examples/example_compare/example_train_editor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5898\n",
      "6664\n"
     ]
    }
   ],
   "source": [
    "folder = 'images'\n",
    "path = os.path.join(folder, '{}.pkl'.format('pos'))\n",
    "with open(path, 'rb') as f:\n",
    "    pos_data = pickle.load(f)\n",
    "\n",
    "path = os.path.join(folder, '{}.pkl'.format('neg'))\n",
    "with open(path, 'rb') as f:\n",
    "    neg_data = pickle.load(f)\n",
    "\n",
    "print(len(pos_data))\n",
    "print(len(neg_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8\n"
     ]
    }
   ],
   "source": [
    "from matplotlib.colors import rgb_to_hsv\n",
    "\n",
    "def rgb_2_hsv(data):\n",
    "    new_data = []\n",
    "    for pdata, cdata, pflow, cflow in data:\n",
    "        pdata = cv2.cvtColor(pdata, cv2.COLOR_RGB2HSV)\n",
    "        cdata = cv2.cvtColor(cdata, cv2.COLOR_RGB2HSV)\n",
    "#         pdata = rgb_to_hsv(pdata)\n",
    "#         cdata = rgb_to_hsv(cdata)\n",
    "        new_data.append([pdata, cdata, pflow, cflow])\n",
    "    return new_data\n",
    "\n",
    "hsv_pos_data = rgb_2_hsv(pos_data)\n",
    "hsv_neg_data = rgb_2_hsv(neg_data)\n",
    "\n",
    "print(hsv_pos_data[0][0].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import hsv_to_rgb\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, random_split\n",
    "from torchvision import transforms, utils\n",
    "from skimage import io, transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CutEditorDataset(Dataset):\n",
    "    \"\"\"Cut editor dataset.\"\"\"\n",
    "\n",
    "    def __init__(self, pos_data, neg_data, transform=None):\n",
    "        pos_data_nor = [((pdata.astype(np.float32), cdata.astype(np.float32), pflow.astype(np.float32), cflow.astype(np.float32)), 1.0) for\n",
    "                        (pdata, cdata, pflow, cflow) in pos_data]\n",
    "        pos_data_rev = [((cdata.astype(np.float32), pdata.astype(np.float32), -cflow.astype(np.float32), -pflow.astype(np.float32)), 1.0) for\n",
    "                        (pdata, cdata, pflow, cflow) in pos_data]\n",
    "        neg_data_nor = [((pdata.astype(np.float32), cdata.astype(np.float32), pflow.astype(np.float32), cflow.astype(np.float32)), 0.0) for\n",
    "                        (pdata, cdata, pflow, cflow) in neg_data]\n",
    "        neg_data_rev = [((cdata.astype(np.float32), pdata.astype(np.float32), -cflow.astype(np.float32), -pflow.astype(np.float32)), 0.0) for\n",
    "                        (pdata, cdata, pflow, cflow) in neg_data]\n",
    "        self.data = pos_data_nor + neg_data_nor + pos_data_rev + neg_data_rev\n",
    "#         self.data = pos_data_nor + neg_data_nor\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        if torch.is_tensor(idx):\n",
    "            idx = idx.tolist()\n",
    "        pdata, cdata, pflow, cflow = self.data[idx][0]\n",
    "        mark = self.data[idx][1]\n",
    "#         sample = {\n",
    "#             'pdata': pdata.astype(np.float32),\n",
    "#             'cdata': cdata.astype(np.float32),\n",
    "#             'pflow': pflow.astype(np.float32),\n",
    "#             'cflow': cflow.astype(np.float32),\n",
    "#             'mark': mark\n",
    "#         }\n",
    "        if self.transform:\n",
    "            pdata, cdata, pflow, cflow, mark = self.transform((pdata, cdata, pflow, cflow, mark))\n",
    "        return pdata, cdata, pflow, cflow, mark\n",
    "\n",
    "\n",
    "class RandomCrop(object):\n",
    "\n",
    "    \"\"\"Crop randomly the image in a sample.\n",
    "\n",
    "    Args:\n",
    "        output_size (tuple or int): Desired output size. If int, square crop\n",
    "            is made.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, output_size):\n",
    "        assert isinstance(output_size, (int, tuple))\n",
    "        if isinstance(output_size, int):\n",
    "            self.output_size = (output_size, output_size)\n",
    "        else:\n",
    "            assert len(output_size) == 2\n",
    "            self.output_size = output_size\n",
    "\n",
    "    def __call__(self, data):\n",
    "        pdata, cdata, pflow, cflow, mark = data\n",
    "#         pdata = sample['pdata']\n",
    "#         cdata = sample['cdata']\n",
    "#         pflow = sample['pflow']\n",
    "#         cflow = sample['cflow']\n",
    "#         mark = sample['mark']\n",
    "\n",
    "        h, w = pdata.shape[:2]\n",
    "        new_h, new_w = self.output_size\n",
    "\n",
    "        top = np.random.randint(0, h - new_h)\n",
    "        left = np.random.randint(0, w - new_w)\n",
    "\n",
    "        pdata = pdata[top: top + new_h, left: left + new_w]\n",
    "        cdata = cdata[top: top + new_h, left: left + new_w]\n",
    "        pflow = pflow[top: top + new_h, left: left + new_w]\n",
    "        cflow = cflow[top: top + new_h, left: left + new_w]\n",
    "\n",
    "#         sample = {\n",
    "#             'pdata': pdata,\n",
    "#             'cdata': cdata,\n",
    "#             'pflow': pflow,\n",
    "#             'cflow': cflow,\n",
    "#             'mark': mark\n",
    "#         }\n",
    "\n",
    "        return pdata, cdata, pflow, cflow, mark\n",
    "\n",
    "\n",
    "class ToTensor(object):\n",
    "    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n",
    "\n",
    "    def __call__(self, data):\n",
    "        pdata, cdata, pflow, cflow, mark = data\n",
    "#         pdata = sample['pdata']\n",
    "#         cdata = sample['cdata']\n",
    "#         pflow = sample['pflow']\n",
    "#         cflow = sample['cflow']\n",
    "#         mark = sample['mark']\n",
    "\n",
    "        # swap color axis because\n",
    "        # numpy image: H x W x C\n",
    "        # torch image: C X H X W\n",
    "        pdata = pdata.transpose((2, 0, 1))\n",
    "        cdata = cdata.transpose((2, 0, 1))\n",
    "        pflow = pflow.transpose((2, 0, 1))\n",
    "        cflow = cflow.transpose((2, 0, 1))\n",
    "\n",
    "#         sample = {\n",
    "#             'pdata': torch.from_numpy(pdata),\n",
    "#             'cdata': torch.from_numpy(cdata),\n",
    "#             'pflow': torch.from_numpy(pflow),\n",
    "#             'cflow': torch.from_numpy(cflow),\n",
    "#             'mark': mark\n",
    "#         }\n",
    "\n",
    "        return pdata, cdata, pflow, cflow, mark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25124\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WzjyYxh6P8oVN1rjc5JeD29jA+PK+t/HZDPt06FmCG5Ba133mz/4DxMtr6EM5OYn5NSQtcUr9tnBgd2+8gvMTzNG8GicfuB+P1dMEcz/DVkPP3BYhbTFyMi3IyN0yTx+WT2ig8hTdtd+3Eq/t5jr26d73wEMQf/bzX4D4Uz/+VyB+7plnIf7OK6/sfT7zKtajQZS1hRBCCCGEEEIMhV4ohRBCCCGEEEIMhV4ohRBCCCGEEEIMhTSUh0SlgrqJ3Rb6A5UreKlnZmYhZv1SmV71K2hl5ULHOg70rmLtwuz0tD3owf3zeHgetJ2yBx0dj0dXwR507MfH4/G5fNYuZAPCwIj3xZ6XRkJSTNdhtALeAfC3VjNpNCyp51rT9uUuaiKr1TrEWf86xM8/hhqlH3lif3z+seNYr8Mq3oz33X8S4l//vc9CvBujOdXF8+cgnj1xF8SpacHy/UztveN6nl9vue6EtD3rmYwmsiCjbn+o+/bITdkTlNuQfox62fFx1JZdvnptf90+tmdC5DGYXzd3tmFZn7z7SPLoymVMru0Ozl9QrbAHNNbjmHwtuc1Zml+A+PpV1MVZySRpr4rOX2CgfOCZM4D3VvJoLg3hAZ/djTyjqWxuY45agunbQcH2N+QJC2KMS5RPLrz5OsQr169CnFDdKpVRQ8ltckRzFsTpQD5NsKwXv/xFiOdPHMd9NbEfYH594ntLuZP9rhPj0Uzbcy494nuf3uJ+GnRqfY90wbkTWDu7vb4B8fl33oZ4fQ01liuruP7SIuprn3z//vwGX/jTPznwOPQLpRBCCCGEEEKIodALpRBCCCGEEEKIodALpRBCCCGEEEKIoZCG8ohIyG8o7qImqN1GnUaj2oJ4YQG1aEuLqMO4fPkaxKypZP3S9AyOiWZ9VMbirwgHsCcen0szHN3orfI1kqzzs9oG1gkOePqQcVaU+XRwo42dNzoJwuf3ZzhkHYeRuNDxljOsi0mEusVaDfXAS9Oo2/iB96Nf1b0n9+vm1CJrdVG38cxjDYjPnL0C8R99CT2OLp6/APHDH0Cfyuoklmdqqbm2WFdY55GSNxbrQo5cH3uImklTD31lH7JeMyA9T5ZgPbvvXtTifuc7L+99brfRG1CIPOoD3rmbu6gJH59Ej+h2B+c3CCnXVWuomWyMoZYszkhDSRpNTg/HjmPufuk7r0DMuZo1lJx7ffMXGHx+ggW9e83y6PDEbnwq7Edrc+vt05g7d4P5GLgNpWtTprkuzr6NHqlJwnpcLK9SQr0va/hD7vsM+FT2+1hPWzvohf72669B/NCTT0DMN4f3lbKBKfcZud5SnPiqccB9UmOwnV+Ap7wjn68gG/x4uH7TCeXWagn7cK+++jLEx+h94otf/hLETz31IYgnJvffH9j7dBD9QimEEEIIIYQQYij0QimEEEIIIYQQYij0QimEEEIIIYQQYiikoTwi6qRD65OX1GYHdR7N8SmIjaSRxs7HMY6HZ5IMx1SzhpIHtGcOx/anZPDHPjesuYyMrjB/QDz7UaVsNElYD6T9//g0JMYTk/bl87wsDmkNSCzgk6Pa4oppLI3XF51QhQ4gDfBeBzR+f2lqHOJ7TsxBPDW3Px6f9UZJivW0maB2+OMf/SDEX/g2+nJd20Sdx6Vz6Et5/wcec4jHyMyIdFjI4XIx2gcf/FwUvfceXQfrPnKfBVMv2EOT41H973B/Vy+jx9o95Hv2Z5/7zN7nbg/bRyFyGWjT2i1sY1rkK9npoj53cxfnL4hjrPfjE6gh79L8B2mc3/6eOHYMlzuGnzvKVx4Npa+N880ZwHMC+PKpXUz7GyzPl9cLGk3a+Qt88xmM2IZ52kyzmDaPaP0O6RY3V3AujFKV+nldzJ+srcvI2zcLUd82eH0rpHHskZ7zrddQ2/u+x98PsaNjSwLW/fmMij251yOPTbkT6Mlf/jkD8mNzPoYR5lfwbGrmOPFswD7yXXo/eIPu7d/8278M8f/3mc9AfP4CzuFycsD/OypJQymEEEIIIYQQ4pDRC6UQQgghhBBCiKHQC6UQQgghhBBCiKGQhvKIKJFXS6mM48/75F21tb0N8dI8+/khvT5qjFLSTPZT0lDOoYbSjA9nqQENky7s0cNfVXj8AAN38Nj/G5e/f34Z6ypMmL/cp5k0mkQPvPqoMo5RYd1fk576boT/6PdQY3R8Bn3cmpOok6yN7y+vVFE73Itx3aiG+5qZRD3Shx66G+L/5ytvQHzl6mWI73/8cYhZY+O79IG5+aRJpAI8Ekv7XLHOw6Oz4OXWr4p3ePD2fGqsvRrVd8voPEjfkySo0amShufNN1AvO9bcr0fsPydEHmPNsb3P5XX0mUy62MawLq1MdW27i163gx6Xzjm3Q7m6H2OrwPlifn4eYu9TR8+R1QHS/AW+8rzQs8YaTKPJ9Ggss5xnl5cV9A40xfnmDxhF53YjjF41X4Nfpv1tr61AXK9gvgyprhrSfO1cwJ7TA/3QtEze6ORduLm+BvFbb6Av5QOPkaaSNZCea2vmquDl3rki8hcXnZ/Al4v9jLA952JPJ9HXrzCHQvXk6mX08x6nuS5abWzzmmNjECfpvlY3T1uqrC2EEEIIIYQQYij0QimEEEIIIYQQYij0QimEEEIIIYQQYiikoTwipsi7b6eFY5SThDSUG+u0PvrAjI2hji3t5Y+5ThMc5zwzRT6UniHZrEVjLyzWZ5mx/OaAPP6ARMgaywDjcFCLQZciIE2MGfLNohPPsfh1GlxcsfIOm5S0DawDrPLxBLhCTPe6VMUtyhWsi+UBfXDqUCv8uRe+BHGLnoPFhUmIH78PPdv++Kuos2vTWH/j0UZ6UNYj8b3ma+XTYbDm0udL6ZN1eLURRckODAyFPdrMI8xesrhCQtoyrnhXrqCuY27Az7RUUmoSN0+jsa8J2iLv2hnyYO720Ltvdwc1l03STNbq2KZ1SGMep6gV5udkdh59e31SMU6VPg9o9qiLjC0l+/9xfsz3ZbaaytzFtDBn2SHgy7WmH1PYazAfvvYs/a7xzexj3SlRvkq6WJciKrBKuThkD1DH+Wm/7nCeJ8tKl/XwWF/5xjchvu/Bh3Ff5Evpsb92YYr/SCgO2dcSNzf3zjefAeO91z5fS7O+b4cY5pXn94DOz7UZP7R87Umre/Ei5t7FxUWIz5Hf90c+8pH9snLmN9AvlEIIIYQQQgghhkIvlEIIIYQQQgghhkIvlEIIIYQQQgghhkJClSOiXMYxy9dWcEwyCxWCAMfOt9voMzk5hlqzlEaQp6TWYmvGehN9Zyo11In0Y9KBEEZD6fG9sTrF3OK9X21YbUA2sCzLXdeU5fNDyj+UwhgfL9aeHbJRZcB1geKs18o9npB0HZ0uHV+GdTsduHmtNpY9OYV+Rg8/8iDE9RrqMFL20SLtbIfK73ZRk1IvoXaZ/UxTPleHeH0mfZXHbJCvyzB17RDltYV9Jwt6XfklJnTu5Hu2en0Z4nsf/cDeZ/bxFSKPwTaMPZk75Pm8toZ+e7PTmFsXSPNYrWAb1e2gV6DxviUP6KWlpYMO2zl3A41+yH6ulOv5weNGxJNOiv6K4MunDDz3Rkjnm0vhiEWXvLdDzs3c5rLnc5hiv441/s0yaiRbNNdGSG1qmZrJiOr+YNWJE9xXt499Ppp2w21ubUJ8/epViBfuOQmx984Zv3A27D7auSUMHj2t6TuwbnGEZO3zljX4/FqpONPPCLAiXr18BWJ+v7hE9/rylf31+33UoA+iXyiFEEIIIYQQQgyFXiiFEEIIIYQQQgyFhrweEfU6DjENQ7zU5UoD4nUahnPXyXsgXtvYpu1x2GGc4c/QaYy/gddp2M701BTE15Zx+FlKww9itgDgn+zpqwkeLsDDggIz5Be3N8MjCgyF4aEIYckzBJan3r61o27MMJvCU5nz9nQzQpouPE1wqAvf2s0tHEa6s4518/f/GIed/tzPfGrv88zSNCxbpOFjv/P7L0D8I8/cD/HCNNbLk5M4hHWDhlskNLzMNwyF62XC076b5YTn3vAILx9F73XhYau5y2hIqhkD5Nk3D6tJ+Znma4nLT799BuLnf/TH9j5z+ybEzVKr4bBBHsrX7+FT3acGMKZ6XSphexrTMER+5PtkS7KwsAAxSxJcRjYePHzNN+xyRM2GL995l+fs0FqW3ORBHREj51oPdCtdie51vYRSo4mJCYhXttDCpkJDFZt9HG79n/zIk7h+Bc/nm69e2vv80vmLsMzcG+qTcb27dP5tiGeP41DuUhmPNU+mdFOYZOrZnusW2+GY880vzm/lUZDB8kbM+7be0nLjOoIne/4C1oVHP/AhiL/57W/h5jd3mLf78RZCCCGEEEII8V5FL5RCCCGEEEIIIYZCL5RCCCGEEEIIIYZCGsojol5FHUc5xPmdS6Tru3AZp+l9Xxd1aleuXIK430cdR0K6DZ5qPKDB/TPTMxBfu34dYt+Uyo6moGbrDq+wwzM+nmdJNrqTwfJYn8lTk3t0cnxoZqz9EWN1HbxGsfH2IZ8vXes+TaPfjrH8lY0tiD/82CmIf/D5ZyEelM5lMZZ9fXkF4ieffBjiU8dRM7nToX2/D6cmf+vyOsQRa2091c475T6RFpRx+KYeN5pMPn6fb4mHPI2l1UC63NhoSIx1kEeDyfunR3hlYwP/YXTTQhSHNZRsl8C5Z7eFmvGA6vX8LOrA22RdlKWoSedp9edmZyEOI8zFCe2PDXMK6/w4t/o296yeFRRlFrUZgW1v8/wFPHfEqHA+bDZx7gzOL702rv/ACax7P/c8at0++uyjuD9qQ3/sY/s2JX/wZ1+CZX/0ua9DfGUN9ZkzdZrng6wkQr5Wvtzr850yThq+ZOtJzl7dIWL6fZ5858NqMLOBz2nuuuZamjaMlvO+PH3acxfQxvDZ538Y4rUt6meVBl4Vcx5S/UIphBBCCCGEEGIo9EIphBBCCCGEEGIo9EIphBBCCCGEEGIopKE8Ispl9H3kMdDjNJbeePaQDpC9sJIYdRsplc8aypC+OpidQe2aETgZ7RmP+c735Al5h1wg+1aaAjzj3Qe2YH2oT8LB19poKm8zfDhZQT8m1rzwrUgS1Pgsk2ay1UUtxdYOxp/94jcg/tSP7msqZ1P0D3z4wbshrpL/apR1IW5fRx+u+0ljudvCet3dRc1JtTGaKVvK4t2C+PZuNJX03LJXl8/70R7AwHJPvfZqWnwaFO+xYBhGEf8DwqvXV/c+96l9E+JmGW+iB/S15VVaA+tWr4ttSKuN7V1E9TZj7RhbNNNz0Wjg8UyS9+A6tb/81CZsFMwycE6tntTLHtDMyOkwOOCzu5Ee887KvSPDetgAc22/h/kuovNfGEP9708//TjEH6A5BZqTkxAnpMAtV/f1wT//Y8/BMu78/86fvAjxM++7D+JvvoW6u84uaonrZazXheF665tPgNcf0SbSVs18H0szh4DRlObE3m5GQb2pOXhcgXXby9fRdz4in/rFpUWIX3/tlb3PnQ5qzmE/By4RQgghhBBCCCFy0AulEEIIIYQQQoih0AulEEIIIYQQQoihkIbyiODx1hOTYxCvkQfbOOkqQhojPdbA7Rfm5iHukw9lSt5YWYAFzs6iNs03JDshjWVMY7wzNr6hAlM21+Ix3qSn8ikrBrUYrIlkiUjIPpWesm83XHesPVIxnV9Eetcwxcd+dWMby4+w/O0t1DV+5LEHIR4b8FytkHa4n+J4+3IZT6YUor6oVKpB3GxifPwu9HQ7v7sL8eQ8+naZKzWiQCgL8Du4gK8t+5qxf5QH6+HmEUzleF3ZZfleVnkeljeEnvnMefSgtPtSiPVwa2e/nqXJiIac4vuWsTHMlWfOnoWYUo0LAppvgJ7xMMJ6WquiTpznM8ioopeovIVZzN3r1P7y1/zs/xpwbJ5baoNY+0U74DbLl3/4H0azPxjTujw1QhjdXs9nhvWnVgZXzMuwXsbKVqE2r93Ge//+B49DfNci9gsrpHULqC/AywddTasV1Ao/+8QjEH/7pTMQ3zuLes719jGIL58/C/GD0+iRaaZ+8OlnC/ZrfHNhFM4gnA85YZnl+T7NebnZ59lc0HrcbMDPZEQayl4ftbw726iHnZ1ZgPjilazRxyMAACAASURBVMt7n9lndxD9QimEEEIIIYQQYij0QimEEEIIIYQQYij0QimEEEIIIYQQYiikobxF1KqoBet3cAxzRl5TbfL4aW2jji2kMdK9Ho6PT5J8H7eZmRkqj6Dx36zTMFoC0ilyXAp5vDt7R7IOI99Ma3B11moFvu9JRtTR+fyIfOvnncuNyvOV74P1uKxjWV1Bn7YqiYye/eDDEH/s2Ycgnprd1zVmMY6vj8nTLaiM09Hl63HmF1Fv9OevvATxTh3v9WJ4T07pfrz2TgxLHI3k0Rix4f4Klu/TaWCMy/i+xwU0H8NgvHVJc8J6pd3NfT8+9ucU4mapVFD7VaLcW+thrn3nnbchfvxR9P47d/EyxLUGeul2Y2zjWFPJuXBhaQni10+jdo2fuzTF4+Xcnvl0gR4fSt7c+FqyPovXp/0Nbm/avxF9fkelYHNbONebi0da8CrNT9DdxjkATj5yAuKxKaxrJfI0DVI2mcZwdX2g35jhwmOLqJN7/CH0uHzkFHoRfvMSzvvx9puvQXzfI6jJDElrbDhMv1PnhtAd0ubm3ub3wzgXezWUeQdYcD4D6wmNIXvXVqqorQ3Ju31tFe9ttYb1LFnf30HeoekXSiGEEEIIIYQQQ6EXSiGEEEIIIYQQQ6EXSiGEEEIIIYQQQyEN5W2Ch383aqjz6HVRYxmTLqNMfn9WM5mvQZpiH0oaGB0nGEdRnlbLmRNi7YGxqbQGgQcd6k0tHiQjsRjrMYOQdRHsU+nRRHLMYrARMT5gvALtj8fus+YlCLFubLQ3IV7bwvHzx2ZQt/HwvailmJpGHWRpQLMUVnHs/c7FKxDXmlivgxLpLBLUYFamcN8vvXkel4+t47E+gV5YiU8vy9pdj5cV6xCN6sIr0snXJhudBetpjU7jYK8sfuaMX11hzSTrpmlxvm2X9buj7bcGNJTyoRTDwj6RJePJhhrx3V2cn2BtHduU5ZWrEO/sbkHc62GbxnWXZYNLS6hdY41kRj7BrJfqp9wm0HNMzz07E5o2hTX9vLqnDTVa6YH17ewBxYRzAfv+5tvwOm51fL6TjGnSCurI+V4ldPz9GH2ZU8ow7BFaYu0b5cse6SK//MJXIY4HNv/wBx7Fssmb/IF7UNu7eBI1leevfh7i7Q5qhy+cQS3yPY/g3As+WA6aZh79KndxvbL7gvmPQ869PoyB+MEe0T58e+Z5RBKqF2mGbV6pSh7Qm9gHrNWbEPe7+/0ybm8G0S+UQgghhBBCCCGGQi+UQgghhBBCCCGGQi+UQgghhBBCCCGGQhrKW0S1ht5YPF671ULfyT7pMGp11FjOkw5jZX0NYtZcsjfjzPQ0Lmehh8cLsai2IGRxGY+X5+HmvL3xbhzVxOjmuXV7ujGscfEZLvEY94iu9bU1rCtd0t/GdHMeft/9EI+No4fpyva+hujaVdRM7rbQZ2s6Qe1uP8B9J3Sqr5y5BHF5fAJi1jutXcb9T55AX69RzaqMfsBTOVjbwLoKn5+U1XWw91Xe8bDHZz6+bxcTc2yeDfhcjc4adR2t9r6WjXVlQtwsrJGsV1CH1uujjq05jnqhHunc5mYwV06MTULc6aKWjHMvC68X5mddHpzrjC+xR8vFvpO+FQLS0mXkQ2x8k2mpaeMGwyNOnrYbcLQ7DM38BfmaeEftWJfmxuiT326vT/ciwy56wh6qVy9Q+VgXP/zEU3ufV6+twLK7jmM9nKjj3AnR1DGIlzexjxpQL+07L6J+c24J5z+oTWPuNxjPUlruyTcp9WF5PgKfFrhwn9fjW2k9qg/WUHqPjcj1tHT20qXUJpXKWK82N9GL/K7pByFuDNQNfgYG0S+UQgghhBBCCCGGQi+UQgghhBBCCCGGwvtCGQTBXUEQfDYIgteCIHglCIK//+7/Z4Ig+NMgCN569++0rywhhBBCjI5ysxBCiDuFm9FQxs65/yrLsm8EQTDunPt6EAR/6pz7RefcZ7Is+8dBEPyqc+5XnXP/8OgO9b0N+y8FFbz0Meki+n3y46ug/1CHPID63R7ELLNgncTsLPUx2N+JxmhzeQkdn88oJyTdhtFUMiy5zBljbsou+MO78ce7hfrMG2F8KFlfyoJTIoqwbgUJamLW17chDklztNpCncfk4jzEZRp/Pz+1P77+5BjWqwun34A4ibFe9QLclyP9zvlLyxCPT6KGcmUZl59+7VWInzrOGsoR4arl1Tw6XiG3eJ82wigLWQ9VwOsqYs9NoxnB9dmflfVCRg8aeJYTu9v7/n5JUc+v70+Um2+CiQn0zd0kH8mx5hjEFWo/I0pGx+axPdwlH8o+xQE9tYuLOP+BTxzGbUqSsMccb2/Eyvm7o+V8vmb6g3xZOOze5laXiynqVqdi9iWmxb42jPt5aYYlhDH2mzLyXd7axXh7B+NaHe99Ywx9n9/3EM538Pqr7+x9vusYahi5n1AbQ63x9Q2c/yCq4DwgWR/redJB7fG3vvQliJ/++CcgDslj0+QrikPug3KC8mgsTb6i5b55QYw81v4D1y8y34LXjxpDfgb7xt8adxbz+wAdzPomzqvxcBN15eGA731e/9jb886y7EqWZd949/O2c+4159wJ59zPOOd+493VfsM597O+soQQQggxOsrNQggh7hQK/ZQTBMEp59wTzrmvOOcWsyy74tx3E5tzjr92E0IIIcQRo9wshBDidnLTL5RBEIw55/69c+6/yLJsy7f+wHa/EgTBi0EQvLhMw9OEEEIIMTzKzUIIIW43N+VDGQRB2X03Yf2bLMt+791/XwuC4FiWZVeCIDjmnLt+o22zLPu0c+7Tzjn31FNPjWYC9x6Gx943yJeyTDq2Gi1fXd+A+Ar57QUlvJUdGs/OzLAnEN2ZhPyTygFXFRqz7fPFydFZOGf1XOzFxQxqMI3m0Cd0I11DQKtzeV7dxIjrHzYZeRXGdDyTTaxbJboAK5uosTx/9RrEj96L/lS1sX0N5av/w7+DZa9+/dsQz3zqgxCfevYRPHaHfkmr5K86PYn19hyd69Y6em1t0vZNjxeWT0fB2oMk82n9PE2eT3Pp83vNWb9wLWOdBmt3fT5g7B3JEhM6ooh2sL21/y6UJvKhvBmUm/00Guivl5An29oqerCtXsc2ZHkZLx9rLrd2UWsWky4uo3yzsIA/GLPOkDWSJfJ949xsc6sn2RpJZX5LYfMV5dsiNYfzvNc003csiC/3Fi3fo051bMnHurkkRJ1gnfzE4xgLOH0Rc23v//0MxB9//imI3//E+yF+883TEG9u7Ofy55/BdVnrW6vgc3J5fRPiRgP1mu0tnLejWsU+bIWuzeoV7LPOn7obV/BpgY2xY/5iX2w0joTtC7B3ZP7h2Afj4Lpr6pXJnaz/ZL/SfF11yDHV8w1qA+v0/lEa1NuOoqEMvvuE/Zpz7rUsy/7HgUV/6Jz7hXc//4Jz7g98ZQkhhBBidJSbhRBC3CnczC+UP+Cc+1vOue8EQfCtd//3j5xz/9g59++CIPg7zrnzzrmfP5pDFEIIIQSh3CyEEOKOwPtCmWXZC+7g32o/ccD/hRBCCHFEKDcLIYS4U7gpDaUYnSbpOHzaqOVV1HGUy+hDGZHmkrVcPfIIYmZmZiFm3xoz/Ju9sMjYJvPpAo3vpEe3EbEukpfvf/bJMILIo5ksqDbz6Tj4Wha0xfTv33c8Hk+jRhXXPz6HnkO7V7Hu/PGffQXih//uz0C8fPrc3udzn/8WLKtHWE/GKqgh4WNtt1H7G9Fy1hI71ot28dg3l/E5GicNJSv1+FbxcvZnZfjepD59rec582k6vRrLHNh3kg+e981aK9bqWhsxXM7WkiVqwza39jU7rCMTYlhSqnjlELs943Vs/3p91IYlCbUxMbYxXWpzuO5y83zs+BIeHz/z/KBk+JwYffGI6lfbZmEcOYbnTzh4/gNuM/L8pA+Dw56fwKepNO01rd+ji5mWqZ/G95rEcz/w7NMQ18kfsFpGrdsTH0Cd5AcGtHYhder6MXqZl8uo9+x2UUM5NTUJ8e4m5uKAOlYx+7FGXK8p9GgWDR6doclnoz4oRODzmSwwH4LJxZ6Yr1XKHpgBtxH5XrMbG+u4nASwUXm/Fch7xA65qyuEEEIIIYQQ4vsFvVAKIYQQQgghhBgKvVAKIYQQQgghhBgKaShvETw+3foZ4bv95gaOXy9VUUPZp7H33R6Oh0/7+Rqk6clpPJ6QtQK+8eakn2ItADPyVxc5A9R511b0Mdqei+oyjvprGvbdpNjcCRo/f+06+kwen5+D+Po66hj//AvfhPg//+W/BvHY5MTe5w//4l/Fslroq9V47DjE3R56trW30dNtronPzV+8egZirqU90m2EWd/lYvSmo+F7akxd8nrIFdxBAbz6TN41PeNG+8VXj3XXffT/cxHe253W/r1nrz0hhoXnGwgo17bIs7lPPpWTpB0rlbHb9MZZ9P6LaXtmfmEGj4ce6pS9bfk5Yo0me8zR/li36NUxsjSMY560oMD0CKy39B7KiL6Sh03RvkBCet2NFt67LvkHJnT3zl1GH+XL13BOgEcefQjiiHSK6UCb24/xWsYp5sZyBZ+TcoBa4qmJCYiv0nPA+YBzsdFMjprMeC4MSvXGBdLk3mJGljbfeeZTKHB6fs/n/GNjfaydHoF0z7S/bot86zNswxrj+x6k/K4Cx3HgEiGEEEIIIYQQIge9UAohhBBCCCGEGAq9UAohhBBCCCGEGAppKG8RPO64VMLx6rUa+vPtruPY+Sot528C4h6OeTaaRhozXW/UIW40MW7xmGoijfN1HiHvkMd0m+HsPp0H+1IOXAGPryT7QnLZvL5Pt+HToBTVph02EWvdSNJTn0TNZJKgbrFex2ZhbXML4rOXsG4++f4H9j5Xfwr1Ru7KJQjb5G21TT6RYYb1rka+XfNzixBvrC9DnAXsjcg6CEfxEd8rb13CypvQ8XNdG8VLi7fkNoSvjVe/RMtZy2WvPeu+sWKWBzSYR+tWJ76f4NyaUcUv0/wE65vYRi0toW/k9ja2h70Oas36faznXJcnxlGL1qijR3Wb50Og7Y2W+ZD99ezDV0yDObj8sH0h8/bl3O3Pvba9xly61UbNJM+FkVB8bQXz2w8/9wTEtSr6UHY7WDfb3X1hYalEmkfaF/dJ6w5z8frKVYgj0sDzle/0UdQYkWdmUVLT6S22fVE9Lns7eicwsMLHm9+Xp1/Cx2J8J41nNOll2Rud4izBi9nZ3YF4vL7fhkbSUAohhBBCCCGEOGz0QimEEEIIIYQQYij0QimEEEIIIYQQYiikobxFVGmse5DheHX2yuK4VMLx6qzDaHdQd9GnMdEsZeBh0JOT4xC3Wi2IeUx3krFPHI3xDvNFk0aHQWO6WXthPIsGjHtYA8nYfeUemvV1NOPTGT633MMxjKr7sJIXFpVivHASNUGvvvwSxAmdQKWCGp9/+Zt/CPE/+W//s73PU9NjsGxjBZuYeAt1FWvL1yGen8F6fWJpCuLJ63hsq6QxYa1yQDqPUY0cM59+iK5dwoKtJN/pkrXHsed4ue7kWbD66xlpIp2v3htjsUJkCW5QHqin0lCKw4Jz6dgE5rpGFecPKNEzzB7Pb589C3FMvpDtDvr8coKJStgmzJMP8PmLF3F7zq2kquRUa/Kdx+vW+lTy+nw0PAfBwXB7ZvohnlxbVIPpK8/rwemhaHkx6RTbHfRmJMm8u7ayAfHf/PGPQnzybpyjIKxiXco6lD8GPFHjmIwaaecl8qGcqmLuvveuYxBfuPIdiOsVzLUt0shPTmM9t/NuID5PaE6tJr1RF5XnK0h5BY9W2Pp709ae/Jq7lLf1zEdgYzoWupqm/+zpX6+tr0J8bEBHXi4f/NqoXyiFEEIIIYQQQgyFXiiFEEIIIYQQQgyFXiiFEEIIIYQQQgyFNJS3CNZ28Rho1nn0ejjefXcHfWF22+gdWKLt2djGjqHGcGZ2GuLLV665PNgLi/VQoWcAfEDXg7VfrNNgbRqMSDc+lLwma8N4/LhPg3ngnm8LxuMzYN0gXXw2ouyhduD6KvpKtluo84hq2Ez80We+BPHDD92z9/mXfu5HYdnaymWIz59/B+LpGdRcNhroCTczgfrNt15+AdcfRz1Uu0faX9YK+HwhKWaVxch4JDzGU26EymY2ZbOrgnKioj5evh0Yfz1YXypKcTiwD6UjD+Uy+fNdI113UMHl9RrOh1CtYZvVJg9n89xQuDi/APH5C/kayiTGVilmXbZ5TD3Pkp1EgELWllHfhSdkGDicgHqYPk1kUV9Jb3m5Sw8BMz8D3osoorp1fR1Xp+Iub2A/r0vnX6tgXeuxRnMH6163td9PHJ/C+Ql6MfqnMmGI9ezxB45D/Pkv4dwLbZq3g63Qb9Axw9Cj7TVdSk+95fW5DxmYquXzXea4oK/l4PoF5zMoSmZ8K/FelowXO66/sYZ9wh//8Z/a+9xoNA/cr36hFEIIIYQQQggxFHqhFEIIIYQQQggxFHqhFEIIIYQQQggxFNJQ3iJ4rP/YGGrHdkgjmZG3FS/nMdFjTRzXzJrMJMv3nZmbQY8go/ugMdnsvcXyLNaueSlq3ggU2zagfYVR/vcqaewRhEZ0bUl4x+PTzfGY8ez5+LRsQR8PYGsNdYy9bfS6iruksezj+SYh1j02Mf3nv/l7e5+rLdTe3n0MfbPGJ7CeTs6gBjIkc6mFOVx+9zRqKq908NjbdC6DPlw3Q5ixDiPfoy0pWM+NbpAL9B0ue87x4oG6YfRHRf1R+aE2+8rf3i5nj07y0wvyXDSFGA6ev6BaRr+8KGJ/Oqy421vY/kXkt1em8tjDmXV1zMIiaihjEndVWENJuTcjDWVK4rVSQU2l1UzSCvk2x7nlG92axwN61F88jJ/1iJ7PjGli6Vb3MmzQVzexLsUJHh9rIl/45usQ/9DTj0Dc3sRcPjWHdal2fb+Lv3zuKi47Rl7mW+ifOj2NufqhLs7bcXwS/VuXUzyXhRN3QxzSc8L3ImTf44J6WqO55HtvRJ2E6fIa0SREvnSaeLwlYV2ek8STe73wudD7QsZzmJBfd0zeu489fP/eZ9aQD6JfKIUQQgghhBBCDIVeKIUQQgghhBBCDIVeKIUQQgghhBBCDIU0lLcJ1jhG5HVVIW+rjLRdOzs43p1JYxrvbayqSEM5O5NfHo3/5vHvxufSA493Lwp4SdLXIkbXMOLXJqy5DMy1oP0XLD9jHYlPm+bxxaxXse40jp+A+NLV8xCfuvsYxFuvn4a4Usa6OV/H8fYXV7f2Psd0NFPTqIFs1lA7XC6hDiOjijpexZv3E0/cC/Gvf/ZViBO+9+TbZWD9Ksv42IiSNYzGh4z0Tvl7d8GI3pCHSkFvLG4DjObSq7HMOXfZUIpDgnV03R76701MoJYsvXwJ4nIZG4XuLuqLeh307TUezZR7Q9LcLy7h/AUBa704pAIT9hkmOH/Z5Ribx9bj82zKCwY/k46NNy44f4A5WONteLQu0db3knV/uHS3hXWFdeMp6V9rpM/90rffhPhbr52D+OknH4K4dAV1jp//J//H3ueJD94Dy+796acg7vZw2/EmauVOLOC1/9nn3wfxqzvYT5i8/1GH5GsiuZoGdDHZ25xzq28uCjN/QeGOWtH1b34DnmfDbkm51tPp9GmF2ec+In3r+ga+X+wMeJPnza2gXyiFEEIIIYQQQgyFXiiFEEIIIYQQQgyFXiiFEEIIIYQQQgyFNJS3iSb5UGbL1yGu1GoQd9s4Fr/TRd0Ge211Om3cofHCwvVnZ1BDacdgkzYsxhHsCZsv+qAB8+zHx1IFMz5+UEIZ8vci7KPFY+tN4fnHWlgfSlt7Ng890rOicrI2eQiFKV6fHi0fp7pWJ80QixXIssjFA3rdywN6Suece66GGslSHfcVRKRxDFAPxM/JRx5fgnilh1frdAfLv+uuUxDztbUaSFeIorIK3p9HoukS+k9otMsIPCeeiufz2fJh9US8Amsk6dhZa1Zo70IMx8Q4aia3aD6CGrWHlRK2UWmMbVyV2rg4Rn1SRk95QELtuQXSUHp0gexZx76TJreS5ikyCYrzJYsqWbfIukjKr3maTY9npW3PeH2aG4KO1UrSPV66uUdjMZac1pgYl5cwWZ6YwbrXWse61KXcm8Wo9/3N//NPIH7wQfR6vPTCtyA+nuzXzakPod7Skb92SvU2CrBeV+v4HHz0Q6dw/VcuQLwaYnkx3ZykRJpKqsaByR90bdlrMcG6kPnupul3HbWoch9+ZlmX6JufwMxhYjo2vEMME/KljOjaXriA82xsrW3ub5sc3NfXL5RCCCGEEEIIIYZCL5RCCCGEEEIIIYZCL5RCCCGEEEIIIYZCGsrbRL1KOo0qjrWvkn9et81eVzhoemV1DeJT93j0STS8fGoKx/Z7h4fTGPCAhlWzVoxjq97y6R6RPP2WVxJp9Jp0bAUNig7bMm/U8kLyOO11UU9bKuFjv3wVva2mJlC3mNL3TnGC2ojBpd0M621zchLijPU5pH8NYjy2qNGEuDKJ8Q99BL2wTmVTEC+3UIOym+G1Scx3auyNRboM0gpbzaMH1kJ4ZYi3T1lodBwFva6KHvqgpvM2nrb4HqdWJ80j6/Ko7l27ehXXJ/1Rmdpb1vWx5jEiDeXSwnHaf75fX8T6K99zOGJC8Wml8+Y38GH0nISVeXsahnybSO/qo2J0ewHmsw8+8RGIy70WxBe2MLd2EuwXrmzh/Af/+7//c4j/0S//LMQbT+57QUbHMK+vrmC9rlewT1qK0IeyVMJ622hiee9/6CTEb6yjNnk9wPI2E7w23M8w0KW1tsfF7iZrJnluDa+mcqQcdbgJzjsVhGdOEs7l6yv4PnGzvzzqF0ohhBBCCCGEEEOhF0ohhBBCCCGEEEOhF0ohhBBCCCGEEEMhDeVtolSmS0/aLNa5dduog2s2UUvG0rQ+ecWw9IDHh8/MzkLMGkb2wUlp+5R0JWaAO8Gj3VlLwVo74/c0WJZHr+lbPjI+DQudrc8bqyjGx5LOr1ZvQJxOoe/Z8blNiNtU9y6euwRxRJ6m1dL+vep0UbPYGCc/1Q7rfyA0V6ZSxmMvVTEuk+ZyqYHL4wTPrZbi+h3SfO70SXtM/k3xiKqbtKB1o/Ha4vIoLvQNYZbvhWXXL2rSme+dZXTVyaAHqUSU4miYIl13lXRutQpqvZIEn5PuLrZxURl1bhmt3++T5rKK+1uYx9xrYN01+/n10buXn2NuUxjr+exZ36N7BNjCktt7ns+Aj73g/AaHrYksCl+aIMR7PXnyFMT3r6Df3ztffwXisQmc26K3i/nsjXOog/y3n/saxH/35z+19/namdOwbHtrA49tCjWRYYTHHpCGstTE3NmI8VjHW6gPDSN8bjob6MGZlrFP23fYd+CUYPqooacPynMCOA9m3o/RctKg1tnrO+nJndyH5P648alkvSjNXWHarB5qeVcH6kosH0ohhBBCCCGEEIeNXiiFEEIIIYQQQgyFXiiFEEIIIYQQQgyFNJS3iQr5TEbkZVWt4vKExi1vbKPHT7eH/kSsueTh3zzGenp6Jvd4WduQ0PbstZWydsKny6A49PkBjqCW8GtKaGw+6yIKDqVnXcVhe+zx8WUJ/iOi742mH0DvxgqpCbZ3dyCOd6iudXexvNZ+3cs6rPVFXYZLUVeRkklaTNcmJq+qJEIPucYc+k62O+jXujSDOowwQr1TSBrNy6t4/BfXMY4938HxUp8vJVcF1kZ45b4FdCE+vzofVr/k0w4jxkuWdBw9J8TR49PU10mH3elhm5JSrutSm1ctYRvTaeH2tQYuXzo+D3GWsYc0tSr0YHHu5TbBSB69HtGeOQlo/0ZTOXC4WcDaL0+DFtG5Jnwt8v2pbzeci0tUF5avXoZ4bAbvfZu0ayHNSTAzswDx+uoyxJ/+1/8XxJvr+/n2Bx9E7XClgveGfSb56gYhLg8pF2+1sN8Q1TH3pjuomZyvY3lpiOfapn7MBvcFPPOCMJwbvTpDT7q7lSp/bhPscu43FDNg5XOn1Owunb+w97nfOzhT6xdKIYQQQgghhBBDoRdKIYQQQgghhBBDoRdKIYQQQgghhBBDIQ3lbYJ9Jll0aGUROMa5VkOvrG6M45q7OeOcb8TUDGrR0oDHbNP4etJUxnzAhNU+kE7DDOoeQSPpMdYKonyfStaaFdVMHjWsaWE9K3sSxbS8s4t62z55OZ45cw7iu2bQH2p5EzVBC5P7y++9ewmWjU+hN1VKPo9hGXUWW+vos9Vtocda0EStb6VGcboO8fQ0erxFXDUSrNftTdSHlqZRT/XaGi5nz8+iWB0HL8fY7M63QQ5e38kRsVcm/1phG3CnqaPE9wpl8o2sNVALViVNZLyCbdAEtWmXLl+BuF8lP70+6uK4Zs9SG1Uuk7bMmEizLyUqtVPSW7GOu+LziC7YLBRZPySdnsnVBHcDrIcz+1TmLb31ROQ3XqpjXbt+GXWHLZoDoE8eoz3SKU5PYl3jE/7d//s/7H2+95c+Bss+8L7jEJdJ78kemqx571A96tL8BEGAcbWC8WQDc3+lgucS46m7lV2sOxe2SXOJq7sgy5/RYNTsx3WL+2FWdLn/XBrfSeo/m36BWb+ooDO/z2s9o3Hx1776lb3Pu7vYBxpEv1AKIYQQQgghhBgKvVAKIYQQQgghhBgKvVAKIYQQQgghhBgKaShvEyGNR+fx6xXyoazS2HvXI1/GzQ2Iu+RnZD16cAz19Mw0LmYNZ5iv40jTfMc9o7PwjQH36SAHPhsfLYpLdK2DgOP88eSHrcPwajZHLD+i803IsCmsojdkML8I8fyDj0FcK6GY4aF7TkFceuWVvc9l8hGrjaM2t7eDuofqBHpjbW2h52USolZ49iTqPi5fX4N4fu4YxJUqXwvScAaoy+ieOwtxk3QdU6RdXmnh+eQriZ3VKvjW57rp3cHN79suVugW8gAAIABJREFUpmd6VN9K/gefCt2LaOC5vN3aJ/G9C89fUK3iM12vorar38dnPEpJG0a5O8tI0xhTbiRhIOf6qWlsE9dWUFfOuTym8r1NREHdt/HAM8aWvHh/uU8j6YP7DZzbM9Ke+fbm1WwWbPN88xm0yUeyVqf5CJbRR/K+45i/rl7D5e0W+jhPj2PdvfcuzOWnz5wd2DeuOzZBfT6upiH5qXZw7oUWrV+fwvLam6hqXFzE3F2tY/m8/4R8KFu7KxAfG8P5Dd7eII9r1td6HgzjkVo0/Zn8mbsybZofe3fNh55yn5J9KqlfRMsTulgv/MUX9j7vkC/5IPqFUgghhBBCCCHEUOiFUgghhBBCCCHEUOiFUgghhBBCCCHEUEhDeYfA3lhRxH5NuP61K1ch7vbQv4jH2lstAsYTk+MQl8hTyOeTE/CAcYpZdhGQJpO1ETyePU/7wP5IQUjXjnbOnpc+rZiVd/L3MB5dnPHGOlz/P742xg2Jrk9WQY1QMLcA8bGpOYjHItRQ/vxP/RWIv/CHv7/3uVxG3UQ4hrqKUmUL4qhEfqpdrGczS3dDfHUb6/XUFHq4VSuoR6qR9jglb6rtbTweox1O8XymqDzWUBo8msnA4//kFXLk1F2u14VrXUHNZdHl7JeXJvv1zGq+hTgaSpRrkwzbuxL58bU72AbFCc5XwB7QMXkJss6OdYBzs9ger65gG8WPVZyw+IxzL+VOz88InN7Yt5nLy1MlmrkTPBpG33wITFGt2a2GrRCjOta1coh1Z6yJ+fCpJz8A8ek334J4bhL1tj/xkz8J8W/9q3+19/kumn+gVsN+QEi5r90nPSiluubUCYh3t1FbN7eAx1YjbTJrmftkPNluYZ82cHit5iewj3phE+tK32eQmj8tiLl3NjUXy3+gaxzBZ/1GmFM1c5ZgmJDOm/vE3C+5du3y3mf21cVyhBBCCCGEEEKIIdALpRBCCCGEEEKIodALpRBCCCGEEEKIoZCG8g6hRl5YrOswvjGkm+gnOP6838fx5/mqP+dqpKubGEe/pLV18sKiMdZ8PBl5zDk6n5DOJyOdI8d5GsqIzq5E61Zp36eOo5bg5dOv47FV8dxZg2m9AVmjaUSTAHsE5XlsOufXifi8sLi8hMfvp3j9xhpYF37iY89BXKmhjvDpH/7Y3udvf+OLsCzrcxODuoduQvWgifrNpILaXrqVxmeSNZONOp5LTNd+bQv1SQFpmfm5q5OeKKLy0jT/Ozpz63PXvsEang0GdZNG50z1sqjPZNH1jd8q61OpvHjAy/YOl0aJ7yFq5C1bKmObxZ7RO1s7GHfRn69cxvL65AnN6YGfq6VjqKF843XUzXEBKeV+9o20uTM/n4UZ57NicwZgPsr3fDZ6TNq3t80p7KlJm5tz4Vw8WkPE59vqUz8uwLoyPYn5h/3B7z+Fcwo8eC/2ZZqTE7j8kQcHls3AshLlMp57otXGep1U0VM6olzdiPHcGjWcz6BUJm910nC2OijSbHfQYzoMyLcyxj7uRIWOn/rALDRME8+9PeT8OFgXfF6xRWtd6ulz8iQmSZ88oOnYI/ITj3vYxhyEfqEUQgghhBBCCDEUeqEUQgghhBBCCDEUeqEUQgghhBBCCDEU0lDeIZjx5OQrWa3i+HP27OHx5+zjFgb5ugf+amFqEsfur61t4AqsXSgqejJaCjoc/gcxqLEMSnjwTz/2GMSf/JHnIWa96t/7B/81xJMLqKE0A96NrO3OFnzZsf0Yz0/i+f7oRz8C8ewkXq9yRFqI+v729z+A1/7cmcsQl8ncqUNj88MG6odiOvZGk/StpINgnQY5tLlel8y0UlyjTnWDPeLq9JxUS1gZuj32njJiBlxe0I+KNT5m+cDuWVdB8tPCWL0R79v3HLD37IgHJMQhEJHPZBLjM89+eWVqI/ot9N/rUZvW62ObYxWN1B7PojYtdewBjTHPX2DEyizcpv2xh53RXHpyMa8fBQMtDWnQeb6DG1wMgE7Vf6weTLfF45ddvFuT7wmdUl079dGPQbxxHvWy9z7yKMSPPYkljjWwLh6/5x6IP/qxT+x9/tarb8Oyx0+S3/TEPMSdAH0kwwnUUMZ0rs0m6jdZQ5lSXajSXAxBhuuvl3HejqjE3uj4nM2OY3nXdli7jM+J8UKPjWmqy8W7mOdXGPSh9OgtaduE52owppmj9UF5DhbWUPL7w0HoF0ohhBBCCCGEEEOhF0ohhBBCCCGEEEOhIa93CGM0lK+ztQtxvYE/53c6+HN+QN8NlGmYjh2mScNkaOjg7BwOeT1z7h3cnn5iz2jYTUo/kfNP+AEN44nMWBceioLnlw4Mtwhp24ceuB/iBllJ8ECUZgOvPdt+mFGLniGuRYfy+b7VMcNoCg5vyOhe1Ml746c/8YMQj9NQlbHxBsR8vSrl/eMbnxqDZaurqxCnZMvBNhwBDdfKejh1eURDWkuV/Gno+zQUfGsLh25vb6NtSFQh2xB6LniI1FQDj2erR0PPA767PH7ac/fZesM7NflAyWbkGz2D3nqV5S5PRxyGY1bPXVuIo6FJuZdHjDYa2P7xc9Bv4zO/m2Lu5iGpxtaJ8sWxpWO43PNgpCkPgc03JWAbELbFMiNeKb/68tvgiFdSBLgS53U61A4PaT3iRsFnEzJq7uVGzmxdR1ush5/9OMTPPYJDWM+8+TLEz3/skxBz/iwN3NswuQ+W7WzgkNIODTltkHVcn4aElklqxJZbIfUzSvS6EZD8ZWNzHeIq5eKMhqyWaPjwVA3jiK52n/bHFjUZj8ZmWxF2W+PHjO+1yW8Dll7UDzjsIay+XM7PlW0xuE2IDliC6BdKIYQQQgghhBBDoRdKIYQQQgghhBBDoRdKIYQQQgghhBBDIQ3lHQLrNNod1I6NT+BYe6MDrJHVhcEjfKAx1bMz0zde712MfspMTe7BM57dWBQYm5H9mO0RdluoYbFgWWN07dGwxTkXsdWD51x9OrfQU96I4+fN/ujaPvfEhyCeHUfdY7fXwu2pmUgTmiZ+QNfYI21vZRzrEetzArK/SYM2xKUK23jg9pUq6iz6NEV/l2xC+NK2W3iu1Sra93Q7eDz9DtaOBrWgoUe7wBpPtkVhCltrDFwgrleFbT0OW79kysvXKwlxK+Dcy9+ysy1VidrvNEZtVzfANiKhKflZo59RLpxfRPsGJqVGkHXdrKlkWNZtnjtjfZGvsQwoPw42iY/eixrAH/rocxB//oUXIH7p7fO5x1Z8/gCMre0Ire/phxw2fD4ffw4tu8ZDrDudk3g9m2No7VGie3HXgC3WxtYOLDv+wJMQf+elr+K+qzj3RC/FPmm1gppL7ldFpKHkyShS0kSuXL8KcXMcbUgqNeqn9TA3hyEe33QT+wZXtkzPjo4vf7GpLEQhGy1PWb7+tJ3PwAg6c4/N/Mc8Z1ye7+J8F/1CKYQQQgghhBBiKPRCKYQQQgghhBBiKG76hTIIgigIgm8GQfBH78b3BkHwlSAI3gqC4HeCIODfv4UQQghxhCg3CyGEuN0U0VD+fefca865vxzY/N875/6nLMt+OwiC/9U593ecc//ikI/v+4YyefhUqqgdq9B49TL58e3u4vj4lfU13IEZz806CfKhnCXtm0dKkKTka+kbch3i+Hr2SmTtRF7M48m3d1AX52OsiWPzjcenEWJ4dB18rD59KfmA8fB19vxkChbvTsygNiGgHaYpfc/EPmWkjeh093UejTHU+sYZ6lmzGK9tK0HdQ408Q1mzyF+B8am326iraLWxfK5H6xvoSzk9O4W7o/2XyN+1XsJ6z9eSsT6SXLf4Oz4snzWYqdFiDPiOUVEJ+2qZY8td7MdoNknrZe5WvvesuGmUm0eAc2tID06dNJZMu435psO6bfKhZF/HNMDn5NixRdoD66UcxeRDmaLuzvdU+XI76wi5DWIP7MW5fQ3o3/iP/2pu2dMTmIsyOpcgIl9gulY+TaX33Ey/ApdbSXx+gXxtEk+BlQjzyeIc5s/uLvokn7gHvSQ5X6TUV4kGdIcNurbL1y9CfPw4and3dzH3lcs4v0AU5L8+sNa338fyIupXdPuUS9vYp+2beo77y/pY75usFeYEx9tj6BL+D2/POkbjtc4M+FAWnBzBzIfg0Ul7n4ucOUmccy42pzrQ384p96Z+oQyC4KRz7iedc//y3Thwzn3cOfe7767yG865n72ZsoQQQggxOsrNQggh7gRudsjrP3XO/QO3/04/65zbyLLsL78SuOicO3HIxyaEEEKIg1FuFkIIcdvxvlAGQfBTzrnrWZZ9ffDfN1j1hr+EBkHwK0EQvBgEwYvLy8tDHqYQQggh/hLlZiGEEHcKN6Oh/AHn3H8UBMGnnHM1912dxj91zk0FQVB695vQk865yzfaOMuyTzvnPu2cc0899ZREMjdJo4Hj1dnrqlIlHRtKxdz87CxuzwPGzdB+vDXT05O0Aq/PXlg8ID1/vDovZ60D60x4jDccD5W9s+vzoUTGyIdxtY26uiAazQvrdsN+T+NTqM+NY9Q2sKaI6x6TDWjzdnZRT5SRnod1FCXS0pZIS5yS/ijuoQYzpuUZaQ5321gXpiZRo7K9uQ3xzCRqKOOE9bR476sV3F+NrLe6Hi0xayoT0mlkHt2jYdCflUUmBeFjK+o1W/Q5AW2uLClvBuXmI4CfGtZNxzGu0WqTN+0Ytq8BJTfWNvOFX5ifw+UeP1nenrVrPAcA+0ryzwpGls3L6QQCKq/d9vj9DTA1Re0taewrlA/42H1NTGj6LXdWNV8gv++sj/kzpPxYq7Hel/pJNL9Bf+B68twHtQrW650Wzj9QLtG8HSGuz3MzcH7ger6zvQ5xifSj9QbOn3B9+QrEzTr20zp0K7sxdoIbdZ6bguaKoOcypVzLmsvRa87BSc3vbV5wTwXn+YhIi8vdDm5TDsL7C2WWZf9NlmUnsyw75Zz76865P8uy7D91zn3WOffX3l3tF5xzf3BTexRCCCHESCg3CyGEuFMYxYfyHzrn/ssgCE677+o2fu1wDkkIIYQQQ6LcLIQQ4pZSxDbEZVn2Oefc5979/LZz7unDPyQhhBBC3CzKzUIIIW4nhV4oxa1joolar3cunIO4XkdvrJ0dHD9+6u57IM54fDvdeR5zPT09QytgaO2Z8sdoZ2asP3sbki4jx1/vu+UNFoZr7rZIUOqhUa3Rf26tgIv1o0XH6nu9s2j4e7mK2oiEdI31Cnl/sZ8h+T1VBq5fEqMHm6k3pOtjjWK3S15VdHPZI47vfYs0kzEdT9zH/W2vb2IBd+Fzk5Cmp1olz9IWaSjrqFPZZBGlRysxssTnCDVCrNcpqqn0IdmkuBNo1jAfsM9krY7LwxDbS56/oNPFfJRRg8zt9/wCaigD9ik22mby5yPNu/HTM7AYjhabvgNrMpGdAS/gHmneK5RblpbQc7NH7Xe1hu0tXws+VKN7K9gesj2g8YT2adMKUqlQfiN/8WYTdYUtmqMgoTkEggDzT7W8v30rxmtbqqDWt0r3ikWQZt6MOP9aRyW817u7/BxRP4TOZeXaKsRTD6LetJVh7g4zPN5qiTWdPD9Bvucz+0oa2MPUo3UeBe+x+PBtzveanwPTHz+gmAKHJIQQQgghhBBC7KEXSiGEEEIIIYQQQ6EXSiGEEEIIIYQQQyEN5R3K/Pw8xF/4yhchZq/A3R30EJqcRB9J1jux7wwL+abJH8p89cA6Dhr/zroLC40/5zHahQRVuPIOaV56PdTlvfLqaxCfu4J+R+z9xGfCnpjJYevWDrm4iASzcQ/vVWsbvRjHxlE/y35TYUjCkgENT0j1qkTeVyF5X6Wdg/WYzjnXI4NV1n/2SXPZ7+Gxsf5phuo164/anR2I4x4dX43qLWlCx0gbfN2qjV0RWDOUkG6FvSKzAz7fFOwNSyWwR6bdvOC5mTg4cJkQt4qZGWz/Ll65BHGF2qAqadFYa7azg22K8YWkx2a8iX57jXoT4t0WtmlcAOvUvR5ynHrD/NzMsfHzG2g3tra2YNncHOpLlxaXIO608VqNT2M/iBs1nn+A243C8xGYDUZLxj7NZY/mI+C+Sq2BdcH2m3BpaNrg/RVqNdRjxl2cX6BSxeWdHubekpl4A4+9TNriThv7pOyH3Sbfy4C6kFsbWBcy4wmN9brbxeVhGZ/LBvU9OtR34HlGDHxtTZy/ee62PrzaYJ9XrSd3m93l66QPQr9QCiGEEEIIIYQYCr1QCiGEEEIIIYQYCr1QCiGEEEIIIYQYCmko71DGx9GHcmcLdW7jYxMQb5MObnoGPXviFAeol3ksPo2SnptD7QJ7/Ngx2qSr8IwRDzwaypCWs45vcEg5a1IuXLkG8X/3P/9ziNkrMSjh2P+AdHC+sfVkb+TSUQVgowpBDKx3xbi9i1oFFmZE5FGU9LEuZcm+liIkP9EYZRZWy1su03LS/1C9DWh5TJ5rvTbeW9Zgsp6I1UUbmxu0P9RllKukAc3wBOsV1IwyXJVY28Dnb7/xyzcdHdmvKgf2oUxijzaraPlQd6SiFLcH1lCyZnFsDHVtpRI+pds7mIs599qaTX55JORbmFuA+J3z7+DWGftQmkaX9n+wzs45+5zznAFMkNMMbG1jbmENZY08PyPS3Hv1nQn3Q46Ww/QW/C5YN/qkoWRP0TLVNe4nZXSvywPXs9vHZXztt7fJM5R8JFPK+8aHmDybsxTrYZueo1KEuTRji2may2J7G300Of9U66iZTEhLPFnBa7Vm5gWhmDpyvmzHfdTEVJWBf3jmKzD95xH7hEbL6zEv57kw+NwOQr9QCiGEEEIIIYQYCr1QCiGEEEIIIYQYCr1QCiGEEEIIIYQYCmko71BYp9Ekj6B+D7ViHfLbq9d4fRzfXqvl3/qpmanc5Wb4vM/7isajsxaCyXiId47/Eusa2BcyC2isfpm8EY1XFO7b5yVl/PTYM4h1H7zcMz6+uG6DLx7G2zvoDcbeW1Y7gXXH7G2g+JR0EylpEEsl9HBLuqhxZA0In3qfdBrdNmlOEnwuqj5tQAmvTauNx1Ov4Pr1GnrCZSl6mFYjPD/G6Cx4BU9dNFpi1jsNXH6/wnE0PRDXW+NP5/P1ou0H9bA+3ywhjopqFbVYXfKrm6D5DTiXjVPuXlhEDWTiMVNk/dTSEmsoz0JspVo+zznSyN/AERbWp/MzudixLn1//9s72C/hXHbu/HmIGw30z/b1ExJug+7wZsO0kaavgX2TpM/5lPSwGc8pwOvvt6lBntjVOddsYm5rtVmzSHMnePotPP9B1sVjSwLSbJK/axrh9pubmxBPT+Nz1qOLGVKftFbKn9+Au6w8Nwc/F/ycWs0kMpgPM092PupqbG6d8dTE4ytF+22i8YwfQL9QCiGEEEIIIYQYCr1QCiGEEEIIIYQYCr1QCiGEEEIIIYQYCmko71B4PPvdd98N8ZunT0M8N4/eWUuLixBfWbsO8fg4aix5CPX0FGoo7bjp/DHXPNZ/1FHhvPtwwBsxI12cb0+smeSx8qzf8mkYi54Za1BG1rIZT1DSEpAOpbWF2oixJmqGKhX0p+pn7DtJGtKB6xmTxjEi3UJAIpdKBfWaWzttiDtdjOt1fC62tlAP2otR77S5g+c60cKYfTO3t9A3rTSDOo3ECC1w+zL5UFrPt2J4HyPWrYywr8P3WMsnoGvHnp5C3A7K5I3LmsrtDrZJvP4W+fqWyR/P6KJDfg7wKV5YQE9oA5XH/nsMaybzNFE3gldPczxjX3rlZYi/8MUvQHxtbRni5iT2O0zzRxcvMpaa5JVbsE1j2Rzvv6g7rm8+hpi8FNtUtyYn8d5HJeyys2aSJafRQH5r0zwbXO/47NgTdIc8MlPa9/gYaotbu7i/Fj83XO+p3pZL+NzstHB+g4lJ7AuU6Dls0fwKjTr2a/jmGhvKI8yHt1vqa/ZvPDR5DpSBfl3OddEvlEIIIYQQQgghhkIvlEIIIYQQQgghhkIvlEIIIYQQQgghhkIayvcIH/rQhyA+cxb9m7ZovHq9jhrJXhe1bQl7IdL+xpqoHWOdSLePfn9xguPp+zGOd3fGA4k8fVgjyTpH9m8aiFkTkpEQIqDvTTLS8Rm3IZ9OzYhgaHXjX0Tw1zissyAhRGJ8JUkjac4/f4T++uoKxMeW7sf9JflatqhE2ocBoV+ffbMyrHdl0lTWqljP0q0NiDs9rNd8bTod1Ct1W6jTYM+3fg+Pj/d/fQO1xs0G6jhqlQaWn3A9Rd1GhXwue2ycxhjfTFzs83YcfG6sjpnxeMEW1Poaf1aPf2vBwxHilsDassU59IF84x2cv6BDuXVsYgLiJum+jeGdI388eg7Yh5I97DKev4CeM24GUkpwnA9Zh8j5hZ/riJ/zdP98zly6CssSFq7VsP31mRuaNoXWZs26z/fRa1zJHpzGqzC/H8Xw/vt9LLBLGv8K+YUnfZzPwOr8qO8woEvsU17nflM5wj5eifyoI1q+tbUKMfuzcq5ij+mtNubqxjh5r1MfdHMd99cjTWezgbk5pL5GnT2oud9G3Z6A+1UuH2+2HKiL3L81fqTeelVQhenrw/IzT/U+TW/ut0f9QimEEEIIIYQQYij0QimEEEIIIYQQYij0QimEEEIIIYQQYiikoXyPsEi+kuyFtb29CfG1a9dyy2OrKvaWYk+f8THUgXTXUUNpxmjTDliz6YOHeGcksgRdJElQjEUm6yo8ugPG+HTxubDuw6fLKAgX55F4Oj4f1tjw9UhT1CKEIY/fR91GTDrEZND7ka5tHGM9SZMKHSqWXSIPzFoD9Uis7b3wztt4rCkeW5ySdpjq5dgYlv/m6+TvOovLA/LkbFTxOYlJI1QxRmlFPU79d/tWwZoYo/Mo6NvFjxVrVoS4E5ifm4P4ay++CHG3i/MFJBvYBjUrqMNmbXMYcQLD52BufpGW5muT4xjbqBuIKHP3Z3SKPtm32X7/Mx2JKduX23ypNOXcnr/6DX5CyZ+fwHd8fvj4WNOP+am9jb7KZdLgZwmeQEBzU/AcBoOn0yfNYVbGepfSjU5iPNYS+bG2STvc6eB8B2srOB+B6YeEGAcBvo6USthX2CL/7CybhbhaJt9Kqvdl6nrUaS6IrunS8s335GpOaIfoYzmqR7TXn9o0CcP91qhfKIUQQgghhBBCDIVeKIUQQgghhBBCDIVeKIUQQgghhBBCDIU0lO8RqnXUlj308AMQX7tyEeI333oL4mMnjkOckhasRKY8GX3VMD01BfHyKnoC8fh4RzoO1jrEFLOKxEX8XcfBuo6Y9s2+WMYD0wg1PBoS49lD2486VH7EsfescTFj/0kI0o9R+7C9vIarn8JrH9G9aHdI99HZ123EKQoRShW8s80J1COVy9gEbe+iR2ZnF3UTEd1r9j/tkhCiVmPfSNyetcHra3gt+r1jtD/ShIZ4/OxhVy6hboWfO6Mp8mh6Eq8ZVnDjzzcoa9SK69VlCPE9wPT0NMS9DmomlxbRJ7Lfx/axVCIvwZRyr0MdNqeX+dkZXM4edlRemuD++9RmcT5L2ROa9m9k4ERIeqtBnSDrKzlXhSPmTm4vjQenx+PZq2H35Pqih8/+g50uzoXRIR/l1g76LI+NYV3MYswvO7R+mu3n304XNY6NMs5HMDt3AuLNTcyFYR/v88kTd0Mc07we7GedktFjGGLfgPWq9Srm7t1dLD9jb0TTTeNeJa7fqOO92OpQrjUCYJf7D94bbw6YOUe4z+pTAxfrhNp5Q/K35udqsNuUV5J+oRRCCCGEEEIIMRR6oRRCCCGEEEIIMRR6oRRCCCGEEEIIMRTSUL5HYG+pMMUR2xP1cYjPX7gA8fHjqKFkHUc5Yh0EKimmZ1BD6dCuz3jU9dno0ng3stchEvCYcpSZoPbB+Nnla0QY1nnw1yxm+HnKelPyFWPJJp9r6BsfXwwun3UifTqeDokD1lZQt1iq4MXuk/ahR35N9al9P6j1ZdTydrZRt9HaRc3H3Cx6SdWrqBXeJk1ir4f6pX5MOg0SYpCMw7XJK6tcpZtNt6bfIY1mn57DCM2tej0sf6ziMUk1S1kI4tFW5Ml7PQZyLHs2fquH6KN1U8iGUtyBLC0tQTxLbdb6Juq8x8dQ+1UmT+c4xgevWsnPjXPzuL+QcjXnXuNDyWIwaiOtGov9ZV0uGbUzeY8xl8VzK4S0NWsOWSNpcj3/RGJyN8WeNqeoD6VtckmzSSUk5JUY93H5Jmn6J2dJ079DGsw+99vm9z6fPYedtoi0vdvr2A8ISe+fkHZ4Y5X6DSXud5BftekXUW6kaxeV8R+tNp5rl3Izeyf26LmoUsKbHMPn8uoGnp+ZzyDIz+UZTzxivNRzVZV5mw61xiBF5zsIqN+VcmfhAPQLpRBCCCGEEEKIodALpRBCCCGEEEKIodALpRBCCCGEEEKIoZCG8j1CSjqIKdJxTExMQHz+/HmIn3vmGYiNbyTBWob5Odyf9cLC8tKYx5fn++b4fHLS4GBtRUhlp0ajyPvy+E6awe9mMDyETz3yCMRXL1+DuDGJmpo3zqO+1aXsjeVGgnUm7COW0b1Z3bwOcRDw+Pn867O1ennvc2t7G5aVK1jW+AR6X5VKqEvo9XD9aq0O8QbpPOIYBZ3dHnqwsa6h10WdBGuJoxKuH/dRt9HrsY8kHj8/N2N11GlE7O/k8ZtiHYetqwdj9EesY/aQkYAopOeKNTFCfC/CPpIfefppiP/F//brEEf0XPzSL/5tiJfJ34914yzdOnYCdXMsiGINPfv/JcabMX/+AuMJzT6TpH0zPshQoEeEaGRno5k8m+LN5vnnbs6VLrbRl/o0mLTcquioPU85ty5DHD6Mmv2QfJDjHs5RcO3C2b3PZWq/WR8aBlhvOPe2djYhLtNcCxvr6E3eauF8AqxB5H5bu40empUyayzx3vS75L/K8xsYfS7G03W8dubessdpmp/wuN+Up1zkZ9BgzcWM9fn1AAAgAElEQVTzV/eUx11ia7FJ/Wv2pR8sP2dX+oVSCCGEEEIIIcRQ6IVSCCGEEEIIIcRQ6IVSCCGEEEIIIcRQSEP5HqFSqUJ8gnwl3yFN5ZtvvQbx+jrqNsZmUMvG46JTEh+MTZMPJcGazIQ0n0a/lZB3Fh1AxBZAtH02oHUz1lLGyo90CjSgnMezh7wvKr9EY+l/8pMfg/gv/uLLEPfSLYjfOFtsfHxR+PyNkIPGx++28Phau6hlKFdQ4zPWRB1Hd3e/vHY5/9qyNiAqY70OQ6w3cYKayJ0dPFbWRJbpXGP20mKPNlq/VMPjaVM97pEXV4M0kstbuLxWQh0K6xINXFdJX8u6SNZHFcFastFzQEXH1kiMSrjFvpVC3AaWFhchfvChByH+xle/CnGH2ow2acuSSczFnK+mp9FjulTBbluvizpy1nr1+pyLIXQB5W5+rFlflXl034PaM25T8tZ1zuq0zdZJfnl8ZAE3WeYnFNIVkpEl+2SOiunH0G86/T7mu4011CWyB6nLsG5NjPGcA/u6xxLnWtL8mX4OaSj7lDv7ZEi9Scda4vkCaA8xzV/A2l+ux82x5v/P3psFW3bd93lr7TPf+Xb37W5MBAEQhABSlEgiMiWZtGU5llWRh6hkx3EeHJcTV6Vckct2JXby4jzkwa5K4tiOo4SR7JITx5YjD6pibFkyKZsKRVICQVKcAGJGz33n8Ux7yANg3fP7VvfZfQ9AoG/z91WpxD/OOXvvs/faa+3Td33rJ/X+kea/Vnh/CzZwhcxqtnv+GBpzPMSpT5picl/c/j5JMi5PeJ/cKj12GuxTkk8ncdd4Tmu2Jl+87X78F0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCPyiNMcYYY4wxxsyEHcpTAmctL/Q027C3oDVzZJ5/4TmpH3hUHcyyUheMezy7sjz9gJiFlXN+PBxLGFytmtydkhrglFicGsXjFtlU2DU8jib+3eVHPv5RqX/98+pMvvjKK1K/9u2v6vbPPCR1Op9+OtTwai2PJLtQv08bW9jdVN/2/offK3UfrsJgeJx9dQj/stvRdtmaUw+i21N/6HBfP8+cyXysHkQ11tytCglL7a52cfSZuj31ROZ66isNh7q/Ab5fo0EXWc9lEzJwhnZPp4aUzIKsy1CdEDvSFKzp3gU957psq7cKt8/vYszdSHdO+7S5mvrFF1/U11c0M5quWLuhfVYXeX/LyJy+eVOzCpNMaLrPuM/pudMb5H3K9RKYv6fjWY3bxYzjROaa/vkevMCnP/RBqfcPtL++tqXn6tJN9f6SDM4TwhzJuj6uwvjADO3tTWZE6+u9Of3+mxs6Xh4eHX//4Ugzlcc9/ezSGXWDIxzHqqLjCDc317GYGc4ZMp459jHfNOjuQrer99UB3eS+OpXNjt4345G+v5Pp63MdXRtiF98nWQ8iua0oJ4fbwszLiHae3LPJaH6ysblInjNqPGi8f3Lthmmf9F8ojTHGGGOMMcbMhH9QGmOMMcYYY4yZCf+gNMYYY4wxxhgzE3YoTwl5rvlEFAHbmC/ebOqlffHFl6X+kR/7fbp9zI+nC7ayojmUnJHNPLwR8pRK5PkVNdlX9LlSlWLyP9S5V9PzliJ2fgZZhCHo3Pqvf+UZqV+6pl7GfWsPSt1YXJMaV/IWCUPTs7joeNKJqaOK+H64Fns7+n0eet8TUtPP7U14iP2+OitjeAj7O+oczrc04zKWyFSDi8t2lZwbfJecuZN0dvD5TkvvI+a3HiFDbhH3BU2HDoLQmg34pzUucuJp0MFMfNqJDSZO4vTcSHrKSat62x1HO5Pm9HF4qH3A+bPavy/MaR/3+uuvS/3Bs98vdY4+q02NEPfJ2pru7+YNOpTw+Eod23Pc6CX7xJKenx4PM/GYS5lNdErJazVeNsdiKpXkPRf1XDz1/sekfuHFb0h96ZL254mzSbetZiyuW8+gvgeGE5/pyd5cvz51ey14hc1Mn/sm1yBgpvP+/q7U/QOtESmd5DCOxsw/5Vir7W6cM0NT6ya+e44npU5Ln8Nurt+Q+vBhdSjbHV0fgY9JEa7xIp6hdw7VuaxfU+DOxzM6k8zo5OsnXs6A7ZJZ63UfR580eW3pf55ku8YYY4wxxhhjzC3xD0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCDuUpYZzrfPROS+ebtxo6/3thXj2Ovb19bBEeRU1+0tKS5vPVyQOcT1+hjvQ0avwuzume9DySzyIXi/9qsrKgWYjMPnxo7azUn39WPYzFOZ3L/4mP/RD2oG7BZ/6/y1L3Vukw1s29h2eBuf9T9dKQugMFHMwGPJedDXgbUdtaAR/2YHdnYme8TnRSdFslwqZGSfYTjVNlTLcYGkeO7RU52l0XGW+ret9cuqT+Ux9eRaut1zKDB1IOdf893LcHAxxwDUkmXBJPNZFDietO9bYuyqpO20hyLk+4fSqZUbKu7Feau5MmXK61ixelXj17RupvPv8tqZ986imp2UcyB5Lj2YXz6g1+g+4zOoUxsnu5nkGSO5lI/XQm8XK4Pel9XONtT8mbDiGE8yt6btfOaibn55/5stSXXtNM6Bs3kOu4otmLZW2U4Ak7uRroxjVb6v31kXvcR/ZiI+ojPHOOq4nx8QiZnPPn9Zkui/Tm9LsOsW9+da6jUTDVkx5f0m71P7SQx9rBWH2wd4BaHcpz5/U5b8j9V/qctgB5OTJvla5xTUZq4lxOvL+gx5xkhU9vZ3U+Z7KsRrJ2RCJZ6qEmx5OmWt8K/4XSGGOMMcYYY8xM+AelMcYYY4wxxpiZ8A9KY4wxxhhjjDEzYYfylNBH/l1WM5+73VO3a3NrU+pxH/Ph5zQPkFO0V1aWpeYM7xLzzYtc56cXyMKqimQCvtZNzBmnezFRc247D57Tyf/EH/r9Uv/yr3xa6oN9nYv/Ax/+kNTnz5+X+tFHHpb6X3zq70m9u6neRndZHc0S/6zT4NR7ODQl5+7Tf+U/E/FUw5XodTTLavuaHi+vdkb5rZq8FnAI4QrQ56FLEJrabvePdqSmO5CPE2ly6rGOCzom+vn5njosB7hPdvraNnpzerx0hYtMvY/5ptabFdzixGWQ8ha+7O2lHzYDOi7cWpbIt1PLlBPqRPQ07E2a08D8nPYRK2fU61td1fqFb39T6u0dHYvP3q/v53oGGURCjj+JbYUbNc0L5FjMTOjpvlaJusFY5Injh1J+i6Odzpl59eAuntXc38/+2q9J3V1Rv5QxxHF+VesTZjinnerJPs4+L+N6BshBbmKHG9euSX3hAfV30z51YltwEvOxfneOzd2enqt+vy81s89HQ+SdYmzmdx0jFzOinTPDtNHQxnQ41LF5v6/PyA+29Zl2NNL7YIj7Yh7PwCG7/bodb/yHgJqLFuDlyc0lQe7cVnhLJPpmXSZ1uqCBlI14fK2njdP+C6UxxhhjjDHGmJnwD0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCDuUpIZ2/ru5WK+p89laT89t1vvjm5rbU53uYi4950ivL6lCmniIyjJB1xRzNvGTmETwNvJ4VNLgm5tMnDofWTTiHi/Debl7TrMHHH9ecsO1tdV4+8fEflvpf/9t/K/WDDz4u9R8885jUX37pih4g85oAp9OnGUEnhF5Jprlq27sb2D48P/wzVH9wnLXYauuLfXgVZ89rO1s8q87LJvyi8Ug/327rseZoZwF+UF7ouaWPWsAb6eC+4n03TDLdmOeEnEm4z/Mt3X9MsqzQ7ukv0XeKzCSd2D5vBPpCiStV41nUZF+dlMTVeqviiDHvAM2G3sMt9Mdz8+pYcn2Dq1fVg3vsyfdJTUeRjuO5s+rg05kv0Ydw7C3geTPrkX1OTUT0LQao27/G54oW+iiu1dAaqyf31a+pjxobeq5/8id+Qup//S9/SeqXdrQ/7y0gx7EmE5pOPjOd0z526uYS+JjDXOO9bR2b74NDSc1vMqe52dCxM4NTee7+B6Tmc8HhQNcPmMPaCxGO4xiOYhv5rez/maGZjznWBX0dY+/Bke4vNOmr6vHlEGzbc/r+dqY3Vp48g9ZcXLaFKYsQJM0ocmzmJ+qcyOkedG2+Kt+QTXntNm8zxhhjjDHGGGPuGP+gNMYYY4wxxhgzE/5BaYwxxhhjjDFmJuxQnhKSKc30NpCf14BT2enq6zvbu1Kfu/8Ctq/7Wz2jmUTJnG6IGEkuZaEeRxp5BzeB8XpJ9OJE1hW8uMWMUolu7NmvfFvq1VXN9foP/+gflfof/KN/LPW/+NXPSH3l5nWp54LO5X/0MXVkSJbMX69zy5A1NSUX8lav53BqOnB+1l+5OmVvIXS6C1I3u8duxPhAncESgs/rr74k9eBI22ExUk/j/Pn7pN68qY5lE85jPqR/ND0ojOei3VXPg47j/u6BHi/dYeRKNnFfdLt6X6Y5ktPvo4rvB+pUTpedUq8C+07aIQ+Vr8PtrTlWY04laPdYriB00IfMz2l/+dWv/rbUv/tHfrfUJRxHqlgXzqt3nhwePp9POO4hhFDSqUxcamyP8lfFPm/K3yWwrYWuZv39sR//Eal/5dOflRpRgOGp979f6u/94JNSNxGK+dpLX5K6s/rE1OOrU9P4nFK3nkFtn1kXx93WR/S9XV37otHU16sG1tLoHl+b8b62g4P9Lak3runaDsMhx2J9TsqH2s4acBQ7XR2bxwM4jjiXZZJ9yHU1kLWO7fdH+voIz32jkeZeNiLWV8CzQq+t2z/AegrpY9sJhNkkKHL6egYVw8oTjxFrL6DlJTGXNZnUyQ+AO8yP9V8ojTHGGGOMMcbMhH9QGmOMMcYYY4yZCf+gNMYYY4wxxhgzE3YoTwlNiBp0uziXP8IlaDb13w6uX1FP7v0fUDeBc7wXl+al7rSRcznWWdoFJMg81/nrVcFsLGbmaR3hhTQmMo84H/y+M+qsbG7uSP3Fr35L94356v/sX/yK1JdvrEs9xnTzEeb2P/XE90j9f/9ff1/qxz7yMakb9DQwXZ2+LNU0eh2E52eAc99b0Dypw719qUcjdR+yFpzMwXFWWMTBd5A9hWYT2k19fYysLOatcnvMDWOGG92AYa4eSSvo9obwLM6cO6f7g6PSyOCs4AuOx7q/Ro37cNLcsjLJipz8nzUOJV5NDo1u7tucE1mfrWXM3cdgoC4VomVDBoe/11NvcP9A3TS6Vzn6Z94nZ8+ewefpLsOfghtGb5xjb5oJjbxAiH4VRccJ34vPKT34pVnQc3njimZCv+/RR8M03vvwe6T+xX/2z6T+8NMfl/rBxz4s9b/6jS9IzZzitEvCcxf80cjBmX+yqdHKY0u9vgba0s6GriFA140P9MXh8XjGfNH5BX1OKkYYG3EuusidHJZ67fiMypMHxTJ57iqRIV3gmZLPIa2O3lejNtYRwQ4bTa2LHM9FA808bTGgtWYNAuZk8llkcj2EDNvmsdSSRESfdOycvj+uh5BJxujt9+W/UBpjjDHGGGOMmYk7+kEZY1yJMf5ijPG5GOO3Yow/GGM8E2P81RjjC2/+/9X6LRljjDHm7cBjszHGmLuBO/0L5d8MIfxyVVXfE0L4vhDCt0IIfyWE8Omqqh4PIXz6zdoYY4wx7wwem40xxrzr1DqUMcalEMInQgj/aQghVFU1CiGMYox/JITwe99828+HEP5NCOEvfycO0oTQ7qh7kBdwEjFfe66nzmMXOZRbW5pB1Kh0fnmFOd6dnu5/YUG3v7m1J3VR6Xz4fKzHOyy1bmH+fFZyvju8vQlvo4m58Q3M9f/o0w9L/ZkvfU3qAFfg6y++qtuDS0ZvLTb0889+/Tmpl89oxmeROJI4HGZd0ZFJ5vKfbP78oK+uRGdtSepWS7uFzXXN2bxwfkU/3zs+3/FAz33WQBcDH2dwpB5GxdA1ZEW14SO1kNM1RjsZI5uK2y/GaHe697ByRh3Kg6G+P8LToPNJ36oJJ6aJHLFhjvxWXns6PtXtczbZjkjifPDzvBR8nW7vSXK43vjAyd5vBI/N7w4HcCBb6OOYb8cM6CtYv2AfzvriyrLUvE/PrNGh1Ne5fkHqUOrYy/UK2AUXSZ+MzDt6hBP/u4VeJR+qB7e+qc8NTz6l6w/8oZ/4Can/l0/+Xan/n3/+Kalfu67rHSxmur/953VsTpRGPPdkWBOgwcxN9qEcQGrWR2CfORzrEbU6+py1hbGYF7+DPPIqO35uazSR4whfczRUh5D9O/NRuS+u08HnMiwnkDzHMHOZsB03sX5Bo6nPHtsb2rbaXT2eLjJRyyO9D8s+XGccDz1DkvHBbuJGzhk4yvfinot4vWRQOyiTZ0JKl9Nf5/IJ8ow75WvfyV8oHw0hrIcQ/l6M8csxxp+NMc6HEC5UVXUthBDe/P/np23EGGOMMW8bHpuNMcbcFdzJD8pmCOEjIYSfqarqwyGEw3CCKTQxxj8bY3wmxvjM+vp6/QeMMcYYU4fHZmOMMXcFd/KD8nII4XJVVV98s/7F8MYgdiPGeF8IIbz5/2/e6sNVVX2yqqqnq6p6em1t7e04ZmOMMea7HY/Nxhhj7gpqHcqqqq7HGC/FGJ+oqur5EMKPhhC++eb//akQwl978///0nf0SL/LYbbV6BCZQZiv3oLXtzCv88s3tzSbscE52pibnyFr6syKLhy4ubkrdZXkUKp7Vhas4X004IbRPZu0H+AhvHhFn59a8No4/TwyHwnzyT/w+PukzscHUn/jJXVi8oZucOmsniu6CYlTSbeMHkbN63UM4LGU9FzG+vrGdTqU6vB02hMOZUXnROsh9l30UKNdNOEMNuAr0d+hK3zUVy+Ep25cMD8VnkhHtzeEf9rAte6gMTVxbuNYPQ1mkFb4PlWSU3l7L+ONHRxvMGkmJ3QWqySH8q1tz7y9eGx+dyjpciEbt9vV8aaJ7NoOshiZkzy/rE4778Jz57T/bSDcln5WCWdyjP6d34cOZoB7l2iCFZ8VjuuLS4vy2s6+OuXPfu1lqff31Fv7jd96VupDjB8vXL8mNTM1zy/qufrNZ78s9dp7H5P6rUbt0qur7yP19dFIjx9lWL98SWq2jQ7W2mhMOPoFMpjzXK/7EA5lA7mSGce2BjMzMRYiY3pQ6bUdo51FuLncPp+B2104lG297/YG2lYuYCwvRvp9Fyut55v6LBICczYBrzXXGJi4WkneKZ9J8fydrC1xQiLu2jLcfu2FW3GnY33tD8o3+S9DCP8gxtgOIbwcQvjT4Y1+5R/HGP9MCOH1EMIfO9ERGmOMMeat4LHZGGPMu84d/aCsquorIYSnb/HSj769h2OMMcaYO8FjszHGmLuBO82hNMYYY4wxxhhjhDud8mreZejdMQsryUNCpk8b89kPjo6k7iMvrzunGT2cw312Vd2E5yt1ITgnvEQOJbOt6G1kzMZCBlJVHjfdCn7nCB7e1156TT/Lf0Ypp0uVuxvbUueV1m3s7wDzzZm1uLSoOY6IAH3LpO7c9DdUyHPaG+n3uXLldak/8KEPSD03d+xQFnBloT2E8Vgb6uGRtuMGXOCMDiUyMkv4QwWkk3ZLv9t4pF5FnvhD2k47bTqL6mns3NDVMQ+uXpH64pIe7z7awgiRcHRQCXPM0qzJiWuLPoN9SBI4VxNnemKLoyYKKwm7spNpTgH0phvo5Lrw2Oh+NeGmXbl0WeqHHn1Iat7jK0vqWNLZPDhUFyzH+MQcSnrkLWTbZugnymRsvv3fJboL6q2dgQP/6paOpXTLPvcVzYzO2sw1Zh6ffv7yhq7t0GzquUo08RNmOpM0MxqbT8ZefX+ro23jAAss3Lyh6xnQGW0jGzJkx9eS6wMUeKYaDNSx7PXYP2sZMVY30e4PN3Ss68zrM+VgT9eioDMZK6yXgIu1sIy8VpzLvTHa9Q1d66J3oG3voYf0mXa3rffNq4mHeMIHt8nzl5za6WMfnUo+VyWO44nX3Zje7u/0rvBfKI0xxhhjjDHGzIR/UBpjjDHGGGOMmQlPeT0ltDB1b4TpCU1Mae0gKmN1VaMrGAmwu7sndRvTaDIs6byULG0+fblsTrvh6wWm6DJOIeL1ySmyWTLzAMtPYxpKlsySwdRAfL7C1ImtHY1++M//9J+U+m/87Cel5rTKisu6Z8nEmKklYcRL4GrXYITv02jrNJkM13Ljhk7JCmgLvYm20utpuxmN9WBijqkbmF6M2VahwLXg9LIOp2ajnY3HbHe8+AqnXC3P6/c5xD/Bfe7X/q3UP/zYeakffUDvu6tXETsS9QvXJHUkVMkUrSnzavhOthsy/VQFznRLp1Zzii0jUDzF1Zw+Wm0di6mDcCZ5b0EjuzoYW2/c0JgrjrUZHtPa2D/H9v19nXbPhIAc0Rt5yVq/QCyhMeDGZ5c6qXCMoAg8/Mh7pH55+yu6bfRZPJc/8IGnpL52U3WMV6/r2Jw30Z8jxoSDK5+LuP+6yJQE9qFToiTe2B8zxHSPRwf6nFZAJWq39TlwafH4+96AblEMcXBIxUif4fT9HYyV3Z6OxWzn/aFOcU2VDOgtmbb73pxur4PxpI9Tt3GoDX/9kqpPHzqjXxhNJazM6bmMm3UORx3Hn6e6kgzVdfrJ1HH/5KRjt56MJObkNvgvlMYYY4wxxhhjZsI/KI0xxhhjjDHGzIR/UBpjjDHGGGOMmQk7lKeEDubGc259i0s443W6AHQsNze2pD534ZzU0P7C2jldYplwvv2Y3gbm/iexIg02zSZeP54fn5X00KZ7Cozp+MBjj0n93PPf1HpXvYzOvDoxn/stfX9Z6Nz8pWU99oM99SB6q7r8dRoNATjhvcZ149vHuZ6vblfdBy4rf7iny2uXcEInnZ4c287Het0z+KqjXJe457LuFIDmsCx6BpegCaG2i/f3Bzv6frSzfKyO49kVvTavH6oHUjb03DWb6hs1W3ofzvVwnyYOJf3Z6dEadEKTaBB9VSsucc991ziWdJGLOq2jbinzt+iBGPNOwPUMDo60T8gaes/32tpHzHW0T1q/qdFDrQYcTd6WuC8vnl+T+vVL6lDyPsvhlZc51idIop8Yk4I1BhKJ8nh/L19W//61y/A7mX+APqBAH7W3syn1zrbWZ+YWpF7fRzzaocZULdasBUFBtKhzJuu6MCaUoeZ6C1wQYQltaXdHx7PlJf3+kw9+fMYaN3SsKwp9xixKrauSx6ZfttNGJEsy3iASjM+oWPshYixd6OrxbO3j2QL7K7A+Q2zpuVs9o+dqblGPf26AsTlDxlfNc9dUOFYmUXbT3x8zPgdMr5MfAAnTx+K6WJN/h/9CaYwxxhhjjDFmJvyD0hhjjDHGGGPMTPgHpTHGGGOMMcaYmbBDeUrowHNjplsBybHZ1EtbtXT+eQtu2+VL6jq8/6nH9fOYQr0Et4zqFr2K8QiO5Ejn75ddnS9fYEJ94ntNzOevcC5ig8GUzNXSbT18cUXqTvMDUn/126/g2HTrX37+Ral7Pc0FOzzakJo+aS/ouazLoUy0uhO6ZwXO1/q2OqLNOfVjEV8VCrgF3fljF4Hu7jDody1LvTbjEb0MbRcVrlW3gxzKrvqsPLd0CulZ0PUlEf/mtsBsrb7eZyPuD95GG8fbyfT77iL/Kcllw/FliTNZ3eJ/vQkjzvBZXok6IYiOTFbSu1BSZxJ7m3jZNqW5W6FTuLu/g9eR4YbWvLKs/f3Gljrq/UPtE3pz6lwy93LtnDqUdJ9L9Pd05Om9F/D4WoHbm55PmE04ljnXM+DYXNHHRMYl/uQxPNLngt09rf/ST/+k1P/9//S39FhzPbd1Hng8YVZuWXOu0hBs5camOqEBXuPyfY9IvQ9HdGFe28ri8vGzzfVrl3BsWNcCLi3XPyhwndkOFxf0OYpjZwPXvsHMT+ZSYnzhM+0Yx8eFPpidONfS/V+8cFa3Dz+12dH7JEZd7+HEP58m7svaFMnaZvc2j5BvU6yl/0JpjDHGGGOMMWYm/IPSGGOMMcYYY8xM+AelMcYYY4wxxpiZsEN5SuD888A8v0Lnd4/gSQwLrTs9dcGuX78pdSyREYRJ1WdWdL58kjGH+fXMthoxh7KYno1VNjh/f+JYq+neQgmn5MHz6ghev6rZWEtnH8CxYW5+CKj1XOU1rzMTiJlBU6MEQ+pM0qmsc9VKOD7/5svP6/YWLkq90FUvI2a39/zmkNG519d80wreQ6OtdbNEJuZYfZ7xSN/PHLG9XfWR2siM47njqS6YqVZqO11s6uuHCBJr4ly1GurANJt6PO3EqeG/8en3T9pKIjtMfiMG2MFPSoMhUcJ/otCUeBfl1JfrmPRdT/pZY94p2McdHapkTjesgbG61dKxt4c+Y3tD+7DOe7QPYfbuhQvn9QCT+xbZwOhDi5wuGtY7gMeXIQOPTv5knYwVXO8AY/P95/S54vLNa1J/83XkCHc1S/D//dUv6PaDXqv5eSnDeKBOZeRaFUnOb83fYE6aQ4m2cmldHcp8RKlTv89BXzNQq1LHw6UJhzIgl3GEdSyaHIsRfc4A7wHO3cKyXotmW39eDPFgNMKzwOKcHnt/oM+0HeS38lmgQB2Ri3l2QY/nDNpaluE+yTEevtWfS5PXuuA9euKNTX01w3NJwWfGuh3WZaHfbr93+D5jjDHGGGOMMUbwD0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCDuUpgXPtOz3MJ9/Q+d90HkdwGOfhgRzsaxbhGN4FFMawjCytW4iFAh3KHBPqx8idbMKpLAqdvz+ZrZXMB4eXwQzL9U31+jY34KO+vC513XxzOpsV8prG+DzilJI8J0qUiRPJw6lzMOmY4vyMcLEKdAt9fj8qoRPXah6SStzCdRvh3AyZN6o+Ej2+0UgdxDnkQpZwXpptfX8LXsfBvu6vgHucjfXLtoK+3oIDuXZWvQwokyFroi00kL2VTW9r2Xc0obFm29P1zre6dWNOBc0GMp6H2ge0mGfXpgOpY+3qopxqz0wAACAASURBVLpn129cl/r8gxekzqP2mefWsJ4B+3P03yNkQo+Q3Vsil7IcYyzG/jM4omGij+PYybGygbHo9/yuj0r97Ne/JvW3r+i54dD43Ku6HkJvXp9Tjgb6Xfd2d6Ve6uq1SuP53movxk4UPmrFsRh1pgPKPvzd0Ujr+aXjttFEFnk14GfViUyW7ci03Zcdbfedjn6A+eBdPLPuHui559g9wPFlGExLPFOO+5rJ2aZDeXZRj7en52OE/Y0LXiv+XOJqGTVMPpilwe1aRj7zYf2DkjIufU/uG/+FC28k8HjuzKL0XyiNMcYYY4wxxsyEf1AaY4wxxhhjjJkJ/6A0xhhjjDHGGDMTdihPKcylbMHT6HTU4+g0dP75wVAzfsZwx44ODqWeW9D57wuL6mDWhScWRYEauZN4fQwPscWsq4n309NAnFAo4a0NMPWdfmqGGehVYBYVHEccG51HZnF1ce0Sz4RKZZgOp8fzE0n2YkVvT4+HGl8707bUaCB3c3j8gVZHvYUsarvLI7Kj0A6qSt9Pp5HZVfQysiacR3y3NuqInMmq1HMzZEYbzzXuo7NnmG2FtlXSnai5urwYybV++6CfWZ4wDTKy3dELqclPpTZizN1IC9m2WROy2Uj7NL4/yWZEf/rqq69J/X0f+X6pK7hmZ86sTj9g3Hc5+tQxnMoc6xlE9NEZ1lcomFk9MYClTrjWa2fVcXz91W9Lff6cZiI/d/mGHgv9/ga+KzrMRgtj75F6c+n6BPibC84916qozfdLwLML+tAso5dIBx9eI7ITu3PH43EHbu/gSJ3DCk5iWTKIUr87x+KFBb2WHazTMYajye8aKm2H7Q7uM6w30MJ9dDjQZ9bOkjqT5xb0eBrZ9L+njeESV0k443QP8p2Ezy21JI5mzdvlu95+X/4LpTHGGGOMMcaYmfAPSmOMMcYYY4wxM+EflMYYY4wxxhhjZsIO5SllaUGzq/IjnZ9ewUncWNdsxYWVJamzqP+2sL25LfXcvLpqKyvIoeQk7Iq5N3AgkX2Vw+NotiA6Yj5/WRzvr4BvmUEqxNT8EFra7At6EMzXy+g5KBU+QKey2dRjH+7puW0vntHtwbuLbzH7inP/65xRugFzi+pejMfqDQ4n8ptW187La81XXpT6YKCfXWjotvMx/B74PjEy8039obmuehKHyLpamNd2v3ljU2p6JYyiOhrotV7AtVqY0/1HnFvmjOWJs0O/VvdHJ4m+7aRjU9du6NKWaNkR9yx1qLzOkUxqY04/7D/b8MYPhzoWczxoYPzheHJ9/abU9OYqZt+uncMWkG1Yah+Scz0DuHD01hstrl8AxxJ9oGTksY+BfnV0cCD1V7+xJ/WopbmSzN9jTmOSuYxng0M48W1mG9Z6cCd7PekDk49PF8mzOOXchhCWsJYF16LIWsetawnZ4ddvXJW6B6dwNEQ7RRh5A9e9KNRZ7MGhHAx0bOV37x/ps0ED9xWfaefnNPN6a13H+ojM6NUVfX/SVnCf7Y051vJaYrysC2ae0rYSnxS9QpE8lHLTfBB4d0Zb/4XSGGOMMcYYY8xM+AelMcYYY4wxxpiZ8A9KY4wxxhhjjDEzYYfylMJcx9EIeXkZ52DrnOr5DvL7Iuez63z3Ei7YEjJ+6EhWnAMOj4P5fvQg+f1yzCGfNM04vbxEFlWjQVeMc+HpeTD7j9lQ+nIy/R0fH8M5OTpEXlLin3L+O+bq4/hKHk/NVH5S8fvi+ywv4FqDOHG83a62qxYclRjVERyj3dLlTXK/IB32+/tSd+d0/3t76ui0OuqznofzeXNLc84uXb4m9c6B5pY9+fD9Ujd76oQWeH9ZaZc7pL5KbxE5ZJPucAipYylt54TRVLwPKvo7bCaxzg8y5t6n2YSTX07PXD6CR76ypF73oK+v9wfah7R62qeurp6VmjmVMaAPQb7eeIgcyrEeb7OJTgouHcePSRetgf4KkZvhAE46B9cKayuk0YF0JrkeAnIbMx0f2k1kL9KrQx9a/xeY6X1m/aenr3dQISN07ayuIRDH6u8WE9d6cUHbWbOhYyFzJZtNdRjZ348Huq+DQ/Vfez11FjeKDanbWFui39fP97HOxsaGusULc/p9xvh8K78g9eqSZkTnEfcBHUrkydZd/doM0mkZ0jWfTcZavM77okzyrqcfWrq/Ez48/LvjmOlTxhhjjDHGGGO+6/EPSmOMMcYYY4wxM+EflMYYY4wxxhhjZsIO5Sklw6Tps2fVo9jc2ZKa89XnkJfXaKhncel1zX969InHpO7BVWs09fNFDvcN88fzER1KuAxjeCi5vl40jl9nRk+GfKAxJq834IpR26MHEelV8O2JB6d1u6suwuXDHanPhun+aTI3nzsAyatwSAO8kiSPEPvrddULjPBfJ3fYamk76La1nZSlOjGjsX7XRkP9oBzvR7MIjZae26VF9SquXVEnku00drQ+s7Ym9dUbeq22tjS7a/WDel80IhxJXKsh/KQBGxNzyOqyHHGt6FroW2s8jcB2Mf3Y6jPbFGZt0WU25jTSbuvY2mhoH9Bsa5927syZqa9HjO1bG5pbvPaA9lFn4dG1ICoWOT08uGNw5/Kc6xcgl7LQ7xf18EMx4aI1cY/n8DUb+O7so9gHFRwK6TjCi+P22tjf/vp1qeeW1PtLnMYaMZ0+KdemqOszmRlNjz3DtV1e0mzJgy3NGy8nMkXnkR2e4blgNNLBaIxM6KLAM15L201vpOd+dUWdxauXLkudRJXj1LRael/1kCH9yqs6FrcQlJz3da2KhQVkXh/p2M781SN8XyxnkOTDcihPrvXkteSyGeFkRGTRJvdNsv4BH3KTB4+pB9SY6JOm6ZX+C6UxxhhjjDHGmJnwD0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCDuUppdXSS1cGdbP2DzSf7+BA8/hWML+dHsjhvn6e//LQgouwAKdyZ1f3xwnyJRzLHI5kiRzKcow5483j16tiWsBPuu/EJZsew5hOcD/hhPcC3sODj7xH6rLQufshw22Z5F7Cy8DX59uTzCJ8vu70tTt6rQvMv5fcNQipK2fPSX39pjorQzg1nNtf0veB35NFzWybg+/ZRRZWWWimWwM5mYfIPesgr5X+0dk1ZMDh5Oc5MuUQPFkwI47XNk4PFc2oPkxrmzX+Tl10VaxpJye9MWpUYGNOBU2MxfNd7XOu9jWv7wgZz91M1zNot9ULv35Vs3DP3a99agfZuguLuv+dHXXJKtyn4xGyC7GeQQHvO29pn9RkVvDEgJS4WxndrulZtnQW6aU16BzWZDoPh/pd9/Ccs8CPJ/l/NX+DSRZkQK5wjVOZOPAoux1tG/QgK+xv1D8e75ZX1bU9c0bb0ZWrr0td5FywQMsc7WR/X5/51s7p2Nhs6Vg7v7Agdb+v9wVDS3tz+v5krMTxhBGeDfCMurm3KTUfw46GHHtxAurGO46XE9caumcaHT5bDOQJSEb3mvrO/vbov1AaY4wxxhhjjJkJ/6A0xhhjjDHGGDMT/kFpjDHGGGOMMWYm7FCeWnSOMz2ORkv/rWAR89UHA51fTodxd3d36ut0IRYXNCMocSiR2cPt0dNgtmQDnsZ4QvzLse0W84GSbKhElND3B76fWVTIicRk+brp7//RT/5xqb/029+Q+rWbmiFKj6TOM6mb25/mI019e2giG5IfmMzKGg/1Oi4sLkpNp3F/X53KvKcOJB0cOjN0YjpdvQ+6Xf389qZmZ7XgK3WQRVVCdmi19PgunNNMOTTb5FwfIdurRFuiE1mrLb4F6AfVKSGJn2SMCYvzOrZev3pTat7j+/vqND740INSX7ukGdB7cNOY51pAbj6zon3S1rZ+vsT4OMbYO0Yf2MZYXcGt49hdTjiWRYksP3Rw7O+StRbeqkuGP5l0kL9dITd5jOeiJrw79pl1uZS1h3fCnMoefNmKz0lYf2EwPB7v5pDBvLSs62i89vrLUudRx8rY0HNXYvGGwyNtZ8u53hfNth5bCz5oxHoGfGbsw4ns4Vpu3lAnsl3p+yP81gKNb4TFJPYLtlXcd/xzHEXIKTptsl7ByR7hTgzV3qr8zvwt0X+hNMYYY4wxxhgzE/5BaYwxxhhjjDFmJvyD0hhjjDHGGGPMTNihPKUwv6mFjJ8284oy/beDiPnjbcxv39rakfoA3sfCos5fX8Z8/MuXNTuLoXP0NJg3WCCnkplIWevY26iKCu+FAxnoZSAbSo80mSvP3EfO7adGUfDjEOHGcFKWFuBpqFaYZlPxeED99Pvp74hxujtQ4fhbE25GDkdwbkGdySxD7iLEg0EOx7Gp7bhATuR4rO/v9/VqtrvT7wM6M2Mcf7urzgrPzcrqsh5fiXY90vfvIYeyYrYVGguTr/IkQ/X21zJ1ZU8SWpmSZMYl26sRQWo9ETua5vTB9QAaDb1r5+bUuz6LfL7lJe1Dej0dD1575RWpP/4jPyx1ifUFziP798VXNV+Q9/0Ixz9CH9gr4Uhy/YM2MqUn3LpW0gWgD4GHF7OT/Y0j6cLwH+q0th/8wY9JvXug48E2HP2IPo5eeeKl8/BQn9TAbDaQP17Ac4QsNzle8VgX5nV9g15Xn+n6fWy7pWMh80r5XHB4qJnP8/P6LLC3uyf1GNnjBc59CceRzwIN5FYu4Bm4QFvjeLY3wH1Q4VmFCxqcdIGDicPLituvQ/EGWLeDz2A1Y22d3lk31p7w0eC2+zXGGGOMMcYYY+4I/6A0xhhjjDHGGDMT/kFpjDHGGGOMMWYm7FCeUuhp0EUYD+BBwMtowGXjFO6DA80UOkIW1tKibm91RXMoE7cAx8cMozGdSTiVFTyOUB3X9PCSGeN0vzD3nfPXOTU+1uQ2JtpEjRgxgKOyCM8wcd8wXz4Genh4f5I5hAPA29P9adlCDiW9w9g4ditGQ31trgfXdkld2411FUbHQz03zUy9jbpzO8LnW209droIw7EebwNu8f1r56W+eUGPd2lJv18xUu+EftV+H95JjYZ4Um1j8lrSEflOw2irmIobxtxzdDrqSHbhbZe4a3e2NWf4xg3N66Pn3e+rizYcqFvGzOnVc+pQEo7FOdYzoBtX0F1DncF9qybG8grjeEmHHn0Ux9rkLx416wfUwbHyiSe+R+obN29IvfXCq/p5Su3cfjJWT3fd6sZe1j20tXLIHEo9wGJijYEcGdHLy+ruzs/rM9ygvy51jvUNSvqkONY+2mmvq8+MXK+g0cD2mvrzpIfvNofMaq6VceGi3gdstxEZ17sDtNVkBQMMaDVrAiTj78T5Spoxn8lqlu0gSTtLstNrHgJrnM1p753Ef6E0xhhjjDHGGDMT/kFpjDHGGGOMMWYm/IPSGGOMMcYYY8xM2KE8pdBBjJjv3Wxqvb6jbgCzsObm1eOg+3V0qK5ZgUndK8sLU19vNDE/nqE+8DZKeBlj5FA2J+bD0/mIQb05TvkuMXc+o2Opbz9xVlQdxUi/y+rymekfqHHhkqn6JefD1+UHapl4LXh/3t+Xunvuvt/53+MDzSstkQW1uKTZVx14FaORehcZzn57Tp3FoyP1i+gWN3Lt4jJ4GhFSTIRPNN/T419dVN91Ht/v4Ei/f1HBoYQKnLStxO9FDceTbTlUb0FcTPJTmd/Kcze9TtoZMtIqSJY1EW7G3JV04XKNR3Ae+/DK53SsHMBJX1pSl+0V5FAe7Wkfs3hW+9S1cxxP6E9pn8j8QHp5ZQ/OJcbbDGNzURz3oWUJDy3XPiDJhK5Zz4B/AaFrlrHD5NiHPuawfyT16oqOL1W64AK2T1dtugRZF9WbrIeAut3W8Wa0vyt1o6Pj06RbVzJfFGs3LCFLfB/bzpCBGTHWbm6qGzzfY0amfpcm1maYa8OxLPVZgO0O0emh29H/sIL1D15b39b9IQtyo8+2N90rPGkM5SR17SChzrkMfL5mu0c+KV3emt3dKf4LpTHGGGOMMcaYmfAPSmOMMcYYY4wxM+EflMYYY4wxxhhjZsIO5SllfkE9jAqzoNstnZ+eNbUejXQ++uqKehd0KLc2N6R++LH7pV5c0vnvnIVdFpizzRxKzO8vkV8VkCFUTdYV54PrvrNExJieBcV/ZUlzKKfPME8zfChK6B4urq1J3aiYyanvZ15THczGOqkl2mzo/g+3NZ+qd/7YoWzA3aWDsrSsftDK6qrU169cw5Hq5+lIJn4n2u083OACbi4z3UYj9ZnCUF9njmYD5ybNV9X97Y20nYck6+qtEaX11uSV8rPTm2ka78p2Tt0oySmbnq86+bp1SnNa4FhLt6w7jwzoyMcuvXHW1jQ/b29vT2q6aitwKFfPaB/LPrLgeMc+a4gs3cSZ1D6s4voHE2N3slYCnMnk5cS7xnMEMjpTp7ImBxKd3NGR9u+Pvueibj9ZXwCbw/6TLpPOO/pQrhFwC6lSmJtTX/dgQ9fGWH3v47q/iWvdH6jbu4yx98LF+6TmM98QY2PW0Ixorntx1FfXd2lR22lZajs6OlCf9eBI12pIMpnxTNmFg7m+rt/3U//6M1J/BM+w21HPR2BmKpcnQFurwnRPUcbfumBJgsG4QsNk5nNVuyBBnfs7G/4LpTHGGGOMMcaYmfAPSmOMMcYYY4wxM+EflMYYY4wxxhhjZsIO5Sml29W59N22zmdvwM3Kx+o5tFoN1Dq/nS7D7q7OZ68woZxuXJK/FOgF6ufzXL2NAi5c0USA35Q536kTmQRR6pagsXHLSUYPtt+ocyqx//muZkmtIRN0EblmO8gpK4qT/TtQFumv6uuJ2oaa2WCHN9StuPDE8es8FTnc1yb8omVmX+1oOxsO9bt3epoTRqfy8FB9IzqTWzuaRXUIr6S3oOee+azlkO1wugMzgkO5O4aXkWRdces1TlDC5LWAd1HUZFRy50WNV1FOLdPvMl25hMdhi9KcDtgHQfNL3ORWW53LAbIQ6T/t72ufuAWH8n3xPVKfX9P1ECLDGhNZWssCYzHduAJ9egGnsqwmHUp4ZhxrawYf9gLMjE5zIOm1AfyHAXzRi8gubKCXyku6bNg8u2ee+5pcysTTA3PIbT7cVIdy/r5HsLfj/ZXV9OvYw3PHItYLGG7c1G3j4EtkLu/s6lg7wFi+gXZ8cKjtvImgyfme5mZefu2q1O0FfTb46f/sp6T+0hfUEf27//hTUldr+v2zCOcTPmwseG1rcpcnFiEoEzk3TId9SsF2XpNVfsLcy+S+vMMP+y+UxhhjjDHGGGNmwj8ojTHGGGOMMcbMhH9QGmOMMcYYY4yZCTuUp5Q2nMl2R+fW95EBNByopxHhUcxh/nl/pPlMh9jeeKiTvhfn6VByUjjcMcgCYziTJerk9Qmvg25YRccDnkWGLCx6GZwhnmRpNegOIH+oJqzq7Bl1ExYWdK7+k4+8V+ovfOObejzJPwNN/3ch5jVNnx+ffv82fNujI/UU168dexytlvqhg1wdxRLXpg2fqDev52IT7m6zg9zIofo7Ozt6bDi1oYX75sJ96mU0kD1FjXBhUe+TItc3FPjAEDmWUHZuQV3G6fRPFxNXL+OVpEvL7KokJpKC0HTRg941M0jZsuriWo05DbBPSSKUc70PBnTikcF8dKhjdQs5l5ubm1JXGG9WML5w+MqayIIMHIu15ljN9Q3o5lUTfWCJY2uwj8HrBdwypvTWr2egr9P/5xaZybm6qs8x8129trt9/e7M4i0Tr7Am+7cmj5Af53jZ370u9cYNrTsTGahsl8MjfaYj7Z4+U46T5ywd28Zjff3wULff62k7j7hY585r/mqyHsMwaTxSvv+J75G63FNH88y8Xssf//jTUv/2vp7bb1/b1f0liiTzXfE6xcXJss6ZJFz3YlrGZZgh5rKmnd4p/gulMcYYY4wxxpiZuKMflDHGvxBj/EaM8esxxn8YY+zGGB+JMX4xxvhCjPEXYozt+i0ZY4wx5u3AY7Mxxpi7gdoflDHGB0IIPx1CeLqqqg+GN+YM/IkQwl8PIfyNqqoeDyFshxD+zHfyQI0xxhjzBh6bjTHG3C3cqUPZDCH0YozjEMJcCOFaCOH3hRD+5Juv/3wI4b8LIfzM232A5tbQV1pc1Ln/OSZRn8f89P0DddNWVtS7aHfUhdvbO5B6c1MzhnoLOt8+w/zyRKfiP2Vg/j1dO3qSk/mCealeQw5ZoAXpkFFPGbadNZKwLBwLXsbbaW20MT39iSfep8cH7/AjH/2I1PsQ777x4su6/4wT7CmS4IDqgjb5MhwguheN7nFW43Csxzo6UoeSh1ahnTQ76jHMLajjWEAI6iEnsrW3o+/HDpcWV6XO0DCzhnaJe9vazs8u4T5jBhuOb/dIX2+19GJk+nLSNt8S9CLoUuHt3Df9JDohicfB94fpflGdk2nuCI/Ndxm9OfWsW039A/HOQN2ufKT3wfve95jUXC9ha0s/3+9rn7uKsZzrJdTpUkXB9Qu0k2o28AfvZM2A45qOISnRBzS4QECNaM1IzcTZbEzvsxaQvTg/p+PNE+95SOovfutFbA9Zg/TOQZLFy0xsvp44oXq++nvq+WUYm5sT3288gMBPQRVj8TLGuqVlzcs+xL7m5pelHvS13VRRd3hmRTM/SzzHjdEOD3Y0/3o01nPxe37vJ6TOC91/Z0mv7UP3X5D6c89eCyehLvuRzyr6drSbugDSmhzKiGNJxvK6fNa3idq/UFZVdSWE8D+EEF4PbwxWuyGEL4UQdqrqd5JML4cQHvgOHaMxxhhjJvDYbIwx5m7hTqa8roYQ/kgI4ZEQwv0hhPkQwo/f4q23/NEbY/yzMcZnYozPrK+vv5VjNcYYY0zw2GyMMebu4U4W5fn9IYRXqqpar6pqHEL4pyGEHwohrMQY/938sAdDCFdv9eGqqj5ZVdXTVVU9vba29rYctDHGGPNdjsdmY4wxdwV34lC+HkL4WIxxLoTQDyH8aAjhmRDCr4UQfiqE8I9CCH8qhPBL36mDNPV0uurh8Z8KOJ17Y13dsPc99n6pmxlcL+xvd1ddtXm4bhQ1mMdEd21EbwNzyhuoJ3Mqk1ws+pZwAyKyBsuMeXvT844yfJ5ZUhWytD7xg5p31Ouqt8E/H6yd14e7j35Q85XyoeaYPXcFz4sUS0CqUE5/fznW8xugYlx44D2/87+vXn5NXmu0tV3SO+jgui4taXAks6yO+vrdm229FmfX1IugextKPXjmTka09OtHem4feVS3nxe6PXoT/ZHuv4tMz314IBWvXV3wJP3eiTrNldQ6UZ+y2+fJ3Wp7JaXMSLm45tjB25WF9V2Ex+a7kPl5dSgXl3VsvHlT74tGE33CgfZ5nTldn2AEN2x3S8fi5SX1yjOMR1iuIDQQDpnn2qeVyMks28h5Rh87uYZBWSG3MQ1R1m0nnRaeG9BppY4hXufYjAUQ7juvXmC7rWPzhz70fVJfwnPTlQ3NBKWHyO4bCmQo2N8n31/LVqXX4gA5zR988vul3t4+nnkwGsBphESZNZFN3qVfyqxzzUtdwDNgs6k/L/gckeeaY5llyLDG2hHrN9Wh/OC/90NSf/yjH5T68m//ltQxQ14srtUNrAuStfU+ZqYpvUR6jMlz1UnGNw6tfJ5Gu2EzyurGen6XJEN6trH4ThzKL4YQfjGE8GwI4WtvfuaTIYS/HEL4izHGF0MIZ0MIPzfTERhjjDHmRHhsNsYYc7dwR6u8VlX1V0MIfxX/+eUQwg+87UdkjDHGmFo8NhtjjLkbuBOH0hhjjDHGGGOMSbjTHEpzl5Mh/6gY012ARwEvYgseRoEcyCPMlz881HpxQd03uhKcA8454nTdKniRfD2fqOlwVDj2Em5Ag/l59C6Sg5vuk3Ku/FJH5+r/+7/nd4dp0DRrtjSLcWlBXYbHHjgj9VV4HHtDdRMIXYAkDZB5hfQEu+pSTLYtehM5zhZzwYZ0ZHAtenP63cvIjFBtJws93f4RHMwh8puYm9kf6PaGQ71PmPFWFnofFDh5QziSvLY82fQyaknch+PtJXmrSRYVLjSFpBq3lrmUdR5GlXw3rSfd5BOfB2PuEtbWNF/v689/S+oRs2sxFj/wwH1S95AJfbivfdrOnnp0Dy3dL/X8gjqVO7uaKV3RM8R4yuMrMB6UOTz5/PjzSX50k6MN+hA46OxTEmccW6Ojz34kw7PB9z6p6xMwP/s9731Y6k/88Mek/vwX1NN77eZNPZ7InEo93rpsX7pxvYYef471HzrIQG0fHV+bw13NrCwo02Lfea7rFWQlch2RqRybGOsjnEg49jl+fjAn8/plXb9g3NDt/Tf/1Z/T7V1/XerRSLeX57r/I6xv0O7pucOjQC1T4ljfqCfaIvNXk/hStvucY22ycbw+/f18HE/us6nc/r3+C6UxxhhjjDHGmJnwD0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCDuU9wsG+ehR0FwYD5PfBdWPeX4U53kdHfX0/PI4zZzXPqQHVLcd8/YoeH/OlmH3VglM5MR++hHdRItOyUerBVPh3lApaW2KSYcJ5RD5fgBf4w09rFlQHDkwd/FeeZkudzIWebu8+5JztX9drXSJrMQv0FnWPJcKyGgHn84w6QuVEtmOFa5HDUYzInRwja2prRz2Py5euSL2+h1zKA/WB9nf09RdfeFHqzf09PR76tLi2RzvqFv8nP/mHpD64/rzUo6F+v/UDvW/aVU2Xe7LYycTb0PciL65mW8nn6UQyRA3EipmeBWps356kuQeZQw5lmaOPKZlrrJ9/8IEHpO4gD7A/UEd+/YZ6excvav+8uqre9/YOHMqK7hzXK0B+IL3I/PbrH3Ath2Zz+mCb9Dk4OQWOFY8ZybllIN+Hn3xC6gvnz4VptDF2n7+wqtt74r1SD5ARfX1fzzVJFVE4n1yfAc8a/fll/TxylluN47aTGOzoz4dwGK9evyH1pUvqNF5f35J6c0/Hyu11/e6vvvqK1IdwHI8O9f0HEP3VkQAAIABJREFUeBb4i//1X5I6FHgmhUs8wjocOe6bbWR4lngOaiC/tcwTUTGchMnxjvnXRZLZzOdldBIF/NXEHVaywGe82+dXvxX8F0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCPyiNMcYYY4wxxsyEHcp7hG5PPbsMjiSzGrf3tqW+/37NrqLrMMp1vvvhkbpqDWQQtdvII4RnQfmrKultwIuskI1VTuZQ6lz5MepmgWaO+etJ7CQzfjAjvcR88xbm6v+u7/9QeCtUcBYjXLg2nJqFOXop8EwaFDWYq4lrAXegE9VnXb7vQakP947dh4htH8KRvPbaS1I/9/xrUn/hS89I/cILr0r9+nXN3IzIoRyhHfX7euwt+KgNeCQjuL6rC+pDLa+qQ7N3XdvWaKTbe/XahtSdVX1/bKqjk43RNjM4qXG6pyiv0fFAzmTWgFdB/wjnNsmZzJiZxnY23d1NPY6J/LpgzCkFjZdjYbetfRAHoJdfVtes09Lc36MDHXu5/gHv49VVZES/gj6D923iwU/PeeazxeTYXSC7sCh1rIoYizm2ZuyTEm0N6yMkkrl+/g/+6Ce4galAwQy9pl67+TmtH1hTp/EmHMrEEaUzyUxsnNsHLy7p9hsPSZ2P1SuM2bE3eGNLx6LDbT22bz6v6w18/jeflfp5vH5jXcfiPMmx1GtPv7TT1rGV7y/G+v4//Id/TOoSz6AHhzVuMDKhX7yp6ylkmd6nbItcF4TXkuNrmU0JssTzb8aceHyUOZINuMXJeInnIIrabNeBnje3N3ksU17zXyiNMcYYY4wxxsyEf1AaY4wxxhhjjJkJ/6A0xhhjjDHGGDMTdijvEXrdBakXF9SbGPTVZdvc1YyfQ+RMNjCffAQXjvPTG/Cxuj31/A4ONAOIGiPz/+hUFvA2csm6om+JbSV5etMzMJucG9/Q15mFtbakOZBnz54JJ4Hz1dNcL3ULoFSGVlNPZrehnkqFmgGEY3obyFI8u6Rt6dd+5TNS/5NfP3Yt6L6unNMMtJuvvi71+pZ6EPtwdel7VmiXMep3Kwbajiv4n+2OOi9sd2F8JOVHn/6I1H/rk78g9Ycevyj1cE/b+TPPflu393vVVW5EdMG4j9C0QwNtsUTGanOi6TKbipmVUCBDgXZPdypjYB5I7uHIvD36TXA4J661EyrNaSWHU99u6T0+P6/jxfWr16V+7bXLUp89o+PJpSvahw6QHzgeax+0sqJeX5K9y/GS4wPWJBih5hoCk+MX+5BWBxnHyNeLzKOmp13zegYn8f3vVcewLncyAV1WxLlpw8lfmVcnvoOxdcCs3khnXt+/uKzOZNmHN3ignuHf/J//D319cJy1+PxzL8tr1y9rruTuvo6VB1h/oMJDW46xtwk3eDjWZ8yA79rBM2IY6LX7yPc+KfXFVT0X689f0v0N9dyM4GwOh/p9ru+hbbZ0+42m3ldDNIYC6yVkeA5r8e91Ex8v+BCHdtbrajvKR8xh1+8ypmvc4mDP3eG7QNpkHzb57mRthtvvxhhjjDHGGGOMuTP8g9IYY4wxxhhjzEz4B6UxxhhjjDHGmJmwQ3mPwJwaqAmhwJzrgwPN4Jmf10ygxUX1PG6s35T66JDz03W++dycbi+EHS3hQhTI4ipz1JgjXk7m72E+Oud4R3hqGeeAMw+PU8QxPb1ENuA5OJPT5pjfimQ6PefLM2cTLgCvxTb8WGYQ0VCjn7rcRbcw1mv7G1/4vNRXbh57hzc3dd9nt/alfvCCZlguNtR7GO9pOynpj+5ruy0oBuK7NXAt2vA8crazI60fvl8dycND3f//+n/+ltSPPHRe6vVd/f7XkEt58dHHpR7DXWCGKPOqWIfq+NqVNWmOdIcbuA/YanJ4zRluDDqTzFijm0UfKU5u/oT3kDF3C4MB8vZa6ort72ufMBiNpr4+HmsfOByqI7m3p33Swb564KvLGJ8SQxnjAcbaik4/PUZkJRYTfWqJPoPjekSfUcIRh2GYjNVcz4BZt9//AfXwTkzJ9QyQzQuvsFmznkFRMTM6w+u6vw7CD4/2dLz81D/5Jan/5ddflbo/cS3nkGd68aKObcs9vRbDDR2reC44vvBaM5exhXPBrPO8qfv/kU/8oB7P3pbUh7v6rDBGBiez0wd4jnn9kjqk82v63MP7bDDQ+4rbH4+Y76r1cKJfGI/0nuY9RKmS7x/juzCLPXmErTg2sw5TmXQuh6Phbd/nv1AaY4wxxhhjjJkJ/6A0xhhjjDHGGDMT/kFpjDHGGGOMMWYm7FDeIzSbeilbcMX6Q53/DVUh3LxxQ2rOwc7hUdBdOMDc/sUFdTDpQDJvr8J88xxzwpnbU07k5lQl5o8zIxPfJtbNF6eWlngaur1xzvnvJ2MIZ7LfVxfggPWBXsuNTXUJnvvmt/T4mLXI3Ekc/5lF9V/3f+hhqV+BBziujnMqc1wnpEqG0NF8pUZHfaOsDeekgEOZKJPwf3J9Q6uh90EDNVpl0jb+wA99VOonzunnP/3QqtTX9vVaduf0Pnj55Vel3tg7kLrCv/El9w1ci4p36kRJL7mRyMG6r1ZLfR3mudLzSPLscKwR/mfMWDMcq7r1/zbmFNFB1i3zW9PcR/08cx/T7cGrbur29/bUY19Z0Xw9OpBV4kLTsWcmNDLrSvhcE957QUedfUgLTmFdAi3HZvZB6FIeeuC+6durocL6Ahn7OPSpOTKcmcNZ4vsx749u3PKyjh8bNzalfu7lK3rA8HWL/vH4mnXVoWzNa770qNKxqNnWZ8rxgGOPHjuHqhIOJZ3JgExpvry6pNnq21v63Yd4LholnqEe3xDX4oUXX5W6dV3d5WTdjkKfVYoKa13g+yZZ6pPPuDhX9JqTPgLHUgTcR1yPAMdS4v3JOiL82yLXFZk84Cljs/9CaYwxxhhjjDFmJvyD0hhjjDHGGGPMTHjK6z2D/gm7i6mFBZYeb2A56q2tbakXFzEd4tVXpOZ0gF1Ms1lY0OkKnJrHP+lrZkAIAdMpGDOiM124fDWmB0Rsq9Jmn0wPwLTNZHoAjuXaTY1U4f455YnThfePdLrw3oGey91dnYqys6dTM16/fE1qTpnlFCdOH+Y0otaSTrPZvLku9fqBtqWiOzH9OImG0HbGdpc19Fo0sIz6kEvY42D5fma+REytzpo6JajBdoep10+9/xGpR9dfkvqjH3hM6s986QWp93GtWrkeT2zrfcrjT6abcRpNcfvlwLk0eBLVw3au7w5lMjWaE4QxFZzzhRnfM/XTOm1nNEomSxtzKuhiauHikk45XVjQentH+4grV3Ua44MXH8Ae9E4aDfReGQ50Wf+lZR3LCae2R46fjA1BnUSDVMfHU+bTxu0QsjB9OvAtHAfsG30a9JGlRX0OqYO7G+G5aYTIhMGRjrWHh1q/8IKOB1e3dGyn6lOiP//IU49Kvd1THeXqjj475B0duyenYcagc0ojppxmiPWIsUa/oEo0PY0mNKBlZZlO5W5h6vYq5sAWI1xraFyM+RijLexCy9qHbrI6vyw1p6wmqhSnrXKKbBKjwvM38VLS7vlRjMVoNxy7k0mpjAmpnVrOKL3J99/+s/4LpTHGGGOMMcaYmfAPSmOMMcYYY4wxM+EflMYYY4wxxhhjZsIO5T1CEhuC5bizjHEMOqf64GBP6jPnzkrNJf7HcJwGR7qk8tycumFUIDn/vMJS6BWjLorbR3NwKW56Z4lKhsnwavXdKg4Bx5bp9g/gUTz3/ItSP/Xk+6UeY/lqLvO+twOHcluvzU0uHf5t3d/+nr4/Bi73Da8QrkS7pdf+EMuFb+yoq7By/3HbYgRJMeaS8oh4ieiCIn1W+D281vAUuIQ+vQ3G6eS4do89ps7kmXl9/+Ual+FLX/mG1GOKJXBy6LeWJZcip0zB+4J+8HFjT5wXnksuDU41ophuZkTcxAWOjfE6iWRDR9NRIeYeIKKdN+FxtxHHwNuuj7G021Pvm57h5s6W1Afw+JaXERtC9y25T+GqoY+p0IfT+5t0x0p+OTrzjFTB21O/E31Moljq9riewArOBRmN9bscJusbqLe3u69j7dYu6m2N9CqGOh7m6O/pq/Yw/hzAY78B/3bxInzZyWuBcxeZsQIY+8H+nWN3hQGE7bSJZ1KOxQViPi4i7ma+q+/fHOt9MoY7TLa21Zkc5fp+PpsUJcfqgHr62h0835ONO3UmGSU0PdonuXJJFBCOjc+8JdZywH3KZwHdnmNDjDHGGGOMMca8zfgHpTHGGGOMMcaYmfAPSmOMMcYYY4wxM2GH8h6hg9zJJtyEPEdWFfKU7r//QalbLZ2vPhgjf2mobsJBX92BhXnNQ+IEdCqSkfl71fT8vXzC66jgIaRz2ZEnlKk1yRnhiUvG+e5wRjbhOP6dT/6c1H/7f/xrUo/hChweqqexva0O5XU4k1945jelfuklzUZszalzk7WYPxWmMjen126IfKp9eClrE45QSb8mVw9hTIcQbgCznip6gGjXJVzeZktfb8Gh7CD38Qju8Me/78N6vHt67vsD9TD6uK92DtWxIWN4HyPcRyVc4sSNSHwl5lBWk8XUYyHJua7oIsPLmOJShHALb4OOzYmOzpjTSbenfc4IfQbzYg8O1YvrdTV7cDTQPqQq1Jvb3dE+7eIDF/SAklxlLSuIiWWlfXoOp7LFXMqJwZ05i+xjmPmc9P/8mwf6NEbjZniweOm1S1Lfd1HPBfugg77234f72t/z3DIj+qVXdH/r2xu6PziaOZ81Ek9P63FTczW34cuu4Nlmcq0MjhUD5DSSyOzCmizDHM81WRP9PzKomdc6KvS7bN24rPsrPiT1+obmYzfgdI76ep+9fkmvDdvieKD7T5416nImk/H29ustpD7rdKeyLsOZ7y+43kGyjojWXDck5c5Ga/+F0hhjjDHGGGPMTPgHpTHGGGOMMcaYmfAPSmOMMcYYY4wxM2GH8h6B89Mb8OaO+updLC6od7G1qfPRm3Ao6Q02sb9d5C112urxcQ4452Qzd4fz0+lU5hPz2Ss4HAFz3ZlLGZEjmSGPKVb63UYj9SquXrspdQV3YHdDv8vPwKn8Yz/1U1Jvbqoz+c1vaa7k5z77Wamf+cqXpB5DJGlXcCjhQDab+v2SvEAkc+4W6gBBLQijiazJguceU+/pvCQ+D+fq07HBtaTX0Ouol8GMNbbrBuSCjz31qNSDI3Vk2JbGfb32V+C7suUzH2qMXLIGs7nQtumx0J2YzLfiPVXvQdw+N+tO4L9OJj4Ucy9v0SsYc6/RhrfNvLvRSMfmHH1Mp6N9wmjE9RC0PkCf1es9JDW6/8SJzND/05VmVi77YPHBuBZCoQ59CDpWJXl8id9Z49TjWD77uWek/uEf+Ki+H0fD3MqtHX2u2Ubm5/XrN6R+5itflvoIGdNZhkduXIxGU8fq5cUVqa9sa1vheDZEFuNkLjRzg+m3jjAWJWM5s8Dp76POmFOJk93GWD3q63f57G9+TeoP/8D3S72xrtdifl6fabeREfr1S1ekZrvmWN/qqbucZEPCLU5en7K+AXMoGzVDbxJpedL1EZL/wmPTV5lRLdm6U3btv1AaY4wxxhhjjJkJ/6A0xhhjjDHGGDMT/kFpjDHGGGOMMWYm7FDeozSRv5fDSdzZ17n9nO+9uKpz9wOzGxN3S9/ewfz4WwRM6efpVHKDOP5qYj7/CFmHc0le0PS57QFuwD68iY3r6peO4IE0M/UeqrbWh4fqPfzs3/37Un/py1/B/tTLGGJu/7mzZ6U+Gqj7UMIr4b8aVbmej+6cXqtXXn1N6n+6pVlaTXh+o+FEJihzx5A9OMa1ajZ1WxmOnYplmk+q36UJd5g0muoznV9ZlnrtjGZwDuCVRIR4Hg712l6+el13iPuG1uBgpM5ODzmUSfZjkg3G8z2xL2a/hpOR/Gtj4kROfTk5Np675GxI6ZRKc28wP6fZgU047Tk86Rb6wHGuXly7B88a41fW0PtqgGzcLjKrD7BGAMf2SJeOuZPoZzRvj54dvDOOxY0ar5quFz4/wlj42V//nNTf9z2PSf0Hfuz3Sz0Y6PFt4jnp9StXpf5Xn/mM1M9/85tS95aXpJ6b0+cqZkQvY22LGzf02eOrzzwr9fyCtq3BEA7lxHMT2wmvBbtjDF1pFmKNY9npqh9LP5RrO3B8eG1fx8a9Hc3rvnJJn5PWLiAvFffR61f1OYbrjtBNbnb0+Ok9cj2D5HW27YmSY2d+0vGOz9O1a1OQ6ftLYzEn1maY8jn/hdIYY4wxxhhjzEz4B6UxxhhjjDHGmJnwD0pjjDHGGGOMMTNhh/IeZa6rXly7pd7EeKTzzYugdetI5+J34F1wDvhRX+e3NyPypeiCNZEPmATt3D7D543/MJGvlGRYomb4YVNfX+7pXP65Uv+d5dxj90tNh2SAc7m4oB7e937wSalfv6Zz/x+6f03qiMzPvX29TcfImmo01DU4gpdXjHE+cK17XXVyblxXD3BzXT2OVkPfr54MchbHyG0cq0OZNfS7lZQBWObTHULmozbgaWQQQ9g0biJPdXVFnZZD5E4+/6pmYeXI3mrA46BXkcPbGDO/NXEk4a3QD564FxCxmdyzKbqt1LmkNIl2BW+DGaBJEFmEpDP5XaxQmnsErmeQ5tnhvkNOZV6qp720rN73ERzJfKR3bh8Z1EsL6vXt7+p4Qe+dfUxZTc8jnHRCC2Q0s//OsR5BhkdSxjaW8P93N9WL29vZl5oPuP871i/ozmvW4PpNzZj+jc9/UepvPKsO403kd587p+sbcHyhRs6/6CzBifzK174h9avIUuz0dLxgW5Cs3xo/v8BzR1lwPOC143oG07PKW7gPMPSHRkv/w+EA7bih37XorUr91df02j373MtSrx/pWMv1Gvj9i0LfX2F8S9YRKSgyTlk3pGZ8Y151okzyPyTLESAbFtmy3yn8F0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCPyiNMcYYY4wxxsyEHcp7lFZLPbcK87vHY2QXwm+aO6MOZsT88cNDdSbn5/X9w9H01LtIZ5I6Fb0NHP9kliLnvudwUBKPAzmRxWBbamgVoUB24jiDQ9nfk/r69WtS/9zP/W96fJj/vrSg2VTdnp7rxaX7pO4j+7APb2KM7MTRELlhmG/fhLvQxfnpH+j2Oz31aYeTjk4ytR+eBbyLNC8JfhG8j9RS0P9CL6ILl5h724en8bf/+a9LvcxMtz11dF66qj5sbDJ3Ej4S3Qe26wJtNZuez5pkXU2+VhccWetU4uPYFy9dVbPB5HimHrsx9wZt5PZ2kc9HL7qFbMIBshXbGNv3Rjr+tJraB+7sqxe+uKheeBV1vEqhe0ePG+NLcfvsw8T5zul6wRkfqt95E+sPHCEnslHpWFbA47v/vY9IffWGfvdnf/O3pL586bJuH1mKZ1d0/YMhnqsO+/Bb6amPkaOJz7fQdnKMD3MtHd8KvD65ZgCvW5LPXUx3exMnn69jbGsi87lFaZKfx9j98k31Y//4f/HfSj0e6Vg8Spx8LWPEegoRzyJ4ruNaGcytTMaoZHydfRQ76Uf5PB2z6RtIr90JHwZug/9CaYwxxhhjjDFmJvyD0hhjjDHGGGPMTPgHpTHGGGOMMcaYmbBDeY9CDyNx10DJLCnM5a/weeZBjeFC9AfqDmR04TiHmzmTIM2aPD6eEtmG+Ujroqt1VaiXMIZz+cv/9FP6Ov7dpQXnkN+N577R0rn3zbZ+fq53Rj8PB6YDZ6bR0e0PB+pUNpGjeXhwIHUOuWB5WfOcOJ9+BIe03VY3Yu/g2EGlFVeWcAKTdsTsKziFMBVyvk53ADJu1mJ+KnIxcXw3djXj8/mtTakDfdwk3JFZjKDGjchKfn/4S9hgcldPCrJ1O0+OZbrPmjiTiS+bSCv4gP/90nz3wfEgGWvpEuO+ef65F6TuI4eYfSr9qKMj9RAXFrBIQNIv1IXk0YXW/eeTfWR+e78yhNSpXJ6D857rWg0PP6hjVVVqpmY+5Pigh/rgg+o87u9j/YQl3f+FNR2bl+GfDvp6bofIFT481LF3xLUlELS5gO+/t6ueINdzSBx9nN844S1ybGVuZOrCsoOfnu9Nh7KB55gyyX1EW8DFGgyxNkSlY+8I15brE2R4vYHhJ/UUkRkNH7VA/irPfRMZ10noqGSvTx9rkyOL05+f08GY29Nj4dCc+LR1fcBt8AhvjDHGGGOMMWYm/IPSGGOMMcYYY8xM+AelMcYYY4wxxpiZsEN5jzI3p55EUSDLClmDw119fR/Zhpx0PSp0Ln+rVM+vg/wk5gMW8BYJ5/dzRvfkHG96BWXJPCFkPcHrGA31u61vaa7X4aF6DG8/0z080oSbkMHDaMPxbMIjbLZ1rv9jjz0pdaer7y9wrbOOeh6TXkxWNze/YBYWciThHSQqQFKjC2NWFLyMEu2Or4/66qMyK5HOIh3P9MrVXFucsCFyy2KD11q/XwPZXnGibfCeIzz3zCmjf5R8vi4sq5ruZUQ6JsbcgzC/rtNhPp+OxRtH2gdwrGVoM+/zrKl9wsbmutSL8wtSR/Rh9LOyZP0CZCmyT584XuYmctyvUJdj9UN7mY69zNceY9+HY+2/c6yv8Mxvfk7qiP6Xmncs9FzO47mqP9L9jQs9/grPXRkk+KNDdTjbbT2AOTiVA7SNxtpFPV5cy8ZkH4v+lr33mO2M1539OZz5ZKRLngWmj/10PHP4qByr2e6T8arugNIgZWE8hKtc8j5UsgzZ7zjfjYnnsmaTmZi6Lca0R4SA5jnzvLFOR2v6Tzvew1wHhM8Zk/fJqH/751OP6MYYY4wxxhhjZsI/KI0xxhhjjDHGzIR/UBpjjDHGGGOMmQk7lPco9DaWljQ/aWNjS2p6HVmjmvp6ybn6mLM9PNL55t1uV2pmI9bBOeWTMC6J88uZr0SPb6/UufqtLm4LjcL6DlAnCip5TlkA+UwjvBymn+vz5+6T+ux9D+nWmf1Il2Iyy5FeXeIG1OUd6Qd4LZlrGflvYvQsAvcH94C7x3eN5XSflRoGN8icygY+UOH1MRyiMJ4uhmRN9TbChPvQgqfRaMKLiPQmsCm8n5mfPHUN7C851yC98nfmaRhzmknXN9A+rY9cYY5fq8uajdiH4z/sM6dS77ReT8fiHH1mC31SrWsNl66c2F6VT3e/6O0NsR7C1UuvSf3Nr35LP5+MD+gv0adh+Ej6X9LpMEOUfSZyh5FzXCATOzSmZ5K+Fy7cytr9Uo8wuOfMIE2SoCeOLRmMcR2T3Em8fYxzjf49g9vLDZRJJil82iRTk5nOvFZwh5mTiXfXZSvSYyzSwX0qdCwjruVkdjubXU6nEWsjlA3mUbMd0omEZ41zk8Nd5n3Zgoc9KRdPO43+C6UxxhhjjDHGmJnwD0pjjDHGGGOMMTPhH5TGGGOMMcYYY2bCDuU9Squlc/WbTXUg6Vg+/vhjUr/nvQ9Kfe3KDal39tXbOER+3vaWvr64qA7nAT5fF2mXzp8/rgs4IPQKkiwseBujXL2ERnbabgv+u1BNgCAYj/X8zC0snWhzk65cZDYUrmuBaxOTCz89q6pCEiRzxBoR7gHchAhPJFFoCp5LHB+CytIoqxpHEx9o4NqVGf0lfJ5OKD2Uid2X9DWT70JvA84K7yu0E94n9DoacEjGcMHorGjWVk2HYMwpZX5+Xup8zPsCHjnuO/aJY3h17PMGyNMLDYqFcM8YlatlKLigAfqoSRePfij7/wKOeH+o32U41n299vplHM3d3k9wgJl+vA8+9IjU45q87ipOH5zjhFtXYmwaUyhNciJx7TgYMX+U4wf6f+aBM1eS/ucYbaWVTT93dDArPFvw+BupxC/Qjy2nLwkQkrG5Yubq8QboChNmNCc2J+9h3pIjruWAPFTc1VXy3KHnqjnpVE6RKP0XSmOMMcYYY4wxM+EflMYYY4wxxhhjZsI/KI0xxhhjjDHGzMRpk8XMHcI52MxPquCCvfTSK1JfvHhe6hsb6lAuLp6TemNnR+oRvJD2gmZfMeemQfeOWZLIcxpN5PR0kNlTwZFMtkWPI5nbH045J/M2Wh3NMiwxvz/PNRctNlZ165OXjk5h4uHptsvEq9N2QAeHwg6zE5mPGgOv7XQnZQzXgI7KNJf3ltBxqeg1Ts+ZrCPNejze3njIXCz6n8iyalBqxPt5suGo8ONNvD/Hd43wNHg8xtyLMNOZfQTdZDqUB30NRmZOJV/v9nr6/px/R6gZL2oGRGbtTn58hGy+JsaWZP0DOJWNBnJ2Tx0nG4sj3Lgx/NdWR9vGaKRjc5K9OLF/5o2GmpxgjnUlnpuSiOZk+IBDWeqxppnQbHd0LHE8jM3k/pl5zfUaOBbz/JxwLK6nus3/vsU7k3uOayfoqzzSJDuc+agVQ6dxLnKtR6OJa5ku7HC8mdu+YowxxhhjjDHGTME/KI0xxhhjjDHGzIR/UBpjjDHGGGOMmYmYztX9Du4sxvUQwmshhHMhhI13bMf3Fj53s+NzNzs+d7Pjc3dyHq6qau3dPojvFjw2vy343M2Oz93s+NzNjs/dybnt2PyO/qD8nZ3G+ExVVU+/4zu+B/C5mx2fu9nxuZsdnztzWnBbnR2fu9nxuZsdn7vZ8bl7e/GUV2OMMcYYY4wxM+EflMYYY4wxxhhjZuLd+kH5yXdpv/cCPnez43M3Oz53s+NzZ04Lbquz43M3Oz53s+NzNzs+d28j74pDaYwxxhhjjDHm9OMpr8YYY4wxxhhjZsI/KI0xxhhjjDHGzMQ7+oMyxvgHY4zPxxhfjDH+lXdy36eNGONDMcZfizF+K8b4jRjjn3/zv5+JMf5qjPGFN///6rt9rHcrMcZGjPHLMcZPvVk/EmP84pvn7hdijO13+xjvRmKMKzHGX4wxPvdm+/tBt7s7I8b4F968X78eY/yHMcau25252/HYfOd4bH7reGyeDY/Ns+Ox+TspRtcdAAAgAElEQVTPO/aDMsbYCCH8nRDCj4cQngoh/Mcxxqfeqf2fQvIQwl+qqurJEMLHQgh/7s3z9VdCCJ+uqurxEMKn36zNrfnzIYRvTdR/PYTwN948d9shhD/zrhzV3c/fDCH8clVV3xNC+L7wxjl0u6shxvhACOGnQwhPV1X1wRBCI4TwJ4LbnbmL8dh8Yjw2v3U8Ns+Gx+YZ8Nj8zvBO/oXyB0IIL1ZV9XJVVaMQwj8KIfyRd3D/p4qqqq5VVfXsm/97P7zRcTwQ3jhnP//m234+hPBH350jvLuJMT4YQvgPQgg/+2YdQwi/L4Twi2++xefuFsQYl0IInwgh/FwIIVRVNaqqaie43d0pzRBCL8bYDCHMhRCuBbc7c3fjsfkEeGx+a3hs/v/Zu9cYabL7vu+/05eZeW7LvZHU3shdiispsmCKwkLQxTAEUgFsmRD1QoLlyAEt0OCbJJZ8gUQJCBy/cCABgiQDCZgQpAQmUCwxFAEShiNBoCjEduINl6IRmlrRZEiJXF5X4mV3n2dm+nbyYmZ3qn7neepMne6uru7+fgCCe56qrqruru5TZ/r86l+Gvnlp9M1r1uWA8iFJn6+0nzn/N2SEEB6V9HpJT0p6ZYzxS9JZxybpFZs7sl77dUk/J2lx3r5P0jdijLPzNuff7b1G0rOSfvN8StK7QgjXxHmXFWP8gqRfkfQ5nXVW35T0UXHeod/omwvRNxehby5D31yIvrkbXQ4ow23+jZolGSGE65J+V9LPxhif2/TxbIMQwpskfTXG+NHqP99mVc6/1EjS90h6R4zx9ZJuiik0l3KeXXmzpMckPSjpms6mETrOO/QJ340F6Jvbo29eCn1zIfrmbnQ5oHxG0iOV9sOSvtjh/rdOCGGssw7rt2KM7z//56+EEB44X/6ApK9u6vh67Acl/WgI4c90Nn3rDTr7q+jd59MdJM6/O3lG0jMxxifP2+/TWSfGeZf3w5I+G2N8NsY4lfR+ST8gzjv0G31zS/TNxeiby9E3l6Nv7kCXA8qPSHr8/K5KBzoLxH6ww/1vlfNcwbslPR1j/NXKog9Kesv5f79F0ge6Pra+izH+Qozx4Rjjozo7z/4wxvhTkj4s6cfPV+O1u40Y45clfT6E8O3n//RGSX8izrvL+Jyk7wshXD3//L742nHeoc/om1ugby5H31yOvnkp9M0dCDF29wtvCOFHdPbXqKGk34gx/vPOdr5lQgh/TdK/kfRxXWQNflFnWY33SnqVzj4kPxFj/NpGDnILhBB+SNI/iTG+KYTwGp39VfReSR+T9HdjjKebPL4+CiF8t85umHAg6TOSflpnf3zivMsIIfwzSX9bZ3eC/Jikv6+zXAbnHXqLvvny6JtXg765PfrmcvTN69fpgBIAAAAAsDu6nPIKAAAAANghDCgBAAAAAEUYUAIAAAAAijCgBAAAAAAUYUAJAAAAACjCgBIAAAAAUIQBJQAAAACgCANKAAAAAEARBpQAAAAAgCIMKAEAAAAARRhQAgAAAACKMKAEAAAAABRhQAkAAAAAKMKAEgAAAABQhAElAAAAAKAIA0oAAAAAQBEGlAAAAACAIgwoAQAAAABFGFACAAAAAIowoAQAAAAAFGFACQAAAAAowoASAAAAAFCEASUAAAAAoAgDSgAAAABAEQaUAAAAAIAiDCgBAAAAAEUYUAIAAAAAijCgBAAAAAAUYUAJAAAAACjCgBIAAAAAUIQBJQAAAACgCANKAAAAAEARBpQAAAAAgCIMKAEAAAAARRhQAgAAAACKMKAEAAAAABRhQAkAAAAAKMKAEgAAAABQhAElAAAAAKAIA0oAAAAAQBEGlAAAAACAIgwoAQAAAABFGFACAAAAAIowoAQAAAAAFGFACQAAAAAowoASAAAAAFCEASUAAAAAoAgDSgAAAABAEQaUAAAAAIAiDCgBAAAAAEUYUAIAAAAAijCgBAAAAAAUYUAJAAAAACjCgBIAAAAAUIQBJQAAAACgCANKAAAAAEARBpQAAAAAgCIMKAEAAAAARRhQAgAAAACKMKAEAAAAABRhQAkAAAAAKLLUgDKE8DdCCJ8MIXw6hPD2VR0UAAAoQ98MAOhSiDGWPTCEoaT/JOk/l/SMpI9I+jsxxj+502Ou3xPifQ9WtuHbbDm8Db6BZIXmxYtFve2vRLTlVbe+WW9//cvN226t7G0BgJ0QY8x9w+M2SvrmG3db3zz0bdbbftmwbF+c6++8P/36V+vt557NbB8AsBJ36ptHS2zzeyV9Osb4GUkKIfy2pDdLumOndd+D0i/+bxftoe19dFBvD2yA6QPO0bj5AHOd3POn9fZiXm9PbXnVR/+Pevu9/329fXqzed85bcf5PvhtGgyvA1d+O8wvJjdzFOibJQcJjRso/EMnJBX2zf/t/3rRHt5VXz62vtn7yoENQJ339S73B9gT60/f+z/U27//P1cuDvb+3Gn7waT3fknf/4a1znN76z832378bbQ8T30wtMxp3vBlvcyU14ckfb7Sfub832pCCG8LITwVQnjqha8vsTcAAJDTum9+nr4ZALCEZQaUtxvjJn8iiDG+M8b4RIzxiev3LLE3AACQ07pvvkHfDABYwjJTXp+R9Eil/bCkLzY9YDCQrt64aM9t2szhlXr7mk1pndjw16fRzGdNe0+XX7Ht3bIu98i65Xll2s93fH992V331tvPLjnlddswiWZ3Je9tize381k0PmUp7NM0mI7xoe+r1n1zGNanuXrfGw/r7emk3s5NafXvDJ8iG+xawCMb1esGSXr8u+vt31f1Aft+IvKdh0vY+imu+yzT+ba5SFuhZX6h/Iikx0MIj4UQDiT9pKQPruawAABAAfpmAECnin+hjDHOQgj/taTflzSU9Bsxxk+s7MgAAEAr9M0AgK4tM+VVMcZ/Lelfr+hYAADAkuibAQBdWmpA2Vqs5xh9Cre3jy1HkYtD+a3M/e62XgZkPrUN5MqSVF6tu+6vLzq81nxswLZIYohLTMfP1a/DDmn73nIy9MZiLh0/f9EeWmbS39tj62u9+8tlJn17yfeEH59nKl9Wb4+PLv57eixgP+RCa03fsXz97o/kIm49b/4yGUoAAAAAwB5jQAkAAAAAKMKAEgAAAABQpNMM5SLUa0l6rsJrWQ18uGvTgH19bwd7vNe5fN4ylffZ473WVvXYfUry6EArlZvxnMTaMhkU4LLWWcKITOUe4b3dWif+3mU6oGGmL0/qVNrjx1Yj+qZlJv1a4bHX1tuveOjiv7/waaHRvtfpbOA36vAbCqybd4h+DeztUaZDXVSWzz24vOHnivXxwVNHF1r8QgkAAAAAKMKAEgAAAABQhAElAAAAAKBIpxnKoHqu0XMRU2sfZWpZeW7R61Amea1MBjNZ33Ic1VnI12zfB5bPXHXVF2a3Y202+GelbKYyGx725QT3gLai6rUekxrN1vYylXPLQDq/n0Fyf4TbHVBtA/Xm9XusfW9me3uNq4eN8Q4td4OCJCOZWZ67sqyu7/v2zyxdJ5bEL5QAAAAAgCIMKAEAAAAARRhQAgAAAACKdJqhjKrnEo8tozi0Od0xU5eydQ7DeCZzZvubWyZzVNnf2Lb1La+qt//Tv7cViDEAm9c2kwnsgaD6PQW8b1zY58TjWElG0h6fRMcynzvPZA6tw/X7J4yOmreHJrnw3B59SXrmcdW5wlydyXFmebI9aycftIYnYJ/R9Llv2/u+6juXbJGkpmjmua+p2Di/UAIAAAAAijCgBAAAAAAUYUAJAAAAACjSeYbytDJ1d2x7n9jw1qd4e24xmfJtmUx3Yu2RbdBzIK6aMZnZut/6unr7//zfm7e1amuaEo1d1ONzJalLuZnDAPaafw5zEUVf32s8e+bR73/gZS89g+n88WO/OEAF36J3lMuadW3gdStteS4zmdS9bNiXLxvYP9g9RPqvZ+9llzzvuqFrPH6hBAAAAAAUYUAJAAAAACjCgBIAAAAAUKTTDKWitKjMyz6xKc9XfM62D3dbzgteWKbSI5Y+/Txb8qdyvH4o939r87b7NlUf2Ep9qyNJCbeVCZUXK+5zHmYDQqjnHj0D6edxUhN62Nx2ufsfeN89zNS9PLzSvD8sY4e+5NpeiCX1/Vb8XEctMo+3W57LTDY93VyGcrHtdSn3iJ+nG8IvlAAAAACAIgwoAQAAAABFGFACAAAAAIp0mqGcR+nm5KJ9YDmLWabw5NCWe07xNFNGZzapt49thSu2/elpvV3NdXgNy6t32bHZUD1uXU0f7Kq+xRBb2fTB9iOqsBPCxt9MvCTUc5GegfS+d2j9n/eHuQxlcn8DayeZSq9zadt78NFKY925N2zOpm9Gsey55T/hDDt8Ptl8ph3L1tel3CPUoQQAAAAAbDMGlAAAAACAIgwoAQAAAABFOs1QRtVrT459+nhmOvmxl9zx3EWmttV8Vm9PLVO5sFdjZHPGq7nIhQ3Fx0f19tG1evvWcwKwb7Y6sIq9Ea3OstedtL5xfGjLM/c3SKJvmXZ2e9a3P/49lXW5f0HH1vglt+nMZE7bTOWgZd3J7P6t3fRWtC0n6j83UZcSGfxCCQAAAAAowoASAAAAAFCEASUAAAAAoEinGUrndSOv2BRtr03ldSR9zreVjdTYMpM+h9yn53v2wnMj1Vpbvu71e+rtl7+63v7z/9eOhenn2BDPI22TXGSkNTKOQMK/I4bWF+ZqQueib0ndyUyGcpD50/dDj1eOzY51RoYSXfFMZfJB8vUz29tkhDTJVFKXcmskg5tuLmz4hRIAAAAAUIQBJQAAAACgCANKAAAAAECRbutQRmlRyTXOLRN5PK63r01tueUunE8Tntj6Y5vzfWDLDzzD2ZDr8IzHtbvr7Ucer7f//OMC0FIy9d+/A8g8bhHerL6KQZpVrgYO7U/NE2v78uR+Btb2vnaRyV/5594zl56pHB1c/LfnO/1WCuixvtedXJZnLJe1zpcrub+AfSiTQvCb/n5f+R0W0BK/UAIAAAAAijCgBAAAAAAUYUAJAAAAACjSbYYy1LMYI68LaVOyp5axTMpQZmplea1Il9S6ytQIqk7v90yH7/vgRvO+c9rW9dq0Tc+eBwAUCnaPAOs7DzL9kUfDvAb03DKTc1vufX8uDuX7P7x68d9XrO89vSWgG37h1vInm7bXUZ1eFvrBJRepnR0JeopfKAEAAAAARRhQAgAAAACKdDrlVVGKlakvpz5FNVPWw2817r+4HxzW27PMFNixrX9wpd6eW9mS6pQgn+LqU3bCgVphyihWxe/evc3nVusbgTPtZnNavznbfGbulhDq/eHcSnjNj+rtK77cprAurO/0vtun1B7bqXCYmeLq8ZTDSt/9yGvry77xFaEv+p7dWbnm55t8A+ZenrYv3ypf7n176zZpSyug8AslAAAAAKAIA0oAAAAAQBEGlAAAAACAIp1mKAcD6VolizG0HIbnLHJtz0Se2jzjpAqIrT+ynKNnKq/69ivt5O7QtrOja7Zvv8Ny5jbpexc1QDHPTCbLrb1NybXOPwZ87rCvKl8Mfv+CA8tIWlPT03rbM5Xe/02tv4zWt3v/l/SHXjakkqH8tifqyz7+74RN2bcLmaSeznp3l9t846u/bE7PP9S96zz7fnwNchdtPX0q/EIJAAAAACjCgBIAAAAAUIQBJQAAAACgSKcZyqB6/Uav5ZjwXIXXehw0t6e2fc9tWGQyne7fkGv0Yx9ZHvTG/fX2wNb3Ol1AqSSf6zGObQpNLmupUMka7PJr39McBwrFO/y3pHkm4zibNC/3/nJhfblnLm/d8SDPWHera5Xtj29kHoz12rfcZJMNZ+Gqu0t2xdvUX8l5sh0hSn6hBAAAAAAUYUAJAAAAACjCgBIAAAAAUKTTDKVCPUt4xTKNXppxav8wyuQyvBbk4bB5eY5nMKvFt7wmps95fuBV9faBBTZP1pyh3I4Z11iFfc5MBq9Xt0fPHVgXzzR6h7Kwtq/v30mntr5/Rx1bf3pkf+o+svXHDR1aem+Gba7EuwK5zr9vuXNcWOFrv/prQr/woDNemS197fiFEgAAAABQhAElAAAAAKAIA0oAAAAAQJFOM5QDpVmIKs9VXLHh7iJXV9JzGpk6lZ6pHGSWzyu5R8+M+LqvfKzePrpWb5+8UG9TuglIkQXukRW/+HuebOudah+Uu0fA0O9/kKlTeZDpaz2y6fza4MSWV7d/7wP1ZcODent+usehc4kPWqfa9WBL93ctHrD0vvxz0/vOus8H2PZD2c8PMb9QAgAAAACKMKAEAAAAABRhQAkAAAAAKNJphjJIOqxMWw5WZ/I08/iRZSEWlsvwXIXXrfQMpdfGss031q1c2LH7dPKXvbzeHlsdSmBdcnUpvd3r/O6yx7bp50ZQEFtonslQegbSv0OiF5XOfA79O8nvteDLvdbkcHzx36/6rvqyG/fW29/4UvOxbJ1Nf8fhzlp2rrnVe10yNLnQsC+B3tVW7PWruZwNFSfnF0oAAAAAQBEGlAAAAACAIgwoAQAAAABFOs1QKliO0aco2/B2YhnGG5ZDjF4nMlPMqikTKaW1tG55TrJ6vHbsnjnxjIc/t3Xn1rYqJ4elBM8vZabL9+lc6FuqAhXr/o5a7+bRQgj1/tEzkrkIjmcm/X4GHsm8mhxAvTnJ1KSuZiYlaVRpP/ja+rIkQ/nlLT/zVp0r3/KXo9c8ytby4atcf+X9fm57yUXoive/dn2uW9lP/EIJAAAAACjCgBIAAAAAUCQ7oAwhPBJC+HAI4ekQwidCCD9z/u/3hhD+IITwqfP/v2f9hwsAAOibAQB9cZkM5UzSP44x/nEI4Yakj4YQ/kDS35P0oRjjL4UQ3i7p7ZJ+vmlDQfVsoWcec/XyBna0non0OeJTWz9knu3AHj+Y1tuTShDkqHlTybF1VAbmJUmOjunfu4sMzEva5kn5XFTwWmyb1fXNoZ5DDPN0eVP7xPtO6/9ydSVzZpaZPPIMZaWI9Ng652sva7ev3kk+l8t+UDPZNvqT7mx7neUmyQW8HazXqu29dV5U78aHMPsLZYzxSzHGPz7/7+clPS3pIUlvlvSe89XeI+nH1nWQAADgAn0zAKAvWmUoQwiPSnq9pCclvTLG+CXprGOT9IpVHxwAAGhG3wwA2KRLDyhDCNcl/a6kn40xPtficW8LITwVQnjqha+VHCIAALidVfTNz/3l+o4PALD7LlWHMoQw1lmH9Vsxxvef//NXQggPxBi/FEJ4QNJXb/fYGOM7Jb1Tkh57XYjVLMUtmybsOYtTy0l4jmLkGclM5jI3LblNrsPzmsm+LUPixwqszBZNv08Orc8ZEGm3S1Ht0nPZU6vqmx/9qyFOJ5WFx837rWYWJenI/jQ99/sdZP507cuT/tP6fu9/vYZ01aPfVW//6f/dfCz90/EHle+F1elzRjJTIzP6CsmxtLzQSC6wt/1Ea3uh1eb5rvqizgOr6ynwcZm7vAZJ75b0dIzxVyuLPijpLef//RZJH1j94QEAAEffDADoi8v8bvaDkv5LSR8PIfyH83/7RUm/JOm9IYS3SvqcpJ9YzyECAABD3wwA6IXsgDLG+G91599b37jawwEAADn0zQCAvug02beIab2qJp6jOLBpwBOrY+k5Cn982ynbXidzXGl7fbtox+aZkCtX6+11zybP5UG3ffY6+mOdkU3PSrWVrUu56oPvcX4VuJMQpeHpRXvqkZvMPQMm1t+NrH3i+7O23+8g6b+sPbca0YtKxtL74m97Xb39e9sUOpcu0ZlnviS5GNgaKz8TK+9t9pozex4seXTLduZ7peffSXewnmQmAAAAAGDnMaAEAAAAABRhQAkAAAAAKNJphjIEaVjJOgQbzp7atOGhHd3EMpGelTi29jXLVHrG0ve/8NpWnous7tvWnZ7W277tq9eb99WmBmYJ3z7T2bERLc+73p23S35O11m5qtuNYZfMF9JzldqTh7Z8av2Z928DO7f8/gW5/i1Xx9nvSeB9ebWv9n77xoO+M2vv+udi15/fNsnEd/ud7l3y6HauDuUqrfidTi6c1ru7F/ELJQAAAACgCANKAAAAAEARBpQAAAAAgCKdZigV6lN7PSM59AzluN723Eb0XIfVjfTaV1ctW+E16o5t/1738lZl+7OJbeu43h4f1ds3HlCnkinU/qcDy6C03v5yD8cGdfre7VlMInltl62jmYlC9AsZma0VpVDpME+t71t4qNIkmcncn6rt1BgdtNyeH9/s9v8tSYdXMsfSN0sHxemdt5a/9cvec6Bp3dyukn+gDuVydv9zyS+UAAAAAIAiDCgBAAAAAEUYUAIAAAAAinSboYz1adS5WlNXrH2cm4KdmaJ8y7IVFnPMZjarucnTW/ZYy1RO7LH3PWbb7rjgUFICKJlAT/5pV2Rrmq7z3Ov5aeOfu+S09/Xb7mDVz9/r1fryXmUse/7m486iNJ9Wml6T2fqzIzvvrAxzcir49pI6lnYtMLR2Lls2r2YoLV/p/XqupmXnkhenb8V3sSltu/LGUyXzmWy98XUXT982yUW9yV1s7AB+oQQAAAAAFGFACQAAAAAowoASAAAAAFCk2wyl8SnYXvfR60nNrO0O7fED275nKzw6MbBXw0pL6mZl/2FaXzaxjQXLWL7slfW2T6fuenZ173Ik2B7bHOnJfe627bk1ZSx7la9E39VyjZmTxWtIX7Xlfg+BZF/+ObT2LeurD6x/auqvfNlo3Nz2vnvjyEziXC622Oa6MXfrhOS0W3WGctfv05F7gasZyx3NU/ILJQAAAACgCANKAAAAAEARBpQAAAAAgCKdZigXko4r2Ygrlon0KdmemZxZbtHlaltFz0rY/ua+/YY55af22KGtOzuptw+u2K43PIU63f+OzWffJ+s+l3bp1Nil55KzT88VSwlBGh9ctL2upHeNnkNM+sNMXMrjWEndS+urve9PHr+487p+XTA6rLcn1lcDxbI1RW39TN+dfIW3jTEu0wck17+Z+qi5g9m1bHBS1Dqz/jpzk5seUJzjF0oAAAAAQBEGlAAAAACAIgwoAQAAAABFOs1QhiiNK/mGmzbt94oNb+eWoZx6jsJqVQ0t15FkKDN1duaTetvrVlYf73W4vC6lH9vR9Xr76svq7VvfFLAZWxRtSCIpW3TsQF8NBtLRtYv2kX3Ovu51l61v9ZrPng3zmtJNGUgp7fsn1rYYZC1z6fdCGB3U2/d8S7298r532Xp92B65DsiW+3menBqZzS1suf8i1HimZTN+ufU96Gx7G/iHes/O++S9XOHz7/o7pPC6il8oAQAAAABFGFACAAAAAIowoAQAAAAAFOk0QxljPesw8tqNNk34ltWiGlk24qZlHke2vuccczV/vPaV50jmldyI5zMn9ljPe97zQL193yP1NhlKAMAmhEG9VvLYcoc3b9Xb3lc673v9fgT+eI8I+XJ//KnXllzced0rdv+Ch7+t3v7CJ7VjWhY7xPr4iT2398auI13uvh9JprLNW53JMbfeQOvHb7m2Gck9yFLzCyUAAAAAoAgDSgAAAABAEQaUAAAAAIAi3dahDGdZjZfatvzEp5fb0U1tjrbnGIMNj33++cCWJ1OaPZdh7YNKncurtu9vegbE62rZczm0OpQA2qMu5eW1LHmGPbKI0q1Krce7rKbzgfelJ/W2fw79/gh+j4Fc3UlvJ5lKa88rGU/ve70O5ZW7tV5ts1Jr/xJru73dz3ptTLbWY132O9tzkP74cOeFyWm28gxk7rzb8vMsZOpu7kFm0vELJQAAAACgCANKAAAAAEARBpQAAAAAgCIbrUOZzOG2o/HMo2cmve1Ttmen9Xbwx2e2n0wpr6yf1MGynMbBUb09tenUSY1M9EZu6nvvcnqZsmP7N5MffRQ4E/srSsNKXWfPOA6trvLUvnP8/gXHvtxjgl5X0utUtsxUzp6vHKvfW8H2fTW5f8G+1W3MJfN2PPvWqXZ1J9tmJrO5yKYMZaaW7Ppt+XnWtg5ln/iJsqK8J79QAgAAAACKMKAEAAAAABRhQAkAAAAAKNJ5kq+WnchMGJ9aBnJuOQ7PUXiOw7c/avlsPVN5cOXO+/JjyW3LM5ebts816na+XNA+vZm4tH3+zKNuIGlc6cOen91xVUnS3E6eqfWtw8zjkzrNtr3ZpF17+sLFf48P68u8r33k8Xp77Pc78ACo2/oOg096ZzKlCZMzqW2cNZepbLOt3tmybPM2fS+s6Vj5hRIAAAAAUIQBJQAAAACgCANKAAAAAECRzutQzio5yIVlF0bjetszk167ykup5OrqeG2rqe3vuuUcJ15rsuFYnNe8nNj6972q3vaamJ4xWTc/Xn8t+zw7PKlxtusRmBw/d3b9+QJYSoz1Wo8T63s9h5gpp6d5rsadfUfPrX3LHu/bC3Z8L5xc/PeNTH/w0Ovq7cNr9fb0WMBqeF+c+wln2b5663KSbbTNVOYKdGe+KHb+QrFB4VPnF0oAAAAAQBEGlAAAAACAIgwoAQAAAABFOs9QnlTmlI8s0+ilq7zW1NxW8CnOJzYFemDLr9p89uTxPsXachs3Ko/3jMfAjvXYjtXrUD74etvX/1JvJ3Pv98wy09f3eeo7UIq6lPvL72/gfa33X57x93sKJDWhb7O/qmPrT73/9LqWE+tvR5XH5+4/cPVu2/b49uvd0aqzVr37oNGBviR3Q4a2m/PPSS6M7NekTXUm945/DpsXJy9eLlO5zTb0XPiFEgAAAABQhAElAAAAAKAIA0oAAAAAQJFOM5QKqk1b9uzCacvhrec6QiY7cWLLR5bbiKf19tBenWpu0ufCewbEeabk/tfU2wPbl9fg3Dd9LgmUO5YVxy6W1rYuZ59fe/RH+9OibR0xdGUR631Y8p1h7YX3nd4Xe9bL2t4Xe+7Rtx8sQ+nbG1b2P7NjsW5dQzvtxlYPu3WaeOu+MPt+fDssUzs9yR737FaUPYoAACAASURBVFoCa9L774zL4RdKAAAAAEARBpQAAAAAgCIMKAEAAAAARTqvQ1nNHh57TR5bf+G5CVvfM5TX7Nl4dsKjDr49r3u5sIxnNefhdbLGXlPTtjWx5YvDejtXt2vfbPOU8raZxL5lLrf5tcfqcBrskVivPenfSX6/galnGK098ysLz2D64kxdS6/z7NcG88r+jg7qy0ZeZ9JO7Lvvr7f/4rNaTi4q3Pn3PZ/k3shkKHPZ5Wx7n+U+d8mF1zoP5jb722aXfK0YxgAAAAAAijCgBAAAAAAUYUAJAAAAACjSbYYySJPKEHbktac8F2G5CZ+SfGDzeqc2PM7NYE4ylBa69Pnt40ptyMVxfdnEduYZSq+7ldSZ3PB062xtRV9/bUdyOdXj7VsG0fU9M7lLeK2B9hZRmpxctL0/877WP2cn1t9Fu0eA3+9gkfmceh1Kv2fBqa1/WMlJRqsrObdjObTHvvo76+1Pf0Srlf0OWvWX1KZ75y3WdYfhu8vUMycz2YYNCMhMrh2/UAIAAAAAijCgBAAAAAAU6XTKawjSuDIdZWxTU3zK6cRu/z326QCZaTLJtJrkXuX1pk+xPbLhdvXhPmXVZ0rMTurtoR2L/zp+9b56+/T55vXXbeN3Os9oMzOlb9Mgs9OL+/ZiAyvRfOKHyvLYu2+c3bZYSC9U+iz/jrppb8fA4yXe99r2R9aXLzKz0bwv9/2NfRprZUrtwdXmfQ9s+uwjrxP2Vd87254fXq/l6rUtiymuCX6hBAAAAAAUYUAJAAAAACjCgBIAAAAAUKTTDOVAUi36YHsPlm3wW40PbEr0ibUnnstYtGvnblVezVR6ntPXlZUVWViO48ie20OW4/j6nwlr0rdMpVv18e3zVP++v9dAX1T7v9H4zsuk25QNseUj6w+D9c3H3vdn7jEwtPXj6M7Lh3bsbm7HNnqZrTD0mys0b6+15PuYLymc463vTtuLg32+kLokfqEEAAAAABRhQAkAAAAAKHLpAWUIYRhC+FgI4V+dtx8LITwZQvhUCOF3QggHuW0AAIDVoW8GAGxamwzlz0h6WtJd5+1flvRrMcbfDiH8T5LeKukduY1UpyEf2ZTkkbUXlnEcWJ3IqWUbZi0ToSeeqbTte63JWaVbntmyhWco7djGtq+pPdcbr6y3Nz1dO9n/muf2d/l8dz2isulzp8/IVPZHyNSlxKUt3TdHSdPqn5c942jrT/1+Bdb/ef/oucZcZtLvnzCy/R00ZCj93ghDr29t7RvX6+2rN+rtW99Qt1p/SfE5ujS+8PdX7sKIC6cLhR+TS/1CGUJ4WNLfkvSu83aQ9AZJ7ztf5T2SfqzsEAAAQFv0zQCAPrjslNdfl/Rzkl782999kr4R40t/l3xG0kMrPjYAAHBn9M0AgI3LDihDCG+S9NUY40er/3ybVW/7I2kI4W0hhKdCCE/d/FrhUQIAgJessm++9c21HCIAYE9cJnX4g5J+NITwI5KOdJbT+HVJd4cQRud/CX1Y0hdv9+AY4zslvVOSHvmrIVbrUZ3acHbgta0sC3FkucSp5xYtd+E8k5nUkrTcR1OuwzOTM8tfBttWtH153S6v04X12bYc3bYd7y7LxSx4b9rwF4sMS0sr65tf+doQTyt90tz6p6v+VnkmsmX/dWjtib31B7Y/r4PZ5Mgzk96P27G+4tF6+4Fvrbf/v4+qpa4zj2331/ZLaos/l3wh40V+LpCZvLPC1ybbDcQYfyHG+HCM8VFJPynpD2OMPyXpw5J+/Hy1t0j6QNERAACAVuibAQB9sczvYj8v6R+FED6ts9zGu1dzSAAAoBB9MwCgU60KbcQY/0jSH53/92ckfe/qDwkAAFwWfTMAYJNaVm5cTojSuJItDJaLmNu0Xa/dmOQobP25ZyptyrRPofa2bz/JeRxf/Pf0xPadyUwObP1TX9/rfnmdL3stsD9yMRCiAOWWfe2I6GAXeV+6sM/J0O9/YI/3vjSXA/f1c5+rpC+v9I9+7MNM33rt7nr7+r3N+94+fEn1Fm8NSrS9sUZS+NeWr+hChlvBAAAAAACKMKAEAAAAABRhQAkAAAAAKNJphlJSbe5ukm2web0LCzFGr/Xo036t7RlMr2vpucSZ1a+KlnP0nGTVwmpgJtksOzbPedz/qnr7iuU6bn3tzvtei5alq7apVmKfj+12tum1Be6MupO9FdT4dnjtRm97xN//Uu3fYaeZDKXXefZj876/WlN65tvK5DO9PTzSem3dx2CLDpjOcW8tW22113I3e2h7M4jkI+0Xme029yJ+oQQAAAAAFGFACQAAAAAowoASAAAAAFCk0wxlXEiTW5V/uN68/sCObpEUu6o3PTOZk+Q2vHaW18ms5DoWB7Zvy3t6rSvPfAwtI/KyB+rtK3fV2zf/0ra35hjDsvPPc7m/Lmsn9j1WscnXBuiPnn9Qd9hA0lHlPgDJd1DLHPfA7ikw8fsjZGpM+/Lk/gq2v1hZnstI+rb8fgaHfl2ydDgr84Xe+9OeDgn9kzsrg32wku8sLrRWjl8oAQAAAABFGFACAAAAAIowoAQAAAAAFOk2QxmlaaXW48iyC567uGLD3Zu+wbZToDO1r5LN2/7nlVdrNK4vG1o7yUV4HS975aM9/sZD9fZffPZOR7keubI0baefM1398nKZH17L7vQ9fwusQgjSUaWP8v7pyD4HJ5m+0x8/91qQtn5SK9JCkkeeyfScZPW//fvT+/1MPepXfUe9/e9t39FrZGJz+ILeGyu/7MmdO5u80Fr3vte0eX6hBAAAAAAUYUAJAAAAACjCgBIAAAAAUKTTDGWI0qiSlZhP68s9R+H1oZJsRGY4HDO1rXy5m1l24mplf0N75TxW4c9t5vu25zI7qbdf8W319p//X82Px/YiEwlg4yrfQ1PrWyct60Seev/kfb1t33OM3r/5/g6872/zHer5S9v2w3+l3vb7I8z2PkOZu/igQwNa2fRF4IrGE/xCCQAAAAAowoASAAAAAFCEASUAAAAAoEinGcr5Qnrh+KJ9/bn6cs9ETib19qFtzzOOPg94btmItgZeh7LSjvbKTX0KdCYT4haWy7j7kXr72r319gt/2by9lfM/PZDhxA4im4x9FFSvAx3t+z5Yf2Zdsw5suWcePVM5seXjTB3KhX8urT0Id17mTmzbQ6+Hfa3ePrpRb1evYfbDJr8UkwrYGzkKbF7+nW9eI3OJjhXgF0oAAAAAQBEGlAAAAACAIgwoAQAAAABFOs1QxihNTy/aJy/Ul3ttRyv/lAx/PeM4s1pXnsMYWOYyDJuXe13M2qRrm5CdZE7sof7cXLB93bir3r7/tfX2TctQrjrlMLAnkMw39+fvNULJomELeTkoMpXrE+1bK5CP2pgYpEWlw/X7EySlFz0TaW9dErn32o+ZWpC55Ul/lKkpXeU1oU/tusHrX7/80Xr7ha/6eZr7kiAHiDtoeyqhAZ+zYrmX6pLnJb9QAgAAAACKMKAEAAAAABRhQAkAAAAAKNJphnIh6WZ1CGtZhaNMLaskh2ghS69Tecsef2DLfTQ9sX848FxGZR7x1EMltu6Jtb3GptfV8ozI3J6rZzTXre1Ufs+eLb1BYAuQubyQiwORkeyxoNob6Oex1530upIH/mZn6hb79r2vd95f+uZrtzew0+zY92V9t2cmva9+5K/U25/9f/b4Q75xvPZYEf8S8puy4MIlu25eQQAAAABAEQaUAAAAAIAiDCgBAAAAAEW6rUO5kE4rtSdPrQ7lVctRXLGjm9+ot29cq7etdJZOLPhxnKk35dkK2fq1dW1nc1t3ZNOzp5kcR5IxsQzmN7/YuPrGUYcSu6htNnify4rt03PdNYNYz0HOrO+cWn+08DrF1p56XclMncnsydMifuv5ztxn2JeP7GYL91oN6NGVent2fPljK7PP3yrAmfxXQG4NPkfFqEMJAAAAAFgnBpQAAAAAgCIMKAEAAAAARTrNUM4lvVCZizuyXMb8Vr0drM7kkWUXPJcxsxzj9LTePj2ptxe2/4HXwWyoQzm26dgTy5yc2mNHR/X20DKU0dpTO9abz9bbXde/S2afL5qXA3shl8/ybPT6jgQoFlWvBekZyrGduF7bceT3O8jsL6nDnMk9eh1M605rj08+Y5n8ZrJv+zP7KyxDeeP+evvrn/cd7rvqC7rmKwMKAaOQ10XmzFkev1ACAAAAAIowoAQAAAAAFGFACQAAAAAo0mmGUnMpVutQetDCghIvWCby4Ga9Ha/awy13OLDtBVvutbUSDZOqh5bvbBsV8AziwIb2Xsdy4SES3/2SUYJchR4/XlzI1Tkj1gGg9yrfU16jeWz9k/cHJ34/Atu0929rDSxlMpJ+7KfWHtpzObDrjMPr5Ye2mzZY34/OFYWSDGUuyL1PCj9W/EIJAAAAACjCgBIAAAAAUIQBJQAAAACgSLcZyiiFShbwwHKIh8N623OEc8tUTo7rba9D6et7ba1gORHPeXg9qknleLxGpdfUnNq2h3Ysvm+X1MA02za721/LXM5lm2zzsWPFOBewparfYxM7j4+8w7H2obWna/5+T+JODcs8M+l968LrVGbuF5D0ZTCrvDrJFfq1Np3x3lj2LPPH7/VtQlb0seGrEQAAAABQhAElAAAAAKAIA0oAAAAAQJFuM5RSba6u13JceC1Frw9l63utrGNrTyy3OLU6lP545/urru5T9T2f6TUxo+U2vNaVT+hezBoXb19Ua+sO+AJ1JnFHvPeXFu3F8jpg2KxqNnAwvPN6knRg7eQ7cMm31rd36MtbPNYzks7zn/597+1BrjPmtK5o+wXZ9kon82bl0HlvsRV/0PgcL41fKAEAAAAARRhQAgAAAACKMKAEAAAAABTpNEMZF/Uc45XDdHmVxRKTOc6emfQco9elPPY6lLY8qS3pmc1KrsRrXE5PbVuWoTy1Y/Wam55Z8Xxndqr/slGAhrpet/uHttGDVUcVqlGJdW4bWKWtz0KvEJnK/oiSZtUMpV0ZnGb6ypmdyJ4z9HfWc4u57/CmupPJP2TqUCbb8vsX2Pojay/fPxDWurBkZhJ7w/uH3JmQP5NylSg519riF0oAAAAAQBEGlAAAAACAIgwoAQAAAABFuq1DuZDi8UVzeLW+2CKLOrUpzVPLQI5tirPnFD074dmIYOt7LiSO7rx8bpnJmbftWEc2oXtuhbyS2lmZGpn7Fr5qyq34slweh4wk+mKdmcqu85ptMy18DPulmtsf2J+aczlE71uHLcsD5nKMycnS8J3vj02O3bdtz9X7Xq8h7dcJnMjLfLOQmcTlrDtz79vr9BI7F+xe+f7Ws1l+oQQAAAAAFGFACQAAAAAowoASAAAAAFCk2wylVJu767WuFja8DZZL9Hm/J5Zl8Myl18bybMTM607a+jMfbleW5zIlE89jWl1Kn/498LynHWuSE9R2y00RX6a2ZNcZybYZTmyxjt/bXA5ykwmjtvsmDdVfMVrdZu/7MifiuGUdSbfK7+wkI5nszNbP5EF9+eq/3/v0qe7aPj1XLKPtxy49s5rPtaEtn3Wda9wB/EIJAAAAACjCgBIAAAAAUIQBJQAAAACgSOd1KAe3LprBazVaIcq5LfcYomccTzJ1JefT5uVT294Va0+qGUrPW9qx+fKp50GN50llx7r09O0lcx/L7n/9OZT+IFO5w9Zc3DH3MWvzMVz1aUeCZHctYr3/HFpf7P3b1PrOw0zdykHLOpQuiTM19CdW4llJ15vrizxTmatDCWDt2lYsTddv3kJa15ILt7b4hRIAAAAAUIQBJQAAAACgCANKAAAAAECRbjOUQVoML5rHlpEcWDZhYqFJrxM5sOHw0HMWXgsyU1bGt+cZzVDJhVjEMa1tZRkSz4N6ravBsN4OtgPPWHoeNHM4+dng686GEcDCLsiVjCN20QJfCn11xTKUt6y/Gnqo0t9Ka3uu0brDxEHmc9aUyc/l9ZO2163M5EE3/xnf5i8dPvPYEvtcHrYQv1ACAAAAAIpcakAZQrg7hPC+EMKfhhCeDiF8fwjh3hDCH4QQPnX+//es+2ABAMAZ+mYAQB9c9hfKfyHp92KM3yHpdZKelvR2SR+KMT4u6UPnbQAA0A36ZgDAxmUzlCGEuyT9dUl/T5JijBNJkxDCmyX90Plq75H0R5J+vnFbC2lUDU88V19+4rWvrICUH+zMcoc+x3lhOY+5tZNshM+Rbljfa1HNfN+eefRtZ2pbRWsPLYQyPVYr2dqIni/N5UiAfbTN8aWWiIz02yr75hil40p/d9VrLdrJ4P2D9y+jzHJl+pNc/9SUqfS+amzt08xnNlc32O93gCZ8i2A18nUllzvX0jqUHdqRm4xc5hfK10h6VtJvhhA+FkJ4VwjhmqRXxhi/JEnn//+KNR4nAAC4QN8MAOiFywwoR5K+R9I7Yoyvl3RTLabQhBDeFkJ4KoTw1MmtwqMEAABVK+ubj5/Lrw8AwJ1cZkD5jKRnYoxPnrffp7NO7CshhAck6fz/v3q7B8cY3xljfCLG+MTR1VUcMgAAe29lffOVuzo5XgDAjspmKGOMXw4hfD6E8O0xxk9KeqOkPzn/31sk/dL5/38gt60wkEbVLOChrZDJFc68lqNlGebWHuZqPdqz99zjyHKLtWxGpraV5zdnmdpXSb7Tnqsfy7qRmQS22x7FPffSKvvmsw1e/Kf3tf6n56Qv9ZrQ3rdaX+7dWbCTc57LWDbUffZ9tZa5n8Dh9SW3D2AF1ps7HNj2uSTOyw4oz/03kn4rhHAg6TOSflpnXcx7QwhvlfQ5ST+xnkMEAAC3Qd8MANi4Sw0oY4z/QdITt1n0xtUeDgAAuAz6ZgBAH1y2DiUAAAAAADWXnfK6EkHSYSV7MbIcxqndBXb6vD3eaztaEMNzhl4bMqkfZcutDKbm03q7qT7VwIbmnueMduwz23aS27BjO7xWb7/w7J2P5TKydSkB7JTdqHSFdQiq5xqTushehzJzMnkG3/uXpL+x9ReZwJLvv3q82frSGbljvffBevvP2m0eQAfSupK5dp3/2rbwL4IdqR25SvxCCQAAAAAowoASAAAAAFCk0ymvLrn1uE9jyUx78Wk5k2NbwbY38jmtZubTbmx71Sm0fuxzG5rPTmzjPsX11B5vy0e2vfGKa3gyxRUA8JJqf5kpc5Xrm+ctIxXJNNPcPfobtt92ZpqvP7b2xNr3vzpzbADWLtqXVDrFtVnI/J6Wbo+L5hx+oQQAAAAAFGFACQAAAAAowoASAAAAAFBksxlKG86GQfNyz2V4jsNziEOb8jybNB/PxDKZM58yXdm/lwFZWDtahnJu+17kMpRH9fYwk//MITMJrACfI+wo72+rRp6hnNkKmbIiuVxjUmYkU/qjKXOZ7MszkrbpXJkQf+7X728+tuW/IxouPABISioNZX8dy2Um0/X9SydZAYZfKAEAAAAARRhQAgAAAACKMKAEAAAAABTpNEMZJA0rE58HllkcWC5jbu2xZyxt+56RTOpU2vq2OMlt+P5rta78lTuwY/H8pmdQfOe2L8+HDjznYQ9nOjcAoEQI0kFDJ+L91al1QIeZ9XMZyrbre63I04YMZdtMY1Jj0zOVS97PAMDych/zNNq8XN3KtWpbPLen+IUSAAAAAFCEASUAAAAAoAgDSgAAAABAkU4zlDFKs0pWMFhWYWbThgfDentoRzu3x4/t8VPPfXhtSK+dZTnGJLdYGX77DOdgx+qPTQKcVndy5m1b30f+uTpeWzoFG8DeIAneG7Ee41m2/8jlGOfWN3uGMtl/pgb1ok2GMlM3Mlez2a8zVl+HEp3JFUztWtvP3R6fa20/5lg/fqEEAAAAABRhQAkAAAAAKMKAEgAAAABQpNMM5WIhnR5ftONzttwyjtPTetsP1udITxtyFVKaa7TYoxaZSdeDyvB76FPvvUamLR9Z2yOV0Q7mdNG8nAni26NvMQ004L3Bvoq3/c+ztucMva/N5Ai9L/bHj7yv9v7O+Paqj/e+1ctKtv4+3o0ScRtCsg3dyGUqB8v+ftblqbuldSn5hRIAAAAAUIQBJQAAAACgCANKAAAAAECRTjOU84X0XCVDOfI6kVYHcmKZSj/YoU+Stu3Nrbaj1370OpbZLEXlAGxXCkmhyHpzclxv+7F5HUuPkHgNzlydLkeOD7gEasphDwVJ42qGMpN5PMl8LjwTObe+3etOztt+zppqR2b6wlydylzf6H190rcmO/QNNG8/vwJfSoBb2OdikO3Mmz9nkc9Za/xCCQAAAAAowoASAAAAAFCEASUAAAAAoEi3dSij9HwlSxgyGcaZFZTygz20dlIbyx4/80ylH5/XwvKsRNOD7di9jpZnIJPaVr4va3sdymS5hzrXjEwmAOyO6nd60r/Y9/2Bfd8f2LY8E5nLZLpk/RY5R68JHTJ9U27b3td53+5tz4u2l8t6EfQGXK4OZbDPTe5Tk3xFbbIW5JbUpeQXSgAAAABAEQaUAAAAAIAiDCgBAAAAAEU6zVDGhTSt5hptkrLXXvQM5bG1J55VsGcztVyhZyR9GvLc2k11Lz2zGC2fOfA8p6+fmRK9sPbhNTsWe7EWmXzosshMYi8RV8IeiJJOK+f2oWcmbf2b3p+1jfRktu+bO80FpBqMbV27jMjXpbTFni89vF5v3zq9/LFdDplKoK18HcnWBWKRwS+UAAAAAIAiDCgBAAAAAEUYUAIAAAAAinReh7Kag/TMZJIztFygZyCv2hRor321yEyhXthwepDJMY4qbd/0yDOGXrvKX2nPb9piH+kfHGVWaIlMJIDNIrPSF0H1HGNS09n7Zq8Taf2R5xYH1k4ykS1jgI33IMhkIMfWPs3UxPRD8774nm+pt2/9ZfP2AHShuX9pW5cSefxCCQAAAAAowoASAAAAAFCEASUAAAAAoEi3Gcq5dPPrlZ1b8akkx2d1Jkee07CjT3KBtrmBhTY91+j7T9qVrIXnNb02lec9/WCSzKT9Q8hsf9XxI3/tsD7kV9do3aWlKPmGHVX9HhpZfzS0/swzkrm+M8drQx62e3iNZyRdru5ksr61h7aDu15Rb3/hE5kDyGr7pcWXEOA8I5lX/xwt5BfxPbpIzhWy3xB+oQQAAAAAFGFACQAAAAAowoASAAAAAFCk2wzlTLpVzVBaUGLomUbLUE4tA+nrj8bNba+VNbTt5WptVZf77OqZ18D0Ol22vo/kFxYi8RqYc3styN0ByseH2ta760cUAehcNYbjfeHc+0aP8GRyibmMZU8iQJJuc2y23I917DWisb24wcEKrfpD3e69SPferrPnnW+PXygBAAAAAEUYUAIAAAAAijCgBAAAAAAU6TRDGRfS5Pl6u7bc61JabtBrMd6y9mGmruWhZyZ9/5mcx7yyvmcofVueefS8pouWoZzb9mZeuJIJ3juD2EbFujONy2YugR1V61/tvPf+bWRtz1Dm+tYcr0vZRrYmprVH1p623H6S/8x+oS9bzHafOwhszqY7w3Y3QMjXocx8jjb9dNvoSSidXygBAAAAAEUYUAIAAAAAijCgBAAAAAAU6bYO5Vw6eeGi7TnBsdeNtGnBA1s+tMzkgS33acRJncnT5v0Fr0NZaXukcej7shCI18CMHrOw4Mbc27a/lUfLiGUAwF4aBOmw0qn4/QommQ4n6VtzOcYVq24/SVpl9p2NOPpiWz7yP8u33WGSd6Iz3hguhLZI8+cmiTbbv0Rbf+Gfuz4Vx21rQ5lKfqEEAAAAABRhQAkAAAAAKMKAEgAAAABQpNMMpaRaAceF1WqcZnIYA8s0HnguMVMLMskxWs7RcyDBg4sNFj5FORN69GNd+NT9FvvGlvPp7s2L91vXLwYvPvZBrPe3p5k6k/4l1Ta36JGeXHuVmcxcnCgbN8oca3t8yaAUVwsv8ldi0PK1me/xa7cq/EIJAAAAACjCgBIAAAAAUIQBJQAAAACgSPcZysoQ1mtdzS03uLBajGOb4rywo59ZXUk3zBxakrm0HMmgsoGBD8VtunayL5/ObduW79vynQPbYLJ/9BdT88vx2gFrN5B0vdKeWX8z8f7G+2rPONrq1p0lchnKNrIl2DL5z+z2G+pTX0qyQ3Jw6KPtqsM4WPL3Ma9LuW3Pvw8YlgAAAAAAijCgBAAAAAAUYUAJAAAAACjSbYYyqBYunNtw1utAJkkCr01l65/crLfHlksceZ3JllOkq9mMuWVMxp5xzOQ2ksylPX4+bj6Wkb1zmfgotsheJWj26skC/VXtf5Ma0NZXDzN1l0+tbV1xYpm6kpJ00NCXzzL9/HTJCGMuo5m/kMkUInarjnoRHcMOWP5jwAdhWfxCCQAAAAAowoASAAAAAFCEASUAAAAAoEinGcowkIaVYleDq/Xl01v1djypt73u5MJyh8HqQU29tqPVtfSco+dEfHl1876u5yiiL1dGJlPpMYvRQW6D3UqeP9m4Ym0jNFuNEmzAxsVYr6eY+/72GtIDjwH64zO1Gn1/3vbuLulfl4k7ZfbtfF/X7rHldmx+r4fsDtadkWy7PlGyLdKmQ92tzndk1d/T0u/155dGm3f4RM8W510NfqEEAAAAABS51IAyhPAPQwifCCH8xxDCvwwhHIUQHgshPBlC+FQI4XdCCD37zQwAgN1F3wwA6IPsgDKE8JCkfyDpiRjjd+ms8MdPSvplSb8WY3xc0tclvXWdBwoAAM7QNwMA+uKyGcqRpCshhKmkq5K+JOkNkv6L8+XvkfTfSXpH41aCNKjWV/S9W+1FH+0m034zOUOPbSzsHzzr4Oa2fnX7Yz/2lhlC3/Xc6375cmuPLX/aNTKTFR0/951KPmz1wQMbt5q+WfX+0e8R4J/TQ1s8tb5yZO25d2AN+5bS/mXhmc2Gvju5DsjkNzOLs17+6nr74Eq9ffrCkjvIfUeuO/NIprI/4jIvfu6x2/3GXtO1WjvY8zlV/aYst6ydHRDskjVlilvRUQAAExhJREFUKrOvYIzxC5J+RdLndNZZfVPSRyV9I8b44m1vnpH00EqOCAAANKJvBgD0xWWmvN4j6c2SHpP0oKRrkv7mbVa97d/RQghvCyE8FUJ4ajG73RoAAKCNVfbNN59b33ECAHbfZX7j/WFJn40xPhtjnEp6v6QfkHR3COHFiZ8PS/ri7R4cY3xnjPGJGOMTg06LlAAAsLNW1jdfu6ubAwYA7KbLDPE+J+n7QghXJR1LeqOkpyR9WNKPS/ptSW+R9IHsloJlH7y2oteFtKMb5epEWp1Jr4XlWQmLYGZrYS0q7VkmMzLM1cnKZFQ8I+I5kEPLaazbLpfoaa1nub+tylT2+uDQLcJZS1pd3yzV3o7DTIbRM5HeVw7nze2J7zoTZExqOzb8KXyS+Y6ZNi9O+zq/X4Ad6/V76+3hpu+py3fshbZFRXfO7j6/K5bkPkiq1br6B/emZyixtMtkKJ+U9D5Jfyzp4+ePeaekn5f0j0IIn5Z0n6R3r/E4AQDAOfpmAEBfXGoSaozxn0r6p/bPn5H0vSs/IgAAkEXfDADogz26Ty4AAAAAYJU6vU1OiNKgMqV94TkLm+49tKPzzKOHImee42hZvylXx7KpBJA/1utKeqbS85+5qfx+LKMV5zR2PkpQ1Ta6tWWZlF5lKrfstQP2UYzSrHIXdr8hu3c3U+u7vS+f2wYW9j1wYP3hxDObXgPatj9t8b0ybdnX5vqDJC/q93rI1Kje+u/E3PFXn2+y7nrq311svmVmcq8LaG+3Ay9cn+F1Kfk9rWJFdSl5RQEAAAAARRhQAgAAAACKMKAEAAAAABTpNEMZZdkKy0l43chooUnPYUQvKOXLbbE/3mIZCn48nnustH2KsWccB0kA07btNTczdSsXvn4SKEWxHS+Ht2MRUewszsRNiQtpelr9h/pyz1R6ZvLY+uLTpHOtN3O1InNRt6bIT+s6ky35c/e8Z5Kh3DdtPsYrym4V7w9ba5jeVaVRtBPT2zt34bcB/EIJAAAAACjCgBIAAAAAUIQBJQAAAACgSLez/RfSfFJpe27QVp9bVmHkOQpbnit5FL1upa3u0/dHmVxjbdueObH22PZ9JVOryiMofizjfc9prNOexSzIWAL7ze9v4Bl974+8zuSp9W+ekUzuGZA5Hl8/aScPqPy3f2Fl8pdNm7rd+snjvZ1Eu3atEOUW2fm6k/tzbh1YNdxDHWYeUX8t5nanEzKUDQqzzfxCCQAAAAAowoASAAAAAFCEASUAAAAAoEi3dSijtKjUugq2d6/7GG3a7tSCHMGDHV43MnM8XpfS1/dSkqFhWS63MUseYNv2jKSt77WuPEOZZF4s47Lq8k5bHUXYn9hB93gtga0To3RS6TO8P5p72/per/3o/dHM+3Zr+/0Rcgb2HV7NcHpfmTw2s+22X2FJ330194AVH8AuyWW32ma7tvpCpWM9v0i8piv2L/XjjelVec18rz9Y3eAXSgAAAABAEQaUAAAAAIAiDCgBAAAAAEU6z1DOKhnKoU95ttxFrnaVZzAHXjfStueZTJ9RvfD9ee2rhuH3wvOctvG5bWthxz7O7Hto64/GttzayfEsiSjC/iJyCuy2GOvZw2MLRc5axqs8Q+l9s8vVfsztv3qtkIvd+XWH5z+zPMZnz+2Vr6q3v/JJf0Bm+3zhXshdeOzdhUmLD8KWC/ZcvQ5lWkey2SypPL9v584SLvk54xdKAAAAAEARBpQAAAAAgCIMKAEAAAAARTrNUCpKsZJf8Gm50bIN0WspWi5jPG5e7tkGn14ebP8e6fRMZf3g6s0kQukbs+cysfbYjn3oz9U25+uPDuvt6bE9oOXU+r2LJuDSiPhgHTiPNieEes4xyex7TWh7vNd+9P7Paz67U2uPvW/O7L/p5PG+03kNTE9a5fh1xf2P+AotNwhc1g6fW0c6qrU9U5mqL1/YVfkk+dbY4RdvQ/iFEgAAAABQhAElAAAAAKBIt1NeVZ8aE2xaTDJF1afNeNunfebm4tn6Pis14dNaKw/wsh7+67lP8TmxX9tv2nP1KUY+nde3P7DlB1fq7eNvaqNyt27fqD4dyy7g9dxJubeVCUO7I0qaVN5wL63hZUCcl6maZEpzJPET6w+9/5z6ydji5PPHJhc9meV+7F7iy/u2g6v2gLYZAb5P8SKvdbdHX7qDJX/v8rIiaZkRz9x5eZ89erFXhF8oAQAAAABFGFACAAAAAIowoAQAAAAAFNlo2RDPXeRydkOf0mylMoY2PI6eybS25yDnmTIi1eNLSoxY27flt1E/sed+xTOXPnXeM5qeqVzxO7nqDGSnpSbIoABLozzM/ojR+uOW8SHPTCZlRHLbs/UnmZOtsaJXJho1sfVH3nnbxv3eDLlyZ4eWoTy6Vm+fvCDsi+yJjwv1D9bQbnoS7YMZshlJX3+P34slMuht8AslAAAAAKAIA0oAAAAAQBEGlAAAAACAIp1mKKPqeYOBZxrtaKJnLDNtnyec1LX0jKU9PDetuJqLTPJFnrPIZB5n9viTJIRZb3odMH+819hM/lSQLbrZTvJatcxcrjSfRbhro6p5217VG8VSeCv3SJTm3p9WF1v/Mct833sG/8DaSeYy119k+tdW30F+rwSvh52pMZ27ULh+3dp319tkKHFp2VqIueW7+y2eZiab7XWGsiP8QgkAAAAAKMKAEgAAAABQhAElAAAAAKBI53UoF9NKc1xf7DmKJCPpGUoLEnoWwkfLSabSp2Dn2ktMwU7qSNpyz5R4SHJgxzL1DIq/k8tOF/dMzJKby9lkzbtV19wE9lHuY0OCpb+irA6lvZme4Y+e2TeesfTvWO+uct+500xGs3r/Be/nc/WufblvO6lf7du3vnhs9bEPLFMJlGv7LRpu+5+S1n+hk7uw8gvwpA78whbn6kw2P59shjJ7448t7sE6OnR+oQQAAAAAFGFACQAAAAAowoASAAAAAFCk2zqUUZpXsoFzzwl6LtCzDb49awfLdQwto+lToGeZbIWrZSU8Z5E7Ns9h2LHO7QETO7bZ1NqeH7Xt+WuR5FHdstPpc/nTJTVNASfy2B/kUXfHqiuccSr0V1Q9pzjy/sczkOPm5XO/X0EuTpXrnzKPr/an3rd6X+qP9b4xe68Fk1yn2OP9OiRrkzcUwHK8QOqmNR1O15117oOUPZzmD0bM9FhzPkhrxy+UAAAAAIAiDCgBAAAAAEUYUAIAAAAAinRbh1I2L9pziHY0SdkXy1gmtbAO7PGWXYiZWo++PKkdGRqW2aZzmUnPOPrjfbr3yUm9PZ3Y4316+Jb/qWCVSYS25YPIAQIpPgb7w+s++v0NPDPp/dvI+3Jb7nWUo/VnLqk7af3bqNL3+74Gdv8B7+fnnoH0etb2XPzxyXWFGW55X4wGfctMLqNnFz5zKzw/Vf0iOM1MLqzldSxzQe2Wlnl9trmmZQO+6gAAAAAARRhQAgAAAACKMKAEAAAAABTpPkNZmda8sGyDj24XuUxjbG7nsg6+fQ8JJfWqqvu3g03yl17LKtf2Gpl+rF530jInSY5jN6do39a6p6Pntk/GEvtg1XUp0SOx/j2W659G1t94JnJk65/6/Q4yJ5NdGiQ5RG9Xc5NeI3OS+/726wRf3+tYZjKT/tqNDm6/3sUGM8vRH6vOTGYvXjZ4IbfhTOWJbtXaE2vnLF0afZ3PN7ftLc1Y8gslAAAAAKAIA0oAAAAAQBEGlAAAAACAIp1nKJt4/ahsZieToZx71iGTfXBJhtNzINVlS06Fb53v9GOz5cn092SHzceDy+tZ+aZe4bXZXbyVu6UpGzjw/sfqInstxxMLQXrm3zOYfs8AzyF6f+hRtnllf36/gbkdy8yOxY/da1yOW16X+HXGtRu5BxjfAR+0zWmbmdzS7NultO3M/aJ0zefxTn1MtjRjyS+UAAAAAIAiDCgBAAAAAEUYUAIAAAAAinSboYyqTXSOll3wupTJw70Opa3vWYmBrZ+Mnn2ask9Lbsoy+GMzNTNDpiamb2+QyX96piU59H5OsQawI4h67ZAoDSr9sfdXc+97vX+zvnxobe+bPSLk+8vFtaZ25TKstAeWeZxlMpQe9Vr4dYlnKP26ouHeCpJ078ubH5/Epfgg9VffL6x6fnhYkbYZy9xYZ0X4hRIAAAAAUIQBJQAAAACgCANKAAAAAECRbjOUQY1zd31acDJL2HMclnWYndbbPlpeJP/QvP8kK1F5vOc5vXaV19FK2nYonqn0fKlnJv2peA4kearUA+wMtRfvjNdmd2XrBndyFFgFr/s4zry580zf6Rca/rkfZeosz6225MzqYJ5U+n7va5PMZOZYnF9neF/v1wK+vSvXmx/vdTDJwW1Q27qT+6xnnfnymf5M0DA3QOiT7HuxnmPnF0oAAAAAQBEGlAAAAACAIgwoAQAAAABFus1QGs9peC2rYEeXTNm29aPVm1rY470uZW6SdVMOMpeZzA7VvfaVvRZJbsOPzadzz5qXE2ACsEnUrdwe3r8NPTdob96BtT0W6PcrWHgm0vtyr7vsG2zIYHpf7NvyvtafW9KX+65t317n0q9Tju6x/R3U216jEz3S55xc3yQXnTuubS3IPlnTsfMLJQAAAACgCANKAAAAAEARBpQAAAAAgCLdZihjfequ15byab0+2h0Ok83VeB1K2fpe68qnfCc5Ea97WX2sZzxsX7laV/NM3Ul/bTxTObc6XNFqbXlmBeijnpWyAqA0s+h1JnMl27z/8vsXLDw3mLkHQK4m9ezOi5I6jydel9IeMLSrIr8Xg0tqSNvBPner3j64Vm9PbDk65Bd5m869JR3iZg6jyKo78xWH7jvP8G9T3UpXeOz8QgkAAAAAKMKAEgAAAABQhAElAAAAAKBIiB2GlkIIz0r6c0n3S/qLzna8W3jtyvHaleO1K8dr196rY4wv3/RB7Av65pXgtSvHa1eO164cr117d+ybOx1QvrTTEJ6KMT7R+Y53AK9dOV67crx25XjtsC04V8vx2pXjtSvHa1eO1261mPIKAAAAACjCgBIAAAAAUGRTA8p3bmi/u4DXrhyvXTleu3K8dtgWnKvleO3K8dqV47Urx2u3QhvJUAIAAAAAth9TXgEAAAAARTodUIYQ/kYI4ZMhhE+HEN7e5b63TQjhkRDCh0MIT4cQPhFC+Jnzf783hPAHIYRPnf//PZs+1r4KIQxDCB8LIfyr8/ZjIYQnz1+73wkhHGz6GPsohHB3COF9IYQ/PT//vp/z7nJCCP/w/PP6H0MI/zKEcMR5h76jb748+ubl0TeXoW8uR9+8fp0NKEMIQ0n/o6S/Kek7Jf2dEMJ3drX/LTST9I9jjP+ZpO+T9F+dv15vl/ShGOPjkj503sbt/YykpyvtX5b0a+ev3dclvXUjR9V//0LS78UYv0PS63T2GnLeZYQQHpL0DyQ9EWP8LklDST8pzjv0GH1za/TNy6NvLkPfXIC+uRtd/kL5vZI+HWP8TIxxIum3Jb25w/1vlRjjl2KMf3z+38/r7IvjIZ29Zu85X+09kn5sM0fYbyGEhyX9LUnvOm8HSW+Q9L7zVXjtbiOEcJekvy7p3ZIUY5zEGL8hzrvLGkm6EkIYSboq6UvivEO/0Te3QN+8HPrmMvTNS6NvXrMuB5QPSfp8pf3M+b8hI4TwqKTXS3pS0itjjF+Szjo2Sa/Y3JH12q9L+jlJi/P2fZK+EWOcnbc5/27vNZKelfSb51OS3hVCuCbOu6wY4xck/Yqkz+mss/qmpI+K8w79Rt9ciL65CH1zGfrmQvTN3ehyQBlu82/cYjYjhHBd0u9K+tkY43ObPp5tEEJ4k6Svxhg/Wv3n26zK+ZcaSfoeSe+IMb5e0k0xheZSzrMrb5b0mKQHJV3T2TRCx3mHPuG7sQB9c3v0zUuhby5E39yNLgeUz0h6pNJ+WNIXO9z/1gkhjHXWYf1WjPH95//8lRDCA+fLH5D01U0dX4/9oKQfDSH8mc6mb71BZ38Vvft8uoPE+Xcnz0h6Jsb45Hn7fTrrxDjv8n5Y0mdjjM/GGKeS3i/pB8R5h36jb26JvrkYfXM5+uZy9M0d6HJA+RFJj5/fVelAZ4HYD3a4/61ynit4t6SnY4y/Wln0QUlvOf/vt0j6QNfH1ncxxl+IMT4cY3xUZ+fZH8YYf0rShyX9+PlqvHa3EWP8sqTPhxC+/fyf3ijpT8R5dxmfk/R9IYSr55/fF187zjv0GX1zC/TN5eiby9E3L4W+uQMhxu5+4Q0h/IjO/ho1lPQbMcZ/3tnOt0wI4a9J+jeSPq6LrMEv6iyr8V5Jr9LZh+QnYoxf28hBboEQwg9J+icxxjeFEF6js7+K3ivpY5L+bozxdJPH10chhO/W2Q0TDiR9RtJP6+yPT5x3GSGEfybpb+vsTpAfk/T3dZbL4LxDb9E3Xx5982rQN7dH31yOvnn9Oh1QAgAAAAB2R5dTXv//9uuYBgAAgGGQf9fz0HMBFwAAAHBEKAEAAEiEEgAAgEQoAQAASIQSAACARCgBAABIhBIAAIBEKAEAAEgGLO8+yjSmhGQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dc77ykO17jIwx/lhcfTDhSUR8MiK+L67++MRxZxhj/OOI+J64+hLkxyLir8WVl8FxB48W7s13h3vz/cC9uQ/35u1wb372HPSFEgAAAAAAAF47HHLIKwAAAAAAALyG4IUSAAAAAAAANsELJQAAAAAAAGyCF0oAAAAAAADYBC+UAAAAAAAAsAleKAEAAAAAAGATvFACAAAAAADAJnihBAAAAAAAgE38fyF7cudLdBZoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut_dataset = CutEditorDataset(hsv_pos_data, hsv_neg_data)\n",
    "\n",
    "def flow_to_image(flow):\n",
    "    mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])\n",
    "    (height, width, _) = flow.shape\n",
    "    hsv = np.zeros((height, width, 3), dtype=np.uint8)\n",
    "    hsv[..., 0] = ang * 180 / np.pi / 2\n",
    "    hsv[..., 1] = 255\n",
    "    hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n",
    "    bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)\n",
    "    return bgr\n",
    "\n",
    "print(len(cut_dataset))\n",
    "\n",
    "for i in range(8000, len(cut_dataset)):\n",
    "#     sample = cut_dataset[i]\n",
    "    pdata, cdata, pflow, cflow, mark = cut_dataset[i]\n",
    "    plt.figure(figsize=(16, 16))\n",
    "    ax1 = plt.subplot(2,2,1)\n",
    "    plt.sca(ax1)\n",
    "    plt.imshow(cv2.cvtColor(pdata.astype(np.uint8), cv2.COLOR_HSV2RGB))\n",
    "#     plt.imshow(hsv_to_rgb(sample['pdata']) / 255)\n",
    "    ax2 = plt.subplot(2,2,2)\n",
    "    plt.sca(ax2)\n",
    "    plt.imshow(cv2.cvtColor(cdata.astype(np.uint8), cv2.COLOR_HSV2RGB))\n",
    "#     plt.imshow(hsv_to_rgb(sample['cdata']) / 255)\n",
    "    ax3 = plt.subplot(2,2,3)\n",
    "    plt.sca(ax3)\n",
    "    plt.imshow(flow_to_image(pflow))\n",
    "    ax4 = plt.subplot(2,2,4)\n",
    "    plt.sca(ax4)\n",
    "    plt.imshow(flow_to_image(cflow))\n",
    "    plt.show()\n",
    "    if i == 8003:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25124\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "1.0\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "1.0\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "1.0\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "float32\n",
      "1.0\n",
      "0.0005521774291992188\n"
     ]
    }
   ],
   "source": [
    "tran_cut_dataset = CutEditorDataset(hsv_pos_data, hsv_neg_data,\n",
    "                                   transform=transforms.Compose([\n",
    "                                   RandomCrop(76),\n",
    "                                   ToTensor()\n",
    "                               ]))\n",
    "\n",
    "# tran_cut_dataset = CutEditorDataset(hsv_pos_data, hsv_neg_data)\n",
    "\n",
    "print(len(tran_cut_dataset))\n",
    "\n",
    "start = time.time()\n",
    "for i in range(len(tran_cut_dataset)):\n",
    "#     sample = tran_cut_dataset[i]\n",
    "    pdata, cdata, pflow, cflow, mark = tran_cut_dataset[i]\n",
    "#     print(sample['pdata'].dtype)\n",
    "#     print(sample['cdata'].dtype)\n",
    "#     print(sample['pflow'].dtype)\n",
    "#     print(sample['cflow'].dtype)\n",
    "#     print(sample['mark'])\n",
    "    print(pdata.dtype)\n",
    "    print(cdata.dtype)\n",
    "    print(pflow.dtype)\n",
    "    print(cflow.dtype)\n",
    "    print(mark)\n",
    "\n",
    "    if i == 3:\n",
    "        break\n",
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "1 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "2 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "3 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "4 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "5 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "6 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "7 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "8 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "9 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "10 torch.Size([256, 3, 76, 76]) torch.float64\n",
      "0.2471909523010254\n"
     ]
    }
   ],
   "source": [
    "# %env JOBLIB_TEMP_FOLDER=/tmp\n",
    "\n",
    "dataloader = DataLoader(tran_cut_dataset, batch_size=256,\n",
    "                        shuffle=True)\n",
    "\n",
    "start = time.time()\n",
    "for i_batch, sample_batched in enumerate(dataloader):\n",
    "#     print(i_batch, sample_batched['pdata'].dtype,\n",
    "#           sample_batched['mark'].dtype)\n",
    "    pdata, cdata, pflow, cflow, mark = sample_batched\n",
    "    print(i_batch, pdata.shape, mark.dtype)\n",
    "    if i_batch == 10:\n",
    "        break\n",
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BranchNet(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(BranchNet, self).__init__()\n",
    "        self.conv1_1 = nn.Conv2d(5, 6, 5)\n",
    "        self.conv2_1 = nn.Conv2d(5, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv1_2 = nn.Conv2d(6, 16, 5)\n",
    "        self.conv2_2 = nn.Conv2d(6, 16, 5)\n",
    "        self.conv1_3 = nn.Conv2d(16, 32, 3)\n",
    "        self.conv2_3 = nn.Conv2d(16, 32, 3)\n",
    "#         self.fc1 = nn.Linear(2 * 16 * 16 * 16, 120)\n",
    "        self.fc1 = nn.Linear(2 * 7 * 7 * 32, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 1)\n",
    "\n",
    "    def forward(self, x1, x2, x3, x4):\n",
    "        # prev branch\n",
    "        x1 = torch.cat((x1, x3), 1)\n",
    "        x1 = self.pool(F.relu(self.conv1_1(x1)))\n",
    "        x1 = self.pool(F.relu(self.conv1_2(x1)))\n",
    "        x1 = self.pool(F.relu(self.conv1_3(x1)))\n",
    "#         x1 = x1.view(-1, 7 * 7 * 32)\n",
    "        x1 = torch.flatten(x1, start_dim=1)\n",
    "#         x1 = x1.view(-1, 16 * 16 * 16)\n",
    "        # curr branch\n",
    "        x2 = torch.cat((x2, x4), 1)\n",
    "        x2 = self.pool(F.relu(self.conv2_1(x2)))\n",
    "        x2 = self.pool(F.relu(self.conv2_2(x2)))\n",
    "        x2 = self.pool(F.relu(self.conv2_3(x2)))\n",
    "        x2 = torch.flatten(x2, start_dim=1)\n",
    "#         x2 = x2.view(-1, 7 * 7 * 32)\n",
    "#         x2 = x2.view(-1, 16 * 16 * 16)\n",
    "        # concat FC\n",
    "        x = torch.cat((x1, x2), 1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConcatNet(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(ConcatNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(10, 12, 5)\n",
    "        self.conv2 = nn.Conv2d(12, 16, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv3 = nn.Conv2d(16, 32, 3)\n",
    "#         self.fc1 = nn.Linear(16 * 16 * 16, 120)\n",
    "        self.fc1 = nn.Linear(7 * 7 * 32, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 1)\n",
    "\n",
    "    def forward(self, x1, x2, x3, x4):\n",
    "        x = torch.cat((x1, x2, x3, x4), 1)\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = self.pool(F.relu(self.conv3(x)))\n",
    "#         x = x.view(-1, 16 * 16 * 16)\n",
    "#         x = x.view(-1, 7 * 7 * 32)\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def weight_init(m):\n",
    "    if isinstance(m, nn.Linear):\n",
    "        nn.init.xavier_normal_(m.weight)\n",
    "        nn.init.constant_(m.bias, 0)\n",
    "    # 也可以判断是否为conv2d，使用相应的初始化方式 \n",
    "    elif isinstance(m, nn.Conv2d):\n",
    "        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n",
    "     # 是否为批归一化层\n",
    "    elif isinstance(m, nn.BatchNorm2d):\n",
    "        nn.init.constant_(m.weight, 1)\n",
    "        nn.init.constant_(m.bias, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "[1] loss: 4.584, acc: 46.601, cost 4.126\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-241-fe4d6e8b70c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0mcdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mpflow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m         \u001b[0mcflow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m         \u001b[0mmark\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmark\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpflow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcflow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "import time\n",
    "\n",
    "net = ConcatNet()\n",
    "# net = BranchNet()\n",
    "net.apply(weight_init)\n",
    "net.train()\n",
    "\n",
    "gpus = [0]\n",
    "torch.cuda.set_device('cuda:{}'.format(gpus[0]))\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "net.to(device)\n",
    "\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "# criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(net.parameters())\n",
    "\n",
    "for epoch in range(2000):\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    start = time.time()\n",
    "    for i, batch in enumerate(dataloader):\n",
    "        pdata, cdata, pflow, cflow, mark = batch\n",
    "#         pdata = batch['pdata'].to(device)\n",
    "#         cdata = batch['cdata'].to(device)\n",
    "#         pflow = batch['pflow'].to(device)\n",
    "#         cflow = batch['cflow'].to(device)\n",
    "#         mark = torch.reshape(batch['mark'], (-1, 1)).to(device)\n",
    "        pdata = pdata.to(device)\n",
    "        cdata = cdata.to(device)\n",
    "        pflow = pflow.to(device)\n",
    "        cflow = cflow.to(device)\n",
    "        mark = torch.reshape(mark, (-1, 1)).to(device)\n",
    "        outputs = net(pdata, cdata, pflow, cflow)\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(outputs, mark)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "\n",
    "        \n",
    "        results = torch.sigmoid(outputs)\n",
    "        ones = torch.ones(results.shape).to(device)\n",
    "        zeros = torch.zeros(results.shape).to(device)\n",
    "        results = torch.where(results >= 0.5, ones, zeros)\n",
    "        res = torch.sum(results == mark).item()\n",
    "        correct += res\n",
    "\n",
    "#         if i % 500 == 499:\n",
    "#             print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 499))\n",
    "#             running_loss = 0.0\n",
    "    end = time.time()\n",
    "    running_loss /= len(dataloader)\n",
    "    acc = 100 * correct / len(tran_cut_dataset)\n",
    "    print('[%d] loss: %.3f, acc: %.3f, cost %.3f' % (epoch + 1, running_loss, acc, end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3d = torch.randn(4, 5, 2, 100, 100)\n",
    "conv3d = nn.Conv3d(5, 16, kernel_size=(2, 3, 3), stride=1, padding=0)\n",
    "print(conv3d.weight.size())\n",
    "out3d = conv3d(x3d)\n",
    "print(out3d.size())\n",
    "x2d = torch.randn(4, 10, 100, 100)\n",
    "conv2d = nn.Conv2d(10, 16, 3)\n",
    "print(conv2d.weight.size())\n",
    "out2d = conv2d(x2d)\n",
    "print(out2d.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.randn(4, 3, 100, 100)\n",
    "y = torch.randn(4, 2, 100, 100)\n",
    "z = torch.cat((x, y), 1).view(-1, )\n",
    "torch.cat(z, z).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.Tensor([1, 0, 1, 1])\n",
    "x - x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.view(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1280000, 7, 7, 32])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.randn(1280000, 7, 7, 32)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 482 µs, sys: 366 µs, total: 848 µs\n",
      "Wall time: 571 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([2560, 1568])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "torch.flatten(x, start_dim=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39 µs, sys: 29 µs, total: 68 µs\n",
      "Wall time: 73.9 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([2560, 1568])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "torch.reshape(x, (-1, 7 * 7 * 32)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 675 µs, sys: 512 µs, total: 1.19 ms\n",
      "Wall time: 681 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([2560, 1568])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "x.view(-1, 7 * 7 * 32).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 42.9 ms, sys: 0 ns, total: 42.9 ms\n",
      "Wall time: 40.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "x = torch.randn(1280, 7, 7, 32).to(device)\n",
    "x = torch.randn(1280, 7, 7, 32).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 42.6 ms, sys: 864 µs, total: 43.5 ms\n",
      "Wall time: 41.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "x = torch.randn(2560, 7, 7, 32).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22612\n",
      "2512\n"
     ]
    }
   ],
   "source": [
    "val_len = int(0.2 * len(tran_cut_dataset))\n",
    "train_len = len(tran_cut_dataset) - val_len\n",
    "train_ds, val_ds = random_split(tran_cut_dataset, [train_len, val_len])\n",
    "print(len(train_ds))\n",
    "print(len(val_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.34 ms, sys: 136 µs, total: 5.48 ms\n",
      "Wall time: 3.94 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_ds, val_ds = random_split(tran_cut_dataset, [train_len, val_len])\n",
    "dataloader = DataLoader(train_ds, batch_size=256,\n",
    "                    shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConcatNet(\n",
       "  (conv1): Conv2d(10, 12, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (conv2): Conv2d(12, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=1568, out_features=120, bias=True)\n",
       "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (fc3): Linear(in_features=84, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ConcatNet'"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(net).__name__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter   \n",
    "writer = SummaryWriter('tensorboard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.],\n",
       "        [0.],\n",
       "        [1.],\n",
       "        [1.]])"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = torch.sigmoid(torch.randn(4, 1))\n",
    "ones = torch.ones(output.shape)\n",
    "zeros = torch.zeros(output.shape)\n",
    "output = torch.where(output >= 0.5, ones, zeros)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [0.]])"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output2 = torch.sigmoid(torch.randn(4, 1))\n",
    "ones = torch.ones(output2.shape)\n",
    "zeros = torch.zeros(output2.shape)\n",
    "output2 = torch.where(output2 >= 0.5, ones, zeros)\n",
    "output2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sum(output == output2).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "only one element tensors can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-227-b1f583804ca8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0moutput2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: only one element tensors can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "(output == output2).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25124\n"
     ]
    }
   ],
   "source": [
    "writer = SummaryWriter('tensorboard')\n",
    "\n",
    "# folder = 'images'\n",
    "# pos_data, neg_data = load_cut_data(folder)\n",
    "# hsv_pos_data = rgb_2_hsv(pos_data)\n",
    "# hsv_neg_data = rgb_2_hsv(neg_data)\n",
    "cut_dataset = CutEditorDataset(hsv_pos_data, hsv_neg_data,\n",
    "                               transform=transforms.Compose([\n",
    "                               RandomCrop(76),\n",
    "                               ToTensor()\n",
    "                           ]))\n",
    "print(len(cut_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "[1] t_loss: 7.145, t_acc: 0.532, v_loss: 0.356, v_acc: 0.585, cost 3.668\n",
      "[2] t_loss: 0.670, t_acc: 0.607, v_loss: 0.334, v_acc: 0.621, cost 3.638\n",
      "[3] t_loss: 0.619, t_acc: 0.661, v_loss: 0.308, v_acc: 0.670, cost 3.645\n",
      "[4] t_loss: 0.592, t_acc: 0.685, v_loss: 0.299, v_acc: 0.693, cost 3.639\n",
      "[5] t_loss: 0.575, t_acc: 0.704, v_loss: 0.281, v_acc: 0.713, cost 3.644\n",
      "[6] t_loss: 0.549, t_acc: 0.722, v_loss: 0.272, v_acc: 0.731, cost 3.648\n",
      "[7] t_loss: 0.507, t_acc: 0.755, v_loss: 0.258, v_acc: 0.760, cost 3.627\n",
      "[8] t_loss: 0.451, t_acc: 0.795, v_loss: 0.224, v_acc: 0.801, cost 3.628\n",
      "[9] t_loss: 0.416, t_acc: 0.818, v_loss: 0.223, v_acc: 0.791, cost 3.634\n",
      "[10] t_loss: 0.400, t_acc: 0.828, v_loss: 0.195, v_acc: 0.833, cost 3.634\n",
      "[11] t_loss: 0.382, t_acc: 0.836, v_loss: 0.187, v_acc: 0.847, cost 3.640\n",
      "[12] t_loss: 0.371, t_acc: 0.845, v_loss: 0.181, v_acc: 0.846, cost 3.638\n",
      "[13] t_loss: 0.365, t_acc: 0.843, v_loss: 0.187, v_acc: 0.835, cost 3.675\n",
      "[14] t_loss: 0.357, t_acc: 0.848, v_loss: 0.184, v_acc: 0.847, cost 3.634\n",
      "[15] t_loss: 0.347, t_acc: 0.854, v_loss: 0.169, v_acc: 0.853, cost 3.646\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-249-5f359b5dad37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mtrain_correct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_ds_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0mpdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpflow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcflow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmark\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mpdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/itas/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    347\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/itas/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/itas/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer_abcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/itas/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer_abcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/itas/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     62\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdefault_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# scalars\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/itas/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_shared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'numpy'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'str_'\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'string_'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "net = ConcatNet()\n",
    "# net = BranchNet()\n",
    "net.apply(weight_init)\n",
    "net.train()\n",
    "\n",
    "gpus = [0, 1, 2, 3]\n",
    "torch.cuda.set_device('cuda:{}'.format(gpus[0]))\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "net.to(device)\n",
    "\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "optimizer = optim.Adam(net.parameters())\n",
    "\n",
    "for epoch in range(2001):\n",
    "    start = time.time()\n",
    "    val_len = int(0.2 * len(tran_cut_dataset))\n",
    "    train_len = len(tran_cut_dataset) - val_len\n",
    "    train_ds, val_ds = random_split(cut_dataset, [train_len, val_len])\n",
    "    train_ds_loader = DataLoader(train_ds, batch_size=256, shuffle=True)\n",
    "    train_loss = 0.0\n",
    "    train_correct = 0\n",
    "    for i, batch in enumerate(train_ds_loader):\n",
    "        pdata, cdata, pflow, cflow, mark = batch\n",
    "        pdata = pdata.to(device)\n",
    "        cdata = cdata.to(device)\n",
    "        pflow = pflow.to(device)\n",
    "        cflow = cflow.to(device)\n",
    "        mark = torch.reshape(mark, (-1, 1)).to(device)\n",
    "        outputs = net(pdata, cdata, pflow, cflow)\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(outputs, mark)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "\n",
    "        results = torch.sigmoid(outputs)\n",
    "        ones = torch.ones(results.shape).to(device)\n",
    "        zeros = torch.zeros(results.shape).to(device)\n",
    "        results = torch.where(results >= 0.5, ones, zeros)\n",
    "        res = torch.sum(results == mark).item()\n",
    "        train_correct += res\n",
    "\n",
    "    end = time.time()\n",
    "    train_loss /= len(train_ds_loader)\n",
    "    train_acc = train_correct / len(train_ds)\n",
    "    writer.add_scalar('Train/Loss', train_loss, epoch)\n",
    "    writer.add_scalar('Train/Acc', train_acc, epoch)\n",
    "    writer.flush()\n",
    "\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_ds_loader = DataLoader(val_ds, batch_size=128)\n",
    "    for i, batch in enumerate(val_ds_loader):\n",
    "        pdata, cdata, pflow, cflow, mark = batch\n",
    "        pdata = pdata.to(device)\n",
    "        cdata = cdata.to(device)\n",
    "        pflow = pflow.to(device)\n",
    "        cflow = cflow.to(device)\n",
    "        mark = torch.reshape(mark, (-1, 1)).to(device)\n",
    "        outputs = net(pdata, cdata, pflow, cflow)\n",
    "        loss = criterion(outputs, mark)\n",
    "        val_loss += loss.item()\n",
    "\n",
    "        results = torch.sigmoid(outputs)\n",
    "        ones = torch.ones(results.shape).to(device)\n",
    "        zeros = torch.zeros(results.shape).to(device)\n",
    "        results = torch.where(results >= 0.5, ones, zeros)\n",
    "        res = torch.sum(results == mark).item()\n",
    "        val_correct += res\n",
    "\n",
    "    val_loss /= len(train_ds_loader)\n",
    "    val_acc = val_correct / len(val_ds)\n",
    "    writer.add_scalar('Test/Loss', val_loss, epoch)\n",
    "    writer.add_scalar('Test/Acc', val_acc, epoch)\n",
    "    writer.flush()\n",
    "\n",
    "    print('[%d] t_loss: %.3f, t_acc: %.3f, v_loss: %.3f, v_acc: %.3f, cost %.3f' % (epoch + 1, train_loss, train_acc, val_loss, val_acc, end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1, 2, 3]\n",
    "a[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "itas",
   "language": "python",
   "name": "itas"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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