using_pretrained_NLP_models.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **This notebook is all about using hugging face framework. how to use transformers and pipeline to get the task done by using pretrained model provided by hugging face.**\n",
        "\n",
        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ERERERERERER0dgxGJ4y3cGw4zgSVqUB4XMZVg5L91zh7hbS5XIZuVyu87FXX331no9//OM/6PlEnrs12tnCbCFNtD0No6X07xwDbrb7vvt777332U996lOvAEBYPbxumqaey65s294Q8i4sLCgAWF5eVmHVcOfjjuNIoVBQAMNhIiIiIiIiIiIiIhovtpKeEl2BsNLVwoVCQSmlOhVpADozhhcWFpTrunBdFwBQLpc7LVHDmcLSbrdl7969X+35ZH72TLShMFtIE21fswngl9LRHvP14kB337t371fb7bYkEgkFQOlQ2LIsZZpmp6V0uMYqACiVSp37O44j3WuziDAUJiIiIiIiIiIiIqKxYzA8JcJQQQBIV9ggIiLFYlHy+bzk83kJwwmp1WoStpAWz/Ngmqbkcjn9CwDw2muv3d3XbOEBQ5cr3PbxaI9HRNNlcTcwm1iN7HjvusA7bt93j8fjd3ued1e73RYAaDabYpomdOcFz/OQTqdVPp/XobAAwcYcPe+9WCyK3sSj12y9wYeIiIiIiIiIiIiIaBx4kXo6CACllBIRgVIKXb93nsN8Pt/5c6lUEtsOWqnW63Xpni8MAO12WyqVyg96DoZfLwKvO5F8UgCAffPArXujOx4RTaf6BeCNU9Ed75ds4N8c6/vu4azh/xGAMgwDiUSiM2sYANLp9BXtpLtaSQOA0u3+OWeYiIiIiIiIiIiIiCYBK4Ynnw57RYcKer6wiHSq0/TtSqVSp1rYdV3R84VzuZw0m03RLaQ9z+u9WnjND9pIRyWZCCoFiYjSs8FGkahEUDV88uTJuw3DgO/7qFQqKJfL0mg0pNFoSL1eFyDYeOO6rqrVauI4juTzedHhsA6Fi8ViZx0f+PMiIiIiIiIiIiIiIupTYtwnQDekW0hf3oa08/fhXGEplUpKVwl7nrfh91wuh0ql0rnz7t27/6DnM/nZt6OdLby0O5gvSkQEAPv3AB81gIvrkRxu/R/+3dux/M/293v/hYWFrwL4YVgxDADQs9oty4Lrup3A13Vdlc/nu+8uCNZpFAoFVgoTERERjVrGyKDVMgAcBpCBSKbzMaVWEVM+VGwVicRxrPrRjTUhIiIiIiKaYKxemgLXmkup20g7joOVlRXR7aNd10U4XxiNRkPK5XJnrnC73Zbvf//7Bw4dOvRGTyex5gP/773RBcPJBHDHYjTHIqKt4+Rp4MTpyA63ft9/fjt2y7/pOxx+7733PvupT33qFQCddtLlchmmaQIAUqmU8jyvsynHtu31rnbSQBgO63bSQND1Qf89EREREUVoZ9YG1ANQ6kFAjB7ueRzAcYh8G2vV/tvOEBERERERTTi2kp4SXaOEO/L5vOi5wqVSCZeHwpppmgoIQuFEIqFyudxXez6BqKuFP8G5wkR0FYu7V5GMsJPAf3t8oLvv3bv3q+12W3w/WP/K5bJcLRSu1+uysLCgdAcHx3Gghws7jiO6nbSeD09EREREEZpfOoL5pdeh1DEoPNFjKAwEVcVHoNQxzC+9hfmlI8M4TSIiIiIionFjMDwluoOEYrEoIiKlUgkA4LpuzLZtcd2NG5sbjYY0m00pl8uiQ+Hvfve7Rjwe/2JPDx71bOF988E8USKiK2Vw68cia+UXq1f2Dzpr+IUXXjhgGAba7bbkcjmUy2WUy2WcPn06hqCNtKTTaVUqlTqbcnQYrJRCOAu+ExLrefFERERENKCdWRvzS28BOIog3I2CAeAoA2IiIiIiItqKGAxPNlFK6Y7REBGIiA4ZgLAV6cLCgnJdV7cy7W4hDSCYL+z7vmq32/LLv/zLv9/zWURdLby4O7pjEdHWs2tHBukd0R3v9eJAd7/99tu/7Pu+tNttAYBcLiemaSKZTCrLsjbctlariZ417DiObvmvlFIQET1vWMBRDkRERESDmV96CkodQxDkDoMOiJ9Cxsjc8NZERERERERTIMJ+nTQsOlC4/M8Ig4VarSa6lSkAeJ4H0zSRy+U6NzQMQwBgbm7uyz2fQNTVwrP8Z3ctJz9IwHtjB3769gzWGnGcfD/e+djivou4eW8bB/c3sbivjUO3NMd4pqRd7zlLz63j5r1tHNrfwsH9TT5nvVjaDbxxPppjvesC77jAzXZfd08kEl/8m7/5m//ta1/72ke6+wIA6M03lmWhXq+LbdvKdV3k8/lOhXBIRERdZy0nIiIios3KGBm0Ws8D6O+bu949gVbLRsb4HFb9CHdMExERERERjR4Tugl2+RxKpRT0nMquimFZWFhQtVpNPM+DZVnwPA8AkEwmVbPZlEqlIoZh4J/+6Z++KCIHejqJnz3DauEh+8ef7oD74xT+06s7UT937SJ+77L/X9zXhnXbefzep8/i1w5FFKDRpmz2Obscn7MepGeB9A6gHtHX6fVi38GwiOz+/Oc//+Wvfe1r/2v335umiVQq1Vlzw64NKJVKWF5e1us0lFKqUCjo9VsHxGwnTURERNSrIBQ+hujaRm/WYbRax5Ax7mU4TERERERE04zlSpNLgEvhcHeVmQ6HgWBeZT6fR6lUEsuypNFoCHCpki2Xy3Xan1YqlR/E4/G7ezqL526NLhjeNw/cujeaY20B//jTHfjWi7vhvTF4y9zFfW089sBp3P/ptQjOjK6Fz9mI1S8Ab5yK7ni/c6zvcHh9ff2f4vH4bxiGoRKJhKpUKgAA0zR19bCybVsBgOu63aGv0gFxoVBQXVXCDIaJiIiIejWXfR6iHhzjGRzH2RN3jvHxiYiIiIiIBsIZw1PCcRzpDoVXVlZkZWVlQytpz/NQLpd1KLwh9H/xxRcP9BwKR1ktnEywWjh08v0EHvv6TfjS/3FTJAGjPqbz13vxsHMzTr7PRgBRG+Zzdv8fLfE5u5b0LJBJRVaRoY7/h9P93jcWi91+6tSpuwCgUqnoWcNoNpuSSqWUbietW0lreia8Xq+VUvqX/n9u0CIiIiLajLnFwphDYQA4jPmlp8Z8DkRERERERH1jMDyhdBAMBNXCYaWZFAoFBQDLy8uqVCoBXQGwrlYDgsBCf8z3ffzKr/zKV3s+iZ99e5BPYSPOFgYAfOflXfiCc3Nk4eLl3nw7ifv/aAnffJEhfFSG/ZydfD/B5+x6btkT2aHknf9vN5qrfd9/7969X/V9XwzDAABJJpMKADzPk3Q6rXTHhlqtJgBEB8S6YlhEpFgsioigWCyKUkrYUpqIiIhoE3YYBkSccZ9G6AnszI5qvjEREREREVGkWKk0ocLAoNNK+rLZwgAgtm2L67qwLEs8z1O6lXQ4VxiGYQgAtNttefvtt/+5p/nCHx4HXoioQ1YyAdz28W0fDDt/vRcvvbpzZI/38H1n8O8f/mhkj7cV/dmze/Dsy7tG9nj2J8/B+bcfID23PrLHnApv1lZxupGJ5Fh3OsCdhb7v/rd/+7eLX/va1z4Cgk03uVxOJZNJVS6XYVmWAoBPfOIT60Awazifz2N5eVnpyuHudtKcNUxERES0SfNLRwEcGfdpdHFx9sS94z4JIiIiIiKiXrFieDJ1QmFdWdb9wXw+LwCwsLCgACCdTqswHJZyuQxdxZZIJJTv+yiXy3/QUygMAP/96Yg+FQC7Zrd1KFw/F8NjX79ppKEwADz78i487NyM+jm+zPvh/PXekYbCAOD+eA6Pff0mPmeXu3lXNKEwAKx8A4NUDX/+85//sv6zYRjQM9wBoNFoiOd5qlQqSa1WEx0K6zbSXRt7AATdINhKmoiIiOgGdhgGJisUBgCbVcNERERERDSNmD5MHtEzKLsrhoFOqNAJEUqlEmzbhuu6AgCmaQKAlMvlzm0Mw8Dc3Nwf9HQGaz5QeWawz6LbNp8t7PzHvUNrQ3wjb76dxFf+z4WxPPY0G3V1d7c3304yHL5cehZIR/Qaaq5C/etzfc8anpmZuas7DA4338A0TYRVwwIArusCCObB67b/juN0Nvp0r/NEREREdB3x1qQGsJN6XkRERERERNfE5GHyKADXCgs68ytt2xbbtgUALMsCAJTL5c4NwxnDePHFFw/E4/G7ezqDd9zez/patvls4W++mIH747mxnoP3xg782bPRzWnd6r75YmZsobD25ttJzhy+3FJ0Xw/512f7Plg8Hr/7Rz/60d2+73daSXeFwiqdTivbDq4R1mo1CecNw3GcDS2ju9d4Vg0TERERXdcj4z6Bq1LqgXGfAhERERERUa8YDE8QpZRcHhCISKf9aD6fh+M4SreQ1jzPg+d5sCxLmabZ+VilUpGDBw9+secTeb3Y3ydwNdu4WvilV3fiWxMS7j378q6xB9TTwP3x3EQ9Z98ZcSvriRZl1fC77kAbYPbt2/d7hmEACGa4N5tNMU0TnufJe++9F6vX6xJ2c+hUDusZwwAu7wTBUJiIiIjouuTwuM/gGg4jY0Q38oSIiIiIiGgEGAxPEBHpVAsrpTqBgepKEa4VIpimKY1Go9NGulKpAABmZ2d7ayP9rhu0ko7CNq4WPvl+YmICRs35j3vZnvg66udi+PP/e7Iqq7/14m6cfH97voauKsKqYbz9Yt93TSQSX2y326LDYQCddtJXofL5PABIPp+XlZUVKRaLopRCsViUQqGg13cGxERERESXmzcOA2pyw9dWy7jxjYiIiIiIiCYHU6IJpduMOo4jIiLdVcMAsLCwoOr1uoTzhQUAms2mhC2kBYCcOnXqbhE50NMDv/ntqD4FYN942/GO0yQGevVzMbYnvo7vvLxrIp+zb32Pz1lHehaIx1YjOdbPngGa/R1KRHaH7aTF932EM4c7wa7neVKv1zcEvfl8HsvLy6pUKukNQAKgM3MY4RgBIiIiIuoi7ckNhQEgFpvUamYiIiIiIqKrYoXShLhaC2n9R6DThlRs2xbdmhSAWJYFz/MAIKbnCgPA+fPnY77vfzMej2++lfSaDzx3a/+fRLdkArhjMZpjTZmT7ydw/x8tjfs0rumlr5/A4r72uE9jokz6c/Yd5x0c2t8c92lMhpOngROnIzmUuve503Jrvq/kvdls/u2hQ4f+nf7/RCKhKpUKLMta775dOp3utP8vlUpKt5QOf3WHwQyGiYiIiC43v3QEwNFxn8Y1xWKPov6LZ8Z9GkRERERERJs1WeVxpGcKi1JK6Uoyx3GwsrIi+XweYbVZZ36lvhuCUEHa7bb4vg8AKhaL3d3Tgw8wc/MKUbZ8nTKT1kL6ci+9Oo8vPRBNsLZV/PD1yZ6//NKr8wyGtZvSwLt14OL6jW97A/LP/9du3Jrv674zMzP3/9Vf/VXmy1/+8mq45sI0TdVoNAQAyuWysm1bB8KSz+cVAHEcR3Wt76KUUuH4ANHjBGgE5peOApik1o/fxtkTk31ReWfWhlKFcZ/GBmdP3DvuUyAiom1OqUn6foKIqD/zS4cBPDXu09gGJv/nPiIi2hYYDE+IMBAQANAzJ7vaR3dXE4tt29BtSsNqYZimiWaziUQiodBvG+mfRdRGOpkI5gtvU95Pd4z7FK7r2Zd34Qv31ZGeGzxY2yq+83J63KdwXf/p1Z340gOn+ZwBQDwWrC+n6oMf6103aCed7L1DoYjs/rVf+7XbAbxiGAYSiQT0jHeE1b+u64pt28q2bZRKpU6F8EMPPRR77rnn1h3H6e4OwXB4pJQNyORcyBX54bhP4YaCC9/2uE+DiIhooigVzZgTIqJxEslAKX6vP2zT8HMfERFtC5wxPEGUUlBKQUQkrCbT1WYAgFqtJgDgui7S6bQyTVMsy4JlWapcLgMAKpUKDMPA3r17/6CnB1/zg5AkCrtmoznOFPrHn+6YuDm1l6ufi+HNt5PjPo2JMS3PmTvhVc0jtSe6r4X652/2XT6fyWS+GM4Zlna7Ld3t/PX6DEB3dxDHcWRlZUWAYMawUsG4YaVU96xhIiIiItJisckOXif9/IiIiIiIiC7DYHgCKKUkrBTTraR1tfCG6jMAsCyrUzGcSqVUo9EQ3bq03W6LYRji+z56biP98xej+nSAxclupTxM7o+nI7w79npq3KcwMablOZv0SvSRSs8C6Wi+HnLi+30vWDMzM/d/97vfzRiG0Vmjc7kccrmcpFIpBQD1el1s2wbCdv8AsLy8rBzHQbFYFP277hRBRERERF3W4/64T+G6lJrs8yMiIiIiIroMg+EJoFuJhtVjnVbS+Xxe9C/gUqWw/l3fP6wW1rOF0V8b6YhGXKR3ALOTXX05TD/+6XRUS0/6TN1RevMX01E97b0xHf+2RmZPRJsb3nWDjgl9EJHdv/mbv3m7XnsrlQoqlQqSyaTyPK9TAVyv1yWfzyOcMwzHcaRrAxARERERXcsMJjt4ZStpIiIiIiKaMgyGJ4SuFgbQqSJbXl5Wy8vLSreSzufzSlefua4rnuehXC4jl8sJABiGAd/3sWfPnvt7evA1H/jweDSfyDaeLQwAP52SFs0n30+gfo4vfwB48+2ZcZ/CpvA5u8y++WDecBQGmK/+sY997H6ElcCGYQiAzqxh3c0BCEYBdK3lnc4QjuMox3FU1wYhtpQmIiIi0lb9VWBiq3JXcfZERD9IExERERERjQZThgmgK4WVUhsqycJfks/nUSqV5F//9V9j6XRa1et1AQA9v7KbYRiIx+PjayMd4ezPaTMtladavcGXf/1cbKrC1kmfhTxS8Riwczaai4Tv9D9fPZlM/kE2m0W73Rbf93Ur6c6GHc/zAAQdH8K1HMvLy0rPGnYcR88a7t4gxHCYiIiISJNYhD+wRkipH477FIiIiIiIiHrFlGFCKKUgIlBKKcdxBAhaSQNAqVRCPp9XtVoNQBA0mKYpOnBIJpMql8tJpVLBG2+8cSAWi93e04NH1UZ63zwQ3755xpkpChgB4J33E1jc2x73aYzVT6cszH/ngwQO7W+O+zQmx827DJxuDH4c3U56p9HzXUVk949+9KO7f+u3fuuHhmF0B8SqXC53NvDYto1SqST5fF65rhtbWFhQXa2kBeEs+fC9gPOGiYiIiC55AcDj4z6JK4i8MO5TICIiIiIi6hWD4QmgW4g6jiMPPfSQOI6jAGBlZaVzm1qt1p24SrlchmmaaDab0mwGQZFhGDhw4MBdPT14pG2kd0ZzHBqJk+8nYB0a91lQL6apunkk0rNB5fDF9YEPpf7l/zktd/zx7n7uu2fPnrva7fYr1WpVIQx4K5XKhnDXda9dlew4DpRSKBaLIiJKKSUMh4mIiIhCa1UX80s+gN538Q2Pj7MnItphTURERERENDoMhsese56kiHRXkKFUKimELUV1qGDbNkzTRLlc7nxM830fMzMzX+zpBKJqI51MBCENTY303OBhGtHY7ZsHTtUHPoyc+P5u3PHHfd03kUjcDeA/ZLNZSSQS8H1fAZBcLgeEQTHC9VrPGQaCQHhlZUWWl5cVABQKBVUoFAb4LIiIiIi2rCKAo+M+iS7FcZ8AERERERFRP1h+NkY6FBYRFItFcRxHCoWCrhbuhL62bUv4O+r1uqRSKWWa5oZQuFKp9DlfOKLuV7smOBRuNIEP6sDJD4G3Tl36VX0/+Lsz54DWxXGf5cgxGAZ2TdnXYOjPWaMJ1E5f/bVy6qPJfK1ENdf8XRdorvZ110QicdfTTz+dSSQSqnvOcDfTNMW2bQUA+Xxe5fN5OI6jNwARERER0fUE1bnXbsEyWqwWJiIiIiKiqcVgeIxERIVtpJVuHx0GxJ2b6D/oUNjzPDQaDSmXy53jtNttMQxDXnvttd5C4eZqEIZEYRLbSJ85B7z9HlCtAR+eAc42gHb70q/GheDvTn0E+O8G4Vej//mt0zavd2dqukLRYZi2r8FQguF1FWyc+Jd3gtfK6trVXytnzl16rZz6aHICYt1OOgoDdFC47777fi9ci1GpVKTdbkulUoHnebFGoyGpVKoTAOuqYcdxOrPzmJ5JAAAgAElEQVTki8ViZ6NQOG9++w5sJyIiIrq6JwHpbydftD437hMgIiIiIiLqF4PhMVJKieM4on/X4bDjOKjVapLP55HP5wEEraQ9z4NlWdDzhcvlMiqVCnzfBwDs2bPn/p5O4J0fRvOJTFob6Qst4K0wvLrQ2vz9GheCYKzP0GvaKnAX901XkD0Mi/vaU/W8Hbyl/40LV3X2fPBa+fAMsN7D1+HMuSAg/mDwFs6R2DcfyWHU2//5dL/33bFjx+3d/68rh03TVADgeV73SAABLnWG0JuBwkC4+88Mh4mIiIi0syeOA+rJsZ6DSDE4DyIiIiIiounEYHjMdOtox3GU4zii/851XVWr1TZUDANBuJDL5TZUDGtsIw3go7WgSrg9QDXjmXPAiVrP4XB6bn1qwtaD+5tTFYgO07Q8Z+m59Wifsw/qwMkPeguEL/fhmeD1Nu7q4YjaScs7L+/u976JROL+9fV18X1fDMOAYRgAgHK5LABgWRYsy9owM7573jAAKKU6lcNA0FWi3/MhIiIi2pLOnngGIuOZ7ytSxFrVufENiYiIiIiIJheD4QkRVgzDcRxI2F/adV3UajWp1+viui5M0xTLspBMJhUAyeVynQDh6aef3h2LxW6/5gNcTVRtpDMRzfgc1Ad14P2+C/42al3sKxy+585GNI8/ZIf291BJvcV98tCFcZ/CplhRnucH9SDUjcKFVl+vlUhF1U66uQq809+6KCIHvve97+3PZrPwfR++76twrb6CbdsolUqSz+c3zBgWkU7nCF05TERERESXCcLZUVcOP8lQmIiIiIiItoLEuE9gOwvzXwCAUkqtrKzEwpBA8vk8arUaAMDzPAUA5XJZmaZ51dai99xzT2/Vwh8eB9b8fk99oz2paI4ziLPnowu6NB0O778JiG2uo+u9d57Dsy+noz2PIbAOnR/3KUyMaXnO7E+ei+ZAUYbCWusi8M4HwP6PR3vcXuxJAe+fHfw47/4QuNnu664HDx68q1qt/twwDPi+L+VyGblcDmGHBwGg9O+6C0S3sKW0njXMZJiIiIjoWs6e+AZ2/NILiMePATCG+Eg+RB7FWjWiXdVERERERETjxYrhMQlnR6pwxjAAYHl5WYVVwwpA9zxKIAwLAKDZbEoulwMAGIYhuVxOzc3N9RYMRzVfODMBoXDrIvDe6vCOffKDTd98Wlo023dGFDJuAdZt56fiOYskzG9djD4U1i60xjtzOL0jmuP0WTEMALOzs511OGwnLQBgmmbnNpZlwXVd0eu74zjI5/OdMQL6FwDOGCYiIiK6nvPv+jh74lYAjwKIaNezJqsQKWJm5k6GwkREREREtJUwGB4DpZTuFi2O4ygRgYggDIVlZWVFSqUSbNtGvV6Xer0upmmKrhauVCqqUqmgUqkAANrttvTcRnqA8GODiGZ7DuTDM4PNFL6RxoXg1yak59bxe5+OoGrxOse3P9mA/ckGrNv6ay18/2fOTkUQOkoP39d7oJmeW8fB/S3c/5mzsG67MNRZxfd/5mw0x6/WBj/G9Xx4ZnwtpSPqXKDecU+j2d9Gk5mZmbsWFxfF9/1OoFupVNBsNjtzhgHAtm0FBDOGXdeN6VnDer7wZd0kGA4TERERXc/ZE890AmLBYD/oirgAnsRM4lasVR2s+kPagUxERERERDQebCU9JkopFItFKRQKKgyFJWwjCh0SAEA6ne60E200Gp3qs2azCQA6HJZ4PN5bxXBU84XTs9Ecp1+ti8CZEVS/flAHspv7XL9w35mhtCb+0gOn8fB9Z64Idb2f7sBL/7AT7usp1M/deK/HY7/PaxuX+8J9Z/CtF3ff8HbpuXVYt13AF+47g4O3XFkd/uzLu/Bnz+6J/Pwe/u0IqnwbzeFuoNBW14CFG38tIxePBVXD9cEqq0WwGx8c76udtIgc+Mu//Mvdjz/++Krv+zAMA7q7g2ma8DxvQxtp27ZlYWFBAUDXjHn9fqDCgJgtpYmIiIg24+yJZwA8g4yRQbt9GIAN4A5AZaCu0m5aZBVKHYfgNCTmIh53GQQTEREREdFWx2B4DPTFfsdxpFgsdgJhx3GwsrLSuZ2eMVyv18XzPJimiXBmpQBBpbBhGHjttdfu6ukEPjyOfiviNkgmgNkx/xNaXRvN4+iq4dSNw+HFfW3c/+mzeOnV+UgeOj23jj//n2vXbCVsHToP69B51M/F4L4+h++8vAtvvj1z1dtGVnm6xaTn1vGF++r4zjUCfeu2C7DvPIf7P7123Wrrh+87A+vQeTz29Zs2FdJvxv2fOYtD+5uDH+ijEbV5PnMO2Ltr03O5I7VrduBgGABU9fun5Wa7r3T7rrvuuhvA9/T/h5t3FABYltU9JkC5rot8Pt+5r+M4etNQcB7BmAHhvGEiIiKiHgThrhv+IiIiIiIioi4MhsdDIWwj7TiOAMDKykonRQnbSOsZlGJZFizLgg6HdbVwIpFQlUpF0ul0j22kI5ovvGvM1cLApls8R+Jcc1PBMAA89sDqpit4b+R6oXC39Nw67v/0Gu7/9Bre/EUS3/uHnXjzF8lOSPyF++p4+L4hzZfdAr7y8IcA0An0b953EffeeQ7Wbed7mu97cH8T3/qjU5GFw5FVeJ+NYEbxZqyvA2sNYNcY2syndwA4PfBhpPZa3yXPu3btuqtarX4vm82K7/sql8uhUql05gx7ngfLssTzPH2XTsUwEKTAAJRSCiKiw2EiIiIiIiIiIiIiooExGB4TfbE/rATTITEACBBUlIUzhgEEYQKAzqxKAKhUKgIAMzMzvbWRjmq+cGbM84XX14ELrdE9XuMCgM21iF7c18aXHjg9cFvhLz2w2lMoqR28pYl/HwadtHlfefjDTkA8iIP7m5E9/5FUeDciqDjuxShfl93Ss6uIxzK4OOAM7Q+P933XeDx+OwBUq1UYRtCxUHd76N7ko9tJ12o1QdBBAl3dI6RYLEIFA+mZDBMRERERERERERFRJKLpdUo9C9tJQ1cMh39GPp/f0Fq0+y4AUKlUVKVSQaVSkWw2K4ZhdIKITRsg9Nhg145ojtOvCyNuidzu7fEevu8M7v90/62u7U828NgDg1c/0nhM1PPfGvFrpTWCWcZXl8FccvCjNFf7Xifj8fjt2WwW2WwWvu+j3W53ZsN7nifhnGG4riuu6woAOI6jEK7xjuOgUCgoPX+eiIiIiIiIiIiIiCgqDIbHQCklumJYVwqH84WlVCqhVqtJWC0sQKf1qNKtSE3TVIZhoFqtqq9//esZETmw6Qdf84Nfg5pLAvExhxbrIw6f+gi7vvLwRzjYx3zYxX1tfOV/YsXvtPvKwx/1VfEb+fM/6qC2OeIK5W57UtEcp8+W+yKy+4UXXjhQrVaVYRhot9vSbDYl7PYgAGBZFrp/X1lZkTAcBgAUi0VhG2kiIiIiIiIiIiIiihqD4THorgJzHEe6W0jn83m4rqsWFhaU53k6FAbQaSOtAwYYhoF77rmnt2rhD34SzScxNxPNcQZxcfJDk/TcOv78D2s9h4OPPXA6mhbCNFbpuXU4//aDnu6zuK+Nb/7RKT7//YqiYhgYqLPCwYMH7wIgulpYt/3vZlkW0um0CmfJw3Ec0bPmC4VC94xhlg0TERERERERERERUSQYDI9B13xhAJdaSAOdeZNSKpVEz6NsNBrieZ5UKpUrktB0Oj2eNtLjni8MjL9ieZMW97XxHeedTVcOf2HAFsQ0WazbzuML99U3dduD+5vDCYVjI36tJMY4vj7YtLI68HEGmMWeTCbvyGazqFarSCQSKpfLKQDI5XLQnR+AS7PkgWBTUKlUguM4GzYPccYwEREREREREREREUWFwfDoSddFf10t3OG6rsrn88q2baTTaeV5HoBgPqUOFCqVCgDA933MzMzc3dOjDxB2bJCMR3Ocgc4hosrAzZrt//HSc+t41nkHX7rBzNiD+5v4ysMf9f04NJm+8vCHsG67cN3bfOG+M/jWsCqF4yNe6mNjfGuJx4D5CNaGNT+YNdzPKcTjB6rVqhiGodrttrTbbalUKkgmkyqVSnXWdSAIhwGgVCp1H0KKxaJ+oxBWDRMRERERERERERFRFBgMj55yHEeHw0oHw8vLywroVI0JgM6M4XK5jGazKeVyWbeQFt/3YRgGYrHY5ucLA9FUDEcVvAxqJj7aACoxeBj+2AOr1wz/Du5v4s//sDbwY9Bk+vM/fO+qVeOL+9r41h+fwlce/gjpufXhPHhqdjjHnZTHu9zO2UwUh1FnKtffyXEN8Xj8V7PZrPJ9X6rVqvJ9H7lcDuVyGY1GQyzLgud5sG0btm3rThHonjMc/r84jtMJiAf8dIiIiIiIiIiIiIhomxtjv8/ty3GczvzIlZUVKZVKCoDoUNi27U4VmQ4QksmkQhgMhK1Jcf78+VgsFtt8K+nmat8VcBtENcMzCrMzQOP6lZiR2bkjksNYt53HS18/gZde3YmXXt2J+rkY7r3zHB6+78zwgkEaO101rp/39Nw6Dt3SHM3zPhMP2ju3RzS3eG7MwXBEa5ScenU39v1a7/cTOfCnf/qnmT/5kz9ZrVarMAwDADpdH3TFcHcraSAYKwAA+XxeACggmDccVg9zcSAiIiIiIiIiIiKigTAYHjHdElREEFaCqbCFqAorhRWADeGwaZool8sAghmVYStpeeutt8YzXzg95tCn2575EQbDqUgPd/+n1zhLeBsa2/O+aw748MzwH2cmAcyO+a0lojVKfVQ+LcDufu77mc985gCA1Ww22wl5u3TWej0+wHEctbKyIsvLy2plZeXSDUWglFKXjx0gIqJtIGMYaLcNyHoG6xJ0wxAxNtxGKR8AEFOrULFVJBI+Vn1/5OdK15cxMgAyaLcNKKWfwwxENnY50c+nSPA7n0/aTq72OrnamqfXO6VWcfZERBc5qIPrFRHdSPc6cb3vUwGuFdMoYxhotYJ1f7PvA0qtYmbGx6ofQUXaNqB/zrvW11epVQCrG77n4deXIsZgeMRERDmOE0PYRloppVZWVjb0Qy6VSrAsS2zbVq7riq4yy+VyaLfboqvP9uzZs7+nB//gJ9F8EpNUMTyfGk0l5K45IMZOrjTF9swDq2vA+pALTz+WHu7xN2M2EbS8vzjg5/q+d+PbXMPevXt/tVqt/gSAMgwDlUoFuVxOksmkMk0TqVSq0066VCqJ3iCErpbROgwOw2ERkcsDZiIi2grmlw4DOAzBHVhXBgSHATHQagUf7x41r67xVrAe7jlqtYD5JQDKB8SH4DhE/RDr4jNAGYGMkUG7fRhKHQZwB9B5Lq8+5uJaz6f++87zieNBIBb7CWT9OOJJlxeGaKp1v1aUugeCw2i1rhYoXHnf9a59l92vD5EXARznWrdJo1ivABeJxHGuV9QXkeOA2lr/dnSQNg261wjBgUvfo8YyG9aJzXyf2v2x7rUCWOX3qmMUBMD2xp9Bup7fy5/PTb0PZFcBdRyC41D4Cbb7+/IV77XK3vBzXrcbfc/T/fVVahUiP4TIcaxV3WF+CrR1MRgePSkUCp2L/yIi+XweQBAI5/N5WJYlnufBsizRt8vlchJWCis9YziVSvVWMbwW0fcfycFn7UZqbxo49dHwjj+TAD62a3jHJxqFWAzI7Bxu1fBMIthEMQl2JFZxtjnQrGFZ+5e+qoUBIJlM3pHNZv+2Wq3CDzbDSvvKDSwKAGzbxsLCAgDo0QIolUpKKYVisSiFQkExFCYi2kKCINgG8AAghwEVXnwBIFFtRBQDgAEFG0qeCB/Xh1LHIfIiZmZcVmtEQF/sgXoACjZarcNX3iiSt/DDEDkMpR6EEmC9BcwvHYfABeRFXhCiqZAxMmi1HgTwCFrtS2vf4Ote8PoAHgQQrHWCF5CYeZrrXJdxrFdAIQyBuF5R7xKJz/E1PEKXvj8NwqvuzTobvkeN7NLE4fBwG79XBVwAP+T3qkMQBMEPArgDkAcvBcCI8PlVGQA2FC7NTdPPa0y9uC02Nwbf7xwB8MCG73cAdNWC9Cn8+gbP14NQSm+0cAG8iJmZF/i6oc1iMDwiYbUX9GxhBBXDndWgVqtJOGtSpdPpDVVj5XIZpmkqAKJD4fDjV/lG/jo+iGCDTjwGzE9QxTAQBFFnzg2vpfTH0sGMVqJpt2c+eK0Mq8J+ad9wjtuPuSRwtjnYMZqrUBc+PC2zH+s5II7H4wcAdOYLA9ABMUzTVOVyGZZlXXPOsOM4IiJKBW8eg30eRDSY9C0PYv3iU+M+jQ1mkveO5Ae++cW3hv4YvYjhSdRPvjDu0+jLzqwdXIxXDwLoqoob6b4fI2zx92BwoX5xci8aTPJznTEyuHjRxsWLj6DVtjde7Bmpw1A4DKgnguqB9cm9yHb25K0DH2Pn0jeg1AMRnE1ExMXZE48O9SHG9TlH/R6j179W+wiA8PUy1LXPgMITaLWewPySC5Hitg0juV71Lor1imga6JBQqXsgMTva8KpvBoAjAI6Em0pcAN/G2RPPjOuEpt7OrI319XsgOLKxM8eIfwYBjmBdjoSbG5+ByLe31HvzhjC4deki3+i+zjYAG63WU5hfOg7gaW6uoBthMDwiYbVXkAiHrQHCOcMAoFzXlYWFBT1nGJZlwfM86Z4vHIbCumJYxePx3spYo5gxPEltpLstfgz4eS36wGvPzsmpgCQaVCwGZPcBb78XfUvpvRO2gSI1E81Fj9NvruLjv9FPMPyrANBut7t/olLhJh9YlqW6OkN0vlN0HEd3kVjXc+iBzuYiVg0TjcP6eiasvtyGJu3zjo3rgnZ/MkYGzebjEHkwbB+GMV5ou4pJe367TeBzvSHcUplgp/6kvDWrDCCT9zWLksJkrcVXm6MYtXF9zhebhwEMfiFxZ9aGUgUoFV4gHcvrxYZSNuYWX0Ay+eS2uUDK9YqIriZjGGg2H4GI3QmvJmp9uEIQds0vFQC4mJkpbpt1fBCX/wwyecUGR6DUkbCSuDjVwf9VN7+N3WEAR8PNFc/wdUPXErvxTSgiorp6xReLRXEcBysrK7KysiKu66JUKnUqxzzPU5ZlqWazqcNh3VIavu8jm81KLBa7Y9OP3lwNfg1qbmbwYwyDDrwSEe512DUH7Ou7kyzRZJqJR/9a2ZuevHbrO2cj+aZHTv/0QJ93zVSrValWqwIA1WpVGYaBZrMpAOB5wfzidDqt6vW6dM0Z7rSRBoL3CnW9OT1ERDR5dmZtzGWfR6v1EUQc9NrlhybL/NIRzC8dg1LHoPDEGCvuiEZDiX3jG13HDsPA/NJRKHUMwGDHiorIg2i13sLcYmHcpzJUXK+I6HIZI4O5xQLml46h1Xor/N50MtbmzQsqTluttzC/dBQZY3I2ik2SnVk7fJ6n5WcQA8BRzC8F78/T9Lzqr/Xkv9/ydUPXxGB4hLqrvRzHgeM4WF5e7syYRFcJQThnWHbv3r0OdAJhFc4Zxl/8xV/sFpHNp5ZRVAsDQHKCi8x14JWaHfxYe9PATXsGPw7RJJqdiSYcjsWAmzKTFwoDwI5ENN+U9bl2isjuV155xchms6parV7zdq7riud5yrZt5PN56JnzerZw922VUqKUmritnkREFOq+QCDqwXGfDg0oCFjeAnAU03cBlah/66r/C4dziwXE268jaAU6eUQczC8d23IXR7leEdHlrgwJt8racASt1utbfqNPL7p/BpnO59mAiINW6xjmlybz+wct2Pw2jV/rI9tigxz1ZIJTvi1HdV/Q75ojCSAIhrvnTHqeB9M0Nxwgl8uhUqmIYRj49V//9d6q2C6c7vvEN5i0+cKX0+HwmXPAB/XeW0unZoNQOIpwmWiSzcSBW28CPjwTvFZ6lZoNNk9MUvvobvFYBvEYcHGwltlq7cRpAfpqHXDLLbfsqlarMAxDAUAikegEvaZpSiqVUp7nddZ+XTGs6XBY/85W0kREE2qHYSDWfiqcH0zTLmh/exQbZkETbSMi9/R8nw2vm4n/ltVGq3UMGSPaWcrjwPWKiLp1WgjHnoCa2ArGKGTCjT5HMDMz/Wt5v64Y2TD1dAVxAcDncPZERFVuEQhmCBeA1hPjPpWB6NeNyKNbasYz9YUVwyMSzoeEhH39V1ZWxHEc5PP5Tljsui5c190QEDcaDdEzhnW1MADMzc31FlREVTEcm5JitV1zQeh1054bh7yxWHD77L7oKo6JpsXHdgHGLwWvldkbtIqPxYDMzkuvlUkNhbXZwfc+Sf1nffeT37Nnz+3ZbBa+70u73ZZKpbJhHQ8p13VRr9cFgNi2LbqjBNAZOzDxV9eIiLatVPZxxNuvs0J4C8gYGcxlnw8rABiy0HaW2XRFbcbIYH7pqSl83RhhODxN53wJ1ysi6rbDMDC3WECrHbaK3tKhcDdjW1ZBTm/V6mYZAF7H/NJTyBjj/7ecXnwQrfZbAKY7FL7EgFLHtt3rhq7AiuEREREopTrBcKlUWgcgpVJJOY4D13XFtm0dDijTNKVcLivTNCWXy0m73ZZcLqfaYQVsOp2+vacTWIto89SkVwxfbtdc8GtdAReaQOti8CsmQDwWBGE3CsOItrqZODBzjdeKDn9TSWBmyt4y5maAc83BjjHA2plIJHSorBKJBHK5HABAb/YJiWVZSKfTyrZtLCwsKADI5/PiOI4Khw2LiKhwgxFDYiKiSbDDMBBvHQW2zA797S244HMUsm0upBJd38XmYQDX/0Z4fukwWq3nMb3BpA6H78Sqvzruk9k0rldEpAUVo48ArSPBdMJterkgqIK0MTPz6JauHu5UhLeccZ/KiDyBVuvBsXX4yBgZNNtHsa4e3JKvLVbdb3tTdpV/+jmOo9+pO6W3KysrG8pwXdfthMP673zfh2EYAgDtdlvi8XhvVWz1CF7fEVTfjU1Mgkrg1LhPhGjCbaXXSiK+CmCwCybN1eBXsvfDJBKJA9VqFdlsVvSmHt/3lWmaSKVSKuwIsaGdNNCZQa+Wl5chwW6iLfgdKBHRFNuZtaHaz2PQ9xiaDPNLT2EdT/DtlqiLEhvAC9f8eCr7OKC+MboTGhojDLfvHfeJbArXKyICOMbk6rbOmICr2Zm10Wodhci0bsbqV1AVnrr5STTeGd33HZ2v99RuftssI5ztPFmtu2kk2Ep6dJSIwHEcpauGw1ahUqvVZGFhQblu0NrdsixYloVyuYxyudxpPeqH72uJREJJr28EzQg2wCanOBgmou0nNRPNBfs+108R2Z3NZlGtVlGtVpWeMVwul6XRaEgqlVIAYNt2Z/2/fM6wbikdFA4TEdHYpbKPB23bWKk19YIWuMewddrCEUVnXV39ekPGyGB+8ShiWyIU1mykbp7sdYDrFREBuoXwUcRbb3GMyVVN95iAa+n8/LHlQ8pri8WewvzSkZE81vb7euvW3Y+M+0RotBgMj4hSSsLfAXQqwpDP5wEAtVpNLMsSz/OU53kAAMuyFADkcjkYhgEjfF/zfR+xWGx/TycQRSvp5ITPEyUi6paI6C3ug/42zcViMV0xDACoVCqSy+WQy+XQbDbF8zwBgHq9LpZliW4jja6OEkDwvlEsFqdkwDsR0RY2t1jYYmHI9rXDCC4cbs25bESDE7nnir/rvG5kNBdmRym44Hx43KdxVVyviChjZDC3WEC8/TqArbcGR2vrhMN6ljB//gAAHzMX3aE/yvzSU9v46/0Mw+HthcHwCIRzIbtDYQGCGZIA4LquWlhYUOl0WiGcNwkAjUZDTNNEMplUwKWKYQAQkc1XKehWqIOa5lbSRLT9pCKaH9483dfd4vH4/sXFRQGAbDYr2Wy2E+7qdd00TfE8D3pDEIDOhiHdUhoACoUCS4aJiMZpbrEAke0yz2trC+ZDHwMwmSEQ0WTIbLioPr90eBu8bp4a9wlcgesVEaWyj6PVfiv4PpQdazZp+sPhS++73BQEADMX78Xqu8NrEc7OHBrD4W2ESd8I6XBYX+B/6KGHOiGBbh+qQ2EdEuRyOQGCuZSGYUi73ZZsNgsR2fyM4SiqhQEgzn0ERDRFIqoYVufePS1Ab3PdAxkAqFarMAxDAZ2qYR0KI5VKKcuykE6nValUknw+r8L3AwlDYRERBbCdNBHR2DAU3joyRiasvJveC4VEo3KxeRiAH8xVx/PbIJAIWkqPcobh9XC9IqK57PNsGd03HQ7fiVU/gmqpEUovPoh1OboN3nc3R6Q49FC4xU1YXZ7BzuzPsVYdfoU2jRWTvhFRSkEpBekCBIFwPp9HPp+HbdvQlWOmaQoAVCoVpWcMA8F8YQAQkQObfvAoqoUBVgwT0XSJxyLZ0CLn3+0nFO5s4Mlms/B9X9rtthiXbVj1PE88z0O9XhfbvrQRtFQqKQBwHEfpLhM6ICYiohFiKLy1MGQh2rx1eQCpm7fXXPVYrICMMRmfK9crIorhJ+M+hSlnoNV6ftwn0ZNU9nGsy3bYjLU5Ii7WqsP7WSwIhV8HQ+GNlHp+qivuaVMYDI9QWDGsL/Z3qoRLpRJKpZIAQcWwDoVN00Qul+vMpPR9H77vyze/+c3eQooL/bVBvUJU8zqJiEYlinVrgM013/3udw8A6MwZBoBKpYJyuazXfAUA6XRa6RnDYStpcRxnQ5cJPaueiIhGZGfWZii8hcwvPQVe9CHqxRHEYpNRPTs6GbTb428jyfWKiAAgkfgGINNV7Tp5gm4Q02BusbCN59tejY9E+9GhHZ2dOa4nE1bcc4PCFsYS0BHSFcP6/x3HUSsrK6IDYi2VSqlGoyHNZrNTNaxbSgPALbfc0tuLsvnRYCeuJePRHIeIaFQSsVVcwEDfyKi1ar+tpAEA1WpVstmsqlarSlcM53I5pFIp5XmemKYJ1+10aOmEw8vLy533DBUMq0f4Z2H1MBHRkO0wDKjW0XGfBkUklX0cUNNxUZCIxkupx5ExvjG21qNcr4hIW/VXMb9UxCTOQJ8mQTeIF7DqD68d8aDYpehqhttCOqgm5yasa441m0sAACAASURBVDPQbB4F8LlxnwgNB0tAh0xXeBWLRSkWi+I4G9f4Wq0muo00AHiep/R84bCNtMrlclKpVDotSPft27f5NtIAsPbzgT8PAECCwTARTZmZxMC72+TC+32HwgcOHNgPdOYMI5FIqFwuh0qlgkajIaZpolwuK9u2u6uF0b1hyHEcFItF4YxhIqIRireOgrvHt4YdhoEYeKGNiDYrg1bryFgemesVEV3u7IlvAJjcQHM6ZNCa4A2f80uPMBS+jEgRZ088M7TjB5057BvebrsTeXBqKu6pZ6wYHrLuqq7uiuEwIJZ8Pq8AwHVd2LYNy7JEB8OmaaLZbEoymVS5XA7tdluy2SxmZ2fHkw7E2cWUiKZMIoJ1a4BW0olEIrO4uIhYLAbf90WHw6ZpqnK5DIQVwrpiONwspHQw7DgOwpbSumKYCzER0bDNLx0BLxRsHUHIzzZoRNSLRwCMvp0n1ysiuhqRR4N57zQAGzuzNtaq7o1vOkI7szaUGl4AOp38oc4VnlssAGDYuVlBxf0zY+ukQkPDiuERuHwupL7Qn8/nUSqVUKvVxLIsAYKKYT1jGAhmUeqW0u12W/99bz8orEWwsWyWewiIaArFx/s2l0wmdwOdiuHOpp5msymmaQKAWJYFXBb46sphIHjPEBFxHIdVw0REw7bDMAAUxn0aFBGG/ETUn8PYmR3t2sH1ioiuJQgzJyvQnEZKTVbV8A7DgMLz4z6NySKrmLl479AOvzNrszq7Zxm0WmxnvwUxGB4RfTFfh8IAUCqVrnaFX8rlsq4kg2maqlKpAACq1aqqVquYmZnpu60pEdG2EkUwHMXmmi6VSgWVSgXdFcNhOAzXdVGr1a6YPR++dzAVJiIatnirALaQ3koY8hNRn9SDI35ArldEdG0ixXGfwhZghJtwxi9jZBBvHQMUu0RsoIY3V3iHYUzc5oDpcWTkG+Zo6FgGOgJh+0+lK4d1MJzP56VUKuk20hvCgdOnTwsApauF9Yzhdrvd+wnUI1hPk/ynQkRTKCarGGM7ttnZ2f0AYBiG8n0fhmEgl8shmUyqcrkslmVtCHtt28bCwoLK5/O6s4SUSiXVPZ++e0QBbRFK3YOd2cnetbqOO8B/erTV7TAMYExzJSl6wYU/hvxE1B+FRzCqVpNcr4joRtaqLuaXXLCzwKAeATD+1s1BBSbX/Y2eCWdqD0esza/5IJQqgJ0LthSmfSNwldnCyOfzG9qGhvMlBQC6W0kDQDhfGEBQNTwzM3Ng+GdNRLQFzMSjCYWbq0Cy/0P5vh8zDGMdCMYCVCoVWJa1DgCe58GyLFiWJa7rKj1mALiys4SIIBg3zIRui7Gh1GT/gM/p1rQdxFujrg6j4WL1HRENIjPCeZRcr4joxkSKE/9z4+Qb/6zhYMYtN6Nu5GPm4vCq4lPZxyEj7wSy1Yz/tUORYivpERARFYbD3W2kOx+37c57urIsC+VyWSWTye5qYSQSCR0C8NIsEdGoNVf7vuvJkydVNptVvu+L7/vwfV+Zpqk8z5NGoyGWZcHzPKTTaWXbNmq1mgDYUCUMBJuMwvcShsJERMPx+LhP4PpkFSLHIfICBC4ELiD9v0FtZUGrs0mvCPCD5xDPBM+nHB/3CRHR5UZwEZnrFRFt1lrVhZIXxn0aU299fXzf8+8wDM64vYqZi/cOtYV0DPyaRyGoGqYtghXDIxBWd13x9zocdl1X2bYtruvqkEB5nodcLodKpdL5HQjakY705LU482gimkJjbiUdi8UOLC4uSrVaVXr9TiQSaDabYpqmSqVSyvM8AEC9Xpd0Oq0WFhYUsHEmfaFQUMViUQqFAkNhIqJh2Jm1odQEXpiXVQieRiLxDFb9q18smV86DOAJQB7gnLKQUo+M+xSuSsSFUi9iZuYZrPpXD/XTtzwIpR6BYlUD0dgp3DP8x+B6RUQ9WG8/iXicr7lBiNjIGJlrrm3DFG8dG/ljTjqR4c0VBoB4q4AxXhfcYlg1vIWwYngEwtafcBxHHMdRAJDP55HP52HbtoShMCzLQndIUKlUVC6XAxC0kwaCFqQ9n8BaBGtrnP9UiGgKTcjalc1mO39ut9uSTCavCHg9z9NjBTqhsOM4WFlZ0aMI1NU2GRERUQQm8cK84GnMJG7FWtW5ZigMAGdPHMfZE0dwsX0ngG+P7gQn2oS1WZRVAE9irXovzp74xnUvRNZ/8QLWqp9DLPY5AMO7SEZEm3EYGWPYF5O5XhHR5p1/14fI0+M+jSmXQas1+lbOQQvpCdyIOkYiLtaqw6vmnV86Arbtjhg3g20Vk3HFfJsoFApKKYV8Pi96bqSuDAMgOhC2LAumaSKXy3VaSbfbbTEMA9VqldViRESbFZOx7gpcX1+XkydPolqtwg+v6ScSCVUulzsJbzhfGACUbdsolUoSbiSC4zhYXl5WxWJRHMdhKkxENDyTdWFepIi1E0/0VMlw/l0fZ08cgcjw5nNNg8lry+oDKghYelH/xQu4ePFeMGwhGq+LzeG9P3C9IqJ+JBIOx4kM7IGRPtoOw4DEnhjpY04+H4n2o0N+DLY+jprCIyPYNEcjwFbSwycAlASDhlEsFmV5eVmVSiUBLrWTDkMBeJ4H0zQ7gXAul4NhGOL7vjIMQwBIPB7fPZ5PhYhoe1LrF08L0PPaG4vFFBBUDCcSCR0OCwAFbFzzu8PhhYUFLC8vo2vOsAo7T+jxBNwkREQUlXnjMNCanAvzIv8/e+8f49Z5n3s+Xw5JzQyHERVrIpvDSMcBK7tkm4zCYhsgjXMExN1uUcdyAxpr4xaWuxf2Hzd7JcNB0N0WmMOLuwv0R2AbBe7d+LZrGVg02xBFZHixN9nsQvQmRlsgjGVnydgub3Qsc8aajBxxxPlJHvK7fxweDmc0kjjkOTyH5PcDTDQZD895hz/eQ77P+zxPpq+d82tlDTOxCJg93pnsGB4S+amCgNF7Z9rWNR2T957GxMQleEs8EoTxgUkF4FSnp8xXgnA76rVLCEXdHsUBIR3ri6cdP01Fr2Am9hJYRK8+mB9onLTEGe+HsxHSplvYw9cjqgB8EcDb8Pl0TExc3vWf6/UIiCJgngfRlz1U22A57g+2iUzwHCIMOwwzA6YIgI4IUNI0jTVNo3Q6jWw2i3w+z6lUapcbLJFItBf+W6KwFUcqF5Ix4/U3Z/D6myF8dN18yd531IB6ahPqqQ1Ejxouj268qW748PqbM8i/dwjvXw0CAGamm3jy4Soe+eKay6MTbIMbFfQgDANALBbjZrNJADgejzMABINBLhaLbTE4n89DVVXO5XJIp9OcTCa5UChY1woA7WvI/qX1giAIQh/U590eQRu74tTWyucRmvsyABf/NjoP8GpPN52o995bxex8J2jXcP8LXlvXdITmHgPwlj1j6gkdwHg70YXxpelg/7zMV04g89XIQB4WdDyA3/8i6sY5gB1eI6YKCJfBrIPoAzDfOk8QKQA+B0ABs3feV9+ZCAxjHoDzXamTigK4EF1tO1QBuAJq/Wv+LNLTY25uhL1g8wD34s2NE0Q5UPMlTARyXW5MyAF4ERElgnr9DMy/y+358VGIMDz0iDDsMJari5ktxzAAsBUJurKyQul0mldWVsjqlpyamuLV1VVfSzggAFAUBbqu7+qpFMaDb78Wwcuv7dajlq77kX93Et/6zhGcPF7Dkw9XoZ7aQHi66dIox4vqhg8/eW8S3/lhGPl3J/f9He1v78F7VwP4xhM3Bjw6wUs0m01qNpvk8/lY13UoikIdUdLtzT+pVAq5XI4A8MrKCgHgZDLJnV3DVrx05+0EQRAEW/DOwrydcWpEz4H5km3HOzBMWF90esFnH2jeI5dK/cBxrLdjffEyQnOvAnCrC1tBIHBxYK4aQfASRJ9z8OAyX9mPzFfCeOCYa7jtYnwDgUAOFf1gG0Ys8YrwFNhLqQj7wWcwCGHYdAsPIVQB8asgXw4TE5fv+FwIzc0DUEH0VBdCse5or7A5Hi+6hXX4+DlUF3tLITGvaxcweW8OExMa3LvOAoCKiKIceH4QPIV0DA8AZr7F3aVpWrtj2IqVBkxxYHNzk0qlEheLxbZTuCUoDG7Qgieobvjwf7wZuuPvvH81CO1v78GT2n3Q/td7sHRd9ns4RXXDh2+/dhiPfHMO3/jr2duKwhbf+eEnsPSxPB7jjBUlXS6XAYCsnuFEIsGJRAJWt7z1r6qan5s6RGArPhqAmUKx3zVFEARB6AtvvMkmytkap7ZWzmEQi123x0Ex5TaElHnnnTNdY7djzd1d+U72rAojClVAdBHABRBl9nxdxPD00SqOdOnJfOUcMl8J44Lf/yJsmUupAsJLIDqN9fIRrC8+jfXFCz2JPhW9gvXFC1hbPI1G4357xucQPID3qpOKAmC43MJEufZzYW3xPKofXrzrc2F98TLWF1/EWvkUiO7QOU8VBBrOx617zS3MdBGBwClUl/qvpti6pmN98SyIXrJhZL1jGMP1vBZuQYThAUBETESdUdLWoj8DphDQcoq1icfjBAB+v5+BtmOY/H4/NxqNDwY4fMFF8u9Ndi30Ll334/Ufz+CRb86JQGwznYLwy69FUN3ofurM/XTawZEJg4Imgid6ve3S0hLHYjHEYjEoioJSqQQrDWJvv3Aul0MrTrrtFAaATCZDCwsLnMlkRBQWBEGwHfJG5B3zqw4c9TUHjtktg1+YJ8MrIgsQCNjbSbq+eBluCv1NifQUuoEqLeG3taBcfgzri09jrazt+XoM64v3txaPnZj77KXuQA+9zFfOIfOVMC6YDsLeN3bsFQDNTYX2YQpY94PIq/HuqiMbfzoZJrew9XxYK5/u67mwVs5hffF+AM+ZDvRObKgtuBvhT5+BVzb+mjyHjfJjtidZrJXPw81rrafqMIReEGHYYVrOLrLcXpYYnE6nkcvl2ve/qqqcSqWQz+dRLBa5VCqxJQ4DkBjpMeW9VmftQRGB2B76EYQt3v+wt8dQGB2i0eiuuTwejyMej+/6nc3NTUKrjz6dTjOAtijciaZpLBXDgiAINhJRIp5xbAUaDnywD7jpGB78goxXeu2Iuu0MO+hx37b9mN0zeAe4MFwQZRDw398Sfrube8zF47PYd/HYQ/h89s8tMl85icxXwvhg1nYc4P1eawNPIHCkbwGwW9bKmuvuxtvhxMYfi2FyC5udv/Y+H9YXX0TDOIUd9/AF22oL7gSzmxHLuyHKOPo3Nxr21RAdHOc3VgiOIsLwAGBmyy3MnfGgrRhpAEAul0M+n0cikaDWF0qlEpdKpbaAYLmHhfHhoz5FXRGIe2Ppuh/a394D9euf7lkQ3jnWhI0jEw5Mkz2xwFQul9t98aVSiQCgWCxSsVhE63tWVXXvHE+FQoE6ncPWJiNBEATBJuqe2U1ecWT3/Lp+2VWxxewbGxxEHlmcYGcEESI3hX6JZhVuhw7gFNbKWs8C4/rii/CRm4ubd6bZtH9ukfnKSWS+EsaLbhy55mvy6VaaQ+/zda+47W68HU5s/LGYqA/BXEQV+Pgxxzp/t67pCAROgfASAg3nneOTigLmM46fpxuIco53KW9d01115Dca3rivhZ4QYdhhiKhzFZ8A0wVmCcLWz6xeyWKxyFNTUwyYcdJWx3AsFiNd18kwjINbxWZsWO9qNPs/huAaIhB3hyUIP/LNObz+5ozbwxHswOW5yzCMqwAQi8W42Wxa87c1x8PqGU6lUsjlcmRdC5LJJKfTaSSTybZ72KokEHFYEATBRrwS5Ul02bmDs3vdbk4utu0HszeEfvI583jW6w4+T+6KMz2rwnBDeAmBwKlWdHB/VD+8CK/GSpMDm4hkvnISma+E8cJ0ee4vunbGA5vuYvfwYqQ0N518r3rOwWPbAFUAPm1L7+2dqOgVrC2edzxCGvCWGO83BrPhze9/0bWNwM6+fgSHEWF4QDDzvov5lgiQy+1cv/P5fFv89fv9XCqVUC6XWVEUds013BAhYhToFIjfk4jjNiIIjzBNe+JBOfCJVTuOo+s6rA0/AFAsFq1+YUscRjabRaFQIACwHMMLCwtsXUP2bDgSBEEQ+sErji12ULwlck8Y9orwMWicejwnJ91OQvHG60XwAFQB8Jy50Guj66zRcNZZ0yuMw24PwTFkvhKE0WCv6GpXX6yd3EnAdoumQ+9VzRhpb4tmvubTtmzs8hZeiZG+MBAhHDCFd3JpYx1DeoaHGBGGB8zCwgJbC/7ZbBa5XI5zuRxUVUUikdjlBi6VSta3pCgKdF2Hruuo1+sfDHrcwmjx+o9n8OTCfXjmL47hJ+9Nuj0c1/jJe5N45i+OiSA8ytgUJU2HPtnTYpDP5+OlpSUAQDAYbFpzealUQjAYZADI5/MAgHA43O6gt1IlLMewhbiFBUEQbMaJeNBeINiyAWlfmJ079t3PfWLAZxz0+QbLoGMf99KoeXuBUxgUOsCnHenM27qmw2uiAQAwH3HgqDJfOYnMV8K4YYmuXhSEd+OtZAgiZzrJJ+rejtglyjjuFB40phjvBcewPpDY7N249VjOS0LH8CKZsgOg5e4ia0E/mUxyMpmEpmmUTqexsrICAJiamuJUKoXNzU0AZsxoq2OYAUBRlN6ipMMKcK3PP6Jm9HkAoRdmpp2Nwc2/O4ln351E9KiBZx5dxSNfXHP0fF7hJ+9N4uXXDiP/7viK4mNDveHqG5RGo7EajUZRLpcRi8Wolf4AXddRLBYpkUjw1NQUNjc3qSUQt8XhZDLJVr9wZ4Q0M5O4hgVBEEYNF3uAncQZMcX7sD0b07zHhCz8CDoCjdMOu2BegzcWdnfw+UbZMSzzlSCMCoHAY65vyrgbgcBF1OuvuD2MDpxKt3nUoePage54960beCdGOjcwt7DFWjmHUKwC2JOaeCAMYx5e3NQn3BVxDA8AZiYAyGQyRETQNA2FQoHS6XT7d6rVqiUKoFgsAtjlGIZhGL2JwsJQE3ZYGLbYG6U8ij3E1Q0f/u6Hn8Azf3EMz/75sYGJwtGjjYGcR3CQYO/vqwzDWLUcw0C7L57j8Tji8Xj751a3vKqqyGaz1NFD38bqGBYEQRCEoWGUxZQ7EQh4e1FWEHpDRyBwyvHFTkc713tklGPxZb4ShNHB66Iw0BqjgxUqvRBR7J3jTQelV0TKWwk0Trs9BIfwRqwxkUuueJdeV8yS0DGkjJ764zEsUbjl9GIABACaZm7MscThcDjMqVSK8vk8EokEACCRSDAA1Go10nUdADgWi9Hm5uYH4XC4+0H0IWq0MQYjUAq7iR4drFPbEogB4JHfWcMTD1fxwKdrAx2D3bz3YRC5n07hOz/8BKobg98Lc9894rZ3lXqjgn77pfqYQ2u12mosFmu7e62eeCtGulgsciqVal8DACCdTjMAaJpGAFjTNImQFgRBGHlc2N09ihBV4IVrZr0uj6cwehDpAxEd/P7LqNcdP43ryHwlCMI4EFGU1jwzDyACwgl4YOrbhd3zYKPhXVF4kN23g8cL97vuYoT7ZbjTa+1MHLvgOCIMOwwRcSv20/oRa5pGmqZxoVCgbDbb/g+pVGrXbWu1GhmGQbqut6NHy+XywQdhhzDcEGHYDQblGN6P1388g9d/PNOOmU49sDVwobpXqhs+vP7mDHJvTbkeF+3mYygAaNjwiaOPOXR7e3u1XC5TLBbjcrkMRVEQj8dRLBaRSCTa8361WiUAyOXM94/pdBqaplkCcd9/giAIgnAbfL4Kmh64VjOcc9YSHfaE+DAI3OxT7oRIhBZB6JWKXkEoqgM0ui5dQOYrQRBGh4gSgWHMg1kB4XNosgLCPOCL3CK6evEtqY8VmKKaPTB7QaDcn8F33w6GiBJBve7++wbCa+6dmz5w5TPfuFYHjQAiDA8IZm53RGYy5hyczWaRTqfZEof3RkknEgk2DIMURSG/38+tjmEsLy9f/dSnPtX9ye0QhgFg2wAOyVNmkJz0gFu300Wsfn4D6qlNqKc2PCd4Vjd8eO/DIL7zw08g/+4hV9zB+/HAcfcfw7HGjo5hm+ZQRVHYMAzy+/2cSCRQq9UIMF3DAKCqKquq2haHLSxh2LqGSL+wIAiCjTQndMAD72mc/EDNY+RG9ooDjxwStCbvdX/BSxAGwoj2rnci85UgCMNIaG4ewDwInwNjHqD5XeIvA2ibozwwx3WFzZ3kzF51UI6uW9grLm3i8evaJfLq8124C6LyDZCWKNx2CFsx0qpqzl2WGJBIJNri8PT0dLNYLJKiKO3b1WoHFJqCNhkQjCZwyJ5DCd3hNYdu7qfTyP10GsA9SD24hUe+uO6qk7i64UPurWm8/mYI718NekYM7mRmygOLzeNMs9l/lPSh3m9+5cqVq9b3uq6TJQ4Hg0EOBoNcLBaRSqWQz+etrnm2rg2WIKxpWlsUBsyKAhGHBUEQbKKxVcHEhNujcPgDNc27tjDHA+66GvT5bodTfaR+v+KukNQYfbFO8AZE+sh31sl85TAyXwlC30SUCBo1FUxfbovAVv1J++U9AksTts+DLr73vhOudd8OAK+8Z2j6ziE096gr53bvPpCNYEOKCMMDoHNB36IVIw1N01jTNLLEYQCYmpriRCJBVpR0PB5nwzBgOc2++tWvXl1cXOx+AGGbXp+1BhCy51BC90SPGli67r2Xav7dyXZMc/SogdSDW0g9sI2Tx2uO9RIvfexH/t1JvHc1gPx7k3j/atCR89iJOIZdZsuGTQuTn+pZXF5cXKxYHcOxWAyA2TNcq9UoGAyy1Slv9QyrqoqVlRXK5XLMzJzJZMjqGN7vWiIIgiD0ydY1HaFYxQMdvwoiSsT2/s5JRQFc7I8kDDoq1RtCAPMJh47r7qIX+7xx/wqjj1dilp3FG68nma8EQbDYJQTzGTOalzp+QdYj7kpImXf1vfftcbP7dhB4xLXq4RhxJ4koCiq6Nza8CV3jPbVpBLEW9Fvfs+UaTqfTKBQKBJj9kqqqcssxBgAcj8fJ7/e3r7rlcpljsRj5fD5m5lUi6s4KbFeUtPQMu0LqgW1PCsOdLF33Y6nVSQyYvbonj9dw8tM1RI82cPJ4DeHpJqL3GHeNoK5u+FDd9OG9q0Gsbfjw3tUAPmoJwl50BN+JkyIKu0+j2f8EOB3t+RhPPPFEJRqNEgAsLS0xACiKQtPT081arUalUqmpqirncjlKpVIUDoc5l8uxpmnta8Vex7AgCIJgN6wDcH+XuWHMA7B3wSbQmHc3KXvAcbCNRs4TDnDgDICnbT8q0ZdddeAxi9AiDAavxCw7icxXziLzlSB0R0RRUK+fAfAo6nV1RwimO91qxLBxg6ivoXihpeYW3Oy+HQROpV8I3bG3S1wYCrytNo0QmqbRwsJCe6E/m82ypmnocAtztVolwHSObW5uEgC2XMO6rrOiKKTruuU6qwAYrDC87a1Y43Hh5PEa8OZwWbWrG75djuK9hKeb+wrEXhfAD0r0nobbQxhvGjbESAPgqftWqdv5toNms3k1Go1iaWkJsViMFUVhANB1nROJBFqOYapWq7fcVtM0ws52XNY0jTKZDDRNG/EVMkEQBFd4G14QhsFnYLcw3Gy6E2VmQTTYnePecYBHMBNTbXdmMNx1IawvXnb1/MI4MfqinsxXziLzlSDcnogSQa12DkSqKQaPOXzw9Z7b0mx6VKCki26PwGE88FlujCFy+72M0AOjpcJ4lJbTiy3XMGA6wDRNo3Q6jZWVFatfmFVVRS6XI6tnOJFI7BIBFEXhWq3mazabH0xMTHQX+TNj0zVJhGFXcCqW2U2qG76hc//2QurBLbeHMN5s2LNjjaY+1dOHhGaz+UGj0egUeNtYUdLW/0+lUgDMrnmrYzibzRJaovDCwgID7d5hEYcFQRDsJQfgKbcHAcZTiCiabXHSZoz0WVuO1SuudGjyZcBlQQKA7UJ/aO6sywKSxMMJgu3IfOUQMl8Jwl4iSgSNhopm8xzqdRU0To7gAULkcr/6bfD7R3ezTESJoF53exTjDZFHN0QId2L0lRkP0BklrWka0uk0FQoFSqfTyGazyOVyUFUVqVSKqtUqJRIJaonCnYdpX7F9Ph83m82rBxqEHeKwREm7gsQRDy+pB0QYdhW75qxDva9pLC8vtz8RGIZBgBklDZji8NTUFLfqAwAAnX3zlkAMoLOOAMwsn+AEQRDsJBDwyg76CAzjvG1H89XcF7vdWYTyRlSeKfTbuUiyYOOxDs6g3d+CMB7IfOUEMl8Jwg4RJYLp6ALqxhU0m9+DJzajjDDMHum63cVl2zaeehGzjkcQhAMiwvAAICJuLeYDAJLJJGezWQbMhf90Os0AEA6HOZ/P89TUFFuO4WKxSAAQj8dZ13W2hAUcNFrJjjjpTdl94wbh6SZSD267PQzhgISnm3hARH13sSnlgI98drWX2zUajXcAIBqNtn/m9/tZ13UulUoolUq7tpHm83kGgGw2C8C8VgBtlzBZVQSCIAiCzZgLJfZGaPYK8zlbFucnFQVEmg0j6gd3FqEaDe8I/fX6K7YcaTq6AMDtnfhvuHx+QRg9ZL5yCpmvBGFSURCaewV144r5ntD12HrBLZg/cHsIgiB4DxGGBwMBO44vAEin02Qt/q+srFC1WqXZ2VkGQPl8ftfif0tAgKIo5Pf7uVwuo1arHcwxfI8Nm2cMcQy7xckRjJMedVIPiJjvOjYJw3Tokz1FSTcajdVjx46Rz+fb5RpWFIUSiQTH4/F2r3w+n0cqlbKuA0gmk1YPPQCAWzuLiIiJyIO5RIIgCEMOkVcWkSOo17+HiNL74t2komCifsnGMfWKO5F1W9d0eEXoB9SWSNI7obmnPCDyA965TwVhdJD5yim8cp8KwuCJKBGE5l7ARP0KALdj3ccQdntjzK34fKMbIw0A7MH7fNyQx2AoEWF4APBOtwBZi/wtx3D7P4TDYc5ms5RKpdDRL4x4PN7+0nUduq4jFovBMIyDRePY4RhuFXZiVwAAIABJREFUNKVn2CVOn9pwewjCAVE/L4+Z62zYkHLwyd431dTr9Q8mJia4XC5TuVwGsOMYrtVqVCqVeHNzk1pR0pzP5zmbzVI6nUahUGhvENI0DUQETdMYAEmUtCAIggP4/S+6PYQO5lGvv9WTc3hHFPbCh3M3xXZvxLMCAJHWs9gyFTsH4IK9A+qRUe6mEwR3kfnKbmS+EsYVKzIasK+aRDggHuxaZR7dGGkT2fwgCD0gwvCAaC3qAwB3LPgTAORyOQaAVCpFAFAsFttOMitqtOUYRiwWIwBYWVn58EAD+KRNFQfiGnaFk8drCE/LfT9MSL+wB7CjY7iPTTXVavXq0tISACAWiwEASqUSxeNxlEolJBIJTE1NMWB2C1tf2WwWyWSS0+k0Ae0oaQC7NhoJgiAIdmJGHr/q9jA6UFCvX0Jo7mxXv231x00Yb8EbojAQaLjn2AoELgDknUUwIg2huUtdi/2TioLp2PfgY29sWCDKjXQ3nSC4iTfnqyuYiXXXQyrzlSC4T2huHqG5tyQyWtgXH4927zqRPOcFoQf8bg9gHCAiazGfYYrBnM1moWkaFwoFUlWVANM1DJgCcT6f50Qi0XYPK4pCllvY7/fzO++880E8Hu9+EMEj9vwxm3UgFLTnWELXhKebUE9t4vU3Q24PReiCk8driB4Vd73rbNgQwd5HDP+VK1euAjsdw+VymZXWenAikWAA2NzcJABcrVbpM5/5THNlZYXS6TQ0TSMAnEwmgZ0kaQBmnHTPgxIEQRBuD9EFMD/l9jA6UAC8gtDcAoAcfPwamqQjENhZ8K7XVRA+h7px1lwU8cglgiiHyjX3FqEqegUzsZfA6C8W1V5U1OtXEJrLwdyEcHnPYxkBoAJ4FKh3J8gMCmYvbZoQhNHCm/OVAuZLrfnqNRBdht+/M6fLfCUI3mEqdg7wyMYMwZuwTzbLCM4y+q70kUSE4QHAzMhkMrSwsMCA6f7qdIB1ksvlKJFIWM5hAKaAsLGxQUA7hpTOnTtX+cM//MMKuo1LuMcmx/BGDYCIk26gfn5DhOEh4ZEvrrs9BMGOGGkAmOnddPXQQw/pHaIwlA6TUK1Wo2AwaLmF2eoWnp2d5Ww2i3Q6jWQyueta0eqpZ2YmEYdHDKaLIPZOjOD+qAC8JJgJgv2slXMIzb0K7z3XFQBn0STTPVzfc41r7z/1EF5YmPf7X0TdOOdB54za+rr1sfQqbrq/BWEc8Pp8xSzzlSB4jYgSQb3+PYC9tTljnJm81xupPYIwaHyy+WAYEWF4QCwsLLAVJ61pGgqFAmWzWaiqSlaUtKqqlEqlAIATiQQVi0WkUileXV316brOLdcwWa7hZrN51efzdffBYUYxI1Frfb5OpWPYNVIPbCE83UR1QxLgvY4qndDuU7NnruJIYpWAwwe9XbPZfOfYsWO0tLTEljhsYRgG+f3+9gp+tVqlXC4HVVUxOzvLqqpSNpvlbDa7yyrMzCAicQyPIj68jbVFb3Sy3Y7QHOA9sUwQ7KfR0DDhf9SDi/PDhI5A4KLbg0BFryA0lwHwgttDGXIuuOr+FoRxQOYru5D5ShgPJhWzcsQr9SGC4CbM8joQhB4QhWkAtFxeAABN03hhYYGTySQDZr9wOp3e9fv5fB7FYhGJRAL5fJ5KpRLi8TisKGkA0HWdGo3GOwcaSB9dmW02h2SX6AgSnm7iD8SJ6nlSD25JjLQXWLchRhoATc0eWBQGgGazubq8vNwWcGOxGHR99xqFlQoBmB3DuVyOs9ksLPewpmm7rh9ExMxMEARBEJxj65oOcMbtYQw53ul3XF98EYC4x/oh0JDXgyAMApmv+kfmK2EcCM3NY0JEYUEQPASPeI/1iCLC8GAwA974FpMXA0CHc9jqGaZEIoFarUaJRAJWl3A8HmfAjCSNxWJoNBofHGgU99mQLrJtAI0ezWoGm/Gu19eBxVXz6xcfA1cr5vfLVaC6bV8E7AhyWpyonkdipA/A3jnhw9UKfvGxOS9Yc8KNzd7mhPV+4xFafLK3jmFr407LNQwA7ShpXdcZABKJxC2327tRCGYvPQBARGFBEIQBIYvz/aB7bmG+0XgaIG8I1cOHuO8EYZDIfNUPMl8Jo8+kogAkorAgdEIkc78g9IBESQ8Iy+llRYG24kGpUChgZWWFrPjQXC5HqVSK8/k8JRKJXQpsqVQiAByLxQgA6vX6B5OTk90Poo+uzF1s1IDwoe5+d9swRZ/qNlDd6v4cfh8wMwkcmQLCk8Chid7GOmKkHtzCA8dreO9q0O2hCPsQnm5KjPTdqG6bYm91q9VZvovbxxp0zgmRacB/F410y4YNJj2KwgBgGMYHADAxMcHRaNTa0LMrQtpKhgCAVpQ0AeCVlRVKp9O75v/WdUNipAVBEAZFo/E0JiZk4e2gEL3quYX5rWs6QnPPAXjF7aEMHV4T+QVh1JH5qndkvho9iC6Cebg2ShCcG++korScwlJ34lXMOdztUQjC4PH7vfX5T+gKEYYHxH5OL03TCAAsASCX2zEm7Ocks9xmhmGgXC5jeXn5Z+FwuPtBzJw44Khvw3oXwnB123T8HUQM7sRoApUN8wswxeGjIfNrzPnyqU0Rhj2K+vlNhKebbg/De2zUTDH4l1Xztd0LnXOC/wYQmQKikf03jTSawLbR/4elcO9awM2bN38WjUZhuYVjsRjK5TIrioJ4PI5SqQQAmJqaagu9VoR0LpdjTdPY6qS34qRFFBYEQRgg5sLOYwDecnsoQ4SOtbLm9iD2ZX3xAmZi82A+5/ZQhgaijOdEfkEYB2S+OjgyX40mfv9zqOjyuAKdorBsWBQODpE8bwRnkbl6KJEoaRdgZmQyGQLA6XQa2WwWKysrlnC8y01WLBZRKpXYMAwCAMMwqFwuIxqN0jvvvDP4KGlgP5ffDtVt4N1fAu8u9y4K73vcLeDKx8A7S6bgvN2w79hDxpMP3xTx0aM889Xh2szqONZ8ULgGLK32LgrvxWiaSQTvLJrzwt75wKY4ej76hdVeb/vGG2+8Y4nC0WjU+jHpus5Wb3wikcDm5iYBZsewhaqqZG0c2jUeiZIWBEEYLOuLlwE87fYwhoZA47TbQ7gja+XzAF51exhDgndFfkEYB2S+OggyXwmjz0R9ASIKC73SbI62y3zYkgVGDxGFhxQRhgeE5fgCYInC0DQN2WwW6XS67RZOpVJkCQWA6RyOx+Pt+FGrX9jn8/HXvva1CjN3Lw7PKEDQhmvB2vatPzMYuHrDfkF4L9uGKTC9t2wKxGNIeLqJJx6uuj0MYQ+P/M46okcNt4fhDTbqFUc2iOzH9XVzPrixufOztW1b3hTSsd8+3MvtmHn1+eefr0SjUXS6hgGwoigEAKVSiWu12i6hN5vNtr9Pp9PQNA0LCwvMzPt11AuCIAiDYH3xAogknvLuPDcUbq1A4DyAy24Pw+Ponhf5BWEckPmqG2S+Ekafqdg5AGfdHobQLey998M08psKRBh2E+l4HlpEGB4Q1qJ+JpOhhYUFBoBCoUCAKQaoqgpVVZHP59mKFk0kEigWiwgGg21FoBVHCgCIRqNkGMaPDjQQO3qGtw2g0SFSbDdMYWZ5gGKlJRC/s2QKQ2OGuIa9xyNfXHN7CO5jbRApfBRxXBDuZNsASis7m0XWavbshuyxY7jRaLyztLREjUaDGo0GAe1NPQQAiqJQPB6nUqnExWIR+Xy+fdtsNovZ2VnOZrPQNA2ZTIY6oqT7/pMEQRCEHlgrayIO3wGiDNYXX3R7GF1R0SsIBE5DxJY78dhQiPyCMOrIfNUNMl8Jo82kosDH590eRu9QBUSXQXgJwHMATiEQOAIgd5cbCnbS5NEWhn0+EYbdpNkcT+feCCDC8ICwuiEXFhbYWtxPJpMMmM6w2dlZbrmGKZ/PkxUjnUgkUKvVyDAM8vv9XC6XORaLAQCWlpa4Vqu9c6CB2BUnfbMl+lii8J3ipZ1k2zCjZPeLkx1hxDXsLVIPbiH1wACFUC9S3QZ+tlgZ6AaRvSytmsJ0dav/N4V9JCzUarUfHTt2DMvLy1heXuaWc5jK5TLrug6/38/BYJATiQRSqRSnUikAZoS0dQxN09rXDOtnLeewqMOCIAhuIOLw7Xh16CI8d8QWWZS8Bd/TrQh1QRC8gMxXd0DmK2EMGLoIaaqAKAdLBF4vH8Fa+RTWFs9jffFFrC9eRkUfcRHPg+5Jos+5PQRHaU547z4fJ3wefM4LXeF3ewDjAjMTEXFrUZ80TYOmaUin0+3fUVUV1WoVAHhzc5NqtRrVajUEg0E2DIMMw6BYLEblcpmsiNLt7e0PQqFQ9wP5pE3XguoWMB00ReFtD8TnXl83han4LDAdcP5824bZY9poml9Wd+ohv/k14XN8HE8+fBPf+WEY1Q3v7O8IA1ABPMLASQaqAJ71AUt3uV03RAE8z+Y5LgF4g+w5rh1of/zx4E9qMFAzzE0Znc9Bvw8I+s1/p4KAfwA64tUbVmKA+70ldo3jnt7cwgCwvr7+zvLyMo4dOwYABICXlpbY+r5UKgEAJRIJBoB8Pt/uGLZ657PZLFniMGC6hZm5vclIEARBcIG1sobwpy+jya8A7P41z31exfricEYbmouSpzETexHM59wejjfwPY31Dy+4PQpBEPYg89U+yHwljAGTigLUh+B9FlVA/CpAF+H3j4Hwe1e6r3wcHAoiSmRkH5vGVgUTE26PYnxh6RgeVkQYHgxWp7APAHNLJbb+YzabJVVVkcvlkEqlkM/nkUgkEAwGuVarUbFYJAAcj8eh6zrFYjFuNpvUaDTo/fff/9kXvvCF7kdil2O4ug1UPCIKW2wbQOEj4NNHgHvD9h7bYODjNeDmNrC2tSPC3QlLmDsyBUSmgUP2XqQs1/DLr/VUg2orUQBPsCkId97zYQDfbvYvDkdbx4m2/n8KwDcYyBPwd+Tu9umBdgtXt80u3cpG96+96aC5SeHoDBA+ZO94DDYjnAcZGz0oeoyRBoCPP/746uzsLAFgyzFsoSgK/P6dS28+n4flGM7lclBVlVRVxezsLANm/YCmmUaslihMAEQcFgRBcIvqhxcxee9lTExcwlA5OGyGKDN0TuH9WCufR2hOB2hhfMV+qoDwGNY+FEeiIHgZma8g85UwVphuYe9ClAM1X8JEIDeygmMvEFXAHlyyMYx5jGr6xNY1HaE5t0cxvhBJeseQIsLwALD6hTVNY03T2m5hC8spZonDFrVajQAgHo8DAEqlElkx0gAwMTHBX/va1z4ol8urRNSdOmjFo9b6vGa7FR3dDR/eMB2UczYIptVts7e0F+HLaJq3q26ZjsrwJHA0ZH7ZhNuu4RSAZxlI3eE9TxSAxsAzPRpXLXE5us9/S7XOvQTgZQJedyFk95mvOvz+12Bg+Sbwy2p3GxL2slEzv66vm272e0KmSNzvRgUrRt5Lm0Ps5L4v93QzZl596KGHfubz+axXBXW6hXXd3EiXSCS4VqtRIpFAPp9vplKptuBricIAdl0rdk5hJlD0NEBBEAShf7au6QDuR2huXOfi57BWHo5O4W5YX3wRobkKgFfcHooL6AgYp6WjUxCGBJmvZL4SxgNvu4Uvg+g5rJVHU2TsF2aPimR8BqMqDAMAWAdofDftuonf79HnvHA3RBgeEJlMhpiZM5mdajIrSrrlEkO1WiXLMQwApVIJ8XgcpVKJACAWixEANJtN8vl83Gw2CQDq9fqPgsHgH3Q9mE/OA9dG+FoAmF2jQO/icD+C8G2P2RKJl1ZNV/ORqb4PGZ5u4vknbkD723tsGOABzgsz1vmRLpdDUww8CdPde1Ce5f1F4U7a4jMD3/AB7x38ND3x7KMV59zC/QrC+7FtmM+/5SpwLNz762PURWGgZ8ewYRg/W1lZwezsLPl8Po5Go7y0tIRYLMYAUC6XAQDFYpESiQQXi0XA7JbnTqcwABQKhXacdOtflo5hQRAAAIahABLZ5CqhuRfcHoIL6AAeG7lOR3PxdRzjWV9FIHAelUVx+QjCsCDzlcxXwngwUT/j9hBuw3NYXxydzYFO4PNV0LRpDc9OGE8BOO/2MByDfJfB7AVhOIf1xdNuD0IQusE75aQjjqZp3IoE5UKhYEVLI5vNth3DFqlUClNTU5xIJLglDrOimHNbuVyGz+fjcrncFgfq9fr/e6DB2BUn7XWWVk1x9yAYbLp73112Lh532zDjd698bApsffLIF9eQenDbhoF1x5MMvN7sXhS2eKYLgXcvj7AZUd0tUQB/1zRF4oOe66BEjxp45tEDPr+6pboNFD8yn8N2icKdNJrmsd9ZMs91EMZFFA72ls5Wq9V+NDs7i5WVFQBAo9GgvVHS8Xgc8XgclmM4kUgQAKpWq5TNZmllZYUAIJvNckscBjNDRGFBENpQc0wjJD3ApKIgNHcJo7ywsh9EGQQCp0ZOFJ6JqZgw3gLQe4fE8KHDx49hffGsRD8KwhAh85XMV8I48ZTbA9gNVUB0WkThLqjXvfpeOYKZ2CgLAl7pdp5HRJHP6sJQII7hAUBE7ehPTdPacaGFQoHS6XRbQLDI5/PW4j8BaDuGAXAsFiPLKby0tIRjx47RzZs3fxYKHSCe+N7eIlKHkoM4h7cbpmA7qJjs6+umKBefNTtg+0D74+t4UrvP0Uhpy5V7p9joOxHGwSKlwzDF5F54pDVOzQfkezvEXdH+u4+dOfDVG6ajdxBsG+YmiOjh7l8joy4KA31tnrl+/fqPmZlmZ2d5eXkZLccwAyDLNVwqlSgejzMAtBzDnEgkKBwON1uVApxOpwGY4rCmabBq6ZkZRASJkxaEAeLJXd8T8mHTDSYVBRP18eoWJsrBbzw9ktGdobmnwHzB7WEMDqqA8BL8/hdFYBGEIUPmK0EYH8xkAA9tAKEKwKexNmKbA51i65qOUKziyS545qcwqnHS3onwjqDROANgjK7ZwrAijmEHYWZqObyotZB/19tsbm7uks2sfmFFUdpR0gAQjUZx7NgxAoA33njjnQMN7B4Pvb8YBN04hzfqpkNz0N3J2wZQ+OjgzuY9RI8aeNYp9yrMLuFvN3sXhdvHYfNY3dBNhPSdiAJ4uWkex26efPgmUg/Y7Cg3uILCtcGJwp0srQL/ch1o3OHOMrgyFqIw0NfmmR/+8IfvEBGvrKzwsWPHLMcwxWIxLpfLZBiGteHHqguglmMYuVwOuVwO6XQa2WwWVh+95Ri2aG0w6rzGCILgJMzeW5D0RkzWeDFuojBRDkSnsVYezT7H6egCxmbBhiqm49t/P9bKmogsgjBkyHwlCOPFRN1jrk7OjFxijON4RqTcy5nRdbMGvHOfN5sec/wLwv6IMDwgrEX9hYWF9up+Nptt//dcbmfDTirVlt8YMEVhXddhOYUBM550eXmZAeD8+fOrzWaze3E4GAHu9dj7DKdZWjUduvtxfd0UZ52I7O2WXmKv9/DEwzcdiZR+kk2B1a5oZq1puoHvRAoHi5C+E88w8A02+5jtwJEI6e0GUPwoMvCNCZ1UNkz38L7iMFXw3nJkLERhoOfNM8y8eubMmT/4+7//+xOzs7PUbDZpeXkZS0tL1Gw2KRqNWh3DpCgKAUAwGORisYhisYhUKkUAYEVJt2KkyXIMW27hhYUFthzELUQcFoRxg/mE4+eYVMZDAO2GsRGFWw4t4BTWyqexVh5NR8FU7ByINLeH4ThEOQDPicAiCEOMzFeCMH4QPer2EDq4IPHRPUD0tttDuA0R1OsLbg/CEdb1y6a73ROoiMhnacH7iDDsAHtdXJa7K5PJEBFRoVAgq2e4QxymfD6PqakpzufzFI/HKR6Pk2EYpOv6Lrfw0tISJiYmGAB+/vOf/6tyufyffT7fZw80yHFzDQNmp+9GfffPbmyaP/cCNojD2h9fR/SofeLdsww8b7PjNoo7u3ijMMVjO3mCgZfXfXcVpLvh299ctk1kBuCteOaN2v7isP7x4N30bvHJeWCmt/dvRHT46NGj/1FV1TfL5fJ//Id/+Ifjs7OzFI1GGTDn7lgsBgCs6zri8TiKxWK7KqDzWKqqUss1zJqmtQ5/q/6byWQIAItzWBDGDud3+JlCqDDyojBVAFwA0WlzQX7x/Ei7QsLRM/DxKC9w6iDKtN3e64sSwyoIw4rMV4IwnngpGSjQyLg9hKHE3OziVc4jNDeiooCHnNqjKsALI4UIww6wt/ex092VTqcpmUxy63uoqkoAkEqlkEql2lHSrZhR0nUdiqLA7/fz0tISW66zn/zkJ1+q1+vfP3LkyP/i9/u/dOBBHvfSBrQB8u6yKcQB5r+6R0Rhi6VV4FrvUcLRowa0P7bnb3qWe+/4vRtP3CFS+vk+I6Rvx0kGXua7u5XvxDeeuGGr8A6DvSMKW2zUzJ5ji6VVYGVtRKNm9uHof9X3YgQRHQ4Gg//qi1/84n/Wdf1/WFpaopYozAAQi8VIURQrShqJRAKJRILy+TxU1dR6ZmdnWVXV9iYi69BW+gQzI5PJ0MLCgojCguA0huHFGF3F0Riw0NwLGFkh9IBM1F/BSN0XVGnFRJuL8evlI1hffBpr5dzIL8hPKgqavlfcHoa9UAVEFwE8h0Dgfqwvmm67UXV7C8K4IPOVIIwz3hDtiHIjWScyCCYmvD6vveD2ABziNbcH0MFZcQ0LXkeEYZuxOoWBHYGYiGDFgSaTSS4UCpRMJnllZYVyuRyq1SoBQDgcZgBIJBKIx+OIx+MMALqut3sp//qv//qwrut/ft99932/J0HY4p55M1LaTSZ8wCG/+e+gaDRNIW6jbv7rZnz07fjwBlDtPRI69eAWvvHEjbv/4h1wUhS2+FYTOLnnZxoDqoPn7UccfvbRCp54+Ka9A/ov1yueEoUtrq+Z7vWVtUq/LvYDM+g5YS+f/m9smxh9Pt/x6enp/7HZbBb+8R//8UQrRhp+v7/tGC6VSqjValSr1QhA+3rQSTqdplbPcPvV0bquMBFZrmFBEMaNRuOMI8c1uwzPO3LsYcO8LwbRv/IqgFPw+R4D4SUQcjZFoemmCIyXADwH4FQrpvP0WC7GT9QvATyAD0C+p0F0GuZ9/qrNj+fF1uP5tCmslI9grfxYy2kni7eCMCrIfCUI40lI8YYobPKG2wMYWszNll5+n622PueMFkTecQwDQL0+Yhu8hFHD7/YARoW9jq3Wor2PiLjl7LIW9zmdTqNQKFAul7PcYfzLX/7St7m52RYHLCeZ1S8MAEtLSw/de++93yai/nvlghEzMvWaC9ep8CQwdxgIH9r5mdEE1raBGxu37wK2i23D7BT2Mv+yAiTvAw5N9HTzJx6+iaXrfvzdDw8ugQ5CFAZMcfY7TeB1At4D8FW+VSh2gpMMfAvAMweQ0tTPb9rfK7y0Ctzc9K4Td2kV8PucH990EDgyBUSmzO8ttg1zg8TiKlAboHh+n/3r/0R04gtf+EJxc3Pzf5qamvqfWz9mwzBIMTcQcqlUYpibirjVOU+qqiKbzRIAzmazpGkaWl/c2mzEwO7uekEQHGDrmo7QnNujuJVm8ykAF2w9ZmjuKQCj32XYDZOKAqoP4r7Qsb54tvX9ZQAX2/8lokRQrysgioBZAdH+u86ZKwDMhXwiHX6/DqAy8g7ggzAoFzxRBmsfWq/L3R+0IkoEhjEPogiazcgdHk/zw5/PV0GzqSMQqIiIIghjhMxXgjC+kBGBVz7dE8lruR+I3gDzIDaY9gaRhtCcjvXFV105f2hu3vb6mrVyDqFYZTAbq7pCxdR957H50SjXQghDjAjDNtISganD1MULCwttN1c2m+V0Ok0tpzBbkaH7OcQAwHIJx2IxvP/++/9mamrqL20d8IkzgxeGo4dNUXgvfp8pDEWmzN/5eL3vvt2hptE0u48f/FTPh3j+iV+hukF4/c2Zrm+TwmBE4U4eYeCRwZ4SKQa+AeCvuhCHTx6vQfvj6/YOYKNeweKqV96o3B4nHfX7bRDp5JDf/AofAt795WDE4XtVR5MUJicn/7Rerz/09ttvP/Nbv/VbH6CjVziRSOz9dapWqwBgPQhsbS5qXWMYAHVWFTAz7a0yEATBLshLHzAtVMzEVNtcn6YobK/QPMwMrmP59t1tprDrrZ3vw8ikogD1Qbjgdfj9t1/48b57RBAEtxnkfLVWvv3mJ5mvBMEdzM2Abo/CWayNj6NPDoDXXbkvIjT3tu0C7Z2YVJRWVc88Isop2zcTEV4F45ytx+wHn28BobncQO9jQegSiZK2gQ63MBERrEX6/ejsF87lcsjlcmRFSHeiKArpug5d1+kXv/jFX9guCgPAiQH3DE8H9xeF93LIb4rDn40CR0POj8urVLf66hsGgOefuIGTx2td/W4UgObBZG2neIKBJ6fu/AdHjxr41tdXEJ628Y4xGCiteE1cGBwTPuD+e8xND7cThTs55Ac+c4/z4wLMzTIO4/f7v/T5z3/++2+//fbx1hwPwzCoVqtRsVikzc1NSqVSpKoq5/N5TqfT1k0pm80CADKZDGUymbZjuGMzElqbkyRaWhBsp+lN5yXzK7Z0F03FzkFE4R1Cc2cxmF5hHeuLcr87zcTAYtxGv6dZEARnGeR8JQiC92g2vbNW5MRYTFH4EgbzPttd1sp2RfM7SQTAJYSjzi+GAWZNz4TxFsyqnghqNSe6ji/e/VcGSgTA9zzfNxya81KMvTAgRBjuk70L8MzcXqTv7H7siACl2dlZbsWDAgByuRxNTU1xsVgEYDqFW/HRZBjGtwOBwNcdGfyMYn4NiuNHDvb7h/ymgHT8iLudo26ytApsN3q+eXi6iZe/udyVOKyxKQ6PE8+s+277N0ePGvj2N5cRPWqzU/WjVW/2Cg+C8GRvGz7Ch8zbOs19X3b+HDCjpX/zN3/zB1euXPlcLBYjXdfb9QEAsLm5SdVqlVRVxcrKCnWIw+2+YU3TOo8HZm5vShLXsCA4APm8usNXQb1+qefiwb7AAAAgAElEQVQPmpOKgtDcJfhY4q12M5jd/cxvD+Q848xMTMVgeqIBH782kPMIgjCayHwlCIK3sNdNtCMKj48AZfase50ImvQ9RzuHZ2IqQnNXQKTtSuEiOtO69tmHNwX5/j6zO8mkomA69j0Ab9n+WAieZ0zVNnuxFuE1TWuLvZlMpjNSGpqmoVAokKqqtLKyQqlUyvoCYAoB8XicAEDXdSiKAsMw/veJiYk/cnTwA3DIATCF3W7cgftxLAwk7wWCY5h8bkVK94ElDquf37zt7zzLZrzyuBGGKYjvxTFReLsBXLvpnR2gg+RY2HQJ+3u87ByZsnc8e5lRzN71AUFEJ06cOPGDH/3oR58FgHg8jmAwyC3ncPtZWa1WaWVl5RYHMDOzpmnUea1p9dlbjmFxDQuCnTB7ud9CQb3+FqajC11/2DQ/nL+CifoVDGoRelgYnFsY8El3m+MwPzW4c/m8tgglCMIwIfOVIAjeYh4RxZ71q3EUhU2GJ53B7By2J43KwvzMeQnMt3eJM9vvGvamIG9+Zp+6bxB1EXencz2A2BSHmL0efS7YjAjDfdIShSmTybS7HomoHfHZSTabxezsLOdyOXTGR1vicKlUAgAoioJSqfTyxMTEV20fcK0CrOlmt3BpgMl108H+bn/Ib4pK4ygOV7eA6nZfhwhPN/Gtr/8STz58azR1FGas8riSYuDJjr8/9eA2/k77yH5RGDAd4ONI9PDBEwP24rRjeEYBrl4058XSBXOOvJYz50yHIKLDJ06c+ME777yjGIZBxWKRSqUSJxIJAkxROBwOs9VDr2kaJ5NJZmZ+/PHH29dvK1ra+h3LQSwIgq141TFsEQGRhnr9CkJzlzAz9wJCc+cRmjvb+jrf+tklhGI3Wh/Oz7o9aI8yuIV5wVkmFQXyPBcEYRiQ+UoQBO8RQb3ev2g3qZiC2PiJwi336hCJw8DZ1ufJ/gTi3YLw3TYhz9sulhqGV6t6IvD5XsB01L1o6d2Pzd73HWprk7QwJoyhymYfzExWdKemabywsGA5tqBpGj3++OOUTCa5FflJaHVEptNp/sUvfuHL5/NIpVLY3NykYrHYjhF9//33/9I2p3CtYoodS2+YIseaS8aE6UD/x7DE4Xd/CdTGLIp3cdX82/vk+Sd+hfB0E99+bafr+Rk2nbPjzDMMvE7AIw/fxPNP3HDmJFuGjutr3osNcZro4e66xe/GhMMGWEsI3o9gxHQTnzhjxk3b6CwmosO/8Ru/8f3vf//7//Xv/d7vfaDrOmq1GqamphgwxeF8Ps+qqpIVH10oFCiZTHKhUKBsNmtdYyy3MFs1Bh3x0qISC0K/+Hw6mjb2zTuLCt7nAzjf8o2wF9PNMDgHNeNzAzvXODIxwMcSAIjG732eIAj2IPOVIAgA4PNVPPaZ4yymozo2ljI93TocPYOm8QrMntVx5VUMX0LTWdTrZzEdvQii1xAI5FDRby8oRJQIGjUVTF8G01kwH+zx9vkWEFEuoKLb48zYuqYjNJeDV+93ojOo189gOnoRPt9LrQ0EzjETU9FsfhnkO9/FY7OAiHLRtsdC8DQiDNvAfs4sa3E+mUwCAKmqSrOzs7yyskLZbLYdIZ3P5wkAEokENjY26Oc///mf2tIpfC0H6K+ZzjcHHW9dY1dH8CE/8GtHTXG44fKbpfCkKXhNBcx4XKMJbNaB62vA9XV7z2W5hnuN4+7gmUcruO+ogW995whObvjwiKwPIwzg5f/2Bk7+7k3nTvKhA4LzhA+4NwzcEzJfGwCwUQM+XgdW1t1/jRwN2SMKAzt/nxvUKruF4xkFOKUBv2aPqYyITpw8efIHL7300m8///zzlWAwyPl8HgAokUgAAHK5HMO8liCbzXIymUQymeRkMmn1DXNnz/DCwsKOBGRuYpJXuiD0Q71+GRMTbo9CcJpGY9CLB2ZEn3zwdgaiRzHIBI1m8ykAXnUoCILgZWS+EgQBAJi9957QjBg+CyCD9cW7zxsRJQLDmAfzAppQx35TaiBwEXXjhV3dusMC0RkAZ1CvA6FYBeC9KVoRgCOo15WdNrOeHu8I6vUFAM/1M9xdEGXA7E1h2ILoDJjPIDSnA8iB6FVbRGLrNQh+FMxnwKyACF0+NgoM4zwAre9xCJ5HhOEe6VxoJ6J2xyMRWYv0SKfTVCgUkE6nsbKygmw2SwCQSqXIcgsD4Hw+76vVarh8+fIjk5OTf9bXwH51Gfin527vfBsFpoPAZ+4B/mXFvTHs54L0t3qUw4dM0fjqDXuFOZtcwwDwyBfXkHpgC2t/fgxYGfNp4KgB/NsVnDxec+4c2w2gsmnvruwjU4Byz62dvdNB8+tTYXfd9VNB4P573Dm306zpwI/OAm9ptgnERHTi93//9//TuXPnHm/VClAikWi/a1NVFblce16nQqGAbDYLmIIwZzKZ1qXoljd6bInFgiD0wdY1vfVhePg+0AvdwzzoiLsIarVXADw24POOB8yDdsSpmLrvPDY/enHA5xUEYdiR+UoQBAAwDN2jm1EVAK8gNPcKgBwIlwFabQnZFTOFgA+DMY+6MS+fmTqo6BXMxF4CY8j7WzmCfR24tq03ncdM7DXb3LNr5ZynXcO7MeskmM8iNAdYrzHGByC6DOYKAoHKLa5tK466UZtHkxQQToChol7v+Ezbw+PDfA4R5UXZvDz6jLki1DutDsf2q6uj3xGFQoFaAjHncjlfLpdjVTXnoVQqReFwmFOpFFkR0gDwgx/84MQnPvGJv+xrUP/8HFDw6Pv6bZvFqciU2Vl61aHY3zvRTTTu0ZApEBeu2ScOV7eA9ToQsiGWGzA7dP9yEbgYAS7a5OocNn73JnBmFZh22Flrd7fw0dDdRddDfuA37gX+v2uDF4eDLWe/nbjtft6PToH49y+ZTuI+8Pv9jxSLxa9/9rOf/WsAqNVqdPjw4aaVMJFKpajzepJOp5HNZqlVXwBN0wgdQrD1/8UxLAh2wZcxHB8shd4ZfLQz0RmE5t5CIPDYHSPahF4YfJed2Rt2GMHgq/J4CoJwAGS+EgTBisB1exR3o1Vb07HEsGuDuiw93ILf/yLqxjkRzO8C8wLs7GQeBtfw/uxUQ1mvrXodt8wN9Xrrm5Y8Zd9LLyKu4fHApnzf8aPTlWWJwhbJZJJbbmGanZ1lVVXJcnqFw2HO5XK+fD5PtVqN4vE4ASBFUbJEdKKnwazpwMVT3hWFneJY2IzRHfQ5u43GPeQHfm3W3vNXNuw9HgCcqQB/tWg6Z8eFowbwJ8vAkzecF4WBCm5s2LfLKjzZvRN3wme6zO2Kcj/IOe2Oft5u2Hs8O1nTge/eD7zVW+1OJ1NTU3956dKlecMwCABWV1d9+XyeOrqGkcvlkM1mKZvNQtM01jQN6XSagHbPMJgZCwsL3LmBSRCEPiF6w+0hCA5z0D4s+5hHvX4FoblLCM2dRWhu8ALBqDF5r3v9mUQa6vVLCM29gnD0THs3vyAIwn7IfCUIwm72xvUKw05Fr4DwktvDGALUVmy5PZju4xGOVHUQ0zUs7wlGHHEM98Bep/DCwgJTy6JlLc4DQDabRTqdBrATA1qtVimVSnHLLcwAaHt7+099Pt9nexrMry4D//djpjDhZTbrd/+dXogeBm5um72qThP0A9HDFQDdLxqGD5lC8qJNjtHlqn2drZ0cNUxx+Mczpnv4+ohODdNN4HerplPYeUHY5Pp6xDa3a9AP3P/Jg93mkN/cQGHXc/BuHD/iTB9wvXGw154bvNXaTHeqv4Sgubm5v/+bv/mb3/7KV75SSSQSnEgk0KofIADcGStdKBRI0zRGa2/gwsICW9cl69++BiMIQic5YNgjwIQ7w4qNcWi9oMJypZs7wi8DqADQQagAZP/F3IoAtCAyP1T4/TqAikSI9YwZCdeks2h29LIxV0BUAdEHjpyVeedDoY8rYJ/5+Pn9ujgCBUG4DTJfCYK3eBtupAgIzrJW1hCaewrmnCvcnhcQUS7a9hmk0XgaExNXbDnWeGH1Pj/t9kAE5xhR9WdwaJrGCwvmGmE6naZkMsmtjuFdq0otQRhWfHQikUA8Hqcf/OAHJ4LB4Nd7OvmvLgP/52mgNgTrNXZHSVtM+ID4UXsjm2/H3GHA7zu4MBU9DNzYtEe8bjSB6rYpODvB76wBD26ZAvGPQ6MjELshCFv8asM+QXPucG+ia/QwcK3q/GsketiMuXaCQWz+sIO3NKCqAw+90vMhiOjEl770pa8D+Pe1Wo2CwSADwObmJm1ubtLU1BSn0+n2g2lFRqfTaXr88cfpu9/9brNVd9COlZY4aUGwAb//MuqG9AwLg2RnUZDb/+MsnXFlwG6BurPryu+/LKLxQWj1spEV9TaAx7JJaD9n2vFzVAFYB7MOH+nyeAqCcCsyXwmCy1wG8JTbgxAcgOhpMF9yexgex94Y461rOmZimVZMtXAwzmIm9qptvc+C5xgR1Weg7BJ8mRmZTIZa3cLt32l1P2JlZcWKAG3fJJFIEACUSiWcOHHiz9CLaLSmm07hYRCFAWcFTSuy+d1l+49tEfT3J3gdP2Lf+G5sOCcMA6Z7+EzFFIl/Og38X+HhFYjdFIQB83m/ummPgNDvc9Bp13DQ74yb3WJ1a3iEmNIFIKz05RwOBAL//dtvv/2/fe5zn/sAretGsVhEKpXifD7P+XyeOrqGOZlMAq3VFCvBIpPJgJk5k8kQEUnXsCD0S0WvIDQnPcMjDQ3JG+uBYwrUnV1X5sK9DubLrZj1HNYXJfrQ83AEwDyI5tv7DHYez8swF6PfQCCQE8eeIAjuIvOVMKY0GhcxMfGC28MQHGCtnMNM7CUwn3N7KJ6GeQER5YJtc7vf/yLqdXFr94Ldvc+Cp5CO4YNBVrcwEe3qFtY0Dd/97nebhUKBrPho618ASKVSSCQSZC3u12o1+vjjjx+amJj4owOPolYxncJej4/ey7Wqc8cOH3K2b/gTfQqx4UNmN6wdXF8HGgPQdo4apqD6V4vAv/4YeHDb+XPaxYPbZn/wXy2aIrcbojBgOsXt4jNd9grfjmMOvj6CfrNX2Cm2G0B1y7njO8FbGlDovUKGiA7/+q//+p8qZqUH1Wo1SiQSAIBUKtUWhQFgZWWFWpHSpGkaWqkVsGKkJU5aEGzlNbcHIDiIFaMsdIsCojMAXgDwFkJzV9o9lW4zOSki/8GZB3AWwCutzuu3MDP3gnReC4LDyHzVCzJfCaPL1jUdYHlPOqr4/RoAeXzvRr3eewzfXip6BUQSidwbKmZisjF+RBFh+GC0YzktLLcwADz++OO+bDaLbDZLKysrlM1m27+cz+cJAIrFYvu2hw8f/rOeRvFPzw2fKAwAlQ1T4HGKTx8BpoPOHPvoTP/HsMtN2WiaruFB8jtrwJ9cM4XWJ28Axx3qjO6HowZwZhX4k2VzrG65hDtZssmhG/T37xKf8Nm3OWEvvUZcd4td9+OA4X86v4qPezdPBQKBP8rn81+Kx+MolUqo1Wq0urrq29zcpFwut+tilEwm2dqMZF2TrA1MRGTFTQuC0C+BwAW3hyA4CPNwXnC8g9VT+b22SBxR3NmZX9Er4gDvm3kwzmNH9D/r2uMpCKOMzFd2IPOVMFqQ71W3hyA4hIiU3WKvILlWzoEoY9vxxglmSTAYUUQY7hJmptYXWt2NbRcWM3M6nSbAdAmn02kGTFdXtVptL8YXi0XE43GKx+P0z//8zw9NTEw8dOCB/MsFM6Z0WLl6w9njx4+aApjd2BHdHD5k39iur9tznINiuYj/3dKOk/jzm+4IsNPNHWfwv/toxx38oEecpZUt3bZubbs2FfTrfN+PY2HneoUBczPJ9TXnju8gRDiM/6e/yP9wOPxHABCPxxEMBtnqG06lUsjlcpTLmYkuhUKBstksLMewpmm7rlMdrmHCnkoEQRAOgNlpJ1FKo4tEIduHKRKbTq5L7uw0F7eNjSjYcea5J/gLwsgi85WNyHwlDD/MF90eguAgIlJ2B/MriCj21cqtlTXIZ/lemMfUfefdHoRgPyIMHxDLMWw5sQqFAmUyGUomk+2ozpWVFcrlcsjn8+2fJRIJpFIpLpVKHAwG2VrsPxBrOvDWkF83Khtm17BTHHKg59ROl6VdcdfVLWfvx244aphO4n/7S+A/fGiKs//6Y7PT98Fte8Xi6abpUv6d9R0h+D98uOMMPl6z71x2ce2mfR9Aj0zbcxy7HfVBPxB1sFcYAP7LynDvnl/T+4qUDgQCf/TSSy8dKZVKuxInAFMcBkCzs7OczWZJVVWynMHWtSmTyeyqQGgh0dKC0A/yIX50IRJh2BlUMF9CaO7SgBfo3x7gucYJS/B/QQQXQbANma+cQeYrYThZX7wMEbBGGxEpu0GBYdgrSDYaT0OivA+Oz7dgq0gveAIHsz9HCyLiDscwMzNlMhl0CsLZbBaqqrYdXKqqIpfLWYv3WF1d9QHA66+/fiIQCBxcGP5pZjgjpPdy5WMgeR8w4ZBp7VgYuLkFVGzqd7VznPeEgEWbUgoXV53tdT0ox2u3CrQbPuC6f/e/G76d/7bRsTflaMtdO93c+Tpe2/l+mNioV1DdsueCGZmy7zk4FbDnOBYPfgrwO7i/aLkKrNeG/41H4UXg154CZnpbi/jKV77ybwD8e5hdw+2fT01NMWBuRlJVFbOzs5xOpy33MANob1rqrEFgZiIiEYcFoVfWyjn8/+zdW2wjZ5Yn+P8hg9QtaTEvstIU7Yw06G03tWOnm7Xo3UG7OvJhandnuqZsNOgu7BRQNrBvOwvbLzvA7oPI1150l/1QwKJfnAUMti9Ed7t6pnr2YQBHdb1U9xTH6TTEzrLZzrCToiVLmRlK6kYqyG8fvgiKykxlisEveD0/QFBexGDw9lGMf5xz5pI2IEZ/fWLHadp1HDr82AbHcA/QX0MkkodtBf3BxgTww4CvY5K9g8PD1zC3lMfu2gi3tGJsKPB6FSxer9joIcpDCJ7tOc4ikddxePgxZKcD9ihCvI24fk3Z54aDdQtzS68D9BF/5utK3A3pc4PeEaYOVwyfknsg3fvrsZQml8thdXWVAMA0TRjG8fft/f19ajQaBMh2oM8888y/7noHdqzRbiHdqe4AawEXAT5/Xl3bZpVVllOaulmsw1A1/CReuPvigawu/s592e75NRv4X+4C/+vW0Zf379+5L3/2t/ZkWDxqoTAArNnqnuCqqoUBtXOAEwHPFa43ga/ujXa1sKdhyxN7fIpEIv87AEqlUgCAaDQqSqWS2N/fJ7jVv6ZpYnNzkwA5bziXy8ENhwEAbtWw8E5u8n9jGGMAAIL/VgBseNmWDQiuGg7emzg8/AhzS1cCvZZIhNswBk+2bOV2rYz1hterfuD1io2WnYoJrmwcb7Zlo9m8Cn6cHyeORkPtjNvdtesI8ZznrsmQnsP0McLBcJe8+cL5fJ68QDibzVKhUEA2m22HwoZhoFarUSaTwczMjIhGo6JcLsNxHJqamvJXLTxONmrAei247YdDMhweRirnsaqqPmbqHDgW7H01HzSjmtpgWJVoAC3bjyEbv94AgPH5haN8zfesYSKa39jY+LbjOAQAjUaD0uk0lUolkU6nQ50nJLlzhtstpd3Lt/8IyBOderkpjDEAmvYeQONx8srpWIg0L4No/A9ch0Ic+veHDuBjzCZWArsGngneTzLs57CFMX94veonXq/Y6CAOr8bewbolw+GJ+mzZHaLXcCaptnq+dvtDAPz66k4ch4fBfXZjfcfB8Cm5FVZetdVD/5/NZlEoFNpVw6Zp0v7+fvvLqxj+6U9/eikUCr3U1ZWPU7Vwp+o2sHcY3PbjM+pm+qq0GFNXzTwKVcOT5vY9ddt6akp9y/VwqPdfNoNuYf7V3TjqTrDXMQg9zBo+e/bsdy3LwoPh8MzMjMhkMmSaZruVtPeVy+WQy+W8amF4719cMcyYArZlT1DVsIVI8yrsdQvAl4PemcDJgwR81n6/EOUCDYd5Jng/6Tg8/DjwSnDGxhWvV/3E6xUbDbJqmE8aGXcH6xYgOBx+HCE+UL5NOVrgXeXbHW/v8IlV44OD4VNwq6sIAOXzeQJAuZxsqe7Nb3TnC6NWq5FhGO25wqVSCQDaB/NTqdSrXe/A12P6O0CzBZQ3ZcvYoCTmZXXjMAmHuGp4XKmsFgbk81e1Zo+tuYNuIb1+38ZGgN0EBmn1Pd8X1TTtu8lkkgCgXC4/9P+GYbRbSRcKBa9yGADwxhtvhIgI+XyehBBcMcyYKpr2HsY+QCQbwOtuKAyIiWmzzAfn+ynIcJgPqPZbHABX4jHmB69X/cbrFRsVHFxNgt216zIcHvfPl77pmHnmHeVb3V17D1w53J3DQ/UhPRuIIUvMhpdXcQU5z5FWV1fJm9/otZHe3NxELBZr/6AXCnt0XadoNNr9fOHPf+J7vx8pNi2DydjU0b81msBeA7jvVqD2Gh6dVt2R4fDyxWC277WUvrnhfxstYUN1S9sLc1AWfnlVw52PJxsM6666D5UX5oIKYP0/l4NuIb3bsHDb7u8H89mofO143z2NJrC1A2ztqruuhi1P9Hmm+w44RHTpj//4j+ffeOON9hmk7nsMARCGYXgz7gmAyGazWF5e9n5U/MVf/AXgziN+VNcLxpgPtmVjbikPYEw/GJENiKvyIIWr2TQRDg9wn/pkd+0a5pZ+CEBtyzJ2MqIcYolPUKuqb1febL6FsPYxIMZnRMVwi7ttWl9x2+Myxk6L16t+4/WKDb/dtes4k3wfQrw96F0Jhlcly+sedteuY/riVYTDH0GOXWGdQqEVxPVrytfr3bVriD1royU+4OfhqRg4kzTcE9rYCOOK4VPoPIjuVgxjeXlZdMwYbldquW2kUSwWCQDS6TRKpRJZljzhR9O07iuG1xW+zhLzsg2sFzp5X7Ep2eL4hQXgt5LA5fOyFXM/7DWArxS2331QbKq323LYVP+mMBuVAb0qQd5/7HS2dmVIr0oQ1cL1Zm+/PAXZQrreBD77pj+/gIVDMuB+KSFPSnnu7KPXxMvn5c+o7Dqw/nPfF/3Od77zKiBPMgKATCbTPhHJ61Zhmiay2SwAYHV1lbzv3nsXEXknOnE6zJgKsv3UGH4gekQoDLhtziakxVkk8jr4jP3+atEHgVRuHaxbCPGMvj7T0WiM6UkzjAWI16tB4PWKDb+dyjsAxrFzz3VEnFcAwVXRnvbM4bF8vHtEAA6COW5Yu/0hms4r4M9/p0A2hOATF8YAB8NPIIQgr1rYO7D+KF4gDLfldDqdPvb/qVRK/PjHP46j22o9laHwwhn71NV+F+ZkSPxSQm3b45Ns1IJtifz8ef9zffcDmoOssvJyr6G2spF1r6rw+RubDqZauOH4/wUqyBbS9Sbw6w3AaQUbDMemgZR78stpb8+UJgNxVXPBexgNMDs7++3OvxeLxVAqlaJ0Ok37+/tkmiYBwObmJnknK+VyOfJOZOoIhQGAW0ozpkqz+dZ4haUnhMJtYjI+LNuWLQ/KjNNjO/TiaDR+FMiWa7c/5PmdfUb0GmKJ1wa9G4yNHF6v+o/XKzYKms1xO2nxJ4hErsJet9BsjuGJtj04WLewu/YKiN4f9K4MEQsR55X2iKMgHIXy/Hw8CZGJiPOKe4I8G3EcDD+BVy1MRMjlcsKb2eh99w6+A3LGYyaTQTqd9uYJEyCruxzHoW9961svdb0Ddz7p9SZIUQ1IxrsPXaa0o6q5oAPi6jawHtBsUa9C0I+6g0AOCsam1FcNN8WTf46pt7XrPk8UCapd807d3/M4yBbSjpChsMr770GxaeDFRRnwnvXRPWBK4e2/6/+kTyLSdV2HZVntufXlchmNRsOrIEY2mxWmaYqFhQVRKBTalxVCiFwuRx1/BxHxgsGYCuNV3WO5H/Qet1gp+uV0BBysW3LWF4fDfUP0Gs4kg2nhvVPJcdjSZy0KJuhnbNzxetV/vF6xYXewbgF4fUx+L30Xu2tvtlsCy9+5xyn0VkNWio/ZScg+EJmIRIINhT0ylL/K78EPsUB0FTuVq315HFhfcDD8GJ3Vwu7fARyFwqurq2Sapvjiiy9ChmGgVqsRAMzMzIiZmRkBAI1GgzRNEwAQi8W6D4Z3FL3WluYBrYeHu18B8e17wVW+Lsb8BbHNFlALqFWFyrCt2QLW76vbHjsdR9jKq4WDmhddq/u73AsLavejjWx8tmEHFgpHtaNAuNf7dDGmpqV0w5ZfPoTD4X/mOA554TAApNNpEY1G2+857slKBMCbN9y2srIiiKh9whNXDDOm0DhU9xCZiDRP80Fvstqa7a5dl8H/hB+Q6SchVgLbNoct/aZjbunNQe8EYyOJ16t+4/WKDb/dtesj3nZZhku7a+89/F/kf+7WONtdu+a2OJ7AKlayAbwrw8g+z4HfqeTQbF7GeFXp+0A2iPKIRF7hmcLjh4PhJ+g4gO5VDbeDYU8sFhO1Wo2KxSIAoFgsUrFYpHK5DAAol8vebMfuw8WakvXHVhbmdgbEQc0g/uoesBdQ++bL5/y1hL2vcHZsJ9VVwxs12ZaX9c/X26NRLQzY2N7v/lIX5oDZiPq9AYBbW3HsNtSfdOF1CHg5oTZkX1C0jvoMhgHEK5UKAEDXdei6TqVSiQBgf3+//R5kGAYKhQKZpikAiFwuh3w+T+57GIfBjAVllA/gEt4/9dm/RJMVDAMy+Ie4iok/MNA3RmBVw8Bov1ZH0w8HvQOMjSxer/qN1ys2/GQL19HrVuRVfZ4cLnHodBKvilU+7pPxeYTIdMcbPeIkgj6R9/tl+T48aScJe4Gwdhk7lVzfg3nWFxwMn6CzkqqzwsqTy+VQKBSQzWYByHDYMAwBAJlMRqTTaaTTaQHIA/gAEA6HL3W9I/4DhCOxafXBy5Qmqwgvn1dTRdep2QJubgQTDvttCXsnwPm9qquGb91Rtz32ePUmsH5f3esryGrhu3tAtzPOo5qcxcxOWcgAACAASURBVBuEr+5ZgXQHuDAnT1wJYr9no2q247MTBBHNJxKJ9puRZVkCAEqlEkqlUnuMASDDYcMw6MGTmVZWVkRnJwzGmGIjdwCXbITE69hZe+fUF9G0yQuGAVmhIWdOTcbBmMELLhgG5GsVeHfyDvIMRLBBP2PjjterfuL1io0GGQ6/gpH4vfSUVZ88Z/jJdteuuZ9HfjLoXQlOx/Pl8eON+menknOrtsf4fvdwIDxJOBg+gTd70TuALoQ4VmnlHWgvFAowTRO1Wo28VtL7+/tUKpXQaDTIcRzSNE14bT/HzoU52ab1YkztdpstoLwZTPXrYqz7gKfu+G/D+ySqq4ZrB8HtKzuuck/tG2Rw1cL+WrQvzcuTKVSrbgMbNV3pNr220ZfP99Y2/3H8dBtQ7G/+5m+e895PUqkUUqlU+/9mZmaE917kzRn23rdWV1fJ+zO3k2YsYO0DuENO0IeIaJdRq37Y1eVsy57YGWAH65Z7MIYFTYjgq7Z2194b8XaMo4SDFsZ6wetVP/F6xUbDKJy0SGQi4rxyqqpPnjN8OrKK9U23zfF4BZVeIDnIKuGTjPP9DoAD4ck0+KPcQ0oIQZ1tpPP5PAFHgXAul0M2m0U2m4VhGIjFYsJr4+lWbqFcLsOyLOE4DgFAKBQaTJlWsxXs9qc04NmzskpPZfVw3QF+vRFMOPzc2e4vs6ZwjuyDVAeCt+4ATa4KDNTWLnB3T1218IW54KqF601ge7+7fb0wF8w88eq2+tfSYgxYvhjc/Td8yHEc8t5b4M4U3t/fJwAoFoviwfnCy8vLIpfLtQNhbinNWMB2194b4plEcrbXXuV13x/4KDQcZ2/325mkgXD4g0HvxoTQEdfVnkT2oLmlNwH6UaDXwSQhfnfQu8DYSOP1qn94vWKj5GDdQiTyCojeH/SuPEB+3jjtqJo2njN8amMTVHqBZHM0Asmxud9dRKac+105OxL3P1OKg+ETeBXDnS2kvZacq6urBMhqYY9pmscOzJdKJfIquTRNE8lkklqtVvcH4mMKjofsNfoTEk5pcqanypAzqHA4NtV9lXPtADhwgjnAq7pquO4A1W1ezINSb8qAU6WgWjYD/vY1iP1RHQp7VcLPnQ2uSrjTnT01r6mo//MJDg8PKZlMAgAsy0K5XD5WNVwsFmEY8iT3zc3Nh05ocrtg8FkjjPXDsZlEwxAQex+6Hzvb67S+VLJLoyKuxzG39CMI8RG4kqh/ms1g7uszSQNzSx8B+AAQ6kfusEcxENf5vmasW7xeDQKvV2y02JaNnco7CIVex8A/c7htgHfXLvv8vMHtpLt1PKh8FwN/DpyadbxCtZsTCIbA8ft9tGY/E5kA3kUkchY7lasKjg2wEcXB8MlICAEhxEPhcKFQEKurq2QYBhUKBTJNE5lMBplMBqVSCQDaB+p1XW+3k/a1Fz0ECMes31ezndNIzMvqPVXVw0GFw4n57lvDWneDq1y4fE7t9tbvxwOZ08xkwFl31G3vwlwwLZsB+brZ2unuMokAWkirDoUHUSV8v8uq65Oc8b+MlMtlu9VqkXfCka7r5IbDBACZTAYAYJomTNMUAJDNZo8FxAAol8s9dPITYywgO5Ucms2rgwuIA2gLJcTkVAyfSRo4dG4BOP0cZqaGaF1Ruj0O+Afr8DDYCnDGxgmvV4PF6xUbRbXbH2J3bTAhFeE6gHd7bgPMc4b9k0Hle+6Jyd4c4iELK8kG4X1Zobo2GhXCTyLv92vua+8VEN7HUN7v9CE6w+DdtfdG/r5nPeMjwicjLxT2Wkl7FcOAnNdYKBRgGIYXDHstPMk7QO84DlmWhWQySZVKBY1G408ikcgPutqLz68Bv3ir91sTDgEvLwHhPj7kzZYMg9ZrarY3pQG/sQhMhdVsDwA2asBX97q7zH/ztI356WDOIL19T939BchZyssX1W2PAfaBhc+/UftB8aVEcMHwjaqNunP652vUrfxXSWUoHNWA58/3v230+n0bt+3eX/fROPCDLtecDktLS3PValUAgK7rApBdKaLRqHBPTBKGYQjTNJHNZtsnJBUKBVkq7JYMe+9t7p+5gpixfppbehOEH0IEeaCXbEB8CKKf8BnAPsX1OA4PV8CB8OAI8VPsVV9Tsq1pXUf48K8BqA2b2emFQm+hdvvaoHeDsaHH69Xg8XrFxsGZpAEh3gToe8F0HCAbJH4C0If8eWOInUkagHgNwMvBfv58JAuyCvwTRCIfwraGLDAN0PRFHeGwAaLvyfu9n10/yHZP1vg5AJNfn+wkASURo887YO7OYRQrKysCssoK2WyW3DbSZJpHr61isYh0Og0AotFokGVZQtd1LxxGs9n8MhKJdLcj5xV9Fmi2gDXb32xdv8IhOXt4JipDoUaPFZZe5bDKcHgxBtzbl22iT+vLu3EsPxNMyJ6YBzZ31c2F3mvI+171DONJ5QgbXymuGg+iOtcjK5u7++VD9XNl/b6NtW01vwCdnQH08/1pG92p3gRuKzqR7pz/NV0IsQ0AiUSCotFoC5DtpAEgnU4jnU5jZmYGpmmSYRiiUCgQjtpGUzabBRF5JziJznCYMdZHu2vXAFxDXI+j2TQgWgZ6/6Bugeg6hPg5iK7zh78eTes6Dvmg/MARvaxkO2eSBoTz1wC4NeggCcEVeIw9Ca9Xw4HXKzYO5OcB+ZngWDhIV/yFVBw2jaTO5wHgnTBwBUS/C4i4/+dDJ7JBsNyuUp8gFLIQDpsTXZF6sG4BuOZ+AXNLVxAK6e3P/qA4hFDxWVMeB4D4EgLXEYmYExXAs55wxfDJvIphAXk/kTdfeHl5WQCyaviLL74IxWIxUavVqFgsIpPJYH9/nxqNRrtq2HEcqlQqdP/+/X8Ti8X+pOs9+fdngYaitfTZs93P1lWh7gDlLRlU9kp15fBeA1hd7+4ysWngxafVXP+D/FQxP8nyMzZmI/zhsle37nbflvlxopqs6A4i6Kw3bdxY6+4xvzAHXD6vbh++qVn48l7vH6rDIRlYLw5g7XKEjdLXcWWtw3/7PWD5bV8XbbVaN5599tn/oVqtAjLwFbquw7Is4f4dmUxGeO9FsVhMAMDCwoLw3rc87slOANrtpDkhZmwYxHUdjqO7ByTjIHr0Oi6EhVDIRqtlIRKxJvpDt2qyUusjAHxQePAsty2bfzPJtxES/lsaMnUI72NnjSvwGTsJr1fDg9crNu5O85lDCBuADeA6f94Yc7JTkg6i+Ck+h8rnxdFnUZuDyB54r0WiOFqtOIhO/gzq3fdEFjTNAmDz65L1giuGT9YZCHvzGNtBei6XI8MwqFgstn8unU5jf38fpVKJADln2LIs7zLi8PDQXy/Vc1eAdUUnYt2+J0OoC3NqtndaU24ApqKlrOrK4dmoDMu7aeFcOwiuEnfR3ZdeK6w7lTeDq3KeFFu7akNhQD5/ggmFgV9vdH8iQELh81lVKBzVgBcuyNdpvzlC3o8q50k/87u+L9pqtbYBIJlMikql0g5yU6kUyuVy++fcVtJkGAYWFhZEoVBAoVBANpul5eVl0RkKuzgUZmxYyA/V/MF6UPoeCpPd37ZiI6e3x2E2sQISOUX7cgr8eD6WALcwYuwkvF4NF16v2LjjzxyskwwXrw96NyYSvxbZAHEwfDIiIiGE8FpxklctvLq6StlsFpubmwCATCYDAKJYLJLbzlM0Gg0ql8uUSqVa5XI5lEwmYVnWp+fOnet+Ty69pi4YBoBbd+RZJgtn+v9BIDEvA54v7/UWfNYdoLwJvLioJuz008K56gbcQYTDz58Hbm6o217d6X8r8XFSbx493qrMRoM5QUOGwug6zFTZ0nprF0pC4QtzsstBv1tHA14orKbLgeeM3lMr6WazeQMAKpUKkskkaZomAKBcLiOVSlE0GhX7+/tULBYBQNRqNQKAbDYrCoUCHqwa7qgU7mw5zRhjk6n/lcIWIs5VYNqG41xxW7rFIcQlEHS3vVgcoDgfvPdhbumHAPoXshDlsVPJYfqijkjkClotHQQdAvPHH0+uRGeMPYDXK8YYY4wx1mdcPvh47WrhXC5Hq6ur5FZdAQA2NzfJO/AOAPv7+1QqlZBKpchxHLIsC7quw3EcAoBWq0WVSqVKRN0liQ0b+IvL6tpJexLzg5s9W3eAm9/0XhU7G1UXDvtt4RzU/fj5JmDvq92mfm4wJwSMuhtVu+tZvU/yUkL9bGEZCne/r1ENeDmhZh+2duXJJ7167uxgWkcD/u/HJ3klB7yy4vvim5ub33/66af/IwDhVg1D13XhtpL2KodFJpMRAFAsFoVhGDBNUwCA2/kCgGwlnc/naWVlRbgjExhjbLLNLX2M/s0UthBpXoW93v3Z4XHd/4F6x5GXFUJ325S97M62Gt6D/7tr3f+SP63rCDsf9y1QF/Qh9iqv+7qs38fz8FC2+JNt52So09PcwL64ht21twa9E4wNFV6veL1ijDHGGBsArhh+DG/GsBACAIRbXUUAUCgUYBhG54F3AoB0Oo1SqSRSqRRSqVQ7FPa0Wq1PwuHwt7vakWgceOFNYFXxuJnqtqyQHUQV6ZQbRPXaWnqvIStrVYTDizHg3r5sE92NoCqHnz8PfFLtror5SW7bcTw1o24+8ySobkN5QLgYCyoU9revqp67KkLhQbaOBoC9QxvlzbjyxxwAXvhhTxe/c+fOV4uLiwBArVYLyWRSeOMKUqmUAOR7ULFYJC8cNk0T2WwWhUIBAJDL5QBAeN9XVvwH1YwxNjZmEyvoXygMAHlfoTCAHmd4Pfqyc0tXALwDoLc3qmEhK7/7FTZYiDrvYs/npYOYyXYmaUCINzEujydj44zXKwPA2xDiNeXbZowxxhhjJxpAf87RIoRAPp8nIkIul0MulxNe1bBpmiKTyVCtVqN0Ok1uKEwAqFwuo1wuEyDbflYqFapWq3Ac51NfO7L8tsqbdWSjBtyoylBpEBLzMpgO9/BU9MLhpoKiN78BWXUb+Kc7tpJ98IRD6sPmZkuGhyr3c5xt7tg9z8R+UFRTO8sXAGp1oPS17WsWbnxWTUvru3t2z6FwbBp48enBhcIbNWD16+7bcJ/GC2/KVtI+CSG2DcO4AQDhcLj9AtZ1XSSTSSqXyxSNRtv/HovFhGEYxzbhvocBANxuGOSe8MQYY5NrWtdB1M8WniZ216717fpOY3ftOnbX3kSzeVnOnRwa3e+LDPn7WQHtP+QPyk7FxO7amwDeHfSuHENQ/Es1YyOO1yu5Xu1UXgevV4wxxhhjfcVHhJ/AnTGMjqPnZBgGLSwsCLcCiwBQJpPB/v4+NRqN9n36YDvpVqtFn3766e+dO3fuz33tzN+/q75q2DPlhlVBzDw9DRWtpVW1lb51R1Y++jGlAb+xqLYi9+Y33VcxP0l8xsILC8PbNnAY1JtA6WvAUVixDQCXz6t9nfVada+ipXWt/glublxCL2e7L8YGNwPbEYB1x8a9veDO1n/jVk/BsOM4/zESiXx/cXERGxsbIpFIIBQKCQDQNE1Y8gR+AUCk02nMzMyIWCwmvPeqbDbbnjHsBcQrKyvtIJnbSTPGJtbc0kcAjCf+nCoh8Tpq1Q/7dn3dmlt6E8AHg94N13Xsrr1y6p+Wc6JvBbg/D7Kwu3a5j9fXvTPJvx6aSjxvriljjNerR5lN/jWI1yvGGGOMsX7giuHHEEK082CvysqrFAYAwzDIMAxkMhkUi0VqNBpULpcBoN1CWtd1WJZFrVaLAODMmTPf9b1Dl77Xy815vLojA9FbdwBH9L9SYEqTlYKxaf/b2GvIuby96qWCue7IisN7CmcDXz7XW0X1o9j7Om7bw1QRMly8tsyqQ+ELcwpDYZIVur2Ewhfmeg+F9w7tnkLhcEiG5YMKhWW1NQINhS8aPYXCABAOh1/95S9/eWljYwOJRALVahWVSqX9/6lUSqRSqYcu54XCXivp1dVV8rpfADIQ5lCYMTaxZAvL/oXCgD3UoTAARCIfDk3VMHW5H+HD/s5HEOKTvl6fP+8PegfaqHV90LvA2NDg9ephIV6vGGOMMcb6hYPhE7ihsDdfGACQzWbJ/Q5Azm6s1WpULBaRyWREuVwW3lzhjgouAEC1WhWffvrp70Wj0R/42qG714H//HovN+l0tnaB0tdx1OrBX9eDvHC4l/bJtQPg1t3e9iMcAi7G/F++2QLKm/6rjh80palvKQ0A6/fjWK+p3+6ocwTw6w1/bZkfR2ULaUfIMLOX55iK/dk7BH69EYffUDjqvuYH0anAEcBX92Qb+iBaR3daN4G/e6unTRDRfCaT+X8e9X+WZcFxnPaJSTMzM+35woZh0ObmJgEyFF5eXvbmC8M98Yk7hzDGJpcQ/Z3BSjT8B5ptywbEkLQa7SLIiOtxAG8Gty+PQGT29fr8cJwheSwBiNBwnHDA2KDxevVovF4xxhhjjPUNB8MncENhIiIQEVZWVsTy8rIoFAooFArY3NwkwzDghsIAgHQ6DUC29XQch5LJJFmWRclkEgDo7Nmzf+hrZ+5eB/72KtDo0++mdUeGJdZdeyCzh725w35t7fQeDi/GZGjVi1t3gO0DNQ/aYqy3auqT3L4nZ8OyI9YWUHfUV48uzfdenev5/BsLu43e9rHX/ak37Z6qqmeig5sn7FUJb/TxxIjytZ7DYU3TXr19+/ar1WoViUQCgJxh73amaJ/FtL+/T8ViEd6M4YWFBWEYBnlVw0KC98UYYxNpEAfmR8dw/G5I4vRBxuFh/9uPhjg46MpOZfiDKcb6gder4cfrFWOMMcbGnKKUYvx4oXAul/NmDAsA5FULe/OFvdnCpVJJpNNpRKNRUSqVCACSySTcUBi7u7v/hogudb0jO1Z/Q+FOmztx3D8YzOzhxRgQmwI+3/I3d3hrR84a9hswh0PApbO9t6b+p604lp9RM3P48jlgdV1WJKv0T1vA9DM2ZiPBtdIdFbfuqG0D7lmMqXsN3bZt7NR760sc1XrbH9lqO+47FL4wBzx7FtD6fG6SI4Cq3d9AuFP5GhCNA//9j3xv4uLFi/9nIpH4Ozccpmg02nIch9xwuAUApVKpfcKSaZrtEQgd7aOPt8NgjLFJ1Gz2s4W0JMSo/K41HPsZ7qKVJ9H30O+3tlZrOO6nxwlPx4HDQe/FaFQrMtYvvF49Gq9XjDHGGGN9wxXDJ/BmLq6srAgAyOVy5B1ULxQKMAwDhmEIAHBDYQKARqNB3pzHSqXifdHMzMz/1fVONOzBhcKeztnD/a4ennUrCv1W7m7Uepu/Gp/pvUq32ZL3nQpBtZQG4ri5EcfeEHwIG6Sv7lnK2n93UtlCerdhYf1+7x/qe3keefOX/bZfTszLmcL9DoXtAwufrtkDC4U9pfeAVf/jszRNe/VXv/rVt905w8KyLHL/XaRSKcpkMiKTyYhisdi+zObmJmWzWayurhIAdLaSdttJM8bY5BGi/8EwoLuVysNrWtcBXBn0bgCwYK+fvq2oEIPY55cHcJ1dOhyGxxJdtQVnbNzxenUCXq8YY4wxxvqFjwg/Gj1YTPXGG2+ECoVCu2q4UChQJpMh9+A7pVIpikajotFokOM4ZFmW196T7t+//4NYLPYnXe/F370lK8yGxZQGPB3rbf6uH3VHtoauHfi7fGLefxBWq8u22r16cVFWQKvw+SZgB1DVGg7J/ZyNqN/2sKtu93YSweO8lFDXQvrTry0cHPZWLRyfBV644O+yvYTC4ZCs4O9394F6U56c4Xf9CEI0Drz2MXDG30PpOM4vLl++/D+2Wi2KRqMtAO1W0ul0WrgVw6JYLAr3fUoA8N7UOt/chBACRAT3O1cRM8Ymx9zSRwAGEQ6/hd21IfoF/wFzSx9gOFpsX8Pu2ulmMMT1OA4P7wW8P49iIxK5LOcyDyF5v3wMoLffHVUgusqtWRkDr1cn4fWKMcYYY6yvuGL4ER4MhfP5vBegt4N072B7JpNBJpMRD4TCQtd1uHOGMTs7+4Oud+Lu9eEKhQEZBt2+B9yo9rd6eEqTlcN+A+nqNnxXgsamZOVwr1SGjs+f733+8aM0WzIEbzSH88NiUIIMhRMK5wrvHdo9h8IA8JzfQiWy8esN21coHHVfw30NhcnG1/dtlL4erlAYkF0gepg3rGnaq//wD//w7VAoJBzHIQBIpVJIpVLwRhm4jv05m80im81SNpslr2oYgBcK+94fxhgbUYOq3F0Z2qrhuaUfYjhCYYDoJ6f+2cOBBQlxHB6uDOi6n0zu2+BDFsDikIUxF69Xj8brFWOMMcZYX/GM4RN0hMHI5XJwD6ijUChgc3OTAMAwDJimiXQ6TaVSyftxAfdgfKVSwa9+9atL4XD4213vwH9+vfcbEZS6A9xYkyFPIq5mfu5pPHtWVh36CfFu3ZGtqf1Uwz53VlYO9zLbd6/h/7IPCodkxefqurptepotYPVrIK1oLvKwCzIUPjujtvV37aD3g8gX5vwH1V9s2qg73X9Y90JhVQH5adTqbvt7ZzgPvAPAugl8bQLP+CtWO3fu3HdbrdYvqtWqAODNGD52VpNhGMKdMdwegwBAeMFwZyDM4TBjbPKI+ICaJ+k4PPwIcf3qUFVuzSTfBsR7g94NV3cH5smJY3A9L97BbMLGXjU/sD14UFyP47DxI4xiyM/YuOP16jherxhjjDHGBoKPAp+McrkcAcDq6ioVCgWRzWYJQDsYrtVqBADFYpFSqdRD96XjOPTZZ5/935FI5N92dc2fXwN+4b+arK+m3Pmp/awE3KgBX/novhQOAcs+A08VAeJ/91xvl3+Q3/vhNKY04DcWxzscDjIUDiIMXduW++xXOAQsX/S3T37vqxl3Tni/5gk7wsatLRv2/jCcbf5kFw3gX37k66JCiO1QKJRIJBIIhUJC0zRhWRZSqZQAgGg0KkqlkgAgMpkMxWIxsbCwIAqFghBCCJIpsOgIhLmNNGNssswlbgE0yPcLC5HIVdjW6efoBmFa1xE+/ACDaat9ku7abZ9JGhDC3xuqKkLkhiJskffFBxiOyjsp0rzc1bxoxsYZr1dHeL1ijDHGGBsYbiX9aMdC3s5Q2GOaRyexp9Pp9p8dxyHHcahcLpPjOKRp2ne7vvbPR+gkxbojK/P62V56MSZn4Ya7fPo2W3I+atNH/rEY6619c7f7ehqLMeDCGfXbBeTj+usN29d9NQq2doMLhcOhYCpkew1XF2P+9mlzx/Z1X12Y62Mo7LaN/nQtPjKhMHBUNewDEc1vbGz8TigUEq1Wy3t/EuVyGeVyGe6cYRiGgWKxKEzTFG7FMOXzeRKSvJAQEELwiWKMMdZfOg4Pb2Fu6QPE9f6/d51JGphb+gDhw1sYrlDYGuoZzCchymFu6Rbmlt4cSKvw2LOvYW7pIzdwGqbfha5xyMLYkOH16iS8XjHGGGNsYnAw/ADv4Lh3wDyXyyGXy3ktOAEApmkKABSLxUSxWAQAlMvl9v9blgVd1/Gzn/3sOSK61NUO7FgyLBg1Xntp667dl4A4NiVDp24D17rjv9r4hQv+A96zCuYUP8pzcdkiOwh1J47PN4PZ9iDtHQK37gTXunFJ4VzhTr1U5V+Y89fWut4EKnb3BwsuzAGXz/cnFK7VgU+rNip2HE4P7d4Hpez/RKD5+fl/3Wq1KBQKCUsWnJE3azidTqNYLJLb2YIAIJvNApBdMKijbzS3kGaMsYF60w2IP8Lc0juYW7oS2DXFdR2ziZWOA/LD0brzuO6r2IQ2LC25dQAf4NCRgX8s8VpgoUtcj+NM0sCZpR9hLnkPrdZfY7gCfinSHHxVImPDhNcrXq8YY4wxxoYAHw1+2LFgmIgol8sBAEzTDJmmCcMwvL9TJpM51kraqxiuVCq4f//+D2Kx2J90de2r7wN//466WzMI/WwvvdcAPt8CGk53l3v2LHAx1p/rC3rGat0Bbn7T/X1wWosxOWd5HNSbsmq8HtB9lZhXO1f4QbfvAeu17i7jhbR+3Kh2f1/1cn3dcIQN646Fe3vBHUDvh2gceOOW/N4lIcSXoVAonUwmhaZpAgAsyxLpdFoAQKlUgmEYLUCe0JTNZlEoFJDL5YR30lMul+tsCzCmLQIYY+wR5pY+wjAemAYAkA3RMhEKfQJqXYcI2dA069Rtp+XB/TiajStokQ7gZYBek3OVh5qF3bXLXV9q+qKOcPhWAPujynUIYYHo5yC6DiFsRCLWqWdMx3Udh4dxAFdAeBkCVwC6MvSPJ1EeO5XcoHeDsaHC69Vw4vWKMcYYYxOGg+GHUef3XC6HlZUV8cYbb4QKhQIMwyCvjXQmk2nPGE6n02g0GuQ4TjsgvnXr1p933Ur6b6+qrxie0o63QW62ZMAZtCnNxm8sxgOfU+snGA2HgJeXgLCPl4BXdWzvP/lnZShsY0oL9oPQXkPeB82AKib1czYWzgz3h7nT8BN0nla/AtHTzvsNh2RIvejjBIhurqdT0MG45+v7NtbvB18hHA6566e7hjWFfK2pfp39zx8Bz/jLJv7qr/4q8fbbb9sA4M4Z9sJdkU6n8fTTT7cAwJ0v3K4aXl5eFrlcDu1+0i4i4nCYMTYZ5pauAfjhoHeje2QDrcccoB/o3OTe9DLbcS55b+iDh0cST7i9I/t4+gv5GZsEvF4NG16vGGOMMTZxAiphHFkkhGgfGM9msyEAIp/PEwAYhkELCwsCAGUyGSoWi8hkMsdmDFuWhWQySZVKBeFw+KWurr1hqw2FF2MnzxV1WsD+IXBvT7ZiDSIorjtx3Fhz9+MpBBYQT7kVud2Ew80W8NVdf0HelAa8sCDvt60d4N7+w0HRdMTC+VkdT8cALRT8h77ZqKzqvXUnmO1bd4H5GRvR8Ah+gHV9dS+4UHgm2p9QGJDh6/k5Gdw+6rk3G5Wty+Vzz9911Jvdh8LPnfUfQp9WrQ5Ydy0cHAZz0CEcAuIzwFPTsl39SVX+3oxqVVX6X/3UdzBsGMarlUrlPySTSWia8H4vfQAAIABJREFUJtLptGg0GlQulwUAqtVqFIvFRKFQoGw2K7yK4dXVVUJHhTC3k2aMTaDrGMlgWMQBGt3fx05ClO9ttqOwAIxgF5GRDVIeL9K8OuhdYGx48Xo1VHi9YowxxtgE4mD4AUTUnjNMRN5BdACy4mpzc5O8SuFMJgNAtutMpVJULpeRSqWE4zj42c9+Fu96vvDd6+puyJMq97SQDD5iU/LvdQfYqMmgSXVL4o2arK4Nsr30lAa8nOiu1e7WLvDcOX9Vw8DR/XcZ8v5rNIFo2EY4FIcW6v+HpgtzbuDtY4byk8VxcwNYfsb//TVIW7vyeRiEqCbnT/fTlCaD6OPPPRlsqpjrW+0iFI5qwPPnj9aSIDjCxld3bNzZ0yHnYakVm5br5UzkdPffhTl5e1W1cL/jf+2fnZ19CcB/AIByuUypVMqbeU8zMzOiWCwCAAzDwObmJmWzWQHIiuFCodA5LkG4J0YdC4wZY2xsyRaZg94LJl3vuYUn0c8hxAgGLWOo55CfsTHH69Xw4PWKMcYYYxNKQYIwHoQQ1DFX2Kueas9h9A6im6aJWCwmAKBYLGJ/f5/S6TTK5bJIpVLwWkl/61vf6q5aGADufKLmxlyY676d65QmK/5eTshq2Ni0mn3x1B1Zzfr5lqxGDMqzZ+XtCJ/yqb25o+Z6pzSvwjCuJJjzazEWXCvfugPctk83W2iY1JvyhIEgBD0/+jSOnntqQmEAqB2c7udi0/L2BxkKb9SAT9fibiisjtdq+5Xk0W3o5v6bcgNxFXb8H4sIhUKXAKBSqSCVSolyuQxvxrDHMI5XI+dyufaoBABYWVnhOcOMscmzUzFlW2Y2YBYizdcVbOdDBdtgvSK8z3M6GXsiXq+GAa9XjDHGGJtgHAy7iEi41cIQQngHztsHz922m2QYBkzT9AJhKpVKaDQa1NlOGgBmZma6qxYGZCtpFS6c6e3y8RkZlLyUUF/ha+8BN9a6b1PbjcUYsHzx+FzlkwQ1k3eQgpzzulmLY/tgtA6i3rqDQGbRDkMoHJQntdwOh+QJGEHe/gPHws1vZAW8yscv6lZbv5SQr5VewvTYlJqTaHoIhjVNeymRSFAymcTBwUEIADUaDQJAxWKRMpkMarUamaZJAODNGfZCYW/GsOCqOcbYJCL8ZNC7MNnIRqR5VUm1Fgf9w+A6dtbeGfROMDb0NO06r1cDx+sVY4wxxiYaB8MdOiuGc7mc8A6Yr66u0vLyssBRJRXNzMwIAEin04hGo95Mx3aP3Ugk0n0w3EM40BYOqavem+oIUFQHxNVt4EZVzgwNgtda+vL5kwPicCi41taDFmQ4/MUW0ByREGmjdvrq126McygMABcfMyv4wpw88SKwecJk47Zt49OqrvSxi00DLy7KdeHCnLrq6rMzarbjc/0nokvVahWVSgUAoOuysDqdTot0Oo1isei9LwkAyGazWF5ebnfDIKk9Y9gbpcAYYxOCq7YGhmxAqAmF25vE+8q2xbplIRLhOZ2MnYZt2bxeDRSvV4wxxhibeBwMu4QQ1HFgHADQ/gfI1puGYRAAGIbRTsW8imFvvrD37+FwuPtgWIXZqPptBhUQ1x3g5oas6AyqvfSFuaOAODZ91GI6Ni1bZo9rsAcEFw47rTi+vDv8c3jqze5m5Z7WuIfCgGzJfjF29HqZ0uRz6SX3tRTUba/VgRtrcazfjyvb5kxUBsJBtbwe8POAiOYTiQSSySQAwLIslMtlajQaVCqV4HWzyGQyZJomCoUCgKM20g9sCwCHw4yxCbJTMQGYg96NyUM2QO9id+260s1q2ntKt8dOy5KV3xZXQDJ2WrxeDQqvV4wxxhhjAMY42fBF5PP5ECDba+bz+c5qYdrc3AQA1Go1KhaLSKfTSKVSFI1GRSqVOnYgnYgCKtccIC8gTszLwG1rV812t3ZlIJSYD66C98Lc+FYHP07CfRqqbt19Z1fHwplgZ8v2qrqtvoX0JITCnmfPyq9+cARQtWWFtypRN8wO+nV/2nnmAfrLv/zLS9ls1tI0Tei6Dk3TRLlcBgDMzMyIYrGITCYDyKphAiA6g2G3QwYBMhzmttKMsYlClIcQxpN/kKnhVgrvVtSGwoCswjuTzEOIFeXbZie5jkjkKuw1DlkY6wavV4PA6xVjjDHGmGsC0o3Tc6uGxYMHxb1qYdM0kclk2gFwqVQSAOCFwo7jkGVZgCw2Hkww3HjCbFAVOgPir+4B9n7v26w7snK4dgAk4sBUuPdtMikxL6tnt3bUbvf2PRvpi+oqO1Wy99Xf3nAIeOHCZITC/VSru10DFK1d4ZCsdn46pq5d9OMMyZxyt5U04WjkQVsmk0GxWBTZbBaQoTDlcrn2z3WGwrlc7pHbYIyxsbVTMXEm+T6EeHvQuzIBLEQcte2jH6Rp7+Hw8IcA9MCug3l+gkjkHa68Y8wnXq/6idcrxhhjjLEO3C7yCEEWClM+nydAttrMZrPt+2hzc5NM04RhGDBNk9LpNAFAo9EgADg4OAh5cx4dx/n/wuHwt7vag79/F1hV0FHot54Fwn18aLd2ZUWqqlB6SpOViqpmdzIZXt38BthrqN2ufs7GwpnhC4dvVNUFjZ7L5yez6jwoQVQJL833LxD23Lqr5iSEH9wDov5eSr/85S/Tv//7v/9lKBQSmqYJy7JEKpVCNBoVQPskJmEYBhYWFgQAFAoFL/wVQgg8YpQCh8OMsckR1+M4PPwYfHA+OEQmNOetQENhz5mkASE+Cvx6JhlRHjuVh+dSMMa6w+tV8Hi9Yowxxhh7CJe+udwD495YYQGAcrkccrmcyGaztLm52U5aa7UaZTIZ7O/vo1QqIZVKwXEc0jRNJJNJ0jTN3wH1M4qORa3fD2a27Em8Ns2qAuK6A5Q3g5uRO4nCISB1QYbDKqvKKzawELMBMTzh8Nau+lA4yDbnk2jv0EZ5M67scYpNA5fPDaaau3agZjs+Q2FPKBQSgJwxDIDK5XK7bXQmk0EsFoNpmgIAstls54xh76SodjjMoTBjbOLYlo3pi1cRDn8EDofV6/dB+Z2KibmldwH8qG/XOTksEL3lzudmjPWK16sg8XrFGGOMMXaCwQ9HHDJCCAghjh0090Jh0zz++2SpVAIAlMtl72A8KpUKHMehZrN5v+srVxUMb9SA5gCO61+Yk/NXVQVo1W238rOpZnuTbsqdj6tyJqrTiuOb+8PVjqmqeJ7yYoxPUFBpowasfq0mvI9qwIuLg5v7XN1WczvOXel5E61W69hJSalUCul0WgBAsVj0/rl9gpPbTpq7hjDGmOdg3UKzeRVA8BWtk8MC0dWBVGrtrr0Honzfr3ecEd5HJPIKhyyMKcbrlXq8XjHGGGOMPRYHwy4iEp0Hyd1qYeRyOeG13jQM49hl0uk00uk0AEDXdViWBV3Xhbu97sOy8y/3cAs6NFty9u8gePOHX0oAcQWtoOuODJG2dnvfFpOPj+qQc217fKuFo5qsFma9cwTw+ablrk29PWfCIfk8fjkBxKaU7F7X6k3ZIUGF870Fw6VSabtarQrHcUjXdaRSqXZA7L1HAQ+/h+VyOZHL5ZDP56mjWrg9c5gxxiYOh8OKkA2i/MAPystA+t2BXf/4cAP+NZ7PyVhQeL1ShdcrxhhjjLFT4IO/R2QPaSGQz+epo2IYACibzaKznXTnjOFSqUReO2nHcQgAvvjiiz+MRCL/tuu9+PdngYai31+HoRWzyvnDw3B7xsXte8C6wtmuLzxtIT49+NaLq+tq5yi/lBhMJeq4qTeBX2+oCe0vzMkZ5P2cI/wglbcHAF79AHjhTd8XJ6K5RCLRbiddqVRa7n8JAMhkMiIWiwkA7RnDALC8vCxWVlaEGwZ7obC3TW4nzRibXNMXdYTDOQA/HPSujKBriDTzfZklfFry8eQ24V0jG4T3oWnvccDCWJ/weuUTr1eMMcYYY93gimGX10IaOFYtDABkGMaxdtLejOFSqYRGo9EOix3HoUqlglarRY1G4ytfO9JDOPCQ6ra6ija/LswB/+1FNYFudXtwldDjJjEvq2FVWb8/+A+utbraUDgxz6GwCvWmjdLXds8h6kxUto2+fH68QmEAeMZ48s+coNVq3fBC4UqlAuCojXQqlaLOimEAKBQKx74TUbultBcOe9XDjDE2sQ7WLeyuvQlZvTU8AecwIzJBdBW7a28NVSgMdFaC/2TQuzIavIpv7TJ2KjkOWRjrI16vusTrFWOMMcaYH3z09zjv/jhWMby6utoOhmu1Wvs+29/fJwBoNBrkOA5pmiYODg5ClUoFd+7c+e65c+f+vOs9+NoE/tPVnm7EQxZjwHNn1W7Tj7ojg117v7ftXDgDXD6nZp8mWa0O3NxQt73lZ2zMRgbXVvrWXWBrR822oppsU8x6I0PhOJzWk3/2JF7b6MWYuv3ya+8QKG+qDYXPXQFe+9j3xR3H+cWlS5f+p2q1KnRdF968+1QqJcrlMgCITCYjisXisZEICwsLolAoCCGEcPtIi86qYa4YZowxF1cPP8k1EP1kZOY4xp59Da3Wj8DVeI/AFXeMDRVerx6D1yvGGGOMsV5wxbBLCEFe1bBwS4e9qmGvsso0TYrFYqJYLAIASqUSAMA9+N6uGAYAy7I+9bUjzxhAVHG2tlEDblRlpdsgTWnACwuy4q+XatWtHa4cViE2JSu6Vbkz4DnQ9/bUbevFp9Vta1LJytreQuELc7Kd9zCEwhs1Oe9cZSgMAMtv93TxZrN5o1qtIpFItEcZABDlclmk02nxqBnDpml6oS+5FcPtjhmMMcYe4FUPN5uXQRiN8DNw7RnCZ7G79tbIhMIAULv9IXbXLgN4C1wNLhGZAN7iijvGhgyvVw/j9YoxxhhjTAkOhjvk83ly22gSIIPhbDZLhmHQwsKCyGQyx2YLAzIcTqVS8Kq0kskkEokEfe9737OEEF/62pHld1TcnOPqDnBjzR54a2lATXvpjZraGbmT6rmzsiJTBVXVur6uexdo9hBAdrowxy2kVeil3XJsejjaRgMy4L75TXAno/TQRhoAdnd3f5FIJFCtVgUA6LoOyO4X7fepYrF4LBTOZrPeCU+iszsGAA6IGWPsJAfrFnbWrqLZvIyJbPFJNog+lO2iK2dH/oD87tq1duAykYG/G+4TXcVO5Sp2166N9OPJ2Djj9YrXK8YYY4wxxTgY7pDL5QSAYxXDhUJBGIbR8qqGDcMQMzMz7SPn6XQa5XIZqVSq/W/VahWtVoscx/mFrx1Zflt91bAUR3VbVg9vHwz2F+lwSM5wfSnhv2q1ug00OcToSTgEXFRUjem04qjV1WyrW6quN6rJ5yXrzdauv1A4HJInK7z4tKxoHyRHyBntN9aA2kEw1/HCm8CZ3jrDffbZZzcAIJFIUKVSEYBsI51KpQAA3vuVaZrtUQmbm5uUzWbRGQqTxDOGGWPsSToriMf+ID3ZAK7hqDrr9ZGqDj6N3bVrDwT+41yVZ4Hw/rFwf9weT8bGGa9XvF4xxhhjjCnCR3+PeDMW2601AXgHzskwDALQrhgulUpIp9NoNBoEAOVymXRdh2VZlEwm0Wq16LPPPvvf5ubm/tDX3nycBz7OPfnnenFhDkjEgalwsNdzGlu7MoRpdBkmXT6vth3yJGq2gE+qaipuLz5l49l4/+cM/9eKmv0flnnco251HdhrdHeZxZgM5QddIewIYOM+8E0NPbXBfpIzOvAvP+opGG61WjfC4fA/ByCSyaQAAE3ThKZp3nxhpNNpUSqVRCaTaV+uWCyKbDYrCoUCcrlcu2rYfQMEAD7jhjHGuiHnEBsg+h4EDED0/3chVWSbzp8DMCf2IPzc0hUQvQaI35WP56giGwQTQvwckciHsK1xDpEYm0y8XjHGGGOMMR84GHa5gXC7jWZnOLy6ukqbm5vt++qbb745llw0Gg3ygmFvxmOlUqE//dM/jX//+99f87VDDRv4i8vye9CGKSCubgObu6cPiBPzvbWkZlJ1G0rajMem+z+ft1YHbm6o2dZLCW4jrcJ/+er0Pxublq/hYagQ7kcg7HllBXilt5N/6vX6j6enp/8PAEgkEgiFQkLTtHZAHI1GRalUQiaTEYAMhDsuLrwuGYA8CcrrlkFEHAwzxlgv5pauADBA9LsQ4gqA3tpDBMcCYAL4BETXoWnXuT3nA+J6HI5zBRCvAXgZgq4MZ/BPNggWhLgO4OeIREwOVhibMLxeMcYYY4yxU+Jg2PWkYNhtJU2ZTIaKxSKl02kAcsYw3HmOXjBcqVTa96vjOP8pHA5/29dO/fJdoPReD7eqS8MS0NQdGVRu7T75ZzkYVqPZklW3vbPxW8/GEe7j0rJRUzP/9cKcrEBnvTtNBXdUA54/P/j1pt+BsOcH93oeGXDz5s1/bhjGjUgk0t5xTdOEO/Ne4KjyVxiG4bWURjabFQBQKBTaYxNyuZzoeP8DuGqYMcbU8Q7WC3EFBB3AyxDQ0Z/A2AKRDSGug7ANgesAriMSsTgE9mlu6QpCId0N/V8GoMvHsy8BjAWi6xDCBvAJQiEL4fB1DlUYY4/E6xVjjDHGGHsEDoaPtO8LIQTy+Tzlcjlks1kqFArwWkkDsmLYDYQBAKlUihzHIcuykEwmqdVqESBnDe/s7PhrJ71jAX97VX7vtyl3zuqgWzSfJiB+cXHwwdK4uPmNmlmqy8/YmI3078zkz7cAe6/37fBzSZ3HPZfCIXkyx6Ki2dZ+1erAvX3gzk5/A2HPKzlZNexTq9X6KpFIpAFgY2NDQLaTBgBUKhWRSqVEuVwW6XQaMzMzYn9/n0qlkoAMibGwsCCWl5dFx5xhIYSAd4IUVw0zxlifxHUdjqODKI5WKw4iNywWcQg8/uxHwrY7BxgQQn5oILIghI1IxOaD730W1+M4PJSPpXw85Z8BQIhLT7w80Zfuz9oAbPffLGiaBcDmIJ8xpgyvV4wxxhhjE42DYZcQgvL5PK2srHgzFr37hrLZLLxwuFarEQB0Vg3v7e2FLMtCZyvpw8PDUDgcFn/0R38U/4M/+IMSEXVX1vp3bwHla8puny9TmgzKBt1m+qSAeDpi4Z89M6yt+UaPqsrbZ88CF/sY+t2oyudIL6Ia8HJCzf6wR7f3jmrAwhzwdGxwc4QdAewcAOs1NSdB9CIaB9645btquFar/bunnnrqxwBEIpFAtVptVwe78+7bFcOZTEYUi0V4VcMAYJpmu5W0WzGMlZUVr5U0wBXDjDHGGGOMMcYYY4wxNnY4GO4ghJCJMBFyuVz7vvFmDNdqNYrFYsI0zRAAkU6nqaOVtPedkskkKpUKLS4u0k9/+tNLv/3bv73a1Y7sWHK+8DCJTcsK4kFWEdcdGTht7QJENvSzcZ4Hq1DdkSFrrxbO2NDP9adiWFULbG4jrd7te8DuITAbAc7ODrYae68hq4P73S76SXqoGq7X6z++dOnSvwOAcDgsALRnDGuaJsrlMtLptJiZmemcL0yGYQivnfTy8rJwT4YiIecpcBjMGGOMMcYYY4wxxhhjY2xAZVvDRQhBXigMALlcrj1fOJfLwZ0vjFgsJmq1GmUyGQE3DE6n00ilUtB1nXRdhxsKI5GQ1YfpdPr3ut6hr82eb5NytQPg1h0ZHN66A9Sb/d+HKU0GeC8+DfzGAofCqk1pUHKf7h32r23U3qGa7cSm1WyHHXn2rHytPnd2MKGwI4Bvdmzc/AZYXZddB4YpFAaA1feAhr+XSzQa/cHGxgY2NjZQrVYRCoVEpXJ0kkQqlTr285lMhgCIhYUF4YXCuVyO8vm8Fwojl8uFABx7P2SMMcYYY4wxxhhjjDE2PjhZA+AVS7l/BnDUWtP7WlhYEIVCgQAgk8k8tA23QouSySQSiQQBsoprdnb2X3W9Q6vv93BrAlZ35NfWLjAXtfF0LI74LKBxjjAWZqK9t2V2mv1r791UFPTNRNRshw3e8dnB/Zt17UfDhtj8L9u09C+6GzUAgIjm//Ef//Hl3/zN37wOAJ2hsOM4pGma6OhoIQDAMAxsbm4SAKyurgId7aKFEAAgVlZWeL4wY4wxxhhjjDHGGGOMjSmuGHYREYgIQggI9wi5VzXszhimTCZDXijszRf2eLOFNU0TAFCtVtFsNikcDn+7qx3ZsYC713u9Of2x24jj1h3g49uyirhWH/QesV7NKghI607/KoZ7DbE9c1E122GD4Qg5I/vmN3K28cb94asOPgF98f92HQp7lpaWfieRSLQ7VCSTSQDw5gsjk8kIt8NF+4Qm0zS9E528E5865woDOBqrwBhjjDHGGGOMMcYYY2y8cMXwA9yD451tpQEA2WxWfPHFF+1/n5mZEfv7+9RoNI4dQHcch0KhkEgkEvSrX/3qd7reAdVtpGPTwNK8rIjU3PMAnBawf3g0d7N20Pv1bO3KrykNSMzL650K975d1l9q2nPHAbIBEXy1porwj1uSj65aHdisWdg+0JUFwVMaEJ8BZqOyBXbn82OvIduXr20DDUUnJXz5IfDqB74uGolEXqpWq+2KYACwLAupVArlchmQJ38JAKJYLMIwjPZls9ksADk6we2aAbedNK2srHDFMGOMMcYYY4wxxhhjjI0hTkRc3kFx988in89TLpfD6uoquTOGCYBw5zRif3+f3DadAIBUKiUAkDtfmKrVKp566qmXut6RL3+q4Na4EvMyFH6QFpKBR2wKWIzJcM3el8FuryFx3ZHVw4CcB7z4lJoqVNYfmqImAk5T3baCFh6R/WSSI2Sb6KOTWnpvXR4OyfXq7Ozj5yHPRo8C4/KWDIp71bBlp4gz3d+MaDT6KgCvYpgqlUormUyS4ziAGwhDvm+hWCw+dPnV1VVaXl5uVwx77aT93hTGGGOMMcYYY4wxxhhjw40TEci2mR0HxeGFwgBQKBTalVUAUCwWBQCUSiWRTqeRTqcFICuFdf34gX1N0y51vTM7ls9b8YCTQuFH0dxQ5MWngZcS8s8qbO0Cq1/L9q5bu2q2yYKlKiRtDvls104cDI8GR8hK3U/XgK/uqel0EJsGLp+X695zZx8fCnea0oDUBXXPnTv+xgeEQqHnEomEqFarCIVCIplMkjfOIJVKeT9GgGwlbZrmsarh5eVl4VYHk/f+x/OFGWOMMcYYY4wxxhhjbHxxxTDkgfDOcNg9MO6Fw2J1dZUA2U66UCicOHvRsizSdV04joNEIkFENJhgOKqdPhR+0JQmg5LEPPBNDbi733vL1NqB/Kpuy+2qCp4ZY+Ov3pRrx9aOum16LfZPGwQ/ypQGXIzJsLpXO1/6vuif/dmf6d///vetSqWCZDIJx3GoUqmIdDot0ul0++disZgA5Ixh72Sn1dVV732vMwymznnDjDHGGGOMMcYYY4wxxsYHl8pBVgy735HP50kI0Z4t7H0HgEKhQJlMhjpbcjYaDfKqht1QmFqtFlWrVREOh5/qemcadm83BgCe6iHs8ExpwLNnZRXx0rwMm3vltZm+UeUK4mHVVDSnNRxS8ETuE1WzYpla9SZw6y5wY01dKBybBl5clOtaL6GwZzHW+zaAntb9Z555pn0WUKVSgaZpQtd1NBoNAoCZmRkBALVajQAcqxguFApeG2lyv3M7acYYY4wxxhhjjDHGGBtjHAxDVgy7X8jlcsI7SO622AQgq4U7DqiLTCaDRqNB0WhU7O3tte/HSqWCUCgkAFAoFOq+YliFmai6bU1pssr3xafVVfp2BsT1ppptMjUcRcGwFhqdVtJsyJCNtW2g9PXwBsKecEiukQM0Pz//nPueA7iBrtdOGgD29/cpFouJYrEoHgiFAciTn7yKYa9jhneyFGOMMcYYY4wxxhhjjLHxwsHwAzqrpfL5PGWz2WMHyN0Zw7S/v0/lcrl9EN6yLFiWbANdqVSQSCT6u+NB81pMq5xBXHdkNaCKVqxMjbqS6tn+VQurCOXqDtDkAsmhUG/KNaG6reYkhagGpBbUB8Kd1LxmfItGo/OVSoWSySR0XYfjOOQ4DgFAqVRCqVQSpmkSADJNUywsLAgA7XbSgKwUdr+E/CvPGWaMMcYYY4wxxhhjjLFxxDOGXd6MYW+2oldFlc/nkcvlyDCMdkCcyWQAQKTTaSqVSu1N6LpOjiNDgkqlMrgD6/a+nH0ZhM4ZxNVtNS2hq9vA3iHw/HkgzIVqA7V32Ps2+llBqSk6t+XgEJhTWGnPunf/wMI/belwWr1Xm4dDcg1M+Jy1floqXi8KJBIJtFotck9O8kYbEACk02mUSiWRyWTw/PPPAwA2NzdpYWFBeKMS3EphAOAFmDHGGGOMMcYYY4wxxsYYB8M4mjHs/hn5fN6bt3jsIHmtViPDMAQAYZomZTIZAYBSqRTK5TL+f/buNrax87oX/f8htzjDmaElO6LpYbdn2AntJJSdxmEM203sMD4BirovCRIwTesD3JwPxkl6D5xzgALJ+AIVleDEOPfLaRvUp2iQJreIm8aE67Rw2ot8sHk6zq2NlHViV8yLGZsj07RpamxpOCONKGqv+2HvtfWQI2c8GuplNP8fIJDiy+YW+YgE+N9rLcCvFnZdF67rbm5nDmWAM41N/iW6o+f8yrt90Uvbzi+jAfHkIeDFU5c+p3VhCXjB8yv7aOeMYt5uzNm+NtKjmH0NAMsMhnfU0uoCfvZ65pK3o4HwtYnRHTTwy7S7o9nOoUv701utFgCI67poNpsAgEajIdlsFrVaTQC/20W1WjUABlpKA+uzhe1TIiIiIiIiIiIiItp72Eoa0PaZKJVKA1lwqVRCqVQKW24mEgnpdrsG8KuGq9WqyeVy4e0bjQbsL+Y35Zr3bf6+trk3R7OdC0nsA34t7YfElxrSdc8Br40oaKGLt7IGLPUufTv7nW1sJT2igx9OnxvNdmhz6p1LP5hg8iAwdZ1fJbwdofDK2ghnIG9+HH2v11sEgEwmI81mE5lMBplMBtlsFrFYTHK5nH5mGQ2EK5WKlMu4GXOPAAAgAElEQVRllEolA4QjFACsfx4SERERERERERER0d7DYHiIflEOALOzs+H5ZDIplUoF1WpVT5HL5dDr9Uy9Xkc2m4XrugZAWC0sIhc/PDdx6UVzAPwK3O0MWScPjiYgbnHe8I7pjigc3T+2fRXD0choWlefWbn0bdDmzJ+9tDm9if3Au1P+e892tTHvC/Cz9ui2dwkHBInIQjqdRqPROK/Mt9fraTtpU61WofOFgcEZw9a2ALaTJiIiIiIiIiIiItqzGAxbpqenwy/NS6USyuWyAEC5XA5vE1RcGQCIx+OioXC/3zeO44jjOKIVw/1+f+6id+K6woVv83a9/CbQ3ebAa/Kg3w76V8Y3FxCveQDM9lWc0ro3l0ezne1uyRwfweOt9Lf/f4V8mw2FNRB+97V+54Lt9Iv5hUsKs23XvA+Ibf5YitOnT5/W83pQkuM4Uq/XEYvFpFaraTtpKZfLplAomGKxiKBiWKanp2WodTQrhomIiIiIiIiIiIj2KM4YHlIqlfRLcVMqlTA9PS1aOVwsFqXT6Zh8Pg8AqFarJpvNAvC/iO/3+6bRaBjXdeE4jhiziYDz8If9kKA3omz0hY5fSXd1fDTbezv2OX4713ccBE6dBTpn3/7s2mgEgGxfxSn51jy/ynwUEvsXtvU1vGrfaPb99LntDxjp4ts+J/b7B57sxGvVF+Cl+QZOL4+otQOAw5d2MNANN9zwXDqdBvwDlgQA+v2+yWaz+lkmgH9QU6VSQaVSEa0Wnp2dNTMzMxAtFeZsYSIiIiIiIiIiIqI9jd8CDwrnLeqw4VKpNNBSutPpGJ0zDACLi4sRwP8iXi8LZg2bn/70p//l4MGD//dF78U/fgR4rXIJf8YGrp9YwHVX7UzgqtWYryxeOCCePOgH2bS95s8CL5269O0cjC0gd932rrPuCvDTEbT1jUaA97uXvh26OGse8G9vYy77TgbCgD9TuN4ZzRxu28ef3XQrac/znkun078ejUYFACKRiDSbTbFCYcRiMQH8DheJREKSyaR0Oh2TTCZlampKSqUSAHgiYowx+vnHqmEiIiIiIiIiIiKiPYitpAGIiIbABlivmgq+MEe5XEan0zHaUjqRSAgALC8vGwCo1+toNBpoNBq6SdNsNnH69OnnNrVDt0xv9k95ay8vTGDuTWBtB77v3+eszyB+d8o/H91g6cVjfqUxbb/5s6PZzqF923/wwYGxjdfTxVrz2E56J0QjwJGrN75Ow+Bb3J1pGa26K/5M4VGHwocylzRf2PO8uXbbPyii1WoNXFev11Gv1yUej0s8HpdqtRqORqhUKqKfZ6VSCSJiZmZmDABhKExERERERERERES0d7FiGOvBMDDYSrNYLEaCOcPaShqdTsdUKhUDALlcztRqtbCddL1eRyaTMf1+3zSbTTz66KMTn/jEJwa/rX+7vnX16NpJ2/Y5wLtSwL7o6Ld9sZZ6QG8N6Hv+frGN784YVcUt4Af/O/E6/vR1oHvu0reT2O8HkLT9VvrA8qr/fuBEgEP7Lr7N9Kj1BWgtAO3u1mz/zm8AN3xm03dfWlr6yrFjx77SbrfFdV1pNpvIZrMe4FcK93o9E4vFJB6PC7B+UJNdLRz8CPyW02E7aiIiIiIiIiIiIiLae1gxDAy0zQxGLUJEEITCKBaL4ezhSqWCQqFgf3Eu2qoTQYDsOI64rovPf/7zC57nba5qeOq/bupuF7TSB557xW/rvBPVw7YDMWAi7lcQMxTeOaOqFnYiCzv2Oo5qhnb3HKuGd8o+Z/39YCK+86FwdwWovbp1ofChzCWFwgDwxhtvnIhGo2EonMlkpF6vh0c36WdTtVoN75NMJgXw5wsXi0UTBMMGgNHPPyIiIiIiIiIiIiLam5yd3oHdSEQwMzNjREQ+9alPRQC/zXSxWATgh8P5fB4IKqx6vZ4BgGw2K/1+P6wYTqfTZm1t7Z8jkch7L3onpj4PvPBN4EzjwrfdjNYicOqs37p58uDWPAbtfitrwPyZ0WxrIr4zM6wBfw3PvTmabb2yyKrhK1lfgObCAjrdrV3PlzgyQEQWr7/++n9Op9MAANf152Nns1n7YKVQIpGQSmVgdr0E2xGdLUxEREREREREREREexuDYZ8xxgTfj4fFVgLATE1NCQBMTU1hdnbWAJB8Ph8BwiqssNU04FcL6zYjkYjMz88/fvjw4f9y0XsUm/DbjP7TRzb9R13QSh946ZRfMfqr79gd7aVpe7UWR7etaw4uANiZcDga8dtAj6KdtFYNs4r9ytIXoH0aeL0L9L2tXccjqBZeW1s7kU6n0Wq1BAAymYw0Gg3JZrPQFtIAUKvVJDiQCYVCAZVKBcViEVNTUwAAnS18STtDRERERERERERERJcFtpL2CXB+S+lSqRTeIAiFoVXD1WpV8vk8crmcqdfrAgBaLdxoNOC6LprNpvnABz5wQkQ2l74dLgBHPr7pP+pt657z20u/dMqvIKUrQ3dldNXCMQcY379zFcPA6NpJA37VMF05OmcW8PwrC2gt+jOOt9o9T17yJrrd7uMAkE6nDeB//iA4SMkOhe37aBtpm37OzczM2AdGEREREREREREREdEexGB4iLbTDL4gl1KphOnpaZmampJyuYxOp2MSiUT45XqtVpNcLod6vS6O40gQCmsrabRaLSwvL//5pnform/41WXbYf4sA+IryUunRretq3ZBde3kQb9yeBS654A3l0ezLdq95s8Cz7WAxhsTW14lrG6ZvuT3dM/z5t7znvd8q9VqhZc5jiOZjL/dWq1marUacrmcyefzqFarAgCdTicMj21DbaSZDhMRERERERERERHtUWwlbRERY53XFpvQMqpCoRBeXygU0O12AQDxeFywHrIPtJYOwuHHs9nsA5vaqdgE8B8eA/7+lk3dfVPmz/o/kweB9ARbTO9F7a7fSnxU0uOj29ZmRSP+mm13R7O9l98ErtoPRJmT7Sl9ATrdBbx2eutbRg87+nHgltKFb3cB/X7/n4OzYRvoRqNhMpmM1Ot15HI5AfzPpmq1qi2kBfC7XpTLZZTLZYiIlEql4QXOttJEREREREREREREexQTjyH2nGH7C3NtJV0ul00QCofXVatVk81mwzbSAOyqYQMAc3Nz/xSNRu/a9I698E3gxH/a9N0vyXh8AYevmuDM1T1iZQ34WXt0wfB4fAHH3gE4kR1qJW0WsLI6gd6aX+k7yjbQqQRw5OrRbY92zuAM4e1//EMZ4OPP+gf7XKITJ05M3XXXXSfT6XQY4sZiMU9nDA+Hw4lEQpLJpJTLZZ0vLKVSCRKUCmuHDCD8DGQ4TERERERERERERLQHMRgeZOwweHp6WmZmZoyGwgD0C3Vtx2ny+TyWl5dNr9cz9Xod2Ww2nDXsOI70+33TbDZNq9W68/Dhw//vJe3dsyXg2ZlL2sQl2ef4laGTB3duH+jSvfTG6GYLD4tGFrDfASKRCYxFF2AwgX1BY4KIWcBYdD0Vc6INRM1gT901acDzJuDJ+u3WPD/IWxOgvwYIFrC6NoFef/26rfTuFHhQxGWsu+K3BT91ZmcCYcAPhe95ciRjAc6dO/dwJpP5z+12G6lUCu12W1zX1VEGGuhKLpcLQ+Fut2uq1aoUi0Upl8th6GsHw3Y7aQbDRERERERERERERHsTg+FBxp4xLCIwxphSyW/9WalUIlp1VSgUTPBlO/L5PBYXFyOxWEx6vZ5dOWxc18Xq6moEAJrN5j85jnPnJe3hTofDgB8QJ/axzfTlaP7saGcLXwn2OcDUYbaUvtycPreA1ukJdM/t7H6MMBQGgGeeeWbqYx/72EkAYSjcbDYlm80KgLBaOBhxgKCVtGgr6VKpJKVSCcVi0UxNTcn09HQYAtuVw0RERERERERERES090QufJMrylt+IT49PS3JZDKc0ajy+TwAIBaLSa1WQywWC6u2XNcFAESjUWm32/Laa6995ZL38JYScMv0JW/mkqz0/YDxuVeAn77un6fdb2UNaI2wzfKVYqUPvLKw03tBb0df/FbizzaBn72+50Lhc+fOPXz77bef1M8UDYUzmQzq9breLAyFgfXPqGFB9wsxxmBmZsZPhIWZMBEREREREREREdFexhK4IfaM4aBiGPBbTIdzhjudjgGAbrdrtE0ngLClNAAEVcNGw2HP80yr1cLKysp3YrHYb1/yjr7wTeCZ/wb0dklgxSri3a0vQO3V0c0VvhL96jvYRn036gtw5hzwWhc7HgTbrisAH31sJDOF1YkTJ6Y+/elPN9bW1ozdQhoA9BQID1SSQqEQXqYHNgF+KAz4BzzpZWwfTURERERERERERLT3MRgeIiJ+EhyEw/bMYW2/qcFwMpmUF198MQL4oXCtVgOC2cOZTAaNRsMAQDqdNq1WC+l0Go8++ujR22677V+MMeOXvLNnGsA/fsQ/3U0S+/0ALbGfIfFuUe/4c1bp0kwdBg6M7fReELA7Zge/lanPA7f9yUg3efbs2Qff+c53/nfA70IBAJFIRJrNJrLZrKcz7uv1uuTzebFaSAOAaKeLjWYMK4bDRERERERERERERHsbg+EN2FXDAFAsFiPlcllKpRJKpZKxvmA3+XzeAEC1WjW5XA61Ws1ks1kMzxluNptIpVIRAHjhhRf+z0Qi8T9GsrNnGsDT/w2Y++5INjdykweBiQPA1fGd3pMr10un2O57VKIR4N0phsM7RdvYnzq7O6vfYxPAbf8TuOEzI92s53lz0Wg0l0ql0G63kU6nB4LhTCYjjuOIzhcerhZWWjVsVwwHYbAJOmQwGCYiIiIiIiIiIiLawxgMb8CuGtaCKmOMKZVKAIDZ2VlTLpdRKBRMpVJBPp831WoV+XxeK4fDcBjw20prK2kEz/nq6uo/OY5z58h2+tkS8OzMyDY3ctpqevKQf0rbg6Hw6DEc3l598auC31zeXa2ih11XAO76xsjmCdtOnDgxddddd51MpVIA/IphDYUBCADJZrNhC+l8Pg/9TEokEqKBsB7gNNRC2h6bwGCYiIiIiIiIiIiIaA+L7PQO7EbGmOEvy6GhsN1a2gqFRUNhAMjlcuGX60HVMFqtlqTTaaTTaUmlUnjuuef+s+d5cyPb6VtKwKde2pJQYiS00u+nbeC5lh9Ydld2eq/2rr4AP32dofBWWPOA2Vf9mba0NfoCtLv+Gn72ZWDuzd0bCmuV8D1Pbsn7b7fb/UKxWDyZSqUQjUZF20gHM4Ulk/EfMxaLSTwel3w+r3eVRCKxYdCrn3H270NdpYmIiIiIiIiIiIhoD3J2egd2KbHbSc/MzBgAA1VW8Ct/pdPphBfUajXJ5XLnVWEHX+CbIBw20WhU8vn83E9+8pNPv/vd7/7/RrbXhzJ+ODz7J371cG9hZJseqZX+elDMSuLRW1r1Zwrvxla7e8nLb/oh8a9c+rhwAtD3FnDq7MSurwy2bWGVMAAsLy8/dMMNN/w5AG0hDcBvIQ0AwSx7ZLNZ6Iz7YL6wFAqFcDvlchkAdBzCwEFPDISJiIiIiIiIiIiIrhxsJb0BOxTWL9C1Unh2djZ8zoIv21EoFEy32w0vX15eNr1eL2wj3Wg0kMlk0Ov1IlY7aQFgTp8+Pbp5w7YzDT8cfuGbI9/0lnEiwETcn0mc2A84XJ4X7ZVFoLW403txZdnnAO9KAfuiO70nl5/uCnD6nH96uYTBwJbNErb1+/3n0+n0rwcHH4lWDAPrwXCz2fS0hTTgH5ykbaQLhULYQhpYbyOt9EAntpImIiIiIiIiIiIiunKwlfQGrLaaouf1C/WpqSkpl8sCAMViEYVCwQBAtVqV4BTxeFzq9boAQKPREK3q0i/zAYRtpa+66qo/X1pa+srI/4hDGeDObwAff3b3tpce1vf8KuJ6x28fq62Ql1Z3es92v+4KMPsaQ+GdsNIHnnvFD+VX1nZ6b3a3vvhrdW4B+LfmAn7a9tfs5RIKxyaAW6b9zgxbGAp7njdXrVY/DQDJZBIaCrdaLbRaLXEcRxzHGZgrHI/HBQASiYTk83kkk0kpl8uYmpoSwB+HEPyEgXBAhk6JiIiIiIiIiIiIaI9iK+kNWK01jYhgZmbGBL2lAcAEIbHMzs6aZDIpnU7HwKq+1lnDNg2H0+k0IpGINJtNk0qlTHDdgy+99JI5ePDg8ZH/Mde8zw8xXvimX0F8pjHyh9gy3XPrgdE+B4jHgKvjwIEYcGBsZ/dtt+iu+IHk5RKs7WWtReDUWSA9Dkwe3Om92R36Aiz3gDeXgaXe8Dqd2Knd2pQbPuOHwlt8oI3neXM/+MEPfvMTn/jEy/Pz8wIArut6zWbTABDXdbV9tACAdqdYXl4GAKlUKigUClIul1EsFjE7O2umpqbECoZRKpVEP+eCDhkMhYmIiIiIiIiIiIiuAOzV+8uZ4fmLOm+4VCqhWCyaIBQGAK3QigStPA0Ak81mUa/XkclkDAA0Gg2TTqfDecMA0Gq1TCqVwi9+8Yv/a0vCYdvlGBBvRIPiq/b5QfGVNp+YgfDuts/xA+LE/iurxfRK31+bS6v+2lzq7fQeXbqjH/fbRm9D5wUNhe+6666TAJBOpyUYPwDXdaXZbCKTyQgQzq5HvV6XfD4fflAlEgmpVCpSLBah4bBWDWv76JmZGTM9PS1DIxMYDhMRERERERERERHtcWwl/RZExADrQbCen56elunpaSmVSiiXy0gmk+Ecx3K5rLODkc/nJZfLCQDkcjlxHEeCWcMSiUTEdV0AgH7p32638c53vvO/b0lbadsNn/EriO/8xuXTYnojK31gYQmYexP4aRv44ZzfenpuIahO3IPtp/vih8E/fd3/mxkK714rfeClU36L6ZdO+WHpXqNtodtd4BenFvBcC3iu5f+97dOXfyh8w2eAe54E/sNj2xYK//CHP/zNT37yk3OpVAoAsLa2FnaWAAD93FA6W3h5edlUq1UkEgl7pjAADITCMzMzRsPgoZEJDIWJiIiIiIiIiIiIrgCsGP4lhsNhu9pKZw4jeA4LhYJJJpPy4osvhmH78vKy6fV6pl6vI5vNot/vGwDhadAaFPrFf7vdRiqVwosvvvjAgQMHHtiWP3KvVBBvxIn4VcUHYn7V5oGY/7tzGS37lT6wsOyH3QyCL2/7HL+yfeKAX0l8ua3DpVWg1wfO9BZwdmUCK/2d3qvRi034gfDU57f1wJl+v//8v/zLv3z6k5/85Fyn0xF7prCOH9CDi7SFtIbCtVoNAKRQKEilUkGxWNQDlQaqhYdnDGs3DIbCRERERERERERERFeOyyiZ2H727EUNidWnPvWpiM5tLBaL4XXlctkUCgUAwOuvvx4B/C/uc7kclpaWIo1GGMAa13XheZ62kwYAk0wmDQA8//zzv33ttdf+hTFmfIv/TN/cd4F//1Pgtcq2PNyOOhADopHBwHifs4BYdGdnnuo81qUecLYHLC4DfW9Hd2lPik34od8bP9rZ/UjsX2+Dvs/Z+bnZffGD36UesLq2gOXVCSwHYfBeX4exCT8Mnvqv/vlt1Ov1Hv7bv/3bL/zRH/3RIgBEIhEBgHa7bQe2EvzAPs3lcqjVahKML5BCoQC7jbSGwEoPbrKqhRkMExEREREREREREV1BnJ3egd0s+MI8rBoulUpSKpXM9PS0aBWWhsJanVUoFNDtdg0AxONx/cLd9Ho94ziOZDIZoy2lAaDX69mBs3Q6HQDAzTff/Phjjz32wTvuuOMfI5HIkS3/Y4983P850/AriF/45pY/5I7RFreDFbh+GrTPAWIOEIv6552I/7sT8cPkqPEv34y+AGtrwJr4FZi9PrCy5p9f7mFXVWAeygAffQzoLQCnfgScOQl0G/76ONPwL9/NYhP+zzXvAxIZ4NBR//Sa961Xgj5b8tf6Tume83/awe9a4T4WXcB+ZwL7HH+tRXUNbvI4nuF1t+b5pyt9/7Jef6Pwd2cPktgu1xWAox/zq4S3ORAGgLNnzz74q7/6q18RERONRgUAPM8znU5H7Ephvb2ej8ViYn2+IJFIiB6QZFUJG2C9UtimoTARERERERERERERXVlYMXwBQdXweV+kG2NQKpXMcMUwAHQ6HVOp+JW3+XzeVKtVAwDZbNZoG2lgoKU00um0WVtbM+12W9LpNNbW1ozneebRRx89cvvttx8fGxv7j1v9tw440wBerezdNtOjoGHxugXEnAn0+gsYDtZ2U+h7IYcy/mzVX9ZKt7ewHhCfaQDdk/75lYX19TJ8eqk07NUf3b9EBkgcXb/Mvu5CdjocvngL2Oesry1jGoiaiSDUvXzX3Ha7rgC8f9o/3QEisjg3N/f7mUzmqVQq5WkYrNen02kB1quH7XD4wIEDHrB+4JHOFdY20nqQUrlcFg2Fxf8gG94HVgsTERERERERERERXWEYDL8NImJmZmbM9PS0DLXgPC8cLpfLAPyZw5VKBblcLlKr1QSACdp+AoCxKofRaDQMAKTTaW0rrbOHAfgVZI1G4/i2zR0e9loFeOH/2dtVxOR7O6HwZvUWNldpvNWzXi+/cJg2YwfbRdtWV1efeuqppz579913nwSAVCoVhrN6YBAwGArrbOF6vY5cLifxeFyq1Sry+bwdDEuxWASA80Jh3f7MzIwB/JbSDIWJiIiIiIiIiIiIrjwMhi9AK4aD87DPA+eHw+VyWQqFQkQrhhE8x7lczgB+6+h6vR5el8lk0O/3TbPZtG9vUqkU2u02glMDAE888cTRD3/4w9vTWnojrCLe27YyFN7tGA7vTdpOfAerg5WILJ4+ffrBiYmJhwBIMpmEVgnrQUDaThrwg+FmsykA9AAiyefzXrVaNfl8/rxqYQSzh7WVtF0prCMQdNsMhYmIiIiIiIiIiIiuTAyG3z4DDAbC9nXFYtEEX8brFcaeN1ytVpHL5UytVkMulwtnC9frdQMAruuGG2w2myaVShkAYTgctBpFMpnESy+99MDBgwePb/Uf/EtpFfHJ7+7+ebN0Yde8z58pfCWGworh8N6xw7ODh1lVwnPJZFL0vVyD4XQ6La1WS1zXhR4klMlkxHEcqdfryGaz4Vzh4UrhZDIp5XJZu1RIqVQaCH1LpZKICLTrBRB+fjEcJiIiIiIiIiIiIrrCMBi+CEH1sIiIAQbC4YHnMagcRqFQCC/vdrsmkUjI66+/HtFwuFarGQDIZDImqBoWAEZbSgPrbaWTyaTpdDrhYzz99NNHbr311m9HIpH3btXf+7a98E0/JH6tcuHb0u5zw/8B3PYnuyJA23GvVoAT/4kV8ZcjbRV9uLDj1cFKq4Sz2exD8/PzkkwmAQDWe7noPGEAsMJhyWQy4WzhWCwmwPpcYQDntZC220cHjx2OPrBHIYCBMBEREREREREREdEVi8HwRdC20tbIRgBhQGwADLST1uuDNp8GAPL5PJaXl43OGs5ms6jX69CZw3o713XheZ5ptVoAgHQ6jbW1NQMgrB4GgDfeeOPe8fHxB3asvbRNW00zJL583PY//ZmrtO5MA/jHjzAcvhzEJvyq4KMf2zVhsDp37tzDX/va1774pS99aREA5ufntcI3nB8MrM8VbrVacF1XHMcJK4UBSC6XC7dZq9Ukn88D8INhKxQOW0gDfpWwjjiQoQ8stpEmIiIiIiIiIiIiunJFLnwTUsEX6uGX6jMzM8aqGhat1CoWi6ZSqUgymZRkMinFYlEASKFQkEQiIfF4XL/sl1gsJtlsFo1Gw/6yXgB/xmQ6nQ5DYZ0/qaHC5OSkueaaax7+wQ9+8JtLS0tf2eq//4IOZfyQ5p4ngU+95IeO17xvp/eKNnIoA3z8WYbCGzmU8dfvLdM7vSe0kdgEkB16n9lFofDq6upTL7744m/F4/HP3X///YuA/9mRSqXEDoWj0agE7+mi7/XNZhONRgP2HHo9E4/Hw8+QarUqlUoFxWIxfFydK6z3m56eDn83xsAYIwyFiYiIiIiIiIiIiK5sDIY3IfiCHdPT0wPFWKVSScrl8sAX7+VyGZ1OZ6Ayu1qtAgDy+bwElcMAgEwmA9d1oS1Em82mRCIRabVa0FC43W5Lu90GAIlGo14qlZJPfvKTcwcPHnzwxIkTU71e7+Et+8MvxqGMHzp+/FmGxLvN1Of914Wvxy93S8lfu1fy3OXdYjgMvusbfhi8i9qfe5439+KLL/5WLBa75/bbb/9nPTAI8FtHB50eBj4fWq2WAECz2ZRYLOZlMpnw4KNcLheer9Vqop8bQQcKKRaLUi6XNRAeDnzDzyjtcqEjEIiIiIiIiIiIiIjoysUvijfJnjOs4bAxBqVSyQCAznoslUpGq7rK5bIpFPzKtkqlYnK5nKnVapLL5QwA1Go1bS1tMhk/jOr3+wYAms2mvlYmlUohCIcBwOjcShEx8/Pz8sQTTxz94Ac/eDwWi9271c/DRWO76Z1zXQF4//Suqq68bDxbAmb/FOgt7PSeXDnsNtHXvG9XhcA2nSM8MTHx0OTkJIwx0ul0kEwmoacAMDY25q2trZl2u60hrmQyGWk0GshkMmg0GshmswIA9Xpdcrkc4vG4VKtVFAoFsQLh8LG1dTTgf+bokUobzBQ2IsI20kRERERERERERERXOAbDmzBceTU8d1hnDms4XCwWw9t3Oh1TqVRQKBTQ7XYNAHvmMLA+d9i4rmscx5F+v2+azSbS6bRptVrQeZSpVMpYATGSyaQB/FbT7XY78vTTT19/yy23PLArA2JgPSQ++fd+SMzQbWscyvgV20c/vtN7cnk70wCenQFe+OZO78nedSjjr9NdODN4mIgsLi0tPfTtb3/7oePHjy9q6Krz3wFIMpk0nU5H0ul0+AGhs4Sbzaa4rmuazaZol4h6va6VwqFarebl83kDDM4VBtaDYQ2Bg7nCYh+spPuydc8EEREREREREREREV0uGAxvgogYY4wEpwPXlUolMz09LTMzM2EwDACzs7OmXC6jUCic95xXKhUDwGKliVUAACAASURBVARzh1Gr1ZDNZsPb1et1A8C4rotmswkAAyExALRaLROEEEgmk2H1cDKZxGOPPXb01ltv3Z0VxLbXgkriUz8C3vjRTu/N5e9Qxp+Te8NndnpP9hYGxKMTm/CrgY9+zA+EL4O23Z7nzXW73YfK5fLDx48fXwSA+fl5mZycNPPz85JKpbTtPwBgOBQGIMF7uQDQamHJZrOIxWKiXSRqtZoUCgUB1ttHA0CxWES5XMZw++jgsya8zAqHGQoTEREREREREREREQAGw5t2oaphbeU5MzOjraUN4H+pr1XDetd8Ph/er1qtGgAbBcNwXXfgMT3PMwAQtCcFsF41bFWtAQAmJyfN3/3d311/22237d4KYtuZhh8On/x7BsUX67oCcNPngSOsEN5SGhCf/C6r3d8uOwh+x/t2fVWwbXV19anFxcWHk8nkwwAwOTmJ+fl5vVoASCqVAgBEo1EJQmCk02lEIhHxPM8EM4XDCuF+v28cx5FYLKazhJHP58MPkqBC2N4N2eC8BG2iMXy6wX2IiIiIiIiIiIiI6ArGYHgE7HnDwe/QQFgFFcNit5W2K4i1rXS1WgWs1yWbzRoND3TesGo0GhEAA1XDOn9YAwrP8waqiAHgu9/97pFbb731uOM4d0YikSMjfTK2Sm/Bryh+9X/7QTHnEw+y57FeRmHbdhCRRQAwxoxvyQP0FvxwePZPeQDDsEMZfz2+49eAwwU/FL7MrK6uPvXyyy8/eNttt50AMDBDWLtHdDodaKVwEAobWMEtALiuG4bB+/fv9/R9HVifKQwA8XhcEolEWClcLBbDYLdcLot2oQCA4bbROkfY+kxiKExEREREREREREREIQbDI7JRBXEw7xEARCuGdeZwp9MZuH232zXVahX5fF5nDoue7/V6pl6vI5PJDNyn3+8bz/NMJBKRZrNp7NnDehu9Xs8P7+/Pf/7zPxgfH3/gsgmIba9V/JD41f+9XmF8JdHqy5s+74dvsYltedizZ88+mMlkHnzkkUeOHDt27Mj+/fvHDxw4cCQajY4bYzKO4xwBAMdxrgcwsRWBrOd5c8HZhdXV1Zc9z1sEsNDv90/2+/3F06dPz7344otzP/vZzxb++I//ePE73/nO0Q9/+MP/uOXr/EzDD4hfrVy56/Ed7wMOf3hb1+SoicjiuXPnHn7xxRf/plAoPAcA8/PzWiUsyWQyvG2n0xmoFAb8ltFaKdxsNuG6LgDAcRxpNBrIZrNhYBu0jw4rhYODgwQACoUCksnkQLg7NFfYIKgYVsEBSgyEiYiIiIiIiIiIiOg8DIZHxJo3rF/Im+Dy4ZAYwezhsLW0Vg4HLUPD1tJaPZzNZk0sFpNer2cAPxBuNBoA/PbSdjgM+BXEa2tr4WvbbreRTCZNJBKRdrttJicnzXAl2Q9/+MM7Dx8+fO9l0Wb6rfQW/DDu1I+AN34MdBt7r7L4UMafxXr0Y34It83B2+Li4hez2exDdtUk4LcuTyaTsC8DBudxP/LII2Eoe8MNN1z/dh7vzJkzi6+++upprbS8++67Tw6FcgP7AGBgP+zKyccee+zoHXfcsfXhcLjzDb+S+OTf++tyL7WbPpTx118i41cDX1e4LOYDX8jq6upTS0tLj//1X//1w/fff/8isN4y2g5oh9aaAP4sYQ2Eg5bRYZWwvl/bgTAAjI+Pe8H7PPL5POxKYTsU1jC4VCoNpMDWZ46xw+HgOobDRERERERERERERDSAwfAIaTg8FAYPBMUaDgetpfWuRkOAcrls7Nvn83lUq1WTzWZNvV4XvTybzaJerxvXdU0QPBjXddFsNpFOp00wd1hSqZTR1qbJZNJ0Oh3RkFgriO15xE8//fSRd7/73XcmEonLs4p4I2/8yA/pTv3YD401QN7tQV1swg/bDhd2PHwTkcVTp059MZfLfUtETDBbVQC8ZTisobBuw66s1JnYdutz+zK9TXC50QBOZ2gH+yD6OPPz8+HjDO+P3h7w26jfcccdfxOJRN67ZU/WW7Er3HVN7nbDAfA17/Mvu0wrgTciIou9Xu+pV1555aHbbrvtxHBl8PC61g4MSucJayCs1cHNZjN8CAASvGcjl8tJrVYLrwve4wFACoX1NvB2EG23kNZweHimMDDYTnoLnioiIiIiIiIiIiIiuswxGB6xjeYNAwhnDuusYcD/gn92dtYA/rxhwK8g1jbTlUpluHoYuVzO1Go1E4QLkUxmPSjs9/tGwwjXdQdaR9sVxMBgW+m3CvT2RBXxL2MHxKd+HJwGv59pbF9wrAGwBnCJo7uqAtPzvLmf//znv3/XXXc9p5d1Oh1MTk6a4ba69szVjSqItbpSpVKpgbUYVLVvGBgDMKlUSuz26O1226RSKdHwWPdNTU5OGgDQ4Fj3eWlp6X/E4/E/HNVztCl2hfuZk4MHLWyH2MT62juUAfZN+OGvtoTeJetvq2h1cLlcfvj48eOLwbo1AIZbRevZ8MAGYD0QBtbnvGvr6EwmIzo/WOkcYQ2F8/m8VKtVyefzplqtCgAUi0WxDhg6L9wtlUraQhrDFcIAwvnCDIaJiIiIiIiIiIiIaCMMhreAVg5rxXCpVDLT09MC+AHx9PS0zMzMmKCl9EBb6U6noy2lkc/njdVaNJLP5yWYPxw+VjabDV/Der1uMpkMGo0GMpkM+v2+AYBms2m0chjwQ2IN3TRks6oyAYQBGpLJpDz22GNHb7rppg8dPHjwXsdx7tyWJ3G30IDY/llZAHqL/vXdt1H1mQgCtti4H77ZgZye36VWV1ef+ou/+Ivf//KXv7ywQcgLe80MB8F2yAvgvKA3aL1rhi/T9ans+a327fVyO1i298uuJN4oND5z5swDBw8ePD6yJ2uU7IMTtLK4e3L9upULHLSwz1pXiaP+6fCa28Xrbqvo7OBXX331e7fddtsJY4ysra1F9EADu/IcWF/T6XTaa7VaRtenvpcC62EwANH3X8BvG12v1wEAuVwuvL3Oj08kElKpVKDBcLFYFOugIE9vr5XCGggHf0f4WQKwUpiIiIiIiIiIiIiI3h4Gw1trYO6jDiEG1r/sD07DAFlnDutdtLVopVIJW0svLy+beDwehsRBm2lks9kwDLZnEOvGNEAbDjg0bLNbS29U7QkA3/nOd47efvvtf7hv377f3jOtpmlDi4uLX7zhhhv+3G7bDAy2cdaW5NrmOQheZWg9SXCZAYChsFjbo0sqlTLtdlt0RrZ9PhqNhut1dXU1YlUMD+yzPUvbri4O9nGgtfSOzB2mbScii/1+//lWq/WVD3zgA08BgLaLtqvb7UrbTqeDoBI93E5wQIKk02k7DDaZTMYD/PdcnSGsgbDuQi6XAwDE43EJqoNNoVAYCHErlYoE7/+iBw2JiGgAbLeODv4uAOuzhFkpTEREREREREREREQXEtnpHdjL7C/xg98lOBGt9NLLAL9iWNtM6130TD6fhwYJ8Xj8vC//g/mV0mg0RENhANB2pta8S63UDKuHNaTrdDpiz89MpVJhSKItsn/v937v5IEDB76QSqVuOnny5D29Xu9hz/PmNvkU0S7ked5cs9m8J5vNPgT4wZMeKBCJRMQYI6lUSrQiWNdMJBKRVCol6XRa2u02xsbGPJ0rnE6noVWW6XRaotGoWFWX4SzsYE5reNtIJCJjY2MesN66d7gdtUqlUuF1w6GeXq4Vw1oZ+qEPfejkX/7lX/56r9d7eORPJO2o1dXVpxYXF7/49a9//aZ0On3PBz7wgaeC2dg6QxjAejW5MUYikYgEazZcq/qzQSgM13VFD8bRLg06RzjYDW0fLfF4XBKJhBQKhfC9vFKpIJlMis4TnpqaCscMlEolGGOM9VkBPS2VSsYYM/D5wlCYiIiIiIiIiIiIiC6EwfAW0i/q9Qt8nTOs9It/EZFSqRSGAsViEYVCwRSLRSSTSalUKnLs2DGvUqkgkUiItpeu1WoCQGKxmMRisbAqDYBo5ZrjOGGb00gkIq1WS6xqyzCcC071d7GCNEkmk7Ar6ZLJJObn53Hrrbee+JVf+ZXPRaPRm+bn53+/1+s9LCKLW/qk0pZaWVn587/6q7/64Pvf//4TGpx1Op0wULWD4CHSbrfD8FYr0vVypetNZ7O6riuu60q73faC9QnXdSUSiUg6nUaz2USz2cTwLFcNloODGmRoPzQctoNipFKpMPCbnJwMK4g/97nPLbqu+9k333zzszzI4fK2urr6VLfb/cJXv/rV62Ox2D3ZbPah++67b0Hfv5LJpBhjxBgjk5OT4f3sFuN263K9LFjz0mq1whnCAMRxHHEcRxqNhhw4cMALDsQJQ+F8Pi/xeFx0VnylUjHdbjccF1AoFFAul7VLRBgKF4tFo10l1MzMTBgGB2Gx/VhERERERERERERERBfEYHjrid1OGkAYEgdf/EsQGIehQLlclkKh4JXLZUxNTUmpVJJOp2OKxaJUKhVUKhVUq1Vo2BCPx0XnDudyOQmqh5HNZqXf75sgxECz2RTXdQEAwakAwNjYmBeEeQD8Kjmd4xq0Bw7DZA3WtMItqCaV97znPd9zXfez11577fWtVusPGBJfXlZXV5/693//9w9ef/31X3zggQd0gK1oS10AGqyG97FbQqdSKRNUogOAXfUr6XR64B8gEomI67ph1brSAxislucChGsVemADgHD+arvd9lzXFa1C3ig4brfb2rI6ZFdBI/g/uPHGG//mW9/61q8vLy8/tNnnkbbf6urqU2fPnn3w+9///s2xWOyeY8eO/a8vf/nLC8DgzGD74JZOp6NtpPW97bwDZlqtVvgD+OtQ3zcdxwnnCTuOI7lcTmq1mtEDdIIwWKrVKgAgkUhItVqVQqEgx44d8wBIpVLRSuFwH4ORApiamhK7m4ReZ7eP1i4ORERERERERERERERvF79Y3j5mOCA+7wZ+X1CduTowd1iVy2UUCgVTqVRQKBRQqVSMBsRBCGGC1qUITg2AcP5wo9EIW546jiP9ft94nmdarZak02mjAXEw4zV8XA0AdWZru902VrB23kzi4HLzyiuv/Pbk5ORvjY2N/bYxZnwUTySNjud5c4uLi1+58cYb/2ajVrQaDOvrrpdblcHQU7vFLrAe6Oo6azabcF0XegogPK+3Caovjd5OtzO8XXtbNtd1Eazn8DJtUx1UMNv7b/TAB52PrOv46aefPvL+97//+NjY2H+8xKeYtsDq6upTS0tLjz/zzDPf+43f+I2TwHp76OBU3uI9SV/jsJIc8NeDzrAG/IMSdI0C66349SAbx3EkFotJr9czABCLxcR6zxXAb/+v3R0Af4YwEI4M0IulVCphdnbW6GxhbR2ts4X1/EbBMNtHExEREREREREREdHFYDC8TfRLfD0tlUoRAJienhZjzMA84lKpZIJT2O1E9XI7WLBD4m63a6rVahgu53I59Ho9E1QPQ2dhDtsokEun01q1iVQqZYD1ME2rMDVU0fNa6QmshzCTk5Ph3/2v//qvHzp8+PC9juPcGYlEjozw6aWLJCKLS0tLD337299+SCuENUR7i1bRYWvdoQANruvKRmFvUKEergmddx2ExOK6rtHL1PCBC41GwwA4LzzW2wbXGX1cva2uYbvas9lsGsAPitvttgSnSCaTerADABg7UPzxj3/8oWQy+YDjOHeO4GmnTRKRxX6///zS0tLj5XL54fvuu28RWA+Dk8mk6MEok5OTYXWwvq76/tTpdMIK9uDgl7DKXFmV7AKszw7ev3+/V6/XDfxW/YjFYtrSH/DbRoedHKrVqgB+q2jAnyUMQPQgHw2B7UAYWK8K1iC4VCqdFwhvwdNLRERERERERERERFcIBsM7JAiIAfiBr/3lPxBWD79lMGzrdDoGCMMHAwDDVcSwKpHt+2YyGQMAjUbDZDKZgXBYw7Th4EQrL+3AJRKJhC2AtaJ4cnLSaOVesJ9IJpMQEfPkk0/efPTo0Q/t37//d8bGxj60uWeRLpYdCB8/fnwxmCPsIVgXdjWl0gMBYLW1tcPgDU4lk8mE99cAVy/TgNd+jOA2ukbD2zYajbAlun3bTCYTBs31et1oyKzr19ZsNo3runaLar3K2K2xhwVBIpLJJP7t3/7tzlQq9QDX6vbxPG9uZWXl8VdfffV7TzzxxHPHjx9f1INMdP61BsMI1qa+1wD+fPSNWp9rtTuwPrMaCOcII1hHABCuPV2HAIYDYeTzeQH899pCoaDtoVEul40VDEuxWMTU1JRoGAysH/yj7/NaIWyzDhxiKExEREREREREREREl4TB8M4x8OcPhwGxVoppFbF1O2h12dTUlADA7OxseAOtHi4Wi+h0OgNtpnO5nInH47K8vGy01am2P7XV63Uz3GLavl4rMO0AWGkVsbJmE4dBsM761Apiva2ImAcffHD8nnvuufPqq6/+0NjY2J2O49y8+aeVNtLv9090u92H3/Wudz1st9fVCnYN0exKcLtdtL7udjVxs9kMW+sOPZYZDtfq9brRkM1xHKnX68hkMuFaazQaks1mw21om16tdrfFYjFZWlqK6HYCYaA8/Ph21bJdDQ+sV8RrNbxebrdOt1sQP/HEE++98cYb/zAWi917sa8B/XIisnju3Lnv9Xq955555pnv3XvvvSeB9fb1dmXwcJtotdE6Btar3YHBKndgoFV5WCEMAI1GQzKZjGk0GmEFcS6XA+DPdQcwMOs9kUiIvvda4fDAn4hgREDwHm9EROyW0RoCD2MoTERERERERERERESjENnpHbiChV/0l0olM1QtDBHRCmEREZmenpapqSmxK4inpqZEg+JCoWBXDYetS6+99loP8IOMfD4v8XhcYrGYxGIxqdfrYbCWzWbFcRxpNpseADSbTf09DEV0hmyr1Qp/VLvdDn9SqVRYddrpdERDjVQqJRoQq/n5ebnvvvsWfu3Xfu3xo0ePfiGdTv/6V7/61evb7fYfLC8vP7S6uvrUCJ7rK5KILC4vLz908uTJew4fPvxb11xzzcMacgJ+BbeGwoD/GmqbXaUVldqGGfArKh3HCWewNhoNrbKU4LbhmglCYQCQer2ORqPh6XWNRsOr1+vSaDQkl8tp9S/q9XpYjakte7VKE/ArNfWxstmsBsfh9cPtqZvNpr2O7VbBcF1XotGoRCIRSafTYrdH10AylUpJKpUSAPjoRz/643379n32mWeemXrzzTc/63ne3Nt6Meg8nufNLS8v/0232/3C97///Zuvvfba648cOfLZiYmJhzQUnp+fRxAKix0Kq06nI5FIRCKRiATzsAEA6XRaotGotNvt8PXV++iativLM5mMZDIZZLNZ0fWczWbD01wuJ7lcDvF4XGq1mlitoiWRSEgQCkuxWNwwwC2VSuF8YcCfGWyHwjMzM+EBQvp+aYwJf4iIiIiIiIiIiIiIRoHB8A4L5g2HQYBdOTY9PS06e9iqKhYAeOSRRzxgvdV0EJoYO5jQNqbValWq1Sqq1SqWl5eNhhvZbNbkcrmB0A2AhiGiLXvtWbGu68J1XXFdV7SSNJ1OSzqdFtd1PTtYTKfTHuC3aE0mkxoaS6fTQafTgYisl0X7ASVExNx///2LN9100+NHjhz5YiwWu8cYk2g2m/csLi5+cWVl5XsM496aiCyurq4+9eabb37261//+k0HDhz4QiaTOWGMCVt66xzhVColwWl4d8CvrtQwrdVqSSwW8wBIq9Xy7EpcDYj1ftlsFkHl70DYq+Gt/q60+lIrguv1ut5HcrkcxsfHvXq9HrbtrdfrMj4+HgbLw+tWD25oNBqi+wFAMpkMtNV0JpMR13XRarXCAx3s0LDdbod/T6fT8TCk3W6bZDKJ3/md35l717ve9XA0Gp169dVXf3N1dfVbXJdvTdfl8vLyQ/Pz85/7/ve/f3Mymbz5wIEDn73qqqv+17333ntyfn5+YE55EJBqKDwwAzsIgcWubgegc6IBrLcMb7Va4eusP47jyP79+71MJiP79+/37AMbcrmcV6/Xw4NndJ1de+21nraLzufzplqtSqVSCQ/GwXo7dimXywgO2gnXU6lUgh7cY3eF0HBYDw6yukgM3J+IiIiIiIiIiIiI6FKxFGl3MMPtQ4erxDQg1nnEsFpMB+GwXo7Z2VnT6XSMhsWVSgX5fN5oq9NcLhep1WqiLVCXl5cNsD4zE0DYclpbTGtIDPitgoH1Fq92m2m7Ve/a2prRCuLh9tJquC1sp9OBziYG1ltPW9cbAPL0008feec73/negwcP3uk4znsdx7nZGDM+qhfkcqIteM+dO3fi0UcfffyBBx5YsNsfK2t2sNGAzW6h7LquZ7+mdotdXQN2O2jAb0GOILjS4FdbltdqNaMVv7oP8Xhc7Pa7AFCtVo3OaQ1+17Mmn8+Ht69WqybYbhgqB2tU9PH0Kclmsxu+t/X7faPrV2chNxoNAwDpdNq0Wi0JTu0W0+H5YE0bAJJMJk3wvIbP8w9/+MM7r7vuut8aGxu7MxKJvPciXsY9pd/vP7+6uvp8r9d77qWXXnrqxIkTJ0ul0uk33nhDJicnw9tZoa++Xw38/1vzybVFtAEgOitYt6OvkTULW+z3JG15bq9dABhuYa5rCQD0PdJaf+H/Tz6fx7Fjx847cKBcLofVwcM0DNb3cj1V1u86YoCBMBERERERERERERGNlLPTO0AAgIGZwiKC4dbSQBgchNVnWnmmLacBPxQO5lpq61IpFAommUx6nU7HFAoFdLtdsYO5Wq0muVzOAEAQxEWwXgWqFXMGwHCoooGLabVaYrdmDQK2sG0rAEQifoF6EFpKKpXSP9rY1azwQzd0Oh1Eo1FvaKaxaMVmp9OZA/B4sG/yxBNPHM3lcjcfOnTovY7jvDcajR7Zi/OKg+rLE8vLy0+9/PLLJ26++ebnJycnN5xDagfuVpvkgba7rut62ipaX8dMJiONRsNooKYtdQHgwIEDnjWnWrLZLLSyNx6Ph2sJgBeLxcKDDnK5HJaXl00ulwtDtmAdDoTFemoFceGfHhy8EM7NHh8f9zQ0zufznobHettsNjswp1irQvUgBxWE4AJAq+CNvYbt4DGVSkm73Q6fW3uOdiaTOQHgxOTkpHn88cevv/HGG+8MDl64eS8GxSKy2O/3n+/3+8/1er3nXn755eeffvrpufvuu29R16Q+N5OTkwbw20Mj+D8OtmF0Ju/w/79dza1BsIbCdit7BNXCwWul70UDLcSB9Tbn2uI8WLtAUKWu7aKtgxNQKBSk2+0aBO8zALxjx44NPA86S9g+OKdcLouWAQcdIKDnAcjMzEwEQRMI6/1fb89QmIiIiIiIiIiIiIhGjhXDu1f42pRKJWO3mNbQ2A6TtaV0UJUWXlEsFg0QBhemUCigUqlAT/X+uVzO2NWc1WrVABio/FRBkBI+hlaTAoC2nAag1aZhBfFQkAMrGA6rj/VUL9fWvVpJGI1GPb0MADqdjkmlUt7a2lrEnl8cVB0CAH7yk5/cfNVVVx0dHx+/eWxs7GZjzMTlUmFsB29nz559/vnnnz9x9913z01OTmKDqsrwfsFzFgZp7XbbpNNpr9VqGbvaUp9zq1JWMpmMOI4j/X7faKW44zgDQZrdFrpWq+kBBQAAu8pS2b8PVWCaQqEglUrFDF8GAN1u11ghskkkEhJcdt7jaJv0arWKXC5nrMplAOtV8LpP1t9jtNV0o9Ew9gEOgL82W63WwEEOa2trxvM8MzY25ul5+/m3XoPw9M/+7M8mfvd3f/fmycnJOx3Hee/Y2Nj1l0NYLCKLwcEIz3ued/LcuXPPnT59eu4f/uEfnr///vsXg7UIALjmmmvC/18NgO2qf5tdzQ6sr0WtZAeAVqsVCYJ4SaVSZmxszGs2m0Zfi1arBdd1wzWsu2y/J21Q5a6Xh9XseqCB7kuwjs4LZ4vFopTLZVMqlbxKpRIpFAoDVcP2DHgEIe/Q+7WIiJmZmTGlUsnDetgcHtxDRERERERERERERLRVGAxfJnQer109HPyubUe1otjY4YSe14AYgLZjDnW7XW0zbazquLDFrwZt9n2Ctr3GDl20Ra/SkC0IiA0AcV03nCOqYdva2prRIEjbT9tSqRQ0eEulUhKcD9tpDwdwwEAL6rAaUc/bs42ffPLJmxOJxPhVV111JBaLHY1EIkcdxzkCANFo9EgkEjnyNl+iTdHQbW1tba7f7895nney1+vNnTx58vkTJ06c/NKXvrQIhDOYTfC3hRWFdstofX70+Rtutxs8t6IVsK7rhtfZr59dFa6BcC6Xk16vZ4aDNK02j8fjAvhVvsH8VeTzeWiQG1xnV18ikUiEj2MdpDBAZ7nqvGxgfb3a2wnuH1Yx5/P5gTUMDLRMNxpsB62okclkDLDeathuL62Pa4Xo4brVVuk2q316eN/h9unqa1/72sQdd9xxdHJy8vpDhw69NxqNHolGo0eNMePRaPTIVh+8MLz+ACz0er05z/MWTp48+e8LCwuLH/nIR05q+2dr3YXzdN+q7fHw/2Rw+7AKuN1uG/1/HjoYBFgPS6FzyzcIgcP3FG0XHbxuMnwwQywW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    },
    {
      "cell_type": "markdown",
      "source": [
        "# **importing pandas to deal with dataframes and manipulations**"
      ],
      "metadata": {
        "id": "l4Jl41bXr1wf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "YreiXHf-UPXh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**# getting our dataset from google drive shareable link**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "iYIXKQvJr_Np"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBiitMA8xl9u",
        "outputId": "191cab8f-6529-477e-edcb-c4f6d37f0858"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=13U7rdoeeqpXGlzp0BtseajWHBnCpIsZh\n",
            "To: /content/bbc_news.csv\n",
            "100% 5.06M/5.06M [00:00<00:00, 57.6MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --id 13U7rdoeeqpXGlzp0BtseajWHBnCpIsZh\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0eKzx_XxriN",
        "outputId": "16cd1c27-fd3f-42c1-c425-b72eee1d29bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: -c: line 0: syntax error near unexpected token `newline'\n",
            "/bin/bash: -c: line 0: `unzip /content/<filename>.zip -d /content/<directory>'\n"
          ]
        }
      ],
      "source": [
        "!unzip /content/<filename>.zip -d /content/<directory>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "l6qwVUvSyCBI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "file_name = 'bbc_news.csv' # Replace with the name of the downloaded file\n",
        "file_size = os.path.getsize(file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1ozr1JkqyJlC"
      },
      "outputs": [],
      "source": [
        "news = pd.read_csv('bbc_news.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "2OD4xu6hyPEM",
        "outputId": "73b451ef-2d3c-4c7f-e425-ae19a7bc3bc4"
      },
      "outputs": [
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              "                                               title  \\\n",
              "0  Ukraine: Angry Zelensky vows to punish Russian...   \n",
              "1  War in Ukraine: Taking cover in a town under a...   \n",
              "2         Ukraine war 'catastrophic for global food'   \n",
              "3  Manchester Arena bombing: Saffie Roussos's par...   \n",
              "4  Ukraine conflict: Oil price soars to highest l...   \n",
              "\n",
              "                         pubDate  \\\n",
              "0  Mon, 07 Mar 2022 08:01:56 GMT   \n",
              "1  Sun, 06 Mar 2022 22:49:58 GMT   \n",
              "2  Mon, 07 Mar 2022 00:14:42 GMT   \n",
              "3  Mon, 07 Mar 2022 00:05:40 GMT   \n",
              "4  Mon, 07 Mar 2022 08:15:53 GMT   \n",
              "\n",
              "                                               guid  \\\n",
              "0  https://www.bbc.co.uk/news/world-europe-60638042   \n",
              "1  https://www.bbc.co.uk/news/world-europe-60641873   \n",
              "2      https://www.bbc.co.uk/news/business-60623941   \n",
              "3            https://www.bbc.co.uk/news/uk-60579079   \n",
              "4      https://www.bbc.co.uk/news/business-60642786   \n",
              "\n",
              "                                                link  \\\n",
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              "1  https://www.bbc.co.uk/news/world-europe-606418...   \n",
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              "3  https://www.bbc.co.uk/news/uk-60579079?at_medi...   \n",
              "4  https://www.bbc.co.uk/news/business-60642786?a...   \n",
              "\n",
              "                                         description  \n",
              "0  The Ukrainian president says the country will ...  \n",
              "1  Jeremy Bowen was on the frontline in Irpin, as...  \n",
              "2  One of the world's biggest fertiliser firms sa...  \n",
              "3  The parents of the Manchester Arena bombing's ...  \n",
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              "      <td>Mon, 07 Mar 2022 00:14:42 GMT</td>\n",
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              "      <td>Mon, 07 Mar 2022 08:15:53 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-60642786</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-60642786?a...</td>\n",
              "      <td>Consumers are feeling the impact of higher ene...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "news.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XlXS2T3I7xh"
      },
      "source": [
        "Link to dataset\n",
        "https://www.kaggle.com/datasets/gpreda/bbc-news"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **creating a duplicate to do our task without disturbing the original dataset**"
      ],
      "metadata": {
        "id": "0bzQcffTsXF6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        },
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      },
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        {
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          "data": {
            "text/plain": [
              "(15244, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df = news\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "rfnoMWsHIXUf"
      },
      "outputs": [],
      "source": [
        "columns = [\"pubDate\",\"guid\",\"link\"]\n",
        "df= df.drop(columns,axis =1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "BLRUMTwiItQJ",
        "outputId": "4b2492f5-1ce1-491c-b1c9-f9d4db44ec15"
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              "                                               title  \\\n",
              "0  Ukraine: Angry Zelensky vows to punish Russian...   \n",
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              "2         Ukraine war 'catastrophic for global food'   \n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing nltk to deal with stopwords and converting the case to lower, pipeline can automatically do this but this just to show the method, it doesn't affect the outcome"
      ],
      "metadata": {
        "id": "E1Q8uj5_tOsB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_lGLrnLLm_N",
        "outputId": "73a73132-6a01-4e47-fd39-5bb6e36d6bfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "stopwords = stopwords.words('english')\n",
        "\n",
        "def preprocess(text):\n",
        "    text = text.lower() # Convert text to lowercase\n",
        "    text = ' '.join(word for word in text.split() if word not in stopwords) # Remove stop words\n",
        "    text = text.replace('[^a-zA-Z0-9\\s]', '') # Remove punctuation\n",
        "    return text\n",
        "\n",
        "df['clean_title'] = df['title'].apply(preprocess)\n",
        "df['clean_description'] = df['description'].apply(preprocess)\n",
        "columns = [\"description\",\"title\"]\n",
        "df = df.drop(columns,axis =1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iYsp04TDLuGG",
        "outputId": "e2f40b25-4e4a-4051-b6fe-9b87f63dfabb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         clean_title  \\\n",
              "0  ukraine: angry zelensky vows punish russian at...   \n",
              "1              war ukraine: taking cover town attack   \n",
              "2             ukraine war 'catastrophic global food'   \n",
              "3  manchester arena bombing: saffie roussos's par...   \n",
              "4  ukraine conflict: oil price soars highest leve...   \n",
              "\n",
              "                                   clean_description  \n",
              "0  ukrainian president says country forgive forge...  \n",
              "1  jeremy bowen frontline irpin, residents came r...  \n",
              "2  one world's biggest fertiliser firms says conf...  \n",
              "3  parents manchester arena bombing's youngest vi...  \n",
              "4  consumers feeling impact higher energy costs f...  "
            ],
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              "      <td>one world's biggest fertiliser firms says conf...</td>\n",
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              "    <tr>\n",
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              "      <td>parents manchester arena bombing's youngest vi...</td>\n",
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              "      <th>4</th>\n",
              "      <td>ukraine conflict: oil price soars highest leve...</td>\n",
              "      <td>consumers feeling impact higher energy costs f...</td>\n",
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              "          const element = document.querySelector('#df-2c8aebc7-d854-4d1c-8670-b0666082ba10');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **I combined both the title and description in order to give model sufficient data to make the decision**"
      ],
      "metadata": {
        "id": "o3-aIGWetp5F"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "sUPxMo10TORI"
      },
      "outputs": [],
      "source": [
        "df['combine'] = df['clean_title'] + df[\"clean_description\"]\n",
        "df = df.drop('clean_title', axis = 1)\n",
        "df = df.drop('clean_description', axis =1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rkrS5nZbTult",
        "outputId": "4b8178b0-8fa0-4de5-fbfe-f5aabe96d473"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             combine\n",
              "0  ukraine: angry zelensky vows punish russian at...\n",
              "1  war ukraine: taking cover town attackjeremy bo...\n",
              "2  ukraine war 'catastrophic global food'one worl...\n",
              "3  manchester arena bombing: saffie roussos's par...\n",
              "4  ukraine conflict: oil price soars highest leve..."
            ],
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>manchester arena bombing: saffie roussos's par...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ukraine conflict: oil price soars highest leve...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8331c649-d948-4799-bfcf-50cc36740287')\"\n",
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              "\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8331c649-d948-4799-bfcf-50cc36740287 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8331c649-d948-4799-bfcf-50cc36740287');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyKgPs_uvSdE",
        "outputId": "15911b3e-5f1e-440f-a82a-fa29777d6fba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "combine    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Installing transformers library to get the models from hugging face framework**"
      ],
      "metadata": {
        "id": "KZMnr3hEt5Q0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NgqsmP-U2Yjb",
        "outputId": "b7ab94a4-1ecb-494e-e9f4-327322455836"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.27.4-py3-none-any.whl (6.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m61.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m61.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.3-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.10.7)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.3 tokenizers-0.13.2 transformers-4.27.4\n"
          ]
        }
      ],
      "source": [
        "pip install transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **we can use the model with autokenizer and automodel but we used pipeline**"
      ],
      "metadata": {
        "id": "SJ3t0w7bsyMV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249,
          "referenced_widgets": [
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        "id": "Ln12WcKj2w7v",
        "outputId": "fcc5c25b-b5f4-4d18-a2a2-17eb0b3fb2bf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/39.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e27212351854cfab07b129728502620"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/953 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2acbded857044095b0d9cee2264c2efe"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/872k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c24b5926504442a78d4a8944e80dcba5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84610395e28f413fa49f2bb42c4545e0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/669M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c87ab57607a404a80b135a348f08b6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at nlptown/bert-base-multilingual-uncased-sentiment were not used when initializing BertModel: ['classifier.weight', 'classifier.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "#from transformers import AutoTokenizer, AutoModel\n",
        "#model_name = \"nlptown/bert-base-multilingual-uncased-sentiment\"\n",
        "#tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "#model = AutoModel.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **importing pipeline from transformers library**\n",
        "It's easy to implement and simple"
      ],
      "metadata": {
        "id": "cPcECBtJuD8f"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197,
          "referenced_widgets": [
            "44d8ad490a0846329488c2d507bda124",
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          ]
        },
        "id": "0qp5e07d5hAI",
        "outputId": "e4052d3d-014a-4795-92dc-96b1790fe3fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "44d8ad490a0846329488c2d507bda124"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dd5611be77944e0863b4373a3de8ec7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d6c5bb476f24bb59a69bb138f82a9f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92d3fb760d7a4ec19925cabe28ff92fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'NEGATIVE', 'score': 0.9964145421981812}]\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "res= classifier('interior is good but service was bad')\n",
        "print(res)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Making a function to pass the input into the model and storing the outputs generated by the model in df[\"results\"]** "
      ],
      "metadata": {
        "id": "KRIORiKruRcM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "urPTVcxIZkUA"
      },
      "outputs": [],
      "source": [
        "def analyze_sentiment_batch(texts):\n",
        "    results = classifier(texts, truncation=True)\n",
        "    return [(r['label'], r['score']) for r in results]\n",
        "\n",
        "# Split the input texts into batches of size 32 or more (adjust as needed)\n",
        "batch_size = 102\n",
        "text_batches = [df['combine'][i:i+batch_size].tolist() for i in range(0, len(df), batch_size)]\n",
        "\n",
        "# Apply the function to each batch of texts and store the results in a new column\n",
        "df['results'] = [result for batch in text_batches for result in analyze_sentiment_batch(batch)]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "MuG_gel8dhMT",
        "outputId": "5f342702-9dd5-4d4b-d544-0662ea5ecc57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              combine  \\\n",
              "0   ukraine: angry zelensky vows punish russian at...   \n",
              "1   war ukraine: taking cover town attackjeremy bo...   \n",
              "2   ukraine war 'catastrophic global food'one worl...   \n",
              "3   manchester arena bombing: saffie roussos's par...   \n",
              "4   ukraine conflict: oil price soars highest leve...   \n",
              "5   ukraine war: pm hold talks world leaders sanct...   \n",
              "6   ukraine war: uk grants 50 ukrainian refugee vi...   \n",
              "7   tiktok limits services netflix pulls russiatik...   \n",
              "8   covid: fourth jab scotland's vulnerable, testi...   \n",
              "9   protests across russia see thousands detainedp...   \n",
              "10  ukraine conflict: guide understanding day 111....   \n",
              "11  russian gymnast investigated wearing pro-war s...   \n",
              "12  ukraine: placards tears, poles greeting refuge...   \n",
              "13  twitter part war effort - ukraine ministerukra...   \n",
              "14  ukraine invasion: volunteers 'working autopilo...   \n",
              "\n",
              "                           results  \n",
              "0    (NEGATIVE, 0.992180585861206)  \n",
              "1   (NEGATIVE, 0.9850346446037292)  \n",
              "2   (NEGATIVE, 0.9923680424690247)  \n",
              "3   (NEGATIVE, 0.9917146563529968)  \n",
              "4   (NEGATIVE, 0.9181494116783142)  \n",
              "5   (NEGATIVE, 0.8106705546379089)  \n",
              "6    (NEGATIVE, 0.980233371257782)  \n",
              "7   (NEGATIVE, 0.9995500445365906)  \n",
              "8   (NEGATIVE, 0.9059380888938904)  \n",
              "9    (NEGATIVE, 0.985632061958313)  \n",
              "10  (NEGATIVE, 0.9748026728630066)  \n",
              "11  (POSITIVE, 0.5727548599243164)  \n",
              "12  (POSITIVE, 0.9937388896942139)  \n",
              "13  (POSITIVE, 0.6345050930976868)  \n",
              "14  (NEGATIVE, 0.9814872741699219)  "
            ],
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              "\n",
              "  <div id=\"df-ccfb3c19-830b-49d3-9eae-ca841d9e3ff1\">\n",
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              "      <th></th>\n",
              "      <th>combine</th>\n",
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              "      <td>ukraine: angry zelensky vows punish russian at...</td>\n",
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              "      <td>war ukraine: taking cover town attackjeremy bo...</td>\n",
              "      <td>(NEGATIVE, 0.9850346446037292)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ukraine war 'catastrophic global food'one worl...</td>\n",
              "      <td>(NEGATIVE, 0.9923680424690247)</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>manchester arena bombing: saffie roussos's par...</td>\n",
              "      <td>(NEGATIVE, 0.9917146563529968)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ukraine conflict: oil price soars highest leve...</td>\n",
              "      <td>(NEGATIVE, 0.9181494116783142)</td>\n",
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              "      <th>5</th>\n",
              "      <td>ukraine war: pm hold talks world leaders sanct...</td>\n",
              "      <td>(NEGATIVE, 0.8106705546379089)</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ukraine war: uk grants 50 ukrainian refugee vi...</td>\n",
              "      <td>(NEGATIVE, 0.980233371257782)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>tiktok limits services netflix pulls russiatik...</td>\n",
              "      <td>(NEGATIVE, 0.9995500445365906)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>covid: fourth jab scotland's vulnerable, testi...</td>\n",
              "      <td>(NEGATIVE, 0.9059380888938904)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>protests across russia see thousands detainedp...</td>\n",
              "      <td>(NEGATIVE, 0.985632061958313)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ukraine conflict: guide understanding day 111....</td>\n",
              "      <td>(NEGATIVE, 0.9748026728630066)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>russian gymnast investigated wearing pro-war s...</td>\n",
              "      <td>(POSITIVE, 0.5727548599243164)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>ukraine: placards tears, poles greeting refuge...</td>\n",
              "      <td>(POSITIVE, 0.9937388896942139)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>twitter part war effort - ukraine ministerukra...</td>\n",
              "      <td>(POSITIVE, 0.6345050930976868)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>ukraine invasion: volunteers 'working autopilo...</td>\n",
              "      <td>(NEGATIVE, 0.9814872741699219)</td>\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-ccfb3c19-830b-49d3-9eae-ca841d9e3ff1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ccfb3c19-830b-49d3-9eae-ca841d9e3ff1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df.head(15)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**copying data from df to our original dataset**"
      ],
      "metadata": {
        "id": "lc355fs2uqm0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "9BB6oCqNPwcB"
      },
      "outputs": [],
      "source": [
        "news[\"results\"]= df[\"results\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **creating two columns for results one for sentiment and other for score**"
      ],
      "metadata": {
        "id": "HKQeKonsu7bP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "iw58SKZsSZfF"
      },
      "outputs": [],
      "source": [
        "news[['sentiment', 'score']] = pd.DataFrame(news['results'].tolist(), index=news.index)\n",
        "news = news.drop(\"results\",axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "okSf6MUjSdF8",
        "outputId": "fae911e2-1f8a-4929-90b5-1fb765f84036"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  \\\n",
              "0  Ukraine: Angry Zelensky vows to punish Russian...   \n",
              "1  War in Ukraine: Taking cover in a town under a...   \n",
              "2         Ukraine war 'catastrophic for global food'   \n",
              "3  Manchester Arena bombing: Saffie Roussos's par...   \n",
              "4  Ukraine conflict: Oil price soars to highest l...   \n",
              "\n",
              "                         pubDate  \\\n",
              "0  Mon, 07 Mar 2022 08:01:56 GMT   \n",
              "1  Sun, 06 Mar 2022 22:49:58 GMT   \n",
              "2  Mon, 07 Mar 2022 00:14:42 GMT   \n",
              "3  Mon, 07 Mar 2022 00:05:40 GMT   \n",
              "4  Mon, 07 Mar 2022 08:15:53 GMT   \n",
              "\n",
              "                                               guid  \\\n",
              "0  https://www.bbc.co.uk/news/world-europe-60638042   \n",
              "1  https://www.bbc.co.uk/news/world-europe-60641873   \n",
              "2      https://www.bbc.co.uk/news/business-60623941   \n",
              "3            https://www.bbc.co.uk/news/uk-60579079   \n",
              "4      https://www.bbc.co.uk/news/business-60642786   \n",
              "\n",
              "                                                link  \\\n",
              "0  https://www.bbc.co.uk/news/world-europe-606380...   \n",
              "1  https://www.bbc.co.uk/news/world-europe-606418...   \n",
              "2  https://www.bbc.co.uk/news/business-60623941?a...   \n",
              "3  https://www.bbc.co.uk/news/uk-60579079?at_medi...   \n",
              "4  https://www.bbc.co.uk/news/business-60642786?a...   \n",
              "\n",
              "                                         description sentiment     score  \n",
              "0  The Ukrainian president says the country will ...  NEGATIVE  0.992181  \n",
              "1  Jeremy Bowen was on the frontline in Irpin, as...  NEGATIVE  0.985035  \n",
              "2  One of the world's biggest fertiliser firms sa...  NEGATIVE  0.992368  \n",
              "3  The parents of the Manchester Arena bombing's ...  NEGATIVE  0.991715  \n",
              "4  Consumers are feeling the impact of higher ene...  NEGATIVE  0.918149  "
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>pubDate</th>\n",
              "      <th>guid</th>\n",
              "      <th>link</th>\n",
              "      <th>description</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>score</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ukraine: Angry Zelensky vows to punish Russian...</td>\n",
              "      <td>Mon, 07 Mar 2022 08:01:56 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/world-europe-60638042</td>\n",
              "      <td>https://www.bbc.co.uk/news/world-europe-606380...</td>\n",
              "      <td>The Ukrainian president says the country will ...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.992181</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>War in Ukraine: Taking cover in a town under a...</td>\n",
              "      <td>Sun, 06 Mar 2022 22:49:58 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/world-europe-60641873</td>\n",
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              "      <td>NEGATIVE</td>\n",
              "      <td>0.985035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ukraine war 'catastrophic for global food'</td>\n",
              "      <td>Mon, 07 Mar 2022 00:14:42 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-60623941</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-60623941?a...</td>\n",
              "      <td>One of the world's biggest fertiliser firms sa...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.992368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Manchester Arena bombing: Saffie Roussos's par...</td>\n",
              "      <td>Mon, 07 Mar 2022 00:05:40 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-60579079</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-60579079?at_medi...</td>\n",
              "      <td>The parents of the Manchester Arena bombing's ...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.991715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ukraine conflict: Oil price soars to highest l...</td>\n",
              "      <td>Mon, 07 Mar 2022 08:15:53 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-60642786</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-60642786?a...</td>\n",
              "      <td>Consumers are feeling the impact of higher ene...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.918149</td>\n",
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              "</table>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-16acf9f8-c053-4ef4-996f-d7517dcc79de button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-16acf9f8-c053-4ef4-996f-d7517dcc79de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "news.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtOkwHGPVU_N"
      },
      "source": [
        "# **text generation**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **I have used gpt2 model for text generation**"
      ],
      "metadata": {
        "id": "9mJ06f-AvHx9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
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        "outputId": "2dbcf9f6-cf02-4dfe-f3b6-a952b4c837fd"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "383333d20d554299b614f40ee7118f5c"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fcafd2b6babd4a2b8a915e8ca8f4256e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "438dcf2d5d434485a0adc1cf405dd1c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c89ad409fa6949b7afb52a8579067e86"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f960741f0eb044649d2b3237f3084932"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import pipeline, set_seed\n",
        "generator = pipeline('text-generation', model='gpt2')\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **testing with sample input, seting seed = 42 means the text generated is reproducible**"
      ],
      "metadata": {
        "id": "SJUsw9RgvPl7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1WW-2yJajFH",
        "outputId": "080f9c89-1c6e-431c-d8c0-ae2f2b87320f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1201: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'Hello, I\\'m a language model, I\\'m a problem solver in languages.\"\\n\\nAt the same time, she said we can understand an idea like \"reactive programming,\" because programming is what you create. The problem is how do you write code as a language. It\\'s just human nature, so we can talk about code as a language with the same basic concepts as Haskell, so we can give an example. If we say, \"We need to solve this problem from the perspective of Haskell,\" we have an idea of how to do it. If we say, \"We\\'re looking at the situation like a problem involving pure numbers and vectors,\" we have an idea of the problem. There are a lot of things that can change the way you get what you\\'re solving. When you\\'re thinking about a problem, you get to see your problem as a sort of abstraction of your problems, and then you get to understand the problem at the exact level that it\\'s being solved.\\n\\nIn the case of our problem at the macro level, that\\'s my problem, we want to think about the context and try to make it work well here and there. But before we move on, do you also think a compiler that can read languages as text is better than a language with other types?\\n\\n\"I\\'m going to work very closely with you, I\\'m going to be working very closely with you, to understand some aspects of programming language that you\\'ve all been telling'}]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "set_seed(42)\n",
        "generator(\"Hello, I'm a language model,\", max_length=300, num_return_sequences=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nk6h4LmtcbaS",
        "outputId": "acd6a139-f359-4cc2-96f1-50b32f2c63d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['title', 'pubDate', 'guid', 'link', 'description', 'sentiment',\n",
              "       'score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "tg = news\n",
        "tg.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **creating a new dataset same as news and dropping all the columns which are not required for text generation. Used description as input for text generation model .**"
      ],
      "metadata": {
        "id": "zsZDpP2Rvd45"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "8zzXvxjIRF5x"
      },
      "outputs": [],
      "source": [
        "columns = ['title', 'pubDate', 'guid', 'link','sentiment','score']\n",
        "tg = tg.drop(columns,axis = 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "zU1M6fADRjWQ",
        "outputId": "26816c7d-cf79-4030-abea-7463e5b2332f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                         description\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "tg.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **As text generation is taking too much time so I've dropped 15230 rows to reduce the execution time, It can be done for all rows but will take time.**"
      ],
      "metadata": {
        "id": "2yquCnFxv4nc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "beARbgcws8nR"
      },
      "outputs": [],
      "source": [
        "tg.drop(range(15230), inplace=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Used for loop for iteration each row and passing into the model pipeline and storing the output in a different column named as tg['generated_text']**"
      ],
      "metadata": {
        "id": "IWQtpylawPCF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgAgttbltDXh",
        "outputId": "a270cb03-9a26-4932-fa40-4aa373ec3a7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        }
      ],
      "source": [
        "generated_text = []\n",
        "for index, row in pd.DataFrame(tg).iterrows():\n",
        "    text_input = row['description']\n",
        "    generated_sequence = generator(text_input, max_length=150, num_return_sequences=1)[0]['generated_text']\n",
        "    generated_text.append(generated_sequence)\n",
        "\n",
        "# Add generated text to DataFrame\n",
        "tg['generated_text'] = generated_text"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **For showing that the model is working perfectly, description in row 15231 is hown below which has approx 35+ words**"
      ],
      "metadata": {
        "id": "x-nLjaQawfM5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Am3H-LdRtKVB",
        "outputId": "a1bb16ef-39ba-4e4c-da5a-c13fc13970fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Former Sinn Féin president says he only recently realised how difficult it was for the UUP leader.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "tg['description'].loc[15231]\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **for the same row, our model has produced text with 100+ words.**"
      ],
      "metadata": {
        "id": "WxwesFh0w0MT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "ghPc1hUgtLQa",
        "outputId": "1fd7ad6b-3c71-4997-a29d-edb92b41b3c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Former Sinn Féin president says he only recently realised how difficult it was for the UUP leader.\\n\\n\"I went to the back of the room and the man sat at his desk and said that if there was a single thing for me to have to be on the front burner of when he took questions of me, I don\\'t think he\\'d think on the eve of the general election.\" he continued to explain. \"A lot of you think that was because he was an anti-Labour nationalist. It was a good opportunity to discuss how, even though we could have a very different approach, we still still had our differences.\"\\n\\nThe Sinn Féin leader has said in the past he does not regret it if \"the'"
            ],
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              "type": "string"
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          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "tg['generated_text'].loc[15231]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **combining it with our original dataset news**"
      ],
      "metadata": {
        "id": "Xx0skyCJxCbi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "tmFINHkb7cLr"
      },
      "outputs": [],
      "source": [
        "news['text_generation'] =  tg['generated_text']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **End results**"
      ],
      "metadata": {
        "id": "hU0m29MbxB5B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
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        },
        "id": "qmSD-I2A7s7O",
        "outputId": "4255d4ef-ade1-438d-f2d1-bbad9f38a439"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   title  \\\n",
              "15231  Good Friday Agreement: Gerry Adams praises Dav...   \n",
              "15232  Paternoster lift at Essex university is TikTok...   \n",
              "15233  The tech helping driverless cars see round cor...   \n",
              "15234        Coronation street party deadline is looming   \n",
              "15235  Scotland 2-0 Spain: Steve Clarke's classy and ...   \n",
              "\n",
              "                             pubDate  \\\n",
              "15231  Tue, 28 Mar 2023 05:20:50 GMT   \n",
              "15232  Tue, 28 Mar 2023 05:13:39 GMT   \n",
              "15233  Mon, 27 Mar 2023 23:13:08 GMT   \n",
              "15234  Tue, 28 Mar 2023 03:12:39 GMT   \n",
              "15235  Tue, 28 Mar 2023 22:06:24 GMT   \n",
              "\n",
              "                                                    guid  \\\n",
              "15231  https://www.bbc.co.uk/news/uk-northern-ireland...   \n",
              "15232  https://www.bbc.co.uk/news/uk-england-essex-65...   \n",
              "15233       https://www.bbc.co.uk/news/business-65036895   \n",
              "15234             https://www.bbc.co.uk/news/uk-65092586   \n",
              "15235      https://www.bbc.co.uk/sport/football/65107499   \n",
              "\n",
              "                                                    link  \\\n",
              "15231  https://www.bbc.co.uk/news/uk-northern-ireland...   \n",
              "15232  https://www.bbc.co.uk/news/uk-england-essex-65...   \n",
              "15233  https://www.bbc.co.uk/news/business-65036895?a...   \n",
              "15234  https://www.bbc.co.uk/news/uk-65092586?at_medi...   \n",
              "15235  https://www.bbc.co.uk/sport/football/65107499?...   \n",
              "\n",
              "                                             description sentiment     score  \\\n",
              "15231  Former Sinn Féin president says he only recent...  POSITIVE  0.993360   \n",
              "15232  Videos demonstrating the 1960s lift at the Uni...  POSITIVE  0.994661   \n",
              "15233  A trial in London used existing street cameras...  NEGATIVE  0.939284   \n",
              "15234  If you want to close your street over the bank...  NEGATIVE  0.993097   \n",
              "15235  Scotland delivered a performance for the ages ...  POSITIVE  0.998982   \n",
              "\n",
              "                                         text_generation  \n",
              "15231  Former Sinn Féin president says he only recent...  \n",
              "15232  Videos demonstrating the 1960s lift at the Uni...  \n",
              "15233  A trial in London used existing street cameras...  \n",
              "15234  If you want to close your street over the bank...  \n",
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              "      <th>text_generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15231</th>\n",
              "      <td>Good Friday Agreement: Gerry Adams praises Dav...</td>\n",
              "      <td>Tue, 28 Mar 2023 05:20:50 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-northern-ireland...</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-northern-ireland...</td>\n",
              "      <td>Former Sinn Féin president says he only recent...</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>0.993360</td>\n",
              "      <td>Former Sinn Féin president says he only recent...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15232</th>\n",
              "      <td>Paternoster lift at Essex university is TikTok...</td>\n",
              "      <td>Tue, 28 Mar 2023 05:13:39 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-england-essex-65...</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-england-essex-65...</td>\n",
              "      <td>Videos demonstrating the 1960s lift at the Uni...</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>0.994661</td>\n",
              "      <td>Videos demonstrating the 1960s lift at the Uni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15233</th>\n",
              "      <td>The tech helping driverless cars see round cor...</td>\n",
              "      <td>Mon, 27 Mar 2023 23:13:08 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-65036895</td>\n",
              "      <td>https://www.bbc.co.uk/news/business-65036895?a...</td>\n",
              "      <td>A trial in London used existing street cameras...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.939284</td>\n",
              "      <td>A trial in London used existing street cameras...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15234</th>\n",
              "      <td>Coronation street party deadline is looming</td>\n",
              "      <td>Tue, 28 Mar 2023 03:12:39 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-65092586</td>\n",
              "      <td>https://www.bbc.co.uk/news/uk-65092586?at_medi...</td>\n",
              "      <td>If you want to close your street over the bank...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.993097</td>\n",
              "      <td>If you want to close your street over the bank...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15235</th>\n",
              "      <td>Scotland 2-0 Spain: Steve Clarke's classy and ...</td>\n",
              "      <td>Tue, 28 Mar 2023 22:06:24 GMT</td>\n",
              "      <td>https://www.bbc.co.uk/sport/football/65107499</td>\n",
              "      <td>https://www.bbc.co.uk/sport/football/65107499?...</td>\n",
              "      <td>Scotland delivered a performance for the ages ...</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>0.998982</td>\n",
              "      <td>Scotland delivered a performance for the ages ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-73f9994b-bc54-4c26-9a9a-84b34aab0533 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-73f9994b-bc54-4c26-9a9a-84b34aab0533');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "news.iloc[15231:15236]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Do share your reviews**\n",
        "https://docs.google.com/forms/d/e/1FAIpQLSepWp917tHUMTwH0g29CQY2FmjQNgeXg0tUNoLdKFr6MW0HLg/viewform?usp=sf_link"
      ],
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        "id": "6ODwP7PCxWPi"
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