ann_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "competent-hollywood",
   "metadata": {},
   "source": [
    "<a id=\"index\"></a>\n",
    "\n",
    "# Approxiamte Nearest Neighbors in Information Retrieval\n",
    "\n",
    "Author: Guilherme K. Gomes\n",
    "- guiligomes@gmail.com\n",
    "- https://www.linkedin.com/in/guilherme-klein-gomes-3218b7127/\n",
    "- https://github.com/GuiliGomes\n",
    "\n",
    "\n",
    "\"*Information retrieval (IR) is finding material (usually documents) of\n",
    "an unstructured nature (usually text) that satisfies an information need\n",
    "from within large collections (usually stored on computers).*\"\n",
    "\n",
    "<img src=\"fig/voronoi.png\" style=\"float: right; margin:0px 30px\" width=\"500\"/>\n",
    "\n",
    "Nearest Neighbor Search (NNS) is a fundamental building block in IR and also in various other application domains, such as pattern recognition, data mining, and recommendation\n",
    "systems.\n",
    "\n",
    "Most modern applications have massive data with high dimensionality. For those cases NNS becomes impractical and **Approximate Nearest Neighbor Search (ANNS or ANN)** takes its place.\n",
    "With the advances of Representation Learning and the production of dense vectors with semantically-rich document representations, ANN gained more relevance in high scale similarity search applications.\n",
    "\n",
    "This notebook containts an introduction guide to some of the most important ANN paradigms and algorithms:\n",
    "\n",
    "**ANN Paradigms**:\n",
    "\n",
    "- Local Sensitive Hash (LSH) - TODO\n",
    "- Trees\n",
    "- Product Quatization\n",
    "- Proximity Graphs \n",
    "\n",
    "\n",
    "\n",
    "### Content:\n",
    "\n",
    "- 1 [Environment Preparation](#1-Environment-Preparation)\n",
    " - 1.1 [Models](#Models)\n",
    "- 2 [Sentence Embeddings](#2-Sentence-Embeddings)\n",
    "- 3 [Similarity Metrics](#3-Similarity-Metrics)\n",
    " - 3.1 [Euclidian Distance](#3.1-Euclidian-Distance)\n",
    " - 3.2 [Cosine Similarity](#3.2-Cosine-Similarity)\n",
    " - 3.3 [Manhattan Distance](#3.3-Manhattan-Distance)\n",
    " - 3.4 [Hamming distance](#3.4-Hamming-distance)\n",
    "- 4 [Document Embeddings](#4-Document-Embeddings)\n",
    "- 5 [Word Movers Distance](#5-Word-Movers-Distance)\n",
    " - 5.1 [Models](#5.1-Models)\n",
    " - 5.2 [NNS with GloVe Embeddings](#5.2-NNS-with-GloVe-Embeddings)\n",
    " - 5.3 [NNS with Bert Embeddings](#5.3-NNS-with-Bert-Embeddings)\n",
    "- 6 [ANN - Aproximate Nearest Neighbors](#6-ANN---Aproximate-Nearest-Neighbors)\n",
    "- 7 [ANNOY - Approximate Nearest Neighbor Oh Yeah](#7-ANNOY---Approximate-Nearest-Neighbor-Oh-Yeah)\n",
    "- 8 [Product Quantizers Methods](#8-Product-Quantizers-Methods)\n",
    " - 8.1 [Vector Quantization](#8.1-Vector-Quantization)\n",
    " - 8.2 [Product Quantizers](#8.2-Product-Quantizers)\n",
    " - 8.3 [FAISS](#8.3-FAISS)\n",
    "- 9 [Small World Graphs](#9-Small-World-Graphs)\n",
    " - 9.1 [HNSW](#9.1-HNSW)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "blessed-heating",
   "metadata": {},
   "outputs": [],
   "source": [
    "# std libs\n",
    "from collections import Counter\n",
    "from typing import List, Tuple, Dict\n",
    "from math import sqrt\n",
    "import sys\n",
    "import math\n",
    "import random\n",
    "import pickle\n",
    "\n",
    "# 3rd party\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ml\n",
    "from scipy.spatial.distance import cdist, hamming, cityblock\n",
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "import spacy\n",
    "from wmd import WMD\n",
    "from annoy import AnnoyIndex\n",
    "import faiss\n",
    "import hnswlib\n",
    "\n",
    "# local\n",
    "from utils.plots import plot_embedding_groups, plot_embedding_groups_3d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informed-closure",
   "metadata": {},
   "source": [
    "## 1 Environment Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "double-boards",
   "metadata": {},
   "source": [
    "### 1.1 Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "whole-battlefield",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_md\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "tough-injury",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_projector.bias', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "embed_tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')\n",
    "embed_model = AutoModel.from_pretrained('distilbert-base-uncased')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "union-dealing",
   "metadata": {},
   "source": [
    "### 1.2 Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "vietnamese-miracle",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sentence_embedding(sentence: str) -> np.ndarray:\n",
    "    encoded_input = embed_tokenizer(sentence, return_tensors='pt')\n",
    "    embeddings = embed_model.embeddings(encoded_input['input_ids'])\n",
    "    return embeddings.squeeze(0).mean(dim=0).detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "coordinated-trace",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "def apply_pca(M, k=2):\n",
    "    pca = PCA(n_components=k, random_state=34)\n",
    "    M_reduced = pca.fit_transform(M)\n",
    "    return M_reduced\n",
    "\n",
    "def apply_tsne(M, k=2, svd_solver='exact', pp = 30.0):\n",
    "    M_reduced = TSNE(n_components=k, perplexity=pp, learning_rate=500, random_state=34).fit_transform(M)\n",
    "    return M_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "round-thought",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_plot_data(ids, sentences, embeddings):\n",
    "    \n",
    "    # xy = apply_tsne(embeddings, pp=5.0)\n",
    "    # xyz = apply_tsne(embeddings, k=3, pp=50.0)\n",
    "    xy = apply_pca(embeddings)\n",
    "    xyz = apply_pca(embeddings, k=3)\n",
    "    \n",
    "    dict_to_plot = {}\n",
    "    for idx, item in enumerate(zip(ids, sentences, xy, xyz)):\n",
    "        sentence_id = item[0]\n",
    "        sentence = item[1]\n",
    "        xy_ = item[2]\n",
    "        xyz_ = item[3]\n",
    "        if not sentence_id in dict_to_plot.keys():\n",
    "            dict_to_plot[sentence_id] = {}\n",
    "            dict_to_plot[sentence_id]['sentences'] = [sentence]\n",
    "            dict_to_plot[sentence_id]['xy'] = [xy_]\n",
    "            dict_to_plot[sentence_id]['xyz'] = [xyz_]\n",
    "        else:\n",
    "            dict_to_plot[sentence_id]['sentences'].append(sentence)\n",
    "            dict_to_plot[sentence_id]['xy'].append(xy_)\n",
    "            dict_to_plot[sentence_id]['xyz'].append(xyz_)\n",
    "    return dict_to_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "italic-yugoslavia",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_knn(rank: int, documents: list, doc_idx: int, big_text = True) -> str:\n",
    "    if big_text:\n",
    "        print('-'*40)\n",
    "        print(f\"Rank {rank} Document: {documents[doc_idx][0]}\")\n",
    "        print('Text Snip:')\n",
    "        print(documents[doc_idx-1][1] + ' ' + documents[doc_idx][1] + ' ' + documents[doc_idx+1][1])\n",
    "        print('-'*40)\n",
    "    else:\n",
    "        print('-'*40)\n",
    "        print(f\"Rank {rank} Document: {documents[doc_idx][0]}\")\n",
    "        print(documents[doc_idx][1])\n",
    "        print('-'*40)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggressive-spring",
   "metadata": {},
   "source": [
    "## 2 Sentence Embeddings\n",
    "[Back to Top](#index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "knowing-lancaster",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = [\n",
    "    \"I like rock'n'roll\",\n",
    "    \"They listen to music\",\n",
    "    \"Chuck Berry has an american musican\",\n",
    "    \"She can hear some melody\",\n",
    "    \n",
    "    \"We need the vaccine\",\n",
    "    \"Many people are sick\",\n",
    "    \"A new virus is causing the pandemic\",\n",
    "    \"Now we have the vaccine\",\n",
    "    \n",
    "    \"I like mother nature\",\n",
    "    \"Summer is cool\",\n",
    "    \"We need to protect the ocean\"\n",
    "]\n",
    "colors = ['blueviolet', 'orange', 'turquoise']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "empty-folder",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ids = ['sentences']*len(sentences)\n",
    "embeddings = np.array([get_sentence_embedding(sent) for sent in sentences])\n",
    "plot_data = prepare_plot_data(ids, sentences, embeddings)\n",
    "plot_embedding_groups(plot_data, colors=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "institutional-particular",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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++OOP/PHHH3z99df8/e9/Z82aNRx//PHccccdnHnmmZSWlrJ8+XIALr30UiwWC+vWrWPZsmXMnTuX1157TXutJUuW0KdPH/bu3cstt9zCFVdcgapWh+yLLrqIyspKVq9eze7du7n++usBWLFiBZdffjnPP/88+/bt44orruCUU07B5XLVu13hIkFTCCGEELr0ycpdtSqZNXn9Kh+v3NXqZZnNZlwuF3/88Qcej4eCggJ69OjByy+/zIMPPkiXLl1ITEzk3nvvZfbs2SHTS06fPp3k5GQGDx7MoEGDQqqJwfbu3ctXX33FE088QWpqKh06dOCGG27gvffe0x7TtWtX/vKXv2A2m7nwwgvZvXs3e/fuZffu3cyZM4fnn3+ezMxMrFYr48ePB+DVV1/l8ssv57DDDtOel5iYyM8//1zvdoWLBE0hhBBC6JLD5Wva49ytn1O8Z8+ePPHEEzzwwAN07tyZc889l127drF161ZOP/10srOzyc7O5pBDDsFsNrN3717tuZ06ddL+PyUlhYqKijqXsXXrVjweD126dNFe7+qrr2b//v3aYzp27BjyWgAVFRVs376drKwsMjMz63zdJ598UnvN7Oxstm/fzu7du+vdrnCRoCmEEEIIXUpNNDftcQnh6ft8zjnn8P3337Np0yYUReHOO+8kLy+Pzz//nMLCQu2fw+EgNze30ddTFCXk77y8PBITE9m7d6/2WsXFxfVWQGs+t6ioiJKSkjrvu/POO0PWsby8nLPPPrve7QoXCZpCCCGE0KUpQ3KwmJQGH2MxKUwdktPqZa1bt4558+bhcrlISkoiOTkZk8nEFVdcwfTp09m6dSsA+/fv59NPP23Sa3bs2JEtW7bg91e3M+3cuTPHHHMMt956K2VlZfj9fjZu3MiCBQsafa3OnTtz/PHHc80111BcXIzH4+H7778H4LLLLuPll19m8eLFqKqKw+Hgiy++oLy8vN7tChcJmkIIIYTQpcvGdsNqbjjKWM0mLh1b0OpluVwu7rrrLjp27Ehubi779u3joYce4rrrruPkk09m0qRJ2O12xo4dy+LFi5v0mqeffjoAHTp0YMSIEQC88cYbuN1uBg4cSLt27TjrrLPYs2dPk17v3//+N1arlQEDBtC5c2f++c9/AjB8+HBefPFFrr/+etq1a0efPn20sTvr265wUQI9lepSWVnZcAtbIYQQQogw2bx5M3379m3Wc+oaRxOqK5lWs0nG0QyztWvX0q1bt1q3p6Sk1FlaloqmEEIIIXTryD7t+eLasZw9Io+0RAuKAmmJFs4ekccX146VkBljUtEUQgghRFxoSUVTRJdUNIUQQgghRFyQoCmEEEIIISJCgqYQQggh4oKiKDTUpE/Elt/vrzX2Z2MkaAohhBAiLiQkJFBUVCRhM86oqorb7WbXrl0kJSU167nSGUgIIYQQccHr9bJv3z7cbreEzTiiKApms5nU1FQyMzPrrGrW1xlIgqYQQgghhGgV6XUuhBBCCCGiSoKmEEIIIYSICAmaQgghhBAiIiRoCiGEEEKIiJCgKYQQQgghIkKCphBCCCGEiAgJmkIIIYQQIiIkaAohhBBCiIiQoCmEEEIIISJCgqYQQgghhIgICZpCCCGEECIiJGgKIYQQQoiIkKAphBBCCCEiQoKmEEIIIYSICAmaQgghhBAiIiRoCiGEEEKIiJCgKYQQQgghIkKCphBCCCGEiAgJmkIIIYQQIiIkaAohhBBCiIiQoCmEEEIIISJCgqYQQgghhIgICZpCCCGEECIiJGgKIYQQQoiIkKAphBBCCCEiQoKmEEIIIYSICAmaQgghhBAiIiRoCiGEEEKIiJCgKYQQQgghIkKCphBCCCGEiAgJmkIIIYQQIiIkaAohhBBCiIiQoCmEEEIIISJCgqYQQgghhIgICZpCCCGEECIiJGgKIYQQQoiIkKAphBBCCCEiQoKmEEIIIYSICAmaQgghhBAiIiRoCiGEEEKIiJCgKYQQQgghIkKCphBCCCGEiAgJmkIIIYQQIiIkaAohhBBCiIiQoCmEEEIIISJCgqYQQgghhIgICZpCCCGEECIiJGgKIYQQQoiIkKAphBBCCCEiQoKmEEIIIYSICAmaQgghhBAiIiRoCiGEEEKIiJCgKYQQQgghIkKCphBCCCGEiAgJmkIIIYQQIiIkaAohhBBCiIiQoCmEEEIIISJCgqYQQgghhIgICZpCCCGEECIiJGgKIYQQQoiIkKAphBBCCCEiQoKmEEIIIYSICAmaQgghhBAiIiRoCiGEEEKIiJCgKYQQQgghIkKCphBCCCGEiAgJmkIIIYQQIiIkaAohhBBCiIiQoCmEEEIIISJCgqYQQgghhIgICZpCCCGEECIiJGgKIYQQQoiIkKAphBBCCCEiQoKmEEIIIYSICAmaQgghhBAiIiRoCiGEEEKIiLDEegWEEPqgqip+vx+/3w+A2WwGQFGUBp/X2P1CCCGMS4KmEKIWVVVRVRWfz6eFS1VVtftNJhMmk0l7bE3B4bKu+2s+piX3CyGEiH8SNIVo4xoLlVAd+gL/oLqa2ZQgWF/IDL7v119/ZeDAgdpymvLchpYtAVUIIeKHBE0h2pC6QqXP52Pr1q1069YNqB0qW6Mpr+F0Oms9rqGQWdf9ElCFECI+SdAUwqAaqlSqqhoSJisqKrRL4fGgpaFPAqoQQsQXCZpCGEBwqAz+b+C+4CplzWBkpKAUrYAKsHPnTjp27IjVam3x+hjpvRdCiLpI0BRCZxoKlQHhvPzdFrTkfSosLKR9+/ZNaoda37Kko5QQwugkaAoRx2qGyuDhhQJaGyoltLROJKqoLQ2oja2P7GshRLRJ0BQiTgRCZSBMBv5t2rSJ7t27A5GrVDZ26ViEXyzboRYXF5OZmdnk9ZGAKoRoKQmaQsRAfaEyWCBQlpeXx1VHHZBgGkvhCKh//vknI0aMqPf+mstqaQW1KfcLIYxNgqYQEdacUBmLL2UJAm1DSzuBtaSjlLRDFUIESNAUIowCobLmsELB4rGjjlQoRX1ac5xKO1QhhARNIVpIr6FSiGiJRDvU4uJiioqK6NGjR53LkYAqRHyRoClEE9QVKouLizGZTKSnpwMSKoUIl8Y+Q4GxYeu6vbHn1bccaYcqRGRI0BSihqZWKisrK7FYLGRkZMRoTcNDviSFntQXMiG6A/ZLO1QhmkaCpmjTWnP5W1EUadsohAFEux3q1q1b6dq1q1zmF22CBE3RZtQMlYH/BovHy98SZoWIXy05V+zdu5f8/PwGHxOJy/zxdF4TbYcETWFIwaEyUKUMd6iUiqaQ/S9aKpYD9jd3fSSgitaQoCl0r2aoDAxwnpCQoD0mHiuVTaXHdRYiUhpqo9kWxKIdal33uVwuEhMTpR2qaJQETaErTalU7tu3j/T0dJKSkiK6LlLRFELoRbjboa5atYoRI0bIeKiiURI0RdwKnKCCZ9Np6uXvaJ2kJGgKIYyupb385TK/AAmaIk40JVQCTbr8LZVGIYyrrV861xNphypAgqaIgXCGyliSQCuEEOEXrYDq8/nYuXNnq4aaasr9bZ0ETRFRsQiVEgCFEPFKzk2R09zvEL/fT1FRUbOHmqq5LBmwv2ESNEXYNBQqy8vLKSwspKCgQPtw6f1Dpvf1F0KItizQDCPaA/Y35blGuswvQVO0WHDP78C/+trOKIqC3+/HZDJFfL2iWdGU6oQQ0aX3Npp6X38RKtbtUOO9iRlI0BRN1NRQqYeDXgghhIhl6A9XQNXD960ETVFLJEJlNKuM0VqWtAUVQrSEHsJBW6DH6nJTL73HEwmabVy0KpV6+zALIeKTHsNBML2Eg7ZA78eSXkjQbENqhsqioiKSk5OjNlWjESuaIjzkhC/aEjnWRVsiQdOgAtM0+ny+eiuVBw4coGPHjhGfqhGMe5nZiNskhIgcOWfED/mBGx0SNA2gKaGyrkqlUT9gRg21QggJB/HAKOdXOZaiQ4KmzrQ0VNbFiB10osmI2ySEiCwjhBsjbAMYZzvinQTNONZQqAweaLY1bSqNGJQkAOpLYH/JCV+0FXKsxwc570SHBM04ERwqg/8buC8cobKmaH7AjBj+jLhNQojIMsI5QwKaaA4JmjFQX6jctm0bHTp0ICkpKSqDn0c7KEXzMr3f74/KsqJFTuxCVJPPgggXOZaiQ4JmhNUMlYFL4MECgdLtdqOqalSmaYw2I36YjbhNQojIMkK4McI2gHG2I94ZL9HEUCBIer1e3G43TqeTqqoqnE4nHo8Hr9erHdgmk0n7F1y5jGaF0aidgWSu85YpKipi/PjxjB8/nn79+jFgwADGjx9Pt27dGD16dNiXN2PGDJ5++mkAHnroIebPn1/vYz/77DPWrl0bluWWlJTwyiuvhOW1WmvatGlh2y4hoskIAU2CZnRIRbOFApXKmrPqBIvnaRoDjBSUjCpaJ8KsrCwWLFgAwCOPPEJqairXXHMN27Zt45xzzonosu++++4GK/mff/45kyZNom/fvq1eVmlpKa+88gqXX355q1+rtZ577rlYr4Iu6T0c6H39jfK9IUEzOqSi2QQNVSrdbneTKpVNEe2gadTOQEacGSiS21Pp9vF7oY9v1xXy++4K/HUsy+fzccMNNzBmzBhOO+00qqqqANi0aROnnHIKhx9+OMceeyzr1q2jvLycQw45BI/HA0BZWVnI3zUpisJVV13Fxx9/DMC9997L8OHDGTVqFHfddRc///wzX331Fffccw9jxoxh06ZNdS4X4Morr+TWW29l4sSJDBw4UHvNYH/729/YvHkzY8aM4e6770ZVVe6++25GjhzJYYcdxuzZs2s9Z+vWrQwdOpQrr7ySIUOGcNlll7F06VJOOOEEhgwZwrJly4DQKi3AyJEj2bp1Kw6Hg9NOO43Ro0czcuRIbRmTJk1i+fLlAMydO5dx48YxevRoTjrppKbsujZJ7yFH7+sPEtBE80hFs4ZApbLmsELBotFRJxqk13Tr6f39+35DEW8s3kVpuZeUXTtRFGiXmkCy00tq6v8/btOmTbzyyis89dRTXHrppXz66aecd955XHfddTz11FP07NmTpUuXctNNN/HFF18wbtw45syZw8knn8wHH3zAySefjNVqbXR9CgsL+eyzz1i+fDmKolBSUoLdbmfSpElMmjSJqVOnAnDSSSfVuVyAPXv2MHfuXNavX89ZZ52lPSfg/vvv548//uCnn34C4JNPPuHXX39l0aJFFBYWMn78eMaOHUunTp1Cnrdp0ybeeust+vXrx/jx46moqOCLL77g22+/5fHHH+fdd9+td7u++eYbOnfurAXM0tLSkPv379/Ptddey5w5cygoKKCoqKjR90rol96/O8AY2yCBOTradNBsKFTu27cPr9dLbm5u1EJlLCqaRqsyRntZ0RCp7fl9dwUv/7SDjCQLJCpkpFXPeV9S5WXZxmKm2tO1x3bt2pWBAwcCMHjwYLZv305FRQWLFy/mwgsv1B7ncrkAuOiii3jqqac4+eSTmTVrFs8++2yT1ikjI4OkpCT++te/cvzxxzNp0qRaj2louVAdQk0mE3379mXfvn2NLnPRokWcfvrpmM1mOnTowLhx4/jll1848cQTQx5XUFDAgAEDAOjXrx+9evVCURQGDBjAtm3bGlxG//79ueuuu5g+fTrHH388Y8eODbl/6dKljB07loKCAqC6GYMwJiOcm4ywDSBBM1raTNBsbqUy0F7MqJeXRevpfX99uGovCWYTCRYTVUG3ZyRbcHn9bCtyarclJCRo/282m3G5XPj9fjIyMrTKYLDRo0dz0003sXDhQvx+P/3792/SOlksFr777ju+++47Pv74Y15++WWtUhnQ0HIBEhMTtf8P5/4Jfg8URdEqtCaTCa/Xq61/8HklEIB79erFwoUL+frrr3nwwQc58sgjueOOO8K2bm2JhIPYM8o+MMp2xDtDttEMtKn0+Xy12lS6XK4mtamMRYiQiqb+lqVXflVl3V4HtiRznfebFYVdpc467wuw2Wx07dqVjz76CKj+3P3666/a/eeccw6XXnop559/fpPXq6KigrKyMo477jgefvhh7fXS09MpLy9v0nIbk5aWRkVFhfb36NGj+fDDD/H5fOzfv58ff/yR4cOHN/n1guXn57Nq1SoAVq5cyZYtWwDYvXs3KSkpnH322Vx//fWsXLky5HkjRozgxx9/1B4vl86NS8KNaGt0HzTDESrrEqugadTlGTH86XkYJQUwmRT89bysiorZ1Pjx8dprr/Hmm28yevRoRowYEVJ9POussygpKeH0009veF2CjsOKigrOOOMMRo0axbHHHss//vEPAE477TSefvppxo4dy6ZNmxpcbmOys7MZNWoUI0eO5O6772by5MkMGDBA64Tz4IMP0rFjxya/XrApU6ZQXFzMiBEjeOmll+jZsycAv//+O0ceeSRjxozh4Ycf5rbbbgt5Xvv27fnnP//Jeeedx+jRo7n44otbtHwhosEoYdko2xHvlIa+wCorK+M2GXzxxRf07t2b3NzckNuDK5KtUVhYSEVFBV27dm3V6zTHli1bsNlsUWuftXPnTiwWS4u/VJvD5/Px22+/MXjw4IgvK1r7rqysjH379mlhIlJWr15N//79sVjC29Ll2e+3sXRrKVmpVkpLS8nIyACqT76FDg83HNmVYfkZdT7XZDI1uj4ff/wxX3zxRaNjVv7yyy8MHjw47NsXaatWraJPnz4kJSXFelWabcmSJYwcOTLWq9EiO3bsAKBLly4xXpOWcTgcbN68mUMOOSTWq9JiVVVVbNiwQWu3rVd79+6lqqpKaxutN4EJXuIlLKekpNS5Ivo6swf57rvvSEhIoEuXLhF7k+XSuT6XFS3x8uFuqamDOrB8exmlVV5t3/j8KsWVHgqykxmUm97IK9TvlltuYe7cuXzwwQfhWl0hNHr/7OmdUc7lUtGMDt0GTYvFgs/ni9hBEouDTwZsDw89X9KO5nK62JO457juvLpoB+t2glrpwa/CqAI7Fx2Wg9Xc8pY1jz/+eBjXVAjjMEK4McI2gHG2I97pOmgGenpGghErcDVJG019iOR+6t4uhYdO6sU3i1bQrVcP2qdZyUhueLxLv99PRUUFFouFhIQErFYrZnPdnYqECLfA5UIRWxLQRFPpNmiazWbDBc1YXDqvOcSTEUTrBGiUE62iKLRLVujZPqXO+10uF2VlZZSXl1NeXo7f7yctLQ2TyYTb7cbj8YQcR1arNeRfIIzW/FvCgmir9H7uMErRQCqa0aHboFlzvLpwawtBE6J3woj2h1nPl7RjuRy/34/D4aCsrIyysjIqKytJSEjAZrORmZlJfn4+Foul3s5Aqqri8XhC/rndbqqqqigrK9P+9ng82jZVVlby66+/agG0ZjAN3BZYrhB6ZoSQZpSApvft0Mu66zZoRrqiCbHpDGTk5RmNESqnbrebsrIyXC4Xq1atwufzkZaWRnp6Ovn5+aSkpNS5/Po+G4qikJCQEDK4eWN++eUXevfurYXUQBB1OBwhYTV4rnSTydSkqqnFYpHjXMQdOSbjg96Dpl7oNmhardaIXzqPBaP2Oo8mI3YGCgdVVWtVKy0WCzabDbPZTP/+/Zs0H3m4BcJpc5bt8/lqVU09Hg9OpzPk78A5QlVVLBZLk8Kp2WyWL584pvdwoKdzRn30vg8CpL1vdOg2aJpMJnw+X8Rev61cOo8mI29bPPJ4PFrbyrKyMrxeL6mpqaSnp9OlSxdSU1O1L4vi4uKYhMyWMpvNmM3mJo9hGZh6tq5wWllZGfJ38HmloXDq9XpxuVzSGUq0SUYImiI6dBs0jdjr3MjjaEZTtLYrnpajqiqVlZVatdLhcGCxWEhPT8dms5GTk9Osy9lGoygKFosFi8VCcnJyk56jqiper7dWOHW5XFRUVOB0Otm0aRM+n086Q4kmM0I10CjfG0bYF3qg66BptIpmtMUi2Irw8Hq9WqUy0MkmJSWF9PR0cnJySE1N1U2QidfjQlEULRzWpbKykt69e4dUVVvSGSp4OdIZqmni9ZhpK4wS0IyyHfFOgmY9YlXRNOJwQ9EWzUpjtFRVVVFcXEx5eTkVFRWYTCatWtm5c+c2Xa2MJy3pDOX3++u8pN/UzlD1VUyN2hlK7wUACTfxQ/ZFdOg2aJrNZpxOZ0SXIZfORWMi8f4FqpXBFUtVVcnMzKRjx4706NHDcJWttnwcmkwmEhMTSUxMbPJzmtsZCqrPmYEA6nK52LZtW53hVDpDicZIQBPNodugGY02mtEmQTM89LRdqqridDq1QFlRUYGiKFq1smPHjmzcuJHu3bs3ueOLML7WdoYKdP5qTmeohiqn0hmq6YwQ0oywDWCc7Yh3ug6aRrt0LsIjmuNbNvcY8fl8IdVKp9NJcnIyNpuNDh060L17d/nSFmFXszOUxWKhc+fODT6nsc5QweG0vs5QDYXTllblJRyIcJFjKTp0GzTNZrPhgqZUNMMnHrZLVVVt+sZAtRIgLS0Nm81Gjx49SExMbPREZ+T9JOJXY52h6tLazlD1dYQK/L8RQoERwo0RtgGMsx3xTtdBU4Y3aj0jBphYzdjj9/tDqpVVVVUkJSVhs9lo164d3bp1k2plHSRIG0drO0MFQmigM1Tw34HOUC6XC4vFwp49e9pkZ6h4YYT3Vc9BM/iHWrzTbdCMdBtNaBudgYwqGu+j2+3G6XSyceNGysvLUVVVq1Z269aNpKSksL3HEsSEUTW3M9SWLVtISkrCbrfXCqdOpzPk7/o6QzXW3jTS50a9n3vlfBQfFEXRxbGk66AZyaGA9LDzWksqSU3n9/upqKjQqpWVlZVYrVb8fj9ZWVl07doViyUyH6eWHoszZ85k9uzZmM1mTCYTM2fOZPjw4QwZMoRvv/2W7OzskMfH6tf97t27ufzyy1m6dGnUl60HCxcu5Omnn+aDDz5o8nMGDBjAggULaNeuXQTXLDYC0wYmJSW1uDNUcBitrKwM+TvSnaGMcM7VcyUwmFG2I97pOmjKpfPWM8JJr6ZwvI9ut1trW1leXo7f7yc1NRWbzUbXrl1JTk7G4/Gwfv16MjMzw7Tm4bN06VK+/vpr5s+fT2JiIoWFhbjd7livVlR5vd6IhX+hL+GYGSoQRgOdoYLDaXM6QwW3TxWxJUEzOnR7FpY2muFZnqiuVjocjlrVyvT0dOx2O3l5eTGfB7w5x4VfVdmzZw/Z2dna5cia1ctXXnmF//3vf3g8Hv71r38B4HA4uPPOO1mzZg1er5fbbruNE044odbrP/300/z3v//FbDZzzDHH8MADD/D666/zxhtv4Ha76d69O6+88gopKSlceeWV2Gw2li9fzr59+3jwwQeZOnVqrdf0+Xxcc801LF68mJycHN59912Sk5PZtGkTN998MwcOHCA5OZlnnnmGPn368OWXX/LYY4/hdrvJysritddeo0OHDsyYMYPNmzezefNm8vLyeP3117Vl7Nmzh4suuojy8nK8Xi9PPvkkY8eO5b///S+PP/44qqpy3HHH8eCDDwLQqVMnLrvsMr7++ms6derE3/72N6ZPn86OHTt4+OGHycvLw+v1cvfdd/PDDz/gcrm44ooruPTSS0O2bevWrZxyyimMGDGCxYsXM2zYMM4//3weeughDhw4wKuvvsrw4cNxOBzccsstrFmzBo/Hw5133slJJ50U8lpFRUX89a9/ZcuWLdr7ccghh1BYWMill17Krl27GDlypHa8/P3vfyczM5Np06YBcP/999O+fXuGDx/e5OMp3kQrHLSkM5Tf79fCaXAQDXSGcrvdOBwOXC4XxcXFtZajl85QRgloRtmOeKfboCnDG7WeUbexse1yu91aqAyEjkC1Mi8vj5SUlCadfGLV6aguflXlm3WFfLRqH7vKXCSqHVjz5xaGDR/BhCPHc8oppzB27Fjt8VlZWcyfP5/XXnuN5557jksuuYQnn3ySww8/nGeeeYbS0lKOOeYYxo8fT2pqqva8b775hq+++oq5c+eSkZFBUVERAJMnT+aSSy4B4IEHHuDNN9/kqquuAqoD3ty5c1m/fj1nnXVWnUFz06ZNvPHGGzz77LNceOGFfPLJJ5x99tlcd911PPXUU/Ts2ZOlS5dy00038cUXXzB69GjmzZuHoii88cYbPPnkk/zjH/8AYO3atXz99de1Klfvv/8+Rx99NLfeeis+n4/Kykp2797Nvffey/fff09mZiZTpkzhs88+4+STT8bhcDB+/HgeeughzjnnHB588EE+/fRT1q5dy5VXXsmLL77I22+/TUZGBgsWLMDlcnHMMcdw1FFHUVBQUGv73nrrLfr168f48eN5//33mTt3Ll988QWPP/447777Lo899hjjx4/nhRdeoKSkhCOPPJIJEyaEvM6MGTMYPHgw7777LgsWLOCKK67gp59+4uGHH2b06NHccccdzJkzhzfffBOACy64gPPOO49p06bh9/uZPXs28+fPZ+PGjY0eU6L5TCaT1hkq+HMT7MCBA5SWltKjRw+g+Z2hAsuJ5cxQRgloRtmOeCdBsx5toaLZFqiqGlKtdDgcWCwWbDYbNpuNLl26tKpaGQ/7S1VVnv1+G/PXF5FkNZGVbMHrN9PjL//EvG8d6cpW/vKXvzB9+nTOPfdcAK1SNnjwYD7//HMA5s+fz5w5c3juuecAcDqd7Nixgz59+mjLWrBgAeeeey4pKSlAdWAFWLNmDQ888AClpaU4HA4mTpyoPeekk07CZDLRt29f9u3bV2v9FUUhPz+fQYMGATBkyBC2bdtGRUUFixcv5sILL9Qe63K5ANi1axcXX3wxe/bswe12hwS7E044oc7Lo0OHDmXatGl4PB5OOukkBg0axIIFCzj88MNp3749AGeeeSY//vgjJ598MgkJCRxzzDFAdZvHxMRErFYrAwYMYNu2bQB89913rFmzho8//hiAsrIyNm7cWCtoFhQUMGDAAAD69evHkUceiaIoIa81b948vvzyS/75z39q27p9+/aQ11m0aBGzZs0CYPz48RQVFVFWVsaPP/7I22+/DcDxxx+vNefo2rUrWVlZrFq1in379jFo0CCys7MlaMZQzXDT2pmh9NgZSrQtug6ake51Hm0yjmbreTweLez8+uuveDweUlJSsNls5ObmkpqaariT6Jq9Dhb8WURGsgXTwW2zmhWy0pIoMvWn66AjeWTQIbz77rta0Ax8qQWPR6uqKm+88Qa9evVq9jpcddVVvPPOOwwcOJBZs2axcOFC7b7gL9D6jrfgxwSml/X7/WRkZPDTTz/Vevwtt9zCNddcw4knnsjChQuZMWOGdl8gBNc0btw45syZw5w5c7jqqqu45pprsNls9W5T8KXKQKUq8P+Bc4+qqjz++OMcffTR9b4OEDLcj6Io2vbWfK1Zs2bRu3fvkOfu37+/wdduzEUXXcTbb7/N3r17ueCCC1r1WiI+tHZmqNZ2hqqoqMDv9+N0OnU9M5RUNKNDt0HTZDJFvKIZbRI0m0dVVSorK7VqZUVFBWazmZSUFCwWC3369GnWeH7NFa33r7HlfLOuEBW0kAlQvncbimIiPTuHr9cWMmDHr3Tp0qXB5UyYMIFXXnmFRx55BEVRWL16tVZlDDjyyCN57LHHOOOMM7RL51lZWZSXl9OpUyc8Hg/vv/9+o7PONEWg49VHH33EKaecgqqq/PbbbwwcOJCysjJycnIAtEpeY7Zt20Zubi6XXHIJbrebVatWceONN3Lbbbdx4MABMjMz+eCDD7jyyiubvI4TJkzg1VdfZfz48VitVv78809ycnLqvWzakIkTJ/LSSy/x+OOPoygKq1atYvDgwSGPGTNmDO+//z633347CxcuJDs7G5vNprU1ve222/j666+19n8AJ598Mn//+9/xer1ae1w9M0I4iPb6R6ozVFVVVZ2doRpra9qamaHCyQjHkh7oNmhKRbPt8Xq9IW0r3W63Nn1j586dSU1NxWQy4XK52LBhQ0RDZjwpcniw1jhp+1xVrP7gaTxVFfgVE5Zh/XnyySfrfQ1FUbjpppu49957Ofzww/H7/XTt2pV33nkn5HETJ07k119/5aijjiIxMZFjjz2W++67j+nTpzNhwgTatWvH8OHDKS8vD8u2vfbaa9x44408+uijeDweTj/9dAYOHMidd97JhRdeiN1u54gjjmDr1q2NvlZgmCCr1Upqaiovv/wynTp14v777+fEE0/UOgPV7IDTkPPPP5/du3czbtw4VFWlXbt2td6zprr99tu5/fbbGTVqlPb+1xzS6M477+Svf/0ro0aNIjk5mZdeegmAO+64g0svvZQRI0Zw2GGHkZeXpz0nISGBI444goyMDN1WnoxEL+f4hjpDBXrP5+fnh9zelM5QgdtqzgzV0OX8SHWGkqAZHUpDB31lZWXcfiJWrlzJc889xzPPPBOxZaxYsYJDDz00Yq9fU3l5OXv27GnRpcuWqKysZNu2bfTt2zcqy1uxYgVDhgxp0gdbVVWqqqpCqpUmk4n09HTS09Ox2Wz1tmkKBM1Am7hI8fl8/Pbbb7WqTuG2fv16cnJySEtLq/P+t5bs4qPVe7En1/5CqPL4yEiy8OLZDb8Xq1at4pBDDmlyEAl8OYTDqlWr6NOnT5MvA8aL1atX06tXryZXiGLF7/czbtw43nzzTXr27AnAkiVLGDlyZIzXrGU2btxIRkaGbscI3bt3L1VVVbXa8erJ7t278Xq9IT9oWqq+zlB1/R0Q6AzVWDhtrDPU77//TteuXes9t8azQEiOh8pwQEpKSp1vtlQ040hbvnTu8/lCqpVOp1OrVnbs2JEePXo0+QMVT9sVDo1tzzF9s/nk1324vX4SLP//HvlVlSqPn0tGdYzGaoo4tHbtWs444wxOOukkLWQagVShYiuc59dwdoaqWTWtqzNUcBitrKyktLQUv98vnaEiSLdBM7gTg1EYLSDVFLx9TqczpFoJaNXKDh06kJiYKB/2JupkS+T6I/N5+rttVHp8WE0mPKofVDiyZyZH98lu/EViyOjHfSz17duXX3/9NdarEVZGOFb0fm6L9SXncHWG8vv92vdQQ52hGqucSpOUhuk2aBqxohlt0fqC9/l8WuPxNWvW4HQ6SUpKwmaz0b59e7p16xbWD2q8dNKJpsN7ZNGrfSpz1xay4UAl2SlWju6TTb9OTetlH0/bIoSRyecs+urrDLV//35tpreaWjMzlF46Q0WLroOmVDTjb3mqquJyubRL4OXl5aiqSnp6OoqiUFBQ0OQB0UW1pu6nTrZELhiZE4U1EkK0ZbGuaIZTfdsRqZmh4qkzVLRI0GyADNjeOL/fT0VFhRYsKysrSUxMxGazkZ2dTdeuXbX5plevXh2VS+LRrGgKIaJL7yFH7+sP1dtghIpcuL8nmjIzVE31dYaqWTWtqzNU9+7dycjICOs2RIJug2ak5zqPBT1UNF0uV0inHb/fT1paGunp6dolCL2fRJtDbz8MhBBCVIuH0N/SzlButztsI39EmgTNBsT6AIyGhoKS3+/H4XCEVCutVis2m43MzEzy8/O1amVTtMW2k+EQje0x2nsmRLyKh3DTWkbYBtDvdgQ6Qull3XUbNC0WS0gDXCOIRUUzmNvt1qqVZWVl+Hw+rVqZl5cnbStrkPciPCTkiuaSz17sGWUfGGU74pmug6ZcOm+5wPSNTqeTdevW4XA4sFgs2Gw2MjIy6NKlS9jL8tJ2UgjRWkb4UaL3c5QR9gHot6KpN7oNmkZtoxkpHo8npFrp9Xq1IR1yc3NJTW3aMDitEc0PtFFOhCAVPyGMxAifZaMENCPsCz3QbdC0Wq2GG94IwnPgB6qVgbaVFRUVWCwWberGnJwcEhIStCkUozn9llQ045OEWaEXej9O9b7+RmKUwBzvdBs0ozWOZjQPxJZ+2Xu93pBqpcfjISUlhfT0dDp37kxqamqdQ1HooZd7SxntZG607RGiNfQeDvS+/kYJaEbZjnin26BpMpki3hkoEIziKWiqqqoN/hqoVppMJq1a2blzZxISEpq8PBH/ZD8JYSx6/0wbKaAZZTvimW6DJhivylPXAR+oVgYqli6Xi5SUFGw2Gx07dqRHjx6tGjjXiBVNOXHoi1y2F22JHOvxQ/ZFdOg6aEZatL8AVVXF5/Oxd+9eysrKqKioQFEUrVrZsWPHZg3q2hgJZCJAwp7QCyNV0/TKSPvAKNsRz3QdNPU+laHP5wupVjqdTlwuFx6Phw4dOtC9e3fMZnPElh9tEmZaRt43IYzDKCHNCNsgokPXQTPSwvkFr6oqLpdL67BTUVEBQFpaGjabjR49epCYmMjKlSvp0qVLWJYZjyQwCSGEvsl5XDSHBM0GtCZo+v3+kGplVVUVSUlJ2Gw22rVrR7du3QxVrWwK+QUcv2K5b+S4EM2l52PGCBVNI2yDiB4Jmg1ozgcpuFpZXl6OqqpatbJbt266mpc0Utr69rdGNCoIsaxSSIVENJUcK0Loi26DZiC0RPqXVV0nNb/fT0VFhVatrKysJDExEZvNRlZWFl27dsVi0e1bG1HyJdF88RbQVVXVJgGwWq0kJCS0ueq8nslnMPbi7TPdXFLRFM0haagBgUvnbrc7pFrp9/tJTU3FZrPRtWtXkpOT5UPXBNKpRZ8CP6zKysooLS3VhthKSEjA7Xbjdru1MW0VRSEhIUELoIF/wX9brdZWDcklhJ4Z4RxohG0Q0SNBswa/34/D4dAGQ//tt99ITEwkPT0du91OXl4eVqs11qsp2pBoB/RA++LS0lJKS0vxeDykpaWRkZFBjx49SEpKAqhzYgC/34/b7cbj8Wgh1O12U1FREXJ7YHtMJhNOp1P78VYzkAb+Kz/kRIBU0+KD7APRVG0+aHo8npBqpdfr1aqVKSkpdOvWjdTU1FivpiFIRTM++Xw+PB4PO3bsoLKyEp/PpwXL5o7dajKZSEpK0sJoU5b922+/0b59eywWC263W5v5KhBSPR6P9niLxdJoxdRsNsuXoIhbRgjKRtgGET1tKmiqqqpVK8vKynA4HFgsFmw2GzabjS5duoRUK8vKymK4tkJEhtfr1S6Dl5WVoaoqHo+H9u3bk5+f3+QpTMPBbDZjtVpJS0sjLS2twceqqorX6623Whr45/P5tOfUddm+5v+3xfalEhJiS95/0ZYYOmh6PB4tVJaXl+PxeLTpG3Nzc0lNTZUPfBRJRbNlWvu+Bar2gWAJYLPZyMjI0H5crVmzhszMzKiGzGBN2T5FUbBarVitVlJSUpr0msGhNPD/paWlIX/XbF/aWDiV9qWxp+fzthHOgVLRFM2h+6AZOOBVVaWysjKkWmkymbDZbKSnp5OTk9PsL1EJRuEl72d0BMJUaWkp5eXlmEwmMjIyyMzMJD8/v82MiBAcHJuirvalLpcrpGLq8Xi0Y9jpdLJ27VqSk5PrvZwfj+1L9f4Z1Pv6G0W8HddtkV72gW6/ccrKylAUhXvuuYfff/+de+65Rxu3snPnzqSmpra68iDBSOiBy+XSgmVFRQVms5mMjIxmTQwgx3rz25euWrWK/Px8TCaTFkKrqqq0DlTSvlTUxQjVwLZ+rhDNo6ug+fnnn/O///2PlStXkpiYSElJCX369OGKK66goKAg7MuTL9/wkvezZYLfN1VVcTqd2mXwiooKEhIStI47PXr0kEu7UaIoCklJSSQnJzf62ED70poV00CTnua2Lw383Rbbl4rYM0JYFtGjq6BptVo577zzeOyxx0hKSmLixImcfvrpEesVLsFIxFpgHNfKykqKioq0yQEyMjLo3LkzaWlpcsLXgeD2pU3h9/u1YBp82b657UtrDhNlhB8heg85el9/IZorKkHT6XRy7LHH4nK58Pl8TJ06lXvuuafZr3PccceF/G2xWEIqAOEmQTO85P1sXKCtcUlJiTbrFEBaWhp5eXmkpKRE7EsqVvtGvnRrM5lMLWpfGhxC62tfqqoqVVVVrF69ut5L+AkJCVgsFtk3ok4SlkVzRCVoJiYm8uWXX5KWlobH4+Hoo4/m2GOPZeTIka16XbPZjNfrDdNa1k2CkYikwHSOgTaWTqeT1NRUMjIytFmndu3ahcViieh4rrH+0pDPWes0p32pz+fjl19+oVevXiEhtCntSxsbJirWx5Fe6P19MkLQlHNO9EQlaCqKoo2R5/F48Hg8YTlIo1HRFOEjFc3/n84xECzdbrfWia179+4kJSXJcacTet1PiqJgMplITk5udvvS4Ippfe1LA80EGhsmqqXtS/Uectr6OVC0PVFro+nz+Rg7diybNm3iiiuuYMSIEa1+TYvFEtGKZlsJRno/ccczn88XMp2j1+slPT2djIwMevXq1axZd4QIh+ae04Lblzalqu73+7WCQnA4raysDPk70L7UZDI1Oqi+UdqXGoURvjOMsA16EbWgaTab+fnnnykpKeGcc87h999/Z8CAAa1+TSO20TTqB6AtBHefz6cNjl5aWorf79eCZefOnVs8ILrR3zdhHCaTicTExCb/iApMgVqzfWlwxbTm+KXr1q3Txi/VW/tSo5zf9b4NRtkPehD1Xud2u50jjjiCuXPnhiVoGq2iGVhmtD4A0VyeEYOmqqoUFhbWOetObm5uk3sZN0T2jTAys9mM2WxuUvtSVVVZvXp1yPilbrdba18aCKXB7UsD1VhpXxo+RjhXSNCMnqgEzf3792O1WrHb7VRVVTFv3jxuuummVr9upC+dQ/Q/UPKFH9/cbnfIdI5VVVWUlZVht9vb1Kw74SQne9FUiqK0ePzS4Mv25eXlIRXUaLUvDayT3o95I2yDiJ6ofCvu2bOHK664Ap/Ph9/v57TTTmPSpEmtfl2LxaK184mEtvBBimaw1WOIrm/WnaysLAoKCli9ejUFBQURP1b09r41l9G3T8RGS9uX1hxYvyXtS4OnIZX2pfFHwnL0RCVoDhw4kEWLFoX9dY3YGSjay9Rj+GuqlpxIArPuBIKl1WolIyODDh06xGzWnWhdOheirWtN+9JACG2ofanZbMblcuFyuSgqKqozoMZz+9IAI4Q0I2yDXuj6Op/JZAppixNubSFoRlO8bVtg4OrApXCHw6HNutOpUyfS0tLaVCUinvaNEPWJp4DQ3PalPp+PtWvXkpmZSUJCQq1qqdvtDime1GxfWt9l/Fi8H/GyD1oqno4jo9N10DTizEBS0QyPuk4ggVl3AhXLyspKkpOTtY47qampzT7xRKszlRH3kYgd+YKNPkVRsFgsmM1m0tPTsdlsDT6+Ne1LG2pjGo4fz0Y4H0nQjB4Jmo0wwgeqIUZuo+n3+7XeqKWlpVRVVZGSkkJGRgb5+fkRnc4xnIz6Y0DEhhxL+hCJ9qVut1vb/4FpThsKp/W1L5WQJppD10HTyMMbGVUkty141p2qqipWrlxJWloaGRkZdOvWLSKz7sjJtvXkPRTNpfdjJhLrH872pcFhNcBsNmsh1Ol0smvXLpKSknTXvjRAwnL0SNBsQFsImtGuaIaT3+8PmXXH4/FowTIpKYlBgwa1ahiSporG+xfpZcT6B45ef1zpdb31TO/vebysf0valwYCaFFREYqi4HA4QkJpzfalTRkmKlZhT4Jm9Og6aBqxjWa0RTtotmY4quBZd8rKyvD5fNqsOx07dgz5Jb93795wrHJckJOhECKWAu1LA+MEWywWcnNz623vWVf7UrfbjdPpDKmgxqJ9afA6yrk1OiRoNqAtVDTjmdfrDQmWqqpqwTInJ6fR6Ryj8T7K/hJCNEdbCDitaV8aXCF1OBwhtze1fWng74be57awH+KF7oOmXDrXz/IaW5bH49FCZVlZGYqiaNM55uXlNWvWHSMFwGidDI3yfgljk4AQe+HeBy1pX1qz01NT25cGAqnf79c6S1mtVl21L9UbXQdNs9lsuF7nsQhIsQoYbrdba19ZXl6OyWQiIyODzMxMunbtGpX2la0Vrf0VjTaasSQhV7QVRgnKsdwGs9lMcnJyk6chDQTT4BBaWVmJ0+lk06ZNTWpfWvNvPXw/xQvdB81IVzSjLdrLjObyvF4v5eXl/Pnnn5SXl2O1WrHZbLRr145u3bqF9YNrpIqm0RnhS1dv5D0XbUVw+9KUlBTt9kBzrH79+oU8XlXVOoeJKisrC2lvGuhvULN9aX2X9NvS5B816TpoGrGNJkS3uhOpbVRVNWQ6R4fDAUBCQgI5OTkxm85RryQ0i3AxwrGk56BslIqm3tW3HxRF0UJiUzTUvjT475rtSxvrkW+kY0SCZgPaQhvNcAlM5xgcLAOz7uTk5JCWlsaBAwdwOp2NzogRDtH6kEZjf0kbTSH+nxynIp6Eo31pYCrk4LAaYLFY6gykKSkppKenR2qzwkrXQVPG0Yzd8lRVxeFw1DnrTl5eXp2z7kR72+QLqemM9OtZiHgmFc34EKv90Jr2pYEQ6nA4tOH99EDXQTPSvc7B+GGlqeFPVVVt1p3S0lJcLpcWLAsKCkhOTo6rk2c0K5rRYPTjUIjmiKdzjdAnPQT++tqX6o3ug2akL51HW7xcOg+edSdQ0g/MutOjRw8SExOb/f4YtaJplEvnsRIvx3xboudjygjHip7ff6PQQ9A0Ct0HTZfLFbHXb0uXzn0+X8h0jj6fTwuWNWfdEf/PKCcqCXtCRId8zuKDBM3o0XXQlDaaLReYdae8vLzW4OidO3duco+75oinweHDyQjjaAohhBCRoOugacSZgSLF4/GETOcIYLPZSEhIoFOnTmRnZ0d8HeTXY8vI+ybE/9N7JUrv628Ush+iR/dBMzBoaiTouaIZGGA2ECyDZ93Jz8/XpnPctGlTVGc4MFpF00g/RkTbIMerEBI0o0nXQdNsNoeMNxVuegqaLpdLa19ZUVGB2WwmIyOD7OxsCgoK6g2TRu2gYzTR6HAk+0aIyJOAEx9kP0SP7oNmpOc6j0eqqtYKlgkJCdhsNjp06BC3s+5E80NtpOAUjyfDcL63RtpXIjri8TMh9EWCZvToOmi2lZmBas66U1lZSWJiotZxJy0trcUfGKN20IkWI26TEPHMCJ83CTiiLZGg2YBYBU2/3x8y605lZaU2nWOXLl1ITU0N64nKCCfumiQARpZ8Ueqb7L/YkfNSfJCKZvToPmgaodd5YNadsrIy9u3bh9frJSMjg4yMDLp27RrRWXfkcnbrRGuu83gcQklO1EI0n3xu4oOe90PgXB2PTeTqouugGek2mpH6gvf7/SHTObrdblJTU7XOO8nJyXTs2DHsy62LUTsDGTHURlJzTrgej4eSkhL8fj+JiYkkJCRo/43mCAaiZfT+udBzQBDxRc/HkZ7WXddBMxoVzXDw+/0hQw15PB7S09PJyMigZ8+eJCUlaY/duXNnWJbZHNEMf0ZjxG2qyefzUVZWRklJCaWlpSiKgt1uJzk5GYfDQVFREW63G7fbrQ03ZjKZSEhICAmhNf9JKBVtVVs4b8Q7vf/g0hPdB81I9zpvycEY+GIOVCz9fr8WLBubdScWU1AadVlGmRkoFlXn8vJySkpKKCkpwefzkZGRgd1uJy8vTxuD1Wq11rtPfT4fbrcbl8ulhdDy8vKQ21RVRVVVrUJ64MCBekOpXi4RCdEYCTjxQSrj0aProBkvU1AGpnMMVCxVVcVms2Gz2cjNzcVqtYZ9meFi1EvnoukCYa+wsJA9e/bgcrlIS0vDbrfTt2/fFk1HajabSU5OJjk5udFlb9iwgZSUFFJSUrRQGmhSEvgXOG4sFkujlVL58jA+ve9jva+/EUjQjB7dB81YtNH0eDxatbK8vDxknvDgio8IZcShlPTaFtTlcmkVy4qKCqB6StLu3buTlJQUtROwoiiYTCYSExPJzMxs8LGqquL1emtVSh0OR0goDbBarSEBtGYwbagiK0Sk6PF8YUSqqsqVkijRdSKKVq/zQIUlECzNZjM2m42srKwGZ91p6TIjOa1mXcuTE198C8c+8nq9WrAsKyvDarVit9vJzc0lNTWV7du3k5KS0mgFMpYURcFqtWK1WklNTW3wsYEqbSB8ulwuXC6Xdvne7XaHzCpWV2U0OJhaLBYJpUIYjHymo0P3QTMSFU2n00lpaalW7Vm3bh0ZGRm0b9+e7t27R/RXkJEvnRt1WfEo0AEtEC4VRSEjI4N27dpF/BiOB4qiaCGxMX6/PySUut1unE4nZWVl2t+BH7TBr1tRUcHu3btJS0ur1fM+3r/A4n39jEzvl2yNcl41ynboQZsPmqqqasGytLQUh8NBQkICGRkZdOrUicrKSgYOHBimNW6cnk9AbVG8jHEZGIs1ECy9Xi/p6elkZmbSpUuXRptztOVgHrh0n5iY2Ohj/X5/yCV7k8lEVVUVJSUl2u2Bc5KiKHW2Iw2+TXreC73Re1AOMMp26IGug2ZLOgOpqkplZWWds+4ELiMGDr5YffFKRVNfy4q0uk6GgR9IxcXFlJSUUFVVpXXg6d27d5NCU7zQ074ymUwkJSWRlJREYmIiHTt2JCUlpc7HBnreB/+rqKhocDiohoJpuKrQenmvjUwCTuxJ0IweXQfNprTRVFU1ZDrHqqoqUlJSyMjIID8/n5SUlHoPtlgchLG4dB6tNqF6ChRNFc1tcrvdIR14kpKSsNvtFBQURHT2KNEyTe15D2idnIL/lZeXh3R6ChxnZrO50Z73Rm8aIWLHSAHNKNsR73QfNGuGpMCsO4Hhhlwulzbrjh6+kI0YxmIhnvdxUwWGzdq7dy8lJSVUVlZit9vp1KkT6enphthGUc1isWCxWOqtjgaoqlrnGKWB4aBcLhcej6fWcFDBgRSqjy2XyyXDQYkWMcIxY6TAHO90HzS9Xi9z586lsLCQAQMG4PF4SEtLIyMjgx49eoTMuqMH0hkofPQ2vJHf7w8ZKF1VVW3Oe7PZTO/evcOynLrIDxx9UBSlWaE0ECiDK6WVlZVUVVWxZs2aeoeDqqtSKsNBCTBO0wujbIce6C5oOhwOlixZwo8//sj8+fPZsmUL7733HieccILu2qbFA6MGDD1sV6BZRyBYut1u0tPTsdvt5OTkaAP9l5eX43A4Yry2Qm+Ch4MK5nQ6cblcDB48WLsteDioQDBtznBQNYOpDAdlXEapBBplO/RAV0HT5XIxefJkhg4dyrhx4zjjjDO48cYbefXVV2O9amGjh4DUUkbetqZyOp1asHQ4HKSmpmK322vNeS9ENAUP25SWltbgY2sOB+VyuaiqqgqZzamu4aAa6nkvX/j6YoT9JUEzeqIWNHfs2MHll1/Ovn37UBSFSy65hGnTpjXrNRITE/n222+1vwPzMEdStIORkS+dR1O8zAwUmMe7pKSE8vJyEhMTtTnDG+qI1pxl6J3Rty8eteYLtqXDQQVfuq9rOKhAz/uGKqUyHFTsGemzKkEzOqIWNM1mMzNmzODQQw+lvLyccePGcdRRR9GvX78Wv2akZwZqK4zaRjMWfD6fNlB6aWkpiqJgt9vp0KEDPXr0iNvewG1h34joCx4OqjE1h4Oqeek+MByUw+Fg2bJljY5RGq+fNb0zSiVQznfRE7Wg2blzZzp37gxAeno6ffr0YdeuXa0OmpGuaEb7AxWLimY0GTHUVlZWap14fD4fGRkZWtVS5r0X8SZev2CbOhzUkiVLGDJkSK1KafBMToHhoFRVrbPnfXAwtVqtEkrbIKMEZj2Iybfg1q1bWbVqFSNGjGjV6xixomnkS+dG+FAHBvwPvhyemppKx44d6du3b5OmPGwuqTbGJyMcz3rV3OGgava8Dwx9F+jkFDwcVH1jkwb+tfX9bpSAZpTt0IOoB82KigrOPfdcHn30UWw2W6tey2QyRW2w8WgxetDU47JcLlfIQOkpKSnY7Xa6d+/Onj17yMjIICsrKyzLaovkZC8iJXg4qNTU1AYfW99wUA6HI2SM0oDAcFD1VUqN3PPeCNslQTN6oho0PR4P5557LmeddRZTpkxp9evJQSIiwev1asGyrKwMq9WK3W6vNUUpVP/YMUq1MZbbYZT3UOhXfcNB1SUwHFRwKK1vOKjA6wZCqNvtZu/evbodDspIn1W9vOd6F7WgqaoqV199NX369OG6664L6+saiVQ0w7espvL7/VoHnpKSEhRFISMjg3bt2tG9e/eYt9+KxvsmJ1whmi542KbGBA8H5XK52Lt3b8hwUC6XS+trUHM4qLqqpfHQ7tsI5wujZYd4FrUjdtGiRbzzzjsMGDCAUaNGAXDfffdx/PHHR2sVdEHa44VPfe+jqqpUVFRowdLr9ZKenk5mZiZdunRp1olc9pcQoiHBw0Glp6ezadMmCgoK6nxszeGgXC4XDoeD4uLiuBkOyijnO7l0Hj1RC5pjxoyR2U2awMi93GMVylRV1QZKLy4upqqqirS0NOx2u8wmJdok+YKNT5EYDgqqe/Q3FkqbeuXGKAHNKNuhB7GvwbdSNA6UaB+QRr10Hk0+nw+Hw0FpaSkVFRUkJSVht9spKCggOTlZVyeYaF06N+JxIGqT/WwMTR0OCqrbndcMpcEzOQWGgwK04aDqC6VGCmhG2Y54p/ugabSTZiy+8OOx3WRzeb1eysrKKC4upqysDJ/PR0pKCl26dCE9PT1iy5aA1npyshcispozHFTNUOp2u0Mu3TudTlwuF0uWLAnp5FRXMLVarXH7+TZSYI53ug6agYMkkgdMIEhE64A0+oDt4eL3+7VB0ktKSlBVVRtmqKCggH379gG0eggtER0S1oWIveCe9/UNB1VeXs727dvp169fvcNBBY9RGmC1WusdozQWw0HJOSd6dB00o8HIvcBjsbyWCgyUXlxcrM2TnJ6ejt1uJycnp0lDkkSCkS5r6+E4EELEh+YOB1WzShrcptTlcmmTrwRet6EpRs1mc6tDqVQ0o0f3QTPSB4pegpgRBTrwlJSU4HA4SE1NxW6307Nnz0YbyyuKYrjB/CNJjnMhRFO0JKApiqL1vG9MYDio4EppYDiowG11DQdVX7W0vlFEJGhGj+6DplGqSW1leQ3xeDwhUzsmJCSQmZlJXl4eKSkpcXlSiMd1EtETL58dIaIpkue94OGgGhM8HFQghAYPB+VyubSCQ83hoJxOJ0VFRaSmpkZ0OChhgKAZDUYOfrEMmj6fTxsovbS0FEVRsNvtdOjQgR49erRqoPRobpfRfuwI45MfSKKl4ulc1JrhoPbt24fD4aCsrKzB4aDqq5TGeiIPPZGg2Qg5IYePqqr4fD62bdtGSUkJPp+PjIwM7HY7eXl5cTHjRVsWqy8Q+YwJoR96veRcczioHTt20LVr11qzOwW+p2pWSps6HFTNYGq1Wtt8KNX9N7vR2mgaaXmqqlJVVaV14HG5XHg8HpKSkujbt2+Tpm9rqWi9j0apNsb6i8MI76FeyHstRP2BWVGUVg0HFZjNKfi2gLqGgwoOpvE8HFRrGCJo+v3+iLWtMFLwi8byXC6X1s6yoqKClJQU7HY73bt3JykpiZUrV9KhQ4ewLa8tMEqYFULon14rmjWFYzuaMhxU8PICc94H/6uoqND+P3g4qLou1weCaVN7+8cL3QdNi8WC1+uVoBkjXq9XC5ZlZWVYrVbsdju5ubmkpqbG7ISkt/dRCCH0wChBM9qCe8g3pqHhoAKX8ocMGRL5lQ4TwwTNSM5XbeTA0txA5vf7tQ48JSUlKIpCRkYG7dq1o3v37m2uLYoEWiGE0J94DsyNDQcVz+teF90HTbPZrI2pFQnR3pnxNjOQqqpUVFRowdLr9ZKenk5mZiZdunSJ2w48RguA0rNdhJOevqREfNFbyKmP3rdDT+senymhGaIRNI1+6Tx4eaqqagOlFxcXU1VVRVpaGna7nd69e0e0cqxH0RgYXk8nlJYw+vYJYTRG+MzqPWjqie6DZuDSeaTEW4UxEsvz+Xzs27dP68CTlJSE3W6noKCA5ORkXX4YpUKnL7Kvokfe69gxwntvhG0Q0SVBsxFGDCxer5eysjKKi4u1ab2cTiedOnUiPT1dl8EyVmQeciFEW2KkSqBRtiPe6T5oms3miF66NELQ9Pv9lJeXa+0sVVUlIyODrKwscnNz+fPPP8nPz4/1aoaVEfZbQDROhkZ6v4SIV0YKaUI0lSGCZiQrmqC/apKqqlRWVmoDpbvdbtLT07Hb7eTk5ISMv+X1enW3fUIIIWJDwrJoLt0HzWhcOteDQAeekpISHA4Hqamp2O12evbs2eg8sEYMmkaaGSheq43yhSNE8+n9MyOfe9FchgiaRup13lQej0cLluXl5SQkJJCZmUleXh4pKSlNPhHICUMEyFznQkRWPH6XCBFpEjQbES9B0+fzaQOll5aWoigKdrudDh060KNHjxYPlB4v2xduRt2uSGlq2HO73RQXF1NUVITL5dIGFQ5MjRb8/1artcnHpeyr6JJwHzt6f++loimaS/dB02QyhcwPGm6xCiyqqoZ04PH5fGRkZGC328nLy4vbgdLbGqNfOldVVRuhoKSkBJPJRGZmJvn5+aSnp+P1erUp0VwuF6Wlpdr/u91u7XXqCqKB/5cvLtFWGOUHlXxeRXPoPq1EuqIJ0Tk5qKpKVVWVNkj6ihUrtIHS+/bt26T5UVsiVgPER/pEJRXNlnO5XBQXF1NcXExlZSU2m63OmaBMJhNJSUmNtgH2+/0h8/UG5uw9cOAAbrebiooKVFVl586d2lzA9YXStjbFaSTI50K0hhw/orkMETT12hnI5XJpFcuKigpSUlKw2+0kJiZy6KGHRm1YG9FyRgi0fr8fh8NBSUkJhYWFWCwWrWrZnPa+9WkskO7YsQNFUcjJyQmphrpcLioqKrTL9G63W3uvrVZrvZfsExISMJvNrVpnISLBCNV7I2yDiC5DBE29tNH0er1asCwrK8NqtWK328nNzSU1NVX78O7Zs8ewH+TA+ykVzaaLxLa4XC6KioooLi7G6XRqlcRevXrFpFlG4JgIhMbGHuvxeLTqqNvtxuFwUFRUpAXU4EAaHEBrhlIJpCLajHpuF6I+ug+aJpMpbmcG8vv9IR14ADIyMmjXrh3du3dvk5cBjXaS1UugDRyLRUVFlJaWYrFYyMrK0qYZLSoqoqKiIiYhs7nvoaIoWjBOT0+v93GBQBp8yb6qqoqSkhItoPr9flRVrVUhrRlKpU20ENWkoimaS/dnz3iqaKqqSkVFhVa19Hq9pKen19m+rS0zyviW8c7pdGptLZ1OJzabjaysLLp27domKnnBgTQtLa3ex6mqWqtTk9PppKysTAuogUBqsVi0AOpwODhw4EBI73uz2SxfwhGk98+03tcfjLENIrp0n3xiOde5qqraQOmBTjyBDjy9e/du9BJgWyQBsPmaGlz8fj+lpaXaHPZWq5XMzEytatnY67TV/aIoClarNWTGrLqoqorP59PCZ2DWrUCnJpfLhc/n0wJpfe1HExMTsVgsEkjbIKNUA42wDSJ6DBE0o9nr3O12h3TgSUpKwm63N/nLXERHtPZDtIJzfcsIjFRQXFyMy+UiIyODzMzMZlct5bhtnKIoWCwWLBYLqamp7Nq1i5ycHFJTU2s91uv1hlyyD/SuD/x/4MexyWSqt/1oJAOpXve3UYKanqmq2iabfYmW033QNJvNEQ2aqqricDjYuHEjZWVlmM1m7HY7nTp1Ij09XU56zRTNiqYRK3Q+ny+kapmYmEhmZibdu3cnOTk51qvXIkascgcCaUpKSoOPC66QBoKpw+HQbgsE0uCOUvUNji/nIn2Q/STaGt0HzXBfOvf7/SEDpbtcLpKTk+nSpQsFBQVtom2bERipollVVYXH4+G3337D4/FobS27detmmMqCHoNmOI4xs9lMSkpKkwJpzQppcXGxdltg0opAu9RAEK152T4wQL6IDSO891JVFs2l+6BpNptbFTRVVaWyslJrZ+l2u0lPT8dut5OTk0NhYSF+v5/MzMwwrnXT1suIH2apaDYuULUsKiqirKxMa+vbs2fPRgdHbykjHmtGYjabSU5ObrRq7ff7Q6qjgdmaAre53W58Pp/WG7++UNqc6UNF8xjhs6b3bdDrd4Ne6T5oWq1W/H5/s54T6MBTUlKCw+EgNTUVu91e5xd5LC7rRWusyVgw4ja1VvCsUEVFRXi9XjIyMsjOztaGwVq+fHnEQmbwegh9M5lMjQbSsrIyduzYQffu3Vs9fWi0Z2vS+3nRCJ8xo2yDno8jvdF90GzKOJoej0cLluXl5SQkJJCZmUleXl6jM5/EKmgamZGGN2rpcnw+n1ZFLysrIykpiczMTHr16lVnoDT6MSGiS1GUJk8fGjw4fs3pQ4Nna5LpQ9sGCWmiuXQfNOtqo+nz+UIGSlcUBbvdTocOHejRo4cuTnhG+NVYFyN2/GiKQBONQA9xr9eL3W5v04P3i/gX6BXflNmaZPrQxklIiw+yH6LLEEHT5XKxYMECEhMTSUlJwefzkZGRgd1uJy8vr1UDpcfy0rlouXioaAamHC0uLqa8vJzk5GQyMzPjdozVWB1zcsLXv2hNH2qxWPD7/fh8PkMH0nhmhJCm923Q27rrMmiqqsq6deuYP38+b7zxBmVlZQwePJi//OUv9O3bl4SEhLAtS4JmeBl52wJVy8Ac4n6/P2yV9Ei/Z7E+cRn1mIhXsdrfrZ0+1Ol04nQ6WbFihdY2PzBbk16mD431Z6219B7SAoywDXoRX5/AJvjpp5+44YYb6N27N0ceeSSnnXYaSUlJXHHFFRFZnpGDkZFFa7/5fD6qqqpYv3495eXlpKSkkJWVFfYfPEK0JfVNH+r1enE4HAwbNgxo2vShgXGWg6cPrSuURmP6UPkuiQ9GCct6EdWgedVVV/HVV1/Rvn17li1b1qLXGD58OIsWLdIum7z11lscOHAgnKsZQiqa4aX3bQsM4B9oa+nxeDCZTOTn59OrV6+InbzkpCjCRc+fv5paOn1oIJSWlZVFdfpQIwQco2yDiJ6oBs3zzz+fK6+8kssvv7zFr1GzShTpKSglaIZfrNtONldg1IJAW8vU1FQyMzPp27cvTqeTffv2YbPZwrKs+sRymst4cccdd5CXl8e0adMAmDp1Krm5uTz33HMA3HnnneTk5HDttdfGbB1nzZrFihUrmDlzZsjtCxcuxGq1MmrUqKity+rVq9m9ezfHHXdci57/3HPPcckll9Q5mHzN+zp16sSePXtatb5N0dKQU3P60IbE+/Sh8UDv22WEsKwnUQ2a48aNY+vWrWF9zUhPQRkLRg6aevhwq6pKRUWFVrUEtGlHa1Ytgzsv6Jke9suoUaP48MMPmTZtGn6/n8LCQsrKyrT7Fy9ezMMPPxzDNazfwoULSU1NDXvQ9Hq99bZBXL16NStWrGhx0Hz++ec566yz6gyaDd2nd5GcPhSqZ/oqLCzU7fShRjjfSdCMLt210ayptTMDNcbIoS8W4nVqSI/HowXLiooK0tLSyMzMpH///o1elhOt09Ax4fT42HSgkuJKD+n5/Vi8eDEAa9asoV+/fuzdu5fi4mJSUlJYv349Q4YMYcWKFdx55504HA6ys7N58cUX6dSpU8jrXnnlldhsNpYvX86+fft48MEHmTp1KgBPPfUUH330ES6Xi5NPPpm7774bgHfffZcXX3wRt9vN8OHD+ctf/gJUN9+ZOXMmdrudQw45pFbP661bt/Laa69hNpt57733eOyxx+jSpQt//etfKSwspF27drzwwgvk5eWFPG/GjBls3ryZjRs3UlRUxPXXX88ll1zCwoULefDBB7Hb7axfv56ff/6ZG264gRUrVmCxWPjHP/7BqFGjeOihh6iqqmLRokXcfPPNHH/88dxyyy38+uuvVFVVcf/993PSSSfh8/mYPn0633zzDSaTiYsvvhhVVdm9ezcnnngi2dnZfPnll9p6vfDCC3Xed//99zNnzhySkpJ477336NChA/v37+eGG25gx44dADz88MOMHj06dB87nbXW/4gjjqhzvS677DLWr1/PXXfdVWv/vv7667zxxhu43W66d+/OK6+8QkpKSoP7ujVaMn1oaWkpZWVlTZo+tK7B8eMhHBklpBlhG/RC90FTLp2HRzRPHvEQ3ANVy6KiIkpKSgDIzMwkJyeHtLS0Jr8XcrIKj7qOia1FlXy0cg9Orx+LScHnN1PphW+WrWHb6sUcdthh7Nq1iyVLlmCz2ejfvz+KonDLLbfw7rvv0r59e2bPns3999/PCy+8UOv19+zZw9y5c1m/fj1nnXUWU6dO5dtvv2Xjxo189913qKrKmWeeyQ8//EC7du2YPXs2c+fOxWq1cuONN/K///2PU089lRkzZvD999+TkZHBCSecwODBg0OW07VrVy677DJSU1O5/vrrATjjjDM499xzOe+883jzzTe59dZbeffdd2ut42+//ca8efNwOByMGzeO448/HoBVq1axePFiCgoK+Oc//4miKCxevJh169YxdepUVqxYwd133x1yGf++++5j/PjxPPzww6xfv57LL7+cCRMm8M4777Bt2zZ++uknLBYLRUVFZGVl8eyzz/LFF1/Qrl27kHW6+uqra93ncDgYMWIEf/vb37jnnnt44403uO2227j99tuZNm0aY8aMYfv27UydOpVffvkl5PVefvnlOtd/1qxZtdbL4/Hw1FNP8dlnn9Xav5MnT+aSSy4B4IEHHuDNN9/kqquuqndfR0vw9KFms5nKykp69uxZ63FNmT4UQjtK1RVKZfrQxsXDd1BbYoigabSKZrSXGc0pL2M5vqXb7aakpISioiIcDgdpaWlkZWWRk5PTqqpltLYn0uLp5Fta5eGDFbtJS7SQnfb/7bK7DziUf3/yLexYwW033cCuXbtYvHgxNpuNUaNG8eeff7JmzRqmTJkCVFeTalYzA0466SRMJhN9+/Zl3759AHz77bfMmzePsWPHAtUBauPGjfz++++sXLmS8ePHA9WXP8ePH8/y5cs5/PDDad++PQCnnXYaGzZsaHT7lixZwn/+8x8AzjnnHKZPn17n40488UQtpBxxxBEsW7YMu93OsGHDKCgoAGDRokVaoOrTpw95eXl1rsO8efP48ssvefLJJ7We2tu3b2f+/Plcdtll2iX4rKysRte/poSEBCZNmgTAoYceyrx58wCYP38+a9eu1R5XXl6uXTEIqG/961qvVatWsXnz5jr375o1a3jggQcoLS3F4XAwceJEbRl17etYaOgz1pTpQ6E6kNYcHD+4U1Nd04fW17mpJYHUCBVNI2yDnhgiaEpFU5+ys7M5/fTTeemll4Dq9mb9+/dn2LBhvPPOO61+fVVVKSsr06qWiqKQmZlJly5dSE1NjdiJ5ocffuC5554LyzYEtPZ4+Mc//sHo0aM58sgj67x/9uzZLF68WNsXsfbrrnL8fkhJCB2Uu9fAoWz5czV7/vyN/v37k5ubyzPPPEN6ejrnn38+qqrSt29fLeg0JPgSd+D9VVWVm2++mUsvvTTksS+++CLnnnsu999/v3bb77//zh9//NGazWxUzWM08HdL2kaqqsqsWbPo2LEje/bsoU+fPmFZRyCknWFwcya/38+8efManeqyqVRVpVu3bvz000+17rvqqqt45513GDhwILNmzWLhwoXafXXta70ymUwtmj7U7XZTXl4eUiGta/rQ4FBaVyA1QkgzwjboSVTr6xdddBETJkzgzz//pFevXvz73/9u9WsasTNQtEUz2AYvKzU1lbVr11JVVQXAd999R+fOnVv1+m63m7179/Lnn39SXl7Onj17SE1N5ZBDDmHw4MHk5+c369J4Y6L13qmqqg1Q3RJ33nlnvSEzHm0rqiItqfbML90HHMqGXxZiSU7HbDaTlZVFaWkpS5Ys4bDDDqNXr14UFhZqbTk9Hg9r1qxp8nKPPvpo3nrrLSoqKgDYtWsX+/fv58gjj+STTz5h//79ABQVFbF7926GDh3KDz/8QGFhIR6Ph48++qjO101LS9NeE+Cwww7jgw8+AOC9995jzJgxdT7viy++wOl0UlhYyMKFC7XxI4ONGTOG9957D4A///yTHTt20KtXL9LT0ykvL9ceN3HiRF566SXteF21ahUARx11FP/617+0cFhUVFTnOje0PfWZOHEiL774ovb36tWrm7z+da1Xjx49KCkpqXP/lpeX06lTJzweD++//36j6xYL0Qw4gV7xNpuN9u3bk5ubS/fu3enXrx+DBw9mxIgRjBw5khEjRtCvXz/y8vLIyMjAZDLhcDjYvXs369at45dffmHJkiUsWbKEFStWUFRUxM6dO9m+fTv79u2jpKSEqqoqXX0PS9CMrqhWNMMRLGuSS+fhEa3l+VWVSo+PVH/18o4++mjmzp3L5MmTmT17Nqeeeio///wzAL/88gt33XUXLpeLpKQknnnmGXr16sV//vMf5syZQ1VVFZs3b+aYY47hyiuv5J133mHz5s1Mnz6d3NxcPvroIyorK3nooYdC1iE/P58LLriA+fPn06FDB1599VXatWvH5s2bue222ygsLCQ5OZknn3yS3r17s23bNq699lqKiorIzs7m2WefpUuXLkybNg2z2cwvv/yCx+PhwQcfrNXD1+FwcMcdd7BmzRq8Xi+33XYbJ5xwQshjKioqOP/88yktLcXj8XDXXXdxwgknsG3bNk4//XSGDRvG4sWL+eSTT/j444/5+OOPcbvdnHjiidxxxx0hr+Xz+bjuuutYuXIliqJw3nnncfXVVzNt2jSOO+44Jk+ezPLly7nrrruorKwkISGhVjj6+uuvmTlzJv/5z3/Izs4Oy35vriSriUJH7WMyt1tvKsqKGXLk/7+H/fv3p6KiQmsv+NZbb3HrrbdSVlaG1+vlr3/9K/369WvScidOnMi6deu0y66pqam8+uqr9O3bl+nTpzNlyhT8fj9Wq5VrrrmGUaNGceeddzJx4kTsdjsDBw6s83UnTZrEBRdcwBdffMFjjz3G448/ztVXX83TTz+tdQaqyyGHHMIJJ5xAUVERt912G507d651Wfzyyy/nhhtu4LDDDsNisfDiiy+SmJjI4YcfzhNPPMGYMWO4+eabuf3227n99ts5+uij8Xg89OrViw8++ICLLrqIP//8k1GjRmG1Wrn44ou58sorueSSSzjllFPo3LlzSGcgoMH7gj366KPcfPPNjBo1Cq/Xy9ixY3n66aebtP51rdfFF1/Mgw8+yL333ltr/06fPp0JEybQrl07hg8fHhKyRf2Ce8U3ZbamdevWkZ5e/UOvOdOHBv9/PEwfKkEzepSGAkZlZWXcX2P44Ycf+OCDDyI2rInD4WDHjh1hvczUmD///JNOnTo1+KEPp9WrV9O/f/+ITtVW5fExe+VePlqxE69qIjnRwoK7T+Szz7/k2aef4MUXX+S4447joYce0i47l5WVkZKSgsVi4bvvvuP111/n3//+N2+++SYzZ87k1Vdfxev18pe//IV3332X/Px8jjrqKH7++WdMJhNHHnkkL730Ev379w9Zl0BP1TPOOIPHHnuM/fv38+ijjzJ16lRmzpxJjx49WLZsGQ8++CCffPIJ5557LieffDLnnHMOb7/9Nl999RWzZs1i2rRp7N69m7///e8kJiYyZcoUli1bxrJly7RtePDBB+nTpw9nnnkmpaWlHHPMMcyfPz9kLD+v10tlZSU2m43CwkKOO+44li5dyvbt2xk6dChfffUVZrOZ0tJSPv30U5544glUVeW8887j2muvDamGrVy5kgceeIAPP/wQgNLSUjIyMrSgefzxxzNq1CheffVVhg4dqr3Hb775JosXL+bkk0/mhRde4O2338Zutze4Ty0WS1g6Hezduxen00nXrl2129bvq2D2it10yazdXm1niZOjemczsiCz1ctujd9//52uXbuGtDcMpxkzZoR0IAqXkpIS9u7dG9VzWri4XC7WrFnDkCFDYr0qLVJaWhr2ZgvRtnbtWnJycuodOzh4tqaanZsCf8d6+tDy8nK2b99e67tBDwKZzWQyxV1YTklJqXOFdN9GU4Y3ar1Ib6PL6+dvX2xg/f5KrArYEs2oioLXrzJrUwJbt25j9uzZHH300SHPKy8vZ9q0aWzatEnrkblixQr27NnDyJEj6d+/PykpKQwYMACHw0FGRgaHH344//vf/+jZsyc+n6/OE4nJZOKUU04Bqnv/XnTRRVRUVLB06dKQtnkulwuApUuXatX4M888k/vuu097TKCTQY8ePSgoKODPP/8MWdb8+fOZM2eONqi40+ms9cNFVVX+/ve/s2jRIkwmE7t379Y6LOTl5TFixAiWL1/O/PnzmT9/vnYJ3OFwsGnTppCgWVBQwNatW7n99ts59thjmTBhQsj6bNiwgY4dOzJ06FCAkC+LZcuWsXnzZj744IOID0BfU83jr3u7VLpnp7K5qJIO6QlYzSa8fpXCcjftUhM4JCe66ydEOBjhu6SxbQieramhH2FNmT7U7/drszWFc/pQvV86VxRFV+tviKDZmrZrjZFL5633w8Zi/txfiT3ZgstV3Y7HajahAFuLqug5/Aj+9re/8emnn2rtw1wuF9OnT6dXr17ceOONlJeXc+211zJw4EDWrFnDgQMHtKpg8I+N888/nyeffJJevXo1eaBqRVHw+/1kZGSwYMGCZm1bfZ01gr3xxhv06tWr3tf473//S2FhIfPmzcNqtTJkyBAt5AZ3+lBVlRtuuIGLL7643tey2+0sWLCAefPm8frrr/Pxxx/zzDPPNGlbcnNzOXDgABs3buTQQw9t0nMixWJSOGVIJ37eXMwv20vx+PyYFIVBXdIZ2z2rVichI7rrrrtivQpxR+8BQe/rD+HbhnBPHwrV3wVNmT7UCPtBT3Q/2Ja00QzP8iJpzpoDWC11lfkVEq0mfL0mcMstt5CTk8Pu3bspLS1l/fr1lJWV0b9/fw499FDtcnhjl1KGDx/Orl27mD17NkcddVSdj/H7/Xz66acAfPDBBxx22GHYbDby8/P55JNPgOqT22+//QbAyJEjtUvR//3vf0Nmd/niiy/w+Xxs3ryZLVu21Bofb8KECbzyyiva/qyrM0R5eTnt2rXDarWycOFCtm/fXud6H3XUUbz99tu1OqoEKywsxO/3M3nyZO6+++5ay+vZsyd79+5l+fLl2rIDn5/OnTvzxhtv8Ne//jVkSJpYSbCYOKJXNteML+Dqwwu47shuHNuvA6mJuv99LAymviG0Arfv3r2b888/H4BPP/2Um2++OWrrFlBSUsIrr7yi/b1w4UJOP/30qK9HsMcee6zRxwSmD01NTSUrK4tOnTrRtWtXevfuzSGHHMKwYcMYOXIkI0eOZPDgwfTo0YP27duTmJiIx+OhsLCQLVu28Ouvv7J06VKWLFnCmjVrKCoqYt26dWzZsoXdu3dTWFhIRUUFHo/HEJXneKL7M3akg2YsxGoczUgpc3qxmOoKsyp+rxeHkszIkSO1KqXNZmPgwIHaYM/PPvssxxxzTJOXN2XKFH799dd6L9ukpqayfPlyZs6cSbt27XjttdcAeOmll7jllluYOXMmHo+HU089lUMOOYSHH36Ya665hmeffVbrDBSQm5vL5ZdfjtfrZebMmbWGHLnlllu4++67Ofzww/H7/XTt2rXWsEenn3465557LuPGjWPIkCF1Vj8VRWHChAmsX79eG7Q7NTWVF198URvDEaq/0K699lqtyl9zfMaEhAReffVV7rjjDpxOJ0lJSXz44YfaMdC7d29eeuklLrnkEv7zn//QrVu3pr7tEWM1m7Cadf+bOG5INSf6OnfuzKxZsyguLo7Ze19aWsorr7zC5Zdf3qrXCRw/DU1/2lQzZ87k1ltvbdZzGlpuU6cPDYwa0aFDhyZPH1pXpVRv04fGiu47A/3xxx888sgjERv/z+VysWHDBgYMGBCR16/L5s2bsdvtZGZGp7PDH3/8QY8ePWpNnRcuj8/bwk+birElWXBWVaECqt+PYlKo8pkYmmfjbyf0DtsH9pxzzuGqq67CZrPVeQk4Pz+fbdu2tXo506ZNY8KECfTv3z/ijcqXL1+utauMhIqKCnbt2kXv3r2b/JxwdgaqqqrSBiDXi0h3BoqU4uJi9u/f36x9HS+cTifr169n0KBBsV4VoDp0qUBO587s2bOn1v2dOnViz549bN26lTPOOIM5c+bw+uuvs2vXLmbOnMmcOXN49NFHef/991m1ahUPPfQQbrebbt268cILL9Q6tiZNmsTgwYP56aefcDgcvPzyy8ycOZM//viDU089lXvvvReAZ555hrfeeguoHlZw2rRpXHzxxXzxxRf06tWLCRMmcPzxxzNjxgyys7P5448/OPTQQ3n11VdRFKXeaVwnTZpEbm4ua9as4ayzzuK6667T1m3GjBls376dLVu2sGPHDv76179y9dVXA3D22Wezc+dOnE4nV199NZdeein33nsvTz/9NAMGDKBfv37ce++9nHHGGSxZsgSAp59+GofDwV133cWkSZMYNGgQixYt4vTTT+fwww9vdJrZhjT1MxA8fWjwZftYTh8aCPrxOPuToTsDGW3A9liI1DY6nU5Gd4T5a9yUepxYTdWXQRKSk/H5weP2cvrQnLB8CAO9ugcMGMD48eNZsWJFGLZARJpUBERTxcu5+ECFm/8s28ncNftxevw4vX7mrtnP0X3bNXo8B+7/9NNPefbZZ5k9ezY+n49HH32Uzz77jNTUVJ544gmeffbZWsOXQfXQQd9//z3PP/88Z599NgsXLiQzM5NBgwYxbdo0tm3bxqxZs5g/fz6qqjJhwgTGjRvH/fffzx9//KENdr9w4UJWr17NkiVL6Ny5M0cffTSLFi1ixIgRDU7j6vF4+Prrr0NGzghYv349X375JeXl5QwdOpS//OUvWK1Wnn/+ebKysrQZtaZMmcIDDzzAyy+/rK3P1q1bG3zf3G4333//PR6Ph+OPP75J08zWp6lV/eDpQxsi04c2TPdBU9poxtfy/H4/paWlFBUVUVpaSkJCAt2ysrhibB5v/rKPSpcbiwJVTi8m4IKRORzSOTzDOGVkZGi/hhsSjmomwHPPPYfT6WTTpk1heb2GGD2MxUuAEKIx+8tdXPv+7xRVuklNMJORbEFVVR7/ZiPr9zmYNr6g3ucGjvMFCxawfPlyPvnkE2w2G1999RVr167Vmgi53W5GjhxZ52sExuENVAIDlbyCggJ27tzJokWLOPnkk7UgOHnyZH766ada4/cCDBs2jNzcXAAGDRrEtm3bsNvtDU7jOnHixHrPR8cff7wWmtq3b8++ffvIzc3lxRdf5LPPPgNg586dbNy4sdlj9J522mkAzZpmtj7hbj4SzelDA3/rKZAaImgaraKptypqVVUVRUVFFBcX43a7ycjIICsri27dumkfhik5cFj3bD74eQOFLoXuHe1M7JNFTkZ4pqaLlWjODBRJejvmhIiV13/eTlGlm4zk/+8trQBpiRY++3Uvxw9oT492tat9wbp168aWLVvYsGEDQ4cORVVVjjrqKF5//fVGlx9o4mQymUhISNBuN5lMzS66BD8/MHpHY9O4JiUl1RvS6nq9hQsXMn/+fL799ltSUlKYNGmSNqpGMIvFEjKCTM3HBNpdNmea2YbE4sd7uKYPzc3NpUOHDlFa69bTTySuRzQ6Axn9C7i5IcPn81FUVMSGDRtYvnw5mzZtQlEUevbsydChQ+nRoweZmZm1fnF1siUyuW8600Z34IKROboPmUKI6Ip1RyaPz8/89YWk1THygcmk4FdVvllzoN7nB86z+fn5zJo1iyuuuII1a9YwYsQIfv75ZzZu3AhUj5Fbc0zephozZgyff/45lZWVOBwOPvvsM8aMGdPkaUMbm8a1ud+HpaWl2O12UlJSWLduHUuXLtXus1qtWjvHDh06sH//fgoLC3G5XHz11VctWr+miPfv9IamDx00aJA2C5pe6L6iGY02mtEWb5fOVVWlqqqK4uJiiouL8Xg8ZGRkkJ2dTffu3ZtVwjda5cxo2yPaBqM3xYgUp8ePXwVznaNogElRKKz0NOm1+vTpw2uvvcYFF1zA+++/z4svvsill16qVfIC4wg315AhQzjvvPO0iR0uuugiBg8eDMCoUaMYOXIkxxxzjDZ6RU0JCQmNTuPanOPnmGOO4V//+hfDhg2jV69ejBgxQrvv4osvZtSoUQwZMoTXXnuNO+64gwkTJtC5c+d6O+o0Zf0aE+sfLG2N7nud79+/n4svvpjZs2dH5PX9fj+rV6+O6pRn27dvJykpKWTYmkhat24dXbp0CWnc7fP5KCkpobi4mLKyMpKSksjMzCQzM7PRsn9DorltK1asiPjA4263m/Xr13PIIYdEdDmR7nXekqlWw9XrfP/+/ZSXl9O9e/dWv1Y06bnX+YEDB1oUYmKtsrKSjRs31junfKT5VZUzXv0Fr08lwVL72C93erl0TB5nDs2p8/kHDhygrKxMd8d6sFWrVtGnT59WfQ/E2r59+3A4HHExfFtzSa/zGJDOQOFZnt/vx+FwaFVLr9eL3W6nXbt2za5aNrYsqQA2XzR+fct+aRv0vp9jWYkyKQqnDenMGz9vx2oOnQbQ5fVjMSkc27f+H9F6f+/BGNVAI2yDnkjQbISRD0av10tJSQllZWWUlpZqMy/07t07YmNqGo2ROgMJEe/iIaidMbQza/ZUsGxrCX6qp0v1+lQsZoV7J/XCntLwlIryWYsPsh+ixxBBM5JtNGMhUuFFVVUqKyu1HuJ+vx+73U5ycjJ5eXnYbLawL7MmqWgKIfTMajZx/0m9WbG9lK/X7Kekykv/TmlMGtCBDukN/0A3wrnPCNVAI+wHPZGgGYfCGcYCVctAW8uUlBSysrLo27evNhTFhg0bwrKstihawTneTuyVlZVUVlaSlJSkjfHW2unohGhMvIQck6IwLN/OsHx7s58bD+vfWnrfhng5jtoK3X8zBMbqiiQ9tdFUVTWkrWWgatmxY0d69uxZ54crmlVGqWjGp6aMPFBWVkZhYSElJSUkJSVhs9lwOBw4nU5cLhc+nw9FUbBarVr4rPkvnFOxCaE3Rjj3GWUb5DwUPboPmnKwVI8jFqhalpeXk5qaSmZmZkjVMl4YMWhGa3uifXL0er0UFxdTVFRERUUFNpuN7Oxsunbtitls1v7VXMfggYZdLhfl5eXs378/ZG7gwDhxiYmJeL1ePB4PxcXF2m01X1cIo9D7d5ZRQpoRtkEvdB80jSjQC7w+qqpSUVGhVS0B7HY7nTp1olevXs3+ABkx/EWL0U5WLpeLwsJCioqKtJEHcnJySEtLa9K2Bs/lm55e/9SiPp9PC6IHDhzA6XSyb98+beaLwPEfPP1azX9Wq9Vw73+06Pl90/O6G+U8q+d9AMbZD3ohQTMO1RX8AhWf4uJiKioqSEtLIzMzk379+rW6ahntS+cNhWgRXYEfLXv37qWkpIT169eTnZ1Nz549IzpOntlsJiUlhZSUFHw+HyaTiR49etRat8AcwIF/paWl2v8HqqNms7neMKq3OYFFwyQgxJ4R9oFRqrJ6IUGzCaJ9QAaCX3l5OUVFRZSUlACQmZnZrOqSiLxodgYK18nR7/dTUlJCYWEhZWVlpKWlkZ6eTnp6esQHnm8ORVG0sNgQr9cbEkarqqooKSnB6XTidru1/dNQGLVYLG3mM2WEoKBXRgg4RtkG+QEaPRI044jb7aakpIRdu3bhdDpxOBxauLRaGx6brTWkM5Dxud1uiouLKSwsxOl0Yrfbad++vdZBzOl0aj9o9MZisWCxWEJmtqrJ7/fXqo4WFxfjcrlwOp34fD5UVcVisYT0pK/ZkUm+nITQPyOEZT0xRNDU6wETqFoGLokrikJmZibZ2dmoqkp+fn5U1yUa9Lqv6hOv2xOYnz7Q3hIgKyuLgoICkpOTw7Le8brtdTGZTCQlJTXYHEBV1VrVUYfDQVFRkfZ3oBKSmJiIw+Fg586d2Gy2WtVRETl6Ou7qovf1N0pIM8I26IUhzoiqqurm4A9UloqLi3E4HKSnp9eqWh44cIDKysqorVO03zejVTSjeem8sfUoLS3VmlskJSWRnZ0dlna8kRQvVe7A0ExWq7XB+cv9fj8ul4u1a9eSlJSEx+OhoqJCG+Yp0AZZhnkKv3g4TlpD7+sfoPdj1yj7QS90HzTj/YAPjD9YXFxMSUkJJpOJzMxMunTpQmpqalysf7S/6OVDHj6BIYgKCwtxOBzaEEQFBQXNuswbL2FPD0wmE8nJySQkJJCdnV1nKK1vmKcDBw5oPesDr9VQ21EZ5sl44uGc39bppTBlFLoPmtHQ3C9gt9utTfNYWVmJzWbTwmVTLqvF4ktfLp23TCy2x+l0apfEfT4fmZmZ5ObmSiexONKSYZ4C/8rKyrT/DwyCL8M8GYP8mIsPEjSjyxBBM9YHjN/v13qIl5aWYjKZyMrKIj8/n5SUlLgf1zKa758RK2eR3h5VVfH5fGzdupXS0lKsVitZWVn06tUrokMQicgLHuapPk0d5ilQHa2vM1NwhTvW58yWkoAgwkGOo+gyRNCMRnCpeWAGeq0WFRXhdDpJT08nKyuLvLy8VncGiNVwSiJ++Hw+SkpKKCoqoqysDI/HQ2pqKvn5+dLZpI1p7TBPwR2ZAq/n9/vrvGzfloZ5igUJOPFD9kP0yDdWEwROzIEe4iUlJVgsFjIzM8PaizeYUYOfEUNtuPZ9oMlF4MeL3W6nQ4cO9OzZk19//ZXs7OyIhUwj7pe2pqnDPO3du5fi4mJSU1NDhnlyuVx4vV4Z5inCJODEnpzrokuCZgOcTifFxcU4nU5WrVqltbXMz8+PaCP9WFw6lw9e9KmqSmVlpRYuFUXRhiCq61KqUfeRHH/RYzKZtN7wHTp0qPMxTRnmCQhpO1pXKI3kjyK9kuM8PkhlOboMETRNJhN+v7/V4c/v91NaWkpxcbHWFi4zM5PExEQGDRoUtUuWRg6abT1U+P1+ysrKKCwspLS0lOTkZLKysujfv3+Dg/LLSVFES3OHeQr+V1FREdKRCaorrfWF0eYO89SWzx0ifCRoRpchgqbZbMbn87UoaDqdTq2HuMvlIiMjg8zMTLp27aq93oEDB8K9yg0ychiLxZidsT6heL1erWrpcDjIyMggOzubbt26Gf4S5B133EFeXh7Tpk0DYOrUqeTm5vLcc88BcOedd2K32zn11FNjto6zZs1ixYoVzJw5M+T2hQsXYrVaGTVqFABXXnklkyZNYurUqTFYy4addtppvPbaa9jt9qgtMzDMU3Jycr2PCVRHA2OM1jXMU/Ag+PUFUqMM8xQP5yNRTfZD9BgiaFosFrxeb5MGpQ5ULQM9xBMSEsjKyqJ79+71njCNfkDKOJrhV9cQRK0ZOzXS+yhSrz9q1Cg+/PBDpk2bht/v1+ZXD1i8eDF33XVX2JcbDgsXLiQ1NVULmjXF03lh9uzZsV6FOgVXR1szzFNgEHxFUfB6vWzcuLFWKJVhnkRTtYXvoHhiiKBpNpvxer313l9VVaX1EPd4PNhsNrKysppcUTLypezA8gIn8mgsK9qX6SP95ROYmSowxFVxcTFWq5Xs7Gx69+7daG9hIzvssMO44447AFizZg39+vXTOqOkpKSwfv16DjnkEBYvXsy1116Lw+EgOzubF198kU6dOoW81pVXXonNZmP58uXs27ePBx98UKsuPvXUU3z00Ue4XC5OPvlk7r77bgDeffddXnzxRdxuN8OHD+fJJ5/EbDbz1ltvMXPmTOx2O4ccckitfbR161Zee+01zGYz7733Ho899hgAP/74I8888wz79u3jiiuu4JJLLmlw+QGvvfYamzZt4qGHHgJCq6hnn302O3fuxOl0cvXVV3PppZcCMHfuXO6//358Ph/Z2dl8/vnnVFRUcMstt7BixQoUReHOO+9kypQpDBgwgAULFuBwODj11FMZPXo0ixcvJicnh3fffZfk5GQ2bdrEzTffzJ49e7Barbzyyiv06dMnjHu75Zo6zNP+/fvZt28fNpstLMM8xYKE4diTynJ0GSJoWiyWkKDk8/lC2lomJiaSmZlJz549WzTuoNGDpmiZwBBELpeL5cuXhwxxZZRLfS1R5vSS2z6Tk869jMtvugcUE3+77z7++P13jj/+eHbt2sWSJUuw2Wz0798fRVF49NFH+fjjj2nfvj2zZ8/m/vvv54UXXqj12nv27GHu3LmsX7+es846i6lTp/Ltt9+yceNGvvvuO1RV5cwzz+SHH36gXbt2zJ49m7lz52K1Wrnxxht57733OOqoo5gxYwbff/89AwcOxGKxMHjw4JDldO3alcsuu4zU1FSuv/56AN58882Q5Z9yyilccskl9S5/3Lhx2utNnjyZww8/nCOOOILjjjuODz/8kI4dO/L000/z/PPPk5WVRVVVFePHj2fKlCn4/X6uvfZa5syZQ0FBgTZf/SOPPEJGRgaLFy8GoLi4uNZ7tHHjRl5//XWeffZZLrzwQj755BPOPvtsrrvuOp566insdjs//PADN910E1988UWd+3DatGlce+219O3bt2UHQQQEqqMJCQm0b9++3sd5vV7cbrd2ub7mME9+v7/WIPg1Q2mkhnmS83p8kKAZXYYImiaTid9//51FixZx9NFHo6oqGRkZzapaNsTIA6gHlmfEzkCRWFZgCKLCwkLcbjd2ux2r1crQoUMjut+i8b6F4/V3lzpZuLEIS0ICi+f/j8nnX0Fun8Es+30Dhdt3MHLkSHbt2sXixYux2WyMGjWKzZs3s3HjRqZMmQJUB/ia1cyAk046CZPJRN++fdm3bx8A3377LfPmzWPs2LEAOBwONm7cyO+//87KlSsZP348UH1lo3379ixdupTDDz9cCyunnXYaGzZsaNL2BS8/EP7qW35w0Gzfvj02m4233nqL4cOHs379eoYNGwbAiy++yGeffQbAzp072bhxIwcOHGDs2LEUFBQAkJWVBcB3333H66+/rr1uZmZmrXUsKChg0KBBAAwZMoRt27ZRUVHB4sWLufDCC/F6vfj9/gb3d6ANbTxq7HMWGOapoeqo3++vNQh+fcM81ayIBkJpS4d5koATexI0o0u3QdPhcPD999/z9ddfs2rVKh555BEmTpxIQUFB2BvEG3lKSJAKakMCQxAVFhZSXFysDUEU3KY3cLuehWP93V4/P24qIiPRjMVsYdKp5zD3g38zdPhIvvniY4r27qV///74/X5eeeUVfD4f+fn5DBs2jG7dulFWVsavv/5KaWkpXbt21aqCxx13nBZ8Ape4TzvtNK1X8zvvvMOIESN4//33+fvf/05ubi4XXXQRZ599Nqqq4vf7Qy5nB0JdwJw5c1i5ciXLly/nvffeo0OHDnz55Ze88cYbAHzzzTe89tprqKrKHXfcwXHHHYfdbkdVVcaMGcOECRO48sorWbx4MTt27MBut9O7d+/Q98btZs+ePWzevJmxY8cyYMAAFEVhwYIF/Pzzz2RmZnLNNdfw6aef4nK5WLhwId988w1jxowJueQfMHfuXN58803eeustoLpN8GWXXcY///lPCgsLOeKII6iqqqJz586MGjUKv99PSkoKycnJlJWVYbFYmDt3Lj6fj+nTp/PNN99gMpm4+OKLueqqq5g0aRIPPfQQQ4cOpVOnTlx99dXMmTOHpKQk7T3av38/N9xwAzt27ADg4YcfZvTo0a0+jhoSrvOUyWQiKSmpwStcgRm5gjsyVVZWaoE00JGpOcM8yXk2fuj5nK23dddll9dHHnmE448/nh9++IGpU6cyYsQIXnjhBa6//vqI9bqU4Bceeqho+v1+iouL2bhxIytWrGDbtm0kJSXRv39/Bg0aRJcuXRrsadvW+FWVDfsqeH3RNr5bX8iizSX4VZXjTjuPBV99QkGvvuzZthGTNRE/Cn//+9+x2WwkJydz4YUX8sYbb1BSUkL79u1Zu3YtP/zwA3369GHRokW4XC527txJz549Q5Y5ZswYrYmM3W7nxx9/pKKigp9++olevXrx4YcfYrVaSU1N5ZNPPmHFihV89dVXbNu2jREjRvDDDz9QWFiIw+Fg586dnH766YwdO1YLl6NHj2batGlcdNFFnHbaaTz55JMoisLQoUO1oOr3++nSpQuTJ09m5syZXHrppSxYsICZM2dy9dVXh6xvQkIC99xzD1arle7du3PnnXcCsGXLFkaPHs2CBQt46KGHWLJkCdu2beOPP/4gKSmJ//znP5jNZv71r38BMGHCBF555RUmTJjAsmXL2LlzJwCVlZWcdNJJAHTo0IHvv/+en3/+mc2bN7Nv3z6SkpJwOByccMIJfPnll7z00kts2LCB119/nW3btvHTTz/x888/c+aZZ9bavw6HgxEjRrBo0aKQ9+j2229n2rRpLFiwgFmzZnHNNdeE54CKE4qiYLFYSEtLIzs7m5ycHLp160bfvn0ZPHgwI0aMYOTIkQwbNow+ffrQuXNnkpOT8Xg8FBYWsnnzZlatWsWSJUtYvHgxy5cvZ+/evezZs4ft27ezf/9+rcOTUc/3danvakXg9t27d3P++ecD1W2Zb7755rCvQ2Pvd33LnTFjBrNmzWrwuQsXLuTKK69s1vrMmDGDp59+Gqhui/7xxx836/nxLuoVza+//prbbrsNn8/HRRddxC233NLs17jxxhu5/fbbtb+ff/55rboRCUa+lB2L5cXjSdXj8VBcXExhYSFVVVXYbLYmD0EUjeMjXn98qKrKjxuL+HVXOS6vD3uyBYtZwa/C2mKV8SecwqqlP+F0VtG5a088PpUlS5Zw7LHHsmzZMi6//HIefvhhnnvuOe644w6mTJmCy+XilFNO0YLN0KFDay03EDR//vlnzjjjDP7zn/8wYcIENm7cyL333ku/fv1Yvnw5qqpyyCGH4Pf7+eOPP3jjjTcYOXIkd955JxMnTkRRFK1X+aGHHsq8efMA2LVrF59//jkrVqxAVVUGDhxInz59OOyww/jwww+54IIL8Hq9TJ48mYkTJ+JyuTjrrLPweDyoqorZbGbKlCk8/vjj7Nmzh6effpqpU6dit9vZvn07w4cP5+uvv+aMM85g6dKlHHfccXi9XgYPHsyqVau0oDlixAj8fj85OTlcfvnlvPTSS0yZMoUxY8bgcDh45pln+Pvf/05VVRXHHHMMAOvXr6d9+/aoqorH42HdunX8+eef9OvXjx9++IF3330Xt9vNOeecw6+//spll12mVd0Cl+iDJSQkMGnSpFrv0fz581m7dq32uD///JOKiooGx940oq+++oq1a9dy8803k5GRUedjAsM8bdy4EavVislkqjXME4RON1rX5fpwtf3evXs3t956a6OhKRY6d+4c9vXyer21KsvR/l6vuQ5tSVS32ufzcdNNN/HZZ5+Rm5vL4Ycfzoknnki/fv2a9To1hzFqrNd5a8Xrl3w4RbPKGC2N7beqqiqtvaXf7ycrK4v8/HxSUlJ0d2kiHFpyDOwpc/H7rnJyMpIornSzv9xNgtmEokChw83IE87lsWvO4PSLr6bK7cNqrn5fn3/+eaxWq9ZTuE+fPrz88su89tpr7N69m4cffpgTTjiBH374gTFjxgDw0ksvacsdNmwY+fn5/PTTTxx11FEUFhbSo0cPfvzxR95++23uvPNObr75Zq0Hd00XXHABF1xwAZ06deLZZ58F4OOPP9bOI7fccgs33HADJ554IgsXLmTGjBm89NJLqKrKkCFD2L9/Px06dOCEE04Aqi/p9+3blwsuuIDLLrsMgF9//VVrRxowadKkkPE6U1JS+PDDDwEYOXIkr7/+Ol999RXnnnsu999/f631VhSFl19+GYAFCxbw0ksvsWDBAk444QQKCgrYsmULiqKwfv16MjMzmTx5Mtu3b9fW8aOPPmL//v2Ul5fTvXt3zjvvvJDXD4ygEPx38LBBwedav9/PvHnztMvPnTp1ikrIjKfPptfr5cQTT+TEE09s8HGBjkyJiYmkpaXV25nJ5/OFdGQKjDtac5inwGvV1ZmpKcM8RSLMNcTp8fH77nJ8fpXGTjNbt27ljDPOYMmSJSG3z5kzh0cffZT333+fVatW8dBDD+F2u+nWrRsvvPBCrWNv0qRJDBo0iEWLFnH66aczaNAg7r77brxeL926deP5558H4JdffuG2226jsrKSxMTEOpvWBJabmpqqXc2aNGkSw4cPZ+HChZSUlPDcc88xduxYrFar9oNjxowZbN68mc2bN5OXl8d9993HX//6VwoLC2nXrh0vvPACeXl5rXlrdSGql86XLVtG9+7d6datGwkJCZx++ul8/vnnrX7dwDiakWL0CmO0w1+sqKpKWVkZmzdvZsWKFWzatAmTyUSfPn0YMmQI+fn5LR7nUu9aus3r9zpItJoxKQoZyVYsJhMef/Wxm5ZoYb83kXHHnMjXH79PVooVq9nEYYcdxgcffADAe++9x8iRI1FVleHDh7N48WKt/dygQYP417/+pXWyCZaQkEBubi4ff/wxI0eOZMyYMfzzn//UHnv00Ufz1ltvUVFRAVRXKPfv39/k7SorKyMnJweAt99+O+R9Oumkk7jrrrsoKCggw55JkcNN/wGHcODAAS1krl69moEDB4Z0EPrXv/7FO++8w2WXXaZ9vh9//HFtQgin08nFF1/MkUceyUcffcTFF1/MYYcdxvDhw3nttddC1u/AgQPcd999/Pzzz7zxxhucfvrpAJSXl6MoChkZGezbt49ffvmFlJQUevXqxd69e7nllluYMmUKp556Kg8++CBHHXUUzz77LEOGDOGKK65g2LBh/PTTTyxbtoyHHnqIkSNH4na7Q64gbdiwgTvuuIOJEyfy4osvarf7fD7uv/9+Ro8ezYQJE7SQ/eWXXzJhwgTGjh3LySefrN0e+BExZswYxo4dS3l5ea39cPbZZ3P44YczYsQI/vWvf2nvW6dOnepcVrBly5Zx1FFHMXbsWCZOnMj69euB6suiZ599NpMnT2bAgAG89NJLPPPMM4wdO5YJEyZonbw2bdrEKaecwuGHH86xxx7LunXrgOpLm9dffz0TJkzgnnvuCbnMum/fPs455xxGjx7N6NGj+fnnn0O2Y+rUqbzzzjvaOtbcjsLCQpKTk8nMzKRTp0507dqVDz74gGeeeYa77rqLiy++mN27d/Pf//6Xiy66iOuuuw6Px0NpaSnDhg3jxx9/ZOnSpbz55puMGzeOX3/9lXfeeYfhw4czYsQIRo0axf79+9m0aRMjR47U9ttdd93FyJEjGTVqlLZPH374YcaPH8/IkSO59tprtff+pptuYvr06Rx55JEMGTKEH3/8sdZ7H6CqKu/+sospLy3jjo/Xcvdn63B6fTw5bxMeX9OH1Pv000954okntPFiH330UT777DN++OEHDj30UO3HYk1ut5vvv/+eK664gquuuoo33niDxYsX4/P5+Pe//43b7ebiiy/m0UcfZdGiRXz66achzaKCl9uuXTuuv/56TjvtNO1+r9fLd999xyOPPMLDDz8MVI8d/Oijj2qPWbt2LZ999hmvv/46t9xyC+eee67WTOXWW29t8nugZ1ENmrt27aJLly7a37m5uezevbvVr2uxWCJ+6dzoQdOIl84DgzsfOHCA9evXs3z5cvbs2YPNZmPQoEEMGDCAzp0762acy3isqjvcPhLM1acRi8lEzw4pVLp9cHBVy51exk65kIrSYtqlV7/Pjz/+OLNmzWLUqFG8++67WuUuMTGR3NxcRowYAVRfHq+oqGDAgAF1LnvMmDG0a9eO5ORkxowZw86dO7Xq58SJEznjjDOYOHEihx12GOeff36dQaY+d955JxdeeCGHH3442dnZIfeddtppvPvuu3QePJ5L3lnDea+vYFtyT0pdKkNHjKwzGK5evZpnnnmGnj178tlnn/GPf/yj3mX37duXvn378t1336EoCsnJyVrvc6gOM2eccQbTp0/nlFNOYe7cuRx//PEADBw4EL/fT/v27TnkkEOorKzkxBNPJCEhgWnTpjF79mw8Hg8pKSn88ssv9OzZk86dO7Nx40YWL17MlVdeSV5eHk6nk6lTp7J06VKsVitfffWVVn3etGkTF1xwAY8++igrVqxg1KhRDB8+HKfTWWdbztGjRzNv3jx+/PFHrb0rwNNPP83MmTP56aef+N///ldnu+fnn3+ehQsX8v333/Piiy9qwznV1240WO/evfn666/58ccfueeee0IqxGvWrOHtt9/mu+++44EHHiAlJYUff/yRww47TAuC1113HY899tj/tXfe8XHUZ/7/bFNv23dVLfeCK9iYYmOaaT7KmSMQQgkE24GEcLQQjuQ4jnJHLgVySfhhwBQHAgRMDI4dqjEEgjHYYAy2wbYkW9tXbXud3x++72R2NCutpJ1tet6vl16SrdXu9PnMUz4P3nvvPdx33324+eab+b/v7u7Gm2++yYsLxm233YaTTz4ZH374Id5//30+W8fW47nnnsNTTz0Fr9eb8XoAwKFDh7Bp0yY8//zzuO6667Bs2TJ8/PHHqKurwxdffIFJkyZBo9Fg9uzZWLRoEY455hjU1NRg0qRJeP7553H33XfjxRdfxO9//3scPnwYe/bsQSgUwvbt23HPPfdgz549WL9+PV566SWcccYZ8Pv9uOaaa7B161Zs374d4XAYmzdv5pdHSmBJ8eKndvy/9zqPRsZVSmj+rwzpz5858OAbB9L+nZB3330Xv/rVr/CnP/0JWq0WH3/8Mfbu3YszzzwTJ554Ip599ll0dXVJ/i0ThV9//TXa2towZcoUAMC5556Lv//97/j6669hNpt5B4i6ujo+vS3+XCnOP/98AEdLStItw7nnnssf29u3b+froC+77DJ8+OGHGW2DYqckCgbYCEq5KHXhB+RW/Mn9WZFIBD09PfD7/fjqq6+g0+lgsVgwZcqUoo1WFupym2rLYOsPo7bi6KWkobIMc5qUuOdPH8HWH4GuWoMzj52A7zqd0PyfIG1tbU3xb+zr64PT6QRwtIabcckll0g2pzB++tOf4qc//SmAo6lAsZC8/vrrcf311w+5/A6Hg//5wgsv5A3gV6xYwTfXiJk/fz5++vIubN5tQ3UiiZoKDSqrqhCbdBxaLvwBHr5kFtp0qdY6xx57LGbNmoVt27bhpptuwsSJE3HppZemREuffPJJvjPebrfjzTffHNQEFYvFsGLFCvzyl7/EySefjNNOO23Q6Mzq6mp+vT766CP84Ac/wE9+8hN0dXXxZSHRaBSHDh1CR0cHbr/9drz77rv47LPPABxNXba1teGKK64AADidTvzgBz/A5s2bMWPGDLS2tvLi/6mnnuI/V6/XS9Zy2mw2XH311XA4HIhGo7xoXrx4Me68805ccsklOP/88yXT7mLrp0OHDvEZManPEjIwMIDVq1fjwIEDUCgUvFAGgCVLlqC2tha1tbWoq6vj32vmzJnYs2dPih0UIxKJ8D9fdNFFkvWS7777Ll/aoFKp+BQqW49oNAq73Y4DBw5Ar9dntB4AsHz5cmg0GsyaNQuJRIKvx501a1ZagaNQKFBVVYUlS5bgF7/4Bb+dm5qaoNfrUVlZiYULF+JXv/oVrrvuOmi1WoTDYQBHj4HXX38dzz77LMLhMPx+P7RaLaZMmYJkMoklS5agv78fM2bMQGdnp+TnR+NJrPv7YWiUCqhFde4VGhXe2uvBNSe0wFo/tLd1e3s7Ojo68M0332DBggXgOA6nnXZais1XOtJZXGVyDxJ/rhQsSKFUKtNmVYey2Rov5DSi2djYyFthAEcvHFardczvq1KpUi4ixMgoVBGTKRzHwe/3o6urC7t27cK+fft4O5djjjkGEyZMQF1dnWzrWYiRxlwxxVSNJMchGv9HGqxSo0ZTQyWatRX49sJmtOureJFZCnzjDuKd/R5UqxV8NLfO2o6w/RuEYwms/dvhQX8jrCsX1jmq1Wr++GE3+aFQq9WYN28e3nzzzYyW9fjjj4fX64XH4wHHcbjllluwadMmvPjii/jss89w1VVXARh8MxT/+6qrrsIf/vAHrF+/nu8IFpOulvPWW2/FqlWr8NFHH+Hhhx/m1/OWW27B//7v//KNTCw1zXjvvffwzjvv4K233sKHH36IOXPmIBKJ8PWOUp8l5D//8z+xdOlSbN++HS+88EKKUBRmMZRKJb9/mGBIJpOor6/HBx98wH998skn/N9UV1en2+SDEK7HCy+8gJkzZ/LLksl6AEhZPuHfCAVOumNpqO2sUCigVCpRVVUFo9GIlpYWTJ48GZMmTcJvfvMbbNiwgW8Yq6qq4qP7yWQSdrsdhw4dQjAYxPbt27F9+3Z89tln2Lt3Lw4dOoT393QgGktAqQQ4cODTHACU/7f8H3f2D7v9WltbsX79eqxatQpfffUVFi5ciL///e84cOBoRDQQCODrr78e8j2mTJmCrq4u/m+2bNmCE088kS8pYfvW5/Px21P8udlAXDbEMjClTk6v/sceeywOHDiAjo4ORKNR/OlPfxq2iDoTKHVePJ+Xrc9iFkTffPMNdu7ciSNHjqCiogKzZs3CnDlz0NTUBJVKVVIisBBnnddXanD6dAN6g1E4fWH0h2Jw+yNw+sJY1NaAVl3p2UC9s9+DRDK1a1U3ZQGS8Rj6dm7Gjs4+BKMJfPHFF0PWrwFHb2Y7d+4EcLQejMFsjBgsZaxQKPD73/8e+/fvxy9/+cthl3Xfvn1IJBLQ6XRjqltduHAhuru78eKLL/L1oJmSrt714MGDmDVrFm6++WYsWLCAr6FkMOuqqqoq7Nu3Dx9//HFWPjcT6urq0NbWhg0bNgA4eu7t3r172L9btmwZHnvsMQD/mFAnXA9WHy4H6Y6l4bbzaaedhieeeIIXWD09PbxQ1ev18Pv92LhxI8rKyqDT6aBUKtHY2Ijp06dj1qxZKC8vx6JFR0tGmM1TVVUVL9jjsRiikQjC4QjCkaPvG41GEIvH4fF64XK50N9/VHCmuwZNmzYNjz/+OK644gr4fD488sgjuOaaa7B48eKU+tt0VFRU4Pe//z2uvPJKHH/88VAoFPjud7+LsrIyPPnkk7j11ltxwgkn4Pzzz08R6cLPPXjw4Eh2hyTisiFhLWcpk9PUuVqtxi9+8QtccMEFSCQSuPLKKzFz5sysvC81A5U+Ygui+vp66PV6TJw4UdKCKFeR2lzZGxUqkwzVMB5XjoPuANyBKGrL1ZhsrIahpmz4P0Zhr5sUA+H40WUWnKIKhQLzr/lP7N3wvzj41nNY8mQdJrS14b//+7+HrEO/4447cMMNN+Dee+9NmSR0++234+abb8aiRYugUql46yfgaORr3bp1+Na3voXa2lpcd911Ke8ZCoX4SAnHcfh//+//QaVS4fTTT8e+fftw8cUXI5lMQqvV4rHHHsvYMueiiy7C7t2709arpYPVuzY0NGDp0qV8qvV3v/sdtm3bBqVSiRkzZmD58uUpf3fmmWfiiSeewLHHHospU6bwtbuZctNNN2H16tV48MEHcdZZZ43ob4Gj8+n/9V//FQ8++CBisRguvvhizJ49e8i/efDBB/HDH/4QTz/9NFQqFX71q1+lrEdjYyPmz58/4mXJhHTHktR2FpaMXHXVVfj666+xePFiaDQaXH311Vi9ejXfjGYymfjU8VD3JaEJfn19PU7WGqD+oAdKKKBSsnOcw4n3vIZkMgkVkphtqYLf74fX68WWLVv4h4nf/va3+Pzzz7Fw4UKcfPLJsNvtaGlpwbZt21BeXo6JEyfi3XffHXJ7CGtKgaMPAezBb9euXXxU+9hjj8U777yT8trvfOc7fOR+7ty52LFjx5DvbzAYsGfPnkGvufPOO1P+LS4bknqd0F2jVFAMdeAEg8GiUDu33HILzj33XMnu1Gxw8OBB6HQ62czgxSQSR6Mh4vnLctHX14eenh5MnDhR9s8KhULo6OjI2NIqFArB6/Wip6cHHMdBq9VCr9dnZEH05ZdfYuLEiaOabz8SPv3007Q1PNli7969vPWSHCSTSXz++eeYN29exn+jVqvHPN4VOBq5stvtBTVXeyg273Hh4a2HoE7GoSnTpAi1SDyJcrUSz1+7gE8PFhoulwuBQADt7e0j+ruLL74YP/jBD7Bs2TJ5FiwDPB4PBgYGcnKtkoNvvvkGOp1O0q+0GEgmk/jkk08yFv1r/9aFZz/uRrlayZ8PHMchFEvi+AkN+O+LpO8DyWQyZTyo0O5pKJsn4VdZWVnae8TOnTsxa9asQVaJxQDHcVAqlQX5gF5VVSW5UNQMlAGlHmEspNQ5syDq6elBX18fysrKoNfrMX369KK8KGSTYph1Ph44ZYoea//WBV8gBo3gkExyHMKxJK48vqlgRSZjJDepvr4+LFu2DLNnz86ryASK/xgt9uUfKdec0IJQNIFXPndAgX8kAU6Y2ICfnjM17d8plUpUVlYOOYGNDSQQilE2aSkSiaQMTxCLUOZVqlKpsmaCT6SnJIRmLlLnuaTUha2YRCLBp8T9fj/q6up48/SxXARytR1LIXU+mvcfT8eokKoyFe6/YDpufn4XAtEEoDhqcq5UKHDmdAP+ed7YGxwLiYaGBuzatSvfi0EUACOdqKNSKnDjqe34zqIm7OjqRzzJYU5jLZq1Y6/dZjPmy8rKUFtbm/Z1iUQiJRrKJjEdOnSIn1cPgJ9XLzWVSa1WF2QEsVggoZkhpVyjmY+IZiQS4VPisVgMWq0WVqsVtbW1dEITBc90cw3uXaZFR7QGnf0J1JSrcOo0AyYbM+9GJsYf+Rh9mG1Gs/y66jIsnyE9DUluVCoVqqqqUsqO3G43jjnmGD6QwXEcotFoSnS0r6+P/zfTF1LRUeFXNkqJSpGSEZqUOh89uVg/juMQCAT4DsP9+/dDp9Nh8uTJstVQ5mq/lfKxQaSnQq3Ame26IaMphUgxH6+lINSKmWI+doZCOGN+KOLxeEpkNBQKobe3l4+SJpPJtPPqWXRUpVKNu2O4JIRmqdVolspBmEwm+UajgYEBVFVVob6+HrW1tcN2bxKpyH0M5vOYK5XjnSCGo9iFcrEvP2O066FWq6FWq4f0UU0mkynR0Ugkgt7eXr6hKZFIHJ2UNEwjUylFR0tGaJaSvVGuyeb6xWIx9PT0wOv1IhwOo6GhAQaDgbcgikaj/Pg1uSmlGs1Sp5TPLyK70PmWX0ph+8spmIU2T0N9vjg6ymyeWHQUgGR0tKqqKmcOONmiJISmRqMpqYhmrhnr+gWDQV5cAoBOp0NbW5ukBVGu60FzQa7Wp5SPQYLIhGI/B4o9Iljs219IvrM4Go0GGo1GcvQqQ2zzFIlE4PP5SGjmA7kjmkQqzILI6/Wir68PFRUV0Ol0mDFjRkYWRLm8WJXKhbGYb04EQZQGxS6Uiw0pm6divKeVjNCkiOboyWT94vE4ent70dPTw1sQ6fV6tLW1jciCKJcXqVxOBqILMFFM0LGaP2jbE+ONkhCapTaCMh9IrV84HEZPTw96enoQj8fR0NCAxsZG1NTUjPpimettWUr7rZTWhSBGSzELtWI/h+mBmhgNJDQzoNSFJrtwcBwHv9+Pnp4e9Pb2QqVSQa/Xy2pBVArkIqJZiBf3WCyWlVFohbhupUwxX8uKedkZxX68F/vyE7mnZIRmOByW7f0VCgU/W7XUSCaT6O/vh8/nw6effoqamhrodDo0NTVBrc7+4VFI4y6JkcFxHILBIDweD3p6eqBSqfjzQqlU8j5x7Dv7eaiZw8L3JohSp9iP82JffiI/lITQpBrNkRGNRvmUeDgcRk1NDcrLyzFnzhx6Wh0FpdbdLv5Mv98Pj8eD3t5eVFRUwGAwYPbs2SgvL+frc9mYt3A4jHA4jEAgMMiqQ6VS8eJTKEbZTGKCIAobSp0To6EkhKbck4GKHY7jEAqF+JGPwFELogkTJqCyshLRaBTffPNNzmZ2l2JEU+7PyeXFXewqUFVVBYPBMOTseakxb2Li8ThvWhwOh+Hz+eB2uxEIBBAMBvHRRx9Bo9GkREWF32neMFHsQqfYl5/IP8V4/JSE0CTD9sFwHIf+/n709PTwFkR6vV7SgqgY14/ILsxA+JtvvkF/fz9qa2thMBgwYcKErE2oUKvVqKmpGeQb5/P5cPjwYcyYMWOQGO3v74fT6UQ4HOYfJocTowRByAMJZWI0lMRVmZqBjsIsiLxeLwKBAG9BlE2xMFZybW+Uq8lAxThTnY0I9Xg88Pl8iMfj0Ov1mDRpUl5uJkIT43TzwzmOQywWSxGjPT09/M+sZrSsrCytGB2JHRdBZJtiFmokNInRUBJCU9iUIAeFLDTD4TCfEk8kEtBqtWhubkZ1dXXGF4RCXj/iKNm6uCcSCfT29sLj8SAYDKKhoQFmsxlTpkzBzp07odVqs/I5I2Ek66ZQKFBWVjbkYACO4xCNRvl6UTZNg4lRdqxLiVD2vVAezOSimMVCMS87XWeJ8UhJCM3xFNFkzRlerxe9vb3QaDTQ6XSYOnUqysvLR/WehbR+2aSUajTHQjwe50eEhkIhaLVaNDU1jckPNdtkc/sJ5wPX19dLviaZTA4So/39/fyYt2QyOWQn/WjPNWJsFPJ5lgnFHhEs9uUn8gMJzQzJ5wUukUigr68PPT09GBgYQE1NDfR6PZqbm7NWk1bsF/B8Uohd57FYDD09PfB4PIhGo9DpdGhtbZWcPz8eUSqVvHBMB5szzMRoKBRCb28vL0ZDoRB8Ph+qqqpGbeuUD+hcJ8ZCIR7TRGFTMkJTbnujXJNMJuFwOHiLGK1WC5PJhMmTJ2d9eUr1wlFKkdpM9lE0GoXX64XH40EikYBer0d7e/uQneAj/YzxhNScYSFffvklGhsbUVZWlrGtk/i7RqOh7T7OKOb9XSrXUyK3lITQLAUfTWaGLfS3jMViIxIKo6WUBFk+yOf2i0Qi8Hg88Hq94DgOBoMBU6ZMoUlOOWIstk6RSIT3ECVbp8wo9tRtKVxni3n7E/mhJIRmsdZoJpNJ3q+wv78flZWV0Ol0mDlzJvbs2YOmpqaSb0qQk1IV0KFQiJ/Oo1QqodfrMX369CEbZIj8kc7WicGspUZr61RRUUGd9EVEMQu1UryeEvJTEkKzmHw0WWNGT08PgsEgb0HU3t6eIipL0QaoVMnFvorH43A6nejs7IRarYbBYMDMmTOh0Whk/2y5KeYbbzYgW6fxQ7FfZ4s9okzkh5IQmrmo0RzLBSIUCvHiciQWRMV+Uco3xdx1znEcAoEAP/oxkUhAp9Nh8uTJJWlKTsf60IzV1ol10rPXMUu4YrN1IqGTX0rhPC2FdSg2SuKOJXeNJjCyg5PjOPh8PvT09PAWRHq9fkQWRLke1UjkH3bceDwefvSjXq9HS0sLDh8+jLq6upIUmUR2yNTW6ciRIwiHw6ioqBhk68SE3FC2TnS9GD2lIJSLffmLeR8wTVBsy18Sd61c1GgOB7Mg8nq98Pl8qK2thU6nQ0tLy6hSVpTOHjvFMBmIjQr1eDz86EepaU50PBDZQKlU8p3uFotF8jXD2Tqx90knRgvV1okYO8Us0oQU8zooFIqiW/6SEJoajSYvqXNmJ9PT04NoNIqGhgZYLBZMmTKl6A4EInew0Y9erxcDAwOoq6uDwWDAxIkTCz51SRQ/wz2wDGfrBBx9sBaK0VzaOhX7tZWWP7/QA3vuKQmhmatmIGZBxKbyKBQK6HQ6TJw4cciL8lg+kxg9hVSjySLeHo8Hfr8fDQ0NsvmiFht0rBcf+bJ1KvbjhJY//5RKVLaYKBmhKVdEM5lMwufzwefzYefOnaisrIRer5e945duvsVDuotWIpHgp/Ow0Y+NjY2jGv1IxwNRbMhh6xQMBlFWVoZAIFC0tk7FLnKKfflJaOaekhCa2e46j8Vi6O3t5WdDV1VVQaPRYPbs2TlLbdKJMHbysQ3Fox+1Wi1aWlqGdRgoBEjIErlkNLZOfr8fgUAAhw4dKkpbp2I/x4p9+RmFfi0uNUpGaI41dc4siNiEFa1Wy8+GDofD6OjoyHn9XKmc1PkkF9uQ4zi4XC74fD7E43HodDpZJjrlYl02bdqEK6+8Eh9++CGmTp0q++dlyqZNm7B3717ccsstWX3fe++9FyeddBJOPfXUrL7vSLHb7bjtttuwfv36Mb/Xe++9B41Gg8WLFwMAVq9ejXPOOQcXXnjhmN87m2SyXGJbp1AoBI1GA6vVyr+G4zicfvrp2LBhQ1pbJ/Y+6cRoLq/txSxySiEaWArrUGyMW6HJrGS8Xi/6+vpQVlYGnU4nOWElH2lLSpUWNpFIhJ8rHgwGUVlZicmTJ8s2+jFXF8aXX34Zixcvxssvv4w77rgjJ5+ZCeeddx7OO++8Qf8fj8fHZPl01113jWWxsobVas2KyASOCs3q6mpeaEpRSjdahUKBt99+GwCGtHUSe4zmw9aJrun5h/ZB7imJFlelUplR6jyRSMDj8WD//v349NNP4XA4UFdXhzlz5mDWrFmwWq2ShsgkNIuTbG/DcDiMI0eO4LPPPsO+ffsAANOmTYNWq4XJZCrK+eJufxSv7XHjDx/bsf2wHx/+/e946KGH8PLLL0u+vqurC4sXL8ZNN92ExYsX44ILLkAoFAIAHDx4EBdddBGWLFmC5cuXY9++fUgkEjjmmGPAcRz6+vpQX1+P999/HwBw1lln4Ztvvkl5/1NPPRVfffUV/+9zzjkHn376KdavX89HM1evXo0f/ehHOPXUU3HXXXfh/vvvx0MPPcT/zaJFi9DZ2YlAIICVK1fihBNOwKJFi/DSSy8NWp/Vq1fjlVdeAQD87Gc/w3HHHYfFixfjzjvvHPRav9+PNWvW4Pjjj8fixYt5cXPTTTdh6dKlWLhwIe677z7+9bNmzYLH4wEAfPrppzjnnHMAAO+//z5OPPFEnHjiiTjppJPg8/nQ2dmJRYsWAQDWr1+Pb3/727joooswb968FDH81FNPYd68eVi2bBl+8IMfDIrwdnZ24vHHH8dvf/tbnHjiifjb3/4GAPjb3/6G008/HbNnz8aWLVv41//617/GKaecgsWLF6csuxCLxYI77rgDCxcuxIoVK+B2uwEA69atwymnnIITTjgBl19+OYLBIL9Nb7vtNv7z2PblOA633HIL5s+fj3/6p3/itw0A7Ny5E2effTaWLFmCCy+8EA6HA8DR/X/HHXdg6dKlOPbYY7F7927ccMMNmDdvHu65556UZWT88pe/xPHHH48TTjgBP/vZzwAcvUdUVFTwziBtbW2YNm0a5syZg4ULF2LRokU49thjMWXKFBiNRpSVlSEUCsFut2Pfvn34+OOPsX37duzYsQO7d+/G119/jcOHD8PlcmFgYIAXq5lQzCK/FKKBpbAOxUbJRDTTCc1IJMKnxGOxGLRa7YgtiEhojl+CwSA/V1ytVkOv12PGjBl5mSuezeOB4zi88rkLL33mRJLjoIACHR/8DRXtC5CotUKn02HXrl2YN2/eoL89ePAg1q5di9/85jf47ne/iz//+c+49NJLceONN+LXv/41Jk+ejI8//hg333wzNm3ahClTpmDv3r3o6OjAvHnz8OGHH2LhwoXo7u7G5MmTEQqF+HVbuXIlXn75Zfzbv/0bHA4HHA4HFixYgC+//DJlGbq7u/Hmm29CpVLh/vvvl1zHN998E1arlReY/f39abeH1+vFq6++ik8//RQKhQJ9fX2DXvPf//3fqK+vx0cffQQA/Pef/exn0Ol0SCQSWLFiBb744gscc8wxaT/roYcewi9+8QuccMIJ8Pv9kg8ou3fvxvvvv4/y8nIsWLAAa9asgUqlwoMPPoj33nsPtbW1OO+88zB79uyUv2tra8O1116L6upq/OhHPwIAPP3003A4HHjjjTewf/9+rFy5EsuXL8dbb72FAwcOYOvWreA4Dpdccgnef/99nHzyySnvGQgEMH/+fPzXf/0X//WLX/wC559/Pr773e8CAO655x48/fTTWLNmDQCkfN63vvUtXHjhhdi4cSO+/vpr7NixAy6XCwsXLsQVV1yBWCyGW2+9FX/84x9hNBrx0ksv4T/+4z/w+9//HsDRZqBt27bhd7/7HX74wx/ilVde4UXiDTfcAL1ezy/r66+/jk2bNuGdd95BVVUVenp60u4HMbmwdWI1pcVKKYi0UliHYqNkhCZLnbPRfcyCSKVSQafTYdKkSWOyICLRV3yMRqyLRz+Wl5dDr9fjmGOOyetUnmxfGHd0DeDFnQ40VGmgVh5970/3vIvJp30LD751COesuAAvv/yypNBsa2vjBc68efPQ1dUFv9+Pjz76CFdeeSX/OmbuzSJrHR0duPnmm/HUU0/hpJNOwoIFCwa99z//8z/jggsuwL/927/h5ZdfTlu/d9FFFw3b5DFz5kzceeed+OlPf4qzzz4bJ510UtrX1tfXo6KiAtdffz3OPvtsPvooZOvWrVi3bh3/77q6OgDAhg0bsG7dOsTjcTgcDuzdu3dIockippdccgnOP/98ya7sU045hU8DT58+HV1dXfB6vTjppJOg0+n4bSCOCKdjxYoVUCqVmD59Oh9JfOutt/D222/z2yUQCODAgQODhKZSqcTKlSsBAN/61rdw+eWXAwC++uor3HPPPejv70cgEMDpp58u+XkulwsA8MEHH+Diiy+GSqWC1WrF0qVLAQBff/01vvrqK1xwwQUAjoo5YYTy3HPPBXA0Qjxp0iSYTCaUl5djwoQJ6O7uThGa77zzDr7zne/w9dFsW2WLsdo6+f1+7NixY8S2TkR2oe2bW0pCaLJi79WrVyMajeLHP/4xdDodGhsbs2JBRBHN4iTTbSis1+3t7UVlZSUMBkPGU52KcV9t+NyJCo2SF5mRwAA833wOv7MLSQ7YplagskyF//iP/xh0URZGc1UqFd/9W19fjw8++GDQZ5144ol4/PHHYbfbcdddd+Ghhx7i08diGhsbodPp8MUXX+Cll15KSYkLqa6u5n9Wq9UpkSImcKdMmYL33nsPr7/+Ov7zP/8Ty5YtS1t3qlarsXXrVmzduhWvvPIKHn30UWzatCnd5uPp6urCww8/jK1bt0Kr1WL16tUIh8P8e7Ljgv0fANxyyy04++yz8de//hVnnnkmNmzYMCiqKRxVm2lp0FBIjb5lqexrrrlmRO/Fjoc1a9bgueeew+zZs7F+/Xq89957kp833LnBcRymT5/OlyKkW3alUply7CmVSln9k0fLULZOH3/8MRYsWIBkMjkiWydx3Wi+OulLIRpYbNfqUqBoazS9Xi+effZZXHHFFTjrrLPg8Xhw/vnn4/e//z2mT58Ok8mUNZ/LfJxYxSheRkK+jdTZ6McDBw7g008/hd1uR11dHebOnYsZM2bAaDQWlC1KNklyHA55Q6gu+8f6dX3yDpoXnIaLHngJZ979PFb99jW0tbXhww8/zOg96+rq0NbWhg0bNgA4un13794NADjuuOPw0Ucf8XVyc+bMwRNPPJE2wrhy5Ur8+te/xsDAwJCRQUZrays+++wzAMCuXbvQ0dEB4Ggnd1VVFS699FL86Ec/wq5du9K+h9/vx8DAAM466yz813/9F7/sQk499VSsXbuW//fAwAB8Ph+qqqpQX18Pl8uFN954I2W5du7cCQDYuHEj//8HDx7ErFmzcPPNN2PBggXYv3//sOsIAMceeyz+9re/obe3F/F4HH/+858lX1dTUwO/3z/s+51xxhl45pln+NfabDa+/lJIMpnk6yxffPFFnHDCCQAAn88Hi8WCWCyGF154YdjPO/HEE/Hyyy8jkUjA4XDwwnTKlCnwer18KUIsFkup0x0Jp512GtavX8/Xi44kdZ4rmK1TbW0tDAYDmpubMWnSJMyaNQvHHnssFi1ahIULF2LWrFloampCbW0tEokEent7cejQIezcuRPbt2/H9u3bsWvXLuzduxeHDh2C3W5HT08PgsGg7JPyiplSEMvFRlFGNDmOw/e//30sXLgQd9xxB6ZNm4alS5dKdqVmg1IXfbkmVye5+HOSySQ/V1w4+rG9vX1M9ia5OD6y+RkKAGVqJRIcoP6/TdT58ZuYsOQiAEAiyaG6TIUVK1bgpZdeSok8smVIJpP8tCyO45BMJrF27VrcfPPNePDBBxGLxbBy5UrMnj0b5eXlaGpqwsKFCwEcFRx/+tOfMGvWLMnlu/DCC3H77bfjxz/+cUbrc8EFF+C5557DwoULcdxxx2Hy5MkAgD179uCuu+7i53v/6le/Svsefr8fl156KcLhMDiOwwMPPDDoNbfffjtuvvlmLFq0CCqVCldccQWuuOIKzJkzBwsWLEBzc3NKp/cdd9yBG264Affee29KOvp3v/sdtm3bBqVSiRkzZmD58uV888tQNDY24pZbbsGpp54KrVaLKVOm8Ol7Ieeccw6uuOIKbNq0CT//+c/Tvt/pp5+Offv28Snv6upqPPbYYzAajSmvq66uxo4dO/Dggw/CaDTiySefBAD89Kc/xamnngqDwYDjjjsOPp9vyOU///zzsW3bNhx33HFoaWnhm5/KysrwzDPP4LbbbsPAwADi8Tiuv/56zJgxQ/J9hrp+nHnmmfj888+xdOlSlJWVYfny5bj77ruHXK5ckqnIEds6pXsvcSe93LZOpXAfJKGZexRDHTjBYFDWo+rll1/G/fffj71792Lbtm2SNVuZwHEcTjzxRLzzzjtZXsKjJJNJfP7555L1anLxzTffwGQySd5I5GDnzp2YP39+Tj5r165dmDNnjuzedU6nE9FoFFVVVfzox/r6ehgMBtTX12ftYvP111/DYrGkNZ3OBt3d3VCr1TCbzVl5v8c+OIx3vu6FvvofUf+BgQHU1taiJxjH7We0Y27T0fVh1wj2XaFQpHyJYf831O+ERCIR7N+/H3PmzBn7iuWQr776Cs3NzbLudzF+vx81NTWIx+O47LLLcMUVV+D8888f0Xt0d3eD4zg0Nzdn9HqLxZKREM4FHR0dqKyszNp5kGu2b9+OhQsX5kzoSNk6se+jsXWy2WxIJBJoaWnJyfLLgc/nw+HDhzFz5sx8L8qIYfsr157emVJVVSV5YOc1ojlz5kw8++yzuPHGG/O5GMNCNZrZRe51Y6Mf7XY7IpEIjEYjrFYramtr6Un2/7hgjhkfdw2gJxhDQ6UaSoUC8SQHbyCG2U21mGWp5qOVAPiL20gucuk6bIX7IBaL8SKGvV68j2if/YP7778fW7duRTgcxumnn45/+qd/GtX70DbNH7nc9qxcZSjrtWQymdJJHwqF0Nvby4tR9j7l5eWIx+NQqVQpwrSsrKzojqdiW95iJ69Cc/r06fn8+IKmlIWmHMTjcX70YyQSgVarhU6ng0KhkP3pO1f7KpufYawpw3+cOxnPfmLHp4cHoAAQTQAXzTfhotlG/F+P0IjFpZB0F/NYLAaPxwO32414PA6DwYDJkyenCFNhVFS83plGSkuRdFZOI2GkqcNCiWYyxsu+zhUjsXU6cuQIYrHYiG2dNBpNwew3uq/mnqKs0cw1hXKClArZEmaxWIyfzsNGP7a1tfEdyS6Xi78IFjtyHIOWunL867I2BKIJBCNxdH79JaoqenCkK8BHQdjXWKMW8XicF5fRaBQGgwFTpkwZ1nIsk6hoJmKUzuHSgERCfhDaOikUCjQ1NQ16zVC2TrFYDBzHDdlJnyv7OKrRzD2y79nzzjsPTqdz0P/ffffdWLFihdwfX7TkI6JZDCdgNBqFx+OB1+tFMpmEXq9P65Gaq21YbNFnYUOPQqFAdZkKNeVqmI5dgFgsllLHJRzTBwyOWgh/FnfpJxIJeL1euFwuRCIRfl+NZAZ8JsfjcGKUoqIEIT9D2ToBR687YjGaD1unYrjPlRqyC81MvOiKgVKv0WSfl4sTcKTrFg6HeXGpUCig1+sxdepUSW/AUmYsx4NUQ49KpRqUFmdCMt3MaPGNoq+vj/+30FIlkUggkUigrq4ORqMRDQ0NstVyDfeeFBUtLWg/5A+O40bdiMJsnZi1U7r3j8ViKdeY3t5evn6UnctDddIPJ0ZJaOaekkqd0wFUOgSDQXi9Xni9XqhUKhgMhhGPfsylWM+FvdFIkRKXY6m5BKSjFslkEj09PXC73Xxnf01NDZRKJR8VZQ4A7D3SdbnK4V0qR1SURWeEDVOZfhYxeoopc1CqyHmM58LWiXRC7smr0Ny4cSNuueUWeDwe/PM//zPmzJmTYmycKbmKwuWSfEU08/VZHMelzBUvKyuDwWDArFmzsma8LxeFdNGSQ1xKkUwm0dfXB5fLBZ/PB61Wy5tLD7c9WFSU3SRYhyuLWDC7FSkxKldTQSZRUTae1OVyoaenB7W1tSnzq4spKlooy0EUF4Ug9IXXh3SZFylbJ1YGFAgEEIvF0NfXJ1kCJGXrRIyNvArN888/f8QecOOFfNVo5gJ2AnMcB7/fz88Vr6yshF6vx+zZs7NSGF5stZNDoVAo0kbdhhKXALImMDmO48XlwMAAGhoaYLVaMW3atBFdlIer5RLfJIR2K8Ko6GhTZyMlFArB5XLB7XajsrISJpMJEyZMGPQ5VCtKlDrFEg0cytaJjRqeOHFixrZOUmK0GG2d8kVJpc5LiXxENHMBq8Hp6OiA3+9HVVUVDAYDWltbi3bkYz4fCnIhLvv7++FyudDf34/6+nqYTCZMnTpVtmNmOO8/lrYW3iRYHZcwdSYlQjONikYiEbjdbrhcLqjVaphMJsybN2/IByCqFZWfYhE6ROEiND3P1NaJXWeK2dYpn5DQLFBKKXXOImFerxf9/f1IJBKwWCyYPHmyrBMOSimiCSBl5CMgr7gcGBiA2+1Gb28vamtrYTKZZN9fmSJsKhgqKsoiE+wG0dPTg3A4jFgsBuBod6v4phAIBNDb2wuO42AymXDMMceMqC54uOUejlxERUvpnCByS6kI/UzXQWjrlI7hbJ2A9BmYXNo65ZPSX0MiY7J5A2I1fB6PBz6fjx/9OGnSJHz11VdoaGgoCNFS6Aj3SW9vb0q0r7y8PGsXKVbGwOoPa2pqYDKZMHHixKLcT8NFK1hUNBgMwu128/6ewnSYw+FIqePKRaQiV1FRghgtxX4sZftBq1hsnfIJCc0CpRhT54lEAr29vfB4PAgGg2hoaIDZbMaUKVOGvRHKQTF/jjhyaTAYUFZWltLFLZUmlnpaTrdvxc0tVVVVaesPSwn2EOR0OuH3+6HX6zFz5kze6F/4unRpM2FUNF3aTA6BPpaoaDweh9vths1mw8SJEyUnMY30s/JBoS7XeKAUouG5jspm29ZpxowZww66KDRKRmjm0gcyFxRL6lw4+jEcDkOn06GpqQk1NTUlsy9yxVBpcbVaDaPRKPl3yWQy5QLl9/vh9XoRDocRj8cBpPrOKRQKBINB+Hw+vrmlra2tpMUlKwdwOp3o7+9HQ0MDGhsbUVdXl/Y4zSQqKjS4Z9ue7QvmOZiuVnSoh4CxIHxPZj3ldDoRCoVgMpkwa9YsVFRUDBKko4mK5vocLwWhU8yUwj22ENchU1unSCRS8C4sUpSM0FSpVEgkErKl+egC9w/Y6Eev14toNMqPfmTjyYajmCON2f6cbNRcKpXKIeuImMhyOByw2+1QKpUoKytDeXk5wuEwurq60N3dLRkRlcvbMhewiK3T6eTtiLJZayq8OaSLVLBmAmH9FnsoEz4ESInR0UZFxaJaq9WitbV1RA9/mdSKss+S+n26fxPFT7Hv00IUmpnAMlfFuOwlIzTVajXi8XhRqn0pCi2iGY1G+bniiUQCer0eEydOHFUIv9SadEaKWFwKPS6z+aAUDofhcrng8Xj4zumJEydKniNS3pbpurhHkp7PB8yOyOPxoKKiIq/lAMM1E0ilzITm0ywqmi49L9z2wWAQTqcTHo8HNTU1kmUrmTLc34iN6qX+tlCjosToKZXrNh1zuaVkhKZKpeIjBHKQ6wNzKN9EuT5PfBGJRCL86EcA0Ov1mDJlSlrbmUIjl4J2uM/JlbiMRCK8yFIqlTAajZg9e/awD2CZeFtK1Sumi8yJBZHc5w+zI3K73VAqlTCbzZg7d27Bd3RmkjITW6yIu1pjsRiSySRUKhXq6urQ3NyMyspK2aeg5MPOqVijUaVEsW//UhHLxURhX4VHgFqtTpm1LAe5vMjlK+oXCoV4calSqaDX6zF9+vSsWbwApRfRHKrZJhfiMhqN8iILAIxGI2bNmpXVfZZJvWI0Gk0RRAMDA3ykDhjsNyf8eTTRRtbc4nK5kEwm+frDbK53ISCOiiYSCXi9XjidTiSTSVgsFjQ0NIDjuEG1otFoFBzHQaVSDVkrKgfZtnNiDztS0dRiFz/FQilct+lhJfeUjNDMRUSzFA9QNvoxGAziyy+/RHl5edGMfhyOfBmp50JcxmIxXlwmk0kYjUbMmDED5eXlWfuMkcBS6+Xl5airq5N8zUjS80IxKkzPM5HlcrkQiURgMBgwderUouvCHCnMi9bpdMLn8/GlK+JO+aG2vbBWlI3jE9qrSI39lHscXyZRUb/fz1tNNTQ0oLq6elDHPNWK5oZSuAeWwjoUGyUjNOWOaOYjdS6ngXogEOBHP7Jar/b29rQ3qmxSahFN4OiNPJFIyC4uPR4P3G434vE4DAYDpk2bVjSlDKNNz4dCIUQiEcTjcXAch4qKCjQ0NMBoNPIiKJlMFqXf53D4/X6+mamurg4Wi2XEIz+Bo9terVYPEqYMFpEWe/0Jx/ENNQEl21HRaDQKp9MJl8uF8vJyWCwWTJo0SXIf0+hPYiSQ0Mw9JDRHQCE154wUjuP4jte+vj5+9GNLSwtUKhX27dtXtN3F6ZBbrLP3rqmpwZEjR2Cz2VIie8KIXKajD8XE43E+gheNRmEwGDBlypSSjOAJ0/PCzul4PA69Xg+TyYTy8vKUOegsPS9MEWczPZ8PIpEInE4n3G43ysvLYTab0d7eLvsULXbcpkM49lPs58oesoQ2WsLvmcyFTiaT8Hq9cDgciMViMJvNmDNnzrCZlXzUio5XSkWklcI6FBMlIzRzlTrPFdn4PDan2uPxoL+/H7W1tdDr9ZgwYcKgm1Yu169YI5ri2jAWvdTr9bzHJfM6YzfkUCjEp4jFJt9iQVRWVgalUpmSHg6Hw/xEpaHGoJUKbDqR1+tNa0dUUVExbHpeaHrMfh4qPZ/vucTxeBwej4evu8xUZOWS4aKiyWRyUJ1uX18f/yAA/CMqKkzLx+Nx9PX1YWBgADqdTrIkYCxku1Z0qPctdQFTCkKzGO89xQ4JzQwpFnHEpp54vV4MDAygrq4OBoNh2FGCxbJ+IyEbF8R04lKYHhd/JruR1tfXS76flJ1NKBRCMBhEPB5HMplEZWUl6urqoNfr+ShTqaaHhXZELII3WgP5kaTnI5HIkNN+0j0IZItkMone3l44nU4Eg8GirzcVjkeVOvaBfzwI+P1+uN1uDAwMQKlUQqVSQaFQoK+vD8FgUNK5IJOo6GjJVVSUyD/FLpaLcdlLRmjmokazUCOabPSj1+uF3+9HQ0MDHwkqxIOykGyH0v3NSMTlSBDa2TCh4ff7EYlEYDQaYTKZUFFRkRIVEo4iE0+bEX7JUSsnF9FoFC6XK+d2RJlO+xE2Lfl8vrQd3COdScxKWJxOJ9/c0tzcjNra2oI8V7OJsFs+kUjAbDZj+vTpKVFbFhVl255lBCKRCB8VVavVaW205CqPGGtUNBwOw+FwDLKtK7aoaLGLNKA01qHYKI67UgaMN6EZj8f5ueKhUAharRaNjY2jHv1YiqnzkWwHOcWlEBZxdrlc8Pl80Gq1aGpqGiQ0NBpN2qic2FdRqlZuuO7tfCC2I5LDhmmsCB8EhuvgFqaHh0vPKxQK9Pf3o7e3F1VVVTCbzWmbW0oJ4VSqgYEBGAwGTJ48OW0ZiDAqmu79xLWi6dwLpMSonOUR4vdNJpPweDyw2+3gOI4vhxjr6M98i6R8f342KIV1KCZKRmjKnTrPNVInQiwW4+eKs9GPra2tGY9+HI5SS50DQ69TrsQls6ZxuVwYGBhAQ0PDqLuHgeGnzaRLD0vNPhffiLOdnkwkEvys7VKxI8qkVjESicDv98Pj8eDIkSPgOI4X+cFgEEeOHOEnF8mZns8XLILn8XhQXV0Ni8WCqVOnjvnYUigU0Gg0Qz6Ise3PzoFgMIienp6UOmm1Wi1Zp5uNqKjP5+PtmNiQi0zrqwu9VrQU7hGlsA7FRkkJTXYRkYN8eTKy0Y9erxfxeBw6nQ7t7e1ZbwzJ5RNePiOauRSXAwMDcLlc6OvrQ11dHUwmU1ZutsORqbk6i8iFQqFBTRsajUYyPZ+JEGJRW6fTCb/fzzegpRMGpQTrnGbCmo39FHZzy52ezxesocnhcAAAzGYz5s2bl/NyjkyOf3HTGBOiw41cTdc0FovF4HK54HQ6UVZWBqvVOqrSpULvoC+FtHMprEOxUTJCU61WyzqyMZdCMxKJ8OJyYGAAer0ekydPltUvsRSbgYB/CMtciUufzweXy4Xe3l6+a7rQUqTC1GK6hiWhubrU/G0pT0Vh93BDQwMaGxtRV1dX8hd15u7gdDr5zumhhPVo0vPs/cXp+Vynh8WwaL3D4YDf74fRaMS0adMKOmItjIrW1tZKvkYqKyAVFVUoFIhGo0gkEtDpdJg8eTJqampkO9+pg37skNDMPSUlNIu56zwUCvHiUqlUoqqqClqtFlOnTpXtM8WUUo0mE5fBYBAulyvlZpzNiBDHcbwlT09PD2pqavgoViGJy5GQyY2YCdH+/n643W74/f6U7uGBgQFEo1HJiFyxNCwNRyAQgNPp5K2YzGZz1iLWmabnmRiSEkKse14qPTzWYzMYDMLpdMLj8aCurg5WqxX19fUlcwMfKioaDodht9vhdrv5eluFQoFIJILDhw8PatqTehiQs1Zazqgo+z/h9LNipFiXu1gpjSs+itPeKBgMwuPxoKenB2q1GgaDATNmzEBZWRnvf5krSuHEE0cuKysr0dLSwtfLiVPDUjWKmY7bY/YsXq8XVVVVMBqNmDBhQsGmNbOJ2I6osbEROp0uZd3FDUs+nw9ut5sfeciielKd8/n0sxwO1i3vcrmg0WjGZMU0FkbSPS+00RpLej4ej/PpYaVSCYvFggULFoyLYz6RSPBlARzHwWKx4Nhjjx1y3dk5wLY/q9kV1krL+TCQjpFGRTmOQ09PD+x2O+LxODQazSDBme69C/E8pohm7ikpoVnoIyilRj8aDAYcc8wxg6I8hdblXqifNVxa3GKxSP6N+CbMJsywcXusWUD4xdKEXq8XFRUVMJlMaG1tHRc3WrEdkclkGtKOKJOGJWGdqJSfpXjKjLBONJc3CiYy2JQis9mMY445pqC65cUI0/PpyCQ9z/6ejQAVpofHw83a5/PBbrejv79/xI1sw50DUtch9kDMSlSEVmZiMSp3VJRFbj0eDxoaGtDe3s6Xg4ivu+K/Zd8LMUVfiiVihU7JCM1CHUHJ6va8Xi96e3tRWVmZMvoxHaVaMwmMfd3GWnM53E1YWKPIGnp8Ph+AozcP1njm9XoRCAQGReSKNWUuRk47okxsbMRTZvr7+1Oi0sKHAfFNeKz7gOM43kw9EAjwddKlNJ1pqPR8IBBI6RrX6/UAjj5wHDx4MCfp+XwRi8XgdDrhdDpRUVEBi8WCKVOmZF0QZfIwIMwMsKYxcVS0rKxMUoyOxsGANbPZ7XYkk8kho9aF3riUDopo5p6SEpqFkjpnzQFerxd9fX2oqamBwWBAW1tbxid+KUc0R0OuGnqAo9EblhpWq9UwGo2YMWNGirF0LBZLEUHMPkjcLCMlhAr5BlwodkTCZhephhlxwxJroBNHg6RS8+lSw6ze1ul0ore3F/X19eOmoQlI7ZxmZQFDzVhPl54XNo0J94FURK5QYA8Wdrsd4XAYZrMZs2fPznvUOpOoqPCBjIlRqX0gJUbZPgiFQrDb7fB6vdBqtZg0adKYx4AWcuPSeDifC4nCOdPHSL4N25PJJF9XKRz9ONSFerjPyyWFmDrPtbh0u918athoNGL27NlpZ00P5+UnFkFS033EIkju6SbpEJvIF4MdUaYNS+IJS2JjdSY6Y7EYP/rQYrEUdTPXSEgmk+jp6YHD4eDtmDItCxhpej4SiUgOFxDW6gpFUC5qdUOhEB+5ra+vR2tra1GVBQz3QAYcfXgUPwywemlWqqJQKFBbWwuj0YjKykr+AUJuX9d8REUpopl7SkZo5qMZiI0Q9Hg8/OhHo9GYtdGPhRxhlItcistoNMqLSwBZTQ0PN3ObXfzFYyaZj1+6MZPZ8lEU+3w2NDTAarWO2kS+EBkqNcwaWxwOB5LJJKqrq2EwGBCNRmGz2XD48GEASNssU8gNS5ng9/vhcDjQ29sLrVaLtra2tIJ9LGTSPS/2dGXngjA9Lz4XxpKeFzb2ACj5piaVSoXq6mp+HwSDQdjtdt6OymKxQK1WS9asSzWOib/LGZnOdlSUuTUAwzcyEdmjpIRmLiKaLLXIRj+yG3S2ZxWXcupc/Fm5FJexWIwXl6zucMaMGSmG2rlAfPEXk27MpDgaJ5UWHurCz6yYvF4vampqxs0oROAf0Tun04lQKASj0YiZM2cOWScq3AfpRJDUfsjUvSCXRKNROJ1OuFwulJeXF0TkNpNaXXGZilTDzHDpeVYWMdrGnmInmUzC7XbDbrdDoVDAarUOyrYNdQ2Uikyzf7P7rtDKSfhd7nMhk6hoJBLhG5u0Wi0qKysHzZwvxg76YqFkhKZGo5EtohmPxxEMBjEwMACO46DVatHS0oLq6mpZu/5KVWgCRy/84hNdLnHJJpa4XC7E43EYDAZMmzZNVgP8sZJJ17YwIsoiEEL7IGE9IkuZVVZW5s2SJx+wyK3T6UR/fz+0Wm3G6VGhmE/33sIJP+ncC6Qi07lqlmHNHQ6HA7FYjJ+3na4kpNAQpufTRVyF2QFxej4ejyMejyORSECj0aChoQGtra2orKyESqUq+TRqMBiEzWZDb28v9Hr9qM30h4tMs4cyKTHKzgWpIQ9y1usyl5Du7m7EYjFYrda0kWsyuZeXkhGa2Y5osq5ij8eDeDwOhUIBs9kMq9Watc8Yb7DIpUKh4G8E7MZbVVWVVduaeDwOr9cLl8uFaDQKg8FQUl3DzNQ/3foIrUk4jkNlZSWqq6sRjUbR1dWFw4cPS/pYym0mnSuYUT/rmjabzZg8eXJWxZ1QBKWrjxuuaSxdNG4sJRLM6cLhcKC/vx86nQ4TJ04cc3NHoSLODggbe5LJJJqbm9HQ0JAiSNnI1XTpeaEIKrZofyKRgNvthsPhgFKphNVqlT1yLXwok5o2BmDQ2E9xvS6AtOn5kdwbYrEYHA4HnE4namtrMyoLKdYO+mKhpITmWCOarHOV3Zz1ej0mTZqEyspKdHR05DS9WioRTam0OJsiImUkDUinIzO52CQSCXi9XrjdboRCIRgMhpK+wYphkVun08mXBcyZM0fyuE3nYym0TZGqiyvk+kRWc+tyuaBSqfI2a1vIcE1j6aJx4lGT6UokhPshEonwqXE2sSbb4rqQGW1jz0jS8+mm/BQCgUAANpsNfX19MBgMmD59ekFlbYarWxdekyKRCEKhEP9AILQ0SydG2fr7/X5YLJasnvuF3EFfDBTGGZIFRtt1Hg6H4fF44PV6oVAo+PSC+OZcKsJvKLJpoi78LpUWT/fUK5WOFPsnCiNxZWVliEajfMpSp9Ohra1N1rKGQkJoRxQOh2E0GjOqPcukNk544w0Gg4NGHKaLiOZSiLKHC6fTiVgsxtdd5rrmdrQMV6srnLnNHsqEPoosQ8A6h7VaLSZOnIiampqCfSDIJqyxh9UejqaxZyTpeWE0TqpGMdeNY4lEgm9qU6vVsFqtRVtznck1SVwr6vV6MTAwgGAwyDtRVFVV8XZNQjEq96CHXERFi7VBeFwKTfHoR71ez49+TEc+hGYuGevnSYlLpVI54prL4dKRTAC53W6+IUuj0UCpVPKj0vx+f94FkJyksyPKprjOpD5xuG5hoZH0SCLTw8Fqr5xOJ7/+pRq5lho1yepOHQ4HBgYGUF9fz58r4XAYbrcbR44ckcwQjNXQuxAQlwawmms5G3syeSAQR+OYnZZwP2QrPe/3+2Gz2dDf3883NBZS9FIOhJZmCoUCvb296Ovrg8lkgtVqRVlZGf9gxsSo1H5ggx6kIqNy1q2PJSo6MDCA5557Ds8++yxeeuklNDY2ZnvxZKWkhGYwGJT8HcdxKeKyrKwMBoMBs2bNGlFRfLFGGDNhNEI6W+IyE8TiikVuxGbamQogsfiprKws6NpEsR1RfX19Xu2IhP59UtHpTCLTI+3YDgQCcDqd8Hq9qKurg8ViKSk7puEIh8Mp03osFgumTp065PqnM1UXW9dIpeYrKgpruICwa76yslK2iT2jIZvd8+nS82xSl91uR1lZGaxWa8Gsfy5IJpPweDyw2WxQKpVobGwcZCUo9WAmRDjoge0LsbWcsFxF/F3uYIX4XrZ//3489thj+PDDD3HppZdi06ZN0Ol0sn2+XJSM0BTXaDI7CzZXnI1+bGpqGlXdBqXOU1+TC3HJIldutxv9/f2or68fVlxkIoCGE6Lp/CvzIUSZuOrp6eGbWoohNZZJZDqTjm3mJhEKhVBeXg6TyYT58+cXTF2c3LC6W+b5ONK6U+F+GOozpDxdhcMF0nXOy70fWJbC4XDwE3uKqWueMZb0fCAQ4Gt2KyoqUFdXh6qqKiQSCfh8vpykhfOJsLFRp9ONKXotjIqm2w/CchW2/cVlQ8LotNjKaaxR0Xg8jtdffx1r166FUqnEqlWr8D//8z9Ffc1TDCUugsFg0RQE/OEPf4Ddbse0adOgUqlQX1+Pmpoa6PV6aLXaMe/8I0eOoKysDCaTKUtLPDTxeBxffvkl5syZk5PPY8bVUiH5TGous4U4cldXVweTyYSGhoacXEjFtYnCr3RNMsLUfDYIhUJ8xzQTV3q9flzYEQGp4ioej6O2thbl5eUpkTkg/9ZBcsEesBwOB2+qbTab8+b5KPZ0FTYvia20xPtjtA9nwpGIDQ0NsFgsshjKFyrCgQLl5eV8A6U4Os1+FqeFpSKjxXROsAcMm82GeDyOxsZGGI3GglgHcXRa/J3VTadLz6eLivb09OCZZ57Biy++iJNOOgmrVq0quoxNVVWV5MIWr0T+P2KxGN577z08++yzOHDgAObPn4/Vq1dj/vz5WbcyKdZC3EyQMlEHwKcS5I5c+nw+uFwu9Pb2ora2FiaTKS+Ru0xqE4UX+aG6tSsrKwfddNMh7JhWKpUwmUxFGbkZLWzKltPpRDAYzKjubqTz5gv9phsMBuF0OuHxeFBXV8eLi3zfaDLxdBVHgMTnhLhcRcq2RmjLw9wpxovfK/CP6yDrnJYaB8qO6XR/Lx59K2Wnla5pqRAiZtFoFA6HAy6XC3V1dWhvby+4UbiZRKeZzzE7L9g5wUZ+HjhwAI899hisVisaGhrQ0dGBI0eO4JJLLsFrr70Go9GY47WSl6KNaHZ1deG+++7Dzp07cfLJJ0Or1UKhUODWW2+V5fNYXYjFYpHl/cUkEgl88cUXmDt3bk4+j/lNNjU1pYhLOSOXfr8fbrcbXq8X1dXVMJlM0Ol0BSkCMkVsG8TS81JG6mVlZYhEIvD5fAAAk8kEo9FYNB3TY4UdA06nE729vWhoaIDZbM7alC1xSlgoSgtl3jyLXDmdTv76YjAYSkpcictVxFGgZDKJRCKBZDLJZ6FYFLtQHwqySSwW46OXlZWVvPiQ4wFDHJ0W/pzOy1Lurm2WxbLZbAgGg7BYLDCbzQUhfOUiHA7jxRdfxCuvvAK1Wo2mpiZUVlaiu7sb3d3diEajqKiowA9/+ENcdNFF+V7cjEkX0Sxaodnb24t9+/Zh0aJFUCqV2LBhA7744gvcdtttsnye3W4HgJwZtieTSXz++eeYN2+erJ/D9j+70On1ej4aV1VVlfWTPRAI8CMQKysreXFZSjfWoYjH43xDQzgc5ieUsOklUr6JhRRxyAasNMDtdvN+j1qtNueCQjxvXnjzZfPmpdLBFRVjmzfPDMUdDgeCwSBMJhPMZvO4ecAABjf2sAcssQgSR6elPEWL8dohFFeBQABmsxlms3nIWtpcLZewa1soRrOdnmfXQofDgaqqKjQ2Ng5q7iw1XC4XnnrqKWzYsAGnn346Vq1ahfb2dsl1DoVCfOlQsVCwqfM777wTmzdvhkajwcSJE/HII4+goaFh2L/TarVYvHgx/2+NRiPrrHOg8LvAM0Wq5tJoNKKiogLRaBShUIgvfhaKH3EqONMbLpvS4vV6+ZrD1tbWorxBjAYmLFjHvE6nw5QpUyTtiKR8E91ud8q+kBKhYxU/csNmzLtcLigUCphMprybqcsxb36oJplAIACHw4Genh7U19ejubk5a9HbYoCVR9jtdkSj0RE19mTSKZyuc76QHtBisRicTiecTieqqqoKpjyCMdyEn2yk55k108DAAEwmE2bPnp13gS0nHMfh008/xaOPPoqvv/4aV199NbZu3TqsHVu+arLlIO8RzTfffBPLli2DWq3GXXfdBQC49957R/w+mzdvxt/+9jf+PbLNUM0ycsBxHHbt2oX58+dn7f2E30eSFmfih6WAhV/CyI/wS6FQYGBgAB6PB2VlZTAajTAYDAV10ZcTKTsis9k85if24fZFIQlRNmebjZkzGo0wmUxpa8yKDakmGaGJN+twZfVaGo0GRqMRFoulpG+sYoLBIBwOB9/YY7Vas153N1xKmNXVST0UyG1Zw3Ec+vv7YbfbEQwG+ehlqdZfS+2LUCgEn8+HSCQChUKBqqoq1NTU8MGL0YyaLHTC4TBeeeUVrFu3DhaLBWvWrMHJJ59c0qUgBRvRPOOMM/ifFy1ahA0bNozqfZjPmFwUo2H7UFZEADI+4IfzJmMpyIGBAX5SA0t3sbRwb28vQqFQSlQ0nV9iMSP0eqypqcl6U9Nw+4KJmnRROCmrmsrKyqylIJnAdjqd/JztCRMmFFxBfzZI1ySTTCZ5S55QKIT6+nro9XrE43EEAgHs2bNHtm7tQkE8b9tisWDChAmy3WQzaViSGrnKmjOAfzQsiffFaMUPKw9wOp2oqakZF6lhIHVfhEIhvjyAGauzEgmhqbrUqEmp1HxFRWF5u0rR3d2NdevW4S9/+QvOPfdcPPXUU2hpacn3YuWVvAtNIU8//TRWrlw5qr9VqVSyps6Lpes8W+IyU9iUHrfbDaVSyY9AFEZshKkW9mQrtqmRSssXy9NtOBzmaw5ZaUC+umWVSuWQN9zh0sGjbZARdkzX1tbCbDaPKzNp4Oi0FofDgd7eXmi1WrS1tQ1ZXzWaefOFLETFE4uMRmPBzNvOxFA9Go0OOjdGWpvIrKnsdjtCoVDWZ24XAxzHwev1wmaz8VnA9vb2lHtPpul5tj/YQ0E6b9d8l0okk0n8/e9/x9q1a3HkyBFce+212LZtW0mlv8dCTvbIeeedB6fTOej/7777bqxYsQIA8OCDD0KtVuPSSy8d1WeMdtZ5phSy0My1uGRWPG63GwCGnS+tVqtRU1MjGdUa6WxzFoHL9822WO2Ihov8iBtkpMy7hRd0FskuKyuD2Wwe8ZzpYkfY1FJeXg6LxYKJEydmdM6NdN78UFE48Vcux60KI3es7nC4iUWFhnDQQ7oBA0PVJiYSCf6rvLwcer0eZrOZLyMaD0SjUdjtdrhcLjQ0NGDSpEmjGgubiam68IGZOXe43W5EIpGUhzQpMZrNAEYwGMSLL76Ip59+GpMmTcIPf/hDLFq0aNzs80zJidDctGnTkL9/5plnsHnzZmzatGnUO0ju1DlQWAPtcy0uY7EYPB4PXC4XEolE1qIVmUyQEUZ9gsEg36gkvKBIRUSz/WTLjMRdLhfi8ThMJtOQArsYGa5Bhvncud1uxGIx/gIejUZx+PBhdHd3S+6LUrKpYbWnDocDsVhMtmk12Zo3LxX1GevNVlgewBp75s6dW/APWqNFSvywJj+73Y5IJMKnxtmDc19fHy+GhPXTUlHqYo14svrT7u5uRCIRWK3WnDxsDvfALHVuZCs9z3Ecurq68Nhjj+Gtt97CRRddhBdeeCFn1ofFSN6P7tdffx2//vWvsWXLlrQHTSaMl9Q5x3E5E5diYWUwGDB16tQxpQP+53/+B3/605+gVCqhVCrx61//Gscdd1za1w83UpKlH1lzTCZd2iOpSUwkElizZg0uuOAC3uNwypQpsqdEmpqa0N3dLetnZApLBzqdTvh8PhgMBsyYMUPyfI3H4yVlos5gZtoOh4OvPZ04ceKoIjbZItNxq0Olg6XmzQ8lRIWNPVqttmTrb4ciEonwpuL19fVobW3NyIJGbOItvlYBSJsOzmWEOhPi8TgcDgccDgdqamrQ0tJSUA4KmZwb6dLzQnuz8vJyPP7447BYLGhtbUUoFMJf/vIXBINBXHfddbjnnnvGVWPfaMl71/ns2bMRiUT4QfGLFi3Cww8/POL32bFjB9auXYuHHnoo24sIAPB6vfD7/Whra5Pl/aXYuXMn5s+fP0hcsi9AHnHp9Xr5NITBYIDRaBzTQwBj+/bt+Ld/+ze89tprKC8vh9frRTQaldWbVNwcw55uxV6JQgHKjNTdbjdvR2Q2myXtiOSiEIQmM1NndjzZ6JoXm6gLU5GFKEQjkQifGs+n56ccSJWtCP0rgaNRn7KyMiQSCYRCIahUKlgsFlgslnFVIsFGItrtdsRiMVgsFhiNxqxGIsWlEsKfhTO2pc6NXDVWsslFPp8PZrMZFoulZKPYrIxo8+bNePfdd7F//34MDAygoqKCj1AzATp37lxcffXV+V7kvFOwXee7d+/OyvuUWo0mx3FIJBLo6+tDZWUlysrK+Jtbtm9yiUQCPT09cLlcCIVC0Ov1aG9vz1q0xu2PYq/Dj0++OAitTsenmvV6Pf+a2bNnY+vWrdDr9di5cyfuuusubNq0CQ888AA6OzvR2dmJw4cP4/7778eOHTvw5ptvwmq14o9//CM0Gg1mz56Niy++GG+88QbUajV+/etf45577sHBgwdx44034pprrgEAPPzww9iwYQMikQhWrFiBH//4x/j666+xcuVKzJgxA3v27MHPfvYzWCwW/ib7ve99D7feeivmzZuHn/3sZ/jiiy+gVCrxne98BzfccEPKurpcLvzrv/4rOjo6AAC//OUvcfzxx+N///d/8Yc//AEAcMUVV+D6668HgLT/ny/EjU1ms3lQIf9YGKpeFxgsRMURUWFDhvhmm60bbSKRgMfjgdPpRCKRKNm08FBlKywlykRFbW0tjEYj4vE4L7iA0pmtnY5wOMyXijQ0NMgawc2kVEL8YDAwMDCosVIqNT+W/cEcBOx2OzQaDRobG4uuBnekcByHgwcP4rHHHsP777+PSy65BPfeey8MBgP/mkQiAafTicOHDyMUCuVxaQufvAvNbFEKQlMcuWxqaoLH45GsuRLWwVVWVo74yZrVWLlcLgSDQeh0OrS1tWU1aheIxPGzTV9j2zc9KFMrkYiY0LHra0w/Zh7OO+t0XHTRRTj55JOHfZ+Ojg68+uqr2Lt3L5YvX46nn34a99xzDy6//HL89a9/5RvKmpub8f777+MnP/kJrr/+evz1r39FJBLBCSecgGuuuQZvv/02Dhw4gLfffhscx+Gyyy7D1q1bUV5ejo6ODtx55534zW9+kxKxisVifA3o7t27cfjwYaxduxbRaBR+vx+fffZZyn64+eabsXjxYqxfvx7JZBJ+vx+7du3Cs88+izfffBMcx+GMM87ASSedBI7jJP8/V2NHGfF4nG9s4jgur41NwwlR8Y1W6GDAcVzaVPBwQlTcMW0wGDB58uSsRPKLCeGs6erqalitVsyYMSPttmP7Q5h+LOQIdSawa6Pdbkc8Hs9Z3eFwDFfPDhzdH0OVrggzOGJBKl6/YDAIm82G3t7eYRs+S4VEIoE333wTa9euRSKRwKpVq/hGZTEqlQqNjY0589YuZkpKaBajj6ZUWpxdgJubmwe9VliPyLpQQ6GQZD2iUIwqlUp+MofL5YLf74dOp5NtOgnHcfj+83vwlcOPaIJDNJEAUAbTlb8CZ/8K/dwRXHPNNfj3f/93XH755UO+1xlnnAGNRoNZs2YhkUjw3qszZ85EV1cX/7pzzjmH//9AIIDa2lp+ZnJfXx/efvttvP322zj55JMRj8fh8/nw8ccfY/ny5WhpacG//Mu/DPpsjUYDlUoFrVaL9vZ2uFwuPPPMMzjrrLNw6qmnpnRph0IhvP/++/jRj36ETz75BMDRB4ONGzdiyZIlCAaDqKiowLnnnosPP/wQHMfhvPPO4yPHK1aswIcffpgTocmOBafTiWAwyNtSFbodx1DdqOLOYLEQBVIjcJWVlVAqlRgYGEBvby+qq6thsVhKPlojRiisWFo4U0ue4bqDh4tQp7PTGq4hQw7C4TDsdjs8Hg+0Wm3ea3BHA9sf6R7UhNerSCQyyN4MOHofikajvF3dtGnTRhXMKCb6+vqwfv16PP/88zj++OPxwAMPDPmAVQjcc889eO211/j99Oijj+ZsRPZIKZkjhxmDy0m2hOZQ4nKoi6uwwFkKcT0iM0kPBAKIxWJIJpOoqKhAXV0dmpub+ZS8HOw8MoD9rgCiidRtplCqoGg6Bt31x+LBB4/Dc889h8svvxxqtZq/0IXD4ZS/YeurVCpTiuKVSmVKFFv4OuE2UigUvH/nxRdfjAsvvBAmkwkGgwEajQadnZ0Z3VAaGhrw/vvv46233sITTzyBDRs24Le//W1KhEGlUmHOnDkoLy/nHwxqampSHgxY/SvHcQgGg/jmm29QUVGBUCiEaDQqW2SeNbQ4nU709fVBq9WipaUFNTU1BX1BzZThbFGYEA0EAnC73XA6nUgmk/wNNBAIoKurCy6XK+PmmGImGAzCbrejp6eHf5DKdlo4k1IJYQROPFpSLESFUbhsRBiZg4DdbkcymSyY6KVcpHOViEQisNvtcLvdqKmpQVNTEziOQzgchs1mG+TyIZWeL7SGpeHgOA5ffvkl1q5di08++QSXX345tmzZktEI7ELgpptuws9+9jMAwO9+9zs88MADo+pvyQUlIzRzkTofC0Jxyd4v2zWXzKy7srIS/f398Pv9CIVC0Gq1MJlMqK6uThlf6Ha7eXEDHH0aFqfkmfXGSNf/7X1ehGPJlP+LeY8ACgU0uib0BGL4YMcutLa2AgBaW1uxa9cunHnmmdi4cWNWtgfrmo/FYti7dy9OPPFEPPLII7jllltQU1MDm802ovSw1+uFRqPBBRdcgClTpmDVqlWDXnPKKafg8ccfx/XXX88L/9NOOw3XX3897rrrLnAchx07duCRRx5BMpnE9ddfj9tvvx2hUAhbtmzBXXfdhc8++wyJRAKffvppWuumkRwzoVCIN1NnDS3ZnFZUDLDOeYfDgUAgAIPBgDlz5qREcDPxdB1pl3ahwcokHA4HVCoVrFZrVmtwR4parYZarR5y3rwwNT/WefOMUCgEu90Or9cLnU43Lssk2DnR3d2NWCyWkchmDUvCqT69vb2IRCKDzhGpCUuFcM2JxWLYvHkzHn/8cVRUVGD16tV4+OGHi+7hQlg+EQgECvr6Q0IzQ0aTOs+FuBR+lnC2dl1dHUwmEyZPnpzyWenSKuwmy0RoKBRK8asUjssTCx+pEzSe5CDeWslYGL1vPIJkJAClSoXmeTPw6O//FwDw4x//GD/84Q9x3333ZVS3mY5kMgmfzwev14tdu3bx89XnzJkDvV6Pnp4eLF++HABQXV2NRx99NON9YbPZcMMNN/CR13//938f9Jr/+q//wo9+9COsX78eSqUSv/zlL7Fo0SJ8+9vfxumnnw7gaNMPS49ffvnl/DSsa6+9FhdddBGAo5GHuXPnpjwYiM3Th6p/SyQScLlcvKG82WwedxNKgNSJRXV1dUOOAczE03WkwwUKIdrDrg12ux1+v7+o6u2G83UdbtKVeMxnNBpFf38/lEolGhsbZR2LWajEYjE4HA44nU7U1tYOO8FKiFDYp7MNEtftsvKVaDQqed0SClI594XH48HTTz+Nl156CcuWLcNDDz2EyZMnF7RAG467774bzz77LOrq6rB58+Z8L05a8m5vlC3sdjvWrFmD559/Xpb3Zx56U6ZMGfJ1UuJSnB7PFiwV6na70dPTw3eGymG/kkwmU55ihTdbYYqLidCdzhj+880jCIqimgxtlQZv/XARVMrszHQX+jzmw44oH4jr30KhEC98kskkysrKUFtbi+rq6pSHg2KJvo2WeDwOl8sFp9PJz9k2GAyyRyzEQlR4nuRjko/Qmok19tTX15f0vheTTCb57vn+/n5+W8fj8UHz5sXCp9RcBgYGBmCz2eD3+3mLqnw8eIqvW8IHBanxkplGqaXgOA6fffYZHn30UXz11Ve48sorcdlll2UsrPNNJlMVAeDnP/85IpEI7rrrrlwu3iDS2RuVjNB0Op245ppr8NJLL8ny/n6/HzabDVOnTh30u1yLy0AgAJfLhZ6eHlRVVcFkMkGn0+X1yVx88fAHg7hxswfuUBJJ0VFUoVZgzQlWXL6oedQ3WHG9YX19PUwm07i7kTIbGqfTiYGBAX70XVVV1aAZ8+zndKUShRB9Gy1sSovD4UAwGITJZILZbC6oqJ14WonwayghyhoxMtknUo09JpNp3EWyk8kkb8mjVCphtVqh1+sHXSOFD9BCwSOuSZR6OCjEefNiWGbDbrejvLwcTU1NBX+NFEephYJU/HDAvgNHzy+DwQClUolIJIKNGzfiiSeegMFgwJo1a7B06dKSjV4fPnwYF110EXbs2JHX5ShYH81sIWwmkQNx6jyX4hIALy69Xi8qKythMpnQ1tZWMHUlUkX/661t+MELX6KrN4REkoNKoUCC43DBtBqcYIjiq6++4m+wUrWIlZWVg9aPbQePx4OamhqYTKZxV28IHN0OTqcTXq8XtbW1kt3SwzXGDDe+UHyDFdbsFgqBQAAOh4M3lZfLRSEbZDrJZyT7hH2x6CVr7CnGjulsEAgEYLfb0dvbC4PBwHdMp0OpVKKysjLta9LNmxcK0UIslwgEArDZbOjr64PJZMIxxxxTNBNshhsvKX44CAQC2LNnD37xi1+gv78fiUQC/f39aG1txRlnnIE5c+agqqoKTqcTZrO5ZO4V33zzDSZPngwAeO211zBt2rQ8L1F6Siai2d/fj4svvhivvvqqLO8fCARw+PDhlIim3OIyGAzC7XbD4/GgvLwcRqMRer2+oG70w8FxHL6w+/GFzYfKMhWWTdahoSo1JSUeIymMwLGHB5aW1Gg0KdOKSuWikQmRSIQ3U9doNDCbzdDr9bI8bIhvsMJ9I4wqSEVE5X74icVifGpco9HAYrFIRqtKDfE+CQaD6OvrQyAQAHD0wYI1A0qJnlKFGYo7HA6+9jJXGZ7RRKnZw0K2S1iSySQ8Hg9sNhu/HfR6fUE+dGWTZDKJjz/+GI8++ii6urpw9dVXY8mSJfB4POjs7ERXVxcOHz6Mrq4uLF68GD/+8Y/zvchZ4dvf/jb2798PpVKJ1tZWPPzww3n39Cz51HkgEMCKFSuyXhDLIpeRSASff/55Sr1bVVVV2sjbaAmFQry4VKvVvA1PMYnLbBCNRnkTcYVCgYaGBlRVVaU0LDELFNblKO6YL8YUsBjxlBqTyQSj0Zj36ASLKojrdcU1u+KI6GiNullK2OFwIBKJ8KnxfG+HXMNKJRwOB9/YY7FYeDstqZSjVBpYLEaL8fri9/tht9vR19fHbweWRi0UxEJUuH+y5WQg9P/U6XRobGwseD/cbBAKhfDyyy/jySefRFtbG1avXo3FixeX/ANnIVPyQjMSieDMM8/E66+/Pub3SpcWB47WIgo7s9l3qYYY9n24qSTMasjtdkOlUqV4PI4nmB2Ry+VCPB6H0WiEyWQass5OaNItjogWUwpYCKs3dDqdvBWP2WwuqpuH0BhauE+kJsYIRY/4XPH7/XA4HOjt7YVWq4XZbC6aQv5sEolE+FGINTU1sFgsI661Y5kDqf2SrjGm0IQoqzl0OBxQq9V89LJYHyjTzZsXC1GphiU2uScej6OxsRFGo3FciKyuri488cQTeP3113H++efje9/7XsEalY83Sl5oJhIJLFmyBG+//fao/j4bNZfiKTHCmyuQ2nyhVqv5LmGlUsmLy/EWoWGRKqfTiVAoBIPBAJPJlDVPu0xSwFKCR26rDanl9Pv9cDqd6O3tRX19PSwWS8HWG46VdI1KLErNHiA0Gg10Oh0MBgM/YKAUt4cUzEzc4XAgHo/DbDbL2tgjrH0Tf+VbiPp8PtjtdvT398NoNMJqtRZUo5dciKddBQIB9PT08L6JUiKUCdFCHvM5UpLJJN5//32sXbsWHo8H3/ve93DRRRcVzTFw5513YvPmzdBoNJg4cSIeeeSRojGGHwklLzQ5jsOJJ56Id955Z0R/k8tu8WAwCIfDAa/Xi0Qiwd80xcbDwohoKV0sGFJ2RCaTKS8TaoTTlMRpYBZ5kzKxz5bgCYfDcDqdcLvdqKyshNlszruDQD4QiqpYLAa9Xo/a2tpBbgZSfpXC/VMMncDDIWxo0el0sFgsBdHYk4kQlRKho63bZTZVDocDZWVlaGxshFarLfr9O1KYD6rNZkMwGITFYoHZbObFvTAiKtw/xTxvnuH3+/HCCy/gmWeewcyZM7F69WosWLCg6I6BN998E8uWLYNareYtiO699948L1X2IaEpeF0uu8VjsRifDk4kEjAajTAajYNqiaQEjzDVqFarB6XkR2J7km+YHZHL5eIjdsVgRyQslRDum+EEz1BlD+yYYP5oZrMZRqOxYNKTuYIdEw6HA/39/RmLKqnubKlaRKmmmELdxkLvT7VaDavVWnQPHMN57WYqRH0+H2w2G3w+X0oN6ngjHo/D6XTC4XCgsrISTU1NaYcNDPc+Ug8HQiGaLlKdj+OP4zgcOnQIjz32GN59911cfPHFuPrqq2EymXK+LHKwceNGbNiwAevWrcv3omSdcSE0TzjhBGzdulXyd7kUl6zW0O12IxaL8V3So62xY+kTKcHD6hCloqH5ulAIEdoysfGHchjK5wOh4BHvG3Fndnl5OWKxGHw+H6LRKEwmE0wmU8E1L+QCoZF4VVUVLBZLViNVUi4Gw0Xe2D7KpV0Ya+yx2+0IBAIF6f2ZTYQP01L7JZFIpJRLaLVaVFVV5Xy/5Bvm2dzf3w+z2QyLxSJrSZV43rw4qyPV2MeEaTb3SyKRwNtvv421a9ciEonguuuuw4oVK0quV+Hiiy/GypUrcdlll+V7UbLOuBOauRaXiUQCXq8XLpcLkUgEer0+q7WGQyEVSWDfc5H+FRMOh3kbnrKyMllteAoZdkywEgE22YI9HGTaEFMKiLvn5a43HAopwcOyB1JNfdlONYobe6xW66giVcUOi2izaTVGoxENDQ2SjWTpnAzYfin2awszmLfZbAXX5CTeH0JRyvZLuohoJvulr68Pzz33HJ577jkce+yxWLNmDWbNmlUQ6z4SMpni8+CDD+LTTz/Fc889V3TrlwnjQmieeOKJeOutt/j/y4W47OnpgcvlQigU4sVlIdRTCRF3ZYvTv2LLE/Z9JBdvsR0Rs+EptafRTGDztb1eL6qrq/korvjCMlRDDIC0tk3FVC4xMDAAh8OBgYEBvns+Fw9fY4HdWMURUWEZSzrBk26/sBpUu92ORCIBi8UyLsslgKOlI06nE06nE5WVlWhsbMyohEZKgA4XectnCjgTQqEQbDYbenp6oNfr0djYWHRZjnSTfMQlEwqFAq+99homTJjAz5h/7rnn8NFHH+Gyyy7DlVdeCZ1Ol+/VkY1nnnkGTzzxBDZt2lTw18DRMi6EZlNTE2bPno0JEyagvb2dP6Db29vR0NCQlYsN65J2uVwIBALQ6/UwGo15aWTJBsKubHH6dzjLJhalGokdUanChDarsTObzWOar81sT8RiJxQKIR6PD+oAForRfEd3wuEwHA4HPB4PqqurYbFY0NDQUJTnhxRSdjShUEjSF1GhUMDv98Pv90Ov1xdMY0+uETa0BAIBPiWczQfRTIWoXJHqTOE4Dl6vFzabDclkEo2NjfzoxFKEZdz6+vrw4osv4rPPPsPBgwdhs9lQVVUFrVaL5uZmtLa2oq2tDVOmTMGZZ56Z78XOKq+//jp+8pOfYMuWLTAajfleHNkoeaEJ/MN/8NChQzh06BAOHjyIjo4OHDp0CL29vdBoNGhpaUFbW1uKGG1qahrS3DuZTKKvrw8ulws+nw9arRYmk6lkrWeEiKM7wWAQPp+PF6Ll5eWora1FbW1tykV8PERqhKnxWCzG113mwqJK3AEsFKS5SP+KYXXJDocDAGCxWMbloAGO4xAKhXgDbfZAwOqsgaMNZONlek8sFoPD4YDT6UR1dTUaGxvzViYgVYuYzttVjnMmGo3CbrfD5XKhoaEBjY2N4+ahw+v14plnnsGLL76IJUuWYNWqVfzI3Fgshu7ubnR1daGzsxOhUAirVq3K9yJnldmzZyMSifAR20WLFuHhhx/O81Jln3EhNIeCRYg6OzvR0dGBgwcP4tChQ+jo6MCRI0cQj8eh0+l4Adra2gqv14v33nsPiUQCd999N0wm07itpWJCe2BggLcjqq6uTonuCFPzwqYLqWhosT69i62Z9Ho9zGZzwd0wpNK/Qk9XoYuBcN+MZJoS2xYOh6NojeWzhXhbpGvsGW56j5RX5WhKWfIJa3Ky2WwIhUIwm80wm80FL6Qz6c4eqRBl26K7uxuRSIS3JiqWfTkWOI7D7t27sXbtWnz++ee44oorcPnll6Ouri7fi0bIxLgXmsPBirE3btyIP//5z9izZw+sViv0ej3f1NLa2sqn49lXU1MTVCpVyYnPbNgRsaYLcW0ou3AXUw0iM1Pv6elBXV0dzGZzwVszpUNqGonYxWCoUYWsBtXj8aCurg4Wi2VcPoABqT6o2WjsySRSnc66Kd8Pb9FoFA6HAy6XqySbnIYTosLaXY1Gw8+ir6mpQVNT07jIgAFHj4PXXnsNjz/+OOrr67FmzRqceuqpeT8+M+Hll1/G/fffj71792Lbtm1YsGBBvhepqCChmQErV66ExWLBypUrsXTpUj7tx6IQnZ2dfFqeRUVtNhvvjzlhwgS0tbVh4sSJfHq+2C4uubIjEls2Cb8LIzviaGgub6iRSAQulwsulwvl5eV893wxXDDHglTdbjAYhN/vRyQSgUKhQHV1Nerq6lBVVZViD1Tq2wY4KgZZmUCuG3tY44WUdVO6qBubqCTHvmGRXJvNhnA4PMhMfDwRi8XQ09PDR7VZFDoej4+6iayYcDqdePLJJ/HnP/8ZZ5xxBlavXo22traiWre9e/dCqVTixhtvxP33309Cc4SQ0MwAjuNGdVIkk0m4XC5ehDIheujQIfj9flRUVPDRUFYX2tbWhsbGRiiVyryfiGI7IjYOM5/pHRbZEQtRKcsm4feRpH6lYLWGTqcTyWSSN1Mv9LSfHLCaZ4fDgWAwyKeD1Wq1ZDRUKrIjfEAo9vGRwrnrrLGn0LpHpaJu4pKJbOwboUVTbW0trFZr0T1UZ4tEIsFbE6WbYCQeJSmOiAIYJETltqHLBhzHYceOHVi7di0OHDiA7373u7jkkksKrpRopJx99tkkNEcBCc08wZoDmPAUilC73c6Lmfb2drS1taUI0erqatkuMMVuR5SuNlSY+pWKhkqJ52Qyid7eXjidTgSDQb57fjzWGgJHo9oOhwM9PT2jmrkutW+EdlriZphCbiCLxWL8xJ6ysjJYLJaim9jDEIuddPtGKurGrgu9vb2w2WyIRqOwWCx580ItBILBIGw2G3p7e8c8fz0TISp0MxB+5UOIhsNhfrpNU1MTVq9ejZNOOqkozwspSGiODhKaBUoymYTT6cSBAwdSUvIdHR0IhUKorKyUTMlbLJYRn9TxeJzvki5lOyKp1C/7LuzIViqVfNSUdYGO16iMUFBpNBpYLBZZygTYNCWpKVdSU3vyUTIhbuxhzSy5cBPIJ+LRnuwrGAzyJS0ajQZ1dXWoq6sbNGN+PMD8UG02GwCgsbExJ+U0UnXV4oeEXAjRI0eOYN26dfjLX/6CFStW4LrrrkNTU1NW3jtXZGKsTkJzdJDQLEI4jkMgEOAjoEyAdnR08DYyVquVj4YKO+YrKyuhUCgQCoXw5z//GRMnToRSqYTBYMjZxKJCJBQK8Sk/jUaDyspKKJXKoo24jQXmCetwOBCJRPjUeD4FlXBqj7gOUViDKN4/2biZCv0/a2trx3WTE8dx6Onpgc1mQzwe5+tQ00VExSNXxWKn2LusI5EI7HY73G43tFotGhsbC+oamokQHSpaPdQxnkwm8cEHH2Dt2rVwOBy49tprsXLlSlRUFJex/EggoTk6SGiWGBzHIZlMwm6381ZNwtS81+tFNBqFQqHAtGnTsGLFCsyYMQPt7e0wGo0lk+LIhFgsxpupK5VK3kxdSjhKzS8XRtyE00eEqfliKugX1hpqtVqYzWbU1tbme7EyQjzlSmyWLryZCgVPupIQYWNPMpnkBVWxC6PRwsS22+1GQ0MDrFYrampqMvpbcV218CvdCMlCbiJjke3u7m5Eo1E0NjYW7bGRidNEWVkZ9uzZg/3792PChAlobGzE3r178fLLL2PatGlYvXo1Fi5cWDTXubFAQnN0kNAcBxw6dAiPPPII3nnnHRx//PE4TR+sMAAAIJRJREFU99xzYTab0dXVlSJC3W43lEolGhsbB9k1tba2FpVoSgdLcTmdTj5al40yAeH0EfENFRg8NlLoT5lPotEonE4n30FfzLWG6RCnfoX7R+xRqVQqEQgEEAwGYTAYYLVaCypClUtYZJs5aFitVlkEldTknnSG6cKHhFxfj4Qm87W1tXxJTSnDzp39+/fjzTffxPbt22G329Hf34+qqiq+nKatrQ2tra1YunRpSYqwjRs34pZbboHH40F9fT3mzJmDjRs35nuxigYSmuOAffv24eDBgzj99NOHTH9yHIdEIoEjR44MMq/v6upCJBJBXV3doJT8hAkTClqcMHNkp9PJG8ubzeaMIzLZ+HwWNRBHQ5nQkbqRylV/yMS2w+FALBaD2WyGyWTKu+jNF9FoFDabDS6XC0qlkm/2ikQifMRNqmSiFB68pGBlJB6PB1qtFlarNa/dwlLR6lw1wzDfYJvNBr/fz9s0jZdzJZlMYuvWrXj00Ufh9/tx3XXX4fzzz+fvI6yXoKOjA52dnWhpacFJJ52U56UmCg0SmkTGsJSRsDlJmJJXq9Vobm5O6ZKfMGECWlpaxmwvNBqEBuK1tbUwm80FOVtbWH8oZdnE7E2koqEjmdbj8/ngcDjQ398PnU43budrA/84lu12O0Kh0JB1qOKuX6FtEzA4Wp1pjVshwR4+mOOF1WrNu5VZJqSrQRQ6TaQrmxhqAEQikYDT6YTD4UB5eTmampqKdhDDaBgYGMBzzz2HZ599FnPnzsWaNWswZ86ccbP+RHYhoUlkBXbBP3z48CARevjwYcRiMWi12kEp+fb29qyKv2g0ypupazQa3ky90G+YQ5EuGspupOm6sVUqFSKRCJ8ar6qqgsViGeTlN54Q1hrW1dWN2edRKlotHDAAYFAjTCGNjmTz171eL3Q6XcmVCgxXNgGk7h/gqMjy+/0wm82wWCwl576RDo7j8PXXX+Oxxx7DBx98gG9961u46qqroNfr871oQ/L666/j9ttvRyKRwFVXXYVbb70134tEiCChSeQE5knJakJZp/yhQ4fQ19cHjUaD1tZWtLW18ZZNbJTncKMnE4kEb6aeSCR4789St50Bjm5XoS1QKBRCKBSC3+/nG2Gqqqqg1WpRVVVVFGbP2SaRSPDROo7jctrYI94/6Sy1pOoP5SpFYY1OdrsdAPjoZaGWvsgJyyawcaHJZBIVFRVIJpNIJpODbLWEjUqF8KCQDeLxON544w2sXbsWALBq1Sqcc845ReGmkUgkMHfuXLz66qtoamrCkiVL8OSTT2LGjBn5XjRCAAlNIu+wqENnZ+egaGh3dzfi8Tj0en1KFLS5uRnffPMNXnnlFSxatAgXXnghzGZzSUVjRgLHcRgYGIDD4cDAwAAMBgOfCh6uG1tqklIp3ERZqUBfX1/BTuxJ10QmnqYkjoiOJi0fDAZht9vR09MDvV4Pq9U6bocPAEej2zabjY/mNjY2Dtoe4rIWYaOS8EFBXDZRqB3zQnp7e/HMM8/ghRdewAknnIDVq1dj+vTpRfUA+tFHH+G+++7jG3N+/vOfAwBuu+22fC4WISKd0Cz8RxmiZGBdv1OnTsXUqVMH/Z5FYA4ePIht27bh97//Pfbu3Yu2tjZUVVXhtddew+7du1OEKBvlqVKpiurCOVKEHo/V1dWwWCyYOnVqyjrX1NRINj6JTdJ9Ph/cbjdCoVDam2ihWzYxg3lWW2e1WjF58uSCXV6VSoXq6mrJWlk2FUYoQPv6+lLKJli3vFT9IfCPaL/dbodSqeT9dQtdBMmF2AeUOWyk2x5KpRJVVVVpH1DYgwLbR16vV/JBQRwRzVdGgeM47NmzB2vXrsXOnTvxne98B6+//jrq6+tzvizZwGazobm5mf93U1MTduzYkcclIkbCuBead955JzZv3gyNRoOJEyfikUceQUNDQ74Xa1yiVCpRXV2N22+/Ha2trbj55ptx5plnoqysDBzHIRwOo7Ozk0/Lb9myBQcPHoTNZkMymYTJZJKcolRTU1OwAmQo2Nx1Zs5vsVgwb968Eae6mMBPV4MmjrZ5vd6U+djM2F4cDc11yk04e5019syePbvoSycUCgU0Gg0/dUeMcNKV8EGBCdFYLIZkMonq6mro9XrU1taO2whmNBqFw+GAy+XinTOy4Tox1IMCgEGNSj6fLyWjIOyYF/u7ZvPaFIvF8Je//AWPPfYYqqqq8P3vfx+/+c1vSiJzQRQv4z51/uabb2LZsmVQq9W46667AAD33ntvnpdqfOP3+0d8c0gmk3C5XJLm9YFAABUVFSlWTaxjno3yLBQhKh5/yFLj+RIOrAlG3KA0lGVTtk24w+Ew7HY772030tnrpUYikYDb7YbdbodarYbZbEZ1dfWQ/pRSTUqlUr/Lykm6u7sRCoV4a6JCqT0Ud8wLzyGpiLXY0SAT3G43nnrqKWzYsAGnnnoqrrvuOkyaNKkk9i9AqfNigWo0M2Djxo3YsGED1q1bl+9FIbIIx3EIBoPo7OxMqQs9dOgQ3yhhsVj4aKhQjFZVVeXkYi20aKqrqyua8YdSIyOFIketVqeNhmbS+OVwOHLe2FOo+P1+2O129PX1wWg0wmKxZDQGMJ3IEU9TEovRQhFq6YjH47w1UWVlJZqamorinBEjjlgLv8SDBo4cOQK3241JkyZh0qRJOHDgANauXYt9+/bhyiuvxGWXXZYz3+BcEo/HMXfuXGzatAmNjY1YsmQJ1q1bh5kzZ+Z70QgBJDQz4OKLL8bKlStx2WWX5XtRiBzCvPQOHDiQIkI7OjoQCoVQVVXFi1Chb6jZbB5T1C4ej8PlcvGjMS0WS1F4GmaKVO1hOssmJnKYa0F/fz8MBgMsFsu4TQMDR49Nl8sFu92OsrIyWK1W6HS6rIkpqZGr7Gc2vzydrVa+6j/9fj9sNhv6+/t5a6JiL58YCjbaMxwOY8eOHdi8eTP279/PNzhZrVbMmjWLv0a1tbVh+vTpKTWNpcCWLVvw4x//GIlEAldeeSVuv/32fC8SIWJcC83zzjsPTqdz0P/ffffdWLFiBQDgwQcfxKeffornnnuu6J6ICfngOA5+v58Xn8yuqaOjA06nEwqFgm+8EEZDW1tbUVFRMehYisfj/PSRYDDIG4iPFw8/IewG6vf74XK50N/fD4VCAaVSCaVSmRINLcWU71D4fD5+BKDJZMqbz+NwQwZyNTYymUzC7XbDZrNBrVajsbExq4K7GLDZbFi3bh1ee+01nHPOOVi1ahVaWloQDofR1dXFT+3p7OxEe3s7rr322nwvMjHOGNdCczieeeYZPPHEE9i0aVPB2aIQhQsb5Wm32wfVhnZ2diISiaC2thYTJkxAdXU17HY7du/eje9973u48sorizLNly1YY4/dbkc4HObHYwojU+KRhOw7S/mydKI4NV+sEWEW4RZ20he66b5wmpLYtglIbSQT7qPhSicYoVAINpuNt2pqbGzMqFygVEgmk/joo4/w6KOP4siRI7jmmmvwL//yL+M6yk8ULiQ00/D666/jJz/5CbZs2QKj0ZjvxSFKCI/Hg/Xr1+OPf/wj1Go1Zs+eDZVKhc7OTng8HiiVSjQ1NQ2aotTa2lqyUTvhfO36+npYrdZRuQII69rEQlTKsqlQ55azkaF2ux0+n4+PXpZCKljcSCbcR+LaQ3E0tK+vj3eTaGxsHHdG88FgEC+99BKeeuoptLe3Y82aNVi0aFFBb4M1a9Zg8+bNMBqNZD00TiGhmYbZs2cjEolAp9MBABYtWoSHH344z0s1Ml5++WXcf//92Lt3L7Zt24YFCxbke5HGPRzH4Vvf+haWLl2KSy65BCaTadDv4/E4jhw5Msi8vqurC9FoFPX19YNS8hMmTIBWqy3oG44YYWMPgJzUoop9D8Vzy/PdACNuZLFarVkd0VoMCGsPQ6EQAoEA+vr6EAqFoFKpUFZWhurq6qLydx0rnZ2deOKJJ/DGG2/gwgsvxLXXXguLxZLvxcqI999/H9XV1bjuuutIaI5TSGiWMHv37oVSqcSNN96I+++/n4RmCcBSy0x8CoVoT08PNBoNmpubBzUoNTc3Z92bb7TLz7qkC62xJ91cbHEDjFQ0dCwCn9nw2O12fsY2m+o0XuE4Dv39/eju7kYkEuGtiVQq1aCHBaGjAZA7b0q5SSaTeO+99/Doo4+it7cX3/ve93DhhRcWZd12Z2cnVq5cSUJznEKTgUqY6dOn53sRiCyjUCig0+mg0+lw7LHHpvyOpSRZA8CBAwewdetWdHR04MiRI4jFYtDpdINS8u3t7aivr5f1JhyLxeB0OuF0OlFRUQGr1YopU6YU1I1faGAvNSmFNcCIp8CEw2EA0gJnKMsm4TaprKxEY2Oj7Puh0InH43A4HHA4HKipqUFLS8sgb9ThpimJbZt6e3sHeVOKR0YW0thVn8+H559/HuvXr8cxxxyDO+64A/Pnzx/XxwVRmpDQJIgiQ6FQoKysDJMnT8bkyZNxxhlnpPw+mUyip6eH75I/ePAg3n77bRw6dAj9/f0oKytDa2srb4UyceJETJgwAY2NjRk3aQiRauyZM2dOxmbThcZQ4wjFlk1M4IRCoUF1h8BRK55YLAaLxVLU2yRb+Hw+2Gw2+Hw+mM1mzJ07d1TbhJ0DZWVlQ05TGm7sqviBQe7Z5RzH4cCBA3j88cexbds2/Mu//AteeeUV6g8gShoSmkVCJhZNBAEcFUoGgwEGgwELFy5M+R27AXd2dvIp+TfeeAOHDh1Cd3c3EokEDAZDShSU+fOJu+S/+uorBINBJJNJ1NfXo7W1FbW1tble3Zwy3LjISCSC7u5uuN1uPvKpVqt5v1ThlB7h92JL944ENsnIZrOhrKwMjY2NmDp1qqzrK/T/TLdMQtumdLPLxfWho91PiUQCb731FtauXYtoNIpVq1bhgQceGPcPHsT4gIRmkbBp06Z8LwJRArAb8LRp0zBt2rRBv2d+hSwaum/fPvz1r3/FwYMH4ff7odFooFQq4ff7UVZWhmuvvRbLli1Dc3NzUTUoZRM2NtRut/MjEBcsWCApIsTp3v7+foRCIcl0r1DkFEq6dyQEg0HYbDb09vbCYDBg1qxZBVN3mOnschYRldpPUhFRcTNZX18f/vCHP+CPf/wjFi5ciPvuuw8zZ84s2YcKgpCCmoFKiLPPPpuagYisw3EcPvnkEzz11FP46KOPcNJJJ2HevHm8kf3Bgwdht9vBcRxMJhMfAZ04cSKfnh+NhVGhE41G4XA44HK5UFNTg8bGxjHNYBene6Usm6SioYVkhZVMJuH1emGz2QAAjY2N0Ov1JfUQIjUyMhQKoaenBzfeeCM0Gg1MJhMikQgOHz6Ms846C9/97ncxe/bskm78uuqqq/Dee+/B6/XCZDLhrrvuwlVXXZXvxSJyCHWdlzAbN27ELbfcwnsTzpkzBxs3bsz3YmXE66+/jttvvx2JRAJXXXUVbr311nwvEiEimUzi1ltvxYoVK7Bs2bK0oiGZTMLpdA4yr+/o6EAwGERFRUVKSp51zLNRnoUiloZCWI/KuqRNJlNObJGkzNFDoVDameXpomxyEIlEYLfb4Xa7odVq0djYOO6GX8RiMWzZsgWPP/44KioqcNxxx0Gn06GrqwudnZ3o6upCLBZDXV0d1q9fT3WZRMlBQpMoOBKJBObOnYtXX30VTU1NWLJkCZ588knMmDEj34tGZBmO4xAIBNDZ2Zli1XTo0CHeX9NqtQ6aKd/W1obKysq8i9BIJMJHL+vq6vjoZaEgNbOcfU8kEinNL0IhOhZPSia6bTYbotEorFYrTCZTUab5x4LH48EzzzyDP/3pTzjllFNw3XXXDem00NfXh9ra2nG3nYjSh4QmUXB89NFHuO+++/jo689//nMAwG233ZbPxSJyDMdxSCaT/ChPlo5nM+XD4TCqq6sHidAJEybAZDLJlpblOA49PT2w2+28kDIajTkzdc8mwuYXYWpebNkkjoamqzN1OBxwOp2ora0tONGdCziOw+eff45HH30Ue/bswRVXXIHLL7983G0HghBCPppEwWGz2dDc3Mz/u6mpiYx+xyEKhQIqlQrNzc1obm7G0qVLU37PxjSyCOjBgwexY8cOdHR0wOVyQaFQoLGxcdAUpdbW1lFF7IRp4Pr6ekyYMAE1NTXZXOWck6knZSgU4usNxaMilUol71NpNBoxZ86ckq45lCIajWLjxo14/PHHodPp8P3vfx+nnHJKUdSgHjlyBNdddx1/znz3u9/FDTfckO/FIsYBJDQJgihoFAoF6urqMGfOHMyZMyfldxzHIZFIwGaz8Sn57du34/nnn0dXVxcikQjq6uoko6HCJpVoNIpXX30VkyZNAsdxsFqtWLBgwbhIbw7lSZlIJOBwOGCz2aBWq/nfh0Ih7N69W9IKSBgNzXfJQ7ZwOBxYt24dXn31VSxfvhyPP/442tra8r1YI0KlUuH+++/H/Pnz4fP5cPLJJ+O0006jUiVCdkhoEnmjsbERR44c4f/d3d0Nq9WaxyUiig2FQgG1Wo3W1la0trZi2bJlKb9nIw5ZTeiBAwfwwQcfoKOjAx6PB4lEAsBRI/Hp06fjyiuvxMyZM4tunny2CQQCsNls6Ovrg8lkwpw5c9JaE4mtgPr6+tJO6Ckmy6ZkMokdO3bg0UcfRUdHB6655hq8++67RdvkZLVa+etrbW0tpk2bBpvNRkKTkB2q0STyRjwex9y5c7Fp0yY0NjZiyZIlWLduHWbOnJnvRSNKmHg8js2bN2PdunUIBAI499xzMWXKFHR3d/N1oV1dXYhGo9BqtYNS8hMmTEBDQ0PJCdFkMgmPxwObzQalUslbE40lKplMJhGNRgc1KDHLJpVKJRkNzadlUygUwoYNG7Bu3Tq0tLRgzZo1OOGEE0pqf3d2dmL58uX4+OOPJQcPEMRooBpNouBQq9X4xS9+gQsuuACJRIKPJhUba9aswebNm2E0GqnGtAgYGBjABx98gAceeEDStJ7BuqqFDUrvvvsuOjo60NvbC41Gg5aWlkEitLm5eVSjPPNFOByGzWaD1+uFTqfDtGnTUFlZmZX3Fna7S8Esm4SjIsWWTVLRUDkasg4fPownnngCW7Zswfnnn49nn30WjY2NWf+cfOP3+/Htb38bDz74IIlMIidQRJMgxsj777+P6upqXHfddSQ0xwnMTqirq4tPybNo6JEjRxCLxaDX61NqQllktL6+Pu8ilHXU22w2xONxNDY2wmg0FlTUjm1jcTRUPK9cPCZyJA1gyWQSf/vb37B27Vq4XC5873vfwz//8z8XzASjbBOLxbBy5UqcccYZuPHGG/O9OESJQfZGBCEjnZ2dWLlyJQlNAsA/JuQIzetZ1/zAwADKy8vR0tIyaJ58U1MTVCqVbEKUTTNyOp2or69HY2Nj0XbUS1k2hUIhRCIRAEctm5gIZZ3006ZNg06ng9/vxwsvvID169dj+vTpWL16NY499ti8PwDICcdxuO6666DVankrOYLIJiQ0CUJGSGgSmcJGGLIIqHCKks1mQyKRgNFoHBQJnTBhwqhGXHIch4GBAXR3d/Oz2M1mc1H6gWYKs2xiAvTzzz/HU089ha6uLvT396O3txdNTU045ZRTMGvWLH47Nzc3S3qHlgIffPABzjzzTMyaNYuPXN999904++yz87xkRKlANZoEQRAFgEKhQEVFBaZPn47p06cP+n0ymYTL5eI9Q7/66iv85S9/waFDhxAIBFBRUcHPkBem5q1Wa8ooz97eXuzYsQM6nQ6VlZVoampCXV1dSUftGELLppqaGigUCkSjUdTX1+O2227D8uXL4XA4+EjzW2+9xTsRbNmypSS30YknnohAIJDvxSDGIRTRJIgsQBFNIhdwHIdQKDQoEspGeXIch+rqasRiMbjdbpx99tn4zne+g/b2dlRXV5ekgEpHf38/nn32WTz77LNYsGAB1qxZg9mzZ4+rbUAQuYQimgRBDAtNDylsFAoFqqqqMHPmzBSHhkgkgldeeQVPPPEEysrKcO6550Kv16OzsxO/+c1v0NHRgVAohMrKykEp+fb2dpjN5oJqBBotHMdh//79WLt2Lf7+97/j0ksvxV/+8hfodLp8L9qQhMNhLF++HJFIBIlEAhdeeCHuuuuufC8WQWQFimgSxBi56qqr8N5778Hr9cJkMuGuu+7CVVddle/FGhV2ux0OhyNlesgf//hHMnUucDZv3ozt27fjmmuuQUtLi+RrOI5DIBDgrZqEDUpOpxMKhQJWq3XQFKXW1lZUVlYWdCQwHo/jr3/9Kx577DEolUqsWrUKZ599dtHUobJ9U1NTg1gshjPOOAM///nPsWjRonwvGkFkDDUDEQQxYi655BKsXr0ap59+er4XhZARjuOQTCZhs9n4VDzzD+3s7EQ4HEZNTQ0fDRV+5dMWqaenB8888wxefPFFnHTSSVi1ahWmTZtW0KJ4OILBIM444ww89NBDWLhwYb4XhyAyhlLnBEGMiM7OTnz22Wd0sxsHKBQKqFQqtLS0oKWlBUuXLk35PetcF0ZDt2/fjo6ODrjdbigUCjQ1NQ2aotTS0jIiX8tM4DgOX3zxBdauXYtdu3bhiiuuwOuvv476+vqsfUY+SCQSOOmkk3Dw4EGsWrWKzjuiZKCIJkEQg/D7/TjrrLNw++2344ILLsj34hAFDMdxSCQSOHLkyKAGJTbKs76+flBKfsKECdDpdBlHQ6PRKDZt2oTHH38cNTU1+P73v4/TTjutJGpLhfT19eGyyy7D//zP/2DWrFn5XhyCyBhKnRMEkRE0PYTIJhzHoa+vj68JFU5R8nq9UKvVaG5uTknHCz0t3W43nnzySbzyyis4/fTTsWrVKrS3txd1enw4HnjgAVRWVuKmm27K96IQRMaQ0CQIYlhoegiRS5ix+uHDh1PqQjs6OnD48GF4vV6oVCrcdNNNuPTSS1FdXZ3vRZYFt9sNjUaDhoYGhEIhnH/++bj55ptxzjnn5HvRCCJjSGgSBDEsND2EKCTC4TBUKlXJTuth7N69G6tWrUIikUAymcTKlSvxk5/8JN+LRRAjgoQmQRDjDvInJAiCyA0kNAmCGHeQPyFBEERuSCc0S6tdjyAIQoBCoUBNTQ2Ao01OsVispJtICIIgCg0SmgRBlDSJRAKLFy/GhAkTcNppp5E/ITEqEokETjjhBKxcuTLfi0IQRQUJTYIgShqVSoW///3v2L9/Pz755BPs2bMn34tEFCG//e1vMW3atHwvBkEUHSQ0CYIYFzQ0NGDp0qV444038r0oRJHR3d2NLVu24Oqrr873ohBE0UFCkyCIksXtdqOvrw8AEAqF8Pbbb1NUihgxt99+O+67776Sm0JEELmAzhqCIEoWh8OBc845B4sWLcKSJUtw2mmnlYQJNtUL5o7NmzfDaDRi/vz5+V4UgihK1PleAIIgCLmYPXs2Pvzww3wvRtZh9YI+ny/fi1LyfPjhh9i0aRP++te/IhwOw+fz4ZprrsETTzyR70UjiKKAIpoEQRBFBNUL5pZ77rkHX3/9Nb766is89dRTOOWUU0hkEsQIIKFJEARRRFC9IEEQxQRdqQiCIIoEqhfML0uXLsVLL72U78UgiKKCajQJgiCKBKoXJAii2KBZ5wRBEEXItm3b8NBDD1GEjSCIgoBmnRMEQRAEQRA5hSKaBEEQBEEQxJigiCZBEARBEASRU0hoEgRBEARBELJAQpMgCIIgCIKQBRKaBEEQBEEQhCyQ0CQIgiAIgiBkgYQmQRAEQRAEIQskNAmCIAiCIAhZIKFJEARBEARByAIJTYIgCIIgCEIWSGgSBEEQBEEQskBCkyAIgiAIgpAFEpoEQRAEQRCELJDQJAiCIAiCIGSBhCZBEARBEAQhCyQ0CYIgCIIgCFkgoUkQBEEQBEHIAglNgiAIgiAIQhZIaBIEQRAEQRCyQEKTIAiCIAiCkAUSmgRBEARBEIQskNAkCIIgCIIgZIGEJkEQBEEQBCELJDQJgiAIgiAIWSChSRAEQRAEQcgCCU2CIAiCIAhCFkhoEgRBEARBELJAQpMgCIIgCIKQBRKaBEEQBEEQhCyQ0CQIgiAIgiBkgYQmQRAEQRAEIQskNAmCIAiCIAhZIKFJEARBEARByAIJTYIgCIIgCEIWSGgSBEEQBEEQskBCkyAIgiAIgpAFEpoEQRAEQRCELJDQJAiCIAiCIGSBhCZBEARBEAQhCyQ0CYIgCIIgCFkgoUkQBEEQBEHIAglNgiAIgiAIQhZIaBIEQRAEQRCyQEKTIAiCIAiCkAUSmgRBEARBEIQskNAkCIIgCIIgZIGEJkEQBEEQBCELJDQJgiAIgiAIWSChSRAEQRAEQcgCCU2CIAiCIAhCFkhoEgRBEARBELJAQpMgCIIgCIKQBRKaBEEQBEEQhCyQ0CQIgiAIgiBkgYQmQRAEQRAEIQskNAmCIAiCIAhZIKFJEARBEARByAIJTYIgCIIgCEIWSGgSBEEQBEEQskBCkyAIgiAIgpAFEpoEQRAEQRCELJDQJAiCIAiCIGSBhCZBEARBEAQhCyQ0CYIgCIIgCFkgoUkQBEEQBEHIAglNgiAIgiAIQhZIaBIEQRAEQRCyQEKTIAiCIAiCkAUSmgRBEARBEIQskNAkCIIgCIIgZIGEJkEQBEEQBCELJDQJgiAIgiAIWSChSRAEQRAEQcgCCU2CIAiCIAhCFkhoEgRBEARBELJAQpMgCIIgCIKQBRKaBEEQBEEQhCyQ0CQIgiAIgiBkgYQmQRAEQRAEIQskNAmCIAiCIAhZIKFJEARBEARByAIJTYIgCIIgCEIWSGgSBEEQBEEQskBCkyAIgiAIgpAFEpoEQRAEQRCELJDQJAiCIAiCIGSBhCZBEARBEAQhCyQ0CYIgCIIgCFkgoUkQBEEQBEHIAglNgiAIgiAIQhZIaBIEQRAEQRCyQEKTIAiCIAiCkAUSmgRBEARBEIQskNAkCIIgCIIgZIGEJkEQBEEQBCELJDQJgiAIgiAIWSChSRAEQRAEQcgCCU2CIAiCIAhCFhQcx+V7GQiCIAiCIIgShCKaBEEQBEEQhCyQ0CQIgiAIgiBkgYQmQRAEQRAEIQskNAmCIAiCIAhZIKFJEARBEARByAIJTYIgCIIgCEIW/j/h0mvO9rMpvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_embedding_groups_3d(plot_data, angles=(10,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "delayed-tokyo",
   "metadata": {},
   "source": [
    "### 2.1 Adding a Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "laden-population",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'I love surfboarding'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "industrial-threshold",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rebuild sentence list with the query\n",
    "ids = ['sentences']*len(sentences) + ['query']\n",
    "sentences_with_query = sentences + [query]\n",
    "embeddings_with_query = np.array([get_sentence_embedding(sent) for sent in sentences_with_query])\n",
    "\n",
    "plot_data = prepare_plot_data(ids, sentences_with_query, embeddings_with_query)\n",
    "plot_embedding_groups(plot_data, colors=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "deluxe-immigration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_embedding_groups_3d(plot_data, colors=colors, angles=(10, 80))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sustained-purpose",
   "metadata": {},
   "source": [
    "<img src=\"gif/sentence_query.gif\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "visible-procedure",
   "metadata": {},
   "source": [
    "## 3 Similarity Metrics\n",
    "[Back to Top](#index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-christmas",
   "metadata": {},
   "source": [
    "### 3.1 Euclidian Distance\n",
    "In mathematics, the Euclidean (or Euclidian Norm) distance between two points in Euclidean space is the length of a line segment between the two points. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, therefore occasionally being called the Pythagorean distance.\n",
    "When viewing Euclidean space as a vector space, its distance is associated with a norm called the Euclidean norm, defined as the distance of each vector from the origin. One of the important properties of this norm, relative to other norms, is that it remains unchanged under arbitrary rotations of space around the origin.\n",
    "On the $n$-dimensional Euclidean space ${\\displaystyle \\mathbb {R} ^{n}}$, the intuitive notion of a distance between vectors $p$ and $q$ is captured by the formula:\n",
    "\n",
    "$$\n",
    "{\\parallel}p-q{\\parallel}_{2} := \\sqrt{\\sum_{i=1}^{n}{(p_{i}-q_{i})^{2}}}\n",
    "$$\n",
    "\n",
    "For neighbors search, it is possible to ommit the square root, since it is monotonically increasing function. Thi is called the L2-Squared norm:\n",
    "\n",
    "$$\n",
    "{\\parallel}p-q{\\parallel}_{2}^{2} := \\sum_{i=1}^{n}{(p_{i}-q_{i})^{2}}\n",
    "$$\n",
    "\n",
    "Here we compute euclidian distance using [SciPy](https://www.scipy.org/) [cdist](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "anonymous-bahamas",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: I love surfboarding\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('We need to protect the ocean', 7.2700892037012155),\n",
       " ('I like mother nature', 7.409277405922192)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "THRESHOLD = 7.5\n",
    "distances = cdist([get_sentence_embedding(query)], embeddings, metric='euclidean')\n",
    "distances = distances[0]\n",
    "below_threshold_mask = distances < THRESHOLD\n",
    "below_threshold_indexes = np.where(below_threshold_mask)[0]\n",
    "\n",
    "print(f\"Query: {query}\")\n",
    "result = []\n",
    "for idx in below_threshold_indexes:\n",
    "    result.append((sentences[idx], distances[idx]))\n",
    "result = sorted(result, key=lambda x: x[1])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "objective-asset",
   "metadata": {},
   "source": [
    "We can also calculate it from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eligible-supplement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: I love surfboarding\n",
      "Distance: 7.270089193714386\n"
     ]
    }
   ],
   "source": [
    "q_embed = get_sentence_embedding(query)\n",
    "test_embed = embeddings[len(embeddings)-1]\n",
    "\n",
    "sum_ = 0.0\n",
    "for idx in range(q_embed.shape[0]):\n",
    "    sum_ += (q_embed[idx] - test_embed[idx]) ** 2\n",
    "\n",
    "distance = math.sqrt(sum_)\n",
    "\n",
    "print(f\"Query: {query}\")\n",
    "print(f'Distance: {distance}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accepting-hunter",
   "metadata": {},
   "source": [
    "### 3.2 Cosine Similarity\n",
    "Cosine similarity is a measure of similarity between two non-zero vectors $p$ and $q$ of an inner product space. It is defined to equal the cosine of the angle between them, which is also the same as the inner product of the same vectors normalized to both have length 1.\n",
    "\n",
    "$$\n",
    "similarity =\n",
    "cos(\\theta) =\n",
    "\\frac{p{\\cdot}q}{{\\parallel}p{\\parallel\\parallel}q{\\parallel}} =\n",
    "\\frac{\\sum_{i=1}^{n} p_{i} q_{i}}{\\sqrt{\\sum_{i=1}^{n} p_{i}^{2}}\\sqrt{\\sum_{i=1}^{n} q_{i}^{2}}}\n",
    "$$\n",
    "\n",
    "Here we compute cossine distance using scipy [cdist](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "occupational-equilibrium",
   "metadata": {},
   "outputs": [],
   "source": [
    "THRESHOLD = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "removed-orbit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: I love surfboarding\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('I like mother nature', 0.4681082333604596),\n",
       " ('We need to protect the ocean', 0.485726817009154)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Query: {query}\")\n",
    "distances = cdist([get_sentence_embedding(query)], embeddings, metric='cosine')\n",
    "distances = distances[0]\n",
    "below_threshold_mask = distances < THRESHOLD\n",
    "below_threshold_indexes = np.where(below_threshold_mask)[0]\n",
    "\n",
    "result = []\n",
    "for idx in below_threshold_indexes:\n",
    "    result.append((sentences[idx], distances[idx]))\n",
    "result = sorted(result, key=lambda x: x[1])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "framed-logistics",
   "metadata": {},
   "source": [
    "### 3.3 Manhattan Distance\n",
    "\n",
    "Distance aslo know as taxicab distance, $L_{1}$-norm or $L_{1}$ distance. The Manhattan Distance $d_{1}$ between two vectors $p, q$ in an $n$-dimensional real vector space with fixed Cartesian coordinate system, is the sum of the lengths of the projections of the line segment between the points onto the coordinate axes. More formally:\n",
    "\n",
    "$$\n",
    "d_{1}(p,q) = \\parallel p-q \\parallel_{1} = {\\sum_{i=1}^{n} \\mid p_{i} - q_{i} \\mid}\n",
    "$$\n",
    "\n",
    "Here we compute Manhattan distance using scipy [cityblock](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cityblock.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "express-austin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: I love surfboarding\n",
      "\n",
      "(10, 156.26491)\n",
      "We need to protect the ocean 156.26491\n",
      "(8, 164.15228)\n",
      "I like mother nature 164.15228\n"
     ]
    }
   ],
   "source": [
    "print(f\"Query: {query}\\n\")\n",
    "emb_query = embeddings_with_query[len(embeddings_with_query)-1]\n",
    "\n",
    "result = []\n",
    "for idx, emb in enumerate(embeddings):\n",
    "    result.append((idx, cityblock(emb_query, embeddings[idx])))\n",
    "\n",
    "result.sort(key=lambda x: x[1])\n",
    "for item in result[0:2]:\n",
    "    print(item)\n",
    "    print(sentences_with_query[item[0]] + ' ' + str(item[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-software",
   "metadata": {},
   "source": [
    "We can also calculate it from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dangerous-southeast",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We need to protect the ocean 156.26490658998955\n"
     ]
    }
   ],
   "source": [
    "manhattan_distance = sum(abs(d1-d2) for d1, d2 in zip(emb_query, embeddings[10]))\n",
    "\n",
    "print(sentences_with_query[10] + ' ' + str(manhattan_distance))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "another-pharmacy",
   "metadata": {},
   "source": [
    "### 3.4 Hamming distance\n",
    "\n",
    "Hamming distance calculates the distance between two binary vectors $p$, $q$, also referred to as binary strings or bitstrings for short. Most used in one-hot encode categorical columns of data.\n",
    "Formally: The Hamming distance $d$ is average number of bit differences between the two binary vectors:\n",
    "\n",
    "$$\n",
    "d_{1}(p,q) = \\frac{1}n {\\sum_{i=1}^{n} \\mid p_{i} - q_{i} \\mid}\n",
    "$$\n",
    "\n",
    "Reference:\n",
    "\n",
    "- [4 Distance Measures for Machine Learning](https://machinelearningmastery.com/distance-measures-for-machine-learning/)\n",
    "\n",
    "Here we compute Hamming distance using scipy [hamming](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.hamming.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "antique-gossip",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n"
     ]
    }
   ],
   "source": [
    "row1 = [0, 0, 0, 0, 0, 1]\n",
    "row2 = [0, 0, 0, 0, 1, 0]\n",
    "\n",
    "dist = hamming(row1, row2)\n",
    "print(dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disciplinary-lighter",
   "metadata": {},
   "source": [
    "We can also calculate it from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "apparent-stupid",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n"
     ]
    }
   ],
   "source": [
    "hamming_distance = sum(abs(e1 - e2) for e1, e2 in zip(row1, row2)) / len(row1)\n",
    "print(hamming_distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "circular-journalist",
   "metadata": {},
   "source": [
    "## 4 Document Embeddings\n",
    "[Back to Top](#index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "secondary-thickness",
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = [\n",
    "    \"Bob Marley\",\n",
    "    \"Peter Tosh\",\n",
    "    \"Reggae\",\n",
    "    \"Cancer\",\n",
    "    \"Heineken\",\n",
    "    \"UEFA Champions League\",\n",
    "    \"Star Wars\",\n",
    "    \"Olympic Games\",\n",
    "    \"Brazil\",\n",
    "    \"Argentina\",\n",
    "    \"Portugal\",\n",
    "    \"Star Trek\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "corporate-douglas",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching Bob Marley ...\n",
      "Processing Bob Marley ...\n",
      "Total Sentences for Bob Marley: 224\n",
      "Fetching Peter Tosh ...\n",
      "Processing Peter Tosh ...\n",
      "Total Sentences for Peter Tosh: 99\n",
      "Fetching Reggae ...\n",
      "Processing Reggae ...\n",
      "Total Sentences for Reggae: 441\n",
      "Fetching Cancer ...\n",
      "Processing Cancer ...\n",
      "Total Sentences for Cancer: 546\n",
      "Fetching Heineken ...\n",
      "Processing Heineken ...\n",
      "Total Sentences for Heineken: 48\n",
      "Fetching UEFA Champions League ...\n",
      "Processing UEFA Champions League ...\n",
      "Total Sentences for UEFA Champions League: 172\n",
      "Fetching Star Wars ...\n",
      "Processing Star Wars ...\n",
      "Total Sentences for Star Wars: 331\n",
      "Fetching Olympic Games ...\n",
      "Processing Olympic Games ...\n",
      "Total Sentences for Olympic Games: 540\n",
      "Fetching Brazil ...\n",
      "Processing Brazil ...\n",
      "Total Sentences for Brazil: 664\n",
      "Fetching Argentina ...\n",
      "Processing Argentina ...\n",
      "Total Sentences for Argentina: 647\n",
      "Fetching Portugal ...\n",
      "Processing Portugal ...\n",
      "Total Sentences for Portugal: 965\n",
      "Fetching Star Trek ...\n",
      "Processing Star Trek ...\n",
      "Total Sentences for Star Trek: 440\n",
      "Total sentences: 5117\n"
     ]
    }
   ],
   "source": [
    "documents = []\n",
    "nbow = {}\n",
    "for title in titles:\n",
    "    print(f\"Fetching {title} ...\")\n",
    "    pages = requests.get(\n",
    "        \"https://en.wikipedia.org/w/api.php?action=query&format=json&titles=%s\"\n",
    "        \"&prop=extracts&explaintext\" % title).json()[\"query\"][\"pages\"]\n",
    "    \n",
    "    print(f\"Processing {title} ...\")\n",
    "    text_str = next(iter(pages.values()))[\"extract\"]\n",
    "    sentences = text_str.replace('...', '.').split('.')\n",
    "    sentences = [sent.strip() + '.' for sent in sentences]\n",
    "    sentence_embeddings = np.array([get_sentence_embedding(sent) for sent in sentences])\n",
    "    text_nlp = nlp(text_str)\n",
    "    tokens = [t for t in text_nlp if t.is_alpha and not t.is_stop]\n",
    "    words = Counter(t.text for t in tokens)\n",
    "    orths = {t.text: t.orth for t in tokens}\n",
    "    sorted_words = sorted(words)\n",
    "    documents.append((title, sentences[0][0:50] + \"...\", sentence_embeddings.mean(axis=0), sentence_embeddings, sentences))\n",
    "    nbow[title] = (title, [orths[t] for t in sorted_words],\n",
    "                        np.array([words[t] for t in sorted_words], dtype=np.float32))\n",
    "    print(f\"Total Sentences for {title}: {len(sentences)}\")\n",
    "\n",
    "flat_embed = np.array([embed for row in documents for embed in row[3]])\n",
    "flat_text = [(row[0], sent) for row in documents for sent in row[4]]\n",
    "print(f'Total sentences: {len(flat_embed)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "scientific-impossible",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = [doc[2] for doc in documents]\n",
    "ids = [doc[0] for doc in documents]\n",
    "sentences = [doc[1] for doc in documents]\n",
    "plot_data = prepare_plot_data(ids, sentences, embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "floppy-wiring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_embedding_groups_3d(plot_data,\n",
    "                         title=\"Wikipedia Pages\",\n",
    "                         legend_title=\"Page Title\",\n",
    "                         angles=(10,30))\n",
    "\n",
    "# plot_embedding_groups_3d(plot_data,\n",
    "#                          title=\"Wikipedia Pages\",\n",
    "#                          legend_title=\"Page Title\",\n",
    "#                          angles=(10,30),\n",
    "#                          save_gif_dir='gif')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ongoing-relationship",
   "metadata": {},
   "source": [
    "<img src=\"gif/wiki1.gif\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "filled-crisis",
   "metadata": {},
   "source": [
    "## 5 Word Movers Distance\n",
    "[Back to Top](#index)\n",
    "\n",
    "<img src=\"fig/wmd1.png\" style=\"float: right;\" alt=\"drawing\" width=\"400\"/>\n",
    "Word Mover’s Distance (WMD) is proposed for distance measurement between 2 documents (or sentences), leveraging that times novelty of Word Embeddings like Word2Vec. They proposed to use word embeddings to calculate the similarities appliyng normalized Bag-of-Words and the representations to calculate the distance between documents. The method has no hyperprameters.\n",
    "\n",
    "References:\n",
    "- [Word Mover's Distance](http://www.cs.cornell.edu/~kilian/papers/wmd_metric.pdf)\n",
    "- [WMD Relax](https://github.com/src-d/wmd-relax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "solid-steam",
   "metadata": {},
   "source": [
    "### 5.1 Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "drawn-planner",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SpacyEmbeddings(object):\n",
    "    def __getitem__(self, item):\n",
    "        return nlp.vocab[item].vector\n",
    "\n",
    "class BertEmbeddings(object):\n",
    "    def __getitem__(self, item):\n",
    "        return get_sentence_embedding(nlp.vocab[item].text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brief-newspaper",
   "metadata": {},
   "source": [
    "### 5.2 NNS with GloVe Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "lonely-recall",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: Bob Marley Page\n",
      "Calculating similar pages:\n",
      "              Peter Tosh\t3.953218460083008\n",
      "                  Reggae\t4.88713264465332\n",
      "               Argentina\t5.883407115936279\n",
      "               Star Trek\t6.038415431976318\n",
      "           Olympic Games\t6.063663959503174\n",
      "                  Brazil\t6.072596549987793\n",
      "                Portugal\t6.098607063293457\n",
      "               Star Wars\t6.173825263977051\n",
      "   UEFA Champions League\t6.239352703094482\n",
      "                Heineken\t6.354133129119873\n"
     ]
    }
   ],
   "source": [
    "# hook in WMD\n",
    "calc = WMD(SpacyEmbeddings(), nbow)\n",
    "print(f\"Query: {titles[0]} Page\")\n",
    "print(\"Calculating similar pages:\")\n",
    "for title, relevance in calc.nearest_neighbors(titles[0]):\n",
    "    print(\"%24s\\t%s\" % (title, relevance))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opponent-receiver",
   "metadata": {},
   "source": [
    "### 5.3 NNS with Bert Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "endangered-tuning",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: Bob Marley Page\n",
      "Calculating similar pages:\n",
      "              Peter Tosh\t4.784566879272461\n",
      "                  Reggae\t5.386720180511475\n",
      "               Argentina\t6.514907360076904\n",
      "               Star Trek\t6.575946807861328\n",
      "           Olympic Games\t6.5948591232299805\n",
      "                  Brazil\t6.596113681793213\n",
      "                Portugal\t6.62761926651001\n",
      "               Star Wars\t6.683879375457764\n",
      "   UEFA Champions League\t6.856717586517334\n",
      "                  Cancer\t6.863679885864258\n",
      "                Heineken\t7.053093433380127\n"
     ]
    }
   ],
   "source": [
    "# hook in WMD\n",
    "calc_bert = WMD(BertEmbeddings(), nbow)\n",
    "print(f\"Query: {titles[0]} Page\")\n",
    "print(\"Calculating similar pages:\")\n",
    "for title, relevance in calc_bert.nearest_neighbors(titles[0],k=30):\n",
    "    print(\"%24s\\t%s\" % (title, relevance))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "structured-probe",
   "metadata": {},
   "source": [
    "## 6 ANN - Aproximate Nearest Neighbors\n",
    "[Back to Top](#index)\n",
    "\n",
    "In some applications it may be acceptable to retrieve a \"good guess\" of the nearest neighbor. In those cases, we can use an algorithm which doesn't guarantee to return the actual nearest neighbor in every case, in return for improved speed or memory savings. Often such an algorithm will find the nearest neighbor in a majority of cases, but this depends strongly on the dataset being queried.\n",
    "<img src=\"fig/ann_benchmark.png\" style=\"float:right; margin:0px 20px\" alt=\"drawing\" width=\"600\" padding=\"0\" />\n",
    "An approximate nearest neighbor (ANN) search algorithm is allowed to return points, whose distance from the query is at most $c$ times the distance from the query to its nearest points. The appeal of this approach is that, in many cases, an approximate nearest neighbor is almost as good as the exact one. In particular, if the distance measure accurately captures the notion of user quality, then small differences in the distance should not matter\n",
    "\n",
    "ANN algorithms are typically compared based on the trade-off between search quality and efficiency. A baseline could be the naive k neareast neighborhood search (k-NN), which is inherently expensive due to the curse of dimensionality:\n",
    "\n",
    "$$\n",
    "O(k * n * d)\n",
    "$$\n",
    "\n",
    "where $k$ is the number of results, $n$ is the number of data points to be searched and $d$ is the number of dimensions.\n",
    "Some examples of algorithms that support the approximate nearest neighbor search are:\n",
    "\n",
    "- [Approximate Nearest Neighbors Oh Yeah - ANNOY:](https://github.com/spotify/annoy) Used it at Spotify for music recommendations.\n",
    "\n",
    "References:\n",
    "- [ANN Benchmarks](http://ann-benchmarks.com/index.html)\n",
    "- [Product Quantization for Nearest Neighbor Search](https://lear.inrialpes.fr/pubs/2011/JDS11/jegou_searching_with_quantization.pdf)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "editorial-secretariat",
   "metadata": {},
   "source": [
    "## 7 ANNOY - Approximate Nearest Neighbor Oh Yeah\n",
    "[Back to Top](#index)\n",
    "\n",
    "<img src=\"fig/ann-tree-full-K-1024x793.png\" style=\"float:right; margin:0px 20px\" alt=\"drawing\" width=\"600\" padding=\"0\" /><i>\"We use it at Spotify for music recommendations. After running matrix factorization algorithms, every user/item can be represented as a vector in f-dimensional space. This library helps us search for similar users/items. We have many millions of tracks in a high-dimensional space, so memory usage is a prime concern.\"</i>\n",
    "\n",
    "Using random projections and by building up a tree. At every intermediate node in the tree, a random hyperplane is chosen, which divides the space into two subspaces. This hyperplane is chosen by sampling two points from the subset and taking the hyperplane equidistant from them.\n",
    "\n",
    "**References:**\n",
    "- [github.com/spotify/annoy](https://github.com/spotify/annoy)\n",
    "- [Nearest neighbors and vector models – part 2 – algorithms and data structures](https://erikbern.com/2015/10/01/nearest-neighbors-and-vector-models-part-2-how-to-search-in-high-dimensional-spaces.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "associate-request",
   "metadata": {},
   "source": [
    "### 7.1 Index Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "regulation-following",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = len(flat_embed)\n",
    "f = len(flat_embed[0])\n",
    "n_trees = 10\n",
    "output_index_file = 'indexes/demo.annoy'\n",
    "\n",
    "t = AnnoyIndex(f, 'angular')  # Length of item vector that will be indexed\n",
    "for i in range(n):\n",
    "    t.add_item(i, flat_embed[i])\n",
    "\n",
    "t.build(n_trees)\n",
    "t.save(output_index_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disciplinary-strap",
   "metadata": {},
   "source": [
    "### 7.2 Index Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "maritime-waters",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u = AnnoyIndex(f, 'angular')\n",
    "u.load(output_index_file) # super fast, will just mmap the file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hungarian-alexander",
   "metadata": {},
   "source": [
    "### 7.3 Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "legislative-jewelry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Peter Tosh', 'He then picked up the guitar and played the song back to the man.')\n"
     ]
    }
   ],
   "source": [
    "query_index = 232\n",
    "query = flat_text[query_index]\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "artificial-somalia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.8 µs ± 1.87 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit u.get_nns_by_item(query_index, 3) # will find the 1000 nearest neighbors\n",
    "neighbors = u.get_nns_by_item(query_index, 3) # will find the 1000 nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "republican-madison",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Query:\n",
      "('Peter Tosh', 'He then picked up the guitar and played the song back to the man.')\n",
      "Results:\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 1 Document: Peter Tosh\n",
      "Text Snip:\n",
      "He watched the man play the same song for half a day, memorizing everything his fingers were doing. He then picked up the guitar and played the song back to the man. The man then asked McIntosh who had taught him to play; McIntosh told him that he had.\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 2 Document: Peter Tosh\n",
      "Text Snip:\n",
      "\" When McIntosh was fifteen, his aunt died and he moved to Trenchtown in Kingston, Jamaica. He first learned guitar after watching a man in the country play a song that captivated him. He watched the man play the same song for half a day, memorizing everything his fingers were doing.\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 3 Document: UEFA Champions League\n",
      "Text Snip:\n",
      "The phrase was coined after a pre-match conference when he was questioned about Arsenal's lack of a trophy after exiting the FA Cup. He said \"The first trophy is to finish in the top four\". At Arsenal's 2012 AGM, Wenger was also quoted as saying: \"For me there are five trophies every season: Premier League, Champions League, the third is to qualify for the Champions League.\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('-'*40)\n",
    "print('Query:')\n",
    "print(query)\n",
    "print('Results:')\n",
    "print('-'*40)\n",
    "\n",
    "for rank, idx in enumerate(neighbors):\n",
    "    print_knn(rank+1, flat_text, idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "postal-insulin",
   "metadata": {},
   "source": [
    "## 8 Product Quantizers Methods\n",
    "[Back to Top](#index)\n",
    "\n",
    "\n",
    "### 8.1 Vector Quantization\n",
    "Quantization is a destructive process with the purpose to reduce the cardinality of the representation space, in particular when the input data is real-valued.\n",
    "Formally, a quantizer is a function $q$ mapping a $D$-dimensional vector $x\\in\\mathbb{R}^{D}$ to a vector $q(x)\\in\\mathcal{C}=\\{c_{i};i\\in\\mathcal{I}\\}$, where the index set $\\mathcal{I}$ is assumed to be finite: $\\mathcal{I} = 0 \\dots k - 1$. The reproduction values $c_{i}$ are called *centroids*, and the set of these reproduction values $\\mathcal{C}$ is called *codebook* of size $k$.\n",
    "The set $\\mathcal{V_{i}}$ of vectors mapped to a given index centroid $c_{i}$ is referred to as a *cell* (also known as Voronoi Cell), and defined as:\n",
    "\n",
    "$$\n",
    "\\mathcal{V_{i}} \\triangleq \\{x\\in\\mathbb{R}^{D}:q(x)=c_{i}\\}\n",
    "$$\n",
    "\n",
    "By definition, all the vectors lying in the same cell $\\mathcal{V_{i}}$ are reconstructed by the same centroid $c_{i}$.\n",
    "\n",
    "#### 8.1.1 Quantizer Quality\n",
    "\n",
    "The quality of a quantizer is usually measured by the mean squared error between the input vector $x$ and its reproduction value $q(x)$:\n",
    "\n",
    "$$\n",
    "\\mathrm{MSE(q)} = \\mathbb{E}_{X} \\left[d(q(x),x))^{2}\\right] = {\\int}p(x)\\,d\\left(q(x),x\\right)^{2}\\, dx\n",
    "$$\n",
    "\n",
    "where $d(x; y) = {\\parallel x-y \\parallel}$ is the Euclidean distance between $x$ and $y$, and $p(x)$ is the probability distribution function corresponding the random variable $X$. For an arbitrary probability distribution function, this is numerically computed using Monte-Carlosampling, as the average of $\\parallel q(x) - x \\parallel$ on a large set of samples.\n",
    "\n",
    "#### 8.1.2 Lloyd Optimality Conditions\n",
    "\n",
    "In order for the quantizer to be optimal, it has to satisfy two properties known as the *Lloyd optimality conditions*. First, a vector $x$ must be quantized to its nearest codebook centroid, in terms of the Euclidean distance:\n",
    "\n",
    "$$\n",
    "q(x) = {\\underset{c_{i}\\in\\mathcal{C}}{\\mathrm{argmin}}\\, d(x,c_{i})}\n",
    "$$\n",
    "\n",
    "As a result, the cells are delimited by hyperplanes. The second Lloyd condition is that the reconstruction value must be the expectation of the vectors lying in the Voronoi Cell:\n",
    "\n",
    "$$\n",
    "c_{i} = \\mathbb{E}_{X}[x|i] = {\\int\\limits_{V_{i}}}p(x)\\,x\\,dx\n",
    "$$\n",
    "\n",
    "### 8.2 Product Quantizers\n",
    "\n",
    "Let's consider a scenario where we need to work with vectors of 128 dimensions. Assuming we accept a 64-bit quantizer, which means \"*half of a bit*\" per dimension, this quantizer will have $k = 2^{64}$ centroids. Therefore, it is impossible to use Lloyd’s algorithm as the number of samples required and the complexity of learning the quantizer are several times $k$. It is even impossible to store the $D\\,{\\times}\\,k$ floating point values representing the $k$ centroids.\n",
    "\n",
    "**Product quantization** is an efficient solution to address these issues.\n",
    "\n",
    "The input vector $x$ is split into $m$ distinct subvectors $u_{j}, 1 \\leq j \\leq m$ of dimension $D^{*} = D/m$, where $D$ is a multiple of $m$. The subvectors are quantized separately using $m$ distinct quantizers. A given vector $x$ is therefore mapped as follows:\n",
    "\n",
    "$$\n",
    "\\underset{u_{1}(x)}{x_{1},\\dots x_{D^{*}}},\\dots,\\underset{u_{m}(x)}{x_{D-D^{*}+1},\\dots x_{D}} \\to q_{1}\\left(u_{1}(x)\\right),\\dots,q_{m}\\left(u_{m}(x)\\right)\n",
    "$$\n",
    "\n",
    "where $q_{j}$ is a low-complexity quantizer associated with the $j^{th}$ subvector. With the subquantizer $q_{j}$ we associate the index set $\\mathcal{I}_{j}$ , the codebook $\\mathcal{C}_{j}$ and the corresponding reproduction values $c_{j,i}$.\n",
    "\n",
    "A reproduction value of the product quantizer is defiend by an element of the index set\n",
    "\n",
    "$$\n",
    "\\mathcal{I} = \\mathcal{I}_{1} \\times\\ldots\\times\\mathcal{I}_{m}\n",
    "$$\n",
    "\n",
    "... and finally, the codebook is defined by the cartesian product\n",
    "\n",
    "$$\n",
    "\\mathcal{C} = \\mathcal{C}_{1} \\times\\ldots\\times\\mathcal{C}_{m}.\n",
    "$$\n",
    "\n",
    "Assuming all subquantizers $q_{n}$ have the same finite number $k^{*}$ of reproduction values, the total number of centrois is given by\n",
    "\n",
    "$$\n",
    "k = (k^{*})^{m}\n",
    "$$\n",
    "\n",
    "The strength of a product quantizer is to produce a large set of centroids from several small sets of centroids: those associated with the subquantizers. When learning the subquantizers using Lloyd’s algorithm, a limited number of vectors is used, but the codebook is, to some extent, still adapted to the data distribution to represent. The complexity of learning the quantizer is $m$ times the complexity of performing k-means clustering with $k^{*}$ centroids of dimension $D^{*}$. So we have $O(m\\;k^{*}\\;D^{*})$, which in terms of $k$ and $D$ is\n",
    "\n",
    "$$\n",
    "O(k^{1/m}\\;D)\n",
    "$$\n",
    "\n",
    "### 8.3 FAISS\n",
    "\n",
    "FAISS is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products.\n",
    "\n",
    "**References:**\n",
    "- [Product quantization for nearest neighbor search](https://lear.inrialpes.fr/pubs/2011/JDS11/jegou_searching_with_quantization.pdf)\n",
    "- [Approximate Nearest Neighbor Search on High\n",
    "Dimensional Data — Experiments, Analyses, and\n",
    "Improvement](https://arxiv.org/pdf/1610.02455.pdf)\n",
    "- [Approximate Nearest Neighbours for Recommender Systems](https://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/)\n",
    "- [Billion-scale similarity search with GPUs](https://arxiv.org/abs/1702.08734)\n",
    "- [https://github.com/facebookresearch/faiss](https://github.com/facebookresearch/faiss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "transparent-possession",
   "metadata": {},
   "source": [
    "#### 8.3.1 Exact Search Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "backed-removal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector dimension\n",
    "d = flat_embed.shape[1]\n",
    "\n",
    "# index definition\n",
    "index_exact = faiss.IndexFlatL2(d)\n",
    "\n",
    "# index training\n",
    "index_exact.train(flat_embed)\n",
    "index_exact.add(flat_embed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "different-population",
   "metadata": {},
   "source": [
    "#### 8.3.2 IVF Flat Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "spread-boards",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector dimension\n",
    "d = flat_embed.shape[1]\n",
    "\n",
    "# coarse quantizer\n",
    "coarse = faiss.IndexFlatL2(d)\n",
    "\n",
    "# index definition\n",
    "n_lists = 4 # number of clusters (Voronoi cells) do be formed\n",
    "index_ivff = faiss.IndexIVFFlat(coarse, d, n_lists, faiss.METRIC_L2)\n",
    "\n",
    "# index training\n",
    "index_ivff.train(flat_embed)\n",
    "index_ivff.add(flat_embed)\n",
    "index_ivff.nprobe = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "light-scholarship",
   "metadata": {},
   "source": [
    "#### 8.3.3 IVFADC Index - Coarse Quantizer with Product Quantization on Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "absolute-spanish",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector dimension\n",
    "d = flat_embed.shape[1]\n",
    "\n",
    "# coarse quantizer\n",
    "coarse = faiss.IndexFlatL2(d)\n",
    "\n",
    "# inverted index with product quantization\n",
    "n_lists = 4 # number of clusters (Voronoi cells) do be formed\n",
    "M = 8 # code size\n",
    "n_bits = 8 # vector compression, must be equal to 8, 12 or 16. The dimension d should be a multiple of m\n",
    "index_ivfpq = faiss.IndexIVFPQ(coarse, d, n_lists, M, n_bits)\n",
    "\n",
    "# index training\n",
    "index_ivfpq.train(flat_embed)\n",
    "index_ivfpq.add(flat_embed)\n",
    "index_ivfpq.nprobe = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bored-obligation",
   "metadata": {},
   "source": [
    "#### 8.3.4 Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "every-eating",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"the current trophy\"\n",
    "query = \"Last album of wailers\"\n",
    "query = \"cells and proteins\"\n",
    "# query = \"fish and wine\"\n",
    "\n",
    "query_embed = np.array([get_sentence_embedding(query)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offensive-creator",
   "metadata": {},
   "source": [
    "#### 8.3.5 Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "universal-prefix",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Exact Index:\n",
      "----------------------------------------\n",
      "Rank 0 Document: Cancer\n",
      "Text Snip:\n",
      "=== Immunotherapy ===\n",
      "\n",
      "A variety of therapies using immunotherapy, stimulating or helping the immune system to fight cancer, have come into use since 1997. Approaches include antibodies, checkpoint therapy, and adoptive cell transfer. === Laser therapy ===\n",
      "\n",
      "Laser therapy uses high-intensity light to treat cancer by shrinking or destroying tumors or precancerous growths.\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 1 Document: Cancer\n",
      "Text Snip:\n",
      "Oncogenes are genes that promote cell growth and reproduction. Tumor suppressor genes are genes that inhibit cell division and survival. Malignant transformation can occur through the formation of novel oncogenes, the inappropriate over-expression of normal oncogenes, or by the under-expression or disabling of tumor suppressor genes.\n",
      "----------------------------------------\n",
      " \n",
      "Exact Index Stats:\n",
      "1.38 ms ± 25.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "IVF Flat Index:\n",
      "----------------------------------------\n",
      "Rank 0 Document: Cancer\n",
      "Text Snip:\n",
      "=== Immunotherapy ===\n",
      "\n",
      "A variety of therapies using immunotherapy, stimulating or helping the immune system to fight cancer, have come into use since 1997. Approaches include antibodies, checkpoint therapy, and adoptive cell transfer. === Laser therapy ===\n",
      "\n",
      "Laser therapy uses high-intensity light to treat cancer by shrinking or destroying tumors or precancerous growths.\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 1 Document: Cancer\n",
      "Text Snip:\n",
      "Oncogenes are genes that promote cell growth and reproduction. Tumor suppressor genes are genes that inhibit cell division and survival. Malignant transformation can occur through the formation of novel oncogenes, the inappropriate over-expression of normal oncogenes, or by the under-expression or disabling of tumor suppressor genes.\n",
      "----------------------------------------\n",
      " \n",
      "IVF Flat Query Stats:\n",
      "916 µs ± 43.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
      "IVF PQ Index:\n",
      "----------------------------------------\n",
      "Rank 0 Document: Star Wars\n",
      "Text Snip:\n",
      "Bounty hunters are often employed by both gangsters and governments. Illicit activities include smuggling and slavery. == Film ==\n",
      "\n",
      "The Star Wars film series centers around three sets of trilogies, the nine films of which are collectively referred to as the \"Skywalker saga\".\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 1 Document: Cancer\n",
      "Text Snip:\n",
      "=== Immunotherapy ===\n",
      "\n",
      "A variety of therapies using immunotherapy, stimulating or helping the immune system to fight cancer, have come into use since 1997. Approaches include antibodies, checkpoint therapy, and adoptive cell transfer. === Laser therapy ===\n",
      "\n",
      "Laser therapy uses high-intensity light to treat cancer by shrinking or destroying tumors or precancerous growths.\n",
      "----------------------------------------\n",
      " \n",
      "IVF PQ Stats:\n",
      "138 µs ± 9.95 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('-'*80)\n",
    "print(\"Exact Index:\")\n",
    "D, I = index_exact.search(query_embed, 2)\n",
    "for idx, label in enumerate(I[0]):\n",
    "    print_knn(idx, flat_text, label)\n",
    "print(' ')\n",
    "print(\"Exact Index Stats:\")\n",
    "%timeit index_exact.search(query_embed, 2)\n",
    "print('-'*80)\n",
    "\n",
    "print('-'*80)\n",
    "print(\"IVF Flat Index:\")\n",
    "D, I = index_ivff.search(query_embed, 2)\n",
    "for idx, label in enumerate(I[0]):\n",
    "    print_knn(idx, flat_text, label)\n",
    "print(' ')\n",
    "print(\"IVF Flat Query Stats:\")\n",
    "%timeit index_ivff.search(query_embed, 2)\n",
    "print('-'*80)\n",
    "\n",
    "print('-'*80)\n",
    "print(\"IVF PQ Index:\")\n",
    "D, I = index_ivfpq.search(query_embed, 2)\n",
    "for idx, label in enumerate(I[0]):\n",
    "    print_knn(idx, flat_text, label)\n",
    "print(' ')\n",
    "print(\"IVF PQ Stats:\")\n",
    "%timeit index_ivfpq.search(query_embed, 2)\n",
    "print('-'*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "available-latter",
   "metadata": {},
   "source": [
    "## 9 Small World Graphs\n",
    "[Back to Top](#index)\n",
    "\n",
    "**Small World Phenomena**:  many \"real world\" networks tend to be higly clustered and have small average shortest path lengh. These networks are known as Small-World Networks\n",
    "\n",
    "<p><img src=\"fig/smallworld3.png\" width=\"500\" /><img src=\"fig/smallw.png\" width=\"500\" /></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "possible-alias",
   "metadata": {},
   "source": [
    "### 9.1 HNSW\n",
    "\n",
    "<img src=\"fig/hnsw2.png\" style=\"float:right; margin:0px 20px\" alt=\"drawing\" width=\"550\" padding=\"0\" />By using structure decomposition of navigable small world graphs together with the smart neighbor selection heuristic the proposed Hierarchical NSW approach ove r-comes several important problems of the basic NSW\n",
    "structure advancing the state of the art in K ANN search.\n",
    "\n",
    "For the selection of the proximity graph connections during the element insertion, HNSW utilizes a heuristic that\n",
    "takes into account the distances between the candidate elements to create diverse connections  instead of just selecting the closest neighbors.\n",
    "\n",
    "**References:**\n",
    "- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://arxiv.org/abs/1603.09320)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "contained-waterproof",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall for two batches: 0.987297244479187 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "dim = flat_embed.shape[1]\n",
    "num_elements = flat_embed.shape[0]\n",
    "\n",
    "# Generating sample data\n",
    "data = flat_embed\n",
    "ids = np.arange(num_elements)\n",
    "\n",
    "# Declaring index\n",
    "p = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip\n",
    "\n",
    "# Initializing index - the maximum number of elements should be known beforehand\n",
    "p.init_index(max_elements = num_elements, ef_construction = 200, M = dim)\n",
    "\n",
    "# Element insertion (can be called several times):\n",
    "p.add_items(data, ids)\n",
    "\n",
    "# Controlling the recall by setting ef:\n",
    "p.set_ef(50) # ef should always be > k\n",
    "\n",
    "# Query dataset, k - number of closest elements (returns 2 numpy arrays)\n",
    "labels, distances = p.knn_query(data, k = 1)\n",
    "\n",
    "# Index objects support pickling\n",
    "# WARNING: serialization via pickle.dumps(p) or p.__getstate__() is NOT thread-safe with p.add_items method!\n",
    "# Note: ef parameter is included in serialization; random number generator is initialized with random_seed on Index load\n",
    "p_copy = pickle.loads(pickle.dumps(p)) # creates a copy of index p using pickle round-trip\n",
    "\n",
    "### Index parameters are exposed as class properties:\n",
    "# print(f\"Parameters passed to constructor:  space={p_copy.space}, dim={p_copy.dim}\") \n",
    "# print(f\"Index construction: M={p_copy.M}, ef_construction={p_copy.ef_construction}\")\n",
    "# print(f\"Index size is {p_copy.element_count} and index capacity is {p_copy.max_elements}\")\n",
    "# print(f\"Search speed/quality trade-off parameter: ef={p_copy.ef}\")\n",
    "\n",
    "\n",
    "# Query the elements for themselves and measure recall:\n",
    "labels, distances = p.knn_query(data, k=1)\n",
    "print(\"Recall for two batches:\", np.mean(labels.reshape(-1) == np.arange(len(data))), \"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "small-algebra",
   "metadata": {},
   "source": [
    "#### 9.1.1 Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "featured-structure",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"the current trophy\"\n",
    "# query = \"Last album of bands\"\n",
    "# query = \"cells and proteins\"\n",
    "# query = \"fish and wine\"\n",
    "\n",
    "query_embed = np.array([get_sentence_embedding(query)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "prepared-deficit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "HNSW:\n",
      "----------------------------------------\n",
      "Rank 0 Document: Olympic Games\n",
      "Text Snip:\n",
      "At the 1896 Olympics, only the winner and runner-up of each event received medals—silver for first and bronze for second, with no gold medals awarded. The current three-medal format was introduced at the 1904 Olympics. From 1948 onward, athletes placing fourth, fifth, and sixth have received certificates, which came to be known officially as Olympic diplomas; from 1984, these have also been awarded to the seventh- and eighth-place finishers.\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 1 Document: UEFA Champions League\n",
      "Text Snip:\n",
      "Five clubs own a version of the official trophy: Real Madrid, Ajax, Bayern Munich, Milan and Liverpool. Since 2008, the official trophy has remained with UEFA and the clubs are awarded a replica. The current trophy is 74 cm (29 in) tall and made of silver, weighing 11 kg (24 lb).\n",
      "----------------------------------------\n",
      "----------------------------------------\n",
      "Rank 2 Document: Argentina\n",
      "Text Snip:\n",
      "Argentina reigns undisputed in Polo, having won more international championships than any other country and been seldom beaten since the 1930s. The Argentine Polo Championship is the sport's most important international team trophy. The country is home to most of the world's top players, among them Adolfo Cambiaso, the best in Polo history.\n",
      "----------------------------------------\n",
      " \n",
      "HNSW Stats:\n",
      "321 µs ± 12.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('-'*80)\n",
    "print(\"HNSW:\")\n",
    "labels, distances = p.knn_query(query_embed, k = 3)\n",
    "for idx, label in enumerate(labels[0]):\n",
    "    label = int(label)\n",
    "    print_knn(idx, flat_text, label)\n",
    "print(' ')\n",
    "print(\"HNSW Stats:\")\n",
    "%timeit p.knn_query(query_embed, k = 3)\n",
    "print('-'*80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nonprofit-karen",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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