bnb-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34717ff4",
   "metadata": {
    "papermill": {
     "duration": 0.038815,
     "end_time": "2022-04-09T15:30:08.879589",
     "exception": false,
     "start_time": "2022-04-09T15:30:08.840774",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Finetuning GPT-2-large with bitsandbytes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9e1b29",
   "metadata": {
    "papermill": {
     "duration": 0.038992,
     "end_time": "2022-04-09T15:30:08.955420",
     "exception": false,
     "start_time": "2022-04-09T15:30:08.916428",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Link to the library: https://github.com/facebookresearch/bitsandbytes\n",
    "\n",
    "Paper: https://arxiv.org/pdf/2110.02861.pdf\n",
    "\n",
    "Awesome video explanation, highly recommended (also can be found in official repository): https://www.youtube.com/watch?v=o-YBDTqX_ZU"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79563b70",
   "metadata": {
    "papermill": {
     "duration": 0.036618,
     "end_time": "2022-04-09T15:30:09.029657",
     "exception": false,
     "start_time": "2022-04-09T15:30:08.993039",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Main idea \n",
    "__Let's quantize optimizator statistics to 8bits and store them this way.__\n",
    "\n",
    "There are also several modifications.\n",
    "- The quantization is __blockswise__: each tensor is split into pieces (~2000 values) and quantized indepedently. This way we __isolate outliers__ and also __keep the maximal values in each block precisely__, which is good since the maximal values are usualy the most important.\n",
    "- Small tensors like biases in linear layers and layernorm parameters are not quantized, since their quality degrades significantly while the memory savings are minor.\n",
    "- Authors use Dynamic Tree quantization.\n",
    "- Authors propose stable embedding layers."
   ]
  },
  {
   "attachments": {
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UT3HDLIwdOzbGDZnHHnssnn+oiqe6j6r0YlSQcb7lvMnNrZ544on0mRBjCM57nAM5fzFGIOfaPM7hVHFR9d6UOnbsGNejJsQNO+20U4yTqCKjoqwmjC/HurDuefxNNT7vQYzFjdqaE70SGBeR3iLEHNw8LB8fgfHwaotbifFYP2KTrCdGY2O/4CZ37BfzzTdf2HfffeM4znnsJ2zjxRdfPMZTQ4YMmaRHSaalfh9SXUz2SVIL8vvvv4evvvoqdhsgeCCBV65OnTrF5A5JJLq5Nnayj8DooosuCo8++mjYe++9Y1KNzyTAJcgm8XT22WfHac8//3x81ISAn+D/pptuCgcffHBcT95nmWWWCZdffnlMtBGk0eU4u2sa22PnnXeOXVqZlyBr3XXXja/PHgR0XBDUZa655ordNvKvzR50aSWRSJcgPocEIwnODMvKTVFIstFtmL9ZFoL8ddZZJyb5Nt9887j8JMtIvGV4nsHBGTgcDPTM52WfTfejNdZYIz5Xl7fffjsMGjQofg5JYT6LbtpsJ96frk833nhj7B5N4o47N5caUBwkX/v06ROTlwTirAsXJnQDJgHZpUuX2FWKbseSJFUiS/4Q25BYAf8fPnx4PIfNM888sbtvTegqStKG13Puy3Cn2yxpxhAas802W/x3MYY6IalDfNRcOPfTgEm8QIKS8ycNscRSxGh0/SyFZcy6PpfCOZ3zM42vxV2imxLrQ9xE4y7bkwbFueeeO322NJJuDA8zdOjQCduABm22Ad/lp59+ms7ZuNgvsuFSVlhhhRgbl0KsRMzF/knDbamuvC31+5DKYbJPkloQkn0EgaAVkgCiXAQktKjyf5Jp2fs0psZqsWUZSZStuuqq6ZQ/0bLPuHHMQ8XfiBEj0meaB8k6qg7ZhiQPqQjIVwo0Zut9Q5B05WKB5CpJQhKYxbj4oZqQbckFEmPNlELilfUsdaG03HLLxcCeQJZqzuxCTZKkulAZnlXz0TBGpR2IE0h+kfT5+OOPY2MYDX08SMzxoCGRBw1azz33XHxdvtcADVgklIhLaDyrKT7hvM3dVzkXNiWW58QTT4wV8VQSUv1GY+Faa60V1l577Ti2MpXzJDjzFY55LCNj7ta0rDRG8hzbjZixOdDoyjh+JC5JblHpz/dSCuvGOIyrrLJKvCsy3+Pqq68+YRvQKMlYiiw/+0BT4HvgvdlO9OTIx3B5+f2G74NGzWIt8fuQymWyT5JaEIIFHiB4qCm4qEkW6JKYaeqkTENabAmO6MJaKgBjHWgtZt2pOGuqYLAU1uOwww6LlXBbbbVV7P5a3PWjWH1b7xuCYJrP5HumspCqvFJYhn79+sXEI9uRwalLIcFaU+BOYpMkIN8pF1kGs5KkchErZFXlnE84HyHfuMk5l3iCqjEe/JsHDUw8aKzi/MN5KB/bECPwN3FD9r41qa2RrjHQhZMbbp1yyikxecmy0oWZ5BgxDQ8SgFlXUM7fNakpOTU58D1xY5Q77rgjVvnTiyPf2yGP4VgY0oUGz2eeeSa+lniPZBnVddl2yBK+Wbzb2LL9gjiSZa4NDbRsb+IqXldKS/o+pEqY7JOkFoSgKEsu0RpO4FQJgmqCJ96HILOxNVaLLevIXWxrQrUaCNaaK7nEBQZdVOj+QSDLWHo1XTw0Rut9Q/A9M7YM25nP5665NaHij2CXeamULIXns+C7GEEu+xOodqztAkWSpDySYAxPQuKFRE82xhvnlizxlQ1F8Y9//KPOB4mnDOcm3pfzEjFTbSqNpyrBuZ5hTGiEI37Zb7/9YkzBuZoGP6r8eTC+IImv1oIYjOFbqORjWxP3cCfemjD0yjXXXBNfR/dYxjEmFiJmYpw/tgGJW2LIppTtF6grhsziGhLG2f4oVQuTfZLUgpBwoYqKIIUW7/x4b3UhYCGgBom+bFy4xtKYLbasX1aF2BIQhJKgo8stXYguvfTSkgOFozFb7+uL7zq7cGF711Z9yIVHlvitqWs330cWGEuS1BhI+nCjBKrySO4xdEcWIzB8BIk/zj2cR3lu0003rfNBr4AMQ4twfuP1jLlW0/mW6TxfU1ySP1+zzDWhArHUe5DQGzZsWPw3vQKofmOct+KKMBJfTZl0bGy33HJLOO200+J24eZojNdcU+zGdiO5RwMn4yteeeWVsWt2dnOWDM9nYzE3lWy/YLmJ72raL8DzLDuxUmtKxErlMNknSS0IgSEBIsEUlXF0XykXY41kyScSfZXc3KMcLaXFtrExJiBBLC3yJOquuOKKOEZdKS2l9T5fEUE1Q20XJzxHchCMWyRJUnNgmA9uFEWyhSpzqv8znI+WWmqpmOwjdnnnnXfSZ8pH42j37t1j3PPCCy/U2KBFxT7xSU3JPqr4ibtIxBHXlMJ7c+fgUkgCksDiPVinmhrgGDeXeKGp8PlZgrG2BFc5uFPyEUccEZd32223Dccff3ytDYvEGWxntjHjI9Z0Q4ssTqoJsU2WAK7vOuT3C4aXqe075WZyzEdvlZpu5CG1Vib7JKmF4aYKjC2TtZKWGxhyQwwCKIIk7mqbr0wj+Msqt0hY1YTAp9Tz2bK0hBbbxsT6HnnkkWHIkCExOUd3FW5IUZOW0nrPdqfrLt8pyVYuNGpCEph9iHmpPpQkqSkRM9x9991hl112iUNOkCTihlfcwCtDYmq99daLiRnihgsvvLDGpExNOG+vttpq8fxGDMTYuaXQ/Te7O2spNI4ydhvnShrzSnnyySdjsq5UwpDGP3o68NzXX39dMklFr4CbbrqpwUm42rAc2Y3d2O5ZQ1+lGN+X74v4gaFJaOSs64ZxJOmopuO7oBG11FAuxEVXX331hDEcS+FzSASz7KxDfbBfMMQM+9jrr78eG6tL4Tt99tln47+JvUkQStXEZJ8ktTBLLrlkWGGFFeK/CUJIstWFAJkEHIMLEyhtueWWE1VxEYARbBOIEmTVFGy+9957JQcobqwW28bQGK2+IKl55plnhhtuuCF2KzrjjDMmqjoopTFa7xuj5Z3vdpFFFonbgXH4Pvjgg/SZSTGGIsvMhQivkSSpITj/EnfQNTZ7UPXGDao4r5KAo0GM8xPnqwMPPDCOicv5L2/ppZcOO+64YzwnkpBhmJBSPRpoSOT53XbbLQwfPjydOv582r9//1g1yPmZO8ayDBmSjpzj6YpKHMM5sxSSkDRkEhdcfvnlExr1wHmaav2DDz44fkap96AqjNezXW6++ebYgJg/v5NwGjBgQL2qFytBrMfdZUHFGj0ViOv4frhxWP5OxjXJhjWh2pI72R577LExSZf/rrMH7zly5Mj4OmIzGpr5Llnfiy++OH5vGRKARx11VPw+2E41YRgUYky237XXXhu/d+JKPo9EajkNqewXO+ywQ+jZs2dchmOOOSbceeedE74T/k/lIt8p8RG9YXbdddeJ4mapGpjsk6QWhu4kdA/lBha0jBJokbCpCYHPqaeeGrvLgPHkiu+UxntR1UWAVVNXF4I2BsjOB6iZxmqxbQyN0erLOg4cODCcf/75cZ2o7tt5553TZ2vWGK33jdHyTiDLzUC4sQbB+6BBg0oGwCwjgTUXMHRP4UYikiQ1BMNHMH4b55XsQcKMKrCjjz46NlRyDqTynXPQ6aefHs99xYgtmJ/EDOdikmokABdaaKF4J3l6ENAASk8FYhvuCFscf3CjsBNOOCGeDzn/rr/++nEar+d9qC7kczbYYIMak30ke0hw0fBHYonPpcp/3XXXjeu12WabxfdgzMBS70FvDF5P/MZ5l/loEKRykf/TgEvsxfs05RAfxAbcQIPtRRKOZBbbIPuOiCfrQpKPKkjiHBK4jKWY/56LHyRyMzQ0L7zwwjGuIcHKtuN74D24oRm9J/g++Y5rwnexxRZbxO396quvxkbYeeaZJ34WyUeSdOWgcZPYmF4QfCfbbLNN3BZ8t4zNzP7A/sJ3ftxxx000FqRULUz2SVILRHBECzWJJQKvzTffPCbSipN07777bmwVpwWVhA5BJXeqK+5eS7Jv5ZVXjkEqN9eg5Tp/5zo+Y6+99opdN0oh6GqMFtvG0Bitvvfee2+8OKCKkaCS4JjX5lusswdJ0Ow9G6P1vjFa3tGnT5+4n+C2226L65OvJqSigkoKvlO+P6ofGJNQkqTGQlxBbEDcQUKGMX25mdejjz4aq9yp8Muq2UshUUZMwrmQ5BDVeJwTSerwHpxXOVeTRCLOIBYpRkUgvRvoeUAs9Oabb8bXE9uQ4Ln++utrTTCB96DCn/M8NyCjkfWRRx6JCSHiJxpDSTbVhEQeySy6grIMjOdLzwyWnxiM9z788MPj+bgp0R118ODBcXmIl4gja0pyNjaSctddd13cXiQeiWv4HqjG5Dvcbrvt4hiObOOa8DriX+JMEnBsu9r2n9qQ4CNBnDV00guCfYrqUfYzktEsT6mqU6kqFC5YJEkt0JgxY5JTTjklmXbaacmeJYVgLZl55pmTQvCTrL322kkh8E0KQVx8rhCkJKusskry7rvvpq+e1DvvvJPMP//88X0KgVPSq1evZJ111kmWW265pHPnzkkhUE8KwW5SCKySTp06JYUAKX3leIWAN1l88cXj63nMNddccTn43K5du8ZpK6ywQlIIxOMybbLJJukr/3TvvffGz+nSpUsyZMiQdOqkrr322riMs846a/LSSy+lU8crBGjJaaedNmHd8w/e+7777ovzjRgxIikEnHG5tt9++zgNvP6II46I26z49aUeHTt2TG699db01UlSCPjj8vMcy8A2WXfddZMll1wymWqqqeK0LbbYIikEs3Edrr766vSVf3r22WeTbt26TfJZLGsh6EznSpJhw4YlM8444yTLkCkErknhAmbCdzLbbLMlhQutuI9MN910cRrrueWWWyYjR45MXzUe22HvvfeO8/Ae3333XfrMxMaOHZtsuummcfnWWmut5JdffkmfUVPi91G8f/Ao/h4lqVr88ccfyeuvv55cf/31yfnnn59cdtllyd1335189tln6Ry1Gz16dPLYY4/F111++eXJ8OHDYyzF+e5vf/tbPB8SB33++efpKyZF7HDPPfckF110UTJw4MAYg7Bc5Ro1alTy4IMPJhdffHFyySWXxGP5zz//nD7bNrC92G5XXnllcsEFF8T4pdzvsCnw/b/xxhvJoEGDkvPOOy/GZSwf8Y1UzUz2SVILR8Jngw02iAm4Uhf/JOpOOumkGGDW5ZlnnolJqXyii2RPjx49khtvvDEGyTUl+/Daa6/F5B5JrPwyMH///v2Tjz76KFlvvfXitKZK9uG3335LrrjiipjUYnmz5WmOZB9B7HXXXZd07949vnc2H/8m2XbOOeckL774Ykzm1ZTsw9ChQ5PNNtssmXPOOWOCkNfzqCTZhy+++CImadkP8svNe5EcPuGEE0ruGyb7WjaTfZLUOCpJ9klStWjHfwrBoySphfvuu+9itxK6RTAeCl1DbrnlltgVYeONNw5XXXVVmGmmmdK5a0b3Xd7n7bffjv+m28sqq6wSx5opB5/HOCp8Pl15uZMe49G0tS6idJmla8qnn34au8iwHelaS5ekyYE7BdMtmO7I7du3n+zLo4ah+xfjPRVjHCbGIJIklYfhNk455ZRw8sknx6E46MpJl2NJqmYm+ySplWIcOQa2vvTSS2MCjnFxLrvsMhMBUhUw2SdJjcNkn6S2yJEoJamV4s523Gls9913jwMLMwgxg2Lnb9IgSZIkSWpbrOyTpFaOO/QOGTIk/PTTT/Eub6uvvnro2bNn+qyk1sjKPklqPAy7MXTo0DDjjDPGu9M7xIWkameyT5IkqYUx2SdJkqT6shuvJEmSJEmSVCVM9kmSJEmSJElVwmSfJEmSJEmSVCVM9kmSJEmSJElVwmSfJEmSJEmSVCVM9kmSJEmSJElVwmSfJEmSJEmSVCVM9kmSJEmSJElVwmSfJEmSJEmSVCVM9kmSJEmSJElVwmSfJEmSJEmSVCVM9kmSJEmSJElVwmSfJEmSJEmSVCVM9kmSJEmSJElVwmSfJNXTN998EwYPHhzeeuutdMrEXn/99XD77beH77//Pp3ScB988EHo06dPuOCCC9IpLUNLXK7Ro0eHHXbYIWy//fbplIZriveUJEmSpMZksk+S6unVV18Ne+65Z7j77rvD//73v3TqePxNom/vvfcO77zzTjq14caOHRv+9a9/hZ9++imdUj6SkmuuuWa48cYb0ymNpyHL1VDXXnttWGuttcJ7772XThkvSZLwww8/xEdjaYr3bGqN8b035b4jSZIkqXGZ7JOkNoKqtHfffbdVJarKQeUk6/Xf//43nTLetNNOGx555JHw6KOPplMarines6k1xvderfuOJEmSVI3a/60g/bckqQIffvhh+Mc//hFWXHHFsOqqq4Z27dqlz4yvAHvyySfDP//5z7DVVluFnj17hnHjxoX3338/jBw5MiaNnn766fDggw+Gr776Ksw555yhU6dO6av/9Mknn8TPePnll+PzvO/NN98cllpqqbDGGmukc4Xw66+/hqFDh8ZEFBWHU045ZZh11lknLNPPP/8cnn/++fDQQw+FeeedN0w//fTh66+/jhWI/DtDdd79998fl/3bb78Nc8wxR4OWqyYkjficJ554In7m7LPPHjp37pw+O359qCZjPUg03XvvvXH9/u///i9uqymmGN9W9fnnn4fHHnssvPHGG2HppZeOCT+Wm+3bsWPH2L34xx9/DDPPPHOcP/++Y8aMmfC+LD/ryvZi2bLp/N2tW7cJ25HtlX/P7O+PP/44fo/5B9u3ffv2YbrppouvRV3bt3j5HnjggfD444/Hz5pxxhnTuSbGfvXKK6+E++67Lzz77LPh3//+d9yevG8533tz7jt8FolS3uu1116L78GyZt+n/sQ+xW+q2JFHHhmmnnrq9C9JkiSphMIFjiSpHu6///6kS5cuyUknnZSMGzcunToefx999NHJjDPOmDzxxBNx2ogRI5KVV1456dOnT9KvX7+kc+fO8fn27dsnSy21VPLWW2/F+cDrBw0aFJ/v2LFjMtNMMyXTTTddsssuuySzzz57csIJJ6RzJsmQIUOSbt26JVNOOWWcj/ft0KFDsueeeya//PJLnIdlnXbaaRMO+9ljiimmSA499ND4/O+//56cffbZyQwzzJB06tQpmWWWWeJ7zD///Mnw4cPjPKhkuWoyePDguLy8P6/n/3PNNVdyzz33pHMkybBhw5KZZ5452WmnnZK55547Lhefw7y77rprMmrUqLgs+++/f9KuXbuJ1mv66adPHn744ThP37594yOTvS/LO99888X3ZbuwLscdd1zywAMPxGXp2rVrMs000yRTTz11cu655074fovfk+279tprT/T52YPt+/e//z3OV+72zZZv5513Tuadd964LnPMMUdy7733pnNMjOUZMGBAfC+2D9uT/alXr17J22+/Xef33pz7zptvvpksvvjicfmy/YZ/b7PNNsno0aPTuZThu8lv8+wxcuTIdA5Jqj4///xzctdddyXPPPNMOmXyuvHGG2OM9uKLL6ZTSvvoo4+SW265Jfnss8/SKQ33/vvvJyussEJy/vnnp1Nap19//TXZfvvtk/79+6dTJDUHm9IlqZm99NJLsfKsEBjGKqw77rgjVkIdc8wx4ZdffonzUGF1/PHHh8UWWyyO+UclGVVTw4YNi6/Jm2mmmcI555wTpzMf3Vp5r9tuuy2+N6i2u+GGG2KF2LHHHhsrDBnjjiohMO9pp50Wbz5BVRrVbVSLdenSJRx66KHhu+++i/NVslylUOl4+OGHhx49esT34vXPPfdcmGGGGeLn5Mc3pOqrEPCHfffdN34+N0Q5+OCD47Jec801sRrsuOOOC4cddlisRLvnnnvievEZq6yySvouk+J9mZftwDJ/+umnYd111w0XXnhhXLZsW1JZRdXmFVdcEecphWrEQYMGxc/NHlQZbrzxxnGZqHREudsX2fKxXsz35ZdfhvXXXz99dmJPPfVUHDOS7cC8bE/2Jb4jlq2u77259h3W6fLLLw8jRowIDz/8cJyHikFuYrPBBhtY2SdJijjXH3DAAeGyyy6LleuT26hRo+J5mGr72lDVvssuu8SYprFMzvGQGxMxALFJPtaR1PSMriWpmdFt8YQTTojdHElykBjaeeedY1dJkh8ERSTQ6JLKfPPNN1983WqrrRYDYLqG5i277LJhu+22iwkzkEjcZ599wvzzzx+7yRIsTzPNNLE7Kq/t2rVrWGCBBeLzs8wyS/jPf/4Trr/++rDIIouEE088MSaAQEKPhA5ddlm2SperGK+nqykJTT6Hz8MyyywTE0wkgOjWnLf66qvHm5xMNdVUcb1IgPE6uhAT/JJQY3347O7du8f1oqspia6a0D110003jXfUpcsq22CbbbaJQfV6660XNt988/h+fE90wSYRRmK2FL4/uhXzudln33nnneGZZ54Jf//73+ONQ8rdvhmWj+XgooGuyCwLy1kKCTqeJ7mZdZllfUi80XW8tu8dzbnvEOTzHS255JJxu/HgdXwPxd19JUmSJNWfyT5JamYkyf7yl7+kf41PGC2//PJxPDqqyf744484bluWvMpjXLr8OGkgIcO4dbvvvnu8Y2qfPn1itRRjCpKoqqs1mmouPpex8c4666xw1FFHxUTNEUccEZN7LNcXX3xR8XIVy15PEi1L9GVIADEm3ZtvvjmhJZ/tstxyy8VkU4Zk0uKLLx5b2Vm3+iCZxufnE2gku0g4MZ3PzbBMYKy5upDQuuqqq8Ill1wSt13//v3j9HK3b4blY3uQ6KvLQgstFOfbb7/9wplnnhmrGtnO5WqufYdtzT5O1eOOO+4YE4T5dZYkqVI0Eg4ePDhW5lONTvV4KVTH8Tzz3XLLLbHKrBTOiTTWcR5nPt6/vngvbmzFuL6cCxmn+eKLL47vW9Ny0kB29dVXh4EDB4a33347jidcE9bppptuChdccEGs8KfxLUPMQoU971eMaTxXU1zDdGIJKiyLEV+wXKwTMQ9YT8bgpZfDRRddFKv3i2+YVgoNtiwH41jn8Vrer9R3VNs6Z1h+xjtmHnoUvPjiixXFRVI1MdknSfVEpROJmSw5VSybnk8egcH1ixM5JLR4P5ImBCW//fZbrE7r0KFDOsd4zJefRrBFcEV1IDdZmG222WKiKEu+Ua2WBWQ14bMIRAnsuEkDgVp2x1mSMyQn6ZJZyXKVkr2eeYvXn8o9km0EefngtlQCMVuvuhJRtSm1rHyXNVXQlYOut9zziirNAw88MH6fKHf75tVWmZhHMpQLA773k046KVbqzT333LFbLftSbZpz3wEJSbYPycFdd901PsfyEpRLklQJhpBYYoklYgPSKaecEhvYGDqDG2xlOIfRAEUDJRXvnBs5R1N9fuONN6ZzjUevgz333DM2fNHbgCFEaKTiRmT1QSKK9+N8Ry+Bv/71r+Hkk0+Oy7vyyivHc2SG5bzuuuviOZ3PZcgMhhEhYVUcY3LOZfgN1oGGutNPPz1svfXW8bUkKsFrmM4wHC+88EKcBv7NNJ6rKXYl+TZgwIB4zi6Os0jC9evXLw6lAhJybC/O5QcddFCs8KfBkB4fJDprQ/KT96IXQR49KTbccMOYPMyUs86gQZltu8kmm4RTTz01fvcrrbRS/O7LSUBK1cZknyTVE0kMkkalKsxIWNEiTFKrOGHF+C8kSfIYv4zAiwozXsN71zTf77//nv41vlWbVk66uzIW4K233hrHuSEQontpOei6SaKNrqO05tKVOP9gbL2sS2m5y1UKr59uuulKvp7EFA/Wn6Rbhm6q+YQT25VpLG++4m9yI9g85JBDQt++fWOwS/IyU+72rQ8SyYznl90Bl4scukVzQcOFQ22ac98B3xcVf9ndi6lw4DvnwoaKTkmSysH5ptzxf0kI0ghFxRfnPcacJTlF4o0xaDM0nFElyLmcBiyGnmBYCyoC62r4qg3nV8a8/eyzz2I1PJ9B3HjppZemc1Q2HnI54+QSdzIP510q7pnGg+QX522eK9WYCnpvEMuw3PntAxr0aAgkcUn8QQMpQ3HQG4DkJhWLDNfCMpOAbUijbJ5jA0v1414vSfVEBRVjmdHqS1CRR6BICyiBaHHihKCIYDNDxRvJGoIyWp8Jnqiu+vrrr2NAmyGQoQKLZFmGpBl/zzPPPDHoyRAsFo8zRwKKYIcESz5wzbrVEijXNDYdKlmuUng9iSiCsnxLM4YPHx67dPB8VhHH+xLo5rtp8NkEeIyNx3h9INnENmysoLJSfJeMK9irV69w3nnnTRJAl7t9G4qLHFrD6U7Ltskudmr63ptz3ynGb2evvfaKF2t8v1wESZJUF85B5Y7/y3lr2223jePaZpX7xA8k9Dj3kGQD1WzcEIxkG8kjEmKc90j2UQnXEFSxcxMtxrlleWgcY1mzczTrQ2KvnPGQWeZyxwDu3bt3bLyj2o3Kfx50wSVpxnM1YRmJJYipSJplSKINGTIkfhbDqYB1otKOOALZ+jEOMnEisXBDlbvObEfiS8cGlv5ksk+S6olEHzc3IGCj2yatoASZBB0EaCT1CDCypFSGBAsBCgkyxp6jawJjuKy99tph0UUXjfNstNFGsVslXTno5sg4JVdeeWV85HHDhLnmmisGtlR1Ud3F/HSnKE6+8X4EZtxAgrHlaF2mxZMkIwEtwS5VWLye92H5GROFdctad8tdrprQtYIEKAEtgTXJO6rL6G7BTR9ofc0jYXr00UfHajACVm7QwZ1xad2lShAE7lQVUilGtxzel4CvObDN+K75PG7Gwd1xqZDjwfYlEVjJ9q0Un8HFDfsS3wXbhqCYz2I8P9T0vTfnvkOSkKoFKhl4HdUN/F5orSfRSPJPkqS60LhXyfi/JIsYA2/LLbcMq666alhhhRViso9kYRYrELsRj/H6LJkEznF05SVpVF+cZzl/Zkg6sYxZt9JsfcoZD7nSMYCJ2aiep9stD/7NtLqwHdi2Dz30UDy/g4Ztzue8Pr9MnO9JZtLLgC6zdD/m3M72bYy7CDs2sFR/JvskqZ4I/kiMEOSQ5CGAJClCsEPlGl0YGK+lGM+T1Ft33XXjjTpIgtCVkhbYrOVxwQUXjIkRgkACK+ajywktqPnkIdVcvJ7uxNxFljv80qLK+xH05PEc87LcJGEY34ZEG1gWun0STDGGG/NmY9wQDBPwotzlqgkJPZJFBIqMYUNwu9NOO8UEIAnD4huX8L60RJO4ohWXrjgEerw2w3gxBLB0pWWcGcbHyY+F05RIihFM0uWG5SL5mz3YvgSiKHf7VoqLBbreUnnABQWJTwYgZxvw3qjpe2/OfYeu2Z9//nk4+OCD40UEF2n8XkiGcmORLMktSVJtiD+oTOfcUtf4v3TjJMbgvEXjIlVzVNWRyCI5RKIIVLGRHCzVtbXUtEpQmZevzgPnxGx84mx9yhkPmflY5nLHAOZzsxud0cC3zjrrTLIspTAvXXVpzKbRlW1DNSXvTcNmhkY7xt2j+yzxAduV7Us8QqUd69ZQlayzYwNLRQoHGklSA40ePTp5+eWXk0LwEf9fCE7SZ/40YsSIZOWVV07WX3/9+PwXX3yRPPnkk8m7776bFAKpdK6J8b7PP/988swzzySjRo1Kp07q119/jfM8/vjjSSEgSqdWrhCYJe+99158n1deeSUpBMrpMxMrd7lqwue89dZb8XMKwWT8O2/YsGHJzDPPnFxyySXJ2LFjk0JAlzz11FPJt99+m87ROpW7fSvBvvTmm2/G9xw6dGjyww8/pM+Upzn3HZbtxRdfjL8T5in1O9F4Q4YM4UpwksfIkSPTOSSp+hATzTnnnMl22203SWwAYoItt9wy6dWrV/Lpp5+mU8cjnujevXsyYMCA+NqHHnoo6dq1a3LOOedMFGc9/fTTyUwzzZScccYZ8e+PPvoo6dmzZ7LbbrtN9Jm85oQTTojzcn6tzcCBA5NOnTolN998c/y7OObL8P6s2+KLLx7/Zn222mqrZKGFFopxYV62nCwDPv7447icO++8c8ltU+yTTz6Jn8O2YrusscYayffff58+Wzvirh49eiQHHHBA/NwFF1ww2XbbbZPff/89Ps+2Oeigg+J3xXk9w/Tjjz8+mW222ZIXXnghTiNO7Nu3b3xk7rnnnvjdDB48OJ0yHu81++yzJ6eddlr8u9J1znz22WfJ5ZdfnvTu3TuZa6654vpIbY2VfZLUCGiRpTWTrrj8v5yxQWj5pCKLsVNoES2F96UCimrArNtqKbTaMg+DKtO9uL5o8aV6j/cp7j6SV+5y1YTPoQWYz6Fqr7aWZlrfqeqj4oyKsNas3O1bCfY1KuN4T7oo5bsLlaM59x2WjSoDfifMU87vRJKkTCXj/2bdSOlVkMVZVJwVjzNMrwwq4+mqSgVghi7AzFu4Zk6nND7Wh/NhOeMhVzJOLjckoSKfLsr0qGBMYYbRoJo+q2isDduMOI+75XKTErY3Q7Fk1ZRsE96bm4/QSyNDNSXjLbP8tSFG4DthCJI8xlHkPTKVrHOeYwNLduOVJEmSJLUg3HWVsd+ycXCzsXDpOlru+L89e/aMySnG7KPLJ8NunHvuubHbaR6NloyxzM0eGBuYMfQYn46x4ejG2tQYFoWEY13jIdM4V844uSTarrjiijgmL+vADUo23XTTOLTMoEGDYvKuLjTGsZ1ZFsaWZpgVGgYzWaMt24zty5iHbGPGMc5ufFIbhvvgPbkL8mOPPRa76TIkCJ81Lh1vEeWuM0OJkNx0bGApJ9b3SZKa3JgxY5JC0JEUgpOKuiK0RYXAPXa/sNuF2iq78Upqi7JuvKWOf+3atYtddPHss88mSy+9dDLFFFPE5/h/nz594lAqGbrInn766cl000034fVzzz137CJKV9GsGy+I0U455ZSkS5cuE95vpZVWSg477LA4rEhTdePNPPbYY7G7LcvIo1u3bsnf/va3uC2ybryZBx54IFlsscUmrDuPaaaZJnYH/vLLL+NwGnSj3XHHHeNQHZkff/wxWWeddZJ55503ef3119OpNaNb8cILLxw/59BDD51kyBmGVtl4442T9u3bx2Xg//369Yvde+vqxot77703rl+2znw3Z555ZjLHHHNM6MabqWudGV5m++23Tzp06DDhed6TLrx8J8XLLrUF7fhP4ccgSZKkFoJByNdbb730rz9R3cDg6ZLU1lEBxo2eqOJiGAqqxag4K0bVGPNNPfXUYfHFF4/VYjWhuyqVYtwxl2FG6GbbXLixSHYnYYYvqW2YFOahWytVjdxwi5tRNMbQIJViOdheVGLSnZfhPEp9BzWhiy2VlLyGda7tuylnnf/973/HLru8L9/hwgsv7JAharNM9kmSJLUwJvskSZJUX47ZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mqWhdccEEYMGBA+Pzzz9MpkiRJrR93vSXGueGGG9IpkvQnk32SpKr1+OOPh+uuuy6MGDEinSJJktT6ffnllzHGef7559MpkvQnk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0lqtTbYYIPQrl278Prrr6dTyjd27NhwwgknxNe/8sor6VRJkqTJ7/HHH48xynnnnRf+97//pVPL9/zzz8fXb7/99ukUSW2JyT5JUqs1xxxzxP8vueSSYYsttgi33357+PXXX+O0UpIkCW+99VY4+eSTwwILLBBOOeWUOH2eeeaJ/5ckSWoJFlxwwfj/Qw89NCy66KLhrLPOCh9//HGcVpORI0eGG264ITaGrrjiinHanHPOGf8vqW1pV7jwSdJ/S5LUqnz++eehZ8+e8d/t27ePLd/8v1+/fmH55ZcPN998c/jwww/DXnvtFaaaaqrwj3/8I/zrX/8KHTp0iJV9U0wxRdhoo43C3XffHd9DaikefvjhsN5666V//YkLua5du6Z/SZKq2Zprrhmefvrp2Fg55ZRThj/++CMmAYldiGduvfXWsOyyy8bzxdChQ8Pw4cPTV4YYE1HZ991334VZZ501nSqprTDZJ0lq1VZaaaXYVSV/OiOJlyX0CHb5NwHv//3f/6Vz/Onee++NQbPUkpjskyTRaLnjjjtO0o23Y8eOcRrJPxo5SQQS4xTHQjR+DhkyJJ0iqS2xG68kqVXbddddYyIvjwD4999/nxAck/Qrleibfvrpw7rrrpv+JUmS1HJssskmMbFXjJiGRB/GjRsXY57iGh7+3mWXXdK/JLU1JvskSa0aY/XRol0pqv122GGHkkG0JEnS5DbNNNOErbbaql5xTufOncOGG26Y/iWprTHZJ0lq1bp06RI23XTTigNhqv123nnn9C9JkqSWZ8CAAROq+MpFg+Z2220XOnXqlE6R1NaY7JMktXr1CYTnm2++sNRSS6V/SZIktTyrrrpqmH322dO/ymODpiSTfZKkVq9v375hpplmSv+qGy3eu+222yRj/UmSJLUk3GiDsfeIXcrVo0eP0KdPn/QvSW2RyT5JUqtHF15asMsNhGnx7t+/f/qXJElSy8UdeYldymGDpiSY7JMkVQWSfeUEwrSQr7baaqF79+7pFEmSpJZrgQUWCEsuuWT6V+2Ihbbffvv0L0ltlck+SVJVWGSRRcLCCy+c/lW7XXfdNf2XJElSy0e1Xvv27dO/SqOab/nllw/zzDNPOkVSW2WyT5JUNQiE67orL91buHuvJElSa7H11lun/6oZyT5iIUky2SdJqhrbbrttGDduXPrXpEgEbrXVVmHaaadNp0iSJLV83Ihsgw02qLW6j+e23HLL9C9JbZnJPklS1ZhtttlCv379agyE//jjjzi2nyRJUmvDXXlratSkQXOTTTYJ008/fTpFUltmsk+SVFUYj+9///tf+tfESAauvvrq6V+SJEmtx3rrrRe6dOmS/jUxGjQHDBiQ/iWprTPZJ0mqKhtuuGGYeuqp07/+xFh9BMHcjVeSJKm16dixY9hhhx1iTFOsa9euYe21107/ktTWecUjSaoqnTp1Ctttt90kgfDYsWPDjjvumP4lSZLU+uy0004xpskj5mGYkrpuUiap7TDZJ0mqOgS8xYHw4osvHnr37p3+JUmS1Poss8wyYZ555kn/Go+YhySgJGVM9kmSqs6KK64Yunfvnv41/u50u+22W/qXJElS69SuXbsY0+RvRrbgggvGRk1JypjskyRVnSwQ5v9IkiRss8028d+SJEmt2fbbbz/hrryMRbz77rvHf0tSxmSfJKkqMYA1ST6sv/76YeaZZ47/liRJas169OgRVl111fhvYh3GKpakPJN9kqSqxHg2PXv2jP/eZZdd4v8lSZKqwa677hr/v/TSS4fZZ589/luSMib7JElV65hjjon/p7JPkiSpWmy22Wbx/4ccckj8vyTltUuyPk6SJFUhxrTJD2IttQYPP/xwWG+99dK//jRy5MjQtWvX9C9JUltmjCOpJlb2SZKqmkGwJEmqRsY4kmpisk+SJEmSJEmqEib7JEmSJEmSpCphsk+SJEmSJEmqEib7JEmSJEmSpCphsk+SJEmSJEmqEu2SgvTfktTm/fHHH+Gf//xneOGFF8Jnn30WfvvttzDnnHOGddddNyyzzDJhiinKayP5+eefw5tvvhm++uqrMNtss4XVVlstTDnllOmzLd8PP/wQ7rjjjvDee++FmWaaKWy66aZhiSWWSJ+tW2tf/8bw+uuvh3vuuSf8+OOPYcEFFwxbbrll3BblKuf1n376aRg+fHh4//33w8iRI0OXLl1Cnz594v7auXPndK6W7f/+7//i8n/wwQehXbt2Yc011wwzzjhj+mzt2M+efPLJ8Morr8T1Z/uss846YYUVVij7t9pSPfzww2G99dZL//oT69m1a9f0L0mSJGlSJvskKYfk1tprrx1GjRoVevXqFTp16hQ+/PDD8Msvv4RDDjkknHjiiWGqqaZK554UyYeddtopDBkyJIwdOzZO69u3b7j77rvDdNNNF/9u6UjS9e/fP3z++edhgQUWCN9++23473//G84+++ywyy671JpEqYb1b6j//e9/4dprrw2HH354mHrqqWOymGTW3HPPHW6++eaw2GKLpXOWVu7rSZKRAGRb9+zZMya6vvnmm/DFF1+EVVZZJQwaNCj85S9/ifO2RKzn+eefH/72t7+F0aNHB8KRWWedNTzwwANh2WWXTeeq2SeffBKTYSQ8e/ToEeaYY46YoB8xYkTYa6+9whlnnNFqEp6lmOyT1NBGo7ainEYjYprnnnsujBs3Lp3yJ14z33zzxYaiPLd/eYgb77vvvtjAS6MjMUi/fv1KxsttdZtSTPDSSy+FoUOHhi+//DLGJ8stt1zYYIMNwrTTTpvONR5FB1yPFKdpmI9rlGz+33//PTzxxBMx7sljfyb+43tQG0eyT+UpXMQmgwcPTg477LBkn332SU477bTklVdeSQonjXSOxjVw4MBkmWWWSd544410SsM1xXs21K+//prcddddSeHCNilcoCVHHnlkcsUVVySvvfZaUjgwpnNJzaMQfCRDhgxJRo0alU5Jkh9++CHZZJNNkhlmmCF5/PHH06mljR49OrnpppuSQiCTvPjii8liiy2W9O3bd6L3a8n4PbKuc845Z/LUU0/Fad9//32yzjrrJHPNNVf8Xdamta9/YygEskkhyEoKAVny3XffxWmF4C5uU7btL7/8EqfVpNzXc3x87LHHks8++yz+jbFjxybnnntuUggi47G0qc5PjYV97Prrr4/71R577JEUAv6kEAynz9aucFGXDBgwIHn66acnrOfIkSOT7bbbLq7/LbfcEqe1VhyHCNOKH6yjkuSjjz6K33F+/2+owoVTsssuuyRbbLFFOkWaPDimXXXVVTHu6NatW4zdCxf4ySKLLNKiYvjJje10zjnnxG3Trl27eIycddZZS55Hbr311mTqqadO2rdvP9FjiimmiI8jjjgindPtXy620wUXXJBMN910yUwzzZT06dMnmW+++ZIOHTokm2++eYypM215mxKb9e/fP+5nXbt2TZZffvlkwQUXTKaccspkxRVXjPFMhu106KGHxnmL99V55503+eSTT9I5kxgjLr300pPMy/tuu+22XkcrMdlXpkcffTRZYIEF4sGLH+cKK6yQzDLLLEnHjh3jxUb+YFaJN998M1ljjTWSG264IZ3yJ05ec8wxR9kXPuVoivdsCIL1lVdeOR6UODmstNJKyfzzzx9Pxlzsfvzxx3E+EhC77bZbsueee8ZkQn3Utq2lutx8881Jp06dkiuvvDKdUrevv/46WXzxxVtVsuu5556Lx7b99ttvoiDh/vvvT6affvrk1FNPTafUrTWuf2OgIahLly7Jfffdl04Zn5jbf//940UI27g2DX39p59+GgPCjTfeOCYvWgOCW/a5SpJ9NSExykXHvvvum05pnUz21Y7GS47JHJsby2+//ZZssMEGMS6RJqeGNhq1JQ1pNCIJs/POO8cETNbACbd/ed56663YEMx18eeffx6njRkzJiZOOT5fdtllcRra8jYlFjvkkEPieSvfYHv55ZfHhCf7YBavZck+tivXybXJkn2bbbaZiT2V5A06ysB4SIUfYfz3I488Estqn3/++diF6LDDDguDBw8OhYuw8Ouvv8Z5KkHXpXfffTeOj1Ws8EMPhYvlsrozlasp3rO+CgelcOaZZ4a33347XHXVVbH8/plnnokl+HRFKxwAJ5TgMy/bm+5adP2qj9q2tVQb9jm6B9IdoVu3bunU6sTvkd/KiiuuGNq3b59ODWHhhReOXSxffvnl2G1ApdGViG1El5RFFlkknRritmQsPc4TheA4nTqphr4edLsuBJOhECi2uXESUYht4mOKVj5mn6S268EHHww//fRTvL7gfICVVlopFC7qY1fUus4Dbcnqq68edtxxxzjERceOHdOp5fn444/DsGHDwpJLLhkfGbd/eYiNGb6Frqh/SYcNIVZmO9Gdl5gyu25ry9uU/fLcc88Ne+yxx4QuuMR1W221VRwP+9VXX43dmqXGZiRcBy6sGKdqzJgx4eKLLw5rrLFG+kyI408df/zxYZtttonjFDz00ENx+rhx42JSiaQVr3v88cfja++6667wn//8J84DDo4fffRRHNeK5BY3BHjxxRdjP35899134bXXXosX3si/LxfbDErO+zLuQTYP0x977LFw0UUXxWVinK284vfkbz6Tzy5+kHzLY1uwjrw3Y0GReMsrXr6nn346zsvylML4Am+88Ua8oN1oo40mSizMMMMMcawixiUi0cey8PlcwGY3T+AEkuGzWS+Wi89krKP8ute1rTP/+te/wk033RQuuOCCOMZY/vtS28LYLjfccEMYOHBg2HPPPeM+QTCZPwZUGwIyxntjnMLZZ589nToeQdvMM88ck+X8FlUax6jvv/8+3tSEbZZHophtyzauqdGiPq/neP6Pf/wjjvPHmJI0Ts0777xh3333bXMJL84XnPs4B6288srpVLUVNGYSC3D+J26gIfGKK64I77zzTjrHxDjHE5sRSxGr1daQUVsMlMU/xDTF70Gs89JLL8Vlk8rRGI0+Kg/XUjSQbbzxxmH66aeP09z+5aMog+Qe11n5uIRtxDVXltRzm5ZGwyTbjViNcfaKcZNArk2z6/bacK7hGpc4SJqA8j7VjDGnCgemZMMNN6yx+2jhRBHLvxknCIUfW+wCUjh4Jf369YtjBxUOhrEP/VJLLRVLnkG3OEp3+RqyB33uKd3FWWedlRQuuOMyIHtf+vbTzYTXZu9L99TCQTR+HtNZHqZTFs3rMsXvSbde5ssvQ/ag612GMZEWWmih2N22cMEf14luZmeffXYsgUd+vdddd904T9YdsBS6PjMv3Xbfe++9dOqkCgeuZIkllphk+Shbzp5fddVV43qwTIwFwXZkLIhCgB/nqWtbF4LzuC68lrJzlpsu2yzb8OHD4zxqW+644464P7HPs2+xj2W/m3K1tm6shQvW5IADDoj7f3FXUcYs5TjDb5HfXDla2/o3hsJFQ7LkkkvGbcU2y3v++edjN1y647KtS6nP6+k6s/DCC8djFmMWde/ePbnmmmtaTRdesD6N0Y2X3y3Hcbq0tPZ9zm68tSvuxss+dOKJJ8axoPbee++4HxCvFC5E43hSjAWcx9AexGQc34mliFmIoYhLirvx1hUD8dmMW8VQB8RZ2e+T74p4be65544xmlSOLD6ma2TxMEF0NWV/PeaYYyY6D6jy8wjnCLqU8vvMrhfg9i8f18a77rprHCKKYzLDiLCNVllllTgWX3Z95zYt7YEHHojnKrZhdj3NNuD6lPMND877nMeIpZ955pk4Tybrxst8XNcSA7ItubdAubG6qpuVfXWgvJvWikJAGCsqSuHuTVTBUDmWR0supbpM//e//x0KFyGx9ahwMIvVG1QIUTlEtcyxxx4bW6HpInzkkUem71AaLSPcdZDqDipA6ApLlRp3M1p00UXjnZCo2DvhhBPiHXoom65J4eASW6P57Oxx+umnx4oSuvGBSr1C4Byr7fhs1oXP2G677eJn0xqel7XcUNpNFRBly6VQtVc4ycb5+D93T7zxxhvj33l0HeQOlIWTRLyrEC3nLCeVLCgc4ML2228f75hKKz0tG1R2sJynnHJKrK6sa1vfdttt4bTTTgs77LBDXDeW+5VXXonbga7PbE9VD1oY6YrPXUx5UAnK7yBviy22iPsTrWmPPvpo/H/W1aCa8XtC4fwQ/5/hbx6FC+OSrY9tDccZjn3ZPkQFM8cNtg0ttLTUFm/DbFq2jUupz+vpOkOlM/s1x0HugHfAAQeE8847L76mraBC46CDDgrzzz9/rMhvK3d/1sT4HRIT8ZskDqCqjzs+Ur2XVfNzbD/66KPjuf3OO++M8xGrTTPNNDF2yysnBuI3u/vuu4dNNtkkxjyFi9dYXcFv8Nlnn413nF5mmWXSd5RqR0UUsSt3Yy8+3nMtwjSuI9QwXE9QCbzaaqvFa7mM2798nTt3jufbtdZaK+y3335hnnnmiXdCprsuvQ049sJtOimqw+mNwfUw2y6/XThf/P3vf4/Xs5xjyB1wnbL11ltPdB1CVSXX/1Sxc86jtx9dqtn2u+yyS7x7v9o2k3114EfCxRIJJ4K5UjjQEQSOGjUqnTIeCUASbiTmeC0l4nSvIsnw+uuvx6CSbllcPPNDX2CBBeJFyiyzzJK+Q2mckPjRzzjjjPHAsPnmm4c555wzdvtiDEHK0BkbgGQFy8U4ADXheT6Tz+ZBIHzppZfG12aJMLqz/vjjjzEJyLgCYHmPOuqouI48P27cn7exn3vuuePysc1Q0/gZbJMjjjginHrqqfHfV155ZewmyfKQ2GN8RLB9WD9OBjw4kbCsjEcFthdBNtPBe9EFmO7VdPn95ptvat3WBP3XX399LCvnoMt2BGN/sA0YK5DvTNUjGzNk/fXXn/DgQrAU9l8CF4IZuobdcsstVZtA4bfDMYGgrPh4Rtc0AjF+SwQXbR2NARwns/1n0003jccJtg2NPHRJKe7Ox/Zj27KNa9LQ13N+IAHB8YvxZNtKQwXB72677RYbdK677roJ5wO1PcQJnLuz5Bpd2knM8VvIut4SgzGcx4ABA+IwIhz7iNWIAYhr8sqNgYgFTzrppNC9e/f4+XQfvuyyy2JcQzwilauhjUaqG9vxgQceiOdZkvT5axW3f/m4TqZwhEZPrulITnHsY3ty3Muun9ymE6P4h0YkCne4vsjOLWA7se0OOeSQeD3bt2/fmE/gOpnCA4YXyhAPcr4hv8C1CuczEn2899ChQydcS6vtMtlXh+zAU1v/d4I8Hvw487joygYrBc8vv/zysfqCisH6IsmVTwjSSkIFA9O50MlwUc6DZFY5CH5pWWCAWi4WeS3rTcIMVCYS3HJQ4XH++efHgw4HKlprMqwzibVy8BmcHEio0WJOYMyFM8tClR2VGuWgmuW4446Lr2WwV6oSqdbj4pjETm2ojuT7YF3OOuusCevIct1///3x+2IdVT34/dBSRgUIDyo1zjnnnPTZ0kg4k4RhfykOVKoJx61S+zyVbGyrHj16TDKWXFtE8EXiINuHaCghKONYzDZiPyFBkMc2ZduyjYvPF5mGvh4kIkhCUJXOeC/VjsQrSRuSPDTc9O7dO31GbRENnjTm5XHM54KSJDo45xPfLLfcchP9lohfevXqlf41PvarJAbi9VRjMMbSgQceGJPuNH7aQKJKNLTRR3Uj+U8ihMZ/Cgzy3P7lo5KM3i9cg1G8seGGG8abUJBwYjtR3czx0W36J6459tlnn1hFzvahaKcc5BA4t9E7rTbkLqjuY5tTvaq2zWRfHeiOSusEwRyBYikcpLgoyyfaQBKuuKqN5BbVZQSI9cXreeTRYsKPm/9n+DePchITdJ3lJgRUC1LhlCUTubBkWaloooWA7o6cHHlwAw6Cai5M8zi4dOjQIf2rPCz70ksvHVsiaCUn8cJ2pTKlpu2e4WBJtzXu3kvQToUeLfokP3ltbYlacDHMelIByHpl68jJi4MkF9YmN6oL+wlBBfs5D6o5+W3Whq5cVLtRuZr9zghYGHSdpEq1oHWR4x6/gfxNbrgrOYlz7nqXHX+qcf3LxTGL42W2D/FvjvdsG7YR24RtlmFbsk2peM7f8Y/52IZZ8Fvp60shUcgNZlgugutqwLGc9eKRPycwoDct2pznSPQtvvji6TNqqzg+l4qRkMVDXHAyT/Hvg98wsVumPjEQyWbiD84zdG3jdyhVojEafVQ7uteT9KdIoPg36vYvD+diii247qNhI4/tx4PtRezsNh0v61nEUA80DPXv3z99pm5sG2LPuq6LwXzMX868qm6eJeqw0EILxYsrughR2VIKFWm0EDGuXx4Ht+KqCi7gqAJsSa0XtDBw4GHZKL0mmZEh8CXRRVUTVW5U3BU/GCegrkRJJTg4McYeCRi2K609NeEgduutt8aEHiXklORT3szd8hiDIwvwa0OwT0UIVTm04BevH92gGfdAbQNdEBjrkUQL1RkEIbRQUk5PsMLYGFkwwpiYCy+8cDj55JMnOqGSgCZRzW+GyloCG8aYZFr+LtItEa2GtDIy9geBCEkjWm6pemVdaS3MVOP6NwYuHtgubD/Wm32I7cc2petvVnXENmMsL+bNVzGX+3oqohl3jH2WZDTHS74TWtUZk5SxXVp6ooFEJxVT7BtUSpL0ZB04rvP/DNWTbBf2P/4NqinpPsQ+yniaXLjxuuzxzDPPxPmkYlS/EjcQ9+RRgZIfwqDSGIjXc/5gX6bhkWFRSBJKlWiMRh/VjMQScQ3xP+eU4gST2788bDeu1TjuES/n0b2X60uOnzSguE3HFwcdfPDB8a7unCeIX4r3vdowZh9jy+av00shtqQxim2bjZmotstkXx0Y64UTAQEdN4/IX9CCarBLLrkkHsy22mqrdOp4tHZwwZUhsOSCjqCQsnHQGsIPnZbj4vduDnwu3VJYPyrjiqsiaBmgWywXkY0dsHKy5UKvFMYy4CDFhSrLwINqQS4E88k/Wum58KOqknFyMpxMhg0bNtE2rWlb092NoJyEbvFNVtT2sH9ccMEFMVlMdQYnVape2Rc5BtDtqzYk8xlXgzGiuOkMiQgqRKk+ogWPxEZLRkBGt7Ntt902Jpt69uwZk38EdFy41tVFv7Wvf2NgG3E8JXglOcw+xNAIJN9IzrGNa1Pu6zn+ESQzXiABHa/jZkdUu51xxhmxEaeSQHJyYNy0nXbaKe4bBMAcu1lH9h8SobXhPMH+xW+WfZXX5B90j5FK4ffCGHsklPPV/1xMkTjPVBoD3XTTTTHRTBdejnVUSRNjZTcGkcpVbqOPym80ynB9RmMQ8Rzxfylu//JQnEHPBrrw8h1wrKQI5vDDD4/HPbYjDeVoy9uUa97jjz8+jvu96qqrxm12++23T2icvPfeeycMM8E+TIEB02jEZP9mHq5FOF8RT2do7KULNfsz83H+YuxYrmOotlx33XXTOdVmFS4WVIfCwSjeQnzaaadNDj744KRw4ZoUflDJPffck6y44orJNNNMkxQuKpLCDzDOP2LEiGTllVdOOnbsGJ9/+umn43sULr6S6aefPtlxxx2T3377Lc7L+xR+jMmiiy6aXHHFFUnhx5y89tpr8bnCATCZffbZkxdffDH+nb1v4WA54fUoXPAk3Ha7cFE4YRnAZ/bu3TspHBTSKRO/Z+GiPCkcnJOpp546GTBgQFI4AE30ePLJJ+NruHV3IdhNCheSyfnnnx9vq144gCeFwDfeJr1wsIrz1bR8Nfn++++TwgEv2XzzzZO77rorLi/bo3CySJZYYol4K/LCgS7Oy7JyG/GuXbsmJ5xwQly+Rx99ND53+umnx++G6bzH66+/nhQuHJPCySWZZZZZksLFZJyvtm09ZMiQZOaZZ463hb///vvjOhcOmEnhZJQccMABE24dr7ahECgmb7/9dlIIEuPvgP2qLeJ3zvpzzBs7dmw6VeXiePzmm2/Wex8q9/U8N3z48HhMZH72X7V+nJcI04ofI0eOTOdo2wYOHJh06tQpufnmm+PfxAknnnhiMuecc8bfQR7zED8ULozi38QoO+ywQ4zJiM34DT311FMxBiB2I5bJlBsDEWvMNddcySabbJL8/PPPcRrfIXHIrrvumowePTpOk8r16quvJsstt1wyxRRTxN/+VFNNley2224x3taf7rzzzngtU3ys5JH/LWe4ZuvcuXNy7bXXplNKc/uXh+vhBRZYIGnXrl3cTvyfa7jjjz8++fXXX9O5xmur23TUqFHJGmusMWG/LH507949+eijj+K8n3zySbyuz7YRD/690EILxWtkznWZ2267LZlxxhknbHsenMM23HDD5N13303nUltmsq9MJIr23HPPpEuXLhN+UPzwFl544WTw4METJdmypBc/al5DMMr8U045ZbLxxhsnX331VTrnePxwF1988XjA4z0PPfTQOL2pk33MS+Ixf4DIP/r27Zu+avyBZ4sttojLmD3fvn37+LkPP/xwnKem5asJJwBOBPPMM88kB7RevXolN9xww0Tr8/777ycbbbRRDM6Zh88G6892ZXmy5erXr19y0EEHJbPNNtuEZB9q2tZ44IEHYjIwvywkcrfaaqsY2EuS1FxM9tWuIck+cF4npiA2Y7vyXsRsa6655iQJgrpioO+++y5ZffXVkwUXXHCiCyyWiQZJ4pZLL700nSqVr9xGHzUNt395aBB+6623YqPjc889N6HBoxS3ad04d3z++efJsGHDkscffzwWneSvifPGjBkzoUCBRiiui6VMO/5TCFpUJsafomsuXUwZw4W7tk1R1E2KcQo23njj2LX3rrvuit1MGVuJ7qJ03y2evzWhPJsSYbqRzTPPPHGdGro+dKktHOzjTUL4N91x2a58RrkKB8V4d6Kvvvoqvp4uOpW8PsP70JX366+/juMqMlgsA3BLktScuBkEY8kWI8bo2rVr+pcagpgjG++SYVsYuqE2TREDSZIkNQWTfU2gONnHzR8kSZLKZbJPkiRJ9WVzpCRJkiRJklQlTPY1Ae62y10Qd99993gHWUmSJEmSJKk5mOxrAlNNNVXYaqutwiabbFKvceMkSZIkSZKk+jDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFUJk32SJEmSJElSlTDZJ0mSJEmSJFWJdklB+m9JarEeeeSR8Pnnn6d/SRObc845w1//+tf0L6n1e/jhh8N6662X/vWnkSNHhq5du6Z/SZIkSZMy2SepxbvnnnvCpptumv4llXbTTTeF/v37p39V7rbbbgs77rhjGDduXDpFmtg222wTrr/++tC+fft0StMx2SdJkqT6MtknqcU79dRTw/HHHx//vdNOO8X/SxmSLzjyyCPDmWeeGf9dHwsuuGD44IMP0r+k0t56662wyCKLpH81HZN9kiRJqi+TfZJavCzZd+ihh4a//e1v6VRpvHPOOSecdNJJjZbse/bZZ+O/pbxlllkmfPbZZyb7JEmS1OJ5gw5JknI6dOgQOnbs6MPHJA9JkiSpNTDZJ0mSJEmSJFUJu/FKavHsxqvaNHY33pdeein07t07nSqNRzde9g+78Uqqzf/+97/w1VdfhTfffDP85z//CUsuuWRYaKGF0mfL8/rrr8ebk/3444/x3LTllluG2WabLX22dfrhhx/CHXfcEd57770w00wzxRuvLbHEEumzdSvn9T///HN45pln4vb7+uuv482UOJ9vsskm8a79ecz75JNPhldeeSUeV9m+66yzTlhhhRXCFFNMWg/D93n33XeH77//Psw111xhiy22CPPNN1/6bOvVHN/LH3/8EWOroUOHhi+//DJ07tw5LLfccmGDDTYI0047bTpXaXw/77//ftzmq666ajp1vE8//TQ89thj4d13342fMf/888fv+i9/+Us6R+vEvsn+xnGE/XK11VYLU045Zfps3Rr6nap6WNknSZIkSQ304osvhl69eoWePXuGDTfcMN7h/cEHH0yfrRuJwquvvjqsscYa4corr4zvd/TRR4e11lorXvy3Vix73759w1FHHRVeeOGFCevI/1nnupT7+ttvvz0mNi677LLwxhtvhOeeey4cdthhMbH0wAMPpHOF8Mknn8RpW221Vbj11ltjI85VV10V3/Pggw8O//3vf9M5x38nF198cVh55ZXDFVdcEV577bVw7rnnxr//8Y9/pHO1Ts3xvZCE23nnncMqq6wSzj777Lj9HnroobD99tvH5OqHH34Y5yuFJB/z7bDDDuGCCy4I48aNS58J4eabbw4LL7xwOPDAA8PTTz8dk4nHHntsWH755eN+0BqR5Nt4443DrLPOGhN8/fv3D2eccUb47bff0jnq1tDvVNXFZJ8kSZIkNRBVOCTnqBgjGVFX1VIxkk70Zlh22WVjUuTll1+OycKffvopnHjiieHXX39N52w9Ro8eHZeddbj//vtjpRbrRlLmlFNOicmJ2lTy+hVXXDEMGzYs/Otf/4qJvldffTU88cQTccxVtisVTyBpRLKO50j8cWOud955J1brkRS5995743wgiXT66afHz+Nzn3/++fDPf/4zVpEdd9xx8fWtUXN9LySY+F1cfvnlsaqPBNTbb78dE6jMQzLr//7v/+K8eWPGjInJQTohlqpmp9Jt7733jglBErv8VqgcnGGGGcIJJ5wQq/5aG/ZTEtBU5ZHoX2yxxdJnytPQ71TVx2SfJEmSJDXQ3HPPHXbbbbdYlTPddNOlU8uXJfb233//Cd12V1pppbDZZpvF5BXJwNaGBAPJNCruqO4ClUv77bdf7OZcV+VjJa9niIU+ffrE7rsZEh1sQ7pEjhgxIk4jUXfttdfG7ynrsktCac899wxTTz11/LwM3URJovB5s88+e5xGl9K99torJhUfeeSROK21aa7vhQQWlZB77LHHhOQ33w9JLbqWkpClu3oxurE//vjj4Ygjjij5W9p6663DeeedF39zGbrMM/2bb76JydvWhu7NVPNR3de9e/fQrl279JnyNPQ7VfUx2SdJkiRJkxHVTVQnkeTLjwtKYoQEFlV9rTHZRxUXyTKq7vJJOLpgkohgnX///fd06qQa+vpffvklJn9mmWWWWPVVG6rIeGQJQKrSSOiRpCLBl8e4cJ06dYqVavnupa3F5P5e2M5sX7Z1cVLrs88+C3//+99jpSWJ2kqSXrwv85cad7HaNfQ7UfUx2SdJkiRJkxFJKW7+wID6Xbp0SaeO161bt5hY+uKLL1rVuFssK8vMsmdVcRnWceaZZ45da2vqnlzf13OTjkGDBsVx3ri5CTdXYgwztmNNGFvuvvvui8kQuvhm+OyxY8dONI4fGEeN6SQSi59r6SbX95JHkpQbayy99NIxEZsh6c2N10jYUeHaoUOH9Jm6fffdd2HIkCHxZiwkuNqSxvhOVH1M9kmSJEnSZETiiHHK6EZafOdNLuCZRkKwtWGZqTJiHfJYH6aRNCs1Zlum0tdTZXfdddfF8dwOPfTQCd0aV1999XSO0ug2StdebhqR3Qmd6jC6hlIt9dRTT8WECvgMxmUkcVLX8rdUzf295DGeHmPL0XWaLqa8JsPNO/guuLFKvotuXfjtMLYid6Ddd999K3pttWjod6rqY7JPkiRJksrAuG+MJUYFEQ/GdKNipqGyrocklOiKmJdNyydFWotsmYvXib95kJyorZtmpa/nb26yQfKHseDoDnrbbbfFO8KOHDkynWtiJO4OOuigOJYfN4XIjxFH4o/qM8ad426v3IGXceS4W/JUU01VshtqS1Hbvtrc30vm22+/jYlYqtDY1ozbl/n666/DaaedFhOzm2yySTq1blRlnnXWWTHJu+uuu8ZHW9TQ71TVx2SfJEmSJJWBO1wyltj6668fH1SNcYfWhiJxxNhwVIsVj6tFxQ6Vf3WNOdfSkAhjmVn2UaNGpVPHYx1Zr2mmmSaueykNfT2VY1R5cXMIKvx4FOPGJ9xUhW6OJIvmmWee9Jnx6BJ5/fXXx6TfpZdeGrsFP/DAA/Guy4zjx2ewDC1RTfvq5Ppe/v3vf4d99tkn3uGYm2tsvvnm6TPjsZ25Y++aa64Zb9xBd2zuJks12s8//xy/K5KFeVRZXnjhhTFxuO2228ZkYXFlW1vQ0O9U1clknyRJkiSVoW/fvrEbIhVSPEhOZN0+G4Jqsh49esRx+4rvTkoVFAmP+eabL17UtyYsM8vOOuRRdcb2Y50ZU6wmDX0924v3IOHBnY7zSIYNGDAgJodINPXu3Tt9ZmIkAG+55Zb4epImjAG47LLLxuTVUkstVdG4cs2ptn21ub+X7C7TdIem2pK7zuZRvUrlJfs+yVfuJsuD5CoJPl63xhprxCrNDK+54oorwsknnxwrARnrL7vjb1vU0O9U1cdkX4qBVbkd9fHHHx9LixkngIM+A31muIPNMsssE2688cZ0SvNqyOdzK+7lllsuDBw4MJ3SMtASwx3GOGnWpqmXn9uRcwt4yr4JBiRJ0nicI+m6VqoqpqlU+3mZMcB22GGHsP3226dT1FrQVW7GGWeMNxXgwb87duyYPls+KpXotpjt33Sxo/si04cPHx6ngWuURx55JN5Nk/HjWhu6aXKHYdYhfyML1pEEEOvMuoNtwTZhG2QqeX0pVH5RHda5c+eJbtDBnY3p2kv3Rq75Fl988fSZmvE5vA8JlcGDB8ckIRVzLTUBW9u+2pzfCwnSgw8+OI7Hd8opp8TjevE24+9jjjkmfPzxx+GTTz6Z8Bg6dGi84QZJyvfff39CF10SfdyE5bjjjovjLHJNOf3008fn2gLWn4YBHvwbDf2tqPqY7CugRJi7Lm288cbhhhtuiIklfiR77rlnbOHhQALGfiBTXlwa21wa8vmclLhbVH1eS0C6++67h7322muiA0dDcWDiwMMt7Vm+2nCSYT6C/6ZAIECrEcnd4nEO1PYQzDC2CS2PjOFyyCGHxLFZ+A2Vi/2bfZb3oRWSAYNbE35zWTcVHvfff/+EC5Jy8Jt+++23w9133x0fNY2TU81o8We/Yf+hWwnnlkqU8/rG2Fcnt4b8Vhinhzv60ZrP+rOv3nXXXd5trpGxPx1wwAHhsssui+fL5lDt52X2+2z9VD2Ik0lo3HrrrbG7JMcozoX8TcKc4zr4/v/2t7/FO4YyXlyGxBHTOKYzP3E/Y5Ex1hrdMRlTrrVhmemuyTqwXp9//nm8AQPrxbpusMEG6ZwhPPHEE3EalVpsI5T7emKUM844I1xzzTUxuUcShIQeCSS656600kqxGg9UuJE04r0222yzmGDiO8oeXBtmOM+edNJJ8TN5HediElc333xzvDZiPL/WqLm+F+JBimmojFx11VVjwvH222+fsK3vvffeCedsnuvZs2espMweJPpIWpJY5blsPEXi0sMPPzzevZqKP67ds/ckEfvZZ5/F+Vobujiz/Kwf171UOnIsYBrHkgyVehwv2M78G5V8p2ojCgFUm/bcc88l3bt3T+add97kvvvuSwon5fSZJCn8QJJ99tknOfXUU+Pfw4YNS2aeeebkkksuiX83t8n1+YWTXFI4iCZrr7128ssvv6RTG64QyCcnnnhiUjiIJ4UTZzq1tMKBL5l99tmTwoErndK4RowYkay88spJ4aCZ/Pbbb+lUtRSnnHIKV3rJoYceGvfBpn5ceOGFSfv27ZPZZpstWW655ZLFFlssmWqqqeI+WAhQSr4m/ygE7sncc8+dtGvXLi43/2cdSs3bEh+FC/tk2223TTp06JAUAodkwQUXjP9mGs+Vek32KAQmyWmnnZZMM800cd15cNx6+umnS87fGA+OI3zOkUceme4x9bPAAgvE9+F4U+pzKnkULvKS3r17x+2wxBJLxH1n+umnTy6++OK4jUq9Jv8o9/UN3Vcn96Mhv5Wffvop2WqrreJrZphhhmSZZZaJ+yvbY/nll09effXVkq+r7yPbPwoXj+ke07SGDBkSP6/4MXLkyHSO5vPuu+/Gc/V22203UZzUlKr9vDxq1Kikb9++8aHq8cUXX8Rjd6nf7owzzpgMHTo0zkcMfOCBB8bj+kMPPRSnZTh2cTyfYoop4us4pu+2227xN9Facdzafffd47qwTqzbsssum7zyyivpHOM9+OCDcZscfPDBcRtlynn9mDFjkl133XXCPNlj2mmnTQYMGJB89dVX6ZxJ8sILLySzzDLLRPPlH5tuumk6ZxLPNRtttFE8t/Ac55xu3bol559/fvzM1qw5vheOdVxHZtu2+MF1+EcffZTOPamPP/44+ctf/hK/k/z554wzzpjwGyl+8F0NGjQonbP1YP3Yh7OYKP9gXU844YR0ziT59ttvk6WWWipZeuml478z5X6nahva8Z/CjtAmUbHGeAGUttLaQAlwMVovCkF+LAtmPlp/aIljAFDubkSL7EILLRRbKvIl/LQm8LrCRfJE02llooWczDuDZNJyTRfWwkEptlYw8CitULRSsDz8P5P/fAabzdDiVDhIxj74vXr1KllKzrp++OGHcZDZrISdcmxaAgoXMLGlivdneRgfgew/aJH85z//GcdYYBnPPPPMOLAn4yEsssgicR7wPrRMsn5zzDFHHFg1v+ylsG1pIeKOWbSCstx0EeJzaKGZd9550znHd2EunGhjSxp3wMp755134najRW/RRReN3YJLdadgG9BSl22rFVZYIX4PKBwYY2Un06kKyQZ2pTWK74ftQJUn02l9evrpp2MrINuCsTpo1cvugKTGd+qpp8ZWwUMPPTTu/02tcGEbf790m2d/BNVDtALTjYYWSbpD1ISWeMYWYR+mcoOKI/Zb/t8aUBlWCCji8jKkAQpBbbwTHa3bVD3XhmMJlVocI/h9UyF4xx13NFnrN1VdLNeRRx4Zj1H1xfGa3zutqvze64tjDePNcOykwoBjKtUcheArvj/Va4sttlg696QqeX1D99XJrSG/FY7PJ554Yjzv0d2T8xLnsKxbD+P3XHTRRfXqXlcK25jtzzk6f/5rKg8//HDJccA4XzEgfHOi2nLttdcOq622WuwBke1rpZQbD3DO5pzMPjz11FPHHhb8BjPlnpfZbyqNtwoXjvGzWS+Wca211qq1+1f2WhAzFa8/MRRjIvEcXfzAMZDfIsvAb5C4Jt+FkGVmsHwQT4LeDnRbo5qFCpcM1WJ8fj6Gy/A5dHOj+oP1YnsXr4txS+vDPsdvg++V4yM3gqgG7K/shxwTuH6qdB8s5/X8jpiH6yPOCxwnuHFBQ3DNQpUU5yyOJ5wDst96NWiO70XNy+9EUUz5tVFUb1ANQWtN4eIqnVqzrLKOjDvVA1QS0MpRCPqS/v37x5YLFE7QyR577BGrAKgOzKMKg8+kohBZy/VKK62UFILaWMXBZ/CeVNO8/vrrcT6UquyjtX2FFVaI82bvWUpxZRzLSDUMLVN77713XBfem1aA6aabLrniiivifFTxUFXCrpJ/0IqQGTx4cHwfKn8KB5T4/0JQktxzzz3pHKVly8ByDRgwIH4ury8E67H1kxYZ5kGpyr5CoBxbRQsXAHG7sQ6FA1lsJS8Ey+lc4xUuJpPCgS62bhQukuL8hYuL5JxzzonPl6og4P3333//uCx8b2PHjo0ViIsvvnj8flhWlpl/b7PNNmXtQ6qf5q7sK/WgwpUqIvb1l19+ueQ8pR633XZb3E9aS2UfLd+rrLJKUghkkw8//HDCdP5N1RjPFQKIiV5T04MKNI6FtJ63pcq+woV7PJ6y7uw32XQq7bp06ZIcf/zxE81f/Gjo6+u7r07uR2P9Vqis6dOnT7LwwgtPtA839JHtH1b21VzZV2488N5778VzLudPzsmFi+b4OProoyecS8s5L//+++8Vx1vsG/369Yufx/uwDFRH1Pa9ss577rlnrC55++2306njEfvxflROfP/99zFuOeKII2KMQUzF8Y/twG/6hhtuSF9VurLvH//4R9we/NbziAXZ/lRNZ1j3s88+O8Y+xEHZ5xCfDh8+PJ0rMW6RJKmNatNj9lHhRasPg91mLcZ1oWWH8acYI4BWY6rj9ttvv3DfffeFRx99NJ2rclSSFALBWBHI+9Ivn0oObvFeE6rZqPRr165dXCYq2ipFC/yLL74Y+/bzeVTJ0Tp+8cUXx5ZqKkMYk4L3prrkjTfeiIOjMnYAqDxhW3B3H26RTgsky0ULGlVYvF9d+Fxa2Kn+4fW8hgo9KosYc6MmVAxdddVVsYLlq6++mrC9WCbG56A6BrTcU4lUCMAD4znwGXzvVE3UVL3DduF9qUZijCK+YyoPL7/88th6z2uZh/cpBOFxDIRSFZWqHoWLzFhBwu+U/btasY5UllDdy+8/w79pGeQ55lHNqMbg+EP1cOGiOp0a4vGGCh+OlVQ11aShr28r+2pNCrFNPFdzTOb8qOZRbjzA/kn1JpVqDIrPuZt4g+pMKjGJOUopdV6uT6UC8RbVPsSAfDbvx++FuIFlK4XfIb0LRo0aNaEKL8P4XcQBVGHymwODpBNvUFHHchOHLLvssrE3AzFUY6DCl7E8uckHMRCf88orr8RKSLY3PU/4HRi3SJLUNrXpszxdRAiEuIgtN+DhwoFuF9y9ie4ilHLz7+mmmy4GT/XFrbLpdsSFHMtC0EiXoVLJMi5kGMx06623jt1T7rzzzom6vlSCJCdd3/gs0FVgu+22i0Eit2onwGVgVObjQdeSBRZYIHYnYNuR5CQ4pitV1q2J9yJoJqjkDsd14TPoJkxXaLAtSPRxsUsCkM8pxnfHenPxe+yxx8aLCYJ+vgsSoHRXybYd3X9Yn9NPPz121WH7Mi+fx8CmxUi4brPNNvHChe7dbGdew3LwPt27d48JYqbxYL25m165CWO1HlyocvdrkshcUHFxyEUUXamqFRe/v/32W7xgzydK+DfTeI55VBrHCbpOcDyYbbbZ0qnjcZ6YaaaZ4var6QYS9X19W9xXa8KwDyRUOE5nyRc1rUriARKB7K+cNznXEgOwX3PeJ7YgiVWcdKvpvFwf/Cb4LLrv8h50FSZ24GYKtcVxJOuIf7JB08F6Z8OQELdlcQFDvdBAmiUjia24eQ6vY/0bivchUcp2Znuz/UD3fmI6GmVYH+MWSZLarjad7MuCMMZ9KRcXvARJ+dZkWlEZbyYL/uqDAJdEX4YAjPFaSt39lqo6xm5iTD+CPQLW+mJcl2zcugzLQYBY08Vohu3G2EUEzllgnyGoZPlp8aairjYsQ/FYXgTUrBevZ1mKceFAJQCt5/nqIxKwBNhcKBDssoxUB7KM5YwXRoJzyy23jC3fJAkZYyfDd7788svH6sYdd9wxbnvG7lB1Yr+lwoSxw7jDJ2MvbbjhhnH/qmYcc1h3xqLhojDDv5nGb6ox78pdjTj+kMBgbKw8jiFMI2E6duzYdOqkKn19W91XSyEpRLUTDUBUdOfP1Wo6lcQDjCFEZSp3xmQ/z9CYQEUx51WqAjO1nZfrgwZFxrbLcGzj3M5YgCxbTUiokdBjPXiAWIQKPsbAo0dChnhw4MCBcblpWKRKl2Qfv22Sbw1FDxCWlUZR7rJ41FFHxSQfFZMkI1kXtqNxiyRJbVebTvZRNUFyiKRRqYRSTTp06JD+azwSgDyouKsvAt580ItS78nfdH0hkCO4pNKjIfiMUp+LutaH4J6LTqob84NigwtSEpYkBep6H7Yn75HHNBKoY8aMiZ9TjOlcNJBozZY3Q/ccprFsbCeWge1UfOFcSpZEpFKSBGwxug1xgwiCbLoTcdFAaz8ViGpd6NbEjQHofs+DAeXzFWv8LqiSYj4ujk455ZSYaCeBQGVpteLCl98Pv69iTOP5fBKwreLYwhAI2f7DUAh01UOWYCp1/ObBvlV83Mqr9PVtdV8tRhKFhCeVkdzUp7aboKhxVRIP0JDIPk7jQR77NOd0zu/8vjJ1nZcrRWxRvIwsN7+jbPiPUjjukexj2alSJG4ksU7DIl18sziGpOROO+0UDjzwwDjUC3ECFY5ZQ3F+3eori2+4+QfJRrro8n+ORST2+Ey2JYxbJElqm9r0FRvjT5HwozsJF0mNiaAwuzDLI4gtnlYJgmECSMa2oWsMY+uVuihvDgTLJNEYw4bAM4+AmQfVFbVd1IIWfoLjPLYTLeC8f3FyFST0+Hxa/4u3Z/Zd8lrmYRl4/3KqkWgBP++882LAvMcee0zSXZFgPhtriMCZ7c96cnfkrKVfrQMVn3Rj2nzzzeODblckb0rhzpfsDwMGDIgXdzyqVfabozIl3wjCv5nGxSrztHUcU7hTcbb/MHwAlTwc+6lWpvKOY1geF+ckOkolRDINfX1b2lfzOFZzt3a6eZ5xxhnxTrxqPpXEA/yf/bv4vE/CkPM3+3e+e2ld5+VK461Sy8iyEEuxbLWh8pBEGcvCOHl0XaareL7akO6zw4YNixWmjEd87bXXhksuuSQm3+tqdGRdUNwATZyUj/WIgdhGJB/Z5+l+nH/QVXiXXXaJ8xq3SJLUNrXpZB9jqNAay0X/DTfcUDJpRlBJIFUpgj8CynxQSrKJC6+GJPtA4Hb++efHLhmM1XLuueeWrH5rLFzcc/FPsJnvOsZ0Wquppii+oBw+fHi89T3P01peG7bT0KFDJwpu2eYMmF3T60nS0jrNYNT5LilsY4JwugxRBcAyrrjiirHLC5VbdeFChDELqZJhmeguzWtLmXvuucNee+0VByQnCUL3MbUeq6++eux2RhcxHlRl9evXL312UlyEMWZlqeR0NaEbHskm9ud8BQq//c8//zw+161bt3Rq28Uxnu5y2f7DtuHiHpxb2HZUI+WRyOCcwDioWdVNKQ19fVvZVzOca0i8kmDhBgiM56bmVUk8wFi7JAa50UX+GMM4k1Slce7OD2tS13m50njrww8/nFCFC+In4gNiK4YQqQ3ViMSNHB+5sQfrRjddfm8Z1hUMkZJP3rHsLGdtOL7yGqpT80je5X/LWXdpGqvZbuUybpEkqe1o08k+glMCHsaNIWm29957h2effTYGq9wNkbvCkRCg5bYSBGqMz0LL8TnnnBMHCie45AYc3DiiMRCUMk4LrdxcYHLziXzQ3Jjo8kJijcTIBRdcEG699dbYZQ1UTzDODoNdM5YOXVZuuumm2IWKQJe7vZXjyiuvjHfW5fV0LeGmGwSzjDtVChcKDKhNyzrdtkgO0pWG75HuLATjWdBOxQ2t8ccff3xsXSc5yHdyxRVXxBb3Ynx/XCyyTFR68Tl0laElnPfggoOWcy42uKsfg4lz4U0QrdaD3z9JYS4qefDvmqqlQGMAF6Jc7LFvZrgAY/8gsVINWDfGnqLig99Uhn+z/nSNrOb1LxfHCaqAsv2H8bw4LoPtx1iiTzzxxEQVxVyYs70YSy9rxGC7sf3yF/KVvL6UmvbV1q7Uvkb1I+OV0cjD8Znuk1mCRY2Pcy7nPOKA7DF48OB4Liw3HuAYws2xSJYRv5AoJzHHuZxEGd9hcRffms7L9Ym3SLgxvh3JNxLqvI6bfqy99toTjbtXE+JCfp9///vfY+8CbvDB+STTs2fPeC6h0pffITEHjbLcFbcuxC2MJ3j11VfHOIt1vO666+Iy5hukOdbss88+sZs+FXzETTSQsu5333137AHCtjBukSSp7WrzETE3gSDooTvDPffcEy+iqFohOcQFBBUWpe7YWpe+ffvG1lMqP2jFpsvw22+/HVukG+tChC4cBNHcyZZAkIqGprjgZnkPOOCAuG0uvPDC2PWRQeBBAE+SjtZouhZzxzcCdQJ+gvL8INg14UKZ7XLSSSfF15Goo6WdxFxtrewE/iQ5CfzpVkNCkoCW5WNbZIkbqmAIlhnjkAGyCW75Pth2tSV3uHC55pprYpKT9+Qih4sSuoox4DgX0VxkUCFw5plnlnWRoJaP3xC/JwYy50KN8aK4szNjHnETBL5z9iVQrcE+yP7HhWOGBA3jJ3Exy4UV+zMNCPzNnbTzFSgtDccV7uZKd1Eu2kn68RsgkU5iheeyLnY1rT8XqFxwcsdsfjM0RHDhzfo3VoNHS9arV6+YAKBaiCpsLvY5F9BYwvlg3XXXTecMcXuw/TiGZNXN5b6+kn21pSr3t1JqX2O/YnzC22+/PTba0YWZsQp5LQ8SIHXdaEqVoXqyf//+sdIu/6DxrNx4gOMH3yUNcSTMSI5Rgc+5lP2ZGKAmxedlknWVxlvsK5yv+R2xTCTDSOCxTNmxrTbEEMxPoo+4g27GeeyjJNvYVtxEjPW77LLLYqyW3TW3JsSfHGvZb7kJG9uQ2Ih1IVbKY/mJbUjocbwgniVm4hhNozUJQaoijVskSWqb2iUN7VNaRRjfhZZQAjjGQ6ErSV3jt9SFizQqYuh2yvvV1aW1taLFmeCRVmOCVQLOSteVRAKJBV5Hy39xy35NCIoJ/Lnw5XNr62JI0EuXO6oVCXL5nivFxSddX+gCQzUYieFyLhBUfyRmuSA79NBDYyKjKbEfkRSmWiVfLcuF06abbhqXI7sDNgkIGgVIrAwaNGhCN2C6YDEvYyQVIyFBFcnKK6+cTml5+D3feOONMZFCAglcaJL840Iy+23XtP40nHBxyjG1WJ8+fWIVVmMiQcAFMdU6XMDWF8dovjOSTiQNGoIKJbYX3zX7ERfdXGyTsOP/GRJdVOawXUk2ZMmJcl5fyb7aUpX7Wym1r3HOoNGHrpSlsO4PPfRQbLRrDHRDZTk53xTfcbYpUKXOmGzFqOZi27RUlcQDVP9RjZadk7Pq2PqoK95iu5EUo6KNykOObcxPAoxlbKyG2AyNHmwH1m3xxRevaN2IL9jPsniotteyvenKy7YkZiUBScI1z7hFkqS2x2SfpBavOZN9GZItJIa5OOJCi4vBhib/WxuSKVSKcZogudFSb8zREpN9GSqC2Y/y44hWopzXu682D5N9rVtxss9klyRJqmZtvhuvJJXCxfTSSy8d1lxzzdhNqy0mT0ju0eWLaryWmuhr6eiGxwD+JIcqTfShnNe7r0qSJEnKM9knSZKkqkbVK+PmMcRAhw4d0qmSJEnVyWSfJEmSqtpUU00VbxzCTT5qGkNQkiSpWpjskyRJkiRJkqqEyT5JkiRJkiSpSpjskyRJkiRJkqqEyT5JkiRJkiSpSpjskyRJkiRJkqqEyT5JkiRJkiSpSpjskyRJkiRJkqqEyT5JkiRJkiSpSpjskyRJkiRJkqqEyT5JkiRJkiSpSpjskyRJkiRJkqqEyT5JkiRJkiSpSrRLCtJ/S1KLdOqpp4bjjz8+rLDCCuHEE09Mp0rjnXvuueHxxx8PRx55ZDjzzDPTqZVbcMEFwwcffBBeeuml0Lt373SqNN4yyywT94+33norLLLIIunUpvPwww+H9dZbL/3rTyNHjgxdu3ZN/5IkSZImZbJPUot39tlnhyOOOCL9SyrthBNOCCeddFL6V+WyZN8UU0wR/y3lvfvuu/H/JvskSZLU0pnsk9TijRkzJuyxxx7h008/TadIE+vevXu45pprwjTTTJNOqRzVo1SRSjVZcsklw7Bhw8K0006bTmk6JvskSZJUXyb7JEkq4HT49ttvh3HjxqVTpInNO++8Ybrppkv/alom+yRJklRfJvskSZJaGJN9kiRJqi/vxitJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkiRJkiRVCZN9kiRJkiRJUpUw2SdJkqTo448/DjfddFO48cYb4/9vu+228Nhjj4UvvvginUPF3nzzzbDccsuFgQMHplNCuPLKK8Oyyy4bn2suF154YejTp0/44IMP0imSJKmtMtknSZKk6Mknnww777xzGDBgQPz/dtttF9ZZZ50w77zzhsUWWyxcddVV4ffff0/nFtge//rXv8J//vOfdEoIv/zyS/jmm2+abVv973//Cz/99FNcjv/7v/9Lp0qSpLbKZJ8kSZIm6NChQ7jhhhvCH3/8EcaNGxeTVrfcckuYYYYZwr777hsOPfTQ8N///jedW6Wwjb7++utY3SdJktTc2v+tIP23JEmSWgC60958883pX3868sgjw9RTT53+1fj++c9/hocffjhsvPHGYdFFF43Tpp122rDwwguHTTbZJLz99tvhnnvuiX8vtNBC8fkMVWX3339/rA789ttvwxxzzBE6deqUPhvCr7/+Gt56660w5ZRTxoo35n3iiSdiFVz37t1D+/bt0zn/9M4774R77703PP/88+G3336L75mfj4TaJ598Eqaffvq4zVg2us7OM8884dNPPw0jR44M0003XRg2bFh44IEH4nL16NEjdOzYMS7DU089FR588MHw/fffx+kkOvNY5qFDh4ZHHnkkvPrqq3HZZ5111tCuXbt0jhCTobfeemvsQrvSSivFad9991346KOPQpcuXeJn8Tfr8tVXX03y4DNmnnnm+Drw9+OPPx6GDBkS3n333bhuXbt2TZ8tLUmSuJwsY//+/cO///3vcPfdd4fXX389vn7GGWeM85G8ff/99+PzTJtiionb/b/88su43XhN8baQJEmtSCE4kCRJUgsyZMiQhDCt+DFy5Mh0jqYxcODApFOnTsnNN9+cTpnYww8/nMwwwwzJtttum4wdOzZO+/3335Ozzz47Tue1s8wyS9KhQ4dk/vnnT4YPHx7nwbBhw5KZZ5452XXXXeNzzD/99NMn7du3T/r375+MGjUqnTNJfvnll+TAAw+M7zfNNNPEeaeccsqkb9++ySeffBLnGTduXHLiiScms88+e3zPLl26xPdfdtllk7feeitZeeWVkxVXXDHZYIMNkmmnnTaZccYZ42etscYaycsvv5z069cvTu/atWucvskmmyQjRoyI7w2+g27dusXPnWmmmZLOnTvH9dpzzz3j8mVeeumluAx///vf0ylJctZZZ8VpL774Yvz7nHPOiZ9R6jtlnTJPP/10stBCC8XPZF34TNaL7Ztt71Ly22LAgAHJdNNNF5e5Y8eOcb0HDRoU5/njjz+S/fffP5lrrrmSN954I331eD///HNclj59+iQ//vhjOlWSJLVGLbYbL+Oe3HXXXXGA6OIH02lF3WGHHcL222+fvqJxMBj10ksvHQqBWzqltHIHQR49enSTLGeG7bTVVluFQpDbrGPolBqMuqWYXNtEagxUVFx//fXh6KOPDoULynD44YeHf/zjHxV1mWO8Jn6jd9xxR7jzzjtjZYvatobsEz///HPcB4855piw1157hRNPPDE899xzcYwwtT29e/cOs88+e6yiy/YjbuJx2mmnxXiHSrUffvghvPLKK7Gqje6sVLVl2G+oOOPYRjUdlXn77bdfuO+++8Kjjz6azhXC1VdfHccH5FzOe1KJdumll8bKQ/ZF4qsMzxG3cSMR/s3+yTLi5ZdfjtWA3GCEzzvzzDPDiy++GLbccstYuch7s3wnnHBCrDKkyi8z00wzhXPOOSe+548//hhfz2ezvvyWKsF6UKVHVV32OP300+M2WnHFFeM8xJR777137C7NcvO5LB/jJrLcVPvVhdc888wzsYqRZaaakPVk/V577bVYFbnFFlvEKsn8uoJtSyXgpptuGte9LWjo+ZI4k8pT9nMeVJbWFHvy3meffXbcj4pxnOU7O/XUU+M+wG+C/Z3fR7n4bVGZyftQFcz+1hj4PbMsLBPHf/aRSpTz+krWv9JzEt/vSSedFPbZZ5/4O+LYVRN+d1dcccUk78WQBhw3uP476KCDYnx28sknx+NOJedClp0qY4ZF4HjD+zaVSvbNUsp9PXEr14MHHnhgHOaBbVTODZ2osh48eHD8Lqlmzqv0O67tt4WWGAPx+ob+Xvkt0UmT38sll1xS4/rzWYMGDYrnhpo0ZN+s7z6gZpAm/Vqcwg6fzDnnnMkUU0wRW0LzD1ojC0FkbJFde+2101c0jsKJJbakDh06NJ0yqaz1lOUrHDjSqaXRSk0rab7VtjHRAk3L9frrr58UArd0atOjRZz1p+W6pZlc20RqqEIQlWy00UYTKmJWWWWVZN55540VHlSifP755+mcpXFsonqESpV27drFipFZZ501Vp2obWroPlG4KEp69eoVz71zzz13rJKi0olqoQMOOCAZPXp0OqcaW0ut7CtcuCfLLbdcsuCCCyaFYH5CNRjn3eJqsMKFVawuK1woxb+p7ONvqvDyVWqvvfZaMscccyTHHXdc/Jvz+EorrZQsvfTSyTfffBOngWPkLrvsksw222yxYi6Lx6aeeurk+uuvT+caL4sFevfunXz22Wfp1CQpXJTEfZr3LlxsplOT5J133kl69OiRFC7k0ymlFS6m4vpTiUiVHPg91VXZV+zRRx+NcRTrU7jQjdPOOOOMWBVJdV8ex/6FF1442WOPPSZ8ZrFsW/DbvOiii9Kp4xUu3GJ1X+GiMM5HVSIx0vLLLx+/T/C+++yzT/ydv/3223FaNWuM8yUx/o477hi3Ob8HKjL5N9PyVaoZvse11lor+eCDD9Ipf+J3x7me4yuVlUsuuWT8HfK7uP/++9O5avbCCy8k88wzT7xuYl34f2PE6FR/LrLIInE78Ztheaiyveqqq+I2rEu5ry93/Ss5J/H+/BaocuW3uMIKK8RrPI4fd911VzrXxG6//faJqpYz2XUp77XUUkvFz+W9pppqquToo49OxowZk85Z2k8//TQhvsuO5Rw3S+0njaHSfbNYua+/6aab4vfEdlhsscWSZZZZJlZis41vu+22dK5J8T3xXvz2unfvnnz00UfpM/WLO2r6bTX0d95UMVBDf6+sF78hfkssD9uddeS3VlyxDY7vnHevvfbadMqfGrpv1ncfUPNo8Tfo2GabbWIrAtnl7EGmmOo7xk/JtwKr+RR+yLG1mZZ5SY2jEEyE/fffP3z44YexwoMWNqo/CheQsUX58ssvr7UlsRAsxGMjreGM21S4MJxoXCm1PQ3dJwoBZVh55ZVjKy/joj377LOxUojKICqvaPVX28IxiP2icGEej1lUElApU7joCYWLlXDUUUfFcQWPOOKIOCYfFRX5Fn72v8IFSXx9huo2xiHM7mZLJRAVD0sssUQcHy9TuMAKq6yyShzjj/0xw/hyVByWMtdcc4VZZpkl/SvEz2EMP6YXLtbTqSEULk7iI39HXdaTasHdd989rLnmmrFHxwYbbBCP0VTQFS7w0zkrQzUGlRhLLrlkrDTic4lvqawD1SfZduRx/vnnx+3LdqzrM9kW/ObzFlhggVjdSGUL31/hojBWNvK98ZvGZ599FisH11hjjTh/tWuM8yU3saHi57jjjotjURYusmPVD9Oo0AfblbEuGdOR/Qn8nyopxsV877334rQVV1wxnvMZ95KqIZaJ4y77PNVu/CZqU7iwjj0CGC+TKiG+44Zin2N9uMszv2WqdakOXX755cMpp5wS96faVPL6cte/knPSSy+9FKtn+Tw+l3E/+Y3NP//88TvLjiFUevF7yFccchygco9KLn5zHId4fyoNeQ8+l0qs9dZbL8ZmtVVMgfWg1xG/beI57mzelMrZN2tT7us5jlOJSazKPFRGMnYo1clUE1PxVQrV3czH2KrFv7tyv+NyflstNQZq6O+V74TfBTeAYt9mu1OpzW+N74lKSc69Bx98cPycESNGpK8McbtQLZ59jw3dN+u7D6h5tNq78RKsZN0gMuzYHIA5IVCee/vtt4eLLrooBi/s8MX4AXNwpuyVktW6TqSlJEkSB6umdJUffT74rAufR/kypa78wPI/xDzWlRMQ3VkoLWeZCQprw/pyIOAAUFtgyA+Q+fLbh+XiBEl5dYbP4+BGgAtO4Lx3fh4QnHOCOO+882KXaw7ExdjufE+sC9uek3p2kK4L8/G5lCLz3TKIeG3dG0kU8/1ffPHFsft3PojPsN9QQn3BBRfEEzYHuvz25cTOZ/I5nNj5rq+99tq4rplsH2B6qS4H5Sw3r2O7F+8HrAMHTz673O2k1okL57XWWivMPffc6ZTxJ1G6VHXr1i0e7+r67a+++uphxx13jCdrTuBSQ/YJLoo4rq222moTBvLnRgF0YSJpkiUK1HZwHuU8xc0dOnfuHLuDEkMwvAqNsJzfssZYzl3zzTdfTObldSi68QMXXzyIqUDcwvmO1xVfmHFRxDQ+N8Oxs1PuRiB5PMcjj9dzbM2/N//mkS0DsRfnay4auXDh4ozkHBeOJNTGjh0b56kUcRG/H7YfsUmWiGQbElsRG/B52Xbk8fTTT8fP5AYidWHbkjzMYxq/V7Zrdg7p27dvmHPOOWOigziD74tuvyQ98onYataQYyO/A+LKeeedN3bRZpvx2HnnnUOvXr1iUoRucT179owJE+blOb5/LoyJB0nkZklqEuAkk/P7KkkqbvhC43pN1wgZ4obddtstHqtJZjcGknEc44lBSLKDpBeJata/uBt4sUpeX+76V3JOIlHPb4rPy7r1k+QngUdSkd8WNtpoo7iMdD/kuoRrrg033DCMGjUqJis4ttCtfd11151o2/Lb5ffC76quhAbHSm6cw/GEmxHljz2Nrdx9syaVvH7rrbeO13z5uJXjJNM5J3DtWIxrZRqGKOgplVgq9zsu97fVEmOghv5es8QeBQKcm8BvZbPNNovX1Vzbs640GrHc7M9c37Iuu+yyS7zW4DtCQ/fN+uwDaj6tNtmXHbx5ZEimcCCmTz0nDH6IHKSZxo5NS3CGf/M8LbXMzwGekwrZ6HIRiJIgojXq2GOPje/BOHbXXXddnQEg473QYs3Bh9YtfmRLLbXUJC0E/Eg4GPK+ZOdZVlpd+THXhOTVYYcdFg9uw4cPjwfompDkpFUq+yGy3LQys/2OP/74CcklDswcKEg4gsTT+uuvHxN6GQ4grBMt4FQi8X9O3iRBMyQHt9122/j+HID4DJaTA0JN4wxkeC3fF60YjJdBywUt7BwoWZ5iHARpqeDgxXgGfAavJzGX4WBIiw0tQ7SQcHLgYMm4QwTcbA/WmXXlM3mOEwuBwgorrBAPtiwH+0A2nYCGA2qm3OVmPpZxp512isuO7GKDeQlamjI4UMvFvsExiyC1tt+z1FxIiPDIgl+1HTQ4MsYdMQsJKJJvXAwTS9CQx4Vy/kE1BTFYJXhPLlRIPmXJt0x20d9YCY2a0PDJOM7EKDTEUT1y2WWXxUohkmT1QTUgF2dcKNPgmb84Yn1JbvLeVEEVb0cexCPFibxiJO6KL+RJRnAOYZtliVY+h7iOOJGKKy7gidmIbVQ34nPutsx4iNnFNvg325HnmAf0hiGxSyXp559/HpMExHbEjrXhO+M9SCpRJdPciJe53mI580k47sRN0o5rJva3mjT09ZWsf/E5ifiZhB7HEmKnvL/85S/xmPXCCy/E6xx+e1Q68dvm80garbPOOrGyj4rYmvAZzDvVVFPFBtmWopJ9s5SGvh58F1yzFMcIfN+Mrcf2ZwzF4oaY2hR/x6jvb6uhSi1Lc6FhiN9O9n1k2JZcX5MH4PoWzEMuhEpE9lWqw7kOJTlaUwNZY6lpH1DzavFbn52SMlOy9DxIpJHUqwkH3nIHfqaq7pBDDokHLAJXDjpU2PEe5ahrEOSaEAyzfLTQEgTzWrLwnMgYADVLvJFsIiFGyxOJKJaRikW699GSVArBKa0wrAdBKeteW3KABBRBIK3voDWH5SPgJLBk2cBBg+dqCgLZhlQo8n50C2HbMz/LQcsQ+BySlQSW11xzTVxW3p/vgtJ9BsGurXKN9eBGJ1QX8t4E/HyvfA8kTHn/PIJzTvKclJiHZSFpwjIQQPA9U8nH+9CCzvIQILPeJOPyBydez3f11FNPxX/Tyk5wwAUM3QJI7jGd9+FExoknq5Ysd7k5YZGUZJ+ihYRtQXn3ueeeG5ORJE89YLYNBMdceJFAz07K/I5oUHAf0ORGZRDHMI51NJao7eACg8Y8qlxoiOR4RMUMFxzEMZxvGwMXKFQEkoTKdwEmLiKWoypuwQUXTKc2DSoHqeyhm1m+MpEYrT7ryXGdmI4Yg9hj8cUXT58Zj1iBBkViPSr76otl5vX5WJa4jviHOCO7uOa7ozGUWIMqG5Zr8803j1UrqhtxLvsjiaR8Qyz/ZhrPZbEwv5m//vWvsXs0cTLVoWxrbhqRNe5miAG53qGYgFifmJ/9prmTSew//PZICGRVcRl+DyRXiJtJLJRS39fXZ/1rOifx2VTg8l3k8dtmOtcuPMdn0NBODE4CnEZ7fof9+vWLVWP5axMSSvRgojcPRSMsJ4UbFGK0FOXumzVp6OvZdkOGDIkNCiR28yiS4LqZYR4qaTQp9R1X+ttqLJM7BuIalu3PeTh/bgK/E/Z7fnvs42xr9mO67tJwxfUk15gkt/PFL42ttn1AzavFXzXS6kJJLhVTPPg3ffFrSshxIKIUm4tjkjG0gPJvWjMJZEDCjIM35bwk10gI0SpDsq+SgzUBEy20q666avybwJREHwEdP65Sy8g0DhD8ULmIzzLyBGAkoTjxZWXtLC/JI36QJCVZThB4UnlWjIw9wTdBHRV7VInVlRggWKZ1jTJkTmYcHGgJo9KQ7UTijmXmeyAZmW9ByGN9SJSxHtnBm21KRVy2TUkosj6Ml8D78/2wDVk/Ak6eq62liJY9El6sP1g3KgmydS5+LcEF3wetcllQy75Aco5ty3pxMKJkmXJj5uHBOpKcy7d48G+q/qhcBFWgBAMc6PluslJxAnWqMDmosv1QyXIzjYCDRC0XAwQ4LB8nLcqs1TaQBGff5ZhHQpjAlHJ/uixIkxvnYBLRVD5wLFP14fxIYo9qNuIJGvM4V6+99trxAooLLC6sQJxF/MQ5jwYw4h8SSzRw0fjK3fnyQ66Ug5iN8zXd96iI51xJDwPiJmIFut019bhyJL24sCUmo9cF68S6sTwk1CrB9iQpwPbkwosLNf6dPWhIBNUXNBozxhLzE9cRk9EgSg8SGi3LceWVV8YqQBq8WWZeS0xEIiOPz6IChkpCYjx+0yoPCQ/iZvZ/4roM/2YaCQHmoeGXGJLfAud0YnmSQ3wv7GP57ui8H0UNXOtwfZJ1geUifXIgtidOz8fDIDHNNJY9a9gupdLX13f9S52T+B6I7bkm4/fFbxB8Bg3pJBmzz+f6hR40/NapjCIRSaM9RQgklKhQytAAwbUfxRQkJRdaaKF4zVBXxW1zKnffrElDXk8BAxWSXAfRQJ2vXmb4I57je6KoohKlvuNKfluNaXLHQCTx2M5UMfJbymN7MI3fHjhfcw3JOYFzAL8JjvckSJsqGVrbPqDm1+KTfSSsOKCQVOHBD5edNn/wySPZR7Imv/OT9eYHwQEbJNQ44LDDkxXPcAAjiVPTexcrZxDkYhwgqZLjB1ecOGN5aK3mtRxkaWli3UmWFf+YizFWBC1gnLBIZOaTlhwMSXCRYedBoJx1H6UFgIw7iTgqzkiCcWLm5Mr2IMAmqOX/jFtA4qkUWuFZd7qlHHDAAfFzsu2d4UfP8tEqTmKTRBYJNP5NEM/nZ5WENeHige6yJBFJrBGk0iWag1rxQYvkK6X6Gb5Xvl+2B0lMtil/s+6cHKggzVcQ5PFdM05Fhv2MYIDpWRIPvCffYbbPZspdbhKkbA8CcC6QuNDhoiq/Hqp+fN+0uLGvsu9wccjvita4UscVqblwkUSyg/MBXXGauiulJg9iEJJNNMxxfqQ7G3EGgTvxBNPzsRLDpXCRzoU1DWvEQcQENEzSqEY8USniPy4YqJwnscg5nUHWubBmeWgwbEokvxhqhG6vVIqwTjTIkXggdqgEyQKO5VwEkSAgcZp/nHbaaXE+YjKqhogTiJGIL0g4MhQIvTyKqzhKyRoYiZXpQUJilNiTsciKE6RcGBI70phIIpdtXG2IpYl7sxi4vmNsFWP/Jxbkt1KMaTzPg++DeI7vMY/rAM7r+W6ixN/0dmE/IR4m/iNWJPGdNSA3p+zaI5/sAn/zYHnZBjWp9PX1Wf/azkkkYrhOo4cMCW96TFBRRuKDeDv7Dvk901BR3KhObyam56/BSMhzfcOxjipj/p+Nk9bcatq3y903a1Lf13OcoUqYcwG9zHhkeI5zCtc9JHKLE8C1qek7ruS31VhaQgzEd8P253qg+LeVTWOf5ffEOZhzVv53xr7PeTQbR7Mx1bYPaPIoL6vVypQ78DOJmmKlptWk3EGQ85hGwpLXFQeq/Pg4+JEoYlk5IPJ+5RxIsgQmlXrFBz2SSbRCkWjKHrRYgWUg8cRr6ZbCiYLugiQeeS9OXiTiSI5xMmTdSiEophKNIJ8TMy02VPgxPh+vB0lDtntW4ceD4JWTJSdxgtD8CbUY3XJJevA5HORIllJJyPpycCve3ixr8TZmu3Pw4+QMWuYo22f9OCAR6HJRQatQHvsPr8vLDralpucPvpUuNwloghPehxZDxkVS28U+yTiaVCIz9AAXztLkwPmAClMaOgjk8g0dqh5U33Ne4jyWnaNojKPCn6RUNjRHMc77DHdBgx43xqIChsoyYoLsxhJcXDCEBUnDPKqWiUGy2ATEA5yjiSGITajO4d90n6NRDZxTOYfTMEYjWR7zMGwI1Tr5C0suBFm222+/faLzN+dkGkJJxmW4SGJYFt6HdWIZSRzQTZaLviw+I26g8o9EQobhWphGtT+fQ4NidiFW/OC9M/yuqOCjuyBDhvDZxGjEErVV3mXbgm1OBSSJAF7L75aGXAZkL4VtR7xEEqM4nmnt2N5UOLJvZvEvFUCNgcQrMTqN2HxOhn8zjf23ODlLwzjfUTndF6lM4nfC75H9v7GSlOVifyK2p4qouJKVxmyuUYipuXYppaGvL2f96zon8Vvnd0fSj4YCEtvE91TO8nvnM4qv40i002OntusRcH1BEp6ED9dtVEDn94OmVtu+XZ99M68+r+f6jgpwtgfXfpwr8sddKsWJYbke4pqP7tp8p1yjso9wnOX4m/88lBt3VPLbqq+WEgPxm6GKkerUfGEJ+F2xPfnt5XFs53zKjZmaSl37gCaPqkz21SXrtkvASZCV4QBDS1J+Wm34gXHQyyPJxw+NAJADZTFODjzHia+4vJgEFA9+oCR6OAnxgy2nNY9EHFU/JM44MbJuGVo+CCRJCPLgOUrTMySWWHcCaRJxBKYsA0lADrxMZ1mZzsm7JlwAUNZMoElLPN16SOhxsxDWi/XhR8+Pn8Cz+MHnFAfrGZaPri5cdNB6xcmagD+7eQXbq1ipbcz3xcEoOwhykic4Jygm4ceFBstKgEGFZUPVZ7mzsdpIuFJRyYGTZVbbxW+HoJX9t3iflpoDyZEBAwbEYzgXT9nQBVIeFxQ0FHJBQWxRSQNqTYjZ6FpH4q25xy0DcQIJANapOT+fYz5xAmNCUaFSW/xVCrEmy011Uk3DgBD3cNFKAyQxXrVhm1HhmMW/PEiANgb2BWJJEtBU4WeI26mC5bniyiKOn3wnxQmmmrD8NPhxvdFUXe5qw2ezbsW9Xkj+sy1J4teWNGro62tb/3LPSSRkSMTxeq7PiPdJznMtRGN68bUa3xm/h3KRXOIYRdf8cq8fG0Nt+3Z99s28Sl/PtQ49u6i4Zkw4rjHZJnlck5EUpcKYRh8eHN+eeOKJ2IjNDRVJsuZj3Erijkp/W5VqSTEQx3Z+O+xzxT3i+K3xnfG7KT5nMBRVcUFQYylnH9Dk0SaTfWT/SUzRykBiKkNZNq215R6sOXCVMwhyHq0hPMeBjVbyPA7SnIyy11LJw0U+fes5QNaGpBFdQWi5YpnowpENnsqPnQMzST8eVI7lD4Z0T6V7Li0uWSs0r+EkyDbhbnS8hiqzcpDQ5ERK9xtavThZkKRgfQj8WR+SopXgO+FERmtKvisx78tYNvnvIEOXGboOZ0i40RLPuhd3ZQFjCtAqRms8681YOQ1V6XLTdZNuQ1wkkfSjqxQnxmwcR7VN/Jap8uD3m5082XeYzqPU/i9VqqZ9iqEn6EbF8Ywgt/jGApJaFy7Y+R3T04B4lIvsrEqx2hDzEcNmMTDxcKVKHRtJiLANaazO37CFf1OlxIV1Q5PDNPTyXiRr8+9FDMn1S3FVT2NjHbhmohdO/joku14hAZ9d67AsLBPLlqnk9aXUtP71OSfxObwPiRCud0jaUA1XnBCpFMlDrge5hsga8Etti6ZQ075dyb7Z0H2b11ANzVBFVB5ToFCqkYfEHu/HNWH24P2ynmBUMdPwkF2fTo64o9S2wOSOgYp/7+zL/HaYnm+84DfGb42x+CkYaQql9u1y9wFNHm0y2UdAQ191DjRUnfEjZvBoxo/jIFaJcgdBziPjTUaeAfgZX4/XklDjjruMAcCBD4ylx/gw3NDj4IMPjpVmdOUgWUZrTjFOWIxxwzJRWceBqbYbXmQ4QVBRRxUfJ8LsrjksSzaGINNYr5rwWsZHIEFFko3gkXWjJYTuOZyAeA+SV8xDt2ISo8zHwZ8bUtDtpKakJgc2glK+Myrj2A5UIfKZdBsqhZMv3ynJT+anlYHWPcalYX2p4COxRoKUZeDgTjcZuhzR0tgYA4pWstwEPnQtIBCh2yb7CMtHlQTj+LFdVd3YT9gH+M0TQPL7oNWTal0S1/y++b2CJDKBKscL/p3heEIXMIJZLug4MfMeVJjyf7U95e4TpfYpjlkMcUCymXGJqATiddmDrjiSWhdiO+IQejMQIxEXtUUNOTaSLGI8N6rFGF+ROJL4jnM48SfPMU85+NwzzjgjXHPNNfE6hHiUaxNiPxIgVCzRiA4urOmuSExNA3aG+Pmhhx6Ky864mjRw04DM38Td+R4/5eI6gPEqH3vssTh+HucBbk7AmFx8fna9ArYX06jsyRIl5b6+kvWv5JxEQoLrJT6T13E9w/XUzTffHBv3aVgvF3EZN0wjucJNc6igohdOliini3CWOCy1LcA1Bvsa13EUFVCVxXfDtMa8M2ol+2ZD923WhSIJko6MF0+yKfsuWK+scIL5ua6i0jJ7cH3IEAJsNxJ+2XVmY8cdLTEGKvf3WtPvneVkGr8r5md/5HfFb40hGfjtVaLcfbPUvl3uPqDJJGmh3n333aTww0+22267pPADSKf+qXCwSfr27RsfmWHDhiUzzzxzcskll6RTxiv8sJP55psv2W+//dIpSTJmzJikcNBOunTpQhlfUjjQJIWTSXLYYYfF9ygEP+mckxo3blxy4oknJnPMMUdS+AEm3bp1S9q1axcfvXr1Sgo/tHTO0suJZ599NimcZOLnZp/fp0+f5OWXX07nGK9woE0KJ7tk+umnj+/PvJ07d0722Wef+PyIESOSlVdeOSn86JPffvstTsODDz4Yt1/hR5cUDgDp1JoNHDgw6dChQ7LppptOeJ+xY8cmO+ywQzLllFMmhYNJnJYpHBSSwkF5wvTCgSBZaKGFJqwPj/bt2yerr756fC4zevTouN1nmWWWCevD/1nWU089NSkcgNM5J/Xtt98mG2+8cXzf7P379euXHHTQQclss82WvPDCC3G+bJuw7nvuuWdSOMHE+VkPXv/VV1/F+ViW7bffPq53tswsy1xzzZUUAoH4PWffNctXCBLi68D0QsCQFE5cSeHAn05N4r5aOCkkCyywQFI4McRp5Sw3rzv++OPj9zxo0KD4ukzhRBI/v3DwjvuTqtdHH32ULLnkkhP9jtgne/TokZx//vnxuJVhv1pqqaXicYR/Z+68886kEDxNeH3+we9CbU+5+0SpfYrjE8frUq/lwTlDTWPIkCElt/nIkSPTOSQ1REOOjSB2u+qqq2I8zLmaB3Ed00pdu9SEczux41RTTTXRMkw77bTJgAEDJsStIP488MADY7z40EMPpVOTGOv37t17otdnjxlnnLHW65racLzZfffdJywb8cmyyy6bvPLKK+kc43HdwTIRG7OMmXJeX8n6V3JO+umnn5KNNtpoQvzN98M1W3E8VY5bbrklbkfeI/ssriu4dnvqqafSucYrtS2y64P867MH2+SEE06I8zWWcvfNhu7bZ5xxxkQxa/7Bdi++psnjfbi2IcYl/s00dtzREmOgcn+vNf3e8eqrrybLLbfchO3P72e33XaL18GV4Hsod98stW83ZB9Q02vHfwpfRptF5QxVfbRy0k2VbraVouWD1qLCDh27qlIdV47CjyRW69CCRTk0XUt5j1JonSKzzngJzFfbWAuTCxl+WhYY7JlWE1pseBQOAOkcf6KqjkG8GVx0zjnnjPMV30yjFLYZ3xefQbdYqt5q2mYZWmWomKLFiG1XvDy0ntDqQEsG+wEtFlmLVWOpz3Kr7WKf5cHYJdl+W87vQ1L1YMxbBpYvxji+DPEhqWXgOoBKNC6puA6ob5doenhQOUTMz5AdjAtWn27HTYFqNpYtG9an0uulcl7fFOvPtQlVWVyf0EWU3jblXqcVo+cNXU+5bmvXrl0cEqqpxkBrLA3dNxtr31bT4PqS3nVU4rWG/VHNr80n+yRJkloak32SJEmqrzY5Zp8kSZIkSZJUjUz2SZIkqcXi7n59+vSJNy6SJElS3Uz2SZIkqUVizC3G8mLML8bMkiRJUt1M9kmSJEmSJElVwht0SJIktTBt5QYdVOy98MIL4ZtvvgmzzDJLWGONNUK3bt3SZ8dX9p188snh6quvDg899FC8o/6zzz4b72jPvNyBMI+7E/7zn/+Mj99//z0+v+qqq4bpp58+Pv/rr7/G7sA9evSI7/Hoo4+GESNGhGWXXTY+eP8ffvghPP7443FbL7/88mHppZee5E7+dS03n8Nd+Pmuipfxk08+idWK3Jmfu45KkiQ1NpN9kiRJLUy1J/tI4h199NHh4osvjv/u0qVL+Pnnn2NS7rzzzgs77LDDhPlI9g0cODBujzvvvDN07Ngx/PLLLzFRdu6554Ydd9wxJuOYduCBB4abbropdOrUKc7He84zzzzh7rvvDgsvvHAYPnx42GyzzcJGG20Uhg0bFn788cfwxx9/xC7CRxxxRFhhhRXCPvvsE9+LaXz+qaeeGg466KD4GeUu93/+858wYMCA8Morr4Tbb789vi9IEG611VZhmWWWCYMGDZqQhJQkSWpMduOVJElSs1tiiSXCI488EqvgqKZ77733YnUdyT2q4vL+/e9/h2eeeSY88MADMUH3zjvvhEUXXTSccMIJ4bXXXovzPPXUUzGpd9xxx8X3Y75vv/02HH/88aFz585xHpCku+eee8Kxxx4b3/fTTz8N6667brwRyOGHHx7OOeecOP3jjz8OK664YrjiiiviPJlylpsk3mmnnRammWaacNRRR4XvvvsuPo488siYpOQ5E32SJKmpmOyTJElSs6JKbttttw2rrLJKmHLKKeM0ursecsghsSru1VdfjdMydLndf//9Y5dczDfffDHRN3r06JgAJIFHco/5eE8q+0AXW6rtevbsGf9Gu3btwqabbhq23377+NnMs80224SxY8fG6sHNN988vs/ss88eq/BI/H300UfxtZUsd+/evcPpp58e3nrrrXDSSSfFx9tvvx0TfTwnSZLUVEz2SZIkqdmRHKN77pZbbhmTeHR1JWlGF1qq4PKogmPsvLwFFlggzDHHHOHNN9+Myb6FFloodt3db7/9wplnnhnH7aOLbjGSfYssssiEZB1mmGGGmCBken58vhlnnDH+nyq+TCXLTXfhfffdN1xzzTXxwb+ZJkmS1JRM9kmSJKlZMc7dTjvtFMfY+/rrr2OlHuPYZUk4xsvL69ChQ+wSm8e0qaeeOowZMyYm9ZZbbrl4I4/ZZpstVtHRtXbuueeOlXRUAObx2mIkAfMJwFIqXW4qBFkulp2xFtdZZ504TZIkqSmZ7JMkSVKzev755+MNMkjEMRbftddeGy655JKw5557hqmmmiqd60/cWZdEWx5JPqrppptuupi8oyJv/fXXD08++WQcq+/ee++NiTg+47rrrktf1TCVLjdj/TGGIF2FqTpk/EDG+ZMkSWpKJvskSZLUrH766af4//nnn39Ct1m64g4dOjSMGjUq/p3HNJ5jngzddEnqkdArrpajW+6GG24YzjrrrDDrrLPGG3o0hkqWm66/WXLvqquuinfrff3112MX4+IKQEmSpMZksk+SJEnNihtmUOnG2HdvvPFG+OKLL8K5554bLr/88nSOSV155ZUxaUb3WW7Kwd10uYkGST0MHjw4HHPMMTHx9q9//StW1V1//fVh5MiRcTy/xlDucpMA5C6+Dz74YLwDLzf04KYgVAAOGjQo3HHHHemckiRJjc9knyRJkpoV4+kx7h1dYpdYYomYRLvsssviHXdnmmmmdK4/0Q129913j2Px9ejRI97kgnH66ELLjTrw3//+N77HGmusEeaaa654l9wLL7ww7LrrrvGOvI2h3OV+6qmnwjnnnBM23njjsNtuu8UqQMb0O+yww8Lyyy8fTjzxxJgslCRJagrtkoL035IkSWoBHn744bDeeuulf/2JKjVu9FAtvvnmm/Dee+/FG20svvjik9yEoxhj9HH3XbrtLrbYYqFz587pM+Mxjt9HH30Uu84yjl/v3r1jorCxVbrckiRJzclknyRJUgvTVpJ9kiRJanx245UkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqhMk+SZIkSZIkqUqY7JMkSZIkSZKqRItO9v3nP/8Jd911V7jlllvC559/nk7908cffxwGDx4c/vWvf6VTWpc333wzLLfccmHgwIHplBCuvPLKsOyyy8bnWrpKlvXll18OyyyzTLjxxhvTKU2juT5HkiRJkiSpJWrRyb5vvvkmHHjggWH77bcPxx57bBgzZkz6zHhPPfVU2GOPPcI///nPdErr8vvvv8dEJUnNzC+//BLXm+dauuJlHT16dNh9993DXnvtFf773//GaRm+uy+++CKMGjUqndI0mutzVB3+7//+Lyar77jjjnDnnXeGkSNHps+Uh33/3nvvDYceemh83HPPPS32t/vDDz+ESy+9NOy3337hxBNPDK+//nr6THnKef3PP/8cHnjggXDqqaeGvffeO87La77++ut0jj8x7z/+8Y9wzDHHxGMG7/ncc8+F//3vf+kcrUNzbNc//vgjbpszzjgjblf2tdtuuy38+uuv6Rx/4nx40003xQaP/OPuu+8uOf/kxn4wbNiw2Kj3xBNPxHWtREO3vyRJklSVkhbs3XffTeacc86kQ4cOSdeuXZNHH300fWa8K6+8MpluuumSwkVMOqV1eemll5LZZ589+fvf/55Oad0KF23JGmuskay99trJL7/8kk4dr3Axl8w888zJJZdckk5pGs31OWrdxo0bl5xzzjnJtNNOm7Rr1y7hUDjrrLPG32S5Ro0aley4445Jx44dkwUXXDBZaKGF4r+ZxnMtyRtvvJEsssgicX2XXnrpZI455khmmGGG5Kqrrorboi7lvn7gwIHJlFNOmXTr1i3p06dPsuSSSyadOnWK899///3pXEny8ccfJ7169Urat2+fzD333MmKK64YX8P2O+CAA5LRo0enc7ZszbFdx44dm/Tv3z+ZYoop4nlw+eWXj/sb25nt9sEHH8T5wGsOPfTQOC/bNv+Yd955k08++SSdc/L76aefko022iie3/n98ejbt29Fv52Gbv+WbsiQIRO2Tf4xcuTIdI7Gw/Z6+umnkxtuuCE+brrppuTOO+9MnnnmmfhdtXW//vprsv3228ffYnN6//33kxVWWCE5//zz0ymSJEnlaRVj9q266qqhcJETLrvsskD1WDneeeedUAj4wyWXXBKGDh0aK3jyqHCgAoKqgBEjRoTbb789XHjhhbFrMFUor732WqxOe/fdd2M322uvvTZ8+eWX6atDePvttydML1W1Ugic43sMGjQoXHTRReHhhx+epNqtlO+++y6+LltP/n7xxRfDCy+8MMmjEATGeTJ8Jut0xRVXxPWmEoRptckqm/LdpKmsKVxAxvfKb28qEF955ZXw73//O/6dX1aqMVgetisVf7yWZWQ7FaN6iu3Ndnn88ccn+W5qwns/9thjcT9g21PBUVsVEN8t1SJsi8IFS8ltwXdauLAJ5513Xqx++eyzz9Jnxm9Pvn+2BdVaTz75ZLj44otj9Va2XZjOMrEu9913X8nvuK7lzj6ncOE6SVUY++ZLL71Uch9T/U0xxRRh6aWXjhVBr776aqwQbteuXfpsedhvqEw77rjjwltvvRW/PyqLmHb99denc01+7KssV+GCPdx///3xN8zvdvnllw+nnHJKnd3wK3n9iiuuGKu0qFjm+MO2pVqrY8eOsdqP3yTY51deeeX43CeffBKeffbZeMzeYostwtVXXx2rJVu65tquHCtmm222cPnll8fjVXZc5VjEPFT7FR9Du3fvHo/HHJezB+e2eeaZJ51j8mOf2GqrrWJVLee4xRZbLH2mPA3d/poY+wjnwZ122ikMGDAg/p/vh9/pHHPMEf7617/G32m1I6Zba621wnvvvZdOGa8QL8fjV3YMay5jx46Nx1P2c0mSpIqMz/m1TFll36677pqccMIJscX+nnvuSZ8tXdlHRdmBBx4Yq0mmmWaa+BoqIKgYyFc1ZBVgO++8c6x4mH766WNVAO9VuICIFXe77757fD3zUX1A5ckDDzwQl4XPzab37NkzKVyApe+cJN98802y6qqrxmqKLl26xPeg0mKZZZZJChe06VylK/vOOuusOK1w8RP/pvqI9+GrKn6wThk+c8stt4zLw7rwmfx78803T7777rt0rkkVAshk9dVXT9Zdd93Yco1vv/02oUpi6qmnTgoXYnEaCkFwXOeHH344/p1fVj5/iSWWmGQZeR9k25vvcv7554/Lx3KybrSU11XNQYUD25/vcqaZZko6d+4c12/PPfecqIow+5zChUqsGOJz+K6Yl8/Of84111wTK2WoJqKqa6qpporvS1UIRowYkRQudGL1zAYbbBCrR2accca4zFQwvvzyy0m/fv3idN6H6Ztsskl8Xaac5R43blxywQUXxO3BNuVvUL3B57IefJaaBtt7v/32S2abbbayK/uyKtbFF188+frrr9OpSfw31Ww8x2+rJXjuueeSWWaZJa5j4YI+nZrESjv2uVNPPTWdUlpDX89r+I1zLOeYXpuhQ4fG38m+++6bTmm5Jvd2zY5Piy222IR9kH2Zyr655por+eijj+K01oDl57dUSWVfQ7dfa9CclX2///57rLQkHvr000/jNKpK33vvvRij9OjRI57/brvttvhctTr99NPjOfuVV15Jp0xeb731Vjx2EndKkiRVolVU9lFxs+OOO8bWZaqjGOOnJlSFUNG36667hq+++ipWoVG9Q6UZY0NRDZChYoIqrcMOOyy21lI1sf7668fneB1VKYwLyL+ffvrpWImwyy67hOeffz5WIjCdij2qsaiyyKorppxyyjjO4Icffhir4ajOouqL+ak4KB57sDasB1VfVGlkj0IwGrp06RKraMD7sW7Dhw8P11xzTVyXH3/8MW4LqmzOOeecklVt4H2ocGJZ2V6gAoSx+FgP1pXtxOtp1Z911llD796943x5TL/55ptDnz59wiqrrBKrnFhWqpwyvA/jRh1++OHh+++/j9VqhQu1uG0effTRdK7SZpppprgebEPWjdezzoxbRWVIHp/DjV323XffWH3Iuhx88MFxXrYPmEYlJzcYYX15P74r3qtwsRPnyXDTD/Y9xgJkvjPPPDN+/1tuuWVYdNFF43bjcwrBeKxUevDBB9NXlrfcVJkx1uEmm2wSzj333LjPFS5eY5UF2/xvf/tbvOmIWg72n48++ih+/1RdZfj3IossEp9jnpaAKjCOexwv2rdvn04NYeGFF46/W/bv4orSvIa+nkpftsUss8wSZphhhnRqaYVzUnzwm2jpJvd2ZTtxrGNblapK/e2332JFUP6cV00auv1UN2KABRdcMBxxxBHxfDXttNOGk08+eZIbphEflNOrgGlU2TMPVfdZpRyvpQIexFFUSn/66afx7wz7OlX2xRV3vCcVnbX1ouC9+Qw+q7aeBazXt99+G6vp+ByqaNmPiA34/CwGQ/Y38xQ/iA94nzx+i4ylecEFF8Q4KD9Wcx6VzsRu9AJgH+d3LkmSVC+FQKLFyir7dtttt9hyf/bZZ8cW+5tvvjk+X1zZR6XDSiutlFBNVri4jNNQCPiTXXbZJVbuZBVzVIBRQbLtttvG5zOFwDFW9lEVWAgI06njq1MYr4XPp7U9M2bMmFjNtdRSS8WKuJrwvkceeWQcpyqrMCynsq8Y4xayTVifrBKvEFzG19Dyy+dkaJVn2zGm0ZdffplOnRTVe7TY33XXXfHvCy+8MOndu3ey3Xbbxao/KpSoDqQykerBbHsVL2tdY/axvamuY7kyhSA9VlQed9xx6ZTyff/998lyyy0Xq4ayyg4+h3X561//OmH7oBDkx3FvVllllVgVUQjSY/XLMcccM9E2y8sqZ9gWn332WTo1iVUPfI/sZ/nvnKpNqh8OOuigdEpppZYbhQuN+J48Lr744rgejF/GPqamw/dPdVAllX1PPfVU/H6K9x/+feyxx8bnnnzyyXTq5FPb8vz4449xXD0e/LuU+r5++PDhsRKYcabWWmut+BsfPHhw+mxpHBcOOeSQeOxt6dVDk2u75lFlTuVy/pjK+1LZRyUxD07xVCxTMcfYay1VpZV9jbH9WoPJXdmXxzY/7LDDYq8JxvTLEGuV06uA75V9leeI25iPqnV6V1CNT1UviFUWXnjhOD1/fhw9enSy3nrrxRgjw2eX04siq/jn82vqWcD67b///hPGcM0ezEdvBuZh/8x6VBDjEOvk580eLEMW17FdiV35TLYd1ahsA5aD42SGzx80aFDcp+ltQLzEdiLWy+I7SZKkSrSKyr7MtttuG6uuaD2mWq4YrbZU5y2xxBKxZT9DRR7VZlSY0GqaoRpiySWXjM8XKwR4oVevXulf4+ctBItxen7cI1q+C8FZbEkuBHXp1BAr5RjLi0rBlVZaKVYfUM3FMtR37BXGeqMSjmWmuqxwURyn0wJNtSNjXlE1dtRRR4Ujjzwy/pv1ZVsVLnrivKVQicR60SLNOtAqT/XeBhtsELcnFW28Dy3TrEep7VUOtiGfxTbLUFk49dRT19jKnSkEwnHcOyrg1lxzzVhByPKxnamay1dLUumy3HLLTdg+oMKucDEZ14f5qcBaYIEF4r50wAEHhMJFVY3LMNdcc8WqpAzLWwjC43T2iQyfxyP/PpUs91/+8pdQuECI25m7UDOGFd9h4WI9nUMtBb93vlu+73wVGv9mWuEitawxOpsDxxwqnwoXmumU8fgdMo0KsNrGzaz09WyX6667bsJdY6lO3XTTTcPqq6+ezlEaVdaMl7XOOuuEwkV9OrXlau7tmkfVE+PVMZYt54T8MZUqYI4jVExTucQxhOrwrbfeOh7bq0VDt78qw7GN8RD5PxVnqKRXAdMGDx4cDjnkkFjpSzX8PvvsEysGqZKrD77rcntR8Bm19SxgvYjZ6OlB/MjxiMo9KgKJH4t17tw5VhNm1X486NGw8cYbx9cvtdRScT7ivtNOOy3ssMMOsRcA24jxJYl9OD6yHUBPkuOPPz6e94nl2I73339/3I6sjyRJUqVaVbJvzjnnDLvttlsMqOgemhR1byCwI7AkiCru1kT3E6ZxAZBHwFYK83Ihkcc0AsJS0/PLwg0V+vXrFwdUZ34SXFyAkRwi4CQRUCluHLHnnnvGxCJdhvPJp1GjRsX1ZrvQhYXHI488EgNYuqyQ1MpfDBbr0aNHmH/++WMASlKPQJMEJcEq60oXGR6sI11+G6JDhw7pv8Zj2xVvv2JsM7rcEERzsxUSdSQ8WRaSlHS5Kb5YYHqxbF72kxlmmCF+P7wnwTgJOPYvEsr5hDDYBqW+c7Yp/88Ur0t9lpskK/sJ+w2DhOe/Z1WObl/8Hkjm8misAeb5fviuswvZPKbxPI+WIPvtF//G+JsH+3Z+Py5W6ev5mwt7fmdcsJJ44je28847x+NRKU8++WQ46KCD4nHo7LPPjsn0loBkEUMZZPsP+xKJMzT3ds3QPZBEKo0wbCsatzLsc9tss01MqJAw7du3bxxe4Morr4xdXukaWC0auv1VOZLLNPZlja1ZzMENjvr37x+fY7tvt9128bzHcyT2+N0ztAaJLBJcxGM0YpHsW2ONNeJ71QfnRxrSsgZY9n/2e34DJOn47Az7Ao0OHIdYThpl+DfHGhpSQZKOdWQduMkNsRMNzKXiRD6LmIF5svnuvPPO2E2ZYx7nbxKQ3KyJGJDkPI2OYDvQIEuskQ2VQmKPBiJ+r/PNN1+cb7XVVouNkSyPJElSpVpVsg+bb755DJy4aCmuViOAJIhjevEFQBacNvVFJEHbrbfeGhN6XBw+8MADcVlJ+hC41efig1bd/fffP1bvUYk299xzp8+MR3BKJQOtxwStxQ/GgGNssZpQqUaLPeOMMeYc49uQkCIJSBBNqz0PAtt8tWNzoSWcsW6oDCKRyvZl7EbGLmSZSmEfyCfS2B+YxnYiyAfBeXY3ZcbY4cKDixNa9htjnKtKl5vkCNUIVO2wjzPWJElC1Q/fP+N3ksilwpbHXnvtlT7bMDQokLjmN5nfz/g300hEMM/kxgUpiW0SyzQK5FHFS3UUv4eaqkcb+nqOTYydSTKARGupZCvVZjTiUCVLRWBLumMsVdgce7P9hweNLZNru3Iu4DjF8YQxPTkfloPjO4lUqo+qQUO3v+ona6jMEq3l9irIel3Q2JUlvMB3lFUL1le5vSiIverbs6Au2bmGsQgZ35DEJ6ggZExg4omzzjprwvZhHpJ7NCaQtGe7Mk5hlmDMyxoHJUmSKtXqkn20vHLBTmLq3nvvnSipR+UULaJUqBFAZWgtpcqNqjgGm25KLA+BLReuBG4ZAmK6Y+QTA+UgSCRAJGlHJRpdUYvRSkwwSPCY77ZSLgJtur2ynW688cZ4gc6FYZYE5OYkXJDzObVVmhFEkwDhYouLsMZCNSYXdCQB8gkULjDYD4qxjdnW+QCehB77BQm+fBdvkCDmRh0k4ehqy0UK31dDVbrcJAZJCNKFl65N7M9891wkqXLs1yeddFL8PWYPktaNoVu3bjHZwL6S7yrIfk+ylue4qUtLwDExu6jM40KcbUJSv7bEZENfz/fAe3BcKB7CgN/kgAEDYhKeCphSN/+ZnDje0RU2239IttE1Ec29XbPEI403VA5lCYVy8B1wfK70/NOSNXT7q3JUlRInZMc2zm/l9CrIel3UVHFfX5X2oqhPz4Jy0OWXG2lRKcj5O6vEy7qSU2HIdslvH7YZ+zD7KMvJvFQQFi8jCdHiaZIkSeVodck+/PWvf40BHXdJy1+8ULVHsMW4KHQJoxsHF+N0nyDI2mijjSZpNW1sBHkEnHwuFX0kagjq6IrBmCyVYN24cxsJIAJaWon5d/bgog/cfZALP7o2czH4/+3dB5gUxdbG8SLnHCSJGIgGFBAliHJRBL2CJBOgRCMoiAkDmJWgYrwqBhRUzIgBRBAQkKgiQVAUREHQK+EDUbmA9c3/0L3MDhtmdwm7w/t7noGdnp6e7uqenqrTp6rZbsaBIUBIJhnbn974YXXq1LEAJY1vsgBp5FJ5JghGw4nlxd71MBbBQSqvXKEO15vx6rKK4COVd+5yS4CXBgcZk+zj2KyOEN2OBw4caN04WR+y9QjCMG4Oxwld8dgnlBmZAWwf3YzY/iOPPNKCNVmVkfUms5CsPro00VigHB944AHrvs3dD9Pbf5IyGkpkknA888jMfuV7yHePR3i+IdhH4J3vWHTglr859uhayTzZAetC4JhGZvRxROCTABKZp+H3moAcgfHoYHdG3p8SGvmUCQ3Z6DLhe8n5moY2gb6ULmQcbJwDOWbC44djiWMKB7JcyVLijuIfffSRnSe4SzvrFi/OdxybsVnhOcX+OC4lYwjYUY8iME/9C/H2Kgi77RIsjw6scT6N7YnBcc0jum4HziMEzkK8Th1jX/aiyAy67dJtni7z1LWis0nZbsqHrsXUy2LLhzphjx497IIjQT/qBbFDzXDMc/yLiIiIZFSODPaFXcPCRlc0BiEnQ4vgCYEqgiZ0h2QQZ4ImVKr2NzJVqPhRAeamC3TDoJKb0QYaFWACUVSyGQiacXCiHywfXDlnnBeCWwSV2G4a1Yy5R1mwzdFdV1LC/AQNmY+uMOH8TOMqPmVO4yotbBsBNAazfvTRR63MWaesorHNwNVc3abbGuvDmDw05sg8jMV6MI4Pg4gTxCQjkQYhWXIXXHCBzUPZ0mW5U6dOFgBm+wmYkoVHmaV0bGVUvOtNEIl1IxjCZ4eZDgR4aeAzDhDdG2XfInhABiWDxhMIpkHFMUEDkv9DBLvpIkZ3YP4GDTgaaQRhwsxbgvoc7zTYeI15sgOydDn+CLyTEfbjjz9aJgrdyvh+s10htptpnCvDxna876f8CFAzUD/BPY5rAnp06eP45bzCuQlcBOF8yLLat29vXd3Cixg8aEBndweqXAlwcB559dVXXbNmzSxD/Y033kgqK875DL0AjmMCD0yjTDnGmYfxXgmKEFzNTsjM4vtHVjqZ2AR+uADDtPAmEMhK+UnWEWjje82Fq7CrLOLtVUBQlqx6LtByTIbY5wxVER3s47zJBTmy4aIDX+xfjukQ7+F8vK96UYT4fL4r8fSSoBsz42cyvAnd6mOzFCtUqGAXf+kZkVI2f4j6FvVEyoagYIhtoHxSu6gpiYNjjnYLdWcuCHPO5nzHOTIjx3J4/PN7wXmT5eYkfKdps3HzHILn1K0yIiPbz7LJyOWz6IJPnSUWy+M3iXoMvcpYJ77PmT2/5FRZ3S/xvJ99RdlSjwxv8MaQDGH9JhbtarK6WSb1cMZ+5rcqp6But2jRImuH0M5LbUzrlOi4lAyJVJgSUqQR7iMHvp86daqPVBqDqQdO5KTlI40VP3HiRPuf5wdC5KToIxUGH/mR88uXL/eRBnjwSs7Hts2cOdNPnjw5rn26Y8cO//XXX9sxsG7dumDqHpEfBR9pHPtIxcBHGow+0pCwaftaRtdbDozIj6svVKgQrcy9Hk2bNg3m8nbs1KtXz0caY8mOI77TI0eO9JEGnc+VK5c9Io1am3agvu/xilQifO/evX2BAgVs+3Lnzu1PPvlkv2DBgmCO3SKNeR9psPr+/fsn+y7E8/5I49j37NkzaZ7wUbRoUd+9e3e/Zs2aYE7v58yZ48uVK5dsvuhHu3btgjmztwNRrpGGvm/evHmy8ol+VKlSxa9YscLm/eGHH+w4ZTnh6/xdp04dH6lQ7pfzW2bxHeF44XsTvT3hOg8aNCiYM2vll5NNmDBhr7LhwXbva9QV2rRpY+ezSMPXRxrL/qWXXvJ33323P+2003y+fPl8w4YN/dKlS4N37P6Nvfnmm33hwoV9r169rNw5R3711Vc+0rCzfbht2zabd9SoUXYu6NSpk480cPyyZcts3xUvXtyXLl3aT58+3ebjuLjuuuts+rBhw/zatWv9jBkz/Jlnnmn7me9C6P7777dl8jmrV6/2kQak79y5sy9WrJidXzjPgN/4smXL2nZF4/f/mGOO8ZHGYjDF+/fee88++8ILL/Qvv/yy/U6wTXwPW7RoYQ9s2LDB1onlPvjgg1Ze4eO1117z33zzjc3HPmSeRo0a+ffff9/qAN9++62PNNT8tddea+UA/q9Ro4avXbu2zffTTz/5SEPWV65c2co++vsgiYfjhX3NsUt9o3HjxnbccMwPHDjQfl/TsmnTJvv+cqyE5wmOVY7bnIL6+nHHHWffaX7HKlWq5EuWLGl1qvR+uzKy/SyLZbLsihUr+gYNGthn8tmsQ4g2QfXq1X2ePHl8tWrVbJ8wf/78+e27G57bEl1W9gvieT+/JZy7+Q0vVaqUP+WUU3ytWrV83rx5rdw5Z0abN2+er1mzpv32sEz2U8GCBa2O8NdffwVzZU9s8/Dhw608wvpP+fLlbZvioeNSMiphg30iIgcClclZs2ZZQDe7V6xpQH766adW+aJylVHxvJ9ABJWWSZMm2QUXKuGJ7kCUa7yoSP74448W4OACA0GE7BZ83tf2ZfllJwcj2Bd+Bo0QGg8EIFq3bu2fffbZFM9vNCzuueceC66FDRf+53333ntv0gVHghXMRyCNeWjUNWnSxN9www0W1AiDfSAIR1CPeZi3SJEifsCAATYtOthHEK5t27bW6GE+/m/ZsqXv16+fXXjJTLCPC8UEV1h/lkeQmYu2bHt0sG/VqlXW4OJzYx+s95AhQ2w+fPDBB/6EE05I2h4ebNMFF1xgx26Ii44sk/LjQQPuzjvvtHVRsC+x/f777/Z9j/6O/fbbb/7888+3wAjn8rTwPRwzZowfN26cXfDneMtJwT4uirOtHOtcoMevv/7qzz77bF+1alW7gJCWjGw/FwWOOOIIf9ZZZ/n169fbNM4/fDbrwDkABJi4UDlt2rSkoBTn3ksuucSCTAT2E11W90u87+d34vrrr/fPPPNMUvlTb+GCB0Gxbt26Jf2WUKfkQgvLJLEF/L7ccsst9vvyyiuv2LTsjLLgYhrbf/nll9vvVbzBPh2XklEK9omIiIhkMwcy2JdVNOri6VVAgI6GThiYJQMwNtgHXqNRThA3rYx4GoQHoxdFRrFeBN4J2pABuXnz5uCV5AhazJ49O0dcPJL9j8AFGUsE2+NFNmzdunVzVLCPC4NcMCDwHv0dJsuVgDsXDuKV3vbfd999FhQaP358MGX397Nv376WYcW6pIVzVZkyZfw111wTTElcWd0vWX0/GdT0tCF4y34FF9f5zbjyyiuTLZOeDWRHc9Eqp2S3EayjbDIS7EvNoXRcSsbkyDH7RERERCR7YJxb7urPHe0ZBze18ZEZx45xaxnvL62xhHmNm/Y0b948zZsd5cmTx8ZoPPvss+1/nmdHrFetWrVsPOf69eunehdixu499dRTbWxExi6UQxfjb3GTtgIFCqT5HUgEjNG6bdu2vW4EyHe6fPnyNt7n9n1woxrGSWNZjCPKeJohPrNRo0Y2PhxjDacl0na2R+4MjMGeU2V1v2T1/ZQz3wPKOldw0yXGVmRMVcbtjV4m4xnz+7Js2bIUx19MdIfScSkZoyNCRERERETkIOJGNC+//LLdWZqbdIwYMcJdeumlFvROVARzVq9ebTfHIVgTrXjx4nYTHm7wQCAuq7ipGoEg7qzPsqMRUGUdWJfUbnSwc+dON378eAtQNW3aNJiamLK6X/bFfp0zZ4775ptv7AJJuXLlbBoXkghoxb6PQC43deLGT9y841ByKB2XknEK9omIiIjIAUfD5J577nHHHHNMMEXk0LVgwQLXt29fu8Poiy++6OrUqeO6dOlimbOJjCAcWVoEhqKR4cs0gjgEc7Jqx44dlhVWqFChvTKL+RymsS6p4Y7vL7zwgmUSt27dOpiauLK6X7Ly/pUrV9pdZkuVKmXfh3B/8VtBoHDq1Klu06ZNNg3c2ZY7trOPWe6h5FA7LiVjFOwTERERkQOO7rxXXnmlq1SpUjBFJDER1Jg9e7abMGGCPSZOnGhZS9E6duxomUl0fZw0aZL93759e/f5558HcySmMJBDN8RoYddEAkZhN86sYBlkhZF1FvtZ4bRwXWJ9+umnrl+/ftZ9dNiwYQnTzT6t4zKr+yWz71+3bp276qqrLDOQsj7xxBODV3YH+y688EL7TvTs2dO9/vrr7oknnnDXXHON27Vrly1vXxwrOUWiHpey7yjYJyIiIiIisp+QhUTW3jnnnJP0ePzxx4NXk6OrIuNf0nj/888/3auvvppq19KcjuBbyZIlLSNry5YtwdTd6JZIdhiZjYxdmFUso2jRotYFNHasOD6HdWBdYhFY6tWrl2WUjRo1yh111FHBKzlfasdlVvdLZt9PF9yrr77azZs3zz388MOuQ4cOwSu7ESC87bbb3KBBgywT9pJLLrG/zz33XHuQtUk37UNBIh+Xsu8o2CciIiIiIrKfMObY5MmTbZwyHgQ1hg8fHryassqVK1twinHmYrOjEgnZWmSYkckVbcOGDVZWhx9++F5j7GUGWU8si/L8/fffg6m78dmsA+tCoCpEQKl79+7W7fSll15ytWvXDl5JDGkdl1ndLxl9fxh4pIvukCFDXOfOnYNXkiNIeOutt7qffvrJgobsy9tvv939/PPP7sgjj0z4G9og0Y9L2XcU7BMREREREdlPwkwngis8yD5Kbyy+b7/91rKiqlWrltQ1kayotWvXus2bN9vzREA3Te6Q+/HHH1smY2jGjBkWAOIO3mR0ISvbzzJYFu9l2SE+k8/mDrEnnXRSMNXZnXm7detmgVYCKtwhPNGkdVxmdb9k5P0E7fr37+8++ugjG8eVLrrRQdfUcAdz5iNAyN1/27RpY3fmTQRk8xKY5hGd2XsoHJey7+S5MyL4W0RERESyAQYbf+WVV4Jne9x8883WVSknYzD18847zxowDRo0CKZmzf5YpsiBwJ0033jjDQta8CBg8s4777g77rjDgiF33XWXq1Klis3LWH4tWrSwIOBZZ52VFASk2yNBFL4HU6ZMse8BQZzly5fb6wSysiuCM7/88ouVAetNdta0adPsBg0EOimHcCyyrG4/WV+Mc0YAqmrVqhYsevLJJ+1cy52PGQ+OfUDWGDdHWbFihbvsssusazXBpPDB2Iq8P5Fldb/E+36y/8jU4yYTdF9v2LChjRsYljU36+D4Zx+wnNGjR7vPPvvMsgIZ15Ju7nTtJVDLdyW9IPrBRlCU449jle3gLtyMW0tmIhmPYXdcgnx0TWb72rZta1m+Oi4lw7yIiIiIZCsTJkyg395ej40bNwZzZG9//PGH79Wrl7/iiit8pEEWTN1t/vz5vnLlyn7o0KHBlKzbH8vc355//nkfaSD7SMM2mJIxaZWx5ByRxrwvXbq0z5UrV9L3PG/evL5Ro0Z+6tSpwVy7ffjhh75EiRK+f//+fteuXTZt586dvmfPnsneHz5y587tBw0aZPNlZ5zXevfu7QsUKJC03ieffLJfsGBBMMdu+2L7v/zyS9+wYUN7jXn4TL5HGzZsCObwfs6cOb5cuXJ7LS98tGvXLpgzsWVlvyCe92/ZssU3b948WflGP6pUqeJXrFgRzO39Qw895AsVKpT0On9feumlfs2aNcEc2dtbb72VbP2jH02bNg3m8n7dunW+Xr16vn79+vY3dFxKRuXin8jBISIiIiLZBHdFbN26dfBsj0jjyZUqVSp4ln2RYRBpeNgdGclSIitBknvggQdsMPz333/fRRp0wdT4qYwTB9lNZOuQzUNW1NFHH31IZuiQ3URWM91J69Spk+rdcbOKO7eSPcZ4b4dqWWdEVvfLvt6vjPtH1ubOnTtdzZo1XYUKFYJXRCSagn0iIiIi2czBCPYx7tLMmTOtEUxX4aZNm7patWoFr+5GFyS6GtHAonvVrFmzrKth8+bNrdEMGmBffPGFDbbOaw8++GDSnTCPO+4463r13XffWQONbnU0vBmfjHnp3sUyly5dagO40yWMblmsG12eli1bZvOceeaZ1gUvFLtMxohiXu4GGStfvny2XWF3Lz5/4cKFbv78+bbu9erVc6ecckrSeFKI3m4GnKecaLB26tQp1SAbd/2k2xqNXLaf5RLU43103eJuk6+99pp75JFHbDB7Pq9GjRquRIkStk5082K9GM+K6c2aNUva5vTKOMQ6UG6sA13mTj/99L3u2shnsSwelDP7kc9iPURERCSHItgnIiIiItnHge7Gu2zZMutClCdPHl+qVClfuHBhewwcODCpi+iuXbv84MGDfYUKFXz37t19sWLFfJkyZXz+/PmtG+KLL75o8/zyyy/+xBNP3Gvd6Y6EefPm2TKGDBliz+k+x2c3btzYn3vuub5o0aK2PNaF7l100W3ZsqVNZ92Yfv755yfrdhe7TLqJHXbYYXutA4/y5cv7uXPn2nysa6dOnXy+fPmsG1rJkiXt7w4dOvj169fbPNHbTXfB4sWL+7Jly1p3tNS6ji1atMjXrVvX1pUyoqz4+6KLLvJbt271ffv23avbIZ8/ceJEW6dmzZrZ/HwW60T3twYNGvilS5fa8tMrY0ybNs3XqVPHuoSyvuxPljds2DC/Y8cOm4cudOxLtjncn3xu9erV/ZIlS2weERERyXl0N14RERGRQxiZYzfddJNl13F3v//+9782EPgFF1zgHnvssb1uFMLrZLZ98MEH1g2OLLzjjz/eDRo0yH311Vc2GD7vadSokTvttNPc119/bV2u6GqaFjLrGKiczDm6M5KtNnfuXMueY/lr1qxx69evt89hEP4PP/wweOfeyG77/PPP7XPDB3d6ZJB4upHRbe/vv/+2geEZ2P/555+3zD2257nnnrNsuOHDh1vWW4jt5kYAn3zyif3N8skijMUg8v/5z3+sqxkZmiyXmy6QpceA62T23X777e6GG26wsho3bpytH5l1lBevMwg7mYp01WU53MSBz+ROlax3emXMvrzqqqvsJgWUK++l/C655BIr18mTJ9t83MXy3XfftfUJt3/dunU2eH505qSIiIjkLAr2iYiIiBzCvvzySwtcEWC66KKLrFsoYysRVCMo9vrrr1tAMMTrdB+lqyfogsq8dKUlAMiYY5UrV3YFCxa0B91G6f6a3rhYLIfgG3dxJODVoUMHWw7rQmCMbqXcfbBjx44WxGK9U0OX1vBzeXDnxmHDhlk316eeesq6+xIgIxh3+eWXu86dO9uy2TYCYtz9kNe4m2SI11kP7hYJ1jF37r2r0gT7CEpyB0nuEMk8PAhAUsaUCcE6umPzeczHOtJ9lgBbuXLlXO/evZO62/JeunSzbwgIsk68L60yJoBH4O7+++93J554ok3j82655Rbbdl4nkMk8LIuAIcsBn9+1a1cLjIqIiEjOpGCfiIiIyCGM8dwYq61JkyYW+AkxZt6xxx5rmXYEhUIE3WJvKEGgiaw8xpkj2JUZBKoINIUYN5Bx5phetmzZYKqzsfZ4kPUWD+br37+/jTFIxl3t2rVtOmP6kXFHZiJBRgJhN998s/39ww8/WEZd7HaH700LQUDG/COYeOmll1q2JGWYEWT1kW13zjnn2H5p3LhxUtB106ZNwVwpC8fzw5tvvpm0XTwYH5CgLOtDhiBZjgQx+/TpYxl/vI/3i4iISM6mYJ+IiIjIIYybOBCgiu22SYYeGXEEhbhbaIgbXIQ3twgxjeAc82Y2WESgMTrYCNaBdeP/EH/z8HHcY471GTx4sN0oY8SIERY4C23ZssWy28IMPx4ff/yxmzRpkt0IhQAmnx1i3cLst/QQPLvzzjstkNqzZ0/LWjz55JMt8zE9dBVu2bKlBSbDjMAGDRpY0JNAanrly74ioMdNSqZPn560XTwoB4KWBHJBliLdlg877DB311132TpyA5T77rvPliEiIiI5k4J9IiIiIocwusRy11qy3KIRVCK7jcBedJCLLMDYeQmqkXVGJh6Bv+yAQN6jjz7qRo8ebYGsNm3aBK/sRrdWtovAFuPpxT4Yz46xAjODMgvHQSTg9/jjj1vw7JprrrHsx9QQzOMOvZT9hAkTLDj4zDPP2NiJ3Ek3OuiZGjL1CNLSzff9999PcdtGjhxp60gwkezBTz/91Mbqe++99yywSJmMGjUqWKKIiIjkNAr2iYiIiBzC6JpKkI6bNkRn8K1YscKy3mrVqpWsey0ZcWSMRXfXpfsnwSICRWTAkRFH0I/AIIHEg2HMmDE2Tt+1115r2XUEtqKdcMIJluVGQIxg5f5CptyVV17pbrzxRutSvGrVKptOoJGgXvRnk63IjTLotsxYfiGCq9w0JLrMUytjppPByLiB7Kd4EfQ977zz3NChQ21MQbo3i4iISM6kYJ+IiIjIIYygF9ldjO9GRtePP/7o5syZ4/r162fjw1122WV7dfF99tlnLTts7dq1ln1222232Y0fCBaBLr10XV28eLF1nyVbjbvYHiizZs1yAwcOtEDbEUccYdvGOvDg5hR002U8Qm7MwR1sueEIAUsCZGS+cRMPuv/SFTajyODjbrZPPvmkLYs7C9M1lzH3yLhjncANOQjUkfVH9uHbb79td82l2y5jBpLRx12RCbgSsIy9IUlaZcxNTMhKpAx4jQDjzz//bAFD9hXlgbFjx9oYhQQFeZ1xDRljkPJhPD8RERHJobyIiIiIZCsTJkxgQLq9Hhs3bgzm2LfWr1/vL7vsMl+gQAH7nFy5cvlKlSr5kSNH+p07d9o8u3bt8oMHD7bpd955p69YsaLNx6N69er+k08+sflCy5cv923atPElSpTwuXPn9vXr17fp8+bN8xUqVPBDhgyx5xs2bPBNmzb155xzjv/rr79sGtatW2fv6dSpU9I6YPXq1b527dq+W7duwZS9lzl27NikbYl9lC9f3s+dO9fm27Ztm7/nnnt8uXLlbDt4nf8rV67s7733Xr99+/ak7WbaokWL7H1pYZldunTx+fLlS/pMllm1alX/yiuv2PKwdetWP3DgQFtunjx5rJwmTpxo2922bVubxnv5v2XLlr5fv37+sMMO83PmzLH3I7Uyxg8//OA7duyYrBxYFvPwOXj++eftveG28yhSpIh91ubNm20eERERyXly8U/kh11EREREsgluqtC6devg2R5kXDHW3P5Cph53giVrjMyw6Btx0IX07rvvths6MJ4cGWqMP0e3XbIDY7P/chKy8ei2ys1KGOvuyCOPtLHvsoIsPTLq6LpbunRpyySM9wYfjDe4fPlyt2bNGuvOS1dqyjkzyFZk7EDef9RRR1kGZnSXZroR02Wb7sN0C6Zbd3S3bREREcl5FOwTERERyWYOVrAvLbHBvszevEJERERE9i+N2SciIiIiIiIiIpIgFOwTERERkXTR9bNVq1Z244pKlSoFU0VEREQku1GwT0RERETicuqpp7revXu7MmXKBFNEREREJLtRsE9ERERERERERCRBKNgnIiIiIiIiIiKSIBTsExERERERERERSRAK9omIiIiIiIiIiCQIBftEREREREREREQShIJ9IiIiIiIiIiIiCULBPhERERERERERkQShYJ+IiIiIiIiIiEiCULBPREREREREREQkQeS4YN8///zjpk+f7kaPHr3XY8yYMe777793zz77rDv55JPdokWLgndl3bfffusaNWrkRowYEUzJvG3btrmuXbu6Ll26BFMSD2V/7733uiuvvNLddNNN7v3333fbt28PXk3bo48+amVNmaclO5Yj292wYUP3zDPPBFNERERERERERA6cXD4i+DtH+N///uc6depkwaPcuZPHKnn+n//8x23evNk9/PDDbty4cRb02xeWLFniWrVq5Xr27OnuuuuuYGrmbN261bVr187+njx5sv2fXS1evNhdd911rnv37hZYSw/B2Mcff9zdcccdLn/+/K5GjRruv//9r1u9erVr06aNBcHKlCkTzL033n/33Xe75557zk2YMMEdf/zxwSt7++OPP1yHDh0ch/CkSZNsWkbXd1+bP3++bWf//v0tyCnZD+cHgrJr1qxxhx12mDv99NNd3rx5g1fT99tvv7k333zTLVu2zI5lvssnnnhi8Gr2snDhQjsP/v77765WrVp27mSb4xXP+1euXOlmzJjhli9f7jZu3OiKFy9uwXrOl4ULFw7m2o15P/nkE/fNN9+4nTt32vnh/PPPd0cccUQwxx6c6ydOnOg+++wz+7tevXpW1iVKlAjmyF6yelzE837KbN68eXbB66effrLy5eLCueee64oWLRrMlbIFCxbYPqpatapr1qxZMHW3jOyXg43fCL67fIf/7//+z5100kmuTp06wavxyer34kDh+G/dunXwbA++Z6VKlQqeiYiIiIikgGBfTrJ9+3bfpk0bf/TRR/tIAyWYuv8tXrzYV65c2Q8aNCiYknlbtmzxLVq0sEd2N3v2bB9pBPnhw4cHU9JGOUUak/7UU0/1P/74o037+++//U033eQLFizon3rqKZuWml27dvnBgwdbWUcac8HU+GV0ffe1SEPcV6hQwQ8ZMiSYItnFpk2b7NyRL18+LnDYg+8g38d4ff311/64447zRYsW9fXr1/eVKlXyJUuW9CNHjrRjN7tgXVgn1q1ixYq+QYMGts6sO9uQnnjfH56PKdMaNWr40047zc7NefPm9c2bN086B2DMmDF2DihQoIA/4YQTbJlFihSx7+vrr78ezLXbhg0b/IUXXmjLrVmzpq9Xr54vVKiQb9Kkif/hhx+CubKPrB4X8bx/x44dvnPnzj537ty+VKlS/pRTTvG1atWysm7cuLH/9ttvbb6ULFu2zMqRY75du3Z+586dwSsZ2y8H25w5c/xRRx1lZcC28P/QoUODV9OX1e/FgTZhwoSkc1X0Y+PGjcEcIiIiIiIpS8gx+9avX++++uor6+aJSAXfMhboFkqGyLRp0yz77NVXX3WRRqXNEyvSoLTsMjLRyOqLlFXwSnz47I8//tg98cQT7uWXX3bfffdd8ErayO4YO3asdWUlyyOl9WN7yO4gi5FuxWQ5kuEQi8y3jz76yD322GPuxRdftOyNWGktiwyoFStWuEgj0/3yyy8u0tByc+fOtYyS1KxatcreR6ZJmBUSaUS69u3bW8YPZUlmRjwoc+ZnH7Av2CfRWA6ZKjyQ0fVdu3at++KLL6ycYlEGZML8+uuvwZTd5Un2y1NPPWXrRHZIPNtCmZCFwrEXjWVHH6ch9gnr9fTTT9vx8/nnn9s0yRoyTS+44AL7XnFcnHDCCcEr8WE/DR482G3atMm+Jxwf7L9TTjnF3XPPPbaPswsyXOlGT2Yz60jG6YcffmjrzjakdMxHi/f9efLkcX379rXzG+dXsvD4Pg4ZMsTKmPNK+B0he/Kqq66y17/++mtbJhlqJUuWdIMGDUp2fmIoBsr49ttvt3MA3wf2G8M0sF7xDglwIGT1uIj3/ZQj2WeUKec0zm+UDb9lzPPAAw/sdY7B33//7YYNG2bn05SywTKyXw42tn/gwIHu008/da+88kq62Yyxsvq9EBERERHJMYj45STxZPZxpZ/sqkhj056TJdK0aVPLCmnbtq1lLZQtW9ZHGqqWGbFw4UKbD7t27fIvvviiL126tM+fP78vU6aML1asmO/Ro4ctM57MvqlTp/o6deokZWDweWSlhNlmqWX2jR071rINyGbhc/mfLLlx48YFc+x+b/fu3e011ov52I7q1av7SMMvmMv7adOm2TqQ9cG2Fi5c2BcvXtxHGn2WIYL0lhVpeFrWA4dJ+GCbBgwYYO9PycyZM325cuVsHsoyNHnyZCuLu+66K81MF16LNLqsrFm3cL3YF+wT9k34/thyzOj6vvPOO5bh8fTTTwdT9qCc+LxJkybZczIs2DeUJ+tDeVJuV1xxhd+6davNg9jMPtb18ssvt6ya6CwnPProo5Y98/nnnwdTvP/ll198p06dbNklSpSw9ePvDh06+PXr1wdzSVatXbvW161bN0OZfewnju0+ffoky4ziuGNf3XvvvcGUg+++++6z7/v48eODKd7WuW/fvr58+fLJjrmUZPX9nJs5R3O+5ZydGr4fnFP5noeftWnTJn/66af7448/3q9Zs8amgfNW165dfZUqVTKV9bu/ZPW4yOr7w983svI4rmO99tpr9jvy3HPP2f+xmX0pSWm/ZDesF8doRjL7snpcH2jK7BMRERGRzMqxmX1cgX/nnXcsYy18cIU+rYwPMtjKli1rmVZkVZFBx3hyTz75ZDCHc19++aWNN0fWz9KlS21Mn0ijyzJWmDc9jLd0xRVXWCYWmX28n4wzxt6pXbt2MNfeyFy58cYb3eGHH27rwPsiDQ/LrhgwYICtC6ZOnereffddy3ghC5D51q1bZ+scjo9Fhg2ZGryXzAXWmzGOLrnkEvfggw8mjROY3rKaN29uWYmU2W233WaZH2zfzTffbO9PCeMnMWZdpIFp2XiUNZmUjHNYuXJly6zKHTPWYkpY55kzZ7oPPvjA1ovtZ/w+Mk3IyEhJRteXccWOOuoo27/R2XXsL44lxoGqX7++TStTpowbPny4rRfrw/Fz6623utdff90yjvYFMnBYJuOfPf/880n7hHLk+OPzOa7k4CCLiuOkcePGltEWOvbYY1358uXtu5YdMs7I7mJdyII67rjjgqm7s/A45jl3kuGUmqy+H5xHGJuU8eHIHEtL5HfI5cqVK+m8wPv4nlWqVMnOYSGWU7169aTzQXaR1eMiq++n/Mj6o/wox2icf8my7Nixo2vSpMler6cldr/kdPviuBYRERERySlybC2exiCBEe72Gj6GDh2aZjecY445xgJb5cqVswYMA183aNAgqeFIg4nAz59//mlBJeYHA/hfe+21yRpiqXn77betG/H999/vzjzzTPscGqkMiH7OOecEcyXH544fP94auXQlChsirBvbSNCH4BNo6LIep512mitYsKBNY3u4GcWRRx5pzwngMR/rEA7wTvetW265xVWoUMFeJ2iU3rKKFCniKlasaPPw/po1a9rA7cyTGoKEdBlj2/v06WPBtH/961/WTeqFF16wwdDjwWfSPTAcSJ59wT6hUUwAMOwaGC2j60tD+uyzz7ZAKw3uEMFEGn10RS5durRNo9sXwdIw+ED3sauvvtqWP2XKlH0ShKMLHUHhyy+/3HXu3Nm6nbItfG7btm3tNbony4HH8cZNZvie8B2KVrx4cQsw8z1N6/xzoHAeIRhNgJp1i8b3g21gW1L6DiEz7+d7ycUXvuOcw7p16+aOPvpod80116QZLOJcyY14uBBAcAucLzn2//rrL+uSH+LzKN+dO3emOZTAgZTV42JfHFd052WYCi5MRJ/rCG5xgYCAHefSfPnyBa+kL6X9ktNl9XshIiIiIpKT5NhgH8EoMrfIeAgfjDNERT41ZJlEN4ao3BPMIbgHGpEEeapUqWKBomg0pNK7CyTvJ1BEoy3MCItH+Lm8LzrjAGTKsY6MyURAiWwzGsIE0sjSI1DF+0P8zTSQcUaAj8w2Ho888og1ymnQkEWW3rIyg7sEcsdiGorcjZYgJmPP8TkXXXSRmz17ts3HWIRkGDIfD8bCo1Eboqxjy5B9QrYPZbEvGmQEIQjAsizWgf95EEzk2CAQGKLsWcfevXtb8JJMEIKBjFVG4JnyzCqyEMkqJPhMkDfcd/zNeIWUGQFaOTgIFhB85diIRnCKaQSnUhoz7UAjQMbxWKhQob2y6lhPprEtqcnM+zkuCcZz0YVx5iiLXr16JV2ASAmfwQUJjnuCgtWqVbPpnO8IonN+j87g4zPIcOU7SvBrX5wD9oWsHhdZeT/j6RFc5eIG5/Ho/cV4rdxx9oYbbkgq23iktl9yuqx+L0REREREcpLE6J8TJxpUPKKR9UB3JRDoomFFdlpsFgRZY+llRtAgI3BYrFgxuylFvMLP5TMIikVjOTREWC7r2bBhQ+vWSVckusaScUZj7L777rNAHuvA/8xP8JNsMLoT86A7LUE0ugojvWVlBo3LSZMmWQYlA6Gfd955lqlGxg+NrYcfftgaXAxCT9cygm082rVrlxQIBGVNeURjGg013p/VoGSobt26lv1IORFM4KYdBCEpm7DbNUEFbnJCdh1lSnkRhA0DwGzXvgg8bNmyxYKKYYZfuO8oT4KoBDtjG6mSHMc/x1EYRKYMyXraF8KyD88XIZ7z4NySkW6S+0vY9ZJjMnZdw2lpHUeZeT834yE7lvInAN6yZUvLhub7ntJ3g+8vmdijRo2yiwM8QpzzCBTyPs4dXCxgWIDu3bvbvuSzD3Q5p3VcZfW4yOz76SrNcA1cvCGbOsziBucxzuNnnHGGO//884Op6Utrv+R0Wf1eiIiIiIjkJIdUsC89BNro3kPQheBbNDKu0ho3Cbyfbp7MG2YLxoP3ESBM6XMJuvFguWFjheAYdyOksffee+9Zd18adjTQwm2g+xVdkrljbOxj5MiRFkhLb1kZRYOJhj6N9dg7nRJg5EHDlO1s0aKFZaWQzceDbnl0qw5R1pRjNIJ8ZF5QVhnpkpYWlsUYg6w34zkxTiLrQgM5zLRh/caMGWMNZ8Z9JPDAHXnJfqGc00M5hw33aGxP9DSyc/hMyj+l/cYYi4xbKKmjuzhdFsMgMg/uVppV7EO+gwR2OX6jcaxyXPKdykiQf39hHehmTvZb7DmL9WQbosfCi5XV99PlnkxhzgGMi0qX0GgEtLnbOAGqiy++2I738LsWOuuss+wcxHoQNKRbMO8j25X1o9sl++RASe24yupxkdn3k03MMAKcjwiodujQIXhlt5deesnOY2QhMwYs45+SdU7QkvMq5znO+dHi2S85WVaPaxERERGRnETBvihc1Sdbi6yIsCssCGKR0RXbGIvF+xlknXGBCKDFi/cRZKNRzPhL0bhZAw1NXo/NSqRhQuYcmRiMP0eXN5bFQOwsi3WOV0rLAg0kGqQEHCmHtDAf3agJYv3888/B1N3ITKOBSiAy7EZFdz26VfPg7+isRsqa9Y/+TPYJDdSUyiKUkfUN0SAm0MZ4i4w7RiAhHCsQBGBZH8YfjB7riTJasWJF8Cx1jLvF+9n+EMFg9nV0sI/gCJmCBGkpQ8k4jiUyM8MgMmXOuGX7AkEsgiUErKPRvZrPIpgdOxbYwUAAm3XhPBTb7Zt1ZxvYFr4nKcnq+8H3iWEJCCxFX8DgO0mm3t13320BdfYNAZiU0E1+7ty5FojhQbdUvhd8xw/0OHJpHVdZPS4y+v4w8Ejwn5tvML5nNMqY8y37jgxJxmTl0alTJzt/8j5uZsTNhUIZ2S851b44rkVEREREcgrVamOQ5UU3Te7mythtBK2effZZe8Sjffv21hDljrZPPPGENSIYe4qGFF1ZU0MDi4YI414RdCLgSDYZXWEZv4qGL8iUYQw3AmGsG9lxZHHQuGMMPtA9lgywgQMHuhEjRtgdGZmX8a7YrvDusfEsi7KgofvWW29ZRiDvIcssNTQiCdyx3nwOQUcy5rjTMJkmZMTQ6IoHZc5nUhbsC9adAAJBydRkdH3BjQSaNm1qgT666LEN7IsQgQvGe+QmKWQ/0mBmffr165duAJiG46mnnmoBDxrQHAtkEdLNmW7V0ThuaLizHjTmCW5Sfqw/mYSMzZWRjNFDEeVN4JpjgAfB59ju4PEg84fjLjq7lG6SHF90rY7eD2FAnszP1ILQBxLrwLqw7qxbiHVm3Qnm0w09xHxsa5jtlNH3p4SAyo8//mj7IAwaEVDirukc+4yHSRZZeuOgguAeFwI4RzFMABdkYjOH97e0jquMHBdZPa4Ievbv398Cn4yNSDfb2OAUzzmvf//99zbWZ/jgPE8mMhnUnIfCLrqZ3S/Z3f44rkVEREREcgyfw0Qq7r5Nmzb+6KOP9itXrgymJjd06FBfoUIFP3fuXHu+YcMG37RpU3/OOef4v/76y6Zh586d/pJLLvF169YNpuz2ySef+OrVq/tcuXLZo2LFiv7OO+/0kYaSHzRoUDBX6pYtW2aflS9fPtK2bBm8d/To0fb6li1bfIsWLewRbdasWT7SkPWRxpq9j/8bNWrk58+fH8zh/fPPP+8jDTFbJvPwiDQ6fb9+/XykERPM5X2kcec7duzoIw3lpPkijR1b/sSJE22eeJf15ptvWhmxLNZpwIABwSspizTIfc2aNZOWy/+RhrK/4447/B9//BHMlbJdu3b5wYMH+0qVKlmZU/a8nwf7hH0TSq0cM7q+ePvtt23bKY+wfKJNnTo16ZgIy+n666/3zZo1882bN7d1wbx58+zYGzJkiD0Hx9zNN9/sCxUqlLQfzjrrLH/TTTf5SCPff/7558Gc3m/bts1HGvG+XLlyycqP4+fee++1418yj3PCa6+95p9++mlfrVo1O05GjRpl0xYvXhzM5f2HH35ox0L//v3tmATnixtuuMEXLVrUzgOrVq3y7777rj/qqKP8Kaec4n/55RebLztgXVgntpFj+8cff7TvVbFixWwb2Bawbdddd51t60cffWTTEO/7v//+e3/LLbf49957zy9fvtyvW7fOT5482c5/BQsW9MOGDUsqP84LpUqVsnP3U0895V999dWkB+UffT7/8ssvrYz5bqxdu9ZPmDDBvmt8D6ZNmxbMlT1k5LjIynHFd599xe/Kueee68eMGZOsDCnfrVu32rwpYV8dccQRvl27dkn7DxnZLwcb50fKkHUbOHCgL1y4sL/00kvtOcfpb7/9ZvNl9bjOLjju+Q2IfWzcuDGYQ0REREQkZTku2Heg0KiYPXu2nzlzZlIgJ6NotE2ZMsUarGk1wqLR2CDoQIN56dKlKTY+CB4tWrTI5pk+fXpSAyclNL5pHM+YMcMazWEDM5SRZWXEjh07bDsmTZpk2x8dPMwIyp59wL5gnxxMBCpZF8oqugEfLxqWHA9LlixJt1HJZxGYYn6CKAryZR1l3rNnz6QgavSDoHB0ID+loAxoZPfu3TspiM77Tj75ZL9gwYJgjuyDgFnDhg1tHVlX1rlXr1528SOUWlAE8bx/xYoV/qSTTkqahwfle/jhh/tHHnnE//3338Gc3j/wwAPJ5ot+EAB/8cUXgzl3B2UJyIT7itcbNGiQ7QJ9oXiPi6wcV5wLubAQllnso0qVKrY/UpNasC8j++VgW716ta9du3aK61q6dGn7DUNWj+vsQsE+EREREcmsXPwTqTyKiEic6FJKN0m6c9LlnTEos6Ndu3bZXWMZo4zu6nRHz4h4308XfR50V6erPXeOjh6DMzPofrls2TIbVqBSpUo2nEHumC6r2U1Wj4ucclzldFn9Xhwo3PU5+sZVIb4TDC8hIiIiIpIaBftEREREshkF+0REREQks3SDDhERERERERERkQShYJ+IiIiIiIiIiEiCULBPREREREREREQkQSjYJyIiIiIiIiIikiAU7BMREREREREREUkQCvaJiIiIiIiIiIgkCAX7REREREREREREEoSCfSIiIiIiIiIiIglCwT4REREREREREZEEoWCfiIiIiIiIiIhIglCwT0REREREREREJEEo2CciIiIiIiIiIpIgFOwTERERERERERFJEAr2iYiIiIiIiIiIJAgF+0RERERERERERBKEgn0iIiIiIiIiIiIJQsE+ERERERERERGRBKFgn4iIiIiIiIiISIJQsE9ERERERERERCRBKNgnIiIiIiIiIiKSIBTsExERERERERERSRAK9omIiIiIiIiIiCQIBftEREREREREREQShIJ9IiIiIiIiIiIiCULBPhERERERERERkQShYJ+IiIiIiIiIiEiCULBPREREREREREQkQSjYJyIiIiIiIiIikiAU7BMREREREREREUkQCvaJiIiIiIiIiIgkCAX7REREREREREREEoSCfSIiIiIiIiIiIglCwT4REREREREREZEEoWCfiIiIiIiIiIhIglCwT0REREREREREJEEo2CciIiIiIiIiIpIgFOwTERERERERERFJENki2DdmzBg3evTovR7Tp093//zzj3v22WfdySef7BYtWhS8I+u+/fZb16hRIzdixIhgihwsKe2L/bHP48VnNmzY0D3zzDPBlKz73//+53r27Ok6deoUTBERERERERER2fdy+Yjg74Mmb968wV/JtWnTxo0dO9Y9/vjj7uGHH3bjxo2zANC+sGTJEteqVSsLwNx1113BVDkYUtoXDz300D7f5/FasGCBO//88911113nbrzxxmBq1vz999+uY8eO7v/+7//cjBkzgqlyoG3fvt1NnDjRffbZZ/b8tNNOc61bt3YFChSw57LH5s2bLfC9Zs0ad9hhh7nTTz99r3P1rFmz3KpVq1xKPyPM27RpU3f44YcHU1T+GUHZjx8/3sq/ePHiVlYtW7ZMsawWLlxo58rff//d1apVyy4qsM8SHcfop59+aufsjRs32jafffbZ7tRTT3W5c++5lslxN2XKFLdhw4Zgym65cuVyRxxxhJVtaN26dXZ8coEmGstr3LixO/LII4Mp+x/fFb4fsdjWUqVKBc9ERERERFJAsO9gK1iwoH/llVeCZwfG4sWLfeXKlf2gQYOCKXKwHAr74q+//vLnnnuub9q0aTBFDrQtW7b4Sy+91OfPn9/XqlXL16lTx/5mGq/Jbps2bfJt2rTx+fLlI4JnjxYtWuxVRjt37vRdu3b1efPm9Xny5En2yJ07ty9ZsqT/5JNPgrlV/vHatWuXHzFihC9WrJgvU6aMb9SokT/mmGNsf3To0MH//vvvwZy75x05cqSVdcWKFX2DBg180aJF/XHHHee//vrrYK7E9P333/vq1avb8VatWjXfuHFjKwOOqWuvvdZv27YtmNP79evX+/r169txGX2ccuxefPHFdiyHJk6c6EuUKJFsPh4FChTwY8aMCeY6MCZMmJD0HYx+bNy4MZhDRERERCRlOWLMvkhF3X311VcuUnm355EGjvvmm2+s+ydX36dNm2bZf6+++upeV+5DP/zwg3vuueesayaZZJFtD16JD5/98ccfuyeeeMK9/PLL7rvvvgte2eO3336zTMRHH33Uvfnmm3utS/R6k2nAej/22GMu0iB2f/zxh/viiy9sGbzvjTfesOVEGjTBu537+eefrcsz3V3fffddyxKL9dNPP9n6kRVHV2iybqKtXbvWyvLPP/+0cqA8KBfKJyVhRgTly3zLly8PXtltf+2LfbHPKbtIQzjpc9hmlkkZpIXPZD4yPELR5UYZsNynn37aLV26NJgjOfbN22+/bes4efJkK8fUsO8/+ugjOxZefPFFt3LlyuCVPdsdabjvtQy2e968eeluj+zG9+Kdd95xt99+u1u8eLGV6eDBg23aSy+9FMwl+fPndxdccIGdw+bOnetOOOGE4JXk8uTJY2W6Y8cOt3PnzqTHihUrXPXq1V3t2rXdSSedFMyt8o8X33fO38cee6z9Jnz++ed2/urfv7/78MMP7bchRDnee++9lv3M+Wn+/Pk2z6ZNm6xsObckKs6NZI7y+8RvClmmnI/JoOb35b333gvm3IOMbc6j4bHKsctvCMdyNLL4nn/++WTHNdnZnTt3DuYQEREREcnmLOR3kKWX2Td06FBfoUIFH2l42vMNGzZYhlSTJk1827ZtfZEiRXzZsmXt6jsZIwsXLrT5EGkQ+BdffNGXLl3arviTKUHGRI8ePWyZ8WSTTZ061bJQyAooVaqUfV6hQoX88OHDgzm8Hzt2rGUVkH3BZ/B/1apV/bhx44I59qw3mRqtWrXyhQsX9uXKlfN9+vTxn332mW1Dt27d/NFHH22ZBZUqVfKRBouPNE78sGHDLHuDsuI9LL9GjRp+xowZwdK9jzRObP3YzvLly1smAp9B5gcoi0gD0La7e/fuSZkjzE/5UE7ME1q2bJmtL+XKclkWj4EDByZlTeyvfZHVff7UU09ZecV+DvsovX0+b948++whQ4bY87DceO9VV11ly+WzKV+W+/TTT9t8oUWLFvl69erZurGtlFnLli1tv7MN0aZNm2bHFhkmLJN5ixcvbvs70hC1zybLh+OBMgn3D5kdZAqS0RJp4Ns0Sd3mzZt98+bNfd26df3atWuDqd7+Pumkk+w1MtokOcqHMkspsy81L7zwgp0f77///mCKyj8jxo8fb+eAe+65J5iy25w5c+y8fvXVVyedB+677z6bl/eEdu7c6fv27Wvzfv7558HUQ8f06dPtnH/NNdcEU/Zk9rVv3z5ZFl9KyOzj927UqFHBlINHmX0iIiIiklnZIrOPm3BwVZ6spvDB1Xayu9JCVlPZsmUte+3XX3+1rLr//ve/7sknnwzmcO7LL790d9xxh2WncNWfMY3ef/99G5OHedOzbNkyd8UVV1gWAZl9vJ9xghhLh8wVkH3B2G6MTcXnMQ/ZGCVLlnQDBgzYK/uL7AvGFmK9yeRjfDpQDoy7dMMNN9h0svTOOecc9/rrr7tIo8517drVxm/iNcYoYhwnlk85/fLLL5YJSIYHGW2UB9llb775pjv66KNt+SG2e+bMme6DDz6wdWX9jj/+eDdo0CDLDsHWrVvdTTfdZJl0ZN3wHtaHjB8y0F555RWbL3Qg9gXi+ZxIo9jdfffddpMNsvD4nEijyfYJZZdZvJdMJzIx+Uy2oVatWpa9R9mAMh84cKDtk7feesvmI9OpSJEitu7RKNurrrrKjhOOCeZl/15yySXuwQcftIxAMkx69+5tGSkcJ1OnTrUsEzJ/+M7ceeedrkGDBsESJTV8P9gPHOfRY5nx93HHHWevMY9kDZmvZFSVLl062VhjKv/4UXaMy8fvDL8JIbL0yEQLy48M5/C3hDIM5cmTx254xPxk/h1qIvUae3DuTAkZ0RxrnEfTwm8+8zG/iIiIiEhOky2CfVSq6RZ55ZVXJj2uv/56C4ak5ZhjjrEuYeXKlbOKPY1LAh9hcI2GEsEkGqAEspgfDDR/7bXX7tV1JyV0xSRwc//997szzzzTPoeB55s1a2aBOD6DQdQJjtFtKmx0sR633nqrBYjoVhWtWrVq9lr58uXtef78+e1/BgtnG3r06GHTWD+6lBJsY7ksv0yZMjYvAbObb77Zui/Nnj3bPp/GIZ9buXJlm4cGI+vYvHlzex5iuX379rVtAOVC+fBZBADZJgJzBMe6dOniLrroInsPn818VatWtQAknxk6EPsC8XwO+4Njis8JB1Nv0qSJdYNj32VWwYIFrcz5PBBEJTDH8RF2vWWgfIKN3bt3txvMsI6VKlWyfVehQgWbJ0RXbAKRHFsnnniiTWPQ9VtuucXm5XW2o3DhwnbjkipVqtjn0334qaeecpdeeqntG0kfgWGOPY5dvmch/mYarzGPZA3fQwLiBJsIhIdU/vGj6zPnjtdee826o3Jhg2ELOAdwbueCC+cVzr+UGedlLvxEq1ixop2vVq9ebefEQwUBPM7/dNVt2rRpMHUPXqNsOJcSJL3mmmuSDdcQosyohzAfv9M1a9a0827sTTtERERERLKrbBHsy5cvnxs1apRV0MMHARQCQWmhkUjQJ0TjhqwIGo6g4k9mAxV2KuvR6tev70qUKBE8SxnvJ9ONwAvzpyT8DOaJzq4AjTbWh7sqErQJcfc/GhyxaPjynjD4BxpzZOoRiBs6dKgFggj4kHVH8IzGBw06Gi5hg4TgGZlsKY3pB7Y7dnt4L0Ep1pWGDp/JfiBIFh2II3uRsaT4TAJVof29L0LxfA7ZfHzOUUcdZdNCBEjj/ZyU8N4aNWoEz3ZjXSivcGwsyo11IKuQBnmIfc44ZiHmISMUZF+G+5XHI488YvubMmacKPD+IUOG2LiN3CWYbSFgTEBX0sfxwXeQDMvo/cLfTGN/hMeQZA7fA8aeJAjVrl07+26GVP7xI7g/bNgwu7jUp08fO4/961//snH4XnjhhaQgKll+nB8KFSq010UMyp5p0RdkDgVkxlNG3JE3OrOU8yR3KOaiIpnZzHfuuefavFxc4+62IX6budhIsJVMagKuXITp16+fZVQfSsFTEREREcm5skWwL7MIQsVmhBEwowsPaED+9ddf1ngioBiNBmbstFgE0miAFitWLNWgSvgZLC86SAfeQ6OLZYTrBKan9tmsazSWzXrQnYhuxHQf5v9JkybZAPdkupHVQVfQ//znP65t27aWdUdDhiyQiy++eK+bb/DZrG80ptFopPHINhG8orEYuz6UL5/HfNFZDvt7X4Ti+RzWi30R2wBOqVGcEXxOSp+N8PMpN+YpWrSoPQ+xPnx+iHUkoMexMX369KT9yoMsHgKLBFaj0W2cYCcBEgIB0UFPSRtlxr6KDrqHmMbr0UEoyTiC/wT7CE6ddtppwdTdVP7xI/DUs2dPu2DDRR2y0biIwzmETF4yuUF5UmYEn6J/XxBOy8r5Lqf59NNPLSDHBRmCpfxuh/h95EJKt27dLHBK5iSBPoZR4PzLeTfEhRSywjt06GAXHMnSJsOfLugMMfLjjz8Gc4qIiIiIZF8J3bqicURgasuWLRZoikaXVzLX0sL7aSQwb2pZJ8xDoyKlzyCYw4NlhEGhjCJoRMCQLAUywegmGv2guy2ZCaBbKQ2YtWvXWle6q6++2oJIjAHIemFr1jYAACYlSURBVITYbrYpGsE7skDYFgJvrDOZI7HzEUxjDCMCdNGZO+nJ6r6IF4FUMv3Iggmz7UKMibe/s4fIAKGMUipftj0UlgcBWTI0Y/crD7JQwqAs77/nnnusuzAZpIxRSCNV4kNZc1yzX6Izc/ibaQRFmEcyj3MOWbUtW7ZMGkogpPKPH1lnXMxhuALutHveeee5yy+/3M7tnJPJLuN8wLmO3wfOc7HnT87lzMt5/FDAkBO9evWy8VzpJRCb1Z0SjjkuilGOXDhLC9l+XGBhWA66VYuIiIiIZHcJHeyjMk8XUYJfYZdJ0MAkUBIdfEkJ72/cuLF1pSVrICXMwxhudDtmrLZoM2bMsKATr8dmhMUr7B5MY4ZB7ONBIIkbdTAWHFkMZPZFB5/YbrY/utFN+TB2UbiuZJER+OMmEdEZfKwDDSO6kmUksyyr+yJerDtdj9kW1j3ENnDjgP3drY1yIXtxypQpFvQLffPNN8nGoKQ8WE+Om3iCdmPGjLFuZXThpdsv3bbp+hveGETSRmOdwAffhejjmYAIAVReoxu7ZA7HOt8vAnrhWJXRVP7x4Xz43XffWQCKDLNoZPryoHs/50vOzzzn9yl6SAUwD+VM5neiZ0xysyqy77j4xPi24Y2z4sF5OMyOTE+YfR7PvCIiIiIiB1vC95ui4Ulg5LbbbrObTzDm2bPPPmuPeLRv397GqOMusk888YQ1osheoVsVmRbgTqk0uuj6Q3cfAloEZ8jKoEsR2QOZRWYXGXp07SKDj20gkEWDkBs4EPxhfQgmMVbfO++8Y68RRGJdaAhxk4rYDA+2n8wx1pVlUj4EFskiAQ1Nbu5BYIk7AdN1iWAm3aQIYF522WV7dfFNT1b3RbwYL4wbXnBXXO6SzH7ijrZkzGQkGzEzKLezzjrL7lY8fPhwC8bRLZfueLEBzY4dO1rXMNZzxIgRljFCmXB3YsqIsgcZU2T1caMV9jcN+AceeMDm567D+ztbMREQbKpbt65lTEYHzfmbcTk5XphHMoeAHUFrxhylnGOp/OND4IkbbpC5x7kgGr8BZCeTAcmQAFzYOOOMM+xCDheWQpwP6JbKjSXYH4mMcWDpmkuXZQJ9KR17qSFox7mZ8oq+mUxKwjLmdzR2eAURERERkewo4YN9VOLp8kjmCcEmbnRAgITgT3g33LTQHY1uQfXq1bNBu7mTbp06dSyQRwYdCOgROGOcNe6UyM0hCIbRKCCQxWdmRatWrWwd6IrLmHxkwHCTi65du1pQj4AgjR2yyRiEnNdoOHfu3Nm6M5HhF3YHBRl5bD93d2QdKRfKh2BmePMMgmK8j2AnN4YgYEiW47JlyyyIxXsyKqv7Il4ELSkvuhMyuDr7jQDlgw8+aA3ljAYpM4JyIzjKWE8EiNlGumATBIy9OyT76OWXX7YMP7L02FeMyUc2Jo111pWsHV5jndkf4Q1G2DbuLvzWW2/Ztkra2C8Ey8nspDwJOpGhSqCVICyv7e9AcE4yb948N3bsWOtizo1+yBzjQgLTlixZEsy1B5nPjCvKhY+UboKj8o8fQX2GIuA3hoA/5/j58+fbhQsuHnARJhyPjr+5GMU5mv3DxShu5MRNKLiYEHtDoURCWTC2IRei+J3i5khkP4ePmTNnBnPuvgsv3aKZxgUusqz5/eMiC+dmfmNBNiQXVriYx82qKPtZs2ZZF2Gy6xnHL7yTvIiIiIhItuYPEdu2bfOzZ8/2kcq+jzQug6kZs2rVKj9lyhQfqfT7SKM1mLrHzp07/eLFi/3kyZP90qVL7fm+xPKWLVtmy1+wYIHfvHlz8Mpuu3bt8itXrvSfffaZjzT2fKTxY9NC/D148GBfuXJlH2nIWDlQHpQL5ZOaNWvW+Ehj3ub7448/gqmZty/2RWa8+uqrvmTJkn7s2LHBlP2Hsv7mm2+s3CIN8GBq6tatW+enTZvmZ8yY4SON0WT7TfYNvj8jR470FSpU8Lly5bLHYYcdZtP29Xc1J6MsevbsaeXDT0T0I3fu3H7QoEHBnLtxLmzdurU/6qij7PyUGpV//MaNG+dr1qyZtA/4n3PXHXfcsdc5+Msvv/QNGza0fcO8BQoU8L169fIbNmwI5khMc+bM8eXKlUt2fEY/2rVrF8zp/euvv+5Lly6d7JjOnz+/P++88+w8Hdq+fbu//PLLrQzD+XgP773tttsO6O8VJkyYkLQe0Y+NGzcGc4iIiIiIpCwX/0Qqj3IIoNsSmXRku3GnR7qQJiK2kwwksupOOeUU6xpHF6w+ffpYBiRdiMnQlEMT2WV0/+NYIKsnzJKSA0PlH5+dO3faEA0M28CNOMgoTylrErt27bKhHMjA5EZNZAhLctzEhOw/sqUZq4+sR7LAU0ImOF3MyWql6y5lH50df6Bwgy0yw2PRpZubQYmIiIiIpEbBvkPIoRLso+FLt8DRo0fbQPcMrE4XaIJ/dFWm65uIiEh2pmCfiIiIiGSWgn2HGG6yQVYNYxwxEHyiIouDm5OQ7cIdPwn0NWvWTFlEIiKSIyjYJyIiIiKZpWCfiIiISDajYJ+IiIiIZFbC341XRERERERERETkUKFgn4iIiIiIiIiISIJQsE9ERERERERERCRBKNgnIiIiIiIiIiKSIBTsExERERERERERSRAK9omIiIiIiIiIiCQIBftEREREREREREQShIJ9IiIiIiIiIiIiCULBPhERERERERERkQShYJ+IiIiIiIiIiEiCULBPREREREREREQkQSjYJyIiIiIiIiIikiAU7BMREREREREREUkQCvaJiEjCevbZZ911113nfv7552CKiIiISM63aNEiq+O88cYbwRQRkT0U7BMRkYQ1fvx499hjj7nffvstmCIiIiKS861cudLqOFOnTg2miIjsoWCfiIiIiIiIiIhIglCwT0REREREREREJEEo2CciIiIiIiIiIpIgFOwTERERERERERFJEAr2iYiIiIiIiIiIJAgF+0RERERERERERBKEgn0iIiIiIiIiIiIJQsE+ERERERERERGRBKFgn4iIiIiIiIiISIJQsE9ERERERERERCRBKNgnIiIiIiIiIiKSIBTsExERERERERERSRAK9omIiIiIiIiIiCQIBftEREREREREREQShIJ9IiIiIiIiIiIiCULBPhERERERERERkQShYJ+IiIiIiIiIiEiCULBPREREREREREQkQSjYJyIiIiIiIiIikiAU7BMREREREREREUkQCvaJiIiIiIiIiIgkCAX7REREREREREREEoSCfSIiIiIiIiIiIglCwT4REREREREREZEEoWCfiIiIiIiIiIhIglCwT0REREREREREJEEo2CciIiIiIiIiIpIgFOwTEZEc6+KLL3a5cuVyK1asCKbE759//nGPPPKIvX/hwoXBVBEREZGDb9q0aVZHeeGFF5z3Ppgav0WLFtn7r7rqqmCKiBxKFOwTEZEci0osateu7Xr16uU+/vhj97///c+mpWb16tVuxIgR7qSTTnLXX3+9TatUqZL9LyIiIpIdHHHEEfZ/z549XePGjd0zzzzj1q9fb9NS8+eff7px48a5zp07u7p169q0vHnz2v8icmjJ5TNzmUBERCQb+P7771316tXt73z58rkdO3a4woULu/PPP9+dfPLJbuTIke6bb75xAwYMcEWKFHHvvPOOW7JkiVV8d+7cacHCVq1auY8++siWIZJdTJw40bVu3Tp4tsfGjRtdqVKlgmciIpLImjZt6mbNmmX1ldy5c7tdu3a5hg0bujZt2riVK1da1h+BwPbt27sZM2a4CRMm2EXPsJ6DtWvX6qKmyCFIwT4REcnRCOp98cUXybq4UMmlUkzwj+kEAqkox2b9Me3NN990HTp0CKaIZA8K9omIyKhRoyyzj6FHouXPn9+mEdCjvkO9h0AgjxDTzzjjDDdlypRgiogcStSNV0REcjS671KhjUbll8BeGAAk6JdS916yAP/9738Hz0RERESyDy5GptQNlzpNmLlH0I/n0YE+UAciUCgihyYF+0REJEe74IILgr8yhmw/xrQpUKBAMEVEREQk+yhWrJh10U0p4Jce6jcMayIihyYF+0REJEejSyNj12S0Iky2X7du3YJnIiIiItlP9+7dk7L44kWd6MILL7QeDCJyaFKwT0REcrwePXpkuCLMXe5OPfXU4JmIiIhI9tOiRQtXtmzZ4Fl8qBPpgqbIoU3BPhERyfHOPvtsV7JkyeBZ+ujCy1h/3KBDREREJLvKkyePZfdRd4lXxYoVXbNmzYJnInIoUrBPRERyPCrAl156adwVYbrwdunSJXgmIiIikn1Rx6HuEg/qQtyYI/bmZSJyaNEZQEREEsJll10WV0WYbL7GjRu7atWqBVNEREREsq/jjjvOHvGgLtS1a9fgmYgcqhTsExGRhHDSSSe5GjVqBM9SR7CPK94iIiIiOQXDj9ClNz316tWLqz4kIolNwT4REUkIBPGoCKd3V14qyh07dgyeiYiIiGR/F198sfvnn3+CZymjjkNdSEREwT4REUkYnTt3drt27Qqe7Y1AYLt27Vzx4sWDKSIiIiLZX/ny5e2GZOll91144YXBXyJyKFOwT0REEkalSpXcGWeckeqg1Dt37nQ9evQInomIiIjkHAxDklp2H0HA8847z5UuXTqYIiKHMgX7REQkoaQ1Hl+ZMmVcixYtgmciIiIiOce///1vV7hw4eBZcvRs6N69e/BMRA51CvaJiEhCoZtu/vz5g2d75MuXz3Xr1i3dMf1EREREsqOCBQu6Sy65xOo0sUqUKOFatWoVPBORQ52CfSIiklC44s14NbEV4R07drjLLrsseCYiIiKS83DhkjpNNOo8Xbt2TfFip4gcmhTsExGRhJNSRbhOnTru+OOPD56JiIiI5DyNGjVyVatWDZ7tpguaIhJLwT4REUk4zZo1cxUrVgye7R60unfv3sEzERERkZwpV65crlevXsnuynvMMce4+vXrB89ERBTsExGRBMTdeLlRBxVicOe6iy++2P4WERERycm6dOliN+QAdR6Cf2GdR0QECvaJiEhCYuwa77393bJlS3fYYYfZ3yIiIiI52ZFHHmndecEFzc6dO9vfIiIhBftERCQh1ahRwx1xxBH2d48ePex/ERERkURADwbUq1fPValSxf4WEQkp2CciIgmrX79+9v95551n/4uIiIgkgo4dO9r/V199tf0vIhItlw/7OImIiCSg7du3uwIFCgTPRHKGiRMnutatWwfP9ti4caMrVapU8ExERA5lquOISGqU2SciIglNlWARERFJRKrjiEhqFOwTERERERERERFJEAr2iYiIiIiIiIiIJAgF+0RERERERERERBKEgn0iIiIiIiIiIiIJQsE+EZEc5p9//nFz5851d999t7vqqqvc9ddf74YOHeqmTJnitm7dGsyVvT377LPu5JNPdosWLQqmZN2YMWNc/fr13bx584IpORNl0rBhQ/fMM88EU0RERLKHX375xY0dO9YtXrw4mJLcwoUL3RtvvOF+/fXXYErWJcrve2Zt27bNde3a1XXp0iWYknX7Y5kikr0o2CcikoP8+eef7oYbbnCnn366e+KJJ9zXX3/tpk2b5h588EHXunVrN27cOJuPSlzv3r3dlVdeae/JbghK0mDYvn17MCXrtmzZ4n766Sf3999/B1NyJsrk559/dv/3f/8XTBEREckevvzyS3fFFVe4d9991y4+RuM5gT4uRC5dujSYmnVZ+X1/4YUX3JlnnumWLVsWTMm+UltX77377bff7LGv7I9l7m8EmP/1r3+50aNHB1Mybl8sQySnULBPRCQH+fDDD93IkSPtSux3333nPv/8c6t4U1n79NNPXYMGDWy+nTt3uh9++MGtXLlyr8p4djBgwAC3du1ay+4TERER2R/IMPzmm2+y5YXPWKmta9GiRd3HH3/sJk2aFEzJuv2xzP2NC9mUT1YClPtiGSI5RZ47I4K/RUQkGyNo99JLL9lVyQceeMDVrFkzeMW53Llzu6pVq7py5cpZoI953nvvPbdjxw537LHHuvXr17vNmze78uXLB+9wVtF5//33rfsvmWQVKlRwhQsXDl51bteuXW758uVu48aNrlixYm7mzJlu/PjxdoX96KOPtnn++OMPN3nyZDdhwgSrPJUoUcKVKlXKXksL67NixQpXvHhxlz9/flsO65w3b17LbAvXiwzAKlWquDx58gTv3I11I9DJ+nz//ffusMMOs88ny/HCCy90RxxxRDDn7nm/+uorm3fOnDlWPpUqVbIyA0FHtrNgwYLJtp/1IDOB7S9dunTS/NF4b/R2RCMzb8mSJbbuVKrBdk6fPt0q2ARp2V72Sa5cuex1kPH42muvuUaNGrkmTZrYtFWrVtk+omyjy4KGQUqfn942h8iU4DihrClHlhPP/pP9j/3xyiuvBM/2uPnmm12hQoWCZyIiBxYXGt955x3XuHFj16xZs2S/X2SLceHxiy++cBdccIE78sgjk/2+E2jhN4ffwf/973+ucuXKe/0uZeT3nd9FglXUQ7i4WbZsWauvhH788Uf3ySefWC8IugETRFu3bp39JvObj3h/L1OTkboUn8t2cOF2zZo1tv3heqS1rvy+f/vtt+7333+3bUR0uZLxGJYr+4D1Z7+wbuF0nlesWDFpf1GnjF5m+JwyZ92iH9R1qHtEl2169b/o7U6tDhmL9yxYsMDmmzVrlvvvf/9r5UkZUYedPXu2++ijj+z9fB7rxXrzN9KrY8WzDLAf2accy+wDyjPcTyE+i2OPZXH8sAzWNd7jRuSAiJwQREQkB4hUgvxtt93mI5VI/+KLLwZT9/bLL7/4E0880XOKj35EKo/BHN6PHTvWRyp9Pl++fL5MmTL2f9WqVf24ceOCObzfsGGDb9q0qW/UqJFv1aqVfW65cuV8nz597PVIhdXXqVPHRypTPlJRtNeLFy/uhw0b5nfs2GHzpGbo0KE+Uinyc+fOteefffaZLaNnz56+Ro0avmTJkj5S8fKRyqXv3Lmzj1QObT7wN/OxzpEKpM1brVo1361bN1tGpKIXzLm7LDp16mTzsjzm5e8OHTr49evX2zzz58+395977rk+Uim1aZQ168h7RowYYc9TEmnw2DKffvrpYMoelEPp0qV9pDJozyMVYitzyosyp7xYlyuuuMJv3brV5sG8efOsbIYMGWLP+ezLL7/c16xZ00caAzYt9Oijj/pIQ8hHGkbBlPi2Gc8//7yPVMx9pBHhI5VhX6BAAVunkSNHBnPIwcTxEvsd5hEeoyIiB8P7779vv/V33XXXXr+NPB84cKD99k2ZMsWmhb/vl112mf3W8pvEbze/S/yWZ+b3nc+56aabfKFChey3i7oJ72Gel19+OWmevn37+ly5ciU7h/K7OHHiRJsn3t/L1GS0LtWyZUv7naV8qN/Uq1fPL168ON11pVxatGhhj1BYrj169PDHHHOMrXvRokXtN/3222/3H3zwga0Lv/NFihSxsnrooYeS9lnsMqmHnHXWWck+P3zkzp07qU6CeOp/6dUhY7E+3bt3tzJk31OelFH16tX9kiVL7Lhj+2LXa8CAAfb+eOpY6S1j+/bttg2UZcGCBZOOK+qlM2bMsHmwaNEiX7duXVs/Pov15e+LLrrIb9u2LZhL5OBTsE9EJAehgkVghsrK+eef7x955BELmFFBCe3cudMvXbrUKlinnXaa//rrr/3y5cv96tWr7fUFCxb4ww8/3Dds2NAqmSDgdcIJJ/ijjz7aKlUIK2pUnKik//rrrzadz2J5tWvX9o0bN/ZfffWVTScIceWVV1rFh0pXWlIK9lH55UGwic/4448//HXXXWeVqLfeesvmw8MPP2wV15tvvtkqcH///bctj2m8P2wM/PXXX9ZA4HOo/LNMyuall16yCtwNN9xgzzFq1CirVFNBZtonn3xiQTS2O62K27p166yyTqCQ9Q1t2rTJn3HGGVZ+lCMI4r3yyiv2Glj3wYMHW6XyhRdesGnISrAv3m1eu3at7W8aHj/99JO9l3L88MMP/aeffmrP5eBSsE9EsqPMBPt4Tr2FQAq/Nfz+EaxjGvWYULy/73zOq6++assOg0vff/+9b926tQW+li1bZtOot9x4441WbyIAR92F+fhdz0gdISUZrUsRDGrfvr39/rL+XCwkOHXeeedZoCutdU0t2EeZ8OACMOXw22+/+TZt2li9iTraG2+8YdtAXYX3EjhbsWKFvT92mazTmjVr7HPDB/XHtm3bWhlRLwLT46n/pVWHTMl7771n9SGOK/YN2B72zcqVK62ORZkRXOTCN+vx7bff2jyIp46V3jL4LOYn8Pr777/bNMqgQYMGtp8pR8rpqquu8lWqVLEy4TkPjoHRo0cnrbtIdqBgn4hIDkKF4qOPPrJAHlcbafxzJZhKBxXiMDC1efNm37x5c7tKS4UnxPsHDRpklRkCO9G4Qk0FMQwyhRU1Ks5UhqI98MADVhkm+BiNYNSxxx5rwam0KskpBfuoJHJFPzorkIpkpUqVLAgH1qlJkya2/WHlDFTg/v3vfydrDMyZM8c+g+1lu0Msv1evXv64445LFui69tpr7f2PP/64ZUHyiA2uxWK5NGz4HD4vRMCM7aGc0kLllwok2YtheWUl2BfvNlPB5Yr/rbfemmw+yT4U7BOR7CizwT5+o6MvivEbfuqpp9pFSc5rGfl9Tw3BF+omBH1C999/vwXVCM5Fy0gdIRbzZ7QuVblyZb9w4UKbhrCsCDxRRkhtXVML9qVUbyIISlba9ddfn2y7nnnmGVtf6pBIaZnRWOYdd9xhn0EANBRv/S+tOmRK6G3AZ6V1wXH27NlW5xk+fHgwJW0p1bFSWwb1ZsqCdQ4DfaE333zT1o1AIeXSrl07O3Zj5xPJbtSpXEQkB2EsEO66y3g2jNUWqVTaXe8ilQ9322232V15I5W7YO69RSo7NsZLpILrIhXZYOpuJ510ko1Nt2jRomTLYHycSOUzeLZ7GYzHg0gFyN1yyy02jhiPRx55xMbkWb16dYbvmseYKqxT3rx5gynOxpBjfLLwzrSRBoCNM8e6RipeNg1FihRxp5xySrKxUribHeOzMO7erbfemrSe/M3NSyIVURurBgUKFLDpxx9/vLvuuutsjJpIRT3Z2EAp4fPOOeccG6tlwoQJ9j+PDz74wMZ3Ofvss4M5d49Fw3g83CWZO8FFGjTu3HPPtfGPGJcmo+WVkni3OVLRtTEfn376aXfttdfauuvuvyIisj/wW9mwYUP7rQ7xG163bl37Tec3MCO/7+A365lnnnGdOnWysQNPPfVUd/3119tYv4wLnJ6M1BFiZaYudcwxxySrU7A9bBdjFzJOXmakVG8qWbKk1T+YHl1mrBP++OMP+z8t1GO4GdwTTzzhbrrpJte5c2ebnpn6X2wdMjV16tSxsQn79OljdVk+h8+LV1brWL/++qvtB7Zh6NChSdvG9r///vu2n9g2ypr9xtiKl156qY2lzXSR7EjBPhGRHIoKFINVP/nkkxas4UYWb7/9tt3gITVUnP766y+rPEff0AEEvKggMii09yQR7cb0fPnyBc+cVXioDDHf9OnT3cSJEx2DIfOYNm2aDXJ8+OGHB3NnTPTngIosj3B9qKxRoeMzYsVO27Jli81LhYx1DNeTAZU3btxowa7oCjKNCwbF5vOodNarVy94JW00Vk488URbPg0DBntm0GoaNrVr17Z5qDg/9thjrm3btlZmBNtoEPB5rDfBWubJqni3mcbAf/7zH1uf119/3SrEDBR+8cUXWyNHREQkJXny5LHfyehAVrRwemxwLrXfbX7/+G3PyO87QbrLLrvMLs7xm0sgrUGDBkmBL+op6cloHSFaZupSXLiMnZf3U57UqTIrtt4E9k9q6x6PcePGOe7h2a1bNytj1hGZqf/F1iFTQ53pueees/rRXXfd5U4++WRXrVo1d99996VbPvuijsX+ZPuoQ7M90ccDxwjHGBegQUCS8iE42LNnT3uN9eVCr0h2omCfiEgC4I67ZKVxpZur2qmholmsWDGr5FKxiUZligeBICqKqWEZVHgIDnG1c+HChXs9uCIcfQV/XylatKhVHLlKG12JphJHoC16GneFo8JNRTGldZw6daqVWeidd95xL7zwglUQuaL86KOPptqYiUZ5tmnTxq4ez58/37IuyU44//zz7fNBxsKYMWPcGWec4ebNm2d3233qqafc/fffb+WYHhpNbFv09oHGUWa3mTvRsb00lObOneuuvvpqq9zecMMNWWp4iIhI4uL3n+ANv8Ox+D3i9456QmyAjt/o6IAL8zKN3yzqCxn5feeOqp999pn91nGXV37LyEK74oorbBnxyGgdIVpm6lIpzUvQknoG82YXlCcZki1atHCDBw9OVp77s/5HPYeeEuEdcLmLMAFc9s+oUaOCuVKW1ToWOP44Hug9Qx0wdru4u2+PHj1sXraPjL/w7sWPP/647fNrrrnGMjpFsgsF+0REchCuOG7fvj14tgdBPio7VFZ4cEWXyjjzckUzxHQqT3RxmTNnTjB1txkzZrhNmzbZ6+FV3JSwjCZNmtgyuIJ6IHG1liAVQTWCVCG2n3WJbgyccMIJ1tigQppe940lS5a4O+64w64CE/SjywoNhw8//DCYI210GaHhQGYl76fLCt2KQlTwqegfddRRSVeGQfchumOnp2zZsvb+6MYVV9bZh5nd5hCVd65IUylmO8jsowEiIiISi2wrfuP4HabeEY06yldffWXZXdFBFgJ2BOeih4vgN3zBggX2m16+fPkM/b5TV0GNGjWSMgj5DObjtzIaARwy8WJ/EzPzexnKTF2KC4J0HQ6xTgS2CByRRYjU1vVAYf0YGqZ69eru4Ycf3itge6DqfwQ/zzvvPOtOy7FBXQkEHtnfBNaiA8cZqWOltoywS3Y4TE68+D5ceeWV7sYbb7RjddWqVcErIgefgn0iIjkElRK6KTRt2tQCUd98841VtKlo9uvXz646tmrVylWqVMm6i9CtgDFlRowYYVc5GcsEZJxRER80aJAFp6hUc0X03nvvtYozXTrT07FjR7viPXDgQFs+lZuff/7ZKvOMHchYLvsDV9K7dOliASky0Ni+5cuX27gqNDCike1I0I7gW9++fe1KLRVUrtByxZcr1gTMqJSzHXTfYJwYyobAX61atWzsHiro6aGBwn7hs+hS3bx582RdWQgEVq1a1YKHXK3mqjXdPdhvsQ2TWFRKGYuIyuzw4cNte1mn22+/3brNRIt3mzl2GKuP+VgW83As0PA68sgjs1WWgYiIZB8E+i655BILpNDFk0wq6iJk2/G7wm8Kv9MEaaLxG81vLdlQ/HbzG75y5UrXtWtX+23PyO87v1NcqGLMPrpYMmbaQw89ZMNTxOL3mQufZF+NHj3afuv4zYv39zI1Ga1L8VvPthAkI/uf3/NXX33VnXXWWcky7lNa1wOBrsvsPz7vzDPPtMxG6o48GB86DFTur/ofn0Gdi/JheRwbjIfHejG0CggIlytXzr311luWQch72F8ZqWOltgyCrvRw4PPI4OP9LIfj+d1337VjneORICF1RIbQ4X2M9cd3gCFRCDQS/BPJNryIiOQYkYqf3QGsQIECXOK2R65cuewOa/379092p07uuNqmTRtfokQJnzt3bs/dZUOzZs2y50xnGfzPHfDmz58fzLHnDnLnnHOO/+uvv4Kpe0Qq5D5S6Uu2Lnny5LHlTpw4MZgrZSndjZc70j3xxBP2PBSp7Nmd3Pr06RNM2X3n3HvuucfuBhiuO3fwizQObBmRimIwp7e7EzMvd46jnMLyqly5so9UyH2kIm93m6OMXnzxxeBdu82cOdPmYxsjlcVgaurYN5HKoi0rpe2PVJx99erVk9aDeblbXrNmzezOyeFnxN6NF5R/pJHgCxUqlFTO3Gn5pptuSnY3XqS3zZGGhF+yZImPVJ6T9n+4zDPOOMNek4NPd+MVkeyK36T77rtvr98Zfru4yym/06Hw933AgAH2Wx3+7vAbzm9S9Lzx/r7v2LHD7lxbrFixpM+uVq2arRPrEH0n/K1bt9pdb/kN5Hcu+jc6nt/LtGSkLsXv/BVXXOELFixo8+bNm9e3bdvWr1mzJpgz9XWlfhB759zU6k3cbbdUqVL+5ZdfDqbsxh1lqSu+8cYb9jx2matWrbI6CusW+2C7ousk8dT/0qtDxuJuvGxvuB94UE/q16+f3Sk3xHbUrVvXPpv14rhCvHUspLYMfPDBB/6EE05I2qfhsi644AK7OzPHTJcuXXy+fPmSXuczq1ataneB3hV1B2SRgy0X/0QOUhERyUG4SxxXxxmfjzuscSMIuu9mBOPEcKWWq5JcqacbSaSyFrwaP64Csy68ly4UdIWIVJKCV/cfPperrGw/V33pXpIarsSShcBd6CKV6KSsgAON9eBKMF10WOd47lAXjewFsh64Mk3mYVr7K71tJlOU5XHnYTIJeJ3Hgdh3kr5Ig8nGDopF1gFZDCIiB1uYKU6GPOclukHSFTUa3Vrbt29vNzRgTD3m5zzGbxj1hZTE+/tOt2HqMfRm4GZZRTIxVhyyUkdIry7FtnLjCLK+yNQjC5LfcbadeXPyb+6+rv9RN6ILLWXEUDTUbcnCi1dW61gh9inrQbYmPR3oKVMipkszw6qQ1UjXXY5TMkVjj32Rg03BPhEREZFshrE2U+pCRoMxVxo30BERyU6ig33cwOBQExvsU0BIRA4UXb4XERERyWbIaiCTIPahQJ+IiIiIpEeZfSIiIiIiIrLP0dV2/PjxrnHjxnYH3EMNw2Rw0wi6BXOH2cwMlyIikhkK9omIiIiIiIiIiCQIdeMVERERERERERFJEAr2iYiIiIiIiIiIJAgF+0RERERERERERBKEgn0iIiIiIiIiIiIJQsE+ERERERERERGRBKFgn4iIiIiIiIiISIJQsE9ERERERERERCRBKNgnIiIiIiIiIiKSIBTsExERERERERERSRAK9omIiIiIiIiIiCQE5/4f3R0GjKku4+0AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "4da0801c",
   "metadata": {
    "papermill": {
     "duration": 0.035667,
     "end_time": "2022-04-09T15:30:09.103742",
     "exception": false,
     "start_time": "2022-04-09T15:30:09.068075",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Scheme of 8-bit optimizer\n",
    "\n",
    "![image.png](8_bit_optimizer_scheme.png)"
   ]
  },
  {
   "attachments": {
    "2ec4f196-0d7a-4b62-a586-43010c292cad.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "5da5fa16",
   "metadata": {
    "papermill": {
     "duration": 0.035727,
     "end_time": "2022-04-09T15:30:09.175442",
     "exception": false,
     "start_time": "2022-04-09T15:30:09.139715",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Dynamic tree quantization\n",
    "\n",
    "![image.png](Dynamic_tree_quantization.png)\n",
    "\n",
    "First bit is sign bit. The number of subsequent zero bits indicates the magnitude of exponent.\n",
    "The first bit set to one indicates, that all following values are reserved for linear quantization.\n",
    "Compared to linear quantization, dynamic tree quantization has better absolute and relative quantization errors for non-uniform distributions.\n",
    "\n",
    "This quantization is defined for numbers in range [-1, 1], that's why we normalize, using absmax."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5091b5",
   "metadata": {
    "papermill": {
     "duration": 0.036079,
     "end_time": "2022-04-09T15:30:09.248740",
     "exception": false,
     "start_time": "2022-04-09T15:30:09.212661",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Stable embedding layers\n",
    "\n",
    "Authors propose stable embedding layers. For that, they \n",
    "\n",
    "1. use Xavier initialization\n",
    "2. add a LayerNorm before adding position embeddings\n",
    "\n",
    "This allows to avoid big gradient variations and to quantize more aggressively.\n",
    "\n",
    "They use 32-bit dequantized statistics to update embedding layer, and the rest is updated in standard precision (usually 16 bit)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf5979e",
   "metadata": {
    "papermill": {
     "duration": 0.03591,
     "end_time": "2022-04-09T15:30:09.320816",
     "exception": false,
     "start_time": "2022-04-09T15:30:09.284906",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8899cd29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:30:09.403754Z",
     "iopub.status.busy": "2022-04-09T15:30:09.403008Z",
     "iopub.status.idle": "2022-04-09T15:30:11.474581Z",
     "shell.execute_reply": "2022-04-09T15:30:11.473969Z",
     "shell.execute_reply.started": "2022-04-09T14:20:34.915276Z"
    },
    "papermill": {
     "duration": 2.118066,
     "end_time": "2022-04-09T15:30:11.474731",
     "exception": false,
     "start_time": "2022-04-09T15:30:09.356665",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import datasets\n",
    "import transformers\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import gc\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = 10, 7\n",
    "\n",
    "torch.manual_seed(21)\n",
    "np.random.seed(21)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e80a86ed",
   "metadata": {
    "papermill": {
     "duration": 0.036123,
     "end_time": "2022-04-09T15:30:11.546954",
     "exception": false,
     "start_time": "2022-04-09T15:30:11.510831",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Install bitsandbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffb62960",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:30:11.628500Z",
     "iopub.status.busy": "2022-04-09T15:30:11.627409Z",
     "iopub.status.idle": "2022-04-09T15:30:11.631918Z",
     "shell.execute_reply": "2022-04-09T15:30:11.631483Z",
     "shell.execute_reply.started": "2022-04-09T14:20:35.753271Z"
    },
    "papermill": {
     "duration": 0.047411,
     "end_time": "2022-04-09T15:30:11.632045",
     "exception": false,
     "start_time": "2022-04-09T15:30:11.584634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'11.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.version.cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78be7461",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:30:11.709456Z",
     "iopub.status.busy": "2022-04-09T15:30:11.708631Z",
     "iopub.status.idle": "2022-04-09T15:30:21.091632Z",
     "shell.execute_reply": "2022-04-09T15:30:21.092386Z",
     "shell.execute_reply.started": "2022-04-09T14:20:35.763326Z"
    },
    "papermill": {
     "duration": 9.424209,
     "end_time": "2022-04-09T15:30:21.092603",
     "exception": false,
     "start_time": "2022-04-09T15:30:11.668394",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bitsandbytes-cuda110\r\n",
      "  Downloading bitsandbytes_cuda110-0.26.0-py3-none-any.whl (3.3 MB)\r\n",
      "     |████████████████████████████████| 3.3 MB 923 kB/s            \r\n",
      "\u001b[?25hInstalling collected packages: bitsandbytes-cuda110\r\n",
      "Successfully installed bitsandbytes-cuda110-0.26.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "# Change last three digits if your cuda version is different\n",
    "!pip install --user bitsandbytes-cuda110"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86008eff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:30:21.234490Z",
     "iopub.status.busy": "2022-04-09T15:30:21.233691Z",
     "iopub.status.idle": "2022-04-09T15:30:26.530250Z",
     "shell.execute_reply": "2022-04-09T15:30:26.531234Z",
     "shell.execute_reply.started": "2022-04-09T14:20:43.128943Z"
    },
    "papermill": {
     "duration": 5.371016,
     "end_time": "2022-04-09T15:30:26.531423",
     "exception": false,
     "start_time": "2022-04-09T15:30:21.160407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-04-09 15:30:21--  https://gist.githubusercontent.com/TimDettmers/1f5188c6ee6ed69d211b7fe4e381e713/raw/4d17c3d09ccdb57e9ab7eca0171f2ace6e4d2858/check_bnb_install.py\r\n",
      "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\r\n",
      "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.110.133|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 327 [text/plain]\r\n",
      "Saving to: ‘check_bnb_install.py’\r\n",
      "\r\n",
      "check_bnb_install.p 100%[===================>]     327  --.-KB/s    in 0s      \r\n",
      "\r\n",
      "2022-04-09 15:30:22 (12.9 MB/s) - ‘check_bnb_install.py’ saved [327/327]\r\n",
      "\r\n",
      "SUCCESS!\r\n",
      "Installation was successful!\r\n"
     ]
    }
   ],
   "source": [
    "# Check if installation is correct\n",
    "!wget https://gist.githubusercontent.com/TimDettmers/1f5188c6ee6ed69d211b7fe4e381e713/raw/4d17c3d09ccdb57e9ab7eca0171f2ace6e4d2858/check_bnb_install.py && python check_bnb_install.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4e422b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:30:26.622284Z",
     "iopub.status.busy": "2022-04-09T15:30:26.620206Z",
     "iopub.status.idle": "2022-04-09T15:30:26.630636Z",
     "shell.execute_reply": "2022-04-09T15:30:26.630186Z",
     "shell.execute_reply.started": "2022-04-09T14:20:46.641104Z"
    },
    "papermill": {
     "duration": 0.056696,
     "end_time": "2022-04-09T15:30:26.630749",
     "exception": false,
     "start_time": "2022-04-09T15:30:26.574053",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import bitsandbytes as bnb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e946954f",
   "metadata": {
    "papermill": {
     "duration": 0.041751,
     "end_time": "2022-04-09T15:30:26.714712",
     "exception": false,
     "start_time": "2022-04-09T15:30:26.672961",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Load model and tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cefd7b82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:30:26.805514Z",
     "iopub.status.busy": "2022-04-09T15:30:26.804812Z",
     "iopub.status.idle": "2022-04-09T15:32:03.802748Z",
     "shell.execute_reply": "2022-04-09T15:32:03.802236Z",
     "shell.execute_reply.started": "2022-04-09T14:20:46.655762Z"
    },
    "papermill": {
     "duration": 97.04508,
     "end_time": "2022-04-09T15:32:03.802891",
     "exception": false,
     "start_time": "2022-04-09T15:30:26.757811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f3478a16cc44ae19bcdbd0ffe71f9f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/666 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fbacfd5e9af477c8c8907ec70f43f48",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/0.99M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "992b67621e0a4a52ad04773e0508421d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5360ad57092404c93a11eaf67391518",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a14ebd504104cd793004b0d9ee858c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/3.02G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = 'gpt2-large'\n",
    "tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\n",
    "model = transformers.GPT2LMHeadModel.from_pretrained(model_name).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee3c4e9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:03.906482Z",
     "iopub.status.busy": "2022-04-09T15:32:03.904747Z",
     "iopub.status.idle": "2022-04-09T15:32:03.907108Z",
     "shell.execute_reply": "2022-04-09T15:32:03.907560Z",
     "shell.execute_reply.started": "2022-04-09T14:21:01.848916Z"
    },
    "papermill": {
     "duration": 0.058429,
     "end_time": "2022-04-09T15:32:03.907699",
     "exception": false,
     "start_time": "2022-04-09T15:32:03.849270",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# There is no pad token by default\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "# To save memory \n",
    "model.gradient_checkpointing_enable()\n",
    "\n",
    "# doesn't work with gradient checkpointing\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5780a9",
   "metadata": {
    "papermill": {
     "duration": 0.044463,
     "end_time": "2022-04-09T15:32:03.996691",
     "exception": false,
     "start_time": "2022-04-09T15:32:03.952228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Prepare experiments configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2e105b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:04.092070Z",
     "iopub.status.busy": "2022-04-09T15:32:04.091207Z",
     "iopub.status.idle": "2022-04-09T15:32:04.092976Z",
     "shell.execute_reply": "2022-04-09T15:32:04.093416Z",
     "shell.execute_reply.started": "2022-04-09T14:21:01.864273Z"
    },
    "papermill": {
     "duration": 0.052047,
     "end_time": "2022-04-09T15:32:04.093541",
     "exception": false,
     "start_time": "2022-04-09T15:32:04.041494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class DotDict(dict):\n",
    "    \"\"\"dot.notation access to dictionary attributes\"\"\"\n",
    "    __getattr__ = dict.__getitem__\n",
    "    __setattr__ = dict.__setitem__\n",
    "    __delattr__ = dict.__delitem__\n",
    "\n",
    "\n",
    "config = DotDict({\n",
    "    \"batch_size\": 8,\n",
    "    \"minibatch_size\": 2,\n",
    "    \"test_batch_size\": 16,\n",
    "    \"device\": \"cuda:0\",\n",
    "    \"n_steps\": 32,\n",
    "    \"lr\": 3e-6,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93006841",
   "metadata": {
    "papermill": {
     "duration": 0.061288,
     "end_time": "2022-04-09T15:32:04.199362",
     "exception": false,
     "start_time": "2022-04-09T15:32:04.138074",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e51bb9b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:04.373050Z",
     "iopub.status.busy": "2022-04-09T15:32:04.372187Z",
     "iopub.status.idle": "2022-04-09T15:32:26.114340Z",
     "shell.execute_reply": "2022-04-09T15:32:26.113807Z",
     "shell.execute_reply.started": "2022-04-09T14:21:01.878990Z"
    },
    "papermill": {
     "duration": 21.829482,
     "end_time": "2022-04-09T15:32:26.114475",
     "exception": false,
     "start_time": "2022-04-09T15:32:04.284993",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cf80a2aed66411bbedf211fca36051c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/2.03k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b62d8af479b4817ae01f92fe174e0a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.25k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset wikitext/wikitext-2-v1 (download: 4.27 MiB, generated: 12.71 MiB, post-processed: Unknown size, total: 16.97 MiB) to /root/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1921df77192e4d6a9e7468d42abe0c53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c1da07a0b1547728fd69f248ba8e645",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83feeeb066f6485797a70bf40c8ee20e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e57fa7efd3a34363919e0aac85d1aa76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset wikitext downloaded and prepared to /root/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea263df5c45943e8bcabd66bc98f9dc3",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c97fbba858c84eaf8b71925adf55f908",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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     },
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    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    test: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 4358\n",
       "    })\n",
       "    train: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 36718\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 3760\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = datasets.load_dataset(\"wikitext\", \"wikitext-2-v1\")\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    \"\"\"\n",
    "    Tokenizes inputs and adds labels for causal language modeling task.\n",
    "    \"\"\"\n",
    "    tokenized = tokenizer(examples['text'], truncation=True, max_length=512, padding=True, pad_to_multiple_of=512, return_tensors='np')\n",
    "    eos_id = tokenizer(tokenizer.eos_token)[\"input_ids\"][0]\n",
    "    last = np.full((tokenized['input_ids'].shape[0],1), eos_id)\n",
    "    tokenized['labels'] = np.concatenate((tokenized['input_ids'][:, 1:], last), axis=1)\n",
    "    return tokenized\n",
    "\n",
    "processed_data = data.map(tokenize_function, batched=True)\n",
    "processed_data = processed_data.remove_columns(['text'])\n",
    "processed_data.set_format('torch')\n",
    "\n",
    "processed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a62d904d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:26.223522Z",
     "iopub.status.busy": "2022-04-09T15:32:26.222782Z",
     "iopub.status.idle": "2022-04-09T15:32:31.384705Z",
     "shell.execute_reply": "2022-04-09T15:32:31.384232Z",
     "shell.execute_reply.started": "2022-04-09T14:21:02.432914Z"
    },
    "papermill": {
     "duration": 5.218063,
     "end_time": "2022-04-09T15:32:31.384836",
     "exception": false,
     "start_time": "2022-04-09T15:32:26.166773",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "collator = transformers.DataCollatorWithPadding(tokenizer)\n",
    "\n",
    "minibatch_size = config.minibatch_size\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(processed_data['train'], shuffle=True, batch_size=minibatch_size, collate_fn=collator)\n",
    "val_loader = torch.utils.data.DataLoader(processed_data['validation'], batch_size=minibatch_size, collate_fn=collator)\n",
    "\n",
    "# Take several examples and fix them. Will be used to track the model progress\n",
    "test_loader = torch.utils.data.DataLoader(processed_data['test'].select(range(config.test_batch_size)), batch_size=minibatch_size, collate_fn=collator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d58a925d",
   "metadata": {
    "papermill": {
     "duration": 0.050386,
     "end_time": "2022-04-09T15:32:31.485501",
     "exception": false,
     "start_time": "2022-04-09T15:32:31.435115",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Train loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af0115de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:31.597096Z",
     "iopub.status.busy": "2022-04-09T15:32:31.595421Z",
     "iopub.status.idle": "2022-04-09T15:32:31.597677Z",
     "shell.execute_reply": "2022-04-09T15:32:31.598108Z",
     "shell.execute_reply.started": "2022-04-09T14:21:04.282341Z"
    },
    "papermill": {
     "duration": 0.06281,
     "end_time": "2022-04-09T15:32:31.598252",
     "exception": false,
     "start_time": "2022-04-09T15:32:31.535442",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_one_epoch(model, opt, train_loader, test_loader, config):\n",
    "    update_freq = config.batch_size // config.minibatch_size\n",
    "\n",
    "    model.train()\n",
    "\n",
    "    test_batch_losses = []\n",
    "\n",
    "    for step, batch in enumerate(tqdm(train_loader, desc=\"Batch\")):\n",
    "        batch = batch.to(config.device)\n",
    "\n",
    "        # Use amp to speed up the computations\n",
    "        with torch.cuda.amp.autocast():\n",
    "            outputs = model(**batch)\n",
    "            \n",
    "            # It we were not using gradient accumulation, the loss would be scaled by batch_size during averaging.\n",
    "            # But now it is scaled by minibatch_size, so we should adjust for that before backward.\n",
    "            (outputs.loss / update_freq).backward()\n",
    "\n",
    "        if step % update_freq == 0 and step != 0:\n",
    "            opt.step()\n",
    "            opt.zero_grad()\n",
    "\n",
    "            with torch.no_grad():\n",
    "                loss = 0\n",
    "\n",
    "                with torch.cuda.amp.autocast():\n",
    "                    for test_batch in test_loader:\n",
    "                        test_batch = test_batch.to(config.device)\n",
    "                        outputs = model(**test_batch)\n",
    "                        loss += outputs.loss.item() / update_freq\n",
    "\n",
    "                print(f\"Loss on test batch: {loss:.3f}\")\n",
    "                test_batch_losses.append(loss)\n",
    "\n",
    "\n",
    "        # Stop after predefined number of steps if n_steps is specified\n",
    "        if config.n_steps is not None and step == config.n_steps:\n",
    "            break\n",
    "    \n",
    "    return test_batch_losses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c4e011",
   "metadata": {
    "papermill": {
     "duration": 0.05106,
     "end_time": "2022-04-09T15:32:31.699566",
     "exception": false,
     "start_time": "2022-04-09T15:32:31.648506",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Regular Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d09e739d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:31.807458Z",
     "iopub.status.busy": "2022-04-09T15:32:31.806850Z",
     "iopub.status.idle": "2022-04-09T15:32:31.810723Z",
     "shell.execute_reply": "2022-04-09T15:32:31.810320Z",
     "shell.execute_reply.started": "2022-04-09T14:21:04.295084Z"
    },
    "papermill": {
     "duration": 0.061067,
     "end_time": "2022-04-09T15:32:31.810837",
     "exception": false,
     "start_time": "2022-04-09T15:32:31.749770",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "opt = torch.optim.Adam(model.parameters(), lr=config.lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "694f082d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:32.086408Z",
     "iopub.status.busy": "2022-04-09T15:32:32.085571Z",
     "iopub.status.idle": "2022-04-09T15:32:33.727451Z",
     "shell.execute_reply": "2022-04-09T15:32:33.727881Z",
     "shell.execute_reply.started": "2022-04-09T14:21:04.311408Z"
    },
    "papermill": {
     "duration": 1.867348,
     "end_time": "2022-04-09T15:32:33.728086",
     "exception": false,
     "start_time": "2022-04-09T15:32:31.860738",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/cuda/memory.py:263: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "gc.collect()\n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "torch.cuda.reset_max_memory_allocated()\n",
    "torch.cuda.synchronize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e90dc074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:32:33.853305Z",
     "iopub.status.busy": "2022-04-09T15:32:33.852353Z",
     "iopub.status.idle": "2022-04-09T15:33:41.963881Z",
     "shell.execute_reply": "2022-04-09T15:33:41.964488Z",
     "shell.execute_reply.started": "2022-04-09T14:21:04.893001Z"
    },
    "papermill": {
     "duration": 68.184703,
     "end_time": "2022-04-09T15:33:41.964679",
     "exception": false,
     "start_time": "2022-04-09T15:32:33.779976",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f53775e5cda64a869d8c5094855a0ab9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batch:   0%|          | 0/18359 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss on test batch: 18.042\n",
      "Loss on test batch: 15.228\n",
      "Loss on test batch: 12.785\n",
      "Loss on test batch: 10.727\n",
      "Loss on test batch: 8.937\n",
      "Loss on test batch: 7.367\n",
      "Loss on test batch: 6.225\n",
      "Loss on test batch: 5.477\n",
      "CPU times: user 51.6 s, sys: 15.6 s, total: 1min 7s\n",
      "Wall time: 1min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "test_batch_losses = train_one_epoch(model, opt, train_loader, test_loader, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39da5003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:33:42.081224Z",
     "iopub.status.busy": "2022-04-09T15:33:42.080603Z",
     "iopub.status.idle": "2022-04-09T15:33:42.085141Z",
     "shell.execute_reply": "2022-04-09T15:33:42.084695Z",
     "shell.execute_reply.started": "2022-04-09T14:22:12.145036Z"
    },
    "papermill": {
     "duration": 0.065132,
     "end_time": "2022-04-09T15:33:42.085265",
     "exception": false,
     "start_time": "2022-04-09T15:33:42.020133",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peak memory usage: 14.0 Gb\n"
     ]
    }
   ],
   "source": [
    "naive_peak_memory_gb = torch.cuda.max_memory_allocated() / 2 ** 30\n",
    "print(f\"Peak memory usage: {naive_peak_memory_gb:.1f} Gb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "799aa400",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:33:42.201826Z",
     "iopub.status.busy": "2022-04-09T15:33:42.200913Z",
     "iopub.status.idle": "2022-04-09T15:33:42.435436Z",
     "shell.execute_reply": "2022-04-09T15:33:42.434983Z",
     "shell.execute_reply.started": "2022-04-09T14:22:12.152932Z"
    },
    "papermill": {
     "duration": 0.29504,
     "end_time": "2022-04-09T15:33:42.435561",
     "exception": false,
     "start_time": "2022-04-09T15:33:42.140521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(test_batch_losses)), test_batch_losses)\n",
    "plt.xlabel(\"Number of updates\")\n",
    "plt.ylabel(\"Mean cross-entropy\")\n",
    "plt.title(\"Mean cross-entropy on test batch\", fontsize=18)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8cca4e62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:33:42.735982Z",
     "iopub.status.busy": "2022-04-09T15:33:42.734300Z",
     "iopub.status.idle": "2022-04-09T15:33:42.738121Z",
     "shell.execute_reply": "2022-04-09T15:33:42.737652Z",
     "shell.execute_reply.started": "2022-04-09T14:22:12.374512Z"
    },
    "papermill": {
     "duration": 0.246448,
     "end_time": "2022-04-09T15:33:42.738244",
     "exception": false,
     "start_time": "2022-04-09T15:33:42.491796",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3325"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del model, opt\n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0542910",
   "metadata": {
    "papermill": {
     "duration": 0.055648,
     "end_time": "2022-04-09T15:33:42.850338",
     "exception": false,
     "start_time": "2022-04-09T15:33:42.794690",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 8 bit Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2992fc2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:33:42.971424Z",
     "iopub.status.busy": "2022-04-09T15:33:42.970782Z",
     "iopub.status.idle": "2022-04-09T15:33:55.857838Z",
     "shell.execute_reply": "2022-04-09T15:33:55.856877Z",
     "shell.execute_reply.started": "2022-04-09T14:22:12.578131Z"
    },
    "papermill": {
     "duration": 12.951404,
     "end_time": "2022-04-09T15:33:55.858008",
     "exception": false,
     "start_time": "2022-04-09T15:33:42.906604",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = transformers.GPT2LMHeadModel.from_pretrained(model_name)\n",
    "\n",
    "model.gradient_checkpointing_enable()\n",
    "model.config.use_cache = False\n",
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f699d1b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:33:55.978808Z",
     "iopub.status.busy": "2022-04-09T15:33:55.977982Z",
     "iopub.status.idle": "2022-04-09T15:33:55.992283Z",
     "shell.execute_reply": "2022-04-09T15:33:55.991795Z",
     "shell.execute_reply.started": "2022-04-09T14:22:23.806534Z"
    },
    "papermill": {
     "duration": 0.078259,
     "end_time": "2022-04-09T15:33:55.992418",
     "exception": false,
     "start_time": "2022-04-09T15:33:55.914159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "opt = bnb.optim.Adam8bit(model.parameters(), lr=config.lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40531f4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:33:56.544946Z",
     "iopub.status.busy": "2022-04-09T15:33:56.543824Z",
     "iopub.status.idle": "2022-04-09T15:33:56.545574Z",
     "shell.execute_reply": "2022-04-09T15:33:56.545994Z",
     "shell.execute_reply.started": "2022-04-09T14:22:23.816701Z"
    },
    "papermill": {
     "duration": 0.49802,
     "end_time": "2022-04-09T15:33:56.546143",
     "exception": false,
     "start_time": "2022-04-09T15:33:56.048123",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()\n",
    "torch.cuda.reset_max_memory_allocated()\n",
    "torch.cuda.synchronize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "efb7cbff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:33:56.683614Z",
     "iopub.status.busy": "2022-04-09T15:33:56.682691Z",
     "iopub.status.idle": "2022-04-09T15:35:03.537849Z",
     "shell.execute_reply": "2022-04-09T15:35:03.536060Z",
     "shell.execute_reply.started": "2022-04-09T14:22:24.255729Z"
    },
    "papermill": {
     "duration": 66.931708,
     "end_time": "2022-04-09T15:35:03.538030",
     "exception": false,
     "start_time": "2022-04-09T15:33:56.606322",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44e09a2afe1b4940bda74070946853e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batch:   0%|          | 0/18359 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss on test batch: 18.451\n",
      "Loss on test batch: 15.610\n",
      "Loss on test batch: 13.034\n",
      "Loss on test batch: 11.175\n",
      "Loss on test batch: 8.998\n",
      "Loss on test batch: 7.386\n",
      "Loss on test batch: 6.190\n",
      "Loss on test batch: 5.338\n",
      "CPU times: user 51 s, sys: 15.2 s, total: 1min 6s\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "bnb_test_batch_losses = train_one_epoch(model, opt, train_loader, test_loader, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52dace44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:03.661808Z",
     "iopub.status.busy": "2022-04-09T15:35:03.660951Z",
     "iopub.status.idle": "2022-04-09T15:35:03.664276Z",
     "shell.execute_reply": "2022-04-09T15:35:03.664694Z",
     "shell.execute_reply.started": "2022-04-09T14:23:30.835075Z"
    },
    "papermill": {
     "duration": 0.067576,
     "end_time": "2022-04-09T15:35:03.664825",
     "exception": false,
     "start_time": "2022-04-09T15:35:03.597249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive peak memory usage: 14.0 Gb\n",
      "Bnb peak memory usage: 9.7 Gb\n"
     ]
    }
   ],
   "source": [
    "bnb_peak_memory_gb = torch.cuda.max_memory_allocated() / 2 ** 30\n",
    "print(f\"Naive peak memory usage: {naive_peak_memory_gb:.1f} Gb\")\n",
    "print(f\"Bnb peak memory usage: {bnb_peak_memory_gb:.1f} Gb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486e6c69",
   "metadata": {
    "papermill": {
     "duration": 0.058791,
     "end_time": "2022-04-09T15:35:03.783042",
     "exception": false,
     "start_time": "2022-04-09T15:35:03.724251",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Model ~ 3 Gb\n",
    "\n",
    "Statistics ~ 6 Gb\n",
    "\n",
    "Quantized statistics ~ 1.5 Gb\n",
    "\n",
    "Expected profit ~ 4.5 Gb\n",
    "\n",
    "Real profit: 4.3 Gb\n",
    "\n",
    "\n",
    "Feel the difference: without bitsandbytes you wouldn't fit it in Tesla K80, which is what you get in Google Colab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e8b93e47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:03.920662Z",
     "iopub.status.busy": "2022-04-09T15:35:03.919394Z",
     "iopub.status.idle": "2022-04-09T15:35:04.120898Z",
     "shell.execute_reply": "2022-04-09T15:35:04.121438Z",
     "shell.execute_reply.started": "2022-04-09T14:23:30.844438Z"
    },
    "papermill": {
     "duration": 0.278897,
     "end_time": "2022-04-09T15:35:04.121598",
     "exception": false,
     "start_time": "2022-04-09T15:35:03.842701",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(test_batch_losses)), test_batch_losses, label=\"adam\")\n",
    "plt.plot(range(len(bnb_test_batch_losses)), bnb_test_batch_losses, label=\"adam8bit\")\n",
    "\n",
    "plt.xlabel(\"Number of updates\")\n",
    "plt.ylabel(\"Mean cross-entropy\")\n",
    "plt.title(\"Mean cross-entropy on test batch\", fontsize=18)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc29e594",
   "metadata": {
    "papermill": {
     "duration": 0.060471,
     "end_time": "2022-04-09T15:35:04.243897",
     "exception": false,
     "start_time": "2022-04-09T15:35:04.183426",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "__Note__: learning rate matters. Adam performs okay with 5e-5, while Adam8bit starts to feel dizzy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9e3f9c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:04.568914Z",
     "iopub.status.busy": "2022-04-09T15:35:04.568005Z",
     "iopub.status.idle": "2022-04-09T15:35:04.574613Z",
     "shell.execute_reply": "2022-04-09T15:35:04.574189Z",
     "shell.execute_reply.started": "2022-04-09T14:23:31.086980Z"
    },
    "papermill": {
     "duration": 0.269476,
     "end_time": "2022-04-09T15:35:04.574735",
     "exception": false,
     "start_time": "2022-04-09T15:35:04.305259",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3682"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del model, opt\n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6c791f",
   "metadata": {
    "papermill": {
     "duration": 0.060927,
     "end_time": "2022-04-09T15:35:04.696739",
     "exception": false,
     "start_time": "2022-04-09T15:35:04.635812",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 8 bit Adam with stable embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "db9ab15f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:04.824757Z",
     "iopub.status.busy": "2022-04-09T15:35:04.823880Z",
     "iopub.status.idle": "2022-04-09T15:35:13.848685Z",
     "shell.execute_reply": "2022-04-09T15:35:13.848220Z",
     "shell.execute_reply.started": "2022-04-09T14:23:31.295098Z"
    },
    "papermill": {
     "duration": 9.09149,
     "end_time": "2022-04-09T15:35:13.848814",
     "exception": false,
     "start_time": "2022-04-09T15:35:04.757324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = transformers.GPT2LMHeadModel.from_pretrained(model_name)\n",
    "\n",
    "model.gradient_checkpointing_enable()\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05b2db0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:13.977389Z",
     "iopub.status.busy": "2022-04-09T15:35:13.976208Z",
     "iopub.status.idle": "2022-04-09T15:35:14.739211Z",
     "shell.execute_reply": "2022-04-09T15:35:14.738686Z",
     "shell.execute_reply.started": "2022-04-09T14:23:39.858416Z"
    },
    "papermill": {
     "duration": 0.82971,
     "end_time": "2022-04-09T15:35:14.739353",
     "exception": false,
     "start_time": "2022-04-09T15:35:13.909643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "vocab_size, emb_dim = model.transformer.wte.weight.shape\n",
    "stable_wte = bnb.nn.StableEmbedding(vocab_size, emb_dim, _weight=model.transformer.wte.weight)\n",
    "model.transformer.wte = stable_wte\n",
    "\n",
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d60f9c5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:14.867696Z",
     "iopub.status.busy": "2022-04-09T15:35:14.866838Z",
     "iopub.status.idle": "2022-04-09T15:35:14.870468Z",
     "shell.execute_reply": "2022-04-09T15:35:14.870043Z",
     "shell.execute_reply.started": "2022-04-09T14:23:40.636036Z"
    },
    "papermill": {
     "duration": 0.070518,
     "end_time": "2022-04-09T15:35:14.870585",
     "exception": false,
     "start_time": "2022-04-09T15:35:14.800067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "opt = bnb.optim.Adam8bit(model.parameters(), lr=config.lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f0d60dc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:15.264127Z",
     "iopub.status.busy": "2022-04-09T15:35:15.263227Z",
     "iopub.status.idle": "2022-04-09T15:35:15.265792Z",
     "shell.execute_reply": "2022-04-09T15:35:15.265386Z",
     "shell.execute_reply.started": "2022-04-09T14:23:40.646849Z"
    },
    "papermill": {
     "duration": 0.334819,
     "end_time": "2022-04-09T15:35:15.265936",
     "exception": false,
     "start_time": "2022-04-09T15:35:14.931117",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()\n",
    "torch.cuda.reset_max_memory_allocated()\n",
    "torch.cuda.synchronize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2ea1b6c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:35:15.419111Z",
     "iopub.status.busy": "2022-04-09T15:35:15.398508Z",
     "iopub.status.idle": "2022-04-09T15:36:22.277671Z",
     "shell.execute_reply": "2022-04-09T15:36:22.276004Z",
     "shell.execute_reply.started": "2022-04-09T14:23:40.935477Z"
    },
    "papermill": {
     "duration": 66.951491,
     "end_time": "2022-04-09T15:36:22.277834",
     "exception": false,
     "start_time": "2022-04-09T15:35:15.326343",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f564ba151d034bbfa1278177e5e0b33e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batch:   0%|          | 0/18359 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss on test batch: 12.851\n",
      "Loss on test batch: 11.302\n",
      "Loss on test batch: 9.818\n",
      "Loss on test batch: 8.689\n",
      "Loss on test batch: 7.623\n",
      "Loss on test batch: 6.383\n",
      "Loss on test batch: 5.374\n",
      "Loss on test batch: 4.542\n",
      "CPU times: user 51.1 s, sys: 15 s, total: 1min 6s\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "stable_bnb_test_batch_losses = train_one_epoch(model, opt, train_loader, test_loader, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae34ad31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:36:22.414499Z",
     "iopub.status.busy": "2022-04-09T15:36:22.411855Z",
     "iopub.status.idle": "2022-04-09T15:36:22.417121Z",
     "shell.execute_reply": "2022-04-09T15:36:22.417711Z",
     "shell.execute_reply.started": "2022-04-09T14:24:47.618900Z"
    },
    "papermill": {
     "duration": 0.07445,
     "end_time": "2022-04-09T15:36:22.417886",
     "exception": false,
     "start_time": "2022-04-09T15:36:22.343436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive peak memory usage: 14.0 Gb\n",
      "Bnb peak memory usage: 9.7 Gb\n",
      "Stable bnb peak memory usage: 10.7 Gb\n"
     ]
    }
   ],
   "source": [
    "stable_bnb_peak_memory_gb = torch.cuda.max_memory_allocated() / 2 ** 30\n",
    "print(f\"Naive peak memory usage: {naive_peak_memory_gb:.1f} Gb\")\n",
    "print(f\"Bnb peak memory usage: {bnb_peak_memory_gb:.1f} Gb\")\n",
    "print(f\"Stable bnb peak memory usage: {stable_bnb_peak_memory_gb:.1f} Gb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6109bd09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-09T15:36:22.570519Z",
     "iopub.status.busy": "2022-04-09T15:36:22.549965Z",
     "iopub.status.idle": "2022-04-09T15:36:22.778546Z",
     "shell.execute_reply": "2022-04-09T15:36:22.778096Z",
     "shell.execute_reply.started": "2022-04-09T14:24:47.628402Z"
    },
    "papermill": {
     "duration": 0.295678,
     "end_time": "2022-04-09T15:36:22.778674",
     "exception": false,
     "start_time": "2022-04-09T15:36:22.482996",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(test_batch_losses)), test_batch_losses, label=\"adam\")\n",
    "plt.plot(range(len(bnb_test_batch_losses)), bnb_test_batch_losses, label=\"adam8bit\")\n",
    "plt.plot(range(len(stable_bnb_test_batch_losses)), stable_bnb_test_batch_losses, label=\"adam8bit, stable embeddings\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"Number of updates\")\n",
    "plt.ylabel(\"Mean cross-entropy\")\n",
    "plt.title(\"Mean cross-entropy on test batch\", fontsize=18)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 385.680936,
   "end_time": "2022-04-09T15:36:26.175353",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-04-09T15:30:00.494417",
   "version": "2.3.3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0017c888b5ec488f80354a194e455fe8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_c272785ee0e7481ca4ad17e6df1cc613",
       "max": 1042301,
       "min": 0,
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