Week_3_Decoder_Only_Transformers_for_language_generation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWA79Y8HDNSH"
      },
      "source": [
        "# Decoder-only Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JYyhDu0umPqp"
      },
      "source": [
        "In this section of the course we want to look at training our own Transformers. We provide you with an easy to follow and adapt workflow and show you the typical pitfals most blog posts and books do not mention.\n",
        "\n",
        "Last session, we introduced the Encoder-only Transformer and this time we take a look at the Decoder-only Transformer to build a  model that can write book reviews given a prompt - e.g text to text."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6smfmA8Tm2W-"
      },
      "source": [
        "## The Decoder-only Transformer\n",
        "\n",
        "An Decoder Transformer esentially reads an entire text and predicts the next token. That means it \"prolongs\" an existing text. This might sound different than writing a review from a prompt but imagine the following text:\n",
        "\n",
        "*We saw white sheep in New Zealand and England. There are white sheep in Germany and the Netherlands, too. Only in Canada we encountered a brown sheep. Therefore, we assume that*\n",
        "\n",
        "How would you complete the sentence? Obviously there are many choices:\n",
        "\n",
        "1.   *we should visit more places to see what color sheeps have there.*\n",
        "2.   *most sheeps are white.*\n",
        "\n",
        "Both options are highly conditioned on the prior text and that makes sense for a book review based on a prompt, too."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In 2018 (one year after \"Attention is all you need\") OpenAI released GPT2 as an Open Source model and the accompanying paper [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). This paper highlighted that Decoder-only models can be used to solve a wider range of tasks then specific Encoder-only models."
      ],
      "metadata": {
        "id": "7cJSYvJtqXvi"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_UXCE96pB_g"
      },
      "source": [
        "### The Decoder Data-Flow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qENUd3_Ts2_r"
      },
      "source": [
        "![decoder.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugrSGbgJs29m"
      },
      "source": [
        "### The Input Transformation\n",
        "Is exactly the same schema as for the Encoder only transfomer!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9bKNvhzs27a"
      },
      "source": [
        "![transformer_inputs_decoder.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_UDMle_s25I"
      },
      "source": [
        "### The Decoder Layer(s)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![causal_attention.png](data:image/png;base64,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s8qCsTpvK7g2dy5c93NVH7O9W+88YYpo14BLmW3K7Nen9/oIZu1Pd1ApZuhLrzwQvf6j3/8Y56ZhYX9tnGV9fGfgqgK+OnmC5WC6plX27VOQV6ezZ/+9CfTjV0/+9nP7Ouvv7Zhw4a5c1yjRo3ssssuc5YKFqvot5zO7xoWV9+butFn7NixppEHFNjUe32X6rnG3k1khXkowKzzqIb51g1ECobq3KxAqJ6jfvnll7vvb83Pq+icqeCpblKKLnqv3wN6PikFAQQQQAABBBBAAAEEEEAAAQQQQCA4gaRlXCq4qKyIO+64w7xghC4Q6Tl2ClIoy8ErutCvgJEy3e69997IRSRdXNRFMQU9VbQdXajTM4luvPFGN00XwPr06eMu5uuioi5w6V+LFi3c/Oj/dPFfF72ii3fBSvOCLGH0CtKjsH0XxUsZGTq+dBFVQVgVXXxVtsijjz7qMko1TUOX3nPPPe6Zp94FVWXU6GKtSsOGDe322293w+aeccYZblr0f8oa9IouEiuzRxd+L7roIm9yIH/VTh3fjzzySCQjRheP9VnTxXZd0PVKsqy8/SnrRkM1qygopyBxkEXPu9Wx0qpVK5e9oyFsR40a5S7Ce8dOdP00RKMyi3Seq1atWuSivTI7dbzphghdtFcm6cUXX+y2o6CxzlHK2FLwVs+Pff75561v377Rm87x+h//+IcLsCtgQkEgUwUKO283aNDAnUOUYemdj/WM4dwBLQ2xquCabs7xvvsLOtdrVAcFQb0scZ0/de7Ss269kRp0Y9Vvf/tb9ztG/aPvC51jNdLD2WefnaPL/Py2ybHC/7/RCAMLFixw72ShGyG0X69eBdVT2f8q0W3Xby+dcwvz0neYtw8FJnWjhp5R7A1bq0CxbtJRkYt+y916662Rmyx0btSw823atHHb0Q1DCnrqJhoVPx6ffPKJu/lI29INN/oNoO9m7/elbhDybNxGc/2njE8FdhXorffd6B36Lbls2TJ37s61KG8RQAABBBBAAAEEEEAAAQQQQAABBJIskLTApbKWdEEt9wW7pk2busCj124Fdv7whz+4TEsNGeYFEnVxTEEDXaTShX+vaFgzXXjyii6cRWcgKfNAFyrzKlpOz22MLtq/SvQ2oucn63UYvZLV9uLsx6+XjiFlvaicf/75kV156ysLJLooEK6LwV7g0rswq2V0gVcXZPM7vrzt6CKusuN0/N93330xwXJvuWT9VTBSQVlvGD/tVxfbdWFfdfUCl0FYKXjXunVrZ66sKNl6F8iT5RO9H11A13Fyww03uMl6lmWlSpVcZrfqqeFsvaIMI12kV3BCF8JVCrtor2V0DtNxoWGYVXQOGj9+vHud138aaltBAa9OeS3DNAQyQaCw87bOcxr+VFnjb39345RGd1BWXe7McgXiFLSMLgWd6xW0VMBPN3pom7rBwfvt4G1DNyHo+8ErCqRpaHqdf6N/B/n9beNtJ/qv6uD9htF3lW6GUHanFzz1U8/otut85screnhVraPhVaO9NM3z0PCweu3dkKL6q8067ynQ650rvXb59dCNb/o9qKLfa/qe0HlTw2frRiIFQr2b3LxtR//VfjXigo4HvdZ59eijj45k/kcvy2sEEEAAAQQQQAABBBBAAAEEEEAAgeQKJC1wqSwGBSFzBwQ1LTrwo8w0XRTTRScFDc466yzTc5s0XRe/dCHQC2aK6kc/+lGOZ0bp4p3fomzLHTt25FhcF9NUcmdi5lgoCW/C6JWEZhd7F3699GzUm2++2WX5KqvtzjvvdPvUMagL19HHlma0bNnSHY9exYpyfGmdyZMnu2eyKttO2cK6wBt0UVv1mcpdFMj0so00L1lWqo+GStWFbwV39RlX1owuqGs4wKACl7qArgChhqWOLrrZQhfhddHdC2hPnTrVZU3qfKWL4F4p7KK9lqtSpUokaKn3utkiuh80zSsaQlb7ViZX7kCLtwx/EcgUgcLO2/ocPf744y7IqBEYFDhUsErDy0YXb2jV6GkFneuVFa1nbetcoGzqX/ziF/bYY49Fr54jaOnN0LlNWY3Rxe9vm+h1vNcdO3bMETD0pnt//dQzuu1+vbzte39z/67zpuuvzqOyjP5u1Ws9ezz6xg9vHb8e0fXWusoSVZasApcaalbPPFcmrAKx+X3v6qYUfccowOuNNsB51esJ/iKAAAIIIIAAAggggAACCCCAAALBCSQtiqLnF2nYMg1nFv2MP2UfaJ5XdEGrW7dupucB6g54DemmC0/KclKwRQFNDdvoFQ2xqO0Wp+iCoy5yaV/ehS3VR8ETL/upONuNxzph9IpHuxK1Db9ezZs3d88lU6bGwIEDbfbs2e54qlOnjjsOOnTo4LIuvHpqfvTx6U3381fDhmooPz3HVcdaWIrao8zK6KKLxcpK8p4zqXnJslLwQc+q1fCq3vNGvbp52UTe+2T+1QV3ZWZFX3DX/r1M1ejgooIRCoLr2W/vvvtuZMhDPxftCwqQ5G6vhsXW8srk9IqMNOShAs3R50ZvPn8RSFeBws7b+g2hmyJGjBgR+dzquZO5i77z/RZ9pjUc9G9+85tI1r7On/odEV30WyK6aD1lZ2qI0uiSiN822r7feka33a9XdP0Le60+0rlJwV2v6AYMBQpzBx81369HdL31XGQNua3zn4aL1T+NlKBnb6ofNExvXkV9oZuLlDGrYWIVCKYggAACCCCAAAIIIIAAAggggAACCAQvkHNstATWRxeO9EylMWPG2NatW132pC40vffeey4TLfeuFUjUULGLFi1ywSXNV+aVLhjqQpQCCmu/GyL2z3/+c7Ezj3R3voIPyuzUM+cUuNHzNjUcpIYMC7KE0StIj8L27dfLywzRsahnFCrzRlm3ypzT8wL1HEpdVNVFXwUdNfRc1apVC9t9zHxdxNax6gVCt2/fbt4/XWQNsuhzpGzBl19+2bVT9VH2qQJg0YGvZFnphgQ9i07ZQcuXL3fnBl1InjlzpstoCspKWdfKhswdgPCCvtE3NyhjR1miyqp98cUXI5ncumivIYV10d77165dO3dM5XXRvrC2KitVmWMKnnr/tI4yuYoSAC1sP8xHIEwCuuFJ38/R/zZu3FjoeVujNOifzr36zaD1lYmnc13uQKPf9mo7+rdt2za3ip6N+MILL7htRt/MoKG29R2gGzN0jlWmtM6puQOX2ki8f9tom37rqWW9kggvZc4riKjzuQK8Gt5Wz7dUQNm7YUznLvWxNwJGUT10c4m+z/Qdon5V270b2rzhZL02Rv9V9q1+C+j7T+djjeBBQQABBBBAAAEEEEAAAQQQQAABBBAIXiBpGZe6YNe3b1/3rD8Nc6j3GiZM2ZUKIOZVFMy49NJLXSBAz6q6/PLL3QVA3UWvi1MKKOl5TgoYFKfoQlXv3r3tb3/7m82YMcNd4FRG53XXXVeczcV1nTB6xbWBcd5Ycbw0BKiX1Xv33Xfb7bff7gKXOk41XFy974b8VPBcx2n0BWk/VdcQo8rC08Vq/YsuuijbtWvX6ElJfa2Am9qr437atGlu3xpCsV+/fi7bJa/KJNJK+9PnUBePH330UZfxrAvael6bAr9BFgW3NQysjgWdgxTwVX8qGzWvwOPVV1/tjim1RZniumj/wAMPuIv2CgrrBgk9o1cX2nUcFLUoIzV3VqqyL7Ufb9jaom6T5REIu4BuKMldNHKDnodd0HlbQ7kqa75///4usK/PbJcuXVwGpoZ21XmnqEWfXZ0PdZOTApPK/FOmnp5bqefynnbaaW6TelawRoTQs28VSFOQ7J577snzpqh4/7ZRBQqr56BBg2KaXpiXN7R6zIoFTND3jfpIwV3v3KffWdEZmArmKpt81qxZ9tRTTxX5t576oE+fPpHvNL1X+6+//nqrWbNmAbX7/lnXuoGpoOdhFrgBZiKAAAIIIIAAAggggAACCCCAAAIIxF2g1HcX1I7EfauFbFB33etu+OJksmnTqrKy4vJ6Tl8hu853tu70V4DKGwYy3wUDmBFGrwAYfO+ypF7KttS/oJ9z6rvBJVhQ2UIKEub1rDE/m423lQLE3rkhiGeNDR482AUk9RxUFWUgKUNLw/6qrQqQK2ipmxs8MwW3dWFegQsVPddSAdgbb7zRZbAuWLDAXbTXOUYX03XRXjds6PyloIqCH8OHD3fr6j9lmT/55JPuJo/IxAJe9OzZ0373u98RuCzAiFnpL1DQuUi/F3Q+Ufazim4qUXBLmcrFLTpX6fypYKjOCzpX6NylLO3ooudma7637+h5uV8n4reN33pG1yURXtq+LDSahZdpGb3PvF4X1cNbXhm1+n0ZxHdIXu1gGgIIIIAAAggggAACCCCAAAIIIIBA0QQCCVwWrYosjQACCAQroAviCjwee+yxLuBRnNoU9aJ9cfbBOggggAACCCCAAAIIIIAAAggggAACCCCAAAIIpLIAgctU7j3qjgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECaCByVJu2gGQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIkDgMl16knYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkMICBC5TuPOoOgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALpIvB/E3GLeXvHC3IAAAAASUVORK5CYII=)"
      ],
      "metadata": {
        "id": "YlPH47kLQHLx"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RuvdXkxKwjb1"
      },
      "source": [
        "### Ouput Tasks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u_34qCQYs2uN"
      },
      "source": [
        "![decoder_task.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2wAAAIWCAYAAAArog5CAAAAAXNSR0IArs4c6QAAAGJlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAABJKGAAcAAAASAAAAUKABAAMAAAABAAEAAKACAAQAAAABAAADbKADAAQAAAABAAACFgAAAABBU0NJSQAAAFNjcmVlbnNob3R9Zk8CAAAB1mlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNi4wLjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj41MzQ8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFhEaW1lbnNpb24+ODc2PC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+Cp+3/74AAEAASURBVHgB7J0HgGxJVf6ruye993YXFnbZZdmFhSWDwAKKKEg0IShIUDABAiLKH0ERBQVUEDGgggpGgiiSRJIkCStIXuKSl81szvvChO6+/+/31a2eOz094b2ZeTvTnHqv595bt8Kpr9I5dU7VbVVyKVwgEAgEAoFAIBAIBAKBQCAQCAQCgcC2Q6C97SgKggKBQCAQCAQCgUAgEAgEAoFAIBAIBIxACGzREAKBQCAQCAQCgUAgEAgEAoFAIBDYpgiEwLZNKybICgQCgUAgEAgEAoFAIBAIBAKBQCAEtmgDgUAgEAgEAoFAIBAIBAKBQCAQCGxTBEJg26YVE2QFAoFAIBAIBAKBQCAQCAQCgUAgEAJbtIFAIBAIBAKBQCAQCAQCgUAgEAgEtikCIbBt04oJsgKBQCAQCAQCgUAgEAgEAoFAIBAIgS3aQCAQCAQCgUAgEAgEAoFAIBAIBALbFIEQ2LZpxQRZgUAgEAgEAoFAIBAIBAKBQCAQCITAFm0gEAgEAoFAIBAIBAKBQCAQCAQCgW2KQAhs27RigqxAIBAIBAKBQCAQCAQCgUAgEAgEQmCLNhAIBAKBQCAQCAQCgUAgEAgEAoHANkUgBLZtWjFBViAQCAQCgUAgEAgEAoFAIBAIBAIhsEUbCAQCgUAgEAgEAoFAIBAIBAKBQGCbIhAC2zatmCArEAgEAoFAIBAIBAKBQCAQCAQCgRDYog0EAoFAIBAIBAKBQCAQCAQCgUAgsE0RmNimdAVZB4HAgQMHRoaenJxMExPffVXc6/XS/Px8arfbaXp6eiQ2h8uz2+2mhYWFFbPrdDppampqxffX54uVcJybm0v9ft90Q/9WOuoROmjHtOdxdFdeeWW64oor0u7du9PNbnazbVvE0pa3Q7/atiBtkDDGCnA+WNdqtdLMzMzBRvM4Oe7966BBiQiBQCAQCGxDBL77uPltWAkbIYnJ/alPferIJGCsbnzjG6fjjjsuHX/88ekBD3hAOvHEE0eGHSfPT3/60+lVr3pVuuUtb5le+MIXXq9Fe/e7353+8z//c1UabnjDG7p+fvzHfzzd7W53WzXs4Xz5yU9+Mv3DP/xDOuWUU9Lzn//8QdZ/+qd/ms4888z05Cc/Od3nPvcZ+G/Fzb/+67+m//3f/00/8RM/kR7zmMdsRRbXW5rXXntteuUrX5m++tWvDmj4x3/8x20rwH/4wx9Or3/969P3fM/3pN/6rd8a0Bw3m4fAm970pvT+97//oBM85phj0l/8xV8cdLxXv/rV6eMf/3j6qZ/6qfTTP/3TBx0/IgQCgUAgEAgcHgRCYDs8OB+WXNBCIKQVhxbksssu8++MM85IH/rQh9IDH/hAT8x79uwpweJ6mBAY1qRVVWXt29VXX534ff3rX7dQ/fjHP/4wUbQ9soH5p50++9nPTne+8523B1GHgYp///d/t7CGFvi2t71tQlvZ7L+HgYRlWUDT+973Po8Tv/RLv7TsfXisH4ELL7ww/e7v/q7rFMFoPW4ljTtaMH5o0kZpmkf5rSe/CBMIBAKBQCCwMxAIgW1n1NO6qITRv+997zsIi0Bw6aWXpu985zvpM5/5jFdS/+d//sdM4h/90R99V5pLDsA5zDc3uMEN0stf/vJlue7bty+de+656a1vfau1Vmgx7nGPe1iLsSzwNvH4vu/7vnSrW90qnXDCCVtO0Z3udCebet3mNrfZ8rwOdwYI6LjHPe5x6f73v7/v4893NwI/+7M/m/gNu//4j/9I73nPe9JNbnKThIY7XCAQCAQCgcB3FwIhsI1xfbMaizkkv7vf/e4WAv7lX/4lsfL7zne+Mz3iEY8Y49LvjKKh6bzjHe9os8PnPe951jQhtGF2tl3dj/7ojx420r7/+78/8RtHt3fvXhcLk9NwgUAgEAgEAoFAIBAIrIRACGwrITOG/j/wAz+Qvv3tbye0bO9617sSzwhzoxwmeuecc0464ogjvBdsPYdLYIJ5wQUXpIsuuiihUWLfHPuz1nKzs7PpvPPOS9ddd1066aST0rHHHmvTn7XiYSJ0/vnnp4svvtj53PrWt1631hDtI3QivJLnSjiwRxD6MDkqB5hA59lnn51udKMbbdqeQNJm/9oHPvAB01XKzqEb/DCnLCaVl19+uXFmPyJ7V5qu0EZ6CALrOXSGMoLjJZdcko4++ug143HIDdhzyMFK6YMZmkP2abGPksM0Cn7QC/5oF3G0GxzpIsSw0FBMdjnghIMYmuV34Maf0u6gHw0EuKzUXkt60FLMyK655hr3i127dpnOo446qpH60luwolzUAfFp5yeffPKK+S2Nncu6f/9+e4MBrpQbmqFh2EEz/QM6aauUEYxGOTAlXfAjDPdnnXVWuuqqq9I973nPUVHsV+qUtoajnEWg5DCUlUw1aTf0I+hG40pdr+Uo/zkaW6g3NKfNdrFW3PK+0Eu+pa6pE9KlTmhv0L2WIw7Y0pdvcYtbLMO12U6bY8BwugUrwkAP7b+0b8I23x9KeYfzW+2ZOmQc5iAbcLjpTW+6rFyrxW++K/0Fv5X6O4fmgDtjBxiOaitNHJlTcNQ/2GMODY3MFyuNJ4SnDWMtwhh35JFHesxmrggXCAQCgcB3AwIhsH031HKjjA95yEPSRz7yETNkX/va15YJKt/85jfTP/3TP5l5L9FgMO5whzukX/7lX06jmFkYBA6H+MQnPrHkREQmbpjERz/60WYyS3rlirBEXjCUTOjFwWhxyAS/UYzpSvnBpD7ykY9clVEjH/bovO1tbzNTVfKE2UCAhdZmnpQJGu9973unX/mVX0lvectbHB8h4uEPf/imCWzQAROCK8wd9//1X/+VOLiEAwGoOzSkHAYCs/O0pz1tILDB+HBACAx0cTCP7I16whOeYCG4+JcrOL72ta9Nn/rUp5bUGwzVox71qIGAWMKX65//+Z+veOgIQhN0sDDQrFMY6wc/+ME+3AC6ED6e8YxnlCR9/Zu/+RtfYfpf9rKX+Z5DLlY6dASmmHY3TD9MH2almAgPM+1veMMbEhrMX/iFX7Bm8xWveIWF9iYhD3rQg2yWVgTk8o52Q11Ae9MhINBW16N5pM2jSW26F7/4xX68613vmp71rGcNXoElbY8DXqjv4sCSA2p+8id/cklb5f1v//Zvu/389V//tfdFcqgJCxr04dUENszs6IfFgTk/3B/+4R+aES/vuCK0ckBKs73hz6LDSuMEjDlxGGNK20C44XAg6gPBd72O8jF+se+RNGhzCA7F4YcFwUMf+tBlGBHm85//vNs+QkBxjB93uctd3F+aQhVpf/GLX/Ti1R//8R9bICxxuJ522mnul7Tr3/u937MQ8s///M+DINTdr/3ar/mZPcRbtTeQspDvV77ylSXthbJgcsuhPasJRAOC6xuEsD/7sz9ze6JeSxlKOPJhPELoLY62iRnzE5/4xMGiC+8Q/IgPRrRpxl/6E4J3cYzBHGRE/KajXK95zWvSl770pSXlIsztb3/79Hj1cwS+cIFAIBAIjDMCIbCNc+2OKBvMMCvhMPgwck2HdodDB2AwYPRvfvObW0PCKXZf+MIX0p/8yZ+k5zznOUsYFjRxMPAwbjADrJgTj8keBpATG7lyyiCMbXHsqYN5YyJHUGHiRRhEc0X4N7/5zWbIfuM3fmOgCSEuq6swl9Bf8mNVl716CAmve93rTHvJp3mlXH/1V39l5gsm5tRTT7UgAxNNGWHGEZYQcJpCW0mDE9z++7//uzwuoWvguYEbVsVxK2n7/vZv/9aMJmGgrzBfCJUwagiRML/sL0OLAUMLU/WSl7zEhx80V6MROmDGSr2V+gZHBAQYJPwOxnGwDUIXTBhCHxpPmDD8YdYxw6XNwbjBUKMtwqGhQaMDfazir0crizBDu4NecOCwEto1abE3DCGO9vCbv/mbI/faER960BQi3NE2YeJhDj/4wQ+ajuaplNT7G9/4RtOLCSttnLjkBYb0G5hRGPLVHGFKualvBBc0C/g36wdaOOkUoRQs6R/gQr+ifjh5lPp95jOfOVJDBS4IvYUhHhY+h2mkzdF+6M/0MfKk7nDDcXnPiYTQhpAJ3bQzsGecoC1CV9PRvxCOaZdofRgnCn6Uhz6N8EX7PRiH8Pfe977X9GI6C62nn366tVssrlCvP/RDP7QkSfypexaUEA6gB6EDGulLaKZoN7RF+hlCxO///u+7bSCgNMtGe/63f/s3p//Yxz7WQifxqWPwLGNsqfOC6RKCNuEBHBBiGb8QmmgvaN9pm7xDOPrGN77h0z3LwtBq2X7rW99y+6G+OAkWIbypOcNCAxxpv+RF+eg7YPjZz37WeLJ4UDTlzbxYhOJHXZ8sIR1BmzGC+H/5l3/pcangRPtl3gFH6Ka9MU8QlrZG/2Mc+4M/+IPBglczr7gPBAKBQGBcEAiBbVxq8iDKgfnPsMAGA8vGdiblpz/96d7zVpKEQfu7v/s7T8asNsNYFQfjCFMAMwmTAyNbHEwQghpXBLAnPelJfoWp0GskECCsMQGjuWpO7GiQYPqYxDniGu1Fce94xztMO+FhnJqHUcCswLTAoIxymIKyUg5jjyDYFIxg7hHmWC2HiRhmvMELIQAmg0MiYFBgjDbLFUaH9EYxrQi44MxKN5oDygBziuCFlgyBB6aqyZzC7CAUw8DCLKMpKQ4cSQ8cqbfmPirqGyxWwrGk0bySPyfhkScCDdozGN7iYIQRQCgH9KDtedGLXuTX5ZRIVsrXe0okmjeEEoQF2itCe3EIQpQXJg8B/nd+53fKq8GVdkVev/qrv2pmnxe/+Iu/6GPraScIbWhrESxxPON+5md+xppOP9R/yoEQLHgMt5tmOO5pc6Xc9AeYeuhvfm4DQQjBgCs08tmOJpMNQ0y9IrBxEAWa3mFHuakThAgE0mHT2eHw5dMg5ZRIDpZZSROE0IiQg6aw2YfAiHxhpBlPyjs0uZQH5h9NcVMzCI3kSVwETBj2shAxTOOo57e//e0+aAlaEXpx3HPAD30dQaXZJ2jTCGu0e9ro7W53u0GyjFPEIwxCGP0JB/aMUQiVlA3NI2kicNKmGce+93u/N6GZxYE3PxaCyimRpc4dYJP/QAfjJeMfizW//uu/vsQ0lbGF8Zt6Y7xeqV4LWbQr6oFyocmlzTcXsBgLOSSJ8Ye229xvi8BP30MIZzyAlqajvVNnT3nKU9IP/uAPDl4Rj37K+EFbQMOPYyGAfkx9sfDU7AcIbRyehYDMmIIWMVwgEAgEAuOKQHtcCxblWhmBsnrZNCHCVAzmCeGIA0qajkkSho5JkwkUbQkOhvljH/uY72E+m8IanjCJD3vYw/we5qmYQbE6C3PBSilmfaTbdKyUY8qEg7kqe0GY1DFnwyE0NYU1/NAKIMSNEqRIAzMcHMxXYSbtoT8ILJhl4fj8wbBj7wTMHSvtaJ5G5TEcp/kM0wrtzR+MCQwUwhHMIBpAygBDO+wQru53v/u5fAiUResB4wSTA7PSZEyJD40wRgjomLAhlOKo94/ILBZHmZvCGn7UN6Z5TbMw/FdzCLowvJggIgw3hTXiYVLKSjwO7dFGHAwo5lE4tB9NYQ0/hB/qGCYT5pO2N+woG3HBuzjCF6YbYQmGEEe/oO3hmlowe+gPfQZTOto7TO5GHdo89v5BG/2jyaSSNsIu383CEXbYRBN/hFYY4B/7sR8zzU2Gm/cbdWihh/sQJ9SWfoG2rTg0YIwZMPbQ3aQF4eznf/7nE22aMsN4H4wDcwT9IqwRlzQL8w6zX8Yd3iHo49CeNoU1/EgLAZ74LM7Qr4rDJLyMZQiYtHX6LhYB7Ckswl0Jfziv9GXKCQZor4f3EbKAguCOI2yzbobppF+hPaUdgxEnVjbri/AIs4xVLBQ0hTXesXAHhvSvz33ucyPbJuNUU1gr8RB6cZSluDLX0K6afZX3zGOMlfS9zeh3Jc+4BgKBQCCwHREIgW071soW08QqJ64wgjA0MME4GOtRDtMiVvsJW4Q0hDdWd2FiV9KMsK8HBuAFL3jBgHH68pe/7Cx++Id/eBljX/JmZRfGCaaJ1VocZjrQzuR9r3vdqwRdckVgGGYGCABjxQo/mimYw1EORhhGA+GIVeRhh3agaFyG3631TDnQKjZ/aCNYbUaAAdei+Sv10kwTLEZpUqgDHPvvRjkEpyKAl3oDR4QQBOXCJA3H5d1KaQ6H5bkIULSflQQ9hGnaQtPUcFRaa/mhecUhOA8z3SUumgYYVVxpb+UdV+KNMr2kDgpjWAQhsIdhx8Gsoi1EAC+O+kJLyW+lspew67kWetHWDS9mlPg/8iM/4rxgVKnPYYcmFpPUrXAIBMOLJeTDIkJZtEH4Kq600ZXGFrT65V1poyXuWlfaNvUz7Io5L+2cfo9jkYiFC/JbafxgLxQabnDFnLvp6H+0G/oy2kCEZfJGSCqCajP84bov7YX+itA5yvGOekPQKmP9cDg0t1goMMbSR5uWDSUs74rmfaXxARrAibmBvjLsVsK+1Fnpd8TDAgOHgIxJeDEbt6f+IJjT79azf7TEiWsgEAgEAjsRgeUz3U4sRdB8UAgw+eGKtoDnskL593//98tWVEviRSOHZg2H2RMOjcbwKqxf6A/MUZOJQDAp8ZtmYCV8ucL8sXKNWRErrkzcJV7Z81PCDl/LfpGmPxoyHJoS9jus5GBocJStMJ88w4gXhoLng3WUZ9Tx9DB81ANlYqV4FPNJXpigoSlrOuoMcyAch2+sFLcwQAW/coU5XSkOaTbLz/NqrqS5WhyEx2HN22pprvSurMCv1n6ISztAWCjhm+nRttZypS0QDs0QgjxaN8zg0GYgFNIm2AcFg0pb3wzX7FcrpUe90WYQQEaVb1jzsVI6h+Jfxo1Rccs40MSu9D0065ibjnJFi17a0agwo/xWqsdCB3EKLYwlON6h0V7JFRpKPZRw1C+WBmjZS5nQ9K+0AFTibfW10Llaf6DMjCGMF6PaC1porBkKVgh1owQ2tHOEAQuEu5VcmWMKbc1wK9VZCVNo4JkFFLTeWCNgTs2vLBigsaedN+eXkkZcA4FAIBAYNwRCYBu3Gl1HeYqZSZnoiiBGVDRRa7myel7ijTo5cqU0YMyKcDgsgAzH4T1MVjFNK4LHWvk1DzcpaRZaWW1vnoZX3g9fSxmLfzEjLc8He2UFfiNmU6OwokwIwLhRGsFhGkuZinnfKE1eM85aODfDFsHxYOI04x/MfanLUZg00yl1VsI33zVN6Jr+K92jUeI0R8z72MeE8MYhDvxgdGFCYd45yGYjjr5RhJe1ysd7BLbSP5r5DpvFNd9t9P5gsEMjw55IXBGYVsu/tNHVwjTfHQwtpY2i+VnPGFDobuZHn2F8QVuHK3XVDHO470v7Xk97gbZR7eUc7W/DOgFTdPYbo8X+iMwn7y8NVtOVvBCqDhXDg6kz8mZvKXsC2TuINpF65MdeZwRHrATYl7eSNrpJf9wHAoFAILBTEQiBbafW3CHSzalahXFh9RLXXDF/6UtfuqZZV9HKFOa8MC/rIYlJFW1V0Q6ttjpd6CzMZ7kWwW2l/Ea9L2VEI8K+oLXc8HHwawk3a6W30fej8gcPGBaYJ1b9Cz4r5UVYXAk3Cqdm3LXeN8PSFlgIOJi20Ix/MPfQz8JCaR8rxS3vS3lXCrdef0wlOQyBH9hgiohmArMvtDKckMm+sVHmguvNg75BPuAI/aultVr5ilnnevPdqnAw55ieskiA6eBq5YGGpmZss2kqmh0WTzjAYi1X9ok2w3FSLNo12hT4Y9KMhnWtcjXT2Ox7aIGm0h5WSr+8H9UfEEI5/AcNOdox9vFymA4arGb4giFjCebNa9XXwQpnK9EOxvwY61icou999KMf9YIF+w0RQoc/l7FSWuEfCAQCgcBORGBzbHh2Ysm/C2lGG8PR9TiENfa54FiZLXsw2POBZmLUj/1bRTtGvHLoANqGlRzmN5zkxY+9H0zwmHLhVtMKcehD0QSW8CU/zHJ4v5IbpSUswimM46iyFT/KTxnXYkRWyvtw+sNQFi0pe6pKGYavME3Neit4gmPTf5h2Vt3X60rdrNYWYLBoB5wYuBFXvrm0Wvsh/UJLCX+oeYIRTGzRTJIODC57HtGacqw4/Ye2A/O4UVfqZ7XyUd/F3KyE32i+WxW/9D367HDbLM+YyoJz0xxus+kpdDAO0XdK3sNX+j60DI8B7NPkJFD6E/umOO4eejEjJ83ry5X6X629QGfZ/1XCN+m9vzRpxZyZUyER0igTp082HQIb8wDp8RvGrjwj0I3CsJnWeu7pc02TfdJlkY/9z5x6W/b1sq+umGGuJ90IEwgEAoHATkMgBLadVmOHSC/CGqeblcMhOLK8aFxIkv1TOBiSUY7Jl4ND+A5b2bTOvh2YGrQLZeP7cFw25nNoCAxmEQqLoMh+lpVMitjvAgOMpqAcnkB+PEPLqJMcyRszplEb3dlvRFxoHXVqIHHRPrLKzMdvyXsnuLXqDaaKj/1Sb+UQBfZ+oOmEIcPsaZTDNG0UjqPC4ldOgPy///s/189wOMzQOKWTtjDKZHU4/GrP5SACTr1cqd1hqsgJkbjS3lZLc7V3MLow6M997nNHChQwqcUUcjM0jIVe2vgoszxo5Th/+gGa4I3srVyt3Jv1rtQX/b2Y8A6njXaSNsox/FvlGH+KJmylvXQICGhJoaUIONBDf+CDz9CPkMB+sJ/7uZ+zwMLCEp/WuL5caS+YCBYhfpgWzAnRQmEdsdIBUSUOwjOngOLYA1pO5uWZ+Gi6cCthyJiOtgsMy4FRjnAIfzjkh77HPDLKlVNdebcZfW9UHuEXCAQCgcB2QCAEtu1QC5tEA8w5gkb5ISSx6ggTjbDFt6dw7AdAO9B05ZtTTOwIS03GinTY18D+BRjEwpyyIsv3mnB8F2p4hReGmrxxHPldHJvZMZNigmXjetkXUd6zwRyGFAdzVAQ9rpweieM4+49//OO+L38wU0PbASM77GBCynH5aHgKM1/CEZeDO3CUb9gksoTbblfKRNk4QpsPOyMYFUc74HtrmEvBaJW6omwc946jXmH0mg6mlUMZ2H+0XsephdQpzBrfcGpqHGhLMFyYZKGdKCcClrSL6VnRqBb/la4w3eV0S5hoBO2mw1wKrQcOYaGcFtkMczD3nBqIqSJlAy/6WdNRrkIDiwobdZx4VzQcfPJhWHOAkM2+ORxHzW+m+SPlxJFncwyw5yH+gammPOy3Y5xo9k+wZMxhzxSLPyudPHiIWS+LVk4oBb/hxQo0gAgItF00SUW4IxE0TYwRfALjIQ95iNOlHz3xiU/0PYsbZayzh/4ULCnj8BhXwmzGlbGVA3bo74ynw3sFOagD80bcgx/84CUm8CvljykkGkQc40qzDT760Y92XbG4V+aUkg51S14sNDAebPTwm7IQxOIF7WfYlTmAuaFoCIfDxHMgEAgEAuOAQOxhG4darMuAILKauRmTGt/Vwfxl2GHSxjsmW5hSJmNMT2A2YEYR2jCFYf9XYbBJA/MZBDXM6xAKmTQxPWKCh3GG6UMLhAlLcTAyfAPrla98pQ9tYEUbDRj7oDBnLKe0capicwWV+AgGrNpyFD5MOd9CIi5M8zm1CR/MPEzKsCMttIMcGsFePejkZDXMNikDtMKoscl9pzi0VdDLR2oRijgWne+SIaAhlMKEwgjznbymZguhAIEaLKgHBGAEk/XgOAob6pSN/5ygSL58iw3mlr13MFq0DxztpZimlXR4Rqh8jT57QH1i5klbWs1xwAfthLSpSxhW0qEu8aPdUrfl23qrpbXWOzTRlI2PxoMxTCLMPP0JrQXmWDCq4F6E4rXSXO09/Yvv53GMOW2djy/Txqk/ylZO+WPRhf6wma7UDZp4vqfFYgBa59VOIFwrf4R0TEcpD0INfResEDRpK0Uzwt7A4W/qrZX2wb5HG8nBGpiG02cQ3MiTw4gYrxB6aMvstysmkSwg0U8oB+PWsGUC30fkO4Qs+JB+2S+L5pU2ggD47Gc/2+XlkyNFaDxY2lcKD53QhbBGP2I/K32ZfsS4hh+OhYtiQrhSWk1/+hiCNAs4zCuUgbwY41ngo68i4LK4BobMEfQFFnrACgybWDXTXu89wij9jX7AmIC1BXMV/Zu+UMrGJ1c2mtd6aYpwgUAgEAhcHwiEwHZ9oH6Y8oRZgAErP5jJsudpFAkwfzCGfFyW/T8wKcWhOYChGja/YuWcvQR8eBuTO4QmfjjyhzlCm1OYH7/QH8xyXvSiF9mUCIGwaLwIxx4LtHDDH4ImLszsM57xDDNarMyjlSmaGcr2eH1EFwZwlMCGEMO3wDC7QiBlsi8TPu9YUYahGXXAR6F7O15hAmGYMMtC4G2aCZ4soRvmqphOFvphShGq0MCBI8JPEZRhODGJglEbhWNJY/jKN7HYo/YaCV4wv+X7W4RjHxkLAsV8qxmXFXvqjHaD6RnM3lqOfZfPf/7zLWii3YB544djZZ+2DmO8nrTWyov3YEzbg9EH4yYumJfSdsB5s06qQ7NA/+BbfSwyFA0e/QOGFU3zAx7wgPWQflBh+EYWeaGxpU4QNppa24NKrBEYMzrGCdpo01yVIPR3Ppuw1dq1Qg7tjXEMzRELCU3tEe2TeizaGsYHxjYc49+o/ZB8lBrBhsUOFiwwB0R44Icg9Za3vMV50J+amudCz2Zc6f+0FwQoxm36Hz8c/fn+WqRjTB0eh1fLm7bMQgWCYDGN5NuAODS7LMiADRghhBdHXYMh7zfq6HPPetazfLgLe2Cb5SJtBFP6wkrfdtto/hE/EAgEAoHtgkBLWoV8Lvh2oSjo2BYIsNKMaQ3MGlqn9TKimP7AACHIwVSvh0GgCSJ0YUbDSj7CxHodcdA4IJSul8aSNqvqMBvEQ9iDOdjpjpVnmFA0a9TbeoVPBCX2vxwKjqMwgw7qBQYVAWMzzfZG5VfaAWVuahJHhd2oH6ZxtHM0CuRF20Hg3ypH/6BPUUfsnULrtZMd5WFxgPIgTIDhesaJrSgzdcgYgHBF22GRaRwc7RMBkvay1ebdzBVlvx8YblV+5EOZEHwZq5lftrqvj0NbiDIEAoHAeCAQAtt41GOUIhAIBAKBQCAQCAQCgUAgEAgExhCB9hiWKYoUCAQCgUAgEAgEAoFAIBAIBAKBwFggEALbWFRjFCIQCAQCgUAgEAgEAoFAIBAIBMYRgRDYxrFWo0yBQCAQCAQCgUAgEAgEAoFAIDAWCITANhbVGIUIBAKBQCAQCAQCgUAgEAgEAoFxRCAEtnGs1ShTIBAIBAKBQCAQCAQCgUAgEAiMBQIhsI1FNUYhAoFAIBAIBAKBQCAQCAQCgUBgHBEIgW0cazXKFAgEAoFAIBAIBAKBQCAQCAQCY4FACGxjUY1RiEAgEAgEAoFAIBAIBAKBQCAQGEcEQmAbx1qNMgUCgUAgEAgEAoFAIBAIBAKBwFggEALbWFRjFCIQCAQCgUAgEAgEAoFAIBAIBMYRgRDYxrFWo0yBQCAQCAQCgUAgEAgEAoFAIDAWCITANhbVGIUIBAKBQCAQCAQCgUAgEAgEAoFxRCAEtnGs1ShTIBAIBAKBQCAQCAQCgUAgEAiMBQIhsI1FNUYhAoFAIBAIBAKBQCAQCAQCgUBgHBEIgW0cazXKFAgEAoFAIBAIBAKBQCAQCAQCY4FACGxjUY1RiEAgEAgEAoFAIBAIBAKBQCAQGEcEQmAbx1qNMgUCgUAgEAgEAoFAIBAIBAKBwFggEALbWFRjFCIQCAQCgUAgEAgEAoFAIBAIBMYRgRDYxrFWo0yBQCAQCAQCgUAgEAgEAoFAIDAWCITANhbVGIUIBAKBQCAQCAQCgUAgEAgEAoFxRCAEtnGs1ShTIBAIBAKBQCAQCAQCgUAgEAiMBQIhsI1FNUYhAoFAIBAIBAKBQCAQCAQCgUBgHBEIgW0cazXKFAgEAoFAIBAIBAKBQCAQCAQCY4FACGxjUY1RiEAgEAgEAoFAIBAIBAKBQCAQGEcEQmAbx1qNMgUCgUAgEAgEAoFAIBAIBAKBwFggEALbWFRjFCIQCAQCgUAgEAgEAoFAIBAIBMYRgRDYxrFWo0yBQCAQCAQCgUAgEAgEAoFAIDAWCITANhbVGIUIBAKBQCAQCAQCgUAgEAgEAoFxRGBi7Ao1O5t6n/lkqr78pVSde1ZKl1ycWldemar9+1Jvbj5VCwuppV+qqpT6fRe/pduW/lX9Kl2799p0YN+sn9vtWp5ttVJb//Q6tarWCpDltFZ4ucS71Vdq7Vaa3j2T9uw5Ik1MtFO3202pXaXJyck0351z+D6EyVVJaS/Jlgf9TDchmi9Fsx77rXbatWdXas3s9nNXZSU1Ze3rZI9o7VR15DfZ0W8yVTPTqbNrT2odeXRqH3uT1D7xpJRuf/vUOvXuKU3PkNG2cQuqy4u+eV66/NyL0jWXXJkOXLsv9ee7qd/rp4W5hdStdN+nVuVUfx3K1+oLF5Vfv5Ywb093Ukv+qaO6r4RP54D898uPuphTWP0UvT2l+t8j4ISVmkHq9nupvUu3u8FavwVhM39UuuCMS9MNzj0ideaUrzLp0H4mlG9HOIuGrn5z/Yl07fyE0p5W1E5Sa0xzqph56kJtotJzq3eUSCazltpc9hNFqacGyLVPnZGe7ruiu6f4LpfCU8fySvshjTaustLW2qrjialOmt41mXZNT6aj9kykGx01mU44Zibd7ma7091vcUTaNaVI28jRZ7ufOC11v3h66p19ZupffGGqrrw8Vfv2purAfhW+m6qu+jL9uO7L24j8w0cK7Uy/ltqaBpPU2rU7tTSutG50TGoff0Lq3PLWaeKu90gT975fau3ec/joWk9OjMlf+3aqzjpfY/UVqbri6jR3+ZWpt28uteYXUtXTQKU+7cGX9mxXro0M5NVRf5reNZ3a6hu0+0rjZ5/OQK/Qc/3Hj/mecOqn6g/0xR5h6qAk0XNvVJBK2OqelFq7NGbMCGPGDI/P6rHE46UdfajyGFNN7kutqavUr2dFEzTTKXPAinFDt309t2Y6GouUyITyaPVMqtMkPaLJ9ZRfe5fyVViPJ52J1J7QIDR5VEozN0zVnpuk1g1vmdLRt5PfdI60nf5efanm46/od0ZK538jVRefnVrXXpb611ye0pz6Mr+eRjR+ci0BUA3qe3RBVguz2ruS2mphVnu3pfFVr0m/1syeVE1p/j7qxindQHV7vOr2pNul1i3urN+dUrrhTQoZcQ0EAoHvEgTGRmDrn/mt1P3316Xee98lblWMt+a/liZrXTzn+cokiasnwfyw9O+uXZoExf0uiFlgss+CWl9MeiWGYBPgUt5M/ZWYgG5XwgXCoyQBTxB6swADugkOZr0nZmcCQVACA84ToASARWZAHIO5fYWZE4MEI5wk3PYvEG5MmPpJOEm7hcmDHpxaP/2o1D75VptA3aEnceV3Lk1f+fBn01mf+1rqzYu2uoLBb0JCySQMa0dMDVnID/bI2KocMFkwc4iuCHapq9oVj0tZzWGp3itJsPqrWNS10pdYZJ6P8Eo3P8g7ZwCoSkDvmGeVVvYnzewkg2des86COkB4K8weAhp+OXyJZV7OfmQDvb7qNeWhVeLDAkJ+L0+lSRn6SoswvNGj3QKBRP+8yrtvn8q0v5fOv1LhFKenttGbkDAn4fV+tzsq/cw9bpxud5PrVzjvffWLafbVf5fm//u/UjV7IBci/q6MQC2wVvR1uWrvdSlddklK53x7SZzWzK409ZCHp5knPC117njXJe8O90N1noTv938s9U8XA68FluzUE9VWJ1We3tysZHD6RT1mr4PAftXzuD2JAEbfcN8kIh2BTpDd4lPud/2uFmAmyafuMAStb4nBY5vOpM5cqR9Z5lJwxhW/V4D6tplNLegxnhBYvRJ6ihvQJg93ZKWl11pbcX6EzKnXEQiv8mkicjotrnOq5+qyHIACa5Gnd/GRCnK3NPnA+2msPqGOfD1dFlSHH3tr6r3uuamSwGZHoSgcbsm9Hjp6IcG5OIIZA49tdaQlcRRgkFb9ojzXr3ZefBWgUn84cLUWpa5OrWsuSukCleKMD2VY6mK2bnBs6jz+Jan9g49Uh7l+x+tMWPwNBAKBrUZAfH1zFtnq7DY//eqaa9LCX/1Z6r3jbZr4YLDlNOYxxnsOs4cmNz1rWvYT/sudAigYss3s7ILkl7k0P6/VX03QBaI2Al/hgpcnsD4fxmNN0G1Nrq2JVpqamk7TYjBmZmaUdBbgOmYeRE5NqMlloK5Z8TyNyYNyyheBg6CEK+SxgtvSSt0EWrPJKa3KopGB9YfZhylSTEeWYCcfHnm28KIHps0OngqMVq6nuL2plKYe+rDUefyvprTnSIU4fG5u34H0uXd9LJ0jQa27IMZMgi31wg/GiR9iGmWenpb2SsIVAjcCDKvnuM6UhDkJJ/J1mROCilbk8aOcVWcuVRNaDUfDNiGBvXVA6UvbKWGstUts17SAUB1RL21hkbTabiajr4f+DdLFX78iHfE1aUznhT1NTlqPtrQefYQiIdsVN3ag1057u1NKQ3WiNtBVhc2Jxh40akHAvFtfq+bUgMrEkkOP9/LpKSxaAKqF64L8+Tmc6pt6ZWGhEsM7D+en/xYOdV9NCiPnkYVYNDLktaAwXbW3vjDoCTvFTj8mwe1Z9z8uHU35DqNDe7b/Rb+T5t7xZhFOacJtCQJqV9M/+ei0+/f+xFq4LcljpUSvk6XDG96Vqk9/QWMWrY1GWgJ79PEw1+t3NQbP04DLy9WvSoPYlYQq+j8LN9aaq8vichbkltPz0KpbdVNFpJ8yNtD+9cgfhnr+OV31ZyegfqixoD0t7Za0XZaucqg6VcUzvYpHNtLSp+m9Snef7vN41coJKVamgxEZDX9nt/raBBOQfdx3HcL5qr+LnrY04C14cwVlWSanoL/KjAWglsrRv3RPOvBxLelcN5UmfuDUNPELD5WW5jBrVQVa77TXp4V/eLrmz+4iNiLdBQMzyiXScxkoj5z+qGkucXWwhh8BIv4AP43Zk095Rerc7+eXg9dALW4DgUBg5yOwowW2/pe+kBae80yZV1xc14SH/Xr0X6wcs70a59cS2DyXwvBq1uhp1XX//v1pfjabJ2Ju00NLI83YhpxIRGODuRyMNxPUrl0zaffu3RKssmlkW4Icrp66PatlvoXy5SnMjIe5DQcVf1Hi5Ge0J7xuS1ibkuklggyCBiZ2Fm700mnixwSo6EZPN6RFam1JCZiFIhRJzySzSf1kHjh50glp4rf+IKVbyfzmMLhLz74wfewN703z12bNqc0eJbBhRtqXcGKBTXRAM/dTYtg6mCL6H37CVXXHynsboUQMT0+VUMkssjMlv0kkMpVTQlrqyEiR64SE9fYBYTZrJqolgQczyLwKrKuiJJknJWGiiPodlS779lVp9xf2KKpUbdQxZo0IiAhs+i0I2f3dKu2T5Fu1p4X5RFpQuDm1ub5wFiGKhsbtKP2oJ8WjLSqM8dc7P6tc6FK6MEaUUflYiBMp4IEgBoNneRvBTTRUalNZs6eISpN7m1WKWVxAWKM98gNFxb/xnsn0kp+4abrnzSj01rvupz6W9j7jCamPZijcYUGgfexx6Yi/fnWauNd9Dkt+6Rtnp96r3pCqa6QZorPi1NYWXfZkhIUhnV+Yl8YMhr8EXgw56o5wLNBMqK9N7Zry2MVzdroqGY/x9shp8pZ+xkpdW+bRjB/QxDDP+JHpKwKb0m9r3NBY0JlRWJkYk6DDkQ7pelClT+q+zcKPTKyn9upe44jMHe1I3IEZeel3ErBmlAdmkRp0HVe3DkIEkcECDQt8xTSbnp/f668iWGMv2ltXH5X2fVJj2OWiTwNA6wZHpKmn/1xq3/FWOe8t/lvN7k2zf/wwmbh+ztBBIxjjyr3phmy9KPcl0Kiwo/yIGPEX8Wvf8u5p+rnvlEB/RAY7/gYCgcDYIdB5odxOLFXvY6elhadL0yMNG4M9A3/zNyiTPD3hMxHXYQbvltzovWcGTaFicjuawGFs+zIrtJZGYZmYndaSeAf/ANOABqglNUwXblwzD3vXMOlDMESEwnkVtqbbHuXe5XXBXGhRKpefCeJiiAmBHYABmRAj3pGgQnk0tSObWCjL4Up4J+J0LLCRhxgB76XSvbV9itfTv6T9QxOf/2RKtzwlpZtsrdnNBV89O/3vv74ndWfnB/XovYV18Quz5OILR7BFUAEE6hEtlx/07PKDQV239iLV2s/MGgxY/UNN1haTheZTocQwKYZ4NPJCKMLE0oIbCUnYWtg/lzoXiFHsKZCqENG3TV6iw4sAymfWwhdMGsyeBCVFxSqV9zzb/DIhJOlZtMOUkDvhaA99pUXrIBxMHFlToVlgyy2HcBVxlSaCWlK7Ij2vNeiR8D2VsS/6K/b2oY10e6Fc5NtK+8RrvvucuXTrG02mWx2NrefWufn3vzPtferjUnWd+nK4w4YAewTn3/mW1LntHVLnlK1dfKlO1/6ll78+tQ5Ia60SWijRlebYdPRfO7Vdj4Xei0qLHg7ZjFXfKy57xehPdJy2x9ilMZemkt9l82TRojzphjjGvgEpg9z1krGBcZGxRemri+stIXNaJYGcDm9Ee0udqSNtIYVzoqRDL86OPk6fY1HIuEADQer3XHzvMM5QHmAyeFMHUBpaDJq7WMKcNrG2tGrTkrVI/xNf0H7k41PrZlu776m67vK073fuoX2I53iRymOQFsUQhtl3V67eq8v4pHe+r98xtuFHOBa5In7GqOBWrqPwq669JC18+NVp8r4/J8Ff+9bDBQKBwNghoNFx57neF7+Q5n7zN2S7eIBtBVnpoSv3zIlMZIPJTs9MjTDYniy5ruQ0cyKoeeJWmGkxszPSfk1IA4N2bVOcCMO0csAYiCj2sWGCyb45GIFi6tck2OUR7TALjdK5nC6aaMfUkp/NbuTZISwaF5ml9EW/sVIMWd3UjMPyEtEgnFdBsMYLvzYcv6SEvkwS+1dfm6pXvjils76+PJFN8rnsnIvSx9/8Qe/FgypMGcGG+wkxSxPSosHU1aySsNMEpwbQQ/vWy/tiXBZhgvCGqaBbgy6qAbUV4aVqBbPcNlQ+nehRyQa0wvZJ9z5wQOGdbZaachLiwSwlqSJZ7bZwh7bNjJjoJOMMpG7qe4clfH62YKlADkvgZZGyF0yf/9eNpi6FvEhLtCo5p+nnnF5+x31dozBHysKCnbDo67knDLvyo1gWSZU+TGivFujmJUD+9oeuSp+9KGuZIXuzHZq1fc94Yqrmty6PzaZ5nNIDd/CnHrbKVdKsVa96o1TC6ImbnWJ5jrRR/3ilpjs1pfEX5t0rGbmVr5hE3b/oFV0ttPUWuiSR+6GjLs/b3vrDXl3v12UsqPsZoUuMAU0aAz1uSFPufbB0xmHXiNSqtCCCybRXSwjIy8VI0IqrvJdYN3Xe9ix/FIVYHvsw0bd/yaQEqq9o0qWp82JNCae9vgsvf2Pqa/Frqxyatb0v+AHtK74m9bRA1Je1SCUNJAda9TC59rX4y9qjflfJTJ2wxGneR/yDx6+/cF267gX31r5faXTDBQKBwNgh4PlsJ5WquvaaNCszSG00E9ka5OGkmcH0Q1gze8pz7cykyjMzx8V3lauYVRhkplQEgMkpmRROT8nEjpT1Rgx5/ulJc2b5rZLislfsCWN/FWSyCoybE+M0q432+RAS8tKPABCiay5nZsNrDzzzT0E8o5c5XN5o78p+iR6TPHu+OHHNidWJEk+OaHWOfi5/EFzJFzJqZCUEKqSS6R6QyaB+6R9fqkMsZOK0yW5u/2z65NtOU6rSU0koc31IgEA4y8wb2EnBJaG6o70nWTNJwUWeyguzxl43cUsuMqaCCGmVGS1JW7oigHLgQCWGBv8suSCkSaMku0kOhpFko3ja3wIOtkvKIGcBUPcIyBKAlIhOYYQmxXEQaKG98IBwqLx1JT9wRWjnmf1plIkwXrm3Ck/pWNCSt5JBmIIU3XrhgRpxu6vbEPc9MXw+CVT3PNPG2ph7cnKg06rzJw3eCzObQDp9UpaDJtGGmWhnepfokVmaSv9sCW1XoRrcZMeeNcwgQ1jbZGAPMjnwdz2oPjbd7d2fqn9+sxeMMMGlpbn9q03TH1d2dZtUO8bEGesDWzowyNevFuOSDr/yQve67WrMW9CY5/3ChKBB16G4ox/lOHUfZJxkjGRhR0mQFb8SMpOrsPgRTn3ZHVIlobMsml8SR84k6V1fpzZqHHHHJM9CqtOu80BQ1BjUULzlNAZ/FYmhjOguRx25pFfy08EdfYavOiFbSZChxkOEtkqn6W66Ez17X/HI1J2/VoIYApjqStciqC36IbDJ38Kaxl5dfd8Q2hbDRvxDwa8noW3vKx6VG8qmV3QkGAgEAtcnAppNdpZb+KuXpeoSnThlRniYdmbChht6bLwZcctMqDmRiVgTUI6a90Ps1sEdHBeNhmfjTuy589HEaiFC2UowRLuGlo1fcdCQ6Sg+unrCzv6L75i8ywRewuZntGqsBPekcTK7ZG1SLgfvmtEG+TWSsqComFmowAJQR1CL41+QiaIkhJQuvyKl/3xtyXTTrl/4wKfS/mt1aqXqgv1oaNQAruzVQLhBuwTNaEWz4Cs/0V7MWbu9OQluCEh6nyUcyW+iWYxRKRfishfvhRHVC4NjxshStZgsbB45ORJM9N5CndGQQKfHRfzUZsyTWQ9oOmlFdDBozA76F7tcSZJwGV+FlFBKDN6x6M5BI9wTBhGfXyX8IQUBC+YXoc/MJ1pEk6+r9qVxmAGxoYgwmBxxIiSr2d4LBy+J9oKFD2MrnCTIQaPbOg1Vby+TfeRL/+8qctxUxwEjsWdtUyE95MSoB+pjs1311ventj6Twmc1JDmpNalF0Um5YSxaxeU+SjPPhzN11J6J4ma5Srz8inavrq5xtSfNnk27aeMlHk178MvtnIEAawRbKkCjvN2vSMn3pEhPlIMQFno0XZBoFj/9hrd2HjO00mJtfYXQxhhW3urqexZvdKOkORVz4Bq32U9hGAsYFOp4y4IwkLFQxxlIIoz8CUo4j5tX703d171rkMVm3cx9+o1p7sIvWwDLwlrWqFmLhlCmxU4LZ/V1INDx3NS+1Vq3vg5XyWEQ/CL+weI3f9GX0txn3rRZ1RvpBAKBwDZBAI50x7jq7LNS58PvS1MSoCqtGFq4suqMaQlWNk9OLpAe8kR1EMWreWnPm0qynhd1o2+a6UhsDpHYq1PObEInzr18pw3Gwkw3zDgT/RrOjIiCwk7DApAPaXXFLOzTvhKYcEwxYTJ6ttlTGCUr9sDhzLCslg9lN/0wLYqlTHrzMr3UqYeTMjFiv1XONRNK2iU5lzl7SxOjF5TJFKqEAsaaGxKXEHNg3/60u63vtn3igyk98GEpnXCLOubGLlddfEU654xve3+DF4qVHQcCSP2oOs8YmyyI1qP1bgKFd7luyF8aJ51n32pJRyRGr80JjLQIxcE8lKJ1FN77JuRXsVouxtD72/QOAFscJoI5E99G6s/nhXdMISX0WBRCuEfrZhlLdaPVbbavVT5cAE+lS1q+0xOVDZbA539c87ML4oRg6sSYmQaMLRWSsvFT7PwtNtKlAAiNlEdkqkAOR3pAJGbIPxqz2pC1dEq6i9koZo+miXxIlziKJGGNNCm7BTYlbDNT4fLus2fTL125kO6gPW2b4Ti636dBbkZikcamIEB9zDzp6Zt35P8FF6fqc2e4XXISa0dtuKc+3FK/KaJT3T2W008f0UtFcVvnkCY0bT65twzQavvuTMtjZx8lwHcKZb6gOUNWEoyntHyliSt5Q0vt5Tw9PsiDsQArhdyv1BcyMY6r3mKtdhthkP3BXhzxq6E/jBTqMxyxi/Ydh7qc3N3J8637LMKYKYGaQp1ucXpsa4WNdSUvPhWCy0tf+dOVhpw9qgrIT3lRvpyaRsD/+3zqP/S+abOO/K/0zdBr3/5cqUEZX3FgCXi6U/nKPVVWSgU1YOn6JUr9IuJvHn7X/tfvpmPu/nAtImqhIFwgEAiMBQL1DLIzylK97U3iqaUFkIYAxtJmY2Im80BfysDor8lAF+5wMOfrLSgTW57c6hnGqYjh12QLw8DhIB2YWjmY2qzxURxl5nu/WeuPp7EcqJ60MzOgeV2CA0fW+0PamtQoI3wCRHkldq2ky3uFL/wAkTH1qaQRs2BiySGXsgQni4KXM/ML+TReEAMewHvhRFTf6ckXc8IPvaMkteHr1z9zhk9FRIiAabKgJTrQ/rRZlRVzZI0R5CFoYBup6wTvJZjBJpgZ0HtwnGdvIPFLkXkpDqIwFGafrHlT4axpU74cJWk7RL1lZZwWpDgE8z0SI+kZSyWue/g2TCWzMKSwvCZ47XguNY9/fg87RRmVhlupQohYBDVOsiQMaxI5PAKcRXALcETJafC3vofBxIS0Js/JIggKM06lxAyS6spatPzx7cEmdoVxXorrk/OUP8wV4CH8vf5r+rjtJjm+s9ZooJuUaiSzIQTUL1wvG0pkMXL14U9rvKBdy+lCX27zsUK1QdofglD91kFo48sdYfICAlr2SZnr0pebPYse1HzOaSiQBmXGfvoR32zM42H2N0E54NK/vFYnt9m1CSJ1MuRHLvmaH9THdcyrtfYKu4wKezgRdU7UXhogLKwRW52MH/nlC+tCWpTSj8mk6eok7OWuznviDzmAkXdnWiOKsSUcfTiPD4RmzOu992NDEQ/9cfaMd+uEWeFb71ljHxr3eR9bQ0OGJg1tma+12SThag1axFd/EBabhp8WD2e/vPna1ENvKREzEAgENoqAZpAd4rS/q/rw/2iCY9IX2TpRy8ylNG1MSJiOlWkOJtMreCoa01rT/9BKa7ZVDEcr7dmz298HmtUeOjRiCFR5/5SmRjEGErFWyKJQwevmDNwMnsOwjw3TPISTSTE4/Z7MD+XMqCiqy9eMpvuc4oh0SacggIClo6ZNI3ubxECVfR05iBM3IzXIxSQtT7etib8l7VJPH731Zwg+/dGUHvMUNpwMUXZwj13V5/lnnquKU63COEEtQrl4DoRm9qOgdMyaR4UpsCoMh5BQ231MHhWe1XHMjBDaEIL58DlCG+xLJUarr9NXfJAKJOKpbz9VXS0EKB9KXKGBk7DjhQG0bRJ6Uao5jvIlDGfRZEFQaUkumhDD0hXG2WTTqSiUHEwYtFKJIhrhPochIbVdtSPy0g6yBv6E12s1KVaoS2q5bYvZUR2wb429MxzLT5po4iZ18IA+BacDRTJ7qSIpDYUXw9sXsQP9hvzIoLKGIN8jKOLnPHQLyS6gbt53/lx6nrDdzcmTG3A+nVAfxQ63/RDgY+W7/+BlqbV7g9/uklardfpXc8OlzauN0rK84CKbPcwU6Ty0Olxu0zlM9sFPd4rHDRca44Q6GSebYupIgo7hIHk+GMQtEYikPtrVoNHWd87Yi2yBTN6LM4bSUV8xgY7HAIMfCx3qM2RNH9UdJDmmPN19CKei+MRYvYQej1kOBtGkK/q1gpL3xopuTp6tUxIIOTzBlJa/16FPp5ThwpmXXPEUfYxLLT4yzTPp2zlzESWLAvZcazxkoanSCZW2lFAaDi0su5/8Upp8/MP1WRIJkRt0137wpRbEwCTTwhUHXYv3Gb3sn+8z3dy7DiP+Msw2it81H/rTtOtUfVg7XCAQCIwFAjtGYKu+8HmdfjQneY0hXodAsFDLyV7s1ZHLq6LcmR3XvKZwnvF5iT9ucJMfV/hbojkGnD+unnswrUHvsaBTBPm2l03TeK33nvPrcNwvd7zkxciX8s+R0VzN6xCL6Xlp9PS9n8y5qLwKgQAi3r4E1c1QinX+JAVSJUdP9NYQiV3vSgAUk16C5sQybtznEjfpXAxpeXtNAABAAElEQVRJfkyxpNdGYJNwObEgBoLv1X3zyynd+Z4EOWR36QWXWLhgz4s1eDITQliB/7AT0GjV2KdmgY6Xcl5Rlr9X8VU3fTEqnPqGIEQ7WNBH0PnwbOay6sSQArklTSXDXj+Eroy3XpC0TR7F+OjWP/4AUKlg7msCySd/002cVx3d7BhMlhtHnQh5Kp2SJjTCwPFsYboORtbm0fQML4fFlOOoGDA5pJ3vclw0Fgjh/tXpWdMm/67yEJ+nOITlpwdd+VgwTKnNvohfp0j9GnRGCPz1f1b4fOriufSAE/l676G77idOU18+cOgJRMwtQ4B6oX4mH/SQjeXxjXN80EVJhDZH66If+GAeLUx4YQUphXbNeKJ2ZuadZp2boq9ljOVKU0STrs6dFubo36RJLs1IPGcfWjnve+rrCHltvnjvSPR95ec0nYDj+I8i4N/XAg57Y1mQyULF0lxa6lz+LhtmkRIEre1XPPf1QQFEgeL31ZnbMotsddijnHsh/qYaenxHOJXRnV7xbNYIRQSsaSQCVgDlmddyiylodpzWnmhF5xMH2eW4gzDCof+1s1L71NvX7w/t0tt/hbbx6VMcmqPAhxp2eRrJFX+8mveNIL4t7yJ+rsuCT8GF5+Z9eV+u5V0Tv57qhjrq7L5xCRbXQCAQ2MEI7ByB7etardWMA2/M6iFajz4riUw+TLw6XMLHMnuyo0aYQHB5mvIpf9nDvuv5UxiFZljyndYeOgQn9jrwYVe0PeyP8CoxI+YGHemxuXwOAVVavUkJV2YMxAyYyXD6ixkNzd2D3AmR2SSx9n6Qhmhek71WmtuTYpRY9TU/0UjLooGkFmMlf/6LY1k0bcIjh0fL1p/TCq60Sh3s7M7+xoYFtisvu6IWehC0EEokeIlBsUCB8KxszBjJvK8l81H0RXjqjcvKvr/JKfavCTMTTwQJLNLcdVoSLiUIEp8i+MRIfai2hckj/JHSt3mTzrrHrNDCnc0iLcVlXCFBdd8CE2FkzR9gKUGwbCFgT4gpQ9oigPLxj9h+VgLFm7RUednUVZ4iQjWj9ix/fq7YLECTJwsF9nc4lVyZu/TUIVWm9GEc6Q+s/tukUvc9laWndmSBjwUPEyoM0KyJ+QVbyDRekKH3ZmRlsmSBl/TxV6gvXz6/cYHti6eTWLhtikBX9bNRga0690K3c4pI27JTO/MATvtE6OJR2inMtWlebnN1UA8x8iQu7/LYCluKIKJDSCalya/mZO5cC22DXNS+nSGxcAhH+Z4Fto4OX+pglgkt9NkcaPA3PysfpcEe6Rbjg4S2Yec+43z0xtp6jS+kq36WexMvyYMLqapP9aY11mhs6FBoRixcoYBrztdyWpYSHWL4j3GiQzrdQkRJSWUSDf2ORganQX9XmMVgzrN/1gUbFtgWrjjTh4O4iFBfF6Vk5Tokt+KvFzXsAz/oivi57rYCP+ooBLbhHhTPgcDORGDHCGz9C87PCGvw9/HJYiqZuNkaMKGTHrRTSWY2GvyxWZPzpM0EoQnbjsvyeTe/G/G3MAjNV8w7nvSVN6dGYtbDiigCFsILppH+EHYz0iHcFwaDo/7RKHb2YJYnplsMuo+Q14w+fIQ0k96w84Z5JFy7mhlQISrTLZplIkiZPKHiX4f0yi4MBs/yB7q8WttgcESD93mJCepKaG1jxnnxBSWFQ75ex/fdyFYSBxoxZAuO3YfR43REBCUkGr+n9Yo4VsKRnPreDAJ6EtpkMqsPGaiOOKiEsspMcEFmWkqwI5MjCsY+lZbisYeL1XIVWVKOdollVVTOg0ajPGwSBUbkhyYOOsQYGTUActvSswRCmDmsqSiIqFZOMI3srdOzuRgSIq7SFT0Z1dqPi8L4xDjdW3hTbH2kAAgkY8MO6n3hhkhTdFNqH4aijHnHM/l1wY9vM9FXTACZClcYZrVXL3aQD/FrB01qGnqnnOTtHB23n869Nn/froQ9lGvvrG8dSrSIc5gQ6J195sZzukSnxw45t321Q/cJXdsc3qN2ZXENbbjuPW4Tj7bXiJ/7TfZwfI0PLL7g35VFgluvI/Ant2X+um+Rrh7o7wuYYqqjt7UYkTPgRU53yV/6ELRqUiGuF3nqAIs5KIweGDoYNyrNQ4NwZI4jMgMIViH0Wfax4cf4ITrdb7nlh6OTomVDU+5Nbdk7p0EM3el9m3Aj5jMWnDitli0DLPF4HFAnzuTkTLivdLDTRt3cld+yOSSpOk0SpIyUr77iT+l5Bk+PTVwbfhF/6/Cbv/LMNHPSvUA7XCAQCOxwBBjad4SrLr/UcxpzI0wkEzWMKidmIcy0+XaWmNk+Wxs0gXua0KSWJyyuazvSZFLBeWrjtswmSsF5SqvTFsM/qVmRkxzJCU0Y4bIpDLE35qCBH3uvDshEaVJ7szrakEwe+ly0LnVp1iiU58yaFJsbgRk/Jkxh1NdeqwmwI72SJFf9mOyz6AviQlNxzGNQYpaA5YiHEFXJNKk/OZ86l238O04H9nOwBavDECEcxJhh/tqXhgzCxI/IL1eKhTb5Uk5/G6+ub+hHaOtwaqMwtEZMzCGCG3u+OoRTuqRi8yIi1A0LU0E0euzPQwCC+QDviv0gnK6pDStoP9nSZuYSKCCABBSFA+G8N06Skt+LSUTIyoIXvBiBGo64EpCIT71AFN9/ywjzjmfSd4t2VghS7Jd01sRx3rpSJvJS+AXlY+GO/Xjyh3VzOQhDXUpYK2G5OmMykWOFnoWBtvfasCDhnJzNJXOZshzy0P72L7no0CJGrMOCQP9iacc26q5Z7duMtEX+qUmpj7Hu0deJru7cS/LN4Widw86LLWrLE5PaD9ef9QIHY6b7uiM0Yuk29yD1P43fLOVMd7JpJFQwR+R+rlzcH+u4LOboHQs0nHDJPf9x9AK6jcugB8YExlT2yJkOpcOCGbEIJRHRY4jOuLcWfJAQieHox6StdLQ2g3wnDTgepMMlY0FyeVzJfsQhB4IUh8DW3q3w7HNb0BuPLzl+Lq/Gm6tWq5+S0urX+evOzQKbx6cmBcoSuiCs4Zb7UTbRFfGF0tbgN3fdOY0aiNtAIBDYyQjsGIGtf9WVHtJgLuHXB0ws45wmbk37umSzL75Bw6TXYoVSYRFWkDEwoFuXY7IhIPk4AhMvDKzShAFmItf9hD4CuksfRWXlFNNIJuwSQ7k65qH8geko/0hz/+z+tKva5RMqG0TVlC3NAcEAl4WrfM9fvNGc+Eb3PWmlWhIqBJ3L5PdiQyjpElcemVQBMQOS00ewUJo9rUIviJFv6cO7h17qnOvc/DViaCR1G0oJp2iHWEmWRqgrjWNfpq8+6MUSELnpIBGF5UPhVRvGjYg41ZhM/rD6Q8xFOwlz6INiVO5J7fMgWT4o3tY7mz3JzKrSV2f7aAvVWhBqZI8qhk1+nT0qmz5EnfYKN2kUaQwCz7IrmIgUK+mOEBbTC2lGjFIbU0sF4xS1rvbU9bSowJH6HGYAlWIF9cN0E7NEmXjBvOifDLfMgPpbRArRVV3Bx1nA8jOH7JCAsKcMcEL6wDt71+DPeN6nFfZiogX/R/g+GlWfsKqT3BSX2rb8qD80DXOCFvCgAeZTFErz4cqgrMLyavLdoOOD2eG2LwKbUj/6YPaajjat9txSP7NGV63OFhJqw/QFXP7r28EfN3090Vf4NpuP+9f3KxGsWIiRd/6NiqxXPS3coG23WaX7HOGbDduEyTP7V+p/Wbuvvpt9l/xlbmCRR4OLfuok6nuZOugvMfDTU6V9bDKNTBP0Sg9cmdY6RcdUMvmQE0YIIVSS0BVYmB+IygBWXuU7UUICCGozuhmeBJqh9Xmajbq5/Rf5u46uJM8PEAdFEFFTZoJ1P3wlc/uV4I0wEX/T8KOOwgUCgcB4ILBjBDZOlmMagFlnomZlzqcyaoLgn81reIcw5Z8mL3HQZeP14sQ5VHEkOuzq+cbMrNLmvzPXnwkxtF1N+GgdON6fQ0h6YmoxU0EYYPLeqKM8CB7l8wHzOnENc0sERJQd1uTVNA6yKzSWayGiQc5gZVbvlLzmc2FFeDs49mG3NJOaR1gMpMisPM+LWelqb9zE3us2LLAtLOg0UMQQCTXUmTVsVIT+T2o1HWGH/Wh8j0wiRLZKhDGxMIkAimmkHiVQdRBEtL+OdLraY4fwwWEl1Fc1V0nGkTqstCeFZxFgYGpIuxJIErtUXtqVLkqXhYFsgplhMLyAKamSlokQZwGK14aPFsGquwQkvbZn7c9Tdk5Ft1wlKrpysh/mtsXE0dHIQ2G49wKE2mCLdkH68iNvZNmOJFVg815LQqt8nNCHUEcKMIIUjZTKEeY2kySO23aNk8PJU8Ibwt4+Mtmgq/bt3WAKEX0rEdiM+qlmZX5Mu7Nzw19Kch5c3RYJxuIKayBo8vl4NYtgjPFuxDnUkvjuBYRVOMbkSqfTzh+QpYM6mbU27g2jxjR3Y48hnDjpE4fJppk6eeuf/XTPjcdjhc/h8lvuFdVjqPNkjNHcwP48L44xaDiA/+R7LQjlb7KJVu2pzU5jDWWtc63QNlrDTz51XOXMHWHo007aBFDGulMWgjQWtqbV/7X/jgUtd/Sacj1kh1XIBl23e01tEpnxWjm5QhhQLg87ym9pWhE/N56Dx486ChcIBALjgcCOEdim9kvrocMgpG4w8kzsMM+ejDSpap705NaSlmRCpms9rWCyf2leG8I1wznYVJUnyJpXMHPALNhVfE214rUbE7zNWZRmHdj7D5TnAgeC1EueCBYTYpSn6+OREQZm5w5YqGQONeOttPnoK9SxCttHE6Rwo9xgJVWctGnx5ogccn6W4yi6+qyAtDxiHGyap7SZ0Fll5kOp1i5JeAEXSoQr14xa9sN/QqdQ8iFqtD4tMREdMTytCQmfKi/CEAyEmQ6kDBGmE/B1leChx7JwixkQYXa1Z7xq3btin9kD0j9UJ31irlKXnfSVIWSLjraYEAQ5DuNAqMCPfz7iGhwUrIedrFa6eY9mFRypF+KYXpJiX5vqHUZvQgcFCMZcN2hm5U912URLWrGWlv7Z78Ipb4nz8SUgUbc2yZKQpuILR/100AnCUOJbOgqKQE/n4n02OYQB1AvRxBVxk58i1b/sD8wIlKTFPQsB5GWzSp71Iw0kVQRKC2JidukasGd8oy6/R/NZH0qitKhbhK5sHpkhFQF6VnBpHr030pSRtMKpn1WUSVx0S/EmFAYTK30+HAo25KoD69C+bCiHiLwRBDalfjh2f+DUJmlsKzj3BwVBeOpwuqv6BKbManbuKcMxLcgpFIILfRXlloW26Ulp+rVzlXHR/azEJP/a0Xf0njcLWvhhOYfxga6JK2N7pjeHdX9neQQBTxnaioFX+sfwZKGK8UX0YAHg/pSJV4okzNKSApKptO59adjafU7qFUbQokTAgJDMN6ZOExpjlsvKAAXdvNdbRg2X0THsvfgHghhStP+XuYb0yZgxKadAUKUii5CNuoVqr7+rBuWkjyuWL/ZzqeTZwNz1UuNf7iP+1uG30N+46asrNv4EAoHA9Y7AjhHYpCJZEyymJuYNa2U0CbfZ4K1JESEEprdMKiQE22lZgInME8ridMb7lZzD6mWZmApz4KOm5d9ZQAOTmVrvG5Nf1gZlEzvMf2BJDsYx56LV6SKAzk2kiRmxGTAZTOr6Jz7BKXrfEZOnJ+l15oDQ459o5vs+MOjCo0khAtrSCX9p2sacfGVmtFHXT7PS5IgOCiVTRBXctKBZsxGjzI0mZD7YV71yeEpPTBfVB4+CMIV5o7g3+UtYkeCjViB/MWWYJ04KqxozmISuwrT5Fpts/TDFYrUeBtAcmRLl2PE2Qqno6bBHUpS0JdySKulgeUQ6fA/NZpWIaNJisfcEvNw2sOek/YEpZQIn/exnUFVW3pGf3vKeXTYI+Vm7RrvNcYgOGghZ9kN4VHvqqc44J4Wr/SUlwi7nb7OJQr5PB13y9+o82VNOpdsWrda8yc/vQJt7Mc4In9Z8kJ+ldJn+1m0bSg/ZaaEg3DZGYB1j7ZrUq38Up+Ykx1/a/6IrwxRjDn3Fb9XPJbcptPwwV+bKQN1wWeOspk88/dPFSU/TzpUO5umLLueen+v7OjyLKi3l0enIRHHg6pd6LtmWvuxTiD1AQM9SmuhO+PRYUETY5MRI+yg9lUmDo57pSPqveYl9ql4MLJnobdOxZsNBVvRn/kGMT40lE0jUuJRv6guPTjw/Y0ruscI08G7IIeVu0C2kOX/o2SO08xdFHquK+KlxhEp2GaBu0b/MUYSP+MJoi/BbaG18Tt5gM4nogUAgsEkI7HyBzROEZjBNDEzfdnqc8KzP6XrMbvM2ibOWhWmDsIqHY75kMRT9FWmMdHo/eNMIAwOO4gWHwIYJI5qJWX3kG6aBPGzCqTj+h3bNKpccZ62/nvyIq19PE/Q8e6uU30x7WqaY+TRCMzowR2hWVFb+Sh9T362SgwpEWmii2NOB8AcDxLH4mTeHVk3qJDgo/NL0OmI6vNpcB2Dv10ZdL2lvBcIo30vikA/tEUwLurJRrWCvVssJlwgd7MVjn5/ZGj1bIwYzw0q9+DbqCKYPcz40kfN81kDqKOOEuSF7zTAVFBj4KYLylKDCMwChoVVbasu00vtrhBF5e1mffIgLd6VgBgvaxJ+h20Kcpk1O6XAatL7zLCDoJLkDyrPiy9bkqDyKQEbxSAaBDyGQ5x6MmtqMKNVP7wmvqJb1RFNfwiYLAHwUm3S6fi9NMIXRfQvGUQInlJAGVxVAP+isNQaUmfyEsRkp5Tmh9oW8TFC0qGiTKT9C5EYdJ+9tB9c64kh9D25W7WHj7XZLyyNTYGgdOGnxKx/OM/DZ1BsEpY07tRPGOzUtj2O0O/9ImT5JS6ftq22pnfs+X9xmOfCGhQvGpo4SKUKTpTPaKsk5Da5ybvdqt+oP9JQF9Vv3X6dN6o6g8R4PPXEVCfQzFn3aOnHSQtEgJFTWTjdo+hlrOhrwvFiWCVAAdRA5wnIAls2odaote1Vb3svGy1xWvdW9+pw6Vqs7o/4ms0Ttu8VlqwXA8qPnEY54bU3iR7zyilT0z4MBdCkCAwJhcFzUj9u76s/dgIee9SUXl4yRhKK3NmFxrdvWopnGVZNQ45oJgBAKAjHl2vSTL/VVx4n4QmqL8ONgsXCBQCAwHgiIg9whztqJlWll0GcWL+YlWX6RNkp8f9WC+dfkoomZcZFpJM9amjjqiZdp18ys/g7GTsI1HIMq2eA8UepqP2XOM8LZ7iN2+32vuz9rc8Tgoy1hkkXz5hVHr9I62Mg/Jc3hl+zBmp9XPiocZnqka03MGtgMp5OfRRHSlibzvr5NhBADwz8ltRFXkWsmIgtkiykAF+xHZjB0g4d+jsK32Dbo+qontGst9ne09IFmMX2cClnxXTUJSF55Fs6YTyFcTqFtU70itGFGZTNICVEtpA2JMrJ3VRIIdKJZcSZUvi4rw9Aq/wULdzJt1T4wa0TJQKH5uC8Cvng0QaSwHAspQQNzVj6+28ekCU0E+8cUw3VGWkqf5kZ95xX5qfTo5zxW7RB6snvdP3w0ffNbVwpHWp2kO+VHmwNXfoCJENY1oyo61Ibg+XhH3fRpP7QpMWNZs6Z38u+JNsx7id+X1IjwyomPFvgg0iAIG+GT26TiOQ+YOKVOXWr1YhJa2Yej70UpKTmxzgKihYko5G7UbYaW7lBoEGYzT/mNNHW/H06d29whtY6+kVOprr0mXfcLD0vdM75wKKlueZzpx/xi2vOHLxvks/+PnpNmX/PKwfOm32xC/bR3y7xae8owQ9YQ46YHnTRDnEdSNS6aV/Erd/QdzL47EywTqC332G+lsPQDN2/F4qaOTbMlLmnSXid9eNCcuivp8E7t3GOKA2YPxyUZaeRkQj2pgN57VpJtUFVHcJZ5L5sSrZMaBC+ByEvaK74R6YOMPBHRc2snwYqolUy+W2jaWN1Bm23nN/m9EvZBJ/bXQ36Vg5EpjsEZNf8IxyGYXeHnvYAOQgKghNP9Ic0Zjjz4w3Y8ymEH8K4cUlc9kROZOVv+lIfan0jF2/cRfyvwyycqA3C4QCAQ2OkI7ByBDYZSk0IRIPIkvQg/a7aeM8yco0nIz5qFxSxrClFEVkh9IIkmVLH+WejwhMLckSdSUmzOI0wzuJIvzDvCGf/sPM8wPSmk7ieU38xuHfcveme1eo+JDMewY5qXBSFSLBO4JrscLae17C+rxC623uQcLbRJlmnPcTqaNDdi0mG+u/X+KxZaTZn/LEtw4JFfKzD/9euLwWhrkzr7npSg0pCn/g4Es0FMhZfARAnKOwsrPG8Co2fzJ8OC4aFWn9k4LyFKYoQokhChX6Wjsc3MID0gaImR4wO3fTUKaIE5A7iWBJqWTB4RkikjvBOCGQ9dTGUpa63tsSmoypC3zlFuYSD1qenRvhTaEXtT+pysqTRaes8R4VovV3oSeMiTTETHhEwvewanSjc87uglwhr0XXgBG8FFjBsxNaF2zU/vaJnEpQzeW6nX1gIoFO2HPWiVaKGOvKVTfn3R1RXjh7YXs0iBAdea64lnHHgYJ5WN9/oPvULHweUprPQOfNiHp/ZLTEK0VD5OXEXYlVJv424T2snBEtHavTsd8bf/liZ/6EHLorb2HJF6Z31zmf928Zi4y92XkNL90ueWPG/6wybUT8UprJO7Un9ffbKrulDdEk0uCwyDsco+6qMK4JGn7gcssORPS0xqbUQHL7nRKh11s+I8atfPSlLtlT1pasLTOoRE+3QRnug3jBFZaCsxdVU84tjCQGOqhTb1K/djvc705pEQP/JluYf9pJi2O3JOZpAo44LN71m8oUCK55SOvXFKx+QFgrR/b6q+c5bSYxxRXzv+pJSOODqndM3lqXX5dzQQCQ8ws1AGJaSTnZ9Il3pCu8ZYU2fjEOq7LOppWHDfLuM0SYEPGDfTq5M96IuGYTkWrFwLui/p5nE4L2ryNr8hdL4v4fAB34gPIluFn5OOP4FAILDjEdgM9mv7gMCop/kL3lkctP6g3dKEIMZ+QqqIij05XIu5HNOHJj541zxg6qZ2npzlSVLLXAnceAkjbmFB+U2h1tujWOIGZvfP+lRJM75wB9CzLMHlHp7sTJ4mM8XzASAKRh6cRqmDIzUpo0UhSU3B0MokT7brzUOhTQu0Syjiu2+pMyfGSJviSYP8S1mHSFziT3wBJiqHQh38IzKYq05RKwQhuBaZtLYnd6uMuqeMIrPvj2nDwonpkRDDSYkctoJ5E8ZUMCkdEkOqIU352VxJYX3apmju9TJDp0AyoVJbURz2qwGKDyhR7/CHtXXYSEur1QhJFQePUE4xSmUfm0up1wZMAlsbgQ1aldCNT7qJX5c/V1x6nfYhUi6lZbxoD/xoIRSPO4Q1RDet/itd78Ekb4Q1rqr3SloyBDrvU9NzTw1AsqSZSacrhpJ2M6gnxUVrSJbkQ30X4Rh8OGV1wifLKU0BzIIDIrK5RtVBR4LvlEy8JjEB3YFu+nFPGimsIbD3vvHVLTUx3ChcSwQ26P3qlzaa5JbHx5RXTS61j5hJLQltPR0GgvBRxgjG11Euj9r0HP126SAkLX6h3e3s0yc7rr7WY7X72cihBk/1GkXm4JzJ6Sn1tazlc1cblaE6CBrn6kY3lAkwWj2dRHvlVU5nODipezHImnX60kgiRKPeqeNWskNkLLakd797p3Tn2+ckT5cm94Jz1bVk6iqTtdYDH5vScSfnd//75pQQ2BgTbAWg+Kw0ZUTsnzt1LqdNO93JC6BGTgpyjQfqs8UENCfO39E0L75f/11f86nJEg62HCH1QqquzMPOrdzz0uPbINrgJuLn+tsS/NZfpREyEAgEtjECO15gK9MUIz8TtacIMepmRuWHdoJZBEZ3YpIPXWNsxgQD8y8mgigEWUzIi5Z4j3J51XDpmyxQKRnlZTZbzC4nj03r5EVWQeckXdn0DElEtBSmRS/rhJYywayAQlcRAgnEd8bwhUwEDw4gmZtXOfU8iVZEjAH7LDIGCk+kNRzpM/l7klBK1j5Km1ShTeGkTae+mEgDInsWraMflJaZk8Xgh3TXllDAYR9YJtpaUdAgLPR7+yyodLR/ryWbww7fLYMAAgEj3KFW9RFikWZ7KgdMAAKGBRXtHVuQv5FGeBFmEvVUPWKAhefCwpxaBiahElP89XX2wwgbhe1pP+KEmDkaRkd597RC3tmt/Nmjw/fn9M0jzDVdb9pzMrVLccRkIUwfc+KxS3C46AKZQio/awvRlFnYprZUt6IXMY8fReJnE0iFtpZNHghrMHDoHLuSSq1RU1vje3g+FU70T6IG0xXB3i0Bhg8BnGzEyFkDQN0rvj+SLYz44K+PAVcYlUZlkZCmDev+nATvFb+t8mbqdNlJTodRzDzx1xYpFv77X/K8NPe2/0jV1fq+44xMb7epQzPYOeV2A+p63/ya9t0dGDxv15s8pqi96l9rl/orWmAJT5zy28EcUP/0ys0z3+SS2DS3jJEPf1Bq/eCpfjHxwU+k7hvfIyEG01zFdlxSL2mov0hCQD7C0VInWdjQGLwwX39Ym5eOMMjYYTu3PyXtfkFuH5U++N175ksyfX7rbuJ+WGJ5MQdhyPNMHYiLAjC+5IUz0cY+ZpkW0+fSCccPAlbfuVj0y18CW5pWpGNOGrxLF5/je8pnrT0qfyQfD1w5mLH1e48a+lMXWgTI24/06/4EowR9mX19vCMS/byGPSd3yH97VuHV0Z0x6eZxTBc7Lgw1kEgx8HcY05KfHbD5zhHs62Qi/sbwy0jG30AgENjpCGjWGA9n4UOzAvuQ8uxgdtcTFTMFjO+EzMSY9zBr5Ihyz1wUnxlhmVvuZ2GNsPqPAIYrAhwsiCdCccU+zl+mOBxCMrNrl+mZn5/3oSEIGBYqluU35FFn73KRodQgecol/yzQdHXIB+Uhn/JJgnwMvqfJoQRHPwKX8YJ+0dyT9rHjzQmZR8ilVJaEG+Eov1edFb+YLI0Itn6vXTKDYu+JBK4eq9QiAAEGLRA7uyRfScYRs0TLhQOgALmqdZU/e/skzLUnJbRJmOKQEaoaARotkj9wq5TYX9JBwOXwD3MSSk+ZcbKkT3iTcGINGoKXwrFPro3QhrAn3ZPUc7qXORDaJwlL1gRiYokJJ+o9fop3zM2Pg/qBu/D8K3QvmjPwbnowU5CQmSrOOVE5wZV0eafwWZOmZ5UPbZqP75dQzUJEV5G7rOajUeRZ7YMDQmiTNj0To4ow7TjsvQNUPpGgsDaDdBsQsPKnvSH7kpTOHVEQpSsPtQ7RzQ+wd5abuOs9Uvu4mw6IXvjw+9Lsv/zt4Lk6sH0FoM6d7kbjHdC65eaQg5w2duPTVZ2E2ozI57RRa332a/FKB4JMaLGFtk2LsnN/qLuFPHjTusVinVXnX+xFBT6WUckU2RIbcZwAf3I/Lel5XFJ7nlQbr7SnrTsv00y1bYXSvxytRO+ccnOTwJ/+t89Xv1cuiue0laCtBzQAMgbm2AqnMHzjk35Cqg5sckoO8tFYz97a9pEyt5AGb+AksOW5RwLbjbSgU+pX9KVLz3Mw9zf1a8arljT2taezgZB8aAr+0Kkr4w2OC/17UsttsgrwIo4emZvq0L7J84pjHPIfa9gU2zjW2ZfEwL/kATrDo0aNmINHfFXJFuJX6iSugUAgsLMR2EECG0N8c5hfCnwRnDxJDEY/zVKOBissHlsTYAcNxIyYda3UwgTbtA5BQO8m0DKI6dd/OyY5x9fF4+kgXSYpEs6OewsVCtzxxKmJVn58o43puyPtTlummG32ROk9+6NgSPiHtgWBgDTY/1BcmeyKIFaeyRUmHIo4NXJBP6IdNXmEB/15CRZTYuqxpnGwkmDjSloItuRJUXOx5Mc9zL9WwnvSMk1P77GfNW1DiZEGDAziBOaTdoq7YYcpIaY20oZ2xOyokGl+FtrISU4CJR/H9r41BCRoQHjim2hwhnhg+tpFg9bVPhYOI4EuYazV7r4OM8GkkvJ3EHicpoQk4egPasuEUq1C79Q1MBtUmTgVrq89NK0FafcQXlqKx7OYUD7i3eZUyaIS1Pf/2mz2Vz2jVbjhTbV3peG+c/6VrhiXSBVHmYANDRqtFGEsr4tTN6oL/GTeiLDYE00cONKl3aJlE6OHhq2r9mPGjZMsEcyUYIUZ2dSUFgcQeJW227JwQtsmulr6TpOap8oiWgkhnFr60d6mOXhEYToTfPyYlEWJwhXNQaM4O+J24p73XkLn/PvfueR5Oz8sMYcUoTtFYKPdNB2tm8WUSWmme/qmJgsgPvBJTZfWy3iyZExVe2+dsGhOXJ3zHbVN9QN1S8ZaDhgiD4Ylxq+h4clZky4LFVO09zSjxZvZ3J8V2O94r7vOKSc5PH+63zpPGnVplmfQ5C+WgrEuO2IqT8YGWTm0NIZACL0oB8nvuffYDrHHLWrXNNCk1mVXKLw6H+PX8TcnueyuvEgDkbSQigw2jAs+jwR63U9LQF2hjcGDwrs09W0JonK3Nc9xQiRdv4SBrsWy+MUh/+np8zImts7f984AmnzTuHIrP9Or+1HvXXC9cpiIn+us4LAR/MA7XCAQCOx0BHaQwLYJUGsigCG2sMKGdiZdVkjFqCKlWTOm8VFznSdgJjam4VpUGExLw5QUYQr/Mh9hPcZkyd6zSfZFTGfhiD1WRTtH+CKQ5bhLmRb8RrkyhHuVVQw2JnuzMpOa1PHfMNo4a9o8KeYycIuAyP/CiCBU2iGhUlgmeXH2mER679e0rhIqwMvMg9PIUfJfJuamG35uvlvfPQe0tCRRITwIHAlfErBZKeZDvGjcVDxWt6Ua02tdVaAsqFFunRgJCQjHql+Wv9nZ1peWrctKt97hj4Bbcby/KsnaNPnnA2mEpdLuiktCeOZj0TqLX6omBdVzTzQ4PCcoYs6EYNSTdkaVbsYK80ytbKP9g1E8+oRjlV9mlyg9aV9y0bUSzkQD2FuDlplHSoKg5ipCQFSaaM44+dF7bJQnwpiKZnNLToGU6Olq8kexxeDmw2BorSw+8JkJkUjG4CRc3bAlzHZkOuoj/C2oUcfSxun9pBhqrVlY4Lf20RTRblVORcfldpXvt/Pfyfs+UIx4NiWc/olHLCG1c+vbp5nH/6r9up+Vqd3Q6ZAtHeYz+eCHpKkffVhqn3hyah97k1Tt25v6l1ycFj76wTT/9jem/mWXLElzpYfWjY5J0w99pPbPPTi1jz8htW54dKquuiL1LjgvLUhwnH/P21c1cVwusJ2+Ulbby99jS240/LWJOo0I89o9nCCpdiUteos+I6eWn/sR7RR3Mwk59H+c9gG3Lr+KriE/FhJIjwOA0CLTJhsDE/cStiqdUIm/963qOokpsCwHWOSCjMUoGvNufXNStuufJYFNNM3LfHpKe+CWimE1bQqJhr46Yo/S0hgvOsocQSJOX1doq9iEetOGlv0itRvyd0CFOP5m3GV3yTn5WmdD3oxnHXXRWurK7/XXAh29mz26FKbGLadLJAmiMyzuUG4iEFX+7CPUhVFzo66PxQF5ky7plYIP7ut3fl3nV4NvAR0CIv7W4wfO4QKBQGDHI7BjBDbkibwyWA/8niTqeUjVwJywsstx2PLEx0h91UTWkYDDB0374uTZ28YJjNkh1CmsfkyYTJwdJprVM3FUpm470lQc5qOJaZliihlG6zInjzmZBTHR++hqmBL5lQ8655VmpvpVnMITn7DQBRPS39dLe45opd0zu60pKp8rYO6EcMhvOvnolWZyCSIIZEV4w58JHhM/9oElCUt8WNpCG2VTQsTM3+aqp/2Sdrk2MzrYe4Qd0gF4mx2KqVNF+GRDGDVpyzjQAFMjHcmhFXdW2oWXP2zNFfqpT2HemfEqM/EFvLSOCq8l+raON+tPsspP2fSTUDSF2ZS0igvsfZPJFvs/wMR7vGyGqW+bSXir1GRanO6BVEYdcA9TJn83LLWjjvbhEe+YkxZNuoDh0guvyuaLasissFsbJoHSgpreU5/wX2jR+tp/pmzdNtDk9cRwYiJprbDKg8YMZgy/jtpWJeGS9kaNUF8TWl3n9LuuhFK+64ZmrSUGri1GDr0d5qVQgXYNrTB71Nj6Nqn3HN+vpFQ8aIQGV4iFUSsxoWubu13/73fTxN3vNZLKmSf/v4H/3qc+LqWGwDZx57ulI17176l90xMHYcoNnwKYvM8D0q6nPjNd95SfTd3Pfaq8GnmdfsRj0+4X/3VCAFzilHbnjndNUz/ysDTzhKel6570mNS/VGZyI9zEXe4x8K30fUcOSNkJrh4Z6jaWmxA9Vq1bbTELbdWs+tisxl21U4YpFiZaT350bvM3aHx3Tm2z9bTHKbb+ff3slD78yVp+UQuWpq2tftb6vruk1q1OSq2TJQBJYEt7tef14stT9ZFPp+rjn3eaPmxIaaAVn3qEPu1w+1vZv31sfXqjaJjSvrnJH7uvx57ey//VppF5cUU03Pk2qX3/e6XWbU7WoVL6fAtE42bnUvvM81L136eldL60ZC4nr0WvFsOqm4k+B9SfC3lPCBCS780WBbbq4vOy3320wHDsSS5j/0KdXvqN9zlO84/jniBz2dvdLwtz7Mf+0j9rUEBDp3TVb1t8nkN77diujReOi+umfrbnIf7po9Gvy9G8luSgMc9nBZGSf7kaIZc54uf2AA7FbRZ+Jb24BgKBwM5GQCzaznAMY4c8x2RJT5OWGFxxojbxYpoQs+tjzBkkxWT35hkuGThh+PVe3mWCY/A0s7EGXJjBWGtWZkhiibEn7ymZqJFVT6cEYqbHZC7uOvPDhCfcGukveV3nQTpzYkKm+RgraaqMzXSYNI1fgyYTUpfPaeo+h9EEz41cT99nw4TU/Doplhf5tf/mOLqt4zReHdItVYWciMCGnOFFdhWpxcZ9ra5TZxzVz0e9zQwJM+lGlb9wl9DSlgrOJqPsA+vNqq51ypzM/zBxRHBjxZvaRbvVgeGQ4MeBLlq3N2NTybyRfSMc6qIc05RtsCTEiR7aAOaU/nCuDrJQBqo+5QezRJJIUWirxHgiBB574lKB7SLtX0MnAIz+YLBbIm0SsUl464eQhmnmAoFQkelwFE6EhKXlo9h9pcsx/v4wNpo+vccfDTFtyd+SUtg5aSD94XNp1PwpAwnerkg+fyBhcEJhEdImlIeSNM46FkKECR04PNFEOUnaVwQ9ldXaV9G4rR1lusNd1kViU7s2/YiflYD18uUC1lBKraNvnI58/TvTvmc9Oc2/9+1Db/UoAXv3c1880OItD7Dowx61o976wXTdE3469c78xuIL3aGNa9/8lgO/3le/rMZBC9n+LveWQmceHNSa1H7oVeqzLBRNqz9rsaA6oDYnoa1zs+NS6063KZEWrwhgdzxFsdUsv3G2mqj6rMYCDg2qHvD9qf2oH7WZ72IE3Un71bo1v1sozL1S7y9fndKBWa0/abFGfXny/t8noXzpgUDE79zhFCfTP+v8NK8xoM0hR/QrCZKt75eANMphPilhDtqr1/xnSqd/JYeiD+mu1Vi44cARlQJfDS6afo9pmExffK781RmnJAyefCfdK9TMrlSd8T71c7RZxNOPC/331EeldGRtNnr2B9Q2JKzJ+TMyjI/6nA0HCTE24TDHJ/LSuvGrQ/rT07hqWurY1mhq/PCVF2QnxzMuC2+iqQ7T1gFO/p5lHfBg41OSkgfpj4ovlIwbYUu+g2sjPodJMS/0se4Qvc20h+nXqKmQGKuvnT90sYCmZcZl+VPX+RRkQo2mX76eY0bFX2/+OfX4GwgEAjsdgR0jsC0HOk8CS0bs5YGW+Hgg1qTHhOVJS5MefrpoUtbgqX8wDj0xRTDtMBgdNBP6h0AAD83tel0Z6C3EKR8OB5nWant/lzRtMtmBIeBbXj6eX4kWLddimVbJTOn5yPl6ckGrNK+V3lkYGTHxlMcEj0qC4rhMKpDwyOWrC8dkKloQgtgbVi0IF51khh8/QhVX9lUYF3k6nfLyEK/t3WLkpMlC+1UUnnxTyJvvqSjmXzEexlaMSDlREopbfHRbEy4ftkb2QAzLH69WHAlYHPPf18mHPe0lJE2+gWRBkLDe6yHsxEh6MkbTpvrxoR0SejDfQniSbJa1jhzvrx/CGZIWaXG1Q9DR4zEnNvau6AUCG/ghlnM1qrphEYHWhuaX+67aXtaKKZAERoeXgGmNnLJDLlRhRUcWQvWktiMv5UubFThm1JDEWmKKLfmqnq1NVK4cqAD/hzyI1Sew0r4t+NIe9CONjg4y4apE9UNYE11kts0dGq0Df/YCU9n5nlMTmq7iFk77QOKHo333L7rA9zNPfVba/ewX+t5/NAbMvvbv08InTkv9885J7RNOdDpTPykNkBx57JFwt/CR9y81aRReR/7Tm2wC6YD6gwnkgVf9Zep95Yupf8VlqXPKbdPMrzwzTYg2XPuEk9Lu573EQps96j/lffHrfvlz5XbbX3MLV9kHowItR08eLNT3aHO0MwkVbS0KVTKRrPZIE/mVMwmW0h1upQZKw5M7+wJ9dqE+GOabZyuuI0sb95jUOvUOOQx/r7wmVV+W0LvvQGrd9uSUJKzhWjpUpP2YH0+9175NfUR9fLf680WX+tc5SWaqxx7tcP3Lrkr9/8/ee8BZelQHvnVD54kaZWmkGQkFUCbnIMAYDMjGOADmLRgvYPMMmMU2+/bZfmA/7Gd+GGzC28UsC8YGjDCIHAQiSCAEQoCQUEIjaTRBmtFoUueb9v8/9X23b/f0BM1IaFq/ru77hYqnzlfhnDqnToWEjK587Y30f/bAEjKIBLTLrLEw0lm/KSX21KXR8ZROPiFVzjk90kfXe85TUrq6lIIC5zIkhcuX5HCulY1308/ECpV0j17UhQDaYmUL+Vr3e7gXrrLiGLW/YwyOsc9CjHTqE2aYtQbSxFu/lNMS3s2/nxkNFHocikcqiPpQTTV9fIeylIO7u3AU4ERy4CrzLEA0zDkhRg3rxfvS6knplOFfT0f2nc2wlK02T7buSeunLk3rJ3K/PH/pG9NQdVW6ZeLitK3xs0hfhdF+9PK3gIIB4n4jbZz6Vq4veV5A/EHirxv/bLp7+uooZ/XAM9OxA49Ly2q0I3LY1bw9rZv8XNo2fR3hGcB6GkqnDv9mOq7/cSzMLSdeJ421NqV1E59Pm6e+F/mU8B/d98h04uCFaXn9VNZ2h9Nke1vaOPmddNvUFxmznRTIk/qvHXp+lHvHxNeina4eeFZaWj8p4t8y/sl019RV6aShX0knDDw1LamdkO5t3JDWT34tbW38dBb+jul7TDph8GlpRf1hDL99aXdrfbpz6rK0afLyLvzRVqxKuFx++ViGCf+iW8TAIgYeGhhYuAybE4CuGI8OZFhyovfnvOKYHaqRUqMQoxp1YPoJIxceQp3V6iCjXQkuMo/5aD8F1Vldzyb8mZRjYrAsJi0SC7LStyFXTXnpTMAaSDRCBMhgSZ7kauVrt3LCNcdF3lEXJ3+Ia4DUcMYEFu+G+KujDmMumUCaSRx+1MFV6kACsTJkZcXyEO9irHtEZAyqEhP+ESVXKcfN6Uo/yijBninuPj8Fo6l5fi2jwbRVZNxgqIIRsQCtMAJLRhbEHnirwOBofbOiaqgUCuHVGgY3BBMJWvjBbLgqX4Vx0ww/phhhRs2LPKlUxj9pIW5CMoVESSuc6FJihIPvxaQfTYUeE/tSZNQCheTlPhz+M8OGpwwRG/JXHD2jakVoCguREZU2CF49X82kpCAp7BLvPnswr3JgasEbWLYi/GTUhFgGTgme7RdoyYuWCwOq2mjJ9KVB6k+bBj3h/G4uSYRkjbQu7humdDHnQs40SvlAmVD373mWnJDIDPqtbWkUlDM8jK+d8TGYrf8eEA696f+eBenUxR9l39gls/xkxobf8F+7fp2J8bTrt39l1nlnrV/cmBrf+Xoa2nBHGvqjN0dcJWAycFOf/Jdu2v7n/cYsZq11w8/Srv/jIs72uqcbp3Xzz5OGT5b92xdT/TFPDP++pzwz1U4+JbXuWNeN16sOqWfr2gWyfw1Ys8zY9qKLBpTvtKJoRkV7ikWfYKKQtqzbkFo33h4qjbW/zzg2deefL06dXaNFOtsi/fVJj4RSn2HWWp/4UupceoWBhHLBVV/8q6nyvKfl56c8NnU4GiBhbdJ9rdP/+C9oD06noT9/daoXDFvjy99J01/4ZsQvJ4kW+9iqT3109mOcbv/Dh1L75+tyGVaLzlF93ctS9VFZIpaWDkfp9reYL9Ycl9N6RcInU2mXFMLO8T2qkqrEFmdGpq3rjZ3dIBYma0MwXDCs0fVIyTiWzvm1MkZKN34RNe2JXK59WLQ7vqM14rhlWc4XzBIZKMPvBxdWIrsVnZthADHLc2ntpPSYobdgjGsojbfvhgn5eVpSPS6N1I5LZw6/PO1Om9O21g0sIg2npbWTGcKOZXzL0soT+p4K85IZ4+H2ccyZeRw6qu/8dBTM1FRnR7q7ch0LAAPpnKFXpePqj4+yd7TWsSawJK3sOzM9su/0dM3EP6atzXyO4QXD/yWtrJ2eJmC+7mpenZZVVwPLCemcJa9JE9Ud6d4WzD/uzIGXpDX9vwIeO2ln6zbgHwDuE2D2XpSG+49PP534HxHPy5EDFwTsq4eelUaqx6btrVtSf2cnDOhR6REjr0xHDT4qHVU7N/yr7KlcBeM6Uj8u3T36p6T2S6V0+sBvpVP6nxfPo+1NfMFGMIoyi1XmovXT34iwuNwH/M8kWnxaxMAiBhYiBhYUwxYTT4Hlg5lzVIUpXfkkMRrMgHeIVFUUqm2YrmAMMpnqxNtlGMoM9nKXgdAohkyYEjUnSlUwdHFnVVnrgQOoR6qa1p5ybs3Ecsy0Rdy9ZJ+9Iw55loM1sKsqKKPYaEwjCEKahPRFp+BHXAWDFz7CwbsVN6Bw5aOQmq2RPLxZKZ5nlckwaDbeaV+Xa5SZDX3K9BF4CJcw1GJuUDUBs5IwuJowNCK3EsyRUBDHehgenAzfCtx2UA3VeExNBq2W96wpCWs1lLChuqRULTI3OYom6BZKQIVT7ZE8QxJn+dQ7n39GXnI3oKFrAUCLn+5fYx+kFhnDAIIol2MCT0cdd3zOs7g2ODh469ZRmCAOAEcC2A41GZkiVB2BV8YrM1vAJTL5bh7k63luWoVsgH/3r7X4rh2+b+xlEwekz+pSpgP4QvpIVQmCWKM9e6/jr8DC/WoaYFCKFsRcMO58Y+K0UZv0+APR2YRhrRLmd86/fC2J4VmVO4xf6ufM7AETzOY8TM/Qm/4yCL2yGpMfePcsZq309z7xnv8vDf3BH3fjK73rMmx8l+E3/1Vv9DT212+Zxax1A1kEGX/X/5uWfeyL2Yvv2I9K5sS7396NUjsXpqTHNX+6cBi2aDRebMvhJLB94Rd6xS6MGE6ngbdwnaU6TNtW0/ikHsn09l2ZWYt0OScP066+4Bn5hWvnc99Mtcuvpr+w1xMDTO631bUu+XqqP5nvvwwJF14VpGHtOzH6wXjb7/hIubWHnRRxIz770EoX4zaJ6qevTa1b7mBxgwyQ9LVvWBdR7A1RR7w7N/wipZJhgyHrBgnGyT3jwAaYshh8DWCs0bBK4TqbDGMxCSu2aduGCA+gfVpxFIZX7ijGbEo+/SnsoSsWg3ZvSWndt2mP5ukvX+2nVTQjqlgs8Tl+xWJWRLpfLhmHUawIEe8xN/Vkjl9WQUxpbd/zglnb2LgyXTvxoYiu5OxpS/8uDVSWc4zmkXTQOgwUB5fTXAaSRyHQIMh7Tf9zuTEOM1rXk8Ze0C6guFP7nh+F/WLqC8x/lvGcYNZkCH80/t401rk7yj9j8DeCCTq1/4VIs34eEjmZNfP83vjbWL9DWkqGjx56QzqyflZaVTsn3du8NaRvMmvNznj6wfg/pF1t2gh1PBYJ2PlDr46ybq1+JY22NpK8lpbX1gQ8zjWXj/4Fy3270mDliPT0JX9H3Ydh9Fbj/5dpEiauzjzwzCXvJnwVzOJa8r4zHV07L+BsdibSD8fflXa2bw/4T+x/Yjpn8JXp1P4XIGm7Aqgd1ymKNrw//AdAi5dFDCxiYMFjgBF3YbiYD+YDNahbA4yxb+eeJwlvpQiOdc4tSr6k0pUo6al6Tpx3LeGKSkwHpsq8XUmV5JBn2JdroEbnZK+VvpJRM37JMFketDBGLvr1Jc4oB2tbeAGAkbvO0VgHwD3OKsfcGw9MOeSZ5TEybe6Bn6IOTv6ki2yt7YyTVNfHn8GlCz8u2S+Hup+phZRKyVYY/ohZgrRGCiuN3AOYMpdDvAcjAXyUE5+WFqqFRmm8+GCoB4VEkg9hnf0u8WH4XrG3jL2BWm2TGe7AAUlnaXq7ggGSkKihhlPhG6sPKB46EIzmJxEnY+j5UTEH+g0hoJq0gQZnOPVhbcT9fHiQhrQwbHBblAHDFhI+1JKEJfbEVdJRrJ73ui0bt7HvzF1iMox+GySglC2z5gHY7lmTtAqmDSMiYXgESVeb3xRMW8P2aJvCOEqHFXbjBQcmI209+BbudQsr48SPb+PiA422nwanam+dvX/K8DKzJgKtv/nwMc2Hn8yazc29ahX3WfJniwi1Kp9h7BaS67WyqDpie+Ods8CvnXl2Grjot7t+7bs3p8kP/lP3fY8H2kLrtl+k2hlZolI9aU03yuBLXzlrz1nj619Kzasu74bPfWjdhESgx9VWr+15Q131nBmGrTPKgc6Uu3AcfSXaSjFi5gED8G1wNLCQ3tp/bWu5jcVig/tNe85FS3dsKtJYc9NyfRZSyZGheE4cdN2+7MoY10kZRoHiyADjab0RJqty3pkRt+KeNbKwxziA9XHkRsUz0nT0w/Ztto1chm1e4Fo/uSGN/fjneZFtgFUQnWNgGW3F0lTt3du25d4cx6vVOmlmHGhTF8cXKPUc2HOYdtoEI9kEZ+7V1bT/jq0JEb25pMrKY2FaYdicMtAcSGdn6YthnZ+wZ86xy7ICJmonUsF9tZ++bhoWXlS51sW4SrjRD9XVJs1cNDmfxCgU+UfmwBJgxD2H39b4Sro9fS1NtrbBdOW0A6giquaom5rYwVEu/Wm6BtMLKvrbhE32paP6z0NadQzqgN9Pxw8+nrF4JNWn+tMRSMxW1E5NE+S3edcP4FmXplOXZMnjLaOfRaB5L4tvjLMAIpN4yqrnEf+UVJ9EA6Wey5cBPCE9OW1CBbLZmUo/nfwAPswJtM9aeyidueTFAdtNuz+VxqY200RhFPnbOnVtmh7YhSrlsrSsdRJlbU1L6seTFsBx1+38MIunE1E+0dlTGd7p5zs/hubGBJ/Z8gmw+YkjDPBUW33pjFW5vNvGv5p2j23swr9l8iepNYC2B4zt0uYJqG7C4Bduf/hPaOUuukUMLGJg4WNgwTBsDGlg25Fvb64M2/tUpLxAK5HBeJhNSBCYvEgSP7KIvU2sfFWxlBfSFfcTFZI5rTpK8JaunPZiEgxPpFIQ9Eq4lPzI3bWUcHT/ckqlKZrfH2DynUbyYxrPiCtrEA+zqtENyaUAbAhGeDNE2F1xq2sBER8NZshkwZNQH2CGcSthzRAweUvgmzDSl4VlAipyLYsELqU0bSj5KgR+5EX0UKkkbRktMrofLsE4C05QIGWGfDOJHL/DAESIqkO2XD6Fh9PG94ERrwGjFhrDaiIm+MOwi9Y5NNNfz4y0EjotTZqB30UVVv0aWJGsgVQZHOtGdpGXTL7nN0kAyfQEoyYXiKomsymHfMM8wUyFtA3pXRuRqccNHNl79hKl3XUnFuvksoLhJR9hpwwhkaSFDYShg1kkvB0SNO8wagAi86YOo/vwPH8tmCwZrHiWwaSNUY06R0dUSeO31kyJ31/VxjoMmYeN60LqRor4lqQnIj8rS3kQKRLPob4LYPEenuEujgAAQABJREFUEPJCWHblvXg9jG8a7FBtsXSta/fcA9b/bFborX/hGt/4UqrNkcqVYd0736F01aOQkpiedtL3nBeW3nGfZn9b/XFPmeXX+1IpGYDCs9ojla0eA/HXc9h37F8r+mtvHofrs62S1oVz7HGRoBxvxLWNyw7seOLV9pXvkeakGalU685NucvkKDFmVU450YzDda64hu5djMmMc/EpXfBAndlya6ohlm5okEIsjb5Dkura1WUIkre7UgcVyQgTFjtZ3MMnVdaemDqc11bT4iNMWpLR07DJCTBVdrTCtdnfFqOti0z0pVmHf9+xgUUjcOHi0jCGRVYqQcpOYySd9gBjGIR8ldWpezbMMGzLj2KLG3Vz/DvjGSkNud8Kt/XWlO6g/lh/DRQ6ZgbYAk42aBhU2IcaRkj0CDRRf+GKQSRnc7DX6sRMP4hhjYxmN1HKFNeBR8JYMDp25NFpyeDxabC2gn20S9JQ7QjCxV8nTYxvCaZlSoYN9A50liXLOGU56skwU+u3fzMdf5wM23D4r1363AD9th1fSdWpajpi8NSQ4JnXyQPP5vesXH7EAojiPtBYnqYmd6U7+76TVi99KkzZb6XTRi5K2yZvSBt2X5G2T94SMZf1nYDUbwWLapNpy86fQjvM1NcI4417WBhdlvqaw6k21ZdWorKo2zV9RxpD8snIy1slrRhao3eaau1Mo7s5T7Bg6kb6joHOQFuCuk2N72RP29GoZOYjII7pQ3Vy+bmkAn9cxaSMpE74J6a2xXN52Rf+yziL90UMLGJgYWMgjwALoA5au1KdyxUxDXU4oWZrjE5WTM3liFUMb+UqI3MEYx6TJCNeR/EDTiI/7qbhZ5SgpWOqzQOkogqlIBW2nYeJdehU97b1Q2nLoMn8mVBBi4S8ZaiKV+1jAOY1pC7kK23PG/k4SephkTB2wsTr0DL2J6DOMjE+DmPgnilZCQhzM1dChjOdjJLxZbKYun0owtinxFPQ40Vd9dm1a3caHhphQrQOxAgGLdfThJ2QigkXeeuByznOPDmNKmFrqGbUhpgB/wPq2pFdk29Q50DwiF3AmXMLr4O/SJSE8w5k8Y14tr4+S+wIsMESHSFN44XvI6Iimo8QbcrQ4sgG96uxxwNqKBKavMIKZ0i6ZIKmwZAJZe5oCJB7mQlk74CHqbdgBD3yocFh2XUoQg9BD6YPuqqKCk8cQu3h2cAQ7DlwrDoyT7wUFe4uDI7o3BMZXx7YZYyESAZehk0+tEP+TQhAzfTbGtS69BlAou6quHb3tJmab1INJpXEcrt+Z8q3BbkooJFLieVcCgDyLFEbxlbwBUk5XTQyPUSsrVs2kAry5J/qkfnZ+8Jwc/eAzacOWTvtjFmVGXjpq5K/A3YuiCCS73AOoue79bqRv3l37+v+nzmSo3RzDY7Mx2yWcQ/He6UfAyAyZezTUdXP7mW7Ck7JfiuRzt3zFsv+7BEVtq5qr6ogjExbCTFNV5X2mo2317oj+95K5/ijUZFqtHfKZOEqrZphitLmrUXUACbJhJVOq5C2+lCnJNjuIJj1RyOBxWBJ1eMC5nMcH5BG4C6IGw4pXSyEmcGRLBYMF5JAA2/fHFobNTt1Tx0RxXDmB2qAdNYO5zti4xVmbGNKDyskrEranLPQEkhnPSeX4/VHn+LCzKJWAcASo4tLK8D0RRpxTVjg3B4shnkv4eXtYJ3SL/MyV53XYpbiqSwgh60aeXg6+/j/BCi1ND69JY1N351GJzalbahOrF75NPy2phZH09SQvE1Xd6V0BGtUSMyOqJyelvWvSRu2X5GmR/HHoXiMsuQpMGinR15btsFM4TsykBn9qeZO5lKYvjluy/RPwqczjjSLNnnr5s+ne3Zcn45d9uh01JKz0tFD58Xvtnu+mu649xtpWd9JEX9i+p5UmXCPO9+tqJdj4nANiS1ucmxbMJDLlq2J913jdyIZzBI8sbJ8eG34756Y8Tf9iv7CfxImbgJFz5HcxmQQJ+cwZGawdRorsbgGe3SrLPRmDHvdP/4j4eJlQWNATaAvfelL6ZhjjkmPecxjFnRdHgjg77333nT55Zen8847L61Zs+aBKOJBz5MZdWG4/mWDqcGZPU1NM8NMheVAxqkwi8vsE8NWzPxlffSBAJBA5elAJqg80Zo+5xf7FiSUcZLUbBwKIY9To0ybC17m7XgpQWIqS4050ftcF4F4cleKI+ulUwoygKqbBIlnBJXHAIRKI+EOx6U0LMrFR799Ofd8TaO+pRRmaAim0EIl7kmUJ+/95TA7d8v3zKM2Ein3TGnkZL9AzM7iwN5KHGVMCizpIDIKgiMK9ZsEUkAlcChZg68MnDKmFUQgpINMG4ROlTPVPJ5IE9K2l5qcFb9gZFidryq9UrSm1AqGSiuUwQhyqyGx6qcNNZCyNTGbXYcAVSWyEgZLwKZSLYisdgtdTTiuDmUMDg6m4ZHZeiib12OBDnWaaI2kl/4q1SFDukZWTYkrGMQGe3GU2skuNZUsUg+tRrrPnqoSh8QSZDyHZA01spAMUidVI/sgWknCQgJ1Z6FD1ssz66SR6wR4V+VRhlFE5rbIXVquaLyUWKQT97ZVy5S4Ns3CcPXz5u5f21PCVh6ufbA16kxOBrNWWb4iVY/MKmwHndeOe7tJ99i/No90sBv5cHzo30FzYUxrYrCHhhxShWg6tiN+8Ux77/b3wnspumP+dLTPGsZaOhhQynvTaJNItqqlOqRxtu3w2nXRpBmb1SxwYaNybCaqjdDpMmxF9B5JXQWrj32MbQ0WZujdEaHO2W5D/+UVAJbfOzt2pfbNt6cK57ulMVSgsSqZtqF299Y35gwpvAXjV3VMYeFu1v41VDfTdn703ThK5PieBR0P02bgijGIsSTUHrduKoDkBsMWhpXOvJDxpsDNHVdT9m0EyuQiSQx8ilZHeuAVEfRz9KEJBY9F/y0z7UF76XWf71WYkjIfi/c5g5GhcM4otU/OOAVLmww8t2y8JN217YdRlnFPOS4zoKr/1clPv2m2Cej6q0vTycsvjPFp013fY8xlVGL81rrjmhXPjjh3bP5GqE1G+YTrJia3pZtuw1BNT/kuw6kho9n+Omrxw31ILRmrR7dvSL/wV/l8WnPMhWn10U9PJ696Ztq4+QoklyLQ4VeJHnME7cBxUPyuWPIwlB6WRn6Tu+6NOWBZ/0kRf3T3pgxTUX7XfxTp6yT5BKbYWrlqTcQfC3+0ehzrcS3mmT3hpwZaDI6Jjj3JMLZR50iR8b43/BdRFm8LHAMyaxdffHH6sz/7s25NfvKTn6Qf/jD3p67nnIcnPOEJ6eyzz57je99ep6en00c+8pEDTnTOOeekxz/+8XvE/9nPfpa++c1vps2bN4eBvKOPPjqdeeaZ6XnPe17QTXskwENDel/96lfTjTfemO6+++60dOnSdNxxx6Xzzz8/WbfSrVy5Ml111VWBj7e97W30l9yfyvCHwn3BMGxpORN133gQmi0tB4J96WoH0WBsHBz5OYj1urDGh4c0Qu/k0hunnGZ6/SJfJn1dDNQklmlruSLsH+9MATxZJpnzr7GKDFlvTrnR5FgldA76OGGGqFCFkkVDX9MEm401aZ/rlSHWP35UIpfYm//en+1k8jlxMLRMW+Qj0Y2nGebs58+A4DI86k/8Fkxgk7PZXNtTqqhE6H53hbQuGAdhKJ0fu+S6Y0MbAZYPesVh7F8DJveIuOIcRxIEtojEd4EHAq8a7PdA8HGYZHFMlrxXYu+ITCCZIWELM9oF2aYkqsL+Ag2YZPVI9rNBU9XleiiX5eAgijDLGeUK4xFLUJPrcRNjk2nHTq24ZQtyAAz6LTkzbQ3qJi7drwaHmBowpIAaBhg8BNh4El9K1/J+M+Cx3jLOwMeaaxBtdepfGwAsccV3kmBtwWT54RUuwwNmFSnwJxsnAxZEjfmTJFsUhfgLEg8viYNw4ir/yLjwO/xvvfvXhLY5l+lhQK+dclq3Iu5vG/tvf9x9P5CHDvtFdXMZP/evTf7rBw4ki26c9t13dZ8PRDrYjXw4PtRQReQIjWqN5Qh/DToNDBw9J8ahosHxbie0DcdTqhw/ow4pM1Rx0WmIEcfxZpJFB9ow/zNORqd4o4XaaMmIu23+gkdk1cUyHFP+pfMIFY2QlK6CKmMfCzdt+rpCfhm+oT9+Wc6LSNNf/naa+tgXwoqwx6Z4hqPSuOoTH1lmwbkdSPBoD23HBsbKWo9qZ7pjc8CZ6W1g7t2/tnFzwCsewphGG0laL8O2koUAz2Y751m5LIwcdX7CEQVW1Y7rZMgtCtCzfGYc6BsoxhJxQoB/MZhwPVQXErayLDMrnsFsLqd4H+hfDnOTGc3td98c0iGjDw4ekY474nE+prFd7A9TxRIwm9NoQ+CG+o9IwwNHpXu2XZcau8f57mwf4GzNwYFVaXjwaM5G35zuvYv8QpSIiuIOvi+fdPnIWs4kXY7gcjzys/zzz34tY/5Quu7Gj4TGzLlnvCryuvrH/xCMm+Xdu+WmYNgc56tIDycrMOS4QeBYVjs5jY3znXB99ZH0sOOeH8+bNl+Z2kgG+6pDaWQwj/sT21HtVPpY1H/p0OqIO7bjrix50x+3bLDw33V3MHgTqFiaaKBvOeWtSaOjGwP+Gls0zjv7NViXXpVuvf0L6a67r4708b2LMsKjeJ6L/xx58bqQMSCjcskllwTjddZZZ3WrcvXVV6crrrii+z7fw8knn3zIDFsLI1n7K6e37CVLluzBsH3hC18IhrOMJ125ffv2dNNNN6XvfOc76a/+6q+STFevu/3229M73vEO+kJexDGNkrQ77rgjff/730/f/va305ve9KY449iwF7/4xekf//Efgyl85jOf2ZvVQ+IZKnWBOPT0a31LGagH0+SuMSbvKWhXCHE+UjBqTtThnJiyK31iYMPLuWyfjrx0TmqqSMaELPmKv8Su9w5nrrScMJmQpa8dHGe5fZVRhpmEXwysTLZK8vrdTI65eQnoSVbYdGEMwwcmchujKQyfW6RR5jotVNrJwmgGFgpZPg7GUNooJiSpnhKeuYnjXSy4LgkcxuPXQf3QQ5prmN13EjkQOObNeh+ewpYJLiJZ5XCBLJ6CJCvgNrDAhThBspT6IITEVYNvpgiLg9CVLSktMqX7vJU8Bb8HYRWGNULFk1gIyCp1CMp4Nw9w7feWc4JDUwlWMd6kEhWYNK2AhVQLdVHo0pwxqk4eyXPEyGyGTYMjEqoeLyA7ZL1k0FzNd99aqD1CBLaQtHZQb2zw7dyr5uq4zCjcIfUjC5PKQNJr815L6qYhEX4khYjkPb4rQLB3JRjK2L9G/ZGoNcSRDK1MV3QG3gMz3LyXxF4WtYUkkpJJa5j1N61yvwXgwKGHUpeuvWH9ntYaaWudsdFQaTReZ2IsNS6/rExyn+6d3Vldq0zUvmvjQedlHr0qkWEsZdOGMusFcY9hKhYLxllYoI9A0LY5X5FDKEE0HTGc7Sn3YVp19upVFdywmVDSEFRBylaHW2t79hl7zTCzm+NzllmnsMwYHmZDv41FnAsfn+Nw7WA8pFcaV/EMtDIPVNE762Go6Gt9LJBo0bW2FsaxCO8w10x/4svscaNf4dwfrOQ6IUWrPPtJ4eelg3TN8UeJl5Z1aycf1w3TeIp1jIWaaVTyeusJw1aGhRqpquY7YBbcO9sP84Zp/9qTfh14YNos56ZvcQYcxH3W49eHoSkqbhcVXRmvMGwe79Kso9KOv90aCCLUOIfqasFg5Trl+Ykc84ePeoYf703GzBYaCDXmuEec9vK0fdtN7P1anlYd9XD8GHNxqg1ODNyTdu+8MyoQhjn6sjrpptuvYi+izBwLUGEFeSTSbMA6pnvHYtGJsPHJLeR9S1q56rT0yHNfn+695ybmgUZaeeTpwaztuHdd2n33BsrsQ2NijGF9JJ1z5u+D6tvIr5OOOvqcyHfb1htSZbyWJie2pZ3b16XlK09J5531B2nb1huZTyfTEUeeAYG4JI2N3pU233YVEtW+tHTZCYDndg32oyGJVeXS+huvv29J5DuBtDhUGW2fMPUjQ3meGL+XsxmRLrqcuHnDD9JxJz4unfPwV0ZdpiZ3Up+HBbM2Mb413XPn9dA9Rdufi2tLmevn+6Jb8Bj493//dxYgGulFL3rRrLps2JDnhVe/+tVpxYoVs8LKF6VRh+r6sWr++7//+/vMRnr1U5/6VDBXZ5xxxqy4l112WTBrah89//nPTxdccEFavnx5+sEPfpC++MUvpm3btqUPfvCD6U//9E+76ZSs/dM//VPkJ9P5spe9LJ166qkhZZNR/fKXv5xuuOGG9OlPfzr97u/+bqR75CMfmVavXh1+T37ykzn3OI8v3UwX+MOCYdhaEKU1Jh9NOA5AQE4z6CVWXBvq72MRsMZg6aTnwBu34sM4p0ntSsfuOUnlwSyYMx4VoaoepmpiOQHJTFVdMTWXEFEQzgTelDKH+JVQjrAo1nT5vbxaQi7FgCJQDyKqqmKoVw2h9KlGg5PZkNFqQhjoanAGqum1ATT23/E8t5yI6EUpEY7soj7uq5ucgsmAu6yNuL9DU/JIaCLWzEXcBQ1feAWIPhNRFSFN/Gfz9dSfOsdeEohiIxg33NxMS//7cA9LiAXDEPCQZ2TLN6HWUW95mBKruXBgIHJUHWC0jhYbxGFuqrSPOPQaXMZeD5hNVSUrbIppT0xzJ4EVIk8HnApqrxUkXDJ9Huarf7WFGXEYqBbW22oQai0YLSfmEFtBO8bh4tOkkRtkP9wRS2cPkHdv2BZsDlCBe/KRiAFwdkfy09iI+9YgRt3/SBtEaYftKuxnoZZuAwojKvZUGK68Xw0YebZZenyBvH40TRkymTHbB362Af01OuCB8PmAV4lN/PhzcvfrZSME4tf0ti1+4rEKdHLr4KUFl6s6ke1pITj3k1WQKpduvv1rhjVvvC71PeFpEa164hqQxgffG5FDex940UtBqu2eaOwlmf7cxfHcWncLHDiNQfErrnrSKXHf26V69LGp78Jf7Qa3b1+XGt//TrwfiLGUbsLD9YFOavsNx4pGBcM/1SqLJBC8nRbfpTVAG4OopdPa8vzTVY45Mu5x2c2qKo2YL0IbhelD9VcLr527tnalY5VnPSFVrr+NPOnfMlGl+72LUjpldX6j7Xf+4xssjNRZx2DBgW9c7T3UnjPeQppMv3CRTqlU6j1agPbgcSF5frF5OD4z9r/gqanSI0ULho16OKTEwtrqTJALRHv9xuhXNcbwCqvPXbVPAzlM2zpWSp3n6KC0sXuQsBx/qjFSOu/pcUtTY6nyU5hHGMOOBTFX2O0Z0nl2XABWmzB/7uVrw9u0WMjqc5zqnQWNdIiuhqVGMqVw65szM9fwKvyiFC7rrv1SWnv2r6YljI3+WuwH3njrd9PQyKp01InnBvO27U6scZZqkRgFqcOw7bp3fZpC0lqFSbFryrChEZlGd2xMuzezJ4w21Fv+beCmecZkOuqEc9LRx54XQI1jAGTzphvSplu/h4q8i28p3XzNZ9IpwDOCNoQ/nczd3euvSetv+haaq2hMUJHbfvLldNLDn5mOOPbMdOTRWbLRbEymu26/Om34Bf1VGoDBdmmPtExJYTmMLFt6UuQ9ObY9dUZRt7Ue+AwvO5ZvxVwMHhqoypYGTTbe8F0WBDvpuLWPDZyYeHJ8e9py508CXxWMq/hX4viA8B8QLF4WKgbuueee9OMf/zjUAGVYSie9IsMmU/LEJz6RNmfLemCcAoCnPe1p+8xcplJJ2Ate8IIk41S6Xbt2pY9+9KPx+qpXvSo99rGPLYOSUrBly5al9773vem6667D9sKueDeCkjcZOelipWglQ3rCCSckf0rx/uVf/iWkbCXDZjoZtY9//OMhEXyoSdkWDMM2zfyl4Yt+J/AlMG0ai9iJSfxRVu+Q/OiCiSgmIgc0HfPXATmZtl6VSifcYKgsj4k89i2ZH7rjGnRocei1HcY4xIjOklf69lIcKxT1P/6TCCwn/vYtN6bWZz8TRLF77VyhcyWjPQgxjzqjuuyWEZOvpdAhfT4QZ7rSyfxVkSj1wfD2D2C7CsIFCicTFWWk+e4UFWMAuAmmzfqSToavXoVhZcKJ2We+tAfrp9l6TfLLIIJda+w3DEmTtfe7+F4UHENUMU7J0PKpCJfhQH1KjgZG1b0LSqvc3N9ild+8a55RhNQx8a4ZcI2TUDEIH3Aj4aelUGrdUowaxy4wCVP/vhEYqrGJNI1BA1AJbIQjLpPRS+4/QTK3ism419216V7KhEGXOJVhBj6+bFiAlHFTotZCpbIBnBocUdXRmldlLmVEbRuqO+Jfcf8aLJ0SrzoMqdq00ncax6HmwdxpKGUKIjljMLeDCmppNKjAad4jSR3lyoCFfzFLHlJ+pBKf4KiJSpHfW4MruR0K1cJwB7J/zZq0bry+y7BVWP3rf+Zz0zTqjPO5of/zz9LQ6/9rN8jDuUuGjUGI87puTLVHaNmNZveYJ4SVR48J2MPxwUbe9cHU9/indoNG//Bl3ecFrw4ZNYlGlJ9sstHC6JO0Tcc5R06PTamqCx7OSPwY/7ru3LNYOKHdEi/dcitqghDuSp6/eWVKr3hxjvawk1J6/UtS5Ts/QiqFoQksOVbOf3hKvUZCvn5lqmzHZLwSbBdu6McVJGndtoxxkNrLXpg6W5CE79idKj/8WWpv5rlwFaxLDv+316bpz38zddiLVjnpuNTHuWs19rj1usrK5Smdtia1b2cf29EwnqUEz0ioRFo9XWXNiXGPi1YstwN3SWzZ31mlqbDfNQyPlAxbmeJHX2SMYf8cwDvvODj6bB+m63IpfkX8quezMU65aFMaJsn9u8zw4O+13Uj/us7xw8JzNX2yuuV9dPed6bo7PpQGl63iGzTT5OgOYHa8qaRtN9/AuDyZpsdg0hLHr5Dupq99spu+hl/Op5NuuvST3Tz1n3Ez5d/5/W+lTfXvpoGlKzHQsRuJ6USkYQQlf3BLoqnR7emGjR9PAyPLkaoO032RomLURANj6KIQh4UB4jkH3f7dS9OGvu+k/iXLgZN4YxjUISz2ZRJPt+3n16d7f/7zeLYOZb13j96Zrln3nnhnuYfwjKUp6n/Nf7wn4ud6zMB/149/mLZe++PUv3QF57ROctwBWgDEdIzuzTsSdy8z6XvxbrpFt7AxoHTK+VemrNepJqnUTSbugWTWesvc2/OVV14ZBlHOPffc9Ju/+ZuzornHTvgf/ehHz2LWykj6P+MZz0CbfCrt2LGjy7CtX78+oigxK5m1Mo33008/PV7HMdi3c+fOkNjp8bjHPS7JPF566aXpwgsvfNBxE0DeTxdmhQXimHAkQBsM8v3s88HebqouGYKBYz5H5QMKOow4SAw4g3ENFwNWzFA99ZRQnce5Py2GPVaHczpJYH2IX458ENT1gUG1F8MIikRwnmwhAph8YtI0b8KVXJSucubDU/3lryxf8/1/vD/ULM06VmQtizwGsSwmyDby2IfGbByHfjvpImkMcObWaXbOvGUM5I6MEhwrgY1x94BUoCNgiiI+8PZUrcwi+xEWgEVxUUdz9PiBFkRUE+aiKvOguMt4uJna5veDuVZkbFk1DSZCRsGVc+98Gy3KVQHOv7zfKuPN4iVGXHDW+RzAxLKzjGX2l3ljgZ4GAz5hziTeAnYYIU1my7i1JlnBZlqswvBU2ZxeQ01Sy41aqlNd0rOdqsy7LdR8xjE8UO8MstcCeLHalSaracmRq1CB6SUk2NqCSX/xX26C9cw1oIJXhDHnvLymiw+oPjVYSXL/S4W2jegs4HZ7hhK2UIPsIw9UHd2PFiqRSB3sFTJvFcJMUoGRdvFBGZ5Ml8xWyYhhziSYWaUJscFdXBLuvkSlhtQq8CyujYNyJj8z9TuLaynCheH23L8GQT+Pm/rUv6bBl/9nOCyQihv+y3ek9ta706xDqgmTkRt63Z/N5ABhN/k/M8FVek5+/H+lkb9+V7xWhobTyDs/kMbe9J9Te8tdZRQWm5amwdf8ySxmzfPVpr8OIV64PWG/pgxaMPeyf2aA7ZiObd6RTCPddhGlrdVD9YELBiUs127ZmtKa1TnZ8qUpXfjkeK7ccAt32qLutjtSuua6lB55dn4/7WQYpZPL0Oznlf7c+Y9Lk6b/dcGw0V9s6m0OsS6HBcMqz2SFmnvn01+Le51z0do3356qp68xGPVa1NL49br23dtg/JCYCCeuwmHeFdQw26//m5R6DJokjJNUxt2XxZhjITB8XYfap7jJdYuZhj5JW0SqX+lpNxF/J+cI/vRbqcrk43gQCzlFSrUyoq9GRPPT0b8ZO6JMxpoYN3NAhHUfD/IhW4kU8gx31KOoSvgFF1mGZ5imxnZE2dlEffab5vw1n7qGacDR3tJnPBWFRKoyfyuBfxFk+qnd2yN6NtBhgC6+cvfenBxn1HN/cQ4LSdd85U+2c36kz4xamc/ey485vcyrKGFu+bnU+fE3PSojz9AUBkbmh39f6cvyo2qLlwWLge9973sBu9YPe92dd94ZryeddFKv9y/9WcbqQx/6UDBVf/iHfxg0bC8QMnM6GbP5nDTqK17xij2CVIPUyYzN53bvxogT7ogjjugya767D06cuP/tlltu6TJ2hi10x7C/MJyqiaH2KPPiyqzjF2pjNTYz1/obbFpmdQzLffqH1AOC3Ak5aAQGyyqzloTsvpwMUux9YDYMZsVJjvLyOV95QoTOYLLsi3W1dmsimMSwtkfelu3kmMucXVL13Atme/DWYhXNgjJsEhKqqyllg3C3nux1aHImTxN/iWgiUydqZQHWfx/OTiDsJZMQqpGocqRJ1TixshkI3EcGc4Is0jyVMgUb66p34aK+1Hs/IJXR93kH4lBtdMKVqJM3pyJe+AcPrChpUESzGb7LoMdKs9/WeBFXTAkn7/qXlJmwmx+cTVX1I6O7zw3GrQ0TJ048OiKB97AC6f41GOQKjFrdDw/D1xyFmWGlvo/V82gjwDKNam4fjF0FvyNXHEEBM27Xzl3sdRjHA4MjmHissFetwfeU4dUqZGuAvT0YMGjBqLZQxQyVUJky4Az1R1YkajLG/KowjZ6rVIXglXFn7YAfmIqGLpOFVM12Ag7cD2EFM7MmHkgX70gT+VO907tMmPttTKH8DQo6rn7jmt/bqkRFDSve9TvM3Symh+/e+hl9bR7XQiVy4n3vSENv+L8itHrC6rTs4q9joORHqXX7ram6clWqP/ZJnJuFblmPm3jP3+9xCPfUxz+U+p/3612JnaqWyy/9UfIMtfadd6TaaWemgCsadZEZK/bjb30z6La9ZjcLdrz2MJZSRlxIdxtSDBR5IcG2VHVBxUUHzrFqy6Cohnb5VahFYtnx5NUztdvCfi2kZ8F42JRtj5+9NCX2jKXnPHWWYZFINAFTiNXH9me+gbrhFuLbdukNJKsqZaPsjuFf/GaqPucp9K1iGiRd58Z10cpjL+37PpZav/eCVMO0f+4DkU0wgtNf+Faa+o+vpoFffXLqV/2ycJ1166P/dZlO/W/fwCWzS4GGk48vYnO7E4Ytd63wc/xzAOyoMlpstC8jd674dGZClbzzJw7LySaGPbt8URVD7cMV9rV2PBNB9VN8hCLPZBHhkC5Z9Y9cKTyqEOOEhcRH4kZJ+vWEd/0i2p7hAdBi+mhvXVwdKv4O6SsvJn4wMaAUTcMcOi0q9rpehu2nP/1pcl/XXXfdFYzTiSeeGCqM80mmevO4P55VS1Sw8JKXvCQNe75kj9OOwi9+8YvwKY2lbN26Na1bty5t2bIl1DxPOYUjOmC65rpHPOIRYeFctcjvfve76UlPelI3ilpj7n3T9apflhE8+kCG7frrr19k2Eqk/DLvcQ4UM64SrJiAJVKdgTHS4Cae4TpWodikODk6EeeGDUDx9qPWFiu9QbtCnJqmcDGRlC8QrXGGGhQwuTG/SMga6MTjz+e8CtakATLnB+HdjzK9jGRzCqLDNMSRHIj9VA6ycIghZSN99bwZAwjmJoHWuf5aY3dJ5JK58rwv9x4NIUFUEjLB6uwkKiOq6FWQwgRsXMo6yGh6uHKghKztJBXKNtxBPwZ+/MVdgw32u5u7ObN1KfBRb6UtMj2kL+iaor4ksCDxUDB3kgnVgth0T5T4HkCNrK51NfMHB4fqJFhkkAPlXi2fiwRKMGduwue9Zr1COgQc3N3HkfcagteQFpmwzAfYzLD4/iGBUzJIg5DZqcsQWyaMm6qD8f2QVHUqWKhjH1elwneosXGi4Ql5dTQgZazNHUmZBgs4bqJaIx4b6I9cOVsdcgsDqNY1yZ7YskN8T+Cb5LtOs9+pNYCRA/bMNGHSGzYsv+8ADB7qjmiHoplJm1K610cdZdiCUQNOCLOKBC8Vd2+a+8s0iKOUTFdnv0/ggbJCrRGJULNNPKRpttVofwERz3x4W3cwq37JoiHUPWOOvAJ3pgEM3w93V0ECXjsj7zcR1ta6m2O/2d7gnnj/O5N73vp/7UU5CuNA/YLHxm++NBPvfnuaeM/f7RlEmxz78z9KS//7x7uqkUrUYo/cE+aJjrrT6GtfMts4CWXXzp4ZK7Rc2cFYwcJztvaytdjxaD9dVzw7ILkgggqyCyRxNMYobfjD/844h2rkMH2dsa9DH7MNmltm2khnx/0ZhkSuuzF1lrIYwgb2sNS6/i72fmFd0uKdL0iUWzcjSyRmrKPvCEH7Eva1XfL1VFuxDMEf5Xqmmsls5yaEYWq/799Sw3CNm7DYomSuvQkpoAw28SZRk2xff2vqQwWzsg2mcr3MGcEf/Wzq8ItBh74e6tcAFbC8598sgGdVs6nHCBK1gM2UAuzgApCPeoYe2W26lY1XSIkJjrUYBcLdNDwjYcODf0owC96iK7PA0+Gnj0mDIcyhxjgk536zco6JMqPQqFqBwwAnyshzlXPRTLgBi+nzHP1A4++QPvRi4gcNAxrV0I1w1qPHM/W6kmFTZbJ8LsM15qFRDvd2Pf3pTy+97/e7lhqVYskgzbfHTSmY9Kf70GTmNCLyox/N1naR9tQQyW/8xm8EHVoCaRqPMDDNP//zP0e6hz3sYcGUypyOoeHkfrjf+Z3fKZN070ceiUo6zn1x5vtQccwKC8O5SBiTTu8kFSu0TkMwMkPsLWNi1GBEa5L9X2ONNIUJYGVhqoqVzsbTdY6SOhqM0oSekOy/l2swd6blv4a0zSw1Ga9RDuUXZhQTVBDCyDiIUD13hggz2/YN16eOey7mcVnSJqHOMaLsbWoOQIxD8auyadkSI0zps1wwZkV9SsavjFBOipIL5u3+tSmIRfe0adAkUGLlxQGMGCXg34u0Iqcer/Dh+AElgDUPGIfDLIoviz2ou3u9gjGL8ssChRkA+TVgTAK/5F6BeawjmYr9bQZP8Q2om99BaRZiM+pSxLd+uLKuIW2S8bJxAHsUp6U1LdpBK1aQvLm/zX1vbC7jR+Jpy0JdEgBkpD0XxwO3/SYNmPZ6bYoDs3tWz0ly9113By0lodSEy5qifpN810na6TSqmE2kxA0WHVoQYG2tPPKrDbI5PdQgkYYKiypkwawRhgEBLT7K5rdgwKKu1FljA3khI+OqoQRUZs3vCR6IGPgQecFYA7fYza3eZ9uvSMo/21PVPX34hF/EL5AYfofvpfaIcyDepWizm6XeWHr23tmPOPr6V6S+Sz6RRv6fd2LBb3VvaH7mG09f9uU0+aH3p+ZVl+8ZXvjIYO389aenwVf+EVK7/7qHZM5oGiuZ+o+PpakPvz+keL2ZeTyA6pSl25uxlDL88L7bXhypaEVxJgVtMNoX12KwUO029oxqVASpUj6QnpUKxpa0E0mZ7dVxqWiJeASfkiXHObsKREGFvWct+mAL5s71JePbT8t05RiY0xPG4ojjXJWFjBZnuWlsJP+Zv+nMhHDmBveYTbO/rV3sle4OdHYOojVuhalGUqa5fxe1MrcIzPFIBPqgk5D7UHkj3IRihXck+xUk+qwuBqpyCLFOPQ3VybURz0vn8k8XeDA/PFwMlNktYsRd/1mOuP2MHYwplub1/nTZ6Ai5Wh/Gh+69KGQG50I3t3T8wrsnXZnPYvrAwP2HvwKhi7cFhwHN1+tKBqS3AiWTpqRK65FKpDTEoWTps5/9bJx19uEPfzgdz1Ep5X6v3vSH+qxU7ZOf/GRk47416dW5rlRnlNl817veFQyUFiKFR0GDDKlM1ec+97mQJP7BH/zBrCyUvl100UXJesjo9TJ71lWjItp9mOuOOgotDZy4eCi5BcOwVefoMzoZ6pxclTRocUl1sz4m4jqc+VSbjdlYAVSdUAmS5tCDWSsmS5momJR5jz8mZhuQzol+zwkmgihLBgcinRVNz+KqIAGpQ/DDHqJmBgHuHifypDji5jIlGiae8/R473/7O1Ltuc9P7e/uneiTuHY/h+lrSF+GKxzcSR2msE45hSpbPtyZsjNIeSoEdvdamaYsN56tiZOptXJC59H9DmOsXC+jg/V5flioF+aJ05gVCH2tsZEjb3t3LaQ1CJbCaAbKqRAle3bYvaeeP0RTrsIdEj+/G88SQGE4xEeYpCB0gqiiLvzBuRBAGhhQMBbETEXzijIywaASDg6CWRVrZbUCL8IBYnyWtoRRCnGdUi17h0Ta5AT2SGAC0yTMN+VDfMos5g8A6ThO/jB2TcyNX/rvn0p9S4lzdJ0jAKbTjvWUWz0CxhZpGrBPkOU432mcBjIJkaYF7xZWLTsDrrbTUmhPfSM8syreD4Gn2X6lEJ5nFZYhkaj5vUIawT3LZ/Hz+9tuZNCodz4knDrIqIGjzH/D8InDjFLqYcsUGdxLZq1oJKLD/XCBl4iRq5vbEh6HsWuyaf/eU5GK3EfXuOwraQe/6nEnptrpmBxfcyqGjTjwfNOG1MYKZPueLQeWo/vbPvhPafJ/vS/yqJ3+iGyAhD1J7Y3rU+sXN8VxAvNl1rr55wcF+3x5Pah+tLFoWvlSgKKnjnsM4DYy2yd9jHYdezFZAEoeAQDz5iqGxjdyKtupbs4b40A4GraWVe3D7Sn6RexPpf9EG8/pIqaNv3CZaYsk9BuZH+GihLgZO+ddYVyTJJgmL6XlwSyGugb9JiLjzwJJ9CvmnsLLbkdm5MG/zJ6LYwbmXA0jJ/eeAStDA+ONwJP/UStSuuhX8SjcTVenyuZ15GN5RCGNwwLTT4/Lc02uAyUU1XSfsUaMAgg7NWNPWa+exAf1GKb2e1NappUr7j5a2lwXUcq4vYGl32L6/LnAzQOBv16ULz4f3hgo92mVDEgJrVpUqj2qJqmEac2aNWVQqBm6X+ztb397qB66v+zv/m4ejZBuioN70KiH6ooyhL2WH3tzK89P0/qjjNlrX/vaWYddK1n7yle+0rXqqPERjajoVHv8xCc+EcZDfPeoAPe1ebSSjJ6qlX/7t3+bnvvc53bN+htPVzK45qEkTgnlQ8E5TSxo5+AfTJuEJkSAq5gVzPcNLGXSh5FqYmij4WTJ7AYfoiwu15ebpK+TYO/EMt8AWcwfZcouvuQhZSxMU8N4RBzGaiG4OLDYjHFBLPNcWXlEqj3rOVFm+7JLc+A818gHf5NXyF+Vw373dyhlI39N+weBTpiwSTQYtySmg4go3n2OcAkkiQcmemvcxEjLNFZ58p49JUzmVDifndz3cLP9qhAv2IykZ7lKLnNx6M2poVqfiEVClmEKEgSIswEMNaGsT/CGvHjUgjgS+nyouaCw0i2hEoyVlfCZdMDLYzAnHSV1EEchgcO/ZN6DUZSwsipSaX7OPr8v8ZG6YaiUFXGAQJrnodVVV9WBo8WBuw3PCJqCoYfp8hy3qlIF8ELrC+jHaCnIAtI00uDGMNKEIfapyNyxAt43DJMPAxf2SjQgIo0FVVaDgDUHGTMPeZNJFuZQawxiN9ejCWBtCL+sDgr4MJhR2agZlfZ78rOm8aUl+CyCnxgOP/BgW45n04Ejn03a2zzweki79mYYNH6Nb++9jx4QAphUW7feHL8Div8QikSTKdrR/JXKI5ZXWliMNTyhfhz6fn30dVWQm0yy9L+O4ueu683Z58LZwYNpk2GCEaJ/evq8w57jYrTjMi73MqXMWG7juR8ZM0ogD9PFM/GNV1e9UL8ysfnZj4oytFbswlIc/2KY/lwyg0c6F3+A0THBLHI53JGymWflz18H8wYcIzMS1rQLNcvLLi6hMteYBxz/rbJl6HyOtRneI3fDzBTpvOcz8hJRAfd+c13z9eRoLQNbFlXAZEFd/8I7B8/EDdz1xltMf7/jz++w6BYmBmR0dCUDUtZCadaf/MmflK973FUnfPnLX57e+ta3hqTNw6bn2ye2R8L74OFh17onPOEJMebOl1Q4SudeM+POdc95znOShlWUJnovGTZN83/9618Pdco3vvGNsw7/lhErw1X91DLk2rVru1n34kscLjJsXdT8sh7KWcARXVfc8Y4zaZixYhpgBqigYlbFUESNZct2ZRymDekXxJOqdtLf4SK5eRYTH5NbMFYGEuZEEm6mmJ4iCSwih2ACMreiZA+/OPOKxtRCpUd4zEY1TaVq9Vf/EcQ/VgE//D9T+8asm1yUkosqygz1TMqNw1fNW6YNJrQDByFz2kBy01RVj9k3mDvr3AVYfEgAFIAHBIILNHiVBIxpJ5FAOjv0A5OqnRlYqymeyvQ5TS+c5bOMSpTD/hMPaq6EdKYMPdg7SIi6ZCLGXAISvKOsjPAQfJVV9h5qlO4BI7L4sy6SYv7JzsWfXJBSKDe8yY15WnaY8Rc3lCdDDIJUqcrMIpgQaRyzAMcMAYi/xwK4go8EUvPbVfQnB1R1hGFqjvPdx1GbhNmrwtypMimhp6HoaTjAcbKYAsZJmLzmEKq7Swgf0nJlK3lGbB+EqoZPKqrA8lcNqR5lodLY6age5l41mDZ+StGisl7dv2b7BlbhpuqpX87SMH94yJr6rIt2AMFngIfBh5EeQoN5I1I2NgJTDD6CKCQs41i0lrnkvBavixiYFwMyJTa0cLSfot9G4wx/W6nOFRjaYm5ovNo33bPmignHStAHOASBPkAvtiEbv7cNWgTeocbIPbZ1snjmCOZykkxSEaWIyJvZ+Ctu9lFBivi2+UhhmzdjU+cc6ozjPjWwPpn9cyb6RV8irarIfcCeVcrLnCiO/ubiVqhYRk8j/5xtXoDj/DmNFs06CoDzxzqfuRgVWoxFsXgjPOJAXKrl7PmL0TGByl4fKFbFUtQVsHurqjVgVeJCoYKdQefh4J1WIoUpvnPkN1+mRSULGANXZZHgdjH9LwN/JcIX7wsNA9KtuoM5AHrt2rWhLqjq4saNG+9Xhu3WW2+NvWTCJrO0N6faYunmWrks/R0Dzj777GDYNm3aFN5qu5UMoeqehvc6x+Lf+73fS8Jx2223pZ/97GfJ+paut1wlcg8V59C+IFxMNgHprCE/5tKYFguVSScoJ9M2DER1GCaqfwkSDAhmD0keI21MwLnKmYlhk7/e/Dlld+cdX/lFdjyGv8lMTyGW42NJlATDgOqJCLWLOXlKLAhb9ZzzUt/b/tbUqc15Lc33vTue515IEk4WQ1kIVAoSNQnpPLHVkWBVBiibiGF6nbCyOuXEV8JT5p33hBX5FkBHXOC3I4eDCHBlWAYnmDCYhjJfwzNuctTeqzyreDGuq8tt8jjkBhUf0FzLzMvyM85nyp+B0Tr7J7EmDsU5sc2BOsnw+smsI+/8ZKzDTwaYdqLoNVj5sByp6qh+/FQ/1CCChZoxkjOlbRJEYfwFJrcyhgfHJPjXV/PMHCS6nNFX9YckTfPVyMmQuCLtVXLH+U81pGvtpRzGPdIfh5lXONWWo4CQiknQcUabUjXKV+1RBi2YNSg0dsrxLLEIgwaRG6b3bQOAFn3Ap+I9c7TiAT9RSaOxjfpi+2q739Pvjo9HGkQm5Ufn3VgDwmtaAv3W/hQQLLpFDOwPA5VhepSSIxaubF8uOsVAYVMLToWHaFm0NB/9lS7aIZMsKrkets3qB00eiXRhSVLmyFHWNhoN1Hd+Dh0xxDl2olGpyiPbmBnoGBjszlGGaSI6Rdq/SUOYC27OB0EgOYADgzFnxtMMYBXmribzxYKJCyTZ1/Lzk3tGHU1Uz44OE7NBGUwc0kY/pDNFNUpgHFRuXpfSEgYCjKZ0Nm9JnR9dC2ycweVqjurJDnA4F1gcBjz/UaNbJT7j3HD8Ovi5TzV6caHanTU4xRiV5VpCbn4H6+YenB24p4qBNzL17kXUlGGOISKN//DLD7PjLKYv8HM/4c9sFt3CxMDSpUsDcA/Pvq9OmmcQo3DSeeVesvuax97iX3HFFRHkHjMNjuzNeSh26fZlsXK5RqNwpURRVcuSPp3LrJX5WT/PfZNhU93yhS98YRmUlCiWrpd5K/0W6v2Q6etfWsUd8cOVw3meEGKyx18JQkzgBOcnmTYIUibYOlb+OlDqVTakB2MhMR6zhpM8CZyxg0jNJcQkUkw8+kjUli7m5QDFdD3Tnq/+mJG0CBZWDE3EfhaOZU/tH16VWt+9PLU+/lGIGCZfXGQTT7Mv5URebsyXOYuVVQBRtVMxs/VostKb9+IJilNgT54Cg1/+cwK3Qj0lBoHD9A1zYj4tpD5yItJVUX9vPdHNO5fgU3al5CqsTZJPvVgNKsMP5j46BRMDDjMuM+YFIxiOEmN4yNKWrgSzy1wZQLB4889/UxT0DpnxTKLMxPG9ZExwdSi6kFBJBEl0gbPcVPiG1M8DvdvuJRuCWGvvRDWSTDh7jU1pqTmM5cdh9jXeM5LGOYS1OQYL1zcCE7uE4KF0L5YLN4zU09gKIF+OmtLScTYIjyI5ZR8gxko6EKVTWKVU5bXdlMoEaPBZ9QdxqJXUMJMATCGE8HsCpXUPKSBtMe76ETYSgOdv5nfK34o87DD8SzhJwNpn/Obm4x6i/MALkWTOugSr/cMiTbzoFjGwPwxwVliNRTQZtjZSaY/OcB+ybcg2l0dO2290zvCLttVtXjwEB2I4bZtFDPeBOV5Vmkix6Q2mjjZLjJyPifOYEQWxKFFDkq39oc5ULrFWqKxH9yFVHiNNRj9gbKXX46X2ggsi5kccgI5uQz4ySzKCzheqINtvIh4DSsTGH+VoQHcfc17w0D9qTVUcj1yYcc81mu3ZXzBuvj211t8JvNSLDtkxrVgSLtXhS6MtpImSgqm0YuGR/cyPQRsU5YWVAA6rxoN9SS1uYXLcFG/3h9NKpM76R1E8x3yaMZH9xYvhUWR+Nk2O59Ni+gcafxnLi9eFiIGS4XG/Vq/71re+lb72ta+lhz/84aH62BtWPrt3q2SAjjvuuNL7kO+216uuuiry2dvetbIQmaU1a9aE8Q+No+zNlWHl0QWmc4ywrNK2xHxpVY3U9ape+t6LrxKH+i90t2AYtjw1xrRXTDcx23WnHucDJ4VgcrwT4tTpdF9hb1C9PsThpUhEOPeoyYpDrKTGRGJKVyNJ0cOh6ItXOf371nUkoxwuFhjOkoqGhZ8GO/qQhLVhDISg8fnPpiY/mYHItLgVife4yQDpQgLI3fLKdDZirTtKMIxLHKgaR4SIYv6WT2xXtGOfm2+GR9nmgxN+4NLSoR2iwR60GgZTBqDQPTqAbMkgokX07sWMe5woiLxJ4PloUgmH2qCarMpnJSYyh4GQwMguAxXVAA6/boDjsw/8gtny0efsFdcIJ3lOQUo4FZMYR65F5sU4lhvl8RL5QzTlvGBk2QsSzDhMbUVpF98o9v9RZyW4fpPqsOb9B9L0Tg7VbmKav4E6APsPG7QFTz2rr6ynJasGUnU5zBCMWv+ARwaMkx9qu4oCUGNU/VETNkpXXSWvWg53lbFkxIJZy2DnOgKg8EveCbMV4z++pfyXL10VR56rqpYZgdoqcMt47MWxeBI/Jbbye2RqOmBYdIsY2C8GbHz2M8zW1xxnEObHXi3PNsxBZGGnjMYY7TRanI05HG/R1LjIuNkvsJBqB4jRWqMkITYiaazEGJlWa5b0jLyAlQuqMv5bTJN9phYZzAIRo5eTR1hXNR1xOi7y8adRqVit4aYLMOOZ/aYsAqpJ3UTMpRGS3DecoXL5HhHCYYtRThZSk7HVskx+5k0WhEfW+Mc/3Z+FGRb7tFKr9Dw6tmVamIdps1jkmGj/dIrQPksecHnBWXdR4bgWtaMAx3f3QE+psk78mFoKcCLRIVzcw5Yhp+bkKagzl6gwHkVYN7CstJF1BiymfyDxF2hevCxIDJTGRuZK2NyjpZrjjh07wmz9fFKkb3/721FnpWwaKLm/nMyQzKBu7dq1+83WvWtaa/QstWc/+9kxLvYmmsKWgmb6daeddlrctSop8+Y5dNdee21as2ZN+PdepOEN082Fo8SXe9cORp20t5zD6flQ6etfXl0c0XTlBB8zoEN9zASswOZpiimJGYtnvJ2sWkEAMwU7Q6Km490JuRlqMgVrQJ7GjVnHySNnmYuL/Mswy8+TrtNzPFtUwBSynJgsa0jzOpi6ZykVqQwsGxmav2ks271eRW2ijJlLLrgEJRMW5E/6mJIjEeQI+dcwZVpzrxyRtVAmCMFA9OYMziQQilwDXstyMtezJFxkPlypqGOyUNiDcS2REGXGJeowAyvpSZeJI2rm9ylWO3rj3PdnV86FmB+VLjGVS6LMIpSazco6p8i+mQGLWoJtMac/8SOJMXn0mZ9nnEmwyahFnPiWxqUk/IVGwjPW832nx/hWw+pjjZXrGsSgVjurSNeQ4SN9a6fBXexT3DGNTcmptL05mnYNjYSRkcFjaCwrOGOvvps2MQpRNwYOJ9lv1mKbHN8SaisMp8isAbct2u9nJ6UU6DdgpBnHM362P+PoojqCLbzeWf0Px2swc/SJTMQShp8MobcyfuC7TOs94phrdjNPpc/ifREDe8eAo2HwXrQzG6CMW1jqZZUgGDcWZnJzy/3TnHKPzS3Na3RFfDU45HmXiLPJyH7BeBdGSdCcaDkAG8bPWzzZePXO44ELSY6NDG2pMslYat8gDd26gDHL51r2L9O555gneB+6mHHNmjCzjaxZmCKOLgwAOQ5mYPO4wrPGfxrUsT+OPDE9fvTXGNS4MS2h6mge5fic69CC0aszb7iYEsds0G/bMGth9dY5jBJj7DIlzGL0/4DJMvS0DgUi8I9xGamdmgHu3TXPXJKRD81V3cNWZCEIs/P1LdetjBNRBR4YC5AX0/8S8FcUsXhbgBhQgqaTMQsarRh3ZGxWrVoVVhrf+973hvXFXpXDa665Jl1yySWR1j1gvUzLzTffnP71X/81wjw7TdP498XJKJbuBLTH9uee+MQnpq9+9avBtH30ox9NL33pS7vjp8zaBz7wgaifkrBeWEz3mc98Jo4oUO2yd6+c6ayDRxsoXZtrzKSUsD0Qxxnsr74PZHiedR7IEu6nvGOyjLwkrYuJNd6d8Bn+XZHEOVllYjVPE7WY+ZjWXFpEFQ3b6kzaSEl2M9VOsKLJ5F3DDxkJq5OZocqTKiQHM3o7qH9zzlNMrMb6WrzHHMybRHQ4J27K1NS/hL2WCrHHGKo8sfIKAYC5CWdSshDuYqLL2Xez8EFZk9kaZLaq9mQHnCzdDqBqV5muJU3he5yA6kF9hXqfKkg6knUn+PAQP3oKMdnH1AnuGlpVAx6lVCFetlDjcZfhDLz6WlRYQkACoOqKL6pOGqhQOnSoLrNOFk3BAWeRI+UKkhRKRhtvenTjSHTxXfGSvc2ETF6NN1nk6Mp36aiTnzajqSBiqJPMV0ei0px8j4bH6n7ggDS0kTYfIwjAkEjaZiTgxAf70yiiyorW4BAHqqMy1GZf3BCr5mkJTN3gbloCeTW3ky97U3iroH7Vbxu0UtHWyM8y4zV/D8k06+Mv8GL5AR6Rwm11Ba8AAEAASURBVBXwF2/ePHdXXOiIzVUmEGc5pg3kRaCehuBpjNy7fMo1y6GR1jiLbhEDB4ABm7AjkO0smpV26H3HyE6F8Vaz+6pLIqaKMcU2Gl0t2mLZ2myXuW3HsBptlz1tWpHkDEIGPzoFh2a3EEm5/zR6IvGjOZOHiXR68MMOFbAQl/PalICpHWl/jXjxYHzbP/2O8VsVSYc0zYVYmehH3Szp8zBcxm1YD+cXKhpF24+BVfP7LfejyrRloAQmwoLZKjjGEkwTBzPrsE+Y5TnWKF3rtFEDtc6K1chbnJp/aI3GwIAHzjnGMSphJTfnQKbsvRVF9Qmhy6NIBjSSHPQljI5YH9xMaYGBgH2WH94xZ0TBgclumsX0Dyz+4gMtXhYkBtwj5oHT4+PjwZyV+8VkwF73uteF6X5N3HvAtIdKuxdMhqo8v+1Rj3pUSLV6Ky+9WIbLCN5XVzJs7q8r99jtKw+lhG94wxvSO97xjvSNb3wjzlKzXgoKNBri0QVK1DyDrZexdE/ajTfeGCb83//+9wcDunr1as4QnorjCkp1T42PzGUcSwlbyfDuC76FFLZgGDaHtANzeeLM6jJOJnlCyXfyYDKtYY1rEIq2AWHemshMhnyd83KsqJJFKRWTGM9nrzHB08CC+NgHIM6vMjWqQlq0E38d4xQhPUECJZGgIREPbO6qSAJWLyM0N3trELUvUGBc/eJgbVTxZNbabZTuPASWYmvusdinc8KHoCAfCZnIjccWao1TEyCCSvSjRlMUJ3A8d99i4u3N3roo6QtjHb0BB/E8HXs1ioTC13UQeCC/a21Of+paxrAOGj0JApErfGTUI5IHAgkxv/g3Va5TSOAUW8VKum8Fw+aHFhWRgYy3bGDGq3JZ95mERVA+qKvsMtDSX5XWyjRSW5KWe6wEzFh9eCKtlUhd2ky7+keTf+3qbuoyRXwJT9h6pXxw4+0qhF3UgfwoV+ZRCLxaV59Vj1Tdlf8ALr5LUHyE9TTOBhGsthWIO4/eZcL08yX7my7XkqdugsgrysSrdDla+bZ4X8TAXjGQ21oRTLtRWm03i37GGOyCh+eOtTW9XzBusdxStPNISZRocmbGk808PzKIaozEQ+61pFrB/H+DIzLijMToLZE8OnAMBHk8j9ywmFhDrVmeRsbN8TIWYyhI8CzeIG1Y5WNKGDfkkRwnHSOK8NzvqEKx4j2tloMLQgFwhPLs3j36L5NL9Gv6Zw62LownjB0uFQYuLM8x1B97iuOcRxbEYgwgfbuJxoZmIRk3YqARDqcuUKEI3r4r3Aw6gWL7b7xbIAxbp+6CHs9cHKcCDuMfioPxdg7J447Mpc8Z/7melBtAxKUoEz/iBZaBZTH9LwN/h/KRF9M+mBhQc0zrildeeWVIqEqGTZhOPfXU9Dd/8zfp4osvTj/+8Y/T9ddfH6Ca5thjj00veMEL0pOf/OR9gt9LM+wzYk9gybB5/tqBOs9Qk2nzXLUNGzYkJYA6GbWzzjor9uHN3WdnPd785jcnVTs///nPJy1IllYkhVvpmWfQyaj2OseX9evXh5dGSR5KbsEwbMgrCrz3Tsj7+BRO1L3EP1Elsp3QtAhWGe5jHqsj4xhnAmHWw78FQxZkbjmjMa/EHOf8IvHshCyhoStu+YUgHoTMSdGJO6wzEqmqeo2TmJMbU5ZlWYpx852JG1hNF668x4uxdJlQEKxwEAIBCmbjq6jm0ebJt42ZfvLmns20F3H3uOVMbPBu4HdCDcuQwNaQ4WOfh+DWaiMQDRkYoO7mYieKd7xMa4g/4ZehPVQ36hEBZXExseccg1kuca9XGQccC6UlNyswQMU7CCFOEUk4Q3JmOokIfvHnZ/SLG0/iQ8VH7tYlfsYyFISwyu33ktnma2HQBtP8EFtK6Nq2Db8Ff53OSal/vJaWcf5f366pdHRnMh2JCmTfWDMd7WHkGCyZ5pBst8hIIsos+WfZjQ7hISmgHsAehI0fGldWxWclqabRM7cpMYDL1BEPHNSO+DTjwhqU4dYyP5cDdVaTzGWYLvIlBxcTZEhnOT/yolvEwIFgIBYbjEgbstnYnn2lTcUCAV3GRYrY3+Z2NI7B6GCcRIlbRPNi0uiv+tGKu+3b3q4fY0WNPaA1+gvS7dRG2tZkSlMaFeyXc4ZxSWuHdmBjEFX6HEe+IBVvT3FEhlwb4ebvGKsmg03flAyEsQe0HYttwG49CLQfFT0pJG1G1dy//dG62r+4xZwiAzaA6rTDV6DCyIyV5h8zAYt6URrxMSUbeVTiwGvKoCzzYQmNBTHqGWLCCdIxhpBZ2/MeYXxl2sw86umYUeLKua2fJaZ+IINBVJPk/nJTWK7M9bS+5mqdy7rnu99J+CM0cFL6i6beuBFjjl/G1oOVvv+MkTT83CO68Ddvn0xjn90adRbawx3+PMYXyBfgRbcgMeC+Lxk2f71qgVZGJuf1r389Y08j9ntpWVFpU6+kam6lZQA/8pGPpLe85S1xEPXc8P29v+Y1r0n+7qs755xzkj+lX9u3b8fo2pJgLEtaZL78XBBTTdKfkjiNk6gBJkPaz7ag+Zwqn1qJVLo2lwmcL/5C8lswDFssJ3aJAFHsJKeLmYLRM0s/sl85SHlnwnL2ZQKLCS4YGuY3Z89BDqTmjJ8m5tjbmP1vN/mRb+xyI7xGnko0suSIzeaEaLR5by5PwFEUF4qliJAKMaH6XFM1hnzjLC+ZG/KOlV7qoD9TGzPwzITWW06XWQtP4ka1jcthyzRgVypk2poSIOQtI3KfnCgiXRMcT01NZ5XL4SHuEjEyIkGKFKVD6PAnyeI5XtYjmBoZ4kN04zBsVSpnjn620gVu/JTWuyRGDOQ94OCxCayFV8RxQs1ZKDUjz3iR1JlxmRjIPpred81bCVocmxBwkHukM29UpBATyAp1oPrafM8Wq95x5l99MJiu5uQxaQr1hcaWbWnJ2HQ6eljT/Ds4p20sLZlckoaX96cWFmy1IVCBUKuwit7i41pfmT8lldbJIrMKLWHU25oErEHA+ZwJPuPleprIX4gxyEdiVcyARzOMHIsb9RTHOcPIuQwlQo4pnWcbCsYNP79sL96Mt+gWMbBXDNCuc4Ox7eWWGA/Rl2xjtnPbJuOIe7aMJgMFAxJMG1K33G6NZqC/shX6jLNj8qhJHzaCko6fDA0MWBsdwGCGooNEtKILFADQ56pInsJaI8kraCnILMlVWUqOla9KtNVmiDGQTmixudcEFAFZDcLC/tkgn96x0hwcS5oshrmfLdLFmAnggsziSyywWSXrYt6Mwy3OeqzLiOlvRBZyPEA8JG8FhME8atrfeZEyIjGxO6wGBe5MS7UogP22BlPnGNMMP3TXKtRczSm+J3fL9Tnu4ioK6vHrDSvG68Mxff9JQ2nl29am6tIZEmn3p++GVsgLYcGsHcbwz8a/GF50CxUDStKUInnW2OjoaDA6c+siDXhfDIusW7cuGLwHQ2VQgym9B1vPrcve3g9UBVPGViej+1BzM6PRYV6zLAlw9nEKVMrDBBUTFwSqXvHsHad0LcK9E+heB2ZCCWw2eUEIQ5RjDCT2cLPXqI6USsuBztdNmJUm5v9DXoLUxHkwT0BOnkw83ZmOd12UPVO6C7kxG3I3yMk7kjC4a6ii7sSPLksT4qKJxUrjqAokQ2HCqFck4LVwmYTwxfqYL/GceNlg4eqEKpeuqMiosT0/dHy7cV1VxkkI9LpywimLcoCXGDC2ls8mJyewdMlKNQyK89IMw1PkQn6BCyZlCRVXgguM9BZzn59bMEJKn6RBuisv1DFL7wJK0QQO/PORF8P5NXt49kxc8Z0BUjhlxMVb2YLCj0t8L/IxN045c7chV+KTX/6qlEC7kfjzMN8ae2a0/plc0edX6XhG0lBqTQ+wD5BctpPunmYa2t5JS6jLypGhNIwa5NRYO01s3536j1ieqjvA1QrAHqEkDs2W4OzQLjA2Ca77UwM/q8UbxJ51p1WIE38E5LrbBgp/6wbCbCe5HVXTQBbh5WzIKdJaMR11C8awyNMWalBIhiNCGVHPme8aJfcERdTFyyIG5sUADZgxNRpetxXORIx+62vmUBR5hZQopPrucXNPGVI3ma9g2KI/5PEvdw4bYvGzr7pAodqfTIQqlpTt3jbVJItVsVy4Hcn4zhHeOMLD0yOzGX2YrVDksDDject9w8O1lb97tqaGgcqyY2yMLMs9bcwh5CHT1R2/KFPDUC3Vpl2kiWxJpFOlmoELbWhcUSaDUtu9ffyEL0vU7e8MEErgY3B03LAc8/AJV2QZ04TPhbeLQvUwjlRG6gnU6yBdQ53Moowom2e/q+NT6cr3uDsI9cbpBaN4LuM/mOlrR/enI9/2sFnMmvBM3LIbGqFg2KzjYQr/XPyXuFy8L1wMXHTRRemd73xnuuyyy2adN3YwNVIl8e///u/ThRdeGPvjDiaPwzWNUrjvf//7yb1uF1xwweEK5kHDtWAYNqUbs2ck2YNi4oSpCD2WcvZwQovd2ESRWZNMZ4JuMuENoJbmROp5V8zosXKaBpGkYfa/DxFrdbSaJltjoR7ogOx+Bzm7GaKB7PbhgkYpVt5CP59MYp6iTAVQsVrLuToehNpWBU5VG8qRhgiHGqKEuLXbmwt6g8BSAiNzItM2yFlfEjimb8B47s91CQriy8CoGqlETWZ2CqZ1gDz6IHhcvXEiDfzHPGWdICZKQIqCQuV0f4XuJ7wJk6wTHudDEOY1aJQSSTJdBgqDcYQtJFS8+6ZzWvXNuMHkUK8pmGT9ct45XAMjpWtSn6ZqoOFnPrLSyltdGacN8GtXwTHUVYd7NcGscaBvA2tpUxixGds1mZZtx4jI9om0pJGZpgr7ZIaWYlFyeiiNY+5/cut06h+jLUxS/jKgWQIr6YHcqEnC9UGAId3UrL9wIL2rA5NnCAqT7ckDs20vpQTQKqvKG2HWLQhMjM9ICAeXbj34lbjxNbAgc+eTeMh4LMlQYxglVuotMzz0Ao7ypfBbvC1iYD4MRDOhgUVzM4KNTVe2v2DUeNef/1io8sH9nA6U/CowYNrZ6CB10yKtcSPHyNyM7Bu8RNvm7niEJcmKe9s4R6DTUI2Q/tpxYSUzOm2NgJiSd/tS5ChTxPigKqSLaTJ8UQ7xyjEnxnLnAf6qSMtUL5RREhTzIyXjJwabWPSSi2oCa+5yGS7TNxr0fRZkZP7KNGbQtpIwYta7zK/GgkuH/X0VJXdEjq4ci1nqeVhGcJaUoSq/qo46KhAZkovBnicpg0HcPp/Jx4UvF3Vm7QU26UE4pU3WPtfFkoVel/2EzUDfyrDyubyXcU1Vxin9Hoz0tWV96YS3npFqq/ZUtxq/dRdMt3NIbh8Z3lzXwwV+4ZiLv+y3eF3IGHAv1vnnn5++9KUvBaM1nxn/A62fdNsrX/nKtL8z1A40v8Mp3uc+97kQWFg/t+881FymjhdCrdqh0wGkTG5BjDqBlx/EicKf01bPpBWzHO8wR4a7Ruq8r1Jc8Hiu3uIRa56o4zg5w9KlAdK1UC1sxQqvE7HEssMgl26ZZInL6jr5uXulDDtFQBWzNhMlgb47qVdhGkMe4qRO3q7aNomn+qVnZhnR+Hn6towy51xfpwuhieMDgjCAYEfaxgJuV3e5PCXePHW9jInvZZ4l02OZAXGs1vLE+/j4WBquLAlmMOpTTMCRvqifz4IDBNApXUDD+2Au0zBsEjcyJEqB3N8XhAZZWxOnS4uRVIiaGZjRgYcPJQzWRvzKzOXnFkxQTkcc4Qf3kRTEWT/3kYWlSZj0kKahMFsLBl8mHwKP8NrAMAeN2x4GU2OaPZCjtBWkZ2yFZAm2lpbsHE2DMGnDMLyDcO/mKK3YN9Sf+iGapmHsag2ZPZg+1D8rMMXtEe5LmPiLs94qLtELKFRXC7hk1mLfJc9hxCaaAX7WwVZCFGGzbTdAjt9BiaB7czJjl+srotx/k4VvRA7cSXD4l1tV4IySbXu2pxKj3mdwGy+Ll0UM7B0DSrjsoHZHm7DtODxsaXja9gzKt2hnMegU8dzDFYsWaESo8aiqZDAZ9Atba+liz1bkiG+URaEa5tCgTz8yc86YDMuKTQyToH9OD4qkwtAdu82Otl5FRd4xgoJIRz6l5CpKxJ+kFRbGqlP2fxbaYO6sTXbeyRMioY8xw76rMSj7k9m7kOL+V01z9xHHeSAzJEDkvma6fJW6Wv1y31oYYzGrARk8IjD3dNSlbjNtk0cuG1jp5wEc1/Bz7KCujjtOV55TZ94NmI3gb4W5wD8xD9opbbJEMivyKOoa76V/eTfKfM+z/fKb+ZX+5f2BT1/lkPXVf3lm6jseWmOOa+1spIltE6B5pq653rPhezDhF2fzlz+nMouvCxIDmsP/i7/4i2Dafvu3f/ug6+Aet7lWFQ86s8Mo4bZt28JAybOe9awwyHIYgXa/gbJwGDZnVCd81RqZjGNkYmLNDBT32DPhsiLxcuDMXbUY4nq6jhOmh03XQz2FSZQN4U0o3RoTmBN+daCPTeQY3BifhrgeT9PsJdB6V434ShzMfW+uHLqDcCiYmyBUIgGMhpwGgWEmH5PQfazq6qaLfQ9BJEOcG8eQiM49xzJmduVEn/dVwGgw2ysVc7jWT/VIdZ3354JZEyQn2CCULFMcgwcIgik2sPZD9LRRjZxvtUKiJyygUVATYKnZ/orcb/iYTBqx8q8kSfjMgYTMcOW9iBlmweUpaD0zz7iSKONLecNDyZ+MW0d8g99yX5YEWcnQ6eeJtXUkZ3WtNSpZhXmv0t5CssbyuyeiTU/xmyZjrLa1J1G5HKXNjZHnJGq0GH0ZxuroUlboh8lvgMm9TVtr2fbQd+znAO1Ee5rGIIkr/FV0cKsQYC2YuyrGXhAEBNFYtVJa+uRbAjq4gKBzVd92UdQp6sFqvvt/3HVpm9G4jcdJmC6I4j6utkOcVxlhD+1VIhtMLJ7BFNP+jS8uJA1l96IZKkXwPdqEBZsLdVh0ixjYHwY4d82VAU3PO0w7dgc5aT+j3+VWRHuKBy+2LW+FX9xdoMDPg6+VECFxaqvuyE92hQZLszcdZUU+PPsaLxSKdkWFA6MNtu947EalqcTNGEY0kfkYNxKmGuOycduT9Df6XO3R56fKmWtTOvaolI5ZxcoLfY5FmSp7VNvf/XFqXXkNcLFYRoexD0WO9ME640eoMtu3gDH6LvA2HadZqHF/mmkExuLjzkPOx1zwZExtMTdUAybjAqYMWxPpTz9MKUycvtFN3csmcxf10tNKimfrBVzg0HmHK6+5fxv7UFyoB1JGHmOEOeMwcBrPM36xuBTBXub6z/abCZ8bT2hnx835zvY7mPQVjl5Z/ZYz0+CpSywkTW/lfMyjZhi38VuzOuQDVf4MzJZe1ru84xN4Nmx2XQ+0/qZcdAsbA1qI9MyyRTc/BjyX7qGOnwXDsDVRG4lDnYPJcW+ZRKYTHBMgq6KaUM8DnVOYA13pZPD8STCgKuYUx79zpBONkguZNT1j8Yx8pVZrSwawSVJH1Q2mDYMksoL9rK4miGvVSpiSAx4zklnS1aCuoU9wcQkaIDMa+kkM5yDLzuMuNrv6hyCy+5HaTKNax1HLTP4aRKkDg0xSbCwXJhIEkQKoMheQ8JRb1rO8Wy6EO/jwlHjP2PBEes8LkjmUWbXOfdTDfXzCQFaFy2nLN6f6PuCaGJ8MlZtBzhYLy0Okd+FZYyfeXUMWb8HwzMJ7mdN9u4/6XclXaCI7AeQn/eHPwCCLwp93iKNgwIjGl8npIqnpZD+oZ8SVVCEDl53FJLito+pZU9Qq824ZMGx8ZdKQjniy521WszuuaKvGyh0eNk17+C5Mm4fwVsZh43ZPp7oqqLSTAeioIfLsq6LaiNXKNqpZnuVX6R/AxD+M2jRSNr51a0K8AgNEZFWT5Ep7YeYS5+rFPhV7plIGmDwtjQbh6leHOstqTbQJvydMX15GsB5Wzbg8a7AAl6UI4Ax0hHJnEHW0H4lpCUlxivOWmTmkvUgXZNyzVJEA0hbRjLroFjGwXwxUVIigOXtGo9p7Ds/ZGIbdsWxQ3mlZ3PKlp5XpZ5jNGKbL8SiIU7uizJvqzWhA0PtzWLTQMn1u+8FAeW5ZdYL+DgANLEp2lpIBKyMwPjGORLooLOfvI/lXTzwxVX7tmSmddDwec9xyiHp+1dNOTtWLLkyt9300pds2Bhxldo7dfRyNoqaDDJlQCg9dmAUc5zLHmrJcQGJwizqxyBK+MnOOhTBsca5o9FuSsN9Ntewqx7ioDuqcptKJZfgY6qS8BFrJKBap9IeBbrM31n1x95drukhqnfiegeuiPt389QcQv53xHIPKZ5+ye/DTa/xpzRvPSEvOZmMxTmZt08duTWvecFYGkevoL3YioVSimutyOMG/P/x3K7H4sIiBRQwsaAwsGIat2j/GmK+CmQS6dClEqe9MCBKwThl5DnBA9ceE5wyR2aSIq2pIOKKah0lkOvw526m24lTvJm9dhQl3YOlwqiN1mxibSpMQ5UOuUkJAy0hpxlkX0jrzggiOfMM35xsLt+RP8LxOEJVsSGTXOFNN2D2AurQWaCL3lZmPeQSoeh6A88BFiZxJGEEJB4kIJYVKGPMZX/vIhIIk9p2ItXDmSm+LPSWaWZW50f+BcFPkrTN/p/pQs8TLM5GKrxJ18ktp4S3wZTwQ1MHgQOBJ1aFArEwaXzTyzHfNeavmWJWxCYbOdkQBME+hpog/GZOvzJ7ti7xgzuTHWpgdb2KpzXtCklaDYKyPsWcNBqzOexVmfoT4mCDhiCgPxYZZwxR3S6ZwEHgxcFOZaMPU9aXJUdS1kMoZVhniJ1PYR76UNd3czUo/cftpT6rlyrgpKWCvW1Viku8YFJmfgCatehZfKePHvZFRHetIm1IKZ9UDRyaTGeNNhpV2JWFnhAhXLZgljWAICaCVEJd+oWqlBGUQgsZZdIsY2A8G7Ih2JRqj7U8lB/eiZamb/ZRnouS7jRDX27QMtxvSDkM6ZWT7hG2XvNqoJUe+SNti3C3zstxypLDfRwNnnFZ9ESlTjAUusLWGiSZQUQj+xd1y1p6cKi//rRxXuPblVi5Ltdf/p9T66/8/tbfdSzbAR1e1aMdaF4Wa0+5T1pOMCHBMc/HMxULni/AmKBYhZdCU4llvQiourDDeVLQ0bHKQ0m4wFvaxyIOuqNU1j2BeC6bOeK7xqUIa1SJSVYlnP+ME45jb9O4Pl1UixXfhAt88C0B4c/HZS/jpWQSWcXNEIxFEWE+UeDH6A5x+zavPSCseiwQV19g5nW58+zVp5WPye3hy2X3bDvavST8cfvCXMO4df90Yiw+LGFjEwALGADPWwnDV4W2sLjJZsTLabg8wD2NyHqlHEPVMbrHSKEOW9W+olANrMbgqYfMtJpF47F6cpsPqH/dQ/SqYAGbY4PWUjNRQQelnVhyvcGbbFJIty4CIVcqlmqXzjI6pFZjMMbucN8//m703gbb0uuo7z3fvfVNVaR5tSZblQTbGYIOxjcHGmMEJsyEBDAGCCd0hYJoEQkw3hHjR6SbJYjmm6ZXudNzAagI2ZDU4CxonwRA72AZPYGzLE57kQZIluaRSTe+9O3z9+/3P9913X1VJluqVZD3VPe/dbzjfGfcZ9t5n77MPSQWpxhuEnXJ1JVQaIjI9cVL6JgS2kjZXJlWnM40wdN4hVkTj/lXX37vXLl3fGsReG+sgdTKWAN/kdPvQSJTVWKYrPCqzmhj6+ICjfp2T4fPwV8/ckamUYctK97wMfUhj78Tb8X2ATzAwfTlNzbImVQEooyIFwi97/8hROLk3hFu0Yq2ZpE2OUJAJdj8aTFisXdq2pD8IxUcadBelU/5Va5hI03jOQeQ9kzLlvDUIzSn7ViZjyuZqNtYgG4ioAXvVRhgKGXFf4T4kDidBYT9ENVpUJGGyBuor2ZcoQ7sKtbQOQba1zvFRQguG7QTMEETZCn15qNEYiLGR58mRVvbsEL/FiELMlusnseU+m8CiwjaqVRJl/MK8kV2jlTk+uwfO/hNCUgYOYlBGNWnwPf1MqV1NiqvwpX7AeIO2Tm+j/GHcBLKJLt1pEGhouxbGPZTyaV/PP4/5HEVXVQOi/oCDNK/jjj7v+BvSn3d1KV4yC+VCWN+756TpM903fZ67AnOlRtjqyfg3LcMbzO7aRa0J0cvbkdI2MkY61GCQREuSURE0Iq45wF637/immkj1KuVDHyuzP//LUj59Z2kefVVpvvjGUp76RBjIDn1eiLTtu76hTP7PVxODUpKp86uZO19mjKHOmQ8JAQhUdZSJdMHGoBaaAmvMpEVzwrlW/1ZNCI2PZA6hsgnLfeGsOVX2s6BCWOf9ANTwFkTHdxd60PBGfVu//ufHs3eROKXcpEGS1tO7qZs1rd69pOVSR5/mdfXz5zn+9S9+UrniBdcECNMTk/K+f/n2cuL2o+XaGx+/CzB3f/TwjsGRvq0eBuW/X/DbVZPlyxICSwjsVwjsG4atrN8JFoA9mmBWHYMgE1U7QG5D1Ng04hFJDK3AHNo5kLKEuSubkqN+6KlSJ9rgM1AKd0h3PnbshowBFIEWxBJe4pVkVjbWyobIEPW3lkMKcywAeu+ueGoG35Xf7HMDaZnmfbnmi59eBt/8rWXw1C8q5bLLKQAluOXTpXzkw6V959vL7Ld/E6QNcaFUi2IoV5TwFk9I/JNDrc99ZUIZiE2ZBmXdk7WJvL21FenaSAbmvuJ23yqarS8T9l2Mh1vQKLSBksAU5nNU9H7kcWqQqQwhf/K9VU0RCFPYWELEH0hLf1TiAEjEmAp1NI7IS7U/mQ0PFJdZ8ygF4euXqHHSLQAr756thJqieyKNA7FDd2JPI6vfWGybH6KNCtIEZm2b/eZTmPWhxJSqWDBso20kjux1GbGCvsbzCm0zQnVXK4+e1rZGwbUyPuv2rHzRs54HkUgJt1fLTa9/V7n7U3fQxlX9UGvdA6hPpbrlBAsSqEe2a1CzElmxIAkQkLTNJN6UsoVpwy971gSWTaI8EeYL+EjaBZnbTvxHOkZeYy1RWnv34QEXKLz8NGZiLNOg5yX8RCJz7oRwkpr7nM8PzWVXlPXv/+/K8MlPLaMbn1IG1z2WjsW88Mmby/QjHyybv/5/lMlb33Tegsi+EtdNhhLzdrWsZykpc/5WpVs/Fxd47fsXj3mmG3bOL5n1cte7leGib2ftgXRiLdLFFH4uQrlw1mtKJDU5O1Olf7cjJFMu7qG23HAkR/aEwQAlomqQh2DaeveGN5fyX/+M8jIHnyTxWz5Thm9/dylf8PjS/Oj3kJ5l4/bEx4AnYM7QRhDn9KV11IRpg0FVqmY9jKMEzMNunZnQn4530uHDDAmgc1/Gp/jLf8ZrpGTiIOMwjxQPCR/CqfpOmDB1HcwEe4R6eefCNOe6pdL1zr6W2e3J7d7DZrUoR1e/JGwZhATzYNrf72lo7903/MLoEeGhjn/NN9xQrvmWG1LUGfP3Ta94R7nnk3cD06YcemJVj/Tj5u0nyiZbA1x0eDiVP7C9H/BLBZeXJQSWENj3EGDG3ydu7XawDRIIkO2gQbqGVKPl0OuWZdqgR6iBKoFiUsWFQBcrBJO5QRukGITi10UHygyClcjHH4TmRNjfka3wCIHLXqL1FTchw/icJAwSJ/dmKN1xEofUhQEQifLtTBwbwUTog5/+mdJ83QsXC5Dn5pprS+HXfNVXl7Wnf0nZ/oV/WmZYFIwcD2Rf8bRYOUU4LX7v4cqmzpur2NZ5Y13mhcpBDGwhBZigcrkic8Bf0jUsEQgdF3VJXjx8WSZU4kE1yu0t64i5f5KSCJEw6uN0Ufd8m4ZxIOE+bWBbaRYbh/L6wR/1kQGrxmOUoiE9Yl+I55FVqtAE7BP8JAyJY5iUVwYOwyIyc+6xUYK4zSJANvRPMdMPccY//jButOeMfWcSggPVsDQwEGaN/ZAwcCP2sa1uzso60t91CJMpUrQYFoDTDmFEG8hkPfOpX12uveqGwEcC5S//89tgzmDrSEtrjtNN1CfJdAVT5IjhEMhZUnflbeFPH6evDVGJHHIERdqKdCVW6Z4hxuyDNqbNLF1hd0/fp8bCTydB5DEBOhccWghIYWjHdy+chJWJDiWw8JtFMlnDC7+esEoC5/Fl9UXfXQ7+3L8szSWX7oaC0vjH35jf6gu/pYz/9E/KsZ/84dIeZrHpvHPpgHTB2i8dsjIxdr/0Ixcd3GdJH1aaHGk3fdp7P33mbvezb+u6rlhv+JkWCZqm3VimZMAeUMezlvLD9BCXnpvoplPnZgaE5u7dmYw0u4BHVNVs1w+V5klPqGG93vzJ0r7xLYQhDy04qkKJVMwz0pr3f6Q0t91RyqOurOEvvrAMLrsYtci7UlwXSBxv1lUV5pELMZTD/aEpD9+UiqlWr1XJgROxZXX+Ur2ZOKrCi9ui+WExVZnGz79Whs19cMzHrVYxKb+4Z86NOUkEN1k8vrnYw4Q0NB8ZZL336KpKpLCv9ezrO0/WdulgkDu59mGpwtz1fn3YnQ8PXvxHPe+68rjv+YJk5WLve/73d5TDH2YBDfisX36grF7kSll1Rz521450ravr57v8DyT/vh7L+xICSwjsbwjsH4ZtINPFaqKU8xrSDPewKRFBujFBcsSpV8xhUrBWCaQXzkVKVczgHdSn+gmYKp8qRYu/DmTM1fhKcqRwRSJV6sAHVcaIqF9zYAPLiWuYcj9ZNvnNyH+o4RL+JhLRJCROPM1htGPw8v+5NM989mmfTvUYvODryhpSse1/9FLSEmVTH5CKyS7guVOj7ULCEgOuCMqWGUuCYU0VICq/tYV6pF/uKzFiJUfCpN4wPTJ6W8A6BAf4rIXh6Ov6OZI6raz35gEtQYaUvTaShZCOSVmUFipN1VCIz9bLvViq+FmfGQfupu2ts5KzUHG1bQ0rMyJM4kNY66LVb5k6/aqBBIgk1CDd0zajzlOJICVNSN6irsTd1faG+9Af3z3nWrngmqffdgg9ElLabm11vTz/eV81Z9as91GMwahWu6KkknaaKbGlH0l02edmx5AMAl8JM/fctYp42RO3TXpayQMEqDiREEYHBm78J6oCBKoXf6Lg7PM4qx1Q8SDBKizJO/XFizeCSiXDNHKTgA0TR8Sx+2YII8EpMeXvfHcH/9dfKWvf/XfvFxhWnvc15dC//r/L0Zd8OzDu2uN+xXwEBKJb7Th7m/W3D+aa5wxxmRD67ZCFk9YDs8N48Nn5LvMp/uHciNen2d+TSpcuaeQcSMafUnD3s2bM2qcXwjkUagksCGFkdhwvStyQlLLqkxC5vOUd3Oqc7rw5ZL7TiEfrYiFjsdyNJd6eYTMJJi/3jFL0Wn6LhnPMhWljP1ubfa2E9QPjKcuNLJysrbColrmDSP4jqYtEJ/McaZC2kvowuQx2pfGsDlFeyq+UzbHNPODd+Vo4yyzWEWsBxGe80Q8pZedvIc7eTYUxLgx48qx5W33zTf7UMd+dPxbC1nLhseBHwJT9wY5/xZc+qjz57z29K2Qp733VO8tn3nsr7zX/QzfuSNcs390fO4x2iQz+Tl0/n+WvTLslq+5zwb8Pt7wvIbCEwP6GQEi7/VCFIEEmdEzzgXgoMQYw3Dg+QCVyiESiRVWyVQKnHr873KUCcnf6B7EwGYdA4FU0tjPhVhQeGPBNBsQ10LkzT1w2hOM91FIkRPVwBuMG8oNdzBndWSEGiYNKO5WceQos4B4sg3/1CsxD1xW9fIFon/3x60v7p29E9Y0V1md/RRl8x9+eR2q+/CvLyhMfX6Z//WEQtdJE6hsgzIPc9wNlYV03SEk5nTVag0GwHlrcbGRE4lvrd6bEZB6AmgCrIbmr1rMtEQEXsCbTvAC+OUF1psTup99EpgoGSlZTlcbRCJVX/GRcwj3wRZbLjH2yeaSFKxFS9zRWhoOyG0xuRUKAl6EmscPzU3cPqiUNfHOsAjQYRBLf2a/oiqtSMc9BGkkEwdQ1qMysQkCOIJxWYK5G7DNchdFaG29hZAQJG9ms0RfW3bsGsXX1VQfL077sSeWLn/OksnEQtasF99m7bitba8dLe3CVJl3JUsP2iRPkD/FF0TzAPYf3Yj2yTDBqYJezIPyyD27EXsrBJgFRqcIwSUSeSOqoAfEJDMzKAd/5pnU8mToPYBIeAZiFcRnAPpAsGUt+7nq+4SGwRpv31I8GkyATlosEbWKfP5eV53/9bmYNWG695tfK+E3/tUw//MEyuPpRZfXbvrus8evhtPLcF5T1H3pp2XzV/3b+ACo1pb/p7Dv2OfpT+mO8ffYTswsTbj45vJlOcq6a07fjkXGotMw52bnFqH3M+mQfr571G2NWCb3WE+3rqhq62MKYzwJWgpoQqTAfGCdO1UiYt2aD+Df9VeYKw7Qf+yj5maNX7v67b82xoPXJxzyqS4DbMSxQojbXqpFAkMxJ6l5TB+tpZK0cM6FRJCRoBqDigmbKfKGB/oELOHGWkbojeYyU0CIQ3HMbM9aRDsZyrfMVjJs5ZPQ6hudIzoRJXzUI/wHgYIN5jT+F95nXa2ZnfR27F9AKAA9LoAvz4ION6jcf+T73F3ad/zzMQxj/0idfUZ72D56ZMlm29736XeWTb7u59hfeLeeFT9jNsN1182GElyIC3Oe5/GeTfy348rqEwBIC+x0CosZ94VzQFAdkX0LDhiIx+ghEC7mstbsJhxa3qKxhv5hZV6QgV8ddTOe907UJES8yji/hK07hLR5M3Eg3KvdFFBBrAnREBWFn7AGSyC+opq0MLsAkPybaj/NDAhK1w8X0aqpl8D3ft5tZO3KkTH/ipWX2yU9QHytFun/xzlIO31EGP/wPuljghu94cWle8S/UYCFfLjKdIBRrdWa3k7mqQiJni19hB8JGYrLKGW26MWeBJc287cTLqxcyGQLDGJsIxVSRsoRGLJyR/iqGMvS99/LMU7vfDyOoNqVo9eBqVEiBdTUvTz58G7GCbfvaqtYrBQ3Cl/mSCLNtLRE/G9u+QDgJtlYRUhhV/CFkhP0Ypkz1R1enTU8iKaavgfeAn/tJhjTAkI9TGLShRBh+qtE23HNGH3l54PnXPO/R5TlPv7RcdOFaOXRBhbMlPNUdPnybPD/EH/BVSkZ3sl7bSLrcZ7iOqX88KCt58B4z/0kEWNA/UcKkalZ+Vrbtj4QboWJWsBwXh0VQVVhVq1TtMhRkx/BTTDLGP4xsR0hJQPHBT/XCO/Cq44ibf8JIAk1ds/PQaYzi4D9/5bzmLQz7sR/9O2X8xj+a+00//IEwb5N3/0U5+PJfmvuvfs3fPP8YtnQmQND1LXtRXDqgfYsfYWTE5n3OAHRXhjH9H28l5pi5zyKL81/mdfyTdvduujJFXed1b5pd132eptWwwCBT6CSqdDrDhuTt/onCc4omQX7TG0rznv/GOwssqEZzACVZeq+LQzUCY1JJ1TNYfFtciPlTjJKQTZKLRFqZO/MR84WLZBXjKIGjUEgVx5TH6Sj0P+nLsimtHzHuTSYSPALIAKpFEFaLcAPUsJuoRbu8wkIfe++c9+QQTU/1bwXykR4KP+ZAmTVXFYdrzHUACiiECU5h93DZdjGoB2JX9/l7n67+C2Gcc51P/I9b+LbrvfuccAth9hL/omsvKc/4cRZGmXd1H/j995YPvfEDtc8tlOeix++oOrt4d8en78BCsgiCqnwey3/W+fewXN6XEFhCYF9DYN8wbFK4zqniHvF2GDDNGudgVFEQhO0KjBwSjxnItp1u8JO47w9BBvGp+rXoJBriKkaor87KIDpyS35hDfgeyqKGlokJwmczVy/NmfJ9GwMUEgQemjpyZVOa+tJLS7MgOYNDKNOfe1lpPy2zZl5dfQg6e+3vlsH3/gDSkW7T+1c8r7S//ArqRd2ptJbFqBwRWHn1J2FAoVOL7tKR3bWgqYDEDOi+UjSkBXOBPt0UIl81PPdHVbPZ1qVDpsReTEfNIT5W2FtgiqAqzxgz9usbqHrilz1RftujW+PQcq04KmHLknvqJYI1bQ28+OgzZYVwsuWpIWXrfpYm7Wd7B1SVGYMYEoZSgspBaQaYGpgzDYywH8R9ZLFcB1HXn30kQzuUmwNptzBBMmsyaVBbaA4i3aItyKWqaJLys59+RbnmmgWDBfidyd11+BZW0+lDlllT/fSVIYx0Q3so/dzcOl7W2aepxZLpFnWnH6tyy0YKysJdCZwcn0Z4RkhLoWhzDiFnM8UaHP1CazWpL4xb40KGRAq3EML2u47hm7dY1xcD2/QpyiYceRbctY8I7/PTrX7Di8rg0dfNK7/57165i1mbf+Bh6zf+r7Lx0n9SBpfX/U3DL3xa7bN1glkM+gh+7npKOthir1n0P6X6C2GzsAY345JNOqBj0LHoIgq3qEwTPnu+7LuMAvuqTiaIl3gpTnJeU/CcNTzpbtLIwlc3Zxi6hicOCyhti/Q6RkkYo8wTLVoczhNJ9hKkL0/9gtJ89XNqNK93YEHwT95Cvo7pzEbEYamJ+Sl8pmXx329UZ4hqpINR/YwwZpTDMo2TJ1+U0hG2X2RSayNzndVnfnLrWpVGwtwxN2TvLYeEkxgwIiMYs4CDNDN3R7oOvnAPG2ll+twp/Vk/TVATPKMj+wDLu3OOrn/uv1XfM1/7MH2ccxB/46ID5Tk/8XyMh1Up5off8KHy3j/8q53ydSUZMk9edN2OhO2eW4+w7xvawsa3Ln3ZuvBnvPVhzmH59wy/MxZ06bmEwBIC+wkC+4dhq1hYnFRdsCeEs6IR1FMGa6qIgclQF2tQa5tOIJwnMm3cXSlFKiHZG6SW2Y/HYDPnYvxxoLua9vzO6xy7JUMYRKQ7Ugzk64GbmI4EEaKWuQrjc5xDh1l5RwQFMnbdEynUD7wEBNpJPkz9df+ptO9/b80nSfZ5kqR7y97/vtI845n1OxYkW6QmnhmmAQgPA/M8tSFAEHcY3dXXWAeTaOG/TzK8SWokddPVzY9EVDVn7QBECHG2hB9BAGRCe5XAMS0J/qp+yPcU03efITYgDDZPbCHdRKUPVR5VFgMXv+/BjYYadqFOlWKjXLYN+dL+gh0+hHJJ+OjPT3/KpiqQ5F0qy0q0QtKZUjTKGYtxfPO7K9tbEH5jGWrykBCTyJEQ8u65R2H0yKhBnUoCbADMBzBpQwqwGqaNd767VrCi1I9cN1CVevTVnO204CbEOXHseLnwkovmvh58ffex26TaqAN9yLOhNPiB9JPjzekDSjA5RJ0jBFbos+myxI62JOWrKr9INgnvc6tKlASfcHGxQguWqAsP4fcqo4m/RKiAA1zWLaa/IzUlTIg5i5eWJR0DdK/GsVfwXlve3nF+utEzvnxe8ZYjMjb/7b+ev5/pYfrX758zbM2hCzBIcUWZ3YnhpKX73BBIfzNY19u0kur4dsyy8CJfo7Qs/Ts8Q50P03HtrEYLE+cDcTNVEE+pG4aEolpI/EjcCJw5zjHE5zilbVH3g6HCqEfzff+IOZyxvc6g4qiUXe5jnyztq1/LmML4BxOCatSOy4jNSW8IvnDfm3MBo5WyWSb82VPs0N9WF5vB6/xWNRdQuaacdR8aYVkgcu+1TJzlU0W04ViRIeqN4q0Zqvktk0PjmIcBVJUbzg/VaRK37hnP3Fli8yDvKRxjVwSLsSen0RHT6lHkzrOArPWsGex+3wlXwfFQxL/iqVeV9QvFLYCEeW3l4Er5spfAdFtMi6fjWYZuvncdL98Tjuejt99Tbnrdu++lzot12V3fmsEOPB76+lu5pVtCYAmB/Q6B/cOwLc6qQF1CememVZrgUiheMjAQs+xM4gWEBppUPaxF+lDai0F6BhLLE4e/ypt0aXnDgS7rw/w+/5DwCjBkCKp6IsQC6mxrGwfY04VBDxDv9DiSEtQpVy66vDTf+M1dWtyUzrz6NaemSkJ9fjwe5vDVPgbqM80ll5TB3Yfxcf8ZeYmAYTjCHPGc1WMqUfcugKgJGcldn0aXml+6WubL6hpEvx7AUUJBJGaAvu6BzU5J5qnVByNCF8DIHD9+FJ7pgrKKUZUwejXA2V+VIEk8yVmq2+OSNOWo0jGYYF95j8RQuBE+Xik54SDoXJl235n1ca+HJvqV2smoIRTknXRYcp+iPqQ5Z60zhhFSguWSOO2kOqRMmaK4Ae+c/ocmkyqQEEb4D2xfyuZ67ZD0ViCofuO3Ps5h2Fga4wyAyT3sezhxd3niU68uX//d32Ch4+4++hmqRyGUcKneqfojVdYqnETeUOkZguIJ5YL6Q8uR/qw8k8bKPjwYZCVfLAVACCJ/mHSr9TDhDfsr29kmiwaaCydZGbS1Ck+Z+qi32sxmRV+lMoTy7s3n6jK0eORLdxWQ/PDo1jjy5Xy6jJ7x7Hl1p++HaINpuy83uPLq+ef2rs8umbU5NE59qL1st6/9DZ9Q8s7TPnO1jzO/On3HgAZj1GnfKVFrvenGSYgw/ZzaJV/nfcYQ/V7ri1llkZlyvcr9n104mbc6FJwBUbm/+qpSrnp0Ut11cS7/D79f2g98JFEb9wfzhJwsaZqGc2ksDDuu3TgGs8UorHlR3pHqmjB5ExeL9Ka+Ydoo3wrj3P1rWXiCgcyYd56nEGE0I2WjzjCgZaTaJguFSNujP58JhfwCKxLmkZUrVCmdM3hmLPdjfFedHuBLtRJJGUksTZX6C4Vk0fmd4dkyzN1DE//i63fUHGXIrn/mDfMS3NfDgUsPlsc+63EJ8uG3fChS0KCkeaSHpvwVvh0sHyD85kVdPiwhsITAvobAvmHYFujJAJxpknuHZWW+fOW9cXWU01hBfbyCxHASyC0Si9n0wi4GMx4J1tggXsOA4CvDgW8S69JOus6QPPgMfky+xPevMo4gRQluCICRK7CuArMaPNAipFRz7970plJuu6USyPp1WYjYdTWrXbNxzmlr74JhI4iIRsuCSgaV/qhOM6Os2UQuMjcZi5mEkiSXLhPLqlf3zYNnVzB2Ynw1gKbshfKjBJHqRhL3fdRTYd8nYpht1Pi2MbW4koNczWBvLhIjgJx9a2FMOnhIaYgpgbPsigyq9VSKJiNrGafdcyRWho/0iVVlmtgflvtJ1y5Pm0iocdaZ/qFiUC80DS0/ShDK4Iw6FUhVIWXWRgRegVlrZNao+4g83Ls2oiBjzmh6/02Hy9rkTpi4zXJgeAKpWylXXrNDuAuZw0c/TbHIwH0wMSRCeZAMKxmTaZSxHKGWs72NBUtgq9qrew9l1vzZivUwXfxlbmXIsBaX6pJUM9qAkUTCvI1RE0FAmZUE5+w20oodHuomkUWOIfDmfDH9V8Ig40CCl/i6vEfsSO7pRNX/vLkyhsdv+CNUIF+fKk/+iv2m9+Fk1obXP24eYvIXb5s/Lx/uCwLpkTVA+ln37jPTUXdxIqRT8uq4YI+bw8mFGqVbzgV2bBetojZpwPz3aRPYudrJklvj5Ml7HX+GYXzb9+t/KVdfb8anO+f1v8Vi3Gtfx963DzGeKQvzac6FjJSLsrj4g3PMatV2SpwMIycd0vcyQjLv2WhTzdXGaWiFRSHCZk+0ETQegjRL1Wn348WQiHmYJ3/txEUfF25YlMQppc8+O7I3emDn3ACT12LS1iNNKrJI8LO+9Gb9TaDWtKtWl6J+qWb3Xt96mOz+9mDHv/Qxl81LcbYPt3/idtqQtsOdWrcHu/y1zEJzL/CrqSyvSwgsIbA/ISD1uk9cPyUuFjdYnJlfBAwiEjWKbKXkxVRIUTzEWKMkM5HUFAkboVQvqUwSMaBgPcutToSm10+KpJln7qaXZxArxLpEa0zJp0iVSOgPLR1ixn3IGS7TNVZnn4PKxYJr3/qWGJkAqy/47n5UfWrRmcUMCZhFcGV4DcmYhDgyQ/xhRGEg+ArTQB1JNilbwJR5J6X4p7xdTZUUAYc11DWVSKmykwNbJYaIG/XARNpJ49QnCXkZ1m3EVg1WC1clMk4N9ADfWzeb0GqqAdmutRrkk3LRdlRSlk0XnlIpmUvsYUJoT95VgbRdoWIQmHHoNYRTDlpnWV0Lou5NC7MG06Ys1jxiGIB302kixaQ/AVs1Bke8DyCiBqyQD2iLCMfwl+cip/yEfMqDn2CjVIHNldd05zThpzt8DIZdXUoWFeYJqQIF8SkjZrfVEql75ya071TLcHhazkj/0ibUjf1trfQZ7a5hkWoZ1XwpN2kNWHG3TlZloPlt0s5RAdZRoi99gQufLLeV8a8nZPFN2nrVZ2559v08cxDYJ37xZ+93pTd+4n+ije3H1U3+csmw9bA4/Z6OeLr3gk9UIRfes2DlnG8fdcKRGckYwkfGLRoIDpsqdavzreHt7NwYT95aGSj7PfPqMOqMjFrPRWR8ZIZhbLXv4dDs97297iu+4jGlfNFXl3LDk4mNg9lqXvQ3S/nEp2Il0n1mAxZQcryAqz7kRxGqo7wKtF0oMXEZSz+JR1bZx8ouVCRnxBFX0d9UfdffI0xcmGs9iJG9ti4QMS1RTuZvj0RgrHPAGt/gxmAKGycn5rcsspkfr1Ev58H9sgX10vZYCtIV7OxvsRLZRydJ0Y7Oevl4prvfM8/w1e9z9yDHv+nN7y2Dt1bsdMb8KYhlfva3PScLmZbLBbM/f+1b5vW4+f03F+t8xvgPcvl7eFquB5q/cZZuCYElBPY/BJjp94cT2d27g6DtMZSbr4OYJXRBqOwtGIQQluA9DhYBAWLefQaSG2CQxD1MYtCYdVbsIqKNQQYe5YykBiDy3S8g4lflJcYYZFa6WTQTqASEZfQu4+LG8i99Fh6dA1GP/+KtZpVzgnrvxbvJlct2rwS2R+pBrAlHhiL4EAqUXUMnLRs6ZNokD2QexDrDReYtEfW2Ljsu4OTixvg19jbMQPhbIKgwh6RTpY1g+7jdcftUTGNAXV0d3jxJG6AWuldXGTYqQT0D5w49pS1kTsyUYkVFUuYqzUV42kq2Robdc9WmqDdOYO7y41k/jwmYsDI946BrjR1WxGfcjsiB0BlCNLVT2hiiKXvWyEN+p1HaRlwPznYZYJV2rj0Hpg4iS1VV96fJMFkSGVlAWy4/lWE7fitdhHaTaXNV3ZVxaK1Yc1Qlk//G/WuoOSnBzMo76du5ZFTtgb4pgfQMQBkx6z5w/6ZMKuXIeXIwDEoZ5HM1tqOkjepFouB2yEhSqUMYN2BIEmF25VA15JP2VxJhnxLmOl+X7t4hwFg4+AuvKGsv/sF5mNknPlY2f/NV8/flw1lAwP5np593RJ4c+J1X5gkHI87p0UOuXYNz0SmGO5wnGG6Onj6NylwYiBGlpErGDWnb0IUTpN3u0fVPpfpsIL0HFVjm4vIR9h9/8w+X8sSnJz82nJX2hkeV9r1I2RhEMmuuDTKAiM34ZN5w4cS0lHS7U3VK2V3QkWnzi9oOq4zXbWMw/yhVN39NybvHNQdoy4RS/AFzxJD9aqwbMoeh6s1Y9oPzd6NqpJggKt4EZizLwDbMEWFyOQKkjUESJ7S9D2YlbGmWDq79NNH7WWed8+zOc20Bw8yb9SGI/753vr8ry06+p+a/dnC9PPe7virhvGgd8j1vfe/u8jtP8u2hLv9e4Dev0PJhCYElBPY1BJzu97cLBd9XAUQkgg37AjEsNsPsvsxWXWU8AjIDuY05OYszrqaYbx62WDmEqtVcushdRFqd07K/zoHYRfmxzCi2h9KNWln8Rbr5HP/kf/V1Bdvufewi4bZ95M6oxvT84Pxj/wASba57TP9GMiD7z34WU8+1mULA4ydz6mHdGlIZou4pu+aqrKXXIWSiHPU5HlxEmb3zi+o2WkkMY8FK8UbDmXbA6tixY6QlE9JRQH2kM9xjJh+4TSBKPO9odQUqYq9OiovSps1SHwkO2U1KbZ0kcqSzzIqmkrEIE0sImWsPPt9CNUr1SH8n/ww7AABAAElEQVQycbGsCKM+RgUy+9WIL5GUNoQBMx3bz71qMjcyXwPycQ+b0rahK9bEaSCmNFyjFpUMm3SZdgzsDlqIk0rMqj5VkFm75IqLo8JKsLjxZKsc3ToMUUd6RG43WVzQ5JsFkDCDALN/mpkSA/PaHrP2Tv4pL5+yF0d1KOomASjBNyPfAavvgzX6Cf9Zs2hhnsnHXlJolxkStyn19/gojacOLbhEaggQ7vZnyyGhqgqWecGpwoPUysi8SQUv3Rkh0Fx4UTn0ileVlRf8jfn3dmuzHP2x7y/tPcw7S7cHCOzMXacnQl+l79KD6e90UQayc53DMWqB9mX7tPOGYxr/3lXUQdph/ojPuFBKJsMX65DOI0jcTC/zTyIygDT93zNs+l1zFee3YSxKi8SON5LKODwAXtnCh32wzldBGzJtfHdeCtPJ2FaC5sLXKAsujE8XkAjjPmjPy1yFKcy+ZBk/9BoHTvBK2DEu4uJdziRl4bGdso94hH67Y1kxHJ9SFuYKh24jA7duXIIswMEqnI1bVIk0PilzIS/y751+/du9PS+G/XzGv+b6K/qi5H7bJ2/DMK94tbqHe/nvDf67KrV8WUJgCYF9CwGn9H3qwM4SmL3ztZ9ZKxmLh+qLYmE+SnCu3sFd5KelxUMQ766IsoYKQlPVLtI3bnXvg4mBvJMFz2J3CeNgOx5FinrlzrNfnDH9+YFDdBdd+6mbsTCG+iWINMjUj5YLZxRdcxVxFixKtp+4GSIehJGEaxiLqXRFxKYFMrLlkXLKiGY1V+LexGr4/trztbu8LT8BVLMZiPgJNGU/xGa1zFHrwffemmYy471Py/1WEkGpLyl5htjeHQQNf5FoWlkdbRCJD3eEX7XZ843vWT2mHYkT9UeYGe11SK9EjRJJaiROrDq3HaMjbCrxZiyLL2MmU1YtQWpS2zppYCTqkTJuxFfOKnPooDHeEEBEshtGhrAQfpZEeFreK6+5nFA77q57PmNFapvRdjNXqOsGHO4wbjLhlLtFKmz+aWaIM8+GG0JpRmXS5HxOHuZpjqQlk0cfr9JfmDcWI1qY0dZV+hCxEp60jw0OkQjtFwIuqmQyZbimZ9TcC8T7DM60jgvqA7En3PIz8NLNIbD27S8uG//j/xJLkL2nzNrxn/6RMn3fu3uv8+xuZ7EXne7sr9XtPJ0easGH4LtS6qLTK+mP/OjLXuoaFX3X7/ZpP7svUybM+ULmiDWSLMyRYGZfF6a+7idNoLpPY1Dmptc7uKvEjXskbsR1fAc33M04XnDN5RiGcj8q46dxNcRZIGVgvsBokOr4MxfWZNr8yjjd0Rih3E5WDHwX4azPjM22zMQU1PmYxRb8XbDJuZnMOZGWWS7HMXOaBoTUGsE0JHGcA9QgYOySd+Bm2S0v88BszbnC/AM0fc/aRcJGeRcZNBPLItCuBrNaO+Hu7bkvyOcr/hWnMGy3furWqECmTvug/PcFv/7b8r6EwBIC+xcCYpd94kQwQbEL5eVdxCMHcQqCSCCwVNRSfOF7kBYbeaL+qJQD1RfP3NIG4GTMviGs9lVmDbCAcUExSTbxRKAiTSjdMA8kKeLlJVlJNEjni2wS6crdDNvgjlvK6ABqjCfdrdC5cIM7yLt5yhf2X3JvPvLh3LUcpjO/VfYrhemz2qzWjpRw8S3VB8GL6LWYZukDLYrXlTBp9BclUbGWJnFhbP5XUM87dMGh7Mc4eeLkGeP18b2vsPLr+Ws9PNzLtlfnno0QHZRLZiEgomwTGS7qZTldOq6MiaqPECAQJNI8Y9pSnkzGZoD1xViIhHBxv4eSIxmaJECZY03NNiZNJWuqgmbPmrATNvi5jyxnr1EvyzJCSpftZwDUlIRriDiX7WHaVKVM+vYRAlxx7e4V28NHMOcPJekaQgqKlMuyNRB7Mwgw1bGmLCIMUF3KGXmk68G/ltE+YFdX2KoREvunoIjaIzBzBX8KRdiw0u5ZQo3HI+DZSvy5Ug9Yq6TWHY+UlTq5V0dGNGe38V2CzrYMwZS0SYK8HSNWOEJXxYpLFwgMn/DkqECOnv3cXRCZfvSvy7Ef/7tl+oHu+I5dX8+TF+fFcC3c+Ovn0r72WXDIWM7FQKe4hVmL+d23eZBdD7x0Qecx+u96iBvcu+XkrEN6phTbOVS1woY5b3DlE+s3rs0xrLw6QxvXvi7jhvRbw0zubyM2lnt370sthz+BVO5uFlrW4dk2GFNK2hiDlphxJbOFsX5+jD3mI51jzB2/gsilFKckrULCmjHHs/OavCxyzmdDEr+iMSsXFyl3VCtRjeQQGcYxCWCISr8yYV5ULdL5yIlQBpI0W1W1zZbnKUZamO3UltyzyzlspuMKYQ/fPlXhlz6wkJF+vct3Xh5G8a+67qq+dLl/+pZbsV9Fw/TuYV7+vpi5nwn+uwIsX5YQWEJgv0FgHzFszuwgoU4qFWQQaIMRgxOcWHnoJlVxRe/6Q6MbztSJk/L1fJ0h556N10HGG0QF0ZYLQZcgPFVTTIjkQtyC4sB+iRqJBvEh8/mYjIMU/Vjxk9Ih3KW796I1h28r6xccYBF0jS0GWPCDEM4mc9KImXliNc9+tjHnbvqe9/ANYqWrjCuZMi6Q+fhZMksJYyajCRfguV4S+hPU6KTuLV1lQGtRE5bwuinEelX2MBRMQnwrkXDgwBo0wKgcvvvuqEaqPimjqnGWEACChrIoWJLIkAPx+0wKaI+ugSmyzGopyaikklI1ElepEFQUhAssiFXMdo2xK838Ju5HlKliRbtaUoM02SShEC60C/FjuY2wtp2qjznHD4ZEM/2qPrryrf+QyuU7z1U9EsMu5OsZeJJi0ihpB8MD6UZgeO84TIm1K67dLWE7fBcMm5Sa0KYwA4jHevgscCXBwWid1XTgbHntgjCJAw+8Jo8JDJaSQAETwSrfzTJ9TpjLtOHRbFMal/Jh6hqYVtW0chC4Ol4yXh4277MVNQH605R6WywZxVQscUmSuttdQlTyKFGpEdbz3TUbGxyM/bKy/sM/DpwXOFj6xtbv/D/lxD//mdKeYL/seezqMRz0K2Dgzz6caYy7L6rgxuUjT/1956F+X7j2QbqYO196jz7AzpfuiQDdt1iVhAnzEPpsX0ZNGROvMGfgAN1F13Cp5c6iiIsmjHv+w+wpbWuvf3KfnDFKufWj4BPSIeyA8dk2LBZNXDBRm4OZgLhDFtpk9zx+JmepkYdpMsK5eDw4ZVLXm/Aj+xRF1hCUU5USdjUtVmA8Z8wTalJ4Iohq6zPG+mzM/A3jOVOd2mM+1rYy//ULXEoUPXrEMY8RWbKwfrzv0U0ySXeJCN9MRsLaF+78B1C++mCbd592wvoNp//nOf7V1+1Y9HXB8k6O4wiY9kn57xV+AfDysoTAEgL7HQKShfvLSRCLAXVz5g0/yOjK0IklTnGZcEVQfoOZcSOPP1QjB6ifrbKJW4t72xxIOpgdhJjwTDGQKESwTM4cuZF3aGYQy5zeCHKq+fX4KXcYs0XXnjxKniB/EPfookNlcuwo5vBhEqCUNVEvEh992YKREojl9s1/SjkofMovvoNY4LeQZbKIVTIZAFdsKaAHME8wCy9UrLO1kBDPWW0L4DEtE69MhpnwLCHvxnbAuc7ZahINSneixoOEqsYhOZ5zJIDUvHBNFj7vzXnQdZgt79IvFpE83KxPplHnqfvTUMGkghNXygnjcQAN0lMJH89mak/CkMK8RhWKdhxB4cj0hCgiPIqSwAMYb1I30rAdkBfCtMGoAZdYhOR5BKEEVcRuQQ2NCENB6f4y4a2rd5k3WeDaNjJ3g3LZo3Yz7Xce/jT7GgEsZVHtUVWtGdYihzBR7hfjMDsIM7Izf0RpbjMbIyEbosaksy3GGirBuUI+YrW8Ze9aLFxKwLkxDuJsyhEDcmAj1WthvBs7rYzcALMGrtCrJuYP7ksjOjKBc4uSbtCTUXM4kVwaluylcXMAN5/PZzd62jPKoVf+ahk85oZdYJje9K5y/J/9VJn8JRYFlw4IOE/Zb+kwzhF5PL3znO5zOvAyBxj/FGfceJ/h2ylBd14dwJaJOYEhyiAi8hHG5eVPqGEue0wpj3pKKbe8jzC1dJnzrAsMU3PFtaU8/et20vPpto+RpGOfuYL9oi6caYa/uFd6Jh4gI8bTgHGsZcqZiy9MXqpwuzilyX+ZR0YdyRCP8FqK1dy/i2+CT+NDHuuiBoISMtX4lcY5hXsQ+IDwTfY6M2hRjcxyXKcuqcDNKjvOB0jv+mbBZ08uFhOFfdLmgcLMmxrv5ONFf98Bg+V1cWoe9mEU/5d+8ZW1nH2V7B/7qPxzmPblF7ZpBO5Lt4TAEgL7HgKQefvE9RO7xfV50cm4RbLhDCtqWHQ7gevhqc5hIEXDKy4Qm0k8g2BXpZZhTjSXX9gTMAXxqWIGmq4JimC7pGPpT+wTynYnv/l+r6N3zcPm6/gE+RDeoqKupmaOq6Vj1F3G5Ln2lc8vzUUXzRNq3/2u0iDhyoRLppVRk1EU+XE3IVwQpIWCuPCGfcEwU6oqOoFLwHuLDM0APNc0xOK6+l1pW1Q8SSWryVwObqyX4+2Jskn5DDcC4yYuCRo2EjD8hY9/s0RMomd9cY+JZeY/xIyrz1F/xHtKZeFZYNJgM/jl/CIZOWpnmAFMmyqGrVI1iJWR5q5DJVA2IqoaqJMnycHYMEO1SagD4FDCpqVseacwLUqkIMQkf2CLCKPyrKqR1BZYAg3iW3+YIKLkZ/vC8Fz+qEtDYJmf7sTRo+UYh4yvXAABR2SKUAkXGLYc6m5zuqfN6rBnJefQsXquQZwE1p+8tCI38cw8CLehe1bM38RgcK2EFuFMfQIMmhkLEjB0UZdCUteiMhWq0D6rFNn9bXLmLFywZEA56PvUXaZZoqpl1d60YvDAcQI4Uklu56Nb/+//YTnwU/9UTnle/fb4sXLyl15eNv/9q2qfmX85vx/aEaPGzkT/TL8MOOoElN5vn8U5bxAoz/XCs6+L3j6f0SWl+Zfdb3Pv3Q+ObdMTBzDmspx15GM7DJsleu4Pl/adv1PKR9+aoPOyPOm5pXzJtxOPMdO59u3/H4ObuX5eRtNl9U/GTfzCPNxO6S+cUxk0BcOk6v2U8aSlWk5iDh5w/uqT8FgCx/9IlWnHPONSWLoIF6m5Cz7MTTPm4CHpKYGLFF1mMGeyMW9ofMQ5wVUv68zcpj73EAkb7CFzRsUffT3O5r64hy1lN8m+7Xqg+T73J1T6Q80tj86FPCzjL8JJ+AgR22/R/yzhZ3JLt4TAEgL7HgKSgfvQzbHCfZQ9KKB+75CThKdqY8YO4gihLYKVS4DA3QCzTSByterFnjYtSSpbUdSg/CVMj3E7V1eQJUrOgPzuRp1i0R3AYmTmW5ClZ3AdHJW1lQvK4MhxJHscPP29P7AYusz+5I/JW7as1qNfrRbJ3auTiZKZ4G+ExUCto03lcEjHPwsgctTV9ATDToKGSWyIByUqAyQ0MgjSXVUlUvjthE8avAbhgnj927OTyEhKSoTsnpaZlWaayfPUxhAnMw2NKGW1nLJSfAdYta6aumf1OoxrCBj8YbRCPJKaBkEkomZbGhhRVcifRA/MG+lrA0QVyEiiyMsz2GTZZdiiKqnRGp4Tj7RCi5CGNbf+Lh7rrrput3Tt9ls+nfKtFCkmYhmOm/vHWs5cCuOk9AtisIHYDcdqe6YOlbGyHFoKlfFUBXQMc6qaVAhIyilTatKu2MukZZ+OsCCPxv7LCjw5hHGLKqj7Xmw3I6lzijNtm9g2Hd/D3hmkAlHLpC0iqZMAPA/dxk/9fNn40X+8q+bjN/yXcvzn/mGZ3fqpXf7LF7ok0uLakRx7LK6o7hfmgYGaucghwLihr2UecVza8eLsgDw4RnL3YcfFuw+6430/n+jr5JPFuyRL+T76B6V59JeWstGN2bVDpfmKHyrlmd+Lhc/PMJZguC5k31rmo4VsPvlXpXnPH6CCSJIyX6gyW+C6rxUPmMIwbQ1q94xtDuf0K+lh/xX1Yxd6kLMxbn1mftHokMlzyfzDuFzlyBWlcUCQOdBJEI0QmDSnPKVxGgoS/7i/bsj4bpCya6RkqKETw5tg0nMOQGMAs/6tkjjhvUfnIdI2WdbEvJPmIn4w+Sz8EGgexoWgrp13/CjiMv6DBr89NvMy+hICSwg8TCCwbxg2NEDiotHSAy9Stf7l3u99nBmrmuCFuHlUkYl7emKtj5VRGTQQrCuZwwGWJKcgb8U9FsD9QNIbc9cVysROZdruuXMeKg9XX4s56D8nbZkeCHAxLsT2yiUXlJUbn1bKjV8wD99C3I//6HWR3oASo4LnR8ue8hN1kb6pdQIh+yDHQNorEExTJYbUzz0PsYrI967EJsczfyLZmmoIKxM2T9NXTXDtYJUIbW1vljHMm8WWiaxMmoyT9elKYKQ9Old+c2g2jEsYNZKekP4WdfBMIdWIotrJCrTST1WDYj2N78NNDGlYFMtBfPdmaYgjq8uU2XJraUAVIvecaWREPw2MqCE7Us3I+pBWoxooTNCItCCDCCfzLEz4BY71PTBMpvjL+Ni+QPDU/Wu3fxqGLX0H2FH2tBPBhzBNnrc0iIouBJbE1BoEIsZpdDJesouNxgpsW4wKjCXQwkXTrnhzlrqNUutpW5D+AOmbxKB79qgdacBkUhOZL+NOkLB5PuFASZsMIvm7qq8F1RYLC9LXFsU9Ox4W7xqDYuHGPXXnmVv/ey/dzawBv+Mv/8dla3m+2r32hKmSW/pi9tZ6dzGC7uueq5wLCAxdRHERoBpJcth2c5gB7cf8n+74duYPpwc9k49pmg9pmEXchD1sf/WqUr7sJ+j0MJq9WwEPXHZ9/7ZzN+LH31bKW3+LOZxBzaTQKCmLESvrytxgGMaSJvdVkVQjI/hnjHI1eMSxPFh3ArA2hGV8EzNjV3VlBnOFD3PwCkZFxluMS32ZlybMCSOZSAa/0rgYK5I509AIzFoO0WZMK2HPpBN9ZtNkvkONesIcNZsjwJ1qPdCn7UwKNpXtLEQp/kIi/Xv/3U/BFYH/TsD+ex++/9K/99+X8c8Ofj08l/clBJYQ2N8Q2DcMW8AsRyLHENdP591r0IXP+vuTgtURfs5Q6ec3rjzKyGXPlupeILNY02tQO9E4iSotEKgDNnSrZudq5XSyyr4izrcyfrC9afXliff80t78IRA3SFTCBdc862tL+/r/N/HcrxRJGHVprrmhlB/56Xk8H8b/5pfFzKLsSmp3CLEPlKz7l4V7QAMSl95RMmPjymyqSgiaDmHk/quege2LbllCNBA+hi3MlTT0Wx2A+FNl3tBXjKqlhAKFCDQTsL4vFOWsH2WNVA9SEkQ2UcecIHUbQ5ioLyjt4Ur3QMYO8MZ0Pe0z4Lv1ykZ84kvYRKomo00Z6x+wgBnzbADoFrzJA6mUlhJXiBPLabRZDg+XASSWPJgtKK9kfbWsKfEZR/zAUsDXf7x5IO0rTpGwfQaGTcvbfg9xI9z8V8rFP9lzIaIZ+dLd7Z/KdiMlpE45H48CjSHGYqGTdpjAaK3AVxvFM9k8a06DAK5uN7E8KXFs3oSRkLOuMsSEcW9MVv8Nz6IF3okzpI8axb4WnlfwQ2C73+98clqCPPCyX9ipMv3j2D/5kbL92t/e8Vs+nQYBZwelWHRJ+mHt4C6XZf8SPTp9jhnOQR4Gjo4WC4jM1cZxSDkkTnP0R8eXw8cHX++/I1LiGcMZYSH2XR8p5Y0/y8LZt5Vy7fP6DHYn7SD6xDtLeTdqkB7RQZmDB2SwHDfuD4NpKzJurrc4VKyP6omMr4GLIViRzHlpHHRtJQfsabam7E7FnzFIWqoxZ29dN4dpZXIk02aSStbd17rBGHaxirFerR4Db+bBxuNZZObAVQ5mDVsNiCv+cp7PJ8e2c80enXvYAkKbt0vL+SKeNpAv3P1WvWv778rZl2X8BxV+tsjSLSGwhMD+h8D+YtiAt/T3jnMl07cFIjLf8ewp1KB9PMO0Ud3en/cWRi37c4xjENPKd8QJ0XFR4qYiJEQwiE8LewWrktUloxonHgtl8P3IXaW8602lPOP5NfjVjynN9/9UaV/zKxDfmyyEQmE/6UtK+ds/UsoBTOl3rn3bW0r7l++AMQDZWha5lgfgskpNeHGlG9QHUPIDJHoybZDxSU9iqKbqVQbCCDzxMGdECSQ5JNnu0QFrVNvQW5wvNenKlBpXDJ24fN6z29Y0No28BfFhmTnpNXvWQthFJQliBoJmCqEyg5FrMabRZD+WlaDMFgrxkExYZT7Ze0cdVHNsZMJUfeSb+9LWAccIAqgydkqRapgBhJLEIvQXsCNNiR3KFGbJNMhqTSIqJBSJDCTINJNv9cdlbWOlXHLlxb7M3Wc/fmeZXEX/m0CwjTAywsq7zKOGA2S0JNiG60dRc7ynrE1v4LwkyVvSZaNLy4HaAzq6WqDWCY2qEMGbm7QpnnbNKcZTVlhgWIFg88BdDbd4rtMAKaQGUCw02xEhKKEkFckhKaskK1fUp1JhOwJMmXR0JG6jTzgs4rTe6X6bMJid3/lwO/CzvwjsAHjnxm9/cxlccllZf8mP9l73ed/6vdeU9u7D9xnmkfjRoeuEkTkiFdSDseqfEy2MibJr92jpNAAUtWvHrn6MV/dmOQuhEJ205n2xn7CI1/slkc95YQyJMPhPvBSyi0SZ2m0MQ73335fmg7+Lyjom3vnNRgdKcxL19qN3lOYod5glpVoeS2Axs4Bjai76maiThmONLiPT5qKS9Rm4CJhFFO/MFWHcZNpYhEJleYAqowdht1uRizPXVJm+WgXOyVqO9LiSsQaKgMsY1cjGc9YY5zFShGpkzlx0DnPvG6tDaiAw9ZMHBSMNyxIjV5wHOj72OYH1OQOoGRCVR0MKDJ2TGa5e9RbYtjXPnX9CduGX8Wk3AfZgws/0l24JgSUE9j0EdiiRfVUVyYBQx9w7kuDUFUPf5wi5QxUQnaBGJkixWEUmoU0zY8YrmKau+MKcgUxz8HZUUMhPi3wuc+J2mJv6fqZr+4bfL03PsBngmS8og2d8VVm59WZUbSAI1g/ujvbhD5TmN/4dRDf5WC9pABnNOqUvhLXAXZ2oUeiX7quI0Z/SF5Gz0ijPZG4Qk0yQFko0GDXMIOEqfu3Ra02kTy9ncBFY62SeuSZBtT0GLgRP/UNQmX8Xv7/XZM7qqiQtPAMX+CYLTvqckSdMJE5sMD/QDjFbLfMYUID4srcNYopyydhIJhADf9K0rESG7ZCeIg3zgWkCvKo81k3/wob6Ug/DVSfByDsSS+sp41Z7npnq+nvXI+lzV11z8Q5MCHHk9rvK9gnO37PsWUWnWgLePspdAy5KfK14DL+4Gi/RBwPWbiJtJGiYReokwRlJF0wETRLJo9JECWCSJl+YTdrL2luXMK32I0VnlDVMLiZRhxpk0Xw/P/uaTGnOolNaQJkaCNCBZzoFFqQjQavMV3HbeeJGX/ylZeWrvnZXbVe+nPHL7345GOetV//a/Qr6SAskk+LIoDfRB33myRcuzmjp7sxPMj4GdKqS6fEwegOojhxin/6m6q5niTmA4mf4JJzkuNx/lzL1cRMthVpIgBCa+b/746XFGMn8K94t46psOAdUPKAl2ahbs+jTryS6CJhJ1X2jDh/X/GTEGDYhzWOQhLGFlEuV7ayhsXyUsxA9O9LFQRZyYvmR8Q/3xcIOc4fzDmN1lQ1z24xfrcWSA5J19lkTPoa0SE/LuAW/AePbRRbB5nwRxtH9bkrwVLnu1BkXKv6AHyfU4T4d7R5YGMjnNFqNsasJ7i2RZfw9w+98W2C7t6609F9C4JEAgX3DsA0efQgpwsly8gTITPoUAnMUbgMJBcgMdABi4iJx4ETvYxAK4fMmoQAhrD/fpQt0Z8Q5YLkg6oazlIZIOaLWCMErAzc4DIJEwrPNmWrjQ6THMQAakiC9HFCqeILEzafc/N5SfvvflPKdSNGUcuggnJtrHlefF67tn/9RaX/7VyoxfhXmoE+My/goe4k2IWKor2pxUWmReu8Kr0XKavkSAgIiP/uaJLTJy0ORt8HSxlFGOMBE/4jKjjWoArOSQ6mBgyysTsK+/vr0+UaRPWA7+99IU7WcjZZ6Q3Rss6dtHf2aqIpSZZnDmcT+Ht0WxkC0/pg2ElZICQEaBdQwCI9bXEIg8QzMpQFkOdLI1FfGLBIzvIwZwlHmjMierWZ7j2Dy3KsG0Ey5EjQQhsbV8Ii1kDkzXVVKVUkSpkrD7EX1O/G77xTWZs1Xqc2rT1GHvPOTt9FvYKjcYyIFZfoyRepgkr7m/WN1lDLmMHP3VMpMYV2udW+lojvDEz3hzJe/kUw0ddqOuTnrQR1gytciiaSkYWYlDik/xJrIO21MOSaUY4BBgwFhwzwGFtSMu3/aGW8nF1FSV9H9QSBq9c7K79U5FuhXD3e38vwX7qmIHpzdMmftO2f77NE5euqsXBNytOxuc3oZ40YeQtfPxzImcfTVPMl0MPs5vuWFNBrlQotqyS7YuONWbiQlJr0au0vDSH3CehE/KMMMwjzUcJW1TNdP1o5no/VR42nQ3tO5wYQ0+OPwZLiqmujzvByEySIMDB5a5czjLItgGEQBX6OVV8b1kHm0hUkrExbuVGEkTnbMMvZViE96bCRlyY24ZEL+mu5XK8CpZOwYZhxlYQ7z/02D3gCLigM/sqAzmbIHTzEfc2qtN5kz10wP8f0cYP4Z5nadVyssgQ9wmz9XSJ75uhju3p7PHLP63lucRf9l/EDgHAzl+4Lk8tsSAksIPIQQOAfT9kNU2g0sX3G22cYG6BWT5RMkFmPOTZOhEElCenKtCNgSqXYTOQlULqidVUreIZCd0+sF5FrFGgbHr95yzXKvyEcxziJCUiohAc8+IDDghNlQ5i1nfUFUROpEAtkTJvKF+C1v/sNSDt9eyotfWsolly9k0j3eeVtp/+Q/lPZN7IvQSfmDu7VKuU4d3LzfwqTECpjpycQoPaIisoXSN5GQ8M0a62JJUOLCd8M5a3uuHCFiEZF6tSB1mZGqkkRMq2uM7tIzvb6C3pOOe1FGqNytgKSnMK/Tk67sQgMIBxm+MFcGPnvXwEDI2NhOUTfSzj1E20SjG6hJdsJR/GBCCJTN/bYRgJB2iNSN7NMb9LfdIe6E1xAYqXoVIyPUvbKCwAfCR4JIYwFhcGlbrUn2cM65a/SJyrxR4fybN/kJddIMc4i/ROXV1+1u58OfZr8L8bNvR2bTOAKuFjLMmU0EVCPJ81D3MGlUvcGynBWjCsCDQMK5I2qVjg0l2ICF7aUVuSHMFTx5mLTalvYH4EB2Q1fy5cIdE9R/CjzTi1SPlGnUXD3h0mdo9RkHyruvLXtwLDbcbqxL8rgnZz32A8N2inTtgdZ58q53PNAoD4/wts8eXZWc2bsySGpq9q1TnHPSLjcP07F7ea+eLf3Umd7xPGAezhyYse/4Z1xMtxhBjluCOTekMxuXsOYj14J/dQmUx5o6Xb//pG/vWQN3pewCJCCpZwwzpzBOBxNqosSNNQ0N/bgwUhdCSICxpYGRlruSM0pKHVycIUw3oTEaGdeeI8kfY3QFtYjJCVSbZQRZCHOxSDVwF1RiAZj0NQY03kKEh7r6CnBxoaqxHB5X4qZWj6WR2WUhJ0WmbOhyo25N+Zy89uhAAZSeMgU6PcBMV2gL8+p3b8872dc49X0Z/1zCbwUcsXRLCCwh8MiAAFPuPnHdaubQTdqomwx5nxxnLwASKPARxCpIHPwgcS2aCLoIcQvixiO8hAiNb9CqHSoxXDehGUZkHmZtESbG8OeN7waPxOEERvOQVLjyuX0QekHLHEralIURKISByAz3fgi3n39JKRdcUsr1jy/t5ZiIPnqklDtvLe0nP9LREBAiCSzCokLUc4gEawiybo8PkJaNYWRgjEJ7w2xQltSL0BII0g4aCJMsH6sio+oLjI6J+62V4aC2mqAesC8i0hfyccXaaskLWs+EJZylCLRgEM3HbzIFMg2rMG0DLJFsehg4da3SIULUAhj4rN0IC51jGMq6N4SVZaVjciDUG0saFLS2l3vLQrBJqPGzPg0Hflt+CalYcpRw8kw9aqORESEc5s2+QnpJSWIme8mEV2oJBCWQeLa+3MO8CRHeobnyrRKawg4omafhDENZ/vNv/rfy5tdo2ISVbfeTkdmI/sr2FIFaXdqKx65PZTFBdVUDYPBmBh020OCLDLuSSwi8VN2LBbW+wMB9ap4fJ/Fmv/aXIwqUnNnpaTyzcEl+yt45rUJW79qeLSpUMtvmk34rQ0c9iEW74gdMAykXEsh7wN67vTrNmrfRd91rSg9u/Hu+8+sf3AwepqmHcd9j2XqGzWTC2NCF+0WgB560o8vZyzuOvuhSQ6TdTlxZ1OEdibTj0THoMKp74OivMnXOF0Ttx6pDde4Y13EG8LHLpnreyzWDqvtmoqpAOjmIUZlf1AJR/TiSfL8rMWcoOUfOOA+tdbGRcZu5dXiccrFgJCJjb9tsAg5h7l85sFqmMF9TmLIV53MSd5xbRpm2BsmZKuRDcdAa6WpghATdC+d2aw/TLpzL1jZoiYiaCBsoMH+3Lgrt0YmLT7IfW3cmsC0yaqdmdeq3ZfzTu92pMFqE4anf7g1+6x6vsXRLCCwh8IiAwP5h2LLpGqsJSBfEz+45GkHQzlZAxicl6rmDpLPvSiTODKZEhCcDV6LVWQ2XxcUaJO8Gd7rseIH4xSfEwJxMSPrRqZNJUPIAgT3k14okt03Q30HpehwImrvSLz2y0f4Ym9ZvupNvIkszlWqXzPfZMlpop2J8JDKklbMXAqIcPA3pj8AIBC2ih2SB7cDHPDrXVQC2jjqKmiVy6tRuCA/qjtl8GV734xFOZi8SGL5bClPr05PACrEV/3qpTJv7p2AAVoE/zE6sFRI5eyMWwp7No4yWG/AlT0L4yOWo4uNybm2oWmHCRZpG+YWzTBa9gGeZtXwEYLQRbWWa4Y9gEoaqEfGOsiEAoe1gWBLGHOknpuPdTGjBjlnDj75kPAmiKtWzTMCMoLabMXoGz/L4boDeqI1FUt2yQKjVr4SgYDOIq4G6jraNd+rgQe425Yw6D1YQt6I3poqrxKgMsyv6SuuyL49SjiAGWyPA1JlvzPZ7/h5c6goGRWIgwT4rw6/1U+K4im8N8YWQAw4SuTD4ngUVFWASUgmUTEnXwhCen3zbnp2b7zBes3QPUwh4nsMenYZswjyRTqT4riTUQXG/U7brVWdE5jLTwDlKTMt+HLVifZzvNJ6Dy7Ef9GfHrHNvi6Ghfp5rsvmVuN2YNZVaLMMm+hkvhuk/G8NZ0vdMFT5lIsCDoaLku+GcNReYlLq1nrNmfibCmHcOcV537E894N5vStyGqkrCuA0PIP2WuWLeX3dGWsnCoKqPQ+bGqPY7TeSsu02mSdSiGcMrns/mHIHWh3utBxMXRmhLxrQWJGfO+8DEsk1dwdqjG5CfZ0FW2JBuniqcenjtvgdiFQ48dhBcxqcdhMWDAb8scO+xnZfRlxBYQuDhAYG9Y+aHqB4tBhA04R6kp8qOiBGiU2MQSqOaLTwg7JW4TVWfA6FJW+agZJi8WNIKqgeFLOAqeYBKAPBQsW9qFGJBL94qYuFBfTeWLmUNJB6aEch1RJmEIt+0wihhUFpNN8c2IciYb6YShO0LxHEkKZRXCVi+8pycJJ91XJX8+Gj9NjRnT3pIarb5Q8AXQmDqajJBQytw6Qkcw/b5puxUMvU0QZKTFNCaYIqlCeqoyJE3ZYyEkrsfQ5YQJ5YSu5JZDw1ghOg/cLBsnjjBfijLATwOxP6/pT5rd4B2Pg4TNYUBl8iIzmc2gdQ8zMeyaSTDhovaKZWyapUYqzCJ6USY3vQBywyDo7ESmS4FVoGPkjaJOn/AIObuIYpk1KL+SJor1HVF2Bo2MDBF24n0aEv30AxMjLYQYlXiJitU/1xWN2wko/YNjQIQLgUxKZveOsmYUziPTmiG9DFXwS0XYT3wWquS5msZa5oSengBB8eATJr7X1opNfxnEKky9yY/dKWdrxJzMZxDuknaDX3CglDmFUM0MH6yce6BiaRCwEZFThhIQsJA7tE1G+yDPKZ0dukejhCwffbqtlRFRjJezya0E/Fu38X51i8EZTjbYeP6e/dm35x7GT8x7clJw0926do/k0ONaDglxVoxdIGPfWIDGSgzY2+mC0GRvhk5CRDGP7+Tobmc5vw09+zKgoczQcpovD5AxigvMGdabHRsa/2RqcIIlclkTCnpDkHd7W8LY9scIx01DDhse3sdNEDZ12F+lSoibbOcI8aoM8/QhRZUH7eQwI0xBqWa48i8V2g/vpUtxjCSuoa9xx5ZY97WfYAFWvev7tUdYL6//QSLkNZb8CZB6qwH/1Wiylued+BnOIsZcBtnGf9Bg9/GAUSrS7eEwBICjwgI7H3WfojAMBth3EMpg6uTzvQS7DI+7mtQnQsqPEhRtRKpY5zBIl3AK+on1jb7fypqkQDIKxijopnqn7hecPr4NU5pWgh03hTbzB35u2JJhpG0SayAcBtMK1uGpBKsLsbEIxhMLFVdZTrmbyljjVeDRp1mXbJaZgNmTCZLqWJUXChKR1CHiaS0WcG1LGZl6S0D2ckveOdLVl1TAlX2JNj1g3gxiOWzzpbWYucbz5G4cbccwm6ApGSKRTINWKiGWQ4e4sve3AbwnXFotNKcMLQW3hVqCbBUxJaiZJbLwvLYQ1LGSb8W0/WRrvJB1VH3edlfRnwEeuGVWN6GnlP6VhOSbouEDVhWCVrtETE4kir5bhjDEzjQqQWo+dd05IRCl3aFEpK2r9FUw9RAgSyR18pEyyQnYQpL0q7Iy5TjKW2ZNkXVKgRb8pQJM1Vc4vlQzfnL34Zh08v2o12mLBwM0leJY7+z6NxizY4OYRlMT6LXxYYJ57sNUS0bYjLchZC4Lo4RR6O9EwCN/eSOz9S0l9eHHQTSPnsslXtcZx4h4VxC90qPTUeH0WAsO67wTp+0X9af48I+zBjyo0PKgTMf7PlY4/HYx3fQZxjXCHRx45g2kqVunjas6TSYblQq535WOnzisTmMcca849hVHZ3EUlSSSTyT8yHJdi+5OSfzIWVMtC4QH/tgDCHng+w/dby5nkI+2ZvGh6wBMrd7+LXnXEfa3ZwkjkZEmKMMMOZogfXVzPNT9jOL3dRC0DiTTOYKiyhbHKw9Ft5K1KnDiLrPwI0DD3/ksO52hbSop0zhCgzb9oV7Z8oPHTxYxoctDQ4YZH7h0bYIuPTjT1D03/2WVy61bQPh+Xe/LeOfO/jZRku3hMASAo8MCOwbhq1ZxZDD8Q+GwJRgzn4BcYUIWaQGgpKZG7ESOQIBTli11EiFkofgCGkBMUEwpD4dkxKUUpGK8oPqOiSUF5Ar9w6thAgmaggOsCsEsNQsv5VNmEYQomdyudo5O8Rn9MdFmGwEB0cnjSCwSNaqVGgnv0qsuCIsIh7ImIVq0J/0VbOBVl5DDXEQU++EY8XVFVNlcQpLrJ/MgYyHxLcIdCbR4h/fomaXN4JaByWUmHhWcuJWr2o2X0mNLJyuXk3a+mcFOk9+q/HXYNj8dlJreJddFv+9XC5G0lWllAKM/K2TOaRJ8JPosUEkrlw9Txn5jqcWy5S8jegflrxafIzcKUyYdJWWsiWWlOCNaLtID4leaS7yBn7CSslslSOaf30nZle1FIA3+pd5ETkL+haDP4kzjxCQ6JQ4iyQ0xBnEFDRV6sLICxNqfxTAXDTbPYYZH0KoKbbVKmPOVlJFlILHSI7SY5z9A1BQbiJTXt9XMSawvcVKPNI1YSectrDm6d12Mo8xCxpK5GQMNSKj9C79xa+EUwLpvsaJkWBvR6rH2Q+7Dry2vndCr7mUsfzxj5D+0j0cIZD22WvBVg9h6Rb1b7oRvQdHf2MMK+XN4fXdB9VunaecixwvGTuEnvsxkrN0lMFUEwuTlP6ZhDPnmZwMgGNVZ3zHYu/6OWzm2DSffLJfExZpsrmoGj1VFT0MHAtivDODJgnr4E9X53AS6MZE5qf4m2MfypC8mY9eStqcwxV2M/+wJkN85xu/WQbGIgyW50rO3G/K/NQMT2RsesSHlh4L+9ZGLExOjvOzrkziLeM+1pJJaJPjCNwZoBQ889g2eAhmrdEAiZYpnZMY9wU8Nbhw74trV1x0eZne0jG9VKN33bQdOPfN1DefsKjPFTBz/z4y92V8x0kPpwqYOZweIPxso6VbQmAJgUcGBPYPw7Z+bXgjVwidvCSSo24i0hTJSrjmA0yXiM29tqiEuCKpGlqCgPNEogb1MgMzkBTPELwQ6qHyfeehIv6KrOPVIW63SYiEg4jEOfi7GhoMrJEJ1FcKxh3KRBUVjJHkoG04LVeckRqJZCuTUVP1mrwsYPKFUQAJ6yt3WokVygFhHwmj1p8huIcY53BvgkZPZiBzSYURiF2mK9bKEt8kKwM3o9ASJf7lE4yeDzIJ2fsB7NyLhsFo0mAV12AUoWdhCcx7ZeCsvwCArIDIaBDEoK4KBdRed53B9uSu8OBZCJlwlOSQdjFF8svKdNqbsgT29IFUhhfgJ8EVJk2mlZqEaZPosn15N46SNzmm7OUgG+soSHSGt756+ZNO6r/xlPfFc/HSNukXlkNX8wjTQ2LpI/rafGakqXwZLvuaTi7P+L7yC4OeyPQFqVsKnD1waXsIOsLnjCb9SSNl90JGqSPPErwy/ILQ3uPfFCZtwr4Wv9n7jOsyRmBh4eyX9BOJXJ1xojamGXLbmP5WgcEBFudgxXZw1aOSz/Ly8ITA4OpH77lggzWPhKiuHwe1Z9Hv0jPpa3Q91opq/1Uzwn6HsxvqXdV/uTMQh2zcdLQkuHNanny3l/vBvkoKxnWImamBcYZJ2mFw6qKMrFjSI61YdDQV5sK6j5M5nFf3u/aSN5mdMHCpDHkRb5cjvC55dc99YSyL+Zko64kEovQuNGr8iak+iydZQHGRh7rClFlnjlsjWwySYP6/adgb7Z40LBSPLsCCJCfOWNcBCzzO7Z65xpIN49xZmUUZ9lQrYcyxMDJsYxZuYOAa5h/PgBtchgGsPbpHX3I1BqIos2ALvClQ4MPd/7xSzzxYbwGDp86bPz8v4z9o8Hv0JVcJ7aVbQmAJgUcABPYPw3bwxhDT4PjgBNkbCU7lR5n4XcHUM9QxGMDDQVUjYe/A7PjJssneNgnRIHPjJhqpKWoiqoyBZt2TFt9N1meRor/eSWNLDOfcHQJkzwB+eBAFtRORoxZC3GgW4yKoJEhwK21z/0+MZ4Cwk77MRE15JwswOojWTFJiMwe5B+ehUzcDQWqyeUDdRO4yca5Ya6SkIW2ZDFdfe/mde7Si9imBgFNy5JOlNe/sASQNq4APfI8I31XTWqLEygUCpyukNxF0cDQXpZpljf0WNz6RL3tzN1yYE+eSftoy5SBHGS0bP5UBpHhZDusdi5fAayAc8JPnUdVRVUilaLarao7ZzwhTOoCosa4ycca17kCyg5lfiO93YCV9ZUUTxg885/w7+lm/+h5AGi4uwEoaufBq2imtAlGMjoyi7omfDUDC8uq2bxrPd8qnQRtN6qvKGd1I28/+3YVX1Ve1shi8yScu1EerkS7jj11qB3Zh4IDBNupSHn4+GrH6DiB7Rjh7BamTiwBK3AS3cBEKWuccA7sV9v8M1ui75H3BOViZHz5u7/2Eyi7dgwSB4Q1P2HPKw4NYwk2/30nKXlVHV/Wzr2Vs2PddaHI88HMK9+4RHOnVcHVjzR7q55+DhV9l5Fy8Mnz+c/F7HXO++pE7CZmWUfPAJVKo+OlJ58a58OTIZkjgRdoZ58an5BmLzJzO/6pUq9+ooyI1l/par11GpJPiZt4gXcaigXNgPWMVPjNz04x5QcNZzkrOXwOMjcCBwWSBBxi7zncDD9ZmHmi3Yc4OohLp3jfCOkcFdpaZyWRKeBnhBmuUJQZIwBVYnnQVrtHSLkLy0VVXLxb2rJ4fe9m1KI84KeHMj59FmDtf8MtPz/6d+zxsH2EZfwcmwkrXw0sY6vr3BwC/x156XY27vC4hsITAvofAvmHYysXPBtgSqcJcxJ9baFgRgkRmkHFqxNe6WQBGgjhY2jsAkbx19wmQIgwOeFY8AwoLmlYl0BXUoL4+YdPHRQ0QP1cxdVMRHg7aNk62ysNLdZFQuCwKO1TcM4DxiCGGSbREqGrbbIKkTdPTM5i3FNZbl5BoVwrGn1iY7zlLTqKcZxk1Ss53CQ0IGRD3AGMkqyD2Fc6n2z6+Wcb8PItrNfsYgAfpWO5K3As7TWTgSCNqcZR9EoYFxiRHCGCNjIpN0eqcoUKTw7ApSqwYkntHaxDfRISBUjnShzkdolY0+tJn1A97uH7R5ah82ir8axY7ElXBK2Mdwoh6CZ78uIR6At4ULswZ5ZeRUtqGIUuILNpHQoWg9pGZliJhiELkWE5gYX4y4rZsrEECJ9tXBi+WGQmTVs+ltgEJE9cEarwaoAtH3N6RI49dmS3mJhdVYo1rejwqXBiGIiM/+690oAXmoQWuSmVlwlSbipn/TmXLriNL6V61Gp74hImBGgjgCeGSD0n5rBRtqIqjcCFvqpWL5zuZ3ZCzrWxzz+1zhOV8QQlAuzTjacCetkuv3Lva6+hpe+8nlGjpHiQInIv2GV70mN2ly9ih3zkc6IfdK32wjg2nvbnj2bm0smswOfZFP+JneI0cOZc59MdoNTg8XKCJOiX+kSTTj8PYdfHCxPGt5mxqdHjHDzeHQZxlSR4pQMKSE3dC4DWIeMz520gOXMamcyxzTBgxw5lYl04fr0udvGo6CWIwZ2NgEW9RgoZA3J/MvOd5bckTaZtH2LSbGBFCVbJROgYQZywANpzRgr1emDO+szCperuRxuCACTjHOUVpmmrYzZhvMG8xRkJdhtdf3xfrrO+PvQSGzUWl3lGx2lY9fL13zkoD78CnuycsZZy7ZfxzDr/HXnrtHLzLhyUElhDY3xAIe7MvqnDZC5jwL4DhQRcERBeUsDDXSwdUBCxy57sIQowuJQBCkyJfuxSGiX1t2xi1GB9vC/xNGYEEN0SCmhqHcI0uoBhURCLVDuIX8YLq+GR6PCXx5GiuMEWuinbv5isilqD353IpSTdwiEPNtY+RwCV9VleVtkViRF6WM78kQBqVSKkWJfEjT0LgIGRE8vxZsgHxGw4VX2UzfQvxIu+5Pd4ua1pWtA4Uy3iq8+jmRAzPFTSmVZ/zQF2GOaAWSYxnZZkEUcWxPofA6e/UQxpGprNdXyvDZ325ofbknnX1GhqtQ7ZsQJxIYEkMyXCRh+WT4EpZhKEwh1nJ2WrcJdpCFFHgHIJNSXrT3sKqhjeO6flu2UnPZ1zi4imjZh7ZH0Zw36KCm3Y3ZCDG1TL6bBjLkwdf8OfZMvaOR9vKw6rDkOtPGLvYPJTtZbw4fO0/6Uu0g7b8hbWqUmlXvxuYmhHONGoPAUIkOkLiLCM3k6H2K/8SllMsrcZIDRI162j3i6opfWcGt+h+mfzROQwXSSrwbJG2DdhHd+W1e1+ZHz3n+RhRwJKq+x6X7mEFAdvF9tmrW7nsCfRV5iRXx3COLfsoD3a4+mxPyyTkB53fuifvdm/u/Rjb+exomw+ULCwwTNOP/VJVuzsru+ZhUC6OdSXQmTcT3fnLN16SH6l6p6xROfbRcPh5F+skrIGQdsEhkSdzgNodlkhGTk0LJP39+ZY5nsX0DWKY/Jmgz3UW5y3lY/MtMOPZQcnQlwlz7pWBc6FlwGJjOz7BKRsnWNjBEAkq95jm5c6iDQtuWtVVq6DlwOzx5maMUw1RoSyrzB9jwqiePyUuCGn4uBvNdU/ukvWLykFUpA+fvJs0cQG0tclbTZt6xr+/C8x8py75BBQCi2X8cw0/28ff0i0hsITAIwMC+4dh42yacs33lHLzrwJ5Z3pR9OlOxCoikKbtw0RCIeciEwMzsso+sxWMhGwfh8GBZtTA4arm+4wjwvRBTAmBG6o2XiB7/ENqiHO6D/JjNWLYCGhxVO700lHGViMkpjvA8hfm1QeYXG5Pgphh1trpGukReoaKo4ygbl5u8g7TB0GAt8R+dGQI32pIRMKdivpVWr45MCrrIO/J6GTZOjEtWzBtrP9GkiZ9URkZHiKpIksldEGwgabQCUHjxvcVmL8RTOz2CUQrEiFKDHEqS/oWJoe75IaruN6bb/xGVFBV+9ybO8CK8guvXSv/8cMn0o6SNdbcl0paCTvKIGGkZIl7pGCUAXonjJqaSzlUweazYjJolpL2lKgK8ZgK811mm19dNXf12i4A3Kxo4mnIxjqbJ/FtcApQiYyabr7hHbP5FtYC2j7mYfiUA8ml8VggSIdLrQhM2iEKKZ9ebhdLIXhFf4lv20SnjLRBo/gtqosmaP0JYvkh1rDTSX+FQTMafrb9VJUoJGNRGSVtyzeGgG7payvzxQjD24IUE/jYzcKg8j19lJTt0x7Y+9jHPx5GcO9TRsNxEKvf+KKy9buvJtelezhBwHaxffbqZNZWr/risnXLO5OUI4Ue2CXrSO7c/MGvCy+GrQOjuy9+M6n6vnOtafc52Itrr3asEjxjmSHiQhCD3CFqGIYKw407c6rMksMve0kJYArOdb4nLRIyLSNnOnbwhuFzgtDb+YW6YTFYTJHFpjBwjCsWRKySCTj/OPwSiXJVHMLHFKpLy4WTAzwzl0VQLs4ghnuGW9cWN4+zcOexCWhywLyN0CJpT2gV0row7sl3ghESDRWNNonJguRwAwaTBZvZNU9DagcuPAfux57x/eXlf/bLVKvCqt5TzaQe2PmNqnALPA2j06866rSMP4eN+KQDUe17wuYs4GfbLN0SAksIPHIgsHfq66GExeN+vJRP/DqzGVgMZBlCXSzG/C+hGozW3fPuMw4zDLnPZqgoOvPBODWYNV47ABI9toXQDgaHM7nFc/ALIFMuPoBMle5wY8LsGRzIZ5DgjFl14F4kXZjHWpBMtnrlgw+syzYwPjFMgi+HozYwVjlUlU3gM1ZDI+Eg6ABVxuwjkgkRU4v0yVuVRjeV11RNmZ83vxFORkCz0OUQe5QIJl1//Ii70vmHyRhAicB/Je1YoUy61M10k1RFmBIl+MRyoAzM1EPJldJg5TKr1iAOa2zWiWj6YhaYiOH3/oC+58T9naccKq/94PEOaVlA25p8dBBFYYyAkUx1rDFSbhmsED60l+0doyL4hTjiO9EIW4kmU1LpTzYoao8dMK3X4q/CX4hACAFj0yCalcYvD9XDIhoxMNZ/0VXY6jOk30xYLccEY5dRRcRBxqQfFViJyUQ3HeAvQWaDWjnzsc9BJLYaMLFAvNqmSgqUAKeM1heCdhXmyuOfWu2FU8CQycCHHoPpfvpl15hhuvmu+umEVXqPbUtoFxz4k6AVVk/6si9Myc7FZf0lP1q2fu81geW5SG+ZxjmAAO1su5wrt/7Y55XNW2HYXPyif6f/pXPTl7vxbC+/N0dxOueDfbp/X7jrR9r2WIIsuF0v+Nd3ZgrK0y28GZrxUMcyZeQga5+j+ci9LobQ8xkoDhUXYeoiF+GSZ60G7BhFMCbfZe4YhMZoY8SEpROGr9L/GC1hDtKSrWM1EnzScZ+c49MqZACn4viZq3Ed+6xGZfpSYInfiLS1EjxrjrCgxDyhCeEo+YNDTnKIdsvh2/xtg2MG7J0eaR53C1xzEkn5E7+asOfGff31zy0//9ZXAoE6V/SzV01dmAsPYWnlhBfPAWZ93/ETfkJwx7+22TL+2cBvCM63bZZuCYElBB45EGDq30fuAgjG674Ppu3XukJLyPsoU8Itz92nhVtdDQ2+BWFAqMrwae6RvQHlQgyTrKAmtjEuJ45U2sI9ArJHphk1GnCnDgUD5QAAQABJREFU1hd1dT8X+WmuX4SqC14iTRF3EBPIupNkiX88FyeiHgpbGbHDvMsEcDjqkB/7FWKSnj0JMoNRe5NLJD3zqHXkMYlRsiBA85KIAZGDmIPoFc8cUKrGyTwzkDP7GFTBc1U55p9NQoYAvUnLWc/BMbkKuP6e1Wa+r7AnYja1jjKtlRkI81pLAuKlNsQdfPO3leYJTzT1c+KefNmofNPjDpU/+OtOOmmbkXIOxrYASkPDrEkY4Q+xpOpjZeom7F3j3SrxkwAQfjl03ff4ewFO1NFDaMP4EFiCckRE210jJRrmUNpmurUNEpm41Ulg6MwjmQnHFFR/284QHdNDWklD74lWRXU1fLpM9675/hQgTS9RRxpWkvpWxE0WSEFte40O+CeTr2GSEJFhsmraMuqjSNlg7mQS400Mnies+A9U05XDV2onUQqcs1OyCyvhat3sK4956o3lkqvPnYno4VOeVta+9TvL1n/8nVqu5fXzDgHbw3Y5V254wTVl5eovgWn7i/SjuvDEAKHvpq/SJRkWGRe5pzfb5zIyForR9V0/6IyX8cVzBo/f01P92rmeAeC1fubBMLxmnFKGPNfPPPK5jtaZeuUJyyBUTd63lJk527HIt3qUh+NGJq2OWZ74RmZEN5bZpnzOTfgp+fIIASVwWQVkTCu9nnGmijgiOEYDVSZheJk6i0KyLeN4wH7sdgzKZvpT/THm/jnKoz3J4twISbwHhA8x149hoRmm/4dsIZicuIejQmDatkjERclnPrM07D07V251uFp+7st/rPz8216ZJJ2PUnHLLQCsB3+9/1xamU+df/fMbR7OeMv4AcxZwe/nn/0/YL353EhRbZelW0JgCYHPPwT2F8MmvL7wX3Ho7h+VsvmpBegtMjUL3jxq0S/Eu94iDzGgCDtSBxEtv4P8DqwXNYFctRwfY2USoZgaieJWV02z10IMIhLqnc8ilrkjHbEt/yL4kARZPZWZ651qbneBtNkDwXk4Q1Z4hxgy8cDtCYyb5tfdd6DEpG5sN55MmuX2uRLxyViCmmBKbiJ1VEVQBy+5fjFHCmxul+2jmxDhEAYyhWB+ooTAcEW0dz2jtvguf+q+PomQFkmbK8QSGaoVWgxjm1Z7xZVl5Sdf1kc9Z/eXfcUF5a23TMqdJ2B2caL2/KPip2piVrEpo5JAGSvbWGmbvKvOshne5uiJI4Flu6Ru1Cur5UnL9KkvfcOfefkuE+O9ZpyHfEsb8BpGzkzwNZ8K0UqqKa0KfPws7Pg3NePMaO+kjF+6o0GsC301/REGyoUFM5doyyHXlDOJGMFGRw/Wvh1ZgYWkH6vSlcql0pbHox7wJKkJkrp+HyOh6GeoU8mo8VP6Wpn3yqACUvqLcKecEJjrBzbK01/4LKOdU3fg5/5FGb/ljWW2PET7nML1bBIbXHFVsT3OtTv4pG8tR275awwZHU1ftw/bPTMPk1m0GboxZ95hfrpCDFkxcBYdqKWgn5duzCSICfmeF0I6zrpAeSJd4+sc0fUr1xqhfuueE4jn+sqVYMapsXhmgJp+1IuTD2Edr7gsmDj+HJvcGZ2ph3MNQjkDEsY0kYIbgfiineyTMy0WDOteN0asi2tK4WTymAM0aGWyqQfZNVofZn5v0ABp1/HF+rHb1GZbHCOzroo/xpdGB1mIwWrvcdRSy8Vleuxo2TxxrKxhGXLwgm+yBOfUveiGF5bf+ujvl/d89oOBgWUVcroK//p2uj/1CIRtmQRexu/67F7g99RLbywvuuHrK0yX1yUElhB4xEAA+rHDPvupSnf9WSl/9rUQonBVmeuDFc9cgzm3xmf3pIlIRJ78i0BkYrJKGt0TMB9Wuqb3oKp4EsQI8lMapcbJgHuQqMSyriOKu5d6m/uLWSsS6gRzZNaVUcQPY5GDsQscIqorZcLegskhpGFg4rGWJLEAxkaFgYf0uCdCpi+ONE3WtPyZpER6CBf2LIDoJRKqtTTiwgSWk1uc2YMMDitjBYGVzJ1/KsYFBvPmNzF/nZMRkqiAodSYiQZIJux/8hww1SOVuK2ubZTVf/trZfC0L+ljndP7O27dKn//dXeolYkmKRIh2m9qWfhTAiZSG0nUUKYRv7W0r3XkS60e28BgTfmWA7UlpajXEBiNYIjd56XKq7AAymWDCmcXnumQvvu8lMKpkqSaZeM+FA+1RsI34OTbhrsGUjyo22Zo2WxyCBWlA0jJVuljMUTDXsKWn5XQsufkgs2y9hT2kD2BvYzTIzDusFzHKKPm/imeTd2wF2WKLqPZYSonDOlsy/0nG9TrQvoOBgZoiwmHYs9YaQ+fBxM4jIou7YOBEMsioacKsO217Xl98SM/OTKhRwAt3wmLIb8GwwZRzwU+VTUXS5uY83/+D35LueKxjzqnbdsnNnnrm8rRH/x2FiwYy0v3eYFAg5XZC37998ro2c99UPI/eefHyi1v+jX6KR2a0eZ/LotTDuPNv0qo1nuYN+cnpU5dyebfXUTDz7ks51M6B7qAYafHefNZ9FZD6ul4qPnX3OpzEuKxzqPGI03C1aNNFstlyv1cTGLGmTvz7Tx8NFfLg5dMaaRhzBJRr3SRJOU0H8PW+cananGYMjOvuWjjvOUvx3A4EeIXLQ3jgVyyPxvputaPW3DX4AQZHmehbRO8cvJC5hbQyZEtjvnYKusv+/tl8OTHmc05d8cnJ8rzX/e95e7te7q0e3j0d73759oiQjZw4to/z2G4EPZMfjWlZfxT4XcxB9a/8RteXQ6yr3HplhBYQuCRBYH9ybDZBrf/YSnv+C6YHS3NMX1Hb+yUxgkS55sUrXO7hLgzvS8Qx9NIKkDqImiQ3zZShVX+hpLtU0hwGLZyDAIdvf8WyZvErPvMgmCTNt91yduEZYQkSmo+Jt+FqGE6wyKtRL8OwyPN7BBIGuJ9jLUvnsv0UBmfxCLYDMaN8EPLEYaNlCRerAsIOpKU5MN7lnFhqAjn2TtDjIZMUbvzXusBoj8K0sYYScHohWqXfAgssq8CqqESKaoHmg3MgoQOd2QwNX/e3Qs1BkYe1j2FuD/4S79Shs97fqryYF3++OMnys/8pzvIE2mkIINgkQEbwESNKLcafw0Miqb614QT5Ryq8ukf7aXFNts9kjj9CKMUbUUiykIThh4g2ccJEOzh6fxMZ4V3QauwU4YNigg4kR5wGKCXdEaGbXCkbKCyCHtFGwJMjkeYhWEDppjAHh/cLCtPgTl8IhZP26MkTrowa1MILfOyCs0mJlPYh1JX2WHY3K82Qc1pG0Z+cgF5y8grNa0qrzLTI/uETSpjyE81K1LjV+upyq3WQ5Xi2lN79TRX/pVkaIVuRLmFhsw4gIrfV774b5Rrv/BxQuVBc9v/5ffL8Z/4oSXT9qBB+N4Tllk7+Mu/WlZf+C33HugcfDl+y03ltre9hmHF/Gi37JyS3YxV+378nXdqgDBfPvbh7bhxehgrt/iACLjXvt0zbX0Aw7kA4X0Q5s85v6ZA9/dr/s3IuHo5J9R0/NYVgJv+/hnG+cMvvqfwesanJmc1Urc+nJkSyWDJBwQhMxfz+zxrlTW5Jx0jMaBJD2+qZx6Ma+YTF4ycz+qRJ8R3lk5BnEv4neB3kkWdTQ7bPgIeOcnc8F1/qwyezpaCB9Ed3rq7fM3rf6AcGd8DOCyQjhpRn/puxXr/+m3nvf+2GH4Z//9n7zzg3CiuP/7ONjam99AxvYTee++EEkJC75CEPyEBQoBAaIEQQuiQEAihQ6ihQwg99I7pHVMDGDBg427f//1mb+72ZOnudFrpVtJ3Pj5L2t2Znfm+lXZ++2belMNvpoHT2/0bX2GzDJpJ4EgQgECDEahfwSZDjHjC7Nmd3XP0cfAehB9//e77n8RSmz4K91LdPwZ48IyQJHqKJM0hSO7NfoPTXVLCxjv8mgc21m+CE9xDNcinC2iUmTwS/UOkRxcDYZ6XTqw8sWw/oc6jzd6R0Hpo8rbpY391roPY0s3Lt+g8+vNokdbqN9hxHvLcvSlat63FhZy8cC3B66aO+gAP6+yBUjyIiW7Vyqe5Sqq3OjqJt8g/6Gms9rs3JpTtC7BOGDfKxo4abeM8wth0E6a1AS4GQx9CsLzjIEYTPZ/mWYTPXkdtC9ERJV4kflwcTJppFpvqD6dYv2WX9XNUPz3z8Rg78s5P7SsfHikxqehr7guSbNFcfBcx7k3yug3yyoqBxLd4hKfTvl2dG3nT1FcIc9O8LRJjwfnqx0tmy8ukEf9au0021HFByHg2PZXXE2/NoesQbO7x847TQHeLpT1sM/T71qb2bd5dahtGKq+kPF7Ozl2146YZYwMW9fgwS3q45f5aokIeO7fpSBfYyanDmkny8GquWYuv2xcEmgu2VoXllrD3QDXBcOq0aZHccQrn7TZ00RqCsHidJ7rHyl+cl7dPHUP/pMWzx40bG2zqu3yLH6GDnMUAF2xaWDus8+blDpphalt7581tjgXn0aFVT/K0jfrV3gyPrDrpjhNoGOR0Z19SNc9ax5mSd2O+eNc+ffpq9/a728e/H351h/+TKzH53QwiSd/pVGb/JvlxnsL3sG1H2OBlhIs8Od6/AW3l6Rgd4Fe4Nir59yDZ23aeZKMf0vab7+Ukki4cnJxLW3y/KhOklJel0ylJ6LXtCp+T86hFyfakev65owLhuPBfuEf4EaFcb0NStVBo8uBMhcjrrQMk6FRm8huv6Lfh9qUyJOj0EFK/9f4QSR/Dgyn9Fsjj9p0ewM1q/Tbcy+d+L9Rx/iq+G+Weth0fPdiGfv2qKt3GPZ7QP7fZJdkiBvEYfw27BaPtuPZ95E8ItHEJfLSlg99yMy1l1651lk2HZy1eLLxCoOEI1LdgkzkmeACPV30O1UeX+4fkZpzcFNwD4Tf4cDNsM1syZEwf4nFtO9pfvMvsndWQPzyC9Ruffhx92yTv+GvYyYCRvri0Dy/0vm8oZmq/qfaX1809V6GDrRB7SuGm7OfRnd2VgYSU/ld9+kclqXpIWYTj/Thtb/VOuYZJujdl8lj/c9HWb+IM3nF38aZ9LgUmhVDv3tH340NI+lCEut/eQQjn9cM050k3PMkadRrULh8WM27sOK/7WOs/wj1IEgOKNub7FNJdSfOaNOe+1YfYhUiZoWOQeNpCp2XTzazfzw/0YC0zhONr9d8I9zid9tDndterXwfxNZXXS8MdFe1QwktduqnkSfMKhUiY3okJi886h2R/kCfe8dKxLlB0nBtDvCTmBmhooyNQxDYdr6AC7ucMr6FzJrEm0eah9oOHzV/7+bBECTaJM4k+efNm7DfKBvv8Eh+k5OV4+V6Pye7tlNdLa+qNG+zrIA0xm2nZmd2UvpD75LH+bMCXd/jWl32QCWS2iV7iOA+/7Z43GUNDIFt8CYgwdHa8XwceUEbeWQlxedcm+/XY6tdm0llLGEiwyZMoD6o8dSE6qhc+1uc1TvJ6J08PZGj/523VddTfo0rKy7bQikvaClutbYOm9YcHNUytX31ho0860sbder1XyitGqg4Bvy4UYERz1lpmyS6QTE8qO2ncdzb8pdtt5IdD/fDONtbvV8Em/2b6AECNSS+R9H3XpaJvXLJ8Szywc9lxqz9lCaeIP7sxv2fWP5Xmf8n3QvvC3LO2ekV5GQ7TzvBr4t83f68hlEohT9jn31v/IQm/yf5Z30V9uYP4CufQb0aSIYhOvU+2pN55mSrXs4ZIsb5HpQzw72oYZhne6/fBv//+26TvefCy+zDvfnpANNfKZkvt4E8ZfeRGDZPuc9d8cIf95sVTwqiP5NRqh9c+fK/VCv8cWtP20v59T+9LHaPjyT8FvwEu5E9b7kjbcb4tnabYkSAAgUYlUP+CLVpm5Mtm755j9vE1ftNSSHiJI/3g+21Q3rL2H3ttScSJ3nVKwavmP3pBvPirhE7401Ha7n9yirjHbYIPk9QUunA/9dP0d69OP4kcP97/T+5HevUPEmu63yQ3Zt1gw606dVCqPvK+SQD6vDbNZ2sd78Mlfahky4QZ/QmqOus+2M7roa5A8PZ4Lz+ItNhhCN47L7+t/UkgCy9TakCeQK/KJBeXk0f4PL2x7sUJT2eTqoRoiyrXKypyEirqA7T6+j0DNtjY+v3Ib/5DFvQ9fZfe+HysXffsCHv4ta99AXQNDXLB5rb1Frpg03sXMd7UieMkklw0eXvCQD/fFoYeedsk1mQB5VU+l702lQsvDWGUt0z2C/n8jRiEgCsSbIIXhJvPYXPxJQ9bnMOWCLaJNnP/MTbIC3WfmR+fdJzkYQuLWMvDNrXPO5tnks22wqweGcYXwfUn0v21AO/IMS6+vHjn3W+i11ABaPz60hw7Cfx+8r5OavOyTdQcR4m2xJ6tGqLqtlSI8uANlFj3tughQwgSE78Huu58HtF4H0I6MQQVkZW9geLnHrYFV1zClt5gZZtlnjl8e9+lSa8OtbGX/NXG33kzi2tnaAYtiq111hS6P8tokL2p4rivP7Gv3/H5ix+/qB+k9t/GtjedivTHDp0+l/6gb25h0jWepORdxzEd77Q/7vWtbb9/IZc+apd+MPR902cdGx7eJHn0Mfxi+j497FHS7Sb8kihPKFu/2MkOvSbv/VW/02GD/+76oTo67kseFCUbQgATL1zlh3OFuviRfpDEm/IO8N+k/v7wZ8Acy1j/ede2lunm9sx9l8a51+/m/91rx756tn05/uvALWmb/+/tEMfw2asobN6KpPEBuPaosd5a/6x97ccLbpPnn22qme2EJX9p2829sQ3S+rIkCECg4Qk0jmCLppo02uyLB3245FM+b+vNJJrk+C+8U+BKa7LPGfKbiLsu/GjvBKhnn05t94hwtw13Ub8LJneS5EYiMeRr9YQbuvePW8d6p3qMhqR5p9778ur8a1iKAoqEJ6regQ6R+bwDrae/IZqYhrm0iauk8FQFND9KSUNa5EHRPLZJ7m2b7MPnxk/nHj7v2Lv3xbzD3m+ye9vC8V6R4JpRRr+tdRJsXp4EXehFqN4qUzdAr6kP75w4xhcPH+1CwWs+wENB9/cgIv0GTetr1LlHb/Y5rWW+ea11iSWtZQUPKjJI3r38pDE+xPP5YaPsjU++s4++HGdffjvBxviaemPcKzXaxct49zpJsAQmsqHbYCpN9vf3EmDqKMmblnjbXLD4o+8wDNL3h06Tdwo07FKdpmT+WhRsPvzS57K5H9KP9zmPLvQGeFk6Rh622fqP9XDK+pwItsTDlgyJnOx2mOCCbdwcE+x7K7pgm0YettHe4fIb7nc+zFVeMp0/Cmtvi7y67hTwBwIaLuv292tA9g8eNy0t4RlancVkvw71oEFtCEFtvPbysiXr8HmL/NwDPLLkgEF+bbo3Ud60wTNMazN+b1abfYG5bK7F5g8BRvJjYW/u6O9s4uMP2cShz9qk9962yZ9+YvLCtX43ykOZ+/e8LbiKbBv+8lT5WtZFD5r8Lwxl1rDWwT5sdlp/yOPes35zzm39F1zEBiy3kg1YY71MFsXOsmnyPo8Z/raNG/Ghjf9uuM+z/caHoPv3wkcDKLCOIib29+tanfVEORU/u+Yhh5/stt1+tKfk/7ZN/uK/j4Wb2nf6F91TEi1VB0kkxKR8vi19Au0KB+i/5HubHO2/r2F7UkZSnvYUnNh/X5K6+MFBgEiQJA+L4n0peO2UzQ/RL1UYIakRHe5V6edRIFsGDPLfbB/aPs2MNnD62WzgTPPZVLMu6tdB/jrww8d/Za+NfMdeHfW2vTFqmL0/5mMbPu4r+2r8Nz7lboyNmTQ2BM2aICGsBvu/KNT0PrF9antg1gZH+5X02+cv9Zxftp3K+w+D3b6D+01tsw6cyWYfNIsNGTyPLTbtEFtq+kVsyekXttkHzhKazH8QgEDzEGg8wdY8tqOlEIAABCAAAQhAAAIQgECDE5AzgQQBCEAAAhCAAAQgAAEIQAACOSSAYMuhUagSBCAAAQhAAAIQgAAEIAABEUCwcR1AAAIQgAAEIAABCEAAAhDIKQEEW04NQ7UgAAEIQAACEIAABCAAAQgg2LgGIAABCEAAAhCAAAQgAAEI5JQAgi2nhqFaEIAABCAAAQhAAAIQgAAEEGxcAxCAAAQgAAEIQAACEIAABHJKAMGWU8NQLQhAAAIQgAAEIAABCEAAAgg2rgEIQAACEIAABCAAAQhAAAI5JYBgy6lhqBYEIAABCEAAAhCAAAQgAAEEG9cABCAAAQhAAAIQgAAEIACBnBJAsOXUMFQLAhCAAAQgAAEIQAACEIAAgo1rAAIQgAAEIAABCEAAAhCAQE4JINhyahiqBQEIQAACEIAABCAAAQhAAMHGNQABCEAAAhCAAAQgAAEIQCCnBBBsOTUM1YIABCAAAQhAAAIQgAAEIIBg4xqAAAQgAAEIQAACEIAABCCQUwIItpwahmpBAAIQgAAEIAABCEAAAhBAsHENQAACEIAABCAAAQhAAAIQyCkBBFtODUO1IAABCEAAAhCAAAQgAAEIINi4BiAAAQhAAAIQgAAEIAABCOSUAIItp4ahWhCAAAQgAAEIQAACEIAABBBsXAMQgAAEIAABCEAAAhCAAARySgDBllPDUC0IQAACEIAABCAAAQhAAAIINq4BCEAAAhCAAAQgAAEIQAACOSWAYMupYagWBCAAAQhAAAIQgAAEIAABBBvXAAQgAAEIQAACEIAABCAAgZwSQLDl1DBUCwIQgAAEIAABCEAAAhCAAIKNawACEIAABCAAAQhAAAIQgEBOCSDYcmoYqgUBCEAAAhCAAAQgAAEIQADBxjUAAQhAAAIQgAAEIAABCEAgpwQQbDk1DNWCAAQgAAEIQAACEIAABCAwAAQQgAAEIFAegWHDhtn9999v33zzjS288MK2+eab28CBA8srhKMhAAEIQAACEIBADwi0tHrqwXEcAgEIQAACTuCNN96wU045xSZPntzOY5FFFrGjjz7a+vVj0EI7FN5AAAIQgAAEIJAJAXoXmWCkEAhAoFkI3HLLLZ3Emtr99ttv20svvdQsCGgnBCAAAQhAAAI1JIBgqyFsTgUBCNQ/gS+++KJoI4YPH150OxshAAEIQAACEIBAJQQQbJXQIy8EINB0BOaZZ56ibZ533nmLbmcjBCAAAQhAAAIQqIQAgq0SeuSFAASajsAOO+xg00wzTad2r7LKKrbEEkt02sYHCEAAAhCAAAQgkAUBgo5kQZEyIACBpiIwYsQIe+SRR+zbb7+1hRZayFZffXVraWlpKgY0FgIQgAAEIACB2hBAsNWGM2eBAAQgAAEIQAACEIAABCBQNgGGRJaNjAwQgAAEIAABCEAAAhCAAARqQwDBVhvOnAUCEIAABCAAAQhAAAIQgEDZBBBsZSMjAwQgAAEIQAACEIAABCAAgdoQQLDVhjNngQAEIAABCEAAAhCAAAQgUDYBBFvZyMgAAQhAAAIQgAAEIAABCECgNgQQbLXhzFkgAAEIQAACEIAABCAAAQiUTQDBVjYyMkAAAhCAAAQgAAEIQAACEKgNAQRbbThzFghAAAIQgAAEIAABCEAAAmUTQLCVjYwMEIAABCAAAQhAAAIQgAAEakMAwVYbzpwFAhCAAAQgAAEIQAACEIBA2QQQbGUjIwMEIAABCEAAAhCAAAQgAIHaEECw1YYzZ4EABCAAAQhAAAIQgAAEIFA2AQRb2cjIAAEIQAACEIAABCAAAQhAoDYEEGy14cxZIAABCEAAAhCAAAQgAAEIlE0AwVY2MjJAAAIQgAAEIAABCEAAAhCoDQEEW204cxYIQAACEIAABCAAAQhAAAJlE0CwlY2MDBCAAAQgAAEIQAACEIAABGpDAMFWG86cBQIQgAAEIAABCEAAAhCAQNkEBpSdgwwQqBMCY8aMCTUdOHCg9e/fv05q3bfVnDBhgk2cODHwErdykvIpv1iXm7ec8+jYaNuppprKBgxovp+xSZMm2fjx48vCNvXUU1tLS0tZeco5WPVRvWQP2SUvqZJrulTeeP1Vm2leGFIPCEAAAhDoWwLN19PpW96cvUYEJB5+/vOfh7Ptt99+ts4669TozPV9mhtvvNHuuusuW3/99W3vvfcuqzG333673XTTTbb66qvbAQccUFbecg8+5JBDgmjbcccdbcsttyw3e90f/9RTT9nf/va3stpx5pln2iyzzFJWnnIOvuqqq+zBBx+0LbbYwnbaaadyslb12BtuuMH+/e9/2wYbbGB77bVXWecqlfeggw4KDydOPPFEm3/++csqk4MhAAEIQAAC5RJAsJVLjOMhAAEIZETgsMMOs+HDh9tvfvMbW3rppXtVarW9mb2qFJkgAAEIQAACEMiMAIItM5QUBAEIQKD2BM477zwbNGhQ7U/cxGfceOONw/DPGWaYoYkp0HQIQAACEKgVAQRbrUhzHghAAAIQaAgCeRry2RBAaQQEIAABCHRJAMHWJR52NjsBBRcYNmxYCMSx6KKLmoIMdJe++uor+/TTT+2bb74JwTdmm202W2CBBabINnnyZBs9enQI0jF48OCwf9y4cfb222+HfDqfgjioDgrkEY9RcId33303bJ977rltjjnm6DKYhMr44IMPbMSIEabyp5tuOltooYVs2mmnnaJOxTZ8+eWX9v7774d8akcl3hy1+aOPPrLPP/881CHrOVXiqXNMM8001q9fEgRXtvjwww9t1llnNfEqZkMxFie1TQEzWltb7bPPPgt1Vb755puvZHCTmFflFguAorK+++67gDYyj59VVyWVMWrUqGDHeEzYUYX/il1Tui50TWnfwgsv3H6txdOPHDnS3nrrrXAdiuHss88ed3X5quGeuvbkidK109Phm1988UXIp+tD+XoSLEVt0Hf166+/tu9973s9zqcGlJtXtlJKX2f6XupPbYztlJ31fdZ1IW4zzzxzyNfVf7qG33nnnVCGvqe6HuNvhThU+/roqm7sgwAEIACBviGAYOsb7pw15wQkui666CJ79dVXQ+dd1ZUAGDJkiO2yyy4mMVWYPv74Y7vmmmvspZdeas8Tj5Fo+9nPfmaLLbZY3BQ6ZSeddJItuOCCdvzxx9t9991nCnKgDtsaa6wRzvH666/bqaeeaksssUSY53TBBRfYc889FwRkLEj5FWBlzjnnjJvaX++88067++67Qye2faO/kQBUcJB99tmnqMjQsRJWZ5xxhkmwxSQGSy65pP30pz+1mWaaKW7u9lVCQAFN/vOf/4RgDTGDxOZGG21km2++edxU0auCQHzyyScmrhJql19+uX377bftZaojvfPOO9uGG27Yvk1vFMDjhRdeCPvmmWceO//889tFlvar07zyyiuHQCyFgvW0004LnXIxWWuttXR4pyTh/qtf/Spsu/DCC4M4i5/jgRrWqCRxKObVTG+++aadcsop4VrU3DnVSdeUbKQkGyuQy49//OMg8s8999wg5iQ8lSQaVlpppXANFLIIB/h/En9nn312p+tO19wyyyxjCgI0/fTTx0M7vcoGl156aThv3CFRtOyyywb2xcS2BJq+F/quRAGsvLq29thjj1hM0dfe5j344IOnCDqi75m+v2K32Wabhfa/9957nX4LVl11VdvLA58UE136rbnyyivD9RtZS+gpiIuuqyOPPDIIZV1vJAhAAAIQaC4CCLbmsjet7QEBeRLOOuus4PGQCFp88cVDp+uNN94IHVF1mH796193El/yCEhYqQOoTqWejCt6nJ6wDx061LRf+XRMMaHz0EMPBXERq1cYFl2daXXqX3zxxVAfPa1Xp1h/6hQqAuAf//jHdq+SylHExptvvjkUKY+DRKa8a/KWvfbaa/boo4+Guhbr1MojdfLJJwePgdqvtkgIyVvwyiuvBIEpBvI8dZfkPVK7VVd1VJdffvnAQJ4XMf3nP/9pY8eOte222667onq8XwLk1ltvDQIoBvPQNp3nsssuC9uXW265KcoT3+uvvz4INIkEdfpVT9X98ccfD546iZxiNpyisBIbJFwit//9739BfMtjpeumknJLnK7kZtlF16TaJhGua0oeKgk6RfyUUHrggQfC90BCQ3UTC107zzzzTLiWikUSlXdI0SJ1zS611FImASzxr2tHguyEE04wBVspfMAgQS+bSRAq37zzzhseFkjIPPHEE+G98qVFm67l008/PXiztX2RRRYJ5er6Vbv0PS4VxbGSvCWh+g49IPjTn/4URKeuMT2s0XUlr7Kie8r+MYJtLOexxx6zf/zjH+Fa0DUXH+zo+3HbbbcFb288llcIQAACEGg+Agi25rM5Le6CgDqZF198ceikbrXVVrbDDju0iyDtU2deYe8lkNQZjB6G6MWSJ0ydyrTgkog75phjQkdOHbDVVlutUw3UwZNnTp323XffPYi9Qg+ERKSGlR199NFhyFosQJ1ZdQ7lTVKnUGJISZ1xdX6V5M1ZccUVw/v43y233GL/+te/Qp64Lf2qTreGbx111FGh4xz3aZigPEDRe6X6dJckGtV5Vif0F7/4hc0444ztWSRmJUQlLuUpLCai2g8u443aJs/ET37yk3b7adibPEuqi+xV7FwSo+rgy4bpeoq/PEYSHipbnsneJtlWHkAlnUfDBuV1icKy3HJl9zgEr1ReiSYJh3SSqNGQQ3l3JY5ikrdKAuK6664LYkvM0kJS16q+AxLAxQSbBJ+uZQlbPSiISR5oXTtq79VXX22HHnpo3BWGW0qYSCTqetX3KCY97DjnnHPCMfJAyUMXkz7LgznXXHPZ4Ycf3mnZgng+2btYqiRvsfLitkceeSR44v/whz+EBwNxu0Swfj+efPLJsOxBZKrfhyjWNt100+DBj0NA5TG85JJL7L///W8shlcIQAACEGhCAskkjyZsOE2GQDEC9957b/AkSVxoSFicB6Vj9WRc635pn4YtquMVk4SYOlmKHpcWa9qvjlkcQikvVWHSkEN5vtSJl4goFGvxeNVH84vSKXowtE3empgkMDSsSl6MQrGmY1ZZZZVwqDrPWrOuWJIXIN2R1zHqgGsNKrVVHfOXX365WNb2bRJ499xzTxAUKi8tgnSQ2rv99tuH4++///72fJW+0bwn2SptP4nrddddNxQtwVksycZa462wnrKfRJWSOuTilpckwSsh1NXf888/X7S6ekBQaOP0moW77rprJ7GmQiJDPWiIc/EKC5cwT4s17ZdojOvzSajroUBMWsNN16sEdlqsab+E5v/93/+Fobv6zum7p6RrT9egkq7JwvmQOp/yFUuV5C1WXnqbrrl99923k1jTfq0DpyGOEmH6XsSk74e+g7pmxTuKNe1XWRLFPZ0zGMvkFQIQgAAEGosAgq2x7ElrKiQgj5WS5pClO06xWG1bc801w0d13GP6/e9/H+beRCEUt+tVnXsNB1OKc1PCh9R/W2+9daehXqld7W8LPXNxRxw+JU9DTBrOp7lA8r4VJtVBQ8xiSs/7idvUiS/sOKf3SSgqybvTVdK8InkmVUfNzyqWIk+VpeAWWSQN4Stmv2Ks0ueTuC3s+Mf9mrelNqg93QnVmKcWr7KFvHNd/RVjLwG7wgorTFHFtFgtfECgg9Oh7ItdOxqWOMTnehZL2qfhwkrx2pHo07BeiRPNqyyW9OBB+RTUQ8MKleJ3VWVKnBVLqn+xNlSSt9h50tvk7Ss2DFPDgWM90/MqY3sk6IolcVl77bWL7WIbBCAAAQg0CYEBTdJOmgmBHhHQMColDZl7+OGHi+bRcEOl9FPyeKDmSMlzoLlA8qZpCJ2GnqmT31XqbjicvHRx+GWpckqJQXmTJBjVNv1p7o6GYXWVinU408drv4YPFmOQPi7y1NwnzV0qldQpVedf4raUh7FU3mLbNQ+oWIoirpjQ0PHycpRKyqu5Z/KIdtfuUmVUY7sCYHR3bRQ7r4YfRh7p/XGbbJKeLxaPifvj58LXrhjqWO3XMMXIMHqdVa7mOpZK0asZ82lOmFJPrtW0N095Ksmr/F2lUtdeOk+8/vSqAEdKEnqlkuYXkiAAAQhAoHkJINia1/a0vICABE/sPJUaMpfOIm+Q8qijKS+B5vwokMeECRPCYRoaKc+AhpCpU6phk8WShuHF+SzF9mtbsXDxpY6N2xXgQVHrJBhj0nnU+VOURM3FKpXSXpRix8T9WiqgqxR5yqOQ9iqUytOTY0rlTW8vHJaa3tfV++7EYk/b3dU5Gn1fTxnGayO+6qFGqflmaWbxGoke5WiT9DHp98X2V5I3XXax9+Vce1oeIA5JloAulXojyEuVxXYIQAACEKg/Agi2+rMZNa4SAQkvzRWRWFNgg+68XqqG8ugpuQJSSJApUMcPf/jDEOVOc2+0X0mBHEolddTkzcgyyfulQA3qBGvIoUSjhpTFjp86yV0Jtu48cLHD253QjHNvNJxT4fS7S8XCnXeXJ8v9WbU7XScN42umFK+NUm2OjOOaZPEakTdPAU66SzHAShzm2d35iu2vJG939Stnv8StBJ4e8hSrZywritr4mVcIQAACEGguAgi25rI3re2GgIYlSbBp2GPsUBZmUbRBeZbik3QNMYzes9/+9rdTBFtQ/uh1KyyrWp+1ppvEmoSS1n8rTN2JCA3pjN7Dwrz6rP1KhYElwsbUf3Eol7wipXjqcA1z0/nS86dSxdTsreZSlUoS5hraqZRudxTbcZhbYf44hK9we6N+7oqh2lx47cShgBpOrO+UAvAUSxJ6OiYKtmiD7rxyxepTSd5idevttviQSB54DVvWOnXFkh7AkCAAAQhAoHkJZPtYv3k50vIGIRBDvStiYakOuBYaPuKII9rD5schh/I2xY5gGofEXxR06e3VfB/rFINsFJ5LUfq6ShKtpY5R51LBRJRKdTBj2QqIoeGcan+sU9wXXzWMVCHZtY5cXycFwkhH20zXR5EWJdQl0L7//e+374oiMwayaN/R9iYdnKZwXyN+liCL10dh+zSXTA84lOK1M3jw4PZ1xxQxsViS90kLR+t7p3mhSgoQI8Gj8kqx13UXz5cut5K86XKyeK9gNkqaM1vsQYrm22kJBRIEIAABCDQvAQRb89q+aVou4aV5Il39xaAg6623Xph3pk77X//61+Bpi6DkAVJYcS0arLTWWmuF1zi8Sh6AwuAGGsqksOtx3o28c7VIsU4SGYXCUwtAa25bTKXqJGFa2PHWmlhag05J0fkUjbKrpHpoqQPVQRyihyrmUSAJzf1TUmTO6K2K+2v9qnpqIeZC0SYvjtbKUlLEvrQwj5H/nn322U75dL38+9//bo9qWKwt0VsUA2oUO6a7bV1d1+l9qk+t0l/+8pcpvgtiqmtASWItLnWhzwrnr3THHXeERbfDh7b/5FVT2H89+NDwyfgQQsFLVl555XDU+eefP8X5JBxVj2KpkrzFyqtkm9YL1FBgBbPR2o7xt0JlSmxqmKjmuZIgAAEIQKB5CTAksnlt3zQt10LY+usqaR6ZOn0SDFpDSfO/nn766RC+XZ07zTWReIlh5xWGP3YcJVzUkVSn++STTw7eFx0vL5UEiubmKFy5QunrKbo6zrvssktX1al4n8732muvhfrLK6HOsTq88k7oib2GSqo98lyce+65Yd7dkksu2X5eHa9j1VlU4BQxUD51ICVuJcTiumTtmUq82WabbcJwLw35ios0a6ikyooRAjW/Lq7HVqKYmmzWvEV5a7RguCJCqp7yDCrapewmDj/60Y861WWjjTYKC0lL+MoLpLZoaQC1T9fEnnvuGQSHxFNhUvkq+1JfgkFzCjXv8bjjjis8rMvPpdYaK8ykpSdU/2onLQehYYgnnnhi4KcojhJr+i5IECuK4m677dapGrre9J3S4tJaKFqLaKuuWnNNawqKneZfHnjgge3zQlWAvkf6nslGWsdQ4llLUujaFVcNr5Q3tNiQwkrydqp8hR8k1rQ+nX5/VE+tKafrQm3XQyDt15xaPUAiQQACEIBAcxJAsDWn3Wl1FwQkxNT5UydaoibtZZIwkwBJLy6sjuShhx5ql112WTg2DiXUfBx1FrXwrUSOojYqmqSG3VVbsK2//vrhXHfeeWcQWjGMueaRqT7af8011wShoWFjhUEN1PHVosrybGhx4uhx0vA1iRqJkO6iAUbE6nAeffTRdtNNNwXBKiGjPyWxU8RKddajtynm64tXeX4233zzwCZdT0UalDdHgVMK66l9mrsoj6QEqLxx+tO1Ik5qnzgWS1oMXZECh7k3SJ6VOC+y2LH1sm3BBRcMi85fffXVwesVl3bQdSCP7B577GHFIiLusMMO4SHItddeGzjKmxuThipLKBcKTgnjY445JvCVN1nCTX968CLBLSH04IMPFhVsleSN9crqVdedHmbI861rR9eReC2//PK27bbbtg+V7Ol3Lqt6UQ4EIAABCOSDQIs/Na7dOJl8tJlaQKDHBPT1kJdEnih5PzRPLUZ+LFaInojreHW25JlKD/HT/BR1XiWGCjv9xcrKYpuCnUisSSjqqX1hQAcJMc0xk7goldR2Bc6QMNFQwK7aX6qM9HZ5KXVelSemvVmyIF1eFu81zFOCWoJMgk1JgkHD1FTHOMS0u3OpbfL4iGd3ETS7K6sR9uv7oGtHLORZ6+m1o+tWXjJ9f5RPDwp6kmQzXa/yssWIqD3Jp2MqydvTc/T0OHnX1ObIS575iy66KHjGe+pR7em5OA4CEIAABPJPAA9b/m1EDfuQgDpM6jDqrydJHdNSHXWJNHkfapnksYlzrIqdN0boK7YvblNQjRhYI26r5FVegnrwFEio6a+cVC9tK6dNlRzb1fehq3J13fbmu9Ibm8V6VJI3ltGbV3nkNQx3yJAhYR6nyij0QEqwKekhEAkCEIAABJqPAIKt+WxOiyEAAQhAICcE9FBIwWkk9iXaCh+iaJ+GLcfhwzmpNtWAAAQgAIEaEkCw1RA2p4IABCAAAQikCWjpixgY5dhjj7UVVlghzL/T3EYF6tGf0nbbbVfSe58uj/cQgAAEINB4BBBsjWdTWgQBCJRJQF4OhU6Pc4bKzM7hEOg1Ac3hVNCim2++OQTl0dIh+lPS9aghzbvuumsQdb0+CRkhAAEIQKCuCRB0pK7NR+UhAAEIQKBRCGjZAwUt0gLtmsemOWu1ClDUKAxpBwQgAIFGJIBga0Sr0iYIQAACEIAABCAAAQhAoCEI9GuIVtAICEAAAhCAAAQgAAEIQAACDUgAwdaARqVJEIAABCAAAQhAAAIQgEBjEECwNYYdaQUEIAABCEAAAhCAAAQg0IAEEGwNaFSaBAEIQAACEIAABCAAAQg0BgEEW2PYkVZAAAIQgAAEIAABCEAAAg1IAMHWgEalSRCAAAQgAAEIQAACEIBAYxBAsDWGHWkFBCAAAQhAAAIQgAAEINCABBBsDWhUmgQBCEAAAhCAAAQgAAEINAYBBFtj2JFWQAACEIAABCAAAQhAAAINSADB1oBGpUkQgAAEIAABCEAAAhCAQGMQQLA1hh1pBQQgAAEIQAACEIAABCDQgAQQbA1oVJoEAQhAAAIQgAAEIAABCDQGAQRbY9iRVkAAAhCAAAQgAAEIQAACDUgAwdaARqVJEIAABCAAAQhAAAIQgEBjEECwNYYdaQUEIAABCEAAAhCAAAQg0IAEEGwNaFSaBAEIQAACEIAABCAAAQg0BgEEW2PYkVZAAAIQgAAEIAABCEAAAg1IAMHWgEalSRCAAAQgAAEIQAACEIBAYxBAsDWGHWkFBCAAAQhAAAIQgAAEINCABBBsDWhUmgQBCEAAAhCAAAQgAAEINAYBBFtj2JFWQAACEIAABCAAAQhAAAINSGBAw7Vp0miz4Q9Y61dPmo1602zMR2bjh5tNHOl/vm/yeLPWCf432Zuuv2ZOrtdb9DeVWb+BZgOm8b/pzQbObjZ4XrPpFrOWWVYzm30Ds/6+L09p4ii38/3WOuKpNjt/bDauzc66BibLxtjZDZuysdtZdpSNB8nG8yQ2nnlVt/GGvn26PFnYv6/YuGcGwcY941TPR9Wxjbknl3Hhpe1cZ/fkMlrJoRCAQPkEWlo9lZ8thzm+fdla3z7b7KN/mk36LocVrOMq9Z/WbN6drWWRX5nNsHTfNuTr5631nbPczte7KBvTt3VppLP3G+w2/rG1LHyw2Uwr9G3LsHF1+GPj6nDNU6l5sjH35OpdGXm6J1evlZQMAQikCNS/YBv/lbW+/Buz9y/1ZjW7xyxl2aq89ad/C+xlLUv/2b1ws1TlDCULde9Z60uHmH14tR/SGM8YSra1T3e0mM23i7Usc2bihatlXbBxjWhj4xqB7sPT9KGNuSfX0O59eE+uYSs5FQQg4IOl6trD9tXj1vrUT5Jhj1izdgR8uGTLqteZzbJGbc75xUNu5518yOOntTkfZ3GxNqfb+Bqz2darDQ1sXBvO6bNg4zSNxnxfaxtzT+6b66jW9+S+aSVnhUBTE6hfwfbpndb65A4Mi+ury9eH3rSsdoPZnFtWtwaf3GStT+/sdh5X3fNQ+pQE+g2yllV8iPHcP5xyX5ZbsHGWNMsrCxuXx6sej66Vjbkn9+3VUat7ct+2krNDoGkJ1Kdg01O8hzdCrPX1ZasbxDr3Vc/TJq/Lo5sh1vrSzursrXV39Txt2LgvrZucGxv3vQ2qXYNq25h7crUt2LPyq31P7lktOAoCEKgCgfoTbBoff/9yDIOswsXQqyI1FGPDodnPadN8pvuWZRhkr4yScSYNq9roxezntGHjjA1VQXHYuAJ4dZK1WjbmnpyvC6Ba9+R8tZLaQKDpCPiM1fpKrS8fhljLk8l82YQQ9CXjOoUAI8xZy5hqL4tzOwR79DJ7qWzYuBSZPtiOjfsAeo1PWS0bc0+usSG7OV2V7sndnJXdEIBAlQnUl4dNYYLvc+8a0SCrfFmUW3w/98C4ly2rkP8K6/7ASl4JokGWa4nqHd9iLRs8m13If2xcPVP1umRs3Gt0dZMxYxtzT86p5TO+J+e0lVQLAs1EoK48bGGdNcRaDq/PyckaeBnVLKyzhljLiGZWxbQm699lVBw2zghkpsVg40xx5rKwjG2stU+5J+fQ0tnek3PYQKoEgaYjUD8etkmjrfWOOVgUO6+XqC/k2bLV52b9p6mshhNHJXZmUezKOFYjtya0y8YDpqusdGxcGb9q5sbG1aSbj7KzsjH35HzYs1Qtsronlyqf7RCAQE0J1I+HbfgDiLWaXhplnmzSd2ayUaVp+P1E/6yUYbXyS0TLPpUmbFwpwerlx8bVY5uXkjOzMffkvJi0aD2yuicXLZyNEIBArQnUjWBr/erJWrPhfGUSyMJGrSOeKvOsHF5LAlnYJ4syatnmZjtXFvbJooxm417L9mZhnyx+72vZ5mY8FzZqRqvT5kYlUDeCzUa92ag2aJx2ZWGjkW80Do9GbAk2bkSrdm4TNu7MoxE/ZWHjLMpoRLZ5ahM2ypM1qAsEKiJQP4LNQ9WSck4gCxuN/STnjWzy6o35uHIA2LhyhtUsARtXk24+ys7Cxln83ueDRuPWAhs1rm1pWdMRqB/BNn540xmn7hqchY18MWVSjglkYZ8sysgxorqvWhb2yaKMugeZ4wZkYZ8sfu9zjKghqoaNGsKMNAICIlA/gm3iSCyWdwJZ2CiLMvLOqZ7rl4V9siijnhnmve5Z2CeLMvLOqZ7rl4V9siijnhnWQ92xUT1YiTpCoEcE6kiwje5RgzioDwlMzMBGHiqalGMCWdgnizJyjKjuq5aFfbIoo+5B5rgBWdgni9/7HCNqiKpho4YwI42AgAjUj2CbPB6L5Z1AFjbKooy8c6rn+k2eUHntsXHlDKtZAjauJt18lI2N82GHateC39pqE6Z8CNSMQP0IttYMOoo1w9qkJ8rCRq0TmxRenTQbG9eJoSqoJjauAF6dZM3ExtyTc2/tLOyc+0ZSQQg0B4E6EmyTm8Mi9dzK1gxslEUZ9cww73XPwj5ZlJF3TvVcvyzsk0UZ9cww73XPwj5ZlJF3TvVeP2xU7xak/hBoJ1A/gs0yEAPtzeZNdQhkYaMsyqhO6yhVBLKwTxZlYI3qEcjCPlmUUb0WUnIW9smiDCxRXQLYqLp8KR0CtSNQR4KtdlA4EwQgAAEIQAACEIAABCAAgTwQQLDlwQrUAQIQgAAEIAABCEAAAhCAQBECCLYiUNgEAQhAAAIQgAAEIAABCEAgDwQQbHmwAnWAAAQgAAEIQAACEIAABCBQhACCrQgUNkEAAhCAAAQgAAEIQAACEMgDAQRbHqxAHSAAAQhAAAIQgAAEIAABCBQhgGArAoVNEIAABCAAAQhAAAIQgAAE8kAAwZYHK1AHCEAAAhCAAAQgAAEIQAACRQgg2IpAYRMEIAABCEAAAhCAAASyJDB58mS7/fbb7emnn86yWMrqJYFx48bZLbfcYq+++movS6hdtgG1OxVnggAEIAABCEAAAhCAQHMSuPPOO+3666+3ww8/vB3ACy+80K2AW2ONNWzppZduz1PJm3feecfuv//+LovQuXTOwjRmzBi766677N1337VPPvnEpptuOptnnnlstdVWs+WXX77w8PbP48ePD/l0buWbeuqpQ76VVlrJVl111fbjCt9MnDjR7r77bnvrrbfs448/tqmmmirkW2655WzttdcuPLz9s4Txvffea6+//nrI19LSEvKpXeuvv77ps9KgQYNMddKxp556qg0ePLi9jLy9QbDlzSLUBwIQgAAEIAABCECgoQh89tlndvPNNwfh9f3vf7+9bc8884w98sgj7Z+LvVlggQUyE2wvv/xyt+ebZpppphBs7733np199tk2YsSIUEWJni+//NLef/99e+yxx2zNNde0n/70p+1iKLZDQuuss86yzz//vD1fa2urffjhh/bEE0/Y448/br/4xS+sf//+MUt4HT58uJ1xxhlB4GmDzqd8Ku+pp54K5zz44INt4MCBnfJ9/fXXduaZZ9qwYcM6bf/f//5nYv3oo4/aIYccYtNOO23Yv8MOO9ixxx4bbLPzzjt3ypOnDwi2PFmDukAAAhCAAAQgAAEINByBa6+91iZMmGDbb799p7Z99NFH4bPEzkwzzdRpX/ww11xzxbcVv8bz7bjjjiYhWCzNOuusnTar3n/729+CWJt99tlt7733tkUWWcS+/fZbe/jhh+3WW28NAmrBBRe0TTfdtD2vPF0XXHBBEGszzzyz7bXXXrbEEkvY6NGjw/E33nijPffcc3bbbbfZdttt155Pwuzvf/97uxdP55PI1RBGDSe95ppr7JVXXrEbbrjBdtlll/Z8enPZZZcFsSZv2Z577mnLLLOMqR7PP/+8XXnllcFbd/XVV9v+++8f8s0///y2wgorBC/bJptsYrPNNlun8vLyAcGWF0tQDwhAAAIQgAAEIACBhiPwxRdfBMEg4bXwwgu3t09CQgJKQ/PkoYpD9doPqMIbebaUdL5SArHwtBJln376aRiSeNRRR9kss8wSDpF4kwCdNGlSmJt300032UYbbdTuLZMnTB64fv362RFHHGFReGpI5A9+8IPQ3uuuuy7k3XzzzcNQSRX80ksv2RtvvBHOcdhhh5mEoJJEmETVgAED7NJLL7V77rnHttpqK5txxhnDfg1vlABUOuigg4LICx/8v/XWW8/kOTzvvPOCh1Hnj/XR8Erl09DInXbaKWbJ1StBR3JlDioDAQhAAAIQgAAEINBIBDRnTOJMIimdNExS3qt55523JmJNc8l0zhlmmKHHYk31lXdKSfWPYi1saPtviy22CCJNnjPNG4sp5tNctSiO4j69brzxxkGkiYFEWkwxn7xqUazFfXpdd911QxvEVHMAY4r5hgwZ0kmsxf0rr7yyfe973wsf47H6oDlxGiL50EMPBS9ePD5Prwi2PFmDukAAAhCAAAQgAAEINBQBzfFSkjBIp+jt0rC8WiTN/1idzBoAAEAASURBVJLImW+++co6nYJ+KC255JJF8yn4SCzz7bffbj+mu3zyLC600ELh+HLyab7bYostNkW+WEapesqDqSGZSrFuei+P3VJLLRWGamqeWx4TQyLzaBXqBAEIQAACEIAABCBQ9wTk0YqBOuaYY45O7UkLtqFDh4agGBp6qKGK8rppGF9Phy12KrjEh/T5JFgUgENRG+VdUrTHtdZaawpPmLxmig6pNPfcc5coOdmnQB8a/qkkYRjb3V0+hdWP+ZRXwUyUusunY9L54vue5IvnUBlK0fOmoCzikLeEYMubRagPBCAAAQhAAAIQgEBDEHjttddCOySKCsPGRwGlIZPxfWy05n8phL7mVCkUfRYpnkPDDxUuX6IqJs3h0vm23nrrTgFAvvnmm3iITT/99O3vC9/Iy6akQCRKI0eObC+/nHwK5S+RqFROPh0fzx3rom2FKZYZj437Y7CRvK7JhmCLluIVAhCAAAQgAAEIQAACGRJQ0A2lKAjSRUcBpZD3Ct6hYXkSG/JUaUFnhaJXcA15jOIQwHT+ct/H88mrttlmm4UIiqqXAp/ccccdYU0yBQ5RMJHoZVJAkZg0dLBU0hppShJcSr3NF/OrjHLOp+Nj3lgXbStMsUzNm0sntVlJywJ89dVXRefqpY+v9fvS5GtdE84HgQYiMGa82XdjOxo0wJcXmSlZ8qNjI+9qTmCs22Ws/0ZPO7XZVG6TPKdv/AHjhOS+F6o5qz/Y9OH3JAhAAAIQqCMC8jQpRUEQqy5Bo2GPGiapEPsKlBGTAnQoQMbJJ58cFqm++OKL7ZRTTom7e/2qcP2aw6UIiQp3H5OGAyq0/V/+8pcwLFNh71dcccXgEUx74RRuv1SK++Jrb/PF/DpP+n3heeO+YueJ+wrzpD8XHpMW1PK+FQuuks5f6/cItloT53xNQeDC/5gdcnFHU1f3ubGPVf5b21Eg73pM4LOvzU75l9lV//Wx7slIjSB89lzf7OKDelxMzQ/c7o9mD73ScdpP/Hqas/gSPR0H8S6XBJ7y+fofJNM6Qv22WcVsYOru++DLfm0mfTob4KHAtlstl82gUhCAQC8IxKF3aUGgYhQ4Qws4l0ryEu2+++52wgknBE9bFl6fuPZYsXMq9P4ee+wRwtuPGjXKPvjgA1t88cU7ebmiB6tY/rgvLoAdPVk6Nu7rSb6Yv6f50udRXgm4rs4XPWvpfDqX7BMX5442K1bfvtqWumX0VRU4LwQgAIHqEJjkw/N/6EL5iTc7l6+HhF+N6ryNTxCoFoFzbje7+uGO0j+/1DsHM3R8/t3V/kCnLRL2tIN87sc/O/bxDgIQqG8CcWigIiKWmxTSfuDAgaZw/IrwWG2vj9Yzm3POOUMgEg2TlGDTmmkxjR2bGjoUN7a9xn1xnl581e64ryBL+Bj3xeMlVCWmJLrivp7k0zEqQ4IsBknpaT4dp3PKRjpnV+ctVmYttvmzPBIEIACBxiRw2QNTirV+PqxQQ1RJfU9g/v19UvnOyd96v6tdfXY9s+O8On96+HLtasGZIACBZiAQg1zECIbltFkenyiY0sE/yimj3GOjcIpeJkWpjN4ozakrlTQvTknDLpUkfmLwj3Lyqc1RmJaTT+eM5y43n/JK5EWhpgAxeUsItrxZhPpAAAKZEXj2nc5F/XRTs88uNRt/vdllv+q8j0+1JzB6nIultj/N+6xVGufzGON59UqCAAQgUC0CWqRaafjw4Z1O8eCDD9pRRx1lV1xxRaft6Q/fffdde+TDYgtPp4/t7v2LL74YznfOOed0eWgUO/F8Giopr5uShkkWSxqGKA+gUjqkfnxfKp+Oj4FQ4rHpMsrNp6UJ0mWGDwX/FTufDknbJ9qsIGuffkSw9Sl+Tg4BCFSTwJvJA7/2UxzxQ38C1xaVeMZp2jfzBgIQgAAEIFAVAjHYSKGHTXOmJHIef/xx05yxYumhhx4Km+VlU4CSSpICi+h8Ct8fRVlheVo0WiH1JdLigtY6Ji74/cADDxQdbqhlCeShUr54bDrfI488YsU8hFpQPK7Vtvzyy7dXJ77X0gZpIRUPeOGFF9oFYjw2fT6J0yjMYh69au25uGB2Op/2xfOkPXzanpeEYMuLJagHBCCQOYFPR3Qucv4kam/njXyCAAQgAAEIVInAkksuGUpWuPh0MIxFF100DOGTF+28884L4eTTVZCwuvnmm8MmhfxPz4F788037dhjjw1/9913XzpbyfcSbJoTp+iIOl+haFOZ0du34YYbhuiVsbB11lnHNLdMwySvvPJKU51jeuedd+xf//LIXp5WWmklS3un1lxzzTCkU2JOZcdhljpW3rNrr71Wb+373/9++8LV+rzKKquE4ZSaj3bZZZd1YqOhl1dddZUOC+1JR9eUWNSwSHn8lC8tkrWA+eWXXx7aL+9htEsoyP+Lgk1eujwOiSToSLQUrxCAQEMQuO1ps3c/S5ryZVvkvdiwsz34Q/+2x1Tbrmo2ZI64p+NVwUjuf8nsaY/sF6P7zTWz2YoLme2+vt9IFuk4trt3qociAMayRvl87Xl9eP8GHk15jw3M5p+tuxLYX4rAZA8c87ZPp3jDR+HIjkv4w+dqL9WgYZvDPjf71COPjvDrZHYf6TSP23NBP79PuyBBAAIQmIKAPFXTTDNN8Fx9+eWX7cJEAuzAAw8Mofu1uPbhhx9uiyyyiCnwhzxhcf02iaBNNtmkU7kSQHG/hGBPk8533HHHhXXXfve734Xzab6Y1oGT8JKYUx122GGHTkVK4Eg0SmDJW/bEE08EkSUR9Omnn4ZjJdQU1TKdVPbOO+9sl1xyiT399NPBuyehJK9aHEIpNnvvvXc6WxBrilh5/vnnmxb5VjRNLUcgoRjbLX777bdfiOwYM2ubyjrjjDOCJ+2www4zCWMFflH7lDQfb9999w1ROmM+vUZxp/PkMSHY8mgV6gQBCPSawEX3mN32TPHsh13asX2xuacUbAqjv8sZZv8r8My958JLUfzOu9PsoC3NztinQ/h1lNj5nY7V+can1lLTEW+5yHjARdwfbjC78P9cBK7XOV+jf9r5dLNHXkta+XXHQ1ob+p7ZfPt1tP6BE80Wmavjc3yn5Rn+4mxfej+Zhxa3K0z+9+cz22kds19va6bgMukksX5a8rDaCoX84r9wwdV28Mne3yi0yZMeZVRLdVz/mJlEd2Fa2Kd37LOR2YFbmM2Q4VDb/f9q9u/nOs725728fWt3fC58p2trw2M6tm60rNmlv+z4zDsIQKD2BOIwQQ19HDZsWLtgU00WXnhhO+mkk+z666+3559/3l55xW9CnuK8sa233trWXruLL70fqyF8PU0anvmHP/zBtDj2o48+aq+/7je2tiTP1MYbb2xbbLFF0TK33HLLMJdNa7TJGzV06NCQUwJIa7bttttuQWzG8uLr+uuvH0LmyzMnr97LL/sN0JNC8GstuD333LM9yEjMo9fVVlvNZp555uAV0/DGV199NewWG4k+5ZPXsDCpTIlSedjee+89k+dQSfkkRpWv2PDSOF9u2WX9hzOHCcGWQ6NQJQhAoPYE/nij2bH/NNNSAF2lc10saG7cNYeZFZsHp4iD+5yXdO67KkeBL/Y82z1EH5mduEvzeGiG+1p4H381JZkJkzpv1+d0ksg64G9mNzye3trxXsL4eRd9+rv1qSSozEKpe/m3YzqX35HT7JNUfUb5cel05UNme5/b9XXxjj9gPtpH6Nz8pNndx5nNlFGAsS1WNPvHvR21ufyBrgXbtY90buMmy3fk7at3+j7EByDyRg4eWP2a6EGAAtrMPUvX55ro19gn/nBGddSxxb7PhSVoXceJ/hsxTzdlp/Pp+lKeYh51eWynnTrx1qbzFL6XR1nHykOfXsOv8Dg+55OAPGQSbPqTEEknea9++ctfhnD08lgphL+G5aWHQKaP13sN/ZMgOfLII22BBRYo3N3lZ4mgffbZJwgXedbktdL5YnTIrjJLmOlPeSS+5B1TQBKJoa7S0ksvHRb+lmdQQxo1J09iK0afLJV3scUWC4JW0RuVT8Myxau7fBomKdE2bty4UE+JWuXTEgnFkta4k7DTIuYItmKE2AYBCEAgYwL7b2q2cVtH9Y/uxdLwtZjO2FtP9ZJPS80Xt5rd7h45dbhj0lpYh21nttaSZrNM50Ma3zbTWlqvubhSuvsFs21O9oWtT0o+p/8//trOYk3D9eTxWWlhswkuKh593exP/zL7ZnSS62QXihqmecLO6VIa9/0cM7onbbakfR9/aaaOqJI6od+bKXmv/9PDGzUUcfUjzCSM0mlR98CJq+wij1ssS4xX/LXZyy6I1cFVUmc8nlcLqKejUuqY+JB6usHJ8fpf18Ve53SUq21aEmJlP+eS8ybX1n/9gXiMNKnrZL+/uKg8XEdWnn6wcrJeW1zw/d4XfZ6F111DMYul6x7t2Dq9t+OHnfuFHTtr8E5e1BP8u6DhxVr3UEk23XoVs9P9e7jA7Mm2NY9MFhXf2tt6/s+Tbfr/pifdm/335PNlvzSTtzAmLcsgb/jGvi3tQZQX9IxbEy+2zqnv3g5ruDd7t87Xk7ykh1/mD10eMUt7eddaIjl23aXimZLXkWOS429+yqPMtv2eKHiRBLV+U9Jr6v3tbrOTPArtEvOYnbmPP5Q5x+yF95JydJ2du7+ZhmPLw/7XuzrErLzDfz/Qr/PFOp9bDzcOvCDxyqseU09ltuqiZqfumbx2PppPeSUgT5q8OxrepwAjMdx9ur4SI8U8P+lj0u/fffddk8ArnIuVPqar9/JwScT0JmmOl9pTbpIoFItykwReOghKT/NL9Eq8dZckpDUcdKONNirqXewufy32+y2SBAEIQKBxCKiTG9OF3nlKC7aDtppyKKM8ORq6GJOetD94YufheJq/ttu6Zj/+sw9Rez458uFXk87ohsvEnElH8dw7Oj6rk/mvI3zNr5QIWMc7gzuu5UPXjjV7f3hy7Nme55BtsvPMdNQgf++uPrSjTrPt0bGA+bJDfM7gqR370u9OvamzWNNQyduOMlvcO8Uxff6N2Y/+lAhibft2tNlvLjX756+TI37pttef0g5+nn89kbzX/6+dm3g5OrYk7+TdiiJQW07Z3b18m3e2pwTUln69xCUk/uNiXp6bLNb6k4jd1a87DedUUrkalvl/XofC9OqHLlA/6Nj6o9XNpvEHD32RJNY2OyERxRqauoJ/f+RF0lxOcRerV525vG2aE6qF7W9xMZQWbPc4x+j5fOClDsEmT6o8qBLJayzR0bqf/tXsIreXhLfEj8qWUDrtFrPn3nXv52/NpvM6KG11kpm+vxKQa3oZ8qprSK6E/ha/d3Hk9pQoUpJgWsevNXm3VLYe9CifHhDI+3rvUP8d8OMl0JQkBmO9Nz0h+b3R74fq8JE/oNCQa4lWiWsJuBUW9HMPM3vlQ/9dOC25FqPdVH9x1DU2sz84kpDUsNf/et3X/53ZFQf7Nb9GOC3/1QGBbbfd1k4//XRTRMVttvEf/AqSFrU+9dRTTcFB5OUi9Z6AAsEo+qXmDq633nq9L6jKOftVuXyKhwAEIJBrAnrKnQ7/f97+ncVarLw6nHoCHjt92n6yPyVPpyMu75izJpF28S86d+7jsQt+z+ysfeOnRFxoqCVpSgLqKMsjGZOGoj1ycmexpn3y3P3neJ/3sFg80uxa7xRrvmBvkoTBfS925Fzv++5l+eGU9pS3Sw8CYlKHXR3wrNJeG3Yu6ZqHO3+On9LeNW1TUJu+SvpOyYMpkfHhRWbPnJZ4o593sSJvsh5UxHmM26ya1FIPViRaYlKwHgkkpYddAMakuaQSa9q3jQsfJT1EkViT9+k/x5m9eJbZk6e6QPTz6rqQly+KXs1HlVhTuu43ybX0+CluM88joaR6q/4xyROna1AetUf/mHht1Q4dr2tR9f7l3+PRHa8SbfKWvXN+0v74MELly1YXHODBkf7m4vX0ZE6scn74RSIE9V4ewv+7IBFr8pS+f6E/SHKh+YGfS3Mlx04w+6mXLa8bqT4IaKidQsnfeeedJcP497QlGuKn4BqaN0aqjIDEmubkiWVPhoVWdrbe50aw9Z4dOSEAgQYgcNdzHY1Q53s77xyVSuqgpT146gjKs6EkT0w6QMTmK3QMwUuO6Py/hkXNOVPHtvPcy0aakoA6t+qcxnTw1kknPH5Ov8qr8rsfp7e4F+TBzp97+knDV288IpmTpnlpf/t56ZyFy0do/lRWabkhSYTSWJ68QB94x74wSZzGpOGGEph9lTbza18PJK53QaQIqzFpiGKcwxa9y+t6PeOcvyiQJYJe/zjxKGmYrKK1jnWho3TP0OR1tUU7yj7xumTbr37Q4YnTlmUX6LgeJNgklvxr2p40nDXOldTw2tfPSzx/x+2YHKI6/rNNIGvIcnq4orx4Cj6kpKGqz7ydvE///+c9zQZNlWxZ2T2JelCjpGHW+27c4YVNe8kiF7VTnkeJ0IsO7HhQJMGrIaUaCaBIpRf8JymT/+uDwC677BJCzku0VZI050xz4coJOFLJ+Ro1r+a43X777bbCCivYqqv6TTnHaUCO60bVIAABCFSdwCsfdJxC67TpyX5XqTAwgTrPGib17qedhYWiBXZX1gLegY1DNjXsSZ3SqV10kDoIvP5Rx3u9U0e3q7TliklHPga6iPMOu8pTbJ88qpssV2xPsk3DE+WZleD/002lj8tij7xsGlKnJM+LvGzy9sU0dFiyvEH8vNt6Hd6puK2Wr3tukJxN8zQlOrT0gv7ueHbKKJsaXiibXe1tuv/FZNhq/N6sv3Qyr1Gi/SkXRBoSqCGnStu2PVjRgxK1X2nEdy6s707ex//jdaB5gBJDGrq49pKJh+/M23wO3AM+vNBF46ruDdOcOM2JjCk9xPQna8atHa/yfGnoq64FHStRFpOE1sJzxk/Jq4KOyMOnYZgaKhrTDIPjuw4BqeA5SvLspcV4sjXZLi/es0WEYjyG1/wRUKCNCy+8MH8Va9IaaY7b2WefXRetR7DVhZmoJAQgUA0CCjigOSoxaW6N5paVk/SUWyk9nEufNf8pHeFP27pL6nDOhWDrhOk17+jHJG9L9MbEbYWvGiqnJRtiR723gi1drmx869NuY++UqzwJtfd8mJw66rVIu6zr8/EuM1NkUSV5fdKC7XoXNOm0+/rpT7V/ryGEv/A+qYLzxKirEjAbuwDW3K/CpGGREmwKJKLjH2p7aCLvm4Y0SrBpGKO8Ws+3CdftPI+SvJvRo6mgI12l+F295bdmv7/OI4m6WNM2BTjRn28O6y1e/qvkIYzaoaS5hOnAIsnWRKx9z+un35DoGYv7pvI8cUhn3BZf08Oqta3YcXoApKSyNTSyVNJvBgkCEGh8Agi2xrcxLYQABEoQUKCKSlPsMMXocZWUp85jeghZJWU1St7YaVZ75p65Z63ScLGY5OWRMO9O6MXj068K967O8nWPdYil9H69lxdFXtfoASvcn8VnDaHTEFoJF6Whw5KhuDHSadoDo2F7Eqx9lSS4Njou8SRJYB3zkySwiLzXEiYDfjRlzRRtUaJIttLQQs071Ge1JQ4blmBTsBl51OQliwFnZnfBFL1cisq40bJTlh+3aIFzJc2t07Gn7ZUIwMffdHH5nNmd/ic7KgLs2+d3DGnWfMZi15DqIs+4kpYsyDLF3wHZsquoo4poS4IABBqfAIKt8W1MCyEAgRIENKdGc0KiF0CR4X6/c4mDS2yOnePC4U/7b1J+BLcYgr7EqZpys0LxRzEch492ByIer+PkzeiNWNN8p6294x6H56ksdfQ3Xc7nRg1JxIMEiUSTQrRXU7Dp3Htv1CHY9Fkh6XWtyiucXu6gL4ONqF7yoGnYn5I8VYoQGVPhguVxuwL0KNqqgoco8qI8mIqmqvluWj5BnlUFG4ki5oerx5xJxMaFfG6Y8gz3BzBLz9+xT+8k+CXEXCsGoadF0O/282jD4dslwxg1lPGgLT3k/p1mv7rIhzd7/fW3lJ87JkWxjEM94zbVV2JOKX1ssqWy/6MgVWRJfQcKh2IrWqjEoiJQxrlxlZ2R3BCAQJ4JINjybB3qBgEIVJWAnuIrQIM6Z0ryqGy6fPK+3P+jcIv5VHZvy4pl8OoddvemxIAOEmyykeaXdZXe9tDnMS2R6nTHbT15VYc4Lda2d5FwyUFTRonsSVlZHKP5dBL06sAraVikBFv0ummbAlxoyYi+TIqSGZMWpV4hfvBXLc8QU+FwUg2LlAD6+z3JEemgKXqvdsYAIPI2ptPPNvO1Di8x+8tdZtuv0RGkRQFFdj7DI0a6SNtg6WRJhtEuxLVWopKiQh62bfJe/w9q6xGJo8ShBKO8f5qneMzViccvCilFdDz04iSvHvSssXhHOVm8UwRMeYo1T03nUVTJuFSEhudqCQAlRY4kQQACjU+g7eep8RtKCyEAAQgUI6AOWBRsekqvOSOKBlkqKSLcc+7ViEmdRT39VmdaHcA4n0adfQWIKDY/Jea9+D4zBUNQkpfhgM2T9/zfQWDxlOASz8sf7JqTuKejKErw9SY98UbnXFojLL2eXnpvnBuV3pb1ewWp2GN9X0rixqRkedUkRNKCTRFM5QXsy6SgHZp3FtfFUyAPBc548q3ECxbrpoXoNdTzJ2slWyRQDryww2Ol+WsxRcEmb5ZEjNZuS6dfuHfsigeTddfW+q0veL9E4lnVnDgNs1QAoD/vleTQPgksRZ7UMhyXP5CshSYhrOOV9tqgI5qlol2Ks/ZrMXbl1/w0LUugkPry4GopkK6+50mp5f2vhxKn7eXrP57pDwruT+omT6CC8Lz0QVKWgssULvJd3lk4GgIQqBcC/eqloo1ST01yPuzSzn8aB0+CAAT6hoACOsSkJ/IxRHjcln5VJ3n7U8yOvCL5UycxDlVSh22XdTqOVhASDVsrlTTvaL+/dJSVjlZZKk8jb49h2wvbKG9K9CxonyL7lVp7SkNbNTwxndLD59Lb0+/TywbE7engGPK0lBpWKREZhtjFjFV83XPDzoUfeknnYBcSdH2dxEpBPTTPTAJLwxGveCh5kKGFno/fMamhwvZLNMUkIbayiz2lsKB1ymOlaJEx6XooFEc6XmukaX0y/xqGddfkhZJYk9i774QOr5s837celYjfAd4DUnRH1U/z5jQfTvX7y8/i2cwU7v+ls314rIth/T7ogY08bnows+EyLhJdUKWjQ3bkrPzdTmsni3jHh0oS5y++n/zmaOkBeXxJEIBAcxDwny5SLQnc/oyv7fN45zP+aQ//rLsMCQIQqDkBiazz7kyeouvkijSniHZ/3ivpOMYKSaztdlbnsOQ/3zzuTV5P3CUJwR0FhQSZOq3puS96QCPvzQF/65z3AO9sNluS5+WrUUmr5d3UMDPN10knzRNTRzwufKzhjorkeecx3sGeoeNICT4Nf4treWmPhqQWW1dP500ndcILhxJq7pTWPFPSfLYzb+0cmVHbNTzzgAumjBD6rXteqpEkHjS3Ky78/LhfRzGJxeYrxk99+6rFy18+J/EGffaNmeaYKaS9hJYE7g5rdsy/StdUi10XS7LF5H8V29OxTULx3P2T9d+0hMAXI32e4ZyJR67jqOSdPICX/tKvqf3cu+7fa12DGgKpYc3phwMxn/bdclQSeEbrLk70BwOaK6dzFiYNsUwPs0zvf+DE9KeO9/LSlWqf7P3auWaa/6eHQDNPm4hIlv/o4Mc7CDQDAQRbM1iZNkIAAiUJqBOpiHEaSqXOpNI5dyRP3VdaKOnIPfxaErwg2Zv8r2h0+22c3pIERzhqBw8PfkWyXR39vb2zdew/E+9BP3+ir8W2C4fQHe15JEyaLSlYwlsuwJQkbJdyj4E6zeoQKzKeBIrS8Tsl85diVE8F2hjy02ROkYbgKdS+hqjJoxKTPCln7xs/dX5VsJl00rCzk65L1sDT4sthqJl7Zi66t+MoeVU1h0reHg2NVGANPYBLnzMefcK1ybw7XQvy/mSZtCZbFGzpcndeJ/tzpcsv972GcCogS4xkGfPr+1a4Le7L4lVBhHpavrzj6aAo3Z1fc9vKOb678srZr4cMDH8shxjHQqCxCCDYGsuetAYCEOgFAYUPv+d4n7viXgHNVVGSqJLnpVjSPJabj0yCPBTuV+Q5zWU74rKOhbTlOdJfsXTwD3wY5i7F9jT+Ns3/iwshq7XfjfMw6+8l7U4HpVDH+rFTEvtImClJDGsYm/4Kk4aQXeYelBggonC/PJ4a+hqHQmoopbwXStE7uuu6buMnzf71RLJd/w8d1rFIc9wqj8we6yfRDSU6lRQkRX9aKy1rwfbjNZNIhungHjpnX0eHVB1IEIAABCBQHQL+LIoEAQhAAAKajzLUPS27r5cEKShGRB6fi3+RzCspFalQHoSDtvQO+2nJE3F5eoolRa277/dmZ+xTbG9zbPvhaglPedrELZ0KP2tYnSLinbL7lMMmYz4NC5SH7LnTk8AScXvhq+ZL3XNC4qErFFTxvHpVWHp5PxW0oljSkMvnz3BP3IG+sLUL9VokDZ+TaEsnDc9TeHcSBCAAAQg0JoGWVk/10LTWmwru5vVQ6S7q+McbvSNwVRIl64vLuziwzna1/LCyy6nR7Fxn5utRdZvBxvpV1PpNLwxLPGMaQiexpuF6GnJVTlKgAkV2k3dG3hst/ivPj+b05DX1hY3lnfrYvZsaNirhJS9lV0nzjsRU85W0NMPyC3as09VVvsJ9mlP46YjE26aohcWCi2jY4wvvJcM3JfAUUEPLBcSAMypT18yrbmetFyavm4ZqLrtA4dmy+bzn2cmQ3Via5kGXKxj7wsaxvrzWjkCldq5dTTkTBCDQFYESz367ysK+LAjE9X2WqdINPYs6UgYEmpWAvCvy+mSxIK06+Pqe813v+mqSJ7Ic3hJX8lLqr5KkuVbyuHWVJMwUbVB/pZKuGc1DrPZcRAVXUQTEmMQtHdQmbucVAhCAAAQahwCCrca21NPck65PAg/o1P+3RY0rwOkgAAEIQKBuCSgQSjrQiRb0VtRDEgQgAAEINC4BBFuNbXvyDR7x7JrkpAojXTgXocbV4XQQgAAEIJBjAhryedV/k8WnFQ1Ti63HJO/gEdvHT7xCAAIQgECjEkCw1diyo3wOy8w+lOfQbcyO+lGNT87pIAABCECgrgh84vPrNCqjWPrppmbLDSm2h20QgAAEINBIBBBsNbamFsf9/S5mpSLH1bg6nA4CEIAABOqQwD4bJesH1mHVqTIEIAABCJRJAMFWJrBKD1c0MxIEIAABCECgJwSG+D1Da/V9OTJZUHzZIWarLZos4N2T/BwDAQhAAAL1TwDBVv82pAUQgAAEINCgBBQ5s5nX6mtQs9IsCEAAAmURKHNFobLK5mAIQAACEIAABCAAAQhAAAIQqIAAgq0CeGSFAAQgAAEIQAACEIAABCBQTQIItmrSpWwIQAACEIAABCAAAQhAAAIVEECwVQCPrBCAAAQgAAEIQAACEIAABKpJAMFWTbqUDQEIQAACEIAABCAAAQhAoAICCLYK4JEVAhCAAAQgAAEIQAACEIBANQkg2KpJl7IhAAEIQAACEIAABCAAAQhUQADBVgE8skIAAhCAAAQgAAEIQAACEKgmAQRbNelSNgQgAAEIQAACEIAABCAAgQoIINgqgEdWCEAAAhCAAAQgAAEIQAAC1SSAYKsm3aYru6XpWkyDIQABCEAAAvkkwD05n3ahVhAon0AdCbY6qmr5dmiQHFncHLBzvi+GLOyTRRn5plTftcvCPlmUUd8U8137LOyTRRn5plT/tcvinlz/FGgBBBqBQP384rbUT1Ub4cLoVRuysFEWZfSq8mTqEYEs7JNFGT2qLAf1ikAW9smijF5Vnkw9IpCFfbIoo0eV5aBeE8BGvUZHRgjkjUD9qKCWqfLGjvoUEsjCRi0DCkvlc54IYOM8WaM6dcHG1eGap1IzsTH35DyZtGhdsrBz0YLZCAEI1JpA/Qi2fgNrzYbzlUsgCxtlUUa59eb4nhPol0EnDRv3nHdfHImN+4J6bc+JjWvLu6/Oxm9tX5HnvBDInED9CLYB02TeeArMmEAWNuqPnTO2SrbFZWGfLMrItlWUliaQhX2yKCNdJ95nSyAL+2Txe59tqyitkAA2KiTCZwjULYE6EmzT1y3kpqn4gAxslEUZTQO8DxqahX2yKKMPmt40p8zCPlmU0TTA+6ChWdgnizL6oOlNdUps1FTmprGNTaB+BNvA2RvbEo3QuixsNAg75/pSyMI+WZSRa0h1Xrks7JNFGXWOMdfVz8I+Wfze5xpSA1QOGzWAEWkCBBIC9SPYBs+LzfJOIAsbTT133lvZ3PUbPE/l7cfGlTOsZgnYuJp081F2FjbO4vc+HzQatxbYqHFtS8uajkD9CLbpFms649Rdg7Ow0fSL112zm6rC2LjxzY2NsXFPCGRxnfTkPBzTewLYqPfsyAmBnBGoG8HWMstqOUNHdQoJZGGjlplXLSyWzzkikIV9sigjR0garipZ2CeLMhoObI4alIV9svi9zxGShqwKNmpIs9KoJiVQN4LNZt/ArP+0TWqmOmi2bCMbVZpm39Cs3+BKSyF/NQjILrJPpQkbV0qwevmxcfXY5qXkzGzMPTkvJi1aj6zuyUULZyMEIFBrAvUj2BSGeN6da82H8/WUgGyTSajo6dzOP+7pWTmulgRklwFun0qTysDGlVKsTn5sXB2ueSo1KxtzT86TVaesS1b35ClLZgsEINAHBOpHsDmclkV+5f/XVZX7wKR9ccp+bbbJ5twtCx/sBbVkUxilZESgxRK7ZFMcNs6GY7alYONseeaxtIxtzD05j0b2OmV7T85pI6kWBJqKQH2pnxmWNltgz6YyUF00doG9zGSbrNJMK5jNt0tWpVFOFgRkD9klq4SNsyKZXTnYODuWeS0paxtzT86npbO+J+ezldQKAk1FoKXVU121ePxX1nr/cmZjPqqrajdsZT1scMuGQ80GzpJtE8cNt9b7ljUb92m25VJa+QQGzWktG71olsXaTemzY+M0jb59j437ln8tzl4tG3NProX1en6Oat2Te14DjoQABKpAoL48bALgwqBl1esITFGFi6HsIn3yerBF1mJNFXFx0LLqNW7nQWVXiwwZEnD+wQ5ZizVVERtnaKgKisLGFcCrk6zVtDH35PxcBNW8J+enldQEAk1JoP4Em8w0yxrWstoNiLa+vGR1Y5AN3BZVS7OtZy2r/BPRVjXA3RSsTp74ux2qlrBx1dD2qGBs3CNMdX1QLWzMPbnvL5Fa3JP7vpXUAAJNS6D+hkSmTfXV49b61E8YHplmUov3GnIhL2c1xVq6HV885HbeieGRaSbVfq/hU/JwVlOspduAjdM0avMeG9eGc1+epdY25p7cN9au9T25b1rJWSHQ1ATqW7DJdBo///JvzN6/1D9M1hZS1Qi4Q9YnM7cs/efs56x1V2fNd3rpELMPr/Yj62vaZXdNy9d+j87pgQlaljkz+zlr3TUUG3dHKKP92DgjkDkupg9tHO7Jh/k9+TLnwz25uhdJH96Tq9swSocABAoI1L9giw369mVrfftss498CNek7+JWXrMgoAU4fU2XsKxCltEge1O3r5+31nfOcjtf732BMb0pgTzFCPhwGq2NFsLtZxkNsti5utuGjbsj1Lv92Lh33OopV55szD25eldOnu7J1WslJUMAAikCjSPYYqMmjTYb/oC1fvWk2ag3k+GS44ebTRzpf75v8nh30EzwPz35a/anf/50rkV/U/k8sYG+KLIvTj5geveezW7mQyxsusWsZZbVzGbfIJtFsaONsnidOMrtfL+1jniqzc4f+5DJNjvrGgh2noidtW5hsPGAxMZa7FY2VhCRwfMkNp55Vbfxhr59uiwsk10Z2LiHLLFxD0HV8WF1bGPuyWVcd9HOdXhPLqOVHAoBCJRPoPEEW/kMyAEBCEAAAhCAAAQgAAEIQCCXBPxxDgkCEIAABCAAAQhAAAIQgAAE8kgAwZZHq1AnCEAAAhCAAAQgAAEIQAACTgDBxmUAAQhAAAIQgAAEIAABCEAgpwQQbDk1DNWCAAQgAAEIQAACEIAABCCAYOMagAAEIAABCEAAAhCAAAQgkFMCCLacGoZqQQACEIAABCAAAQhAAAIQQLBxDUAAAhCAAAQgAAEIQAACEMgpAQRbTg1DtSAAAQhAAAIQgAAEIAABCCDYuAYgAAEIQAACEIAABCAAAQjklACCLaeGoVoQgAAEIAABCEAAAhCAAAQQbFwDEIAABCAAAQhAAAIQgAAEckoAwZZTw1AtCEAAAhCAAAQgAAEIQAACCDauAQhAAAIQgAAEIAABCEAAAjklgGDLqWGoFgQgAAEIQAACEIAABCAAAQQb1wAEIAABCEAAAhCAAAQgAIGcEkCw5dQwVAsCEIAABCAAAQhAAAIQgACCjWsAAhCAAAQgAAEIQAACEIBATgkg2HJqGKoFAQjkn0BLS4vpjwQBCEAAAhCAAASqRQDBVi2ylAsBCEAAAhCAAAQgAAEIQKBCAgi2CgGSHQIQgAAEIAABCEAAAhCAQLUIINiqRZZyIQABCEAAAhCAAAQgAAEIVEgAwVYhQLJDAAIQgAAEIAABCEAAAhCoFgEEW7XIUi4EIAABCEAAAhCAAAQgAIEKCSDYKgRIdghAAAIQgAAEIAABCEAAAtUigGCrFlnKhQAEIAABCEAAAhCAAAQgUCEBBFuFAMkOAQhAAAIQgAAEIAABCECgWgQQbNUiS7kQgAAEIAABCEAAAhCAAAQqJIBgqxAg2SEAAQhAAAIQgAAEIAABCFSLAIKtWmQpFwIQgAAEIAABCEAAAhCAQIUEEGwVAiQ7BCAAAQhAAAIQgAAEIACBahFAsFWLLOVCAAIQgAAEIAABCEAAAhCokACCrUKAZIcABCAAAQhAAAIQgAAEIFAtAgi2apGlXAhAAAIQgAAEIAABCEAAAhUSQLBVCJDsEIAABCAAAQhAAAIQgAAEqkUAwVYtspQLAQhAAAIQgAAEIAABCECgQgIItgoBkh0CEIAABCAAAQhAAAIQgEC1CCDYqkWWciEAAQhAAAIQgAAEIAABCFRIAMFWIUCyQwACEIAABCAAAQhAAAIQqBYBBFu1yFIuBCAAAQhAAAIQgAAEIACBCgkg2CoESHYIQAACEIAABCAAAQhAAALVIoBgqxZZyoUABCAAAQhAAAIQgAAEIFAhAQRbhQDJDgEIQAACEIAABCAAAQhAoFoEEGzVIku5EIAABCAAAQhAAAIQgAAEKiSAYKsQINkhAAEIQAACEIAABCAAAQhUiwCCrVpkKRcCEIAABCAAAQhAAAIQgECFBBBsFQIkOwQgAAEIQAACEIAABCAAgWoRQLBViyzlQgACEIAABCAAAQhAAAIQqJAAgq1CgGSHAAQgAAEIQAACEIAABCBQLQIItmqRpVwIQAACEIAABCAAAQhAAAIVEkCwVQiQ7BCAAAQgAAEIQAACEIAABKpFAMFWLbKUCwEIQAACEIAABCAAAQhAoEICCLYKAZIdAhCAAAQgAAEIQAACEIBAtQgg2KpFlnIhAAEIQAACEIAABCAAAQhUSADBViFAskMAAhCAAAQgAAEIQAACEKgWAQRbtchSLgQgAAEIQAACEIAABCAAgQoJINgqBEh2CEAAAhCAAAQgAAEIQAAC1SKAYKsWWcqFAAQgAAEIQAACEIAABCBQIQEEW4UAyQ4BCEAAAhCAAAQgAAEIQKBaBBBs1SJLuRCAAAQgAAEIQAACEIAABCokgGCrECDZIQABCEAAAhCAAAQgAAEIVIsAgq1aZCkXAhCAAAQgAAEIQAACEIBAhQQQbBUCJDsEIAABCEAAAhCAAAQgAIFqEUCwVYss5UIAAhCAAAQgAAEIQAACEKiQAIKtQoBkhwAEIAABCEAAAhCAAAQgUC0CCLZqkaVcCEAAAhCAAAQgAAEIQAACFRJAsFUIkOwQgAAEIAABCEAAAhCAAASqRQDBVi2ylAsBCEAAAhCAAAQgAAEIQKBCAgi2CgGSHQIQgAAEIAABCEAAAhCAQLUIINiqRZZyIQABCEAAAhCAAAQgAAEIVEgAwVYhQLJDAAIQgAAEIAABCEAAAhCoFgEEW7XIUi4EIAABCEAAAhCAAAQgAIEKCSDYKgRIdghAAAIQgAAEIAABCEAAAtUigGCrFlnKhQAEIAABCEAAAhCAAAQgUCEBBFuFAMkOAQhAAAIQgAAEIAABCECgWgQQbNUiS7kQgAAEIAABCEAAAhCAAAQqJIBgqxAg2SEAAQhAAAIQgAAEIAABCFSLAIKtWmQpFwIQgAAEIAABCEAAAhCAQIUEEGwVAiQ7BCAAAQhAAAIQgAAEIACBahFAsFWLLOVCAAIQgAAEIAABCEAAAhCokMCACvOTHQIQgEDTEBg5cmTRthZun3766Ysex0YIQAACEIAABCBQLgE8bOUS43gIQKBpCWy66aY2wwwztP9FEOltOoYEAQhAAAIQgAAEsiKAYMuKJOVAAAINT+C4447rto09OabbQjgAAhCAAAQgAAEItBFoafUEDQhAAAIQ6BmB1Vdf3Z588smiB2vf448/XnQfGyEAAQhAAAIQgEBvCOBh6w018kAAAk1L4Pjjjy/ZdrxrJdGwAwIQgAAEIACBXhLAw9ZLcGSDAASal0AxLxvetea9Hmg5BCAAAQhAoJoE8LBVky5lQwACDUmgmJcN71pDmppGQQACEIAABPqcAB62PjcBFYAABOqRwBprrGFPPPFEqDretXq0IHWGAAQgAAEI1AcBPGz1YSdqCQEI5IxA2qOWfp+zalIdCEAAAhCAAATqnAAetjo3INWHAAT6joC8bEpEhuw7G3BmCEAAAhCAQKMTGNDoDaR9EIAABKpFAM9atchSLgQgAAEIQAACkQAetkiCVwhAAAIQgAAEIAABCEAAAjkjwBy2nBmE6kAAAhCAAAQgAAEIQAACEIgEEGyRBK8QgAAEIAABCEAAAhCAAARyRgDBljODUB0IQAACEIAABCAAAQhAAAKRAIItkuAVAhCAAAQgAAEIQAACEIBAzggg2HJmEKoDAQhAAAIQgAAEIAABCEAgEkCwRRK8QgACEIAABCAAAQhAAAIQyBkBBFvODEJ1IAABCEAAAhCAAAQgAAEIRAIItkiCVwhAAAIQgAAEIAABCEAAAjkjgGDLmUGoDgQgAAEIQAACEIAABCAAgUhgQHzDKwSqRWDMmDH23nvv2UcffWSzzz67LbjggjbTTDNV63RVLXfy5Mk2bty4cI6pp57aWlpaujzfxIkTbcKECeGYwYMHd3lsNXem61HueXpTb7VZ5+zfv78NHDiw3FPW5HjZUfacaqqpbMCAfP4U6ruj1JNrrStoI0eODN/B//3vfzb33HOH7+B0003XVRb2lSDw3Xff2fvvv2+ff/65jR071madddbwu7bAAgt0+3tQokg2QwACEIAABLokkM9eSpdVZme9EPjPf/5j999/v3366afW2traqdqzzDKLrbTSSrbjjjuGDnOnnTn+8MILL9jZZ58danjuuefaDDPM0GVtH3nkEbvkkkvCMZdddlmXx1Zzp2xx7bXXln2Kfv36tde/nMw333yz3X777bbOOuvYfvvtV07Wmh17+umn2xtvvGF77723rb/++jU7bzkn+vnPfx4O/9Of/mRzzjlnOVmDGJUdHnvsMRs+fPgUeeeYYw5ba621bNttt0VoTEFnyg3ffPNNuKYfeuih9oc26aPmm28+22677WzllVdOb+Y9BCAAAQhAoGICCLaKEVJAIQF5Lv7xj3/Yk08+GXbJezH//PPbkCFD7LPPPrN33nnHvvrqK7vnnnvszTfftIMOOig8oS4sh8/FCeip/s9+9rOw85xzzrEZZ5yx+IGpraU8XfIwyROmVMwTJsFGqj8CEhfnn3++vfbaa6Hysq082/PMM0/wdMvjLQ/RTTfdFL6DBxxwgE0//fSZNfThhx+2iy66yBZbbDE7+uijMyu3koKeeOKJwEQcjj/++LKKGjVqlJ1yyin2ySefBHG7+OKLB0+lvlfiKM4ffvih6SHOXnvtZRtssEFZ5XMwBCAAAQhAoCsCCLau6LCvbAIaCnfCCSfYxx9/bOrs77TTTqHzkhYDEgkvvfSSXXDBBWFo0XHHHWfHHHOMzTXXXGWfjww9I7DZZpuZ/gqTPG9XXXVV8HL+/e9/L9zd0J9XWWUV0zC2eeedt6HaKbGm79SIESOCXffdd19bddVVw/DU2FCJdD1Qkff3lVdeCcfre5ulaIvnaoRX/VZJrGko929+85sprhkxl0B98cUX7corr7SFF144PKRqhLbTBghAAAIQ6HsCPD7vexs0VA3kNZNY07wgec4kEtJiTY2VkFtuueXsd7/7XZj/oTkh119/fUNxoDH5J7DJJpvYrrvuaossskj+K1tGDTUMUmJNc9SOPPJIW2ONNTqJNRUlr7eGQx5++OE27bTT2pdffmm33XZbGWdpnkP1EOrVV18NDdYDqGICX17uAw88MPyeSQw/88wzzQOIlkIAAhCAQNUJINiqjrh5TiDhpXlLShtvvLGtuOKKXTZewQ922WWXcMyzzz5rb731Vvvx8sJpGJLKjEkdobfffjt0hvS0W8d0l5RHw7+ee+4501PwvCYNc9TwUHHQEKvCOX+q96RJkwITcYlJfPQ5BqeI26vxKpYKtvD000+H4V894V+qHuPHj29vSwzKUnis7DV06NDAReculSKDyEyvCnCjeg4bNsx0rmJJzMQuXfbo0aPb66V9pf7SedJla/u7774brrdvv/02vavL91988UW4rp9//nkrJ19hoRpyrDlWSttvv323YlRDFrfZZptw/H333WeqR0zxekt/B+O++Krhz2IUGcfPup6VxCMyjMzid1vbY9Lxmk+o76nEZqlUTp1UF6XCOsUydP5S1176/LqGYt3lkS2VFBhmhRVWCLv1O1Uq6fqUnSTq9HArXreljo/bdW3KG/rUU0+F6ztuF7u0DbQ9Mu7KdvE7GDnF8tKv4qMh7LKLAteUSpFxvA50nI7X9Syx29PfXl378lLqfB988EH4zSt1zrhd/MRRPPXbSYIABCDQiAQYEtmIVu2jNj3wwANBYOnp/eabb96jWijwiIZCKnqdxN4hhxwS8ilQyW9/+1tTYIRTTz3VLr/8clMAj3SHQEEYFJRBc1IKk467+uqrTXNpYmdLx+hcW265pa277rqFWfrks4SnhlKpk5/uuE0zzTS21VZbhb8YiVIdn9NOO61TPcVIafnll29n1+mADD6o06eAKRKTaZbyoq6++uq2++6726BBg3p8JnWqFERD4kDerUMPPbRT4BkJ7AsvvDAMQYuFyksrcaHhfQpYk06//vWvg2A977zzgki74YYbOgl9eZA0r0jDAtPpzDPPnCLoyMknnxzEaPq4Yu8150ttj0kdVg2FU4CPNCM9lPjBD34QvFnx2PTro48+atdcc00nkSZ7yyu22267pQ/t0fu77rordHIVDEcBX3qSNN/q1ltvDcyUX/ZUitebIrsWXnexXH3HHnzwQdtiiy3C8GcNr42CUcfoupbnSWn//fe3tddeO9hV89r0O6GhhvqTuE5f/zPPPLPtueee7QIoFOD/6aHOH//4xzA0MQb/ifviq7z18vTroZHact1119m9994bdwchEOvUk/lm6cBCr7/+epi71l5YwRuJX9lObSuW/vvf/4bgP2mxqvIVqET21py4wqTfRjGScEwzknhU/e+8887Ab4cddrCtt946ZNeDleOPP970O6K5jMWSPLF33HFH+C3U9yqdJAKvuOIKe/zxxzuJJs2BVBvT177y3XjjjXb33XeHIFJqi+bWak5fOq233nrBo13st0LXmobnFgouReBUm4rNCRQLtf2WW27pFARGvw+6zn70ox+lT897CEAAAnVNoPhdpa6bROX7ioCeiCotvfTSPQ7br86phmapk114g4/tkFiQGFSHXZHYJHL+v717WZmkSMM47sBcguBWBMEDuhVEpFFw50LUFjc6C3EhIiIeUFDEw0pQFypulAZBceNaEO/ApRcwl+AdzPQv4WmiczLr1F+1VT3PC9VVnZkRGfmPyPzeJ96ISI4TUceR/Pzzz68TDHpzCQK9rpyh++67bxoexnn0+f7776ee9ccffzyn+Fu+OanmjXH2zR265557pvISLMrJ8bSYweuvvz4JGj34rl/vuWtjBAEn7/bbbz/KNYhU4WvIHNH04IMPTqsVOr+ICEHMiX7zzTd3WjhG3RHgoijyMmx2HDLLoVXfRI95QMS4nnoc/vzzz8lZJ1Lnos3FEwraESY6AkTQrOpJcH799de33XHHHdOctU2gNq3EqF2JABjSK6+YBXS0N+3RHKd77713cpTVobpUx6I6804CQo1AYvLTvtUtnoTfGO3KubZ9c9QZcTpy3ZSOA20+H+G1dg9uSj/uUy/aKOa4yFunCyOcR3OtRLN6VWd333331CEj0qy9EWSXL1+eOljGdPv+Jv6USYQq7Tj1t8urDRwrD21WnYk6ERBLfA2NXFsESDvQ6UTM5T4SaRM1s5qu9kVIjqLNPguZaMv4aSMYE28+7qWLvve1O+2ZeHLdnkte7SHSpn0Rj9qz5/bcrEZqcRbPNPcgFqLkuLs/MZt3RBB6uGoPBJpOHIyS7sqVK1Pd6cCKOb/nkrbjuWg0Bw6eS54VOiCI4hdeeKEroAZav0ugBM6aQAXbWVffaRVeTzDb5PQulTjHc/D8oR97YDkNHBlRFE5OjOOvl94fZVGKxx57LLumP/7+cDteJEQvc0yPseiNiB0xcNddd2XX3t+cqTHvpQzWHGAOLUfE9ZrPZ9XH0aG1op2VNjkkFgbhrHBoP/nkk+ndT1kl0hylNQdxqTz7biOeOFscauLKd4wYEdUiVERWCMtNRtB/9tlnk+gRhRBxGZ1T9S9KxXETOXVMDC+OIifum2++meY/Zl++CVxzjER3E5XkpH/wwQfTsvY4Oucme/XVVxd3//XXX1PEws7nn3/+uqiuKBMGnMaXX355cm6TCUGrg8DHKqlWS2Wc34g1y+obvjiaqMEvv/wybtrpt3Kw3FM7Jbp6UARM7uFd082Ps6y9T1aJFAVaWyVSPbuH8Bwj8rZrdwSkOjXM8EYWJBLh9MkqkaJEH16NPu1jr7zyytQ55H5V38pFmFstksDQuSDivGYWeCHWiC2jCNIOHK9T5Msvv5wi2ITGU089NWVDlHhGEGvO4Rk4Pm8IW6J27RmzVpZt292DnrtEvPtlfB6rE2UyKsDzc3weyJfwxMR9lMikSJgop2iY9M8+++y1PAk8+9S5iCgxl3vXNq8i+fXXXyfeonoEHSPyPBvNJ/TcEQWO6fDAU1mUcd5RkuP6XQIlUALnRKBz2M6ptk64rP4o6y1m+/b45o+tPOJwjpdKrIxizT6OgsgZG51MjrDohJ5zTv/o4DiWCIhzKGp3I/btt99Ovbx6etc+41Cs8VyGfxKbnBrO4CjWHMc54WQyi0EQLDfbzCPhFHKgCMS5c8YZylAqc1Ucu2aiTXreRaieeOKJKb9RrEnHceMQ2z+KNfvwUQa8OGSc3LlpI4bmxeGzX/0nEjC2k3naTf83vJZjLMJiWJfyxUQZRUpF1uZizTGGJYrkatuc1Zjl9Jlo9Fys2U7EPfTQQ37ubDo2CFSWe2rXxImCqZ+b2dbUTe7HlFUE07vxtC9OO/H6d5vIFuGvXWq3omzmWhFuhmiKjHkGGDasrkcTLRZBYoYwjmLNNqIjHQmiUK6ZEXiEEyFIlMyfZcokv4s0Atq9LLI2F2vOc+nSpemjjGN7ThmU1X0QsWa7+9E94Bs3HUAxwzLxIUgDSbEVAAARTUlEQVQNXx3vXe2AeJUXpqLlTMRdm7DfM2He1nVsWUyIEW21EiiBErgVCFSw3Qq1eGLXMHdYthVvPN4f4bmtOa4cFsbJjJkLwTjvcxGUY+LA6/XOfCNOObGw9NHTvWSGCnG4N33mIif5eK0Bs1KhIT1LRnwYGqSHfdMiBktpL2JbyvjAAw/cdufV6NCSEc2JUub4+XFEjaFbhIBedM7U6JjleM4im4u17Fef5uoxzuzc5vNqsn+pnWTfLt+iYwSnfAyxGi1lFiU1bGzJ0t5Edzm62rshvezSVQd4zbSNfWy8d+L075p+2z24az77Hqc9rFleQ0EYnYIRVjqBzM8SQRLVN9QSd88N0V/7vFJBRDamo0D0WPuddzzlGFEp0SOdAmlThvYxkdu11y0Ydrg0PDj57vudczvnGFkb80l7NrJhbDeOEWlc6rCzjQhkOhZiqdulOWqO8Ww0r9RL7nN/65Qzx06kdC5+k68hwcSjUQBGY9RKoARK4NwJ/PPcL6DlPw0CHHDDsMxxMMxlH9OLzOSRoVlJb9u8B3Xc5/fonGZuF2fHe6WWLE4GJ4tjxZl47733VsttJcs4j2N+etXHnuRxX37rhTaZfjTnzzUvLRGeY833EPngcIg8EgU30xLt5JRuMvsJmhw/Hsthsqod1ox4I4Dn0TW95hHeeI3iY8wvjnCiueO+RInGbePvsZ2M2zf9Ft0ktDjThoTOF5NIeyNW19pbBL/ol+v0f5EGtuZwbts3JZ79w6kXWRa5PfQeFClcE56z093wf9XxLtdP6LumXeab3XChdshAOQwX9GE6VIg18y8JHs9AAsNrS4ietBGdQx999NHqGRId1bZ1kqSNb2KEoWcIQXgRlrKKZrl3lyztWZ0o89gxtukeTCdN7kOiK/f8pmvUrkfBmjK65rV7biw3jmsdZ+Nx/V0CJVACp0yggu2Ua+fMyuaPImdlyXHfdCk5nlM8n8jvj/zcud+UVxwXPdU+24zDQLA5x9wZT9o18ZD9+35zQCNgtvWO20+w7XIt+5Zj2/FhmZ7xteOzP8ePxxm6qKf7ueeem+ZkmW9jONN8GOA4TMpiCtssjt543Fr9jcfs89uQUKvfcboNSVsS57lm3/m96RzKnaiu45byTHrn9UlbyfZN3zpNRGNzT206dtyX4/ed+zbmse9vwnDTvT2ywfZUBNv8Ol2H6I+PVRcN7TVfk3gTqUq7UI86NrZZ2nYiUSOHpbTb9i+lWduWsrofx3ty7XhlHQXbPvfgmP8+15Ayeo7uw3PtGrq9BEqgBM6BQAXbOdTSmZTRAgOiERwVK40tDY2ZX4reVgsUsE3vOJqnW/u/aJwojqFWmQO2dqzt6bm1KtrNMg5OHHFOy50rww2VJ05NJtvfrDI6j3Ny5FOGtXPHgVoqI2f2tddem+Yb6pm3iqP5exzZ8brHnvmPP/74Wr2snXOTo7+WZp/thKU5isycnLUIgPbGaTQPaz4Xa+l82htHMyZiuBbR4uDvI9bkiSnBZrivBVi0s20mSmIeHhvrZFs6+8fXbOxy/HgMDiKNBP2SRbDYl06BpePm226kTPO8/N9COYbheR1I3lm3dJxt2gDR5tqk0c4zQsDCKe+8885a0mvbM0Ta/eTeGzlcO2j4sW3/cOh1PxPlHTcqK7Fp3vAuQ3L3EVrjefwe04rWie7uYuFp0ZcsvrQp3Xzu36Zju68ESqAETpXA/04YOtWStlwnT8A8BH+EOeZW9trFDJczfEskbZsztEt+Gfpi6Bknb+ljVcU4w8d2/JfK7FoTycirEJaOM2QoQ9ty/NJxx9qWlfm2rUKXa8jxY3m8kymLw3ACiXLtwypzY6SJUxWHjcO9VG+26cHfV8SM5dnlt6iBVeacx4qHrmHN0t44nGtldk/Iy3Upv/YXkbY27Mz5Nu1bK495jwQQwbDrojoWZjCsT3Rb+lgiyxnClu3jd4b2jtv2+f3vDdHUXD9W6Vi5GWWal18d45N5h/P94/89T1LWbE8bIc7V/Vo7wVk7yTXmnt8URXIvLd2f8+GHKcv4nSGX47aUdVN7dn1pzzfy/JRPonNL15ByWbBFJ46VIVnKSKiusbSdIB15Jr9+l0AJlMA5EqhgO8daO9Ey6xmO6OIsmpS+yThkloNnFhbZt3d/KW9zPzgrhrOtRYYs766n2wIBcWyW8jrmtiyeYRXJMeIynlMkiqgxFMwqajfbUkbLZ68temKuS5zubXPsOKIvvfTSNAyOg2YJ89GyIMPayppYcNzUnZXsjmHO4b1XIsTmKFmtcZOlzCJUmV83P95y5so8vsBY5IXp2Micynm6OKjz7Zv+L6qd9wtagS+LSKylsehD6sHqlxHNjs9v99GSMDNUd5OYWDvnuH1Tx04EZxiPZSKqM5dpzI8IsYroRVoW1TEvdm1hnZxPu8nw0nRU6MgQQSb6lhbLkda1vPXWW1M7yZDItBH3WPLMefItkrrU7lJ3On0ifJPGtzrNAk3j9rDWkabTa8nM69SevR7lRi0LAv3++++LWemwch94/iQK71lI6Gl/a+3bdjzff//96+Y4L56kG0ugBErgDAhUsJ1BJZ1TEUXZODgcX3/QOY16a0cTafBOJMthc044F88888x4yMG/DV2zyqDeVUuxz3uRzalSJvbII48cfJ4bTSja5LqxUc4MK0y+HJi8p0uUJxEZ+0VQ0gufCFzSXeQ38WsFTEZsGOI1GofPCorM/J04tuMx89/qx7UzgjRiz/8t4e3atA3vIBsjO9qTberTML8s+CDdRZr3f3k9gUigoZDbBL1rVhY9+epxXh/mdHJwWVbX81udik7Yb+il64sRcKIKOh0OsSeffPKaQLDUfCJoY17Ew2+//XYtkiiaMx9CTPxlSCVhNZaRgFPuNbGZdOZejunGMvj9xx9/3PbTTz9dl488LTyjvWnnWMXM6cy9wJEfhz8SS8ok6rRkKROBszQccCmNbVYctIqj9ugF7ITkUnqdEIZPMsMZLS/PXEOeb+rV0v+j6bDR7pTbeTLkz/vntC/ntYjJ/LUURIl0SyaSlzl/OqhGTupk3uaSBwHlvNqHKDOmoxF/hnyyi3h+4oIPkf3DDz9c1w6U2WsTXD+e999//3Re0fi0VS8jn79ORP3Ki4mOp96nDf2nBEqgBM6UQOewnWnFnWqxDfnyolwvX+XYeN+Uj15mTjCH2xC6OFUcFKstcjAuyryYlfPEGbb6o1UMOaQcwEQKCJFd5hxdVJnm+XAivOeIEDLnzguwOWeGz3GKUk5CKBGT5MHR19us1/3TTz+dhmARV3mXU467iO8XX3xxctxEALy0Wx1iqXdbPXKuvS/Ly493NVErTqs8OVxWetNuOONenMvZsjCJFTadj0MnupHhTeatHGNeikiilf4Y4fX222+vXhLnMbzNFXMtok3vvvvu1N7Uj/YWASdaOdaj/d475T4hUEW61D/BKpoguqHjwZzQfY2jbjVCHSZEH6ceU8ugW1FQp4XyRhATnCKfmTuV82mjlq7XcaADQTndNxz+f18dykhYWD11KUqW4bGuPy+vV7ej0NaOzUOSXuTJkvAEsuvXkaHeL1++fG0InHJpJ+ZWiQp6Z5lIkLZveJwyaUOGdYpqzk27lb/ye/ehNmQxnIcffnh+6HX/V04rhGqnrufKlSvTXEzPFUJCuySm8m5AZdAOxgWUiD517DpF9pVF54WOGu1GXbj306ZSAO85/OKLL6bngeeqNFbSda3uf+1Ivc47U1yn55s5o95LqR3gpIPMc9FwRMI+0dWcz7dVcV2PevBcck517f8RcF4ncOnSpTHZQb+1RwsQ6YwRWVefEbquifjCXxsaeZqj7JmAqY4/7U19KLfrYxjnfWwHFa6JSqAESuCECFSwnVBl3CpF4VT96+oLXfXWEm3+gPpD6sP8AeYEGPLDeeeYXaRxmLwL6eeff54WXyCAMiyIk2bolwUELvq8+14D55cI4lCbH8MBYZwtzoZI1KOPPrqYrRcL//jjj5PTxlmdRzEXEx2wkUP44YcfTivfGfY3sjRPhLP79NNPT3W6a/baB0fUtSfimQgEJ/DOqwtnEBicUg5ZjEPvXOlpz/aL+jZ8LGYhjiyznm3j98hbJIozL1IkYsQBz1BBQ7c4zj7z9iYazdlWjzoYCEYmgkQIEcGHCLbkYbEX0RV5yD8f+wlDnNXfpsUlEgEhqkSCDMGT1r1NOEfgynM0zrOOE9E97ZPjTdiMpp0rI1FBfFkan+EkvetfqmuRWGlFekSCOPnKZKic96QRlkvGqSeuMVkr01I62wgc7wPTiUAMEn25X5OG4HK/4pkhidmnvMSYYZJEko6rDHN0vcS560p0LekIGvefZ4SIdu4/z1CdXYQn0T8XbNJ7fuhQcT5t2/3r3nMfiR6vDe90zxP82qWOFXknf50BBLH26ZouwghHdWd4vI6EMQJJ0BOQ/l6MRry98cYbk9gn9KTzYa5RPfjbkijjmLa/S6AESuAcCfzj6gP9P+dY8Jb5fAjoPRaR4Zxzbv3xHXtLj30lnDMOEifK+eeO87HPv0v+bkO99yIrnLRTHcaDpegfQcmJPaaJwhL5HH1O5LHPd1HXQpxgRNCKwOzS3gyxc38QeJz2i3KGc02GJcofz0QjOP27mrpwD6kL9+8+aefnUA7RIo71d999N+1WPoLSs4JY2+X5oEw4EyPSyO9mmDKKjDm3b6JAPeO6axlwxMF9rr53ud89I9SB6/WMyLm++uqrSYwR18TP3JRXOuJW3SXd/Li1/xOnnk37tOe1vLZtdx8QXtq/Z8wuXOSpc0W63D8EfK0ESqAEbiUCFWy3Um32WkqgBErgxAksCbYTL/JJF2+bYDvpwrdwJVACJVACOxG42LFoO52yB5VACZRACZRACZRACZRACZRACexCoIJtF0o9pgRKoARKoARKoARKoARKoAT+BgIVbH8D9J6yBEqgBP5fCZifZA7cLnP7/l8Z7XPdOOJ50fMe9ylDjy2BEiiBEjgugc5hOy7f5l4CJVACJVACJVACJVACJVACBxNohO1gdE1YAiVQAiVQAiVQAiVQAiVQAsclUMF2XL7NvQRKoARKoARKoARKoARKoAQOJlDBdjC6JiyBEiiBEiiBEiiBEiiBEiiB4xKoYDsu3+ZeAiVQAiVQAiVQAiVQAiVQAgcTqGA7GF0TlkAJlEAJlEAJlEAJlEAJlMBxCVSwHZdvcy+BEiiBEiiBEiiBEiiBEiiBgwlUsB2MrglLoARKoARKoARKoARKoARK4LgEKtiOy7e5l0AJlEAJlEAJlEAJlEAJlMDBBCrYDkbXhCVQAiVQAiVQAiVQAiVQAiVwXAIVbMfl29xLoARKoARKoARKoARKoARK4GACFWwHo2vCEiiBEiiBEiiBEiiBEiiBEjgugQq24/Jt7iVQAiVQAiVQAiVQAiVQAiVwMIEKtoPRNWEJlEAJlEAJlEAJlEAJlEAJHJdABdtx+Tb3EiiBEiiBEiiBEiiBEiiBEjiYQAXbweiasARKoARKoARKoARKoARKoASOS6CC7bh8m3sJlEAJlEAJlEAJlEAJlEAJHEyggu1gdE1YAiVQAiVQAiVQAiVQAiVQAsclUMF2XL7NvQRKoARKoARKoARKoARKoAQOJlDBdjC6JiyBEiiBEiiBEiiBEiiBEiiB4xKoYDsu3+ZeAiVQAiVQAiVQAiVQAiVQAgcTqGA7GF0TlkAJlEAJlEAJlEAJlEAJlMBxCVSwHZdvcy+BEiiBEiiBEiiBEiiBEiiBgwlUsB2MrglLoARKoARKoARKoARKoARK4LgEKtiOy7e5l0AJlEAJlEAJlEAJlEAJlMDBBCrYDkbXhCVQAiVQAiVQAiVQAiVQAiVwXAIVbMfl29xLoARKoARKoARKoARKoARK4GAC/wWqxZqbgSogpAAAAABJRU5ErkJggg==)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dh7Hxsjba6Gz"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5PfZRbn5ugbw"
      },
      "outputs": [],
      "source": [
        "# Setup\n",
        "# This cell installs all the dependencies used in this week - imports happen as we go\n",
        "%%capture\n",
        "\n",
        "!pip install transformers datasets accelerate python-dotenv openai tiktoken annoy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-Hu61xpwwAK"
      },
      "source": [
        "# Training and using a Decoder Transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dv8TFZp6yvdF"
      },
      "source": [
        "Let's get started writing book reviews - we use the famous Amazon review dataset and reuse the cool streaming feature of the datasets objects introduced last session."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_4UWqarDaik"
      },
      "source": [
        "## Data Exploration - Create Your own Book reviews"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oWNQmW4ADb4X"
      },
      "outputs": [],
      "source": [
        "# Load the dataset and buffer the first 200 shuffled\n",
        "from datasets import load_dataset\n",
        "\n",
        "\n",
        "amazon_us_reviews = load_dataset(\"till0r/nlp-in-5-weeks-reviews\", streaming=True, split='train')\n",
        "shuffled_amazon_us_reviews = amazon_us_reviews.shuffle(seed=42, buffer_size=200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yufjg9FfvWtE"
      },
      "outputs": [],
      "source": [
        "# We can use itertools again to gather the first n records from the streaming iterator\n",
        "import itertools\n",
        "\n",
        "N = 200\n",
        "amazon_us_reviews_sample = list(itertools.islice(iter(shuffled_amazon_us_reviews), N))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJ6HgWKfzWqt"
      },
      "source": [
        "In this section we want to show you how to convert a pandas DataFrame to a Huggingface Dataset. That might be handy for your daily work."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "pGg-2p92wEC_",
        "outputId": "2dcf83e8-1b46-4e02-80cf-cda2695c0440"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    marketplace  customer_id       review_id  product_id  product_parent  \\\n",
              "0            US     45232107  R3SJYLG07EHNE9  013146308X       332774292   \n",
              "1            US     51197187  R10X8G16DAJH9R  0595368239       687973923   \n",
              "2            US     24577728  R30O48IC9RP08J  192904738X       469025369   \n",
              "3            US     49441053  R34X92SSRBI0N4  0966170229       396093604   \n",
              "4            US     12256167  R3NJ8RGXZRKKGQ  007142959X       822648026   \n",
              "..          ...          ...             ...         ...             ...   \n",
              "195          US     52960913  R1MZ3K8XOMPWEV  0525948775       535890305   \n",
              "196          US     18755160  R1WIS16TAYHPP8  0515116475       437179605   \n",
              "197          US     51917717  R37OAEJ59YZR20  0385324162       390234841   \n",
              "198          US     23711977   R2Q6UPS8ILT1T  0061091316        56455717   \n",
              "199          US     12069644   R773N4Z0CRDJU  0060736267       892654736   \n",
              "\n",
              "                                         product_title product_category  \\\n",
              "0                       The Pterosaurs: From Deep Time            Books   \n",
              "1                   VAMPIRE: A sensual romance novella            Books   \n",
              "2    13: Classical Baguazhang: Sun Style Baguazhang...            Books   \n",
              "3    PowerNomics : The National Plan to Empower Bla...            Books   \n",
              "4    The Successful Investor: What 80 Million Peopl...            Books   \n",
              "..                                                 ...              ...   \n",
              "195                               The Death Collectors            Books   \n",
              "196                 Mallory's Oracle (A Mallory Novel)            Books   \n",
              "197             A Breath of Snow and Ashes (Outlander)            Books   \n",
              "198                                          Lost Boys            Books   \n",
              "199      A Tree Grows in Brooklyn (Perennial Classics)            Books   \n",
              "\n",
              "     star_rating  helpful_votes  total_votes vine verified_purchase  \\\n",
              "0            2.0           16.0         34.0    N                 N   \n",
              "1            1.0            4.0          5.0    N                 Y   \n",
              "2            5.0            4.0          5.0    N                 N   \n",
              "3            5.0           26.0         29.0    N                 N   \n",
              "4            5.0            6.0          6.0    N                 N   \n",
              "..           ...            ...          ...  ...               ...   \n",
              "195          5.0            2.0          2.0    N                 N   \n",
              "196          5.0            1.0          3.0    N                 N   \n",
              "197          4.0            6.0         12.0    N                 N   \n",
              "198          4.0            3.0          3.0    N                 N   \n",
              "199          5.0            2.0          4.0    N                 N   \n",
              "\n",
              "                                       review_headline  \\\n",
              "0                                Warmed over leftovers   \n",
              "1                             Unpleasant Vampire Story   \n",
              "2    A Great Translation of An Internal  Martial Ar...   \n",
              "3               Live on your feet or die on your knees   \n",
              "4          The Successful Investor - William J. O'Neil   \n",
              "..                                                 ...   \n",
              "195         The resurrection of a long dead psychopath   \n",
              "196                                    Another Success   \n",
              "197                                 Redeems the series   \n",
              "198  A book that reveals the coplexities of everyda...   \n",
              "199                                          Beautiful   \n",
              "\n",
              "                                           review_body review_date  \n",
              "0    To his discredit, Dr. Unwin virtually brags in...  2005-10-14  \n",
              "1    I must worn potential readers that the vampire...  2005-10-14  \n",
              "2    The author performs a great service in transla...  2005-10-14  \n",
              "3    Mr. Andersons' book provides a thorough examin...  2005-10-14  \n",
              "4    This book was written for those who want to le...  2005-10-14  \n",
              "..                                                 ...         ...  \n",
              "195  Jack Kerley's \\\\\"The Death Collectors\\\\\" is a ...  2005-10-14  \n",
              "196  I started the series with Winter House and am ...  2005-10-14  \n",
              "197  The Outlander series are my favorite books of ...  2005-10-14  \n",
              "198  I read this book mulitiple times. The way Card...  2005-10-14  \n",
              "199  This book is so well written that you feel lik...  2005-10-14  \n",
              "\n",
              "[200 rows x 15 columns]"
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              "      <td>0966170229</td>\n",
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              "      <td>PowerNomics : The National Plan to Empower Bla...</td>\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"marketplace\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"US\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15132409,\n        \"min\": 12065385,\n        \"max\": 53094054,\n        \"num_unique_values\": 187,\n        \"samples\": [\n          23711977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"R3TGTEWOVE8GH2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 198,\n        \"samples\": [\n          \"1872197108\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_parent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 295072946,\n        \"min\": 10090993,\n        \"max\": 991355343,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          334546428\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 198,\n        \"samples\": [\n          \"Hitler's War and the War Path\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Books\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"star_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.168041783516412,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"helpful_votes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.20869263335391,\n        \"min\": 0.0,\n        \"max\": 155.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          51.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_votes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.838459171649326,\n        \"min\": 0.0,\n        \"max\": 184.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vine\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"verified_purchase\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_headline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 198,\n        \"samples\": [\n          \"A book for the ages\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_body\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 199,\n        \"samples\": [\n          \"\\\\\\\\\\\"The Face\\\\\\\\\\\", the first book in this volume, is probably the best of the five Demon Princes novels.  The reasons are manifest.  Villainous Lens Larque provides some of the best challenges and intimidation for our hero, Kirth Gersen.  There's the usual helping of exquisite writing and fine descriptions; the menu items during Gersen's restraunt visits are particularly noteworthy.  Most importantly, \\\\\\\\\\\"The Face\\\\\\\\\\\" is the most morally challenging novel in the series.  It presents, to some extent, the best argument for Gersen abandoning his single-minded hunt for revenge in his abortive attempts at romance with two characters.  Lastly, \\\\\\\\\\\"The Face\\\\\\\\\\\" has a killer ending, laugh-out-loud serious if there ever was such a thing. <br /> <br />Another notable element is the planet Dar Sai and its inhabitants, the Darsh.  Dar Sai is odd even by Vance standards.  Giant umbrellas of water protect folks from searing daytime heat, the women grow mustaches, food is prized for its repulsiveness and gender wars are taken to a whole new level.  Vance scores particular points by showing us the spoiled tourists who visit solely to mock the backwards Darsh.  An just dessert is served for them at the end, of course. <br /> <br />The last novel in the series, \\\\\\\\\\\"The Book of Dreams\\\\\\\\\\\", is competent but less biting than \\\\\\\\\\\"The Face\\\\\\\\\\\".  The new villain, Howard Alan Treesong, is less impressively hateable than Lens Larque or Viole Falshune from \\\\\\\\\\\"The Palace of Love\\\\\\\\\\\".  \\\\\\\\\\\"The Book of Dreams\\\\\\\\\\\" contains vignettes set on numerous planets, none of them particuarly brilliant.  The best parts of this novel are the introductory segments to each chapter.  Short paragraphs cover a wide range of ground, from the hilarious philosophical basis of the Institute to the stunning character introductions for Treesong's various Paladins.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2005-10-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "df = pd.DataFrame(amazon_us_reviews_sample)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92z9Q07fzjeW"
      },
      "source": [
        "First, we need to design a prompt that is followed by the review."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vpfNmUd2p39m"
      },
      "outputs": [],
      "source": [
        "def create_prompt(row):\n",
        "    return f\"Write a {row['star_rating']} star review for {row['product_title']}: {row['review_body']}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jCFVJ8O2p8dC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b60bce9b-f7a9-4b15-ae01-c780b75cbe78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    marketplace  customer_id       review_id  product_id  product_parent  \\\n",
              "0            US     45232107  R3SJYLG07EHNE9  013146308X       332774292   \n",
              "1            US     51197187  R10X8G16DAJH9R  0595368239       687973923   \n",
              "2            US     24577728  R30O48IC9RP08J  192904738X       469025369   \n",
              "3            US     49441053  R34X92SSRBI0N4  0966170229       396093604   \n",
              "4            US     12256167  R3NJ8RGXZRKKGQ  007142959X       822648026   \n",
              "..          ...          ...             ...         ...             ...   \n",
              "195          US     52960913  R1MZ3K8XOMPWEV  0525948775       535890305   \n",
              "196          US     18755160  R1WIS16TAYHPP8  0515116475       437179605   \n",
              "197          US     51917717  R37OAEJ59YZR20  0385324162       390234841   \n",
              "198          US     23711977   R2Q6UPS8ILT1T  0061091316        56455717   \n",
              "199          US     12069644   R773N4Z0CRDJU  0060736267       892654736   \n",
              "\n",
              "                                         product_title product_category  \\\n",
              "0                       The Pterosaurs: From Deep Time            Books   \n",
              "1                   VAMPIRE: A sensual romance novella            Books   \n",
              "2    13: Classical Baguazhang: Sun Style Baguazhang...            Books   \n",
              "3    PowerNomics : The National Plan to Empower Bla...            Books   \n",
              "4    The Successful Investor: What 80 Million Peopl...            Books   \n",
              "..                                                 ...              ...   \n",
              "195                               The Death Collectors            Books   \n",
              "196                 Mallory's Oracle (A Mallory Novel)            Books   \n",
              "197             A Breath of Snow and Ashes (Outlander)            Books   \n",
              "198                                          Lost Boys            Books   \n",
              "199      A Tree Grows in Brooklyn (Perennial Classics)            Books   \n",
              "\n",
              "     star_rating  helpful_votes  total_votes vine verified_purchase  \\\n",
              "0            2.0           16.0         34.0    N                 N   \n",
              "1            1.0            4.0          5.0    N                 Y   \n",
              "2            5.0            4.0          5.0    N                 N   \n",
              "3            5.0           26.0         29.0    N                 N   \n",
              "4            5.0            6.0          6.0    N                 N   \n",
              "..           ...            ...          ...  ...               ...   \n",
              "195          5.0            2.0          2.0    N                 N   \n",
              "196          5.0            1.0          3.0    N                 N   \n",
              "197          4.0            6.0         12.0    N                 N   \n",
              "198          4.0            3.0          3.0    N                 N   \n",
              "199          5.0            2.0          4.0    N                 N   \n",
              "\n",
              "                                       review_headline  \\\n",
              "0                                Warmed over leftovers   \n",
              "1                             Unpleasant Vampire Story   \n",
              "2    A Great Translation of An Internal  Martial Ar...   \n",
              "3               Live on your feet or die on your knees   \n",
              "4          The Successful Investor - William J. O'Neil   \n",
              "..                                                 ...   \n",
              "195         The resurrection of a long dead psychopath   \n",
              "196                                    Another Success   \n",
              "197                                 Redeems the series   \n",
              "198  A book that reveals the coplexities of everyda...   \n",
              "199                                          Beautiful   \n",
              "\n",
              "                                           review_body review_date  \\\n",
              "0    To his discredit, Dr. Unwin virtually brags in...  2005-10-14   \n",
              "1    I must worn potential readers that the vampire...  2005-10-14   \n",
              "2    The author performs a great service in transla...  2005-10-14   \n",
              "3    Mr. Andersons' book provides a thorough examin...  2005-10-14   \n",
              "4    This book was written for those who want to le...  2005-10-14   \n",
              "..                                                 ...         ...   \n",
              "195  Jack Kerley's \\\\\"The Death Collectors\\\\\" is a ...  2005-10-14   \n",
              "196  I started the series with Winter House and am ...  2005-10-14   \n",
              "197  The Outlander series are my favorite books of ...  2005-10-14   \n",
              "198  I read this book mulitiple times. The way Card...  2005-10-14   \n",
              "199  This book is so well written that you feel lik...  2005-10-14   \n",
              "\n",
              "                                                 input  \n",
              "0    Write a 2.0 star review for The Pterosaurs: Fr...  \n",
              "1    Write a 1.0 star review for VAMPIRE: A sensual...  \n",
              "2    Write a 5.0 star review for 13: Classical Bagu...  \n",
              "3    Write a 5.0 star review for PowerNomics : The ...  \n",
              "4    Write a 5.0 star review for The Successful Inv...  \n",
              "..                                                 ...  \n",
              "195  Write a 5.0 star review for The Death Collecto...  \n",
              "196  Write a 5.0 star review for Mallory's Oracle (...  \n",
              "197  Write a 4.0 star review for A Breath of Snow a...  \n",
              "198  Write a 4.0 star review for Lost Boys: I read ...  \n",
              "199  Write a 5.0 star review for A Tree Grows in Br...  \n",
              "\n",
              "[200 rows x 16 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>marketplace</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>review_id</th>\n",
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              "      <td>013146308X</td>\n",
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              "      <td>The Pterosaurs: From Deep Time</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>Warmed over leftovers</td>\n",
              "      <td>To his discredit, Dr. Unwin virtually brags in...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 2.0 star review for The Pterosaurs: Fr...</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>US</td>\n",
              "      <td>51197187</td>\n",
              "      <td>R10X8G16DAJH9R</td>\n",
              "      <td>0595368239</td>\n",
              "      <td>687973923</td>\n",
              "      <td>VAMPIRE: A sensual romance novella</td>\n",
              "      <td>Books</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>Unpleasant Vampire Story</td>\n",
              "      <td>I must worn potential readers that the vampire...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 1.0 star review for VAMPIRE: A sensual...</td>\n",
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              "      <td>R30O48IC9RP08J</td>\n",
              "      <td>192904738X</td>\n",
              "      <td>469025369</td>\n",
              "      <td>13: Classical Baguazhang: Sun Style Baguazhang...</td>\n",
              "      <td>Books</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>A Great Translation of An Internal  Martial Ar...</td>\n",
              "      <td>The author performs a great service in transla...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 5.0 star review for 13: Classical Bagu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>US</td>\n",
              "      <td>49441053</td>\n",
              "      <td>R34X92SSRBI0N4</td>\n",
              "      <td>0966170229</td>\n",
              "      <td>396093604</td>\n",
              "      <td>PowerNomics : The National Plan to Empower Bla...</td>\n",
              "      <td>Books</td>\n",
              "      <td>5.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>Live on your feet or die on your knees</td>\n",
              "      <td>Mr. Andersons' book provides a thorough examin...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 5.0 star review for PowerNomics : The ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>US</td>\n",
              "      <td>12256167</td>\n",
              "      <td>R3NJ8RGXZRKKGQ</td>\n",
              "      <td>007142959X</td>\n",
              "      <td>822648026</td>\n",
              "      <td>The Successful Investor: What 80 Million Peopl...</td>\n",
              "      <td>Books</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>The Successful Investor - William J. O'Neil</td>\n",
              "      <td>This book was written for those who want to le...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 5.0 star review for The Successful Inv...</td>\n",
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              "      <th>...</th>\n",
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              "      <th>195</th>\n",
              "      <td>US</td>\n",
              "      <td>52960913</td>\n",
              "      <td>R1MZ3K8XOMPWEV</td>\n",
              "      <td>0525948775</td>\n",
              "      <td>535890305</td>\n",
              "      <td>The Death Collectors</td>\n",
              "      <td>Books</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>The resurrection of a long dead psychopath</td>\n",
              "      <td>Jack Kerley's \\\\\"The Death Collectors\\\\\" is a ...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 5.0 star review for The Death Collecto...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>US</td>\n",
              "      <td>18755160</td>\n",
              "      <td>R1WIS16TAYHPP8</td>\n",
              "      <td>0515116475</td>\n",
              "      <td>437179605</td>\n",
              "      <td>Mallory's Oracle (A Mallory Novel)</td>\n",
              "      <td>Books</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>Another Success</td>\n",
              "      <td>I started the series with Winter House and am ...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 5.0 star review for Mallory's Oracle (...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>US</td>\n",
              "      <td>51917717</td>\n",
              "      <td>R37OAEJ59YZR20</td>\n",
              "      <td>0385324162</td>\n",
              "      <td>390234841</td>\n",
              "      <td>A Breath of Snow and Ashes (Outlander)</td>\n",
              "      <td>Books</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>Redeems the series</td>\n",
              "      <td>The Outlander series are my favorite books of ...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 4.0 star review for A Breath of Snow a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>US</td>\n",
              "      <td>23711977</td>\n",
              "      <td>R2Q6UPS8ILT1T</td>\n",
              "      <td>0061091316</td>\n",
              "      <td>56455717</td>\n",
              "      <td>Lost Boys</td>\n",
              "      <td>Books</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>A book that reveals the coplexities of everyda...</td>\n",
              "      <td>I read this book mulitiple times. The way Card...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 4.0 star review for Lost Boys: I read ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>US</td>\n",
              "      <td>12069644</td>\n",
              "      <td>R773N4Z0CRDJU</td>\n",
              "      <td>0060736267</td>\n",
              "      <td>892654736</td>\n",
              "      <td>A Tree Grows in Brooklyn (Perennial Classics)</td>\n",
              "      <td>Books</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>Beautiful</td>\n",
              "      <td>This book is so well written that you feel lik...</td>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>Write a 5.0 star review for A Tree Grows in Br...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c794753e-5c01-43f8-a86e-936a2bcdd5bf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c794753e-5c01-43f8-a86e-936a2bcdd5bf 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-c794753e-5c01-43f8-a86e-936a2bcdd5bf');\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",
              "\n",
              "\n",
              "<div id=\"df-a9f6ff76-417b-4afd-afce-3c5195646c30\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9f6ff76-417b-4afd-afce-3c5195646c30')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a9f6ff76-417b-4afd-afce-3c5195646c30 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"marketplace\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"US\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15132409,\n        \"min\": 12065385,\n        \"max\": 53094054,\n        \"num_unique_values\": 187,\n        \"samples\": [\n          23711977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"R3TGTEWOVE8GH2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 198,\n        \"samples\": [\n          \"1872197108\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_parent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 295072946,\n        \"min\": 10090993,\n        \"max\": 991355343,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          334546428\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 198,\n        \"samples\": [\n          \"Hitler's War and the War Path\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Books\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"star_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.168041783516412,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"helpful_votes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.20869263335391,\n        \"min\": 0.0,\n        \"max\": 155.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          51.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_votes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.838459171649326,\n        \"min\": 0.0,\n        \"max\": 184.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vine\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"verified_purchase\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_headline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 198,\n        \"samples\": [\n          \"A book for the ages\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_body\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 199,\n        \"samples\": [\n          \"\\\\\\\\\\\"The Face\\\\\\\\\\\", the first book in this volume, is probably the best of the five Demon Princes novels.  The reasons are manifest.  Villainous Lens Larque provides some of the best challenges and intimidation for our hero, Kirth Gersen.  There's the usual helping of exquisite writing and fine descriptions; the menu items during Gersen's restraunt visits are particularly noteworthy.  Most importantly, \\\\\\\\\\\"The Face\\\\\\\\\\\" is the most morally challenging novel in the series.  It presents, to some extent, the best argument for Gersen abandoning his single-minded hunt for revenge in his abortive attempts at romance with two characters.  Lastly, \\\\\\\\\\\"The Face\\\\\\\\\\\" has a killer ending, laugh-out-loud serious if there ever was such a thing. <br /> <br />Another notable element is the planet Dar Sai and its inhabitants, the Darsh.  Dar Sai is odd even by Vance standards.  Giant umbrellas of water protect folks from searing daytime heat, the women grow mustaches, food is prized for its repulsiveness and gender wars are taken to a whole new level.  Vance scores particular points by showing us the spoiled tourists who visit solely to mock the backwards Darsh.  An just dessert is served for them at the end, of course. <br /> <br />The last novel in the series, \\\\\\\\\\\"The Book of Dreams\\\\\\\\\\\", is competent but less biting than \\\\\\\\\\\"The Face\\\\\\\\\\\".  The new villain, Howard Alan Treesong, is less impressively hateable than Lens Larque or Viole Falshune from \\\\\\\\\\\"The Palace of Love\\\\\\\\\\\".  \\\\\\\\\\\"The Book of Dreams\\\\\\\\\\\" contains vignettes set on numerous planets, none of them particuarly brilliant.  The best parts of this novel are the introductory segments to each chapter.  Short paragraphs cover a wide range of ground, from the hilarious philosophical basis of the Institute to the stunning character introductions for Treesong's various Paladins.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2005-10-14\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 200,\n        \"samples\": [\n          \"Write a 5.0 star review for Goodbye Doesn't Mean Forever (A Bantam starfire book): In Goodbye Doesn't Mean Forever, Lurlene McDaniel tells a heart-felt story of a teenage girl who is struggling with cancer. <br /> Melissa finds out early in the story that her leukemia, which has been in remission for two years has relapsed. Jory, Melissa's best friend promises to help her have the best senior year ever, because of all the stress and pain in Melissa's life. Melissa's older brother, Michael, who is also Jory's life long crush, is asked to donate bone marrow to Melissa, because that is her only hope. Soon after, Melissa becomes well and in a couple of days, the doctors say she will be able to go home. Jory goes to visit her two days later, only to find that Melissa has caught a fever and that her body is rejecting the bone marrow. At school, a boy named Lyle tries to help Jory with all the sorrow of her sick friend. Eventually, Jory falls in love with Lyle, but still has feelings for Michael. Will Jory find true love? Will Melissa overcome cancer? To find out, read Goodbye Doesn't Mean Forever. <br /> I highly recommend this book to any girl ages 12-112 that enjoys romance and drama novels. It is an amazing book and I promise that if you read it, you won't be disappointed.  <br /> <br />\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# We can use Pandas apply to create a new column from create_prompt using existing columns\n",
        "df[\"input\"] = df.apply(create_prompt, axis=1)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KktCESq3z5Ge"
      },
      "source": [
        "As we have discuessed the Attention mechanism has a limited context length due to computational complexity and the trained positional encodings of a pretrained model would not exist for the new sequence positions.\n",
        "\n",
        "If you train a model from scratch it is just a simple parameter - [see here for the config of GPT-2 in HuggingFace.](https://huggingface.co/docs/transformers/v4.26.1/en/model_doc/gpt2#transformers.GPT2Config) The parameter is called \"n_positions\".\n",
        "[\n",
        "Therefore, keeping an eye on the length of your texts is important."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "7tXdkeWGrP4u",
        "outputId": "324f2989-b59e-470d-ce32-08641fdb8a98"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# We have some very long reviews in this dataset\n",
        "df[\"input\"].str.len().plot.hist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYNrYSzL0k0W"
      },
      "source": [
        "Let's imagine we have this dataset stored as a parquet file or need to store it. Pandas has built in Parquet support."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Mh35E-YD2YM"
      },
      "outputs": [],
      "source": [
        "# Write parquet\n",
        "df.to_parquet(\"df.parquet\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_inIKFgOweyT"
      },
      "outputs": [],
      "source": [
        "# Read parquet\n",
        "df = pd.read_parquet(\"df.parquet\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bghDwp5hzgyP"
      },
      "source": [
        "#### Two ways to achive the same result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNL4YPO41Blx"
      },
      "source": [
        "Now, we need to convert the DataFrame to a Huggingface dataset. There are two ways to achive exactly the same result here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O085vrCXy-xn",
        "outputId": "64b0a54e-e2ec-482d-ed62-efc30a7b0376"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['marketplace', 'customer_id', 'review_id', 'product_id', 'product_parent', 'product_title', 'product_category', 'star_rating', 'helpful_votes', 'total_votes', 'vine', 'verified_purchase', 'review_headline', 'review_body', 'review_date', 'input', '__index_level_0__'],\n",
              "        num_rows: 170\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['marketplace', 'customer_id', 'review_id', 'product_id', 'product_parent', 'product_title', 'product_category', 'star_rating', 'helpful_votes', 'total_votes', 'vine', 'verified_purchase', 'review_headline', 'review_body', 'review_date', 'input', '__index_level_0__'],\n",
              "        num_rows: 30\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Option A - use a train-split defined somewehere else\n",
        "from datasets import Dataset, DatasetDict\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Let's assume we have a given train-test split and therefore two Pandas DataFrames\n",
        "train_df, valid_df = train_test_split(df, test_size=0.15, random_state=42)\n",
        "\n",
        "# We can create two individual Huggingface Datasets\n",
        "train_ds = Dataset.from_pandas(train_df)\n",
        "valid_ds = Dataset.from_pandas(valid_df)\n",
        "\n",
        "# And combine them to a DatasetDict\n",
        "ds = DatasetDict()\n",
        "\n",
        "ds['train'] = train_ds\n",
        "ds['valid'] = valid_ds\n",
        "ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHbxJh4FwkVq",
        "outputId": "d6b7c182-f025-494b-8627-8b1eecc15a53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['marketplace', 'customer_id', 'review_id', 'product_id', 'product_parent', 'product_title', 'product_category', 'star_rating', 'helpful_votes', 'total_votes', 'vine', 'verified_purchase', 'review_headline', 'review_body', 'review_date', 'input'],\n",
              "        num_rows: 170\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['marketplace', 'customer_id', 'review_id', 'product_id', 'product_parent', 'product_title', 'product_category', 'star_rating', 'helpful_votes', 'total_votes', 'vine', 'verified_purchase', 'review_headline', 'review_body', 'review_date', 'input'],\n",
              "        num_rows: 30\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Option B - we let Huggingface do the train-test-split\n",
        "from datasets import Dataset\n",
        "\n",
        "# This automatically created a DatasetDict\n",
        "ds = Dataset.from_pandas(df).train_test_split(test_size=0.15)\n",
        "ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aousxtz23HLm"
      },
      "source": [
        "## Using a Decoder Transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can read more about GPT-2 in [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf), which is its accompanying paper.\n"
      ],
      "metadata": {
        "id": "Q3HjwRieJQge"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296,
          "referenced_widgets": [
            "0baad1d80b11449da235e388ce59c413",
            "63932c350b204b4493582b5bd78af36f",
            "41de399025de418bb7b4684874423f96",
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        },
        "id": "YXRQ_FS90_15",
        "outputId": "e8159613-9b5c-44bf-e2ee-19ea2d16252f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45442e91082749459f45ebc506fb9617"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62ff78b35387408998a871c1a80e194b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Loading the Model and Tokenizer is as simple as with the Encoder models\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# We have to pass this model a few extra parameters\n",
        "model = GPT2LMHeadModel.from_pretrained(\"gpt2\",\n",
        "                                  bos_token_id=tokenizer.bos_token_id,\n",
        "                                  eos_token_id=tokenizer.eos_token_id,\n",
        "                                  scale_attn_by_inverse_layer_idx=True,\n",
        "                                  reorder_and_upcast_attn=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJYnWPBuzrrG",
        "outputId": "62edaf32-40e6-409b-c6de-289f2aede95a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPT-2 size: 124.4 Million parameters\n"
          ]
        }
      ],
      "source": [
        "# Remember this code to see the size of a model\n",
        "model_size = sum(t.numel() for t in model.parameters())\n",
        "print(f\"GPT-2 size: {model_size/1000**2:.1f} Million parameters\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMtO_qY331gz"
      },
      "source": [
        "GPT-2 uses a different Encoder than BERT, but the idea is the same. Split parts of a word into tokens and maximze compression on the training dataset. With the GPT-2 BytePair Encoder the split does not happen on character, but on Unicode Byte Level."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laBfHxX8tZ2R",
        "outputId": "dc4da8f0-38db-4b64-e402-dd02c450891d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [12295, 587, 881, 329, 13226, 2106, 11, 475, 262, 3918, 11, 1449, 21319, 11, 290, 3572, 286, 2426, 2300, 1282, 1978, 845, 16576, 287, 428, 1492, 13], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "tokenizer(ds[\"train\"][0][\"review_body\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NHKthYcdl0C"
      },
      "source": [
        "Byte-level BytePair Encoding is very powerful, because it can represent any text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6ureGVJdfFg",
        "outputId": "9c89aa8c-0f6b-4251-8315-e05b3d5a5370"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [8582, 249, 240, 26486, 230, 37929], 'attention_mask': [1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "tokenizer(\"🛒✈️\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4MU5XjZ4McH"
      },
      "source": [
        "Have you wondered how the model learns to stop predicting? There is a special Token for that. Note, that the Tokens of BERT and GPT-2 are not the same."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfcDUTayEMAZ",
        "outputId": "f6544cea-14a6-444c-dd1c-53076e8cc427"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [12295, 587, 881, 329, 13226, 2106, 11, 475, 262, 3918, 11, 1449, 21319, 11, 290, 3572, 286, 2426, 2300, 1282, 1978, 845, 16576, 287, 428, 1492, 13, 50256], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Can you spot the id of the <|endoftext|> token?\n",
        "tokenizer(ds[\"train\"][0][\"review_body\"] + '<|endoftext|>')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kY5oF0xi4oZ7"
      },
      "source": [
        "To keep things fast in our limited time, we reduce the maximum length of the texts to 500 tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3gbVEg3F4w6g"
      },
      "outputs": [],
      "source": [
        "context_length = 500"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X6zjZU284yzJ"
      },
      "source": [
        "It's a good practice to write a preprocessing function that does all the necessary changes to strings and tokens your usecase requires. Here we are truncating the encoded texts and add the <|endoftext|> token."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IjtaoeIzyt9x"
      },
      "outputs": [],
      "source": [
        "def tokenize(element):\n",
        "    outputs = tokenizer(\n",
        "        [item + '<|endoftext|>' for item in element[\"input\"]],\n",
        "        truncation=True,\n",
        "        max_length=context_length,\n",
        "        return_overflowing_tokens=False,\n",
        "        return_length=True\n",
        "    )\n",
        "\n",
        "\n",
        "    input_batch = []\n",
        "    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
        "        input_batch.append(input_ids + [tokenizer.eos_token_id])\n",
        "\n",
        "\n",
        "    return {\"input_ids\": input_batch}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZvoXMDZ5P02"
      },
      "source": [
        "Again, we use the Hugginface Dataset map method to tokenize all splits of the Dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "id": "mLaIVYFCy43L",
        "outputId": "a3ce90ec-aa22-4379-cf68-856fe23e9f25"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/170 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae3cb637bd19475a8f3fb5d364402063"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/30 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9616fad7809544df9e8ae06a606f8043"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_ds = ds.map(\n",
        "    tokenize, batched=True, remove_columns=ds[\"train\"].column_names\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlOj9TBSEKng"
      },
      "source": [
        "## Learn to Use the Hugging Face Trainer\n",
        "\n",
        "If you thought there is no object from Huggingface coordinating the training you are wrong. The Huggingface Trainer is one of the best Training coordinators available and supports a huge range of options.\n",
        "\n",
        "When using the Huggingface Trainer we need a Huggingface Data Collator that connects the Trainer to the Dataset. The Data Collator has two main jobs: Batch the Dataset and apply necessary functions to prepare the batch - like padding - e.g. adding special tokens to shorter sequences so that all sequences are the same length within a batch. This is necessay for GPU usage.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BUduyRc8zI3h"
      },
      "outputs": [],
      "source": [
        "# Of course there is a special Data Collator for exactly his task\n",
        "from transformers import DataCollatorForLanguageModeling\n",
        "\n",
        "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQmYEGGx0Cnz",
        "outputId": "f56a7080-922b-4d13-e1f1-b5f2c342309d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_ids shape: torch.Size([5, 305])\n",
            "attention_mask shape: torch.Size([5, 305])\n",
            "labels shape: torch.Size([5, 305])\n"
          ]
        }
      ],
      "source": [
        "# Let's see the shapes of the first 5 records when run through the Data Collator\n",
        "out = data_collator([tokenized_ds[\"train\"][i] for i in range(5)])\n",
        "for key in out:\n",
        "    print(f\"{key} shape: {out[key].shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6_11haF6fSm"
      },
      "source": [
        "A Huggingface trainer needs Training Arguments that describe the Hyperparameters of the Training as well as the loss computation and all aspects of logging and saving of models. It even offers additional features that make training on multiple GPUs or smaller GPUs easier:\n",
        "\n",
        "\n",
        "\n",
        "1.   Gradient Accumulation\n",
        "2.   Multi GPU Usage via Distributed Data Parallel\n",
        "\n",
        "\n",
        "Training a model on the full dataset might take a long time with Google Colab free tier. After this short training we will provide you with a way to load a model trained by us."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "617aU-wi0GMG"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"gpt2-book-reviews\",\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=6,\n",
        "    logging_steps=6,\n",
        "    gradient_accumulation_steps=8,\n",
        "    num_train_epochs=4,\n",
        "    weight_decay=0.1,\n",
        "    warmup_steps=1_000,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    learning_rate=5e-4,\n",
        "    save_steps=6,\n",
        "    load_best_model_at_end=True,\n",
        "    save_total_limit=4\n",
        "    #fp16=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    args=args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=tokenized_ds[\"train\"],\n",
        "    eval_dataset=tokenized_ds[\"test\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "rdW-0FMj009d",
        "outputId": "6b817d44-85ed-4ab7-b3b5-280203c054c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='20' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [20/20 01:26, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>7.088900</td>\n",
              "      <td>6.461815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>6.709500</td>\n",
              "      <td>6.111723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>6.259900</td>\n",
              "      <td>5.611014</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "There were missing keys in the checkpoint model loaded: ['lm_head.weight'].\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=20, training_loss=6.605344295501709, metrics={'train_runtime': 90.9354, 'train_samples_per_second': 7.478, 'train_steps_per_second': 0.22, 'total_flos': 124592410368000.0, 'train_loss': 6.605344295501709, 'epoch': 3.7209302325581395})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "trainer.train(resume_from_checkpoint=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkViIvgPvQzU",
        "outputId": "ca6c67f2-5e84-47c5-f8e7-11284c559c25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        }
      ],
      "source": [
        "# Let's try the results of this mini trainig\n",
        "# First, we tokenize the prompt\n",
        "tokenized = tokenizer(\"Write a 5 star review for Harry Potter and the sorcerer Stone:\", return_tensors=\"pt\")\n",
        "\n",
        "# Then, we let the model run multiple times though the generate method until\n",
        "# it encoutners a <|endoftext|> or reaches a deined max_length\n",
        "generated =  model.generate(input_ids=tokenized[\"input_ids\"].cuda(),\n",
        "                            attention_mask=tokenized[\"attention_mask\"].cuda(),\n",
        "                            max_length=context_length, temperature=0.6,\n",
        "                            encoder_no_repeat_ngram_size=3,\n",
        "                            early_stopping=True,\n",
        "                            do_sample=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-t9hS58_wLEt",
        "outputId": "d838d66b-f0d3-475e-8a8f-f6cfba04d20d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"Write a 5 star review for Harry Potter and the sorcerer Stone: This is my first Potter book review ever, but I think it is the best. <br /> <br />I'm a huge Harry Potter fan, and I read all the Harry Potter books in the last 10 years.  I read all the Harry Potter books again in the last 10 years, and I'm just now getting into them, and they are all excellent.  I've read the whole series, and I've enjoyed them all, but they all seem to be the same...except for the fact that each book seems to have a different ending.  This book is different, but it is the same.  It doesn't have the same ending, and it doesn't have the same character. <br /> <br />The main differences are in the way the books are written.  There are brief descriptions of the usual things that happen (like potions, potions, and so on), but the main characters are written in very short, almost too short sentences.  This is a lot like a mood book, in that the main characters are very well developed, and the story is very exciting.  The same goes for the characters about the teachers, the teachers' families, and the other supporting characters.  The main characters are also very well developed, and the story is very well told. <br /> <br />The main difference between the books is that the first book in the series had a little bit of a beginning.  This book is a lot more of a story, and it has a lot of detail, and it has a lot of characters, and the plot is very well developed.  The book is very easy to read, and you get a lot out of it. <br /> <br />So, what's the difference?  The main difference is in the way the books are written.  There is more detail in the book, and it is more concise.  The main difference is in the length of the book, and how the characters are written.  The length of the book is actually dictated by the story, rather than dictated by the book's content.  The length of the book is actually dictated by the book's content.  The story is written in a shorter style, and the content is dictated by the book's content.  The length of the book is actually dictated by the book's content. <br /> <br />Now, how\"]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Now we just have to decode the tokens to characters\n",
        "tokenizer.batch_decode(generated, skip_special_tokens=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "77a1bba6fb074af19568e96a4a528895",
            "7e2ab9ba196c41078b1b9560c34752a2",
            "30d48d28f0ef463eb66bd2f8b0bdda88",
            "3150e85a67ff4f18ab898b62ccd352ec",
            "835d8d42d5cf429b9801021029feaa1c",
            "fe5cfceeef2f489d8cbf94f77784cb81",
            "872fc715c8fe42af9eef6a5926aa503d",
            "ff6c1f1f775f413bb9d404405e2872d9",
            "e6559cc579ef4b20b85c0482a963b6cd",
            "e79a161e4287475ea68b7a81a09a4164",
            "25f8100fffee4cdabc30b524eae11757",
            "6626ab9890364e958cc77b90354bb7df",
            "7b3db5d520794948a2136d35cd2f88b0",
            "88f003ac4e92417399edadccc15c37e1",
            "32da117fe2cd413ca5ea2f449ff7dff8",
            "8bb7581b19d94e18a0acd3814bfa1457",
            "2d44409d923f43a4931bb70add72a266",
            "632dfdefbbd844428d1e0b538715049f",
            "e5c4c80ba67048149c74acc75a34b465",
            "feb64bc9f1ac47ba87370860eed8e63c",
            "8eb52f054cec49a4a0253d7163a4f3c8",
            "887c462da0684d1fb67d1234af3fdd82"
          ]
        },
        "id": "KcRxZKMP8nGc",
        "outputId": "2f8dbcd1-c0c2-48bd-c1e7-f4045e12ada0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/936 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77a1bba6fb074af19568e96a4a528895"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/510M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6626ab9890364e958cc77b90354bb7df"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# If you want to, you can load our model here\n",
        "model = GPT2LMHeadModel.from_pretrained(\"till0r/nlp-in-5-weeks-gpt2\")\n",
        "model = model.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XD2gVopbdpGJ"
      },
      "source": [
        "# Using OpenAI's API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZuPMQ-3ioCP"
      },
      "source": [
        "In this section we will make use of OpenAI's API via their dedicated libray."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load an OpenAI API Key"
      ],
      "metadata": {
        "id": "jpeQY1nUkJB8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from dotenv import load_dotenv\n",
        "import openai\n",
        "import os\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
        "\n",
        "\n",
        "# Loads a file and dumps its context into the os environment variables\n",
        "load_dotenv(\"env\", override=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQTlHBw-hI3f",
        "outputId": "25068f22-5fa9-497f-c250-7902c77baab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "client = OpenAI(max_retries=5)"
      ],
      "metadata": {
        "id": "KkfsoYZPVRZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uF9Ydoajd0Gk"
      },
      "source": [
        "# Emulating Week 2's classifier with OpenAI"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## But first, let's do the embedding test!"
      ],
      "metadata": {
        "id": "PWMrW6N-XoHY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_embedding(text: str, model=\"text-embedding-3-small\"):\n",
        "    # replace newlines, which can negatively affect performance.\n",
        "    text = text.replace(\"\\n\", \" \")\n",
        "\n",
        "    response = client.embeddings.create(input=[text], model=model)\n",
        "\n",
        "    return response.data[0].embedding"
      ],
      "metadata": {
        "id": "0ZDIZ3_Dsj4x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One 1536 dimension vector per word ...\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2)"
      ],
      "metadata": {
        "id": "gG48Y12jVsJ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's create some embeddings\n",
        "import numpy as np\n",
        "\n",
        "queen_embedded = get_embedding(\"queen\")\n",
        "king_embedded = get_embedding(\"king\")\n",
        "cat_embedded = get_embedding(\"cat\")\n",
        "dog_embedded = get_embedding(\"dog\")\n",
        "tree_embedded = get_embedding(\"tree\")\n",
        "monkey_embedded = get_embedding(\"monkey\")"
      ],
      "metadata": {
        "id": "lUlE5OOnV5Kw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sub_space = np.vstack(\n",
        "    [queen_embedded, king_embedded, cat_embedded,\n",
        "     dog_embedded, tree_embedded, monkey_embedded]\n",
        "    )"
      ],
      "metadata": {
        "id": "Ag_RHV4XXbsg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One 1536 dimension vector per word ...\n",
        "sub_space.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hj0fycbYV6W7",
        "outputId": "a4b072ff-c81f-4bff-c84c-30d03d811393"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 1536)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ... turns into one 2 dimensional vector per word\n",
        "redcued_sub_space = pca.fit_transform(sub_space)\n",
        "redcued_sub_space.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "408MGbQ2V8BE",
        "outputId": "84db9f26-e05c-4341-89ef-03f9acef408d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A scatter plot is the right choice to visualise the embedding space\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "sns.set(style='white', context='notebook', rc={'figure.figsize':(6, 6), 'figure.dpi':111})\n",
        "\n",
        "sns.scatterplot(x=redcued_sub_space[:, 0], y=redcued_sub_space[:, 1], s=150,\n",
        "                style=[\"queen\", \"king\", \"cat\", \"dog\", \"tree\", \"monkey\"],\n",
        "                hue=[\"queen\", \"king\", \"cat\", \"dog\", \"tree\", \"monkey\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "-bbdvloCV8v6",
        "outputId": "3991d5f3-83c9-4f85-9cb8-41b821c29c56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 666x666 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Zero-Shot Classification"
      ],
      "metadata": {
        "id": "WeO2ljLgZJXO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def cosine_similarity(a, b):\n",
        "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))"
      ],
      "metadata": {
        "id": "OjCDVdB3YvrX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# First let's embed our labels (hint, a neutral sentiment is a good idea in sentiment analysis)\n",
        "labels = ['negative', 'positive']\n",
        "label_embeddings = [get_embedding(label) for label in labels]"
      ],
      "metadata": {
        "id": "56VF6o8hYaWT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def label_score(query_embedding, label_embeddings):\n",
        "    # cosine similarity of the query embedding and the positive label minus the cosine similarity of the query embedding and the negative label\n",
        "    criterium = cosine_similarity(query_embedding, label_embeddings[1]) - cosine_similarity(query_embedding, label_embeddings[0])\n",
        "\n",
        "    return  'positive' if criterium > 0 else 'negative'"
      ],
      "metadata": {
        "id": "7b37y1enZGBJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_embedding = get_embedding(\"Today, I feel totally awesome!\")\n",
        "\n",
        "label_score(query_embedding, label_embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "dU8Q8iRcZg3I",
        "outputId": "17ef25e2-716d-4bcf-a054-db175f2c0eef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'positive'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_embedding = get_embedding(\"It is raining - better take an umbrella or you will get wet!\")\n",
        "\n",
        "label_score(query_embedding, label_embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "0tbl9UKyZ-3H",
        "outputId": "e5179362-0b30-4121-b664-12e5fd38f948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'negative'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generating Text with OpenAI's API\n",
        "\n",
        "[See this page](https://platform.openai.com/docs/api-reference/chat/create) in the API's documentation for more details on the chat completion endpoints."
      ],
      "metadata": {
        "id": "vpShVkBualDW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = client.chat.completions.create(\n",
        "    model=\"gpt-3.5-turbo\",\n",
        "    messages=[\n",
        "        {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": \"You will be provided with text, and your task is to translate it into emojis. Do not use any regular text. Do your best with emojis only.\"\n",
        "        },\n",
        "        {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": \"Artificial intelligence is a technology with great promise.\"\n",
        "        }\n",
        "    ],\n",
        "    temperature=0.8,\n",
        "    max_tokens=64,\n",
        "    top_p=1,\n",
        "    n=2\n",
        ")"
      ],
      "metadata": {
        "id": "ruVWsrFoaopG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQbolKR0bYI_",
        "outputId": "99ed7694-9720-4bb1-a645-82bb81d8bd86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ChatCompletion(id='chatcmpl-9OqkCqu9CHJsdUv9JXzAaBQvcQgIg', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='🤖💡👍', role='assistant', function_call=None, tool_calls=None)), Choice(finish_reason='stop', index=1, logprobs=None, message=ChatCompletionMessage(content='🤖💡👍', role='assistant', function_call=None, tool_calls=None))], created=1715709648, model='gpt-3.5-turbo-0125', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=16, prompt_tokens=52, total_tokens=68))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response.choices[0].message.content, response.choices[1].message.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEs-q0H6baoH",
        "outputId": "17204da1-44f5-42b0-ab83-830cff9a68f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('🤖💡👍', '🤖💡👍')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = client.chat.completions.create(\n",
        "    model=\"gpt-3.5-turbo\",\n",
        "    messages=[\n",
        "        {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": \"You will be provided with a block of text, and your task is to extract a list of keywords from it.\"\n",
        "        },\n",
        "        {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": \"\"\"The Artemis program is a Moon exploration program that is led by the United States' National Aeronoautics and Space Administration (NASA) and was formally established in 2017 via Space Policy Directive 1. The Artemis program is intended to reestablish a human presence on the Moon for the first time since the Apollo 17 moon mission in 1972. The program's stated long-term goal is to establish a permanent base on the Moon to facilitate human missions to Mars.\n",
        "                        Two principal elements of the Artemis program are derived from the now-cancelled Constellation program: the Orion spacecraft and the Space Launch System (as a reincarnation of Ares V). Other elements of the program, such as the Lunar Gateway space station and the Human Landing System, are in development by government space agencies and private spaceflight companies. This collaboration is bound together by the Artemis Accords and governmental contracts.\n",
        "                        The Space Launch System, Orion spacecraft and the Human Landing System form the main spaceflight infrastructure for Artemis, and the Lunar Gateway plays a supporting role in human habitation. Supporting infrastructures for Artemis include the Commercial Lunar Payload Services, VIPER rover, development of ground infrastructures, Artemis Base Camp on the Moon, Moon rovers and spacesuits. Some aspects of the program have been criticized, such as the use of a near-rectilinear halo orbit and the sustainability of the space program.\n",
        "                        Orion's first launch on the Space Launch System was originally set in 2016, but faced numerous delays; it launched on 16 November 2022 as the Artemis 1 mission, with robots and mannequins aboard. According to plan, the crewed Artemis 2 launch is expected to take place in late 2025, the Artemis 3 crewed lunar landing is scheduled for late 2026, the Artemis 4 docking with the Lunar Gateway in late 2028, the Artemis 5 docking with the European Space Agency's ESPRIT, Canada's Canadarm3, and NASA's Lunar Terrain Vehicle in early 2030, and the Artemis 6 docking which is expected to integrate the Science Airlock with the Lunar Gateway station in early 2031. After Artemis 6, NASA expects yearly landings on the Moon to occur from then on.\"\"\"\n",
        "        }\n",
        "    ],\n",
        "    temperature=0.5,\n",
        "    max_tokens=64,\n",
        "    top_p=1\n",
        ")"
      ],
      "metadata": {
        "id": "OTeVfmePcCNu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(response.choices[0].message.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BInIgBrycekr",
        "outputId": "4dfa4d14-1848-453f-b88c-e9ecdcc65177"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Artemis program, Moon exploration, United States, National Aeronoautics and Space Administration, NASA, Space Policy Directive 1, human presence, Apollo 17, 1972, permanent base, Mars, Constellation program, Orion spacecraft, Space Launch System, Ares V, Lunar Gateway, Human Landing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Putting it all together - RAG (Retrival Augmented Generation)"
      ],
      "metadata": {
        "id": "xJdKQTZrgTKk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The RAG pattern is older then you might think - see the original papaer from 2020:\n",
        "\n",
        "[Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)\n",
        "\n"
      ],
      "metadata": {
        "id": "kKWa_WabJut_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Please download a Plain Text UTF-8 a copy of [The War of the Worlds by H. G. Wells](https://www.gutenberg.org/ebooks/36) and upload it to your Google Colab instance.\n"
      ],
      "metadata": {
        "id": "JWRU-2ojFQtJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Embedding the book"
      ],
      "metadata": {
        "id": "COn3Zj-PU74q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First, we need to read the text file\n",
        "\n",
        "file_path = 'pg36.txt'\n",
        "with open(file_path, 'r') as file:\n",
        "    content = file.read()\n",
        "\n",
        "\n",
        "print(f\"The book has {len(content)} characters!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkKOnnbXiGrE",
        "outputId": "e9a23533-93af-4e7b-c6c6-50c2e1c6f28c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The book has 356668 characters!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Second, let's chunk the book into windows of length - note the overlap\n",
        "\n",
        "def chunk_text_by_characters_with_overlap(text, chars_per_chunk, overlap):\n",
        "    start = 0\n",
        "    while start < len(text):\n",
        "        end = start + chars_per_chunk\n",
        "        yield text[start:end]\n",
        "        start = end - overlap  # move start up by chars_per_chunk minus the overlap\n",
        "\n",
        "chunks = []\n",
        "for chunk in chunk_text_by_characters_with_overlap(\n",
        "    content,\n",
        "    chars_per_chunk=3000,\n",
        "    overlap=400):\n",
        "\n",
        "    chunks.append(chunk)\n",
        "\n",
        "print(f\"We have {len(chunks)} chunks with a mean of {int(np.mean([len(chunk) for chunk in chunks]))} characters!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nrj5ucEzTK8v",
        "outputId": "0615cb58-867c-4430-8e85-8e0bd33cf338"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 138 chunks with a mean of 2981 characters!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, let's embed these chunks with an OpenAI Embedding model\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "embeddings = []\n",
        "\n",
        "for chunk in tqdm(chunks):\n",
        "    embeddings.append(get_embedding(chunk))"
      ],
      "metadata": {
        "id": "uqOlljueiL_G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e4bdf4b-c7ae-4286-cfb1-1fb2c1816136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 138/138 [00:31<00:00,  4.45it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(embeddings[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzbfvVlOU3NY",
        "outputId": "02044e82-9f4f-4a15-c6c9-8e4895758cbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1536"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from annoy import AnnoyIndex\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "dim = 1536\n",
        "unique_id = 0\n",
        "\n",
        "# Setup the index\n",
        "t = AnnoyIndex(dim, 'angular')\n",
        "\n",
        "\n",
        "for embedding in embeddings:\n",
        "    t.add_item(unique_id, embedding)\n",
        "    unique_id += 1\n",
        "\n",
        "t.build(10)\n",
        "t.save('test.ann')"
      ],
      "metadata": {
        "id": "vo_o4iPugS7N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8e9dc1a-6772-47c7-f772-a2aa5adb9c7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Answer questions with knowledge from the book!"
      ],
      "metadata": {
        "id": "77NoMw3hWLtk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First, we embed our question to find the most relevant section in the book\n",
        "# This is so much better then just using keywords\n",
        "\n",
        "question = \"What weapons do the invaders utilize?\"\n",
        "question_embedding = get_embedding(question)"
      ],
      "metadata": {
        "id": "r3hEE6E5Wj6Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Second, we retrieve the relevant chunk\n",
        "result_idx = t.get_nns_by_vector(question_embedding, n=10)\n",
        "result_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-e1pohlW5qZ",
        "outputId": "d79dd80d-d007-4e17-8cc9-b7828e09e682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[57, 58, 128, 44, 77, 24, 62, 40, 86, 35]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_hit = result_idx[0]\n",
        "print(chunks[first_hit])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkBTki6nXAMX",
        "outputId": "dd73ee79-9ee4-4379-e931-c4db690b44d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "other was\n",
            "watching the fugitives stream over Westminster Bridge, that the\n",
            "Martians had resumed the offensive. So far as one can ascertain from\n",
            "the conflicting accounts that have been put forth, the majority of them\n",
            "remained busied with preparations in the Horsell pit until nine that\n",
            "night, hurrying on some operation that disengaged huge volumes of green\n",
            "smoke.\n",
            "\n",
            "But three certainly came out about eight o’clock and, advancing slowly\n",
            "and cautiously, made their way through Byfleet and Pyrford towards\n",
            "Ripley and Weybridge, and so came in sight of the expectant batteries\n",
            "against the setting sun. These Martians did not advance in a body, but\n",
            "in a line, each perhaps a mile and a half from his nearest fellow. They\n",
            "communicated with one another by means of sirenlike howls, running up\n",
            "and down the scale from one note to another.\n",
            "\n",
            "It was this howling and firing of the guns at Ripley and St. George’s\n",
            "Hill that we had heard at Upper Halliford. The Ripley gunners,\n",
            "unseasoned artillery volunteers who ought never to have been placed in\n",
            "such a position, fired one wild, premature, ineffectual volley, and\n",
            "bolted on horse and foot through the deserted village, while the\n",
            "Martian, without using his Heat-Ray, walked serenely over their guns,\n",
            "stepped gingerly among them, passed in front of them, and so came\n",
            "unexpectedly upon the guns in Painshill Park, which he destroyed.\n",
            "\n",
            "The St. George’s Hill men, however, were better led or of a better\n",
            "mettle. Hidden by a pine wood as they were, they seem to have been\n",
            "quite unsuspected by the Martian nearest to them. They laid their guns\n",
            "as deliberately as if they had been on parade, and fired at about a\n",
            "thousand yards’ range.\n",
            "\n",
            "The shells flashed all round him, and he was seen to advance a few\n",
            "paces, stagger, and go down. Everybody yelled together, and the guns\n",
            "were reloaded in frantic haste. The overthrown Martian set up a\n",
            "prolonged ululation, and immediately a second glittering giant,\n",
            "answering him, appeared over the trees to the south. It would seem that\n",
            "a leg of the tripod had been smashed by one of the shells. The whole of\n",
            "the second volley flew wide of the Martian on the ground, and,\n",
            "simultaneously, both his companions brought their Heat-Rays to bear on\n",
            "the battery. The ammunition blew up, the pine trees all about the guns\n",
            "flashed into fire, and only one or two of the men who were already\n",
            "running over the crest of the hill escaped.\n",
            "\n",
            "After this it would seem that the three took counsel together and\n",
            "halted, and the scouts who were watching them report that they remained\n",
            "absolutely stationary for the next half hour. The Martian who had been\n",
            "overthrown crawled tediously out of his hood, a small brown figure,\n",
            "oddly suggestive from that distance of a speck of blight, and\n",
            "apparently engaged in the repair of his support. About nine he had\n",
            "finished, for his cowl was then seen above the trees again.\n",
            "\n",
            "It was a few minutes past nine that night when these three sentinels\n",
            "were joined by four other Martians, each carrying a thick black tube. A\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finally, assemble a conversation for the OpenAI chat API"
      ],
      "metadata": {
        "id": "lgdQfJS4WRS8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = client.chat.completions.create(\n",
        "    model=\"gpt-3.5-turbo\",\n",
        "    messages=[\n",
        "        {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": f\"Answer the question based only on the following context: {chunks[first_hit]}\"\n",
        "        },\n",
        "        {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": question\n",
        "        }\n",
        "    ],\n",
        "    temperature=0.5,\n",
        "    max_tokens=64,\n",
        "    top_p=1\n",
        ")"
      ],
      "metadata": {
        "id": "QfT2Zd-2WjVU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response.choices[0].message.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "ANvlxiuNXzTg",
        "outputId": "acfca03e-2ab5-4f68-ba72-cd320b2033ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The invaders, Martians, utilize Heat-Rays as weapons. They also carry thick black tubes, which are likely some other type of weapon.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Summary\n",
        "\n",
        "\n",
        "We hope you enjoyed this course - let's quickly recap what you have seen in these three weeks.\n",
        "\n",
        "\n",
        "\n",
        "1.   A short recap of NLP history and the problems each \"epoch\" had\n",
        "2.   Symbolic and Statistical methods to work with language\n",
        "3.   How the idea of a word embedding changed everything\n",
        "4.   How transformer models work on embeddings\n",
        "5.   Encoder and Decoder transformers\n",
        "6.   How a transformer can be task specific\n",
        "7.   How generative transformers can work around task limitations through in context learning\n",
        "\n"
      ],
      "metadata": {
        "id": "Kb6GNzfj5oN4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Where to go from here?"
      ],
      "metadata": {
        "id": "K99y17qJYIWH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   **What is temperature?** You can play with temperature in the GPT2 example or OpenAI examples above. It is a very simple mechanism that shifts the propabilities of a model's output. This is done by manipulating the model's internal scores per class per token by simply dividing them by the temperature value (0 means no division). This way the higher the temperature the more random the next token becomes.\n",
        "2.   **What are LoRA and QLoRA?** You might have heard about Low Rank Adapters or Quantized Low Rank Adapters in the context of fine-tuning larger Open Source models. In general these two aim to reduce the number of parameters that need to be trained and therefore memory consumption and compute needed by introducing new sets of parameters that can be \"upscaled\" for inference.\n",
        "[See this link for more details on this topic.](https://lightning.ai/pages/community/lora-insights/)\n",
        "3.   **How do I run LLama models on my computer?**\n",
        "The fastest way to get up an runnining is [GPT4All](https://gpt4all.io/index.html) if you need a frontend or [ollama](https://github.com/ollama/ollama) if you want to build your own or get fancy. Both build on [llama.cpp](https://github.com/ggerganov/llama.cpp).\n",
        "4.   **What is LangChain?** [LangChain](https://www.langchain.com/) is a controversial wrapper-framework that tries to bring common tools used with LLMs together in a simplified programming interface. I recommend you to use such tools directly to increase your understanding and make your solutions more flexible. Instead of following a LangChain tutorial, try finding a similar one in the [OpenAI Cookbook](https://cookbook.openai.com/).\n",
        "5.   **How do I finetune smaller models?** As you saw in this week or follow the great [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/) included in your Safari subscription.\n",
        "6.   **How do I finetune bigger models?** This is a constantly changing topic - a good start is either the [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) initative or the [HuggingFace Blog](https://huggingface.co/blog/ram-efficient-pytorch-fsdp).\n",
        "\n"
      ],
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        "id": "EHftAVYUYL52"
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    }
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