Context_Aware_ChatBot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "## Retrieval-Augmented Generation (RAG)\n",
    "Retrieval-Augmented Generation (RAG) is a technique that combines document retrieval and generation capabilities to produce more accurate and contextually relevant responses. In RAG, a retrieval component first fetches relevant documents or passages from a large corpus based on a query. Then, a generation component, typically a large language model, uses the retrieved documents to generate a response. It's particularly valuable in applications like chatbots where the quality of the response is crucial.\n",
    "\n",
    "## Workflow\n",
    "The workflow of the Context-Aware ChatBot using RAG consists of the following steps:\n",
    "1. **Document Loading**: Importing text data from various sources such as URLs.\n",
    "2. **Document Splitting**: Breaking down large texts into manageable chunks for better retrieval performance.\n",
    "3. **Vector Storing**: Converting text chunks into vector formats that can be efficiently retrieved.\n",
    "4. **Retrieval**: Fetching the most relevant text chunks in response to a query.\n",
    "5. **Question Answering**: Generating responses based on the context provided by the retrieved texts.\n",
    "6. **Interactive Widgets**: Experiment with different queries interactively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup and Installation\n",
    "First, ensure all necessary Python packages are installed to run the examples provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install transformers sentence-transformers langchain faiss-cpu ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tahaei/.local/lib/python3.10/site-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm, trange\n",
      "2024-06-14 15:58:47.735100: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-14 15:58:47.844983: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-06-14 15:58:48.282041: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2024-06-14 15:58:48.282086: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2024-06-14 15:58:48.282090: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
      "WARNING:root:USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from langchain.document_loaders import WebBaseLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import FAISS\n",
    "from ipywidgets import widgets, Layout\n",
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document Loading\n",
    "We begin by loading documents using LangChain from predetermined URLs which provide a rich dataset for our RAG system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 5 documents.\n"
     ]
    }
   ],
   "source": [
    "# List of URLs to load documents from\n",
    "urls = [\n",
    "    \"https://en.wikipedia.org/wiki/Natural_language_processing\",\n",
    "    \"https://en.wikipedia.org/wiki/Artificial_intelligence\",\n",
    "    \"https://en.wikipedia.org/wiki/Machine_learning\",\n",
    "    \"https://en.wikipedia.org/wiki/Deep_learning\",\n",
    "    \"https://en.wikipedia.org/wiki/Data_science\"\n",
    "]\n",
    "\n",
    "# Initialize the WebBaseLoader with the list of URLs\n",
    "loader = WebBaseLoader(urls)\n",
    "\n",
    "# Load documents\n",
    "documents = loader.load()\n",
    "\n",
    "print(f\"Loaded {len(documents)} documents.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document Splitting\n",
    "To enhance retrieval performance, documents are split into smaller, more manageable chunks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split into 722 chunks.\n"
     ]
    }
   ],
   "source": [
    "# Initialize the text splitter\n",
    "splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
    "\n",
    "# Split the loaded documents into chunks\n",
    "chunks = splitter.split_documents(documents)\n",
    "\n",
    "print(f\"Split into {len(chunks)} chunks.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunk 1:\n",
      "Natural language processing - Wikipedia Main menu Main menu move to sidebar hide \t\tNavigation Main pageContentsCurrent eventsRandom articleAbout WikipediaContact usDonate \t\tContribute HelpLearn to editCommunity portalRecent changesUpload file Search Search Appearance Create account Log in Personal tools  Create account Log in \t\tPages for logged out editors learn more ContributionsTalk Contents move to sidebar hide (Top) 1 History Toggle History subsection 1.1 Symbolic NLP (1950s – early 1990s) 1.2 Statistical NLP (1990s–2010s) 1.3 Neural NLP (present) 2 Approaches: Symbolic, statistical, neural networks Toggle Approaches: Symbolic, statistical, neural networks subsection 2.1 Statistical approach 2.2 Neural networks\n",
      "----------------------------------------\n",
      "Chunk 2:\n",
      "2 Approaches: Symbolic, statistical, neural networks Toggle Approaches: Symbolic, statistical, neural networks subsection 2.1 Statistical approach 2.2 Neural networks 3 Common NLP tasks Toggle Common NLP tasks subsection 3.1 Text and speech processing 3.2 Morphological analysis 3.3 Syntactic analysis 3.4 Lexical semantics (of individual words in context) 3.5 Relational semantics (semantics of individual sentences) 3.6 Discourse (semantics beyond individual sentences) 3.7 Higher-level NLP applications 4 General tendencies and (possible) future directions Toggle General tendencies and (possible) future directions subsection 4.1 Cognition 5 See also 6 References 7 Further reading 8 External links Toggle the table of contents Natural language processing 67 languages\n",
      "----------------------------------------\n",
      "Chunk 3:\n",
      "5 See also 6 References 7 Further reading 8 External links Toggle the table of contents Natural language processing 67 languages AfrikaansالعربيةԱրեւմտահայերէնAzərbaycancaবাংলা閩南語 / Bân-lâm-gúБеларускаяБеларуская (тарашкевіца)БългарскиBosanskiBrezhonegCatalàČeštinaCymraegDanskDeutschEestiΕλληνικάEspañolEsperantoEuskaraفارسیFrançaisGaeilgeGalego한국어Հայերենहिन्दीHrvatskiBahasa IndonesiaIsiZuluÍslenskaItalianoעבריתಕನ್ನಡქართულიLatviešuLietuviųМакедонскиमराठीمصرىМонголမြန်မာဘာသာ日本語ଓଡ଼ିଆپښتوPicardPiemontèisPolskiPortuguêsRomânăRuna SimiРусскийShqipSimple EnglishکوردیСрпски / srpskiSrpskohrvatski / српскохрватскиSuomiதமிழ்తెలుగుไทยTürkçeУкраїнськаTiếng Việt粵語中文 Edit links ArticleTalk English ReadEditView history Tools Tools move to sidebar hide \t\tActions ReadEditView history \t\tGeneral\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Function to clean and extract meaningful content from chunks\n",
    "def clean_chunk(chunk):\n",
    "    lines = chunk.page_content.split('\\n')\n",
    "    meaningful_lines = [line for line in lines if line.strip() != '' and not line.startswith('Jump to content')]\n",
    "    return ' '.join(meaningful_lines[:100])\n",
    "\n",
    "# Print the first 3 chunks as examples\n",
    "for i, chunk in enumerate(chunks[:3]):\n",
    "    cleaned_chunk = clean_chunk(chunk)\n",
    "    print(f\"Chunk {i+1}:\\n{cleaned_chunk}\\n{'-'*40}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector Stores\n",
    "Document chunks are then embedded and stored in a vector format to facilitate efficient retrieval:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tahaei/.local/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "/home/tahaei/.local/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
      "  warn_deprecated(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vector store created with embeddings.\n",
      "Example vector:\n",
      "[-0.0155906  -0.07476962  0.07039002  0.00700213  0.03098843  0.04333629\n",
      " -0.00559796  0.04472222 -0.02328821  0.02334314]... (truncated)\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Initialize the SentenceTransformer model\n",
    "model_name = \"all-MiniLM-L6-v2\"\n",
    "embedding_model = SentenceTransformer(model_name)\n",
    "\n",
    "# Wrap the SentenceTransformer model with LangChain\n",
    "embedding_wrapper = HuggingFaceEmbeddings(model_name=model_name)\n",
    "\n",
    "# Create embeddings for the chunks\n",
    "embeddings = embedding_wrapper.embed_documents([chunk.page_content for chunk in chunks])\n",
    "\n",
    "# Create the vector store\n",
    "vectorstore = FAISS.from_documents(documents=chunks, embedding=embedding_wrapper)\n",
    "print(\"Vector store created with embeddings.\")\n",
    "\n",
    "# Print a few example embeddings to visualize the results\n",
    "example_vector = vectorstore.index.reconstruct(0)  # Getting the first vector as an example\n",
    "print(f\"Example vector:\\n{example_vector[:10]}... (truncated)\\n{'-'*40}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the embeddings of the document chunks can provide insightful information about the distribution and grouping of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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P5cpUrDlUYfEEqbQec4HC4kyl9VAoFIrqooSZYk2hdiBeIZXW4+CZqYKfqbe0HgqFQrHaUcJMsWZY7g7E1WZpW2lpPRQKhWItoISZYk2w3B2Iq9XStlLSeigUCsVaQQkzxZpA6w7E+3eH6WqyZb0eicVXda6vek/roVAoFGsJ9eRVrAnK3YEYisaYD0aXtLSFo3Hd2loL6jmth0KhUKwl1NNXsSYot7D4nD9CIFy8bqTK9aVQKBQKvVDCTLEmKLeweFxCLF48j5fK9aVQKBQKvVDCTLEmSO1A7G625X2/0A5EgwCjoXgeL5XrS6FQKBR6oYL/FWuGcnYgNjeYsVuKp8RQub4UCoVCoRdKmCnWFKXuQLSajDTZTHQ321SurxVKbv455XRWKBT1jBJmijVHageiVsxGg8r1tULJl3/uNmeYUY9fXTeFQlGXKGGmUGhA5fpaeRSq9OA1RfnagXMrPv+cQqFYnahZRaHQiMr1tXLQWulhpeefUygUqw81sygUipoTisaYnA8yPhdkcj5IKFo8d9xSaK30oPLPKRSKekO5MhUKRU2pRB3Scis9KBQKRa1RwkyhUNSMQnFgy61Dmqr0MBcoLM5U/jmFQlGPKFemQlEF9HbVrQYqGQdWbqUHhUKhqDXKYqZQLIEEJueD6TxYzQ1mrKbiSWczqYSrbjWgNQ7s/t3hktKbwJVKD2cmvCr/nEKhWFEoYaZQFGHU42fGF+bLT54qS1RVylVXCXITsZYqQEul0nFghSo9OG0mPn1r/fS7QqFQZKKEmaJuqbZQyCUlqrbFo+lYpVJElVZX3Udv2lRzy00trHrViAPLl3/uxJFZJcoUCkXdooSZoi6ptfsvU1Rta1z8vhZRVUlXnZ7UyqqXigM7eGaq4Gf0iAPLrfRwcllHUygUisqiAiwUdUdKKBw8M8VcIIKUV4TC1w6cY9Tjr3gb9MiDtRJSNtQyEWsqDqy7Ob8oVXFgCoViLaKeeIq6ol4ytushqlKuumLUOmVDrROxpuLA9m7roNluTris7Wb2buuoq/g7hUKhqBbKlamoK+rF/adH/FO1XHVQPB6v2Hv1YNVTdUgVCoXiCkqYKeqKSgsFrRsK9BBV1UrZUCwez2wUPHl8vGCsXr0kYs2NA1MoFIq1ihJmirqikkKhlA0FmaKKPF5TraKqUMoGvTYyFAvcPzYyxwevW8/RS3MFd5V2NForatWr9c5ahUKhWGkoYaaoKyrl/itn52FKVL32ymWao+ZFosrVaNWUeFarq65UEVMsHi8uJW+PztFoM7O7t5mXz2b3Z+au0kpZ9Wq9s1YLSjgqFIp6QwkzRV1RCfffcvKJ9bY20Oaw8IU7dmSJqmlvkG8fGtYsOpZy1ZUjYorF40VikvlAlFfOTfHF7TsXCTO4EqtXCaveSkisuxKEo0KhWHsoYaaoO/QWCsvdUCAg63W9RUe5xysWjyeBaDzOrC+CzZzfApQZq6dnAP5KSKy7EoSjQqFYmyhhpqhL9BQKem4o0Ft0LOd4xeLxBGAyGGi2mwhG8hdMz43V0ysAv1521hZCQt0LR4VCsXZRTx1F3ZISCt3NdrqabGVPknrmE9M779dyjpeKx8uH2Shospu4tb+DIbcv72f0StWRSz2k4ChGLC5rmrtNoVAoiqEsZopVj54bCvQWHcs5Xioe7/JskM4mG30uB3azkUAkxpDbx6UZH/fs6uJ7h0cWfbeSWfXrJQVHQepcOCoUirWNEmaKVY+eGwr0Fh3LPV5vawMfvnED3z8ywvcOj+D2BnE5bezb4eKXb9lMi93Eng3NFUnVUYhqJtYti3oXjgqFYk2jhJliTaDXhgK9Rcdyjzfq8fONl84zNhug1WGhxWFGIHhnbB6P/zyfvqu/6ln1q5VYt1yMBlHfwlGhUKxplDBTrBn02FCgt+hYzvEyNw4YhMBqyrbwZAaxVzvIvtKJdZeDgLoWjgqFYm2jhJliTaHHzkO9RUe5x6v33Y/1XAOznoWjQqFY2yhhpljTlJv5XW/RUc7x6n33I9R3Dcx6Fo4KhWLtUlNhJoT4e+B9wKSUcnfytTbgUWAzcAH4sJTSU6s2KlYvy838rrfoKPV4db/7cQVQz8JRoVCsTWq9NPwmcH/Oa/8/4Fkp5QDwbPJvhUJXUpnfD56ZYi4QQcormd+/duAcox5/rZu4JMXymKVQQewKhUKxsqipMJNSHgRmcl5+P/APyX//A/CBarZJsfrRmm0/HI1XuWWlkdo40N2c3+KjgtgVCoVi5VGPT+wuKeXl5L/Hga5aNkax+tA7e38tSQWx793WQbPdnIiTs5vZu61D1XtUKBSKFYiodXZrIcRm4EcZMWazUsqWjPc9UsrWPN/7deDXAbq6uq5/5JFHqtNgDXi9XpxOZ62bUTdIEmVwkIBI5JGqZdRTNCYZnQ0s+bneFjsmo6j49dSjf5ZzjHq7PpVG3Z+r65qr67m6WCvXc9++fYellDfke68ed2VOCCHWSSkvCyHWAZP5PiSl/DrwdYAbbrhB3nXXXVVsYnEOHDhAPbWnliw3wL4STM4HeeLJU0WD5pvtZu6/cQddTbaKXs9a90+tf18vStldu9bvz9VyzVOs9eu52lDXsz6F2ePArwJfTv7/h7VtjqJcUgH2mbFcqQD7MxPemrna6qVkUK37Z9Tj56+fP8ulmUDKcEIsHq759SmV1SY0Kkmtx5xCoViamsaYCSG+DbwKbBdCjAgh/h0JQXavEGIQuCf5t2KFUc8B9vUQNF/r/glFYzxx7DKHL3o4O+nl9Pg8Zye9jM4GCEZiK2YDxGrYXVstaj3mFAqFNmq9K/OjUsp1UkqzlHK9lPIbUsppKeV+KeWAlPIeKWXurk3FCqDeA+xrHTRf6/6ZmA/yb0fHmPGGicbiICEaizPjDXNh2kcwEqv7DRD1JDRC0RhTC0HcC0GGZ3yMePyMePz4QsUTAFeTWo85hUKhjXp0ZSpWASshK30tM79Xu38yY7BAEpcQieUXLKFIHLc3hM1sqOn1WYp6KUk16vFzZNhDk93M0+9M8MaFGeJxyRaXg2s2tPDA7nVscdU+mHkl3JMKhUIJM0WFWClZ6WuV+b2a/ZMbg2UzGdmxrpHP37uNb71ykVPjC4u+Mx+IYu801fz6FKMehMaox8+3Xr3ITVva+G9PneHMxAKRaJy4hPH5EG5vmJOXF/jd+7axpaO24myl3JMKxVqnHvOYKVYBKit9carVP/lisDyBCI+/NcY/vHKRj757I+3Oxb8Rjce5ZmOLrtcnFI0xOR9kfC7I5HyQUDS2rOOlhEYxKik0Uq5UV6OV509PcmJsnlAknrRKgjcUZWIuyImxeX5ybBx/KFqRdmhF3ZMKxcpACTNFRaiHAPt6phr9UygGSwD+SAxfKMprQzPcvrVj0Xf7XU7263h9Rj1+Hjk0zJefPMUf/uAYX37yFI8cGl5WcH6thUbKldrncnLgtDuRFyyHaV8Yu8XIT98ZZ6bGsVvqnlQoVgbqDlRUjFoH2Nc7le6fQjFYZqOgwWJkxhfm0oyPe3d10+60YBSCdqeFB/f08IX7t7O+xb6s309RqZ2TtRYaKVeqzWxgfD5/wuJILI7RIBia8hGPS92sheWi7kmFov5RMWaKilLLAPuVQCX7p1AMlkEIXE4rF6Z9DE74cNpMfPG9O7GZjQQjMaYWQqxrtmHWoQ1ad05+9KZNZZ1zSmikYugWghEaq5THLOVKDURitDmsTM6HFn3GbDQQi0ucNhNxCX/4g2M1z7Om7kmFor5RwkxRcWoVYL9SqFT/FAv2tpmNbG53EJOSWX+Yf37tYpagWdeij2Coxs7JWgmNlCt1cMLL3gEX/3p4ZNFn2hwWPP4wd+/oJBaPZ1kLa5nQVd2TCkX9ooSZQrFKWarCgc1s5I6BDnaua+LPPrC7IoKmWjsnayE0Uq7Uf3j1Avt3dvLmJQ/nJn3p9xssRtodFqSUvP+aXt4eyRaoy7UWKhSK1YkSZgrFKiFfvch7dnZxZsKb15XY3Wzj3l1dtDutFWvTak/R0NvawK/esplDF2b4j+/ZwdMnJnjt3DQAmzsa2NrZyF07XFiNghcHFwvkauRZUygUKwslzBSKVUChepEP7O6uWQwW1E9d0krS29rAzzXamJwPsq2rkU/dsYVwLM5CIMpsIILVZOQHb43lFaf1kNC1lALwqx3VF4p6QAkzhaKOKGdiWKow9Wf29dcs2Dvl7itmtVsNKRosJgPr2xoIR+N4/Il+brVbuDjo5pkTEwUthrW2FtZ7AfhqCqV67wvF2kEJM4WiTihnYtCy6/GpE4k4pqXcZZWaBGu5c7LaZMa6haIxwrF4UTduLa2FSwn6WqfPqKZQqve+UKwtlDBTKJaBXmKm3IlBr12PpU6CpZ73UjsnV6MLqZ6thZVOY7JcqimU6r0vFGsPJcwUijLRa0W/nIlBj12PxSbBy7NBPnn7lvRvGZJetwOnJ3ltaKak8y60c3I1u5BcjVY+dUcfz56c4K3hWQKRKM12S83Pr14KwOej2kKpnvtCsTZRwkyhKAM9V/TLmRiWu+ux2CS4qb2Bm/va+cdXLzDiCeANRbEaDfS02rmtv4OWhsTvLseSsZpdSCnBOeT2sbndwS/etIHWBguNNhPdzfaaWl/qoQB8IaotlOq5LxRrEyXMFIoS0XtFr3ViEMhFLj+H1bisXY+FJsFmu5m9Ay6++twgo7NBtnY6MRsFgzNeXj8/w5GLHj579wCP+xO7DZvtZvpdTqa8IQwGgVGIJd2Rq9mFlCs4RzwBXjqbuEbdzbaaC856TmNSbaFUz32hWJsoYaZQlIjeK3otE8POdU34wnGePD6c5fK7ua+NOwZcZccxFZoEd/c2c+CMm4vTfhAgkURiYDYYeP81PVy7qZXmBjO/cssmLkz76HTaeOrkBN87PEKb04LLaV3SXbdaXUgrQXDWcxqTaguleu4LxdpkZS1DFYo6oJwVfSgaY3I+mLeIdWpiKESz3cxd2118/eDiQuA/fWeCJ49f5t/dvqWswtSpSTCXPpeDV88lJiqTwYBAsKm9gU/v62chGOXPnzjJr3zjdb7y7CCXZgL4I1HOTiwwsRAkLqWmIuWr1YWkVXB6/OEqtWgxtSgAX+weyGSp+wH0FUq16AuFohjKYqZQlEipK/qlgtuX2r13a387hy968hbJBjhycRaX08rD12/g/t3rSspVVshaYDcb8fgSwqHJbqLDaWH/ji7+y09PcdbtBUAgWAhFefRnl3BYjHxu/wBfe/4cgiuWjGLWodXqQlopgrOaaUxK2eBRi92saymli6L+UcJMoSiRUlwfWoPbi00MN/W18ZdPDxZt02tDM9y5vbNkl1+hSTAQidHqsOANRXE5rbxrfQsvnJlkeOaK9ctsFBiEwOMLMzEf5+AZN3fv7OT4aLa1qJA7crW6kFaS4KxGAfhyNnjUQihVoy8UCi0oYaZQlIjWFb1ElhRrVGhimPGFK2qByTcJXp4NcO+uLt4cnsVmNtLncvDoG8NYTAZicUlMStocCcEUicUBeOXsNH/6/qs4MTavqW31nOdrOaw0wVnJAvDLiberhVCqZF8oFFpRwkyhKIPe1gY+s6+fwUkvBiEwGw1EYnHiUjLQ6WRdSwOT88GSg9vzTQz5LDDNdjO7e5vpczmwm41EYnFMhvInrNxJ0GgQBCNxPP4I43PBtGvTKAR2ixGjELQ7rUSicSwmI1JKApEYDdbFj5Ri1qHV6EJarYKzHJa7wUMJJcVaRAkzhaJM4hLOuX0cvuBhNhimxWbh+s2t9Luc6ff1sHTlWmA2tTewd8DFgTNuHn1jGI8vTF+nk/dc1c19u8oXM/kmwZRoisTitDotzPmjtNlNdDitGA0CKWFds40ZX5h2p4VgZHFA91LWoWpbRiQwOR+saJWB1Sg4y2GlxNspFPWEEmYKRRnkxs1YjUYCkRgvDU5xdjIRN2M2GnSJNcq0wATCsXR+sYvTiXgvq9mAUYis315q4tdaAiklmuYDER6+fj0HTk+lY8tSdDZa8Yej3NrfwZDbl/V9rdahallGRj1+ZnxhvvzkqYpXGVAxSysr3k6hqBeUMFMoSkRr3MzD12/QLdYoZYE55/bywhk3F2f8mIwGmuwmXE4rNrMx67eL5cgqtQSSxWSgo9HKA7vXcX7Kv+i8bWYjt/S18+CedfzwrTEMAt2sQ3rW0EyJ6W3xaFooVLrKwFp3xa20eDuFoh5QwkyhKBGtcTPvvXqdrrFGva0NGA0GvvPGCNu7GxGIRdar1G8XitlZTgmkpdxznY02elsbdLMO6VlDM1NMb2tc/H61k76uxqLt+VDxdgpF6ShhplCUiNa4mVhcViTWKBiNYSsyiReK2dEjI/1S7jm9rEN619CspyoDq7loez5UvJ1CURpKmCkUJVJq3IyesUbLidnRS5xU2j1XiZJG9RKEvpqLthdDxdspFNpRd4VCUSLllIxJiZnuZjtdTbayJ6TllKupF3GyFJUoaVSo9FQmlQ5C1yo4w9F42cfXUvKommS2adYfpqXBvOx7QKFY7SiLmUJRIrWMm1nOb6+UHXKVEJD1EIReSXdqPbpH67FNCsVKQAkzhaIMahk3U+5v14M40UIlBGSmoCWPQaoaQeiVslhmukeb7WZu7e9IJx4envFjNhrorPLO0LXqslUo9EAJM8WKppa726oVN5PvHMv57ZWyQ65SAjIlaF975TLNUXPVg9ArITgz3aP5Eg+3Oizcu6uLj9y4kQ1t1RFClYgRVCjWEkqYKVYs9eAqqXQg/FLnWOpv62Hpq7QYrqSA7G1toM1h4Qt37Kh6EHolBGfKPdpsNy9KPAww7Q3zr4dHmZgP8fl7BqpyX9TTDliFYiWihJliRbIWXCWVOsflWPqqJYYr4SpOCcpYXGISgoiUSAmz/nBVLK2VEJwp9+it/R0cOOPOEmUpovE4Y3OBqlmpVsomE4WiXlHCTLHiWAuukkqfYzmWvlKF4nIta3q6ilOCctobZpuM8bUXzvLO2Dwmg6Cz0VY1S6vegjPlHu1zOXj0jeG8nzEZDAhE1axUK2WTiUJRryhhplhxrAVXSb2dY6lCUS/Lmh6u4pSgDIRjPLSnh3PHzvLDU2NAos6olHDwTKRqllY9BWfKPWo3G/H48qcPabKbMBtF1axUK2WTiUJRr6xMc4JiTbMWXCX1do5aheJcIJwWQgfPTDEXiCDlFcva1w6cY9Sz2N1WKTIF5e7eZg6ccWflCQtF4ri9IeJSLjuPWCnoldcu5R6NS0mrY7HQsZoNuJxWDEJUzUqValN3c35BXS+bTBSKekXdGYoVRz0kC6009XaOWoUiUNEkqqWSKSj7XA5ePbfYijMfiBKJJQRuqYlr64He1gb6XU7u3dWFyWgAASajgTanhc3tjnSB+2paqVIu273bOmi2mxOubLuZvds6yrZK1mMCXYWiEihXpmLFsRZcJfV2jlrjhkLReF25YDMFZSF3XzQeR5IQZivV0trZZOMjN25kYj7E2FxgUYH7WlipKhEjmM81rlCsNpQwU6w4Vko+ruVQb+eoVSgaDaKuXLCZgjIQiSXdfQFiyd83CIE5GRwPK9vSuqGtgc/fM1BXxcL1jBEstOnkrqbqWF8VimqhhJliRVLLzPt6oGXHYj2do1ahaBSirnbkZQrK0+MLXL+plXhwDm8oikEITAZBm8OC2Zhoz0q3tK62YuFaNp3MW6KEo/EVe44KRS5KmClWLCt1Eiplx2I9naMWoRiKxurKBZsSlMdG5vjx22P8xp39jJ1KpJWIS4nJYMBpNRGOxtnc4VjxllaofNLjaqJl00kgHMPjX7k7sBWKXJQwU6xoVsIklGkdC8diHBqaSe5gTFiVlkoaW0/nuJRQrDcXLICr0crH3r2Rfzt6mSeOXea+NisPX9/FibF5hAApJddtauXDN2yoe0vrUvhCETz+xE5YIaCtwYzRaKhZ2bLlomXTSSwul+Uar2VZN4UiH0qYKRQVJNM6NhsI4w/H2NHdxEN7ejg4mJ2pXY/EuNWYZJYSivXkggWY9Uf44VtjbGp3cOd2F/6Ls3zs3RuJxCTRmMQXjnJ5NoDNvLItZUNuL98/MsLzp9y4vUFu6evgYzdv5K1Ls5y+vFCzsmXLQcumE6NBlO0ar4eybgpFLkqYKRQVIjdoORyVXJjyc3bCy7GRWT579wCP+8eyJp3l7Fisp0mmnlywcQmjswFGPAFePjvF3sYQ33t6MOszBgH7dnRWvW16MeT28gePHWNwwgtAu9PCfVd18Z9+cJzJhRDbuxtxWEwrrmyZlk0ndouxLNf4WijrpliZrOwlokJRp+QLWpYkUjMAXJz288IZN7t7m7O+V+6OxXpK6ppCrySqy6XecsLpjS8U4ftHRtKiDOD2rR28cNrN0JQPbyjK+FyQWPzKuKp2Prly0ZKstslmKnlsaa1kUe/9o1idKGGmUFSAfEHLgkTdwhSvnJuiz+XI+kw5AkFNMsVJWV2KsZJ3Y874Ijx7apKYlMRkIiPbtZtaeSkjme6UN0w4ln39V0oy3aWS1ZqNpU9jWitZrIT+Uaw+lCtToagA+YKWzUZBk93EjDfxsJ/1RdJZ2VOUIxDqra5mvVFoQ0Kz3czu3mZ2rmtkfaudWX94xQV+j3r8xOKSC1M+onGZTgHisJiY9obSn8snyldSMt1irvHBpb++iHoreaZQZKKEmUJRAfIFLRuEwOW04gtFCUXitDjMBCNXyspk7lgsJYhfTTJLb3rI3JBgnJhic3sD917VzYw3RDQmGZsNEonFiUvJQKeTdS31H1uUcl//yi2b6Gy0MTYXIC4l4ZhkPhCh3WHFnRRn+Vx9tXLflrtBRc/dyVorWaxU97ZiZaOEmUJRAQoFLdvMRja3O3B7Q9wx0MF5t49me3aQfqlB/Gt9ktHaXymry8GDF9h37RbOT/l4/fwMrw1N4/GFaXVYuKW/A6PBgNEg6Gyy1/CsipPpvr48F+Q9u7v4Py9fSL//+vlpbu1v54dHxwDocFqw5Lj8auG+rZcNKvVW8kyhyEQJM4WiAhTL52UzG7l+Uysfv3kzDouB975rXdotU85OsXInmdWQv6nU/rKYEqJrYj7I/3j6TFa6kmlvmB8dHePYyCx/8HM7aWmw1m2y2Uz39fOnJvnIjRt4fWiGE5fnAThwxs0X7t/BsdE5JhdCdDfbMBquCPNa5JOrp12Q9ZhvT6FIoYSZQlEhSs3npTWIPzfPWTmTTL1YLpZDuf0Vj0t+fOxylijL5OK0nyeOXWZTu4N2p7UibV8ume7rVBqQ//zzV/PDt8b4yfFxJhdC/OjtMf7sA7t569Ispy4v1DSfXLnXqpLUW749hSKFEmYKRQUpJZ/XcoL4S5lk6slysRzK7a+4lLx0trB1EeDFs1P86q1baM/zXj1YGnPd168NzTDiCXD3jk4+eF0vZoOBaDxOZ5OVaza04vHXNp9cvW5Qqad8ewpFCiXMFIoKozVoeblB/FommXq0XJSLEIL3X9ODzWwkEIkx5PZxfHQuK9auUH8tBKJFj+0NxPK+Xi+Wxnzu6xFPgH989WL6773bOtLXsda7cet5g0o99I9CkYkSZgpFnaBHEP9Sk0y9Wi5KZdTj58nj4zx2ZBT3QjAduJ9b6ipffwkEG9vtnM1IyJrLhnb7ovxY9WRpXGkxUmt9g4pCUQr1cdcqFIqqJEIt13IRisaYnA8yPhdkcj5IKJrfolQMPY4BVwTSy2eniMTjxOWVwP2vPjfI3gEXzfZEpv98/WU0Cvbv6MRaoDam1Wzgnp1dtDmufK8ek/gulXg1JRL16vflsNqT/CoUeqIsZgpFnVANK0g5lgs93Hd6uQAzBVJuXjgJDE35eP70JDu6Gzk/7cvbXwJ479XrOD2+wNujc8wHokTjcUwGA012E+/qbeaB3d1Z39PD0pgZm2YyJGo8+kKxZcWqLeW+1tP1upzYupVm4VMoaokSZgpFHVHpnWKlptbQw32npwvQ4wvzxnkPwWgcQcJ1u7ndweRCCLc3RDAc48AZN3/xoXdxw+ZWzMb8rrHe1gb+w73bePrEBD+74GEuGKbZZuGGza3cu2txPy8nRioUjTExH2QhGMXjCzPlDdHdbOelQTfn3T5CsfiyBFMh97We/a5F4JWS5FftglQoCqOEmUJRZ1Ryp1gplgs9Ngroudlg1ONnNhDhyLCHcOyKhavdYcFuMbKpvQGjEEgkTquJv3/pPHaLsaAA6W1t4GPv3sQDV69bsp/LjZEa9SRSb/zb0THG54K4Gm3cv7sbp9XEW8OznJ/2sbndQSwuefrEJMdG5/ntfVvpabEta6en3v2+lMAzCHjqhPYkv2oXpEJRGHU3KBR1SMoK0t1sp6vJpuvEpTU2SY9Cz3oVi06JA/dCiEabCSREY3GmvWEGJ7wIYGIuxPC0H38oxpQ3xFwgsmTsl9Z+LidGatTj56+fP8t3D48wNhskLiEci/Pozy7xX356mo++eyNOq4nJhRAef5izk14OnJ7kn1+7wOtD04x68udZ04Je/a5F4D15fJyXz05z8MwUc4EIUl4Rbl87cG7ReVRybCsUqwF1RygUa5CU5eILD+zgzz6wmy88sIOP3rQpy7qhR4oDPY7hC0V48vg4F6b9nBlf4Oa+K9nF4lKyEIoy4wsnBBtwa38HQ25f+jNaBMhSpCyN3c3548dyY6RSgubSTID5jNQcDVYjHl+YoSkfB8+4ubW/nfH5IJKE0ETCi4NTxCV5RY1W9EpPsZTAi0vJMycn6GjMn4i3Fpsi6mGzg0KxHJQwUyjWKEtZLlLuu2IsleJguccY9fg5O+nlsSOjnB6f54dvjXHr1nY2tF2pYxmXkhlfmAZrwp155zYXx0eviAmDSPyXmqyjcVnWZK3V0ghXBI0EovErosQoBJFY4u8Xz05xzcZWQpE4xozzn/VFsJmNyxI1elw7WFrgRWKSS9MBbObCblc9hLFWRj1+Hjk0zJefPMUf/uAYX37yFI8cGl6W9VGhqDYqxkyhUORFj0LPyzlGyn350J4e3AtBkDAxH+Rbr1zk3+/t5/WhaV45N0UoGqelwcJ9u7q5al0TBwfd6ViwTe0N7NvRyQ/fGuPE2DzzwQh3NQV55NBwWQHnWmOkUoJGACaDIWENA2JSYjYaCMfizHjDOCwmzEZBLMNy1eIwE4wkhGO5OeX0KtK9VGydBFoazFhNBn75lk3Y8yT7rVbi2HrKM6dQLAclzBQKRV70SHFQ7jEyY5sCkRitDgvT3oTV5dT4An/1zBnuu6qb33/vLowGgT8cZX2rnb9/6UJaRDTbzewdcPHNl88ndwomrEOxuFzWZK0lU3xK0MTiYZrsJmaSbfeHEucyMR+kzWFhIRShzWHBH7piwct0xZYravRKT7GUwDMIeODqbi5M+fjbF87h8YUXJfud9Ucqnjh2NVW0UCiUMFOseOqhduFqpbe1gd/ZP8DluQCRmMQXijI2G8BoENy2tUOTqCknTUJmbNOQ28ct/R386OgYJqOBRpsJm8XIK+emOXzRg0EIdvc2MTYbzLLs7O5t5uVzUwQj8byutkpO1pmCJjPX2kIwSleTlYVAhAeu7gYJ/+nBXZiNBryhKBemfFy3oZV/PTICLC8bvh7pKZYSeF2NVnb3NPOfnzyZFs6pZL/HRmb57N0DTC4EK544drVUtFAoQAkzxQqnXmoXrlZS/fvm8CzTvjBNNhPXbmzlPVd1l9S/paZJyIxtujjt4+Hr13Pntg4sJgNzgSivnpviuVOTRGKSq3ubePj69Zxze2m2m9MC5PpNLXz9hZkl458qMVnnCprN7Q7c3hDzgcRGhY++eyMfuKaX7x6+xNPvTODxh+l3Odm/swthSLgH5wKRZWfD1yM9RSGBd/X6Zvasb+Z7R0bxhhbXHr047eeVc1N86o6+oulU9FhU1XMtToWiVJQwU6xYVExJZcntX4vRQDAS59Vz05yf8pXcv6UUi065AlsaEu7IHx+7zDm3l5OXF5AS3t3Xxh/83C6Gp33s6G7iJ++M89CeXq7d2EosnhAgcSkJxYoHzldyss4VNDazAXuniVv627m5r52/e3GIizN+XE02Wp0WfKEY//z6MAdOT/LZuwd4zTitSzZ8PYp05xN4JoPgK88OLhKemVUURj0BCvWunosqVYtTsZpQwkyxIlExJZWl1v3b3GDm5r42OhttfPW5QeaD0fRuxpYGM2cnvfjDUT51Rx+PvzXG8Iwfq8nIB67pTVte5vyRmk/W+QRNo9XEdw9fYnIhhN1sRABub4SFYELUjM4GOTzs4ZO3b6GzjtxuuQJvfC7I6GwAAJvZSG+Lnc5GiUQiEJiNgnAsnlf46r2o0muzg0JRD6gZS7Ei0SuB5kql0rmaat2/VpORu7Z38vK5KS5O+2mwGnEvhBifD3J6YoHT4wu8NjTDj45epqfFzuhsgMeOjHJ8bC6dIgGoi8LZuWlJvKFoVt+mRM3WTifbuxvZ2unk0oy/oKWpENXO35WbksMgBFaTAZvJiNVkwCBEXuFbiYLwpeaZUyjqGWUxU6xI1nJMSTXi6uqlf8c8AdqcFkwGA5FYHEPShWYxGTAKwUtn3Xz+nm3MeMMYhcBmNqYtL5dng3z4xg11Vzg7X98mRM0VAeMLRUvq21rEWpZrpapUoL6qxalYLShhpliRrNWYEr1cQEsFXddD/8YlhGJxelvs6VxlKXEmSOTQmpgP0WBJtDsz/xfA4KSXd8bm+M07+3nu1JXJ2mgQ7N3WUbPJWu++LTYmLs8G+eTtWwB037VcbkqOckW/lo0CqhanYjWghJliRbIWY0r0ivtKWVeG3IkC2gNdTlodFhptJrqaEsWz66F/MwWM1WSgpcGczgcGiYz/TTYz3mQOsNxSTADPn3Jzc1971mR9/PBr7K1h7KGefVtsTGxqb+Dmvnb+8dULjHgCeENR3S1p5VipyhGmxSyCueix2UGhqCVKmCl0pVo5xfRKoLmS0MMFlLKuWE0G9m3v5MAZN4+8MYzHF6a72caDe3p479Xr6G1tqHn/ZgoYgxBZ+cBS3La1nbeGPelSTI8fHcs6xkIwQiwusybrU0lXaK3Qc+wWGhOp5LpffW6Q0dkgWzudWE2Giuxa7m1t4OHrN7B/ZxdSghDQ1mChwZp/eilVmC5lJb6r6cp4UDkNFasBJcwUulHtOJe1FlOy3LivlHUlEI5x784uvvrcIBenr9QQHJsN8t3DIwxN+fjMvq1L9q+r0crkfLBik2CugLGZjVlpGTa2WNm/s4tjI3N89u6BrFJMKerVna3X2C00Jnb3NvOzix6u2dDCr93eSrvDQigaT5dK0nNXban3fSnCVIuVeN4SJRKNM7kQVDkNFasCJcwUulCrnGJrKaZkubFJKevK7t5mDpxxZ4myFPOBKJdmAulJu1D/TnuDfPvQcMUnwVwBYxCwa10Tu3ub2b+jk3AszognwONHx/L2Sz27s/UYu4XGxLvWNzPtC/HsiUm+/MQpApEoHU5rVqkkPZLrlnvfaxWmWqzEoWickdkAf/fikMppqFgVKGGm0EQxF0Gtc16tlZiS5cYmpawrfS4Hj74xnPcz0XgcicyatHP7t9oivJiAGfX4Oef25hVlK8Gdvdyxm29MNNvNOK0m/vCxY5yb8mExGrCZjVmlkn573wDff3OEcDROKBory9K53PteizDVYiW2Gg08e3Jc5TRUrBqUMFMsyVKuClWnrnrs29HJ7t5mFoLRtFsqJUqKFQSf80eIxuJ84tbNuBqtGAp490wGAwJR0CVaKxFeSMCsZHe2HvFQ+dyCu3ubefrkBJc8AYwiEU+XutwxKTk1vsBPT4zT53JwcdrHc6cmyuorPe77pYSpFiux1WzgrWH1/FGsHpQwUxRFi3XEaDDURc6r1UymOJ5cCBKNS67qaeKha3o4ctFDu9OSd3LN/N5sIIw/HOO2rR18/t5tfOuVi5waX8j6fJPdhNmYPzEo1Gex6JXoztYzHjNXnPa7HPyvF4ZwNVqZC0QwJq9jTEoC4RgxKXlx0M1/f/gaTlye5+Wz5Vk6q5HrTouV2GQQ+CNRBIVjCdXzR7GSUMJMURCt1pH3XLWu5jmvVjO54thqMmKWktPjCRfeb+8boLfFvkiI5H5PIDAKwRPHLvPqOQsff/dGvvLsINPJFBRWswGX04pBiIIu0XpJPJvLSnJnV8IVnClOw9E4RqOgp8VOJBYnFIkjgXA0Tix5XYLhGO1OC8dHEyK7HEtnNXLdadkoYDAs0GK3qOePYtVQv0tKRc3Rah0xGURdlL5ZjRQSx6nyNx5fhINnJjV/z2Y20tZgIRyN8/rQDLdv7cBkNNDmtLC53YHNbCwam5VbhicfahIsTCXKEaVIiVOrKSGw7cldrG1OC0aDSKQNMRrobrIlNweEs8RMqSW2UtasYuhx36csgnu3ddBsNyfcvnYze7d18Om7+jEbDer5o1hV1K3FTAhxAVgAYkBUSnlDbVu09tBqHYnLeM1zXq1WynUdpr7XbDezu7eZPpcDu9lIIBJjyO3j7ZFZ3Ashfv3OPibmQwQiUZrtliXdafWQeHalkRlLFo3HeXN4tujnl+sKzrxGqTqcHU4rwUjCjekPxdja6eTE2HzW90q1dFYzl2Axd/UgqOePYlVRt8IsyT4pZeEZQFFRtLoqJILeVvuKDcKuZ8p1HcYltDaYuWPAxYEzbh5NJpFtdVi4pb+DD1zTy4uDbrqbbPzRg7s0x2atxcS+yyE3luw37+zn1Pg87Q4rNnP+QP/luoJzr5FBCAxCMDYbJBqLs6m9gdu3Lk7GW46ls5qbL4q5q1fyJpClUElz1x71LswUNaRU68hKDMKud8qN4zEZ4K4dnfzl02ey8pVlpkz4/L3bMBkFrsbSLDOreRLUk3yxZN5QFCnhwrQv7TrORQ9XcO41mguE2dzRwM7uJu7c5sqbjLdcS+dy73u9hMdqfP7Uoji9ovbUszCTwFNCCAn8Lynl12vdoLVGOdaRlRSEvRIo13Votxh5fWg6bxJZgIvTfg6dn+H6ja1ltWs1ToJ6UiiWbMjt45b+Dn50dAy3N0Rvix1DjgjTyxWce43C0TiHzs/kTca7XEtnufe93sJjNT1/apW0W1F7RL1uIRZC9EopR4UQncDTwGellAcz3v914NcBurq6rn/kkUdq1NLFeL1enE5nrZuhG5FYnPlgNLHVPi4xGgR2i5EmmwmzcfVPxLW+npFYHPdCiEhs8b1qNgpcjdZF1yEal4zNBgiEY8Tz3OIGkRBvPS12TIWSmq1SqnE9o3HJ+FyQWE7nGw2CFruZiYUQkVgcq8mQJcwKXU+9qKd7uZxxnY9a35+VQAIzvjDeYLTgZ5w2E20OS5EkISuT1Xg987Fv377DhWLn69ZiJqUcTf5/UgjxGHATcDDj/a8DXwe44YYb5F133VWLZublwIED1FN7lkKLKyEcjePxr03rSD1cz0zLQqbr8M4CloXxuSCP/OAYgXAsXVsyGo9jMhhosptwOa00WIz82f7ddDfbdWvnSoiHqcb1TPV/vnXvJlMDe6928cIZN5MLQcLR+JLXU0+qeS8XGg+haIxHDg1z8FJhS/Dezg5N6Tvq4f7Um8n5IF9+8lTREIbmqJkv3LFj1VgIU6zG61kqdSnMhBAOwCClXEj++z7g/6lxs1YlWl0Jq8lFUC61FB2lug5TsWlSQm+Lnc5GiUQiEJiNiWBwrbFMWs9bxcNcoVhs4MVpP4/7x7hxcyufvH0LZqOo6mJHy72sx1gvNh7sZmPdJSquJ+o1X6CiOtSlMAO6gMeSk4YJ+Bcp5U9q26TVh4ph0E49iI5SxHFmbFoi59liAaYllknreZczlkLRGB5fmGhMIoEGsxGn3VR3FrZyWCo2cC4QIRyLp2uR1hN6jPWlxsOn7uhjqTXBQjBCPC6ZnA/WtQW2ElQjea+ifikozIQQVwP/G+gFngS+IKX0JN87JKW8qVKNklIOAXsqdXxF7QuPryQisfiKE7B6pLXQKrbKGUujHj9PHLvMs6cmGZ7202g3cfvWDn7u6nWsb7XT2aSfe7UWrNS0Inos1rSMh2dPTnDDplaeObk4OXIKi9HAxEKQf3zl4pqzwKp8gWubYk+F/wn8CXA1cAZ4SQjRn3yveOpvRd2jNXFpKZnAVyKhaIzJ+SDjc0Em54OEorFF788HoxXJ1F5plsqYXmxyKyVD/VJjqdluxmI0MLmQ6OfhGR8vnnHzb0fHODvhJRyNM70Q5odvjvGlH59kcNLH5dn8u0lXEsvp/1JZahxrPYYeVQm0PFuOj86xs6ep4PvBSIzeVjtHLs4yF4gg5RWB+LUD5xj1rPzxUYyUsO9uzm8hr1dhr9CHYq7Mxgz34X8VQhwGfiKE+GUSm0YUKxgVw6DNZTPnjxAIF5/k6jkWpty0FqVUHJBFxtKm9gb2JpPcvnR2ikAkhj8co6/DwW/c2c+3Xx/OKqR+cdrPMycm2LfDRbuzNm4+PWMJ9UgrslR79HKz61WgXsuzxReK0u6w0t1sWyQE41JiMxu4tb9jURJcWDvWfJUvcO1SNMZMCNEspZwDkFI+L4T4EPA9oK0ajVNUjrUew6DVZROXLEp5kEu9C9hyNm6UItwNQuQdS812M3sHXHz1uUFGZ4Ns7UxsgR9y+3hzeJbDFz18bv9AViF1gFfOTbF/Zycef/XFbq7IcVpN7O5tZv/OLpptJhy20mPglrNxZinRpWecqF6LNa3PFovJkFd47OppYnt3I8+fmkwfIy4lkWQsogDeHJ7l/t3r6nIxpCcqX+DapJgw+wtgJ/Ba6gUp5dtCiP3AH1W6YYrKspZjGEqJiTKIRO6pYqxGAVuKcC80lnb3NnPgjJuL037anBbMRkE4JonE48SlZGjKx8Ezbm7f2sEP37piGZn1RTAZDVUXu7kiJxiJMTzj57WhGZ48fpnP37ONy7MBbtvaURVrxVKi63f2D+gaJ6rXYq2UZ4vFZFgkPIxC8J+fPMmsP9GOYGRxypcpr6kuwwcqgdoRv/YoeLdKKf9FSvlanteHpZT/vrLNUlSatRzDUEp8XXODGbuluIVEi4DVIwaomqQm12KkzrvQWOpzOXj13BRWswGX04pBCARgNlxJqvri2Smuyak+0OIwE43Fqyp2c8V6MBLjwrSPGW+YaCzOxSk/z52cxBuKVSTGKXd8+EKRJUXX5bmApoLoWuNES7nmxcZzqc+WlPDobrbT1WQjLmU6/1vudUBCNBZHSrjk8a/6WDPF2qRe02UoqsBajWEoxWVjNSWyoueLhQHtuxtrnWqjVErdVZhvLDmtJoQgqyak2ShoaTDjC0UJxyQzvjBOa7bwvbW/g7iUVbXWZor1uJS4vSFCkWyLzCvnpvji9p28fHZK1xinfOPjV2/dxKGhmaLfi8Qk074wliLZ8Utxs2u55g/s7sa9EFxyPC/n2ZISiAdOu/NeB0iMkVOXFxicWGCjprNTKFYOSpitcdZiDEOpLhuzMX8sjJZJphK54qqV6LbUyTV3LEXjkh3dTSxklJUxCIHLaWUhEMXjD9PmsOANXbG2bGpv4J5dXfR1NFR1DGaK9UhMMh9YXApn1hdJC0y9NnwUGh/BSJy3R2fZ2Ja/0DkkAuibbCaCeYRLilLd7MWu+X27uojEpObxXO6zJSUQj4/Oc/LywqL3N7U3cOc2V3pjQK9LP5f3SqhcoVj9LCnMhBC3SSlfXuo1xcplrcUwlBNfV84kU4lccdW2vpV63pljKRSNce3GlkX9bDMb6XM5mFwwcUt/O8dG5mhvtHDH1g7eW6M8ZpliXQLR+GKx0+IwE4wkRKQeGz6KjY9AJEaDxVSw0DnA2GyAaze28uq56YK/UU6caKFrLpE8cmi4pPFc7rOlt7WBT+/bitNq4pVzU8z6IrQ4zNza38Gd21wcHHQzF4hgEOiWI2AlWrYVqxMtFrOvAtdpeE2hWBGUm/yz1ElGr/QDKWpVqaHcybVYP9vMRm7Y3Mqv3bYFi1Hwy7dsosFiTO/WqzaZYl0AJoMhEdOUwa39HQy5fYA+Gz6KjY8ht49b+jv4yfFxOhtl3soNRoPgPVd1c37Kp3sS23zXfHI+WJUySimrldUo+PCN63nomh6iMYkvHGXI7ePxo2Npa3ejzYweVbxVFRRFPVEs8/8twK2ASwjxuxlvNQHKtluHVMIMXy+m/VLaoeWz1Yiv0zNX3Eqq1JDZ/3azkd+8s5/nTtV3HGOmiBybDdBkNzGTkcIj132mx47lYuPj+OgcD+3p4djoLDKPSai72ZbeHVqtONFq5D7MtFrNBsL4wzF2dDdxV9JKdnE6O9h/z4ZmjOHZsn8PVta9pVgbFLOYWQBn8jONGa/PA79QyUYpSqcSZni9j1muyCulHaMeP0+dmODwBQ+zwQgtNjPXb27lvl1d6fJBqTZYTQYevn4D9+9eV5H4Oj1zxeltfasU+a7VzX1tPLB7XcX6WS8yRc6hoRmOheewW4yL3Gd67VguNj7mAhEODrr5/D3bOD2+wImx+YKiq1pxopXOfZhrtRIkUmc8fWKcYyOzfPbuAR73X7GWpa7D4NGLZf1eipVybynWDgWFmZTyBeAFIcQ3pZTLG/mKilIJM7zexyxX5JXSjlGPn//x9BneHp3Lynl0ZnKBd0bn+O27t/LG+RleG5qpSgyJnrniVkKlhkLX6qfvTHB0ZG5FuIOuiJx1iVJRvhAnx+b50dtjxCXs3dah23jJHR/NdjO7e5vpczmwm40EIjHMBsHHbtrIQihaVHTpHSeabxFVydyHhaxWNrORze0O3N4QL511c3VvM8dG57Lu28GSfy2blXBvKdYWWmLMrEKIrwObMz8vpby7Uo1SaKcSZni9j1muyCulHRLJE8cu8+rQdNb2+mgszow3zMtnp9nictLWkCjzemt/R3oCHJ7xYzYa6MwIWs+clMp9HOtZyLreKzWsJndQpsjparKxrtnOvh2duluiMseH1WRIl6569I1hPL4w3c02HtzTw4a2hqoK2mKLqAd2d1ekMHsxq5XNbKS3xc74XIhfvXUL733Xurq1bCsUeqBFmH0X+Fvg74D6zoq5BqmEGV7PYy5nwi6tXqPk2VOTeXMeSRIr4qffGefLH3oX65rtWRNgq8PCvbu6+MiNG7GaBE8eH8+alG5zhhn1+MuaHPWKAar3Sg2r1R2UzxKldy3Nz+zr5/JckL/4yWnOub2YDAZaHBYabWZeG5rhwrS/atZGLYuoz+zr56kT+sa0LWW1MghBKBrDbBS6jx8tlkuDYFVWQVHUJ1qEWVRK+T8r3hJFWVTCDL/UMeNSMuUNEY7GmZwPFp2YljNhl3Ju0ZhkeDp/FvB4MqfWfDCCySj46nODWUHE094wP3hrjMuzQX7++vUcvTSXXj3PBSJ4TVG+duBc2ZNjZgyQQGIQBqLJ+ptL9V9KBADs3dbJqfEFJudDiz5X60oNa8UdVIlYzjanlZ+8M4HZaGB7dyMCgdko0ikyqmVtLGURpXdMWy2tVlotl/0uZ9274hWrAy3C7N+EEJ8GHgPSM4KUsnhaakVVqMQDrdgxU3XrzAYD59xeHn9rrOjEtJwJu5Rzk0ga7SamF/KXn4lLyT27unnm5OSinV0ADWYjb4/O0pRcLb98NtsytdzJMWV5SUzsY5o3MmSKgF3rmnj4+vUcGZ4tGgxeC9aCO6hSKRXm/BGOjcxhLTKuSrU2lmPVK3URpZflKhSNEZeSf3fHFtwLIYbcPo6Pzi0aS5W0CNeb5VKxttEizH41+f/fy3hNAn36N0dRKpVwcRU6ZqpuXSgS58E9PQy5fUtOTMuZsEs5t4VAJFEM+82xvJ8zCMG+7S7+/uXzed93WE2Mzgayyu7kslxXXKkbGXI/+87YPCOeALf2t/P5ewawmAxV2eGoZZKvd1frcqlkDJ3e1sZyrXrLbUc5YjCzrZMLQeYDEa7b1MZDe3qy0mNUwyJcL5ZLhWJJYSal3FKNhijKQ88A82LHzKwfmJvTCQo/tJYzYZdybk67iZ+7eh1vDc8usogZhGCg00lno41RTyBvG8xGQSwus8ru5LIcV1ypGxkKfXYuEOHJ4+P4wtGqTBBaJ/lKjMN6opIxdHpaG5dj1VtOO8oRg7lttZqMNNoSNUlPXp7nt+7q5/lTk/S5HFWxCFfCcqlQlMOST0khRIMQ4g+TOzMRQgwIId5X+aYptJIKMN+7rYNmuzmxWrWb2butY1lxUZnHjMUlZoOBB/f08Nm7B9I5nTI5emkOjz/blZiasLub8z/IlpqwtZ6b1WRkfaud371vOw/u6aHdacEgBK0OM+971zr+fw/swGgQmPMUfLaaDZiMBsxGQ1bZnVyW44rTOrF7/OGSPltJUhPnwTNTiVJF8sok/7UD5xj1ZAvgSozDcghFY0zOBxmfCzI5HyQUXf6epUrG0KUWL8XQYm3UKv7D0fy1NcttR6njpFhbUzswzUYDZye9fG7/AB+9aVNVxs5aiZNU1D9aXJn/BzhMogoAwCiJnZo/qlSjFKVTiSSTmccMR+Occ3sXlUTJpNBDa7k7E7WeW2eTnVhcsm+Hi307XAgEgUgMfyiKyWhgciHIe67q4js/G8EbimIyGGiym3A5rVhMiX9nlt3JZTmuuFIe+hJR8wmiXNddtZKdFqJUy41W91slY+j0sjYu16pXTjvKHSfF2moQAqtJcGJsngf39FRt7KyFOEnFykCLMOuXUn5ECPFRACmlX6iRWZdUohh56piT80Eefyu/IEtR7KG13Alb67mta2nAaDBw1u3FIAQxKRnxRDg4mHClfuj69Zyb9DGxEIScGJJ39TZzz64uvnd4ZNFxl+uKK+WhL7jy2Xxb94fcPi5O+yo6QSxnkq/EONRCqW68UkRcpWPo9EiroofFp9R2lDtO6tE6tdrjJBUrBy3CLCyEsJPMsymE6Cdjd6ZibaDHQ6saE3YoGuPJ45cLtvPZkxN8/OaNvD0yx9sjiyces1GwZ0Nz1qTktJn49K3Lc8Vp6b+r1zdjMgjCsTi/eusmLkz5aXNaePbkZFbOtVv6O3j/NT20VXCCqMeJsxilWG6gdBFXjRi63MWL0SBwWEx4Q1HG54JLBtTrZfEpZRFV7jipR+vUao+TVKwctAizPwZ+AmwQQvwzcBvwiUo2SlF/rJSH1lIr+IvTfn741hif2z/AA1fnr92YOymdODK77BiXpfqvtcHMnvXNfOXZQUZnA7icFh66ppdvvXqRY6NzBMKJOKlpb5inT4zjDUb4D/faCrZruUlQazlxllOwPhyN0+9yEouTN9UCZCQjhrLdtJUuGJ65eBn1+Hni2CXNblk9LT5aF1HljpN6tU5Vsyi8QlEILbsynxZCHAFuBgTwO1LKwneTYtWyEh5aWlbwY7MBIrE43c32vO/nTkondWpbof7b1dPEznVN/Ojty4wkd40OdDXx5PFxDp2fYX1bAxNzQSSkY+I8/kjBrfuFXHR37+jCYTESjsklBU+tJs5SC9anUy14Q7gXgtzS17Eo1UKKlOUmFpdlu2mrFUNXzu5KrYsniWRyPqhL5YJyx0k9L/RqHSepUGixmAHYAE/y87uEEEgpD1auWYp6pd4fWvXoIskkt/8Azkws8L3DI1lt7nM5ePSNYSTgC0XZ2ulEQlZMXD7xUGhCf+qdCQ6ecfOJ27bwgzdHkZKigroWE+ey8rxJmPNH+dHRMY6NzPLZuwd43J8dE5m+7hrdbwJZ0HpXSZf8cnKmLbV4MhsFjxwa1q1ywXLGST0v9GoVJ6lQgAZhJoT4C+AjwDtAap+1BJQwW6PU80OrXl0kmWT23+R8kB+8uXhThd1sxOMLI4CFYJSuJhblV8qN3Sk0oWcmBn7u5CRX9SQqGyyV26qaE2coGuOpExNcnPYjSZjmCyX3zJfnzWwUNNlNiczxUz6ePz3JVT1NvHJuOv2Z9HXXIN53rmvCF47z5HH9RIxWlru7stDiadob5K+ePat75YLljJN6X+hVAj1rrSpWJ1osZh8AtkspVcC/ou6pZxdJPgq5XgORGK0OC9PeMNF4HMniIPtcy1++CT2VGNifjFF76ayb33/vrnRlg6WymVdr4pyYD/LTd8a5MOUnGo9npTJJJfxNiRGR/Hcm4WicJpuZyfkQvnCUZ09O8ofv20kwEsNmNmZdd6NBFBXvzXYzd2138fWD57LqkuohYrSgx8aL3MVTSvhWonIBLG+c1PNCT28qUWtVsfrQcgcOAeZKN0Sh0It6SXSqhZTrNZcht49b+jsAMBkMJJJoZJNr+cs3oYeicSYXEsLMG4oyOhvEaBBZSXSXSlabmji7m+10NdkqEk/lXggxNOklGouDhGgszow3zIVpX7qtKTGSe54pi+DEfJBN7Q10N9mYD0awmowsBCPc0t+edd0FFE16fGt/O4cvevIWi4elE7Uul0JjIpNS3fHVSFpc6XFSCSqRjLgQ5STiVaxNtFjM/MBbQohnyS5i/rmKtUqhWCbVdpGU654o5Ho9PjrHQ3t6ODYyy0IoitmYPQnns/zlxtcFIzFC0Tj+UDRtb2tpSLx/YdrH5nYHNrOxpmkvUu7XTe2OtIUw6/1IHLc3RG+LnWa7ZVGet8xSYQATsRCNNhP9nY50JQiDAFdjtghzNVr51B19PHtygreGZwlEojTbLezZ0MxNfW385dODRdudz5XoC0Xw+BMTrhCJnbYOa+lr2swxUSiPndNqLMkdv9LSn1SDalqvKllrVbH60CLMHk/+p1CsKKrlIlnOA76Q69XjD/PMyQn+w73bOHF5gRNjc/hC0aKxO5kTekqw2C1GzEYD4VhCuNyxtYO3hj15BU8tSFlyYnG4pb+DHx1dXIR+PhCls1GmLYQSmT7PSEwyH4imPxuNxfH4wty+tYMXB6d4+WxC3GzvbkqPhUgszrcPDTPkTojTX7xpA60NFhptJrqb7cz4wiWLmCG3l+8fGeH5U27c3iAup419O1z8/HXr6XM5S+qT1JjwhqJcta6ZA2fcWXns9m5z8dGbNpY0gdf7pphqs5yaouVQyVqritWHlnQZ/yCEsADbki+dllIWf2opFGsEPR7wucHT7oUgkbjEZjZgEIINrXZu2tJKuyNROqqQ5S9T5F2c9jMfiCIltDosTMwH6etwsHebi688m7AG5QqeWpCy5GRaCHPTXETjcbqbrVkWwtR5XphOxKRlsqm9gTu3uXg8KfIyRVTKbXrwUsJCOeIJ8NLZhJDtcFr59L6tmI0Cp9XEQjBKITJFzJDbyx88dozBCW/6/Yn5II8cusThix6+9MGrSxZnva0NPLB7HV/68QneGVtIx91J4MTYPN946XxJ4mElbIqpFuVar5YTtK8slopS0LIr8y7gH4ALJMIzNgghflWly1CsdfR0T6Rcr/u2B3B7QywEowy5ffxrRhqN7mYbn76rv+ixUiLvyePjuL1BvIEYA10O9m3v5Oa+Nr79+nDaXZhP8FSbTEvOwUE3n717gBfOuHnl3BSzvggtDjN3DHTw8Zs3Z4mQzPOc9YWZWgjR4jBza38Hd25zcXDQne63lIhKXS9i2ZNfMBLD7Q1x8vICTquJe3d1sr7VzpvDs+mNB7mkRIwvFOH7R0ayRFkmgxNeHntzlN+6s58Gq9bsRImx9eKgm7gkmSpFIjJKiJXq+lppm2IqSTnWq+W6PZXFUlEKWp4U/w24T0p5GkAIsQ34NnB9JRumUNQ7ersnJJLnT08WtGponYx7Wxv44LW97OhuxGRMfM5pNfLT4xO4vSGMQhQUPNUm05JzcdrP4/4xdvc288XtO7GZjQQjMQwCelsWJwPubW3gYzdt5IbNrQQjcYLJ+KvHj2anH0mJqFl/mKOX5tiT8dTLTCUC8Mq5Ke7d1cUNm9o4fNGT3tWZSaaImVwI8vwpd9FzfO7kJB++YUNJwiw1tlIFvfNRquurnvOGVZNSrVd6WMWVxVJRClqeFOaUKAOQUp4RQqhdmopVQ7kuCr3dE3oKPafNxDm3Nz0RpILIv/jepQVPNcm15MwFIrx8diqdzmMpK2GDNZFSI3fiTJEpotLXK+lVzN04ADDri2AwCA4OuvmNvf0cHvZwacZfML5PSnB781tMU0x5S880VCnX11rMG5ZLKdYrvaziymKpKAUtwuxnQoi/A/4p+ffHgZ9VrkkKRfXIdFHMBsI0mE1cs7GF/Tu76G2xYSki0FLCZlO7Y9GuuVS9xlLcE3pOxssVPNVkuZYcrd/PTUORu3EAoMVhJhiJpa13N25u5cM3bMBsFHlFjBDgctqYmC8szjqc1pL7pJKur7WUNywfpVivUlbWYmhdLCmLpUIrWoTZbwG/DaTSY7wIfK1iLVIoqkSmiyIVZzQfiPLa+WmePjHBF+7fzrrm7CSdmZa1RpuRD17Xyw/eHMvaNXdL/5V6jZvaGzS7J/SejFfSRLBcS46W76cmZC4nNgVIWLRx4Nb+DobcPiDhrnru1CT7dnQWrKva2mBm3w4Xjxy6VLBtd+/spN1Rmouqlq6v1Z6ZvhTrld6WS2WxVGhBy67MkBDir4FnSZRkOi2lLD8LoUJRB2S6KHLjjADOTXr5t6OXuaqniZa4XBT829ti5/3X9PBPr13k6Mgcc/7Ew3vaG07Xa/zd+7bT19Gg+aFbick4NRG89+pw8rzjGA0CY9JNU08T7nItOUt9PzUhvzB5CkjsZDIZDImktizezQlLC2GH1czPX7eewxc9eTcADHQ5+eC1vSXFl2W2tdqur7WSmb5UK6uelst8VRn0KiqvWB1o2ZX5c8DfAudIPMu2CCF+Q0r5ZKUbp1BUilQ8V744oxSvnJti/85O5mfi/PXzZ7MywW9qd/D40cu8fn6GDW0NWIwGPP5IOq3BQijKycvz3Li5TXObypmMtVg3LCYD4Wh8TUy4S9Hb2oCr0crezg7eHJ5lymtCSvLu5gRtQrjP5eRLH7yax94c5bmTk0x5Q3Q4rdy9s5MPXttbcqqMzLZW0+JZ7dxetaYUK2ulLJdahfBqt2IqstG6K3OflPIsgBCiH/gxoISZYsWSclHkizNKMeuL0NJg4VIgwqWZQFYR8T6Xg0ffGCYUiTPjC9PTbKfdac1Ka3BibB6Pv7SEkaVMxlof6qtxwtU6UeX7nNloSE7I6whH48z6wwiRqLe5d5srHSNotxg1W6X6XE5+685+PnzDhvRr7Q5LyZayXKrl+lqrmem1WlkrYbnUel+uFSum4gpanhoLKVGWZAhYqFB7FIqqkHJRBCKhRXFGKVocZkxGgTcUXVRE3G424vEl3IOpRK3WnIdzbuyJVjGhZTLW+lBfyRNuof7SMlH5QxHc3jALwSgeX5jByQUuTvvpcznoicXTE/Kox89rQ9M8e2qS4Wk/jXYTt2/t4Jdu3sj6VjudTdp3rTZYTcsWYvmoRrB+vWWmrycLUSUsl1ruy5fPTnFLfwd/+8LqWlQplkbrrswngO+QiJd9GHhDCPHzAFLK71ewfQpFRUi5KJ4+MZkVZ5TJrf0dGIBYXC4qIh6IxNK1HaPx+CLhBtmxJ6WueotNxqWIrXqbcLWSr79u7mvjhs1tfOOl8wUnqt/ZvxVfOMaTxy7z/SOjXPL4cTlt7N3WwT07u3jp7BTmWIhRjx+DIC1u7WYjfa5EItfT41784RE+s29rDXuguiw3yF1PIVWPFiK9LZda7stYXPLTd8ZX5KJKsTy0CDMbMAHcmfzbDdiBB0kINSXMFCuOlIvi1PgCkwtBZnKKZ6cCwae8YSwmw6Ii4kNuX7q2o8lgWCTc4ErsSamuxKUmuVLEllzGhFsrq0Wh/vKGYnz70DAXpnx5M/IHwjEueQJ88+ULvHlpNp3CYmIhyHcPj/Dm8Cy/957tjJ0a5qkTE/S7HOnfyE3kOjkfWlOT3nKC3PUUUvXsdtfTcqlFCPe02PnOzwrv9oX6XFQplo+WXZm/Vo2GKBTVpre1gc/s28qTx8d5/K1RJuYWl/XZta6JJpsJgzd7Qsqs7bgQii4Sbp1NVvZu62QhGOHJ4+OMzQYw5JnUcle9Wia5UqwbBiHKmnDz7ULdv7OTzR0OLEZjxYRaMWtgKq5Pksgfl9ufV/U08YO3RplcCKXdzJmcdXt55uQE11mMvHbBw0Bn8aD8tTTplRvkrqeQWslu91LRIoQdVhPzwSgWY+FzVfU1VydadmVuAT4LbM78vJTyoco1S6GoDr2tDfzKLZu5b1dXIh7JH2Zwwsvzpyfpczm4bWs7x+fMdDfbFk8+g25+977tnLw8z4mx+XTsya6eJq7b2MJjR0a4flMrjx0ZJRKP43Ja81p6jl6a471XhwlH45omuVKsG+VMuLmT7ab2BvYOuHjulJsjF0/TZDfT73IuEmoOq5FAOEY0rs3Cls8itxAobA1MxfUZDIZkTF+2MOtzOfnmyxfobbMTyeOaBjh4ZopbrjEwF4yky1UVYi6QuCbjc8GaxzlVmnJ3BOsppFaq270ctNyXZqOg3WFhIZh/cxKo+pqrFS2uzB8A3wD+jUQeM4ViVWExGdjY7iAcjePxh9na6cyKITltNOQN/t3U3kBfRwM3bm7D40/EnoSjcQ6dn+GfXhtmLhDhtq0duBcSOYp8oSib2x2LxNlC0vqldZIrRWxZTIaSJtzcybbZbmbvgIuvPjfIxWk/ABvaGrhre2eWUGu2mdnicvDuvnYOnJrE448UdWcVsgzu3dZJa0N+0ZmO6/OF88b02cxGZoNheqQds9FAOI84m/KFMIhEe/PFFaYIRmLEpOTitI9vvnKhLuKcKk1Ho5XfuLPvyoaJCS8Xpn30uRx5z1tvIVWpMlS1olgogBYhvK7ZzrUbW1R9zTWIFmEWlFJ+peItUShqTLEYkqWCf7uabISiMR45NJz1IM3cJBCKxHF7Q4vccI02M6FovKRJrhSxVcqustzJdndvMwfOuNOirN1p4b1X9/CVZ89waSaQPMc4074wr5+f4dVz03z27gEePzpW0J2Vz/3l8Yd5+sQkx0bm+OTtWzh6aY43LsxkCbRUXN9Pjo/njekLRmL0dzgJRRN9nq9MUofDSlzGuX5zK/ECE3wq4fB9u7o5M+FFyvqJc6oU+UuTNfOJ27awvsWOOY/FaykhFZeSKW+IcDTO5HxwSYtjJctQVRstIQlL3ZedJd7nitWDFmH2V0KIPwaeAtIZNqWURyrWKoWiDlkq+DefBSFzkwBkpta4Mrns2dCM0SBKshaUuoVf666y3Mk2FdeV4vatHbxwepIL036MQiABtzfEpvbE712c9vPCGTe7e5t5+ezUIndWPvdXZjmsk5fnaLAk6pW+7109HBycTAvAVFzfpRkf8Tyaasob5L6ruviXQ5cSLqBABH8klvWZvds6iMbHuW9XFwbBIhd1KuFwd5NtURUAWF1xTilyhbJAEIjEePXcDOen/AWFaDEhlbqmZoOBc24vj781tqTFsZZlqFLoseGllLi7pe7LlVRWTaEfWoTZ1cAvA3dzxZUpk38rFIok+SwImZsELk77F6XWSK16jWUE6Ze6hV/LrrLcyTYzXxvAtZta+fITpzLOWRIMxzBmtOuVc1N8cfvOdMH0TEtfrnjNLIcVk5JILM5LZ93csLmV//LTU3z+3gHc82MEo3HmAhHeuTzHH/zcLl4cdC+aqG7f2kEwGufFwSlGPAE2dziY8YWZ8YWJxCQ71jXyoevXM3N2Kj2h5U56drOJvQMd3L51cRWAFKslzgmWFydWSEhlXtMH9/Qw5PZpsjjWqgxVCj12l5bTn0vdl6q+5tpDizB7GOhT9TEViuLksyCkNgl89u4BXjjj5tD5GYxC0GzPfuiHorGyrAV6Jx/NnWwzXbEADouJuUAYS4YVwWgUxDLcgrO+SFYcXaalL1O8ZpbDiklJIJyI65pYCNHmsOANRXnhtJsNbQ1cmvFzU19bur/WtzYUnKhS5ZEOnnHjsBgZ6Gzjpr427t3VzcbWBl4+f2VCy530IjHJMycmePzoWEGRvJLinJZiOXFi+YRUo83Erp4mPvrujbQ2WOhwWjhy0UOzPXFfLGVxrJWFSK/dpZXawFCNJMOK+kGLMDsOtACTlW2KQlE79HBhFLIgXJz287h/jN29zbz/2h66Gm0YDNliYilrQSr9xqw/nP6tSuwQzG1Hris2EInS3WzD47siWtoaLPhDV1yGfZ0OnFYjv3zLJuxmI5FYon4oZIvXSEwyF4gQkxIpE5NPLC5ptVuYDSTE3dhcgF+7bQu+cJRtnY3pzPrFJqpSyyNlHmtyPsix0blVEeekheUG3GcKqWlvmGs3tvLMyQn+7uAQQoCUcM3GVh7a08PBwUSs4lLCpNoWIj13l662DQyK2qBFmLUAp4QQb5AdY6bSZShWBXolyCwmruYCEc65vbznqi7WteQv81PIWpCZfuPE5fmK7xDMbMeQ28e+HZ2cvJyo+3lpJsBtW11poWa3GOlqsjHiScSB7ehu5Lfv3spPjo/z9IkJPL4wfZ1O3nNVN/ft6sLVaE2L17iUeENRIlFJNB5HAGaTgXt2dSKl5HfuGcBpSTyiTo7N09ti11zyqNzySPUQ51RN9Ai4Twmp0dkAf/PcIINuL/5wjIVglGgszognwLGR2cSmEP+YJmFSSQtR7iLMKARDbl/R72i1cq2mDQyK2qHlyfXHFW+FQlEjtLgwSmG5rphca0Fu+o187auUOPvoTZvw+MMYBXz5Q+/ihdOTnJ/KFmoupxUhwGiAZruFT9y2mW+9epEjw7NEY3GsZgNGIXhpcIqzk4n23reri2Mjc3imwiBBJi1mEtjW1cjPX7eeJ96+zI/evsxcIMz1m1vZ3O7k5v52XI26n2oWtY5zqjaZQjSedOVKSIhko8AghCYhKpEcPDPJ+HyIibnQolQkmZtCjo/O1UyY5FuE7eppYt+OTg5m7D7ORauVa60Je0Vl0JL5/wUhRBdwY/KlQ1JK5dZUrHi0ujA2lnjc5bpiUtaCfOk38rWvUjsEM60WHY3Q27JYqL09khCf16xv4ef29HByfIFjo4kYmzanJSupbqq9D1+/gY+9eyPPn3YTi0tOjy/Q5rSwb3snH3v3Rr72/FkOnHFjFII2h4VRT5BTl734QlE+f89AxXeiraWdcCkhemxkjrdH55gPRInGE67nJruJd/U2axKiqdgqs1HQZDctKnEGVzaFGA1oFiZ61+DMtwg7cNrN0ycm+J27B7g442Ndix272UggEmPI7eN4cjxrEZNrTdgrKoOWzP8fBv4LcIDEQuqrQojfk1L+a4XbplBUFK2Bur2u0uNB9HDF1Fsm9HxC7YGrr4jPeFzyxNuX2djegECkLS657d2/o4sfvjXGju5G/uIX3sX0QogpX5hINMb3j4zw03cmEi4hp5U2h4WJ+RBNdhNT3urVr1xLO+HMRsEHr1tPo83MK+emmPVFaLabuLW/g3t2dS0qN5aPVGyVQQhcTiu+UJRQJNtqNuuL0GgzaRYmetbgLLYIMxsFzXYLwgCjswG++coFPL4wrQ4Lt/R38NCeHhZCEc1ici0Je0Vl0OLK/APgxpSVTAjhAp4BlDBTrGi0BurmSTJfFfK1L9fdNBcI1yyQOFd8js8FCUZj2IpYNBaCESSJCXDEE2Bw0sveARdHLnq4ua+dJ4+PYzEZaHdYWN/WwOXZAEYDuJxWDELUTIiuVkLRGE8eH+fopTl29zbzxe07sZmNBJPWou8dHmHPhuYlxXBmbJXNbGRzuyOdmy5lgdvc0YDLadUkTPQuZl5skdPaYOF971rHf37yFEKQ3oE87Q3zo6NjnB6f50sfvLokUb6WhL1Cf7QIM0OO63IaUKNLUXOW6+bQGqibJ8l8VchtX2Yi1szJLhytj0ppmgOfufK5zB2rWzud9LbYcTmteMOJwHG7xZjlDq2HHW16utdqTUqwzAUivHx2Kp17LhMtYjg3tspmNtLbYqezUSKRCAT37Oykuzn/xpdMKlHMvNgibHdvMwcHpxiZ8bO104nJaMhy5xqE4MVBN+tbG0oSVmtB2CsqgxZh9hMhxE+Bbyf//gjwZOWapKgX6nkC0sPNoTVQ1xie1anVpZHZvsyknSmisTg7u5sSudEMouYuEq392eawZH0uJQr6XA58oSiTCyE6nFYcVhPNdvOi8lW13NGmp3utHtArvUO+2CqDEOkKF93NNu7dpc2FmWndKrQhoVTLabFFQ6q6hcVkxGwysrXTmRaT5f6eQrEctAT//54Q4ueB25MvfV1K+Vhlm6WoNfU8Aenl5tAaqDt49KKu7ddKqn2nxhc4fNGzKGZnU3tDumyQLxzVPfaqVGGutT8brKa8n0vlTHv6xDidjVYsxsXnUssdbXq71+oBPdM76BVblRKL+SzETXZT0q1NSZbTYouGVHWLFocFS564SKgPS61i7VBQmAkhtgJdUsqXpZTfB76ffP12IUS/lPJctRqpqC71PAH5QhGePD7OhWl/1go6RaluDi2TyaCGdi3Xuljo+72tDfz63n7++bULvDiYCMxucZi5tb+DO7ddKRuk94q+XGGudXLO97mL0z7ef00P3mAEj3+xUKjljrZKuNfqAb3TO+gRW2UQYDUaODk9v8hCPOMN4wtFuWZ9S0mW02KLhkAkRnezjUabOa8og9pbaqtNpb0l9eyNqQeKWcz+Evhintfnku89WIH2KGpMKROQRFb15hr1+JnyhnjsyCgTC8GsFXRmCaBSRcpyJ5PlWheX+r7DYqS3pYEvvjc7MDuzbJCeK/rlCnOt/Znvc20NFv7Dvba629Gm5w7ZepqUMgVLIBxjd28zfS5HOl3E1EKI27a2VzW2ymE1ssXl4PXzM3nfD0Xi9HU6aSwxgXChRYNBwIN7enhtKP/vwdrKPVZpb0k9e2PqhWIju0tKeSz3RSnlMSHE5so1SVFLtE5A+7YHeP70ZNVurpRYeGhPD+6FIMjsFfTmdseyAsTLnUyWK2K0fN9sNHBsdI6X8gRmp9BrRa+XZUhrf+b7XD3uaNMrFqseJ6Xe1gZ+Z/9WRjwBfnzsMo/+bJiFQJSN7Q3s39FJvMoevEA4xrv72nn13HTehK+b2hu4aUsbvnC05OoOhcZWv8vJhWl/QRf8fbu6kEgm54N1IagrRaW9JXon9F6tFBvVLUXeW3prjWJFomUCci8EcXtDWe6PSro6M8VCblFtSKyg3d4QvS12DEJUze2wXBGj9fsPX79Bk7up0Wpa9sRRL7nTcgVbKBqr6aSoRyxWJSe95VrhIjHJdw+PcGkmQIfTistpw2wUvDY0w4Vpf9XCF0LRGIFIHKfVxJ///NWcGJvnOz+7xNCkL8uFf+DUJAOdzrJ+o9BioJAL/oHd3URikkcODdeVoNaDzHFjNgieOlE5d32lEnqvRooJs58JIf69lPJ/Z74ohPgUcLiyzVLUiqUmoLiUROKShWA07/uViLXJFAu5RbVTzAeidDZKrCZtJWT0blchiokYrd9/79Xrlgyqv2PAxTMnx/GFYvS02HFYTZjnBOua7XSWIKDqsQhzPViZlhuLVckYteX2T6ptk/MhrHl+u1rxc6nzeOO8hyPDHlobLOzb4eKP3reLWFwy7QunXfigLRN/KRSyprkXgnUbc5tJqeI8d9x84tbN/PSdcYxCZIWGZLKcRVklE3qvNooJs88DjwkhPs4VIXYDYAE+WOF2KWrEUhNQJCbZ3dNUtOiv3haVTLFwfHSOh/b0cGxkNsvNEY3HkciqBogvV8Ro/X4sLouu6O8YcPGzCzN0Nto4MuzOyly+d5uLj960kT6XNutCvRVhrpeNKMsttVMpS6Qe/VMPVtLM84hLidNmYmI+yCOHLvHquelEAfSMmMq92zoqsvjKZ6ldCZs+ShXn+caN2WhgaNKL2WTICg3JZDmLsnpP6F1PFBxJUsoJKeWtwJ8CF5L//amU8hYp5Xh1mqeoNqkJqLs5/wO4u9nG7QOudP24fOhtUUmJBUhOOoNuPnv3AA/u6aHdacEoBF2NNvZt76zq6jWzXYUoJmJK/X5qRf+FB3bwZx/YzRce2MHD12/g+OgsnY02vvrcID86Osa0N0xcJjKXP3ZklC/9+ASXZvIXZ84lJcyLUS2LpNZJsVoJdlPieO+2jmR+NWi2m9m7rWPJcVcJS6Re/VNrK2nueaTKOlnNiekpswA6VHd3rlbR6vEvrg1aLVIi6+CZKeYCEaS8Is6/duAco57se7/QuEmFiaRCQ+J5rvdyFmVan3e1SuhdT2jJY/Y88HwV2qKoE4pZZ/Zu6+SxIyNVtajkWvEys8WnSsjYzAa2dTaWHAysZ7vyUUzElPP93BX95HwQXyjGkWF33kBpgHfGFvjpO+P8yi2bl5zM6qkIcyUsOaFojGhcMj4XxGQAu8WILxTT7P4pd2NCJSyRevVPra2k+c4jt6zTK0NT/NHOXRgNVMyFnc8VWGvRuhTlWPQKjZvMMJHM0JBMlrMoa24wc3NfG95QLGvnb6pQ/FwgUtOE3vVE9WYxxYqi0AQkkbQ7i9+YeltU8omFzBIy3c02Pn1Xf1VFWaF2ZbKUiNFDBMUl9LTY+eYrFwp+JhqP8+awhwf39GgSMPVShFnvSTHl7jHOBXn14Dnu2tHJ60PTnHf7CMXimmOzytnBq3e+MNCvfyrRtlIodB6ZZZ2EkGxqd3DTltJSd2ilkCtw347OunLt51KOOC/U31lhIjN+ZI5PcbmLMqvJyA2b2/j2oWEefWN4UaH4dy7P1TShdz2hhJmiIIUmoGJiorPJyt5tncz6wxhE6RaJQtSLWNC7Xcv9vkGAw2rC4yvsSjEZDCwEo0Un6FxrQUejteYpK/S05GTG1OxrhjsGXPzl02e4OO3Hak7E1EhJxWLXKmGJ1Kt/am0lLXYeqbJOzXYzFpOhYqKsUJyew2Ji57rGus1xVo44L9TfmWEiL511Mz4XIhSN6facHfX4+cZL57kw5UMCBoOBaV+Ynxwf59KMjz/4uV2aE3qvdpQwU5RMITGxq6eJ6za28NiREXyhaNkWiWK/W2uxUIl2Lef7zQ1mzHNiUQqRTJrsJtocloITdD3sesyHXpacXHeP3WzkwJkrrt/cdCuVCujWe3Ghp6WrlgufWlrslnIFvnJuml+6eSNDUz4m50OL3q9lNQooT5wX6+9UmMgHru1hW1cToM9zNrOf8xW4j0vSheIVxUsyLZB/f4QApJSyqWKtUtQ9uWIiHI1z6PwM//TaMAAP7empiEViuVnFK8Vy21Xu960mI+ua7ezd5uKxI6OL3zcbcDmtXLuxJe/EVs+5tcLRGHdt7+SOAReRWJzLs0GePz3JiCcAaJ8Uc909VrOBV09kT0q5MTV67kLMyhVlNPDw9Rt479XriMWXN+npbemq1cKnlha7pVyBc4EIB067+fW9/Rw8M1lX1nooT9Qu1d92i5F+l7PgBrByyO3nzAL3KVL3XDWppyocmRQUZlLKxmo2RLHySImJUDTGI4eG0w+H27Z2lGWRqNebpN7pbLLx0Zs2MuT28s7YAtFYHIMh4f7pcFrZ2N6Qd2Kr59xa5yYX+O7hEZ56Z5wZX5g2h4X9O7v48I0bePOih5iUmo+V6+4RQixy/abSraRYKjZL61ittDVSb0tXrRY+tbLYaXEFnrw8zy9ZDHVprS9X1Fa7v+txE0W9egqgBFemEKITSN+xUsrhirRIseLIXQ31uRw8+kb28FjKIqFHksx6EnXVbk+fy8mfPLSbnxwf540L00x7w5iNBq7ubeb+3V2YjYvdmJXa9Tg2G+Rvnj/L+FwwXWS+FCvcuckF/u/vHuX42DyQWF1PzId49I1LHB2Z5c8/eDUb2xyaJ0WzQfCJWzdjNhoIRGIEh4/R3+lkcMKb/ozJYEBk7NMvFpuldaxWKwdbvbn4yx37pZ5H6neisUSppHLuMa2uQImoW2t9uSKrmuOm1jt/c6mX/IiFWFKYCSEeAv4b0ANMApuAk8BVlW3a6qPexINe5K6G7GZjSRYJPepNapkoq9X/tVqJmQzQ2mDmY+/elFXs/J9eG8ZuMS7qx0rsejzn9vLCGTcHTk/mLTK/lBVuIRjmu4dH0qIs0U5JOCYhBm8Oz/K9wyP89r6tmiaQUY+fp05M8NN3xhma9NLqsPDJ/gif3T/A1547y6nxBSARh5cpXgvFNGkdq9VOTFovomG5Y1/reWT+zh5TgCeePFXWPVbt+LZKPYPKFVnVGje13vmbyUpIGqzFYvZnwM3AM1LKa4UQ+4BfqmyzVh/1bDZdLrmroXz1LAtZJJZ7k2idKKvV/6WKTL0e1KFojKdOTBR88M0FIov6sRK7Hu/d1cVLg1NFi8wXs8J5/BF+crx4/uonj4/z0Zs24lwiWWXmtTAKgdlkYNobxuOP8MKrF/nEbZv5Lz89jTcUxeW0YkieZyH3TyljtR6y6VebalkhFv1OY/m/s9z4tlLu30o/g+pFnOej1jt/M1kJ96YWYRaRUk4LIQxCCIOU8nkhxF9WumGriXo3my6X3NVQvnqWhSwSs/5w2TeJ1onygd3rqtL/pYpMPR/U5TxsKrHrMZ+1NDfGsJAVLhSNAfBH79vFQjDKofPTHDjjxr2QvRtuYj4ES4jF3GuRmbAUghwbnWNXTxO/c882fnx0jHAsvqT7p5Q+lnUYU1NJqmWFKPV3lhJOoWgMu9nIp+7o49mTExwfncMXimpyBZZy/+o9B2SeF0jMBgOx5FjSurirtgenXlIe1WO8Wy5ahNmsEMIJvAj8sxBiEihcKFGRxUowmy6X3NVQbj3L1M7AfBaJ5dwkWibKN4dn2bOhpSr9r6U9Q24fC4Ew3nAM90KIjW0OojHJO2Pzy3pQl9OPeq1iM887n7UUsmMM81nhUpPc60MzvHBmEofVzB0DHXzh/h38n5fP806Ga7OryQpLPDQLZZPvbbFjNXnZ2N7AqcsLfOi69Vy/qVWT+6eUPjYIUVcxNZWmWlaIUn4nGosvEk4397Vx1/ZOIPFsnvGFOTE2z+mJBbZ3NfKJ2zbT7rBiMRmKjoVShJbec0CmIJxcCBKNS67qaeKOARdHLnpod1qWFDq18uDUQzxkvcW75UOLMHs/ECRR1PzjQDPw/1SwTauKWplNa70aenHQzefv3cah8zMMTXoLWiSWc5NomShnfGEi0eKTuF79v1R7NrU3cPvWDr735ihPvH2Zk+PzuJw29m7r4H3vWsdLZ6e4OO0vSyyW2496rGIzzzuftRSyYwxzrXCZk1w0HqfDaWN4xs/3j4zy1qVZPrd/gL/4yam05eyB3d20OYpb8QpdC4NIbEawmYyEojGEQPN1L6WP6ymmphpUywqh9XfC0Th/+0K2cGppMNPZaOMbL53n4rSPd8bmaLCYuKW/g7u2dXJw0M0zJyfTlUSKuS9LEVp6zgGZ90owEuPCtI9QJM7ZCS9vDc+mi70XW9zV2oNTa5frSrg3tdTK9AkhuoGbgBngp1LK6Yq3bJVQC7NpvayGjAbB9RtbWQhFC66OlnOTaJ0ofeFo0Xbr1f/F2tNsN3P71g7+8tlB7GYjg5Ne4hImFoJ89/AIbw7P8nvv2c6s/zJzgUjJYnE5/bjcVWzmeedaS1OkYgxzrXC5k5zJYGBdiw2PP8xCMMqQ28fBM27u2ubiu4dH2N3TxC9cv37J+LJKrIpL6WOLyaBbTM1K2DRULSuElt9xWE1M+0JZ/d5sN7N3wMVXnxtkPhglEovjD8cJhMP86OgYx0aSosY/tuTCqFShpdcckHmvxKXE7Q0RilwpUJ9Z7P3ls1MFUxLpZb1bCeMyH/UU71aIJX9ZCPEp4BDw88AvAK8JIT5Z6YatFlIPkmLoaTZNrYYOnpliLhBByiuroa8dOMeoJ3+ha71IrYa6m+24Gm00WE3pv7uabIsGe+omKZTMsNhNkpooi3HtxhYuzwaKfkav/i/Wnqt6mnjm5AQeX0L8RGLxrPfPur08c3KCq3oSeZvnAmHC0Tjjc0Em54Pp+KtCLKcfIfu65btOxcg878yyLg/u6aHdacEoBJs7GrhnZ+ei1Xi+Sc5hMbGrp4mNbQ3YzAZeOjvFHQMuPnX7Fv7rw3vo71w6xaKWsVHqqrjUPk5ZI/du66DZbk5MXnYze7d1aLZKjHr8PHJomC8/eYo//MExvvzkKR45NFzx+7hUKtHf5f7O7t5mTma4vlOvpXIrNliNTHnDxDOEUKaogYSw8vjzJzstVWjpNQdk3iuRmGQ+sHjB+cq5KfpcjoLnoFVUFjr3FCtlXBZCj3uzkmhxZf4ecG3KSiaEaAdeAf6+kg0TQtwP/BVgBP5OSvnlSv5epaim2XSlxrOV607TsvJ5z1XdvHqucN+Dfv1frD19LiffPjRMm8NCJCYxGw2Ec8TZwTNT3LOri+dOTRKTkovTPr75ygXNFs9aBdfmnneqrMvu3ma+uH0njbZEyozuZvuicVdoknNYTGzuaKC31Y5BkIyh6VjSUlaoTbmUuyoutY+XY42stcupFKplhdDyO/t3dvHXz2VXXMzMrWgUgnA0htmYPf29cm6KL27fyctnp4pasEq1Duo1B2TeK5JEeEAus75IevdzvnPQw3q3ksZlMeoh3q0QWoTZNLCQ8fdC8rWKIYQwAn8D3AuMAG8IIR6XUp6o5O9WgmqaTVfCNuBClHuTaJkoDaKDoyNzVTFbF2qPq9FKo93E1EIYm9lIq8PCxHx2e6Z8IWxmIxemfdy3q5szE94si6eWh16tHjb5zvv46BxGA0VFYbFJzmQwYDIkVrJWs1GzKCvWpkabGafNxKdvLX/yKLWPy4mpWYmLrGotDHJ/BxJjJPU7drNx0d6QzN3CMSmx5HG5ZYqaYhasUoWWXnNA5r0iSNwf0ZzFXYvDTDASK3gOS4Vb7O5tZluXs2DS3kqNy0y3aDQuCUVjVXGL1jrerRBahNlZ4HUhxA9JCPX3A28LIX4XQEr53yvQrpuAs1LKIQAhxCPJ311xwgyq98CqZjxbJeILyr1Jlpooq21JyhdrF47GaWuwIBBImXgIeoPRrPi3docVfzhGd5ONO7e5eDwngF7rQ6+UftTzOpYjCittUc7XphNHZpd9zSv9QF+pi6xqLQwyf+fYGx7uv3FH+ndC0diiMZW5W9gfitHhtLAQzHYFZoqazDGX7x65b1dpQkuPZ1DmvWI2CprsJmZydj/f2t/BkNu36BzyHSOTTe0N7B1wceCMm6dOjNNgMdJityxqXyXGZW5M9F1NQR45NLwqcnyWixZhdi75X4ofJv9fyVqavcCljL9HgHdX8PcqTjUeWNUKwK3HZLlLTZTVtiRltmfU4+fspJeeVjsvDk5hNhpY12xjW5eT8fkQ094QkZhk/45OItE4n717gIOD7rzXUc/JuBLXsVTBUg2Lcm6bTub5TLUCmX2hCB5/IvZTiESlBod1sSVwJeRaKkS1rBCp3zlpFFm/l29MZe4WDkfjrGuxEYn5s4LnU6Imc8wVu0d+Z/9Wnjw+rlloLfcZlHteLqcVXyiaPodN7Q3pBV2h+yZf32RujBifD7K53YEgfxm1SlQLyXWLxuJyxblF9UbU440thPgF4H4p5aeSf/8y8G4p5WcyPvPrwK8DdHV1Xf/II4/UpK358Hq9OJ3Oqv+uJJEewhssvAvRaTPR5rBQrjSLxOK4FxJCIhezUeBqtGI21odrZblIEg+JgM+L3eHEaBAl91uqv+JJK9n4fJBQNPEgNQqB1WRAAhajgc4mK4FIDG8wSixe+L7sbbFjylP7spx21ct1jMTizAejBMIxYvGEldFuMdJkM+nejtz7s1q/HY7G07tNo3GJySBotJnyTs7RuGR8Llh0HBgNiV2uJsPqyIVWLoWet5nXFRLpMqaTQf8GIYhLSTQuicUTMZ9dTVYisThOa+K6a7lHTEZD4hpJQKD5GZF6tmR+jzyv5TtWJBZPP0MECREfl4nx5A3FMBnFkmM3s2/sZiPhWBxvKIrJINL5JjNJzRsxHcdlofnKaYzijZmyfnc1jvB9+/YdllLekO+9ghYzIcRfSik/L4T4N2DRVZBSPqRjG3MZBTZk/L0++Vrm738d+DrADTfcIO+6664KNqc0Dhw4QK3ak28FkqK72bas2JpQNMYjh4Y5eKmw22lvZ0ddxb2US24tvqP+1pKtSbn9tcnUwO1Xd/DMyUTppClfiB1djdyzq4t9V6+j3WHhvz99hk0uB30uB3azkUCy3uXx0TnmAhGa7Wbuv3FH2RaJUDSGNxDlsbdGOTDlThcZh8TDPRJLZBvb1+7iIzduIBSJEY1rsyIt1+qUEi6Vtmhm3p9L3i86rdiH3F7+4LFjDE7ESOxnusJAl50vffBq+lxXxEV67BRx8e7d1sHeFXSvVcoqWex5mzmmzEYD/nCM505dcSc6rCZ29zZz985OmmxmGm3mtDu0Us+6fFa4nesauX5TKwdOuzlxeX7JKgJPnZjg8AUPs8EwzTYL125sYX/OOSxFqm/C0Th/8eQpfOFoXlEG0Bw184U7dtDSYNZtXE7OB/nyk6cWeQb2Nro5uODK+t16ctdXg2KuzG8l//9fq9GQHN4ABoQQW0gIsl8EPlaDdqwYUg89o8HAb9zZx4UpH8+enGRsNqBbPNVKjXspFb1q8eX218VpP7P+y1zV08Q9uxJBypGYZNe6JjoarYSjMd5/TQ+PH73Mo28M4/GFaXVYuKW/g4f29HBw0M2m9oay461SE0K/y8l3D19izh+lyW6is9GKlOD2hpgPRInG48z4QuxZ30wwGufAqUk8/kjRMaSHW7TagbjVCrD3hSJ8/8gIgxPevO8PTnh57M1RfuvOfhqsiUdyPpdTpnDuabZx947a5loqhVqFP0gkApAIYnFJZ5NVkzuxUs+6fAuBifkgr5+f5ifHx/nM3QMMuX1MLIR4+sQkp8YX+My+rek+yv2+1WgkGInx6rlpzk/5+PRd/bQ7rZrakrrfxueCBKOxgqIMrrgn9Qw9WMnu+kpTUJhJKQ8n//kzICCljEN6x6S2K18mUsqoEOIzwE9JLC//Xkr5TiV/cyWT76F39fpmPnHbFhqtRmISXawPa+FG0nOyztdfc4EIr5yb5pVziY3NBgF/9oHdALgXQvzL68O8OjSdjhuZ9l5JgPm7922nr6OhrOuY+UDf0d2ExxtOtC8QxWExMT4XTOdWi0nJ8LQfbyjKXz93Np1NvJAw1bp9vt4SUlZroeHxR3j+lLvoZ547OcmHb9iQFmaQHTB+aGiGc1NejMLATVvauHtnJ29d8mAQVFTY6HHNUuMjEI6xu7c5yxr86rlpbtsK61r0P4fliMFKPOt8oQhPHh/nwrQfAenawalEsUNTPp4+Mc66FhuvnZ/GZDAwuRDkyePj/Motm5HIiiwkSo1N1msz1UoojVQrtAT/PwvcA6SWe3bgKeDWSjUKQEr5BPBEJX9jNVBoUnxpcIqzk/oGT66FG0nPydpkgP07OlnXYs/rloQr/ZUShB5/JF1sO2W9MhkMLISinLw8z42b20o+p1yxmblDrdFmSvxWMJEqoMNp4aYtbdzS305Pi51fu30LHn+YGze3pY+RWyRaayH5J49frqsNI8Um31TqgH6Xg3A0njd1gFYS1sj8/SNJWMImF4JIWJQmoLe1gQd2r2OgqxGDEASTY+ixI6PMBSK8NjRTsQBpPaxcqfFhNRm4d2cXB864F1mD+1wOOpw2zDpa/0rNtZUrQEHq+qwb9fiZ8oZ47MgoEwtBjIZELdV2p4VQJE5MSgLhGAfOuPniAzt57Mgo0VicGW+Yx98a5b5dXdjMxoosJMrZGd3RaOU9V61j/44uJIl0JE12bS7UzN/9wLU9xCVZz0ej+0o7al0aqVZoEWY2KWXaBi+l9Aoh1t42iTqk2rmOVkKNseWi50o5EIkzOhvgm69cyOuWvDjtT/fXrD+cfuimim13NsqkK0ZgNgpOjM3j8ZduvVkIRLAYDfzyLZuwm404rEbu3dXFvx4epcFqZGw2QDQu2d7t5GM3beL505P8z+fPMR+MEIlL7trm4pdu3sTg5AIXp/1ZD38tQvbQ0AwDXY1Z46YeElIWWmhkpg743uER2pwWXE5r2UJSCHA5bYvy1sWkJByNE41LupttBMJRHjk0kfUboWiMJ49fLnjPVSqfmV5JROf8EYbcPvZt7+Srzw1mlelKWYNPXp7nyx96F1s6HLq0vdTnYj4B+oFre9i5rpHXhmYK/o7WZ12qLx/a04N7IRE4HwjHmPVHmA9E6GpKlCCLxSUzvjBOa7b4n5gLsRCMYjYZKuKxKNU9qZdbemohxOnxBf7taKIMVur5eIPdzCZTA6FovOalkWqFljP2CSGuS/0hhLgeKF7jRlEV9CqvoZXllv2pFKFojMn5oObyRcXQq3zKqMfP375wjjeHZ/GGosTllYnoq88NsnfAxUCnM91fuYLQkNyxaTMZsZoMGIQo2028EIpxesLLf37iJJ/9lyN86ccnuXVrB9dtbMGS3H3W7rTwsZs28dXnBvnJ8XEkMOUN414I8a+HR/mfB86xd8BFs92c1Y6lhGxcSs5NeQvGr6QmyXB0cRbzSpOvvE9m6oAfHR0jEo9jMoissmaXZvxMLSweb4XGYWuDmX07XFm/k7KQhGNx4lJy/1XdjM8FF5VOq/Y9njoPLcJGyzWLS9jc7kiXQ8rHObeXZ3UcA1r7bC4QLljC7gdvjrGrp5nWhvzPgkLPutwx4AtF0n0ZiMRobjBnXfdAJMaMP2G5jklJW4MFbyj7+dXiMKeFW6nPJq3PRq0livQq+Zc6zmtDMzTazLQ4LMwFovzo7TEmvSHe9651fGbf2kyVAdosZp8HviuEGCOxO7cb+EglG6XQRi1ivmpV9qcQegcV62EVzJzYupps7N3mYnN7A1azEW8oytFLs5wan+eTt2+hM2n9qpSbeNTj52vPn+XFwal0lvDBCS9fe+4sn7htM+fcXmb9YW7ua+eVc1P4wjG2dDgYnQ2kS0aZjYJhz5VagsdH59LtWKrdkZjEKAzpxJ35qNWGkXyWgsyailazAZfTmiUqL0z5+M7PLtHTbOPZU5Pp8XbHgIt3xuZ4/pQ77zj8+evWc/iih8EJL5LEjrhY8r7cta6J91/Tw6NvJFI3Zlp0anGP6+nONwgY6HLySLIcUj5MBgNvDc/y4B59xoDWPgMKCtC5QIRnT07wsXdv5O2ROd4eWfpZl+9Z9Ku3buJQ0up2btLLjZvbuDA9kv5OLC5ZCEZZ32pnbDbIbVvbeXPYk3XcW/s7GJzwsr27saRnU6nPxqXyrOnlock9zmIPwTRvj8yxu7el4DFSx6mnmFU9WVKYSSnfEELsALYnXzotpSw+6hVVoVYxX/VSY6wSNdv02HWUmtgyXWLfPzLClDdEW9Jcf1VPczp3EVTGTZx6AE55Q+ks4e1OC7dv7eDaTa3YzSb27+ji7h1dAHzpxyewm42MzAbwha7kFmpzWPCHYulagkYD6XYs1W4J3LSljSG3L2tXYSr4eTmWQK19kPvwziR3odHvcmS5L1MlegCCkRgXpn1MeUN88b0709aCp96Z4CfHx/mNvf2Jc85TRqvP5eRLH7yax94c5ZmTEwxP++lptnP/7m7ef00PL5+dYsRzxRGREj61uMf1FIPNDWZaHZZ0OaR8NNlNBCJR3caA1j4LReNFBejFaT8/fGuMz+0f4IGr1xV91hV6FgUjcd4enWVjm4M3h2d54Op1HL7oYWgqkZ0/GpeYjWA2GuhzObh9wJVV5zOVNPb505MAmp9N5T4bi+2M1kuw5ztOwkMg0v8+OjLHA1cXPk49JjnXEy0WM4Abgc3Jz18nhEBK+Y8Va5UiL7mTjMNqrFnMl56pDcpZ+VQyvi5fLT672cg1G1vYv7MLV2PhTcmhaIxQNM7H3r2RRpuJp09M8Oq5Kaa9YQQCjy/CE29f5p2xOf7rw3vSW9srkQE/9QA0CEFno413rW/m/qu6ef60m//yk9NMLiQser9400bu2dnJ+SkfvlAMQSL5bUxKGsxG2hwWJuZDyLik0WbKasdS7e5ptnH3zk4eeX2Y0dlA1oaGJnuiuHlXk60iG0YKPbx7cuoLZi40wtE4bU7LokSbcSnTu+ei0Ss1FVOvz3jDaYviy2ev3I+Z47DP5eS37uzn4evXE4lJIvE4E3NBHn3jUpYogyvCp8Vh0fUe13Kv6SkGrSYjjTYT3c02xmYXj4+UVbLZbtFtDGhd5BgNYkkBOjYbIBKL091sL/iZYs+iQCRGgyWxwcYgBN8+dJHP7R/g4Bk3L56dYsYXxtVo4YHd3VzV08Q3XjqPxxeh3Wnh1v4O7tzm4uCgmz6Xg2Z7QhAu5bGo1LNRi2CfCyTuofG5YMHxtVzhv1qKqBdjSWEmhPgW0A+8BaT8ERJQwqyK5Jtkbu5r444B17Im81qbg8td+Whdvb3nqlAyy3dp55earPdtD3DiyDS/+K4NDE54+ebL5+lzOYq6Mt4472FoyovZaGB7VyOf2z/At18f5tT4QsZngzx7cpJfueVK+gu93cSpB2AwEiMmJfu2d/IXPz3FkNuP2SiwGA1M+8IcPOPm6t4mBroaOTW+QDASo8luTtTLa7Aw7Q0BsKXTgctpXdSOYu2+e0cXP7swzVsjs1nlb1I7znyhKPt2dJa1eCg2dos9vPe3hBj1+LPOI7XQmJxPlLrJFSWRmGQ+kLAiZtZUzHw9ZVHMFGaQbUVosJqwhaL85TOLE2tmkhI+egp2rfea3tbbriYbD+7p4buHR/IKc5vZqOsCUmufGYXIEqCpnbiZ6TwuzwayLNv5KPYsSpWC+snxcTqckiG3j688O8jtWzv44gM7cVqN2C2mhGv85AQfun49v763j1A0zpDbx+NHx7BbjFnXuJjHIhSN4V4Icei8h1A0npVAOpNywgeWEuyp58zFaR/ffOVCwfG1HOFf7Q1vtUKLxewGYJdcycmpVjiFJpmfvjOB2xvi392+hRcH3SVP5rU2By9n5aNl1eVeCDLtDfONl86XdX7uhSD/6+AQ2+IhvvvyhfTrI57AovZlnktcShxWEyfG5jlxeZ6jI7N8bv8AX3l2kOlk0eEmu4njo3OLdlnq6SY2CLAaDZycnuf+q7p54bSbC1N+DEAsJgnJeDLQV3B63MuO7kZC0URQskBgMgiicYnDakQguGdnZ0HLQaF2SySb2h10N9nyBn93N9m4bmNLyedWbOx2NFqLPrwjMVnw4V1IlEggGk8Iy8xC0Zmvz/oiWa7PFLmr/1KFjx6C/dKMn688O8jYXDDtRi50rxWqp5gSLY02E65GK3JxQZi8WE1G3nv1OoamfFyaCWTtNDYIUZFNQ1r6LLPYeWbYQWY6j3t3dRGMFN+UUOxZdHx0jof29HBsdBZB4r6f9ob54Vtj/PCtMTa1N/DZuwf418MjXLexlcPDHi7N+PGFokWvcT6PReqeuKqnmTcuTGMQ2eI3k3LCB4qN25Sb/75d3ZyZ8OZ156fOYTnCf60kOdcizI6TCPi/XOG2KPKw1ArhyMVZXE4rD1+/gft3F4+DyCRXFCVyKYV46sQkx0fn+dz+ATa06SPOCsX5LGflo2X1thCM4PaG0p8pxdyd2e/bGou3Lzfxo0EIzEZDOrh7aMrHwTNubt/awQ/fGku7b3yh/HE1ermJHVYjW1wOXj8/w7WbWvnzJ5Llu5ML0biUNFiMmAyC46NzfPC6XibmQ8xkxAMJAZFYwspw5/bOopNxZrtD0RizyXIvcQl/+tBV/PSdCZ46Mc6sL0KLw5x21Rw47aa3tUHzOS8l6H/jzr6yH96FrC2CRJB6b4stXSg68/VoLJ5lScskd/VfjhVsOYJ9cj7Io28MJzaA5LFW5bvXMoXNtDfMdZtaeXHQzVMnxjEZEq7xUoRhb2sDn9m3taqbhpbqs9R1uDwb5Oa+9kXpPLyhKG8Oz+LxR4o+L4o9i+YCEQ4Ouvn8Pds4Pb7Amxc9HAvPYbcYs1yVF6f9hKNxPnn7FiSUfI1HPX6+9epFOptsNNpM/LcPX8NcIMKbFz28cSGx+SBTnJUTl1ho3Kbc+d1N2fdGitzxtdT4NxsFdxYQ6mshyTloE2YdwAkhxCEglHqxwrUyFUm0rBBeG5rhzu2dmie2XLEXjMSyEpqem/TS3Wzjl2/elN41WC6FLBt7t3WmrUeFKLbyKbbqSj0obsuwbGSixdxdyspMJP+diSHpJvGFEgWrXzw7xe+/dycvnp1KT4iVTsYbCMd4d187r56bxmExpV2SAK5GK/ft7OL2ARcOq4lQNIbHG+bT+/o5eCZhfXUvBInEJbt7mrh9wMVjR0Zod1qyLA75XImZ13zSG8K9EOT2rR383NXruHO7i2hcphOlPn50rKQHqRZXxkIwymwgEdNXiGK/mc/a0uG0cO3G9Vy/sZWDg+70JGw2ivTGilsLjLd8q/9yrGDlCPZRj5/hGT9Pn5hI78rNdCNvbnekE5fm3mspYTM6G+Bvnh9kfC6E3WzEIApb24pRrU1DueOypUj4Qm9rA5+8fQv/+OoFRmeDINAkXDNZygJ0cdrP5dkAH7tpIw/u6SEcjTPtC3FybJ4fvT1GXCbqS96zs6us520oGuPIsIeb+9o5cMbNT384zuW5RG7C2/s7+I07+3ni2GX84Stll8rZSDTnj2A1GfnUHX0cOD3JseROVbvZxN6BDm7f6sq6NzLJHV/Fxr8rsJB3PIWiMaLxOL95Zz/eUHRRsu4UKz3JOWgTZn9S6UasZJba9bVcKrFCyBQdKRN0bvzPU++Mc0t/O5FYvOzVbDHLxrHRed73rnWMeAIFrV7FzqvYqisSk7Q2WPKu3lIsFX9WSr9LFgcRm42ClgYzsbgkLiXhSJx2h4XeFnv64XhzXxuNVhOT88GKxPhF43Dg1CSfvXsAm8VId5Ody3NB9qxv5pO3b+Gls1P86b+9gz8cpcNp5faBDq7Z2JqOrXN7E4kth9w+Hn9rLH2dvKEoD+xel3afZ8Y83rC5jW+8dP7KNZEw54/ywzfHeGt4Nl3eKfOaN9u1P0i1CGaPL0yD2USgSIqOpR7e+USEAP7+pfNZVhWDELicVtod+cdbsXxXVpOBn7u6h/07ujAaBAaDvkIlJWJ3dDfl3RUZisRxe0P0ttgL3msSycEzk3h8Eax52lVqTE+l66GWG54x4gmwtdO5yM2aotgiUYsF9LatHTRYTemSW11NNtY129m3o3PZAnUhEKGlwcJfPn2Gi9N+TEYDXU1WLkz5eOyt0WQoxTaeODbGXCBKd7ONvdsWW7+1LLTmgxF6W+zs39nJ/h2dWEyGRGjAiYn0fZ1v93W+8VWoesArL11cdI6pNrw5nEg1JCWLknWnWOlJzkFbuowXqtGQlYjWXV/LoRLb5VOiI3OnWS6zvggGIcoOpNRi2XhpcPEutky0TJ75Vl27eprY2unk4Jn8qzdYOv6slH4XLP5sasL2haKEIokErqFoPP2wv25TCzdsbuO7hy9VLMbPIBJ1Gh8/OsZD1/TwCzes561hD79400b+6plBBicTBT0aLEamvWFeHJzCH47x6bu28trQND99ZyIdW/TQNT3JoutxWuxm/vq5QTz+SPp85gIRvKEY3z40zIUpX9ptkmlRujjtz7tzsZQHqRbBPDjh5ZqNzbx6bnlZ2/OJiF++ZdOi8bZ3W0c6jxkk+r3UfFepz+ppPbqStsWRLsGVy3wgSmdjYiGT715bSTE95casxmVisZFPeKZYavFbqgVUT4EqETx7cjItTqKxODO+MJs7HMz4wox4Ahw8M8mNm9sY8fjzWr9HPX6eOjHB4QseZoMRWmxmrt/cyl3bXDxx/DJHLs6mf2/EE+AfXrlId7ONT9/Vj9Vk4FjScpXrfUlZHwdczqzxVeweyCX3urY7rFyY9qVrCH/27gEe9ydEYa2SnOtNQWEmhHhJSnm7EGIBsqS1AKSUsqniratjSt31VS6VyG+VEh2TC6H0jrJcUvEy5T50l3qgm42C42Pz3D7gKijMtJxXPsuGySD4yrODi1IQpNASf9bRaNXc7xKZ97M2szFd9/KOgQ7Ou3002wtYlvK0YbnjJ3PsPP7WGA/t6UEAT78zwemJxA5RizFRVSAV9zY5H+Kn74zjsBjzBkRvcTnZs6GZ/Tu7efXcFJcz2t/ncvDoG8NISFsGcwVq7s7FUh+kWgTzhWkfn7htC+en/CXHsCxFMXfc+tYGbtrSvmjzQ8oiajIkynT97QvV2eqfErGpnYE/ymM9jsbj6fGb715bKTE9y9mtp9fit1b5HUPRGG+cz16EBMIxJmIhGm0mtnY6cS+E+M27XDx/yp1l/b48G+QjN23gb547y9ujc1mC6szkAm8Oz/LRmzZwbtK3qH9Sffrw9RvYs6GZp96ZyOt9mfGG6d1mTwcWLCWg72q68v181zXzuTo6G+SFM25u3NxKOBZfNXnMCo4YKeXtyf83SimbMv5rXOuibKmHQGrXlx4lRipRBik1YWfuKMslFS9TavxPqvxHOBpnNlA4hswgBGaDoNGWf21QynmlVp/dzXa6mmw4bSb6XPnr7qWshNdvaisaf5bYhait34tdI5vZyPWbWvn4zZv5uXd184UHdvC+d/Xw4qBbl5I3xchsVyoI+d5dXZyZWMBiNGA0CBxWE+1OSzrWCODN4Vk2tTuyShNNe8PEJQSjMf75tWG+8uwZ7t7RSbP9iuvebjbi8YWZD0SJxK6MmdSDtM1pwRuMYTMb85Z70UK+Mkq59LkcrG+xFywx42pcnPKjFHLHW2qM5r7uXgjyyKFhvvzkKf7wB8d4+sQEj76RsCjmQ+/yVCnBcXx0jru2udjUvvicTQYDPc32gveaXiXKKs1ySldpGVNaF7+FxoZe5CuxZDQIYnLxmElZzs5OehlyJ3IUvnx2KktgbWpv4Mljl3llaJoZbzgRhygT33UvhDh4xs3PLnq4cXNr3vYcvTSHLxzl7h1d2MyGvN6XTe0N3NrfwYHTk/hD0SUF9Hwwmr4HCl3XVLWArZ1OJheC3D7g4qM3bVoVogyWcGUKIYzAO1LKHVVqz4qg2uZ9vfNbpSbs46PznJv0poOCU6SyTT9+dEzzQzfXNP2JWzfjD8cwCpE3hQCAq9GGy2ll77aOolvaS82zpkf82f27w+l+f+2VyzRHzUX7vZRrNDkfzBo/+WIy9Bo/i9sVJRyLs7OnKW0ty42nSQS7W/mXQ8PpmJVGm4kGqxG72YjRIBiZDSxySwYisYTLzBdeFL+SepA6LCZ6W+x84YEdZVkTil3buJR0OK3s3dbJQihRf3D/zi727ehMtMGUyMuWL4ZFb/JZBda12PnmKxfwZgTd56LncyPTYnpw0M1n7x7ghTNuXjk3ld4Ze99V3Xz4hg0FnyGVsNhXguVY9iqR3LkSFHL/3b2jk/4OJwvB2UXCKC4lZqOBzR0NzOYRpTvXNfEnj79DIPmszsUXjvLsiQn++KGreObk5KL3F4IRYnGJw2LkE7dt4bmTk1njK3Pn6aw/wq1bO5acOwPhWDqNULHrmqoWEE7ma6v19dGTosJMShkTQpwWQmyUUhYudrbGqFWNSj3N5L2tDXxu/wDdzTaeemdxCoPU7pq92zqWfOimJqFEluzElHxibJ7tXY08c3KCze2OdJBopvjYs6GZ7mZ7wfNaTp615cSfZV673tYG2hwWvnDHjiX7Xes1yhw/hWIyorG4buMnN7N9X4cTXzhasLB4m8OC0Sh49dwUdksi8/+0L8zYbIAtHQ4iSSH/yrkp9u3oTAuzzGSa+XZEGoTgxi2ty7Yk5Lu2FqOBvk4nN21p463hGbZ3N/HDt8Y4cHqSEY+f/397fx4ex3Xf+cLfU9X7gkYD3VhJggQIkOAikqIki6JEiYtkyYss23FiJ3bi2Fk8ie2Jc+dej68zz82MX2ecmUxmXifjZBInca5jR07skSzLkrVQoqidEvcdIEACxNoLet+r6tw/qqtZ3V3d6G50Aw3ofJ4nTyyA6K46p+qc7/mtbrsJD+/owif2rqv5eyullEVdsShKFLmg+8I5qOe6oRYcE/44norPYEevA1/bMgyTnodEKTa7bWUzAVeLaFmqO7LZegAXUs79ZzXosKevFcFEpmgtcWYPVFu6WjAyHy36XKtRhzFfFDpOe/44QjDui8Fm1JYKypgKEsX0QgIf39uLX33fBgBy3N6V2XAuKYAjcruyxfZOUaK5d2Cl2g6uNJVkZToBXMyWy8jZ4N/L5TJW6mGpd0bT+jYLPnN3H/YNtIMjJK+EQaWBlMomdMMXy1sUgrE0vnxkM85NBeGJpGA18pgLpXILxvYeO+4bdOc+W6tY4lLbbtQSfwYUzx3RuD71/Rda9Cpp7Nxi0mM+nCwZk2HScXVzaQG3np2UIOLOTc6yFpA9G1ph5OXvb7MacMMXQzyb4RhKZOC0GOCNpDATTOYFTCvFNG8uxCBpaIt6buKFYvNmII4rsxG8NebHB27rxld/cg4XpsPgOQKLgcdsKIm/e+063h734wtb8mNY6t35opRFPWdRjKZzQfdKf0CFeq8bhYLjzTEfLkyHcoKjkvIMzSha1POmFEFeqmWvWXoAF1IudEaiFMeuevGb927EyLzcbeRWM3ACQmShs6+/vaSHwGEyIJYujjXmsuulvYwre9d6B9osBkwFExj1RvAPb1zPFefdN+DCA0NutFqiCCUy6Gk1gxDZch1IZPL65arhOZJ7B1aLxbbeVCLM/kPDr2KVsdwPSyPbJnW0mJARJc1Ms0oW3VA8gxPjC0UCYz6cxA/fvonfO7gZr1z1YioYx2wgiXbrrR5wz16YxWO7e4u+o55tNwrFbEoQ0e+2lhVmlc5drRY9h0WPnescuHAipBmTAQB7+9pwwx9Dl8Nc1/IJoXgGh7Z2YKjTjsuzkaI6QIp40vMctvc4cGEmhGRWIHKEIJIS0NdmQSwlwGrS5dLjldpWF2dD+PoHt9XUiaJaDDoOrRY9Hj8xmXsXP3N3H544NY0L02EA8qaUFiXoOIK0SHFhJozAeg6xZAaxtIjZkGzljaUEzGTb7+zf7FrSdUpULsy7f7Mrr70PADy4rQuPn5jMBd0X0ohNph6Co5lES+F790BLEj8/N7vk9nRA48t5aLHY+l5K6Kut7ZRQ/P4Dm/HshTmMe6JIixLsJnmd2bXOgafPzWoaElKCiIPDbjxzbq7odwTyeBzc4kZKKC490+Uw4aFtnfBEkvib42M4ORHAQjbz1x9N52VNnjIG8MAWN8Y8UXQ6jHjrur+oXpyC2cDn3oHVYrGtN+WyMk0AvgBgM4DzAP6OUqqdwvceYymVi6uldFxBJ6wGHmmRLlmsLWXRFSnFmC+qKTBmggn85UvXsHt9K77+gW3wx9JFVjmbUVcksBoZw1fJi35oayfCiTQESV4otRxLS7HoGXU8HtjSgV9cmMstZGqUGL+jlz3Y0tVSl42i8Dky8hw2ua346O29OHbFg0A8UxTbt3tDK96ZWIDFcOu54gjJpeLf3d+OK7MRiBKF02bI+/t1TkvVz1MtB5BQPIPTk0GkBAkUQFer7JonWbcJAGQECQajLtfpN5oU4I2m8YO3J3B8xFt0wn/yzLTmgaFSDDzBY3t6cfSyJ6+9z72DLnz89nWYDyfw6qi/yN1bbpNZ6uGsHoJjJURLIVrvnSjRJbenWykqOdxphc4U1p98dzyAxH4RTosBv7avD+udcg9ep8UAfzSJlIb13WHWI5WR8JHdvbgwFcINfxwcyX8q+13yGhFNCnCYi+Ns22xGPH5iEp5wKi/7WmHCH8fJiQCObOvE99+8AYkCj+7qwZnJICb88aJCx10OE1pMuqKuF81msW005Sxm/wggA+BVAI8A2Abg3y7HRa0GaqlcXC2lNv/nL87j+IgXn92/CU+engalWPJDWuuiK0oUPNHecC1GHtfmo/BH07h/ixs/eKs4TFFLYDU6hq/c3N036MaZmwG8fMWbWyj329J55U8KLXpazY/HvFG47aVjqexGHf7NAwN45aq3ZLDsTDBRl3gjrecoKUi4OBPGfDiF3zs4AJtRB4c5v13N4eFOPHthFhO+/B6XibQIUyuPh7Z3IpkW8bG968BBjk1TCmhW+zzVan1MCSKuzIURjMsudJ4QzIQScuFgUFCKPLeqnPBAMRdK4IlT07mfF57wX7/mw2N71tV0uIqlRXzv9et5hwt/NI2fnp7BldkIvv7BYQy4bbgyF1m0J+JSxqYeNNJaX8u1NKI9nfLZ9b7PxT6z0sNdYeiMVv3JVqsekaSA16/58Po1Hw4MuXIH3u7W4vVuuLsFD2xx49JsGNt7HPj9g5vxwmUP3hrzI5YS0G6TPRtHtnVivdOMVosRm9y2ojFVJzKpy1io49wsRh5vX/cjEJevXysRRaQU9w268OC2Toye9ReNZTNZbJeDcsJsG6V0JwAQQv4OwInluaTVQ6mHpR5ZX6UWIfVJ6aXLHmzvkbPiGlEHqRKMOh53bmrTrJHEE4KMKGH/5naMagSeAtoCazli+MpVdlcKrwLZwqk6AX/58jX8zoGBrJVSgoHn4DDr0WrRazY/vnfQhfVtVmxylS7b8fIVD/rarblg7EJrYjUV8QtRNgVCCJ69MIeZYKIolkOxfh0f8RZZLVOCCIuBwzcf24kXLs/jtVEfphYSsJnkHn/3Dbrw969dxw1fHP/nw1vw1JmZmsVCrdbH6UAcU4EEaDa9HwAyooTOFnPOLSmB5lkA2qwGAAn4SrQDU4rgDnXaihrMV0JKEPHSlXkkMxKMGuUDrs5F8NTZGfz2ff0QskHO5TaZesRaVnLNlVR8X05BqEUj2tMBjRG+i31mNeEahaEzGZEW1Z8sbAem1QJJWe8IKGJpCX9zfAyecArj3hiObO3Evk1tOLjFDZOeB5d9afpdFnS0mAFox9kWHqKV7Gt1nNtwdwv+9d2buX+jlYgiiBKGu1vQbjNitMSYNoPFdrkoJ8xyo00pFdZa1kO9aNTDorUIFZ6UCot1VtsepR4QUBwe7sD5qWBeWwxAdnMOddpxYKgDz56f1fx7LYG1XDF8hU23Hz8xmSfKFCRKcXIygB+8dQO9rRa8OxGA227EL92+DoQD/uy5q3n37o+m8dNsEcevHBnSXNwdFj363VYcH/EtqcCuFupN4SO7e/DEqWlkJKkolkOhcBFXtz/xhJO4Y2MbvnJkCO02OTvz7M0g/seLI5gJJvHhXT0Y98ZqFgu1xhMqfydKyCueOjIfwcM7OvF3r92ARGVRptdxELNmM4dZD7MhjVOTgZLX9MaYD/dvcddkrVTe21LWgxazDjf9cQgSXXTdqGesZSlKCYj7BuUYUHXF90YVwq2URljSGyF8Z4NxvDnmR1+7FVu7WpDIHrjO3gzlPlPPc1WFa6jDLwrrT6rLGylojYM6AejZC7L7EZCF0o9PTclWf6sBJj0Pk57DUIc9ZwEvhdYhWiljoWAz6hBOCjDwqnCVRCZn3VM+5xuP7Sj7Xe8lyo36LkJIOPu/CQBz9r9Z5f9lQGsRKjwpBWOZoo12uduj2M16BONpzRpJBwbd2DfQjh+8NZHXQFeNlvhYiYDPcgG2aUHOlHx11IevfWAYR694cHIiAJfNAEohNz8uQMdxmAulSm6cjbrHwo3GpOfhjciV52Ml6mepF/HCvyeE4CenpvDDtyfR127BFw8N4uWrHswEk5obQrViodZ4QvXf/dLt6+CyGbDRZUW71YDb1rXCbtTjBycmEYrLpTSUGJtNLitsxhTevlq6XVMwloHVoKvJWql+b9XWA4dZh13rWjHUZYdZzyMtSNl+maXdZY2ul1hKlBy76sXxES8+c/fGshXfF5vjersH621Jb4TwTQsiZkNJXJgJ480xX178otLX8cXL83h4Rxd29jqwSRX+UNiUu7Bsj+KOfOd6AEaez1mv1eWNKhkHreeqUCg5zHp89ZGtiwqzSg7Rep6g3WpAJFk6RL3SeWsm13ojKTnqlNK1d7erCK1FqPCkpLRNUrPc7VGMOh63b3Di+29OYH2bJc8tNxtMQMcRmPU8khrJAeXEx3IHfGoJYcVCKdnk/1aEsNL/caPLiu8eH4fdpCtqEt1i1i1aKLbe96i10ahLNKibVqtFsrIoVtL+5NiIB3dvasdwV4vmhgBUJxZqtYIof7ehzQLCyYH+3z0+jjFvFP0uKx7e2Y2/+NQe/NNbN3DsqhfdDhMe2dGFX7pjPd598zVkpNIlAFqteuh1pCZrZeF7yxGCoU5rzt39k1NTiCQF3L7BiTs3OcvOcyNjLcuJkoxIcc0T1exrqlBujtOCiOlgEkcvz+PMZBDxjIBWs2HJ7269LemNEL7TwST+9BdXMaayvBfGL16ZCyOWEjEVjONxVfhDYVPuQrGiuCPfv70L/mga3mgqL/Sh0nGo53NVyQGz22HGng2tS563mwtxPHdxDqcngwgnM2i3GrBnQ+uaTACopFwGYwXQWoQIZEuMEk9TGFcArEyxvV6nJdfc+akzM3kCY2O7BV95cKgm8bGcAZ9aQrjQQqkIYaX/o92ok4VAhw0B1TQofSc5QipqfryUe8yv53Qr9k25j8I+iVr1s5RFMRhPl21/0mGn8IZT+NRdG/DGNb/mhgBUJxZqtYJwRO7HeWDQjT977iqmg0m0WvTY2t0CnhAcH/FhzBPFHz64BV97ZBiiRNFmNUCv43CG5zQzyBTuH3Kjp8YyJYXvrcOsz7W2UtzdbTa5btRi7rJGxlqWEyXKAbAwVEJNqTmeD8Xhj2XwnZfHMBOS4/8oKFIZCcdHMktyg9bbyryYQJEohS+aQlqQ4AknF7XOpAQRRy/PY8yrHU+rZCge3OrGXx0bw9mpYMnyEk/FZzTFikHHocthhihR/PDEZE3jUMlz1dNqhp7n4I+kEM+I8t7DEzithqIxKHXAvLu/DQ9s6QBHgANDHbgwHYYvmirynFQyb+PeKL7580u4OBPJhQXMm3XwRlIr5lpvJEyYNSlai5BiqVmIpjXdSMDKFdtbTGDUKj6WK+BTSwgXWijVQlipnN5mM0JHOIBAsy5PJRtnrfdYVAJDx8NtN+ZO3cF4BmY9j1++Yx380RQmA3EkUiLUBUDUi6J6o9LKNB33xnB6MoBIQigZF1fpPSvUagVxWPQ4PNyBl67I1gWzgYdRx2E2lEQglkZGlHB5TofuVjM+c3cfep23xrfFJFs7ARTFgG3vseOTd22oqPCqFoXv7Y5eB46NeHOiTC3agfLuskbGWpYTJcoBUCtUQkFrjj3hBBbiAp48M42Xr3qQESXoeQ5OqwHEJhc7XWpcnJYI4DlScd1FNeUEilIjTM9xGPNGK0puCcUzODMZzDs8F0II8MaYH7OhZMnyEq+MeHHPQDv2DbSXHKNepwX/9vBgTXX4Fnuu+tot+MQd6zDhj+Hn52fx2jUfIgkBG9otOLy1Ax/Y2Z2XoR6KZ8BzHN6/vRsf3NkDiUrgOQ7xtIhnL8zi7M0QnBY9Ht3dgxPXF/LqrJUbU+Wz04Js6d/YLtef9Gd7eiqlNgAse2x1o2HCrInRWoQG3Tb0Dplxz4CryI200sX2ygmMZs+o0RLCygYFaAfYXvfFcXhrB67MRbClyw6C4r6TjRLKpeKDTk4EcH4qiH/3/i2gEvD85Xm8ds2LD+zsxuvXfLg0EwalsvAqXBSVjaow03QhlkarRY99Ay584o71aDGXXzaquedarSBGHY+NLitOTVyFjueKOhTw2Szb5y/OYd9AOzKilMuGI4TgC/f3YyGWxoXpEN4aX4BEKfZscOL927uwvm1pJ2/1ezvgtuFH705Cx2sX0wRKu8saGWtZTpQoB0ACFIVKKBTOcUoQMRVIYCGWwdPnZpHOCpO0KGE+nEQkkcFgpz0X9L6UONjCQ+CFk2/hQA2bcimBos58V5JbAECUgMmFOCRKYdTxRRY0iQLxjJA7PGuxpcuOn56Zzq4TnGaCyOW5MH5z/8ay4kqdoOOPpdFi0uWe38XEabnnymHW4+Edsqv0v78wkpfUdG0+ipsLcVydi+ArDw6BI8Dzl4oTRx7a1omMKOKvX7m1PoUSGTxxaho7eh1FddZKZSMr9zcbSmAulML7NrXhy4cH8c9vT+LKXAQAcuEZpyeDeHhHd1PvMdXAhFmTU6qsw7GrHgTjcv+x1VZsr1kDOAuFcCiRxkaXBa2WDL60Z7BICF+YDuHTd29AIiPmMpwkSnPFTnuyxWrrLZRLxQcpG+p0MIl3JwLggJwL85onins3u/CFBwbgthnhthuLugo4LLL7ocNuyrneREqRFiSEkwJu+Ccx7o3hjx/dhg1tZkwuFHdPqEUs1BprZ+B5tJj14DgCfzSNeNbNrOPkhsY8IQjGMuAIwanJACQKvHRlHnwoicd/dgk2ow47eh34w4eG0GLSw27S122ulPfWE07CbTfCbTNptp8Byrt+Kx2bat+pclYTxVW/vaelKFQC0J7jaELAz8/P4t7NbvijqaK/iWdEzIeTMOmt8EVTkLR6dlWB+qB3hbvVwLqacdASKOrMd+UwdmoygEd39eQOKoQAW7taiuKbOAK0mg1IZaSSbvJWsx6xtAgOcikXjhD0OPLLSxh15RtyFx7KDDyHZEbCm2N+XPfFKm5Xp/VcPbanB9GkgOOjvqIse0AWQuemQ3j2whxazfq850fduzMQTxetT+oEA3WdNeDWvAFAWpDwly9fgyecQkqQIEjAbCiBJ85M4+xUEF8+PIhvHx2FPyt+wwkBC7H0ssZWNxomzFYBWtamx/asw/1bOlZdsb1mq41USKEQTgsSTr79hmY8ldnAY53TjC8e3IwXL8/jxPgCxnxR8ITDXZvacGi4A2duBuR4qDreW6n4IGVD5TmCFy/N47fu68/9zh9N4xcX53BlLgKTns8tjGqUjgR/++p4TpQl0iLE7IJn0fOIJDP4yckp/PKdG3IWuHokZtQSa8cRoMNuAoXct1NptKxU/RcpRbvNAEGU0Gox4NtHR7EQS+OAXS48G0kKeZtZu81Y9XWXw6DjYNBx6LCZlhQnttjY1PJOGXU8Dm3txIXpMGZCyaK+hRtdVnzyrg1498aCZsX3ws+NZ0S8ds2HPRucaLcZ4Y2oxBnNCp6InHgSSQqYjyQhUVrX96KWcSgUKL6s+/LDu3pyouz2Dc68GEEdz8EXTRfFCN4SuxlsctmQEkRIFBAlCYm0CIDAYtTBYdJj3BdDMCtECi2p5eoXVptJWk6oaj1XlALXPBG8OVbafR5KZPDWdT8+/b5b64dEKTKi3GSs3WbEj09NwaznNQ8iQL6VWD1vd250gotncM0j9/0EAJ7IzdajKQHjvhiOj3hx72YXfnpGPnQKkgS7qbYs6maFCbNVSrO7BrWYDsTxly9fw82FBChkV6EoFS9wpVguS5vSgzEUz8Cg42E18nhscw+OXvZgJpjI26CU4ouP7OjGYKc9rxn8E6em5UVsfKGuwanl4oNMernP3NW5iCxUSsS+nZ4M4vDWTuh4rmgsZwIJtNkM8ERSkKh8Im+zGtBmNWAhlsZLVzzYv9mFoU4bPnRbD4D6HA6qfaaVjfD5Sx5IWfGYFiQIEs3179zb50RakHB81IeZUAImjeelkfX/6hUnVmpsllKc98zNAB7d3YOXLntw4voCRCphwGXDXf1tea21KjkAEgCRhIDTEwHcO+DCE2eyXRWyAplmm40DwF2b2nBqIogxb/2CtpdSj0wtUNKChDFvNJftWBgjCCCvz6n62VEscOenQjiXLXuREWX3pN2kw/aeFhh1HNrtBpybDqGzxYiFWLqoLVG556HSTNIP3ZZBMiMuKlQLn6u5UBJ6nivKMleTESWE4xm4bEZ8Zl8fjDoO4UQG704EcHzUC0LkeoJbu1pgLhGjqFiJ1fPW127B7X1OvDaZwaWZMERK0WLSw2KQwxZu+GKIpgS8es2Hrz0ynBNmOo7Dng3ONdXInAkzxrKQEkQ8c34WJycCRQU33TbjopvjclraZoNxjHqi4AiBnueQFiQYKfD5e/thMXAQKfI2KLlg42zJzbfeG/9iWVWEELnGGqhm7FsyI2I6GMdsKIn/dXwsbyx5jkNKlNDZYsrW8pI31nhKxHw4BUGUkM5I0PEc/uWdm/jqIy3ozIrT5UbZCC9MhzE6H0E0LeSsewCwsd2C/ZtdoKB4+7of7dbSFrF61P8rdXBoVJxYrXW41JuhkuRxf7biu0QpNrtt6MgWIg3FM6BUtsY6LKVdvTqeYEO7Ba9d8+HLhwdxdiqIcV8MUlaUAYDFwKOjxZSL1QwlMnV5LyhQFytSZ4sJnnAST525ZR3vd1vxo3fyW8npOC6vz6n62dHzBB+9fR3sJj3eGPPBH03DyHO4o8+Jw8OdCCcF3LvZhRPjC7jhi2Gjy4p5MZWLldrb5yz7PFRS6oIj8r/57qvXNWvUXZgO4/cObobdyMNuzj/cckQWXkqJncJxliiFPRuHGklm8M2fX8J0IIFWqwH3Drjw5UOD4DkCs16HlCCBAJoJJHaTXK5GmTcle9kXTSOZkXIxismMiFhaQDwtYn2bBWOeKBZiadiMtz5ze48d79/etSo8RpXChBljWZgPJ/GzszN5QbHqzJqN7daSm+NytKVR8IQTGPfF8dIVb65A5G8PpfDuVS90PIfBDmuREGl0IdBCFrPCEAD3DbpwwxcvshApgc02ow6xtABK88fyd+/vR4tJj/lICjcDcc0O7krZkHKxUctl3ex1WvClQ5vhshnw07MzWIil0WY14L7NLhwYcuOf357E5+/rRyojQseVdnUstf7fYgeHRtTkq+W5KxRzhYVFAeCxPT3Yvd6Jl67MY9wbw8Z2KwY7bXBaDbCbdOhsMRXNpdNqwOGtHfjeGzfwz29P4suH5YLTr4x44Y+m4bYb8bHbe/HgcGdeWEA93gtRonWzIhW+W2Y9X7JGoUIoIVvavJEUnjo7iwvTIdy2rhX/fmgYPEcQSmRwamIB/+lnl7B/czs2d9jw5cODOD7ixdW5CBzZZJr7Bl34tbvLB/1XUupib58TRy97NNv5eaMpXJ6VrelDnTakRQmHtnZmW81R6DkCUaJ5JXYA5GJNCSFw243objXjmfOz8ETSkAB4I6lcDNgfP7odH7qtC29dX4A3mimqmwjIVmJ19wPFMnnHRid41XuaFiWY9TyiKQHRpLxPRFMCYikR7TYD7h9y45N3bagqYadZY5zVMGFWAathIpuZlCAikhRKnmiV06JJzxVtjsvRlkb9XVOBBP78+fwWS4JIczWGvv7BYbRajHnf1eim64UsZoVZ32bGB3Z2458KmsarA5sf2lZcA28ulMQNXww71znguewpmfavlA0pFRu13HGEViOPD+/uwd4+eVGPpkScmQzkAoRFiaKv3YqMSPMWfTVLqf9X6cGh3jX5annuFhNzcv9XOR7PbtLh4JYOHBvx5gqhdjlM+PCunrySCYD8TH5gZzeuzkVwbjqE77w8hvf1O/F/f2AYDrMeHJF7Lf7w7UlMBW4ljdTlvViiFalwntTvlrpAM1Bc7iSZkWMwJ/wxiBLNtT/zRlIghOCaJwpBlCBSmnPD3TvoxrePjuaScQbcNkwHE7jujcFqKP88VOIa39bTgn94/Ubez9SZpsCttmN/e3wcx0e8+Oz+TXjy9DRsRh0+vrcXR1Rt9tSxpuudFthNOuzrb8d/+tklxFICLAYeKSpBohTjvhh+dnYGv3HPRozOR3EpFCmqm6hYiUWJ5uZNsUzyBOg36qCcCEVJbqtm1HEIxNNo77Dh4Z1d2NJpx5/+0m1y4sQK90VtBGvH9tcgpgNxPH5iEt969gr+6Mnz+NazV/D4iUlMB4ozVhjahOKZXHXrUoQTAsz64gDOSq0Cgbi8cKYEEZ5wEnOhJDzhJFKCdrq/FkpmmVY2EiDXGHrm/CwiBZuAcootR7WtYha7B8UKc2DIldv4HGY9Dgy58MWDm7HOaYbZkH94UArmKtlmF6aLx/XoZQ8e2NKB9W1mtJh10GXrUPW2mbGh3YK7+tvwkd09cFr0+I17+iBRmneNikg5PuJDKJHJs8h959hYQ96bjAj85N0p6DgOx0d8+MuXRvH0Wbk364d39aCrxYjBTlsuLkiLWsuaVHpwSAtSLp6ny2FGZ4tpyQeJWp67xcTcjl4Hjl72IJ4WcoVxnz47A380DYkCM8Ek/vXkFP7y5WtFc9nrlItJ/+pdG7DJZcXIfBQ/PT2NkxMBEELwo3du5okyreuriQrGoZQVSUE9T+p3azaYwL4Bl1ySxWbIa2mmiJ3hrhaMzEdz7c/CCQEL8bScoS3eSp7hCMm54ZR+uv/Pkxfhj6Xx/TcncH46BIryY6Ecyroc2mKky2FCm9WAaOpWYezCHsuA3MXEqONwwy/38HzpsgfbexyYCiTw4iUPelvN+JOP7cTn7t2E23pb0Ntqxs5eB37lzvX49Ps24PmLc4imBIgSRVqU8qzRb4/7wROC37x3Ex7Z0QWHWZe3Pqk9HL2tZrnuWqsZdpMOY74o7GYdNrttuTGjkMvf8ISgr82CD93WjVarHtu6HVWLsuVem2qFWczKsJwutLWMRIHR+WiReVyNIEnYvaG1aHOs1CrAk6WfhpTMsnK8es2H37hnE9pVP6tnIdBq7mExK0yh+8xh1uORHV0lWykBkJMbjDy+eHAznr0whzeu+TDmjSEtSLi7vw1HtnXiylwEfe0WPHt+Dte80dw1PrKje9msm2o4AgTimVywtrot2Lg3hn89OYUj2zoRiGWwoBHUvJQ4r+V2Y6up5blbzBXW77biJyen8JHdPUVB7wrhhICbCwnNuex1WvCr7+vDIzu7IUkU85EkTk0Ec4kwi11fLd4JniM1WZEKUc+T8m6FEmncPdCOWErIq1qviJ0uVcxcv9uKnlYzKAB/LA0DzyGdkZNRjDoOPEfQatEjmrp10FK31at0jVjMNa7nubw5LuxgonxvKJHJs6B9bcswpgJx3L7BiX96ewL+aArD3Q785r39aLMaYDHw4AnB7/3gJKIpEZtcVngjKQTiafAcgQFyklCLWY9wSsATp6Zx50YnPnfvJuh5UrQ+tVr0+MjuHjx1dhbj3ihmQ0kEZzM42CLhDx/aiaOXPXhzzI9EWoTTqs+5ejdli0NXw3J6XuoBE2YlWG0T2cxwBLjhj+Hgllvm8UIG3DYc1tgcK20fEkmJ+O6r40sS0UpmWTmiiWLrVb0CvGs5CJTLZCwUboJI8eqot2QrJUC2YIhU/ttHd/Vga5c9J47P3Qzih29P4vxUCOvbzPi3h4fgiaRy1zjUaceJ8eLm4OpU+neuB/D+7V3octQvYUAtUArjpRSC8TS+fHhQrmM271u0/l+lAmG53dhqannuFhNzZj0PkUoY6rLjJ6emNP+NkpVYSnCqn0mJUox5o5rPm/r6UoJc5yySFBCIpXFzIQ6Tnke/24q2bBugwjlQ5kgUKQ5u7YDVoMMbY/6i79KyImlROE8GHQe33QS3HfiDI4O5gqey1UuHj+zqwb6Bdhy7Kh9yZoMJHB7uxHdfHc/W1JNj7+bDSaQECUYdhwODbpyZDOS+QwkLqPZwUO5QlhLEvDku7GACAHf3t+PkxK3rCMYysJt0OSvpuC8GPc/BE0njJyenEE4K6G014U8+thMc4ZBIpzEnJmHW8xhw22Az6ZDIiIinRKQFinAig1Aig3Q2kUjrvryRFH749iTeHPeDJ8D+ARd+enYG0ZSAP39hBI/u6sFD2zphMfBIClKuDVstrOQBqhaYMCvBapvIZsZh0aPfbcXxUS++dEgOCn5jzIdgLINWqx73DLjw4V3dWKfx0lViFTg83IFjVxd3UywmopXMsmvz2r3uAGB9uzlXX0dNtQHehRu/1cjj9Wu+uh8E1JtkShCRFqWyIlc5taszTRVhBchJHBTAzYVEUZNrjhCM+aLodpjzYnDUlc2NPJ+L+6qXtVlLoKhbStlNOrjtRnS0GPGpu/pw/PgNfOPQjpJxXtVYLRvZz7ISqn3uFhNzEqUYcNk0g94VlKzESgRnJdc3HZBDBH52dgZzoSR6Ws04PNyJvRud+Jvj4zg3FcRtva15ZTzUc7RLl8AzL4xiuNuOT9+9AceuenF5NlzWiqRFuXly2414aFsXblvXiowoIZoScOZmEC9d8eKOPmdW9BHs3ejE0+dMGPfF4I+lsdltQySRQTwjotdpxpHhTnz3tXHoeA69ThMODXdgNpgoOnRVcjAodSgrnOPCHstyKEMH/vbVMXxkdw/29DnhMOvhtBhwwx+HL5pGPC2CUhHJjIhERkRnixGeSAqXZsI4uNWNx0/chChShIQM/LE0+t1WTGcLTivdEsqJTcXwEYhnsLHdiouzYXz2no24Mh8BsIDR+Sh+8PYkbEYdJEpxW68DX3lwqGYjyEoeoGqBCbMSrLaJbAT1SnpQForvHBvTdDf5Iil0O0zQa7x0lVgFNrqsePK0totUoRIRrWSW3VyIa1btNuo5HBnuRFuJWLlKA7y1Nv5tPS3Y0mVHX7ulZIzbUg8C1VhYPOFk7mDCEbkaeUqQEEkKuSiYwibXiYwInnDIiHKwb2HAMQDYTDy80RR+eGKyrqEAagHgj6Zxe58Tr4568fylOeg4gg67KbdJ6zhS0mJXrdWykf0sK6XXacEn9q7H4a2dufqAbVYDLEbt5b2cWBpwy3XMCoPe1ShZiZUKznLvhVLb8OREAAvRNHQ8Bwrgu6+O4+lzJnz58CCueaIY9UYRz4iYDSbxy3eux9+9pgrityNXL3DcF8PvHBjApw0cKEpbkbQoNU+z2dIyf/qLqxjzRqHjOKxvM2PCH4ckUbw55sOXDg3CoOPwg7cmchmXr1/zIxBLY8+GVmzpasHtfa340TuTcNmMODLcicPDHWgx6fG+Tfk9MesRoK6e49OTQfiiOlAqW+juH3Lj0mwIn757I1656sGfPHMZeo5gsNMOl82AP3xoCP/w+nWMzkchShQLsTTCiQw2umQ39//9wWG8OebHhD8OnhC4WozZJuflxaYateFDKdNyfMSHLxwYQGAsgo4WA8KJDDZ3WLGp3YYj2zrzMmGrZaUPUNXChFkJVmIimyn7s97ZK4WbwZtjvrwTbXdr6c9c7NTNc1xdRHRhZpli5SEEaLMZcFuvA4/sKF8vZ7EiqaU2/pevevHshVl86eAgnopruxrrcRCo1MKidTApdIkUNrke98Zw56Y2jHoimgHHwC3XTSNCARQBMB1M4H++PIq5UCpXfVwtrh5o0W4wXUv4QqPqlFWzFtRa8b6UWDoy3Ik3x/yaMaHqrMRqBKfWe6GM982FRC4Gym7SwR+T22upq7z//PwcOuwUHS0m/O9TU5gJJjSrynvCKRwf8RQ9V7XO03QgjjfH/LgwE8aYR7akC6KEYDwDk55DJCnkGo9/6q71eOf6Qq792R99aBitZj0EiUKUKPQ8h8/f14/e1tLJH/WMa741x90QRLmtWiojdyM4PNyJX5yfw2tjPsSSAja6rPBni0e/cyOALx8exD+8dh2xtAiDjkMiLWIhlpazUCWa83ycnJA7Q7TbjPjQbT0lxWYhWuvLuxMLODmxgM/2E3ztkWHYjDqsc5rx6qgPPzk5hV3rHTWvF81wgKoGJsxKsNwT2UxpvI1KelhKyYByf+sJJ+smopXMshcuzePdGwGEkmlYDRn86l0b8OC2pc1FuY2fAJgOJIvcg7Xcw2JUMg9aB5NCl4g6cBmQe4d+9PZexFICZkPJooDjwkbwjQgFoKA4PuJBIJt1VshcKImwQchlSaqpNXyh3nXKqlkLlvKuljpE9Dot2L9ZTgS4PBvOWYnU3SOWkjCRu87seKsFv8XIYyZ4K3MzV+X97AwoaC45wWk15JVgUFPquSo3Tw9t60SbzQhPOFkUXtDRYipqURRJCuhsMSKeEkEhW49/5a71OSujuv2ZGodZj68+srWse6+e4QzKHE8H4nht1IujVzwIJzLQ8xy2dNnxHz64DS9cmsPb1xfQbjPCpONwwxfD66M+HB7uwA/evgmeEJgNPCIpAYMdNvhjabx81YPDwx34zf0bYdBxVZd/KVxflAQFRfT+t6cuot1mwNc+cMsiv5T1opGFnhsBE2YlWM6JbKbsz0YnPSyllVSpv623iFZnllFKceHkWzjwvqVbdspt/EoD8kL3YK33sBiLzYPWmCrXqBQJVqxfCqFEJhdk/9zFOXijSQSjt+IIC7NBGxEKUEmdLgLAE0lCx+W3o1pK+EK96pRVsxY08l3tbrXAZTPhWx+/DUcvz+PMZBCJjACH2VC3A6My3mrBzxOCjKp2nlJeQolrM+t5eKNJtFpLl8dYrCl84Ty1WQzwRJJ4/MSkZniBzahDIJbOVb4HAFEQ4Y+lsa7NglhKQDQpQhAp7h104dVRX177MzX1aLe0mDhRW1t1HJDISPjrV+RnSrEgX/NEMbkQx+h8BL97/wBOTQbhj6bQajFgIZ7GqckADg135A5hfPZAeM9mF25b58DmDht4Ti5hUYtnp3B90UpQKFxflrpeNKrQcyNgwqwMyzGRzZb92Yikh2rcMrW4cxshotXC5QpH6jL25TZ+pQG5ko1WyHKf6LTGVLnGWEpAV4sJDw53YmIhhs/s64M5GycykG3n89E9vdjaZYeO53JlKwqzQRsR01FujPvaLTgw6Mbs1Tn852cuIyVIedYos55fkuV1qf1rq10L6vmulnrvNrms+PV9G/HhXfUrjKugWE1EKZ0T/CKVXX5KSx45m1LMxbUlMiLcNlNeS6RCFnuuCudpsfCCP/noTrTbjZj0x/P6sKYFCWlBwpZOu1yY16zHp+/eiHhahCecKvrexd7hesQ1F1pbD2/twHQwgRu+GExZUUYIyQnMK3MRHB/x4uO39+KlK17osmM8FUzAZpTrGCribFtPCw5u6cCLl+bx1vjCkjw7lSUo3LKuA/VZLxpR6LkRMGG2CI2eyGbL/qxlcSgnpqp1y9Tqzq2niC68n3rZdBaLWzTpeezsdcCk5+Aw61f8RKc1pp0tJhzc2oH9m9sRSQq4Oh/FP701WdQA2203YswbXfaYjlJjrPTi+4uXRvFgWwahhACjjsuzRn3h/gHc3d+G5y7OL+maa40VrXYtqFeC0nQgjtev+SBKFD2tZliNOuhDBN3ZqupLFZylUFtNFMEfT4m5EhMAcN9mF85PB3NxbePeGA5udePiTLjk51bzXFUSXnBxJowDgy78vedG7ncSpUiLFFy28v+dm5y5sfryoc15vXYzolwVf7DDVjaWdqlxzVoCs7vVjO+9cQNRVZN0Arn2WyItQpAojo148bVHhjEyPw6DjkNvqxkEQCIt5rWL+sQd6/HMhVmcmQzmfa8oAZMLcUiUapY1KUWpBIVWi4gv7RksqrVYr/WiUc9zPWHCrAIaOZHNlv1Z7eJQSkw9tK0TEkXFbpl6uHPrIaK17me/LY3pQHzR719sQ67E5XpXfxuGOuz46iNbm+JEV2pM58NJ/P1rNzATSsBpNUDPEyQFKW++HtpWHytmNUKn1Bgrvfgm/HHwLlKU4TUXSuKlK/N4ZEc3zk6Far7mpRwuql0L6pGgNB2I48kz09jeLY/P9964kevScWDIjU/dtQH92Srs9abQarKx3YqFWBoumxGRZAY9DjMODXfgqdMzOSuyJyxnZQbi1+tiHa8kvOD7b07gP35kO45d9WLcl9/GrNWsh9Oix6Gtt75TosCYN4aTNwIIJtNoNRmwd6MTA4uM41JCMkoJTKXsiUQBbzSF3lazfF8mPYJx+blR3MVKkP91XwyfvrsPbrsRf/mrt2M6mIBZx+OdGwt5okyxQB8b8eJH70yCEGBrVwv2bGit+CCpTlBICxJmQwl4Rk7DoOPw/u1dSGSt7Z5wsqliwBoNE2YrTLOl8VazOJQTU1aDDoF4uiK3DAWtmzt3KSK61P1EdQK+c2ysrDhUNuTTk0H4Y2m0mPTYs6EV79/elWuwW6nL1WLUlSx1sJwUCqLWbKHPlCDipSvzWIili5qkA/nztVQrZrVCp9QYK734jHoOOo5oZvTJ1qjumq95qYeLateCpcZWpgQRr1/zYXu3A3/x0mhemRZ/NI0nTk1j3BvFHz+6o6om0dVQaJU16TmYDTp8YGcX9vY58fIVD9KiBIe5dFN4AEW/r5RSYlip3dduMyKZFmDgOfxfD2/FiesLuDQTQjQloMWsx77+duzZ4ITFcMtDoH4GjDyPREbEa6M+XPOUfwaWEpJRSmCqy56EE0K2byUHl80ITySJeFrMuYuVIP/eVjPu3NiGY1c9+NCuHrjtRthNOoz7onCY5edTbYFWnhsdz8EXTVcdG61eszOihOspAX/188vwRpNw20w4uNWNX75zfdV13lYzK7/6v8dptjTeSheHxcSUy27Ej09N5YJNtVDcMiT7v8vRaHfuUmL9lMX4hi+WV0z15EQAb4758PUPbstZHVZLAGo5QaTnuYrnaylWzFqFjtYY24w6EAJsbLeCI8XxP8Ata1Qt11yPWNFq14KlxlaG4hmIWVdWqdp5F2cieO7iHH5938aiz6nX5lhqvAFgndOiOQfqvzn/TgAP37m1JsuylhhWF0VOiyLcNgMiyQx6W824fUMrdq1vhdUgx1Qq/SIppXV5BgrdewuxNOyqQ16p9aGUwBz3xnJlT5SODYBcTHuTywZ/NIX7Bl04NxlEu82AewZc+MDObmRECaGEgD9/fgRJQYRZLxdpfnRXD46PerHOaSl6btSfX0ts9HQgjr977ToGJQlOqwGtVj0ICC7OhBGIX8+9781UwaBRMGG2wiy2uHa0GHFgqAPBbJPu5TgZVCIe1AVItTDreUz64+h320qmtSsbIQWp2Z1br80hFM9g3BvD/s0u9LutMOv5nBmd95ZO11YW4xu+WFExVUGUcPZmCI+fmMRv3defa7jb7AGoiwmiL9zfX9V81WLFXOomV9SOSqLY2tWCSLJ0Wx61Naraa65HrGgtQmspQl+icjuz771xo+S/ESQJpycD+PCunqKA+XpujqXGu9wcKH9zmSc1H9gKxXBhUWRKgdv72nDiegDHRjz43QP9+Oe3JzAdkNtHCaKEgQ4bvvXx2yDR+hwwe50WPLKjG7vWtyIjUMTSAmaDCbw55gNHXJrjW8raemE6hEd39eD8VBDTwWQuaULHEaQyIgY7bPj03X1IZETsH3JhNphEUhDxP14YQSQloLfVnKsDeHIigPNTQXzp0CB0PMGP3pnM+y6eI6AUSAoSCIDTk0E8vKO7orlRv+9DdhSVupkLJfH6NR/2DbhyGaYKhXGiZj0HQVrdljQmzJqAUovrtp4W3L6hFU+cmsKl2fCyngwWEw+FJzR1P0QC2YRuN+tyJygtlI2QoDZ3bj03B44QHNzagWNX5XgJJc5m34ALd5j16NNZcHMhXiQOQ/EMTk8GNYupKrwy4sUHbutGq2r8mjUAdTFBlEiLSKRFGHU8ggm51IGeL3YNLtX9Xg+hU9iOas+G1oZZptXvQ+G7oIzPUloY3d3fhge2dACQNyn1plOr0JdrdelKtl4C5Ey5SFLIu+6VLO+jdRBbCmoxPBNMFL3H/S4rDm3twF++dA2XZsN44dI8Blw2XJi6lXwQiKfxylUP3r+jq27JGIXjq3B2KqQ5vqWsraFEJtcK7+RkADcX4oil5LIn+ze7MNzdgh+fnMpZvvZvduHoZQ/mwsmsdVl+h5V4O6WY7i/tXZf33IiUwqbXYSGWhi+ago7j4IvqkBa018RCKnnfRYniuYtzJdemG74Y/uXdm+hxmHD0imdVW9KYMGsSChfXtCDhxPUF/NNbkznBsty1zcqJB/UJrbAfoo7jcHIigHv6XRj1lO47qWyEFLRqd269N4dISsBfHRvLVfcG5Dibp8/OYP3WNA7sdOPlq54isSFRwB9LFxVTVROMZZARKALx+rti6x1rUW6BVIJ9L82G0Wk34u3r/qLCowpLdb/XOylGvQFDY69YajkS5X2YDyeL3gVlfDpbTDW1MNLzHOJpuXdpqUNILULfYdFDHyIlWy8BcvulNqshd90rWd6n1EGsR6xs8y+FIoZ/cWEOr1/zocWkg82kw87eVty1qQ2/uDCXC/rPFbw9I5dxUDohnJsK4ci2zooPmKXe21rHt5y1dcIfx1u8H5+7dxMokCfe/dEkrrVbEIxnEElmMNRpw/OX5nIZnArqUjlvjPnwSVUxXTH7DjrMesyHUwCVvQWUAjcDcbn5eB0SX3pazfiXd29q/k6xdPqiKXztA8OgdOVqgdYDJsyaCGVxTQkiHj8xWVKoNHLxq3SjV05oz1+c13ThPX9xDv/x0e2IpgQsaJzICzfCalw49dwcUoKIaELAi5fnIFEKp9WQc1EopAUJr4x4cXi4o0hsKBtyYXFEALmClE6rHtFUBpJU38zaRsRalFog1cG+0bSAr75/K85OBTHhj2MhmkZMlY5fj5prjUiKUTbgt96YhUOobzkSh0WP4W473r7uL3oXlPE5uLX4+SmFWmhNB+I5943DLBfrVdztkwtx6Hku5yavBqOOR7fDjANDbjxxarr491nRsWdDa+66l6u8T+E6BADff3Mi76CnbLyHW1MVZU2XgyPAQIcNvU4zjDoOKUFCIi2CV7n+DDyHYDwjF7zl8w8kkaTcaaKSAyYBiorZqmvp1Tq+i7m1tZ6R7tYCl79IYTHwmnXiTHoeG9ut8MdSICDYN+DCsxdmYdPr4DDrsRBL562b9wy4cGU2gtH5yKJrcSXvu9WoQzgpJ2KoUbd/E4RMUR3I5a4FWg+YMGtCVqq2WTUbvVHH49DWThwf8Wq68GxGHXQch1+7ewPOTAZxajKABSVbsc+J9xe0N6omVqZe46Pc74Dbhh+fnEIgJve/62wxYiGWRiJ9q93QyYmFXPsRNQ6LHJh7ciKQtyiJWaunIFHcvsGJszdDcFoNkLLB5UulUe6kUgukutyEjufw0hVPrl/eG2M+BGMZiJTivkFXXVpXSZTi8/dtgjeSwrg3hgvToaJrqsUq1+u0oM1qwFfv0y5HshQL5N4+J35xYU4zkL6rxYTbN7RWda3K9bx4eR4zwQR6W804tLUTx0Y8ee72B7d14lfu3FBT5mRHiwmfumsDxr1RXJyJFFn5NrqseSJ7Ocr7FK5DNqMOAx02PLq7B6JE4Y+l856JjEiXtPEq79KEP45rnmjee9zXbsHvHxzEmckg5sJJtFn1oAA2d9jyXPh2k+xSXeyAed+gG3//2nVNgTkyH8Vv3dePxc4a1XY2WMytrT4EeMJJtJoNJQWSQcdhwG2HxcDj1/f1IZYScHEmjPlwqmjc1AViF1uLK0l80fME7dmDsxqlnRNQ3CZOYTlrgdYDJsyakHovfpVsNp5wEpMLcWzvcWDXulboeLmydTgp4M0xP/ZvRlFxRKuBx2f3b8JLlz25zVndfueZC7PY3t2CB7d1YntPC5KChFA8g2ueKH5+bgbv39GdVyOp0kWlXhWyFWGztasFgahc6yeSlItcrmuzYD6UBIWcweQw6zUXN6OOx/u3d+HNMV9OLIqUIpGWC0/2u6w4MOTGldkITk0EMeZdulm9WothPeqAKeUmANm9NR1M4KmzM9jR68DXtgzDpOchiBKGu1vQbjPWfG/qTdkTSSKcyOD2vrZcNpgiepZilSPQDipfigUyFM/g2FVvkVhVvw/HrnrR67RUnVRwYnwBiYyIfQMu/OkvLuOGPy5bcHQc/NE0fnxyGvPhFP7gyGBNz1W/24Y/fnQHnrs4h9OTAUSSAtqsBs16VI0q75MSREQSGURSIr7z8jX4oqmc6JkPp3ByIoDnLszh3zwwgH96awJbulpyzwTStW+86nepsOUYILsBj496cGi4A//89iTuH3KDJwS/dd+mvAQhm5GHwyyvVaUOmIe2duLMzUDJ8I65UBJHL8/jjj4nXrzsKXnN1XY2qIZyAkkJWTkw6MILl+YRiKfxybvW440xP45emsdsMKnZfo0jWHSvqiTUoNth1owTVbdzKmzjpLCctUDrARNmTUg9F79KNpubC3H86J1JvDbqAyGy8Nne04L9m1145vws3HYT+t1WuGwm6FUbYVqkePL0NLb33Nqc1e137CYdhnta8H/8y1lc01iMjo/68M2P7swTZ1qLSrFbgy5pfAqFjbrWD08IKIBYSsDmDhsoAD2fgtteOj5ofZsFX//gNjx+YhKvjHgxG0qizWbAfZtdODDkxrGrHnxwZ0+uJVE1blYtQVWpxfBDt8nxf1rzf2hrJ6wGXq5ervrsUrEqSqFKxb2lZGq9fs2X6+vJEeAbj+0oe13lKKoBpeNhN8kNoi/PhvFvHhjAy1c86Hdb6x7Qu1QLpESBS7NhTAUSeWJV/T4om0O1LcrOTQfx0LYuvHLVk4t1SosUokRhNvCAJGEmlFiS1Wh9myXbeqmn7KGoEeV9lDXKwHO4Oh/Fq6O+nMUOQC52aC6cxPERL7Z1t+CnZ2ZyGYJTlydr3njV75I6jkrtBXhzzI+vPrwV56dD+Njt6/DM+Tm8cGmuqBCvVikP9ViGE2m8fMVb9nouTIfw2f0bywqzRpZPKvX+KzFcXS0m3LvZnVvLxr0xbO9twbd+6TZEEgJiaaGo/Vqle9VioQYdLSbNa1PaOfW2moraOCksZy3QesCEWRNSr8Wvks2GI8C3j47izXE/2q0GXPfGEM+IuOaJYsIfx79/ZCvCCQHBeAbeaAo9rebcZ8knIeRtzmoe3d2DJ89M4+p8JNcEV83ofBRPnJ7Gb+7fiLRANTcpLWH52J4eDHfb8db4Qk3jUyhs1LV+APlFjyQFdLbIadtkkc8DZKvDb93Xj0d2diOaEhCKZzAyH8GV2Qg+uLMnr71INW5WLUHNc9yiFkOOyFbF7756vWj+n784j+MjXnx2/yY8eXoalCJPrGu5lTOihP4OG3hCNHt5Aktb/EpZAU16ueBlRqS45oniy4cHYTdpWy9rpR4xi+rDVKn3wWHWIy1IJeOLCoVfShCxEEvDYtBhT58Tf/LM5bzfK+5ym0kHArJkd00llpZ696VVr1Gf2deH771xIxeXx3NyQ/NkRgSX7e+oDr5XMgR36Pman71C67sSR6VO4IgmRXS2mPAfPjiMH7w9gWfOz+dcvhTApZkw/u6163niXWssBWlxS38sJaDdakSXw1TT+NYjGajw/Q8l5AD/h7Z15VnCACCWFvDE6Wl4wimsb7NoPvfVCMnFQg201iaXzYA9G9Zh7wZnURunWq6hGWDCrAmpx+JXyWbz/KV5DLitmAklYdHz8MfSiGdE2Iw69LaaMe6TTz671rWgx2lBIiNiciEGnhC0WvSLCshuhwkvXfGULDArUopnL8ziQ7d1478+d7VokyolLJ88PYOP712Hq3MRBOLFL2G1zYLVtX4UV5m6WKKeJ7hvayfCiXRRfZzChbCvzYIrcxG47EbYTDrN5t3VuFnV960I6t+9v39Ri+HePqec9l4w/+o6TS9d9mB7jwOvX/MVWYYKT/06Tm6R8tpoY8pNlLMCcoTAqCO4NBPGh3f11D2Atx4xi5Ucprb1tODE9YW8f1POKheKZ3BpJox9Ay5YDTr4o8WFcQVJth7r+crKcdSDehVJLlyjFKusgkHHYTaUBE8IdBxBWqS59kEKb4z5sHcbV/Ozp+WdUA4DHXYKCgqn2YA2qwGvjnpx3RfPWtIpCEguzmwx8Z4SRAiShC/cP4BoSigZN6kcOmoZ33omA6nf/7QgYcIfw8h8tGgt4wjBoNsGp0WP/Zvbsb2nJefevTAdgtnAVx1yUCrUQOvaFPFGAPz9a9e14zvrkIy03DBh1qRonVrMeh12b2jF4WyT6HJUstmcvBHAYIcNBHLGy3QwAQMvN7G97ouh3y3HRxl1HB4/cROvjHhhNfD4pb3rsW+gHW1WAw5u7YBZz+Ot8YWiRUbPcwjE0poZPkoc1nQgKZ88aXGhwDfHfLlF22HWY0evI5eNxnMEn7t3E05cX8C5qeo2h8LFWF3rR4kPiiZFOM0G3LnJCVc8gjM3A3j5ije34B3c6sb2HgdeHfXmLYTbelqwpcuOFy/Ol6ymXo2btZC5UBI3fDHsXOcoK5K29bTgH16/kfczdfYSIG9qX9synDvlFm4uhaf+h7d3wm7UocthgtNigC7bb9IfTWMmmMD+ze15i181p/eV7hm7s9eBTQWFhdUb52LfXUmh6Ns3tOKf3pLj9Aprnc0Ei12REgXenQjgw7f1QJDk9kDeSL44M+k5uGwGcIQsq7umHkWSC9codUgBAPBEbrRtMch9HEWJ5toHKQRjGXCE1LzxlhLUymEAAO7c5IRBx+Gt8YW8nxdSSryr27VdmQuDUmDfgKsobhK4dbgx6LiqxrcRyUDK+z8XSuJ7b9yA1uOvlNB5+aoH/+35q7gwHYbdrMO9m1349N0bsM5pRkeLufgPl4iWRfIz+/rqclhohlZPTJg1Mcrid3BLApGkgEA8jdH5KL73+vVF42wq2ehCyQx0PAc9L5/8MqKEzhYT/LE0rEYdfu+BzYilBPzX58ZwbiqEnb0t+K37+nF8xIt/ensCkkRx2/pW3NHnxO/e34/r3hhevDwPKesa0/EEXS1mzIfzNyoKuQSFSCl6WkzIFJSamAsl8dzFOViz/ecKm+UqsR0P7+jCJ+/cgEd2dle0eCkvnUgpfuOePpycCOY23wl/HE/FbwWzu21GtNuM4Ajw0rGreOF0ftyC3ajH1584D07l2lPcWG+O+fCZuzfiqfhM1Wb1SgT10csefHb/JlzzlLaoyhtY6ewlQN7UCt2S5SxDEgWCiTR+cXEOo/MRgBDcvakND23vAs/Jv1eo9vS+kj1jY2kRU8E4Hi8oLKzeOCv57nKWpANDHXji1FTJun8tZh1OjC/kVUpXQgWOj3rxsT29+NBt3fjBW5PIiBR6nqDNakC7zQhdtnzAcrtrllokuXCNUkIKfnFhDnaTDnqeg92og0gpuGwfxweG3Dg3FQSIHFe00WWBjk/UHG9YqXdClGhNB4dCwdRuNeKGP4anz96KkVPWiULLTqXj2+jacqXeTXUJnelgEgMdNvS7ZWvi1bko4ukpfPHg5qq/rxRq0aTjALOBRywl5kSUy25c0mGhmVo9MWHW5HgjSfyv4+NFL91UIFH2JFTJRucw6SGIEjhCoOM5GPU8HGY9Rj1RPLanF8F4Gu9OBDDujaHDbsTn7+3Ht4+Oym2KOAIdT3B1NoJL0yH8/Nws/sOHt+HLhwdhNepgN+mREUUc3OrG4yfyiwJK2RY5APDwji7MaywopyeD+MQd6zSb5QKyleYHb09iOpjAV44MLfriFL50Rp5Dj9Oct/kqwkrJnHRa9Xj8xCRQUA5EKR0xOh9Fm82Qa1sijztBMiPijTEfdvQ6imIuqnWzajETTMBu5Mu6O/Q8VzT/6uwlQDu1vJRlSN0P9Lo/lisl8vyleVzzRPGlQ4P4/psT+My+PnAEVZ/eV6pn7HQgjr85PoaTE4FcNp5SWFi9cVb63aUsScF4Gpdmw0Utf4Bbtc7Op0N5ldLVY/KvJ6fwkd09mAokMBdKQqQU8ZSIVEaE06Jfle6awjVqwh/Dp+7agENbO0Ah39/pyQB+fn4OwXgaQ502PLitCz85OYUtXXYQEBwZ7oA+M7Gk66i0BZ1yrYXWe8XCOuGP5Yl3LcGkjmGbDibxyogXd250Ii1KNQuARpdXKvVuqkvotNkMMBR0APGEUzUJQgq5SoAiuFotengjqdz8OC16PLC1A2+P+3HdG0NKlJYsolaym4UWTJg1MUs5CVWy0e3d6ISU3YSNOrl+l47nkBEl3N3fDgNP8Pb4AgRJwsGt3Tg+6sVYVpSJEs2dIrtbzTg3FcJPTk7h9vWtuGezC+02Iww6Dh+7fR1OTgQwOp+flSlRim3dLfjI7h786J3ias6RZAZWgy7v5S9Ex3GYCy3+8mu9dElBwqWZMMa9UXx2/yb89PR0ztJX2A90V8Fboi4dEU4I6LDTPPeGSc9jOpDAB27rxoXppblZtbCb9BBpeXdSShCL5l/JXlLqDWmllmtZhtS1tLzRFNIZKS+ZQwnCXt9mycUt1rNyOdCYOBHlvjzhlGY2nnJf9wy0Y99Ae8XfXcrSYTPqMLkQL9m6y2zg4Y+l0NligkHH5Y3JVCCBp8/N4pf3rs+521MZEUO9DtzV37Yq2s4UuomsRh5397fhuYvz6Gu34ODWDhy9Mo8T1xcw5omCIwT3DbnxHz40jJG5CDZ32vHi5XkkMiJMOrmQ8YPbOjF6dmnCDFjcNauspxP+uKb1ft+ACx/Z3YM2lXgvJZjUMWyeSBKfu3dTbs5roRFhAIXWqUNbi99NZR1UZ2oXUq0gnA7EsRBL41vPXkE42zj+I7t78MO3JxGIy6L4vkE3/scLI5jwx2HUc9jYbpUtyzWKqJXsZlEKJsyamFpOQsoLBQAHhjpwZS4CT7g4aLjLYcJD2zrBEeQygDrtJsQzIsx6HnajDhyRX+i0IGFffzu++czlXE0a5RUXJQpdtjT3y1e9eHC4M+8h7nfb8M2P7sQTp6fx0mUPfFF5E7x/0I1Hd/fg9Ws+TAUSRdfXZjVAryN5IqiQFrMOer58Nlq5l86k5yFRiqtzEfz7R7ZCpNDuB2rL/zt1kLI6SUBNWpTQaTfhq49oZxeVwmGRs04lipLxTmrrTSkRoCV01HWaCgtAKmhZhpTnsNAVqkaJV/vxu1MY7LBp/huFaiqXW42yOD+crYqeEsS6xXyo3y+tbDwdx+HyXBi/uX/jkkWPwyJbWcplEt8z4MLlmTC6Hebc2BSOydPnZrC3z4mP7OlBu1U+/FQb27USlHIT3TfoRiwtYFO7Df/w2nWMeKIIxNJosxrQYtbjylwE8bSA37xnE/7q2DVc98ewtaslr8baaJ2usZzr0Kjj8dC2Toz74vjz568WWe9fuDSHaDKDrzxoyj0r5QSTEquWFiToedJUXTK05urgVjc+f++mXExtJFv4V8cRbHLJ77vSvFxdeLcaQagcoIckIXcvfe1WPHV2Fm+O+7Gx3Yr9m115B/VURoI3msp5LWoRUStV0L0cTJg1KSlBRFqQ4InKvce0GkUD+Q9+4Qu1rbsFn9i7Dqcmg7g0Ey5puVEv/L5oClu65MrOKUGExahDJCXAZpSzwgiRrWWAbIHhOZJzS/oiKZgMfNFD3O+24d/cP4BfvmO96rrT+NE7NzVFGQDs2dCKHocZokQ1myyrT2nlXv7FXjqO3Mr2K3zplAWvEHWQso7jNJMb7CY9OI5U/SL7IilcnYvgZ2dnMBdKFsU7pQSpYsuRltAZdNvQO2TGPQOuotTyUlYpZYMpdIWqUeLVlLjFclRauTwtSPDHUrg8E8ZfvjRaVNZjqWiVSlBn4xHIm2c9RI9Rx+PwcCeevTCLCV+x9VcRyq+MeHBkW2eeK2epsTMrzaJuooMD+NE7NxGIZxCIpyFIcvZlNCWAJwTTgQTWOS34/UObkUhL6HKYlmRhqpU2mxFPn5tFJCVAx3NFXRIC8fwahcsVN1nPMIBymfAXp8P43L2b8PAOOaZXlCj62q245o1q9oY1VVHGRH2AHrLf+rlyMFcEmNZBvdBrUa2IWunEIy2YMGtC1K2CvJEkQnFBs1E0IL/YPEc0X6iLM3LBy3sG2vEHRwZh0HGai7pWA/WpYALBeAb7N7vwxKkpRFMC2m3GonR2u0mHYFz+mctuRDIt5j3EapO4UcflslymA8gJukIUgdDRYkJSENHfYcMNX1zzxVfGoNTLv5SXTlnwMJtvVVLXPVOsdoXUEg+lnkO7SS93SkgIePrcDC7PhvHVh7eg22Eq6sBQjlKp5ceuehCMy1W5F3OzKhtMMpPKc4WqUeLVlLjFclRSuVwQpVx/SDX1jPnQ2jgLs+4c5volHDhMOvzBkaGSnTJGPREcGu7ET8/M4NJMeMUDkOtBJW4iXySFCV8c5mz2pYGX50EZdaX37t4+J37w1gS+8diOFRGloXgGl2cjReJdfWhWi4JGxU1qZQ7WIwxgsbka9UTx7IXZnPD0hJPodZpx4votK7C6N+zGdisODLkqur9SB2i1dyKcEGDU8UUH9UKvRbUiaiUTj0rBhFmTod6cRQnY1y8LgMJG0Qq71jtgNejwzPmbmi9UKJHBsxfmEEsLZc27hWZ8PU9gM/Kwm/U4czOAE9f9ODDowk/PyCJFfph1aDHpcy1GDm5xY9wXyz3Ei2W5VFKrp7PFhPdv78LRyx7NhVAZg1Iv/1JeOsUd+IrnSt7PlbpnV+fC4EixJbOWeKjCRVHLenNuKoQdva0Vf6aClovmsT3rcP+WjoosMMoGc+yqt6hljYISr6aOWyzFYpvRcsV8LHfCgdWkw2wwgfVtlqLOAC9f9eDglg587/Xr2Q1XfqZWMgC5HlTiJsqIFAvxtCyKeV5T2Adjcszpoa0dECSKuVAyJ0qWC+WQV65khloUNCJustSa+siOriXXlqvGpddq0ePYVQ/2D7hwaiJQFAOcykgw6Tkc2lrZ/ZU6QKu9E4IkIZkR8kqqACjyWlQrolYq8agcTJg1EYUbUmHh00J/uvJiR1NC3X3k3a0WuGwmtFtT+NOP34ZjV7zY0duCC9MhzIaSaLcZYTfpcMMXQ1qQMNRpw5HhTvz83Cx2rXeAoLLMvMVcNEpsR7nSEOUWN/VLV1g7ShF45V66XqcFbrsRBzpcuQUPACKpDL750Z15MRe11M3JjY3Goli4AZybCuGRnfWJc6im1IF6g0kLUlGQvOKGe2vcn8vKXErlcm8khe09DvS1W0sW4qxHzMdyJxwYdTz2b3bhO8fGirJ192924fUxH5IZSbOzwkoEINeDSizWsZSAFpMO8bRYUvh3t5pgz/Zo/bPnriKaEnKipGcRC229qOWQV69ivMDiLuEvHhxYksu7Gu9CKJ7BW+MLaLXoS/aHPTTcAYuhsnjQUmOr9k7oOA6j89G8Li0AirwW1YqolUg8WgwmzJqIws25VOFTq0GHOzc5cy/2XCjZEB+5Xseh12mG225EX5sVGVHCdz69F0cvz+PFy/O4OB2G3azHB2/rxpHhTrx+zQezgcehrZ04drW46rxC4Saz2Oa6lMVNeenOT4VwbjpUFAtxW69j0ZdOz5cu9rjOaalL7E8zxjmoUc/BifEFjPmi4AmHuza14dBwB4LxND6zry8vbvH5S/M4eSOAUDIDh0mPvRudeGjb4pXLT1wP4J0bfjgtxfXEFOo1FvXcOJfyfXv7WvE3ryyUbHcFLH8Acj2oRMzMBBPYs8GJN8f8mtmxOp7D4W2dOHbFg9OTwby6gcdHfDjcmsJ0IN5wa2KtlpV6FOOttJPLp+7qq/n5qEZ4KutVKJHJq/+otgL/9PT0oolACqXGVm2ciKQEXJoN47HdvTljRWFGaK0iarnXgcVgwqyJ0NqcCwufKi4udfBro33kBh0Hl6rTQJfdiIe2dSGSEhCIpXBtPorjI95c0VulE0A5qt1klrK46XmCj96+DnaTPneqc5h1uGfAhSPbOjVjxAopJSCXWmRTQZ1ooFUj6cK0LNirncN6VrK+NQfdkCS5Fp1ZL/cLdJjz54IjwIDbisEOmxwoLUqQKIWO076mhWgqZw1ICRI4wmnWE1usMXIt91uPjbMatL5PohSpRSw/KynMa6USMcNzBO/f3oXrvhjmQsmi7NgdvS24a1Mb/t/Xb2gK14xIl8WauBTLylLXiUZkDpYrX1IKRXgG4+ncnlOqP2w18ZnqsYXqNVCME3/40BZcng3j0kwYr4568QcPDuHE9QWMe6JIi1JdRNRyrwPlYMKsiSglsNQPvsOsx1cf2Zr3sCx/rIweVpPckDkQN2Ko0573EDfKglfL4pYSRDx7YQ5nb4Y0T3U/OTmFXesdFS3qjWzX4bDIKel2o16zRtKju3oQSWWqmsN6VbIuvO/WRe5by+UCyC5PnuNw6ewsLs9GQAhwR58Twz0taDHp8eiuHlycCePcVBCeyC2XVq5Ztapgr9bzfHMhjucuzuH0ZBDhZAbtVkNeWYVy1EtgV0rh96kLmJZiuQOQ60ElYmb/ZleRxcKk52DukFvQHdragf99agpJobRwXS5r4kpZVuptUS9XvsQbTeHURLDob9TCsxF7jjK2b70xC4egz41tX7sF/S4L7tzYhkBcFk08R7B3gxORlFBXEbXc60ApmDBrImp92FfKR17qIa7Fgtco0aOcNEud6oDKFvVGt+sw6nhs73Hg60+czyvGq1iNrs6F8c2P7qyqvUg9KllXe9+lXC5KB4c/f/4qIikB9wy044GhDhwb8eLvX78OfzSN29Y5MNBhw2O7e2E16nDsqkezr6fW8zzujeKbP7+EizORnKt63qyDN5JaFYHzzRiAXC8qFTPluiZcmYuU/Y7ltCauhGWlnl6RxdaGz9+7CW6bEW+NL5Scq0btOb1OC9qsBnz1Pu36j4VrtMW4NiXM2ryrVcpSHvZm8pFXu8k0UvTU46SZEaWGt+tICSJeHfWCIwRtNkNRLBxHCF4d9WKd07LktPdKA8lrEXelXC7qDg6dLSYcGOzItdkSKUU0JWDMG8N0MIkzk7LrMhhLYzRbIykYl7PyDgy5ip4LTziJfz4xmfe96rR9AE0fON+MAcj1pFIxU+qw12zWxOW2rNRLuFeyNrw66sUn9q5fNGu7UXsOQbEAe6/BhFmTsZSHvVl85NVsMtVu/tVa1pZ60kwJIsJJoW6lG0pdvyJoTHoe27pbsK27BYOdNpj0OqQE2e067o0hEF/cXVOPeJRaxV0pIawuDHl3fxuOjXjygvk5QrAQS2Nzpy3nuty7sQ3xjIgOO4XDrEOXw4Rd61vzvi8liJgNJXB8xKt9H9lM5tOTwbwG4c1IMx2uGkGtYma1WRMbYf2vl3CvfG2o7F1plj1nrcGEWROylIe9WXzklWwy1W7+tVjWlEVdiTHTCqovt6iH4plcw+5SVBrfUu76eY5DOJlBX7sl14vvJ6em8uLMDm7tQAV5CnWxEtYq7koJYXWhyNv7nPgfL46o/oZAxxFkRJrrwal2XRp1BHdtatOs9h6KZ5ARb3WH0PFy0WOLkQdPCERKkcpIWIilV0XgPNvoillMlOh5gvubxJrYSOt/PYR7I7K/m2XPWUswYdakNMPDvtST32KbTKWb/wd2yu15anEnGnU8HtnRhc0ddrxwab4oqP7je9dhsMNaclGXKHItqEpRyUK2mGXwd+/vR2+rGQcG3TkXn4ISZ3Z5Noxvffw2uOxa33CLUuJIXcfNaS5fmHOxBVyiFL5oSm4bFk7mno1S1g11ocgWsx7B+K3PJpCfd46jELPjqLR4AspbAyQq18FyWg2Ip0W0WQ3wx9KYCSaQESXoeQ5OqwGb2i2rJnC+Gd79ZqOcKHEnIk1hTaxXXGc5lircm7HKvZqUIBYVEK5XgtVqggkzhib1OvmV22QqPb0BWFLMVEakeOLUVK6OmQQglBCydeEy+MqDQyW/nyNyOn85FlvIKrEM3vDFcGhrB16+6i2qoq0QiKfxylUPelvLu021xFEyI+aVIHhsdw9G5iMQJVq2DZPWAq58lp7jMOaN4qkzM3nPhpZ1QykU+cKlOQiiJAsoVSFRnhC4WowgkK1eDosOgihpxpQVXudMMIF7B11490YAN3wxxDO3LJxpUYI3kkKv06LR0ZTRrGgdCkuJkjdem1jpy122bhXA0oR7M7uFlT2HDyXx+JPnV307sqXAhBmjiOU4+QGVn95SglRzzJSyYAbiGc0ed4WNhwtxWPQwa1Svdpj1Odeo224EpfJ3aZ3uFMug+m8K3alHL3vwuXs34dTzVzXvTymkWEn1/0LXTzIj4oY/lstw7Gu34N7Nbjx5egZmA685n6UWcPVnfXhXD8a9Mc1no9C6MeGP4SO7exBNZnDdFy+q3m3Uc+iwG2HQcbAZ9XhgixvD3S24a1P7oiKU5wg+uLMbl2cjeaJMYbDDhv2bXTh21YPH9qwr+VmM5mCxQ2EzWhMrsf6Pe2MIJzKQKG1IyZ1KaNYkE/Wec8BOQenqb0e2FJgwY+SxnCe/Sk9vPEdqjotQL5iletyVixEz6ni0mHR5LYbUcWBPnp5Ci1mPDrup5OlOooDTosd92b/RqlH26qgXPEfQYpablxdmZSpN27Xus5R1Qam+/9zFOQgCRbvNkGuYfXzUmysOqTWfWgu4RCkWYmlYDDrsWm/Dr9y5HgvZ2K4L06G8Z0PLutFmMeArD5rw+jUfDg934Px0ENOBZFFT+r52Mx7Z0YV2mxGLobQ58kdT+I17+rDeacar13xYiKXRZjXgwKAbD23rxKujXgTjGdy/paPs5zWyVh1DG2XMASCWFvE3x8fgCadyv18NG/Ri1v++dgsODLnx5JkpXJqJVOyFaMTz2GxJJsu556wWmDBj5NGICtOlqPT0xhNSc1xEPYJd9TyXW8jGvTEcGHLjr46NIRBPw20zylmVZTYPHQc8sLUD/+OFEc3YsfNTQfzBg0PgCNBhN0HPc0WWPaXlSOF9LmZdeHRXT676vlJU96mzM3ljWWo+CxdwfyyFLocJg5127O1rxZ/8/BLmI6m8tknqz9JyufQ6LXhszzpEkhn82Sd24ehlDy5MhxBLCTVvDr1OCyRK8a1nr2B7rwNfe2QYNqOczTrhj+Ppc7MIJTLgCMrOc6Nr1TGKUY/5zl4HpoJxnJwI5Al1hWbeoMtZ/5Uafn91bAx6noNRibFdRHA2OpGgWZJMlnPPWS0wYbaGqeW0tdw9G3udFnzx4ABGPVFwhEDPc8hk2/cMdtjQ3Spnb9YaF1GvYFdlIQsnMnjyzBT0PJdrJq9GvXlQyM1+eY7g7XE/bvjjmnFOE/44TlxfwJ71rbn71LLsFd5nJS5nPc/he2/cQLnpKjef6gU8mRExOh/Bm2N+/PnzI7kYMXXbpKfPzSz6bBh0HNptRrTbjPj1fZZcNW9AFsGiRPMSCirBqOPR0WLC5dkILs2GiwQtUH6el8t9z7hF4Zhvclvx+DuTufpzG9utReKsWTfoctZ/pYZfIJ5Gb6u56PdagnM5nsdmSTJp9j7BKwETZmuUWk9bK5G1I1FgzBvDyRsBBJNptJoM2LvRiQG33AB3KXER9Qx2Neg4SJTi0kwkd+rVwh9NYzqYwPERD87eDOHje3txcSYMAkCkt0pCKBj1HMY9UcTSYsX3Wan5/0O39Sx5Pg06Dq0WPb7/5gy+++p1CBo9HXO1x/qcVT0byuawVOuAw6LHng2tNc0zc6UsLylBRCCWRjCRwYPbOnNxluqSKkr9ucLDT7Nu0OXWqH63FU+ensprtl2IWnC+157HZs8UXQlW/6wyilBOW8dHfAglMnmBlN85NobpgHbWH3BLyJSjnlk7yrW+NupDIiPCyMtB8a+N5l+r4lY7MOSCw6yXLYBmPQ4MuUqeHlOCiEgigwNDHWizGiBpLOjVBrsudrpzmPW4vc+J//nyaG78dRyHcW8UvU4z7CYdOI4ARM4+bLMZsLHdirQogVJa8X1Wav7PiJLmfDrMeuzf7MJn9vXh8/dtyiUvlCIUz+DCdAgt5tJnuTfGfBjuaan62VjK86qgbIxdjmILgESpHG821IFwIo3Cp6DSsQzE02X/DWNxpgNxPH5iEv+/py/jt//xXfznZy5jciGOR3f1AACc1lvPTjghICPmz1Yzb9Cl3l233YgWs16zAbuCWnC+157H5d5zVgPMYrZKKeWmXOppazmzdqq91mriItQWGKdFj0d39+DE9QWMe6JIi1LN8UyLne529Drw6qgXc6FUzqqWyIgw6XnMh1Owm3ToaDHByHMg5Ja7Tb3hVHKf1ZQaKZzPapMXlO+LpgS4bUbEUkIuw1NNIi2i3Wqs6tlYyvNa+A647EZ88aCc8KAENRt4Dv0dNty1qQ3/9OYNBOIZ7LelMR2I5+5TpBTeaAoSlWuqFbpAlbFsRkvNakLtnksKEtKilBdn+R8f3Y57B1346Wk5W1eQJNACGb1SG3SlYSFa7y6lcuxopRah95prr1kzRVcSJsxWIeXcPmY9v+RAyuXK2qkl6LOSuIjC+IxQIoMnTk1jR68Dv7avD+uz/SZrCXZdzDXa77bi+UtzMKtOx0oNr6fPziAQSyOSFLC5wwZj1jWaEiRs62mBjiO5khuL3Wc15v9ep3lJyQvq76MU2NhuzdVEI9kxsZvkzEqC0mVDtKg18LfcO/Cr2Y0xLUi4GYjjymwET5yazo1VVCfgO8fG8HsPDIAjgCeSwkI0jflIsigLtnAsGbVRKMAJAB3H5dziE/44nrs4jw/u7MaZySAm/HHoOA5EFZW5kqUcqnGzF7671cbIvhdde+o9h5/3gSNYU+3IqqXphBkh5I8B/DYApfnd/00pfWblrqi5WCwo9Lfu68di72slp63lyNppxMmwlAUmlMjg9Ws+vH7NhwNDrprjMxY73dlNOui4fIvLhekQHt3Vg/NT8oYjSBII5JpgsbSAda0W3LvZhWueKFKCmEt6KEe1sXO1JC+ox0f9fSY9j95WMzpbKARRgi+axuRCHDt6ZGthWpQa2iKmksBol92Ix09MlhyfuVASz16YQ6tZj3BSwJ2b2vD02Zm85ufq4PP3miul3hQKcD1P0GLWYUFVZPj5S3O4f4sbXzo0iFdGvLg8F4ZRR+Awr9wGPR2I4/tvTqCjxYRHd/fk1R/8/psT+My+vkWvqVqLUDMXgW0kyhp1/PgNfOPQjvd0O7KmE2ZZ/jul9M9W+iKajUrcPkcvz+OOPidevOwp+TmVnrYanbXTiJPhcqRel20PYzcWuS1CiQyOj3pzG86pyQAACqOOw/be9qLSEzzHgecIOlqKM7gUajH/V5q8oDU+hd/HEQJJkjC5EEcqI6Gv3YL7h9y5UhyVZo5V+wxUk/RQ7jmQKMWLl+fxhfsH8MaYP084A/nB5z2t5kUtNaz+WXkKBThHSJFbPBjLQJAonjo7g3sG2vGb+zfCoONWbINOCSJOTQZwd3+7Zv3BB4bcODUZgNte3MO1kGq8EO9l155Bx0HHEXQ5Sq997wWaVZgxNKhEdFyYDuGz+zeWFWbNctpqxMlwueIzSlkUKajmPU3443gqPoMdvQ786vs2gOcIxr3RkqUnvv7BYbRaysdr1eJyXsr4qL/v9GQQ08E4bEYdHtqWX7QWqDxzrNpnoFLhfWhrZ9n7zIgUN/0JmPR8kXB+Y8yHYCwDPcdh/2YXHtnRVVZgNrr+2XKIvkZ/h5YAN+n5PLe40oZr13oH9g20r7j7KpLIoNViWLT+YCiRhtu++CGvGi9EsxWBZSwvpNkCCLOuzM8CCAN4F8D/QSkNaPy73wHwOwDQ2dm59/HHH1/GqyxPNBqFzWar++cKIsV0MLHov+tpNcEbSRVlNAGyC8FtN0LPr/xpiwLICBI8kRQyogRCSF6dr3LXSpFtLk4BZPtZEiDXALdc43GeI+hymKBbpAemQrXzmcn2Zyw1/h12E7zRVF4j70JaLXp02I2L9ukESo+FFvUYHwpAECVkRCrHyGUkJDJi0WdWOs7q8eI5ArOeh1EvW0pAKYx6PvcZhe9A4b+n2etpMeswH05p3qeNFxAWeAgSRbfDlBPFWp9lUn33YtdeSD3etYwoIZwUkEjL48tzBGaD3I2iXu/wcnwHBbAQSyOaFDR/RymF3aSH06IHV+b51aJh661E4Y0s/p667caK15JqqebdXis0aj6bjYMHD56klN6h9bsVEWaEkBcBdGn86usA3gLgg/wofgNAN6X0c+U+74477qDvvvtu3a+zVo4dO4YHHnig7p/rCSfxrWevlHX7OMx6fPWRrRBEKe+01dNqxuHhDmx0WWHg+WV1t2idxheiKTx/aR7+aBq39znx2qgXF2bC0HME7kUyBBULxbg3ho3tVgx22uC0GmA36eCyGvD0uVk8d3G+5PUoMWZKAdjFrAS1zKfailI4/gDwW//4DvzRTMlFtt1uwHd//U6sb6vvyTgliGVjrwBUFIM3F0rij548X7ZwLUeAbzy2oyK3xHQgjlOTAbRaDDh62YN3ri9ApBIGXDbc1d+WexbU74A6u/TNMV/OzXTvoAufvrsPF6ZDePL0TNF3HbB78UKgHVu7bOhpteD1a9pjobxLpVze9RrLcmNSGEun0OUw1aXI6HJ8h/L+pwQRU4EELs9GcGE6lLeOLeW7GrXeTgXi+N3/9yTmw9pucwDobDHhrz+zt+R7ylzc1dOo+Ww2CCElhdmKuDIppUcq+XeEkL8F8HSDL2fVUI3bx6DjcmZzngCRlIhjVz148vTMsrab0RJRrdnrc5j1OHszhKlAAjt6Hbh30J3L7utymEuWwfjOsTEYdRwObunAsREvHs/GfnQ5TPjwrh4cGe6EN5rCqYlg0d93OUx4ZEcXvJFkQ91PardF4fj/3gMDmAvJ1hyDjisqOAsA0UTpmmJA7Qt+veJX6h0fKLdVAr59dBQzoQScVgP0PEFSkIqC+netd+DszRAODLrxFy+NFrmZXh31IZ4W8dv39ePidBijnmjR961vM+MDO7vxT29NlrymxdzojYxnXI4io8vxHYVuXiPPYZPbio/e3otjVzwIxDNN657jOQKRFpeFUSNSqaS1jLX4YtRK08WYEUK6KaWz2f/8KIALK3k9zUS1m6q6qvp3Xx1f9nYzpUTUTDCBDrsRd1x2VQAAKPJJREFUj+1Zh4/e3ouXr3hyGZPALStDIcpGkkiLeHC4s2hTngkm8a8npzDui+G37+uH22bEW+MLefEZD23rREaky9J+p9T4R1MCNrmsuDQbhihRmA18kThb324u6UZa6oJfj/iVescHpgQRL12Zx0IsDZOGwFSLhCPDnbAadDg24s2bf0DuouC2GeEJp/DqqBefu3cTnr0wm3efNpMOX7xnM/S87LLTEpeVCNRGxjMuRxJLo79DyxqXFCRcnAljPpzC7x0cgM2og8PcnJl3PCEYcNkQSQY16/YZ9RwGXDa5YHQBrMUXYyk0nTAD8F8IIbshuzJvAPjdFb2aJqPaTXWl2nuUElEipUiLFJOBBP7l3Zs4PRnAr+/biKfitxprn70Zwvu3p+TMP5VFKJKQNxKl91zhpgzI1cJvLiTw6qgXn9i7Hvdv6SgKzn/8xOSyjUfh+DvMesTTAh7Z2Q2OEGRECdGUIMdoZeOUjHoOR4Y70WYtFjX1WvCXWg6l3plj1YiEXqcFd/VT/PjUFHQ8B0GSNOuPyf++u+g+L50K5sZoKQK1kfWmliOJpZHfUW7d4QjBQiyN4yPepm4r5LDocVd/G4KJTC5BofBZu6u/rejw8V5rqcSoP00nzCiln1npa2h2qtlUl+PkrYXyvVoiSmmNtBBLwx9P45URL3b0OnIWM28kCX80jb977XqeRejAUAecFj363Vb86B1tF5RSLVzZlAvvyRNOLut4qMdfiYl6Y2wBu9Y7IFKKyYU47CYdulrkEhsGHYfbeh14ZEeXZqX7ei74pcqhVFPlvF6ZY9WKBAPPw2LgsbnDBgqq2bRc+feF93m54B5qFaiNrDe1HEVGG/kdK7Xu1BP14cOg49Bhp3nPWqkyKmvh3hkrS9MJM0ZlVFpjrJINL5SQq6TL9amWHqCaEkSkBQmeaAqbXMUiSraEUTkLjxC8MebD17YM4/VrPiQzIiJJ+YSqbBiKRejCdDhX5FFpdlyIUi281El/ududKN/nMOvzYqLOTNrxuwcG8Po1L94a98MfSWPnOgfe19+GB7dpi5rlWPCrdZPWqxBxtSKBI0Cr2VAXUVFrvb5G1ptajiKjjfyOtdJWSOvwYTXqsKPXgcPZTiuFnS7Wyr0zVg4mzNY4i214yYwIkVJM+GP43hs3lhygqmzsA24bvJEkCAFmggnoeDnInSMEOo4gLVLoeQKRUgRjGZj0PKRsz8L3b5MTdn9z/0Y4LQboeHlz9UfTCCYycJr1cFoNuTIHalrMOuh5UnJTLjceilh0mPUQRFpVa6FSKN9XaDm8MhfBt4+O4N7NLvzhQ1vQbjXAbOAx1GGHxaj9WjZ6wa/VTVqPQsTVioRmqY7eqHpTy1FktJHfsZbaCqkPH2lBgj+WwuWZMP7ypVFQiqK5Xkv3zlgZmDBb42htYBKlEEQKQaK44Y/h/dvlxZnSpQWoqjd2UQL29bsQTQmwm/VYiKZzQe4GHQdRomizGhBPiWi16pHMiMiIFLdvcOKRnV342dkZXJ4NY9wXAwHB+za14fC2DkwtxDHYYcPHbl+Hvz0+nvf9SuA3R0jJTbnUhp7MiLk4kkd2dOW1FloKyvf1tRdbDv3RNF675oPFqMPePif0PIeFeBo8TzQFYSMX/JWOi6lWJDRTdfRGtS9bjiKjjfqOZhHO9cKgk/t6/vUrxaVFCtfLtXbvjOWHCbM1TuEGpggQjhBEUwJazXrsG2jHUwX1nirZiNWxSADFmDeKRFou86D0h7wwE8K9Ay48cWYaaUGCSS+LM6fFgM4WE+bCSTw43InZYAKP7OjCng2tuTpVN3wxxDPy5z1xZhpnp4L48uFB/PDtCXzyrg04NRnAyYlAUeB3uU1Za0NPZkTc8MdyrYXuG3TjJ6emQABYDDwcYu1uXuX75sLJIvfr1i47fvXuPrw17sd/fuYyAvE07tzYhrs2tWluio1c8JshLqZakdBM1dEb1b5sOXrWNuI7mkk414NqDi5r7d4Zyw8TZu8BlA3s2QtzeOrMNEKxDIZ77ehxtGL/Zhe+/8YErvtjeU2bgdIbcVoQMR2U+3KemQwinhFg0evQ6TDi0V09OD4qu+yOj3pxZGsn9vY5cWk2jGueKHiOyNWybXK7oU2udvz6PX2wGXXgOYK/PjaG2VASiYyYE2UK474Yjo/6MOCy4s0xP/74w9vx8/OzODMZRCIjwGE2YOc6Bx7Y0gGjjocnnNQUUqVaCx0Zbsc9m9vxj2/egChJ+NRdfXj9mhebpQQef+IcWs2Gmjb9XqcFIqXo77Dhhk9uYt5pN+Ez9/Thr4+N4eaCXMlex3MIJYSSFstGLvjNEhdTKBIAQM/LFlat+VwO4bLSNLpnbaO+Y7mEM4Wc1NPIIq7VHlya6dDAWH0wYfYewWU3YqjThj98aAtMeh7tVgN+dGIS3z46movVUpo2K5ltWhvxbDCO2VASf/qLqxjzRnPWKpfNiLeu+3FmMogvHRrEU/EZTPjj+PGpKdw/5MZ/+aXb8PJVLy5Oh5ASRDg0RM5cKIFz0yFYjDxmSrSeemvMj4e3y67OR3Z249f3bcSHd8mbclqQcMMfw/dev47pYKJsvJyyoR/e2onZUBKRZAZvXZf7VgLAlw8P4ttHRzDui+ErO0QkMxJCqN3N29liwvu3d+HoZQ8oKO4fdOONa/6cKANuxcfJY6FtsWzUgt9McTHq+m+VJCIsh3BpFlZbJflGC+fpQBwLsTS+9eyVmou4VjKmtRxcKrn31TafjOWBCbP3CKF4Bk+evlUr7DP7+vDqNV9eAH04IaDDTmHUyZtv4UY8HYjjzTE/LsyEMZatpi6IEhaiadiMOnCEYMIfzyt/EUpk8NTZGTjMety50YmvfWAYep6UWKDlxAAdx4EQoKvFBIdZL1fglijCyQzSggSzgS8qhTAdiON/Ha+uiK5Bx0HHc/irV65hKpDAQnYsPrK7B69c9WLcFwMgL8qCKEHSceAIqSneyqjj8dC2TlzzyNauoS47fnJq6tbvVfFxCqUslo3Y7JotLqaZCnQ2y+a5WivJN0o4K8/IkCQUZXBX+oxUOqa1HlzK3ftqnU9G41k79n5GWQpPfOPeGPYNuPL+jVIDTEG9ESsxFi67EW+OFW/ekaQAi4EHBfDGmA/9bmve70OJDNKihM4WE7ocZnS2mIqEBEeADrsJJj2HAbcNgkQx6oni/HQIY94oJAps6ZKb26oXwUrjP9JCcfVujgAWvQ7hxK3mynv6nHhtzAe33YhP7F2H9U4z/u2RIfz6vo3Yv9mVayUViGuX7CiFYu06MOSCzahDIJ6GjufQZjMUuZHlMS3tOlQW/FJjWS2Km7TLob2JLGdczFLms95MB+J4/MQkvvXsFfzRk+fxrWev4PETk5gOFBc3bvR1fOfYGI6PyIcddaLOd46NLfv1rDT1eEaqGVPl4FKOag4ubD4Z5WDCbBWQEkR4wknMhZLwhJNICeX7KGqhnPgULkyH8MCQG33tt05mSg0woHgjVmIsStUQiySFXA9MpfyFmko2dmXxoxSYDiTgi6QgSXKtM0GimA8nsb7NCpuRx92qitvl4j8kSpESJJy4HtAcO4dFj90bWiFItxZwq0GHrhYjvvrwVqSy9d2+8P138SfPXMLkQhyP7uqB06KvKd5KsXZ1OUy4c2MbNnfY0NtqLhovYHHXYT2ei8JrU4Sjw6yXrUNmPQ4MuZbVQlVpPE+1wrhammXzbCah2iws9RmpdkzreXBh88lYDObKbHLqZe7WclUFE2n8ycd2Ip6WN/iLM2EE42kMdNiKPl+xuCUyomYNMUGU4I+msLHdApr9b45g0fgntZtIxwEHt3TgtVEfREqzTYRviZ9+lxV397fh6GUPPnXXhtwiWCr+Q10Cg1KK2VASR6/M512LUcfj8HAnXrg0f8s9K0n4rfsG8N+ev4oJfxx/uJMiI1H4omk8fXYG56eC+IMHh8CXaF68GAYdB7fdiLs2tdXsOmyUG6QZgumbIRFhpcuHqGmGjNlmY6nPSC1jWq/4TjafjMVgwqyJqWecjTqjz6jjcGDQjWMjXvzt8esQJAm9TjPu7m/HR29fh3WtZug13IwtJn3OBfr02Zmi78iIFDzH4chwB4a7W/CNx3aU3di1xMV9Qy585cEh/PTMDF66Mo/5cAotJj32b27HgaEOPHN+BqCAernViv9Ql8AAgHabAbG0dsZjb6sJX314C352dhZvjPmQSIk4Nx3CdDAhu2epkCuOCwAT/jhOXF/A3g3OisZ+sfmoNsOy0fFXKx1M3wyJCM20eTaDUG02lvqM1Dqm9Ti4sPlkLAYTZk1KI07svU4LvnhwQDOrMpWR8Pb4Aib8cc2NXbG4nb0p1yc7PxUsaiLeYtZhfZsZD27rRLvNWPZaSomLH5+chklP8JUjQ7hv0AWeI0ikRYx6ovj5uRnE0yL0PMlbtAqtgUoHAUWUAcA9Ay6Me2OaY2fQ8eh2mLC9pwX3b3FjndOMp8/PwGrQIZqSY88MOg7KEm/Ucxj3RBFJCSWr9FdCLSdwdXP4/Ztd6HdbYdbzSGREjHtjuDAdWvUNkpshEaGZNs9mEKrNxlKfkaWM6VIPLmw+GYvBhFmT0qgTe5vNiF9cnIee57Cly17U/LmU4FNbeI6PevGlQ4N4ZcSLN8Z8CMYycg2z3b14ZEfXotaacqKTALg4HcFPTk6jy2HCT05NIZwQIEhSTkQOum15i1ah9Skj0rxg/r52C+4fcuMplZWvcOy6Wy3YNwC8eHkeNqMOV+ciWN9mQTCeBs8J4LPZokoh27Qo1WVTrvYEHopn4I+m8eiuHhwb8eJH70wiEEvDaTVg34ALj+7qwanJAALx1esGaYYCnc20eTaDUG021M8INEKxFntGVnJM2XwyFoMJsyalUSf2UDyD81MhGMtsauXKNCgWnpeverCx3YrDw9vgtBhgN+nQ5TBXtFmWE516nqDFrMNr17z4gyNDuRIWwK3SHL1DZhRuh+prO3E9AEop2m0G3DPgwv1Dbhwf9eZtsuXcFJ5IEjt6HQgl5M4IRl28SMTWc1Ou9gR+e58Tf3F0NM9i6VfFv33p8CAIVrcbZKULdDbT5tkMQrUZUZ6Rt96YhUPQV/WMrOSYsvlkLAYTZk1Ko07sSxV8ingJxKuPsVAC/VOCBG80BR1H8up2AQBHCFw2I8bmo7AYijMV+9otuGfAhWNXPXhsz7qi4qvqorGxtIBxbwxPnZ0pGsdyborCwHyOEJgK6lbVa1OutkYWTwheHfUWuZEVJvxxvDbqxc7e8qn9q4GVTERots1zpYVqs9LrtKDNasBX79ta9TOykmPK5pNRDibMmpRGndjrIfhqibFQB/p/ZHcPFqJpZCQp199SDc8RbOttgVmvQ7vNgGAsg1arPs/6FYxncP+WjqLrMOg4uFuMOHplvuaxq3RTpqBLagVTS2ZlRpJwcSZc9nMvzISREVe3xUxhJRMRmm3zXIpQbZYiuY2AADU/Iysp/pshA5rRnDBh1qQ06sS+Ei6awkD/MW8Md25qw9NnZxBLCUXFVSUKDLitSAkivvaBYZj0PJLZ4HbF+sURlLTq1WPs1JsyP+8rKv2h5wkePzFZc7mKajIr1ZuqIFLoeQ5Wow6xlFD0uUY9Bz1HgBpdmWt5A6+FZts8l3ooYhXmi1lJ8b/SGdCM5oQJsyamESf25XbRaAX6X5jOz+ws7NHZ4zDh/i0deOLUNEKJDBxmPXb0OtDvtmJ7TwsSGRGzwUTZOmL1GDtlUz5+/Aa+cehW6Q9/NIn//9FrSKTF3HUpmZFvjvmxf7OcTFDNmKhRJ2B4I8m8TfWz92xERpTQ3WpCNCnAF03nJUa4bUa47aaa4t/YBq7Nat48m6m1FYPBqAwmzJqcRpzYl9NFoxXoH0pk8jI7T1xfgChROG1yY/NDWztx5mYAoUQGfe2WXM01dQbig9s6kcyUr4y91LFTrEegcpyZw6IHBcXzl+Zh1HF4cLhTMzOy322Fy2YqqgVXbkwKOXszhINbEkX9P0fmo9ja1YIXLs2hr92KVosBFBQk22dUkCi29bRAxxGkBLFiaxfbwNcezVQkl8FgVA4TZquARpzYl8tFUyrZYMIfx1PxGezodeD+LW4MuG0w6LjcNXAEuDgdxt397fiLl/IzEKMpAacngwjEM4sKhlrHTrEenZ4MYpcujid+dgl7NrTiyHAnCIADg+6i61IyIy/PhvGtj9+GTS6r5mdXkoARSqQRSQpFm6ra2ujLWRo5JDMiZkJJOC0GbO6w4dtHR9HvtlYktNkGvjZppiK5DAajctgq+x6m3o2wtSjs0akmlMjg9Ws+PHVmJnctyjX0Oi343L2b8O7EAqaDSYCgqOF3o3rKKdaj5y/O49JsGClBwtvX/fjuq9fxn352Efdv6cDb1/0lMyPHvFEcLXNd5cZEwazXafb5U1sbDwy6YDXokBZERJIZ7B9w4d/cP4DjI15MBRIV93Rslt6U1VDvPqHNQj3vq5mK5K4m1uqzxVg9MIsZo6EsNdlgKpDA5g5bzl2nLoYLVH7irzSoXbEe3fDFci2dKJUbvNtNOvhjafijaUz443DZjQjGMxDEfAGm4zicmQziw7u0r6uSMdm9oRWj81HN3ynWxp29Dnz2nk0IJTLwRlOapUEqsXattg18rcbC1fu+mqlI7mphrT5bjNUFE2aMhrKUZAOJym7LUsVwJUrhi6aQFiR4wsmSYqvUYvvQtk602Yx5gk2iFKcng3ktnTgCdLYY4Y+lMRNMIJzMYCaYQDIjorPFiIVYGon0rVN1i1mHREZYUtbo4eFOfO/169qDCtlydn46hAe3d+LvXrtedvNdTLyupg18rcbCNeK+Gp2BvdYyeNfqs8VYfTBhxmg4tSYblBMMyYwIbzQFPcdhzBvFU2dmND+v1GI74Y9j3BfH0+dmcXk2khNsn79vE7zRVK6lk47noOMJbvhiiGdk8RWKZ9Bq0WPcF0MglsZGlxXzYgoOsw73b3FjX3877CY9BIkilsogkRYhSPmb12Jj4rYb0e+2YiqQKDmuu9Y7wHNkydauZqpyX461GgvXqPtqZAb2WrMsrdVni7E6YcKMsSzUkmxQSjAkM2LOzfjhXT0Y98Y0T7alFluHWY8Dg278+fNXEUkJuVIdoUQG3kgKJj2PlCCCJwR2kw6CSHOiDAAuzoawe4MTZ26GkMiIWIilccdGJz68qxuvX/PjW89eASHAZrcdG10WvK+/HceueBCIZ/I2r8XGpJJNlSdkydauZqtyX4q1GszeyPtqRAb2WrQsrdVni7E6YcKMsWxUmyGpJRgkSnNuRq3m5OqTbanFdkevA8dG5LZGOp5Dh53CqJOFy7g3ht3rW3FyIgAKwGLkIUj51iZJonjfpja8ctWDG/44bCYdPranF//z5WuY8Mdh1HNY32bB+ekQ3hr3480xP750aBBPnZ0p2rzKjUklm2pKEOti7Wq2KvdarLZYuEpp9H3VMwN7rVqW1uqzxVidMGHGaGoKBYMv67788K4ezebkwK2TLS2x2Pa7rfjRO5MAAEGSQFVV8i9Mh/DpuzfgrTE/Ls2GwZP8duD9Liv2bHDiqbMz+PLhIRwf8cJu0uGVES+mg0m02Qxw2YwIJzK5yvwT/jheGfFiR68Dr1/zVbV5Lbap1tPa1WxV7gtZTbFw1bAc91Wvkjtr1bK0Vp8txuqkOVZcBqMMimD46iNb8c2P7sT/+fAWrG+z4KmzM5olK5STbamyFGY9j0BMLv2g4zgQ3FpsQ4kMjl314usfHMbHb+9Fi1kHjgBuuxEf29OLLx8exE9OTmHSH8fPzs5gfZsFH9rVA28khc0dNvS2msETAl80v7TEG2M+9Ltv1TWrpvzEYmVNFPF6YMgFh1kvx7KZ9Tgw5KrarbQcJVRqRXFtl6MZYuGqZTXd11q1LK2mOWCsfZjFjLEqUASDJ5zEU2dmKjrZFsaoKa2dWi16/Ldf2Y1QIoOrcxHcXIgjkrzVd/LiTBhWA4/P39uPcDKD8XNRfG3bMM5NBfH9tybAEwKTns/VYXtffxuSgpjLHpVAIUj5JTSCsUxeP9B6b17Nbu2qB6slFq5aVtN9rVXL0mqaA8bahwkzxqqimgxCg47LLbZGHZdr7fTcT+cwG0qASsDB4Q48trsXx0e9edY3k4FHu82IdpsRExzB0REP5kIpmPV8Xh21LocJdpMOrWZDbrMikC1x6vpmrVY9kqoEgkZsXqu5p2OlrIZYuFpYLfe1WjJ4a2G1zAFj7cOEGWNVUe3JttdpwRcPDsAbTeH1a37csdGJg1vdyIgUb4/7cXzEi4vTITk4Py5b4go/w6Dj8JUjQyUXbJfdmLdZ6XmCFrMOCyp35j0DLox7Y7n/Xq2bVzOwVq2Dq+G+1rplaTXMAWPtw4QZY9VR7cm23WbEbCiJ2VAS//vUFBZiabhsRryvvx1/+NAQfvj2Tbwy4sWdG51Ii5LmZ1RT2oIjBG6bEbGUoJk9uto3r2ZgrVoHV8N9rXXL0mqYA8bahgkzxqqkmpPtdDCJP/3FVYx55BZHBAT+aBrPnJvFxekQfv/gIF64NIt7B91lA96rKW3BEWD3ulb0d9hw16Y2HLviAQAcGHKtic1rOVhrleXXEsyyxGA0DibMGKuWSk62KUHE0cvzGPOW7jv56qgXQ50t0PNkSRuL1mZlN+oQSwsY7LCxzasK1lpl+UayUgKWWZYYjMbAhBljTROKZ3BmMlgUjK/mjTEfDg9vq0swvtZmZTGy16walqOy/FqxxjEBy2CsPdiOwWgKGrVRShSIZ4SiYHw1wVgGTouBBeM3ActRWT4jSnj8xOSqFzNrsTUSg8FgwozRBDTy1M8RoNVsQCoj5YLxC+l0GGE36ZiLscFUIr4bXVl+OhCHN5LC8Zu3yj2sRjGzVlsjMRgMJswYK0yjT/236i5lsLHdCm80hXBCgCBJ0HEcWsw6PLq7F10Ocz1uh1GCSsV3IyvLK2IGovbfriYxs1ZbIzEYDNaSibGCVHrqTwvasWGVoNRd6nKYYNLz6G01Y3OHDVu67NjcYcPePice2dHV9BvxakYR38dHfAglMqD0lvj+zrExTAduFfYt1UZLTa3FeSsVM5W2ylpJ1mprJAaDwYQZYwVZro1S3UvSaTHArOfQaTfhwW0d+OLBzavCdbVaqVZ8N7Jn4VoSM40UsAwGY2VhrkzGirGcGyWru9R4tGLIIonqXG7VVJavNmFkLYmZtdwaicF4r8OEGWPFWO6GyKzuUuMoFUN2YKgDTkv5OS4U35VUlq8lYSRnjZudKXktq0XMrPXWSAzGexkmzBgrBjv1rw3KJXBcmA7j0d09eOLUdElxpiW+y1k4a00YUcTMK54rmtex2sTMWm+NxGC8V2HCjLFisFP/6mexGDJfNIUT1xewo9eB169pC/BS4lvLwrnUMhG9TgvcdiMOdLjWhJhhLnoGY+3BhBljRWGn/tXNYgkcHCEY90Txa/v6NIVZteK7HmUi9Dy3psQMc9EzGGsLJswYKw479a9eKkngSIsS1jstODC0dCtVvRJGmJhhMBjNChNmjKaAbZSrk0oTOAy6+lipljthhMFgMJYbZpJgMFYBKUGEJ5zEXCgJTziJlCCu9CUBqK7umCK+uxxmdLaYarKINrLOGYPBYDQDzGLGYDQ5jewlulSWO4GDJYwwGIy1DhNmDEYT0+heovVguRM4WMIIg8FYyzBhxmA0KUstDbGcLHcCB0sYYTAYaxUmzBiMJqUepSGWk+VO4GAJIwwGYy3CjpcMRpOylppuMxgMBqMymDBjMJqUtdR0m8FgMBiVwYQZg9GksNIQDAaD8d6DCTMGo0lRSkN0ObTjqFhpCAaDwVh7sOB/BqOJYaUhGAwG470FE2YMRpPDSkMwGAzGewcmzBiMVQArDcFgMBjvDdiRm8FgMBgMBqNJYMKMwWAwGAwGo0lgwozBYDAYDAajSWDCjMFgMBgMBqNJYMKMwWAwGAwGo0lgwozBYDAYDAajSWDCjMFgMBgMBqNJYMKMwWAwGAwGo0lgwozBYDAYDAajSWDCjMFgMBgMBqNJYC2ZGAxGSVKCiFA8A4kCHAEcFj2MOn6lL4vBYDDWLEyYMRgMTaYDcbx4eR5nb4YQTmbQYtJj13oHjgx3otdpWenLYzAYjDUJE2YMBqOI6UAc3zk2hrlQMvezUCKD4yM+jMxH8XsPDDBxxmAwGA2AxZgxGIw8UoKIFy/P54kyNXOhJF68PI+0IC3zlTEYDMbahwkzBoORRyiewdmbobL/5uzNEALx9DJdEYPBYLx3YMKMwWDkIVEgnMyU/TeRZAaU0mW6IgaDwXjvwIQZg8HIgyNAi0lf9t/YTXoQQpbpihgMBuO9AxNmDAYjD4dFzr4sx671DjgthmW6IgaDwXjvwIQZg8HIw6jjcWS4E10Ok+bvuxwmHBnuhEHHlg8Gg8GoN6xcBoPBKKLXacHvPTCQq2MWSWZgZ3XMGAwGo+GsiDAjhHwCwB8DGAZwF6X0XdXvvgbg8wBEAF+mlD63EtfIYLzX6XVa8Km7+vDwjjQopSCEwGkxMEsZg8FgNJCVsphdAPAxAP9L/UNCyDYAnwSwHUAPgBcJIUOUUnH5L5HBYBh0HDpbtF2aDAaDwag/K3L0pZReppRe1fjVRwA8TilNUUqvA7gG4K7lvToGg8FgMBiMlaHZfBK9AG6q/nsq+zMGg8FgMBiMNU/DXJmEkBcBdGn86uuU0p/W4fN/B8DvAEBnZyeOHTu21I+sG9FotKmuh7E02HyuLdh8ri3YfK4t2Hw2UJhRSo/U8GfTANar/ntd9mdan/83AP4GAO644w76wAMP1PB1jeHYsWNoputhLA02n2sLNp9rCzafaws2n83nynwKwCcJIUZCyCYAgwBOrPA1MRgMBoPBYCwLKyLMCCEfJYRMAdgH4OeEkOcAgFJ6EcC/ALgE4BcAfp9lZDIYDAaDwXivsCLlMiilTwB4osTvvgngm8t7RQwGg8FgMBgrT7O5MhkMBoPBYDDeszBhxmAwGAwGg9EkMGHGYDAYDAaD0SQwYcZgMBgMBoPRJDBhxmAwGAwGg9EkMGHGYDAYDAaD0SQQSulKX8OSIYR4AUys9HWocAHwrfRFMOoGm8+1BZvPtQWbz7XFe2U++yilbq1frAlh1mwQQt6llN6x0tfBqA9sPtcWbD7XFmw+1xZsPpkrk8FgMBgMBqNpYMKMwWAwGAwGo0lgwqwx/M1KXwCjrrD5XFuw+VxbsPlcW7zn55PFmDEYDAaDwWA0CcxixmAwGAwGg9EkMGFWJwghnyCEXCSESISQOwp+9zVCyDVCyFVCyPtX6hoZtUMI+WNCyDQh5Ez2/z6w0tfEqA5CyMPZd/AaIeTfr/T1MJYOIeQGIeR89p18d6Wvh1EdhJC/J4R4CCEXVD9rI4S8QAgZzf5/50pe40rAhFn9uADgYwCOq39ICNkG4JMAtgN4GMB3CCH88l8eow78d0rp7uz/PbPSF8OonOw79z8BPAJgG4BPZd9NxurnYPadfE+XWFilfA/yvqjm3wM4SikdBHA0+9/vKZgwqxOU0suU0qsav/oIgMcppSlK6XUA1wDctbxXx2C857kLwDVK6TilNA3gccjvJoPBWCEopccBLBT8+CMA/jH7v/8RwGPLeU3NABNmjacXwE3Vf09lf8ZYfXyREHIua35/z5nXVznsPVybUADPE0JOEkJ+Z6UvhlEXOimls9n/PQegcyUvZiXQrfQFrCYIIS8C6NL41dcppT9d7uth1Jdy8wvgrwB8A/JG8A0A/w3A55bv6hgMhgb3UkqnCSEdAF4ghFzJWmEYawBKKSWEvOdKRzBhVgWU0iM1/Nk0gPWq/16X/Rmjyah0fgkhfwvg6QZfDqO+sPdwDUIpnc7+fw8h5AnILmsmzFY384SQbkrpLCGkG4BnpS9ouWGuzMbzFIBPEkKMhJBNAAYBnFjha2JUSXaBUPgo5GQPxurhHQCDhJBNhBAD5IScp1b4mhhLgBBiJYTYlf8N4CGw93It8BSA38j+798A8J7zRjGLWZ0ghHwUwF8AcAP4OSHkDKX0/ZTSi4SQfwFwCYAA4PcppeJKXiujJv4LIWQ3ZFfmDQC/u6JXw6gKSqlACPkigOcA8AD+nlJ6cYUvi7E0OgE8QQgB5L3sh5TSX6zsJTGqgRDyzwAeAOAihEwB+H8AfAvAvxBCPg9gAsAvr9wVrgys8j+DwWAwGAxGk8BcmQwGg8FgMBhNAhNmDAaDwWAwGE0CE2YMBoPBYDAYTQITZgwGg8FgMBhNAhNmDAaDwWAwGE0CE2YMBmNJEEJEQsgZQsgFQsi/EkIsJf7dGzV+/h2EkG8v4fqiJX7eRQh5nBAylm3p8wwhZKjW72kGCCEPEELuKfG7rYSQNwkhKULIv1vua2MwGJXBhBmDwVgqCUrpbkrpDgBpAF9Q/5IQogMASqmmYFgMSum7lNIvL/0y866JAHgCwDFK6QCldC+Ar2H19+V7AECpcV4A8GUAf7ZsV8NgMKqGCTMGg1FPXgWwOWu5eZUQ8hTk4so5y1X2d8cIIT8mhFwhhPwgK5RACLmTEPIGIeQsIeQEIcSe/fdPZ3//x4SQ72ctP6OEkN/O/txGCDlKCDlFCDlPCPnIItd5EECGUvrXyg8opWcppa8Smf+atQCeJ4T8iuq6XyGE/JQQMk4I+RYh5Ney13meEDKQ/XffI4T8NSHkXULICCHkQ9mfmwgh/5D9t6cJIQezP/8sIeR/E0J+kb2n/6JcEyHkoey9nspaI23Zn98ghPxH1f1uJYRshCyKv5K1YN6nvmFKqYdS+g6ATC0Ty2AwlgdW+Z/BYNSFrGXsEQBK9fXbAeyglF7X+Od7AGwHMAPgdQD7CSEnAPwIwK9QSt8hhLQASGj87W0A7gZgBXCaEPJzyP30PkopDRNCXADeIoQ8RUtX0N4B4GSJ330MwG4AuwC4ALxDCFH6L+4CMAzZ+jQO4LuU0rsIIf8WwJcA/EH2322E3LdxAMDLhJDNAH4fcl/mnYSQrQCeV7lOd2fHJAXgKiHkL7L3/kcAjlBKY4SQrwL4QwD/Kfs3Pkrp7YSQ3wPw7yilv0UI+WsAUUops4oxGKsUJswYDMZSMRNCzmT/96sA/g6yO+1ECVGG7O+mACD7txsBhADMZq06oJSGs78v/NufUkoTABKEkJchC6CfA/gTQsgBABKAXshuybka7udeAP+cbZ02Twh5BcCdAMIA3qGUzmavawzA89m/OQ/ZCqfwL5RSCcAoIWQcwNbs5/5F9t6uEEImACjC7CilNJT93EsA+gC0AtgG4PXsGBgAvKn6jv+d/f8nIYtJBoOxBmDCjMFgLJUEpXS3+gdZIREr8zcp1f8WUd1aVGgFowB+DXKf2r2U0gwh5AYAU5nPuAjgl6r4TgX1dUuq/5aQfw9a11jp5yrjQQC8QCn91CJ/U+34MRiMJobFmDEYjGbhKoBuQsidAJCNL9MSHB/Jxmu1Qw52fweAA4AnK8oOQrY4leMlAEZCyO8oPyCE3JaNy3oVwK8QQnhCiBvAAQAnqryXTxBCuGzcWX/23l6FLCCRdWFuyP68FG9BdvFuzv6NlSyeNRoBYK/yWhkMRhPBhBmDwWgKKKVpAL8C4C8IIWcBvABtq9c5AC9DFi7foJTOAPgBgDsIIecB/DqAK4t8FwXwUQBHiFwu4yKA/wzZ9flE9jvOQhZw/xeltFqX6CRkMfcsgC9QSpMAvgOAy17jjwB8llKaKvUBlFIvgM8C+GdCyDnIbsyti3zvzwB8VCv4n8jlQaYgx6n9ESFkKhvHx2AwmghSOjaWwWAwmgtCyB+jyYPbCSHfA/A0pfTHK30tDAZj9cEsZgwGg8FgMBhNArOYMRgMBoPBYDQJzGLGYDAYDAaD0SQwYcZgMBgMBoPRJDBhxmAwGAwGg9EkMGHGYDAYDAaD0SQwYcZgMBgMBoPRJDBhxmAwGAwGg9Ek/H/woFl8avsgtwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensure that the embeddings are in a suitable array format for PCA\n",
    "embeddings_array = np.array([emb for emb in embeddings if emb is not None])\n",
    "\n",
    "# Standardize the embeddings before applying PCA\n",
    "scaler = StandardScaler()\n",
    "embeddings_scaled = scaler.fit_transform(embeddings_array)\n",
    "\n",
    "# Reduce dimensions to 2D for visualization using PCA\n",
    "pca = PCA(n_components=2)\n",
    "reduced_embeddings = pca.fit_transform(embeddings_scaled)\n",
    "\n",
    "# Plotting the reduced embeddings\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1], alpha=0.7, edgecolors='w', s=100)\n",
    "plt.title('2D Visualization of Document Embeddings')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot illustrates how similar documents are clustered together based on their content, offering a visual confirmation of the effectiveness of the embedding process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieval\n",
    "We employ retrieval techniques to fetch document chunks that are most relevant to user queries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the FAISS retriever\n",
    "retriever = vectorstore.as_retriever(search_type=\"similarity\", search_kwargs={\"k\":5})\n",
    "\n",
    "# Initialize a local LLM for generating responses using HuggingFace pipeline\n",
    "qa_pipeline = pipeline(\"question-answering\", model=\"distilbert-base-uncased-distilled-squad\")\n",
    "\n",
    "# Define a simple function to generate answers using the QA pipeline\n",
    "def generate_answer(question, context):\n",
    "    result = qa_pipeline(question=question, context=context)\n",
    "    return result['answer']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question Answering\n",
    "Finally, we demonstrate how the chatbot responds to various queries by generating context-aware responses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tahaei/.local/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 0.3.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question: What is Natural Language Processing?\n",
      "Answer: providing computers the ability to process data encoded in natural language\n",
      "--------------------------------------------------------------------------------\n",
      "Question: How does machine learning impact data science?\n",
      "Answer: generalizable predictive patterns\n",
      "--------------------------------------------------------------------------------\n",
      "Question: What are the key techniques in artificial intelligence?\n",
      "Answer: search and mathematical optimization, formal logic, artificial neural networks\n",
      "--------------------------------------------------------------------------------\n",
      "Question: What is the future of AI in healthcare?\n",
      "Answer: increase patient care and quality of life\n",
      "--------------------------------------------------------------------------------\n",
      "Question: How does AI contribute to personalizing education?\n",
      "Answer: responsible collection of data and documentation of algorithmic rules\n",
      "--------------------------------------------------------------------------------\n",
      "Question: What are the ethical implications of AI in surveillance?\n",
      "Answer: long-term effects, ethical implications, and risks\n",
      "--------------------------------------------------------------------------------\n",
      "Question: What are the challenges in developing AI for global languages?\n",
      "Answer: to ensure public confidence and trust in the technology\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "questions = [\n",
    "    \"What is Natural Language Processing?\",\n",
    "    \"How does machine learning impact data science?\",\n",
    "    \"What are the key techniques in artificial intelligence?\",\n",
    "    \"What is the future of AI in healthcare?\",\n",
    "    \"How does AI contribute to personalizing education?\",\n",
    "    \"What are the ethical implications of AI in surveillance?\",\n",
    "    \"What are the challenges in developing AI for global languages?\"\n",
    "]\n",
    "\n",
    "# Process each question and generate an answer\n",
    "for question in questions:\n",
    "    # Retrieve relevant document chunks\n",
    "    retrieved_docs = retriever.get_relevant_documents(question)\n",
    "    \n",
    "    # Combine retrieved documents into a single context\n",
    "    combined_context = \" \".join([doc.page_content for doc in retrieved_docs])\n",
    "    \n",
    "    # Generate response\n",
    "    response = generate_answer(question, combined_context)\n",
    "    print(f\"Question: {question}\\nAnswer: {response}\\n{'-'*80}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Widgets for Query Input\n",
    "To enhance user interaction and provide real-time testing capabilities, the following interactive widgets allow you to input a query and generate answers directly within the notebook. This feature is particularly useful for exploring how different queries are handled by the chatbot and to demonstrate the dynamic nature of the retrieval-augmented generation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c187951cc43543b0b0a2c56594cedfdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='What is Natural Language Processing?', description='Query:', layout=Layout(width='80%'), style=Tex…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc5d013994b340eda84cb43dec3afeac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='info', description='Generate Answer', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "602537833b21420a8cc947386cbb34d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_input = widgets.Text(\n",
    "    value='What is Natural Language Processing?',\n",
    "    description='Query:',\n",
    "    layout=Layout(width='80%'),\n",
    "    style={'description_width': 'initial'},\n",
    "    disabled=False\n",
    ")\n",
    "query_button = widgets.Button(description=\"Generate Answer\", button_style='info')\n",
    "\n",
    "output = widgets.Output()\n",
    "\n",
    "def on_button_clicked(b):\n",
    "    with output:\n",
    "        output.clear_output()\n",
    "        question = query_input.value\n",
    "        retrieved_docs = retriever.get_relevant_documents(question)\n",
    "        combined_context = \" \".join([doc.page_content for doc in retrieved_docs])\n",
    "        response = generate_answer(question, combined_context)\n",
    "        print(f\"Question: {question}\\nAnswer: {response}\")\n",
    "\n",
    "query_button.on_click(on_button_clicked)\n",
    "\n",
    "display(query_input, query_button, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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