Rasa_2X.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1OYE_7Q-MAtT",
        "outputId": "41677d0f-1cf1-4d3f-e854-c8a6c377d88e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "  Building wheel for mattermostwrapper (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Created wheel for twilio: filename=twilio-6.50.1-py2.py3-none-any.whl size=1208699 sha256=ef2c5e9928d8b0782d39ba2da4ce553d03a0449c7b1c3e1f4a1ceb37a59b3805\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/db/cc/c5c9ef20439073ba2a5403f5ce292446d3a01547007927df87\n",
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            "Successfully built mattermostwrapper docopt pyTelegramBotAPI clang termcolor twilio wrapt\n",
            "Installing collected packages: urllib3, requests, cachetools, google-auth, websockets, uvloop, ujson, sanic-routing, numpy, multidict, jmespath, httptools, aiofiles, absl-py, wrapt, typing-extensions, termcolor, tensorflow-estimator, tensorboard, sanic, ruamel.yaml.clib, pytz, pamqp, keras, humanfriendly, clang, botocore, attrs, async-timeout, tzlocal, tensorflow, sanic-cors, s3transfer, ruamel.yaml, requests-toolbelt, python-engineio, python-crfsuite, pymongo, PyJWT, packaging, joblib, docopt, dnspython, deprecated, cryptography, coloredlogs, bidict, aiormq, aiohttp, webexteamssdk, twilio, terminaltables, tensorflow-text, tensorflow-probability, tensorflow-addons, tarsafe, slackclient, sklearn-crfsuite, sentry-sdk, scikit-learn, sanic-jwt, rocketchat-API, regex, redis, rasa-sdk, questionary, python-socketio, pyTelegramBotAPI, pykwalify, pydot, psycopg2-binary, networkx, mattermostwrapper, kafka-python, jsonschema, jsonpickle, fbmessenger, colorhash, colorclass, boto3, async-generator, apscheduler, aio-pika, rasa\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
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            "    Found existing installation: attrs 22.1.0\n",
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            "    Found existing installation: joblib 1.2.0\n",
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            "  Attempting uninstall: dnspython\n",
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            "      Successfully uninstalled dnspython-2.2.1\n",
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            "    Found existing installation: aiohttp 3.8.3\n",
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            "      Successfully uninstalled tensorflow-probability-0.17.0\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2022.6.2\n",
            "    Uninstalling regex-2022.6.2:\n",
            "      Successfully uninstalled regex-2022.6.2\n",
            "  Attempting uninstall: pydot\n",
            "    Found existing installation: pydot 1.3.0\n",
            "    Uninstalling pydot-1.3.0:\n",
            "      Successfully uninstalled pydot-1.3.0\n",
            "  Attempting uninstall: networkx\n",
            "    Found existing installation: networkx 2.8.8\n",
            "    Uninstalling networkx-2.8.8:\n",
            "      Successfully uninstalled networkx-2.8.8\n",
            "  Attempting uninstall: jsonschema\n",
            "    Found existing installation: jsonschema 4.3.3\n",
            "    Uninstalling jsonschema-4.3.3:\n",
            "      Successfully uninstalled jsonschema-4.3.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "yellowbrick 1.5 requires scikit-learn>=1.0.0, but you have scikit-learn 0.24.2 which is incompatible.\n",
            "pydantic 1.10.2 requires typing-extensions>=4.1.0, but you have typing-extensions 3.10.0.2 which is incompatible.\n",
            "nltk 3.7 requires regex>=2021.8.3, but you have regex 2021.7.6 which is incompatible.\n",
            "jaxlib 0.3.25+cuda11.cudnn805 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "jax 0.3.25 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "cmdstanpy 1.0.8 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\u001b[0m\n",
            "Successfully installed PyJWT-2.6.0 absl-py-0.13.0 aio-pika-6.8.2 aiofiles-22.1.0 aiohttp-3.7.4 aiormq-3.3.1 apscheduler-3.7.0 async-generator-1.10 async-timeout-3.0.1 attrs-21.2.0 bidict-0.22.0 boto3-1.26.27 botocore-1.29.27 cachetools-4.2.4 clang-5.0 colorclass-2.2.2 coloredlogs-15.0.1 colorhash-1.0.4 cryptography-38.0.4 deprecated-1.2.13 dnspython-1.16.0 docopt-0.6.2 fbmessenger-6.0.0 google-auth-1.35.0 httptools-0.5.0 humanfriendly-10.0 jmespath-1.0.1 joblib-1.0.1 jsonpickle-2.0.0 jsonschema-3.2.0 kafka-python-2.0.2 keras-2.6.0 mattermostwrapper-2.2 multidict-5.2.0 networkx-2.5.1 numpy-1.19.5 packaging-20.9 pamqp-2.3.0 psycopg2-binary-2.9.5 pyTelegramBotAPI-3.8.3 pydot-1.4.2 pykwalify-1.8.0 pymongo-3.10.1 python-crfsuite-0.9.8 python-engineio-4.3.4 python-socketio-5.7.2 pytz-2021.3 questionary-1.9.0 rasa-2.8.33 rasa-sdk-2.8.10 redis-4.1.4 regex-2021.7.6 requests-2.28.1 requests-toolbelt-0.10.1 rocketchat-API-1.16.0 ruamel.yaml-0.16.13 ruamel.yaml.clib-0.2.7 s3transfer-0.6.0 sanic-21.12.2 sanic-cors-2.0.1 sanic-jwt-1.8.0 sanic-routing-0.7.2 scikit-learn-0.24.2 sentry-sdk-1.2.0 sklearn-crfsuite-0.3.6 slackclient-2.9.4 tarsafe-0.0.3 tensorboard-2.6.0 tensorflow-2.6.5 tensorflow-addons-0.14.0 tensorflow-estimator-2.6.0 tensorflow-probability-0.13.0 tensorflow-text-2.6.0 termcolor-1.1.0 terminaltables-3.1.10 twilio-6.50.1 typing-extensions-3.10.0.2 tzlocal-2.1 ujson-5.6.0 urllib3-1.26.13 uvloop-0.16.0 webexteamssdk-1.6.1 websockets-10.4 wrapt-1.12.1\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google",
                  "numpy"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "!pip install rasa==2.8.33"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**If you didn't get an option to \"restart runtime\" on above cell. then just click on run time.**\n",
        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)\n",
        "\n",
        "**click on restart runtime.**\n",
        "\n",
        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a3j7j5U4DGhh5e72IfKjULb6xbBt3gLWK/cSUmdM9Xy2vh+DGnB9bRtb39TPy+6BtbUDrtNDie1fynqlsX5yaxwmziVs4lzCPoDrZ1Qk7Y4hZuV6UneH4xbcn7OJ2rHY87dHE6MGRi1lW0IkSr3nY/WwtsZh1AxCP+pP6cfubdMr34ADzkpgH6Sq1VB/RPhfk1j2tLZ9lCtjCNXdW+Dw1Fpitw9h/r4EQlcG4vnBLdJKmulkfj6Ma3x0+vw87UCijHSoc+Fj/fBclirWs2JfDNuOhRDmlMrWf6gAHyKDPZt/P0CTbCOnCGh0KE41+Se072Y4Nnfk/bZR09Nef/jI7OxsunfvXvt01169etGzZ0+uXbtWmyMvhBBCiNvTKXrkR9jY86zLwwY/U1x4mMu/WPJLaRXnLmkMvn78rRe5v/7B4GvcSAUOA26vC9V6aiTrn9ONgx4UXe+BQWrUB7XvZvoYvwH1pCq1yevrMdiVGS/EkrI7UpuLrXdzo1qtW84M3zpBfB25mTR9bY0ozSfp/QX4LtzW+IOMjGZUXCd1dYj2wuPBSGIWueq1jwMha2LxAdTrQli2u3XHR9+2KfUW4+irSN+mTesJHTW87kc9PAl50wdQEZWYivrgNlaogSdDmGnswVitYH1SauNPcf01ndTNAK64D2+f31xupaSkBKA2MC8pKeHgwYPk5ORw7NgxDh482KCMPOFVCCGEaB2dIpAHWDxqBn26W9aZZlFly90/jwcTU6rLm59KYNkdHh/XvRVqZ41PTYrN/8IJfc/wsz/LqgxOhl+TiHmzfmhdysnNKwgcOYQFxoLY+z21o5SQg7p+EaPBcylJayJbJ5D/NZWYhTEkvR9J1GEj7X8lla0fa9/WD4ivH1xG4LvalJrI98NwrX+V4xhC5D+UgJqYp5eR9Kuh5ZdSWtqCNJJ9y4hu5OIgPy6S8GOAIhQfZcPeYYfpIYQA6rejCVkXAyhYGtS6vxrUp357GTHHjH16nYw1kawHeDAQz/qjJnUQ/SC95qmvpaWl9OzZk549e1JaWlr79Fb9YF8IIYQQt6/TBPI2Fr1ZPGpGnWn9fpuEiakpJiamUF2FptpYpGxY0LjuWPc0aZ0K6lJsFEDGy6Gs+LYmuByO63PadyvWxHCy/jCS6iSWTfXV9krXYc31or0kZOUT8/RMlu1reNNn6fZ4beDGTFx1PcHDR4Vq37wVRUxuvQC3Sk1ShA++61rpBlLHQML+pgBUrJg9k+hv6wXG6iSW+QUSrQZGRRKoP5rPlQwiF0drU2rejCbsfsO/VbguiiHqQUAdQ8jquj3S1/+3Avc+fenb14HAZt8UqybGz8dAu5Zy8pP5eM3bBoDPm2GGc97v9mHuq9pfYZL2AaPCmTmxjYfMJIPwhwNZn1WvnavUJL3uy4y3VICCsDdDGx+Rpx2dO3cOgJycHM6cOUO3bt1wdnbG19cXX19fnJ2d6datG2fOnCEnJweQQF4IIYRoLablJ3Z3dB1qvTDiEZysBwLQ5/pwrH4dgcbUBExN0JiYcKOq6YH8oLtNefTB1h0Z42aKTQbLXogm4wqANT6L45mrADYvwHmYM4GLo4l+N5rwYHeG9PdlxZm5xG9c2mB5roviiQ9SgDqVFT79GaKcT/i70US/G8585RD6zohBjYK5CWH4WNfUIUw7D9tYMNwB57nhRL8bTfTi+bgP74/v2/nMTYin4dpaogeer28jcqIC1EmEP9CX/iN1Q2sqh2DS35cVX6pB4UPUJ2Eoa+Pc62SsDmXFMeDBSKIXGU85AldC349ECajfDWTZwZuhvOrgMjIAUJOwL73x9J56QlavJcQxgxU+/ek/0pcFr+vaaFhfnIPXkw8oX9jL2qeM5Zr3wDMgHKXuf40NOdk6ZrA2IRJPEpg/sm/tsbDseV+c7frj+1YqahT4/COJSCNj2Le3yspKrl27ee/KPffcw6RJk3Bxcamd5uLiwqRJk+rcCFuTVy+EEEKI23Qx4amOfihVHVtO/1dz1/rHNG5vfK0ZF35GM+6VQs34V4s1Dy37RfPQ679qJr51UeO54sotXynZlc1bcaNPdtVTslcTqmj45NCyk/GasImK2qeK1rwUE8M0W/P1ll//qZyVZzUpq2doHOrNB2hwnKGJSjXwDM/LOZr4RZ56T0nVvRSemrBtP2puPi10hia+sHnNYJDROio0w+dEaVLqVbHs66UaV9CAq2bp10155muZJnOl7kmrijBNymXd1KNRGk+F9km6kfWemnurJ7vOSDir0ZSka6JmOzRsV4WnJiwhp+ETc+sr2aoJqVcnw24+nTUyo5FiBo+BuvvK2HFk9FjoQEVFRZrExETNnj17NEVFRU0qv2fPHk1iYmKTygshhBCicSa/RPTX9Fvxc3tcMzSZ52fr6H/WDxNTU0zNzLT/mpphYmqCqZkZpuaND7Zj3dOE1XM6ptfy+hkV6fkXAOjr4I7r4CbW43op+bmZ6Galr4MbwwdbNz5CypV8VEfzuaCdAfcRDvRoy3GIqq6Tn5Wuq2NfHB5wxcHYDbetuM7Sqh5YN6kZ1SQE9CdwM8xIOMvWOdobQq//ehLVibOUAZb9nXF1VDSpnVTvueP2cgauqzPJ/Fv7JrPUPY6acCx0gGPHjtGtWzfuvffeJo8LX1lZyQ8//EBlZSWjRnWSRH8hhBCiizJRL+qp+UNYBubGxmkXosswHMi3yPVUljl4sULtQ+yp+kOOCtAG5S19sNPtzCuEEEIILVOA6vMFHV0PITqV/E3R7TLkZFd2O4G4BPFCCCHE7TMFqPz5eEfXQ4hORMW2NUmAK5FPt+2Qk0IIIYQQLdVphp8UbUe9aSYmJibNeq34X0fXuuOUbo/RjjH/cChzH+xsmelCCCGEEFpteWuk6Cx6O+A5ybNZs1h30SPDeqAnnpO4jZtw1aQezMdzkieuL8zE2OCUQgghhBAdzUS9qKeml3cEvb1bZ+RxIYQQQgghRNszBeh2j4xYI4QQQgghRFdiCmBmY9/R9RBCCCGEEEI0g7lJDysZQ14I0SQajaajq/C7ZmJi0tFVEEII0YmY9xg5taPrIIToxAwF7xLQt6+aAF6/3SWoF0IIYW4x4vYD+YKCArKzs6moqGiFKgkhOgsJ4jsPQ4G7BPNCCNG2unfvjouLC/b2nTMN3URzG9/KBQUFLF26lL1797ZmnYQQQgghhOg0fH19WbFiRacL6FscyBcUFPDII49QVFTU2nUSQnRCR44c6egqCGDMmDEdXQUhhPhdGjhwIHv27OlUwXyLA/nHH3+8tid+3LhxPProo/Tu3eKn8AghOgn9U4JGo0Gj0XDjxg1GjRrFsGHDOrBm4tSpUxw7dgxTU9PapzDXkDQbIYRofVeuXOHzzz/n8OHDgLZn/rPPPuvgWt3UokC+oKCAUaNGAdogfvfu3a1eMSFEx6gJ3mte1dXVVFdXU1xcLIF8Bzt16hQDBgzAzMwMMzOz2mC+flAvhBCidU2dOrU2mD927Fin6ZU3bclM2dnZte8fffTRVquMEKLzqOmJv3HjBtXV1R1dHaFTXV1du1/kxmMhhGgf+vGufhzc0VoUyOuPTiPpNELcOeoHhvrBvOgcjAXxEtQLIUTb0Y93O9MojS0K5IUQdzb91Brpke9canrk9feREEKI3ycJ5IUQdRi62VWCxc7D0D6R/SOEEL9PEsgLIQyq3ysvOgfpjRdCCFFDAnkhhBBCCCG6IAnkhRC3JD2/nYfsCyGEEDUkkBdCNEoCx85H9okQQgjo5IF8+aXyelMyWTlkCEOCEynpkBq1MXUiwUOGMGRVZp3JDduhiyi/RLkMdiK6gC77N9bl3OHn8EbdiduexcbFi1kclcK5Rqe1wpo+W8zixVGktOZCRSfVNsfQnaodA/kSEoOHMGRI46/gLdpT3KXkJYwaNZzJH+W1XxVbpGnbVT84b6qObofC+FkMGTKEh96/xcMPCuKYNWQIQybEkF0NXEpmyehRDJ8SS2ffg01Xs6+DSVR3dF06uW9XGvlbGMVk/+dYuSWTkk5ykdfRf2NtKXNVE85NNa87KsBshppjtdFz9O/tb78K1aeLWbx4MRu+reroygghGmHe/qu0w+MJP1ysDH/ab7AFABZWVlhgwUDbu9qxbrej8e3C2bZFSzXcDiUkBo9lSdp8tvwYgVuLltw0dj7z8H4tk+TNaWQ/74KLmeFyeWmJZAJuz/hpy1hYYWUBFoP70VX24J2uZEswYxenMT/xRyLub6eVuvoROs7u5v/P55GclEzs4mTiPg0lPjEMt57tUxVj29/1zjVNZ+saSugL+lMukb09jrQiA+erfo5YtHP9RCd1+QjpOdq3uenpXL5/An06tkZCCCM6IJAfypRnwghQNF7KQhnBsR8j2qdKraJp29VcHd4OfT142AeSkxJJywnFZaShQnlkbMsG3PCvCdoslER89yNdaQ+KNvDAPMIW1b3UDPt7CWl/n0VwfAwrtwewJdDOyMzto8P/xtqQnXcYYd76U0pIPBFHWlHbnK/EneHysaMUYIlNXzhfdIQj5ybg1a+jayWEMKRT58iLzsAK71lBQCGJaUbSa3KSiT0B+MzD37496ya6JDNbPJ4JRQlkpmT8PtM5hOi0ivnqcDFYjmTanAfowzmOHi3u6EoJIYzogB75Jvp2JUMCYvFY/Q0bZjUhLUWdQey70XycnEnJNbAa6k3AwgUs8HHBykg6SEcqyYhlRdRadqkuQV9HvGcvIGyGgYJ12gFdSk3Nh7HMGhILtG2KjYVyCkE944j7904yn3fBrV57Zn+VSCHg7e3BzV/qM1k5ZBaxHqv4ZkMAdfbgpWx2fbCWtZuTybsAFgo3vP8axtIQJbZmAHnE+U/mjdOhbMkKq7Ndef+ZyuTllwjb+RWh+r8OnI5j6qQ3yHt6CyeXtGWyUUM3UzZOEnA2mug1iSSf1u3Xvy7nzedrtktPveO1YRvouWV7gX57H1h0ieiX15J8upzQrSeZd1pbvxqxAUOIhfZNsanPph92QIbepMxVQ5j1kaF61aSSebAqfYOuF/nm9n71jh3Jq2ra0gLb+wMIi4wg4F5tokjN/qnRYPsNnWvUiQS7L+H033azZVQGK977mORvSyjHCkfvp1j+ZihKm0tkb4/mjX8kkqkuv/l3vNAPx/rpQk3ah51Ic47PlpSvr/oS2UlrWVvzt4MVjt4BLHhhAX7OxvIVO4+Sw7GsWKN/jDSx7rrjjNVf8c6A5JvHWU9b3KaHsfxvAcbTNdvKzyqyLoDNQ0qcBoNr30McOpJBnu8sHNtifVXnUe3eRMrxYs5drQJTc2wGjcF7zjRc+xoof+M8qu2bSP6ugPMVQHcbnDzmMOcv9ljWKVhG8TfJ7PhaRfG5MqoA814DcPKcxuzxdctmfbaYjcf74f1yMDYZTVl2FeczdxC3O5Piq1VgbskAZy+m+fYg5e0t5P5pHqsfH1Gv/B42JR+h4EIVYI7lQCe8fKczYWi9pKUbl8n773b2puZql91YWUOaM39ZMUf27+BQTdvXlPWbzYTBui2+eIh/rdhD8cBHeG1hvRSrG7lsWb6BI4wh+I1ZOOm6hqt+U7Hn82SO/HSeqhs17e7L9D870qde93HZmUNs3rafLLX+vpcewebovIF8M5R/G82sv8aQbeGG32Oh2PUqp/BwIrEvJpOWvYXdS9w6Ve5n4ZZgJi9Oo7ynLR6BobjYXCJ78ytM/oxb1NMK5QvxxAeX8OW7LxF7wpuIjfNwwbZtTrA1LJT4P25B3Edx7FdF4FYnyMombVMhEMSjk5vwjVO0i+f8XyK53BaP6aF428ClnEQSVwWSfHQVBz4MwM7MEbcpdvBuGtmnw3AbWjNzIRm7tL8KJB7OJnSkS+1iS45/STYWzP9L+wbx+tJenUrsefD2CSL0oUIytu8i+b1ATldtYfcivWMwJ5apASvJxqXO8bqrThvoyjapvfQqkRfL83PyyLNwxGWkBT0AqwcXEL/xKUrSVvLShmy8l8Uz716wHUqHKeY8asAAACAASURBVM9J1wbxfe66vb/NvFhCJhZy0dkb/xAl5TmJJKbEsWRGIT2+2ICf4va2vzBmFhOwQjk9gPnKS+Ql7SQ5OZrAvItEeKWx8rOLeEyfT6jNJbI3J5L80UuknbXgm39537yobe4+7GC151O09XXRr++uhvc1NLd8Q5dIfnUCz225hNW4IEJ9rOB8NonbY3kpOY2sxN1E3N+ZzuD6ysl8bxaB72dj4epH0At2WFwtJGN7LC890vS6p/19MhOwwsXbn/nKcu1xFr+Eqf/LY8vOiHa7jwQg9/BRztMP7wcGAPDQuAEc2n2CY7mzcHRq5ZVV5LHjvY9I/w3MFU643mcJ14o5kZvOpncKOP/Si3jVSf+6zKF/r6Ks0gb7e12xryrmxA/nyD0QwwbL1wj9c02YWUbWZ6vYeLwM87vscXKzoRuVnD+dS9buGE4XB/PaY071AiDjy/7ILJwXJ97MLTr3RQxRB4rB1Jx+Tq4M6FlGcdYeYrIMbWQZWf8XxcbMy5jfZc8INxu6VZwnLzeLPf8+zZl5S5g3wrK2Dukfv82O01WY99Nrjx8MlTWkGfOXZbFx9UayrprTZ7ATrvd1g5p6fXCa4r++xhxnc7jLlQcG76H4jArVxQlM0L+V6IcsTpSB5dhRtUF8WdYmoj5Tcdm0D/bOrth0r2n3jzhdMI8lj4+ovSgqy9rIqo1ZlGFOn8EjcLSB86ePsOmd9Ea2UdTX9QP58kyiX44he0wEB9bOv9kTtiiMRyMnE/jRImJ96/Xetok0lrgPYYmhj57ewo81vcQXkon+exrlPT1YtX8DAQN1ZRaGkfHuLAIbHTnDAjtXJXaUULgBwA63cco2vdm1hpvvAuw+iibui0wi7tdb44k0EovA4okpKG/5fVVC4usvkdwriA37l+NR200fxvz/C+ahiCWsTJrCuqlWuDzgjQWxfKkqIWhoTS9pBl+qwKKnBYWb0sh+zgVtKF9OdmYaEIR7m+9n4/LwYMvXel+6L0/jjTHBxL2/n8yFbijNtHVN27SS7GsuROzfzfx7dWVrjtcNbxCb4c/ycRY0p71qFRUycOVXHHhMP+/cDeVAKFFrF2D3JyXKjuqJry6nUBXHymdiKcSRsGf0At6WKCpk4OoDHJhVs71hBP1nFpOXp/F5WiF+j9lhMfB2tt+bd778J341bb9wAbtensBLu2NZWT6f+PQIlDUbsDCIuIDJvLH7c/a/6q375aAF+7Aj1ZxPqXd+0tV3ckQMge+6c+wNpfYCrLnlDTm9k7VbLsGsDXyz2qO2XNgziQRPWELs24kEbQ2ize+k+GgWQz5q3izl30az6P1slMsOsC5Y72bhlx9l5ZRAYl+Oxf/LUKODBNSyCWDdVr3jo/b7IJZF6/356gWXRmdvNTdyycoug4FejNHFrX3GKnHavYUj/1Mxzcm1VYOGvN1xpP8GA/7yIqGTB9Que9qpLfzr4yMkJx5iTJ1e4DKwn8WSeWOw0QWOc37aw6q1hyg4eoTzf/bCBuCnQ+wvMKfPn+YQ/rjrzd70GwXseSeGQ5kpHPF1wr1O93IZZXd5E77Qi371ll18RMW5id70A7iYzuYvisF0AN76Fxo3LqNKiGLT8bI621iVtZnNmZfp9+dQQv31evbLVGxctYmszXvJddb1Zv/0FSmnq2DoLF57Zkxt2TnqZKL+kULW7hSKRzzCAGMN2oz5C9L2U2DehxGPhTPPTe/iQLfNqrQjPOLsTh/6MOZBJ3acyUV1/DITxt9stNzjJyjDhglKXVdiRRabN6u4fLc7oaHTsL+5sag2rmLT8c3szR3BLCfgRh57t2ZRhiUj6lygVFF8IIZ/fSHpXE3VATny2oC3saEnm+NSykZiiyBoXlC9n7MtUM6ZjwuF7M9sj2Hl7PB4IpTQFwy8XG8mllzK2Meua+D26nK9Lz3AzArlX+fj0Q41bRFnDwIGQvlnO8nQG3I7Oy2RQiwI8GrCrx45icSkgUtIkF5Ao2U3fR5BQPKRTMoBXKcQ1BPSvs7kkq5MyeH9pBHEO28HQNF+Mk/X1oLvtgNT3XHrwI67+W/W6znrqWTidIA8CmsP7XLKLzWYFbBA+cwHxG/cgP9g3aTmtFcN5XKWP9axN4/W8dGsun/nw4bzUMBKklESFh9PqPNtLl+5nOWz6m6v4zh/XICMM62Qff/4vJtBPGjz+721d4+6hATcDOIBzBxx+4sdkEZekW5aS/ZhB6o5nzY4PwF2jy0nwhXK//M5aZdaVt6g8ksY/HigP8sT44lfpGyfka9c/Qyfv18IJfSFIDwG1p/hEmlxsRQSxLzAeiP+9FQSEOKiPU+dacK6ffzrHh9mVigXRRAEFG5O4xaD/7aaqmPpHCkDp3HuN4Pn7m64OgM5KjIrWnFlN7JIP1oGlmPw/suAOhcIlsOm4ekEFOVxss46bXCfcjOIB2DQYO4B+LmYn2uneRO+9DVe0w/iAUztGWwHUEZZ3XgbsMHrsZtBvHY5IxlxF/DbL7XjmV/OUlFwA/r8eVrdXwtM++A6eQJ17wm+zJFDWZThxISH66XnWLry0P19oOzkzfNF+XUMDvapmMCcv85hzsMjG+/4aMb89g+H89rS1+oG8XCzPa+VUdNE5iNccTKFYpWKy7UF87QXfX1H4HqPbmuPfkVWGTh5PKIXxANY4urxAH0o4+SpAu2k08c4cRVwnsbcOr8ymDNgshf6iUmicZ1q+MmaoSebI+/4LgDigocTZ6zQ2ca+RVpL00aBqKmv232dKNhqCjMXvENciF6eyP6jESjHWUB1NmmbC2FgGAG37o6nJOc7CgGWT2bIciOFis9xCbA1c8F9OsTGp5O52hsPi0tkpKXB1HV4TLYgiDgSD+cRNNQRTmeTdg08xrvdXu/u7epWf4IFVr3qT7PCI2g+jrtjWTljLOnTn2KKhxKl0gU7WxeUel/mzWqvm6ukx+1uR2uqM/xkOXlJsSSfdiRiU/zNXyNuh6Ht7WVBywZ7bRqrftqvatteDcPLfrZDQbvXgBbuww6kPT/ZMdHV0PnJDqWfC6h28d0P/8T7/uaXN+g+P0K91rJkSzBjT/sRMMOTKQ8qcRlsi939yrbvia9hYISlm2pG+9Gflsd3uwHiCB5u9NuHwgstrI+FCy4eQNp3nC4BlzY/QKrIPJYLpiMYM0o/NDDH9cEx7Mg5QsY3lxkzvpUGovz1F87dAOwH4digS9GcMcGrGdNgJnPMm9r9eOMyxUdT6uTIN87Qsi2xrPfVVpCvDUTtHZqSx11A3hmAXLYsW8wWI6XO14QoQycw4Z4jJJ/ewlsrjuI0aiRjRo7AcaANA5xdjffE08L5Lxdz5Av9HHkjursyZsQmco/rpdecOsaJMug3fkztcmvaJjcxgsWJRpZ1UXspcK7gDGVAv4ED7oDUkI7VaYefbJ62GcO97Xjg2KB3p/Nz9AjAbfkbJKZkEjFOiYVqJ2uLwG6Rx61/OtbjNj0UpbHtrx3L2gI3pR/EJ/JdznI8hmaQmgR+a5VYWVgwMRDidmVQ+IQjPVRfan/aH9fZ9rNhFvdHsOWgO4kfrOXj7StJi9dOt3INIuKtMALq3SDXtPbqpOoFR+XjYXJALNFxaQS95dG5696KutY+HIqtjeFP7upl6G+sueXrMbMj4MNDOG7/mI//nUjsa7uIBehpi8cz/+QdQzeLdyYDPQia7mK0E2FEi09LttjpYsXr7fHwtIvpZOQCZLFxyWKDRYq/OcK58V606kiU3Xu2QSByjkNr/8meoir6DHLFa6YTNro/soLDm0j/6XaXb8Mf725GccsBOA3vh7Hsdntr3RvTfngtWMIf0/aQ8t9csr4qIOurPdpcfLfZBM9wxaaxxmrO/OcO8a9/7qH4Rh/sR3kxy8lG1xdVQHpCOgX1Fj3i/jFYHj9Sm16Td/wEZQzA64H6R4MlA5yd6GesR8m+7h3M/RQyruntumMuhEbPDrv9n+nbg4UFkEHhWaCrjeFsP4UAjzdYsnk/mUuUWHwRRzkuzPdpXv7mQK+nCPO5dd+5lZs7Huwi7XgeT53dxy68+acul0Eb5H9JhjqAfv8vDUYux60LtafVUA/mR3swf3U5l0oKyT6cSPTfY1nySAYlew7UOZab2l5dgcX9TxHqEcuS+JXEzvHoGn+zraBr7cPTlJzH4PnpXMnphhObXd4AM1vcZkWwblYEXLtE4ek0kj9Yy8r3ApmQt67uzcOdzmgCFoXS+lnsJRTqoqke7XAhU3w4neJGgrAydS65Px/lyM9ePHJPK674RmUrLkwnaz97iqqwvD+Y1wLq3qHbLev2Anlzc4Dz/PIrzfgOH8CEx5o46o+5DSP+Mo8Rf4Gqssv8UqTi6L4U0r/dxKpfy3jteffGH87VxPmzDuyhuMqSMcGvaXPWa3XjRIKBm02dRjHS8ghHVCouj7+HY8fLYKArrgby3gb8eQ6zht1iM820lw0XSs4DRnoCRJN0+XHk7Rw9gEISU9ori/D2ON7nDZSTkVN4y7Kdjy1TpnvDtTj2H05j/2fl4BqARxNHP7EdrO153JWUZjgntj7FRKZ4QPZXGezM2AUeE1HqLuatxj2MH2nsT9tJ+nawm+LWtiP3tJZLmcS9F010ku6+DTMLrBSOKGdFEL82CMhjbbL2UfHNbq8uwRb/5+djRx5r/51cd7ssOle/dGvoavvQ8U9+QCFfqgydnwrJ/KIQ8GP0vS0rb0hhcjTR78WRWZOC0tMKu5F+zF/3ActHQvnuz0nrlA8bsMPRAyhKJO1EGyy+PJvsNGDgaIa2+Y+NxaiOnwfLkfj+dQ5zHmv4Cp7xAH04z9GMVrrn7O4/avPRT58m90bDj3O3vsVbKzaQ3oLUpHNqbUb7UOeGw+xUGlhXcwzQ/Uzyc0FTbsbsx4B+QNkJjuXeunRxxiY2/d8esi5q/29u2YcBwyYwbWGwNp3lzFGO/NYa85/jFzXAUJzqN5HRCytHHri/DxSpUB3L5WQZOLnXvajQ9q6XcUJ16421GTgAS6C4oKAJaU+iMV0+kLf1ehS/nlD43hvEnKh3y9ilDKJfjCWzE32DWikfxq8nZL79Bon6+ZbVl8j4NJY0o3MaUs6ldr5LzuqhafgBcS8+R9w18Jgzpek5rK7+LBgKJEXz+u56X/zVhSRGLGFXnd/zbHF7yAXS3uCNePDwmXgzj9hKycNTIe2DGJKvWeD9QDuN6nC7ellwaXsMMYtX1t3/ANXaneloo+t7bHZ7NU35lY69tVLbKw/lu1cSpxcAaS9yITmj7kV5+Q872d+8P4xGtev2t9E+bCtWXo8SZOj8BBRueYOVKrB44lE8rFpW3pC7KvKIef8Nlvy73k2/1ej+b4dVp+yOt2XKLD8sKCT67zFkX6v76aXD0bz0USaXmpIWk7KfjDrfU+VkvreSOMBxjkcb9PbXk5vB0Qtg+adRxjtEBo3E9S4oO37MYODdbKYjcH/AEsqOkJx6rs5HVT8nk3z0Mpf7ODLS0Fjyt2BpqU1iOZ2pQv+e1svHNrLD4BCRTddnhCv2pnD+6y2kqPU+uHEZ1YFD1N2SfoxR2gNlHNm+hdx6N9heztxEzPZcLuva06rqHKrMQ2zenVWn3txAF+z2oU8j3fFNn98Sy54Ap8k8plfyxmVUCTsw1kT2o12xoZiU7Ue5bOqE65/qJnX0e8Ade1Mo+3YHWxpsrIpNa3eQW3O37NBRjOwF5OwgIUu/bBXFB1KM1kE01PVTa/p6886n88kJiCXafyxfTg9AOdBCNw5xGiUW3gy7NB+3Nv8iOM3+f0dTaDRPP4Awbzvo603Y3z1IXpzGkilj2T89QDeOfCJpl7TjyN86zLDF8V4LSIvjpacgyNUO98fn17lRss1YefDoExbs+k855XgzzasZKzVzIfSTVXw3ZQm7XpxMxq4AbT74Ve2Y65nlLoTOKAf7mz2zjg9MwY5sCnFj4gP667JC6e0Hu3dR2HM+U1xbbxPblJkL8/8xn50BsXr7H8qLMkjcngl9A1jg51hbtrnt1Rht73AacWHB8Lgbdg/MY36H3FdgS8CiMGLSool+L5EA3QPDrDymEdRzF3HvzWLsce22XvohmZ3JhZT3BK7darm3WGtHbH8r78M2Z6Ek4tNQMv8aU+f4vJSTSGJKCTiHEv83vaEkm1veACufCFbtSmPJR7MYe9SPgHF2WKA7J6otcFkSgEcnaZ76rHzeIf7pHGZ9FM3U8V/iN12JXa+b22/hM4yLNOEm/LNxBLtn4P2Yh3Z+VRxxhy/B0PmseqLtOym0wwhaMsalsd817Rn5pz4c+voI6cem4eR2++GDo88cxuRt4MiBKCKOOzHynppxz89RZToA74AJjaeRGNHnAS9cv9iAKmsTby0/hON9fbj8fZ72AUmmwI1zFJ+DFiX73+XO7L8cJepAMcn/jEB170jdOPK5nDPQtdznz8HMy1/FxuNH2PDWSeydHLHpDmU/nyBXXYX5wH6U4UQfoM+f5+D9XRTJxzfy1gp7nIba0A3dsiugj3ICY7o3st1Nnr8PYzxcSf5URVbCW7xxyBGn3pfJzSumrOYi7VwxDZronjE80O8QyefKwNmVkfXrcpc7wY/nsWpjFkc2vMXJwU442nSrs0/7XQWnPoCpI74zR3BiYxZZG9/ircFOunHkcym4LH30zdH1A3m0Nw8eSJ+oe7JgLJnXwELhgsfj/2TB843cBNuqCkn7T4zxHvWnJ2oDecBu1gYODdA92TU+hrS+jnjPfocDj1uwdsJz7GrC2twWbiHidDDRKXHEZPsx4vHW2o5bscDNKwCL/8RRPnUaHs3tLRkYwIZ0F91TLuOISQH6OqL0iCD+b/NR1s85vM+DgIHRRPf1R1lvkAArpSfe7CJ5unuzbrbtaBb3R7D7S3fi1qzl4+0xpNUcr08bOF6b216NuT+MLUvyCF6TRtz72fitndfKW9YMI4MI84nmpaQ3WHtYN25+Tw+WfxFPv+Vv8HFyHDEpFtje782C+HhcvhpLYDPH+G6go7a/NfdhO7C4P4zdX7hrz6e1x6cb3kv+afBJrc0t30D9m13f1z3ZdZw3EauXdtDFZlNZ4LbkAN+M0z3ZdUMm5Vhg6+xB0L+a8WTxxzdwaFwmr6/aSGyO7smugau0T3Zth4dBpWdqh4EccYuRpOydhmP59RFyj2VS5jbG6M2bTWbpxKywJQzdvYmU43moMnVPSR3xCL7+E3Bs6QA53Z2Y8/LT9Nu8hZQfisnNBPO+9rj7z8H9QhxR+4q16TcjWnajZb+/vMiSu7cQtzOT4lwV58wtGeD8CE8ry9jx75R6vfKWjHj8NZbUPNk1S0UBYN5nACOm1nvaqWk/vBYuYUDqHpL/m0tWZgFgjmU/e9w95/CI2y1yyZsxv7nzHMKf6cfmxBRyi3JRmZpjM8idaXPcOf+fKJJ/PscvDZqoH2PGDiB5dzEj7jf8TAHLEfN47ZWaJ7tmoToDdO9jcJ9ajpjHkud1T3Y9k4XqJ+2TXec878Tpf27gSFN3yO+ciUaj0TR3ph07dvDkk08C8MknnzBt2rRWr5gQov1pNJraV3V1NVVVVVRUVFBRUcHFixcZNuwWdzCJNnXq1CnuuusuunfvTvfu3TE3N8fMzAwTE5Pal+hC1IkEuy8hTf+hgaLrytrI4o1ZWI59muUzu8RdW6IZOmvs2+Vz5IUQQggh2su5Lz5i07HLdSfeOEfKF1mAJSMbTVESonXdEak1QgghhGiZxYsNjxsv6lq9ejVUFXMi5wyqorc4karL69fLQ7f802x8dSPBSLu2jtWrV3d0FTo1CeSFEEIIIZrCfABeC17FSffU2BOZ2qfGmve1x33qNLzHDrj9eweEaAYJ5IUQQoiOoAhgw48BHV0L6fFsLtM+DBg7jdCxjedIS7uK9iA58kIIIYQQQnRBEsgLIYQQQgjRBUkgL4QQQgghRBckgbwQQgghhBBdUItudu3e/eZzea9cudJqlRFCdD76DxlqwfPjRCuqeViX/nvZJ0II0fYuX7757IBu3bp12Lm3/oP/WhTIu7i41L7//PPPefzxx2+vVkKITks/cKw/TbQvQ0G87AshhGh7n3/+ee17Z2fndj/31gTw+us1MTFpWSBvb2+Pr68ve/fu5fDhw0ydOpVHH32U3r17t05thRAdQj9QvHHjBlVVVVRXV1NVVcWDDz4ogWMH02g07Nu3D3Nzc8zMzDAzM8PU1LT2BF+/p0YIIcTtuXLlComJifz3v/8FwMfHh0GDBrX7d6FGo6lzjjcxMdFO07SwJgUFBTzyyCMUFRW1WiWFEJ2L/unhf//7H05OTh1YG5GTk4O7u3vt/yVwF0KI9jNgwAB27NjBoEGD2nW99TtrTExMbr5aGsiDNphfunQpe/fubZ2aCiE6Ff3Tw9GjRxk2bFgH1kacOnWqo6sghBCigzk4OLROIF+joKCA7OxsKioqWqN+QogOop86c+PGjdq0mqqqKtzd3SWQF0IIITrQqVOnGDRoEKamppiamrYsR74+e3t77O3tW2NRQogOduPGjdogvrq6moqKCioqKrh48WJHV00IIYT43auurgZu42ZXIcSdy8TEBFNT09r3JiYmmJmZSSAvhBBCdALV1dW138/mknMphKhRf3jDml75qqoq+vTp08G1E0IIIcSpU6dqRy8zl5xXIUQN/fz4mvSayspKKisr+e233zq4dkIIIYQYPHgw3bp1o1u3bph2dGWEEJ1L/Qc/ydjxQgghROeh/90sgbwQwiD9E8WNGzc6ujpCCCGEQPuruQTyQgghhBBCdGESyAshbklSa4QQQojOQf87WQJ5IUSjJIgXQgghOpea72YJ5IUQQgghhOiCJJAXQgghhBCiC5JAXgghhBBCiC5IAnkhhBBCCCG6IAnkhRBCCCGE6IIkkBdCCCGEEKILkkBeCCGEEEKILkgCeSGEEEIIIbogCeSFEEIIIYTogiSQF0IIIYQQoguSQF4IIYQQQogu6HceyKtY4+WF1ws7UXd0VYQQQgghhGgG846uQA11xgbWxO1D9f15KoDudylwGfc4z4Y8jONdHV273wf19hcIXJvTxNL+RKUsxLVNaySEEEIIIYxpViCfm5vL999/z3333YeTk1Pr1KBazc5Xn2TNdxVgaYOrpz+D7qpAfexrVHujeOZgEgtj38d/QOus7k60c+dOAPz9/W9rOb0dJuA/fVidaervdvLNTzBI6Y+rQv8TF/5wW2trjJqdLwSyJkcuFsTv2+bNm3nllVfYtGkTY8eOBWD16tXs3r2bTz75BEdHxw6uoRBCdE27du1ix44dhIaGMnr06I6uTos1K5D//vvv2blzJ/7+/q0UyFegWvcCa76rwGZSBO//zQuFWc1n4Vw5soYnX93JmrAPGRb/LM5mjS3r96vVAvlRs1k4qu401b+0gbzr9IUs7LrHufgd+uabb5gzZ06D6ba2tnh4eBAUFISLiwsmJiYdUDshhBCNuXHjBt988w179uzhl19+4caNG/Tu3ZsxY8bg5+eHlZVVbdkLFy6wcuVKbGxseOmll7C0tGzz+tVcCDSm5iKhZlt27drFuXPnMDMz47777iMgIAA7O7vbqkfHptb8tJM128+D7WyW1wnitXqPWcjyWSm8sGUzOw4H4/xQ946ppxCiy/Ly8sLFxaX2/7/99ht79uxh165dvP3220ybNq0DayeEEKK+69ev88knn3D06FGsrKy4//77MTc356effuLLL7/k2LFjLFiwgMGDB7dZHcrKyrh+/Tq9evWie3fj8aeTkxM2NjYGP/vDH/6ARqNh79697NixAxsbG8aOHcv169fJycnh3XffZeHChbf162qHBvLq7w7xE+D86DSjve3Oo8fDln18fSIbHtJLsqi+Qt7BD/lwfQqq8xVAd2xGeRH89LM8fF/vBsu58v0+PvzgQ1KyrlDRvTeDRvvz7IsuDcrVupjHvvUfsiFVxfky6H7XIFz9nmVh0NgGFxy/e01oqyvfreHJxTs57/ws8f+cfbMNq9VsfimQD3Ns8F/9DvafPsWa2jT9nYR77UTy8cXtmDx5MrNnz64zbd68eTz77LN88sknKJVK/vjHP3ZQ7YQQQujTaDQkJydz9OhRlEolTzzxRG0grdFoyMjI4NNPP2Xbtm0sWLCg0SD7dhw8eLBJqTdeXl6Nfv7TTz9x8OBBhgwZwqJFi+jZsycAeXl5rFmzhu3bt/Piiy+2eDs6dNSasz/lAN0Z5qAwXmhMOCkpKexboB/Eq9n56myeeXcf2b1deXi6P/6+rvT+fh9Rz88mfFfdMWiufLeGJ5+PYl9WBQrlw/j7jqd/8WYi5v2dfYbWWbyT8MBniDp4CoXSH//p/oy3O48qLoInX96Juro1tv4O0cS26j36WSKm20DOBlbsvbl/1HtXsCEHbPwiCB49kLEhK1kZGc7DAwBcCY5cycpIH+w7ZOPEncrR0ZEpU6Zw8uRJCgsLAW3u+fjx48nLy6tTNi8vj/Hjx7N69WpA20vz0ksvMWPGDE6fPk1kZCSurq7ce++9PPHEE5w6deqW66+uriY1NZXAwEDuvfdehg8fTkhICMePH0ej0bT+BgshRBdRUlLCoUOHUCgUBAQE1AlwTUxMGDNmDM7Ozpw6dYrvv/+et99+m7CwMH777TdOnTpFaGgob7/9NmVlZXz33XcEBwezZcsWNm3axNNPP82qVasoLy9vt+0pKCjgypUrTJgwoTaIBxg6dChubm6cOXOGoqKiFi+/A3vk1fyUB+CIfSNxvME5d63Q5tX7RfHJi67U9r+HfMOaZyLY+a9X2TzqE2YPAqpz+OzdnZzHBv/Vn7BwdE3pcH7a8gLPrKs/SouanavWoOruRcS/I/CyrZm+EPWWFwhct4aog+OJmmL4Z5Tfl+a0VXdcQyLwPxzOznUb+NozgvF8zYZ1OVTY+hMRot2PvUeNRYEadNCr4gAAIABJREFUdQLsKx6Es3Ks9MSLVmdiYoKdnR1VVVVUV7fsyjw/P5/g4GAcHR154oknKCkpYdu2bYSHh/PRRx8Z7eWvrq7m/fff54MPPsDX15f333+f0tJSPv/8c5544gk+/PDD2htbhRDi96a4uJjS0lImTJjAXXc1HLbQ3NwcFxcXioqKKCsrw8/PD7Vazc6dO+nVqxc+Pj7Y2NjUuQBIS0vj+vXrWFlZ0bt3w6yNtnThwgWAOkE8aL+HBg4cyKFDh2rLtITBQD43N7e298mQnTt31t5gqW/x4sWtN5qNUTnsiMuB7l4sfFoviAe4ayzPLvBi3xspfLY/h9lPO8P3h9hXAt0nLeTZ0XV33qBZwTy8Lpw6W5Kzgw05oPjr43qBqZbCNwCvdX8n5btsKqaMp70z9o21e43g4OAG0/z9/W/7JlijmttWlq48u8Sfr8N2ErVuLBWsIaXCBv+/BePavn9X4neuoqIClUrFgAED+MMfWjb+0tWrV3n11VeZNWsWpqamaDQa7r33XpYvX05OTo7RQL64uJjjx4/z3HPPsXDhQszMtHlmEyZMIDg4mJ07d+Lm5ka3bt1avH1CCNFVFRYWotFoGDDA+HCFkyZNYtKkSbX/v+eee9i/f39tPn39m1179+7NsmXLUCia2XPcCmoC+GvXrjX4rE+fPgCtH8h3aupTZF8ExozF1cBNyd1Hj2c8KaScOIUaZ/g+myuA833DmhR4q3Xlr3z6JF6fGit0nvNA+x8OnUtL2qr7qGeJmP414dtXshKw8YsieLRE8aL9lJaWEhcXx/bt23nqqadwcHBo0XKGDx+Ol5cXpqbaDEUTE5Pajoxff/3V6HyDBg1iw4YNDaZbW1tz3333ce3aNaqqqiSQF0L8rvXt27fVljV27NhbBvHfffcdMTExDabXn1Y/Z97QPPrlhgwZgqWlJYcOHWL06NG1gX1paSlffPFFczelAYOBvJOTk8Evmpoe4dbp5VUwyBHIyaNATfOjYoUNBsM/y94Gpw8b1LwVNBw3XY/tIMPrbmPG2r2mJ97QPmsPzWur7rg+Gozr9ihUOPP4o64d0pbi9+OVV17hlVdeqTPN3NycefPmERoaWtsj3lxmZma1QXxzVVRU8PXXX7Nx40aOHDlSJ1/Tz8+vRcsUQghhWFPO8927d68z+sz169e5du0avXv3rpOmU/+mVGOj1tT82mtvb8/o0aM5fPgwb7zxBsOGDePGjRtkZ2fXfofczkVLh/bI9x/kDORwKl8No4xEgkei8Hp1H92nR9W94VV9nivQMAi8eB79W1276/bdqeLzMLrpee3/n707j4uqXh84/pmFgYGRTXHjQkmionndRc01d00yUstr4pbpTVNKo/Rm/iyvmGXXPdLSdktcoTJvP1Ojn4RLUu5iUCjlhoAsA8Msvz+8cy4jIKsi+rxfL14vOXPme55zcM4853u+5/k26jWRGf0lxSyPih2rHOLWruAIACeIWr6bHpF9kScOxK1StPyk0WgkJiaG4OBgZs2adVtqDd/IYrGwZs0a3n33XcaPH8/UqVN54IEHsFgsvP7667c9HiGEuBOVNBTlVmrdujVvvfWW8ru9Tvy4ceOqVLVGo9EQFhZG/fr1+fe//82PP/6Iu7s7Q4cOJScnh127dlWp8k6NJvINO/TEnxOc+Go3qSFj8C/hgunE4TgAerT+T6nIhoG08oATiQkcMfalxw3fw6ajCRwBDK0Dr3fyN22FgROcPXEcU0jZ49ob+gei4wQJ38Zxtf9gSTBvojLHKmffChZ9Z8L7sbeYq1vE7C/e4q1v27GovxxpcWsULT9ps9nw8vLi7bffZsCAAQwZMuS2x3P06FHef/99Xn75ZcLCwpQJqYxG4y0royaEELWFn58fKpWKP/74o9R1vv32W3bt2sWTTz5Jx44db2N0laPVannkkUd45JFHlGU2m421a9diMBioX79+pduu0fKT+D/KjMe8IWU9s1YlkHND8YicAyuYvzkHfEYyvLv9C64lQ57wB9NuVqw9Qo7DG44QtWo3Jvx5alDL68ua92SwD5i+XUHUYYe1Sd28vnj5ybb9eNQHOBzFW984lrHEkkr0a//DztQq7fXdo6LHKieOFYt3Y/IYzOzx7Wg3di6P+phIePstdl8taQM55BhvXfji3qNSqXjsscdo06YNH330EZcvX77tMaSkpJCbm0uLFi0cZpW1Wq0YjfIfXghxb/P19cXT05Off/6ZrKysYq9bLBZOnjxJbm6uw+yutc358+c5efIkTZs2pV69epVup4YfdtXR7u8rmZE6gRUxcxm5z5923dvRUGfiQmIcR1JyMOnbMWPpVIcJo/xHRDLj8ARWxMxmZGIwfTs0RGe6wJHvEkg16mg3M/J66UkATUueevFRdkfsYEfESI507Uu7hnDh8G4SLlC8h17TkqlLZ3D86RUkvDmBR3f2oG+gAeztWxry6NAcBvvLsJuKHasc4pYvYrdJR9+XpxJsAGjHxBcGs3vOTt56czftlCE2DfFvqoMTu1k0Cwa3vI/gJ8YQ7HOzYIQonwYNGjBhwgTCw8P5+uuvlV7xwMBA0tLSlBMrXO8xSUxM5OLFi9W2/caNG6PVavnxxx/p1KkTarUaq9XKV199xZ49e2rkLoEQQtwpfHx8CA4O5ptvvmHTpk0OE0IBHDp0iGPHjtGiRYtiM7tardbbHW6lnDt3jnXr1lFQUMDDDz9c6We1oIKJfPPmzXn00Udp3rx5pTdYjKYhj0ZG027fJqI+28GRr3ZgAnTeTWk3dhRTH+uL/41lRP/znlb/mdl15zb7zK6DmV3CzK6GDjPYsCbw+syu8TvZoTPg32Eki5Z04ciU54i+MSbfR1n5eav/zFa6mx3HAL03TduOYdH0iQTfYeVqbll5yfIo57GyD6nRdZ7L1F7//fsYOk9l9sO7+Z/v3mL+tlasfOz6G9pNWsqY5DlEH9vNjtRgAp+ogX0Td60ePXrw8MMP89577/HQQw/RtGlTOnToQGBgIAsXLuTIkSO4u7tz9OhR4uLiMJvN1bbt5s2b07VrV1avXs0vv/xCy5YtOXDgACdOnMDZ2ZnU1FTy8/NrZPy+EELUNJVKxSOPPMKFCxeIj4/nzJkzBAYGolarOX/+POfPn6devXqMHTtWSfDd3Nxo2LAhJ0+eZM2aNTRr1oyBAwdWKY7+/fvTo0cP3NzcqmO3OHLkCIcPH1b2QavVMnr06CqXba9QIt+iRYtbUydeY8D/4Ykserh4HfSbvafpoNm8NWh2uVY3NB/M7OWDuXHt4K27mVrSGzyaMviFtxj8QvlDqim3MpFvN3M3u2eWsVI5jpWh11x27p5b0iv0+MdOdv/jxsUtmbh8BxX4HyFEudWpU4ewsDCmTJnCxo0bmTt3Lv7+/ixdupQ333yTTz75BDc3NwYPHsyWLVuYP39+tW3b09OTZcuWsXr1arZs2cL+/fvp06cPmzZt4uOPPyYuLo709PRqLb0mhBC1iaurK9OnTychIYGYmBgSEhKw2Wx4eHjQt29fhg8f7tDZodPpePzxx1m7di3Hjx/HZrMxYMCAKsWg1+urtUPl3LlzxMfH4+npSe/evRk6dGi1nOdVNpkPXAjxHzabTfmxWCyYzWZMJhMmk4msrCwCAwNrOkQhhBDinpWUlISHhwc6nQ6dTlfDD7sKIYQQQgghKkUSeSGEEEIIIWohSeSFEEIIIYSohSSRF0IIIYQQohaSRF4IIYQQQohaSBJ5IYQQQgghaiFJ5IUQQgghhKiFJJEXQgghhBCiFpJEXgghhBBCiFpIEnkhhBBCCCFqIUnkhRBCCCGEqIUkkRdCCCGEEKIWkkReCCGEEEKIWkgSeSGEEEIIIWohSeSFEEIIIYSohSSRF0IIIYQQohaSRF4IIYQQQohaSBJ5IYQQQgghaiFtUlJSTccghLhD2Gw25cdqtWI2myksLKSwsJC6devWdHhCCCHEPS85ORknJyecnJzQOjs713Q84j/y8vJ4/fXXyc/P5/XXX8dgMBRb56uvviIyMpL/+Z//oV+/fsrywsJCli9fzvbt24u9Z+XKlbRr185h2fHjx5k9ezbZ2dkOy/v3789LL72Ei4sLALt27eKNN97AZDIVa7dhw4a89dZb3H///Rw5coTnnnuOOXPmMHToUH777Tdmz57NhAkTHH7v168fU6dOJSsrixdffJETJ04Ua1er1RIREcHgwYNRqVQVjjs/P5833niDb7/9tljbdvY4ASX2G4+TvR1AaTs3N5f58+fz448/FmuzTp069OnTh7CwMBo2bFjqtm9mz549LFiwALPZrCzTarV07NiR06dP89prrykx2o9h+/btmTp1qkM7X331FRs2bFD+PvZlkZGRpW67f//+RERE4OzsrLTdpk0bxo0bh1otN++EEEKIO4Gzs/N/E3mtVlvT8Yj/+PPPPzl+/DijRo3Cw8OjWBIL0KZNGxo1asThw4fp27cvTk5OwPVk7+9//zt169blm2++4Y8//lDeo9VqufHv/Ne//pXIyEg+/vhjEhMTlURdrVY7rD9kyBAeeOABPvnkEw4cOOCQQKtUKmVdrVaLSqVCo9Hc9Pei7avVaod9rFOnDp07d+app56iefPmJe5/eeIuqe0b2eOyH5+i+1L0uNkTWPtrHh4ejBw5kp9//hlvb2+cnJz4/fffAcjJySE2NpakpCSWLFmCj49PqdsvzcMPP0x2djYffvghly5donnz5kyZMoWGDRvywgsvOMRoj8++7zfu3437ZF9WmqLHUKPROLRttVorvC9CCCGEqH4OudXFixdtNR2QuPdkZmby/PPP06FDB6ZPn17T4ZTbb7/9xqxZs+jTpw9TpkxRLqTgeg/5kiVL+Pe//827775L+/btazDSyilpaI3JZKKwsBAnJycCAwNrOkQhhBDinpWUlKR8J+t0OrQ6na6mYxL3IJ1Oh1qtRqPRUJv+D544cYJz585hsVgwmUy4urqiUqkoLCzk/PnznDt3jsaNG1O/fv1atV92RRN5i8UiQ2qEEEKIO4w9idfpdDK0RtSMG4fe1BaBgYF4eHjw6aef8umnnxZ7XavVMmvWLB544AE0Gk0NRFg1RRN5+zAc+9CaouP2hRBCCFEzig6DlURe1Igbx8zXFu3bt+eTTz7hk08+Yf/+/Zw/fx6AevXq0bNnT/72t7/RsmXLm45Fv5OVlMgXHWYjhBBCiJrl8HyiyWSSMfJCCKD40Br7GHmTyUReXp6MkRdCCCFqUFJSEq6urv8dWiNjYIUQdkUTeZvNplSukfNE9Tl79iwTJkxg2LBhREREVGvbJpOJ6Oho1q9fT0pKCqNGjWLBggVImWEhhHAUExPD9u3bmTZtGh06dKjpcCqk6HezJPJCCMXdlMgnJCQwevToYst9fHzo3bs3Y8eOpVWrVrV2GFRJNm3axKuvvkrbtm2ZNm0aDz74oCTxQohayWq1kpCQwJdffsnFixexWq0YDAY6d+5MSEgI7u7uyroZGRksWrQIb29vwsPD0ev1tzw++4XAzdgvEmw2GydPnmTLli38/vvv2Gw2vL29GTp0KN27d6/wEGOHRL4qOyGEEHe6vn370qpVK+X39PR0vvzyS2JiYoiMjGT48OE1GF31MRqNHDp0iNatW/POO+/QoEGDmg5JCCEqJT8/nw0bNnDw4EHc3d3p2LEjWq2W1NRU9uzZQ2JiItOnT1cmPLwVjEYj+fn5uLm53bQKXYsWLfD29i7xNfuM6AcOHOD999/H2dmZnj17YjAYiI+P56OPPiI5OZlx48ZVukCG9m7qjRJCVB+VSuXwU1sNGDCAkSNHOiwLCwtj6tSpbNiwga5du94VSW9+fj6pqak0adLEoadKCCFqE5vNxq5duzh48CBdu3Zl3LhxSiJts9mIj4/ngw8+YOvWrUyfPv2WlXr+9ttvyzX0pm/fvjd9PTc3l507d2IwGIiIiFBmfh80aBD/+te/+Pnnn/nzzz/5y1/+Uu7Yin4317775UIIUUVNmzZl0KBBnDx5knPnzgGwZMkSevTowdmzZx3WPXv2LD169GDJkiXA9V6a8PBwQkND+fXXX1m4cCHt2rWjWbNmjBs3jqSkpHLFYLPZ+P777wkJCSEgIIDg4GCioqLIz88vtm5aWhpz585VtjNmzBj279+PzXa9VkF0dDQdOnQgMTGRmJgYWrVqVWxfUlNTlTYCAgIICQkhNjYWi8VSbF8jIyOJjY3loYceYuTIkWRmZgJgsViIjY11iPlf//oXGRkZFTj6QghRusuXL/P999/TsGFDRo0a5ZCoq1QqOnfuTMuWLUlKSuL06dNERkYya9Ys0tPTSUpKYtq0aURGRmI0Gjl8+DATJ05k8+bNbNy4kWeeeYbFixdTUFBw2/bHZDKRm5vL/fff7zDju6urK61btyY7O5uLFy9Wun0ZWiOEuOeoVCr8/Pwwm80OiWxFpKSkMHHiRJo2bcq4ceO4fPkyW7duZfbs2axdu7bMXv6PPvqIHTt2MGjQIHr16sWBAwdYsmQJV69e5aWXXlJusyYlJTF16lQ8PDyYMWMGALGxsYwfP57FixcTGhpKly5dWLNmDW+++SYNGzbk6aefRq/XKz0/P/30EzNnzsRqtTJ+/HgaNmzI119/zcyZM/n111957rnnHG7rbt++nU2bNtGwYUMaNWoEXE/iV65cybvvvsvIkSMZP348iYmJrF27lkOHDrFixQrlNrIQQlRWWloamZmZ9OzZEw8Pj2Kva7VaWrVqxfnz5zEajYSEhHDhwgV27NiBm5sbQ4YMwdvb2+ECYO/eveTn5+Pu7o7BYLidu4NarUalUpGdnY3JZFLG79tsNq5evYpOp8PNza3S7UsiL8RtkpCQwIwZM+jTpw+vvvoqrq6uNRbL9u3bmTdvHpMnT2batGm1cvKqqjCZTBw5cgRfX99KJ5+5ubnMmTOHESNGoFarsdlsNGvWjAULFnDixIkyE/mGDRsSFRWllPQ0mUxERkayceNGBg0aRPv27cnOzmbp0qW0atWKRYsWKV9ATzzxBHPmzOHDDz/koYcews/PD4PBgIeHBz4+PnTp0kX5ssjMzGT58uUYDAbWrFlDkyZNABgxYgQrV65kzZo1tGvXjl69eimxGQwGVq9eTYsWLZRle/bs4cMPP+Sdd96hV69eqFQqQkND6devH9OmTWPnzp089dRTlTqWQghhd+7cOWw2G76+vqWu079/f/r376/83rhxY7755htlPP2ND7saDAZeeeUVpXPjdnJ3d6dHjx7s2LFDuaOp0+k4dOgQBw8epFmzZgQEBFS6fUnkRa12K0v5Vbd9+/Zx+fJl/u///o8//viDpk2b1kgcRqORvXv3kpuby969exk7dixeXl41EktNyMzM5OOPP2bbtm1MnjxZSWwrKigoiL59+yoVfVQqlZL4Xrlypcz3Dxw40KEuv06n49FHH2Xz5s38+OOPtG/fnl9//ZX/+7//Y8WKFQ69SK6urnTv3p3vvvuOP//886YXDadPnyY+Pp5XX33VYV81Gg2hoaHExsaya9cuunXrprzWr18/mjdvrvxeWFjIt99+S+fOnenUqZPDMxOtW7cmMDCQEydOUFhYiJOTU5n7LoQQZanO76Xg4OAyk/jDhw+zevXqYstvXHbjmPmS3lN0PZVKxeDBgyksLOTrr7/mm2++Udbp0qULYWFhVRrnL4n8PSYjI4NJkyaRmJhY4utvvPFGsQcDRfXo1asXW7Zs4aGHHqJx48bV1u7FixdZvHgx9913H+Hh4WWur9fr6d27N7t376Z37953/YORL730Ei+99JLDMq1WS1hYWJXuRmg0mmovy+nn50dAQADnz5+nsLCQpKQkcnNzmTRpUqnvKWusZ2pqKoBD77qdj48PrVu3VrZjp9FoHJL1nJwcTp06RWJiIq1bty5xO/7+/pjNZknkhRB3nPKc53U6nUP1mfz8fPLy8jAYDA6J9o1Jd2lVa4re7T116hTff/89Xl5edOzYEVdXV3788Ud++ukn7r//fvr371/pohKSyAtxmwQHB5OQkFDt7WZnZ3Po0CFlLHN5DB8+/K4pu1iWouUnjUYjMTExBAcHM2vWrNtSa7gi1Go1Go2GvLw8zGYzcL1XKiIiotSKBmXNtpuSkkKDBg3w9PQsdR2LxaI8OHszQ4YM4cknnyzxC8fd3f2WVY8QQtx78vLybuv2WrduzVtvvaX8bq8TP27cuCpVrbly5QqffPIJPj4+PP/888qw2sGDB/PBBx+wbds2/Pz8CAoKqlTcksjfo6ZOnXrHD0URojoULT9ps9nw8vLi7bffZsCAAQwZMqSGo3NUUFCAyWRCq9Uqvf0ZGRncf//9BAcHV6rNJk2acPHiRaXyTFE2mw2r1VrutrRaLR06dLjjLoCEEHcPPz8/VCoVf/zxR6nrfPvtt+zatYsnn3ySjh073sboKu7333/n0qVL9OjRw+HZOK1WS/fu3Tl06BAHDhyodCIv5SfFTdlsNo4dO8azzz5LUFAQAQEBDBkyhM2bN2MymRzWjY6OLrF8X0ZGBqGhoUr5vqLLoqOjSU1NZebMmQQFBREUFMTzzz9fYikmk8lEbGwsI0aMUB4OGTduXJXKNtlZrVbi4uIYM2YMzZo1IygoiJkzZyrDEuySkpLo27cvw4YN488//3R4bf/+/bRr147Zs2crPQn2UoUBAQHKT3h4OEajsdRYLly4wJtvvklwcDABAQH06tWL1atXc+3aNWUd+/ELCAhgwIABpKWlERUV5bCdgIAAhzsARd9j/yn6NykqIyODUaNGsXXrVs6fP88rr7xS4WN6J1KpVDz22GO0adOGjz76iMuXL9d0SA4uXLjA77//TkBAAM7OztSrVw+AQ4cOlavHvCT+/v7A9Vu7N7py5QrHjx+nRYsWN63koNPp8PHx4ejRo3fcMRNC3F18fX3x9PTk559/Jisrq9jrFouFkydPkpubW6uGhpY0y7aTkxMajaZYPlURksiLUtlsNrZs2UJoaCjffPONMhb31KlTREREMHfu3Crf+lq/fj1jxowhNjaWgoICCgoK2LFjh1ID1i4vL49XX32VmTNn8tNPPynDDv7880/l35VlsVj44IMPmDRpEvHx8ZjNZgoKCoiNjeXRRx91SIYDAwOZNWsWp0+fZvXq1cqH79KlS6xcuZL69eszY8aMSlekSU1NZdy4cbzzzjtKwnTu3DmWLl3Krl27qrSflfHRRx8xefLkGtn2rdKgQQMmTJjATz/9xNdff60kyIGBgaSlpXHy5EllXZvNRmJiYrVcLN5o165dDjXnjUYjmzZtAq4/AAXw4IMP0rlzZz7//HOHuOD65/Cdd94pse58Uc2bN6dr16588cUXnD9/XllusVjYunUr586dY+DAgTcd2+7m5ka/fv1ISUnh888/d/jSMZlMrFu3jl9++aX8Oy+EEKXw8fEhODiYCxcusGnTpmJJ7qFDhzh27BiBgYHFZnatyB3G28XLywu9Xk9CQoJDzmS1Wjl48CD5+fm0bNmy0u3flqE19nFGRQ0fPpyQkJAqtZucnMzSpUsdEr66desyd+7ce6oKR2VERUURFRXlsMzX15cNGzYo1VR+/fVXli9fjp+fH4sXL6Z9+/aoVCp+/fVX5s6dS0xMDP3792fgwIGVjuP06dMEBgYSHR1Nu3btSEtL44UXXiAxMZELFy4oVTZ2797Npk2bCA0NZdasWTRs2BCbzcYvv/zCtGnTKn8ggF9++YWVK1cyYMAAIiIi8PPzw2q1Eh8fz/z58/nggw9o3bq1kpwPHDiQZ599ljVr1tC+fXseffRRPvzwQ06dOsXq1auVHlC4/mDpsmXLWLZsGUajkTlz5tw0lsOHD5OUlMSqVasYOHCgMl46Li7OoUfWy8uLrVu3AuWv3FP0PfaHnsuSlJREQEAA7733Xpnr1iY9evTg4Ycf5r333uOhhx6iadOmdOjQgcDAQBYuXMiRI0dwd3fn6NGjxMXFVflisSQXLlxg3LhxDBo0CDc3Nw4cOMDBgweZMWMGbdq0Aa5/oc2YMYPnn3+eCRMmEBoaSsuWLTl48CDR0dEMHjy4zNg8PT2ZOXMmM2fOZOzYsYwaNQpfX1+2bNlCXFwcM2bMoHv37mXGO3ToUA4fPkxUVBTHjx9n2LBhZGdnExsbS0pKCs2bN8dms9XqWYCFEDVPpVLxyCOPcOHCBeLj4zlz5gyBgYGo1WrOnz/P+fPnqVevHmPHjlWey3Fzc6Nhw4acPHmSNWvW0KxZsyrlJnC9xGWPHj2qVOMd4L777qNTp07ExcUxf/58OnbsiJeXFwcPHiQ5OZnAwEDatWtX6falR16UKi4ujosXL/LKK6/QqVMnpUpHYGAg8+fPp27duvz444+VnlAHoE2bNrzzzjt06NABtVqNn58fffr0wWKxKO0ajUZ2795N69atefHFF2nUqNH1aYnVagwGQ5Urh8THx+Pt7U1ERAT+/v6oVCo0Gg3du3fnqaee4uTJkw49shqNhmeeeYbBgwezevVqoqKi+OCDD/jHP/5B165dqxSLt7c3Wq2W+Ph4ZbZMV1dXBg4cyKBBg6rUdmW0bt2apUuXKonl3aJOnTqEhYWRmZnJxo0bsVgs+Pv7s3TpUlq0aMEnn3zChx9+SIMGDdiyZQtt27at9hheffVVFi1axKFDh1i9ejW//fYbERERTJ061aHCQrdu3YiOjqZv3758/vnnzJw5k0OHDvH666+zePHick1u0r59ez799FO6du3K2rVrCQ8PJyMjg3fffbfYZFClcXV15bXXXmP58uVkZGQQERHBihUr8PPzY8eOHfTs2VOSeCFEtXB1dWX69OlMnjwZrVZLQkIC+/fvJysri759+zJ//nyHWVJ1Oh2PP/449evX5/jx45w+fbrSwxHt9Ho9Xl5eVX6IX6NO44VwAAAgAElEQVTREBYWxtNPP41Op+Pbb7/l888/5+rVq4wcOZIXXnihSvPK3LaHXfV6PbNmzapS0fsbBQQEKPU7jUYjy5Yt4+rVq9XW/t2sPA+7Xr58mfvvv7/Eeue+vr40atSIjIwMh5nKKqpr167F/k8U/XDC9RJQqampBAYG3rTyRmUUFBSQnJxMSkoKvXv3LnEdrVZLenq6Qw1u+0lm6tSpLF26lNDQUIYMGVLlRKZ79+5Mnz6dVatW8cUXX9CpUycef/xx+vfvT506darUdmUEBwdz//33V2n8Xk0JDg4mOTm51Ne7devG0aNHHZY9+OCDfPjhh8XWtd/JgP/eZanMNgGaNm1KXFyc8ntp/++K8vf3Z9GiRSxatKjUdYrecalsGzfGdiOdTsewYcMYNmxYmTELIURVqNVqunbtWu4Osvvvv7/Y+a1Dhw6sX7++xPVDQkKqNDKkIu+v6L5UhPTIi5vS6XR3TF1onU5X7XW7rVZrpYdN2Gw2JcFNS0sjJyenyvFoNBpmzJjBvn37ePnll8nOzmb27Nl07tyZrVu3VrmHQQghhBB3D0nkRakMBgNnzpzh9OnTxV5LS0vjzz//xNfXFxcXF2X5jWXubDYbhw4dIiUlpdJxaDQaDAYD6enpDj3D1fEgol6vp3HjxrRs2ZL9+/eTnJxc7OfMmTPFyltdunSJ+fPnExAQwPvvv8+pU6f48MMPqzTMqKhGjRoxceJEYmJi+O6773jwwQeJiopyeFhRCCGEEPc2SeRFqbp06YKzs7MyjtdisWC1Wjly5Ajz5s0jJyeHvn37KsNJDAYDZrOZnTt3kpeXR35+PtHR0URERJRYQqq83NzcaN68OXv27GHr1q2YTCays7NZtWoVc+fOrfKDiMHBwZw5c4bIyEhSUlLKTMbz8vJYsmQJp06dIjw8nN69ezNlyhTWrVvHtm3bqtRr/t5777F582auXr2qtFO/fn06duyI0WgscRZPZ2dn9Ho9u3bt4tChQ3fkU/tCCCGEqH4yIZQoVZs2bZg0aRIrVqxg1KhRDq9ptVqmT5/u8BBkq1atCAwMZMOGDWzYsMGhnaok2xqNRqldP3/+fObPn6/EMGLECI4cOVLptgE6d+7M448/zhdffEFsbGyx10NCQoiMjESv12Oz2YiOjmbr1q3Mnz+ftm3bolKpePLJJ4mPj+ef//wnjRs3plu3bgAkJCQwevToYm3GxMQA0LZtW95//32lytLVq1dLHcM8YsQIfH19iy1v0KABnTt35rPPPiv2d9q4caMykVB0dDQvvfSSw+uJiYlK9aKQkBAWLVrkcIdFCCGEEHcu6ZEXpdJoNDz33HMsX76c9u3bo9Vq0Wq1dO3alffff5/p06c7VLvw9/dn8eLFdOrUCbj+0GpERATvv/8+DzzwQJViadu2Le+++y4PPvggcP2hxLVr1zJr1qwqzzKp1+tZsGCBw36WJj4+nmXLljFgwABCQ0OVuxGenp6Eh4cDsHLlSi5dulSpWCZPnszLL79MixYtgOsXK+3bt2f58uUsXLiwxH3V6XTMmjWL5557Dj8/v0ptVwghhBC1j8p2G56ei4mJYdeuXdVetaaoolVrpI68EJVjs9mUH4vFgtlsxmQyYTKZyMrKIjAwsKZDFEIIIe5ZSUlJeHh4oNPprhcBqemAhBBCCCGEEBUnibwQQgghhBC1kCTyQgghhBBC1EKSyAshhBBCCFELSSIvhBBCCCFELSSJvBBCCCGEELWQJPJCCCGEEELUQpLICyGEEEIIUQuVPoVlNTMajSxcuFD5ffjw4YSEhFSpzeTkZJYuXYrRaFSW1a1bt0ptCiGEEEIIURtIj7wQQgghhBC1kMpms9lqOghx99m+fTvz5s1j8uTJTJs2DY1GU9Mh1SpGo5HXXnuN7777jn/9619069bttmzXZrMpPxaLBbPZjMlkwmQykZWVRWBg4G2JQwghhBDFJSUl4eHhgU6nQ6fTSY+8qH5Go5G9e/eSm5vL3r17uXbtWk2HVK0SEhIICAhw+ElISChxXaPRSHh4uMO64eHhDsPBSpKWlsYPP/zA5cuX+eGHH27FbgghhBCilpNE/h6SkZFBaGgoS5YsKfH16OhoevTowdmzZ6u0Hb1eT+/evXFzc6N37964u7tXqb3Kys3NZeHChcyZM6fMxPlO4+vrS/fu3fHx8aF79+41HY4QQggh7kC37WFXcW8ZPnw4w4cPr9EYTCYTP/30E/7+/tXabnBwMMnJycD13vnRo0eXuq5er2fZsmUsW7YMo9HInDlzyrUNvV5PZGRktcQrhBBCiLuT9MgLIYQQQghRC0kiL0plH4oTHR1NamoqM2fOJCgoiKCgIJ5//nkuXrxY4vpFx4OXNoynom0D5Ofns3nzZkJCQggICKBdu3bMnDmTpKQkZZ2iY9I7dOhAYmIiMTExtGrVyiGu6OjoYvGsXbuWIUOGEBAQQFBQEOPGjSMuLg6r1VoNR7N8KjL+vjLHMC8vj6ioKHr16lVsOzf7ewkhhBDiziNDa0SZ1q9fz4oVK0hLS1OW7dixA5vNRmRkJHq9/pa3bTKZWLhwIZ999pmyXlZWFrGxsahUqirFYTQaWbBgATExMcqygoIC4uLiiI+P591336VPnz6V3MNbr7zHMC8vj1dffZWtW7fWVKhCCCGEqEbSIy/KdPr0aVxdXYmOjubs2bPs27dP6e2+cOGCsp6Xlxdbt24lOTmZw4cP07Zt22pr217F5W9/+xvHjh0jOTmZpKQkvvzyS1q2bKmsZx+TXjSGkJAQjh8/TnJysvIzcuRIhzgaN27Mu+++S2JiotL2mjVrcHNz49tvv6WwsLAajmTZ7OPvk5OT2bhxY7neU95jePDgQWJiYhgzZgzx8fEkJyfz008/MXnyZDw8PNi4cSMRERG3ateEEEIIUc0kkRdlatOmDe+88w4dOnRArVbj5+dHnz59sFgsWCyW29K2q6srHh4eHD9+nLNnz2Kz2dBoNLRs2ZJnnnmmSncF9Ho9ERER9O/fX6mwo9FoCA4OpkmTJuTl5WE2m6u0n7dSeY/hpUuXcHJyIjQ0lAYNGgDg6enJY489houLC6mpqTW1C0IIIYSoBEnkRZm6du1KQECAwzIfH5/b2naDBg146aWX+OOPPxg+fDj9+vVj+fLlpKamUh1zml24cIE333zTYey4vVf7TlfeY2gwGDAajezYsYOrV68CcPXqVT7//HMuXrxIvXr1bku8QgghhKgeMkb+HpSZmUlhYSFOTk4Oy6vau36rdevWjT179hAXF8fWrVuJiopi+fLljBo1ildffRVXV9dKtfvrr7/yzDPPkJKSUs0R31l69uzJkCFD+Oijj/joo48cXgsNDSU4OLiGIhMAZrOZ9PR0bDYbXl5eODs739LtWSwWMjMzMZlMwPW7UN7e3mi198bXQkFBARkZGahUKurWrXvb9js3N5dr166h1WqVC+6MjAzy8/Nxc3OrsXk3hLjT1NRntLaRo3IP+v3338nNzcXT01NZZjKZOHLkCBqNBo1GU4PR3ZyrqysDBw5k4MCB5OXlsXz5ctatW0evXr0YPHhwpdr8/vvvSUlJYfLkyfz9739XjktGRgaTJk2qzvBr1LFjx0hISKBPnz6cPXuWc+fO4efnx+jRowkLC8PV1bVa7m7cKa5du0Zubm6Jr2k0GvR6PQaDAZVKdZsjuzMSN3sSr1Kp0Gg0ODk5yRelEKJCzGYzWVlZFBYWYrPZsNlsqNVqXFxccHd3R63+78CP233es18I3Ox7TaVSVarj5PLly5jNZtzd3XFzc6tqqFUiQ2vuIQaDgRYtWnDw4EHWrl1LdnY2cL2aycaNG9m2bRtBQUHK+Ok7yU8//cSSJUtISUlR7hw4OzsTHByMm5sbOTk5xd6j0WgwGAzs27ePffv2lXrHISMjAwB/f3/q1KmD1Wrl999/Z+3atZw8efLW7dRttm/fPvz8/JgzZw47duxQHoydOnVqpe9m1AZqtRonJyflR6PRYLFYyMnJITMzs6bDqxFmsxmz2YxKpcLT0xMfHx+HC3shhCiL0WjkypUrFBQUoFKplHOs/bX09PRbWijCZrNhMpkwmUxldkIV/Q648afoxUZtJN0v9xAnJycmTJhAQkICUVFRREVFObzepEkTXnjhhUonddHR0bz00ksOyxITE5XthISEVLpMZGFhYYkx2+Nu3759seV16tShW7du/PDDDzz77LMOr73xxhtK5ZouXbqwYcMG5s2bx7x5824ah3121qKlKgGH2V2Ltl3azK/297dt25b3338fLy+vCrddUZ6eniQmJjJgwACH5Vqtlk6dOjFt2jS6dOlSqbbvZDqdDi8vL4dlOTk5ZGdnU1BQQH5+Pi4uLjUUXc2wWCzYbDZUKpX0wgshKqywsJDs7GxsNpvSw26/u2mxWMjIyMBkMhW7+1+dTCZTuYbeqNVqPD0979pz3d25V6JUgYGBbNiwgaioKHbu3ElWVhY+Pj7069ePKVOm4O/vX9Mhlqhdu3asW7eOjz76iAMHDlBQUICfnx/Dhw9n7NixJT6oqVKpGD9+PDqdjs2bN3Pq1KkS2+7SpQtvvvkma9as4dixY3h4eDB48GD+9re/8emnn2I0Gm/17t0Ww4YNY+fOnfz222/4+vry66+/UlBQgNlsJj4+XrlT07Nnz5oO9ZZzc3MjPz8fk8lEYWEhLi4upd4qvXFMc9Gx7HXq1FGqGtkrKRkMhlJvtd443Cc3N5fc3Fzc3NwcLqDNZjPXrl0rs12j0Uh2draSmKvVavR6vcOX6o3st7fheo/W5cuXARz2Oy8vj5ycHOUulkqlwtnZGQ8Pj2K9Vzk5OeTl5ZUaw83G/tuPh4uLi3KxZbFYuHbtGgUFBUovm0ajoU6dOsU6Acq7/wUFBcWOZ0Uu3goKCsjOznboXdTpdHh4eDgkB1arlaysLIfYnZycqFOnzi1/5kGI28l+3tPpdMU+bxqNBjc3N8xmMwUFBWRmZjp8jxY977m7uyvnXvt52Ww2o9frb+ud4qLneb1eT25uLhaLRens8PDwQKfTOZw/4fo57Nq1aw7nz/Kcl0rb5xs7ncpDZbubBsUKIUpkNBqZP38+KSkprFixgkaNGimvWSwWPv/8c+bNm8eUKVN48cUXsdlsWCwWzGazcusyKyuLwMDAGtyLiikpSSzq6tWrFBQUFPsyKW8ib7VaUalUyhhzm83mMFylpETRfvFgv4DS6XQ4OTmh0+nQarU3bVetVuPh4aG0a7+rYL+lDdd7yaxWKy4uLnh7e5d4XIxGIwUFBRiNRlQqFXq9HpVKhYuLCzqdzuFiw37b2d6uk5MT3t7eSjKflZVFXl6ew7omk0mJwcvLC4vFUu5E3mq1cvXqVUwmE1qtFicnJywWi5JAF/3blHf/7b12FotFeQbIfkztSf3NevPy8/PJzMzEZrOh1WrRaDTK0CStVouXlxdOTk5K7IWFhcp69s+QvUfQ2dlZHnYVd4UrV65QWFiIwWCgTp06xV43m81kZGRgtVqV569KOu8V7USxs5+P9Hr9TXvcy3oYtiIPy9o/l3YajQa1Wo3FYsFqtaLVaqlbty4FBQUUFhaSn5+PxWLBxcVF6RjQ6XTlPi+Vts/luXuRlJSkXFjodDrpkRfiXpCWlkZ8fDw+Pj5kZ2dTv359NBoNVquVCxcucOzYMeD6MKV7gT3BAir9cLfNZsPZ2RkvLy9UKhU2m01JQksbruPi4oKLiwsZGRmYzWacnJyUxM0ez83aNZlMuLi4KLesNRoNnp6e6HQ64PoXhv2Wdmkx6PV61Go1+fn5qFQq3NzclC84k8mkJPju7u5Kj1jRBDs7OxsPDw/y8/OVNopeuBSNoaCgoEK3s41Go3Jc6tatW+yCwWg04ubmVqH9z87OdriwsPeIFb0IKY3VaiUnJwebzabMZWH/G9mT77y8PDw8PJQee1dXV4eeN/t2cnJypFde3BXMZrNDb3VJil6o2pV03ivKyckJLy8v5ZxcUFBQ/cGXQ506dTAYDIBjR0BBQQF6vR69Xq/EptPplM6FypyXb9znypBEXoh7QL169QgICCAuLo5BgwaVuM7AgQPp16/fbY7s9jOZTMowC51OV+nJxNRqNW5ubkrCZu+BKc+DV5Vt1z7UpaCgAKvVil6vV74s4L8PdOXn5ytDhirC3svk7OzscFtbrVbj6upKYWEhBQUFWCwWJR69Xl/si0mn0ym3iys6LtU+dr/orXr7sBr7svLuv1arVe6SFH0/XL+oKmvYnMlkwmw2K0Ob7OztFRYWKklNQUGBcgu9pO1UxwR6QtxpqvNBUb1ef9OEtujd0KKKDhG0x1S3bl3ld6vV6vC6XUkVa7RarcMdWZ1Oh1qtxmw2F9vujSpzXi5rn8tDEnkh7gGenp4sW7aM6OhovvzyS06dOoXZbMbZ2ZnOnTszcuRIBgwYgJOT011VghKuJ6d//vmnwzKbzYaTk1OJY75rA3vvvdFoLDUZrczf0Z5oFv0SsrN/oVmtViUpLa1Hrujt4YrMiuzs7IxGo8FkMnHp0iXlDob9FvKNbZa1/0Uf6r1x3ozyKDrG9cYvW3vPHPz3C9xms5Genl5qPHfbZ0uI202tVjt8juz/LnrxXNI5vaTPv0qlKnHdypYkvlXn5bJIIi/EPcLLy4tnnnmGZ555ptR17sZEo2gSZh8brdFo8PLyqvVVDJydnUvdh5KS8bLYe6/Lurgp+v+kOi+E7ONQ7Q+75uXlkZeX51CXuuiXbHXvf2nK22Om0WhwdnYuMREoz3EVorYpq5e6Ot04XKesMfD2jonbXbXmdp2X7Gr3t5gQQpShaPlJ+3jzgoICcnJyan3tdK1WW60PR2q1WuXBrJLYx+wXVd1f5PaLLPjvmP38/Hxyc3Ox2WwOf7Oy9r+6xtiWd0iMvRRfbb9AFOJm7A9zFxYWlvrZKPqwq/1B73tFdZ+XyyLdA0KIe4b94U61Wq3UkK+N7D2+VR2PfyN7z7PJZCr2mn1MvFqtVr7I7Xc4bpSdnc2VK1fKfJj0RvbKEfbt20s82meItI9ZL+/+azQa5eKjIkN8bny/fThRUQUFBaSnp5OZmamM6bdarSUeOyHuNvZhmPn5+SV+Bosm+ffKnahbdV4uy71xdIUQ4j/s464tFovSyws4VG6xs5dMu9OGHLm4uChlIYvWpYfrZRmvXLlSqcTVXkqtsLDQIQm3Wq3k5eUpFXU0Gg06nQ6NRlPsgqiwsFCpPnNj0l/02JaU9NqrPtgrxdgVHV9uL9NWnv3XarXodDpsNpsSv11pCUhR9rKg9pmAi8aTm5uLyWRCpVI5jOG315+2s5fftM+kLcTdwP6QZmFhIdeuXXP4LFmtVnJzc7FarTg7O1fq+ZTa6Fadl8si9/+EEPccV1dXCgoKMJlM5OXl4ebmhrOzs1JS8fLly6hUKoeHOquT/aIhLy9PqWVfkbGTRUtQZmdnk5+frwyLsT8DUJkhL/YqPrm5uUrZRPvDp1arFZ1Op9SMdnFxUcaxZ2Zm4uTkhEqlUobmODs7K7fTnZ2dKSwsJCcnR0n6rVZrsRgNBoNSo/ny5cvodDqlQo59m/Za8OXd/xvbLFobuixqtRqDwUBmZiZ5eXmYTCaHCkIajUap7mPfjtls5sqVK0qPZWFhoVKD/k67IBSisuznAvtcEPaqTUUfMrdPFmVX0nmvKpM+6XQ6pS77zZ5jsVqtZGZmlviavdRuRdkrYuXk5GA0GjEYDLfsvFxmLNXeohBC3OGKJqx5eXnKLIL2nqTCwkJlQqY6deoU612pKnstdPtQkcqUJXR3d0er1ZKTk0NhYaESs/2h0MqWNCutXfvMhEVvk3t4eKDRaJQkF1BKNRoMBuUCyGAwYLValZ56uH5r3v43sLNPOJWVlaX07MN/S3IWLQFZ3v23VyeylxwtWk6yPEN/7PXn7XXi7UN7StpO0Qd17ePz1Wq1Q01+Ie4Wrq6u6HQ6srKyHMrjljbDcnWc94qy3w0rj6KzMt/YRmWSa4PBoJxPbDab0satOi/fjMzsKoRQ2Icw3A0zuwohhBB3mxtndpUx8kIIIYQQQtRCksgLcYfKz89n/fr1DBkyhICAAOUnISGhUu1lZGQQGhrq0NaSJUtKXX/Lli20atWKtm3b0qlTp8ruhhBCCCFuERkjL6pVXl4en3/+Odu3b+fUqVNoNBr69OnDhAkT6NChwz1ThqqqLBYLb7/9Nu+9915NhyKEEEKIO5SMkRfV5vfffyc8PJyff/65xNc3btxIcHDwbY6qdjp79iwTJkwgMDCQ1157DV9f32qtnJKRkcGkSZPo0qULERERyvLSxshv3ryZXr16yRh5IYQQogbdOEZeeuRFtbDZbGzatInjx4/z/PPPM3bsWDw9PbHZbFy6dIno6Oh7ppZsdUhPTyctLY25c+fyl7/8pabDEUIIIcQdSMY5iGqRn59PWloaDRo0YPDgwco06iqVigYNGjB9+nTat2+vrG8fr13SGO3o6Gh69OjB2bNni7125coV/vWvf9GrVy8CAgIIDg4mKiqqxDJyNpuNY8eO8dxzz9GuXTsCAgIICQlh7969xcpNWa1W4uLiGDNmDM2aNSMoKIiZM2eSmppa4v5euHCBN998k+DgYAICAujVqxerV6/m2rVrxda1WCx89913Dm1PmjSJgwcPlllX2l4jtySVPYZCCCGEuDtIIi+qhYuLC76+vqSlpREdHV3hqdnLIykpiSeeeIKVK1dy7tw5AC5fvsySJUv46quvHNa12Wxs2bKF0NBQvvrqK7KysgA4duwY8+bNIzk5WVnXYrHwwQcfMGnSJOLj4zGbzRQUFBAbG8ujjz5a7OHS1NRUxo0bxzvvvMPly5cBOHfuHEuXLmXXrl3F4li3bh1PP/20Q9t79uwhMjLSYZKKJUuWKA+hjh49GoDRo0c7PJwaHh6u1NYWQgghxL1NhtaIaqFSqRg3bhzHjh1j3bp1fPnll4wbN47HHnsMHx+fKrefmZnJwoULOXfuHDNmzGDMmDH4+PiQl5dHXFxcsZ7txMRE/vnPf9KgQQP+8Y9/0Lt3b5ydnbl06RJfffWVw6QMv/zyCytXrmTAgAFERETg5+eH1WolPj6e+fPn88EHH9C6dWtlQpfDhw+TlJTEqlWrGDhwoDIhTklxZGVlsXv3bvr06UNkZCT169fHarVy7tw5vvzyy2qfMVQIIYQQ9w7pkRfVpn79+qxbt47ly5fj7u7O4sWLeeihh3j22WdJSkqqUtunT58mPj6eyZMn89xzzykXB66urgwcOJBBgwYp69psNv73f/8Xs9nMP//5TwYNGoSLi4syzGfixIk0adJEWT8+Ph5vb28iIiLw9/dXZvTs3r07Tz31FCdPnuTixYvK+t7e3mi1WuLj48nIyCg1Drg+g2i9evU4c+YMJ06cwGq1olarue+++5g2bZoyBAkgIiKC5ORkkpOT2bhxI3D9AWH7suTkZJYtW4Zer6/SsRRCCCHE3UF65EW10ul0DBs2jKFDh3L69Gk2bNjA9u3biY+PZ/Xq1XTr1q1S7drHqvfp06fMKY7t4/VbtGjBX//615uuW1BQQHJyMikpKfTu3bvEdbRaLenp6Ury3717d6ZPn86qVav44osv6NSpE48//jj9+/enTp06Du91dXXlhRdeYPr06UycOBEfHx+GDRvG448/TvPmzaUcpxBCCCEqTbIIcUuo1WqCgoJ44403lFrob7/9NlevXq10m87Ozmi15b/21Ov1ZSbKVqsVs9lcoTg0Gg0zZsxg3759vPzyy2RnZzN79mw6d+7M1q1biw2vCQwMJDY2lo8//phevXqxZcsWhg4dyoQJE7h06VKFti2EEEIIYSeJvLilVCoVHTt2pEePHly8eLFYIn/p0iUKCgqU3zMyMvjhhx+KtVOvXj1yc3P5+eefy6z0otPp8PLyIikpqdSqM3Z6vZ7GjRvTsmVL9u/f7zCMxf5z5swZOnbsWOy9jRo1YuLEicTExPDdd9/x4IMPEhUVxfnz50uM6aGHHmLJkiUkJCSwYMEC4uPj2bx5803jK4/yHkMhhBBC3F0kkRfVIjMzk7fffptjx445VFW5du0a//u//0tcXBxNmjShfv36wPVebYPBQEJCAseOHcNqtfLbb78xe/ZsYmNji7X/wAMPEBgYyLJly/jss8+Uai+FhYXs2bOHb775RllXo9HQqVMn0tPTmTdvHvv376ewsFCJ84MPPiAlJUVZPzg4mDNnzhAZGUlKSgoWi+Wm+/ree++xefNmrl69qlxU1K9fn44dO2I0Gh2S6pSUFBYuXMixY8eUGLRaLW3atKFBgwbk5ORU6DgXVdFjKIQQQoi7i4yRF9XCZrPxww8/sGrVqhJf9/DwYOrUqbi7uwNQp04dunXrxg8//MDIkSOV9ZydnRk4cCDHjh1zeL+fnx9TpkzhH//4B/PmzWPevHkOr7/xxhsOv/fu3ZvQ0FA2bdrEU0895fCar68v3bt3V37v3Lkzjz/+OF988UWJCXBISAiRkZHKQ6ZXr15l0aJFJe7niBEj8PX1VX63WCzs2rWL9evXl3hMevXqVWI75VHRYxgdHc1LL73ksCwxMZGoqCjg+n4uWrQIFxcXMjIyePrpp5U7IDabrUqxCiGEEKL6SY+8qBaenp4sXryYiRMn8sADDyjLH3jgAf7+978TGxvr8KCrSqUiLCyMmTNn4uHhgVarpUePHnzxxReMHz++WPsqlYrHHnuM6Ohohg4dioeHBwBNmjThlVdeYejQoQ7ru7q68tprr7F8+XLat2+PVqtFq9XStWtXFi1aREBAgLKuXq9nwYIFDuvezOTJk3n55Zdp0aIFcL2HvX379ixfvpyFCxc6VJUJCAjg7bffdojZx8eH0aNHs2PHDoKDg8t7iEs8JhU5hkIIIYS4u6hsZSsFGVgAACAASURBVA04FkLcM+y97zabDYvFgtlsxmQyYTKZyMrKIjAwsKZDFEIIIe5ZSUlJeHh4oNPp0Ol00iMvhBBCCCFEbSSJvBBCCCGEELWQJPJCCCGEEELUQpLICyGEEEIIUQtJIi+EEEIIIUQtJIm8EEIIIYQQtZAk8kIIIYQQQtRCksgLIYQQQghRC0kiL4QQQgghRC0kibwQQgghhBC1kCTy4q5mNBqZM2cOwcHB7N+/v6bDuWNs376d1q1bs2LFCiwWS02HI6poyZIl9OjRg7Nnz972baenpzNmzBgmTJhAdnb2bd++EOK/kpOTmTZtGlFRUbd8W0ajkcjISF588UUyMjJuum56ejpz585l3rx55OTkOLyWkpLC66+/ztNPP83TTz9NYmLirQz7rqOt6QCEuJXS0tL44YcfuHz5Mj/88APdunWr6ZBqnNFoZO/eveTm5rJ3717Gjh2Ll5dXTYdV7RISEhg9enSx5T4+PvTu3ZuxY8fSqlUrVCpVDURX+2RkZDBp0iT8/f2JjIxEr9cDoNFo0Ov1eHt7o9XKV4oQZUlPT2fbtm0kJiaSl5eHWq3mvvvuY9iwYbRp08bhnBQVFcXRo0eZNWsWAQEBtyU+k8nEd999x+7du7l69SoA3t7e9OzZk/79++Pi4lLhNrVaLU5OTri5uaHRaJTlly5dYtWqVWRlZREUFISPjw+NGzcmIyODRYsW4e3tTXh4uHK+qQh7G+np6aWuU7duXebOnVvh78Ca+LuURs66d7DU1FQmTJhASkoKkyZN4uWXX3b4ABR18eJFFi9ezH333Ud4eHiZbW/bto3NmzezYMECmjZtWt2h3zIVjdvX15fu3bvz3Xff0b1799sQ4Z1Pr9fTu3dvdu/eTe/evXF3d6/pkG6pvn370qpVK+X39PR0vvzyS2JiYoiMjGT48OE1GF3t5+npyXvvvVfTYQhRKxw9epR169aRl5fHAw88gI+PD/n5+Zw8eZKVK1fy8MMP88QTT9yyi2KTyURubi4uLi4lJsd5eXmsWLGCM2fO4OPjQ3BwMGq1mtTUVLZv386PP/5IeHg4Pj4+Fdquh4cHCxYsKLb83LlzZGRkMGTIEEaMGKEsL6uHH8BqtZKdnY1arcZgMJTaKePu7k5QUFCJ+ZObmxs6na4Ce3LnkUT+Dvbzzz+j1+sZMWIE8fHxXL58mYYNG5a4bnZ2NocOHaJRo0blajspKYnU1NTqDPe2qGjcer2eyMjIWxhR7TR8+PB7JoEdMGAAI0eOdFgWFhbG1KlT2bBhA127dqVBgwY1FJ0Q4l5x6dIlPv74YwAiIiJo1qyZ8lpOTg6rVq1i3759tG7dmr/+9a+3JIajR4+yevVqhg8fTkhISLHXv/76a86cOcMjjzzC8OHDUauvj8C22Wzs2bOHjRs3sn37diZOnFgt8aSlpQHQpEmTCr83KyurXL32DRo0ICwsrFK9+rWBJPJ3KKPRyO7du+nUqRODBw9m0qRJnDx5stREXghRfk2bNmXQoEGsW7eOc+fO0aBBA5YsWUJsbCwbNmxwuNtz9uxZJkyYwLBhw4iIiFCeu0hNTeXNN99k48aNbNmyhdzcXLp27corr7xCYGDgTbdvMpmIiYlh/fr1nDp1Cg8PDwYPHsy0adPw9fV1WDctLY3Vq1ezc+dOcnNz6dSpE9OmTaNr165lDgsq73vz8/P57LPP+PTTT0lJScHHx4cnn3yS8ePH4+Liwpw5c4iJiQEgMTGRmJgYQkJCiIyMJD8/v8QhNzabjePHj7NmzRr27NmDxWIpcfv2IVBvvPEGDRo04K233uLYsWP4+PgwZcoUnnrqqVrfYyYEwPfff8+VK1d44oknHJJ4AIPBwLBhw1i5ciVxcXEkJycrnzmAhQsXotfrmTVrFo0aNWLZsmVcvXqVUaNGsW3bNi5evMj06dPx8PCodHwmk4nff/8dvV5P27ZtlSQeQKVSERwczPfff09SUhI5OTkOn8vMzEw+/fRTfvnlFywWC/7+/oSFhSkJ+o1DZfLz8x2GvaxevRqADh06cO3aNZKSkoDrd1CnTZtGYGBgpYfYlEdycjJLly6ldevW9OzZk08//ZQLFy6g0Who27YtYWFhGAwGYmJi2L59u/K+on8X+xCbP/74g40bN3L69GksFgv169dn+PDhdO7cWTnv2YfmjB07lt27d5OcnExoaChDhw6tcOzysOsdKi0tjcOHD9O5c2eaNm1K8+bN+f777x0eTMzIyCA0NJSAgAAGDBhAWloaUVFRBAQEOPwkJCQAEB0drSyLiooiLS2NAQMGOKwbHh6O0Wh0iCU1NZW5c+fSrl07AgICCAkJYe/evVitVmWdhIQEevTowc8//8zChQtp1qwZL7/8MllZWaxevZqgoCCee+455XaZPfY1a9aQlpamtB8UFMTMmTOVq/TKxp2QkFDqcbiRPfakpCT27t1LSEgIAQEBBAcHs379ekwmU7H3pKamMnPmTOWYlHdbN2M0GgkPDy/xb2Dfn6Lt2o9hdHS0Ek9QUBBBQUE8//zzXLx40aGNov9f7D9LliwpMZaMjAxGjRrF1q1bOX/+PK+88kqF9+dOplKp8PPzw2w2V/ph35SUFCZOnEhKSgrjxo1j5MiRHDhwgNmzZxc79kVZLBbWrFnD3Llzadq0KUuXLuXJJ59k9+7dhIWF8euvvyrrJiUlERYWxqlTp5gxYwZz5szBaDQyfvx4tm3bdtP4yvvevLw8XnnlFRYvXkyrVq1YunQpQ4cOZe3atTz77LNkZmYyadIk1qxZQ5MmTejatSvvv/8+kyZNummC/fXXXzN27FhSUlJ48cUXHba/ZcsWbDabw/pr165l3rx5dO7cWbmgWbhwIZs2bSrrTyHEHa+goICzZ89Sp04dh6F+Rf3lL3/5f/buPa7H+3/8+KPTm04qVGi1FUVo5FBCkyHGnMI2hznO4SOjDTnM4bPNtOWTYQ45k88YOaTY2MEQWka1ybHURy2nUKHepHfv3x++XT9vlUPl/Lzfbt1u3tf1ul7X832l9/t5va7n9bqwt7fnzp07uLm5MWLECGrXro1KpaJHjx4MHjwYGxsbpX12djYrV67k+vXrWFlZlbscR6VSUaVKFfLz87l48WKx9ZUrV6ZPnz707dtXJ6HOyckhJCSEy5cv4+HhQf369UlPT2fp0qVcuXKlxH2ZmprSr18/mjVrBkCHDh0YMWIE7du3p1u3bvTv3x8zMzNsbW0ZMmQI3bp1eyon9EVXLKpXr46XlxfW1tYcOXKEtWvXotFoaNKkyQN/L8nJyXz99ddcvHiRdu3a4evrS0FBAcuWLWPnzp06+8rPz2fNmjWkp6djZWVV5vcnI/LPqfj4eFQqFQ0aNMDKygpPT0/27dv3wPKaJyEuLq5YYp2YmMjQoUMJCAjA399fqTvLzs5mxYoVyn/WnTt3oq+vz+bNmykoKGDnzp107tyZd955R+lrz549bNmyhdTUVGVZVFQU2dnZzJ8/H0tLy6fyPjMyMpg+fTpxcXEUFBQAkJmZyaxZszA3N9cpzUhKSmLUqFE6MT9Lq1atYsGCBTq/o+3bt6PVanVGSMsiLCyMRYsWcf78eSZOnFgR4T4X8vPziY+Px87OjmrVqpWpj9zcXKZMmULv3r3R19dHq9Xi4uLC559/zokTJ0ot1ymqNf3ggw+YOXOm8vfj5eWFv78/u3fvZvTo0dy4cYOQkBAaNGjA7NmzMTMzA+D9999nypQprF27llatWpW4n8fZdufOnURGRjJ79mx69eqFnp4ePXv2xMfHB39/f3777TcGDBjAa6+9hoWFBdbW1rRo0eKB/6/S0tJYsGAB7u7uzJkzRznG77//PjNmzCAkJISGDRtSr149ZZvq1aszd+5cpTywX79+jBgxgujoaHr27ImpqWkZfktCPB/y8vK4evUqZmZmpd6XZGFhwWeffaa8rl27NgkJCZw/f56GDRsqI75FAz2FhYW8++67dO3aVRk9T0lJKVecbdu25dixY4SFhZGSkkKnTp2oXr06cPfG9ntPQori0Gg0dOjQQYlDq9USHh7Orl27OHv2rLL9vVQqFe7u7qSnp3PkyBFcXFxo2rSpsr5WrVrs2rWLKlWq0KxZs6dWFmNgYMCYMWOUKyY3b94kJCSE5ORkrly5wmuvvcZrr71W4u/l5s2bfP/999ja2jJu3Djlc7dr1658++23/P7773h4eChJv0ajwcPDg0GDBpXrJEUS+eeQWq3m4MGDuLm5YW1tjZ6eHh4eHixfvpz4+HglEbaysmLr1q1A8cv/JenTp4+SkJZWRnCvomRapVIRFhaGl5cX+vr6pKenExwczIYNG/D19VW+jHNzc9m9ezdLly7F1NSUAQMGsHnzZmbPno2Li4tSHnRvIh8XF0ejRo3YvHkzjRo14saNG3zxxRdEREQQHx9P27ZtHztuAE9PT+UDrbTZS+53+PBh/Pz8mDhxItbW1uzbt4+xY8cSGxtLt27dqFSpEhqNhk2bNnHt2jW+/vprZZTg7NmzTJ06FZVKxYIFC8qcHJbF6dOncXZ2Jjw8HHd3dzIyMvj0009JSEjg4sWLyqXNe/+/FM1A8jBJSUk4OTm9VDczZmdns27dOrZt28bw4cPLVJsJ4OrqSrt27ZQvUD09PeVvobRRKLj74a3RaDA3N9e5dN2qVSuio6MxMjIC4OzZsxw8eJAFCxYoXwgAJiYmyg3cFy5cKDGRf9Rtq1SpwsGDB2nevDnt27fXKbdxd3enU6dOpKenc/v27cc6NrGxsaSmpjJ16lSdvwUTExP69evH7t272bNnj04i3717d517fCwtLalduzZpaWnk5+dLIi9eCmZmZsrfeHlZWlrSunVrnc+R+xWVjNx/lTciIkKnRKSoZr5OnTqMGjWKtWvXsmfPHvbs2YOZmRnNmjXD19e3xIHE++PQ09Ojdu3aAA+8Ovm41Go18+bNU8puihSV3xQpKsMpkpSUpLO+SEkz1tSrV0+nNLLoqsDly5fJzc19YHypqan8888/ShlOERMTE9zc3IiKiuLSpUtKIm9sbMzbb79d7isNksg/h86dO0dsbCyffvqpchZat25dXFxciI6Opn379hX2QfAg//vf/4iLi2P27Nk6M744ODjwr3/9i8GDB3Py5EmdL+POnTvTunVrMjIysLOzo0GDBnTs2JFLly5RuXJlZbS7iI+Pj86InaWlJcOGDSM6OlpJ5J+WwYMHM2HCBExMTIC7tXru7u4UFBQoZUT5+flkZmZSv359OnbsqEzD5ezsTI8ePQgNDSUrK+upJvKNGjUiJCREGRWwt7enbdu2bNiwodxzxLu5ufHFF19Qq1YtcnJyKiLcp27SpElMmjRJZ5mhoSEDBw7UuaL0uAwMDB74BVoaOzs7vLy8WL58OWfPnuX999+nUaNGWFlZ6VyBSkpKIjc394EnXKUl2I+67a1bt0hLS6NevXrFEmVzc3PmzJmjvM7Ly3vUt0hqaiq1atXCwcGh2DpHR0ecnZ1JSUnRib+svwchXlX6+voP/QwyNDTEyspKySXy8/O5efMmJiYmOtNI3jviXb9+fYKCgkhKSuKXX34hMTGRvXv3sm/fPmUE+d5tHyWOiqCvr4+5uTlVq1YF7l6RuH79urK8aCDi/kGS0matKWnGGn19/TJPSZyamopGo2H16tWsXr26xDb3l+pWxPTHksg/h44cOQLcTaKKWFtb07x5cw4ePMjFixext7d/4nGkpqaSm5vLuHHjGDduXKlt7tWqVSudD4R27dphbm5e6ll5vXr1iiW9NWrUoFatWsWS/ietY8eOShIPYGRkpHxgFDE0NMTExIQTJ06wZ88e3n33XQwMDDh9+jSbN2/GwsJC50z8afDy8io2j+3jTg1WGk9PT954440S7xN4Udw7/aRarSYyMhJPT0/Gjx//TGYxMDY2ZtasWXh7e7N48WIl2W7evDnjxo3TuRHUysqKwMBAXnvttRL7etBNtY+zraWlZYUNDqjVas6fP0/VqlUfWBp37wmyEK+K27dvP9X/9w4ODsyaNUt5ffToURYtWoSvr2+Js9YU0dfXp27dutStW5fCwkKSkpL44YcfiI2NxdjYmA8//PBphK+jUqVKjBkzRnn9sLnmiwYfnuasNUZGRrzzzjullkCX9Qrwg0gi/5zJy8vj4MGDXLp0ic6dO5fYJjEx8akk8k87kS5y586d5/Zpo0ZGRgwcOJB9+/Yxfvx4xo8fr6wzNDRk9uzZMpXhc+be6Se1Wi1WVlbMnTsXX1/fUv/GnjSVSkXXrl3p2rUr169fJz4+nsWLFzN48GDmzZunxJWVlcUbb7yBp6fnY+/jUbYtuvk8OzubO3fuVEgyb2xsTK1atUhISCA7O7vYg1YKCwuf279vIZ4UExMTqlWrxqVLl7h582aJpWI5OTksXLgQU1NTRo4c+dQHGm7evMnJkyextLTUOdEvSuoDAgL45ptv+Ouvv+jSpYvOwJe4686dO9jb2+vU+z9pMmvNcyYtLe2hjyeOjo7mzp07TzyWosviK1euJCUlpcSfTz/9tML3e/r0aeVhFM8bjUbDzz//jL6+Pt7e3lhYWGBoaEiTJk1YtmwZfn5+5b5UlpGRoVNycOvWLX7//ffyhi5AuZGzUaNGhIWFkZmZ+dRjOHv2LDExMVy/fh24e9m3TZs2zJs3j9q1a7Nnzx5u376t3CB25MiRYjO8PMyjblu5cmUcHBw4d+5csfpPtVrN999/z+bNmx/788bR0ZHz58+X+MyHc+fOkZKSgouLy0s7r7MQ96tUqRJ16tQhJyen1O/4CxcukJ6ejpGR0TOZcvXKlSuEhYURERFR4lVYlUqFubk5+fn53Lhx46nH97wrGrQ4duzYY39ml4ck8s+ZI0eOcPPmTbZu3VosaT516hR9+vQhNjaWCxcu6GxXqVIljI2N2b17N0eOHHnopTszMzMyMjIIDw8v9Q/yjTfewM3Nja+//pro6OhiN8tUhOvXr5OdnY1Wq0Wj0XDw4EFlVNvb27tMcT9J169fZ+/evXTq1Im5c+dy4MABzpw5w+bNm/Hx8SlXnWBR2c6pU6f4448/KCws5NKlS3z11VcsW7asAt/Fq61oOrO4uDh+/PFH5QPX2dmZjIwMTp48qbTVarUkJCRU6A1bZ86coX///uzatUvnw/7OnTvk5+ejUqnQ19enYcOGeHh48MMPP+jEBHDq1CmWLFnCrVu3StzHo25rbGxMq1at+PPPP/n111914omPj+c///kPt27d0hmpLywsfOiXlKenJ46Ojqxfv17n3oq8vDzWr1+PmZkZb7/99sMPlhAvkdatW2NpacnOnTs5c+aMzrq8vDwiIiIoLCzE29u7WD3300gMa9WqhaOjI0lJSRw6dKjYPjMyMrhw4QLW1tY602A+Sc97+d29x8jV1RUbGxv+/PNPTp8+rdPuzJkzhIWFlfqZXR5SWvMcyc3NJTo6Wpmb/H4qlYpmzZoRHh7OX3/9pXMjma2tLR4eHqxfv5733ntPZ7sNGzYUu7zu5eWFhYUFy5cvZ/ny5cryooe8GBsbKwnPlClTGDRoULF47OzsHjp7zMOsX7+e9evX6ywrKlEpuuv9ceMuemDPvQ/TAHRmrvnmm2+KPe3zURgaGmJhYcHKlStZuXKlzroHPdTnURgZGdG6dWs2b97M6NGjdfbZpk0bEhMTH7vPe4WHhxe76TMhIYHQ0FDg7jGcPXu2zk1MLytvb2/efvttVqxYQatWrahTpw5NmzbF2dmZWbNmER8fT5UqVTh27BjR0dEVWmbWpk0b/Pz8mDZtGjExMXTq1ImMjAzWrVtHdnY23bt3x8jICGtra8aOHcsnn3zCkCFD8PPzo379+vz555+Eh4fzzjvvlBrX42zbpUsXYmNjmT59OrGxsbz11lv89ddfbNmyhTfffBNfX1/g7kl0vXr12LZtG+bm5ri7u9O9e/cS9+/g4MDYsWOZNm2aMpuWiYkJGzduJDExkdmzZ1O3bt0KO6ZCvAhq1KjBgAEDWLlyJcHBwbi6umJhYcGtW7c4c+YMubm59OjRQ+f+uDp16nD48GFWrVpF3bp16dSpE+bm5mWOwc3NjZCQkBI/51UqFR9++CHz5s0jLCyMn376idq1a6Ovr8+VK1dISUlBX19fuZ/sSQzuFTE1NaVGjRqcPHmSxYsX4+LiQseOHXWuVFhYWDBt2jT09fUf+L116dIlwsLCSryh3tTU9IH3CpSmpN+LjY0NPXv2ZPXq1cybN49GjRrh5OTEyZMnSUxMpE6dOk+kZFkS+edI0SwxgwcPLvUP1d3dHTs7Ow4cOKDzn1qlUjF+/HiqVatGREQE6enpD9xX48aNWbRoEUuXLuXw4cOlzn7RvXt36tSpw9KlSzlw4MATnbnEwsKC1q1bM3LkSBo0aFBiicqjxv2kmJub8+GHH3L48GGqV6+uTD0Jd+sbf/jhB44fP86yZcvKVCvfsWNHZs6cyeLFi7lw4QINGzbk008/xc7OrsIeiS3u/h4HDhzIyJEj2bBhA1OnTsXBwYGQkBDmzJnDf//7X0xNTXnnnXfYsmULM2fOrLB9m5iY8MUXX1C/fn02b97M9u3blf/7//rXv6hfv77StmXLloSHhxMaGsoPP/xATk4O9erV48svv3zoA1IedVsTExNmzZpF/fr1+f7779myZQuOjo58/PHHfPDBB0odrJGRER999BHnzp1TZkTq2rVrqfvv3Lkzr7/+OosXLyY4OBiNRoOXlxfff/89zZo1q5DZGoR40TRp0oTXX3+dbdu2ceTIEfLz8zE0NMTBwQE/Pz9cXV11/jZatmzJmTNniIuL48aNG7Rp06ZcibxKpXrg54a1tTUzZ85UZqk5fPgwhYWFmJiY0LRpU959990yDVSVJc5evXqxbNkyjh8/jlarVQYViujr6z/Sk2yvX79e6kMaq1WrRqdOnR47vpJ+L3D3amStWrXYtGkT8fHx/Pnnn1hYWNC9e/diJyIVRU/7NAt5hPg/RfOYt2jRotR5759HZ8+eZcSIEXTs2JGAgIBij6ieMWMGO3bsKPEqyItAq9UqPxqNhoKCAvLz88nPzycnJ+eBs6QIIYQQ4slKSkrCwsJCOSmTGnkhHkNcXBypqancvn2bmzdvKvVxd+7cITk5mdTU1HI9LVQIIYQQ4lFJaY0Qj6FOnTpYWFiU+sAHQ0NDJk+e/ETmihVCCCGEuJeMyAvxGBo3bszGjRvp27evzlz+1tbW9OnTh61btzJkyBB5SqUQQgghnjipkRdCKKRGXgghhHh+SY28EEIIIYQQLwFJ5IUQQgghhHgBSSIvhBBCCCHEC0gSeSGEEEIIIV5AksgLIYQQQgjxApJEXgghhBBCiBeQJPJCPCWxsbF4enoyefJk8vLynmksERERuLm5sWDBAjQazTONRQghhBBlI4m8eKElJyfj7e1NcHDwsw7lofbt20dmZiYHDx7k/PnzzywOtVrN3r17yc3NZe/evVy/fv2ZxSKEEEKIspNE/hWTlZWFn58fTk5OJf6Eh4c/6xBfWm3atMHa2ppWrVpRq1atCuv30qVLfPLJJ8ybN++R2hsbG+Pj44OpqSk+Pj5UqVKlwmIRQgghxNNj+KwDEOJV4enpSWxsbIX3e+PGDY4cOULNmjUfeZsePXrQo0ePCo9FCCGEEE+PJPKvqFGjRhEYGPiswxBCCCGEEGUkpTXigbRaLYmJiYwePRpXV1ecnJzo3LkzmzdvJj8/X6dteHg43t7eJCcn6ywvKue5t469aFl4eDhpaWmMGzcOV1dXXF1d+eSTT7h06VKxWPLz84mKiqJ37964uLjg5OTEoEGDSmz7uAoLC4mOjqZ///64uLjg6urKuHHjSEtL02mXlJREu3bt6Nq1KxcuXNBZd+jQIdzd3ZkwYYJyM6tarSYgIECnfCkgIAC1Wl1qLBcvXmTOnDl4enri5OREmzZtWLRokU4t+70lUr6+vmRkZBAaGlqsVOreKwAllVWVdm9BVlYW7733Hlu3buWff/5h2rRpj31MhRBCCPFkSSIvSqXVatmyZQt+fn7s2rWL27dvA3Dq1CkCAwOZOnVquWdfWbVqFf379ycqKorbt29z+/Zttm/fTlBQkE6ym5eXx4wZMxg3bhxxcXEUFBQAcOHCBeXfZaXRaFizZg3Dhg0jJiaGgoICbt++TVRUFN27d9dJhp2dnRk/fjynT59m0aJFysnM5cuX+e6777CxsWHs2LGYmJiUKZa0tDQGDRrEkiVLyMzMBCA9PZ2QkBB2795drvdZFmFhYQwfPvyZ7FsIIYQQDyaJ/CuqpNHb+0fTz549y/z587G3t2fjxo0kJSWRnJzM7t27adq0KZGRkURHR5crjtOnT2NiYkJ4eDjJycns27ePpk2bkpCQwMWLF5V2v/32G5s2bcLPz4+DBw9y9uxZkpOT2bp162PVhpfk77//5rvvvsPX15e9e/dy9uxZkpKSCAsLo2rVqqxZs0bnhKVjx46MHj2aTZs2sWPHDjQaDWvXruXUqVPMnDkTBwcHpa2xsTHz5s0jJSWF48eP061btwfGcvToUZKSkli4cCFJSUmkpKSQmJjIkiVLMDc3V9pZWVmxdetWUlJS+Pnnn7Gzs2PUqFGkpKTo/Hh6epa4zdGjR2ncuPFDj01SUhImJiasWLHicQ6pEEIIIZ4CSeRFqaKjo7l06RLTpk2jefPmGBgYoK+vj7OzMzNnzqRatWr88ccf5ZqHvFGjRixZsoSmTZuir6+Pvb09bdu2RaPRKP2q1Wp+++033NzcmDhxIjVr1kRPTw99fX3MzMzQ1y/ff+OYmBiqVq1KYGAgDg4O6OnpYWBgQOvWrRkwYAAnT57UKd8xMDBgxIgRvPPOOyxatIjQ0FDWrFnDZ599hpeXV7liqVq1KoaGhsTExJCVlQWAgi+vNAAAIABJREFUiYkJHTt2pFOnTuXquyzc3NwICQmhUaNGT33fQgghhHgwudn1FfUoN7tmZmbyxhtvUKdOnWLr7OzsqFmzJllZWeTn52NsbFymOLy8vHByctJZZm1trfP61q1bpKWl4ezsjKWlZZn2U5rbt2+TkpJCamoqPj4+JbYxNDTk6tWrODo6KstMTEwYM2YMo0aNIiQkBD8/Pzp37oyenl654mndujVjxoxh4cKFbNy4kebNm9OrVy86dOigMyL/tHh6evLGG28Uux9CCCGEEM+ejMiLB1KpVBgZGT3rMIC7sZR39P1+hYWFZa6x12q1SoKbkZHBzZs3yx2PgYEBY8eOZd++fUyePJkbN24wYcIEPDw82Lp1K1qtttz7EEIIIcTLQRJ5USozMzPOnDnD6dOni63LyMjgwoUL2NnZUblyZWX5pUuXyM7OVl5rtVqOHDlCampqmeMwMDDAzMyMq1ev6owMa7VaEhISyjVrjbGxMbVq1aJ+/focOnSoWI15SkoKZ86coVmzZjrbXb58mZkzZ+Lk5MTKlSs5deoUa9euLVeZ0b1q1qzJ0KFDiYyMZM+ePTRs2JDQ0FD++eefCulfCCGEEC8+SeRFqVq0aEGlSpWYPXs2R44cQaPRUFhYSHx8PNOnT+fmzZu0a9dOKScxMzOjoKCAn376iby8PG7dukV4eDiBgYHk5OSUOQ5TU1Pq1q3L77//ztatW8nPz+fGjRssXLiQqVOnlnvWGk9PT86cOUNQUBCpqakPTcbz8vIIDg7m1KlTBAQE4OPjw8iRI1m+fDnbtm0r16j5ihUr2Lx5M9euXVP6sbGxoVmzZqjVamXmoHtVqlQJY2Njdu/ezZEjRygsLCzz/oUQQgjx4pAaeVGqRo0aMWzYMBYsWMB7772ns87Q0JAxY8bo3ATZoEEDnJ2dWb16NatXr9bppzzJtoGBgTJ3/cyZM5k5c6YSQ+/evYmPjy9z3wAeHh706tWLjRs3EhUVVWx9t27dCAoKwtjYGK1WS3h4OFu3bmXmzJk0btwYPT09PvjgA2JiYvjqq6+oVasWLVu2BCA2Npa+ffsW6zMyMhKAxo0bs3LlSqysrAC4du0as2fPLjHO3r17Y2dnV2y5ra0tHh4erF+/vtjvacOGDcrMNeHh4UyaNElnfUJCAqGhocr7nD17ts4VFiGEEEI8v2REXpTKwMCAjz/+mPnz59OkSRMMDQ0xNDTEy8uLlStXMmbMGAwMDJT2Dg4OfP311zRv3hy4e9NqYGAgK1eupHbt2uWKpXHjxixdupSGDRsC0LBhQ5YtW8b48ePLfKNtEWNjYz7//HOd91mamJgY5s2bh6+vL35+fsrVCEtLSwICAgD47rvvuHz5cpliGT58OJMnT6ZevXrA3ZOVJk2aMH/+fGbNmlXie1WpVIwfP56PP/4Ye3v7Mu1XCCGEEC8ePa3cPSeE+D9arVb50Wg0FBQUkJ+fT35+Pjk5OTg7Oz/rEIUQQohXVlJSEhYWFqhUqruTgDzrgIQQQpSusLCQXbt20a1bN5ycnOjbt6/ODeVCCCHuioyMZOjQoRw9evRZh/LUSI28EOKlVNr9CdbW1vj4+PDhhx/SoEGDcs/9/6RFR0czduxY5em9Tk5OmJqakpyczJAhQ+jatetDnwkhhBDPA61Wy8mTJ9m6dStpaWkUFBSgUqlwd3ene/fu1KhRQ2mblZXF7NmzqVq1KgEBAeUuo30UkZGRREREPLCNv78/TZs2VV4XFhaSlJTEjh07sLCw4KOPPiq2TdH73rJlC+fOnUOr1VK1alV69OiBl5dXuabWlkReCPFSa9euHQ0aNFBeX716lR07dhAZGUlQUBA9evR4htE9XGxsLNWqVWPJkiXKvRNCCPGiKSgoICIigp9++onKlSvz5ptvUrlyZTIzM/nzzz/5+++/GTZsGE2aNHliMajVam7duoWpqSkqlarUdvXq1aNq1aolrqtWrRoAOTk57N+/n99++43r168DdyfPKMnRo0dZtmwZlSpV4q233sLMzIzY2FhWrVrFtWvXePfdd8s8qCSJvBDipebr60ufPn10lg0cOJBRo0axevVqvLy8sLW1fUbRPZhareb8+fPUrFnzuY1RCCEexeHDh9m9ezfOzs6MGTMGMzMzZd2ZM2dYuHAhW7ZsoXbt2lhYWDyRGH755RciIiKKjarfr127dg9cr1arWbx4McnJyTg4ONC6dWt+//33Etvm5eXx448/YmZmRmBgoHLVoVOnTnz77bfs3bsXT09PbGxsyvSepEZeCPHKqVOnDp06deLkyZOkp6cDEBwcjLe3N8nJyTptk5OT8fb2Jjg4GLj7AR4QEICfnx9nz55l1qxZuLu74+LiwqBBg0hKSnro/jUaDXv27KF///64uLjg6urKsGHD+Pvvv5XnB8TGxtKgQQMiIyNJSEigadOmODk58euvv+Ln54evry8ZGRmEhobi5OSkxFfkxIkTjB49GldXV1xdXRk9ejQnTpwo9r6CgoKIioqiVatW9OnTR+rvhRAVLi8vj19//RVjY2P69eunk8QDODs74+XlxcWLF4mLiyMoKIjx48dz9epVkpKS8Pf3JygoCLVaTUpKCv7+/ixevJjdu3fj7+9f7ufVPC4DAwN8fX2ZO3cuM2fOfOBVhMuXL5OZmUndunV1BmRMTEzw9PQkKyurXA/NlEReCPHK0dPTw97enoKCgjI/jTc1NZWhQ4eSmprKoEGD6NOnD4cPH2bChAkPfNqwRqPhu+++Y9SoUdjY2PDdd9/x73//m6ysLAYNGsThw4eBu19sK1euxMvLC0dHRxYvXsy6deto2LAhU6dO5ZtvvqFatWp07dqVdevW6ZQIHTp0iP79+5Obm8u///1vxo4dy8mTJ+nfvz+xsbE68URERDBjxgyqVKlCzZo1y3QshBDiQYqS2ddff12nDr6Inp4eDRs2xMrKihs3btCtWzf69++PmZkZtra2DBkyhG7duumUw/z9999s2bIFIyOjJzaCXxqVSkXTpk0fab9Xr14lNzeXmjVrFiufsbW1xcDAoFxPqJfSGiHEKyc/P5/4+Hjs7OyUesfHlZuby5QpU+jduzf6+vpotVpcXFz4/PPPOXHiRKmlMBkZGfz999/861//YuzYscqzGN566y2GDh3K9u3badKkCVWrVqVFixZs374dtVqNp6en8uCwGjVqYGlpSeXKlbGzs6NVq1ZK/xcuXCAoKIi+ffsSEBCgfPH5+fkxZswY1qxZg5ubm9LezMyMRYsWSf29EOKJKUpmq1evXmptupubG//5z3+U17Vq1WLXrl1UqVKFZs2aFbvZVaVSMWbMGFxcXJ5o7E9SUa1+VlZWmfuQRF4I8UrJzs5m3bp1bNu2jeHDh+Po6FimflxdXWnXrp0y24Cenp6SDF+5cqXU7RwcHFi1alWx5ZaWltStW5e8vDwKCgowMjIqU1wJCQlcvHiRLl266Hxh2tra0rx5c3799VeuXr2qLG/fvj1169Yt076EEOJxFA1GVIR69eo99NkmR48eZdGiRcWW37/s/pr5krYpqV1FUavVZd5WEnkhxEtt0qRJTJo0SWeZoaEhAwcOxN/fX+fpxI/DwMCgzFOG5efnEx0dTVhYGIcPH+b27dvKum7dupWpzyLHjh3j6tWrdO3atcT1dnZ2OvszMDB47qfgFEKI++nr6z/0s0ulUunMPnPr1i3y8vIwMzPTGei4/ypBabPWlPUK7pMkibwQ4qV27/STarWayMhIPD09GT9+/FOZl/h+Go2GxYsXs3TpUgYPHsyoUaOoXbs2Go2GL7/8skL28frrrzNx4sQS6zcNDAyoUaMGFy9erJB9CSHEoyrPyHNZ3F+uUzRP/KBBg8o1a01FK893kSTy4omIiIhg+vTpDB8+vFyjnq8qtVrNF198wZ49e/j2229p2bLlsw7phXXv9JNarRYrKyvmzp2Lr68vnTt3furxHDt2jJUrVzJ58mQGDhyojCip1eoHzmv8OAoKCnBxcaFOnToV0p8QQpRHtWrVMDU15dKlS2g0mhJzgmPHjrF27Vp8fHx49913n0GUT19ubi75+fnlKjmSWWtEhVOr1ezdu5fc3Fz27t2rPCjhZREbG4uTk5POz/0zgRQpmqrw3rYBAQEPHZXIyMjgwIEDZGZmcuDAgSfxNl5Jenp69OzZk0aNGhEWFkZmZuZTjyE1NZXc3Fzq1aunc1m4sLCwQkarrK2tycjI4OTJk+XuSwghKoKNjQ3W1tYkJyeTkZFRYpsTJ06QlZVVoXX0z4Oik5h//vlHmV64SNGJTXmeEyKJ/CskKysLPz+/YvNNFwkPDy9xHu3HZWxsjI+PD6ampvj4+FClSpVy9VdWubm5zJo1iylTpjz1y3nlZWdnR+vWrbG2tqZ169bPOpyXStFUZnFxcfz444/KB6uzs3OxBFir1ZKQkFCuqcHuV6tWLQwNDfnjjz8oLCwE7ibxO3fuLPWBIg+i0Wh0vhy8vb2xs7MjLCyMCxcuKMu1Wi3R0dGEh4eXecpNIYQoCxMTE9q2bUtubi7r16/n5s2bOuuTk5M5ePAgNWrUoGHDhjrrij4nX1S2trbUqlWLU6dOcfbsWWV5Xl4esbGxWFlZlXnSBZDSGvGE9OjRQ2de62chPz+fuLg4HBwcKrRfT09PUlJSgLuj83379i21rbGxMfPmzWPevHmo1WqmTJnySPswNjYmKCioQuIVxXl7e/P222+zYsUKWrVqRZ06dWjatCnOzs7MmjWL+Ph4qlSpwrFjx4iOjqagoKDC9l23bl28vLxYtGgRf//9N/Xr1+fw4cOcOHGCSpUqkZaWxq1btx5aM2ljY4OjoyMRERHcuXMHT09POnbsSO3atfnkk0/47LPP6NevH++99x52dnb8+uuv7Nq1i1GjRkkiL4R46ry8vEhLS2PPnj1MmTKFBg0aYGhoyJUrV0hJSaFy5coMGTJEubfH1NSUGjVqcPLkSRYvXoyLiwsdO3YsVwwdOnTA29sbU1PTinhLj8TY2JgOHTqwbNky5s+fT8uWLTEzM+OPP/7g4sWL9OjRA2tr6zL3LyPyQohXjrm5OQMHDiQ7O5sNGzag0WhwcHAgJCSEevXq8d///pe1a9dia2vLli1baNy4cYXt29LSknnz5jFw4EDi4uJYsWIF1apVY9OmTXTt2pXMzEyd6SFLU6VKFcaOHUvVqlVZs2YNp06dAv5/+VB4eDiurq4sWLCACRMmcP78eZYtW6Yzt7wQQjwthoaG9OvXj/Hjx2NjY8ORI0c4dOgQ//zzDx4eHsycOVPnvh6VSkWvXr2wsbHh+PHjnD59ulhpyuMyNjbGysrqqX8GNmvWjE8//RQbGxt+++03IiIiyM/PZ+jQoXTp0qVcM4fpact7VMQLIysri2HDhtGiRQsCAwOLrQ8PD2fBggWsXr2aOnXqKO379u2Lp6cnISEh/PzzzwB06tSJyZMn69R1FbVPSEhQlo0aNarEfT1u33B32qgdO3YQFhZGYmIiFhYWvPXWW4wZM0aZS7Zo1DsyMvKBx+Kbb75RboAsiic8PJyIiAhOnTpFpUqV8PDw4KOPPqJVq1alTjNYNCK/YcMGPD09H7jPe0fkg4KCShxxLWmEv7S+y3IM8/LyCAsLY8OGDaSnpxfrc+TIkUycOBGtVotGo6GgoID8/Hzy8/PJycl56Jy9QgghhHhykpKSsLCwQKVSoVKppLRGPNyqVatYsGCBzg0q27dvR6vVlpqQVnTf+fn5zJo1i/Xr1yvtcnJyiIqKQk9Pr1xxqNVqPv/8c53k//bt20RHRxMTE8PSpUtp27ZtGd/hk/eoxzAvL48ZM2awdevWZxWqEEIIISqQlNaIhzp9+jQmJiaEh4eTnJzMvn37aNq0qfIEySJWVlZs3bqVlJQUjh49+kjlCI/ad9EsLv369SMxMZGUlBSSkpLYsWMH9evXV9oV1aTfG0O3bt04fvw4KSkpys+9o/Fw9wbEpUuXkpCQoPS9ePFiTE1N+eWXX7hz504FHMmHK6q/T0lJYcOGDY+0zaMewz///JPIyEj69+9PTEwMKSkpxMXFMXz4cCwsLNiwYUOJV0+EEEII8XySRF48VKNGjViyZAlNmzZFX18fe3t72rZti0ajKfdNc4/at4mJCRYWFhw/fpzk5GS0Wi0GBgbUr1+fESNGlOuqgLGxMYGBgXTo0EGZYcfAwABPT08cHR3Jy8ur0JsdK9qjHsPLly9jZGSEn5+fUnJjaWlJz549qVy5Mmlpac/qLQghhBCiDCSRFw/l5eWFk5OTzrLy3GFdlr5tbW2ZNGkS58+fp0ePHrRv35758+eTlpZW7ptfAC5evMicOXNo06aNMt970aj28+5Rj6GZmRlqtZrt27dz7do1AK5du8YPP/zApUuXqF69+lOJVwghhBAVQ2rkX0HZ2dncuXMHIyMjneXP+5R0LVu25Pfffyc6OpqtW7cSGhrK/Pnzee+995gxYwYmJiZl6vfs2bOMGDGC1NTUCo74+fLWW2/RuXNnwsLCCAsL01nn5+f30Jt1hRBCCPF8kRH5V9C5c+fIzc3VWZafn098fDwGBgYlPjr5eWFiYkLHjh1ZunQpR48eZfjw4WzatIl9+/aVuc/9+/eTmprK8OHDiYuLU2rUH7XO/0WRmJhIbGwsbdu2xd7eHgB7e3sCAwP54osvynwiJIQQQohnQxL5V4iZmRn16tXjzz//ZNmyZdy4cQO4O5vJhg0b2LZtG66uruV6VPCTEhcXR3BwMKmpqcqVg0qVKuHp6YmpqWmxp8TB3Tp3MzMz9u3bx759+0q94pCVlQWAg4MD5ubmFBYWcu7cOZYtW/ZSPeZ+37592NvbM2XKFLZv367cGDtq1ChJ4oUQQogXkJTWvEKMjIwYMmQIsbGxhIaGEhoaqrPe0dGRTz/9tMxJXXh4OJMmTdJZlpCQoOynW7duZZ4m8s6dOyXGXBR3kyZNii03NzenZcuWHDhwgNGjR+usu3ce+RYtWrB69WqmT5/O9OnTHxhHafPU3zv3+719l/bk16LtGzduzMqVK7Gysnrsvh+XpaUlCQkJ+Pr66iw3NDSkefPm+Pv706JFizL1LYQQQoinT0bkXzHOzs6sXr2aDz74QHkMsrW1NX379mX16tXP7QN/3N3dWb58Od7e3lSqVAm4Wxby8ccfs3HjRmrXrl1sGz09PQYPHsy0adOoV69eqX23aNGCOXPm0LBhQwAsLCz44IMPiIyM5P33338yb+gZ6Nq1K40aNcLCwoL69esrx7GgoICYmBgGDx7M/v37n3GUQgghhHhU8mRXIV4BarWamTNnkpqayoIFC6hZs6ayTqPR8MMPPzB9+nR5sqsQQgjxHLv/ya4yIi/EKyAjI4OYmBg0Gg03btxQ7hcoLCzk4sWLJCYmAnfLlIQQQgjxYpAaeSFeAdWrV8fJyYno6Gg6depUYpuOHTvSvn37pxyZEEIIIcpKEnkhXgGWlpbMmzeP8PBwduzYwalTpygoKKBSpUp4eHjQp08ffH19MTIyqpAHbAkhhBDiyZMaeSGEQqvVKj9SIy+EEEI8X6RGXgghhBBCiJeAJPJCCCGEEEK8gCSRF0IIIYQQ4gUkibwQQgghhBAvIEnkhRBCCCGEeAFJIi+EEEIIIcQLSBJ5IYQQQgghXkCSyAvxnLp16xarVq2ic+fOODk5KT+xsbFl6i8rKws/Pz+dvoKDg0ttv2XLFho0aEDjxo1p3rx5Wd+GEEIIIZ4QebKrqFB5eXn88MMPREREcOrUKQwMDGjbti1DhgyhadOm6OvLueOj0Gg0zJ07lxUrVjzrUHRcuXLlWYcghBBCiP8jT3YVFebcuXMEBATw119/lbh+w4YNeHp6PuWoXkzJyckMGTIEZ2dnvvjiC+zs7NDT06uw/rOyshg2bBgtWrQgMDBQWV7ak103b95MmzZt5MmuQgghxDN0/5NdZUReVAitVsumTZs4fvw4n3zyCR9++CGWlpZotVouX75MeHg4RkZGzzrMF8bVq1fJyMhg6tSpvPbaa886HCGEEEI8h6TOQVSIW7dukZGRga2tLe+88w6WlpYA6OnpYWtry5gxY2jSpInSvqheu6Qa7fDwcLy9vUlOTi627sqVK3z77be0adMGJycnPD09CQ0NJS8vr1hbrVZLYmIiH3/8Me7u7jg5OdGtWzf27t1LYWGhTtvCwkKio6Pp378/Li4uuLq6Mm7cONLS0kp8vxcvXmTOnDl4enri5OREmzZtWLRoEdevXy/WVqPRsGfPHp2+hw0bxp9//snDLohVrVq11HVlPYZCCCGEeDlIIi8qROXKlbGzsyMjI4Pw8PASE+vySkpK4v333+e7774jPT0dgMzMTIKDg9m5c6dOW61Wy5YtW/Dz82Pnzp3k5OQAkJiYyPTp00lJSVHaajQa1qxZw7Bhw4iJiaGgoIDbt28TFRVF9+7di91cmpaWxqBBg1iyZAmZmZkApKenExISwu7du4vFsXz5cj766COdvn///XeCgoLIzs5W2gYHBys3ofbt2xeAvn376tycGhAQgFqtrqAjKoQQQogXmZTWiAqhp6fHoEGDSExMZPny5ezYsYNBgwbRs2dPrK2ty91/dnY2s2bNIj09nbFjx9K/f3+sra3Jy8sjOjq62Mh2QkICX331Fba2tnz22Wf4+PhQqVIlLl++zM6dOzEwMFDa/v3333z33Xf4+voSGBiIvb09hYWFxMTEMHPmTNasWYObmxsmJiYAHD16lKSkJBYuXEjHjh0xMDAoNY6cnBx+++032rZtS1BQEDY2NhQWFpKens6OHTsqtO5dCCGEEK8WGZEXFcbGxobly5czf/58qlSpwtdff02rVq0YPXo0SUlJ5er79OnTxMTEMHz4cD7++GPl5MDExISOHTvSqVMnpa1Wq+XXX3+loKCAr776ik6dOlG5cmWlzGfo0KE4Ojoq7WNiYqhatSqBgYE4ODigp6eHgYEBrVu3ZsCAAZw8eZJLly4p7atWrYqhoSExMTFkZWWVGgeASqWievXqnDlzhhMnTlBYWIi+vj6vv/46/v7+SgkSQGBgICkpKaSkpLBhwwbg7g3CRctSUlKYN28exsbG5TqWQgghhHg5yIi8qFAqlYquXbvSpUsXTp8+zerVq4mIiCAmJoZFixbRsmXLMvVbVKvetm1bndH0khTV69erV48333zzgW1v375NSkoKqamp+Pj4lNjG0NCQq1evKsl/69atGTNmDAsXLmTjxo00b96cXr160aFDB8zNzXW2NTEx4dNPP2XMmDEMHToUa2trunbtSq9evahbt65MxymEEEKIMpMsQjwR+vr6uLq68s033yhzoc+dO5dr166Vuc9KlSphaPjo557GxsYPTZQLCwspKCh4rDgMDAwYO3Ys+/btY/Lkydy4cYMJEybg4eHB1q1bi5XXODs7ExUVxbp162jTpg1btmyhS5cuDBkyhMuXLz/WvoUQQgghikgiL54oPT09mjVrhre3N5cuXSqWyF++fJnbt28rr7Oysjhw4ECxfqpXr05ubi5//fXXQ2d6UalUWFlZkZSUVOqsM0WMjY2pVasW9evX59ChQzplLEU/Z86coVmzZsW2rVmzJkOHDiUyMpI9e/bQsGFDQkND+eeff0qMqVWrVgQHBxMbG8vnn39OTEwMmzdvfmB8j+JRj6EQQgghXi6SyIsKkZ2dzdy5c0lMTNSZVeX69ev8+uuvREdH4+joiI2NDXB3VNvMzIzY2FgSExMpLCzkf//7HxMmTCAqKqpY/7Vr18bZ2Zl58+axfv16ZbaXO3fu8Pvvv7Nr1y6lrYGBAc2bN+fq1atMnz6dQ4cOcefOHSXONWvWkJqaqrT39PTkzJkzBAUFkZqaikajeeB7XbFiBZs3b+batWvKSYWNjQ3NmjVDrVbrJNWpqanMmjWLxMREJQZDQ0MaNWqEra0tN2/efKzjfK/HPYZCCCGEeLlIjbyoEFqtlgMHDrBw4cIS11tYWDBq1CiqVKkCgLm5OS1btuTAgQP06dNHaVepUiU6duxIYmKizvb29vaMHDmSzz77jOnTpzN9+nSd9d98843Oax8fH/z8/Ni0aRMDBgzQWWdnZ0fr1q2V1x4eHvTq1YuNGzeWmAB369aNoKAg5SbTa9euMXv27BLfZ+/evbGzs1NeazQadu/ezapVq0o8Jm3atCmxn0fxuMcwPDycSZMm6SxLSEggNDQUuPs+Z8+eTeXKlcnKyuKjjz5SroBotdpyxSqEEEKIiicj8qJCWFpa8vXXXzN06FBq166tLK9duzb/+te/iIqK0rnRVU9Pj4EDBzJu3DgsLCwwNDTE29ubjRs3Mnjw4GL96+np0bNnT8LDw+nSpQsWFhYAODo6Mm3aNLp06aLT3sTEhC+++IL58+fTpEkTDA0NMTQ0xMvLi9mzZ+Pk5KS0NTY25vPPP9dp+yDDhw9n8uTJ1KtXD7g7wt6kSRPmz5/PrFmzdGaVcXJyYu7cuToxW1tb07dvX7Zv346np+ejHuISj8njHEMhhBBCvFz0tA8rOBZCvDKKRt+1Wi0ajYaCggLy8/PJz88nJycHZ2fnZx2iEEII8cpKSkrCwsIClUqFSqWSEXkhhBBCCCFeRJLICyGEEEII8QKSRF4IIYQQQogXkCTyQgghhBBCvIAkkRdCCCGEEOIFJIm8EEIIIYQQLyBJ5IUQQgghhHgBSSIvhBBCCCHEC0gSeSGEEEIIIV5AksgLIYR45Vy9epX+/fszZMgQbty48azDEUI8IVlZWUycOJGgoCDUavWzDqfCSSIvXmpqtZopU6bg6enJoUOHnnU4z42IiAjc3NxYsGABGo3mWYfzRCQnJ+Pt7U1wcHCZ+wgODsbb25vk5OQKjOzZqIjj8SLKysrCz8+PgIAAnS9xAwMDjI2Nsba2xtDQ8BnSBwLNAAAgAElEQVRGKIRQq9Vs2LCBTz75hKFDhzJs2DCmTJnC/v37KSgo0GkbGRnJ0KFDOXr06FOJrehEYOjQoaxZswatVvvA9rt372bo0KH4+/uTkpJSaju1Wk1QUBATJ04kKyurzPFJIi9eahkZGRw4cIDMzEwOHDjwrMN5LqjVavbu3Utubi579+7l+vXrzzokIZ46S0tLVqxYQXBwMMbGxs86HCFeWf/88w8zZ87k119/xdzcnJYtW9K8eXPUajVr1qxh8eLF5OXlPbH9FxYWkpOTw40bNx6apB87dozMzMxS1+fl5REbG1vRIT6QJPLPsbS0NNq1a4eTkxNfffXVA0dOL126xCeffMK8efMeqe9t27bRv3//F26k8XHjtrOzo3Xr1lhbW9O6desnHN2LwdjYGB8fH0xNTfHx8aFKlSrPOiQhhBCvoLy8PNauXUt2djbDhg3j888/56OPPmLUqFEEBwfj5eXFX3/9xcGDB59YDDk5OcyaNYuFCxdy69atUtvp6+uTnZ1NfHx8qW3OnDlDeno6BgYGTyLUkuN6ansSj+2vv/7C2NiY3r17ExMT88CzwBs3bnDkyBHy8/Mfqe+kpCTS0tIqKtSn5nHjNjY2JigoiNjYWFq2bPkEI3ux9OjRg2PHjjF27Nin+oHzrN1bXpKYmMigQYNwcXHB3d2doKAgbt68CUB4eDhOTk6EhoaSkZGBr69vsRKbjIwMpk6diru7Oy4uLvTv359Dhw4pIzpqtZqAgAD8/Pz4448/6N+/Py4uLhw5ckSJIygoiKioKFq1akWfPn3Izs4utZzn/tKYov7Hjh3LqVOnGDduHK6urri6ujJu3DgyMjKA/19a4uvrS0ZGBqGhoTg5OT20xEaj0RAVFUW3bt1wcnLC09OTb7/9VrkEnJeXx4QJE/Dy8iIxMVFnuy+//BI3Nzf279+vLL916xarVq1SBifatWvHqlWrin1x3t/u/v0CxMbG4uTkRHh4uM629x7zCxcuEBAQQNOmTUlISCAyMpIGDRooJTalldxotVoSExMZPXo0rq6uJf5u749h//79Osdp1apVj/xZLMSrLD4+npSUFFq2bImXlxd6enrKOpVKxbvvvou5uTkxMTEcOnSIoUOHEhERAcCiRYt0SmxCQ0Px9/fnjz/+4KuvvmLYsGHs3LmzwmKtU6cOtra2xMXFlVhrr9FoOHjwIMbGxjg4OFTYfh9GEvnnlFqt5rfffqN58+b07t2bc+fOcfLkyWcdlhAvhaioKEaOHImDgwOjR4/mzTffZPny5cyfPx+NRkOLFi1Yt24dXbt2pVq1anzzzTfMmTOHGjVqAHdPKAcOHMipU6cYO3YsU6ZMQa1WM3jwYLZt26azr6SkJMaNG8c///xDgwYNdOqxIyIimDFjBlWqVKFmzZplei+7du2ib9++6OnpMXLkSDp06MBPP/3EyJEjSU9Px9TUlKlTp/LNN99QrVo1unbtyrp16+jRo0epfWo0Gr777jsCAwNp3Lgx//nPf+jYsSPLli1jzJgxXL16FRMTE0aMGIGhoSE//PCDkrj+/fffbN26lffee49WrVoBd5P+adOmsXTpUnx9fQkJCaFBgwZ8/fXXzJgxQ/lSLGr39ddf06BBA0JCQujSpQvLli1j9OjRXL58+ZGPi5GREcOGDWPx4sU4Ojri5eXFypUrGTZsGCqVqtTtfvzxRz788ENSU1OZOHGizu92y5YtxS69L1u2jOnTp+Ph4YG/vz92dnbMmjWLTZs2PXKsQryqTpw4gb6+Pk2bNtVJ4otUrVqVOnXqAGBra8uIESNo1qwZAB06dGDEiBE4Ojoq7fPz81mzZg3p6elYWVk98G/9cVlaWtKkSRPS0tJIT08vtj4jI4PTp09Tt25dqlatWmH7fRi5w+c5lZGRwdGjR5k6dSp16tShbt267N+/n7feeksZQc3KymLYsGEkJCQo24WGhhIaGqrT14YNG/D09CQ8PJxJkybprPP19dV53a1bN4KCgnRqRtPS0ggNDeWnn34iJyeHhg0b8umnn/LWW2+hr3/3XDA2NpYJEyawcOFCoqKiCAsLw8/PjylTpvDf//6XhQsX0r59e7744gusrKyU2Nu3b0/37t1ZtGgRP/30E7du3cLX15fAwEDs7OwAyhR3bGwsffv2LfE43K8o9lWrVpGRkcHcuXNJTEzE2tqakSNHMmDAgGIfBmlpaYSEhLB//35ycnKK9Vnavh6k6MZcoNjvoOj93Ntv0THs27cvnp6ehISE8PPPPwPQqVMnJk+ejK2trdJHSf9fRo0aRWBgYLFYsrKy+Oijj+jduzeNGzdmwYIFTJw48bHez/NMpVKxaNEi6tWrB9y9ojVx4kTlype9vT329vYcPHiQypUr4+7urnyZ3LhxQ0lEZ8+ejZmZGQDvv/8+U6ZMYe3atbRq1UopWbp9+zbTp0+nd+/eyt9L0Wi7mZmZThxlYWpqyvz58/H29kZPTw+tVsuWLVuYOnUqkZGRjB49mmbNmmFpaUnlypWxs7NTEuzS7N+/n7Vr17JkyRLatGmDnp4efn5+tG/fHn9/f3766ScGDBiAs7MzH330EV999RXt2rWjWbNmLF26FBsbGwYNGoSBgQFarZbw8HBiY2NZvXo19evXB+5eFQoPD2fatGl06dKFNm3asHPnTiIjI5k9eza9evVCT0+Pnj174uPjg7+/Pz///DMDBgx4pONiYGCAm5sbr732GhYWFlhbW9OiRYsH1sOnpaWxYMEC3N3dmTNnDtWqVQPu/m5nzJhBSEgIDRs21Pl9Va9enblz5yonYv369WPEiBFER0fTs2dPTE1NHyleIV41arWaK1euYGJigpWVVYltKlWqxJgxY5TXtWvX5vLlyxw5cgQXFxeaNm2q016j0eDh4cGgQYOU7+3y3Eh6r6ysLN5++23279/PoUOHcHZ21jn5OHz4MGq1mlatWvHHH39UyD4fhSTyz6n4+HhUKhUNGjTAysoKT09P9u3bR2ZmpjIq+DTExcXpXKYHSExMZOjQoQQEBODv76+cWGRnZ7NixQrlUtbOnTvR19dn8+bNFBQUsHPnTjp37sw777yj9LVnzx62bNlCamqqsiwqKors7Gzmz5+PpaXlU3mfGRkZTJ8+nbi4OOUO+czMTGbNmoW5uTl9+vRR2iYlJTFq1CidmJ+lVatWsWDBAp3f0fbt29FqtcVOCB5XWFgYixYt4vz58y9VIt++fXvq1q2rvDY3N8fJyYnjx48r5TWlOXv2LAcPHmTBggVKEg9gYmJC69at2bNnDxcuXFASeVdXV9q1a6ck8Q+KoywcHR1xc3NTvlD09PRo374927ZtIzY2lg8//PCx7oO4c+cOv/zyCx4eHjRv3lzni8rNzQ1nZ2dOnDjBnTt3MDIyonv37uzZs4c1a9aQkZFBdHQ0X375pXJp+caNG/z222/F3quenh5NmjTB1taW48eP4+HhwcGDB2nevDnt27fX2a+7uzudOnUiPT2d27dvl+t4PUhsbCypqalMnTpVSeLh7u+2X79+7N69mz179ugk8t27d9e5mmJpaUnt2rVJS0sjPz9fEnkhHkKlUlXY34mxsTFvv/32A0fi1Wo18+bNIykpSWf51atX8ff3V147OzsTEBCg08bW1hZ7e3vlplcbGxvgbp19XFwc9vb2uLi4SCL/qlOr1Rw8eBA3Nzesra3R09PDw8OD5cuXEx8fryTCVlZWbN26Fbg7wjdkyBC6du1a4ggrQJ8+fZSENDg4mKioKFavXq2MNN6vKJlWqVSEhYXh5eWFvr4+6enpBAcHs2HDBnx9fZUvtdzcXHbv3s3Spf+PvXsPi6raHz/+hsFBUARNROXAkUlQVI5XGAEJL4RGR1ROevTY1+uxLEw0i8yj+c1ICsvMKwpeu3gByURP2ckrKmIKmJgoCIlhIl8FLzgyMMzvD3/McQKVm4L6eT3Pfh7Zs2btz94zzqxZ+7PWWkmTJk14+eWXiY2NZf78+bi4uDBx4kROnz5t1JBPTk6ma9euxMbG0rVrV27cuMG8efPYtm0bKSkp9OvXr9pxA6jVasO0T5X1zlfm6NGjBAUF8fbbb2Nra8v+/fuZOnUqSUlJBAYGYm5ujk6nY8uWLVy9epWPPvqIwMBAlEol586dY9asWSiVShYvXmzUCHjYzpw5g7OzMzExMXTv3p3c3FzefPNNUlNTuXTpkuG2493vl/Le+QfJyMhApVIRHR39UM/hUVMoFJXexq2KjIwMioqK7nv97m5sKhSKShvxtY3jfqytrenSpQu7du3iypUr1WrI37x5k/T0dFJTU3Fzc6u0jKOjI6WlpTRq1AgbGxtef/11Jk+eTEJCAkFBQQwaNMhQ9vLly2RnZ3Pw4EHWr19/z2Pevn2bnJwcOnbsWOFL3crKigULFlT5HGoqOzubtm3bVprf6uTkhLOzM1lZWRVeXyFEw/Ggz1RTU1OsrKwM6S9lZWVcv37dsL/8+VZWVhU+u5VKJV5eXqxZs4aUlBQGDhwI3OngzMvLY8SIEVhaWj6Es7o3acg3QOfPnycpKYk333zT0JvaoUMHXFxcSEhIwM/Pj0aNGj30OH799VeSk5OZP3++0Ywvjo6OvPbaa4wbN47Tp08b9U4FBATQp08fcnNzsbe3p3PnzgwcOJC8vDwaN25cYT7Yvn37Gt3CtrGxYeLEiSQkJBga8o/KuHHjeOuttwz/CXv27En37t0pLS2lrKwMuJN/l5+fT6dOnRg4cCCNGzcG7vxyHzp0KJGRkRQUFDzShnzXrl359NNPUalUADg4ONCvXz82btxY6zni3dzcmDdvHm3btq00hehp1bx5c0JDQ/nTn/5U6ePOzs6POCJjJiYmKBQKdDpdjd8DAQEBjBw5stIvxWbNmhn1eHXr1o3nn3+e2NhYhgwZUukX2fjx4+nfv3+lx7K1tTX828bG5pF8vv2RRqPh4sWLtGjR4r53Au/+PBBC1F5paekjHRz+x3SdgoIC5s+fT4sWLZg2bVqFu9h/nPqyS5cu2NnZkZSUhI+PD2ZmZhw+fBgbGxu6d+/+SM7hbtKQb4COHTsGYNQbZmtri7u7O4cOHeLSpUs4ODg89Diys7MpKioiJCSEkJCQe5a5m7e3t9F/ggEDBmBlZUVeXl6lz+/YsWOFRm/r1q1p27ZthUb/wzZw4ECjBkijRo0qDFgxMzPD0tKSX375hT179vDXv/4VhULBmTNniI2Nxdra2ijd4lHw9PQ0NOLL3d0wqg21Wk27du1kBo4/KCgooF27dvcdB1GfKwjqdDo0Gg0KhaLGPcZmZmb07NmzSqlZhw8fNswkER0djZubW4XGsLm5+X1z88vzWAsLCw1pO4+ShYUFbdu2JTU1lcLCwgo5u2VlZU/s4mlC1AcLCwtatmzJ+fPnKSgoMBrTVa64uJioqCiuXr1KcHDwI+0kuxdra2t69OjBrl27OHv2LC1atODChQv06NGjzr57q0NmrWlgbt26xaFDh8jLyyMgIACVSoVKpcLZ2Zn169eTmZlpNNXbw/SoG9LlSkpKGuwXZqNGjRgzZgyWlpbMmDGDDh060L59e1588UVOnTrFuHHjKv0wEk+Oli1bAnd+cD9o8ZD6cuPGDc6ePYujo2O1v/iUSiW2trYPXPikXF5eHsuXL6d///4sXryY5ORkwxgNuDOg19ramhMnTlBYWHjPeho3boyjoyPnz5+nqKjI6DGNRsNXX31FbGwsJSUl1Tqf6nBycuLixYuVTnF7/vx5srKycHFxkQWkhKgjnTp1oqSkhCNHjlT6eVpYWMi5c+cAGtT/Ow8PDywsLDh06BCJiYmUlJTg5eX1UFIlH0Qa8g1MTk6O0awilUlISHioX2blyvNEV69eTVZWVqXbm2++WefHPXPmDGfPnq2XX7YPotPp+OGHHzA1NcXHxwdra2vMzMzo0aMHq1atIigoqNb/kXNzc41ycG/fvs3evXtrG7qoobKyMqMvmC5duuDh4cGmTZsqTAmbnp7OihUr7ruoyIM4OzuTm5trVLderyc1NbXSO1vZ2dkVyv7444/89NNPqNVqrKysjMrrdLr7/gBp0qQJfn5+ZGdnG00rCXdSy6Kiovj5558NdW3evNkwRsff358hQ4YQHR1tGEhma2tL//79SUxM5N///rdRWsrNmzdZuHAhFy5cwMLCAm9vb3766Sd+/PFHoxhTUlL45JNPuH37No0aNaJVq1Y4OjpWWDvjt99+4+TJk5We1x9fx8qo1WqcnJz4+uuvjVLJbt26xddff03Tpk3vmR4khKi+rl274ujoyOHDh0lMTDT6P1paWkp8fDzXr1/H09OzQspefXU2wp3FJjt06EBqaip79+5FpVLRrl27eolFUmsamGPHjnHz5k3i4uLo1q2b0WNarZY5c+aQlJTE77//bjQgy9zcHAsLC3bt2kX//v3p0aPHPQfYwZ1estzcXGJiYpgyZUqFL3uAdu3a4ebmxkcffYSZmRm9evWq81/E169fp7CwEGtra8rKyjhy5Ajz58/Hzs4OHx+fGsX9MF2/fp19+/YxaNAgJk+eTOPGjetstH152k56ejpHjhzB39+f/Px8li5dyldffVUnxxDV4+bmRmRkJGFhYfTu3ZugoCDs7OyYOnUq06dPZ/z48QQFBdGpUyd++uknYmJieOGFFygtLa1xSkvPnj1xdnYmLCyMlJQUmjVrxsmTJ0lISKj0i6uoqIjg4GCGDRuGWq0mKSmJjRs30rNnT4YPH274YdmqVSucnJzYtm0bJSUlqNVqw0CtP3rxxRc5fvw4kZGRnDp1isGDB3Pjxg3i4+PJzs6mQ4cO6PV6jh07xtq1awkKCqJXr14oFArGjRvH/v37WbZsGeHh4VhaWvLyyy9z4sQJ/vd//5cjR47g5+dHbm4uW7ZswdTUlICAAMNxk5KSDJ9zzz33HCdOnGDr1q385S9/MUw7W75i85YtW9BoNKhUKq5cucKOHTu4du2a0Wdn06ZN6dixI9988w1WVlZ0796dIUOGVHrejo6OTJ06ldmzZxsmD7C0tGTz5s2kpaUxf/78Ws8yJIT4r6ZNmzJ27FiWLl3K6tWr2bt3L3Z2dpSWlpKRkUFBQQFeXl5G4+WcnJxQKBTExsZy4sQJ/P39a9WItra2Zvbs2ZiamhrGvT2IQqHA29ub1NRUQ298Xc5ZXx3SkG9AioqKSEhIoHv37hVynuHOLe9evXoRExPDiRMnjBrydnZ2eHh48PXXXzNixAij51U2p7mnpyfW1tZERUURFRVl2H/3fOx2dnaMHz+ed999l7Fjx1aIx97e/oGzxzzI119/zddff220z8zMjPnz5/Pss89WKF+VuMvnY9++fbvRc++euebjjz82mlKyqszMzLC2tmb16tWsXr3a6DFra2teeOEFw6Iw1dWoUSP69OlDbGwsr7/+utExfX19a51SVdl8/KmpqYZ1BwIDA5k/f36VP8ieBr6+vkyaNIkNGzZw8eJFw2wsXl5exMTEEBkZyaZNm7h27RodO3bkgw8+MMxkVNMceUdHRz799FMWLFjAl19+SZMmTXjhhRfYunUrc+fOrVC+S5cuzJkzh3Xr1hmmSitfi6F8ajS4M0B16tSphrLW1tb3bMhbWloyb948vL29iYqKIjQ0FGtra5577jkWLVqEo6MjBQUFLFu2zGjOeLgzz3P53PI+Pj787W9/o1WrVqxYsYJNmzbx1VdfsWPHDmxtbQkMDOTVV181pCtZWloSFhZGp06d+Oqrr9i6dStOTk688cYbjBw50tAjp1QqmTFjBo0bN2br1q18//33uLu7s2zZMhISEkhKSjKcS6NGjfjnP//J+fPnDQPABw8efM/rHxAQwJ///GeWL19OREQEOp0OT09PvvrqK3r16lUvt86FeJK1a9eODz74gG3btpGYmMi5c+cwNTXFzs6OSZMmoVarjTomy6f03bNnD0ePHsXDw6NWxzc1NcXa2rraz3NxccHBwYHbt2/TpUuXWsVQK3rRYKSlpel79eqlX7p0qb6srKzSMhkZGfo+ffroQ0ND9cXFxUaPXb16Vb9w4UL9c889p3dycjJsR44cqVBPWVmZ/tChQ/oxY8boO3bsaCgbEhKiv3XrllG5kydP6qdMmaLv1q2bUb19+vTRZ2Rk6PV6vf7IkSN6Jycn/ZYtW4zi/OPfH3/8sSHWYcOGGdXXrVs3/ZQpU/QnT5685/lXJe5bt27pQ0JCjOr+41Ye192x//E6ldfzx2uye/dufadOnfTPPfec3s/Pr0LdgYGB+kuXLlUa/4OUlpbqv/zyS72Xl5feyclJP3jwYP3evXv1Z8+e1ffp08coxvJrWH5N77Zlyxaj16d83/2uSUhIiL6oqEiv0+n0+fn5+iFDhujDwsL0BQUF+ry8PP3Zs2drdE7i4Sh/fw4bNkx/9erV+g5HCCHEI3D27Fl9Xl6evqCgQF9UVKQ30esb6Ggt8UQrn8e8d+/e95z3viE6d+4cr7zyCgMHDmTatGlGt9IKCwt577332LFjR41Wdm0I9Hq9YdPpdIZpwbRaLdeuXav3aRXFf5XfecrJyWH16tX3XBlRCCHEkyMjIwNra2uUSiVKpVIGuwpRHcnJyWRnZ1NcXMzNmzcNA3NKSkrIzMwkOzsbe3v7BjFFlhBCCCGebJIjL0Q1tG/fHmtra9auXcvatWsrPG5mZsbMmTMNq6kKIYQQQjws0pAXohq6devG5s2bWb9+PQcPHuTChQvAnSn2+vbty//8z//QuXNnGRAnHjoLCwsWLVpU32EIIYSoR5IjL4QwkBx5IYQQouGSHHkhhBBCCCGeANKQF0IIIYQQ4jEkDXkhhBBCCCEeQ9KQF0IIIYQQ4jEkDXkhhBBCCCEeQ9KQF0IIIYQQ4jEkDXkhHpGkpCTUajUzZ87k1q1b9RrLtm3bcHNzY/Hixeh0unqN5Z4KC+G118DJCUxMZJNNNtlkk+3J35ycYNQouHSpSl+V0pAXj7XMzEx8fHyIiIio71AeaP/+/eTn53Po0CEuXrxYb3FoNBr27dtHUVER+/bt4/r16/UWy31Nnw6RkfDrr/UdiRBCCPFo/PorbNp0pzFfBdKQf8oUFBQQFBSESqWqdIuJianvEJ9Yvr6+2Nra4u3tTdu2beus3ry8PKZPn17lVT4tLCzo27cvTZo0oW/fvjRr1qzOYqlT27bVdwRCCCFE/di3r0rFzB5yGEKI/0+tVpOUlFTn9d64cYNjx47Rpk2bKj9n6NChDB06tM5jqVOFhfUdgRBCCNGgSUP+KTV58mRCQ0PrOwwhhBBCCFFDkloj7kuv15OWlsbrr7+Oq6srKpWKgIAAYmNj0Wq1RmVjYmLw8fEhMzPTaH95Os/deezl+2JiYsjJySEkJARXV1dcXV2ZPn06eXl5FWLRarXEx8fz0ksv4eLigkqlYuzYsZWWra6ysjISEhIYPXo0Li4uuLq6EhISQk5OjlG5jIwMBgwYwODBg/n999+NHjt8+DDdu3fnrbfeMgxm1Wg0TJs2zSh9adq0aWg0mnvGcunSJRYsWIBarUalUuHr68uyZcuMctnvTpHy9/cnNzeXyMjICqlSd98BqCyt6l5jCwoKChgxYgRxcXH89ttvzJ49u9rX9KHQ62WTTTbZZJPtydtqSBry4p70ej1bt24lKCiI77//nuLiYgDS09MJDQ1l1qxZtZ59Zc2aNYwePZr4+HiKi4spLi7m22+/JTw83Kixe+vWLd577z1CQkJITk6mtLQUgN9//93w75rS6XSsW7eOiRMnkpiYSGlpKcXFxcTHxzNkyBCjxrCzszMzZszgzJkzLFu2zPBj5vLlyyxZsoRWrVoxdepULC0taxRLTk4OY8eOZcWKFeTn5wNw4cIFPv30U3bt2lWr86yJDRs2MGnSpHo5thBCCCHuTxryT6nKem//2Jt+7tw5Pv/8cxwcHNi8eTMZGRlkZmaya9cuevbsyfbt20lISKhVHGfOnMHS0pKYmBgyMzPZv38/PXv2JDU1lUt3Tb20e/dutmzZQlBQEIcOHeLcuXNkZmYSFxdXrdzwyvz8888sWbIEf39/9u3bx7lz58jIyGDDhg20aNGCdevWGf1gGThwIK+//jpbtmxhx44d6HQ61q9fT3p6OnPnzsXR0dFQ1sLCgkWLFpGVlcWpU6cIDAy8byzHjx8nIyODpUuXkpGRQVZWFmlpaaxYsQIrKytDuebNmxMXF0dWVhY//PAD9vb2TJ48maysLKNNrVZX+pzjx4/TrVu3B16bjIwMLC0tiY6Ors4lFUIIIcQjIA15cU8JCQnk5eUxe/Zs3N3dUSgUmJqa4uzszNy5c3nmmWc4cuRIreYh79q1KytWrKBnz56Ympri4OBAv3790Ol0hno1Gg27d+/Gzc2Nt99+mzZt2mBiYoKpqSlNmzbF1LR2b+PExERatGhBaGgojo6OmJiYoFAo6NOnDy+//DKnT582St9RKBS88sorvPDCCyxbtozIyEjWrVvHv/71Lzw9PWsVS4sWLTAzMyMxMZGCggIALC0tGThwIIMGDapV3TXh5ubGp59+SteuXR/5sYUQQghxfzLY9SlVlcGu+fn5tGvXjvbt21d4zN7enjZt2lBQUIBWq8XCwqJGcXh6eqJSqYz22draGv19+/ZtcnJycHZ2xsbGpkbHuZfi4mKysrLIzs6mb9++lZYxMzPjypUrODk5GfZZWloyZcoUJk+ezKeffkpQUBABAQGYmJjUKp4+ffowZcoUli5dyubNm3F3d+dvf/sbzz//vFGP/KOiVqtp165dhfEQQgghhKh/0iMv7kupVNKoUaP6DgO4E0tte9//qKysrMY59nq93tDAzc3N5ebNm7WOR6FQMHXqVPbv38/MmTO5ceMGb731Fh4eHsTFxaGvxYAYIYQQQjxZpCEv7qlp06acPXuWM/6KnZ0AACAASURBVGfOVHgsNzeX33//HXt7exo3bmzYn5eXR+Fd83/r9XqOHTtGdnZ2jeNQKBQ0bdqUK1euGPUM6/V6UlNTazVrjYWFBW3btqVTp04cPny4Qo55VlYWZ8+epVevXkbPu3z5MnPnzkWlUrF69WrS09NZv359rdKM7tamTRsmTJjA9u3b2bNnD126dCEyMpLffvutTuoXQgghxONPGvLinnr37o25uTnz58/n2LFj6HQ6ysrKSElJYc6cOdy8eZMBAwYY0kmaNm1KaWkp3333Hbdu3eL27dvExMQQGhrKtWvXahxHkyZN6NChA3v37iUuLg6tVsuNGzdYunQps2bNqvWsNWq1mrNnzxIeHk52dvYDG+O3bt0iIiKC9PR0pk2bRt++fXn11VeJiorim2++qVWveXR0NLGxsVy9etVQT6tWrejVqxcajcYwc9DdzM3NsbCwYNeuXRw7doyysrIaH18IIYQQjw/JkRf31LVrVyZOnMjixYsZMWKE0WNmZmZMmTLFaBBk586dcXZ2Zu3ataxdu9aonto0thUKhWHu+rlz5zJ37lxDDC+99BIpKSk1rhvAw8ODv/3tb2zevJn4+PgKjwcGBhIeHo6FhQV6vZ6YmBji4uKYO3cu3bp1w8TEhJEjR5KYmMiHH35I27Zt8fLyAiApKYlRo0ZVqHP79u0AdOvWjdWrV9O8eXMArl69yvz58yuN86WXXsLe3r7Cfjs7Ozw8PPj6668rvE4bN240zFwTExPDO++8Y/R4amoqkZGRhvOcP3++0R0WIYQQQjRc0iMv7kmhUPDGG2/w+eef06NHD8zMzDAzM8PT05PVq1czZcoUFAqFobyjoyMfffQR7u7uwJ1Bq6GhoaxevZpnn322VrF069aNlStX0qVLFwC6dOnCqlWrmDFjRo0H2pazsLDg/fffNzrPe0lMTGTRokX4+/sTFBRkuBthY2PDtGnTAFiyZAmXL1+uUSyTJk1i5syZdOzYEbjzY6VHjx58/vnnhIWFVXquSqWSGTNm8MYbb+Dg4FCj4wohhBDi8WOil9FzQoj/T6/XGzadTkdpaSlarRatVsu1a9dwdnZ+dMFUNgOQfFw99s6dO8crr7yCt7c3c+bMaTCD6YUQT67IyEhOnjzJjBkzKsyU12BU8TsvIyMDa2trlErlnUlAHkFoQgjxyCUlJVVY9EylUqFWq3nnnXdIS0t76LMAxcTEoFKpjFYHfpzqr43MzEx8fHyIiIgw2q9UKrGwsKBly5Z1PguVEOLxpNFo2LhxI9OnT2fChAlMnDiRd999lwMHDlRIzc3KyiI4ONiQFvooYgsPD2fChAksWLDggdMxp6amMmnSJCZMmMDx48cfenySIy+EeKINGDCAzp07G/6+cuUKO3bsYPv27YSHhzN06NB6jO7p4+DgwI4dO+o7DCFEA/Hbb7+xePFirly5gr29PZ07d6a0tJT09HTWrVtHamoq//znP7G0tHwox9fr9dy8eZOysjKsrKzu28GQlZXFr7/+iouLS6WP63Q6Dh06VGcz2FWFNOSFEE80f39/hg8fbrRvzJgxTJ48mbVr1+Lp6YmdnV09RSeEEE+vW7dusX79egoLC5k4cSKenp6GsWdarZb169dz5MgRDh06xPPPP/9QYrh9+zZLly7l6tWrzJo1yzD5xB+Zmpqi1Wo5fPgwzs7OlS4AmZuby5kzZ1AoFI+sMS/3NYUQT5327dszaNAgTp8+zYULFwz7dTod8fHxBAYGGtJwPvvsMwoKCoyer9VqiY2NJSAgAJVKRffu3Zk1axa5ubnAf9N6ymcJGjVqVIUUmIyMDGbNmkX37t1RqVSGmZnuvm1bnp4SHh5OfHw83t7eDB8+nN27dz+w/rtpNBqmTZtGUFAQR44cYfTo0bi4uHDs2DFDrDExMfd8Tvn5R0RE4OPjQ1paGpGRkajValQqFYGBgRw9ehS9Xk9BQQFBQUH4+/uTm5tLZGQkKpXKkGJTWcpNREQEffv25dixY3z22Weo1WpcXFwYO3YsaWlplJWVsW/fPqPXJTIyktu3bxvFXNXXTwjRMKSkpJCVlYWXl5dRIx7upOH99a9/xcrKisTERC5evMjbb79NWFgYGo2Go0ePMmHCBEOKTWRkJMHBwRw5coQPP/yQiRMnsnPnzjqL9U9/+hN//vOfOXXq1D0/U44ePYpGo3mk48mkR14I8dQxMTHBwcGB0tJSQ6+JTqdjyZIlrFy5kuHDhzNu3DhSU1NZtWoVx44dY/HixTzzzDPodDqWL1/O8uXLGTRoEJMmTeLs2bPExcWRlJTEqlWrcHZ25osvvuDAgQNERUUxe/ZsOnToYPhwP3z4MMHBwTg5OTFz5kxsbGz4/vvvmTVrFhcvXiQ4ONhoRqht27axZcsWWrduTZs2bVCpVPet/14yMjIICQmhcePGdO7cGTMzs2r3GhUWFhIcHEzr1q0ZMWIERUVF7Ny5k5CQENauXYtKpWLWrFlkZ2cTERGBl5cXI0aMwNbW9r71Xrx4kfHjx9OvXz9GjRpFfn4+cXFxhISE4Ovry/fff8+gQYPw9fXl6NGjREREoNVqDdeqqq+fEKLh+OWXXzA1NaVnz56V9nC3aNGC9u3bc/XqVUxNTfnHP/7BhQsX+Pe//42DgwMDBgww+n+t1WpZt24dAM2bN0epVNZZrObm5ri7u7Np0yZOnz6Nt7e30ePXrl0jOTkZBwcHHBwcSE9Pr7Nj34805IUQTx2tVktKSgr29vaGL4EDBw6wfv16VqxYga+vLyYmJgQFBeHn50dwcDDfffcdL7/8Mjk5OWzbto2RI0cyd+5cQ4Pb09OT4OBgdu3axeuvv463tzcXL14E7qyxUD6ff3FxMfv378fDw4P333+f1q1bA+Dn54eVlRU7duxg2LBhRlOJNm3alGXLlhmmJQVwcnKqtP77KS4uZs6cObz00kuGPNDqDpQtKipi/PjxTJkyxfAl6ePjw6uvvkpiYiIdO3akV69e2NjY0LhxY+zt7St84VWmtLSUsLAwhg8fbvhC7927NyEhIYYv5/K81Bs3bvD222/zww8/MGLECFq3bl3l108I0TBoNBr+7//+D0tLy3ums5ibmzNlyhTD361bt8ba2ppdu3bxzDPP0Lt3b6PyOp0ODw8Pxo4dW6eNeLizzkv79u1p1aoVhw8fxt3d3egYaWlp5OXlMWLEiEoXb3xYpCEvhHiqFBYW8sUXX/DNN98wadIknJycKCkp4T//+Q8eHh64u7sb9Qy5ubnh7OzML7/8QklJCTqdDp1OV2FQlLe3NwkJCQ+cTtHc3Jx33323wn6FQoGbmxt79+6t8CXg5+dHhw4dannm4OrqyoABA2o1W0ybNm0YPHiw0ReYg4MDdnZ25Ofn17hee3t7evToYXTtXV1dsbe3x8/Pz2gtCisrK1QqFadOneLmzZvVev1kukshGhalUkmTJk3qpC4LCwv69+//wEZ8ZGQkR48erbB/xowZhn8/88wzzJo1y2iRxGbNmuHi4kJSUpLRoNfy3HkbGxu6d+/OkSNH6uR8qkIa8kKIJ9o777xTYUVbMzMzxowZY0jLuH79Ounp6aSmpuLm5lZpPY6OjpSWlmJvb4+npydRUVGcO3eOv//973Tt2pXmzZtjY2NTpZj0ej0ZGRmsX7+eH3/80agBXNnqvQqFotLbztWlUChqPeWjqalpncRSHRYWFkapRn908+bNKr9+0pAX4slWlc+npk2b0qJFC+DO5/GNGzcoKyujWbNmhs9IGxubCp+XJiYmeHl5kZSUZDTo9ddffyUrKwu1Wv3ANMK6Jg15IcQT7e7pJzUaDdu3b0etVle6KnBAQAAjR46s9IugWbNmKJVKFAoFYWFh+Pj4sHz5ciZOnAiAu7s7ISEhFQZsVSYxMZHg4GB69OjBvHnzcHV1xcrKiu3btxMdHV1HZ/70qcrrJ4RoWMoXHnyUXn75ZUOqnUajYdGiRfectUaj0Rj93a5dO1QqFSdPniQ/Px9bW1sOHz4MgJeX1yPv6JCGvBDiiXb39JN6vZ7mzZuzcOFC/P39CQgIMCprZmZGz549KzTw/0ipVDJ48GAGDx7M9evXSUlJYfny5YwbN45FixZVqPdu169fN8z4smDBAqysrAyPPax5kp8WVX39hBD1r3xhuPPnz1NQUFDpNMDFxcVERUVx9epVgoODG8SAdaVSiZeXF2vWrCElJYXevXtz9uxZVCoV7dq1e+TxyPST4qHYtm0bbm5uLF68+JEujPCk0Gg0vPvuu6jVasMvfVF7JiYmDBs2jK5du7JhwwZDSotSqcTW1tbQw3I/586dIzExkevXrwN3enp9fX1ZtGgRzz77LHv27LnvQKcrV66Qk5ODSqUyasTDnRQRUX3Vef2EEA1Hp06dKCkp4ciRI5WutF1YWMi5c+cAGtQP9C5dumBnZ0dSUhJJSUlcvnwZLy+vernrJw15Uec0Gg379u2jqKiIffv2GRo8T4ryebfv3h40f/fdZadNm1bhVt0f5ebmcvDgQfLz8zl48ODDOI2nlp2dHePHjyc5OZl///vf6PV6mjRpgp+fH9nZ2WzatMnoNq9WqyUqKoqff/4ZgLNnzzJ69Gi+//57oy+ekpIStFotSqWyQl7l3T9mra2tadmyJSdOnODq1auG/enp6Xz55Zc1Oqfa/Fhu1aoVjo6OHDt2zOi8f/vtN06ePFnjeu+OrbIv6LpUnddPCNFwdO3aFUdHRw4fPkxiYqLRZ0VpaSnx8fFcv34dT0/PCncsy8rKHvpny71YW1vTo0cPLly4QFxcHK1ataJLly7Vrkej0dQ6rUhSa54iBQUFTJw4kd69exMaGlrh8ZiYGBYvXszatWtp3759jY9jYWFB37592b17N3379qVZs2a1CbvGioqK+OyzzygqKuK9995rUL/mH8Te3p4+ffqwZ88e+vTpU9/hPHF8fHzo378/0dHReHt70759e1588UWOHz9OZGQkp06dYvDgwdy4cYP4+Hiys7Pp0KEDer0eX19fgoKCmD17NomJiQwaNIjc3Fy++OILCgsLGTJkiGFApbOzM02aNOGTTz7B29ubYcOG4eTkhJ+fHxEREYwePZp+/fpx8eJFdu/ejVKppKCggCtXrlTp/2Bl9atUqmpdi/L32pYtW9BoNKhUKq5cucKOHTu4du0a3bp1q9E1btWqFU5OTmzbto2SkhLUajUDBw6sUV1VUdXX71Hnrwoh7q1p06aMHTuWpUuXsnr1avbu3YudnR2lpaVkZGRQUFCAl5cX/fr1MzynZcuW2NjY8PPPP7NkyRK6dOlC//79axxD48aNmTJlCmVlZRXukt6Ph4cHBw4c4ObNm/To0QNra+tqHTcrK4tPP/0UOzs73nrrLWqaWCk98uKhGDp0KCdPnmTq1Kn3nW3iYdJqtSQnJz+w97u61Go1WVlZZGVlsXHjxvuWtbCwYNGiRWRlZXHq1CkCAwOrdAwLCwvCw8NJSkrCy8urLsIWd7GysmLMmDEUFhayceNGdDodlpaWzJs3j88//5yCggJCQ0NZvHgxDg4OfPvttzz33HOYmJgYys2cOZMzZ87w2muvGb5MvvrqK6P53N3c3Jg+fTrZ2dlER0dTWFiIiYkJkyZN4pNPPkGr1RIZGUlWVhYLFizg/fffByAnJ6dK51FZ/dWlVCqZMWMGY8aMISEhgeXLl5OVlcWyZct49dVXq11fuWbNmjF16lRatGjBunXrHvriKFV9/YQQDUu7du344IMP8PPzIy8vj8OHD3Ps2DEaN27MpEmTmDBhAmZm/+13btasGS+99BKNGzcmNTWV7OzsWh3fxMQEKysrrK2tqzWrl729PR06dKBp06Z4eHhU+7hKpRKlUomlpWWtPptM9PV1X0I8co+qR76hKD9fR0dHwsPDH0qPfFJSEqNGjWLjxo0PXJCnPO8deGjx1JZerzdsOp3OMJuAVqvl2rVrj3TZaSr7YJOPKyGEEE+iKn7nZWRkYG1tbfghID3y4p4KCgoICgoiJiaGnJwcQkJCcHV1xdXVlenTp5OXl1dp+bvzwSMiIuqkboDbt28TGxtLYGAgKpWK7t27ExISQkZGhqHM3TnpPXv2JDU1le3bt9O5c2ejuGJiYirEs2rVKgICAlCpVLi6ujJ27FgSEhIoKyurg6tZNdXJv6/JNbx16xaRkZH4+vpWOM79Xi8hhBBCNDySIy8eaM2aNSxevJjc3FzDvm+//Ra9Xl/rnuWq1q3VagkLC+Prr782lLt27Rrx8fGYmJjUKg6NRsP777/P9u3bDfuKi4tJSEggMTGRlStXGuXnNTRVvYa3bt3ivffeIy4urr5CFUIIIUQdkh558UBnzpzB0tKSmJgYMjMz2b9/v6G3+9KlS4ZyzZs3Jy4ujqysLI4fP16lQXJVrbt8Fpd//OMfpKWlkZWVRUZGBjt27KBTp06GcnfnpJfHEBgYyKlTpwx57VlZWYZ5xcu1bduWlStXkpqaaqh7+fLlNGnShP/85z+UlJTUwZV8sOrk35er6jX86aef2L59O6NHjyYxMZGsrCySk5OZNGkS1tbWbNy4sdKUKyGEEEI0TNKQFw/UtWtXVqxYQc+ePTE1NcXBwYF+/fqh0+lqPUd8Veu2tLTE2tqaU6dOkZmZiV6vR6FQ0KlTJ1555ZVa3RWwsLAgNDSU559/3jDDjkKhQK1W4+TkxK1btygtLa3VeT5MVb2Gly9fplGjRgQFBRkW3rCxsWHYsGE0bty4ygMshRBCCNEwSENePJCnp2eFKe1sbW0fad12dna88847XLx4kaFDh+Ln58fnn39OTk5Oncwje+nSJRYsWGCUO17eq93QVfUaNm3aFI1Gw7fffmuYv/zq1ats2rSJvLw8WrZs+UjiFUIIIUTdkBz5p1BhYSElJSWGua7LNfQVWL28vNi7dy8JCQnExcURGRnJ559/zogRI3jvvfdqvLz9uXPneOWVV2o9hVVD99xzzxEQEMCGDRvYsGGD0WNBQUEPnHVHCCGEEA2L9Mg/hc6fP09RUZHRPq1WS0pKCgqFot7mfa8KS0tLBg4cyMqVKzl+/DiTJk1iy5Yt7N+/v8Z1HjhwgOzsbCZNmkRycrIhR72qef6Pi7S0NJKSkujXrx8ODg4AODg4EBoayrx582r8Q0gIIYQQ9UMa8k+Rpk2b0rFjR3766SdWrVrFjRs3gDuzmWzcuJFvvvkGV1dXQ/50Q5KcnExERATZ2dmGOwfm5uao1WqaNGnCzZs3KzxHoVDQtGlT9u/fz/79++95x6GgoAAAR0dHrKysKCsr4/z586xatYrTp08/vJN6xPbv34+DgwPvvvsu3377rWFg7OTJk6URL4QQQjyGJLXmKdKoUSPGjx9PUlISkZGRREZGGj3u5OTEm2++WeNGXUxMDO+8847RvtTUVMNxAgMDazxNZElJSaUxl8fdo0ePCvutrKzw8vLi4MGDvP7660aPffzxx4aZa3r37s3atWuZM2cOc+bMuW8c5Ys63T1VJcCoUaMqrbt8wag/Kn9+t27dWL16Nc2bN6923dVlY2NDamoq/v7+RvvNzMxwd3cnODiY3r1716huIYQQQjx60iP/lHF2dmbt2rWMHDkSa2tr4M7AyFGjRrF27dpHu3JnNXTv3p2oqCh8fHwwNzcH7qSFvPHGG2zevJlnn322wnNMTEwYN24cs2fPpmPHjvesu3fv3ixYsIAuXboAYG1tzciRI9m+fTt///vfH84J1YPBgwfTtWtXrK2t6dSpk+E6lpaWkpiYyLhx4zhw4EA9RymEEEKIqjLR18WUH0KIBk2j0TB37lyys7NZvHgxbdq0MTym0+nYtGkTc+bM4dVXX+Xtt99Gr9ej0+koLS1Fq9Wi1Wq5du3ao/2hV8XlqoUQQojHXhW/8zIyMrC2tkapVKJUKiW1RoinQW5uLomJidja2nLjxg1atWqFQqGgrKyMS5cukZaWBtxJU2rQKvugE0IIIZ5S0pAX4inQsmVLVCoVCQkJDBo0qNIyAwcOxM/P7xFHJoQQQoiakoa8EE8BGxsbFi1aRExMDDt27CA9PZ3S0lLMzc3x8PBg+PDh+Pv706hRozpZYKtONG4Mt2/XdxRCCCFEgyU58kIIA71eb9jqPUe+Xz/Yt+/RHU8IIYRoKNq1g0oWqvxjjrzMWiOEaJhWrID7zDYkhBBCPJFat77zHVgFklojhGiYOnaEJ2hBLiGEEKKuSY+8EEIIIYQQjyFpyAshhBBCCPEYkoa8EEIIIYQQjyFpyAshhBBCCPEYkoa8EEIIIYQQjyFpyAvRQN2+fZs1a9YQEBCASqUybElJSTWqr6CggKCgIKO6IiIi7ll+69atdO7cmW7duuHu7l7T0xBCCCHEQyLTT4o6devWLTZt2sS2bdtIT09HoVDQr18/xo8fT8+ePTE1ld+OVaHT6Vi4cCHR0dH1HYoQQgghGihZ2VXUmfPnzzNt2jROnDhR6eMbN25ErVY/4qgeT5mZmYwfPx5nZ2fmzZuHvb09JiYmdVZ/QUEBEydOpHfv3oSGhhr232tl19jYWHx9fR/tyq5CCCGEMPLHlV2lR17UCb1ez5YtWzh16hTTp0/nf/7nf7CxsUGv13P58mViYmJo1KhRfYf52Lhy5Qq5ubnMmjWLP/3pT/UdjhBCCCEaIMlzEHXi9u3b5ObmYmdnxwsvvICNjQ0AJiYm2NnZMWXKFHr06GEoX56vXVmOdkxMDD4+PmRmZlZ47P/+7//47LPP8PX1RaVSoVariYyM5NatWxXK6vV60tLSeOONN+jevTsqlYrAwED27dtHWVmZUdmysjISEhIYPXo0Li4uuLq6EhISQk5OTqXne+nSJRYsWIBarUalUuHr68uyZcu4fv16hbI6nY49e/YY1T1x4kR++uknHnRDrEWLFvd8rKbXUIiHrfy9OW3aNDQaTX2HUycqO6fMzEx8fHzuO9ZECCEeJmnIizrRuHFj7O3tyc3NJSYmptKGdW1lZGTw97//nSVLlnDhwgUA8vPziYiIYOfOnUZl9Xo9W7duJSgoiJ07d3Lt2jUA0tLSmDNnDllZWYayOp2OdevWMXHiRBITEyktLaW4uJj4+HiGDBlSYXBpTk4OY8eOZcWKFeTn5wNw4cIFPv30U3bt2lUhjqioKP75z38a1b13717Cw8MpLCw0lI2IiDAMQh01ahQAo0aNMhqc+iQ1jB62pKQko2tXvqnVat555x3S0tIe+EOqtmJiYmo1QLm+6xdCCNGwSWqNqBMmJiaMHTuWtLQ0oqKi2LFjB2PHjmXYsGHY2trWuv7CwkLCwsK4cOECU6dOZfTo0dja2nLr1i0SEhIqNMhSU1P58MMPsbOz41//+hd9+/bF3Nycy5cvs3PnThQKhaHszz//zJIlS/D39yc0NBQHBwfKyspITExk7ty5rFu3Djc3NywtLQE4fvw4GRkZLF26lIEDB6JQKO4Zx7Vr19i9ezf9+vUjPDycVq1aUVZWxoULF9ixY0ed5r2Lyg0YMIDOnTsb/r5y5Qo7duxg+/bthIeHM3To0HqMTgghhKg5aciLOtOqVSuioqLYtWsXy5cv56OPPuKTTz7Bz8+P6dOn12qg5JkzZ0hMTGTSpEm88cYbhoa4paUlAwcONCqr1+v58ccfKS0t5cMPP+S5554zPGZnZ8eECROMyicmJtKiRQtCQ0NxdHQEQKFQ0KdPH15++WXWr19PXl4eTk5OwJ10FzMzMxITE/Hw8KBly5aVxgGgVCpp2bIlaWlp/PLLL7Rs2RJTU1P+/Oc/ExwcbFQ2NDTUMPA0KSmJUaNGyQDhOuDv78/w4cON9o0ZM4bJkyezdu1aPD09sbOzq6fohBBCiJqT1BpRp5RKJYMHD2bnzp3s3LmToUOH8uOPPzJixAgOHz5c43rLc9X79etn1JtemfJ8/Y4dO/KXv/zlvmWLi4vJysoiOzubvn37VkjD+OCDD7h48SJXrlwxPKdPnz5MmTKFzZs34+XlxejRo4mLi+PGjRsV6re0tOTNN9/E0tKSCRMm4OnpSVhYGKdPn66Qpy8enfbt2zNo0CBOnz5tSNOCO2lW8fHxBAYGGtJwPvvsMwoKCoyeXz6TT/kc/927d2fWrFnk5uYC/03reeedd4D/pkjdnQKTkZHBrFmzDOM3AgICiI2NRavVGsqU52CHh4cTHx+Pt7c3w4cPZ/fu3Q+svzJVOWZ57DExMRw4cMDoWqxZs8aoLNzJHQ8LC6N79+64uLgwefJkTp48WaXXISIiAh8fHw4dOkRISAguLi5888039809L39O+fiP6sZb0+sihBANkfTIi4fC1NQUV1dXPv74Y/76178SEhLCwoUL6dix430HcN6Pubk5ZmZVf8taWFg8cN76srIySktLqxWHQqFg6tSpDB8+nO+++45t27bx1ltvYW5uzocffsiwYcOMUmacnZ2Jj4/np59+4ttvv2Xr1q2sWbMGHx8fFixYQKtWrap1fFF7JiYmODg4UFpaik6nA+404pcsWcLKlSsZPnw448aNIzU1lVWrVnHs2DEWL17MM888g06nY/ny5SxfvpxBgwYxadIkzp49S1xcHElJSaxatQpnZ2e++OILDhw4QFRUFLNnz6ZDhw6Gu1KHDx8mODgYJycnZs6ciY2NDd9//z2zZs3i4sWLBAcHG/1g3bZtG1u2bKF169a0adMGlUp13/orU91jrlq1Cq1Wy/PPP4+vry+HDh0iLCwMpVLJyy+/DNxJeXv77bc5cOAAPj4+uLm5kZ+fT0hICEVFRYY7XPdTWFjIm2++CUDnzp0xNzev/gtaxXjr4roIIURDIg158VCZmJjQq1cvfHx8SElJ4erVq0YN+cuXL1NcXGz48i4oKODgwYMV6mnZsiVFRUWcOHGCrl273je3XKlU0rx5c5KSksjJyaFLly73LGthYUHbMaxkigAAD1BJREFUtm3p1KkT0dHRtG7dusrn1qZNGyZMmMCECRP49ddfefvtt4mMjMTd3R0HB4cKMXl7e+Pt7Y1Wq2Xz5s188MEHxMbG8vrrr1f5mJWp6jUU/6XVaklJScHe3p5nnnkGgAMHDrB+/XpWrFiBr68vJiYmBAUF4efnR3BwMN999x0vv/wyOTk5bNu2jZEjRzJ37lxDI8/T05Pg4GB27drF66+/jre3NxcvXgTuNFDLU6SKi4vZv38/Hh4evP/++4b3nJ+fH1ZWVuzYsYNhw4YZvYeaNm3KsmXL6Nixo2Gfk5NTpfVXpibHbNmyJQsXLqRNmzYA/OMf/+CVV14hISGBYcOG0aRJE3bs2MGePXv46KOPGD58uOH/5YsvvlghdexeioqKGD9+PFOmTEGpVALUaLalqsRbF9dFCCEaEkmtEXWisLCQhQsXkpaWZjSryvXr1/nxxx9JSEjAycnJ0PusUCho2rQpSUlJpKWlUVZWxq+//spbb71FfHx8hfqfffZZnJ2dWbRoEV9//bVhtpeSkhL27t3L999/byirUChwd3fnypUrzJkzh8OHD1NSUmKIc926dWRnZxvKq9Vqzp49S3h4ONnZ2YYe2nuJjo4mNjaWq1evGga3tmrVil69eqHRaCguLjaUzc7OJiwsjLS0NEMMZmZmdO3aFTs7O27evFmt63y36l5DcUdhYSErV67km2++ITAwECcnJ0pKSvjPf/6Dh4cH7u7uRj8U3dzccHZ25pdffqGkpASdTodOp8PKysrojo+3tzcJCQmMHTv2vsc3Nzfn3XffZeXKlUY/HBUKBW5ubhXeQ3CnYdmhQ4can3NNjjlkyBBDoxjAxsaGZ599lvz8fLRaLRqNhmPHjtGpUyeee+45o2vm6upqGFPyIG3atGHw4MGGRnxNPSjeytTkugghREMiPfKiTuj1eg4ePMjSpUsrfdza2prJkyfTrFkzAKysrPDy8uLgwYNGAxHNzc0ZOHAgaWlpRs93cHDg1Vdf5V//+hdz5sxhzpw5Ro9//PHHRn/37duXoKAgtmzZUuG2ur29PX369DH87eHhwd/+9jc2b95caQM4MDCQ8PBwLCwsALh69Srz58+v9Dxfeukl7O3tDX/rdDp27drFmjVrKr0mvr6+ldZTFdW9hjExMYac6nKpqalERkYCd85z/vz5NG7cmIKCAv75z39y4sQJw0qvtYm1Pr3zzjsVztvMzIwxY8YY0iauX79Oeno6qampuLm5VVqPo6MjpaWl2Nvb4+npSVRUFOfOnePvf/87Xbt2pXnz5ob1Ex5Er9eTkZHB+vXr+fHHHw3TmAJG759yCoWi1jMc1eSY93P79m1ycnJo37491tbWNY7L1NS0TmZvqmn6S3WvixBCNCTSkBd1wsbGho8++ogtW7awf/9+zp07B9zpSff392fUqFFGK5SamJgwZswYiouLWbduHUVFRXh6ejJjxgw0Gg1vvfWWUf0mJiYMGzYMFxcXVq5cycGDB7l27RpOTk6MHj2aF1980ai8paUl8+bNw9vbm/Xr1/Pzzz8D4O7uzuTJk1GpVIayFhYWvP/++3h5eRnK3i9vftKkSdjY2LBt2zbS09MxMzPjL3/5C2PHjmXgwIFGPYsqlYqFCxeyYcMGQ8y2trb4+fnx6quvVimH+F6qew2fVndPP6nRaNi+fTtqtZoZM2YYfpyVCwgIYOTIkZU2LJs1a4ZSqUShUBAWFoaPjw/Lly9n4sSJwJ33VkhICJ6eng9smCYmJhIcHEyPHj2YN28erq6uWFlZsX37dqKjo+vozOv/mI8DuS5CiMeZNORFnTAxMcHFxYXZs2cze/bsKj3H0tKSkJAQQkJCKjyWkJBQ6TG6dOnCkiVLqlR/+Qw6gwcPrtOyzZs355VXXuGVV155YFlTU1Pc3d1xd3evUszl1Gq10aJV91Kdazh8+PAK0zD+UXnve/PmzdmyZQulpaVotVq0Wq1hUa3Hzd3TT5af28KFC/H39ycgIMCorJmZGT179qzQwP+ju98v169fJyUlheXLlzNu3DgWLVpUod67Xb9+ncjISNRqNQsWLMDKysrwWPlaBXWtPo75OJDrIoR43EmOvBDiqVF+Z6dr165s2LDBkEahVCqxtbXl5MmTRqkVlTl37hyJiYlcv34duNNT7+vry6JFi3j22WfZs2fPffOqr1y5Qk5ODiqVyqjhCNRqzMT9PIxjNm7cGEdHR7Kzsw3X4nFTH6+FEELUJWnICyGeKnZ2dowfP57k5GT+/e9/o9fradKkCX5+fmRnZ7Np0yajwZFarZaoqChDetbZs2cZPXo033//vdFKviUlJWi1WpRKZYVpT+8eQG1tbU3Lli05ceIEV69eNexPT0/nyy+/rNE5PWiA9sM4poWFBQMGDODkyZPs37/f6FqcPn3aaEB5dbVs2ZLWrVtz4sQJw8B2uDNDU0pKSo3r/aOHcV2EEOJRktQaIcRTx8fHh/79+xMdHY23tzft27fnxRdf5Pjx40RGRnLq1CkGDx7MjRs3iI+PJzs7mw4dOhgG/QYFBTF79mwSExMZNGgQubm5fPHFFxQWFjJkyBAaNWoE3FlDoEmTJnzyySd4e3szbNgwnJyc8PPzIyIigtGjR9OvXz8uXrzI7t27USqVFBQUcOXKFdq3b//A86is/rvHf5Rr3rx5nR3zbgMGDDBci4MHD6JSqfj1119JSEio9voMd7O2tmbAgAFEREQQEhJC9+7duXnzJt999x2///57nQ1CfVjXRQghHhXpkRdCPHWsrKwYM2YMhYWFbNy4EZ1OZxgg/fnnn1NQUEBoaCiLFy/GwcGBb7/91jDFYnm5mTNncubMGV577TWWLFlCly5d+Oqrr4zmc3dzc2P69OlkZ2cTHR1NYWEhJiYmTJo0iU8++QStVktkZCRZWVksWLCA999/H/jvSsYPUln9lanLY97N0tKSf/3rX7z66qskJCSwfPlybt++zcqVKxkwYEC167s73nHjxhEaGkp6ejqLFy/m6NGjzJw5k7CwsBrXW9lxHsZ1EUKIR8VEf/f9UCHEU618sKter0en01UY7Hq/lUOFEEII8XBlZGRgbW2NUqm8k8pZ3wEJIYQQQgghqk8a8kIIIYQQQjyGpCEvhBBCCCHEY0ga8kIIIYQQQjyGpCEvhBBCCCHEY0ga8uKJptFoePfdd1Gr1Rw+fLi+w2kwtm3bhpubG4sXL37gYkJCCCGEaJikIS+eaLm5uRw8eJD8/HwOHjxY3+E0CBqNhn379lFUVMS+ffu4fv16fYckhBBCiBqQhnwDlpOTw4ABA1CpVHz44Yf37TnNy8tj+vTpLFq0qEp1f/PNN4wePZrMzMy6CveRqG7c9vb29OnTB1tbW/r06fOQo3s8WFhY0LdvX5o0aULfvn1p1qxZfYckhBBCiBowq+8AxL2dOHECCwsLXnrpJRITE8nPz6d169aVlr1x4wbHjh2jTZs2Vao7IyPjsVyxsLpxW1hYEB4e/hAjejwNHTqUoUOH1ncYQgghhKgF6ZFvoDQaDbt378bd3Z2XXnqJ8+fPc/r06foOSwghhBBCNBDSkG+gcnNzOX78OB4eHrRv354OHTpw4MABo/SagoICgoKCUKlU+Pv7k5ubS2RkJCqVymhLSkoCICYmxrAvMjKS3Nxc/P39jcpOmzYNjUZjFEtOTg6zZs2ie/fuqFQqAgMD2bdvH2VlZYYySUlJ+Pj4cOLECcLCwnBxcWHmzJlcu3aNZcuW4erqyhtvvEFBQYFR7MuXLyc3N9dQv6urKyEhIeTm5hrqrkncSUlJ97wOf1Qee0ZGBvv27SMwMBCVSoVarWbNmjVotdoKz8nJySEkJMRwTap6rPvRaDRMmzat0teg/Hzurrf8GsbExBjicXV1xdXVlenTp5OXl2dUx93vl/ItIiKi0lgKCgoYMWIEcXFx/Pbbb8yePbva5yOEEEKIh0tSaxqolJQUlEolnTt3pnnz5qjVavbv33/f9JqHITk5uULDOi0tjQkTJjBt2jSCg4NRKBQAFBYWEh0dzc6dOwHYuXMnpqamxMbGUlpays6dOwkICOCFF14w1LVnzx62bt1Kdna2YV98fDyFhYV8/vnn2NjYPJLzzM3NZc6cOSQnJ1NaWgpAfn4+YWFhWFlZMXz4cEPZjIwMJk+ebBRzfVqzZg2LFy82eo2+/fZb9Ho94eHhWFhY1LjuDRs2sGzZMi5evMjbb79dF+EKIYQQoo5Ij3wDpNFoOHToEG5ubtja2mJiYoKHhwdnz54lJSXFUK558+bExcWRlZXFDz/8gL29PZMnTyYrK8toU6vVAAwfPtywb/Lkydjb2/PDDz8YlV20aJGh4VfemFYqlWzYsIGMjAzOnTvHvn37CAgIYOPGjWRkZBjiKSoqYteuXaxcuZIvv/ySoqIiYmNjmT9/Ptu2beOZZ56pkB6UnJxMs2bNiI2NJSMjg+TkZIYOHUpCQoLhXKsbN4BarTY8tnHjxipd96NHjxIYGEhiYiKZmZmsXr2aJk2akJSURHFxMQA6nY4tW7Zw9epVPvroI3755RcyMzPZtWsXPXv2xNPTk59++slwzR+FM2fOYGlpSUxMDJmZmezfv5+ePXuSmprKpUuXDOXufr8cP36cbt26PbDujIwMLC0tiY6OfpinIIQQQogakIZ8A3T+/HmSkpLw9vY2NE47dOiAi4sLCQkJlJSUPJI4fv31V5KTk5k2bRp9+vRBoVBgYmKCo6Mjr732GqWlpRUa5gEBAfTp04dWrVphb29P//79GThwIJaWljRu3NjQ212ub9++REdH06NHDxQKBTY2NkycOJFnnnnG6EfLozDu/7VzxygKQ0EAhn8eCiJKQAmKEAhCIAghSBCxFAurQI7gDbyDvXUukTPkBKmTUrANljYmyhZLZINstVG2mK8cHvOmnHkMb7fjcDgwGo1QSuF5HvP5nLIsn2tEt9uNPM+ZzWZst1s6nQ5KKSzLIggCzufzc33oU1zXJQxDPM9DKYVhGKzXa+73+5//iHcch+PxiOu6DVUrhBBCiKZII/8PJUkCfDdRFV3XWSwWJElSe2V9p9PpxPV6Zb/fv+yA+77P5XJ5WS/5OXwAbDYb+v3+r3fYts1wOKzFxuMxk8nkpel/t2rgqLTbbQaDQe1Mq9Wi2+2SpilxHFMUBY/HgyzLiKIITdPo9XofrXu1WjGdTmsxXdcbyb1cLjFNs5FcQgghhGjWF2BTF77D4LCTAAAAAElFTkSuQmCC)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQBckLcBnpPu",
        "outputId": "55994e30-359c-4dbb-9e27-523eebccf301"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tzlocal==2.0\n",
            "  Downloading tzlocal-2.0.0-py2.py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.8/dist-packages (from tzlocal==2.0) (2021.3)\n",
            "Installing collected packages: tzlocal\n",
            "  Attempting uninstall: tzlocal\n",
            "    Found existing installation: tzlocal 2.1\n",
            "    Uninstalling tzlocal-2.1:\n",
            "      Successfully uninstalled tzlocal-2.1\n",
            "Successfully installed tzlocal-2.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tzlocal==2.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9KslZIuw1p5",
        "outputId": "a2b2bdb6-d53f-49d3-82ad-6412802eb082"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting nest_asyncio\n",
            "  Downloading nest_asyncio-1.5.6-py3-none-any.whl (5.2 kB)\n",
            "Installing collected packages: nest-asyncio\n",
            "Successfully installed nest-asyncio-1.5.6\n"
          ]
        }
      ],
      "source": [
        "!pip install nest_asyncio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uiSWgI3xCeK",
        "outputId": "17e39c5d-b1fe-4e6b-c61b-683d0ec6ee9b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "event loop ready\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import rasa\n",
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()\n",
        "\n",
        "print(\"event loop ready\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "aR2PFEHUzgpR"
      },
      "outputs": [],
      "source": [
        "from rasa.cli.scaffold import create_initial_project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "d20WNabxzf0t"
      },
      "outputs": [],
      "source": [
        "project = 'test_project'\n",
        "create_initial_project(project)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwgC5CVaCn8U",
        "outputId": "c933e375-753a-447f-f468-f1ae900ed41e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['domain.yml', 'data', 'config.yml', 'actions', 'tests', 'endpoints.yml', 'credentials.yml']\n"
          ]
        }
      ],
      "source": [
        "os.chdir(project)\n",
        "print(os.listdir(\".\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7UH5pC_RMX-w",
        "outputId": "85a812c4-de91-4a4d-bfa5-efaf7ebdb68a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "config.yml data/ domain.yml models/\n"
          ]
        }
      ],
      "source": [
        "config = \"config.yml\"\n",
        "training_files = \"data/\"\n",
        "domain = 'domain.yml'\n",
        "output = 'models/'\n",
        "\n",
        "print(config, training_files, domain, output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDSNIVJ0OcjW",
        "outputId": "74e3b4e6-d10c-4ad0-8ef2-de4b87b6619d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working\n",
            "  from collections import Iterable\n",
            "/usr/local/lib/python3.8/dist-packages/past/builtins/misc.py:4: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working\n",
            "  from collections import Mapping\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[94mThe configuration for policies and pipeline was chosen automatically. It was written into the config file at 'config.yml'.\u001b[0m\n",
            "\u001b(0lqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqk\u001b(B\n",
            "\u001b(0x\u001b(B Rasa Open Source reports anonymous usage telemetry to help improve the product \u001b(0x\u001b(B\n",
            "\u001b(0x\u001b(B for all its users.                                                             \u001b(0x\u001b(B\n",
            "\u001b(0x\u001b(B                                                                                \u001b(0x\u001b(B\n",
            "\u001b(0x\u001b(B If you'd like to opt-out, you can use `rasa telemetry disable`.                \u001b(0x\u001b(B\n",
            "\u001b(0x\u001b(B To learn more, check out https://rasa.com/docs/rasa/telemetry/telemetry.       \u001b(0x\u001b(B\n",
            "\u001b(0mqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqj\u001b(B\n",
            "\u001b[94mTraining NLU model...\u001b[0m\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/rasa/utils/tensorflow/model_data.py:750: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  np.concatenate(np.array(f)),\n",
            "Epochs:   1%|          | 1/100 [00:06<11:05,  6.72s/it, t_loss=5.77, i_acc=0.0469]/usr/local/lib/python3.8/dist-packages/rasa/utils/tensorflow/model_data.py:750: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  np.concatenate(np.array(f)),\n",
            "Epochs: 100%|██████████| 100/100 [00:25<00:00,  3.93it/s, t_loss=1.19, i_acc=0.986]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[94mNLU model training completed.\u001b[0m\n",
            "\u001b[94mTraining Core model...\u001b[0m\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[93m/usr/local/lib/python3.8/dist-packages/rasa/core/policies/ensemble.py:444: DeprecationWarning: Please configure the max history in your configuration file, currently 'max_history' is set to old default value of '5'. If you want to have infinite max history set it to 'None' explicitly. We will change the default value of 'max_history' in the future to 'None'.\n",
            "  policy_object = constr_func(**policy)\n",
            "\u001b[0mWARNING:rasa.shared.utils.common:The UnexpecTED Intent Policy is currently experimental and might change or be removed in the future 🔬 Please share your feedback on it in the forum (https://forum.rasa.com) to help us make this feature ready for production.\n",
            "Processed story blocks: 100%|██████████| 3/3 [00:00<00:00, 288.77it/s, # trackers=1]\n",
            "Processed story blocks: 100%|██████████| 3/3 [00:00<00:00, 269.89it/s, # trackers=3]\n",
            "Processed story blocks: 100%|██████████| 3/3 [00:00<00:00, 152.01it/s, # trackers=12]\n",
            "Processed story blocks: 100%|██████████| 3/3 [00:00<00:00, 63.58it/s, # trackers=39]\n",
            "Processed rules: 100%|██████████| 2/2 [00:00<00:00, 427.31it/s, # trackers=1]\n",
            "Processed trackers: 100%|██████████| 3/3 [00:00<00:00, 227.96it/s, # action=12]\n",
            "Processed actions: 12it [00:00, 281.42it/s, # examples=12]\n",
            "Processed trackers: 100%|██████████| 2/2 [00:00<00:00, 184.89it/s, # action=5]\n",
            "Processed actions: 5it [00:00, 706.52it/s, # examples=4]\n",
            "Processed trackers: 100%|██████████| 3/3 [00:00<00:00, 219.14it/s, # action=12]\n",
            "Processed trackers: 100%|██████████| 2/2 [00:00<00:00, 1390.22it/s]\n",
            "Processed trackers: 100%|██████████| 5/5 [00:00<00:00, 978.24it/s]\n",
            "Processed trackers: 100%|██████████| 120/120 [00:00<00:00, 1731.19it/s, # intent=12]\n",
            "/usr/local/lib/python3.8/dist-packages/rasa/utils/tensorflow/model_data_utils.py:383: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  np.array(values), number_of_dimensions=4\n",
            "/usr/local/lib/python3.8/dist-packages/rasa/utils/tensorflow/model_data_utils.py:399: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  MASK: [FeatureArray(np.array(attribute_masks), number_of_dimensions=3)]\n",
            "Epochs:   0%|          | 0/100 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:447: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/cond_grad/Identity_1:0\", shape=(None,), dtype=int64), values=Tensor(\"gradients/cond_grad/Identity:0\", shape=(None,), dtype=float32), dense_shape=Tensor(\"gradients/cond_grad/Identity_2:0\", shape=(1,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:447: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor(\"gradients/cond_1_grad/Identity_1:0\", shape=(None,), dtype=int64), values=Tensor(\"gradients/cond_1_grad/Identity:0\", shape=(None,), dtype=float32), dense_shape=Tensor(\"gradients/cond_1_grad/Identity_2:0\", shape=(1,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  warnings.warn(\n",
            "Epochs: 100%|██████████| 100/100 [00:11<00:00,  8.74it/s, t_loss=0.14, loss=0.0196, acc=1]\n",
            "Processed trackers: 100%|██████████| 120/120 [00:00<00:00, 900.81it/s, # action=30]\n",
            "Epochs: 100%|██████████| 100/100 [00:12<00:00,  8.15it/s, t_loss=0.464, loss=0.288, acc=1]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[94mCore model training completed.\u001b[0m\n",
            "\u001b[92mYour Rasa model is trained and saved at '/content/test_project/models/20221211-180700.tar.gz'.\u001b[0m\n",
            "TrainingResult(model='models/20221211-180700.tar.gz', code=0)\n"
          ]
        }
      ],
      "source": [
        "model_path = rasa.train(domain, config, [training_files], output)\n",
        "print(model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OufYvqf6Objd",
        "outputId": "bd4cef29-e138-49e8-b3da-440a285fe797"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:rasa.shared.utils.common:The UnexpecTED Intent Policy is currently experimental and might change or be removed in the future 🔬 Please share your feedback on it in the forum (https://forum.rasa.com) to help us make this feature ready for production.\n",
            "WARNING:tensorflow:5 out of the last 5 calls to <bound method RasaModel.predict_step of <rasa.core.policies.ted_policy.TED object at 0x7fdf7c553f70>> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Your bot is ready to talk! Type your messages here or send '/stop'.\n",
            "hey\n",
            "\u001b[92mHey! How are you?\u001b[0m\n",
            "i am fine\n",
            "\u001b[92mGreat, carry on!\u001b[0m\n",
            "i am sad\n",
            "\u001b[92mHere is something to cheer you up:\u001b[0m\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<img src=\"https://i.imgur.com/nGF1K8f.jpg\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[92mDid that help you?\u001b[0m\n",
            "yes, thanks\n",
            "\u001b[92mGreat, carry on!\u001b[0m\n",
            "bye\n",
            "\u001b[92mBye\u001b[0m\n",
            "/stop\n"
          ]
        }
      ],
      "source": [
        "from rasa.jupyter import chat\n",
        "\n",
        "endpoints = \"endpoints.yml\"\n",
        "\n",
        "chat(model_path.model, endpoints)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xdl2olO3M6cN",
        "outputId": "704c2037-7121-4003-d5d5-d25e73cf93be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "absl-py==0.13.0\n",
            "aeppl==0.0.33\n",
            "aesara==2.7.9\n",
            "aio-pika==6.8.2\n",
            "aiofiles==22.1.0\n",
            "aiohttp==3.7.4\n",
            "aiormq==3.3.1\n",
            "aiosignal==1.3.1\n",
            "alabaster==0.7.12\n",
            "albumentations==1.2.1\n",
            "altair==4.2.0\n",
            "appdirs==1.4.4\n",
            "APScheduler==3.7.0\n",
            "arviz==0.12.1\n",
            "astor==0.8.1\n",
            "astropy==4.3.1\n",
            "astunparse==1.6.3\n",
            "async-generator==1.10\n",
            "async-timeout==3.0.1\n",
            "atari-py==0.2.9\n",
            "atomicwrites==1.4.1\n",
            "attrs==21.2.0\n",
            "audioread==3.0.0\n",
            "autograd==1.5\n",
            "Babel==2.11.0\n",
            "backcall==0.2.0\n",
            "beautifulsoup4==4.6.3\n",
            "bidict==0.22.0\n",
            "bleach==5.0.1\n",
            "blis==0.7.9\n",
            "bokeh==2.3.3\n",
            "boto3==1.26.27\n",
            "botocore==1.29.27\n",
            "branca==0.6.0\n",
            "bs4==0.0.1\n",
            "CacheControl==0.12.11\n",
            "cachetools==4.2.4\n",
            "catalogue==2.0.8\n",
            "certifi==2022.9.24\n",
            "cffi==1.15.1\n",
            "cftime==1.6.2\n",
            "chardet==3.0.4\n",
            "charset-normalizer==2.1.1\n",
            "clang==5.0\n",
            "click==7.1.2\n",
            "clikit==0.6.2\n",
            "cloudpickle==1.5.0\n",
            "cmake==3.22.6\n",
            "cmdstanpy==1.0.8\n",
            "colorcet==3.0.1\n",
            "colorclass==2.2.2\n",
            "coloredlogs==15.0.1\n",
            "colorhash==1.0.4\n",
            "colorlover==0.3.0\n",
            "community==1.0.0b1\n",
            "confection==0.0.3\n",
            "cons==0.4.5\n",
            "contextlib2==0.5.5\n",
            "convertdate==2.4.0\n",
            "crashtest==0.3.1\n",
            "crcmod==1.7\n",
            "cryptography==38.0.4\n",
            "cufflinks==0.17.3\n",
            "cvxopt==1.3.0\n",
            "cvxpy==1.2.2\n",
            "cycler==0.11.0\n",
            "cymem==2.0.7\n",
            "Cython==0.29.32\n",
            "daft==0.0.4\n",
            "dask==2022.2.1\n",
            "datascience==0.17.5\n",
            "db-dtypes==1.0.4\n",
            "debugpy==1.0.0\n",
            "decorator==4.4.2\n",
            "defusedxml==0.7.1\n",
            "Deprecated==1.2.13\n",
            "descartes==1.1.0\n",
            "dill==0.3.6\n",
            "distributed==2022.2.1\n",
            "dlib==19.24.0\n",
            "dm-tree==0.1.7\n",
            "dnspython==1.16.0\n",
            "docopt==0.6.2\n",
            "docutils==0.17.1\n",
            "dopamine-rl==1.0.5\n",
            "earthengine-api==0.1.334\n",
            "easydict==1.10\n",
            "ecos==2.0.10\n",
            "editdistance==0.5.3\n",
            "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1-py3-none-any.whl\n",
            "entrypoints==0.4\n",
            "ephem==4.1.3\n",
            "et-xmlfile==1.1.0\n",
            "etils==0.9.0\n",
            "etuples==0.3.8\n",
            "fa2==0.3.5\n",
            "fastai==2.7.10\n",
            "fastcore==1.5.27\n",
            "fastdownload==0.0.7\n",
            "fastdtw==0.3.4\n",
            "fastjsonschema==2.16.2\n",
            "fastprogress==1.0.3\n",
            "fastrlock==0.8.1\n",
            "fbmessenger==6.0.0\n",
            "feather-format==0.4.1\n",
            "filelock==3.8.0\n",
            "firebase-admin==5.3.0\n",
            "fix-yahoo-finance==0.0.22\n",
            "Flask==1.1.4\n",
            "flatbuffers==1.12\n",
            "folium==0.12.1.post1\n",
            "frozenlist==1.3.3\n",
            "fsspec==2022.11.0\n",
            "future==0.16.0\n",
            "gast==0.4.0\n",
            "GDAL==2.2.2\n",
            "gdown==4.4.0\n",
            "gensim==3.6.0\n",
            "geographiclib==1.52\n",
            "geopy==1.17.0\n",
            "gin-config==0.5.0\n",
            "glob2==0.7\n",
            "google==2.0.3\n",
            "google-api-core==2.8.2\n",
            "google-api-python-client==1.12.11\n",
            "google-auth==1.35.0\n",
            "google-auth-httplib2==0.0.4\n",
            "google-auth-oauthlib==0.4.6\n",
            "google-cloud-bigquery==3.3.6\n",
            "google-cloud-bigquery-storage==2.16.2\n",
            "google-cloud-core==2.3.2\n",
            "google-cloud-datastore==2.9.0\n",
            "google-cloud-firestore==2.7.2\n",
            "google-cloud-language==2.6.1\n",
            "google-cloud-storage==2.5.0\n",
            "google-cloud-translate==3.8.4\n",
            "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz\n",
            "google-crc32c==1.5.0\n",
            "google-pasta==0.2.0\n",
            "google-resumable-media==2.4.0\n",
            "googleapis-common-protos==1.57.0\n",
            "googledrivedownloader==0.4\n",
            "graphviz==0.10.1\n",
            "greenlet==2.0.1\n",
            "grpcio==1.51.1\n",
            "grpcio-status==1.48.2\n",
            "gspread==3.4.2\n",
            "gspread-dataframe==3.0.8\n",
            "gym==0.25.2\n",
            "gym-notices==0.0.8\n",
            "h5py==3.1.0\n",
            "HeapDict==1.0.1\n",
            "hijri-converter==2.2.4\n",
            "holidays==0.17.2\n",
            "holoviews==1.14.9\n",
            "html5lib==1.0.1\n",
            "httpimport==0.5.18\n",
            "httplib2==0.17.4\n",
            "httpstan==4.6.1\n",
            "httptools==0.5.0\n",
            "humanfriendly==10.0\n",
            "humanize==0.5.1\n",
            "hyperopt==0.1.2\n",
            "idna==2.10\n",
            "imageio==2.9.0\n",
            "imagesize==1.4.1\n",
            "imbalanced-learn==0.8.1\n",
            "imblearn==0.0\n",
            "imgaug==0.4.0\n",
            "importlib-metadata==4.13.0\n",
            "importlib-resources==5.10.0\n",
            "imutils==0.5.4\n",
            "inflect==2.1.0\n",
            "intel-openmp==2022.2.1\n",
            "intervaltree==2.1.0\n",
            "ipykernel==5.3.4\n",
            "ipython==7.9.0\n",
            "ipython-genutils==0.2.0\n",
            "ipython-sql==0.3.9\n",
            "ipywidgets==7.7.1\n",
            "itsdangerous==1.1.0\n",
            "jax==0.3.25\n",
            "jaxlib @ https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3.25+cuda11.cudnn805-cp38-cp38-manylinux2014_x86_64.whl\n",
            "jieba==0.42.1\n",
            "Jinja2==2.11.3\n",
            "jmespath==1.0.1\n",
            "joblib==1.0.1\n",
            "jpeg4py==0.1.4\n",
            "jsonpickle==2.0.0\n",
            "jsonschema==3.2.0\n",
            "jupyter-client==6.1.12\n",
            "jupyter-console==6.1.0\n",
            "jupyter-core==5.1.0\n",
            "jupyterlab-widgets==3.0.3\n",
            "kafka-python==2.0.2\n",
            "kaggle==1.5.12\n",
            "kapre==0.3.7\n",
            "keras==2.6.0\n",
            "Keras-Preprocessing==1.1.2\n",
            "keras-vis==0.4.1\n",
            "kiwisolver==1.4.4\n",
            "korean-lunar-calendar==0.3.1\n",
            "langcodes==3.3.0\n",
            "libclang==14.0.6\n",
            "librosa==0.8.1\n",
            "lightgbm==2.2.3\n",
            "llvmlite==0.39.1\n",
            "lmdb==0.99\n",
            "locket==1.0.0\n",
            "logical-unification==0.4.5\n",
            "LunarCalendar==0.0.9\n",
            "lxml==4.9.1\n",
            "Markdown==3.4.1\n",
            "MarkupSafe==2.0.1\n",
            "marshmallow==3.19.0\n",
            "matplotlib==3.2.2\n",
            "matplotlib-venn==0.11.7\n",
            "mattermostwrapper==2.2\n",
            "miniKanren==1.0.3\n",
            "missingno==0.5.1\n",
            "mistune==0.8.4\n",
            "mizani==0.7.3\n",
            "mkl==2019.0\n",
            "mlxtend==0.14.0\n",
            "more-itertools==9.0.0\n",
            "moviepy==0.2.3.5\n",
            "mpmath==1.2.1\n",
            "msgpack==1.0.4\n",
            "multidict==5.2.0\n",
            "multipledispatch==0.6.0\n",
            "multitasking==0.0.11\n",
            "murmurhash==1.0.9\n",
            "music21==5.5.0\n",
            "natsort==5.5.0\n",
            "nbconvert==5.6.1\n",
            "nbformat==5.7.0\n",
            "nest-asyncio==1.5.6\n",
            "netCDF4==1.6.2\n",
            "networkx==2.5.1\n",
            "nibabel==3.0.2\n",
            "nltk==3.7\n",
            "notebook==5.7.16\n",
            "numba==0.56.4\n",
            "numexpr==2.8.4\n",
            "numpy==1.19.5\n",
            "oauth2client==4.1.3\n",
            "oauthlib==3.2.2\n",
            "okgrade==0.4.3\n",
            "opencv-contrib-python==4.6.0.66\n",
            "opencv-python==4.6.0.66\n",
            "opencv-python-headless==4.6.0.66\n",
            "openpyxl==3.0.10\n",
            "opt-einsum==3.3.0\n",
            "osqp==0.6.2.post0\n",
            "packaging==20.9\n",
            "palettable==3.3.0\n",
            "pamqp==2.3.0\n",
            "pandas==1.3.5\n",
            "pandas-datareader==0.9.0\n",
            "pandas-gbq==0.17.9\n",
            "pandas-profiling==1.4.1\n",
            "pandocfilters==1.5.0\n",
            "panel==0.12.1\n",
            "param==1.12.2\n",
            "parso==0.8.3\n",
            "partd==1.3.0\n",
            "pastel==0.2.1\n",
            "pathlib==1.0.1\n",
            "pathy==0.10.0\n",
            "patsy==0.5.3\n",
            "pep517==0.13.0\n",
            "pexpect==4.8.0\n",
            "pickleshare==0.7.5\n",
            "Pillow==7.1.2\n",
            "pip-tools==6.2.0\n",
            "platformdirs==2.5.4\n",
            "plotly==5.5.0\n",
            "plotnine==0.8.0\n",
            "pluggy==0.7.1\n",
            "pooch==1.6.0\n",
            "portpicker==1.3.9\n",
            "prefetch-generator==1.0.3\n",
            "preshed==3.0.8\n",
            "prettytable==3.5.0\n",
            "progressbar2==3.38.0\n",
            "prometheus-client==0.15.0\n",
            "promise==2.3\n",
            "prompt-toolkit==2.0.10\n",
            "prophet==1.1.1\n",
            "proto-plus==1.22.1\n",
            "protobuf==3.19.6\n",
            "psutil==5.4.8\n",
            "psycopg2==2.9.5\n",
            "psycopg2-binary==2.9.5\n",
            "ptyprocess==0.7.0\n",
            "py==1.11.0\n",
            "pyarrow==9.0.0\n",
            "pyasn1==0.4.8\n",
            "pyasn1-modules==0.2.8\n",
            "pycocotools==2.0.6\n",
            "pycparser==2.21\n",
            "pyct==0.4.8\n",
            "pydantic==1.10.2\n",
            "pydata-google-auth==1.4.0\n",
            "pydot==1.4.2\n",
            "pydot-ng==2.0.0\n",
            "pydotplus==2.0.2\n",
            "PyDrive==1.3.1\n",
            "pyemd==0.5.1\n",
            "pyerfa==2.0.0.1\n",
            "Pygments==2.6.1\n",
            "pygobject==3.26.1\n",
            "PyJWT==2.6.0\n",
            "pykwalify==1.8.0\n",
            "pylev==1.4.0\n",
            "pymc==4.1.4\n",
            "PyMeeus==0.5.11\n",
            "pymongo==3.10.1\n",
            "pymystem3==0.2.0\n",
            "PyOpenGL==3.1.6\n",
            "pyparsing==3.0.9\n",
            "pyrsistent==0.19.2\n",
            "pysimdjson==3.2.0\n",
            "pysndfile==1.3.8\n",
            "PySocks==1.7.1\n",
            "pystan==3.3.0\n",
            "pyTelegramBotAPI==3.8.3\n",
            "pytest==3.6.4\n",
            "python-apt==0.0.0\n",
            "python-crfsuite==0.9.8\n",
            "python-dateutil==2.8.2\n",
            "python-engineio==4.3.4\n",
            "python-louvain==0.16\n",
            "python-slugify==7.0.0\n",
            "python-socketio==5.7.2\n",
            "python-utils==3.4.5\n",
            "pytz==2021.3\n",
            "pyviz-comms==2.2.1\n",
            "PyWavelets==1.4.1\n",
            "PyYAML==6.0\n",
            "pyzmq==23.2.1\n",
            "qdldl==0.1.5.post2\n",
            "qudida==0.0.4\n",
            "questionary==1.9.0\n",
            "rasa==2.8.33\n",
            "rasa-sdk==2.8.10\n",
            "redis==4.1.4\n",
            "regex==2021.7.6\n",
            "requests==2.28.1\n",
            "requests-oauthlib==1.3.1\n",
            "requests-toolbelt==0.10.1\n",
            "resampy==0.4.2\n",
            "rocketchat-API==1.16.0\n",
            "rpy2==3.5.5\n",
            "rsa==4.9\n",
            "ruamel.yaml==0.16.13\n",
            "ruamel.yaml.clib==0.2.7\n",
            "s3transfer==0.6.0\n",
            "sanic==21.12.2\n",
            "Sanic-Cors==2.0.1\n",
            "sanic-jwt==1.8.0\n",
            "sanic-routing==0.7.2\n",
            "scikit-image==0.18.3\n",
            "scikit-learn==0.24.2\n",
            "scipy==1.7.3\n",
            "screen-resolution-extra==0.0.0\n",
            "scs==3.2.2\n",
            "seaborn==0.11.2\n",
            "Send2Trash==1.8.0\n",
            "sentry-sdk==1.2.0\n",
            "setuptools-git==1.2\n",
            "Shapely==1.8.5.post1\n",
            "six==1.15.0\n",
            "sklearn-crfsuite==0.3.6\n",
            "sklearn-pandas==1.8.0\n",
            "slackclient==2.9.4\n",
            "smart-open==5.2.1\n",
            "snowballstemmer==2.2.0\n",
            "sortedcontainers==2.4.0\n",
            "soundfile==0.11.0\n",
            "spacy==3.4.3\n",
            "spacy-legacy==3.0.10\n",
            "spacy-loggers==1.0.3\n",
            "Sphinx==1.8.6\n",
            "sphinxcontrib-serializinghtml==1.1.5\n",
            "sphinxcontrib-websupport==1.2.4\n",
            "SQLAlchemy==1.4.44\n",
            "sqlparse==0.4.3\n",
            "srsly==2.4.5\n",
            "statsmodels==0.12.2\n",
            "sympy==1.7.1\n",
            "tables==3.7.0\n",
            "tabulate==0.8.10\n",
            "tarsafe==0.0.3\n",
            "tblib==1.7.0\n",
            "tenacity==8.1.0\n",
            "tensorboard==2.6.0\n",
            "tensorboard-data-server==0.6.1\n",
            "tensorboard-plugin-wit==1.8.1\n",
            "tensorflow==2.6.5\n",
            "tensorflow-addons==0.14.0\n",
            "tensorflow-datasets==4.6.0\n",
            "tensorflow-estimator==2.6.0\n",
            "tensorflow-gcs-config==2.9.1\n",
            "tensorflow-hub==0.12.0\n",
            "tensorflow-io-gcs-filesystem==0.28.0\n",
            "tensorflow-metadata==1.11.0\n",
            "tensorflow-probability==0.13.0\n",
            "tensorflow-text==2.6.0\n",
            "termcolor==1.1.0\n",
            "terminado==0.13.3\n",
            "terminaltables==3.1.10\n",
            "testpath==0.6.0\n",
            "text-unidecode==1.3\n",
            "textblob==0.15.3\n",
            "thinc==8.1.5\n",
            "threadpoolctl==3.1.0\n",
            "tifffile==2022.10.10\n",
            "toml==0.10.2\n",
            "tomli==2.0.1\n",
            "toolz==0.12.0\n",
            "torch @ https://download.pytorch.org/whl/cu116/torch-1.13.0%2Bcu116-cp38-cp38-linux_x86_64.whl\n",
            "torchaudio @ https://download.pytorch.org/whl/cu116/torchaudio-0.13.0%2Bcu116-cp38-cp38-linux_x86_64.whl\n",
            "torchsummary==1.5.1\n",
            "torchtext==0.14.0\n",
            "torchvision @ https://download.pytorch.org/whl/cu116/torchvision-0.14.0%2Bcu116-cp38-cp38-linux_x86_64.whl\n",
            "tornado==6.0.4\n",
            "tqdm==4.64.1\n",
            "traitlets==5.6.0\n",
            "tweepy==3.10.0\n",
            "twilio==6.50.1\n",
            "typeguard==2.7.1\n",
            "typer==0.7.0\n",
            "typing-extensions==3.10.0.2\n",
            "tzlocal==2.0.0\n",
            "ujson==5.6.0\n",
            "uritemplate==3.0.1\n",
            "urllib3==1.26.13\n",
            "uvloop==0.16.0\n",
            "vega-datasets==0.9.0\n",
            "wasabi==0.10.1\n",
            "wcwidth==0.2.5\n",
            "webargs==8.2.0\n",
            "webencodings==0.5.1\n",
            "webexteamssdk==1.6.1\n",
            "websockets==10.4\n",
            "Werkzeug==1.0.1\n",
            "widgetsnbextension==3.6.1\n",
            "wordcloud==1.8.2.2\n",
            "wrapt==1.12.1\n",
            "xarray==0.20.2\n",
            "xarray-einstats==0.3.0\n",
            "xgboost==0.90\n",
            "xkit==0.0.0\n",
            "xlrd==1.2.0\n",
            "xlwt==1.3.0\n",
            "yarl==1.8.2\n",
            "yellowbrick==1.5\n",
            "zict==2.2.0\n",
            "zipp==3.11.0\n"
          ]
        }
      ],
      "source": [
        "pip freeze"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KPOFvQZEkT1A"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sy-tClxxkWbg"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cNNQgcMtkYgo"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}