thainlp.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/San-Maansson/NLP/blob/main/thainlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install spacy-thai"
      ],
      "metadata": {
        "id": "7sCa1xQqJhpT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "hWEqoWICIbq2"
      },
      "outputs": [],
      "source": [
        "import spacy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "C8IYS732Ibq6"
      },
      "outputs": [],
      "source": [
        "import spacy_thai\n",
        "import deplacy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp=spacy_thai.load()\n",
        "doc=nlp(\"ไก่และสิ่งมีชีวิตวางไข่อื่น ๆ เก็บเลี้ยงอย่างกว้างขวางทั่วโลก และการผลิตไข่ไก่จำนวนมากเป็นอุตสาหกรรมระดับโลก\")"
      ],
      "metadata": {
        "id": "SlRYW_CMKD7x"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for t in doc:\n",
        "  print(\"\\t\".join([str(t.i+1),'{:<29}'.format(f'{t.orth_}'),t.lemma_,t.pos_,t.tag_,str(0 if t.head==t else t.head.i+1),'{:<8}'.format(f'{t.dep_}'),\"_\" if t.whitespace_ else \"SpaceAfter=No\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMmmHtGcKMQ_",
        "outputId": "18de5bee-2a3b-463f-a2c1-6858fa7f1407"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\tไก่                          \t\tNOUN\tNCMN\t7\tnsubj   \tSpaceAfter=No\n",
            "2\tและ                          \t\tCCONJ\tJCRG\t3\tcc      \tSpaceAfter=No\n",
            "3\tสิ่งมีชีวิต                  \t\tNOUN\tNCMN\t1\tconj    \tSpaceAfter=No\n",
            "4\tวางไข่                       \t\tNOUN\tNCMN\t3\tcompound\tSpaceAfter=No\n",
            "5\tอื่น                         \t\tDET\tDIAC\t6\tdet     \t_\n",
            "6\tๆ                            \t\tNOUN\tNCMN\t1\tcompound\t_\n",
            "7\tเก็บ                         \t\tVERB\tVACT\t0\tROOT    \tSpaceAfter=No\n",
            "8\tเลี้ยง                       \t\tNOUN\tNCMN\t7\tobj     \tSpaceAfter=No\n",
            "9\tอย่าง                        \t\tPART\tFIXV\t7\taux     \tSpaceAfter=No\n",
            "10\tกว้างขวาง                    \t\tADJ\tVATT\t7\tadvmod  \tSpaceAfter=No\n",
            "11\tทั่วโลก                      \t\tNOUN\tNCMN\t16\tnsubj   \t_\n",
            "12\tและ                          \t\tCCONJ\tJCRG\t13\tcc      \tSpaceAfter=No\n",
            "13\tการผลิต                      \t\tNOUN\tNCMN\t11\tconj    \tSpaceAfter=No\n",
            "14\tไข่ไก่                       \t\tNOUN\tNCMN\t13\tcompound\tSpaceAfter=No\n",
            "15\tจำนวนมาก                     \t\tDET\tDDAN\t11\tdet     \tSpaceAfter=No\n",
            "16\tเป็น                         \t\tVERB\tVSTA\t10\tacl     \tSpaceAfter=No\n",
            "17\tอุตสาหกรรม                   \t\tNOUN\tNCMN\t16\tobj     \tSpaceAfter=No\n",
            "18\tระดับโลก                     \t\tNOUN\tNCMN\t17\tcompound\tSpaceAfter=No\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "deplacy.render(doc,WordRight=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0TYx69lmLRrn",
        "outputId": "6ec29031-8ba0-4c11-df7f-7a4c8ebf5d12"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   nsubj ╔══════>╔═╔═════ NOUN  ไก่\n",
            "      cc ║       ║ ║ ╔══> CCONJ และ\n",
            "    conj ║       ╚>║ ╚═╔═ NOUN  สิ่งมีชีวิต\n",
            "compound ║         ║   ╚> NOUN  วางไข่\n",
            "     det ║         ║   ╔> DET   อื่น\n",
            "compound ║         ╚══>╚═ NOUN  ๆ\n",
            "    ROOT ╚═╔═════════╔═╔═ VERB  เก็บ\n",
            "     obj   ║         ║ ╚> NOUN  เลี้ยง\n",
            "     aux   ║         ╚══> PART  อย่าง\n",
            "  advmod   ╚>╔═══════════ ADJ   กว้างขวาง\n",
            "   nsubj     ║ ╔>╔═╔═════ NOUN  ทั่วโลก\n",
            "      cc     ║ ║ ║ ║ ╔══> CCONJ และ\n",
            "    conj     ║ ║ ║ ╚>╚═╔═ NOUN  การผลิต\n",
            "compound     ║ ║ ║     ╚> NOUN  ไข่ไก่\n",
            "     det     ║ ║ ╚══════> DET   จำนวนมาก\n",
            "     acl     ╚>╚═════╔═══ VERB  เป็น\n",
            "     obj             ╚>╔═ NOUN  อุตสาหกรรม\n",
            "compound               ╚> NOUN  ระดับโลก\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import graphviz\n",
        "graphviz.Source(deplacy.dot(doc)) # Note: Unable to render properly in Github but looks alright in Google Colab"
      ],
      "metadata": {
        "id": "RR1fj5RkSZry",
        "outputId": "72e2cb70-a11a-47c1-fb01-1f66489589fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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font-size=\"14.00\">ไก่และสิ่งมีชีวิตวางไข่อื่น ๆ เก็บเลี้ยงอย่างกว้างขวางทั่วโลก และการผลิตไข่ไก่จำนวนมากเป็นอุตสาหกรรมระดับโลก</text>\n</g>\n<!-- r7&#45;&gt;x17 -->\n<g id=\"edge1\" class=\"edge\">\n<title>r7&#45;&gt;x17</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M1151,-727.91C1151,-722.11 1151,-711.66 1151,-701.5\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"1154.5,-701.21 1151,-691.21 1147.5,-701.21 1154.5,-701.21\"/>\n</g>\n<!-- x13 -->\n<g id=\"node3\" class=\"node\">\n<title>x13</title>\n<text text-anchor=\"middle\" x=\"303\" y=\"-274.3\" font-family=\"Times,serif\" font-size=\"14.00\">ไก่และสิ่งมีชีวิตวางไข่อื่น ๆ</text>\n</g>\n<!-- x17&#45;&gt;x13 -->\n<g id=\"edge2\" class=\"edge\">\n<title>x17&#45;&gt;x13</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M1331.59,-655C1375.96,-646.94 1414.85,-635.17 1429,-618 1462.64,-577.19 1465.36,-537.41 1429,-499 1403.83,-472.41 1136.32,-485.58 1100,-481 818.43,-445.51 493.49,-343.31 362.38,-299.42\"/>\n<polygon fill=\"gray\" 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-->\n<g id=\"edge10\" class=\"edge\">\n<title>x13&#45;&gt;x12</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M253.24,-259.92C216.21,-247.21 165.53,-229.82 126.38,-216.38\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"127.29,-212.99 116.69,-213.06 125.02,-219.61 127.29,-212.99\"/>\n</g>\n<!-- x10 -->\n<g id=\"node10\" class=\"node\">\n<title>x10</title>\n<text text-anchor=\"middle\" x=\"303\" y=\"-191.3\" font-family=\"Times,serif\" font-size=\"14.00\">และสิ่งมีชีวิตวางไข่</text>\n</g>\n<!-- x13&#45;&gt;x10 -->\n<g id=\"edge11\" class=\"edge\">\n<title>x13&#45;&gt;x10</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M303,-259.82C303,-249.19 303,-235.31 303,-223.2\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"306.5,-223.15 303,-213.15 299.5,-223.15 306.5,-223.15\"/>\n<text text-anchor=\"middle\" x=\"315.5\" y=\"-234\" font-family=\"sans-serif\" font-size=\"10.00\">conj</text>\n</g>\n<!-- x15 -->\n<g id=\"node5\" class=\"node\">\n<title>x15</title>\n<text text-anchor=\"middle\" x=\"651\" y=\"-191.3\" font-family=\"Times,serif\" font-size=\"14.00\">เก็บเลี้ยงอย่าง</text>\n</g>\n<!-- x16&#45;&gt;x15 -->\n<g id=\"edge4\" class=\"edge\">\n<title>x16&#45;&gt;x15</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M625.4,-581.94C565.43,-572.49 512.12,-557.8 493,-535 408.02,-433.63 558.34,-278.92 623.29,-219.96\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"625.66,-222.53 630.76,-213.25 620.98,-217.32 625.66,-222.53\"/>\n</g>\n<!-- x11 -->\n<g id=\"node6\" class=\"node\">\n<title>x11</title>\n<text text-anchor=\"middle\" x=\"961\" y=\"-513.3\" font-family=\"Times,serif\" font-size=\"14.00\">กว้างขวางทั่วโลก และการผลิตไข่ไก่จำนวนมากเป็นอุตสาหกรรมระดับโลก</text>\n</g>\n<!-- x16&#45;&gt;x11 -->\n<g id=\"edge5\" class=\"edge\">\n<title>x16&#45;&gt;x11</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M876.69,-581.82C892.3,-570.01 913.21,-554.18 930.3,-541.24\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"932.48,-543.98 938.34,-535.15 928.26,-538.4 932.48,-543.98\"/>\n<text text-anchor=\"middle\" x=\"931.5\" y=\"-556\" font-family=\"sans-serif\" font-size=\"10.00\">advmod</text>\n</g>\n<!-- x14 -->\n<g id=\"node7\" class=\"node\">\n<title>x14</title>\n<text text-anchor=\"middle\" x=\"651\" y=\"-108.3\" font-family=\"Times,serif\" font-size=\"14.00\">เก็บเลี้ยง</text>\n</g>\n<!-- x15&#45;&gt;x14 -->\n<g id=\"edge6\" class=\"edge\">\n<title>x15&#45;&gt;x14</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M651,-176.82C651,-166.19 651,-152.31 651,-140.2\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"654.5,-140.15 651,-130.15 647.5,-140.15 654.5,-140.15\"/>\n</g>\n<!-- w -->\n<g id=\"node8\" class=\"node\">\n<title>w</title>\n<polygon fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"38,-0.5 38,-46.5 1872,-46.5 1872,-0.5 38,-0.5\"/>\n<text text-anchor=\"middle\" x=\"67.5\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">ไก่</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"38,-23.5 97,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"67.5\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">NOUN</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"97,-0.5 97,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"126.5\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">และ</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"97,-23.5 156,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"126.5\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">CCONJ</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"156,-0.5 156,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"242.5\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">สิ่งมีชีวิต</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"156,-23.5 329,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"242.5\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">NOUN</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"329,-0.5 329,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"380\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">วางไข่</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"329,-23.5 431,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"380\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">NOUN</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"431,-0.5 431,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"468\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">อื่น</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"431,-23.5 505,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"468\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">DET</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"505,-0.5 505,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"533\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">ๆ</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"505,-23.5 561,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"533\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">NOUN</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"561,-0.5 561,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"598\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">เก็บ</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"561,-23.5 635,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"598\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">VERB</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"635,-0.5 635,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"686\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">เลี้ยง</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"635,-23.5 737,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"686\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">NOUN</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"737,-0.5 737,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"781\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">อย่าง</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"737,-23.5 825,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"781\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">PART</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"825,-0.5 825,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"897.5\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">กว้างขวาง</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"825,-23.5 970,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"897.5\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">ADJ</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"970,-0.5 970,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"1028\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">ทั่วโลก</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"970,-23.5 1086,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"1028\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">NOUN</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1086,-0.5 1086,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"1115.5\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">และ</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1086,-23.5 1145,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"1115.5\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">CCONJ</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1145,-0.5 1145,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"1203\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">การผลิต</text>\n<polyline fill=\"none\" stroke=\"black\" 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font-size=\"14.00\">DET</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1508,-0.5 1508,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"1545\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">เป็น</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1508,-23.5 1582,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"1545\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">VERB</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1582,-0.5 1582,-46.5 \"/>\n<text text-anchor=\"middle\" x=\"1661.5\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">อุตสาหกรรม</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1582,-23.5 1741,-23.5 \"/>\n<text text-anchor=\"middle\" x=\"1661.5\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">NOUN</text>\n<polyline fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"1741,-0.5 1741,-46.5 \"/>\n<text 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class=\"edge\">\n<title>x7&#45;&gt;x6</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M1469.66,-415.98C1524.4,-405.26 1582.31,-389.54 1600,-369 1634.99,-328.36 1630.6,-260.92 1624.61,-223.37\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"1628,-222.43 1622.83,-213.18 1621.1,-223.63 1628,-222.43\"/>\n</g>\n<!-- x9&#45;&gt;w -->\n<g id=\"edge18\" class=\"edge\">\n<title>x9&#45;&gt;w:3</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M270.47,-93.85C258.93,-84.54 247.16,-71.89 243.31,-57.05\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"246.76,-56.46 242,-47 239.82,-57.37 246.76,-56.46\"/>\n</g>\n<!-- x9&#45;&gt;w -->\n<g id=\"edge19\" class=\"edge\">\n<title>x9&#45;&gt;w:4</title>\n<path fill=\"none\" stroke=\"gray\" d=\"M343.35,-93.98C359.25,-85.42 374.18,-73.35 378.65,-57.07\"/>\n<polygon fill=\"gray\" stroke=\"gray\" points=\"382.14,-57.38 380,-47 375.2,-56.45 382.14,-57.38\"/>\n<text text-anchor=\"middle\" x=\"398.5\" y=\"-68\" font-family=\"sans-serif\" 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            "text/plain": [
              "<graphviz.sources.Source at 0x7fbc8686eda0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# \n",
        "!pip install sklearn_crfsuite\n",
        "!pip install https://github.com/PyThaiNLP/pythainlp/archive/dev.zip\n",
        "!pip install fastai==1.0.46\n",
        "!pip install emoji"
      ],
      "metadata": {
        "id": "gEx2UrjswxLb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dill"
      ],
      "metadata": {
        "id": "ggR6q96wySVn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline\n",
        "\n",
        "from pythainlp.tokenize import word_tokenize\n",
        "from gensim.models import KeyedVectors\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager as fm\n",
        "\n",
        "import dill as pickle\n",
        "import pandas as pd\n",
        "\n",
        "model_path = './'"
      ],
      "metadata": {
        "id": "JiqmHnE6xqBq"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#load into gensim\n",
        "# model = KeyedVectors.load_word2vec_format(f'{model_path}thai2vec.bin',binary=True)\n",
        "from pythainlp import word_vector\n",
        "model = word_vector.WordVector(model_name=\"thai2fit_wv\").get_model() # load thai2fit_wv from pythainlp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "f722d6fb9c274ce3994f2291e8f4800c",
            "92524094ae464a94bb9e4ef69fd4e830",
            "6e17e506b2474813ac0d5aee0a8409b9",
            "f93da565b7af4d649cd17faf0ce77437",
            "e8b47ef22a054f78a106d18188985bb4",
            "198e51aecb7e4f6fb13eb9871b16c95f",
            "71fc3c9b30ea434d8f52b5f70a5f6a62",
            "ca311e1509c84567bba322dee648f5b2",
            "71e74ce9e0f944219ac2f964fe8ca988",
            "ce1357b675f046d88fbf88cc4ba7caae",
            "5e3ed9c4a6fd43939ad2dbbb5a34a72f"
          ]
        },
        "id": "7VYUfsoJygj-",
        "outputId": "9701714c-3e45-42a6-aa1e-5aab1995b1a6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corpus: thai2fit_wv\n",
            "- Downloading: thai2fit_wv 0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/62452646 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f722d6fb9c274ce3994f2291e8f4800c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create dataframe\n",
        "thai2dict = {}\n",
        "for word in model.index_to_key:\n",
        "    thai2dict[word] = model[word]\n",
        "thai2vec = pd.DataFrame.from_dict(thai2dict,orient='index')\n",
        "thai2vec.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "rAUnXfEEym61",
        "outputId": "a8632497-b80f-4db2-ccd4-8e67b5cf343d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2         3         4         5         6    \\\n",
              "ที่   0.308956 -0.097699  0.116745  0.215612  0.015768 -0.064163  0.062168   \n",
              "และ   0.010751 -0.618971  0.129665  0.035460 -0.007560  0.027607  0.397824   \n",
              "เป็น -0.015736 -0.258926  0.052953  0.153728 -0.005985 -0.021081  0.041088   \n",
              "ของ  -0.189711 -0.174774  0.171124 -0.186771  0.054294 -0.114150 -1.109456   \n",
              "มี   -0.156962 -0.231863  0.080312  0.323157  0.215695  0.055145  0.420794   \n",
              "ได้  -0.428813 -0.031194  0.041922 -0.036608 -0.008106  0.076470 -0.782270   \n",
              "\"\"\"\" -0.287710  0.064193  0.205076  0.146356 -0.071343 -0.039451 -1.845461   \n",
              "การ   0.239587 -0.303620  0.079953 -0.453045 -0.528826 -0.161692  0.235725   \n",
              "(    -0.120522 -0.355783  0.168180 -0.377733 -0.158624 -0.047249  0.361140   \n",
              ")    -0.086848 -0.155231  0.133015 -0.039913  0.183761  0.115142 -1.940854   \n",
              "\n",
              "           7         8         9    ...       290       291       292  \\\n",
              "ที่   0.039649  0.864940  0.846904  ... -0.142418  0.033241  0.171581   \n",
              "และ   0.026543  0.254075  0.168328  ... -0.105786  0.180930 -0.101630   \n",
              "เป็น  0.057312  1.633230  0.442729  ... -0.009408 -0.252576 -0.305512   \n",
              "ของ  -0.094466 -0.447015  0.042377  ... -0.168676 -0.148738  0.680404   \n",
              "มี    0.016842  0.256759  0.832864  ... -0.044267 -0.147186 -0.105424   \n",
              "ได้   0.033361  0.606864  0.440520  ...  0.024458 -0.025031  0.103389   \n",
              "\"\"\"\"  0.163763  1.018096  0.272786  ...  0.051024 -0.532856 -0.131856   \n",
              "การ  -0.099673  0.691668  0.536159  ... -0.110436 -0.297495 -0.217414   \n",
              "(     0.161460  0.913314  0.345037  ...  0.116285 -0.318218 -0.356664   \n",
              ")    -0.066565 -2.399744  0.146722  ...  0.019406 -0.181474  0.099863   \n",
              "\n",
              "           293       294       295       296       297       298       299  \n",
              "ที่  -0.624864 -0.009358  0.449131  0.120130 -0.122195 -0.450617 -0.071318  \n",
              "และ   0.070885 -0.037263  0.183606 -0.049088 -0.672288 -1.293044  0.592576  \n",
              "เป็น  0.372542  0.049151  0.568470  0.266586  0.400800 -0.784650  0.197369  \n",
              "ของ   0.097702  0.020270  0.182967 -0.083949  0.006287 -0.707434 -0.070234  \n",
              "มี    0.907078  0.009299  0.550953  0.139337  0.031696 -0.670379 -0.008048  \n",
              "ได้  -0.078255  0.034323  0.459774 -0.748643  0.337775 -0.487408 -0.511535  \n",
              "\"\"\"\" -0.090323 -0.058895  0.151262 -0.420358  0.055971 -0.930814  0.163908  \n",
              "การ   0.045158  0.066647  0.190095 -0.304333 -0.724927 -0.995488 -0.716609  \n",
              "(     0.519889  0.130475  0.125772  0.101328 -0.382658 -1.205359  0.340139  \n",
              ")     0.516092  0.201697  0.249139  0.252957  1.138815 -0.018209  0.232265  \n",
              "\n",
              "[10 rows x 300 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2201f7dc-d060-4af5-8ca0-38c29df8db29\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>290</th>\n",
              "      <th>291</th>\n",
              "      <th>292</th>\n",
              "      <th>293</th>\n",
              "      <th>294</th>\n",
              "      <th>295</th>\n",
              "      <th>296</th>\n",
              "      <th>297</th>\n",
              "      <th>298</th>\n",
              "      <th>299</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ที่</th>\n",
              "      <td>0.308956</td>\n",
              "      <td>-0.097699</td>\n",
              "      <td>0.116745</td>\n",
              "      <td>0.215612</td>\n",
              "      <td>0.015768</td>\n",
              "      <td>-0.064163</td>\n",
              "      <td>0.062168</td>\n",
              "      <td>0.039649</td>\n",
              "      <td>0.864940</td>\n",
              "      <td>0.846904</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.142418</td>\n",
              "      <td>0.033241</td>\n",
              "      <td>0.171581</td>\n",
              "      <td>-0.624864</td>\n",
              "      <td>-0.009358</td>\n",
              "      <td>0.449131</td>\n",
              "      <td>0.120130</td>\n",
              "      <td>-0.122195</td>\n",
              "      <td>-0.450617</td>\n",
              "      <td>-0.071318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>และ</th>\n",
              "      <td>0.010751</td>\n",
              "      <td>-0.618971</td>\n",
              "      <td>0.129665</td>\n",
              "      <td>0.035460</td>\n",
              "      <td>-0.007560</td>\n",
              "      <td>0.027607</td>\n",
              "      <td>0.397824</td>\n",
              "      <td>0.026543</td>\n",
              "      <td>0.254075</td>\n",
              "      <td>0.168328</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.105786</td>\n",
              "      <td>0.180930</td>\n",
              "      <td>-0.101630</td>\n",
              "      <td>0.070885</td>\n",
              "      <td>-0.037263</td>\n",
              "      <td>0.183606</td>\n",
              "      <td>-0.049088</td>\n",
              "      <td>-0.672288</td>\n",
              "      <td>-1.293044</td>\n",
              "      <td>0.592576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>เป็น</th>\n",
              "      <td>-0.015736</td>\n",
              "      <td>-0.258926</td>\n",
              "      <td>0.052953</td>\n",
              "      <td>0.153728</td>\n",
              "      <td>-0.005985</td>\n",
              "      <td>-0.021081</td>\n",
              "      <td>0.041088</td>\n",
              "      <td>0.057312</td>\n",
              "      <td>1.633230</td>\n",
              "      <td>0.442729</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009408</td>\n",
              "      <td>-0.252576</td>\n",
              "      <td>-0.305512</td>\n",
              "      <td>0.372542</td>\n",
              "      <td>0.049151</td>\n",
              "      <td>0.568470</td>\n",
              "      <td>0.266586</td>\n",
              "      <td>0.400800</td>\n",
              "      <td>-0.784650</td>\n",
              "      <td>0.197369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ของ</th>\n",
              "      <td>-0.189711</td>\n",
              "      <td>-0.174774</td>\n",
              "      <td>0.171124</td>\n",
              "      <td>-0.186771</td>\n",
              "      <td>0.054294</td>\n",
              "      <td>-0.114150</td>\n",
              "      <td>-1.109456</td>\n",
              "      <td>-0.094466</td>\n",
              "      <td>-0.447015</td>\n",
              "      <td>0.042377</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.168676</td>\n",
              "      <td>-0.148738</td>\n",
              "      <td>0.680404</td>\n",
              "      <td>0.097702</td>\n",
              "      <td>0.020270</td>\n",
              "      <td>0.182967</td>\n",
              "      <td>-0.083949</td>\n",
              "      <td>0.006287</td>\n",
              "      <td>-0.707434</td>\n",
              "      <td>-0.070234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>มี</th>\n",
              "      <td>-0.156962</td>\n",
              "      <td>-0.231863</td>\n",
              "      <td>0.080312</td>\n",
              "      <td>0.323157</td>\n",
              "      <td>0.215695</td>\n",
              "      <td>0.055145</td>\n",
              "      <td>0.420794</td>\n",
              "      <td>0.016842</td>\n",
              "      <td>0.256759</td>\n",
              "      <td>0.832864</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.044267</td>\n",
              "      <td>-0.147186</td>\n",
              "      <td>-0.105424</td>\n",
              "      <td>0.907078</td>\n",
              "      <td>0.009299</td>\n",
              "      <td>0.550953</td>\n",
              "      <td>0.139337</td>\n",
              "      <td>0.031696</td>\n",
              "      <td>-0.670379</td>\n",
              "      <td>-0.008048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ได้</th>\n",
              "      <td>-0.428813</td>\n",
              "      <td>-0.031194</td>\n",
              "      <td>0.041922</td>\n",
              "      <td>-0.036608</td>\n",
              "      <td>-0.008106</td>\n",
              "      <td>0.076470</td>\n",
              "      <td>-0.782270</td>\n",
              "      <td>0.033361</td>\n",
              "      <td>0.606864</td>\n",
              "      <td>0.440520</td>\n",
              "      <td>...</td>\n",
              "      <td>0.024458</td>\n",
              "      <td>-0.025031</td>\n",
              "      <td>0.103389</td>\n",
              "      <td>-0.078255</td>\n",
              "      <td>0.034323</td>\n",
              "      <td>0.459774</td>\n",
              "      <td>-0.748643</td>\n",
              "      <td>0.337775</td>\n",
              "      <td>-0.487408</td>\n",
              "      <td>-0.511535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>\"\"\"\"</th>\n",
              "      <td>-0.287710</td>\n",
              "      <td>0.064193</td>\n",
              "      <td>0.205076</td>\n",
              "      <td>0.146356</td>\n",
              "      <td>-0.071343</td>\n",
              "      <td>-0.039451</td>\n",
              "      <td>-1.845461</td>\n",
              "      <td>0.163763</td>\n",
              "      <td>1.018096</td>\n",
              "      <td>0.272786</td>\n",
              "      <td>...</td>\n",
              "      <td>0.051024</td>\n",
              "      <td>-0.532856</td>\n",
              "      <td>-0.131856</td>\n",
              "      <td>-0.090323</td>\n",
              "      <td>-0.058895</td>\n",
              "      <td>0.151262</td>\n",
              "      <td>-0.420358</td>\n",
              "      <td>0.055971</td>\n",
              "      <td>-0.930814</td>\n",
              "      <td>0.163908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>การ</th>\n",
              "      <td>0.239587</td>\n",
              "      <td>-0.303620</td>\n",
              "      <td>0.079953</td>\n",
              "      <td>-0.453045</td>\n",
              "      <td>-0.528826</td>\n",
              "      <td>-0.161692</td>\n",
              "      <td>0.235725</td>\n",
              "      <td>-0.099673</td>\n",
              "      <td>0.691668</td>\n",
              "      <td>0.536159</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.110436</td>\n",
              "      <td>-0.297495</td>\n",
              "      <td>-0.217414</td>\n",
              "      <td>0.045158</td>\n",
              "      <td>0.066647</td>\n",
              "      <td>0.190095</td>\n",
              "      <td>-0.304333</td>\n",
              "      <td>-0.724927</td>\n",
              "      <td>-0.995488</td>\n",
              "      <td>-0.716609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(</th>\n",
              "      <td>-0.120522</td>\n",
              "      <td>-0.355783</td>\n",
              "      <td>0.168180</td>\n",
              "      <td>-0.377733</td>\n",
              "      <td>-0.158624</td>\n",
              "      <td>-0.047249</td>\n",
              "      <td>0.361140</td>\n",
              "      <td>0.161460</td>\n",
              "      <td>0.913314</td>\n",
              "      <td>0.345037</td>\n",
              "      <td>...</td>\n",
              "      <td>0.116285</td>\n",
              "      <td>-0.318218</td>\n",
              "      <td>-0.356664</td>\n",
              "      <td>0.519889</td>\n",
              "      <td>0.130475</td>\n",
              "      <td>0.125772</td>\n",
              "      <td>0.101328</td>\n",
              "      <td>-0.382658</td>\n",
              "      <td>-1.205359</td>\n",
              "      <td>0.340139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>)</th>\n",
              "      <td>-0.086848</td>\n",
              "      <td>-0.155231</td>\n",
              "      <td>0.133015</td>\n",
              "      <td>-0.039913</td>\n",
              "      <td>0.183761</td>\n",
              "      <td>0.115142</td>\n",
              "      <td>-1.940854</td>\n",
              "      <td>-0.066565</td>\n",
              "      <td>-2.399744</td>\n",
              "      <td>0.146722</td>\n",
              "      <td>...</td>\n",
              "      <td>0.019406</td>\n",
              "      <td>-0.181474</td>\n",
              "      <td>0.099863</td>\n",
              "      <td>0.516092</td>\n",
              "      <td>0.201697</td>\n",
              "      <td>0.249139</td>\n",
              "      <td>0.252957</td>\n",
              "      <td>1.138815</td>\n",
              "      <td>-0.018209</td>\n",
              "      <td>0.232265</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 300 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2201f7dc-d060-4af5-8ca0-38c29df8db29')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2201f7dc-d060-4af5-8ca0-38c29df8db29');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thai2vec"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0bb_G9ufzfrO",
        "outputId": "74f53f31-c61a-4a44-898f-6e6e80589c43"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 0         1         2         3         4         5    \\\n",
              "ที่         0.308956 -0.097699  0.116745  0.215612  0.015768 -0.064163   \n",
              "และ         0.010751 -0.618971  0.129665  0.035460 -0.007560  0.027607   \n",
              "เป็น       -0.015736 -0.258926  0.052953  0.153728 -0.005985 -0.021081   \n",
              "ของ        -0.189711 -0.174774  0.171124 -0.186771  0.054294 -0.114150   \n",
              "มี         -0.156962 -0.231863  0.080312  0.323157  0.215695  0.055145   \n",
              "...              ...       ...       ...       ...       ...       ...   \n",
              "ไลต์ฟ       0.003361 -0.016570 -0.087134  0.010009 -0.024552 -0.023878   \n",
              "ไหนว่า      0.004052 -0.017273 -0.086782  0.009518 -0.024050 -0.024528   \n",
              "ไอซ์ที      0.003284 -0.018590 -0.087451  0.010155 -0.024508 -0.022246   \n",
              "ไอบูโปรเฟน  0.001181 -0.018887 -0.089136  0.010626 -0.025262 -0.022990   \n",
              "ไออุ่น      0.003489 -0.017758 -0.086213  0.009525 -0.023742 -0.022018   \n",
              "\n",
              "                 6         7         8         9    ...       290       291  \\\n",
              "ที่         0.062168  0.039649  0.864940  0.846904  ... -0.142418  0.033241   \n",
              "และ         0.397824  0.026543  0.254075  0.168328  ... -0.105786  0.180930   \n",
              "เป็น        0.041088  0.057312  1.633230  0.442729  ... -0.009408 -0.252576   \n",
              "ของ        -1.109456 -0.094466 -0.447015  0.042377  ... -0.168676 -0.148738   \n",
              "มี          0.420794  0.016842  0.256759  0.832864  ... -0.044267 -0.147186   \n",
              "...              ...       ...       ...       ...  ...       ...       ...   \n",
              "ไลต์ฟ      -0.016818 -0.004438 -0.009342  0.021095  ... -0.087733 -0.014504   \n",
              "ไหนว่า     -0.016455 -0.003854 -0.009640  0.020903  ... -0.087552 -0.013441   \n",
              "ไอซ์ที     -0.016468 -0.006843 -0.009797  0.022212  ... -0.087593 -0.013227   \n",
              "ไอบูโปรเฟน -0.016576 -0.002285 -0.009721  0.021497  ... -0.085806 -0.013042   \n",
              "ไออุ่น     -0.016291 -0.003616 -0.009784  0.020153  ... -0.088554 -0.011662   \n",
              "\n",
              "                 292       293       294       295       296       297  \\\n",
              "ที่         0.171581 -0.624864 -0.009358  0.449131  0.120130 -0.122195   \n",
              "และ        -0.101630  0.070885 -0.037263  0.183606 -0.049088 -0.672288   \n",
              "เป็น       -0.305512  0.372542  0.049151  0.568470  0.266586  0.400800   \n",
              "ของ         0.680404  0.097702  0.020270  0.182967 -0.083949  0.006287   \n",
              "มี         -0.105424  0.907078  0.009299  0.550953  0.139337  0.031696   \n",
              "...              ...       ...       ...       ...       ...       ...   \n",
              "ไลต์ฟ       0.013926 -0.028222  0.025204  0.116887  0.010768  0.001158   \n",
              "ไหนว่า      0.014196 -0.029132  0.027147  0.117508  0.011206  0.001051   \n",
              "ไอซ์ที      0.015222 -0.028785  0.026722  0.118564  0.010984  0.001583   \n",
              "ไอบูโปรเฟน  0.015676 -0.029257  0.028669  0.121909  0.010290  0.001157   \n",
              "ไออุ่น      0.014781 -0.028147  0.029460  0.117833  0.012079  0.001301   \n",
              "\n",
              "                 298       299  \n",
              "ที่        -0.450617 -0.071318  \n",
              "และ        -1.293044  0.592576  \n",
              "เป็น       -0.784650  0.197369  \n",
              "ของ        -0.707434 -0.070234  \n",
              "มี         -0.670379 -0.008048  \n",
              "...              ...       ...  \n",
              "ไลต์ฟ      -0.028861 -0.022301  \n",
              "ไหนว่า     -0.027981 -0.023563  \n",
              "ไอซ์ที     -0.028450 -0.022971  \n",
              "ไอบูโปรเฟน -0.028039 -0.023236  \n",
              "ไออุ่น     -0.028659 -0.023365  \n",
              "\n",
              "[51358 rows x 300 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>290</th>\n",
              "      <th>291</th>\n",
              "      <th>292</th>\n",
              "      <th>293</th>\n",
              "      <th>294</th>\n",
              "      <th>295</th>\n",
              "      <th>296</th>\n",
              "      <th>297</th>\n",
              "      <th>298</th>\n",
              "      <th>299</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ที่</th>\n",
              "      <td>0.308956</td>\n",
              "      <td>-0.097699</td>\n",
              "      <td>0.116745</td>\n",
              "      <td>0.215612</td>\n",
              "      <td>0.015768</td>\n",
              "      <td>-0.064163</td>\n",
              "      <td>0.062168</td>\n",
              "      <td>0.039649</td>\n",
              "      <td>0.864940</td>\n",
              "      <td>0.846904</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.142418</td>\n",
              "      <td>0.033241</td>\n",
              "      <td>0.171581</td>\n",
              "      <td>-0.624864</td>\n",
              "      <td>-0.009358</td>\n",
              "      <td>0.449131</td>\n",
              "      <td>0.120130</td>\n",
              "      <td>-0.122195</td>\n",
              "      <td>-0.450617</td>\n",
              "      <td>-0.071318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>และ</th>\n",
              "      <td>0.010751</td>\n",
              "      <td>-0.618971</td>\n",
              "      <td>0.129665</td>\n",
              "      <td>0.035460</td>\n",
              "      <td>-0.007560</td>\n",
              "      <td>0.027607</td>\n",
              "      <td>0.397824</td>\n",
              "      <td>0.026543</td>\n",
              "      <td>0.254075</td>\n",
              "      <td>0.168328</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.105786</td>\n",
              "      <td>0.180930</td>\n",
              "      <td>-0.101630</td>\n",
              "      <td>0.070885</td>\n",
              "      <td>-0.037263</td>\n",
              "      <td>0.183606</td>\n",
              "      <td>-0.049088</td>\n",
              "      <td>-0.672288</td>\n",
              "      <td>-1.293044</td>\n",
              "      <td>0.592576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>เป็น</th>\n",
              "      <td>-0.015736</td>\n",
              "      <td>-0.258926</td>\n",
              "      <td>0.052953</td>\n",
              "      <td>0.153728</td>\n",
              "      <td>-0.005985</td>\n",
              "      <td>-0.021081</td>\n",
              "      <td>0.041088</td>\n",
              "      <td>0.057312</td>\n",
              "      <td>1.633230</td>\n",
              "      <td>0.442729</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009408</td>\n",
              "      <td>-0.252576</td>\n",
              "      <td>-0.305512</td>\n",
              "      <td>0.372542</td>\n",
              "      <td>0.049151</td>\n",
              "      <td>0.568470</td>\n",
              "      <td>0.266586</td>\n",
              "      <td>0.400800</td>\n",
              "      <td>-0.784650</td>\n",
              "      <td>0.197369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ของ</th>\n",
              "      <td>-0.189711</td>\n",
              "      <td>-0.174774</td>\n",
              "      <td>0.171124</td>\n",
              "      <td>-0.186771</td>\n",
              "      <td>0.054294</td>\n",
              "      <td>-0.114150</td>\n",
              "      <td>-1.109456</td>\n",
              "      <td>-0.094466</td>\n",
              "      <td>-0.447015</td>\n",
              "      <td>0.042377</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.168676</td>\n",
              "      <td>-0.148738</td>\n",
              "      <td>0.680404</td>\n",
              "      <td>0.097702</td>\n",
              "      <td>0.020270</td>\n",
              "      <td>0.182967</td>\n",
              "      <td>-0.083949</td>\n",
              "      <td>0.006287</td>\n",
              "      <td>-0.707434</td>\n",
              "      <td>-0.070234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>มี</th>\n",
              "      <td>-0.156962</td>\n",
              "      <td>-0.231863</td>\n",
              "      <td>0.080312</td>\n",
              "      <td>0.323157</td>\n",
              "      <td>0.215695</td>\n",
              "      <td>0.055145</td>\n",
              "      <td>0.420794</td>\n",
              "      <td>0.016842</td>\n",
              "      <td>0.256759</td>\n",
              "      <td>0.832864</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.044267</td>\n",
              "      <td>-0.147186</td>\n",
              "      <td>-0.105424</td>\n",
              "      <td>0.907078</td>\n",
              "      <td>0.009299</td>\n",
              "      <td>0.550953</td>\n",
              "      <td>0.139337</td>\n",
              "      <td>0.031696</td>\n",
              "      <td>-0.670379</td>\n",
              "      <td>-0.008048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ไลต์ฟ</th>\n",
              "      <td>0.003361</td>\n",
              "      <td>-0.016570</td>\n",
              "      <td>-0.087134</td>\n",
              "      <td>0.010009</td>\n",
              "      <td>-0.024552</td>\n",
              "      <td>-0.023878</td>\n",
              "      <td>-0.016818</td>\n",
              "      <td>-0.004438</td>\n",
              "      <td>-0.009342</td>\n",
              "      <td>0.021095</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.087733</td>\n",
              "      <td>-0.014504</td>\n",
              "      <td>0.013926</td>\n",
              "      <td>-0.028222</td>\n",
              "      <td>0.025204</td>\n",
              "      <td>0.116887</td>\n",
              "      <td>0.010768</td>\n",
              "      <td>0.001158</td>\n",
              "      <td>-0.028861</td>\n",
              "      <td>-0.022301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ไหนว่า</th>\n",
              "      <td>0.004052</td>\n",
              "      <td>-0.017273</td>\n",
              "      <td>-0.086782</td>\n",
              "      <td>0.009518</td>\n",
              "      <td>-0.024050</td>\n",
              "      <td>-0.024528</td>\n",
              "      <td>-0.016455</td>\n",
              "      <td>-0.003854</td>\n",
              "      <td>-0.009640</td>\n",
              "      <td>0.020903</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.087552</td>\n",
              "      <td>-0.013441</td>\n",
              "      <td>0.014196</td>\n",
              "      <td>-0.029132</td>\n",
              "      <td>0.027147</td>\n",
              "      <td>0.117508</td>\n",
              "      <td>0.011206</td>\n",
              "      <td>0.001051</td>\n",
              "      <td>-0.027981</td>\n",
              "      <td>-0.023563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ไอซ์ที</th>\n",
              "      <td>0.003284</td>\n",
              "      <td>-0.018590</td>\n",
              "      <td>-0.087451</td>\n",
              "      <td>0.010155</td>\n",
              "      <td>-0.024508</td>\n",
              "      <td>-0.022246</td>\n",
              "      <td>-0.016468</td>\n",
              "      <td>-0.006843</td>\n",
              "      <td>-0.009797</td>\n",
              "      <td>0.022212</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.087593</td>\n",
              "      <td>-0.013227</td>\n",
              "      <td>0.015222</td>\n",
              "      <td>-0.028785</td>\n",
              "      <td>0.026722</td>\n",
              "      <td>0.118564</td>\n",
              "      <td>0.010984</td>\n",
              "      <td>0.001583</td>\n",
              "      <td>-0.028450</td>\n",
              "      <td>-0.022971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ไอบูโปรเฟน</th>\n",
              "      <td>0.001181</td>\n",
              "      <td>-0.018887</td>\n",
              "      <td>-0.089136</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>-0.025262</td>\n",
              "      <td>-0.022990</td>\n",
              "      <td>-0.016576</td>\n",
              "      <td>-0.002285</td>\n",
              "      <td>-0.009721</td>\n",
              "      <td>0.021497</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.085806</td>\n",
              "      <td>-0.013042</td>\n",
              "      <td>0.015676</td>\n",
              "      <td>-0.029257</td>\n",
              "      <td>0.028669</td>\n",
              "      <td>0.121909</td>\n",
              "      <td>0.010290</td>\n",
              "      <td>0.001157</td>\n",
              "      <td>-0.028039</td>\n",
              "      <td>-0.023236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ไออุ่น</th>\n",
              "      <td>0.003489</td>\n",
              "      <td>-0.017758</td>\n",
              "      <td>-0.086213</td>\n",
              "      <td>0.009525</td>\n",
              "      <td>-0.023742</td>\n",
              "      <td>-0.022018</td>\n",
              "      <td>-0.016291</td>\n",
              "      <td>-0.003616</td>\n",
              "      <td>-0.009784</td>\n",
              "      <td>0.020153</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.088554</td>\n",
              "      <td>-0.011662</td>\n",
              "      <td>0.014781</td>\n",
              "      <td>-0.028147</td>\n",
              "      <td>0.029460</td>\n",
              "      <td>0.117833</td>\n",
              "      <td>0.012079</td>\n",
              "      <td>0.001301</td>\n",
              "      <td>-0.028659</td>\n",
              "      <td>-0.023365</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>51358 rows × 300 columns</p>\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using t-SNE, we can compress the 300 dimensions of each word into a 2D plane and plot their relationships."
      ],
      "metadata": {
        "id": "_R_ABJ2I0BM7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/PyThaiNLP/tutorials/releases/download/thai2plot/thai2plot.pkl\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-zODZ5B0I53",
        "outputId": "24ccc0cd-3152-4cf3-b86c-083b5fd6abd0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-04 03:31:39--  https://github.com/PyThaiNLP/tutorials/releases/download/thai2plot/thai2plot.pkl\n",
            "Resolving github.com (github.com)... 140.82.113.3\n",
            "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/190712505/9dc470e1-2b8e-40dc-9517-eb6fbb3e4295?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230504%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230504T033140Z&X-Amz-Expires=300&X-Amz-Signature=d57daf7600c1441b420bbb272c79c0ee3767877804b0142132ad77f3a8190882&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=190712505&response-content-disposition=attachment%3B%20filename%3Dthai2plot.pkl&response-content-type=application%2Foctet-stream [following]\n",
            "--2023-05-04 03:31:40--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/190712505/9dc470e1-2b8e-40dc-9517-eb6fbb3e4295?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230504%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230504T033140Z&X-Amz-Expires=300&X-Amz-Signature=d57daf7600c1441b420bbb272c79c0ee3767877804b0142132ad77f3a8190882&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=190712505&response-content-disposition=attachment%3B%20filename%3Dthai2plot.pkl&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 411060 (401K) [application/octet-stream]\n",
            "Saving to: ‘thai2plot.pkl’\n",
            "\n",
            "thai2plot.pkl       100%[===================>] 401.43K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2023-05-04 03:31:40 (11.3 MB/s) - ‘thai2plot.pkl’ saved [411060/411060]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = model.index_to_key\n",
        "\n",
        "# #tnse\n",
        "# tsne = TSNE(n_components=2, init='pca', n_iter=1000)\n",
        "# thai2plot = tsne.fit_transform(thai2vec)\n",
        "# pickle.dump(thai2plot,open(f'{model_path}thai2plot.pkl','wb'))\n",
        "\n",
        "thai2plot = pickle.load(open(f'{model_path}thai2plot.pkl','rb'))"
      ],
      "metadata": {
        "id": "BXlNAhgj0PFS"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels[:12]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-aSNivCE0ngG",
        "outputId": "439fe4cc-e21d-47e0-c370-cdaf2627c281"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ที่', 'และ', 'เป็น', 'ของ', 'มี', 'ได้', '\"\"\"\"', 'การ', '(', ')', 'โดย', 'ส']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/jeffmcneill/thai-font-collection/raw/master/downloadable-free-thai-fonts/dip-sipa/TH-Sarabun-New-Regular.ttf\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AaudNMip1C_F",
        "outputId": "29452bb0-7d30-4736-a5b8-f1cb27695374"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-04 03:35:34--  https://github.com/jeffmcneill/thai-font-collection/raw/master/downloadable-free-thai-fonts/dip-sipa/TH-Sarabun-New-Regular.ttf\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/jeffmcneill/thai-font-collection/master/downloadable-free-thai-fonts/dip-sipa/TH-Sarabun-New-Regular.ttf [following]\n",
            "--2023-05-04 03:35:34--  https://raw.githubusercontent.com/jeffmcneill/thai-font-collection/master/downloadable-free-thai-fonts/dip-sipa/TH-Sarabun-New-Regular.ttf\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 479628 (468K) [application/octet-stream]\n",
            "Saving to: ‘TH-Sarabun-New-Regular.ttf’\n",
            "\n",
            "TH-Sarabun-New-Regu 100%[===================>] 468.39K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2023-05-04 03:35:35 (12.4 MB/s) - ‘TH-Sarabun-New-Regular.ttf’ saved [479628/479628]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#stolen from https://blog.manash.me/how-to-use-pre-trained-word-vectors-from-facebooks-fasttext-a71e6d55f27\n",
        "def plot_with_labels(low_dim_embs, labels, filename=None, figsize=(10,10),\n",
        "                    axis_lims = None):\n",
        "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
        "    plt.figure(figsize=figsize)  # in inches\n",
        "    for i, label in enumerate(labels):\n",
        "        x, y = low_dim_embs[i, :]\n",
        "        plt.scatter(x, y)\n",
        "        prop = fm.FontProperties(fname=f'TH-Sarabun-New-Regular.ttf',size=20)\n",
        "        plt.annotate(label,\n",
        "                 fontproperties=prop,\n",
        "                 xy=(x, y),\n",
        "                 xytext=(5, 2),\n",
        "                 textcoords='offset points',\n",
        "                 ha='right',\n",
        "                 va='bottom')\n",
        "    if axis_lims is not None: plt.axis(axis_lims)\n",
        "    if filename: plt.savefig(filename)\n",
        "\n",
        "plot_with_labels(thai2plot[200:500],labels[200:500],axis_lims = [0,30,0,30])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "9XCGkRHQ1HIu",
        "outputId": "8b9f06df-270f-4fc3-d009-ff7db37a969c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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27eLWrVtkZmayf/9+zfh+fn706tWLiIgIrl27hkqlolu3btnGXr16Nd7e3oSFhbF3717OnTuXv98QIYqwDLWab67HULpVACYdu6FnbAKAoZMrakChr8/ZNp2p7uWl20CFECKfSfIjhHhtx44d49ChQ2zbto0qVaoAoKenR0BAAOPHj9eU97WysqJnz55UrVqVpUuXEh8f/8JxDQwMSE5OJj09nbS0NM2p2YsWLWLgwIEYGBhgb2/PnTt3ADh48CA+Pj50794dAENDQyZNmsSFCxey3evZsTMyMjRjC1ESHI9P4nZK2nOvq4HolDSOxycVXlBCCFEIJPkRQry2I0eOEBAQgLm5OatXr8bJyYmAgAAePXqEj48Ply9f1vSdOHEiERER9OvX76Xj9ujRg+joaLy9vYmJiQEgOTmZoKAgunbtqun3dPVux44dWbduXbZxLCwssiU/PXv2JDY2lho1akjiI0qc2NTcbbDObT8hxMupM9Ukh8fz+GwsyeHxqDOL1M6TEkMKHgghXpu+vj76+voAzJ8/nxMnTjBz5kx27txJ7dq1SU1NfaVxjYyMWL9+PQAqlQqAP/74Az8/P031nbzKzMxET0+PUqVK8euvvwLg6+v7SmMJUVzZlMrdj//c9hNCvNiTi/eI3xpORsL/fh7qW5ZC+bYzxp5ldRhZySMzP0KI1+bl5cXJkycB6N+/P97e3sycOfOVNko/efIEIyOj515fvnw5vXv3ftVQ6dKli2YPkhAlVQOlGbZGhjyvDqMCqGhkSAPlq33IIER+Cw4Opk+fProO45U8uXiP+6uvaCU+ABkJqdxffYUnF+/pKLKSSZIfIcRr8/Hx4cKFCzx+/JguXbrg7OzMqFGjcHZ2ztM46enp7N27l1q1auV4PTo6mtDQUJo3b/7KsX7//fd8/PHHmmRNiJJIX6FgsqsdQLYE6OnrSa526EuZeiFeizpTTfzW8Bf2id96XZbAFSJJfoQQr2TJkiW4uLgwbdo0SpcujZeXFyEhIXTp0oVKlSoxefLkPI13584dPvjgAy5cuPDcQ1F/++03Pvjgg9c6N8jR0ZENGzbwwQcfcObMmVceR4jiLqCcksWeKioYGWq12xoZsthTRUA5pW4CE+INknIjIduMz39lJKSQciOhkCISkvwIIV5J/fr1uXz5Mhs3biQsLAwHBwfGjBmDgYEBK1asQE8vd3+9REdHM3jwYFxcXPD29ubSpUsMHDgwx75BQUGvteTtKXd3d9atW0fXrl05f/78a48nRHEVUE7JyYbV2OjtzIJqDmz0diakYTVJfESR07RpU+bNm6frMPIs82Hu9rzmtp94fZL8CCHyzNraGmNjYwwMDDAyMiIzMxMDAwOioqJYvXo1Bga52yS9d+9eateuTZUqVfjkk08oV64cZcqUeW7/SpUq5Xkp3fN4e3uzevVqOnTowM2bN/NlTCGKI32FgkZW5nQqb0UjK3NZ6iaKpJs3b/LWW2/pOow80zMvla/9xOuT5EcIkWe9evUiOjoalUpFkyZNcHNzA6Bfv355On35zJkzrF+/nkGDBjFixAgWLlxI7dq1mTZtWo7982PW51kNGjRgyZIl3L59O1/HFUII8WpSUlKYNWsWderUwdHRkerVqzNu3DgePnxYaDFMmzYNlUrFe++9R0pKiqb9jz/+4L333svTWEaOluhbvjix0bc0wsjR8pViFXmnUD9vcb2OJCYmYmlpSUJCQp5+iRJCCCGEEMXb22+/TfXq1fniiy+wsrIiJiaG2bNn8/vvv2Nvb09wcHCB3j8+Pp4GDRpw5swZ+vfvT6dOnejQoQMA7733HnXq1GHkyJF5GvNptbfnKdOjqpS7foH8zg0k+RFCCCGEEDp35coVPDw8cHZ2Jj09HXNzcypUqECzZs24fPkyUVFRBZ78qNVqPDw8sLGx4c6dO+zfv5+MjAymT59OSEgIBw4cwNjYOM/j5nzOjxHKt50k8XmJ/M4N5PQyIUSJkZmZQdSVSyTFx2GmtMKuqgd6evq6DksIIQTg7OxMQkIC5ubmQNa5b+Hh4Zw8eZKwsDBKly5d4DEoFAr27t3L1KlTefDgAT4+PlhbW9O5c2eCg4NfOQZjz7KUrlaGlBsJZD5MRc+8FEaOlij0ZI9dYZOZHyFEiRD691H2L/+FpAf/O0zOzLosLfp8imt9Hx1GJoQQIjfUavVrHXUgiqf8zg2k4IEQ4o0X+vdRtsyaopX4ACQ9uMeWWVMI/fuojiITQgiRW5L4iPwgyY8Q4o2WmZnB/uW/vLDPgRW/kJmZUUgRCSGEAAgMDCQwMDBb+7Zt22jcuDHOzs44OTnRvXt3IiIitPokJibSqVMn7OzsaNiwIXFxcZprzyZJKSkpjBs3jipVquDo6EiVKlUYOXIk8fHx2e579OhRWrVqhaurKyqVirfffjvbWXDDhw+nYsWKNG/enISE7AeTBgcH4+vrm6fvgyhckvwIId5oUVcuZZvx+a+H9+8RdeVSIUUkhBDieebOncuCBQtYtmwZ4eHhXL16lQYNGlCnTh2tBGjOnDkcPHiQ69ev4+fnx4EDB3Ic79NPPyUqKoq///6bGzducOHCBSwtLfH39+fZnR+bN2/m888/Z8aMGYSGhhIeHk737t1p3rw5J06cACAuLo4//viD8PBwfHx8WL58eUF+K0QBkeRHCPFGS4qPe3mnPPQTQghRMO7cucPMmTNZvHgxrq6uAJQqVYqhQ4fy3nvvMWfOHK2+7du3p2rVqhw5coSWLVtmG+/JkyckJyfz888/o1QqNeONHz+e2NhYLl3K+tArLS2NwYMHs3jxYmrWrAmAvr4+77//PmPHjmXKlCkAWFlZ0bNnT6pWrcrSpUu1ZptE8SHJjxDijWamtMrXfkIIIQrG3bt3ef/992nTpg0TJkzQutaqVSsuX76seR0QEEBERAQnT55k//79WFpmPyTU2NiYdevWYWRklO2avb099+/fByA2NpaePXvSt29f+vbt+8L7Tpw4kYiICPr16/dazyp0R5IfIcQbza6qB2bWLz5DwbxMWeyqehRSREIIIXLi6elJQEAAAB988IHWNUtLS1JT/3dGTrt27fDw8KB8+fKoVCqGDx+e5/s9XfZmZ2dH7969iYuLy5bU/Pe+oviTc36EEG80PT19WvT5lC2zpjy3T/Pen8p5P0IIUQTMnTuX0aNH4+Li8sJ+58+fZ//+/dy6dYsKFSq89n0XLVpEz549qVu37muPJYo2mfkRQrzxXOv70H7El9lmgMzLlKX9iC/lnB8hhCgi7t+/j52d3Uv7bd++nfbt2+dL4pOX+4riT2Z+hBAlgmt9H5zr1s+q/hYfh5nSCruqHjLjI4QQOrRkyRJWr15N3759GTNmTK7fl5ycjLGxcZ7vN3DgQHbt2sXdu3fz/F7xZpCZHyFEiaGnp09ljxpUbdSMyh41JPERQggdq1+/PpcvX2bjxo2EhYXl+n3W1tbcu5f9GIPExERMTU1zfM++ffuIiori8uXLVK1a9ZVjBjA3N+fJkyfZ2h8/fvxKSZkoPJL8CCGEEEKIQmdtbY2xsTEGBgYYGRmRmZmZ6/e2atWKDRs2sH//fk3bw4cP+eqrr2jVqlWO70lPT8fY2BhDQ0OMjIywsnr1Kp/Nmzdnw4YNXLhwQdMWGxvLDz/8QLNmzV55XFHwJPkR4gWWLVtG3bp1cXR0xNvbm6CgIM21bt26sWTJEmbOnImdnR116tQhMjIy2xjLly+nT58+hRi1EEIIUfT16tWL6OhoVCoVTZo0wc3NLdfvrVq1KmvWrGHy5Mk4Ojri6OhIzZo1UavVLF26NMf3tGrVCj09PRwcHPD09MTLy+uVY69VqxazZs2iX79+ODk54ejoSLNmzWjYsCFffPHFK48rCp5C/ezxtkVAYmIilpaWJCQkYGFhoetwRAm2e/duAgICqFKlCitWrCAhIYGOHTsSFhbGmTNnmD17Nlu2bEGlUnH+/Hk2b97MpUuXtA5hg6zkJzg4WHMS9Jo1a/jmm2+oWLEiq1evxt7eHoBRo0ZRuXJlBg8eXMhPKoQQQghRNOV3biAzP0I8x7Zt2/jxxx/ZsGEDU6dOpUWLFjg7O3P+/Hk+/vhjOnXqpDkpun79+kycOJH4+PiXjjty5EhCQkJo3ry5JlHKyMhg+/bteHt7F+gzCSGEEEKUZJL8CPEcNjY2mv9vaGhIVFQUSUlJ+Pr6smvXLr766isA+vfvT3h4OJMmTcrVuCqViv79+7Nhwwbatm3Lv//+y4cffoirqytNmjQpkGcRQgghRP7KUKs5EveQzXfiOBL3kIyitZhKPIckP0I8x2effca6det49913GTNmDFOnTmXAgAEYGBjg4eHB/fv3X2ncrVu3Ym9vj76+Ph9//DFt2rTB1dWV9evX5/MTCCGEEKIgBN2Np86xy3Q+G07/y5F0PhtOnWOXCbobr+vQxEvIOT9CPEfZsmU5cOAAkHWewMqVK4mOjs7zOE+ePMHIyEhr3BkzZjBjxox8i1UIIYQQhSPobjx9L0bw33memJQ0+l6MYLGnioBySl2EJnJBZn6EyIU7d+5gbW2NmZlZnt+7c+dOatWqVQBRCSGEEKIwZajVjAuNypb4AJq28aFRsgSuCJPkR4hceJWiiImJiQwZMoQtW7ZgYPC/Sda1a9fi4uJC06ZNuXXrlqZ99OjR/PDDD/kSrxBCCCHy3/H4JG6npD33uhqITknjeHxS4QUl8kSSHyFywcDAQOvwtYyMDAwNDXPsGx8fz/jx43FyciItLY2YmBgmTZrEo0ePABgxYgR///03zZo1Y/bs2QBkZmayY8eO1zpzQAghhBAFKzY1PV/7icInyY8QuWBra0tSUpLmJOcdO3bg6emZrd/58+fx9PREX1+fsWPHUqZMGZRKJYaGhprkqVKlSowePZotW7bQokULEhIS6N+/P5UqVZJTod8gjx8/pm3bttjb22fb33X48GGqVKmCl5cXV69eBeDatWu0bdsWlUqFk5MTPXv2JCYmRhehCyGEeA6bUrnbLp/bfqLwSfIjRC7o6+uzdOlSunbtikqlIjAwkIULF2brd/78eWbNmkVgYCCffvopx44dw83NjUGDBmFubg7An3/+iaGhIQ8fPqR///54enpiYWHBpk2bCvuxRAFatWoVXl5ehIaGsmjRIq0zoEaNGsWWLVuYPn06EyZMIC4uDj8/P3r16kVERATXrl1DpVLRrVs33T2AEEKIbBoozbA1MkTxnOsKoKKRIQ2Ued8jLAqHpKVC5FLHjh3p2LHjc6/36dOHPn36aF6bm5uzb9++bP1sbW1ZsGBBAUQoihIDAwOSk5NJT08nLS2N+Ph4lEql5npKSgrJycmUKlWKgwcP4uPjQ/fu3YGsc6UmTZqEtbV1tvcJIYTQHX2FgsmudvS9GIECtAofPE2IJrnaoa94XnokdE2SHyGEKAA9evSgZ8+eeHt7Z1u+1r59e+rWrYuenh6XLl3Cyckpx8TawsJCkh8hhChiAsopWeypYlxolFbxA1sjQya52kmZ6yJOkh8hhCgARkZGmoNrVSqVpv3ChQssXLiQ8PBwevXqxb///ouTk5OOohRCCPEqAsop8S9ryfH4JGJT07EpZUADpZnM+BQDkvwIoQNqdQbx8SGkpMRiZGSDUlkXhUJf12GJQjB06FC+//57KlWqhL29PRERETRt2lTXYQkhhMgjfYWCRlbmug5D5JEkP0IUstjYXfwTOpGUlP8thTIyqoCb69fY2LTRYWSioG3atAkDAwPeeecdTduzJdSFEEIIUbCk2psQhSg2dhcXLg7USnwAUlLucOHiQGJjd+koMlHQUlJS+PLLL/nxxx91HYoQQghRYknyI0QhUasz+Cd0Itq1YTRXAfgndBJqdUahxiUKx5IlS+jQoQNVqlTRdShCCCFEiSXL3oQoJFl7fF50aKWalJTbxMeHYGXVoNDiEgVPoVAQFBTE33//retQhBBCiBJNZn6EKCQpKbH52k8UH2q1mvHjx2NmJofeCSGEELokMz9CFBIjI5t87SeKj4iIiBzbly9f/krvE0IIIcSrkZkfIQqJUlkXI6MK/O8M6P9SYGRki1JZtzDDEkIIIYQoMST5EaKQKBT6uLl+/fTVf68C4OY6Xs77EUIIIYQoIJL8CFGIbGzaUN1zPkZG5bXajYwqUN1zvpzzU0JlqNUciXvI5jtxHIl7SIY6p4qAQgghhHhdsudHiEJmY9OGcuX8/r/6WyxGRjYolXVlxqeECrobz7jQKG6npGnabI0MmexqR0A5pe4CE0IIId5AMvMjhA4oFPpYWTWgQoX2WFk1kMSnhAq6G0/fixFaiQ9ATEoafS9GEHQ3XjeBCSFEIUtNTeX777+ndu3aODs74+DgQMOGDVmxYoVWv9u3b/P5559TtWpVHB0dcXR05O233+bQoUPZxlSr1axcuZI6derg5OSEq6sr/fr14+7du5o+iYmJtGrViooVK/LZZ59lGyM4OBhfX1/N65SUFMaOHYurqysODg40aNCAPXv25N83QhQ4SX6EEEIHMtRqxoVGveDIWxgfGiVL4IQQbzy1Wk27du0ICwtj06ZNhIeHExkZycKFC1mwYAHDhw8HIDo6Gl9fXypVqsTx48e5ceMG169fp3///nTt2pVNmzZpjTtixAj279/Pli1buH79OmfPnsXKyoq6deuSkJAAwL59+yhbtiyRkZFcuXKFM2fOvDDW4cOH888//3D27FkiIyOZPXs2vXr14urVqwXzzRH5TpIfIYTQgePxSdlmfJ6lBqJT0jgen1R4QQkhhA5ER0fj7+/PggULcHBw0LTXqFGDP//8k59++olHjx4RFhbGtGnTGD58OJaWlkDWIdLt2rXjxx9/5LvvvtO899SpU+zfv585c+ZQsWJFAExNTZk2bRqenp4sW7YMgNatW5OSkoKbmxtnz57VJEU5yczMJCgoiAULFmBqagpAw4YN6datG7t27cr374soGJL8CCGEDsSmpudrPyGEKK7s7OwYOXIkEydOxNnZmYEDB2qulS9fnvLly3Pv3j2aNm2Kv78/Xbt2xcXFhaVLl2r61a5dm5iYGM3rx48f4+fnR/369VmyZInW/Vq1asXly5eBrIRo06ZN3Lhxg5o1a74wTj09PSIjI7Gx0T6PT6lUEhcX98rPLwqXJD9CCKEDNqVyV28mt/2EEKI4u3r1Kjt37uTq1avcvn2bvXv3aq7p6emh/v8lwCtWrEClUnH27FmmTJnCo0ePsvUBaNKkCXXq1KFcuXIEBARo3cvS0pLU1NRCeCpRFEnyI4QQOtBAaYatkeELjryFikaGNFCaFWZYQgihE8/O2kDWErOX9VO/ZE/kzJkzmT59OhUqVHj9AMUbQ5IfIYTQAX2FgsmudsDzjryFSa526Cuelx4JIcSb5dKlS7i7u6NUKmnVqtVz+61evRovLy9GjBih2XuTk/v372NnZ1cQoYpiTJIfIYTQkYByShZ7qqhgZKjVbmtkyGJPlZzzI4QoUWrWrEl4eDhLly5F8YIPfvr160d4eLjW3qDXMWPGDJydnTl+/DibNm3KNgsl3iyymFwIIXQooJwS/7KWHI9PIjY1HZtSBjRQmsmMjxCiRDExMeHhw4c5Xnv48CGlS5fW9Ltz5062PomJiZo+eREeHs7atWu5ePEic+fOZfny5Wzbto0WLVowYcKEPI8nij6Z+RFCCB3TVyhoZGVOp/JWNLIyl8RHCFHiVK9enejoaJYvX05GRgaQdfDpjBkzKFOmjGbfjq+vL7/99ht///235r0PHjzg22+/pWnTpnm+b1paGoaGhhgaGmJqakqnTp24evUqHh4e1KpVK9shq6L4k+RHCCGEEELolLGxMTt37mTz5s24uLjg6OhIlSpVOHXqFNu3b9f0q1evHvPmzWP48OGoVCocHR1p0KABdnZ2zJ49O8/3dXd3x9fXFycnJ37//XeGDx9OqVKlGD58OEeOHOHUqVP5+ZiiCFCoX1Yqo5AlJiZiaWlJQkICFhYWug5HCCGEEEIIoSP5nRvIzI8QQogS4+rVqzRt2hQXFxe++eYbXYcjhBCikEnyI4QQosT47LPPmDBhApcuXeL48eMEBwfrOiQhRDGSmakm6loc/4TEEHUtjszMIrWASuSCVHsTQghRYqjVagwMDFAoFOjr6z/3IEUhhPiv8DOxHFoXyqP4FE2bqdKIJt1cca5po8PIRF7IzI8QQogSY/78+YwdO5aqVavi5eVFixYtdB2SEKIYCD8Ty86FF7USH4BH8SnsXHiR8DOxOopM5JXM/AghhCgxPD09OXz4sK7DEEIUI5mZag6tC31hn8PrQ3H0KoeenhxVUNTJzI8QQgghhBDPcTs0PtuMz38lxaVwOzS+cAISr0WSHyGEEG+0mzdv0qtXL9zd3bG3t6dhw4b88ccfWn2CgoKoVasWKpUKV1dXvv76a9LS0jTXf/zxRxwcHGjZsiWJiYma9sePH9O2bVscHBz48ccfAUhPT6d79+5UrFiRzp07a43zVJ8+fVi+fLlW2+7du2nZsiUVK1akXLlycsCiEEXEo8QXJz557Sd0S5IfIYQQb6zw8HDatWvHO++8w5UrV7h58yazZ89m1KhRzJ07F4BDhw7xySefsGzZMiIiIvj77785deoUkydPBiApKYkffviBa9eu0aBBA62kZfXq1Xh7e3Pt2jV++OEHkpKSuHDhArdu3SIyMhITExOCgoJeGueSJUv49NNPGTNmDFFRUdy9e5cVK1bw448/vtLBjUKI/GNqYZSv/YRuSfIjhBCF6OlMgb29PTNmzNC0f/LJJ3z00UcAREREoFKpAMjMzOT777/H3d0dlUqFl5cXv/32my5CL5aGDRvGyJEj6dixIwpF1lr8Bg0a8OuvvzJx4kQyMzPZtGkTX375JV5eXgBYW1vz7bffsmXLFgD09PRQq9Wkp6eTkpLCgwcPtO6RkpJCeno6mZmZGBoaUqNGDapVq4arqys7duwgPj7+hTEmJyfzxRdf8Oeff9KqVStNnNWrV2f16tVMnjxZqtIJoUO2rkpMlS9ObMysjLB1VRZOQOK1SPIjhBCFaNWqVXh5eREaGsqiRYtISEgA4ODBgxw6dChb/5kzZ7J582YOHz5MREQEa9euZdy4cezfv7+wQy+WateuTVBQEPXq1SMuLk7TXqdOHTIzM4mJiWH27NkMGjRI631KpVLT38TEhC+//BJvb2/WrFmj1c/f35/58+dTtmxZPvroI4yMjNDX1+eXX34hIiKCt95666UxXrx4kYoVK+Ll5cXZs2fx8vKiVq1aXL58GXd3d9RqNTExMfnw3RBCvAo9PQVNurm+sE/jd12l2EExIcmPEEIUIgMDA5KTk0lPTyctLY24uDiuXr2Kg4MDDg4OXLlyRav/H3/8wdy5cylbtiwAVatWZfDgwWzdulUX4Rc7Y8aMISIiggEDBqBUKrWuWVpakpqamqtxPvroI8LCwujXr5+mTa1W8/777/Pzzz/zyy+/EB4e/kox6uvro6+vD2Qtf5s+fTpTpkxhwYIFAFhYWOQ6TiFEwXCuaYN/P89sM0BmVkb49/OUc36KESl1LYQQhahHjx707NkTb29vzaf5u3fvxt/fH7VazZ49e2jfvr2m/9GjR7ONoVQqOXv2bGGFXKzt2bMHpVJJnz598n3slStXYm1tzYcffkhwcDARERG5fu+TJ08wMsr6Jcrd3Z3r16/z+PFjPvroI3r37s3FixdZunRpvscsRHF06dIldu3axYgRI3Qah3NNGxy9ymVVf0tMwdQia6mbzPgULzLzI4QQhcjIyIj169cTGhpKhQoVgP8lP/7+/uzevVvHEb5Z7t+/j52dXb6Pm5SUxMSJE5kzZ46mLbf7cpKSkjh27Bje3t4AGBsbU6tWLY4cOULNmjVp06YNjRs3pmfPnvketxDF0dmzZ7l06ZKuwwCylsDZVbHCrW4F7KpYSeJTDMnMjxBC6FBqairXr1/Hw8MDyKpOllNpZFF0KBQKpkyZQo8ePXBycsrTe69fv07fvn1JSkri0aNHmvamTZty4sQJQkJC2LFjBwcPHtQshROiJEtLS2PVqlUMHjxY16GIN4QkP0IIoUNHjx6lUaNGmteNGjXKcambKDru37/Pnj17OH36dK7fExoaysSJE/n9999ZunQp9vb2DB06lCNHjgDg4ODA4sWLuXfvHocOHaJMmTIFFb4QRdqxY8f4/PPPuXPnDhkZGQB069aNgIAAHUcm3hSy7E0IIXRo3759+Pv7a177+/uzd+9eHUYkXmblypVMnToVY2PjXPVftWoVzZs3p127drz99tvY2NhgY2NDenq6po+BgQEhISGsW7cOW1vbggpdiCJv+PDh/PLLL1y7do2IiAj69+/P+vXr2bBhg65DE28ImfkRQggdOnv2LPPmzdO89vPzY8KECTqMSLxMgwYN6NixY677X7hwgb/++gt3d3c8PDzo3r076enpLFq0SKtfixYtqF27dj5HK0Txkp6ezs8//8yZM2eoUaMGR44cYevWrXz55Zd06dJF1+GJN4BCrVardR3EsxITE7G0tCQhIQELCwtdhyOEEAXG0dERJycn9u3bp9XeokULbty4wY0bN3J83/LlywkODmb58uWFEKUQQhSeoKAgjh49yrfffsvBgweZPn06UVFRzJkzh+bNm+s6PKED+Z0byMyPEELoiFqtpkWLFtnaW7RoweLFi3UQkRBC6FZAQIBmf0+zZs1o1qyZjiMSbxqZ+RFCCCEEAPHx8bRv354nT56wfft2ypUrR0xMDLNmzWLGjBm6Dk8IUQLld24gBQ+EEEKIIkCtziAu7jgxMVuIizuOWp1R6DHs3buX9u3b07JlS7Zs2QLAn3/+ya1btwo9FvFmCwwMJDAwUKstLCyM/v374+bmhqOjI25ubvTu3ZsrV65ke/+jR48YP3481atXR6VSUb16dWbMmCFHBRSSP//8k5o1a6JSqahduzaHDh3SdUi5JsmPEEIUYRlqNUfiHrL5ThxH4h6SUbQm60U+iY3dxZGjTTl95gMuXR7O6TMfcORoU2JjdxVqHM2bN2fTpk3MmDEDT09PIiMjmTJlCl999VWhxiFKnpCQEPz8/PDx8eHcuXPcuHGDy5cv07FjR95++21OnTql6ZucnEy7du2wsLDg77//JiIigh07dhAcHMy7776rw6coGf755x+6detGtWrVOHr0KNOnT6dDhw5aFSyLMtnzI4QQRVTQ3XjGhUZxO+V/n2TaGhky2dWOgHJK3QUm8lVs7C4uXBwIaCe2KSl3uHBxINU952Nj06ZQYjExMaFBgwYoFAo2bdrEnj17WLlyJZ6enoVyf1FyhYSEsGTJElq2bKlpMzAwoFOnTqSmpjJr1izWrFkDwPz583Fzc2Po0KGUKlUKgEqVKrF+/XpcXFw4e/Ys3t7euniMEuH8+fOMGDECX19fvvvuO2bPno2enh4PHz7EyspK1+G9lOz5EUKIIijobjx9L0bw37+gFf//z8WeKkmA3gBqdQZHjjYlJSXmOT0UGBlVoJHPQRQK/QKP58aNGwQFBaFUKvHw8MDb2xuFQvHyNwrxHxcuXODjjz/m33//pW3btixZsgSA4OBgAgMD8fX1JT09nYyMDDZs2EBqaiq2tra0bduWAwcOcPPmTZRKJQ4ODoSGhhIeHk6FChWwt7enXr16JCUlsWfPHtauXUudOnU09+3UqRNdu3bl/fff19Wjv/Hi4+Pp2LEjMTExLFmyBAsLC/r06aM1O5efpNqbEEK84TLUasaFRmVLfCBrbkABjA+Nwr+sJfryi2mxFh8f8oLEB0BNSspt4uNDsLJqUODxODo6MmjQoAK/j3jzDR06FBsbG44dO0aLFi148OAB1tbWWn127dqFvb09Z8+exdTUlBEjRvDtt9+yYcMG2rdvz8mTJ2ndujUZGRnUr1+f4OBgLl26RJcuXXB2dqZVq1bZZiUtLS1JTU0tzEctcZRKJcHBwZrXU6ZMoX379roLKI9kz48QQhQxx+OTtJa6/ZcaiE5J43h8UuEFJQpESkpsvvZ7HadPn8bb2xtvb29OnjypaVer1VSqVIkHDx4UeAzizXHnzh1UKhWOjo54enpmS3zUajX//PMPCxYswNTUFIC1a9fSs2dPzRlnFy9eZN68ecyZM0fzPg8PD4YOHcquXbuYO3cupUuXLrRnEjkLDQ3FwcFB12Hkmsz8CCFEERObmrtNo7ntJ4ouIyObfO33OiZNmsTs2bMB+Pbbb9m8eTOQ9em8UqnM9surEC/y1ltvkZaWRnh4OIaGhtmuKxQKhg8fjo3N//5s3759m8qVKxMXFwdAnz59ALRmGSBr5kFfX1+z30fkr/T0dAwMcp8iFLEdNC8lMz9CCFHE2JTK3Q+d3PYTRZdSWRcjowr8bzfXfykwMrJFqaxb4LHUq1ePzz77jP79+9OpUyeSkpJYu3Ytn332GQsXLizw+4s3y6RJkwgODsba2hqVSsW6deuy9VmyZAkuLi5MmzZN0zZ//nyWLFnC9evXCzPcN8bVq1d57733cHNzw9bWFi8vLxYtWqTV58SJEzRp0oQyZcpQqlQpLCwsqFy5MiqVChcXF2bOnMmXX35JpUqVsLa2xtnZGScnJ1QqFVZWVnz77bda412/fp3PP/+catWqcebMGQBmzpyJnZ0dderUITIyMtveweXLl2uS28ImyY8QQhQxDZRm2BoZvuDXYahoZEgDpVlhhiUKgEKhj5vr109f/fcqAG6u4wul2MGYMWMYNmwYZcuWZeLEidSsWZMdO3awc+dOGjVqVOD3F2+WxYsX4+joyP3794mIiKBbt27Z+tSvX5/Lly+zceNGwsLCgKyCBT4+PnzzzTeFHXKxd/DgQfz9/enSpQtXrlzh9u3brFmzhoULFzJu3DgAwsPDeeuttxgyZAgKhYI//viDzp0706JFCyIiIggLC8PHx4epU6diYmJCu3btaN68OdevXyciIgKVSsWCBQuYO3eu5r4XL17k888/Z+7cuUycOBGAWbNmce7cOfr166eZUS4q5GNDIYQoYvQVCia72tH3YgQKtAsgP/31eJKrnRQ7eEPY2LShuud8/gmdqFX8wMioAm6u4wutzLVCoaB///7079+/UO4n3mxbtmzRKkX9X9bW1hgbG2NgYICRkRGZmZlYW1tTqlQpSpUqVWzOjClKxo0bx4cffoi/vz/6+lkfmHh6erJ582aqVq3K559/TlBQEL1796ZZs2YMGDCAn376ifPnzxMTE0Pnzp15++23uXPnDk2bNuX69eucPXuWlStXau5haWlJv379mDBhAkOHDgXA29ubH374AWNjY83en/Hjx1O/fn1SU1O1ypcXBTLzI4QQRVBAOSWLPVVUMNJeK29rZChlrt9ANjZtaOTzF7VqrsGj2mxq1VxDI5+DhZb4ANy/f1/z/+Pi4sjMzCy0e4s3T3JyMsbGxs+93qtXL6Kjo1GpVDRp0gQ3Nzd+/PFHVq9eze7duxk/fny294SEhODl5cXIkSMLMvRia9y4ccTHx+Pu7k50dLSmvXLlyri4uHD16lWGDBnCd999h42NDRMnTmTbtm1ERESQlpbGzz//TJ8+fWjatCnR0dHs3LmTxo0b07VrVyZPnqwZz83NjZiY/31QY29vz7Rp01i2bJmmrX///oSHhzNp0qTCefg8kJkfIYQoogLKKfEva8nx+CRiU9OxKWVAA6WZzPi8oRQK/UIpZ/1f6enpfPDBB+zbt4+FCxfi7e1NQEAAALt378be3l7TNzMzk169enHw4EFq167N+vXrZdO5yJG1tTX37t3L1p6YmIiZmRlKpZJ9+/ZpXXv//ff5559/AKhWrZqm3dfXl+DgYJo0acLy5cu5desWs2bNyvG+y5cvz7+HKGb8/PwYMmQI8+fPp2LFilrXlEolKSkpOb5PTy9rLmTLli2ULl2aFStWMGXKFDw9PTE2NqZGjRps3bpVs3Txaf/iqnhHL4QQbzh9hYJGVuZ0Km9FIytzSXxEvgsMDMTS0pIrV67w/fff065dO5YtW8aoUaOybUjes2cP6enpREZG8uDBA3bs2KGboEWR99ZbbzFlyhTNXh6AmzdvMnPmTFq1avVKY6anp2NiYoKpqSlWVlb5Feob486dO8TFxdGhQ4dXer+BgQEZGRmkpKQwZswYvvrqK9566y3++usv1Gp1vlZ1e/LkCUZGRvk2Xl7IzI8QQghRgi1ZsoQLFy5QtmxZ9uzZQ1RUFG5ubjRo0IAvv/yS6OhozafIBgYGpKamkpGRQVpamqYksRD/9dFHH5GSkkKXLl1ISEgAwMzMjF69ejFkyJBXGnP69Om8/fbbGBoasmrlKpLD48l8mIqeeSmMHC1R6JXsD4fyKyE8c+YMZcuWxcXFhZ9//hl3d3e6d++Om5tbvowPsHPnTtq1a5dv4+WFJD9CCCFECfV0WVLZsmV58uQJXbp0ITIykkWLFtGoUSOcnZ2JiIjQJD8tW7Zky5YtVKlShYcPH+oydFEMvEoBjcDAwOdea9q0Kf/88w9PLt4jfms49xIuaK7pW5ZC+bYzxp5lXzXcYkutVjNw4EB27drFgwcPSEhIwNLSMtfvf7rfr2rVqoD2fq233norV0sJJ06cSGpqKmZmL65CmpiYyLhx49iyZQvt27fPdYz5SZa9CSGEECWUsbExiYmJqNVqduzYQa1atfjjjz+YPn06AIaGhqSmpmoVP5g7dy7Xr1/X7AsSojA9uXiP+6uvkJGQqtWekZDK/dVXeHIx+z6jN93+/fuJiYnhypUreHt7c+PGjTy9f86cOZpxTE1NNfu1TExMsn3IkZiYCJBtyVqnTp1Ys2YNERERPH78WOtaeno6RkZGjB8/HicnJ9LS0oiJiWHSpEk8evQoj0/7+iT5EUIIIUooU1NTXFxcuHjxIk5OTvz111+agyefNWjQILZs2aKjKEVJlqFWcyTuIZvvxHHkfiJxW8Nf2D9+63XUmfm3N6U4SE1NxcTEBAMDA0qXLp3nggRPy4qXLl2acuXKUbVqVZKTk7ly5QoHDx5k+/btZGRksHz5cu7cucOGDRto2rSp5v3W1taYmZnh5eVFRkYGf/zxh2Z/UGpqKnv37gVAX1+fsWPHUqZMGZRKJYaGhjqpKinJjxBCCFGCtW3blgMHDuDt7c1nn33GihUrGD16tFafyZMnM3HiRIKCgnQUpSiJgu7GU+fYZTqfDaf/5UimBP9D5n9mfP4rIyGFlBsJhRRh0dC6dWtSUlJwcHCgSpUq1KhRI0/vf7oHy9vbmwkTJmBoaMjOnTu5evUqRkZGdO7cmdKlSzNw4ED09PRITk5m1apVmvcPHTqUHTt24OXlxRdffMG6detwcXHB0dGRcePGERsbyw8//EBgYCCffvopx44dw83NjUGDBmFubp6v34vckD0/QgghirSUlBQWLFjA6tWruX//Pvr6+tja2jJgwAC6d++u6ZeZmcnPP//MwoULuXfvHqmpqdSsWZPAwEB8fHx0+ARFW8uWLVm8eDEBAQHMnDmTbdu2Ub58ea0+1tbWbNu2jVatWmFgYECbNoV3/pAomYLuxtP3YoTWIc9lU3I3o5P58MUJ0ptGX1+f33///YV9goODn3vN1tY2WyU3FxcXfvvtt1zd38HBgRMnTuSqr7m5ebYS54VNkh8hhBBFVlpaGi1btsTLy4tNmzZpzpy5evUqo0aN4uzZs5r9KX369CEyMpL169dTpUoVMjMz2bFjB127dmXTpk3Ur19fl49SZDk5OXH+/HlNies6derk2K9ChQoEBQXRunVr5s2bV8hRipIkQ61mXGgU/0117hnlrpqbnrmcPSWeT5a9CSGEKLKuXr2Kn58f8+fP1zps093dnQ0bNrB06VIePXrEsWPHOHToENu2baNKlSpA1kF8AQEBjB8/nqlTp+rqEYo8fX19rl69ytChQ186Q2Zvb8/WrVv59NNPOXXqVCFFKEqa4/FJ3E5Jy9Z+xkqfO0YKXrRLRN/SCCPH3Fc6E/krIzODkJgQtl/fTkhMCBmZGboOKRuZ+RFCCFFkVa9enerVqxMREUFgYCBHjx5FX18fJycnhgwZgqmpKffu3ePIkSMEBARgbm7O6tWr+frrr6latSrr16/Hx8fnuafBiyympqZ88sknuerr6urKli1baNasWQFHJUqq2NT0HNszFQpmVjVi+tlkMsn5E3zl204l/rwfXdkbuZdpJ6Zx5/EdTVt54woMqTAad2NPTC2MsHVVoqfjfz+S/AghhCjSIiMj8fX1Zfr06fzyyy8YGBhw+vRpRo0aRWRkJGq1Gn19ffT19QGYP38+J06cYObMmezcuZPatWuTmlqy9gDkhUqlIikpKcdrz9sn4OnpqTkbRIj8ZlPq+b+eHihvyGhvGHklhfLP7AHStzRC+bZTiTznpyjYG7mXEcEjUD+zWNHxfg0aRbzDrVQ9bnEZAFOlEU26ueJc00ZXoUryI4QQomhbuHAhffv2pVu3bpq2OnXqsGXLFs3GfC8vLzZs2ABkHazo7e1NTEwM165d00nMQohX10Bphq2RITEpadn2/QAElzcktLIxf1Wwg4dp6JmXwsjRUmZ8dCQjM4NpJ6ZlS3xa//NRtr6P4lPYufAi/v08dZYAyZ4fIYQQRdrt27epVKlStnYzMzPKlSsHgI+PDxcuXODx48d06dIFZ2dnRo0ahbOzc2GH++bJzIAbh+DChqx/FsE1/OLNoq9QMNnVDoD/pjNPX09wq4SJsxUm3jaUdlZK4qNDp2NPay11U6gVNIp4J+v/Z/s3mOXw+lAydXQekyQ/QgghijS1Ws3XX3+Ni4sLS5cuzbFP6dKl8fLyIiQkhC5dulCpUiUmT55cyJG+gS5vgTmesOIt2Phx1j/neGa1C1GAAsopWeypooKRoVa7rZEhiz1VBJRT6iawYuDmzZv06tULd3d37O3tadiwIX/88YdWn7CwMPr374+bmxuOjo64ubnRu3dvrly5otUvJSWFsWPH4urqioODAw0aNGDPnj1afe4+vgvA/T33ufb5NSLG3yT53pPnJj4ASXEp3A6Nz5fnzStJfoQQQhR5HTp04MyZM3z77bc8fvw4xz4ODg6MGTMGAwMDVqxYkedTzsV/XN4C63tBYrR2e+LtrHZJgEQBCyin5GTDamz0dmZBNQc2ejsT0rCaJD4vEB4eTrt27XjnnXe4cuUKN2/eZPbs2YwaNYq5c+cCEBISgp+fHz4+Ppw7d44bN25w+fJlOnbsyNtvv61VyXH48OH8888/nD17lsjISGbPnk2vXr24evWqpk85k3JkpmRyf899XKe6YlvFnoMX/3hprI8SU/L9+XND9vwIIYQo0sqUKYOhoSGlSpVCX1+fzMycC90aGBgQFRXFrl27MDCQH2+vJTMDdo6GHHdcqAEF7BwD7gGgp1/IwYmSRF+hoJGVua7DKDaGDRvGyJEj6dixo6atQYMG/Prrr7Rp04bBgwcTEhLCkiVLaNmypaaPgYEBnTp1IjU1lVmzZrFmzRoyMzMJCgoiJCQEU1NTABo2bEi3bt3YtWsX7u7uANSyqYWNiQ1hhKHOVJOS+YTHKQ9fGquphVH+PnwuycdiQgghirRhw4axf/9+3NzcGDhwIGZmZs/t269fPywsLAoxujdU5NHsMz5a1JAYldVPCFFk1K5dm6CgIOrVq0dcXJymvU6dOmRmZhITE8OAAQNo2bIlf/75J76+vjg5OVGrVi369+9Peno60dFZ/+3r6ekRGRmJjY12YQKlUqk1tr6ePl81+QqbDjaEfxNO9LlI0vRStQog/JeZVVbZa13IU/IzdepU6tati7m5OTY2NnTs2DFbJR1fX18UCoXW12effZavQQvxPHFxcYwbNw4PDw+cnJxwdHSkSZMmbNq0CYDExERatWpFxYoVGTRoUI5jBAYGEhgYWIhRCyFepHLlypw4cYLIyEiGDh2qdS0iIgKVSgXA8uXL+eqrr7K9X6VSERERUQiRvkGS7ry8T176CSEKxZgxY4iIiGDAgAEolUqta5aWlpqy/z/++CMzZ85k3rx5XL9+ncOHD+Pj48PQoUNRq3NfiOBpXz8HP5Z9tYwmPzbBppMNsWaRWdefkwA1ftdVZ+f95GldwMGDBxk4cCB169YlPT2dL7/8ktatW3P58mXNdBjAJ598wsSJEzWvTUxM8i9iIZ4jLS2NZs2a0bFjRw4fPoyVlRUAf/31Fz179kSpVJKQkEDZsmWJjIzEz8+Pc+fO4eXlpePIhRCiiDErn7/9hBCFYs+ePSiVSvr06fPCflOnTuXEiROaSpomJib07NmT9PR0VqxYkev7rVmzhosXLzJt2jT8HPxoXrk5E6MnciTuCK7vGnN7dyaP4v93zpqZlRGN3y1G5/zs3LlT6/Xy5cuxsbHh1KlTNG3aVNNuYmJChQoV8idCIXLp3r17DBo0iE8//VSrvWnTpgwePJht27YxadIkVq1ahZubGw8ePNCathVCiIKyfPlyIiIiis+ssoMPWFTMKm6Q4ye3iqzrDj6FHZkQ4gXu37+PnZ3dS/s97wgBR0fHPN3vgw8+oFevXowbN47Jkyejr6ePo6UjkeaRtGnhQ6avmtuh8TxKTMHUImupm65mfJ56rT0/CQkJAFhbW2u1r1mzhrJly+Lp6cnYsWOfW5kHskroJSYman0J8SpsbW359NNPmT17Nk5OTnz99deaa2XLluXBgweYmpqyadMmbty4Qc2aNXUYrRCiIGRkZhASE8L269sJiQkhQ86keTV6+uA//f9fPOekFf9ppOvonA4hxOurVq0aPj4+XL9+/ZXHUCgULFu2jMuXL+f44Y6engK7Kla41a2AXRUrnSc+8BrJT2ZmJsOGDaNRo0Z4enpq2t9//31Wr17NgQMHGDt2LKtWraJHjx7PHWfq1KlYWlpqvipXrvyqIQlBUlISs2fP5tKlS/z999+yzl+IEmRv5F7abGzDR7s+YvSh0Xy06yPabGzD3si9ug6teKrWHt5dCRa2/HwylSrzknCc+xCHHx6hWmSES/sRzJw5E4Bt27bRqFEjnJyccHJyol27dpw4cUIz1LP7LXPaBxwcHIyvr29hPZko4UJCQmjfvj0VK1akQoUKVK1alVmzZj23kuSbatWqVYwcOZJvvvnmtcYxMDBg7dq1HDt2jG+//Tafois4r1wLdODAgVy8eJHDhw9rtT+75Kh69erY2trSsmVLwsPDczxpe+zYsYwYMULzOjExURIg8cqebrxTKBTo6elx9uxZzWZoIcSba2/kXkYEj8i2uTb2cSwjgkcwy3cWfg5+OooOevToodloXKxUa0+0RU3GTa7O+hmjaNrMFwPnplrlrffu3cuwYcPYsGED3t7eABw7dozOnTuzdetWvL292bdvn9Z+yzNnzsjsu9CJXbt20bt3b+bOncvmzZvR19fnxo0bfPbZZ0RGRmrOwnnTWVtbY2pqipmZGenp6a89XqlSpdi8eTP+/v6kpKRQtWrVfIiyYLzSzM+gQYPYtm0bBw4cyHG94LPq168PZJ0kmxMjIyMsLCy0voR4Vebm5gwePBh3d3cuXbrE5cuXdR2SEKKAZWRmMO3EtByrCj1tm35iuk6XwB0+fJgBAwbo7P6vw6CUEQMGDWHWH6dwafUh3bq/z5YtWzQfNp0+fZqff/5Zk/hA1lkgn3zyCatXrwagdevWpKSk4ObmxtmzZzXL5oUobEOGDGHx4sV069YNff2sJN7R0ZHffvuNZcuWcf/+fR1HWDh+/PFH2rRpw8iRIxk/fny+jGliYsK2bduK/AxanmZ+1Go1gwcPZvPmzQQHB+dqU9TZs2eBrP0YQhSGL774gi+++IJffvmFv/76S9fhCCEK2OnY09x5/PySy2rUxDyO4XTsaepWqFugsSQmJjJz5ky2bt1KXFwcZcuW5d1339VKDIobGxsbTQXXzMxMjh49ypQpU9i4cSMrVqxg1KhROb7P3t6egwcPAmj2WwKyvE3ozL1797h9+zYBAQHcvHmTbt26ER8fz8KFC2natCkuLi5cu3YNH5/iW8ijT58+z6309uxWgPfff5/3338/Wx9fX1+Cg4NfeI8XFW6xsLAgJCQkF5HqTp5mfgYOHMjq1av59ddfMTc3JyYmhpiYGJ48eQJAeHg4kyZN4tSpU0RERLBlyxZ69epF06ZNqVGjRoE8gBDPU61atRfO/Jibm2v+7D7r8ePHGBsbF2RoQoh8dPfx3Xzt96rS0tJo2bIlZcuW5fDhw0RERPD7779z9erVbFUoC1JERESBJRh6eno0btyYLVu2sGbNmpf2z8t5IUIUNH19fc0ZlL///ju9e/dm8+bNfPfdd0DWL+7FcnmqyJM8JT8LFiwgISEBX19fbG1tNV/r1q0Dstb77d27l9atW+Pu7s7nn3+uWfMrRGHz9PTkn3/+ITExkRs3blCmTBmt635+fvzwww/cufO/T4wvXLjA+vXradKkSWGHK4R4ReVMyuVrv7xQKP5XuWjv3r2cP3+eH3/8EU9PT7y9vRk0aBDVq1fHzc0t3++tSwYGBmRkSCU9UbxYWVmhVCoJDQ2lS5cuLF++nBo1alC7dm1dh1YsZWZmcuPGDS5cuMCNGzeK/HK3p/K87O1FKleurJniFkLXlEoln332Gc7OztjY2LB582at60OGDCElJYWWLVvy+PFj1Go15cqVY9q0acV6yluIkqaWTS3Km5Qn9nFsjvt+FCgob1KeWja1CjQOX19fTUl9yKo+GRoayokTJ7C2ttZKlPJbYGAgKpXqpQcbFpRPP/2U/fv3s3z5cho3bgzA2rVrs50wL4SuPV3W9cknn9CzZ0/i4+MZOXKkrsMqdi5fvszOnTu1jqixsLDA39+fatWq6TCyl3vlam9CFAczZ87UlGL9L4VCwahRo567Xl0IUTzo6+kzpt4YRgSPQIFCKwFS/P+ZNKPrjUb/mQplBeG/y2XNzMyoWbMmNWvWpF+/foW2BEylUr10zf6run//Pt27d+fWrVuatnv37hEdHc28efNYuHAhiYmJDBo0iMjISK2jLmbMmMHChQuJiopi06ZNuLu7y4HootA1bdqU48ePY2JiwtSpU/nrr78wMzPTdVjFyuXLl1m/fn229sTERNavX8+7775bpBOg1zrkVAghhCgK/Bz8mOU7CxsTG6328ibldV7m+qmCnPkpLHPmzKFt27bs379fM8NVtmxZypYtS6dOnfD09GTIkCHY2NjQrVs3zTOHh4ezdu1aLl68SGBgILt378bHx4e+ffsSFRWly0cSJYyDgwMnT55k1KhR7NmzBycnJ12HVKxkZmayc+fOF/bZuXNnkV4CJ8mPKLEyM9VEXYvjn5AYoq7FkSknlQtRrPk5+LGr8y6WtlnK9CbTWdpmKTs778x14hMYGKhVxWjNmjU4ODhQrVo1zpw5k6sxTp8+Tc+ePXF1dcXJyYkqVaowaNAgrZkSgH///ZeWLVtiZ2dHQEAAaWlpQFaxgmfPJhs+fDgVK1akefPmxMfHPzfmwMDAQlnylp6ejomJCaVLl6Zcuf/toWrZsiUVKlQgJSWFW7du0alTJ1q2bImVlRWQVQzC0NAQQ0NDTE1N6dSpE1evXsXDw4NatWqxYsWKAo9dCMjar3b27FnmzZtXpM+iKaoiIyO1lrrlJDExkcjIyEKKKO9k2ZsokcLPxHJoXSiP4lM0baZKI5p0c8W5ps0L3imEKMr09fTzrZz1jBkz2LlzJ//++y8TJ07Mtm/wv7Zs2cKIESOYNWsWS5cuxdDQkOTkZH777Td8fX05cOAA9vb2AHz99dfcvXuXmzdv8uGHH3LhwgVq1dLekxQXF8cff/xBeHg4kydPZvny5QwbNixfnu1VDRkyhHfeeYcpU6YwadIkICuxiYuLY/z48Tg7O+Ps7My4ceMoU6YMGzduBMDd3R1fX1+cnJxQqVRs2LCBUqVKMXz4cN5++23eeecdrK2tdfloogRxcXGhU6dOug6jWEpKSsrXfrogyY8occLPxLJz4cVs7Y/iU9i58CL+/TwlARJCEBgYSMeOHUlNTcXBweGl/c+cOcOmTZu0jnYoXbo0H374IbGxsSxcuJBvv/0WgDt37tCwYUMcHR2pV68e1atXzzaelZUVPXv2pGrVqqSkpNCvXz+uX7/OpEmTOH78OJmZmajVaho2bJh/D/0Stra2HDt2TKvN0NCQIUOGaLV98MEH2d47ffp0pk+fnq3dxcWF8+fP52+gQjyHr68voaGhOV4rqL1yb5Lc7o8qyvuoZNmbKFEyM9UcWpfzX3pPHV4fKkvghBB06tSJa9eusWzZslz1/+abb6hevTrLli2jYcOGODk5Ua9ePUaPHo2hoSHR0dGavm+99Rbx8fFcvXqVDRs2YGhomOOYEydOJCIign79+vHgwQP8/f159913uXjxIleuXKFp06bs27eP5cuX58cjC1Ei3Lp1izp16tC6dWsePXoEZP03efjwYR1HVvQ5ODhgYWHxwj4WFha5+sBIVyT5ESXK7dB4raVuOUmKS+F2aHzhBCSEeKOMHDmSLVu28Ouvv3L9+nV2795NpUqVGDdunFa1t/79+/Po0SPKlCmDSqXi+++/f+nYp06dIjY2lkGDBuHu7k6DBg34+++/8fb2Zvr06SQkJBTko+Wb4no2iHhzzJs3j8mTJ+Pk5MT27duBrGVa6enpOo6s6NPT08Pf3/+Fffz9/dHTK7ophix7EyXKo8QXJz557SeEEE89evSIJUuWcOfOHYyMjICs88YGDx7MvXv3tDYAb9++nbi4OGJjYzE3N8/V+E2bNuXAgQOUKlUKgNjYWIYNG8aDBw+wtbVl9+7ddO3aNf8fLB8V57NBxJvj2Q8ins78iNyrVq0a7777brH9b1mSH1GimFoY5Ws/IYR46t69eyiVSk3i8yxHR0et5GfLli306NEjW+IzZ86cbFXdfH19UalUqFQqSpUqxcyZM5k9e7amutr3339PmzZtNL/QRURE4OvrS0REhNY4ly9fpnv37jx48IAJEybw0Ucf5c+D51JxPxtEvBkyM9V09u/BxwN7cOPmderVq6/rkIqlatWq4e7uTmRkJElJSZiZmeHg4FCkZ3yeKvoRCpGPbF2VmCpfnNiYWRlh66osnICEEEXKkiVLcHFxYdq0aXl+r1qtJiYmhipVqtC6dWvu37//3L7JycnZDkXNrVmzZrFnzx4UCgVXrlzRJD0vO0do0qRJTJ8+nf379/P555+/0r1f1ZtwNogo/sLPxLLyy6OcXH+P/s3m4G3fjC1LD3P5+E1iYmIoU6aMrkMsVvT09HB0dKR69eo4OjoWi8QHJPkRJYyenoIm3Vxf2Kfxu67o6RX/wwiFEHlXv359Ll++zMaNGwkLCwPA3NycJ0+eZOubnJysWYL2rP379+Pv78+cOXOeex9ra2vu3buXrT0lJYW0tDSt84YSEhLYvHkzP/30E3v27MHNzQ0PDw9OnTpFo0aNGDhwoNYZRbdv3+b27dvY2dkxefJkzTgGBgYkJyezbt26HM8MKkhvwtkgonh7Wun12X2/9d1ac/JKME1b16dh7WY5Vl0Ubx5JfkSJ41zTBv9+ntlmgMysjKTMtRAlmLW1NcbGxhgYGGBkZKSZhfDw8CAmJoZNmzZpZlmSk5OZMmUKjRo10rzfysoKQ0NDjI2NMTMze+Hm6Xbt2jFv3jytw1Pv3r3LwYMHNQeDPhUeHk6LFi2wsbHh7bffpkuXLnTu3BlDQ8McZ5e2b99O6dKlCQ8PZ8WKFTx48ADIqhz3/fffM3/+/Dx9X/57+Gt8fDyffPIJ1atXp3LlynTu3Jnbt2/n2Dc6Oppu3brRuHFj5syZw9q1a7Vivn79Oj/88ANz584lNDQ029kgffr00apkd+3aNdq2bYtKpcLJyYmePXsSExOTp+cRJc/zKr0621ZnbNdFTHh/Db72vaTSawkhyY8okZxr2tBrig8dh9ek1cfV6Di8Jj2/9ZHER4gSrFevXkRHR6NSqWjSpAlubm5A1lk927ZtY8WKFbi4uODo6IiHhweRkZGsXr1a835LS0u++eYbvLy8WLZsGUOHDn3uvfz8/Pjuu+/o378/jo6OODo60qRJE8qUKYOTk5NWX4VCQUZGBmFhYVStWpVBgwZRvnx5zMzMuHr1arYleu3btyc1NRVPT09u3bqlmeVxdHTk0KFDTJ069bW+TwMGDOD333/H1dWVa9euUaFCBfr375+tX1paGu3ataNWrVocOnSIoUOH4uLiwq+//kpGRgYAJ06coG3btvTo0YN9+/a98GyQuLg4/Pz86NWrFxEREVy7dg2VSkW3bt1e63nEm08qvYpnScEDUWLp6Smwq2L18o5CiBJBqVSyb9++HK9Vr16dP//886VjjBw5kpEjR2Zr79OnD3369NFq69q1a7bqbIGBgdkOWnR2dubSpUvY2tpiaWkJwN9//62ZZfHz8+PSpUua/uXKlaN8+fKEhYXh4OBAt27d2LFjB2XLln1p/Llx4MABwsLCmDlzJvv37+fTTz/N9mwA58+fx8TEhNGjR5OZmYmlpSV16tTh5MmT3L17lwoVKuDj40NQUBDp6emo1eoXng1y8OBBfHx86N69O5B1uOqkSZOwtrYmPj4epVKZL88n3jxS6VU8S2Z+hBBCiCLMwsKCJUuWaA5bvXv3Lvr6+lhYWGBra/vchA2yZo369+9P27ZtX1iAIS9sbP43Q25oaMju3btp165dtn61a9fm6NGjgPbZIKVLlyY5ORkAe3t7+vfvT8+ePTE2Nn7hhumOHTuybt26bO0WFhaFvodJFC9S6VU8S5IfIYQQogg5ffo0P/zwg9Zytk8++YTevXvz4MED9uzZQ+vWrQGws7Njz549LxyvRYsWjBo1Cn9/f+Li4l47vkmTJtGoUSOuX7+Oj48PP/74I4MGDXrp+56eDWJgoL3oxMLCgoCAAAwNDV87NiFyIpVexbNk2ZsQQghRhFSqVInOnTuzceNGunTpAsCQIUMoX6E8vXr1YunGpbzX+z1MU0yxsLDg6NGjdO7cWfP+x48fZyuj3bVrV5KSkmjTpk2OS9Tyon379rRv3x6A5cuX06hRI2xtbXP13mrVqlGpUiXatGmDp6en5myQmzdvvlZMQrzI00qvOxdefG4fqfRackjyI4QQ4qXUajXLli1j8eLF3LlzB2tra/z8/Bg7diwWFha6Du+NYW1tjYGBAcuXL+fevXv8/PPPWFtbE/EkgsUnF5OakcrhE4eJeSsGZaqSa0eu4eroSnh4OM7OziQkJDBjxgwMDAxQqVRERUXx66+/MmDAAD788EOSkpKYOXNmvsUbGRlJlSpV8vw+W1tbKSssCtXTSq+H1oVqFT8wszKi8buuUvCoBJFlb0IIIV5qyJAhHD58mE2bNhEeHs7u3btRKpU0a9aMx48f6zq816JSqXQdgkavXr14+PAh9+/fZ/DgwRw8eBDvtt7MnzufSwsvoW+mj1EFIxR6ChJLJ2LxoQW37txi3bp1zJkzh7p165KUlESlSpUICwujb9++/P7773h5eTFy5Eh69OhBrVq18hzX8w5/fVr6+1U9ePCAgIAAGjVqRFxcHBs2bCAtLe21xhQlw3/LoOeGVHoVIMmPEEKIl3j48CELFiwgODiYhg0b4unpSefOnXn48CFNmzbF1NRU1yG+MZRKJcOHD6dz585MmzaNUkal2Pl4J65TXKn8WWUyEjMwq55VDlqNGtMqptgPswdg2LBh/PPPP/Tt2xdTU1MMDAwwMTHhs88+49KlS5QuXRoPDw8MDAzo3bt3nuLK6fBXyDo49el5SAAZGRl52rszZ84cmjRpQnh4OE5OTsydO5eqVavy008/vfCcJCFe1dNKr251K2BXxUqWupVAkvwIIYR4IRMTE+7du8f169e5ceMGp0+f5qeffsLDw4PU1FRdh/fGefa8IdearjxUPtRcU2eoMa9u/r/XqEm0SKS8XXlNW+vWrTE2Nsbe3p7Q0FB69eqFmZkZkydP5s8//+TYsWO5jiU1NZWzZ89y4MABqlatyoULF+jatSsrVqwAwM3Njb/++ovIyEiGDRvG999/zw8//ICjoyNvv/02hw4dyjamWq1m165d1KlThx9++IFZs2YxePBg9PT0WLx4Mfv27WP//v3UqlWLgwcP8vXXX+Pk5ISjoyN16tRh0aJF2cabMWMGrq6uODg40KBBgxyLQNy8eZNevXrh7u6Ovb09DRs25I8//sj190II8WZQqF93zjqfJSYmYmlpSUJCgqwjF0KIYkChULz28ieRs+3XtzP60OiX9pveZDrtnLKXmyYzAyKPQtIdMCsPDj6gp5+re6vValq1aoW9vT1Xr17l33//5YMPPqB79+58+umnNGzYkJkzZ9KjRw82bNiAubk5nTt35pdffgFgx44dfPTRR/z000+88847mnErVaqEp6cnS5cuRaFQ0KlTJ65cuYJCoSAyMlJzllGNGjW4fPky33//vebA2HPnztGjRw9GjBjBhx9+CIC5uTmNGzdmw4YNmJqacuzYMd555x0OHDiAu7s7AOHh4XTo0IHJkyfToUMHFAoFx48fp1evXgwcOPCFB9KKoqlPnz74+vo+t4DH0xnJF5VPF8VDfucG8idCCCGEKKLKmZR79X6Xt8AcT1jxFmz8OOufczyz2nMhOjoaf39/li5dytGjR7l58yZTp06lRo0a/Pnnn/z0008kJyfTv39/1q9fz4MHD1i0aBEKhQKFQkG7du348ccf+e677zRjnjp1ijJlyrB27VoqVqyIra0tx48fJyEhgcaNG2vOMnr8+DG9e/cmNTVVKzHx8vLim2++YevWrUDWL7jW1tasWLFCs/yyYcOGdOvWjV27dmneN2zYMEaOHEnHjh1RKLKWOTVo0IBff/2ViRMnai3dE7r377//Uq9ePSpVqsTUqVNf2v+bb77BxcUFlUqFg4MDKpWKqlWrsmrVqle6/7Zt22jcuDHOzs44OTnRvXt3IiIisvXbtGkTfn5+ODs7o1Kp8PDwYOLEiSQlJeXpWV5l/5J4dZL8CCGE0AgMDCQwMFDzes2aNTg4OFCtWjXOnDmTqzFSUlKYPHky7u7u2NnZUb58eTp16sTly5cLKOo3Vy2bWpQ3KY+CnPclKFBQwaQCtWz+U8Tg8hZY34uMxBiOWHqzuVxLjlh6k5F4B9b3ylUCZGdnx8iRI5k4cSLOzs4MHDhQc618+fKUL1+ee/fu0bRpU/z9/enatSsuLi4sXbpU06927drExMRoXj9+/Bg/Pz/q16/PkiVLtO7XqlUrzZ8RExMTPv/8c9avX4+LiwuffPKJJkEpW7YsDx48ALI+1Y+MjNQ6eBWy9k49e6ZR7dq1CQoKol69elrtderUITMzUytGoXu///47LVq0ICwsjKVLl77wfKq///6bFStWsG7dOq5fv05kZCQRERFcu3ZNs7ctLS2NHj164ODgwMiRI7XeX7lyZa0DgOfOncuCBQtYtmwZ4eHhXL16lQYNGlCnTh2tBGjixInMnTuXqVOnEh4eTkREBPv27SMiIoIWLVpoCnfk5VlE4ZDkRwghxHPNmDGDnTt3MnfuXCZOnPjS/hkZGfj7+3PmzBn27dtHVFQUUVFRvPPOO7Ro0YIbN24UQtRvDn09fcbUGwOQLQF6+np0vdHoP7uULTMDdo4mqGxj6tRfR2fvufSv9jWdvedSp/5agso2gZ1jsvq9xNWrV9m5cydXr17l9u3b7N27V3NNT09Ps9xxxYoVqFQqzp49y5QpU3j06FG2PgBNmjShTp06lCtXjoCAAK17WVpaZttDNnr0aI4dO4ZCochx/1BujRkzhoiICAYMGIBSqXzpfYVude/enZCQEDw9Pbl16xYJCQnP7VumTBnee+89xo4di7OzMx9++CEHDhzQ6hMUFERmZiZXrlxh27ZtnD9/HshKnEqVKkWZMmUAuHPnDjNnzmTx4sW4uroCUKpUKYYOHcp7773HnDlzgKwloRkZGWzbto26detq7lOhQgWWLFnC48eP2b9/f56fRRQOSX6EEEI8V2BgIB07duTTTz/N1SeW69ev58mTJ6xfvx47OzsgqyJYz5496dOnD3Pnzi3okN84fg5+zPKdhY2J9uxGeZPyzPKdhZ+Dn/YbIo8SVMqZvtUmcduorNalGKOy9K02kaBSTll7gV7ivzMiz1se9my/l+3/mjlzJtOnT6dChQovvX9mZiZ6enro6elx6vRpjoXf53DYXRKfpJGRmft9Znv27EGpVNKnTx/NsjdRdFWoUIF9+/YRFhb20j8nT0uw7969m9DQULp168aXX37JN998o+nj6urKoUOH6N+/PyYmJjg4OPDXX3/Rq1cvrXOv7t69y/vvv0+bNm2YMGGC1n2enZlUKBRMmDCBgwcP4u7uTuvWrTWzRwqFglq1amn+m8jLs4jCIcmPEEKI5+rUqRPXrl3T7MV4mSNHjtClSxf09fX57rvvUKlU9O7dm8zMTHx8fGTp2yvyc/BjV+ddLG2zlOlNprO0zVJ2dt6ZPfEBMh7eYZzzENSoQaH9Y16t0APUjHceTMbDO7m696VLl3B3d0epVNKqVavn9lu9ejVeXl6MGDHiheXP79+/r0mMX2bq1KnUrVuXP7ZtZ/pve+m+6Djz9odx+XYijafvZ+fF27kaJy/3FMWXgYEB/v7+rFq1ijVr1mjaPTw8+PXXX3n06BEPHz6kWrVqBAYGMn/+fDp16qTp5+npqZmR/OCDD7TGzmmGcNiwYRw8eJCAgABmz56taf/vjKcoWgx0HYAQQog3h76+Pvr6WUuwVq9ezaVLl+jbty9nzpzBwsJClhe9Bn09fepWqPvSfscNKnK7tMlzr6sVekSXLs9xA3Ma5eK+NWvWJDg4+KX9+vXrp7VfLD/06NGDst4t6TNpMQlH1mL8zLWYhGT6rz7Ngh618Pe0zdf7iuLNwMAg2zlRTZo0oUmTJi9979y5cxk9ejQuLi4v7Xvnzv8+QJCiGcWHzPwIIYTIN15eXpw8eRLIWuvu6OjI9u3bNevni6qRI0dqfXJbnMVaV8m3fiYmJjx8+DDHaw8fPqR06dIv7JeYmKjp8yoyMtVM2HoZgzL2pN2/pXXt6efqE7ZeztMSOCFeJC+zhElJSfj6+rJ582aGDBlSwJGJ/CLJjxBCiFf2tPqRu7s7O3fupHnz5hw8eBBAc/bLd999VyTObXv8+DHfffcdtWvXxsnJCRcXF5o1a8ayZcvYvHkz77//vqbvo0eP8Pf3p3LlyvTr10+HUeedjVGpfOtXvXp1oqOjWb58ORkZWQUSUlNTmTFjBmXKlNHsYfD19eW3337j77//1rz3wYMHfPvttzRt2vQVniLLiRsPuJ2QjIFFWUBN2oMo0uPvoG+c9edJDdxOSObEjQevfA/x5ggPD6dhw4bZlmemp6fTu3dvHBwc+OKLL7SuVa5cmXv37r3yPa9cuUJwcLDs5ylGJPkRQgihZcmSJZpNxC/ztIDBjh07GDt2LI6OjigUCrZt28a5c+fo3Lkzn376aUGH/FJPnjyhUaNGPHjwgK1bt3L9+nXCwsKYOXMmQ4YMoVq1apQvX17Tf/Xq1Xh7exMWFsbevXs5d+6cDqPPmwZKM2yNDFGQ82yIAjUVjQxpoDR76VjGxsbs3LmTzZs34+LigqOjI1WqVOHUqVNs375d069evXrMmzeP4cOHo1KpcHR0pEGDBtjZ2b3WjFrsw2TN/7f06U7MmtEkHF2Led1Oz+0n3izm5uY8efIkW3tycjJGRkZabYGBgYwaNYqNGzdqFbbYtm0bqampXLp0iS1btmiqvYWEhGBgYEDZstqFQXLL2Ng4TzOez3uWx48fY2xsnK1dFAzZ8yOEEEJL/fr1+fXXX2nUqBFdunTBxcXluT+0vb29AXjnnXd45513SE5OxsLCgq5du9KiRQtmzJhRyNHn7NatWwwdOlTrNPiQkBDGjBnD1q1b6dq1K48ePdJs1DcwMCA5OZn09HQyMjKK1dkc+goFk13t6HsxAgVopUCK///fSa526Oey6pmXlxd//vnnS/s9/TPwMjkdFglZBz0+++8HwMb8f79AWtTtgEXdDjm+99l+QLa9RzmN/bJ4RNHg5+fHjBkzmDNnDpaWlkBWYZUDBw4wefJkrb7p6emYmJhgamqqKV8N4OTkxLFjxxg9ejSGhoY4OTlx5swZevfunasPeZ6nZcuWfPnll0ydOhUzs6wPE3bt2sXBgwf5/vvvc/Usf/31FwcPHiwyf1eWBDLzI4QQQsPa2hpjY2MMDAwwMjLSbOL18PAgJiaGTZs2aaoYJScnEx4eTrVq1ShTpgyrVq3Cw8OD2NhYVCoVQUFBunwULW5ubvTp04ebN2/y4Ycf4ubmRtOmTUlPT9ckPc8ufenZsyexsbHUqFGjWCU+TwWUU7LYU0UFI0OtdlsjQxZ7qggop9RNYHlUz9EaW8vSzzniNSuZs7UsTT1H68IMSxSiqVOnUqFCBerXr4+TkxOOjo6MGTOGZcuWZStKMH78eL744gtat27NlClTNO01atTgl19+ISwsjMTERJycnOjbty9TpkyhW7durxzbsmXLSE9Px9vbG5VKhUqlYtq0aaxduxZHR8eXPouTkxPjx49n9erVqFSqV45D5I1CXcRq8SUmJmJpaUlCQkKRWCMuhBAlSXx8PJ07dyY0NJQPPviAqVOnaq5duHCBcePGcfHiRc35K40bN2bq1KlUrFhR08/X15dPPvkkW6nY4OBgAgMDc1U5rCD8+++/NGrUiMmTJ/Puu+9iaGhISEgIX3zxBYcPH+b69evY29ujp6f9uaCvry+BgYH4+vrqJO7XkaFWczw+idjUdGxKGdBAaZbrGZ+iYufF2/RffRrIaRaLXFd7exO+F0KURPmdG0jyI4QQIt+lp6djYFC0VlZ//fXXZGZmZlsqk5iYSIUKFbh8+TIjRoxg9OjR1K9fX3O9OCc/b4qdF28zYetlbif8b2+PrWVpvnm7Wq4Sn6C78YwLjeJ2Str/3m9kyGRXu2IzCyZESZXfuYEsexNCCPHKfv75Z6pUqYKjoyMODg6oVCpcXFyYOXMm586dw8PDA3t7e1auXJnj+319fQttJujWrVs5nt1hYWGBjY0NAN9//z0ff/yxply3KBr8PW05PLoFv33SgLnvefPbJw04PLpFrhOfvhcjtBIfgJiUNPpejCDobnwBRS2EtsxMNVHX4vgnJIaoa3FkSol2nShaH8sJIYQoNqKjoxk3bhzr16+nadOm2WZ6hg4dyqBBg+jWrRve3t706tVLR5FmUavVfP3110yePJkvv/ySjz76KFsfR0dHNmzYQIcOHVi7di01a9bUQaQiJ/p6Cho6l3l5x2dkqNWMC43Kse6dmqylc+NDo/AvaylL4ESBCj8Ty6F1oTyKT9G0mSqNaNLNFeeaNjqMrOSRmR8hhBCvxMDAgAEDBjBr1ixcXFzo1q0bW7ZsQa1Wo1Zn0L17QxYtmk2DBjW5devWywcsBB06dODMmTN8++23PH78OMc+7u7urFu3jq5du2pK4ori6Xh8UrYZn2epgeiUNI7HJxVeUKLECT8Ty86FF7USH4BH8SnsXHiR8DOxOoqsZJKZHyGEEK/ExsaGiRMnApCZmcnRo0eZMmUKa9b8yOAhj0lJieG7mQAG+LWE2Nhd2Ni00Wm8enp6lCpVCn19fU0lu5x4e3uzevVqOnTooHVeiCheYlPT87WfEHmVmanm0LrQF/Y5vD4UR69y6OnJ3zWFQWZ+hBBCvLanld8WLx7Ehg17SUmJydbnwsWBxMbu0kF0WYYOHUpwcDBubm4MHDhQcy7H8zRo0IAlS5Zw+/btQopQ5DebUrn7jDe3/YTIq9uh8dlmfP4rKS6F26HxhROQkJkfIYQQ+UOtzuD6jW95wYQK/4ROolw5PxQK/cIL7P/Z2dlx/PjxHK8976DLFi1a5Hi4qygeGijNsDUyJCYlLcd9Pwqyqr41UL44ERbiVT1KfHHik9d+4vXJzI8QQoh8ER8fkuOMz/+oSUm5TXx8SKHFJEo2fYWCya52ANkOSn36epKrnRQ7EAXG1MIoX/uJ1yfJjxBCiNd2//59Onbsz0cfvrywQUqKbO4VhSegnJLFnioqGBlqtdsaGbLYUyXn/IgCZeuqxFT54sTGzMoIW1dl4QQkZNmbEEKI1zdnzhxat26Kq9sDevfKngAZGytIScnEyEgPI6P/lXV9/PgxxsbGhRnq82VmQORRSLoDZuXBwQf0Cn95nsh/AeWU+Je15Hh8ErGp6diUMqCB0kxmfESB09NT0KSbKzsXXnxun8bvukqxg0IkyY8QQojXlp6eTpky7pibVUCpjM52vVYtY9asjuejj91RKuuiVqvZtGkTN2/exNPTUwcR/8flLbBzNCQ+E7tFRfCfDtXa6y4ukW/0FQoaWZnrOgxRAjnXtMG/n2e2c37MrIxo/K6c81PYFGq1ukgdL5uYmIilpSUJCQlYWFjoOhwhhBC5cPv2bd555x3+/fc6738ArVubwzNbzBMTM1m27AEXL5gBWUtA3N3dmThxInXq1NFN0E9d3gLre0G2LfH//0nsuyslARJCvLbMTHVW9bfEFEwtspa6yYzPy+V3biDJjxBCiHwVG7uLf0InahU/MDKyxc11vE7P+clRZgbM8dSe8dGiyJoBGnZBlsAJIYQO5HduIMvehBBC5CsbmzaUK+f3/9XfYjEyskGprKuT8tYvFXn0BYkPgBoSo7L6OTYptLCEEEIUDEl+hBBC5DuFQh8rqwaa1xlqNcfjHha9zeZJd/K3nxBCiCJNkh8hhBAFKuhuPONCo7idkqZpszUyZLKrne7LDJuVz99+QgghijQ550cIIUSBCbobT9+LEVqJD0BMShp9L0YQdDdeN4E95eCTtacn2xGYTynAwi6rnxBCiGJPkh8hhBAFIkOtZlxoVLYaavC/umrjQ6PI0GXdHT39rHLWQPYE6P9f+0+TYgdCCPGGkORHCCFEgTgen5RtxudZaiA6JY3j8UmFF1ROqrXPKmdtYavdblFRylwLIcQbRvb8CCGEKBCxqen52q9AVWsP7gFZVd2S7mTt8XHwkRkfIYR4w0jyI4QQokDYlMrdj5jc9itwevpSzloIId5wsuxNCCFEgWigNMPWyPBFpQSoaGRIA6VZYYYlhBCiBJPkRwghSrjFixejUqmYO3eupu2XX37h8OHDrzWuvkLBZFc74LmlBJjkalc0zvsRQghRIkjyI4QQJdyqVas4ceIEP/zwAwAZGRlMnjyZsmXLvvbYAeWULPZUUcHIUKvd1siQxZ4q3Z/zI4QQokQpIguthRBC6Mp7772Hu7s79evXB+DLL7/E19cXd3f3fBk/oJwS/7KWHI9PIjY1HZtSBjRQmsmMjxBCiEInyY8QQpRwtWvXpmzZsnh5edGyZUvc3Nz45Zdf8vUe+goFjazM83VMIYQQIq8k+RFCiBLu+vXrfP7551SuXJlRo0ZhbW2t65CEEEKIAiHJjxBClHDvvfeerkMQQgghCoUUPBBCCCGEEEKUCJL8CCGEEEIIIUoESX6EEEIIIYQQJYIkP0IIIYQQQogSQZIfIYQQQgghRIkgyY8QQgghhBCiRJDkRwghSqALFy5Qr149KlasyMcff6xpDw4OxtfXF4CUlBTGjh2Lq6srDg4ONGjQgD179uR5HCGEEKKokORHCCFKoKFDh2JjY8OtW7cICwvjwYMH2foMHz6cf/75h7NnzxIZGcns2bPp1asXV69ezdM4QgghRFEhyY8QQpRAd+7cQaVS4ejoiKenJ9bW1lrXMzMzCQoKYsGCBZiamgLQsGFDunXrxq5du3I9jhBCCFGUGOg6ACGEEIXvrbfeIi0tjfDwcAwNDbNd19PTIzIyMlu7UqkkLi4u1+MIIYQQRYnM/AghRAk0adIkgoODsba2RqVSsW7dOp2OI4QQQhQGmfkRQogSaPHixTg6OnL8+HFKlSql83GEEEKIwiAzP0IIUQJt2bKFvn37vnbCkl/jCCGEEIVBkh8hhCiBkpOTMTY2LjLjCCGEEIVBkh8hhCiBrK2tuXfvXrb2xMREzMzMCn0cIYQQojBI8iOEECXQW2+9xZQpUwgLC9O03bx5k5kzZ9KqVatCH0cIIYQoDFLwQAghSqCPPvqIlJQUunTpQkJCAgBmZmb06tWLIUOGFPo4QgghRGFQqNVqta6DeFZiYiKWlpYkJCRgYWGh63CEEEIIIYQQOpLfuYEsexNCCCGEEEKUCLLsTQghxGvJzFRzOzSeR4kpmFoYYeuqRE9PoeuwhBBCiGwk+RFCCPHKws/EcmhdKI/iUzRtpkojmnRzxbmmjQ4jE0IIIbKTZW9CCCFeSfiZWHYuvKiV+AA8ik9h58KLhJ+J1VFkQgghRM4k+RFCCJFnmZlqDq0LfWGfw+tDycwsUjV1hBBClHCS/AghhMiz26Hx2WZ8/ispLoXbofGFE5AQQgiRC5L8CCGEyLNHiS9OfPLaTwghhCgMkvwIIYTIM1MLo3ztJ4QQQhQGSX6EEELkma2rElPlixMbM6usstdCCCFEUSHJjxBCiDzT01PQpJvrC/s0ftdVzvsRQghRpEjyI4QQ4pU417TBv59nthkgMysj/Pt5yjk/Qgghihw55FQIIcQrc65pg6NXuazqb4kpmFpkLXWTGR8hhBBFkSQ/QgghXouengK7Kla6DkMIIYR4KVn2JoQQQgghhCgRJPkRQgghhBBClAiS/AghhBBCCCFKBEl+hBBCCCGEECWCJD9CCCGEEEKIEkGSHyGEEEIIIUSJIMmPEEIIIYQQokSQ5EcIIYQQQghRIkjyI4QQQgghhCgRJPkRQgghhBBClAiS/AghhBBCCCFKBEl+hBBC5MnRo0dxc3Pjo48+0nUoQgghRJ5I8iOEEOK5/v77bzp06ICbmxvVqlXj448/ZurUqfzxxx88evSIe/fu6TpEIYQQItck+RFCCJGjXbt2MWDAAL755hv++ecfzp07R8eOHTl37hyNGzfGxMSEsmXL6jpMIYQQItcUarVaresgnpWYmIilpSUJCQlYWFjoOhwhhCix2rRpw9mzZzE1NcXQ0JAyZcrg4eFBjRo12Lp1K7t379Z1iEIIId5w+Z0bGORDTEIIId5A69atw9zcHH19fdRqNVFRUVy6dIkjR45w//59Ll26hIeHh67DFEIIIXJNZn5KoLS0NAwNDYvcWEKI4kWtVqNQKHQdhhBCiDdYfucGsuenGHn8+DFt27bF3t6eGTNmaNo/+eQTTdWliIgIVCoVAJcvX8bHxwcXFxdUKhUODg44OjoSEBBAYmJinu69efNmPDw8cHJywsHBAQcHB5ycnPj88881faKjo+nWrRuOjo6oVCo6dOhAaGio5npKSgpffPEF3t7eVK5cmdatW3Pt2jUg65eoihUrEhYWBsCYMWOwt7fn3XffJSMjQysOlUpF1apV+eeffzTtCQkJ9OvXD2dnZ1QqFS1btuTkyZOa6+fOncPDwwN7e3tWrlyZ4zPKL3FC5N6jR49wdHRk6dKlug5FCCGEyDVJfoqRVatW4eXlRWhoKIsWLSIhIQGAgwcPcujQoWz9P//8c5o3b05ISAgRERFERkZy48YNdu/ercmc9+7di4eHB7Vq1eLcuXOa986bN48vvvgCgPT0dD7++GMmTpzIlStXiIyMJDIykuvXr/PDDz8AWTNA7dq1o1atWly/fp0bN27Qrl07AgICSEtLA2DChAksWLAAa2trzp07R/PmzenWrRsZGRlMmTKF3r174+LiwrVr1/jrr7+IiIjA0NCQ7du3A/DkyROGDx/OoUOH6N69u9YvXd27d6dUqVJcu3aNiIgIhg4dSvv27TWVqJYuXcqgQYM4e/YsX331VX7/qxHijRYSEkJAQAAVKlRApVJRr149WrVqRatWrbTKXR89epRWrVrh6uqKSqXi7bff5vz581pj/fjjjzg4ONC8eXPN32H/9d8PIlauXImnp6fmg4+nf+8IIYQQeSXJTzFiYGBAcnIy6enppKWlERcXx9WrVzUzMVeuXNHq37VrV6KiomjcuDENGzZk5syZxMXFafUZOXIkmzZtYvDgwUyYMEHTHhQURI0aNYCsxGb48OH8+uuvVK1albfffpvVq1drkhqA8+fPY2JiwujRo1EoFCgUCvr164eJiQmXL18GYNu2bZw8eZJ3332XX3/9lQEDBhAZGcnBgweZN28enTp1AkBfX5/09HTS09NJTU3l/v37QNasT7NmzahcuTL29vbcuXMHgLt373LlyhVmz56NgUHWNrb27dtTr149jhw5AsBnn33G8uXL8fHx4d9//823fydCvOn27dtHhw4d+PDDD4mKiuLAgQPExcVx+/ZtjIyMNP02b97M559/zowZMwgNDSU8PJzu3bvTvHlzTpw4AUBSUhI//PAD165dw8fHh+XLl7/0/uvXr2fKlCls27aNiIgIdu/ezapVq1i2bFlBPbIQQog3mCQ/xUiPHj2Ijo7G29ubmJgYAHbv3o2/vz/+/v7s2bNHq/9HH33E8uXLuXTpEuvXr+fx48fUrFmT6OhoTZ+qVavy+eefM2/ePM1MyahRo3jw4AHvvfceAMbGxowfP56NGzdy/fp1Jk2axK5du2jVqhVPt4zVrl2bo0ePZotZqVRqEi4bGxtNu6GhIbt376Zdu3a0aNGCcePGaZbyubi40L59e6pWrcqBAwc071mxYgV9+vTRvH5673LlynHjxg1N4pPTvatWrcrff//N1atX8/AdF0IMGjSIxYsX06VLF3bt2kXt2rX57rvvOHbsGMuWLeP27dukpaUxePBgFi9eTM2aNYGsDzHef/99xo4dy5QpUwDQ09NDrVaTnp5OSkpKtg9jcrJx40amTZumWc5buXJlvvrqK7Zs2VJgzyyEEOLNJclPMWJkZMT69esJDQ2lQoUKgHby86Kys5UrV+brr7+mcePG7N+/X9O+ePFi6tevj0Kh4JtvvqFhw4ZkZGSwd+/e5xYy8Pb2ZtWqVYSFhREVFZXr+L/99lu6dOnCxo0bee+995g0aZJmz5CHh4dmhgdg3LhxhIeH89ZbbwEQFRVFWFgYvr6+ub6fEOL13Lt3j6ioKNq2bcuOHTv49ttvGTlyJJs3b6ZChQp4enoSFhZGbGwsPXv2pG/fvvTt21drjFatWmlmf01MTPjyyy+pWbMm69atAyAyMpI6depQp04dIiIissWwbt06OnbsqNX27AcbQgjxJvP19SU4OFjXYbxRJPkpxlJTU7l+/ToeHh54enoSHh6utRQtJwYGBqSnp2tem5qaMn78eE6ePElkZCShoaF8//33mJubv/T+/x3rZerXr8/FixfZs2cPZ86cwdTUlFq1ammuv6jw4KpVq/jggw8KrCjBkydPKFWqVIGMLURxZWBggL6+PpmZmfTv3x8fHx8+++wzbty4AWTNCmdkZGBnZ0fv3r2Ji4ujX79+WmNYWlqSmpqqef3RRx8RGhrKxx9/DGTN7PTr149PPvmEP/74Q9Pv9OnTrFq1quAfUgghRIki5/wUY0ePHqVRo0aa140aNcpx6VlRFBkZSZUqVXLdf+XKlWzdurXA4tmxY4dWIiaEyJphUSqVLF68mDJlyhAbG0vdunWZNWtWtr6LFi2iZ8+e1K1bN0/38PX1pWvXrtjY2PDbb79piiAMHTqU9evX58tzCCGEEE/laeZn6tSp1K1bF3Nzc2xsbOjYsaOmVPFTycnJDBw4kDJlymBmZkbnzp01G9NF/tq3bx/+/v6a1/7+/uzduzfHvpMnT8bZ2Zlt27Zpte/fv19T7e3Zqkzz589n5MiR2cZJSUnh3Xffxc3NTbPv6FXk9nipsLAwRo8ezfXr1+ncuTNBQUFA1nKcDRs2aPodOHCA9u3ba8p6u7m5cfLkyVyV9N68eTMff/wx5ubmPHny5NUeSIg3VPPmzZkyZQqjRo1ixYoVhIeH06FDh2z97t+/j52dXZ7Hr1WrFkuXLsXMzIxWrVrRtGlTIOsDCVtb29eOXwghiqK8rJwR+StPyc/BgwcZOHAgx48fZ8+ePaSlpdG6dWsePXqk6TN8+HC2bt3K77//zsGDB4mOjuadd97J98AFnD17Fj8/P81rPz8/zp49m61fWFgYf/75J5cvX6ZZs2Za10aMGMHGjRsZOHAggYGBmvbt27dTvXr1bGOtXr0aW1tbTp8+Tfny5V85dgMDAzIzMzWvMzIysi072717N0ePHiUuLo4vvviCWbNm0b17d2JjY7X6rVixgmHDhjFs2DD++ecfIiIiCAkJQU9Pj0mTJhEfH5/t/mq1mo0bN1KnTh0GDRrE1atXcXR0ZPXq1a/8TEK8iZo0acLt27cJCAgosHs0a9aMPXv2EBoaqim5b2ZmVmD3E0KIwvLzzz9TpUoVHB0dcXBwQKVS4eLiwsyZM3N1BuF/5bQH6L9bAtLT0+nevTsVK1akc+fOOW6JWL58uaaIVGJiIq1ataJixYoMGjQox/sGBgZq/Z5YnOUp+dm5cyd9+vTBw8MDLy8vli9fzs2bNzl16hSQddDkkiVLmDVrFi1atKB27dosW7aMo0ePcvz48QJ5gJJKoVBgY2ODpaWlpk2pVFKuXLls/xE8TSwMDQ0xMTGhbNmymmuurq58/fXXLF68mNatW5OcnMyECROIioqie/fu2e6bnp6OiYkJpUqVwsjICCsrq1eKv0qVKhw7dkyzxGXnzp14enpq9dm2bRv169fHwMCA8+fP06JFC5ydnbl9+7ZWv3v37rFt2zZatGiBnl7WH2lLS0tq1apFxYoVNRurn+Xv78+CBQtYuHAhJiYmWFlZUaZMGfkkRoj/sLe3x93dPd+TEUNDQx4/fpytPSUlJVvlRiGEKI6io6MZN24cCxYsIDQ0lMjISCIiIggLC2PMmDFaZxCOGzfute71bGJy4cIFtm/fzsqVKzExMWH79u3MnDkTOzs76tSpQ2RkpNZ79+3bR2JiIn5+fly4cIFz585x6dIlOnXqhLOzM46OjixatIgLFy68VoxFxWsVPHj6i6u1tTUAp06dIi0tTWs2wt3dHXt7e44dO5bjGCkpKSQmJmp9iZdTq9W0aNEiW3uLFi2yLSmrUqUKPj4+qFQqHj16pLVUbsmSJTg6OnL//n1NOdl///2X/fv351gAoEePHpw+fRoXFxc+/PBDreQrL+rWrcsHH3yAl5cXKpWKK1euMHbsWK0+T0tj+/n5YWJiQlRUFElJSXh4eGj1+/zzz4mOjsbLy4uGDRsSHh6uuaZSqXJcnmdqasrOnTupXbs2n376KS4uLpw6dYpevXq90vMI8abS19fX/B2fn3x8fPj111+1fjY8fPiQCRMmaO1lFEKI4srAwIABAwYwa9YsXFxc6NatG1u2bNH8nvbsGYS3bt3Kt/vWqFEDU1NTevTowY4dO4iLi2PWrFmcO3eOfv36MXv2bK3+rVu3Jj09nU2bNnH27FmuX79O27Zt6d69O2FhYdy4cYOuXbsSEhLCggUL8i1OXXnlj9cyMzMZNmwYjRr9H3v3HRbV0T1w/EuXIiyI2GGpoqLYuxE79t6jYoya2E1MYowFey+JiUaNYo8aey/RaCyxxS4aEQGxYqFKh/v7g5/7uoIoSvd8nsfndefOzj2X1wBnZ+ZMHc0n9o8ePcLQ0BCVSqXVt0iRIm/cHzJ9+nStwzXFu0mrJCyklIhO69OD2bNnM3v27FTt5ubmzJw5k5kzZ77TfU1NTTlw4MA7x5leeca3TaF+8cUXHD58mMDAQNasWcP06dMZNGgQ+vr6dOrUiZ9//lnTd/jw4axfv57bt28zYcIE1q5dy8qVK/H29k6VDL7++ptvvuGbb75552cSQny4Bg0aMHPmTEaMGEFwcDCKomBoaEijRo34/fffczo8IYT4YDY2NkyaNAlI+b351KlTTJs2jS1btrBq1SrNGYSQeunah9DT08PFxQVvb2/NYdLjxo2jRo0axMfH06hRI63+pqamDB06lKNHjxIYGMitW7cYOXIkXbp00fRRqVQ0atSI5cuX8+WXX2ZarDnhvZOfwYMHc+3aNU6cOPFBAXz//fd89dVXmtcRERGUKlXqg8YU+YO1tbXmkNPY2FhWr16tdUDrq14tqvHqXiIhxIdJ74yJV9tf/oB9nVqtfuOHNT179qRnz56ZGpMQQuRGurq61K1bl507d1KgQAFWrVqVrff/8ssv+fLLL1m5cuVbv3/WqFEjzXMVLSwstM5kzKveK/kZMmQIu3fv5u+//6ZkyZKa9qJFixIfH09YWJjW7M/jx481h3K+zsjICCMjo/cJQ3xEHj9+jJWV1Rv3HSiKQrt27bCwsHh70YLkJAg6BVGPwawI2NUGXb0siFoIIYQQ4n/09fVJSkrK6TDe27tW683NMpT8KIrC0KFD2bZtG0ePHsXe3l7repUqVTAwMODw4cN07NgRgP/++4+7d+9Sq1atzItafHTe5T+2Q4cOoVar0+/kuxP2fwcRr8wgmRcHz5lQts2HBSmEEEIIkQfFxMR8NJMRGSp4MHjwYNauXcv69espWLAgjx494tGjR5qzUSwsLOjXrx9fffUVf/31F//++y99+/alVq1a1KxZM0seQHwc0iqNbWBgoHltYmJCZGRkqvdFRERQoECBlBe+O2FTb+3EByDiYUq7784siV0I8X6Sk5MJCAjg6tWrBAQEyJJWIUSe9ezZM5o2bUqZMmU+eKz+/ftTtmxZrT3YixYtom7dugQEBLzXmPv37/9oDnvP0MzPywoPr68D9PHx0dQKnz9/Prq6unTs2JG4uDiaNWvGokWLMiVY8fEqVqwYkZGRXL9+nXLlynHw4EGtqm+NGjVi4sSJ/Prrr5pS3ufPn2fDhg0pp8QnJ6XM+JDWDJIC6MD+0eDaUpbACZEL+Pr6sn//fq0KoObm5nh6elK2bNkcjEwIITJuwYIFNG/enG7duuHs7JzqupmZGTExMRgbG2u1x8bGppqRGTZsGB07dsTDw4Nbt24BKXuK/P396dChAxcvXgRSkqRnz55RsGBBze/pL0VHR2NsbExERARjx45l586dtGnTJs0D3/39/Vm6dCnx8fH54gy2DM38KIqS5p9Xv6AFChTgl19+4fnz57x48YKtW7e+cb+PEO9KT0+P3377jfbt22Nvb8+6deuYNWuW5vqsWbNwdHSkTp062NvbY29vz+DBg5k/fz5169ZN2ePz+oyPFgUi7qf0E0LkKF9fXzZt2pTq6IOIiAg2bdqEr69vDkUmhBDv5+U5iQUKFKBw4cKprjdu3JgpU6ZoEg9FUdi5cyf+/v5a5yBaWVlpxnm5JaBAgQJUrFiRZcuWcffuXW7fvg2kJElz585l2bJlWoVnoqKiWLx4MY8fP8bBwYGEhAQePXrE5MmTqVevHj/99JNWIakRI0aQmJjIvHnzMrUqXU6Rk+REntGxY0fNXrLXGRsbp1+yO+px2u3v208IkSWSk5PZv39/un3279+Pq6ur5lBjIYTI7YYNG0aHDh2YNm0akydPTnV9+fLljB07lgoVKmgOXHdxcWHXrl0ULFhQ02/cuHF8+umnTJs2TXOESadOndi6dSsPHz5ErVaTnJysSZLatm3L6NGj6dChA2FhYURERPDixQsKFChAx44dqVOnDqGhoahUKgwMDPjiiy+AlBU10dHRKIpCREQEAwYMoE6dOqmOs8mLdJRcVrYhIiICCwsLwsPDMTc3z+lwRH4RcBxWtXp7vz67wb5e1scjhEhTQEDAO5WA7dOnT6qiO0IIkdXSK9+fU+OFhYXRsWNH/Pz86NmzJ9OnT+fixYt8+umnxMbGMnv2bDp06ACkHEvw+eefs379erp06UJkZCTt2rXj9u3bfPXVVwwfPjzV+L6+vnTr1o3o6GgWLVpE06ZNPyjejMrs3ECSH5FvJCkKp8OiCIlPxMZQn5oqM/ReTs8mJ8ECt5TiBmnu+9FJqfo24qrs+REiB129epUtW7a8tV/Hjh0pX758ptxz2LBh7NixgzZt2vDTTz9plnX89NNP3Lt3T2uJrRDi45aRZCUiIoK2bdsSExPDtm3bKFasGIMGDaJu3br06NEjw+N9rDI7N5A1AyJf2PMkjKr/+NLxkj9f+gbR8ZI/Vf/xZc+TsJQOunop5awBeH296v+/9pwhiY8QOexdN9Nm1qbby5cvc/36df777z98fX25cuUKkLLefsOGDVSvXj1T7iOE+PisW7eOrl270q5dO80ZhNHR0cTHx+dwZB83SX5EnrfnSRifXwvkYVyCVvujuAQ+vxb4vwSobBvoshrMi2kPYF48pV3O+REix9nZ2b31kz1zc3Ps7Owy5X4ODg7cvHmT5s2bExYWhrOzM7dv36Zbt24UKVLkjfsMhRAfj6SkJJ4/fw7AuXPn3vl9ry6uevHiRZp9cuOsT3JyEsHXr3Dj5DGCr18hOTnvHsqaFil4IPK0JEVhrN/99ApYM87vPp7WFilL4Mq2SSlnHXQqpbiBWRGwqy0zPkLkErq6unh6eqaUqH8DT0/PTCt2ULBgQY4cOcKMGTM4d+4c5cqVo1ixYnz66ad88cUX+aKykRDiwwQHB+Pl5cXRo0epVq3aOycsPXr0oG3btly+fJkRI0ZkaYyZxe/MKY6sXELU82eaNjOrQjT0Gohzjdo5GFnmkeRH5Gmnw6JSzfi8SgEexCVwOiyKOpb/Xy1FV0+KGgiRi5UtW5YuXbpk2zk/pUuXxsfHJ1PHFELkbQcOHGDRokU8ffqUyMhI4uPjefjwYYbGUKlUHDt2jKlTp+Lv7098fDz379+nUKFCWRT1h/E7c4qd86alao96/oyd86bR5qsx+SIBkuRH5Gkh8YmZ2k8IkTuULVsWV1dXgoKCiIqKwszMDDs7uywvb/3zzz8zb948bt26hb6+/IgU4mP0008/ceTIEZYsWULRokVJSEhgw4YN1K1bl2fPnr19gNd07NiRPn364OzsTL169fD09MyCqD9McnISB3/7RbNq5nUKcPC3X3CsVgPdPL5aRr6zizzNxvDd/gm/az8hRO6hq6urVc46Pj6ehQsXsn79esLCwkhMTKR48eJ88cUX9OnTR9Pv7NmzfP311wQHB6Onp0eLFi2YNm2a1lkZ8fHxzJgxgzVr1hATE4O+vj41a9bk77//Zv/+/ZrEJyIigj59+nD27FlsbW3Zu3cvlpaWAOjo6JDLCqYKITLBtGnTMDQ0pFatWhgbG2NtbU21atXo06cP3t7eGR7P1dWVM2fOZH6gmSjY9xqxEeFpJj6QkhDFRoQT7HsNOzf37Awt00nBA5Gn1VSZUczIIN3/WIsbGVBTlTmVoYQQOUNRFFq0aMHt27fZunUr/v7+BAUFsWTJEhYvXszIkSMB8Pf3p1WrVkyaNInAwECuXr1KdHQ0Q4YM0YyVnJxMy5YtuXbtGn///Tf37t3js88+48yZM8TFxWFsbKzpu2DBAo4dO8adO3do3Lgxf/31V7Y/uxAie924cYOgoCACAgK4evUqPj4+1K1bl7CwMJycnIiMjMzpEDNdkN+tTO2Xm0nyI/I0PR0dpjiXAN5YwJrJziX+d96PECJPevDgAZ6enixevFir0luFChXYsWMHixYt4sWLF+zZs4c+ffrQoEEDAExMTJg7dy47d+7UvOePP/4gKiqK33//nYIFC9KpUycOHTrE1atX6d27N3PnztX0ffz4MW3atKFMmTKcPHmSRo0aZd9DCyFyhKWlpabYiZ6eHo6OjrRv316zJDazSu3nJoq+Yab2y80k+RF5XsvCKn5zU1PUyECrvZiRAb+5qWlZWJUzgQkhMk2JEiUYNWoUkyZNwtHRkcGDB2uuFSlShCJFivD06VOGDRvG7Nmztd6rUqkICwvTvD5+/DidOnUCoGXLlri7u6Ovr09wcDBNmzbFz89P07dly5YEBgZy/vx5jhw5goWFRdY+qBAi18uPVSBLlXUjWd8gzeq5kLLnJ1nfgFJl3bIzrCwhyY/IF1oWVnG+Vlm2VHRkcVk7tlR05FytspL4CJGP3Lx5k/3793Pz5k0ePnzIn3/+qbmmq6v7zvtv9PX10dfXZ+XKlZw7d44vvviCunXrcvPmTYyNjUlK+t+ZFi1atKBcuXIUKVIEtVqtWV4nhMjffHx8qFatGvb29lSsWJE9e/ZornXt2pXly5cDcOHCBXr06IGzszP29vY4OTkxYMAAAgICtMYLDg6md+/eODk5YW9vj7u7O7/88gvJycla/VavXo2bmxtqtZoyZcrw008/Zf3DAmp7e3TtSwOkSoBevta1L436lX2YeZUkPyLf0NPRoY5lQdoXsaSOZUFZ6iZEPvPo0SOt16//0vCuKlWqxL///ouPjw/Dhw+nXr16XL58mebNm6fqe+XKFY4cOUJwcDCBgYHMnz//ve4phMg7Dh48yIABA4iJiWHz5s3MmzeP7t27ExISwoEDBwgPD6dXr14cOnSIbt260aVLF3x9fTV7hMqVK0f16tU1CVBcXBxNmjShWrVq3Lx5k4CAAP7880/+/PNPvv/+e819N23axLRp09i9ezeBgYEcPHiQNWvWZEspfl1dXZr36E1sCUcUfe2VNIq+AbElHGneo3eWV9zMDnn/CYQQQnw0rl+/jqurKyqViiZNmrzXGA0aNODPP/8kIiKC6dOnc/PmTXbu3EmBAgVS9d27dy9t2rShaNGiHxq6yGEXLlygYsWKVKxYkfPnz2vaFUWhZMmSPH/+/L3GjY+Pp3v37qjVaqZPn651bdSoUfz8888fFLfIfrt372bhwoVs3ryZ6dOn07BhQxwdHbly5Qr9+vWjffv2GBoacu/ePTZv3ky7du0wMEhJGIyNjRk+fDhdunRh6dKlAFy7do327dszdOhQTSXJwoUL8+OPP2pmkAC2bNnCjBkzUKvVAJQqVYoffvhBa89iVipbtiwdPh+IbuW6RNu6EFPcnmhbF3Qr16XD5wMz/Yy1nCL1f4UQQuQZlSpV4ujRox80hq2tLQYGBrRq1eqtfWNjY7Wqv4m8a/LkyZqZu6lTp7Jt2zYg5TBLlUqFlZXVe427b98+jIyMuHHjBhUrVmTQoEFYWFgQExPD1q1b2bRpU6Y9g8geNjY2mr8bGBhw//59oqKi8PDw4MCBA9SvX5+BAwfSt29f/Pz8qFWrFtHR0fz2229Uq1YNgCpVqnD8+HHN36tUqZLqPra2tlrnBm3cuDFVH5VKRWhoaGY/4hvl1Blr2UmSHyGEEHmCiYnJG0vMRkZGpjlz8ya2trZUrVr1rf2srKy4dSt1adeIiAhMTU3f+X4i51WvXp0vvvgCHR0dxowZQ1RUFLt372b06NGsW7fuvcd1c3NjwIABNG/eHFtbW8zMzLh8+TIjR46kdevW7/TvTOQuX3zxBZ07d2bRokWsWbOG6dOnM2jQIPT19SlXrpxWwjJu3DjGjh1LyZIlGTx4MCdOnAAytg8xt3n9jLX8Jv+kcUIIIfK18uXL8+DBA1auXKkpShAfH8+sWbMoVKhQhpam6enpvdMn/U2aNGHz5s0cOXJE0xYZGckPP/zw3svuRM4YPXo0I0aMwNramkmTJlGpUiX27dvH/v37qVOnznuP6+joyO7du7GxseHevXs4OjoyYsQI+vfvz48//piJTyCyi7W1NX/99RdXrlyhdOnSrF69mv79+6fZ99W9iO+7D1FkL5n5EUIIkScYGxuzf/9+xo8fz8SJEzXt1atXZ+/evVlyzzJlyrBu3TqmTJlCv379gJTEydPTkxUrVmTJPUXW0NHR4csvv+TLL7/M9LGrVasmy9vyqcePH2NlZZXu2T5DhgzB1NSUX3/9NRsjE+9Lkh8hhBB5hru7Ozt27Mjw+15ffvKmfUMeHh6prjVq1EgONxXiI/UuS9d8fHzw8PB45zGfPXtG9+7dCQ4O/oDIxPuSZW9CCCGEyLfi4+OZO3cuVapUwdHRETs7O2rVqsWqVas0ff7880+cnJxwdHTkwIEDqcYIDAzUVOB6KSIiglGjRuHg4EDJkiUpXrw4vXr14t69e1p9mjRpQvHixRkyZEia8Xl7e+Pt7Z0pzyoyn76+vtZytqSkJE1lN3jzXsSIiIg37kNcsGABzZs358iRI7J3MAdI8iOEEEKkIzk5WXN+R0BAgKzrz0MURaFFixbcvn2brVu34u/vT1BQEEuWLGHx4sWaQ2sXLlzIwoUL2b9/P999991bx42MjKRWrVq8ePGC8+fPc+/ePe7cuUPFihWpW7cuYWFhABw+fBhra2uCgoK4evUqly9fzsrHFVmgWLFiREVFcfXqVSClup+bm5vmeqNGjZg1a5bWLM6tW7f49ddf+eSTT9IcMzExERMTEwoUKPDeVQbF+5Nlb0IIIcQb+Pr6sn//fiIiIjRt5ubmeHp65pszL/KzBw8e4OnpyahRo7TaK1SowI4dO7C1tWXKlCl88803DBo0iJiYGBISEt467s8//0yZMmVYvHixpq1AgQJ8/fXXnDt3jpUrVzJixAiaNm3KmjVrcHFx4fnz59laslhkDj09PVasWEHnzp2JjY3F2tqaJUuWaK6PHDmS+Ph4PD09iY6OBsDCwoJBgwbRo0ePNMccNmwYHTp0YPr06UyZMiVbnkP8j46Sy+rwRUREYGFhQXh4OObm5jkdjhBCiI+Ur69vupvYu3TpIglQHjFp0iRWrVqFp6cnv/zyi6bd1taW48ePY2dnB6Qsb/Pw8CAwMFDr/a+3N2vWjMePHxMREYGTkxP//fcf3bp1Y+bMmVSqVAkbG5tUy+c8PDzw9vZOtTfk5ZI3WfomRNoyOzeQZW9CCCHEa5KTk9m/f3+6ffbv3y9L4PKAmzdvsn//fm7evMnDhw/5888/Ndfe9yyWBw8eULp0aQ4fPszff//NzZs3OXnyJDdu3MDf3x8LC4vMfAQhRCaSZW9CCCHEa4KCgrSWuqUlIiKCoKCgfH0YYH7w6jks8H5nscTExGBkZKR5XaVKFTZv3kxwcDAODg4UK1YMExMTevToQcuWLbX6CiFyF5n5EUIIIV4TFRWVqf1Ezrp+/Tqurq6oVKr3Opx23759VK5cWfO6T58+2NnZYWVlRWxsLFFRUZQoUYIZM2bQrFmzzAxdfESSkhX+8X/Gjkv3+cf/GUnJuWpnSr4hMz9CCCHEa9I70PB9+omcValSpTee7ZSepKQkfHx8+P777+nWrRvJycno6upSq1YtgoKCOHToELVq1eLnn3/miy++AGDlypWZG7z4KOy/9pCJu3x5GB6raStmUYAJrcvi6VYsByPLf2TmRwghhHiNnZ3dWzfWmpubazbKi9zLxMTkjUsYIyMj0zyLJTExkeXLl1O2bFkWLVrEo0ePePLkCYcPHwZSKrupVCo6duzI4MGDNYlPegoWLEhMTEyq9ujoaIyNjTP4VCI/2X/tIV+uvaCV+AA8Co/ly7UX2H/tYQ5Flj9J8iOEEEK8RldXF09Pz3T7eHp6oqsrP0Zzk19//ZXSpUtjb2+PnZ0darWa7t274+fnx8qVK/H19aV58+ao1WoKFSqUZlnrqKgoqlWrxtixYwkNDeXhw4eoVCpUKhWJiYmsXLmSypUr8/jxY65cucK///7Lf//9pzVGUlISCxYsoGrVqtjb2+Pk5ISvry+jR4/m8ePHmn5Xr15l06ZN1KtXL8u/NiJ3SkpWmLjLl7QWuL1sm7jLV5bAZSL5ri2EEEKkoWzZsnTp0iXVDJC5ubmUuc6FHjx4wNixY1m8eDF+fn4EBQURGBiIv78/J06cYOPGjVSoUIHz58/z6NEjatSoQa9evejatavWOM+ePcPS0pI+ffoQHBxMdHQ0xYoVQ0dHhwcPHrBw4UI2b95MiRIlOHjwIL169aJBgwY8fJjy6XxiYiIHDx7Ez8+PrVu3EhAQwO3bt9m9ezcJCQmUK1cOBwcH7O3t6devHzNmzKB27do58SUTucDZgOepZnxepQAPw2M5G/A8+4LK52TPjxBCCPEGZcuWxdXVlaCgIKKiojAzM8POzk5mfHIhfX19Bg0axLx58/jss8+oUaMGPXv2pHXr1ri7uzNw4EDMzc3ZuHEjHh4eLFq0CLVajZWVFWFhYahUKtRqNYqisHz5cq5evUpiYiKWlpasXLkSDw8Pvv76a1auXImDg4PWWUCenp5s2bKFIUOGUKNGDb788stU5/aUKVOGS5cuUaJECa5evYqpqWn2foFErhQS+ebE5336ibeT5EcIIYRIh66urpSzzgNsbGyYNGkSkFLO+tSpU0ybNo0tW7awatUq2rVrR7t27VK9z9zcXJP8vNSrVy+8vLyoUKECoaGhmva5c+emeW9bW1uePXsGQPny5SlfvjyBgYF4e3tz6tQp9PT0cHBwYNiwYZiamvL06VNJfgQANgVT7zn7kH7i7ST5EUIIIUS+oqurS926ddm5cycFChRg1apVWtffVPntZTU3Q0ND1q9fD4CHh8c73fPVw1KDgoLw8PBg5syZLF26FH19fS5cuMC3335LUFAQ0defEJtogZG9BTq6Ou/1jCJ/qG5vRTGLAjwKj01z348OUNSiANXtrbI7tHxL5u2FEEIIkS/p6+uTlJT0zv07derEmTNnPvi+S5Ys4fPPP6dr164YGhqiq6tLuQJqln4yDmODAoTtusPTZVd5NPMsMdeefvD9RN6lp6vDhNYp+wdfT4Nfvp7Quix6kiRnGkl+hBBCCCFIWdbWr18/zp8//0HjPHz4kJIlS2pex1x7yrO1NygQq08hY5WmPSk8nmdrb0gC9JHzdCvG4k8rU9RCe2lbUYsCLP60spzzk8kk+RFCCCFEvvLs2TOaNm1KmTJlMvQ+e3t7Nm/eTM+ePbl48eJb+69YsQJnZ2eWLFmi1a4oCuPHj8fJyYnlvy0nbJd/uuOE7bqDIqWMP2qebsU48V1Dfu9fkx+7VeT3/jU58V1DSXyygCQ/QgghhMhXFixYQPPmzTly5EiGCwu4urqyceNGOnfuzJUrV97YLzo6mqlTp3LhwgW6d++e6nrbtm25ePEiUydNIepp2oesvpQUHkdcQHiG4hT5j56uDrUcC9G2YglqORaSpW5ZRAoeCCGEECJfSUxMxMTEhAIFClC4cOEMv79ixYqsXbuWtm3boqOT9i+grxZHMDU1xdLSUnOtUKFCGBgYYGhoiJ6OLslK8lvvmRwZn+E4hRAZJzM/QgghhMhXhg0bxsqVK6lYsSITJ058rzFq1qzJ8uXLNYeXvs7MzIwhQ4bg4uLCiRMn8PLy0lwbMWIER44cwcXFhYG9+2NqaPLW++kWNHyvOIUQGaOjvFqbMReIiIjAwsKC8PDwVKdqCyGEEELkJUqywqOZZ0kKf/PMjp6FEUW/qyZlr4VIQ2bnBjLzI4QQQoh868yZM7Ro0YJSpUpha2tLrVq1WLduHdn12a+Org6q1o7p9lG1dpDER4hsIsmPEEIIIfKlQ4cOMXr0aGbMmEFwcDB3797l559/ZsGCBQwdOjTb4jB2s6bQp2XQs9Be2qZnYUShT8tg7GadbbEI8bGTZW9CCCGEyJfKli3L33//jaIo9O7dGz8/PxYsWEDNmjWxs7Pj9u3bFCuWNaWEk5MVHvqF8SIiDlNzI4o5q9AB4gLCSY6MR7egIUb2FjLjI8RbZHZuINXehBBCCJHvPH36lNDQUKytrWnXrh3Ozs6sXr2aTp06cezYMcqVK4e/v3+WJD/+F0M4vtGPF2FxmjZTlRH1ujrjWMkm0+8nhHh3suxNCCGEyGaPHz9m2LBhuLm54ezsTKNGjdiyZUtOh5Wv6Ovr8+TJEy5dusTOnTu12gFMTExITEzM9Pv6Xwxh/5JrWokPwIuwOPYvuYb/xZBMv6cQ4t1J8iOEEEJko9DQUDw8PKhVqxYXLlzg1q1b/Pjjj6xYsQJvb++cDi/fUKlUlCpVigkTJlCuXDkiIyOpV68eP/zwQ5bdMzlZ4fhGv3T7nNjkR3JyrtpxIMRHRZa9CSGEENlow4YNBAcHM3bsWMaOHYtKpcLOzo6WLVvy22+/0b17d0qXLp3TYeYLDRo0YOXKlaxatYpevXpl+f0e+oWlmvF5XVRoHA/9wihR2jLdfkKIrCEzP0IIIUQ26t27N8+ePcPf3x9/f3/+/PNPvvvuOxISEihRogR79+7N6RDzjXr16qGnp0fbtm2z5X4vItJPfDLaTwiR+WTmRwghhMhGpqamWq8tLS2pUaMGNWrUYPjw4dl2/szHwNbWFldX12yrHmtqbpSp/YQQmU9mfoQQQohcREcndenjZ8+eaf4eGRlJfHx8doaUZ+np6VGoUKFsu18xZxWmqvQTGzPLlLLXQoicIcmPEEIIkUkOHjyIk5MT7u7u3Lx5U+vaq0nNhQsX6NWrF87Ozjg4OFC6dGmGDBlCcHBwqjFHjBiBk5MTP/30E0+ePKFBgwa4uLhw8eLFLH8ekTG6ujrU6+qcbp+6XZzRlbN9hMgxkvwIIYQQmWT06NHs27ePmTNnMnHixDT77Ny5ky5dutC5c2d8fX25c+cOly9fpkqVKnh4eHD37l1N399++41bt24RGBjIoUOHqFatGmPHjmX58uV07dqV5OTk7Hq0PMnDw4OjR4+mee3o0aN4eHhk+j0dK9ngOdAt1QyQmaURngPd5JwfIXKY7PkRQgghMkloaChxcXHExsZqzpN53cWLF9m6dSsVKlTQtBUoUIC+ffsSEhLCkiVLmDp1KgBLlixh6dKlWFhYsG3bNnx9fTXvU6lUnD59mtq1a2f9g+VhwcHBtG/fHisrK7Zt24apqSmtWrVi9OjR1K1bN0vu6VjJBnv3winV3yLiMDVPWeomMz5C5DxJfoQQQohMoigK7du3p1ChQqxbty7NPhMmTEBRFHx8fFi6dCmPHz/G2tqaBg0aUKRIER48eKDpe+PGDcqWLYuiKPTt25fTp08za9Ys2rdvj7OzM4GBgZL8vMXPP//MlClT2L59O3v37qVz585ERUVlyQGnr9LV1ZFy1kLkQpL8CCGEEJno0KFDqNXqdPuMGjWKO3fusH79euzt7QkLC2PNmjV89913dOnSRdPPxMSEsLAw7t+/T3JyMufPn6dZs2a0b98eAwMDKXzwFkmKwr2YOE6GRvIoLoHIqKicDkkIkcMk+RFCCCGyQFhYGIqiYGmp/en/ixcvWL58OY8fP8bIKGVfiEqlYujQoTx9+pSgoCBN31q1anH27FmqV6/OjRs3WLx4MSVKlMjW58ir9jwJY6zffe7VakrYhFEkBQdyobgDRZ6E5XRoQogcJMmPEEIIkcmSkpLw9PQkPj6e06dPY2hoqLn29OlTVCqVJvF5lb29vVby07x5c/766y9at27N/PnzadOmDf/880+2PENetudJGJ9fC0QB9IoWp9CS9UTMmUTo/Xv0u/AfBvcfZGsJbCFE7iHV3oQQQohMNnPmTCpXrkzz5s354YcftK4pisKjR48oXbo0TZs21TrD53WNGjXi5MmTPH36lO+//57169dTtmzZrA4/T0tSFMb63ef1o2ILNGtN7PHDPPXqQFzlmpR1c8uR+IQQOUtmfoQQQohMdO3aNdasWcO5c+cwMjKiZs2aaZZbPnLkCBs3bmTBggVMnjw5zbEcHBy4c+cOzZo144cffqBly5ZZHH3edzosiodxCanaDctXotBvm7T61bEsmJ2hCSFyAZn5EUIIITLR4MGD+fXXXzEzM8PAwIBVq1YxYMAAzXVLS0sMDAwwNjbGzMws3apjenp6PH36lEaNGtGhQ4fsCD/PC4l/typu79pPCJG/SPIjhBBCZKK2bdtSv359zWs3Nzet5MfCwoIJEybg7u6Oj48Pw4cPf+uYI0eOzJJY8yMbw3db1PKu/YQQ+YuOoiivL4vNUREREVhYWBAeHo65uXlOhyOEEEKIPCRJUaj6jy+P4hJS7fsB0AGKGRlwrlZZ9HTk0FEhcrvMzg1k5kcIIYQQ+Yaejg5TnFPKgb+e2rx8Pdm5hCQ+QnykJPkRQgghcrHk5CSCr1/hxsljBF+/QnJyUk6HlOu1LKziNzc1RY0MtNqLGRnwm5ualoVVOROYECLHyYJXIYQQaYqJiSEpKQljY2PCw8OxsrLK6ZA+On5nTnFk5VKinj/VtJlZWdPQawDONWrnYGS5X8vCKjytLTgdFkVIfCI2hvrUVJnJjI8QHzmZ+RFCCJGmjRs3MmfOHIKDg6XSWA7wO3OKnfOmaSU+AFHPn7Jz3jT8zpzKocjyDj0dHepYFqR9EUvqWBaUxEcIITM/QgghUiQkJLB8+XI2b97MixcvePToEdWrVyc2NjanQ/voJCcncWTl0nT7/LVqKY7VaqCrq5dNUQkhRN4nMz9CCCEA6Nq1KyEhIezcuZN//vmHixcv4uzsTIsWLXI6tI/O/RvXU834vC7y2VPu37ieTREJIUT+IMmPEEIIbt68yfbt21mzZg3lypWjQoUKdOnSBRMTE2rVqpXT4X10osJCM7WfECJzxcTEEBUVRVJSEs+fP8/pcEQGyLI3IYQQODg4EB4eTsGCBYGUH+z+/v6cP38ePz8/ChQokMMRflzMVJaZ2k8Ikbk2btxIYGAgXl5eeHl5cfTo0ZwOSbwjmfkRQgiBoaGhJvEBMDY2xs3NDS8vL3x8fNi3b18ORpf/qNXqdK+XKFMOMyvrdPsULGRNiTLlMjEqIfKv1atX4+DgQLNmzbRmai5cuEDt2m+vnJiQkMCvv/5K48aNqVWrFhMnTuTGjRuyJzIPkuRHCCHEW+lIlaxspaurR0OvAen2adBngBQ7EOIdjRs3josXL1KzZk3WrFmjad+0aROVK1d+6/tlT2T+IcmPEEJ8hLy9vfH29tZqu337Nl9++SUuLi7Y29vj4uJCnz59uHHjRqr3b926lcaNG+Po6IharaZcuXJMmjSJqKioVH3v3r1L7969cXV1xdbWllq1arF9+/YserL8w7lGbdp8NSbVDFDBQta0+WqMnPMjRAa4urrSvHlz1q1bR6NGjQgJCWHy5Mns2LGD8ePHp/te2ROZv8ieHyGEEJw7d47OnTszefJk5s2bh7GxMYmJiezatYvWrVuzceNGqlSpAsCkSZM4fPgwc+bMoVq1agA8evSIMWPG0LBhQ06ePImBgQEA/v7+tG3blilTprBq1Sp0dHQ4ffo0vXv3JigoiOHDh+fYM+ekwMDAd+rnXKM2jtVqpFR/CwvFTGVJiTLlZMZHiAzatGkTM2bM4MCBA7Rp0wZTU1NatmzJP//8g0qlSve9sicyf9FRFEXJ6SBeFRERgYWFBeHh4Zibm+d0OEIIkS+9nPV5+b+LFi2idOnSNGrUKFXfjRs3snPnTtatW4eiKHh7ezNq1CitPUIAiqJQvnx55s6dS7NmzQBo3bo1HTt2xMvLS6vv+fPnadasGU+ePEFXVxYhCCHyLkVRZGlwFsrs3EB+4gghhGDQoEE0atSIHTt24OHhgYODA5UrV+bLL78kMTGRBw8eACl7fyZOnMixY8dwdXWladOmPHv2THOtcuXKPHr0SDNulSpV2LNnD9WrVyc09H9lmatWrUpycrJW3/f18OFDWrZsiYODgyZeIYTIDoqiULx4cZ4+Tf9cLpF7SPIjhBACgIULFzJnzhx+/vln7ty5w4kTJ6hduzbDhw/n9UUCI0aM4NixY7Rs2ZL58+dr2nV1dbX6jh49msDAQAYNGpRqaYmFhQXx8fEfHPf3339Pp06duH37NnFxcaxdu/aDxxRZZ+XKlan2mwnxNtHR0TRv3hxbW1tmzZqlde3EiROULl0ad3d3bt68yeXLlylXrhy2trasXr06zfHSmql5+PAhtWvXpmTJkkyePFnr2vXr1+nfv7/WnkgvLy8mT57MJ598grX1//bmZWRPpMh+kvwIIYQAYPr06fz++++4ubkBYGJiQq9evZg9e3aqvo8fP9b8PTk5+Y1jHjp0CJVKhZeXV5YtC1EUBX39lC2s+vr66cYjhMib1qxZg7u7O35+fixbtoywsDDNtW+//ZadO3cyc+ZMJk6cyIoVKxgyZAiXLl3ihx9+eOd7/PTTT/Tu3ZsrV64wZ84czT3++usv2rRpQ7Nmzbh69SoBAQHcvHmT4sWLM2HCBLp06aIZY9KkSfz4449Mnz4df39/AgMDOXz4MIGBgTRs2JCEhITM+pKI9yTJjxBCCCDlU8+SJUumare3t0/VFhUVhYeHB9u2bWPYsGFvHPPZs2eUKFEiU+N83dSpU1m3bh1OTk4kJyfTq1evLL2fECL76evrExsbS2JiIgkJCVrJD0BcXByxsbEYGhryxRdfsHLlSmrXrs29e/cyfI+EhASSkpI093jy5AlbtmyhU6dOGBkZoSgK8+bNIyAggOXLl/PHH38AKR/EJCUlsXv3bk0xGICiRYuyfPlyoqOjOXLkyAd/LcSHkWpvQgjxkVq+fDlr167l888/Z/To0QCULVsWlUrF2rVrcXBwSPf9aZXAzgklS5Zk//79OR2GeEevF78Q4l18+umn9OrVi4oVK6baK9imTRuqVauGrq4u169fx8HBgTNnzgAZO6Ns5MiRfPrppyxatEhr7+DLmZ3du3czd+5c/Pz8ePLkCf3790dfX18zE/5yT+Tu3bsZNWoUtra2/P777xQqVCjNPZEiZ8jMjxBCfKRq1KiBr68vW7Zs4fbt20DK0pJRo0YxYcKEdN9rbGxMZGRkqvaIiIhsK/v6stACQGhoqCx3EyIfMzIyYtOmTfj5+VG0aFFN+9WrV1myZAn+/v7UqlUrQzM9L7383mFlZcXevXu5deuW1j0AlixZwqRJk5g/fz7BwcE8f/6cqlWrMmLEiPfeEylyhiQ/QgjxEbKyssLY2Bh9fX2MjIxITk7GysoKU1NTzMzM3loxrVGjRowZM0ZrA++BAwc4duwYNWrUyNLYExMT6dq1K6VLl2bLli2aX3rKli3L3bt3Nf1Onz5N3bp1UavVODo6MnTo0FQJ2+ufCh89ehQPDw+ttoxstAayZLO1SHHgwAFcXV2pXbs2d+7c0bQ/ffo0zSWbIv8bPnw4c+fOpWTJktja2r7TGVoxMTEYGhoCKYlP48aNNR8AvcnUqVNZv349FStWREdHB1NTU7y8vJg5c2aqvu+6J1LkDEl+hBDiI9S7d28ePHiAWq2mXr16uLi4sHDhQpo1a8aoUaMYN25cuu/38fEhMTGRihUrolarUavVzJgxgw0bNqS5RygzeXt7Y2FhwY0bN5g7dy4tWrTAx8eHb7/9VrOkys/Pj9atWzN58mQCAwO5evUq0dHRDBkyJMP3y8hGayDTN1uL/xk9ejT79u2jf//+LFy4UNO+efNmKlasmHOBvYM9e/bg4ODAt99+q2nbunUr27dvz7mg8ritW7eir69Phw4dNG3vkmzs27ePypUrAymzMbNmzaJdu3ZaCfXrgoODcXJyStXu7Oycqu1d90SKnCF7foQQ4iOkUqk4fPiwVluPHj3o0aNHqr4eHh4cPXpUq83a2prFixe/9T5eXl5v3OPxLp/QpmX58uVcvXoVa2trDh06xP3793FxcaFmzZqMGTOGBw8esHv3bj777DMaNGgApFSumzt37nslZmlttH61bPerG60BvvjiC7y8vPjxxx8/eLP1206e/9hUr16dDh06EBkZyZIlSwgNDeX3339n9uzZHDhwIKfDS9eSJUs4fvw4derUYcqUKRgaGjJ79mwp+/2e4uLiGDNmDDt27MjQ+7Zt20a/fv2oVq0aMTExGBsbU7VqVRYvXkyrVq3Yt28fdnZ2ab43L+6JFKnJzI8QQog84+VBgtbW1sTExNCpUyfatWvHyZMn0dHRwdHRkcDAQEaOHJlqOYpKpXqv2ZRPP/2UBw8epLvRunv37pp9UmXKlOHMmTOaZXDvauTIkRw8eJB69erJQa1vsHDhQrp06YKVlRUDBgygVq1aXL9+nRMnTuDi4pLT4aXrs88+o0qVKhgbG2NoaMiiRYswNDSkadOmOR1anrR8+XLatm1L6dKl39pXURS2bNlC1apVGTJkCDdv3sTe3l7rTLB69eqxYMECPD09CQ4OTnOcvLQnUryZzPwIIYTIUsnJCg/9wngREYepuRHFnFXo6r7fmT/GxsZERESgKIpm6cqPP/7IqFGj2LlzJwYGBplycOqrXm60BlCr1Zr2Vzda9+7dm3v37r310+C0JCcno6urq9ls/fp9xP8YGhry/fff8/333+d0KBlWpkwZSpQoQe3atWnXrh0GBgZs27Yty86/ys90dHTYs2ePpqLb23h6epKUlMSSJUvo1q0blpaWFCpUKNWHDE2bNmX69Ok0adIkVfJSuHDhDO+JnD59OmZmZsD/9kTOnTs3A08qsoIkP0IIIbKM/8UQjm/040VYnKbNVGVEva7OOFayyfB4pqamODk5ce3aNRwcHJg/fz6JiYlprsXPamlttP7kk0/e+r60NlsvXbo0R57hXSQkJGBgYPBOfQ8ePMigQYMwNTVl48aNuLq6aq7p6OhoKl1duHCB+fPnc/r0aZKSkjAwMKBJkyZ89913lCpVKkueI6fdvn2bzz//nOLFi1O9enWKFSuW0yHlWYqiMG7cOE1i8TampqZs2rQJfX19BgwYgJOTE2XKlElzT167du148eIFvXv31mpfuHAhnp6emJubs2HDhnTv5+Pjw7hx46hYsaImUbK3t8+WPZHiHSi5THh4uAIo4eHhOR2KEEKID3D7wmPl54GH3/jn9oXH7zXud999p/z444+KoijK2rVrlSJFiiiPHj1SFEVR6tevr/z1119vfO/rP/Zef/3XX38p9evXf+P77ezslICAAGXLli1KkyZNNO19+vRRfHx83no/RVGULVu2KDVr1tS8PnfunFKuXDnF398/1X3eVVBQkNKrVy+ldOnSSqlSpZSaNWsq27Zt0+qzd+9epXLlyoqdnZ3i5OSkjBs3TomPj9dcv379ulKrVi3F0dFRsbOzU2xtbRW1Wq00adJE8zM5ra/PhAkTlAkTJiiKoiiVKlVSbt26pezcuVNxdXVV3N3dlZIlSypNmjTRfC2GDRumFCxYUNmxY4fm/jExMcqgQYMUfX19xcHBQTl79qxm/FOnTikeHh6Kk5OT4uDgoMyYMSPNr9PatWsVW1tbpUyZMsq///6b6mv0tv9vRdZKSkpSIiMjczoMkQdldm4ge36EEEJkuuRkheMb/dLtc2KTH8nJGT/zolGjRpw8eRJ/f3/mzJnD7t27KVKkyPuGSvny5albty4BAQFASqlqZ2dnunfvnuYSupcbrV+tNvauXm62LliwIDExMQBam62DgoIyPKa/vz8tWrSgQ4cO3Lhxg7t37zJ//ny+/fZbfvzxRwBOnTpFv379WLFiBYGBgZw5c4Z///2XKVOmaMb5+uuvadCgAefOnSMwMJCgoCACAgI4ePAg5ubm7xRLaGgocXFxrFq1Cj8/P6ysrLh8+bKm8ARAUFAQHh4etGnTRjOjVKBAAQ4cOMCYMWNwdXVl2rRpAISEhNCiRQv8/f0ZM2YMf/31F8uWLUuzQtqsWbPYv38/P/74I5MmTcrw11Fkrbt379KqVaucDkMIKXgghBAi8z30C9Na6paWqNA4HvqFZXhsBwcHrly5QosWLfjll1+oWrXqe0aZYt26dXz99deMHz8eAHNzcy5evIiNjQ1r1qxJ1T8jG60h8zZbv8mIESMYNWoU7dq10+wfqVmzJuvXr2fSpEkkJyezbds2Ro8ejbu7O5ByztPUqVPZuXOnZpzOnTtz//596tatS61atZgzZw6hoaEZikVRFNq3b8/evXvZu3cvXbp0Yf369QwaNEjTp127dlhaWuLj40OtWrVwcHCgevXqPH/+HB0dHZ49e6a574kTJ2jWrBm3bt3il19+wdbWltatW6eZJHp7e9OuXTsGDBggZcJzUFxcHPPmzaNq1arY29tTvnx5xo4dm2YBgPwsKTmJc4/OsffOXs49OkdSclJOhyT+nyQ/QgghMt2LiPQTn4z2e5Wenh43b95k+PDh1K5dO8Pvf5WlpSWmpqaYmJiQlJSElZUVBgYGGBoaatpe9XKj9dvOQXqVp6cnixcvZsmSJZiYmLzXZuv0VKlShT179lC9enWtZKVq1aokJyfz6NEjZs+eneq8EZVKpdX/s88+Y+XKlVy/fp1NmzYRHR1NpUqVePDgwTvHAnDo0CFq166Nra0tAAYGBhw8eFCrz/nz59m5cyfr16/nzp07HDx4kG7dujFp0iSuXbum6Wdj8799YQYGBiiKwpEjR/D09Ex13/bt2/Pff//h4+OToXhF5urUqRNPnz7l0KFDBAQEcOjQIRISEmjbtm1Oh5Zt/gz6k2ZbmvHZgc/47vh3fHbgM5ptacafQX/mdGgCKXgghBAiC5iaG2Vqv1TvMzWlf//+7/XeV/344480bNgQS0tLNmzYgKurK82bN8fR0ZHy5ctrZoNeUjK40fplrB+62To9o0ePpl69egwePDjVuUAWFhbvVf2uVKlSjB8/nlu3bnHkyBE+/fTTDL1/6tSptG/fniJFirBjxw7q1aunuRYXF4efnx8XLlzAyMiI5ORkVCoVixYtonDhwpw5c4bY2FgA6taty6pVq3Bzc8Pb25stW7agVqvfedZNZK8bN26wZ88ebt68ye+//07BggUpWrQo9evXp1atWty/fz+nQ8xyfwb9yVdHv0JBe0lvSHQIXx39inke82hs1ziHohMgyY8Q+cKoUaMoUaIEI0eOzPJ7JSYmoq8v3zpE+oo5qzBVGaW79M3MMqXsdUap1WqioqLSvPb6YayvUxTtX0h69epFr169tNoWLlz4xv08bzqYdeXKle90v2+++YZvvvkm3Rh79uxJz5490+3zqkOHDqFSqd54mOyH0NfXf68zh6pWrYqFhQURERGcPXsWU1NTzbXIyEgMDQ0xMjLixIkTLF68mDVr1qCrq4u9vX2q8sXLli3T/N3Dw+O9y1zHxMRgZPR+ybZ4N46OjoSHh1OwYEEg5Wvu7+/P+fPnuX37dr4/4yYpOYkZZ2ekSnwAFBR00GHm2Zk0KNUAPV29HIhQgCx7EyLPiI6OZvbs2VSpUgUHBwecnJyoX78+Pj4+bNu2jR49emj6vnjxAk9PT0qVKsXAgQNTjbVu3TqqVauGra0tarWaVq1a8c8//2j1efDgAU2bNsXJyQm1Wo2dnR1qtZp69eppTq1PTk5m7ty5uLq6olarcXd35/fff9eMERERQZMmTShevDhffPFFqjiOHj2Kh4dHJn2FRG6iq6tDva7O6fap28X5vc/7Ef/z7NkzSpQokdNhaJk5cyaVK1emefPmzJs3L9VMTUxMDKVLl2bSpEnY2Njg5eVFcnLyW8cNCgp671mfl+dCiaxjaGioSXwg5VwuNzc3vLy88PHxYd++fTkYXda7EHKBx9GP33hdQeFR9CMuhFzIxqjE6yT5ESIPiImJoU6dOjx//pxdu3Zx584dbt++zZw5cxg2bBhly5bVqna1du1aKlasyO3bt/nzzz+5fPmy5pq3tzfHjh1j165d3L17lzt37tCvXz/at2+vOcgRYPLkyZQoUYKTJ09qKj8FBgbyzz//ULJkSQDmzJnDtm3bOHHiBIGBgWzYsIGxY8dy5MgRAA4fPoy1tTVBQUHcuHGDixcvZtNXTOQGjpVs8BzohqlK+9N2M0sjPAe6vdc5PyJ7TJkyBUdHR3bv3p3qWlrV8JYvX46TkxPh4eFcu3aNNWvWMGvWLLy9vblx4waPHj1KNc7LvTtmZmaoVCo+//zzVDNlr3vb9bTExcUxbdo0fv7552yftfbx8aFatWrY29tTsWJF9uzZA6TMIL56kO1ff/1FmzZtNB82ubi4MGrUKEJCQjR93ufDpG3bttG0aVMcHR1xdHTE3d2dGTNmEB0dnSXPmx5FUShevDhPnz7N9ntnlyfRTzK1n8gakvwIkQcEBwczfPhwpk+fTvHixQE4d+4co0ePZteuXZw+fZoXL15o+uvr6xMbG0tiYiJJSUmaTc337t1j165dLF26lKCgICpXrky1atVwd3dn+fLlTJgwQTNGkyZNMDAwwNPTk0qVKuHt7Z1q4/P27dv58ccfsba2BlJOMB86dCi7du0CUjZwx8XF4eLiwqVLlwgPD8/Sr5PIfRwr2dB7Wm3ajaxEk35laTeyEr2m1s6yxCciIiJLxs0KubUa1O3bt9mxYwe+vr7Ur18/1fW0quHVqFEDX19foqOjGThwIL/++itmZmYYGBgwcOBATp06pXm/kZERenp6GBsbY2ZmRmJioqYk96pVq9KNTV9fX2uG6OUBqWmJiYlhzpw5ODk5cerUKZ49e8auXbvw80u/BHtmOXjwIAMGDCAmJobNmzczb948unfvrpXQQMozjxgxghEjRnDr1i0CAwM5d+4choaG1KxZU1O5LqMfJr0sdT59+nT8/f3x9/fnzz//1CRR77Oc8XXe3t54e3trtV2/fp3+/fvj4uKCvb09Li4ueHl5MXnyZD755BPNz4uIiAjat29PiRIlqFWrllbxjZdVC1/18OFDateuTcmSJZk8efIHx54VCpsUztR+ImtI8iNEHvDyh8fdu3fp27cvLi4ufPLJJyQmJvLixQtMTU21Pk3r1asXISEhVKhQQesHyunTp3FxcSE6Opr27dszduxYJk+ezKJFi2jQoIHWLwUdOnRg6dKlXLx4kYMHD2JpaUnNmjW1ZpFOnTpFlSpVtGJ9tYKUqakpW7duJSAggEqVKmXVl+et4uLiSEhIyLH7f+x0dXUoUdoSl2pFKVHaMkuXulWoUCHLxs5MubkaVFJSEoaGhhgYGGBiYqL5ZRVIsxqelZUVxsbG6Ovro6OjQ6NGjbSSpkaNGmn22iQnJ/PPP//QvHlz3N3d8fHxYfjw4ejo6LBs2TKio6P5999/3xibi4sLhw4dAuDatWvExsZSrFixVP3u3btHhQoVCAoKYubMmahUKiwsLDTJVnbYvXs3CxcuZPPmzUyfPp2GDRvi6OjIw4cPtfo9ffqU3bt307BhQ3R1U34ts7CwYNq0aZQpU4aNGzcCGfswKT4+nufPn7Nnzx6t79GFCxdm2rRpmJub89dff2X6M7+cwWrWrBlXr14lICCAmzdvUrx4cSZMmECXLl00fRcsWMCxY8e4c+cOjRs3fms8P/30E7179+bKlSvMmTMnV5Yzr2xTmSImRdAh7e9xOuhQ1KQolW1k+WVOkuRHiDzi3r171KtXj4YNG3L16lVevHjBrFmzmDlzJnfv3kVRFM0nooaGhqxfvx5/f3+tpENfX5+AgAB+//13rR/AL3/Jeb2s70uFCxdm+PDh9O7dWzOrk1dcvXqVBg0acPPmzZwORWSi7du307BhQ5ycnHBycqJv3774+/tr9Tl27BgtWrTA2dmZChUq0KdPn1R9csLLalCv7w14WQ0qpxOg0qVLU7t2bdRqtWb/4EsVKlTQVMO7fPkyvXr1onfv3jx48AC1Ws3IkSNZvXq11njVq1dn4MCB2Nra4uDgQHJyMr///jvBwcH8888/FC1aFEgpYX7y5Elq1Kjxxthmz57N8uXLUavVdOnSBR8fH03C8Cp/f3++/PJLFi5cSNeuXYmLi0OtVlOrVi3KlCmTSV+p9L1epvv+/ftERUVRrlw5rX5ff/01Dx48wN3dnVq1amn9G61SpYpmyWBGPkwyNDTkt99+01QnrFChAk5OTnzyyScsWrSIQoUKZbiE+bt48uQJW7ZsoVOnThgZGaEoCvPmzSMgIIDly5fzxx9/aPo+fvyYNm3aUKZMGU6ePEmjRo3SHfvlioaEhASSkpJyZfKjp6vH6OqjAVIlQC9ff1f9Oyl2kNOUXCY8PFwBlPDw8JwORYhcZdy4ccoPP/yQqj08PFwxNjZWAgIClPbt2yunT5/Wul6/fn3lr7/+UhRFUQIDAxUDAwPF09NT6dKli/LJJ58oNWvWVO7du6coiqK87VvChAkTlAkTJqTbx8fHR+nTp0+q9lfjeOmvv/5S6tevn+5472vv3r1KjRo1lI4dO6a6r8jb5syZozRv3ly5c+eOoiiK8uLFC2XZsmWKWq1WChYsqCiKomzatEmpWbOmcvXqVUVRFCUuLk75448/FAcHB+X69es5FntiUqLSaFMjxW2lW5p/yq8srzTe1FhJTErMsRhF5njy5Ini4eGhlC9fXrl06ZIyePBgZd68eYqiKEpAQIBiZ2en6VujRg3l2rVryvbt25WePXtq2t/0Pfddvp8mJCQoNWrUUMaPH6+EhoZq7jto0CAFUHx8fD74Gd8U365duxQPDw+lRIkSiqGhoTJ48GBl1apVioeHh6bPnj17lPr16yvPnj1L9f60fhY9e/ZMad68ueLs7KwYGRkpAQEBHxx/VjkUeCjVf+eNNzVWDgUeyunQ8qTMzg2kXq0QeURwcPAb19+//IRx7ty5tG7dmpUrV6Z56r2dnR0lS5bk0KFD3L59W2vDbX7y/PlzOnXqROnSpZk8eXK2fdIrssesWbMwMjKiYcOGGBsbY21tTdWqVenbt69m/8GcOXO4e/cubdq0wcDAAGtraypUqMCQIUMYOnQohw8fzpHYM1INqlrRatkYmchs1tbWmqVcsbGxrF69+o2zLY8f/+/fxLtUvXsXR44cwczMjIkTJ2ra1Go1v/zyi9ZBspltyZIlLF++nKVLl+Lu7k50dDR//PEHI0aM0FqW2qJFC/bs2UORIkUoUaIE7du3Z/78+anGS05ORldXFysrK/bu3at5jtyssV1jGpRqwIWQCzyJfkJhk8JUtqksMz65hCQ/QuQRiqIwfvx4pkyZwpgxY/jss89S9bG3t2fz5s20bduWDRs2pLk0ol69ety8eTNDPzxWrFjB9OnTiYqKSrN0dnpmzZrFkiVLuH//Plu3bsXV1VWzzCWr+Pv707p1a7y8vFi0aNEbz2wRedPNmzdRqVTo6OiQlJREYGAgV65c4fjx4zg5OREZGcmBAwcwNzdHV1eX5ORkgoODuX79OidOnODZs2c8ePBAUzwkO0k1qP9JSlY4G/CckMhYbAoWoLq9FXr5tPT548ePsbKyeuPhuIqi0K5dOywsLFi7dm2m3PPhw4eaypyvs7e3z5R7pGXq1KkcOXIEJycnIGW5npeXFwkJCaxbt07T78qVKxw5coTg4OA3/kxITk6mcePGLF26VDNeXqGnqycfYORSsudHiDykbdu2XLx4kalTp76xVKmrqysbN26kc+fOXLlyJdV1W1tbqlev/s73jI6OZurUqVy4cIHu3btnKF5/f382bNjAtWvX8Pb25uDBg9SuXZvPP/88S0/6dnd358WLFwwbNizDMYvcz9LSUlMNSk9PD0dHR9q3b8+8efO4deuWpnTyy70gurq62NnZ0aJFC6ZNm8alS5fS3CSfHaQaVIr91x5Sd+YRui87zfANl+i+7DR1Zx5h/7WHb3/zWyhKEqGhp3n0aCehoadRlJyvoqe8Q4nuQ4cOcf78eVxdXd/YZ9asWTg6OnL69Gm2bt2aZgnxV++5Z88eHBwcGDx48HvF/T6Cg4PTTFScnbXP/dq7dy9t2rRJ98MwXV1dZs2aRbt27bhz506mxyo+TpL8CJFH2NjYYGpqiqGhIXp6eukujahYsSJr166lbdu23L17V+uanp4ehQoVeuf7vlxyYGhoiKmpqVblp7dJSEjAwMAAAwMDTE1Nad++PTdv3qRcuXJUrlz5rWVt35ehoSG7du3i1q1b1K5dO0vuIXKvtMrkvq2PoihalRGz6iwSqQaVkvh8ufYCD8Njtdofhcfy5doLH5QAhYQc4OSpT7hwsSfXfUdy4WJPTp76hJCQAx8a9gd5W4luExMTIiMjU70vIiKCAgUKAO/3YVKpUqW4efMm9+/fz7Klni/PeJoxY4amrWzZstSuXTvdhCU2NhZjY+O3jl+1alUWL15Mq1atCAoKypSYxcdNkh8h8ojhw4dz9OhRXFxcGDx48BuXT7xUs2ZNli9fnqqsakaZmZkxZMgQXFxcOH78OL17937n97q6uuLh4YGDgwN//PEHI0eOxNDQkJEjR3Ly5Ml0S9oKkZ43HR4J0LVrV5YvX05cXBxjx46ldOnS2NvbU7p0aUaNGpWqStT9+/epVKkSNWvW5MaNG+zevRtXV1dat26d6SXSP/ZqUEnJChN3+ZLWPMjLtom7fElKzvhhpiEhB7h6bTBxcdqzIXFxj7l6bXCOJkDFihUjMjKS69evAylnAL1a9a1Ro0ZMnDhRK+k+f/48GzZsoG7dukDGP0wqVKgQBQoU0FTzzKoS3y/PeNqyZQu3b98GYM2aNYwaNUrr7LjXWVlZpfkhQ0REBKamplpt9erVY8GCBXh6ehIcHJy5DyA+OrLnR4g8okSJEpw+fTrNa4GBgWm2N2zYkJiYGK221w+ke9WblmYMHz6c4cOHv1OcXl5eeHl5aV7PnDmTmTNnpurn5OSU5rI8Id7m5eGRpUuXZvPmzYSHh9OuXTtu377NxYsXCQ8Pp1evXvTv3x9dXV3OnDmDSqUiPj6emTNn4unpyT///KOZ/enatSujRo2iTJkydOjQgYIFC3Lt2jW+++47pk+fzvjx4zM1/sZ2jZnnMY8ZZ2doFT8oYlKE76p/R2O7xpl6v9zkbMDzVDM+r1KAh+GxnA14Ti3Hd5+hVpQkbvlNgjemVTrc8ptM4cKN0dHJ/sRST0+P3377jfbt25OQkECJEiVYsWKF5vqsWbPw9vamTp06xMfHAymz/fPnz9ckP69+mKRWq9m8ebPmw6TWrVvToUMHrKysNGM2b96cdevWYWtryyeffEKTJk0y/blePePJyMiI5ORkChcujKmp6VvPVGrSpAnTpk2jQ4cONGzYEIDIyEh++OGHNGNt2rQp06dPp0mTJmnOkgnxrnSUd1mImo0iIiKwsLAgPDwcc3PznA5HCCFELjNs2DDKli2Lh4cHY8eOZfPmzVSqVInZs2fj5eXFuHHj6N27N15eXqxevVpzwOZLDg4O7Ny5Ezc3N65fv06PHj00h/feuXOHQoUKYWFhga+vL+3atePWrVtZ8hxJyUkfXTWoHZfuM3zDpbf2+7FbRdpWLPHO44aGnubCxZ5v7Ve50josLWu+87gifWFhYXTs2BE/Pz969uzJ9OnT2bhxI9999x3m5uZs2LCBsmXLAnD06FG8vb05evSo5v2HDx9m6tSpBAQEAClJoqenJ5MnT8bS0jLNe65bt47evXvj7++f66u+icyR2bmBzPwIIbJFcnIS929cJyosFDOVJSXKlEM3n/+il1+o1eo3zi7mhDcdHunh4cGBAweoX78+AwcOZOPGjWm+39bWlmfPngFw48YNzS9nN2/epFu3bhQsWJAtW7bg7Oycpc/9MVaDsilYIFP7vRQXF5Kp/fKCadOmsXz5cqZOnUq3bt04ceIEM2bMYPfu3dkWg0qlSrWXqGvXrnTt2jVVXw8PD63EB1KW+73tcNPX9ezZk549357oCvEmkvwIId4oOjoaXV1dzYbbp0+fZqjgwUt+Z05xZOVSop7/b323mZU1Db0G4Fzj7QUJEhMT0deXb1cixRdffEHnzp1ZtGgRa9asYfr06QwaNAh9fX3KlSunSWzS83LRg4mJiWYP0KJFi5g9ezYBAQGsXbuWr776KtP3/HzsqttbUcyiAI/CY9NcoKYDFLVIKXudEUZGNm/vlIF+uV1kZCR79+7l77//plmzZnTr1o3ExESioqJyOjQhcj0peCBEPuTt7a21t+fEiROULl0aNzc3zfKel0aPHo2dnR29evXS2vNz+PBh3NzcaNGihWYdtrOzM5MnT051vxcvXjBu3DjKly+PWq2mfPnyzJo1i4SEBPzOnGLnvGlEPX9KSEQUy/4+y9TdRxizZhPdunbh1N5dmnGioqLo1KkTTk5OqNVq7OzsUKvVVK1alUuXLmn6xcTEMGHCBNzc3DT3+/XXX9Pcs/Qulb9E3vLy8MgrV65QunRpVq9eTf/+/d9rrBo1anDhwgUURcHR0ZENGzawZ8+ePHemSF6hp6vDhNYpM22v/5f58vWE1mUzfN6PSlUNI6OiaYz6v9GNjIqhUuW/mbY3HXuQHyjJCrH+YURfCiHWPwzlPQphCPE6+ShViI/AN998w44dOwgODmbixIls3boVgFu3bnHixAkCAgLo0aMHBw8epFmzZjx9+hQvLy8OHjzI6dOnKVOmDA0aNODOnTvUrVuXTz75hPr16wMp5UpbtGhBq1atOHPmDCYmJty7d48BAwZw6tQpmhdTARAdn8CSY2do5e5KJdsSJCUnc+i6H16f9+fmvfvo6urxyy+/8OLFC44cOYKtrW2az5KUlESHDh2oXr06Z8+excTEhKCgILp27UpiYiJDhgzJlq/pxyQ3LXl73dsOj3ybQoUKYW9vz9WrVxk2bBhffPEFkZGRtG7dOpMjFS95uhVj8aeVmbjLV6v4QVGLAkxoXRZPt4yfwaSjo4eL83iuXhtMSgL06i/JKQmRi/O4HCl2kBUKFixIs2bNqFGjRrqz4nn5INmYa08J2+VPUni8pk3PwhBVa0eM3TK+AkGIlyT5EeIjoK+vT2xsLHFxcTx//lzrWkJCAklJScTFxWFoaAjAxo0b6dChA2XKlKFMmTJUqlQJd3d3dHR0GDx4MOvWrdMkP7/88gsuLi4MHz5c8/6SJUuyadMmHNRqHCqXoYSlBXeePENtbUkl25RNzHq6uniWL8247QfxPfMPbrXqUq1aNYKDg+nSpQsRERG0atWK/v37ax2Ot23bNh48eEDfvn0xMTEBwM7Ojh9//JG+fftK8vORyUjNnsGDB3PgwAGePHmi1d6oUSNOnjxJcHAwly9fZu/evTJjmMU83YrRpGzRTP3F3MamGeXdfuGW3yStctdGRkVxcR6HjU2zzAg91xg3bhyfffYZ5cqVIywsjMDAQK0z3PZfe5gqwSz2AQlmdoq59pRna2+kak8Kj+fZ2hsU+rSMJEDivcmyNyE+AvPmzaNPnz4MHTpUq93BwYHo6GgKFixIdHS0JqF5dRP45s2b6dSpE3379kVRlFSbwK2trdHX16ds2bKcP39e025mZkaFsmV4HJGyBt2tRFF61Up9cGMBfX0e3b8HpJTm/vnnnzl9+rRmxqlZs2YcOPC/8zkcHBwYOnQojRs3ZvXq1Zr2ChUqaM6YEO9n+/btNGjQACcnJypWrMjgwYN5/Pjx29+Yg952eORLhw8f5v79+/j6+lKmTBmtaw4ODpoKVTt37tQqFyyyjp6uDrUcC9G2YglqORbKlBkJG5tm1Kn9N5UrraNc2flUrrSOOrWP5bvE56XixYvTqVMn3N3dWbRoET/88AOQtQfJZjUlWSFsl3+6fcJ23ZElcOK9SfIjxEegWrVqXL58GR8fH6328ePHU7VqVW7cuIGfnx+6uinfEl7dBD5nzhz+/fdf4uPjuXjxIgYGBppzKAD69OmDrq4uTZo0wc3NTWt8Sysrkl75xfRNTMwtUrWZm5vTt29fxowZwx9//KFpr1y5MuHh4TRs2JBevXpp2o2NjWVz+geYO3cuy5YtY8WKFdy+fZuTJ09SsWJF6tWrl6sToGLFihEVFcXVq1cB2LdvX6p/h5BSNMPY2BgDAwOMjIy0yujq6elx4sQJfv/9d61KciJv0tHRw9KyJkWLtsHSsma+WeqWFh0dHX777TeCgoI4e/YslStXztKDZLNDXEC41lK3tCSFxxEXEJ5NEYn8Rpa9CfGR8vPz4/fff+fKlSsULFhQazanVq1arF27Fkj5ZHHx4sXcuHGDUqVKaU4ofyk8PJw1a9bw9OlTzbK3l0wsVBBeMN04dPV0Kerk8sbr+vr6qQ7K8/Hx4Y8//njr0qTY2Fj09PLvLz6ZadasWRgZGdGwYUOMjY2xtramatWq9OzZk1GjRrFmzZqcDjFNenp6rFixgs6dOxMbG4u1tTVLlixJ1a9JkyasXr0aOzs7Wrdujbu7u9b1Ro0aUb58+ewKW4gsk1UHyWaX5Mj0E5+M9hPidZL8CPGR+vrrr5k6dSoFC6YkJ6/unWjcuDGDBg1CURR8fHxo1KgRbdu2pXDhwqnGCQ0NxcrKKlXiAymfSpapWx9uXU517aUCpgUzfN5PVFQUxsbGb+138uRJ7O3tMzT2x+rmzZuoVCp0dHRITEwkKCiIy5cvc/z4ca5fv05cXFyqw0Jzi3bt2tGuXbt0++jq6rJu3bo0r3l5eeHl5ZXmtVx2DrgQbxUS+ebE5336ZTfdgql/lnxIPyFeJ8vehMinli9fjpOTEzNmzNBq19HR4eDBg0RERNCjR48031uwYEEcHBy4ceMG8+bNo2zZsowfP/694iji6Eybr8ZgZqW9ObVgIWvafDUG/Tf8Qr13715cXV0ZN25cmtdbt25NkyZNCAoKSnUtKSmJtWvX0rt3b4yMjFJtcBepWVpaambS9PX1cXR0pEOHDsyfP58LFy6kmdwKIXKfrDpINrsY2VugZ5H+9xs9CyOM7FMvlxbiXWQ4+fn7779p3bo1xYsXR0dHh+3bt2td9/LyQkdHR+uPp6dnZsUrhHhHNWrUwNfXly1btmgVAjA2Nubbb79l4cKF6b7fwcGB4cOHc/36dXx8fDT7gd6Hc43a9P9lOV3GT6PFsG/oMn4an/+8PN0DTkeMGMHhw4f55ptv0ry+aNEi+vTpkyo5unTpElWqVKF///78/fff9OjRg/nz57937CLFN998I19HIfKAlwfJvvnEo5Sqbxk9SDa76OjqoGrtmG4fVWsHdPJIyW6R+2T4t5kXL17g7u7OL7/88sY+np6ePHz4UPPn999//6AghRAZY2VlhbGxMfr6+hgZGWlVwzp16hQeHh5v3d+gp6eHv78/q1atypR9M7q6epQqV4EydepTqlyFty51S05OxtjYGFNTU6yttWeNrKysKFKkCEWLFiUuLk7rWrNmzZg6dSo2NjZYWVlhbW2tVaBBvFl0dDSzZ8+mSpUqODg44OTkRP369fHx8WHbtm1aM4UvXrzA09OTUqVKMXDgwByMOnMkKQonQyPZ9jiUk6GRJMlyN5FHZdVBstnJ2M2aQp+WSTUDpGdhJGWuxQfL8J6f5s2b07x583T7GBkZUbRo0fcOSgjxYXr37s2OHTtQq9X07NkTF5f/FRQoUKAAEydOfKdx+vfvj6mpaVaFma7p06dTpUoVrKys2LZtm9a1KVOm4OnpibGxcaoPV4YOHUrLli3x9vamYsWKWFtbaw51FW8WExNDnTp18PT0ZNeuXRQvXhyAc+fO0bBhQzw8PChSpIim/9q1a6lYsSI7duygbNmyXL58OVURgbxiz5Mwxvrd52Hc/6oFFjMyYIpzCVoWVuVcYEK8p6w4SDa7GbtZU6BsIeICwkmOjEe3oCFG9hYy4yM+mI7yAbs5dXR02LZtm9ZGUy8vL7Zv346hoSGWlpY0bNiQKVOmaB289aq4uDitT24jIiIoVaoU4eHhmJubv29oQgghMuDWrVucOnVKa+P/uXPnGD16NOPGjaNz584EBgZqkuHly5dz9epVpk6dSrly5Vi5ciUeHh45E/wH2PMkjM+vBaYqC/zy16vf3NSSAIk8KylZydSDZIXICREREVhYWGRabpDpBQ88PT1ZvXo1hw8fZubMmRw7dozmzZuTlJSUZv/p06djYWGh+VOqVKnMDkkIkQkSEhLo0KEDTk5O7Nu3L6fDEZnMxcUFLy8v7t69S9++fXFxceGTTz4hMTGRFy9eYGpqytOnTzX9e/XqRUhICBUqVCA0NDQHI39/SYrCWL/76Z6HMs7vviyBE3lWVhwkK0Rel+nJT7du3WjTpg3ly5enXbt27N69m3PnznH06NE0+3///feEh4dr/gQHB2d2SEKITHDlyhWMjIxYt24dCxYsyOlwRBa4d+8e9erVo2HDhly9epUXL14wa9YsZs6cyd27d1EURbN/zNDQkPXr1+Pv70+lSpVyOPL3czosSmup2+sU4EFcAqfDorIvKCGEEFkqy0tdOzg4YG1trVVt6lVGRkaYm5tr/RFC5D4VKlQgLi6ONm3apCoyIPKHpUuX0qtXL3r16oWRkRG6urrUqFGD3bt3U6BASlncTp06cebMmRyONHOExCe+vVMG+gkhhMj9sjz5uXfvHs+ePaNYsdy/uU4I8WYGBgZs3bqVjRs35nQoaUpKVvjH/xk7Lt3nH/9nJCXLUqWMCg4OxsnJKVW7ubk5NjY2AMydO5d+/fpx/vz57A4v09kYvlvNn3ftJ4QQIvfLcPITFRXFpUuXuHTpEgABAQFcunSJu3fvEhUVxTfffMPp06cJDAzk8OHDtG3bFicnJ5o1a5bZsQshcoC+vj5mZmY5HYaW/dceUnfmEbovO83wDZfovuw0dWceYf+1hzkdWp6iKArjx4/HycmJFStWpNnH3t6ezZs307NnTy5evJjNEWaumiozihkZpHseSnEjA2qqcte/d/F+Dh48iJOTE+7u7ty8eVPr2ssDfgEuXLhAr169cHZ2xsHBgdKlSzNkyJA3LsvfvHkzDg4OuLq6cu7cOU17XFwc33//Pc7OztjZ2VGzZk0OHTqkuX7v3j2qV69OyZIlmT59eppjq9VqAgMDP+CphRCvy3Dyc/78eSpVqqRZ4/3VV19RqVIlxo8fj56eHleuXKFNmza4uLjQr18/qlSpwvHjxzF6wynuQoi8pW7duuzevTunw9DYf+0hX669oFXOFeBReCxfrr0gCVAGtW3blosXLzJ16lSio6PT7OPq6srGjRvp3LkzV65cyeYIM4+ejg5TnEsAbz4PZbJzCfR0ZJN4fjB69Gj27dvHzJkz31juf+fOnXTp0oXOnTvj6+vLnTt3uHz5MlWqVMHDw4O7d++mOe65c+cYNWoU06ZN07SPHDmSW7ducenSJYKCgpg/fz69e/fWJF5//PEHDRs25Pbt26xYsSLPFg4RIq/J8Fy+h4cH6VXHPnDgwAcFJIQQ7yopWWHiLt83VuvSASbu8qVJ2aJS5egd2NjYoKuri6GhIXp6elqH476uYsWKrF27lrZt22p9ap7XtCys4jc3dZrn/EyWc37yldDQUOLi4oiNjUVfP+1ffy5evMjWrVupUKGCpq1AgQL07duXkJAQlixZwtSpU7Xeo6enR3x8PLGxsZoEJjk5mT179nDu3DlNefhatWrRtWtXDhw4gKurK927d6dnz564ublx7949wsPDsbS0zKKnF0K8JAuZhRB51tmA56lmfF6lAA/DYzkb8JxajmmfNSb+Z/jw4XTs2BEXFxe++uqrty5vrFmzJsuXL6dly5bZFGHWaFlYhae1BafDogiJT8TGUJ+aKjOZ8clnFEWhffv2FCpUiHXr1qXZZ8KECSiKgo+PD0uXLuXx48dYW1vToEEDihQpwoMHD1K956effqJx48ZER0djZ2cHgK6uLkFBQan6qlQqTYJUtGhRDh8+DKQsbxNCZI8sL3gghBBZJSTyzYnP+/T72JUoUYLTp08TFBTE8OHDta4FBgam+Qtaw4YNiYmJyZMHnL5KT0eHOpYFaV/EkjqWBSXxyacOHTrE6dOncXR0fGOfUaNGsXPnTtavX8+dO3c4ePAgJUuWZOzYsVorX17OjDZr1ozr16/j4+OT5fFnxM2bN+nWrRsuLi4UK1YMd3d3li1bptXn7Nmz1KtXD7VajaOjI0OHDiUyMlJzfcuWLTg4OFC9enXNnqf79++jo6OjWQLo5eXFypUrs+25hPhQkvwIIfIsm4IFMrWfEOLjEBYWluYemxcvXrB8+XI2bNiAvb09kDJbM3ToUL755htNvxMnTtCrV690l4ZmhpiYmPfaM33s2DE8PT3p1KkTN27c4OHDh6xbt44lS5YwduxYAPz9/WnVqhWTJk0iMDCQq1evEh0dzZAhQzTjfPfdd5w7d47+/fszf/58IGV7g7m5uVbxBiHyEkl+hBB5VnV7K4pZFEi3WlcxiwJUt7fKzrCEELlYUlISnp6eNGrUiPj4eK1rT58+RaVSpZlwvEyGIKXwi1qtxsvLK8sSoAsXLqCvr0/RokUz/N6xY8fSt29fPD090dPTA8DNzY1t27axYMECQkND2bNnD3369KFBgwYAmJiYMHfuXHbu3KkZR1dXl/j4eOLi4nj+/DmQUjVvzJgxHDx4MBOeUojsJ8mPECLP0tPVYULrssCbq3VNaF1Wih1kEkVJIjT0NI8e7SQ09DSKkpTTIQmRYTNnzqRy5co0b96cH374Qeuaoig8evSI0qVL07RpU549e/bGcaZOnYpKpeLzzz9PtxDU+zh27Bjt27fHxsYm3RjeZOzYsYSFheHq6qq1T6lUqVI4OTlx8+ZNhg0bxuzZs7Xep1KpCAsL07xesGABDRo0YObMmUDKUr9///2XESNG8O+//2b5zJcQWUGSHyFEnubpVozFn1amqIX20raiFgVY/GllPN3kgOXMEBJygJOnPuHCxZ5c9x3JhYs9OXnqE0JCpMKnyDuuXbvGmjVrmDVrFt7e3hw5coSjR4+m6nfkyBE8PT1ZsGBBuuP9+OOPAAwcODBTEqAjR47QsGFDPD09OXLkCJ06ddIsN8uIxo0bs3fvXn755ReKFy+udU2lUhEXF/dO47Ro0YKbN28yefJkIGU2qmLFihgZGVGxYkUuXLiQ4diEyGlS7U0Iked5uhWjSdminA14TkhkLDYFU5a6yYxP5ggJOcDVa4PhtaLicXGPuXptMOXdfsHGRg6yFrnf4MGDWb16taaS4apVq+jQoYPmuqWlJQYGBhgbG2NmZsaTJ0/SHU9HR4dly5bRo0cPRo8ejbGx8XvHNnDgQJ48ecLSpUvp3LkzVlZWFC5cGH9//wyP9fjxY0JDQ2nbtu17x5OWgwcP4unpCYCnp6fs+xF5kiQ/Qoh8QU9XR8pZZwFFSeKW3yReT3z+/yqgwy2/yRQu3BgdHb1sjk6IjGnbti3169fXvHZzc2PAgAGaYgYWFhZMmDABd3d3SpYsybZt2946pp6eHmvXrqV9+/ZERUW9d2yRkZH8/fffmJmZMWXKFCpVqkSxYsXYunVrhsfKqvOCDh06xNq1a4GU5Kd3797Y2tpmyb2EyCo6SmYvVP1AERERWFhYEB4ejrm5eU6HI4QQH7XQ0NNcuNjzrf0qV1qHpWXNbIhICPEmiqIwePBgDhw4QFBQEM+ePcPCwkKrj4eHB97e3m8sT6+jo5NqCd/KlSvZt28f/v7+nD9/XtNepUoVnJycaN68OV5eXpn9OEIAmZ8byJ4fId5gxowZqNVqunXrprU+evv27XTr1i0HIxMi+8TFhWRqPyFE1jly5AiPHj3ixo0bVKxYkYCAgEwb+8qVK5rKcC81aNCAK1euZNo9hMgOkvwIkYawsDBWrlzJjRs3KFCgAPv379dc27BhA1WrVs3B6ITIPkZGNpnaTwiRWnKywv3/Qrl17hH3/wslOfn9FuXEx8djYmKCvr4+BQoUQFc3837N8/f31+z3ecnT0/O99iQJkZNkz48QabCwsEBXV5fmzZvz+PFjpk+fzr1795g5cyaBgYG57iRvIbKKSlUNI6OixMU9Ju19PzoYGRVFpaqW3aEJkS/4Xwzh+EY/XoT9b4WBqcqIel2dcayUsQ8VmjZtyooVK7Czs6Np06ZUqFAh0+I0NDSkXr16Wm316tXD0NAw0+4hRHaQPT9CvMGDBw+YPn06x44dIzIyEisrKzp27MhXX31FgQIF3j6AEPnE/6q9gXYClFJNT6q9CfF+/C+GsH/JtTde9xzoluEESIj8JrNzA0l+hBBCvFVIyAFu+U0iLu6Rps3IqBguzuMk8RHiPSQnK6wec0prxud1ZpZG9JpaG10p2y8+YpmdG8iyNyE+QFRUlOa8iBcvXqCrq/tB5zwIkVvZ2DSjcOHGhIWdIy4uBCMjG1SqalLeWoj39NAvLN3EByAqNI6HfmGUKJ01paszKklROB0WRUh8IjaG+tRUmaGnI4mZyFuk4IEQQHR0NM2bN8fW1pZZs2Zp2vv3789nn30GQGBgIGq1WnNt48aNuLq68vXXXxMTE0PLli1xdnbm1KlTWmOvXLlSUwI0IiKCJk2aULx4cQYPHkxaPDw8tE4cP3v2LPXq1UOtVuPo6MjQoUOJjIzMnAcXIgN0dPSwtKxJ0aJtsLSsKYmPEB/gRUT6iU9G+2W1PU/CqPqPLx0v+fOlbxAdL/lT9R9f9jwJS/d9iYmJ2RNgJktISMjpEEQWkeRHCGDNmjW4u7vj5+fHsmXLCA8PB+DYsWMcP348Vf///vuPr776itOnT5OUlET58uXp1asXGzZsoGfPniQlJaV5n8OHD2NtbU1QUBDXrl3j0qVL6cbl7+9Pq1atmDRpEoGBgVy9epXo6GiGDBnywc8shBAi55iaG2Vqv6y050kYn18L5GGcdkLwKC6Bz68FsudJGL/++iulS5fG3t4eOzs71Go1Tk5OzJkzB0j5udm8eXPUajUODg706tWLR48eaY33888/Y2dnh62tLbt379a0e3l5sXLlSq2+169fp3379ri4uGBra0vDhg35+++/U8W+evVq3NzcUKvVlClThp9++knr+vHjx6lcuTKOjo6o1WrUajX29vb06tUrzyZuIn2y7E0IQF9fn9jYWBITE0lISCA0NJSHDx9iZ2eHoijcuHFDaznbihUr+OKLLyhZsiQLFizg888/x83NDYCiRYty+vRp6tSpk+o+TZs2Zc2aNbi4uPD8+XPCwsLSjWvPnj306dNHc7aCiYkJc+fOxd7ePvMeXgghRLYr5qzCVGX01j0/xZxV2RdUGpIUhbF+99Os9aiQUvZk9KmLPBw7lk2bNvHJJ5+gr6/962VoaCiNGzdm1qxZdO/enYSEBCZNmkTXrl05duwYkLK6Yvjw4fz+++9UqVKFkSNH0qpVqzRjOnPmDP379+enn37Cw8MDRVE4cOAAXbt2ZfHixbRr1w6ATZs2MW3aNPbv349arSY4OJgOHTpQsGBB+vbtC8CAAQMYOnQoffr0wdTUNJO+aiI3k5kfIYBPP/2UBw8eULFiRc0nUQcPHsTT0xNPT08OHTqk1d/X15eqVauiKAp9+/alXbt2rFixAgBHR0cCAwPTvI+pqSlbt24lICCASpUqvTWuYcOGMXv2bK02lUr11qRJCCFE7qarq0O9rs7p9qnbxTnHix2cDotKNePzKgV4nKTQum8/5s2bh5OTE127dmXnzp28rKl17NgxateuTffu3QEwMDBg8uTJXL16VfPz7MmTJ9SuXZsFCxbQvHlzBg0a9MZ79u/fnwULFuDh4QGAjo4Onp6eLFy4EG9vb02/LVu2aA4sByhVqhQ//PADO3fu1PTp168fx48fp2LFijRq1Ihff/2V6OjojH+hRJ4hyY8QgJGREZs2bcLPz4+iRYsC2snPwYMHtfqbmpry9OlTLl68SFxcHL6+vvz6669AyiySrBUWQgjxNo6VbPAc6IapSntpm5mlUa4pcx0S//alX7qWVrQZNZrdu3dz584dhg4dyq+//qrZ79quXTs2btyY6n3m5uaa5Mfd3Z2QkBBmzZrFrVu3Uh2o+tKLFy9o1aoVM2bMoGXLlsTHx2uuNWnSBF9fX83rjRs3amaBXlKpVISGhmpejxo1it9//x0/Pz8WL17MzZs3qVatGi9evHjrc4u8SZa9CZGG+Ph47ty5Q7ly5YCUvTevJjSNGjXi8OHDNG/enNu3b7Nw4UIMDAwy5d4xMTEYGeX8Gm8hhBBZz7GSDfbuhVOqv0XEYWqestQtp2d8XrIxfLdfFV/209XVpW7duuzcuZMCBQqwatWqd3q/oaEhPj4+eHh4UKhQIYyMjPD390dPT7uwiqmpKSNHjqR+/frMmzdP65BVCwuLD/rw0cXFhQULFlC3bl0uXrxI3bp133sskXtJ8iNEGk6dOqW1Z6dOnTpaVdzatWvHhAkTKFSoEL/++iv16tVj7dq1H3zfBw8ecPv2bVxdXT94LCGEEHmDrq5Oriln/bqaKjOKGRnwKC4hzX0/OkAxIwNqqsy02vX19d9Y/OdNRo4cyfr16+nUqVO6/TZs2ECdOnXeODv0ofT19aXYQT4my96ESMPhw4e1vql6enry559/al4XLlwYOzs7/v77bwYOHMjw4cNp3749Z8+e5eLFi1y8eJGoqKgM3fPy5cs0b96cggUL8vz580x7FiGEEOJ96enoMMW5BJCS6Lzq5evJziU++LyfR48eERgY+NbEB+DZs2eUKFHig+4nPl6S/AiRhkuXLtG4cWPN68aNG6cqS/1yU2fZsmWZNm0aISEh9OjRg+HDh3Pw4EHs7OyYMmXKW6fgL1y4QNu2balSpQqLFy9m6tSpjB8/PiseSwghhMiwloVV/OampqiR9vLuYkYG/OampmVhlabt2bNnNG3alDJlymToHrGxsTl+SPjgwYNxcnLi4sWLORqHyFqy7E2I1+jo6GBjY4OFhYWmTaVSUbhwYa3ZHD09PczMzDSFDh48eIC9vT2fffYZJ06coHbt2uzcuZNTp069sVzn9OnTuXPnDosWLeLBgwfY2NgQFRUl0+1CCCFylZaFVXhaW3A6LIqQ+ERsDPWpqTJLNePzslpbt27dcHZOv5rdqywtLQkNDUVRFHReGzMiIgIzM7M3vDNzHD58mPv37+Pr68snn3ySpfcSOUuSHyFeoygKDRs2TNXesGFDfvvtN622fv36aT6pqlixIqVKlcLOzo4KFSrQu3dvvLy8aNSo0RvvFRAQwPHjxylatCi6urp4enpiYmLChg0bMvehhBBCiA+kp6NDHcuC6fZJTEzExMSEAgUKULhw4Xce28LCAnd3d77//nvGjx+PiYkJAHv37uXkyZMsXLjwg2J/m8TERIyNjTEwMMDIyAhra+ssvZ/IOZL8CPGaN53RM3bsWMaOHat5/fpp04DmrJ9XvTzA7XVHjx7Vet2oUSNu3779TjG+PDtBCCGEyE2GDRtGhw4dmDZtGpMnT87Qe//44w/Gjx9PhQoVNMUSnJ2d2bJlS5bv8WnSpAmrV6/Gzs6O1q1baw4uF/mPjpLLfouKiIjAwsKC8PBwzM3NczocIYQQQgghRA7J7NxAZn6EyMUUJYmwsHPExYVgZGSDSlUNHR29t79RCCGEEEKkIsmPELlUSMgBbvlNIi7ukabNyKgoLs7jsbFploORCSGEEELkTVLqWohcKCTkAFevDdZKfADi4h5z9dpgQkIO5FBkQgghRP6RnKxw/79Qbp17xP3/QklOzlW7QUQWkJkfIXIZRUnilt8kSPMsbQXQ4ZbfZAoXbixL4IQQQoj35H8xhOMb/XgRFqdpM1UZUa+rM46VbHIwMpGVZOZHiFwmZY/Po3R6KMTFPSQs7Fy2xSSEEELkJ/4XQ9i/5JpW4gPwIiyO/Uuu4X8xJIciE1lNkh8hcpm4uHf7hvuu/YQQQgjxP8nJCsc3+qXb58QmP1kCl09J8iNELmNk9G5T7e/aTwghhBD/89AvLNWMz+uiQuN46BeWPQGJbCXJjxC5jEpVDSOjooDOG3roYGRUDJWqWnaGJYQQQuQLLyLST3wy2k/kLZL8CJHL6Ojo4eI8/uUrrWvh4ckMHXKPr0aGEhYWkf3BCSGEEHmcqblRpvYTeYtUexMiF7KxaUYhK2+8vUdx6dJzEhMVSpY0xNbOgj59PqdgwbIcPnyYTp065XSoQgghRJ5SzFmFqcoo3aVvZpZGFHNWZV9QItvIzI8QudDDhw/p2nUyffos4ty5I5w7t5np038h5LEzEyeuYN26dTRs2DCnwxRCCCHyHF1dHep1dU63T90uzujqvmn5ed6kVqtzOoRcQWZ+hMiFVq5cycOHDxk16hsALC0tcXBwoHv3HkRHx7B9+3asrKxyOEohhBAib3KsZIPnQLdU5/yYWRpRt4uc85Of6SiKkqvq+EVERGBhYUF4eDjm5uY5HY4QOSIqKgoDAwOMjFLWGz979oz//vuPf/75h0OHDtGxY0f69++fw1EKIYQQeVtyspJS/S0iDlPzlKVu+W3G5yW1Wk1gYGBOh5FhmZ0bSPIjRB6kKAo6Ovnzm7MQQgghxEuZnRvInh8hcomEhIR36tepUyf69OmTxdEIIYQQQuQ/kvwIkQF3796ld+/euLq6YmtrS61atdi+fbtWn3379lGlShXUajXOzs6MHz9eK7Hx9fWldu3aODk5oVarsbOzw97enpYtWxIR8b/y1Q8fPuTzzz+nZMmS2NnZ4erqStu2bQkMDGThwoUZjuvEiROULl2acuXKcfHixTSfz8PDg6NHj2penz17lnr16qFWq3F0dGTo0KFERka+3xdPCCGEECKHSfIjxDvy9/enRYsWdOjQgRs3bnD37l3mz5/Pt99+y48//gjAqVOn6NevHytWrCAwMJAzZ87w77//MmXKFM04X3/9NQ0aNODcuXMEBgYSFBREQEAABw8e1EznPnjwgJo1a6JWq/Hz88PX1xdnZ2euXbuGoigYGxtnKC6Ab775hh07drBgwQImTZr0Ts/bqlUrJk2aRGBgIFevXiU6OpohQ4Zk1pdUCCGEECJbSfIjxDsaMWIEo0aNol27dpr9NjVr1mT9+vVMmjSJ5ORktm3bxujRo3F3dwfAysqKqVOnsnPnTs04nTt35v79+9StW5datWoxZ84cQkNDte41efJkunbtytixY7l37x41a9bE2dmZ//77Dx0dHbZu3ZqhuAD09fWJjY0lLi4u1f3SsmfPHvr06UODBg0AMDExYe7cuVrPIoQQQgiRl0jyI8Q7qlKlCnv27KF69epayUPVqlVJTk7m0aNHzJ49m2HDhmm9T6VSafX/7LPPWLlyJdevX2fTpk1ER0dTqVIlHjx4oOlz/PhxunbtSnBwMB06dODzzz/n5MmT6Onp0ahRI/z8/DIUF8C8efPo06cPX331FQCJiYl06NABJycn9u3bl+p5hw0bxuzZs1M9S1hY2Ht89YQQQgghcp4kP0K8o9GjRxMYGMigQYNQqVRa1ywsLIiPj8/wmKVKlWL8+PHUrVuXI0eOaNr19fXR19dnzJgxmJub8/nnn2NqakpYWBjGxsYkJSVlOK5q1apx+fJlli5dCsCVK1cwMTFh9+7dLFmyRPOe8PBwJk6cmOFnEUIIIYTI7ST5EeIdHTp0CJVKhZeXV6aXmdbX1ycxMVHzulKlShw7doy9e/fSoEEDypcvT926dbG0tMy0uMqUKcOFCxfo2bMnAwcOJCEhgZiYGEaOHKlZ6iaEEEIIkZ9I8iPEO3r27BklSpTIlns1aNCAuXPn0qVLF6ZMmcKdO3feWKTgfeMyNjZmz549uLq6MnLkSNzc3Lh16xY//fQTn3zyyYc+ghBCCCFygI+PD9WqVcPe3p6KFSuyZ88ezbWuXbuyfPlyAC5cuECPHj1wdnbG3t4eJycnBgwYQEBAQJrj/v3335QuXRp7e3v27t2bLc+SFST5ESIHTZkyBUdHR3bv3q3VXq9ePe7evUv79u2z9P729vasW7eOmzdv8t9//+Hu7o6ZmVmW3lMIIYQQWePgwYMMGDCAmJgYNm/ezLx58+jevTshISEcOHCA8PBwevXqxaFDh+jWrRtdunTB19eXgIAArl69Srly5ahevXqaCdB3333Hjh07WLZsGT/88EMOPF3mkORHiBxy+/ZtduzYga+vL/Xr19e6ZmtrC6QULchsBgYGREdHp3ktLi4OAwODTL+nEEIIIbLe7t27WbhwIZs3b2b69Ok0bNgQR0dHrly5Qr9+/Wjfvj2Ghobcu3ePzZs3065dO83PfWNjY4YPH06XLl00+4Nf9bJqbGxs7DtVjc2tJPkRIockJSVhaGiIgYEBJiYmWFtba67p6ekBKaWyM1v58uW5ffs2q1ev1uwzSkxM5I8//sDPz4/y5ctn+j2FEEIIkfVsbGw0fzcwMOD+/ftERUXh4eHBgQMHNDM2ffv2xdjYmFq1auHu7s65c+c076tSpYqmUuyrfvzxR/r168fIkSOz/kGykCQ/QuSQ0qVLU7t2bdRqNS9evMDT0zNb7mtubs6+ffvYuHEjdnZ2FCtWDDs7O3x8fNi9e7fmoFUhhBBC5C1ffPEFGzdupEuXLowePZrp06czaNAg9PX1KVeuHM+ePdP0HTduHGPHjmX16tVaCY2uri6KomhevzwvsHLlyvz7778cOnQo+x4oC+gorz5dLhAREYGFhQXh4eHyS5gQQgghhBDvITY2FhsbGx48eKDZz6ujo6NJbDw8PPjmm28oWbIkX375JadOnQJg5cqVHD16lJUrVxIYGMiAAQPYsWMHxsbGAAQGBuLh4UFgYGC2PEdm5wb6mRCTEEIIIYQQIhd5/PgxVlZW6RYyGjJkCKampvz6669pXler1XTq1Ik2bdqwa9cuChQokFXhZhtJfoQQaUpKTuJCyAWeRD+hsElhKttURk9XL6fDEkIIIcQ7eJfFXT4+Pnh4eKTbZ8CAAURFRdGuXTt27NiRSdHlHEl+hBCp/Bn0JzPOzuBx9GNNWxGTIoyuPprGdo1zMDIhhBBCvAt9fX3Nfh1IKbT0akVXExMTIiMjU70vIiIi1QzPV199RWRkJB07dmTevHlZF3Q2kIIHQggtfwb9yVdHv9JKfABCokP46uhX/Bn0Zw5FJvKj//77j3bt2uHo6Ihardb8qVChguYgvn379lGlShXUajXOzs6MHz+ehIQEzRgjR46kePHiNGjQgLCwsFT38Pb2xtvbO5ueSAghcodixYoRFRXF1atXgZTvpW5ubprrjRo1YtasWQQHB2vabt26xa+//prmYecTJkzA1dWVXr16ZX3wWUiSHyGERlJyEjPOzkAh9VT5y7aZZ2eSlJyU3aGJfCguLo7GjRtTs2ZNLl68SGBgoObPyzMpTp06Rb9+/VixYgWBgYGcOXOGf//9lylTpgAQGhrK9u3b8ff3p3bt2qxcufKt950zZw5qtZpWrVoRHh6uae/WrVu+WNIhhBCQcmzGihUr6Ny5M2q1Gm9vb5YsWaK5PnLkSFq0aIGnpyf29vbY29vTpUsXBg0aRI8ePdIcc86cOVSsWDGbniBrSPIjhNC4EHIh1YzPqxQUHkU/4kLIhWyMSuRXoaGh1KtXDx8fHwoVKkS1atXw9vYmKChI02fbtm2MHj0ad3d3IOXsq6lTp7Jz504ALC0t6dWrF2XKlGHFihVpzvy8fs9Fixbh6+uLtbU1q1atAiAyMpJjx47l+R/qQgjxqnbt2nHz5k0CAwM5f/481apV01zT1dXl+++/5/r16wQEBBAQEMClS5cYMmRIumMuWbIk2yq9ZQVJfoQQGk+in2RqPyHSU7RoUdavX89///3H8+fPmTJlCo8fP6ZSpUqa08Vnz57NsGHDtN6nUqm0ThefNGkSgYGBDBw48K33NDMzQ0dHhy+//JIzZ85Qv359/Pz86Ny5M926dcPOzi5zH1IIIUSuIsmPEEKjsEnhTO0nxNts2LABR0dHWrZsibu7O4sXL2bv3r3Mnj07S+5nYGDAkSNHMDU1BaBVq1Z06dKF1q1b5/lNvEIIkdmSFIWToZFsexzKydBIknLX8aDvRaq9CSE0KttUpohJEUKiQ9Lc96ODDkVMilDZpnIORCfyo2nTprFnzx6+++471qxZwzfffEPRokW1Chp8qJiYGFQqlea1nZ0dixYtyrTxhRAiP9rzJIyxfvd5GPe/78fFjAyY4lyCloVVORfYB5KZHyGEhp6uHqOrjwZSEp1XvXz9XfXv5LwfkWm+/vprWrRowd9//82LFy8yffzExET+/PNPKleWhF0IId7VnidhfH4tUCvxAXgUl8Dn1wLZ8yQsZwLLBJL8CCG0NLZrzDyPediY2Gi1FzEpwjyPeXLOj8hUffr04c6dOwwfPjzTx378+DE9e/bk6tWrWof9zZ8/H3t7+1TV3rp378727dszPQ4hhMhLkhSFsX7301j/gaZtnN/9PLsETpIfIUQqje0ac6DjAVY0W8HMejNZ0WwF+zvul8RHZBlDQ0NNgpKcnKx1EF9GPXjwgKFDh+Lk5ETFihW5fv06gwcPBlKqvS1cuJBr165RqFAhTbW3qKgojh49qqkqJ4QQH6vTYVGpZnxepQAP4hI4HRaVfUFlIkl+hBBp0tPVo1rRarRwaEG1otVkqZvIUqVLl+bixYsoisLZs2dxcXF5r3H+/PNPqlSpQunSpenfvz+FCxemUKFCmutmZmYoisK3337L2bNnqVOnDo8ePeLTTz+lXbt22NvbZ9YjCSFEnhQSn5ip/XIbKXgghBAix7Vt25bNmzejVqtRqVTvdFhpWi5evMj+/fupV68e9+7do3379kydOpVZs2YBKdXeDhw4wOTJk4mOjqZNmzYYGRnRv39/vvvuu0x8IiGEyJtsDN8tPXjXfrmNjqLkrgV7ERERWFhYEB4ejrm5eU6HI4QQQgiRZQICAujYsSOFChVi165dFChQgOvXr7N7925JyEWOSFIUqv7jy6O4hDT3/eiQUvXtXK2y6OnopNEjc2V2biDLQUndhAAAXytJREFU3oQQQuSoxMREhg8fjp+fHz4+PqjVar7++uucDkuIbLFlyxbGjBlDoUKFOHnyJABr164lLCwsZwMTHy09HR2mOJcA4PXU5uXryc4lsiXxyQoy8yOEECJHrVu3jpMnT7Jo0SJq1KjB4cOHKVu2LHfv3s2S+yUnJ3H/xnWiwkIxU1lSokw5dGVPm8ght2/fpn379vz333/cuXOHx48f06FDB86cOUPRokVzOjzxEUvrnJ/iRgZMzuZzfjI7N8ibi/WEEELkaQ8fPqRXr17cvn2bx48fa0pd9+/fH1dXV+7fv58l9/U7c4ojK5cS9fypps3MypqGXgNwrlE7S+4pRHoKFSpE+fLlsbe3Z/r06Vy4cIHdu3dL4iNyXMvCKjytLTgdFkVIfCI2hvrUVJnl2RmflyT5EUIIke0uXLhAZGQkY8aM4ZdffiExMVFTba1Lly7Mnz8/0+/pd+YUO+dNS9Ue9fwpO+dNo81XYyQBEtnu7t271KlTh0KFCuHu7k6ZMmVyOiQhNPR0dKhjWTCnw8hUkvwIIYTIdi1atCAqKoqAgAB+++03qlWrxt9//029evX4559/Mn3Zc3JyEkdWLk23z1+rluJYrYYsgRPZyt3dXc6XEiIbSfIjhBAi2+no6NC1a1ettk8++QSA2rVrU7t25s7A3L9xXWupW1oinz3l/o3rlCpXIVPvLcTb3Lx5kwEDBvDgwQN69uzJxIkTczokIfItqfYmhBAi34sKC83UfkJkpi+++IKJEydy/fp1Tp8+zdGjR3M6JCHyLUl+hBBC5HtmKstM7SdEZlIUBX19fXR0dNDT0yM5OTmnQxIi35LkRwghRL5Xokw5zKys0+1TsJA1JcqUy6aIhPifX375he+//54yZcrg7u5Ow4YNczokIfIt2fMjhBAi39PV1aOh14A0q7291KDPACl2IHKEm5sbJ06cyOkwhPgoyMyPEEKIj4Jzjdq0+WpMqhmggoWspcy1EHnMnj17cHBw4Ntvv9W0bd26le3bt+dcUCJPkORHCCHER8O5Rm36/7KcLuOn0WLYN3QZP43Pf14uiY/IFvfu3aN69eqULFmS6dOnp9lHrVYTGBgIpPyCX7lyZdRqNc7OzowfP56EhIRsjDj3WrJkCcePH2fTpk3Ex8cDMHv2bIyNjTP1Pl5eXlKAIp+R5EcIIcRHRVdXj1LlKlCmTn1KlasgS91Etvnjjz9o2LAht2/fZsWKFYSGvrm64PHjx+nfvz8+Pj4EBgZy5swZ/v33X6ZMmZKNEeden332GVWqVMHY2BhDQ0MWLVqEoaEhTZs2zenQRC4nyY8QQgghRDbo3r07586dw83NjeDgYMLDw9/Yd+vWrYwZM0ZzAKqVlRVTp05l586d2RVurlamTBlKlChB48aNadeuHX/99Rfbtm1DR0cnp0MTuZwUPBBCCCGEyAZFixbl8OHDQMrytvTMnz8/VZtKpUp3tuhjcvPmTXr37o1araZy5cqUKlUqS+7z66+/oqsrcwX5ify/KYQQQggh8hQLCwsuXrxI27ZtqVevXpbdZ8aMGaxfvz7LxhfZT2Z+hBBCCCFyiZiYGIyMjHI6jFxHURQ2btzIypUriYiI4Pnz59jY2KS7dDAj7ty5w5QpUzh9+jTR0dE4Ojry5ZdfZsrYIneR5EcIIYQQIhe4cOEC+vr6FC1aNKdDyXW++uorYmJi2LBhAyqViujoaJYtW0b16tU/eGx/f3+aN2/Ojz/+yLJly9DR0eHcuXOMGDGChw8f4u3t/eEPIHINWfYmhBBCCJHDjh07Rvv27bGxseHZs2c5HU6uEhkZyc8//8zBgwepVKkSbm5utGrViidPnuDp6fnB4y9dupSQkBCGDBmCq6srNWvWZMGCBfTt2xdFUTLhCURuIsmPEEIIIUQOOXLkCA0bNsTT05MjR47QqVOnNIsdfMxMTEx48uQJd+7cISAggAsXLrB48WLc3NxISEhApVJ90Phjx44lJCQEf39//Pz82LNnD4MGDSIsLAxnZ2diY2Mz50FEriDL3oQQQgghcsDAgQN58uQJS5cupXPnzlhZWVG4cGH8/f1zOrRcRU9PTyvBMTQ0pHTp0pQuXZpu3bp98OxMwYIFtV4XLlyYwoULU69ePb799luZ/clnJPkRQgghhMgBkZGR/P3335iZmTFlyhQqVapEsWLF2Lp1a06Hlqdk9dk+cnZQ/iLL3oQQQgghsllgYCCnTp3CzMwMgJ49exIYGMg///xDsWLF0nyPWq0mMDAwG6PMedHR0TRv3hxbW1tmzZqlae/fvz+fffYZkPK1fHlu0siRIylevDgNGjQgLCws1Xje3t6aAgavjj179mytfj/88APFihWjcePGxMfHZ8mziZwhyY8QQgghhMiV1qxZg7u7O35+fixbtkxT2vrYsWMcP35cq29oaCjbt2/H39+f2rVrs3Llyncee+nSpZqxz507x4EDBwgODiY5OZkjR45kybOJnCHL3oQQQgghRK6kr69PbGwsiYmJJCQkEBoaysOHD7Gzs0NRFG7cuIGxsTEAlpaW9OrVizJlyhAXF8fAgQMzPLaFhQXLli1j8ODB6OvrY2try+PHj7PjUUU2kZkfIYQQQohcJjlZ4f5/odw694j7/4WSnPxxbrr/9NNPefDgARUrVuTRo0cAHDx4EE9PTzw9PTl06JBW/0mTJhEYGPjWxOdNY8fGxrJnzx46d+6s6ScFD/IXmfkRQgghhMhF/C+GcHyjHy/C4v6vvfsOi+ra2gD+zlClDQJSLNShiFhREDtCFIk1JqKxJsYYYzcmlpsoGhV7iVETkyheezeWiMaC0dgSUbEHRpqCWOmdOd8ffM51MoioAwPM+3senpvZZ5991uFOJqzZ+6ytaDM2N0D7EFe4NLfWYGSVz8DAADt27AAAxXM9R48exYIFCyAIAqZOnYqePXuqbex9+/YhMDBQ8SwW1Tyc+SEionJbv349WrVqBScnJzRr1gyHDh0C8OYPHANAcnIyQkJC4OTkBEdHR/Tq1QsxMTEq523btg1+fn6oW7cu6tSpg7Zt2+LgwYMVcZtEGiO7/BARP15XSnwAIDstHxE/Xofs8kMNRVYxTp06heDgYLi6uqJJkyYYOnRomaW+CwoKcPfuXTRq1AheXl6QyWQoLCws9/Vyc3NhYGDw0uPh4eEYOnToa90DVS9MfoiIqFyOHj2KTz/9FLm5udi1axeWLl2KAQMG4OHD//0x9roPHBcWFiI4OBgtWrRQbGAYHByMd999V+kPmhkzZmDevHn47rvvkJycjIcPH2LBggWYMGECywJTjSGXCzi9XTXxf9GZHTE1Zgnczp07MXXqVCxcuBAxMTH4+++/0aNHD3Tp0gU3b94s9ZyzZ8+ibdu2itdt27bF2bNny3W9oqIiHDt2DC1atCj1eHJyMmJiYuDv7//6N0PVBpMfIqJKcOTIEXh4eKBNmza4e/euov3x48eoX7++BiMrv4MHD2LlypXYtWsXwsLC0LlzZ7i4uCAlJUXR58UHjtetW1fqzM+LoqOjYWRkhClTpkAkEkEkEmHkyJEwMjJS/PFz7949rFq1CocPH0arVq0AlOy70a5dO3z//feYPXt2hd0zUWVKiUlTmfH5t6xn+UiJSaucgCrY4sWLER8fj549e8Ld3R3+/v44fvw4xowZg7Fjx5Z6zvHjxxEUFKR4HRQUhGPHjr3yWqmpqRg4cCCuXbv20md4tm7dioEDB3JfnxqOyQ8RUSWYOnUqDh8+jBEjRmDlypWK9l27dqFZs2aaC+w1WFv/71kDPT093L9/H1lZWWjUqJFSv9d54Njb27vUb23Nzc3x7NkzAMD58+fh5+eHevXq4ffff4eHhwfatWuHlJQUtGnT5qXfEBNVN9kZZSc+r9uvqjty5Aju37+Pu3fv4tatW9iyZQt69OiBR48e4cmTJ0hOTlY558qVKwgMDFS8DgwMxJUrV156jeTkZIwdOxZSqRTNmjXDjRs3MHr06FL7Hjp0iEvetACTHyKiSuDj44P33nsPc+fORffu3fHs2TOsXr0aCxYswNKlSzUdXrl89tln2L59O/r164epU6ciLCwMn3/+OXR1K7Z2jo6ODnR0dAAA3333Hfbt24dBgwZhy5YtMDMze631/kRVmbHZy59FeZN+VZ25uTnE4pI/RcViMRwcHBAcHIx58+bhypUrKpu9ikQiWFtbQyKRKI1Rp06dUmdrjh07Bm9vb7i7u2PEiBGoU6cOLC0tXxpP/fr14eLioqa7o6qK1d6IiCrBypUrsWTJEuzduxeffvop9PT0EBAQgDNnzqBevXqaDq9crKyscPLkSQAl5WD/+9//lvrN7Kvk5ubC3Ny83P2bNm2KS5cuAQDGjBmD7t27QyaTKWIhqinsXM1hbG5Q5tI3k9oGsHM1r7ygNEQulwOAUlIjCAI6d+6s0rdz5874+eefVdovX76MiIgItG/fHvfu3UOfPn0wd+5cLFy4sNRrctZHO3Dmh4ioEujr62PatGm4ePEiZDIZbt++jVWrVlWbxOffUlNTYWFh8drlYF/1wHFpnJ2doaOjg5iYGAQGBqJly5YYMGAAOnXq9JpRE1VtYrEI7UNcy+zTrp8rxOKq/UzKvXv34OPjg/r16yMsLKzUPsOGDVMURJk5cyakUikcHR3h4OAAR0dHNGzYEBs3blT079SpE8LDw/Gf//xH0fbs2TOMGTMGGzZsgEgkgqenJ+bMmYO8vDyEhoYiOzsb7du3R3FxMfbs2YOCggIAJQVU5s6dq3gNlFSsHD58OBwdHREfH69oDw8Px7Bhw9T3yyGNY/JDRESv7U02/SvPA8cv06FDB1y8eBGjRo3Cw4cPsW7dute+PlF14NLcGkEjvWBsrry0zaS2AYJGelWLfX527tyJzp07IzY2FuvWrVM8v1eaCxcuYMOGDdi+fTvu3r2LhIQExMfH486dO6+cienduzfMzMwQHR2NuLg4XLhwAffv31c577PPPsONGzcQGRmJuLg4nD59GufPn8f06dPVcr+vo6ioqNKvScqY/BARVaD8/HwsX74cLVu2hJOTE6RSKdq3b4+tW7eq9D148CDatWsHFxcXODs7Y8CAAUrfQAIlhRPs7e3Rt2/fUp91eXG/neeePXuGcePGoWHDhnBwcEDLli2xa9eucp37Mrq6uoplKQBQXFwMPT29Uvu+zgPHL+Pg4IDvv/8eV65cwf79+2FoaPha5xNVJy7NrTFkXhv0ntgc7wz3RO+JzTF4bptqkfgAwIABA/DXX3/By8sLSUlJSE9Pf2lfS0tL9O/fH9OmTYOLiws++uijci1pvXfvHqRSKebNm4datWoBAExNTfHdd9/hwIEDyMjIAAD8/fffOHjwICZMmIDatWsDKFnC+9NPP+GHH354oy9yACAxMRFDhgyBh4cH7O3t4efnh3379imOZ2Vl4f3331eZ0WrZsqVKgYbnxVvq16+Pb7/99o3iofJj8kNEVEEKCwsREBCAmJgY7NmzB3FxcYiNjcVPP/2ErVu3YsqUKYq+K1aswJo1a7B+/XrFsrjWrVujZcuWigTozp07+OOPPxAfHw9DQ0P89ttvr4whOzsbXbt2hbu7O6Kjo5GQkID169dj2rRpr9wgNCEhAR06dIBUKlVK1q5cuYL3338fmZmZuHHjBoCSPYD+XfUNeP0Hjl9GV1cX169fx+7du7nzOmkFsViEeu614dbKFvXca1f5pW4vsrW1xfHjxxEbGwtbW9sy+0qlUsyfPx9Hjx5FTEwMQkJCMH36dMycObPM8+rXr49ffvlFpV1PTw/W1taK2SZTU1OEhYVh8ODBSmXxbW1tYWxsrCjVL5cXI+lGNArz85D8zy3I5cUvvbZMJkNwcDDee+893Lp1C4mJiVi2bBm++uorrFixAgCwatUqZGdn48SJE4iPj1fMaF25ckWlwud3332HIUOGIDo6GosXL37lFgH0dpj8EBFVkNu3byMwMBCrVq2Cvb29ot3DwwO7du3CunXrkJ2djdTUVCxevBg///wzXF1L1vvr6+tj/Pjx6N+/P5YvXw6gpOpZUVERioqKUFBQUOZSkufWrFkDCwsL9O3bVzEz07hxY8yZMwerV68u89xly5Zh4MCB+P3335Vmao4cOYJGjRrh559/Rp8+feDk5ITNmzeX+hDx5cuXsWPHDowZMwaTJk3Cjz/+CG9vb8yfP/+Vsf9b//790aBBg9c+j4iqB11dXQQFBWHjxo3YvHnzW431fEbH3d0dJiYmsLW1xdSpU5X61KpVCwUFBYi5cBY/jR6OHbOnIzcjA4e+W4SfRg9HzIXSN0+dMGECJk+ejN69eysKMrRu3RpbtmzB7NmzIZfL0apVK7i4uKBfv37w9PTEV199hZiY0jew1dXVRV5eHgoLC1FcXMzkp4Kx2hsRUQVp3LgxGjdujPj4eISGhuLs2bPQ0dGBs7Mzxo0bB2NjYzx+/BiZmZn48MMP0bVrV/Tt21fpG8933nkHq1atAlDyDWnPnj3h6emJrKwsvPvuu4iKisKgQYNgY2ODvXv3qsTg6+sLIyMjtGjRAps2bVJUSmrSpAliY2PLjL9hw4ZYs2YNDh48iN69eyM7Oxu7du3C6tWrERkZCScnJ/Tt2/el54eGhiI0NFTxun79+vjrr7/K9buLjIxUGetl3nTZChFVTbq6ump9NiY8PByjR4+Gvr6+yrG4y5cQtW294nWRXA6xSISsp4+xf+k89Jw0Ha6+bZTO8fb2xqFDh7B69WocOXJEsZyuZcuWkMvlePDgATp37qz4vM3IyMDu3bvRtWtXrFmzBl27dlUab+LEiRg0aBBWr17NZ4IqAWd+iIgqUEJCAjp16oRu3brh+vXruHHjBmbNmoUFCxYgISEBgiDAy8sL7777LgBg4MCBSudLJBKlikRff/01YmNjFTuch4eHY8WKFWjdurVSwvDbb7/h2LFjaN++PW7duoXPP/9cqUTs8288yzJy5EhMnDgR2dnZiIyMhIeHBw4fPozjx4/DycnpbX81RESVIisrS/Fc0L+d3fW/Jb2Ps7KRV1gEU8P/FZs4uWGtyhK4qVOnIj4+Hp9//rlK2f5/f2YDgJmZGT766CNMnz4dO3fuVLQ/f27SwsICv/32G/75559XLhOkt8fkh4ioAv3444/45JNPEBISAn19fYjFYrRs2RL79++HkZGRot+KFSswZcoUSKXS1xq/S5cuGDNmDM6fP4+OHTsiPT0dqampioQIANavX48vvvjilWPl5eWpbFg6dOhQnDhxAnfv3kVSUhK2bdv22jESEZWHTCaDn58f3nnnnVKPjxgxAp6enjhy5IjKsfz8fPTr1w9ubm548OCByvFRo0ahTZs2SsUGigsLkZP+FABwMzkVv5z+C0b6eniWnavok/nkMe7fuqE01u7du2Fubo5hw4YhLS1NqfhLWV6c0ZLL5QgMDHzlDDypH5MfIqIKlJKSgvr166u0m5iYoE6dOorXT548eaM9f7p374758+ejqKgIrVq1wocffghzc3McPHhQURggOzv7pd96vujPP/+s8jM6crkccXFxuHbtGuLi4sr9RwcRaYapqSlyc3NV2nNyclQ+l0JDQ/HVV19h9+7dSpubPjdu3DgcO3YMY8eOVTm2adMm2NnZISoqCjY2NirHp0+fjoULF6JXr16KZbRyeTFS0jIxa/8xrDvzN3ILCmFmaICD0bcAAEeu/4Mj1/9BVlrJ85VFRUUICQnByJEjUVRUpEjWPD09kZiY+Fq/F7FYjIULF6J37964e/fua51Lb4fJDxFRBRIEATNmzIBUKq2wvWn69OmD06dPIzY2FocOHYKBgYFK2ekWLVrgvffeK7VIQn5+PlauXIkvvvgCRUVFij9UkpKS0LFjR5Vqb1evXlXMKlWmmzdvYvny5diwYQN2796NDRs2YPny5bh582alx0JE5RMYGIiFCxcqlbv+448/cOrUKfj6+ir1LSoqgpGREYyNjVWqQlpYWMDIyAiGhoalPuf3/Fx9fX3o6+tDIpEonWtjYwNbW1ulZ2rEYh3899wl5BUWwlBPF5929IWpoQESn6QpjW1iXvJMT2hoKCQSCcLCwnDjxg0EBwdj/fr1+Oqrr8rcCPW3336Dh4cHvvnmG6X2li1bYs2aNejevTsSEhJeej6pF5MfIqIK1qtXL1y+fBlz585FTk6OWsbU09Mrdaz8/HyVxEdfXx/bt2+Ht7c3li1bpnTsxIkTaNiwIebPn487d+7Azc1Nsav6kiVLMGDAABw5ckSp2tvRo0fRsGFDtdxHed28eRM7duxQ7N3xXEZGBnbs2MEEiKiKCgsLg62tLXx9feHs7AxnZ2d888032LRpk8q+Yt988w2+/PJLdOnSBfPmzVM5tnTpUrRq1QoLFixQuc6gQYMQFRUFqVSKjz/+WFGEACiZ9Rk3bhyCgoIQEBCgaNfR04NYJMbX3QPgZmOFtacuICUjEwZ6/1v+a2BkhHoNS8r4//LLL5g3bx5MTU3RpUsXHDhwAH5+fvjoo49w8+ZNJCcnl/o7mDBhAo4fP44vv/xS5Vj79u2xfPlyBAUFISkp6dW/UHprrPZGRFSBLC0toaenB319fejo6KhtmVabNm2wdOlSdOzYUbG/zuPHjzF//ny0bdtWqa+1tTWsrKxgY2OjVGr14cOHGDhwIP773/9i1KhRqF27NiwtLRUP67q6uiI8PByRkZHo0qUL5HI59u7dixUrVpRrE0J1kcvliIiIKLNPREQEPDw8IBbzOz2iqsTQ0BBhYWEICwt7ZV9PT09ER0eXeqx58+aKfcVKY2xsXOqzQEDJDMvt27cBKFeOjI+Ph8f/by9gaWIMX2d7JKdlIO+FDaQdm3pDLNbB48ePAZRskFpQUICTJ08iKioKP/30E9q2bQsXFxeVTamfk8vlqFWrFoyNjWFlZaVyvEuXLggLC8M777yDzMzMl94jqQeTHyKiCjRhwgT07dsXW7duxaRJk9S2QedHH32EjIwMfPjhh3j06BEEQYCRkRG6d++OOXPmKPUNDQ1Fs2bNYG1trVQOOzc3F2vXrsU777yDjz76CC4uLnB2dsb+/fsBAJ9//jmAkoIJjx8/hrW1NXx8fHD48GHFfkSVISEhQWXG598yMjKQkJBQ5Z9ZIqKqZeGSJZgwdgxq64rhYFkbO/+OxriAtjC1tIK7nw0s65fsLVarVi1kZGRAEARER0fD0tISu3btwuTJk7F//37o6em9tIJmWFgYvL29YWFhUeqWBAAU2wkMGTKkwu6VSoiEKrZBQkZGBiQSCdLT02FmZqbpcIiISMOuXbuG3bt3v7Jf37590bhx40qIiIje1IULFzB79mzExMQgPz8frq6uCA0NRbt27QAAixcvxrJly2BnZ4fdu3fDwcFB6fzns9Hh4eHIyMhA3759cePGDfTp00exJ9qLOnXqhNDQUHTq1AnA/2Z+QkNDUSwvRtTDKDzKeQRLQwucXncUJyNP4YcVy1CvYSPMnv2t0jlNmzbFpk2bUFxcjLFjx6JNmzYoLCzE0qVLVa5D6qPu3IDrA4iIqEor72yZumbViKhi7NmzByNHjsR//vMf/PPPP4iPj8fUqVPRv39/xYzz0qVLcfXqVYwcOVLlGcV/O378OKysrJCQkIDr168rlbF+lWMJx9B1d1d8fORjTDk9BZ/8PgKro8Nh6VUPDRo1gViso3JOt27dcPLkSTRr1gyfffYZNmzYgClTppTrenPmzIGDg4PKzDxVvtdOfv744w/06NEDdevWhUgkwr59+5SOP69sZGdnh1q1aiEwMFBpjTkREVUfVaG0tIODwyu/7TMzM1P5hpiIqo6CggKMGDECEydOhLe3NwBAJBIhMDAQq1atwsyZMwGUFDbw9fXF7NmzkZaWVuaYXbp0QX5+Ptzc3HDlypVX9n9u9drVeNfnXVzfcV2pPbsgG0fij+BYwrFSzwsICMCff/4JmUyGxYsX4+DBg6WW1S7N1q1bER0dje+//x4AIJcLuH/nGf756wHu33kGubxKLcSq0V47+cnOzkbTpk1LnVoEgIULF+K7777DDz/8gAsXLsDY2Bhdu3ZFXl7eWwdLRESVp6qUlhaLxQgKCiqzT1BQEIsdEFVhubm5+O6773Do0CEEBgYqlav29/dXFDMYNWoUZDIZvv3221eOaWxsjD179iAuLg7NmzcvVxxyQQ7YA9J5UmRcykB+av7/DuoAEIAFFxegWF6M4uJipeqZzs7OiI6ORnBwMFatWoWWLVuW7+YBDBgwAE2bNkVqaipklx/iv9PPYt+yy/j9l5vYt+wy/jv9LGSXH5Z7PG2Se/OJWsd77f9SdOvWDXPmzEGfPn1UjgmCgOXLl+Prr79Gr1690KRJE/z3v/9FcnKyygwRERFVXVWttLSnpyf69eunMgNkZmaGfv36wdPTs1LjIaLXI5FI0KpVK9y8eRN79uxR2sTUzMwMhS9UWKtIOXo5KBAXQCQWQaQrAl6YcDGwNUDWnSwkZyTjwr0LOHXqlKKaJgDo6Ojg9u3bGD9+PNq0afNa1/36668V1eAifryO7LR8pePZafmI+PE6E6B/yb3+GE+331HrmGqt9hYXF4cHDx4gMDBQ0SaRSODr64tz586hf//+Kufk5+cjP/9/b4BXVfQhIqKKVVVLS3t6esLDwwMJCQnIysqCiYkJHBwcOONDVE3s27cP3bt3R506dd54jNzcXBgYGLxxf5/uPvhhyw/4Z/I/kPhJYGD7v2NmLc2QdT0LMV/F4D2j9/B+r/fRq1cvpfGMjY0xYsSIN4pdLhdQy6DsZxPP7IiBU9M6EItFZfbTBoJcQNoBmdrHVWvy8+DBAwBQWf9oY2OjOPZvYWFhmDVrljrDICKit1CVS0uLxWKWsyaqpnJycmBoaPhWY0RERCA4OLhcfZOTkxEbGwsPDw9Fm5OtE5ymlP4ZIhKLUO/jegCAdV3XoZVtK6Xjjo6OyMrKKvXcyMjIV8aTEpOGRcN+LbNP1rN8pMSkoZ577TL7aYP8uHQUp5dePvxtaPzrsmnTpiE9PV3xw91tiYg062X/cX/TfkREz/34449wdXXFunXrXuu8jIwMjBs3Dvv374eu7qu/u7969Sq6desGU1NTPH36VNHewroFbIxsIELpMysiiGBrZIsW1i1eK77yyM7If3Wn1+hX08kz1Z/4AGpOfmxtbQEAqampSu2pqamKY/9mYGAAMzMzpR8iItIclpYmoory/vvvIyoqCnPnzkVubu4r+6elpeGbb76Bs7MzCgsL8eDBA3z77bfIzs4utX9UVBR69eoFb29vrFmzBnPnzsWMGTMUx3XEOpjqMxUAVBKg56+n+EyBTimlrp9700ptxmblW65X3n41ndhUv0LGVeuyNycnJ9ja2uL48eNo1qwZgJJM/cKFCxg1apQ6L0VERBXkeWnpspa+sbQ0Eb2u2rVrIy8vD/r6JX/UFhUVldk/OjoaXl5e+OSTTzBt2jQ8e/YM5ubm0NPTK7XsflhYGO7evYvVq1cjOTkZ1tbWyMrKUrlOoEMglnZaivkX5yM1539f2NsY2WCKzxQEOgT+e2gF2eWHOL09RqlggbG5AdqHuMKluXWZ92Pnag5jcwOVYgcvMqltADtX8zLH0RYGThLoSPSBh6Unum/qtZOfrKwsxMbGKl7HxcXhypUrsLCwgL29PSZMmIA5c+bA1dUVTk5O+Oabb1C3bl307t1bnXETEVEFeV5aeseOHS/tw9LSRPS6hgwZgp49e0IqlWLUqFEwNTUts390dDS2bNmCfv36ITMzE71794abmxsmTZpU6rlxcXE4ffo0bG1tFZ9jRkZG2LZtm0rfQIdA+DfwR9TDKDzKeYQ6RnXQwrpFmTM+sssPEfHjdZX255XagkZ6lZkAicUitA9xLXWM59r1c2Wxg/8nEotg3sMFab/8rd5xhRcLrZdDZGQk/P39VdqHDh2K8PBwCIKAmTNnYu3atUhLS0O7du2wevVquLm5lWv8jIwMSCQSpKencwkcEZEG3bx5ExEREUozQGZmZggKCmJpaSKq0VJSUvDJJ5/g1q1b6Nq1K1as+A5bZlx85azN4LltXpm8lDZ7ZFLbAO36vXr2SBulno+DrZ+z2nKD105+KhqTHyKiqkMul7O0NBFpnWHDhqFjx44YOnQoPvnkEzR29YZ+QsNXntd7YvNyVWqTywWkxKQhOyMfxmYlS90441M6decGan3mh4iIahaWliai6kAoLkbO35dQ9OgRdOvUgVFLb4h0Xr6E7ZXjCYKiqpyuri5ycwpQnsfvy1upTSwWsZy1hvDrOyIiIiKqtjKOHkVsQCAShw5F8uTJSBw6FLEBgcg4evSNx5w7dy42b94MqVQKuVyOD0MGlus8Vmqr+jjzQ0RERETVUsbRo7g/fgLwr6c4ilJTS9pXLIdZly6vPW79+vURERGheC2XC6zUVkNw5oeIiIiIqh2huBip88JUEp+SgyVtqfPCIBQXl3vMJ0+eKP752bNnipLazyu1lYWV2qoHJj9EREREVO3k/H0JRQ8evLyDIKDowQPk/H3plWMVFRUhJCQE7u7u2L17N2QyGfz8/ODp6YnExEQAgEtza9j45WHFwQmYsflDhG4dhB1nVkLHqFhR5nrx4sWoV68eWrZsiYSEBJXrxMfHw9HRUant5MmT6NOnD1xcXODs7AxPT09Mnz4dT58+fa3fB5UPkx8iIiIiqnaKHj1SW7/Q0FBIJBLcunULS5YsQXBwMNavX4+vvvoKw4YNAwDExMRg1OShWLNhOaL/voVjB8/AzsMYZx9vUpSoXrp0Ka5evYqRI0di+fLlr7zu6tWrMWHCBEyYMAExMTG4e/cuzp8/DysrK7Rv377MzabpzfCZHyIiIiKqdnTr1FFbv19++QXXrl2DlZUVfv/9d9y/fx9ubm5o3bo1pk+fjuTkZBw8eBAff/wxAgI6AwDqudfGD42/V6qI+c0338DX1xcFBQUICAh45XVv3bqFw4cPo27duoo2MzMzTJo0CTExMdi+fTtGjBhRrvuk8mHyQ0RERETVjlFLb+ja2qIoNbX0535EIuja2MCopXeZ4zx+/BgAYGVlhdzcXLz//vtISEjATz/9hLZt28LFxQXx8fGYOHGiyrnm5uZIS0tTvB41ahRGjRqF8PBwREZGvvIeVq5ciYKCAixZsgRbtmxBWloabGxs0L17d5iamiI5OfmVY9Dr4bI3IiIiIqp2RDo6sJk+7f9f/KvQwP+/tpk+7ZX7/dSqVQsZGRkQBAGHDx9GixYtsG/fPixYsAAAoKenh4KCArXH/9yAAQMQExODQ4cOQSaTYe/evYqESCgtqaO3wpkfIiIiIqqWzLp0AVYsR+q8MKXiB7o2NrCZPq1cZa6NjY0hlUpx/fp1ODs7Y9myZSgqKoJUKq3I0AEA//zzD65evYrY2FhFm42NDUJDQxEXF1fh19dGTH6IiIioRps8eTLq1atX6rIlqv7MunSBaUBASfW3R4+gW6cOjFp6v3LG50XdunXDyZMnMW7cOHz22Wf44osvcPXq1QqMukRKSgrq169f6rEXnyUi9eGyNyIiIqr2cnJysGjRInh7e8PZ2RlSqRQdO3bE+vXrsXfvXnz44YcAgMOHD8Pb2xuOjo5wdXXFjBkzUFhYqBinqKgIw4cPR/369dG4cWOlb98dHR0RHx9f2bdG5SDS0YGxrw8k3d+Fsa/PayU+ABAQEIA///wTMpkMixcvxsGDB2FjY1NB0f6PIAi4fPkypFIp+vXrh/z8l2+iSurB5IeIiIiqtdzcXLRt2xZPnz7FgQMHcPfuXcTGxmLx4sUYN24cPD09YWNjg7Nnz2L48OFYt24d4uPjceHCBVy6dAlz5sxRjLVlyxaEh4fj77//xoQJE7B3714N3hlVFmdnZ0RHRyM4OBirVq1Cy5Yt32o8U1NT5ObmqrTn5OSgVq1aSm3Gxsa4fPkybG1tsWnTpre6Lr0akx8iIiKq1pKSkjB+/HiEhYUpSgb/9ddfmDp1Kg4cOIDz588jOzsbe/fuxdSpU9G0aVMAgIWFBebOnYv9+/crxkpNTcUHH3yAjh074scff0Tfvn01ck9UuXR0dHD79m2MHz8ebdq0eevxWrdujT///BN//PGHoi09PR0LFy5Ex44dFW2WlpbQ09ODvr4+TExMUFRU9NbXprLxmR8iIiKq1tzc3ODm5obExETMnDkTf/75J5KSkuDj44Ps7GwYGxvj8ePHWLRokcq55ubmePbsmeJ1165dsWnTJhw7dgwNGjSozNsgDTM2Nlbbnjr16tXD1q1bMWvWLMTFxUEul0NPTw/vvvuu0kxj48aN0adPH0ilUnh6euLrr79Wy/Xp5URCFauhl5GRAYlEgvT0dJiZmWk6HCIiIqoG7t27h7Zt22LOnDno168f9PT08Ndff+HLL7/EmTNncPfuXTg6OqqcFx8fj06dOik9y7No0SJMmTIF9vb2aNWqFXbu3Amg5JmfyMjIUschooqh7tyAMz9ERERU7a1duxaDBw/G4MGDFW2+vr44ePAgbG1tyz1Oamoqvv/+e9y5cweurq4VESoRaRCf+SEiIqJqLykpqdR9WczMzGBtbV3ucU6ePInWrVsz8SGqoZj8EBERUbUnCAJmzJgBqVSKdevWvfE4eXl5KtW4iCqSIBeQJ0tDzpWHyJOlQZBXqSdSahwueyMiIqIaoVevXpg3bx6aNWuG/v37w8jI6LXHsLCwwOPHj0s9lpmZCRMTk7cNk0gh9/pjpB2QoTi9QNGmI9GHeQ8X1PKy0mBkNRdnfoiIiKjas7a2hrGxMfT19aGjowO5XP5G4/j5+eHSpUvYsWMHnteEys/PR1hYGOzt7WFlxT9IST1yrz/Gk023lBIfAChOL8CTTbeQe730JJzeDpMfIiIiqvbGjx+PyMhIuLm5YfTo0W88Q1OnTh0cOnQIGzduhLOzM5ycnODp6Ynbt2/j119/VXPUpK0EuYC0A7Iy+6QduMslcBWApa6JiIiIiCpRniwNj3+69sp+ViMaw9DFvOIDqsLUnRtw5oeIiIiIqBLJMwte3ek1+lH5MfkhIiIiIqpEYlN9tfaj8mO1NyIiIqIXyOUCUmLSkJ2RD2MzA9i5mkMsFmk6LKpBDJwk0JHoqxQ7eJGOxAAGTpJKjEo7MPkhIiIi+n+yyw9xensMstPyFW3G5gZoH+IKl+bl3yyVqCwisQjmPVzwZNOtl/Yx7+EMEZNuteOyNyIiIiKUJD4RP15XSnwAIDstHxE/Xofs8kMNRUY1US0vK1gOaggdifLSNh2JASwHNeQ+PxWEMz9ERESk9eRyAae3x5TZ58yOGDg1rcMlcKQ2tbysYOhpify4dMgzCyA21YeBk4QzPhWIyQ8RERFpvZSYNJUZn3/LepaPlJg01HOvXUlRkTYQiUVaX866MnHZGxEREWm97IyyE5/X7UdEVROTHyIiItJ6xmYGau1HRFUTkx8iIiLSenau5jA2LzuxMaldUvaaiKovJj9ERESk9cRiEdqHuJbZp10/VxY7IKrmmPwQERERAXBpbo2gkV4qM0AmtQ0QNNKL+/wQ1QCs9kZERET0/1yaW8OpaZ2S6m8Z+TA2K1nqxhkfopqByQ8RERHRC8RiEctZE9VQXPZGRERERERaocomP3///TeCg4PRoEED2Nvbw8/PD5s3b4YgCIo+KSkp+OKLL9CwYUM4OTnByckJPXr0wOnTpzUYORERERERVUVVNvkJDQ3F/PnzkZSUhMTERHz//fdYvnw5xo4dCwBITk5Gp06dUL9+fZw/fx5xcXG4e/cuRo0ahQ8++AB79uzR8B0QERER0XOFhYXo1asXvLy8cPv2bQBAQUEBPvvsMxQVFWk4OtIWVTb52bBhA+zs7NCtWzdIpVKkpKTg8OHDWL9+PVJSUhAbG4v58+dj4sSJkEgkAACRSITg4GCsXLkSixYt0vAdEBEREdFzV69eRYMGDTBmzBhs3rwZAHDy5ElcvXoVurp8DJ0qR5VLfp48eQIAsLS0xIgRI+Dq6opz585h0aJFsLKyQqNGjSCTydChQwcEBQXhgw8+gFQqxbp16xRjeHt748GDB5q6BSIiIiL6lyZNmuDu3bsYO3YsvLy88OzZM0ycOBGzZ8/WdGikRapc8qOjowMAiI6Oxv79+xXtz78RMDIyUkyNbtiwAY6Ojrhy5QrmzZuH7OxsAIBYLFZ6NoiIiIiINEssFsPf3x+urq64desW/P39MWvWLLzzzjuaDo20SJWbYzQ3NwcAhIWFoVGjRsjMzET79u2xevVqlb4vzu4w2SEiIiKqutLT02FoaIgpU6bA3d0dX3/9NZe7UaWrsu+4w4cPY8OGDRg8eHCZ/TZt2oQ9e/Zg0qRJMDY2rqToiIiIiOh1WFpaKgpXEWlKlU1+dHR00KtXr1f2GzlyJEJDQys+ICIiIiIiqtaq3DM/z7m5ucHMzKzMPkZGRsjMzFRpz8jIgKGhYUWFRkRERERE1VCVTX4sLCxe2adTp07YunUrLly4oGh7+vQp5s6diw4dOlRkeEREREREVM1U2WVv5eHj44Pvv/8eEydORHJyMkQiEfT09NC9e3d8++23mg6PiIiIiIiqEJFQxcqkZWRkQCKRID09/ZXL3oiIiIiIqOZSd25QZZe9ERERERERqROTHyIiIiJSq3v37sHHxwf169dHWFhYqX2GDRuG8PBwAMCdO3fQrVs3ODo6wtnZGYMHD1baz3Hz5s1wcHCAp6cnoqKiVMaKjIxEp06dKuJWqIap9slPsVzAOdkT/HrlPs7JnqBYXqVW8RERERFpnZ07d6Jz586IjY3FunXr8OzZs5f2ffbsGQIDAzFkyBDEx8fjzp07cHR0REhIiKLPwoULERERgRUrVmD27NmVcQtUQ1XrggcR11Mw68BNpKTnKdrsJIaY2cMTQV52GoyMiIiISHsNGDAAAwcOhJeXF+7du4f09HTUrl271L6nTp1CmzZtMGDAAACAnp4evv32W1hYWCAtLQ3m5uYIDQ1F7969UVBQAAcHh8q8Faphqu3MT8T1FIzaFKWU+ADAg/Q8jNoUhYjrKRqKjIiIiEi72dra4vjx44iNjYWtrW2ZfXv37o3t27ertJuZmSEtLQ0A0KdPH9y5cwfr16+viHBJi1TL5KdYLmDWgZsobYHb87ZZB25yCRwRERERESlUy+TnYtxTlRmfFwkAUtLzcDHuaeUFRUREREQakZubCwMDA02HQdVAtUx+Hma+PPF5k35EREREVH0dPnwYLVq00HQYVA1Uy+TH2tRQrf2IiIiIqPrJz8/HvHnz8P3330NXt1rX8apSJk+ejGXLlmk6jApRLZMfHycL2EkMIXrJcRFKqr75OFlUZlhERERE9C+mpqbIzc1Vac/JyUGtWrXeaMzc3FwsXrwYUqkUZ8+exZMnT3DgwAHExMS8bbhaIycnB4sWLYK3tzecnZ0hlUrRsWNHrF+/Hnv37sWHH34IoGRWzdvbG46OjnB1dcWMGTNQWFioGOfWrVt499134e7uDgcHB0yePBnFxcUAgE6dOiEyMlLR9+LFi2jfvj0cHR3h4uKCsWPHIjMzs1Lvu1omPzpiEWb28AQAlQTo+euZPTyhI35ZekRERERElSEwMBALFy5Eenq6ou2PP/7AqVOn4OvrW+5xioqKAJRsoNqkSRMkJCRgwYIFMDc3h0QigYmJiaIPlS03Nxdt27bF06dPceDAAdy9exexsbFYvHgxxo0bB09PT9jY2ODs2bMYPnw41q1bh/j4eFy4cAGXLl3CnDlzAJTMvAUHByMqKgqDBw9GVFQU/vzzT6xcuVLlmjKZDN27d8fs2bMRHx+Pa9euIScnB2PGjKnUe6+WyQ8ABHnZYc2gFrCVKC9ts5UYYs2gFtznh4iIiKgMBQUFWLJkCby9veHi4gIHBwf4+flhw4YNSv3K8219fn4+QkND4eHhAXt7e0ilUnz66adITExEWFgYbG1t4evrCycnJ5ibm6Nr164AgP79++P3339XGuuHH36Au7s77t27h7Zt28LR0RFSqRSLFy8GUPJH9KhRo7By5UqEhIQgPz8fjo6O8PPzQ8OGDSvwN1ZzJCUlYfz48QgLC0PdunUBAH/99RemTp2KAwcO4Pz588jOzsbevXsxdepUNG3aFABgYWGBuXPnYv/+/QCAGzduoG7dukhMTMSRI0egp6eH/v37IyEhQeWahw4dwtChQ+Hv7w8AMDIywpIlSxRjVRqhiklPTxcACOnp6eXqX1QsF87GPhb2Xb4nnI19LBQVyys4QiIiIqLqTS6XCwEBAcJnn30mxMfHK9qvXr0q+Pr6ChMmTBAEQRBiY2OFOnXqCCdOnBAEQRCys7OFjz/+WBgyZIjinKKiIqFLly7Cd999J+Tk5Cj6LViwQLCyshL++ecfRd9Ro0YJ7733npCVlSUIgiCcPXtWsLW1FW7duiUIgiDcv39fsLS0FI4fPy4UFhZW7C+BhISEBGHYsGGCq6urYGhoKHTo0EE4ePCg4ODgoPS+eFFcXJzg4OAgCIIgJCYmCm3bthUKCgqEDh06CFlZWUJwcLBw9OhRQRAEoWPHjsLJkyfLjOFV6cjr5gavUm1nfp7TEYvg52KJXs3qwc/FkkvdiIiIiF4hOTkZQUFBWLNmDRwcHBTtTZo0wa+//orVq1cjOzu7XN/W79y5E+7u7hg7diy2bt0KqVSKAQMGYOLEiRg2bBiWLl0KAJDL5Th06BDWrFkDY2NjAICfnx9CQkJw5MgRAICuri4+//xzLF26FFKpFCEhIdi/fz9K/kYmdbp37x7at2+Pzp0749q1a8jOzsbChQuxYMECJCYmlut33qBBAwQEBMDDwwPBwcG4desW7t+/j3feeacS7uDNVPvkh4iIiIheT7169TB58mTMnj0bLi4uGD16tOKYjY0NbGxs8PjxY4wbNw6LFi1SOtfc3BxpaWmK12fOnEGjRo0QFRWFMWPG4PTp03B3d8eRI0fg7++vKEIgFouRkJAAa2trlfGePXsGALC2tsbs2bNx8OBB3L17F2PHjsUPP/yAYcOGVcwvQoutXbsWgwcPxuDBg2FgYACxWAxfX18cPHgQhoblr5g8a9YsyGQyTJkyBd9//z1GjRpVgVG/PSY/RERERFro9u3biIiIwO3bt5GSkoJjx44pjonF4nLPtujq6iIuLg7Lli1TVPkCAD09PRgZGb1xEQKxWIx27dph//792Lx5s6K9WC7gnOwJfr1yH+dkT1As56zQm0hKSoJUKlVpNzMzU0lQyyshIQHu7u5vG1qFYvJDREREpIUePHig9Foul7/ROM2bN8fp06cRERGBMWPGIDAwEImJiQgMDFRHmNDV1VUkVRHXU9BuwQkM+Ok8xm+7ggE/nUe7BScQcT1FLdfSJoIgYMaMGZBKpVi3bp3axqzqmPwQERERaakbN27Aw8MD5ubmb/ychr+/P86dOweJRILFixfjxo0b2LZtG3R0dNQaa8T1FIzaFIWU9Dyl9gfpeRi1KYoJ0Bvo1asXLl++jLlz5yInJ+etx9PV1VVKoouLi6Gnp/fW46oTkx8iIiIiLdW8eXPIZDKsW7cOItGbFY2yt7dHgwYN0Ldv3zceozRPnjxBly5dFOWrZx24idLmFZ63zTpwk0vgXoO1tTWMjY2hr68PHR2dN575e5Gbm5uidHlKSgr++ecfuLm5vfW46sTkh4iIiEgLGRkZqezX81xmZuZrPfRub28PHx8fdYUGAFi+fDm6deuGEydOoJaRscqMz4sEACnpebgY91StMdRk48ePR2RkJNzc3DB69GiYmJi89Zj/+c9/cPHiRTg4OKBDhw6YN28e6tSpo4Zo1UdX0wEQERERUeVr3LgxkpOTER4ejsGDB0NHRwcFBQVYvnw5LC0tYWtrW+6xdHR0YGlpqdb4ioqKYGRkBENDQ5iaW5TrnIeZL0+QSFm9evVw/vz5Uo/Fx8e/9DxHR8eXHq9Xrx6OHz/+WnFU9nNCnPkhIiKiau3nn3+Go6MjVqxYoWhbu3Ytzpw5o8Goqr5atWohIiICe/fuhVQqhZOTE9zd3XHp0iX89ttvmg4P48aNQ3h4OJo1a4aRE6eW6xxr0/LPVpF2EglVrCxDRkYGJBIJ0tPTYWZmpulwiIiIqIrr2LEjdu7cCT8/P8hkMhQXF8PJyQlHjx6Fh4eHpsMjNSiWC2i34AQepOeV+tyPCICtxBBnpnTmhvdVlFwuICUmDdkZ+TA2M4CdqznE5fj/St25AZe9ERERUbXWv39/eHh4wNfXFwAwffp0dOrUiYlPDaIjFmFmD0+M2hQFEaCUAD3/83lmD08mPlWU7PJDnN4eg+y0fEWbsbkB2oe4wqX5m+0p9Ka47I2IiIiqNW9vb1hZWaFp06YICAhARkYG1q5dq+mw6A0IcgF5sjTkXHmIPFkahBeqtwV52WHNoBawlSgvbbOVGGLNoBYI8rKr7HCpHGSXHyLix+tKiQ8AZKflI+LH65Bdflip8XDZGxEREVVr27ZtQ3p6Oho0aIDWrVvDwqJ8D8dT1ZJ7/THSDshQnF6gaNOR6MO8hwtqeVkp2orlAi7GPcXDzDxYmxrCx8mCMz5VlFwu4L/Tz6okPi8yqW2AwXPbvHQJHJe9EREREb2gf//+mg6B3lLu9cd4sumWSntxegGebLoFy0ENFQmQjlgEPxf1VpajipESk1Zm4gMAWc/ykRKThnrutSslJi57IyIiIiKNEeQC0g7IyuyTduCu0hI4qh6yM8pOfF63nzow+SEiIiIijcmPS1da6laa4vR85MelV1JEpC7GZgZq7acOTH6IiIiISGPkmWUnPq/bj6oOO1dzGJuXndiY1C4pe11ZmPwQERERkcaITfXV2o+qDrFYhPYhrmX2adfPtVz7/agLkx8iIiIi0hgDJwl0JGUnNjoSAxg4SSopIlInl+bWCBrppTIDZFLbAEEjvSp9nx9WeyMiIqIaQRAE1K1bF9euXYOVldWrT6AqQSQWwbyHS6nV3p4z7+EMEctZV1suza3h1LROSfW3jHwYm5UsdavMGZ/nmPwQERFRlRUaGqr0vwBw48YNLF++HKdOnUJhYSH09PTQpk0bODs7o0OHDrCyskJRUREGDx6MU6dOwc/PD9u2bYOenp7S2MOGDUOnTp0wbNiwyrshKlUtLytYDmpYyj4/BjDv4ay0zw9VT2KxqNLKWZcZh6YDICIiIiqvkydPomfPnujatSuuXbuGuLg43L59G3Xr1sXMmTPRr18/AMC1a9eQlJSEhIQEGBkZ4dChQxqOnF6llpcVbKf4wGpEY1j0d4fViMawndKKiQ+pFWd+iIiIqNp49OgRdu/ejWbNmgEoWeq2dOlSxMXF4ZdffsHOnTvRt29fNGnSBJ6ennB1dUVWVhYCAgI0GziVi0gsgqGLuabDoBqMyQ8RERFVG89ndg4ePIglS5YgJiYGjx49wogRI6Crq4vU1FQAgI6ODtauXQsAXNZGRApc9kZERETVyo8//ojZs2dj2bJlSEpKwtOnT9GyZUtMmDABgiBoOjwiqsI480NERETVyty5c3HixAlIpVIAgLGxMYYNG4bCwkJs3ry53OPk5ubCwKDydpYnIs3jzA8RERFVK0lJSYrE50WurmVvpviirKwsnDt3TvHs0Is6deqEyMjIt4iQiKoqJj9ERERUpf3yyy+QSqWYP3++os3T0xNt2rTB3bt3X3u8u3fvomfPnsjKykJ2drY6QyWiKo7JDxEREVVpvr6+uHnzJnbv3o3Y2FgAwMaNGzF58mTMnDmz3OPExMRg8ODB8PT0xCeffIL9+/dj/PjxFRU2EVVBTH6IiIioyrKwsECtWrWgq6sLAwMDyOVy1KlTB8bGxjAxMUFRUVG5xtm4cSP8/f0RHByMHj16wNraGtbW1uU+n4hqBhY8ICIioipryJAh+PXXX+Ho6IiBAwfCzc0NK1euRFBQEMzMzLBt2zaVc3744QcsW7YMBQUFkMvlePLkCXJzczF+/Hh4enri66+/Rrdu3WBlZYXt27dr4K6ISFNEQhWrCZmRkQGJRIL09HSYmZlpOhwiIiKqRpKTk9GkSRPs2LEDHTp0gK6u8ve848ePh4eHB0JCQtCsWTMkJiaqjNGpUyeEhoaiU6dOlRQ1Eb2MunMDLnsjIiKiGkNXVxeff/45li5dCqlUipCQEOzfv1+x/89nn32G8PBwtGnTBklJSRqOlogqG5e9ERERUY1hbW2N2bNnAwDkcjnOnj2LefPmYffu3diwYQMaNmyICxcuAABEIpEmQyUiDWDyQ0RERDWSWCxGu3btsH//fhgaGiI0NBRZWVkwMTGBg4ODpsMjIg1g8kNEREQ12j///IPi4mJs2LBB0cbniom0E5/5ISIiohrr5s2b2LFjh0p7RkaG4jgRaQ/O/BAREVGN9OjRI/Tp0wdPnz59aZ+IiAh4eHhALOb3wUTagP+mExERUY00e/ZsODo6YsiQIdDT01M5rq+vjydPniAhIUGpPS8vDwYGBpUVJhFVIiY/REREVCPl5uZCT08Purq6MDY2Vjnu7OyMP/74A48fPwYACIKA/fv3QyaTwcvLq7LDJaJKwGVvREREVCONGDEC77//Ps6cOQN/f3+V4z179sSJEyfwwQcfKMpeu7m54cCBAzA1Na3scImoEoiE57t+VRHq3sWViIiItJNcLsfy5csVxQ1KY2ZmhgkTJvCZH6IqSt25Af9NJyIiohojIyMD/v7+aN26NVJTUxEUFIRDhw7h2rVrpfYPCgpi4kOkRbjsjYiIiGqMzZs3IyQkBGlpadi0aRO+/PJL1KtXT6XggZmZGYKCguDp6amhSIlIE5j8EBERUY0hCILi+Z3s7GwAgLm5OTp06AB/f39kZWXBxMQEDg4OECDCOdkTPMzMg7WpIXycLKAjFmkyfCKqYEx+iIiIqMb48MMP0atXL1y9ehUTJkxQtIvFYjg5OSleR1xPwawDN5GSnqdos5MYYmYPTwR52VVmyERUibjIlYiIiGoMc3NznDp1Cl9++SUSExNRUFCA+/fvw9LSUtEn4noKRm2KUkp8AOBBeh5GbYpCxPWUyg6biCoJkx8iIiKqcfr27YsbN27A1dUVNjY2CAoKAgAUywXMOnATpZW6fd4268BNFMurVDFcIlITLnsjIiKiGsfDwwMXLlxQab8Y91RlxudFAoCU9DxcjHsKPxfLl/YjouqJMz9ERESkNR5mvjzxeZN+RFS9MPkhIiIirWFtaqjWfkRUvTD5ISIiIq3h42QBO4khXlbQWoSSqm8+ThaVGRYRVRImP0RERKQ1dMQizOxRsrHpvxOg569n9vDkfj9ENRSTHyIiIg3LyclBt27dYG9vj0WLFikd+89//gM7OzsEBgaioKAAAJCRkYHQ0FA0btwYzs7OcHR0ROfOnbF///5Sx8/OzkZQUBAaNGiAkSNHVvj9VHVBXnZYM6gFbCXKS9tsJYZYM6gF9/khqsHUnvyEhoZCJBIp/Xh4eKj7MkRERDXGxo0b0bRpU8TExGDt2rVIT08HAPz11184cuQIkpKSIJfLceLECeTk5KBDhw4oLi7GsWPHcPfuXcTHx+Pbb7/F5MmTsWLFCpXxN23ahGbNmiE2NhbHjh3D1atXK/sWq5wgLzucmdIZW0e0xor+zbB1RGucmdKZiQ9RDVchpa4bNWqEY8eO/e8iuqyoTURE9DK6urrIy8tDUVERCgsL8ezZM0gkEvz0008YPXo0dHV1YW9vj9TUVMhkMowdOxbDhw9XGqNt27bYvHkzevbsifHjx790/OLiYjx79qwyb6/K0hGLWM6aSMtUyLI3XV1d2NraKn6srKwq4jJEREQ1wqBBg5CcnIxmzZrhwYMHAIC8vDwcOnQIH3zwgaKfIAho3LgxPv74Y3z22WdwcXHBnDlzFMe9vb0V579o8ODBePjwIZo0acLEh4i0WoUkPzExMahbty6cnZ0xcOBAJCYmvrRvfn4+MjIylH6IiIi0iYGBAXbs2IGYmBjY2toCAPbt24fAwECYmJio9D9y5AiePn2KW7du4cCBA4iJiQEAiMXK/1mXy+UAAH19fWzZsgUymQzNmzev4LshIqq61J78+Pr6Ijw8HBEREVizZg3i4uLQvn17ZGZmlto/LCwMEolE8dOgQQN1h0RERFTthIeHY+jQoaUee3F2RxAECIJQar/3338fFy5cqJD4iIiqI7UnP926dcMHH3yAJk2aoGvXrvjtt9+QlpaGHTt2lNp/2rRpSE9PV/wkJSWpOyQiIqJqJTk5GTExMfD3939pnxMnTsDDwwPvvPMO3NzcSu2zZMkSDB8+HH///XdFhUpEVK1UeCUCc3NzuLm5ITY2ttTjBgYGMDAwqOgwiIiIqo2tW7di4MCBEIlevtdM9+7dER4eXuY4Tk5O2LVrF3r16oVt27ZxyRsRab0K3+cnKysLMpkMdnYsHUlERFQehw4deumSNwAwMjIqdTl5RkaGyheKHh4e2L59Oz744ANER0erPVYioupE7cnP5MmTcerUKcTHx+Ps2bPo06cPdHR0MGDAAHVfioiIqEaqX78+XFxcXnrcz88Pf/zxB3777TdFW3Z2NmbMmIEOHTqo9G/WrBk2bdqEXr16lVmEiIioplN78nPv3j0MGDAA7u7u6NevHywtLXH+/HnUqVNH3ZciIiKqkcqa9QGABg0aYOfOnVi6dCmcnJzg5OSEJk2aICsrCxs3biz1nNatW+OXX35BSkpKRYRMRFQtiISXlYjRkIyMDEgkEqSnp8PMzEzT4RARERERkYaoOzeo8Gd+iIiIiIiIqoIKr/ZGRERElU8uL8b9WzeQlfYMJua1Ua9hI4jFOpoOi4hIo5j8EBER1TAxF87iRPhaZD19rGgzsbBC52GfwtW3jQYjIyLSLC57IyIiqkFiLpzF/qXzlBIfAMh6+hj7l85DzIWzGoqMiEjzmPwQERHVEHJ5MU6Ery2zz8kNayGXF1dSREREVQuTHyIiohri/q0bKjM+/5b55DHu37pRSREREVUtTH6IiIhqiKy0Z2rtR0RU0zD5ISIiqiFMzGurtR8RUU3D5IeIiKiGqNewEUwsrMrsY2pphXoNG1VSREREVQuTHyIiohpCLNZB52GfltnHf+in3O+HiLQWkx8iIqIaxNW3DXpOmq4yA2RqaYWek6Zznx8i0mrc5JSIiKiGcfVtA5dWviXV39KewcS8Nuo1bMQZHyLSepz5ISIiqoHEYh00aNQEDdt2RINGTZj4EFUhoaGhCA0NVbw+c+YM3N3d4eXlhatXryraHz58CD8/P9SrVw8rV65UtO/evRvOzs7w8fFBUlISAOD+/fsQiURITEwEAAwbNgzh4eGVcj/VCZMfIiIiIiIN+vLLL/Hrr79i2bJlmD17tqJ91qxZ6NevH86dO4e5c+cq2qdMmYK//voLI0aMwLJlywAAR44cgZmZGX7//fdKj786YfJDRERERKRBurq6yMvLQ35+Pp48eQIAyM3Nxb59+zBixAjY29sjNTVV0V8sFqOgoAD5+fl4+vQpAODo0aOYPn06jh49qpF7qC74zA8RERERkQYtXboUQ4cORUZGBhwcHAAA+/btQ2BgIExMTFT6L1++HP7+/sjOzkZAQADkcjkuXbqEDRs2oFGjRpDL5ZV9C9UGZ36IiIiIiDSoVatWuHr1KtavX69o27BhA4YNG1Zq/+DgYNy+fRvffvstACAqKgrNmjWDgYEBmjVrhqioqMoIu1pi8kNEREREVIXcv38fMTEx6NSpU7n6Hz16FEFBQQCAoKAgPvdTBiY/RERERERVyKZNmzBw4ECIRKJy9f/999+Z/JQTkx8iIiIiokr2yy+/QCqVYv78+UrtYrEYGzduxNChQ8s1Tm5uLjIzM1GvXj0AQP369ZGeno7c3Fy1x1wTMPkhIiIiIqpkvr6+uHnzJnbv3o3Y2FhFe1xcHGrXrg0XF5dyjRMdHQ1/f3+lNn9/f0RHR6s13pqCyQ8RERERUSWysLBArVq1oKurCwMDA6XqbPHx8S8tdFAamUymWPL2XFBQEGQymbrCrVGY/BARERERVaIhQ4YgOTkZjo6OaN++Pdzc3BTHjIyM0K9fv3KPpa+vj/bt2yu1tW/fHvr6+mqLtyYRCYIgaDqIF2VkZEAikSA9PR1mZmaaDoeIiIiIiDRE3bkBZ36IiIiIiEgr6Go6ACIiIiIienPFcgEX457iYWYerE0N4eNkAR1x+cpkaxsmP0RERERE1VTE9RTMOnATKel5ijY7iSFm9vBEkJedBiOrmrjsjYiIiIioGoq4noJRm6KUEh8AeJCeh1GbohBxPUVDkVVdTH6IiIiIiKqZYrmAWQduorTKZc/bZh24iWJ5laptpnFMfoiIiIiIqpmLcU9VZnxeJABISc/DxbinlRdUNcDkh4iIiIiomnmY+fLE5036aQsmP0RERERE1Yy1qaFa+2kLJj9ERERERNWMj5MF7CSGeFlBaxFKqr75OFlUZlhVHpMfIiIiIqJqRkcswswengCgkgA9fz2zhyf3+/kXJj9ERERERNVQkJcd1gxqAVuJ8tI2W4kh1gxqwX1+SsFNTomIiIiIqqkgLzu842mLi3FP8TAzD9amJUvdOONTOiY/RERERETVmI5YBD8XS02HUS1w2RsREREREWkFJj9ERERERKQVmPwQEREREZFWYPJDRERERERagckPERERERFpBSY/RERERESkFZj8EBERERGRVmDyQ0REREREWoHJDxERERERaQUmP0REREREpBWY/BARERERkVZg8kNERERERFqByQ8REREREWkFJj9ERERERKQVmPwQEREREZFWYPJDRERERERagckPERERERFpBSY/RERERESkFZj8EBERERGRVmDyQ0REREREWoHJDxERERERaQUmP0REREREpBWY/BARERERkVZg8kNERERERFqByQ8REREREWkFJj9ERERUpsLCQk2HQESkFkx+iIiIKlFBQQGWLFkCb29vuLi4wMHBAX5+ftiwYYNSv4sXL6J9+/ZwdHSEi4sLxo4di8zMTMXx3bt3w9nZGT4+PkhKSlK0C4KAIUOGwMHBAVOnTlW0y+VyLFmyBB4eHnB0dETTpk2xdetWpWvu3bsXjRo1grOzMxwcHODg4ABnZ2d88cUXFfTbICKqXLqaDoCIiEhbCIKA4OBguLq6Ys+ePXBwcAAAREdH49NPP8WVK1ewbNkyyGQydO/eHdu3b4e/vz9ycnIwduxYjBkzRpEkTZkyBX/99Rf27NmDZcuWYenSpQCA33//HUVFRYiLi0O7du1w584duLu7Y/Hixdi/fz/OnDkDKysr3Lp1C927d4eNjQ06d+6MoqIiDB8+HD/99BO6d+8OAwMDjf2eiIgqCmd+iIiIKklycjKCgoKwZs0aReIDAE2aNMGvv/6K1atXIzs7G4cOHcLQoUPh7+8PADAyMsKSJUuwf/9+xTlisRgFBQXIz8/H06dPla6Tn5+PoqIiFBYWQl9fHwCwb98+rFixAlZWVgCAhg0bYuzYsThw4ACAkqVtEydOxJYtW9CwYUP06NEDmzZt4pI3IqpRRIIgCJoO4kUZGRmQSCRIT0+HmZmZpsMhIiJSu9mzZ2PDhg0ICgrCqlWrFO329vY4ffq0UmL0IpFIhOf/2f7tt98wadIkZGdnIyAgAOHh4QCAzMxMODs7IzMzE4MHD8ZPP/300jjCw8MRGRmpOPdFV65cwZIlS5CUlISTJ09CJBK9+Q0TEb0hdecGnPkhIiKqRLdv30ZERARu376NlJQUHDt2THFMLBajvN9JBgcH4/bt2/j222+V2keOHInPP/8cR44cgUwme+M4mzVrho0bNyI2Nhb3799/43GIiKoSPvNDRERUiR48eKD0Wi6Xq23sU6dO4fbt29i4cSOSkpIQHx//1mPq6uqiqKjo7YMjIqoCOPNDRERUyW7cuAEPDw+Ym5vjnXfeUcuYcrkcEydOxMqVK6Gjo6NoIyKi/+HMDxERUSVr3rw5IiMj1TaeSCTCzz//jMaNG6Nt27ZvPV5+fj4GDx6MK1euqMxUERFVZ5z5ISIiqkRGRkZK+/W8KDMzE4aGhq89piAIWLRoERYuXPi24QEANm3aBDs7O0RFRcHGxkYtYxIRVQVMfoiIiCpR48aNkZycjPDwcBQXFwMo2fh04cKFsLS0hK2t7WuPuW/fPowePVptiUpRURGMjIygr68PAwMD1K5dWy3jEhFpGpMfIiKiSlSrVi1ERERg7969kEqlcHJygru7Oy5duoTffvvtjcZs0KABxowZo7YYBw0ahKioKEilUnz00UeQSCRqG5uISJO4zw8REREREVVJ3OeHiIiIiIjoDbDaGxEREQEA5HIBKTFpyM7Ih7GZAexczSEWizQdFhGR2jD5ISIiIsguP8Tp7THITstXtBmbG6B9iCtcmltrMDIiIvXhsjciIiItJ7v8EBE/XldKfAAgOy0fET9eh+zyQw1FRkSkXkx+iIiItJhcLuD09pgy+5zZEQO5vErVRyIieiNMfoiIiLRYSkyayozPv2U9y0dKTFrlBEREVIGY/BAREWmx7IyyE5/X7UdEVJUx+SEiItJixmYGau1HRFSVMfkhIiLSYnau5jA2LzuxMaldUvaaiKi6Y/JDRESkxcRiEdqHuJbZp10/V+73Q0Q1ApMfIiIiLefS3BpBI71UZoBMahsgaKQX9/khohqDm5wSERERXJpbw6lpnZLqbxn5MDYrWerGGR8iqkmY/BARERGAkiVw9dxrazoMIqIKw2VvRERERESkFZj8EBERERGRVmDyQ0REREREWoHJDxERERERaQUmP0REREREpBWY/BARERERkVZg8kNERERERFqByQ8REREREWkFJj9ERERERKQVmPwQEREREZFWYPJDRERERERagckPERERERFpBSY/RERERESkFZj8EBERERGRVmDyQ0REREREWoHJDxERERERaQUmP0REREREpBWY/BARERERkVZg8kNERERERFqByQ8REREREWkFJj9ERERERKQVmPwQEREREZFWYPJDRERERERagckPERERERFpBSY/RERERESkFZj8EBERERGRVmDyQ0REREREWoHJDxERERERaQUmP0REREREpBUqLPlZtWoVHB0dYWhoCF9fX1y8eLGiLkVERERERPRKFZL8bN++HZMmTcLMmTMRFRWFpk2bomvXrnj48GFFXI6IiIiIiOiVKiT5Wbp0KUaMGIGPPvoInp6e+OGHH2BkZIR169ZVxOWIiIiIiIheSVfdAxYUFODSpUuYNm2aok0sFiMwMBDnzp1T6Z+fn4/8/HzF6/T0dABARkaGukMjIiIiIqJq5HlOIAiCWsZTe/Lz+PFjFBcXw8bGRqndxsYGt2/fVukfFhaGWbNmqbQ3aNBA3aEREREREVE19OTJE0gkkrceR+3Jz+uaNm0aJk2apHidlpYGBwcHJCYmquUGqXrKyMhAgwYNkJSUBDMzM02HQxrA9wDxPUB8DxDfA5Seng57e3tYWFioZTy1Jz9WVlbQ0dFBamqqUntqaipsbW1V+hsYGMDAwEClXSKR8E1OMDMz4/tAy/E9QHwPEN8DxPcAicXqKVWg9oIH+vr68Pb2xvHjxxVtcrkcx48fh5+fn7ovR0REREREVC4Vsuxt0qRJGDp0KFq2bAkfHx8sX74c2dnZ+OijjyrickRERERERK9UIclPSEgIHj16hBkzZuDBgwdo1qwZIiIiVIoglMbAwAAzZ84sdSkcaQ++D4jvAeJ7gPgeIL4HSN3vAZGgrrpxREREREREVViFbHJKRERERERU1TD5ISIiIiIircDkh4iIiIiItAKTHyIiIiIi0gpVLvlZtWoVHB0dYWhoCF9fX1y8eFHTIVElCQ0NhUgkUvrx8PDQdFhUgf744w/06NEDdevWhUgkwr59+5SOC4KAGTNmwM7ODrVq1UJgYCBiYmI0EyxVmFe9D4YNG6by2RAUFKSZYEntwsLC0KpVK5iamsLa2hq9e/fGnTt3lPrk5eVh9OjRsLS0hImJCfr27auymTpVX+V5D3Tq1Enlc+Czzz7TUMRUEdasWYMmTZooNrT18/PD4cOHFcfV9TlQpZKf7du3Y9KkSZg5cyaioqLQtGlTdO3aFQ8fPtR0aFRJGjVqhJSUFMXPmTNnNB0SVaDs7Gw0bdoUq1atKvX4woUL8d133+GHH37AhQsXYGxsjK5duyIvL6+SI6WK9Kr3AQAEBQUpfTZs3bq1EiOkinTq1CmMHj0a58+fx++//47CwkJ06dIF2dnZij4TJ07EgQMHsHPnTpw6dQrJycl47733NBg1qVN53gMAMGLECKXPgYULF2ooYqoI9evXx/z583Hp0iX8/fff6Ny5M3r16oUbN24AUOPngFCF+Pj4CKNHj1a8Li4uFurWrSuEhYVpMCqqLDNnzhSaNm2q6TBIQwAIe/fuVbyWy+WCra2tsGjRIkVbWlqaYGBgIGzdulUDEVJl+Pf7QBAEYejQoUKvXr00Eg9VvocPHwoAhFOnTgmCUPLvvZ6enrBz505Fn1u3bgkAhHPnzmkqTKpA/34PCIIgdOzYURg/frzmgiKNqF27tvDzzz+r9XOgysz8FBQU4NKlSwgMDFS0icViBAYG4ty5cxqMjCpTTEwM6tatC2dnZwwcOBCJiYmaDok0JC4uDg8ePFD6TJBIJPD19eVnghaKjIyEtbU13N3dMWrUKDx58kTTIVEFSU9PBwBYWFgAAC5duoTCwkKlzwIPDw/Y29vzs6CG+vd74LnNmzfDysoKXl5emDZtGnJycjQRHlWC4uJibNu2DdnZ2fDz81Pr54CuuoN9U48fP0ZxcTFsbGyU2m1sbHD79m0NRUWVydfXF+Hh4XB3d0dKSgpmzZqF9u3b4/r16zA1NdV0eFTJHjx4AAClfiY8P0baISgoCO+99x6cnJwgk8kwffp0dOvWDefOnYOOjo6mwyM1ksvlmDBhAtq2bQsvLy8AJZ8F+vr6MDc3V+rLz4KaqbT3AAB8+OGHcHBwQN26dREdHY0pU6bgzp072LNnjwajJXW7du0a/Pz8kJeXBxMTE+zduxeenp64cuWK2j4HqkzyQ9StWzfFPzdp0gS+vr5wcHDAjh07MHz4cA1GRkSa1L9/f8U/N27cGE2aNIGLiwsiIyMREBCgwchI3UaPHo3r16/zeU8t9rL3wKeffqr458aNG8POzg4BAQGQyWRwcXGp7DCpgri7u+PKlStIT0/Hrl27MHToUJw6dUqt16gyy96srKygo6OjUrUhNTUVtra2GoqKNMnc3Bxubm6IjY3VdCikAc//vednAv2bs7MzrKys+NlQw4wZMwYHDx7EyZMnUb9+fUW7ra0tCgoKkJaWptSfnwU1z8veA6Xx9fUFAH4O1DD6+vqQSqXw9vZGWFgYmjZtihUrVqj1c6DKJD/6+vrw9vbG8ePHFW1yuRzHjx+Hn5+fBiMjTcnKyoJMJoOdnZ2mQyENcHJygq2trdJnQkZGBi5cuMDPBC137949PHnyhJ8NNYQgCBgzZgz27t2LEydOwMnJSem4t7c39PT0lD4L7ty5g8TERH4W1BCveg+U5sqVKwDAz4EaTi6XIz8/X62fA1Vq2dukSZMwdOhQtGzZEj4+Pli+fDmys7Px0UcfaTo0qgSTJ09Gjx494ODggOTkZMycORM6OjoYMGCApkOjCpKVlaX0rV1cXByuXLkCCwsL2NvbY8KECZgzZw5cXV3h5OSEb775BnXr1kXv3r01FzSpXVnvAwsLC8yaNQt9+/aFra0tZDIZvvrqK0ilUnTt2lWDUZO6jB49Glu2bMGvv/4KU1NTxfp9iUSCWrVqQSKRYPjw4Zg0aRIsLCxgZmaGsWPHws/PD61bt9Zw9KQOr3oPyGQybNmyBcHBwbC0tER0dDQmTpyIDh06oEmTJhqOntRl2rRp6NatG+zt7ZGZmYktW7YgMjISR44cUe/ngHoL0r29lStXCvb29oK+vr7g4+MjnD9/XtMhUSUJCQkR7OzsBH19faFevXpCSEiIEBsbq+mwqAKdPHlSAKDyM3ToUEEQSspdf/PNN4KNjY1gYGAgBAQECHfu3NFs0KR2Zb0PcnJyhC5dugh16tQR9PT0BAcHB2HEiBHCgwcPNB02qUlp/98DENavX6/ok5ubK3z++edC7dq1BSMjI6FPnz5CSkqK5oImtXrVeyAxMVHo0KGDYGFhIRgYGAhSqVT48ssvhfT0dM0GTmr18ccfCw4ODoK+vr5Qp04dISAgQDh69KjiuLo+B0SCIAhvm6kRERERERFVdVXmmR8iIiIiIqKKxOSHiIiIiIi0ApMfIiIiIiLSCkx+iIiIiIhIKzD5ISIiIiIircDkh4iIiIiItAKTHyIiIiIi0gpMfoiIiIiISCsw+SEiIiIiIq3A5IeIiIiIiLQCkx8iIiIiItIKTH6IiIiIiEgr/B9Q1fi/6GMbbgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Word Arithmetic\n",
        "You can do simple “arithmetic” with words based on the word vectors such as: * ผู้หญิง + พระราชา - ผู้ชาย = พระราชินี * นายกรัฐมนตรี - อำนาจ = ประธานาธิบดี * กิ้งก่า + โบราณ = ไดโนเสาร์"
      ],
      "metadata": {
        "id": "daqqskvI1uAy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#word arithmetic\n",
        "model.most_similar_cosmul(positive=['พระราชา','ผู้หญิง'], negative=['ผู้ชาย'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMmCs1NN1yB9",
        "outputId": "6e19700b-63f3-4265-aaec-b3db39312715"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('พระราชินี', 0.7954867482185364),\n",
              " ('กษัตริย์', 0.7382754683494568),\n",
              " ('พระเจ้า', 0.7046602368354797),\n",
              " ('เจ้าชาย', 0.6979373097419739),\n",
              " ('พระมหากษัตริย์', 0.6972416639328003),\n",
              " ('เจ้าฟ้าหญิง', 0.6871017217636108),\n",
              " ('พระเจ้าแผ่นดิน', 0.6827988624572754),\n",
              " ('พระพุทธเจ้า', 0.671796977519989),\n",
              " ('มกุฎราชกุมาร', 0.6711804866790771),\n",
              " ('นายพล', 0.6694187521934509)]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_words = ['ผู้หญิง','พระราชา','ผู้ชาย','พระราชินี']\n",
        "sample_idx = []\n",
        "for word in sample_words:\n",
        "    sample_idx.append(labels.index(word))\n",
        "sample_plot = thai2plot[sample_idx]\n",
        "plot_with_labels(sample_plot,sample_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "1kT2qdjx18D4",
        "outputId": "c1a4aa96-ee31-49ef-ee15-d499050c818a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.most_similar_cosmul(positive=['นายกรัฐมนตรี'],negative=['อำนาจ'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LhfSt3S2Ect",
        "outputId": "512faea7-93fb-432e-e542-8d9d3a70e2b2"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('รองนายกรัฐมนตรี', 1.4945054054260254),\n",
              " ('รัฐมนตรี', 1.4007558822631836),\n",
              " ('ประธานาธิบดี', 1.3626699447631836),\n",
              " ('พันเอก', 1.3437265157699585),\n",
              " ('ผู้บัญชาการทหารบก', 1.3405416011810303),\n",
              " ('ผู้กำกับภาพยนตร์', 1.3339321613311768),\n",
              " ('นักฟุตบอล', 1.331659197807312),\n",
              " ('เอกอัครราชทูต', 1.3306005001068115),\n",
              " ('แห้ง', 1.3243674039840698),\n",
              " ('สุภาพสตรี', 1.3231494426727295)]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_words = ['นายกรัฐมนตรี','อำนาจ','รองนายกรัฐมนตรี']\n",
        "sample_idx = []\n",
        "for word in sample_words:\n",
        "    sample_idx.append(labels.index(word))\n",
        "sample_plot = thai2plot[sample_idx]\n",
        "plot_with_labels(sample_plot,sample_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "vH_TAYqg2EYP",
        "outputId": "298a7280-abb7-4dad-b49a-ac4c9b4de101"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#word arithmetic\n",
        "model.most_similar_cosmul(positive=['สัตว์','พืช'], negative=[])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqvI82H12Pnu",
        "outputId": "da4f8549-19e2-4852-e76d-ef6ca8c51cc3"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('สิ่งมีชีวิต', 0.537461519241333),\n",
              " ('สัตว์เลี้ยงลูกด้วยนม', 0.5080006122589111),\n",
              " ('แมลง', 0.5048903226852417),\n",
              " ('ผลไม้', 0.4839755892753601),\n",
              " ('มนุษย์', 0.47641509771347046),\n",
              " ('ผัก', 0.46431201696395874),\n",
              " ('สัตว์น้ำ', 0.45941096544265747),\n",
              " ('ต้นไม้', 0.45185261964797974),\n",
              " ('พันธุ์ไม้', 0.4504697620868683),\n",
              " ('ไม้ยืนต้น', 0.44425836205482483)]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_words = ['สัตว์','พืช','สิ่งมีชีวิต']\n",
        "sample_idx = []\n",
        "for word in sample_words:\n",
        "    sample_idx.append(labels.index(word))\n",
        "sample_plot = thai2plot[sample_idx]\n",
        "plot_with_labels(sample_plot,sample_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "45qO7h9R2Vtt",
        "outputId": "e789a905-056c-43f0-ae32-b4a275a08cec"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Doesn’t Match\n",
        "It can also be used to do word groupings. For instance: * อาหารเช้า อาหารสัตว์ อาหารเย็น อาหารกลางวัน - อาหารสัตว์ is type of food whereas others are meals in the day * ลาก ดึง ดูด ดัน - ดัน is pushing while the rest is pulling. * กด กัด กิน เคี้ยว - กด is not verbs for the eating process Note that this could be relying on a different “take” than you would expect. For example, you could have answered ลูกเขย in the second example because it is the one associated with male gender."
      ],
      "metadata": {
        "id": "LXHRdVvL2jdU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.doesnt_match(\"อาหารเช้า อาหารสัตว์ อาหารเย็น อาหารกลางวัน\".split())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zoZVh_vu2nor",
        "outputId": "8e9fd45f-fa3a-4fc7-b39a-cbd54c9eb899"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'อาหารสัตว์'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_words = \"อาหารเช้า อาหารสัตว์ อาหารเย็น อาหารกลางวัน\".split()\n",
        "sample_idx = []\n",
        "for word in sample_words:\n",
        "    sample_idx.append(labels.index(word))\n",
        "sample_plot = thai2plot[sample_idx]\n",
        "plot_with_labels(sample_plot,sample_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "PvXzt5WV2t3H",
        "outputId": "ebe5a533-94ac-4867-d180-f58db2590065"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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FixZlxIgRmT59+loXSwAAANgcaiqVSqXaQ2yKpqam1NfXZ+nSpamrq6v2OAAAQJVsaBtsdVeHAwAA2BQiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoChVjaD+/funpqamzePyyy+v5kgAAMC7XKdqD3DRRRfl+OOPb32+3XbbVXEaAADg3a7qEbTddtulT58+1R4DAAAoRNV/E3T55Zdnhx12yL777ptJkyZl9erV692+ubk5TU1NbR4AAAAbqqpHgk4//fTst99+2X777fPQQw/l3HPPzfPPP58rr7zyTV9z2WWX5Wtf+9oWnBIAAHg3qalUKpXNucMJEybkiiuuWO82Tz75ZIYOHbrW8ptuuiknnnhiXn311XTp0mWdr21ubk5zc3Pr86ampvTr1y9Lly5NXV3dpg0PAABstZqamlJfX/+WbbDZI2jJkiX54x//uN5tBg4cmM6dO6+1/PHHH89ee+2V2bNnZ/fdd9+g99vQDwoAALy7bWgbbPbT4Xr16pVevXq9rdc++uij6dChQ3r37r2ZpwIAAHhd1X4T9PDDD+cXv/hFxo4dm+222y4PP/xwxo8fn6OPPjo9e/as1lgAAMC7XNUiqEuXLvn+97+fCy+8MM3NzRkwYEDGjx+fL33pS9UaCQAAKEDVImi//fbLf//3f1fr7QEAgEJV/T5BAAAAW5IIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKEq7RdAll1ySMWPGpLa2Nj169FjnNs8++2w+8pGPpLa2Nr17986Xv/zlrF69ur1GAgAASKf22vHKlSvzyU9+MqNHj86NN9641vo1a9bkIx/5SPr06ZOHHnoozz//fMaNG5dtttkml156aXuNBQAAFK6mUqlU2vMNbr755px55pl55ZVX2iz/8Y9/nI9+9KNZuHBhGhoakiTXXXddzjnnnCxZsiSdO3de5/6am5vT3Nzc+rypqSn9+vXL0qVLU1dX126fAwAAeGdrampKfX39W7ZB1X4T9PDDD2f48OGtAZQkH/zgB9PU1JTHH3/8TV932WWXpb6+vvXRr1+/LTEuAADwLlG1CFq0aFGbAErS+nzRokVv+rpzzz03S5cubX0sWLCgXecEAADeXTYqgiZMmJCampr1PmbPnt1esyZJunTpkrq6ujYPAACADbVRF0Y466yzctxxx613m4EDB27Qvvr06ZOZM2e2WbZ48eLWdQAAAO1hoyKoV69e6dWr12Z549GjR+eSSy7JCy+8kN69eydJ7r333tTV1WXYsGGb5T0AAAD+r3a7RPazzz6bl156Kc8++2zWrFmTRx99NEkyePDgbLvttvnABz6QYcOG5ZhjjsnEiROzaNGifPWrX80pp5ySLl26tNdYAABA4drtEtnHHXdcbrnllrWW33///Tn44IOTJPPnz89JJ52UBx54IN27d8+xxx6byy+/PJ06bXibbehl8AAAgHe3DW2Ddr9PUHsTQQAAQLIV3CcIAACgGkQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBFE8S6//PL0798/n/70p9Pc3Ny6/Pbbb8+nP/3pKk4GAEB7EEEU7ZVXXsnNN9+cJ598Ml27ds306dNb133/+9/P/vvvX8XpAABoDyKIotXX16dDhw750Ic+lF/84hcZNWpU/vCHP+S0007LvHnzcsopp1R7RAAANjMRRNFqampy3333Zfjw4dlmm20yZsyY/NVf/VX69u2bBx54IN26dav2iAAAbGY1lUqlUu0hNkVTU1Pq6+uzdOnS1NXVVXscAACgSja0DRwJol2sWrWq2iMAAMA6iaCtwMqVKzN58uSMHDkygwYNSmNjY0aPHp1bbrmlzXYzZ87MQQcdlP79+2fQoEE57bTTsmzZstb1t912WwYOHJhRo0ZlwYIFrcsrlUrGjRuXxsbGTJgwoXV5S0tLJk+enKFDh6Z///7ZZ5998r3vfa/Ne06bNi177rlnBg4cmMbGxjQ2NmbgwIE566yz2mz37LPPZty4cRk6dGh23XXXjB49Orfffvtan3XOnDk56aSTMmTIkAwYMCBDhgzJsccemyeffLLNds3NzTn33HOz2267pbGxMX/5l3+Ze++9d51/v29+85tpbGzMfvvtl6effrp1+eOPP56/+Zu/yaBBgzJgwICMGjVqrc83fvz47LTTThk7dmxeeeWVtfZ94YUX5sILL1zn+wIA8A5V2cotXbq0kqSydOnSao/SLlpaWiqHHHJI5Ytf/GJl3rx5rct/85vfVA444IDKmWeeWalUKpU5c+ZUevXqVfnpT39aqVQqleXLl1c+//nPV8aNG9f6mkGDBlVefPHFyg033FAZP3586/IZM2ZUPvOZz1TWrFlTGT16dGX27NmVSqVSueKKKyoHHnhgZcmSJZVKpVJ54oknKgMHDqz85Cc/qVQqlcqqVasqPXv2rPzgBz+ovPbaa2/6GebMmVPZc889K9OmTau0tLRUKpVK5eGHH67stttulauuuqp1u5kzZ1YaGxsr//zP/1xZsWJF63v8x3/8R2XQoEGVX/3qV63bnnTSSZWPfexjlVdffbVSqVQqDz30UKVPnz6VJ598ss17v/rqq5VBgwZV/vSnP1W+8pWvtP69Fi9eXOnXr1/lX//1X1tnmjdvXmXMmDGVa6+9tlKpVCovvfRSpX///pUVK1ZU/v7v/77yjW98Y63PdsEFF1QuuOCCN/3sAABsORvaBo4EvcMtXLgwhx9+eKZMmZLGxsbW5XvvvXd++MMf5tprr83y5ctz11135dhjj83YsWOTJLW1tZk8eXLuuOOO1td06NAhK1euTHNzc1566aU279Pc3JzVq1dn1apV6dy5c5LX75Nz9dVXZ8cdd0yS7LHHHjnttNNy5513Jnn9lLfx48fnu9/9bvbYY48cccQRmTp16lqnwp155pk5++yz89d//depqalJkvzlX/5lvvvd7+aiiy5KS0tLkuSXv/xlbrzxxhxzzDGtFyTo1KlT/uZv/iaXXHJJrrzyyiSvH6G66667MmXKlHTv3j1JMnr06HzqU5/KjBkz2rz3G++3evXqvPbaa3n55ZeTJI888kjGjx+fo446qnWbxsbGXHbZZbnxxhuTJD179swxxxyTPfbYIzfddNM6jwQBALD1EUHvcDvvvHPOPvvsXHTRRRk0aFCbSzY3NDSkoaEhL774Yk4//fRMmjSpzWt79OjR5j/cr7rqqowdOzZXXHFFm+1Gjx6dBx98MHV1dRkxYkQGDBiQJHnooYcycuTItfb5Rkh069Yt5513Xm677bY8/fTTufjiizNjxowcdthhqfzZ9TZGjhyZu+66K6NGjWp9bZLsv//+aWlpyaJFi5IkJ598cg455JD88Ic/zMEHH5yBAwdmv/32y0knnZTVq1dn4cKFSV6Pufnz56d3795vOtsbamtrc/7552fffffNv/3bv7Uu/9CHPpTx48ev9ffedddd88c//rH1+UUXXZR58+blxBNPXGtbAAC2TiJoKzB79uxMnz49s2fPzvPPP5/77ruvdV2HDh3aBMf6fPjDH87s2bNz8cUXt1l+4okn5uSTT86MGTMyd+7ctz3niBEjcuutt2bOnDl57rnnWpdPmDAh8+bNy8knn5wePXq0eU19fX1WrlzZ+vyaa67J17/+9fzjP/5jnn766fz85z/PmDFjcsYZZ2zw50zSZttx48bl97//fS666KKNfi0AAO8+nao9AG/tjSMlb3jj9LHN4Wc/+1lmz56dW2+9NQsWLMi8efM2eZ+dOnXK6tWrW5/fe++96dGjR4477ri3fO1ll12WmTNnZpdddkny+pGcY445JqtXr17rQhDr8y//8i/57W9/m8svv3yj598Yf/rTn9YKOwAA3tkcCdpKPP744xk6dGh69OiRww47bLPss6WlJePHj88111yTjh07ti7b3P74xz9m55133qBtn3/++dYA+nNvnKK3oT772c/mueeey1e/+tWNet3GWL16de67777st99+7fYeAABsfiJoK7Hvvvtm7ty5uemmm1p/yL8pampq8u1vfzvDhw/PgQceuMn7a25uzlFHHZUhQ4asdeRqYw0bNixjxoxpcznrjVVTU5PvfOc7eeKJJzboEtZz587N6NGjNzgwFy9enM9+9rN57LHHnD4HALCVEUFbgdra2jb3+/lzy5YtS9euXTd6n5VKJZMmTcrEiRM3dbwkydSpU9O3b9/MmjUrDQ0Nm7SvW2+9NWeffXYuuOCCTdpPp06d8v3vfz8PP/xwLrnkkvVue+GFF+bv/u7vctttt603MhcuXJjTTjstgwcPzogRI/L444+3uVgFAADvfCJoKzB8+PAsXLgwN998c9asWZPk9RuoTpw4MTvssEP69Omz0fu8/fbbc8opp2xysLxh9erVqa2tTefOndOlS5f07Nnzbe1n++23T/fu3bPtttu2+V3R29W5c+dMmzYtM2bMyJQpU950uzfm7969+5v+xue+++7LyJEjs/vuu+f4449Pr169ssMOO2zyjAAAbFkujLAV6NatW6ZPn57zzz8/X/va11qXjxo1Knfffffb2me/fv1y6qmnbq4Rc/TRR+djH/tYBg8enJNOOin19fVvaz/XXHNNPvjBD6a+vj7f//73N8tstbW1+dGPfpRDDjnkTbc577zz8ulPfzorVqzItddeu85tfv3rX2f69Ok56KCD8oc//KH1/kWb62gaAABbRk1lK/9BQ1NTU+rr67N06dLU1dVVexwAAKBKNrQNnA4HAAAUxelwsIEqa9Zkxa8eyeolS9KpV6/U7j8yNf97aXEAALYeIgg2QNM992TxpZdl9Z9d/rtTnz5p+PtzU/eBD1RxMgAANpbT4eAtNN1zT54748w2AZQkqxcvznNnnJmme+6p0mQAALwdIgjWo7JmTRZfelmyruuH/O+yxZdelsr/XrocAIB3PhEE67HiV4+sdQSojUolqxctyopfPbLlhgIAYJOIIFiP1UuWbNbtAACoPhEE69GpV6/Nuh0AANUngmA9avcfmU59+iQ1NeveoKYmnfr0Se3+I7fsYAAAvG0iCNajpmPHNPz9uf/75P+E0P8+b/j7c90vCABgKyKC4C3UfeAD2fnqq9KpoaHN8k4NDdn56qvcJwgAYCvjZqmwAeo+8IFsd8ghr18tbsmSdOrVK7X7j3QECABgKySCYAPVdOyY7geMqvYYAABsIqfDAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQlHaLoEsuuSRjxoxJbW1tevTosc5tampq1np8//vfb6+RAAAA0qm9drxy5cp88pOfzOjRo3PjjTe+6Xbf+c53cvjhh7c+f7NgAgAA2BzaLYK+9rWvJUluvvnm9W7Xo0eP9OnTp73GAAAAaKPqvwk65ZRTsuOOO2bUqFG56aabUqlU1rt9c3Nzmpqa2jwAAAA2VLsdCdoQF110Ud7//ventrY299xzT04++eS8+uqrOf3009/0NZdddlnrUSYAAICNVVN5q0Mvf2bChAm54oor1rvNk08+maFDh7Y+v/nmm3PmmWfmlVdeecv9n3/++fnOd76TBQsWvOk2zc3NaW5ubn3e1NSUfv36ZenSpamrq3vrDwEAALwrNTU1pb6+/i3bYKOOBJ111lk57rjj1rvNwIEDN2aXbRxwwAG5+OKL09zcnC5duqxzmy5durzpOgAAgLeyURHUq1ev9OrVq71myaOPPpqePXuKHAAAoN2022+Cnn322bz00kt59tlns2bNmjz66KNJksGDB2fbbbfNnXfemcWLF+cv//Iv07Vr19x777259NJLc/bZZ7fXSAAAAO0XQeeff35uueWW1uf77rtvkuT+++/PwQcfnG222Sbf+ta3Mn78+FQqlQwePDhXXnlljj/++PYaCQAAYOMujPBOtKE/fgIAAN7dNrQNqn6fIAAAgC1JBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBG0lvva1r2XAgAH5wQ9+0Lrssssuy5NPPlnFqQAAYOsjgrYSM2bMyF133ZWrr746SbJ06dJMnDgxffv2rfJkAACwdRFBW4mxY8fmgAMOyF577ZU1a9bkpJNOymmnnZYePXpUezQAANiqiKCtxEEHHZQddtghvXv3znve854MHjw4F154YbXHAgCArU6nag/Ahnn22Wfzla98Jf3798+Xv/zlbLvtttUeCQAAtkoiaCtxwgknVHsEAAB4V3A6HAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBFUBatWrar2CAAAUKytPoJWrlyZJHnve9+bQYMGpbGxMaNHj84tt9zSZruZM2fmoIMOSv/+/TNo0KCcdtppWbZsWev62267LQMHDsyoUaOyYMGC1uWVSiXjxo1LY2NjJkyY0Lq8paUlkydPztChQ9O/f//ss88++d73vtfmPadNm5Y999wzAwcOTGNjYxobGzNw4MCcddZZrdtceOGFa9309JVXXsnxxx+f4cOHp1+/fvn4xz+e559/PkmyfPnydO/ePcuXL0+SLF26NCeeeGIGDRqU/v3755BDDsmvfvWrTfiLAgDAu9tWHUGVSiWf+MQnkiRTp07N3LlzM3/+/Fx//fWZMmVKxo8fnySZO3duPvrRj+aiiy7KvHnz8thjj2XFihU59dRTW/d1zjnn5Je//GWOP/74fOMb32hdfu+992b16tV55pln8uCDD+app55Kknz961/PtGnT8vOf/zzz5s3L97///Xz1q1/NT3/60yTJ6tWr84UvfCEXXXRRnnzyycyfPz/z58/P008/nW9+85vr/Vwnn3xy/v3f/z277bZbnnrqqfTp0ycnnXRSXnvttfzd3/1dJk+enO7duydJPvOZz6Rz58556qmnMm/evJxxxhk58sgj8+KLL26+PzQAALyLbNURtHDhwhx66KFJkl133bV1+d57750f/vCHufbaa7N8+fLcddddOfbYYzN27NgkSW1tbSZPnpw77rij9TUdOnTIypUr09zcnJdeeqnN+zQ3N2f16tVZtWpVOnfunCS5/fbbc/XVV2fHHXdMkuyxxx457bTTcueddyZ5/ZS38ePH57vf/W722GOPHHHEEZk6deoGnQp3//33Z86cORkyZEh++tOf5oQTTsj8+fPzgx/8IHfddVc+8IEPJEmWLFmSJ598Mt/4xjfSqdPr97098sgjM2rUqPzXf/3X2/qbAgDAu91WHUE777xzTj/99CTJPvvsk1NOOaV1XUNDQxoaGvLiiy/m9NNPz6RJk9q8tkePHnnllVdan1911VUZO3ZsrrjiijbbjR49Og8++GDq6uoyYsSIDBgwIEny0EMPZeTIkWvt8+WXX06SdOvWLeedd15uu+22PP3007n44oszY8aMHHbYYalUKuv9XL17927939tss03uueeefPjDH87RRx+dT37yk7n22muTJL169cozzzzTGkDrmgMAAGhrq46gJPnd736XJPnVr36V559/Pvfdd1/rug4dOrxlcLzhwx/+cGbPnp2LL764zfITTzwxJ598cmbMmJG5c+e+7TlHjBiRW2+9NXPmzMlzzz233m0vvvjiHHjggXn66aczZsyYXHPNNa2n7u2555754x//+LbnAACA0nV6603e2RYvXtzmeUtLy2bb989+9rPMnj07t956axYsWJB58+Zt8j47deqU1atXr3ebI488MkceeWSS5Oabb86BBx6Yvn37tq7f0LADAADWttUfCXrD/vvvnx49euSwww7bLPtraWnJ+PHjc80116Rjx46ty7a0+fPnZ/fdd9/i7wsAAO9W75oI+s1vfpObbropNTU1m7yvmpqafPvb387w4cNz4IEHbvL+mpubc9RRR2XIkCFZtGjRWutvvPHGDB48OJdffvla6xz1AQCAzWurj6Da2to3Xbds2bJ07dp1o/dZqVQyadKkTJw4cVNGazV16tT07ds3s2bNSkNDw1rrDzjggDzxxBO57bbbMmfOnDbrOnXq1OYI1Jo1a1qvUAcAAGy8rT6Chg0bliT5l3/5l6xZsybJ6zdQnThxYnbYYYf06dNno/d5++2355RTTllnsLwdq1evTm1tbTp37pwuXbqkZ8+ereu23377dOvWLZ06dUqXLl3WOuVu9913zwMPPJDm5uasWbMm9957b/baa6/NMhcAAJRoq4+gbt26JUl+9KMfZfDgwRkwYEB23333PPLII7n77rvf1j779evX5kaqm+roo4/OrFmzMnjw4Hzuc59LfX1967px48Zl4cKF6d+/fw466KAMGTKkzWs//vGPZ9iwYRkyZEgGDx6cTp065YQTTthsswEAQGlqKlv5j06amppSX1+fpUuXpq6urtrjAAAAVbKhbbDVHwkCAADYGFv9fYJKtqalkpnPvJQXlr2W3tt1zagB26djh02/Oh4AALybiaCt1PTfPp+v3flEnl/6WuuyvvVdc8ERw3L4Xn3X80oAACib0+G2QtN/+3xOmjqrTQAlyaKlr+WkqbMy/bfPV2kyAAB45xNBW5k1LZV87c4nsq6rWbyx7Gt3PpE1LVv19S4AAKDdiKCtzMxnXlrrCNCfqyR5fulrmfnMS1tuKAAA2IqIoK3MC8vePIDeznYAAFAaEbSV6b1d1826HQAAlEYEbWVGDdg+feu75s0uhF2T168SN2rA9ltyLAAA2GqIoK1Mxw41ueCIYUmyVgi98fyCI4a5XxAAALwJEbQVOnyvvply9H7pU9/2lLc+9V0z5ej93CcIAADWw81St1KH79U3hw3rk5nPvJQXlr2W3tu9fgqcI0AAALB+Imgr1rFDTUYP2qHaYwAAwFbF6XAAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAUpd0iaN68efnCF76QAQMGpFu3bhk0aFAuuOCCrFy5ss12//M//5ODDjooXbt2Tb9+/TJx4sT2GgkAACCd2mvHs2fPTktLS66//voMHjw4v/3tb3P88cdn+fLl+frXv54kaWpqygc+8IEceuihue666/LYY4/l85//fHr06JETTjihvUYDAAAKVlOpVCpb6s0mTZqUKVOm5Omnn06STJkyJV/5yleyaNGidO7cOUkyYcKE3H777Zk9e/YG7bOpqSn19fVZunRp6urq2m12AADgnW1D22CL/iZo6dKl2X777VufP/zww3nve9/bGkBJ8sEPfjBPPfVUXn755XXuo7m5OU1NTW0eAAAAG2qLRdCcOXNyzTXX5MQTT2xdtmjRojQ0NLTZ7o3nixYtWud+LrvsstTX17c++vXr135DAwAA7zobHUETJkxITU3Neh//91S25557Locffng++clP5vjjj9+kgc8999wsXbq09bFgwYJN2h8AAFCWjb4wwllnnZXjjjtuvdsMHDiw9X8vXLgwY8eOzZgxY3LDDTe02a5Pnz5ZvHhxm2VvPO/Tp886992lS5d06dJlY8cGAABI8jYiqFevXunVq9cGbfvcc89l7NixGTlyZL7zne+kQ4e2B55Gjx6dr3zlK1m1alW22WabJMm9996b3XffPT179tzY0QAAAN5Su/0m6LnnnsvBBx+cXXfdNV//+tezZMmSLFq0qM1vff72b/82nTt3zhe+8IU8/vjj+dd//ddcffXV+dKXvtReYwEAAIVrt/sE3XvvvZkzZ07mzJmTXXbZpc26N67KXV9fn3vuuSennHJKRo4cmR133DHnn3++ewQBAADtZoveJ6g9uE8QAACQvEPvEwQAAFBtIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoiggCAACKIoIAAICiiCAAAKAoIggAACiKCAIAAIoiggAAgKKIIAAAoCgiCAAAKIoIAgAAiiKCAACAooggAACgKCIIAAAoigjaSNdff31mzpxZ7TEAAIC3SQRtpB/84Adpbm6u9hgAAMDbJII2wlNPPZWnn346o0ePrvYoAADA29Sp2gO8k1166aWZOnVq/vSnPyVJunfvnn/6p39Kp07+bAAAsLVyJOhNzJ49O3fffXd+/etf5/e//31++9vfZocddsi5556bJUuWVHs8AADgbXJI402sXr06L7/8cj7zmc/kj3/8Y/bee+8MGzYsvXv3zoMPPpiPf/zj1R4RAAB4GxwJehN77bVX3vOe9+Scc87Jvffem8bGxvziF7/IrFmz8oEPfKDa4wEAAG9TTaVSqVR7iE3R1NSU+vr6LF26NHV1ddUeBwAAqJINbYMijwStWrWq2iMAAABVsskRtHLlykyePDkjR47MoEGD0tjYmNGjR+eWW25ps93MmTNz0EEHpX///hk0aFBOO+20LFu2rHX9bbfdloEDB2bUqFFZsGBB6/JKpZJx48alsbExEyZMaF3e0tKSyZMnZ//990+SHHjggfne977X5j2nTZuWPffcMwMHDkxjY2MaGxszcODAnHXWWa3bXHjhhbnwwgvX+lz/+I//mMbGxuy666750Y9+1Lr8uOOOy80335wk+c1vfpM999wzu+66a/75n/95nX+fmpqat/gLAgAAW9ImRVClUsmHP/zhzJkzJ//xH/+RuXPnZv78+bn++uszZcqUjB8/Pkkyd+7cfPSjH81FF12UefPm5bHHHsuKFSty6qmntu7rnHPOyS9/+cscf/zx+cY3vtG6/N57783q1avzzDPP5MEHH8xTTz2VJPn617+eadOmZcaMGUmSm266KV/96lfz05/+NMnrFzb4whe+kIsuuihPPvlk5s+fn/nz5+fpp5/ON7/5zfV+rnnz5uWMM87IpEmTcv/99+eGG25Y53Y33XRTTj311Dz66KP5yle+8vb/kAAAwBazSRG0cOHCHH744ZkyZUoaGxtbl++999754Q9/mGuvvTbLly/PXXfdlWOPPTZjx45NktTW1mby5Mm54447/v8gHTpk5cqVaW5uzksvvdTmfZqbm7N69eqsWrUqnTt3TpLcfvvtufrqq7PDDjskSXbfffecdtppufPOO5O8fsrb+PHj893vfjd77LFHjjjiiEydOnWDToVbsmRJxowZk6uuuiof+tCHcvLJJ69zuy9+8Yu5+eabM2bMmPzhD3/YiL8cAABQLZt0ieydd945Z599di666KLccsstOfzww/Otb30rSdLQ0JCGhoa8+OKLOf3009d6bY8ePfLKK6+0Pr/qqqsyduzYLF++PIccckjr8tGjR+ezn/1s6urqcswxx2TAgAFJkoceeijJ6z9++vN9Pvroo0mSbt265bzzzmtd9+ijj2by5Mn59re/nfvvv3+9p6nts88+eeGFF3LjjTfmPe95z5tut8cee+QXv/hFEqe9AQDA1mKTfxM0e/bsTJ8+PbNnz87zzz+f++677//vvEOHbOjF5z784Q9n9uzZufjii9ssP/HEE3PyySdnxowZmTt37tuec8SIEbn11lszZ86cPPfcc+vdtnPnzvnOd76T97///enbt2/69++fNWvWvO33BgAA3jk2+WapixYtavO8paVlU3fZ6mc/+1lmz56dW2+9NQsWLMi8efM2eZ+dOnXK6tWr33K7N06l+8QnPvG23+tPf/pT6+l7AADAO8NmuUT2448/nqFDh6ZHjx457LDDNscu09LSkvHjx+eaa65Jx44dW5dtCYsWLcq8efM2KYCS5Mc//nH222+/zTQVAACwOWyWCNp3330zd+7c3HTTTZvltzE1NTX59re/neHDh+fAAw/c5P01NzfnqKOOypAhQ9Y6cpUkN954YwYPHpzLL788SfLaa6+lW7dum/Se06ZNyxe+8IVst912+dOf/rRJ+wIAADafTY6g2traNvf7+XPLli1L165dN3qflUolkyZNysSJEzd1vCTJ1KlT07dv38yaNSsNDQ1rrT/ggAPyxBNP5LbbbsucOXPSs2fPvPzyy+v8PVNTU1O23XbbN537tttuy/77759TTz01s2fPzoABAzJ16tTN8jkAAIBNt8kRNHz48CxcuDA333xz68UDVq5cmYkTJ2aHHXZInz59Nnqft99+e0455ZR1BsvbsXr16tTW1qZz587p0qVLevbs2bpu++23T7du3dKpU6d06dIlLS0tqa+vzz777JNzzz03K1asaN327rvvzn/9139l9OjR63yfNy4Xfv3116e2tjY9e/bMDjvssEG/QQIAALaMTY6gbt26Zfr06Zk2bVoGDx6cAQMGZPfdd88jjzySu++++23ts1+/fm1upLqpjj766MyaNSuDBw/O5z73udTX17euGzduXBYuXJj+/fvnoIMOypAhQ5Ik//7v/56XX345e++9dwYMGJABAwbkqquuym233Zadd955ne/TvXv3TJ8+PSNHjswJJ5yQwYMH55FHHsm4ceM222cBAAA2TU1lQ69h/Q7V1NSU+vr6LF26NHV1ddUeBwAAqJINbYPNcmEEAACArcUm3yeI161pWZNZL8zKkhVL0qu2V/brvV86duhY7bEAAID/QwRtBvfNvy+Xz7w8i1csbl3WUNuQCaMm5NDGQ6s4GQAA8H85HW4T3Tf/vnzpgS+1CaAkeWHFC/nSA1/KffPvq9JkAADAuoigTbCmZU0un3l5Kln72hJvLLti5hVZ07JmS48GAAC8CRG0CWa9MGutI0B/rpJKFq1YlFkvzNqCUwEAAOsjgjbBkhVLNut2AABA+xNBm6BXba/Nuh0AAND+RNAm2K/3fmmobUhNata5viY16VPbJ/v13m8LTwYAALwZEbQJOnbomAmjJiTJWiH0xvNzRp3jfkEAAPAOIoI20aGNh+bKg69M79rebZY31DbkyoOvdJ8gAAB4h3Gz1M3g0MZDM7bf2Mx6YVaWrFiSXrW9sl/v/RwBAgCAdyARtJl07NAxf9HnL6o9BgAA8BacDgcAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBAAAFAUEQQAABRFBAEAAEURQQAAQFE6VXuATVWpVJIkTU1NVZ4EAACopjea4I1GeDNbfQQtW7YsSdKvX78qTwIAALwTLFu2LPX19W+6vqbyVpn0DtfS0pKFCxdmu+22S01NTbXHYT2amprSr1+/LFiwIHV1ddUeh3bkuy6H77osvu9y+K7L8m76viuVSpYtW5addtopHTq8+S9/tvojQR06dMguu+xS7THYCHV1dVv9PzA2jO+6HL7rsvi+y+G7Lsu75fte3xGgN7gwAgAAUBQRBAAAFEUEscV06dIlF1xwQbp06VLtUWhnvuty+K7L4vsuh++6LCV+31v9hREAAAA2hiNBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBRRBDt7rnnnsvRRx+dHXbYId26dcvw4cPzq1/9qtpj0Q7WrFmT8847LwMGDEi3bt0yaNCgXHzxxXERyq3fgw8+mCOOOCI77bRTampqcvvtt7dZX6lUcv7556dv377p1q1bDj300Pz+97+vzrBssvV936tWrco555yT4cOHp3v37tlpp50ybty4LFy4sHoD87a91b/tP/fFL34xNTU1ueqqq7bYfGw+G/JdP/nkkznyyCNTX1+f7t275y/+4i/y7LPPbvlhtwARRLt6+eWXc+CBB2abbbbJj3/84zzxxBOZPHlyevbsWe3RaAdXXHFFpkyZkn/8x3/Mk08+mSuuuCITJ07MNddcU+3R2ETLly/PPvvsk29961vrXD9x4sR885vfzHXXXZdf/OIX6d69ez74wQ/mtdde28KTsjms7/tesWJFZs2alfPOOy+zZs3Kf/zHf+Spp57KkUceWYVJ2VRv9W/7DdOmTct///d/Z6eddtpCk7G5vdV3PXfu3LznPe/J0KFD88ADD+R//ud/ct5556Vr165beNItw32CaFcTJkzIf/3Xf+U///M/qz0KW8BHP/rRNDQ05MYbb2xd9vGPfzzdunXL1KlTqzgZm1NNTU2mTZuWv/7rv07y+lGgnXbaKWeddVbOPvvsJMnSpUvT0NCQm2++OZ/+9KerOC2b6v9+3+vyy1/+MqNGjcr8+fOz6667brnh2Kze7Lt+7rnncsABB2TGjBn5yEc+kjPPPDNnnnlmVWZk81jXd/3pT38622yzTW699dbqDbYFORJEu7rjjjuy//7755Of/GR69+6dfffdN//0T/9U7bFoJ2PGjMlPfvKT/O53v0uS/OY3v8nPf/7zfOhDH6ryZLSnZ555JosWLcqhhx7auqy+vj4HHHBAHn744SpOxpaydOnS1NTUpEePHtUehc2spaUlxxxzTL785S9nzz33rPY4tJOWlpbcddddGTJkSD74wQ+md+/eOeCAA9Z7euTWTgTRrp5++ulMmTIlu+22W2bMmJGTTjopp59+em655ZZqj0Y7mDBhQj796U9n6NCh2WabbbLvvvvmzDPPzGc/+9lqj0Y7WrRoUZKkoaGhzfKGhobWdbx7vfbaaznnnHPymc98JnV1ddUeh83siiuuSKdOnXL66adXexTa0QsvvJBXX301l19+eQ4//PDcc889+Zu/+Zt87GMfy89+9rNqj9cuOlV7AN7dWlpasv/+++fSSy9Nkuy777757W9/m+uuuy7HHntsladjc/u3f/u3/Mu//Eu++93vZs8998yjjz6aM888MzvttJPvG96FVq1alaOOOiqVSiVTpkyp9jhsZo888kiuvvrqzJo1KzU1NdUeh3bU0tKSJPmrv/qrjB8/PkkyYsSIPPTQQ7nuuuvyvve9r5rjtQtHgmhXffv2zbBhw9os22OPPd61Vxop3Ze//OXWo0HDhw/PMccck/Hjx+eyyy6r9mi0oz59+iRJFi9e3Gb54sWLW9fx7vNGAM2fPz/33nuvo0DvQv/5n/+ZF154Ibvuums6deqUTp06Zf78+TnrrLPSv3//ao/HZrTjjjumU6dORf03mwiiXR144IF56qmn2iz73e9+l8bGxipNRHtasWJFOnRo+39WOnbs2Pr/YeLdacCAAenTp09+8pOftC5ramrKL37xi4wePbqKk9Fe3gig3//+97nvvvuyww47VHsk2sExxxyT//mf/8mjjz7a+thpp53y5S9/OTNmzKj2eGxGnTt3zl/8xV8U9d9sToejXY0fPz5jxozJpZdemqOOOiozZ87MDTfckBtuuKHao9EOjjjiiFxyySXZdddds+eee+bXv/51rrzyynz+85+v9mhsoldffTVz5sxpff7MM8/k0Ucfzfbbb59dd901Z555Zv7hH/4hu+22WwYMGJDzzjsvO+2003qvKMY71/q+7759++YTn/hEZs2alR/96EdZs2ZN62+/tt9++3Tu3LlaY/M2vNW/7f8buNtss0369OmT3XfffUuPyiZ6q+/6y1/+cj71qU/lve99b8aOHZvp06fnzjvvzAMPPFC9odtTBdrZnXfeWdlrr70qXbp0qQwdOrRyww03VHsk2klTU1PljDPOqOy6666Vrl27VgYOHFj5yle+Umlubq72aGyi+++/v5Jkrcexxx5bqVQqlZaWlsp5551XaWhoqHTp0qVyyCGHVJ566qnqDs3btr7v+5lnnlnnuiSV+++/v9qjs5He6t/2/9XY2Fj5xje+sUVnZPPYkO/6xhtvrAwePLjStWvXyj777FO5/fbbqzdwO3OfIAAAoCh+EwQAABRFBAEAAEURQQAAQFFEEAAAUBQRBAAAFEUEAQAARRFBAABAUUQQAABQFBEEAAAURQQBAABFEUEAAEBR/h8gdGF6dlwf/gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.doesnt_match(\"ลาก ดึง ดูด ดัน\".split())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "xRV9LP_C2yoq",
        "outputId": "2d12ca16-e535-45b0-c26a-ea5d8ef773f7"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ดัน'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_words = \"ลาก ดึง ดูด ดัน\".split()\n",
        "sample_idx = []\n",
        "for word in sample_words:\n",
        "    sample_idx.append(labels.index(word))\n",
        "sample_plot = thai2plot[sample_idx]\n",
        "plot_with_labels(sample_plot,sample_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "g8xGRpPV23ZC",
        "outputId": "55c96a49-c94f-4fd1-b49b-19eac2325ca3"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.doesnt_match(\"แมว หมา หมู หมอ\".split())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "SQaRNdvg29yf",
        "outputId": "a676d8d6-162c-4a86-ed98-57606e3b66c4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'หมอ'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_words = \"แมว หมา หมู หมอ\".split()\n",
        "sample_idx = []\n",
        "for word in sample_words:\n",
        "    sample_idx.append(labels.index(word))\n",
        "sample_plot = thai2plot[sample_idx]\n",
        "plot_with_labels(sample_plot,sample_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "zOc_BsP13Dg8",
        "outputId": "afab0d00-3b50-4383-9639-878dc87e81ec"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cosine Similarity"
      ],
      "metadata": {
        "id": "dm9vwdbd3JAJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('China + Beijing:', model.similarity('ปักกิ่ง', 'จีน'))\n",
        "print('Italy + Rome:', model.similarity('โรม','อิตาลี'))\n",
        "print('Beijing + Rome:', model.similarity('โรม', 'ปักกิ่ง'))\n",
        "print('Italy + Beijing:', model.similarity('ปักกิ่ง', 'อิตาลี'))\n",
        "print('China + Rome:', model.similarity('โรม','จีน'))\n",
        "print('China + Italy:', model.similarity('อิตาลี','จีน'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Duyhxc_23Hkt",
        "outputId": "8c04ceb9-352c-4410-b226-45a000158fe2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "China + Beijing: 0.3135956\n",
            "Italy + Rome: 0.42819628\n",
            "Beijing + Rome: 0.27347285\n",
            "Italy + Beijing: 0.17900795\n",
            "China + Rome: 0.02666693\n",
            "China + Italy: 0.24352394\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_words = \"ปักกิ่ง จีน โรม อิตาลี โตเกียว ญี่ปุ่น\".split()\n",
        "sample_idx = []\n",
        "for word in sample_words:\n",
        "    sample_idx.append(labels.index(word))\n",
        "sample_plot = thai2plot[sample_idx]\n",
        "plot_with_labels(sample_plot,sample_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "qZZe5dcB3Qvs",
        "outputId": "adf9bbd9-bd5d-45a8-b729-702cc9c4651c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spellchecking\n",
        "\n",
        "Originally contributed by Sakares ATV, adapted from Kaggle Spell Checker using Word2vec by CPMP."
      ],
      "metadata": {
        "id": "mJD9IW_U3drT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "words = model.index_to_key\n",
        "\n",
        "w_rank = {}\n",
        "for i,word in enumerate(words):\n",
        "    w_rank[word] = i\n",
        "\n",
        "WORDS = w_rank"
      ],
      "metadata": {
        "id": "Zp9_iY-b3gG_"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "thai_letters = 'กขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤฤๅลฦฦๅวศษสหฬอฮะัาำิีึืุูเแโใไ็่้๊๋์'\n",
        "\n",
        "def words(text): return re.findall(r'\\w+', text.lower())\n",
        "\n",
        "def P(word):\n",
        "    \"Probability of `word`.\"\n",
        "    # use inverse of rank as proxy\n",
        "    # returns 0 if the word isn't in the dictionary\n",
        "    return - WORDS.get(word, 0)\n",
        "\n",
        "def correction(word):\n",
        "    \"Most probable spelling correction for word.\"\n",
        "    return max(candidates(word), key=P)\n",
        "\n",
        "def candidates(word):\n",
        "    \"Generate possible spelling corrections for word.\"\n",
        "    return (known([word]) or known(edits1(word)) or known(edits2(word)) or [word])\n",
        "\n",
        "def known(words):\n",
        "    \"The subset of `words` that appear in the dictionary of WORDS.\"\n",
        "    return set(w for w in words if w in WORDS)\n",
        "\n",
        "def edits1(word):\n",
        "    \"All edits that are one edit away from `word`.\"\n",
        "    letters    = thai_letters\n",
        "    splits     = [(word[:i], word[i:])    for i in range(len(word) + 1)]\n",
        "    deletes    = [L + R[1:]               for L, R in splits if R]\n",
        "    transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1]\n",
        "    replaces   = [L + c + R[1:]           for L, R in splits if R for c in letters]\n",
        "    inserts    = [L + c + R               for L, R in splits for c in letters]\n",
        "    return set(deletes + transposes + replaces + inserts)\n",
        "\n",
        "def edits2(word):\n",
        "    \"All edits that are two edits away from `word`.\"\n",
        "    return (e2 for e1 in edits1(word) for e2 in edits1(e1))"
      ],
      "metadata": {
        "id": "hKbWrnRF3zlw"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correction('พัดนา')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "fyYT81em3647",
        "outputId": "35458d65-5386-43c6-8bb0-09632327c6bd"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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