALOhqIkvkM9Urh0tAFbXoOaQGp7ZRnXBUL3Dm1C3f/z0nhJ1WxIs+A3IjyDLgotZqimJLaO4Zk4Xyg8ZPiunlFnkLXkuL/Ke3B+IpBFY6gLZhDYkEqYQhqqI/xEJCsjTk0Ql87xJ1xqr/96JGcNOYJ5+XyPOujEnue56LCVcFb5QTmnKsQz3HvFckkaLQB6rcBSUQzlpRhPMY8Q6zCFHHDMcY/ZBb9wqnG9zUOxnA8ODhQ+71+T04nzWeaQ5QbtEBhH4KaP5zLPSNCPPIRA1LKMzD+spSOBBz3VwevmCOcW3SXoXsU6c2UEtYiuKHVND7bPbhkwT0jgxRtPpf0hJR/zqMM7nBZIWFqeJIk1uASl0IqMAaV3nOKET5lBnRWwqDyQO3POKZARU7gmjQHdX4MGXOOdu0Uctl1up6EQeA2uwvZQoL1yUWX9CHjKX734ZZHin0OnnQCtw1KE2qIhR7OyX6jMYhfyLi6fbh+vSUZyAw5yeGnT8ry6BQOi5AYtJrq/zlytzGOCQKuLyyrYrW2zjNCG0w4R6/EjW8h34+KuFBDzhnmq8enQy46H8ilbgbpQ9zV+G92NV/imlxtPMht81x9N8W8nM9w/wj3eSExSnCOCDmvN5Q8lW47EY6vQboQBcw/NM8uMLdayPe6TTpPaWxMR4rHWQBXWKxV3rJdX4bX/wpYzqMQMZQSiwDzrAEpfYA4VKfcDQ4wTeRtXl33nkGeEyCfSPFsMUMMo8NRVsQzvyKHgOyFzmmQXm00kHN5+n0rlqyG8SiBq84MDmRnU811xs0QuTHjVFDjWLuaJNUPAtcqZHsZZD/jFBJYSGU++lBSyls34WYLGTxMetzmhvouQB6bB+qXAdaZ4ZnaaxPtwqesoJgjKfLGOmJ3DTmHj76rYT51MpWhiHEJpeCYL9yug3I2B0lUhnicYDuNHDntZuEcGHprlEd5vouK54KYbsA5pJO4xwYcXXOM59mMW4BQho91DmmbV/kd8jW4fnEN7mPrkV4hlQoh9290VJcmykGdzsd9lNWHMeZKHFN6p/omU60ddM0mPPR/jGcttqv3EjmNMW0MBoPBYDAYDAaDwWAwGK4h7KWNwWAwGAwGg8FgMBgMBsM1xCvLo5bSB588QFC9InCG6JJAiQNlB+22aHBbkEQ12qI71biDPGnD0CGloBxfXIhCNRwu6NkJKEvzoSjbz59rR/GLc/3dxqZkUHU62cx132NQ77i7dwBqVQ3Uql4XsoAGKJ2QBTi0zZISnq+RShyFvrtZ0Me8UO1HScEYdLQIO2c30Mb0jYhJ3wb1OKbiAy4VI9DhJ6AkR6hDHENaVfxpCJpkv6nxMT7RzvmDCzkkbHRFX95ogfrWQR/OdSdBXXUZgB55gfZokFbcUH9SopJkdLJxV4a8oP/6Aaie4CqmdGkjNRaVynBMiHkWgL43xTw+g2zsHPTpCkcS/U5nlaW8Lnefl0wt/oEuSOrrEHWhDAuM8IrzUIb6YnN6NwbzNIWrwMefPC7Lh3Acy0DtX0pB1u+z8Pm5nVdkBPgdMZR9S15pCGp8iD6vgbbKMTwcgrY85Q74cA+Dq0WnoHaDretenEAqNZf71q19OT1sY3f/Aa7Zb2sO7e7LbSoDhXz0DE57VZstlUklRqy5TMa7Tnie54KgkLlBMkKpjKuUsYYFaucEXOrMo9wPLjU4T1VUCwp5hLXTVxxNKD+9WMS6NFaMzHzEkQgUYtRxDn1OTgcMSAzTFO5nU+QDlBOA1p2ONX5S0Kinc5Yxxos+XWd/Bp7vNgqZ7yRlfNRYohzNQTJKCYefU/ZMrQTkK4iVc7gu5ZjtzK8S1IE5lV+Mczrn7d9RDvX7/d8vy08/lAPb33z3L8ry4WeSTY3PFPsGp5L1THc1j9ttuJE5yjLVHt2+5nGzrzyKMuNkDqnqGuG5zMVuEV+yGWJ5qviXzrGewLEpQTqc+RifkfLFvKnYFTUpPdF8SegGg/VycA63RYz/PF9ctwY3lBrmHCf6xbnq0q2rbUm9z+AwlUE2k06ZZyrvqSNGNuLVa22SwSEW8yN3q6UAXwSe80q3NQ9zLoMsJOQcwlpUkTQzZtC9FDEsgkwmgzw1wb0P4LaWzNSeFclKIUWMfEpXNP44b5Mhngm2JSFud/UslCWK22Nc/yJVfjtBPB8P8CwCd69t5L2xr/HHXDquv/Jj4Eshz3M56UIeNR1DSoJ5c3D4oixvbun3FGv7FJL8RqA2ooMRc9qc10LfBZCOO/xtXtjGZphPNcRxRr+M8hgPOT/Wcbpj5ci7HJ5Bc6xzHst47qAscjKFtBjPXvdeu7uoY8xafjF4vnNR4R7bhBvpCGvFFA55lP004bQUYp1zjltJcGsHHZHi3qcTOE9jfjUbas8NPFsvt87IsRa3GsqJmk3IzREjPEjsapBQUr5EZy4fDlNTSOJm3BYmZc6O9yQVR0jkgy+R1RjTxmAwGAwGg8FgMBgMBoPhGsJe2hgMBoPBYDAYDAaDwWAwXEP8Bry4BZWHbgFeTlq9jkyw63oOJwMfNNkaHGPqkAyR5u88UuJBfcN1c1DYZuBLLplKdKmZTnWO589FyTuFNOJmheKE8pzOQ6KpzShRCEGnw27THUi+2qD/B77kPaRIp/n6xRhxreZeu3d7UZ+J6JjTEFRYOspgN/OLTPcboA/Zn/WhKLhzSBkySKueHEmSNp+JvrgHxy7nYwf8QsLkN3SOFPTYKdppAtrZx49F/d7dEmW7A3ocqX2kIOakp6PuEbqkCeeBCLRAMtyu1D2qqFYKSVqIvghAe+am5HRdoTMJZV0zyhnx+ymoulMc4zvGA7VXCzTDqKgbnY+qEkfQIHEOUhVZ9xQU1gR18b0LAAAgAElEQVR/m6BeI8gtDo5FD/7+Tz8oyw8/e16WhzMK/9B+RX3WOSPzPHfzcuxC9gUqtVdxP6JkhhRc9CdiZcqJnOFvIdVoNijPUYz26BgDl5LpeHH+4RyyKtB+J6CAxoHae29D82N0oXgX7CsGtVGXIWWwtFohnVa/VmIlabYVJzd3VfDkCMV5Bv2ehzbPKhIjlc8gNxzjnumkR5kj18U55GxxT5T7AK4Ks/MjnX+2kAROXihG9vYllepA1uKDhu6FdMag3QNo3YipXqU9kCdAVuFznGJ9JZ2Y8ueokCN53vqCa+6cmxftnEAaS2lSRMka5CvsWz/lPGYOw8jBemcrf6Z7Bd0OeZalGREp1U24Nza7cIOCDDnD3KVs5P9j701+ZcvSLK99Wuvt9q/3Njw8+owoFQWolDCpQiVggARIDPgPGCAhMYMJA0aIGWLIAAEzJggJZqRQUlVQqspUZkRkNN48f/7a+25r19rTbQZ27lm/E2FX7i+ePbiR+tbEt9s7dmyf3Xz72+eutVdxJUnU2TNJRg+PtC73IafjmjKHI93pqb77i18rzmaQSITvQFbj3No9qhPUMRwuNVmGGOa1/ifoR5p+eOp2K60JJZx8lkvIjSBtuoRLzatncsQpkQNXmC8nl2vHvclS9e2i/y9PtT4x5x3jaIGULmfIqShDo6tJgs6LISOgvKE70P0vq80yMn/d1sH2omsQBi6tZUsx1pZirmeho+UExxTMoYeuKH1AHsu+bUkbIYNYQL6WYy3MITejBVjo1jlKSLkdpPcrjBvuCSiN6Y00LnPIDVcOfYg55CEbmc7gtAfpd4S+6mBMxanun4TvRh4VhYEb1A4+ry40J4pM88bj+IgFZD+XV5IPUQbm0b4l9mjcf/kQ0pOA+Qjk4i0HMZWvJYQl3bjgmJcwRlM+iIXfw7UoSeEChxzM53q+gPq+FfJPuqZieq2Qo0aQQt1972H9m5BWviUCFzSuvRHc1Vp7Yrp14lHoJMX9UQk3ruBGeRSPU8HYxoaKR6tw3i2m6/ESx1i7Mee7mPMrrFsZ5jmd4RI6Y1J6xnFGx2XEGsYAHmlAt03K3YbVN8dRY9oYDAaDwWAwGAwGg8FgMNxC2Esbg8FgMBgMBoPBYDAYDIZbiDfkxQUuuKYOk4LecpIi7Ud0zxCU6S7cSHJ8XpSQ34AF7loSFkqlIEPCyc/kZBfXdCNQqTugJ16ci8J/eiL6OGlerZOsQaFawgWpAN2VriEs8xTqfl+U85g8Y0ea//YJ/Z1Ox3303e8455yLXosafzLjSfd0rlB/ztA/QakOCkH7I00xd6JBvn6ttn3y+kVTvnNH1OteV+15OcGp74v1OOrv6iT87FT99vJcJ+rn0OednOk3p6DWPjy8q3tjnE0Xer4c1Fo6g/FU+T7kbg/f/6ApR3A+KoN39V7Uu+uxUtJdJIO0BmM+otUSJi/p/3NQgh3GvA9VvkI7lrhPB9TJDqi3/Q5oqHV9YrrnQNqVgbaf00kFcpKYbg8ca6AhZpQwgm361fOXTfkzOEYtMkouSd3Ud9+FtsY7mKrR9avcLPuIUAnfkuBtlpKSGhyAAh/A9iAD1bYgJRTxdIF5kV0W9bVq49EIjkmQDF5ciAI8iBQLJheSR339tdymhrtyqfFw7+MYrfDgbIOq5biF4M3nfodSxUa21/oRStso04KcDeXzK8Xg44mkKo/eU4wMEN8YWiKsJ/2+ZDEh5FFhB+vSZN0H8yvEyGefN+UxpKp+F+5VjpRjzBXEixx0b49xEuK7cctxkLRhPVPIuaiPXad2ywu26B+1LAr3y7O1bHeAGJOizrtwvehAQtVyusMcDekQgTWE8k1KculYkyGekR4eIM5WdS40nWqe5WjL/kByiwL9c3oseXII5y7O6Refayzs7YmGPhpA2h4qtv/mi8dN+b/77//npvxPf/5r3R/uPwni6TZ9pJI4dvfvruNIAdnMAk5SMSZOv5J8gX/DbLn9QNri58pLFvnrpnwFOcXJidaZFaRSOdbXz578tik/r11zDveUl9BV78UL9QUp+QXy4v19xc4+jhYIM8oNQfN3AMZdtyvZ/mhfkvInJ3SVgiOcr9tmy+vjtaw1Q9tXlF2ir04h/TuDW9edHcXElgsc1ByU2NCdkW6qbB9KKJLo93O7MqQzGSR5uebZoFUX9A/yH0/ZONogh7RogeMZXl/p88/O1B7PIEsK+pACYh3ppNtzGyJ8VbmilhtNIb1kPnc2wdEQ2Ede4ZiFK0ilKGXi2klTSUreelgXO/hujKM4cmw2V3Wc9pT/eOQ/6K+IsZsulYy7kBaHMx5FoPrSWZnSp4r3RyzjOKGj4Xi4jmVha/P8dvAucGW5bs8Csqa85NoP52HK1Nzmdd1XnDc41gTPvsQ8y3DMymCg+MSjHU5PdczJar4eL5RQ8t4Z5EicwotLOCYuVPYD9aHHcS6uQxnaZil3yz2q5D5D36SDXUtzfAOMaWMwGAwGg8FgMBgMBoPBcAthL20MBoPBYDAYDAaDwWAwGG4h3vjY8OBakkHqMt1LQCtbgtaUgA+WpqIbzeeirV5ciqq9B7cf0snJfJ9P9d0M1NOnj3Vi//GrNT1wORMliqe3D4eiyaUpnoOOAZBPrECDnS/1+3Sy4entnrR4UKJ6oOq1KFEVKdXh9U22htw7d1xLaJKuaFm9TFSysmV/pGIJOhpddgLQ5nhK98VUEqbnJ3JAiPt69pfnoqH98rGkEjPQ4+KahnZnJMr+LBNl8hnuHeDU/UNINTyopKQgxl1RGenYQrplSOkdqIe9+6Izj7/zHi7R9Ynb3knubQTu+p1ry1ytpR6kixocSCCV8jxJnY41GRwLMAhWpPhhMkaRPu906Kyi+uT1uMvmkC+t4BKwEq13vCuJB2n7vqRT0mb3qJbkoNTzTZeQYpEijTET3CBjaVQv26SBe9G2g5bU5wanGTpDVZRhgG6N8RmEN8xpXD/nyfyg2iYRXb/0zU53/d2WA0dXbby7K3eZ777/UVM+GKk/Z7/R3D6+VJ8fnkm20e9oXvJZKX1iV7T6Dc3H66MtOpwQQeBcUN87qiCNjDe3eUppaaHrzyHJ/A3ovj8uJL3kI5BlDP8l5zBHuxPNr+lrUdEv6/XwcKiYWkJKVsJVIRhByoSB2jbYgaMF1n0HeUaxpKsC5yglnfqqR14ReMhyqnU53KLebbrK3P/1+VPnnHP7yAM+vaPxvANZN80t6d4WhZvXQhptlS0KOXoOA5cxoAc3Erp3pfX4msCN8firJ0353oeafwHmM91/lpAhDBFnq2Otqc+//E1T3nugeTxOJcl5+lSSoD//i79oyidTjYsQcbZ6R15uSZy6e4drF5WLS433yaXmE2U2jGMlxmoeQQaP/s0LyJ0q5S7nkHBcXCnvGXT03RfPv2jKL58oR53VLlThUveehOqXk1PJsMZ7mq+LuXKOq0hjpADbPqooC1AdK6/7J/qq60IWGY3v6bsnlFbBZbCWSG61N71vHFgip3Ug8+oTSrlfT9FWE12z2oN8eolFARagRQz5FY5eyJj/U/qJfUwIx74wXjdiy1Uu4H6C0iuNsxXkTnTXK5D/LmaS/y8Wkn9dYk/z9Fif/x9/9XPVPYIbGCRBlET1OsqTt4myKt20lpmEXbULXVdnudZ/bvNm6N+LU82zHI54dEMLK8pV4QJGJ07EzsEQLnhwW8qqdcxIQ+SzTMHoMAunJ7q2BpDLVpAIJQvNvwKSvnJFtzfI4pCkUiaYFSp3E/Vpv94fhdt0AwsCF9RHHAx3FBvOcbRG+zgN1X+BeRO3FnbsMzDP6MacZ5QYaWBM8eyTU435TqJrRsP1+ApjyhOxh4d8u0vnMFyT40iAAFJFzt0YuVuEMefpEkX5oxMGu3L4HB3c1z+YPMpgMBgMBoPBYDAYDAaD4Y8T9tLGYDAYDAaDwWAwGAwGg+EW4g15VB6ORqBE8aRzUItznlwPSUoPDgjkQ79+JVeh9x992JTDiBIjOMlACpDjPvcfPmjKf/KznznnnPvysaipPchz7kDisncgl4TlElRSnkIN2uJsJnqWB50uRhskCeq40udpojYgvZo002tHkm26ngRx7KLDNa15+lS0S8pkwAB0EWRwXdCbc9D4XsNVagVHrclEdN8U9MH3HorWe36pNvz5r79qymegyoU1PfzJM9QXZDPK8PqgBt59CNcLuKhc4iT7fKnfL+j61dP1QawTwwM4gN15T8+RQv7hbnAPe1egBC+gw9DvXKUSaPuYWxGoeaSNt8YkxonPKHMSzTeA5GYGevjFZN1/g57u0YW0pjuAkwdovR5OLZTKUP5FpzXWlxIOxqOWS9QNcsB2m70L6yHvvOyjbrzm90vuBrFT22kpwjt5dJsrGa9bjhwcC6C2Iv5dU8Jj0MA7Hf37LiSXuyPF2dG+5tPBI8XcLz//rClfwDHO7YE+TGlfaz5RKnVT/4Me3SKobhOBC+qlNKD0kJ0UUgKEv5WgbQton758KXnKyUwxdWcM2SieLVlq/q0uJc9YYjxM4RA4OFi7cyVw7EoxF+lGFSLWU9bScsbDmArhdOFQrwJSADrFZSUlenSKg8yUMbWsP9/iwlg555a19KFM9VsJ3HRabmUYSwEcfegEmSJuVRmlD5CGxYzFGDuUUEFml8IBLEnXn79+Kar6L/7ynzdlSjx+9Ss5EGWQTc4QIyg3H2Ach8jLjl5pXDLneobxukD/cy5STnuDQd4WEDhfrtfoYV90fsrnW2sy6pGjvRz6aAlKfoWUeY7nnMDBiw5MuyPNr+6nP23K9x99tyk/P19Ly14+k6xsdq42P9iH/HRf944CrLPLC5RVl9hhbsE1JS/UHmkfbo+QX82hs5rOFFMqyJTeBYIwaOb+AJKeJSQlFccw9haXuGYKp68h5PRxrhwOhqUuyClrEWiOW2BETyGDG9Xx1NPdCXIYulFF2Le0jnhA3R1y6uV0grLy1cmVrj/H53PkOYMd5aUpdHDXTkPOORf57bkNEWEYum4trdnZhQNiwLwEHYC1arXUGHt1LHng7L6OzRgXep4IEpqATku4T5CrI88hFa6Q08/ra5IY7r5Yl9NQ14aIndlc/V69lhQzxPpewuHLz7F3RB3pWldiFK6QU8+RazO/+Ztf/so559xyuU0/vsC5eq6NIY8i34NyX+YBbYdT5AqQtTEnXyHmeg+ns1Lliws922yiuZNCHrVXyyIxJNwE82OJfObOHY2nHu5Roc9nmNMBQl8/VCxm3u0jHF2ANgixdvRGckKOMC+DipFnM4xpYzAYDAaDwWAwGAwGg8FwC2EvbQwGg8FgMBgMBoPBYDAYbiHeULsRuGADJZmONRXoPQvQMeke1YU0KACF+PilKKEznsA/1DWep4SD5trv6ZT5Tz/5XlN+8GAtYfnNb/+m+ezkXHS7ko4NbjOVPKEMC9TTDDQ1F5GGLykAaZEJ6M88vT0M6fii9g0bStn2uMRpv+8+rCVjvyr0LOePRZ92dJGg6wXaig5Ml6D6LWbkm2pg7IzVPxx1SU/Ut7t3RVVbvZSTWFFL4lJIzbJcbR/jFH+65JxBvnZwJDpoEkPCh/oWsMkJKtFySdO/8+j9pvzBJ5/qelowYEx/m9PA/3BsmIs3/R8vRb+wehHaxVMiR20NxielSgEdKCBx6KHtypryewZXj4NYp/hTNkkZZEkuPa2BWpITSHtASc0XGstzUFjJ3Qw85YltQv/m8vZwHTtb8i70T8U2pmMNOq7lnIT7FHRdQvzNWvIoVgbOGJRsYoxcO8ilsC7qUMoGbeUc9PBuoZhI97gKTmNnkFamkKEWcCegPKft9EVXo812asE7m4uBC2uXOK5nYUjpCzuP7m0Y56jf8wtR35+9Fj3+A8jMPDuvwtqSgM69p7g37ip2BbVctcTfbeJWXFCfYglruR0loDyHkFuUBeSnpcaAyyFRWZHCDXkRqOp95A8+QV/f3a+fYXvOfIFzLq7X4V5fa1UEBwpKI+ik2HKaxOcZxuRqpe8uEc8KtHOBmITp6paQEKeR2u2olmS4uX7/yeeSGO8eyqFiNoXrJajniwS5SgFZAZxLxkgCzl5L8gwTQPfkhdyjslbMYpxCjAvfTTx1PnSuqqUNkPF0UsQfTMUS8aGEJGUBOQItEKtC83tBuS1y2sFYMoJeR/KUTk9rXXdPHdzfX/fTwY4o8+evf6t6ZcqFu3Qww9o6h9vjYq7518PDBph/q0L5bXeIxTuVpPyLr5WDXV6pPaqKycR1G2yvP4MgdJ1aEp+g/hFiKB0fCyxQGfYflLUt4JwVrpCT0IEI96QMPIJs4v49OWq9OJZr25Mna9e2OGYcRt54V5Jg5iqzuerlIfMPEFNyyLwWkFBdYf9xgXhacj8BGXOEtamXwknqHUn4i7xwJ6/WYyiBS8855Lut9IMLDZbqi4nG8wTuWYcZnH9S5HwITHQp7OGIhhVkaVFfY+Zwdz0HOzjiIoaUt8J48XBHiighPoMzG/Z5fg5HVjq5od/pTsTfYs6W43dD7MP+7M/+T+ecc1dXerZt4HpPlWJd7PYVT68W6k+mOe3dCWI/5msFd1f2f4k8g+q9CO8COrFymyUkZk/O1vWJKPfHOt4bKA6nXawLiDU0wCwK9ecStsLdFH2FnIt7CGacEY7ZoDyK60tYmjzKYDAYDAaDwWAwGAwGg+GPEvbSxmAwGAwGg8FgMBgMBoPhFuIP5sX5G+iQBeg9M0hYhqDQR5BBkA59Bcrak6dPmvInn4jCFDpR4rKVqGGTC1HornCq+tn5ms5bZapvBErWDKfizyc40Rs0/x4cUQrQ2uZz1TfuiFqVg4aagWeV4kTyklRoNCVpmfk3M6XeGFEUu8H+mqr5nb/7rzSfz0FNO4OjC9laSzhmLHK1Q56r3aoU1GhQ2s9LtVUwB2Ua3Lf7j0QZS3v67tPna9lciWt7oJ52PGiSoN5NIPd4fKYT3YfpZnkCT2IPQWFNQQX84U//TlPeO7rflIuWXIUSiXdzMr9zUvgEnIo3SJ9i8BYpDwtbdEbKY+AaBnegbkcUv/FAdMkQFL8e5IR7cH/YG66p4iUozCH6NApAJcW4y+mkQklUTKmUinSbYkyZXIGS2ppbm923iJvi3dvAO8mfKGtip7Q+bn0OWn9BByjcn657iGeUtVUtRzjOb5zwDxp2ka9jbkpXOfRxB5T9V6eKC9/7BFoK9GEKl5ocLkJlSdmCvkpFEIZxi9rOE/jbArd3JMkgWCe0P2WvFWILXRWKQG13ulJ7/c3zr5vyTz5ULOrnkLDN1UgdDxmSlhznMS+LmvJdwmkiziGB6KhPi67meUxpMyShFeacR1pB6ZCHhKPC7yaI5WlP8SWHpGeJNgsO17KgIN5ebA1d4Pp1wByhDlFCKYWuv0GB5wrE0BwapwVo1SvMvwI3XaH9GSMrSBEXmOvPXqwdm9h+l5B133v4YVP+6ANJeb+Ga+TfnKtMFWoHY6U31Jh7fSzJzE6kMfLZV0/1TNXmxmm5Fr4j96gwjFyvt15nFgvlhJGD9Bnxx3Neon43yVJnIVxcIKEYDSWDojz3aiIJTbaAIx7md7la1yfxune30hic4nsLyACSPtxTQPMvkWuHMR3JIDcM9Pu9sWQGJyvV/fkr5cZZjnmPIPcuYmoQOJfUeVMCGV2/ozqsplifkEAv0W+Um1JqsizVhpRelxXXS+QzcIlJEPPu35VL7ep6L4L8q8v4WGx22pxDBlzmKkeecjRIYxBTZnQGxCIZIEZ3kK+lXI8wL6N3JBsOgsDF9X4mgRxrMpXb3QIuse06cb3UPU8gDztcSMrnIkoV9XHJdkGs3RvAVWugNrp2ygyZI6GOGeZ/2YfDXhdOgZSiL3HMBuW1brPkKmjJr+DCiLrH3F8gf7i8XLdN+S0kNt8agXO+DoDsw509ueJNz45xOeXrm48voDNTRekp52trj6DqJLgmRPxNcZ/GpBnDOkE+08e+pYO9PaWYBXIoHhXRWiMo+Qo2O2xWODeiP5Isa4Cyby2G3xxPjWljMBgMBoPBYDAYDAaDwXALYS9tDAaDwWAwGAwGg8FgMBhuIf5geRRpWQHoTjc5ScVwCeqAfryCtGa+FG3wsy/lZnR09z39FqiNzx/LseA13IboTnVxuab/np1KHjOZivZ5OVf5bCJnhDv35WR050iynQCnWmeZ6j4AhbKA80AAmnGIk8Qncz1HBjodpVJZfSJ5yw3lLeFd4HxNr+uM5HKwc6TT7Z/+6heqDyibPbDRg1icsX5Pwyj1ooO9OlefvLxQ2yYdfd5LxN/vet5Hv7UbrulsOSQZK9BNZwWpr/p8VYniNp3Lsegu6MC7oP4HLb2FHnYwFpVyOOZYoMxrk+tXW2b1rhCAP0gJTQLXg7QlcUo2XgM1W8tJZtDj86iPMjiMeAxcGKk5n+l/ytrJKeVJ/3DvWuF7dGwoQ1J/cU24WWayhPHHk+eibk6mcGrwN/RLS2rG8jvoR9+Okc1P+c0yqDZln7TuzbI+yv1y0GvpxnQ5UZtMp4q/LWnHAi4ctaylRCxIcTI/usrtIs5fTDUv98aKg/2R+m2nC7oz6dFsIzoyOY5LUqv5rByM70Bvur6xq4Jyw8fsC+r3eA37FzIYcLw/eymZx8mF5ss9uBdQ0nrx2b9oysOR7rN3+KgpX4fJDBLfAlLhIFXfde5+1JSPvid5qH//O6o7JKdJAKdIxOYpXDiu4MLY6eq3riAXmMAZ4yUkz/PatWcFqcjbIgyc68brvqCUdAkJ9gz9M4DEu+Wmg2KF/ykjyBPRDnRXSjlE8LlvueYgLtay0Qxy7MVM7fr6+aumvLMvJ6k7R5LS/GWudZmuX/sdrXPDXa1/i4Xa42qu53iC32J7ULob0QkI8xuefm+NIAxcXK9vOx2t84uFckXKTaqIdHeu/8hXu8pRKuR2Fcb21ZkcSV/86ldNeQlnzf6OKPG7+5LWJN11PUs4rZVjSa99ou99+dvfNOWXz5SnffCh7veDH/+gKQ9Gym/OX2O+TCGbGaidjr9WfacLSKto4cJYd9M6+hYIXODiui9SyNcHkOFSVlOgH15PID3C9ibAffpwMgo6yldPLxQLn3/1rCmP4PoyggwtwbjoDNafp8hbppAeTpF7XE6Vi05W+s3Du+rng131SQ5JSB4gPkIGc4m4ydw1gWMSrWALSDGzd9CH699O3IOH63F5AQcsrsk56sH1uYCTXYE1cop4fLnS88RdXZNAqjs9VUzrwmExxL5mnmFsJ3ldFd2vM9O1Hi6Jyxiy9JHGUW8ITTLaP4ecv0wRIzPsI5AvO+xxsHy4DPnYa+xlT88u6q9Biv6W8F45aACXsT3sF1+/hDQW+yzn6YRFJ1usbWjDbKlnvziHG9pS10dO6y7POAjDAuX1b3H9XSK3nV9iDHXUfljSXZTweBTsS9GfFW0IabNMyRekqjuHcp5LIVtkDC2/BY3GmDYGg8FgMBgMBoPBYDAYDLcQ9tLGYDAYDAaDwWAwGAwGg+EW4o3lUdd03hbDG3SgFgUWUooYDgcRKEYsB6AWvobbz8tjUVv3h6JlzeekrUGiBcrrsL+mh16diga1BN1tDtoiqbzTSzgP4GToBLqdCDTRHuRfNE+IQf32kSh0x3C7mkNmsshIo66/t+UD+su690LQvQu0CU+3n4HGXqKv+pAgpH3RTaMcLg1kjK30W6ev9exhqN/d2ZNcawRKdnS4lk1Q3ZCAAuhfSW41O1M5hk5nfCTKYkT5CU8sx6nsMSQfd+6Jetztg/pILjzZf2TLvzN5lHdBLUOgMw2doQZ9UDYhYUsSSKXg9gPGpktwSv6wyzCh784vRe1dzkBnBbU8wqnt170+GKheZabv5Su4EOGadKD7teSXoGzTbejlicbXZ49Fc17Bjs0jNtG1ILhBEvUufIe88430hzKo4IYJX7XcV4KNZdaZ4yLG+/mrqSjwlxeX+FyU1AA6tC5cA7o76/FfYm51IGsM4NI3HGiuLEFlfX6hOXqvJxnG+7uSR8Fsx91E9o3odsYmwJ8i6Mbg3AYJ05bg69/xdCJDfG05EGChiVtOV3AgRFz6+kzx+IuXmnPvP1R7VRPRkk8ff6HPx1p/dmJdn3TX8ZVxvzzROkvZ1PREa/H+ex805UEoeVTRojyD7o3noPPNZK51ZdziJYNOfE904l8dSwry+ZO1/GS50Hh9WwSBc0kt21xAvvTiTL+x/+GHTXkFSUaGuNKB9JNrCx0iVoh5FSj+CeMmJA4lxkUG16FmGQOn+uJcMoT/+5/8s6b8Fz9X+1WQr3lIqygr7GPuDsaS2Iz2Ub4P2dwdrZHH5xqvHgO/A/3XHiQEk6vt9WPlK7cs17+fwiUqK1SnALT9KII8Aw5TbYmzxuS9HeafmiO/ffHbpvz8qdzeDg7ULg++85Om3IHkf7d2IQqgOV3A6fDlV5I+Hr9W7A5eyoXnEBL+O3fl7JJ5ON9ATlMi9Z9ClnA2gSyBcb21AmKMb5KFviWiMHTDOs8KkXtTRtlLVB9Kg748liTp+ZU+f4SknMcnUOLkNbTdOVylZi8lsY57cMCD3G16tp53FWQ3dPxZwC3v5EqSxO6BfvTeHcW7HiTsJ3ON0XM095Nzrd1TSEkpV44i/l1eX6bMI8XebJuI4tjtHq2d/i6eQCaInDDH+pMiFy3gOjjDWrGA9n3F8UwX0lx9N7+A21qEvUkK59NY/VjWdaA8anGh9S9ArCrg8JViPnWH+p0YsuHVSm2wQAwq6Y6KPvI46qHMkAXR2QrfndXrIWX0b4vAORfWeRbvOtg5aMqU/byGvDKkcyCel/kZusSlXUgY+5qjGY+hKLUviCDXahtJrtskRtwOW+6WdPHS+AihQUs7kMfBGTpNKbnET7aONMD+qq997O6u4nJ7b0G56TfzaIxpYzAYDAaDwWAwGAwGgwwIHdAAACAASURBVMFwC2EvbQwGg8FgMBgMBoPBYDAYbiH+YPeoFr0HuhW6IfQHoqD1hypHMWhNoMSRgrlYiOb78pVooHdw6v7BoeQ0dw9EW6U7w8tXa3lE3qKJwm0hE222dKK7JaAcz2eifHVAvQNTymUrOEKAtkX+1+lEz/T8RBTJywWkJaBllu+CzR9452rpC/stAZVzAOnIMSVoCeQIkINFoD0nPV1zdCQ663e/+1DXe9HWVjPQX1M4ScEpIK8b4uxE7ff6VG4NFWR1gzui7XVgd9WBqonyOc/T9UO42uyIbnzvPVGZQ4xdD3lGW5bCj9+de9S1a1sMCmyvh2cYqA2H6K8eKPy9jq5PQY9PKHmEewndifhbMaWQFWmOPZTX9emwLpCbJYgFgx19HoM2uaogLSDNEW5HL09wov6l5i6dV9rOUCiCA9ommb4b96iiHtt0SKKsqUUlRf0jSC/CVjnYeE3r/pAsHO1JMnMIeZJHPIgjSOvqe6YYB7ugGh/sKCa//11JadKx7vHkqZwBF5AtRKGor58/ESV9mmN8j0Q3HWH8den8RqmOezfUb8I71zjy+ZajHMug1cJdJwS9mXIvOh9cYG35xVdyavjT+4p1Rw+1Ll7dEQ23v9T4r47h8lCvUSkcEOmM2H2ovhv+nb+v8g9+1JTLvhxOKLWtAlDIOQZR3j2Um9GTp6oXJRzf+Z5ccHpfS+YYvKolEFvULPogcHk9hk7gwDTAmrQLScvV06+a8oq06oTjEHMIXOoY0qeKmlTQ2q+mkt7MmR+AJR/XsrIA9wshfT26L9r/nfuisB9D7uZaMi8999EDjaf9Q40zF2uuf/IjjYV/4x/8I9T9f23KJ+dap+ky+f6R3Dk/fyrnqbfFcrVyv/7i184556jqvXgtydLhnmJIGEDaTVkfYsi1i6dzzhWQ6Oztq33/PqQtP/lI43ZxoZwleq11yR1r3sVP17JEpH4uPFd+uHf6pCl/b6m4+MkPJE+8/53vNuXeQG17ifbXKHJuCgeal2fq99lCeY+HjI6pjoNDqA/5D9uB9975Oi4uS+XkcaK2pwPmJFPDPTmRTPTnX0vu+dE99fnOrtp+ZwAXLcj8776n8X+CPDyOIDfdV1t1hrUD2AIuNRPN4RzypT5+594juYQlWM9WuH4Ch80n57rnr5/q+QrPnE79EyJItpQXDDvVu5ENB0HgwjoeUgKUQc5GJ68ScpoM7mCvjjWGDwZy77p/V7nGGPuFoKt2HOCYhYHTd7up2iiDFrvM1nXT3ZwLeITHru4xRC4S7+h+MWJ6jD3WHPs/T+dDbPQ88rQCLnczHFmxgASvQi73bnYa3oX1M7SOQYFk/i5yhQvsy7IJZGWUbPF9AWT2A6wPPeScBfaUJaScFZzHYuy5r6XFUBu3Ha4xJ3h8Q3cX+5kOncwgrUWfFDgKoGodb8C9MepVYd2H/M+j7mHV0nlthDFtDAaDwWAwGAwGg8FgMBhuIeyljcFgMBgMBoPBYDAYDAbDLcSby6OuWUA0zvGbLnAuofQC5RAOUyXoRgE4mDEkGVeXoopmmaiCQ7gaTE5FPZtMdIL8VX3C/2wlWuFkIXcZSqXChHIC0c7yDPXCe66yIv0dlDhIonxHdMpnr0QDf/FadaT7BE9B9zWNfrvuUd6FNe01RBsf7Yrqe9IT7f3VUtTlq0KURUpmSFMf0rkLff5gX1TCLhxCioXauQJNkQ5WrqZT9iPRAjuhKL39HiiCuHdAei+o/963uKG6HvXdPRDFeLQDlxY8LN94knLXOp082D592Lm1GiuqpTA80bzbFU2zi+fpgYY6gjyJspIIczHEieYlT7GnpA600Q7L4CUO6TzUW9ctBQ2yA6pwB7RWjs2yosMH+gsuFhHm3P6hpJIJ5p+DE1rb/g5919JHvTtp2/pnvSvqOcU5zljSdiWj45XbeE3LvYhOW5BB0JkigmyD8iuHsYBw7dL6fzqglY4xtoZj3SN3oqrvdkQl//iR+uf5qWL71VTz+69/8WVTJv09hZxuDDr5CPTo0UgU5tFY5TEcybaLwMlRBWsCxmfoNtNeKVWjaxj7McPnXxyLcvzqQmvXB0eSn+48+lj3fKF27IJmHNZiiXkMIvhQ8p/9n/5pUx7/8O/pez2tEys6HIWMi4gpBanIav/9Dx415V89ftyUX7yS/OPH/7rq8MGnkn+cPVvLTJIIMp+3ROm9u6olCVDDuNGuxupgD7KTZ1rLKZ/upmrjKIEMDv3Z6WzWKbDPuwOtOaMVxgWkgtdTtIDcrgua/vd/8GFT/vDjT5ryz3/+66b85WO4t/TVnw8/1HjqwIVwtoIjE9bOjz7UmDvYlWyhO9T4Ws0lDxp0KEDYHrJ85Z48XcuJcuR5vVhzZTQEDT7G80MenZCeX0Dut9SYi3uSRHVG+/hcc2QAh5/qUmUPF7CilvzmkCwFS7qXQp6/C87/kWSQJY4iqOh8BQkB85LTMzgITihXgGsncgMP+TFl9deSx22mqN57l9VHHCxy5YE9bFfimA5W+u4E7qt/9dvHTfmnD5XTHmAd6PTgKgrJzPBA7VBg7Zyeql8mM+0pOvU1RakceQqJ4wqfH96XNHQAuc0C10yuNFZenWuv8OuvJPN7cabPk5HGQidkfKFjGPYxyNfovLtNlFXpJrP1vmxypXa7xPN0E8UBukcNIMPMIVc9OZbz5AVyh9EAe4qIEibd32MfVxa6ZxxwDV4PpjKGM9QAezvIT0O4CpUB5E5oT7qjzqfaozrI34JyczK3xDEfk4XqO9zRWHZ0yK33KVGkdtkGro8tYS0raO2GY8W+ozuS+z25VKz0Bff5kA3TaRnjlk7SdPstsb/MEUPzXPFsVUsqee8U46yDszIS5K4JHOmYCvP4gQpSthyydY9cKIf07cvf/Fw3StRv3/l7chJsS6jMPcpgMBgMBoPBYDAYDAaD4Y8S9tLGYDAYDAaDwWAwGAwGg+EWIvBvoL0JguC1c+6rb7zQsG184L0/+ubLvhnWh/+/wvrxjx/Wh387YP34xw/rw78dsH7844f14d8OWD/+8cP68G8HNvbjG720MRgMBoPBYDAYDAaDwWAw/H8Dk0cZDAaDwWAwGAwGg8FgMNxC2Esbg8FgMBgMBoPBYDAYDIZbCHtpYzAYDAaDwWAwGAwGg8FwC2EvbQwGg8FgMBgMBoPBYDAYbiHspY3BYDAYDAaDwWAwGAwGwy2EvbQxGAwGg8FgMBgMBoPBYLiFsJc2BoPBYDAYDAaDwWAwGAy3EPbSxmAwGAwGg8FgMBgMBoPhFsJe2hgMBoPBYDAYDAaDwWAw3ELYSxuDwWAwGAwGg8FgMBgMhlsIe2ljMBgMBoPBYDAYDAaDwXALYS9tDAaDwWAwGAwGg8FgMBhuIeyljcFgMBgMBoPBYDAYDAbDLYS9tDEYDAaDwWAwGAwGg8FguIWwlzYGg8FgMBgMBoPBYDAYDLcQ9tLGYDAYDAaDwWAwGAwGg+EWwl7aGAwGg8FgMBgMBoPBYDDcQthLG4PBYDAYDAaDwWAwGAyGWwh7aWMwGAwGg8FgMBgMBoPBcAthL20MBoPBYDAYDAaDwWAwGG4h7KWNwWAwGAwGg8FgMBgMBsMthL20MRgMBoPBYDAYDAaDwWC4hYjf5OIg6vogGTnnnPNBgH/xusZXKJdNOapW+tGg0FejaNNtHG8fhfwtoSz1W97ry3GMx6pvVBS5PsI9ojD8vWt/9yrvNj+rw2+i2LrGVyqXlepbBapjFXY3fu5d3Tb5pfPFYnMjvCF63dSPBz3nnHNxpN8KI7VDWarfArf5GT0+jwJ9N0Q5QL+xnaNQfR6x/wH2Z1WPqbIoN/57gH7jb7a7ivfT5xX6pNU/GLtVpXKSpk2501G/Ffjucrlsyqssa8qXk/mJ9/7IbQGdbtcPBuu5yI5hXSuOT3yX86PX66ncxfMUmqN5rmdgO/KaqmR7qS0ijLHr9kripPkMw8UtFoumPJtN9ZvosBDjqFWO+P4Z4w6fBxybGCZ5jmfFM3FcXddhuVy4PM+2MhfDIPDXj1C15pbQGtutMp6lNRdQZgxrXYMf4Oc31DMINvxLq5I3fPGGy9ufb75RsHkaO+83/1h7TnMhUTFCuSirrc3FOAx9px5n/O0K15Qoc6QmHGN40vKGFksQO9lGjNMhyjHiK8dDWcfU1ri7IV6G4eYxWPmbBgGvwRrNOmL8xjesE3k6bspF/0D3CdcxZXX21BXTs63MxUE39Xuj6/iHtQ3tlySKW6xniDZhTCJafRVsHuft67/FY9XXBK0RdfNM+4Ziqz+r1lrIz3V9kSunykvFzRKD6qa4w5zqxevJ1ubiYDTyuwfrsRIFqCzqVyHGpzHnBy/HOoDxyZyJY6MVljC2O91OUw4xd9m+12uLx1zhv69Wyp2XKHPNZS7MMdhJ9fut/sKazv7lsyaJch3WJ0UOdD1OT88v3XS2nRx1EMV+v55rFUboCu2TI7oGrfl3Q64OMPawrW7MRRkPQuYQv/+4/oaci33LPDO84X5FwXxqcw5e+s1ztP3DKjJOpcyRcP9Xeb61uZjGke92rmPmDblLKyhw34S+Lje3RdBeAHHN5n2CvyFRaW/dNo2Zzd/jp2zbONq8Bwo5vkKtJUlP61zQ6zflKti8xnC/talCk+NnbjE5306OGob+eswHrbxCdeh2FBPbbRJuvD5kftCax7rmplWsHTc3z6mmFowLN6zR7Tl8wz6D6zWvxlgpUeHVSrG1xFjkvosxhfsPZofTZbZxLr7ZS5tk5NIP/z3nnHNZ62WLKpkU2rCm2UVT3l1+1pQPUn0eDkdNucCE7CDDHvWw8KGhJpOrppxh0To80HNeT5STk1fNZzEW89FAkyTCIlV6vlRBYyOAeCYqXEArfZ7NVb6aYVOaKgmdDb7blBfJXf2uGzjnnCs+/x/dtjAe9Nx/8G//qXPOuaOd3ebz/lCb98nVZVOOb3jpxJdgw0Sb/T6CTrejfhv0h6rDSEFqZ7zTlBkUctx/uZg755w7P9W44e+nqQJgit8sMCbyAuVM5els3pQvlnpRMFtpbM1X+vzBo0dN+TuffK8pn1/NmvIvfv2bpvz46ddN+X/53//ZV25LGAxG7h/+m//O+n9KtpXqMcfLoxKBZ3dvvyn/9Cd/0pR/9H09z9nFWVN+8expU16tNIbPz06a8uRcY2a5UDzY3dE4/+STT51zzt25ozEepwpSv/ybnzflf/KP/7wpF3jx1e9rfPX6A5UHKjssELsYax3Mb77kefnqdVM+PjlV3fBy6ToQ/+U//6duWwhD50a9dWyZ5Xyhq7olEZPoLsoa55FXjOlGSAidYnRZqA3TGIsmYljsNi9OacJktp6jGnLtjTyTJ7d5UXNIZArP1xlYWBNs3j1eKFdciBWXZ0s999VClYsTXT9Qk7mT06utzcVOFLrvH67H2XKu355iJ3cV6Dk1gp17gD4t0BenWFNDrFd3MZ672ASGXpu5fqjr74wUd+NY4/+ijg3zkjEd8Xel5+j3VEcmHhk2tp5/bMD4nWWKFzmeYxiqMw6HmmfNi2jn3PH7/7Apn/zJf6h69u8555z7xX/9b7ltYW/Udf/xv/sv1f+ndhiP1d737iqvYN4w6Gjt7CNX4ctUJq2djp4XXciw5UK8TOBmH/s0F9X9mcT6Td96VYikFn3FN3Ul6rjI1OdXc62LGdbO+Vzll8cvm/LrM8XNyUxjMcPaFKZ8maex+F/8t//b1ubi7sGB+4/+s//cOefcONHYc1daqxanquujA+VAHSTYZ6da/9K++uto97ApDzE2cvYj/vjx8aefNuX+QHNxuVj+XrnEC5n5Qu3/2RdfNOXf/Oa3eqRL5CvIP7pdjc2PP/q4KZ8+e96Un7/Umr4s9Lsp/ohz78FD3R950vuP3mvKnTr3+i//m//BbQv7SeL+k/c+XP8u/gjxRak6PK/0vGmqxm9tIBFvIozzflf90EVsG++oP7lG8eUY532CWHi9mSwwhvhSZZWpv6dz1Z05TIh84/RM+dQcm8AcecJkqfG9wsbPI7/lgjyINOceIl7vzvTd/+r5s63NxW4ncf/y9z9c/w9+r9PVM8dYKyq8SMwyjcnXZ+dNeb5UXaOEcVT9O51NmjLbpXTcNON3mafUa3D7ZTNiMXIdpBZugBxpd6znS/HSdoC9jhs8aIp3f/oPdM+f/N2mPIs1Nkeh+m6IF7Gu9dJx/d//6T/99922EIWh2x+t1+TYqX/u7msefPLBvaachGq3IfZihwPkLVjoZoXGcIK4yRyCL2cWiIurpcbIxZVioQvW4yLCutjFmst8Jk34CkS/M0C/9bBvSPHeY4HxOlvpu589foZ6aR4f3tW+i3/wfvFS61Hl9bt//quvNs7FN3pp44PAFc0gUSUTJJi9ShOsk2uR6KVguqARlnioEi9HegMtHh3+NRKbjz4C68FQg+Luwd7vVx4b8uVSQTNEYI35O/xLPBq4xfa4gdVQcB+CTo4TBIIML0ZCvVBKkMxm0boc3Pje8c0RRlGTiJJZMZ+C2YBFaoBEgwMtyzSphnhRs4NNBV/O7CJgdcFQiW74y7HraIz06sSgi0WHCynZFK2//fIvYdj4zRea7Fx4HQJ/FOINKQLR8kovc6YXGuuDvjYbbAP+pWubqLx3yzrJrnIke1MFtQ7asIsgdPeONh8ffaAEbILn+eI3v2rKj7/QC9dhX/dZTLU4LuZKSgYjJcL37ivJ3Ttcf350Ry9y+IfpGOWH9/CCmX/9wHgpsMkvuEHB5mOC/ko4F8ky44sGbDLmSBy69Vz5Nn8A//YIXVQvxqG7gQmI/0uRwKbgbkR+8wsBbvYrvDRIMEuSlC+IkFjhr6m9rspx/RKJ7KcuEn126CUW0iUTT8zd+VLP3forWsG/zGAeY41gx2WYo6HDX8n5F+hqq52nOjnfvLTP0Ec5N9h4a5UggZhgjJHdF+I+u1jbOkx+8ZzjIeb3rpLGPubL5bn6Y1C3Ywf9VWBT1Mf9olaShaSZfwlEAs3XBkmqWPDy9LgpzzGu47F+604X7TdT3Cnmj5vyVbpek7h2vz18s/HiehuUGrfLieJjnCvZjJAElnO1d6+reRFjDpUYz0lX7dZiRZHphOvD1npSt2G5wCebWbIhP7/hD7VxlzEUDNFTbCDnmPdYO0fIAVbYWFV+8x+2oujdzMXAeZeG69+sMo33bKZnGGANy7F2nh3r5T3zvLSv3OVqpvXkaqn5kiBfvYeXORfnGjPPX+gl1xAvJw/qP6J0d5S3nuDFUgcvYY7uaIPkSuWNGf5QcvfOnaZM9sgKm4wY83V/RzkeX9oMh/qcf/A4P1fdHt6/75z7VmTLN4B3vo5/GWLiAnPRt95wqkwW0Bh/iBxirA6wUUuRI3UwR4fIe8li6iNnJpPgmg0V4GVrhPIcY2WCtmc+E2Fu9w9VlwlemJ1PtXfJMLf4sijHvCeTcoU/JudY68tge/sLwlfeZXUc6fXVbkyi5nh5ib/TtBiazM/Yp2SxXeFFWIkXMiE27q2/KJHVhHa5/lt9UIGNwT8mOX4uFGQj4l84h/rY91ReMfvilV7KHn78I90fMWIWkKWjYh8x+zq/3W6O6p2v5yDvO0CciDH/dtA/9w70kiLFUs2cY5AqV6F6p8JDFohbKeb9EmM4xt7w+o8Zg5HabzTU7+zgD7wd/MHfM/8igQB//F7O1W99xBrG2Uf3FaMzvBBf4A/q/CNzwnF8pWe9CXamjcFgMBgMBoPBYDAYDAbDLYS9tDEYDAaDwWAwGAwGg8FguIV4I3mUc4ErgzUlKPaitXdz0KoXkkQNvXTB1P9dTUUZ4gFxfWjbd0C/6vJwTVCoHj0QDemDDz5qyvcf3G/K17TBz78QxfTxV4+bcobD2cjD4wGqPMslq3iYG6RSoFNRPl7i+TwkN50KNKilNHAVNCJVTa8uWsdYvh3CIHBpTRWlzr2A9rPfV9sPUC5xTQVK124P1DOcUTTi+SNoz4RsTNSBB3UFaIekbodxqvsVNxyk1zpwGD9D6QV1iR3Q7ZIYZyphZlyhLnOc6XL8/EVTPnyga8ag4g1Bkd8mvPdulV/Lo9SGPZ4phD4iBe89aNUvzjVHA0iDIoy5CtLCIhfFb4hXvgf76vfBrujkvQ401cN17BjicJEZ5JEBJAfjDs5j4UF0oFZmpCdjjvagWSc/eA6aMeV1PGB0iLgzBZ2xP1hTW286aPQPxbVsL6AEArGk0zoHBM8IKd+oJ5rmfk/P0k1BA4/VPx2MZ0qfeDZUFzTwPmnjdfsEkDd0cG0OWcElzlnIECtnS83diwv1/wKyxQLXX61wLhjG+gKxdQX5V4q2caC8puEbLnffElXl3bSWfxX4OwgPmAtbdF/IULC2UarZw/kGjgeBU+aC84s6uD5syXxB7YYUIKznRT/QnN8Zoc8RI2eQuwQFDuMF3dwjH2D881gbfKk48vK15mKEM5bujDGunOQk5aXOCStGH67rslV5VNCs+dlC9bw6g9y0RGwtQHWvFDNS0OG7POwTSzglj0EIWSTO0Qsg4WgdnOhA567jH6WVHO+t9uFBvDyoE9KPADF0DDb+HBLKxURzuoKOH0tna00tSp5jo2t43shWURXOz9fynelE63Mfv9frQHZwqXPyljgjZIDchQfkL1HmmTaDjta8c8iMjyFz2j+QVLh7IAnTdcrCfDJErHr4QBLmA5yp89cLxEWcgTKAzIbnSjy8r3M0OkhwSkhLB2O1DdfIPtbFk6kkYqev1xItnjH4tvDOubKOcwtIgOYYwwVy9SFkCvs76of7+5JmHuAsmhG0GpRTpWg3lnnsWgw5E79b1uc+ZZhzEeZw5VWvZSbZyIpmDshnSpzPcHYpGTrP76mwn+C+aIq9xQJjakHDBSwvRfzu/nZ/PbLmmFsZYghl1h1KyzgmkbvuQ3IzhyTqagaZEKR8XGsp3eHB0Jdzjeeg7l+kEy15Ks/NoFyIyqsMcZSHxI8gc1xAfvrsuc6p6rx80pSjDzUXM0irQh5kjfOLimr93DwI/q3hdQxI3IGsCWe37eHcvLv7Gufcw+9gX8JzadOhnovnO/F8W44Rrl1LnGlDOdVgtK7Dzlj1GiBH5VpFeeyKZ0Qhzl/xeAb85gpnvXE1O0LcccjZX51LHk6J/A7a7OxKa9ZNMKaNwWAwGAwGg8FgMBgMBsMthL20MRgMBoPBYDAYDAaDwWC4hXhDvrh3YS0PSipRiLsrUXp6uShAYSxqEK1QS0iSSK0fg0pNCU0ECu8DnIz/ox/+sCl/H+WDO6KQzmoK3Q5kPmB5ua+fSZq0oEW0LnERPYnozU67VPrHl6LT5aQl3+AOkuSiP8ZzWUSntRdMVn3zidJvgmtaXw6KHm1Id2F9SFlTBdoz3XdGKdygSA1sObfAyYInw0eb3xuSEhfV9M2Ip8KHqi/twdkndM+g01dAaQyYjzu0c2zZHEPOgJPmZ3DcevlcFpqdkehulJptE977RrZHpwtS6Wdz9e+n70s+GEZqu8uJqKF9SJL2d0XN/MEn+u6A0h2UDw7lCDWFU06JsXF9+6tzuXSsZmrDg4Gu7cIBroAbFOfoBU51H4FamVe0AFX7H0N+s5yJ/piiPQ4P9BzlCamY6+urrUoynAtqum3YcijQb3RBrxwnKu8N1M8P4P7xYHeIa+ikARpqyz0DJ/BDJkZXjS4+79VxgioxD2eDAn2/ymCJC8noAo5R05namza4dO56CWvbr04lZzi+WuB6SDVadq363XflHlU5565rS1kXTTlo4YymaPrfOecc4lKAig+6cBBDXKJjD4aJOzuVPAMKOTegm0otqRvQEh39XKI9PerOhXGMeHy0o/k3gERvSteyTGPq6lKyo3mmcjoQ/X2PtuMTrdNpsR4PYcsu/u3gvXdZtu6ZAutiDCv1PKT7DlxBRmqfYaw6pw6W0+hPOia6nPIhdVYUqD0D9FEYqG2D2hI2CnVtBbeKEo41EWn9iGF0HgywtoUYizD3cleIQSvIozjWW8ZQFdcmfey32HeELwuXn6/tvYs5ZCVwEqkwR6eQ+jg8P+U+lFwPIR/qDzX/zs4kM15g3N57+Kgpl5nu//yJ8oVrl7Fd5A105Luzpxj52+NfN+Vnjx835dVEzzq/wLEEaP/DsWj795EjP3ut/J02thyntKxmbvTq1fq7Rb49eZQLnHN1XrhC/VvSoxiSXaz9++jnu5CvHY3gGJOoH2LEtjShYx7kyiFdaiDzT+j8ti575suQsuZwvc0hp1zQ5h3SCyyjLg4h90ZuwHs6uKP6QhNwCWc5rjtluLm8TVTeu0UtG16uFIuyfLN8aBd91MMYG2KeRXiKHHJ6pus9BKAupFLfeaTjNCaYL1/heI9FHSdLyCl9geM5EMS4dwkg5+J+ZAUpOPPYCvkNHZSf/D9/1pQPsT3fvae678DZahhrLFW1pDPcojzKO99IkijBo1X2LuRRfUi86fo8gJRwQKkipEE9OLYFdFWLNs8/HqdBp86kzvnpRlZgr1BhzmVYqxI6NmL94x4mTzFHsS/PuAfjES4PdVTLqlIdpnBTzZBvBOE356jGtDEYDAaDwWAwGAwGg8FguIWwlzYGg8FgMBgMBoPBYDAYDLcQbySPCn3hutWaetrJ5BKVll815SAW1WwFOjqpfF38aq8HhwVKD0C3vP9IFKM/+YlkUD/+8U+a8uF90cdSSqFWa8qdJz+fp0FDlvD8WLKNy7mew6MuHnInutoUOEmalOAKdEYwzl2Apg9A+Ysz0f9Dtz5JfLZFeVQA96gKlK4B6LgDnFTuSBXH6fZ9nK7fol5DHuXKG+jTpNvf4HQSgPoW1c4opOtWoN2H4JJ6/hDHEz5uuWdQP0HpT4vOJ9rmdIFxUak8m4lmHeGU+ITPuk1476p6XE7gwNOHW9V773/QlAegXtONIIQb2rCra8Z4u8emKAAAIABJREFU5nsfSR41BvV0BIrkPdA3z+eiAb6e/v6J7JknbV/t/4OPP2zKvejjppxDNjPFqe7PT0UDX6J/j69EYTy90LPSDYM0V1IxK8zjELKfy+m6jatqe/KowDkX1E5HIcJTAopkH24Rh2PNy3s76Odd9dWjHY29vb6+uwt5VB9jO6FNGt0b0Lc9XN+5/hyuHnTX84gFJeR2OSjGK/xmhvG6ynT9VaZrhkPIRqBtzZ/LmeVsARcexhT8XSIrt0cbbiEIXFmPlWWpsRdh/AQtRwm0EWjYXP86aP8++mUIGavPSbFVf1AS1enCEQwxM1qs41VcYixEKEMSkMa6912MqYf3jpryCH1aIaZcoM1HC43ByVjj9ArPcTIFVRgylmqlfCOe1+Vqi4413jdOkjmo1EGleBNBvt2lexpkEB5Oe57repdjQb/L2MOlK0COEuAfKDEKatetis5hGCsc7R4yLIcYR2kj3QND5AZJqfZIEahaRG7GU0qeUfcIY8p/Cxr4H4SqckEtC4uQt6WQU2eQZ64gZQogLfPI2/YOJdXtwlXq9elJU57N9d379+XOOL9UXnBxrPUqDilHX8uWih06qqlfXr9WXvrLv/55U15eat1PEV+mJ6pXDhnISSlJ1u6hZIijA8mm6MSZU16OuB7Dbehaut6y0nlbeMmDcoyTGO6GQ/TDCFLeIa7ZhcNNH2MyDCBxwpiklIbzsiWDwjUJfutawkF3SzrT8O/jYcW6QO6INfUKzosx+navrxia4RiDCutrvoTzIuQ5OZzqcq7ZW3bEvEZZVW46r3M+OldlcHSM1Ud0eG0ZzGJ9WFVc53VNQmdNOJZ+cv9uU/7xx3Jh+/xL7VlXM7X1Rd2O0xxyGkfpi36TDmwRtDjcF7JpC+S6c0iiEqx557/6F015dq7PRz/7WVOOF1gXe3o+f1SvT1i/tgHvfj9vYnwKuW7BDS2KKR/E0Rr4bgw5VavMeYYy94Ax9qAhxpGvr6kwbrjmteYiOohPmeA50i7KkDByVx7i6IUKMlvGiN0drSMrSNhLuonm3ywbNqaNwWAwGAwGg8FgMBgMBsMthL20MRgMBoPBYDAYDAaDwWC4hXgjeVTgC9fN15T07kouR6EXBXQJBwRKkiLwyqIOpDWg6cWgFT2896Ap/+ynkkH9+Cc/aspHkGSkOHk6BJ3R1eV9uE61mFIBT4n/m6ZcPhWVlFTFEqd+U1lD6UUFWVAZkPIH6iSkYzHomqETRTbM1jRX0ibfFkEgemsykDSh11Wf0HWJz9KDlIFSqQANmuAE/qRFdwOFG3KLiJRUSolAp0tqGlyC4ZrxNHC0q89RBpUyBq2x4vOhHED6FuL5Bn24aXU1FjLc/3KhzxdzUeST6I2m2LeG9745jb7APNu9K6ozHShOQOXugMp5BDlNudA8Dr366whU6mGyWaqxvyfq33CM9n35qimfX63H9ghuR5SnjeAkt4sT5itI9BaQSo1xzVcvJJ+YePXLINWznsP6pg8nnQ5oltduc861T62nVGprCCh9QBykHKav+bE70vMejNRW+wNIiWC7N4zUtkPQh0e4JoHcyFNXE1MeBfeoRrazOUYQFeWxkCosAtCdQTLtBJC7QMqzhDPG7kB1GfUo20LMR90YZxfZN1NP/xD4yjeU7xX7EWteB00U0a4jVJ1iSg8hDx0yvpGGi+eMIBGgCuxipv+Zwk1uXM/jJIWEp5AUaG+k9twZw6nsvtxZDvZB7Ud9g1BzazzVfO0v9Hxn+6rXb0EVv5xhHQVF2Xs4VM7WsuFwi7Jh730zRnNQlFO0MWMAZZQ5KNOxu2Htx/oTe/VVGGDdhSQuwvwLMP5xexfX9SkcYhOdGakfiDbLV8KI7pZwuypAsc82P1+UKAalEXI6yIyiFfJB5jzvaF0MnHdJPS4qtH+FNfLyAms18ohyqc/39zdLoi4gN7o417g92JdUcHImiXsO2WIUsE/VFpNX67X5M0h/W3ISzIMM61+fcsaE+STkoZB/LTFOT8+VD/TOlDM8gESZ+rcKkhvG8lHt1hTd4AL6h8AHzuX1MyyxJ4iRb4yQQ+50VR7hmrSl0oPcj9JPtiHGcIKcIEFunODogBh1COv5SqlwSNki5TYlHLroHJMi1vRUZs4TIh4dwilrPlPfzhKMEazpRYU5hzHyblbFtdRuXueodAwO6AwbcgyrfhxjPH4hjdh3invjHbVFF/ulP/3Z93UN+vT0hfrmDlyr0loeFVzRPRF9Ci3Q3hiOiX3dezJVHKET6Ary0ynklCXmqEMMnn39V0351VB9+mD//aY8eoRjBOo1O7wh1v8h8M65spZM0q1phViymGnPuherH1gLurG19nm4yLckwdhfBpRmwp0Krk4ObnLX8qiCiy4cEF2MEY+6MIOkk1SMWNzBnqqgUy/d8/LNR270cGxGr6d53+8hNlSS/N8EY9oYDAaDwWAwGAwGg8FgMNxC2Esbg8FgMBgMBoPBYDAYDIZbiDeWR6XL9Un2USGaaOnoqIQT1clCgpNIBOpTCjnV3X3RNH/0g0+a8qeffqcpH909bMqkrQYxqX+gPNWfBzi5eY9uRxEojKgX6XxXV5K7XOCk8bKgCxHo0iiXrfdi1FPhek+nJNCJG9eG7bmeBEHgOrXcoQeqoQet+iYXiwhcNqgX2ifwg7LWg9tGp69yAppYDLopKf50Crg+vT8o9fshKHkR7pHB3SnL4AICSqov1CcVKbSQR/kWZ1QXdfBb4VxzIEKbTeHmlPREF9wmwjByw/5aZrQzlHyrjzkxgIuaa7nUiNrYAfU+AmXzCifXz8FmHN8VDZx9RDcQjp+9kaRQjYSFUwKnwfch1+v00M5wgYjhpuThZLLI1M6vTkUxDArN1x24L5WBxlpL9odnOp+I5n54cLD+93i7tP5mClLJQPo2JAgdSg9BJYaZh4tB301ASU1xUZduUD2NFw+5kae0seWkkdbVVb2830yw5in6wZJSKY2tEhOnZN0xiGLcP4QMNUajpTGlJRrrVaY4nlXvhghewXkIobwtj4E+04M+28UzjPAMXSpFKfNAnKYJ4mSiWJdBTtGhvK6nsd2pf3cASv77dzUW9ndUgf199fXePa3R/T2VQ8T9gk5ocHILwVF+ALnFFxO56kwR1yl+SkLNxXSxzkHCanuy4TiO3eHhOrd4BanrCvIZysvOOVZDrW2kz7ckjzQ1bLnYQZ4N2UBVUhIDySZkWUUtQ8oxrrmG0ZkvxIIdY344/GaBcRlAShhA2hFBitWFbCTuSkKbIs5Gx4o7JydaF+PNisq3h6+cL9b5Wh/OaVkm2cEU8uUruITGaMfBUOvJJdwWTyCJGvS1ts0hoU+QU3ZA219O9VsTuBoua/kLJVMR5J4JZDbMtejS1eV6gFizWum5Q0ibC7gNffVUTjoTSNs+/u53VR/EJrr8vAvjoco7N69z6xmDCaRPXTzjiA55kIkFdHVDWwVYCxPkGR04ZqatnABl5K685lryX9CZFPO2WmjMBZQeQjJHlXGSYA2DrLDEPO4jkdofqu7LldrsAmvt0jNOIAfe4v6C8N67Zb3+tpwROW6Rb/FYhiUkmZ0YMkxInHYwJoep2ujD+wdN+Sc/lDzq5BWOCEC/70FaFdRzMVtBbkw3IATyR3ua/x+/96gpf/VUUv2Tpb47wzNh+WvNuSUk/NxTfvnkL1V3tNMPd/61prx/tF6Pg2061notEZT+vX4N57x97T8i5AQtTfAN7oKU8NIZKok2SxhT5MAR4myAYzau5xTjKaVazFehWHMhXcLgAkpZZpd7KrTzbAW5GN4dFHhHQIfpBcZFiaSxdN/cd8a0MRgMBoPBYDAYDAaDwWC4hbCXNgaDwWAwGAwGg8FgMBgMtxBvLI8K8jUtqvCQm1Q86Vv0HrphRNBKBV70wGFPcqcP3pdj1IcfPGzK+/s7TTkFnY4n1vO0aZKhms/hTDOEVArKLlfdF8Uph4vM5AqOAaRLZ3LbycGzgorHFbDyCNBOpG7R7aOCXOWaar1N8mIURm44XNP6yhVPMIc0iFIDUNYCtCulZKQDpy2JE8uitaWQ8IT4PIarWIr+upa4lXBigAFOc3K/c86FpN6tcNr5CvIEDJUcVLkYFLucUinQAvms7EPK3VyLfrc9hxMiDAPXrem5Kdq5BynReCz6poPTSgDJyNFQ7byTwG3qidxaYjxbF/2boI9WaK8E9dmD5DGuZYazmajCpAzSgY0SQ8p/AjhftaQfu3rWh/dE1V+AepovQVsMGfrgYAUngQUp9ZDdbQtB4FxUUywrxlPHuBZt/LxFPYVOJiQjFfKokLR6zhe6LkHiclMMuJYzsl6UibbsbUBtD7AWhAnWAtD0Pb4b0jEPAbLCb3G8UJJH+S1dC1pz1G1PWuOCoKHf07iBcTvD84w5PxBPBpQw4oGWJR1+EK8QyHqg2Y/hjLYz0vPD+Ml9dLC+5t4dfXi4r7g8Gut+/X1dE+1rboUj0aJDOgmB5x+PMa4hFdmFLGX8WnPuYgFZECZ+tVTMWF2tpVLVFuVRaZq6R++955xzbon1/vgrSbcup3TlUf0T9Fvo1PYe8amLse0Rb0hlD2LF7hBlOi+mzKnq/CChAyIp4ZhbJcdQsXluUfpUQgLjuYZB/j6AJGMaUrqisTCCdHcKCV9BXvoW4X3pVtm6/3Z34OgE+RIdAgu0xcGB1qoK7fjq5XFTjkjPx5zrddTvCWLj8krjZApJlMs1HvrXcwdxq2LwaOUZuoZSXToOMu8uYsZLzEWExTlkG6dffq7v4mcfPVQ+7jHeFvN1n1bV9vRulfduWceQ9gzHOEc5iX8/Z3bOuQi5AqVhAeRUUV/lGA5AEWRQCWRwKeTuEeRRUZ3H0t3JQXoczHTvHLHMRxqLzEgibgpod4tYQ8lHCpeoEeJ/f67fPYPjT9lyNXpHroreuaLuxxh5eQ9HDIwRQxbIsUrMyxBSGcbCMeSZDw4UZ3764x825aO7d5vy2SXamk4+fcWieb7O+fbgTErJWwTny4fYl/7oIzk60XkoONMecYH5n0N+s4TbKTePHEv+Srnob588bsrp579syv/q4E/Wv7lNGXggCdMC+fME+4n5ctm6/hphyFiFY0jQn9y3R609ZYwyjzDhdyl/+n0pVsi9K8otiTkTZjrgIW+MID3tYI/q8Zsd5tS6i5vhOJXXp5J4f/1M+6tzOGyuim/OaYxpYzAYDAaDwWAwGAwGg8FwC2EvbQwGg8FgMBgMBoPBYDAYbiHezArFVy4o1xQ2MuoC3oZSBlCGWnRG0JD270mq9OC9e015DzTsTgc0QyeqVNWS61BjRDphXXW6IIG2NeiLqleNRX3M791pyj/89NOmfAVJ0TwTRewsAyUc1MYSVKySsi3SskB596RFNu1EIuTbIQiCxgFmeiXqHpikLdkBnWMiunJB1hSAPuZJ96ajEB8X9L0Q11BWk4CGHNSDikqHBPqoEN1dQvpDaQTpwwUoxgHGQsS60wUG9PCCJ/BjLJKW20tIhd9e3/0urtUROeizB6B1drqglcKBoNfR9Y/gxvZgT6fuv39HUsXxCBKKO5oXPBGeYzsFbbUitbSmOY4xz1agZnOckx7pW3RPuJeAhn+0Jyp8AreHpCdq8/NTjfdX5ypPF5vHzHKpfp9N1/TUqtweDTwMw0biFkSiSFaIZSvEleUCJ9EPKJtCbAs4X+kQIbSkZyhHeLYALhUBrgmv53rrkHvKAXEqfgEnBNybLnQtIzE8R4U4WOA+pPJPeGI/nfwQcyktqDBftyqPAgJIlhLMyz1Q+PfpfIL414XExJMeTrdFBOpRqvLRQHPhzq4o5we7uuejI82Lh0fra7pDyipU7vTgmAIXwICOcVj2PRcQupORCn2gOh7cU5/eeSm5wPETzcsiI4Vft3fztYOP3yINPIojt1vLY4aIT1+jP49nGm8ruG6NZ2qTnS5iIiThlMdFkEIGpGHHavMylFQgQOxeck7V96e7DNQTroJLTRhAvkqKOcZoibmVl5AqoJ1LOC+mGCPdWGME07K1viZ0p1u+m/nnnXNlPV9KuGHN4NzEeEmHxSEcLp8/f9WUM8gCDg8lw+iGep4CY2M+V7vnkKTEmMcB5khZXsvgKf/53aeqP0ci5bFOZEW24Wrn4oh5N+SyqEsPlP/puej8TyHDGO6obSJE7et23ea66ILA+Ws3IKlCXAHpSEGnLax5rTZuzS06UCpHipArUCpMOX8M2XgAmQeT2rBu5wBjIqDMGfuPGJ+niC8rrnmYi8ES38W6nGBOV1hTKLnsYq5HiBOUs8dblLb9LnydKHBtO4R8i05SkwVkNpD45ZTNw/mtN9Z9DsaSKj04lCSq5diDwJRA5t/BGOh013O9D1nxcoF4jZj68K72rvv7usf4VDnnEDZRA7izMqZ6aCEZ67MIMRL97heSa776xT9uyifJelwXS+bUb4fAeRfU42xVaP2e4ZpLHHcwL1Uecc5xL4bgxhWcbpithJU5HF0Y8T9Ry/34Ol6jzbgw4h6MFzzKhE6OPHOgQs7lSjwTpWCIC2dzBbCnL183Za4vSyQ3eUsXuxnGtDEYDAaDwWAwGAwGg8FguIWwlzYGg8FgMBgMBoPBYDAYDLcQbySPqnzlVrUbC10ySMOn3KVF6wTrZwRa7Xs43fv+XcmjhgPRMXkKNU+p9znJVXwUSqHW17NaFf49BG29B2nJDuRZ74GqPgNVrwCJ9Zeff9WUJ6BEke5ERwZKuygdo1ORrtmef5T3lStX6/oFFR1/RF/MQK9L4QoSBZSv8GRw0m5vkgPRyYLuMagDnp3XNDR4UgrBpWu1TkuGtZlWTDccSuVacqpws4SDzhuUkUW4vgNXpcFQ8qNtwjvJaOZTURLLQvTtDiZpH3TQEWSL9zn/jiR9Ip2x7cajcVK2KITQy6At2I9x7aZC6VmfUhbOD84zyNPyAvWCk0kMRwJSm6uAMr7TptwZidr6+NnLprzMRYXfGasty5pmy1j0tgiC0CV1vcOWfE9jbL5QLJnEOGV+KApuMIajGQMtZkYBOjQlacECkkfGdFDF6c6WN5JQUDopiUL75ZCP0rkkx+9XkHuQH+sr1StfaoxMJ2iPK7UHmdVFsTleJgmlbxsv+YMQBJJuxuD13g005+5iTLoQ8lDKVtDOFSScpAf3Mf7uDHXPD+5r/H9wF64au/p8b6w1tVPLQgq0SWvthgzAc/47xno9n/OUs0Ii0KfDDWRE5+r3sIO1M5OMJYMsoMghIzs7d861ncTeGl5Szj4cTUo87wnGXkFpWh8yKNDxO3AISihTAQ07xZrWwdihkQXzgwIU/yJfk9QDWgFxbNGlDfGcaoiEUkU4moS5JkiGOha0FMroRgOnOF3Rcviky2E3fTNl/rdH4K61m3PIEWYz9V0JWvvRgfK816fnTfkF6OuDLtzNTuQAMz3VukvJJ5+tk1JyjbgEun5a981N8qUczp4rSHYpA60QO0u/eU77G9LIiE4tmKMF5tdkItniEWTURb1Ob9Ph1DmvPJg5AepTQI7lNy95rT0H1Ost59GYpxHQeY2nLdC9KcMaxYuuJQ6h5k1JR1ms6R6VCRB/PfYiHkcOUErnsNY6uNG22p9yZjQO07WMznvvSMEfusD16vG3Bxni0Y6kTAu61uJoA0opc7qNYmyXC83Lu/vK5wbIBa8uMV+nEvVk+K3lUrFhPFrnVRHmcIo+7UfaIz66q3y5h7U7wV5jeiHHoGwF2WTLEZJ7PgBxiq5xjscyZFp3Z6v155TLvi28k2yzxLiC6stdwCGP0tBqvFnvFLVkoJA1hZvjYwVpMeVjsePxK4gH1/OV0n/UhD9Tto4HwF4FkyVmXtp6jyBw/8c9ZbFSvGYMvUI7ZWjLivuoG2BMG4PBYDAYDAaDwWAwGAyGWwh7aWMwGAwGg8FgMBgMBoPBcAvxxhzVqiYFUTJB6QnlAyT6xKBE7cGN5hFcmg52RZvrdUUBgyLDhRGof+ENEiNSpeq6tepLSmIkKh2dDiQ+cG4f/MH3QU/kaeTTleh2v30suneW0XlI92w5nFBehnIj79km99R758s1NSsGBY1MzyXkXYMRae83yKMiyuP4HnCz3CW84YFukp5cU395Aj9pbS2HrhbVcDM11Lf+B7/v+Bw3ODmQWtdyrNFVO2NRNfcO5Wq0TVSVd7NaE7Jcip746vhFU354qFF851A08P2hqKfDka7p9CCFi2gNoyLpiS3KYQi3J0rRYn5+7bDQmggo8hR1UhshicLczZzmXFVpnnUiPd+4LzprNxVVtoO+G6ANTr9W+81nateGmr3Fueidb6RQlJGxXVe5YsxyhVPsS8Qtt5kmn9HiD7IlSkIT0HSjko5F+C3o2bJrLickHi3JaovfS5mcPl0iVpKC7+m2QZmjDzaW8xbFGWOn5JfhlJWAZr5FBM65a9OgHaxJuxirecsnQeUh+PlRSEcdxCKPeQn5C6arGw4hoYLzRQR5UkVXt+56zNPJhGsS+8JjRLbKoKoHdC2DPHTVg/Njqd8f7lOfRumO2iCDFHKZQaJ3snZhoFT1bVFVlVvN1/N9Ce1cDneXy7naeDFVvBmnot0fQarYhcQhhUxmBdmUh4THJ1p3mf/EjMWg+PurtZynZFCCzDlE24eQxpW4X+UhcUJuVYScl4gFWDtz0P19rL7IEbMY11qS52CbSc3voB6XK0gvFnCA6Y2VcyKEuGfP5e7x+lhSqU6idWBnoPvsQj67A0kdXShDWKUMBoo/PbiWduvjAjrsO8bcmdr54uwSz4Sxw1UDv9+SMAfZxutbDpeUQkIqlWWQwOKew+G6DcJvQev/tvBe7oF0qkrpRktHl5a7FvJS5CRJsvm73CuUGJMh4zUkrM5jwLQS+uD6i/pnyqMoh8G9C0g/CspTsenxkCq7Dh0GIXPmcQuttRnzFfOPucGKi/MWEQTedevjKQZ0t0JbzC40nrtYn1PIiculYl6CRW+MGPnRQ7mdJhj/x68kc/zq8ZOmfHau3x1iHged9TiZY871MHa+81Cx42ik76V0T0U/Ti4UR+ZwzOPxC1HLHQl7aawTGXLmBdKY1Y7qMP3kfeecc2Vne3lO4J0L65yfYxVVc1dwX12tWt51TamgzJFOzpT4+c1zpII9MLqWocq1TwWo3fgwnULsUasbZKXt/SLfMyAm0nmYuswbjt8I6MiKCrGZlpRifYt10Zg2BoPBYDAYDAaDwWAwGAy3EPbSxmAwGAwGg8FgMBgMBoPhFuKN5VFB/Z6H9M0WT8mReiiQPrkDStfBoU6iHw0lj0o6ovaSKhhEpH6Bwk1KIChG1zKaAO4GlGxUlE3h1PEEdPPdECfqg8pEGvX5TFTFCWQVq+PjppyDWtmWEQA0DanrvlV1lKtcWUtJSB+bQhI1hdPCzmC3KReUsoB66kC3Dmh7QdevljPM5oenhCoAJfW6O33LPYruAaC1hpvHJSnAlO+Q1s9jxcOW0wOK+G7RcvoSBqBbxlukKhLee1fUUr2dPc2b0RBSCo4cUNZ3d+RolXZ0qj9tnapo85yO6FRSbJb4sY2qTVLFiPMW9GT0bxhoTLWojZjbdOyoQDeNQWftoooDyENenutU/5DyHoyr6YxuA+s5U904cd8c3vvGeclDJkSaJudcQCkn6O1FDkkUeKtL2GSECeQ2qfo87qpMSjKlREkM56Pr/o9JB+Y8EyL0p4s1/kgrX6HuOZykckjlIjj1jMaq72gE+cdUJ/NTNkM3rSTaXt8RsXNu36/H3B7d1SA/XUD2cx/tP4bDmw9ZP/QdaMOdPuQCexoPPbRFF243MZwaHcvdun9LOnzByYPyrAqyDcz5Cs4IpP6GdMHBs/qE8ihJSLtjrTFVRUmUxsMZXHNGNYW98tuj9VdV6Rbzq9+7b6cv6eSy0Lo4g8Tv+YU+vz/RM456KidIZ+IY/dOVbDVM0A6IfyXa03cpSVv3S0QpR6gfijto477uHcAmrlip7lVLQknHDLgTQvKxgGshf7cA5Z1zmvKobfYd4b1z19O/hDQ9Qgzr9rU+v3jxrCl/9bWkFKenWh8O9tRH/ZHyVZ9CnwiHnwCxsd/X3DnEOt2DFOqaWt+SMiFexFhbB5AepsiFPcoF4jHnUDmjI5h+ysONpoC0tFhS9gAZD9aYQT0/oq26KgZNjj4IFT9WWAcYY8qWSgFuiFjDYkiMPNa5PNQ1IfJ8yr0ppwoDjNuWU9X6d6uWcyGPbNhcpqtbQtfLimskZeDqhzTX9REcjlpyt5YNHY+TwDNxLG4RYRA0EtE9SNALOmAhvzk60Nxa4TlXmeLMAPKo73/y3aa8C0eq8zPN3b/661825VO4wx0eSua0i6MDHj//2jnnXI715uE9Oax++sEHTXkH8T1o5aXCaqG9IN0W+1gXL7GWZC2pn+bZCnLVHOvQZCnJf95b96nfJhUjCJwP18/pEdcL1BOqL5chn2Pf5hlc1dS0LoHcj3Pa054acv4A+ijf2mdwb1rHU8ogq1Zmqs+xV3DY/wfV5vWJTqkLzMtZzj6kCzLdQTHPYsRrODUO42+WmRrTxmAwGAwGg8FgMBgMBoPhFsJe2hgMBoPBYDAYDAaDwWAw3EK8kTwqcIELaxp0CIoRqXY8ULmA9MHhpO/hjtyj+kPRaqMUbiPBZreNABKjmJ/jalILrylSkYeLDSiMJb55ORWV7fJcdPv5leh5s0tR7ArQfe/fEYXubKLvnkN2tJqQwg+qMOQZdE1x1xSxln3R26OsX9V5UFrPr1Q3OgUs8Ixd9GfeOi17s1SqJY/CQxTks8KBi24FcUI3nfV3K/x+hXv4lkyJdlCbZRsVHJDookDpC90tSowzuhpRqkOZ0WhHtPRi253XVLByVe0e5UCZ96XKPJk/gHxiAMo/jQMuLkW1dHC+CdGnPTwn1Ryh3zyG6WxyffB6gXfFBSQWLNN8p1DdAAAgAElEQVS9iPK3Jej51+5ZzrVdloagQpPaPIQ8KiS1ESf8p6BL5qBIFm77UsXAyXUggbwkQPyKQF0eQUYSgl65zHT9dIWYCMp+N1WfBz3JA8qYLhW6vgPnmS6kqlXdiZRWknpOV7eWSxRkL3msOs7QxrMV5l8BOi3GwmCgujx6IDrry+nzpnx1qnFPpMG7+RtFEIQurdt6Rie7QuP5LqjUR2hn0uOXodoryLQWxXBJ84meeYIYeJnp2e6hrRNQ/luOFfE1hXizw14FmvPqVGtYeYw5R2cgNG0PTkWju4/0+Xv/b3vv8SRZml35fU+6VqEjdZbsqu5BN6Yb5AyJMRuaDbnghjT+U1zSjCuuueGSG+6GYzSCwxkaMAAaQKO7qkukjFShPFz7k1z4Cz8/B7LYTKQnGQ27Z1Nfer144hP3u8/9nHtAP67DKWSgeNloa47P5hfrdjLV/hQ3VtR2b4vj6Xuea1QxL0Tso5R6Y9ti3JzrH8Ml90isC8TQBfjrdKEKFnrexlwXazTgSEUJdyXziWqUxKrvF7jJ6ViSgRliJa+/GCv+h7nGOfIo7UDf0MEH676GeNCDVDhF3jUdv32Nvi+KonTT2Wq9xNg36k3lnBcj5XNfffN43T6BYxQp9Dkkj/AOcgnykTliXYyYmmXqo+FQ110gBlw7m46RK9KZktJDUuw9SjEha1qyn6FduER+e36lexkv9fkIMv8F3InilvaPDvKHqGpzj35flA5yc+YVdKBhHla+PZ8LIMcMsUZC7J0++jCiVIry7I05T/c8yi+u1zRcL+moibWyIXlG/A0gqyshT2xB7hpR8g+Zmu9Lyp1yD6KzrxNamEfM6Zw7ddtCEIRuZ2clPdrblQTp/ELX6LQgFYaE6g3es9o45rPPJIl68PCjdZtr8Ze//mrd/v6Z5I+3bt1bt+/df7BuPzuRq9RkuIqTB7t6n/vFz366bu82kSNh7BKs12ZL4zWAI3IyVqzdH2ifOxkpNucbWj829XwNvjtiTbtJFbOLLWapXuD8ePU8ux3sbQkcAjGfGQfnM7xPDymnVyyO29r7PUhYKav2IRtk3utxLbIsRpVT+RvOUPyuAHkQYgTfZ/KE0ic9x8VQ6+w55tbZCO9akCSyhAZdBUPkFTUc/+BIbsNPz/A+BhjTxmAwGAwGg8FgMBgMBoPhBsK+tDEYDAaDwWAwGAwGg8FguIF4Z/co/y1V4jfIWKB0kTIZQXaQgTb+zbe/XbfPX7/S30aiR0WgYx7siFb24JYqgPdAoStARb6uqp/monOxAvt3jx6v23/6H365br96I6pyDfSsXgv0ZHQFHVzoGjFo697Hc9FfKa3a9DRhpfq/+8n7w/N8F1WUsDnouBeXonfVQ/XlaCoaLSlxMzhnUbbR65BGK5pgA/I4uqTQgcTBpYJuBGuHIVDSSY1jO4crBd0YCnw/mYJKOYGjwmKp9nCk8b8YnukYVJXnnO7tigYeN/Tc07HodNuEX5auU0lLLl+I3jnFPe2Hn63be324h+A8L0+05jzQ5jPIh169EZ2V83bQEfXz1sHRut3DnKdrUVStixQzfjoBrTdV307w+bMT0RAvRqIMUtrYwrzrNyWh6dNVB5T0Tk1rdIjnO59jPuZ0MLu+5+1RT0M/cHud1VyZwdkgTUC1hVtCqy1a6TLR2n01Fb12MoaUbajnfX2lud06VR+2GnQ3UR/eORY9eH8Ah6lrWjUq+o8RC16fnq/bb061bi5GGs/pTH08A512CblbUGBeYByKUP1xABnibkvymbMz/S3p5wPQzJ+77a3LvCzcRbWn1dAvx5ByHTQ0LiFo8HQvrIGS3+hpLI72NO6Djp4hh4RsAfnQ9y9E5y1L7ZHHkAIElZuXhxSghLRnPtY5EowX56YH54e4D3loV2NURzuFe1cBKUIMh709uJntHd1et88n367btYqu7W3RsaYsi3X8n+LZJ4g3XgkHNATRaQaZ8axEW/OW7hlvhnIpOrl4+zw/3Fc/PLwtV5X7x4qzg8pJrdXX+OS57nc20jVPLxQvvn+mY54+V9xZzpQD9DugbN/WOotb2ueKUufElrfhQuchF+v2ILPKKDTaHsrCueVydR26WNWx5p6evFy3v3+udoK53cY6m0JuNJqoTwc99RGl9bOpznMFxyPGhkEXTqkV5X8EyRJdSpivxFgfOeQ5Z5fKV66wd46Rv12Cwj+D9InOi3R27MPNJ4Vr3G+/0Vq8u7ei8yfJ9sazdKVLK8e8BM4qIfb1jZIMcIyCst+NEbccYlW/puNrlI/CpS9AGQbKJjbcLpFTXssv/A37Vz4V5W44pKDEkH9KlxzK4FBuYclcF1fCe1dOuRby4Z225re3+QKyNfi+52qV29lsAqkH3B33+4ondUhmR1M9/35H8edwX86nr84057/+7vG6/etvv1m3B/uSm+zuaz5PUDoggNzop19+6Zxz7otPf7T+7MEdxdzlRLlWisk2htNa5r3dEWwx0982YjicYs0t51j32N+CjXn6dofc8zer3GvDnes9EXjO9apH+Bc/lTRtsVD8+M0336/bE+RzT14p/5s9er5u3759vG7fmetZjvBcu/twDMWaaiFHijckUXThXM1zD+OT4D1sMYcEFPd7jvj4+Nkzff5GzzE8U3779PnjdfsC7qU7e3rXaiFe+xuOq2o38H3B57eVr/3Jr75zb4MxbQwGg8FgMBgMBoPBYDAYbiDsSxuDwWAwGAwGg8FgMBgMhhuId5NHeaqMTIcY32eZa1HwfPAD65BJgDHqWqCJLRaiMD15LDrVGSo2t+Aw9Uf/RNXDf/HTH6/b7b4odEF9RUNdQvpyfiY5xN/8+f+1br98omuGdVHS+11VPj8+0rlvHaodgXr47TOd54IyGzg4JImom6RIUtLjedsURumc1zKRi+TtblZRTQM0gaNEiorXoc9xEw3t5MXLtx7T74gG2YdUp9OFk1hTVPoaKm1fS1PoAEa5EyUGCaRv07koy5dwCHoDOcwEzmAB+rtwpNDpPKQJ15ug8u9qLni4txkqqG8TzVrsfnZ/JR/409/qeXz0RR/yjCPQShPI4i7PNT8jyBavztRfF3CVOr0UPZBU4U/v3V+3P3n4YN3uwClusLdaR4sJnCuutCaGmGuPHj9Zt5+cyBkoquuZOj1RzGk2F0GqmIIWubur47sd3Vcd8ozxyzfrNh3Brl2etvktdxT67riS1SVDuJtcqh/qMaR/cFoawoluDplMVuieM9Cqp0ON+eSVxtZHxx3t6h7moPt6vuib/c7qfvKl+vgZxuc3X0vu+uK1+jIH3bsFWVNcE027jHTOGZ5vNtN8CWu637ijZ21DBtVBe+9QlOhPP9Ec/Zunurf3RV6UbjxbjUG/jzUHp4luUzPHh1SUFN9mAFlRW8cc9NVH93b1zDstxdESe+1wpJgzhxvMeKg+japxr4Em32jBSSXQmOcNSEvBvC4bupf6kfbIDujk8b5kdmmsPWAx1jl3IUXpeJqnwyvIE1PF/qi5WseUxb4vkiR1L56vpKLPn0q+NIXTUIS1kuHSaaF7OxvrmOenoltHkHOMsV8+e63nPcff/uaR4tbXA93D57e1dr/4aNXn9z8WpRpmc+7pd5K+/sVfar4/OVGcDxH7ui3F8wSaiTdwVWophLpsCWllW+ssgvSUMpYaHHwaDTrWbA+lozsl+hzSyyeQR9FRyafbD6j1HqQ/+YX2q9EM0h3IUCizqcMFqIf9eH+gMWhXzkznF+rnMfKGCWRbGXKUGWL08ArSJ/xtllDSB6ciuuAw74LObcNZBfn7ApKrqyp/4t703vCcu1ZKsJQBXcmiDYdIteeQDJ0h3p0tdM/1ISR7bU3oAfalNsaq3oILVUFHKOSg1fAHkG8wJheQLKWQBk4nWv8jSIiZc83xruAhR1siL51OdZ4Ccj66jfUQ6x/cUox+Dvn5VlE651XvFR7kYU248w0amodNlLg40zJz2H7cyVPlhU+d1jH72kdMS7EWHz2SrO+4p/3qZ198sW7ffXh3dS/IM8tcfV5gnV8hpvzyq6/X7a+e6B6v4MrbQf/34Cq1e6VjJnOt4xRyyjDUdZuYd2Gpezh7vMq9Mkhx3xeB71y3GoD/9Eu9b3vI584gGZqjxMKjE83hN+dqf/dKxzd/ozHZw/v03bu31u2jPX1+/0jz9sG9uzpPV3JyV8mQ0pnu5dUzzZUTxP+nL7XXv8BznGNMaBvJkhCHR7r+zoZrqmJNXmruxJBo0sm2h7zvywd67h+CMW0MBoPBYDAYDAaDwWAwGG4g7Esbg8FgMBgMBoPBYDAYDIYbiHeSR3me5/yqSvOGPIqyEshswO5yh7uiHv7Tf/Llun1rR3Sj3z4SrWxM2uAYdK+6aFkXcCc5fSFJUgMuIc32ilq1yEVZOn8lOn8LN9lvixL3CDKfb79XdewWKHw//vTBuv2zz0Qduweq+PBzfX6GiuV0LcpIOS9Zct77O/99f/ie5+LKRYR0U1LQPNxDuy7qVr8renuroXaOaTQDlZMOROeX4ju+gmyijT7fgzPY4Z76sFXRrQun+00gUyL1+XQouc/rc82PJe6FNO0BZE0t0LpLp3POF6Kh0j0qg1NVHedMQaeeQOaxTdTC0D2squFfjEWPfw06/8cfPVi3e6APPj9RZfQ3L0SNPX2m9ZdCYlSHhK0GNyYiaImSOoNcJy70+XS5ov8u5pBHQRJ1eSl6eA4qd3egGDEHbf3NueiMKWiOu623uyB12hqjDuR6xzjmO9AoWX2+Ha+eI9jiWozCwB1V8qgwVUX95VQ06QD9UBR0ldJc7cLtrUXJCuJgAdp25EG+4CP4gA5/iljVea2+DSunkyzB+gOtmy4HuztaW1O6RMF5IAH1NIK7Vw+uEr2O7iuBw1jidM5OrPvZ7ehZf/GTT9btjz8WnfV//Nf/3m0LpXMur6ZFqwnqfUd90YJ7lF/HWEDuWwvgGALJ7DlkQmWgvishZXiwpzVCV60L8Mzp+DesXHC6Xc0XygeDme7l1TPF6/ML9fkIVOj0tT4/nuqc90utxd4+pFiBrnUEV4+dHY31b7+R7LMIFXf81vblUVmWu9PTVfwZXWk+1yAv8NtwjhnBaQ+y3azQ8RdD7Rv7NY3nADLj1pH2vMuO5v/jl4qFJ690reGl4tO8WlO9FqRpHbgkfa9xO32jsfdiXdNvI0lraJ7lkEf5iB11uGHMIcVdYA9uwmGQopkM8TREv24b1/PCxzNQTnqBXAThx0WgrNchny3p7uLTyUfnjyCbyCHndJD8z+CmmUy1XvZ2VvP/bKq98PVQ93iGXHgOp58CEoUU8TVDKQCHPSOmk+PGwKgTWhjTJfKYJnK/nR2t124Vs4MtrkXP81xYSQk6AXIPyAtCrKEmXKUGPd1nA7KmM+Qcv32itTW8kPNmA+e8C6nGZx/Lxe7oUGunAVnZ9d6VwWFuSbdVONa8eAWp4lOt57NLzdEcrmv7R8oNbu1DwtXDOvY0XxZLrbMOpDTtFh0J9XzPX34YeVRZFi6v+qCJXOQAOfcx3guHyCP2IZn97J4kMXt7Or7WlTOUhzWaB5rPzN0vmNvh/avJdXHtEgpZ3gxreIh1+Rwyn3Pk3X2+SzU1H6/wHrt3pJx9toDs/UrzJEb0HLQpUVffXPq6tzfP/9Y551wGZ9H3hu+7spKwtzraZ3YgH3wAKdMVnHZ3kcMdH+mYJqRhM3xfsIT07+SV9v43eBe/eqm2j/zjwcd6zw4q+fkS+c4YDqsXl3ADxjn4nrPX1r2HcAM7HCj29bAXZpA8Dsc6/xvs18sSpRrwPvYQa/rLzx663wVj2hgMBoPBYDAYDAaDwWAw3EC8WyFiV65/vfDpFY/vfvjrQxcFF4/7+maphUKR36Fw5Z/9WkWJRqX+9lt869bDL8x9FHc87umbvwMUCOpXv5548INP5/omMkedtiTVNWelfvU6mehbugGKcv7m68e6TqRfuD79/LN1+y5+vXx4pG+GT091zmWib4MLHz+lXH/r68i+eU943vpXrkYDhd329C1jOtM3njttfZtfC/XLAgsOv0YBveESBZbxLep+R98mHuKbdhYrnk70TeQ5frmqH6y+lQ7wS/QcxfDenOKXYBSenc317ecFCvWlub4hr4Fd08Yv4L2WnnUX3wx3Bvp29WqJQs74VjxBtc4ErIJtIgj89a/kX95/sP68jyKE9x7qW9saGGJBoL4tU30bnWLsRniGJ0+0/iKsv+Nj/QLisNYDFJeL8evl9Y8+AX5kDcGc81GUPARzaTHUWL9ALBjh19MYv9xmc82BNn6VOAp0v72O4tF9MDC+eqZfnaZPda3P9ldr90+2+AtxEPhrdl/Y0zi8aqBPEFf6NfXPbkvrKcQv29NL9ZWP9RpjbpeF5uStI7GM9lHwNp/pV9+rS7HXjis2XF4q9pYotjYYqF93A/0yc4V1+eS5+jgI9Hwp1kpY6Pi7x/qlkQWNz8c6fgeFNaM7ii9/+IV+gWnimG3C85zzql/rGognzVjXa3CeN95euH+JIprnp1rHL6/AUirUvntf+8m//IXG+osBiv43NV/foLjlolzdQ0td67yBxn+JX4avRjrfr77Vff3yMRhYPf3tHzvtJS+fidX6H//xP123B7f1rLv4FfEQ7Kxh8N263e7pl8lmZ7WO/WB7azHNcvfyYrV2JlPN5zrGZ7eFdKnQMdOl4mkLlYA9jNUFigxPEZ8umYuApdPd0y998VLzyC80Fte/qAaertNqar8+Ptb8iBBT/vZEY/irp2JdPrylfa5VB3MNbNheT/vIbKI+mIPdkYC5UTrtI0XKvfAdU8//l/A8zwVVIcoSOcSCxXmnpDZzrXDfUn9lKBwdgF3rx1pzSaZnm4ANWCCmbaR2YCQnFTNnDobzDH83XmiOXK9b55wLUSw8y5FzsCgw5i+p7yXuJUDccWBp8PgWnpUFm2vVfPS2ygYPXD1exZBJidwjALMZTBQW9SRz9NlL7Vtfofh+1Fce8HquHCIEM6u7p7X47KXyxQSMndu3FJOuC8uy8PPlBrtc9/L8ja45nOs5np7p+SZ4JzgZ6fOnWK+fHt9bt/stFHPvKzeYwwjFoWjtEiYk6fbq1m4g9D3Xr94x7uHd59MD3Z8rUeQ7UvuPfvRg3W6AUXqGQuAvUGh9DEZNhqK9d5Cjfoq9pXvIvQMMw2r8qAAYTdT/VyjKHmARxdjDHn0n9laKeO2HOubxt9rbqK5o9VCgFrnmrV3lVUs865zvW8vqHWeLRcHzvHCjqlj7Vy+VP/yrXyhn/vED9fGvv9K8enBLx5A5fY5CyQn2v4mG303BZP38rphufeTtE+Sl05HmVL9RrUsYJBUoSD0AK7mPc7+e6Zg//cu/0bkv9O6YgFGatHUvh1AMHe/rWX0PcRw5QL+jcTs80J49OLZCxAaDwWAwGAwGg8FgMBgMv5ewL20MBoPBYDAYDAaDwWAwGG4g3pGj6q392T2PVEtWRxMFiEUxu5CbpBmKDE9EPeqhsNbDI1H/wkBUtTaKf7VQ6HEBuVbGopvxim4WoIhZE1SmvlhK7nZNtO7egeh8tyEh6LZQjNfTs+4c6Jx1FGk6BLXvkwf31+1HJ6J9DmcoSpyioN11X25THeVUtKvegHyirf5ZRrqfdh2yNrRHKI7nUISuuRDH7eSpikPv3RV97e4dUdJY0LoOecwCRflcNdfCmujLyVDX8UNd/2Bf41ZHsbGzU1Fcz1BY9dPPPl63pxNdM4P86tYuCu+BEpd6otN5oOJlqebF7APJo8IwdHvVs7ZQYHcH/dkGZTZAQbr9PT1P7cdfrNt9yMAeQSaUn2oCblCvIX97/kSU0AAFDHd6OmejKtxVsNj2BSQboB6ygHMBynG3rjnQjjWn6qBId/Dcuyh012ChScyZva7u8e6B1vo01Xy8d3dF9Yzj7UkyPOdcXC3uQ/T9Hqi5CRZ/DxKn413FKhbBvMxRcBtyjnaPVE6d/95t0TEbNcTQqf62ACU8WdPJdWyMddCBDDaq6Tock5BFAxEApnPRfhsoYE0pWxTrnEWJvQOyvSakgEcDxevkA/HAPc+5erX+w1Dj5ccoAhqjvyDzXCJWDBGvlpAbtFiAE5LPhwd31H7wk3X7q1/9W/ytrvUaErWdShbie+qfqCW6f9nQdeZjre3zE8UFH8X3jncVd+8c6pxBqjH15oiXiB3tEIXbWWAeMpsH9zRPo8PVc3tblCqmae5evFxR70P0fYwCwh6q1h50FXPLAHMVBaeDpf52OdFz7aPIaTPT8TuQMP38J6Kcl8gJXl0qFi7GK6p+hOK4vYHOfU/KQHcHhcM//lhJz8/PdO9dyvbGWlttPKuL8TsfpCgp5sIclHfPz9GmtOcD/V7ola6s1p0Hqf4SlHxWR/Yhj/KxLwWQTJSe+ivAWAcsUAyJUY55UkLOhNribvdAa+3urdVYf/NE0pcMci4mgMy1S+Sf2N43JKe4vMPHG0WaS+wZJaRdYQ4ZFPJ9yqOuCyBvGmi8H/KydKPKCGGZIm8JUQy5oXeFVoy9DTKc9r7GcF6TTHMy0/ws+tyXtI4+/0jS8iDRWpiNlUdeDZWjdPurnIrS+OGl5twUEpteR2N/AAnj5fCv123PKVYf9ZWvpXPJcxrIx/s7kiKnKD9xMdY9Rii4G+IdqVF+mLUYh6G7t7+KR3vYh2vYC+vIp3ZgRJLjfe4MRX6fPH+8bk8hN8ohVQrwbMd4HwixLn3EohYKWeeVhNPDgmLJD773dljYGeebjrR2gwjFqiFX/xryqBHWXA3FsHsD5a4FpLZDFMpPQsaj6/b2pIp5WbjJcvU8v4Ehz3/1r/543f7ks8/X7RevtT6O8N7cxphfnmgO7yHvbbfVnra1z335hc7fDbWO0rneHSZLreluNRY0hOG7yk5bubOHNdSDtH820tq9Guh+jzBHa4ibvTbMj3oo1VBozj1+hXIOdf3tAO+XlLv9EIxpYzAYDAaDwWAwGAwGg8FwA2Ff2hgMBoPBYDAYDAaDwWAw3EC8szzKVdSzAo4lOSiBpF322qKpHx6IvtdHJel6/dN1ex+M0BJVt/d/+uN1u9WBhKmt2++2RL+K4J8eVtKdOihzh/uiHnc6onDtUKoBevAXd0DNrsEdZSA61+CINEs9B92DxqC230GF8Wev5eu+RL9m17TF7bHdXFmWLk1X91GDPIoU67AAxRvj0IQzyo8eSlY0A6VrmWgu/PwzSW9qdV0rAAWw0dRY+XA6uADd+lqKUYKyiNvakEMMdkQ9HaC/d3uiGs6moheGEQYL9g7NCLR1OBBlcA2J8RzO03kSuGRMk99Nd/uHwA8D16ykP7Pnoo96mD+kQ9PJqwPZyuCW5vOtY8nWPv5ElMTzoaq0p1z3GZxBQEXc7YtCGEeaM2FFFQ0TVrfHOoerGKmn0cc6Ry3SPC0h1Qog0es09bcBxvHaVcQ558ql7jfCc3Qg0du9J/lJ52Dv753vvVEUzi1WNMyjPdF49zpwKIPTTA+OJjstSDLgEHQEB5Ql6MARZDL9geiY7RbWPdURU9BQ0c9e5VJCV5gI67aOSddpwQkBFOQO7nFGF5VUsb0D16wAEqoStOkpHMv6kKVkmAs9HJN9GPMoFzrP7VSxqZ6zX3TftQhOUpHGtwXKeh+OPR4kCBHkVG1fNOA7xzp/d649pFfX+vv+uRw28kwxuF/Jz7K5xraAxKMGSVavq3v8yWfax3/ehSPgXc3f4/t61v4taXTCpv42qKkPogZktwn1sqBOP1Q8arRX+QPdBN8XZelcVl07pEwQc7/AemrXNFZ7uxqrASTbkwusP6yzg47O2e2gryBnrLWUi8xL9VWKffr1dLXPXIwltb4byrGjjngxu9QxjZn2i/sNOCNB7tOCzCSEPPECuYEfQWIDeUyEGOnBgYiSg8Jtb+w24Dnnqi7KQF/PUq2bGLKuGiRGYcHPdcoYe0KE/SegGxMkFD4kFF7A/Ud/u3+oPOWokou/GWvMn11oPTOn5p4eQgbDFNHHODJPjxGbcOuujjymif/RwedtzLsF5MrZdZzytpekls65pNKwBU6xbK8lV9Zb+9rDfEgmWnCx28c+OtjVO8flufKZLId7YaZ2HfOiDUl8wZQPsqKg6p/Cl6yiwLh1IE/OS+2LJZzffvK5yifMFlqjMfbC27c/WbcP9xVPXa59dDKTRIVqxt09zbkIMrvu9lSmG/Ccc7WqDzzk87UAcv6e9ny+IzA+dPC+2N3VfKAkkS60KNbhmpAnNSAv73TgiIf3uEUl0U6WmuMF+qpJGQxk9f1Y75S39/ROeY65xnn35X3F6XM4tg3TR+u2h+tejTUfZonWPXMdyXS3y8UoKunZxblkQmNI1nLIdxsoDdKGVH6APOyTQO8Zpcf3QrVrDYwPSqL4ifLhEd45Akivi0klccN7RgRTvBj92oKD8hz7+08/Ut6S3IE8FtraAPtLE3Hei3SeGlz3IpRnKLEHB/jCYDGlLPbtMKaNwWAwGAwGg8FgMBgMBsMNhH1pYzAYDAaDwWAwGAwGg8FwA/HOXP9y/d/yLZ86V4tJFRb17RDUvOND0aNC0KSpmqC7BFirzkP1/rgGGigcqULQjbyKuh+Ckt7pi9bdhKylC6eZFI4pMRxL/DrocaBthaAKO1QDTwKdZ7er/jhAFeoG+my00N96W6ScEte9VgcFLVnQIQGV00HdKyApacLJqQ56e9mGywDotd4GfQwUa1xrciVJWkmTokpylUa6fgGXkRxzxcdc7KO/W3jWYkfjTImNF+hvG6Dee6CeXqIaP7/xzEC5m4NaOWcF8y3CC0JX31lJdrJncp1I55g/6KMSrl5NUJ0DUnzBWB/Aya2JvsvxnKSN0p2q1dQaIYJqDYKx6lpwY+t2307rpgsdpTIp+tmDTUYDVFnywJNSfZAsRGP2IO8L6Bfz7J8AACAASURBVJ5R043u3l1JpYJ4exqbKAzdYUX3fXBb9NqTI8nE0ue6T1L2W6jG34czX4y+KtFXHh3eICcM8TwJ3LumWIttyChrldwtgaTMd4zDkAfpdl0d9NgWONuLlJX2MTFynl/PMYfcNIKcsY1z7hyKnnwAajXj0TYReJ7rVjTYAHErBsW7jn2rDspsHbGwAacddJeLm3r+gy7oxy3ISaeSQZ0NRWOG+sk1GacrufAcrgtLtD04GR18IqlMcx+yJrit7MBhqNbiRXWPaR0yNwSbha/7urrCfrkvCnljX1LbuNqD6eTxvvA8z8WV60QAud8c0u8QEqAB1tBOU89yd08UaDfQmgaT2vWRdtWxpwWQUzishRx0+Ajy3LDaF4cXot1PIavwW9jHG3AAxBrNSqxXyqWRW2GYXbbAfgm5TxOOHA1IIQvITEo4rCwWH0Y27DzPhZUUMYN7V4S11Y6R09DtFPrQbqw9bMMpFbFoucB6wV4UgWYfMr/Evvj5jyT3++SjB845504hQ/7qu291PtwjTZoiJEkeP8c+USAfqiNONxD3W3De64DCvwdZXAuTwMM6jq6litH2NDZB6dwgW/XbMpaLbPfoX6zbvduab+nor3Q/kG41cj1vrac5udPRPM+wFrMCskjkBDXsM+kSUkU6aVb96XPOQZ7YhUtNDJfaINJ9hZR+I1cJcX1Ka3NIlGczPbefYy4iv+7D4a8GV5sO1v12UTqviml0AM2mmEuQKcUdvBcg92rBJYouWXRepHS1cG+XJzYRlxoNzX+fMsNqSS/ndJvDfoDPG3BG9bAWmyy5gLnWchr3XU0HF1xK8tOEXHq00OdTOkuiNgRlZMV1rNnqa6O3zhUur/Tu89Wvv163u5qSro5njxCImP/daRyv20GgP/YQY3zkBwH2qNmC+yXuEoFxOV0N4iKhVFF9lsJpLMY7fIx3+zrfvRFbPaz5HPG/hAP0EuvPw2BwWJZLyaAW+K6hHv7unMaYNgaDwWAwGAwGg8FgMBgMNxD2pY3BYDAYDAaDwWAwGAwGww3EO/LFS1dW1CJSfUqP8ijRzpo10Y06oAR2mpKn1OASU4Ba5bEGOOlJoIOVqahSyznkEQ3IMyoKbwhqeAzaVIkeaLLCeyZavUO186BOajuoT5BV5KAe5nQSoMMCaIsRpCUx6FF5dn38dmVSXnV/NdCbc9CHF4moWz4oiCmogTnK6NcgcQvgDFNDpXXKSjzIFEZXckyYTUQHLHDdsuqHAlXKHaQ/yQwU/7na3bboi60mqstjHHJUtS8whuVSz5rgnBndoCCnSnGeMdyppqDNbROBH7pWcyWtqYfq/yyEbAyU2ZzPjHVWg9OaD2pmgPO0m1q7FEVSEuaDhppB2pRCXlBU4cbHvIt/QPpEZ6425FmxT64+5QS6szKlPE1zeTlTe44q7SUkbF3MU7qV7e6v5J1huEUaeBi4/t5qDGt0gLotGvPplSjzORxKwPDcoOC3QPulTNQH9bSO+FjibxdjzdsUa82DU1VUzYWMsaBEG4xxShjjBuLdhiQI6xg05KzEOED6tgRtPYfmpER7dyBpX4g4u01pG+F5vosq+VMGWRf5uzGmDYZooy8CrCEoqFwnxlhgP3l+qc+fXopiO8Zab/exX+E3muh6JUN6w5hex17YghtU8EoyrBSUd8/TAxZXkN8EmlPRnsbF64EGjnFPEcsfHv/huu03RalOKqlDGWxvLYaB5/Z6q76agJa+QMC7mMNFZKz5dh9rJYCUtsOBxvz34ZAVdLRHhZBJJzhnciU3mBKfdyqHrxao5MVS/78+UA4T1hXDY8yhJKOLhW6XOr8l9sLJDC6JBRw+kAPUYskZlpBzZQhavv9hfi8MgtB1Oqt5tsSevD9QznnZUkz1Pc3VBhbpfkd9F+N5UuSOl5fDdZsuWVmuPgoRA378sRxJ/ov/7F+u23fvrcoF5Jg7j76VPOq7J090buzAgXu7PBlbofPxTHU42G242UHq3odT5k5fczNmAIt0TO/Wylk13KI8yi9L16z28IISX8gXugfaIy8nyBWnyic9KBVjuvFBAlbAsSbH65APFxyHHGKCMgyUnl27FwUOfRzqHJSmtjuKg3TRYx/SMcxBbkGXnCnkrOUc8iO4pjYhy2p21Gezc81dHxKVbcLzvPX+y9xljntd4l4bKMUQhnhXghSc73ZFjbna23MdH/M8xPE+am4sLiQn9q73SMTZGs69gKx+MpFcqI+co1ZHCY9Q/e9hP7g61zx9rcu7CGs6TTS+CUpApL7Os+HDep1LbLGsRuE8t6iud47SC89evV63//kfKK61z9X3dCirI2+OIAn0mf9jrOioVCDmTq6QC224R0J6fZ2ELt7ubsu8ntdpIefJ6FiKd1EmuEtcPmOOOkV5FMhmU7w78l0zwj00m797LRrTxmAwGAwGg8FgMBgMBoPhBsK+tDEYDAaDwWAwGAwGg8FguIF4N3lU6VxZvEUeRc2Eo7wHdH6wxv2cVf1B9QIn3MOdeaAzlaBS5xNQe0Hzp1znmv7vg4ZPSYxfsto76Ix0QNjwQdHxIWQY2ZJVpXX+Bap+zyibgRSIbk1037qmhvpbpLuVZemySlZWgq5cUo62UTib9Hk8I/rQQ6X3BqQs3b4ogwVowgtIHFLQDcv87W4I13OKMhCyRz3wgXk+ytE6cEJgZfLZTOOwHOq+5hu0NrULTmT00xwU8hHGNklw/Bbh+75rVq5m+7tyG4prcCAAHS8nfZsyRFByQ9BQHe67RP/S0SyGtCN7e7H1jTFzlbQpL0T7rNVEVS7zt1+TMpugBjkJpYSQYSU+5BmgaC7hPLRAO4PMcheU8GZL/Tpor+Zy6JOS+X4oytKNq7n14vxs/XncgnMJKKMz3LOkk85xIlKeWkP/0HUvgg6CtOII14oQiyOs6VprJbNI4TqWlaJaRz8Qh+MQ1GQ6YyDeQZHn5hjzJWwFN6SNnC8IGB7ophPQYmcT9d82EfqeO6yoz1mJNYe+pSR0w7ENThAe1p+PNdrAWFB6OILEj+4ZLex1IRx7YuzN1xIZuhtEJWSLMRxO2pBNNRTbSjgJLRPQltH/Ual2jHnkQS6dO1HFWz05Lvm3P1u3zyHddPWVg0jhv6O6+/8BYeC7wWC17mavRHsvSHvOdb3Tkebes5egyUdw69nX3zYxz/0AewvziYbiTRBjrz1TH568UF8lk9We88Uu5RaQLcOZr16XG5wHWUUHY7KAZGA00THzJfKWUufk/OZemGG/LCEhTigtLjY2+K0hDCO3s7uS7LwZa30cHMix9OpA4xVcKO56cI+Ksc+3MM9yxJkSeVsbDn7UPzawv/7ix3JAOxxonykrqvxeV3H/o1tywJvDVYry0GIjZcP6gGsS96sIe0OEPKGDXPfoQHOwAXl7whwAeXJtsDp+q858Xum8aNX/i/nz9cdPH/+JrltTnOgkkg9mqZ4xXdZwPCQZJV1qoKGiwyncgGYz5BOQEJeF5ksxWY1LCrs+j7Ed073egFSkTucmJlHIG3meAnsY5DPZVG2H3Jx5/flQ+/Tr5y/W7SVdsLYIzzkXV/kiZb1Q5LoZpFJtvCs18F7oRbq/AOdpBZB5h8wzIanjmGK9JpDOLKcsf7DqO75zUnpYQqZ0ca5514dT8gDvPQnygcUldFCIkQlk+5x3dDGkpMdDXkVvWn+91rddTmN1vgXu7THkUf/JL75ct+lGOs/w3sQxQeypMyZBhhohniR8L8SLBqXAPvKPepVfZZC4lTPkZXR0gvSpgVhZYp4VmDfMBwrOC8y/HG9AdGqky3GN3zW0NV8W7nfLTI1pYzAYDAaDwWAwGAwGg8FwA2Ff2hgMBoPBYDAYDAaDwWAw3EC8M6fRq+hBBaQ1GaUMOJZyoMUcziQL0cGyheiBPqikcSiqFKmfpJ8XqKruFZDFpHAzqe5hOhY1MJlLVhWSwo+bp5MN6XF5Cno+KHbJRM+UQHIzHetaV1ei5V6NRHMmnY3foskdYJtU4tLlFQ06IY0SjDoPrlikeuWUU5GBh7ZfsqI+aGVsQ8ri49E2HKNwnmuqIimWmz2FNm6MshdKpWpwQshTtRPwjb0NyzLMb7RzzPbZAg5WkLFEW6TwE57vu1p3JfnqHu2uP2/nomkmU9DwM62JGizTClAeIxrfQG5Rol8SUGlLOLnlAav3v706e1iNX7zhYqF1vsB45WhnXP8IWR6dpGi2QFoknV1wng3ZGu63C9r6YOdg3e70B9WzbVMe5dy1IY23SDY+vwbdQih3S0EZpYuS5/RcASnGHiUzOj8p/pRLkj5fh6OIX1/1f4x1U2/CXQ9rNIb0rYGq+MkC41NS4oT7ogyq4F4DdwV0FNcuHftmkJE9O9MesE34vueajdWzTkGHTeDGlJP2WlBLgnHJIJPFPM/hhnE1pTMa5jCcZ2Ks6RAuXB6kAHl1yhSatKyk9AMSuiZoy6QKg+6boM8zhDxKLAI4bCQOLglwZWocPdTHR3KM8iCvS4NVjGP8eV+UnufKam1TdsJtIMcEnUDp8/JcsbUfw90H6+y4B8ednsaTbpQtuOPkkCQFcFWZjdW+mq7aqaeY79UglWrC0aTLnETrgLHYgc6e5ZSSam9L4epSg8MOt+YS8kdHGSXyjQ8lj/KC2DV691f316aURRKjB588WLfrL3l/kFPXIOvz4KwJ149BXePY3pVsrAW5eAtxb38gifbJk0fr9ni6yhGfPZdL1E5Hffvp3VvrNuVRU8gjE8wRny5xcG1ptrSOez3db7NHKaSumyBnSwudp9vXvlhrr9obe/F7wvN851fWT6UHqbl7qTakigX6pJyrv4sWHGgQKwLIIAJIvB1ylRTy+CUcoxYJ3UBR2qGaIpRB5Znua7kc43P1d92HDBnvPEWKOIsrZuXbHRNL7gWI45OpJDnPv/7Vut0M6ODzYXJU3gvl7j5eGHLcdwqpVB6oz33EflfHPIerZYg4mmOMcsQul8IRCKU1MryLLSt5VIhz012tieucjiQ5XUI2F/V21u0Yrn1liBwc+ytdcVkKgO9VMSVRMeb4RqmR4O/+2XvDL52Lq7HjPPzu1em6/eiN5tgIkukIfbK7h3ci5Bx8P2DuSNl24PN9Be/ixdvfU6//lFJWfkeRM3feeBdlDol3csQIOsY5yLMSrPUF1uUSEv7FRo6vePl6DFeuf/cX7nfBmDYGg8FgMBgMBoPBYDAYDDcQ9qWNwWAwGAwGg8FgMBgMBsMNxLvLo6r/bsqj6EIEChqkFPOJqGTpEq4XqaheUSJ6IG+MVb9ZPZ2Uw9HZybo9GYquVe+u3AGeff+9rgMZ1p2PPtf9xnCVcnRAgHsJirfnc9GaspmobwtUI5+M3i6PSuBqQqeqDLSspKr2vuVa4C6oaOAZZRXok2IJGjikbI2maILFD0iGUlDAxkONOVh8bgr52PhKxyzmulaMSuL1RkVJZEX3kJQ5XX8+V9/nkCrMUaGdsqkM4+DlkEfhnNmGjIySDM2LCeiWSzhYRfH2aMMb8D1XVOMR9EW7LmageoK+6VFWshQlcUG3N9D/S0d6LvoXNMAh5kYTrkudntoe6IzXwxf9gKypRP+fn4t+OQaVdK8r6mk9ElW1hGxxBveuArIwl3J8yT3V/ewey7Wj0xMN3NVrmw+xBfhB4JrdlezKm2n+zDBXHajcCeZtAue6FI4ZWU5ZKWQYrN6PTs8L0jdBt4dUYgG3mbQa/6LQvQSQXjG28tx0P2Afgp26Ic/ZoIRTngdq6xLrm2ZaGWi2I8Sjc0hVt4l5lrtfna9idQy3nD04ClJiWIRwVdiQhGGtoC9mWNNT6HKSBOt4gXgFaUuzzpipubGW+aH/KRksPaxRDFI5RBwd6r6yDhwE9Zcu9OkaBgklxnE81754cPuu7retuJY09tftYVh97m1vLeZ54Ybj1TzPN9xadAyvlkE+OkHfvxki3nhoY69odQbrdhvr0se+cfpc8e/Jd5KFxDXFv7JytXl5pnl960y5T4MhbqpjLofqb68Ft6k29ly4UI3GOtEcEk26/WWQpGeQTi4RlxNIToItxtFNhK4IVn3UO5DU7myqZx4jb4zqur+mpznchWPbblcyM39PfXGwD3fBvuRGLAXAZ744l+PKGfa3a8nVxQU+wx7Qg2TJhwtmsaN5RG1pPdbxjYaeI4R0b0PyrLO4BNL4FHM8hUSzsyu5VthY9Y23RVdF55zzKhdCz4ccbCaJ23gq2Uk0h0NrW9GnRQcmSvYocaDkn8603F8xhgmkWG04Bl0738T4O15mM//Eu0XGNQEnRbgw0qlxidhUUiKGdg6HnSzjexr2bOzT3Be2idKVLq2kujTAjTas5iCPQmyhJDOA3DBACQVK1ZlnF8hX6PaVQTY1v9LeNcN7ildJoSLI9kPIs9otjfnFWOco4XBZ4h4DuiNBEhPSHhlr18MciBGDYk/zelTStQ8OlddrcItuw84rneevnodlCp5caJ/5n/+NXN2Oe4o9X95X/nz7UPvWoKtYWWD8NySMGNsS/RNA7tnC+uN89qu5kNFVFS6JTC7nKGuS413Q1Si9ElLs4wVlUNj/ppi7syXl7DqeMvf/5V//H+v2k5faI34IxrQxGAwGg8FgMBgMBoPBYLiBsC9tDAaDwWAwGAwGg8FgMBhuIN5ZHlVU3CKPtEK6R8G5olUXxSmDbGUO+VCjpTarpyc8P+6SlKQpqE1PHj9ft0ltPDhY0apPHks+Vfq6TndXcoh2X3RTKHg25EkZqG+k82WsNj8DPQq0yBmcBzxIProt0MBJRV/35facFjzPWzsasVI52K8uBNVwkuhZ6nAfqdVF1wty0fgcKN5TSFMSUDPpLlR4kHxsUEU1d2oVPTEEZW6DALhhfQVqKOQZE1AZI0iroGZwOWRQCdxYJgkodA3IVRL132RC+qv+NvpQhfl933kVDXoy1X08+e7xuh3AUe24gerzV7q/ka+J7kFjwqr+w4WOOZ1Czob+/exHkhk2YvXRElTOrJI/kYY4hXwwDDV2M1BZH72SPOC8IenVTlM0S+rvctD2I86ZOHxrew7Z0esLUef9zm1da8v0b+cqB7DmyjEmwHpKoZlpIjZcjERJnZP2DFpp6jj/MbkRt3LMiwx0XMYkUnkvX+u61yEpxhpK4KIXQVaTk+4MSSwdMFJQ2Cm5TaHb2Whjf0khLclBJZ4gHlxe6t4nSzp/bA9JUbqns9V91fFs97AnFJiTCebSRn/BSs9zoLIn+ByxJYE74wj7axtU4d6GhAgU7qy6B6ybkBRv3FcZQLILBvGbc8WCJqQFXktrOllK0uAgbygyrd3TC8VmD32w20Q83tX4ZvPV/QRb/MmpLLVEClCwKQEKfcgKuSfDXcSDG8YVYtubWPN/dweuJ8h5hpBYP3qiPvnlG/XDJDlft5NKfne1FKX6sPut7utAsTJLFdfOXjzVrSNH27stCvsC8YWSjCnWX0npNBxNpnBPLJBYhIg1qUfS+RbhOedV87Xek6ti785H6/brS/XXJSTruzVR6CPEkzqc9FpwxNuH0yBTkBwSI0o1z0/PcBCOqaSQM7qRYs0HkEC04UYVYl2mkO34PjXHbENawwQKxwewFpwjFw36kiLEA/VrGVX3s0WpoitL51d7col1lkLeXAs1PvMcZRKQN/Qgt17SlQd5XpFC+ku5H5yM0ikkOQXWLiVG9WrOlXCD4p4Eme4C67wRag2FkBJSJkMpNONRUuicsxIOV8jjlnBMypHCRHB+8z+UPKpw7joUFMibqbylDMbL9D+yGcYiVBxN67rv8vLNup0s4EKKl7f5CFJQ5EBTyKboSBRWr8RBAIfTunKwBtyguJ4mE11nib244J4B1+I5coMxNtUUOdgS477ccHDFLXgoWfFB1qKDnaLm0hLP/uvHys8ve5rzdMu7HGme73S4b6g/Q2qR6R6KOe8j52NZjivkQvVq0l2gZAVd/5oovbFALJjiPSf08B6uu3IZYiLLBkyWGFvEoClzYLwDj1AK5NmpctTLMcWqb4cxbQwGg8FgMBgMBoPBYDAYbiDsSxuDwWAwGAwGg8FgMBgMhhuIdxJvrKqBr8hCfvkDFaq9t1c6Hy1EDZqBPtZBpeUypDsR6P85XDVAyw9RJb/eUiX/FPSkrPKy+PizH68/C0Cx6x/KlSKAOxIrxheg+1JmUILWtgQNag4a/hIuGTnoZQndQZY6zwL0q+vq/eX21FHOuXItcXOg3HmQUkSo1k6JxRiULh8SjgLHTFugni50/npdlLReQ7S5LiqAL6fqt/PKjcU55x6fPHHOOReChn56+mrdroWUmmlsg4AUZFDtMeccHRIwb84nuv4IMq9WJEr0YgrZFOi3DVSe97ZZyR0oneeyikoZ9zX3lz5o3aDDziF3SuFURCZ1CrebIdzezq/UFwXo0909yQmvriR9ePLk0bodOo1Zp7GSRCTo50v8HQvqh5AZ+KBFP3/zbN0+w/qPIS+KQZtsYi5HcJbI4JoyX+r44Uj3dquhuelXtHRvi64nnuev11qI2DDY17PvQ95z+Ua0frol0fWL8YPyuBiSnBLtlI4VoHsPBpJKeLi36zUYQfYQYxsJoVmh09MYlGWf8gFW46f0CTGFbfiBbEg4EtB1x9xrIOGj7GWbiKLYHd9aOao8f6b5eQkafA4JXp6h/0PEXfRFAA55e0fxJPe1F51ewiUD67uIWvqc7nBwlUqrGNivIQBAeuwjXnqxztG6c6TrwJHx6lRynhiONd19OHNAokpXhZPT79btCYLAXu/Oul0PJQvaqeZY4LYrscmr/vdBUfd9jgnlC/q7ONI/anXI3SCxXlAyPdT5h9B+Jw6Sha72mStPsprfnuukw+qUXZiiLcpv1u1PjiRB22nqOnf3763bF0NJDJYvFOfLuuL2FVzKlpBO+8z1MowF8qJGU3M3gIyTzo7bRlHF6AD7A52k7n4KCnqpY+7vKRfsY5/zE3Vwibz0Yqj+ykDhn1K2Anl5jpyJTmrL6/6iqyI2Zu45lLAGmHfMPynzpvugF2lMHfZXh3PmOL7AIYM9SaL8+t+XdP7Q68A/BL7nuXqV00XIUadwrlwkih8hjhlDsneJeOMiPUyH8lTInRLsLpTwhx3Ih3D+FMKJvNq7ygB9j9ge1ylHUnuCnGsOV7MaJOYl4vKm5BhzDu8olIIsse8ssB+zDIMfbV/67ZxzzvNcWY1jimv7cBSsNxAfkMcvsG7SCXJ3rJEm8sIAdQgW2dvdGXcPVArDYQ5fvdC7hFe9m06xjzfb2k8vL7QPzeHIl+L6Q7jAxXOUB8B8PIdrLl2oRninoFPrEouxgCuoh7Hzr987PtA7B19E6eiEt2M3wTwcQnp6dannHbaUH7Tb2udYlmO6UN9OIT1jvsg5RdnS11+v9sAAuY+PNT+F2xznzfBc7yKtrt5RY8zRAvnkDJL/Cb7TWPB9Hut1w3EbMTrLKIn63TmNMW0MBoPBYDAYDAaDwWAwGG4g7Esbg8FgMBgMBoPBYDAYDIYbiHeUR3murKjyGWjMQSDaDylAv3kix6ZDyGA+e3B33U7mqPQNRxc/AE0Ibk8+qPp+XTSro3v3dZ+g6npVtelmW3SnOu6l1hL1rSxI2YWsKadLFGioKemGcEpidXrcbwpK2beP5eDw+kzUsTru011Xdfcg53lfeN5anlCCAlri+7sS1N2clGaPz6Jxvhrr/iNIiZoN0UDpsDEDhc7HPOrvi57cwBhdVLS1Gap1H4Pq2IEzEumCS9DOMrhVFAkdWHTOKSjsp5Dt1Hpw9kEfzEA95cwJPphllLBMEvft0xWFswOXgs9//s/X7WIimubFr3+pv52JbljM9QxjOGCdTUQDdZSf9QZoa+20IS0Mc9B/4TBz+nol76HMrgDFu7cjqmQN8psSFPvpWPf+cihaawP04wbo5JNIfdP0dP64I0nZ/R/9ZN3uH368bg+O5B6VVXTycsvU07xaaz7uOW6oXzt06qCzAKVBkPjN4VJRepR26L49jKeHuVPA0aQOiV+3qftpNFf9TMfAJCRBVuAxpG9TkldAhuMcnimlPPbtzgkzup3B2S7Btkb3Fu8DyaMC33Odao4ucb3XV4qFc1BmG4iFaU5XLZ3TA22/Hmscuz09Z9TAMdgjKbM4H2p/HU8U0zqVa8dBS/EyoCQDjnkF6MG1u8fr9sNf/NG6/c3/+n+u2xevQTO+p3Xmw0lxcqH2+YnilNfr6zyPfrtuLyARCperPdJbQv7wvvA8F1T07AKTjG4VlMaUmJOctwWkGksM6ALOKCncFpeF2o199VW/p+P3D9WfJ+o2d1btOidwHVo+0R724lL3/p//R5+t2/FtxbWgqXt881oOnGGBmEs6PlyiwljzL8CcZoyk+w/bRb5VzTeu7Vy+nsdw94mUV+3eUl+UTs/Tb+j4fgiXkCu5o4TY8xPQ7HPQ9kvICLyNPBl7Wvn35eWcawj1rsC5Z4jvOcoPoLkhp18WdErS+SPECw8SYspMI+RvjY72Tsqm8moOllt0OHVOUsQaZGoJ2nW4aM0mkhAvmZdCBs7+SbHXNun2BPdKjkULUiV/R5LDOdbd+ZtVLpLhvYFyn0Gnj8/V9yXHFhLidKpze3BYnE+0vi9HOn4Ct6slZMYzlGrIa9rHZzO4jX0gNY3zfedXuXkC+WCJMYrwztfuKedPM8WTEaT6Od4BNlyLGYPxQJ195U85pcgdze3Du4qHeeWgOISj4Tkk+QuUVuhDQsPpfwqXuEZLcy2DnOY1pcKUv5XM6+CmiXniI2emdPLa0dfbontU6Zwr3HVJFPY3ci/MnwXythHm5+lr9cmgrbGlZI1z9eJC0tNkDiffMaSqiKEh8tjrO+N3EUucY4w85HyicTgd6h57fY3t3pHmUL2rNTTFXGQpgBnkwTStK/ldQMY4jr1jIzl2b4UxbQwGg8Fg5qfGkAAAFpJJREFUMBgMBoPBYDAYbiDsSxuDwWAwGAwGg8FgMBgMhhuId9JxeJ5zQUWrTkGViiFJiUBhfPFGEovvX8mlYA73mALSoxSyorAAhT9HBW64ZJSgnNdQPdpjpfLqe6l4Sho+pEz4FKYeLgOttAQltoD0KqN7FKhvdEEpQV9b4DyXoHmRTkUC/zXNeJvEU89zLqzonoWHiuRoJ+B00dkqh9ziYFdU7hoqczdroN3i88tL0dAouYpqornWIV9o10VD++jByskmgtPNAu4j8yUrd8O5IYVzA+ZZC1XKNyh0oCmCJewiOBOlpHWjz8i/jUDPZJXwbSIMQ7e7s6LtNWqQoeEZFoX6yItZpR9uQ7nWQppRhgc5DVyFPIxXsyupVC3U562eqIo1T1TI6+r2cSpnogDSi7hGZxJdf0IZGlyfygUkOgvJpiJQJf0ATj0Y6xhOdUd3JYnq7X+kawVcHxUNfItU4tJ5Lqu4paT6OsgW6XRHZ48NNzDKhzDdSLsM8bcduKqEgfphCeq9D+mnBzeUfPH3OZukVxdw2HE1VO/HHpHmdLdQ23OUn0A+gFMmiL+Uhfnopw0XQqzL0v8wssU8z914vKLok+59hj3nNWSIvabGLi+15pYZ4ttCc/jipWjGf/1UFN4RpB21tp4fQimXI5B1+5I43O6t6L+Hx1qLfgx5ACUQ0GokiHklHI5GkEgPEIOwhFyJffHkmaSNswu4PUL+OH4td6rUSSoVVecvF6LNvy8831u7XqVj3Q83XzoBlqCgJ1grU6zLJVwvm57GapirvSwwbgi/ARzW9o+11x7NIP/IV3214f0S6IYP7kvKRmfMFxeSk/icQw3NBbpdZdjT6xFiB+RR4YaLCeImJXeQCqXJh5FHra7z92MUHfNCSNB378lJq4acL/DhMOJDkzaGJRgcsEq4GTnsMwyHGeRy2RLyz3T1+RLygAx7HvVOGdzMCljjRJRMbMjeBUoaCsxfStTpSFQbSK7SbKnN86RVn5VbtDhdFWEoqntDjP+BfQaqLzdF7t/BM1JeMoI8IoFMAWajLoK7FvO/GpyEOjvKV64dixqIjy2P0hjIlyA9L/B5TtenDSdFvGfAYXWGvTijMy3m/xWk6E2s7709xYbJ2dvlze+LoizdrMpNWuj/DBLSEoNag2Sp09Te4g0VZzLkBRvOvEjMYpTNWCKnyOFmFEca005LkjevvRozvosskXOO4GQ0xRoeXSFGwKXRgyTNIV7mfE+CkxVl8pSQcu8Msf4C5Oa97mqNvgw2doT3RLle7wHiGh2qKI9a0hX5B0ovXI707jscQYZ2cYHjdYwHPaYPt8UIMsMkpVPb6vMUkryMelPkhxli5RXKZswutUaXkMruhwfrNuVRV5A2jiGbnSGeLpEnLBPmgLq1ku+UP+AkZUwbg8FgMBgMBoPBYDAYDIYbCPvSxmAwGAwGg8FgMBgMBoPhBuLd5FFOFEKwkVzOyuCgozU7oobNQeGnHCgFJTAEPSmIQNsH9ej0RLTq+ano0d6U5wGFtJJN+XA9SGJSvPW560CesyMK7cGhXI2oj8hKiKvo1AKGU4B/TBeiTUV1PV/bezudTU4U26USr6uB496ygrQ2PdccYzWaiw5251j0yk5HfdXuimqYoLr+6XM4GV1pPB9+KieHAJTBM9BAvfHquqyUnoMeS7rlDLS6sxdyL6MLwaefyGms0RMl8xyOKX6peym8DV7uutmp6bkHDd1vA88xGW/R4QQoi8Jl1XjMULE9Ag2wDtpnDWPkzUH39VFRHy5NOeieZ1fq06d/9fW6ffLo9bp90NG1eg04NsFV5Np5yANN/PSlaIgj0A2nqNJ/OdU6z+A2dDiQPAtmEq6FPmBVeR8SsRBjV2uIikvJG53q1s5KW2f1r65HxzFK9ujgUQdFfZFRYqTjp3DA87GmdzA+dUjcZjPN26s3kk10KbIpNRfyirbvg0seQtZGianf1jnaUQ/H61nnoNMmkBgEWGc5tqkU8ijuQQHGtoR70sZwbZU2LIRR4PYOVnPxChKzKRjTX59rPt/ta07WIU9cprrvV6AKz+Cw+MtHWotBW+e5jSflWqAMsIY+utNaHdTGXjl/rfEPd7TnpZD2XH0lh6Fv/s3/vm6fD+Ww8/k/++m63emIkn+BmPLnf/Zn6/b0QpKvqK55WlDO5mGvLat5km9PeuoHgWtW7iUB9qcJBjGCjIRxaJFpro6h1UhL5BOYzy9HcBWbqs+zQNdl3jDBoyeQioeV9G8P7m57PbjeNLTn/fo7OaA4SBU+PtY434Nj1SyTDOASuU0Te20NMYApTJJpHVPaQXnUMqEwfXvwnHNBRaH3IYmioxXdZSLIXQrEHyhvXdGS808Olx7mIykkEdOp1ugEuVSCk3qF+jqpctQZ3Y4g/SiQX9OptdXCfgZpSRC+Pc4FPiVRkO0vMJcRD/aaWosh3ATpUHjtzrdNedRKHLUaI0p8p1PNyacncl8dtBWfRii9oMjjXIg8pAf5NpV0w1OtxflI49lsaN/t9/B+00KeU0kCC8ja5tijx8gtxyPkM5AvsbREZ1fXdCG1+vgYUhEP/X85RfyC/KqcqG+eP1O8DuD4uk2kWe5enq36tA6J0SxTv80pd8a7Y4M5pKf1x9INOV3a4NRF96b0XGPaCd8uvyqR/l273C6RXDC/WkDyM0IcP4XkdID9vQtHrAQSRneJ8g75i3W7wPtwDOnTFO9SDvE1QgyOq/a23cCuu5kr3KNUyvGdWJ+OECuvZmqfQaZ2MOU7Md5LEIeY6DV8jWFUak3HkPnXKifREm5hU5R+qGNf2HDCQ3/Xumo38U7L8htTOA+/PtV7zPlYx1zBQWu5pGxc99tsawJGWCeXr/X+ShjTxmAwGAwGg8FgMBgMBoPhBsK+tDEYDAaDwWAwGAwGg8FguIF4ZzsN/1pag88orZmC7nd4eKTPQdMbgjJKOlUOehwrP6egxI3GqPw+Fj2p54m+2a2LklYxpVwECvEFaKjDc7UzGAPUSAYboJI53EG8CJXtUUI7CEijFs3r7BJyLhxTOMjLWPG6cifYIvPUlaVzedXprEi+BB1tseEYpeeiPOo16PvtFiqxo996cBe5c6C5kC0lcYsjUQkPP3qoG0U/XyvS8pQ0YfHalqhMfgX53OSVaJK3DkQD7w1EnL1aiqqXgPoWB6DH4R77fTl5eJBhfPnZj9ftGqi4iwU0Ev/D/+S2hSIv3LRyIKs14foCx54SjEoHim8J96AmZDm1LqjIlPjBbesKjnA7TZ3nzq1b63YO167RUMe/fPnEOefcPAXFvAW6I6ikpC3nV3AvAoV9/2PJoxpwLAlRmT1jAXbI1gb7d9btuCH6I5ieG25GYcX/99w2uafemsLvga5egnrqg5bOGJbPII/CQ/qk0m/IhzQnA7q7gH7uY+3GmBdN3EOyrO4T8kS6lRSQrMzhBOQg1WjXFBcCUFUXoNOG2GFKUIDplEXHCA90U+4pBV3QfkCG+r6o1eru/icrmeebM9GkyfB9NNR4vZpDykTHNDxPgWc+fqC11bwQrf1vIXkZXioG3+qrHUMSMYA71PFgJQ/spNgrd+SMsIS0Z/lS+9bZrx+t23uQXu3ek1y2dV+xNoHb0L//k79Zt//qP3yre8TcrO8w8MARBxYL9WruUf73vggC33W6q3s9vnu4/vwrSENLcKkzSIbGiDc+AqeH8RzkesbRWHP4m1farxI4ZoZYf6Tkz5Dz1Cp5xA5kiHt0l/pG9Oo54kunDSe3kWTLQ8yVl5AEPJpI4vtgR3tn5wfk1cwO6Ui3hHQlzz7U74W+89e5IORRSKIKjAtdwHzIJ0qn8eLzlHX1v4d4RflViLXbQW6Ux3ATSnB8lXf4kCFSHlUWlN7rOUJajSJ2+z5jHpwIMb50uKJENoXkJqbkFPsx89G4cn+k2+P7onTOpVXcXrCUwoaVGyQTLMMA2QldorpN5XCB0zm7HcU/f6H209fau+gYFJSSVWeQagSVbqlM6JgD6U8Kl5oLvOfMdPydO4qb3ZZym8xnGQY1M8g/ZnCjPTnVmp5i/cVN5s+Q9i3e7lLzvijK0i2q2DgJdQ2+/+1gs54h/27lnOeahylKUtQhn/WwGJqQf795qfeUFOO4iOFO1B7qnLtVfEM+4WGtFgVy+7nOsYDzW6erGNnC+j+9Uhy9ROw4h4NSmtJViHJGJ8B9s4QET5KlrfoNr1/Ayo3zIv9DPKVk7QJx5RX20bCuuPwR9s7jI+Ufu3314fiNxuf0sVyoGwVyY7gNZ/7qRb6AlNfRdZputDFysVvKS3d39J7XhAx9hlItowlKOOD5JnB1S+kCiNy8jXeRL37883X79kc/Wrf/+//uv3VvgzFtDAaDwWAwGAwGg8FgMBhuIOxLG4PBYDAYDAaDwWAwGAyGG4h3lkddY4MMGdCVRfQxFrEfoqL+s3PRnb58KNlMHSctQ7ggwPnk05+JPpRciqpUjESF9ECFCiumEimd7b7O1+2C1r0vSmKtI0pU6UTty6E5KUARy0HzT0EVPr0QtfzJidx25inor3RFghRsm7Io4poyW+I+i4x0WfVlQZcmcGQfP5eLyG5ftLIeqKodOHZ14Ja1C8eG81ei+4dwAfBBK7y+agHqaRPU3QJV8YeQR3XqcN6BxCeHo8VkovHx0eE+6JlLSPKWoa7V7oJyS6omqO2DHpzHtogkTd3TF6v59PFnqq5PaUhZaF6FXd3HDNX18xyOWaDh9ruQPh3q/Id9URhfvZb0KYSTxf0HkLmFmjOXo9XxpU86pdqXcG3JnqmiPqmYn967t2636nQjgQtPCZ0jKvbX2qI/H9x+gHvU3Mw9SB1IOa/oz9sVR5VrSmuB+eZTFgIJwjUV3TnnJkPN4QTyKK+gxE/XKiDn4PxvtCCbKhUXI5bVxz141+sYYxiAgt+A61QNp2jAvSzE+RiDcmrZ0NEJHLGmcDpZ4vAQdOYErNgM8yvY6ujh2rWaO3zwiXPOueiXf7n+fABHCwfZwS+fK+b0PhINt+/BdQd9UUKq8uBQsTYFbfsKVN00V6zrwEHOr0FaVsXscl/Xb3/+JY7V+r/69d+u27cfaL9s/IFiwXwp2nK4o2t+80jx/d/+b3+xbmdj3XuzC2mjE0rsPSFkcde7h7/F/dH3nKtX0tLbd9Qnp69FXX91Iqo9nRdncPYouRYDjWd7qfPcQr7Ua2rtXGKfodx6F7nQ/QO44FTx7Bixugk5bgLXGRgGum5L996DDPk19tHHQ/X3KQblfgi5D/aXHPGC8qMpHJMukKPNZnQf2R5K51xaTQzKijaOoayyxNwL6cYDBzrEHw+OSh76utZUfKMuOUNc9+BO4jfVnley83hJt8K3u0dx0pcF3dMoHYJcesMxDzkeHOnmmL8epMIB+iOA/KQewH2pko5ThvW+KJ1zSSVtW2A9LTw976s3iit15AEFZCoOMuw+pNyXI/VJA+NWg3vm7kD9sEQ6UW9o/veOb6/bwd4qR0ojSJshJZ8/V16awVG00VWs2T/e1d9ivS6w0RWIkEs44jx7KSnkm0s40tGxbSF50Gymd7B09mEcTp3znKvmRQ3vBVcLlMqAzLuN+NdowlULUhI6a/o+pX+a84f72pc6vv52/ErxG+Z/Ltx4T6hV10HehfeYDDEygHypgfeVFOUuXl+qb5+cqf+/PdF+eTXTOAYouZAuue7pYFvieN1np7Pa0+matw141XPyPZixZEPWjXeoMeRup8hPmnhXePVS/bC/J0lUb0fvHC30fw2uvg57SBfyqPX3DsgZ5pCdNRDPg47209a+YkHUgSSP74twjJpBVoiKIm6MZ13gXTrG3N1H3vUHn368bv+X//V/s26bPMpgMBgMBoPBYDAYDAaD4fcI9qWNwWAwGAwGg8FgMBgMBsMNxDvLo95q2ABXjjGoXtlS1KAUFcN/+1jSmn/2xYN1uwsKoy+2k/NAD2bV8waob0UDFZvhYJVWFMmC7jKgfsdNSSZCUNy8SLQp0mzzDDIosNcKX8eMpqLEffv943X75IXkUYlHmiskAqh4vU3Kqc7prSl1GbQ0Oei4rHidF6R7635O4R51AgeMwY76tl0XjbaA7CSEA0KEa81fqn/abTjMVHRfD25XrO6ejlAJHhXLS0rZ0F5iXiYLcV9z0vEhp+p1IZvDvIhxDyHWQFBovia4n22icM4tq9v987+WK0uM+6AzzX5P97rMNY51zOFGzL+FhLAt2m4L50wmOs/slcbg6UgOM3Ffa3SUrvo6iEAlHat/zl+JTlxCPvijh1+s27sDURjnC62zrEDcIVUc8qi4BSp0S/MrB822dKLuknIelP9gJekPoixFgyf9nE4nMeZhE+5Rz68ksRn3tM7qLdI6FXMnoEDX8CgR5nOzp7Vb83A/oPiXa3coSLhAo2/gOTy6mECqwVizIYkCKCuYp3wOzZcC9xBALknJHd2j/A/0G0UUxW7/+L5zzrmdQzkPtRuQbGXq22/+4t+t2/cGGtM/OFA/zuD68Q0kOikccY6OtEZbl4hFU/XdPpw3DiDF+uQPP3XOOdc7ltzJg2PJyV/+at1uQO55+JkkAamvOZhDuuogifn+z79et3up7n0AF755qfPUIePLQPOO4PhSr/ZIf6suGaVz1XqJsSYePHywbr94LkkB53CI+2Q3kEI+gmT7EGu6VaPrGeetzt+D9KiNddRuVs5zAfY57NE+HNv6A8XHAT5fzDUvX4x0/efnWLv7mkPDS+2dHcwtr6v2JVwyX18pTxjOdZ8vXkrCsU2UXulSfzUIJeQL3kab1oSQf2Pf9n0cA2UCZYO5o9sX3GaQtzHv2JA5Ic9LKweYDE4w3HsKzCPmojSDoqSWEgU6EdLZaI74kkGK5wWUVtFKEcdgvmeVdLPcopa/LCVxpfQ3wX42h4vL2ZnckgLcxxLtYUf7fYQ11+7p/C3sYxEkVxlkfctz5ShnmC/B+UrOQWlgdq45ng8lSa85OCDBvapowV0MY75cMHfV5y+QLz8/kZw85ZxGrj2D29Hrl0/X7Vbtw+yLZVm4RSWF6vXvrj/34Cp5PlSb+yXfHdo99IuvvlhAThzRzRL7fK2Dd0rEgAyuXRvvQZWcZQH54BLOx3PIo3J8HjbgsLpATjvVuH9/rs+/h1xukUGeiHTIZ8kFyCJD5ADNtnLadiVBD4LtyqPeXhSA/Y14g2OXkCcNIStqXWocHj16vG73+8qFKHlsIuY29yWbWozgeodnvpbTs2QF3/kj7K0e5r7X1PE53jXHiDV0+jofaj1dQdo3hSRqOtJeeIiyLLcPkf+cK37l498tVTSmjcFgMBgMBoPBYDAYDAbDDYR9aWMwGAwGg8FgMBgMBoPBcAPhvQut0fO8U+fckw93O4YfwP2yLLdiQ2Rj+P8rbBx//2Fj+I8DNo6//7Ax/McBG8fff9gY/uOAjePvP2wM/3HgreP4Tl/aGAwGg8FgMBgMBoPBYDAY/r+ByaMMBoPBYDAYDAaDwWAwGG4g7Esbg8FgMBgMBoPBYDAYDIYbCPvSxmAwGAwGg8FgMBgMBoPhBsK+tDEYDAaDwWAwGAwGg8FguIGwL20MBoPBYDAYDAaDwWAwGG4g7Esbg8FgMBgMBoPBYDAYDIYbCPvSxmAwGAwGg8FgMBgMBoPhBsK+tDEYDAaDwWAwGAwGg8FguIGwL20MBoPBYDAYDAaDwWAwGG4g/m9bHvTSAkQUFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper display function\n",
    "def imshow(img):\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "\n",
    "\n",
    "# obtain one batch of training images\n",
    "dataiter = iter(celeba_train_loader)\n",
    "images, _ = dataiter.next() # _ for no labels\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(20, 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",
    "    imshow(images[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Pre-process your image data and scale it to a pixel range of -1 to 1\n",
    "\n",
    "You need to do a bit of pre-processing; you know that the output of a `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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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",
    "    min, max = sorted(feature_range)\n",
    "    # scale to feature_range and return scaled x\n",
    "    \n",
    "    x = x * (max - min) + min\n",
    "    \n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.9765)\n",
      "Max:  tensor(0.8588)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# check scaled range\n",
    "# should be close to -1 to 1\n",
    "img = images[0]\n",
    "scaled_img = scale(img)\n",
    "\n",
    "print('Min: ', scaled_img.min())\n",
    "print('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.\n",
    "\n",
    "## Discriminator\n",
    "\n",
    "Your first task will be to define the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. To deal with this complex data, it's suggested you use a deep network with **normalization**. You are also allowed to create any helper functions that may be useful.\n",
    "\n",
    "#### Exercise: Complete the Discriminator class\n",
    "* The inputs to the discriminator are 32x32x3 tensor images\n",
    "* The output should be a single value that will indicate whether a given image is real or fake\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv( in_chnls, out_chnls, kernel_size, stride = 2, padding = 1, batch_norm = True ):\n",
    "    \"\"\"\n",
    "    Helper method to quickly create convolutional layer with batch normalization\n",
    "    :param in_chnls : input channels\n",
    "    :param out_chnls : output channels\n",
    "    :param kernel_size : kernel size\n",
    "    :param stride : stride for the convolution\n",
    "    :param padding : padding for the convolution\n",
    "    :param batch_norm : batch normalization flag\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_chnls, out_chnls, kernel_size, stride, padding, bias=False)\n",
    "    \n",
    "    layers.append(conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_chnls))\n",
    "    \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Discriminator Module\n",
    "        :param conv_dim: The depth of the first convolutional layer\n",
    "        \"\"\"\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False)\n",
    "        self.conv2 = conv(conv_dim, conv_dim * 2, 4)\n",
    "        self.conv3 = conv(conv_dim * 2, conv_dim * 4, 4)\n",
    "        self.fc = nn.Linear(4 * 4 * conv_dim * 4, 1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: Discriminator logits; the output of the neural network\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        x = F.leaky_relu(self.conv1(x), negative_slope=0.2)\n",
    "        x = F.leaky_relu(self.conv2(x), negative_slope=0.2)\n",
    "        x = F.leaky_relu(self.conv3(x), negative_slope=0.2)\n",
    "        \n",
    "        x = x.view(-1, 4 * 4 * self.conv_dim * 4)\n",
    "        x = self.fc(x)\n",
    "        \n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
    "\n",
    "#### Exercise: Complete the Generator class\n",
    "* The inputs to the generator are vectors of some length `z_size`\n",
    "* The output should be a image of shape `32x32x3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deconv( in_chnls, out_chnls, kernel_size, stride = 2, padding = 1, batch_norm = True ):\n",
    "    \"\"\"\n",
    "    Helper method to quickly create Transposed convolutional layer with batch normalization\n",
    "    :param in_chnls : input channels\n",
    "    :param out_chnls : output channels\n",
    "    :param kernel_size : kernel size\n",
    "    :param stride : stride for the convolution\n",
    "    :param padding : padding for the convolution\n",
    "    :param batch_norm : batch normalization flag\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    trans_conv_layer = nn.ConvTranspose2d(in_chnls, out_chnls, kernel_size, stride, padding, bias=False)\n",
    "    \n",
    "    layers.append(trans_conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_chnls))\n",
    "    \n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self, z_size, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Generator Module\n",
    "        :param z_size: The length of the input latent vector, z\n",
    "        :param conv_dim: The depth of the inputs to the *last* transpose convolutional layer\n",
    "        \"\"\"\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        self.fc = nn.Linear(z_size, 4 * 4 * conv_dim * 4)\n",
    "        self.deconv1 = deconv(conv_dim * 4, conv_dim * 2 , 4)\n",
    "        self.deconv2 = deconv(conv_dim * 2, conv_dim , 4)\n",
    "        self.deconv3 = deconv(conv_dim , 3 , 4, batch_norm=False)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: A 32x32x3 Tensor image as output\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        x = self.fc(x)\n",
    "        x = x.view(-1, self.conv_dim * 4 ,4 , 4)\n",
    "        \n",
    "        x = F.leaky_relu(self.deconv1(x) , 0.2)\n",
    "        x = F.leaky_relu(self.deconv2(x) , 0.2)\n",
    "        x = torch.tanh(self.deconv3(x))\n",
    "        \n",
    "        return x\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the weights of your networks\n",
    "\n",
    "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
    "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
    "\n",
    "So, your next task will be to define a weight initialization function that does just this!\n",
    "\n",
    "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
    "\n",
    "#### Exercise: Complete the weight initialization function\n",
    "\n",
    "* This should initialize only **convolutional** and **linear** layers\n",
    "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
    "* The bias terms, if they exist, may be left alone or set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"\n",
    "    Applies initial weights to certain layers in a model .\n",
    "    The weights are taken from a normal distribution \n",
    "    with mean = 0, std dev = 0.02.\n",
    "    :param m: A module or layer in a network    \n",
    "    \"\"\"\n",
    "    # classname will be something like:\n",
    "    # `Conv`, `BatchNorm2d`, `Linear`, etc.\n",
    "    classname = m.__class__.__name__\n",
    "    \n",
    "    if \"Conv2d\" in classname or \"Linear\" in classname:\n",
    "        nn.init.normal_(m.weight.data, mean = 0.0 , std=0.02)\n",
    "        if m.bias is not None:\n",
    "            nn.init.constant_(m.bias.data, 0)\n",
    "#     elif \"BatchNorm2d\" in classname:\n",
    "#         nn.init.constant_(m.weight.data, 1)\n",
    "#         nn.init.constant_(m.bias.data, 0)\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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "def build_network(d_conv_dim, g_conv_dim, z_size):\n",
    "    # define discriminator and generator\n",
    "    D = Discriminator(d_conv_dim)\n",
    "    G = Generator(z_size=z_size, conv_dim=g_conv_dim)\n",
    "\n",
    "    # initialize model weights\n",
    "    D.apply(weights_init_normal)\n",
    "    G.apply(weights_init_normal)\n",
    "\n",
    "    if torch.cuda.is_available():\n",
    "        device = torch.device(\"cuda:0\")\n",
    "        D.to(device)\n",
    "        G.to(device)\n",
    "        print('Models moved to GPU.')\n",
    "    print(D)\n",
    "    print()\n",
    "    print(G)\n",
    "    \n",
    "    return D, G\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models moved to GPU.\n",
      "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",
      "  (fc): Linear(in_features=2048, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc): 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",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 32\n",
    "g_conv_dim = 32\n",
    "z_size = 100\n",
    "\n",
    "D, G = build_network(d_conv_dim, g_conv_dim, z_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training on GPU\n",
    "\n",
    "Check if you can train on GPU. Here, we'll set this as a boolean variable `train_on_gpu`. Later, you'll be responsible for making sure that \n",
    ">* Models,\n",
    "* Model inputs, and\n",
    "* Loss function arguments\n",
    "\n",
    "Are moved to GPU, where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on GPU!\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import torch\n",
    "\n",
    "# Check for a GPU\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "if not train_on_gpu:\n",
    "    print('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Training on GPU!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Discriminator and Generator Losses\n",
    "\n",
    "Now we need to calculate the losses for both types of adversarial networks.\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",
    "\n",
    "### Generator Loss\n",
    "\n",
    "The generator loss will look similar only with flipped labels. The generator's goal is to get the discriminator to *think* its generated images are *real*.\n",
    "\n",
    "#### Exercise: Complete real and fake loss functions\n",
    "\n",
    "**You may choose to use either cross entropy or a least squares error loss to complete the following `real_loss` and `fake_loss` functions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being real.\n",
    "       param, D_out: discriminator logits\n",
    "       return: real loss'''\n",
    "    labels = torch.ones(D_out.size(0))\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze() , labels)\n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being fake.\n",
    "       param, D_out: discriminator logits\n",
    "       return: fake loss'''\n",
    "    labels = torch.zeros(D_out.size(0))\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze() , labels)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "#### Exercise: Define optimizers for your Discriminator (D) and Generator (G)\n",
    "\n",
    "Define optimizers for your models with appropriate hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "lr = 0.0002\n",
    "beta1 = 0.5\n",
    "beta2 = 0.999\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\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. You'll use your functions `real_loss` and `fake_loss` to help you calculate the discriminator losses.\n",
    "\n",
    "* You should train the discriminator by alternating on real and fake images\n",
    "* Then the generator, which tries to trick the discriminator and should have an opposing loss function\n",
    "\n",
    "\n",
    "#### Saving Samples\n",
    "\n",
    "You've been given some code to print out some loss statistics and save some generated \"fake\" samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Complete the training function\n",
    "\n",
    "Keep in mind that, if you've moved your models to GPU, you'll also have to move any model inputs to GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(D, G, n_epochs, print_every=50):\n",
    "    '''Trains adversarial networks for some number of epochs\n",
    "       param, D: the discriminator network\n",
    "       param, G: the generator network\n",
    "       param, n_epochs: number of epochs to train for\n",
    "       param, print_every: when to print and record the models' losses\n",
    "       return: D and G losses'''\n",
    "    \n",
    "    # move models to GPU\n",
    "    if train_on_gpu:\n",
    "        D.cuda()\n",
    "        G.cuda()\n",
    "\n",
    "    # keep track of loss and generated, \"fake\" samples\n",
    "    samples = []\n",
    "    losses = []\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",
    "    # move z to GPU if available\n",
    "    if train_on_gpu:\n",
    "        fixed_z = fixed_z.cuda()\n",
    "\n",
    "    # epoch training loop\n",
    "    for epoch in range(n_epochs):\n",
    "\n",
    "        # batch training loop\n",
    "        for batch_i, (real_images, _) in enumerate(celeba_train_loader):\n",
    "\n",
    "            batch_size = real_images.size(0)\n",
    "            real_images = scale(real_images)\n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            d_optimizer.zero_grad()\n",
    "            \n",
    "            d_out = D(real_images)\n",
    "            d_real_loss = real_loss(d_out)\n",
    "            \n",
    "            z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "            \n",
    "            g_out = G(z)\n",
    "            d_fake_out = D(g_out)\n",
    "            \n",
    "            d_fake_loss = fake_loss(d_fake_out)\n",
    "            \n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            \n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # 2. Train the generator with an adversarial loss\n",
    "            \n",
    "            g_optimizer.zero_grad()\n",
    "            \n",
    "            z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "            \n",
    "            g_out = G(z)\n",
    "            d_fake_out = D(g_out)\n",
    "            \n",
    "            g_loss = real_loss(d_fake_out)\n",
    "            \n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "            \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, n_epochs, d_loss.item(), g_loss.item()))\n",
    "\n",
    "\n",
    "        ## AFTER EACH EPOCH##    \n",
    "        # this code assumes your generator is named G, feel free to change the name\n",
    "        # generate and save sample, fake images\n",
    "        G.eval() # for generating samples\n",
    "        samples_z = G(fixed_z)\n",
    "        samples.append(samples_z)\n",
    "        G.train() # back to training mode\n",
    "\n",
    "    # Save training generator samples\n",
    "    with open('train_samples.pkl', 'wb') as f:\n",
    "        pkl.dump(samples, f)\n",
    "    \n",
    "    # finally return losses\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your number of training epochs and train your GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch [    3/  100] | d_loss: 0.4432 | g_loss: 2.3757\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    3/  100] | d_loss: 0.6075 | g_loss: 1.5393\n",
      "Epoch [    3/  100] | d_loss: 1.0891 | g_loss: 0.7653\n",
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     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch [  100/  100] | d_loss: 0.0349 | g_loss: 6.0392\n",
      "Epoch [  100/  100] | d_loss: 0.0008 | g_loss: 10.7171\n",
      "Epoch [  100/  100] | d_loss: 0.0051 | g_loss: 9.3664\n",
      "Epoch [  100/  100] | d_loss: 0.0596 | g_loss: 9.0301\n",
      "Epoch [  100/  100] | d_loss: 0.0393 | g_loss: 9.9458\n",
      "Epoch [  100/  100] | d_loss: 0.0123 | g_loss: 10.8526\n",
      "Epoch [  100/  100] | d_loss: 0.0678 | g_loss: 8.8420\n",
      "Epoch [  100/  100] | d_loss: 0.0237 | g_loss: 7.9122\n",
      "Epoch [  100/  100] | d_loss: 0.0080 | g_loss: 10.1088\n",
      "Epoch [  100/  100] | d_loss: 0.0286 | g_loss: 6.4996\n",
      "Epoch [  100/  100] | d_loss: 0.1013 | g_loss: 7.2605\n",
      "Epoch [  100/  100] | d_loss: 0.0390 | g_loss: 7.5703\n",
      "Epoch [  100/  100] | d_loss: 0.0030 | g_loss: 8.0342\n",
      "Epoch [  100/  100] | d_loss: 0.0211 | g_loss: 8.8177\n",
      "Epoch [  100/  100] | d_loss: 0.0053 | g_loss: 6.3496\n",
      "Epoch [  100/  100] | d_loss: 0.1257 | g_loss: 5.4610\n",
      "Epoch [  100/  100] | d_loss: 0.0669 | g_loss: 6.9028\n",
      "Epoch [  100/  100] | d_loss: 0.0020 | g_loss: 8.9699\n",
      "Epoch [  100/  100] | d_loss: 0.0039 | g_loss: 11.7622\n",
      "Epoch [  100/  100] | d_loss: 0.0537 | g_loss: 9.6376\n",
      "Epoch [  100/  100] | d_loss: 0.0021 | g_loss: 8.1018\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 100\n",
    "\n",
    "\n",
    "# call training function\n",
    "losses = train(D, G, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training loss\n",
    "\n",
    "Plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdc0016c910>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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",
    "View samples of images from the generator, and answer a question about the strengths and weaknesses of your trained models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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)\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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load samples from generator, taken while training\n",
    "with open('train_samples.pkl', 'rb') as f:\n",
    "    samples = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What do you notice about your generated samples and how might you improve this model?\n",
    "When you answer this question, consider the following factors:\n",
    "* The dataset is biased; it is made of \"celebrity\" faces that are mostly white\n",
    "* Model size; larger models have the opportunity to learn more features in a data feature space\n",
    "* Optimization strategy; optimizers and number of epochs affect your final result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** \n",
    "\n",
    "\n",
    "The generated images have eyes , lips and other facial parts which is good but although it doesn't look good enough.\n",
    "To improve this model we can try below things :\n",
    "1. Increase Z_size to improve Generator loss\n",
    "2. can use high resolution images so can use deeper network too.\n",
    "3. can add more unbiased images like less white faces and increase number of brown and black celeb faces\n",
    "4. can try changing the hyper parameters of adam optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"problem_unittests.py\" files in your submission."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}