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        },
        "e4dadd15f1594efdb063e34c6fed0b7f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
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            "_view_module": "@jupyter-widgets/base",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Goal:**\n",
        "Build a QA system over a small document collection.\n",
        "\n",
        "The system will evolve from:\n",
        "\n",
        "* naive keyword retrieval\n",
        "* to embedding retrieval\n",
        "* to chunk-based retrieval\n",
        "* to top-k retrieval\n",
        "* to reranking\n",
        "* to final answer generation with context\n",
        "* to more advanced RAG improvements"
      ],
      "metadata": {
        "id": "GsZHJ4ykSPhK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Core pipeline**\n",
        "\n",
        "A RAG system usually has:\n",
        "\n",
        "*   Documents\n",
        "*   Chunking\n",
        "*   Embedding model\n",
        "*   Index / retrieval mechanism\n",
        "*   Retriever\n",
        "*   Optional reranker\n",
        "*   LLM generator\n",
        "*   Prompt template\n",
        "*   Final answer"
      ],
      "metadata": {
        "id": "Co8QDL3yTLI7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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eRuvck04/ozHk7FAZvFj6x4bH8fj1fD6OK/wb7s+67Gkca/llZSPldfyoV8x9s9SpqQ6hdfZHCLmb/OAcNyfll+xclgfC+tAfPssPa53Uvyw3sXih3UN08lyPM6DMoZ9P62PWNpJ7Rf2t/heDHmUmhel1SVpfhW2RdLvVs54Ppd2XZxHOfep4opAJQRsnEcfjMfh8VwijGO/x9eEcyzimJvF+IvnJLHQ0gqNkUJDpCQRMcmwKTQ8sjGaffXM33FZQl+1YX5W9jQfv1l86prxk33tT/6WblJZYgZJQmzSsTCtkG3IkjgYndZi+O6779xHH33k3nrrLTd8+HD3wgsvuPfff9+PPXDdQLk///xzvwiz9957u8svv9xXg3dcJjG5LqGiAi8PbessN998s9tll138DYGgi9jboUMHd/zxx7v99tsvkV9DBmKlJhC12FAqUz4ErL+kreLa4DDJSjtesS0VJxbrbSBiorgNvGzgEq/GxmJ2koBdbGU4TD/Mw34vll4Y33wrGTubpMQTlHilN8vKb9JqfFiuOK9wsBrWI+l4zLfUALLU+XiwbS9jG1AnDXYtzTDtMJ1ieebT45WKCIhASyZQjoCZhZNNULLETYpT6fX2zMxrHJc3n4ZwKTFMzpxkFuGzVGKVplHp9Wl9plS5dV4EREAEshJIM2Dg+nAhNuvv4SJ+KOKVSi88H5Ypvi5cRLff42PF/jaByuqT9HeYbphWbDgQXps1ntUnFCfDdEnH/oXpF/vC0YS7hlyXNV0T80xYTbPSTBMDKWNY51hsTBJYk0TZrP1a8ZongawL1uHzKonE6NGj3VVXXeWuv/56b+U7//zze0te9jXr0qWLGzp0qJtrrrkyWfJmsuClELhjQMhFVcb9AqrzRhtt5O69996COm5WoLG1UC02ZyUD9kquLXdwncdkKyv/pIlUfCyvSVvWMimeCITWLkmCsgm3pQTsLJ+OZRG000RtWzAw4Trp8ywTx5ME/aQFhCSRP1xYsLKE8cLz4SJHWG4ra9i7QoubcLEgfHGF1iGxpYgJO1mPh9Yjdq2xMyuQ8HiYfizCh1ZHcVr2d9rxJEG/lNVMUrnCdCgPz0p8d7HRaPv27etZRhn38F0Sl4M4MaOQS/h7XMfw77Tfk/JOSsfKYROAYmUqJ82sccN3pvUt6l7Je1hPVREQAREoRsDGuuEYPBwPJ/3ekPPhNSZCUa5QkLJy5pWnPcvDn+HvSXWP5yKx2JWWVtK8IukrFbvexJPwb/s9ZBILe3H5Q6HMeCYJdDZfjfMrdjwUCC3NUunEeSfFj9s8rGMY39rHhLawH5uQlSTyxVZ+oQAXinnFxLUkwTCMnybmFbsu3lja6h0LpWHaxeJYveI4YX3Dc2HZkuIkcdbTUwQai0CSeJg0/m2sMXG5OpbNKyrRj5LeI1n5V3Jt1jwaEs/m2fCETbyR2tlnn+21V9w4LLzwwt6odoYZZnBvvPGG69SpU0lL3qICr2V64YUXugEDBnirXQoxZswYd/jhh7szzjjD1wnn0uEDuiEV1TVNj0DWFYtKalbJA6uSayspc61dm/ZwK/XQq+RhXGsMVB4RmFoEeD/m+YmQDQps0BQLw+GCiNU5vobjxURsSzOOk5RnmqAfPn+z5pWUflr9LG657Zq0iBCnkeXdkRYny7VZ3p2l0il2vti5UoPzYmWrpExhX0xrs1JcGlrntPyKvQMreT+WupbylIpT7Hypd3MsxoX1b+h4IEuZiVOqbMXSKcWk2LWx+FfucyEU4EIBLcwzSVRME/bS0guPlxIHk0TBUFy0smXJy+KmiYhxXlnaolzGii8CIlA5gcZ+T1Ze4tpLoZLnWyXXlkMiy7u0nPQUt7YJmKFWuKCEL96ddtrJPfjgg17kHTdunLfgxciWeKbTJtUsVeA1Hw8jR450K6ywgjcTZpKKuHvYYYe5M88806eX1RdEbWNV6URABEoNGqY2oVLiQkPKV400G1KOcq8pJdKUm14xQSBLWg0d8DT0OsqkwU+WllEcERABERABERCBLAQaOg6uZCxZybW1NhbMwrhacSoZTxYrU7XSzYuDxsJ5kVQ6IlAbBOyLWrPqPe+889zBBx/sFlpoIa/DshfaNddcU7SwiQJvqAgvueSS3ix43nnndR988IHbdttt3W233eYTlbhbGx1BpRABERCBlkagoROxxubE5K2xJgiVTBQbi0tTKGNjsVA+IiACzY9AYz3vKyXXVMpZaT3LuV5iWTm0FFcEREAERKBaBEKdFd+8e+21l1twwQXdJ5984l588UXXu3dvvwlb7N6B8iQKvBbZEltsscW8s182VCNRibvVakqlKwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0BIJmFEMC7IHHnigw21u27Zt3corr+wGDx7skSS5aphC4A0jsWPbN99842addVZvEvzss8+6VVdd1fvczdOnYEtsMNVZBERABERABERABERABERABERABERABERABERABEIC4Zeg3bt39z54J06c6EaNGuW6du2a6FFhCoHXrHcRc1dffXWHyIu4u8kmm7h77rlHxEVABERABERABERABERABFoQAT4XJEw77bQtqNaqqgiIgAiIgAiIgAhMPQKmzz7++ONuww039KLuscce6wYOHJjopiFV4N17773d1Vdf7R36fvzxx+7NN990Sy21lPzuTr22Vc4iIAIiIAIiIAJNjIBtmMAnVvyTn8cm1oAqrgiIgAiIgAiIgAiIgAhMZQJ9+vRxw4YNc8sss4x74403fGliNw31BN54czXcM/zyyy+uV69e7umnn57K1VH2IiACIiACIiACItC0CbASHwYE35Yu+ib5EMuzlUmff0mbUeSZT3NMy9rmxBNPdLPNNpvbf//9XWNuHtkcmapOIiACIiACIiACIpCVgFnxnnnmme6II45wbdq0cW+99ZZjr7R4s7V6Aq/t1saGavPPP79bYIEFvPXujTfe6HbaaafUndqyFkzxREAEREAEREAERKAlEDBhbPTo0X4/g549e6buX4Bgxv4GfP7ekj6Bh5FZNjMGtXFoc7V0NjcHtHdTEfatH+P7DWsRa7Nqi/It4RmhOoqACIiACIiACIhAKQIm4o4YMcJvssacgU3XDjjgADd58mQv+FqoJ/Dahc8884xbc801XadOndyECRPcF1984dq1a5e4S1upwui8CIiACIiACIiACLQ0AiaALbHEEu69995zM844o+vRo4fr3LmzW3TRRb3bK85169atHpqWKJzxtRh8wsCY1KxuG2rhbCwRVF955RW3/PLL+yzKYcy1CLPhNQjQeQjxVkcTextaz2reW1bvTTfd1D344IPu22+/dR07dpTLtmpCV9oiIAIiIAIiIAIi8F8CZgDx22+/uXnnndd9//33brvttnO33HLLFOOxegKvqb/nnXeeO/jgg90ss8ziJyNyz6C+JQIiIAIiIAIiIALZCdhg7OSTT3bnnHOOm3XWWd1nn33mv4aKw6qrrur23HNP/7VUuQJk9hKVjmmuDKptXRpa7u6+++5u0KBB7rjjjnPrr7++dwsWi6cwK1dUDfPYYost3H333eeuvfZat9tuu2X6Ig1hl3xbt25dGlyJGNYXEJn32Wcf179/f8deF3GwvpHE39qmXA6VFD4UtTH8wABk7Nixbp555in042r3lUrKr2tFQAREQAREQAREoKkTCMdj+N99++23vdECFr3xvCHRgvfAAw90l112mVeDmZgceeSRmQbDTR2cyi8CIiACIiACIiAC1SLA5ggLLrigO+WUU/zi+TvvvOOGDh3qRo4c6bPEuveuu+7yg7ap7ee0mvnbQPXXX391HTp08HXFyMACjNZZZx23ww47uNVXX71wnHFpVvcNobUDX6SNHz/eXXrppW7fffed4nO2sL2T/PXimuCFF15w7777rhs1apTDBxouNyyPUv3FvpDbeOON3cMPP+yj4zdttdVWc7179/Z1ZFPjMMQ+1UrlUY3z4YSCcg4fPtzhxm3OOeesl101+0o16qU0RUAEREAEREAERKApEbAxJ8YQgwcP9uNG3OkS6n1lVjco+8sqZhdtv/327v7773eYAA8ZMsStssoqEnibUuurrCIgAiIgAiIgAlOdQCwWzjzzzG699dZzd955Z72y8anV8ccf7y666CJ//JFHHnEbbLBBo4m8oeCK8LzhhhtOMWDME2Y4EF122WW9ZXPXrl39Jl4IsQipP//8s88SMRGr28MOO8yfzxpMIH3uuefc2muv7f2VIZ5vtdVWqWPaWFS94YYbHF+1vf766z7btm3bukmTJnkLVixZ40F1WtksXT6l22WXXfw+Fz/88IP/ZwGXHVtuuaXbeeedvQuPeGzO3wzoV1ppJf+FXVZxOSuvpHhhOy299NLeYgTXbe3bt3eI8/QVJhpY8Zbj9qKSMulaERABERABERABEWhpBGwsydd+N998sx+Lfvrpp34D4XBMWM+C1wZnDISff/55N/300/uLGmsg2dIaSfUVAREQAREQARFo3gRs0GUba2HFizUoFqtYo4YuABDwTDBDQJxrrrlyFfIY59m6vlnChsIcriKwKMZylsFjtYJZfJ511lnuhBNOcOPGjfOiYRgYh1555ZW+HFbGm266yWGEUE5AMEXYxWgBQRJr2VjIDd05kDZC+1FHHeUmTpzos1p44YXdv//9b/877Xnssce6gQMHNkjUpM6HH364H5jD+euvv/blYr8LCxhWXHDBBW655ZYriPy48Lj66qvdiiuu6C1pCdUWVcP08fmGuM1iBCGpr1S7POW0u+KKgAiIgAiIgAiIQHMhYGPXQw45xJ177rne9RtjU8bPJQVePhljYM0nch999FGjDCKbC3jVQwREQAREQAREQASMgA26EHRZOMfH7EsvveRPm9AZ+pg944wzvGuss88+2zGIy+NTfdvMK9xl1/I34Zm/2ZkX8ZmAxSyuCJIsRUOhuNzNxkIRcI455nDffPONe+CBB9wmm2ziBdUZZpihng9eNmCDyWmnneatcHEddswxxxTtYMYMy9sVVlihYHGLD1wshkOmYXmoO4LwmDFj3HzzzeddaWy++ebe0OHVV191P/74o1t88cV9euUG2+fiiiuu8H54seS97rrrCsm8+OKLXoh+9tlnfV6EE0880fsmJuyxxx7ehzABVw9seEaIRdVQwM9axrT2DNNmAkF72eeALFQMGzbMZ0E/pb/mYVWctAiRtR6KJwIiIAIiIAIiIALNkYCNXf/1r3/5sfDss8/uBV42KS4p8NoAn9V5BppJA8jmCE11EgEREAEREAEREIE8CdigC+tHBmFYZZqAxzk+b0eUw8IUFwCPPfaYd89w9NFHe4ExSeA11w+UE0vcNL+0aT5rESoRLQnkHYq8WNRecskl3g/wo48+Wm8MmOSf1gS5rEJvKBqSB6Ir4i2itgXKgwsAPjszC2dEcSxbEXkRYDFCSBIUQ3+wNp7Ft/Hnn3/ujRb43a4Ly4KYfcQRR/gimIVuWj9oiIhq7YhoC+Mbb7zRb6pHv6Ddw/Dmm2/6jdhefvll3wfoC4TLL7/ci93t2rVzr732mmdjdeAnedAO9Ck48XuxdklrT/KK3S6Q5yKLLOIom4WkvsK5uN9Z2WxDNn4mBcofb9qWdCzP+1NpiYAIiIAIiIAIiECtE7BxJONCxql85Ye3BYw3Mgu8WC3ce++9uazI1zowlU8EREAEREAEREAE8iYQC7zdunXzvkzTgm3Edc8997gttthiCoE3zUoy3ugq/Pv99993+H994okn3IcffuitZhFXERAJWT6tj9NHMOXzMIQ/QjkbbVnc7bbbzt1+++1+34dNN93UnXPOOW6NNdZwPXr0mALPM888430DI/wWE3htAGyWsqeeeqoXdq+55hrvBgFXAyasI4wT2FwYlwiIi3DfbLPNpsiffC0gmiI+2/Wl+kzoAoJ6Yn377bffuo4dO6ZeiqC91lpreatmBvBsFJcWirVfmsgfX8A0C+wAACAASURBVIMfZCx0saC29jThP3YvkqWtLU6WuHH/wf8y/onxVawgAiIgAiIgAiIgAi2dgI1vzdUX41kseDP54DWLBwahDLrz+OSqpTeI6i8CIiACIiACItDyCNgYip9Yanbv3t2NGDGiHojvvvvOW8viFuHLL7/0fmYRZAmhhaZZ63IcgRXrTzYeM6EwFtWw4txrr73quQKwjcIQzxD14jzIj8FiKMyF48Dzzz/fbzzGtYiBuFa49dZbC5bIWUTPcFPf2267zfNg8y72gMCNxe677+59vOLvlTpi8Ww+gbH0xeI3KVi6CKJY+FI+XDzsuOOOnifMcL0QWkWTFhayWDQjgOPSwZhTJnwh45ahkhCKqQsssID3v4wrNKwvcH3w1VdfeRF61KhRfoM5XB/gXoK69+3b18cl0J5Y7YZW12HalJ/2gQO+nOHIxn5hG8e/Y1GMCE6ZCKHrCONk1uekST81q23KYX0ltAJHDDeh2LjxSSG+lhHeY+tgqwPtgyuKxx9/3F9G38bPXL9+/SrBr2tFQAREQAREQAREoEkTsDGZfXGGu7BPPvmkPAteBu34RZPA26T7ggovAiIgAiIgAiIwlQiEAi0Wkghi+M3CQnHOOed0WCuy0RZiIn/z2fu+++47hShnxR8yZIjbddddC5t+cRxRFCvd0P0Ax3EHgVCIqIhIhmUqFsSIbybElrKwDAVE27nXxLeffvrJC4NY3bJRGCGLNXAs8L711lt+rIn4jQA7fvz4guBo9YYNm64lWdcSJxyrLrnkkl4sNctghEk2sCNd2OMOA2YjR470gi4WuVgI4wIiFH+XWGIJ995773nfxOTLOQRj2gzhsdz6It6zYVuHDh18GpQZwZa2jwN9BQtnxPQs7i/+7//+z28OFwaE1Isvvrhef+K8uUhYc801fb1xvbDlllt6y2Lqi8jKhm4mKGPxTXlM/E3zy2zCLa5IYIngTOB3E6nZLI52tH5naSHys4EcgS8IyQ8fxZSBaxG68/BHPZUeA8pWBERABERABERABBpMwMZAtlcHFrwIvIwjM7tokMDbYP66UAREQAREQAREQATqCZ5zzz23F6lMGDU8WI8iOK677rreN2taYJMtxDcCYmuXLl28qwWsNnG3gICJgEwgD9LFNxcC5ZNPPlm0NWxwiIhHGbGYDQeMZgXbtWtXd/3113sxjk/DyB8BkM23FlpooUxGASYCb7311u7uu+/2+z1gscumEVh62qf5bPqLCIkVKu4qsgidCNkwPOCAA9yFF17o64xrByxPEdVhYvUy4XfgwIHen1noTxhhESGcY0lhv/32876KsxhB2KD8qaee8lbKM800kxd4aXMsqhEv4Yr4juUxIjA/LVgefJbHRmy2SZ+lizEGAjTc2JQNq47hw4d7lxdY09I/BgwYUK8vbrvttu6OO+7wiwWDBg0q5IU4jkUxLiQs0M6UhzRIyzaM22233bxYjnV0yMFcd2CRa7s9MxGhLFirmyV1KCAj7JMu/QGxmYA1M0zYVI++IYFXD1QREAEREAEREIGWSCAXgXejjTZyDz30UKbBa0uErDqLgAiIgAiIgAiIQDECoUUrIhnWuoh8WIdiuYnoOHr0aPfOO+/4T/IJiHR8Ok8w4ezOO+9022yzjbfSRbxDLLbAp/iIdGzCtffeexcEOARFxnKIcAhsbMyAoGch3MDKyolgi0Ut11hACEQQRDjEXUKY90033eRdLJiYWsqCNzzPZnJsKof4ithKuO+++7xLBSxlEYCpt4U0MdWOIzxTPwTdhx9+uHAdBguMZxEQbdM2xHBERQRWBEysai0dsy4lPdwFcA3CJLywwkV4xDqadiCUqrMNyrH6RfBEhEbIRmzGUjUtxK4QEJUvu+wyL94jElu6WLxirUxZ11lnnUJybIi20kor+X6FJfeyyy7rzxFvvfXW8/3QrGw5jm9jrHl79uzpFwws/TfeeMNbVx988MFeNDZhFpGftrdN5yxjXCuQBvFp46WXXtpvzmZWxvQZ2tjSscUDFjAQjS2Q16GHHur9J9PHJfCmdhWdEAEREAEREAERaMYEchF4bYCcxTqhGbNU1URABERABERABESgQQRCFwi9evXywhmfVCHUhgF/swidCH9Yw2JRimUpActHrCIZj+GrlQ23zM0AouWiiy7q/ZviKgFXBGFgYzFcPvD5PQEr25NOOslbAFsILVdNbAvFULO0xap3hx128EIplsL2qX85YEIxFPcIuEkwq00T/LAgRbQ0IRs3AvjBTRJSbYz6wQcfuKWWWsq7YECQRDhFxEWQZhM1OCA89unTx1snm3sGrGVhSsgy3sXC+YYbbiiInVlER4tjm7mFYiv5Ui8T240lbI2vXc9GcKTBggBCrAX6Ev2BPkCgfbiWNrJr8MWM/1tC7969PVvcdGA5jIU3IrD5OTaLavOl+8ILL/g41iet7yGmI4JTPrOu5hw+oVlQYDGDTfhwH4KvYSyPEZyPOOIId/rpp/uy0G6UgbLyj/PLLLOMb58XX3zRtzvuNoxTQ/pcOf1TcUVABERABERABESg1gjkIvCy6v7II49kGvDWGgCVRwREQAREQAREQASmJgGEO/vEH/EKa1rGVWaxioCGC4VQtEKcw/UClqK4PsA9wb333us/W0dMw/oT61b8nBKOOuooL5YhaPLZP+IwFraIlbYJlglpWH/yiT2Bz/kRes1i0sRVPodn0y0sMxFgCSbEYtmLiEpcC5YPVq4IhqXcKIQiLYI0PoUp1z777OPT5XrjgTUy/lpxZQAz4ofBBrv8pFwwQwhH2IWRBRgjNMIFH8dYQuOCAGF04sSJ3ooYi1Z4WXtRhnDDOITL/v37ewtjOOMWAcvgLAKv1dksiY0jaZpQXmxzOsuDDenYgM/6AfWj/Fgic/3bb7/tfyc/+hEuJtgMDx/MuLuANUI3bY/gS1pYxmK5S0BMxY8vlr0Ey5dN3xDGzYLXBF7reyxaYPlNgD2iOXljafzaa695a3WCbdZGnljlEswtBxbXxKV9TOymz2PVizhcyld0vY6hP0RABERABERABESgGRHIReBlsMugt9SnZ82Im6oiAiIgAiIgAiIgAlUhsPPOOzs+T2cDK3zpIvAi8CFeIZSaFSNWsrfeemvBNy0CKJ/nX3rppYUNsyigfcJOuoh2p512mrfY5FP4tIBbCCyDzz77bB+Fz+yx2EREJVA2NsLCncPRRx/tj5kFL35wsV7FPUAlIfT3S3pm0RkKtuZLGD+3+++/v88O37pmeRxa26611lp+kzfEYY4jMOIWAJcE+DXGfQRuJrAEXn311QvjWrNWxiIa0TQWkMkT9w3kCw/aCwth8hk7dqwXokuNkcPzWKdixYqFMVaqWcRhymDxqCN1xV0GYi+BTdHwU0tZsFDG/QJpW8CCGXcJCLAIsbaZGa4irA/QJxCIEVItP37SLxHu2eSMjdLoZ7SXCbxs4IavY0RaFgYIiNf0KcRc3IzgbsQWDzjPQgQb5uGShICwj2UxVr6LLbaYP4YF91xzzVWoA2I1zCmjLHgrufN0rQiIgAiIgAiIQFMkIIG3KbaayiwCIiACIiACItDkCYSiHmIeVqVYUZp4GrpfiCv7yiuveDENEQ0REaHr008/9damCKtYUfIT37QIdohlCGIIfVhKYm2LiEdgQy78vrIJVrhpl+VJORAuN95444ILh3vuucdttdVW7vjjj/fWlAQTCfmdvImPYIjohqUs7iXwsYpFJwJqKVcHNki1DbjMojO8LvzdBEbyxwoVa1QCwiH58Sk/Yi1iIiIgFtBhoP6cRwA/8sgjCwIljBEjaSNCjx49vLsAhE2EXSxbze0Bx6k77YMbDayfS9WTNMO+gB9b8jIL3HIFXkRQrGxDH8O4e8AfMG1CO9FnsHymvvgNpr70DcR62pq2ok8hlHIt6ZUK5oPXDD/izd1ClwtYWlM+2gD3CwR48o88N910U9/XKBv3BX6jcSES9sFS5dF5ERABERABERABEWhJBHIReLF6wJdbKeuElgRWdRUBERABERABERCBYgRsEGbuDvhcHfcMWJaeddZZ3gITq1x8lCKSYu2IeIf7BjZLI5jbAhuD4TbBrCQt71VXXdW7b8DnKYFP69lY7fbbb/duCMw6kvNcz0ZmuHYgT44htCEE8pk/IjHWqfg7XXLJJb3bB4Q7s75ETMYy2DYWS6o/5ce9Vynh0s6feeaZPg8TvOPrQmEQP71YEuOD9rzzzvN54JYBbtQBy+gwhG4KEBAREhGuEb1DNwxcQ5q4DMD1QhiwakYwx+8u7hmMLz57actyBV785uJCw4TmUpysLJYPbYG7iX/84x/eIpmAsI6lMkL+QQcd5DfCM1Haruc45y0gTiPg4ycXsdlcMnCePokVMBbCWDbTD83dA+2Bta9Z+prgTJ9mMzQLffv29Vbg+OiNrbLN3QOiOy4iCCxMIEJjIX7VVVd5S2QL1I9+RX2xFEY8zspNTykREAEREAEREAERaA4EKhJ4GZhhDcFOvAzy4oFwcwCkOoiACIiACIiACIhANQjYIMyERdwNcIyAL1g+9Ue0SwqIrLhiQMSLA+Iq4zIEYyxPzZLVBECEYgQ5rCH53B3hFvHz/vvvL1pNs8y0SLh+wNISK8x4kR8/t1iKIoZikYnohxUxQih1yxIszR9//NFv7IUQXczdgflfxcUC4h8uCLAuxgUAIvFhhx3ms0UAtc/4Sc/aAXETa2GEa3MNQPywbljzYnFKXm3btvXWpbRFGHAvwDksquPri9Xb2od2xcWBieTlGFAYAxYAEIoR9wn43cUdhYnkiLHXXXedt6zG5zAWzvwkhH5sEX3NHzMWvYi2+MxF6LcQbsyGb14sgHHnYVbOsKavbLbZZn6jvyyCN2kj1mNEYkIxea655pru3Xff9VmzESBpY43MvWLBNn/Lmk+Wvqg4IiACIiACIiACIlDrBHIReFnRf+KJJyTw1nprq3wiIAIiIAIiIAI1Q8CEOwRHBFeEKzZMQ4BlEyoGabYZWYcOHbxoiUiH2wPE1nKCiV3FNqFi0R7RD/cCCLRYpiL6YYWKkItFMaGY4FhrohpCLSIzQjchqf5hfbBYxQUDYmRYl1JGDKSLaAozrJjZ9A6Bsxxxtpz2zBrX8jeBF+tuLFyTQmgJHZab+mB9+9xzzznEdoRehHr6BG5Cktx6lCof6duGccU2jrN0wnbDhQmbquHHF4Ge/onwyz1BeRDXFURABERABERABESgpRHIReBlUMWnfqUGvy0NruorAiIgAiIgAiIgAtUigAAZbyjFWMzGY/aTDbBCEY3jJh7bhmMmJJcqayi02aZW8bWkbxvChflyjLhZ87KyWHmxcM6yeRZ1I98wn1C8LFXHYudhTloWrH6UK4tQmSVvs9quZLMwY0BZ4YYYikWzbYqHJbKxTGuXhojT1iepZ9hW9JW4/UrNG6gDaVg6WctTbBEjC3/FEQEREAEREAEREIGmSEACb1NsNZVZBERABERABESg2RAwAdOErFDUCkXOULyNRdtKYYRpI8RZMKGW81kF1krLkuf1JsiGdUpL3xjE4nC55UkSmMtNI6uYWSpdG+izERyWrrhbGDBgQFn+aZPqA1f+WV8Nxdy8yp5UN7tXwv5PH6WMDV1AKMVQ50VABERABERABESgKRDIReBdY4013NNPPy0L3qbQ4iqjCIiACIiACIhAzRMw60YraBbL1bwqNTXzzqsOSudvAuZmAl+7+D7G3zM+fhuyAVnYLxqzP6YJveHxqV0e9TcREAEREAEREAERmNoEchF42ZiBDS1KfWo1tSur/EVABERABERABERABESgpRAIrWnZ/A3/ufjjJVTT0ral8FU9RUAEREAEREAERKBWCOQi8LI785AhQzRYrJVWVTlEQAREQAREQAREQAREoI6A+aTdYost3H333edGjRrlFl988XqbyAmUCIiACIiACIiACIhA0yaQi8DLjrXsrEuQNUDT7hAqvQiIgAiIgAiIgAiIQPMhYIP9G2+80fXr189tuOGG7uGHH9aYvfk0sWoiAiIgAiIgAiIgAgUXXGeccYY78sgj3bzzzus++eQTx94F5rYLTNPUrf4Xtgw2Ebdv377uxRdfdBJ41ZNEQAREQAREQAREQAREoPYIhMYXXbt2db169XKIvTLKqL22UolEQAREQAREQAREoKEEcrHgXXnlld3QoUN9GTRYbGhT6DoREAEREAEREAEREAERyJ+AuWmwn/nnoBRFQAREQAREQAREQASmJoGKBN4+ffq4YcOGOSx5n3/+eQm8U7MllbcIiIAIiIAIiIAIiIAIpBAIPsbzMaaZZhqxEgEREAEREAEREAERaCYEchF4e/fu7V01EGTB20x6hqohAiIgAiIgAiIgAiLQrAgwTie0atWqWdVLlREBERABERABERCBlk4gF4EXS94XXnhBAm9L702qvwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQKMSyEXgXWmllbyrBoIseBu1/ZSZCIiACIiACIiACIiACIiACIiACIiACIiACIhACyaQi8C74ooruuHDh0vgbcEdSVUXAREQAREQAREQAREQAREQAREQAREQAREQARFofAK5CLy9evVyL730kgTexm8/5SgCIiACIiACIiACIiACIiACIiACIiACIiACItCCCUjgbcGNr6qLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAg0bQK5CLwrrLCCGzFihCchH7xNu0Oo9CIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAk2HQC4Cb8+ePd3IkSMl8DaddldJRUAEREAEREAEREAEREAEREAEREAEREAEREAEmgGBXATeHj16uFdeeUUCbzPoEKqCCIiACIiACIiACIiACIiACIiACIiACIiACIhA0yEggbfptJVKKgIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAL1COQi8C633HLu1Vdf9QnLB696mAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAg0DoFcBN7u3bu71157TQJv47RZLrn88ccf7q+//kpMa7rppqt3HNGef3GYZppp3LTTTjvFcdJOituqVaspjielS6QscZPi5AJHiYiACIiACIiACLQ4ApWMSQxWPDZJSzNprFNOXK7PWt6s8SzNpPEhY76kuk3tuGnjWcoal7e5xmUylxSSGLS4m1oVFgEREAEREIEWRCAXgXfZZZd1r7/+uscmC97a6D0MYvnH4C4WbGujhPmUIqm/lSNIVzMuNxcTIgtMghDEY1E8bcKRJKCXW95qTbzSWq8aE9ssYn9YnqldhnLKm7ZIkTQZr9V0yxUO0iah8XGl+zeRcjgkLa6RRtaFOOImiThJ6fJsSXq3pAkNjRU37RlJ3ZIWL5Oe00nvTdItJ25au2VdQE0rb9bF1nLfFZWWN4/3VTl1q8Y7KJ9RiVIRgdomoHlabbePSicCIiACIiAClRCQwFsJvRq81iagSRN6istEnXNvvvmmu/zyy938889fODZhwgS3+OKLu1133bUgKjDR/fHHH90NN9zg2rdv71q3bu2mn3569/vvv7sFF1zQ9e7du15cJv2jR4/2cSz89ttvbpZZZnFzzjnnFAsAP/30k2vbtq0XovnH9aEoWoOIVaRmQkB9rfEbUswbn7lyFAERmPoE4mdfsWdhLMDFccPzjKEYb1kcxlvt2rXz4ykTzG1xMGvciRMnurFjx9Ybx02aNMmPF0k7TPeXX35xn3/+uZthhhkKkH/99Vcfd8YZZ6wXd9y4ce69995zM800U2Es+PPPP7ulllrKzT777PXifvnll+7999+fIi4GJbPOOmu9uKNGjfJGJoxRjQ11XWWVVXw5wvK+88477q233qqXLnHXWmutwhiVisDspZdecsOGDXMzzzxzIV3Gw1tttZVbYIEF6qX73HPPeVd1YVzG1Ntss42bd955fVxb5H/kkUfckCFD/LjYykvcfffd14+rGafbQttdd93lXn755UK6HIfZfvvt58tg4fDDD/ftwLVt2rTx8eeee253zDHHFPqC9QNb/GnOxh9T/45XCURABERABERg6hDIReBdZpll3BtvvOFroJXhqdOQca7ffvute/DBB90111zjll9+eXfeeef5gR8Bgffdd991l1xyiR/kMiGg3biGgfZRRx1VbzD6ySefeNGXSYQNUr/55hvXt29fn0Y4cGXwvMYaa/iBveVFuptssom78cYb68Vl8MwAnME6gcEm6e6xxx7u0ksvrVfeJ554wm277baFATgD2PHjx7u99trLDRw4sF7cF1980fXr18917NjR50d9SXe77bZzp5xySr24DJwPPvhgny6DXyYpDJ433XRTt9tuu9WLiyh+8sknuznmmKMQl4nBhhtu6OOHfD/++GN3yy231BvAMzFYddVV/b9wAP/pp5+6J598st7EgHR79uzpuLfCyQl1HjlyZL2JDOXt1q2b69SpU724TNIQ22kLJhbUj2OdO3d2HTp0mELE/+yzz3x5mUDAl2tmm2023y5hGXhofP/99/Umf0zoiAvrMO7kyZMdExcmhWbhRd3DyWDYd4lveROfvhkuFjQ0bm3clSpFOQRsEmqLVfRHFpaS+oNZjYYLW/SlpLhmMW9xLR8Wr+Jg93Q4MbYJdByXfk75LB3S5W+eF3HgWWDPJntvUgdEjjj88MMPBWEgKW74zuV+p942cbdn8xJLLDHF+/nDDz/05QvjEolFvvhdTlwEHmsD7nfS5lkWx33++ed9usYXXuTBs75UXBjALynunXfe6S1orbzUk2fbFltsUS9dynjHHXf48oVxeQZtvvnm9eJSTnvXUF7amXryPuCdF5aXuPa+C+MiovC+CePynOVdQbDFS44hyBx44IH14tIXeC9ZmvacXmihhdwBBxxQL+7XX3/tDj30UF8vE4B4V/CeOProo+s9e3nnHXTQQZ6nxaWPsmfCkUceWe9dPGbMGNe/f3+/2EqgLDzjV1ppJXfmmWfWi0tf4L0LT4v73Xff+ff+SSedNMV4YOutty7EtXc87XD22WfXe2fi5ot3tIlelIGxQ1Jc+hhxeY+ZaMa7ceedd/blDd/FjAcQ2cK4cNxzzz0LYwezRh88eLDbfvvt/X1oi+Gky5jEymtxH3jgAbfLLrv4+9vifvXVV575CSec4PsqgToj6G255Za+X9n9gICJ+Hb88cfXi8u4Yffdd88Ul4V3ykC6du9QBsp6yCGH1Et30KBBhXTDuPA67LDD6sW94oor/PVzzTWXLy99CMH33HPPdXvvvXe9uIwxBwwYUIhLu1GGCy+80OcXGh1wr9HXLV2Le9ZZZ7kddtihXroXX3yxO+2006aIyz1InwjTveyyy3x+pMtx0qXdTj/99CnGZueff7676qqrfLtZXO4V8qMPh33nyiuvdHCzNubepE9SXsa/YVzS5RnFOMi+nuMeuuCCC/xYLozLWPj+++/3cSmDGVLQFxjLheND6vbMM8/4cbLF5T7mXuvatWvhdWFjW8a9PJ9pL37SZoy30uZm1nfj947+FgEREAEREAERaJoEchF4EQVZEY8nb00TSdMu9a233uouuugix+SBiRzi42abbeYHqLUuvjNxwhoEgY8JRSwqfvTRR36yxESbuEyMsYyILSkYVD/99NNevDZhk3SxjMBfdJgunBiUIwQhGDDYZQDPxHajjTaaYhKM1TOiggkcTGQ22GADLz6HA/jhw4f7CTcDeBMamETsuOOOftITxmVSyYTbJhHUkQkok2gsOsK49913nxcIwokiZUCUZ+Jl4hVpMFHEygMBwiZ0WNAwMUA4CCegTOiY5DGxhQMTDgRfJi1M3sK4TLD2339/N8888xSEHMpw7bXXelE8jMvkgoki7WQTW+LCkUlzGJdyIYpYusaBdOPJHxO3f/3rXz5dE6lIl/7PRDqc/B177LG+HmF54UudLV0Tn2kHJno2AbUJ3b333uvWXHPNeukeccQRXvAJ06WN6U/rrrtuvbjwYjIeThQRRViEQcwK241+w2TV0qUM9GlEAizmw/6ASBFPKpn8Pfroo26FFVaoF5d+A0srA0867gvuFZ7h4aSS/syiCpNKy4+4Tz31lFt66aXrpUs73n333YWJrYmAiDALL7xwQfwgPybmQ4cO9WKLpYughvUTk9WwDAh3lIEJK8dNiH3llVfqfXlAuquttppfZLR73uKOGDHCL2iE6bLYxITZFoBMNMb6KhR2SKNXr16O546JWRYXazErl4ln66+/fmEizrX2ciWu9X8TPVdffXXHM8IWWojLM4i4ofhB3fr06eOPm2UYZUCIo76huGQcEHnD8jK5py1MeLBFGPodi0sm6nHfw4+41j7EpWzE5VnJeY5RBljRPpTbRALirrfeev75ZYs4PCt5Xj322GP+uW5xSYO4CDEWlz5Ge9HXCRaXNBAK6dsm2hMXroi5tIHFRfAkLtfwLrF+zgLYbbfdVi9dFsd4HlN3E+YRYhFXubfDMQ3HEX1DEZ/rF1tsMS/ihHEpAxZ1BBNuLV2eR2FcngM8v0OBl3QXWWQRLwCGcXnnsVjLs9GEY/Lq0qXLFF/ecPycc86ZIi4CPkJo+B6kDDw7rd/Ak/LyzmQMEcblWQRH6zcWlzKss8469eLSXixe2oJvuMjYo0ePknFpY97vcVyOIwhbv7F055tvPt92YXnpBzwz4rj0B/paPM5gcZa4ZhFri5f2Lqc9yI9+QFw4hHHp63Zfh251aLvY0tbup3hsVk7cpj1iVekbi4A9H7GKRsjm+RYvjjVWWZSPCIiACIiACIhA9QjkIvCy4vz222/Xm4hUr8hKOSZgkwMmdEyYEZMQrooFBnsmeoTxmKjYpNiOm1hhExs7nrbJmlpIBIoRCAWSOB4Wu6ElJcJLmrVvHJe/1uxO5AAAIABJREFUTXQIRRHEEibMoWUwk3aENI6FE3zuIUSJ8B6wz03p72lxwwk+n4TGccmfcsTpIkjEn9KGZSiVLmUlflp5Qw5JdUNctc88Q5EBoToUvUgHvtTN0rTnQVrdEFDigDhEnmEbwzcpLi8ns86zPDmW1B8oa/w8Sus7JhaHFrwcS7L2tcWO2II3zapcd74IiIAINHUCacYAScfLMRyw92cSn0p859tiYVK6SXsexPHMECAuA+8bypxlL4W0uLYYE+aZNP42y/CsZYjTtXdVOG7n96T8WQBmcZhFSlxF8FNBBERABERABESgeRCQwNtE29Es1MyKJ6kaZumaNMCrZrXTBvGVbBJlnxrH5U7b1TmpDHlsAmOWoVYOmxjEvsySBvBmyVvpJmtpbZfENyluOROyavYTpS0CIpAvgXLu7axxw0WN8Lkaiu+xqJAkoMTP6rT3hC1qJAkdacJQGNfejVnjxs90riu20BmLPeSdZfO2vNKt1rutqaVLGyWF+P2aNnbg2nI2H6wkbrWETeqQdbyVNV6xNDmXVYhNG49UWo6s45x8n6zNM7XjjjvOuynhq5qVV1653tc2zbPGqpUIiIAIiIAINH8CuQi8Sy65pMP3qg0MNQCrbscptjmHffJeTPitbumUei0RSJpMlSO0lzOhq1bcSsubZt2TtjiQJBwkTe7ThINyhIDGLEMe5a2UQzllqFQISOqP5Qgiivu/J1nYFnq/19ITXmURAREQgfIImO9hxh+48sIPM26ANIcrj6Nii4AIiIAIiEAtEqhY4GWjLHwnsmmXBgfVb+LQ6go/eqy+4ycvqzVW9UuoHERABERABERABERABERABGqRQDFDkVosr8okAiIgAiIgAiKQjUAuAi+bjIwaNUoCbzbmFcWyQRkbavFZlYT1inDqYhEQAREQAREQAREQARFocQRCa14qH25I2uJgqMIiIAIiIAIi0AwISOBtQo1ojfXAAw+4fv36uR9++EGiehNqPxVVBERABERABERABERABGqFgL4ArJWWUDlEQAREQAREoHICuQi8iy66qHv//fclNlbeHqkphAOwueaay919991+UwRrwCpmraRFIBcC5gs3yY9rLhkoEREQAREQAREQAREQgbII2Bzjtddec3POOaebb7755PqtLIKKLAIiIAIiIAK1QaAigbd3795uxIgRbpFFFnEffPCBBN4qtqk11OGHH+6effZZh+9jrbpXEXhK0uGO8o2fu3IUAREQAREQAREQAREQgfwITJ482bVp08bh/u3zzz93Dz/8sAxI8sOrlERABERABESg0QjkIvAuvPDC7sMPP5TAW8Vmw/oRy8dnnnnGLbTQQtpYrYqsSyWNH2SzRmVH+WrtKs/NWc30S9Uzr/O2EDFmzBjHrs3aFDAvskpHBERABERABERABCojYHOM77//3nXq1Mn99NNPfrwmQ5LKuOpqERABERABEWhsArkIvAiOo0ePlsDb2K2n/BqNgA1yk1xicIyBcFN1PcDA3jbv4ychHNRPN910FXG2iQMuRdgU8LvvvtOzoiKiulgEREAEREAEREAE8iMQjtV22mknt88++8iKNz+8SkkEREAEREAEGoVAxQLvyJEjXefOnSXwNkJzIbqZEFctq9FGqEaTzMIGvmuvvbZ76qmn3MEHH+y22247t8IKKxTqk7eP2SeffNKtuOKKrn379oV2b2rwQqF48cUXd//+97/dpEmTqiLw6v5oar1D5RUBERABERABEagFAjYhvOyyy9z999/vHnvsMQm8tdAwKoMIiIAIiIAIlEEgF4F3gQUWcB999FFVRJsy6qKoIlA1Aibwspng5ptv7t577z2fF31/wIABXvC1UMknbeG1WAUfccQR7vTTT3e//vqrM0vavNw2WF58kvfZZ5+5ZZZZxg0dOtQ98MADrlu3bu7NN990Sy65pOvfv3+D723j9ttvv7nZZ5/dTT/99O7rr7/2ricq4VSqoW0hpFQ8nRcBERABERABERCBlk7AxmuMBxdbbLGqLca3dM6qvwiIgAiIgAhUk0AuAu/888/v8K9JqKZoU00QSlsEyiHw6aefuiOPPNJhZTt+/Hh/6amnnuqOOuqoiu4DBtiIn4i7M8wwg+vXr5+78soryylaprjhfcqOyVaHpIs5hzhrg/9MGfw3kj1gXn31VdezZ09v7f/JJ59UxCjO3+qy3377eRcQQ4YMyTX9cuqruCIgAiIgAiIgAiLQlAmw0L/JJpvID29TbkSVXQREQAREoEUSyEXgnW+++XIXbVpkayRU2hro6quvdq+88orj06kkP7Di1TgEEDkRXxFh11xzTbf88su7M8880+21117uqquu8u4Uhg8f7i1fi1mRIkpaSHO3gfC67rrruptuusl98cUXbsKECT5vXB0QQpE2S3ohofDaU045xf3++++uY8eO7pBDDnF77rmnu/DCC73oO8888xQuixdvSvkepv7Ead26tRfC11lnHbf00kt7y+Cw/KRraXENvoxjf8Yh97AexOd64pP222+/7fN5/PHHE0Ve8zec5le4XI6N0+uUiwiIgAiIgAiIgAiIgAiIgAiIgAiIgAikE8hF4J133nm9b81YdBL4yglMnjzZtWnTxm92gLiHwGvHKk9dKTSEgFmyLrzwwv4zNvyUESZOnOgtHp555hl3++23u2222WYKkRcB0QRJ2tP89ppoTDqDBw/299NJJ53kcJ+AewOLx3ncQ3Tt2rVgURsLrWliaJa6Uo577rnHbbHFFlNED30MlxKvqSMiKmXHLQPWIJtttplbbbXVCha2ZJBmFZyUvm0ARxntWrN2tsIOHDjQffjhh+6GG24oxOEXBOBSXxeEHMN24Xr5vM7SexRHBERABERABESgqROQIUlTb0GVXwREQAREoKUSkMBb4y1vAtj666/vdtxxR7fzzjvLgncqt5kJhUsssYQXWu+77756ovsJJ5zgTjzxRPfcc8+5VVZZpSBiFhNFrZ25jusJWL5iQYtQzGZuffr08X/H1q2V4jARFith3K1gKd6jRw8vzrK4YIJqnA+uFhBvKZPVLa2OiMZbbbWVO+2007xrCx481MPSxkL9kUce8ZvK4XeYAGfO8+/zzz/3ZSOEVrv8TTlYZKKspQJW0NSTtgtDsbaRP99SVHVeBERABERABESguRCwTWvzHm82Fz6qhwiIgAiIgAjUKoGKBV58a84111wOn6QmysjaLb/mNuGPT/7xxbryyitL4M0Pb0UpLbTQQm6ppZZyDz74YMHC1gbD+IPF2vrnn3927dq1q9dmWJmee+65XkBFmLz11lu9gEsYNWqUF3hx+YDVNsdvueWWxHKa8IjLg91339299dZbrm3btm6XXXZxF1xwQea62UPg5Zdfdr169XK//PKLT8f6ngnaO+20k7fAxYXDdddd53bbbTd/7yO+Yq0bWuMiVFNujh9zzDFepN1hhx38Zm4ItWaFjmuILl26eItlRGXz1Tty5MhC+YmLkHzAAQd41xEW2Hiub9++/pqXXnrJl92CleXiiy92pEV5P/jgA7+RHNxHjBjh2YZlHjdunK/bQw895DfPIw71mzRpkudRygI4M3BFFAEREAEREAEREAEREAEREAEREAEREIEcCeQi8OIrFOGGIBEkx9YJkurUqZN3BdCtW7cGbXZVnVK17FRx0bDooot6f6+xGAoZNknbdNNN3R133FEAtcYaa3gXBf3793f8jruFs88+23311Vfef28YNtpoI+/2gfiIkuZmAMteAqLpvffe67bcckvfL3bddVcvliJS7r///u64447LtBhgD4FBgwZ5odhcIYQ/WbTBRy8iLVa4M888s7eCRZDGh+/RRx9dKDoiMNbLCK4wQCBFoEWE/fHHH71YaoE4WNTy/CAPfOjiS9eEYBOx8XWMZfH999/vmT711FNu7bXX9mIx/CkL9VhppZV8HPyCE2CBD2PK0KFDB58HTIn37LPPFp5X+E3u3bu3vwaLaUTha665xou9pB/7DW7ZPV+1FwEREAEREAERaG4EbCzL+Ijx2D//+U/NOZpbI6s+IiACIiACzZpALgLv7LPP7kUaggTe/PpLyBIhkY2qFlxwQQ228kNcUUpY79JG7777rk/HNvDiGO4CBgwYUPCZzHn82uLOAUtRLEMJWIniluCSSy5xWP0i5BIQRBEw2ejsjTfeSNyw7fXXX3fLLbecO/zww90ZZ5xRqEv37t3d2LFj3ddff53pnrSHwGGHHebFUSxdOUZARDarZCxsEbURQxGU8RWMKIoVrlncXnrppX5CgFXzxhtvXCgT1rLEoS5Y0RLuuusu949//MNb+J588sne2hmx2oRgY0r+WPGSP+4Y+vXr57kixOLr2AJiMAIurh422GADf/jQQw/1dcJXMr6RsU4+77zz3MEHH+zbi/iIzriZQASOLYHZ0O6HH37wbabnW0W3iy4WAREQAREQARGoYQLhpJCFdBa4te9HDTeYiiYCIiACIiACEYFcBN455pjDf6YtAaR6/UsbHlSPbTkpIyKaywDESCxoQ8HW0kI0xIoU4ZBNvz7++GO3yCKLFPzyWjyz6F1rrbW8gE+wtsZSFfF49OjRhSLiogBXCVjTYoWKADts2LDCeYRVriOYL91ifYf6IGzOOOOMbrvttvMCKBuiJfmd7dmzp3eHgCCNyEvA3QQWyNSXgOCMRT/ir1mCUGbEbsJRRx3lTj31VP87AjlxyRNhlYAw/uKLLzrysnLb5OL99993yy67rBfB8eOLJTEBgdmsmmeddVaHUG0Wxbh1wE0DFsdYo2AJj8iMCM6iFC4ySIf4sShN2ojLBx54oDvnnHMyWUOX05cUVwREQAREQAREQARqhYCNu6644gr/pRRfoGn+USuto3KIgAiIgAiIQGkCuQi8s802m7cYJMiCtzT0hsR47bXXvLiFxaEYN4Rg/teYz9p33nnH4SOZgBiLFStWogSsVhErsR5dc801vUUuFu8E3CHceeedbu+99/YCorklsE3POI/F73fffefjcy2CqG3eRjqbbLKJw7UCwSx6cc9w2223uc0228yxeVnoZ7YYBcqJeEv5f/rpJy+czjTTTF50RfBlIQdrVsRqfNQScGFAGcydA5bmWCqfddZZ/jz9FstbBFnSxN/wt99+688hTuNXGjcK+A8mP+ImBa7FdQMs6P+dO3f2TOERBixusY7m80LC5ptv7i14sfTF4pdggvbQoUO9T2vKQBvgNxi3FgTywQ/y3Xff7UV2cwGBT2EFERABERABERABEWhuBGxSyLjo4Ycf9uMnCbzNrZVVHxEQAREQgeZMIBeBF2vCL7/80nOS+JhfdwlZwpjP+RHQsgp2+ZVEKVlbYKmKBShiJG4UcI/AZl/cSIiUFhBnsWzFotQCVrK0HaIj1qIIjVjCIqoiduJDF7GW9PBdS9oIpViwYqGK5S6CsLlxYDM2RMn11lvPTZgwwVvymiUwG6CxsVi8YZqVxepDGdgMDUEUa2QC6VM/AlbCpEuZ8J1rG52Z1SzCK9ax99xzjxd2cSuB1QdWs2y8eOaZZ/qFCcRn/NjyO6I14jPuHRDJqV+8MSOc+Ie4fPPNN/u6Yw3NQhICM2IvdWOTNdIz0RyR+vvvv/dxCAi+1JFN3MJAfNw43Hjjjf7wOuusU7Cgpi1MsO7atav3k6xnm54BIiACIiACIiACzZmATQqvvfZaP65jEV8Cb3NucdVNBERABESguRGoSOBF/MFCDxFLPirz7xqhwIvvU0Q4PjGXwJs/61Ip2o2CX1msGiywMRr9n0/9EWoRWLFotUAbEhAwcW+A/1iET/zQ8gkcvmwJuC1gII17Amtfs37FatR84hKHzdcsYHmKtW67du28X1l8zhKwksVdAha866677hQDdKsP126//faF9BA0sWrFZy1+c7GonWWWWfx5LIcpLxb7Yb1wv0BeCNSI3KusskrBZQPnzj///EL6iKosUlx00UVedMWnNOH444/3YuyYMWO80Izwi19e+vsuu+ziff9iHY34amGPPfZwjz76qF/4YLM1Au4gEODZII3ANWyqhrAMVwJuF6g39xO+fC1gXYwIjbjLs23ffff17ckmbbrnCpj0iwiIgAiIgAiIQDMkYGPDG264wS/+s7mzBN5m2NCqkgiIgAiIQLMlULHAi5iCBd9XX33lIcmCN7++Egu8Q4YM8Z/GS2zKj3HWlKwt8DWNhSpiLj52EXiTQigmFssjyddtGP/000/3YiTiKWIuVrR5hLBvIcp26dLFC6GIt3kEBOYwrbR6kidCqlnSIr7ilxgRG9+3zz//vOvfv78XvrFihysib2zxW0mZ08qGhTab35G/JjiVENa1IiACIiACIiACtU7AxjrXX3+9d1/1wgsvaPxT642m8omACIiACIhAQEACbw13h1CEQ4BjEy58j0rgrb1GM5cClAyRkn9xIA7CJAIlvyMsmk/XrG0a9gl+Jw3y4qelTx6VCKE8FCwf0jIxleOUPayblcFE13iBh2ssjbC8sKlkMcj4kW8o+IaWxeSRJrSnHTf/x+Z6wvzvZm2f2uuZKpEIiIAIiIAIiIAIlCZg4zJcYOF6j82BKxmrlc5RMURABERABERABPIkkIvAixXj119/7culgUB+zROy7NChg3vppZccn9BLbMqPcbkpmZBrQq25DMhqURoLkKXyN1HVrEyThONy0wzz5Fr+WT3ysI6NRd+4jmG/joVuE2U5Hm9oVg7jcuMykfniiy/c8ssv74trG9aZ/2M910r1VJ0XAREQAREQAREQAREQAREQAREQARGYWgRyEXjx/zl+/PiCZWKSCDW1Kthc8t1nn328n1I2mpLY1FxaVfUwAiZS83cszlarv4fp4vrks88+864g2PyNzevOOeccv3kdoVplUA8QAREQAREQAREQgVoiEH9xVUtlU1lEQAREQAREQATSCeQi8M4444zum2++kcCrniYCItCkCJhwO3DgQHfcccd59xP4Or700ku9yMsx+d9tUk2qwoqACIiACIiACFRAoJIvwyrIVpeKgAiIgAiIgAhUSCA3gRcLXvP9KQveClsl4fJKfKrmXxqlKALNjwBuGbbeemv30Ucfeb9zo0eP9pWU9W7za2vVSAREQAREQAREQAREQAREQAREQASaE4GKBd4333zTzTDDDN4HLz4z5R+2Ot1DAm91uCpVEYhF3PPOO88LvZ06ddLzTN1DBERABERABESgRRCwOdyrr77qLrnkEnfNNddoHNQiWl6VFAEREAERaC4EKhZ433rrLdemTRvvogHLXQm8zaVrqB4i0LIIyFK3ZbW3aisCIiACIiACIvA/AjYpHDx4sPvnP//pv2TSvE49RAREQAREQASaDoFcBN7WrVv7Tdb4qYFAvo1votOoUaNcly5dvJW0hKh8GSs1ETACPL/sGSZXM+oXIiACIiACIiACLYWATQrZcHb//fd3WPJqXtdSWl/1FAEREAERaA4EchN4seCVi4b8u4SJufPNN597+OGHXffu3TXYyh+zUhQBERABERABERABERCBFkvAJoXDhw/3G86+8cYbMippsb1BFRcBERABEWiKBHITePHBi6sGrfRWpxssvPDC7qGHHnJLLrmkGFcHsVIVAREQAREQAREQAREQgRZJwOZwI0aMcP3793fss6KvBltkV1ClRUAEREAEmiiBXAReLHcReKeffnqJjzl3BBtYLbroou6BBx6QwJszXyUnAiIgAiIgAiIgAiIgAi2dgAm8uGjYaaed3McffyyBt6V3CtVfBERABESgSRHITeD96quv3AwzzCCBN+fmN4G3a9eu7r777nNLLLGEGOfMWMmJgAiIgAiIgAiIgAiIQEsmYHOOyZMnu/fee88ts8wyEnhbcodQ3UVABERABJocgdwE3nHjxrm2bdtKfMy5C9hgq2PHju6JJ55wPXv2FOOcGSs5ERABERABERABERABERABERABERABERABEWiqBHIReNlt/ssvv3Tt2rWT+JhzTzCB96CDDnIHHnig69y5s1bTc2as5ERABERABERABERABERABJyfZ/z111+O+Z2CCIiACIiACIhA0yGQm8A7duxYN9NMM0ngbTptr5KKgAiIgAiIgAiIgAiIgAiIQIGABF51BhEQAREQARFomgQqFnjffvtt16pVK/fFF1+49u3bS+CtUj9g44NpppnGs1YQAREQAREQAREQAREQAREQAREQAREQAREQAREQAQjkIvAiPCLwzjzzzBJ4q9SvJPBWCaySFQEREAEREAEREAEREIEWTsDcwo0fP94dfvjhbtCgQS2ciKovAiIgAiIgAk2LQG4C7+eff+5mmWUWCbxNq/1VWhEQAREQAREQAREQAREQgRZOAGMS/O5++OGHbqWVVnLffvutJ2LCbwvHo+qLgAiIgAiIQM0TqEjgXXHFFd0777zjXQd8+umnbtZZZ5XAm3OT26Bq1KhRrkuXLm666abTQCtnxkpOBERABERABERABERABFoyARN42Vdl9dVXdx988IEE3pbcIVR3ERABERCBJkcgF4GXWiPwduzYUQJvzl3ABlvzzz+/u+uuu/yKuh3LOSslJwIiIAI1SYCFLkKePsh5+ZFenmnWJDwVSgREQAREQAQyELD5xZdffun69OnjxowZI4E3AzdFEQEREAEREIFaIVCxwPvuu++6v/76y/373/92s802m8THnFvWLHgXXXRRL/B2795djHNmrOREQATyJaAduPPlqdREQAREQAREoNoETOD96quvHF9pfvLJJxJ4qw1d6YuACIiACIhAjgQk8OYIsxpJ2WAL9wx33nmnBN5qQFaaItACCZhVLAt0/ItDY1u3Uh77Z3nzk2N77723u+qqq3KZaNqi2a233uo22WQTN9NMM8ntTQvs/6qyCIiACIhAfQKhi4YePXq4cePG5fLeFWcREAEREAEREIHGIZCLwMuEmVXeOeaYQ9alObebDbaWWGIJd9ttt7lll11WjHNmrOREoKUQMAGV+uI7nc1U8gwmnv7yyy9+Yrjwwgv75xV5FXOFUGoDl9lnn9299957jp+VuqixvHr27OkuvPBC17dv34rTzJOh0hKBWiBgzwr8/icFWenXQiupDCJQPQL4311sscWql4FSFgEREAEREAERyJ2ABN7ckeaboIkZuL94+OGHvQ9ea7R8c1JqIiACLZHApEmTHJ9jfvbZZ278+PH++dK6dWu3xRZbeBzl+L+1Z9N9993n/u///s/7Zi8VQnH3iiuucIMHD3Yzzzyzu+SSS1y7du385fPNN5979tlnHa5qKhV47frVVlvNnXDCCW6NNdbwaSJAY8lsQnRoUYzIJV+9pVqy+Z4v5x6Y2hRKLZZM7fIpfxEQAREQAREQAREQAREQgeoQyE3g/fjjj91cc81V8eS7OtVsuqnaZO2ggw7ygslCCy0kxk23OVVyEZjqBF577TV3/fXXu6eeesp9/vnn7ocffvBlmmWWWdyMM87orW1///139/zzz3vrHV4SWPryLDKL3yTrvnDTstdff927P0A0Ju7EiRNd+/btp6h7KEatvfba7q233nJ77LGHO+2009wFF1zgn3mEBRdc0N1xxx2uV69ehfJQTgLiLMKsWRryN8HKzDl+p3wEs1zecMMN3W677eb+8Y9/uF9//dXNMMMMhfJpEW2qd9MGFyBJ4Iz7SEMSpx9Zn2vI9eVcQ3mtH7OwkGZFW06aWeMaP+6JoUOHunXWWafepXaer7a+/fZbhyV8Fiv9rPkrngiIwNQnIAv9qd8GKoEIiIAIiIAINIRAbgLvRx995Oaee26Jjw1pBV0jAiIgAlUkYA/6I4880p1xxhlu5ZVXdltttZXbZptt3E477eRFzn333bdBJYgFtaefftqLsVjjYnk7duxYhw/xF1980W/EGca3ch111FHulltu8Zt1Ev71r3+5s88+22FdTMA1zUUXXeRWXXVVLz4jeIXWtg0p+JZbbundMxxyyCGFy0kb62ULQ4YMcYjVO++88xRlb0ieuqa6BEJf0nmLsZVajpequS1ANFTMtfuKhZUBAwY4FkwaUma7J4cNG+b69OlT8M9t6U+ePNm1adPG3zf33HOPGzNmTEGMztvlSylmOi8CIlA9AvoaoHpslbIIiIAIiIAIVItALgIvkwgE3nnmmadBE4pqVa45pSsLmebUmqqLCDQuAZuo4TIBa1p8elvYeuut3bbbbutF3thy9aGHHnIHHHCAf65fe+21XjQivPHGG97aFnHYwuWXX+6F16OPPtohAuFe4eabb/ZWt1gGxyGcPGI9jGUxPnsJb7/9tjvssMPco48+6v9ms5dzzz3Xrb766lOkg59ALAl79+7tzw0aNMiXt3///t4P8M8//+xdO1x55ZW+fLvvvruPhwuK9dZbz+2zzz7uuuuu85bD/I5rCNJAdP7tt9983Hvvvdfn3RDBrHFbWrmFBKyPffjhh96HM+JnOZa41t4jR450//znP91LL71UlsuShrbG119/7R544AG/4LHZZpu55ZdfPnO5559/fr/AgjjbkP5qzwCs/HfdddeCwGtp2c9NN93UjRo1ysE2z2CWg6QpwThPskpLBERABERABERABESguRPITeAdPXq0m3feeRs0oWjukPOonwTePCgqDREQAQggciHCTj/99F4IwhXCdttt561jsXxECH3mmWfcjjvu6HC/g7sGrFi//PJLD/CJJ55w6667rhd5l1pqKbfiiiu67777zrt9WGCBBRybrK2wwgrunXfeKQCPhTV7+XAN+RM3TZAifQTk5ZZbzm+0hmA7fPhwnzYCGGnho5ywwQYb+HrhB/iqq65yzz33nBd111xzTX8etxRYFm+//fbe5Q3iMOLvk08+6dZaay1fBuqPGIjIplD7BKwvPfjgg94SnTYOA30GMZ9A6+PwAAAgAElEQVQFB/o9fdwswEOL2dA1AuKibQ6IOMziRpyu5UH+5qc39Nds723yMuvi2ELXROjzzjvPISSzuIFP7M6dO/s033//fX/fsWCSZFEX+wfGrQr+qtMW3OM6hiJq+Fw49NBD3TnnnFMoN+csL64hH7himT/nnHO6vfbay5188sn13DWkucYw9xPh5oulNnWr/V6oEopA0ydgzxieabxLH3/8cf8clDVv029b1UAEREAERKBlEKhY4MWCg0Sw4mDS3BCLkZaBWrUUAREQgalLIPRPa8/qVVZZxVuvYq0XuihAYEK4Of/8890pp5zi5phjDu+7114at99+uxdmcbuw+eabu1NPPbVQOcTeJZdc0rtnSNuczPJ/5ZVXvHj2/fff++tD36Pm9xdrW3yB4qYB1wpXX321LxNWurib2G+//dwOO+xQ2CAOFw8nnXSSQ/BDmMY/PEIZbimwxMUq+cADD/R+fkmXSWwY+DydfGaddVZff8RshdolYH2JxYhFFlnEvfnmm27ppZf2BR4xYoR3RYLP2GIhTcDgnrB+RD8lbSzSWeRgUTtNdM26KZ9dj1U9AvIjjzziuCeTgm0GaEKrCdXENaEWf9UsliAIY4GOoGznilkvG0OLw+IOiybmozse23Xo0MELQLfeeqs77rjj/M93333XuzjJko/Vz3x8hy41sFxmkQa3XxKWave+U8maHwG733jucY/zBYwE3ubXzqqRCIiACIhA8yWQi8DLQIDPZDt16iSBN+e+YoMtJmxYzDB50oQnZ8hKTgRaIAETbNhsjH/777+/F4QQVhDFEE/xsXnEEUd4K90TTzyxHiUsXhFH27Zt6y12CfZs4m+eV2bxyOfcAwcO9L50kxYBV1ttNe86AgvGpLDLLru4G264wVvkUi4EpZdfftm7cMA1A+L03nvv7cVc3ClQl9NPP937AcbtAp/W4yoC4ZcJK9aSHOdTeNw4ICDjEiIOfOqOr1EmurirQPTWImZt3iwmKq600kreqhsXG4T111/fW2Zb+zLoYdNSFqdZlKBfWL9lYQLLVeLQd+jbJjyyCR8LCSwQYEEeujnBTzN9hUUNFhpM3MUynHIhltLHuY9YJAnvFRuEseiA1Xu/fv38efom6VK2bt26+fFVqfDNN9/48mEFHAfjw0IH9/RMM83kLr30UjfzzDPXE2RJA8tcxFvqYZbxpMdYj3Twwcs/3JnsueeeU+TFvYzAjOUxi//4zg4DzxlEXJ4RYeCe5d+PP/7o72usg0NWpeqv8yIgApURCOcXLGCxSKt7sDKmuloEREAEREAEGpNAbgIvkwY+zdXkN9/mM55M7rCQwVpNjPNlrNREoCUSsIc/AlfXrl3djTfeWBB4EUGxXkVwRZQl8NzBehE3CVj83nbbbd73Or55jznmGMcGbqeddloBJQIPIjCCEe4fsASMF6jCySQiLs84/I0iECHA3X///V5owkcvfnhxG0GgHAh5CMlYPG600UZeLMKPMK4VEPSow1lnneWGDh1acLWA1SXiHgIY/n4RuhDUqCui08Ybb+ytNbHeRajDPzEBQZi4fN6vyW5t3i3Wn+mDWJcjjtI/seS1BQhKTj/CXQc/ceeAgEFfZQyz+OKL+z7wxRdfOPxV03exCiYgGuOSJA70bfJjkQSBGF/Sd999t++TWH/fdNNNXijm3c29RPwwWLkpC30V613um/Hjx3sXIvRj7lHcS+DOhL6NBTsbECIKYz1PuizA4EqCcrIY8thjjzlEafxk83UVgYVirJCxXOdeYGEedw4E7vU11ljD54cgzSIP/rRZPCEgBuOHGFYspFBHrO8Zm+CiC/EX0Rv/3DwLWHzBepjnBuexrCbg73q33Xbzv1Mnysj9hsX98ccf71599VX/jFEQARFofALhO5kveHAXIwvexm8H5SgCIiACIiACDSWQm8DLxELWTQ1thvTrTMxl4oQAg/ghgTd/zkpRBFoaAXuOnH322V6kYaO10Nc3wiliF+IvAhXCFcIuloiIoohfuGcgPP30014UQ/ixl8rBBx/shVFEM3t2JX19EH7OPWHCBC8cIZT17dvXi7iEu+66y/v5pZwWn3Jjtdu+fXtv6YfghfhFwPqScwREWawNLSCeIXJhkYtFJZ+0E7AwxlIX0QwBDBcOWDNj4YgwzQImrhsI+oqi9u4W689YhtKm9BP8LPOTxQgCbYsV+K+//uqGDBni++tnn33mz9G+9G9ERgLXmBhLe+PmA7GX/mYB61QWFhj/cJ8QEDjpt/RHFhIQmLGCRQBOClZuFhPo+1gcY21sPqPDa7DOvfDCC72gi7sRLM8RcCk79WZRBCvi7t27e7ciWKcj9iLschw3KwjNWLljmY7Ay4IGAYveY4891ouzXIcPX+4D3LAguvbs2dML0FyPZT/5kx/3N/Hwic0CDWlisc+9w2LKCy+84EVc3KuQL2IugjhuM8wPMvcvYxvuR+JiGUw+CiIgAo1LIHy38bxA4MWSX++8xm0H5SYCIiACIiACDSWQi8DLBJoJDpMqiY8NbYrk64wnkygsgSTw5stXqYmACKQTYFKH0ItYg+CK71HCTz/95IWupOd9OBFEsEUgLRXijaJKxS/nvG1uFfr4DK8v5i8U1w7UlXcbn+Ur1C6BsN8hRmK1ilUq//jUGAtThFFERxYA8PmM0Mg7FTGWfoqVORaqcSBtBE/ES6zJ2UwMK1SOI+ayuBEGFkQYE9HncK9AHyLEiw1hmXFJgNUt6VuwDeFYYCBgrYvYimsUFigQdrHApQ74v0VcZpGDxRcLCL0IpwMGDPDWvbYZGuViI0LqizUvFsBspkjAcveMM87waeGfGjEY4RW/1wQWSFjUQQznH+XGih+LXurNgjTCMMIvjBF+yYs8l1lmGf83AatpFowQlC2wGIS1PIExT5LQXbu9UCUTgaZNIHwm8Szi3uR5JoG3aberSi8CIiACItByCEjgrfG2NgHFfPcxcZWIXuONpuKJQBMiEG68Fha71OZR4XnSQCjlk+ykkPWZRbywPAhkWA8llTFMM87fXmyUJa6HneMn6bMBFXH4m7yoQ9pktpgY3ISavNkW1doWMRHrVtwe4FrAAq44nnvuucIGbCEILGcHDx7s/TsjDuMGBHcJWLYT+FwZtw+IxfQbLGYRWOk/iMZYC+PCAT/PpIWlsFm+2iJD3K/Cv1lEsX0M2BCOfojlOX0bsRWBlnLhWuLcc8/1lsNjxowpLD7gtgEL344dO3pXJbhZwE81YikWvKTHtfi3jQPlxGIW0dbcN+A/F4EHq2VE2ssuu8z7GUZkZhyCKwyslikflryIt9QH4Rn2COqEUMQlHayiEaipL3GxosbKmDIgxiNAE8iX8g4aNMinF97TzbYDq2IiUEMEWDTC8l9BBERABERABESg6RDITeBlgI+/uKwT+aaDaOqW1HiymzSfKTMJ00Rn6raJcheBlkIAwcasa0MxFMEKYatY4Los8WqVZVx3E5trtbwq1//EfERMxFJ81rJxGWIjVrC8QxE/EWM5j59YfN0ilGKhevHFF3t/zbhwQBDFkhVrVQIuBe68805v6YsrA9u0zURdrGZx8YGfWvxGE3A1grsC3EEkLRyEAi9CLC4QcOmA/14EU6yGEXlt4QS3JAisWJPjIxffvAQEbcqFtSvWxLgnQZyhjmeeeaZ3b0Jgg8JrrrnG4T4FP9cIrljpwgVBGAF3vfXW8xa2WOgj9iJiI7CyyIyoTdqUg0Bdse7FnzVMsGzGlzW+j0NhCEGaDepgTB35KoD6nXDCCf4fbibgiHUvIjaW1vxEJMeyGNFd4x7d4SIgAiIgAiIgAiIgAiJQnEAuAi8DeiYTbKKhQXi+Xc4mgOxUze7eZjFTSlzJtxRKTQREQAREQASaDoE0K2w2VWPzMzZew0IXa9vQLUJaDRnbYHGK4Iu/2lKhMay9s+YRxsMNA24QZp99di/usrFamvuSUnWs9DxC+DnnnFNPDKZsiNe0Ce4lVl11VZ+NPhGvlLauF4HyCMhgpzxeii0CIiACIiACtUAgN4GXTwCxKpHAWwvNqjKIgAiIgAiIQMslkOR7uZggGlpsGzWzWo+vC31Gm3uQkLRZ3JbjW5qxUxjIOxRecR9ibkRM8LR8QhcmVh6uNQbF3I6QFvEoKwvHoeuSMD+zxrdjaeWJLfeNq5U1tDzGyhe/wVhXS8Btufeqal6bBMp5ftVmDVQqERABERABEWh5BHIReNkIhM/runTpIoG3Sn1IA60qgVWyIiACIiACzY5AmmAY+nlGpDQRtxQAEyqzxrf0ak24ZNAXjidgwD8Tje33mEclYxCzBHzttde8X91rr73W+zLGBQYbs5kQbUJzmFeaX+9S7aXzIiACIiACIiACIiACItDSCOQm8LIhCZt+yIK3Ol2ICZJ8QFaHrVIVAREQAREQARGoPgFcMPTr189n9OSTT3o3ERo3Vp+7chCBLATCBamtttrK++zGt3itLVRlqYviiIAIiIAIiEBLJJCLwMuGJAi8iy++uAbqLbEXqc4iIAIiIAIiIAIikIHAl19+6bDm3XDDDTPEVhQREIHGIhAKue3atfMbUM4555wSeBurAZSPCIiACIiACFRIIBeBl03WXn/9dbfEEktI4K2wQeLLbbBlfKeffnoNtHJmrOREQAREQAREQASqSyD2z1vd3JS6CIhAuQRCgbdz587u1VdfdbPNNpvmHeWCVHwREAEREAERmEoEKhZ433//fTdp0iRvjcGGGfrULt+WNN91nTp1cjfffLPfUVqM82Ws1ERABERABERABBqHgPlBbtOmTeNkqFxEQAQyEQgFXuYdCLxzzDGHBN5M9BRJBERABERABKY+gVwE3okTJ/qdkCXw5t+gJvDC9vrrr3crrLCCs2P556YURUAEREAEREAEREAEREAEWhqBWOB9+eWX3dxzz615R0vrCKqvCIiACIhAkyWQi8D7yy+/+FXepZZaStalOXcFE3Pxb3zTTTe55ZdfXgOtnBkrOREQAREQAREQAREQARFoyQRCgXeeeeZxw4YNcwsuuKDmHS25U6juIiACIiACTYqABN4aby4TeBHPb7jhBtejRw8NtGq8zVQ8ERABERABERABERABEWiqBEaNGuX3VlEQAREQAREQARFoOgRyEXh//vlnb8G7zDLLyII357Y3gZdPpO6++27Xt29fMc6ZsZITgeZEwDYy+uuvv1yrVq2aU9WafF1oE8I000yjtmnyrakKiIAIiIAIiIAIiIAIiIAIiEDtEMhF4MUH78iRI92yyy4r8THntrXPpfbff383YMAA16VLF1nw5sxYyYlAcyHA8wLxkH8KtUsgFHprt5QqmQiIgAiIQEslgIGJFiNbauur3iIgAiIgAk2VQG4CL474u3fvLoG3qfYElVsERKDZEBg7dqybMGGCw3e3Qu0QwF89X7zMOeecvlAIvRLja6d9VBIREAEREIG/CbBgTNCXQOoRIiACIiACItB0COQm8I4YMcItt9xyEnir1PZaSa8SWCUrAs2AQLgxyu677+4GDRrUDGrVfKuw+uqru2eeeaYwidYEujbb2gQOK51ZXselTTteTq3M+j4p7Yb0j7IXDuqM/uts/1OL3JAylFN/xRUBERABERABERABERABEaiMQC4CLxZJCLxsAGYJVlYsXR0TgCsTLE2y1DdEQASSng/TTTedO/fcc90hhxzi2rRp43r2XN5tutU/XJvW07s///hPnXrzP3+8f/31h7ce/bPu3191Vjr87kWquv/9+Se///dY3YE///jv33W//x2n7lidYU/d//vfTdziFNdOU3e8zlFE3TlLo660/mTd/6apO1YXh6v/Tor06z4DTWhSYrWattV/LVz/53ZimlZ1/mv/qqsL1cEdBdf+9+c0vo7/8z3sRa4oTt1D1F/693HS9X/U/fw7zVaF43+XKjxeyKcQv64sJoz91y3G32WYMvznP787diVfaIH53WqrruzWXntt98QTT/iIoUCv3p0vgdAntaUciqmhr2oTRe2n3rf/a4s0sTsWt8sVqmE97bTT5tvoSk0ERKBiAttuu60744wz3IILLqh3VMU0lYAIiIAIiIAINA6B3ATe4cOHu+WXX14Cb+O0m3IRAREQgSnEwW7dlnbvvvu2O+rY492Bhx7pBdvf/zPZi5b1Q4IIGR2qk1b/e4lXMAvBBNV66UXJ10+qyEZvhVNpEu9/c/n7S9Ei4e8If0vHUYgP+L/rBOak1P67CZrFSIzDycTyFC8kSU+a9Kvr3GkOd83V17p99tzDvfTSS65Xr156b5Zq3gaer4ZP6t//+N39Pvl399tvv7nJv092v//+u/vjP3+4/9QtojCgCn//+9yfdefqrvn9P+6vP/50f7C4Urdowk8vPtctePzxZ93vdcf+5Fzd33/WxeM/Fm2mazWdazVdK/9z2rq/p61b9Gg9Xeu636d1rVu39v+ma10Xb9rpCsf5m+PTTjPt3wvDddfzc9pWdX/Xiamtpv1bVGUxwv9dd/tNW/ff1Ar6FHxqkVe+IlCfQLjYOO+887rHHnvMb6BtGz6LlwiIgAiIgAiIQG0TkMBb2+1TWDUfPHiw69Onj2vfvr1W0mu8zVQ8EWhMAuGEbLbZZnOzzz6HG/ry6+7rceO8v9eyP9VuzMK3wLymn6Gtt7Dutmgnd8wxR7uTTz7FTZ482R9TqA6BL8d96T7996fu5wk/1/mm/tFN+OGnup8/eD/VEzj2w4//z951AEZRdd2bntB77x3UX7GioFhQEBQUVBQREVEQLGBBULGgKNgLKhY+BUQFRRGxF1BQ7KhYQZAmvdeUTfLfc9+83cmmbZLZZBfuhGXamzdvzpt63nnn0q49u2nPTp7fs4f24sfXzoEDB4TIzeD6SU1lQjc7nZiKDU8hIyRXppOJaWMhh+OZSE4Aecy/xOQkSkxKlF9yIqaTKDk5mcqlpFBKSnlKKZfMBHM8JXG6lJRylFwuhZJTknka6/nHy5N4PpnP/xRnXLlaZWp3WDtelxIhR6/FUAQObQTc7xOHH344zZo1i9q1a6cE76F9WujRKwKKgCKgCEQRAp4QvHv4YwgK3uOOO06VSB5Xvm01r127Ns2ePZs6deqkL1oeY6zZKQLRjID7g6xRo0Z0Tq/eNH7Cw/T3339REpMwOkQWArint2jZmk4/+QRq1aIZvf766/rc9LiK3NfE4MGDacqUKTn2IDYfCVCvgryME+IxiYnL+KQEJihBYoKM5DFISf4lYBkT8Im8PoHTyS85QdKJepbzwC/OIUMxhuwdStlYVs3CtcNGo8e+MY2x2IVg2v5Yneuf523F7kNcRmAF4mwjabEsTmxSjIWKsVHxT7MiWJTAWA41MJ9zmIYyWJZDMSxKYasm5uVQFztpoSjGeYr8MtLSKT0tg3xMcmek+Xg6nXw8j0YJXyrWmfVpTICn7k6l1AP7Kf2AUThnZph8snnsw75Z6Yz5YI58wgMT6NbRt0odqV2JxxeDZqcIFAEB9/XXtm1bevPNN+mwww7T744iYKhJFQFFQBFQBBSBskTAM4J38eLF2tU0DDVpCV68aL366qsSyE67SoUBaM1SEYhSBHJ0qaxbh/r0vZTuuvcBWvHPclWFRmCd4v7duHETOq3T8dS6VQtuuHtLCV6P68m+2CxctJBOOfkUatmmFd3zwQSq3aSuEIhxsCsgaFXNH5sUCCHLNKr8YcqaeLjHxkgk/zWB9dZyxJiG5K/5DazJnSbnkpxz1owE+wnYm+Q2QwksMVN5macE8jBTgf9t3o4btaASWGbnAmuNQQqWM1ksWmfzP/6Amn/O8ede+8dqWvD6ZzR9/P/oxpEj6RH2EFeC1+OLQbNTBIqAQDDBO3PmTLVoKAJ+mlQRUAQUAUVAEShrBDwjeL/++ms64YQT9EPV4xq1ZG7Lli2lq5QSvB4DrNkpAlGOgPuDrFq1KnRB3/50/8RHacUKJXgjsWrx0G3atDl1PeNkql6lIn38yaf63PS4ouw1cdGFF9Ebb75BM/55k1o0b0Ob929i0SwTkgj4Z/7zj92BBoXCtQbM+RoxBxdaIg/KYMnY/C2mC/Cl9hiLwrNzUceuY81xDE4gQpNXXn7Zzl6cVSa4IQTKDq3sjCWIoflPVMnJScnUkBrTbTeMpDeenEm+LJ9YQ2gjduG1pikUgXAj0Lp1a5o+fbqId/SaDDfamr8ioAgoAoqAIuANAiUmeJcvX067du2ir776ijp06KAfqt7Uiz8Xt4IXXXmPPPJIfdHyGGPNThGIZgRyBkVhBe9FrOAdxwpeJXgjslrx0G3evDn17tWd9rEf7Pfff6/3dA9ryn09xLLFQZ/r+9LoJ+6kVVv/FTuFvAYhHTFEEu/qISZlkpXDEAuRns+AYHVVa1aj9f+sowEtL6Jff/2VjjjiCH2PLJMK050qAgYBew9dsWIFNWnSRAIy6qAIKAKKgCKgCCgC0YGAJwTvzp07adGiRRIEzGYYHYcf+aW0BG+NGjVo3rx5SqJHfpVpCRWBUkXATWg1qF+fzr/wYrVoKNUaKNrO4F3akj14B1xyAa1csYz++OMPJXiLBmGBqe0zc/WqVdSkaVN6fe3bVLtBXdq1YwfFchAwHSIHAdy7EtjbGEHYTo/pQLNnzabeF/bWoIORU0X+kuC6stdWSQk/1DsGq/D2+nCD8w/3/rwuv+anCCgCioAioAgoAopAcRHwjOBduHAhdezYUQne4tZEPttZ8ub666+nESNGULNmzZQM8BhjzU4RiGYEclg0VKlCvS+6hCY++iT9s3yZevBGYMWC4G3Vug1ddcVl9OuS72nZsuXqO+phPVkSahUTvE2Z4H1l1Wyq3bgO7duxlwleleh6CHXJs2KiL5uDydWuUJtOjD2KnnnqaRo6/BrKyMighISEkuevOYQFATxzxMualZ1ix1GMAZYoxd02r93ZMoE0zos49mp/hxJZjHupDQ5ZjCrWTRQBRUARUAQUAUWgDBDwjOD98ssvqVOnTkrwlkEl6i4VAUXg0EXATfBWrFCezu/Tlx5/5jlavkwJ3kg8K0TBy96GV1x6Ma1c/ict/e13bbTzsKLsS83ff/1Nbdq2oRkr36TaTevR3h17lOD1EGdPsmIhpy/GR60qtaKOldrT/fc/QMOuHaYEryfgepOJbTB54YUXaMOGDTRq1ChKTk72Z16UXnv2WXXgwAFKSkoSIrakhCnKB/I23qXOD/aL3bNnD1WsWFHK7EUQP+QB4tNLgtqb2vI2Fy+w8rZEmpsioAgoAoqAIqAIFIaAJwTvDu76CAWvEryFwV389dqSXnzsdEtF4GBGwP0RVr9+XerZ6wK690EOsqYK3oisdqgTW7VpS+d1P5MSWFD6yacaZM3LirIvNUuXLpXo768wwVtHCN5datHgJdBe5IW4dHHZVK9CPeqYeDSNv2883TzqZiV4vcDWozwsWfrJJ59Qz549KTU1VXrr3X777XT22WcXiTRF41YiW3IMGTKEqlatShMmTPBUFPLxxx/T+PHjCYKTc889l+bOnSvlO+aYY2jixInUpUuXIjWmuZ+tSnZ6dEJpNoqAIqAIKAKKgCIQVgQ8I3jxQnXyySd7+rIW1iOPssxRUfl1PYuyQ9HiKgKKgIcIuD88oSgafftddO2IG2nVv//mY9Hgj35UQPijkhXQ3xFeJkLvFo/Ovtkl2zVvzcdn/nk2FPd4ggsAsqQK22iksoLtuPbt6PDDDqMlS34uEkni2UEdpBnZl5off/yRjj32WJq+/A2q16IB7dmxUwneCKtz8eBNSqAkVoR2iTmJnnzsSbpuxHVK8EZYPbmLA6J0zpw5tH37dkpLS5PYEIcffniuElvLBDyTrGevfVbdcssttHv3bnruuefyPFKrynVvG5zQ5gX17pNPPikWZhjGjBlDN9xwg6hra9WqJcuOP/54Gjt2rJC+ed2TsT95UrGi2K0ExjLrPYzlWI88zzzzTJoxY4Yoh61VRV5KYrs90kXT+7vF9tJLL6Wbb76Z2rdvXyRiPIJPXy2aIqAIKAKKgCJw0CPgGcH7xRdf0CmnnKIE70F/yugBKgKKQCQhYBVW6PZarlw5uue+B+jO20fT8tUbKSYulmKcj0v+wiQQqNK1FKQr/5MpsVAMzMNS0bgqmmUmFbYz21u+Fh+tMmRjPXsyIp0syjYkrZ3GRmBbzUpnMbbhj2P/4rxoXVMKzt5FEWMrKZJsbwbHA9I9cjaKwdg5nmzxinR8H/0cNySE/C/Llo09QTGPhbKKjwsLnPRSYme9bCHLcewYmUROVv6iWYdK4IWAUjVrVqORw4fTC889w8HWWrEH799mO94+XEGHHKAOiZF9qfn++++F2Jm6bBY1aNmQg6ztzEXeRDIgrquljIpprvtwDlm+LEqukEyZ6ZnUo8LpNGP6DOrXv58SvOEEvZh5Q7kLa4aRI0dSvXr1CCTt5MmT6dprr6VJkybR0KFDhfDE8yXYIsHtf7t69WoaPXo0QRHcqlUr+uGHH+iSSy6hqVOnyj0Qad1B3PK6L7qXgXDdsmUL/frrr3TEEUfkODoQ0MvYqghEJcqPYH4Iavnoo4/Sddfl3ZBQmFfvtGnTZHv0WrS2D/lBGlz2aOmJZ8vdsmVLev755+m0005TgreY141upggoAoqAIqAIlDYCnhG88+fPp1NPPVUJXo9r0L5off3113T00UfLC7YSAR6DrNkpAlGMgPt+0KRJY0rPyKQxd9xJ9Rs0ZLVoNUpLT6MsHyKg+/j+nMnkSTplcI+ATF8GT/t4OoMy2TYADwOsz8zCOp5GIB1Z5uN7DsZZQrwYtRNTurwsO5vTZDIdirFrGvOZspz9EZnxBEmaxdthnM35ZmaZ5Rhn8RjpsRzkKHhYUMkxYJ6ZVI4FIW1YaCGZ7TymkdimjQUBLYtiKQ5kdmycUU0hDYIBcR4gDuO8uzQAACAASURBVOy6xIR4iotPkGBOWA6FVjxPx8uyeErAOBHTibIuDj/kF8dj/sUzeR4bjyBDrPrifYFMj5N9Co3MpcWxCnUrWMRx2n3sBTn7zZk0d/YbtJMVbFCdPfbYY/rc9PD6sy81P//8syjPZqyczRYNdVnBGx4PXqc5o8hHEAp96pz1Rc675BsUfFSFH3MoR8dXBgjeiinkS82gcyqeQe+8PYd6nteLbFf+kh+H5uAVAva6uuaaa4QoBcmJYf/+/VS/fn0heUGkusldNLLAkuHII4+UtG+++SZdeOGFcr+F6ve2226jtm3bUuPGjalSpUr+ou7cuZN++eUXWWdVuMHHYZ97sHmAahe2D7CNaNiwoT/psGHD6Nlnn5X7d48ePSRN8+bNJfiiDeK3du1aORaU8/LLL6d27drJ9suXLxcievbs2aL8/fvvv+mzzz7LkT/SQclcrVo1SQ/yuFevXjmKCnx++uknsbCLlsFie9xxx9Hjjz8ulhzBpH20HIuWUxFQBBQBRUARONQQ8Izg/fzzz6WVtygBFw41sItzvPZFq2bNmvTuu+9Shw4d9EWrOEDqNorAQYyA/fiCqgg9KaJhsN1vhXTlnyFoWZfrSGMtMSwCYBDC0OyCMRVC2Kh/s0EaR8PB5lHG888/n9566y1Zo4123lWivRY2rt9IddmT+qW/XqMWrVvS5q2bxQ4gHIObziyYlDXUaDBB6mjAzbmA/9BY4h47a/I+1/PT+rpT57RJCShzc2p0TSpuuLBjx2LFbm3XuFPlxtN9NAWjLQRv5RQ6sHc/nV+5m3inqtVXOM7Qkudp3+0HDhxIderUEf9ckJfoNfLPP/8QyMA1a9aIqnXdunXUuXNnuU9Xr16dzjjjDElviXuoQhcvXkwvvfRSroI98cQTQtha24dnnnlGrFYKIhhB0kJJDM9dbAciuTUHskR8EHj9XnbZZdS1a1fq379/jv099NBDEjQO4hQQv7BduPjii6VcCCiHZ2mTJk2oRYsW4jeM39atWwkqZLyLQzWM/X700UeCA4bffvuNDmPrHQzIz+4T1hYoE8hleINHMmHqJngfeeQRwSGSy1vys1tzUAQUAUVAEVAEDh4EPCN40bJ9+umnK8Hr8blhX7TwYvjGG29E/Iuhx4ev2SkCikAREdi1axe9//77rLIqT23atPJ3S4fSFEQpVLi48WOMD24Z8w+q3nTu0prBy7A8LS2d0/E6qH2xPtcPeZjt3T98CBrVr1GuQulrfQz96/jDX5b505lI6LaLLqbtzy6z6l+7XEgmKHWdaOZ2GmpdUfmK8tdR88o8/BV5HZS6osRl1a4ll51lhmyGQtd4MSLSOxS8iazkxTRUXvaH+fj4RO5VYZZB1YYeFpi2abEM0ygLyufLzKLVq/4VgsSq2grrElzE6j/kk7vJ8qpVq9HhJx1BT7z3HG3xbaZ9O/b51eDFAypAXvqJ2lict2bOMekINDrwOr+TiWMpIopzR6HuTBkVOv5wjgrByudmDJ+L/Ce+oNkY8zLMizmJsVYxWyG1mKjIOmkocWxVrH0JCGX8QWWPMZpLnCXmOnOWiXo/hq9fKPb5KHzZGPMyIZwdtT3SOvM4Nve+ZP/yZ8ol16GzLC+8D/gOUIvarejz1z+gMZfcImXBoGRS8c7OcG5lydkBAwbQvn37RNlqbRuwXxC8gwcPFpUsfMbHjRtH119/vXjtVq5cmTZu3Ei1a9eWIr744os0a9YsQlA09/1v+vTpYp0AohiKXpCn33zzjdg4hNoIBuuIhx9+mP78809+/rWR/fXt25eOOuooIY7d+WAf99xzj9hO2AHbwMMXSmXct6+++uocXsGvvfYaTZkyhT7l4JgrV66UwG1QA0MFDNsJ2EJANYxejfgmAsnbr18/yR5qZBDdgwYNiuhvJYsRMEMPExXvhPPK0rwVAUVAEVAEFAFvEfCM4MXLDlrpVcHrbQW5vbCgSgApoB8/3mKsuSkCBwsCoX4EHyzHG+3HofXlfQ2Cioz1seY0nuj0wV1p/pSPqceAnnTT1DFUiaoQN1/46c/A3gMErfguuwhQM22oW0tdYtoSm2zq4VCyIGANOYs/LMfP/Nl5tgMBeeukM6nD7XTrPcbIEUYt3FREaYxnmvxxQw//cbMQ/wFlbgASmhgp/RR2jsIkMkLtqTV1aH88Va1dnT748AN9vwlPdZU4V0vEgpiFdQHITTuABLzxxhtpD1vQQEX78ssvC3mL4aqrrhJC9+mnnyZYJmAAkYtliN2BbayfL+wR/ve//4k69r///hNSFoTp5s2bpfHMvvvaMfYF4vmCCy7IcXywFDjmmGMk+BoGWC9AiQsyd9GiRYQeh3feeSc1aNBAiFpLBCMtAqjByxeEbY0aNYSoxTxI7fLlyxOCN0LlC0uG33//XRTDd9xxB917772iHMaxI/AcrBpwDCCnMYDghuXE+vXrpTEwkt/jbdlOPPFEOS6Q2JFc3hKf3JqBIqAIKAKKgCJwECHgGcGLlzm8GCnB6+3ZYQkAvBhCwYuXSX3R8hZjzU0ROJgQwP0B9w0MVjlqj88uDz5ef8C0oBXYviRD8PbB82UVVAw4uMticckPh8IwcG+XF2b2OK0a2dpTlBTfwsp1qK7PZC9peB7/76NpdGW3y6lyciWq1aQ2db/hPEqpxOQK+1QL9vB7hnEzD+LBzD94L5eLT6GUhBQqn8iq7DhnnJAsy+QXn0RJPJ/ICu4E3iYxjolb3h/m42PjxY/ZEdWaCIG4jOKirzayWHEO1T+8ueHjnZ7OFC5U/xizb246+3unpULFD49u9ARgsjeDl/Ev1ZdGuzN2066MvbQrbRft5Ol0XzrbqrACmK0ZiNXNm5dsoC/f+Jy2Htgu3fzRTV7fbyLrPLHvoOgZAusFBDQDaQvSD3YEIDBBhCJIGhSrX331lVge3HXXXaJ8BdmK9N26dZOeHhg++OADuuKKK4T0HD9+vGyLYGjnnXeeqHdhpwClK/JCPgjGBoLYfl/YMfxhob7F+zECv4EEhnIXpDE8uLEMAxS5sHGAJQ7sBpAeZYMvL7x1QQSDREbQONzLv/32W9kO+UGpjHQoO5S6sGcAWYx0OA6QyyB/MaxYsYJAiiKvJUuWSNwM2DXAYm3BggVyXB9++GHIauSyOhNsncMLGeprfU6VVU3ofhUBRUARUAQUgaIj4BnBCw+qs846SwneotdBgVvYFy0EcQDG6AanH0Aeg6zZKQIHEQJugregwwrloy2UNNhHfkRtqNuHA/78yGzsq6hEbqjpC0sHPJAGeLkjxYfj+A/lPN3K6DNOP4M+n/+5wFEprgJlcLBB44ELqwPHekB0psYeQAfvEYBKGRpmqJftgICE+7L2UaPGjQjd3kGMqV2J99iXNEf7kYAAZvfff7+oXfGDChVBDEF+2sBkdl/wsH3hhRfonHPOkUBqGG699VZR8CKgGq5P2DiAiIX9GIhSeNMeOHBALB42bdokxO5JJ50kwdag1B07dmyexCgIYdggQBmL+yoaCUAou1WyS5cuFXszrAcxjXdpO9x999303XffibUOfNGh9rUD/IZBRMNP2C1egWIZxDLOV0v42m2gTIbvLshhqJwROwOEMvYDAhuevyqEKelZqdsrAoqAIqAIKAKKQH4IlIjgPf7440V1gUAGluDV6Mfenmz2QxVd2BBxHdGKtVuvtxhrborAwYKAEiQHS03qcZQUAfdzEgRPnbp1qSYrDUMZMjIDPtVoMIF6D6rfDFapGv9qVqw63tPoQm69rLPgK83kFa5DaWhhBWwmK2AxLR7TUMTCv1b8qc1697xtnLHprXe1va6D/abdSvC81tllaEwQdTIrEPEDmWWnJcAhPKmdRgd3njYAos0H21ils83P5o15O62NF6GcZQdPGnu+4xwIdUAQMyhdrRd5qNuFmi74WQi1MQLAgWDGUNCzMhzPUSh94esLK7toEWmgnMG9gELFX9MpAoqAIqAIKAKKQNkgUGKCF12Stm/fLt2O0P3IdmMqm8PRvSoCioAicGgj8Bd3df2WI5SDjAHRgkA47iBo/sBqEkiNnTNdwdbwQMDPBkOzwdKE1EJANCeAmo+7WGeyEhK93P1Bz6BOdcitHEHSUB15LA8OomaCPTnqWleQNUsuIQBVLEeWCsybaXegtXzTgsCygdfy2Ab96BMSDAkWy13s4+NNkDUJbMVd7820WWanLUFmSC+k4TEINKRjAg15xHHXfUuoAbuFixYK5vfcM86vZCsrm4pD4SoJB1FzKOBWmscoxDffH5QULk3Ui78vW194RmBwNwzYXPG8MLf9LLn/2W1sQ0Lw3i3hifTY1t4Tca+1jR35kcf+xhPHlsjuI7/7qn3uYH3w/uzxuC117DJ3md1lRH7uc9etzoXwBd9FGI499lhR89blhqZoEWlESzmLfzbrloqAIqAIKAKKwMGHgGcEL7pYWY8tvNDp4C0C2pLuLZ6amyJwMCFgP8TQNRYRuosyiMuu2JEaT1Izy1RrtvzPM5h2cjSLZZFx+dWhKAg0bNiItrKHZWrqAdrE6jV4M0aLmqsoxxlJaa26MFQlWr4+1XJN5DwyGDzIdZPX4KQNXCfBV4zx/jX/ezOYkuC6zcduomSW2pI78kZwOP/gv2fkzrygxgs30VaWVi7eIH/w5+K+LrxolLLEbH6kr9eI4mPHqtO9ztudn72fQ6EMD+Dhw4dT3759xeoBPR4xKHEazhrQvBUBRUARUAQUgUMbAc8I3vfff5/OPvtsVfCG6XyyigEvXqzDVETNVhFQBMoAAftBid4ULVq0oNPO6EIDBg6mIzgCefnyFUR1K4GkLCfj8Ex+tauzPDsrm2JYIRvnCj5lNnMRRsIbGfIoOxtdz5nuYeWU6WaOYExZ/AxIFxWWj7uxZ2IsXdTNen8XdA7YhK7skk66qptATlAFi6rKdm+XgHEmaByXzhDQKD/K7OfLsM4OrjTBdRHEpOG4cLwxrNKNE4Uv/zg4lhCB6LYOxS8UvDwdl2CCb8XzOB6qXFHyYrnpsm7VvaJmYwVwHBTAnM6vBGN8sK/69RvSnl07qUHtqhKcB4Ez9WM/vBcNzrHiKHmDiVs5fZyAbPmSusGH4gGhmi86obaw2PO+KBbDxWCdcf9wDyC/gwe3RzVUj7jW0IMA46J07w/vGaO5KwLeIABvXwhfYGOHez3u+dHgv2vfKeBHfOWVV0pgOm2I9Oac0FwUAUVAEVAEFIFwI+AZwTtv3rwckWbDXXDNXxFQBBQBRYD8H4w333wzPfLII/TTb8s40ExLWrtujZCvBQ3GEiEXDeNfELwqh+YQXCrIUJBYTGzylNEyCimM/7CEBxClWMMzWO8sNaphh0w2m9kEubdzlzA/zizv5XkvdShqQww7INhldqEg5/xnRtAu8h/sI3gBAnQZUgtdzE1iM4sx1J4mrR1A5MIuo0mTZnT+OWfRd99+xeSW6easJK/3V7JVHJZVoyhqFuHcuOmCf9xIYc4YZyowj/BuCPCGNWaMdGba6GXNuWXPT5xqpp0jcG7ZVHaJuc7MFWmvOTtvr0BuipBGE1yjSMdNGfzHDRsSCo0bKOR/MzbzOXS7nldYcUh4zwuhGSoCHiMAYnThwoV06qmnepxz+LKzZC4C040bN06Cw0UDMR0+RDRnRUARUAQUAUUgehAoMcGLKLHbtm0TbylEzFUPXm8r3374L1iwgDp06CCReZUM8BZjzU0RiGYE7E18zJgx9PjjT9CfK9eKDQAIk9Ilt5h6Mv+KOThb5hwVM6+SbeYXMMpEaHJGk7TgtKirphzl/bGHJtCE++7xE8B6Ty9ZfRW09d8rltHzk56jtStXy7MzT/cCYUFN3cWw/wjryIWWZX05ZTJhj6ksKNYdAhaB00Cw+ulY8Y8WjbmfkgWh6m80cApo1ufU/9oGD9sUYW0W3KkKFQL7rxnrC2HI5MCAxgbZufwn69xqfGdpFivyoca3nqlI7cdMlPpm+2xOJ0Qx+7XgJ7QwvKuheMc4Jo4S4hIpkVXsWGb8s41KHv4ueI/59rvvKdWXSn36XkC3jhrFDR9NwncSaM6KQBkgEK2qV1tuKHfxXoHemUrwlsEJpLtUBBQBRUARUASKgYBnBO/cuXPp3HPPVYK3GJVQ0Cb2w78GR/9GwIZjjjlGu0p5jLFmpwhEMwL2Jj5hwgQmeJ+kpX+vpDVrVjtcY2jkZDQffzSVHQHumjZtTi9MforuGDNKCd4wVJ6bLB816hZ66KGHZS/N2regRA5+l5eqPROBvtgiBIpsf6BAH0hQBPIzmtecBKyZt2Qs8nfoYdcRFUrL5nP0oXopMFXs2BzExBklPSxFrPVKHFuI2CE2gcsLRT0TrxKoENNQ3UNdj22xHZZjA+d4ZdvgQ8gJgktXHCC7jbrdob6dHgQBstkQy5msXK9evQZVovL05qtvyK7mz58vKsdoJcXCcCprlgcBAoUFiYvEQ7TXYKdOnYTg7dGjhxK8kVhRWiZFQBFQBBQBRSAPBDwjeN955x3q2bOnErwen2b2Y7Vly5YEEr1t27b6AeQxxpqdIhDNCNib+Njbb6enn51Mv/+zmjasX18GCt5oRrF0yi4Eb7Nm9NAD99HDE++XezmINiW1vMPf3eABcmLI+OF06W1XUDkmE2E2kFcAMmhtA4pck8L+n1/JjGg2VDLWu+Nz5xRQ+xo21hDRjrqWp90K4vzMSgJHYFMU95hy7sExZsn3wKGQrkKV6dbBI+jtKW9SG363+fOPPyS9KtrDc75oropAKAjY59GJJ54oBC++7VTBGwpymkYRUAQUAUVAESh7BJTgLfs6KLAE9kOnOXfrhQ1Gu3btlAyI8DrT4ikCpYmAvYnffPNN9NJLU+m35auU4C3NCijCvkDwNmrUhGZMf4luGXmdEFlQUSrBWwQQQ0x6eOt2tD1tJ7276jPalr2d9u3Ykz8f63CT4gltuFLXqLhK3BALWqJkDl2dw40hj2Ul2kd4NsY5X7FmRUrfl04D6l1IiUmJtGnLJtmZErzhwVxzVQRCQcC+U0DBO2LEiKgJDhfKsWkaRUARUAQUAUXgYEfAM4J3zpw51KtXL1XwhumMad26NUEl3aZNGyUDwoSxZqsIRCMC9iY+dOhQepeDXSLI2n/r1sLqslBP2Gg83mguM+qqbr169PEH79OVl/dTiwaPK9NNDDav35yOvOAYGvvEOFq5ZQUlJid5vDfNrqQIwKqhcbVmdHXXyyhmo4++++UHE8Sw1P3DS3okB+/2uKZswELbIGWPNtz1FLy/SEA51GO2Viml64PvDUL2Prp582aqUqWKWNvooAgoAoqAIqAIKALRgYBnBO9bb71F559/vhK8Yar3qlWrikfdUUcdpQRvmDDWbBWBaETA3sSvGnwlfT7/S1r801Jas3oVxbE/pw6RhYDPl0516zag996dQ0MHD6T9+/dTSkqK3tM9qiY3wdu4ViM68crOdMsDt9OabSspXkkKj1D2LhvcuxpWbUxDug6k6gcq08dffqzXgnfwFisnEJhQV+Naio+PL+VAncUqckRvBCytFY8EGiwkEGdEH4wWThFQBBQBRUARUAQiHgHPCN7Zs2dT7969leD1uMrtB+sojjKNrlL1WP2l3Rc9BlmzUwSiGAF7E7/yyivp2+9/oC++/p5WrlghH+c6RBYCsGho0LARzXrtFbrx+mG0des2DjZVTUktj6rJ/WysFF+Bzhzeg2574h5as5UJXrYA0CGyEPBl+KhRtUZ09WmXU7m9ibTw+6/0WijDKsKzBA2DfqsSpyxbNm3iwJ1raCOP9+7bRwe4YSqOG0ySkpKKTFjGWhsUzjvGITsDAQudHWI5E8yi5sYiR9XtnoYHdgyWQ2HsrJetnW2sEtwGOXPDinXSAIogg8gHY+eH6KQyzWlkmseCB8rkTNtyu/PEvadK5cpUuVIlqlWrFjVq0IDiEhJy1aYPgQf5F4e8I5zsBXY4diWly/Ci1F0rAoqAIqAIKAJFRMAzgvfNN9+kPn36KMFbxArQ5IqAIqAIlAQBexO/bvhw+uSz+bTouyW0atW/quAtCahh2hYEb8OGjemV6VPo1htHqAevxzi7CV4QSReOvIRGPXoHE7yrlOD1GGsvsgPB26RaExrRfShlbc+khd8sVILXC2CLmEcwkffvv//SXLZd++Cjj2jxd9/R7h07ipijJgcC8eXL07H/93/UvUsXOufss6k9By2zA8jqTCbUtSFWzxVFQBFQBBQBRUAR8BIBzwjeN954Q4z4MzIyKCGPVmsvC30o5qUt6YdiresxKwKFI2Bv4oMHD6IFXyxigvdnWrtmdZg+HKE+kn+lNJRsf+FSHrGWq1j+xqirmjVr0ueffsoevJfQnj17qEKFCkpqeXQ2uQnepJgE6jnyArr10bG0VglejxD2Nhs0eDSr3oJuu2gk+Tal0idffKbXgrcQF5qbO8Djz0uW0I033SR2YHZoWL8+HcFBfo/mOBD/x+PDmjWjxrVrU3kmL7kVsdD8cySAehXKWOdnlbdZrBQF4Yl5DFDY5hibGaPmzW+9W/GLRFDdQnFrFbmyyARMROMPprKhTnUtl7TubbAeaaG0xbZWyZvXUTtlTmN186Zt22jJ33/T4t9+o7mLFtGfK1fKFvCyvfrii2kU98Zr2L69ORb8uOzhelYVrYJypkY9BKu5S5KfbqsIKAKKgCKgCCgC4UfAM4J35syZdNFFFynBG6Y6Q0Wpf1eYwNVsFYEoRcBNaNXnD/Fk9nP97uc/acP6/2jv3r0hfZyZ71ahLGVwf9CZbquyNE+E8voodS/LuR47wj87dmcZWOfvwet8hNtutM7Gpoyu/82Xus3LdunFfLYhDKT7LuYcYsFJKiR1lnxe+wtigwnZBSZNTjo7OA0+zs3e8F+AhAhOh9UgUxqwgjc9dQ+1bNKQFixYQJ07dyb7IM4TZF0YMgLu66FyQiXqPqIX3fTQGFq7jRW86sEbMo6lldCXnkFNqzejWy68gbI3pNOniz5XgreUwLdeu1ZB2pff32exUANDt1NOocu6daMuRx9NtRo1IipXzpC5uNelpRExMc83rVz3xpCKbolSm9hl2WBu686zJnh5fpmHmj7oPp5ndpZ8dq90iFs/MV3YQcIaCfcatq+QH3DasIHe/fprepat7D5gwhfDqcceSy8/+yw15rE8GxzbhsKyL4319nkE0c6AAQOoZ8+e+owqDeB1H4qAIqAIKAKKgAcIeEbwvv7669S3b18leD2oFM1CEVAEFIFQEHATWkdwV9Dfli6lW2+7g2657S6Kj4uXLqAxsaBUnY9mh8eVeYf0zM6Gqoo/MPEhy4Qn5o1/IT62TUR7t/ch6FVR9vCP3Q952vgVSpfTLFZiZWZxXoi8zssQYAb5QV2FFCLgwnbYObbFyMzH8DzKgHlJg7ykHMiHA/9gn9ijLQ/IWRehKmQ0H6vhCGLY4zCOYuPjKD423ngqxvEyJrKtoisW07wsJiaOl5v1MTgWx4NSlFtQcDnzfNRCPsRyvgAM5LWQ4da7kUuHfODViGJgP/jLBoZ83DE8BscQS5l07dUDacarr/mD76iveihne+Fp3DjWqliDulzbnW58YDQTvGuY4FVP6sIRLN0UNsja0G4DKWV7Ai38bpESvKVQBfa+invYqlWr6Eh+duzm3gQjhgyh0Uzq1W7Z0vRQ4EZCYs/dTCZ05Z7MP7nvoYx2XMTyBprTQtgwFFI2hGxCShKiH27eTZ2BPdgGPxC2GBKY8I0BQc7+vCDJ0xjvB159le5hchfDPdddR3c++aRMQ8mM501ZD/bD8ES2lLjhhhvoYlYdayNkWdeK7l8RUAQUAUVAEQgNAc8I3tdee01eAtSiITTgi5rq/fffpzPOOEOCWigZUFT0NL0icPAiYLvYfv7553KPwNCQVVcnduzE35PxfE/2MWmCH0dG92USgrxk+tKFMM1IS5d7dkZGOkFNl86EsA/TPM4EUYttMjPMNBOuNmCND11qkQYf/bLOZ0jcIn29R3admOA74DHMB7eQxfEJ/GNCmJdJhHn+gUjHL4FVW4mJCc44ie/VHIQoOYWSeXkyj3HvXvffOlr0xXwhSqa/8gr1v/RSvZ97eBq4n40tG7SkjtecTtfdPoJWbf2XEjXImodIm6wCevXgrP1NSgXuM4PvOfWrN6Tre15FVXZXpI8WfKwEr+e1lH+GsFZDz7sTuRfBgpdfpkTuAUJsL5DNZG8GAq459gSRaB9QijCVaFegefFsxLMzHo1+HISNqlShdCZ6+917L81mn+NTWSk9/4sviP16IoLktR+Gp59+Og0bNkzs95TgLdFpoBsrAoqAIqAIKAKlhoBnBO+r3CJ9ySWXKMEbpqqrWrUqffnll3TEEUfoB1CYMNZsFYFoR2Dr5s300ONP0rffLKZ9e3YyWQtFkKNmlajdRoEqSlZWCglZyeMEEJRMQMI/XUjKBCYrQV7yfHx8opCZ+InK1dk+Ns4oWIUAFWUs/ApNBPYYXme8C82+RPnK+zHKVh47EcTjJI9YKYObRAjwxMa6IWAgEaihXFyyy6YBqayfoxELM1EtqmBDVFuVsBDW+HH5oHbGAzGLyXBDcGM6i3wYY3uokXneEt6yDNswAe7zcTomxjN8PA2iHKQ5k+ip+1Npf+oB2R5DZVZxnX12D7rqqquoWrVq0X66RVz53QRv68atqPqRtenZuVNo5baVTMYbRbWcG8VxkS6J8XTQCey2GMlhNxIiogWV3xQzUFjHzTTEnJEsmJy1qDlZGANq410qiwLp7b5CaefJ5Mam9Nh06lD5RDrnmDMofU8a/bXsL7lGZQ8hKiqLcGCalBGwvqpTp06lgQMHUmcm8Ra89x7RP/9QJgdTy5bngqrdw3Wy4DmDIY7f6YnJ3peef54GjRtHbbhR9nfugQMCuKztGpTgDVfta76KgCKgdh+WdAAAIABJREFUCCgCikD4EVCCN/wYe7KHltxl7oMPPqAWLVpEBMGLjzDbzc+TA9RMQkYA2INEA/72I1j9mUOG76BNKN1nQ/UtPGhRiJ4D054Y3tdVDoKXg0ItW7aMHl8wmU7ufCqt27PGGCUzMyn0JNR0QlYaKw6/1bTrGjJd0V2Ep7MuYEudW6nqJ1T9FihG6WosShx7Epc/tDEvcSxRZBvR/BmKFnYlDkxYEpMlunKYpPCP7T9kzHPiJW0IV2PIgrX4mQaXIIq2QOCxH9iqOGGv/GU3btXWsoUtU2CsItYrzj4dbLBfNNrgj/ee4w87tmWOrxBP7eLb0fL3f6WuPbrSrWNupQn3T4iI9xvvz8zIyDG4t0fdBg1o3YcfUiw3XmWwalcDJJdePaEXTBwaOhs3prlscddr5EjqxIHXFv70kxSiLJ8P9sMQ/vBQ8MJ+TxW8pXdu6J4UAUVAEVAEFIGSIOAZwTt9+nTq37+/KnhLUhsFbNuMIxd/8skn1JyjGLujHodpd3lmC0IR3bmhtsNPh8hCABczBlXfRFa9lGZpcG9wD7YhIK8y2EjlXpcvZ0y2vB0L/UtzT0hx8lLs5r0wuPTuLfOQXAYtCtY5Gl/i/AazMhR1os3BBqyzY6tOFI9gvYd6fepJfvalBupEqBQrV6xE4z54iDp0PEmUu6y5ljGmxPMZRCV7PFtVtyVLbRAq+EvzaphUO42axgda/J8dT2nxseYBxKbfC5rPbbHyANnJRE4Ce0HHxjiWHqx0hzenkKC8LCGWVfOclvXy/MP/5g/T/LR1lrPK3XpphwW54mfKBi2USunOHyvYKYP285I0nmLdu2BuqXAgz/0DaHvWFnpz5Ks0+clnqXuPs+m9ee9LAcqS2Co+ApG/pcU1NTWV8D65gQN/zZ00ic495xzKWLtWrGV0KF0EjK0RN8zye/3Tjz9O1z7wAI1mQvWBp58u3YIE7c3eQ2H5dOWVV1K/fv2U4C3TGtGdKwKKgCKgCCgCoSOgBG/oWJVpylatWtFH7NXVtGnTUid48WGAXzBxuJe7H2/bupk2bdxIO3fuCHx6Sq9qp1u2081SunHbAEjmUzjwfw7VoY2gbFIE9bp21YGbZgmiXAqezVWPuSioQhcEJQgUWfKW3edZ8GCCKMjBUFRYphs5yAM/iSAsglFKocv47t27CB9pFStWpAb1G1C9ho2ofv36OY6rrBoByvQi0Z0fdAjk2yu+uN3l82CO8ySTDzokD50DskQWiNoxt4+hV6ZNp//Wr6eEcolUrlJ5St13gINGMenIVhpFYutLCUKjvLWqWxDA4mvCZLEJ4GcaB8w0PD3jxEaFaWDHQkUabvwPT6P0tY0jCCxoH7xGtex63vrVyYFlNr3ZygQPRFmcOSGoJTWCEzqtH1n8rPJls76Xn2PpmWxTkp1GqVlp0u1cBh4lpCTQrg07KT01jerWq0sr/llBKez/qs+t8J1kFtuRrBR9nMnEzh060IIZM8RzV6I/au+P8IFfQM4geWGXRHXqUI8BA+j9BQtoycKFdFSnTmVGqtp76NatW9lFohLbNin5XyYnh+5UEVAEFAFFQBEoBgKeEbxQygzglxMNslaMWshnE7eSBS9ZixcvpsMOO6xUP4KCP7jenD2bZs54hb6c/zFt3rnfu4PVnEqEQJXkWOrWoyddf9OtdOKJHcx3NH84qI9hiWDVjRUBReAgQODnX36md9+eR0nsL43GRml3FIsGh6w0zKUQoS7aMzfp5Wrby2HOYJe7GhfdzXdWLe9Wzfu9cnnCeupKW571z5XlptFP/jkRDMUaCXWCsfvHhLaPvZ4RODEdftBMYIunND8H0B0cgRCz2CtaAic6QZ9AxqLREGNkaoMo2nztPNaDMBf/6nxYcRNokDXHcfDwhpc363TZyzspgQMO8i8xwfi6ouESAQf/+fcfDuqVTJOeeooQY0CfV+G70Ox73K5du6gxe73u2r2b3n/ySTq7Z0/yrVsnXus6lB0CUj/syb7lv/+oFqtmOxxzDC3+4Qd9jyu7KtE9KwKKgCKgCCgCUYuAZwTvSy+9JAEblOANz7kwduxYgvICgXlK60PIvZ8XOBDEjdddQ3vTs6hS1Rp0ZMez6IiTzqBGbY+mGg2aULkKVSDi8Q9Z/IHp44BDmRjzD+OM9DTKOLDfjLGcx5kZabLel450gfmM9FTK5iAsJthRhgQzkjEHPJLgR7wuG8GQMM/rsvnjVT5K+WNVusviw1XmOZ18BJuPWtuVNo67yHLUJwkMJd1q8bHvRIzGNJaZwFDcjRaBotCd1hnHoKstLxfFFMgCqKd4WXx8gsmHP2zj+SM3nj9qY/mjFstjeVoCVfGHVGwcT2M5lvEHcBz/4vmDF8Gs4jhtXALnGZfI+ZrAVrG8LJ4VW7wLXi+8hAz43t+/lwU469fSj5+9Q3OnPEw7Nq6m0zufQm/Pe48qISKzkrzhuSCjINdgu4aiFjlcFg5FLUd40+P6D+8e3LlbTNUzO/yY59fzJPx71j0oApGFgH3Rf57f44YMGUJt27ShP958k7grEGWx7ZY2BJd9fUkdNWlCI0aNoidYWb3o00+pI5O9Zel9i3cIE0hV+7mU/RmiJVAEFAFFQBFQBEJDwDOC93//+x9dccUVSvCGhnvEp/J3c2USsXPHE2jh4u+o24WD6KKR46leuzpMSMLrkLiLJY/TzbRb2OMXRIEnNVyp+Tmht/G+aIkV/7ujTScqKh6cbqYSxMVRRomIiZc7YiZH2eRK7xCfOQB2+2Y629v1/v04C7DacZLw78NPAIn1RKBcWC7FClouy4wgyuQR6JlqlluVlyi0nLROGR1uWtJIOv45zg3SizI4+DtI36QUovKVoL4i+mbuVzRu4On80ZZOPy35mY466kh/1OyIP+m0gJ4gkMmNHDg39aPMEzjDlgkevkr0hg1ef8aW6LXBKcO/x8jcgzsoZ2EltJ7RhaXLa31ww1BeuCMNPKgxVj/q4qAc+jYWf+Dc9cwz6WMmDm+7+moaf+ed5Fu1StW7oUMZ1pQQKkDFu2rFCmrKvsgX9OpFb8yZU6aN9PZaLsn9IKygaeaKgCKgCCgCioAikAsBzwjeKVOm0KBBg5TgDdNJVppkgO3Ol87Kjoa1qlNqbDI99v4f1PSoGrRrJ6tG+ScBvcA4QrEqXVuDZXBgJnOCYbqbuoZcPppYkJecTmjXwIauSdtNtnDb3Hxken62lbMPKh52aBfl6JYacrltDqEZf+YZQMe/aX4yQ3j2GjYYXow12Io3dTfRuH7n0E9fvEdfLPiCTmFFr3obhunCjLBsLWFii7Wbu+Kmszo+P7I3uCs25o3SnbtjO+p3c42b7ttmnTmvZRpMspNelvq7jJvu5TF8bmahIQR5mQSSxgYbQ7mQB8590whkznNR0PMWWB8cqMzMQ3XP662nqHPA9tI0+UF5lDsYZAzU8NIq49w9kA7TCB4pjVA2UJY5btlfkF+plNEUNNcdK7gLva0LHMvevXvl2BtxN2kdFAFFQBEINwKBxvpsql27Nm3ZsoW+fvllOrFjR/Jt3qxBWcNdAaHmb99F2Yv3tL59acG331I2v4NzBen7W6gYajpFQBFQBBQBRUAR8Pf+mThxIo0ePZrq1atHq7hRHxZqbk6IA0kHmDD7wnj88cfTypUrOUbDNnrxxRcl2qpaNITnrCotgtfdpf/kkzrQV98uoXfWplEsq0S3rWNSlxmPOBAg2mUrPBVdglzhkYjzpEKVBKpWm+i2c/vQNx+/RVu3b6fq7HGoJG8JwI2CTd3k7iDuTTFt+lSu8zxaLaLgWA72ItaqVYseeeQR6t+//8F+qHp8ioAiUIYIWBXHLz9zj5727akBNy6tfestfpGLoyy2aNCeHmVYOUG79jGhG9+wIU2bNo0uZ1u2Tz78kLp07VrqNg32nEGvzF6sJD7vvPNKvQyRUytaEkVAEVAEFAFFILoQ8EzBC2+vq666Sgne6Kr/XKW1J8Q778zhl7rz6eF5P1G7k9vTplXplJikkXSjoXoz2Z84pUKc2Db0aVSdo2X/H33wyXy1aoiGyitmGW3DDLulUL+LLqHZb7xORx99LA2/4UaqVKUy+1tn5NkoEwgoBWWto7q1Kluej2EVbSbmRSVulL2iwHWWwdPaBF9y1iEIE9S/vEzSOkGZkD4LKl4J7mT8sU1wJ7sM23EgKCak3QpiGwxKtpO8nWBTvF+7T5QrEwbgzr7Nfk15pJwoI9TE8mf8X6DKFYUv/0RBbL24kY79uY1qFx0TOB2vgxI4AV7Y3PIZD6UvjxMcT214bCewp3ac+G6bdGghtT94aWPAdVmXW1A/+fA9euC+cbJs5syZdNFFF+nHczHP+1A2sxYNoaTVNGWDgNo0hA939OBAwLu3mdTt3acP9erShea88AJlb9yoDfXhg71YOYtNQ5UqtGPDBqrG9XT9ddfRExwMr7R9eO3+TjrpJBo8eLD0ziztMhQLQN1IEVAEFAFFQBFQBLxT8D733HN0Nft6qYI3PGfVO++8Q927dxfSIFyBs9z5tmnemJLqtKHH539E65ZnMXlhjXHDc3yaq7cIZKT7qFbDePr588V0W5+T6Lfff6PD2h2mKl5vYY6Y3Kx6d/CQa2jK85PpjrvvpRtvHs0xdA5Qalq63+YgZ4GD1b3WHiGQyhgQWJNpMxkwHHGmcoxyLkNOxugAA9OsLqcRv7mByxfbLDPlCqTlpe6i+vPABPsNS+pAxu59mP267U0CGfmX+he58pDNXDt1yG7QxMYDXKakkGLfIuS4MdU2Y5DXWO+MzdFLEMhatWvSfXffSRPvv5fatm1Lf/zxh5QyXPf1nHV+aM0FW5YcWkcfXUer53946ssSc0898QRdP2IETeDfrTfeSOlr1lAiv0/qEDkIyDWABkEmeet06EANmzen72HV4HhVl1ZJ7TnTldXDl19+OfXr108J3tICX/ejCCgCioAioAiUEAHPFLxK8JawJvLY3P3BU7lyZfqWX/TacPTjcHW1t/mu4CAPLVq0oAfnLqG2Jx1F2zdydF9Hheb9UWqO4UDABmir05To0rat6awTj6Rpr87Sl/RwgF3Gedr7BK5fXKfw2Vn47c904MB+2rNnjwQzKv7gMpx2BwssfoZh3TLfWN92RS7/bFOcfBaXqKzGxSZniVBXycnJVKdOPep4/P9JBPuV7IkkZcDHvVrflAhz98ZuUuS7776jNWvX0O5tu2nvvr18bRygtNQ08adO5W7qaJjOyvSRDz7TrLT2OYpxvCBBeS41GR/H6m1WarOaO5YbPO04HvPOuoREo+aOZzU3GmPjeDnG8bxMFN2s7jbKcNNg6q9v27bgjKVRJK+TOccyp+nEaYMxx+54WefpZZ8/tE6Tij+B9dnOsYW7kSWHxX4BNwZ7YdkGkKA8EpISqFa9WnTaKaeV8D7l2Wlz0GVkX/LH3nEH3Td+PH38zDN0Zrdu5PvvP4pnZa8OkYWAqHibNKGLBg6kdxYvpjR+hpf288GeM2dyUD7YNCjBG1nniJZGEVAEFAFFQBEoCAHPCN7JkyfTkCFDVMHr4fnm/uBv2bIlffbZZxKcJ1wErz0ZHhg/ju4cN5HmbthHu7ahazGr5JR48LBmSycrH6t46zaPp1mPPk9vjh9GOw6whzIPSiSVDv6ltRd3fdaoXp16X3AxTXz8KVr25x9sq5JUWsXQ/YSIAOwyWrRqQz3O6ky1a1ant9+eow0vIWIXajL3NdG5S2f68rMvZdO4pHi+JhL8ijgTsM+QooGx2Yuoyd3ibywUsXaApXQTodms5oaeG//MlJl2JN8SkA/WISCRQeLAskMHg0BCbAK99sZr1Kd3n1JXKx7sdWDf625k1e5jjz1G6959l+pzA76Pvfm14T7yal8sNZo2pYcefphGPf64XA+l/d5mz5nOnTuLPQNUvGrREHnnipZIEVAEFAFFQBHICwHPCN5nn32Whg4dqgSvh+eZ+yO1Kb/wffHFF2EleC1xfOpJx9GBKi1p4juv0pq/Wb3L6iMdog8BKNEqVo+j9f/8R8NObkBruEtmQw7gEa4GguhD6OAosfs+UbFCBTr73F70zAsv0Yrly8V7UYfIQgAf7PXq16cj2jSj7medRVOnT9ePZ4+ryL7YjHt5At11xRhq2aolXfnoMDqlx2myJzdJ6/Guc2QXbB9i983u0H5faLPMeFdn+eBBzYQOW3mI/QdbfIi83PG0Zn5YSONYZyxkMvgfcMViCWJIZJhVs6W18NPiI81yYOu3Dd2vzLMa2FLVfjIbnLbjPw3vaRmEAzdkN+xH2PGasninTFHz9jyH9dxJQEhybMPTYokdwwWAEFm8qrFXVj87jDmOnpfS9l3b6Zu3F9GLY56lzRs3E3oPNWvWTJ9RHp6U9loYdu219OzTT1Mm9wSL5Ya/TFaxI2CuDpGFgNRXnTr0xSef0Kn8TbV+/XqqW7duqV4T9pxBcLULL7yQLr30Un1GRdZpoqVRBBQBRUARUATyRcAzgnfSpEk0fPhwJXg9PNmCFbzz58+nBg0ahOVFL4cdRHIM9btnGp0//DLa+C+/bCYqwethtZZaVqjThCTuSszVd2HjGHp/3rt0do9z9EW91GqgdHbkvnZBzpxxxpk0/Y23aR0T+qrQKp06CHUvqKs4JrwqV65EbZs1oL4XX0JTX35Zr8lQAQwhnft6aHfUYZR6IJWe/2s6lY+pwL0YtklwP6+HQI6h5Z3LgkFIVF5q7Rb8ymFHRWwkxoYe9VsyOMvcacGx+g/OTd4FU9rB/tt+52vZOpdlg81T1Mn+kjhLHaWyzdKSznYbcNQgqmHT4La1ZhI6gRXVDeIb0zdLFtJVR19G9913H91+++1inaGNU96cpfYlf8RNN9FTrODN/PNPor17xR5GLWG8wdjLXKS+qlWj1cuWUZNevejvv/6iVq1bl+ozwt5DYV8DSyEdFAFFQBFQBBQBRSB6EPCM4H3qqafoWlYIaJA17yo/hzKvYkX66aefCFYN4VBg2jz37t1HFStWoAfn/Uptjj+Cdmwumf8ujkE+CFkpkrO3q/kQxnefe8j92VkYnkXYIrRv77y9DwssRnA/3sLKnP/6/HIyGLq3C0F5w1/TMXExVLk60XkNK9Kkhx+gq4ddqx/Pxa+eiNzS3ieEPGS/3U6dTqHX336XNnIkbv2Aj6wqQx2BuErg32HNG9HAQVfQlBdfLNWP98hCxPvSBJ5le/lZVpGeWPgsndipE63euIoSUhKN9UIYhkCuBefvdLgu2nMv1GdXGI6ryFmG8GiyecL3OKlcCitJ46lb0in09FNP07Brh+n1UGTQ89/AvuSPYQ/el9lKbcMvvxCtW8cKa+i4dYg0BDLxHC9fntJ276Zkvm8tWbKEjjrqKL0mIq2itDyKgCKgCCgCikCEIuAZwfvkk0/SddddpwRvmCp63LhxNHLkSPlgDYeHqj0Rfv55CbVvfzRN/y2dUiom0IF9mUwShRakCV1Ls7K4WyZIXSaasJ3tAYgyiywI/UKDeMq8eMs84hPJVn6Fk83D+Zb25+H+ts7jO9sRQokMCav99LAz4did5a7FHMqjwOrg7f1rnBVuxVKOTPOLS5PHh7x/UYEf+QZfW35095WuujzUbJBAfZrUoPFjb6aRN4/WazRM12hZZWsJrX379lEFtmg4sWNHmjXnA9q0Yb0SvGVVKfnsV1T1CYmUwqqots0b0BDugvs0d5tWf0PvKspeD2tWr6HGTRrTjFWzqXbjOrRnx15WTyul5R3SJc8J10NSShLbTMRQt+TO9A77Ufc8r5c2QpYcWn8O/iBr99xDM5jgXfn995S1di1bafD7mYf70ay8QUAI3pQU4hc1ijnuOFq4cCE32nYqk2cE7qXoFaQNxd7UreaiCCgCioAioAiUBgKeEbyPczCAG264Qcmj0qi1MOzDnggfvD+Puvc4l+ZuzKb9HLw3I6PwyO7GO5Aj/7KVQ4UqMUwMM9FoeF5K24fANsSqNePLB8WuEKtOd80Y6b5plmOZLOdtZdqObddOVxqnxyd7D8IFEOkd30HLcsKzEDiB6OR18PyzOzDbOjsVopcJbBPUPNBNVhawAlbGrPly/AgDQXn8G/DLr2GLpeesq4stuGx8QNllxqPQeCYKz83zlrC2Cl1/WimLsy02A3bIzyGtJZ3TMzaYtLZ5gJfPSCeqwPXRrWodmnj3LTT8hpv0Gg3D9VOWWVpCC92ak9hb8fTTu9D0mW+zd986jUxflhWTx76tyrpaterUqkk9uuqqwYTeL0rweldRfoJ3LRO8jRrTlKUzqOnhzWjnjp1qWeIdzJ7klMUBXJMrJVPG7gw6t1oXskIBtWjwBF7JxGJ5F4sEnuHAXVt+/5344aAKXu8g9jQnIXjLlaOs/fsp7sQT6SP24j2rS5cyeUbYAG/mvVcHRUARUAQUAUVAEYgGBDwjeBGdd8SIEaq8CFOto6LQih6ulnR7Ikz734t05bDr6Z31+2nXVpCtrMYtIBCHKHZj4qh6HUPoLv9xFX0z72VaumAebdu4hvbt2CJK3pRKVblbcjL5mHH0ZaSRj8koIV7B4vI+zIukQ8padauVxzriX0DrWiSEarCoNdiwIXge6XMod131ldcrbPCyvNK4VTCFpUdaKRPIXWdsewz7yWFejno2gW8c9YSQzIGoOAhcE8sKnJgYHvNydM3H+ti4BEpmRrdi1VpUs2Ezat7+ZDrurD404uw29PRD46lf/8soLS1N0qs/a5gu1lLONtiD97w+F9JzU6bR6lX/ah2Xcl2EsjvUV4OGjemods2o17nn0OTnni+Tj/dQyhqNaeyz7M+//6R2bdrRiz9NpxbtW9H2bdvVTz7CKhQEb0qVcpS25QD1rH0W3X7H7XTfvffp9eBhPVnbtHH3308PMsm79++/iTZu5Hev7LC9T3pY/EMuK0vwZjDBm8gE7zvvvks9zzlHv60OuTNBD1gRUAQUAUVAESgeAp4RvI8++qhYCKgSqXgVUdhWpUXwTn1pCvtCDqZ3HAWvrwAFbxarZxNTYqhKTaLF7yykKbdeQmv/+48qJMVRm2M7U+2mbala3Uas6K1C+3duowxfhhBOcXGJElkbBGVsfAIvS5DDR/ChWPbii41jcpPXwZcvBtNMIMNLNhZjsKAgMiUyuJk2Ub9tNzJeinWyDIS4WWeihjvp/MSwjVIOYtkoesVSQdS/Zl6IZyiUeT6GiRmjBMYiyIrd85AsG4oapHUMtpHI5050dMYKCfzWCbIv2Cjgx1HJsWOodNmTEGpoEOdZ3D1Oflkcr5zHbsNibBcgxAPTmbz9nm0bacvaFbTu719oM0gNTunj3+WXXUovT3vFf6pp97vCrrroWO8meOvVq0Pn9bmYxj/4CC3j4CzRF6gIkv6cDTf+BpT8fFuio5r8pcS9vGmz5nR2l87UsG5temvOHH1ueliH9h3kO+6KfsLxx9O0H2dSg6Ob0E4leD1E2ZusQPCWY4J376Y91Ltud7rxxhvpkUce0evBG3glF0vw3vfAAzRu7FhKX76caOtWebcIl2DAw+IfclllojccW7Ed2LWLyp18Ms2cOZMuuuiiUiV47T100KBB1KNHD+rTp49ek4fcmagHrAgoAoqAIhCtCCjBG60153G57YkwfepUGjBwIL29LpvSUtHFP2+LBpC7yRViOAo20aThg+jT2S/RsZ260OX3/o8aH9aQylUK2CyAa7Wq1YDprVnv7vnlWMaaI3NUu7JdPt6zsriw4DOG+yw0WU4SqRBwncQF+te5VhYUG80qfv3WxI7tgl/V6y5KCL3khNfmbZBfKttj7NzM8VSWL6UfP55NsyeNY0eLbLr44otp0qRJVL06R18DhAy8fuh5fEGVUXatW7eiGrVq07sffi4Er6i6cQIGkaZFKV6+53kIFw0aYmTIp4tn7q6faJzJXTprt5JfuW1X0nyPyzbe+BPkZ4IdyKGwW0t++wrw0Hkjh8jkbdu2o0cn3kuvTJtKa+CHqddhUU7JAtPaZ9ln8z+jLmxXMvWH16nhMU2V4PUMYe8ysgTvns17qE8dJnhHMMH7mBK83iEcIHgfmDiRbh89mrJA8O7YoQSvlyB7mJcQvJUqcUP9Nqp06qncKD+NG+cvK1WC1dp6nMwE82W876uvvrpU9+8hnLmyknggQUNe7w+FvlN4UMjg9x/3vL6TewCwZqEIKAKKwCGKgGcEL1QXUF+ogjc8Z9KsWbOod+/eooANByFn6+2V6VPpsgEDafaabMR4oIy03OSfRIJPihWv3Xt6d6EfF31G106YSr1vHEB7dxDt2wXfN9aM5ooO5jBNhUYxB51TxPAfRUxerFoqLuOTg1QKgaXN/fpZLDxwroCARz0llyPatp7oveceoukPjZI9TJ78HA0ZcrWSS8U6GSJrI3v99urVi+bOnUu//PEPNWaV6J5du0EdMmnqeEnbFgBeKmcijLHlH645M22mnJYCTFmO1knrrLWm1SaYHxtLi9jdGkNjCgp17AMqdfFTCYxlP1a1ns/Yj7DTOuRuBBE1vtwmpKRm4J0E0jjTNg0U/o7SX4LGcG8Aa2JtbFwcZT4mYZIdWORaxyr+HMcI1JwWGbm1GeW/Ef27fGVcpwqKsJ8J3laN6tCLL77A199Qo87H5trQ4kKq+JP2Wvjok4+o21nd6PmfplGr9q3VoqH4kIZtSyF4K5ejfdv30fk1u9E9d91Nd959l75Heoi4VfCC4L2NCd7sP//kl7R9lMUveEoieQi0R1kJwVulCu3YsIGqsffuC88/T4OvuqpUrwl7znTr1o369etHAwYMKNX95wdlMDlrSVg7tuvzOq/x3Ic1WTQNeDdwE83BdlyWENbrOJpqVcuqCCgCikD4EfCM4H3ooYfo5ptvjoiXgPDDVjp7cD/MK3KXrV9//ZWaNm0qhIDXLyr2RHj1lekcAlxAAAAgAElEQVR06WUDaPbqbPIx75CempPgFfKCiYg6TWPpmRtuprdfeITGTv2MOvc9nTb+m02ZvBEsFvSFo3TOkQL3AgsJCWLHBB+TX8kV46hmA6IfPvyV7u/XkXbs2Uu3jBpFD/KHnw7RjYC9fhcsWECnnXYa1W/QkM7vfQEddfSxci3igy0Tth+w/+CL2GetP5hgyWJbDx/OlQwfZcAihNchPx9bqmCcmcHzbBOCPLC9D+n5Ose88KCODYkJjOhYj8DeRCxKYFliAh2aeWODAhsTzBubEqyDfQmWB9IK6SoEriWZHWsWMLru5bBisWl4OaaNbBlELk/y/SiOfcBjOZ2xfoFXNdu/8Lr4ePai5mnMx8eb5fGSBvYxvM6xjInjdQncWgLLC/zQeAJvcdjK2PyAM/ITr3RZz2UQKxXXwPsEdpnpB2jE9cOpRo2atGbNGkmgBK8312DAouE7tmg4gaYtm0UNWjbiIGs71JPaG4g9ywXnfHK5ZLnOusR3pOeefY6uHnp1qXZH9+xgIjQjq8a831HwZiPI2oEDSvBGaH1ZgncL253VOussevrpp2nYsGGl+m1lCd6uXbvSpZde6jnB6yZq3QSmm6gFeYl597dEOOKQ4HmBawSxKfDDtJ3HGFggDcb2Z9NgeUGDfV/AOCEhQd4dMMb7Q0pKCiUnJ/t/CJCL+aJ+2wGj4EZia1On32ERepFrsRQBRUARCDMCnhC827dvp4n88njLLbeU6ktImLEp8+zdH/zNmzenRYsWUd26dcNM8E5jgvdyenMVvzQwORhM8IKcqF47jn5b9AONPv84umzUBBp0/6209i8fq+dAbhRHoVrmUB8SBRBfX1821WkeT38uWkbjeh9J2/elspJ3MisJh4TlvDokgI2wg3yOg3YNGzbU+DrrELEI1KhRg1577TXqwiqtcDTaReyBh7lg9qXmm2++oRM5SBEI3rot69Gu7bsoMSGxaHv3O3kU0n2j0N4dgQSFJhW2v2jFzJU6xB4tOZMVslEBq80q/j/E/dry+tIyqGqN6vTvHyto0GH9CHV2wgkn6HtkCavfvbm9Hh6YMIFuGzOGslkowABTFpNZSgB5CLRHWUl9VatG6//9l+p37y6e1KXdO9ISvKeffjr179+f4MVbUO9MS9gGK2rlVuZYIthzTXrQ2NgZJcQMee/evZsdR3bIb+fOnbSNrS22bNkiY6yzv32sWt+zZ4/83MssgWtJ0hIWqcibg/B1/0D8wj6tatWqVIWV3HhPqFmzJtWqxYGTeYxvwEaNGlHDhg0LjbFgFcD2W1KJ3yJXj26gCCgCikBUIuAZwTuBXx5HsRpQLRq8Ow/cBG+TJk1o8eLFpUDwvsIE72V5Knil2zF/wdVgFeh1xx0har5nlvxJ27nrv489eZXc9a7uw5UTVJKZrESo2yKB3n/+VZo04lKKS06i1atWU+3atZVoChfwpZwvFCYbuIsnPhTc3fhwT8mACjdIlWIUu75cKharVEllMiADKhcoWhxVCz4efFAAs7pXlL6O8hcfeYF5sx7rsG+bBtM2jZ0O9rwzHtsS1tAx8TYgIvBinBNAUQI1OkEYsUzWiZLWqGSgsE3kDyiobRMSWJHL81iH5VDuxvF0ArZJYDsTKGt4mVHYmLQ2nVXfpKQkU/kK5SkxHkocNGg5yl/kJ37H5hfnVwhDKZyz0QvHCZUOPuR08B4Bq1icxMq36669lu56fTxd3Pcy+mffMspINarzHAPcS8yZFfjfsTIJBOyEjbQ7gCdE4taMHcpybG7HNndHfZ7nIQaY0Lw50fzW+13bXRdFoCEnJy+cmyUuaH0gVKdzwTnuIyagKNT2xnbE8DU2GKIzdoKR2qCkmA0MjlWL2YF/QH5J5ZOoUUpjGtTpYlr2/d+0O22PrFc1u3fXhX0nH89B1u647TbK/vlnEyBWCV7vQPYwJ0vwrmGv5MY9e9JEfFvdemuJvq1wPeGX2/M+74KD9ISa9OyzzxZruKvYImL//v3ybLR5uMclbSjA/ixRC5IWoqGtHAgQv82bNwthu2nTJlluiVyk27t3b7GRxzO4XLlyfkWtm2SFmtatsMU6LHOrcDEPDILfa+w7DsZQBOPYrCIY/vuYxhh4HmAlvf0V5UCgBK5QoYJ8D9apU0cIX4iAmjVrRq1bt6aWLVsSen3mN+Acs/fYcKiii3IsmlYRUAQUAUXAewQ8I3gf4JfHW0v4EuL94UV3ju6PnBYtWtDXX38trbjhUHvZE+G1Ga9Qv/6X0VvswZse5MEL9W6VGnH076//0IizWtLdr82n47udSlvWseKACRIdogMB45VKVKcx0f19+9L8d2fRjWyv8gjbrGgDTXTUYUGlVHIkeuowHPfy6Dn68JTUff63a92WtuzcRtdNGkknXnAK1YipydQk24PIrg3xaJyUHT9l46Isy/E/0uKP6RH5k+kMnkKDBkgTWJLAcgMNETwNiyLxlpZus8Z2RGxJuAEUM+JLLXYknDcsSxzbkuwsEAWO36J/GUhV41XtT4ftkBeXTWxJ4uCVzYo4bqQQwkWmQUSbefhOCzHN68VWWxognPViyw01HfyqOT3sTWSarUc4PfYCEiSW7UkwD8sSNKhYG5IYWJrwH6eWMZuS8C7wv5ikCLIBR2/jlW2QNmuxFdLMnDiNJoweT0OGDqHJz07WZ5DHl4V9pt93//009vbbKXvJEjkXleD1GGiPspP6YtXmyj/+oObnn0/j77uPbuN6i4Z3M5CZIF1B1u7atctP2q5fv14anDH+j60nLFFryVqQnqEOIFlBXoLgxK9y5cpUjRXP+FnVq12GdPZXiQPXIb0la92EbajEd6hlDDUdCGDgBbIXmFn1sSW1QWzbH7ACpiC6oUbOb8Cx4FhB/kIY1LZtW/m1atWKjj766DyJX0v44n5vFdahHoOmUwQUAUVAEYg8BDwjeMePH09juPtXNLyERF415F0i94cqXkx+Z++0xo0bh5fgfXUG9bu0P835L5tSD3CQtfSAB6+PPTrrcvf+BwdcQX9+/Qm98Mc62rnJqG0kiJMOUYMA6rJa3Xj6feH3bLVxPDVv2YL+WcbRtXlQgjBqqjHfghalu2FRokUXJW1+H02hfkzlqwoCsVZIFeUup5UOhsNCJhCIrihnjn5IFQWtoqW17yHffvstdejQQTauXbcOHdX1GCFSoeTN4HsgyFkE+oLvpSFumaxlz2kZM5EqyjdezwyrjLPtMhC7IGqxDdToCIYjZK+Z9w+GQfaTmvCYznEGSmMb/ydBCE1aWZJDAcvnF/Zvc5F1CGxoSFgMEiRRnsHOEoycWTNpVbQ2eKLZzgQJNGSz5OqodA25bZ790p2aSWMzdlTqIHfFaxq+1fjBj9pMi4oeivZEVs0nslqele5xPBZFPC+Lxzw3CCdVSKEln3xPW9Zsptp1atMP3/9ADRo0CMv7TdHOnoMrtb0W7uV39DvvuIOyf/pJGgIyWUWI3g46RBYCluD9k6002l14Id1911101913F+vbyr7LIc+lS5eK0hPfErgW3XYKbtUpSFqrLsVyEI8gFaGmBTGLn1XZgnQEKWnXFUVRC4UsiFhLxsJ+AHYE+EHIYqcxtpYFUNyWds+XwvyC5f4b1EsneFlJFc7uMxR4r127llasWEErV66UMX6WQEed5DWA9AXZDeL3iCOOoFNOOYVOPvlkUf4GDyDcleyNrPuClkYRUAQUgaIg4BnBex9ambn7lxK8RYE/9LT3s/oCPlzoNhUOAs7W2+uvvkKXXHoZvbM+mw7sDxC8UB1B5VO1DtHlzarRmZffQIMeuIv+W8Zqg0RV74Zek5GREqoyvHRWqk506xnH0e+//EBr162jBvXr6wd2ZFRRRJSiKIRuQQXOQXqFeGT57TtUgrig3XjxweVFOUKEQpOVAIG7mRx58qknqVxCClWpUFmUtaIijcnmoIIgdDkAIY8xjWCCQvCCUIVnuWNNYLs4i6ZXGhhyNzEgT1GoOsGBil3kfNsgrPYVjAL/XFxuDu8DIZWdtA65nM3HikCblnhwn7uWxIWyzQQKZCJWlLpGjSukLat6YUEiSl6ejoESWQIbmsYNydvZpyyzNDZPx4udiv1BIYaAhXHcgJxKLdu1pKHXDaNOHTsVGy7dsGAE7LvdPffeS3ffeSdl//ADpNlK8EboiSP1xWTnL1xPR/XrR2NHj6Zx3EOyON9WdpsPPviAurOfbzgGt1oWJCLmQcjC8sv+MI+fVdmWL19evmW8GKShzWkRMz0jAlYUwUHa7P3Pvd+CnuNevCfYfeX1DpSXZzHSu32Li+JZDH/h5WztATEQfr/99hutWrVKCGE849ATFGPc65EW9hBQMv/f//0fXciNCT3ZEgQNAO7Bkr1FDf7mRd1qHoqAIqAIKALFQ8Azgvdefnm8PUq6ERUPqoN7K3sizGSC92ImeOduzKb9bIeXwQolvORA0ZRSOY52b9lNg46uTPe/+R0d3vk42rGJX0b5Q1CH6ENAFNnN4um5G26i2S8+Sl8jKJEGuIm+isynxHiRx0+u33z899wfQO4PnWAC6KABxaMDyU/VY7N3k9N5YW9xxzp8OOnHk0cVE4ZswJdmZTtKX7FlMNeV9ZqWyOo+jrTO91Mhip1pPFOxLhNEMRPGLno231KKorYweXohxyi2Cg5Ri+vYnl+YNh7Uxmfa/tzpjX+0sWXAX2kO4Wi4Ls3yR+q+7Lvd2LFjCUIMJXgjtaZMuaS+WMH6E8fcOGbAABpz0010/8MPF4vgdR8plLeffvqpX30Lcs/6weJeBsIVZJ/1n4VaFoQtFLP4WfWsJXGhBsUyLwZL0trnpn1mhvJ+YvfvJRnrxTF5lUcwMSw2Pw6RjX3Ye3ZBRDXqfsGCBfTRRx/RT6zgX8diDtQpyFxYN0CBDesMnBOwdejatSv1Zfs2rLOD9FThc9Ptw+zVMWo+ioAioAgoAt4i4BnBO27cOLqDu38Vp5XZ20M6OHMDruE0w/cTvK/NoIv79ad3N2XTnt0IyGUIXl+6j2o2jKfPX51Djw8/n2ashFKJKO1AwMKhJMiLL6ErgwJETHilkX3rECICTC4Iti6AQTakM/lQq1EiLZr1Hj0w5ByaO28endujh17DIcIaycnwARBJClOoHtENPoPVkdlMlqF8mSijeJWaefcP3eORLniw3QYDAa+gCDRBy9xBsOy0XY580AMBXZLtukiuPy2bNwhY5a1thLTnmDv3g5UY8AbB0HMpSKGWXy6W0Annu03oR3BwprTvdqO5h91EVoIqwRvZ9Sz1xerX77/6io6//HIaNWIETXzssYh/LwtW0gLlgohabUT29jx0k+S2scw28AXvyRK+UHZ/+eWX4u0LtTUscrAN5qH8BQE8cOBAGj58eA5lL87R/PL29qg0N0VAEVAEFIHiIOAZwYtukHdy9y8leItTDYVvU1oE76zXZ1DffpfTPFbm7t7lJnjTqW6LRHr5tvH0ybSH6aXlO2j3Vn6BQ3fXYvi4CcHDxA7USgjsAp8+DkjP0waLTF9OTPwEpbEMlAE2A2Y6b9VTTsq4AIxLqJgqvPY8TlGAsMqGtzHiKxtsRyZNp1lnWxDkUGVXqRVHy7//i0Z0bUtvvfUWnc9BPRD4ASoOHaITAUvuwjfvQQ6ct4M92+BvB2VhRka6eI9CWejj+XSOpIj6xjgjw0R8zkjjZVAe8ku8L9MoEzP4XMnwpfM0d2MXxSK6smcQemZL44x0ZXdIWnRUdwJFyTK+R/jcvqRlDKtYk9oAVLg0+P4j3cvtMnRBh/IRFwlIYUyz4jGZI1cnJCVSYgKrnNhTNAHz8Bbl9BKIim9gopYUf1KjlES6xARsB8VkkuSRlJwk981t7JWHD6qRI0dK/UQaKV/G1eTp7t1dXt0Zh2odUhSrkqKk9fQgQ8gs1EafUNMF71LJ8hAqoZST2Hfym0eNkkCq6sFb9ArAK2Jp6dktwfvtokXUgcm1m2+4gR56/PESf1vZHj3WQqagaxXKXqhz0WUf74RXXHEFpbJns20o094+RT+HymoLS/yi/lFvUOAGD39wQL9Zs2YRCF9YPMADuX379pL2559/FouHY489lq6//no699xz/Zsr0VtWtar7VQQUAUWgYASU4NUzRBDwe/DOmE79Lr+S3t2YTrvZqz+Tg8YYBW86NWidSI9eOZz+WPwxPbd0OW36l9chOncRBpBB8ECM44jcFarGUPlK2DfRflYLH9ibRensyxcfx+RISmLOYDQgYuLEOo6JFDONmDIghJlfMT6AtiiOuldGLqWvX/XrirfkX+1O5xyPWBbmUL3mzC/HYUuwnOIN7oA6/jL7A+648rXEtrGPNAP2a4/XKQO4NMTK4Z7BMhYe3c4708AlI9VHVWrG0/rlm2hopzr0yy9L2YvrcMnWqirVfqN4dVpWW6HeQDIi4Aa62tno1DWYQKxYqQqTrXwCOL6cMgapKapWJ5gSrico5LHMmcaXrXTXDhrn+ECUa8/Vqds2MEgjgxOEBPvytzTYaymoI7htgHA+p91C/cAp77TIBDXMiC+q67pAifyNPP6L00yY4FLO9eOvLCwNNBZJfnZX6JpvvVex2Jk2MalAcJuxLYJdhq75QoAL+Y39Zcs9c9fOnRychlvIeEBXV3SRTGTy19ZfWZ0/XuzXBviL5CByoRK7bjyKS3p6gWlZ7tuL8heUh7sulCD2Hm37boeGpMeZKBSCF894btg7ZIOsOS9dQno5/tD5Iu9Ki3u5kL1+T+mivf+GUruW4F38xRd00pVX0k2snnx40qQSE7yh7NumQUMvyL3TTjuN+vfvT1dyOVS8UxQEIzetm/C1Vj7u0sKb95VXXqEpU6ZI8LY2bdrQ4YcfTgje9hWrynHNjGZfaMSEsYO1AjuYn1ORW6NaMkVAEVAEciPgCcGLGz/Uu3dxtFd9CQjPaTZjxgy65JJL/H6aXn8I2Xqb9tIUGjh4KBO8GbR7p5vg9VG9VvF0T88+lJa6lyZ++hGtRYC1EP13hduAHyiTT9XqGvLxlwW/0K/z36J/l35P+3ZtRwohPxKTy1PFarUcZbBEjGGiOd1093LgjWGFXCxUd+iaDcUdq/BEUSgEFtR0zPryshhmgrEOBJaQViCZpDu3Q2iBxRLPQictp4/jiOHINzYmXqKHW+1GrOQBtorXIY0o/TgflIOnETk85xAcDR1rczPB8HeE4lEitYMIskEj/D5bUEGympIVl5n8y4J6Un7p7O/I0d9ZfZkp69MpG9Hg4RfJOCBIkOTrIr7ioTpkfBHpPHXvbjrAlZzOZsu//7qEep/dhS6/cjCd2a07pZSvKIeSn/ItPGe55lpSBGzXvLPOOos++eQTeuixJ6njyZ2pWfMW0nAiBGbwWeonSgPrDPfpOlfdrRCu7XOJ34OIVJO0BK0fJQXEvX2eti55LbRyd7Nxrk94p6XIjMz2eTvGQAGcex3uQSDet2zeQh998C6NuvEG8cY8GD3s3T7QbrsML6s11Lysh2BedgBeP09DLZP7HhvKNsHq4EhWC4dyPMEBhMLdUymUMh1saey73fDrrqNnmCjMXrJEGprCTfDaHh3SE6Konlp498H7B7/HxKNVX26yed9li1xfeJbhx+9C3FWJlQXsRQuyGz01gt/hkA7LkI4b4pj1NNtyeo5SRVn7ORKx00ha5HLks4EleL9iz9ROgwfTiKuvpseee65Uv63wfEKDY7du3agfB3obwF7A+m3nVQ1HVj7WxghjCazp6pEJm4annnqKZs+eLcvPOOMMaZCex3Zu69evp2uuuYbGjx8v5woGnCNl/ZyPLHS1NIqAIqAIlA0CnhG8COAAmwZ9CfCuIt1BR+CF9Ndff0mX3nCovGy9vfj8ZLpq2LU0b0NOi4ZM7qJdt1kcjeEXPhCGEz/9mNaFSPDK+zQTjeUqxRAHMad5z71Gn73yKNVu3IradOhCjQ87hqdbUvkqKdzl2ZC/3DPcT0XhtR48pVGk8os/K4DR7RsWDZkOmQkvT0OAgtQ0wW5AisL7U8aYl+XOPMhPLEd6WY/8fJKvtHAjMI7fG9Q1zfvPzGIiFemERHXGUg6WIqOg4vUJYtnx+0QXbktGgyR2guCYM8VEKY91EdHYXshk2YYJaaxjcg4fH/LyJNMJPM0/LEvgH/bB4MlyCaSTJN3KQTwLoeEQ4fL9AiUmuCeeZsho7/bdtGfnNlo4byYtevcV2rnqD+rdszt3C3yKGjRuKqWEArgYThzeXQyaU6EIuO8XOHdG3nIrPfrgBPp9+WohFK2ystCMipgg12d3yERqEXfkefJ8NPeuxfmkKHFJcB0jyEmzZg2o80md6I+lv9AuJgzMteaNr3mJC1nEDGy5d7I6GecfoqkHD1CG2aAwRcy+RMmjFdMSHXQUbxzNdiWR1vvFvttdPXQovcBEYTZ3ucYDPYtJyhI3bICIdRqi8Y5hOm6YB4A0zYP44WdPkQa8MCIPkKkI4gUSle8bXg0obyy/CKWx5cAKDizV7uijiW/GxMajlM0/rJegl1wGwa56dbr5/vvpjfnzqT83nFbld/EjW7Sg03i7+EaNiLtiUDaTxKJe9ICEtgTvIt7fyVddRTcMGkSPs5rS5ya7vQIjn3zsOYNgW1DwXnbZZfptF2bMIyX7/O5fIHXHjBkjwdj69OlDTZs2pbfffpt++OEHuuWWW+jBBx80171zP9DAsZFSo1oORUAROBQR8IzgRYC1e+65R18CPDyL3B+lzZs3p2+//ZZq1KgRVoJ38tNP0TXX30jzoOB1efCCCK3DBO9tXc+i3Vs20KQfl9KmVaFZNIAIrVA1jlL3EY099wi2Y9hON01ZQEec0lKIw1R+f0/nH0hdzPvfkd1EkSOq89swMM45CEfTg9zI5Yxwzj8t793ueaxyedG6/Wn9CmGk93cXD5q2ebnq2iXmM0vzEy0GMUYy68ggc6xy9TaX7Jx5jG3+Is51fnlN+5dhF3YfyMduB1z4GPEdxfagYpcBTJf/uptmPDKGvpr9PD08/m4aMep22aWSJB5e3GHIyl0/8WyBcs/4iTRw0BBavfpfIRN1iBwErJq0cZOmdEHPs9myYRstXfpb5BSwiCVxK/3RnfP3338XgveUU06h7t27Uw8O3tiwYUN/rtaDvbTVPuhiOvP11+mDuR8R98+QGx4a2EwvD9OgZlSleEigZ4aRYCMopaixeZlRzZreGaJyF14HDXGufPwqbyc/3jibfXX8DXeu/KR3ibtRwTVjvKBNIx3KaHuaSLBALmveH9FuVXmQwjwfwbqpGKx0CmLLg4ZVc/c3SVyWJWI7Aq9t/BybEjkPQB5ie7E1QZuns8yxNjGNo8a2BH7eu3bvpt379lBKlXJ04YUX0MQJE80jjtdHc5dfe46H2supiJdcyMntSz58VF9++WXK/uUX8W0qMcFre3Xg2QIidu9eo2zFgJcKJlF3sPVM1apVi0TQynOMFbMHOL+bn36anmIPWun1wOdKPOdbLEWwCy3JPzmZtm7eTPV795b8Rlx0Ed1wwQVU+8gjhejN5F6JuLaEDOZjW8G+pHPYE3fiq68KsbuMPUmXrVtHvU46iV5lBSPUwFncQFdiwpzLaQneLz79lE5lUv56Vs8+MXVqqRK8VsF76qmninp3EJPMKt4J+ZI7aBKC7MX14vbtRVA22L1AcATiv2PHjtIDafXq1TRnzhxRfcujIkobqg+aytMDUQQUgUMaAc8I3ts4Qu+9996rLwEenk7uB2STJk3oxx9/pOqsJgiHgjedlQqJ/EL+2OOP0k03jWKLhpwKXgTkqtU4jm7vfhZtWPkHTf5lHe3ZVniQNRxDcnn+MuYPvqHtq1Cro0+lO2fP4ZdVoh0bjD8lPlrFTgHY5dcLLw8pXYHqury6ihfQUzxX13V35uGS8bnPlQI/vE1CB6EcZ1huzCybnftE9O/CvS8HE/OhDguNeFZZx1LVWkSb1hM9eM0AOrxmLL089WXnpU2VvB5e4p5mFazgve+Bh2jQkGtoDXezs13oPN2hZlYiBFBfDRs2ok7Ht6dWLZvTvPfeC8vzszh+s8U5MBBaIEWg4H3ttdfomWeeod9+C5DWCNoCsuByjgwvpI8zWFVvODx77TXx+aefs/3M5bRuzTrZa9cre8g1sW/3Praq2U+pe1J5fIBJJR7v2U/pqRxQMJ2DC3LAQZCQYjVh2c3igFPANtwng//Y4kesf7j3hxDOxkIIjKrpqg7vemO7g94mPFdsz3ePi+9kFyNd6SXYoPQgMdOJybAESpJxcjkel0vmHwcbLMfBBsunSHDVZCbb1v2+mn5e8JN4Zh937HH03fffOc+b6FGz23MNDfEgdY855hg5BlwX1tO0LAhr+5J/KSsxX+XrUhS8sHIqoYJXjpfJ3Z1bttCtkyfTMzfdRHE8n7FvHyVwY85t48bRFFb9bWKrIKhwoUDFuYzGiRwkLYh+WGs56/C6BYI3jVWxFbt0oQNMrMY1a2aUvNvZyovzR+OL6WlluhbZAJnIQ3ptobs5n484dqTNYfOAhglOF8NdzTOZRH5q5kwa8+KLkrYzE7wz2Gqu9mGHEUsVzbmNfdSvTzvZ1/4i9h39GMeDcjDJ240D1/3Hx7+Uu7BzFDLKcnoolOQak/qqU4fmf/wxnc5d4K/nenti+vRSJXjtOYOAWn379hUVrxK8JanV6N42r8YqfI/ivEAAtscee4zbd/aKL+8JJ5xAH330kTTwiq0eGko9ULZHN4JaekVAEVAEShcBzwhedN1AK56+BHhXgcEKXnSFwYdxOAjeDP6YTUiMpwkPPvz/7F0HYBTF+33phN57lSYighTpvQnSFBQQC91CEQUFpCoIKigi8EMREOGvoqAURRGkiYACIooKSO9dekIa+b9v9uayCQkkZC+5hFm47N3u7MzsN7OzM2/evA+vvjoE38SRaBAWbp4ifhjeqt5FL1sAACAASURBVCn2b9uAj3aGcPBLYkZ4wgMwS+osGvmL+RAYbs4ffpiw7juc3E+iR0ikWiZn3vvO1RcnYlLcNKLvMkjKmDUAebkC8e0+Y5D52kl8MXuaNd6RAZgTiZk4HLWAbheuXg1F5swZMf7td/A0GbyHDxuA11FDOxCZHvjkzZcPNSqVR4EC+bBl62/pjvVymiw5YQ0K4CveuGUT4FIYvcICEic+9s3OBE6ume3vz2zZs3FFyiUUylcQI78bh7qVG4JwrrR2CkJVhFPVslkgVIT8iw6j089wgr38HsY9P5EEfK9dDUMon7Ewfq5d5j7E9ZvgcNi1MK5EiVLgcGQEpX/C+Z2gj/yO4m/1neej5LfIA8lyVgGPBXRS8kHWknfRkleDajo5VVRhNW+nBZ3Vt5gVJgJgUaZHpH8EHPYluKr2IssjgKvI/LjOiWSPP0FVf77r/YIC1DtfWJHyOyCQv4P8LRCWgGwgHZ0GEnwNkr36BBGsFcDWuk7kgNR1jM+P1/oHWjr0/qJjD/7mP9kEvpYcW+8M+3flclEdI6yOk+eP490u47H++3X4eM7H6Pp01zTVn9R9XwE9xGeCsB9lkqNcuXLuqqwAR24pyVzX+epAlurXX3+N6/IcEtxNLhgpfQS/TJlwggBPQT7P39KB20OtWoEeI8EXEPLw2f6KY4J61O2MPHkS/jlzWlIIZGwrSQN55lwsbR+RcxH9KLJnVX0nwCnSB4UZ32gyjy/wmt2HD2NIly4oWbo0wsmWDRSWsOji8pm6zvjkmVGMdklDzgkQLBNJMlEigKwLRNaFIc+dL1fERRA4fpBM2ZUEqYZ99BHepIOpyWQN96dmMb2VWv3trFmxZ+dO1O3bFycJWkdTdsKHx1C2LEqVKYNJDNu6TRtEsr1LLmNbA7yrCZI1fv559KfvjclkDqekRINuO6/ShplYxmYzFhALxAf0fkJ2edeuXVGnTh3lh2fGjBlYsGABli5dCpkgkM0TY1ZTIsYCxgLGAsYCCVvAMYBXvGqK2LoBeJ2rbvYBakYuE9u/fz/7vfk98rKMiAhnnzgQr497C6NHDrsR4GVnPnchX4xo2QS7tqzBrN30Psx+eNi1hAFeYf1my+OHvzf+jvefa4UP/zqGUPbfr4XScQYHoWbzbgtI+QmTrHApH/Su0xQdH++I11/oqTSLRQfYbN5lAd2JFgalTARNnjodHTqRYXH4kGHweldRuZktefLmQ9X77kaJ4sXwyy+/eATgleW2+l1iZ9Ikl1WTEONWDwLtyzrF/ML4Ee/cy5cvx969e1VfQTx0P/PMM4oJJPJDTm66L/Lnn3+iIpl5ebksYfz6SShf/l4cPWsxeUUyQTa1czGN9H0p7XICpJaEg7AECZa6AUoNVFq6oxaAKe80ix0ofynmoP5av2J0cfRqEX1WL1uxForEXi4Sn1PE+GykV3dIehSDsBiK7uUwMWe54Nx13oJddR6t71b6AnBbogtxvwkQrs+4ZBkUGC2gtCXHoByFKl2GGAedotegIHR1a7JSxLoDJd3g2iTtErlLYeX332NwywF4e8LbeHnQy7fdn0wsa92JJfXxlcf69evxLEHDf/75RwG8ok/ZSsBP15aSrF79HLRp2xbf89mLEICXoGZyAV7lqE1AXsqHtenUCYXz5MH/JkxQgOs8yqBMI6v1F4KhoJQBeG4f013PT9v69ZGjYEFEUwbBhxMIIPD798aNOEJw9EGZ8CFw2o6SAN9s2qQmPwqwXahABm9Z6t2+QrCzsOjeuiSHDhF0zcq+cQ4CrUoigmkfOHAA2/79F+25WmDBxx/jPEHK3gRfBegVB7W6zBXASyD6BIHjCgSRz1LzFszXBTqhu5tAck+CU2PpPDqK5/2yZ8chtllNBgzAHrbTauP1eyn5Up7XbqdGbjlK00Tx3Xujs934akjCxyLZXvszHyu/+w7NCBz3p20nc4IsUljJxhFC0oxpQnvEAvEBveKMTyZyZUJXVsfIb3HCJpNcqr03kg0eKQsTqbGAsYCxQHwWcAzgHTx4MMbREYEBeD1T0WSAMICdS3lxeuJFGR4ewbgDMOL18Rj32ggsjSvRwJdzzoIEeFs0wM5f12H2Li7HJUkiLDRhgFeYTOKY7Y1Oj6Nai/Zo8kR7nDpEL6tkG5ktbVhAQN5M1E8+f+IU+jWpiE37juO+nL7WwM4MNryqEGMYvFdJoMqMGbNmo0Wb9jhGhpWRaPCqonIDvPny5cf995ZGxfvuw0ou/XWK6WJ/RzzwwAM4deoUZTIzuh3tWTqy1qa/24/FZy0BRrQuqjDlQmTJdJzNDhpLHrTDFmGBibyQ5OE8gR1hhsn5c2TpXSRrT45LnRU2kNbwS26J6b6IgGzlueT67eXvoUHzpjh67hDZpnx53WSTJd5uC7lxSAvyVFsMNuk+oHRnuYni0A1rHLRGvF77cIslEG5wNiHJInve3bK5cbSEbDq6rpJ2Zd11ItY9uHRzdc7juwfbbVnZt91EnPtxaxYnqLkU2/jCVM6YLRgh/4Xg4TwPYgWXpzdt2tQj/Uk7+Kt0hF3PgmbV62fiZuBvQpMjcSc9tm3bppjqolspz4z0k4UMIZ7o9Sb11L6M2WnQWT8HLVq0wKq1axFOAFPYrckGeHkDKm46/p1HUGcEQc6DBLYF+CxJoPZd9lfbCrBK+/6PEggjyI6tSrD7HwKwfxJ4zVGgAEAphh60yewlS5SObUke20tQ+P3Zs9WkyvsLF+IXXperRg2Lhcv2Qti5vxMobkV5hJNsS3LQlgUJEi8gW7gs0/2aab1JxuuDbPemkrFcnc/+vGHD1ATSdYKn2r6qD8NrT7kA3tOUyFEsX2Eas23LRQb2F4yzCeshyBA+ffw47iLY2pJ5CWa+95Pd+y+vXTR+PGo1aoRo/nbC0ZoGeFcwP83798cLZF6/RxkJBfCqxy7l1k9J++0J6Zzktu3meu+xgL3PIu1cfU7gdOJzMozPnKzQkfe+SDUJs92p/o333L3JibGAsYCxgHdaINkA7759+5TmnnjRfPPNNz3SIfdO06WvXEVG0oEFl1wOGf0GJtCx1tLjsZ2syYA3ZwFfDGleF7s3/4yZf0UgOIs/l6qSBcFln3E3GSwFZ/bFhVPn8fHwbhj8f4txiX1z5ZMmMYPW9GXeNH03srS4yN3+GNW+C7LlK4KlM94kwEFWoG+gV0o13KmDEt15DuNS8gzUvZwxaw5atGqHE8eOEtCiN3OzeY0FNJhUgEytSveURt1aNbGQYIRTE6Qa4H2dOpjTp09XjG4ZYCndSpeXa83CsR9LjIEEIJEJg3/JkrsVKJxQfOL0TzT6RH81SPQ2uWT8KJl+cv9NqLvpBNitn4f9e/dzWXdJLL2wEhmyZMDViyFk2aUcSJIYm97pYYRZmSVHNpw6cBKP3/UI5s6dqxz4OPU8pLZ9L1FiYPTo0UqrUrZGBATl2RQHRXE3p0EQHV9jApW/kHF6laCzUwCvlmm4QlkG0cuNJkv/J35aUovzClm5fMCxnYzX+0XDdcsW+FWqhPzU5/2CDpnrU9t1GHV7J3E5d8jq1QrsbUtAcw7l3nLccw+9vmZCBep5fkq73ccJsEgydP05CSTgqw9B1vZMb+E77yg94UmcGBrNzwWyXkOohZuZ9lXnCSQrarkAw9oBnKsDqvJOtvBhroy7iyBqBPPgw3btMoHkLGTjfrNoEQZNnIjdlEoQe10hmJyleXOUJ5s4mCDzcd7zZab1DmUUepEtLNITihWczA6uAngJmn9P0LslQXJxADcplQDe1H5uTPppwwLSxsgmk1iXKZ8iE6qyF1nBHj16QJybyqSu9BvSS5ueNkrG5NJYwFjgTrWAIwCvdF5FXP2tt94yjbeHapIUlFoy6qHZew3wDhoxFpPffA2L4wK8BGZz5AMGN6mBPdt+xbStF/k7K0IuskMbj9yCaA4WKOGPLydMQcjl8+j2xkgc30u2BzX7zJa2LCAs3qyU2jjw+18Y9XRL7Dx5GOTeeDWLV4NXYmlPPjfeVJJxnax9MHMOWrZuh5MnjidbF9Cb7jO95EUGReJkrWrFcniQzNKPPrIc/SRXw1Hso+vCSC4x/uOPPxR77RrBCAFSZZ9U5yeSJ/nIAE6kF+QjwKzSvLRtwvbS52Uv12jnawIya8crh8l8E2dUv5NNeJL6nLJVq1YNzQmgPPzww6hMT/XJXamigS1hL4u00f/tX4j8JQoQqLmk9N/N5j0WkP5Czlw58e9vO9G7ald8NGsmenbvkeTnQdeZGgQApX5JuUv7rycSpM4KyKD3why3f4RZK3VUf7ISAJTzMiEhcci18tG/9TMgxxL73MrkxfDhw7F582aI40GRchCJEknHE5t+DuqTTbeNgMtlArCidavAyOT2J2WyiB9fTlRVpg5vy5o1sZrxt6tXD6+QuSygavs+fXCG6YljtQ2US+nSrBk+ppybaO4WItvvc4Ko9bicG7t3W+xZjicUIMtyKNGyJeaR4VuHTOAd69Yptm7hYsWQl+DtoVWrECzavQSSQED4foLAPchS7tuzJ3yoi3uATODilG4II7M2gOUTF3hVAC/rgwDEPmT7bqF8TFVer7R7CS4PHzQI85jGIbLJlQM1fnIxP4cWL0bWChUAMnpP0YFpDQK8fdlmDSQ4Hcm0ElsPEirruADvQLIhJ4pEAy/wdzmV80Q9iS9OvWIjpdIz6aRtC9gnp7QWuUwEyyqGZWSkiyM2Wc3j9CRW2raayb2xgLGAsYDzFjAAr/M29UiMKQXwvjxyLN4b/zoB3nBcukAQj0snraW5QPa8wCuNqmLvH7/h/V/OIW/hnLh6IQGAl53mfMX8Mbpda7R65lXc17Am2bx0VmG0dz1SPzwZqcvHD3LS70m3KndjwswFeKJhBa/tpMXXeZSBvx78J3tQ60ljJyNuOyAmYMSkqR/ikQ6P4eCB/UaiIRl29dSlUl7Fi5dA3RqV0ahhfUydOi3JgJan8uapeFcRMBFHT98SfBGQNycBnfbt2yutPgG7nNx0O/AfGXmyTHTe3gUoULIwAd4LBuB10tAOxCUAb65cufHH+t/Rv15vLP2WDnoeap3k50GXuUwcdOjQgVjhJbXEXHSopQ8l5/X+dtnnt3u7MhmSh1q0hcnOlDwcOnRI5U/yJPWzAkFDYb2JF/pp06Yle4JD51O/F2rSCdIuLpU+TyYtl905A/AyETWIoLTCDwRwHqRmbEkyXPcSBBXgVLRyS3DSZhElxiqRrRzBew6Q59zlTK0hgdHTZMZ+TSmEQpyEOkZWbDjtUUE0dWmT+wm4vsPVgY2oc1udOtpdGFd/OkDzL1UKn3AJeBcBhs+cwQRKMrxFx3ZHmW4GAuU+BJqFzZuNTiyjCSqJ3IN70/I0ouMrH4bp+vTTuJvl0oUs589+/BEzvvkGRxnvNspOlBd5CAGdeb4cy2YuHUlVo56xciZXvDjeIgt5KfV7NzDtSK5CcATgJWC+nPZsQdD4ZeoOvy1O1lIQ4NUDw8fIHn700UfVx6nJx9t9fsx1acMC9v63SNKIvKBo7o8YMUJp9J7hcyUTzqY+pY3yNLk0FjAWSJsWcAzglRk6achNo502K0KMBu8bGD/2dSw5HoZL5+MAvHmAgfUr4dDff2DKrxw0F8oRL8Arcg5+AeKcBnj94SoY+vkWet72pTfyhPV606bV7pxcywC8SFl/jHmyJ8ky92LqqAEE/6mn7CVMON2pFDZgAQ42hbn1NAdtbcj8qVWrVqyC0pqHcdmHab007QBv8WJFUahoCSz5biWOHjqIKGFZJcTWEvA7OTd/W9fbUoz/a3JylOhr3cN+9SVxy/YtMyYurAoZj921Pm2xYsVRo3IF1tEa+JQAhdPvT4kvpTfN2NX3LcydyZMnK9kFvZUuXRqvUEOzO50p2e0TQSdJ8tuJZ1O3CWcJxAiw9vHOz1Hs7uK4QIA3uSBMSts0vaenAd4/N25Dv9rPYMOmDahVo5bjz8Ot7GiXLZH6o59T2ccHEMuyYwFpBZiV78JQk73oU8sx/ZHjImUmHwGbhSEskxsCdhwkC/TYsWPquGz9CJK+//77jty71huWZ6oKwcnDBFrOkDkseraOMHiZX/XeEWdp3HwI3g4iGDthzBhco/RBhkKF8CH1dJ+lTu1ytgH3Efz9jpINcwi+ziPgU5znm1GGYB1XGQSxL3FZHKK1a4cPCd4Kg7cL2b8/knVcmYDvPoKn/xIg4ssdX1OyoBcdumWjZMM12q0cHa/NJiu6GJeGg87aAijRsPmDD3B/lSqIIoBs76fo/J5huCq9etFJYJSaABXJhYxk9JYmkNuTbOSenCAAnatdoM1ysx/RgPe2mXrej5B1/Cg/h8jgXUm28mLqDm+mTnA1gspRZB4nt08U18naEILY4+XdQPumFINXv4fq1q2r2uhuLFOn3023ehbN+bRrATvzW4O8R+gLYiAnQxZR+kS3dYbJm3bL2OTcWMBYwLst4BjA+wJn1SdyqZXpBHimwD+mlljXrl0VGyW5S1fjy6G8cKWTO3b8W9SKG4Wlx67hIn1aREUJKGsBGllzAS/VK4+jO//BlJtINMjAIVN2PxzeuR+fDO+Osd+vxX8nBGAy4rueqR2ej1VLbnw9bS4ObFmJ5QvnEeAN52DG+7RdN1L7T5bArhGv2NxkSZgsF+vdu7da+q03PZgXMCkhpzmet6yzKegO88MPP4LFixdhyLBRGDx8JPZxkBrpAs90ivqeNcAWd+92/MM2J9aTK1Ix7mz7WM+165gwpfR1VvzWleqv688N3+WEK2jMFa4UYu1udOoUkxMuFdZusWyesMSdVGxnWdYR93H1xQqhjlr/ratc36Ojhf0tzpgs/VqZwNLAiXKsxeOuK1RcFltcjtjCubRv7bYXb/CXLl5A2eKF0OLBB/Hd99+nq/enOMqSiV9xciabLH9/5JFH0Iegjcgx6E07YnP6OYw76fPpgYXIV7wgGbwXDYPX2WYn2bFZEg25sXPrX3iuWjeu1t+NMlxmf7v9SSl7ac+8tV0XvWnR4xWtYXEyKJISrVu3Rh2ybGsSKFSthwNe5+1x3EeA8gzTPSEMXsqWKGmF5Eo0qIaPbaK8I/ie3UwwtkLJkvS/kBnX6ZhMSaGQibuIrP1hM2YglP3M4gRom1MSYSA1eAN4TuQQFENWAG7Jj7BqRXaBYOsF5rMrnTfXpibuIGoy+8g5guWkAKrw1wiQC2OXs7qKFRwtjG3GJ45FC3FSRzl8EH1Qmy6uum+RluHxT5YvRx6CuBVKlEARsmbZSAGU5QDflcIMVtdyGzhlCnZxxYE09ycIBF/j+dwEoO8nk3go9XcLsK4qWQm13Cl5/VxV52mj1cxbY7aVQ7t0wTjKR6QGwCuSOTJR/jhB5tt9FpPdOJgI0qQF7CCv6I+/Rt1tmeCSFQrC4P3555+tfhOfGW9tp9Ok4U2mjQWMBYwFaAHHAN7+XEr0Dh0emE6Ac/XK3jkXkOoAvQ/nzZvXI0vjdblNfnciBg4dSSdrIbjI/qoCeOWW/HzpBIUAb51yOLZnF6ZuvYxseTMjNB4NXokrd0F/bFy0Er9+9ylemTcHJ4z+rnMVIxVikgFz9tx++HPT31g5dRDWrvxeMYBItUsClzFlMy7MKhlAy3LXXbt2qcSlYzmAjCEBmbJzYKc3YQ7qTqbsnWAQpuzdWqnp53jkqNEY8/pryMbB6qgx49Hz2eeV3Mr1aHrFlkF0VLTSUI4S8JLP+PXrka69gJg87mKvWedcjrlkIC//BOh0AZ7XGU9EBJc/R0VyH4EofmSvvvNa+AggKjmzrhEQ1gJJNWAqeRKMNAZAlQF4NPMZzbgVCK/OuY7pOCRfkne93FY1UQLoEKwn4Cx6i8r7tw8hYO6j/VimRJF9XGC+Pu/D81Z42VvfVfnLIF1d76fYngHUDg8MzKD0ZQMDqeMZFGAB2S4QyU/SlfDCPqUMjVwjziflu58f9WuZrnznUEalJVq4O//egddGvIq1a35UDFd5h6bl96f9fdWqVSuluSebAFbCApNJFpEO0ZunmfQa4D3I92YJMgfnH12CXAVzcdXJVQJPiWdgp8ZzfKelKU7WsubIjsO7DqJruc5qSW9JAoXJYXjpSZik2DKpsg1xw8tvXa/tdV3ysIXA6hQChfPmzVNZKs7l/c9Tv1UANOnXeWKzP5P3UALiKpm7hwRYIXvVMYBXMi7tsLxXBKylVq0CSKUN1YCngK1yXhydiaM06TsQKI1mWFldopyAynlp523vYgoT0/kDO54SDwFUCS/tbCTfSYqFz/ZY4rpOsFevHFB2dGnrqpm2+EBXOS7XiuavnJd8Sb5FxoPpS1wCAisAXPImQLSkJyC0XKvTlu8EraIJLqv3ajLBXfc7nADvWk6QNaR0zTACyGPpQC41AN6mlKyQtrszZSLS8rvJE8+WifPWFrCDtyL3Iat4hMkrk73ilF3YvaZe3dqOJoSxgLGAsUBSLeAYwCvLyt59913TWCe1BG4S3t45v4sDVNGVk6V9yRn0JJScrggfTH0f/V8ZhiVHL+MCJcYE4JHNlwBvJvqzeLFOGZzcvwfTfruKLLkyIvSKMGViO9qJjIhEXurvfjt9LhkYx9B17FA6WKNnYOkomy1NWkDqYnAmXxJ/LmPpqI5YziWWsiWWZaSXuDo1U68GYHHYRzpuXZftIK0s015Ab93ChJeBtmyi/TlkyBBUrVo1TZZJfJnWbcNvf/yDRvVrK4aobCVKlET2nDkQcuWqAmMj6ThPJDYiOTiOioxQx6J4TGx3/Tr3KgyHlG76a7oxkcdvxHJERlA3IEjtA/wtp2TS/sn3LAQ4jh0/htOnTqJ06ZIYz1UTUheT8jx5/CZuIwG9CmQQnRPJZK8sx+zFJdBlRU/TtWktbKfZuvFlV7cDwiAWr95LLq4gszCYTj9DnGEu3oaNzCXxW0DqRYZMwbh49gI6FmiDP//4ExXuq5Dm+5Pbtm2j88SPMGfOHOXcsBgdhInetHiWl8lG+6blVJx0Cmp/P5elI7IoSkfsXbvWYvDKhJSTFVLAbRdzOta72XVcAFCZOFPvGKYrk146nB2Md18rqyHkI+F5nT28bit19uOmJ5OXkp66v4RAVxcYLwCvNblnWSNuXJJXybOvLZyacBRgmedkYlH1NRwAdyV91W5RF3gdtYAb0AHfKK7cG81+S2oAvMIolzZcJiEMEOfkw3LnxGUfrxaiJIsweMX5q2juiwM2kWvyxJj2zrGwuVNjAWMBY4EbLeAYwNu3b1+15Mx0ApyrZvbOuQwMduzYwdVjWT3yMtTlNmfWR+jV7yUF8F6MA/Bm5Mq1F2uX5jK/vZi+/TqCMvogLCRhgPerd6YjY9ZsaNHzcZw+xE4rWXBmS5sWENamP1lv4RzRfP5yB3y3ZKG6kcQCvN5017I8VgbdonMoS8ZEo7cnPW/fRy/cop8oUiWyZDatb9v+3oM/f9+Kc6eOY/v2bTJ0tZip1qjX8iouDFeXBIsFLKghruucxWa1PtY1SkLBxW7V9rEPiONf8mtJMMS/cDWhcze7xp1ynCK6uZKwwqpj/rivtQ7diGRbh2KO20GImO8iyeASdVAsY2E7W8+FCqP0iS3JBgHOxYLC3s1OVpqw9+7l0uP0sum2QJ4hATzsDEYBf+VYSjLj9Tvt3z3/omyZsnh31TTUbFQbx07TERIZ2InekiVQHV8qlkSIlgqJNx+OpxknQlt1T2xSbjAwtl5L4swYS13lRqmVqPAoZMmTFXlAx1o+5TD/8/no2KljmulP6rovKxcmUBv2e8qt6CXIwkQWeSABdcWZmn3Tz4Vm/yfOmIkPFYskQBmBYDZOfxM4BLXqHQd4E5EtqWuOgsqJSDOtBdEA78+rV6Mu680Y9k2Gs7+SGgBvR8podOrUCQ/TGZ4Z26W1muQ9+dUA7nnqYYv2+Gd0Gvjrr7/iyy+/xHFqWcuWFscS3mNhkxNjAWMBY4HYFnAE4BVHEs9yptkpxxCmkCwLxGXw/kFHFLK0xRMvQl0RFn+9EA+3fxTfnIzGVcqgRUSQCSGdcjJ4M1KerH/N4rh87hQBXuqryYq7eByniZ5e/hL+mDt6HHIXKonm3TvizBECvGSzmS1tWkCvcgwmi3t0u/r4ddVSMq6y3bIu6roqOoNvvfWWcn6mQUC9vFX2GhDTcgD2Y3ZnNzIg1hIAsg+lzp8ASsKOEsc28lvvJawcF0BNjsleNmFTZiaLMojLOAXMlbyJnIPkS9KS5bKnyHAync60WVfTWq4VO0yB6+kL+vCUtm5iy9ftZO0cnazlzoPy95XH1D9mU6rDH5dCL8Sr+yfyGW49aJHtsIOZWota7yUjCbD2Elbh1JrUWtc6ob1EHlMf7JrXKln3dElMSrFrT+wr4qhou2KIP5f2eOKCvyKyItuNALV1XIe3wunQljq2/qsmPJRMiyUPI1vmwKwg3x1Te07E3Fmf4PSZ06rM0gqzS+dTJuErVaqEhg0bQpYki3apvGvsm6dBXXtasUgCBJqz8X335w8/4LqAKrG01BP7VJlwnraABng3kmldm+Du+GeewRA6jEtJgFffo9FH9XRp3znx6zGmSNQ8RdkRkRy8++67lb8M+aSVtv7OKTFzp8YCxgJp2QIG4PXi0rN3zjNSi0y0i4QB4okXoa4I69evQ716DfD5v9HwI8kpLNTS4PUJ8EUw5dD6VS+CiGsh1OA9p2TUIsLECVvsoaVyyFXSHx8MGIwi5SobgNeL61hSsiYMxFwF/TCgQU2sXDAHRUuVvWVd1HVVvITfwyWiAqQmZRMAVoDYYDpXkb1sdraTBsY0M1D2+ph9r+UbRMtalsYKE37fvn3YuXOnAoXFy7nEL0vIvvnmG5QrV+6W95aU+0jpsJphqoHz9AYeprQ9nU5Pt+3pEdjVkzQpydaNr3zs70+RaBCphvvrVMGQ+aOQNXUX3AAAIABJREFUpxC17EWPWgO0SgfapT0t+s6UK1Ga0W69aGsSKkY/Wjvbkwtj6LBuB32iG23LlPu70re2rlXxi0SK0gklAKpkU7jsm1rZUaKJLftICcu8iBY186WuU8vDJT8M69KpVnv5LekqfW2XhrXrniyNa0nPYpkrrVOJw6Kbx9wnw0n+7K90P67cEBkmH+5lObpM9vpST1rrTbv36ryEo0RJED+BIktC2DYDJUrImJbf6pOBsiX8LSzqgIBAZMycEduWbcaU3u/g4PFDeH/K++jXt1+abn/j1kcBdZWdUtihp/0ZKEKZr3x8/2399ltcP3HCALxON+oOxacB3l/XrUMNsr7f5kqPl+lHQOkOSxrpbCLQIbOZaNKQBUQWTfrgQ4cORbNmzSgJzgk96nR7YmybhsxismosYCxgLOCYBRwDeGUJ2tSpU80yHseKJnZE7733nnLCI4METzB43Q5pDh6kXmcJTF61D8XuvQuXz5A3IM6CAnyowQr0qVpQOQmavPko5Rno34IM3xsB3nAUKhOIiV2fwd0PNELzHh1x+rBh8HqoaqRYtAI+FLjLDz2rPoBv503FvZUfSHKHTEAWYaHrga5MXAh4azmlShkGo/Zg/gkdlwizt3bt2mjTpo36iBSK2YwFjAXShwX0u1ImmAa9/AoWff4VwvjPbN5pgclT6Gywb3/vzFwichV3Yi2lZUniZtHeVyzEd1tRLo/etGSJYfAmoixTK4gGeDevX4/q3brhXfb7X6QTztQAeHV9Tqm+WWrZ3KSbMhbQ48z/6OxRyEpffPEFZs2apRL/gSsLDGM8ZcrBpGIsYCyQ/i3gGMArGpb/+9//DMCbRuuMfvEK00i0OQdMXojm3drj9EEuHxZP8C6A9/n789JRTTZM2riH3sgtJ2zaOYW+9UiyVQqXDcTr7TuhUqN2eLBHJwPwptF6Yc+2NBaFS/mjW+XqWPjRBFSrVS/Rz3tyO26aPZcYM1pstyg341euEe0vcQIpnnvlPoRNLE6gRGNOWL32LT2wCPTy+MTYy4RJPQukRwZv6lkzcSn//sfvOHnkuJqojLW5JpiUcIGvpQHt78eJJ9GpFnaqD1mrZK/68JyeoJLz+reUpb8vw0tY20oC/V3FJcxXYc0yHsV2VXrW8tMP0Uoem2IKfow/juNSyZKwcoXpq7jBQsL1EdauxQ0WJi5/Kr1n2RTL16Uh7ZbCkbBMQ7F8hcXrChd3rxjGZDfL9UpTWjGZLeawdU6ft9KXcxJnFB0zqhUD7BMoh41Kh9py3ujmMystaouJLNeIdETotVB1byJrIE5k1f3wfFoFlbwp7/a8FChSBKUoP7R+8WJcpw49K6nRw01ck5GioTTAu3XDBlSjc7P3X3wR/cSBtWHwpmg5mMQ8YwHdv3777beVs7U1a9YoPxiyok6ciaeH/rdnLGdiNRYwFjAWSLwFDMCbeFulakhP6zTaBwKlihRA/spN8PqX83B0DwFecY7jAnifvS8ncuQriok/bceF0xwvykAxrkQDAbSClGgY3vIh1GzTFQ92fxSnjJO1VK0/TiQeGRGJIqX90b1aLfzf5NdQu2HTRAO8etAu9cy9LDpOphI6LsFud7C/fPlyjB07Fhs4WJJtwIABGDFihHL0YN9SUhfRibK4WRzeBDB4+l7TQ/ymvDxfinrCQ1YKmM17LWCeBWfLJhbASwZvGQK8677+2gC8zprZ0djUoIzl9PumTahMrdLpr7yCZ+m/QAG8KbTKSd+QAdscLVoTGS1gb5PEEev48eOVwzVxtvbTTz8ZFq+pJcYCxgLGAg5YwDGAVzwET58+PUmAjwP5v2Oi8DTAK4bUlWFA3+cx9cPZWHL8Gi5f5PEI0eP1UU7Wet2Tnfq65fH26g04c5gsGzKR4m7C2Mlfwg+DGtRB4ycHoOnTHXDGQwCvkhAkc0hYxLcLAt4xlSiZN6oA3jL+6PFAXcx+eygaNGvplc+7sHVFLmbMmDHKIZuwAsaNG6fYunoTFpqc00y89FZ3QulcbseOv3HwwD5s3/67ciTnY2MsRov2p2Li3fhR+pzyESdJisZntQ2K2ejSLJXvfgTL/MThlEtXUulz8iPPooD1dttqpqoc1+dUWNdv6zpOJJEdqTWWLcDf+i1f7eHtYXQcwn9MifGv2/GU+iJMRK3NarmRso7pvaXdqrRVORkmjv7OnDmL/Xv3olGTxhg8eIi6IrkM92Q+2nfM5SnGbE/Yy1oq21o7eUtaNhQD1y4qnLTLbxlanpHUljO4ZSbTYAA7mFKwaFHcTSenq7/6ClFk8Ka2RnYaNGeKZFn1w6lH+tdvv6FC586YOWwYenCSOpLvbH++a1Ni02MBka3qRpmIhx9+2Cv7eilhC5OG8xbQ9evjjz+GrP7dvHkzRJf377//VqvrzMSC8zY3MRoLGAvcWRZwDODt3r07PhBPr9I5MSyZNFmLdNltZceyGl+2Y79Yh8rN6uHM0XA6RwlE5uxA97LZUKJCDYxf8QOO7Yu/rGWJZp6ivnihRkU89OxINHmqvccAXgF+MnCFfThlFcNDItUYVAFLssQ2jW/WElkuy+Xmw+XBCThsT7G7dAO81evh47eHoH5T7wF4dYdQWADiKE028dQ7jIOjMmXKuG2kgEoX+JhihkuhhLQNPv30UzzxxBPuVLNmz45sWbIpgDFmk8XRVv0SJ0/KmZQ4lxIwUjmWEgdOMcCl+5g6Zy2xVs6hzJYoCwRTa1r0poOCMqhl/yEhV3H27BkUL15cOf8SHWozqEmUKZMd6FZgultOwAZoWjU94frOqQV3vqwnK4EtuY9MskBWtws4W+a0qIN1KLZruJuY2nWLN7vXpEya3Wz1RrIL/A6OIBbASwbvPQR4fxSAlw57DcDrnRVD9cPpCHbPjh0o06ED5r7xBp589dVUAXjr1KmD5+nk7fHHHzdjO++sLmkyV/Z2KTv7p0OGDFHgrjhbEy1e0xdKk8VqMm0sYCzgRRZwDOCVWd4PP/zQdAI8VLgzZsyAOLJTw0wP6dPZ4y1eOD/8sxbE9K3byNS1yDvZcgNdS2fB3dWbYvSir3F8/80B3n5V7kGrPmPQvGt7j0k0ECvBgR3bCTpXQu7CQOgV4OJZcf4WqRiGqQ2K3m51EHBXCJfBmS1cIfTq7cbk3HV2gHfuO8NRp1Ezr3zet27dqtgA9i09A7u6TZC9gCpVqlTBtm3b0KPXM3jiqe4oS0aE0viMh4KnNDwJ1ijGrvpuaXe6dTutH9aVai+BhanqCicsX7JTpUMugHAkNTeVBqeAxXJMNDi1LqcGjblXx0S/M57z1yWcYr1Gqvol7NcI0fcU1rECl+W8S/uTx6KpCxpFoFo5hHHdz23VevXMEbISb/eijyqMZJceqkywyGoFfx73DwhAgD8/gfwE+COQoG1QhiBk4D4wMEiVgV+AH88FIjhDRhVGsRNdjgTFhrLq4D8CvI3q1ECp0qWwceNGw+K9rUJL/EWqjsoyZ5mAlpUfsSY8rHgEsFRgrQCYDvt8tCnRWs+PSk0/lbF/yxPGWuLOhISUnKln3J1TnWdr73B2E29YB0LaV1QY4NEBg7qisPfpCnMlSzku/V+5cKEBeJ0zseMxaYD34M6dKNGuHT5/5x10eumlFO1r6YFho0aNFMDbgUCzIe84XtR3dIS6Pi1keySEhGXLlqFJkya4dOmScsRsQN47unqYmzcWMBZIpgUcAXivXLmi2HIfffSR6QQks0Dsl+vOubzo5IUnnsBz5MjhMYBX0tYVYsGChXjssUfR/62P8fBLXXH430jkykcHW6Wz4966D+HVLz7F8ZswePMW8cWz95dB235v4MFuHtDgFTCHQEpgRmDMI7Xw59ZNaNC6E5o8ORBVmlVVOM9/xy3AKilMIgeLL1lRXSewlY32Xjr1A2TNmRt1H+2ACydD4EsmtZ849rGTxFy2SFaCibjYrcFLBu/8Ka+het2GXv28S11WwMcdIt+h24sHCG4fYVux++AJiLfi/86dcztVilvMCTHnFFiUkN6B2DNWRISeXIx5kYGwlBVc0go2iQV1TK5zhbGqsEgzWMCaOLZyQ1j6mHI8pS6y4tTfdfouLM5C5KxgydrcDEmXkyobY9Japi4HCC7LX6UP42I9u0BsS/LCxYh2sZwFmJbN7ihQjpUpVw6rln+H9u1aKda03KenJu+SZZP0cHEy28gwRCFM/bvGT7j6Gyp/r4cjPCqcExARiODEg+wjr/N7JD8E8cNlTykYmXCM4Ccqgs7H5LdMVqjjnBQRh2QykcHf1yNdDHoBn5UzNEsaQUulCHPe7WhNT8SoKilOzqhvrp4B6zmxHMCJbIk8n5YcinL0Rkdw/pywkOXeauJBnMP5BSLIN0BNYgRwH+THyQvuA1z7QO7VJ4CTGZzg8OfkRSA/GdQnGMGc2AgKCLLiZxzimO6mTOab1CnzDDj3wMWatC9dGmWoPb/CMHidM7AHYlJ9cJbTsf37Ufihh/AlnVc/+txzbEsiEZhCqyPtAG+fPn3Qvn17r+7reaAYTJQetoC9bcqcOTMmTZqkSGIC8mpnyGY1sIcLwURvLGAskG4t4BjA++STT2LmzJmmE+BgVbG/AEVHVJawyFJeTw6A7HE3adwYq1avxrBZy9H4qeYcfAKdCuVE5caP4OVPZibI4JVBaO7Cvnjm3hJo9+KbaNGjI04dZKc10FkHN5LXoIzisRzYuvwHfP7G8zh7fD/TLoUuwz9Ag06Nce4kEBFK8IRMPL1ZDEEBfmXgG1vTTI2nhS3IvRqoapDLBhCoMIxDNEMTyxBWY3Clw8l03bqlsSuLYiBykzTDw8JR5O5AjO/4FAKDA/HmZzNxiuxkYgS4ROd2HMuT2RguoRUzUEAlN7jmYB20RyWNReFS/uhZvQ6+nD4OVWvW87rnXWyo63BaBPaTU3S6MX/qqSfxy69b8fOv23CAGrz+ZJumyiZlkaSEFZKlrtFPa7JWpCcp7dsLHB8QflNwXOFvPgr0K1CgIL5fthTP9uzqZkx7sm2/vTtMB1e5iLCXo69gxsQPMOmtSQQjA5VkhnvyxwWERvlQJ1n+RRLGJTgbJWx1xTi3tJQVyK/2rKGM10eOC7B6nVxct8wJbRZpSZ5IWStJiGirRls8XQFuFW3ezeLVoK3F6rU/AXbp25inQZ4szf3VT5mdoa/VoK00U29TQDPIfudH/VUAMwFlf2HE+xNA9hfFauQtkhcPtWuNoYOGIEs2iv2bzREL2NuTkpxQKpk1K1bQyVrU4cNGosERCzsfiaxc8eOy9dPUSc7XvDkWzZmDdk8/zb5fJFeOONuHTij3ui9Rq1Yt9O/fH506dfK6vp7zljcxprQFdD0bPXo0vvjiC7xCh4IyoRASEqKyYvpDKV0iJj1jAWOB9GIBxwBeWWIxa9Ys0wlwsGbYX27FqJ+2k0u2ZFDq6ZeeXhoTyQFq+bKl8e++/ejcfzR6ThyF3uXLoFSVBhj08Qwc3xM/aCsAaG7KoPYoVxjtX5qAlr0749QB5wFeqwNAB3D+PihSGhjSrB3aDRiPc8cO4cOXHkEFMo1fmr1A6fOGhVjsJgmfIZMfl1VTszcUuEbdXh8XkCt2leF1ppw+7EgDV+hgLiKMQ241SLcBqIxIXc94rfG+dU5YtxKXfJdOuoDKAiBbjuCuI1NWX2TMCoQQqA29xGvVGF9xAfmfg99ARThmWB8EUVe4QBHgvT4jse/39WjU5QX8/uNi3FWxGpp27aPSFsmMDJRwuMb4hKx67bKAvm4/WA7WRCsquacCd/njuVr1sHDmBFSsUl2xz2SwbrbUt4BuzPv164cVK37Ehq3b6WTtgBnIp37R3JCD8PBwFOek3YTxYzHrw6k4f56NjRnQOF5SWm937qb5eLpWZxV/m76PIEPGYLbt4Qi7RiA3lCzcEIK6IdcQcY1s3FCyacM4yRdOYFcm5ciylfbb/ZGJCzkus4B2+RJL1BrRMoGnmN4yEaidFUrbbjkttIJZzFz3Xn7Ewz6/gbGu2PEClArzne8Xtr3CwHWz5pWTQmsSQTkdFMkVhheCveRLTS7qD99P6ruA2xJGxyfvQwXAihwJP5yYDSBr19fferf58H1rXSN7RqxkTFzXMIz1nS9QYderD6+RtATaVmGt/In9IvnCylkgF1bM+h4H/9mPvJQQEE3qXLlymSW6DjwN9knjeypWREGuAPpx8WJEHTqkJobN5n0W0ADvOfoTyN20KZYuWIDWlEiQd0Ygyy8lNt2XaNWqlZKGE2drRqIhJSx/Z6Whx5rifDZDhgxYsmQJnuZkhjB5H3vsMVPn7qzqYO7WWMBYwEELOAbwdunSBbNnzzYNsoOFE5fBKwMfeQl6GuC1Aw0yAG3bpjW++XYZitxVFtcunEDtdr3w/JSJOCESDTewcsUxky9yFaRDttJ50eHlSXS01oUAbzjDeqZzKvbImptO3R64C93Hf4JmneriJJm7bz35MC7/dxzjfvgVVy9YwGeO/MCZI1HM+y4ULF0eeanb+x/Dhl+LRkAGH8YD7Fj3F84e2YdqLdrCP4MwgDmGZdajCZ5GkDQrur/H9+5BgZKlrRXbQuaKADLSCR2JXypM1pwEXKmbe82aiEaOfMDhXSfx+8qluLdOUxQlu/nqeQLFjF9Iwlz9agHRBBdyFfDHpsVrMfnZpupaYT0VKn0fchcrjfvqP4QGHTur635ZugrLZozFrl/Xolm3l9D9jXdw6WyUGpx7YhOwI38JPzxfpy6WfDIF91D32OhkecLStxenbsy7PN4ZW3/fjg2/EuA9aADe27OmZ6+SwXpJth/Dhw7Cl5/OxX8X2EBxS4m23bN35j2xux0vnjmGQmzoqzekc9DV7yEncoHqzgpw1Bq4FvvVJb2huLEWB1ZgUsK87n8UUKAEAwFeLqWwJDe0HIfMzFmArZvJq/Wrlb61BnPlGoKbMmGodKRd4W9Js7Vx4Xm9Bewy/2pyUvSixe4asBWE1wXyKlBXvjOAr0xWWlINFrDLc+KQ1PVd9iK+Iv8o4uCSWLCsZNkpruiC/bcOI1frsHZ42pJfseBtLbvCSVH+C0YGHLx6AC/c2wv7Du5H3759MWXKFNOXdOhR0s9BpQceQA5Kg6xZutQweB2yrSeiUQAvmdYXz5xBdmrgfv/dd3iwRYsUfR70e0iAt6AgshnMZizgIQvo9qlt27ZqdUfRokWxefNmbNq0yfSHPGRzE62xgLFA+reAYwBv586dMYdLicwsr3OVRneyxKZZ2eE7SdRS9ikFAtjTmfPJJxjz+mvYv/8A2j87FL0njsNJcbJ2E4D3qRI58NjgyWj9/FMEeCMY1gNLxV3SCcJm7VOVAO+4ebivYW3Fai1wF/BS3SYoWKocXpgxBeePA4unjMFXk0Yiex4O8onEVmneEb0nfKQKLSgYmP5iH6z5v/9RFiEAwVmz492fTys276i2ldHzrfkoSV3hy+ejMaBmVry2dDcKliyIM8cuoDC1iTctXc207kXJSnkxd8Q41Gj7FPNQWMW7cclyzBjUCWWrNsAeMnJfnLEKVVtUwo+ffI+zx/bgkYH98XSxrMiZvwgmb/4bf2/Yje2rFuH04T20WxDGME//EWgOIfOX8omYwXyu4LGnRk1BjrxFkJ3XlalcmQAxgYOEtFOTWTWFtZanuB/61a2H7z7/AKXK3mMA3mTa1MnLddvbmrp9+w8fxboNmw3A66SBHYxLA7yjRwzBxx9Nx9UQPtzcUqptd/BWvDYqmZDyIxDatlVbLKUUxqrojQq6PEtP3Yp5yk0RYi101GKcuo5a0jsWUKpOuTRsJYxSoJb/9nZWhbOYsjERa9O4GLau9Kw4Y4OfSTVijB605VfQQpddsbj2FiTsOh4HQNbMzpggMXq/bpDaxUJWYSz8O9ZmweG2wzfVY7HyEV8Q0aMumq84vpn7Nd54ejQaNWmEVStXmWchqZUigfD6vVCzfn34X7yI9QQMFYNXtK3M5nUW0ADv1fPnkblePaxcsQJNyOQ1YyuvKyqTIQcsoAHev/76SzkIlpXAXbt2hfj2EUKTIZE4YGQThbGAscAdZwHHAF7RaPqEIKDphHimDk2dOlUxW9SQjaBmSumLxn25NqhFB2Z578Wo+XNwLF4nazEM3ieLZ8djZPC2e6EbGbOeA3iFxZQpG9Dznhx4dtJXeKBlI5zYf4UyEZmVQ5tn7suOGX9dxZbvluDt59rh1ZnLUbt9c5zYewXT+rZGcJasmLByCf43aDy+mTYa038PQ2YycCc88Qi6vTlXebwfUDsHJm24SF3crNi1aTdebn43Zu0IU8tMZw/ujN6T5uPJckHo8+Ys6gJnwbv9H0Prp/pi4Jwp+HfLf+hXPRdeW7IN7dvcj1b33o/6jz6HbqN6Y96bn2Dzd/MQnCm7YlPt+W09Wcj/x/w1Qk5KEX4+aQ7PL8HoJYtwbE8EMmYLQEbKMnw65jUsen80Xpj+Ldo++5CqFyQdkz1sSUokVhs4KbVVtItzF/TDC4056Fj4CYoWL2E6X0kxoIfD6me1YYMGOHfhElb99AsOkRFnBvIeNvxtRK8kGkrchffefRsTKdMgmrwp3bbfRrbTzCX2d2Qh6gVV61gTI6aNxaEzB7hSI4kTjS5UMkmK0kkSn05SYFcZxHZzGKtgbnIqMQXodqGYzHgSk5aEERkLX0pB5M2YF62KNELbxq3x0Rwj95VY+90qnO6TN+PE36WDB/HLypUG4L2V0VLxfBT7936ZMuHapUsIrlMHa9asQQO+01NjbCV9CsXs9xBpIBXNbJL2EgvEelcXKqQ0n0WiYfDgwXjmmWdSpd57iWlMNowFjAWMBW7bAgbgvW3T3TkXCqNHAAlZqtW6eSOcCyyMNxbOxZF/b8LgpQZvt1J50abPWHQY1NtjAK84dBMHOPmK+aFv1fKo1KAVBn7wFs5zxXMWSib8+OnPGPdEXXxxJBqrP5uLLd/PxQdrfsRugqFFSwKnToSgc6FM+OxoNF6sVRpPj5mAR55shwOUbciaw5JO2LJ8Jz4Y0A5z/t1NzUBgYL0m+HvjKszdL6wnYORDlHooXpayDX8hU/ZcZOxmRtVmj+Lnrz/GzG2bMK77SwR515I1XBC7t65FyYq18cJHPyid4tX/9w0mPN8GzR7tiUlffoRXnnqWLN6S6PTqyxAfass+nEEHcl/gLTKaTh0Edm3+ERUbNlESGBuXbMGMgR0py3CSOsfD0GHgMEWyCrnCAbONfeZUTRVbZ8/ni4HN6mPtt58jf/6CBuB1yrgOxKMb84ceaoGjx0/ix59+xSE6WROJD7N5lwWkPS1atBg++7+5eHlAH7VUX7aUnLzzLos4mxu7HfNkzo12Izvi2Vf64uDZA9RP94xUkLN3cGfFJuVVKFthtCvbHL0f64WRY0aagb1DVUC/Fx6jn4x9W7bgNzrONU7WHDKuB6JRAC99bURevYoAOjn7af161CXQmxoArwduz0RpLHCDBXTdHjp0KNatW4cHKCcj+99//930iUx9MRYwFjAWuA0LOAbwduzYEXPnzjWdkNsohMRcktqdOz1grkIna7kqP4QhMyfhaLwAr+U4LHchX/S4uyAe7PEqOg7uS0atZxi8AsAGZQTyUlv3i3c/xW8rvkKXER8oeYNfl/0f/tn8M3qM/RBdhvUm43UhZg15HO/9chlZcgQRMD2J//VrizxFS2Hs0k8xsGFLHN/3D/pMWULHL8Vx/tQR6hz6I2+JMuhbJQP6Tf8O239cRFZwGA7t/A1dhk9H1eY10CK7D9p2H4B8BHlnjHwOc3ewY54xAC9UL4T5x0/j5cZNUaNVZzz+Snfs+SMCpSsGIIyEPcHdpvQdiWUfjMFnBKCz5QXmjZ6C3VtWYsQC6uQxzJrPvqLO7htYumMbFi76BZN6tsKcvWex/88TKFAiP8oX9cGmbZcxZ2QParYdo/O79QjM6EvHQc6yvBX8xD/CbB7yUD1sXLEEOXLkIMDLwYh4zTFbqltAtxGdO3fE3//8izU//0pJlX10gmcA3lQvnDgZiCTAW6BIEXy7+Gs816ub5WzLALyOFZMd4C2QLR9qdq2PYZNfJ8C73wC8jlnZoYhEvcEvGnkz50O94GqYOPYt9B84wPQlHTKvfi/0HzQIqxcuxF8bN+I6JRr44la6yGbzLgsogDeYul7Uv/WpXh2bfvkFNbhPyTGAbj/PUyZC+nlmMxbwpAX06rPDhw+jTJky+N///ocePXrQ+ex5ZM+e3RBJPGl8E7exgLFAurSAYwCveLycN29einZC0mWJJHBTKdm5iy8LusN3f7lSyEOAd/DsyTiWEMBL0C93UV/0Ll8cjZ4YgM7DBuCk0xIN4uCG2omB7AdP7NqC0gbrCMoWw4XTR5VH8Oz5i6JKk/Z4sNdQ5C4chCv/gUtzgQ8HdMf2tUuQkZoOkURQa7frgY6vjlaevc+fDsfMVx7HjvXf0nt4AHV8r6BG6ycw5P/m4dvpn2He68/w91MYPG8a3us9kGbiPb4zAf2rl8foxdvx728bcf7kETz0zBO4xPTe7foQek6YT6du+zC83f3o/vp0sm/bYs+WdVj16RR0HPIe08mAs0d3o37HDgilbvDpw8fw5VsD0HfaAp6j7MKugxjYpARqtuiAnRtWoMPgSXji1e6Y/vKb+HTiUDz1ygTqAtekk7hQvNGtKfq+PQ/Nuz1Bx3FkV4t4sEOb4E+iL5mBshHD2tXFljUrkClTsJldd8i+TkSj24i+ffrgx9VrsWHz7zhwwEg0OGFbp+MQgLdgkaJYtPBLvPB8T7UKwQC8zlnZDvCWyF8cpZvfg3GfvIMj5w7FoxvvXLompqRbQMoqgO/bjBkzoaFPdbz3znt44aUXTF8y6aaM9wpZLRBIB7ejxozBrMmTcXT7dlw/dkx5nTUAr0NGdjCauADv5q1bUY3apCk5BtCAWz1qAA8cOBDKrvXzAAAgAElEQVTiACsl03fQnCaqNGaB8uXL45FHHsHnn3+Ol156Cc8//7ype2msDE12jQWMBVLfAo4BvB06dMCnn35qGuLUL1OP5EAPmGtWqYjofHfjzcVf4PDu+CQauMyYAG/eYr7Uvi2Fuh2exZOjBnlEokFAR2HvbluxHNdCLqPw3ZWQj2zcTFl9kImkA3Fjc/EsJQsuW2zWAK7MzUIG6qVzYPhwZMsTiIxZgf/ofC2K2rVBWXyQleev0pFZxDUCmvRBIg7SBHil6oLyhC6AcshFsJ7L8WtkAmdQYYVJLB+RKrtMeYhAgsniUYYSZoqZ+/vKX/H5G/1w+b+zBKILoXSV+mjW9RXkL54V4WHMD9OQDMt1ErewZSn9i+x0HrdkyjTs/u0nPPTsSJQjmCz5C2K4dV/Ox4o5E/HfiSPImjsfqrXoiNbPDVP6u5HhzjpbE1a2siHtPYoA7++/rEUgHRiZJeUeedxuK1LdmHft+jR+2/aHpcF76MBtM3hjOWK6rRwldJFb1DRep0vJSyombh3PzRRO3QDHjV+SlA1LojBhuCTueQFdilHDevaMDzBsyEDFUJHnyzgUSZLZEwxsb5dKFyqJUg+WwxuzJuDQuYOecfbpTLbvyFgUwBsUgEC+oJv618Hro17DiNFGosGpyqDfC6Nefx3j+Qn/91/g3Dn206w2x2zeZQEt0RBBgkFg7dr4888/UaFChRQdW+n3UM2aNSHL5tu0aZOi6XtXiZjcpIQFdDv17rvvKnD3nnvuwZEjR7CakjJmnJESJWDSMBYwFkhPFnAE4A0JCcHDDz+Mzz77zHQCPFQ7ZsyYgd69e6vYU+Nl59Zxe6QNft5xAPP+3IETB6n16nYbHnPjAvDmK+6L5yqVQ822XfH064OpT+u8RINilXJ8ImBuIMFVrmhDGJ3RR1G7NioiSjlA8/UnS8WHSC1BT9GQFdsFBvlDDkm4yIhIfqdbGUYkTsTE4Yt/oL+KV0h1Eo8PA0fznA8R32hBbHlSzvv5+SgQFr7U4uVhiV+QWR9qL/j4yHdxdsYwAvLmscDia1cJFpMFKwCugM+hBJ991DV+CpgVRz7WkMtX6fuKg/dsBHkzEFglhk29XTnIeyI5NzvjFJavaPX6E7xmFnleHK1Zns21Q3cnqqQCeP3pg57pjupQGzu2boD44E6NuujE/aTHOPSgrFatGrhy9Rq2kqn1z869bgde9ntWdSMeT3yWg76YmmMHAGKDAdYzECtOVtZYUUpcrnSsGLXzP1f83Kn04j1nhdXn1DemF5NkzI9Yx6xHx73pZ8n9MOhbkwfEpTvCtQBWeDcSbH1Rf2P+qPZAXaT+i/627SNti/x2tTEaHFdNgjzV6rhEIE4o5RkNR1kOYPo/1wufzfvELEV0+IHU9pc6W6ZwKZR7uBJGTXkDB87uMxINDts6udFpgDcoMAhN/Gpj4IsDMfHdiaYvmVzDuq7Xfbex48ZhxDDq9FPXUhqh6+wwGYDXISM7GE0UZ/j9smXDxVOnkL1xY+zduxclS5ZM0ck/3ZeoQ+1fAXgfooM+w+B1sJBNVDdYQNe548ePK3BX2LsC9l64QMaM9JxS0Lm4KR5jAWMBY4G0bgHHAN527dqpWTfTCXCuSugXWhg74qKDdfr0aWTOnDlVXnR6md+b48Zi6LAR+OpQtHI4Fn7tRq1X0WXNTwZvn6oVUO2hx9FtzFCPALwKbhEQVlBWAikKbFaIjnj9lX387BQ7MzG+AU7s87Z4BK3RqJYANpKCAobj17sVsMeH5915ZHhxeGXlVwBTAZLjyaMtHXWtILgKzCYI7AKClXM5iYegmkhSqAEbUWZfBS7HAdocqJKSnn8A74UmHtOpDv7e8rPpdDlgVyej0G3vm2++qQZlH878GI92esKa6JAJC9VLlsdD4EnXwyPTCzJnwCMCUKpJDtnzGZZ9lPrNuh5lgZlSx2QSROKU4+qYyKVowNMVTn5bYXQ4hnH9ljgF1r2u4rWlo8PzvEgWqPilnuv0edy6hvmSfHAv+VHAqkrXyruVTwFb7enHHBMjyGSNn4DL8kyx3fCTSRvRpFQew2WyhXuZ+OHkjmhMy3Msz5mfPH+c2PFT3/0VG9Sfe1liLpIoAZxpkWMB/C763XKtxOdHtrssk1aTRLyP4OAMOHfmLEoUzoumTZvihx9+MM+Tgw+DHeAVBu/9j1fH4AkjjQavgzZ2KiopK3musmXOjkZBNfB056cxc85M05d0yMC67zZr1iz07NkTZ1auRO6CdJBK4MTP6LM7ZGXnolHv8dy5sYfM3TKPPoorly8jE/v9Kbm6ww7wDuOkQIsWLczz6FwRm5jisYB9HHXXXXehU6dOmD59Or7//nvUqFHD1D9Ta4wFjAWMBZJgAccAXtFomj9/vmmEk2D8WwXVLzwBLOSFt3v3bgUSpMZMpq4oW7ZspYfTanjt09Wo1rIhzhy9Ues1ishv/rv88EKNKri/8SPo9sYwjwG8t7Khd50numahws5mSwHCQk10krMbO4sK4A3yVfIRE7o2wPaNaw0g5WwpJjs2e7sg7fHSpUsRTG/c+fIXQIYMohlCQFOBn8I2tQBXa28xTKOJ3itgjMcEhFVMVQ3gEjwlFms5A1MgqgZTBaglA93NZmU4DfK6wkQpANbNk032faaNCAgCC+OdgK5MuvgHCMAbjCC23/kLFsD9lSpj0dcLcfXKZVziAF7a9ZQcwKcNG95+Lu3PQrF8xfDgK63Ra+BzLg1eLncwm1dZQMqrcLYiaFuuOZpVbYLZ82abvqRDJaQB3m/4PmjD98JPM2eibsOGiDx58rblexzKmokmHguovjYB+BUsr+YvvJAqDjj1u6gKtX9Hjx6N1q1bm+fR1FaPW0CPM3v16oVj1Am/ePEiRCZk4sSJ0O2YxzNhEjAWMBYwFkgHFkg2wCvLh0JDQ1UH4MsvvzSdAAcrhR3gLVq0KPbv36+YYqkB8NrTzBYchBJ07DXhx7U4RWfMaim1DVuUSlWolD8GNWiAcjWaoPv44Ti+x3mJBgdNbaK6hQU0wCsyGFN6NcaWn1cZgNfLa812SjQsWvQVzp79j6zXSIRdC0N4ZAQlGyKVREBERATC+ZG9MHevc2JGcXEJxirWrADA1BcRTUDZFJPXxYq1QN4YFq9iy7oaAas9iJlw0JIPcffxmU8Y7fpaHY8+pq8Xtp9slpyEMGljJkz8yUgTVpowciWcYs9ybzFpdTjZW/ckcShWropLwlkSFRLWSlf2wvRlfK68xb7GCm+XthC7yG/ZCyNYSzfIoFmA3kP0YP/VV4tQuHBhrCSb7u67706VNt3Lq2+ysqcBiguXLiBHthx4ckg3vDh+MPad3EO9VwK88c2x3Uys2WrtbH/jyd4tr0/WLaXMxTeZe7xBCiWhHCVx/lJkkjLmyojAKwFokrUOxrz2OoaPHGH6kg6VuO7k7/jjD9xXqRLe6NsXrw4ZgvCDByltRY0ns3mVBRSQRULH6JEj8fZXXyHk/PkU72vp9lOYu4MGDUJjSkWYCUivqibpMjO6rZJ+0ZNPPolHyWAXDd6///47Xd6vuSljAWMBYwFPWSBZAG/16tWxZ88eXLt2TWk0LViwwHTKHSwpDarKXjS4xNYCYKQGwCu3pSvL6/TGPIqdz3FfrkflFnVw6kA4/GT5seveI8MjUaScP15t3grFK1RHj/EjDIPXwXqRGlEJwBtIBi+lXfHBc03wy7ofU3zQkRr3nRbTTK32IS3ayuQ5fVrA/gwIwHvl6mUsC1mDXIF5cC7qDCcziMaqOQitE60nJcQeAuhru8RoUsfoQasQcdZLuNWe4zFoDOKZ8BqL+M8kBStNGF92aUzbcmYd0cdj/7LOxJyL/c0WViZ43HFakiiyKa1pxfSPSSGhWibSMAjyQ21Ux/BnBuH9GVMw/4v56PhYR8PYcujR1MBc6NWryMil/k3puGsFHSJHnzihJq7M5l0WuE6ChG+JEqjVrBmuZ8+OX9avTzVw1bAmvatupPfc2N/bOXPmVH5nPvjgA5zkagNZhWYmGdJ7DTD3ZyxgLOCUBRwDeFu2bImFCxcagNepklEDJUvbVQB0edmdPXsWGbnkOrUAHHu6eXJmQ1ikD2b8dgaZcwTgv+N0DEHnZMK6iwyPQNFyARjdrhMKlr7XMHgdrBOpFZUGeC+HXsfsfi3x8+rlrsF8/PrDqZVPk65lAcupl/WJT+fZ7kgtQfAlHidsEtZbHPPYtbLj3oOSknBoczIubT+xoZa/0Ixkh7JronFZQNf9v/76CzWq1EBUQBT6TXoRDZ9opljbwlrHdTrBVDrTBCNdetBKssSt62xJmCg9akqPKOa60oCOOW7pP7skT+S7i/GussFLFDveJXeiDikZFJFHcQWQbAg72HaM0SmpFKsaW3rWlmSKJfMTTR1tEUQXIFpY5gqoFgl6kepxfdfsc+VgVHTp5bgiv1sseZEQ0ZvSbRemutKlFga7xY4XvWlLj9piuKtzKh5XHPq7hOcx0ZqWui361OLg9KbtjDDdmYdr50Lxw4jFmPrBNJWdXbt2oWzZsmYw79CTbH8HlCpVCoe59Dl07Vr4BQcjig6SZWWC2bzDAqqslJwSH+P778eQV1/F+DfeMOMq7ygek4sUsIBurxpwBWiBAgWwnhMcc+fORaNGjcxzkAL2N0kYCxgLpA8LGIDXi8vR3jGfNm0a+vTpo3KbWgCvpK0rzIaNG1GHTJDipcph3Lc7kLOAH04fcel3cilysXuC8OYTvZC3aCk8PWawkWjw4nqWmKy5Ad6Q65j3UiusWfFdqtfFxOTbhHHeAjcDVp1PLf4YvQVkvt371TZM6/dxu/efUtdpuYyrIVdRr1YDbPtja0olbdJJogUyEHDsR/mAt99+O4lXmuC3soDutz1DRtyMjz7CN++/j1aiq0qw19/INNzKfCl2PpJySf6U7vlh2TI8yP7+xp9/Rk32s1PLebUwJrVkUYoZwSR0R1tAs8bHjh2Lb775Rk3GCrgrvw2j/I6uGubmjQWMBZJgAccAXtFq+op6UanVEUnCPZugDllg8ZIleLhdO3qED8aYBVtQuUl5cByN86eikaeoD6Y+/wLyFC6JzsP648S+cHqXN85tHDJ9ikcTw+CNwqcD22DV8mUG4E3xUjAJGgsYCyTVAvZlnceOH8PfO/7GtdBrFttV6K2uXUSYpUctn8gol1Y1NaulT6OPK/3qiHD3MYk7kh9pH4UJfF3rV1PPOpK61yJioI7xXBT3wua1GMCusHIdr5fj1l5+a+1rXq90sK8j8nqkOx4rDGm/WiRCM4GTahhbeCW2IPG4iO/C1JWBtT+ZuMLKlb1MRohElPgB8BNtax4PCghCQGCAOhaUIYhOBIOQISgDgoKClONAWVYr36WPkEHO85z1PVAlJfeRJ28eFCtWXGlSm80zFtD98m8JmLRu0wZtqKm6ZPZsgDINaktgtYZncmNijdcCetUJHaw16NAB67ZuZVshz3nqkjpMaRkLpKQFdFu1adMmtG/fHk2aNMFB6oX/9NNPqUpuSkkbmLSMBYwFjAWSawHHAN7mzZvToc8iA/Amt0QSuN7bgHPNjNpDJ3sN69bCsZNncH+9lnh8yDsodd/dKFMImPrGFA4cMqNt/27U6aVnYEo4mC1tWsAN8NLJ2ldD2mPZkq/MwCNtFmWScu2EDMLtSBwkRkIivhtJynWGPZukqpCmAwuQGOPAL03fSrrOvGG2e6Z47au+8ufLh1OnT2MPJ+hL0cFjFKW/xDGl2VLXAoq9mz8/tm/Zgvs7d0aXJ57A/82blyqglq4vIZTwEFk4sxkLpJQFdN2TMa9INHTp0gXz589XOryypeYK1pSygUnHWMBYwFgguRYwAG9yLZhC13sbwBv3RTv749l47dVXcPjkOZ7yRfnazfHf4d3oOvoD1Hm4Kc4eNQBvClUVjyQjAG9AIPUSIwnwDu+MJQs+N50tj1jauUilI6wnYuwgq/17QoCKBkrT6/JMfd9xweebgdFyjR1AlrAJgcQJAc1yXK7T5WL0d52r77eKST8PtzPhcKu4E3M+vjoR91hamHSwT/roupyY+79ZGKX9K1q/Rg82uaZM8Hrdhxw6ZAjefOstPEGJhnnTpyP6yBGrXTMsXo/Z/lYRS//KR0D23LlRr2NHrN+8GZsog1ajZs1UIc3oVQ/16tXDa6+9hoYNGxpN7FsVojnvmAV0/6hy5cqoWrWqIo+JFu/dMiHFyVrTb3LM1CYiYwFjgXRqAccA3mb0+Lp48WLT+KbTipLQbcWdTT167DgWzP8U/+zYgbUrvkHjAVPxyHNdcPKgkWhIy1VDyjk4ky8d/YVibt/GEA3muCB/Wr6/9JR3vYRblk17cpOOtlqiTqZFaGiocgYpGmkxS831kvOYvT6n86WcPXEpuAA7+qOPxd3rJeKyTFyuUUvI+V0+lmMn61haAokMG8WTNTRtx+0NWtd2C6al5yptl7xncq+BkcuXL6NwoUK4xP26WbNQj3INEdTiDTAsXs8Y/laxigNH9q/8ihfHNGoj933zTTQgoLpm9epbXemx8/q9VKlSJbzzzjtozDrijSQTjxnARJyqFtB1rS812Xfv3o3TXHEg33v16mV0eFO1ZEzixgLGAmnFAo4BvE2bNsUSLvkys2vOFb198D916lT1gvNGUC0hQGnR/LmY8s02DJ/9Ho7+axi8ztWMlI/pOoG8jNn9cPSffZgz4CFs+2uXV9bFlLeMd6cYFsaJFWplynb16lVcunQJFy9ehAzyZS+D/Evcy/crV66o43ov50K5RFOA2xACuGHcx2iRurRKCe7q5e92cDUuA1izXaOjhaXnlvpUKqJx2bQc68pRlWfNtrTkCalH6mIl63ZQyYbypI+PLwIDBOj1U3s/gtv+NgBYliBbv6kfyvMBrnNKT5TnRC802KUXqnVD1V70QqkhGsTvGRhG9kGyd2mMynVaazSQ+qOB1CPVx2J+Z+AxC5SWc/LS3bVrN4oWLYKsWbN6dwUyuTMWMBZINxbQ/XPpT/br1w+lSpTATi5/9s+SBZH//WccrqV0SfPdFcH3QUDRothKjdHq3bvjOidJt2/fjooVK6baeEqPPapVq6YAXmHymrFdSleOOzc9DUwIc3fQoEEoWbIkihQpglmckDITDXduvTB3bixgLJB4CzgC8IaFhSkhdAPwJt7wiQmpO1nCjsuRIwfOnz+vHJR4K+tLgBbpBAoIJPn85qsv0H/MVMzevB5H9hDgNQyRxBS7V4URAE1AOHEulKeIP/YuXYr5k4dj49Y/3cCcYXZ5R5HZ24WPP/4YY8aMwYEDBzyWOQFHM2XKpJ51ATPFiZIAmJpRGyjMWhfD1mLaCsOW4KocJ3NXIFy9xNvaU1JCnErJXv9WDqksSYNoOqaKiqSjKmEOuxxaiYOrKDqgkheZANFh18JxLYxMYr6TFKOY9dabt7Xr1qE+B89m814LJEWH+lbyD3FlPuLe9c1kP+KGvZXe9K3youO7WZ5ulZ+4eYgvT+b94D112/6OEM/0a9asQdPatbFi5kyZaUPkhQtqIszINXi+zOTZkvdWAJ0LniBL8f6nn8Yp6oyOHzcOQ4YOTdV+vh3gnTRpEurUqWMAXs9XCZOCywJ6MuHUqVMQFrlIhOylv5fNlC7R8g3GWMYCxgLGAsYCCVvAEYBXluVKA/wNPfSaWV7nqpvuZIlNS5UqhX379qnlx94K8Oo715Xq9y2/on7zh/D5nrO4SGlepTNGpp3Z0o4FBOCVErscGokHKvljeo+uOH7qLL759lszk+5lxaifux49emA2PaTXqVcfTZu34ORQTreEgQzchUGrgFIBSflMRnGQGRkZoTrOUVEERFnofoLqcxOwVckqRFnA6nXXJM51AVsjKM9AcFXORUWKXEMEwVYCtCqcFT5SzrkAWUvOIYqewXmM52XqQEBdqm8yvPqlQF8fXenku2ovJIRURF+3TqefaHaSrevLjPoyjCXvIL+t71quQfYBBJV9RL5BrhEJB4bxY1gtAREQIKC0Jffgy/M+EpfEqT5yjeiDynHKSejf0cyXSzfUspTiEgtG4toEqFa0Y3VKgde8uygC1pL24UMH8eVn83Dg4AGs+WkjGtStad6dHnyepJ7rjwYc4wM27YCm1oW1qp55b91O8cTVPbbbPJat+WzJ88Un7naSMdckwQJuqQau5rjnnntwlPIMLevXxzLKA3DGDpEEGVV7aep8Eqya+KCaCCHvGl+yEvf8yn4y2dQnWA7dCfLOmjPHeveyzUqtdkenXb58eUybNg0NGjQw76fEF7EJmUwL2Ou+sHdr1aqlJqOOHj2a6s9GMm/NXG4sYCxgLJAiFnAM4JUOwLcEfQzA61y56ZecFFIJLqU7ePCgAi5Ss+OXmLvT+RNmd45MGfDehpPIVTgfQi/Sk7mwQ8yWJiyg2bsCUF2KADpXACrmz42+Lw/FoIEDjRaWF5WivU24l4P2fQQOd+09ghx02nLpwkUFZloqBwpJ5WY5NZLvFqRC8FQBmxbQokLIOYKcruAqrIJala6CusSKR4VVf2OIX/JTgE35p9i3PMc9YTbru8CdLuBTZUv90RHHY1gXVhpzxnU/6oBKyMpPDKaqfqi8q3hlR6do+qgOz/uQPAogrTY3QCuArM6XK1W5D50Bl2REPDmNfchtPOuwukOmlSt3dmz99TfUr1UVjRo3waofV5p35y2NeXsBPMn4kbIMjwhXbWFYWAT3YdxT0iQkVMmbiBf60GuhPK4lTSQMdao50RHGsBGcJImICFO/ZeVLOD8RjE++K3DUxWi/zokBmVSRe1GTLS6Wu/27mjxQkxHykUkOa0JE+gzWMWHQ6/PWcX8/SooEBUAmOQLJyA+gxEhQQBD8KSli7fmbrHxh6weRpZ8xcyZkzshP1izImCGYkiVk7geLbEkQmfnJ1Pzm3FKUj3Ggc3u1PGlX6X76ETpYq1GjBo4fP44K5cphGfVfizzwAEDmXBQle6Kl/sjEmgF7k2bgOKEt1SFr0lPZk+9lLn3Bws8+w6OvvsqKH4X+lGCbPGWK9RpKRXDXnr6szBxHRvEDrBNmbJesKmAuTqIF7I7+ChYsiJUrVyrpEpFqMHUxicY0wY0FjAXuOAs4BvDWFwbAsmWm4XWwCqVVBq+9c3oXgd12fUfiwX59cGI/l6Nx4OgGDh20lYnKMxYQQOritShULOGHImf/RZGSZdVSKZlVN1pYnrH57caqO70y2baf0gy//r4Thw7ud4GviYzVDmIqcDaR190ymDBr4w90eyyl2ICsG5e9ZT5SN4AF8BJopjEKcNDSrEEtXCGT7ohhpnikYOzg7lLKy5w6fQpFCxfF5YuXceH8BTX5cY0TkaI7ffXKVYTwczXkCn8TnKVmtehWh4RcVb+vMkw4wdgIAXLJXOffGMDfI7lPO5ESGqYuNcFigsJBQQSECQpnob60aEy7P9ms79myZUO27NmQPXt2ZMmUGSXLlUTB++5CXmRTNyzTKC7OftoxQBrMqe6nifRXiwcfxK9c/hyQMSPG0JHR4McfB9g+seJzdveSmnCQCTq1ikI+afB+UzLLanJQlnMIqMvv/jJ5Sv125MwJahnhyJYteIoSSmu5l2fiE0oqtXvkEav+K01577Cw6eOlZK0xadktIBOhIvk1kGSSTZs24cSJE0oP+hE+J6ZemrpiLGAsYCxwcwsYgNeLa4jugMsgMzdn/KUjLo5/Unt2PzEm0y/nF18cgB+XL8O8nXuwZUc0MgdazDsD8ibGiqkXRhE9OcYgsQyhXM7/zH2+eOrxx/DTL1txcP9+lbG0UA9Tz4Ipn7JuzAe88AKWLV+BX7b+Qdb/gVRb5pnyFkg7KQpgUrJ0abw34S1MGPc6rnEwY54pZ8vPvZLkWhiqVquKv/76y51AYHAg9aHpmC+YutEEJH0CKOXB3wJM+mcI5HuWjNYgOsdjODkfKE71XN8DBMhUICZhTe79xamfyIKIkz+R/3AzZgnqaMasSIe42PHqvIslrzMkTF2ZoJF9rO+Kses6rvSoyerlXmZeFJvX5XRQXyfMXpE4sZi9Ci2yJFF0OJFPUTIqko7IpTBOOSe/RY5FfRfmsCUn4r5WpFUYnci5REUwbv6W9kYcRFnyLPKRc9Z3pY2t2MiWPnak/i7MZL5P9L1qhNw32B9PvdINs0Z/KGsJzLvF2Uchwdjs7/CxY8dixMiRqs5kpc+Hfh06oM/DD6NAmTKKbcrCBOnnoLi5tbcarNhxq1UhtwAn3dI18cwexo0vhezgWDIyiykf8TchNmN7whkPyyZ0Yrdq9Wq8Rr3j9X/8oY6NGT0aw4cNoyaSn1uP/vYmPB27gxsi8ibA2XN3aWL2Ngvo/uznn3+u/Elk5OSTMMrf5CoDA/B6W2mZ/BgLGAt4mwUcA3jFy+p3331nGLwOlrC98z1jxgz07t07zYAAmk145uxZ5M2Th+DuPzierxxOn4hCpiB2ZmXsyY938BQcLLR0EJW9XM5cjUSPyv449c+fqFyzHiZOnIBeZPiYDpb3FbQd4F3y7TJs2f43DpLJK4CS2bzLAjIBdhd11ce/PgqTJr6lBvcWXpJ6uoveZaHk5UbsKJuAJR0e7YCvFn6FXME5cHft8hj02avInSefWBv+INDLv4Rclf6r/LVKQomL2PbWEX3GvtdnYuAqm5SH7TZcQiEJ3ph1/Y1vRJdgiuu6G0ExfYWlah1/HDFHb3zramGU+FmzLpkTV8wiXBLzj7N/Smwl9p3b49Hn9LHYV9PxKv/9++dufPjcZGzduBUPPtgc33+/3DwLyav+Sbravtz5JFlyo197TWm4yySUbGUpD9acMg61KlTAfZyUKpY/PzLmymWBmAJm6tlgeeYEyJS9HNMfiUTeQS4GcKy90gFyxaGdu8kx+9KRuEs/EgMCJybMzax0M0kK+7m46Ud8MrUAACAASURBVHA1gADh0WQ+7+WqjD/37MEGLivfQEB3865dKsVKlDsYSDmGJ5580p0Db11yLvkSNrG3gc5JquAmcJq0gH4mRJqwatWqyg9NLrY7slLYjD/SZJGaTBsLGAukoAUMwJuCxr7TktIv6O50+rR7+1Zs+O0PfPDHdTKlfJCJH839MECvF9YMDrBOX4lC64r+KIkraNJSlg9GY+WKFarDb4Ao7ysz3ZiLPvLCRYuw9Y9dBHj3G4DX+4oKkQR4i91VCn16P42FX36hZBsEaPPWgb4XmvCmWdJ2/I+subz58yEg0h8PtKiOscveUTrQl65Ql9rFFNVgsGK7Jlnr4yYXxMFiExu1W8kkwdnPVHREJgRFUbJ2ZcEN5trzGuf7DY7TdByC4/GTJUNWZEAwPp86B+/1m4DFixajbbu2ZhCfgg+dYoILDusCLy9fvowv5s/H98uXY8369Th/5ky8uQkSx5S8RrG7FTtcHGjeWtdH0hFGu9rzIxIG8vFjfP7i4E1Y7oxTaUdrAFmARsmF61rlMNT129q5apr9vItRLGklyCwWJrwLjNaOMVWbEOe43VGjZtCLRrFyHsz0w3n/p7jKLu6WLV8+1K9bF106dsRjZEXbN7GZbGYSNgUru0kqTVjAPsYoWrQoKlasSGnwU9hMKRnZzBgkTRSjyaSxgLFAKlnAEYBXZvpr166N5ewMmgGqZ0oyLc5Y2l/ApbnMb1Df5/BM/xfx+d5onLkQiRzB7NTTe/2thwOesamJ9UYLyDgnnMtqQ677oUNFH2S7eBq9+ryIv//Zpdi7jRo1Mh0rL604uo2QJZ8fz5mLP3buVRINZvDofQUmS90LUQv2yc7tsWrlD4bB63AR6X7IpcuXkC1rNpS7n97gt80EhQNw6dwlAkn0YK/SjAErHc7CnRldHBQ7Npc5fmbzdUo+BGUKQpHgomhSrA4KZS2AbTu2mfdMKtSghBib/3El1lE6YhOnbGf5/SIBYOUI0EJarJxqgFV9vXESIlZYuUzfn0unVoM2sheJj2uhoUofW/ahru8i9aEkP+Qa2bs+IhWifos8iO24fFeSIa5j8ZnUX0BlF1Dt/u4CmgNc5+S4yLcEUyItiNILGbmXJeMZM2dGZvnNfRZ+RF86C49no7504QIFULhwYUq4xHY+KP1iyY+8l7393SxAf5YsWVKhJpok73QLyHNiOQf1QeXKlVGsWDFs3bpVtUG6rTDM8ju9lpj7NxYwFkjIAo4BvLVq1cIPP/xgAF4P1bW0CPCql7A4FHJpsomMR7uWzfHSkGE4ynMbDtN/x2UggA5rfNnd9/XlMj5qE3r7S1sB0rFQaWF7WAV/U7A61smEQ7rPxA3iYkYlLGuRcAB1Rv2JPfAS3UURa9TMlWjqSObOC1TJAeykdvLUGTNx8dIVPPZYe0qEPOuh2m2idcICuo0YOnQo/jd9OvYcOoGj7Ax7i8MWJ+4xvcQhoEMhDv47d2iL9evWqvemGbQ4V7r2ycVCOQug6Yut8dKIwdhzZjcCqa9rNu+ygOjy5s9RAI/e0xp1ytfCZws+MwzeVCwiS1M5Kk2AkKlopkQnLe9m1QUT1nIakEzS7WeVKlUg8nCyN+SdRBe3CeiQBXSftnPnzjh8+DAOUHLst99+QwFOnpj66JCRTTTGAsYC6dICjgO8ZtlEuqwnybope51o3rwFNvy0BlPefRtNOvbEueCMOEdsI0yceV2lfFko8Ub2hZUHYtfm5v1o4V4et2Oft8MAjg8kjeUbJAYRtXBRWeGn8kOAVH93RaLJKmrvwk+VDx2Nt9rDx4PB+sSz4jYeLFalrpcQx8WX1TnXn7g+TJQpLYfOalN7kcjjzo/kkkA6dw6iPxDB1/2lDHZtwIoFH+NCiOVERxwbdO3aNVl1wFzseQvYAd73Jk/GviOncfLEccUO9fZJE89bx7tSkLIqTm3Lbk90xMrl3yPMOFlztIDs75yCBHjvqlsG7yyZiuPnjym2ntm8yAIyP+oXjdyZc6NRxpp4/LHHMXvObAPwekkRuR35JUJ6wcksawafPc6477K4k5f237f7znNLtrgS1vrodp10nc7N3q06TFrTsLW3nWXLlsVnn31mAF4nK7aJK9EW0M66hw8fjlWrVilH49OmTUPjxo3N+yHRVjQBjQWMBe5ECzgC8EokNeiEYQX1OQ3A61w1stvyvffew4ABA1wAXdpzxGOfbX3n3UkYNPAlFMyTHU245D9/qQoILFQBhSvWQ67CeejF3OV3w2VKATsVYCrYql5R6wZcrbCyxQJoXQioGyaOAw67AVINekoELiKu3T+Ikn/jKXX5/7N3HoBRFO0bfyEkIIo0qQKCBWyIBRB7b1hRrH8+C/bPXrH33gt27BUL2LGLnw1FRVG6IIggINJbqP/5vXtz2RyX5JLshbvLOxrubnd2duaZmd2ZZ955Xu83hLjuO0QpZLRzOh7/xOu4ej3HE7m7YAWeytlOiHd0jjuGduUqPgNv6Xg9J2Fc1eD1nE/VfnM/arh4gWVtwMbWdCD4LUtov2EZvcqJGDKR0eOOoa2BF3eYWgcU390/zmLE6eTl4+0dT++OxOW0+2Pn4FJnQT1nxgyZOm64TP1lsMyf+JssXDBfGrVoLWuvXVd6HHaY7LvvvtE1akspbQj4h3mfPpfKQw9jwTtN/p46xSx404Z4xRNm4rLxxk625rz/yisvPSeLlzjnPDyCzMlaxUENXVnMgrdJS6nftqE8PfQVmTbnb7eQtQY1bCMpXY4lwvuzxippuW5L2b/xrnLUEUfJI48/YhP4HKvmqi5OIlGb6v0rSgynmn6mxws/O7fccksleLfaaiuzmMz0isvB/Pkxbf/+/eWOO+6QBc554UXOxwQOx7N1V2sOVpMVyRAwBDIQgcgI3q7OM+wnn3xiE9QIK9kPtBYtWiSNGjWSefPmSYHTActWEgBSM2xh0e/JJ7XNjPltuIwfPVKWOLK0QdNm0qzNxtJyww4Bmwu96UhStNVWLocsXa4kqVpTQJLq1ubYdxfPhzwlPB3JieQDZKebQAafATHK8cCygk9HjHLMTfxruDh63ll5QcyiM4fuW/hzxfKleozzq1Y47TclcCF1nd4bx8gjbK0nZ12mApdyUMUBoxzw1eSPaNgoU6YabhAdlE3L5/KlVylhS57zHTnhtCPd75poSNZ0xG0tl4BXiHCJ4gce8pfy59Vc5cjcGu7PeYlnW6C7rlZ+bb1zXkFtV6alMvLHIc5xwd+ap44dNpLNt+4snbbZRrbdemvZb7/9AvwT6i3CJm5JRYhA3MnahRdK/zcGyLDfxsmkieMD6ZO0BNp9WhJOe6KBc574P2m/X+INli4tlFZtNpBn+j0hN1zVRwpjHuuz9dle5QCWccMwji3Way71WtSX5359TabPmWYEb6ZVlssPC8BtGmwgx2xxiBy090Fyz/332AQ+A+vJspT7CCQSvK84Z3sQvbYlPvfrPtNK6Me0o0aNkl2co8LWrVvLHnvsIffcY++HTKsry48hYAhkFgKREbzbb7+9fPzxx1lLPmZWtQS58QMtKmmTTTaRCRMmKBGZzSSA90ScbJssjjQKnU5DbUderlzlSFJMYxUHR/AuC0jeIk/NEKkBEYrFLJ6bwxYbnshVYjRG7AZWrnhlDohdPHjnQe7GdH+VFHYkL0QwBCq/IVSV9FVvz4HXZ1hZJYZjlrJqOqxkdMnquJnYvmTlUlm00DkwKVzqZBrWdk5C6q6WTVslz8iaS5opPwHr3r272872mYydOFVwkrLAWWTHdf9CDm1KKlnYginR+3hgJR9u5/SvQFvQh2LXJzrbCTniCeIXmeK7lOKHVstbTH+kSP86sMH3lvVx7ZLYheFnQfh7sHjiryy6S3gbrv8ee/gUOQOqQFNIRiSTPvWxZccO8uRjT8pZZ5yiToTqOMc9NomuAMhJLgm/I5s2bCLNOrSUfkNeMAveaOCNPBXVpG7QSnp2OFB22XpneaH/C0bwRo6yJWgIlI1A+NmJY6u3335bOnXqZO+msqGzGBEj4MdDWO7CMajTQmd8895779n7IWKsLTlDwBDILQQiI3i7dOmiGjnZTD5mWtV6LNnOu9FGG+WU91Bz4hHyIh1reMU3DlfGNLJyW5C9Q5CA1K5cWpnWp3I5P/5hfuutt8gVV1wp11x/s1x65RUyZ9Y8WeosRNWCXS3aA6twtVTXBY1gkYMFDZUScZQmlrlqKc8XFz8wmEdKJGZxrhbmSIQEiyw1Yp9IkiA7giV6YNUenNeFGP10afJfTKIkML7nGhUpiVm9+1oq3vZqOiv8+GJNUAgtS0BCe5mSYEFHF2NcudiOT1lZmAnkTSCFY/ImfNMDLORALytTHdyDdRy+x48Fv71lvpYiZqXPd/2f32CnP9Fw4TNmkR+4kHcW/qucJ/baSky/9vKLcvEF50j79u1lxIgRMT45++R3MrFPhcchTeo3kbZdNpS+n/STv2dPlTw0aixkFAI8J5o1aC6HbbS/dNpgS/ngsw9tAp9RNWSZqY4IYC2JkzUMTGzxsTq2gDVb5vB7nEWGWbNm6Vx48ODBazZjdndDwBAwBDIcgcgI3s6dO8tnn31mBG+EFR4meHF2gAdRpQlyTKfRW9h5y7lExxke0mKWdRHiHE6qpHun43YlkaerUbsV5Xq9XnFC5hMtGv1pX3YjddNR2+lPM/xcuOCCCwTd7q06bSNnnXuetGzVWhbOX6CyHEsKC2WJsxhdsGChs+Cer1a+ixa6704KZsmSxcKCUuGSJc7x1xJZ6qy7lzvpEV4UK5Y6p3tOfmSFk0rR3/w5yzu+EwcyGAITSRXyEljYQ3gGZOgqR9DCfipZHCNz4+eUD/WyJiVjFbcWxroe+jVG7Ko+d4yQDTSpHckLaes+8zyx7Y7nOesPNKohfvleK3Ys3wlS5zv5kgI+C/heoN9r1a4t+bXypbb7nqfnakvt2kXnOM61tTR+geTXzg8+Y8dJU8+zI8Ddt07dujJnziy56/ab5Zdhw7SgAwcOlMOc1rVZy0fXR8J9Yb21G8kOx+0m1z5xs0z+90+pVWBO1qJDOpqUeI60aNBSjujQXbZpu7W88+E71h+igbZapuLHODaWqVz1G6lbOfzs6soj4N/lBx98sLz77ruy2WabyciRI3NyLlx5tCwFQ8AQMAQCBIzgzeCW4F9sbE9p0qSJzJ07N6s1eDMYasuaIZBzCLz22mty1ln/lX/+mVmuskGi1nISAkqGOv1mJSchRGP6zxCVeU77Ofgd6DsTB1PfQNMaghWngGhH8793BEg2vPZ1YJ3rPYwrcRuzlsVofNWqwArXx/GfgeVvTLrFWwbHfntdbnSw1YYWclmNggP5Fi/ngoWxWipjaYyjQ9W9hpTGKaI75olrlYRZ5v5cXI6pdTJpoQVO3EAmoiKhwJGMPQ7vKZdcfLF6KLcQLQLFnKw1aiEHXd1TTrngdPnznz+dHE1BtDez1CqNwAq3w6BFo9Zy5j4nSeu85vLWB++YxWClUa2eCZjPgGjr3fCMFk9LrXwIeJLi3HPPlQcffFCaN28uf/8d+A3JNWOn8iFjsQ0BQ8AQKBmBSAheVnm33XZb+fzzz+2BG2FrC7+8nnrqKendu3eEqVtShoAhkIsI8NwI5AgCghQLXZ7RaLxi0QQhiwNClTawkFYEPLnsJWmoGyylWbCzkD4EwhZ87dtsIp2P31FuuOkOGTl/uFqiB+rNyULFJWlW99lXzrSwQi+WpdR/pQ/JopRVdiQQHwlCkq0m8UOl7jopfhLnooW1lsneDfaQE/Y/WoYOHSr//PtPcIsc261UFfVUne8Rbi+89+rVq2dOYivRIHhv+UXYSiRjlxoCFUaAHWU4F7/rrrvkkksukfr168vo0aOV6DUL8wrDahcaAoZAjiNgBG+OV7AVzxAwBKonAtm25T8sHVLRGsumLble6zqZw8mKlt+uK0LAt/9jjjxa+r/+qtz1wYPSeb/tZR2phxpyAsUbswqPX+6svFf7Ho5T/Hvwq4j8VO3l+JFwXFWgVkvwIARW5no1+tRevznsCDCQeA7dIdB3Du7og9d89r+9lXvofOxryB1icMT7NvRW9LGD3qEip1X2xFvfx7W8A4ekcWkULPXRSon9W/wzOMOxon+Db8vcf62kpQwb+LWzaj9cDj7kYHn7rbdt8h6vW/uSCgJhcveyyy6Thx9+WObNm6eX2kJBKghaHEMg8xDw7/E333xTevTooQYK3333ne58yrYxbuahazkyBAyBXEUgMoJ36623li+++MIGUmloKQxO+TMiIA3gWpKGQA4j4KUJEotYlVrTFYU3FbI2ClK4ovlL9bpk2uFmFZUqehWP5617/po8WbbptK3MnD3TWbGvJYdfeKS03Hh9WbZ0mToXJHh5kECOBOkRNJr5XsvpNedJPsdUv9kdR8OZc86JXy2VKnEyJvw5uRKN587zXY+5vzz9Tlx3PvZdj/nfqs8ci5+P88PACVzcCh+5k4BeDfKp+Q2OBCFM8yazyseBIWkEtDP/QW/zr/8NnR0c8WeKzkOGo729DC3ulcvcpHqFc9pY6DS6l7pjjp51Ot2Fy9x5p++9zEktgCvHlrvvOHdc7iym9Y847jgSKOqI0EmdIIlCWf7+epK8+nx/KXD61hP/mCgtWrQwgrfiTb9aXulJ3H333Vf+97//SaHTmocUOvTQQ40IqmCLmD17tjRs2LCCV9tlhkDlEfAkxc8//6ykLv38nXfekYMOOsj6deXhtRQMAUMgRxGIjODFwyWDKlspT09Lsa0o6cHVUjUEDAFDwBDIfQRwZtfvxSdl6Cffy5L5i5QmDchSkRVoK69yjgJjtCeEZ0B6ZnbwpK8TXXH/OXLYZRfiGeJarWvRy8YhodPQDshs96f600V/qiuN9XD8eJElMeQuKDh6NjIsyIVzOyi1a9ZWcnzOsrkK8hFHHKFWl02bNrVt9Znd7DIud+F5R9euXXULNwskm266qVr7EWxuklq1hXFq3769DBo0SDbaaCNbcEkNPosVMQJ+7jt9+nTB2Ti+aPr27ev8S5xlBG/EWFtyhoAhkDsIRELwMiDYaqutlOC1YAgYAoaAIZAZCFTWm3g2WMhmBtLly4V3XJOKlXL5UrbYiQhUlthZgTM+Z7W6HAtW94njPQZOTDyXOatUnO5huYqzvsKlhfod3UDOrYg58KO+ib/SOexbqQ79Akd/gcM+LFmD85Cseg5HgO5aHPutctescNfoJ47/uN6d83Egbr21r1qGxyQUAmeH/I45NoTk1e9Y/xLc95BMcHAMIrgIQayMAyeKgcUxFsn5tfKdBXNN/V07v7bqI2J5W7vAEbbOAjk/P1/j8FfLfa/FMX6770VWxyW3U3PqZH24Igj4fn7AAQfI4MGDZYMNNpAxY8aopnPnzp2NDEoR1PDzsnXr1vLll19K27ZtjeBNET+LFi0C4fbYpk0bmex25Fx66aVy++23W5+OFmpLzRAwBHIIgcgI3o4dO+pAwEJ0CPgXG5O6yy+/XO644w5NvLIT1uhyaCkZAoZAJiLgZV3SRSCmK91MxLIyeUokyMPO7+xZXhlky3ct9cBgB3kFpBcsZB4CXk4GQtmCIVBeBPzCwIEHHijvv/++7LXXXrJkyRJ1tIYVqo2bU0M0jNOGG26ohjutWrUygjc1+CxWxAiE2yPO3IcNGyYnnniiPP3000bwRoy1JWcIGAK5g0BkBO+WW24pX331Ve4gkwEl8S+2hQsXynrrrSd4BcZqxgaqGVA5lgVDIEMRMAu4DK2YULbYbtisWbPMz2iO5VBdpxV5T0updMk0lBMvTCVOSjcrIRLv/PLoZpOfNbUIU1o+k53z2BmxW5kWUr2vDY+J2caNduwmm2wiV111lXTv3l0WLFgga6+9tpGUKTSTRIKXeV3Lli0NuxSwsyjpQSBsnf/BBx/IIYccIm+99ZbNhdMDt6VqCBgCOYCAEbwZXIn+pcZ2Twatf/zxh+bWCN4MrjTLmiGwBhEIPxseeughefaZZ2SJczYDq6XELx7u89imHTiSKvoLtm6Hj3lrU906HtLsLKbfGXMA6eMUK3p8K3jpgOg2daJwj9hnYPnqnEO5reqxQ7qdvfgxYrPVnUsDtVQ+vSVg3HpWNUYDzVEfP34vTZF//PUgFHNbpSc47rDBKZT/7b6r9qnb7h67SqPVcJaHebHt8rpNPoZzXuyzhkuELe2Fbgv/2LFjVdfwo48+Eqyk7Jm+BjuN3doQMASyGgE/kfn8889lzz33lG7dugmLaBMmTNDn9E033SRXXnmlWfylUMvhdxGGJTi3MgveFICzKGlDwOvwHn/88fL888/LjjvuKF9//bXez8ZOaYPdEjYEDIEsRiASgpeJ82abbSbffPNNFkOReVkPE7yQAWgP2Qst8+rJcmQIZAIC4YFuz5495Y033tBsdezYSTbfsqMsWrRIlixe7D4XyoKF86XQbV8tXFIY6IUud57vlznP987b/VL3ucxpieLpPtODm7sX0x9lMo/jJpxKsR2fz1poiKILmhdogublOx1R9EAdY6v6pY7RVQdVMa1StTJ0/0N2e5K7Vn6BppMX0yKt4c7hvApSl+vyVN807NwqUDRVBtj9qcMuR1bnO53SObNmyVeDP5UlS5dJuw03kvHjf1eS2SYq6W1tfhEiUSYjvXfN/dSjsF72aQT91yQacr/VRFfC8HMT/V0s/CB4x40bJzNnzhRIoTfffFPmzZtn4+cUYA/jufPOO8uAAQPU8aG9n1IAz6KkBQFPVFx44YVy7733qvPEUaNGWX9OC9qWqCFgCOQCApERvDxwv/3221zAJGPKECZ42W42adIke6FlTO1YRgyBzELAP8z79+8vxxxzjKy7ztpy7kV95PyLL3NkZE1Z7pwzqZMj/nekozqJcrqkyx25i3WsdySlzqDUmZTTLXUOpXAWBQG83FmeBnGCYyvV0RTOnmJOo7wzKKx63fdV7niqAdtirF9rQphiWawOohyJ6n6royicOtV0Tp6cw6aajsDF2VMtp6Va05G4yNbUdN85r+SdWsx6Z1NBerVI08XJ4zN2LSa5yuW6+/IlsMrlKza8wXfIW/IGYIGRL1bGAYZqtRsroB4jxCyB/fHE8mMfDMH8/ttvyTVXXiy/j/tdnn7+JTmx17FmXZZqYylnPG/JvaYkC8qZ3Wof3Yikat8EygWAt+4bOXKkIBWHtWmjRo3kr7/+UoJ3+PDh0qlTJ/nuu++ka9eu9pwtF7oW2RBY8whghIAzz9tuu0390SAZMmXKFJsPr/mqsRwYAoZAhiKQFoLXBujR1LbHEf0wVtDnzp2rlmeGbzT4WiqGQC4h4B/m//d/veSll16UCy7uI9ffcpuMG/O7s9JdEpCeFNiRnMpF8heTaVCrVb7r6YBcVZ4TUQeVblAm1H3Xi4LPWPxYSu63/s+JANbYR8oYqxpDIMlQ9FVVU+MHgu/+GGRrTHohJsOA9IIPcYkGvQQi2n1xVrRhiYlieQtJRKRuvBwTdA3pupYl8QrWrZ2H92lugtJ12y3kwO4HqoWZr7+U8bKIZSJASwlatTipkiW6C2YjZzVtZG+Z0JUrQpHEipdDKasX+EdETVlQuEgmuIWOBfMWyNZbb61OsSwYAqki4AneXr16CRINPXr0UCtedqyMGDFCGjZsqOQQx1n8tPFzqsgG8UzTv3x4WezoEfBjo2effVYdrLGA8/vvv2vf9v0/+rtaioaAIWAIZC8CkRG8aMQOGTJEkbABVPQNAi/AbD+zYAgYAoZAMgT8w/zss8+WZ597TsZM+Ev+/fdf9SSOlWtJIUyEViZO0bXld2Sl18YJ4dSZ4TBhXZ5WsaYJvsVOKmOrTlvJtU4X8pknH3b1NNveneWpwBTihschaHBeffXV8avc8gWiGaHfIgWS7xYusBIPrMOR48CiPM9Zj2NBrosJzkIbPWevzRw33Gbc4/6KLLe9ebfXaY5doneM23vrNUFyLm1WIHQBwv12FvCBbjTnOOZS1kWK4FoOB1rRoTuqGHWQPtet1PRImv4Y6FsHZfYlDxZInP19HIfUFzZSqIByRlmr7lp6xSsvv6JOdGzBo5wAVsPontzhk3fcpZdeKu3atZNbb71V5yEQvhtvvLGcc8450rdv37hWu81RUmssRp6lhpPFSi8C/l3Awg3z4Dp16shvv/2mfgzsPZFe7C11Q8AQyE4EIiN427dvr1ugCDZ4ys7GYLk2BAyB7EXAb2O72ZFZjz72uPwyeoKM/31cqeRu9pY2u3NOXTE5uemGa+XJx/rK/PkL7d0ZcZX6wc39998v559/vvS+4TTZ7di9Ze6MWVJY6CRHljjN6ZgcCdrTy1WKxEmP8B2JEvdbJUnc30oXf6n74/eKZcHfKnUwWGRBDqmqx/RL7Lg6APRW425shHRJzFngasVV6/iAsI0ZzwdRYseC70VXBbrPCEjHZFeQONHzyJRAYQfX6mJGTG6ECIH8CQcCCZK4c0AkT5y8iWpXQ26rZApa1i6+I7hVLsUdy9N4WPgHerk1XHxIcKRRuJZPpFOCYyXr6YJVfu18mTlphrxy8wvSsOa6sqjGYrdLqUB+/fVXsxyMuD/kYnL+nYfePLrzWPV9+umncscdd6i2PITQ5ptvrs7WeN6+9NJLcuyxJoeTi23BypS7CPh3+U8//STbbbedFvT777+XLl26GMGbu9VuJTMEDIFKIBAJwcv90Yg1grcSNVHGpbZKmT5sLWVDIBcQWOY0cpFweeCB+6RPn8tl0rR/5a/JfwVkjoWMQmC5Ix/abbyJnHDckTLkq//Jv7PNgjfKCgovMjdYt4Em/da8T6SOs9Nd7P5zis2oIevxoHd4YtX/Ura12PkiYZMwz+r7VmIfC18bRclKs60tOpcYK/nvktIKLHqDsHqa4bNFJSq6ouhYYBEcpFC6PNpalgAAIABJREFUTTBna7tamS/z5L5T75B3+g2UbbbeRn4a9pNebcYCUbSd3E3Dt48DDzxQ3n//fV0UeOCBB+SRRx5RSTN2vqG/S+jYsaMudg4bNix3AYm4ZBDmWEBbMATWJALeknzixIm6YMMOqI8//lj23ntvI3jXZMXYvQ0BQyBjETCCN2OrpnjGbKtUllSUZdMQWEMIeIK3X78n5PQzzpA/p86Sf2b+oxpla1qSYA1BkrG35cXbpm1bOfLQA2Ts6FEybdp0I7QirC3/vix0RHqd2rVlj8P3kpvfuEem/DNZrU7L4B1TzElqOrMpJrYGo6UuiZKOTEIR161XV9oVbCjH7nO41J2TL18N/cYseNMBdg6lGSb/WcQ84ogj5PXXXxcs9h988EGZM2eOWvB27txZS+2tfKdNmybNmjUz7c4S2kIY18aNG8e1Tm2xJYc6T5YVxb/PWbTBEh/pMfo6fd6Mn7KsMi27hoAhUCUIVJrgHTt2rFqI8dAdOnSoTVKrpNrsJoaAIWAIFEfAE7yPPfqonHHmmTJh8gyZv2C+cNwI3sxqLUxYWrVqLQcfsKdMc97eJ5tH6EgrKExGNF1nPel5Yy85+YIzZNLMP1QWwEJmIbC0cKm0Xa+dnHHAidJyZVN568N3bOKeWVWUcbnxkxcvv/DRRx/JPvvsI/369ZPrr79eZrtdER9++KHstNNO8bwzV7nssstUo9eIoeRVGn52NmnSRLCaXHvttc2aPuN6QPXJULhNrr/++jJ16lR58sknpXfv3taPq08zsJIaAoZAORCIhOCFPMCxwQ8//GAEbznALyuqf6nxedFFF8m9995r+JYFmp03BKopAp7gxXrp3HPPld8dwbvQCN6MbA3egrfnQfvL+PFj3IRlmj3bI6ypROu+U275r5x2+X/lz5mTpKB2QYR3sqSiQABt4/Ubri9n7N1bFoybI2MmjbX+EAWwOZyG19+977775IILLog7UOvfv79qbi9YsEAGDhyo27hxNIpjJsjd1157zT1zx5sFbwltI5HghUxD+skseHO4M2V40cJtD4fuGJYxH6af++dAhhfBsmcIGAKGQJUiEBnB29ZtN/3xxx9tUB5h9fmX2qJFi2S99dZTTTEbaEUIsCVlCOQQAn6ge+eddzoN3stk4pR/ZNbsWSbRkIF1vHy50+Btt7Gc2Oto+fbLL0yDN+I68ls6VziHZrXyasn/XXqCnHf7pfLXzIlSywjeiNGufHIQvC0atpTzDjhDxn05Sv5dMMvGkpWHNadT8H38559/luHDh8vxxx+v5X333XflxBNPVELymWeekUMOOaSYlR+WvQ0bNsxpbCpTOCN4K4OeXZsOBMJtslu3burv57rrrpNrr71Wd6gxL7ZgCBgChoAhUIRApQneMWPGqKadEbzRNyv/UqOScGL3xx9/2KQnepgtRUMgJxDwD/Orr77KbVN9Skb/MUXGjhkdeLo3R2sZVcdLlxZK+/abyW033yAP33+XzF+40J7tEdaQJ3+WLV8mBfkFctS5x8pF918hU/79S2oV1IrwTpZUFAhA8DZ3BO/5+50uo/73q8xdPN/6QxTAVqM0/Hj5888/l0MPPVQtdnG4dswxx9g27nK0gzCZttZaa6mWcW2nY24WvOUA0aJGjoBvf927d1fniexqveuuu8yCN3KkLUFDwBDIBQQiI3g32GAD+ekn83wcZaPwLzQs89A4njx5sk16ogTY0jIEcggB/zBn2xqOZn4e8bs0cpb/U6c4Ust5D08MPF9KDu5c8H/OBXUpFfcrVX4HU0XXl//aMJht3Duz+167ypy5s2Tc2N/t2R5hS4tb8K5yFrw1a8nhZx4plz58jUw1gjdClKNLaqXTpG7WoIWcs+cp8scPE2TmvJnWH6KDN6dToq+vWrVKy8h77ttvv1Ut3vr168sNN9wgJ598cpzg9e8806Qv5c3vxgUeH/SLBw8ebDsHc7oHZUfh/Du9V69e8uKLL8qpp54qjz/+uC3eZEf1WS4NAUOgihGIjOBt06aNDBs2zAblEVZg2IIXghdnB1ji2Up6hCBbUoZAjiDgH+aXXHKJWjZsskl7eeal12TDDTeS6dOn6aSN5wefwV9e/FiNmjVE/9NPF89xl8RVy9+Y9S9n+J+g330IfS1+qPQ4SWEP5unxsEoSDhQ/WXJMvSx0tf/NpyMDglTdZ/x37Ejst7/Wn/dxIRL0v5VcG/sjNfd9JZFjRIOmHr5nqBzLnVXpBk6eYeBrL8upJ/1HTjnjbHnikQfdc32FImvkQ+U7pJ8Malt17ffws46SPn2vNgveykOblhSor+YN1pczdztBZo2eIZOm22J2WoDO4UT9+w+pOHR3kWFgsRM9enOolsMVb0WrFgh4CbLzzjtPLfOPO+44JXrD7/pqAYQV0hAwBAyBFBCIhOBl1bx169ZG8KYAeHmieCJ3odu+izdbtkoVFBQYwVseEC2uIVBNEPAPc7avsY2tS5ftZNr0GXLXvQ/LTrvuJgvnz3MWuatk2dLlzunMYlm8eJHb3laoGmYrnAzMcrdNeuUKPoPvK9z3FStW6ufSWBy2UnN+lbOMDLjQgNis4QjPlatc6p70xPpXGc7wsaI4vkoC6QhHNtdwacQIaAhlPepY5lWOcHZ0c8Ax63Hi1nDHOR8Q0BwTd32eEtZOjiKvpn7PywvI0jynwcpxSNqa7l2Vp9dxHMI7ILv1Gj3O9TXcd0ju4Dfp8FnTxakFMe7SQNe1Zq0899sd57f7rJXvjrk4nlRUwlwJ8hghDmvurKPqrrOOjHPSGb2OOlT+mjJVpXeQOLKJSnQdNbwIWrtGvvS46Gi5+K4rZJLT4DUna9HhHFVKgZO1VtJ752OlfuE68tXQb6w/RAVuNUnHv/9+++032WuvvaRBgwaqxXv55ZfbNu5q0gasmLmLgNfavfHGG+Waa66Rgw8+WN5++22dDxNsYTx3695KZggYAuVHIDKCd/3115dffvlFc2AWpuWviLKuwDPw0UcfXVY0O28IGALVFIHwc3ffffeVb74Z4kjchfo8hvDE4ZSFNY/AOmuvI4XLljqifak6Bnr22WeVGDed5OjqJtwXmq3bTA69pqeccvGZMmXmn+ZkLTqYI0sJgrdVwzZy4i5HS4f6G8sb7w4wq8vI0K0eCfnJzNixY5XgRaKhR48eAiFkjpgq1gZs0bFiuNlV0SPg+zDyY1jm77777oLetnEO0WNtKRoChkD2IxAZwduqVSvBm609bLO/UVgJDAFDIDsRCBNbSDWw6LbDDjtI165dlTBRKwf9P2a56uUXvByDFptzvvxJ9BeyE5pYrotLPgTGH8mVhpNpFHutRy/zEP9NKsU0jVfXMEZntI5zWoOF2cMPPyxbbbWVDBw4UOvEFkWjbVRhPDds0U52O2dfufiKy2XktN8CJ2uJzbrCYtOxC2PNyiej3UfvUQmd5gpeGi5aKQInyQGvCA7Ocr9C5fblc5lcVrhMNmnRQS7rfYFM+HiMjJtsmtTR9ojcT82TkZMmTZJdd91VHYPhbO3OO+80gjf3q99KmOMIeImGp59+Wnr37i2dO3eWoUOHGueQ4/VuxTMEDIGKIRAJwZufny8tWrSQ4cOH28O2YvVQ6lVMVvlL5igpDbezJA0BQyCLETBr0OyqPKuv9NSXH9wcdfhR8vHXH8uLfwyQJnWbSqEskWWOkkS0o3goYlSTfVOVEL0g+Oa/e0I1OOJ/FVePdqIieqWz0w7dMvgFOVqkaV0yJRs+U0wDO5Zy2SgWZ7WLfhW/Z/E8hrlw7lq8HEV4FJUlKGdA+QaK1YQiEjjxOxIr+e6/htJIeuy8v9RfUk++/eFbk2gou0ItRggBT/DOmDFDFzQZMx9yyCHSt29fI3gr2FKmTp0qLVu2rODVdpkhEB0C/n0+YMAAOeKII2TTTTeVUaNGBW+XkGPA6O5oKRkChoAhkL0IREbwNm/eXH799Vd72KapLZiTiDQBa8kaAjmIgF8UYtt/2Mo0B4ualUVisQ5Cwju9y8pCZHim406XfvpROm/XWWrXKpAeZx8pOx+1m+S5RemVzqJ9hdOOVmvsFY6SdBImqiGNAz33G0kTPvW3m0Cu0O9OUxpdaj5ZeI0d41Ov57dLA8mB4Dxa1qTnrkHjWtN2x7genWu0q9254HuMFPWO/7jef9d88Zv8uv9JBxJV/w/yq7/dOe7BhavUP2KMCsa5otOSJrlAu9p94RNTfXcQvWnVio45VUQuuiYJqI41OtNOo9ppTqMxrZ9oUefzHS1qp03tNKnRrq7pdKjRts4jfh7XOQ1r/jjujqletbsu0LF2xG6dAlm+dIWMHzpW3nv6TfljxB8ycuRI2WyzzYzgzfD+lWnZ8wTv3LlzpVOnTlJYWCgHHXSQPPHEE6bBW47KCvv+2HjjjWXKlCm2y6Qc+FnU9CDg3+cff/yxIEG2wQYbqONxI3jTg7elaggYAtmNgBG82V1/lntDwBAwBNKOQFQkcTLZg1QzX1VONKpCC9c7o6uqMqWKca7Gg+S5os/lMnP2v7laxKwvV4smLeSd99+R7TpvZ5rUWV+bVV8AT/AuWbJErfvQ7ESL97nnnjM953JUhyd4Icgh0aZNm2YkWjnws6jpQcCTFd99951069ZNLcv/+usvXZQ0C970YG6pGgKGQPYiEBnB26xZM9UWtNW06BpD+KV13XXXCX+Gb3T4WkqGQK4iwLPDk7KlkbPES0Zocg3koz/nP3OJkCyJbA7jlaixWxb563FLpV2Z5E4qKEUbZ+68ubJ82fK4NC71tbRwqf4tWbpELf3834plWNVigYvF7nKdRGKFC5HEcbWS13Puu35ijYtVcGAJnBicVG1CSE0hF6vZWrXy1ZK2Flawrl/m1cQi1lnGunP8pi3VcGa3tWLWtThVVGvb0Kda2NZw1zlr3WLnauSpJa9eS3ouHW+lq5a5atWLdS/nsAImncDqN27x60qm1sJqGsxzI4nTwLhccUy0wQFS4Kyqx/7+u8x39dKlc5doK9tSq1YIhMfLEJP0wR133FFeeeUVswYvR0vwOC5atEjat2+vJJrNO8oBoEVNCwKerGCnMP4LGjZsqG2zbt261r/TgrglaggYAtmMgBG8GVx7fqCFRQIvszlz5qjjCFutzOBKs6wZAmsYgUzc+s9km+cWLxzdwq5b11cnwjxJ5Mll/9tLGfjPXCCazYq36jqKSRxVHdaVuZPVU2XQq97XhsfFbdq00YWPbbbZRh1Zeuve6o1QaqUPSzRsvvnmgtM6I3hTw85ipQ8B/26YMGGCIB0CsTt58mSdG1v/Th/ulrIhYAhkJwKREbxNmzaVESNG2EAgwnbgB1p8brTRRjJ+/HjTwooQX0vKEMg1BBIdds2cOVPmzZunVolsueSPBaPFixfLYve5xH1iqVPs++IlLl4Qh7ic99dwPVtfGVAvV01RPgPrxoC4xUwPHVKQDUhdPznUT68lGhwsGX40Pv3Z8HeOhX4nWhYHv2uoZSMBHVDVAnUWj976EWvH4FhMJzR2zh/jfH5Bbald4Fw/Oa1W/goKCuLfE38XnStw8fJ1Ea5OnTrxP37zR7y11lpLrU7YXtiuXbtca34ZXZ6wVXtJGS2PFElJ1u9VAUJ5LMVLy09ZFulVVZagWwfWyBYMgYogECZ4ebbyPN9kk01k0KBB8feQta+ykfVk2axZs5RI49O/ww2/svGzGOlBwLfLv//+Oz52Yiy13nrrGcGbHsgtVUPAEMhiBCIjeHnImkfLaFuCH7BSSQxY//jjD92KaRa80eJsqRkCuYCAJ3chY0877TR58cUXAodLVRxqsS08320ndySrkp0FdZxjJUeUOvKzIB8S1JGlSp4W6PMsDykI96eOrGJWvjzz/B/b3pc7QnnZUkcsu63yy5cul2XLl2nZVq5yBDNb6YnPdnrvOKuKy1yR2+Hd/ayzzqrIpXaNIWAIGAKGQAiB8Li4Q4cO+p5p3LixDB482AjecrQUjyPO6o444gj55JNPjOAtB34WNT0IhBceWrdurYYGWPAiD2kWvOnB3FI1BAyB7EUgEoKXgRQEL96PCUZARtMgEi14f3dadVglGL7R4GupGAK5gkD4mbDhhhvqYtCee+0tBx56uGy++ZaumM6q1v1buMRZ4DqtUQhTdEOXoxnqSNFlTpd0KVa97lzhYmflW7g4IFjdIFrjcF5JVa7DQtfpj/Ln0uD7itixwLJ3Wew9UCTFoNaTXqtUr3PyDI6gVf1SMoZAqTO+Rf8TbU/0RlXzk+96LND/VDJYrXKx9MNa11vpuvMcJy6fqg+KFmhwHdcH6QbH852mKZa4tSCa3XfeYZDQBRxDz9Rpj3IvTdPlhfvlKRmdp1gGxwONYp7JKiUBsc390EzNdxbDxHHXqG6pKyME/DKHabMmTeS9d96UG667Rp5++mk58cQTzQlQmjtiMjmQyt5yTVrwlpb3qKx7K4uPv74snW/Oe0v7qO5p6VQ/BMLvQDQ62cJNGDJkiM1Lql9zsBLnGAKexF2wYIHugOLzzz//lFatWhnBm2N1bcUxBAyByiMQGcHbqFEjGT16tA2kKl8n8RT8gJWt0lgisN2aQasRvBGCbEkZAjmAABIMkJR4DD/hhBPku6E/yeadtnHyDAscqbtYrV3VV5KSpo4cdT8gHv1noGzgSMrAm1LgVCn4J/4JTHos+BIKsaPxY8EX/RdJhmKfnCg6FjtFxFi6sbRWQZjGDkPKakQYYC6HpC1eaY4+Dg4U+0iwXk48B+nKBQmfLnvxhIq++jz728RkKHz5vCwFv/0fx8IW1C7PWCnnOfIY3biu224uf7sthvMXLNRE7blevE6j+OUlQmxrcRRopjcNnN+pkzeTaUgv0Dmaevj52aVLF100YOz8yy+/2PM1R+vcilV9EPAEL2Pd5s2by+zZs9WQoW3btrZAXn2agZXUEDAEUkQgEoKXbbgNGjSQMWPG2EAqReDLG23AgAFy+OGHl/cyi28IGALVAAFP8Pbr97iceurp8ueMOfLvP/867dxFOtFdPTgr2lIkcKOCTBU1yyBslKtVxtZr95Z196rJe1m58OeLtIK1sGVextZCvLy//+7bclrv43VhlC3F5mCqTOgqHIHdRc89/6zUWFlTmjdrriR8YvDLErqwoCFY4FDLcv0ZqltvbZ7HYgOW5c6KGwtx918Nd8w7CdS+x2KKt0TX+Fh6c7iGs/KulbxM4QWMhMWM1S5wRYEYJd08FmnUmjywWg+s12PHyKeL48+XCWYSdZeiBRGuDhZH9FsIT9rxCqfPjeX/yuWBY0XdJeCkVbD653ehk5FZ4c5Pn/GPNG7WWE4+/WSpk1enzCxZBEOgJATCBO8uu+yi2vNoxpvhScXajNcsTz5+qFiadpUhUFEEwv27idsFhcETu1rxT2Njp4qiatcZAoZAriJgBG+u1qyVyxAwBKoNAv5BzoAXxzIfff61tHUD3znOQQqSAxYyBwEmKuvUqyeL3BbDbTt2kO+++066du1qk5Q0VdHdd98tF198cTz1WnVqOcJxZXEjdHdW9apVSyNNGbFkAwRi6ioqw+LAXrFspTRu2EiuufZaOeHEE6R+/fpKGGeCAzirsuxBIGytv99++6l1H+/FCRMmaCFsh0T21KXl1BBIRCDcf1u0aCHTpk1To7L27dvb2MmaiyFgCBgCCQhERvAyKB87dqwNpNLUxGyFMk3AWrKGQA4g4J8PI0eOli222ExefG2g7LTTrjJjxnQjeDOsfqkrJI1+d+/L/ffeVfAEvf7665uOXIT15PvDLz//Iltvs7U0btBYzn38YtntyL3EuflLeienJC1IfTjXffF/YXtXOKvTVao1HUhuqJ6v+1ylutMuNjrS/OY88dCbdudwGOiviWtOO2KZwHHSUSPY+LXoUYfScefQuE4lhFRFoLJKvCR+prRkncVwXi1nXYx+NH/oTzuniSrggjVyzGoZuRdnEhyXfsEIH21qrJXzarlPvtdyetfoXNdxf3nOuaL7DytnH5xbRPlz/B9yx39ukl++/VnWb7W+/DX5LxtHplLpFqcYAmGL04MPPlh9gvAcmDRpkrWnCrYV3k1onFowBNY0AmGClx1Q6O+OGDHC+ZjY3AjeNV05dn9DwBDIOASM4M24KkmeISqKbZ+mT5clFWbZNASqEAH/IP/hhx8E/cEnnnlB9j3gQJn+9xRH1CQntKowe3arEALUFU5Jfxs+XA7pvrcgr4HDN/MEHV0zWe4wreU0qa+++Vq56aob5Jy7L5TTLzxHJi0e76x3S7EOjXGPSkLG5BnIlTrs0y/6I/bda0HH9KI1+4GGdRGFGVxXpLIQnFldyCOkW13MtrgsfYboMFs9Jchu9YAYP1XECxd9K4oRfNN/A6+CMQIcYjwgvb2VpU+QeM3rrS+jfx8h53Y8ReYsmWsLHums0hxP2z9De/bsKbwLCRMnTtRPs+BNrfI9hlg+77HHHkaQpwabxUozAuH+yy41dqsNd2Oojh07GsGbZuwteUPAEMg+BCIjeNddd10ZN26cDaSyrw1Yjg0BQyDLEfAPcrb7d+vWTZ567mXZa9/9Zfq0v82CN8PqVgnepk3lh++HyFGHHSRo8mItaQRvdBXl+8PlV18ut910mwxa/Llamy6ev1h1b2Mu8xJumPxoibkq1bg2OFlilNQMc6MDpLIplS0tXYzULna74tLF7lQRaQ3iOFdru15bufbUK+SDfu/I8lXOstkIucrWWLW83vf74447Tr788kvVn2dnIQ5I7fmaWpPwOEGMQ/AidWH9MTXsLFb6EAgTvFtuuaVa7w4bNky23nprI3jTB7ulbAgYAlmKQGQE7zrrrCPjx4+3gUCEDSH8Qrvkkkvktttu0wGrWSJECLIlZQjkAAL+Qf7TTz/JdtttJ6+/+b5s53Rd/5kxwwjeDKtfT/D++N0QObLHQbJw4UKpW7euERAR1pPvD9ddca08+srj8v6EwTJ57iTbARMhxlEltdw5X2vWuIV88tr7cu1RV8Qdttk4JyqEq0863tlo7969ZdCgQYIDaKz8MEAxgje1duBxgtjdZ5991FLSCN7UsLNY6UMg/D7YZptt5Oeff5Yff/xRtt12WyN40we7pWwIGAJZikBkBG895zTGBgLRtgL/QmPQ2qBBA6enOUMg0m3iEy3OlpohkO0I+Ac5Fg0MeD8e/JW023BjmWVO1jKuar1Ew6+OeDjUSTTgLKRZs2ZGQERYU74/XNnnSrn3kfvk83lDZPq8aVJjFUKxEd7Ikqo0AhC8TRs3l6+f+Uz6nHSRTJvh+kMT6w+VBrYaJsBuCORuzjzzTHn//fcVgaFDh0pTt2PCCN7UGoTHCYOdgw46SEaNGmUEb2rQWaw0IhCe9yJDhgQLfbtz585G8KYRd0vaEDAEshOByAjetdde27zVRtwGwi+0DTfcUAdaWCQYwRsx0JacIZDlCPgHOdvW2L729dBfVOd1/vz5ZrWYYXW7wkk0rOuckk77+2/ZbcfO5igkDfXjLfnuvPVOuezay+WrpT/KPwtmqA6s6dinAfBKJBkQvM3k6+c/lz7HXyQTJk6Qdhu0M0KuEphW10s9wXveeefJhx9+KIsWLZKvvvpK2rRpY+0pxUbhCd5ff/1V9t57b5k+fboRvCliZ9HSh0B43rvjjjvKt99+K0OGDJHtt9++2hK8XtPexjTpa3eWsiGQrQhEQvCutdZawh+i/AQjIKNpDmEcW7durRrHderUMXyjgddSMQRyBgH/IB/pCN4tHMH71Xc/SdNmLWTOnNkm0ZBhtQwJ0dRZ7M78Z4Z06bS5m6R86yYp3artJCUd1eOJnqefeFpOOfsU+abwZ5k+31nwrnTOv5yzUguZgwAEb5PGTWTIq1/KJUdfIJMmT5I2rYyQy5wayp6c+H5/2WWXqQXvv//+K4MHDxacMpkFb2r16OcdaPBed9118swzz6R2ocUyBNKIQHg+vMsuu+jCDSQvPif8+DeNt8/YpHFUWsM5drVgCBgChkAYgcgIXohHE+OPtnGFX2jt2rVTC14jeKPF2FIzBHIBAW+x+Mgjj8h///tfuf2ue+WU08+W0aNHSr7T7c4UUispteYPluB4as36oyrl7rFT5ckfz/QGDRtJ/XXry3FHHipfDP5MiQcsMIyAiK4neqLn2aeekRNPPkm+WPW9zFu0QFY4ct2sXaLDOYqUPMH7zdtfSZ9Dz5fpM6ZL0ya2pT4KbKtbGr7f33DDDfLyyy/HCd7NN9+8WpNA1a0dWHlzD4HwfBjnfyzcfP3114I1b6oEL/EIfgxQ3rGAWczmXruyEhkCuYpAZAQv0gGs+BLMgjea5uJxpJLQ4J06dao6izB8o8HXUjEEcgUB/yA/++yz5aGHHpIO7TvIywPflbbtNpSFTqZhJZaLzmM9f/xLcAv/CV/0aPy8/xI6EosfpLT6j1jM2En9Ff4evmfo8qLUilshBJcW3UlCp2vGf8QOxkhiH6WGF1oNMcpF50g3diLhfBAnFtN9xNMpdnj1exZdl2BJEboNeNeps5ZzqjZfzjvrNHlzwBtCfT344ING7kbcET3R8/gjj8np/z1DPln5tRQuKZRlS43gjRjqSifnCd4v3x8sVxx4sRQuK5SCWgXWJyqNbPVLwPf7u+66Sx5++GGZOXOmfPHFF4JTplRJoOqHWvISG5llLSGTEAjPe/fdd1/5+OOP5X//+59gzVuRvo3lK88LHJfzl46Qrrm6L+8999yjfjZuuummCmGQjjJbmoaAIZAZCBjBmxn1UGYuPvjgA9l///3LjGcRDAFDoPoh4B/k3oIXSZfatQvk4EOPkI5bby35BbVllbMehbBkh7paLrg/iNIaNWEy3Rn32/2SmjXcJzylO+5/w3nqd40b0K7EDnxWuTN6DZ+BEyv/PS+Wvp7nWnchn/CeXKupxnbN67WkwdFYWkEUnzcSkJ+oAAAgAElEQVQuXykrnY4qgQG6EtLwyCtX6LGV7kdAXPPJ30p3zl0VOh6Q3cFxH9efJz7nV7j0+GSAjmUtaQXfSYvflMWl4Y6vYIsccTVPwfWaPvlyv1esCNLwg/2+D9wro0eOEHTVR44cqbrqZr0bbZ/1RM+tN90qV11/lXyx7AeZs2BW3Fo62rtZapVBwDtZ+3LQZ3J594ti/doMBSqDaXW9NryT5dZbb5XZs2fLRx99JDvssIMRIOVsFLyveL+li/wqZ3YsejVHILzg0L17dxk0aJBa8e62224p922s+hs3biwQxOFQnsWMJUuWpLSTNhifBu+xqPuQH+/fcsst8uWXXyoWFSG5q3mTsuIbAjmNQGQEb0FBgUyaNCn+QCvv1oecRtkKZwgYAoZAGhEIWwocfPDB8u6776bxbtEkHXDIAcFcMw+yOSCBi+TEiux0A4PbwBzW0cpBBtwHpKr+jh1yU1IliFc6YjU4HETy1sSOfo0d10hKzlZ1aNqkifQ4/HC59957VbveNNSirwFP9EDw3nzvzTL43+9l6rwpplUXPdSVThGCd/3GreX5vk/Ji5c8KbMWz7FxZKVRrZ4J+AnNs88+K+jwQsa8/vrrstdeexkBUj2bhJU6RxAIk7CHHnqovP322/Lpp5/KnnvuWWrfDo+Nm7ixF1b9zZs3l7p168oRRxwhffr0UdK3tOAX4H/88Uc56aSTZPjw4ZG+o8pDMHNj/5xDIxvHyq+99po933KknVsxDIGoEIiM4K1Vq5ZMnjw50odeVIXMhXSoKEhzI85zoTatDIZAehHAA/Zff/0lHTps6ixF26kVAQHLRp4lfDJo9b8hxBYsWCCLFy+W+U7SYbGbGC8tLBSOh/+YMHMNx5Ys4ZyL574XEtf96XV6LvhcvHiJO7dE7899ITP55Dd/mtay5e7YMt0+v8x9X6UWsljMxixw+Y4FrlrUBpa6ZQUsJuJWwEoiu2dnzMo4sEKGVM5z+sT5zgldTcnLz1etYq7jXRb+zHfn6q69tqzj/tZ2f3Xruu/ruE9+u0nCOuusEz+PhE69evVUSofPddap5+KvJXUckVvbLYImOsMwcresmqzYeT+w+cxpHO+1x14y8N8PpX6jBvLv9H8kr1YJ2zHD6hpFKh0uA6U4MEk4FdiflxCSnCo6tFpC5S/4amsVq4mcpJ5mKK2w1MrqCRQTayn5dCnrKCudhfsGTdvKabv/R5ZMXiwjx4+0cWTqNWUxQwj4fg+pe+aZZ+rz9sknnxQWPc3CrXxNhfHB33//La1atTJZuPJBZ7HThIAnWiFmBwwYIJ9//rnsvvvupfZtf82QIUMEbW6cLzLOxfIVKRekHu644w655JJL4rn2Wr0cYCzIOI25N36GkIRgbJ0YvMU798PgjcB48cILL5Qbb7xRx9s8j/xYnE/GmqSbbBzo43Pep02a3l8D155++umabr9+/ez5lqY2Z8kaAtmKQGQEL5PgP//80wbmaWoJ4Yd9mm5hyRoChkCWI5Auza+qgCUgmyGeg62hflCrMgghmYRU8qJyE17awcs9JHwSh0GyH2Snkm5UcXjxkr+ot+5Flb9sTyfcDwpq1JKu3XeQvu89pZbcrjUxTXJ/ycjJMCmaeD6wDicgduKmau5bETG7mlZ1CXcIY1saBRuIfpRCGIf1skutsCKF6OLRvNCKP1q8BGGyenXiuqQ0g+NF6Bb95ribJrv/PK58c5NcyZeFMl92qrGt9Dru/+T5F1+wyWq2d8A1lH8/oYHEOe6449RKj50SRx99tLWpFOvEE2Lom0Ig4dzZJIRSBM+ipRUB3w6POuootVpNheD1z4THHntMzj//fCV3EwNjMeQbjjnmmBKfE3PmzFFyuHfv3ir5MmbMGN2BhcY3iyDJwu233y6vvvqqDB06tFLGWYkEsJegwpoYOTaIa1vASmvTs8QNgaxDIDKCl4mqX9XKZpIh62rQMmwIGAKGQAgBbx3rD4Wt/r0uWCJgJR0nrUSrUyVqYhYN4XQS4yX+ztbdB97iwpc7/BnGpyxMwnhkKxbZ1NHiE7vHH5czHFGxXqv1ZLMdOjpna0sC2Y6YhjKazqrXjOakWo0H1uK6yIC+Mr9jcQKtZs4HfUD/0ISO6T0jLK3EbCAwrfIhSDLrr7g2dAxF0ieO3ic4xncfVOeZyziJZHYx8BG6xrrI617H9LRVwJqdPlipB/etUTOwWEYORY+pznWQoG+TKpUS00bB2j1s/e7Pqd52LI1VyKogQ6038DeKEeB6PLCS1ySdyHYNxSX4rkXUxQ13zB0vXLRExv8yVuZMnyO//PKLbLXVVjZZzaaOlkF59X0ebc6DDjpIGjVqJGxjhpQxAiS1ivI4ffPNN3LGGWfodnSb06WGncVKLwKe4GXBBuK0PATvZ599JhdffLFa7V566aW68MNOMxYysODFwhdnjIT+/fvL3XffLV27dpW+ffvqMTgO+gGkLukg+7LBBhtI27ZtdRHk5ptvlhdeeEF3CyBX+cknn0jTpk1XAwSnuk8//bQcdthhcs011+h5SNonnnhC/ex4eamLLrpItnb+M/7zn/9oHKQYuH/Hjh3jaYLDTjvtJOeee64939Lb9Cx1QyDrEDCCN4OrLDyouuCCCwSnEXXq1LHBVgbXmWXNEDAEVkcgTJKWhE9JJHNV4JmMxPb3NTK2Kmogvff46MOP5KG+D8m8f+ZI/Qb1Vf4jXOeBg7+Ykz61II9ZkXt5kBiBq070YmSvOuJjoSOW9eUqg1KcLHZiIzHyN0Yg+/Ri14RJ2xVKKq9QXehVLg+BLAmfLi/unisCVlmPq4M/dR4YI5ohksl3yLmglhHrd40fu2GcQF7d3jisosB1gcNEnCAWd6JYA8K2RkwCBZ5YiVxvMZ+nv/OUJHbfHYnL95o1nZ1uvpNEyS9wFvP5ipPa87pzi520y3rN1pO77r1LOnXqlN6GYKnnNAJhchKipWHDhmq1d/bZZ8eJk5wGIILCeQy//vprxW3YsGFmwRsBrpZE5RHwBO+xxx4rr7zySrkI3vfee09lGJBk8Ba32267rZKwkK4bb7yxZhBCl99o/B5//PGyxx57yG233aYGbMy/+Y0EWjj8/vvvst9++6njXP5w4tajRw+ZOHGiLFq0SLbcckuNjlwDRPJzzz2nBPH1118vJ554omoCn3POOXLllVdqPCTPuBdOeDfbbDMleqdPny4tW7ZUQveBBx7QeAceeKBaHUMC2wJW5duXpWAI5BICkRC86MwQpkyZop+22htNEwnjiM4jGscMWG27VDT4WiqGgCFgCBgChoAhYAgYAtmPgJ/QsCX6kEMO0fEyJA0O1/y25uwvZXpL4DFEoxTDkh9++MHmHOmF3FJPEQE/9+3Vq5e8+OKL5SJ433jjDSVRp06dqtrSEKX77LOPIN3gw+zZs9XqH4KXxQ0seCFPuc4HHLUhz0C8cGBB9Kyzzopb/HIOa2Es4CF06UddunQRiGZIYnaqYEXMTgPyAKHMOcKRRx6p8jI4i9xoo43kcOeU984779Rz3Ofnn3/WxVD0gNEaR47GCN4UG5FFMwSqCQKREbxYX/HgJBjBG03rCePIqiAvCohewzcafC0VQ8AQMAQMgdxHIOw0pbKlLc3aO3HCl+xe1dEi3FvwJ7PSDzueQQ/bgiFQUQT8hIaxMtZtjJfZxoxMgxG8qaHqMfzggw+U2Bo3bpwRvKlBZ7HSjIBvmyzaPP/88+UieN99910lQufNmxfP5Y477qi8xejRo9Vitk+fPmop26FDB7nvvvvk5JNPlltuuaVYqbCiRUoIopfnClILWNjyrMHafZNNNpGFCxeqgzU0e3Gw9u233+pzCDkHSOqXXnpJrXUhhAkzZ87U6yCYuaZnz55q+ct3ZBwghwksVKHryzGsfyGpzzvvPEGT2AjeNDc+S94QyDIEjODN4AoLE7lo/bAtBO/sRvBmcKVZ1gwBQ8AQMAQMgRgCqciTVAewSiK2bTxTHWq/asroLfwgJbGSw/kz5AekiBG8qdWBxxBSCl3Qp556ygje1KCzWGlGwBMWyBpg3VoeDV40pSFESSPs3BZ9bqx7586dq1q8OG9jB0A4QLZiUUtAToH7Ip3A4iTyDFjZ8h3t7912200lFmrXrq1yDLvvvrtq/Z5yyimyYMEClZYIB85BLnvZCb6TV/SAmfMjI3HttdfK66+/Lg0aNFC5CJy8sVjKvfbcc089bwRvmhufJW8IZBkCkRG8DNLZ9kCwAXs0rSDRgpdVw3r16hm+0cBrqRgChoAhYAgYAoaAIWAI5AACnpxELxMShN9sd77//vuN4M2B+rUiVG8EPGFx0kknyTPPPJMSwevn0VjJol17ww03KIhhqUOsdbGmZUEIWYYJEyaotS+fyCZAqmLdSzj11FOlX79+amyFFS0a3wQsdJE06datW5xshdB9+OGH1akbeWcnLrtU0AfHadqnn36qUhPcC1IYPV2s5iFtIXAhjV9++WW555579BgLVQScvF199dXyzjvvCFrZjzzyiC3CVO+uYaU3BFZDwAjeDG4UYYK3fv36ulUKQXjT4M3gSrOsGQKGgCFgCBgChoAhYAhUKQJ+bPzPP/+oBRwBjctHH33UnKxVoCbMWKcCoNklaUPAExZYwz755JMpEbylZcaTqIlxkHP45JNPpH379qrTi3yCD1yDBe/OO+8sBQUF5S4rhC4Wwmjo4pgNyQcLhoAhYAhEjUBkBC8JoV1DsEFB1NUk8tlnn+mWM69XVx11/KJH1VI0BAwBQ8AQMAQMAUPAEMh2BDzBi+UcOppYyzFuxtrPtjCXr3aZx0Fmhbezly8Fi20IRIuA78OnnXaayod89dVXcdmFsvTbac/8Jcbzx5lTM79OVWM/zHWEZZjCc/PwPUsikz1CPi73D/c57z/A5530ly5dGi+HcQHRtjFLzRDIFQQiI3gZWE2bNs0I3lxpGVYOQ8AQMAQMAUPAEDAEDAFDIAsQ8MYl6O3it2KttdZSz/XoXtrOtyyoQMuiIVAKAp6wOP300+Xxxx+X7777TiUVoly88USrf5Z40tcTqWFCOJHMLYtsTZZ2ZRZQPLFc1n2tURkChkD1QyAygpcB1YwZM4zgTVMboqJ4iNuDPE0AW7KGgCFgCBgChoAhYAgYAlmJQHj3YIsWLdST/VZbbSUDBgwwgjfFGvUYLl68WP79919p1aqV7cpMETuLll4EEi14f/rpJ5ViiZLgTW8JLHVDwBAwBKoGgcgIXrYMoHtFMImG6CsP6wNWEo3gjR5bS9EQMAQMAUPAEDAEDAFDIHsRCM89ICbxXdGmTRsZNGiQzksINoYuvX79pPCFF16Qvn37ypAhQ4xAy94ukVM5TyR4f/vtN9liiy2sfeZULVthDAFDIAoEIiF469WrJ0uWLDGCN4oasTQMAUPAEDAEKoWA7XioFHx2sSFgCBgCWYdAmODFY32jRo3UiveLL74wgjfF2vSTwv79+8tjjz2m/j/MQjJF8CxaWhFIJHhHjx6tWtvWPtMKuyVuCBgCWYhAZAQv23lmzpypEJgFb/Qt4bzzzpMbb7xR1l13XcM3engtRUOg2iJguwOqbdVbwQ0BQ8AQyBkEwnOPzTbbTAleJjloddrcJLVq9pPCF198UZ599ln56KOPjEBLDTqLlWYEEgnecePGycYbb2ztM824W/KGgCGQfQhUmuAdNWqUboNatGiR6jXZICq6RhAerELsslrZsmVL0xKLDmJLyRAwBGIIlOQ8ojwAQRYTKuM4Itn98EDs0+Z8SR6T/TMTBxwHH3ywoMOYuOBYmnUv5xK9GJen/BbXEDAEDAFDYM0gEH7Wo83Jewj/IL/88ovNTVKsEj8pfP7559U53XvvvWcEWorYWbT0IuDb5imnnCJPPvmk/PHHH9K2bVtrn+mF3VI3BAyBLETACN4MrrTwYHWjjTZSK4T11lvPCN4MrjPLmiGQLQj458uUKVOUCA1rE/JiYHIM2bkmA6QufwUFBSllw3tK33zzzeWyyy6T448/XuWDateuXeGygJPHI2riOqVCWSRDwBAwBAyBMhEIj5m33357WbBggT67x4wZYwRvmegFEfyk8JFHHhFkGgYPHmwEWorYWbT0IpBI8P7555/SunVra5/phd1SNwQMgSxEIDKCl4HU7NmzbRAVYSMID1ZZpfz++++ladOmRvBGiLElZQhUVwT8w//SSy+VO++8Uy1eL7roItltt90UEqxmeQalQmp6YpVtnXXr1pUePXoosYqlbUnWtmXhnmh5++2338rtt98uTNwvv/zypFI1Ph8777yznHPOOXL00UfHb+PTu+GGG+S4447TrX3kMT8/P+7A8t577xWu7dKlS6XzX1b57PyaRyDX5aSi0iYEJ54HJT0LTOZlzbdly0FxeTjeY1OnTtX3xPjx421ukmID8c+MgQMHyjfffKNjg6ieIylmwaIZAkkR8O2wd+/e8vTTTwvGCexqtfZpDcYQMAQMgeIIGMGbwS0i0YIXb7ZNmjQxgjeD68yyZghkIwJff/21XHLJJQKJuv7668ttt90mvXr1SnlSXFhYqFayJ554olpNvf7665HAwAvq6quvFqyJIJEOO+wwwTrXE7yeiMbSGAtk/8xEf5EtfFgmP/HEE3LLLbcoQUW8DTbYQCByDz/88HgePTHcrVs3PQ7pHQ5ooEN+45Hdx+W8eWaPpJrXaCK0MUJlLdbNsd8arUa7uSFQ7Hm899576xZunvsTJkxI+V1mMBoChkBmIpBI8E6bNk2aNWtmc+LMrC7LlSFgCKxBBCIjeBcuXCizZs2yQVSElZmowTtixAjdjhImGCK8nSVlCBgC1RAB/zyB1F1nnXWEiTHWT61atRKI3zp16hSzlvU6tZ5cDVvo3nTTTfLhhx+q1c8bb7whEK1YWxC4D5PtMBEbloXw0Pvn3lVXXSWPPvqobLrppnLGGWfECWcfj7R8ehwLW3EgafP++++rLvxOO+0kTz31lJx00kl66T777COdO3dWS10cyPTs2VN22WUXPXfCCSdo/v7zn//IW2+9Jfvvv78ccMABapXcoEEDtQjzesCJ1oxezsIThmF5i1SsoKth08u4IqPXSXte09IkfuHAf5KnsqxoE8GkHX711Vey++67Vxhn/2ygX7O4fPrppxfrZ76vIoXCOfqaX+yp8E3tQkOgEgj4NnvggQfKjBkz5J9//pGJEyfa3CRFTD1+P/74oxLkvB9tzpEieBYtrQj4MR6GBDgAxLF748aNrX2mFXVL3BAwBLIRgcgI3vnz58ucOXNsEJWmVnDhhRfKjTfeKGuvvXbSrclpuq0lawgYAjmOwNKlS1XjlkEzGt933XWXlthbzk6ePFnWWmutUgfRw4YNk+7duwvvAWQPIFjbtWsnBx10kJx99tnlemZ50mirrbaSX3/9VaZPn67SNAQmmpxHVsEH7lmvXr1itQSxzBZTyGG28XH9Cy+8oHmBxOP6Dh066PY+yCuIgCOOOELJKwjc9u3bq7UupDDHCTgUJd3SgrcSzvEmk9XF8+0Lx7BbbLGFSnWwmOClSShcqtIN4Xi0N6RJCGvSmteTMWyvpk9WZlzm09pyyy3V8j3scClcdgjx++67T7B0TxW7rG5ElvmMRcBPauiLSDPwfhg7dqw+861tll1tHj/GAe+++65q8PoxQtlXWwxDIH0I+LbJ7jLkwDAqa9iwoRG86YPcUjYEDIEsRcAI3iytOMu2IWAIGAJRIOBfAljhIWmAzq0/BsmLRS763z5gOYHkAaQq5ChWriNHjpR+/fopUUqATE0MEGrcAzJ41113VataiKHESXf4t9cHhmxNlFUg/R133FFlJZo3by4vv/xy3Fpxww03FCRtIHZ9eq+++qr89ttvarULEXXsscfGs/jJJ5/Ip59+Klgt7bXXXtKnT58SoQULyLPzzz9fCWSkKbyXdi7CWuzII49Ua9///e9/uoUQy2gLmYOAJ+JpI1deeaV89tlnOlGkveGcj5AKGeTj+EUH6p5dNpUNpMcfpDH9hPaF9AltHMvE0izqfN/9+OOP5dprr9W2WlJ8bxXsJU7C+Q4vVqy77roqXfLMM8/Enw0+zXnz5kn9+vU1b8ccc4xavh966KEpY1hZrOx6QyCMgG//tEX6N217+PDh+r4yS9Sy24rH75VXXtH+/sEHH5jGadmwWYwqQMC3zaOOOkpee+014d1j/boKgLdbGAKGQNYhEAnBy8QIK5G5c+faoD5NTcCcmKQJWEvWEKjmCPiXAE7WsLp94IEHZPHixUpcIlcAiXnHHXfIIYccIhdccIFaTgwYMEAH1vvuu696KEe+gAC5g14uk8Kw1Q9WVFgAYg2LpR8O0HimQRaX5SCD8/fcc48Sb127dtVJO4G8fPnll/LDDz8ooYyswu+//67Ww1gis72UPCLJ4PPLdcgwbLfddnLuueeuZpmEJSdlv/vuu9XS12/X5/6UhzxQRvDAqpGye3IbfH7++WfZZptthAkI0g5ghyUZW+XJlxEMmdnZ0HZm8YFFBMigQYMG6eJBmOT0pCufkEaJUg5YfUOKsBhBO0AGhH7hSVT/DvfXJiLh2wZk7tZbb62yIJ06ddL2w4IIJDT5ueKKK7QdeVLWfye9sKTD888/LyxqvPPOO8Xasr9vOA1fJi9NwTn6Hc8AAsdp+7feemvcOhkpBiz76QO08y+++EI/WdBgYQhnhsTxOJF3QliuhPvwx3PGy7Ukk23JzFZjucpEBMLbuL/77jt9nvOO4J1gz9+ya8zjh8QSYwH6dVnv6LJTtRiGQOUR8P2XxUYWP1n0ZFer9evKY2spGAKGQG4hECnBC8mbzCIrtyBbM6XhBZY4OVozObG7GgKGQC4h4DUzcbIGsYWVazhA8rz99tvSpUsXlShAMoFAvI4dOyq5+n//93967KWXXpKHHnpItXsJEJto3TIgR3Lhuuuu0+MQw1i3IudASLSWhExlS22iFioO1pB9gGCF/CI9vy0eq1tIXUitRo0ayZ9//qmWs2zBf/PNN5WQJRx99NHqqA3rRhYlsbokLQIWnOi6YTUJMQDZt/3228uDDz6oBBuTCras7rHHHgIhDi44X+M+119/vbRt21a3xT/88MOa3l9//aWYQXAnK2cutaNsK0u4zaE3DVHJZBGLdch6vHQfd9xxSqoSStLlpf33799fsJglTTSrIXZZCEHbOVVyxE9S2XYKGcX2chZcSgol5cungyU+W9NxNuglI8hfWDObtBm3+QUafifKjJAe12DNx+KIlzjx+cIif9ttt1X8ILnp1zwjsGIvLSSzkDaJk2zrRZmXX9/f0G1ntwU67Fix4zzUiKCy68vjR3/nvclfqs+wslO3GIZAxRHw/Zf3MwuXjB8Zn1q/rjimdqUhYAjkJgJG8OZmvVqpDAFDwBAoFwJ4JIY8hahC+xaiFLKWYwymIX9PPfVUJS0hOiE2ITqRNcBikYDl7uWXX64yDBBUED+QqFgk/ve//5XTTjtN9ttvP2HLN+QpVn4Qrv5F5D+xUsRakMklOrkQTGzHgyjmOOlssskmMnv2bHXmhn4q5BMOdXCuBlE3adIkJcogc9He5f4EroWQxSoXqQmIWyQaCPzG2guLX6wxIW3R3sVql7IjQYEDOYhgb3kIgQbBh/QDZDb5ZHKMdjH3RcLiiSeesElyuVpj+iOHCUbIn59++kmtTwm0IyxxsRDHEtcH6hECGMdl1C2BreATJkzQBYXnnntOFy8SAwQ/Vq2QTWeddZZ069ZtNSI1nB8WHLzsB2nSxiFiWYyBeA5rQYcJWvoUCykELNSxGkeOJFlAQgQ5Evo9kibeoSJxWWChL9B/KCtW6ZC2LJwQ0LXGyh3LYohv+hbHEgNkL7Iv9FWeFcii0P98WSGzscRH1gK5FwuGQGUR8O8QJHTo0yz6sShnOyhSQ9bvvOEdPHr0aH0XmgZvathZrPQi4Ilcdkcx1vQ7T4zgTS/ulrohYAhkHwKRErxMtsMe0rMPjszLsZ8IYQnHZIstkKloA2ZeSSxHhoAhkEkI+EExhOXNN9+sE2CIKrRysehjizqkFCStD3vuuad8/vnnajUB0QWxCgGLJStEGKQqVqyQoZCiOIfEshYSFTKJwCeEKlauEKNsAfV58c82rGchwphcQo5hzYtlJPmBWCZAGpM+hDIkF4Qc5DTWmDwr+/btq+QbjtIgXk8++WS9DkL2lFNOUZkILHUhoXwZuR6CDlIMTWEskrEWgTiDxOI5DFkwbtw4LSMBgozt/RC8WJSw3R9yDkkKtghDODMhMSuoTGr9RVbjWI3iiZs65XPhwoW6QMDiAiQHEhwE2j51TXuGcIX0ZAHAB/oLlt7If4TlD1ggQFqEdoRcCP2NvoFleXhi6r976QPyxXgKwpZrIX2RIEGKhDzi8Iw+Qt+ArKVdYnFMG9577721fdLvIG8hn5FTYMs1pCyB/oIjJeREuAf9gWtIG8IbXWus9nke0O695b6XkNhhhx108QR8cNLI/QlY1dMH6J9YUaLPy1Za+jvPFRZJyDNWlTvttJP2PeJyDfjRjwg2zsms/pItufHPWSRFeG6zWMm7irZqz+Cya9E/h9Ckx1qfBUoj0MrGzWKkFwEvP8Q7kfcb7dPvYrF3RXqxt9QNAUMg+xCIjOCF3OUPfTV72EbXEPzKOYQDFmEQHjZIjQ5fS8kQqK4I+EkbTsogcCBztthiC2nSpEkxSBhEe7KJE1ioYoHrA88oLGy9diaWsRBYkF9Y//lAGhCgEE0+eDKtInUAkYyFEcRv4jZzrLYgoSGfk72PIGSZvGINTDrhreFYfkF0Q3ojQeFD4tb0cJ5LeiZD+r3++uuaD5skV6SW03dNuF1ghYpFENblPiBv0KFDB20b11xzjVq4MqkkQPZjpQ356QMSIFi1kgbHIT3pF8gs3HbbbWqpTuBeLEpgIUaRRsUAACAASURBVBtuN759QOJCqtCPIHaTBRYOWOxgwRdSFDILgpXxF/0YLWEcndEPkJCA3Hr88ceVEKY8jz32mJKvLJCwKIMVPmnilJA4ENmQ2ASkWyCCsdClHZM+Zbn44ov1PG0c0pe+TIDEZmGFBRX6FyT5iSeeqJbHEMl8QoLTP8m3J8lx0oY1L/cOT+bT1wIs5VxEwPepG2+8URc4RowYoYseLJDY2DkXa9zKVB0QCL8TGFcyZqU/E4xzqA4twMpoCBgC5UHACN7yoLUG4vpJH0QG2o4QD/ZCWwMVYbc0BKoRApBAXvfba3YmDqK9RAGwlKRP6iFLdFblrynpOu/QCsLKxyENSFaIMwLkG6QWW8B9SBeJGs5/oh56SVqoEF5YDiNXYc/szO48WHSzuAHBiDUssgiQsFjrQs5CpELeHnjggXL//fcrQYrcAguuWKESsNLFSR+kLAQwxC7kKHIgWLASeIcjcUK7TmwXfjCGZS5yBSy8QE7xB7mKJTvWsCz2Yk2LhS0kK4QoJC7tDXkRyGj6BSQsCzHIShAgnbFMpr1CQp955pkq9YD8CnIOvl8hpQDxSpxHH31USVq0dSF2IaWxCMYyknL17t1btbjpE1yD7i7HWOzBAh7Ldaz16bcE9LJxtNirVy/t1+CMRTKYMGmHXKYOjIjL7P6SybnzbYd+yiLM33//rQuOaKlbu0q95szvR+pYWcz0IxAmeHnnsihZkg+H9OfG7mAIGAKGQGYjECnBy4SHrXe2mhZdpSc6gmHyhyVCuoiM6HJuKRkChkA2IMCzJExSekI1mTd7nkee7PTnE5/33qqCshPHx0s2YSzrXeEH9R5H0vL3h3BiAg8xxz3DZHB4i3yiNaAnj0mTsnKdD+FzHAuT2748yerUv0jfffdddTiH7jBWjRCHQ4cOXU1vNRvaRa7mMdzmIHDZecTCKRZBWJdDpGJhitUqTtIISHlgkYqEAxbrWLFDpKJLTf0ScLoHkYk2LyQm+p+0TdIYMmSItlHkEJAlIR2sbLEkT9SfxnK2Z8+ecfgbNmyoBDOfXIcFLISotzznfnyH7EWKge2rWB/jLBBiFt1cJEuYEGPVCBELaQ1hjdawD8g3QM7yeeWVVyqhyzXci3JB7JJXrIvRJkbGARwgmJE9gQjG+hhdba6DVIPohvjGgpeATAPkM/re9F3kUyCKCZDg7FJCYsXGN7na+9JfLt+f6CcsSNBf+Y7EihG8ZePv3/Hh92LZV1kMQyC9CITf2yzWIA1kzmvTi7mlbggYAtmLQKQELw4zmBCXNWnPXrjWTM59JTHpwpoHbUCbAK2ZurC7GgKGwJpFwD8PcdgE4fviiy+u8Ym7fx5jLcYCHGQVxMKzzz6rhK8RC2u2zYTvHh6f0IaoJ6xWsWbFKhZSNlnA4hYyE6tUH7CY9U7YIFXRksWhGPrPnhwmLsQ/jsRoGwRkEcgHVr4+P+HP77//XhcFcMZWkpU7msHcKzEgy0C6PkCmYr2LdAKkrA9YF0My41gOAhciGC1tSGFkHziGjEPdunX1EjRzkWMAL3SEScsTt5xH4oGJN5bELHAg55AsQOKCRThgHEAdYBlMsDFk5vSXbMuJf9YijUP7ZQEBZ6FY39tzONtq0/JrCAQIhN8J6N+zmMl7w94X1kIMAUPAEFgdgcgIXshdHrZs87PBebRNzeOJ4yG2WGJ1gyWP4RwtzpaaIWAIZD4CnkyFXMNy8JFHHsm4ZyHWlDiTYgu6hexDwFtyewvusERHeSaUidcl/k4FGS+X4i3R2SWVSPomW/BNdq9EPW3ShBDGwWJJoaxxRmnnOUdey5JwweIXTWEcEtridSqtwuKUhICf1GBdj9NBxspYlyMfYgRvye3G92MWKbG8HzhwoJFn1s0yBoHwewZNeRYRkS4qz/s4YwpjGTEEDAFDIM0IRErwsiUQxxllTQjSXKacTN5P1tgaicUQW0kN55ysaiuUIWAIZCkCRk5lT8V5EjdRwsNLioQlSsLSHV52JJEE9nXvJU/C8h7he3B9afqWiRIikKOJcinhbdSJ+Q/LkyTKr/ht18m2Yftj3Mtr5hLfXxMuX1ifm/hhLBLzFs57WEcbp2+Qy5BvLNJgtcxWeiPhsqcPZWJOffvBwRq7J7BMv/TSS+WEE06wtlVKhfn+jaUzki7sPrG+mIktvHrmKTzfRS6I9wqSQEbwVs/2YKU2BAyB0hEwgjeLW0iySVoWF8eybggYAoZAyghkqhMYb3HpLReTaRmnXEiLmFUIZNuiq+9DyUjkdAHvB5333HOPXHTRRapt/OGHH8rzzz+vjtaMVEoX8tUjXd9+0NTGmV+DBg3ktNNOU6tU5EW8M8HqgUZqpfTkLv3wmGOOUV1yI85Sw85iVQ0C4Xcr8kD0ayQLrZ1WDf52F0PAEMguBCIleJEOqFOnjm2xS2MbCFvn+NuY1VgaAbekDQFDwBAoJwKJVpXlvNyiGwLVAoFRo0apbjFO5dDStmAIVBYBP6nB+SFWu/Xr15cePXqoFS+W6UicWChCwM8fpk6dqrraOIRES9vmFdZKMgmBMMGL7AqORZH2MYI3k2rJ8mIIGAKZgkBkBC/6uxC8SAfYwKBqqhdLMVba2YJGMNyrBne7iyFgCBgChoAhYAhUHIGK6BFX/G52ZXVBwI+Df/31V7XgxcnafvvtJ9dcc40RvAmNIEyaMY+47bbb1No523YiVJe2XZ3LGW6T9GkWBrHSt8X06twqrOyGgCFQEgJG8GZh2/ADWAh1vFE/+uijcW/WRvJmYYValg0BQ8AQMAQMgWqGAANQr/EL4WtyJtWsAaShuH4MjL5z9+7dlQA66qij5MYbbzSC1+Ht9bK9ljhVwDEsnrHctWAIZCICYYIXffs999xTPvroIyN4M7GyLE+GgCGwxhGIlODFa3jdunXNkrQKqtW/7BCZZ1C28847y5tvvlmmt+oqyJrdwhAwBAwBQ8AQMAQMgTIRMAusMiGyCOVAIGwAwdh40aJFagCBdWp1lWgIO270zh+TQWpW9eVoaBa1ShEIE7xoxh922GEycOBA4xuqtBbsZoaAIZAtCERG8M6aNUv+/vtv3Q5lVqRVX/1ojT333HNy/fXX61Y0gn8hUh++TngxhkMy79yJuTermqqvT7ujIWAIGAKGgCFgCBgChkDqCPix7sKFC2XTTTcVSEsseHHql8kEr1/oSGX8XVJcrk0crycjbZ955hnFBIMcnxafpZG/qdeAxTQEokcgkeA99thj5aWXXjKnnNFDbSkaAoZADiAQGcE7c+ZMmT59uhG8VdwowmT6uHHjpE+fPvLCCy/EB25RkLPJ9Li89+3E4ibzxp0srt+OmZg/GmSyEN5O5s+TLumEgx+kJkuXc2GCm2sZ0JYWN7xtNFkeypPfkuImG1SXhK/fzhouc7riRjHhSNb+cjndKu7+djtDwBDIYgRK07pM9pws6X2ejrjZ/JwOjwuSvTMpW+LYgWZU2bgVfReH81LSeCBZfpPFLc84ozzjl6VLl662QyyVMZTvnsQtKChYbbxWWjsLt/ewFWpilw/j4PsUZG6rVq2kXr16ss8++8gjjzwiS5YsUUfQiWPGxLGhP5/YHkpqN8naTnniVsUj7Msvv5THHntMd/p17NhRPv/8c8WitGdQVeTL7mEIpIJAIsF78sknS79+/YzgTQU8i2MIGALVDoFICF7E+dGDnTZtmg6mzIK3attRSVscfT2MHTtW+vfvL/vuu6906NBBSfhkZGXV5truZgjkFgLlsarxk79Ei3oQScWqnnhMmEsiJPz5MMJVNVktz8TW4gY15MmLZO0hWb0lIySSLa6VhG9Vxq3sYiD4pJpfj1+qfag8/S3KuInEVRQLsbn1NLXSGAIVQyBssbrxxhtL8+bN1SFT3759K5Zgll7lx//XXnut3H333bLBBhvI/vvvL5dddpk0adIkS0tl2a6uCCQSvGeffbY8+OCDwsJT4sJRdcXIym0IGAKGgEfACN4cagveotWTt36A5y17IeGxsub81KlT5fTTT5c777wz/oK86KKL5KmnntLBH9cSb/bs2apztMsuu+gxAoTDhRdeqJbCTZs21RVU4s6dO1deffVV2WmnnYrFveKKK+Sdd96R9dZbL358wYIF8uyzz6olAen6Cfytt96qlgUNGzYsFveBBx6Q9u3bF4vLoHX48OG6qMCgngEAaaG1xqJDeEDA4H7y5Mlq2ezj8nnllVeuZsXAvcCpQYMGWlb/h3dhBhLhdCkv2tP169fXeN6S5NBDD5XatWsXi/vxxx/LnDlz4vfzGKMl5UkeP9H/4YcfdDuhT887o+natWvSuJSFshHf56Nt27arxQUD7uvT9Zi1a9dutZ5AXn198+nrH1wSAxIt3Nfnv7S4yRaAStJ+SxbXLE5y6KFlRTEEDIE1ikBpC/KJz+Uo4qKJunjxYn2X+vQLCwt1fOCDf8aXFTe8uM2WfOLzHuJ9yR+/IfkS36+Mf7hnfn5+/Bzv24022ihpXNImQCZwHX877LDDanF//vlnIa4vmx9rJIv7448/CuMgAvfGuhR8GTsk5veDDz7QuD6/pY0dBg0apGMxn1/SJR+nnnqqvp/D709kvebNmxfPr6/fZHEZb7FTD6tP0gNf8nP55ZevNi6666675N9//41by4LbWmutJYwFE/Nw8cUXx8dF5Bls1113XR2bhuOSt0suuUQYazBe5Pf8+fN1rPjkk0/Gj/n6P+OMM+T111+Xxo0ba/46d+6sYzKs/sL4giuY01Y8UUT7hBB97bXXisXlfocccki83sCSMfImm2wiH374ocb1GFL+3XffXevV1xv32mKLLYQ6CsdlDLnjjjvGrREpN/XSqVOn1dLFkIYxNhiQLp+Ua8KECfLwww/L//3f/8XH5KTD+B8MGBOH+xffbWFpjT567eblQCD87qHNs1DBfDGTZVfKUTyLaggYAoZApAhESvCiwcvAzCx4I62jtCTG4JHBIYNuP+CH1PQDeH9TBqcQq8QNT6YYNDJJ8hpevHAZIHfp0kXWXnvtYnG///57wRkclsM+DQbQBx54oLaXcLrvvvuujBo1SknbcNzjjz9eiedw3Mcff1xGjx4dT5dJB22PCQeEa3gic/XVVxfLgyeVIX59fv1gl0nI+PHjdbBPB2FywufTTz+tg+Rw+77ggguUZAZHBhr8MUF5+eWXpU2bNsXi/uc//4nn10+QGJizZS5s+c45PMR6QtpPeijT4MGDizkyJC/EnTRpkg7yuT/1AK4//fTTapIpOB0ZM2aMltlPAKkX6sgT+0yQSJe6/PPPP/V+BNJt2bKlelvmXh4HJj1Mnpjo+O2PxIVghqgOT2QgjSGpucZPpojLZOq7777TyblPl4kyeQhPFGmPzZo107hgHs4D6dKuyRvBa/B98cUXxfLAc8pPpvwEiQnxdtttt9pkijrAgSGBNk7+iEt5mXSHy0b/YeLlH6qcY0LHIgbkfjguZWLST9vyMiFgw/UsqITjMnHbddddtQ/SRskDk9y99tpLLfO9lSbH6Tt777231jvxwQJMsN5/6KGHisWlD/fs2VNxJHA9eQCbJ554olhc2gETW1+/4TzQh8J5oD+gj0Yb9FbGPFe4nkl7eKGI9kG/YNEgHJd83XzzzcXiDhs2THr16rVauj169NBFnXC6I0aMkKOPPjq++OPzSx5uv/32YnFpzzxfEvNAvq666qpicel/EAeQCj6/TM5Z/IF88Its1Olnn30mZ555ZnxhizwQt3fv3vqMCueXdFlwYyIOlsQFM/LFIlQ4bknpnnPOOXLuuecWi/v2228LBEq4b9NOmRydcsopxfI7YMAAlfhh0c73K+JSLvIWLluyuCwgcj1bJ8uKS7pgQFz6i+9bLBped9112sd9HsCM+j3iiCOKxYWooY2AGc8FwpQpU+TRRx/Vuvfbz6mLe++9V7Bkw5rP90+IErZte0KE68NxW7RoEV9kIy79hzYRThci64YbbpBwXMrG4iULd+G4N954o7Z/ysZzmjoGM/rwAQccUKxsvIOwTiK/JcX1+aUu6YNhzEpKl7ik6+OSBs8injlsYw/XBe2JBd/wIi7P+Pfff1+fieG4EIK882hn4QVfnpHdunUr1iZPOukkLXN4IZlnFM9Invfhto5zLBZRw3F5/n766af6DPbvcZ6LxxxzjC4k0x58+6NdfPPNN6rFGo578MEHa//073P//OJZkPje5nnKcwoC2luI81xlITrcr8CSMQ1jEv9+9eQD/Ttx7LDffvvJH3/8EV8cJg88t+mzib4sjjzyyPi7mOcO7w3ey2+99ZbeKzwm4dnJ2IznL7gQn/Sef/751Raz6X9+HEf+SZdnPBqtieOi888/v9j4kLLxDmdBPDyOJB2MBegH/n1BuuTh/vvvj48l/XiLPhx+bzN+YjzEs5fgx3GUg7icp+yeROe5zbMsHJfv9EH6PHXEteSffPD8TYzLGMxjSB37PNAvE/MA5vRdyubHD+R32223XS1d2l54txyYkfdtttlmtbi0m/AiOXG5R7KFeuJSfj92IB/kn77KsZIWwSm/N1jQDFgwBLIEgUSCl/cu8zojeLOkAi2bhoAhUKUIVIrgZTAOWcTAlUkYkyAjeKu0/lK6GYM9bwGabEt3SolYJEMgIgQgd/2kx1vikLSfFIdv4+P6dusn7kwWEwMkd3iC5B9ukNKJE7qJEycW01/2k1WImvLGDecDUj2s60weIAM222yz1dJNjOsnwSyohPNAmSE3w9qBJcXlfliRgStlou9DiDDJhRAJL5BAfkCqeEKcOmHhBfIHsjwcl7SwUPKkPHE94Z+YLsQ6BAyTfq9hXVJcJvYQJd6y3qfbunVrnQSH80A5/AJHON31119fyflwXNoNeaBNheMmSxdSm3TDcSHmsfzbcssti6ULcfb111/HF5XIL3Gps80333y1uF999VV8saq8ccERi8LEPPCuJQ9+EcynSxtDgieMA+QNiyHhuGDDogPlC8fl/f3tt9+uFhcrsg033LDCcSFdyYNfzCO/5IH6hbwI54F+SVtPLBttDD3NcFwWDUeOHKnjD/ocf/QVMID0CceFbOL54BcZyQP4YnlXVly/eJksLnVBnsOLl7R1CMXExUsWlsLEG/mlH4NB4gIq/SJsLUkevFWhJ3B4RnhLv2Rxaet+x4mPSx+ivZNfbwHIM4MFC//M8XHp88ksbVloTHxGUg6eJ4lWuT5uRK8OS8YQKBOBsNU5i5UQ9vRJiPPqGMILJtWx/Fbm3EDAE7x+PMfiKgtJJtGQG/VrpTAEDIFoEagUwQsJwEoyFhNMoJjAMKA3C95oKynq1PzE10/kfPreiiXxfqnqH3JdSU7WkjkICVs2+HuWx+kHcclzeR2n+XuV5SAkjA/3Kckhm08vnI9Unc2VhFm4jsL5LakufBzbchd1b7H0DAFDwBAwBAyBwJq0tHettxJOhlXiuzkcNzw+SnzHlzQu4x5l6aqH0010yEa6ycYZftwTzq83EkjUCC9rDJXK2CzReZsncJI5kCNu4riV38mcrHGcXTIsNHIdi23hHTPhOqqsYzq/AyGx3j25mng8maFFuuJavzUEcgEBzyvQhzFKQI6EHVLhHXO5UE4rgyFgCBgCUSAQKcGLpY7XTjVL0Siqx9IwBMqPQNhyLfHq0iZ02R432QQpGSFOOVONW5nJdRjPsibi6YibSBqU5Kwrsd6TTVZLIgMsboBepuLgLd7XZB2XJw+VievJpMouSKZzoTPZ0zxT85vL74qKls0WUMs/HqnqK/yiP/0K+Sp2XWCxzu6EkqQLqjqPdj9DwBAoHwKe4GXHFDt8kJE54YQTjOAtH4wW2xAwBKoJApESvGw/ZLujWfBWk9ZjxTQEDAFDwBAwBAwBQ8AQMAQyBAFP5Hbv3l19ESDTxKcFQ8AQyE4EPK+AxBCGZDhQ9Lr8yaz9s7OUlmtDwBAwBKJBIDKCFy06HBIlOpKIJpuWiiFgCBgChoAhYAgYAoaAIWAIGAIlI+DJIBwdoh2PLjfa9ASz4rWWYwhkHwJhp6v4icDZp3dOagRv9tWn5dgQMATSi0BkBC8OWpBoMII3vRVmqRsChoAhYAgYAoaAIWAIGAKGwOoI+InNUUcdJYMGDVJnlUbwWksxBLIXAU/wwjPgcBXHuLvttptJNGRvlVrODQFDII0IRErw/vXXX+pwzSQa0lhjlrQhYAgYAoaAIWAIGAKGgCFgCKyGgJ/Y9OrVS52rsaV72LBhGs8seK3BGALZh4Dv0xMnTpR27drJ999/L//P3nkARlV0UfiGhA7SqwI2BAQsqNhRRMWCXREbVhRFULH3hopYURSxY++9YsPe/VGs2AARkCooUgP/fLO5y2TZTTbJbnaT3JF1N+/Nm3KmvJkzZ+5stdVWRvBWvKK0FBsChkA5IJBSghcTDU2bNjWCtxwKzqIwBAwBQ8AQMAQMAUPAEDAEDIHVCOjE5rjjjvPKXUhdU/BaDTEEKi4C2qYnTZrkTa5MnDhRunTpYgRvxS1SS7khYAikEYGUEbx//fWXTJ061QjeNBaWBW0IGAKGgCFgCBgChoAhYAgYAvER0InNwIED5ZdffpHZs2d7xV/NmjVNwWuVxhCogAhom/7mm29k0003lZ9//lk23HBDI3grYFlakg0BQyD9CKSM4J0xY4Y/ZK1Zs2am4E1/uVkMhoAhYAgYAoaAIWAIGAKGgCEQIKATmzPPPNObZkCA8sknn0j9+vWN4LWaYghUQAS0TX/xxRfeNAMmIddee23jGypgWVqSDQFDIP0IGMGbfowtBkPAEDAEDAFDwBAwBAwBQ8AQSDMCOrG5/PLL5aWXXpJ58+bJl19+KQ0bNjRCKM3YW/CGQDoQ0Db90Ucfyfbbby9z5861Q93TAbSFaQgYApUCASN4K0UxWiYMAUPAEEgdAtgsDF21atXWCDzWj3rIRr+J0kqay5LebAiXNKxatWqN8snJyVkjb9niNx5ulENsWeAv0345NDYevvHSa34j1bAy48CgOZ7Ly8tb43IiHOL5TRRuvHpWkjSkwm82pKEkmFEQ+L/11lvlwgsv9KYZvvvuO2nevLksXbpUcnNzo2WVbLj0p+FzGkA8fOP5TdSXVQa/2uZjG0BJ3kE8G4tvovdVSfyWJA2p8JtoFBbv3VYZ/JZk/BRvvJXsOFLJijfffFN22203+ffff6Vu3breREOisUM8fEuSXvMbQbA8cUjdLMZCMgSqNgIpI3hnzpwpU6ZM8QMoBrXxBkJVG2rLvSFgCFR0BMIJOwNTJgQ4CKAaNWoUyh736WDVj95Mh1/CZBAWO1ktywQpTHcqJifxBokVvT5Y+g0BQ8AQMASyCwHee7xvHnzwQenfv7/Uq1fPm5BDwWvOEDAEKh4CSla88sorsvfee/uFX8ao2tbLkqNUhBEv/pKEWxK/ieKKR8aWRISQDX6ZY8U6nd/EziHK6pd44i3qJBtuOL8KRQBcZy4Wpreo+WBZ/JKHZcuWxZ1nxoabKr+xc92iwk1mXlzU/LUsc+iiwo1X17VP0ee0flRUPjOlBO/vv/8uLVu2NIK3LG8ae9YQMAQyhoC+sBMNKDKWMIs4axFYsGCBV4hRZxgsohCDUIg3KCjKrw44dFAY65dBXJ06dfzAMfTLdd69tWvXjqZh8eLF0rp166jNSR34//fff952Xa1atQr5bdOmjQ87DHfRokXebxjukiVLZN111/WLGaFf4mOBt3r16n6wyQB54cKF0rFjR7+NMvTL1krUdGoPk7TxfKdOnaRJkyaF/M6ZM0d+/PFHr9TRCRDpItymTZsW8oudTU7YBnuNj3A5aRtiJ0zDrFmz5KeffirkF2w4vGWttdYq5Hf69Ony22+/RdOg6d1ss80KPU+5ET+ne2veqA+kd8cdd/TnE4Rp+OGHH4RPbHp79uy5Rnq//fbbuHmL5xdsY/NGufXq1WuN+oB9UupObBp23333Qvklb/iljMN6Qri77rrrGjhgJ5G6E/qlXvTu3Tt60JXWSQ6/AuPQL20Iv9TTEDO22UPUqV/aHOHuueee0Tqp7YetvByuRf3Vfp16iV/aZjih/vDDD4W6Fut3jz32iLY3DTfWr6YPv/gJwx0/frz8888/vl3gdAJBGvRvDTeeX/IHmRHr95133vEKNj00jHzRL4BZrN833njD90m6+FeU37ffftvXV01vUX7HjRsny5cvj/Zz/Kad7rLLLmuk4bXXXis0L8AvZUidjE1vvHCpn9T1WL+QPVqPdJLZqFEj395wTz31lBxyyCG+D7rlllukXbt2vjzAnHpDW99pp53WCPfzzz+Photf6jl+O3fuvIbfTz/9tJCCkHDpb+jPYtP78ccfF5r447dBgway8cYbF/IL7oRL+arDL/3jRhtttEb/Txui7ioW9HuIbTiEKmw/TPgIl3YV+qUvTSZc+kjmeBtssEGhcKmnHGYXppc6R58X2/cm8ksZaf+vbSKeX8qiRYsWHrcwb2D266+/Fnq34bdVq1Zr9HukbfLkyWu820obbkjk8N7j/ajjAcqN94EumGv/wH3e8eG7WOtktNCDH/HGA/HCJXzqeDgmCcONHWfESy91PVvdM888IwcddFDcXTXZmmZLlyFgCBgC5Y1AygheBugM/o3gLe8itPgMAUOgLAgw4NVBb7ztmuEk7fnnnxcm4gzgmSQwqWAwzaTypptuKjRJY3IxePBgP5GEUGASwASJgyGuu+4671d3OzDIPuyww/yEWSfXTLSZSN1zzz2F/M6fP9/71VViJg5M9plQ3nHHHYX8srPigAMO8HHrSjXp7dq1qzzwwAOF/NKH77///lEomYBgu3DrrbeWRx55JEqSkBcmcxAaOqkEN/wyYb/33nsL+YWc2nffff1EhvySDsg4ro0dO9ZfwxEuBA4khZJsXIMQJF3333//Gn7ZqsfkkkcU0QAAIABJREFUX8Mg3AMPPFCeeOKJQn6ZWDORZ1JIvggX8ueEE06QO++8M7qlnHxAMJA3Jqe8ICkPwuXAnuuvv76Q3xdeeEH69u3rJ5ykAb8cOHrWWWfJsGHD/PM4wn322Wd9ucX6HTp0qFx99dWF/GI38uCDD476Jb2k4dRTT/VpCMNlwnPccccV2j0DgXnxxRf7NId+7777biE+yF8lZwj3hhtu8FiEfiFELrrookLvdPyOGjVKjjrqqEJ+b7zxRrnssstknXXWieIAYUY9j/VLvTvnnHPWyNuVV14pAwYMKBQudemCCy5Yw2+8cEePHi3Y3FR8wQwcwOuII44oFO7tt98uV1xxRSG/1IfbbrvNt5cQB8qRPIfh4pd606dPn0J+iYuw8auDK9LA85B6seHed999vtx0myltiDxDUIXtgrKkDSbjl3JAuRj6hRygrdGWw3CpH9RhCB5NA33Ro48+KhDYxfmlLyJdW265ZSG/Q4YMEUg9CBtVYND3QbhBzms/QL904oknyrvvvutJuNAv9RrSKfR7yimnyFtvvbWGX/pl+srQ75FHHhm1u0q4tHv6TPKr9VQXYQ4//HCZMGGC7x/UL98vvviisPgR7koL/XJdy5k2G7uDbb/99vOkE8QnjjbHu+PVV1/174wwXOodJ8PjV9PKNzjGHl4M6Tt16lRPkuKUwIHw5HkNl+ch62m32lcrqYhfJcqVoKKO/vnnn1G/vMOI++WXX/aEkYZLGIRL+StZCKnIuw2/hKd+KXf88j5TNY8uQJGG0K8uAtAONFyeh5gF3/Cd+ffff/t3Rew7k/fgk08+6f2C81577eXrYfv27X0/TNyUO+nhGv1yGC7vm5133jlarqSPNkF7iPXLnIcFDiU2CZd0bbPNNvL4448XChe/vB9DcpV3cbdu3eTpp58u5JdFF8LVMtNweYfRf+qCBddZUKI+KJHKNfCjLOmjQr8s/oCHkqNFhfvNN9/4/i0MF2yop5i+0N1MYPrBBx/IPvvsE1100/cr6umRI0cW65dweW/fddddhfoRwqUN6SKh9uknn3yyXHPNNYXCpd7RNmP7f/oi+vowvfQB9A/hfJV+etCgQXLVVVcV8ksd6tev3xrh8o7n/Rj26fTdAwcO9O9XrpNexmC8Q84+++xCfhnX4Ze+iDoMjowdLrnkEm9WJAyXugSWYXpp07wHGWOGfnkXn3feeb4thu943m3HH398Un55hzGmCMMdM2aMTy/4anrBjHzE+iUPvM9Dv4wH6P9j36+83ynLWL+0CcZ9YRrIL/5pk/TLtGHwpU3EjjMY5zAm13B5hrbJ+I7FIn3XUEZgyPtR+1n80rfxXunRo0eh+oBfxqKUBTjgl/bGmJN+LqxnvF+p02G4vIupq/QR4fv1mGOOkeeee873VXqdvo8FOhaew3cb9ZHrhKvjWeYjPM/YPgyX+k+/pe94OhrCDf3q3ID2wwKmvosJW99Bse9B2ivvTF3MVgXr66+/Hj38TsNlkY1FX/oSLU/SCw70wWHejj76aKHv0TmAjsEZD5CH8J1JGuhX9f2q4grKTecF+o6nLumis76L6bfBIdY2OzggLCCM8L1NuLF+6SMZD5IG4qJOUDcefvjhQgvJXD/00EP9fFHfg5QDfcVDDz1USI1OHvEbvl/JG2MGwlWBhb63mVfQFvXdwnubMoz1y7uA+UoYrvplzKfiEV2opj/UhWRw0PFA7FyX9NIudGFJ/VKX6f9xurBFeeI3DDd2Xqx+uU4a+DvRHFr9Ejd+8af1jufXW28936eHaSAfvBM0XO7xDNd10Q2sKettt93Wf8KFe23j4JTNO1NTQvDS6BgUsiLKamnYAD2q5gwBQ8AQqCAIoH5jwMhLHsVOOPjgZYWSjT6PF4m+aHmRMaCMfZGNGDEiOhjWFwjPMugK/fLyZ9CIH305MRElXF70oV8mwfgNSVv80vcyeA79knYmM/pyVIUpL65YpRXhMuBi0g9RSd54nvTGKpe4zqnkqhJRlRODCibYoUqEvKEwYrAUhovfWFUjaQ+VQJpeBnvx/EKeaLhKzpOmUEmk1S6eqoU8xttuxIQ+VllDmEq+V5CqbMk0BAwBQ6DKIsDCAWQtfbnuMKyyYFjGKzQCqghORSZCsqK48NQv35Ay4bgI0gmCT11I4jAmjd3ZFM8v40OIlaL8hmlkDA65znNgwrgunpKZ+6QvVFOrEj82vZDPkL+aN+7jd/3114+SdFxjnMtiBASZ7vbgOvFAJOnOkFi/seHiN3YnFkQ1hLXioGlAha+mKDRc/EJEER9xQ+RQNt27d/dj1HD8zaIh/nUxT+chLPaqXyWpWAACBx0/Ew5jfcjoWHMYkLDEqePnovyyCKXEppLGlA/hxu6mYXcKGOvOMRXgIAiJ3SEDmRwuHCqhBekazoP4zUIpdTJ2Nw1+Y/PGIjL4hnkj3aQh1i8LQOCAI35NDwsysWlgAUgXfsiLEtIsNsX6ZfFfF1I0bMoFAjzWLwv6Yd0hDupcvDSUJFyEHyEhrSTz6aefXigN1JHhw4dHF0XBSHccIuYI00varr322kJ+aWsQqmeccUYhv4RBuDon1rrLXBCSOAyXMAhXSVf8QvCygMbCVOiXPLHgRhsMzSyCL4ufhEWZsoCxww47eAFOIl5T65z2Kdn0bQRvNpWGpcUQMATKDYHYLboMHCEtIRPZ1o0aL1bpVW6Js4gMgSQRKM1kLZmgSxJuvPBit4KqH70e75nY1fCK5ldX9mPzFm+l3/xGUFJFT6oxK0m45rfkZZEIs2QPLSPGsvgNd97E60sIG3ME2223nSc8+A0BwuQtjDdeGhL1OxW9f0pXf5rM+ySVflJJdobpKus7L5V5tLAKIwDZA+GGEpmdTKHNU8PKEDAEDIF0I6CEKX3Q999/7/siXLa+N1JG8KqdOuTmpuBNdzWz8A0BQ6CsCGjnx+rnSSed5Lez8w2pm8glImXiTVaLmoDGOwwt0eQrWb+sRMY7hTpeuKnwm2iAHc/2rG4ZC3GNd7gB95P1q1tPNcxUHQqnL+x4daAsE/xsCLckaUjkN96WpHh1LJu3LpW177DnDQFDIHsR0Hf7119/7c2H8Pf777/v1TjZrLjJXkSLT1mypHgq3kGEkex7qDz9potAL+9wS4JtPHxTkd7YNGi7RdGHCQsdJ4Y77MJaWtL6GG88G2+MGjvuDMef8caHmQ433niaNCd7uFgm/MbDLN4ieboWZhPN89K1IFmScNOFQ7LzzJLOdWPTW97z4pKmN/ZNF86VlcjF1Acm9Njti4kPteNf/FuyfH2kjOAlw2yBwu6PEbzlW4gWmyFgCCSHQKLBYOzTqAXinUCaXCzmyxAwBAwBQ8AQMAQygYDOQTj0cJNNNvFbYbFZiV1bI3gzUSIWpyFQNgRUwYuNeUyfsf0al63qubLl1p42BAyBbEfgvffe8+e1YGNdTTRmU5qN4M2m0rC0GAKGQEYQUPtRoV3bjCTEIjUEDAFDwBAwBAyBUiOgBC8H0WETHjIIW/QcBGYEb6lhtQcNgYwhoIcpYtOTg8uwg2sEb8aKwyI2BKosAuEhhnpODQc8c7ZONo0vUkrwcuBN7EmLVbYGWMYNAUMgaxDQVX7s7LLT4Oabb452xIlshWZN4i0hhoAhYAgYAoaAIZAUAkrwcrI4hxRxqMrjjz/uTxDPpglYUpkxT4aAIeAPnOKALA5ieuyxx2TGjBlG8Fq9MAQMgYwhoOMMDu9jjNG8efOs2lGQMoL3zz//FAheO5QoY3XNIjYEDIE4CCi5+9lnnwknx3L6a9euXc2UjNUWQ8AQMAQMAUOgkiGgEy8mXW3btvUnw48dO9afKq5bvStZli07hkClRkAJ3tNOO83bvZwyZYoRvJW6xC1zhkD2IwC/gJ3e8AyYbDEbk1KC9+eff/aDKbPBm/2V0lJoCFQFBMKOtm7duvL00097mznpOoW5KmBqeTQEDAFDwBAwBLIVAX3vM8Fp2bKlzJ07V0aPHi0DBw6MKgGzNe2WLkPAEFgTASV4Bw8eLOPGjfMHHNnuO6sphoAhkGkE9OBFPWA8W/iFlBG8f/zxh1fwGsGb6apm8RsChoAioItNJ554osyZM0eeeeYZW4Cy6mEIGAKGgCFgCFRSBMKF3datW/vt3JhlQv2nRFElzbplyxColAhoux00aJB88MEH8vXXX2fVduhKCbplyhAwBCosAikleH/55Rdp166dESgVtjpYwg2ByoOAkruYj8F0zMKFC6V+/fo2KKw8RWw5MQQMgSxFAJJNiTYOrzRnCJQXAiHBu/7663u7+9dcc42cd955ZqKhvArB4jEEUoiAEryo8L/44gv/sd3CKQTYgjIEDIFSI6B90VdffeUXkt9///1Sh5WqB1NG8E6bNk0w0WAEb6qKxsIxBAyBsiCgkzzs8P3444+y1VZbmWmGsgBqzxoChoAhUA4IMDDV/jsvL68cYrQoKhMCIcG78cYbyw8//CCXXnqpXHbZZXbIWmUqaMtLlUFACd7jjz9evvvuO/nkk0+M4K0ypW8ZNQSyG4FwzLHWWmsJZ/507Ngxo31USgneSZMmybrrrpvRDGV3FbDUGQKGgCFgCBgCVQMBVXHyjX0qtVGVjtyXpz0+Vuv5oEzVTyrzlC2HNJQmT5r2f//9V4YMGSJ33HGH1KhRw3ZOlAbMUjxTketOKbIb95EQg80331wmTJgg5557rgwfPtwI3lSBbOEYAmlGIHynK8F75JFHyuTJk72ZBiUw0pwMC94QMAQMgWIRCM3I0DeNGTMmo32UEbzFFpl5MAQMgYqMQKwB9IqcF0u7IVCREYAU5QCCdKkyM3W4AfGSN1yq8papvJS1fulWNXZOoGR4+eWXZa+99irRQPfzzz+XF154QQ444ADp1q1bWZO0xvNK0HMjXSR9yhNdggArat0pQRaL9BoSvNtss418+umnfrFh5MiRJaqHqUqPhWMIGAKlQ0D7MiVPDj30UJk1a5a88847hdpyeS7wli4n9pQhYAhUZgR07Pvll19K3759/blkuEwtuqeM4J06dao30bDeeuuZgrcy12DLmyFQwRCo6pPdClZcltxKgIAOLB577DG577775MILL5QePXoUyllFVN/oAO63336Tq666Ss4//3zZcMMN1ygx8lYWZW9ZbAsymFRsM2H7VtP+33//Sd26deXpp5+WAw88sFhiTZ/75ptvZNNNN41iivmvtddeu8zjSlWT5+TkpFVJnunmW1S70rqRbjV9pjEIJ1Q77bSTvPfee8JBqyhqytK2Mp0vi98QqKoIKMHLoh+7Q954441i3ylVFSvLtyFgCJQ/AuG4g/N+Jk6cmFGrBikheJs1ayYQvNi53GCDDWwAVf71ymI0BAwBQ8AQMASyAgElUThoQIldSCW2V5599tnSuXPnaDrLQriEqp3tt99edtxxx+g2bOJjcSeVJKcOmE455RQZPXq0zwPhY+ezT58+ctJJJ/kBnbqSrNwrDgsWLJDWrVv7QxpQrxJnafOSicUtzcf8+fOlcePG8swzz3glbnGEvt6HDP7+++/l3Xff9YdjYuIBu4vFPV9UxVdzGpC7OMoFZTFjVn6zfV+vp7K+lFdjVMznzp0rbdu29fYpu3btWqa6U15pT1c82vZ23313Twb1799fxo4da/OTdAFu4RoCKUJA+3ra7KJFi/wioV7bd999/UGJr732mr+mC6kXXHCB/z1s2DBr4ykqBwvGEDAESoaAjsWY69B/Mf4oyxynZLEX9p1Sgvenn34STqzNVGbKAoQ9awgYApUHAe2DDj/8cDn66KOld+/e1i9VnuK1nFQgBFDeQIY+8sgjfqs0rkuXLnL33XfL1ltvXSZiDQJT7fu2b99eGjZsKGzv14FNurZtotwdP368Nz2wdOlS4eTcV1991ecFRe+NN94o++yzj/87WZJV+6y///5bGjVq5IlNCGNVLhWXF42HCfGoUaOE08YbNGiQdPypqlKaj99//92PB8EJFWVxylIlVlu1auVPIT7vvPOkadOmcvnll8ugQYNKTfCGJDvKaxYYIJ1DR9ntsccepY4jVdiVNhzFfM6cOYLg4sEHH/SLKRAh2D8Ox+TXX3+93z4IEVySBYjSpi1Tz2meWXiBzCfPjz/+uI0DMlUgFq8hkCQC+q5g3MBi6uuvv+6JElxI8Oq7kess3p1++uly0003Rfu9JKMzb4aAIWAIVDoEjOCtdEVqGTIEDAGd3HXo0EFuvfVWPzgsiwLMEDUEDIGyIfDoo496oveSSy7x26UhAHv16iUvvvii1K5du1RkEwQVEzs+2223nSdvlEQuW2rXfFr7jy+++EK22morTyRvueWWhTy+9NJLcs4558gPP/wgxxxzjDdPgQuJNDUVwPXQjIOGr6rjO++8UwYMGBA3GxB3us1e7RpzrU6dOnLllVd6jFE0cxBNbPzqn+uhvWC1S8u1eCpW0peMeQPNByedQ+R//fXXsskmmyRN8FavXl1eeeUVr95FGQ3hDVFdUuFAWDfY0tuvXz9P9KGwhjj/888/fTnxbqDMYnHib8UqmXwXR8Anwp144pVnbP0oqj4r5hAeELosphx22GFrPPLkk09Gyd1ffvlFwDq2boYPJaNmzlaSWOvLIYccIk899ZRfcMGuc7amN9X9lYVnCFRUBLTtzps3T5o0aeLfH7xHcLRj+jkUvEuWLJFatWrJm2++KbvttptfZK3IC3UVtbws3YaAIVAYgWwYZ6SU4GWwjHqlpANxqxiGgCFgCKQSAe1cN9poI3nuuec8UWD9UioRtrAMgeQQQOFas2ZNr8S5//77BdusuLfeekv2228/vwUTUrZ79+5xyRclHiHZID8hpXSrfZgCCM1JkyYJh7C8/fbb3mwUYXMYy8477xxt/0qwhoRcMgej6WAJRWrPnj3l4osvliuuuCIuCEqkXXrppXLZZZcl3CpPfmLNSGA2YLPNNvPkMf0X6qWZM2f68w0gTcGyKEeeUbyyHZ2+D4xDRXNI2sWLP7lSjZCffLQsCBfSOZYInzJlileLFtX/hoNh/O66666elDvqqKPktttuKxMpx/Z8SFzqDbZYOXSrOBeSw+o3dsAeEt5qAgK/8cyCxD5bGtyJL6yzYB3bDr799ltv1gNlPGNxSBAOJMJ0Gm0Dd+aZZ3qF+Q033CBDhw6N1k0tHw2T+HBh29C2owsTaj5EVfTF4Vqe97UeHnvssb7fgQAaN25cmepSeabf4jIEqioCYX/Je/2jjz7yi5X83nvvvf27hL5NFbyoelkoxrwRB3vaWL+q1hzLtyGQPQjo+DiZhfJ0pDplBC+DeAhetkla55qOorIwDQFDIBkEwsEhW4SZ1NnCUzLImR9DIPUI6CADswZsn0R1Ezq2kj/88MPyv//9zxOb4fgh0VhCw/z444+9jV/+Rr2qBFunTp38dUhRSEJMHtAvQKxBjJXGaVpmzJjhbeSyDR5VKDb/IHFDx2QUe8Cnnnqq30EQu3sAQrt58+Y+feoIA3MEOBSrEOF8gwmkJItUqsq8/fbb/bN77rmnV38SHwQmcapLtGOB65999pm0a9fOH16m7o8//vBmHe655x5p2bLlGuM4yOIttthC2rRpU+QYT+PFhi7E+sKFC4UDJ4obF+p9nayTZ8oXV1o1BPWK+rXtttt6kqAopyYuQpMas2fP9ocHoyCrV69eaapN9BkWOjhdmXcS+IYO1TVEPPFAUmBDlzrLokdRTrG+6KKL/KF/Yd3BXAl1h7xTd1j4UBfiWVy5JGtipEzgpOFhxYbFDtqLKtqLU1qnISkWpCFgCJQQAW2/V199tT+k9bjjjvPvJswi0YYheHFqlgab4xzQWZb3RQmTaN4NAUPAEMhaBFJC8DJRmTx5shG8WVvMljBDoGohoBNYiF22TXfs2LFYgqFqIWS5NQTKBwEdZGAfD7MDquAN7edxGAGEaUgGoiRk5Rv/mHbAfipjDQ7cYvs+TolUtm2yhRP1DifXxnMhUUWfgDKW7ey77LKLVwUV55QI++uvvzxBhwKZraHXXHONJ+ZQSXKwGFvfp02b5u3OoiQmDxo3h8UcfPDB0ajYPv7EE0/4v1FTQoBD4mLOgkNjlLSLTRtkI+GgTAxVnKh2wTKRu+uuu7x5DHWoqlHI4tS0w2OPPebJwFChCvbkTxXJ+OdAL8qTvEIUEy42c7Vcn3/+edl///193nFFkbThPSbwlA04E57eIxzKAFeU4lrLSQ/4gzAmLeruvfdenzfFIayHYTowj4GdaHUhLsRPmJDkkKiQ/sTD3yiPY/N7yy23eLvC6lDRYgtXHWVIOVAPw0P6VLWGP+w8Uy9IL6YvTjjhhOjzQ4YM8QsJWncw0aGLBQkrQ8wN2hiLDCh+UYlTv0aMGOEXM7T+0haxY4sqFkd9op5S7yi34sjiZNOSCn/a75x11llerYxZFRY2iquLqYjbwjAEDIGyIaDtF9J20003FWyzT58+3fdLLIKxMwNHP4pddRZaIYLNFFvZcLenDQFDoGwI6DiSXVOM55hnlFakUJaUGMFbFvTsWUPAEMhKBEKC99lnn/UnimfT5DMrQbNEGQJpQEAHGYMHD/aEIEpGXHhAGn+jOMQsAaSpOpSuuqUee9qoVGnHkF3Y3sPpQVKQXBC3kJGErVva1aasEqEoeyH/IItRU37//feeQOZAxqL6CL2H7VYIZux5olAlHFTJkGKdO3f2Sk/URhzuiNO+SMldCGqIQwhiTBHwDckcOuwOov6FpAMvSGLCIS8okNluzmFamHNAHYvtQewVtmjRwmMEDqSD9Cn+qBhRM3J4GaQ0dlqPOOIIYVs/6YaYZgcWRC+KUJ7TrfjYE4bEw5Zt3bp1vY1cJcUhGykDrlMu6h544AGPQTIErz6jZBx/8zzq6zAdxVXPcBANIQBWENDqCAt8cIol+EJoQ+CqO+igg/xBbHwOOOAAX56kR80W4A/8wRdzHMSFGQ0cJCzKbiWOlYDAnAdmPZRkV9MVPIMpDg4CheyHKB0zZownjSGMcWeccYbcfPPN3lY17zLKCmwxtRDrULJDdPCJV3dQMpPWAw880D8KacICCWXHZIR2RNwjR4705RouGrCIAqGNGRDIUkyV4Fjw4BlcJiYy8eqF1nvIbuo06WaXQDalsbj6bPcNgaqKQNiPsAOBHcL0Z7zDONAdczs4xBv8zbuQMYKN86tqjbF8GwLZgYD2QYzV2IHA+CoTC08pJXiZKDFQtQ42OyqZpcIQqKoIaB8EYcEEPHbrd1XFxfJtCJQ3AjrIQMGIklEJP1UF6kEpEGOQrBBFEEYQSNgShYR86KGHosmGqMXcAwOnUGVK+Bxqhgo4kYPIZJyCOQJVAUOUQe4p8ZyIoNJ8KBGK4pjt/7/++qsny1AakU7Sq06fUWIxJBJVFav2h0MlKeQhYUL0hQND7ddQ3kJKYzKBQ9/Uadoh3siPmiXQbawQqNddd533rurnkGjcYYcdPKnL5FnTgy1jiEYlKCGTISEhAiEBcZo//uY6jt8Qk7olvjhctbxRomJPEdM6EIxKyBLmtdde64l9VcPGs4sLcUu+2ZKPmhayVhcBCANyG8wgKfEL/hzkBkEOWc6CINhjYoLFAByHsEGQQ3Cro26CP2XCwgXlyaGBmM6AtMfpYgDKWNTPOLYWY1oDpSzljOvTp48/AI60qgJY40FpDCnMc5DAOCXnCR+FbVh3WGjgmVjTIFqPuIe5DeLHUccwzXHyySf7PIYOdS54aR2l7kO2sFiBYplFCNLOwUaqNs7ERKZQogv+0HQMHz7c9xe0fRYzcNlCQsdLt10zBAyBCALahs8991y/m4CFS5S79DX04bpIimkiJXytbVvtMQQMgUwioON0xqCMPRgjZWJcZARvJmuBxW0IGAJpQUA7WFb0IW8233xzW3hKC9IWqCFQNAJKrkEsQpShFKQ9KrGrT+t9JRwhebGlyyFp6tSmakhs6iFubHuHrApJOMhi1LBskULtiB81KaATSIhezAEoiZVoIBa7ZRQVLmpcdfxm+39IlCnxpuYPsF+LQ2WJ6gh1KCpRXDgxJe+QqISn1yGl9XkNj0ktk1swUHUz35h7YDKsB2spIUxeVdEMIakmGjSdkIIQtErCky760MWLF/tD63CoSLEXGype1QQBh5lhXgHHllnUk0URvKE9VFTMlAHEK6Q55nUgqjFzgVPlNWYIIDjj2VLVMoIkxfyFko6qZA3JYi03MMXerdpvRJFMXsln48aNPREK+atksZYHyvIJEyb4dKBspa5Sv6mDbCHGsXiA8hfciRtcMWeGP8hqTS+LAuRJ6yamEKgjhE9bgXTG3IU6cIboAHeU8doGuA9JzcIDCwDx6g5mPaijlCmOeoLCBFUvCnDiZhGF/ONQgkNIs1D64YcfRklvSF1IaRz+iQ+iOBMTmSgwwQ9Nh9ZNxCe0OZyRQPEQs2uGQHYhoG0YMhcTK/R1XPv888/9B3MNLDbFms/JrlxYagwBQ6AqIVDpCF4mC2yhsC0SVakaW14NgexEQDtYbEOy7ZWVNNtZkJ1lZamqvAiEZhhQz7FlCaIVZac6CFkUOqgHWenW07Ax2aCkK35RobKFHFuwkHGQX9jmU6IYhQ/mB5T44xlIMuKDeIKQI0w9nIX7kIDjx4/3BBnb6lFiFkfw/v777960g5o6CNWhkFyQpig5Q1MTqB7Zzk8eyTukIZNTCF5c7Gm7+GfrqZK/2BXGzi+HfmGXFnUsNo1JK2YIVA2taceeK+pKsIB84xniZds+WEDioZaGpMMpwQvJh7kBiGXsrGKaAFU0hCt5ZhssRCbEMbsjcJBmkKJ8KBdUnqhYIXgxSVCUiYaQaCOdSnwS7qhRo/yEHpvCYE5doMyJP5HTPp5FAuoKimTMccRzmB5AoYqJArV3zG/eGYxlIWQxVQF2EKKUSZhe6h75VZMMxIFbdUY4AAAgAElEQVQyljLVPEOOooAlH9j0pa6QH+pHiDvXIHH/+eefQtdRTaNUDw8mxJ4s7QVVNfVbbU5rHYDIZAGEdOEwS9CtWzdvMxnCWlVvWqaUL+VM+2IxJHSQ0ZQlZDRlo/ljnE9dUEddYEGCepltBC91mcUX6i95xhnBm7AJ2Q1DIGsQCNspi2T0Xyx+sosDE0m8M9gFobsRrF1nTdFZQgyBKotASPAyT6jwJhoYgLP10QjeKlunLeOGQNYgoAM9Jr9MeNm2aoO/rCkeS0gVRQDSEEKSAQ8EKB8liiAgQ5ui2HSFzIM0Zbs8B6lAkqLoZWLHIVNsXVf1IopaSDTISlSqejgLBB9EH+pdwueQKFSOmCtgzIJiFIIPcqooMlIHbZCjpE3Vt3rwF/nCffnll96WMAQghCQOxSXXIdmKctpHsSBFuvR53aKq6YPkI/0oTVGgatqUXFNVJgQdRCDb03mW/Bbn1P4sacZMQmh2AmxRQ2MDGOIRcw2odNX0BOWixDc2YylTTXO8BTa9psSy2v/VNKIcVUIeQloP/QoPlovNjxKdmEXAhjGEAHYbIQJQyGK+QVXBmDegTKiDEMKoWiFNYw9Bi41DcWDR4OOPP47eVmKZ9w6EN9ijitXDvRJhT94gZJWQ10UD1NCQ8JDlqJkpd0h/VUljskFtKOszarP6k08+8dGxEABZrvaDtV2o7Wc1FcLiCmmgfCF7aU+YlSB+yhmn9Q4bzJQNeKH0RVVNHiFQw8MMi6tr6byveLBoc9JJJ/l2QnvABIkt9qYTeQvbEEgdAqEtbd45vHtZEGNBiQ8HkfL+zpaFpdTl3EIyBAyBioiAji/YXcfCeKUgeBloMiGzwVNFrJKWZkPAEDAEDAFDoOwI6GSLbd19+/ZNGCAkKAd4oSrVQ9N0/PD8888X2paOXVS2Y+L00CkIJn1ObbhiPqBBgwZ+a3xITkHKQbqp2QLUvBCYbP1UpaJueY83WYxVm2IHFhIzno1cts9DkEKeoYBU9TKqXJRHKB5Ra7LlnTBQjd5zzz1RW7F62BjmEjBpgamB0CQEW1Ih1dROrKZBsUOBiuJUt93jD3J8u+2289tZIbswIwBZftNNN3nFp5LrKKpJH8+GhKvmXxWfWqioP0k7jjQRPiQf6SZcyoyyKwpT0g3pTnlSLqRBCUmIRMJTohdlLeQ338Ut2rF7Aywxb6EOhTLkPCpnyh7C/rvvvosSk5CaKGcpXwgFCExMfVBumLCAbOV5lNrU7fAgO+IAd5ThkMtKtHOYHvWRuDH3AMEM3jvttJNX+GKnDVuxaqIhxIo4aEfqMLGgB/iBAWQ7hKsudEDoslhB+mkTENBqL5owwBasuUf+cXoAX9hQIXohT8BP3c8//+yxITycppPFC8IibAjy4sql7D1M8SFoW4DAR81OvaTekD6boxSPn/kwBLIBAW2riMjgF3gX8M7B/ji7ffSAU2vT2VBalgZDwBDQcRFn/yDOYFyciQWolNngpfNlkMwEJhMZsSplCBgChkAsArFbnw0hQ8AQSD8COtlie/hRRx0l3bt396YV2OEDyYqqEiIvdLFKWO5x+BUkadu2bf1W+dChRoVMC52SYag1UWEyuMKFqkLIVsgrlP2hgwSEzCOe4ggqJeJQkibyiwoyTDNEGyv5sSpeFEnXX399IfulEGUQw4QBCQ4JCNEZOkg5TAckSgP5BGt1kNmQ3+F2f+5RDnfccYdX4Wq5zZ8/35ONmNSI58gDalXKFDI9dIwDUQzjUNyiYth2220TkmpaNthZxA4zhCvb6VFdY5dXzQawDRebw+QXlXFRrrjy02cJB1xRg4fjVj2YKzYO6i9Eb2zdwZ/GySF/1C9deMA/BLfWbw2TugFpT5nwDDZ4WeiI51DGQk5SHqF6GZKeskJ9rGVHWCiHaTuYZqDusNgQtgM1sRGrhKZMKVtUxyw6qEtGlQsJDoGdTU7LhLqDqp6FD9pbsvUjm/JiaTEEqiICYVtlFwM7cnTnBAugLFAauVsVa4bl2RDITgS0z2IMjmkZxtiZGHOklOBlWxoTLiN4s7PSWaoMgaqGQLyDeKoaBpZfQyBbEYA4QlmLeQM1caBpjR0Q4ZcPW6xjXeiXA9MgqGLJqXgkVboGXUWFC5GJEhKSG0Ut5mNCp+lkizmqTNSWqXQQzSiWUfESfzw8Q+xiMY/NWzb3sWqjOCQytc5BwhblqJeYEIFEZVyLDeTQETbYhfgVRYRCTECaQ76CezJOzVuE6ddFS64lMlWBEpr0JVt3EhEksXmkrDWPyeY7mXym008y5HQ647ewDQFDoGwIKKegu0cwrcMCFv0pB2QawVs2fO1pQ8AQqHwIGMFb+crUcmQIGAKGgCFgCGQcASWE9LA1JYWUnCqKXCTxTNyUoOEZ9a/hhqRwLJGjxHEYB8+FhGR4ryRq/2T8xvopauE7doIa5kXTjF1hdVzT8BJhGBtmovjDg/A0fMU9jDOsTMmWCyR1PPI+XsVUkwyUM+GTr7CecE2vJ0pXvHC1vMN7YT3iemwdAZPYBQf8xat3sXHGEt7J4K55i0c6h3UWPMJ0FdcO4tUdbVdhewrzFq+9JdORZDPJEo8oTyZP5scQMAQyj4AuKvJOZ2cL5hnUZn829zuZR85SYAgYAplCIJl5QjrTljKCl22UyJFNwZvO4rKwDQFDIBkEdNDHFmY+qK9sIJgMcubHEKi4CCRSGGZLjkKyjjTFEnZhOtORl5LEny2YpTMdRamPlcTW+Isqq+LSWN64qzmIWBK3uHRW5vvpUutXZswsb4ZAtiCg43dsi2Mr3+xpZ0vJWDoMAUMgHgKZ3t2WUoIXEw3YXjMTDVbZDQFDIJMI6GAQW45jxozxB9FYv5TJErG4DQFDwBAwBAwBQ8AQMAQMgZIhoOP3119/3R+eqqp8W7gpGY7m2xAwBKoGAikleL/55ht/qIcRKVWj8lguDYFsRUAJXk72vuGGG6RHjx7WL2VrYVm6DAFDwBAwBAwBQ8AQMAQMgTgIKJGLTfRLL71Ubr75Zu/LCF6rLoaAIZBNCCgHeuqpp/qDlXv37p0R/sEI3myqFZYWQ8AQSAkCSvBycjYnuXMauy08pQRaC8QQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQKEFCuAf5hyJAh0r9//4zwD0bwWpU0BAyBSodASPBee+210qtXr4x0sJUOWMuQIWAIGAKGgCFgCBgChoAhUM4IJDo4spyTYdEZAoaAIRAXASVW4R1OOukk6du3b0b4h5QSvF9//bV07do1IxmxemYIGAKGgCKgBC82wSF4+/TpY/2SVQ9DwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBBIKQJKrPbs2dMTvP369csI/5AygvfXX38VbPAawZvSemKBGQKGQCkQULtcP/30k7Rp00bq1KljtrpKgaM9YggYAoaAIWAIGAIlR6A4+6DF3S95jPaEIWAIGAKGgCFgCGQKASVWd955Z0/wHnbYYRWf4J0wYYJsuummGclIpgrS4jUEDAFDwBAwBAwBQ8AQMAQMgYqNALt/Vq1aJXl5eWXOCAQurlq1anHDMoK3zBBbAIbAGggU1+5KC5m119IiZ88ZAlUHASV4d9xxRzn++OPlmGOOyQgvmlIFLyYaNtlkk4xkpOpUHcupIWAIJIsAk7WcnJyEE6xkwzF/hoAhYAgYAoZAVUWgrOQGkw2IzkRkZ6pxVZKnKII11XFaeIaAIVB5EShrH1h5kbGcGQKGgCKg/cTMmTOlQYMGUrt27YzsIDaC1+qkIWAIVFoEjOCttEVrGTMEDAFDwBBIMQLLli2T3Nxc/wknLNlOlDKpQnnLh9/Vq1f3i7t6rThiWSdlv/zyi/z888+y5557lmpSFpJA22yzjYwdO1Y6dOggei6Afs+YMUP22msv+eqrr3w6wb1GjRopLk0LzhDIfgRiFbd6kFoyC0La7mlD2kfNmzdP1lprLa/CN1I2+8vfUmgIGAKpRyClBO///vc/2WyzzUzBm/pyshANAUPAEDAEDAFDwBAwBAyBCofAqFGj5PDDD5fGjRt7MjOWRM50hnQydMUVV8gbb7wh77//fqnmMkrgLlmyxCt3xo0bJ7vttls0LI3no48+ku23317++ecfqVevXqazb/EbAp4MxemiCL9jyVeI1HDxp6ywERdOCVri47f+nSh8JYFJi/rVtnXOOedIy5YtZejQoaVqw2Gc2p4///xzefLJJ2XEiBHRxZqy5t2eNwQMgcqLQKYFZkbwVt66ZTkzBKosAjoou/TSS71KZuutt7ZBWZWtDZZxQ8AQMAQMgWQQuPPOO+WQQw6RRo0aRckRyI1dd91VOEy5adOm/l2qpEysMjZU1HFP38WQna+++qpw8Eg8F89uppI4KPHUNm6sqk8VekxmZs+eLShjx4wZ4+NiiyT+OR+kY8eORY4BdDJ0wQUXyNKlS+WGG25ISA4VlX8NB5N1CF4gcrfddts1CN7Ro0fLKaec4sm0L7/8Un777TePe6wriQJR8dIwklFAJlMnzE/lR6C8TaiAqNZtFkNo46Hd60mTJslGG22UsK+I1+/oNQjeBQsW+H5A22NpS1D7r48//lgGDRrkFfclaZOljdeeMwQMgYqNQKb7iZQSvHR8m2++eZk71IpdpJZ6Q8AQyDQCOijDJviFF14ohx56qPVLmS4Ui98QMAQMAUMg6xAIJyKo4S6//HK55JJLPNFZs2ZN6dWrl6y//vpy1113rfEehaAM1XeJTCFwAPPJJ58sO+ywg7z99tte1dqpU6cocavkDpOSkqgEdRJz9dVX+3d9u3bt5MADD/Qf4oSw7t69u3DgiY4LlIRG/ReSoqThyCOPlC233FJOP/10Wb58uTf1ELrYSVuYf9LCp1atWvLyyy9Lnz59vLmHDTfc0F/H6X3SSpp5/sorr/R4QyJh1gHcMdegakaeU1zjkbjcT0b1mHUVzxKUlQj8/vvv8uOPP/q6i8kSflPv5s6dK3379pXzzz/f1+OiFhCKIzfC+23atJH77rvPLyLhLrroIrnqqqtEz/XRdhsL1vDhw+Waa66RhQsX+tPq77jjDu+FaxMnTpRHHnkkqXF/orSquRf6CdJy7LHHeoIXR5r40GfoQlaoRk6lyjkrK4klyhAwBLIagZQQvGyF4EVgBG9Wl7UlLo0IqKKDKOzFnkagkwxaB4S77LKLnHHGGbLPPvskNdBLMnjzVo4IFDdRSEdSiFMn5DqAT0c8FqYhYAgYAtmAgE4Gzj33XPnwww/lgw8+8MmaNWuWV6L++eefhbZNY5Jt44039gRwrEONCiGMu+eee7waFtKIOFq1aiXNmzcXTCGwu6YoB9GJArZnz57yxx9/eLKJ37hQuUsf/dxzz3mC97vvvisyzJCQDT3q9T322EOOPvpoOeywwzyBo3Z8w3Ed6sApU6b4Q6XDtIThPfDAAz4c8hxvTDhw4EB59tln5a+//vKPkc+1117bE9SJXDLvQsKELEPJ/Pzzz0vr1q1t91I2NLAsT8ODDz4ot9xyi5/Hs7hAm/r333/9IkX79u2lfv36nkjdYIMN5NRTT43mhnajRKe2Bb4hPWPvhRDoGH3+/PnebAv9TLNmzaKLHV988YVsscUWa6Cm7ZQFGFTwtHfMoKCuRbHfokULufnmm+XNN9+Ul156yS/S0IZ1Aak4+75KXBOPErc8O23aNGE+gbI41iXqU7K8yC15hoAhkAYEdCw1ePBg32cccMABGeEfUkrwss2oW7duGclIGsrIgjQEihxo60ChBgqPAgP/OsDhXrjdyKAsXwR08Mh20CFDhng1T1m3apVvDqpWbPG25yoCyUxq041WIgVJuuO18A0BQ8AQKA8EtI/DnAEmBRYvXuyjPe644zzRcffdd/u/IW95r6JshYDFVu16663n72G7FtKWdy1E5eTJk+XWW2/1hDGE8LBhw9YwQwA5ctRRR3m1XZMmTfwuwHvvvVfWWWcd+eabbzzRitIV5SAEEMpYlLmaXn2vY0YCZSHEznvvvecPN/vkk098PniONMTbAv7ff//Ju+++6w9Vw6EwHjlypCeXVMEc4g+pdN5553m7uaQVEkkdaYNcQgUMGQ0epEHd7bff7knds88+26uhIV+JK9Zdd9113g9K57333ls+/fTTKJmMXwhcsMQPRLE6cIGU23fffX35cJ+8Z8M7tDzqsMVRMgTCRZKGDRt6pT5qcl24QMkO0ZrIlWVcpM9i9gUTanPmzJEnnnjC77b79ttvpXPnzmtEq8/QL8A1QDjXqVPH+4PEpX2xSwDV/osvvug/ydR9wuFQtqIc5l+6dOkSXZCh36Ed9uvXz5Pf2NK+6aab5PHHH/cLYg8//HDJCsN8GwKGQKVAQMckO+20k+8f6JMycYiqEbyVojpZJsoLAbUDF0vezp49VxYt+k/WXbdNoaSUZQBUXnmqjPGECl5W9g866CAjeCtRQRdFCJclm+FkgIk8arP999+/LEHas4aAIWAIZD0CYd9Xt25dT5JCckKcqJkBMgFhiG1LFk1Rp/BhSzUCDwihZ555xitW+H399ddHbe5C0EJWomoNJzuQJqhzIWghPCEuIWpQDEOcYPMXVR7mC+KRMDqJUZMIpBGFMOkjHTyPepgxACQ1ceBIC+QypBZEKkpanuvatasnjogbchhyG9IGQumdd97xBLaS36iXCZsw2R7ORI7xBkrc+++/32OgRM/xxx8vDz30kN9RBDnMYU18IGj//vtv//3YY4/5tKGMRiUJOU5cKCghi3FMGEnPaaed5okxcCONYE1ZQZajHlZn6sKsb3pZmcCpU6fK7rvvHjXPwJhazZVQb7FVTT1mUQaFfjhOwlwCJCuLNNRFVd2HGdV2+9Zbb8mNN97o6zRt7rXXXpPevXvHNY+iz9CWsamNGQfs92ISBXIYky+0X3YNPPXUU94ON+lmcadHjx7R6GlDLKJgGoL7bdu29SYY6Cs4twMTNbTb/v37e4UwcaEO3m677fwCF+30iCOO8CQ0/RLPYgKGvoFFH/J94oknZmW5WqIMAUMgvQhoP8VCK2MQTLtkQmCWUoJXt1RkIiPpLS4L3RBYE4FXX39f3n5nvEyd/IO85gYS/yxcIKukumzZfXu5atiFsvtuvfxDK1fmuwF4rkFYjghoH8SAjINMUNRYv1SOBZBkVErEQw6wvZbBPU7LioE5ailUYjiIgdA+Y6y9R12ACf2oTUf8sl0vmdPbOe390Ucf9XFhP5FBPBMZJUFiVSGxCzmhnUS1UxduDdTfLBSp3Tbdxhvah0wSRvNmCBgChkCZEdB+l8O+UPRBfECSYD4AN2rUKE/mQo7+8MMPXi2KQpUt1ihgIVgga0KnfSX3IW7YWg2BygciZN1115UXXnihkEIVFR+kDSpc+mDIFvrkeAvmmmbi5T2BQjaRgzgmLsLBrigENiQV268HDBjgSZsGDRr4XT8oGXEQPiiKIaFR5LClnHyi+mX7uiqbIXVRDEIE4zikDlxQKKJO5MAobJiSH9SAkMnkSQlgFhMhjziMjjBQPKuSkXKAcGMrOhjyHlSybfvtt/dEHOnEdATENoQ1+QET8mfOEEgGAdoqYyQWLqh7KPWpuzi1R42taNrGK6+84ts8Cyu0m2uvvdbbwaU9YOYFUw8sEkGSMo5jLB62X223ELvUVcZXl112mV8Aiqe6DRf1aX+0SXYahCp7XcxgkYkPCyrsJKDtsHDEGJO2DrGrJiEwtUIfxCILbZ/x3plnnunVuDjIXfoM2iwKeeJkUQX1LotDOOJhYeXpp5/27U+dCXySqXXmxxCoXAho38b4gIWvE044ISP8gxG8lateWW7ShIAOOObPXyjHn3iavDHuJem6YW059vCtpM/uXaVZ86aSV7eOTPr2D3n0mY9k1F3vySZb7iJPPvGYNG5UP6ltQmlKepUMVsuLLaKsyjPJSWarVpUEK4OZ1hcQJC7bUFFIqT1rCE8G5gzQUTEx4S/KpUqpxMQBtRVpYYKDkgo1FmQGTu0yJjpMyAb1GaxQFrUhYAiUGgHtu77//vso4QrRA8mBg1yEfMVsA4pSDkpTR58JoQMxqaQiZChEJw4yk63LnHAPQYQ/SBiITtR0EDu4n376yZNBLLBBXEJ8vv766z498RZp9RoqGd4BHLAEyQtZCmmDCQae5R2Dqg/yGtKafp1706dP96YMcKQDggeClw8OZS6HOTGW4LwRiF5IIQ0XP5BfkLDYvcURBuQPeED6QNpCioMrxDaKXxS45BF82WYOGQxZBoEECYy5CUxT4CCkiHf8+PFeDQmpznsRchjCHXUh5FToyB82eLGtynvM3kulbhZV4kHGx9Q7vhl7UcdR12N6QOs4iwq0c9o0inF11EuUarR31LW0X0hSHCp0CFDGd/EIXhaNsFUJqcwCDH1LorG6tnV2A9DWn3zyybhlQ7uDzGXMRr+y1VZb+b5k0aJFXpELCUs/g6M/oF/QhXb6Ou7RpmiXkLr0JaSJHQSEoYchhpFDaKPYo31DVA8dOtTfTtW4tEpUQsukIVAJENB+inf+fvvt59X8mRCYGcFbCSqTZSG9COigZNasebLeBhvJgb3XlpFXHSGNO2zoIs5xo/v/RJYu400uUs+RUDl13fUlcu+NT8qwUV/I8y++KV07bygrnWqkmhs4mTMEDIEIAjr4ZUsqA322+6rilkkG6icmy7RBCFVsr6FUYiLMdjgO1gknA0yC1Q4kk3EcJAJKLSbEbC1kWytKrPA5/Y3iiYkJijUm2uqwy8hzbEWM50gnh4+Ejok9k249GRrFCAplFC2kn23PqNlQlEAWoPBSxz225ZozBAwBQ6C8EAj7RBZGUdVBduh1FKqQGHq6PX0XfSLkLSTKNtts44kcCCDIVcgR+lIITkiP2267TS6++GJPXtJPQwRB0vA8ZBL9IKQM/TBEMo5t25jLSbQLRycxqABVTcszxA1Zg7kJ+lZ28qAIRpmHnWHC513CuwfCF9u5EMkQNRCikDu8n8g7i32kDzUORC/P47D3CybY0UVJS145oAoyieuoHfGLApAFSu5BmHGdbeC879g6Djbdu3f3KkOeBQ/yovZFIc0gqsAedSTkGqQvC6CQ5qQZB0lMGikTykC3yit5ZYvc5dWSKm48Ot+ZN8/Nd5xtbRS8ELtqVgUTJJCkkLo4FjcYG1H/IXMZ79AOUJszbqLNU18xZRLWP40HFTBtAyUvJC/tBuVbPKfPo9ql/lP36RsYe3H4GX0H6neU/LQrzKHojgL6AYhazL1AZGOSBfMRmIhg3MhiCOFwj/YDQUveUPdiLoZ2Sx9Au1TFPKpdDpPkGXWEBaGDWp/+AVLYFlcqbnuwlBsCJUVAxyTMUfmw2FrhCV62LWBzKhMZKWkBmH9DIBkEwgHJxhtvJRcObi9HnHycG9XMkFXzF8nyfHeYWq57uRe84N1cQFa5a9VruYPXGreQad99LT37jJGXxn0sHdq3MRVpMqCn0E9xassURmVBlRCBsG1BJDBAVzMI3MN8AQolbBMyuOZQGgbOKDN4z0CeomZSkhg1FJN4Jhq8iyAaGJyzisqWPibBTM6xv4YySuPXwTeTBiYaqtwNswMhy2QZQgN/kL+QtajPUK1AHqMEQ7nGfdLHBJsJOEQC2xfZ0otShe2MKMKwFYfSBBUcttwgGHCQEMccc4wnOyBGbFJewopl3g0BQ6DUCGh/A+lJ/8biWEhQ0CejPsVBQLIAB4lDH4vDJibkCQt0mGBQRxj0zfSxkC5qyoD73EPdSni6UKb9OqbfuEZ/mmjrtp52T/+Mv0QHJvE8/TP9aqyD0IIgVffBBx94chibmqFDwYgNTkhk7qNqRnnIvAc7oJBAekgVz/HO4T2EuhfCB/IYckqxwqwCpBPfpDskixQDyDUcBHHo2G3y2WefeZUxjnceBBwmj8Af3FBMk2Z7j5S6SVSpB7Wt04ZZJGcMQ33VeT3jHfoE6hPtFSKT8QpjHJzaosUGL22KOku7YKwW9iP6m3EQKjcWN3QBibCot/GI0VARS10nbFSztBGUuSysY4OXsRZjKHXco89B8a5mJlgwgdhl8QhVPQIA2gpjTd2htc8++/iFIBa8eJ48QXCzOMOiPeYnGLeymAT5e/DBB/s+Dv+ET39pnEiVakKW2SqOgL5r6RvpIzM1j0upgpfBBavQ1plV8dpdibKvdfmhh1+Qrz64QW4c7QYdM6aIVMuR3LzEatxVK1dJvlv9zWvdUn6d8K0ccsxz8qVTcrDOawPt8qsgRvCWH9YljSlUY0ASoHzA0eZwELxMCphEc1I5W01Rv6LYCE8Ox6+acmBbHZNlVBWoMNg2iHqELbhMBNiOF+u0jRMH6l0Ua2rXDTIYZRpExy233OKJY7YWo1SDyGAyrmoRVUlBYkD4cio8g3vSAbnM1j4lTCB21aE0Y2JAPDj8MOFhkmF9RUlrlfk3BAyB8kAgdpxP/8cnkemaRGmK18elYg6h9tg1fD0YN1SzkiaI0JA0ipee4oim2LxpHCFZG/op6j73dMeKYplomzcEF6STEbjlUeOrRhxh/WcRe4899oibccZrmM4Kdx7hkQUOTKSwA4mxVIcOHeI+v9LNkaq5eRQkKYv1qHdxHOCYm5vn7FYen1D5GttG586d40jWpoXiYcGEcPOd4CY3t5onZvkw1ox1kMPc011f3Nc2p7aHuYYdXg6ZVMdYUBeAIJUhqVnQIV7Gcyx8gY8peKtG27FcGgLZhIARvNlUGpaWrEIgHETs2ms3efD2naXVem0l3yl3c/OqFZ9WN1BfvsydPNu6jVx23khZVXNnty3vIlsAKR4581EFEIjdDsyWPyYH6vRUdt0OzHUmFZC2bHtl2wuKCxQi2G1DAYaqCuUV22JQcuBQV2GXMSSQdbLP/fCgNxYo1QYw99hyiC1GVmBR8UI4o8Jimyzpw6HW4p+jDP4AACAASURBVMAhJgJ8s30PFQvbFJlkkC9IXByruTzP1mSdOEAGs52RiRATdtKrBIBNDKpAQ7AsGgJZiEBoKidMHtchRSEvlYwMD7UM+0+uK0lJf68kMOGFfbDe43r4jPbPsdfiwVVUGOofP7h4JHT4PuI3H+KNzYPuLiEc9Ude9LeGTxyaJvBSwjnEKkxPUYt54XuAree8+7BDqmaD2AofL3wtnxDrLKxqlqQKhEC8xYb8/BVSzclXclw9R93LIjxjqdXtzt13ZO6ajms6l6Jt8nfoL9Je47lI3Wanl9stWeBWraLNOlN5UiC+cX+vDk7DxuwKJr9cm3Xp5n7kEGwvv/HzM+3fZJXr43yfRASr02UHZ1egCmtJNQQyhECmBWZG8Gao4C3a7EdAB9W/TZ4hffffXr6YMExkjjtoyQ8g4g1W1szTSjcAqebs8i5yW4j69HtA3hn/hfdkyrz0lr+W3RVXXOHJNOyCGVmWXsxLE7q+gLCVxnZTDpxhmyyKVuzTspWXezhsOkLmql1CzDFAnkLsoprFtiNqE7UVp+lh+yr2fQk7Xh0I2yKTElQZqDHYVsvhG5C8bDVWgphtwNxjayzpY1stp5bzN4dvcJgPYcSmg/SwbY8DclCRKMHLFl/CgJRGiXXWWWd54trqa2lqlD1jCBgC6UZAiUniKaliN91pK4/wiyKK0xF/+I5iCziLl5C72BzlvRY7pqzq5ZOOMqioYYZ1gTzo4nHx+VkVEaNwbojamGXqw/yF80YKwvLkaE41yXcLIjWr57qxdm9nv/Z0N57ZUxYvyXcErFsMKkIPU7DmwqZI79jAhX8n4oVvTewK/MPhqtN49Jr/O/Tnkk1yue9nce4+t6P+40zrCrIa8bcqP2pCBTJZiV7gUWIYwhmM+ZtvyGJcrJo/VqlfFfvR4uug+TAEKi4Cxe3kSXfOUkrwMnFl0JGK7VXpzriFbwgUh4DW46effUOee/gcefCpS2XFjOmSV4ORR5LOjSL8qnKzhnJQn6vk4cffklp1m0cHBkmGYt5KiICWHcoWbJ6efPLJ1i+VEMPy8B5OXA877DBv2xC1LHYUUYFgw0yJTmxCQoJyQA/f2LtlexxkKHYNIWc5sAf1LIpYnufgDuylcRgG2+tw8RZXwmuYf2CbII76g/oXhTDqWrU9CelLOrDzxn0OxWGrLOQsW/iw0QuxjF1ebOtyjcN9yBcHe2DXTQlgbDOi8MXGLwpgwuQQOCN4y6MGWhyGgCFgCFQsBF555RU/rmELPXMue1dUrPIrj9QmUuCnO+5rr73K7bD6Xh599GEX1RL3gUpd7j6Y3sKWtH74O58RWcFHf/Md/uZ5/i5gl6MZiP2bG7HXIFdhefWbuRt/8x3+Vj/4cwdlC+YisHfNZ7VCOJ3YGW+STnQtbEOg6iFgBG/VK3PLcZIIaOMYNfohWTDlAblw+CmyzBG8NUpC8DJ0cUvU1Zo1kZ23O1fufuBVp97rYAPyJMugtN607DhRmi39nEBtA6jSopnZ58ItgZy8zunj2HaDLEX9CsELKcwJ6BCsqJratm3rzSlwIBsnQKO6RYVblHI+kZ1Dcg85jG3c++67zx8oEs9hj450YJuXw+BQ9XL4BgeoQdpC+kL4YuctnloDcw7YbeOwOZyp/DNb7yx2Q8AQMASyCQF7J2RTaWRvWhKOZTBrsNIpSzkjxO0uXOkUqStXrJR8vjGh4q7lr1zmfi9zv5dJ/oql7jf+l7jfS2TVisVOsOI+K90nf6njU91952+VI23zly+VJg1ryRdf/yGXXvuCPHFXfz8WynfmEFxEDqx89yxmXSK4rf52JhMw74Cq1vGsXnRbIMX13/5fxFRCNfe3iolznOTXb6YsuI/ENmJJIeJb1bv+Gsrdau6aPsx9b36lwCQM34TnAoxcr+biQnnsfler7v52v3Oru2s13N/uk1sr8p3jiODc2pKTV8eFX99913XX3T2uVavjAs2TXEzZEJ6XKK82QTF/3hwZ/+4HbjzYwo8NzRkChkDlQEC5hqFDh3qTNSzGZoJ/SCnBy/ZUDp/JREYqR7WwXGQTAlEF73NvyJSJI2Xoxcc5Be/MEil4/UEC2OttWFs6tj9b3n7/f9K6VUsjeNNc0Fp2KEAheVFgWr+UZtDLEHzsltfQtmEZgi3xo7HxooziGiQxtoAvu+wyb7aBbXfqN9EWvGQjV1MNqMyJj9Onra4mi575MwQMAUOgaiGg28F5B+n7p2ohYLktDoEff/xBBpw0RD547xPn1ZmWK1eHChb1rrlkEGjYqKm8+NxTskOPnQod+JjMs+bHEDAEsgsBnb+x03SXXXbxwqN4JvvSnWojeNONsIVfYRHQbW8Tvv5Vrr2ynzz61LmSP3O25Do7U8k6f4Jro7ry12/TpdN2N8m8uTP9o6bESBbB0vkLCV62ww8YMMBIs9JBWa5PFWc7kHLFoYAND5Dh73BLIr/1MBzulXQLayzhHE8RE7bh0L8edqMH7+hkXA/aiT20R9W8KHuxKdyxY8cSp7dcC8kiMwQMAUPAEMgoAjaGzCj8WRm51glMeOy9996ydssGcvt1h8t6bZt69Wv06JACW7Mr81d5JS02dfn2tmmRxbrvlQVqW8S3KGKjftzf2NvVAxO96tc/HwljxfJ8WeFUu7kuvpUuQFS7fjHcH4rGAWfu4xay8/03imJ3j29336uIVzr/2LclPJ8Owi0cjr/u0uS//SJ8WByFzTTEM+QQW3h+TJYbOVDRfXkFL+peVLdRRa+74UTABapeFL7OL9ecn9xc/q4mufh300NUu+6SEwZUkxruU716nvswb4ykBhzWalBbum+9oXz84S+y3V4j3NUV7jyJidKpUxcb/2Vl67JEGQLJIaD8w6GHHupNBnIoaiZEO0bwJlde5qsKIhAOoHfv1UseG9tbGjdvLSvmLZQ898Iu3rmNS4tXSI112sqVF90un09sLC88/3BGGnrxaa1cPsIVtD333NNvkc9EB1u5UM2e3NA29YTzMFXpPPympDbtkkmL1kkOdWM7D2YacDZ5z566ZikxBAwBQ8AQMASyGQFdxF606B93PkEDaeYOq5016xbHNtZzyUbBm9zB0CXLY2yY+ncycSV6lhQk83yY0pL6j5fLZKhgfS70q7+Lej7ePWe6YrEzc1G7qTw19i055JiRjpTfU1566RVT8ZasEppvQyCrEAgJXg4AHzJkSEb4h5QSvB999JG3JWNESlbVNUtMGRDQuvzyq+/LrdeeIK+NH+Zeyv/J8r//cyQvq7zxBxZ+hXv5Sslr3VSWzp8lHTe5Sj756gdp4Q5bM/KmDAWS5KOK8cyZM6Vu3bpSv359wz1J7Mxb+SEQHiD34YcfyrBhw0y9UX7wW0yGgCFgCBgChkCFR0C3AN922y1OMXaa3DFygJw0pLcsnjq9QD2aHVlEbJs0HVsSzjXd2Usy0QVmgYtPDeE5dXNeTScWalxPOna4QCb9+pesWLbQqYLzbBxYPILmwxDISgSUNzrkkENku+22kzPOOCMjvKgRvFlZPSxR2YjAWecNk4kf3Suvv3mxO1x1LZE5c32jjVj/d/txCvYJseW6WgNnYL9WA5n1/deyz5H3y+XDx8oeu+9oJGM2FqylyRAwBAwBQ8AQMAQMAUPAEKiACOhi8SGH9pOnnnhcls2+S6rXzZNV/y5xZgU4usxcNiKwfNkKqe7OZbniokfl0quekvnz50jDhk2M4M3GwrI0GQJJIKDEat++fWXLLbeUc845xwjeJHAzL4ZARhG4YtjNMvKGC2TsyH7Sp38vlxZMNbAmDcvLkix2llbKv79OlktGvCBvfLRY7r3/Ydlqi429HSk9HTajmahCkTPoDW2eVqGsW1YrEALe9pyzNWcH5lSgQrOkGgKGgCFgCBgCWYCAErxbbLWzrNdygTz14uXuzJBp7syQZMzJZUEGqmgSVizLl7xWDeWL8T/KVj2vkAV//+3s8zYwgreK1gfLdsVHQHcQY3KPA7rr1KmTEXFfShW8H3zwgWBvwkw0VPwKajlIjMC3306SwaedLq0bL5K++3SStms3lOo1q8u//yyRqdPmy4tvTJSf/8iVI/qfLINPPsoHZGYZMlejDPvMYW8xGwKGgCFgCBgChoAhYAikDwEleDt03loO2GUtGX7rYFk2Y5rUqGEEb/pQL3vI+Ss4iLu2/DN9oay13hD5+eefZcMNNzQepezQWgiGQJVGIKUELzYEsTdhBG+VrlOVNvMo7FZyOmxuZMD0w6RpcsKAwfL5Z59K3TrVpWatWu5U1B5y/XUjZKMNGkVxMIKx0lYJy5ghYAgYAoaAIZAeBDhh3oWsJ9anJxIL1RCIIKAHl2JmzFzFQkAJ3vYdNpe9ejSQkXedLitmTJc8I3izuiA5ryW3Xk2RRcslp9lJ8trr46T37rsZj5LVpWaJMwSKRyDTO4iN4C2+jMyHIVBou8ycuf/IiQNPkw/ee0V26dFVOnXsLNNnzpMaeflSu+ZyqVOviXTbej/Zd589PHI68DIYyw8B7dguueQS6dChgxxxxBE2YCo/+C0mQ8AQMAQMgVIiwKIwn5zcXMlNcJBrKYO2xwyBIhFgMWH58uWSl5fnjpYworeiVJcowdtxc9mvZxO5fvRgR/D+aQRvlhegFwDVquFWV3Ilp/4x8tBDj7r5Sj+br2R5uVnyDIHiEOBdimPhNBPOCN5MoG5xVigEQgXuVcNvk/FvPCwnHNZRDtlnC6nWrJ7IP//Ksn+Xypvv/ypvvPubTJ8xS+rXzZXGLbvJKaddLuu2bWEkbzmXuHZs++67rzcbc+6554qeMlzOSUl7dNTPZJ2+cJL1j7+SPENScnJKdE7yGkmJnkK8xnHESZ9PXJLspcFvkP+Cn2VDJEiiGzCs4hDHEqa6tAOMZJ/TOoh/syNcwsIx74ZAgEC8HT9/z5sjP//6uzMB9aesyl/hJ/8rVrodRe43CrCV+Stlhfu9Kn+V5K/Kjyh+3X3s/q/kt/6N3+BevrvP8QHsTOJ6jvvk+/vBc+HfLvyImhhlsft28TKH4e8wLB+mxpPDH0QTCVPfKf69UnA96p8f/CPt6t9f8gH6kw58XLxoinjt5eZVi553kMPZCK7D9Lb4+e3/uV/VCn7HXFe//jkedff99Mz1u2TFXYiGxZkKGg5EfI4jaaLPF4Srfnz8TPQIs5rzx8+CaxyCpelbpXHqNR+u+wR+fDAu7mhavJ9IWvjPp5m/fX/Ms7merOU+/XMu7xCXVu5Vg8h1z6y7bjvZdNNNC5G6tvus4nRNSvBu3LW79OhWU+4Ye5YRvBWg+Hwbq1FdpE4tyalxpNwyarQMHjSw0s5XKkCRWBINgUqBgBG8laIYLRPpQkAHTUw4um3VUzZsOUuefOZMkZrOBMOiv0WWrnCDbDdYr+4+dWsz/ZD/vf2D3PvY5/Lforny76JVctKQ62WXXXr4iVGyhEm68lNVwtWO7aijjvKnWJ522mmVbkWcPOJQ2pgzBLIFATswLltKwtJR4RCAuCxQTX7xzji577575Y33P5afJ09NX1Y8e7laYRK7eFTcmKW4+xGetAQKllj/8R4tLrgIj1zYxVzzdHE8fwmQ9l4LCOqiCiOZcBP5WYOzTiK+VFeMDht3lpNPOkmGnDrIk8o2bk01wukJT+cqnTpvJR3brZBnX7lUVsycIXnMTcxlLQIr3dyyWq7rdRuv5Uz9HS4XX3adnHfOUK+i54Amc4aAIVCxEFD+4ZxzzpH27dvLgAEDMsI/pJTgff/992WHHXbISEYqVvFbaisCAjpg+mPaH9K2TSc5e+AWMmL0WSL/zpYVCxdLNT9w0pkGahpnS8mpI3KareWur5Rnx34mjz37iTPf8Jecdf51st9+h/j5QYbU+hUB8pSlUTs2TDNsscUWMnTo0ErTL5VFVeNFU25AiUpr5Uqn+HLfK5Y7FVh+vleAreR7Rb7/O999r3B+VvK9Yrm7j1IM9ZhTi/nrEdXYckc0e//+Ge67b71fEAdqq5VOWUb4bpnDKbQiKjOUWF4xli+yokBpRjvy17hPnKoq8+o0FGMx6rRQqZbwd+S51aoz99tPnp16gq1xkAqonAIllP/tlU4uvU4NlavKLZRRqKJQSnlVFOqoCInhFVM8g1LKffLoDwoUWV4t5Z7N8+Hyu8DmYYGaS+MmzNw817cUkA8M/nMdiZ9bjY8L26nT8pwd8Dyuu2+vyCr4EF8192ye84u/au47z337Z/W7IDzvl4+Lvzrhu/urnHws192P5C2kfYpumr9Nnix/z58vG6y/vjRwJ0DjylJPU9YRWECGQAVE4MIzz5Crb7w5knLXPvfZtacc3/cA2XrzrinITXKsZnK+UpCcooJIdSKKI4bTnJ14wZc8SSV/orhs8S6cNWeefO8OeHr+tbfliZdf82PbV15+Sfbca6/iHrf7WYCAvm837rKlNKq7QD78dITk/zUzMpYwl7UI+HJjLlm/ruTVPEJuvOl2GTL4ZCN4s7bELGGGQNEIKP9w8MEH+10xF198cUYU+UbwWk01BOIgAEmV5wZGs2fPlubNW8ig43aUUfecLTJ3uielIFriOsgoR3xVq+5IkiZNZPKE3+SMC5+R337/XW6++UbpufsR7DT05I659CEQErzdunWTM888MyMdbKpzGJJm7733voy5c4z89OOPMuOP3+W/BQv89lzHoTpyze9oFffTfyq7UxFaRE3G1tSIOQeIVr0WVZJ5ctUzun55ZhXEsz/JyNG/fmuyp4E9iJHtzZUdveLzF0E1QvgyXfS/3Y8ajhRetHSlLC8IombN2nLwIQfJPXffLTVr1jT1V/HQmg9DwCOg76w77xgtJ518iuTWqiM9ttxMLj71RNl84w7y35LF8vfCf6NmDgw2QyBVCPA6rFm9htuEVlvqO6Lpn0WL5cjTzpW3P/xYXn75ZdnLkbx2eHaq0E5PODo27Ny1m6z4b6b89OstsnL2XD8GMpe9CERs8Dqlbl51yalztNx771g59tj+lWK+kr2oW8oMgfQhoO/KY489Vjp37ixnneXM5TjxU3nvtk0pwfvee+/JjjvumJGMpK+oLOSqhkB4KFpOXgPZt1c7ef71YSLz/5JVjvhF1Ves88pIRxK3aijL5i2S/sc/IFP/+FWGj7hZeuzS1ysTUcqZSw8C2rH169dPunTpIhdddFGFHzCF5O6QIYPl1ltHRcHbeKsdZYtdD5GatWt5hS6k5orlS2T50qVOabtc8pctdf3yMve9xF133+7DN7YXvZoW9S1qW1e/PcGJKhe9q/v29hax9eiUvkwEI1vwXTvABqQqar39xojy1nmO2mlUe43efiL2HlHv+u8CW4uE7S9H7D6uvu6tLSa2/4tXmo9TnBZYdfTqWIhZ/41tQzex8d/4UVKXqCIGDCPbhr1iFxgjkyDftgvsLPK3t3br0obNRr8qozYYI55X28ItIJJR80ZsH0ZUuDlOORvxGrHPuNo2ogvPtX/SGrGvGLmPMpg8oar11wv+9ukq8OuTURCW1w1HJMIR/4SBqrjAzmIkzIjKOPI7YovRq5a9v8gzudXcBMPbqcRvwccrm9X2YyT+iBVMtZFJPfN/ybTvPpev333edZFzZKm7stFGHeSHH773cZmSNz19nIVaeRAI20iLFi1l1qxZblH5SLlk8AD33lohs+fN931hZGHZCJvKU/LZkpNIv049zHc7ZVo0bSTL3Fhgkz0OlJq1asm8OXOi9+3gtWwps8Lp0D6kS9ctZOH8qc5e961OkLLAlWlkN4657ETAl1udms7cnztUs8kJ8tpr46R3792MR8nO4rJUGQLFIqD8w9FHH+0JXkw1GMFbLGzmwRBILwLhRKv7Nj3lr6kTZMr0u9xBagtk1X/LJccp1pJ2qHmXOyLXHcSW/98yOfKY++WPaVPkyaeekVZtt7SD15IGsuQetRznzZvn7VjVr1+/whNN+oK4/fbbZdCgQdK0QV1p3mZDOenGZ2Xj7dfzg3g3J4s6L9wo2GavKtTIgTiRT9S4SKBQ9f7gPeFplUoouK/3MKfgeVzCKfgdDRflMAfncE+/eR5/BSnjuQi/G0lI9BA3SGAShYcCwrckB7wRvB6Ms5rARaWLZJ44I2Slj6TApEJE1VuQ0chlvV1AeEYSHTWrEuGEvYvyvQVdAmfeeO6Y83PcN+s3XAudz5/iyo+I0DgKTpS6icPhFCTbP++x1GcJIsKXFwo84K2jZapeNCxNTzSPkTOXoq449bJPeoQDl7+miIy7f4S8df918uesOXLiiSfKmDFjMjKwKYy6/WUIZDcC2rdj5qxHjx5y5MH7yx1XXihT/pwhS5Zii9GI3ewuwcqVumXO/uf6bdaR868fJWMeeFheeO452We//awvz+JiXq3g3UKWL5ohk367VVa693A1FonNZS0C7PjMbVhbFs/6T+q0PUUmTpzoRSmZIISyFiRLmCFQgRDQttu/f3/p1KmTnH/++RlpzylR8LZq1UomTZokpuCtQDXQkhoXAR0kXehsplw9bJjM/GWktNigiTusYGGpDyvAlmlu03qybMFS6XPQrdKieR158PEPoWUqPOlo1ah8EAgXHlgR/GXSD9Jm3Q4y7JXvpGHzajJ7Omrb4owxhIygcoEFytUwGzofKMQQrvYQIThRnhaEEfiPXlPvMQRnkUqSYB6Sil2FnkjGhYxlguLy4uFELuZeIQI0DF9J1vDbq5WTcSHLnny6C0cfJ6Nxo49S98kkLCk/KH9r18+VJmuLTHjrZ7n84M3cYliezJ+/wD9vKt6kYDRPVRQBHYiPGDFCzj33XPn2jReldq0a8vc//3j72OYMgfJEALv6azdvJm9+/LkcOfgsueGGGyrVOQbliWV5xRU9ZK1Ld9myU448+KQjFWZMl7wa1n+UVxmUJh5P8DavL1O/mSrtNrtAZs6YIS1atjQBUGnAtGcMgSxAIDzkvUOHDn4HcSYWbIzgzYLKYEnIDgR0gDTpp29lh+22luFXHi7HnbKn5LtD0nLLoqBxvEu+M9eQ26qB/Pr1n7Jtz8tl0ClHyqXD7vDb4Th8yVx6EIBYwlX0bYVaN/9xE/6111lHlixcKOeNfU12OKi3TP91qVSvUaPA1mx6cCwcagFrGKv2LFPUSbCwZQo//W2sEC8eJbZLoPgvU/4y/7A/oM/1cxtsWkNuP+1SeeyWK9xW89nSrFlTm6xkvngsBTEIREzN8P7N/CFEOhAfcsYZ8vJTT8pXrz8rv/8xLbGtfytNQyCNCKxwbaNB3boy39l83nLvg2T06NEycODAjExS05jNShN0uIBavXYzuWLo9nL+VcfK8ul/SvWaRvBmc0GvcObT8lo1kbtv+VAGnDa6wISZLYpnc5lZ2gyBohDQ/niBOxeHs0hqOTNHmRC5pJTgfffdd/32skww1VbdDIGyIaBGPUUOPrCnrFq2UJ5+YagzzbBEVjobeGU+qACbvBzc5lZm7xr1qpw4+G7505l/aN1mU0d+rHCTTBuEla384j9d2QjeZcuWSz2n7Nqgy+Zy/fivZOFsLBpEbO6aMwQyjcAK11c2a5snbz/yolx/yr7uYKglUtsNcEK75plOo8VvCIQI8I7QwXemFgKXLVsmNdwi3ZFHHyMTP/tYPnjmEfll8hR/0Ks5Q6C8EaA9YNqqQf16sk73nvLMs8/JAfubiYbyLoeSxMdiFePAjhtvIRusvVxefmOEyLwpsmJZvjPTYP1ISbAsL7+cf5G/ZJbUaHeG7Nr7GJn04/9k6pTJPvpMEELllW+LxxAwBNKPgBG86cfYYqgACGhDGPfyg3LqqYPlkfvOkC13bu9MMywotWmG2GyvKjj8Spo2lW6bnuVsnNaTCV9/U+BtNcFcAeCyJJYzAuFgr2Ht6nLguTfLkecPkmmT3MmctgWvnEvDokuEAARvy3Xz5Klb7pXnR5wify1YYpMVqy5ZhYD2pfEWHRgH4PxBhOV4MpGmZZfdd5eZk3+VD59+2NnfnZ4V6uKsKjxLTLkgoARvvbp1pN02veTBBx+UI4880sQ75YJ+6SLRRaK777lbBpwwQEYNP1oGnbuvC8y9g//+L3K2gbnsQYCDC2q7902t41yaanlyHvM8w4cPt3aWPaVkKTEESo0A4zradXmOJcPEGsFb6qKzBysLAoXsm3ZaX5o2qi3vfnSlyGwnj0QYmUJ1pLe31LS+zPljnjRbb4g8/NC9cvgRx9oLPcWVSTu2K6+80vHpTeXkk09OO8bpVIKFdbSmq4/9rxgjfc88UWb8uswRvDVSjJ4FZwiUDgEI3hbt8uSFOx+VB88/XBYuj0wqTY1SOjztqdQjoO+Gq666yttG292RqkOHDnUnl/cuFFl5kb3hLpNuW3aXZf/8LeOfHCvTps8wgjf1xW8hJoEAdbJGjepSq0ZNWX+H3eX2O+6Qk086Ke1jqCSSZl4SIBC+Y1uvvY7McOYZDj2opwy/aE9Zd7N2BU/FI3ljzUgle25AUUWxxkkFFajcwt1wJd0Zl8gkVyym+Fssi+duIO98Xk2uuOwM+fTTT2Xx4iVuO7fteKpAlcWSaggkRCDTO4hTSvCOHz9edtppJxsEWIWvUAioiYSZ0z+SVmtvL2++eL706rOJU+/Oc+rdVJtOWOXs8TqSt2UrGX7Zw3L+5S86m0umckt1hdGODdVJmzZt5JprrhFVOKQ6rnjhpZrsDQfvuY7g3W/ghXLKDcOc/V1T8JZHeVocySHgTTS0yZOPXnhHhh29i+S7baNMZcxEQ3L4ma/0I6B9KVuaH330URk5cqR89tlnPuJ+/frJ6aefLltvvXVcsjcvDQeeFVpg7rqJ5K1cJm8/ep/8YQRv+iuDxRAXgQjBW0NqOjMNG+zYW2688UY5w9mHNvN72V1htC+ZMGGC7LDDdrJo0WKf4Nq189yhjbniLAJERCsFLmdVjjuD1pGx7lpOrjqBWgAAIABJREFU5H9ebeZ1LQXfBSoX97c+uvq0gdWbHPTEXfd81KPbBeEDWn1NwyV64sspOE23pDQqz8dS1ZHzbFcTqWtQ2Rx+6y862+uxxeguRATOkYMlMHvmf/jfetjEKrdQHbnu/+9++/8Kvn2avNfV/n2QwOsyGI3T/QC3xUtXOEJ39cHIKHdR8NpYKbvbmKXOEKgoCBjBW1FKytKZNgRWOFVtXl41uXbYQDnv4nvdS/p+kYX/FdjeTf0hSQwSqlV3NrEa1pdGDY6WA/ueIPfcNcpUbiksYe3YUO62atVKLrnkElm+fLm3K5dqpwOyL774Qh5//HE577zzpEmTJtFouA+ZUJZtv7EK3l37nyanj77ZKXiN4E11eVp4pUeAdtekZZ58/e7/5MKDusmcOXNdW2hsk5bSQ2pPlgMC9NF3OJXi1VdfLdOnT/fviQEDBsgpp5winTt3LpQCFgo5mC1Vh7OFfftGG28s9d2Brm88dLdMNRMN5VDyFkU8BFYreGs5Be9ucu2IEXLO2WcbwVsBqova4p03b57c48w1/PLLL9K4SUupW6eun2MoTbvKsZFL3ZiYcTF9Gu9ufvNZod/5HEQZOYxylesjWbD1v90cJt+xxZ7cdH+vdL+Z1/A734Wzgr/5ds+vcGeM5Ltvrq++5w5kdX6IhzBK6yCIc6rl+kVktkJ7wpgt0Y5k9n9zPbzGbwhX950LEe3+yHF9OUR3rrN3Xi23mpsL5rl7rn93c8Jc9zuP8N29XPed5+5Xi15z99zfXPfXAj88l+POValJWO5dUt3dq+G+czROx/CC2a+//OjxZvfIMccc42Gw3U6lrQ32nCGQHQgo/4CwrKU7d+nYYzOzSzslBG/r1q3lp59+ElPwZkflslQkjwAvU12p7tShlWzWtb08+tRQp96dngb17up0cdp8XsvG8vZLX0qvfa6TKc6wftu27YwISb7oivQZErzNmzeXyy+/PG0Erw7IlrgDpSB433nnHV+Ou+22m1eEoYRRV9ptv+Ggr16NHNl63+Pl/LF3y/TJjuBNg6osRcVgwVQxBKjfjZrnyU+ffS9n9+ksk37+RdpvuEGlIgZ021WmipZJdLpdecSRiFgqrwMjdZcFRG3YR3/33Xdy3XXXydixY30S69WrJ4MHD5ZBgwbJ2muvHU12qrbfheF02WwzqZ+bI68+MEZ+n/qH9e3prugWflwE1AZv3Tp1ZN1te8lVbuHjgvPPL9ddUFY0pUfASMLSY2dPGgKGgCFQFgTCHcSMGa+99tqMvDtTSvC+/fbb0rNnz0o1mStLIduz2Y+Aqi9XLf5aqtXZTF598izZ4+CtZMWMOek9vIpJOvP0Zi1k2y1Plxr1u8i777zkV8DLa4Kb/aVT+hRqxzZw4EBvg3fYsGFpI3jjpfL333+Xu+66y5O9dPCHHXaYHHTQQYW8KtkLwVBcmYcD9qb1a8mmvQ+X8x+8V6b/ZgRv6WuJPZlqBDzB2yxPfvnfJBm6Rwf55ptvpWvXzgnHBPR3KFiKq/9FpVPV8cnmpSRxJes3U4coJJtn81c6BH788Ue59dZbvSmHBQsWeBNkqDEOP/zwlCl4SZn279222lrq5qyQV8eOkUmTpzjlV6pNRJUOB3uqaiGgBG/9enWl7da7yBVXXCEXX3xxuY6hqhbiqc8tcxs+vJu8kCWJs0QSvUuTeVZzEM9vRXg/Jlq4TbTYmeh6SbBm7K/hlGWHX+prj4VoCBgCpUUgFJgx/+esh0yYN0opwQuZsfPOO2ckI6UtCHuuaiOgDeDtl250StozZcWsMZJbt7rkL1rqtt6k3jxDiHbEFu9a8uPnv0un7hfJxInfSpcunU3Fm4IqqfZ2T3IHgzRo0EBGuC2GoQ3eVKvw/LY1tqe5AbXf4uUGburGjRsn9913nyO7vpH27dvLEUccIYccckihXEJyMcBLtO03JHjXad5Q2vc4WC580Cl4jeBNQW2xIFKFQJTg/coRvHt2kAkTvpFNN+0ad0xgKqOSoa59TPgNhvGuh/0RsSTyk+h5VbeG4YTX9Ll4/sJntE/Ua+pfn1fzNbHhxItL/caGGV6PDT9RuEWFpcRI7dq1pVGjRr5PnjRpkiBgmDZtmrObuFgaNmwo+++/v+/XU+F0obnXbr1l1aIF8tLY0fLDpN+cstgI3lTga2GUDAHaDfW+UcMGsu42veTiyy6TS9wktTzPMShZis13tiOQ6jF3KvNbEQjoVObXwjIEDIH0IRAKzDDRcJl7f2bi3WkEb/rK2ELOegRWyrIVOVIjL0fOGNxHnnj6C/lz+iiRuXPdirez45Reftdb/F/lbFPltGgtu+x4jixZ1U4++mBcylW8JRlYlWRrbkn8prMqxCoGVLHABAXVFX/Xr18/5biWJk/vvvuu3H///c4kxxTZZJNNvBKse/fuSQWlhNiGbVvK+jseLOfePUr+/MVs8CYFnnkqFwTURMOkz7+Ts/bu4gjeCY7g3bTIRd977rmnEAEJ2RUSdSEJqL9jiUH8x7sWkoFFPav+EhGQ4bOx5GZx4UZsFBYmYuORrvHI1thr2pcXRcyGZC5po3+MF04s6at+yqWiVLBIGjdu7E01YIaHz8KFC30O1CxZWQ/G0YH4Hnv1kQUz/5A3Hr1Xfv5tsploqGD1pLIkVwneZo0byQY77C7nXXSxXHTBBSbeqSwFbPkwBAwBQ8AQSAsCOp5DYAbBm04TkUVlwAjetBSvBVohEGDS7Y3xi3Rs31I27dJeHn/W2d+dMSO95hkCcPLdAW+5LZyK9zOn4t36IvnJKYU2cirPsk4YKwT+WZrIWOJDCafwO/yNfzrSkCRSFRjf3OODIozD1yBcJk6cKM8++6y8+uqrnjjYdddd/ent7doltsOsBG+XDdvK+r36y+m3DJNpk5a5urraxm+WQmrJqiIIUM8bu0PWvvtggpy3/+ZxbfBq3/bII494u6Z///13FUEn+7LpD6Ip2Lob+1tTG97nWln/Jgw1S6Nx8u1Pby9Ij37rttXw79hn9LmShhuGw7P031616FS7LVq08IqLr776SmbPnu0OC5wjS5cu/T975wEgRZH18bc7YRMgaYmKgEgSQUVBRRQFDBjuM2AAlTvBnDkVjGBAlKQicEQBObhTVBBJRpInQUBAgpKD5Bx3d8Lu91711NIMu8vObs/0zOy/telUXeFXtT3V/371iurVq0d16tRRed29eze99NJLyvVOcX+v9f1tbriRdmxaR/+bNJ62bNthqRuI6Gt9yFG0EpC5uBIScqhKpXSq36oddX3pZRZ4u0PgjdYKQ75AAARAAASigoAWVrt06ULp6ekkk63FvAXvjz/+SNdddx06AVHRxJCJMxGQWUwTeQZUom1sxVuDBvfrTA8/dyMLvHsiJvCKFa9a06vSxRc+QbXqXUtfffFvS/+GQhkKrS3hzAKlnJMHlj6nj/Wsu1rENN9rtlozW+RpEdQcX7AYao5PwpnT18dma71gC7lgCztt9WYeJqz3pY0ED/HVL/tmizY9hKsgIUKLHzqsFh5kK+lpX6MiIIiQK2Lv3LlzqX///pSWlkZff/01tW7d+oxiQa2q5emiO56hp/r3pO3rvNxWXWdq6rgOAhEh4PP4qOLZTlo883/Uo8NVtGPHTqpatcopbVqLWTK8feLEidSwYcPcvOX192UW/sx/Y8HCpFnoMwuC8rdXUBzmeAqKQ7tPMQuhwaKk/lvPL73gvOjy6nLlVSZ9jzlMXiJrXnkP5ml+JpmvmfObl6h6pvsKcz04jbzqJSKNtBCJfPHFF2qitUWLFil3DDLLufhzFxdk4Vj038QNN95Eq1Ysp6XTPqdde/fxB2gWvsORIOIEgQIIsL5LCfzcrF6lMl3Y5jZ6+vkX6OWXu1naL0UFgAAIgAAIgEC8EdCay4EDB9S7h/QhQ9FhrOJhqQUvBF6rqgXxRIKAn8VDB/tLXbPia7r33kfp06FPUpOra5Nv91EeGhm51yoRRZxVK9F/Rn5PHR4ex2LgCVX84jwQ9L3yEi2TwsgLq8wUHiysBlurhspdhEoRLvXQZi3AinjgcrnUKumKX1p9HGy9JWmafWCZBVLztWCLKy1E6PjOtJX7JYy+TwsOed1nDqOFFc3G7BJC9rVQoV1WaJFH16GcFwFXhmqIFe+CBQto+vTp6lz9+vUpOTlZTQQn1mFPPPFEnlVgbgsV2Ud060feood7v0w7IPCG2mQRPowE5FmWfq6Tfpownfo9fjNlZHkpmf2IFtfCMYxZRtQgoAhMnTqV3n33XZo/f746Fsvczp0704033njKBEXSlmWR57r+TSkuQv33ccutt9L/5s2jVT98TQcOHc6dJKm48eN+EAiFgBZ4q1WuRPWvvZm6vtCNXoEFbygIERYEQAAEQAAEbCMAgdc29EjYbgI+n0yI5aCZk/rSo8+8T7/Pe5PKVClF2UcyKNEROYFXiXepSUQ86VpCuS405ZtpdOst7Sy1lli1alWuuJmX5VawdZU5jNST2XrNbCmm61CHN1u6asHTbEGrw5v995qt3szXzaJvsKhqTlfHFRxGRGXzueAXc3Mcsq/D6hf34OvFba8yCaUM1Vi5ciX97W9/U8N7a9WqVahozQJvGVcC3fnyYOrQ/QnauQEuGgoFEIEiQkAE3iq1nPTN8P/SqBfuoxPZPDqBl7w+VmnL+YhkzKJEpBzmZ4pF0RYYjdmCNxLpWf3cyyvPkWZozoN2xSDn5GPb+++/T5MnT1ZBWrRooWY8FlHXvOgPmPrDn9X1oDvit99xJ303czqtnTODDh4+AoHXatCIr1AEtA/eiuV5QteWN9Irb/Sgl7vBgrdQ8BAIBEAABECgxBPQmkN+k6eHGxAE3nATRvxRS8Dny2GBN4F6vtyB5v68gn769gUiFn2zs3xs5cmOeSO4ZLMv3kSebK1pkyeofuObaPy4EZYKvBEsSlQkpQWlQYMGKd83Xbt2LZZFdGEKFSwMyz3ia/e9996j//znP5Samkrd+CWpe/fuyppZFu2yQgse+f0QmAWycmzB23nAN9Tm3htpzza2/nZhpvXC1A/ChJ+Az+unc+o5qPffn6TtC6fRqvWbVaLFGY0Q/lwjhZJEQHd6ZXI/8ZHWqFEjNXKiU6dO6hmtFwkni1VWugUx1nnqeP+DNG3KV7Rm1nTaf/AQBN6S1DCjqKy5Ai+7k6p5RWvq98GH9NSTT6BPGkV1hKyAAAiAAAhEL4G8jM8imVtLBd4ffvhB+ZDUkUayIEgLBEIiILNIsG8UWW6+4RJyOpz09fQXKGfvAUoInA8pvmIGNtw0VKU3XxxJQ/69jnbvXJs7BLSYUefOoF7ceEK93y4rLUlXRN2kpCSSWSxlGTZsmJokR9xFmK2HQymT2aK4oPt28iR9gwcPphEjRqgJesR/owi7Zv+NoX7Z00N4xSDSxR8fun0yg66560bavTn+ffAaNqBYIvvJKXTe2dw4k1IMn6F31UqgF/ijSl/2L23H5AKh5x53lBQC+mNDRkYGHTx4kKpVq5Zb9HBb6ubHWP+NdGSRecWCX+iXyRNo/ZZt3C+ROQKwgEBkCcjfgfSV0lKT6dzL29BXbOF+O486wrtdZOvBytT0XBeF7cdamTbiKpiAdvUmf3cy8hB1hBYDAiBQXAIQeItLEPfHJIGTE6wdpWtbXEw3tm1G3XreRv7dh9gvb+TcM2h4hsBbkb7+92x6Y8BKWr50rroEy7eiNS/xCyxWsi+++KKyghowYIDyFawtZ4sWa9536Travn278tkoAm/t2rWpQ4cO1L59+1NukgduUSzCzO2gdFIiNbjqFur33RT6az0bnXvj34r3TOLmma5bWd+RjEuL27EgcnuzPFStjpu+7D+Yhr3xFC1cuIiaNbsMokAkGwzSCpmAngBTRk/YNZROf8C75trW5PaeoClj/kWr/9zAIhtGZ4Rcobih2ASkn5JevhytXLeRbnrgYfr++++pTZs2eJYXm2zkI5BnS15u1SKfE6RYWAJ5jQYs7L0IBwIgYC8Bs7Aqc+w8++yztvx2QuC1tx0gdZsI6BeqXdtXUDu2sOz+/J10d+eryLfzADlteKnSAu/EMbNp5Of76Nvpn0PgLUbbMAu8Yh310UcfhV3gPXbsGO3evZvOO++800RdOaEncytqsbTIe9Ott9HMqd/Q4++MoPYvdaEj+4mOHuSPAT4P8bQ/Kvq8BU+RCQv4eFHApYR8YiyoLGxInZuTU4TKwIFM5BK8BJ/L4RNnEjfzikfiPeW+nJOFk/D6KL97C66jwF1nypipEoLrI/eYdwoSp+Wavn7q9tTEVXmC6i/v6iwg0/nCUFPuFIiE55uisyq7yJ9J9HCTcuR2JdKuvdwwecFHqqL+xeO+cBLQ1rrFfS5bkUf9N3L5lS3o6L49NP/rCbSJLXhlNJFdI2GsKBfiiE0CXv5oXPOcatR7yGjqN3QE/bFmFdWr39CWl9TYJBgduTb/9u7Zs5s+/fS/tH7TVnKxleiZRrIlSgeOV1M37mQXgzsXMlbH6OOpYKqLoM6Z+iJGv8QIpPdVCP5fTickqIh0kNMsV0/t1+i8nJonc/7M/ZTCfPQ/vTcUOGO6kLt7+iXxsRboZ+q5Bk7Wu5+tcaX/ms3/+Hl0k4xwUqs/cF5GkfIi9bB37w7+mOeg669vS/fd1xH9puj480EuQKBIBLSwKi7AypQpowzM7BjJaKnAi6+8RWoLuMkGArrhL5z3Bd3b8WH69/CnqMWN3IHdddAWn6aeTC+5q59DLzw9mMZ89ift27MZP/LFaBda4P3nP/+pXDOIL95wWfDmlU15mGurCauswvRHiUnLt1OnVk3o6KH91KLtLfT3d8ZS7QvLk0Pc+nKvVvqNqnMbJKQmmI953ywy5t5juk8Fl0ABfU91R7UIGNx7NmuAZlFW3y9RcW9dOuzqXSAgbMpWBiGfci6fcLn3Gu8ERlyBeMz7Oh3JrpyXVUvb+j5dZ+ZimPe1OGq+L/ges+Aq4QSBbM1itvlc8L4Zp1+/PPBWxcX/yCr7co3fCXLjFjFVxWUKq9NV95rCqjnOTAUzlzH3xSW4LgNMVXnl5SuwzQ0W4J8bL6fHRkLk9xD1eehB+mnSOHq/bx966YUXIQjoRoMtCBRAQD/b77rrLvryyy9p2qfD6bJGDWnLjh2U5HKf6fsK2IKAZQS08JdeoTy16tCZtm3dRieOHeUfaheLU2wNCrchlrEOZ0TaCnTr1i10Z/tOtHjRHHb5QtS4fhXVR8hrUXNCiCDJnQ4RI40tH8t5nrPEn8M/9PzD7+c5Q7LlHF+Xe1RYvi9HCZjSZzHOq2t8fKZv4eHkEE1xu3iCZLfbRcks5oqgK4uDO6x/7eK/rwCl++7rQBMmjFfX8HE8mmoPeQGBwhHQ+tJzzz1HaWlp1KtXr4jqDzqXEHgLV18IFWcEdMMfN7oXT4I1mGZ83pVqNKhEvv3HlYuG0zokWuhQYkdhvg3nD0w6Q/JjrqwTZVZ23ndUraDOJSR0oGeff5E+HNAH4kgx2pz+WiYuGo4ePUpDhw4N+xc06YyZZ2gvRvbzvFW7jd7M7ebLpR6aN6oXzRjVmzzseqJSjfOpfrPrKCUlVbUbYjVXvajxmsDtLUfU3UCHWyLPMRqfESb4uvlYqYVGXAnSWZc2KvvyF6K23H4lDqUe51Ci7AeuGekE7gucl78sJeZKGBYKtbgrYQ1DD+O6EmwTtT1yAIf6uzHCqPeT3D9SrY6ePKnOSBkDKqQRwrCckBv98taRr1Wq6a+fd8UPpspJrtgZsCAR6zoJyhk/KZYbPmhz45YwcqQCGKFyLfICvr7V00ZUaRWC/zt5gzqfa4mrlGvJhyissi9xG8fKEkZdCxB1SN4MkjmGqQwXm6nzvqo1tmiWJ8/JNWB5IxY7Omwgz/Iip17WJC1pA0pYlpS5vtl3eXa2j/Zv30xLZn/DVuQ+evaZZ+nDjz40iHP7gT85hQILCORLQP9efcCWHl35o2TDhg3pvx+9S5VYZNu+a0/uByr5m8/bMr8guKavNOqJgCXSBE7+Xp3622LkI3LyV4EjVvhZLdflmV2v1rk0Z+FSuu2hx6nHC89Sz74f4lke6UZjQXqTJ0+i22+/gxqcX5lGD3yImt/YmGOViSQL0+aCw5jfe6T/pK/rVqU/Mwf6XeqrtNG/NL5OB/Z5Imt1jq8rAVn6FCIec5/McJeTTR4O4+Otlydt9XI42Xp49fN5uZ7FFuYyoavse/0+FY8SoJXAzPfy1ifpnGGRfpPTkUAOea5yn4n/V6MmnNxPMo5la/SJjH0Ox1sHn0vkvpaEdyc5KCXJRUnJvPLoz+QkJ7uCS+TVyXFzHLzPJtMckPtmsp8gbnekn6bd7zCLY4doxW9baMZPm6l7zzE0cODH9PTTT+Ed8EwViOsgEIUEzAJvSkoK9e7dGwJvFNYTshSnBOTH38mfs9/t8SDN+HYh/TjtRXJXKMU/tBnG5GvKRE6bwqlxNgaJwKdvY2MIRoZYdhKU2ZhRd98NwYo7BSLKiI9fWVPYMseZpH7oj23ZSY2v7skdgCq0cf3vgaQgjhS1+Wlh6cSJE6qTJMMkYl1sknalXs65vS09nEjHU4i8x4i2LpxPiyYPp2VLllBWZha3R7/RaeaOsRLnlPBsWGEooS5gdSHWGNrKQp8zLDhOWl/kXg86J2GU8KssN4ytSlOsOQv5+lDUusV9kSUgryJueZHhf2TiHRevybzKs0zWrCwvZbCV/NWt2tCgocOpcuX0yGYQqYFAjBPQFrzHjx+nqjzZqnyUvLBRI/rg1X9S8yYX0Al+rmdmZfJvmXxiM/oc6hEsR7kfvgIDLAJCnTyMcwU9834erIzHuRZoTgY49RvaqYJPXhLRaecKVBSjsNKC1O+CxXD5qJa3YG52q2F8J9Mf99RBvgU/Jb3cD4CB4Opi4IOhjuHkl8VTPiLmphK4nvsJ0pR0sNslmdxJnuulS6Wx790NdOX/3UeN6p1Pv61cpfIc6/2nKGxtYcmSFhd++kkmPW9L/3fDpTRpZg9O6zjRoSPsRol7aHo4V0E5yG07eQWS337T+UDbVGeC/2jMAfXHa20ko8KaPkAF3Dnk+mwIuIk4+SdzWqKBTOiGHbwtDGLzUyu/fR1PXg+0wDklZAeex4H3Q2MoFt+rh1XJVq3GhxTj0cB/ifIuWEreBavRDa2epj+2+GnLpjWGgUQxDYoKQwBhQAAErCOgn8HPPPMMpaamshHhexB4rcOLmEAgfwLmjuq9d13Jw8+y6D9D7+YfUx95+QVKuggi/oqfKgdvpePLn2PFiaqY8xmddScfS8clUeQPWZWEG0hUvsyaO/GyLx0HtqyUlcUQ78ETtOWvQ/S/BRv4q+0aWrpsNf3t5ubU9+OpKi78sKMFF0xAy6jROwmPEg246YtVpwgTPrb0zJahfbz1ifgsW7bAkB9Dta+viSDNVhjG0L+AOC1+ywIis4ghxtC/gJAdsJyW89qKOjcMX1MWzYFF7hHfaHJdluDwJ9OUtDlsIN2805R4jHzptIvbarVoKlYk4gMv91gsSPj5o14GZDIo09bBzyexMpbnlKwOflY55f7AsTNRzouFCluWcDjZOtmiRD3b+Foib93uJLY84TU5SYm4SckpPDTcGEIY6qLrCZa7oZJD+JJMQPdL5syZQ7fdegsdOcpf73i5s92NdGOrK+nc6tXUy4KyLhMrs8DffYJYmAWEPznvVs8A/hsPWPjLh2Xps8gzQ3oh8lyRrRYO1DMlYK1vuLxR/5wcTaBHFeSOQNC1xCFVhHJsWOkZh0b8ajGiUtfVuUCYkwFOfhzX95wqsegbTC0jD2X5ZG/rFAXzjM0pWGo1RqioT/dGvoP2cz1uBgqjhXFjdAPfJWz1x0+5Xf1mCQPjA6jqCep7VfyBGNXtAssonBEF/87xuVM+zpo/tJrvV5gCo3bk900qUv0u6vNGpOrzQECPknjFP6iunaNHM2gDu2OY9uNsmv7THLqwSRP6beECciQlq99MsXLEEt0EzO82DRteQGvWrKYNv/el2o2qUea2/fy77laGJlYuhpaZxx9lbiL5X8u9coYPQSfD5R8w2LCmUEbKgWeUmUfuIy0USIGRWqfdIga7p5w0jsQK2IxH/hYzMryUWqMKDe07mR5/aTL/ubIgL2gxCiqUmkBYELCdgBZ4n3zySX6nSqb+/fuHfQRxXoW21EXDd999R23btsWwAtubFzJQEAHzD+a/hgyhqdO/o2aXNqHjGWy9q19ilB0i+xuTbY6H+91ZfOzl/ZPnc5Qo5VXDhqQb7XQmsICSQAdYvBWrRhGzjmV42BrHQ4e583wiw6c64Vk8pL5smSQ6v2Y5Or92BWrXphFd1Oo6/tW/WWVbfuzVkGssxSYQbrcJxc5gESOQ0W+qiYhllskSwBxd8Jd/CG5FhI3bCkVAC+nqgxgWEACBIhM4duQQfdy/L301dTotXrYid+RQUSIULUGJwfwx2hhuLEOP+SORDE3mYcYJMoxYPiap4cccRn1MMoYnGx+T5OMQn5fekZyTsEoUNoYr662EE7HZ8D0vw5eNj1KJAafo4lxC0lL5UB+oAkOg9X1yPRC/xCVWfOpDVuBjl/YNa4g9IoYako4hmAYkZL0fOKkOlcuhwDZwq4xqUaNQghZj4j1DlDWspA3/ovp+w12NMfRcT5wkH/d8IpaqD4IsyvIIGR/vywgaGXYuWzmWj4G5HwTVqBf52GiMsFGjaWSYuXyADORLxaU+RooQezJsthrWzh8tC9LUitJQAvdUrl6d3mefgZ06dVJnIDAVA2aEbzXXVYUK1em8mmm0aEkvon0H1McHa6XdCBcuzpPzsdsJZ5XytOj7FdT8+t5sB5SpPrzr0R1xXnwUDwTihoB+Dh84cED1h8qWLWvL7ygE3rhpUihIKAS0P9PX33id5s79mVpf15aOHjsW+LJTmOJNAAAgAElEQVSaoD5SZHm8yqxe9r1sdeuTzrefBV3vcbX6eHy8V/mEEtnWSaVKl6LUlDTatPUAv8TkUGqai6pUKk3VK5eh6tXKUs2zy9J5tSpSrRqVKKUK+9xNqMZZlrW8yjrrwWK7e+rX3VAKhbAgkAcBLQBHEs6ZZmgOzkuo4fMri5Q1XEPaJI/RIJKHWsbC5jsvbqGwjAY2kWzjSAsEwkEgL0Ht8KFDJO6GMjLZTQP3Q7zcJ/EH+ibibsrPfijlnI/PSZ9EhEUP91nEP7uXXajIR2Wv12P0X7ij4WExIZvvkb6NxKe2elXnxQcmx8n3yEdsNQJD0uSP2vJBW4264OehvseIKyBkSji+R9JQ4YLSkLyqERwSj4zqUIJlwF2QuPwJiJ7aXZBYxopIapj/BtZwqZuWVaghTGvBWgneAV+esq9GZyiR2xDZE3mEhYzCcKgRFYYAL6PIZHRGEo+ocPOoDJes4uvTlaTc5Lh58j0XT9gk1+WaWAq5k3j0BVvcqnvUiIzAvjuZ941rpdgFg0z8ksTXxT+gPOOFtcRRo0YNOuuss3IpQFyyrEFELCI9+u+y5q2ocT0vjfq0G/l2/kVO9g+LJXoJyHPZUSGN9m3YS+n1/0mbNm2imjVrQuCN3ipDzkAgqglA4I3q6kHmwk1AZph18pBlmVgtkTvXejHmMjIsVAzhwvA3KdYqCdz5VlYw7KahOH0mnhNAuWPiPr4xYRGLwpgCJdw1jvhBAARAAARAIHoJaJcwIvDB6i7/esp1qRNw05M7IaRYyIoFLLs8MATkgF95sdAVVwl5WO+aUwnFdbDUj/QVjYmaAhbQ0leU/UAf0rByjp1RWfJiqN0DRe9fCXJ2OgGxGjcmC2ve/FJKc2XQTz/3It/uneqDAZboJaAE3vKptH/zQapY9zn65puv6ZZbbsOI6OitMuQMBAokoF0BykgmOxYIvHZQR5pxTUANw8t1NndqUU/6uxNBV1+TV4TY6fzHQuXpB9vAgQNpz5499M4776CjFAsVhzyCAAiAAAgoAnr0RfAIB6tGPFiFOdjS33wcS8KmVTzCFU9we7C6HRj9U23UEK5SIN7wETgp8La84nzavddHa9e/zy4aDvKHDanX8KWMmItHQAu8+1jgTWeBd/To0fT3v//dFt+dxSsJ7gYBEBAC+vc5lJGQVpKDwGslTcQVcwT0FxYr/wDxQmN/M/B4PGqyqG7dutGOHTto3Lhx6CjZXy3IAQiAAAiAQJwTiJRbIKsFznBWC/qY4aSLuBUB8dnMxiIOHg14bYvzaO1mL23fzgLvgePKzckpk3sBWVQRUAJvxTTas24/VW7wPI0cOYI6d+6i3ASK+xQsIAACIBAKAQi8odBCWBAAgZggoDtFb731Fv311180fPhwdJRiouaQSRAAARAAARAAARAAgZAIiMAr7kLIQ21bNqQla07Qgd19iHiS52wvS78w4Q0JZyQDa4F35x/7qFqjrvTvf39KHTs+AMOUSFYC0gIBCwhoYbVv377Kh3b37t1tGUEMgdeCykQU8UXAmEk5W808bV70efgmi/76Ngu8W7du5a/hIyHwRn+1IYcgAAIgAAIgAAIgAAKhEsi14CVqe8359OvvR+jQnr5Ex7Ig8IbKMsLhtcD71+rddE7jF+nrryfTbbf9zRZhKMJFR3IgEFcEtLD65JNPqnINHjzYlg81lgq8M2fOpBtuuAEPpLhqqigMCMQeAe2ioWfPnmo22rFjx0Lgjb1qRI5BAARAAARAAARAAATOSOCkD97WV9ejlWuP0u5d/eCi4Yzc7A9gCLylaMvvO6nmxS/R7Nmz6ZprroGeYn/VIAcgEBIBLay+9NJL6r4+ffrYoj9YKvB+++23dP311+OBFFJTQOBoISAWumKdm5WVpUzq+/fvr47FxF5mQdy3bx+9/PLLNGLECJVl+eORRd8XbPEr1+Te4Ekr5Jys2hIYw6asbwH6wfb222/Thg0baMyYMbZ8QbO+ZIgRBEAABEAABEAABEAABMwETgq817ZsSpu37aJNmwfwJGuHMMlalDeUkwLvDhZ4u9HKlSvoggsuhJ4S5fWG7IFAMAGtP7z44otqorV+/fpB4EUzAQE7Ceg/yh9++IHatWunBEEt0orAK8P+ZeKu1atXU4MGDQrMarCwq0Vg+WMPnmxDC8h2lj1e05Y6y8zMpNKlS+cK8fFaVpQLBEAABEAABEAABECgZBLw82RqDkcitWjRkjzH/6Jflw0g/+5d5HA6SiaQGCm1dtGwjS14a7DAu23bVjr77HNyDYxipBjIJgiUeAJaS+ratasSeD/44AMIvCW+VQCArQT0H+UDDzxANWrUoF69eqmvpyLualFWZjNduXIl1atXjw4cOKAsQ7/88ktq3749Pffcc7mWuTp8RkYGpaSknFKu3bt307x58+icc86h5s2b21pmJA4CIAACIAACIAACIAACIBDbBLTBSPMr29C5FQ7S59+8Qb6d28npPnVOkdguZfzlXlvw7tu0l9LP70o7d+6kKlWqQOCNv6pGieKcgNaSXnjhBaUhffjhh7aMIIaLhjhvaChe4QloK9s6derQsGHDqHXr1uqPU7tSEF+utWvXpuPHj5M4z/7ss8+oZcuWVKFCBWrTpg099NBDucNpxM3D1VdfTYsWLSIRhUUEvvXWW2nFihXUpEkTZQksiwjFkyZNovPOOw8WpoWvqkKHlDqVL2gi0mMBARAAARAAARAAARAAgXgkoEcJ1qh1IT3wt+rU68NHWeDdCYE3yivb5/WRs0olmjJhNv2t4zB+b/GqHOv30ijPPrIHAiAQIKD/ZkUrkg9uZcqUseXvGAIvmiQIBP2I1q1bl6ZPn04i9MofiKzJycn0/vvvK1P7Xbt20bnnnktbt26l/fv3U/ny5U9jKPf+3//9n/K90rdvX/UFZ/v27XTllVcqMXfcuHHqnoEDByoB+amnnsKX2jC1xLzcYoQpKUQLAiAAAiAAAiAAAiAAAhElYBYDExIcNHLAA9T5+Zspa8deSkqCBW9EKyOUxNgIhRL4hgoVqXrlztT0ihtoyuTP+N3TT0641giFJMKCAAgECEDgRVMAgaCvLmJVO2jQIGrbtu0poquIvK+99ppaZXnjjTdIJvG68MILlfArFr+yiKArsyfefffd3KlKos8//5xef/11evXVV6lcuXI0ZcoUZfmLBQRAAARAAARAAARAAARAAASKQ0C7Zziwfx9rhek0sO/f6ekX7iLfnm1sCprDVmQ5lChCYkELG5zIcqZgRc6nEX3uEq50WDI9dckuco4LfeMpafLowcIufq+fkmpUp0kT5tIdHT+iZcuXUpPGF2OCtcICRDgQiEICdo8ghsAbhY0CWbKHgP5jEAF3xIgRJL5yZdm7dy81a9aMzjrrLFq2bNlpmfvPf/5DMluiuGkQn7za7YL43x0wYADddddddPvtt6v70tLSaNq0adSqVSvldFt89coKFwLW1rmuy5EjRyqfyWJBrc9ZmxJiAwEQAAEQAAEQAAEQAAH7CJxqweukCxtWp2WzXqXE9FRWbJM5YyKn+nkNlj/NYmRgX4KoYPyPWJjqRV0Oup+F41OXwPEpGmfgXG68ed/CSrRxQUd5mlLL14JE4twTZrU4z/3AjfpaQepynko43yD3BN+njgPXcrdy7rSMBgodHEkK+XlOl5RKj1DTZpfT/F/mqnBwz2Df3xJSBoHiEpDRw+rJwBqPHQsEXjuoI82oJGD+MRX/uTIRmji5F5cMItJOnDgxN98i3Pbo0YOWLFlC4tJh3759lJ6ersRbsert2LGjcrsQvMgf+pAhQ+jxxx+H4BjGVuDxeJSf43fffVfVkfhAhsAbRuARiFr+PmV1OjHUMAK4kQQIgAAIgAAIgEAMEdDvMXPnzqFrrmmlct6gQW1q1rgGNW1SnRrWq6jcwinhQVbeFwMTcQXg5r6Vy23suxxyPpESHdoIhY/5/SUxkSed5nOyr687WAxVEgZH60hIpJyEHD6W+4xE9L4ca7HDuBYQPji8ulmfyxVgtWgqLgz4eg6Lvzl8TjTgBP5HtkpEERE6sJFjFSzHCC7H+jKfkJyJHp3A59UtARHbzyeNZI1/fT4Oy7s5fF5FKeElSWWZm8DnsknuUdHLNd76/NJHZUtp3sr9Pva/KW4WVDiZD4TvFCtricPPrv/kvhMZHsrKSqCHnv2UDh89Sn/8sYaNhOrDZV8M/c0hqyAQjQQsFXhnzJhBN954I4SUaKxp5ClkAuvXr1c+dhs3bkwpKSnqfj0ESraPPvoojR8/njIzMyk1NZUuvfRSmjNnDi1YsICuuOIKeuWVV+j+++9X4u/o0aPp4MGD1KlTJ3WtcuXK+Dobco0U/gb9YBPL3V9//VXVEwTewvNDSBAAARAAARAAARAAgdgksGXzZnrkscfou29/kLcXXmWyYbHk1WaxPpEyeS28O4HikRBhWQRU2UpenCwWJ7J2K4KxiMmi4xr7WkRWAjSfM0RpLagGBFcRajnrfhGUA0JtrtGxCLx+I7xfjAOUQCuirIi2fkPYVZbHwkUpxYGt3s/LdLh4pT/17qQAAyPd886vRZ+OGcnztLRQwq9dVn9WlhBxgUBJJKC1hv79+yt9SNxz2qE/FEvgveyyy+jPP/+katWqqS0E3pLYlOOvzPJ1VbtO0KUzvtpytyTw5dtc6qysLOVr17wsX75cWenK34XMoNi0aVN68MEH6bbbbos/YFFYIv1g++ijj2jhwoU0YcIEWx6wUYgm5rKk61J8WR85coS6dOmCDnDM1SIyDAIgAAIgAAIgEAkC5hGJHk8WnTh2lMqWr3ha0jk5fvJ6vGxt6iOPbNmyVNzHyT0ej7Hv9Qb2PRm8n0EeFi083kzK4vBeFY6PefVmyTaLMjOO8zaDLVN5X65leXjNZLHDowQP2apwvC9hJB3Jr48FWSXG+nLIxyKsn9VYOfbzcTYf+8QKls1q2Ub4ZDnEsJc1WhFwlWWsmN3KfSKS8n8OtkaWecrE8liEYoczYJmshGMnH/PK+zIyLJGPE3nrZItmhzOJj93qvMvFcbj4vOw7XZSclEIpaak8+XYqG/8kKwMgYw3s8/Vk3k8rlabe/0qX5rVMaSpTqhSf5/uSktlS2h2JZoA0QAAEIkxAv7M+//zzdPz4cRo+fDg/44xRxZFciiXwNm/enNasWQOBN5I1hrQiQkAPB5fE8vKRKxa88pXVPFxci8DB4rA5w8ZQHp7oIA+hOCIFKyGJ6AebuNKYP3++cq9hxxe0EoI7rMXUVvMrVqygJk2aGEPueIF/srBiR+QgAAIgAAIgAAIxSkD6vPKuEXvvG9qqVlsXy1bO6VVb3spW+3MQpTcQhEVewy2wWP6ySy+2ElYuHpTVsKyuwBo9FSv9WunrwgVZ9NQJcgICRSGgtQax3BWBV0YSy4cyl0ueO5FbiiXwwoI3chWFlKKTgJ4lUURdcydK/rC0MCw/3PLHLdfx4x2ZetQPtoEDB9L//vc/+uyzzyDwRga95amYhdzzzjuP3nnnHbrvvvtQn5aTRoQgAAIgAAIgAALxRECEQyV35jHZj/5gnld5C7qm3Bpks5Cq3eSqCAK+dMUvLeupJz39qsSNPOiJx0zzDmn/vXI9v2nJIlkfarxm4J/8GASf1yM/zfkMNuYx88+rLmJPiI9krSAtEIgNAmaB99ixYyQjiWNO4G3WrBk7BP8DFryx0eaQSxsIFOTawYbslLgkhf+JEyeoFA+NgsVn7Fa/Ht7y9NNP07p162jmzJkQeGO3OpFzEAABEAABEACBEkPgDH5+A3OmncRRRB+4gdv03Uo8znMS+4JntjcGWUaD3FxiGggKCgJxQUALvN27d6ejPHHi4MGDIfDGRc2iECAAAiAAApYS0D+YX331FT3xxBO0a9cuFT9Ee0sxIzIQAAEQAAEQAAEQAAEQAAEQAIEQCZgteA8fPkyDBg2KPR+8sOANsdYRHARAIKIEtAsNB0+agCV2CWg/vDt37lQjRuRYhrNB4I3dOkXOQQAEQAAEQAAEQAAEQAAEQCAeCOj3UmMCSQ9PsljalnfVYvnghcAbD00RZQCB+CYgfrDy8ncV36WOr9KZhVypS3ENVK9ePSX0QryPr7pGaUAABEAABEAABEAABEAABEAABEInAIE3dGa4AwRAIIYIQOCNocrKJ6tmgTctLY2mTp1K1157Lfzwxn7VogQgAAIgAAIgAAIgAAIgAAIgEBcE7B5BDIE3LpoRCgECIGAmoB9s48ePp1mzZtHIkSMhBsZ4E9Eib40aNahXr170wAMPoE5jvE6RfRAAARAAARAAARAAARAAARCIFwJiXCaLXSOIIfDGS0tCOUAABHIJ6AebzF4p1p4zZsyAGBjj7UO7Y7jkkkvozjvvpFdffdUWx/UxjhHZBwEQAAEQAAEQKCEE5OO4XmTugsIu5vsKe485nBY4CntvqOGD45X8RlJMkfwWxNOcl7zype8tav0UlivCgQAIlDwCEHhLXp2jxCAQ9wT0g23UqFFK4J00aRIE3hivdS3wXn311dSyZUtlxavrOcaLhuyDAAiAAAiAAAiAgKUEpI8k4qLMVSBCYrBoW5AgGnwtFHHY0kLEYWR6+LYUTc8jYa6rOCwyigQCJYKAfi8dMGAA7d+/37Z3VQi8JaK5oZAgULIImAXeb775hiZPngwxMMabgK7Tdu3aUf369Ul+PCHwxnilIvsgAAIgAAIgAAKWEjDPW2BpxCFHxtbD/L8v28fiso/7bCwy+3nrl62f/Ep05mt87OfVx+dy+JzX6yU/i9MeXuXjvvT1fD4vn+djHx97PeQNnFf3ZvuNOPLY+nOyKYfvyfbnkOxncxg/7+fk8Lls3nJ6RrqBIdWJCeRyOdk618Hia6Jp61THcs3tSiKX201Jbhe5k9yU7E4mt+wnJ1GpUqWodOk0Sk5OpZSUFLUmJDhCJocbQAAEYo+Afi/t1q0b7d27lz755BNbRptC4I29toMcgwAInIGAfrANHz6cvv76a5o2bRrEwBhvNR6PhzvQbmrfvj2lp6fTkCFDUKcxXqfIPgiAAAiAAAiAQHgI/Pjjj/wxvD+tXbueKqafQ1WqVlP9JhGAfSKuBgRUJaKKQMpCqOyLEOrzsMDqzSIvb0Vw9Yio6vEqoTWLhVaJJ0eEWhZM+R8uAG9Fzc1d5dgQTbG4GEEyJaWlUNmzzqLy5cpR+YoVqDQf79u7jTyZmXRJ06bUp08f1b+NHoEeNQcCIBAKAa0/9OjRg/bt20fiKlKeny6XPAMit0DgjRxrpAQCIBAhAvrBNnr0aJoyZQpcNESIeziT0T+QHTp0oNTUVEycF07YiBsEQAAEQAAEQCBmCbz++uv0zjvvBPKfRM0vqUbp5ZIpQbnhTaBEdt3gdDrI6eB9tWXrVKdhsZrk5mM552RrVd53qf3A1hW4xvcZYU6uErOKnn39GtsETkkOjbhcvMrWIVsWPMQ9gbKS5UzJNkFZzfI9bPHq4PwlqGviYkLOycqxcpx8qMrAp9Qq7iPklBFGBaEc/kfyIHHLRXE/rMIHwokPXbHila2y5FXHbO0rWxG/WZvO5ut8go+NfSMMWwLzRRHCvVksfrPI7RWrYhHOxUqYr/mUlTGL4rz1cBgRxY8cyaT9B4/TgYPH6PDRE5SV5aENm/bT/KU7OEceVU+VK1eiNWv+pHLlyqr4tfuGmG2EyDgIlDACZoFXLHjFGAkCbwlrBCguCIBA+AlkZGSoIVL4Ih5+1uFMwSzwli9fngYNGgQL3nACR9wgAAIgAAIgAAIxQ0CLC1999ZWajNblKkdtr61Lk0Z3Ine16vmUIy8rW/O54OtnCp9XMnqit/y2+p6TE8IZZyQtXk/LjunEadkxhS+UAbFSiw31V21z/8njfOCaDqtCmCeuK2hfScs6Ad6K2wZ2O7HbR59+c5j6DhhKf65ZSo888ggNGzYM/duY+atDRkHgJAH9DH7ttdeUiwb5W9YjUCPJyVIL3m+//Zauv/56PJQiWYNICwRAAARKAAGzi4ZatWqpoWzwwVsCKh5FBAEQAAEQAAEQKJCA2YihadOLaenSZdTyiiY095cXlajo33VYWZ8WZRHZNSB/FuX23HskdbMEalw4/UzuDZJo3jedlg8r8hccaYG0lFuKvJf8eAUXJVEslEtzzlNqc0TN6cor29Pvv8+no0ePqIhhmFKs5oabQSDiBMwWvHv27KF//etftryrQuCNeNUjQRAAgUgR0DPVYphTpIiHLx39Y9WmTRuStXv37rZ8FQ1fCREzCIAACIAACIAACIROQA/pl1Fr6emVuH+UQJt+f5Oq16tM3u0HyZUcWR+QoZegZN4hLh28GYcouUYdWrbMQRdffDsdO3aU0tJKwU1DyWwSKHUME9AfZbKy2H85u2mRSRft+FADgTeGGxGyDgIgAAIlhYB+ebnsssuoS5cu9Oijj0LgLSmVj3KCAAiAAAiAAAjkS8AsIpQqXYlaXl6dZnzfg3J276QE9pOLJXoJ+H055CjrpgNb91CFut3pwIED7Ie3HATe6K0y5AwEopoABN6orh5kDgRAoLgE7PhyVtw84/7TCeh6bNiwIfXs2ZPuvvtuW4a9oG5AAARAAARAAARAINoIiAsGmWjsggub07WXp9CgEc+Rb+cOcrqd0ZZV5MdEwO/LJkfl0rRh8Raqc9mryndnxYoVIfCilYBAjBKwewQxBN4YbTjINgiAQP4E9IPtyy+/pIkTJ9J///tfiIFx0mDOO+88Gj58OLVu3Rp1Gid1imKAAAiAAAiAAAgUj4D+EH5+/Uvo0gZO+s+kVyDwFg9pRO72eXzkrFqO5k5dQdfc+h5t3bqZzjnnXAi8EaGPREDAegLa37l8cLNjgcBrB3WkCQIgEFYC+sE2duxYGjNmDM2aNQtiYFiJhzdysxV29erVaerUqeyn7GJ0fsOLHbGDAAiAAAiAAAjECAHdV2p80eVUrdxRmjnrXfLtYgteF1w0RHMVeljgdVdNp4mfzKa7Ow+hTRvXUc1adfDeEs2VhryBQBQTgMAbxZWDrIEACBSNgH6wieWuiLwzZsyAGFg0lFFxl1ngrVChAv32229Uo0YN1GlU1A4yAQIgAAIgAAIgYDcBv5+H+jsS6eqWLciXtZd+WdQXAq/dlVKI9A2BtxIN6jOVnu42lrZs3kg1zq0FgbcQ7BAEBKKJgNYfPvroI9q1axf17t3blr9jCLzR1CqQFxAAAUsI6AfbZ599Rp988gl9++23EAMtIWtPJGaB96yzzqIdO3bwDMNptsxMag8BpAoCIAACIAACIAAC+RPw+f3kdDjob7e2pm1bN9HS5QPIz5OsOTDJWlQ3G8NFQyUa8M4k+ufrE2jnzu1UpUo1vLdEda0hcyBwOgGtP7zyyiu0fft2ZWTm8XjI7XZHFJelAu/MmTPphhtusEWpjig1JAYCIBDVBPSDbfz48TRy5EjlosHPHV8Hd3yxxB4BXXdHjhyh2rVr0759+1QhMIFe7NUlcgwCIAACIAACIGA9AZ+PBV4Wcx/ocCP9tnQ1rfxjIARe6zFbHqMWeN9/4wvq/vZndPjwQSpTpizeWywnjQhBILwEtP7w1ltvKWOkoUOHktfrJZfLFd6Eg2K3VOD97rvvqG3bthB4I1qFSAwEQCCYgHmSNbHgnTZtGjpKMdxMtMC7adMmatGihfrRhMAbwxWKrIMACIAACIAACFhKwMMCr5sF3ocfuoMWLVhCy1d/TP5d28kBH7yWcrY6MsNFQ2X68J2v6Hm24M3MPEFJSSl4b7EaNOIDgTATgMAbZsCIHgRAAASEQGZmJiUnJ8PaM4abg/7BXLx4Md155520ZcsWkhlK7ZqdNIZRIusgAAIgAAIgAAJxSEAJhW4ndXn4EVq2aAYtXj6EffD+hUnWoryuPVlcb9Uq06gPvqEuXcdy/9bPOU6EwBvl9YbsgUAwAf2+2qNHD2WMNGLECFjwopmAAAiAAAiAQH4/mGKJ3b17d/r999/R8UUzAQEQAAEQAAEQAIEAAS0u3H1vF9qz5SeaPf9D8u1kgZdFXyzRS8Bw0ZBOkz+dTbd3GsYCr09lFq7lorfOkDMQyIuAfgb37NmTtm3bRqNGjYp9H7xw0YDGDgIgEE0ExEerWHrC92401UroedE/mKNHjybxq/zDDz/AFVDoGHEHCIAACIAACIBAnBLQguDVrdpRrfQ9NHbiGxB4Y6Cule/kymVp+ZzVdFGrd5QgJD47IfDGQOUhiyBgIqDnhjlx4oQaQVy+fHlbRhDDBy+aJQiAQFwTwFD+2K9e/UP13nvv0apVq2jcuHEQeGO/WlECEAABEAABEAABiwhoQfCaa2+hKmV30GeT3mSBdxsseC3iG65ofB4WeCum0aHth6hcrWfpzz//oLp166GfGy7giBcE4pwABN44r2AUDwRAAARinYBYM7jdbnruued4hmgn9evXz5YhL7HOEfkHARAAARAAARCITwK6r/RGjzdpyMD3aN/BCUR791AOFzchMSE+Cx3jpfJ7s4kc/H/6OfTNf7+j2+4bSLt376JKlSrDgjfG6xbZL7kE7B5BbKnA+/3331ObNm3wxanktmeUHASigoC2YpCh/J988glNmDABHaWoqJmiZUK/tNx///3UpEkTevHFFyHwFg0l7gIBEAABEAABEIhDAn6/j12SOWnb2qlUo96tNGvaa9SqXTPK2fMXv5uzzAuNN6pqPYErxJmeRuRMY/+7c9n/7sfUoGFDWr1qpcqnHu4dVZlGZkAABM5IQP52ZUlMTDxj2HAEgMAbDqqIEwRAwFYC+sE2ceJE6tOnD/36668QeG2tkeIlruvzlltuoXvvvZdE6NXnihcz7gYBEAABEAABEACBOCCQ7aecRAfLhrvpbzc0oynfbaXF379GTdtcaiqc2PPKal6Cj+VaXufOdE8oDHNbjkwAACAASURBVM8UfyhxRVvY/JR0fV62sjrJu2s7/eO5z2n8Z7NUISZ+/jnd1b49+rjRVqXIDwjEEAEIvDFUWcgqCIBA4QjoB9vUqVPVcP7Zs2dD4C0cuqgMpS2yr7rqKnrnnXeoVatW6PxGZU0hUyAAAiAAAiAAAnYREMMxw2hsOd1z57P0+VdzqEa1MtSudSOqeW4Ftgrl6wkcgPXFBEqkhIDWKOcS+MZE5cpBXeGAEk/QvsnVg/l+CSxxKQlTtiri4Ps1FUknTIQMwznrliLmU1nwqf/5v+wcnvBZttmq7+r1etndmIPWrN1Bwz79WeW19nm1qdc7vZQRAxYQAIHYJKD1h6FDh9KOHTvorbfesuV9FQJvbLYf5BoEQKAAAmaBt2/fvjRnzhwIvDHaYszDXBo1akSTJ0+mOnXqoD5jtD6RbRAAARAAARAAgfARUD53A9GvW7eW+vUfQvN+nk+HDh+hbH82+VlozGZ3DsY2h3y87/V5ye9lC2C/l+/08SpKqcTkD+yHL78lK2YHJbrOoqQkN5UrV5Y63nMbdehwD1108SUlCwNKCwJxSEDrD6+++ipt3ryZxo8fb4tLQQi8cdi4UCQQKOkE9INtypQp9N5779Evv/wCQTBGG4XZB5kIu+Juo1y5cvBNFqP1iWyDAAiAAAiAAAiEl0A2u2tISGB3DWHxu8siMVuhiiWqlwVhHwvDsu/z+VkslmMRjFlAZuH45L6fsvmcL1vO8/0iMMvWx0Iy59G4n6+rlcVmvnbyWMJy3D6PkYaK38N5MIRpda+I1LJV+fKxqMLpCYMQMIuc7WDLWie7uUjkrcORSE72aZzo4GM+53Y5eU0iN4uzLp74N5lXVxIf81YE29SUVEpJ5TUlhZKTk3O3aWlpVKZMGSpbtiynkL9JMFyPhVBZCAoCUUhA/w336tWLtm7dSsOGDVPPJZfLFdHcWirwyoRGrVu3tsUUOaLUkBgIgEBUE9BD+sVFg/jgnTt3LgTeqK6x/DNnFngbNGhAa9asUYEx+USMViiyDQIgAAIgAAIgEAECIpIargEcLFLKiiV6COTk5CjxRyZi0mv05A45AQEQCJWAWeAVC94RI0ZA4A0VIsKDAAiAwJkI7N+/nypUEL9j2bbNZnmmPOJ6/gS0WH/gwAG67rrraNmyZRB40WBAAARAAARAAARAIAQC2uVVCLfkGVSEyYKWM13X90p+TvrqPRmj3G/X7PPFYaPLE5x/cxmDyxuL5SwOI9wLAvFMQAu8PXv2VC4axowZE/suGmDBG89NFmUDARAAgcgT0ALvpk2b2E9ZB5o/fz7E+shXA1IEARAAARAAARAAARAAARAAARDIg4AWeHv37k3btm2jIUOG2OLZwFIXDT/++KOysIIPGbR5EACBaCAgX9PlSzqGpUVDbRQtD1rgXbFiBXXt2pXkQ6I+V7QYcRcIgAAIgAAIgAAIgAAIgAAIgAAIWEtA+w8XX9x2jCCGwGttfSI2EAABEAABCwnoH6mff/6Z+vfvT5MmTcJHRAv5IioQAAEQAAEQAAEQAAEQAAEQAIHYJwCBN/brECUAARAAgbgloH+kpkyZQl9//TWNGjUKAm/c1jYKBgIgAAIgAAIgAAIgAAIgAAKxScDuEcQQeGOz3SDXIAACBRDQQ/jnzZtHAwYMUFafGNYfm01G/0iNHj2a1qxZQ3369LHFYX1s0kOuQQAEQAAEQAAEQAAEQAAEQAAEIkFA3EPmNYFkJNKWNCDwRoo00gEBEIgYAf1gmzZtGr3yyiu0fPlyCLwRo29tQrouP/zwQ8rIyKCXX34ZAq+1iBEbCIAACIAACIAACOQSEAu0SC8iitixhFuIkfgLSkPKrcuemJhoBwKkCQIgYDEBO0VeSwXeWbNmUatWrTB81uIGguhAAARCI6AfbDIh1+uvv07z58+HwBsawqgJ7fF4yO1201tvvUWVK1emRx99FL8xUVM7yAgIgAAIgAAIgEC0EhChVvrEZxIYzcKiOax5H+Jj4WrZLI5r4dYsXsv1YMZOp1NNxqTfX8C6cKwRCgSiiYD++5XRw1u3biUxTtLnIplPCLyRpI20QAAEIkLALPC+9tprtGDBAgi8ESFvfSJer5dcLhe98MILdMUVV9Cdd95py4+l9SVDjCAAAiAAAiAAAiAAAgUR0P4sRST1+0UcJWXxKq7XtPWrnqlehFMRRxMSEnlrWM7q1eFwxAxoO63/YgYSMgoCUUZA6w89evSgjRs30rhx42wZdQqBN8oaBrIDAiBQfAL6wTZz5kzq3r07LVu2DAJv8bHaEoO24BXL3Y4dO9LVV1+NurSlJpAoCIAACIAACIBArBH49tsZNGvWbNq3/zB/ME8jL1v0Znk95M3ykpe3Ho+Pz3nVx3Mvj5ry+Xj1ZPG5LPLwdePYy30vI0x2tk/1w/y875Mtr+qcL5uyed/Hlqg2eHiISLWIeOxwOEksbhN5dTvdaut0usjJ5x187FSri5KSkpm3m0ehuZShQlJSkto3VjclJydRSkoqrVu3jlavXkPn1qhInR68nx5//ElVFi1aR6RgSAQEQKDYBLT+8O677yqBd+TIkfyMNQyVIrlYKvD+9NNPdO2118K6KpI1iLRAAAROI6AnVJszZ44a2v/jjz9CFIzRdqJ/pETcFWvsBg0aoC5jtC6RbRAAARAAARAAgfAT0OJgjzfeoLfeflslmJzkoLq1K1GyW0RKXp0OcrFVq9OZoPZFgHS5WIRMOotc7lLk5NXhSiGXM4kSHUkchgVM2Soh08XnOI6A4CnxGdazbDEr55UFrUw0JD5lc/geNrvlRVnT8qlEZVnLx4kO3ldX1DnlmoCPZV8uJHJgDqmOsxPYV212wE8vb/go13etct/L/yhrX/mPwykvwrLV1yQIH0jY7IC/Xzn2y3V13lizc0So1lvel/B+FraVmO1VYnY2b2VfrSyAZ/szye/NYEH8BIvhGeTzHlPCuBgp+HwsgPO9Xt56vYYgLpbIEm8Wi+zlyqZR+fJl6cupv0lGqEuXLjRixAjFCyJv+P9WkAIIWEXALPBu2rRJ/R3HvMA7e/ZsuuaaayDwWtVKEA8IgECxCdjh+6bYmUYEuQS0WC8Cb9++falatWro8KJ9gAAIgAAIgAAIgEAeBLQo6GdR0ulMZsvQqjRxdGe67LqGHFosyUR0FVVVVlE3DRH25CKuDOSadmkg9+jVyftu05rE+8mBYwmv44+lycJECpbVF9h6eesJrOZ9fyCMnJOwspVzsioVOXC/xKV5GsK2scg5fayvy71yLpk2LF5OV7QbSHv37qCpU6fSzTffDE3FRA+7IBDtBLTm0LNnTxKBd+zYsbHroqFq1aq0du1agsAb7c0O+QMBEACB2CKgX1TEoqFPnz5s5VAeAm9sVSFyCwIgAAIgAAIgECECWmTo2+8DeunFruTZN4xcFSoQHTxAfk9AjFQWsKI5moVdLUJqoZKPc30taBHUJGLm3qvFTX1/oKBihZsrJJsE5dzzEi5YXM5LGDaLpEFpqKSUrW7QIvfIeeWwVwoaWAPn9HHuNZ0/HY3pWOVX58ssjpv2DTPkoHD5VLhYL8ulwC1icZzN7i3c1c+mMf+aSf94Yhjdccft9OWXX2HEWj4IcRoEopGA2TXCli1baMiQIbZ8pLHERQME3mhsYsgTCICAnpghliZWQK2dSkALvJ06daKBAwfSWWedBYEXjQQEQAAEQAAEQAAEggiYh/SnlqpErVtWp29mvElZ27exywV2h8D/Ka8JIS9FusmUSl4ibF7icsgZC9wQLAKbFNTToixuWXSEeZWpaPnPYSE9IZmtn9n1RWqFx6nWeXVo1e/LVWRw01A0prgLBOwiYPglz1a+d+34+7VU4BV/lzIBDoZE29WckC4IgAAIxCeBe++9lz755BNKTU215ccyPqmiVCAAAiAAAiAAAvFCQLu1On78KJUqVYY+G/Uo3f1Qa/Lt3MsCr7hXKMoSLMQWJY6CBNeixleU+6wqi047L2G5KPliIZeteBMrV6U2175KW3c6ae0fy9DfLRpK3AUCthIQX97ib9yuBQKvXeSRLgiAQEQI2P2QjUghS0Aid9xxB02cOJEn6nCgw1sC6htFBAEQAAEQAAEQCI2AFnh3795NVapUoU3L36OaF55D/j2H1URqWKKXgMfjI3fVs+m5Lv3ph0VeWrniF7hoiN7qQs5AIF8Cdo8ghsCLxgkCIBB3BHQHd8GCBSSOzmfOnIlOUgzWsnlYi0w2MW3aNFUKO4a7xCA+ZBkEQAAEQAAEQKAEEdAv9lu2bKKaNWvTstk9qck1ddmC92AxLHhLEEAbi+rN9JGr+jnU9dEPaMqcI7T+j8V4d7GxPpA0CBSVgN3GZZYKvHPnzqWWLVvCRUNRWwPuAwEQsISAFnh/+ukneuqpp2j16tXoJFlCNrKRmIXc66+/nr777jsIvJGtAqQGAiAAAiAAAiAQIwT0i/2GDeuoTp26NPeb7tTyliYs8B6AwBvldWhY8FZngXcg/WfqX7Rz+59RnmNkDwRAID8C+h3WDqMkSwReGQKybt06gsCLRg4CIBANBLTA+7///Y+effZZWrx4Maw+o6FiQsyD+UexTZs29MMPP0DgDZEhgoMACIAACIAACJQMAvrFfv26P+n8uvXp6/Ev0W0dLmGBdz8E3ihvAj4WeJ1Vq9HTDw+k0Z/9QceO7ECfN8rrDNkDATMB/fwdMGAAbdq0iT7++GNbDF8tFXjnzZtHV111lS0FQfMCARAAAU1AC7w///wzPfPMM7R06VIIvDHYPLTA6/V66YYbbiCxyMYCAiAAAiAAAiAAAiBwOgH9Yr927R9Ur14D+vRfL9ADj10KgTcGGosh8Fahx/4+iIaNW0w5/iMQeGOg3pBFENAE9PO3R48etHHjRho3bhx5PB5yu90RhQSBN6K4kRgIgEAkCGiBV0YVPPHEE7Ry5UoIvJEAb3EaWuA9duwYtWvXTo0SwQICIAACIAACIAACIJC/wLuOLXjrsgXvyI8ep87PtGKBdx8seKO8wfh8fnJWrkQPdRxEoyfMpZwcPwTeKK8zZA8EzAS0sPruu+8qgXfkyJEkRkoulyuioCwVeGU49JVXXgkL3ohWIRIDARAIJmB20dC1a1dauHAhfPDGYDPRAu+hQ4fopptuovnz58dgKZBlEAABEAABEAABEAg/gVwfvOvXUp3z69G/+v6dHnuhHQu8eyHwhh9/sVLws8DrqJxO97cfSOO/mMeGKT5KSHDg/aVYVHEzCESOQFwKvDIcukWLFhB4I9eOkBIIgMAZCBw9epRKly4NTjFIQAu8hw8fVgLvL7/8EoOlQJZBAARAAARAAARAIPwEtMCwefNGqlXrPBoz6GHq9GQbWPCGH32xU9ACb4e7eJK1LyHwFhsoIgCBCBPQz98333yT1q9fHx8uGuTl+4orroDAG+HGhORAAARAIB4JQOCNx1pFmUAABEAABEAABMJBQAsMO3f+RdWqnUOfj3qc2j90LXnYgtftdoYjScRpEQFD4K1EHe76gAXeReyiwaNiNk84bFFSiAYEQCAMBPTzt1+/fmqStcGDB9uii1rqogECbxhaCqIEARAoMgHpFOXk5JDD4ShyHLjRPgJmgbdt27a0aNEi+zKDlEEABEAABEAABEAgigloF2W7du2gqlWr06LvX6XL2lzAFrwH4aIhiutNsmZMslaVOncYQF99u5UO7t8CgTfK6wzZA4H8COhnsR0faCwVeMU/4uWXX26LUo3mBQIgAAIgEF8E9I9iZmam+m1ZtmwZOrvxVcUoDQiAAAiAAAiAgEUEtKiQkZFBqamp9Nuc1+miqxuSbzcLvE4YO1iEOSzReLN85Kp2Nr305Ec06acjtG7Nr/C/GxbSiBQEwktAjMsSEhLCm0gBsUPgtQ09EgYBEIgEgePHj1NaWlokkkIaFhMwf/Vs3LgxrVixAgKvxYwRHQiAAAiAAAiAQHwQMPebqlSpRg/efRH1GfgIZW7bTsmpBc/kni0I1D+y5ARtA2dyr5tC8Wi5wiwSKtySh+Q6sTCZMYdJNO7IN298+fRr5jNBVzl8yHng9LNOeCjpnHPpqS796cf5GbRmFSaIDrUqER4EooGA3SOILRV4FyxYQM2bN4cFbzS0LOQBBEowAW3BIM+k559/nmR0gR1DJEpwFVhSdHOdNWzYkBYvXqwsUlCXluBFJCAAAiAAAiAAAnFGwMND/cXf7rtvd6NX3+jDrso+5xJ6yLvzkJIxRY40i62GKMrn2cA3ISGREiWAUigDSiWfU8cihKpr+jwf5O5LALPQaZY485JOg88VIJjm1o8WnYMr7HQx+tQQwfdpQTpwnq3tKFv2A1s5lkMRrtVWYgtc02J2rqbNO4F9IwpxDafF8JxTOOs8BYvQKhf8jyudjVFcpSk16S7q0OkxGjl8CDSVOPvbRHFKBoG4suCFwFsyGi1KCQLRTkALvOIX/B//+Af9+eefEAWjvdLyyJ9ZyK1fvz7NmzeP0tPTMWQtBusSWQYBEAABEAABEAg/gexsP+uurNZme1m0TaIWzc6jn398lahUBU7ca6iJSrHVSm5A3MxVM7V6Gdj6+B6PX61+Xj1eH3llq/Zl61NrFh9nZHp49ZOXw/j8OeTlicN8as1W22xWQf183s9CaLba5lCOP5vPicYqx34+JhXGEElEdGbxmVVnh9ryMe+LEO3Q1/i8w8ESdUIOi9N83plITr6YyOKzU/blXnZPIS4qktyJlJTkUgK4y5VILj4nW7eLtxyH0yXnJSxfk3N8Te5VQnYST1InE9UFLH4NfsGr1K/mqq8FFNxcq2jhqkVnLbVLWB890Xk4/euTH2nzxk10bq2a6O+G/88FKYCA5QTk2SXvsDIHkB1GSZZa8C5cuJCaNWuGr02WNxNECAIgEAoBLfD++uuv1LlzZzW0344HbCh5Rti8Ceh6Ewve6dOnU82a6PCirYAACIAACIAACIBAfgR032n16pXU9NKrKDPjMN12YyO6ufWFSlrct/84HT6SQQcPHacjRzPp8LFMY3s0gw4fPkHHM7yUmeWlLF7FIhXL6QRcrgTWe1k0ZsE4mcXflGS9dVNyipOS3S5KTXHxeTelpSapNYWP01KSKDWNVzlfKonFcqL5SzbTJxPmqkTmzJlDV199dUDgDrdTC9QsCICAVQS0sNq3b1/avHkzDR482BZdFAKvVTWKeEAABKKGAATeqKmKYmdEv6RccsklNHr0aGrSpAksGopNFRGAAAiAAAiAAAjENwFRZg2BcNzY4TR0xERatnwV5fCpZLebXEluSnLxKgJlMguOqSlqTUtLpdK8ppZKo1K8LZUm51JYnExS9yWLkMniZBLfLwKmm61enWzY6mQLWDFuFYNXB1vNihExG8LyuQR1TnlzYMtasahNdMpWwrOFrDrnCJwTVxFiocthlIWuuIwwLHcD5rxGkZT5rpjznkEAFQRsEWwYJ7NVnbIUFgth2bLlMBvQen05LMKwtTFbIxv72bzllQ2XxepYzmWydbLXm60sljNY/D5xIpPXLDqRwVteM/j4OE9sd/x4BmXweoInBz524jhl8bVMtmrOyspiwTyLrZyzyKOEc97yvpctn08c26Pq6KabbqLhwwfT2WefE9/NEqUDgTgloIXVnj170oYNG2jcuHH8d+7hEQPuiJbYUoFXrOUuvfRSW5TqiFJDYiAAAlFNQAu84nu3U6dOtHbtWljwRnWN5Z85XZdXXXUV9ejRg9q2bYvfmBitS2QbBEAABEAABEAgcgQwei1yrK1Kyc+qs7icwAICIBBbBLSw2qtXL9q0aRONHDmSPwx52dVLwRNcWl1KSwVemQCnadOmePm2upYQHwiAQEgEtCgobmOeeuopko9P6OSGhDBqAusfKbFsuOOOO+jhhx/Gb0zU1A4yAgIgAAIgAAIgEM0EpP8rqyxOMbUNWvQ18Rspi96Gq0zK5rZAw9u8L552trDeC/RcankWKG//E1a7pVB+hAPWxmK1nNci9SDsxW8nFhAAgdgjYBZ4xUXDiBEjIPDGXjUixyAAAtFO4OjRo1S6dOlozybylw8BPbTloYceosqVK1Pv3r1tGe6CCgIBEAABEAABEACBWCagxdy8ypCf8BjL5Y22vOfHH8JutNUU8gMCoRPQAu8bb7yhfPB++umntryzwoI39LrDHSAAAiAAAhEioAVecc+wbt06mjBhAix4I8QeyYAACIAACIAACIAACIAACIAACBRMQAur/fv3p23bttGHH35oyzurpQLvkiVLSCbC0ZGiEYAACICAnQQw3MlO+takrX9PxowZQ6NGjaJ58+ZhkjVr0CIWEAABEAABEAABEAABEAABEACBOCFgqcC7dOlSuvjiiyHwxknjQDFAAARAwG4C+kdq1qxZJG4axGm9DGXTvszszh/SBwEQAAEQAAEQAAEQCD8BPZ+GzFL/5JNPUnp6OubYCD92pAACIBACAbvfUy0ReCtVqkQbNmwgCLwh1DyCggAIRIQARhREBHPYEtET5ol7hssvv5z279+v0sKkeWFDjohBAARAAARAAARAIKoI6H6fzK3RpEkTWrFiBZUqVQr9waiqJWQGBEDA7hHElgq8v/32G1100UWw4EW7BgEQsJWAFgXlmfTMM8+oYf0QBG2tkiInruvy2LFjVL58eTp8+DClpKSgPotMFDeCAAiAAAiAAAiAQGwR0KLFr7/+Ss2aNVOjuWRB/z626hG5BQEQCC8BSwXe5cuXU+PGjSHwhrfOEDsIgMAZCGhRcOHChXTffffRxo0b0QGM0VZj7rinpqbS3Llz6dJLL8XvTIzWJ7INAiAAAiAAAiAAAqES0H379evX0/nnn09er5ecTifmZQgVJMKDAAiElYA8q+T91eVy2aI/WCrwLlu2TA2ZwJDosLYZRA4CIFBIgVcseO+//35atWqVLQ9YVFTxCcgPpPjblbVOnTr00ksv0SOPPEIej4fcbnfxE0AMIAACIAACIAACIAACUU1AC7w7duyg6tWrU0ZGBiUnJ0PgjepaQ+ZAoOQQ0Broe++9R1u3bqUhQ4bYootaKvCKL5wLL7zQloKUnKaDkoIACJyJgNlFQ8eOHWn16tUQeM8ELYqv6x+q22+/XblpGDVqFH5nori+kDUQAAEQAAEQAAEQsJKA7tvv2bOHKleuTMePHycZ2aXPW5kW4gIBEACBUAno91WZBHLz5s00ZswYWwySLBN4ZQi0uGiAwBtqU0B4EAABqwnozt7ixYvp7rvvhosGqwFHOD5trSs/mFOmTFETeqJDH+FKQHIgAAIgAAIgAAIgYBOBYIFXJluTSdbQH7SpQpAsCIDAKQS0sNqrVy8l8I4YMUK5khFXDZFcLBV4xYK3UaNGsKyKZA0iLRAAgdMI6M6eCIGPPfYYLVq0CBa8MdxO9A/V9OnT6Z577iHp1MuCiTViuFKRdRAAARAAARAAARAoJIFggffgwYNUtmxZCLyF5IdgIAAC4SVgFni3bNlCw4cPh8AbXuSIHQRAoCQSOHHihBrChSV2CehOvfxY1qxZk6ROU1JS0KmP3SpFzkEABEAABEAABECg0AR0X3Dfvn2Unp5O+/fvV267YMFbaIQICAIgEEYCWuB97bXXlA/eTz/9NPZdNPz+++90wQUXwII3jA0HUYMACIBASSNgttSVydamTZtG7dq1w29NSWsIKC8IgAAIgAAIgECJJBAs8O7du5cqVqwIgbdEtgYUGgSij4AWeAcMGEAyGWS/fv1seVe11EXDypUrqWHDhrYUJPqqGDkCARCwm4AIgzk5OeRwOOzOCtIvJgGpRxF3xc/75Zdfrvwa6R+wYkaN20EABEAABEAABEAABKKYgBZ4Dxw4QBUqVKCdO3dSlSpVIPBGcZ0hayAAApEnYJnAu2nTJhILXgi8ka9EpAgCIAAC8U5AT7TWrVs3mjBhAm3bti3ei4zygQAIgAAIgAAIgAAIMAEt8Mo8DGXKlKHt27dTtWrVIPCidYAACEQVAW2UZFemLBV4V61aRfXr14dVlV21iXRBAAROI6CFQaCJbQL6x+rnn3+mli1bqgnWxKIXE63Fdr0i9yAAAiAAAiAAAiBwJgJa4JV+fVJSEsm8DDVq1IDAeyZwuA4CIBBRAnaPILZU4BUXDQ0aNMCDNqJNCImBAAgEE9CdwOXLl9Ojjz5KCxYsgBAY481E16nX6yW3202zZ8+ma665Bh8UY7xekX0QAAEQAAEQAAEQOBOB4PkYNmzYQLVr10Y/8EzgcB0EQKBEEbBM4N24cSOtWbOG6tWrB4G3RDUhFBYEoo+AFgOXLFlCd955J23evBkCb/RVU0g5Mnfsr7rqKmXF27t3b3TsQ6KIwCAAAiAAAiAAAiAQewSCBd4///yT6tati35g7FUlcgwCcU1AjJHETYMYJNkx0tQygVcElNWrV6sHrRZX4rrmUDgQAIGoJWC24L3nnnvojz/+sOUBG7WAYjRj+gfr7bffpkmTJtHSpUtVSez48YxRhMg2CIAACIAACIAACMQcgWCBV1xDYu6fmKtGZBgE4paAfk995513aMeOHTRkyBBbPkBZKvCKBe/5558PgTdumy0KBgKxQQACb2zUU6i51D9YixcvpltuuYV+/PFHuuCCC2z58Qw17wgPAiAAAiAAAiAAAiBQeAJmUTdY4BU3bI0bN87tA8p1WRITEwufAEKCAAiAgEUE9Hvq66+/rgTeUaNGkR1zAVki8KanpytH5xB4LWodiAYEQKBYBLTAK0KguGiQ5xOsPIuFNCpu1nUow16aNWtG7du3p5deegkCb1TUDjIBAiAAAiAAAiAAAtYS0DPSBwu8v/32G1100UW5fUC7Z663ttSIDQRAINYIaGH1rbfeUgLv0KFDSdw1uFyuiBbFMoF369atSuCtU6cOLHgjWoVIDARAIJiAFnhXIL0aCQAAIABJREFUrFihJlmbP38+BN44aSa6A//ss8+q35zvvvtOlQwCfpxUMIoBAiAAAiAAAiBQognoPp0WKoL7eQkJCSRGHE2bNs0VeDMzMxWz5ORk9AlLdOtB4UHAHgJxKfCKs3OZzRI+eO1pVEgVBEDgdAJ2fDlDPYSPgP7xnDp1Kr3yyiv0xRdfwPd7+HAjZhAAARAAARAAARCIKAHd1/voo4+ofPny9MADD5wyWkvcMCxcuJAuu+yy3CHQXbp0ofvuu49at24NLSKitYXEQAAEhIB+bsn76c6dO2n06NGx66KhYsWKJBa869ato1q1auGhijYOAiAAAiAQFgL6A+KePXvorrvuouuvv55ee+01WGuEhTYiBQEQAAEQAAEQAIHIEtBCyc8//0wyYmvJkiVqVnqx3JVFZqefM2cOXXHFFer46NGjdOWVV9Lvv/+ujjGqK7L1hdRAAAROCrwDBgwgeU997733bHEjaImLBhF4//rrL1q7di3VrFkTAi9aOAiAQFQQkA6edAgdDkdU5AeZsJbA008/rdw0fP3115SWloYOvbV4ERsIgAAIgAAIgAAIRJyAWaAtU6YMLVq0iOrXr5+bj5SUFPrhhx+oRYsW6pxYzImh2cSJE20RVCIOCAmCAAiAQD4ELBV45cF67rnnQuBFcwMBEAABEAgbAf3DNXPmTHr55ZepW7dudO+996JTHzbiiBgEQAAEQAAEQAAEIkdA9/Vuuukm5aZh/PjxuR/yRfSdNm0atWzZUmWodOnSNGnSJGrTpg36gpGrIqQEAiCQBwG7J3y0TODdtm0bbdiwgWrUqAGBF00dBEAgaghkZGSQfOnHEj8EzJNv3HHHHVSuXDkaO3asKiCG5cVPPaMkIAACIAACIAACJZOAec6FW2+9VY3I04sIvDJ669prryX52H/zzTcr/QH9wJLZVlBqEIgmAnaPILZM4N2+fbsaGgGBN5qaF/ICAiWTgPbTunz5cnr44YfV0C4If/HVFvSPV58+fdSQvMGDB1OzZs3wgTG+qhmlAQEQAAEQAAEQKIEEzP128b07dOhQevTRRxWJUqVK0ZQpU+i6665Tk6qJ4CsWvLpvWAJxocggAAIgoAhYKvCKBe/ZZ5+NF2w0LhAAAVsJaIF36dKlaiKujRs3QuC1tUasT1zX8YoVK+jJJ59UE22I2Gv3sBjrS4oYQQAEQAAEQAAEQKDkEdBCRefOnem7774jGTEsi4zMk0nW5MO+iL/ff/893DOUvOaBEoNAVBHQH6WysrKUHpqammqL/mCZwCuTrG3atImqV68OgTeqmhoyAwIlj4AW/5YtW0YdOnSg1atX2/KALXnk7SmxdPx37typZitt3LgxfoPsqQakCgIgAAIgAAIgAAKWEdD9+fXr19P5559Pf/75J9WtW5eSkpJo5cqVSvAVC17tvgGj9SxDj4hAAARCJKCF1R49etDevXtpyJAh5PF4yO12hxhT8YJbJvDu2LFDWclVq1YNL9fFqxPcDQIgUEwCEHiLCTBGbtc/YN98840auifibu/evWHFGyP1h2yCAAiAAAiAAAiAQGEI1K9fn5o2baomWxOXDGLE8eqrr9K+ffuUBS/cMxSGIsKAAAiEi4B+Br322mtK4B02bBh5vV5yuVzhSjLPeC0TeMUH7+bNm6lq1aoQeCNahUgMBEAgmIAWeBcvXkwyCdfWrVthwRuHzcRsqdGlSxc6cuQI/fOf/6TmzZvjdygO6xtFAgEQAAEQAAEQKFkEtFjxxRdfUPv27dVH/AsuuIAGDBhAHTt2VCLKnXfeCYG3ZDULlBYEoo5AXha8MSvwVqhQgcSCd8uWLVSlShW8WEddc0OGQKBkEdACrwzfeuGFF9QMuxi2FZ9twGzFK1YdMtGn+OLFAgIgAAIgAAIgAAIgENsEzP330qVL06xZs1Q/T/ZnzJihNAhZ0M+P7XpG7kEg1gnod9I33niDDhw4QIMGDYpdC14ReHft2qUE3kqVKkHgjfXWifyDAAiAQAwSeOyxx+jEiRP00EMPUatWrfBbFIN1iCyDAAiAAAiAAAiAgJmAFk5krgWZVyMtLU255nrllVeoV69esN5FcwEBELCdgH5Ovfjii0rgHTVqVOz64BWBVya4EUfn6enpeKm2vXkhAyAAApoAvujHf1vQP6iTJ08m8ccrs5Z+/PHH8V9wlBAEQAAEQAAEQAAE4pyA7svLCL22bduqj/kLFy4kcRGJ+X/ivPJRPBCIEQL6fVTcxxw+fJjefPNNWz4+WeKDVwTe3bt3Kz+XFStWhMAbI40Q2QSBeCcgHULx1eVwOOK9qChfgMCzzz6rnNlLh79r164YsoeWAQIgAAIgAAIgAAIxTkCLFoMHD6annnqK7rrrLpo4cSIm1o3xekX2QQAErCVgmcArLhrEghcCr7UVhNhAAARAAATOTED7XRaLjn//+9+UlZVFIvbKRBz62pljQQgQAAEQAAEQAAEQAIFoI6D7cvv371d6g8yvccMNN9hiIRdtbJAfEACB6CEgxmWyJCQk2JIpSwVeGSZRvnx5vEzbUpVIFARAIC8Cx48fV766sMQ/AT2E76233lId/j179igfbVhAAARAAARAAARAAARil4DZ5VqHDh1owoQJqjBwxRa7dYqcg0A8ErB7BLFlAq+8SIsFLwTeeGymKBMIxBYB/ZV/yZIl9Mgjj5Bs0QGMrTosSm51HYuoLw7uy5QpQ+eeey49/vjj+PBYFKC4BwRAAARAAARAAAQiRED6cQUtYhFntooTSzltLRfuLCYmJoY7CcQPAiAAAsUmYJnAKz54d+zYQWXLlsWLdLGrBRGAAAgUh4AWeBcvXkz33HMPbdiwAQJvcYDG0L267j/77DNatWqV+vD48ssvU926dfHbFEP1iKyCAAiAAAiAAAjYT0CLrlpMtcpgwizUmvejUUg1C8/FEZUlHpkXRMobjeW0v7UhByAQuwT0szEjI0PpDjKC2KrnZShULBF4xWpXLHh37txJZ511Fl6iQ6kBhAUBELCcgBb5fvvtN+rYsSOtXr3alges5QVDhCERePrpp5UP3nnz5tH48ePVvXb80IaUaQQGARAAARAAARAAARsJiIgpfWlZtCBpY3biMmkRYWQRoRdib1xWMQpVwghoYfWVV16hI0eO0KBBg8jj8ZDb7Y4oCcsE3r179yqBV4bEYkKbiNYhEgMBEAgiYHbR8OCDDypLTgh7JaeZ6Lo+fPgwde3alZo2baqsuPv374/ZlktOM0BJQQAEQAAEQAAEQiAgwq6IAy6X67S7tv71F21at462b95Mu3g/4/hROsEusbJFCJbJhMSFgXKhkKhWj9/LE956yOvzksfroyyPl7xeL2Xy6uN7pK8mWz+nKfvSd/fxVuLzyzawL+dy1HV2x5CQQwnZLD7LuZzAeXHT4DeOsyUuWTkOmeaIc6OypvLF/8n/Mv9RQu5VKSbfr4KoEJTI2xxenVyGJJdTsUjmNcnlILfTRW6Hkxy8r86zcON2uSnJ7SKXXONtUlISZbIF38GDB0n6oU4+LsMjnGvUqkX1G11IzVq0pPNq1TyFrzCH0BtCQ0VQEIhCAlpY7d69Ox09epQGDx6snnl5PU/DmX1LBd5du3ZR6dKlIfCGs8YQNwiAwBkJaIF34cKFykXDZu6MQuA9I7a4CqDbwE8//UQzZsxQv03ij7dTp06YcTmuahqFAQEQAAEQAAEQKA4BJaby6nQ6c6NZ9vvv9PmnY+nbr76ktRs30zFzAuXTKbFUaUpMTqZEdjmQw1ZqOV4PZfPKaq7aT/CzhSrHmSBCLAuuIswmUja5+NjBrnaV7hqIU+8boqxIrqdeN+akJzJsio3r4hFX36f2Df2WhVLDT69yg6AS0MdETs5roiORnIlOSnQmkkNZz/I+n5N9QwjmrcNQgj28ZrCwnMV5P8EiMpdSidc5iVxmjtwnUnEC7/P9OeJ6gVdWf3mV+Pg4m4VmTyb5Dh0mz8EDRN4slf9qrkS6qu2N9CgbIVzXunUuWRjJFacV414QsJeAFlZfffVVOnbsGH300UexK/CWK1eODhw4oCx4S5UqBYHX3raF1EGgxBMwu2gQC85Zs2ZB4C2BrUKL+v369VNfT9euXasmXGvUqBF+p0pge0CRQQAEQAAEQAAETiWgJynTfnCHj/qEPuj5Bv3x13YVsGLzq6jiNW2ozMWXUXLtOuSqUIkSkpLF1NWQWWXDAqgyjZVFT3zGQi53tihHXBGI0MmWbAnig5aFX4cIv9l8nvezfXyOLXPTOL4Uvt3N8SVztDKoOYn31TGLqi4Wh918Pon3xUpXhFZHQMw1RFz+Vx+LCMv/Zav8Gfa6YpWbIIIui9givmbz1svnsjisN7BmcrxZfI+Hy5PJ92TyjVk5HIb3M3hfVhUbh1fl5fRy+Lq2ElYwtNIsLBQXsS7mLVs0+/fvo+NrVtP+Od/StmmTybvzL2pQqSJ9MHoM3dDuZoUPIi/+QkEgNgmYBV6Z8PvDDz+MbYF3//79yg9vamoqHkyx2SaRaxCISwLoKMVltYZcqEceeYSuu+46mj59OvXt25cqV66M36qQKeIGEAABEAABEACBeCFgHt321eTJ9OQ/OtGuQ0eoLI94qv1kNzrrxr9RQsWKrM+yWJpxgrJP8MoWqSLcGoKutq3Vtrh8Sixh9aL3lXWsIQaLHW+OCqLFUONepYXKVq7zKZaHjfOBVHLdJ5jhq+Q5BG91eHVZRNVAuJNBDPcNOZwQ2yurBJVYq7bGPQHJOtcymO1xlaWwGPSq9ZSKD2RYC9u513TKgTKqjbiwYOvhJLeyfHakplGiz08Zf/5OG4d8QLsm/YdaX3oJTfzhJyrH8xmJIGyeeO6UZHEAAiAQlQS0wPvcc8/RCX5WDh8+PHZ98IoFrwi84oc3JSUFL81R2eSQKRAAARAoeQT0y4v4QhLr3XvvvZfGjh1Ln3zyCVwKlbzmgBKDAAiAAAiAAAgwAbO4++A//k7jxoyl8uXKU523B1DqTbdTDo988u3fSzn/z955AEZRdW34TU9IgQQIBEjoiEovCtIEFFFEEFB6L9JBBH6lSVMEQYqCoJ+AAgqCgIKASJEuvUondEJLSO+7+c+ZcHFYE5KQ3ewmOSPrbnbuzNx57szszDNnziVRwakWNHGriVnKsauXuE+iydOlOTxeRqeK/ztlsstNcWB/nPKQPEKfDuJRuce+/HcG+mU8JorTXJfUC3AeYW3gd34RXwevfHDOXwCxJw7hYJe3YR90E1t27UGdui+J5M0Ea5lUCFiDgBK83OcL597lXLzqu6ysj1ly8KoUDSx4XSkXj+S6zMomlGUJASGQGoGUcooJrdxHQP0mBQYGYsyYMejUqROWLFmCn3766eG5NsWLpPdiJffhkzUWAkJACAgBISAEchAB/dNtderWxd49e1D+jRYoNOVLwLsgEm/f0nLHgjoUS7fMNQOf1OSt6axNJXB6p8tsFZ8onzM4c61rN4riZdnr4FsYzhQkd7ZPW9zctA4bN/+Bpq82EaeSQaZSXAgIATqsUFoczqc+depUTTIXKVJE64+I0xXqj/129KjAo2Onulh+4YUXcPbsWSjBe//+fa3nSBG8smkJASEgBISALRHQ52X+9ttv0axZMy2S9+effxbJa0sNJXURAkJACAgBISAELEZAf53OHXxtp85oK783BPnGfAZDaAiMoaHUG1nWil2LrWw2mLEmWDg/MaW5dPUrhoskea+u+wVHjx9HlUqVxKtkgzaUKgoBPQHTvOZZTcesgpfTNDg7O8uBKKtbUZYnBIRAigRY6t28eRMBAQFyXJJt5NFdy02bNmHz5s2oXbs2fv31VyxdulSjI/maZSMRAkJACAgBISAEcjIBJXg/HPURpk75DJV79Ue+8TMpavcmRe3GU9Suw6N0BjmZg62tWxJds9g5u5DkLYKjLV+Gxz/HcDWK0mPQIMFzttZaUh8hkDoB3l9Z8jrQsdQag9kE74MHDxASEqKFA8tByBpNKcsUAkJAEVCibvfu3ejVq5f2pIEcl2T70EvcRYsWaY+rlC9fXpO8y5cvl5No2USEgBAQAkJACAiBHEtAXfgfO3ESVStXQplGTVB08a9IvBuEpIR4SsdgHSGRY4FncMWSDAlw8MwHR+qUbXsFP7zfry++mPe1BCBkkKMUFwK5mYBZBS9LXjbVIlJy8yYl6y4ErE9ACd79+/ejR48e+Oeff+S4ZP1msZkaqN6J582bh6CgIFSvXl3rdG316tVyk9JmWkkqIgSEgBAQAkJACJiLgP76/JX6dbFrz17UPhQIo7MrDOFhsLNStJm51i8nzEdL10BR1M7FS+LB2hU4Obg7bty+Db9ChUTy5oQGlnXI0QTUMTYyMlJbTw8PD6v4B7MJ3lDK18MvO7rjJII3R2+7snJCwOYJ6AVv7969ceLECTku2XyrWaeCnIf3OOU5a9myJaZMmYJly5bBx8dHTqSt0xyyVCEgBISAEBACQsACBNRF/6kzZ1DxuedQZeIM5O05EAmBF0E5FiUtgwWYP80sVRCCm19RbKsagE4v18f3q36R65ingSnTCIEsJKCOsR988IHW0dns2bPpfg3dsKHja1YOZhG8efPmRXh4OMLCwrS6i+DNyiaUZQkBIWBKQAnev//+G/369cPRo0fluCSbyX8IqN8qTtGwbt06dOnSRetxlCN7ixcvrm0zfNOSXzIIASEgBISAEBACQiC7ElAX/QN79sTcH5ag0T+3ERcR/jA1g312Xa0cWe8k6nTNpURp3J73OW58Ogb3+G9Kgyl9ReTI5paVyiEE1DF2+PDhmuCdNWsW9Z+YACcnpyxdw0wJ3po1a+LcuXNgwRsREaFF8IrgzdL2k4UJASGQAgF1ArRz504tRcPFixdF8MqWkiIBJXn37NmDGTNmYNiwYfjyyy/Rvn17LaqXBzmhlo1HCAgBISAEhIAQyK4E9MFXRZwd4dqlL0pMmoW4wAuwy2L5kF0ZZmW9k4yJsPfwgj0F0O2s8yyW/7gMbdt30KQR93ckgxAQArZHQO2fI0aM0DpZmz59evYUvBcuXICnpyc41wTn4BXBa3sbm9RICOQ2AkrIHTx4EBMmTMD69etF0uW2jSAD66u2l5MnT2Ls2LHo378/Nm/erN1x5bQNInkzAFOKCgEhIASEgBAQAjZFQJ3n8HV7uXLlUPv33XAs9QwMIfdhJ8LQptqKK5OUZAT1eAdO07CrRml0qP8SFq1cJYLX5lpKKiQE/iWgBO/IkSO1L6dNm5a9BW90dDSCg4NF8MpWLgSEgBAQAtmOgIpuiY2NBedtbtiwobYOGzZswLfffgtvb2/tJoG9vb2kbMh2rSsVFgJCQAgIASGQewmoPJDfzZ2LXoMHo/H5YMQ8CAEZQ/KIkp7BFreMJMrd6VL6GZwf0Alu2zfgwgNJhWmL7SR1EgKKgBK8AwYM0CJ4OeVftsvByykaVARvTEwM7t+/L4JXtnEhIARsioDqrMCmKiWVsUkC+kcYOXKX0w41bdoUM2fOxJAhQ9C4cWP5jbPJlpNKCQEhIASEgBAQAqkReJR/t0sn/O/gcdTffgxRnJ5BondtdqNhwetcvCTufjMbVyd/hAfGJDhRlxCSNsxmm0wqlssJqOMsp2bgVCpDhw61StR9pnPwKsHLUU/37t2Ti99cvmHL6gsBWyLAwo4Fr4ODgy1VS+piwwT0J85bt27FokWL8N5772HVqlVwdXXVOmHjQTpgs+FGlKoJASEgBISAEBACjwioc5vGlSviRMlnUfmb5Yg5fxZ2Wdy7uzRJ+gkkJcbDsVBRRO/eikPdWuPS5asoVSJABG/6EWaLknw9oQa+ZuXBtHNn9f2/BflTctnHP5msstZHdHo7in44v39na3F+6e3EWl/uPyx0vPgpSxn4wYzkXN18zfrhhx+iSJEiuHLlipZ6UH+da0cwHzW3inJSEbzu7u5afom7d+9qTPVRUAJZCAgBISAEhEB2IsA/d/w7xjcG+MkU/nGsXbs2vLy88Msvv6Bfv35o0KCBtkrS4UV2almpqxAQAkJACAiB3EVAf11ewTc/wt7ujHITZiDm4jnYi+C12Y0hiSSNvU8BGC6cxr63GuDosWOoUrmynHfabIulv2K8Tyqxy9ca6RWd6V9C7ivJ124sL5mltQO7VNtaSzibTfDyjO7cuSOCN/ftT7LGQsBmCfDBPiQkBPnz55cbTzbbSrZbMf1dTs7De4xOrtu3b48tW7ZoNzUnTpyonUTohbDtro3UTAgIASEgBISAEMhtBPSCt7RXHtj1/wjFB45A7JVAieC14Y1BE7x588Huzi3salwd+/btQ61atUTw2nCbpadqTwoMSYxPIEEJOFCk5aOBQiwNdM2RkJCIhKgYGGJiER8Xh8TYBPpMf0fEwBgdDyNNa4hPpPc4GGgczyuJ/jbw06wkPrVrFUMSvdNnSveRZDSA3pDEAb4O9jDa0R/UsZ/RwU77LomDYe3pCVj6G06OsHNyoBtCTvTiz8l/a+/a3zSOIkbtqLydvR3saSX43Y7m60DzTKLvHRzpM0XYan2Z0MuBvuPPfB1lT2X5byeahyPNk79zdHSCkwN9fjjvf3kkISoyEtGR0XCk+nh55dXK6AclezmK1RoDXz/yYC3RbDbByz8eQUFBInitsRXJMoWAEHiMgBJz27dvB/dkefDgQXmkSbaRpyKgl7zXr1/H+PHjUbduXQQEBGD16tVo0aIFmjRp8ui3T1KCPBVmmUgICAEhIASEgBCwAAG94PV3dYb7mGnw69IH8deuiOC1AG9zzTKJ3Io9PSVtHxmBnS89i107dqBu/foieM0FOIvnY9pR87EjR7B+3Xrs2bMH5wIv4ebdIMSTtNQyL7BcZWfJnvDfDA5ZXGMbW5wmoumVaFIvFwf4FSiMys8/j/r0dOXbrVqhfPnyjwrlxswCZhO8fFF769YtEbw2ti9IdYRAbiSgF7yc4Pz48eMieHPjhmDGddaL3m+++QZHjx7VonnPnj2LM2fOoGPHjqhRo4a2RGvfuTXjasushIAQEAJCQAgIgWxMQC84yni5w27YeBTrMZAE72URvDbcrklGiuB194JdaAh21a+IoyQEq1StKoLXhtsstarp98GNv/+OUR+NxrGTx/8t7uaEMgWKonrhkqhauAQCvArAmSJftXBeyqHLH/mdI0I50tWR0zpw1CuZYActYjY5zYMjf8efqbQTTcTl7PmdI2u1//7N70txtMnZeTmiVvs+eRm8KJ6Xfpz6niN7ubRy0Py9FgFMHpC/45sS/IkTu/InYxLZaY4W5msjLqN9pvQU/JlTKtB4I31npKjiRHrnvw203fN3PGjzolk8nLsmv+OMCQinKOWrEfdx+t4NHL97FceCLiMxguS4tgZAg/ovY/oX01G9evXk+dCysiINhmpnTu/H+W7z5s1rlSeIzSZ4Gd7Nmzc1iLnRlGfDY41UWQjkWAJKxu2gu93Dhw+XCN4c29JZu2J6ycs3NGfMmAFPT0/UqVNHS99w+/ZtLT9vmTJltIqJ6M3a9pGlCQEhIASEgBAQAo8T0F+XP1fAG3F9RqBEv2GIvSopGmx5W9EErwcJont38Hejqrhw8SJKlS4tgteWGy2Fuun3vy6dO2PJ0qVaKa/CBfFa6cpoX742ahcpi8L5CgHObsmG0sBWU3V/pe/1TDOnJoPJeO1P1WGaybs2pb4ztfT0qJZa52u67/WduaXasRuNUOMe9fv28EOyXX4ktB9bwcf6iHs4Dy5PIlsz39QZ4YPQu1h/+QjmH9uKvVdPA2Gx2ixGjx6NyZMna5+zwk8qsdq/f3/kyZMH06dPR3x8PJyzONe5WQSvm5ubdkfhxo0bWQYwm+3bUl0hIASykIBe8HKKhv3790sEbxbyz+mL0oveQ4cOgfPzPk+PBj333HPYvHkzXFxcwJHjnPuZBxG9OX2LkPUTAkJACAgBIWCbBB6L4M3rAfsPJqJot76SosE2m+tRrZJz8HrDeOMq9jZ9EefOnUO5cuVE8Np4u+mrp79eeLHmizhw6AAKlSiGYTWaoeez9ZGfona1RLjx0SQCKbcuRadyxGqyCH3CkNZ4k0n/W1wLCbbS8LihfqJiftLIh+M4b6+rsyvg6kmyNw6/ntqF0XtWUnTvdSQ9iMKgAQMw56uvsmRdlVgdPHgw2I9OnTpV67OFo3mzcjCb4OUkxpyfkIesMORZCUmWJQSEQPYioA5smzZt0kQbP0av/5HNXmsjtbVFAqY9pP5Oj1ytWrVKi+Zlsbt79264U+60nj17onjx4toqqKT/3KmAtXpWtUWWUichIASEgBAQAkLAMgT01+WFqcOk/F98B9+33kF80HXYOTpbZqEy10wT0ASvD3USffEs9javrz0pVrlyZRG8mSabNTPQ73d169TFnr170L15G8yt0xFueSgAJCYc0bFRFKxL6QPIt3KaBe6cTIaMEdCSQnB6B0oP4UYi1cmT2MZGo+u6OfiBZC9Co7Dwu+/QvUcPi7sA5R/YPbDgnTJlSvYWvByxdOXKFRG8GdsmpbQQEAIWIKBk7oEDB7BgwQJ8Rwd2EbwWAC2z1E60+QkWlduJtzWOGOeTcBa9HOHLQ5cuXVCpUqVHxHg6nsZaPaxK0wkBISAEhIAQEAI5n4A6/+W4OW867yj5v5+R9+UmSLwTRILXOr3M53zqmV9DTfAW8IXh5BHsa/Mq9tG5Za0XXhDBm3m0WTIHJXjfHzIUs+bMxjfDJ6B3qVdIOIYggiJ2WebaccBHltQmdyyEc/4mGg3wcnEH8uTFiA3zMH37Cni6eOLatWvIly+fRX2AErzDhg3TBO8nn3ySvQWvq6srLl++LII3d+w/spZCQAgIASGgI6B+1NVXP/74I/bt24dnnnkGfn5+OHHiBCIiItC6dWstylcNfOHFkb16SSxghYAQEAIaP/ihAAAgAElEQVRCQAgIASFgDgJK8MZSLsi8FJBV8ec/4F65BhLv3xXBaw7AFppHEuUWdfQtgvgDO/F3xxbYvnMnXq5XTwSvhXibc7Zqnzt1/AQqVqmMBZ9+jj6FGyHm8nkYnaijtORe02SwEAGO5nWhm1eO1Fld3zUzsGDbrxg8aBBmz5mTJYK3V69eWh8tM2fOzL45eDl6lx9FDQwMFMFroQ1VZisEhEDGCZg+Rp/xOcgUQiD9BFQKBk5ZpIYNGzZgy5YtCAgIQIkSJXDy5EkEBwejQoUKmuz19vZ+VFaietPPWkoKASEgBISAEBACaRNQsiksPBz5qVf36uv3wLV0GSSGhIjgTRuf1UokkZB3LOKPuO0bsb9XW2z480+8/sorInit1iLpW/BjOa+ffQbF/Ytj64CvEPP3ASR5uMI+SdIwpI9k5kqx5HV3oty85CmfmzMAZ+5eRWJYFBwojYOlnurV577NS8favn37WmV/NUsOXha8Hh4euHTpkgjezG2LMrUQEAJmJMA/sio60oyzlVkJgScS4O2OX3rRu5MiLzhPL/eqyrI3Ojpa+83kxPtvvvkm6lFUhn4Q2SsbmRAQAkJACAgBIZBZAkpmcGfo/v7+qPXHfjgWC4AxNFQEb2bhWnB6TfAW9Ufs+lU4MLgH1vz2G1o2b24VYWTB1cxxs1Zybd2vv+Ktli1xYe5qlAl1RkRCJJwcKACEOwcTx5sl7R5vSIBXXl8cu3kGVacPxPLly9G2bVurRNVmyQo/XIjZBK+XlxcuXLgggjcrW0+WJQSEgBAQAjZNwDR1A+eq/5VO+rhT0iJFisDHxwdXr15FUFAQKlasiOZ08s4CWA0qKljy9dp0M0vlhIAQEAJCQAjYJAF1HnL29Gk8+/zzqLPtGOwKFoIxkgSvveTgtclGo0ppgpdEfMyK73HwQ5JT1JFvW3ryy/S80lbrnxvrpY/erfJCDcSHhOH0iMWICbpFN1MkLUOWbxOUAo/z8ubx9ceLk7vBuVIAdm34UwvCsWRn19Z+gthsgpeTFp87d04Eb5ZvubJAISAEUiPAUQsh9AhawYIFLX4wl1YQAk8iwNsin0yozti47MaNG7Ft2zZ6eshFi6oJp8cnWfRyWc7d26RJE5QpU+ax2SYkJGjz4HlZ8uREWlMICAEhIASEgBDI/gTUxf5B6qTrhVq1UHfXP0DefDBGhYvgteHm1QQvPd4f9cN8HB43HPOWLEO/Th1E8Npwm6lo+YjQMHh558OsjoMw5IU2CAsJ0nLCypD1BBKos0JPn0JYc3gLWv3wCQx0HWVPbWGpNA28hjxvHqzVkbZZBK+zs7MWhXT27FkRvFm/3coShYAQMCGgDmy/0eNMn332Gfbu3WvRA7k0gBBILwHVqZo+fQN3ULp27Vrw45Pc62qBAgW0GxJ3795FbGwsSpUqhcaNG+N5irzRD1yGt3XVQZsI3/S2gpQTAkJACAgBIZA7CKhz4l3bt6N+o0aov+8cjJQuykipouzoZrEMtklAE7wBJRD57SwcmTwaU75ZhA97d0M8nfc5iyy0yUaLpzZjL7aCUgG0a98el8YuQinPwoiMiYCj7GtWabNEulbycHHHg7hw+HzcHuvXr0ezZs1y9I0Sswne/Pnz48yZMyJ4rbLpykKFgBDQE1AHNs55OnnyZOzbt08Er2wiNkVA5elVclZVjm+UbqeLsGvXrsHV1RW+vr5aHumbN29qeXuLFi2KKlWqoHbt2lrnpiJ8bapZpTJCQAgIASEgBGyKgDon/oPOiZtSzv8GBy/C4OgCY6wIXptqKJPKsOB1CiiJ8C+n4Oj0SRg7bz4m9nsPCfw9SUQZbI+A2tcGDBmEeXO+QtKMTTDExCDOSFGjdnIzxRotxikanOwd4OTuCedhTTHo/4ZjxpSpFhG8KvXD7du3tWs4znBg6XQQKTE1i+DlTmL4IvSff+iRDxqssSLW2GBkmUJACNgmAb3gnTJlCnbv3i2C1zabSmpFBFKK6mUwnNd+x44dmuzl31lO5cBRuqHUMUpUVJT2d7ly5VCzZk1UqFDhsfQP6reY9wWVGkKldhDoQkAICAEhIASEQO4goKIK1/6yEm+3eRcNj14BJXsiwRsrEbw2vAlogrd4aYRNH4djc6Zh+Owv8fnggTm+gygbbpI0q6YcWLV6LyExMAgnRi1FdPAtSamWJjnLFtCieAsUQaWJHeFdrRx2bNhsES+g/EPPnj219JD8FLE6/lp2DR+fu9kEb6FChXDq1CkRvFnZerIsISAEUiSgF7zTp0/XIiItmWtHmkEImIMAnxhytC6/9CkceN63bt3CwYMHtSdlOK80y11+xVBkAMtenoY7O+V0DtxZW+XKlTUpbDrwvqEGldJBUjuYo/VkHkJACAgBISAEbI8A5+7n84HlS5agfZcuaHj8OhIMRhjjRPDaXmv9WyNN8JYogwefjMCJ+XPQZ9osLBgxxCKRh7bMIbvUTR/gWLBMAJp4lsSyPlMQcfc6nBwk/6412zHBQHl4CxZDq3kjcMQ+FFdOnLZIQKryD/369dME78SJE6GOv1m5/mYTvH5+fjhx4oQI3qxsPVmWEBACTxS8K1euBEfwHjlyRASvbCvZjkBqkb28IrxNHz58GNevX9cEL0fnKqHLd4v5RNPT01NL6VC6dGkt0jcgICBFBmo5PFKkb7bbTKTCQkAICAEhIARSJaAEw6Jv5qPHe/3Q6NRNxMYlAPFxEsFrw9uNErwhYwfjn8UL8O7kz/HT6OEwcN8LkoPX5lpOL3id/fLjo+ebYEKrgQi7e0M6WLNya2kdrfn6Y+hPn2Hx9UMIvXLLIs5SL3g5u8GECROyZwTvpUuXtAtCvog8fvy4RWBZeZuQxQsBIZDNCKho3Z07d2LVqlWYM2eOCN5s1oZS3X8J8EkjD+rdNLqXt3d+gubkyZMIDAxEWFjYozvTSvqqyF0PDw/4+/ujTJkymvTlm7MpDXrpy/KYX3oBLO0jBISAEBACQkAI2D4BJXjnzvoCA9//AI3+uY1YevrHLiGe7+ra/grk0hpqgrdUWdwb0RsXf/wejT/+BBvGj4KRn8QiwSstZ1sbhl7wOvnmw5d1OqBv4/YIu39LBK+VmyqO9pm8JHgnrZ2LGRf+QujlmxZxlo9yMA8YAM5uMG7cOKtE3Gc6gpcFL1/4cWTQ0aNHLQLLytuELF4ICAEhIASEgM0QUKkcWMKy7E0pxcL58+e1dA6cx/fu3bvaDQ5O+J+Hes7mdA6c2oEv+vgzd9ZWpEgRLb1D2bJlUbx48VTXVcSvzWwGUhEhIASEgBAQAmkSUDkgP6dOh0eOHYtGp+8gNpqe/KFzABG8aeKzWgFN8JYsg3vDeuDyz8tQddRE7PlkLMgYwSiC12rtktqCHxO8BfNiUcOe6FS/BSLu34aTRFxbtb2U4J322wJMubAVDy7dsIizVGK1c+fOWgDNtGnTsm8ELwteviDkR0Z5kE7WrLoNy8KFgBB4SEBFPEqOUdkkcjKB9Ajf4OBgLcr39OnT4N5do6Op9+yHaR04ly9/5ovAWOp0hQcWwfnz59fEb4kSJVCyZEnt79QGvfjlMpLqISdvcbJuQkAICAEhkF0IKOkwcfRofPzpFDQ6Q4I3KlIEr403YFIiCd6A0rg7pBuur16OUiPG4fi0CdQzLwleyukqEby21YB6/2Xn5YK5jXqg/ysdJILXBpopOUVDMXywfDq+ufY3Iq4EWcRZqmMtp4dkwdutW7fsHcHLF3+HDh2yCCwb2C6kCkJACGRDAkp8OTg4ZMPaS5WFwNMR0AtffW5e07lxx20c4ctP4vDn8PBw7QYt7y88Hb9zhC+fsKi0Jyx+Oa8Un7jwkzuc7oH/Tm3g6Xla/c0WSfnwdO0qUwkBISAEhIAQyCgBJR3GjhyByTO+SI7gjYwQwZtRkFlcPonOvZz8S+D24C64s3YlCg0bg7MzJpHgpXMqOwcJvs7i9khrcUbquNDeIVm727nY4bNXe+D/mnYnwXtTUjSkBc/C47VO1goURYeFY7Hh3jmEXrNMDl4Lr0a6Z2+WFA28NO7EhXv45kEieNPNXwoKASEgBISAELA4Af5dVi+OrjXN46sqEBISgitXrmivmzdv4t69e1pUrzpZUNJXL255XhwFXKBAAU328hM9Svzy96kNPA8lo1UZkb8W3xRkAUJACAgBIZCLCKjf7/8bOhTT5s6lTtYoB29kuAheG98GWPA6FiuOOwM64d761cj3/mhc+mIyiRYSvBDBa2vNpwIhEB0PO3cXjHq9Az55oy+laLglKRqs3FiJtM945C+CV+YOxtHI2wi2sOC19hPEZhG8fJHGnbXs379fBK+VN2BZvBAQAv/eZOKIRBZUfANKbjzJliEEHieg5Cr/hvOQmvTlcZzH9+rVq5r4vXPnDu7fv6/l8eW0Dix9+cXimAUtn1ioTl34Oy8vL+TLl0+Tv5zygaN/+TPn/k1rUKkf1P6rOnuTTt/SIifjhYAQEAJCQAhwylYShXQjdtiAfpj1v4VoePIWYiNE8Nr6tpEseP1xu18H3N/wGzyHjsLFmZ/AkWSVgQTvw2BRW1+NXFM/JXjjbz+Ai58PBjVuhTkthiLiAQlee8dcw8EWV5SvIfJ4F0KVGT0RZBeLO5evW9RZ8rbA1ynWShFpNsFbvnx57Nu3z6KwbHGDkToJASFgewTUgW3x4sVYuHAhdu7caZUcOLZHRmokBJ5MwFT6qlQNqU314MEDXL9+HdeuXdPSPLD8ZfEbFxf3KK2DmpbnpaKI+WKTxTKL37x582ov7nGWxW/BggW1aGA3N7c0m0tFAfPJlDqREgmcJjYpIASEgBAQArmEwKOe3Xv1woIlS9DgZBBiw8JgZ5BO1mx5E0giKeXoVwS3+7RFyOYNcBv8Ic7PngJXTfBSGi26gS6D7RAwkJB3oHPbiCMX4VW9LLo2eB2LW32IiNAgEbxWbCYjLduBrjdcPPOh1Oc9EEfpM24GXs3RztJsgvfZZ5/F3r17rdh8smghIASEQDIBdWD78ccf8f333+OPP/74j2wSVkJACKSPgHrUSL2rqZ4U8cv7IMtelr5BQUFaBDBH07P85Q7elEjmCGA1HxX9y3+r71j8enp6alHAevnr4+MDb2/v9K0AlWIBzC8eRASnG5sUFAJCQAgIgWxOQJ0T9+7SBYtWrkSD4zdF8GaDNtUEb2E/3OrdBmFbNsNp8Cicmf0JvDTBa0+C1y4brEXuqaISvMF/HkKBJjXR+qVGWNXuY0Q+uA1He+kLxlpbgjHJCBeOePd0R6HPu8PN1RVXLgVaRPCqpw35+odT1HEQizWeIDaL4OWKV6xYEbt27bJW28lyhYAQEAKPCKgD27Jly7B06VJs3LhRInhl+xACZiaghC9H0ao0D7wI1Ulbaovjsix7lfTldA98MhRGEUWc7zciIkKbHwtfvZRVApjnzwKYl89pHlj+crQvC+D8+fNr4lf/yshqm6aEUNPqcwPrJXFG5i1lhYAQEAJCQAhkNQF1TtytfVssW7se9U9cR2zoA4rgpZueEgWa1c2R7uVpgte3MG71aoPw7X/CftCHODVnCvJLBG+6GWZlQSV4b6zYDv92jdC0Rh1s7PwJIkPvkuCVaOusbAv9sljw5rF3QpyrA3ymdkORvD5aB9M8mFu+qmNtx44dUaZMGUyYMEFLZefs7Jylq282wVupUiXtMWgZhIAQEALWJqAXvMuXL8e6detE8Fq7UWT5uYaAPtLXNOo3LfmrICnpyyKYO35jGcwCmCOAObc27+McicvvKs+VitJlOcwnU/w9f/bw8ECePHke3U1nCawigPnuOstglsQZHZQMNp3ONEWECOGMkpXyQkAICAEhYC4C6py4Q+tWWLlpM+ofvUYRvCJ4zcXXUvPRBG8hErw9WiPiry0wkuA9QYK3MHWxZqDnziVFg6XIP918VQ7e8/PX4Jl+rVC/Sk3s6DYV0WH3zZuLlbrNMNrRBkD/rJXj9ekIWWeqRNqPPBxdEOFkgNekjni2WAmcPn3aooK3e/fumuAdPXq0VfyDWQQvb9BVqlTBjh07zG7CrbMpyFKFgBDIzgTUgW3+/PngNA2Sgzc7t6bUPScSMM31y+uYVr5fPQdO88DSl+UvS+Dg4GDtpVJAsARWcpnzAeslK3+vcvayCFZ38DkVBEcC82NVLIBVfmAVDaz+Zkn9NENqQlitu/5dpPDTEJZphIAQEAJCwJSAOidu27IF1m7ZirpHRPBmh61EE7wFC+Nmz1aI3rEVsQ8FbzERvDbZfInkwxzp/PD0Z0vx/Eed8eLzVfB3z88RExmKJO5wyxy1pqAFI83LiV4JtH3wMu1oxtTNscjeVPgmUgSvh4MrgvMkocBHrVCx9HM4ceqExQVvuXLl8NFHH2Vfwcs/HDVq1MC2bdtE8Jpj55V5CAEhkCkC6i4qp2bYs2cPJk+eLDl4M0VUJhYCWUcgpdQPSsKmNwKYa8vpHkJDQ7UXdwanJDCngGBBzBHB/M4REAkJCVrHcCoaWKWcUMtVKSG4LItgjgh2cnLShLA+TzCnjOCIYSWDudyTchWnRVV1IqdPgWE6TUoRwyKI0yIr44WAEBACuYOAErzvvtUcv27b/lDwZp9O1rTfP36R1FK/d6rlnjQue7euEeSl4ORbCDd6sODdhuhBH+HonE9RkgRvIo2Tx/5tq4WV4D0xYSEqj++JKuUr4GiP6XRuGQUDNaY9bb+ZHXh7d3Ny1sSuYx568szVHYiPAVlkRNI77SEPczNnflmZrautTK8E71X3eJQY2QZVy1fBkeNHLSp4e/bsiWeeeQYjR47M3oK3Zs2a2Lp1qwheW9mapR5CQAgIASEgBHIoAVMJrP5mAcwS1vQi8EkYWASz7OWoX44IVjKY/+aIYBbEUVFR2iz45Jq/43eVC1jl5+V3JX15vEoj4UodOrDk5fL8YgHMkphlMAtg/ptTRPCLP3N5lWIiM80ncjgz9GRaISAEhEDOIKAEb+s3m2H9XztI8F6lFA3hlIM3IZvk4KWc/I5OSKKAspQGO/p9TWncI/nLz7LbPXzyhufBT+GkIIttq7WTBa8DCd5b3VoidtdfiBg8BodnT0IZEby21VQPa8OpAFi6Hxu1AFWn9sVzpZ/BP71mUABBHEXbUrd4HGqbicGo3eMwUmBBPry0cDgCw+6h43N1UKNwabQqVwMuPoWBiDBExVHggs1v35kAkcFJk1M05MFZlwg8+1F71KhYDQcPH7ao4G3dujWef/55TJw4Mfvm4OUfjlq1amHz5s0ieDO40UlxISAELEdAPQb+tI9UW65mMmchIAQsSUCf+1ffCZyKyFWRuhkRwTwfjv5l2ctCWEUG87uKCub3yMhI7YkBJVhVigh9dDCPV3Vk+ctl1TScIoLlL3+vpmHxy99xxDALYBbD/DeLYjVOyWEllDPLV9VfiW01P9NOKVKLIFblJUdcZltCphcCQkAIPD0BJXhbNXsDG/7ahTpHr2QLwas6W7VzdoEhPBQOedxJepL15I5dCYcd38ylaEZDaDAcPPMhycACWBuT3FGrAz227pIHcHaCMTxMk7oOeb1hjKWbtNFRySXNEFX59C3zpCl5Pemx/vwFcas7Cd7dOxDx/lgc+WIiSovgtQzyzMyVmivRngQvpUo4PGIuaswaiHLFy+IcCV4+t4szJmRe8NK2n4f2hcREAxqt+ATlffy0wPa/g87hXHAQXi9VFd837498Hvnp6bQHVBMH2lIopUMSdabIf+mkr5aqjPclDvSllwPdAKG9KflvGox8UySVHL9qHK1Q8jQpDDSpNhfaA7VP2jku1YNvWvBXPKW+PsnjeY+k0ZzOwoz7pSZ4nfLglHMYKn7YEbWqvIB9B/cn15DGmfMcVR1r+cnhsmXLom3bttk3gpcfbXzppZfwxx9/mB1UZvY1mVYICIHcTUAEb+5uf1l7IZAWgbREME//NDKYp+OTepbBnAaCxS9HBPOL5TB/z5HDPI4jhlVUMNeHhTD/zSed/FLpIfT1UPmKlYRV68GRvyqKmevAn1WkMItjlWNYRRCrlBIsh9V4cwlixf5Jolg7mTfpXVp/wZ3axbc5T8jT2kZkvBAQAkIgOxNQ0qHl669j004WvBTBG0ERvHT9Tgdgm101lrn2dCMz4dYN3B7QEUWXrIediys9kh6vyVl7Ty/Enz+DOyN6o9jPf9KPLn3LkpfWiSN6nQoXQfDcaWxx4DN0NAwPQhD6/ddwb/Q6XEqVhYEZ2Oz6PxS8BUjwdm2B2D07ETFsHA7PmCARvLa4xeoE7/6hs1Dr6/dRsmhJXOw9k7bVJMTRvpbZ85YE2qY98/li9u6f8dXhTbgwcWPy9h4Vius3zuPDnSuw9cpJHOgyGQHehRARGwkneycKCqCbHFQuJj42WWpqaR6caN/ypjwfFM2eQOI1JgyxidRfBd1UMJKFdbB30KKRYxOp3qxjeZ/iJ9NonBONc3FwQhw9AZBA8+WyfBTh+Wr+9qHz5XQRybdbkgc3lVKCRS6lrYij+hk5LJkGnjKPM6WboHNW/p5lryaHzZBpgnMVezq544jjfVT/sCvqvlALu/bty9HO0iydrMXTgbZu3brYtGmT5Lm0xYOO1EkICAEhIASEgBB4agJ6EaydjHIE0cNoA30EwNPKYFUxnieLX34pIczvLIhVlDBLYY4SZknMJ3FKoPJnPh/jQd9hnWnd1XjtRJ9OfNX0nF5CRQ2rtBP8N8tfFscqephFMEcQp/TiciyI+cXlLDmouuvZqXXi+vN6PUke6zmY1jOzF2KWXG+ZtxAQAkIgIwTUxX6Lpk3wx669qHPkMmLpN8TmBS/9pjn6FkbUrq0ImTMF/r9sR+LdIE3KssB1LFIMkb/+jAc/LYT/yi1IvH6FUjk4ami08UUDcKNtE0Tt34OylJbCjn6Xzj1XCPlatYXfzMVIuHFZS/1gk4MWXUlijQVvl7cQu3cXIj+YgEPTx4ngtcUG0wnefUO+wEvzR6Bo4aK43Gc2iVIHxFCahsyeVyQYSfB6+eKXY1sxeMtinOo5DUduX0HjZ18AKGoX0XTDYsTLmNtiIPrXakGR6lGwJ7m7n6RvYfd8KF7QH3HRYXBx80RSfBy+JEm84sw+OJHk/e3tEcjj5IJ4ijTO40a5fdmsJlBuX8/8SIqgIISEWK0DOWePfBy9QOejIfQEmQ8JYmfE02cDpaBwc3FLno7PjXm/ovoaSRCzMHYkmbw/8CjmH/kT26+exoR6bdC12quIIDnNstiZ5nX11iXcoXV4IeBZWnYCohNjMx31zJuKJnidPXAgKQgvju6J+i/VxY7duywqeNV5d2bb/Gk3dbMJ3gYNGuD3338Xwfu0LSHTCQEhYDYCSrjcu3dPkyKlSpWy6IHcbBWXGQkBIZBjCJhGB2sXnQ+lsOlK6oXs0wLgyF8Ww0r+8rv6zFJYRQyrqGKWwdpjeg/TRfD0fFKoz2Osj75V9denudALYh6vl8T8t4rA5UhhFr784jIsglke66OKWSAriawkMZdT5c2Vmzi9fPV5nk3bLrXH+lKLOE7pe2ud+Kd3/aWcEBACOYOAuthv3uRV/Ll3H+ocohQNURHZQPAmwKlkGdzq35FEbxH4jpuGhCsXtbQMWoRu8dK42bMlXCtUR/6ho5BwNTB5HKdn0NIx5MO1dk0Qc+woApZvgJN/CVyq8xzyvtMBhT//Fok3rz0SwjbX0nrB+zCCN3LkJByaOkYE71M0lsWf6NQJ3t1DZqDe/JHwK+SHS31mwc3BGdHmELwULeuZtwA2nt6NsbtW4YOazdBhyVTUeb4qYij69siti+heuTEWvtFXE6zx1Ola3SUf4y5F50bExWDOq93R8cXmOE3Ct+FPkxDgVRAN/Z+FB4nZ92u8DmcHOicjIXz+znU0+HEibofdQbtKL2Nps/5wcKbIeXK3a07vRe+N3yCGond9XNwxqPprGFnvXS1qfsvZ/Qil/L9taryGCrP64LkCRfDzOx+R6DWg34Z5WHRyF7pWqAc3ksLNS1dF41JVEB0fjTxeBTB607eYd3QLjXNBXf9y+Ln1cMTERiffqM9kugYleA8m3cYLo3qgUYOXsfWv7Rb1AnxerQI+nmJzzfQkZhO8L7/8MtavXy+CN9NNIjMQAkIgswTUgW369OnYtm0bNmzYYJUcOJldD5leCAiB3EMgpSjhJ0lhHscy1lw5BFn4sgRWkcIsgpUMZkHMLyWR+Xv+rAQxH3P5hJZTS/C76uhO1Y2/U9HGqkWV4NTXXy+MVad1LHhVRK5+a1BRxSyJVYd3SjDzOH3KCf6sBLH+M5dTZdVna25xqUll/j61qGTT+j5pe3jSOBHO1mx5WXZOJ6ByrOtzlmfVPqfOiZu9+gq2kuCtffgy4ukYbssRvJyegaNx7d09ce2tOig8fQGcy1WAISyYNhV6aJyO3Xb0+3e1ZQMU/WYFHP2KwRhJ0pqje0koOXjmRfzVK7g7iuQURVI6l3lGy9N757Ox8G7XGYWmLkDiDYrqfRjxa3vbny4Hb7fkFA2RH36KQ1M+EsGbicZS/R2oFFiZmNXjk+oE786hM9Dg6xEo5OuHwN6ztMhYswnefIXw0+GNGLrlB9wZtQoDf/kcc//6FSNfa4dP6rWlSFlP0N0bkDVFg28/QDnK0/vt8KWYSp2y/X7xKHYOmo9dx7ej/oL/w7gmHTHhnZF0IklPW92+RJ2zRVJKLx+U/fI9vFqiAka+0Bxdf/8a92IicHr491h3aBPeWjgRE5t1xYhazXEkKBCd1s/Fy8Wfx8Jun2LW+q+x9fo/8Keo321XT+FaeDB2dRiP6mWr44W5/XDq/k2EDFoA13LVgeDbiLp/He7evlh6YAOGbPkewZMo5cSDO3CZ2h73Bn9DeXNdKUVEvBkin5MjeA8n3UGNUd3x2iuvYNOff+ZoZ2kWwcsn+a8QrCRyvUMAACAASURBVF9//TVHwzLbQUBmJASEgEUJqAPb3LlzsWPHDvz8888ieC1KXGYuBISANQk8jRzObDqJlNaXL574nJAFMMteJYZV2gmWyEoSq3cup6KHeT34+M3v3L8Df88vlTKCl6k+szDhMimlYuByenmiT+egZKnKV5ySPGZhrPIVqzzILI+VSFapKEzFsYo2VpHHSjir71UEM//NL1seniSbn0ZMpedGhAhoW94ipG7mJKBEkzpWPc0+ld76qHPiN199FVt278FLJHjjYqJtW/ByigWKgIz6axOCv5yGgLU7YLgTRClCKYxQS7/gj/A1yxC2YjH8V2yFgaJxk/iGJ0FJot8Fju69P200Yk8cRsGPZ+La6y/AuWRpeLzRCtF/74I/5fNNeJjuIb0cs7YcC176HctfmDpZoxQN1Mla9OjPcGDy/4ngzUBDqKdtzp8/r507cMdXauD9ggd+msgcA3fmxXlrd38wC/W++gAFChbClfdmw52iUs0ieDkHL+XW/X7/egzd9j0ejFmt5cxdvG81hm5dgpUth+BVipAF5drdHXgCr6/8DI1JvgaToD11/wbWtx6BOgHPU6eDLrhy9yparJyGMyE3MZiidyfXbwtXSv9w/951PPfdMLoxsgpwzwvERMJ3Umv82W4ULpF8/ezAbzgwcjkJ4UCggB/iQ+/DZWJzJI1Zi43nD+KNpePxdsV6WE2Ctu3cgahTtBwGv9KF8gSH4+PtSzBxzypUL1wKMxt1Rb1nXtTSQFRa8L4mwQvm8cTO62fQt0pjTKVo46joCDqP40y+mUvEq0XwunjgiOEOqo/ujjeaNsXvGzdaxFmq7e3y5cvImzcvfHx8LBopnNp2m2nBGxgYqJ3EN2nSBGvXrrUILHPsdDIPISAEcg8BdWCbM2cO9uzZgxUrVojgzT3NL2sqBIRABgiklKNXpZJILaWEmr0lJHFKVVeil0WxXhDrhbH6rMryuamKKFYRxPzOL33ZJ8lj/i3RRxqbdgCncjErhnqWXJYvHPml5mHKk//m8SqKWJVV7ypdBk/PQliludCLYiWdeRoljtV3+shm/qwXzqosv1tSLmVgU3yqoqa5mHkmes7mzIWXlqROa3xGVjA7t0lG1jOnl+XjDe97mzdvxrlz5/AqSdby5cv/Z7X5WGOOVD0p8XyUooFy8G7esVNL0RAXG2PzgtcpoCRuUvSqa406yN/vfUrPcBl0ENQ6TeOI3RsdmsCzeVvka98dCdevauKKx3FUrqNfUQTWLAHvviTaPhyJy7Wqwt4tDwrN+BbXWjdGiT8PUx5S6qyNXrbZ0RrlxyfH6+BbCLe6tUTMrr8QM/Zz7J84HGWpQ6pEGscyUYYnE1D7319//YVRo0Zpv7edOnVCnz59HpuQzwl4P+XX0w6JFDnO+Xb3jfwSL80aivwFfXGFcvB6UNqDaEqRkNljupaDl6JjN53Zq8nbhOHLNPnp4JUfe8/9jTo/jME4SpcwodUHmLh2Jo7fvYZfqMO1jce3oSGJXVe/UkBEMAyR4bDnJ6NIvgZfOIgBfy7GgZsXsbPTOLhQp2y+0zqjY/UmlOrhPThTp24FP22Dxc36wZXGvb32C4R/uILuPBAn6gytG0Xwnrp3DYc++AHDqU4z9qyBYfQq2Bcsho9+mYHbUWFY1HE8Qm9dQD4tt24iFu38EeP3rUHPii9jeM3mKP3NEAQN+R+OUYoJV5pv+WdqkaSOgTH8PuJonTmnb2Z+W5XgPRp/C9XG9ULzN97Eb7+vs4izVMfat99+GzVr1tS2Od62+BwvKwezCd7XqWfOX375RSRKVraeLEsICIEUCagD2+zZs3Hw4EEsXbpUjk2yrQgBISAELETgSZLYVLiZVsG0k7qszlvGy2cprFJUKPnL0cGm0pgFsyqnRLOSxupdRSGrTu+4HM/L9AJF/a0vr+eovxjUf9Z38KcX7aaPfvOFrD6Fh4pUVuX0nQTyfPRymP9Wcln/rqQ1C2YVpayX2PrPSlbpJTWvh7qIVmX1YlovnU2FtYU2XZuebUbyeJtrRdK6kE5rfHrrkVnZkd7lWLucOr6dPXsW8+fPx0aKHLt06RL8/f0xZMgQvPHGGyhXrtyjanJ5fpkrqpBnrM6JW9K1+sbt25IFbxyJTcoLyrkzbW7g/LOaELHD5brlUGzZRng0awTjPRKyfCwlOWWMCMel6v7w/2038tSpDmMwPc1hoPGJJNS9vXD/88kI/mIiSu47T2kevGC4d0cTRQ6FCuNyvedQZP5yuDzzHAyhIZTqwTwRnOblmCx4nUjw3ujRCjE7tiF2/Azs/3iYRPBmAvThw4cxbtw47N27F82aNdM+6/c/lUrlaVI4JNLNHO6IbP/H36DWp32Rz7cgrvWZA09Xd7MIXo4Q9qAO0o7ePI+Gyyfh9sCv4WTnSNHBsfD0KYoLN8/ixe9HIWjAfIRRbttCc/pgTpPuGFS7Fe49uI3ZhzagGAniThTl6/l5J4yq24Zy6DZFIYrUrfH9aDjbO5LI7YvOJG1fKV4BU/b/Ch9XT1QsGIC/2o+lkwJH1Fk8GudDb6OohzduR4eiZuHSWP7WYLjnL4IFO1fAQPtY/zqtQI8IYO/10/hox3Ls6D8PHZeMwdYrp/B7m5GoVCiAPv+D178dhwsjF+D9bUtwn3KC7+s8QWvZFWf+xnaadmqDdnB3ctM6frO3e/rjlMaNInjPxt3Bsx93R8u33sIaC2UdUMfaDh06oEaNGhg2bJhV/INZBC8/gsc7yapVq6yyEpnYz2VSISAEciABdbds4sSJOHDggJYf3Bp30HIgWlklISAEhECWEkhLHnNlUoo0No3c1EfqWksuqdQT/HvE0le9+GScX0oqp/SuJLQSxioq2VQQq3mpcoqNXgArZkr6chk1neqgSM/VNKJbdcynl/Nqo1CCXl0gK6Gr5K7Kz6yWo4SWvkM/9R3PU0VgKWGtF9f6XM9KZqt1UpFYSkqrVBtKOKcmpvVR1KZl9NHSahx/p9J96L9TddN3Wqi+4/VSAtxcwjRLd0ozLUwffZ3W0wIZXWRqXDPK2xLHihs3bmDJkiVaVO+DBw8QEBCARo0aoWvXrvD29tZWVe0PmYkoVMweRZW9+SZ+/3Mz6h4mwcuilG4+2azgpf3KnjrevPpWXcRT5K5H/UYwUKdLidevw0jvhad9jftTxiDu8kUa1xiGqEgad5UicuPgt2AFbvVtD4+GTVB41veUi/cC7KkjKY7udSrij2utXoZbrfooMOoTJFykR/dtNF0O5yF2LFgYN0nwRu/cioRJs3FozGAUpwheA0f3piHnU/rtzOh+lJ7y6d1HzFWf9B4r9OX4d0T9dvA63b17F6NHj8b333+PQoUKYfjw4RgwYMBjN1ZUCgfFIK2bLkrwHv78B9T4sCt8/Arjam/zRfBqT/zYcb8LlKo2LgoF3fMhhm/S0Pd0Wwjubl6IpHQMDlTAzcMHRy+fQvvfv8SdyFBKUeBGIjcfPnrxLbSq8gpWH9uCsTtXaika3OhmcBlvP3z/Rj8tYnYI5ff9e8RSBJ7dR8IWKOtXkoRtLGHgOw5uuHz/Kh5Q6oZSeQshX4GiQGQYYg1xcHV2px+2JOrLgZ4OoDq6UIeH92hcQU8fPIgKxZCtP2DN+QOIpDr7kSB+u0xNzH29N9XfiLZrZmJj4DG4U2dueZ3d0KpcTUyknMKGJHrqKpMdrWmC19UDlyPvotSkbuDo2tWrV1s0gjfHCN7mzZtLnsv0HAWljBAQAhYnoC4I+abTlStXtB9udYJr8YXLAoSAEBACQiDbEEiPQNbLTrViKQlkNU5JpPRe+GYVLCWOlUxWneOpvMdKGnM5VUblOtZLaP1nLssslPRV81Bl1Lt+PkpwKq5K9ppGFavxpjJb9VBtylkfIZyWGFNyUS8BUsrnzN8peczz18vn1KS0fjswvbGgJINp6g/FQJXXT8fLV3XTj08pulov1vVS3VRsm6b2UBHTKh2Iyjmd0jz03+nXRy+z9cz065JV27q5l2OaCiS9kknVQ+Xc5eh3vWTmSMKVK1fixIkTWiBCvXr1MHDgQBQpUkSb1By9savz3zYUufbbxg0keK8izkCPPnN+c1uM4KX1Zrnp4OmFRIq8DV//C4z371HHapROxqcASdkzcH+1OVwrV0fY8kUwhobBztUF9vm8kXD+NDyavwMHyn/q6OEFB5/8MFAgmpabl9bZsUgAwlf9gLuffIiS209SeLOBIBMLG+SgCV6KOL7VozUi/9qCpClzcfjD/iiaTsFr7n0gp86P+5CaOnUqrtPNg4YNG6Jfv36oXbt2hldX7WdH561EtQHvokAxPy1Fg7ly8CZRUmZOycCxrBwpHE/SmuWuZlPpnUWoC0XZGqickdJFuOWhHLqOzoihVAdufIODpCp3wGag3LYOHjTOgfoBiA6nbZ+md3QFSBDvPLENH2xbioP9ZlP+XeqsjfaLGEqXkHy8o6WzPHbOk7y/UMR8JKeeePi9USvDs0sux385UcoFTi3hRh2maTl9aV6gmzAsiuHkCCOlcNCy7HpS3SJDKPUIbfMUZczrxB0qxpHgffrY3eQmTBa87rhN8/eb1BmtW7XCKso6oFxBhhv6CROobYDTgLz44osYNGiQVfyDWSJ4uTONVgTrxx9/tMpKmLNhZF5CQAgIASEgBISAEBACQsDcBJ4UwZSSMEpPZDLXUS+MTCMUbU0yp8ZUn+pCCWP1nRKpSrLpZbE+ElpFRvO7EsumEc7qEVz1biqn9ak2lLTWz09FCPN66COb+Xuel368WlfVJmqZSuYqGauinlV5fWSyvn1NpbBp22sX1w9FVUqyWvHTc1Rs9Yz1baSP6E5JPnNZJb152XpRreqnjx7Xy171Wa2vksz6tB56UWwqm/WdHqr0Hip1CItUvbRWZVUkt35Z+ihzFcGd1ftNcHCwdh39xx9/YOfOnWhKHQEtX75ca09mmlJ7pvf4pC723327JdasW4c6JHgNJICMtix4Oe8mH9sob669d37Kq0s5P5MdE21wJLAoRQMSKQo5r/e/4zS/RLGMEREkcyllAx0HyJprHbNpgpfnyR2xubgiZu8OOFeoAntXEk90k8pmBa8vCd5ebRCx/U84fb4Ah4f3gW8uErz6p0XUjUh1TNf/Tph+1t+01N98VOVUJ67u7u7azRQ+Fu3atQvz5s3DoUOHtLQNnD6FH7MPDw8Hl+vYseMTdzm1n51asg4Vu7wF34AiuNxzltaBmDk6WVML10Tqw7y0j//e037BvvdhQSPJUdqwSa46a/t7LO0TPM6BooATNXFqT0KYbh7Sd7G0D3i6uePM/evo8NuXOEppFaJJDPOyOCJYLUe7UaVEru73JiUw6riVLHt5OiOcKaUEH28T6aZKAtWB66LttTTO1YHSslAF42i/5ohkFa387xql94j3eLnkFA15KOo5Ej4ft0Pr1q21rAOWFLycupZv1mXbHLzcS1wEHUjfffdd7XETiZJ7uo1PphICQsD8BNRFTFqRPOZfssxRCAgBISAEhIBtEEjPo7FPikhMbZxeaKYkxExls61GNpu7lZREUJJBL5v1IoLFsT7iWUVO66Oe9VHW+gjrlKRFSt/pBba64NanxVDfMQPTz+o7U2mttgf1vT49Bk+jF7p6tnr5rJaVUp3UPNR8U3rXpABduKvI2JRuBpiKZX2KDMVFSeaUIqhVPfRl1PqoTg9VB4n6HNZKGKcknfk7noZfriQXWSi7USqCAgUKaLPm9eDHh8ePHw+Wvl9//bX2SLG+LTK6vapr87ZtWmH1mrWUg/cyRfnRDQl67NpWI3jV+pKp5RC8/66y6gyLoxhNBx7HEorkuLYN8Es/sLDy9EQSRfayALZFuautP0czFvbDrZ5tELZtM/LMXIhjQ7sjXxqCV4mrU6dOgUVTUFAQ8uTJ85+bL/obG/qnH9J6CiC1svrtXqWrUU8C6J8KUTfl1I06NU5/LNRL3JRu8OmPYWpfVvuP6bi0fv+4rnnz5tXyYhcsWBBeXl5aXn4WvmFhYdqWs3DhQnTv3v2JUlDtZ2fWbMNzrRrDr7g/AknwutL8zSl4M7r/a+tP2zxH1poO2jgOkU2ygzPtN44U1ZtvZnesaz0c9UpVQWRUuFk782Nxy/94nzONzE2uJ0cAZzZm9/G1TBa8rog0xsNzdBs6ntJx0MIRvJMmTUKlSpXQokULq7jRTEfwKsHbrl07LY+JCN6M7nZSXggIAUsREMFrKbIyXyEgBISAEBACmSOQ1oV3anNPTTg/SVKnJaNNl2Uqp9X43CKp9TxMI6qViFFSVS+wTSOxlaBWwkZJaNNoPG47VTY16a0X4Hq5q7Yj9W4qj/WRZCrlgZJRvJ4ptak+WprL6P/WItAoZ7Ze5Kv56KWZPupN8dTXUUURcp14YNHEopcjCvnp2K+++gonT57Ec889p0UVsgjm6TMaXayuzTu88w5WUuRancMkeOnRaSPnyrTB1ASZO6qkPbV2nGDmHM2b3HBpT2SFEprg9SuC233aInjzBuSd8wNODOoMd06JQ/VJKwcvV5n7ITly5IiW21lFr+uj2dX2qk/hoi+nPptK3ZRymetv9OhvpqjtVf9kgulTC1bAm+oiOXCRo+c5mr5MmTJaypRixYqlWUW1n13Yug/lXnkJxUuWwKVeM7XNiyNkM7rfprlAMxfgKF/3vAXx/u/zUCZfIQyo1RIRkcGUZsEWOyFM/8rzMZOjqA3kjR1Ht0KLt5pj7eo1FongTX+tLFvSbIKXw9b57oYIXss2mMxdCAgBISAEhIAQEAJCQAgIAdsgYG5RzWv1JFmdmbVOS1zzvG1dRDzN+quc0koiq3zU+qhofd5oHq8iDFWktWmEtV7wqvnFxsZq0X9KKit5q0+FoebLUbzcwRO/mDlH7PLj4OfPn9fSNAQGBmLw4MHo1q0bqlSpoonozAjeLh3aY9lPy1HvUCAMlKsztwrep9l+rDFNEuUudSzsj9v92iN40zrkn7cMJ/p1gAuJOHq4nQSvNWqVc5fJuXiXLl2Kq1evonHjxlr/LfnzUz7YdA4GuunjQDdprh4+hRI1KqJc6bI412uGdhyJM5LgNXNkajqrle5ifIzinLmOnC+XtrE4yrGrboymeyY2WJDXwYUikx1cnJFnTBs0bPoKfl/7m0UFrzonsNZvqdkEb+fOnfG///1PBK8NbthSJSGQ2wioE2DuqZhPukuWLPlUJ8W5jZusrxAQAkJACAgBISAEnkZap0dKp6fM09A3zUNtrQvrtOrO8pfzA6vh9OnTmDFjhpbmkM9XOWKwa9euWidP9+7dQ/369R91uJbWvFMbry72e9J8F/3wAxocuIBE6q3eGEOdj+XCCN6n5ZjV0yWRMHQsGoDbAzrg3u+/ovA3y3Gqd1vYc/5kErzpaTqV9/tp6p7azSD9vNJT5mmWbe5pVMQ/p4xQA++LHKnLUvfs2bOoWrUq3nvvPTRr1uxRGcWPo+vTGlRqjHtXb8K3RDE8X7YcTlEEb0JCLHU0RjlvbVzw8vppN6YedubGf2eX9n1S23B+XxbXTu5eyD+2DWq8XAd/rPvdooJXPSlird8hswhevtvIeUkWLFgggjetvV/GCwEhYHECHG3BkRF895VzTy1btkzrnZi/k0EICAEhIASEgBAQAkIg5xLIiKB+mpQf6SGnHkNPSZKsWbMGc+fOxYkTJ1CxYkVN6r5DKRQ4DYPpkNnOgNTFfr8+fTD/22/x8v7zSHSldA+Ug1YEb3pa0jplNMFbrDjuDOyM2+t+gf//VuFkz9ZadGV6Ba91am7bS2Whu3jxYly6dAnly5fXOk/jVKN6icv7DO+3GenDRe2n4SGhyJvfG9XLV8Ch3l8glvLYGjm3rG1jeVQ7LUVuNqlreqrJ62NH4totXwH4f/wuytWqiq0b/rCo4E1PvSxZxmyCt2fPnloieEnRYMnmknkLASGQHgIcBcF3aceOHYu7d+9qN5/Ud+mZXsoIASEgBISAEBACQkAICAFzEYiJicGUKVO0lIa+vr5atGCPHj2081X9wNfSasioZEqprurafDDlEv2SpHL9v8/B6JZHBK+5GtZC82HB6+RfArcHd0HQ2pUo88NaHO7cQssfbKT8wTlJwlkI4aOnNzndybRp03D48GGUKlUKXbp0eSxSl5evonVVJ3MZrZMSvDERkcjj5Yl6FapiZ5+ZiHxw16wdlWW0XlKeO+Q0IE+Bonh+Ynvkq1Aae7Zst4jgVU8Qc5odzqfu4+NjlSeIzSZ4+9BdQU4GL4JXdiMhIASsTUDJ3NGjRyMkJES7+SSC19qtIssXAkJACAgBISAEhEDuI8Bil3N8NmnSRMuryxf/alAd0Ok7fjMnIXVt/sH77+OLWbNQf88ZGD28SPCGUwRv2o+em7MuMq/0E0hKjIdTQGncHdoV135ZgUrL1mFvhzdF8KYf4SOJ99dff+H27dtapK5+UJ0l8k2WzKYjUILXEJ8AR3dnNKvwEtb3mEKC9zYJXocM1FqKmptAIt0U8fAthgbT30NkMS8c3rHHIoJXHWtfe+017Vj/wQcfWOUJYrMJ3r59+2LOnDkieM29Rcr8hIAQyDABJXNHjRqFyMhI7dgkgjfDGGUCISAEhIAQEAJCQAgIgUwSOHLkCKpVq/ZoLiyDlBDKyGPgT1MNdbE/6sMPMWXqVDTY/Q8MXvlgpEfHRfA+DdGsmSaJUss5lSyDe8N64PLPy1D7543Y+k5TEbyZxK8i5J82Uje1xT9KpZJEKQHcHdGlciN833EMIkKCKAes3EjJZLNlavIEQyI8ff3RZu4I/OMegzP7j1hU8LZq1Qqvvvoq+vXrZxU3ahbBGxYWhkGDBuGLL76wykpkqsVlYiEgBHIcAZVvlw+s/Egc51qSHLw5rpllhYSAEBACQkAICAEhkG0IcLABiyVLS109EHWxP37cOEyYNAkNdp6CwdsbRupDxy4dnUdlG7g5rKKa4C1VFvc+6IlLy5fgtTV/4teWr5DgTaQUDY6SoiED7Z3Z9AvpWZR6PJ/L2nk44cNarTDl7YGIuHcLTrKfpQehxcokULoTz0L+6LdoIjbGX8OVY6ctkjpBHWtbtmyJpk2bggNgrZHdwGyCd8iQIZg+fbpVVsJiW4PMWAgIgWxJQB3YOM8ZD5zjzBoH2GwJTyotBISAEBACQkAICAEhYDYC1uxVXZ3/fjJ5MsZQ3xQNdhyHwceXBG+oCF6ztbD5Z6QE7/3/64uzSxfi3XXb8OObDamPNcrRLILX/MAzOcfHBK93Hsxr0BX9XumAsPu34CKCN5N0Mzd5HAnevBTB+/GqOfg66CDunrlkUcHbunVrTfD27t3bKv7BLIL3wYMHWm/1U+mxD5EomdsAZWohIASEgBAQAkJACAgBISAEhIAQEAKZJaCeYJvx+ecYPnIkGvx1DIb8hUTwZhashadXKRpCxg3FqYVfo9emnfjmtXoUwJtIflce+bcw/gzPXi94XQr7YG6dDujVsB0i7t+UCN4M0zTvBPG0z3hRDt4Z67/F5HNb8ODSDW0B+jYzxxLVsfbll19GixYt8D7lPbfGE8RmE7wfUl6fTz75RASvObYOmYcQEAJmIcAREzxk5aNwZqm4zEQICAEhIASEgBAQAkJACGSSgOqDgjtD55SK9bYeQVLhojCGhkgEbybZWnLyZMFbGg8mjcCJBV9i6Ja9mNm4trgWS0LPxLz1sjBviSLoUqQavuw0ChF3r4vgzQRXc0z6KIJ3zZf49ORGJNwOtYjgVXmYJ0yYgFq1aoE7W3uUm9kcK5LOeZhN8HJnRpMor49E8KaTvBQTAkLA4gRE8FocsSxACAgBISAEhIAQEAJCwEYJKMH73aJF6EUpy+r/eQjGIv4ieG20vVS1NMFbvDTCpo3Bsa+mY/SOA5hcvyY4GtFZInhtsvWU5H2mWiV4Bcfi4MiFiA4O0vJuy2A9AlonawWKou+yKVh4fhfi74dbRPBabw0fX7LZBO9Yyukzfvx4Eby20rJSDyEgBISAEBACQkAICAEhIASEgBDItQSU4P3pp5/QoUMH1N+8H0b/EjCGSASvLW8UyYK3FMJnTsTRmVMwY99hDKtVTQSvDTeaitZs+U5rrF/zKxI/WwdjQhxiH3auaMNVz9FVMxiNcPfxQ4MvB+Jk9B2EXA+yqOBl0c+DtcS+WQRvCP1ATJw4EWPGjBHBm6N3D1k5IZA9CKg7qOfPn4erqysCAgLMnmcne5CQWgoBISAEhIAQEAJCQAjkVgJK8K5esxatW72Nuhv2ACXLkeC9LykabHijYMHrGFAKEXM/w9FpE7Dw0Al0r15RXIsNt5nKtzp+3DhMoCfbj34wF1X8yiAi6gGc7CVvsjWazphkhDOxd3R1Q4HJHeBbvBhOHzthUcFrzU41ecXMJngnU8+cnKZBUjRYY9OVZQoBIaAnoH5gO3XqBF9fX3zxxRdWSXIurSIEhIAQEAJCQAgIASEgBKxFQJ0Tb9i0Cc1efx11ftsJu7LPiuC1VoOkc7nJgrckIr/5Akc+GYO1J86gRcXy4lrSyc8axdS+9htF77Zo1RLjmnfFhCY9qKO1IMnDa40GYdlJ0bQebp64GhqEEp/2QNt27bCcnmawRm7crEJgNsH76aefgjtaE8GbVU0nyxECQiA1Auo41LdvX/j5+eHjjz+GimAQakJACAgBISAEhIAQEAJCIDcQUOfE27b/hcaNGqL26i1wer4yEu9LBK8tt78meP2LI2rxPBwePxK7zl5A3WfKiGux4UZT0vBOUBAKFymCsiVK4fyAr5AYH4t4ygNrbye5eLO6+ThntVd+P3y15xcMWjEH386bj1793rNI4Jd6gvjUqVMoQu3v4+NjlSeIzSJ4g4OD8fnnn2P48OFy0MnqrVaWJwSEwH8IqANbz549UaJECXCOcBG8sqEIASEgBISAEBACQkAId6h24gAAIABJREFU5CYC6px4776/Ueel2qi1YhOcq9QkwXtXUjTY8IbwSPAu+xaHR7+PI5cCUbVUSXEtNtxmSvBxFatVrYqjx45h43uT0bT8S4gIvStRvFncdsakJDiSVHfO44Ua/3sfh8+cwvWLl1GsdAkYEg1wcHQwa43UsbZu3bpavvP+/ftbZX81m+DlR6Dff/99q6yEWVtGZiYEhEC2J6AObL1790bp0qW1pwtE8Gb7ZpUVEAJCQAgIASEgBISAEMgAAXVOfPjoUdSoVg0vLFsH15p1kHjvjgjeDHDM6qKa4C0WgKhVS3B4RH+cCLyMiiVLiGvJ6obI4PLU/vYZPd3+0ejReLnqC9jeZQriKA9vkjEJsLPL4Byl+NMSiDckwMu7MDaf2YvX5o9Gwxdewrb9lIOcBr2Mf9r5m06n2r5p06ZoR6kgunXrZpX91WyCd9asWRgyZIhFwp3NBV3mIwSEQO4goA5sb7/9NipUqIBJlOhe5UXKHQRkLYWAEBACQkAICAEhIARyOwF1Tnz67Hk8/+wzeOH7NXCt3UAEr41vGJrgLeqP6PWrcGhwD5y5eg3lA/ytIoxsHJVNVU+laQgNDYW/fzFEGuLwc9sReKdaU0SEUC5eB+lsLSsazEC5d92dXABnF1T8ZihOnT+Lnev/QL1mTSy2D6lj7WuvvaZF8Hbt2tViy3oSQ7MJ3q+++goDBgyQKLms2GJlGUJACDyRgPpxnT17thbB++abb+boZOqyOQgBISAEhIAQEAJCQAgIAVMC6pz4+s2bCChWDNW/+xnuDUhy3LlJEbzOAsxGCSjBG7V5HQ737YiL166jNAnDRIMBjg7mfbTcRhFk22qpfW7kyJFaGtOiJQJwqttU5HPPh8ioMGo/kbyWbFyD0QAne0c4e/ti0K+z8NWfv6Brm7ZYvHK5tlhLRO/yfPURvJ07d0bHjh2zt+D9+uuvwR0ayWPQltxcZd5CQAgIASEgBISAEBACQkAICAEhIATSJqBk032KKPT19kbV+Uvh8cqbInjTRmfVEkmJFMFbqBiidmzG4Z7v4PKNGyhRtKgErFi1VdK3cL1ALEuBRhcDA1G/UnXs6PwJ2UUDwmPC4ezglL6ZSal0E2DuiUkGeLm6Ax4+mPznQoz9dSEqPV8Bx0+dtKjc5Zmrp4Vr1KiBPn36aC9rPEFstgjeBQsWaCshgjfd26AUFAJCwMIE+KTWjnId2dtLr6UWRi2zFwJCQAgIASEgBISAELAxAkrwxsTFkfhwRYUvF8OrWSskBl2XCF4bayt9dZISE+HoWxjRf+/E4c4tcPnaNRT39xfBa8Ntpq+akrznL5xH9SrVEBkThRYvvoy1rf4P1LsXIiKCYU8dgDnINWqmWtSIJC23sYHEbh56IsHRqwAQG4neG+bjf3+tw7Pln6XO7o7CxcXFYpG7agXUsXbcuHF49dVXUa9ePavsr2YTvN999x169OghgjdTm6hMLASEgDkJiOA1J02ZlxAQAkJACAgBISAEhEB2IqCkg4EqnY+CHkp+8Q3yt2yP+KBrInhtuCE1wVvAF7FHD+Bgu9dF8NpwW6VWNSV5T58+jfp16yH4QQheeL4yVrUcCn+/skBUKCJjo2AgQcmiVwtH4j7YSPzmhtAk42PgqAO6/wzJHdLpWTBTni6J/p9EH5wcHeHm7Aa4eQKU73jNqZ3os3ou7hNr7otn9S+/aB3bWSotgy1ulmYTvIsWLdJ6ipMIXltsZqmTEBACQkAICAEhIASEgBAQAkJACOQmAnqxUYBEh+9nX6JQu+6Iv34Vds6Sg9dWtwVN8OYvgJjTJ3C4VWMESgSvrTbVE+ul9r/w8HC0e+ddbNz8h1b+o6btMbZeG7h5+4EEGj3fH42khHgkGBNJ+JLEpFdKyvO/C0sulb6yJlNneCK9kv2vgk7WsakPmrZWk9GxSHPZuuL0zG3yF1q9kkWuGpiGHX3vQqktHPi4pXWg5kpSNxHBIbew8vRefLL1Z9wIvgsfv0L4dvaXaPXOO9rkWS13eXk8WOsJYrMJ3iVLlqBTp04ieLPloUcqLQRyFgF1ID9y5Aj1YOqPggULZvnBPWcRlbURAkJACAgBISAEhIAQyG4E9HLD38UJecZMRZGufRF/7YoIXhtuTE3wevsg7tJ5HGxeD4FXrqB48eJWeeTbhjFli6qpKHqu7JbNmzF2/Hj8vW+fVvcm5aqibZX6aFiqIgLyFoCDSx4SlyQv/5Ojl+xmkqmRfahH//M9lzVF8/CL1L5PiWQSzV8ZWF4GRRYnz5i/VPpVV4a/f8z7qnEPy7P4pGhluij/d120unO5h9+T+NUGXha/7Hkcv6hjQX6PDsOt0Hs4efcadgaewqqTe3D+zg1tkpLPPYPRQ4ejZ+9ej9ZGzz6rNhZrP0FsNsH7008/oV27diJ4s2rLkeUIASGQKgF1YGvYsCFat26NgQMHWqUXS2kiISAEhIAQEAJCQAgIASFgLQKPdfiUzwsYMhr+vYcg7kqgCF5rNUo6lsuC1z6vt5Yr+cCrNRF4+TKKlyghgjcd7GyxiLo2VXW7dvkKFi79AUtXr8SlU6eAxOQxTiQ7Xeyc4O2aBx6UeoAyzFIkKHtOR0rdy6LTHo702cmR8veS9HRycIAjpSlwpry+TvRysKe/6Tst5QO/a33RJP/N5R1ImDo4cO5fHkcvh3/jaO1o3GNRuDRfO0cSq05cAQfY0zKTaNpEqpOBBC+/c6dmRoMRCRRJm0gdyCUm0ov6wEmkSGR+N9DfcQlxiI2NR2x0DIzxyVHKxgQaR+MTuAzNg8tp5TlymSOYOf8Cy2ASwEbDQ7lN34ckxvzbvG4OqFS5Kt5u+ia6deqMEqVLPRrHvB1o/bkvntw2mE3wrlixAu+++64I3ty2Bcn6CgEbJKAObM2bN0ebNm3QtWtXEbw22E5SJSEgBISAEBACQkAICAHLEdAL3kqFCiK8w3soPWIcYgIvwl5SNFgOfCbnrAlebxK8VwNx4PWXcIkEbwkRvJmkav3JTUWvqtGlS5dw4XIgbgXdQnh0FGJiohGfmKAJ03iSohEREYiMikI0fZ8Qn0CveMQb+D2RysYgKjoScbEkUqkzxbi4eJKnJFhZmrJopW2JZaqR3smiamkNkMARs1nMg6N7nUhCOznBmY496uVKEcv82cWVXo4ucHV3hZeXFzw9POHh5QFPd6/kd/rbr6Av5S8uimefeQZFihX7zwpwulgW3tYQu/oniEuWLAlv2n+zOj0EAzGb4P2FEhi3atVKBG8W7yeyOCEgBP5LQB3YXn/9dXTo0AGdO3cWwSsbihAQAkJACAgBISAEhECuIqAXDFWL+eFuw2Z49vNvEH3xrAheG94SkhLj4VioKKL3/oVDXVpqKRpKSooGG26x9FdN6yjs4cuJZKc1ZCSFzVJkbLIATqJIWYMmkykql6Nr+Z0kMI/nCFvjQ1FsoO8ND4Uxx/o6UHQv55nVIoXp3Y4igh0pype/Y8nqyNKW1s+RXhxF7EjRv/xuT+9augUzDEkU4cvymgdrR+wq/1CtWjUMHjxY658sNaFvhlVPdRZmE7xr165FixYtRPBasrVk3kJACKSLgMq3w4K3e/fu2tMF1jjApquyUkgICAEhIASEgBAQAkJACFiAgL7DnxfKlsaV0s+h0uI1FMF7gR6/drLAEmWW5iCQRBGaTv7FEb5iMY7+30Bcpk7WSlC/ItbIKWqO9ZF5pE5ACUqWlby/mgpf/t60w66UpDB/Z62OvczVvooFz4/XWw2Ki2LB62pr66tPEdm/f3+8Q528WcM/mE3w/vbbb+DHoTksmu9EyCAEhIAQsBYBW7mDZq31l+UKASEgBISAEBACQkAICAEmoKTgG/XqYtedENTafgzR1y9TOk/KrymDTRLgXKWupcrg1tghuLJwPm6FhcGHHlsXwWuTzWVTlVI3ddKqlF6gplXWVLialk9vFPKTymV3Oa38Q4MGDTBo0CAtTWS2FrwbNmwAR8uJ4E3P7iFlhIAQsCQBdfIzadIkvPHGG6hevbqcEFkSuMxbCAgBISAEhIAQEAJCwCYJKMnQr3s3LFi+Ag2P30BsWDiZ3wSSvI91e2+T9c+NleKOppyL+ONSuyYI+3s3HtBj9NxS1sjpmRv5yzoLgYwSUMfZxo0bax28v/3229lT8AYGBiIkJASbNm3Ca6+9piWB5iTJMggBISAEhIAQEAJCQAgIASEgBISAEBAC1iOgrs9nz5yJocOGocHOkzD45IcxIowEr6P1KiZLTpEAy107Fxctt+mJus9Tp1KFcfLcOa2sCF7ZaISAbRJQgrdixYoYOXKk1foAynSKBiV4//zzT7zyyitWsdS22cRSKyEgBKxNgCN5bS0/j7WZyPKFgBAQAkJACAgBISAEcg8BdcG/fedONKLHh2su+BFur7yJxNvXKQ+vBGbZ2paQRB1eORYsjIQjB7CvXVO82749Vvz4ozyNaGsNJfURAjoC6gnicePGaalra9asaZV9NtOC99KlS3jw4AG2bt2KRo0aieCVzVwICAGbIcAHOK13T3n8zGbaRCoiBISAEBACQkAICAEhkHUElHiIiotDQVdX+HfujaJT5iLuykXYSd85WdcQ6VySkfo0ci5eCqFTRuH4vC/w9bffom+vXvKkdDr5STEhkJsJmE3w7tixA/Xr1xfBm5u3Jll3ISAEhIAQEAJCQAgIASEgBISAELAZAvrH+l+sXAln7t5Djd1nEfcgGHTxLnl4baalAC09A0l3B3d3XGleF9cvXsCVGzcQULSoVaIBbQiNVEUIZAsC1n6C2GyCd9euXahbt64I3myx2UklhUDOJqBOZPnGU7Vq1eDp6Sk5q3J2k8vaCQEhIASEgBAQAkJACKRCQF30T/nsM4z66CPU/fUv2JWvCMP9u//f3pmASVWcbfudhRlWZVhkUUA2UcAFBAQBP1wIIBo1/jGoSYwaXJOY5NNoXBA0rjH5zSIag4oGTVyixiUgKgpBRSGugIDKMiIgEAFlH4f5zlNMdU6f6YYZpme6e7jPRV/NnH67TtVddarqfertOsE2DezDmykNpyyI3s1ve4Btefmf9t7oUdbvuGPtXy9Pc9lj/91MqSXyAYHkBOqEwLt+/XqbOXOmHX300Qi8tHYIQCDtBPxP0Tp16mTjx4+34cOH0zelvVbIAAQgAAEIQAACEIBAOgj4ufHna7+wti2bW5ezzrX9f32vbVvykVkQMZqTjkxxzTgCZWVllhO88gKBd/U5p9qCV1+0P/7tCbv0O6fjx9BWIACBShFISQTvhg0b7I033rB+/frR+VQKO0YQgEBNEvCT2F69etldd93F4lNNwiZtCEAAAhCAAAQgAIGMJ+AExJwc+96oUTbp0Uftf2Z/YqWFhbZj0ya2aUhz7ZXp+uXRu5unT7Vl5/4/29r9UFs2730rCj4qDbZuyOOZImmuJS4PgeQEfIT9rFmz7OCDD7amTZumJeq+2gLvRx99ZBs3brS33nrL/RTaJ0jlQwACEEgXAS/w9uzZ0yZMmGD9+/enb0pXZXBdCEAAAhCAAAQgAIG0E/Dz44+XLrWuHTtatx9eYm1v/L1tW7zILC+I4iWMN211VLaj1HILG1hug4a24vQhtnj+PDv7iSn20OnD3D7JxjYaaasbLgyByhDwOqj0h+uuu86+853vpEV/SInAuylY9Zs9e7YdccQRaSlEZYBjAwEI7D0E/AS2e/fu9tBDD1mfPn14MMHeU/2UFAIQgAAEIAABCEAgAQEfZXb1VVfZLbfdZoOnzDLr1NVKV68KHu5VALM0EHAPVguum9+hk62++ke2duKfbMt3L7Q5f7nHugXnid5NQ6VwSQhUkYDXHwYOHGjXXHONnXjiiWnRRlMi8G7evNnefvttk1pNBG8VWwLmEIBAygn4DrZ9+/b21FNP2ZFHHonAm3LKJAgBCEAAAhCAAAQgkE0Ewg/q6nNQV5u7YbMNemuhbQ22XCz76kv2463lynTibvAq6NzN/vPn/29rrvtfW9NvkF07+RW7vlm+uc/ZmqGWa4XLQaDqBLz+MGDAALv22mtt5MiRadFGUyLwbtmyxd555x1TtBwCb9UbA9+AAARSS8B3sNoX/O6770bgTS1eUoMABCAAAQhAAAIQyFICfp78RSDqdg32iczp098Of/xFK9m+NYjkXe22A0BUrNnK1X7I2nohp359y293oK2fdK+t+d+L7Mtefe2ER6fYU12bBRkoM8X25tZsVkgdAhBIAYGwwDtmzBgbMWJEWrTRlAi8W7dutffff98OOuigtBQiBfVBEhCAQB0kUBI8rKBe8GRgDghAAAIQgAAEIAABCEBgJwEvRixdVmw9DuwQiIwd7IhJz1pO1yBga9VyK9u8yQKV13LY+zWlTUYRuRbst2u5eZbfspXl5OXZmpuutPUTxttXx42wI+552F7pWmR5qqPANA91N6X8SQwCNUXA96l69s/YsWNt+PDhadFGqy3wLly40LZt22bz58+3zp07p6UQNVVJpAsBCEAAAhCAAAQgAAEIQAACEKhrBPx2DYuXLbOjDznIPt+y3XqNucX2OeMHZvs2tdIv19uOL7+ystKS8gewBWqjExwD4de9hdVHCZdVIxTEsO7yCD/zbadtkgtU8bouqYTC6e7V1F0+h66czc6s7gjib8sPJ+oGLz3FrrDQ8pu1sNyGjW3bjBfs88svsnWfFtvXl4+z439+lU1qU2CNdn49Hm/V0GINAQjUMoGwwDtu3DgbNmxYWrTRagm8+vnzggULbPv27e79wAMPZJ/LWm5IXA4CEEhOQBNX/QQqL1gd54AABCAAAQhAAAIQgAAE/kvAi7zbS0rt9G+OtOemvGBNmjSxjudfavuedqbltu8U7MtbYGVBQNeObZvNtpfYjq9LdiqQemmrAQmX/giLvgn3jg1UUJmXf2WnpBr84f4uPxm87XzsWPlR/lm5rLzzZJwWGxFmQ+nH0qggApefiDsvSTZ4eWVWZdPh3rVdws6P/d//TdunFUpMZQ9euQUFllNQ3/IaNLK8woDjlxtsyxuv2qo/3GYr5rxppYf2ti633mU/G9bfLnDuSnCdHWXBV3cvNtOOIQCBzCHg+9K+ffvanXfeaXrYWjq2r02JwKufQS9atMjatWuHwJs5bYycQAAC5QQk8uaEJ5+QgQAEIAABCEAAAhCAAATi/Pdnn59sV/7kUvtw8RJHpvVhh1uL/znBGnXvZYVdu1nuPkWW27iJ5RTWD/YPyLdcbeEQbDkgccMJnwqu0BYEgUi5UyzVa+dnZTpXLpZ6oXRHmWzLRdPYZ4HAWf4dp+2Wf3dH6LtuD1t9UZ+5/0poLhdktbdB9PB7HcgfCF65wfYT7tDfucGrLMfKgnd3vtzGbVGRt9M++I973/m3bMpf7jvBdwMOucG2C9q/WBnaUbLdSoMH0X8dREFvW7XCNrw3x754bbqtnTbFMWp4yKE24Lpb7KxRI+3MIMkGrpzBtgxBufMQd7krIZBVBMIPr2zTpo299NJL1qNHj7RooykReJXIxx9/bG3btk1LIbKq9sksBCBQqwTWrVtnRUVFtXpNLgYBCEAAAhCAAAQgAIFsIaCfFytq1AdEzHzjDbv3rrvsxX88Zas2BpG74UO/jJPAK+FTz7oIvhuL5pUw63Yj8OJuIIruVHNj4q4+yimPjnWarARRBWOUh87qlI9f9XG8PqhW0q3OhSXc6N/JmO9uS4g9qisvFAdit9tTwf9qUFHOgcirQ/nbv0GhdTz0cOtz+hk28Ntn2eCObWy/2AXLgv12g/Ir4nePMsGXIACBdBIIC7wjR460iRMnWsuWLd3CV21H46dE4NWAsHjxYmvVqhUCbzpbFteGAAQqEOjSpYtNnjzZunbtSv9E+4AABCAAAQhAAAIQgEACAoqKlV+fH3mw2ifFxfbh3Lm2+JPFtnbVStu0YYMVFuRb40bBbrGK3JUsGfz7uuRrK/m61L4u1XtJ8PNkRfYGryCiVgKm/l8a2Osl4aM0OK9z4a0ZKmzX4BTSBDvfKuo2vI1DuDyRCFhZ5gfCa15QrnpBJG9+eeRxfrmgra3cJGwrElcvRenmBhG7sb91LnjlBefzgvM5itoN0tH/c4P/K+JWkb/6XNtI1Au2YqjfoKE1LWpm7Tt1sjYdDrSifZpUJB6w/lppB58QtcstCQEIpIJAtQXeDz/80O1xuXTpUmvRogUCSipqhTQgAIFqE/Cd24gRI2zo0KH285//PC374FS7ICQAAQhAAAIQgAAEIACBWiIg8dVtuSBhNCL21lIW6uRldgSCrntJbJawS8RunaxnCrV3E/B9Z21H7nrqKRF4lVhxsLKnn0H7p8ft3dVK6SEAgXQT8J3b/fffb3rNnDmT/indlcL1IQABCEAAAhCAAASyhoB/YLECulL5c+N0PBtj57691T/EYXf517W8wCNbFyHM3rrVh08KEMhQApnycPdqC7zz5893ndVnn33mnriJwJuhLY5sQWAvI+AnoduCp/62bt3aVq1aZYWFhSmdnO5lSCkuBCAAAQhAAAIQgAAEIAABCEAAAhlIICUCr/atkXjSoEEDBN4MrGSyBIG9lYBfcDr++OOtb9++duutt7JNw97aGCg3BCAAAQhAAAIQgAAEIAABCEAgRQR8UJke7D432Kt88ODBaQ0oq7bAO2/evGBD8Txbu3atFRQUIPCmqKGQDAQgUH0CXuCdPn26TZs2zcaNG0cfVX2spAABCEAAAhCAAAQgAAEIQAACENirCZSUlFi9evXse9/7nm0IHkD5zDPP2Pbt2502mo4jJQKvMi+BV0IvWzSkoxq5JgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFDTBLz2uXnzZvc8Mu1qkO7nkqVE4K1fv76tWbPG7cWLwFvTzYj0IQCBqhKIbnqujk/9FQ87qCpJ7CEAAQhAAAIQgAAEIAABCEAAAhAQgeHDh1vHjh3t7rvvTrseWi2B96ijjrL333/fGjdu7AReHal8uibNBQIQgEBNEtATbnf3FNyavD5pQwACEIAABCAAAQhAAAIQgAAEIJA9BHxg60033WR/+tOfrLi4OCP00JQIvE2bNrWVK1dmRIGyp0mQUwhAoDYJ+MUndXr33XefXXjhhe7y2jcnPz8fobc2K4NrQQACEIAABCAAAQhAAAIQgAAEsoSA1xPCQa3HHHOM3X///dalS5e0R+8KY7UF3nfffddatmxpy5cvR+DNkoZJNiGwNxIId8SDBg0y7ZXz9ttvOxSK5NWLLRv2xpZBmSEAAQhAAAIQgAAEIAABCEAAAhUJKFrXR+zquWOZfKRE4G3VqlXGhCRnMmzyBgEIZA6Bn/3sZ3bPPffY008/bcOGDUuYMYnCOhB+M6feyAkEIAABCEAAAhCAAAQgAAEIQKCmCHhRV7/03ZUWEH3WT03lp7LpVlvgVQRcu3btbPHixe6a7MFbWfTYQQAC6SAQ7qNeeeUVO/XUU6179+72xhtvJOzDFNmrDn5Xh/bxja7m6Tv6bvSoKdtED41TWb1IHc5HMttk+Y0Oan4gi5Yt0Ypm2Da833GigTJRXnWN2rT141iy66ajzXJNCEAAAhCAAAQgAAEI7AmB8Py6NufUmTCvr0oekvl7ifybytom85lUj9F0q2qbqGyJ/Mxk/mCm2ta2D626SMQieq9pG4Zf//rX9thjj9mhhx4ai+jNNP0zJQJvhw4d7OOPP04ojuxJJ8R3IAABCNQkAXXE6si94Dhx4kQ7++yzrV69eixS1ST4DEu7MgOyn2yprSQSuqNF8g/ui9omm6wkEts1MCc66rKtVsejR21zqEoe6qptsgluZR0Z1WFlbavqyCRagEqFg1TddJP1DZVNNxXMqpuHVNRFVfKA7c7eLtM5RNtwojGoKmNbttnW9hhUl8f4aNmSCU66L2qLw67yEB3ja8pW5a2KALkntvwKMcOcD7KT8QT8VgwLFiywu+66y9566y376KOPrFu3bnbxxRfb97///YzWPast8M6ZM8c6d+5sixYtyuiCZnxLIoMQgECtE/AdYPjCvlN/77337Pjjj7cDDjjA9tlnH2vUqJF7hVdb9f+1a9e6zv60006LCRs6/9JLL9lf/vIXa9GihTuvCda6devshz/8oQ0cODDOVtHDzzzzjDVr1ixmu2HDBjvllFOsX79+cbbvvPOOzZw50/bdd9+YGC3bkSNHWqdOnWITRV1v4cKFNnfuXGvSpEnM9quvvrKjjz7a2rRpE2erB2Xqlxgqo59Abt261a1Q+u/7ibeu95///Mfq168fQydbLfap7OGtLWS7fv16a9y4sXuYnRfR9bef2PrJpzipTgoLC2u9LXBBCEAAAhCAAAQgAAEIQCCzCeg5KgUFBbFMyn/Qgln4nP9w06ZNcX6FX4T2vkY42GPp0qVxaehB3PKL5MtFt+1bsmRJXLrbtm2zpk2bWlFRUZytzv/73/+O+UzyeZT/tm3b2oEHHhhnq/Py8xo0aOCyL1vlX76ofKxwHr744gvTL1GVP28rH+/ggw+2Hj16xNnKV5Wtyrx9+3b3mfy43r1721FHHRVnW1xcbP/4xz/i/Mwvv/zS+vfvb3369ImzXbZsmbOVPyhWYrt69WrnPx977LFx/uv8+fNt0qRJjpGuL39R/uHQoUNtyJAhcbazZs2yu+++O+ZDy1b5/fa3v+38XV+HOi8fWlsuen/b255xxhl24oknxtm++OKLdvvtt7vnh/k05JfK/73ooouccOsX9uSzKmJX5Rs9erSdcMIJcX5vJt8h1RJ4VdGzZ892arYqLeqsZ3LByRsEIAABEfBbMPiIgfBA/8knn9iKFSvca+PGjbZly5a46DR9RwKmOn31h+EB59VXX7W//e1v1rx589hPODQYn3nmmaanbYZtJe7++c9/tv322y/25EsNxhpsRowY4WyVTw1Z8ivyAAAWSUlEQVQ2Dz74oP3+97837X3uxWjZ3nTTTW6QDNv+7ne/cyuPslVnr0FvzZo19tvf/jY2QPp0de62225zEw4N0l68vvfeeyvY/vKXv3R5kEgctn300UddfsOD41VXXWV33nmntW7d2iQC69Dg/+STT7rtMcK2l1xyiRvQvfisPIjvc889V2HwFxuxELOwgC6W0UnFT3/6U3vooYdifFVvmqxo/+VovZ1//vluMBcz3xZU7zrnf46jMihvysPzzz/vJhUqh0/3iSeesCOPPDKuji+77DJ74YUXYiK+uGuS9fe//90tkvrJqdK44IILnIivSZCvT6UvZpoMhm1VF5oIaUKp88qzbNWeNCEM2yoPEvy1YBGORtEERpOdcNu//vrrTe1fQryP7tO7fprkFyL8YkfU1rfL3/zmN3GLC+Kmdqb61+RV6clW7frqq692k+pwHu644w63gBK21TXVpvw5vzigCZsmtn4hQRN9TXrFUv8Ppys2qlPvCKgNa9Ir2+g8ZsKECW5yLVulozrTtTXZS2Srz3VdlUl1rIWYk08+uYKt2pNsfZSQ/95JJ51UwVb17jnpQ9lq0UUT13AedD21PTHxkYFyLJSH4cOHx9mKh9qeyqWy+4UXtbm+fftWyMP06dNdXn26updle8QRR1Sw/de//hWXB9mqfR5++OGVsk2Urso/Y8aMmDOlMipdtVs5MmEOslUeVCafXzlN6it69uwZ55wkslW6uqejtrqvdF+qjSltvcRcfbz6t6jzp8iPsLOputC94/sW5dm338ra6nqy9Y6pvq+2LA5RB1T5kW14IU55EAflOZzfqtiKg4I6ogt86rejjq3uLf3CL7xoJ777779/zHn0HJLZakzyDqG31T2gdKN50JZx0QVJOYXaTi680KnxXPUbrTeN0XKuGzZsGHvwqmzlMKs/DTPTWKqFYN9Hqi5kq/YYXUD9/PPP7YMPPqhgmygPyWw1/kTbzmeffebq2C/Mqr2rrWthOMpBwoX69Ogirpz78DmVQ2zl8KqNi7XqXI64nlcQbWcK9FEePAefBznh0XqTyPHhhx/G2ap/Vbp+DPJ1rId4a7yK8k1kK19UC9rhPChd9afRPGg+4fOr+0l1qrZ33nnnxcZGnwfNETwzH82s+00L9X4c9bZPPfVUbJG8qrZ+3FYeovWmX5jNmzcvVjZdT/fQ5ZdfXoGZxjbdm+FxW3V35ZVXxok1SkNzQ4kzvu5929acQu0/PGZee+217lk7/rz6TbUNjedRW31f7TLVthqf1YaVrg4vYGhup7rw8w59pnmcHv7uRTI/viroQn2Gt1U9ffe737VVq1a58U3tQPMItZl//vOfrozeVmlozA0HN3iRTvNA3TeemfpZzYUV1OH7KNmqf1L7C49Xaqeas+q6frxQG2vfvr1JjNL95POgNCSERW2VroJKwray8bZ+fNe5Ll26mPwTHT5dzckUeCKmstUh28MOO8zNW8O2qtvBgwfHfgXp5+nqRzSvCduqfem896+8oKcgk8mTJ8fZqr8ZMGBAbJ7hbXUtMfPzUJ3XPSlhUv9X2npX3y1RccqUKXG26st69erlyhW21Zzo2WefjbNVf6O68G3MC4Xf/OY3XcBOOA/q9+RzaX6lduT9q1GjRrn5d1SAlNjox0fvX+mXo5q7hm01zznrrLPcXMGLoBprzj33XOfnRW11X/jxXG1Ytj/4wQ/cPDlsO23aNJOPpTFP+dWYLGbqc3Q+bKv5nu9ffBtRuur3LrzwwjhbicbqXzSmi6/asNqO7iuxCKer+ZMXV/39qzm+OIhP2FZjhebfYWbKr/xGjQFhW0XXPvDAAxVsv/Wtb8X8YjU2cVdfqjl1eFzw96PaxCGHHBLX97lGGjp8X51oISFqm86/qy3wvvnmm24yo5si3GGls1BcGwIQgAAEspuAF//kMGuA9+OL3r2oERbpdF4TcE2K/OEn25q8amLgRUF9rgn4QQcdFJuU+3QVySxbTQQ02ddLE3BNUr0A6W3lqCpt78jovBwvOdfhaym/ckDlnIQdaeVBDkPUVuJUWAT1+ZXDLGchLDJoghqOvNbETQP7OeecE+cYKw0JemHHyzu3mjj6dL3gJOdPDlLY+dOE6oorrog5tslsveD+i1/8ooIQKyHX50F5El/vKEZFWzmKqg+d9wsxqhcJxz5fPg/Kl2x9vandqF7kHEfLpoln2ElTPaiNaYIYFZm1mq+JrXfSVL9qY1Fb1YnS1WRVeVB+NcnVZPrxxx937cxPlHU9Recrj36S6MVKCbThrWLUpuVchG3l5ElAUn2G09X1ZBt20tR21c7lnOjweZBYozwofe+YajFFiw1qU2FbOanHHXdczNmQE6l0JXpJAAnbylGW0xN2vJSuoj4eeeSROFvV1ze+8Y2YrdJVGSR6aQIeTvfTTz91tt5h986fFmiiZZNDJ2En7KR5508LVmEnTba6r3wEjNJVeeWASjgI20pAkq3y4BdOJMbJ4ZAjEnVO5CyEnT+1Odned999cbYSFFW2Xdn6hThFqqhsUedPbe8Pf/hD3AKfnHg5OOGoFrVl9Q1adIs6imoPPqrFO6ty/nS/+YUmOZBTp051zphfXJOt+qsf/ehH9qtf/SrOVm1JeQsvSMr2Jz/5id1www2VstUDUbWIFF4M1P53cjZ1f/k2rbYnIUr9TthWbUmLW7L1To/qTQ6wHNuwrRZl1ZdEbeXAXnrppXG26lt0Pd2L4XTHjh3rnOZwunL4VV6/KCqOyoPOyRkP26qN3njjjXG2YnbLLbc4xz9sq7rxC6g+D7JVGnKCdc5vuzJmzJjYoqhfkFRbVzmii84ql+5X1Zvanvop3cdq51oACrcd1bn6OLWdcLoqs+77sO3NN99sWggO2+reHD9+vLvnwrYq71//+te4hdlktuKoeo4utqqf9guzfrFn3LhxTuDyi6LqZzSXELPoAqo4vP7667HFVj/Gi4NENd8PqA/VeCWf2C/M+nFbttHF1mS2YhZNVwvvWhwIC8oaL8RSix/h+YBsw7/a8vMoiSrhYAPlTczU/3kx2LNXm/LX8uOr+hbdXxoHw2Ox7gsJRWExWO3Rj5l7aqt7MzoWqz2qD/XzBC+i616Lbq/28MMPuwUJnzeVTenJNrrgq/FDPP3CrF8cji6gipmEXB8sob/9L84k5uoIc5Ao6fum8OJwdLFV31MbC2/XonSV30SLrVrQV9l93ezOVv2zOPjFZI2LaufRQz8/90KsL5vyoKjRaNm0iODHeH2meZzakdpj9FAfE7VVfflf8YXtq2Jb4UKcgEANENA97H2ayuzRWwNZ2KMkUyLwKipDK63RDmCPcsSXIAABCGQAAe/YJ9tHMZzFRJ2+d4ijRUlm6x0Fb+9XhH2UpD/vBxu/f7DO78o2en2/R2w03fDENPydROd9GtG0k6WRAdVJFiAAAQhAAAIQgAAEIAABCNQagar4V9FfAvlMJkpD5/QK+4PePpGP5/1Z/+7TjO43ncjP9NHElbWVXXX910T+dyr87WR+fTYJuLtrvCkReBX6rp8/6cDB3x1yPocABCCQ3QT8BCRaCh9REBWIE5W2tm2TTRSi+fDCfjTPiR6G4yNeKmubKA/hiAmfTrLFgUS2PporUV1Ey5YpttEJqY9OTJTfytoqEjU6yU2Urv95VWVsxbSy6XpbvYfLoetE206yNubzG71/ErUbH8ETvlaydJXerib7/nqeSW3dm8n6kShDP7dM1msmuodrytYvbiVilN29et3NfTa2s5pqv3uSbrj/qew4mKh/0rUTjZm1batxsLLjCrY7W0yyuUNVHjYanb94cSrZ/DDd85eqlK26tsnmktFxO9k9lGiMr23bRH3Drsbu2ppnZEoe6u4IS8kykUC1BF7tlaKfCugnBNr/wt9ETHwzsarJEwQgAAEIQAACEEhMIFnkSCocpGTzwtpcLKpKHqpiKz6Z6KxmgrCZiraTiG9VypYsD1Wp47pgW5V7DX+OUQICEIAABCCQnQRSIvBqb0IJvUwIsrMRkGsIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgOwmkRODVkxf1ZDwE3uxsBOQaAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyE4CKRF4Bw8ebDNmzEDgzc42QK4hAAEIQAACEIAABCAAAQhAAAIQgAAEIACBLCWQEoH32GOPtWnTprlN63WwB2+WtgayDQEIQAACEIAABCAAAQhAAAIQgAAEIAABCGQVgZQIvEOHDrWpU6ci8GZV1ZNZCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAINsJpETgHTZsmE2ZMsUJvETvZnuTIP8QgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAthColsCrh6u9/vrrNnLkSHvuueestLTU8vLysqXs5BMCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQFYTiAq8bdq0saVLl1pBQUGcXptTFhy+pD5Sd9CgQfbaa6/ZKaecYk8//TQCb1Y3BTIPAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZBsBL/DecccddsUVV1jbtm1t2bJllp+fn1jgDW/DMGDAAJs1axYCb7bVOvmFAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE6gQBL/DecMMNdv3111urVq2suLg4eQRvWODt1auXvfvuuwi8daIpUAgIQAACEIAABCAAAQhAAAIQgAAEIAABCEAg2wh4gffHP/6x/fGPf7TmzZs7gbdhw4a7j+Dt2bOnzZs3jz14s63WyS8EIAABCEAAAhCAAAQgAAEIQAACEIAABCBQJwh4gXfUqFH2xBNPWJMmTWz58uXWqFGj3Qu8vXv3tnfeeccGDx5sM2bMcEDCEb51ghCFgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCQgQTCWuwxxxxjb775phUVFTmBN+kevGERt3///u5Lhx12mL333nsIvBlYyWQJAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJsEwgJv165dbeXKldasWTO3RUNYx9X/c8qCw2MoLS21vLw8O+200+yZZ56xFi1auC/n5ubGhf3WTWyUCgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA+gl4nfaLL76w9u3bO822Xbt2Nnfu3F0LvNu3b3dPYbvmmmvs5ptvtvr169ucOXOsR48e5vd8SH/xyAEEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgbpLwGuxr776qh133HFu391BgwbZ5MmTKwTixkXweoH38ccftzPOOMMRuvfee2306NEIvHW3vVAyCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAIIMIeIH3hhtuML10XH755XbrrbdW0GkTbtGwbt06F/q7ZcsWO/XUU91T2njIWgbVMFmBAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE6jyBgQMH2rx582zDhg1uS92TTz551wJvWMQdMmSIvfbaa9a0aVNbsmSJNW7cGJG3zjcZCggBCEAAAhCAAAQgAAEIQAACEIAABCAAAQikk4Dff3fFihXWpUsXKyoqMgXk6m9ptf5zn8e4CF6d9Ns0PPLII3b22WdbTk6OjR8/3i666CIrKSmxevXqpbN8XBsCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQJ0l4LdnuP322+3KK6+0ffbZx4466iibOnWqK3N0p4UKAm/YoGPHjk4ZPuCAA+yTTz5xCUQV4jpLkoJBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKhFAmFttnPnzrZp0yb7/PPPTcG4Z555ZsLnpFUQeJVfrxLff//9dv7551t+fr6dddZZ9uCDDyLy1mKFcikIQAACEIAABCAAAQhAAAIQgAAEIAABCEBg7yHgddl77rnHLr74YmvRooU1atTIli5d6iAkek5aQoE3bNi7d29btGiRbd682W677Ta74oorEHn3njZFSSEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFaIODF3fXr11unTp2sfv36tnLlSps0aZLbStd/Hs1KQoFXRn4rhoULF1r37t2tZcuWLhx4zJgxNm7cOJeOEtUevXl5ebVQRC4BAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKDuEQiLt0OGDLHZs2c77XXQoEH28ssvuwInit7V+aQCb/hLEyZMsNGjR1v79u2tuLjYTjrpJHvsscesQYMGCL11rz1RIghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAWCJSVlTkht169eu5qF1xwgd13331WVFTk/l6wYIHbpmFXz0XbpcAbLoO2Z7jqqqusXbt2tnr1amvYsKHdeOONdumll8YVVRmSmqyoXkX36sjNza0FHFwCAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEDmEpBuqkPvEne9sKtzo0aNckG1Ene1TYOieLV97q7EXX1vtwJvOPR34sSJdu6559q+++5rTZo0seXLl1uHDh3chr/nnHOOtW7dugI9ZdRnOHPRkjMIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAjVLIBwU6680ZcoUF0QrrVWBshJ9p02bZn369Em6LUM4l7sVeKNFmj9/vlOTP/jgA9tvv/1clK725i0oKLAjjzzShg8fbgMGDLDDDz/cfc4BAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC/yXw6aef2pNPPmkPP/ywzZkzx2mr27Zts379+tnkyZOtWbNmlRJ3lWKlBV4fPuy3W3jggQfcFg1Llixx2zU0btzYZWLjxo0ubLhRo0Yu0lcRvs2bN3f/33///d15RfVyQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoKwSkeUo73bp1q23YsMGJtNJEFbX71VdfmUTdpUuXuteKFSts8+bNsaK3bNnSxo4da5dccok7t7ttGcLMKi3w+i9FE3/++eftwQcftOnTp7u9eRXRK8XZC8ESfb04XFcqi3JAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgOgfz8fOvWrZudd955dtlllzkhuKriruyrLPDqSxJs9VImwsfMmTNdSPG7775rxcXFTvDVQ9e82CtxmAMC6SBA1Hg6qHPNTCFA+8+UmiAfYQK0S9pDZQhovukf2lsZ+2ywoe2nvpbqWhvxhAiSSX1bIcXqE6APqz7DvSEF2sneUMuUsTIEdC8oQld6qDRUvRo0aOCiert27eq2Yhg6dKj17NkzlpxsNbfxWmplrrPHAm940uEfwlbVC1c2g9hBAAIQgAAEIAABCEAAAhCAAAQgAAEIQGBvIoBQnv21LaFWW9kq+FXirh6cVlhYmLBgJSUlTtT1EbxVLf0eRfAmuogUZjU+L/gmeiJcVTOHPQQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBukRAOqpeEnX9qzrlS5nAmygT/KyoOlXDdyEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFsJRCNxPZbS6V6J4QaFXizFT75hgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCQDQQQeLOhlsgjBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEEBP4PDJQm/KADzLEAAAAASUVORK5CYII=)"
      ],
      "metadata": {
        "id": "ldeSoJyMUTsR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "documents = [\n",
        "    {\n",
        "        \"id\": 1,\n",
        "        \"title\": \"What is NLP?\",\n",
        "        \"text\": \"Natural Language Processing, or NLP, is a field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language.\"\n",
        "    },\n",
        "    {\n",
        "        \"id\": 2,\n",
        "        \"title\": \"Transformers\",\n",
        "        \"text\": \"Transformers are neural network architectures based on self-attention. They became the foundation of modern language models because they capture long-range dependencies efficiently.\"\n",
        "    },\n",
        "    {\n",
        "        \"id\": 3,\n",
        "        \"title\": \"Retrieval-Augmented Generation\",\n",
        "        \"text\": \"Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces an answer grounded in that retrieved context.\"\n",
        "    },\n",
        "    {\n",
        "        \"id\": 4,\n",
        "        \"title\": \"Vector Databases\",\n",
        "        \"text\": \"Vector databases store dense vector representations of text, images, or other data. They support similarity search over embeddings, which is useful in semantic retrieval.\"\n",
        "    },\n",
        "    {\n",
        "        \"id\": 5,\n",
        "        \"title\": \"Fine-tuning\",\n",
        "        \"text\": \"Fine-tuning adapts a pretrained model to a downstream task by training it on task-specific labeled data.\"\n",
        "    },\n",
        "    {\n",
        "        \"id\": 6,\n",
        "        \"title\": \"Cooking Pasta\",\n",
        "        \"text\": \"To cook pasta, boil water, add salt, and cook the pasta until it reaches the desired texture.\"\n",
        "    }\n",
        "]"
      ],
      "metadata": {
        "id": "RXAZ07GMUTSz"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Frg3qdJvSIdx",
        "outputId": "ff2db030-7e4c-44c1-873c-77d23fb791cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] What is NLP?: Natural Language Processing, or NLP, is a field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language.\n",
            "[2] Transformers: Transformers are neural network architectures based on self-attention. They became the foundation of modern language models because they capture long-range dependencies efficiently.\n",
            "[3] Retrieval-Augmented Generation: Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces an answer grounded in that retrieved context.\n",
            "[4] Vector Databases: Vector databases store dense vector representations of text, images, or other data. They support similarity search over embeddings, which is useful in semantic retrieval.\n",
            "[5] Fine-tuning: Fine-tuning adapts a pretrained model to a downstream task by training it on task-specific labeled data.\n",
            "[6] Cooking Pasta: To cook pasta, boil water, add salt, and cook the pasta until it reaches the desired texture.\n"
          ]
        }
      ],
      "source": [
        "for doc in documents:\n",
        "    print(f\"[{doc['id']}] {doc['title']}: {doc['text']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Baseline 1: naive keyword retrieval**"
      ],
      "metadata": {
        "id": "40tfKlmZVJwa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from collections import Counter\n",
        "\n",
        "def tokenize(text):\n",
        "    return re.findall(r\"\\b\\w+\\b\", text.lower())\n",
        "\n",
        "def keyword_overlap_score(query, document_text):\n",
        "    query_tokens = tokenize(query)\n",
        "    doc_tokens = tokenize(document_text)\n",
        "    query_counts = Counter(query_tokens)\n",
        "    doc_counts = Counter(doc_tokens)\n",
        "\n",
        "    score = 0\n",
        "    for token in query_counts:\n",
        "        score += min(query_counts[token], doc_counts[token])\n",
        "    return score\n",
        "\n",
        "def retrieve_keyword(query, documents, top_k=3):\n",
        "    scored = []\n",
        "    for doc in documents:\n",
        "        score = keyword_overlap_score(query, doc[\"text\"])\n",
        "        scored.append((score, doc))\n",
        "    scored = sorted(scored, key=lambda x: x[0], reverse=True)\n",
        "    return scored[:top_k]"
      ],
      "metadata": {
        "id": "fXWl5k0OU1zI"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"How does RAG use external knowledge?\"\n",
        "results = retrieve_keyword(query, documents, top_k=3)\n",
        "\n",
        "for score, doc in results:\n",
        "    print(f\"Score: {score} | Title: {doc['title']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWjovy2rVOmB",
        "outputId": "952c24a5-6c1c-4ae0-bf5d-0d05026c06fd"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 2 | Title: Retrieval-Augmented Generation\n",
            "Score: 0 | Title: What is NLP?\n",
            "Score: 0 | Title: Transformers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WJct3qk3WC8b"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Baseline 2: semantic retrieval with embeddings**"
      ],
      "metadata": {
        "id": "smT0TvrRWaCS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q sentence-transformers scikit-learn"
      ],
      "metadata": {
        "id": "VGGAQ6WXWbrS"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encode documents**"
      ],
      "metadata": {
        "id": "yDSbn1h6XC9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import numpy as np\n",
        "\n",
        "model = SentenceTransformer(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
        "\n",
        "doc_texts = [doc[\"text\"] for doc in documents]\n",
        "doc_embeddings = model.encode(doc_texts, convert_to_numpy=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174,
          "referenced_widgets": [
            "e4d57f38007645cfb1b4203f6aacaf0c",
            "3ab7ad6721e34b99929e5e98155d74c2",
            "b66448a1fd4e4b39854570550162e3d2",
            "909701d1cd2a4da8b4f7ec1100e47c3e",
            "0185f23aa38c4781978e5781da8b029b",
            "a061ae74708d48008661a931373972f4",
            "0a818371b0f04be0a05889369876e831",
            "0fd4d2a2c89949ef923ba6d121641ad2",
            "75b15d8fba5e499ab1e40aeb39611b66",
            "fc98f481baec4a618d0210ec348dc6d3",
            "69c2987d8b7e41f3afb06613ef28dec3"
          ]
        },
        "id": "YH9Rw7grWfFb",
        "outputId": "b1f82ed0-06b6-4d48-db36-a4bc56e7de7f"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/103 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4d57f38007645cfb1b4203f6aacaf0c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "BertModel LOAD REPORT from: sentence-transformers/all-MiniLM-L6-v2\n",
            "Key                     | Status     |  | \n",
            "------------------------+------------+--+-\n",
            "embeddings.position_ids | UNEXPECTED |  | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Retrieval function**"
      ],
      "metadata": {
        "id": "Ncrz1VL9XFI0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_semantic(query, documents, doc_embeddings, model, top_k=3):\n",
        "    query_embedding = model.encode([query], convert_to_numpy=True)\n",
        "    similarities = cosine_similarity(query_embedding, doc_embeddings)[0]\n",
        "\n",
        "    ranked_indices = np.argsort(similarities)[::-1][:top_k]\n",
        "\n",
        "    results = []\n",
        "    for idx in ranked_indices:\n",
        "        results.append((similarities[idx], documents[idx]))\n",
        "    return results"
      ],
      "metadata": {
        "id": "AvsJJZz-W4ZN"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"How does RAG use outside information?\"\n",
        "results = retrieve_semantic(query, documents, doc_embeddings, model, top_k=3)\n",
        "\n",
        "for score, doc in results:\n",
        "    print(f\"Similarity: {score:.4f} | Title: {doc['title']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXtygy0nXK0O",
        "outputId": "4602e62b-220b-4e18-f704-ab522c70ce01"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Similarity: 0.5618 | Title: Retrieval-Augmented Generation\n",
            "Similarity: 0.1457 | Title: Vector Databases\n",
            "Similarity: 0.1321 | Title: Transformers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Why chunking is necessary?**\n",
        "\n",
        "\n",
        "*   long documents contain multiple topics\n",
        "*   one embedding for whole document can blur meaning\n",
        "*   retrieval becomes less precise\n",
        "*   the LLM context window is limited"
      ],
      "metadata": {
        "id": "upS9xd02XVPe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "long_text = \"\"\"\n",
        "Retrieval-Augmented Generation is a framework that combines information retrieval with text generation.\n",
        "Instead of relying only on the parametric memory of a language model, RAG retrieves external documents or chunks\n",
        "that are relevant to a user query. These retrieved pieces of context are then passed into the generator,\n",
        "which produces a more grounded and accurate answer.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "yPox8YYjXloR"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def chunk_text(text, chunk_size=40, overlap=10):\n",
        "    words = text.split()\n",
        "    chunks = []\n",
        "    start = 0\n",
        "\n",
        "    while start < len(words):\n",
        "        end = start + chunk_size\n",
        "        chunk = \" \".join(words[start:end])\n",
        "        chunks.append(chunk)\n",
        "\n",
        "        if end >= len(words):\n",
        "            break\n",
        "        start += chunk_size - overlap\n",
        "\n",
        "    return chunks"
      ],
      "metadata": {
        "id": "8Y9g6BQaXM4l"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chunks = chunk_text(long_text, chunk_size=20, overlap=5)\n",
        "\n",
        "for i, chunk in enumerate(chunks):\n",
        "    print(f\"Chunk {i+1}: {chunk}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ORjzG_LXnwe",
        "outputId": "270e0071-8421-4eee-ada3-25a40f22425a"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chunk 1: Retrieval-Augmented Generation is a framework that combines information retrieval with text generation. Instead of relying only on the parametric memory\n",
            "\n",
            "Chunk 2: only on the parametric memory of a language model, RAG retrieves external documents or chunks that are relevant to a\n",
            "\n",
            "Chunk 3: that are relevant to a user query. These retrieved pieces of context are then passed into the generator, which produces\n",
            "\n",
            "Chunk 4: into the generator, which produces a more grounded and accurate answer.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* without overlap, useful information may split badly across chunk boundaries\n",
        "* overlap preserves continuity"
      ],
      "metadata": {
        "id": "MDBvXRgWXxbO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build chunk-level index**"
      ],
      "metadata": {
        "id": "XG0nCIfJX4Qn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chunked_corpus = []\n",
        "\n",
        "for doc in documents:\n",
        "    chunks = chunk_text(doc[\"text\"], chunk_size=20, overlap=5)\n",
        "    for j, chunk in enumerate(chunks):\n",
        "        chunked_corpus.append({\n",
        "            \"doc_id\": doc[\"id\"],\n",
        "            \"title\": doc[\"title\"],\n",
        "            \"chunk_id\": j,\n",
        "            \"text\": chunk\n",
        "        })"
      ],
      "metadata": {
        "id": "x5287zeeXq0q"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for item in chunked_corpus[:10]:\n",
        "    print(item)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgodpDYWYCEH",
        "outputId": "0c5c4cc3-eec3-422f-e0c6-95f2f1f55859"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'doc_id': 1, 'title': 'What is NLP?', 'chunk_id': 0, 'text': 'Natural Language Processing, or NLP, is a field of artificial intelligence focused on enabling computers to understand, interpret, and generate'}\n",
            "{'doc_id': 1, 'title': 'What is NLP?', 'chunk_id': 1, 'text': 'to understand, interpret, and generate human language.'}\n",
            "{'doc_id': 2, 'title': 'Transformers', 'chunk_id': 0, 'text': 'Transformers are neural network architectures based on self-attention. They became the foundation of modern language models because they capture long-range'}\n",
            "{'doc_id': 2, 'title': 'Transformers', 'chunk_id': 1, 'text': 'models because they capture long-range dependencies efficiently.'}\n",
            "{'doc_id': 3, 'title': 'Retrieval-Augmented Generation', 'chunk_id': 0, 'text': 'Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces'}\n",
            "{'doc_id': 3, 'title': 'Retrieval-Augmented Generation', 'chunk_id': 1, 'text': 'information, and the generator produces an answer grounded in that retrieved context.'}\n",
            "{'doc_id': 4, 'title': 'Vector Databases', 'chunk_id': 0, 'text': 'Vector databases store dense vector representations of text, images, or other data. They support similarity search over embeddings, which is'}\n",
            "{'doc_id': 4, 'title': 'Vector Databases', 'chunk_id': 1, 'text': 'search over embeddings, which is useful in semantic retrieval.'}\n",
            "{'doc_id': 5, 'title': 'Fine-tuning', 'chunk_id': 0, 'text': 'Fine-tuning adapts a pretrained model to a downstream task by training it on task-specific labeled data.'}\n",
            "{'doc_id': 6, 'title': 'Cooking Pasta', 'chunk_id': 0, 'text': 'To cook pasta, boil water, add salt, and cook the pasta until it reaches the desired texture.'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encode chunks**"
      ],
      "metadata": {
        "id": "gXhGG07PshuC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chunk_texts = [item[\"text\"] for item in chunked_corpus]\n",
        "chunk_embeddings = model.encode(chunk_texts, convert_to_numpy=True)\n",
        "chunk_embeddings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1MjjPjVYDiE",
        "outputId": "11f9765d-cccd-4da0-d1be-cdf25c5ba675"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.00232506, -0.02046783,  0.04117717, ...,  0.11483683,\n",
              "         0.10635287, -0.01068684],\n",
              "       [-0.01222023, -0.00478948, -0.01016358, ...,  0.20164487,\n",
              "         0.05373789, -0.09423851],\n",
              "       [-0.063749  , -0.04680438, -0.0020209 , ...,  0.1026561 ,\n",
              "         0.017344  , -0.00324976],\n",
              "       ...,\n",
              "       [ 0.00613086, -0.04005997,  0.03257215, ...,  0.05440363,\n",
              "         0.03676422,  0.03476672],\n",
              "       [ 0.01590589, -0.06565252,  0.00062193, ...,  0.13069053,\n",
              "         0.02280299, -0.07617868],\n",
              "       [-0.05657782, -0.02696108,  0.00962706, ...,  0.13093704,\n",
              "        -0.01146962, -0.05079087]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Chunk retriever**"
      ],
      "metadata": {
        "id": "djYkweevsqHq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_chunks(query, chunked_corpus, chunk_embeddings, model, top_k=5):\n",
        "    query_embedding = model.encode([query], convert_to_numpy=True)\n",
        "    similarities = cosine_similarity(query_embedding, chunk_embeddings)[0]\n",
        "\n",
        "    ranked_indices = np.argsort(similarities)[::-1][:top_k]\n",
        "\n",
        "    results = []\n",
        "    for idx in ranked_indices:\n",
        "        results.append({\n",
        "            \"score\": float(similarities[idx]),\n",
        "            \"doc_id\": chunked_corpus[idx][\"doc_id\"],\n",
        "            \"title\": chunked_corpus[idx][\"title\"],\n",
        "            \"chunk_id\": chunked_corpus[idx][\"chunk_id\"],\n",
        "            \"text\": chunked_corpus[idx][\"text\"]\n",
        "        })\n",
        "    return results"
      ],
      "metadata": {
        "id": "O9tfzcRcsoYJ"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What does RAG retrieve before generating an answer?\"\n",
        "results = retrieve_chunks(query, chunked_corpus, chunk_embeddings, model, top_k=3)\n",
        "\n",
        "for r in results:\n",
        "    print(f\"Score: {r['score']:.4f} | {r['title']} | chunk {r['chunk_id']}\")\n",
        "    print(r[\"text\"])\n",
        "    print(\"-\" * 80)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETH4gVQ6suLG",
        "outputId": "a443967f-6f21-45b9-cbf5-035b009a05d5"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 0.6394 | Retrieval-Augmented Generation | chunk 0\n",
            "Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "--------------------------------------------------------------------------------\n",
            "Score: 0.5105 | Retrieval-Augmented Generation | chunk 1\n",
            "information, and the generator produces an answer grounded in that retrieved context.\n",
            "--------------------------------------------------------------------------------\n",
            "Score: 0.3464 | What is NLP? | chunk 1\n",
            "to understand, interpret, and generate human language.\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2U9gyQ0GswcB"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build the generation step**"
      ],
      "metadata": {
        "id": "27zxCPPRs77S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prompt template**"
      ],
      "metadata": {
        "id": "_1DvncVKtVGq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_prompt(query, retrieved_chunks):\n",
        "    context = \"\\n\\n\".join(\n",
        "        [f\"[{i+1}] {chunk['text']}\" for i, chunk in enumerate(retrieved_chunks)]\n",
        "    )\n",
        "\n",
        "    prompt = f\"\"\"\n",
        "You are a helpful assistant. Answer the question using only the provided context.\n",
        "If the answer is not in the context, say you do not have enough information.\n",
        "\n",
        "Context:\n",
        "{context}\n",
        "\n",
        "Question:\n",
        "{query}\n",
        "\n",
        "Answer:\n",
        "\"\"\"\n",
        "    return prompt.strip()"
      ],
      "metadata": {
        "id": "OgSX6kdjs9xI"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What is the role of the retriever in RAG?\"\n",
        "retrieved = retrieve_chunks(query, chunked_corpus, chunk_embeddings, model, top_k=3)\n",
        "prompt = build_prompt(query, retrieved)\n",
        "\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lhhVKxlta41",
        "outputId": "6b78fc52-8709-4013-fc66-1b53e3c40683"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a helpful assistant. Answer the question using only the provided context.\n",
            "If the answer is not in the context, say you do not have enough information.\n",
            "\n",
            "Context:\n",
            "[1] Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "[2] information, and the generator produces an answer grounded in that retrieved context.\n",
            "\n",
            "[3] search over embeddings, which is useful in semantic retrieval.\n",
            "\n",
            "Question:\n",
            "What is the role of the retriever in RAG?\n",
            "\n",
            "Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What is the role of the retriever in RAG?\"\n",
        "retrieved = retrieve_chunks(query, chunked_corpus, chunk_embeddings, model, top_k=3)\n",
        "prompt = build_prompt(query, retrieved)\n",
        "\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0FbJ2_DNteev",
        "outputId": "aa9c8aaf-9822-4661-e79a-95f3fb6e04a1"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a helpful assistant. Answer the question using only the provided context.\n",
            "If the answer is not in the context, say you do not have enough information.\n",
            "\n",
            "Context:\n",
            "[1] Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "[2] information, and the generator produces an answer grounded in that retrieved context.\n",
            "\n",
            "[3] search over embeddings, which is useful in semantic retrieval.\n",
            "\n",
            "Question:\n",
            "What is the role of the retriever in RAG?\n",
            "\n",
            "Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Plug in an LLM**"
      ],
      "metadata": {
        "id": "EnSC47Bbt5kU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers accelerate sentence-transformers"
      ],
      "metadata": {
        "id": "vlrgagTftoey"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "import torch\n",
        "\n",
        "model_name = \"Qwen/Qwen2.5-1.5B-Instruct\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "llm = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    torch_dtype=\"auto\",\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "\n",
        "generator = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=llm,\n",
        "    tokenizer=tokenizer\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2669c92dd8ac4354a9c0fc826073b33c",
            "2109650fbef645008b73c2c2fa7f45f9",
            "61ae2343acbe4b94a12c06113b3c9986",
            "0251be32bbca41108a9af49b561c5fe3",
            "cf930cf94fa34e608ea53edcb3c90de7",
            "992e09daa2384e829c73d1db510f4341",
            "76a471cea93f420f95dbc27db322bcfc",
            "64335d0ac5d04813b58466a35caf203f",
            "f5671ee4322b4b71ae60cbe45ae3e713",
            "e1d6615326a2457480be9543ff00c01e",
            "18e88cd9e54a4bc39a7f7df67f857a7b"
          ]
        },
        "id": "yjGZsvYGtzAq",
        "outputId": "03e9a9f0-9338-41b4-926d-814e8d502bc8"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/338 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2669c92dd8ac4354a9c0fc826073b33c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What is the role of the retriever in RAG?\"\n",
        "retrieved = retrieve_chunks(query, chunked_corpus, chunk_embeddings, model, top_k=3)\n",
        "prompt = build_prompt(query, retrieved)\n",
        "\n",
        "response = generator(\n",
        "    prompt,\n",
        "    max_new_tokens=150,\n",
        "    do_sample=False\n",
        ")\n",
        "\n",
        "print(response[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4AwhlVut9wk",
        "outputId": "900550f4-19db-4c91-ed5b-e275ffdc91b2"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Both `max_new_tokens` (=150) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a helpful assistant. Answer the question using only the provided context.\n",
            "If the answer is not in the context, say you do not have enough information.\n",
            "\n",
            "Context:\n",
            "[1] Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "[2] information, and the generator produces an answer grounded in that retrieved context.\n",
            "\n",
            "[3] search over embeddings, which is useful in semantic retrieval.\n",
            "\n",
            "Question:\n",
            "What is the role of the retriever in RAG?\n",
            "\n",
            "Answer: The retriever in RAG plays a crucial role by finding relevant external information to support the generation process. It acts as a bridge between the internal model (generator) and the vast external knowledge sources, ensuring that the generated output is grounded in factual data rather than just abstract concepts. This integration allows for more accurate and reliable answers by leveraging diverse sources of information. Additionally, the retriever helps manage the complexity of large datasets by efficiently filtering out irrelevant content, focusing on what's most pertinent to the task at hand. In essence, the retriever ensures that the generator has access to comprehensive yet focused information, enhancing the overall quality and relevance of the generated responses.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sxJk6wx_vGga"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Wrap it into one function**"
      ],
      "metadata": {
        "id": "gbUBiN98uLgX"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DGHn_kcIvGCo"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rag_pipeline(query, chunked_corpus, chunk_embeddings, embedding_model, generator, top_k=3):\n",
        "    retrieved = retrieve_chunks(query, chunked_corpus, chunk_embeddings, embedding_model, top_k=top_k)\n",
        "    prompt = build_prompt(query, retrieved)\n",
        "\n",
        "    response = generator(\n",
        "        prompt,\n",
        "        max_new_tokens=120,\n",
        "        do_sample=False\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"query\": query,\n",
        "        \"retrieved_chunks\": retrieved,\n",
        "        \"prompt\": prompt,\n",
        "        \"response\": response[0][\"generated_text\"]\n",
        "    }"
      ],
      "metadata": {
        "id": "xWy0387ruByc"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = rag_pipeline(\n",
        "    query=\"Why is chunking useful in RAG?\",\n",
        "    chunked_corpus=chunked_corpus,\n",
        "    chunk_embeddings=chunk_embeddings,\n",
        "    embedding_model=model,\n",
        "    generator=generator,\n",
        "    top_k=3\n",
        ")\n",
        "\n",
        "print(\"RETRIEVED CHUNKS:\")\n",
        "for ch in output[\"retrieved_chunks\"]:\n",
        "    print(ch[\"text\"])\n",
        "    print()\n",
        "\n",
        "print(\"FINAL RESPONSE:\")\n",
        "print(output[\"response\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HEIbkbSuuOuV",
        "outputId": "3ee964f4-79fa-4812-ad95-9bebe2a292a1"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Both `max_new_tokens` (=120) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RETRIEVED CHUNKS:\n",
            "Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "to understand, interpret, and generate human language.\n",
            "\n",
            "models because they capture long-range dependencies efficiently.\n",
            "\n",
            "FINAL RESPONSE:\n",
            "You are a helpful assistant. Answer the question using only the provided context.\n",
            "If the answer is not in the context, say you do not have enough information.\n",
            "\n",
            "Context:\n",
            "[1] Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "[2] to understand, interpret, and generate human language.\n",
            "\n",
            "[3] models because they capture long-range dependencies efficiently.\n",
            "\n",
            "Question:\n",
            "Why is chunking useful in RAG?\n",
            "\n",
            "Answer: I do not have enough information. To determine why chunking is useful in RAG, we would need more details about how chunking relates specifically to this retrieval-augmented generation model. Without additional context on the specific application of chunking within RAG, it's impossible to provide an accurate explanation for its usefulness.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NUWN3J8BvLOo"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = rag_pipeline(\n",
        "    query=\"What is RAG?\",\n",
        "    chunked_corpus=chunked_corpus,\n",
        "    chunk_embeddings=chunk_embeddings,\n",
        "    embedding_model=model,\n",
        "    generator=generator,\n",
        "    top_k=3\n",
        ")\n",
        "\n",
        "print(\"RETRIEVED CHUNKS:\")\n",
        "for ch in output[\"retrieved_chunks\"]:\n",
        "    print(ch[\"text\"])\n",
        "    print()\n",
        "\n",
        "print(\"FINAL RESPONSE:\")\n",
        "print(output[\"response\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHgA1e5CuQ4d",
        "outputId": "a4a6ec59-517c-4ee6-97eb-b05ad4446d99"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Both `max_new_tokens` (=120) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RETRIEVED CHUNKS:\n",
            "Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "to understand, interpret, and generate human language.\n",
            "\n",
            "To cook pasta, boil water, add salt, and cook the pasta until it reaches the desired texture.\n",
            "\n",
            "FINAL RESPONSE:\n",
            "You are a helpful assistant. Answer the question using only the provided context.\n",
            "If the answer is not in the context, say you do not have enough information.\n",
            "\n",
            "Context:\n",
            "[1] Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "[2] to understand, interpret, and generate human language.\n",
            "\n",
            "[3] To cook pasta, boil water, add salt, and cook the pasta until it reaches the desired texture.\n",
            "\n",
            "Question:\n",
            "What is RAG?\n",
            "\n",
            "Answer: RAG is a system that combines a retriever and a generator to find relevant external information and produce human language understanding, interpretation, and generation tasks. [1]\n",
            "\n",
            "This answer can be directly extracted from the given context, which defines RAG as \"Retrieval-Augmented Generation\" (RAG) as a system that integrates a retriever for finding relevant external information with a generator for producing human language tasks such as understanding, interpreting, and generating text. Therefore, there's no need to infer additional details beyond what's explicitly stated in the context.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ask_qwen_direct(question, generator, max_new_tokens=150):\n",
        "    prompt = f\"\"\"\n",
        "You are a helpful assistant. Answer the following question as clearly as possible.\n",
        "\n",
        "Question:\n",
        "{question}\n",
        "\n",
        "Answer:\n",
        "\"\"\".strip()\n",
        "\n",
        "    output = generator(\n",
        "        prompt,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        do_sample=False\n",
        "    )\n",
        "    return output[0][\"generated_text\"]\n",
        "\n",
        "question = \"What is RAG?\"\n",
        "direct_answer = ask_qwen_direct(question, generator)\n",
        "print(direct_answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZsiXTwbu5nR",
        "outputId": "1b3a6a66-dc20-43f5-dc67-ee8bb30a6255"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Both `max_new_tokens` (=150) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a helpful assistant. Answer the following question as clearly as possible.\n",
            "\n",
            "Question:\n",
            "What is RAG?\n",
            "\n",
            "Answer: RAG stands for Read, Analyze, Generate. It's a method used in software development to break down complex problems into smaller, more manageable parts. The process involves reading and understanding the problem thoroughly, analyzing it to identify its key components or requirements, and then generating potential solutions or ideas based on that analysis. This approach helps in structuring the solution process systematically and efficiently. RAG emphasizes breaking down the task into smaller steps, making it easier to tackle each part individually before combining them to form a complete solution. This technique can be applied across various fields such as programming, project management, and even personal problem-solving. By using RAG, developers and professionals can enhance their ability to solve complex issues by focusing on systematic approaches rather than jumping directly\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tDtb50jivMTb"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Improve retrieval with top-k discussion**"
      ],
      "metadata": {
        "id": "2xEAUqHZvfyh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for k in [1, 3, 5]:\n",
        "    print(f\"\\n===== TOP-K = {k} =====\")\n",
        "    results = retrieve_chunks(\"What is RAG?\", chunked_corpus, chunk_embeddings, model, top_k=k)\n",
        "    for r in results:\n",
        "        print(f\"{r['score']:.4f} | {r['title']} | {r['text']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iXgotbTvjqS",
        "outputId": "7647055f-6c9e-4285-8d95-1a716d23ff12"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== TOP-K = 1 =====\n",
            "0.3824 | Retrieval-Augmented Generation | Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "\n",
            "===== TOP-K = 3 =====\n",
            "0.3824 | Retrieval-Augmented Generation | Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "0.1178 | What is NLP? | to understand, interpret, and generate human language.\n",
            "0.0852 | Cooking Pasta | To cook pasta, boil water, add salt, and cook the pasta until it reaches the desired texture.\n",
            "\n",
            "===== TOP-K = 5 =====\n",
            "0.3824 | Retrieval-Augmented Generation | Retrieval-Augmented Generation, or RAG, combines a retriever with a generator. The retriever finds relevant external information, and the generator produces\n",
            "0.1178 | What is NLP? | to understand, interpret, and generate human language.\n",
            "0.0852 | Cooking Pasta | To cook pasta, boil water, add salt, and cook the pasta until it reaches the desired texture.\n",
            "0.0627 | Vector Databases | search over embeddings, which is useful in semantic retrieval.\n",
            "0.0510 | Transformers | Transformers are neural network architectures based on self-attention. They became the foundation of modern language models because they capture long-range\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Add metadata**"
      ],
      "metadata": {
        "id": "UTqINGVYv6fB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chunked_corpus = [\n",
        "    {\n",
        "        \"doc_id\": 1,\n",
        "        \"title\": \"RAG Overview\",\n",
        "        \"source\": \"lecture_notes.pdf\",\n",
        "        \"section\": \"Introduction\",\n",
        "        \"chunk_id\": 0,\n",
        "        \"text\": \"Retrieval-Augmented Generation combines retrieval with generation.\"\n",
        "    }\n",
        "]"
      ],
      "metadata": {
        "id": "JGTZJ0J-vnLb"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O9Z98iAewNpy"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Simple RAG system using wikipedia articles"
      ],
      "metadata": {
        "id": "XPUaAysFzYFO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the Wikipedia URLs"
      ],
      "metadata": {
        "id": "CxiP_nOezVUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "urls = [\n",
        "   # \"https://en.wikipedia.org/wiki/Retrieval-augmented_generation#RAG_key_stages\",\n",
        "    \"https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\",\n",
        "    \"https://en.wikipedia.org/wiki/BERT_(language_model)\"\n",
        "]"
      ],
      "metadata": {
        "id": "Lvh8LZaRwxou"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers accelerate sentence-transformers scikit-learn beautifulsoup4 requests"
      ],
      "metadata": {
        "id": "RXtrdiALwyf1"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import requests\n",
        "import numpy as np\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "import torch"
      ],
      "metadata": {
        "id": "mj2mgysAw1UI"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download and extract clean text from a Wikipedia page"
      ],
      "metadata": {
        "id": "R8tfTBXpzTF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fetch_wikipedia_page_text(url):\n",
        "    \"\"\"\n",
        "    Downloads a Wikipedia page and extracts the main article text.\n",
        "    Returns a dictionary with the URL, title, and cleaned text.\n",
        "    \"\"\"\n",
        "    headers = {\n",
        "        \"User-Agent\": \"Mozilla/5.0 (compatible; EducationalRAGDemo/1.0)\"\n",
        "    }\n",
        "\n",
        "    response = requests.get(url, headers=headers, timeout=20)\n",
        "    response.raise_for_status()\n",
        "\n",
        "    soup = BeautifulSoup(response.text, \"html.parser\")\n",
        "\n",
        "    # Get title\n",
        "    title_tag = soup.find(\"h1\")\n",
        "    title = title_tag.get_text(strip=True) if title_tag else \"Untitled\"\n",
        "\n",
        "    # Wikipedia article body is usually inside this div\n",
        "    content_div = soup.find(\"div\", {\"id\": \"mw-content-text\"})\n",
        "    if content_div is None:\n",
        "        return {\n",
        "            \"url\": url,\n",
        "            \"title\": title,\n",
        "            \"text\": \"\"\n",
        "        }\n",
        "\n",
        "    # Remove elements that are usually noisy\n",
        "    for tag in content_div([\"table\", \"sup\", \"style\", \"script\"]):\n",
        "        tag.decompose()\n",
        "\n",
        "    paragraphs = content_div.find_all(\"p\")\n",
        "    text = \"\\n\".join(p.get_text(\" \", strip=True) for p in paragraphs)\n",
        "\n",
        "    # Basic cleaning\n",
        "    text = re.sub(r\"\\[\\d+\\]\", \"\", text)   # remove citation markers like [1]\n",
        "    text = re.sub(r\"\\s+\", \" \", text).strip()\n",
        "\n",
        "    return {\n",
        "        \"url\": url,\n",
        "        \"title\": title,\n",
        "        \"text\": text\n",
        "    }"
      ],
      "metadata": {
        "id": "MdQK6XE7w3Fp"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load all URLs into a document collection"
      ],
      "metadata": {
        "id": "smiNCrMjzQpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "documents = []\n",
        "\n",
        "for i, url in enumerate(urls):\n",
        "    doc = fetch_wikipedia_page_text(url)\n",
        "    doc[\"doc_id\"] = i\n",
        "    documents.append(doc)"
      ],
      "metadata": {
        "id": "WiOHDKYdw7ly"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(f\"Loaded {len(documents)} documents.\\n\")\n",
        "\n",
        "for doc in documents:\n",
        "    print(\"TITLE:\", doc[\"title\"])\n",
        "    print(\"URL:\", doc[\"url\"])\n",
        "    print(\"TEXT PREVIEW:\", doc[\"text\"][:500])\n",
        "    print(\"-\" * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6kv_oyIxAhf",
        "outputId": "c9a337bb-ea6b-4c66-e952-fc50635ca568"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TITLE: Transformer (deep learning)\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "TEXT PREVIEW: In deep learning , the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens , and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplif\n",
            "----------------------------------------------------------------------------------------------------\n",
            "TITLE: BERT (language model)\n",
            "URL: https://en.wikipedia.org/wiki/BERT_(language_model)\n",
            "TEXT PREVIEW: Bidirectional encoder representations from transformers ( BERT ) is a language model introduced in October 2018 by researchers at Google . It learns to represent text as a sequence of vectors using self-supervised learning . It uses the encoder-only transformer architecture. BERT dramatically improved the state of the art for large language models . As of 2020 , BERT is a ubiquitous baseline in natural language processing (NLP) experiments. BERT is trained by masked token prediction and next sen\n",
            "----------------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chunk the article text"
      ],
      "metadata": {
        "id": "AZGGPBg4zMf1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def chunk_text(text, chunk_size=120, overlap=30):\n",
        "    \"\"\"\n",
        "    Splits text into overlapping chunks based on words.\n",
        "    chunk_size and overlap are measured in words.\n",
        "    \"\"\"\n",
        "    words = text.split()\n",
        "    chunks = []\n",
        "    start = 0\n",
        "\n",
        "    while start < len(words):\n",
        "        end = start + chunk_size\n",
        "        chunk = \" \".join(words[start:end])\n",
        "        chunks.append(chunk)\n",
        "\n",
        "        if end >= len(words):\n",
        "            break\n",
        "\n",
        "        start += chunk_size - overlap\n",
        "\n",
        "    return chunks"
      ],
      "metadata": {
        "id": "2p7Ua5oexC8W"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a chunk-level corpus"
      ],
      "metadata": {
        "id": "GEohPP_VzJ8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chunked_corpus = []\n",
        "\n",
        "for doc in documents:\n",
        "    chunks = chunk_text(doc[\"text\"], chunk_size=120, overlap=30)\n",
        "\n",
        "    for chunk_id, chunk in enumerate(chunks):\n",
        "        chunked_corpus.append({\n",
        "            \"doc_id\": doc[\"doc_id\"],\n",
        "            \"title\": doc[\"title\"],\n",
        "            \"url\": doc[\"url\"],\n",
        "            \"chunk_id\": chunk_id,\n",
        "            \"text\": chunk\n",
        "        })"
      ],
      "metadata": {
        "id": "NrQw_lodxHJi"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Total chunks: {len(chunked_corpus)}\\n\")\n",
        "\n",
        "for item in chunked_corpus[:5]:\n",
        "    print(f\"TITLE: {item['title']} | CHUNK: {item['chunk_id']}\")\n",
        "    print(item[\"text\"][:300])\n",
        "    print(\"-\" * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-49xRJ0xJL7",
        "outputId": "3e4958d8-6507-4668-a4d4-40a135f929d0"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total chunks: 124\n",
            "\n",
            "TITLE: Transformer (deep learning) | CHUNK: 0\n",
            "In deep learning , the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens , and each token is converted into a vector via lookup from a word embedding table. At each lay\n",
            "----------------------------------------------------------------------------------------------------\n",
            "TITLE: Transformer (deep learning) | CHUNK: 1\n",
            "advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLMs) on large (language) datasets . The original ver\n",
            "----------------------------------------------------------------------------------------------------\n",
            "TITLE: Transformer (deep learning) | CHUNK: 2\n",
            "in large-scale natural language processing , computer vision ( vision transformers ), reinforcement learning , audio , multimodal learning , robotics , and playing chess . It has also led to the development of pre-trained systems , such as generative pre-trained transformers (GPTs) and BERT (bidirec\n",
            "----------------------------------------------------------------------------------------------------\n",
            "TITLE: Transformer (deep learning) | CHUNK: 3\n",
            "in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable information about preceding tokens. A key breakthrough was LSTM (1995), an RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient \n",
            "----------------------------------------------------------------------------------------------------\n",
            "TITLE: Transformer (deep learning) | CHUNK: 4\n",
            "for long sequence modelling until the 2017 publication of transformers. However, LSTM still used sequential processing, like most other RNNs. Specifically, RNNs operate one token at a time from first to last; they cannot operate in parallel over all tokens in a sequence. Modern transformers overcome\n",
            "----------------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7NlcVVx3xPbL"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create embeddings for all chunks"
      ],
      "metadata": {
        "id": "qM5jGMEqzGwW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_model = SentenceTransformer(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
        "\n",
        "chunk_texts = [item[\"text\"] for item in chunked_corpus]\n",
        "chunk_embeddings = embedding_model.encode(chunk_texts, convert_to_numpy=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174,
          "referenced_widgets": [
            "3b95fe5eac994ac3ae178be22a7b91eb",
            "bed48d66a4124497ad5c4cac62fea42f",
            "f66a7a161da143a3b292904b853f9c81",
            "4474c836ad3f49a7b17243a2f361197e",
            "484cd51acbee47e3a9810aef4b2e5316",
            "04fe4e2d9a0d40519126b51f15f26a91",
            "9c12e04e2d97427b8c200a34bb1e45fa",
            "8dd810a7bcf345ee8fe76772a9632733",
            "13663016da3a4da0bcae1e486ceceead",
            "e363a4d6ae734173beedcb0b2f7d7dfa",
            "cffb89d776194cd5a74af91e5b8b3f67"
          ]
        },
        "id": "YwjpQWVwxLoR",
        "outputId": "29e6b641-cb1e-4bbb-9f3b-d27064e17422"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/103 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b95fe5eac994ac3ae178be22a7b91eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "BertModel LOAD REPORT from: sentence-transformers/all-MiniLM-L6-v2\n",
            "Key                     | Status     |  | \n",
            "------------------------+------------+--+-\n",
            "embeddings.position_ids | UNEXPECTED |  | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Embedding matrix shape:\", chunk_embeddings.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBvJS2tBxQhP",
        "outputId": "f4cd14b5-9ef8-44c4-e0d0-e4afb195e22b"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Embedding matrix shape: (124, 384)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Retrieve the most relevant chunks for a question"
      ],
      "metadata": {
        "id": "1CIDJwhOy_El"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_chunks(query, chunked_corpus, chunk_embeddings, embedding_model, top_k=5):\n",
        "    \"\"\"\n",
        "    Retrieves the top-k most semantically similar chunks for a user query.\n",
        "    \"\"\"\n",
        "    query_embedding = embedding_model.encode([query], convert_to_numpy=True)\n",
        "    similarities = cosine_similarity(query_embedding, chunk_embeddings)[0]\n",
        "\n",
        "    ranked_indices = np.argsort(similarities)[::-1][:top_k]\n",
        "\n",
        "    results = []\n",
        "    for idx in ranked_indices:\n",
        "        results.append({\n",
        "            \"score\": float(similarities[idx]),\n",
        "            \"title\": chunked_corpus[idx][\"title\"],\n",
        "            \"url\": chunked_corpus[idx][\"url\"],\n",
        "            \"chunk_id\": chunked_corpus[idx][\"chunk_id\"],\n",
        "            \"text\": chunked_corpus[idx][\"text\"]\n",
        "        })\n",
        "    return results"
      ],
      "metadata": {
        "id": "AcYLMM1mxSw-"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What is the main idea of retrieval-augmented generation?\"\n",
        "results = retrieve_chunks(query, chunked_corpus, chunk_embeddings, embedding_model, top_k=3)\n",
        "\n",
        "for r in results:\n",
        "    print(f\"Score: {r['score']:.4f}\")\n",
        "    print(\"Title:\", r[\"title\"])\n",
        "    print(\"Chunk:\", r[\"chunk_id\"])\n",
        "    print(\"URL:\", r[\"url\"])\n",
        "    print(\"Text:\", r[\"text\"][:500])\n",
        "    print(\"-\" * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81NanW_XxVsQ",
        "outputId": "bcf8b769-9a75-4e34-954d-4ad68465eb7e"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 0.3666\n",
            "Title: Transformer (deep learning)\n",
            "Chunk: 81\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: when a user uses an autoregressive transformer to generate a continuation to a sequence of tokens, the model would first perform a forward-pass on this sequence, whereby the KV caches over this sequence are computed. This is called prefilling . Hyperscalers serving large Transformer models may use disaggregated inference , wherein prefilling and decoding are performed on separately specialized hardware. FlashAttention is an algorithm that implements the transformer attention mechanism efficientl\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Score: 0.3484\n",
            "Title: Transformer (deep learning)\n",
            "Chunk: 60\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: the encodings generated by the encoders. This mechanism can also be called the encoder–decoder attention . Like the first encoder, the first decoder takes positional information and embeddings of the output sequence as its input, rather than encodings. The transformer must not use the current or future output to predict an output, so the output sequence must be partially masked to prevent this reverse information flow. This allows for autoregressive text generation. For decoding, all-to-all atte\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Score: 0.3417\n",
            "Title: Transformer (deep learning)\n",
            "Chunk: 48\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: an attention head , and each layer in a transformer model has multiple attention heads. While each attention head attends to the tokens that are relevant to each token, multiple attention heads allow the model to do this for different definitions of \"relevance\". Specifically, the query and key projection matrices, W Q {\\displaystyle W^{Q}} and W K {\\displaystyle W^{K}} , which are involved in the attention score computation, defines the \"relevance\". Meanwhile, the value projection matrix W V {\\d\n",
            "----------------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_name = \"Qwen/Qwen2.5-1.5B-Instruct\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(llm_name)\n",
        "llm_model = AutoModelForCausalLM.from_pretrained(\n",
        "    llm_name,\n",
        "    torch_dtype=\"auto\",\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "\n",
        "generator = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=llm_model,\n",
        "    tokenizer=tokenizer\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "da062f8cf91f44608d00c0bd391adf95",
            "ab2e073e569c450ba5730f239f57e15e",
            "60382ed0419e4495806f8f0b19727eba",
            "b11d2472a5fd4dbdafebc310774245d3",
            "4a33ff8499554abb886c474b0d0e29b9",
            "d112538a96e24de99fbba15358f51355",
            "267aa4d0a1814afaa2c4259fc171d9df",
            "c6983eb44f77416a938b3e3fa481d190",
            "0b962102f196494db38dbd89ca1ac600",
            "d94f7ce92cdb4be28516edd04c93b339",
            "e4dadd15f1594efdb063e34c6fed0b7f"
          ]
        },
        "id": "nIEhs_QExYBN",
        "outputId": "21954aae-3d39-4dcf-fc2e-55339d2be8c3"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/338 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da062f8cf91f44608d00c0bd391adf95"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ask Qwen directly"
      ],
      "metadata": {
        "id": "nHtL-1i9y6mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def ask_qwen_direct(question, generator, max_new_tokens=180):\n",
        "    prompt = f\"\"\"\n",
        "You are a helpful assistant. Answer the question clearly and concisely.\n",
        "\n",
        "Question:\n",
        "{question}\n",
        "\n",
        "Answer:\n",
        "\"\"\".strip()\n",
        "\n",
        "    output = generator(\n",
        "        prompt,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        do_sample=False\n",
        "    )\n",
        "\n",
        "    return output[0][\"generated_text\"]"
      ],
      "metadata": {
        "id": "fK8-fMWexijq"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build the RAG prompt"
      ],
      "metadata": {
        "id": "a4EicJFDy3Uo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_rag_prompt(question, retrieved_chunks):\n",
        "    context = \"\\n\\n\".join(\n",
        "        [\n",
        "            f\"[Source {i+1}] Title: {chunk['title']}\\nURL: {chunk['url']}\\nText: {chunk['text']}\"\n",
        "            for i, chunk in enumerate(retrieved_chunks)\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    prompt = f\"\"\"\n",
        "You are a helpful assistant.\n",
        "Answer the question using only the provided context.\n",
        "If the answer is not in the context, say that you do not have enough information.\n",
        "\n",
        "Context:\n",
        "{context}\n",
        "\n",
        "Question:\n",
        "{question}\n",
        "\n",
        "Answer:\n",
        "\"\"\".strip()\n",
        "\n",
        "    return prompt"
      ],
      "metadata": {
        "id": "7P0GR2Igxmf-"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ask Qwen with RAG"
      ],
      "metadata": {
        "id": "1fWnh9kiyzsJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def ask_qwen_with_rag(question, generator, chunked_corpus, chunk_embeddings, embedding_model, top_k=3, max_new_tokens=180):\n",
        "    retrieved_chunks = retrieve_chunks(\n",
        "        query=question,\n",
        "        chunked_corpus=chunked_corpus,\n",
        "        chunk_embeddings=chunk_embeddings,\n",
        "        embedding_model=embedding_model,\n",
        "        top_k=top_k\n",
        "    )\n",
        "\n",
        "    prompt = build_rag_prompt(question, retrieved_chunks)\n",
        "\n",
        "    output = generator(\n",
        "        prompt,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        do_sample=False\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"retrieved_chunks\": retrieved_chunks,\n",
        "        \"prompt\": prompt,\n",
        "        \"answer\": output[0][\"generated_text\"]\n",
        "    }"
      ],
      "metadata": {
        "id": "CVBQT4qOxp_c"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compare direct vs RAG answers"
      ],
      "metadata": {
        "id": "kK8za1p8yxUc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compare_direct_vs_rag(question, generator, chunked_corpus, chunk_embeddings, embedding_model, top_k=3):\n",
        "    direct_answer = ask_qwen_direct(question, generator)\n",
        "\n",
        "    rag_result = ask_qwen_with_rag(\n",
        "        question=question,\n",
        "        generator=generator,\n",
        "        chunked_corpus=chunked_corpus,\n",
        "        chunk_embeddings=chunk_embeddings,\n",
        "        embedding_model=embedding_model,\n",
        "        top_k=top_k\n",
        "    )\n",
        "\n",
        "    print(\"=\" * 120)\n",
        "    print(\"QUESTION:\")\n",
        "    print(question)\n",
        "\n",
        "    print(\"\\n\" + \"=\" * 120)\n",
        "    print(\"DIRECT QWEN ANSWER:\")\n",
        "    print(direct_answer)\n",
        "\n",
        "    print(\"\\n\" + \"=\" * 120)\n",
        "    print(\"RETRIEVED CHUNKS:\")\n",
        "    for i, chunk in enumerate(rag_result[\"retrieved_chunks\"], 1):\n",
        "        print(f\"\\n[{i}] Score: {chunk['score']:.4f}\")\n",
        "        print(\"Title:\", chunk[\"title\"])\n",
        "        print(\"Chunk ID:\", chunk[\"chunk_id\"])\n",
        "        print(\"URL:\", chunk[\"url\"])\n",
        "        print(\"Text:\", chunk[\"text\"][:500])\n",
        "\n",
        "    print(\"\\n\" + \"=\" * 120)\n",
        "    print(\"RAG-BASED QWEN ANSWER:\")\n",
        "    print(rag_result[\"answer\"])"
      ],
      "metadata": {
        "id": "MiQaFRyNxse1"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "questions = [\n",
        "\n",
        "    \"What is the main architectural idea behind transformers?\",\n",
        "    \"What is BERT designed for?\",\n",
        "\n",
        "]\n",
        "\n",
        "for q in questions:\n",
        "    compare_direct_vs_rag(\n",
        "        question=q,\n",
        "        generator=generator,\n",
        "        chunked_corpus=chunked_corpus,\n",
        "        chunk_embeddings=chunk_embeddings,\n",
        "        embedding_model=embedding_model,\n",
        "        top_k=3\n",
        "    )\n",
        "    print(\"\\n\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kR1DeYbAxvr2",
        "outputId": "e28c48cd-3ec2-495b-9758-7253101b298b"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Both `max_new_tokens` (=180) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n",
            "Both `max_new_tokens` (=180) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n",
            "Both `max_new_tokens` (=180) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========================================================================================================================\n",
            "QUESTION:\n",
            "What is the main architectural idea behind transformers?\n",
            "\n",
            "========================================================================================================================\n",
            "DIRECT QWEN ANSWER:\n",
            "You are a helpful assistant. Answer the question clearly and concisely.\n",
            "\n",
            "Question:\n",
            "What is the main architectural idea behind transformers?\n",
            "\n",
            "Answer: The main architectural idea behind transformers is to use self-attention mechanisms, which allow them to weigh different parts of an input sequence based on their relevance to each other. This enables transformers to capture long-range dependencies in sequential data more effectively than traditional RNNs or CNNs. Transformers also have a fixed number of parameters that can be learned during training, making them computationally efficient compared to recurrent models. Additionally, they use multi-head attention, allowing for parallel computation across multiple heads, further improving efficiency. Overall, these features make transformers well-suited for tasks involving natural language processing and other sequential data analysis.\n",
            "\n",
            "========================================================================================================================\n",
            "RETRIEVED CHUNKS:\n",
            "\n",
            "[1] Score: 0.5610\n",
            "Title: Transformer (deep learning)\n",
            "Chunk ID: 1\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLMs) on large (language) datasets . The original version of the transformer architecture was proposed in the 2017 paper \" Attention Is All You Need \" by researchers at Google . The predecessors of transformers were developed as an improvement over prev\n",
            "\n",
            "[2] Score: 0.5222\n",
            "Title: Transformer (deep learning)\n",
            "Chunk ID: 36\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: on its neighbors, much like what happens in a convolutional neural network language model . In the author's words, \"we hypothesized it would allow the model to easily learn to attend by relative position.\" In typical implementations, all operations are done over the real numbers, not the complex numbers, but since complex multiplication can be implemented as real 2-by-2 matrix multiplication , this is a mere notational difference. Like earlier seq2seq models, the original transformer model used \n",
            "\n",
            "[3] Score: 0.4710\n",
            "Title: Transformer (deep learning)\n",
            "Chunk ID: 14\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: is All You Need\", a multimodal transformer architecture, MultiModel, was published by most authors of that paper. Other examples include the vision transformer , speech recognition, robotics, and multimodal. The vision transformer, in turn, stimulated new developments in convolutional neural networks . Image and video generators like DALL-E (2021), Stable Diffusion 3 (2024), and Sora (2024), use transformers to analyse input data (like text prompts) by breaking it down into \"tokens\" and then cal\n",
            "\n",
            "========================================================================================================================\n",
            "RAG-BASED QWEN ANSWER:\n",
            "You are a helpful assistant.\n",
            "Answer the question using only the provided context.\n",
            "If the answer is not in the context, say that you do not have enough information.\n",
            "\n",
            "Context:\n",
            "[Source 1] Title: Transformer (deep learning)\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLMs) on large (language) datasets . The original version of the transformer architecture was proposed in the 2017 paper \" Attention Is All You Need \" by researchers at Google . The predecessors of transformers were developed as an improvement over previous architectures for machine translation , but have found many applications since. They are used in large-scale natural language processing , computer vision ( vision transformers ), reinforcement learning , audio , multimodal learning , robotics , and playing chess . It has also led\n",
            "\n",
            "[Source 2] Title: Transformer (deep learning)\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: on its neighbors, much like what happens in a convolutional neural network language model . In the author's words, \"we hypothesized it would allow the model to easily learn to attend by relative position.\" In typical implementations, all operations are done over the real numbers, not the complex numbers, but since complex multiplication can be implemented as real 2-by-2 matrix multiplication , this is a mere notational difference. Like earlier seq2seq models, the original transformer model used an encoder–decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding layers that iteratively process the encoder's output and the decoder's output tokens so far. The purpose of\n",
            "\n",
            "[Source 3] Title: Transformer (deep learning)\n",
            "URL: https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)\n",
            "Text: is All You Need\", a multimodal transformer architecture, MultiModel, was published by most authors of that paper. Other examples include the vision transformer , speech recognition, robotics, and multimodal. The vision transformer, in turn, stimulated new developments in convolutional neural networks . Image and video generators like DALL-E (2021), Stable Diffusion 3 (2024), and Sora (2024), use transformers to analyse input data (like text prompts) by breaking it down into \"tokens\" and then calculating the relevance between each token using self-attention, which helps the model understand the context and relationships within the data. The plain transformer architecture had difficulty in converging. In the original paper, the authors recommended using learning rate warmup. That is, the learning rate should linearly scale\n",
            "\n",
            "Question:\n",
            "What is the main architectural idea behind transformers?\n",
            "\n",
            "Answer: The main architectural idea behind transformers is their ability to perform attention mechanisms across multiple positions in the sequence, allowing them to capture dependencies between elements in a more efficient manner compared to traditional RNNs or CNNs. This allows transformers to effectively handle sequential data without the need for explicit state representation, making them highly effective for tasks involving language modeling, image generation, and other sequential data analysis.\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Both `max_new_tokens` (=180) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========================================================================================================================\n",
            "QUESTION:\n",
            "What is BERT designed for?\n",
            "\n",
            "========================================================================================================================\n",
            "DIRECT QWEN ANSWER:\n",
            "You are a helpful assistant. Answer the question clearly and concisely.\n",
            "\n",
            "Question:\n",
            "What is BERT designed for?\n",
            "\n",
            "Answer: BERT (Bidirectional Encoder Representations from Transformers) was designed to address the limitations of previous transformer-based models in understanding context, especially when dealing with long sequences or sentences that contain many words. It does this by incorporating bidirectional attention mechanisms into the Transformer architecture, allowing it to capture information from both ends of a sequence simultaneously. This makes BERT particularly effective for tasks such as language modeling, text classification, and question answering where understanding the relationship between different parts of a sentence is crucial. The model's ability to learn contextual representations across the entire input sequence has significantly improved its performance on various NLP tasks compared to earlier approaches.\n",
            "\n",
            "========================================================================================================================\n",
            "RETRIEVED CHUNKS:\n",
            "\n",
            "[1] Score: 0.6201\n",
            "Title: BERT (language model)\n",
            "Chunk ID: 14\n",
            "URL: https://en.wikipedia.org/wiki/BERT_(language_model)\n",
            "Text: mask out all the tokens as \"Today, I went to [MASK] [MASK] [MASK] ... [MASK] .\" where the number of [MASK] is the length of the sentence one wishes to extend to. However, this constitutes a dataset shift, as during training, BERT has never seen sentences with that many tokens masked out. Consequently, its performance degrades. More sophisticated techniques allow text generation, but at a high computational cost. BERT was originally published by Google researchers Jacob Devlin, Ming-Wei Chang, Ke\n",
            "\n",
            "[2] Score: 0.5769\n",
            "Title: BERT (language model)\n",
            "Chunk ID: 5\n",
            "URL: https://en.wikipedia.org/wiki/BERT_(language_model)\n",
            "Text: 64 {\\displaystyle H/64} self-attention heads, and the feed-forward/filter size is always 4 H {\\displaystyle 4H} . By varying these two numbers, one obtains an entire family of BERT models. For BERT: The notation for encoder stack is written as L/H. For example, BERT BASE is written as 12L/768H, BERT LARGE as 24L/1024H, and BERT TINY as 2L/128H. BERT was pre-trained simultaneously on two tasks: In masked language modeling, 15% of tokens would be randomly selected for masked-prediction task, and t\n",
            "\n",
            "[3] Score: 0.5687\n",
            "Title: BERT (language model)\n",
            "Chunk ID: 4\n",
            "URL: https://en.wikipedia.org/wiki/BERT_(language_model)\n",
            "Text: the initial token representation as a function of these three pieces of information. After embedding, the vector representation is normalized using a LayerNorm operation, outputting a 768-dimensional vector for each input token. After this, the representation vectors are passed forward through 12 Transformer encoder blocks, and are decoded back to 30,000-dimensional vocabulary space using a basic affine transformation layer. The encoder stack of BERT has 2 free parameters: L {\\displaystyle L} , \n",
            "\n",
            "========================================================================================================================\n",
            "RAG-BASED QWEN ANSWER:\n",
            "You are a helpful assistant.\n",
            "Answer the question using only the provided context.\n",
            "If the answer is not in the context, say that you do not have enough information.\n",
            "\n",
            "Context:\n",
            "[Source 1] Title: BERT (language model)\n",
            "URL: https://en.wikipedia.org/wiki/BERT_(language_model)\n",
            "Text: mask out all the tokens as \"Today, I went to [MASK] [MASK] [MASK] ... [MASK] .\" where the number of [MASK] is the length of the sentence one wishes to extend to. However, this constitutes a dataset shift, as during training, BERT has never seen sentences with that many tokens masked out. Consequently, its performance degrades. More sophisticated techniques allow text generation, but at a high computational cost. BERT was originally published by Google researchers Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. The design has its origins from pre-training contextual representations, including semi-supervised sequence learning , generative pre-training, ELMo , and ULMFit. Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain\n",
            "\n",
            "[Source 2] Title: BERT (language model)\n",
            "URL: https://en.wikipedia.org/wiki/BERT_(language_model)\n",
            "Text: 64 {\\displaystyle H/64} self-attention heads, and the feed-forward/filter size is always 4 H {\\displaystyle 4H} . By varying these two numbers, one obtains an entire family of BERT models. For BERT: The notation for encoder stack is written as L/H. For example, BERT BASE is written as 12L/768H, BERT LARGE as 24L/1024H, and BERT TINY as 2L/128H. BERT was pre-trained simultaneously on two tasks: In masked language modeling, 15% of tokens would be randomly selected for masked-prediction task, and the training objective was to predict the masked token given its context. In more detail, the selected token is: The reason not all selected tokens are masked is to avoid the dataset shift problem. The dataset shift problem arises when the\n",
            "\n",
            "[Source 3] Title: BERT (language model)\n",
            "URL: https://en.wikipedia.org/wiki/BERT_(language_model)\n",
            "Text: the initial token representation as a function of these three pieces of information. After embedding, the vector representation is normalized using a LayerNorm operation, outputting a 768-dimensional vector for each input token. After this, the representation vectors are passed forward through 12 Transformer encoder blocks, and are decoded back to 30,000-dimensional vocabulary space using a basic affine transformation layer. The encoder stack of BERT has 2 free parameters: L {\\displaystyle L} , the number of layers, and H {\\displaystyle H} , the hidden size . There are always H / 64 {\\displaystyle H/64} self-attention heads, and the feed-forward/filter size is always 4 H {\\displaystyle 4H} . By varying these two numbers, one obtains an entire family of BERT models. For\n",
            "\n",
            "Question:\n",
            "What is BERT designed for?\n",
            "\n",
            "Answer: BERT was originally published by Google researchers Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, generative pre-training, ELMo, and ULMFit. Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain vanilla language modeling task. It was pre-trained simultaneously on two tasks: In masked language modeling, 15% of tokens would be randomly selected for masked-prediction task, and the training objective was to predict the masked token given its context. In more detail, the selected token is: The reason not all selected tokens are masked is to avoid the dataset shift problem. The dataset shift problem arises when the number of tokens being masked changes during training compared to testing. This allows B\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xALDzHS5ydgP"
      },
      "execution_count": 126,
      "outputs": []
    }
  ]
}