notebooks/05n_NLP_on_HF_03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bviA42ZlzFtP"
   },
   "source": [
    "# บทที่ 5n - การเทรนโมเดลบน Hugging Face พาร์ท 1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B6RJcBQozISX"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/ai-builders/curriculum/blob/main/notebooks/05n_NLP_on_HF_03.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CL-CtUdbN9Oc",
    "outputId": "687cf0a0-88f5-4285-fba3-89dac731ab34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: multidict, frozenlist, yarl, pyyaml, asynctest, async-timeout, aiosignal, tokenizers, sacremoses, huggingface-hub, fsspec, aiohttp, xxhash, transformers, sentencepiece, datasets\n",
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      "Reading package lists... Done\n",
      "Building dependency tree       \n",
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      "git-lfs is already the newest version (2.3.4-1).\n",
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     ]
    }
   ],
   "source": [
    "!pip install datasets==1.17.0 transformers[sentencepiece]==4.34.0\n",
    "!pip install accelerate==0.23.0\n",
    "# To run the training on TPU, you will need to uncomment the followin line:\n",
    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
    "!apt install git-lfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v42-SYbS-v5Z"
   },
   "source": [
    "# ทำไมเราต้องเทรนการเติมคำ (Mask filling)\n",
    "\n",
    "ในปี 2018, มีงานวิจัยงานหนึ่งที่ชื่อว่า ULMfit ได้มีแนวคิดเกี่ยวกับ language model ในการเพิ่มประสิทธิภาพโดยใช้เทคนิค \"การย้ายโดเมน\" (domain adaptation) โดยที่ขั้นแรกจะต้องให้โมเดลเทรนบนโดเมนทั่วๆไป (เช่น Wikipedia) จากนั้นก็เทรนแบบเดิมอีกครั้ง แต่ทำบนดาต้าเซ็ตที่สนใจ (เช่น รีวิวหนัง) จากในนั้นขั้นตอนสุดท้ายก็ Finetune บนงานที่สนใจ ทำให้ language model มีประสิทธิภาพสูงสุด!!\n",
    "\n",
    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NOUJP1nZuxm1CXuvL70zHQHKE6ZbEhpEAiTgVAKUJ5y68vSbBEjAdAQoT5huSSJhELWJSFDnnM4mQHnC2etP70mABExEgPKEiRaDppAACTibAOUJZ69/rvfUJvgeIIEIEKA8EQHonJIESIAEvBGgPOGNCutIgARIIAIEKE9EALqZpqQ2YabVoC1OIkB5wkmrTV9JgARMTYDyhKmXh8aRAAk4iQDlCSettruv1CbcifA1CRhGgPKEYag5EQmQAAn4JkB5wjcfXiUBEiABwwhQnjAMtdkmojZhthWhPfYnkJCQ0LhxY/GT8oT915sekgAJmJtAu1YtxUDKE+ZeKFpHAiRgcwI1Lk3PKJcsTlKesPliF+AetYkCwLCaBMJGIDo6+p133qE8ETbAHJgESIAEAiAw/qF/DLvuWulAeSIAcGxKAiRAAroSKF82eeHsmyhP6ArVYoNRm7DYgtFcexBITk6mPGGPpaQXJEACVidQpEiRZ8Y/SnnC6utI+0mABGxAoHq1dMoTNljHoF2gNhE0OnYkgZAIUJ4ICR87kwAJkIB+BChP6MeSI5EACZBASAQoT4SEz+KdqU1YfAFpvpUJUJ6w8urRdhIgAVsRoDxhq+WkMyRAAlYmQHnCyqsXku3UJkLCx84kECIByhMhAmR3EiABEtCLAOUJvUhyHBIgARIIkQDliRABWrQ7tQmLLhzNtg8ByhP2WUt6QgIkYHEClCcsvoA0nwRIwD4EKE/YZy399oTahN+o2JAEwkaA8kTY0HJgEiABEgiMAOWJwHixNQmQAAmEjQDlibChNenA1CZMujA0y2kEKE84bcXpLwmQgGkJUJ4w7dLQMBIgAacRoDzhqBWnNuGo5aazpiZAecLUy0PjSIAEnESA8oSTVpu+kgAJmJoA5QlTL4+uxlGb0BUnByOB0AhQngiNH3uTAAmQgG4EKE/ohpIDkQAJkEBoBChPhMbPMr2pTVhmqWioQwhQnnDIQtNNEiAB8xOgPGH+NaKFJEACDiFAecIJC01twgmrTB8tRoDyhMUWjOaSAAnYlwDlCfuuLT0jARKwGAHKExZbsMDNpTYRODP2IIHwE6A8EX7GnIEESIAE/CIg8sTgq/tJ69WrV3fu3Hn//v1+dWYjEiABEiAB/QhQntCPpRlHojZhxlWhTSQAApQn+DYgARIgAZMQgDzxwpP/urrvlWIP5QmTrAvNIAEScCAByhM2XnRqEzZeXLpmeQKUJyy/hHSABEjALgSiooq+/OxTlCfssp70gwRIwMIEKE9YePF8mk5twiceXiSBSBOgPBHpFeD8JEACJOAiQHmCbwUSIAESMAkByhMmWQh9zaA2oS9PjkYC+hOgPKE/U45IAiRAAkERoDwRFDZ2IgESIAH9CXjKE2PHjtV/Go5oIAFqEwbC5lQkECwByhPBkmM/EiABEtCZAOUJnYFyOBIgARIIloCbPDHhwiPYwdgv8gSoTUR+DWgBCfhDgPKEP5TYhgRIgAQMIEB5wgDInIIESIAE/CHgJk8gdAIChT8d2caEBKhNmHBRaBIJeCdAecI7F9aSAAmQgOEEKE8YjpwTkgAJkIB3ApQnvHOxYC21CQsuGk12MAHKEw5efLpOAiRgLgKUJ8y1HrSGBEjAwQQoT9hj8alN2GMd6YWDCFCecNBi01USIAFzE6A8Ye71oXUkQAIOIkB5wgaLTW3CBotIFxxHgPKE45acDpMACZiVAOUJs64M7SIBEnAcAcoTVl9yahNWX0Ha71AClCccuvB0mwRIwHwEKE+Yb01oEQmQgEMJUJ6w9MJTm7D08tF4RxOgPOHo5afzJEACZiJAecJMq0FbSIAEHE2A8oR1l5/ahHXXjpaTwCWUJ/gmIAESIAGTEKA8YZKFoBkkQAIkQHnCou8BahMWXTiaTQIuApQn+FYgARIgAZMQoDxhkoWgGSRAAiRAecKK7wFqE1ZcNdpMAvkIUJ7Ih4MvSIAESCByBChPRI49ZyYBEiCBfAQoT+TDYYUX1CassEq0kQQKI0B5ojBCvE4CJEACBhGgPGEQaE5DAiRAAoURoDxRGCFzXac2Ya71oDUkEDQByhNBo2NHEiABEtCXAOUJfXlyNBIgARIImgDliaDRGd+R2oTxzDkjCYSLAOWJcJHluCRAAiQQIAHKEwECY3MSIAESCBcByhPhIqv3uNQm9CbK8UggogQoT0QUPycnARIggYsEKE9cZMESCZAACUSUAOWJiOL3d3JqE/6SYjsSsAoByhNWWSnaSQIkYHsClCdsv8R0kARIwCoEKE+Yf6WoTZh/jWghCQRMgPJEwMjYgQRIgATCQ4DyRHi4clQSIAESCJgA5YmAkRnbgdqEsbw5GwkYRYDyhFGkOQ8JkAAJFEKA8kQhgHiZBEiABIwiQHnCKNLBzENtIhhq7EMCliBAecISy0QjSYAEnECA8oQTVpk+kgAJWIIA5QnTLhO1CdMuDQ0jAR0IUJ7QASKHIAESIAE9CFCe0IMixyABEiABHQhQntABYhiGoDYRBqgckgTMRIDyhJlWg7aQAAk4mgDlCUcvP50nARIwEwHKE2ZaDZct1CZMuCg0iQR0JuApT1x//fU5OTk6T8PhSIAESIAECiNAeaIwQrxOAiRAAgYRoDxhEGi/p6E24TcqNiQBKxNwkyc+/vjjoUOHUp6w8pLSdhIgAasSoDxh1ZWj3SRAArYjQHnCVEtKbcJUy0FjSCCMBNzkiffee4/yRBhxc2gSIAESKJgA5YmC2fAKCZAACRhKQOSJUiUTZdaxY8dOmDDBUAs4WR4BahN5JPhfEnAAAcoTDlhkukgCJGANApQnrLFOtJIESMABBCBPfPvZsJIlKE9EeLGpTUR4ATg9CRhMgPKEwcA5HQmQAAkURIDyREFkWE8CJEACBhNoULf0/JmUJwym7j4dtQl3InxNArYnQHnC9ktMB0mABKxCgPKEVVaKdpIACdieAOWJiC9xdMQtMJUBc+fO5f4iU62InYw5cuSIedwReeKGG25YuXIlrELuCTxPnTo1KirKPEbSEocT2Lhx4/Dhwx0Oge47gYDIE/D0k1lz8Lx69erOnTvPnz+/VKlSTnCfPlqCAL4e40uyJUylkZYjsHPnTvPYLPJE56umHDiYCauQewLPY8aMMY+F9raE2kS+9d2zZ8+iRYvyVfEFCdiUAOUJmy6sfdw6ceIEP5Dts5z0xCcByhM+8fBi5AmsX7+eH8iRXwZaYAgByhOGYPY+Cfd0eOfCWhJwAgFu7nDCKtNHEiABSxDg5g5LLBONJAEScAIBbu6I1CozbsI7+cqVKw8bNsz7NdaSQGgEYmNjQxtAz96MntCTJscKG4HHH34wbGNzYKcTKFemjEkQMHrCJAtBM3wQ6NCmcv8ra/towEskEDSBChkpQffVvSOjJ3RH6s+A1Ca8UypXrtwtt9zi/RprScBeBChP2Gs97enNyJtvtKdj9IoE8hOgPJGfB1+ZjkCThmXvub2V6cyiQSQQBgKUJ8IAtZAhuaejEEC8TAJOIMDNHU5YZfpIAiRgCQLc3GGJZaKRJEACTiDAzR0GrzK1CYOBczoSMCkByhMmXRiaRQIk4DwClCect+b0mARIwKQEKE8YuTDUJoykzblIwNQEKE+YenloHAmQgJMIeJUnDh8+7CQG9JUESIAETEGA8oRhy0BtwjDUnIgELECA8oQFFokmkgAJOIOApzzRo0ePo0ePOsN7ekkCJEACJiJAecKYxaA2YQxnzkICliFAecIyS0VDSYAE7E7ATZ746aefunXrRnnC7stO/0iABMxIgPKEAatCbcIAyJyCBCxGgPKExRaM5pIACdiXAOUJ+64tPSMBErAYAcoT4V4wahPhJszxScCSBChPWHLZaDQJkIAdCVCesOOq0icSIAFLEqA8EdZlozYRVrwcnAQsTIDyhIUXj6aTAAnYiwDlCXutJ70hARKwMAHKE+FbPGoT4WPLkUnA8gQoT1h+CekACZCAXQhQnrDLStIPEiAByxOgPBGmJaQ2ESawHJYEbEKA8oRNFpJukAAJWJ8A5QnrryE9IAESsAkByhPhWEhqE+GgyjFJwFYEKE/YajnpDAmQgJUJUJ6w8urRdhIgAVsRoDyh+3JSm9AdKQckARsSoDxhw0WlSyRAAtYkQHnCmutGq0mABGxIgPKEvotKbUJfnhyNBGxLAPLEtGnTatWqJR6+9957Q4cOzcnJsa3DdIwESIAEzEqA8oRZV4Z2kQAJOI4A5Qkdl5zahI4wORQJ2JxASkrK+++/T3nC5stM90iABKxAgPKEFVaJNpIACTiCAOUJvZaZ2oReJDkOCTiCQIkSJShPOGKl6SQJkIDpCVCeMP0S0UASIAGnEKA8octKU5vQBSMHIQEHEaA84aDFpqskQALmJkB5wtzrQ+tIgAQcRIDyROiLTW0idIYcgQQcR4DyhOOWnA6TAAmYlQDlCbOuDO0iARJwHAHKEyEuObWJEAGyOwk4lADlCYcuPN0mARIwHwHKE+ZbE1pEAiTgUAKUJ0JZeGoTodBjXxJwNAHKE45efjpPAiRgJgKUJ8y0GrSFBEjA0QQoTwS9/NQmgkbHjiRAApdQnuCbgARIgARMQoDyhEkWgmaQAAmQAOWJ4N4D1CbyccvJyZHX586dy3eBL0iABAogQHmiADCsDpUAP4dDJcj+ziNAecJ5a26Qx+obck4OvyEbxJzTWJ0A5YkgVrDI+fPng+hmxS5nz57deuGxefNm/HfHjh0HDx48dOgQnvE4fvy41+/BxS480tLS0tPT8VyyZMmKmkdqaqoVUdBmEtCdAP4nGjJkyIYNG2Tk6667burUqVFRUbpPxAHtQQBfc/EhrD6Td+7cuW/fvgsfxq4P5KysLE9P4+PjkooVSy+OR2qJ9PRSJdIrZJSvmFG+UoWMKpUqlipZwrMLa0jAgQRw93jnP8Z+MmuO+N6yZcuvv/6a31gc+E7w0+WTJ0/iu/GWLVvkM3n37t34ID5w4YGvx0ePHvU6TmpKXHJSXOlSxUqkJZRITyxXJqlq5bSqlYtXqVQchWKJMV57sZIEnEbg97X7Ol815cDBTHH8qaeeGjNmjNMg+O+vnbUJfLAuXbp01YXH77//vn79eiX6+g/Id8vSpUvXqVOn9oVHo0aNqlWrVqRIEd9deJUE7EoA/8dRnrDr4obuV2Zm5s8//7xixQr5TF67du2pU6dCH1Y7QmpKct1aterWrlmnVo2G9erWr1s7muqYFhDLTiJAecJJqx2wr9u3b1+2bBk+jfH1+LfffoMkEfAQPjvguzDkiYb1yshfq+YVMsol++zBiyRgZwKUJ/xfXbtpE/jxbd68eUuWLPn+++8hRvgPQpeWCKxo1qxZ8+bNW7du3bBhw6JFuWVGF64cxDIEKE9YZqkMMfTw4cPffvstPpDx+PXXX3VXh307kZiQ0Kxxw5bNmrRq3qxVi6ZxcXG+2/MqCdiMAOUJmy1oiO6sXr16wYIF8oGMb8shjhZo90oVUtteVrFdq0qXt69ap2bJQLuzPQlYnQDlCT9X0A7aBEJ/Fy9e/MUXX3z11Vf45PXtOb6eli9fvmzZsgh5wD55hDimpKTgOSEhITY2FiHoKGAEjHnmzBlseEGc24kTJ44cOXLswmPv3r179uxBtBtqfE+EmOMOHTp0uvDARL4b8yoJ2IYA5QnbLGVwjuBjEzLEl19+OXfu3B9//NHrXjk1MuIaypZKzyhTsnypkmmpyempycWTk1KTixVLwO6N2EsuKZKUmPuBjEEyT59G4UTm6cxTp48cP3H42PGjx0/uP3Rk976Du/cf2HfwiO/9iRiufevLOndo1/XyjtgAogxggQTsTYDyhL3Xt1Dv8A0Wv9jhAxkPbKPz3T41oWiVEtGVS0RXSo8qlRSVXqxoiWJF8ZwcVzS66CWx0UUSYnNDg0+dPX82+3z2uUuOnzl36OS5Aydyn/cfz9lxJGfrwextB7OPniokIUXliqk9u9To2aX6FR2rceuH70XhVTsRoDzhz2paWJvAT3D4Re7DDz/89NNP8eucV2+jo6MvvfRSbLXAc/Xq1atUqYLQBq8tA608ffo0VOeNGzdu2rQJm/T++OMPaBdeB0H0RKtWra688NBrdq8TsZIETEKA8oRJFsJgM5YvX44P5A8++KCgb8DY8lYlo2yNyhVqVM6ojr9KECXSi+qxDy4rO3vX3gMbt+/6c9vOP7ft2rBl+94D3v9RABNs9+jbs1vfXt2RosJgRJyOBIwnAHni9vse+GzOXJmauSeMXwLjZ4Qk8fnnn0+fPh2/2+H7qlcDkuKKNKwQ2zAjtn75GPzVLhsDbcJry0Arj2SeW7836/ddWat3n8Xzqp1nT571ntsuPi66d/eag6+u36trjYT46EAnYnsSsBwByhOFLpkltQnsjnvzzTfxJXj//v2eHiYmJmI/RYMGDfCMRBCIhvBsE44abN6DYXhg5x4CKzyngFDSrl27wYMHd+3aFWXPBqwhAdsQoDxhm6Us1BEoEf/973+nTJkCldazcUx0dIOaVRvXrt64TvUGNaslF0v0bBOOmn2Hjqxct/G3DRvx/MfWnV6jKpo2ajDk6v4D+vZKTkoKhw0ckwRMQuBsVtYtd9z71fyFYg/lCZOsi+5m4INu/vz5//nPf/CjndeEPmVTotpVj2tdLa5NtbgGGbFR+mgRhfiBCIvfd51dsunMj5vPLN54BhEWnh0QPdG/d53hNzbt0KayHnq15wysIQGzEKA84XslrKRNYHsFfpGDKvHTTz95eoXgiMsuuwwRCvXr14/4nf+uXbuQhhOPlStX4nwQN2uxxeOaa67BQQaI43C7xJckYBsClCdss5ReHUHk2uzZsydPnozf5Tw3bpQrVaJNk3ptm9Rv0bB2QqQTPRw6evzHlWuW/Lp66W9rj5046eZOQkJ8vyt73jjomuZNGrld4ksSsA0ByhO2WUqvjmDHMSQJfCAjmNetAQSIVlXjutdN6F43HnpEZO/8cTbgbzvPzl176uu1p5dtOXPOI5yi5qUlbh3WdNjgxqVKGiRku+HiSxIwgADlCR+QraFNIMXDpEmTXnvtNc+9GzVr1kRKh8svvxxZJHz4GalL0K1/+OGHhQsXQqdwEykQ3tylS5eRI0e2aNEiUuZxXhIIKwHKE2HFG6nBES2MQImJEyd6pnaHJNGtbfMubZrVqVY5Uub5mBcayk+/r/96yS8Ll604dtJ1mpdq37RRw1HDh13ZrWuUMT8mqolZIAFDCFCeMASz0ZOsWbPmhRdemDZtmtuXzKJFLulQI35Ak8RrmiamJRoSIBGg6wig+Oy3U9OXn/x+E/K75euMvR5DBze6747WtaozX1s+MnxhGwKUJwpaSrNrE0jogGNg8ZnrdtY9Qg+QwKFbt24VK1pjwzDOz/vuu++w/Q+bPtwWA4eP3nHHHfCF54+6keFLGxCgPGGDRVQu4LR7SBKvv/66m0ycmBDfo12LPpe3qV+jqiU+x7JzcpauXPvZt98vXr4Ku/GVgyggU+bttwy74dqrea6HFgvL9iBAecIe6yhe4Fvl008/jcg1N6fqlYu5qXXSoOaJpZKj3C6Z8+VfR3M+Wn7yP0tO/rEvS2shQjz69Kg19t52OIJUW88yCdiDAOUJr+toXm0CW5f/9a9/vfPOO4gcVqbjWy+O5+zduzf2buBMDVVvocK2bdugUCCDvVvuzLp1695zzz3du3e3kC80lQT8IUB5wh9KJm+Dk4meffbZl156CXvrtKbWrV7l6q4durVrHvGNG1qr/C8fPHJs5vwln837fve+A9peGeXK3nP7iCED+8fGxGjrWSYBqxOgPGH1FYT9CMV97LHHcDid1pf4mCIDmyb+rW0SdnBo6y1URk6KyUtOzFiReSY7XxxFjyuqP/noFU0alrWQLzSVBPwhQHnCk5IZtQns4MBnLjbOaWMlkNKyZ8+e1157bYUKdlBPkTYZ5zl99NFHblkzmzRp8sADD7Rp08ZzqVhDAtYlQHnCumuHmK/nnnsO4RJHjx5VXkAmvrxl4xv6dm1Y61JVad3CufPnFyxdMW3W16v/3KL1okJG+TH33HnNVb1x3JK2nmUSsDQByhPWXT7E3j700EP4iUvrQsmkord1SL6tfXKJJDt8Uu07nvPqouNvfX8CR5Nq3byqV62n/tmlTs2S2kqWScDqBNzkiQkTJjz44INWdyoU+82lTZw5c+bFF1/897//ffz4ceVVsWLFkDny6quvTk1NVZX2KGDz8+LFi6dOnYqtK1qPED3xyCOPVKpUSVvJMglYmgDlCcstH1K+I/0w1FKcl6yMj46K6tu5LVSJSuVKq0rbFH5bv2nyJ1/8sGK11qMmDes/8ciYFk0baytZJgFLE6A8Ybnlw7+hjz76KPLBa6OJK6RFjemeel3LYgkxRSznkW+DM8+en7r0xPPzju08fDF6Ojq66B3DW4x7oFNa8Xjf3XmVBCxEwE2eePXVV2+//XYL2a+vqSbSJmbNmoVNDVu2XPzZKj4+HoESgwYNSrL1AW+4Afj+++/feustbPdQqxsTE3PLLbfcfffdOBJVVbJAApYmQHnCQsu3fPnyu+6668cff1Q2I3agV4fLRlzbp3xpmycn+/2Pza9/OGvZb+uU7yj0791z/EP/KFvahoqM1k2WnUOA8oRV1hpixMsvv4yAYuytUzaXL56rSgxrXSw2ym6qhPIRhbM559/87sRz847tPXZRoUhPS3ji4c4jb2pWFAk/+SABWxCgPKGW0RTaBDZxIBnkjBkzlFnIJTFgwIAbb7zRfrESyke3AmIocCo1FHEcBKUuYQMLUoF26NBB1bBAApYmQHnC/MuHTRz//Oc/EcKm/XWuc6umd93Qv2JZB92ZL1/zx0tTP1676aJknJyU9M8H7hs6ZKAl8n2a/51GCyNOgPJExJegUAN+++234cOH//LLL6plakLRh3qk3to+yX6xEspHt8KprPOvLTo+Ye6x42cu7vJo3aLCWy/1rVe7lFtjviQBixKgPCELF2FtAiEDyCtx//33a3cyX3bZZXfeeaczdzRgVwuCqN977z0kpFD/a0Gmwa1CWlqaqmGBBKxLgPKEmddu3rx5ONgYqYiVkTWrVLj/5kHN6tVUNc4pIA/FnIU/vvLeZwcOX8y10apFs4n/Hl+9WhXncKCnNiZAecK0i4vvgUgJjyTE2dnZYiRON/5bm6R/XlkcCSZMa3b4DEMeinGzj05ZekIdOBobG4VTPMbe0z4uzpLZ8cPHiiNblADlCSxcJLUJBAjcdNNNOLFCvYHKlSt37733QptQNc4s7N+/H/F7CxcuVO6XKlUK6eg6deqkalggAesSoDxhwrXD9+DRo0e/8soryrakxIS7bhjQv0s7h2eCzDx1+u2P57wzex6i2wQOjhcdP3b0zdcPZgCFerewYF0ClCdMuHYIl7juuuvWrl2rbMPpG/83KL1BhtNPDlq54+ydHx5avu2sItOwXpn33766bi0GUCgkLFiYAOWJiGkTs2fPRj4F3ITL2wfffZHwEjXIMWHhN5SupiMJxQsvvHDgwMWT7W6++eaxY8fia7Gu83AwEogAAcoTEYBe8JT4Hnz99devWbNGNbn8siYPDh9SMs1uGYiVg4EW1m/e/sTr0/CsOnbp1OH/nn6iZIl0VcMCCViUAOUJ8ywcAoqxpW7MmDFnz7puv5Pjij5+VfFb2yUxu4IsU865S7DF47HPj5w86zpqND4u+vknut3+txZFmIDCPG9lWhIsAYfLExHQJvBpi00ciAtQS1atWjV8CteqVUvVsCAETp48+dprr0HHUUBq166NGhBTNSyQgEUJUJ4wycJNmjQJERPqe3B6avKYEdd3vqyJScwzjxk5Oefenf0N0mSezXKFWEOYeH3i0x3atjaPkbSEBIIjQHkiOG769sIvdjfccMPXX3+thu1RL2HSoHScx6FqWBAC2w5lj3rv0PwNF3dAX9mtxrTXB/AID75DbEDAyfKE0drErl27EB+xdOlS9b7BS2xvjo2NVTUsuBH44Ycfnn76aZWfGYeqIp4C54y6NeNLErAcAcoTkV0ypL0cMWLEu+++q8xo16zBP0cNgzyhalhwI/Dntp0Pvzh5847dUo+gv4fu+/tdI2/h/g43UHxpOQKUJyK7ZD///DO+Em/f7grOio8pMqF/2oh2SYwFKGhdkHji5YXHH5l5BMd5SJuqlYt/Om1wo/plCurCehKwCgHHyhNROJTIsEVatGhR165dN2zYIDOmp6cjzQ8SPeJUDsNssOJEFStW7NGjB7LTQdmB/VlZWYikQG6kVq1aOXwfuBVXkzZrCeCI3F69eiG1CkQK1P/+++8bN27s168f39haSmEqb9q0CR/ISH4p48fGxIy+ZdB9wwYmxnPXmC/kJYqnXHVF2+OZp9Zs3Ip2CMD+7oelazf8gS0eFNl9geM10xPAl7HePbquWbdh05atMBZfOXB8GI5y52ZbA5Zu8uTJECYOHTokczXMiJ19R2kETVCY8AEfcC6rGndlg4QfNp3ZfyI3H9CRo6envL+yUoXilCd8cOMlSxAoU6pYzy41ps9cm3kqCwbPmTOnTJkyLVq0sITxoRhpnDbx9ttvDxo06Pjx42Ju48aN8eN/9erVQ7HeOX0TEhJwFxETE7Ny5Up8FYbjP/30E27kunXrhkrncKCn9iNAeSIia7p48eLOnTtv2+Y6ILNC2VKvjrunfbOG/PHfn+XALVzbpvVrVK6wZMXqrAsp9P/ctGXuvAXdLu+YksKQE38Qso1JCVCeMH5hcFrzPffc88gjj6hjm29qnfThrSXLpvB3O79Wo0xK1NBWSTsO5/y+K/cWLjv73Kdz1p86ld2lYzUqO34RZCOzEnCmPGHEng7cSz/88MNPPfWUWnrI8LfddhvDJRQQ/ws443r8+PHHjh2TLvXq1fvf//5XunRp/0dgSxIwIQFu7jByUXBKMc5IQgSWTIrb7MfvviWlWKKRNthjrm27945+5rUtO/8Sd0qXKvnuW682ql/XHt7RC8cS4OYOw5YeG+uGDBkya9YsmTE2qsiL16bd3CbJMAPsNNFr3x1/cMaRrLz9HQOvqjv19f5Ik2knH+mLAwk4bXNH2LUJ5FcbNmzYBx98IG8mnDHx4IMPXnHFFQ58b+nlMs5efeihhxD6LgOWL19+ypQpNWvW1Gt8jkMCESFAecIY7P/+97/xA52aa/g1V44Y1Kcof11SRAIsZJ4+M27SfxcsWyH94uPj3v6/id06dwxwGDYnAXMRoDxhwHrg69yVV165fPlymatcatT0W0s1q8z8a8GzX7rlzLVvHdh/PEeGaNOy4sx3B5csQeU9eKTsaQYCjpInwqtNQA/u37+/SjicmpqK6An81G+GZba0DQA7btw4bOsQL4oXL47oiSZNmFff0qtK4y+hPBHWNwFC2HBGEjbTySzRUVH/HDW0V8dWYZ3UCYOfO3/+/6Z98s6sb8RZZEt5+dknr7mqtxN8p482JkB5IqyLu3Pnzk6dOiHvj8zSICNmxsjSPI8jdOZbD2b3e23/hr2uwMD6dUrP/fiGjHLcbRc6Wo4QSQLOkSfCmG/i6NGjffr0QSIlWUkkdHzppZcuvfTSSC6sXeZGjokuXbocPnxYEouePn36888/b968eYUKFeziIv1wIgHmngjfqmMn8x133KEOb04ulvjSQ3d1bNEofDM6Z2Qk6WjVqG5aavKPK9YgGxA0oC/nzS9VokTjBhTinfMusKGnzD0RvkVF6GvHjh23bt0qU3SpHT9zVOlSyUwwoQPy4olFBzcv9tPWM9sP5UZP7DtwcuaX6/v3rpOawjTPOuDlEJEi4JzcE+HSJiBMIE0jDr+UJaxTp86LL75YsmTJSK2o/ebFt+E2bdpER0f/+uuv8A57Z3B4R7NmzaAB2c9ZeuQcApQnwrHWECaGDh363//+VwYvnV789cfuq1ejSjjmcuyY9apXqVm14vylvyKMAo9vFixKSU5u3oTqj2PfEXZwnPJEOFYRicyR3RxxEzL4oObF3rulVEJMkXDM5cwxcfzqoBbFNuzJXr8nN3ri8JHTH322pm/PWulpCc4EQq/tQcAh8kRYtAlkaoQwoXYcNGrU6JlnnklOZjyV/v9rgG1SUpKgxqmiOGAG0ROUJ/QHzRENJEB5Ql/YuE+++eab33nnHRm2XKkSr4+/r0pGWX1n4WggAKoNa1Wb9+PynJzc0+wWLF6SGz3RsD7hkIB1CVCe0HftEDEBYUKOhMfIt7ZLeu26ElFF9Z2Eo10SVbRIv8aJWw9ly+Edx0+cnTF73cB+9Rg9wTeHpQk4QZ7QX5tAKoS+ffsuWbJE1h63yhMmTMDNhqXfCmY2Hvk7SpQosWzZMtyBQJ6YO3cu4inKlStnZptpGwn4JkB5wjefgK6OGjVq8uTJ0gVnhb79+OjypRnCFhDCABpnlCnVtG4NRE/I2aLfLPyucsUK9erUCmAINiUBkxGgPKHXgiBWAls5VMTE3zsnP391elEGTOjFN/84ANu3YeK+4zm/bj+LK5AnZs3dcM1VdZOTuLkjPym+shQB28sTOmsT2FkwYMCAb75xpQRr2rTp008/jbM5LLXo1jO2Vq1a2C8jO2iwBIie6Ny5M3fQWG8habGGAOUJDYzgi2PHjsV+OukPYeLN8feXLpEW/HDs6QcBRKa0aFD7y8XLJHpi7rcLalavVqtGdT+6sgkJmJQA5YnQFwancnTo0EHlmIAw8XT/NB6RFDpYHyMAb496CUqewOaOr+dvurZfvcTEGB+9eIkETE7A3vKEntoEfrcfPnz4xx9/LCvaoEEDbOWgMGHM+xtniOIYFERPYDrIE5CHcDAV99EYA5+zhIkA5YkQwU6aNOnhhx+WQZBj4s1/jS5bMj3EMdndHwIQgBrWdG3uwL+Mc+ctaHNZiwoZ5f3pyzYkYE4ClCdCWRfEFGMrx5o1a2QQbOVAxASFiVCQ+tlX5InNB7JX787NPYHUmD/8tOO6gQ2io7mRxk+EbGZGAjaWJ/TUJh5//HH1A13t2rWfe+45buUw8u2MhKOxsbFyUPbJkycRRtGvXz/UGGkD5yIBfQl4yhNbtmzBGxu5YPWdyH6jzZo1C2kmcGMM19JTk9964h/YbmA/N03rUUaZkjUqZXx7ITVmzrlziJ7o1fWK9LTipjWYhpFAoQQoTxSKyGsDZCO+9tprFy5cKFeR/BI5JriVwyurcFTi+0LvBonr9mRJaswdu479uenQNX3r8ntEOGhzTMMI2FWe0E2bmDZt2t133y3rgWQHOC40JSXFsOXhREKgYcOGSES6bt06vDxw4AAUehzjWrQotWG+QSxMwE2eWLVqFSJjERZEecLHokKjBKKsrNyfiRLi4l4bd++lFfmjvQ9gYblUOaNsudIlFv60EqOfPn3m20WLB/S5MjGBieLDQpuDGkOA8kQQnO+55x58SZaOOC4Up3Iw+WUQGEPpAiWoT6OE7ze6DhZds37/6TPZXTpVC2VM9iWBiBOwpTyhjzaBcyL69+8PYRiLhH0EiJ4oU6ZMxBfMmQa0bNkSWaB37NgB97dt23bixIlOnTo5EwW9tg0BN3nil19+oTzhY3EBB//XHz58GG2KFiny7D9ua1qvpo/2vBQ+AjWrVLjkkiLL1/yBKY4cPbZy1epr+lEvDh9vjmwEAcoTAVF+++23H3nkEenSICNm5qjSPC40IIB6NcbJHUiNOXvVqYMnc89RWrJsR41qJRrW492KXoA5TmQI2E+e0EGb2L9//xVXXCHfg6Ojo5999lnkPojM+nBWfAsuUqRt27Y///zzwYMHwWPFihXVqlXDFhuyIQFLE6A84efy4bAenJS0evVqaf/ArUN6tr/Mz75sFg4COLZj974Df27bicF37NqdmXnq8vZtwzERxyQBwwhQnvATNb6MXXPNNfLTXbnUqLl3lSmVHOVnXzbTnUB8TJGe9RM+/OVk5tnc3Y5zv93Yp0fNMqWTdJ+IA5KAkQRsJk+EGu2PD9xBgwap85Duv/9+bCswcj04lyeB+Pj4J598Mi3NlY3/wQcfXL9+vWcz1pCAtQjgrNz3338fp9KI2a+//jpOx5R8CtZyJKzWPvDAA4sWLZIpBnbvhL+wTsfBCyUAvfiR22+sX6OqtHztP1NmfvFVob3YgARMTiA2JmbyKy9079xJ7ET8bLdu3Y4ePWpys400Dz/dXX311UhPjkljo4pMv7VUhTQKE0augJe5qpSI/nB4KcmDmXkqa8DQD3F4h5d2rCIBSxFoULf0t58NTUl2nYyJr8evvfaapTy4aGyo2sSjjz66YMECGQ+pDXr16nVxbJYiRwAHiI4fPx6/bMCEU6dOjRw5EtkxI2cOZyYBfQhQnvDN8ZNPPnnhhRekTYOa1e67eaDv9rxqDIGY6Ohn/nEbMpLKdHc/+MimLVuNmZqzkED4CFCe8MEWuvn1118vG2zR7KVBac0qMze5D2DGXWpzadyEAa5f7zZtOXzznZ8ZNzdnIoGwEcAGpa9n3GgDeSKkPR34dW7EiBHyuyUOiVA3w2HDzoEDIFC2bFmEweOnDPQ5cuQIUmPi/KoA+rMpCZiSADd3FLQsu3btgjoMLRIN0ounIP9lSrHEghqz3mACxRLi61av8sV3y/AvZlZ29i8rVg65pn8UExUbvAycTm8C3NxREFFkXlO/W97cJunhnqkFtWS98QRaVonbtN91quiGPw+WLZ3UvAnTRRu/DpxRZwIVyqd07lD1o8/WnDmbmwJyzpw5yP/YokULnacJ83DBaxO43cW9rsTvJSUl8WCOMK9UMMPXq1dv64UHOuPMDgTD16hRI5iB2IcEzESA8oTnapw7d27AgAFr167FJWwimPjgqAtZGD0bsiZiBMqXLoHfmX9atQ4W7N23H6eodGzbOmLWcGIS0IkA5QlPkL/99hv2O0uaiYYVYj8cXjKaR4Z6YopoTbe6CbNWnTpwIjcv5vzvtgzoU7dUCar5EV0STq4HARvIE8Hv6bjttttUmonRo0eXLl1aD6QcQ2cCyACC/R0y6NixY/fs2aPzBByOBCJBgJs73KhPnDhx/vz5Ujn0qm4tGjD9rRshU7y88apuzfLOTHnlrf/+8NMvpjCLRpBAaAS4uUPL7/Tp00OGDJE0E0i++L9hJeKii2gbsGwGAomxuUuDPCAw5tTp7OtHfJKVlatT8EECVidwWbMMS2/uCFKb+PTTTz/88ENZvJ49e15++eVWX0i72p+SkoLDq/A7KhxEqIs6yMqu/tIv5xCgPKHW+o8//lD/a9eqWvG2wX3VJRZMRQBHuj7+97/JXhuEutwz5tFTp5iGzVRLRGOCJEB5QoHDBud163LDo/CY0D+tTtkYKfPZbAQaVYgd38e112bFqj0TXvzebBbSHhIIjoCl5Ylg9nRgHwd2NZ84cQK8kNRgwoQJMTH85A3uzWNEr3LlymELupwpuHnzZmzr4CGvRnDnHOEnwM0dYIz8BTijbtOmTSjj9gBpJtJTU8LPnjMESaBYYkL5MiW//fFX9D9y9FhWdlandm2CHIvdSMBMBLi5A6uB3Rw33XQTlEeUe9RLeGZA2oXfhsy0TrRFQ+CyqnHfbzyz7VA26pb8tGPgVXVLcmeHhg+L1iVg3c0dwcRN4Iy6v/76S1YL5YSEBOuunEMsv+WWWzIyMsTZcePG8ZQvh6y7E9xk9MRbb72lDg0dOahP5fJlnLDulvaxa5vmnVo2Fhde/8+UVWtys4TwQQI2IODw6AkkmMDXrezs3Bvd5LiikwalU5gw+bsaC/TadekJMbnBxWfP5gz/+6zz501uMs0jAX8JWDR6ImBtYsmSJW+++aZQwW6OZs2a+UuI7SJHIDY2FiqSzI8DO5566qnI2cKZSUBnAk6WJ/bt26f+165ZpeINfXgWj87vrjAN9+DwIUmJubJ+Ts65+x56TH5lDdNcHJYEjCTgZHni5ZdfXr58udB+/KriFdJyz3Hnw+QEqpaMHtfbtbNjybIdb011raDJzaZ5JOAPASvKE4FpE/j+dPfddwuLtLS0UaNG+cOFbcxAoHHjxr179xZLPvjgA8nnbwbDaAMJhE7AsfLEo48+KmFQRYsWfXTUjVFRgX2kh06eIwRHoFR68b/fOED6Im7ig09mBjcOe5GACQk4U544ePDgY489JsvRulrcre2STLg0NMkrgTs6pTSpGCuXHvn3/KPHznhtxkoSsCIBy8kTgX2RnTp1qpKER44ciTyLVlwkx9qslgwb1JGrybEc6LgtCThQnli5ciU2dMhqDuzRsU61yrZcWbs61b9L+3rVq4h3/37+xRMnT9rVU/rlQAKe8sRVV12VmZlpYxRISIyM43AQEvH/DUrnmaEWWuvoC0smG3D2H8h8/NlFFjKeppJAoQSsJU8EkAvz+PHj+Kfl5IXvT7Vr177nnnvk9IdCibCBSQjExcUhOcjSpUthD85/RUZMJsU0ydLQDF0IOC015uDBg7du3Qp0OPfhuQduj4t1/eyjC0wOEm4C+Ae0eqWMmd8uwUSZmadyzp3r2LZ1uCfl+CRgGAG31Jjbtm378ccfBw4caMvs6b///vuIESPwww/wDm+bNKw1gyYMe6PpM1H54lFbD2av2pWF4X5ZuXvQgPol0hP1GZqjkIAJCFgoNWYAcRPPP//83r17Be9dd91FYcIE77SATejbt2/VqlWl29NPPy0ZmwIehR1IwKwEnBM9MWfOHJUC87YhV6UkFTPrmtCuAgk0qFmtZ/vL5PJbU97dvcf1L2yBHXiBBCxFwC16YsGCBdhbasvoiTFjxiARJhYnNaHouCuLW2qVaKyLwL/6FE+Ky02KmZV17tF/LyAXErAZAatET/irTSBQ7cUXX5RF6tKlS/369W22YA5xB79j3HnnneIsfsT49NNPHeI43XQOASfIE/h1DvHDsqZVMspe3bWDc9bXZp7edeMA3L/BqTNnzrzwyhs2847ukIAT5AmEo37xxRey1g/1SC2R5O9Xa749TEWgXGrU6K6upJgffbZmxao9pjKPxpBA6AQsIU/4+wGK39hVxrW//e1vodPhCJEi0Lx5c+TFlNknTpyYlZUbwMYHCdiJgO3liZkzZyLZhCzZbYP7MgWmdd+9pdOLX9uzk9j/3vQZO3e7zue2rke0nATcCNhensDR7OIyDua4tT13c7itv5Ve3tEpWUlL459ZaCXTaSsJ+EfA/PKEX9oETp2cNGmSuNytW7eMjAz/3GcrkxK4+eabxbLdu3d/+OGHJrWSZpFACARsLE/gvCQVNIFzQ69o1TQETuwaeQI39e+REBcHO7Kys5/9v1cjbxAtIAG9CdhYnli4cOHXX38twB7olpoQk7spgA+LEsCejjHdXKETM7/Y8MuK3RZ1hGaTgA8CJpcn/NImIExICszo6GgGTfhYbKtcQtxEy5YtxdrXX39dNklaxXjaSQJ+ErCrPIHg4TVr1giEEdf2ZuofP98Ppm1WPDlJhU58/Nnsv/buM62pNIwEgiZgV3nimWeeESaV0qOHtWben6DfIGbpeEu7pDIpUWLNhBe/N4tZtIMEdCVgZnmicG3i1KlTr7zyigDp0aNHmTJldIXDwSJDYOjQoTLxjh075s6dGxkjOCsJhJmALeWJZ599VrAhaKJji0ZhRsjhjSAwrF93FTrx5v+mGTEl5yABwwnYT56ATPzll18KyAe6pcRGMWjC8HeV3hMi8uX+Liky6ozP123eeljvGTgeCZiCgGnlicK1iSlTphw8eFAoDho0yBQ4aUTIBBo0aFC3bl0Z5q233gp5PA5AAiYlYDN54ueff/7uu++E9Y1XdWXQhEnfdgGahWNW+nRuI52mfTD9ZGZmgAOwOQlYg4DN5Akk7RLuJZOKXteSQRPWeBMWauXNbZJw3gqa4UzYia/+WGh7NiABixIwpzxRiDaBbPDqk7d169aVKlWyKH2a7UlAKU0rVqzADY9nA9aQgD0IiDxRrVo1cQf7mEaNGiUH0VvOQfWBXKZkWrc2LSxnPw0uiMD1vbsULZL7o+ux4yfe+fCTgpqxngSsTsA28sTevXvfeecdWY5RHZOZacLq70xlP7JODG/nymn633dXHjp8Sl1igQRsRsCE8kQh2sT8+fP//PNPWYbBgwdbbj065j0sZ7kBBnfo0KF8+fIy0bvvvmvAjJyCBCJFAPLEe++9V7lyZTHAovLEvn37PvnEddc6uFdnHs8RqbdTOObNKFPy8lZNZOT/vccUxeFgzDHNQsAe8sTkyZPPnj0LpvExRUa0SzYLXNqhB4E7OibHXNihk3kqa+oHv+kxJMcgAZMSMJs8UYg28cYbruPWq1atqg6eNClamhUggaJFi/br1086zZkz58iRIwEOwOYkYCUC5cqVw6k0lpYnsMNODv2Ni43pd0U7K9GnrX4QGNSzs7TatGXr0p+X+9GDTUjAqgSsLk8g8u4///mP0B/UrJg6eNKq60G78xMolxrVr3GC1E1+Z0X+i3xFAnYjYCp5wpc2gaNDZ86cKfj79u2ryzrg0Mq8UIaOBQ2InzelzXPPPee1jRpkyJAhqkGhw6qWBRVCH6GgkU1bj+SmMTExMA/a/2effWZaO2EYbinlYWYjaZvJCVhansBX4bffflsIX9G6WXKxRJPTpnmBEmhSp3ql8q5s01M/mB5o94i0L129vvxFZHZOamkClpYnEFa8adMm4X9TG2aasPQ70bvxN7d2betYvW7fDz/t8N6ItSRgFwLmkSd8aRP4jU7C1XD72qVLF13gYxNBcrIr8m35cu+/C/3yyy8y1x9//OF1UlVfs2ZNrw10r4RcIg/dRy50wLyZ3yu0ZRANUlNT27ZtKx0xURAjsAsJWIuAdeUJpMBUH339uzBowlrvO7+sRWbTAV3aS9PPv/oGiSf86sZGJGBZAtaVJ1QS8XrlYlpVjbPsCtDwAgl0qhlfrWS0XH572q8FtuMFErALAZPIE760CXWzevnll6ekuA7UCZ2/EhQ2bNjgdTSlWRTUAHET0rFWrVpeR9C9Entb5KH7yIUOmDeza3NNoe0DbaAiYkB7/fr1gXZnexKwHAGLyhPvv/++oK6SUbZJnRqWw06D/SHQq2Or6KgotDx9+szsuV/704VtSMDSBKwoT5w4cUKFFd/c1vXruqVXgcZ7EkBu4pvyQic+mbXuzJkczzasIQGbETCDPFGgNoFYtV9/dcmEXbt21RF98+bNZTQlMWgHd9MjlE7htY1Wm0D6enloWwZUzhtgYkC9rN64SZMmSBMoXnz++edWd4f2k4A/BCwnT+Tk5MyYMUNc69G+pT8+so0VCaSnJrduXE8sn/XFV1Z0gTaTQKAELCdP4MvS6dOn4WbRIpdc25Tb6wJdcMu0H9zCtbjHjp/5eoFrC49lrKehJBAUgYjLEwVqE9Onuza7YgtG06ZNg/LOeyd1NoRXbcJNjHCTKmREFdisQjBQ3yzv4X1WP2rzBmjmR1v7NEFGTCTaEH+QEdM+jtETEvBJwFryxLx58/bv3y8OdW/Ho0N9Lq3FL3Zt4/o3aPEPSw8fOWpxb2g+CfhFwFryhPqG3KFGfKnk3EAnPmxJoGJadMsqrg07H3662pY+0ikS8CQQWXmiQG3io48+Eltx4xod7dpw5Wl9EDVKUHCTIWQoESxUQISnNnH8+HFpo01dEYQZ7KIIqGQimzdvXrdunapngQTsTcBC8oT6QK5drVLFsqXtvS4O965Di0axMbn/5mbn5HzxzbcOp0H3nUPAKvIEvoV+8cUXsi4DmzFowubv0EHNXUv82Zz1p05n29xbukcCeQQiKE94Fx127NixYoXryJzOnTvn2anPf0VTwIc7hoP0oGQIGV0EC9wzJCUloaxCJNTcqkZpHHJJ/fi/aNEi1bigAmbHGR9iw/jx4zt16oSWniPAgPvuu087iGqDSu1EGGr27NnI4in2w0eYh90rffr00XbHaNIAMRrYQqK9hLK6Onr0aIzg59RugwTxsm7duqVLl963bx/6fv3113Xq1AliEFN1OXr0KGJAfv/99+15j3bt2iHx53XXXYeCm6k4/gM1qH/33XfREWlWpCOeG1x43H777ZUqVXLrhZdo+f3336MLnj2vombMmDHoi4JMgcK2bds8W3q9GpALMqb/9kh7cRbGi/3wEe6CAyh5GmnXGpEnBg0aJEvz+uuvw9NXX30VWQnN4zJO6FAxTV1aOyuwyzyrYJglSYkJbZs2WLAs95/gud/Mv37gAMOmDvdER44em/XlV6tWr926Y+e27Tu27djZoW3r4ikpQwdfg4Lb7Dj+AzWo/3jKW+g47YPpv61eu23HDjw3ql+3Uf16d428pXLFCm698BJHnHy3ZOmRY8e+W/Kj51XUPPqPe9EXBZkChX0bvfwi6vVqQC6o2f03SbqIv4t+WCouwE3426Ftq6GDB6oxbVkQeeKWO+79av5COLhgwYLevXtjA0ViookkgG+++UY2dEQVvaR/YxMZZsu3RMSdwhKP/uTw+fOXnMzMWvj91p5dqkfcJBpAAsYQEHmi24Bp2NOEGUeNGoVnua8JqwHetYmvvnJtc8W/B40aNdLdAty3yy06hAatNoE7fBU3gQLa4BkP3KgrG1QkhbajuupnAQkmRZiARiDChJ8dvTaDnePGjZMBpYGYvXDhQqgV0D5Ur+uvv14cxzMemF1dgl+owUs4C0VDyupqWAu4DWvdurUkdsJZAHfffXdYpzNgcNylT5gwQTuR3H7jBg833k899ZT2kpRxo/7ahQcK6irkCTzQC9+NtPIE2mApcUm11L0QkAtB2AMg+JTROisyDpzFJdyc6+6RaQc0vzwBpXjv3r0CsEPzhqYlScP0ItC+mUub+H7pT2ezsnDDptfIkR0H+sLjz76gtUHuvSFY4K77uSfGaS9JGZ9Rk96YPOnNybhdV1chT+APvb757COtPIE2A4cNxyXVUvdCoC4EYRKYDP/7/Vp/IeLgD/5Cc3l70vO6O2WqAc0vT6igCRzPkZZYYPSxqajSmKAJlEuNalwhdsWOsxjhq/kbqU0ETZIdrUggIvKEd21CffLi5lnfDR2yMAgokHtv7f08Lqkbcq3uAP1Cq03gtl8G0bYJaL0xC2IcpAsiFHz0xU3LyJEj0QBahjSTl9ou0BSUMCGyAq6iEsIECnhGX9ULPKE7yOz4lV6rTSiT5Fdrf6bWmhFi+bLLLhNtAglQ8V0QIQYhDhjx7pAS8JAoCdx1434bz7AK9/yIDvAMDbigQuRqDeiCBiCALnigBkCgWmgVDSVMIDLiyiuvxEQYHHf1ShCy0CEgAABAAElEQVSBrIgRME4oHC544JcLgdoDZ5UwgVmEhqgwMFgcgWuhGG+tviaXJ7788kvhWaZkWrWKF4Vaa0Gmtf4TUOkwT2ZmLvvl1/atL/O/r8lbQkqoXKlixzatYCfutxct+RHPKCOyoGH9up5xASJDoAECKBAugSALFU2Au3fIFlpFQwkTiIzo26s75sq9pf/iKyWIIFwCI2CcUCgF5EKgJsFfJUxgIgEiQgxshjzx+LMV4F0o9pu/r8nlCYSXCsPudRPMD5MWhk6gR70E0Sa+/Gbji0+GPh5HIAErETBenvCiTWRlZSHvmmBr1Sr3C4TuD6U1IKxAe5eodAftfg1VKWYUtKfDfyMhCkhjSAbKEq/dcVXMU9qE1lrpokIwIDpolQ5oDc899xza4GYYl9REGEFkCEgkeIg8AR+lEs3QGL38mdqrzcFVwoyYmBgs/blz5xA6ITYEN5QZegGyZ9AR7sZx1w3z8Oy5jqjH3TjqlS6DESBJiNwgIoW4hgWViAntLLjDR3soFLiKZhjE0wDp7uezdnDVxasLQdgDpyRiArNoNRcMNXbsWEwHx3EJTqmpbV8wszyhAtnaNsmNcufD9gRKpRevWaXCH1tzb9q/XbjYNtrEjYMHymYK7QoOv+t+3HKjZtaXX3tqE6jHrTg6Fk9NkV4YAZKEyA2QNtRQUDckYgKDqFlwe48yFApcRUsIE+qS6hhQISAXgjAJfknEBLzQyi5TP2g1+pHcGEz4jkvwKyCzLdfYtPLE6tWrsetZeHavG285sDQ4CAJY6Kfm5kbU/rHp4Oath6tVSQtiEHYhAesSMFie8BKNhhtmFc6An9PDgVJJD0pokFkgVaCA23IcDqJiCqRSmSF7OqSNqvS/AAkADsosod+BazdiaIUJjI/B1fgSQyFGKvUBL5VKIje0qMENoTQz+Dk+Pr5hQ1es+I8/Xvy2Z7AZek2n9AXtgEos0AoNbg3cOqouEnMhjUXgQNlzsVSNaqMdP6CymyXSV9mjdUHNpWZXE6ka1QaXIKxId0gPWmECl9Deaxc1oL0LIk+o9B/IPQExCLkeIut1Zmam+l+yDbWJyC6GgbOrtca2DgOnDe9USl/QTqPEgoLSQ6CBW0fVRWIuZDRIG1KAfKAdH2VVIyKI29WAXrpZIn2VPW4uBGoStBUZAdKDVpjALNAj8CfTOeRwWZEnunfuJF5L7gl8HsrLSD3DDJm6bEpUg4zYSJnBeY0k0Lzyxc07C77fauTUnIsETEJA5ImU5DixB1+P8StmmGzzok0sWbJEJsNddKlSpcIxsVIWIIJowyJENVCqhOzakEoxQ4QJlJW6EZB5mE5FQCBoAgpIQN09GyvblAyhbYOtK/JSmS0v1e0fuuMBq0S80MoW2nGMKTdu3FgmchODjJndgFlC3GGhLFS6gOeAqkYCK1QXvQpqfO2Agdqj2qv3oXY02QWDmjC5oJ3LhGUTyhM///xzdnY2WCEvTNO6NUwIjSaFg0DTujVl2DXr12NnRzimMMmYIe6wUF4oXcBzQFUjgRWqi14FNb7bgIGapNorGUI7IHJhyssweaGdyyTlXHni5YntWrUUe8wgT6hvyO1rxJkpY7JJVsyeZiDpadtLXbdkS5bl7g7mgwQcSMAwecKXNuH1RkivxVDiwl9//SVjqht43KJLjWqjLqk4i+CSTSA8QUJCIH+EngITRqrbeK/24D5HHJFJpYxnrQaB0AmEckgDr/eKqle4C2q5AfnYsYtZx8I9L8c3mIDSJtSKaw1Q+zhk04f2kkPKZpMn1FfhqhllU5KKOWQV6GbDWtWKXrj1yck59+vKVQTiBAJIpSFuehU7KlesKFdxBIkTaIiPsbGx7771qnnkCfWB3Kaa62bVOWvhZE9b5y33kqWuHT1OpkHfHUvAGHkiYtqEZ0wBIghksd3iJlCpJAkVZOFVC/D9XkFf2TqBcAm3/Re+O/q4euLECbmqbNY2VkYq19RVyBAStYFLYpVWsFDNjCzg6NCiRXPfDwhiR0ZMI6fmXEYSUKKDCpHQzq4ECyVhaK86pGwqeUItROM61R3Cn26CAE4SvbRShqBYtnwFmTiBgPpw9jxRFe4rwUKFVziBCXxMSIg3iTyB3Z07d+ZmgcFD3azKSz7bm0CbvLgJpJzYf8DOgWz2Xkd6FzoBA+QJd21i69at+/btE9PVXUronniOoIIjVEyE6A64Y1e39CpuQkkSqrFXLcBzFm2NZKZEDXQBNbu2QRBlZU+gfWGAipIwQ9AE7EfKCQUcZxYG6pE52+OrHm7tJKUlDrPQxUj1/4U2iYOMrPZBeL3tD272Ql0I1B5lZHD2OKSXeeSJZcuWCfNGtalNOOTd53KzUe1LpfTrb2E8rjgiTJHuETfYktLymmG36mKDunX3TCqhNkF4vecPbnZ/XAjUJGVncCbZuJdJ5ImlS12BLclxReuXZ7IJG7/j3F1rWik2LrqI1C5b7tKn3BvxNQk4g0C45YloN4y//fab1OBOVUV3u7XR5aW6DVYxERJcoOoxC0QKSBW4dVdxB1JQ4oX/lqCj9EUXr7kh/B/Ka8uOHTt6rfdRCTPUHhNIFeGwysfsXi8B7Pr163Fp3bp1XhtYqBLCASSJcNyHQ3eQYbF8OEBUy0QlhtFFm/DThaDtUUkftS6wrAiIPDFo0KBt27ahEqkx8fzqq68i74NqE+7Crl27Dh06JLPUubRyuKfj+KYiULd6lUu+WgST1q7fYCrDQjEGwgEkiXDchEN3kGGnfvBx357dtUZiRnkpZ5dqLwVR9t+FoE0qXZ0n8rivjMgT1986SrLDSu6Jzz//PDEx0b1p2F6vWuXaXdWoYgxyEPDhHAKxUUXqlY/5dftZuLxqzd7e3V35gJxDgJ6SgJaAyBPdBkw7dvwM6pEaE88qYb+2ZRBl9w9XdS9XtWpVifAPYlB/uuBuXDY1SEwEBAgpqL0eMohIFRKeoKIntPqFP3OhDbqoWAmVDtPPvmFqBpfxkMG15TBN58+wl17q+plOFAp/upizDQ7CxP8n6s2MyALcwOOIUF2sRcCLHKKBiAzEYkChQAHPKEskBa6qoJigZ/TfBWPsCdoRS3eMePSE+iocGxNduVwZS8Ok8YESqJ63p2P3nr1Hj7n+sQh0EFO1xymYODFUCRMIK8DdO44I1cVI5I+UQzQQkYFYDJzfiQKeUZZIClxVB3YEPWNALhhjUtC+WK5jxKMnLn6pYNCE5d49IRvcIG/RoU2EPBgHIAHLEwhf9IR73IT6KqxuU8MHD3qBxDLgWd2lu8VEQKrwbKNUBv9tgw6CWzjZ1oHckwhScJvI/6G8tsSpH17rfVTiblZdhftQTPTKgqGGDbSgFh2bKk+ePFmsmCUT74lSAN+hEUCP0MoEEyZMCJSJZ3s5ehPagey2wHTaNpgU+U1FvNDWB1QOyIWg7dFLrAnINcs1jmz0hPoqXCWjXBR/pwvq3dP8GteH8y8fvxHUABHrdGnF8kiHee7CKbZr1m9o09J18FPEDAptYlEKMAY0gkf+ca/2KIrHn30htLFze8u5m9AOZLcFptOOiUmnT3lbxAttfUDlQF0I2iS99JqAvLNE48hGT6jI4voZMZbAZTYjE+7aLiadmlTJbLYVao9adCdoE0XSxwuQ84fGFUrGDA2efmnJ9Jlrlq/8C8Z06VjtwXva4tkMhtnbhjBFT7hrE2vWrBGO6jY1fFiV7qDN2uAWE6EUBG0bVRmQbdAjEAQoSgeEgIkTJwbU3Xdj7Q2w75ZyFWZAIkEZviAeBNoEXmKQIGQXf6bzsw2CZaQl0mFu3LixUaNGfnY0VTMl+rgJEzoaiUAMSVomBUg5GBxlbPHAIoYoTGCoQF0Izh69gq90BGvOoSIoT6xevVqY1KxSwZxwaFX4CMTFxlQsV3rb7tzf6Nb/8afVtQlstRBWbsKEjgAb1a8HYQIDIiIDhW07dkoZWzwQMRGiMIGhgnAhOJPuGnmLjlhsNlSk5Al8T5P9feCpfkK3GVu644NAgzxBCukwz57NiY2N8tGYl4wkcO3N06fPXKtmnLdoc5dO1ahNKCBhLXjKE9j4fNttt4UyaT5tAnekmzZtkuEM2Iuu7sPxiS/SA27UZaOHcklJFXIDL/VBJMKUjoi6V1EYCxcuDP0YUViiBgzIKvy0LiYh4AK+yzYT3JFGNnQCgRIlS5Y8cOAAbENWVItqEyqQIVDBSFbEn2fRDhCwgM2u/rQPtE2gLgRkD7a3yPh41iUvRqDeWbF9pOQJSISCq2oF14HEJqTnNTBBVYrB86e8kFLM17bwzsPuPXbyYvJztxgHt9FkzDrVKicnJV7WsE6X1s0yypQ0IZnQTcK6izaxdduO0EeL7Ajf/eAKZNBGTOhrEnZwYEBEK3zz2Uf6jiyjBeFCQCZhh4uEe+AZ5XC4YI8xIyJPbNmyRdGrVTbfl2dVH/GC18AEVSnm/fV0heKJ7hu6tZaXe3DnkcxzqsYzxsFtQGnZpGJsWmLRzrXiBzRJrFrSpHyUU0EUapVxBcvk5JzfvvNo9WrpQQxihi4ILlAhBriNr1YlrVrlNDzjfn7gVXXNYKFXGwoK5XhzynIlTMD+Zo3LozvcwXNBXbyOz8qgCbjJE3fccQfu5UM5fyDfx9Nff/115kxuTgs88F1cCuF7VroDbs4xNSZSNWpSuCcSBlJmQsJAfXBBEzIg5AOVclLkADVRcAVljDaso9ChIGeIogF7tCYhdELl1Ch0kDA1UILRjh1W/SosEQ1h4iPDSl6J8CWLDdSFgOxBkIV4ofYLhJWVbQYXeUKJtkiNiZwm0HPD6iAkQhm/fOkSYZ0o3IP/tMpXet11m7dphQk/jUEvDDvpnRlX3fHwk2+8E8QIfk4UwWblS7s0l+27dkXQDF2mlogGXYYqaJBZX3yFS5UrVSyoQYj1QbgQkEnqXA+VkiNEg23c3fjcE0qbwI19Sny+L8/W4jx/w2kfBq/YcVYrTPho6XYJHTHyI7OO1B2/+84PDgU3iNuYpnpZNiUqPsaVCXvL9iOmss1PY6BKdO0/rXnnN7EDAqoE/tBx89bDKOAOH9EH+PNzKPM0m/6ZK2JiwrguH/134IN3t8WfmUUW86DT0RJt7olz584NHTpU/QYfxCz5Pl7V92BkwSxdunQQwwXUBbfBEiUB3UHuydWtvnYc3L3jJW7+5X7eU7/QNi60jN/SZVLMKL82F9rFRwOVuRNDiXTio7G6pGQRUZVgj1JMQjdJzRJcoWzZstJRHeId3Dhm6CVbLcJhiYyMe/tw39776UJA9qhYCZwqEqgIEg6YFhrTYHkCSrGItkCk7lEthEtrqm9t4qdV67WNfZTvumGA/A3r171lwzoqFmPGN4v7jXoYaoWPvla8pDSpHTt3W9F+rzbLVguvl0KslJFXrV4T7nt7/10IyCR1jMikNycHoYOESM9y3Q2WJ5Q2UbWEtYMCfGsT89f7Ui7c3iRP9C0uf/d3TUHEhArHmLzkRJ3xu6FWuLW3+ssqeUu/Zdthy/kCAaLrgKmiR8D4Zo3LjRjWDPfzuJPH9oe04vGoPHwkgNU3CQHlEdwxiUnONENHecK7NlGqVKnoaCM+fEVoUHf1XnUH9Uu+LLbby0DfAeiuQv0hBPgZpyByBuYSfURNCt1E9BS4cP/993uNnkA0BB6qC8rSLDdk4oLsgkvKJFx1M6mgqdWA+hZUvIx1tQl17y3RBIqPn/f5qr2PgkyBG/vevXvjh3Tto2HDhvg53U1jUqEK6pBRGRwjuNVIfaAuBGQPGos9mB3qmFd5Bfa7ueCDhqMuGSlPqL3NIGzRPQvYdiFvj2U+4ybUVdW+oDcVJAn5g0Lx6j/vwVaRh0beIAoF4iaefP1dm0VPqHXfZtlANrWUapOChBKoev/v81WXggoyBe7qu/a7Fsdwav9qNmuDI0Jkh4XqruIU1CGjcgkjuNVIfRAuBGQSGotJMGDgsOFeFRa44OaFcseBBSPlCfXrXeW8G1RrAceeCzG4EG0iL6pCtffhJiQJ+YNCMefO0tgt8vLgdFEoEDdxx/t2i56onO66M9pqtbgJBEcgJkKkB6gS33x64y/zR7zxQm8IE5An8PLQ5gdRg20dPpY7spdgpPwVZIbIK9qrhXbRNmY5dAJ6yRP5BIhdeVGjBgRNCAKVDhMvcRPuI25CIfPaRl31p4AgBZEAICjgBsyfFA8QEZCfAoOPGzdOdAQoCBgHxkCSwANDQXEYMWIEatAYvkiNbEXRnuKhbvm0W3GgmIhVmAKHiWjzdPqY2h9nA22jln7Pnj2B9jWmvdebeZkaqSixzwLPkk8Bp3LgxlvuwyFMuEkVoViLLJsQILyKHbjhx0TyUBFNMEkkAJgkWR7QDDVip6clgboQqD1PPfUU3n5iA+QVIIJggRSeUgPDUOApHp7rIjUiTwwaNEi0A2zuQP2rr76K9D8FdQmuXn0gIydi8eSk4AaJbC/kg4DcgHCGXXsP4E/dabtZJVEViIPYtXe/26VCXw7o2r7OpZXuGP8iVAlM9Ok3iyFeFNrLKg3KlnRtaT52/MTp02fi4+NMa7nXm3mxtm+v7sgB0bdnN0mmgFM5cNctN+EQJuSAT138wvEWw7fv8Cp24G4fE134+/rjKW+5DOvZXe7/YdKiH5YibOHIsWOoETs9TQrChUBNeu6Jx6BKwFqYAYUFlCBYFE9JEcMQEoJLPMVDuzSG5Z5QUWwV0yyZBBHJICA3IJZhy4Fs/BWUEkKUCwRBbDmYreXsZ/mWtklNK8X2enkftAnMhQAKiBd+9jV/s0rprqX/a+8J81urtXDkvZ9fFCZmDPW8jUdjaBb40/YyVTmI3JZBdDGVy1Y0RuSJbgOmHTt+RjZ3wAvtDa8/TuXTJg4ePCh90tJyk4gY8NAGQXgNmoANbmKEijUI2jwIB9AX1Hmil19+eaFj9u3bV7QJKA5qRwY6innPP//8Y489JvEOUCjw0Nqm1VwgTEgzzOg2KUyCYoKOubkoli9XV31MrZ1Fr3J6uuur8OHDJo1Y83EOKO6xoU2AJO75oQ6AiWgEAkdECtEIQsSFW3fczGM1JWBBO5qSG1CAjCJnYcAkWCJTox4P6QI5AA3UJTVOoC4Eag9QQDdR8goME9uUATBMcKkaFrQEjJEnJCst5k21pjABy6E1IFelbLVAcAR0BC1GKavtHnWqVVJlz2Y+aiB/DOvfA4kn0GbKp3PtpE2kpSQrxw8fPVouPux7LdV0gRZ8nAOKG2xoE0iB+d2SpaJEiEwgU1y4eoloBIFO6tYen4Qd27ZGWIEKcFANlNyAAmQUOQgDh3fAEpka9aoNTvS4a8Qt6pIaJAgXAjUJNHDW6fC77hOFBba5kYFtaKNMYgEEjJEn1DfkUkmW1CagNUBxkH0WECBuKelF71YhFU0qxapyoO8xKCCju6Qg8QQ6PjfvmJ20ifRirmDz/QdOBoolgu2RZkJtfHhjYh+vwkQEzePUNiMQujyRT5s4dOiQAEpJMUjm1OoRKneD5yLhRh2366h30yk8W/pZgyAFdZ4obtKUEFBQdzRALMOsWbNEoYCkgjM+lPGw6v3334eyAFUC0oMyFTcwcAotIU9gZOgaXoMmZFJt6ITWJN9TF2Rw0PVq6Y8csWSmH3EcP2JDF1D3/CJY4IYf/N1uwoMANXbsWIyDu3cc0uF5A49volBPZKFhgGgTaIw1RSVUCRggd/7oC5NQRvyFp1X+uxCEPfAasy9evFiAwACRS1AJVhBcELgBw4KA45wuBsgT6gM5PfXiDaq1CCNWonbuto7FMHt9bjIIL9rEus3bxak6l7o2gAThY/+u7UWbQPTEvB+X4+QOr4MgLQXkD4njQANEaqClV8XEa3fjK1OTi6lJDx06XK6MebUJZaePwtuTnp/0Rl11zy+CBQSCaR9Md7sD9zFIQZdGPzIeqgRu3XFIh+fdO8INnnj2BdkNAQNEm0BjCAGYHUETCElITcFtfz30lQNHoQ54WhWQC0GYBO9gwM8L5sLUVavXbt2xUxQTVFauWLFD21Y4DxVmFwTBsfUGyBP797uiutQNqrVoI1aiSRfXto6CMkGs2O7KENE0bwNIcD4iekK0CURPzFiRiZM7PMdBSAXkD4njwFU54AMdPVuap6Zknix18NAp81hVqCU4lUPaII4gxMgIBF/IGR/YJLJ522E8S66KETc18xqkgAbIu7n8t93QR2ADjs9o1qgcdo645Ybwp5nboRu56TP6T9P6rhpoK6V8/tA4t0o4gvSf8xa6EoIWZBh6ybDwDjtEYKccC4ICXnp12W0iZ74MUZ4oos0zf/XVV8+YkfvTE6IvsD3BmUAd7jVuU2+88UaBsHbtWpwq6nAgbu5D8pDADWgHuIF3uyovIU8g64SUtSkDvDYOsdJs9oTojuW6I8pXbe6A8TjSWcfNHU8++eTDDz+MYS9rVOeVR+8xLRx1wKf24E9VOfOVf+McDRiPrBBID+Hpxah/vSjhEriKk0SlgXYo1KjR3Oq1o415/k2oEqhB3ASyUahLqi92lEArUfWqgLCLV8bdo9JqqnqTFDrc8PfM02dgDHYieIYDmMTIiJuBUAgJ3IB2gLt3r/ZAnkDWCbm0b+Nqr210rDShSTp6Z86hTp06ff2to75f+pOYh+BW/ISQmOjlxjgI+zMyMiTu9cPhJfs20mfMIMzw3UWd7qk9+FNVrh1XHodoYASkhEBuCM+hrnx5n4RL4CpOEpUG2qGkRg3oeUmNef1/DkCVwEvETSAbhVtH7CiBVqIaqwJiLr64s7TKqanqTVL48JeTN005CGNqXJr+x893mcSqQs3APbzETUjmy0Lbo4G6z3e7q4fQMGb8PK8jQG5AAgvtJdzGYy+JtkaVtTf2fjZzM8lTm1CDexbcvEBflX3DrTEO+MBhH9pKpU1Afxl572zZGoMGWhe07VlWBJYt3yWbO1CDQzamTp3q5+aOfHETKmJN/XiuJmDBIQS0v5ZjWwe1Cbd1l4AIVBYkTOCSlqFbd91fms0e3R00+YBhjZ5QH8ipSab+Kcn3GkEREFFA8kF4ZrsUYQL1IaoDCLsQbUIFYrgZBmECs7RsWDslKVdynffDctlsgueX35mBnJpu7U3yEjt6RJtAygmTmGRCMyQgAoYVJEzgksHhBiY0yYQLp69JYY2eUMGkacUsuacDqKEIiCggySA8s12KMIH60NUBRF6INqFiMbRrnRvEUTG2c+34tITcXRKfrMiUUA48I+ACCTW1jc1TTs9bemvFTYgwAYwhBk3IQiDEoFrlNMmaifABDI5nXILEgPFVQATqlTCB+oFX1UObw0dOoV5iKGQ0P5tJY+0zbIDUghqllchLbRt1SVuJ2ZUwAV/E4OUrd0+fuRbN8IxenkMh9OPam3OPXMVDwiXSUhPkJZ8LIhB09EQ+bQJH1skEsbGuuK+C5mO9XQlol/7sWbud/xT6qqn8lyhg+4PXAVU6Cc9sFF7bh1JpNntC8cWifcMnT+Tk5AiT2Jh8H9SWA4WUE9hMAbNxVqibNqESTEAyCNEvlWjz+IncH+s8HzjXA5s4VD3CK2DVk2+8gxoUhvXroUZQbcxQSEp0fQE6cdJKO5wNRqfyX6KArSJeZ1fpJIwJPzGhSV6x2KwyfPJEZqbrgyU1Xue0x0YuAbZOTD6Qq3LirFA3bUIlmIBkELpJKtfm4cxznqPhUA9YouoRW4EtHnd+cAg1uekzu6So7qqNGQpxef8U5+R4ccoMFnraoH7nx6Vmjcp7NgioBnfyONrDrQtu9V039p+tVdrEm//LDWPEwzNYQ7QMuepnM2msfYasIJYoAcLTMHVJ2xGVwgSmagM9VPgGYkNwCeNre6GLKC/KQe1VlgsiEJw84UrrIoOeOuXaQJWQQDWoIM42r9cu/enT1jvoONzLoxJMIMsD9m54TofMESpbp4/YCs+OwdWYzZ7gvLB6L5EncJSsOIKTO5BnVLtdLjgHjx07Jh0T4sx7OoM/rl1IOZHbcN0mpJzI97h4emgIySZkRBV2IdEQ+aa58EIrTMhVZJpQlRJz4dkr4jV6H/8ScYfCYoBKMHH/I+Oxd8NzDmSOUNk6cdyGZwPda0xoku4+mnNAkSfatWop5iG5GI6jUsqCOW02zCqlR/y6w/3HJ6VNhJhsQnxRkRdec1tohQlpj0wTqlICLgxj4v9EibGum6YTJ93p+T+IwS3xg7+aMfQsmF5HePDudjIFgiDUXKrsKRnkRl7k3fn72UwNG2JBpQWFAVphAsNCdFC6g0gtbnPhjFXVwO0SX/ogIPJESnLu91g5uQMJ+Hy0x6U8DdB3K14lARK4QAC5LXHbiSKCI9q3b4/ICKUOQKqQVJeCCnku8Qg3NrPZE25/TTt+OKIn8CEu/kZHWzWEWOxH3IQUVJSEWkdEUki5oOyVqmWYCphXrIJKYs4DPuLzwhgz8348CBMKSw+L3JbD77ofLiA4omXnHh3atFbSAE7fRKXKaomzNvBngLMmNMkAr00yhe7RE8hlrlwrFpfvVz1Vb4mCuv9XSoQyG5EUUvaaulI1C18B84pVeDbn6R6xef8U5+ScDx8Hy43sdauINl7Dh0d+NvMxQkCXlBTiVWXARhVET2BAbPEIaFg29k0g0OiJfNqE+h04ycrbm30D4tVCCSDHxMkLwcPqZ9tCuzinAUIhnnrqKURG4H8WPHASBx6e7kMywMOzXvcas9mju4MWGlB3eUJtsouxuDaBvRLYyoFwBqScgBCgQhUkAwWWWNUYv9xKNyloJ4jxJrnNGB3t+mc6Kyvb7RJfKgJIM/HcE7kncSBoAn84iQN/6qoqQC/A4aDqZVgLJjQprP6abXB95Qm1ww5uWvrzGHslEDqBWAaknIAEoKQKyUAB71SN8Quqpva6DcR4ezxnjI66uJ0nK+tcTIyFVSpP7/StQWCC7N2AHODjPAs/m+llGw7mkKG86inIYSFXDVZM9PLOzOMEJE/k0ybM7BVtIwGTEEA0BBQBSBKIkkC6B3X8J9JP4CEHcKJgmLVms8cwxzmRhQggnURe4sntSolQYRRKIDDeI5VjoqCdIMabxBmDI4BoCMgBkCS+W7J0244dKlAC6ScqV6rYsU2rvr26F5SKIrgZC+1lQpMKtZkNbE8A6SRciSe3n1VygAqjUDXGc1A5JrxuAzHeHs4YCgHoEW9uzY1BQDYKpJzwGqeAq342C8USbd/DR125C7zKJUqwUOEV2r4sG0Ygnzahzhc4cYL5wA1bAtNNJEETMIvHtRS0Nvg/JXfDRvi3bBRkgFu92exxM88hL3U/TzQuL81EVrYrKaZ1SeIQDTFeu3VCpZ8IPRGmdckUanl2titcIsbiKVEL9TT0BjiJw7AtG35aa0KT/LTc6s30PU80Kiovmv+SS6z+eazSSWi3Tqj0E7okwrT6m6cg+7M1WzkcHjSByAJkskD6Bhy9oT13Q6GDGCH7I9ASB3YgwSSO50SlyjQhLf1spoYNseDV1BDHZHd/CAR0nmg+bcKf0dmGBEiABEjAjYDuwgTGx3HQMku21b8La3ZtqFgJuCbJJpDD0u3wDje2gb5USTED7WjO9qfzzktKZI5qc64QrTIfAX2FCfiXnJysvDx5xpUJSNVYq6AiI1SsBOyXZBNIYKmSZerllEqKqdeAERznbN7PBFGazR0RtCciUyNP5NMvfV/oTT5iEJBsUh2Kgf0dkCfkCAyEUaicmn42093TIunjdR+TAxZEICBhAoPk2yulzmhQB3YUNA3r7UpAu/Tx8RdPeLKrv/SLBEInEA5hAlapwKVTeac7h25qpEaAXuC2lUP3ZBM79x4Q72rnxWhEyll95z3PnGv6AuVodieguzBhM2AQC9zkiXAkm9hywBXwpcI0bIAx86xLlkoqFmsVd7TnhmoP7wzOfkRAYI+GEiagLGBzBLQGr6MhJkLOtlBKBJohmKLrgKnahA5+NvM6BSvNTyBQYQIe5YubUCHEZ/N+qDG/z7RQXwLapY/Nyw+v7xQczZOAOn5y2zb3QxY9GxdUo8sgBQ3O+oIIhEmYwHQqivisLZIgIqmEBE2s25ybckIFUCjNoiDCftYfP5kpLSuUKelnF0s0O5Hp2h+bVKyYJQy2k5Glq9cXd/ZtXB2KX3qNE4oNDukbPmEiMTFRDiI9etryeiG0CQmaWHEh5YQKoFCaRejvFugdMojKIhH6mBEf4UxePuKoqHy/7EbcMB8GaHWBzdsOu22p8NHR8xKyMMg2DYzplkIC8RGe7VGD6RA9gT90nP7ZWsnjAGkDL7Vni/rZzOsUwVUWpKcENxp7FUQgCGECQ+XTJkqUKCGj84CGgijbvh5nTygf09JcGWtVDQskQAJaAuETJjCL+kA+aosEQCqphKScUMkm9EqEqQbMKFNKu0a+ywjfkAZ6SSS+pwvi6tHjrvRPKclJQXRnFxJwDoHwCRNgWLx4cdEmDp/Mi+y3LNncpBKzcq2XlBMXk03U0i1aVo1ZtUS+Gw0fzJScoaNE4mO6IC4dylv6EukJQXSPVBdEN0ikA5695oD007A3/5eb2xIPN2FCKn0/IzgCfwi7EHVj+sw1Wm1C9fWzmWofdMHr7EGPxo5eCQQnTGCofB8ZF78Ka25Qvc5niUqcRz37wmP37t2LFi0qyOYNGzYsXLgQz8uX5/5f16xZs1q1anXq1AnPnl0wpjTGmLgtwTOa4cjV5s2b9+nTR7sj0bOvJWqULIWD63CYqCVs9tNIFVYg7VetWqWSv3odoWHDhlqlJpSIBq/js9LqBMIqTADOxQ/k4yetzgr2I6kEdnbIMaJ4iegJPOOYDHVSRig+Yth5P7q+NnVp3cz/odZvckUqmTNLRVZ2dubpM+JOifR0//2yUEsVUyA2/7H8B+SP9GF/zWZtcEqoahBiRIMahwWrEwirMAE4JUuWxFc+FPafsHa+CbiApBLY2QEtQEVPoBIBDnrFOGDkGStcsu+AJokY3J+HkjNMm6JCLX2JNH+d8sfxcLeBHiHaREGKgJ8GqNMroCD42cWtGUQN0SbEHrer6qWfzVR7/wtAIV7gORSZxv8ZHdsyaGECxPJFJaXnffVRN6gWZQr5YNy4cb17937jjTfk35KCHEGDESNGvPfeeyJMoBkKeInK5557zrMXtA7U4xnNZGQRNTDOkCFDMK9nF2vVqKXHTwTWsjxQa3ECqI8uOBlUK0z4aMlLziQQbmECVNUH8qGjx+0BWcUm4MBO2dMRkI7gA8Kn3yyWq5giILEDQRzSUZ0k4mMW4y8d1chS6emOCGT77ocffXDGyaBaYcJHS15yFIFwCxOAWaqUKyDr0EnLaxNwR8Um4MBOUSj8FxEKfWtNXuKK9sIs/usdamuJaVNUHDjhCpmxVtxEl07VZMmgCCh9odBF9GygTRLhedWfGu0GEx/t/WzmY4SCLiGERC75FkcK6s56PwmEIkxginzahPqZ7vDhw35Ob8Jm0AggTPgjE0BlgAwhLiD8YeTIkYh9UOESECAwjlcHEVuB8yPRHs+qPUIq0B5ShdcuVqk8dOiQmGr7DR2+tQnfV62ymrQzTAQMECZgOX6mE/tVVH+Y3DFsWHX/P+8HV4yDqgnFBigdUz6dKyPc1K+7/0Mh2kKJGnqpJP7P7k/Lw8cuylJpqan+dLF6m++WLPXhwndLfCkXPjryko0JGCBMgJ76hrw/7wbV0kjV/f8neQEOqiZEvyB2PDfPFdl0f1dfMVDaiRBqoRQNHVUS7RShl5UsVaqklcKKESCg7snVwRmh0Ag6p6bq6DvthZ/NgnBByTQ4aiR0qSUIA5zQJURhAojyaRMZGRlCbd++fdbFh3AG7K2AfICHDy8u7PaYjQZoPH78+DfffBNCw+jRo1HAs+zOgMChxAsZCmN+/vnnEydOFGECz2iPh9rN4dbehwHmvKSWvmzZsua0MESrGjRoICP4Vh/UVdU+xHnZ3TYEjBEmgEt9IJ85m3UkL+mApTGq+3+1/0JFUgTtF4a6Y/yLkjZiQNf2/g+ILtqOAUVbBG1toB33HHCJxUg2ER8fF2h3C7VvVL+uWLvIp/qw6AeXcqHaW8hHmhoOAsYIE7C8XDnXL647Dls+3wTcUff/avOFiqQIZZkwWq+X90nmiFvaJvk5Jtpre/kfahGKqUH03X7ItfTlylgs+49K/Yh4ARyT4TV6ApXIB+Hjjl1tgsAxolp6SkrQVsreDW0NyjhDVGrUUH42cxsn6JdKpoGb4OA1egImebUq6Ekd1TF0YQK48uWbqFKlihDcv39/dnY2Mg5YEag2tUTHjh0LckGJCFAikF1C2wzREwiCwDYNVKIZNAt1VUVJqBoUJOZC9oBAzkBfJVVom1mijPsusbNChQqWMDhQI5FjAnIDtmxsv/CoVKmS1xFEm2jXrh1aeW3ASmcSMEyYAF5thpRdew8Ut34qRMkuAV/wBwclA0VAb6Qpn32l2mM7BrJFiCoho915wwB11bNw4wNPIh9nSlLu711InIlNJdIXmSZ8d/QcyrAaAYXpKlesaNikEZkIn8yQG7BlY9uOnfirXNH7P0ASN9Ghbett23dExE5OaioChgkT8Fp9Q952MO+0BlOxCNAYyS6Bkz7lsE/JQBHgGJc8/83FzC/YkYGEEaJKYBwM+ETfArcGt3lmD/JxpiXk/j6KXugrHZFpwkevQM3Tvf22Q66lr1KpQNd0n1SXAXFPjsMyID1gtFx5ov80RFLgeFHELxw+cgo1y3/bLaoEmhU048B+dUXUQPDF8pW7mzUuj5YQJpDGwrML5kIzTCGhCpgFioZSMVQqSj+beY4fdM0bE/vIIabwunnnN8EBcNKKJ2g5KCkn6Fmc2VEXYQLo8qkP6pP33Llz+P28fPnct50tH1AQJFsEfHQTJsRf6BEIrEAbCA0oQK3wzQGDqPwUf/zxh++QDd9DRfbqnj17xAC7ahPQGqA4QJuAmxAgtMKTIq8NmlBldZUFxxIwUpgAZBzqjF/qRC7cve9AvepVbEAep3LMyMsN0aWNr9A2r85OemeG1/q7bhgwrLDdHNj6gT+37tBHJtw/wpyJMGHq7n0HxeCKFWz7z7E4CK2hY9vW0CbwEqETQwcPlHrts9rQARVDlbUNWHYUASOFCYCtWrWq4N1iC20CviCoYfIBV2KIq/3OWKl9jz0y64j2pSpDX/C9mwP7PvCn2ksBcsa7fytp2kSYMHJr3tJXrWy97D9IYIk7cOgFIhDk6hErXT9GqoXwvdUCI8xbuFmCJvCsoidkw4jnaBA7oGW4xWjI+aPaifxspowMsQBrv5kx9Nq/TS+IAywUj0KcyGnd9RImwC2fNoHvwfg2fObMGVzAF2IbaxO//PKLvGl8iA6QGyS2AikkfDSTcawbKCH2q2eRbPCyok1/poM2obZpiEKhfFcFVa9aqktuBbxD5syZg/aSOBOqBx7QOwo6AQTNXnvtNbQXyQNRG5gCXdyG1b5EF8yC9m5dvKoq2o4s60vAYGFCjIdenKdNuO5R9XXK+NGw50JpE3WqeY9a8tMqyAoIxEDGiv5d2/sQF6Y989BPq9YjUAJREpKAE+PndqxWGcZgG4if00WkGTQpmbdS3o7LiJhhwKSIlWiYt61j1QWFwnNSUS5QX+iGjqkfTJ/15derVq+RxJmIs+jYptWNgwcWdAIImk16czLGF8kDURuN6tfr0LaVpw3aGvSa9sF0bDNx6+VVWNF2ZDl0AgYLEzBYaRP4hf/Y6XMp8fn2RIfukfEj5GoTeUkrm1SKDdEAKAuIxUDSCmzlKEhf+OGBsvPXu8IrVOZL9EJfGIOOIdoQ1u57juWczjovU1S1WtyEmD3wqrr4w4YF6Ai4MxfVAPfhaakJeEYcBK76ZvjRfwdiXwYCJUSJgMQgp37KmNq+m379O8QLaBkqIgNTIEIB7bXChJ/NtCOHXoYlmNeTQ7XKaYjyGHhVvfAl4wzdeHOOoKMwAQeLnD/v+j9NvK1Xr97atbk/XNx9990DBviKjzUnHTer1J4O7UYPtMExHJK0EpkmvMZNoA1iKyQXJjSa999/321kz5dqLjS2qKxz8uTJXr16iWuzZs1q1KiRp5vWrVER8osXL27fPveGBAoCThL19Oj6668XIQBXcZKoNHA7QxT6wtixY5WKoR0Ew7777rueugZUDHTxffyH2ywwY9SoUV67XHnlla+++qp2XuWg2yDaNiwHRyAiwgRMHTp06LRp01Do3an1Y3feFJzx7GVdAlf//Z/bdu+F/RMee/hvNwyxriM+LFdniP68YG6Ly3ugJRQEnCTq2eWaYbeKCoCrOElUGridIQp9YfQjjykVQzsIhp0+5W1PXWPWl1+NfmS87+M/3GbBsLBk+N/v99qrb8/ub096Xju18tFzHG0zlv0kYLwwAcMQRZuS4srs+N39ZVtUCfVm3k9n2cwkBBb9ebrH/+Um44uKKpK56+HY2CiTGEYzSCCyBPQVJuBLvrgJvFbaxKZNmyLralhnV6dpqORGntOpUAgVSuDZRtVAyJAyVAmLChOwf8uWLeJFkSJFqlevLmX7PSNaAQ8EUOCeH+KCp4ggwgTq/7+98wCPqljfuJBGQkIgIfTeA6GE3kRAAUGKiBQRBBXxKuJfUREVRS4qIIhyEQQBgQBKUfACKgKX3qSGFgXpEJReEggJKf83TBjWzSbZ7J7dPXPOuw/POjtnyvf9vuNm992Zb7JZ/gD9QkgGaCMWSmBAqA+oxANXreQJoWXgEnhidqyVwDPKmEtMl5kzukhhAo3FQglUYhY0xvOYMWOGDRuWuSNrtCXgKWECXkRERAhfjpw8q61THE3/BJAD9cxf6R+F8ahWpbIoGPgZqxXwDwso8IUf4kJmEUEIE6jPZvlD934DhF6ANukLJQoUwIBQH1CJf7hqJU/c1TIyhAnMjn0leAZkuRrCJnD0ksIE2ouFEqjERGiP51HjSr3/1us2+7LSSQIeESZgMz4T4gcAIf0fOJdEbcLJOCrX/UDsHWFz1UqFKUwoFz4a7CICmgsTsNNam8CvxIsXL8YFA2sTEL9lhGzmthRXLXNGQJ7IXnHAKgPRK8fdH3JqHRZk0PFNOH9+lU5Iyi1MSANiww50ASttQioF2Wy1kMsf0AaLF6SEAaUAl4RCAeEA8oQ0DC+FMAGJAc1kl5deegn1cnWGbI+CZZfRo0fLS7Acs+AltodgNKFxyKssaEvAg8IEHJE358nYv1JSUr28lF9FrG10jD3asTPnUu8tbKxRraqxnRXeQRrAdgyUIUNYaRNCmMAlbNDICoVc/oA2M/7zmZQwhr/1Oi4JhWLUuM+/nzNdjoCXQsuAvoBmssvgF59HvVydIduLgmWv8R+NkFejFjTGRHg5adpMDChkDnmVBecJeEqYEJZjManQJg7e+5rqvEccQRUCMug1qxdRxWbaSQIuJeAKYQIGW3/SlR+F8RM6MmK61CVPDY5clbmdWmz5ttkLKyaGDBmye/duXIUwIX7cttlS/5VSm6hWrZr+rXXGQnmfYxmC1ThSm5BtrBqI9RGohL5gKUyIGogIQnfAOHJwFMSw0BFkAzms1ClkDQpWXSwv4R6Tt5lYQ2F5lWUNCXhWmIAjUrRKupN86q/0tf18mIfA0dOxwtkSxYoGFwgyg+My5QTWIFj5m+PpoWJ9BHpBX7AUJkQNFAShO0DjkIOjICQPiAiygZxX6hSyRhSsellehR4h1lCgctnP6Wso+NCQgGeFCTgiPxVg3YSGfnEoJQjIoNeqUVQJg2kkCbiUgKUwgYmioqKwZlyTGa21CZli4Pbt2/gOpskchhwE2SXEAzkpIExg/QXOIsVDaWflVpfw8HClHcnReLkmQioRsousQUIHWWlZkHIABILMsgJqpHAgh5IFeclyQJvl7LtI+6X8YXMQVjpDwOPCBIwvWbJkSEiI8ALZHJ1xh32VIxBz9KSwubo5Fk3AWaybEC5v3LpNFOSzXDeBbA6y0rIgtQCbOS8hNKBetJdDyYIUFCwHzKqcfS+ZQVMqIFmNw/pcEfC4MAFrpVi878ydFGP+eJermJiocVJK2qFzGXs6qE2YKPB0NQsCVsIEfqnVSpjAhNbaBNLCFymSsVqJX3uyiIiNamz6wBd7sXrCxmUVqqBGyRUlkZGRKpjsuI1YvyB+AMF+CqkCYDi8FLe9/PKfeQ75/0VWbWS9HFl2kb+6ZB7Wqkb2tdlF7uMQ+0Ss+vKl8wT0IEwILxo1aiQK+/446rxfHEEhAvv+yMj6VLd2TYXMdsZUrF8QWzmwn0JKABgQL8VX/Ww2dEgtAEdy2LRB1sslGLKL1f4Rm91lpexus1fZe0dcXbtxQ3ZhwUkCehAm4ELjxhm3Vlxi6kEunXAyqEp133M6KTE54+iARvXSU9LwQQKmJZBZmMD+dA1pWGsTGLpZs2ZiAvmFSsP5DDPUi/ce2MeBVBTIYbF8+XJs7hBZDFR08/fffxe7eJAIs27duiq6kCubpYJgeZ9LRUBezTymbJ9VG1kvW8olSDaFhsxToEaKDnI0y2ZyHGmw5VWWnSSgH2ECjsgbIPp3ahNOBlal7vG3Eo7d29PRqJ7BxWLLwEj1QQoHuCqXUUh9wbKLKO87eEgU5AhWbWQ9zhYVl06dOSMKtSJqWDXO5qV8c5YDWjaWgoWltmLZgOXcEtCJMAGz8atAqVIZ30u3HU/MrSNsry6Brccywl2lYmhY4QB1HaHlJOAkAVcLEzCP2oSDMcLifPHAPg4cGgqlQgw0bdo0eWaHg0N7qJv8Il2lShV5UJaHbHHHtDa/3ksI8gthZlOk0JD5klWN/Agrh828B8Sqi3wpu8gaFtxDQFfCBFyWYvGJ2L9vxN90DwTO4nEC+w8fF4kwkQC1bp2Mk4w9bpUbDJDf7eXyBEwqdQqbcoCwCvkm7DRPJL+0HDar1BI2B5TG2LzKSm0J6EeYEH7JN+St1Ca0jbS+R5NSVLPGpfVtKa0jARcScIMwAeutz+lAlXznxT6FixcvhoWFudBLowwNnQJ7OoQqgaUTLVu2VM6z6OhoYXP9+vWVM94Bg6X6YLn0QJShIEjlwoGRte2CQ8u0HZCjZUNAb8IETG3QoIG3t3dycnJaWtqemD9bNqyTjf28ZBgCe2IycjbXqFYtf4CJfqZr0fReyokt91NOiDUIUBCkcqGHQBeplHHErx6MMaQNehMmABmfkBcuXIjCpj8TcYpOnjyGBE+n/kEAuUW23Fs30axR+unvfJCACQm4R5gAWBvrJnB2Jo5xFtB/++0349EvXrx4bp2yp0vnzp3FsDKjZG5n8WB7JJvYv3+/MKBJk4yPhh60xw1TQ4CwkiewzEGsVpD1bjCDU+iHgA6FCcAJCAiQ/0tu3XtQP7hoiUsJyFg3b9zQpRPpbXAIEHJxhJAk7iebuCdb6M1m2uMKAjoUJuBmq1athLN/30g5EMvTOlwRed2NuetU4tVbGblPWzUvpzv7aBAJuJ6A24QJuGJj3YSPj88jjzyydOlSXN6+fXvHjh1d77JbZ0B6CDkfsldCi5EvLQuWEoNlF8s2lmXLcbIZ1rKLfsow+M6d9BTEefPmbdGihX4Mc6kl0CDEQglIErKMGXWlTQwbNsylEDi4IKBPYULY1q5du02bNqG8hdqEOe7Xi1euHTmZsUPh4ZYPmsPp+14iqYRQJbB7AjqFTDYhj8C439Sjpfffet2j8xt5cn0KEyAeERFRunTpM3czlfwac7tWKV8jh4G+3SWAQAsSSDZRoVwhUiEBsxFwpzABtja0CdR26NBBaBP4yoq1xFhRbLAwQGvAjhU4hRyWWbkWHx8vLuF80KzaGKZeLpBBFkz7cyKo7r7UIKBQIMesTPEg6206CD4ykYTNBvKqPE1DNsOl3OLVNvmttIQFSwJ6FiZgZ/v27YcPH47C+UtXj585V6H0fXXV0guWDUNgW3RGskbs5mhU3/iZia0Cl75uYtznqETKicEvPi/zO8gTRq3ai5dYcCETSdhsIK/iNBCrBriUq5QTojtssxqHLzUhkHTnzrODXtu8fYcYDUsVVqxYgRVkmgzu/CBt27adOXMmxvk1JuGttgWcH5Aj6JzAykMJwsL2bSrp3FSaRwKaE3CzMAH7bezpQC1+phO+3bp1a9++fZr76fEBke5R2GC5OMLKKnlJNrZqYPXSUuYIDAy0uqrnl9jHvm1bxs5e8yyaQESQVEIoBXL1BCohKGTWFCzDJ1NRiF6Wl0RZahxyHCl2yEuZe1nVyC5ZzWLVni8dJqBzYQJ+4UzfokWLCgc37srYe+Wwv+yofwKbdh8QRmJDh6+Pj/4N1tZCJJUQSoFcPYHxIShk1hQs55VnbYhelpdEWR7PUbZMRjY7uXlEXsrcK3ON7JXVRJm7sMZ+AhAmnh/0+tqNm0UXvQkTsAq/3gnbtp+4v9TffgfZUi0Cf11PiT6bsXmnXWtqE2pFj9Y6S8D9wgQstq1NYMUaPg0Lh9auXeusZ/rrL9M9ZnOmhrwkG2fvh2yPZmottYiJiblw4YLwDj8IZO+mwa5KCQCqgVABHnvssex9zFGbkGqCHFyKFPZrE3IW+7tkbzav2iSgf2ECZuNYX3lbrtm226YjrDQMAZweumVPhjbxaJvWhvErV47IjJhYNCEkgM4dMn4yyWocmSbT8oAPy8ayXh5EWu7eAgq5NMOyfVZlOVGuemU1GustCQhh4te160WlDoUJGNamTZt8+fKhgBSJS6NvCVP5bFQCCDGSnuKRP8CnJZNNGDXM9MsWAY8IEzDEtjaBCz169BB2btiwAds6bNmscB3O0RD5PrGzA8dqZPYEQoNYN4HdH/YcuoFFE3Ice9pnntGDNWvWrBGzV6hQITw83IOWuH9qKQH89NNPYnZZk5Ux8lsiIi63b8jGqJF3gmwpx7TZRfa1LEhd46uvvso8i2VLlh0moIQwIbyTb8h/HD995u8MJdFhx9lRzwQ27tyXdCf9b663l1eHNg/r2VTX2Sa//y/75Vcxi6zJatLO7TPEi7kLFsvtG7IxalAvXkqZo1ZEdVETZauL7GtVkNLGpK9nZp7IqjFf2k9ACWEC7uDTo1w6sXg3tQn7I6xky4W7MkLctWO4fz6j7XBXMiQ02i0EPCVMwLkstYnu3bsL3/Gte8+ePW7h4L5J8KcFp36K+fB1cfny5ZZzQ5UYP368qOnUqZO8hHq0tNy7IS5B4HjjjTdEAgvUyAM7ZEc9F1JTUyE/CQvld2k9G6ytbdJlqU1IUSCriSA0iDaQDJ5++mlL4cCyBiPL5RIoi80jp0+fHjNmjGUXzAL1IfNcmEIoGmJMm6sncOvikbkva+whoJAwAXeQn1ge5/zr5p32OMg2ihJYvTVjacyDTRsXKhisqBdOmi3lg2U/Z2gTciVFViNDvBC7LaAXdO83wFI1sKyBhCH3hqAsNo+cOnP2o3GfW3bBLJOmpecUyPzALEIoEcPaXD0BsQP/MvdlTVYEVBEmhP3yE/LGP29fjEvJyinWq07gzNXkHScThRc9Hq+huju0nwTsJOBBYQIWZikBVqxYEWkRhSqxevXqhg0b2umPKs2gTYjFEdAaoERAdBAHbSD9p8w0AWFCShjwC9kx0RIPtMSuDQgc0COOHDki26MNulge2KF/Gnv37r18+bKw03hnsuTIH/IBHpAM8EBjyAH25KrE2RlClYBk8OCDDwoZAiIClAKhO2AQy/M18BIpLaFKYAq0waYPoVZgUpTF1JlNHT16tJwFoRGaCIbCFJgXDxQsZ8k8AmuyIqCWMAEvvLy8nnjiiWnTpqG8ctOOAU8+lpVrrFeawJXrcTIRpvx+rrRHjhkvsktAMsA/jAAtwJ5clTg4o/vBQ5AMoBc0bP2okCGu3UhfMSF0Bwzy/tD7h2vg5eCBz4+6m3cTjYPUUgAAOdBJREFUUsKGLdvAvGCBApgUZTG1TfvHf/ShkD8wUZvHewhZBB0xF2qQvQLT8RQPm+hsVqolTMAF/EXGtg4cvp6a9sDC3bdeaRlk0y9Wqk5gwc6MRRMFgvzatqqouju0nwTsIeBZYQIWZqlN4Bq+lgttYt26dYMHDy5QwGjpiD/77DMIDSJPBPQFS4lBuP/iiy9aRlFmuIR+gYflJVFGe0stI3MDHdYsW7ZMWAW1pVq1ajq00NUmYYWCXH0AycCe6SATzJ8/XwgHQpKw7AWxY8qUKXi2rIQ2AQ1CTISC5VoJGCDkBsv2KItZXn75ZSFeCD3Csg10CrSxrGHZHgLKCRPCqaeeekpoEydj/977+5+R4ZXtcZZt1CLw84btySnpP8Pmy+fX6VFzZf+xihRO5ZBLD+R+Das2Vi+hESyeM0OoBlAHZHfRDHrHjEkT5KIJUYmzNqBBiJYoWK6VwPoIvDPbXBYhJhoweIjQL9DGqhlUD7SxMo8vbRJQTpiAF/g02KVLl4ULF6I8e2s8tQmbkVW9EmkmZm/LOLCvW+dwPz8v1T2i/SSQIwGPCxOwMDttol+/fu+++25SEv5w3EFKAvxql6NLajXAwoeRI0dCZYD4ghUQQm4QayKQMyJzPkvUfPfdd3K1hWyPcXAJKyZQUIsAPnht2bJF2KycqqIVakttwv6v+mi5adMmsQhCJL8USgHUDTxQzmwe1kHgkugC8miDqfEA+XfeeQfSQ+YuchZchUIhJkIlhA90zGqizOOwRhJQVJiA/ThDB2cGYaEWykvXbKY2IWNqmAKOTFqyZpNwp2O7NgWCVDrvSfMotGjWWIoL9n/PR8sda1dioQQyX4okmpAJcIRH5/Zt5Q4OK1PHfzQCV6MWfL9x6zYoGmiP/SOY/Zle3d8cPtJKdJB9MdHOdSth4f6DMSfPnBVzobJs6dLom9VcsjsLgoCKwoSw/IUXXhDaxKG/7uDAjsbl/RhTgxFYf+T28UsZ6fYG9DXdWc4GiybdsYeAHoQJ2JkHH4ayMbdnz56LFi1Cg/Lly8+ePTublrykIgH8ZcUv/LDc19d3586dBQsWVNEL2kwCdhJQV5gQDo4bN27o0KEo+/n6rJz+aVD+ADsdZzMlCOyJ+XPgBxmpjpZ9N6dxg3pKmE0jScAxAuoKE/AXH54rV6587NgxlPs1Dpz6dIhjENhLtwSemX1J5DqNCC9yYMtLurWThpGAJgR0IkzAlyxzYQo/Bw4cKAonTpyIjo7WxHkOohMCyIL5448/CmPwCzyFCZ3EhWa4iIDqwgSwYC2bj48PColJd37832YXgeKwniKw8JeME7srli9HYcJTUeC87iGgtDABRDja+bnnnhOsFu6+eTk+1T3cOIt7CPx1PeXH6AQx1/N9It0zKWchAU8R0I8wAQI5aBOtW7eGMCxILViwwFPIOK8rCGzcuFGeLYLUCa6YgmOSgE4IGECYAMkiRYp069ZNIF3w89qUFH4a1sn9pYEZsecvrdu+VwzUv3dPDUbkECSgVwKqCxOC6/PPP481pyjfvpP29eY4vcKmXY4QmLwh7k5K+rryAH+fZ3rVdmQI9iEBRQjoSpgAsxy0CQjDQ4YMEWy3bdsmcvIpgppm5kBAbJVEo8jIyAYNGuTQmpdJQFkCxhAmBH75hnz+0tVVW3cqGxMabk1g/oo1qXe3WCLNRJ+eGQqUdSO+JgH1CRhDmEAcihYt2qdPHxGQKRviEu5kt0Va/biZyIP4xLQZmzOyYD77dJ2QQv4mcp6umoyA3oQJ4M9Bm0ALrCIODQ0VkZLfZk0WOAO6i9yKMTExwjGkdDKgh3SJBO4SMJIwAYcgIyIppojt3P+uzj5hEG8BVQjciL+5fO1WYW3fXt3zBzCTiCqho525I2AYYUK4LcXiS/Gp3+64mTsWbK1XArO2xl9PSF+WmCfPA0NebqJXM2kXCThLQIfCBFzKWZvw9/cfNGiQ8H7lypXnz593lgT764BAVFSUsKJ06dKPPvqoDiyiCSSgPYHLly8joe+pU6fE0P/617+Q/BXLwbSfyY0jvvXWW2K2IyfPbNi5z40zcypXEZjz468JiYkY3cfbe2D/vq6ahuOSgEcJGEyYAMsaNWq0b99eQP101Y2ku7sAPMqYkztLAOtfPltzQ4zyRMfwCuUKOTsi+5OALgnoU5gAqpy1CTQaPHhw/vz5UUhOTv7mm290SZhG5YIA0pru2LFDdMC3NS8vHtqcC3psqgoBCBNPPfWUwYQJwO/QoQM+EIsofL1oBZdOqHJDZmXntbj4Rb+sF1effLxT8aJFsmrJehJQl4DxhAkRC3F2EsqnryTP2calE+reoRmWz9wcf/5Gingx7LXmyvtDB0jAFgHdChMw1i5tonDhwpAnhGurVq2KjY215SbrlCEwa9YsYWuJEiXwq7IydtNQErCbgBAmDh8+LHoYY8WE8CVv3rwfffSRKGPpxP+277GbChvqkcCsJb/IRRNvvfqyHk2kTSTgHAGjChOg0rJly3bt2gk8n666zqwTzt0pHu6NTBNjVl0XRnTpULV+ZAkPG8TpScAFBPQsTMBdu7QJtHv77beDg4NRwMGTXDrhgvvEfUPu2rVLHgeLrZLiSEL3Tc+ZSMD1BAwsTAh4Xbp0qVOnjihPXbCMB3a4/p5y1QwXrlxbvHKDGL139ydKlSjuqpk4Lgl4iICBhQlB9MMPPxSFs1dTpm/KyKHoIdic1ikCk9fHyeNgRwxt6dRY7EwCuiSgc2ECzOzVJgoWLPjaa68JyGvWrDl48KAugdOoHAikpKRMmjRJNCpbtmzXrl1z6MDLJKAaAcMLEwgIUmbIpRMnY//+YfVG1aJEezMITJq7BN/c8MLPz+/1QS+SCwkYjIDhhQnEq3HjxthqJwL3ycrr8sutwUJpeHf+up4yfnXGookej9eIrFXM8C7TQbMR0L8wgYjYq02g6RtvvIEDk0QU8f2Wm5xVvKGXLVt28uRJYTnWwnh7e6voBW0mgawImEGYEL4/9thjDz30kChP/e6/OOghKyas1y2BA0eO/7LpN2HeC/2eLlEs4y+sbg2mYSSQKwJmECYEkDFjxojUXTjfYeRP13JFiY11QuCD5dewpwPG+PjkHfVeK51YRTNIQCsCSggTcDYX2kRQUBDefAWgP/74A2d2aAWL47iHwI0bN+R+HMj8+G7jnnk5Cwm4h4B5hAnB84svvhBnjty4eWvaouXugcxZtCIAfX/8NwvFaGGFQ4dw0YRWZDmOPgiYR5gA75o1a8rj2L/ZGn/wXPpiKD4UIrDrVNL8e6fAvjqwUZWKoQoZT1NJIEcCqggTcCQX2gRaP/PMM/Xq1RP+T5s2Dd91c2TBBvohIEOG7zMjRozQj2G0hAScJ2A2YQLEkHJCfhpGzoLfj2ccleo8TI7gBgJL12w6dPSkmOi9N14LvHsYlhvm5RQk4AYCphImBE/ss8P2Z5RTUh94deGV1PQf4PlQg0Dy3ZCl3Q1ZWOGA9996SA27aSUJ2EdAIWECDuVOm0B++IkTJwoOV69enTJlin1M2MrzBJD/csWKFcKOXr16Va9e3fM20QIS0IiACYUJQW7UqFEyS/GoKXOZFFOjG8rlw1y8cm1i1A9imlo1qvfq1sXlU3ICEnAXARMKE0AbGhoqk2JuO544fTOTYrrrhnN6nsnrb+w9kySG+ei91sEF/JwekgOQgF4IqCVMgFrutAl0aNas2cCBAwXvX375Zffu3XphTzuyJpCUlPTpp5+K6zgR9p133sm6La+QgGIETCtMIE5FihSR/2vjPNF5y1crFjyzmjt2xnc3E27Dey+vvBM++RC6v1lJ0G+jEbASJlq0aIHfRQICAozmpy1/XnnlFbm4+P3/XsOxHbZasU5fBE5cSh65IiMFZrNGpV94JmN5uL6spDUk4BAB5YQJeOnI5yF8FC5ePOOcM5QTEhIcwsVO7iMwc+bM2NhYMd/IkSPFD63um54zkYDLCJhZmBBQsa1DJsWctnD5qXPnXQabA2tDYPXWXet3RIux/vVcP6yb0GZcjkICniZgJUw0bNgQGbhNIkyAPdJh4uOWyDIel5g6eOEVsU3A02Hh/FkSQIBe+vZKwp307Ry+vl4z/tM5T54sG/MCCahFQEVhAoQd0SbwzXby5MkiPH///bfc5aFWwMxj7d69excuzMi41qZNm44dO5rHd3pqbAIUJhBfpI/5+uuvcQIlyvhi8O7n0+8kJxs77kp7d+Hy1dHT5gsXypUpPfTVQUq7Q+NJQBLILEysWrXKbL+F1K5d+8033xRMVh5K+Jo7O+T9ocvCxLU3NvyZvoQNj+FvtKhWubAo85kEVCegqDAB7F5yd1yuYhAeHo6jOg4dOoReR48eLVeuXPny5XM1Ahu7hwDyleLw11u3bmE6ZGmaNWtWYGCge6bmLCTgUgIUJiRe7HPOly/f6tXpGzouX7uRnJzSqFa4vMqCfgikpqW9MXbKydi/YRL2ccyaMrFCuTL6MY+WkIDDBChMSHTNmzdfsmTJpUuXULP+yO0utQPCAr3kVRb0Q2Df2aR+sy+n3E2BGVmr2JwpXb28uGpCP/GhJY4TUFeYgM+OrJsQqKZOnVqqVClRHj9+/IULFxxHyJ4uI/DZZ5+JP5CYYfTo0cWKFXPZVByYBNxHgMKEFeshQ4a0bt1aVEb9d9XOA39YNeBLPRCY+99Vuw8dEZYMeuHZpg3r68Eq2kACThKgMGEJEErxd9995+vri8rbd9L6z7mcmHz3669lI5Y9TeBWUhqEiaS7yoR/Pu/5X3fz8XH8O5GnveH8JHCfgNLCBNxw/P9D/Ag/b948kcErPj7+gw8+uHOH5znfvzP0UFq8ePH69euFJT179uzQoYMerKINJOAkAQoTmQHirTgqKiokJASX0tLS3ps488KVa5mbscaDBKBKTP72R2FA7Yjqw14f7EFjODUJaEWAwkRmktjZMWbMGFG//2zS64uvZm7DGs8SGPTdlcPnM762TPi4XXgV7ubwbEA4uzYEVBcmQMHBPR2CH7ZyJCYmbt68GS/x4zxOFW3atKk2aDmK0wT27duH07bxLQUjYccNdqQLFd/pgTkACXiSAIWJrOgXKFCgUqVKixYtQoOE24n7Dx9/7KHGXjwAIite7q2HVDTo35/fup2IaQP8/RfPmV44NF1I4oMElCZAYSKr8DVu3Hjr1q3Hjx9Hg+gzSSULekWWTl9JwYceCEzeEDdhzQ1hSZcOVcePaqsHq2gDCThJwADCBAg4pU2gf8uWLTdt2nTy5EmUjxw5UrRo0cqVK6PMh2cJQCrCGm+RZsLf3x8rXEqUKOFZkzg7CThPgMJE9gyrV6+OFDPbt29HM+RcvBF3q3m9mtl34VU3EEB20lc/+o88QmXKhLFNuJvDDdw5hYsJUJjIBjCyFD/66KMLFizAezKarf79dttw/xIFmXgiG2ZuurT1WGL/2ZdS7+6zqVi+0E8Ln8aeDjfNzWlIwGUEjCFMAI/jezoEWxyYhDMgZOIJZDfYv3+/y7BzYLsI3L59+91338UyFtF67Nix1apVs6snG5GAjglQmLAnODjXWR4puvjX9fhnTy+2cR0BLF4b9dXcg3+eEFO89Fy/Lh3auW46jkwC7iFAYSJHzmFhYT/88INYsoq8Bt2nXzx7NSXHXmzgUgInLyf3nHExOTV9kgB/nyVRPQsVzOfSGTk4CbiBgGGECbByVpvAEJZvvsnJyfhWfPr0aTeEgVPYJJCamvrvf//78OHD4upzzz3XpUsXmy1ZSQIKEaAwYWewvL29oRfLdVLjZizYvPuAnX3ZzBUEpi/+6ecN6StZ8GjWqMH7bw8RZT6TgLoEKEzYGbsGDRpMnjxZNP7resoT0y7cuH33a7Gd/dlMUwLXbqV2mXLxUnxGCL7+olOtGkU1nYGDkYAHCBhJmAA+DbQJjNKwYcMZM2aIaMTFxQ0dOvTaNaZh88DdiSknTZq0ZcsWMTd+Pn3vvfc8YwdnJQHtCFCYyBVL7K1btmwZNnOhFw6tfGfC9D+OUy/OFULNGv+0YfvXi5aL4cqVKT1j0gRvLy7q1gwvB/IIAQoTucI+YMCAwYMzEt8eiL3z9MxL4kf7XA3Cxs4TEEtXjlzIyH/59v81e7o79zw6z5UjeJjAgZgL7bvPuxGXns0KjylTprz00kuirOizs/kmpNtISoxE8eJUCBzbER0d3bp1ayZflHzcU5g/fz5SS4i5wsPD58yZg7Os3DM1ZyEBFxGgMOEAWKybqFmzJvJiYkNBckrK+h3RLRvVCQ7M78BQ7OIwgY07970/8RvIQxihUMHgpfO+KVmiuMOjsSMJ6IEAhQkHotC2bVt8KhYLWo9fSsa/zrUD8uRxYCR2cZBASuoDODF0Vcxt0b/H4zWmTujIEDhIk910QwDCROsuc65cTRAWGUCYgCOaaRMYq0WLFkiKieMhUEYuRiSegDyBBcaCF59dTWDJkiW4KcUs+OEUx2uHhoa6elKOTwIuJUBhwmG8VatWxZGiv/zyC0ZISExat31v68Z1g/IHODwgO+aKwM4Df7w1biqyYKKXr4/PdzO/qlWjeq5GYGMS0BsBChOORQQ/3WF37a+//vrXX39hhEPn7lyMS3m0hj+/GzvGM7e9oA8PmHd58Z5bomOzRqWXzuvp463NyvHcGsP2JKAVASFMXLqccWMbQ5gAHC21CSQl7tChw+7du48ePYqhL1y4EBMT8/DDDyNfplZh4DhZEVixYsWECRPE1aCgIAgTODc0q8asJwElCFCYcDJMjRo1ksc830y4vWn3/taNIvMHpO/14MOlBJD58pVRExOT0hcP42vJ1C8+ffihB106IwcnAVcToDDhDGEfHx/IE0uXLhV5yvecTopLTG0TzndjZ6Da1RfCxP8tujJn+03ROiK8yMrFfQoE+dnVmY1IQK8EjCpMgHcerPjVFjvOrezYseO6devEsPXr1//444+5s0BbyFajYW/5559/jiyYqA8MDMS2jsjISKs2fEkCahGwEiZ69+4dFRVFodOBIGLn4dSpU0XHUsXCvh75RpHQQg6Mwy52Etj7+5+vffIlxCDR/stxn/To2tnOvmxGAvokQGFCk7icPXu2WbNmMmH8q62DxjxeiKsnNGFrcxAhTEzfHC+u4sTQjT89W6JYkM3GrCQBVQgYWJhACLTXJjAoDnNu06bNjh07RIyRimL06NH583Ors0vu+cWLF3/55ZdiaGhAs2fPbtKkiUtm4qAk4C4CFCY0JA0Bun///lB2xJjFw0KnjHitdLEiGk7BoSSBHft/f33MZLFiApWfjny//9M95VUWSEBFAhQmNIwaVha3bNkyNjZWjPlC88AveoTkZe4JDRHfGwo5JgbOv/ztjowVEyWLB21bNaB0yQL3rvO/JKAkAWMLEwiJS7QJjHv9+nXk/pHyBPIyfvrppwUK8B1B4/8N5s6dK09IgTAxa9aspk2bajwHhyMB9xKgMKE575SUlGeeeebbb78VIxcJKThp+P9VLFNC84lMPuCGnfveHj8NyUcFh1Hvvf3is31NzoTuq06AwoTmETxw4ED79u2lPNGzfv4ZfUOZ/UBbzjiVo//sy0ujM7biQ5hYv7x/pQoh2s7C0UjAzQQML0yAp6u0CQwNeQKb6zZs2CDCVrp0acgTyB7v5igadTrs4MA+DuzmEA5iK8c333yD7eVG9Zd+mYQAhQkXBRryxCuvvCI3dyAp5vihL9WrUcVF05lw2MW/rh83Y4E4lQM5JsZ+OLxf7x4m5ECXjUSAwoSLoonVE0jHJjd3PFIt3/znCxfIx+yM2vC+diu1x/SLm45mnKqIrRzrlvXniglt4HIUzxEwgzABui7UJjA6ck907dp11apVIo7BwcHY3FGjRg3PhdUgMwPsiBEj5LKUggULYisHc0wYJLomdoPChEuDj80db7zxBjRNMYu3l9cHLz/T4aHGLp3UDINDj/jP3B/mLVstnIUwgRwTT3bpaAbf6aOBCVCYcGlwkXsCmzuOHTsmZqlZ0mfJi0VKFWLyeGepn7yc/PhXFw+fT09FjEd68svv+2DdhHjJZxJQlIBJhAlER8tzOjIHG3mJe/bsCXn44MGDuIqM8WvWrMHSiQoVKmRuzBo7CZw/f/7NN9/EmkDRHjyxVDsiIsLO7mxGAvokQGHC1XHBUUrt2rXD2/LatWsxF75Rr9sRnZqaVrdGFVxy9exGHf/W7cT3vpixbO0W4WC+fH6zJk/s1L6tUf2lXyYhQGHC1YHGNmd8QkbmeHGw6IW41O/33GpeMV+JgpQnHGe//URihy8vnr6SfngzHk0bloYwUaxIoHjJZxJQlIB5hAkEyLXaRPoEXl7dunVLSkravHkzXmJdMXZ54Gf/evXq4cclRW8RD5q9a9cu/PIp/pLBDCxCWbhwYZkyZTxoEqcmAecJUJhwnqGdI7Ro0aJKlSrLly8XJ/vsifkz5tjJZnVr+vn62DkCm0kCp86df3nk5ziYQ9QUCSv8fdTMZo0ayAYskICKBChMuCdq2I3bp08f/Hp3+PBhzBifmDbvt5vFCnhFlvZ1jwEGm+WrjXH9Zl+Ou51+aB0e3btU/3F+Lx4XKmjwWV0CphImECbX7umwvA+QshFH2SUnZ2iZderU+fDDDwsV4lF2lpCyK2M99vz582fOnCm+UaBp69atJ0+eHBAQkF03XiMB3ROgMOH+EG3atAn5gK5evSqmxtmi4976V+Wypdxvibozrvtt74gvZ9+6d1Zo1UoVv5v5VamSzKmkbkhpeToBChNuvg/wo93rr78+adIkOW//Jji8o5CfN5ezSSQ5FBLupL2y4Io8kgOth77abMyIR7giMAdwvKx7AmYTJhAQ92kTmAwrJrCADVsSxJ0QEhLyzjvvNGzYUPc3hucNxFeITz75RCaYgEFIazdkyBAsS/G8cbSABJwgQGHCCXhOdcU+Z+QDkrvDfH18Xu//5JNtH+L+jhyx4svbF3O+X7RyvWzZoe3DyDERyKOyJREW1CRAYcJTccMvT4MGDcLeZ2FArZK+s/uHhhfjcracA7I/NunZOZdj/spIMOGfz3va55369qyVc0+2IAF9EzChMIGAuFWbwHw4M+nJJ5/cvn27vBnw8sUXX/T15QI2icS6sHXr1rFjx167dk1cyJ8/P7LZYd+4dTu+JgHVCFCY8GzEsL1u4MCBWJAlzWher+YHL/cLCWbaMInEuvDnqbPvfTHz+Jlz4gI2J7475NXBLz5PTceaFF+rRoDChGcjtnPnTnwklod35PPJM6ZroYHNA/njf1ZxSUt74Mv1ccP/ew0nhoo25csWXDq3V+2Ioll1YT0JqELAnMIEouNubQJTIvcEMiZ8+eWX8uZAasxhw4ZVrVpV1rAgCNy8efOrr77CznAJpFq1aqhhMlEJhAV1CVCY0EnssJYY6XXxzizsgTAxbODTrRtF6sQ8/ZiRkpI6f/nqqQuXJd3J2JxYODRk6oSxLZo10Y+RtIQEHCNAYcIxbtr2unjxIjJQyOPtMPijNfwn9Qzh+R2ZOZ+6kvzyt1fWHr4tLz3WtvLcqU8UKphP1rBAAooSMK0wgXh5QJsQdwm+bz///PN4FxYv8dMT1GLU5MvH95SM/4+QPRTrIy5dupTx+oEHnn32WeyC8fPzkzUskICiBChM6Cpw+/bte/rppw8dOiStatUo8u0BTxUuFCxrTF744/jpj6bOxbPk8EjLFv8Z+xHkCVnDAgkoSoDChH4Ch+RiX3zxBX6xk3pxkF/eUV0KvtA8MC8TUNyNU0rqA1M2xI1cce1mUsZyiXx+3p991Pal5xpwjYl+7mRa4jABMwsTgOYxbQJzI/FE//79V65cKYNXvHhxJARq1KiRrDFnAZIN1pWsX79euh8WFjZ+/HgchS1rWCABdQlQmNBh7G7fvo3VE0ivK20LDPAf3OeJro80N/mZSsh2OeP7n+YtXyPzEEMgHvnOm88+3Yv7OOTdwoK6BChM6DB20It79+4dExMjbWtc3u8/PUNqljR7BoroM0mvLLyy+1TGQj/wqVWj6HczulWvGiZZsUAC6hIwuTCBwHlSm8D0kIe/+eYbbPG4fv26vI2gTSDRoznPxUQapAULFnz77bf4niCBPPHEEx988AHPNJFAWFCaAIUJPYdvzZo1SAB0/PhxaWSVcqXeeLZnvRpVZI15CqlpaT+t3zb52x8vXb3/F6pxg3oTPh5ZqUI583CgpwYmQGFCt8HF58B///vf48aNkyfceeV94LmmgR88VrBwYF7dmu06wy7EpYxYfn3O9nikmRAPX1+vd15v/s5rD/r5MTG868BzZPcRoDAB1h7WJkS0//77b2QnXrJkiQw+jp/AF/K+ffsGB5tlRTF+kVu7du3XX38tzzEBjVKlSo0ePbpFixaSDAskoDQBChP6Dx8SZEIMxaJinGwnrW3duO7gPl1LFysiawxf2H3oyMSo72OOnZKeBgUGfjB0yDNPdedyCcmEBaUJUJjQf/iwgGLAgAG7du2Spgb753330eAXHgz09zHLHg8cEfrVhrgxK2/EJaZKDk0alJo+sXONalwuIZGwoDYBChMifrrQJoQpy5Yte+21106cOCHvLOSe6N69e69evQIDA2Wl8QpYPILUEtOnTz916v6HYB8fH2Tf+L//+7+AgADjuUyPzEmAwoRCcd+9e/fgwYO3bdsmbcbOjg4tGg3s0alEkVBZacjCgSPHkfDyt32/W3rXtWP7ke++VayIidQZS/dZNh4BChOqxBQyMfb5fvjhh/K8NlheoqDXsHbB/Zrk9/UyskKBAzi+3hg/fs2N8zfua+Uhhfw/eq/1i/3r5WUGDlVuYtqZEwEKE5KQjrQJ2IQdDfix7uOPP46Li5Mm4shMpMns1q2b8dZQYK3Epk2boqKijh49Kv1FAeeDDh8+3Jy7Wiw5sGwkAhQmlIsmZFNsMRs6dOjZs2el8d5eXp1bN+vTuU2Z4gb8lr7vj2Mzf/h5696D0l8UImtFfDR8WIO6dSwrWSYBpQlQmFAufPgb+v7772N1reWKNpzfAYWid8P8xltDcSspLWp7/Gdrbpy9el+V8PbOO2hAgxFDW/IwDuVuYBqcDQEKE5Zw9KVNCMuwxQPyMPJQ3LlzR9rq6+vbvn37Hj16YJuDrFS3gG2Ev/zyy6JFi86dO2fpRWRkJL4JNG3a1LKSZRJQnQCFCXUjiC0eSMQ7YcIEy6xA2NTQqmEdKBS1qlZU1zVpOfJKrNu+d+6yVQf/vL9wD1dLlSwx7LVXnuzS0eTZQCUoFoxBgMKEunE8cODAu+++u2LFCksXkH7iXy2C/vVgUKgh8lAgrwSO4Zi+Of7Kzfs7OOBvlw5VR3/wSHiVwpa+s0wCqhOgMGEVQT1qE8JEJGNDEqB58+ZZKsT4QNykSZOOHTs2btwYOSmsnFHiJTZu4I8KTie5ceOGpcHVq1fHlhasmLCsZJkEDECAwoQBgoi1xEjJNnHixJs3b1q6U71SuW5tWrRtXt9fzbONL1+78d+1W35cs/nchfunNcPBksWLvfbSwKe6d/X1MXtWfMtws2wAAhQmDBDE7du34ze8X3/91dKXfD55utcNeK5ZIE70sKxXqLzlWOLMLfFL9t5KTL6X7vKu9Y8+XOmT9x+OrFVMIV9oKgnYQ4DCRGZK+tUmhK3Y7IBkkFAo5DnPoj40NPSxxx5r27Zt6dKlM3ulwxr89rhx40aoEtC8rcyrXbs2UoHCF+ZXsyLDlwYgQGHCAEGULly6dAkLKKZOnXr16lVZiUKAf75Hmzfo1KppROXySryPJaekbI+O+fF/mzft3p+S8o+f5sqUKvnS8/369OiGg0ItfWSZBAxAgMKEAYIoXcCnyrFjx/7888+yRhSqF/d5tklgz/oBYUFq/Ib31/WURbtvfrPl5pEL95dLw5c8eR7o9GhVnMTRuL4RVkxbhYkvScBKmMAaVZxcSSx61yZEhLDLY9KkSV999ZXVB2JcrVKlSsuWLVu1alWiRAkdhjMhIWHr1q3r16+HyG0lr+AT/COPPILj+ho0aKBDy2kSCThPgMKE8wx1OEJ8fPysWbMgUpw8edLKvOJhoW2b1X+kab3wCmWtLunhJVL8/Lb/99Vbd6//be+Nm7esTKpbu9bLA/o91raNF07q44MEDEeAwoThQpru0KFDhz7//PO5c+dafchEmsgWlfM9ERnwZN2AQgF6fE+7GJfy476Exbtvbj6WKI8FFTHK5+f9TK/aQwY1qVrJ4KmXDXlP0il7CFgJE/glftiwYfZ0NHwbNbQJEQasJUZiNuQB2rFjR+bAVKhQoVGjRtjrERER4e3tnbmBO2tiY2MhRuARHR1t9dcCZmDRB7J79u7du1y5cu60inORgDsJUJhwJ233z4XddsuXL585cyZ+tcN3fisDIFI0jazRLDKiQa1qHt/uceV63LboQ1v2HNy+L+ZG/D/2pMBsf/98jz/Wvm/PJ+tH1rbygi9JwDAEKEwYJpQ2HcHx80jThjfkY8eOWTWA1opdHu2q+7ernq9mSV8sRvDgAxrEvrNJK2MSVsXc/u1EYuo/tm6k21WlYugL/er261UnrDBPqfNgoDi1awlQmMiGr0rahHQD2yKgUCxcuPDixYuyUhZw6GatWrVq1qyJ52rVqiGJprzk0sLp06dhGB44jNoqw6WYF4pJ8+bNcSRqmzZtPK6euBQFBycBChPmuQfOnDmDZRRz5sxBkqDMXvt4e9esUr5OtUp1wivVrFIhKL+bPm5euHIt+vej+w4fxfORk2dx5khm2+rWrvlUt65PdO4QZOhjqjM7zhqzEaAwYZKI441u7dq1ECmWLl2KdbuZvS5WwKt5Jb8mFfyaVvCDTuGeJWLJqQ8ciE1CLoltxxM3HU3EconMhuUP8OnaMXxA37otmpb1rHqS2TbWkIC2BChMZM9TSW1CuIRf7f73v/9BocBbcOa9HqINJICKFStiSQWeK1WqhHUKhQoVyp6InVdx0AbO1UM6DEjU+ER+5MgRq9yWchwkeMdqDmTHQArPggULynoWSMCoBChMGDWy2fu1e/duvCFjdRvUCpstsZGtXMlilcuWqly2ZCX8K1OyaOGQvFp8Dr2TnBx7/tKfp84ePR3756nYwydOn7/0j4wYlvbUqlG9c/u2nTu0K1dGjXRFlsazTAK5JUBhIrfEDNAeO++Q4Gzx4sVY14bPqzY9CvTLU6uUb62SvhElfPCvWjGfYH9ttn5cu5X6x/k7B2LvHDyXhOf9Z5NuJtlQh2EV9m50bFelV7eIDm0q++fz8JJnm5RYSQLaEqAwkSNPhbUJ6RuOGt20aRPef5Gy+ODBf5xLL9vIAtKbITNFsWLFihQpgr0VwcHBBQoUwLO/vz9WWODsDxTQGGMmJmL/Wxo2kuAtHjnqIT3ggVVzSH6BZRGokWPaLECGaNGiBXJh4IGJbLZhJQkYjwCFCePFNFce4W1zz549OCMZpxFt27Yt83YPy9G8vbyKhYVAoigRVrhQcFBIcFDBoMDgoPz5/fPl88OStzyBAelvyBjk1t2P1/G3bt9KuH0tLv7qjbjrcTcvXrl27sLlcxcvXbh8zebKCDkXhnuwSaPWLZq3afUQsl3KehZIwNgEKEwYO745eodPsGvWrMEbMh5ZqcZyEGgT5UK9y4Z6lwnxCgv0CsmfNzR/XjwH+eX1zvuAr3cef9/0DSEJSWlJyWlYDRGXmIpjPi/Fpz9jNcSZayknLyWfupJ8PcF6l5+cQhTKlg5u/0jl9o9UevihClgxYXWVL0nAqAQoTNgTWSNoE5Z+Yi0D3oW3bNmyefPmP/74w/KSG8pYlFGvXr369evjoFPsKMGKCTdMyilIQD8EKEzoJxZ6sAQr2rC6DW/IeECwwGI3d1oV4O9fr06thvUiG9ev17hBXZ674U74nEsPBHAGzaA3hy1ZnnGOQ8OGDVetWoUfY/RgG21wPwH8erdu3TrxhoxPy242oEyp4GaNSjdvXKbVg+XDqxR28+ycjgQ8ToDChJ0hMJo2Yek2viYhG+X+uw+kgYBUofknYyy+CA8PR1YLPHAUKDaPKHF+niUllklAKwIUJrQiachxcI7yzp079+7dK96TY2JibG6Hdsb34AJB1atWrV6tSnjVyrUjatQIr4p1Gc4MyL4koC4BCBOvvPXOD8t+Ei5QmFA3lK6wHCnSfvvtN7wbiyxpmQ9dcnJS7NUrX7ZQrRpFxT8cAlqyeJCTY7I7CahLgMKE/bEzsjZhRQHnZeDNFw+kh8Az1rbhq9SVK1fwjEdcXJzNtcf57z6wICIkJATPhQsXLm3x4O8PVpD50rQE8D/RU089dfjwYUEAx9BERUVhk5RpgdDx7AlAKcab8N235PT3ZPyId+HChbtvxhlvyNhYl3kEbM0IzJ8/BFvmCgaHhoSEhYaUKlmidMkS2KaB5BFhhbl7LjMz1piRAIUJM0bdCZ+xfxnvwydOnBDvydi5jDfiS3cf+Hh8/fp1m2MHF/ALCvQrEpY/tJB/aEhA8aKB0CPKly1YrkxBFLhZwyY0VpqQAIWJXAXdRNqEPVxw/NKAAQPQEr8wIIeQPV3YhgRIgMIE7wFXEMA2EOySEyNfOJpDLiFXGMAxSUBFAhQmVIya/m3u27fvvHnzYOcrLzSYNLaD/g2mhSSgBwIUJnIbBSZE+Acx+TMvU0X8gwtfkEDWBChMZM2GV5wiwPdhp/CxsykJUJgwZdjd4bT8hOzlnqNH3eET5yAB1xKgMOEAX2oTDkBjFxIggQwCFCZ4K5AACZCATghQmNBJIGgGCZAACVCYcOweoDbhGDf2IgESeIDCBG8CEiABEtAJAQoTOgkEzSABEiABChMO3wPUJhxGx44kYGoCFCZMHX46TwIkoCcCFCb0FA3aQgIkYGoCFCacCT+1CWfosS8JmJQAhQmTBp5ukwAJ6I8AhQn9xYQWkQAJmJQAhQknA09twkmA7E4CpiNAYcJ0IafDJEACeiVAYUKvkaFdJEACpiNAYcL5kFObcJ4hRyABExGgMGGiYNNVEiABfROgMKHv+NA6EiABExGgMKFJsKlNaIKRg5CAKQhQmDBFmOkkCZCACgQoTKgQJdpIAiRgCgIUJrQKM7UJrUhyHBIwOAEKEwYPMN0jARJQhwCFCXViRUtJgAQMToDChIYBpjahIUwORQKGJRAXF9e3b9/Dhw8LD3v37h0VFeXl5WVYh+kYCZAACeiVAIUJvUaGdpEACZiOAIUJbUNObUJbnhyNBAxIAMJEnz59Dh06JHyjMGHAGNMlEiABRQhQmFAkUDSTBEjA+AQoTGgeY2oTmiPlgCRgKAJCmIiOjhZeUZgwVHTpDAmQgFIEKEwoFS4aSwIkYGQCFCZcEV1qE66gyjFJwCAEKEwYJJB0gwRIQH0CFCbUjyE9IAESMAgBChMuCiS1CReB5bAkoDwBChPKh5AOkAAJGIUAhQmjRJJ+kAAJKE+AwoTrQkhtwnVsOTIJKEyAwoTCwaPpJEACxiJAYcJY8aQ3JEACChOgMOHS4FGbcCleDk4CShKgMKFk2Gg0CZCAEQlQmDBiVOkTCZCAkgQoTLg6bNQmXE2Y45OAYgQoTCgWMJpLAiRgXAIUJowbW3pGAiSgGAEKE24IGLUJN0DmFCSgDAEKE8qEioaSAAkYnQCFCaNHmP6RAAkoQ4DChHtCRW3CPZw5CwkoQIDChAJBookkQALmIGAlTNSuXXvVqlXBwcHm8J5ekgAJkICOCFCYcFswqE24DTUnIgFdE6Awoevw0DgSIAEzEbASJiIiIlavXk1hwky3AH0lARLQCwEKE+6MhLc7J1Noru3bt5ctW1Yhg2mqQgSCgoIOHjyoK4MpTOgqHDQmM4EilSIyV7KGBDQhsHb59xHh1TQZSpNBMgsTa9euDQsL02RwDkICThKYOPU3/HNyEHYnAZsEHmpWdv3y/jYveaqSwoSbyXPdhJuBczoS0B0BChO6CwkNIgESMCsBChNmjTz9JgES0B0BChPuDwm1Cfcz54wkoCMCFCZ0FAyaQgIkYG4CFCbMHX96TwIkoCMCFCY8Eow8aWlpHplYn5NeuXLl9OnT+rSNVqlOYNeuXS+88AK80M+eDgoTqt9UxrY/ISHh8OHDxvaR3nmQQGRkpJhdJ3s6KEx48Gbg1DkSwMdjfEjOsRkbkIADBObPnz9+/Hh01M+eDgoTDsRRky7MN/EPjCF3H/+o4gsS0IjAtWvXNBpJm2EoTGjDkaO4jIC/v3+dOnVcNjwHJgEdEaAwoaNg0BRbBMrcfdi6wjoScJbA+vXrnR1C0/4UJjTFmbvBuKcjd7zYmgSMQYDChDHiSC9IgAQMQIDChAGCSBdIgASMQYDChGfjSG3Cs/w5Owl4gACFCQ9A55QkQAIkYIsAhQlbVFhHAiRAAh4gQGHCA9D/OSW1iX/y4CsSMDoBChNGjzD9IwESUIYAhQllQkVDSYAEjE6AwoQeIkxtQg9RoA0k4CYCFCbcBJrTkAAJkEBOBChM5ESI10mABEjATQQoTLgJdE7TUJvIiRCvk4BRCFCYMEok6QcJkIDyBChMKB9COkACJGAUAn8eu9K6y5xLl28Jh0aPHj1s2DCjOKeYH9QmFAsYzSUBxwhQmHCMG3uRAAmQgOYEKExojpQDkgAJkIBjBI4ev9Kq82wKE47R07wXtQnNkXJAEtAdASthokePHlFRUV5eXrozlAaRAAmQgNEJUJgweoTpHwmQgDIEIEy07DQ79q84YTFXTHg8ctQmPB4CGkACriVgJUx06tRp7ty5FCZcC52jkwAJkIAtAhQmbFFhHQmQAAl4gACFCQ9Az2lKahM5EeJ1ElCZQGZh4vvvv/f19VXZJ9pOAiRAAkoSoDChZNhoNAmQgBEJUJjQZ1SpTegzLrSKBDQgQGFCA4gcggRIgAS0IEBhQguKHIMESIAENCBAYUIDiK4ZgtqEa7hyVBLwNAEKE56OAOcnARIggQwCFCZ4K5AACZCATghQmNBJIGyaQW3CJhZWkoDaBChMqB0/Wk8CJGAgAhQmDBRMukICJKA2AQoTOo8ftQmdB4jmkUCuCVCYyDUydiABEiAB1xCgMOEarhyVBEiABHJNgMJErpG5vQO1Cbcj54Qk4EoCFCZcSZdjkwAJkEAuCFCYyAUsNiUBEiABVxKgMOFKupqNTW1CM5QciAQ8ToDChMdDQANIgARIQBCgMME7gQRIgAR0QoDChE4CkaMZ1CZyRMQGJKAGAQoTasSJVpIACZiAAIUJEwSZLpIACahBgMKEGnG6ayW1CYWCRVNJIEsCFCayRMMLJEACJOBeAhQm3Mubs5EACZBAlgQoTGSJRpcXqE3oMiw0igRyQ4DCRG5osS0JkAAJuJAAhQkXwuXQJEACJJAbAhQmckNLF22pTegiDDSCBBwmQGHCYXTsSAIkQALaEqAwoS1PjkYCJEACDhOgMOEwOg92pDbhQficmgScJUBhwlmC7E8CJEACGhGgMKERSA5DAiRAAs4SoDDhLEEP9ac24SHwnJYEnCZAYcJphByABEiABLQhQGFCG44chQRIgAScJkBhwmmEHhuA2oTH0HNiEnCGAIUJZ+ixLwmQAAloSIDChIYwORQJkAAJOEOAwoQz9Dzel9qEx0NAA0gg1wQoTOQaGTuQAAmQgGsIUJhwDVeOSgIkQAK5JkBhItfIdNaB2oTOAkJzSCAnAhQmciLE6yRAAiTgJgIUJtwEmtOQAAmQQE4EKEzkREiB69QmFAgSTSQBSYDChETBAgmQAAl4lgCFCc/y5+wkQAIkIAlQmJAolC5Qm1A6fDTeXAQoTJgr3vSWBEhAxwQoTOg4ODSNBEjAXAQoTBgm3tQmDBNKOmJwAhQmDB5gukcCJKAOAQoT6sSKlpIACRicAIUJIwWY2oSRoklfDEuAwoRhQ0vHSIAEVCNAYUK1iNFeEiABwxKwEiZGjRo1bNgww3prAseoTZggyHRRcQIUJhQPIM0nARIwDgEKE8aJJT0hARJQnICVMPH2228PHz5ccZ/Mbj61CbPfAfRf5wQoTOg8QDSPBEjAPAQoTJgn1vSUBEhA5wQyCxNjxozRuc00L0cC1CZyRMQGJOAxAhQmPIaeE5MACZDAPwlQmPgnD74iARIgAY8RoDDhMfQunpjahIsBc3gScJQAhQlHybEfCZAACWhMIC0t7ZW33vlh2U9i3IiIiLVr14aFhWk8DYcjARIgARLIiQCFiZwIKXyd2oTCwaPpBiZAYcLAwaVrJEACahGAMDF0xCgKE2pFjdaSAAkYkgCFCUOGVTpFbUKiYIEE9EKAwoReIkE7SIAETE9ACBNzvl0kSHDFhOnvCAIgARLwGAEKEx5D766JqU24izTnIQH7CFCYsI8TW5EACZCAywlQmHA5Yk5AAiRAAvYRoDBhHye1W1GbUDt+tN5gBChMGCygdIcESEBdAhQm1I0dLScBEjAYAQoTBgtoVu5Qm8iKDOtJwN0EKEy4mzjnIwESIIEsCFCYyAIMq0mABEjA3QQoTLibuOfmozbhOfacmQQsCFCYsIDBIgmQAAl4kgCFCU/S59wkQAIkYEGAwoQFDOMXqU0YP8b0UP8EKEzoP0a0kARIwCQEKEyYJNB0kwRIQP8EKEzoP0baWkhtQlueHI0Eck2AwkSukbEDCZAACbiGAIUJ13DlqCRAAiSQawIUJnKNTP0O1CbUjyE9UJkAhQmVo0fbSYAEDEWAwoShwklnSIAEVCZAYULl6DluO7UJx9mxJwk4SYDChJMA2Z0ESIAEtCJAYUIrkhyHBEiABJwkQGHCSYDqdqc2oW7saLnaBChMqB0/Wk8CJGAgAhQmDBRMukICJKA2AQoTasfPOeupTTjHj71JwCECFCYcwsZOJEACJKA9AQoT2jPliCRAAiTgEAEKEw5hM04nb+O4Qk9IQBECycnJffr0iY6OFvZ26tTp+++/9/X1VcR8mkkCJEAChiIw4pNxm7fvEC5FRESsXbs2LCzMUB7SGRIgARJQgcC5v+Nadpod+1ecMPbtt98eM2aMCobTRs0IUJvQDCUHIgE7CSQkJFCYsJMVm5EACZCAqwlQmHA1YY5PAiRAAvYQ+PPYFdmMwoREYaoC93SYKtx0Vl8EuGJCX/GgNSRAAiYmwBUTJg4+XScBEtAXAQoT+oqHG62hNuFG2JyKBCwIUJiwgMEiCZAACXiSAIUJT9Ln3CRAAiRgQYDChAUM0xWpTZgu5HRYDwQoTOghCrSBBEiABECAwgRvAxIgARLQCQEKEzoJhKfMoDbhKfKc17wEKEyYN/b0nARIQGcEKEzoLCA0hwRIwLwEKEyYN/b3PKc2cY8E/0sCbiFAYcItmDkJCZAACeRMgMJEzozYggRIgATcQoDChFsw630SahN6jxDtMxIBChNGiiZ9IQESUJoAhQmlw0fjSYAEjESAwoSRoumML3nS0tKc6c++JEACdhK4ePFicHCwr6+vne3ZjARIgARIwHUE8J4cFhbmuvE5MgmQAAmQgD0EYmNjS5YsaU9LtjE8AWoThg8xHSQBEiABEiABEiABEiABEiABEiABXRPgng5dh4fGkQAJkAAJkAAJkAAJkAAJkAAJkIDhCVCbMHyI6SAJkAAJkAAJkAAJkAAJkAAJkAAJ6JoAtQldh4fGkQAJkAAJkAAJkAAJkAAJkAAJkIDhCVCbMHyI6SAJkAAJkAAJkAAJkAAJkAAJkAAJ6JoAtQldh4fGkQAJkAAJkAAJkAAJkAAJkAAJkIDhCfw/rM8Pcjr9nqgAAAAASUVORK5CYII=)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177,
     "referenced_widgets": [
      "200ad20ec1404a04ba736d98df14b45b",
      "c2d7c2a86a7843709008f379e3e25ca5",
      "75880c99a66043508f9bb1c58866a2bf",
      "30b6c075f4dc4d91af5dc8ae42be61fc",
      "c8f7c8f1d3644390a1f52e30f471c2a5",
      "c0f52706952f4666b01422b0029bede3",
      "8cd19efb1f8d4d8199659785d1ef8b78",
      "423ee81c2d8b4588aa9b42d767931a1f",
      "485f539cd66c4737b1e7ee53c6ac39d7",
      "b6cd4a9bfd714de49286844d30978497",
      "70c0b50a6cac49ab8a12926ff0b0639c",
      "d3dece75ee164837ac9020877a7592ed",
      "6a67af2cf44f4695a6af6a4a9d83281f",
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      "528dce7267ee4d538d6e630c4b5c66f5",
      "a4710c4a61d843149003f91b09ff8f36",
      "d9b15d73cdff4eddb2302ed8e8b5f005",
      "85ebe9fb127f49d781484315c1dcebfa",
      "3c3ee2e5329b40f3b1e51f8e240e87d2",
      "c808681a97d34991a583710b51819259",
      "3cf6632ddd394e1fbb2b2c333829d1df",
      "175f80a769724c7c92e365f654db583d",
      "32e7a296a4764db2a70f95bfeac62748",
      "9a4beaf7e68243fc8c305a64e1e29bb1",
      "0ebc10c7eb734e1e86f208f256834ae7",
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      "02cd574ab2034dca94d5d49e8fa55ddf",
      "2381ee40169f43f9bdbd656f91ba254a",
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      "941f40f10a6644adaae49007426d9247",
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      "3f1511285838434baa6f8c5b9b7e1585",
      "4ca404bd4fab43db90d96d1b77508806",
      "c73da9cb8b85492d9999d5d098dc1bbb",
      "141a5c25a6ba452ba8e01456d6b2dcb2",
      "973756bcc4c24ac3b99d46a37ce56cd5",
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      "19ff20b0ffd242978b999503fbf3653f",
      "f6fd83e3bdf84551abb248b965360a61",
      "d0c4fff16d6a4a5083f82e729ee2ed1f",
      "32fa236590a3450e8944a63bd31d322c",
      "97b4f9d9f5fa4e17a13d6351c987344f"
     ]
    },
    "id": "meHFi8lLOKgx",
    "outputId": "53e60ab4-cd09-4f3f-b686-6fe005fed185"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "200ad20ec1404a04ba736d98df14b45b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3dece75ee164837ac9020877a7592ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "175f80a769724c7c92e365f654db583d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2381ee40169f43f9bdbd656f91ba254a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f1511285838434baa6f8c5b9b7e1585",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoModelForMaskedLM\n",
    "from transformers import AutoTokenizer\n",
    "import torch\n",
    "\n",
    "model_checkpoint = \"distilbert-base-uncased\" # ขนาดเล็ก เทรนง่าย\n",
    "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v61kdgga0r5E",
    "outputId": "82cfdf9e-0988-45f6-eaef-a93006b37cbc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DistilBertForMaskedLM(\n",
       "  (distilbert): DistilBertModel(\n",
       "    (embeddings): Embeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (transformer): Transformer(\n",
       "      (layer): ModuleList(\n",
       "        (0): TransformerBlock(\n",
       "          (attention): MultiHeadSelfAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (1): TransformerBlock(\n",
       "          (attention): MultiHeadSelfAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (2): TransformerBlock(\n",
       "          (attention): MultiHeadSelfAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (3): TransformerBlock(\n",
       "          (attention): MultiHeadSelfAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (4): TransformerBlock(\n",
       "          (attention): MultiHeadSelfAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "        (5): TransformerBlock(\n",
       "          (attention): MultiHeadSelfAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (vocab_transform): Linear(in_features=768, out_features=768, bias=True)\n",
       "  (vocab_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "  (vocab_projector): Linear(in_features=768, out_features=30522, bias=True)\n",
       "  (mlm_loss_fct): CrossEntropyLoss()\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fDqfjxno_3vS",
    "outputId": "fbac8911-d684-44b3-ffc1-0479334643b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'>>> DistilBERT number of parameters: 67M'\n",
      "'>>> BERT number of parameters: 110M'\n"
     ]
    }
   ],
   "source": [
    "distilbert_num_parameters = model.num_parameters() / 1_000_000\n",
    "print(f\"'>>> DistilBERT number of parameters: {round(distilbert_num_parameters)}M'\")\n",
    "print(f\"'>>> BERT number of parameters: 110M'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4RRGqXW23LK"
   },
   "source": [
    "# ทดสอบการเติมคำ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sl5Dk4rTqWJs",
    "outputId": "a5fa3a00-6d77-41fd-bc1d-60455f085f47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([[ 101, 2023, 2003, 1037, 2307,  103, 1012,  102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1]])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rgtGXneI04Nm",
    "outputId": "5ac32bbd-3d3a-4b26-9674-7d27701fa014"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[ -5.5882,  -5.5868,  -5.5958,  ...,  -4.9448,  -4.8174,  -2.9905],\n",
      "         [-11.9031, -11.8872, -12.0623,  ..., -10.9570, -10.6464,  -8.6323],\n",
      "         [-11.9604, -12.1520, -12.1279,  ..., -10.0218,  -8.6074,  -8.0971],\n",
      "         ...,\n",
      "         [ -4.8228,  -4.6268,  -5.1041,  ...,  -4.2771,  -5.0184,  -3.9428],\n",
      "         [-11.2945, -11.2388, -11.3857,  ...,  -9.2063,  -9.3411,  -6.1505],\n",
      "         [ -9.5213,  -9.4632,  -9.5022,  ...,  -8.6561,  -8.4908,  -4.6903]]],\n",
      "       grad_fn=<AddBackward0>) torch.Size([1, 8, 30522])\n"
     ]
    }
   ],
   "source": [
    "text = \"This is a great [MASK].\"\n",
    "\n",
    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
    "token_logits = model(**inputs).logits\n",
    "# หาตำแหน่งของ [MASK] จากนั้นคำนวณว่าจะเอาคำไหนมาแทน [MASK] ด้วย token_logits\n",
    "print(token_logits,token_logits.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IA9bVbSw1ev8",
    "outputId": "152f5a4e-23df-42c5-c6d8-138cd23287cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 101, 2023, 2003, 1037, 2307,  103, 1012,  102]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs[\"input_ids\"] # 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8kiN8pqJqjcf"
   },
   "source": [
    "# 101 กับ 102 คืออะไร??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5Aa5kWoj1CG8",
    "outputId": "cfe8b451-48fe-4b7d-bd7d-e95ed62a4012"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30522"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.vocab_size # 30522"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "DPgcd-_D1Qgn"
   },
   "outputs": [],
   "source": [
    "mask_token_index = torch.where(inputs[\"input_ids\"] == tokenizer.mask_token_id)[1]\n",
    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
    "# เลือกคำที่จะมาแทน (tokenizer.mask_token_id) ด้วยคำที่มี prob สูงที่สุด "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k_ndEPD01nSh",
    "outputId": "39716b82-4bc6-402c-ffaf-79f6ccdcf1c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-4.8228, -4.6268, -5.1041,  ..., -4.2771, -5.0184, -3.9428]],\n",
       "       grad_fn=<IndexBackward0>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_token_logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vq3s_IrR_4iW",
    "outputId": "764885d9-7e5a-42e7-ebe4-58fa5122a7d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'>>> This is a great deal.'\n",
      "'>>> This is a great success.'\n",
      "'>>> This is a great adventure.'\n",
      "'>>> This is a great idea.'\n",
      "'>>> This is a great feat.'\n"
     ]
    }
   ],
   "source": [
    "top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()\n",
    "for token in top_5_tokens:\n",
    "    print(f\"'>>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y9phPSx428TU"
   },
   "source": [
    "# โจทย์วันนี้: เทรน Mask filling บนดาต้าเซ็ตรีวิวหนัง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442,
     "referenced_widgets": [
      "d0640683c10647d1922e0a4df9b28904",
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      "e5f35fe4ba76423cb24051c70ce36ef3",
      "7bf45325f21e45adb3dd9928526a4ebb",
      "58b8a6b99da64d7b82b709554a6e65d9",
      "166b357c384743f8a4f0e20a96992c8a",
      "500b0cc9e53b4ba2833b4fb3c682fa3e",
      "221aea6d9a2744399480b7ee21ee76b5",
      "5c01ee96c88d427bb5d673e7a26a16d8",
      "ac05545352d04c7399d4eba6fe6dd50a",
      "f3e23c6a293446aba713aeb192d2847d",
      "454d7f09ed114f8e8389ca4ea21a0a79",
      "dcf496e9753f466cbd504c692f94f665",
      "a214bc1444c842138ea2f79c1ae8b9e3",
      "4aab47abfcf64badac54824dfd32fae2"
     ]
    },
    "id": "VhBeqjLPAFzr",
    "outputId": "e94dc264-2e66-4acc-e3af-96d64c98ef51"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0640683c10647d1922e0a4df9b28904",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.79k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9db18a070404a29ae8a6b7d3b0f2e7d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.02 MiB, post-processed: Unknown size, total: 207.25 MiB) to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffb0798c91eb4374bcb64621bc259932",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/84.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "0 examples [00:00, ? examples/s]"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset imdb downloaded and prepared to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1. Subsequent calls will reuse this data.\n"
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      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
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      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 25000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 25000\n",
       "    })\n",
       "    unsupervised: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 50000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "imdb_dataset = load_dataset(\"imdb\")\n",
    "imdb_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KW_lcQhbAJb_",
    "outputId": "7d3bee09-7615-4e77-f97b-3e91a5164815"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "'>>> Review: There is no relation at all between Fortier and Profiler but the fact that both are police series about violent crimes. Profiler looks crispy, Fortier looks classic. Profiler plots are quite simple. Fortier's plot are far more complicated... Fortier looks more like Prime Suspect, if we have to spot similarities... The main character is weak and weirdo, but have \"clairvoyance\". People like to compare, to judge, to evaluate. How about just enjoying? Funny thing too, people writing Fortier looks American but, on the other hand, arguing they prefer American series (!!!). Maybe it's the language, or the spirit, but I think this series is more English than American. By the way, the actors are really good and funny. The acting is not superficial at all...'\n",
      "'>>> Label: 1'\n",
      "\n",
      "'>>> Review: This movie is a great. The plot is very true to the book which is a classic written by Mark Twain. The movie starts of with a scene where Hank sings a song with a bunch of kids called \"when you stub your toe on the moon\" It reminds me of Sinatra's song High Hopes, it is fun and inspirational. The Music is great throughout and my favorite song is sung by the King, Hank (bing Crosby) and Sir \"Saggy\" Sagamore. OVerall a great family movie or even a great Date movie. This is a movie you can watch over and over again. The princess played by Rhonda Fleming is gorgeous. I love this movie!! If you liked Danny Kaye in the Court Jester then you will definitely like this movie.'\n",
      "'>>> Label: 1'\n",
      "\n",
      "'>>> Review: George P. Cosmatos' \"Rambo: First Blood Part II\" is pure wish-fulfillment. The United States clearly didn't win the war in Vietnam. They caused damage to this country beyond the imaginable and this movie continues the fairy story of the oh-so innocent soldiers. The only bad guys were the leaders of the nation, who made this war happen. The character of Rambo is perfect to notice this. He is extremely patriotic, bemoans that US-Americans didn't appreciate and celebrate the achievements of the single soldier, but has nothing but distrust for leading officers and politicians. Like every film that defends the war (e.g. \"We Were Soldiers\") also this one avoids the need to give a comprehensible reason for the engagement in South Asia. And for that matter also the reason for every single US-American soldier that was there. Instead, Rambo gets to take revenge for the wounds of a whole nation. It would have been better to work on how to deal with the memories, rather than suppressing them. \"Do we get to win this time?\" Yes, you do.'\n",
      "'>>> Label: 0'\n"
     ]
    }
   ],
   "source": [
    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
    "\n",
    "for row in sample:\n",
    "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
    "    print(f\"'>>> Label: {row['label']}'\") # 0 = negative, 1 = positive"
   ]
  },
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    {
     "data": {
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (720 > 512). Running this sequence through the model will result in indexing errors\n"
     ]
    },
    {
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       "  0%|          | 0/25 [00:00<?, ?ba/s]"
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      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
       "        num_rows: 25000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
       "        num_rows: 25000\n",
       "    })\n",
       "    unsupervised: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
       "        num_rows: 50000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def tokenize_function(examples):\n",
    "    result = tokenizer(examples[\"text\"])\n",
    "    if tokenizer.is_fast:\n",
    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
    "    return result\n",
    "\n",
    "\n",
    "# Use batched=True to activate fast multithreading!\n",
    "tokenized_datasets = imdb_dataset.map(\n",
    "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
    ")\n",
    "tokenized_datasets "
   ]
  },
  {
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   "execution_count": null,
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    "id": "YpxD2KSU2k3p",
    "outputId": "f7fa4150-71b8-4be6-f667-9747c90e6f5a"
   },
   "outputs": [
    {
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       "  14229,\n",
       "  2040,\n",
       "  4122,\n",
       "  2000,\n",
       "  4553,\n",
       "  2673,\n",
       "  2016,\n",
       "  2064,\n",
       "  2055,\n",
       "  2166,\n",
       "  1012,\n",
       "  1999,\n",
       "  3327,\n",
       "  2016,\n",
       "  4122,\n",
       "  2000,\n",
       "  3579,\n",
       "  2014,\n",
       "  3086,\n",
       "  2015,\n",
       "  2000,\n",
       "  2437,\n",
       "  2070,\n",
       "  4066,\n",
       "  1997,\n",
       "  4516,\n",
       "  2006,\n",
       "  2054,\n",
       "  1996,\n",
       "  2779,\n",
       "  25430,\n",
       "  14728,\n",
       "  2245,\n",
       "  2055,\n",
       "  3056,\n",
       "  2576,\n",
       "  3314,\n",
       "  2107,\n",
       "  2004,\n",
       "  1996,\n",
       "  5148,\n",
       "  2162,\n",
       "  1998,\n",
       "  2679,\n",
       "  3314,\n",
       "  1999,\n",
       "  1996,\n",
       "  2142,\n",
       "  2163,\n",
       "  1012,\n",
       "  1999,\n",
       "  2090,\n",
       "  4851,\n",
       "  8801,\n",
       "  1998,\n",
       "  6623,\n",
       "  7939,\n",
       "  4697,\n",
       "  3619,\n",
       "  1997,\n",
       "  8947,\n",
       "  2055,\n",
       "  2037,\n",
       "  10740,\n",
       "  2006,\n",
       "  4331,\n",
       "  1010,\n",
       "  2016,\n",
       "  2038,\n",
       "  3348,\n",
       "  2007,\n",
       "  2014,\n",
       "  3689,\n",
       "  3836,\n",
       "  1010,\n",
       "  19846,\n",
       "  1010,\n",
       "  1998,\n",
       "  2496,\n",
       "  2273,\n",
       "  1012,\n",
       "  1026,\n",
       "  7987,\n",
       "  1013,\n",
       "  1028,\n",
       "  1026,\n",
       "  7987,\n",
       "  1013,\n",
       "  1028,\n",
       "  2054,\n",
       "  8563,\n",
       "  2033,\n",
       "  2055,\n",
       "  1045,\n",
       "  2572,\n",
       "  8025,\n",
       "  1011,\n",
       "  3756,\n",
       "  2003,\n",
       "  2008,\n",
       "  2871,\n",
       "  2086,\n",
       "  3283,\n",
       "  1010,\n",
       "  2023,\n",
       "  2001,\n",
       "  2641,\n",
       "  26932,\n",
       "  1012,\n",
       "  2428,\n",
       "  1010,\n",
       "  1996,\n",
       "  3348,\n",
       "  1998,\n",
       "  16371,\n",
       "  25469,\n",
       "  5019,\n",
       "  2024,\n",
       "  2261,\n",
       "  1998,\n",
       "  2521,\n",
       "  2090,\n",
       "  1010,\n",
       "  2130,\n",
       "  2059,\n",
       "  2009,\n",
       "  1005,\n",
       "  1055,\n",
       "  2025,\n",
       "  2915,\n",
       "  2066,\n",
       "  2070,\n",
       "  10036,\n",
       "  2135,\n",
       "  2081,\n",
       "  22555,\n",
       "  2080,\n",
       "  1012,\n",
       "  2096,\n",
       "  2026,\n",
       "  2406,\n",
       "  3549,\n",
       "  2568,\n",
       "  2424,\n",
       "  2009,\n",
       "  16880,\n",
       "  1010,\n",
       "  1999,\n",
       "  4507,\n",
       "  3348,\n",
       "  1998,\n",
       "  16371,\n",
       "  25469,\n",
       "  2024,\n",
       "  1037,\n",
       "  2350,\n",
       "  18785,\n",
       "  1999,\n",
       "  4467,\n",
       "  5988,\n",
       "  1012,\n",
       "  2130,\n",
       "  13749,\n",
       "  7849,\n",
       "  24544,\n",
       "  1010,\n",
       "  15835,\n",
       "  2037,\n",
       "  3437,\n",
       "  2000,\n",
       "  2204,\n",
       "  2214,\n",
       "  2879,\n",
       "  2198,\n",
       "  4811,\n",
       "  1010,\n",
       "  2018,\n",
       "  3348,\n",
       "  5019,\n",
       "  1999,\n",
       "  2010,\n",
       "  3152,\n",
       "  1012,\n",
       "  1026,\n",
       "  7987,\n",
       "  1013,\n",
       "  1028,\n",
       "  1026,\n",
       "  7987,\n",
       "  1013,\n",
       "  1028,\n",
       "  1045,\n",
       "  2079,\n",
       "  4012,\n",
       "  3549,\n",
       "  2094,\n",
       "  1996,\n",
       "  16587,\n",
       "  2005,\n",
       "  1996,\n",
       "  2755,\n",
       "  2008,\n",
       "  2151,\n",
       "  3348,\n",
       "  3491,\n",
       "  1999,\n",
       "  1996,\n",
       "  2143,\n",
       "  2003,\n",
       "  3491,\n",
       "  2005,\n",
       "  6018,\n",
       "  5682,\n",
       "  2738,\n",
       "  2084,\n",
       "  2074,\n",
       "  2000,\n",
       "  5213,\n",
       "  2111,\n",
       "  1998,\n",
       "  2191,\n",
       "  2769,\n",
       "  2000,\n",
       "  2022,\n",
       "  3491,\n",
       "  1999,\n",
       "  26932,\n",
       "  12370,\n",
       "  1999,\n",
       "  2637,\n",
       "  1012,\n",
       "  1045,\n",
       "  2572,\n",
       "  8025,\n",
       "  1011,\n",
       "  3756,\n",
       "  2003,\n",
       "  1037,\n",
       "  2204,\n",
       "  2143,\n",
       "  2005,\n",
       "  3087,\n",
       "  5782,\n",
       "  2000,\n",
       "  2817,\n",
       "  1996,\n",
       "  6240,\n",
       "  1998,\n",
       "  14629,\n",
       "  1006,\n",
       "  2053,\n",
       "  26136,\n",
       "  3832,\n",
       "  1007,\n",
       "  1997,\n",
       "  4467,\n",
       "  5988,\n",
       "  1012,\n",
       "  2021,\n",
       "  2428,\n",
       "  1010,\n",
       "  2023,\n",
       "  2143,\n",
       "  2987,\n",
       "  1005,\n",
       "  1056,\n",
       "  2031,\n",
       "  2172,\n",
       "  1997,\n",
       "  1037,\n",
       "  5436,\n",
       "  1012,\n",
       "  102],\n",
       " 'word_ids': [None,\n",
       "  0,\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29,\n",
       "  30,\n",
       "  31,\n",
       "  32,\n",
       "  33,\n",
       "  34,\n",
       "  35,\n",
       "  36,\n",
       "  37,\n",
       "  38,\n",
       "  39,\n",
       "  40,\n",
       "  41,\n",
       "  42,\n",
       "  43,\n",
       "  44,\n",
       "  45,\n",
       "  46,\n",
       "  47,\n",
       "  48,\n",
       "  49,\n",
       "  50,\n",
       "  51,\n",
       "  52,\n",
       "  53,\n",
       "  54,\n",
       "  55,\n",
       "  56,\n",
       "  57,\n",
       "  58,\n",
       "  59,\n",
       "  60,\n",
       "  61,\n",
       "  62,\n",
       "  63,\n",
       "  64,\n",
       "  65,\n",
       "  66,\n",
       "  67,\n",
       "  68,\n",
       "  69,\n",
       "  70,\n",
       "  71,\n",
       "  72,\n",
       "  73,\n",
       "  74,\n",
       "  75,\n",
       "  76,\n",
       "  77,\n",
       "  78,\n",
       "  79,\n",
       "  80,\n",
       "  81,\n",
       "  82,\n",
       "  83,\n",
       "  84,\n",
       "  85,\n",
       "  86,\n",
       "  87,\n",
       "  88,\n",
       "  89,\n",
       "  90,\n",
       "  91,\n",
       "  92,\n",
       "  93,\n",
       "  94,\n",
       "  95,\n",
       "  96,\n",
       "  97,\n",
       "  98,\n",
       "  99,\n",
       "  100,\n",
       "  101,\n",
       "  102,\n",
       "  103,\n",
       "  104,\n",
       "  105,\n",
       "  106,\n",
       "  107,\n",
       "  107,\n",
       "  108,\n",
       "  109,\n",
       "  110,\n",
       "  111,\n",
       "  112,\n",
       "  113,\n",
       "  114,\n",
       "  115,\n",
       "  116,\n",
       "  117,\n",
       "  118,\n",
       "  118,\n",
       "  119,\n",
       "  120,\n",
       "  121,\n",
       "  122,\n",
       "  123,\n",
       "  124,\n",
       "  125,\n",
       "  126,\n",
       "  127,\n",
       "  128,\n",
       "  129,\n",
       "  130,\n",
       "  131,\n",
       "  132,\n",
       "  133,\n",
       "  134,\n",
       "  135,\n",
       "  136,\n",
       "  137,\n",
       "  138,\n",
       "  139,\n",
       "  140,\n",
       "  141,\n",
       "  142,\n",
       "  143,\n",
       "  143,\n",
       "  143,\n",
       "  144,\n",
       "  145,\n",
       "  146,\n",
       "  147,\n",
       "  148,\n",
       "  149,\n",
       "  150,\n",
       "  151,\n",
       "  152,\n",
       "  153,\n",
       "  154,\n",
       "  155,\n",
       "  156,\n",
       "  157,\n",
       "  158,\n",
       "  159,\n",
       "  160,\n",
       "  161,\n",
       "  162,\n",
       "  163,\n",
       "  164,\n",
       "  165,\n",
       "  166,\n",
       "  167,\n",
       "  168,\n",
       "  169,\n",
       "  170,\n",
       "  171,\n",
       "  172,\n",
       "  173,\n",
       "  174,\n",
       "  175,\n",
       "  176,\n",
       "  177,\n",
       "  178,\n",
       "  179,\n",
       "  180,\n",
       "  181,\n",
       "  182,\n",
       "  183,\n",
       "  184,\n",
       "  185,\n",
       "  186,\n",
       "  187,\n",
       "  188,\n",
       "  189,\n",
       "  190,\n",
       "  191,\n",
       "  192,\n",
       "  193,\n",
       "  194,\n",
       "  195,\n",
       "  196,\n",
       "  197,\n",
       "  198,\n",
       "  199,\n",
       "  199,\n",
       "  200,\n",
       "  201,\n",
       "  202,\n",
       "  203,\n",
       "  204,\n",
       "  205,\n",
       "  206,\n",
       "  207,\n",
       "  208,\n",
       "  209,\n",
       "  210,\n",
       "  211,\n",
       "  212,\n",
       "  213,\n",
       "  214,\n",
       "  215,\n",
       "  216,\n",
       "  216,\n",
       "  217,\n",
       "  218,\n",
       "  218,\n",
       "  219,\n",
       "  220,\n",
       "  221,\n",
       "  222,\n",
       "  222,\n",
       "  223,\n",
       "  224,\n",
       "  225,\n",
       "  226,\n",
       "  227,\n",
       "  228,\n",
       "  229,\n",
       "  230,\n",
       "  231,\n",
       "  232,\n",
       "  232,\n",
       "  233,\n",
       "  234,\n",
       "  235,\n",
       "  236,\n",
       "  237,\n",
       "  238,\n",
       "  239,\n",
       "  240,\n",
       "  241,\n",
       "  242,\n",
       "  242,\n",
       "  243,\n",
       "  244,\n",
       "  245,\n",
       "  246,\n",
       "  247,\n",
       "  248,\n",
       "  249,\n",
       "  250,\n",
       "  251,\n",
       "  252,\n",
       "  253,\n",
       "  254,\n",
       "  255,\n",
       "  256,\n",
       "  257,\n",
       "  258,\n",
       "  259,\n",
       "  260,\n",
       "  261,\n",
       "  262,\n",
       "  263,\n",
       "  264,\n",
       "  265,\n",
       "  266,\n",
       "  267,\n",
       "  268,\n",
       "  269,\n",
       "  270,\n",
       "  271,\n",
       "  272,\n",
       "  272,\n",
       "  272,\n",
       "  273,\n",
       "  274,\n",
       "  275,\n",
       "  276,\n",
       "  277,\n",
       "  278,\n",
       "  279,\n",
       "  280,\n",
       "  281,\n",
       "  282,\n",
       "  283,\n",
       "  284,\n",
       "  285,\n",
       "  286,\n",
       "  287,\n",
       "  288,\n",
       "  289,\n",
       "  290,\n",
       "  291,\n",
       "  292,\n",
       "  293,\n",
       "  294,\n",
       "  295,\n",
       "  296,\n",
       "  297,\n",
       "  298,\n",
       "  299,\n",
       "  300,\n",
       "  301,\n",
       "  302,\n",
       "  303,\n",
       "  304,\n",
       "  305,\n",
       "  306,\n",
       "  307,\n",
       "  308,\n",
       "  309,\n",
       "  310,\n",
       "  311,\n",
       "  312,\n",
       "  313,\n",
       "  314,\n",
       "  315,\n",
       "  316,\n",
       "  317,\n",
       "  318,\n",
       "  319,\n",
       "  320,\n",
       "  321,\n",
       "  322,\n",
       "  323,\n",
       "  324,\n",
       "  325,\n",
       "  326,\n",
       "  327,\n",
       "  328,\n",
       "  329,\n",
       "  330,\n",
       "  331,\n",
       "  332,\n",
       "  333,\n",
       "  334,\n",
       "  335,\n",
       "  336,\n",
       "  337,\n",
       "  338,\n",
       "  339,\n",
       "  340,\n",
       "  341,\n",
       "  342,\n",
       "  343,\n",
       "  344,\n",
       "  345,\n",
       "  346,\n",
       "  347,\n",
       "  348,\n",
       "  None]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_datasets['train'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sAvFKYf5AuFd"
   },
   "source": [
    "# maximum length = จำนวนที่โมเดลจำกัดในแต่ละ input\n",
    "จากบทเรียนก่อนหน้า \"การใส่ input ที่ยาวไป จะทำให้กินทรัพยากรเพิ่มขึ้นกว่าปกติ\" ทำให้เราต้องการหนด maximum length ของโมเดลนั้นเอง (ปกติแล้ว ถ้ามีการ์ดจอแรงๆก็ใส่เยอะได้ ไม่มีปัญหา)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zdV7DMPKAN_S",
    "outputId": "765cee85-f547-4bf3-a6d4-db2896e7c397"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.model_max_length # distil-bert = 512 คำต่อ 1 input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TetsQoFJAcE_",
    "outputId": "07b418e8-2d57-44ca-8491-cd7fc263080e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'>>> Review 0 length: 363'\n",
      "'>>> Review 1 length: 304'\n",
      "'>>> Review 2 length: 133'\n"
     ]
    }
   ],
   "source": [
    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
    "\n",
    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M2ZGYPbl2_nF"
   },
   "source": [
    "Review 0 + Review 1 + Review 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1uCUrB6T2zM5",
    "outputId": "147d9ab7-a4dd-4dac-d6f1-b763a90f89da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['attention_mask', 'input_ids', 'word_ids'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_samples.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wWBbAAS1AefJ",
    "outputId": "8cbd974a-81a0-46ba-c22d-0fc958d7c3ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'>>> Concatenated reviews length: 800'\n"
     ]
    }
   ],
   "source": [
    "concatenated_examples = {\n",
    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
    "}\n",
    "total_length = len(concatenated_examples[\"input_ids\"])\n",
    "print(f\"'>>> Concatenated reviews length: {total_length}'\") # แต่จะเห็นได้ว่าจริงๆแล้ว input จะเกินกว่า 800 คำ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sED3zXyB4FF1"
   },
   "source": [
    "# ถ้าเจอ input ที่เกินจะทำยังไงดี?\n",
    "= แบ่งเป็น ก้อน (chunk) สิ!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rd7LfcK8AVXc",
    "outputId": "4577b19f-b9a9-48b0-99a7-9630c7a94b9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'>>> Chunk length: 128'\n",
      "'>>> Chunk length: 128'\n",
      "'>>> Chunk length: 128'\n",
      "'>>> Chunk length: 128'\n",
      "'>>> Chunk length: 128'\n",
      "'>>> Chunk length: 128'\n",
      "'>>> Chunk length: 32'\n"
     ]
    }
   ],
   "source": [
    "chunk_size = 128\n",
    "chunks = {\n",
    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
    "    for k, t in concatenated_examples.items()\n",
    "}\n",
    "\n",
    "for chunk in chunks[\"input_ids\"]:\n",
    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z88zkfyuApxz"
   },
   "source": [
    "# เทคนิคพิเศษ\n",
    "= ปกติการแบ่งเป็นก้อนๆมักจะมีเศษอยู่ที่ก้อนสุดท้าย (จากตัวอย่างจะเหลือแค่ 32 คำ จาก 128 คำ) โดยเราจะเก็บไว้ก็ได้ (แต่ต้องไป padding ซึ่งออกจะยุ่งยาก) เพียงแค่ในบางตัวอย่างมันอาจจะเหลือแค่คำสองคำ ทำให้ไม่เหลือข้อมูลอะไรให้โมเดลได้เรียนรู้มากนัก ทำให้คนจึงนิยมที่จะตัดออก"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "k7Gj83sYAleH"
   },
   "outputs": [],
   "source": [
    "def group_texts(examples):\n",
    "    # Group ข้อความทั้งหมด\n",
    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
    "    # วัดความยาวทั้งหมด\n",
    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
    "    # เราจะเลือก chunk 1 จน ถึงอันก่อนสุด (ถ้า chunk สุดท้ายเล็กกว่าที่กำหนดไว้)\n",
    "    total_length = (total_length // chunk_size) * chunk_size\n",
    "    # แบ่ง Chunk\n",
    "    result = {\n",
    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
    "        for k, t in concatenated_examples.items()\n",
    "    }\n",
    "    # สร้าง Labels\n",
    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 356,
     "referenced_widgets": [
      "314dd1b4c4234530a2a671a24f144d8b",
      "c1c74866043a456eafa423eff0214959",
      "e47ae445ba084fdab916ff3d8ee71f74",
      "8027bdb044d94764955b8264a48950c3",
      "c23a508cf68d45c38ce4ccd065b28de4",
      "1cb102c8efec409e80741f6066fec2e1",
      "bdf4fc84d86c49dc8e24b133534dd4ff",
      "98b27562c12a4c7180553675ba6cb515",
      "16ed1de7a12142ac889929203cbe4a85",
      "d72d15798ba74ac2a2f548b19d927106",
      "b16fc1589f1247948ac1ba21e46549ec",
      "232e87769d484077b74a36545de47bef",
      "99824c3497b24ba98cf43c960a6f2383",
      "d0854d4efec248c2b192cf256e1437f5",
      "c450fa117d364a05aebfd923a7b605a1",
      "ff1d11edf3794767abb2503e4325c54c",
      "b831b7df8ee6437186b81bf176e0b127",
      "8393a349cb4a46de81ccf38c0000cced",
      "3455101a66d7408db759d18528d1136d",
      "57d6a96950084d9c85e80116d719d091",
      "30ef4a29299a40aeb26171ffe4f56dc3",
      "b425edae66e349e0be36e3726ea77e2f",
      "ca3070e5bd1c4d01ab81de3538291950",
      "53e94e9cf3ad4faea515a42c37ab0b68",
      "b74a7292cd6e4f7ca617e398b618dd78",
      "5ab180011afa401f92bdd2b88d86bc96",
      "4f79e3564fce41b7a3be58826d966daf",
      "1ec764f599ae462e8abd5f4fd8d8f3d3",
      "8acfd6c28a0340368bcbb80374096eff",
      "acab58363acb415e84a2fa4f926df559",
      "820e978c3076478db9a3ad1e3a3cf9e8",
      "780192fdf7aa4b8aac8fee79f6ac93b7",
      "2352504291b34f2e82746b4b1d6bc31a"
     ]
    },
    "id": "_34BZpYlA1YM",
    "outputId": "4f71972c-5c05-4897-df71-0fd5311abde9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "314dd1b4c4234530a2a671a24f144d8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "232e87769d484077b74a36545de47bef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca3070e5bd1c4d01ab81de3538291950",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 61291\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 59904\n",
       "    })\n",
       "    unsupervised: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 122957\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
    "lm_datasets # ได้ข้อมูล X,y พร้อมแล้ว "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "id": "GqTdyQR0BIl3",
    "outputId": "846f9e81-4c54-45a5-8956-a93bf71974e0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"as the vietnam war and race issues in the united states. in between asking politicians and ordinary denizens of stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men. < br / > < br / > what kills me about i am curious - yellow is that 40 years ago, this was considered pornographic. really, the sex and nudity scenes are few and far between, even then it's not shot like some cheaply made porno. while my countrymen mind find it shocking, in reality sex and nudity are a major staple in swedish cinema. even ingmar bergman,\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(lm_datasets[\"train\"][1][\"labels\"]) # ต้นฉบับ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "id": "9WJnTBcdBI-J",
    "outputId": "1589d361-49c6-44c5-8294-fdd96ed00576"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"as the vietnam war and race issues in the united states. in between asking politicians and ordinary denizens of stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men. < br / > < br / > what kills me about i am curious - yellow is that 40 years ago, this was considered pornographic. really, the sex and nudity scenes are few and far between, even then it's not shot like some cheaply made porno. while my countrymen mind find it shocking, in reality sex and nudity are a major staple in swedish cinema. even ingmar bergman,\""
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"]) # อันที่เราอยากจะเติมคำ แต่เราต้องแอด MLM เข้าไปก่อน!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oOhhq5HRBJb3"
   },
   "source": [
    "แทนที่คำที่เราอยากให้โมเดลทำนายด้วย [MASK]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "PDcRQwYFBGoR"
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForLanguageModeling\n",
    "\n",
    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15) # ใน 1 ประโยค จะให้ทำนายคำแค่ 15% ของทั้งหมด"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "33tEgA56BOyv",
    "outputId": "d0ef8381-c0ea-41a4-ef30-419bc929fd34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "'>>> [CLS] i rented i am curious - yellow from my [MASK] store [MASK] of all the controversy that surrounded it when it was first released in 1967. [MASK] also [MASK] that at first it was [MASK] by u. s. customs if it ever tried to enter this country, therefore being anco of films considered \" controversial \" i really had [MASK] see this for myself. < br / [MASK] < br / > the plot is centered around a young [MASK] drama student named lena who wants to learn everything she can about life. in [MASK] she wants to focus her attentions to making some [MASK] of documentary on what the average swede thought about certain political issues such'\n",
      "\n",
      "'>>> as the vietnam war and [MASK] issues in the [MASK] states. in between asking politicians and ordinary denizens of stockholm about their [MASK] on [MASK], [MASK] has sex with her drama teacher, classmates, and married men. creating br / > [MASK] br / > what kills me about i am [MASK] [MASK] yellow is that 40 years ago, this was considered pornographic. really, the sex and nudity scenes are few and far between, even then [MASK]'s not shot like some cheaply made porno. while my [MASK]men mind find it shocking, in reality sex and nudity are a major staple in swedish [MASK] biography even ingmar [MASK],'\n"
     ]
    }
   ],
   "source": [
    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
    "for sample in samples:\n",
    "    _ = sample.pop(\"word_ids\")\n",
    "\n",
    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MK4xU6zuBjAT"
   },
   "source": [
    "แต่ปัญหาตอนนี้คือตอนที่เราแทนคำด้วย [MASK] มันดันแทนแค่คำย่อยของคำนั้นๆ(sub-word)ไม่ได้แทนทั้งหมดเช่น \"bromwell => [MASK]mwell\". \n",
    "เพราะฉนั้นเราจะต้องทำในสิ่งที่เรียกว่า \"whole-word-mask\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "M9srLU9VBYgn"
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "import numpy as np\n",
    "\n",
    "from transformers import default_data_collator\n",
    "\n",
    "wwm_probability = 0.2\n",
    "\n",
    "\n",
    "def whole_word_masking_data_collator(features):\n",
    "    for feature in features:\n",
    "        word_ids = feature.pop(\"word_ids\")\n",
    "\n",
    "        # Create a map between words and corresponding token indices\n",
    "        mapping = collections.defaultdict(list)\n",
    "        current_word_index = -1\n",
    "        current_word = None\n",
    "        for idx, word_id in enumerate(word_ids):\n",
    "            if word_id is not None:\n",
    "                if word_id != current_word:\n",
    "                    current_word = word_id\n",
    "                    current_word_index += 1\n",
    "                mapping[current_word_index].append(idx)\n",
    "\n",
    "        # Randomly mask words\n",
    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
    "        input_ids = feature[\"input_ids\"]\n",
    "        labels = feature[\"labels\"]\n",
    "        new_labels = [-100] * len(labels)\n",
    "        for word_id in np.where(mask)[0]:\n",
    "            word_id = word_id.item()\n",
    "            for idx in mapping[word_id]:\n",
    "                new_labels[idx] = labels[idx]\n",
    "                input_ids[idx] = tokenizer.mask_token_id\n",
    "\n",
    "    return default_data_collator(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "17hulOsSBiJ2",
    "outputId": "9472799a-cd0e-4040-d4d8-ca8df3abe5f2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "'>>> ('[CLS] i rented i am curious - yellow from my video store because [MASK] all the controversy that [MASK] it [MASK] it was [MASK] [MASK] in [MASK] [MASK] i also heard [MASK] at [MASK] it [MASK] seized by u [MASK] [MASK]. customs [MASK] it ever tried [MASK] enter this country, therefore being a [MASK] of films [MASK] [MASK] controversial \" i [MASK] had [MASK] see this [MASK] myself. [MASK] br / > < br / [MASK] the plot [MASK] centered [MASK] a young swedish drama [MASK] named lena who [MASK] [MASK] learn everything she can about life [MASK] in particular she wants to focus her attentions [MASK] making some sort of documentary on what [MASK] average swede thought about certain political issues such', 128)'\n",
      "\n",
      "'>>> (\"as [MASK] [MASK] war and race [MASK] [MASK] the united states [MASK] in between asking politicians and [MASK] denizens of stockholm about their opinions on politics, she has sex with [MASK] [MASK] teacher [MASK] classmates, and married [MASK] [MASK] < br / > < br / > what kills me about [MASK] am curious - yellow [MASK] [MASK] 40 years [MASK] [MASK] [MASK] [MASK] considered [MASK]. really, the sex and nudity [MASK] are [MASK] and far between [MASK] even [MASK] it'[MASK] not shot like [MASK] cheaply [MASK] porno. [MASK] my countrymen mind find it shocking, in [MASK] sex and nudity are a major staple in swedish cinema. [MASK] [MASK] [MASK] [MASK],\", 128)'\n"
     ]
    }
   ],
   "source": [
    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
    "batch = whole_word_masking_data_collator(samples)\n",
    "\n",
    "for chunk in batch[\"input_ids\"]:\n",
    "    print(f\"\\n'>>> {tokenizer.decode(chunk),len(chunk)}'\") # หมดปัญหา"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w1tg-3dt5V9Q",
    "outputId": "c14b4e50-3781-4bd2-c04d-a5c8c5cbf99b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 61291\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 59904\n",
       "    })\n",
       "    unsupervised: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 122957\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_datasets # 61k ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ypfLXrn4C_UN",
    "outputId": "fe1e3c7c-3cd7-4c0f-f02a-faa9e9ded754"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 10000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
       "        num_rows: 1000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size = 10_000\n",
    "test_size = int(0.1 * train_size)\n",
    "\n",
    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
    "    train_size=train_size, test_size=test_size, seed=42\n",
    ")\n",
    "downsampled_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387,
     "referenced_widgets": [
      "0601f991805e43f68101d0047446b2d4",
      "181598533da74d8387cf0d0bd5073403",
      "e7a8a02040af48aa876ee845f301e7b1",
      "a596d8672c1340648cd2d5af04702e9a",
      "07c737cc7ec14390a0490b9d7f8e6bae",
      "ce350edd1a4144daa19b4f4f750ecd99",
      "b7a2b397f00a40dc815c4467b41f12bf",
      "bbad97acdf1249c4a0fbfe97f10f55ed",
      "4f85a5eb7a3e43a69d9ec0d63b0375b1",
      "f02498c447ae4e40a3ddf61fd8b1134f",
      "7c2d75c758b1482eba8b69b38a6f45ec",
      "6fc49828d6394875a4a3bc3f1d86a020",
      "ba2f28ce250749daa828ea2bab25e93d",
      "3c5b5d391598406f9edcb731123941bf",
      "2ad2b0da3af1421082443701907985b5",
      "042d9de05f064d819d943ab019fba36f",
      "d2201000bbe74fc29318caa25a85c66e"
     ]
    },
    "id": "ZW6IR4l5XVor",
    "outputId": "2f32123a-7907-42b5-ac9c-c04c74fa5df7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Login successful\n",
      "Your token has been saved to /root/.huggingface/token\n",
      "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
      "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
      "\n",
      "git config --global credential.helper store\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login() # เอาไว้โยนโมเดลขึ้น hugging face ได้เลย"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "9qlilX1xDePR"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "batch_size = 64\n",
    "# Show the training loss with every epoch\n",
    "logging_steps = len(downsampled_dataset[\"train\"]) // batch_size\n",
    "model_name = model_checkpoint.split(\"/\")[-1]\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=f\"{model_name}-finetuned-imdb\",\n",
    "    overwrite_output_dir=True,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    weight_decay=0.01,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    push_to_hub=True,\n",
    "    fp16=True, # สำหรับคนใช้ GPU\n",
    "    logging_steps=logging_steps,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436,
     "referenced_widgets": [
      "90a9a4f571ab4161a20419763d23da46",
      "9720869a36f440a2b0f22dd87388fc07",
      "1ce7217de615462bb827b26db3ea72b2",
      "f54434534a7c4193a9a6f4f970068338",
      "98dff38af0384f27847c8844100a7b74",
      "59ea6e0700534090b1b4be549036fe63",
      "43cc9a3a82594599a3b463fe5da1169e",
      "b08ad8cd82834c5f86ae32e7b7c0f7b4",
      "2af8c1b15d514621977bc0d16dd113b7",
      "e97394f9e66346bfb3a7913bcc6b34d0",
      "c0d8346c976745829247e8f9833b1b59",
      "a6a6b059a4b14cdab82f21a570b8f056",
      "d5f603b40c714d589f47908297a9f2b1",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning https://huggingface.co/mrp/distilbert-base-uncased-finetuned-imdb into local empty directory.\n"
     ]
    },
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       "model_id": "90a9a4f571ab4161a20419763d23da46",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "128b457f47df4bafa55685ec48e59e3b",
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using amp half precision backend\n"
     ]
    }
   ],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=downsampled_dataset[\"train\"],\n",
    "    eval_dataset=downsampled_dataset[\"test\"],\n",
    "    data_collator=data_collator,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9OLbDMf3EFOq"
   },
   "source": [
    "# How to evalute LMs?\n",
    "# วิธีประเมินผล language model?\n",
    "เราใช้สิ่งที่เรียกว่าคะแนน \"perplexity\". Perplexity คือคะแนนที่ช่วยประเมินว่า \"คำที่โมเดลเราทำนายนั้นถูกหรือไม่\" (ยิ่งน้อยยิ่งดี) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 146
    },
    "id": "HBGgOfsfDwrC",
    "outputId": "0d29fd92-d2d7-4a3d-d0c3-9a5c4d73230b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set  don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: word_ids.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1000\n",
      "  Batch size = 64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [16/16 00:20]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Perplexity: 21.94\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "eval_results = trainer.evaluate()\n",
    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\") # โมเดลก่อนเทรน"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 688
    },
    "id": "-0X4PqIfD6Qt",
    "outputId": "2a98a10a-5ae5-462e-b0f7-ed9cd9f0276e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the training set  don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: word_ids.\n",
      "***** Running training *****\n",
      "  Num examples = 10000\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 64\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 64\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 471\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='471' max='471' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [471/471 31:54, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.707000</td>\n",
       "      <td>2.488302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.572000</td>\n",
       "      <td>2.423960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.537700</td>\n",
       "      <td>2.435494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set  don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: word_ids.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1000\n",
      "  Batch size = 64\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='32' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [16/16 11:03]\n",
       "    </div>\n",
       "    "
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set  don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: word_ids.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1000\n",
      "  Batch size = 64\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: word_ids.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1000\n",
      "  Batch size = 64\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=471, training_loss=2.6047206907009115, metrics={'train_runtime': 1918.5724, 'train_samples_per_second': 15.637, 'train_steps_per_second': 0.245, 'total_flos': 994208670720000.0, 'train_loss': 2.6047206907009115, 'epoch': 3.0})"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 148
    },
    "id": "hR6QbICUD9Vr",
    "outputId": "4086d097-72ba-49f6-860f-b8b65ea942f6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set  don't have a corresponding argument in `DistilBertForMaskedLM.forward` and have been ignored: word_ids.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1000\n",
      "  Batch size = 64\n"
     ]
    },
    {
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       "      \n",
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       "      [16/16 00:20]\n",
       "    </div>\n",
       "    "
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    {
     "name": "stdout",
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     "text": [
      ">>> Perplexity: 11.84\n"
     ]
    }
   ],
   "source": [
    "eval_results = trainer.evaluate()\n",
    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\") #โมเดลหลังเทรน"
   ]
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to distilbert-base-uncased-finetuned-imdb\n",
      "Configuration saved in distilbert-base-uncased-finetuned-imdb/config.json\n",
      "Model weights saved in distilbert-base-uncased-finetuned-imdb/pytorch_model.bin\n"
     ]
    },
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file pytorch_model.bin:   0%|          | 3.37k/256M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "3f3fec68eb1946ffa6286d804af1d32e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file runs/Jan19_08-04-53_ff8fe0df0c19/events.out.tfevents.1642579623.ff8fe0df0c19.76.4:  68%|######8   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "a2e632e10085485d9fe052002f67ef12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file runs/Jan19_08-04-53_ff8fe0df0c19/events.out.tfevents.1642581569.ff8fe0df0c19.76.6: 100%|##########…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file runs/Jan19_08-04-53_ff8fe0df0c19/1642579627.893799/events.out.tfevents.1642579627.ff8fe0df0c19.76.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "9383caac9b4c499a9c7421e6440dee7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file runs/Jan19_08-04-53_ff8fe0df0c19/events.out.tfevents.1642579562.ff8fe0df0c19.76.3: 100%|##########…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b75887daeab4686bc7162c5978550d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file training_args.bin: 100%|##########| 2.92k/2.92k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/mrp/distilbert-base-uncased-finetuned-imdb\n",
      "   b0e7435..01c80f6  main -> main\n",
      "\n",
      "Dropping the following result as it does not have all the necessary fields:\n",
      "{'task': {'name': 'Masked Language Modeling', 'type': 'fill-mask'}, 'dataset': {'name': 'imdb', 'type': 'imdb', 'args': 'plain_text'}}\n",
      "To https://huggingface.co/mrp/distilbert-base-uncased-finetuned-imdb\n",
      "   01c80f6..6985945  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'https://huggingface.co/mrp/distilbert-base-uncased-finetuned-imdb/commit/01c80f63e00b89c0a2c303d9233306a9dd042236'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub()# โยนขึ้น hugging face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8fxVVoYA9gjB"
   },
   "source": [
    "# โจทย์พิเศษ: ในเมื่อทุกคนได้โมเดลและอัพขึ้น hugging face มาแล้ว ลองดึงเอาโมเดลลงมาใช้ดีกว่า !! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "H3aN1hWo9hGL"
   },
   "outputs": [],
   "source": [
    "# Your code\n",
    "# วิธีเรียกใช้งานให้ดู 05n_NLP_on_HF_01.ipynb\n",
    "# https://colab.research.google.com/drive/1iOylkO475Ki1C1Lf4RNbvpx05tNYqmjP?usp=sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_uSzs7HVwSzd"
   },
   "outputs": [],
   "source": [
    "#### #####"
   ]
  }
 ],
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