diff --git a/README.md b/README.md index 2ec4b1a..a4274dc 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ ## Лекции -| Дата | Лекция | +| Дата |Лекция | |:----------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 13.02.2024 | [Вводная лекция](lections/OATD_lec_1.pdf) [pptx](lections/OATD_lec_1.pptx) | | 20.02.2024 | [Клаcсификация данных. Основные понятия](lections/OATD_lec_2.pdf) [pptx](lections/OATD_lec_2.pptx) | @@ -12,14 +12,21 @@ | 19.03.2024 | [Ансамблевые методы классификации](lections/OATD_lec_6.pdf) [pptx](lections/OATD_lec_6.pptx) | | 26.03.2024 | [Кластеризация данных](lections/OATD_lec_7.pdf) [pptx](lections/OATD_lec_7.pptx) | | 02.04.2024 | Кластеризация данных (продожение) | -| 09.04.2024 | [Нейронные сети в задаче анализа данных](lections/OATD_lec_8.pdf) [pptx](lections/OATD_lec_8.pptx) | -| 16.04.2024 | [Сверточные нейронные сети](lections/OATD_lec_9.pdf) [pptx](lections/OATD_lec_9.pptx)
[Рекуррентные нейронные сети](lections/OATD_lec_10.pdf) [pptx](lections/OATD_lec_10.pptx) | -| 23.04.2024 | [Решение задач выявления плагиата](lections/OATD_lec_11.pdf) [pptx](lections/OATD_lec_11.pptx) | -| 23.04.2024 | [Решение задач выявления плагиата](lections/OATD_lec_11.pdf) [pptx](lections/OATD_lec_11.pptx) | -| 30.04.2024 | Лекция | +| 06.04.2024 | [Нейронные сети в задаче анализа данных](lections/OATD_lec_8.pdf) [pptx](lections/OATD_lec_8.pptx) | +| 06.04.2024 | [Сверточные нейронные сети](lections/OATD_lec_9.pdf) [pptx](lections/OATD_lec_9.pptx)
[Рекуррентные нейронные сети](lections/OATD_lec_10.pdf) [pptx](lections/OATD_lec_10.pptx) | +| 09.04.2024 | [Трансформеры](lections/OATD_lec_12.pdf) [pptx](lections/OATD_lec_12.pptx) | +| 16.04.2024 | [Решение задач выявления плагиата](lections/OATD_lec_11.pdf) [pptx](lections/OATD_lec_11.pptx) | | ## [Журнал группы](https://docs.google.com/spreadsheets/d/1zqJdQO_HKVQjHRK1knrDeaT2SGrptDQ38FjlflW-Rn8/edit#gid=1516016995) +## Экзамен + +* Консультация 20.05 в 9:20, аудитория М307. +* Экзамен 21.05 в 9:20 М-200з +* [Программа экзамена](assets/TDA_exam_program.docx) +* [Задачи к экзамену](assets/TDA_exam_tasks.docx) + + ## Лабораторные работы ### Лабораторная работа №1 @@ -45,4 +52,24 @@ * [Методические указания](labs/OATD_LR2_metod.ipynb) +### Лабораторная работа №3 + +| Группа | Дата | +|:--------|:----------:| +| А-01-20 | 09.04.2024 | +| А-03-20 | 16.04.2024 | + +* [Задание](labs/OATD_LR3.md) +* [Методические указания](labs/OATD_LR3_metod.ipynb) + + + +### Лабораторная работа №4 + +| Группа | Дата | +|:--------|:----------:| +| А-01-20 | 09.04.2024 | +| А-03-20 | 16.04.2024 | + +* [Задание](labs/OATD_LR4.ipynb) diff --git a/assets/TDA_exam_program.docx b/assets/TDA_exam_program.docx index 4a5b084..74e2193 100644 Binary files a/assets/TDA_exam_program.docx and b/assets/TDA_exam_program.docx differ diff --git a/assets/start_notebook.md b/assets/start_notebook.md index 424d2ed..13b453f 100644 --- a/assets/start_notebook.md +++ b/assets/start_notebook.md @@ -2,7 +2,7 @@ ## Локально -Скачать и установить python (рекомендуется версия 3.9) https://www.python.org/downloads/, не забыв поставить галочку Add to PATH на самом первом экране +Скачать и установить python (рекомендуется версия 3.10) https://www.python.org/downloads/, не забыв поставить галочку Add to PATH на самом первом экране ![Установка python](install_py.png) @@ -11,14 +11,23 @@ ``` python -m pip install --upgrade pip python -m pip install notebook -python -m pip install sklearn +python -m pip install scikit-learn python -m pip install matplotlib ``` Готово. Запускать Jupyter можно из командной строки командой `jupyter notebook`. Рабочей папкой будет та, из которой данная команда была выполнена. +## VS Code + +Jupyter поддерживают некоторые распространенные IDE, в том числе [VS Code](https://code.visualstudio.com/). Поддержка нативная, однако рекомендуется установить расширение (extension) "Jupyter". + +После создания нового файла с расширением `.ipynb` необходимо выбрать интерпретатор Python. При этом, рекомендуется работать в виртуальном окружении [виртуальном окружении (venv)](https://docs.python.org/3/library/venv.html). + +Более подробная [инструкция](https://code.visualstudio.com/docs/datascience/jupyter-notebooks) + ## Онлайн [Онлайн версия Jupyter Notebook](https://mybinder.org/v2/gh/jupyterlab/jupyterlab-demo/HEAD?urlpath=lab/tree/demo) - может использоваться как временная замена локальной установке. -Допустимо вместо локальной установки Jupyter использовать [Google colab](https://colab.research.google.com/?hl=ru) \ No newline at end of file +Допустимо вместо локальной установки Jupyter использовать [Google colab](https://colab.research.google.com/?hl=ru) + diff --git a/labs/OATD_LR1.md b/labs/OATD_LR1.md index 00d880c..a38b7c7 100644 --- a/labs/OATD_LR1.md +++ b/labs/OATD_LR1.md @@ -26,7 +26,7 @@ * Метод к-ближайших соседей (`n_neighbors` = {1, 3, 9}) * Наивный байесовский метод - * Случайный лес (`n_estimators` = {5, 15, 50}) + * Случайный лес (`n_estimators` = {5, 15, 50}, `random_state`=42) 9. По результатам п.8 занести в отчет таблицу с результатами классификации всеми методами. 10. Изучить, как изменится качество классификации в случае другого разбиения выборки на обучающую и тестовую. Для этого повторить пункты 6, передав в параметр `random_state` новое значение, и пункты 8-9 дважды. diff --git a/labs/OATD_LR3.md b/labs/OATD_LR3.md index dfbdfbb..35e99d7 100644 --- a/labs/OATD_LR3.md +++ b/labs/OATD_LR3.md @@ -9,7 +9,7 @@ 1. Загрузить выборки по варианту из лабораторной работы №2. Стемминг проводить не нужно. 2. Используя GridSearchCV произвести предварительную обработку данных и настройку методов классификации в соответствие с заданием, -вывести оптимальные значения параметров и результаты классификации модели (полнота, точность, f1-мера и аккуратности) с данными параметрами. +вывести оптимальные значения параметров и качество классификации модели (взвешенная f1-мера) с данными параметрами. 3. Перевести выборку к векторному представлению word embedding согласно варианту. 4. Провести обучение и настройку тех же алгоритмов классификации и с теми же параметрами, что и в п.2, но на векторизованной выборке 5. По каждому пункту работы занести в отчет программный код и результат вывода. @@ -22,10 +22,10 @@ | Вариант | Метод | Word embedding | | :--- | :--- | :--- | -| 1 | KNN, SVM | glove-wiki-gigaword-100 | -| 2 | RF, MNB | glove-wiki-gigaword-50 +| 1 | KNN, SVM | glove-wiki-gigaword-50 | +| 2 | RF, MNB | glove-wiki-gigaword-100 | 3 | KNN, DT | glove-wiki-gigaword-200 | -| 4 | RF, KNN | glove-wiki-gigaword-25 | +| 4 | RF, KNN | glove-wiki-gigaword-300 | | 5 | LR, MNB | word2vec-google-news-300 | | 6 | DT, LR | glove-wiki-gigaword-200 | | 7 | RF, SVM | glove-twitter-100 | @@ -60,12 +60,13 @@ * метод нахождения экстремума (параметр solver: ‘newton-cg’, ‘lbfgs’, ‘sag’, ‘liblinear’), * регуляризация (параметр penalty: ‘L1’, ‘L2’) -Обратить внимание, что разные виды регуляризации работают с разными методами нахождения экстремума. +Обратить внимание, что разные виды регуляризации работают с разными методами нахождения экстремума. +Предлагается сначала настроить модель для одного из типом регулязиации, затем для другого, и выбрать лучшую модель из двух. **Метод опорных векторов (SVM):** * функция потерь (параметр kernel: ‘linear’, ‘rbf’), -* регуляризация (параметр C: {0.1, 1, 5}) -Обратить внимание, что разные виды регуляризации работают с разными функциями потерь +* коэффициент регуляризации (параметр C: {0.1, 1, 5}) + **Мультиномиальный Наивный Байесовский метод (MNB)** * параметр сглаживания α (параметр alpha: {0.1, 1, 2}) diff --git a/labs/OATD_LR3_metod.ipynb b/labs/OATD_LR3_metod.ipynb index c8081fd..e8773d9 100644 --- a/labs/OATD_LR3_metod.ipynb +++ b/labs/OATD_LR3_metod.ipynb @@ -99,7 +99,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Далее необходимо создать объект класса `GridSearchCV`, передав в него объект `pipeline` или классификатор, список параметров сетки, а также при необходимости, задав прочие параметры, такие так количество задействованых ядер процессора `n_jobs`, количество фолдов кросс-валидации `cv` и другие" + "Далее необходимо создать объект класса `GridSearchCV`, передав в него объект `pipeline` или классификатор, список параметров сетки, а также при необходимости, задав прочие параметры, такие так количество задействованых ядер процессора `n_jobs`, количество фолдов кросс-валидации `cv`, метрику, по которой будем судить о качестве модели `scoring`, и другие" ] }, { @@ -108,7 +108,7 @@ "metadata": {}, "outputs": [], "source": [ - "gs_clf = GridSearchCV(text_clf, parameters, n_jobs=-1, cv=3)" + "gs_clf = GridSearchCV(text_clf, parameters, n_jobs=-1, cv=3, scoring = 'f1_weighted')" ] }, { diff --git a/labs/OATD_LR4.ipynb b/labs/OATD_LR4.ipynb new file mode 100644 index 0000000..26f7f1b --- /dev/null +++ b/labs/OATD_LR4.ipynb @@ -0,0 +1,2422 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "vOpSJBZfFKuo" + }, + "source": [ + "![](https://camo.githubusercontent.com/518a06d7ca808cd4ad8d5b6deb4ef15983d4649737618153432479f977935bba/68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f79616e646578646174617363686f6f6c2f6e6c705f636f757273652f6d61737465722f7265736f75726365732f657870616e64696e675f6d696e645f6c6d5f6b6e5f332e706e67)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ha8cW_h-FIND" + }, + "source": [ + "reference:\n", + "\n", + " - [nlp for you](https://lena-voita.github.io/nlp_course/language_modeling.html)\n", + "\n", + " - [YSDA Natural Language Processing course](https://github.com/yandexdataschool/nlp_course/tree/2023/week03_lm)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H2QPNriQIvTw" + }, + "source": [ + "# Лабораторная работа №4. Использование нейронных сетей для генерации текста\n", + "В ходе работы мы будем обучать нейронные сети генерировать тексты, похожие на стихи поэтов.\n", + "\n", + "## Цель работы\n", + "Получить практические навыки решения задачи генерации текста.\n", + "\n", + "## Указания\n", + "1. Для работы рекомендуется использовать Google Colab и среду с GPU для ускорения расчетов. Для установки среды, использующей GPU в Google Colab нужно выбрать пункт меню \"Среда выполнения\" -> \"Сменить среду выполнения\" -> выбрать аппаратный ускоритель \"GPU\".\n", + "\n", + "2. Выполнять работу следует последовательно запуская ячейки, анализируя код и приведенные комментарии и разъяснения.\n", + "\n", + "3. В ходе работы будут встречаться вопросы, на которые нужно ответить, создав после него новую ячейку. Вопросы отмечены заголовками 3-го уровня.\n", + "Для ответа досточно 1-2 предложений. Но будьте готовы более подробно его пояснить при устной беседе.\n", + "\n", + "4. Обращайте внимание на комментарии `` - здесь вам нужно будет вставить значения параметров либо исходя из анализа кода\\выборки (где указано), либо попробовать разные варианты. Парамеры, приведенные тут по умолчанию, не обязательно правильные.\n", + "\n", + "## Варианты заданий\n", + "\n", + "Четные номера по журналу - Пушкин, нечетные - Маяковский.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "96PukQZbFNwL" + }, + "source": [ + "# Загрузка библиотек" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "nyb7MBAsFNhh" + }, + "outputs": [], + "source": [ + "import copy\n", + "import torch\n", + "import numpy as np\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "from random import sample\n", + "from IPython.display import clear_output\n", + "from torch.utils.data import DataLoader, TensorDataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eyO1tDFiGwFu" + }, + "source": [ + "При использовании Google Colab следует выбрать среду выполнения с аппаратным ускорителем GPU, что существенно ускорит расчеты. Для установки среды, использующей GPU в Google Colab нужно выбрать пункт меню \"Среда выполнения\" -> \"Сменить среду выполнения\" -> выбрать аппаратный ускоритель \"GPU\". При этом, следующая ячейка, проверяющая доступность CUDA (платформы, использующей графические ускорители), должна возвращать `True`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XtIrqKT4GSXf", + "outputId": "376ab52b-e0d9-4c48-a01a-0c1191d9fa5f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vXAr_sdxFSKm" + }, + "source": [ + "# Загрузим данные\n", + "\n", + "В соответствии с вариантом" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Krp456VbE7yP", + "outputId": "bd470eb1-d3e8-4059-a5cb-f24831c3d9dd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-04-05 17:50:23-- http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/pushkin.txt\n", + "Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n", + "Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1048627 (1.0M) [text/plain]\n", + "Saving to: ‘poems.txt’\n", + "\n", + "poems.txt 100%[===================>] 1.00M 625KB/s in 1.6s \n", + "\n", + "2024-04-05 17:50:25 (625 KB/s) - ‘poems.txt’ saved [1048627/1048627]\n", + "\n" + ] + } + ], + "source": [ + "!wget -O poems.txt http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/pushkin.txt\n", + "\n", + "# Маяковский: http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/mayakovskiy.txt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BFnX81iNFFEf", + "outputId": "f2efb98f-d09e-49d6-f8b4-2952ae48c075" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество стихов: 720\n", + "\n", + "Пример стиха:\n", + "\n", + "Возможно ль? вместо роз, Амуром насажденных,\n", + "Тюльпанов гордо наклоненных,\n", + "Душистых ландышей, ясминов и лилей,\n", + "Которых ты всегда любила\n", + "И прежде всякой день носила\n", + "На мраморной груди твоей —\n", + "Возможно ль, милая Климена,\n", + "Какая странная во вкусе перемена!..\n", + "Ты любишь обонять не утренний цветок,\n", + "А вредную траву зелену,\n", + "Искусством превращенну\n", + "В пушистый порошок! —\n", + "Пускай уже седой профессор Геттингена,\n", + "На старой кафедре согнувшися дугой,\n", + "Вперив в латинщину глубокой разум свой,\n", + "Раскашлявшись, табак толченый\n", + "Пихает в длинный нос иссохшею рукой;\n", + "Пускай младой драгун усатый\n", + "Поутру, сидя у окна,\n", + "С остатком утреннего сна,\n", + "Из трубки пенковой дым гонит сероватый;\n", + "Пускай красавица шестидесяти лет,\n", + "У Граций в отпуску, и у любви в отставке,\n", + "Которой держится вся прелесть на подставке,\n", + "Которой без морщин на теле места нет,\n", + "Злословит, молится, зевает\n", + "И с верным табаком печали забывает, —\n", + "А ты, прелестная!.. но если уж табак\n", + "Так нравится тебе – о пыл воображенья! —\n", + "Ах! если, превращенный в прах,\n", + "И в табакерке, в заточеньи,\n", + "Я в персты нежные твои попасться мог,\n", + "Тогда б в сердечном восхищеньи\n", + "Рассыпался на грудь под шелковый платок\n", + "И даже… может быть… Но что! мечта пустая.\n", + "Не будет этого никак.\n", + "Судьба завистливая, злая!\n", + "Ах, отчего я не табак!..\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Загружаем текст из файла.\n", + "# Стихотворения в файле разделены токеном ''\n", + "\n", + "with open(\"poems.txt\") as file:\n", + " data = file.read().split(\"\\n\\n\")\n", + "print(f\"Количество стихов: {len(data)}\\n\", f\"Пример стиха:\\n\\n{data[10]}\", sep=\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "urxR6VN2FbVl" + }, + "source": [ + "# Подготовка данных и сводные статистики" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OhWos8xuFZZj", + "outputId": "635e93d6-6614-4c2c-c6c0-b0c7e70ea6ae" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество уникальных символов: 143\n", + "{0: 'PAD', 1: 'EOS', 2: 'a', 3: 'g', 4: ';', 5: 'R', 6: 'у', 7: 'И', 8: 'д', 9: 'ю', 10: 'V', 11: '?', 12: 'd', 13: 'з', 14: 'ы', 15: '–', 16: '(', 17: 'H', 18: 'Г', 19: ':', 20: 'm', 21: 'é', 22: 'ж', 23: 'c', 24: 'ц', 25: 'l', 26: 'Ф', 27: 'ф', 28: 'â', 29: 'п', 30: 'b', 31: 'г', 32: 'k', 33: 'B', 34: 'S', 35: \"'\", 36: 'z', 37: 'р', 38: 'ъ', 39: 'ь', 40: '!', 41: '\\n', 42: 'й', 43: 'Б', 44: '\"', 45: 'н', 46: '_', 47: 'P', 48: 'к', 49: 'F', 50: '»', 51: '*', 52: '—', 53: 'Ц', 54: 'L', 55: 'ê', 56: 'щ', 57: ')', 58: 's', 59: 'y', 60: 'С', 61: 'Ш', 62: 'Р', 63: 'э', 64: 'i', 65: 'x', 66: 'У', 67: 'è', 68: 'à', 69: 'p', 70: 'л', 71: 'T', 72: 'I', 73: 'û', 74: 'в', 75: '„', 76: 'Z', 77: 'П', 78: 'ё', 79: 'Л', 80: 'ш', 81: 'М', 82: '…', 83: '-', 84: 'З', 85: 'n', 86: '.', 87: 'В', 88: 'х', 89: 'с', 90: 'Ю', 91: 'C', 92: ' ', 93: 'е', 94: 'j', 95: 'Х', 96: 'а', 97: 'Н', 98: 'Д', 99: 'M', 100: 'и', 101: ',', 102: 'б', 103: '<', 104: '>', 105: 'А', 106: 'Т', 107: 'N', 108: 'о', 109: '«', 110: '\\xa0', 111: 'o', 112: 'Й', 113: 'Q', 114: 'U', 115: 'W', 116: 'ç', 117: 'т', 118: 'Е', 119: 'O', 120: 'О', 121: 'ч', 122: 'e', 123: 'u', 124: 'f', 125: 'D', 126: 'E', 127: 'К', 128: 'v', 129: 'Ж', 130: 'Щ', 131: 'м', 132: 'A', 133: 'Ч', 134: 'h', 135: 'Я', 136: 'ô', 137: 'J', 138: 't', 139: 'я', 140: 'r', 141: 'q', 142: 'Э'}\n" + ] + } + ], + "source": [ + "# Составляем словарь уникальных токенов\n", + "vocab = [\"PAD\", \"EOS\",] + list(set(\"\".join(data))) #список уникальных символов.\n", + "\n", + "# Формируем два словаря, реализующие перевод символов в их индексы и обратно\n", + "id2char = dict(enumerate(vocab)) #словарь индексов в символы\n", + "char2id = {char: ind for ind, char in id2char.items()} #словарь символов в индексы\n", + "print(f\"Количество уникальных символов: {len(vocab)}\", id2char, sep=\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xeTO4fBQFfBS" + }, + "source": [ + "Рассмотрим длины текстов" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J-u-IxOeFZXY", + "outputId": "3ac61929-c08f-447f-df37-0f65030d3a57" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Максимальная длина текста: 8948\n" + ] + } + ], + "source": [ + "lengths = list(map(len, data))\n", + "print(\"Максимальная длина текста: \", max(lengths))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "Ym7S8tmNFZUg", + "outputId": "67470c7b-ad81-4445-85d0-d3b1a4621e33" + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "plt.hist(lengths, bins=30, range=[0, 6000])\n", + "plt.xlabel(\"Длина текста в символах\")\n", + "plt.title(\"Гистограмма длин текстов\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JuD6onVvFZR8", + "outputId": "49d24414-289e-4775-ac16-2f33f8873ac1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Средняя длина 808.9125\n", + "Медиана длины 453.5\n", + "Мода длины 221\n" + ] + } + ], + "source": [ + "lengths = np.array(lengths)\n", + "print(\"Средняя длина\", np.mean(lengths))\n", + "print(\"Медиана длины\", np.median(lengths))\n", + "print(\"Мода длины\", np.bincount(lengths).argmax())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wjXxroS0IDXu" + }, + "source": [ + "### Вопрос 1\n", + "Где можно будет использовать знание о параметрах распределения длин в выборке?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zfkSHd-8FlE1" + }, + "source": [ + "Выберите длину для генерации" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "y-Q7-EPuFZO5" + }, + "outputs": [], + "source": [ + "# Устанавливаем, сколько символов будет генерировать модель (максимальная длина генерируемого текста)\n", + "MAXLEN = 512 #" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TSfv4n08Fpiv" + }, + "source": [ + "# Преобразование данных" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vtelHtjyFq1N" + }, + "source": [ + "Создадим функцию для преобразования текста в вектора одинакового размера для подачи в нейросеть. В этой функции добавляется токен EOS - конец последовательности. Если текст короче заданной длины, то добавляется специальный токен PAD. Если текст больше заданной длины, то он обрезается." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "Hf9iaFRKJUGq" + }, + "outputs": [], + "source": [ + "line_ix = [char2id[c] for c in data[5][:512]]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "2emYDlcVJnwi" + }, + "outputs": [], + "source": [ + "data_ix = np.zeros([len(data[5]), 512], 'int64')\n", + "data_ix[5, :len(line_ix)] = line_ix" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QA5lQz2eJ8Oq", + "outputId": "e5df533b-ff5e-494a-d6ae-b2e8e4ffd442" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 ... 0 0 0]\n" + ] + } + ], + "source": [ + "data_ix= np.transpose(data_ix)\n", + "print(data_ix[5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def to_matrix(data, char2id, max_len=None, dtype='int64', batch_first = True):\n", + "\n", + " max_len = max_len if max_len else max(map(len, data))\n", + " data = [text[:max_len] for text in data]\n", + " data_ix = np.zeros([len(data), max_len+1], dtype)\n", + "\n", + " for i in range(len(data)):\n", + " line_ix = [char2id[c] for c in data[i][:max_len]] + [char2id[\"EOS\"]]\n", + " data_ix[i, :len(line_ix)] = line_ix\n", + "\n", + " if not batch_first: # convert [batch, time] into [time, batch]\n", + " data_ix = np.transpose(data_ix)\n", + "\n", + " return data_ix" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DbkCcqotFoRP", + "outputId": "97e86d8c-26f4-407a-c587-58d0df5867cd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Исходный текст:\n", + " Так и мне узнать случилось,\n", + "Что за птица Купидон;\n", + "Сердце страстное пленилось;\n", + "Признаюсь – и я влюблен!\n", + "Пролетело счастья время,\n", + "Как, любви не зная бремя,\n", + "Я живал да попевал,\n", + "Как в театре и на балах,\n", + "На гуляньях иль в воксалах\n", + "Легким зефиром летал;\n", + "Как, смеясь во зло Амуру,\n", + "Я писал карикатуру\n", + "На любезный женской пол;\n", + "Но напрасно я смеялся,\n", + "Наконец и сам попался,\n", + "Сам, увы! с ума сошел.\n", + "Смехи, вольность – всё под лавку\n", + "Из Катонов я в отставку,\n", + "И теперь я – Селадон!\n", + "Миловидной жрицы Тальи\n", + "Видел прелести Натальи,\n", + "И уж в сердце – Купидон!\n", + "\n", + "Так, Наталья! признаюся,\n", + "Я тобою полонен,\n", + "В первый раз еще, стыжуся,\n", + "В женски прелести влюблен.\n", + "Целый день, как ни верчуся\n", + "Лишь тобою занят я;\n", + "Ночь придет – и лишь тебя\n", + "Вижу я в пустом мечтаньи,\n", + "Вижу, в легком одеяньи\n", + "Будто милая со мной;\n", + "Робко, сладостно дыханье,\n", + "Белой груди колебанье,\n", + "Снег затмивший белизной,\n", + "И полуотверсты очи,\n", + "Скромный мрак безмолвной ночи —\n", + "Дух в восторг приводят мой!..\n", + "Я один в беседке с нею,\n", + "Вижу… девственну лилею,\n", + "Трепещу, томлюсь, немею…\n", + "И проснулся… вижу мрак\n", + "Вкруг постели одинокой!\n", + "Испускаю вздох глубокой,\n", + "Сон ленивый, томноокой\n", + "Отлетает на крылах.\n", + "Страсть сильнее становится\n", + "И, любовью утомясь,\n", + "Я слабею всякой час.\n", + "Всё к чему-то ум стремится,\n", + "А к чему? – никто из нас\n", + "Дамам в слух того не скажет,\n", + "А уж так и сяк размажет.\n", + "Я – по-свойски объяснюсь.\n", + "\n", + "Все любовники желают\n", + "И того, чего не знают;\n", + "Это свойство их – дивлюсь!\n", + "Завернувшись балахоном,\n", + "С хватской шапкой на бекрень\n", + "Я желал бы Филимоном\n", + "Под вечер, как всюду тень,\n", + "Взяв Анюты нежну руку,\n", + "Изъяснять любовну муку,\n", + "Говорить: она моя!\n", + "Я желал бы, чтоб Назорой\n", + "Ты старалася меня\n", + "Удержать умильным взором.\n", + "Иль седым Опекуном\n", + "Легкой, миленькой Розины,\n", + "Старым пасынком судьбины,\n", + "В епанче и с париком,\n", + "Дерзкой пламенной рукою\n", + "Белоснежну, полну грудь…\n", + "Я желал бы… да ногою\n", + "Моря не перешагнуть.\n", + "И, хоть по уши влюбленный,\n", + "Но с тобою разлученный,\n", + "Всей надежды я лишен.\n", + "\n", + "Но, Наталья! ты не знаешь\n", + "Кто твой нежный Селадон,\n", + "Ты еще не понимаешь,\n", + "Отчего не смеет он\n", + "И надеяться? – Наталья!\n", + "Выслушай еще меня:\n", + "\n", + "Не владетель я Сераля,\n", + "Не арап, не турок я.\n", + "За учтивого китайца,\n", + "Грубого американца\n", + "Почитать меня нельзя,\n", + "Не представь и немчурою,\n", + "С колпаком на волосах,\n", + "С кружкой, пивом налитою,\n", + "И с цыгаркою в зубах.\n", + "Не представь кавалергарда\n", + "В каске, с длинным палашом.\n", + "Не люблю я бранный гром:\n", + "Шпага, сабля, алебарда\n", + "Не тягчат моей руки\n", + "За Адамовы грехи.\n", + "\n", + "– Кто же ты, болтун влюбленный?\n", + "Взглянь на стены возвышенны,\n", + "Где безмолвья вечный мрак;\n", + "Взглянь на окны загражденны,\n", + "На лампады там зажженны…\n", + "Знай, Наталья! – я… монах!\n", + "\n", + "\n", + "Преобразованный текст:\n", + " [106 96 48 92 100 92 131 45 93 92 6 13 45 96 117 39 92 89\n", + " 70 6 121 100 70 108 89 39 101 41 133 117 108 92 13 96 92 29\n", + " 117 100 24 96 92 127 6 29 100 8 108 45 4 41 60 93 37 8\n", + " 24 93 92 89 117 37 96 89 117 45 108 93 92 29 70 93 45 100\n", + " 70 108 89 39 4 41 77 37 100 13 45 96 9 89 39 92 15 92\n", + " 100 92 139 92 74 70 9 102 70 93 45 40 41 77 37 108 70 93\n", + " 117 93 70 108 92 89 121 96 89 117 39 139 92 74 37 93 131 139\n", + " 101 41 127 96 48 101 92 70 9 102 74 100 92 45 93 92 13 45\n", + " 96 139 92 102 37 93 131 139 101 41 135 92 22 100 74 96 70 92\n", + " 8 96 92 29 108 29 93 74 96 70 101 41 127 96 48 92 74 92\n", + " 117 93 96 117 37 93 92 100 92 45 96 92 102 96 70 96 88 101\n", + " 41 97 96 92 31 6 70 139 45 39 139 88 92 100 70 39 92 74\n", + " 92 74 108 48 89 96 70 96 88 41 79 93 31 48 100 131 92 13\n", + " 93 27 100 37 108 131 92 70 93 117 96 70 4 41 127 96 48 101\n", + " 92 89 131 93 139 89 39 92 74 108 92 13 70 108 92 105 131 6\n", + " 37 6 101 41 135 92 29 100 89 96 70 92 48 96 37 100 48 96\n", + " 117 6 37 6 41 97 96 92 70 9 102 93 13 45 14 42 92 22\n", + " 93 45 89 48 108 42 92 29 108 70 4 41 97 108 92 45 96 29\n", + " 37 96 89 45 108 92 139 92 89 131 93 139 70 89 139 101 41 97\n", + " 96 48 108 45 93 24 92 100 92 89 96 131 92 29 108 29 96 70\n", + " 89 139 101 41 60 96 131 101 92 6 74 14 40 92 89 92 6 131\n", + " 96 92 89 108 80 93 70 86 41 60 131 93 88 100 101 92 74 108\n", + " 70 39 45 108 89 117 39 92 15 92 74 89 78 92 29 108 8 92\n", + " 70 96 74 48 6 41 7 13 92 127 96 117 108 45 108 74 92 139\n", + " 92 74 92 108 117 89 117 96 74 48 6 101 41 7 92 117 93 29\n", + " 93 37 39 92 139 92 15 92 60 93 70 96 8 108 45 40 41 81\n", + " 100 70 108 74 100 8 45 108 42 92 22 37 100 24 14 92 106 96\n", + " 70 39 100 41 87 100 8 93 70 92 29 37 93 70 93 89 117 100\n", + " 92 97 96 117 96 70 39 100]\n" + ] + } + ], + "source": [ + "# Проверяем работу функции - кодируем один из текстов и смотрим как он выглядит в кодированном виде\n", + "encode = to_matrix(data[:1], char2id, MAXLEN)\n", + "print(\"Исходный текст:\\n\", data[0])\n", + "print(\"Преобразованный текст:\\n\", encode[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nSrGA_gLLh3A" + }, + "source": [ + "### Вопрос 2\n", + "Пояснить, что хранится в переменной `encode`.\n", + "\n", + "Как будет выглядеть ваша фамилия в кодированном виде?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eQ-s7rzHFu9T" + }, + "source": [ + "# Подготовка нейросети" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c3qNOFx8Fwec" + }, + "source": [ + "![](https://raw.githubusercontent.com/tensorflow/text/master/docs/tutorials/images/text_generation_training.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "atZoEO6VFyQf" + }, + "source": [ + "Исходя из архитектуры нейросети на картинке, нужно задать 3 слоя:\n", + " - Embedding\n", + " - GRU\n", + " - Dense\n", + "\n", + "\n", + "Для этого воспользуемся pytorch, ссылки на подробную документацию каждого слоя:\n", + " - [nn.Embedding](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html)\n", + " - [nn.GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html)\n", + " - [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "3rAXL2-QFvM9" + }, + "outputs": [], + "source": [ + "num_embeddings = len(vocab) #количество эмбеддингов должно соответствовать длине словаря\n", + "embedding_dim = 32 #определяется размерность эмбеддинга\n", + "emb = nn.Embedding(num_embeddings, embedding_dim) # Определяем объект emb как слой эмбеддингов заданного размера" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mGb6aUqfOmav", + "outputId": "ec17500b-0522-4c56-a6f9-0a5da11f7346" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "143" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "num_embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "esvotgUlMADX" + }, + "source": [ + "### Вопрос 3\n", + "Почему количество эмбеддингов должно соответствовать длине словаря?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hHK3n4noF2OP" + }, + "source": [ + "В качестве примера пропустим через этот слой первые 5 букв первого текста." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GvwHFBOWFz-U", + "outputId": "0a58cf39-3151-48d5-846c-6f55d1dfc863" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер тензора: torch.Size([5, 32])\n", + "\n", + "tensor([[ 1.5029e+00, -1.7916e+00, 2.2026e+00, -2.2513e+00, -6.9742e-01,\n", + " 3.0790e-01, 6.8104e-01, 7.7460e-01, -6.1624e-01, 1.5153e+00,\n", + " -4.1424e-01, -3.7886e-01, 3.0690e-01, 2.3166e-04, -1.3926e-01,\n", + " -4.1106e-01, -3.6484e-01, 3.3451e-01, -3.3457e+00, -6.2611e-01,\n", + " 8.0940e-01, 8.7477e-01, -9.3242e-02, 1.0553e+00, 1.0860e+00,\n", + " -4.8333e-01, 1.1123e+00, 6.2818e-01, 2.5269e-01, 1.3213e+00,\n", + " 5.3855e-01, 4.6009e-01],\n", + " [ 5.7508e-01, 1.6589e+00, -1.1679e+00, -2.4309e+00, -3.9557e-01,\n", + " 3.8376e-01, -9.8238e-01, -1.1509e+00, 2.2251e+00, -4.6321e-01,\n", + " 6.5630e-01, -1.9887e+00, -6.1972e-01, -1.6304e-01, -9.7050e-01,\n", + " -2.5951e-01, 4.9236e-01, -1.9583e+00, 1.9546e+00, -1.0485e+00,\n", + " 1.2174e-01, 8.9455e-01, -2.3779e+00, 2.3129e+00, -2.7838e-01,\n", + " 9.1432e-01, -2.7290e+00, -1.1626e+00, -8.9565e-01, -8.5091e-03,\n", + " 8.8071e-01, -9.9323e-03],\n", + " [-8.4068e-01, -6.1268e-01, 6.7328e-01, -9.6503e-01, 6.8494e-01,\n", + " -8.4349e-01, 3.5604e-01, 1.7478e+00, -1.7844e-01, -7.7445e-01,\n", + " 1.3449e+00, 1.1115e+00, 1.1597e+00, 7.3386e-01, 9.6546e-01,\n", + " -1.7147e+00, -3.4931e-01, 1.7341e-01, 3.5583e-01, -2.5318e-01,\n", + " -4.7672e-01, -1.4090e-02, -5.9210e-01, 5.4945e-01, 1.6417e-01,\n", + " -4.4186e-01, 7.3914e-01, 1.8834e+00, 3.0256e-01, 6.1556e-01,\n", + " -9.7063e-01, 4.4972e-01],\n", + " [ 5.6335e-01, 1.9862e-01, 3.7290e-01, 9.5874e-01, 6.3197e-01,\n", + " 2.9606e-01, 1.6983e+00, -6.7355e-01, -3.7383e-01, -1.0147e+00,\n", + " -1.2620e-02, 6.6465e-01, 9.1266e-01, 1.2363e+00, 1.9966e+00,\n", + " -1.1470e+00, -5.4097e-01, 1.3002e+00, -1.4012e+00, -8.1303e-01,\n", + " -7.5828e-01, 2.8108e-01, 1.0428e+00, 4.5049e-01, -1.2042e-01,\n", + " -1.2361e+00, 2.9283e-01, -1.6573e+00, 3.8987e-01, -2.1059e-01,\n", + " 1.4592e-01, -2.3350e-01],\n", + " [ 7.5370e-01, -1.6429e+00, -8.4913e-01, 3.6310e-02, 1.4648e+00,\n", + " -9.0935e-01, -2.1241e-01, -3.0972e-01, 6.5774e-01, 6.8460e-01,\n", + " 4.4648e-01, 6.2579e-01, 7.1138e-01, 1.8680e+00, -1.3650e+00,\n", + " 1.2340e-02, -1.2517e-01, 3.1195e-01, 3.0871e-01, 5.0507e-01,\n", + " -1.2373e+00, -1.0363e+00, -4.4852e-01, -8.9384e-01, -4.5717e-01,\n", + " 1.0538e-01, 4.1036e-01, 2.9193e+00, -1.1410e+00, 8.8568e-01,\n", + " -3.3892e-01, -7.0943e-01]], grad_fn=)\n" + ] + } + ], + "source": [ + "# emb_out - 5 первых символов в виде эмбедингов\n", + "emb_out = emb(torch.tensor(encode[0][:5]))\n", + "print(f\"Размер тензора: {emb_out.shape}\\n\")\n", + "print(emb_out)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-YOv6t-qMOQi" + }, + "source": [ + "### Вопрос 4\n", + "Пояснить, почему получен такой размер `emb_out`?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nkS5ZQ8fF4XP" + }, + "source": [ + "# Создадим ячейку GRU" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZBfkiOYmM9yr" + }, + "source": [ + "### Вопрос 5\n", + "Обратиться к документации к [nn.GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html) и ответить на вопрос, за что отчечают параметры `input_size`, `hidden_size`, `num_layers`.\n", + "\n", + "В следующей ячейке задать значения для этих параметров" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5bJ7fRAYFz71", + "outputId": "08fe5314-c7fb-4901-9947-4eabbd4c2326" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер output: torch.Size([5, 64])\n", + "Размер h_n: torch.Size([1, 64])\n" + ] + } + ], + "source": [ + "# Определяем ячейку GRU в переменной gru, предварительно задав рамерность скрытого состояния и количество рекуррентных слоев\n", + "input_size = emb.embedding_dim # вход в GRU должен соответствовать размеру эмбеддинга\n", + "hidden_size = 64 #\n", + "num_layers = 1 #\n", + "gru = nn.GRU(input_size, hidden_size, num_layers)\n", + "output, h_n = gru(emb_out)\n", + "print(\"Размер output:\", output.shape)\n", + "print(\"Размер h_n:\", h_n.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4W5Q3O45F6wh" + }, + "source": [ + "Выходом GRU является 2 тензора:\n", + " - output (используется для классификации)\n", + " - тензор скрытого состояния h_n (используается для последующей передачи во времени)\n", + "\n", + "Теперь используем output для предсказания следующей буквы, пропустив через линейный слой" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p16NvYn2Fz4-", + "outputId": "a31d5f60-dfe0-4a7e-c078-ee7700750aa6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер выходного слоя из нейросети: torch.Size([5, 143])\n" + ] + } + ], + "source": [ + "in_features = gru.hidden_size\n", + "out_features = len(vocab) #предсказываем букву из всего словаря\n", + "linear = nn.Linear(in_features, out_features) # Определяем линейный слой. Почему заданы такие входные и выходные параметры для него?\n", + "linear_out = linear(output) # output - выход GRU\n", + "print(\"Размер выходного слоя из нейросети: \", linear_out.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6no89JxCNwvX" + }, + "source": [ + "### Вопрос 6\n", + "Что содержится в векторе linear_out?\n", + "\n", + "Определить индекс символа, который наиболее вероятно выдаст ячейка GRU на первом шаге?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z2lThHhjOBI2", + "outputId": "50a18b7f-094d-41e1-a728-c47ef9561474" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 0.0909, -0.0186, -0.2154, -0.2624, -0.0282, -0.1714, -0.0423, 0.1505,\n", + " 0.0645, 0.0452, -0.0173, -0.0822, 0.1414, 0.1803, -0.2480, -0.1756,\n", + " -0.0447, -0.0136, 0.1097, -0.2245, 0.0189, -0.0559, 0.0215, -0.0040,\n", + " 0.0454, 0.2437, -0.2015, 0.0818, 0.2883, 0.2263, -0.0109, 0.1681,\n", + " -0.0642, -0.0497, 0.1583, -0.0795, -0.0376, 0.0674, -0.1307, 0.0640,\n", + " 0.0966, 0.0140, -0.1366, -0.1685, 0.0397, -0.1600, -0.0497, -0.2244,\n", + " -0.1075, -0.1048, 0.0275, -0.0144, -0.1456, 0.0394, -0.1426, -0.0605,\n", + " -0.0743, 0.1303, 0.0337, 0.1010, -0.0651, -0.1233, 0.0282, 0.2729,\n", + " 0.0485, -0.0911, 0.1994, 0.0610, 0.0911, 0.0940, -0.0973, 0.0747,\n", + " 0.0031, 0.1500, -0.0295, 0.0127, -0.2865, -0.0251, -0.1395, 0.1547,\n", + " 0.0830, 0.0782, -0.0181, 0.1317, -0.0537, 0.1293, -0.2458, 0.1477,\n", + " -0.0610, -0.0592, -0.2878, -0.1622, 0.1336, 0.0987, -0.0834, -0.0815,\n", + " -0.1764, 0.0202, -0.0097, 0.0176, -0.1874, 0.0716, -0.1070, -0.1364,\n", + " 0.1748, 0.1479, -0.0478, 0.0862, 0.0648, 0.1126, -0.0995, -0.1324,\n", + " 0.1023, -0.0247, 0.0439, 0.0212, -0.0608, 0.0402, 0.1468, 0.2311,\n", + " -0.1142, 0.2198, -0.1181, -0.2196, -0.0653, 0.0870, -0.3388, 0.1208,\n", + " 0.0851, 0.0590, 0.1963, 0.0710, 0.1099, 0.1648, 0.0260, -0.1451,\n", + " 0.0012, -0.0973, -0.1058, -0.0628, 0.0585, 0.1859, -0.0573],\n", + " grad_fn=)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linear_out[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NZnJy28VF894" + }, + "source": [ + "Теперь определим класс со всеми частями:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "1GinxRWgF9KG" + }, + "outputs": [], + "source": [ + "class CharGRULoop(nn.Module): # Описываем инициализатор класса.\n", + " def __init__(self, num_embeddings=52, embedding_dim=16, hidden_size=64, num_layers=1): # В методе __init__ определим архитектуру модели, создав необходимые слои\n", + " super(self.__class__, self).__init__()\n", + " self.emb = nn.Embedding(num_embeddings, embedding_dim)\n", + " self.gru = nn.GRU(embedding_dim, hidden_size, num_layers, batch_first=True)\n", + " self.hid_to_logits = nn.Linear(hidden_size, num_embeddings)\n", + "\n", + " def forward(self, x, hid_state): # Здесь описываем стурктуру сети - как сигнал должен по ней проходить\n", + " x = self.emb(x) # Проходим через эмбеддинг-слой\n", + " if hid_state is not None: # Проходим через GRU, сохраняя hidden state\n", + " h_seq, hid_state = self.gru(x, hid_state)\n", + " else:\n", + " h_seq, hid_state = self.gru(x)\n", + " next_logits = self.hid_to_logits(h_seq) # проходим через полносвязный слой\n", + " next_logp = F.log_softmax(next_logits, dim=-1) # Используем функцию softmax\n", + " return next_logp, hid_state" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1aW5mneXF_cy" + }, + "source": [ + "Определим модель:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "_U6Q0oosF_o7" + }, + "outputs": [], + "source": [ + "model = CharGRULoop(num_embeddings=len(vocab), embedding_dim=64, hidden_size=192, num_layers=2)\n", + "# Можно попробовать создать сеть с другими значениями параметров" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nnS2cn8YGBPw" + }, + "source": [ + "Количество параметров:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "zp7I2GVIGCOw" + }, + "outputs": [], + "source": [ + "!pip -q install torchinfo" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24-GKj6nGDc4", + "outputId": "8bc88d9d-0105-4ad6-ab6a-5a90c5d2a4bf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "=================================================================\n", + "Layer (type:depth-idx) Param #\n", + "=================================================================\n", + "CharGRULoop --\n", + "├─Embedding: 1-1 9,152\n", + "├─GRU: 1-2 370,944\n", + "├─Linear: 1-3 27,599\n", + "=================================================================\n", + "Total params: 407,695\n", + "Trainable params: 407,695\n", + "Non-trainable params: 0\n", + "=================================================================" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from torchinfo import summary\n", + "\n", + "summary(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gIEsZBJbUNY5" + }, + "source": [ + "Каждый раз, когда вы вызываете модель (forward), вы передаете некоторый текст и внутреннее состояние. Модель возвращает прогноз для следующего символа и его нового состояния.\n", + "\n", + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "afVs3q7tGFPO" + }, + "source": [ + "Функция для генерации последовательности символов (текста)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "meZeWXwUGFss" + }, + "outputs": [], + "source": [ + "def generate_sample(model, char2id, id2char, seed_phrase=' ', strategy=\"greedy\", max_length=100, temperature=1.0):\n", + " \"\"\"\n", + " model - нейросеть\n", + " char2id - словарь преобразования букв в их индексы\n", + " id2char - словарь преобразования индексов в буквы\n", + " seed_phrase - начальная фраза для генерации\n", + " strategy - стратегия генерации (жадная \"greedy\" или сэмплирование \"sample\")\n", + " max_length - максимальная длина сгенирированного текста\n", + " temperature - ???\n", + " \"\"\"\n", + "\n", + " x_sequence = [char2id[token] for token in seed_phrase] # кодируем начальную фразу\n", + " x_sequence = torch.tensor([x_sequence], dtype=torch.int64) # создаем тензор\n", + " hid_state = None # задаем тензор скрытого состояния h_n, при такой подачи вектор заполнится нулями\n", + "\n", + " with torch.no_grad(): # отключаем подсчет градиентов, поскольку сеть уже обучена и не нужно проводить обратное распространение ошибки\n", + " for i in range(len(seed_phrase) - 1): # подаем номер буквы и hid_state в цикле\n", + " _, hid_state = model(x_sequence[:, i].unsqueeze(0), hid_state)\n", + "\n", + " # начинаем генерацию\n", + " for _ in range(max_length - len(seed_phrase)):\n", + "\n", + " logp_next, hid_state = model(x_sequence[:, -1].unsqueeze(0), hid_state) # подаем последнюю букву из фразы\n", + " p_next = F.softmax(logp_next / temperature, dim=-1).data.numpy()[0] # нормируем выходы модели на температуру и применяем софтмакс\n", + "\n", + " if strategy == \"greedy\": next_ix = p_next.argmax() #берем токен с максимальной вероятностью\n", + " elif strategy == \"sample\": next_ix = np.random.choice(len(id2char), p=p_next[0]) #получаем следующий токен сэмплированием с вероятностями\n", + " else: raise ValueError('Хулиган, не делай так! Выбери \"greedy\" или \"sample\"')\n", + "\n", + " if id2char[next_ix] == \"EOS\": break # если получили токен EOS, то прекращаем генерацию\n", + " else:\n", + " next_ix = torch.tensor([[next_ix]], dtype=torch.int64) # создаем тензор следующий буквы\n", + " x_sequence = torch.cat([x_sequence, next_ix], dim=1) # добавляем предсказанный токен в конец последовательности\n", + "\n", + " return ''.join([id2char[ix] for ix in x_sequence.data.numpy()[0]]) # возвращаем декодированную строку" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IJohkcbJGG5O" + }, + "source": [ + "Попробуем что-нибудь сгенерировать:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b2hqsv33P2gW" + }, + "source": [ + "### Вопрос 7\n", + "Выполните следующую ячейку несколько раз с одной и той же SEED_PHRASE, запомните выводы модели и объясните результат - чем отличается стратегия greedy от sample?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5bqp2xRjGIdW", + "outputId": "431d70ec-9bca-4765-aae6-3ae6e143fb11" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Я люблю машинное обучение!\n", + "ддZгàuгuuZsàCCCCОааа___IIшCCОаа!___IICйCCОаа!___IICйCCОаа!___IICйCCОаа!__\n", + "\n", + "Я люблю машинное обучение!\n", + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "EPOCH = 200 # количество эпох обучения\n", + "history_train = [] # список значений лосса трейна на каждой эпохи\n", + "history_test = [] # список значений лосса теста на каждой эпохи\n", + "model.to(device) # И модель, и данные должны находиться на одном устройстве.\n", + " # Поэтому при работе с GPU нужно следить и явно указывать, на каком устройстве проводится работа.\n", + "\n", + "\n", + "best_test_loss = float(\"inf\")\n", + "for i in range(EPOCH): #цикл по эпохам\n", + "\n", + " loss_test = 0\n", + " loss_train = 0\n", + "\n", + " for batch in dataloader_train: #цикл по тренировачным батчам\n", + "\n", + " optimizer.zero_grad() #обнуляем градиенты\n", + " batch_ix = torch.tensor(batch[0], dtype=torch.int64).to(device) #делаем из батча тензор\n", + "\n", + " predictions_logp, _ = model(batch_ix[:, :-1], hid_state=None) #подаем батч в модель\n", + "\n", + " actual_next_tokens = batch_ix[:, 1:] # таргеры\n", + "\n", + " loss = loss_fn(predictions_logp.permute(0, 2, 1), actual_next_tokens.long()) # считаем лосс на батче\n", + " loss_train += loss.item() # добавляем лосс с батча в суммарный лосс\n", + "\n", + " loss.backward() # делаем обратный проход\n", + " optimizer.step() # делаем шаг оптимизатором\n", + "\n", + " history_train.append(loss_train/len(dataloader_train)) # добавляем средний лосс за эпоху в список\n", + "\n", + " for batch in dataloader_test: #цикл по тестовым батчам\n", + " with torch.no_grad(): # отключаем подсчет градиентов\n", + "\n", + " batch_ix = torch.tensor(batch[0], dtype=torch.int64).to(device)\n", + " predictions_logp, _ = model(batch_ix[:, :-1], hid_state=None)\n", + "\n", + " actual_next_tokens = batch_ix[:, 1:]\n", + "\n", + " loss = loss_fn(predictions_logp.permute(0, 2, 1), actual_next_tokens.long())\n", + " loss_test += loss.item()\n", + "\n", + " loss_test = loss_test/len(dataloader_test)\n", + " history_test.append(loss_test)\n", + "\n", + " if loss_test < best_test_loss: #сохраняем лучшую модель по лоссу на тесте\n", + " best_test_loss = loss_test\n", + " best_model = copy.deepcopy(model)\n", + " best_model.to(\"cpu\")\n", + "\n", + " if (i + 1) % 5 == 0: # выводим график лосса каждые 5 эпох\n", + " clear_output(True)\n", + " plt.plot(history_train, label='loss_train')\n", + " plt.plot(history_test, label='loss_test')\n", + " plt.grid()\n", + " plt.legend()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iWGz3oH3Rowq" + }, + "source": [ + "### Вопрос 8\n", + "Достаточно ли обучилась модель? Имеет ли смысл изменить количество эпох обучения?" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k924KVVaGU4t", + "outputId": "01db571f-54df-421e-cdf5-6c6c4ce7bf72" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "perplexity (best model test): 7.181648032772434\n", + "perplexity (last epoch test): 7.421088521940808\n" + ] + } + ], + "source": [ + "print(\"perplexity (best model test): \", np.exp(best_test_loss))\n", + "print(\"perplexity (last epoch test): \", np.exp(loss_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5fwRbhzDGVxl", + "outputId": "230be0e7-f04c-4c63-8576-2e2971a82b00" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CharGRULoop(\n", + " (emb): Embedding(143, 64)\n", + " (gru): GRU(64, 256, num_layers=3, batch_first=True)\n", + " (hid_to_logits): Linear(in_features=256, out_features=143, bias=True)\n", + ")" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = 'cpu' # Обучение обычно проводится на GPU, но чтобы не тратить его ресурсы, работу по генерации текста уже обученной моделью стоит перенести обратно на CPU\n", + "model.to(device)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0PWo3arcGWvv" + }, + "source": [ + "Погенерируем текст. Сначала зададим стартовую фразу.\n", + "\n", + "Выполнив следующие ячейки по несколько раз, убедитесь, что правильно ответили на вопрос 7 \"Чем отличается стратегия greedy от sample?\"" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "dtUW5hVBGX9p" + }, + "outputs": [], + "source": [ + "#Если вы не согласны, вы можете поменять стартовую фразу, но что думает об этом высказывание машина?\n", + "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nr24uuQRGZqQ" + }, + "source": [ + "Sample strategy" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bY_iYC82GY28", + "outputId": "1a41d952-882b-451a-b321-f2066f03f255" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Теория автоматического управления - лучший предмет,\n", + "В вечерчят, может и ребких ряд,\n", + "Когда в тихает Наздним не рафстились взор,\n", + "Где боющей слус во знали цвю,\n", + "Еврожачи в пустыню твоей, прочей,\n", + "В Ажертрянный лес пропариду улещенье,\n", + "Истревняешь литее молви от меня за Eилей\n", + "В и ждох эти пыраматься спомнорой,\n", + "И вишел ее пеельчим на пытом.\n", + "\n", + "Зевители, моей пужко образ роды погоглавляек;\n", + "Скворный, сокружлен ускоре вином:\n", + "\n", + " EOS любится каз ним меня дороды!\n", + "Всях казы-тро ты сладо мною рошу.\n", + "Нет, и красота тебе прироща\n" + ] + } + ], + "source": [ + "print(generate_sample(best_model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=\"sample\", max_length=MAXLEN))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i6n4dUScGbk9" + }, + "outputs": [], + "source": [ + "#prompt = \"<не сдерживайте себя, сгенерируйте что-нибудь про соседа>\"\n", + "#print(generate_sample(model, char2id, id2char, seed_phrase=prompt, strategy=\"sample\", max_length=256))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gyB9g3KOGe8k" + }, + "source": [ + "Greedy strategy:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HuTB1fdlGd0b", + "outputId": "83440df4-700d-4c3b-935b-8e3aa5f65bcb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Теория автоматического управления - лучший предмет,\n", + "И в тебе под него не след не внемлет он мой под сон,\n", + "И в тебя с полей под сенью покой приветный страсть устались,\n", + "И в тебя в тебя с поле под сенью покой,\n", + "И с тобой страсть и страсть у него в поле молчаливой,\n", + "И с тобой в тебе с полей под страсть и страсть на волненье,\n", + "И в тебя в тебя с поле под страсть и страсть на восторга,\n", + "И с тебя с невольно прости страсть и страсть на восторга,\n", + "И с тебя с невольно прости страсть и страсть на восторга,\n", + "И с тебя с невольн\n" + ] + } + ], + "source": [ + "print(generate_sample(best_model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=\"greedy\", max_length=MAXLEN))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "_tKKZheZGfu8" + }, + "outputs": [], + "source": [ + "#prompt = \"<не сдерживайте себя, сгенерируйте что-нибудь про соседа>\"\n", + "#print(generate_sample(model, char2id, id2char, seed_phrase=prompt, strategy=\"sample\", max_length=256))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "RpVEloyFScaa" + }, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jxpdQXl1GhyL" + }, + "source": [ + "# Эксперименты с температурой" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LdKkJc5vGjIt" + }, + "source": [ + "В функции `generate_sample` есть параметр `temperature`.\n", + "\n", + "Основываясь на прошлом пункте, выберите стратегию, которая больше понравилась и запустите ячейки с разной температурой" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "h88cuY0HGfsa" + }, + "outputs": [], + "source": [ + "nice_strategy = \"sample\" #" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9uZZRmP-GlS2", + "outputId": "46f77170-a06e-4e72-98ca-f30d76e0c5f2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Теория автоматического управления - лучший предмет,\n", + "Нет.У дух преБЦя! убиием?> Скутxщ:: —\n", + "мбебле) был за цев чещу\n", + "Па. ТДа чевтовых: а домите,\n", + "Хтон живлчивые Посов, я , куке, лесь,\n", + "<пучалу> лябесимlим КлOE<жуе оRлки-пом,\n", + "Бедпятные пъе! сrакиса: ебя.\n", + ". нелаться, лены яхтну…\n", + "М зигранном дня, <судую,\n", + "Афием, П*\";c——\n", + "ДТрома, нуй, чтоко и; Дьбл\n", + "Еу! укнавнпиdусем.и.. ь,я\n", + "\n", + "Гдю Увер!ы\n", + "Дррикаявь, твое гофJ —!\n", + "Уе.ш. И, Бстоъ тиханью,\n", + "Лю??\n", + "Хласуя âлучит хожделz штон!…Л.\n", + "…»>\n", + "ожестгу<чиг\n", + "Ясь.…а хванни дупгухНвеннемляцы. —\n" + ] + } + ], + "source": [ + "print(generate_sample(best_model, char2id, id2char, seed_phrase=SEED_PHRASE, strategy=nice_strategy, max_length=MAXLEN, temperature=2.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DwIORQ3OoiKN" + }, + "source": [ + "### Вопрос 9\n", + "Сделайте выводы как влияет изменение температуры на генерацию текста.\n", + "\n", + "Выберите оптимальное значение температуры" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "id": "4DoOXClVohes" + }, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "htAW_ABDGnc6" + }, + "outputs": [], + "source": [ + "# По завершению работы с рекуррентной сетью, очистим кэш\n", + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rlcgdw_gGpiO" + }, + "source": [ + "# Bonus track GPT" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FND3cMDMGra3" + }, + "source": [ + "Дальше происходит магия, чтобы все вышло:\n", + " - здесь лучше перезапустить сеанс (Среда выполнения -> Перезапустить сеанс)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HEZqH-4rGo84", + "outputId": "881559c1-9659-4157-97fc-ccf9425169b3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.3/297.3 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m46.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m62.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "!pip install -q transformers[torch]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "5ccG2XtoGtDT" + }, + "outputs": [], + "source": [ + "import locale\n", + "import torch\n", + "import transformers\n", + "import numpy as np\n", + "\n", + "from warnings import simplefilter\n", + "from IPython.display import clear_output\n", + "from transformers import Trainer, TrainingArguments\n", + "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n", + "from transformers import TextDataset, DataCollatorForLanguageModeling\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "xerwrDTGGtAc" + }, + "outputs": [], + "source": [ + "# Задаем некоторые настроечные параметры касательно кодировки и отображения предупреждений\n", + "locale.getpreferredencoding = lambda: \"UTF-8\"\n", + "simplefilter(\"ignore\", category=FutureWarning)\n", + "transformers.logging.set_verbosity_error()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "ygm4wl7EGvLX" + }, + "outputs": [], + "source": [ + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "model_name = \"sberbank-ai/rugpt3small_based_on_gpt2\" # Опередлим, какой моделью будем пользоваться\n", + "tokenizer = GPT2Tokenizer.from_pretrained(model_name) # Определим токенайзер для нашего текста\n", + "model = GPT2LMHeadModel.from_pretrained(model_name).to(device) # Загрузим предобученную модель трансформера rugpt3small от Сбера\n", + "\n", + "clear_output()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jfAXAgtfGvIs", + "outputId": "a9f47a68-7a4a-4edc-be98-cbf539e277cb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Теория автоматического управления - лучший предмет, который я знаю.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'\n", + "input_ids = tokenizer.encode(SEED_PHRASE, return_tensors=\"pt\").to(device)\n", + "out = model.generate(input_ids, do_sample=False, max_length=20)\n", + "\n", + "generated_text = list(map(tokenizer.decode, out))[0]\n", + "\n", + "print(generated_text) #Так работает предобученный трансформер" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "By_ukdhOp_F8" + }, + "source": [ + "Давайте дообучим трансформер на нашем датасете - мы хотим генерировать стихи" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "YLIYVZ1AGxzV" + }, + "outputs": [], + "source": [ + "train_path = \"train_dataset.txt\"\n", + "\n", + "with open(\"poems.txt\", encoding=\"utf-8\") as file:\n", + " data = file.read().split(\"\\n\\n\")\n", + "\n", + "with open(train_path, mode=\"w\", encoding=\"utf-8\") as f:\n", + " f.write(\"\".join(data))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "ulVcdoROGyy2" + }, + "outputs": [], + "source": [ + "train_dataset = TextDataset(tokenizer=tokenizer, file_path=train_path, block_size=128) # Создание датасета\n", + "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) # Создание даталодера (нарезает текст на оптимальные по длине куски)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "nTkPLolOGzqH" + }, + "outputs": [], + "source": [ + "\n", + "training_args = TrainingArguments(\n", + " output_dir=\"./finetuned\",\n", + " overwrite_output_dir=True,\n", + " num_train_epochs=15,\n", + " per_device_train_batch_size=32,\n", + " per_device_eval_batch_size=32,\n", + " warmup_steps=10, # рекомендованные значения (warmup_steps нужен для \"разогрева\" сети, c его помощью learning rate постепенно увеличивается до заданного значения)\n", + " gradient_accumulation_steps=16, # рекомендованные значения\n", + ")\n", + "#(обычно мы хотим положить батч по-больше, чтобы сеть побыстрей сошлась, но мы ограничены памятью gpu, gradient_accumulation_steps накапливает (суммирует или усредняет) градиенты за прогон на 16 батчах )\n", + "\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " data_collator=data_collator,\n", + " train_dataset=train_dataset,\n", + " optimizers=(\n", + " torch.optim.AdamW(model.parameters(), lr=1e-5), # рекомендованные значения\n", + " None,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MN4IUz0cG06R" + }, + "source": [ + "Эта ячейка займет около 15-20 минут" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MIDjczLBG1x4", + "outputId": "4cd93a79-50a4-4f67-dc0d-1736a9055395" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'train_runtime': 512.7748, 'train_samples_per_second': 43.499, 'train_steps_per_second': 0.059, 'train_loss': 4.064910634358724, 'epoch': 10.21}\n" + ] + } + ], + "source": [ + "output = trainer.train() # Дообучаем трансформер на наши тексты" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jGSF1YRpHzUw", + "outputId": "76ecac12-dd33-4005-d1bd-4a6e27a0f74e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "perplexity: 58.25970187428917\n" + ] + } + ], + "source": [ + "print('perplexity: ', np.exp(output.training_loss)) #расчет перплексии" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CssQW8rLHzUx" + }, + "source": [ + "### Вопрос 10\n", + "Какое значение перплексии получилось у трансформера?\n", + "\n", + "Какое значение перплексии получалось у рекуррентной сети?\n", + "\n", + "Почему у рекуррентной сети значение было существенно ниже, но качество текстов хуже? Почему нельзя сравнивать значения для рекуррентной сети и трансформера?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZEG2FYNsG4oy", + "outputId": "0bad01e8-fb2a-4a34-c1ba-39dd01017a66" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Теория автоматического управления - лучший предмет,\n", + "Когда в нем все подчинено закону. \n", + "\n", + "И только законы в нем\n", + "Ошибки и воры\n", + "Так искусно ведут против\n", + "Нимфы его дела;\n", + "Они как раз в ней с самого начала\n", + "Узрели: человек есть душа,\n", + "Коли вдруг он не становится ни грешным,\n", + "Ни преступником,\n", + "Ни преступником.\n", + "\n", + "Он как будто в ней живет,\n", + "И в ней дух живет,\n", + "В ней свет есть для всех он,\n", + "Она его питает,\n", + "Она о нем поет.\n", + "\n", + "Но для него ничего не значит\n", + "Свой день, свои жертвы,\n", + "Он не может быть свободен,\n", + "И он ни над кем не властен:\n", + "В нем весь ход земного рая\n", + "В нем его предел.\n", + "\n", + "Он хочет жить, он хочет умереть,\n", + "Готов он ко всему и не может\n", + "Предать себя сомненьем;\n", + "В нем человек в конце концов\n", + "Перестает жить и для себя.\n", + "Ведь он же человек,\n", + "Он ведь даже в своем законе\n", + "Омертвляет грех силою мысли.\n", + "\n", + "Но все напрасно: с нею человек живет:\n", + "И в нем живет она со своей душой.\n", + "Но\n" + ] + } + ], + "source": [ + "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'\n", + "input_ids = tokenizer.encode(SEED_PHRASE, return_tensors=\"pt\").to(device)\n", + "model.eval()\n", + "with torch.no_grad():\n", + " out = model.generate(\n", + " input_ids,\n", + " do_sample=True, # sample strategy\n", + " temperature=1.0,\n", + " max_length=256,\n", + " pad_token_id=512 # указываем id токена\n", + " )\n", + "\n", + "generated_text = list(map(tokenizer.decode, out))[0]\n", + "print()\n", + "print(generated_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kK91Afxmqsnr" + }, + "source": [ + "### Вопрос 11\n", + "Проверьте работу ячейки выше для разных стартовых фраз и разных параметров `temperature`, `max_length`, `do_sample` и объясните, за что отвечает каждый из параметров. Подберите (субъективно) лучшие" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "URWWyeN_G92m" + }, + "source": [ + "# Beam Search\n", + "До этого мы использовали обычный \"жадный\" поиск\n", + "\n", + "![](https://huggingface.co/blog/assets/02_how-to-generate/greedy_search.png)\n", + "\n", + "Для каждого слова следующим считали _наиболее вероятное_.\n", + "Но, возможно, пройдя по не самому вероятному слову, в итоге мы получим более вероятную __последовательность__ слов, чем при жадном алгоритме.\n", + "Такой подход называется Beam search.\n", + "\n", + "![](https://huggingface.co/blog/assets/02_how-to-generate/beam_search.png)\n", + "\n", + "[подробнее тут](https://huggingface.co/blog/how-to-generate)\n", + "\n", + "Для использования Beam search передадим в функцию генерации параметр `num_beams` который характеризует количество рассматриваемых \"альтернативных\" путей" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "id": "f5qj2KVoG56d" + }, + "outputs": [], + "source": [ + "SEED_PHRASE = 'Теория автоматического управления - лучший предмет,'\n", + "input_ids = tokenizer.encode(SEED_PHRASE, return_tensors=\"pt\").to(device)\n", + "model.eval()\n", + "with torch.no_grad():\n", + " out = model.generate(\n", + " input_ids,\n", + " do_sample=False,\n", + " max_new_tokens=75,\n", + " no_repeat_ngram_size=3, # устанавливает вероятность 0 для повторяющихся n-gram (таким образом решается проблема зацикливания)\n", + " pad_token_id=512,\n", + " num_beams=5,\n", + " num_return_sequences=5, # количество возвращенных сгенерированных текстов отранжированных по вероятности после beam_search\n", + " top_p=0.9, #\n", + " top_k=20, #\n", + " temperature=2.0 #\n", + " )\n", + "\n", + "generated_text = list(map(tokenizer.decode, out))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "af8foFo1f6Hj", + "outputId": "0f5328b9-201b-486a-b569-30ce7715278a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applicant 0\n", + "Теория автоматического управления - лучший предмет,\n", + "Который я когда-либо читал.\n", + "\n", + "Я знаю, что это не так,\n", + "Но я не знаю, как это объяснить.\n", + "Я не знаю даже, как объяснить\n", + "То, что я знаю, и то, что не знаю.\n", + "И я знаю только то,\n", + "Что знаю я, но не знаю\n", + "И того, и другого.\n", + "\n", + "\n", + "\n", + "Applicant 1\n", + "Теория автоматического управления - лучший предмет,\n", + "Который я когда-либо читал.\n", + "\n", + "Я знаю, что это не так,\n", + "Но я не знаю, как это объяснить.\n", + "Я не знаю даже, как объяснить\n", + "То, что я знаю, и то, что не знаю.\n", + "И я знаю только то,\n", + "Что знаю я, но не знаю\n", + "И того, что знаю я.\n", + "\n", + "\n", + "Applicant 2\n", + "Теория автоматического управления - лучший предмет,\n", + "Который я когда-либо читал.\n", + "\n", + "Я знаю, что это не так,\n", + "Но я не знаю, как это объяснить.\n", + "Я не знаю даже, как объяснить\n", + "То, что я знаю, и то, что не знаю.\n", + "И я знаю только то,\n", + "Что знаю я, но не знаю\n", + "И то, и другое.\n", + "\n", + "\n", + "\n", + "Applicant 3\n", + "Теория автоматического управления - лучший предмет,\n", + "Который я когда-либо читал.\n", + "\n", + "Я знаю, что это не так,\n", + "Но я не знаю, как это объяснить.\n", + "Я не знаю даже, как объяснить\n", + "То, что я знаю, и то, что не знаю.\n", + "И я знаю только то,\n", + "Что знаю я, но не знаю\n", + "И того, чего не знаю я\n", + "\n", + "\n", + "Applicant 4\n", + "Теория автоматического управления - лучший предмет,\n", + "Который я когда-либо читал.\n", + "\n", + "Я знаю, что это не так,\n", + "Но я не знаю, как это объяснить.\n", + "Я не знаю даже, как объяснить\n", + "То, что я знаю, и то, что не знаю.\n", + "И я знаю только то,\n", + "Что знаю я, но не знаю\n", + "И то, чего не знаю я\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Выведем _num_return_sequences_ сгенерированных текстов\n", + "for i, seq in enumerate(generated_text):\n", + " print(f\"Applicant {i}\", seq, \"\\n\", sep=\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AWJBlaMSHzU0" + }, + "source": [ + " Дополнительно можно изучить смысл параметров top_p и top_k [по ссылке](https://huggingface.co/blog/how-to-generate)\n", + "\n", + "Опробуйте разные значения параметров, какая схема выборки получилась лучше?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NgxotZZrHBLI" + }, + "source": [ + "Сохранить модель при необходимости:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H8CjiUvzHBn2" + }, + "outputs": [], + "source": [ + "#torch.save(model, \"gpt2_finetune.torch\")\n", + "#mod = torch.load(\"gpt2_finetune.torch\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Cn991FqHzU3" + }, + "source": [ + "### Вопрос 12\n", + "Вместо вывода добавьте лучший сгенерированый текст за лабораторную работу и напишите при какой архитектуре и при каких параметрах он получен:\n", + "\n", + "Например: tuned gpt3 with params:\n", + "\n", + "* do_sample=True,\n", + "* max_new_tokens=80,\n", + "* no_repeat_ngram_size=3,\n", + "* ...\n", + "\n", + "\n", + "В мире, где порядок стремится к хаосу,\n", + "\n", + "Теория управления - свет во тьме.\n", + "\n", + "Автоматы, системы, в них неспроста\n", + "\n", + "Скрыт закон, управляющий временем.\n", + "\n", + "\n", + "Регуляторы, обратные связи,\n", + "\n", + "В этом мире - как волшебный ключ.\n", + "\n", + "С их помощью мы можем, без отказа,\n", + "\n", + "Двигать системы, чьё движенье - дым.\n", + "\n", + "\n", + "\n", + "В каждом узле, в каждом переходе\n", + "\n", + "Скрыта струна, что звучит на удивление чисто.\n", + "\n", + "ТАУ нас учит, как в море перемен,\n", + "\n", + "Найти путь, где устойчивость - не миф, а быль." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lmhlmlk5lTD3" + }, + "source": [ + "# Контрольные вопросы\n", + "1. В чем особенность рекуррентных нейронных сетей?\n", + "1. Типы рекуррентных сетей - обычная RNN\n", + "1. Типы рекуррентных сетей - LSTM\n", + "1. Типы рекуррентных сетей - GRU\n", + "1. Что такое и как вычисляется перплексия\n", + "1. Что такое \"предобученная\" модель и для чего ее нужно \"дообучать\"?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ia7mf_pIlpKe" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/labs/OATD_LR4.md b/labs/OATD_LR4.md deleted file mode 100644 index d23d063..0000000 --- a/labs/OATD_LR4.md +++ /dev/null @@ -1,46 +0,0 @@ -# Лабораторная работа №4. Использование нейронных сетей для генерации текста - -## Цель работы - -Получить практические навыки решения задачи генерации текста. - -## Задание - -1. Загрузить выборку стихотворений одного из поэтов в соответствии с вариантом. -2. Познакомиться с данными. Проанализировать статистические характеристики исходных данных (среднюю длину стихотворения, среднюю длину строки). -3. Подготовить выборку для обучения. -4. Построить нейронную сеть. Тип ячейки RNN выбрать в соответствии с вариантом. -5. Обучить нейронную сеть на разных количествах эпох (5, 15, 30, 50, 70) при зафиксированных параметрах embedding_dim = 256, rnn_units = 300, T = 0.3 и сравнить результаты генерации (тексты), перплексию и статистические характеристики сгенерированных текстов. Выбрать оптимальное количество эпох -7. Изменяя параметр температуры T проанализировать изменения сгенерированного текста. Выбрать оптимальное значение параметра. -8. Проанализировать зависимость перплексии, скорости обучения, результатов генерации от параметров нейронной сети embedding_dim, rnn_units: -embedding_dim = {vocab/4, vocab/2, vocab, vocab * 2, vocab * 4}, где vocab = размер словаря выборки. -rnn_units = {10, 100, 300, 500} - -## Указания - -Для работы рекомендуется вместо Jupyter Notebook использовать [Google Colab](https://colab.research.google.com/) и среду с GPU для ускорения расчетов. Для установки среды, использующей GPU в Google Colab нужно -выбрать пункт меню "Среда выполнения" -> "Сменить среду выполнения" -> выбрать аппаратный ускоритель "GPU". - -## Варианты заданий - -### Поэт - -Четные номера по журналу - Пушкин, нечетные - Маяковский. - -### Тип ячейки RNN - -Остаток от деления номера по журналу на 3: -* 0 - https://keras.io/api/layers/recurrent_layers/simple_rnn/ -* 1 - https://keras.io/api/layers/recurrent_layers/lstm/ -* 2 - https://keras.io/api/layers/recurrent_layers/gru/ - -## Контрольные вопросы - -1. В чем особенность рекуррентных нейронных сетей? -2. Типы рекуррентных сетей - обычная RNN -3. Типы рекуррентных сетей - LSTM -4. Типы рекуррентных сетей - GRU -5. Что такое и как вычисляется перплексия - - - diff --git a/labs/OATD_LR4_metod.ipynb b/labs/OATD_LR4_metod.ipynb deleted file mode 100644 index d7d453e..0000000 --- a/labs/OATD_LR4_metod.ipynb +++ /dev/null @@ -1,1854 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "srXC6pLGLwS6" - }, - "source": [ - "# Загрузка библиотек" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "yG_n40gFzf9s" - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "from sklearn.model_selection import train_test_split\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "import time\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "При использовании Google Colab следуюет выбрать среду выполнения с аппаратным ускорителем GPU, что существенно ускорит расчеты. При этом, следующая ячейка должна возвращать текст, похожий на \"Found GPU at: /device:GPU:0\" " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LshgkZ0cIOor", - "outputId": "903898c0-4205-47d7-a6e3-ca22e79ffba9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found GPU at: \n" - ] - } - ], - "source": [ - "device_name = tf.test.gpu_device_name()\n", - "print('Found GPU at: {}'.format(device_name))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "vWkRnHV0DK0L" - }, - "outputs": [], - "source": [ - "RANDOM_STATE = 42" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EW8HRqz8Oz_b" - }, - "source": [ - "# Данные" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UHjdCjDuSvX_" - }, - "source": [ - "## Загрузка данных\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "pP1Ou4nq_W1o" - }, - "outputs": [], - "source": [ - "# Выбираем поэта\n", - "poet = 'pushkin' #@param ['mayakovskiy', 'pushkin']\n", - "\n", - "path_to_file = f'{poet}.txt'\n", - "path_to_file = tf.keras.utils.get_file(path_to_file, f'http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/{path_to_file}')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "aavnuByVymwK", - "outputId": "1c4c379c-ab1e-4b84-939c-66a7471c0210" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length of text: 586731 characters\n" - ] - } - ], - "source": [ - "# Загружаем текст из файла.\n", - "# Стихотворения в файле разделены токеном '' - сохраняем в переменную\n", - "with open(path_to_file,encoding = \"utf-8\") as f:\n", - " text = f.read()\n", - "\n", - "print(f'Length of text: {len(text)} characters')\n", - "\n", - "EOS_TOKEN = ''" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Duhg9NrUymwO", - "outputId": "8f485753-1712-47a4-c328-a4a714ec0ea4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Так и мне узнать случилось,\n", - "Что за птица Купидон;\n", - "Сердце страстное пленилось;\n", - "Признаюсь – и я влюблен!\n", - "Пролетело счастья время,\n", - "Как, любви не зная бремя,\n", - "Я живал да попевал,\n", - "Как в театре и на балах,\n", - "На гуляньях иль в воксалах\n", - "Легким зефиром летал;\n", - "Как, смеясь во зло Амуру,\n", - "Я писал карикатуру\n", - "На любезный женской пол;\n", - "Но напрасно я смеялся,\n", - "Наконец и сам попался,\n", - "Сам, увы! с ума сошел.\n", - "Смехи, вольность – всё под лавку\n", - "Из Катонов я в отставку,\n", - "И теперь я – Селадон!\n", - "Миловидной жрицы Тальи\n", - "Видел прел\n" - ] - } - ], - "source": [ - "# Посмотрим на текст\n", - "print(text[:500])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dLZNbAnzj2lR" - }, - "source": [ - "## Подсчет статистик\n", - "\n", - "describe_poems - функция, разбивающая файл на отдельные стихотворения (poem), и расчитывающая их характиеристики:\n", - "* длину (len), \n", - "* количество строк (lines)\n", - "* среднюю длину строки (mean_line_len)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "C7G_weaWnMSg" - }, - "outputs": [], - "source": [ - "def mean_line_len(poem):\n", - " lines = [len(line.strip()) for line in poem.split('\\n') if len(line.strip())>0]\n", - " return sum(lines)/len(lines)\n", - "\n", - "\n", - "def describe_poems(text,return_df = False):\n", - " poems_list = [poem.strip() for poem in text.split(EOS_TOKEN) if len(poem.strip())>0]\n", - " df = pd.DataFrame(data=poems_list,columns=['poem'])\n", - " df['len'] = df.poem.map(len)\n", - " df['lines'] = df.poem.str.count('\\n')\n", - " df['mean_line_len'] = df.poem.map(mean_line_len)\n", - " if return_df:\n", - " return df\n", - " return df.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "8t4QIKLgj8_y", - "outputId": "4ffe0325-70be-4a3f-9fd6-910be40e5dd3" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
poemlenlinesmean_line_len
0Так и мне узнать случилось,\\nЧто за птица Купи...253610923.114286
1Хочу воспеть, как дух нечистый Ада\\nОседлан бы...554317033.372671
2Покаместь ночь еще не удалилась,\\nПокаместь св...427913133.451613
3Ах, отчего мне дивная природа\\nКорреджио искус...443513133.364341
4Арист! и ты в толпе служителей Парнасса!\\nТы х...389310638.642857
...............
714Чудный сон мне бог послал —\\n\\nС длинной белой...8603822.833333
715О нет, мне жизнь не надоела,\\nЯ жить люблю, я ...196723.625000
716\"Твой и мой, – говорит Лафонтен —\\nРасторгло у...187530.333333
717Когда луны сияет лик двурогой\\nИ луч ее во мра...269732.750000
718Там, устарелый вождь! как ратник молодой,\\nИск...256541.833333
\n", - "

719 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " poem len lines \\\n", - "0 Так и мне узнать случилось,\\nЧто за птица Купи... 2536 109 \n", - "1 Хочу воспеть, как дух нечистый Ада\\nОседлан бы... 5543 170 \n", - "2 Покаместь ночь еще не удалилась,\\nПокаместь св... 4279 131 \n", - "3 Ах, отчего мне дивная природа\\nКорреджио искус... 4435 131 \n", - "4 Арист! и ты в толпе служителей Парнасса!\\nТы х... 3893 106 \n", - ".. ... ... ... \n", - "714 Чудный сон мне бог послал —\\n\\nС длинной белой... 860 38 \n", - "715 О нет, мне жизнь не надоела,\\nЯ жить люблю, я ... 196 7 \n", - "716 \"Твой и мой, – говорит Лафонтен —\\nРасторгло у... 187 5 \n", - "717 Когда луны сияет лик двурогой\\nИ луч ее во мра... 269 7 \n", - "718 Там, устарелый вождь! как ратник молодой,\\nИск... 256 5 \n", - "\n", - " mean_line_len \n", - "0 23.114286 \n", - "1 33.372671 \n", - "2 33.451613 \n", - "3 33.364341 \n", - "4 38.642857 \n", - ".. ... \n", - "714 22.833333 \n", - "715 23.625000 \n", - "716 30.333333 \n", - "717 32.750000 \n", - "718 41.833333 \n", - "\n", - "[719 rows x 4 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "poem_df = describe_poems(text,return_df = True)\n", - "poem_df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - }, - "id": "TmCI6rv1f49T", - "outputId": "444fe362-1a5f-45b0-dc21-146c08e094cb" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
lenlinesmean_line_len
count719.000000719.000000719.000000
mean808.03755229.46453427.445404
std1046.78686239.2440205.854564
min74.0000005.0000008.250000
25%280.5000009.00000024.125000
50%453.00000016.00000025.758065
75%852.00000033.00000031.522727
max8946.000000437.00000048.923077
\n", - "
" - ], - "text/plain": [ - " len lines mean_line_len\n", - "count 719.000000 719.000000 719.000000\n", - "mean 808.037552 29.464534 27.445404\n", - "std 1046.786862 39.244020 5.854564\n", - "min 74.000000 5.000000 8.250000\n", - "25% 280.500000 9.000000 24.125000\n", - "50% 453.000000 16.000000 25.758065\n", - "75% 852.000000 33.000000 31.522727\n", - "max 8946.000000 437.000000 48.923077" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "poem_df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rNnrKn_lL-IJ" - }, - "source": [ - "## Подготовка датасетов" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3mOXOtj1FB1v" - }, - "source": [ - "Разбиваем данные на тренировочные, валидационные и тестовые" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "MM5Rk7B8D1n-" - }, - "outputs": [], - "source": [ - "train_poems, test_poems = train_test_split(poem_df.poem.to_list(),test_size = 0.1,random_state = RANDOM_STATE)\n", - "train_poems, val_poems = train_test_split(train_poems,test_size = 0.1,random_state = RANDOM_STATE)\n", - "\n", - "train_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(train_poems)\n", - "val_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(val_poems)\n", - "test_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(test_poems)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6QfP2RCpqdCS" - }, - "source": [ - "Создаем словарь уникальных символов из текста. Не забываем добавить токен конца стиха." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IlCgQBRVymwR", - "outputId": "a9769da4-3417-44e4-e491-cd1a09a51869" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "143 unique characters\n", - "['\\n', ' ', '!', '\"', \"'\", '(', ')', '*', ',', '-', '.', '/', ':', ';', '<', '>', '?', 'A', 'B', 'C', 'D', 'E', 'F', 'H', 'I', 'J', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'Z', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'x', 'y', 'z', '\\xa0', '«', '»', 'à', 'â', 'ç', 'è', 'é', 'ê', 'ô', 'û', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ж', 'З', 'И', 'Й', 'К', 'Л', 'М', 'Н', 'О', 'П', 'Р', 'С', 'Т', 'У', 'Ф', 'Х', 'Ц', 'Ч', 'Ш', 'Щ', 'Э', 'Ю', 'Я', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я', 'ё', '–', '—', '„', '…', '']\n" - ] - } - ], - "source": [ - "vocab = sorted(set(text))+[EOS_TOKEN]\n", - "print(f'{len(vocab)} unique characters')\n", - "print (vocab)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1s4f1q3iqY8f" - }, - "source": [ - "Для подачи на вход нейронной сети необходимо закодировать текст в виде числовой последовательности.\n", - "\n", - "Воспользуемся для этого слоем StringLookup \n", - "https://www.tensorflow.org/api_docs/python/tf/keras/layers/StringLookup" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "6GMlCe3qzaL9" - }, - "outputs": [], - "source": [ - "ids_from_chars = tf.keras.layers.StringLookup(\n", - " vocabulary=list(vocab), mask_token=None)\n", - "chars_from_ids = tf.keras.layers.StringLookup(\n", - " vocabulary=ids_from_chars.get_vocabulary(), invert=True, mask_token=None)\n", - "\n", - "def text_from_ids(ids):\n", - " return tf.strings.reduce_join(chars_from_ids(ids), axis=-1).numpy().decode('utf-8')\n", - " \n", - "def ids_from_text(text):\n", - " return ids_from_chars(tf.strings.unicode_split(text, input_encoding='UTF-8'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Wd2m3mqkDjRj", - "outputId": "88ebef4b-6488-465e-d2f7-612e27efc0d2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Корабль испанский тр\n", - "tf.Tensor(\n", - "[ 87 120 122 106 107 117 134 2 114 123 121 106 119 123 116 114 115 2\n", - " 124 122], shape=(20,), dtype=int64)\n", - "Корабль испанский тр\n" - ] - } - ], - "source": [ - "# пример кодирования\n", - "ids = ids_from_text(train_poems[:20])\n", - "res_text = text_from_ids(ids)\n", - "print(train_poems[:20],ids,res_text,sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uzC2u022WHsa" - }, - "source": [ - "Кодируем данные и преобразуем их в Датасеты" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "UopbsKi88tm5" - }, - "outputs": [], - "source": [ - "train_ids = ids_from_text(train_poems)\n", - "val_ids = ids_from_text(val_poems)\n", - "test_ids = ids_from_text(test_poems)\n", - "\n", - "train_ids_dataset = tf.data.Dataset.from_tensor_slices(train_ids)\n", - "val_ids_dataset = tf.data.Dataset.from_tensor_slices(val_ids)\n", - "test_ids_dataset = tf.data.Dataset.from_tensor_slices(test_ids)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-ZSYAcQV8OGP" - }, - "source": [ - "Весь текст разбивается на последовательности длины `seq_length`. По этим последовательностям будет предсказываться следующий символ.\n", - "\n", - "**Попробовать разные длины - среднюю длину строки, среднюю длину стиха**" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "C-G2oaTxy6km" - }, - "outputs": [], - "source": [ - "seq_length = 100\n", - "examples_per_epoch = len(train_ids_dataset)//(seq_length+1)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BpdjRO2CzOfZ", - "outputId": "515651a7-765d-4bbf-9e90-8eb9a9380759" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Корабль испанский трехмачтовый,\n", - "Пристать в Голландию готовый:\n", - "На нем мерзавцев сотни три,\n", - "Две обезьян\n" - ] - } - ], - "source": [ - "train_sequences = train_ids_dataset.batch(seq_length+1, drop_remainder=True)\n", - "val_sequences = val_ids_dataset.batch(seq_length+1, drop_remainder=True)\n", - "test_sequences = test_ids_dataset.batch(seq_length+1, drop_remainder=True)\n", - "\n", - "for seq in train_sequences.take(1):\n", - " print(text_from_ids(seq))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UbLcIPBj_mWZ" - }, - "source": [ - "Создаем датасет с input и target строками\n", - "\n", - "target сдвинута относительно input на один символ.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "9NGu-FkO_kYU" - }, - "outputs": [], - "source": [ - "def split_input_target(sequence):\n", - " input_text = sequence[:-1]\n", - " target_text = sequence[1:]\n", - " return input_text, target_text" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WxbDTJTw5u_P", - "outputId": "f44e70c6-b600-4fb3-8073-ba8d774d8832" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(['П', 'у', 'ш', 'к', 'и'], ['у', 'ш', 'к', 'и', 'н'])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# пример\n", - "split_input_target(list(\"Пушкин\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "B9iKPXkw5xwa" - }, - "outputs": [], - "source": [ - "train_dataset = train_sequences.map(split_input_target)\n", - "val_dataset = val_sequences.map(split_input_target)\n", - "test_dataset = test_sequences.map(split_input_target)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GNbw-iR0ymwj", - "outputId": "888b397b-5370-4ae4-d2ba-98d54595ee72" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input : Прими сей череп, Дельвиг, он\n", - "Принадлежит тебе по праву.\n", - "Тебе поведаю, барон,\n", - "Его готическую славу.\n", - "\n", - "\n", - "Target: рими сей череп, Дельвиг, он\n", - "Принадлежит тебе по праву.\n", - "Тебе поведаю, барон,\n", - "Его готическую славу.\n", - "\n", - "П\n" - ] - } - ], - "source": [ - "for input_example, target_example in val_dataset.take(1):\n", - " print(\"Input :\", text_from_ids(input_example))\n", - " print(\"Target:\", text_from_ids(target_example))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MJdfPmdqzf-R" - }, - "source": [ - "Перемешиваем датасеты и разбиваем их на батчи для оптимизации обучения" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "p2pGotuNzf-S", - "outputId": "d9ae90cb-d904-4528-d0ed-eb11caeb43d3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Batch size\n", - "BATCH_SIZE = 64\n", - "\n", - "BUFFER_SIZE = 10000\n", - "\n", - "def prepare_dataset(dataset):\n", - " dataset = (\n", - " dataset\n", - " .shuffle(BUFFER_SIZE)\n", - " .batch(BATCH_SIZE, drop_remainder=True)\n", - " .prefetch(tf.data.experimental.AUTOTUNE))\n", - " return dataset \n", - "\n", - "train_dataset = prepare_dataset(train_dataset)\n", - "val_dataset = prepare_dataset(val_dataset)\n", - "test_dataset = prepare_dataset(test_dataset)\n", - "\n", - "train_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r6oUuElIMgVx" - }, - "source": [ - "# Нейросеть" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "22uVCbSyPBjD" - }, - "source": [ - "## Построение модели" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m8gPwEjRzf-Z" - }, - "source": [ - "Модель состоит из трех слоев\n", - "\n", - "* `tf.keras.layers.Embedding`: Входной слой. Кодирует каждый идентификатор символа в вектор размерностью `embedding_dim`; \n", - "* `tf.keras.layers.GRU`: Рекуррентный слой на ячейках GRU. Выходной вектор размерностью `units=rnn_units` **(Здесь нужно указать тип ячеек в соответствии с вариантом)**\n", - "* `tf.keras.layers.Dense`: Выходной полносвязный слой размерностью `vocab_size`, в который выводится вероятность каждого символа в словаре. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "zHT8cLh7EAsg" - }, - "outputs": [], - "source": [ - "# Длина словаря символов\n", - "vocab_size = len(vocab)\n", - "\n", - "# размерность Embedding'а\n", - "embedding_dim = 20 #@param{type:\"number\"}\n", - "\n", - "# Параметры RNN-слоя\n", - "rnn_units = 300 #@param {type:\"number\"}\n", - "dropout_p = 0.5" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "wj8HQ2w8z4iO" - }, - "outputs": [], - "source": [ - "class MyModel(tf.keras.Model):\n", - " def __init__(self, vocab_size, embedding_dim, rnn_units):\n", - " super().__init__(self)\n", - " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", - " self.gru = tf.keras.layers.GRU(rnn_units,\n", - " dropout = dropout_p,\n", - " return_sequences=True,\n", - " return_state=True)\n", - " self.dense = tf.keras.layers.Dense(vocab_size)\n", - "\n", - " def call(self, inputs, states=None, return_state=False, training=False):\n", - " x = inputs\n", - " x = self.embedding(x, training=training)\n", - " \n", - " if states is None:\n", - " states = self.gru.get_initial_state(x)\n", - "\n", - " x, *states = self.gru(x, initial_state=states, training=training)\n", - " x = self.dense(x, training=training)\n", - "\n", - " if return_state:\n", - " return x, states\n", - " else:\n", - " return x" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "IX58Xj9z47Aw" - }, - "outputs": [], - "source": [ - "model = MyModel(\n", - " vocab_size=len(ids_from_chars.get_vocabulary()),\n", - " embedding_dim=embedding_dim,\n", - " rnn_units=rnn_units)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RkA5upJIJ7W7" - }, - "source": [ - "Иллюстрация работы сети\n", - "\n", - "![A drawing of the data passing through the model](https://github.com/tensorflow/text/blob/master/docs/tutorials/images/text_generation_training.png?raw=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LdgaUC3tPHAy" - }, - "source": [ - "## Проверка необученой модели" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "C-_70kKAPrPU", - "outputId": "02de9cf7-29d5-4345-8e02-b8c940ca5c1a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(64, 100, 144) # (batch_size, sequence_length, vocab_size)\n" - ] - } - ], - "source": [ - "# посмотрим на один батч из датасета\n", - "for input_example_batch, target_example_batch in train_dataset.take(1):\n", - " example_batch_predictions = model(input_example_batch)\n", - " print(example_batch_predictions.shape, \"# (batch_size, sequence_length, vocab_size)\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uwv0gEkURfx1" - }, - "source": [ - "prediction() предсказывает логиты вероятности каждого символа на следующей позиции. При этом, если мы будем выбирать символ с максимальной вероятностью, то из раза в раз модель нам будет выдавать один и тот же текст. \n", - "Чтобы этого избежать, нужно выбирать очередной индекс из распределения\n", - "`tf.random.categorical` - чем выше значение на выходном слое полносвязной сети, тем вероятнее, что данный символ будет выбран в качестве очередного. Однако, это не обязательно будет символ с максимальной вероятностью.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "W6_G5P7W1TrE", - "outputId": "5fdc06a0-9be3-42b4-8054-454e997586ee" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example_batch_predictions[0][0]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0yJ5Je4J1XRb" - }, - "source": [ - "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAhsAAAHhCAYAAADHxnxfAAAgAElEQVR4nOydX2hbx77vv/tyHirSmqimcJHAuTKOCZgTavQgB7m1H4K5BiOaguGKPJzrpCV+SdKeE2xSTLJzAt17aYdi2hTc4yY+fXB1scAF1z0GI6hNEJHZx7gIxDW2UC7mWvtCsRWck8jnad0HdyazRjOz1tJfp5kPBFqvPzPrN7/5rVm/Gc0XpuZ3xf7+vjkyMmJub2/X7f4XLlww0+l0Xe4vKuvChQvm/v6+8LjsWQ3DMAOBQEPqydZHZJt6t8nvgbm5OTMQCJhzc3PNroqj9pqbmzOvX79uvnjxooE102heXf4LNBoXeL1exGIxLC4uolQq1b2s+fl5zM/Pw+v1lh2fnp7GlStX0NHRUXZsbGwM+XweoVCornUEgFKphE8++QTBYBDRaLQhZf7eGB4eRj6fx/DwcLOromRtbQ3t7e2Ix+O4ffs2PB5Ps6uk0bwS/ME0TbPZldBoNBqNRvP7RWc2NBqNRqPR1BU92NBoNBqNRlNX9GBDo9FoNBpNXdGDDY1Go9FoNHXllR5skF8BrK2tlf2tvb0d7e3tlmMajUaj0Wgazys92Pjqq6/g8/mEPzWMx+OufvqYy+Xw3nvv0UHKJ5984uinnfzg5r333kMul6PHyU/lRMcAIBaL0eOxWKyi+3744YcoFovC6/hnYcs7DoMx9jl4G6hQtVexWMSHH34ovC9fHm+HRCJh+Ttvd/a40zKrec56wdpP5Jcy+OdkfU9kW96/WPs5tY+qTSrtt82CtZ+bvsf360QiIbwn3yZO7ROLxSx2tYsjfJlsfexiVzOQxVkVdjaw69OsjVS25a+TxXb+nrwPHbcYU0azN/qolHQ6LdxU58WLF+b169er2sypmnvMzc2ZhmGYpmma29vb5gcffEA3B9re3jZHRkboBlXsuaZ5tBEV2dRobm7OssFROp2m5+7v75vXr1+n9zEMgx5zU3eZDZtFpZtf8c/s5j78uXybsPD2Ys9Vlcm314sXL8yJiYmmbfLFl+9mgyrDMCy+xfqpnT8ZhiE8bmcfVZvwz1Vt368nJB6k02nXG+OxdubjiqpNWGT2Ie3P+q/KluQYKYM/VxW7mgEfO53a3o0/8f1/e3vb7O3tFV7Lxmvy/8RebmK7Xcw5bhsJOspsqL6GY7EY/u3f/o2OttiRmOqYbMRNRm78iI3/eo/H44hGo3XZVOfw8BAHBwdobW11fe2TJ08QCAQAAHt7ezh37hzddMrv98Pv92Nvbw/FYhFLS0v48MMPARyNWNPpNFKpFEqlEtra2vDll18il8vR5yX39Xq9mJycpBtd9fX1oVAouP6i29nZgc/nOzYbE+3t7QGAa7tX017JZBJnzpwRbgzGwvtcqVRCKpVCOp22fHmIeOONNwAcbUIGAJlMBtlstqL61gKPx4O7d+/SZ+7u7sbBwQEODw9trw0EAjAMA8ViEcViEfF4HG1tbbbXFYtFFAoF4UZYtbJPNX7QCDo6OvDDDz+gs7PT9bXDw8N0wzO/349Tp07R/uK0TUT2yeVyWFpawtWrVx3XhfjJ+fPnARz5Uzgcxs7ODgAoY1czCIVCGBsbA3AUO3t6emhdawUfuzY2NnDt2rWyzHqpVMKzZ89o3AeO4veTJ09o/WSxPZPJAADOnj0LANja2sLKygo2NjZs63MccDTYILsx5vN5LC8vl+0e+e233+LBgwfI5/Po6elBMpm0PdbR0YFHjx7R+/p8PmQyGXi9XkSjUayurtJ7rK6uIhqN0gYgzl5Jp1VB0rXBYBCDg4O2LyACO3AqFAoYGhoCcNTQm5ub9GWUyWQwOzuLvb09izPkcjlcunQJH330EQ36oVAIMzMzGBkZQVdXF6LRqHR3xdXVVYTDYUsQj0ajwsEhm4aLx+P4+OOPK7JVLSGD2YGBAVy5ckW4W6gIVXs9ffoUAwMDyjQuP+AjTE1NlaUi2UBdLBZx8eJFDAwM4OTJk7QtZWV6PB5MTk4CALX77Oys4+esNxsbGzhz5oyj+gwPD2N8fBzBYBCRSASxWMwSUBcWFtDV1VWWct7a2gIAfPHFF2Wpdyf2EbUJodJ++6qyu7uLg4MDGv/s2kRmn1KphPv37+PKlSt4++23hWWJ4ggZHJI2JQMc8sJ0E7saTbFYxObmJrq7ux1fI4ulgDh2kQ+R58+f0/cC+Vj2eDx466236ACBDMZkH4tsbGc/DhOJBOLxOG7dukXtLqvPscFJ+oPoFpB/rFYFmwLiUR0j6Sz2vqJUEp9WMs2jFNXExIQwXatKffFlylK+fJrQDXxKmrXdxMQETQ+TKZWffvqJ2pOdZkmn02Zvby89t7e3V1gfuxQ4ex/RMZnuSDOoVHfFrr3m5uaEz2mXnif1mZubo2nJn376iaawValKtkxSP1KWYRjHxu5ufYB9rnQ6LdWf4Z+ZP5e1nRv7sG0iK/M46KuoqEZfSHSt2zYRTdXapd35OEJiEnkffP3115bpNCexq5Y4ie3V+ocqlrLtQspRTbmSuvb29pozMzPCOCR6l5BpFXK+LIY1UsPKKbaZjVwuh++//x7Ly8s0s3Hy5MmqBznT09Po6emhmY3R0VF6zOv1wufzIZlMYmtrCz6fryYjNKK1QcqcnJwUTiF4PB5Eo1HLiNEpJLVIsi9E8yGfz+Mf//Ef8ezZM7S2tqK1tRVPnz7F8vIy/Yrb29tDS0sL3njjDayuruLatWvo6OhAR0cH7t27R6dYCIlEAqlUCn/605+kUyGdnZ04ffq08NjZs2fR1dVFv8ybDclquU1x2rVXd3d3mc8Sv+azGnx9enp6ABx9zbW0tODbb7/Fw4cP0dHRoUxVsmWSL1GSRbp69Sra2trol2GzWFtbg2EYePDggaP+Rb7YxsfH4fV6EQqFYBiGJQtJIKl1lkgkQlPAxJ57e3uu7MO2iajMSvvtq0CxWMTly5cxPj5OMxdu24TYh2QjxsfHadZjZWUFAwMDwoWFfBxhM9Pz8/P4j//4Dzp14yR21Rq72F4qlXDz5k2Ew+GKsyyqWCqKXew0f1tbG81esHV99OgRTpw4UTbNJIrtbW1tmJqaQiAQoNNC7LS9XX2aje1gY29vD3/4wx9oQJ2fn8fTp0+rKrRUKqFQKFAj5XI5/Pjjj5ZzPvzwQ6RSKcTj8bIXQmtrK549e+ZojrnS+vHzjGQFMb86mIdME5FUI3v9nTt3EA6H4fV6adAkKTJSJjsdwgZN3mlisZjtQIPUp6WlBX6/v+yY27lxpzaoFNF8s5My7eaF5+fny6YJ5ufnLetpRORyOTx+/Bjd3d305dnT00PvI7qvrMxisUgHJ4eHh66CAJn6quUvLRKJhHKgoSqTrbvsxU7asq+vD8BRoD44OMDu7i6A8qkAp/Zh24Sn1usDyOp+9pcW9UZWZi6XQyQSsQw0WJy0CWsf/uW8vr6O/v5+LC8v0xcZiyqOJBIJFAoFOpDk6+D2hUemfWr1Szky7akaaDgpU2UDNnaRWMEvBeCnuoGj9o7H4/QjFZDH9s7OTvT391PfV/UFN+up3NigKuxSHyQlRNJTX3/9ddlq2UqmUUi6T5SGY8t1+4uTSlek81NF/PXkvnx6l00nBgIBS0qLt53snvw0kugYWy5fJnt//jrWfk6nkVQYhiFNJVYCkYInKUX+vjK7q9qLf04+zciv5mdh7yuqD1tf9r52ZbL+7lZKXWaDShFNYfJ1IufwPqLyIf4ZeX9nj/O2VdlH1SZ2/bYaSD+r1S8pRHaX2YH3D9bveNur2sSpffhpFFUc4e/L20cVu5wgs0Gl8DYQ1Ymcw9rHzgaq2MVfy9qI9XX+nqrYzh/ny7SLpXbU2u48x/anr3bza/X86atGznFb5/G64ObnqZra8KqsAfm9cRzXG7wO1Nvux3ZTr0wmg52dHUt6iSUUCsHn8+Grr75qcM1eT0ha3c0cv6Z6yC+dnEyZaWpHIpFAV1dXVXP8GvfEYjEEg0HpdJGmPjTC7n8wTdOsy50rJJfLYWRkBAAwMzOjnFMvlUr4/PPP8Q//8A+Wn3PdvHkTCwsLAI52EtVOq9FoNBpN8zh2gw2NRqPRaDS/L47tNIpGo9FoNJrfB3qwodFoNBqNpq7owYZGo9FoNJq6ogcbGo1Go9Fo6ooebDCw6raiLXtlkJ3XeGVbshsg/4/s0EZ2x+SFqZzUhxV/Y0W/VGXy5fFiWc2Ar68bu6vsI2sTwCpGpxKq46/l7cfaXXWs2uesByolZztY27L+I/I90X1jsViZfVT1YcsT2Za3vax/yQT5Goms37q9nvcfWV9wagNRm6jqquonqvoAVj+p127EouerpO856bd8m/C2Ya8VxWBZHBa1CXu9bGdbmY80nbrs3vEKkk6ny4RynGxuwm9yJdtsjNyX3aWPFULid7VU1Ye/DyvgpirzuG94ZicGxaKyj6pNeBvwZRqGUSZyJRKu4uvAwwsvsbvuvnjxggryHQdUPmt3LvucTu5DNiezE/2SlcGjEmYTlV2r3UArwU2/VV0/MTFheQ7+udidm534rKhNeB/lN5VT9RO72MXvQF3vNqk0tjvpt7I24VHtpi3yS1GbkLr//PPP0s3mnNanGejMxm+EQiGqCUB0S5zs6b+zs4NoNEo3uSJaDyLdlmQyiTNnztA9QYaHh+mGQX6/H6dOnaL6EKr68PfZ2NjAysqKULiKP/c4oxI241HZR9UmmUwGAKiOw9bWFlZWVqjkcyAQgGEYKBaLZfoCbW1t+PLLL5HL5Wx1OFiBJKKTMz09DcC9Jk29YaWrVZBnJgJTRAQsnU47+jrN5XJYWlrC1atXa1If4Kj9enp6HG28JROtahRu+q2I6elpDA4OWjRIisUilpaWqH5UsVhEOp2mwmd2PitrE4/Hg7t379K6dnd34+DggMY1VT9R9U2v14vJyUnaN/v6+qTy6rWi0tjupN+K2oQnl8thc3NTuEEl337kfFGbED0b1b5RTurTLPRgQ0CxWMTm5qZQ4Ianra3NomiYTCaxvr5epqQqcioWXphKVR82aMZiMTx58gSjo6NlHUhWZjQarSh9Xi9IinNgYABXrlxxvTspbx9Vm7AvskQigXg8jlu3blHhqOHhYYyPjyMYDCISiSAWi9HOHQqFMDMzg5GREXR1dSEajVpecmzauVAoYGhoCMBR4J6cnAQAtLe3Ix6PU6XfZsGmeuPxOFVcVXF4eIiDgwO0trZScauBgQGcPHmS+vvCwgK6urrKUsOlUgn379/HlStX8Pbbb7uuz9TUlDCVvbq6ijfffJNeq5oKYNukGTjttyLW1taE9WcH6LlcDpcuXcJHH31EBwYqn7VrE5aNjQ2LsKCqn7DYxVKZQFm9cBPb7fqtrE145ufnMTg4KOzv/ADUTZvwOK1Ps3itBhv8XJponoyosw4ODjrKBpBt00mA/fXXXxEMBsu+WlUZhmKxiLGxMeGLVlaf58+f45NPPrHIDfPwZZLOQ5Qe4/E4bty4Ufd5bH7ek59rHBsbo8qThmG4GgCJ7OOkTUiwn5ycxIkTJ+jfiRrq+vo67t27h4GBAVqftbU1jIyMYGZmBsvLy7hx44blWVjZ7XA4jJs3b6JUKtF5VgDI5/Pw+Xy4ePFi3eeq+bUOrF1Z1c/x8XFcvnzZcX22trZw6dIlxGIxnDt3jv49FApR38pmswBA5QQWFxcRDoelX2Wq+gwPD1vUSdPptMXuyWQSDx48QD6fx71793D//n3ar2VtUi/4OXl+TYKTfstDMge3b9+WvpQfP36MsbExPHz40PLBovJZuzZh78EPAFX9hLWFKpYStdhGvRzdxnZVv3XSJoD7rIbTNhHdy0l9mkqz53GOE7UQXtre3jYnJibKlPxkKqOqOURZfXiFQtFaDFWZbNlO10g0Cn6OWYXT9mLbRKRsSOZTRXZk51P5eVe79Tlkvpefmz9ua2ecriERqc+q1h0Q28lUZkWKoU7qw7YD3yZ265fYOfhG46Tfqq7j//X29pr//u//XqbOy/qlzGcLhYKjNhEJL9r1E/Yc1TqFRgoLVhLbVf1W1Sa8cq5KFV2lHC1rE9F9ndSn2bxWmQ0VJC0sE14io1zV6mmSoRgaGrKMLufn53Hu3Lmy0XQul0MkEhGK36jq093djf7+fvoFQ0Tr2C8aWZksyWQSLS0t8Pv90nPc2qAa+LlfVZl27cWex7ZJZ2cn+vv7aRo1l8vh8ePHlrQqm9Ym0yui/1elv5PJJICX877FYpGmvA8PDx2lztln+PDDD+v2yyHRXLSoTI/Hg3A4jJ6eHpqBm5+ft6TX2evj8Tj6+vosWQuSnejv78fy8rLw6161poVvr76+PsuUGZ/uZ+HbxA6SkZOt+neLk34rKpPN7OTzeRiGgdHRUTx69AjBYBA9PT10KoKsy2CnJkQ+e/LkSds2IdkLmfCirJ/Y9c1YLKYUFiQZuVpN8TqJFbIyZf1W1SYk5qrERIkfs1kNt/2ExUl97Ki13cto9mjnuCAaGbIjetFXnWlaR6OiUaQqw2AYRlmZZLRvVx/yhc7/XVUmeQa+LDcYhlHT0TJrA9F9ZXZX2cdJm/T29gqP818XrI14+/Ff+OSeoi92tr34zIodMhtUiuoZ+XNEx9g247+C2WeUfbHzGTW7+rBtLWpPtj7stXZtYge5vpar+lX9lj2u8g9ZFkHkWyqfZbFrE/7eqjZT9U2+TUS+4sQGbrCLpew5vM867bduMztOfoXDtwnflqp+Vskvr2ptdx4txKZxxdrampaZbwKJRELLzDcYoiCtZeYbS7FYxOXLl7XMfIOpt93/ruZ31PwuIY4IwP1A41//Ffg//6dONfud8t/+G/A//gdy//f/YmRkBMFgUA80GkgikcD4+DgMw9ADjQYSi8UwNTWFeDyuBxoNpBF215kNTf04PAS6u4HNzWbX5NXk5Engf/9v4L/+12bXRKPRaKpCLxDV1I9//Vc90KiGp0+PbKjRaDSvOHqwoakf/+//NbsGrz6CnWg1Go3mVUOv2dA0jv5+oK+v2bU43qyuAisrza6FRqPR1BQ92NA0jr4+4I9/bHYtjjd//KMebGg0mt8dehpFo9FoNBpNXdGDDQZWS8Lpbo28/gQrBsVrgsjEz4hYFCswBbzcTZDfPVNVpqhubHmsdkOtdkasBl5Lwm2dZLtrsjbi7UeE30TtoaoPK+ol8xG2XLY9VWU2g2r8gLcReRb+7ypRNP4Y31f4vkDsKrK5rJ/w14ru22hYfSa3PiCzAa/5xNqBbxPR86vsw7aLqlxR/Kn3zrduqCS2i64nbcbbnLcfH6NZ+4jeC6wvqPoJXy57XTVlNoS6bBX2CsLrXFSyA5tp2msz8Fok5G8TExOW8oh2wM8//2y7c6SozHQ6bY6MjJgjIyN0hzmy69/PP/9ctQaMI27fNk3g5b/bt8tOYbVQnOi5sJAd9QzDKNOG4Hdble2WKmp3J/UR6UPw9ZChqo8Tm1VLNX5ArhVdw+vapNNp6tO877M+y2uW8NooZLdFkZ6GXT9x2iaNgPgT8U832jgqGxiGUaZTwmrHsHGF10pR2Wdubk4ae8hOoLJnkPXNZlBtbBfFUhGsbVVl2GkqyfoJH3P4cysts1HozAZA9QSi0SjVF0ilUkin0641QNzICQPA9PQ0BgcHcfbsWcu5w8PDmJycdLSJE19msVjEN998g08//RQtLS30PLL3/nHaLGd4eJhumuT3+3Hq1CmqRWDH4uIifD4f+rhFpzs7O4hGo9QeRHviUPDLDlZy3k19WLl14MjmhULBkeoiX2ajqcYPtra20NPTI9zoqq2tDV9++SVyuRztU0RSnff9jY0NrKysYGtri2qVTE9PAyjXRhkbG5NqQ6j6iZs2aQQdHR344YcfLFooTlHZIBAIwDAMixppW1sbSqUSnj17ZtHf6OvrozomKvuUSiVkMhnEYjFhLNvY2MC1a9ekPiTrm42m2tgui6U8KnVXN6j6SSaTAQD6rtja2sLKygo2NjaqKrNR6MEGrC8OItozMDCAkydPOn7xAe7lhNfW1qqWWBaVSQYwTgV4jgu7u7s4ODhwFIxzuRyWlpYssteEtrY2izhXMpnE+vo6bUs2FclLZ9vVh6Qqg8GgxcZbW1sAgC+++EI4BeO0zOPO6uoq3nzzTWH6PBQKYWZmBiMjI+jq6kI0GqWDkidPntCBRywWw5MnTzA6OoqdnR14PB5MTk4CALXP7Oxs1dvh27XJ74Xh4WGMj48jGAwiEokgFoshFArB4/Hgrbfeoi8j8uItFAoolUpK++zu7mJ3dxfLy8tlUwTkhf38+XOa7menblR9s9FUG9udxlLRR+bU1JR0emphYQFdXV1l0zqqfsJ+pCQSCcTjcdy6dcsigldJmY1CDzYYtra2cOnSJcRiMZw7d8719W6yGuQLpNqvLr5MMg/3qm2xTNRZr1y5YvuSKZVK+O677/DZZ58Jzw2FQvD5fLRj/frrrwgGg/RLmVVXHB8fx+XLl8u+cmT1IeqK2WwWqVTK8vJaWFjA0NAQVWxcWlqiL2InZdYafg63VnO0yWQSDx48QD6fx71793D//n2USiWsra1hZGQEMzMzWF5exo0bNyz2ef78OT755BMEAgHLVzpZVwAA+XwePp8PFy9erIl9VG3ye4Gos66vr+PevXsYGBigbf3xxx8jHo+jvb0dAwMDePfddy1ZNZV9tre38c4771B/LxQKFh/65ZdfsLy8jHw+j2g0iunpadu+WS/4tQz8y7SS2O40loo++FgV1vX1daTTadoXQqEQPZbNZgEAX331Fb1W1k8IZBAyOTmJEydO1KTMRqAHGziSnG5pacG3336Lhw8foqOjg456RTLXItbW1pBOp6Vywt9//70lq5FMJrGwsIBgMIj29naMj49jampKuNDKaZnky4WMbru6urCwsIBoNHosFmnJcCsAlMlkMDs7i4GBAbS3tyMajdJROwkQY2NjtHMNDAzgrbfeEkqLnz17Fl1dXZavHCf18Xg8iEajlq+KSCRCU5zEp0RfT6Iy6wEvO12r6TN2iqq1tRUHBwc4PDzE6uoqrl27ho6ODnR0dODevXs0wxQIBPDP//zPNNtRKpVQKBTQ1tZGM0jkS/jq1atoa2ujX97V4LRNXlVIlmF8fBxerxehUAiGYWB1dRWAdZD76NEjnDhxgn45A2r7nD59msYWj8cDn89nkZUnUxPAUTaxUCjgr3/9q23frAe8PDuZWqs0tjuNpaVSCffv35d+ZJK69fT0CI95PB6Ew2H6/6p+0tbWhqmpKcsghM2EVFpmo9CDDbw0fk9PD3WY+fl5nDlzhv4/+foSrXhn5wVFDjc/P49z585ZUnH8i8AwDIyOjuLRo0eOpj9EZZJ0NDuCjUQiiMfjjtd/2JUps0Gl5HI5RCIR6YtdVCY7Ss/n84jH44hEIshms2X3IBmKoaEh4fPz6wPs6sPWi12T0NnZiYODA+zu7gJQTwnxZdrRjFX9sjL7+vosU1QbGxuWfsIOvtgXU3d3N/r7+6k9MpkMdnZ26P8Xi0X6Ejg8PLRcWylu2kQEWcHfyKmXSstk7cW2AX/veDxOBxAq+/j9frS0tNABX7FYxObmJrq7u2m8JAMa4Gh6LRwO4/3333fcN2WIfkVXKU5iu6hMp7GU+LFqrUYul8Pjx4/R3d1ddoxkuMnaFlU/6ezsRH9/P72P6r5uypTZoOY0bWnqMcQwDDMQCJiBQKBsVS9ZCSxanc3/+oHF6S8s+JXE6XSa1oX8U/3iQgS/epn8P39ft6vie3t7nf1ixMEvK1ibi55TZXcCv9Ka/GIiEAiU1ZU9xpdlV5+5uTml3dg2Y8u1K9POZk5s4AYnfkDqLKorayNRW5FjfH1Z+6iOBQIByy8meLuz/dNJPxG1iRPILy4q+VWaCN4PRHUi9eV/7aOygcq/2OcXtaXKPuT5Rf7Bt3UtfwUhs0E1qGK7ab60r92va0Q2kP0yy66NncQRvp+wbcLft5oy2XPq9StFPdjQuMLJIIfSgJ9x/u6Q2Ez0k0dNfVG9TDT1gwye3HwEaaqn3nbX0ygaR5C0umEYePDgQUMXf73O7O/v47333kMqlcKf/vSnY/ETzteBRCKBrq4uhMPhV26x9atMLBZDMBh0vH5LUxsaYfc/mKZp1uXOGs0f/wjcufPy/2/f1toodmibaTSa3yFaiE3TOFZX9YvTDmbRnUaj0fxe0IMNTeNYWdGKphqNRvMaotdsaOqHYF8LjUaj0bx+6MGGpn78z/+pBxzV8t//e7NroNFoNFWjF4hq6svmJvC//leza/Fq0tOjBxsajeZ3gR5saDQajUajqSt6GoUhFotJFfNUkC2GefVD/pjovmSLWNFW1GSrbl5IixcdEt2XLfc466IQcrkc3nvvPcd2J+fLnpEVIVO1Ca9Fw9uc3zbazkfYct34UKNhn9Pt1tjsM/I6EaztZL4uahP+HHbLZLs2kdWHP9Zs1Vf+OdzoIDm1gaivE38X2ZyNJazN+bjFxyBVXBNdW09dFDvsYoXTa/n24n2LPW4X9wG5JIDKZ530W7extFHowcZvkM7AKuY56SBkn/n19XXk83n09PRgenpaeCybzeLZs2fUIYl6n2EYwvtevHgR4XBYKKR18uRJqriYz+ct6oCsCiQrSnRcKRaL+Pzzz9Hf3+/4mo6ODjx69MiiYpjJZAC81IAgzz8+Po47d+6gVCohl8vhz3/+M7XdzMwMPv/8cxqEFxcXqc2Xl5fx/fffWwIIIPeRWCyGVCqFbDZb1ibHCeJbH3zwASKRiKtr19bWLM8YDoepeuRXX30Fn89HfRIADYiqNmHvvbS0VOYHdm0iq08ul6M6NKTNUqlU3dV2ZfB6G9euXcP8/Lyja1U2UMURIkX+8OHDsmO5XA6XLl3C+Pg43n33XcsxXn9ofX0d/f39VM+HPb6+vo7NzU3Li5joodRaBLASVLFCBVGwnV/m7DAAACAASURBVJmZoe1FFI4Jo6Oj9BlZXSs7+5RKJdy5c6dMLE3ls076bSWxtFHowcZvhEIh+nIginlOxKC8Xi8mJyfpjpp9fX0oFAoolUpUZZQMPngBrrGxMekLaWtrCz09Pa53LyyVSshkMojFYq/MLp/T09MYHByk6pNuOTw8xMHBAbXrzs6ORaCOiBodHh5ib2/PIorn9/vh9/upCNjw8DC1ud/vx6lTp+gxlY8Ui0UUCgXcvn37WA/sgJcKmW5fAKz4n8fjoYqj6XQaf/vb3/Ds2TOLsnFfXx8VBVO1CXBkv2+++QaffvopWlpaLOXK2kRVn2KxiNbWVmSzWSwuLgI4UloGIFT/bQYyxU4RKr9UxZHh4WHpx0ZHRwd++OEHR8J0yWQSZ86cEYpEulXIbiZ8rFDh8Xhw9+5d+szd3d1U4dgNIvssLi7C5/OViaGpfNZJv602ltYTPdgQwCocuoWoH3o8HvolAwDt7e2Ix+OYnZ11NAhYXV3Fm2++SVOcfArv6dOnVMaZPba7u4vd3V0sLy+/Eun8tbU1FAoFDA0Nub6WpBuDwSAGBwdpUGhra7MokyaTSayvr2Nvbw+tra3Y3NykX7dErl4kO65SCeV9hKhjfvHFF8ciZV8P2EBNvrIGBgZw8uRJPH/+HG+99RY2NjYAvByYkIG3qk2Al0HSTvGYbRNVffb29uD1ejE7O4tUKoX29nY8efKk6Vk+Ni1fqd+7Va+tlmKxiKWlJctAEng5pTgwMIArV65Y4hqRlT8u07iyWOEGXuEYAJWgF8VZmX1yuRyWlpbw8ccfl5VRjc9WE0sbwWs12ODXOsjWSdy5c6cih0wkEpbGJvNrwFHq3efz4eLFi47TuMlkEg8ePEA+n8e9e/doCo+McNl07NjYGL3v9vY23nnnHZo2LBQKTZ0z5ecvyUuYTDNVmg0YHh6mz5hKpeh9Q6EQfD4fDXa//vorgsEgWltb0dHRgcHBQQSDQbS3t2NxcREXL14s+9Ih0vR8EAXkPrKwsIChoSGa/lxaWnI8J/8qsbW1hUuXLiEWi+HcuXP07x9//DHi8TgNsO+++y58Ph88Ho+yTYhv2mXxZG0iqw87FUn6Qb1ffPzaCn6dBJvSD4fDuHnzpqv6qPyyXsiyGmNjY9TXDcOg7chOIZBpCzK1VS/sYrssVjiFTAOyAwRyT3Zalb2vyD5kauazzz4Ttl+lPlttLG0Er9UOouQlLaNUKuHmzZsViS8lEokysSzyBXL79m0AwNWrV1EoFLC1teUohc2mnVtbW2kKj3em7u5uLC0t0f8/ffo0zp8/D+AoFejz+bCzs9O0eVMSfHiSySQWFhawsLBg+fuPP/6ImZkZx4M9j8eDaDSKVWarbza1nMvl8Le//Y2mz9mUdLFYxJ07dyyDjWKxiMuXLwtFiVQ+EolEaPryjTfeQEtLC/b29ir6ijqOkGf69ttv8fDhQ3i9XjqYam1tLetfiUTCMk0gahPTNBGPx7GwsICpqSl67sLCAiKRCO1Pojaxq8/W1hba2towNDQEj8eD27dv45/+6Z+wu7tbtzYh2UyS0VRx/vx5pFIpYZ8WofLLepHL5fD999/jL3/5i/Qcr9eLaDQqjDEejwfhcJhOp9ULu9jO1oePFXasra3ZClCSaVXZMWIfAJidncXs7KzlnIWFBcTjcQCoyGdrFUvryWuV2VDBjihFAw3yxSJa0U0WBopUOYvFIk0VHx4eOloHAhzNd7NpZ1EKjzA/P0+P+f1+tLS00LR+NVNCPCobVAL7ZZDP52EYBkZHRy0LrZyUSVL2ovlv8iVIOi9/3Z07dxAOhy0pzkgkIgzoKh/p7OzEwcEBdnd3AdQ21S1btV5PRGWSF0dPTw+1F+t7LORLkAx6+XuTNnn77bctiyaz2SwikQji8ThNH8vaxEl9dnZ26Bz73t6eK78lGbl6TYeJ1pDIylT5ZT2Zn5+3rHESQb6q29rapMf4tQkqRL9IqhWyWCErkyy2t1O6zuVyePz4sTDOsvbhF97G43G6mJa0ayU+6ySW2lFPuwMA6iJc/woyNzdnBgIBy78LFy6Y+/v7pmma5osXL8zr169b/maaprm9vW329vaWXZtOp03TNM10Om35+9zcnLJMwzDoccMw6N+vX79uvnjxwjRN09zf3zcvXLggvEZUJ1KXWmAYhtnb22tub2/X7J6Eubm5smeR2Z23HfuMrH34upL7yWzD2py3vZ2PsG1dSxvJbFDt/WQ+a5ovbcj6HYG1Edte7PPz16naRFQ3ti6qNlHVxzTVfmIH6Uf8PSuF75ei+xIbsnHCNN37Jbk3H3/Y6/g4wrfN9va2+cEHHwjbiq0P3558mW7jj8wGleLEB8g5sjgiiuHsfXkbqOzDPyvfV2T1ddJv2Xu49dta251Hb+qlcYWTlKKm9oim6TT1pZppVU3lNGO6SFN/u+tpFI0jSFpdDzQaC/n1gh5oNJZEIoGuri490GgwsVgMwWBQDzQaTCPsrjMbGo1Go9Fo6orObGg0Go1Go6krerCh0Wg0Go2mrujBhkaj0Wg0mrqiBxsajUaj0Wjqih5saDQajUajqSt6sMFAhHPcipex2h/8TpesVgK/KyCvGcKXyessiHa3E13L3pcXcLMrs9FUUx9Ze7F24e3A21Sko8DqLPD2Y4+LdvSUtQkrwCW6Z6Nhbed210DSZryvs8+oEuCKxWJlNqi0n/DtKbKtrE0ajZO6qnDq7/x9ncQ1t21CIG0uEyFrts1rVR/R7pq8HgvfH8g1bD8QxR/2HDsfcdImxxU92PgN4kSsqI6TAEy2ol1fX0c+n0dPTw+VlCfbW3/wwQeIRCJl17Jb166vr2Nzc5M6Frs1NjmH/f0z2SI9m80in89bNCf+/Oc/Y3l5Gfl8HjMzM/j8889RLBbL6prNZvHs2bOmvvhUNlChai9+697l5WWcPn3aon8Sj8fpcVZVkd0WOp/Pl233S7Y4F+kgyNqEiC/NzMxQ4TwiqtcsiEgU2TI5Ho87qk8ikUA8HsfDhw/LjrEiY0SAK5PJlF1fKBRw+vRp+rdq+sni4qKlj9y7d8+ikSFrk2ZAdFNYAUUneh4A6IuFXMv+DQBGR0fpMdZnncS1StqEnPP555+jv7/f8vdKY2m9qLY+a2trWFpaKnvO6elpGify+Tyi0Sjdgj4Wi+HJkycwDMNyDe8DZGtxIlio8hEnbXKc0YON3wiFQjQQEVEdJzomXq8Xk5OTdJOrvr4+KqtNxIGcbJJC9FPIC3Fraws9PT3CDYWKxSIKhYJQ4W9vb8+iZeD3++H3+7G3t0c1GMhgKJPJIJvNlimeNgveBirctNf8/DwGBwcdbUS2sbGBa9euSdtscXERPp+vTOtB1SYejwd3796lbdLd3U1F9Y4DOzs7NNjZMTw87EjympV/JxBp7atXr1rOraaftLW14csvv6RZK1b3QtUmx4EnT54I9Xx4eIn3YrGIdDpt0U6SYddPqmmT6elpDA4OUvFBp2U2mmrqUywW8c033+DTTz9FS0uL5VggEIBhGJYPOaIPw4oO2t2fbVse1kfc9JPjiB5sCKhGvGx1dRXhcNhxcCPpvYGBAYts9OrqKt58801hOp+IrH3xxRdlsu2tra3Y3Nyk6bxMJoPZ2Vns7e3RUTMAtLe3Ix6PY3Z2tum7gcps4BRVe+VyOWxubpYJgkWj0bIphFKphFQqhefPn9PpADY1SgIzKzNNULUJj0pUr1GwKWBeOrsaSOo4GAxicHDQIqh3//59XLlyBW+//bbr+8p8JBQKYWZmBiMjI+jq6kI0GqUDdDdt0ijYqaZCoYChoSHba9gBVi6Xw6VLl/DRRx9ZBqxTU1O20wR8P6mmTdbW1hzVv5ZCkLXAbX3IgEokZjY8PIzx8XEEg0FEIhHEYjHXA4FkMokzZ85Y7l+Jj7wKvFaDDX6OTTSnTNLkMgdTQdKRbpyDpLPX19dhGIYlvZdMJvHgwQOaHmZT7wsLCxgaGqLXLi0tIZfLoaOjA4ODgwgGg2hvb8fi4iIuXryI1tZWOt8HHKUUfT4fLl68WBMFVxX8nDsf8FU2sMOuvfisBp+mjMfjuHHjhmXq5pdffqHTUNFoFNPT03Qq5LPPPpMOEmRtwtuili93FfxcPmtX8pWUz+cxPj6Oy5cv11TJN5vNIpVK0bYm0x2VfpXJfGRtbQ0jIyOYmZnB8vIybty4YfEvJ21SS/g5d34en51qCofDuHnzpuPptMePH2NsbAwPHz60qAmz04ZkmoDvY6J+UmmbkK94u4xRNbHULfWI7ey0rAiiCLu+vo579+5hYGDAVeySZTWq8ZHjzN81uwKNhARYGdUIL1UrlOX1ehGNRrGzs0M7fzQapS+21tZWy5dMJBKh6cs33ngDLS0t2NvbQ0dHB4aHh2n9i8Ui7ty5g9bWVip7fvv2bQDA1atXUSgUsLW1VdfUHJlzt0NkAxV27bW2toZ0Oq18sXd2dlrmqoEju5M2bGtrQyqVwl//+lfMzs5idnbWcu7CwgLi8TgAdZuQ+jRSW4b1AxVnz55FV1cX9vb2alYvj8eDaDSK1dVV+nL65ZdfMD4+Ts9ZWVnB6Oioq3UUvI+srq7i2rVr1Mb37t1DPB6nA367Nqk1ZDBLMogqzp8/j1QqhcPDQ2XMaG1txdOnT7G8vIzZ2Vl4PB5sbW2hpaXFIk8PvJwmYBH1k2raJJlMYmFhAQsLC5a///jjj5iZmUFHR0fDRexqHdvJlNzCwgKmpqbo3xcWFhCJRHDr1i2kUimMj4/D6/UiFArBMAysrq46jqXT09NlWQ0epz7yKvBaZTZUsAsyRc5Ivlj4LxXg5SK0aoSy+Dm/vr4+y5wsm3rv7OzEwcEBdnd3AYAOItivHVLnO3fuIBwO05dIsVikadnDw0NXc6kqG9QC3gaqMp20VzwetwzYRCSTSbS0tMDv98Pj8SAcDmN1dZUeJ9Ni77//vmVRVzweRyQSQTabRSgUsm0T8hVUyUBD9euXWiBau1Ntmez6CTaLQr6++/v7sby87HrBpshHnjx5Qv+b9Wen/UQGycjVa+qFLCZkBwyiMskAgkzPEtuKpmtzuRweP35Mpwlk/aSaNuEXYBuGgdHRUbow1a5v2iH65Uc1OKkPXyafAc1ms4hEIojH45Y1S6y/sX5oB2kn2VoNgshH6kWt7V5GXYTrX0Hm5ubMQCBg+XfhwgVzf3/fNE3TfPHihXn9+nXL30zTNLe3t83e3t6ya9PpNL1GdMw0TdMwDPq33t5ec3t721In9vj169fNFy9e0GPpdFp4LV8mKUt0XSAQMOfm5lzZyTAMYV0rxc4GMrvbtVc6nS67hr2fzK78ccMwhPVOp9OO22R/f9+8cOFCWX2d2l5mg0rh68M/B3uO6hn56/k24X2PvffIyIjUZ930E/5a3kayNnEC6dsyH3ALHytE9yX15X2Df072OGt3/hnt+gnBbZuwzM3NWZ7FaZkyZDaoFCf1IefIfJbYgz2u6keiMlkbGYYhbH+Vj7hpk0qotd15tOqrxhWNngrQHFHtNJ3GPY2eCtAcUSwWcfnyZS0z32DqbXc9jaJxBEmr64FGYyEr0/VAo7EkEgl0dXXpgUaDicViCAaDeqDRYBphd53Z0Gg0Go1GU1d0ZkOj0Wg0Gk1d0YMNjUaj0Wg0dUUPNjQajUaj0dQVPdjQaDQajUZTV/RgQ6PRaDQaTV3Rg43fYMVvZHvri+C1EFjBNP6Y6L5EYIoXULKrD6s3ItvRU7YjHHttvXaldAqvmyITkVJdyz8/rwnCtwuvo0Dsw/9dVCe+TVnb8uWSXSD5ZxRd22hYv3NTF9GzyGzg1Ged2EfWT+yOOeknjUIVK5wge07eb0XPKdsRVuazovvyPlJpmzSaSmM7oO4nfHuytuPtyre1LI7YxS5VvHTiB02lLluFveKIdotzCrubnt19+J33DMMQ7t7G32d/f9+8fv063QFPtBtdOp02R0ZGzJGREUv5c3NzNduJstbwuxiqmJubM69fv27+/PPPts+zvb1tjoyMmPv7+0Jbqspk24TsGChqo+3tbXNiYoLuIMi3UaXP2QhEu6FWci5/jPVvJz5L4O2j6ifpdNpSxoULF1z1k2bCP5ebc1kbGIZR1sdZHyU+bxiGpX1UPmvXTyptk2ZTTWwX+Td55u3tbfODDz6Q2odFFUd42NjF+/OLFy/MiYkJWqadHzQbndkQcHh4iIODA4tWhFOePHmCQCBgex6v+FcsFpFOpy16KLL6eL1eTE5O0o21+vr6UCgU6HXFYhHffPMNPv30U7S0tND7lEolZDIZxGKxY7kpFyulbcfw8LBFo0AFq/yayWQAgIpzbW1tYWVlBRsbG2XX8fL0W1tb6OnpEW7y1Nraimw2i8XFRQBqTQORrHQz2dnZgc/nq2rDMFaLhuh3pFIppNNpFItFW59lYe1j109CoRDV8iAaIkSvwk2ZzaBWsSIQCMAwDBSLRaF2zOLiInw+H/r6+iz3Vfmsqp9U0ybNpprYzvcTVujQ7/fj1KlTNIapUMURHjZ2kVgyPT0NoFzTyM4Pmo0ebDCQFFYwGHQli8ym6UQS89FotCxdxr5Yc7kcLl26hI8++sii7Oq0PkQsjHSC6elp4fm7u7vY3d3F8vLysUlxAi9TlQMDA7hy5UpNB0L8gIENGIlEAvF4HLdu3RKKKPHy9Kurq3jzzTdpqpJNb3q9XszOziKVSqG9vR1PnjwRDoZkstKNhk25upW8X1hYQFdXlyUlzQZxInw1MDCAkydPCgMw77NsvVj7OOkn7LWbm5tUhMxpmY3ELlaIsLPB8PAwxsfHEQwGEYlEEIvF6C6QuVwOS0tLwvZV+ayqn9SyTRpFpbHdaT8RifxNTU0J46wqjrDwsYuIwwGg9ZmdnaXxSeUHx4HXarDBz2nxc3dEzTCbzSKVSjlWe+zo6MCjR4+Qz+cRDodx8+ZNlEqlMuXAeDyOGzduWBzr8ePHGBsbw8OHD8vUKJ3UJ5FIWIIWGczIRs3b29t455136H0LhULd1w7w84z8c4yNjVHlScMwaloffsBAiMViNLieOHGi7Dq+oxOSySQePHiAfD6Pe/fu4f79+yiVShZlSWJX0dxwI7Ma/Pwva1dW9XN8fByXL192NL8bCoUsSpgA8NVXX9HjW1tbuHTpEmKxGM6dOyetl+xFK7OPqp8ALxWOZS8SVZm1hJ+L5+fNZbHCCTIbEEXh9fV13Lt3DwMDA1hbW0OpVMJ3332Hzz77TDiAd+Kzqn5SbZvUknrFdif9pFgsYmxszPKhxCrjrq+vI51OW8qUxREWPnYR3wKAfD4Pn8+Hixcv0vrI/OC48FoNNnhZZVka3uPxIBqNupIMJpCXEz/CB44kr0+fPg3g6Kvg6dOnWF5epqPTvb09tLS0lKXeZfXhxblIKpuMqLu6urCwsIBoNIpPPvkE//mf/4nTp09bRso+n6/uKU72BZXP56UDIa/Xi2g0WrP6rK2tIZ1OWwYMbW1tmJqaQiAQoKlePp1dKpVw//594SCFlaxvbW2lX3NbW1toa2vD0NAQPB4Pbt++bZE3B44GMN9//33Dshq8FLjsK+fs2bPo6upylAJm8Xg8CIfDAI5S7y0tLfj222/x8OFDdHR0CKfFVIJyIvs46Sd2gmmNFLHjPzDm5+elmTpVrGBR2cA0TaRSKYyPj8Pr9SIUCsEwDKyuriKTyWB2dhYDAwNob29HNBqlWam1tTWlz6r6SS3apNY0IraL+okT8TIylcQiiyMEUewi2ROSXbl69Sra2tqwtbVFpy1FfnBceK0GG04hL23+BfTJJ5/YrvC1m6tvaWmB3++nDkjSuqRMUZpXVJ9YLFYWQPlAl81mEYlEEI/HMTk5ifb2drS0tGBrawuA+xSnUxtUimiesdIy2TUEbLDv7OxEf38/feZcLofHjx9bbJDJZLCzs1OW1ejr67OsqdnY2MCZM2fo/Xd2dmjA2NvbK6vv/Pw8zp075/orT/ZLglrBz/06LZO0V19fHx149PT0UHvMz89b7CPyWRaRfez6Cft1Lnqp2ZWpgmTknH4Fu0UUK0RlOokV7ACdvEj5QX48HkckEkE2m6UvRpnPqvpJtW1ih+xXdLVAFEudlsn3k1wuh0gkYitexscYuzgii13AUZ8jg53Dw8OyDzORHzilnnYHoH+NQpibmzMDgQD9x69WJquY+V8+bG9vm729vfQ6dgUyuYYc41fx88fZlcOq+vBl2tVZda3bVdmGYZi9vb01+yWFYRi0LqL7yuyeTqfLnp+1bzqdlv5KhbUBXyYpT7aKm60v3552bcauVneDzAaVQlbDy56DPYc/xtud9x/WPmxfsPNZlX3c9JNAIEDt5LSfyCDX1+oXLKpYQSD25f1PZQMn7Unu7dZn7fqJ2zZxgswGlWIX29lz2GN2dmV9nT+HLVMU11RxRBW7+P5XiR/IqLXdebTqq8YVa2trWma+CTRyKkBzRKOnAjRHOJma0NSeettdT6NoHEHS6nqg0VjIrxf0QKOxJBIJdHV16YFGg4nFYggGg3qg0WAaYXed2dBoNBqNRlNXdGZDo9FoNBpNXdGDDY1Go9FoNHVFDzY0Go1Go9HUFT3Y0Gg0Go1GU1f0YEOj0Wg0Gk1d0YON32AFkkR76zshFosJRXVYbRD2vrxmCC+KRo6Lds9ktQD4Hd/Y+8pEfo4LdjYQwetPyNqM1QZh70uE30SaIU7qY7e7Ju8HovrWazdQp6hs4ASZDdj78rZT+SxrI37HTl7jhfdpVT9h69XsvsD7gdv6EDvwNlf5LF+mLMbI7CeLXfx9+TZT+UGjqSa285oron5C7s8/p6y9ALnP8uWp2pO3eSWxtJHowcZvsAJJRGCKyCw7gQg9Ee0T9u9EHIffs5/dTnh9fR2bm5s0+BClxYcPH5aVRZQWx8fH8e6775Yd+/Of/4zl5WXk83nMzMzg888/r8v24rVAZQMZ/Lbs+XwehmFY5J/JNtXZbBb5fJ7qOwAvhd/IFs7xeJwGA7v6EHEpXuuAIPMDAIjH47baDY1CZQM7ZDYggZgVnyJ/U/ks2d76gw8+QCQSEZY5OjpK6/vo0SO6pbmqnxBUbdJIeL+9du0a5ufnHV1LBNEMwyg7pvLZr776Cj6fjx4HQF9SZLt5Ept6enqofDk5Txa7FhcXEQ6Hkc/nsby8jO+//56WqfKDZlBpbCd+Pj4+Tp/lm2++scSDYrGIzz//HP39/ZZrVe1l57MnT56k8ZuNXXb9pJJY2kj0YEMAK5ftBCLjfPXqVcvfS6USMpkMYrGY7SZYvGjV8PCw9IXU0dGBH374Qai0uLe3Z9GX8Pv98Pv9rkW2moFIuMsJvCx5sVhEoVDA7du3bV/orJS2k/osLi7C5/Ohr6+v7HyZHxx3VDYQIbNBKBSigZHoZxCtBpXPEhGtSjYTUvUT4Hi3CS8AqGJsbMwyYJbB+mypVMKzZ88swnZ9fX1UM8Pr9WJycpLGpr6+PhQKBZRKJdvYNTw8TDc78/v9OHXqFC1b5QfNxk1sJwOSs2fPAjhSNF5ZWcHGxgY9Z3p6GoODg/Qcgqq97HxWhpt+UmksrSd6sMFA0l7BYNCxLDJRCL1y5Qrefvtty7Hd3V3s7u5ieXlZmtoi6caBgQGLRHGltLa2YnNzk2YyiPLjcR5sVGsDXpacCM198cUXwjQvm6qMx+NURdGuPuTFxZ8PqP2AEI1GK562qDV2NpChsgF/fzcif3YQJWM36WEnbdJo2JR+LSXvRT7r8Xjw1ltv0ZcjEfgiAwqe1dVVKqbmJHYRiBqpaCBZaz+olEpiOzsIJ9mIW7du0cHa2tpaTduQ8PTpU6rU63aqrdbvk1ryWg02+Pkwfi6NSHJns1mkUilHao8knSgbbW5vb+Odd96h9y0UCpYXDUlnr6+vwzCMql9CHR0dGBwcRDAYRHt7OxYXF3Hx4sWmjnD5uUTertXYgM9qEBYWFjA0NETvu7S0RDstK0c9Pj6Oy5cvW6aZRPUplUr47rvv8Nlnnwk7sMoP+PR5PB7HjRs36p7i5Nc6sHa1s4EIOxuw5925c8dxULeD9Es2LV+LvlkP+LUM/Jw8m9IPh8O4efNmTdbuyPrQxx9/jHg8Tl9A7777rjCLRaaa2BenXewCjvrf2NiY8MVWaz9QUY/YTiBTIpOTkzhx4gQtLx6PO8qeuoHtl2SqbWxszPE0eK3fJ7Xk75pdgUZCGtIOj8eDaDSK1dVV5XnE4X755ReMj4/Tv6+srGB0dBQffvghTp8+TaXKPR4PfD4fdnZ2ygKg1+tFNBoVHnMLm+IsFou4c+dOUwcbZC7RjkpsMD09bclqECKRCE1tvvHGG2hpacHe3l7ZeWfPnkVXVxf29vbKgiVbHwCYnZ3F7Oys5ZyFhQX8y7/8i9IP+HRqZ2dnQ9YPsH6gQmUDFpIlE9kgHo8jFArVXbyMpOXtsOubTqYkKoEMLCcnJ23PPX/+PFKpFA4PD2v2wuL7EB/zEomEUF5dpL1jF7tUwl2NFrGrdWwHgLa2NoyPj8MwDPoMZOormUxiYWEBCwsLlmt+/PFHzMzM1Gxw1d3djaWlJdfX1fJ9Uiteq8yGU0i6ke2U5IuF/VLhR6Hr6+vo7+/H8vIyxsbG4Pf70dLSQtP6qpQiCY5tbW01fY47d+4gHA7XJJ0mskEtEdlAVWYul8Pjx4/LshqdnZ04ODjA7u4uAHWaN5PJIJvNCgdjbH3YxVckOxGJRJDNZnH+/HmlH/Akk0m0tLTA7/c7tovq1y/VIrKBqEyVDUKhEF3AVs8XDGlzu7S8Xd90AsnIufkKdkMymQRwNBiuVZmqOLK2toZ4PE4HEMDLhdT8QMMuduVyOUQiEeFAo1o/IBm5enyVi2K7rMzOzk709/dbnpn4HpttIwvUR0dHLYuXa8H8/DzOnDnjOn5X8j6pp90BO7MsxwAAIABJREFUAHURrn8FmZubMwOBAP2XTqctx1+8eGFev37dvHDhgrm/vy+8x/7+vjkyMmJub2/Tv21vb5u9vb3C+xqGQf/e29truS6dTlvqEwgEzOvXr5svXrww9/f3zQsXLliOketJPWXPUS2GYZTVtdr7yWxgmmq7G4ZhGoYhvC9rP/a+vO2ITZ3Wh70/fy2B9wO+TWTXyXDie26wswF7jqquvA34PhQIBGid3fgs77vsfd30E/55+L5pB+m7Mh9zCx8LRPclzzM3N2f5u8i25HqncYS3C18f3u5OYxd/f5UfOEFmg0qxi+3sOfwx1gaqeDA3N2dpT1V7uYnt7D3t+onT2CWj1nbn0aqvGlesra1pmfkmIEt1a+pHo6cCNEeopmc09aPedtfTKBpHkLS6Hmg0FvLrBT3QaCyJRAJdXV16oNFgYrEYgsGgHmg0mEbYXWc2NBqNRqPR1BWd2dBoNBqNRlNX9GBDo9FoNBpNXdGDDY1Go9FoNHVFDzY0Go1Go9HUFT3Y0Gg0Go1GU1f0YEOj0Wg0Gk1d0YMNBqKY50ZZkr+eVenjRZl4gSBeKIu9llWHFAkL8dfKVE357WfZ+7pVFKwHvI2cbtPMXydqL9ZG7HGVffhjom3SVduHi8rkheiOg/Ir6+tu60KeR2QbVR9ibSsqj7UTa1s7H1HV57jBPqOb+qr8slZxhK+PKla8Sm1iZwMVfN9VxRH+WdnjqvcC309UbWLXb2VlHgvqsi/pK0g6naZbw5ItY91s9T03N2dev35duE217D78Frcy+Ptsb2+bExMTdOvh/f198/r16+b+/n7Zuew2zS9evDAnJiZo/Uid3WydXWvm5ubo9rjb29vmBx984GibXX6rcsMwLNvsGoYhfDaVfch1bHux9WOvF91fViZPJVtn1xPV1us8xGd+/vnnsi2oVX2ItG06nRb2rbm5OemW1iofUdXnuMH2U9NUb7fPo/LLSuMIaQtSH9YP7GLFq9omdjGZhW8v3iaqNiHbnIvKUcUuVZvw8MdUZR4HdGbjN0KhEBVoIsqSRO3Tjlwuh6WlJVy9erUudTs8PMTBwQEVymptbUU2m8Xi4iIAq6BTJpMBAKp4urW1hZWVFWxsbMDj8eDu3btUKKi7uxsHBwc4PDysS72dwCqT+v1+nDp1Cnt7e8prSqUSnj17ZhFg6+vrw5MnTwAcje4LhYJQ/lllHwAIBAIwDAPFYlEoZrS4uAifz4e+vj7LfVVl8iSTSaFSbbPY2dkRyo6LGB4exuTkpPBcVR/q6OjADz/8IBTDK5VKyGQyiMViwp1pVT6iqs9xw+v1YnJykj5jX18fCoWCo69sO790Ah9HdnZ2EI1GaX1I2xAVWlWseFXbhLeBCiKQNz09DaBcsFDVJhsbG7h27ZpQDVcVu1RtwsP3W1mZxwU92BCgUmflKZVKuH//Pq5cuYK3335beE40GpWmvaampmzTacFgEIODg7Tje71ezM7OIpVKob29HU+ePKGdm3XARCKBeDyOW7duUWdm2djYqEhRsF6o1FlZPB4P3nrrLTpAIEqOJHATpcovvviiLM1rZ5/h4WGMj48jGAwiEokgFovRzksGlR9//HFZnVRlshSLRSwtLZUp1TYaNt0aj8eFz1Tt/Z32od3dXezu7mJ5edl2GtOpj7wKrK6uIhwOOx7kyfwSqCyOtLW1IZVK0cFOMpnE+vq6cLCvihWvQpvIbKDC4/FgcnISAGg/mZ2dpTaQtUmpVEIqlcLz58/p9A2ZDrGLXXZtIuu3qjKPC6/VYIOfYxPN3RFZdqcOubi4iHA4LBxNEmdlJblv3LhB59FYmeL19XWk02nLC4ocz2azSKVS9Bgr4ZzNZlEoFMqeJRaL0UHIiRMnyupG5KZr/ZIRwc97yl7CY2NjuHLliqPBz8cff4x4PI729nYMDAzg3XfftYzyFxYWMDQ0RG27tLRkmb+U2SeRSMAwDKyvr+PevXsYGBjA2toaSqUSvvvuO3z22WfS+tmVCTQ2q8HP5bMDXVaCfXx8HJcvX65ZYHLbhwBge3sb77zzDvX3QqEgnI924yPNgJ+PlwX8RCKBQqGAoaEhR/eV+SVQeRwJhULw+Xzo6upCe3s7fv31VwSDwbKvflWsOC5tYhfbZTZQQdoSAPL5PHw+Hy5evGhZKyNrEwD45ZdfsLy8jHw+j2g0SjMkqthl1yZ2/VZW5rGg2fM4xwkyn+dUYlckm62Sj7abq+fXHbCw8+H8XB17X5FMMH9ffl6w2VSyRoaHnS/l7cPO06rsI5rPJfPhIlloVuJZVSbBzZqURsLPRTtB5kN2fUjU1tvb2+bIyIjlXrzP2vnIcfNpFW7XSqn8UoTTOMLDrwUj58vs+qq2icoGLLxfsu2gahPRMdXaC35dGF8Hvk0IbL91W2YzeK0yGyrYbIFI5ZGMctkvFXaUSb4q+vv7sby8TOeuWZLJJFpaWuD3+8uO5XI5PH78WJh2Jqm2QCBA/7azs0Pn8fb29midOjs70d/fT+/D35eMxitRbhXZoFpyuRwikYhUbdBJmeTL6/z58wCObHBwcIDd3V0A1jSvnX0AWNbqkOmVUChE25lkqSKRCLLZLEKhkLJMwvz8PM6dO+c6q6H69Ust4OeiKy3Trg/J8Pv9aGlpoVNR/BSMnY/UC5KRc/oLKSfEYjGlgq+qTJFf8riNIwSSoRgaGqL1UsWKerYJycjV49daMhvIyiwWi3QK4/DwsGwdn6hNPB4PwuEwVldX6THZlBkfu/iy+TZhYfutmzJl1NPuAHRmgzA3N1f2xcqOysnIUTVS5zMX5BpyP36UyZbZ29tr+bLk6yNavS87TlYl8/eVZWKcZnJM8+iria9rNRiGUVYfUYZA9MsHmV3543x9ZfYxzXIbOV0J7qTMSrMaTnzPDU6ekZyjekb+elUfEvkeayO2TXh/VvmIqj7VQurk9BcjTu8nyowRRJk301S3WaVxhL2nXT/gY0U920Rmg0qxi6XsOfwx/llE2TZV3BJlulWxy02b8NeqynRCre3OoyXmNa5YW1urODOiqZxEIqH8ItbUnlKphJs3b7rO1Giqo1gs4vLlyw3PZL3u1NvuehpF4wiSVtcDjcZCNiTSA43Gkkgk0NXVpQcaDSYWiyEYDOqBRoNphN11ZkOj0Wg0Gk1d0ZkNjUaj0Wg0dUUPNjQajUaj0dQVPdjQaDQajUZTV/RgQ6PRaDQaTV3Rgw2NRqPRaDR1RQ82GGKxmK0QFA+vhfDee++V6WGw2iCiHRnJzxtlAkqi68g1ojLZ5+B3hFNd1wx4+zndrZF9DpF9WNuxu4/yOi0ygTy3bcLrkLD25Z9R5geNROUjKmR2Fz0j/5ys7d22CbGvyG7ken6XWVWbNAMnsUIGbyPeL1ltENF9ZTvCOokHop0lZfVx4gfNoJLYzl8n80lRe6qOqWKXnY/wGjBO63MsqMtWYa8g7H751Wh18JoFc3NzjnYdnZiYKNtljt8lkK0fu1OpSFtCdB9eA8OtRkM9YHUBKt1lk9cF4DUZVBoBIr2aStqEh20TkW7BcaJSDQW75+J99vr167RNDMOQ2o5vE3KuyF/J337++WfbHVZV/aQZqPxHhaj/9/b2StuBtJNhGFJNJXIf3j7pdNocGRkxR0ZGpPe303yq9DlrRa1iu5v+7/SZ3fQh/lze7s22sx06s/EboVCI6pl4vV709PSU7YPvhCdPntB990ulEjKZDGKxmHQTrOnpaQwODuLs2bP0b2T//mg0Co/HQ+WD0+k0isVimXLoxsYGVlZWqLYECyup7vF4cPfuXXpdd3c3Dg4OqMZKMxgeHqabJvn9fpw6dUooca3i8PAQBwcHVNtjZ2cH0WiU2pzok4ieU6TCWkmb8MzPz2NwcPCV2PyM9RE38HZnKRaLWFpawocffgjgqE9NTk5Se/T19VFZbR6+TcbGxoRaQ8CR/0xOTjqq+3FrEzZWuIH0D2L3jY0NXLt2TboZ0+LiInw+H/r6+ix/t4sjxWIR33zzDT799FO0tLQ4rg8L7wfNoFaxvdJ+okLVhwCrj2QyGQCgcWlrawsrKytUrv64owcbAnghKDvYtBgrG727u4vd3V0sLy8LU3hra2tCmWnWAYm41cDAAE6ePIm9vT2LAxKp9NHRUdqB2FSbSkZ+Y2MDZ86cOTbBVyRepoKkDYPBoEXOvK2tDalUir7Ikskk1tfXywYxokBYaZuw5HI5bG5ulokrRaNR19MW9cKpj4iQ2Z1FNIhjkYlE1evlJGuTRiOLFU4gKf2BgQEq6U4Gvc+fP6f3ZaeTcrkclpaWhO1rF0fIoFvWhqL68Nj5QaNxG9vt+snU1JR0ekZ1TNWHZD7CDnYSiQTi8Thu3bplEeVTldlsXqvBBj/fJZpHLJVKuHPnjrKT8XR0dODRo0fI5/MIh8O4efMmve/29jbeeecd5PN5ZLNZFAoFrK2toVgsIh6P4/bt29KR8tbWFi5duoRYLIZz585Zjj1//hyffPIJAoFA2Vcfq0Y7Pj6Oy5cvl319E7VBNy+ZSuHnd0XrMojCoSxoiRgeHqZ2TaVS9L6hUAg+nw9dXV1ob2/Hr7/+imAwWPb1wAfCatuEwH9BezweTE5OWhRjb9y4Uff5VH4Olx3gOPERGTK7E+wGDIlEQvqirdfLqVFZDX7OnV9HoooVdoyNjVF1acMwLO35yy+/YHl5Gfl8HtFoFNPT0yiVSvjuu+/w2WefSZ9bFkfIvVVbtavqAzQ2q1Gv2K7qJ6QfEBuk02naF1TH2OOiPmTnI2RgODk5iRMnTpTdU1Zms/m7ZlegkRDHkVEL4aXz588jlUrRlP3p06fp15TH44HP58POzg52dnawsLCAhYUFy/U//vgjpqam0NLSgm+//RYPHz6E1+ulL6bW1lYEAgGMj48jHo8jFAqhVCqhUCiUpUmBo5RbV1cX9vb2aMBptJgakWeXUa0AkMfjQTQatcgrs6n3XC6Hv/3tb3jjjTfo8Vwuh++//x5/+ctf6N+SyWTFbUJYW1tDOp1WDuI6Oztx+vRp18/pFnaKSoXIR5wgsjtw9EUsGzCoBOVEbVILnLRJrSADy8nJSdtz2VjhJjXv9XoRjUaxs7NDU+pkeg94mdn761//itnZWczOzlquX1hYoDLrojjS09ODeDyOhYUFTE1NWa6LRCJlbcfWh+2/Kj+oNY2I7ap+QqZnZHWTHZP1IQLrI21tbRgfH4dhGPQZZFNxqjKbxWuV2VBBUuMyZyRfLPyXCk8ymQQAvPHGG/D7/WhpabHMgZIUHjsKzefzMAwDo6OjePToERWA6unpoU49Pz9Ppzy6u7vR399PpxsymQx2dnaE0w+ZTAbZbJa+EBOJRMUDDac2cEMul0MkEpEONJyUSdZTiDodyZgMDQ1ZAuT8/DzOnTtnCYTVtAlbD3a9iIhkMomWlhb4/X5HNpL9kqBW8D7itEyR3XO5HB4/fiz8mo3FYkpBOVGbVIvTNhFBMnL1+jpkY4WbMkkGrq2tDR6PB+Fw2PKyIlNU77//vsWf4/E4IpEIstksQqGQNI78/d//vSUTl81mEYlEEI/Hhetj2PoQVH5gh+jXL9VgF9udlinqJwTyvKLpGdUxVewCrD7S2dmJ/v5+ep9Ky5RRa7uX0ewVqseFubk5MxAIWP6xq9vJSmB+xTtZCU6u4VcD88edrDomGIYhvW86nRbWk6y2Jsf41efsMfKP/BrECYZhmL29va5/MaK6H18fts4yu/PtxdqVfU5RXZ3+6qWSNhH9IoI8g+j5nCCzQaWofIQ/R/TrD5ndTVP+KxO+H/DXq9pE1DdJGWw/ED2PrE2cQOpcqxX+drHCNF8+D98nWb/jfZr3L1l9Rb86ksURFtEvJlT1IccrtZvMBpViF9vZc2RxRNRP2PvyNnB6jC/TzfvETZlOqLXdebTqq8YVjZ6C0Ryhmn7Q1IdapN417ql2WlVTGfW2u55G0TiCpNX1QKOxkJXpeqDRWBKJBJ060wONxhGLxRAMBvVAo8E0wu46s6HRaDQajaau6MyGRqPRaDSauqIHGxqNRqPRaOqKHmxoNBqNRqOpK3qwodFoNBqNpq7owYZGo9FoNJq6ogcbv8GK38j21pfBan/wO13yuiC8OA4RM5IJ58h2cuR1L9gdB9lnee+99+i22rxug9vnrAd29lHB2kBlH9Huo2y5/LWszgKxn8h2/LWqNmHb+TgIsVVaH1U/4W3E+h6vXcG3NX+crY+dj8j8gG8Pvk6NRmUft9eyvsU/p+i+TuII20/sYoWqTfhrmy0IVk1sV/UT3mdFu76SsokN7OKInY+wdpf5D1/msaEuW4W94oh2zJOxv79vXr9+ne5Gx+6cxx978eKFOTExQXd243eoNAzDsnsbqYdhGJbd67a3t82JiQnLzqCkHFEZ5Fo3z9UM9vf3zZGREUc73/G7IbK25HeNFJ0r2y2R7NDnxEZsmao2sat7s6m0Prw/zc3NWfw3nU5b+oKsbfn7qM7lj6n8gGd7e9scGRmpyS6stUBVV9G5xLb8bqt295HFEVU/cRsD2Tbhdw/l41ozqSYGiuxDnkt0X2KXiYkJZfuo2o+PMWy7i/zZaZnNQGc2BLBy4nZ4vV5MTk7STa76+vpQKBRQKpWo5sH09DQA6976vCJisVhEOp22SKMvLi7C5/OVCay1trYim81icXERgHX/fI/Hg7t371J9ie7ubhwcHFBhuOMMkWq3szurd+HxeKjMdjqdRrFYxM7OjkULg2g/HB4eolQqIZPJIBaLCTcm29jYwLVr12w3tuHbT9UmPKxU9HGg0vrw/aStrQ1ffvklzQSpNB9YMpkMAFBRsa2tLaysrGBjY6PsXNZH7PyAp1HKr06RiWiJYEX1/H4/Tp06RW1hhyyOqPqJG/g2efbsmUUTpa+vzyKD3kzcxHYetp8QG7Eim+FwGDs7O/T86elpDA4OUr8WYaeMy/rI3t6eRTvI7/fD7/db/MBJmc1CDzYYSEoxGAy6kiFmISJIHo+HKkACQHt7O+LxOGZnZ+H1ei0dNJfL4dKlS/joo4/owCCXy2FpaUmoVOn1ejE7O4tUKoX29nYqNyx6WWxsbFjEwoAjhcjjks4HXqYqBwYGHEnMswGDiCwNDAzg5MmT2Nvbo6qXZNCWTCaxvr6Ovb097O7uYnd3F8vLy2VpXvKyev78OU27ygTgeCl0uzZhU67xeLwhCqQqqqmPrJ+EQiHMzMxgZGQEXV1diEajlt03nz59ioGBgbIUMBvEE4kE4vE4bt26ZXlBiXzEzg9YcrkcNjc36cuhWbAp/UKhgKGhIdf32N3dxcHBgUV4cWpqSjhtoYojqn5CUMUKUZt4PB689dZbdKBIBoTkA6xZVBrbZf2EfESwIpvxeJz67NramqP25eMIIPeR1tZWbG5u0niUyWQwOztL28tpmU2j2amVRuJEgMo0zbIUmVPYKQv2PiSdZRgGTVuSFNhPP/1U9rdCoWCZCuFTxeQ55ubmaBmiZ7EToUqn0zUVVZPBi2XJ7Eqeyy7FSVKFP/30E00rilK5pLyvv/6aTmmQaRJR+lNkS1GKU5Tmd9omxB61ElVTYSeaVm19+H7C+hNvZ1HdSJnExmz6XZZaZn3EiR8QGpXK50XRVHbl44UT7PoI74eqOGKa8n7Co4oVfJ14IcSZmZm6p/TrHdtNs7yfsKJoFy5cML/++mtzbm6ubApV5ct208a8j7B9emJigrav0zKbyWs12HADO9/sBFHg4OfU2BebSFWTBIPV1dUylUL2hSEafPBO6+QF4maNRKPg5/1FiFRQVfPx7HoK0XnkRSSad5UFaJHiq12bsPVnXwLNppr6sP2Ef6Gr1oKw7SBSm1QNDoiPOPWDRg3u3KJa1yM738lgnNhOpIirGnjy6474slWxQtVvnfTpRuI2thPs+olhGGY6nRaqzIqUWJ0o46p8hD3mtMxmoqdRBIjmm8kqYVFaPRaLSYWyisUiTXMdHh7SOT2v14uenh465ULKDIfDeP/995HP5+m/eDyOSCSCbDZL1xLs7OzQecO9vT1LnRKJhCPBtGQyiZaWFvj9fsd2kdmgFpBUZFtbm7JMMj/a09NDn29+fr5suojcc2xsDENDQ/B4PPD7/WhpabGkPzc3N9Hd3U3vu7q6Sq9np8WAoxTn48ePhXOsqjZhYdfuOLWL6JcEtUJUHydlivoJO/XBzl/zsO3V2dmJ/v5+dHd3A3hpY/L/LKyPOPEDdl2H27UaZOW/6FcGtUC0rkdWZi6XQyQSsRXKYm0XCoVs4wiB7yeiuspihajfss8Tj8ddTV+RKY96TPHK1hI5KVPVbxOJBAqFAs6ePYvh4WGL3Q3DwOjoKB49ekSnS1RxhEW29qtUKuHOnTsIh8Pwer2OyrSjnnYH8HpNo6iwSzmLvqJM05pKE12vmkLgU66y0b/oC1FWXz6dyN6bL6+SXyAYhlHT0TKbxhXdV2Z3/lr2C4FP4/L35NuMbWveRvyXh+prxGmbuLW7ygaV4KQ+osyb6hnZeoqmEPgyeRuybSL6AlT5iMwPTLO6rAapU63S0bzfie4ryvKYpvUZ+XZj20TVN2XTsaLr7GKFqk3YmFdJjJHZoFKcTCeSc/hfk6j6CXtft782kcURlY/wbaLKcFUyjVJru/No1VeNK9bW1rTMfBNIJBJaZr7BlEol3Lx5U8vMN5hisYjLly9rmfkGU2+762kUjSNIWl0PNBoLWZmuBxqNJZFIoKurSw80GkwsFkMwGNQDjQbTCLvrzIZGo9FoNJq6ojMbGo1Go9Fo6ooebGg0Go1Go6krerCh0Wg0Go2mrujBhkaj0Wg0mrqiBxsajUaj0Wjqih5s/AYrftPe3u56t0ay+xorhER2A+T/sTu0kev48sjOmeQaVriKL4/f0ZMIJInKY+9br50R3cA/p9M68dex4lOAtT152wHWtuFtL2uTan3kOGFnHxkqu4v8nfgeK2glutbOL1mf5tuavb+sTWKxmKvnrAd2fVoF29/Za0V25cUFZWXy95TVSbSzpKw+smc9DoKPle7EK4sVvO1JHLZrE0BuH1WbOHmfAC/7tqifNJW6bBX2iiPSyFBhGIajnfJEYmFEx0C0Ox2vM0F2hON3RVRpULDHyI54P//8c8ViRLWGfc7t7W0qqGUHvwMfq6XB25nXy2BFwGT3tRPJcusjxwle48GNIJjK7io/VGlr2Pklvxsir5vCCh6KyifPdxx1gJzu8ujmXNY+qjjCI9OVGRkZMUdGRiy+rqoPKwZ3XLDzERl2sYK3ieyZ+fjk1D4qzSeZIOTIyIg5MTFx7ITYdGZDACtdbUexWEShUMDt27dtN1zi5YTHxsYwNjYmPLetrQ1ffvklcrlc2X7+Ozs7Fq0HIjVNdDlYWPlur9eL+fn5Y7VZzvDwMN00ye/349SpU2Xy4DylUgnPnj2z6Ar09fVRXQ7ezhsbG1hZWcHW1hZKpRIymQxisZhwYzJVm7C48ZHjhsfjwd27d6l9uru7cXBwIPQfFju7V4rKL4vF4v9n7/1C20jafP/vxV5EzMRE8RnY1wJnZTwmYHaI0YUclHftCyMwGDETMByTi3MUT3AuJpnJrpHxrJmcEJjd1muCzyQvJOt34p0Lv+Jn8XrArxcfvIbEGBHpQjgYdI6xhbwrsPZAcDo4m5EvDuh3kalKdam6ulv/Z1IfCMy41V3VTz31qOqpUn2xtrZGy9R1Hclk0iCNvrq6io6ODgwMDJTdTyTWb9y4UVUd68HBwUGZRke1ZLNZ7O7uUi0SWRzhWV5exvDwMO0Xuq7j0aNHuHXrFtra2mzXYW9vD/39/S11GJrMR8ywihVerxeaptFshpk+DN8mTuzDtwmLSJ5+fn4ew8PD+OSTT2y/Z6NQgw0GksLy+XwYHh62JWBDBL3u3bsnXQrgg6YVfr8fCwsLCIfD6O3txdjYGHXOzs5OQ7Dd2NhAOp2mX9JsGi8Wi+HatWu2ymw2h4eHOD4+poMnM1wuF06fPo3t7W0A78SVCoUCisWiIYhHo1EcHBzg+vXryOfzODw8xOHhIdbX16VpeTMq8ZFWZ3t7Wyhix2NldwBYWVlBb2+vcJnp1atXCAaDjpYQiE+3t7cjm83i6tWr+Pzzz+ngiAwmRD5eLBbx4MEDTExM4OzZs7btUU/Y5atCoYCRkRHb9z58+NDSZ/kvJ1kc4evFfiEC7764zHzcrD6bm5v48MMPaQxq9vKVzEdkWMWK0dFRTE1NwefzIRQKIRqNCgfMfJvYtY+oTQii75NUKuXYpxrJezXY4NfS+GBIlPMymQwSiYTt/QMrKysYGRlBLpdDOp3G2tpamfOIRqEyUqkUwuEwFhYWsL6+jsnJSVofv9+Pjo4OGtRfvHgBn89HZ9lkppjL5TA1NYXx8fG6qLTahV9rNBuMRSIRTExM2DoK/dq1a4jFYujq6kIwGMSFCxdoBgcA3rx5g6+++gper7csU7G/v4+PPvqItnWhULC9plypjzQDfv1X9I5EldNuIJbZnVUZzWQyAID79+8DMPpkLpfDzZs3EYlEbPvls2fPEIlE8PjxY0Mm74cffsDXX38t9JnV1VUEAoGGZvL4tXh+P1V3dze2traQy+UQCAQwPT1ta/8Aq+qZTqeRTCaFyrD8l5MsjrDwX4jEV8xm31b12djYwPfff49cLofZ2Vk8ePCgrvubzGJ7sViU+ogVslhB1LXT6TRmZ2cRDAaF+ydEAwY79nGS1SCZFTsZ9mbxF82uQCMhAc8Kl8uFsbExg9S4jFAoRNNWp06dQltbG46Ojgxywn/84x/xu9/9znZdNzc3cfPmTfqM2dlZxGIxKgHNpvuz2Sz+4z/+o0yGGAA++eQT9Pb24ujoqGl6JuRLyIxKBID4tozH4zSb4fV6MTU1hVgsBr/fj2KxiEKhQFOoH3/8Me38LpcLHR0dyOfsmWXmAAAgAElEQVTzjr6UnPpIM2CXqERUIqonszsLkX83W2Lp6+vD2tqaZXnt7e149eoV1tfXsbi4CJfLhb29PbS1tWF/fx+Li4tYXFw03LOysoJ/+qd/QiwWw/PnzzE1NUWvPX36FNevX7e1VFYJLpcLc3NzmJubs/zs0NAQEokETk5OHH1BuN1u9Pf3G/5Gsjj8l5NVHAHe+kEymaQDTpKxWllZwcOHD+mzVlZWEAqFyjR6RPVhl3nb29tpJqpeX4RmsT2VSpn6CIkPMsxixSeffIJEIoGpqSm43W74/X5omobNzU36TLM2Aaztw7cJi+j7ZGNjAysrK1hZWTF89s9//jMWFhZaIgP7XmU27CJa2yQzFn6m0tPTg+PjYxweHgIQLwUsLy/j4sWLjhucDdT5fF74GZIRYIMHy87ODjKZTE32FpjZoBqy2SxCoZDpQMNOmWR2ToJCX18fBgcHaRvs7Owgn8+jp6cHHo8HbW1tdPlL13Xs7u6ir6/PUb2t1r9rSaW76GWQWZnZQMNOmbzd+ftjsZjpGvny8rKtpRvyRRYIBOByuajdA4EA/uZv/obOrnO5HGKxGEKhEDKZDIaGhgyZlHQ6jcHBQayvr9seaJCMXL2yVxsbGwBgmCTYKTObzeLZs2cGnyU+LmoLWRwh9mS//MiAic1ShUIhxGIxzM3NlcUZvj4DAwOGZV67y3QE0a9fKoXNtvE+wsYbUZl2YgVrT35gbdYmVvYRtQmL6PuEzTTlcjlomobr169ja2vL9vdOLe0upNk7VFuFpaWlktfrpf/4XxmQ3cyincnJZJLed+nSJcPuYNkvLPgyvV4v3UFMyiN/Z8slu5lF5bHXvF6vYec1/0yzd5WhaVpZmdWgaVpZfUR15u3O2ly0u5y9zt+7v79funTpkvD9ZW1i5SP1QuZ7lcD7CPnH7o4nn+FtK7M7e423D18mu1Peyi/562a7+Cv9NYwZxE9qtauf9zvRc4kNRb/IMYsxxD4iu8jiCCnPyq9Ev7yS1adUMvZrJ7/+kNmgFpj5CHkfvk/LYoWdWCv7dYqsH5m1id1f7Dn59RJbbr3sXiqVSkr1VeGISlLviuqJx+NKZr7BFItFTE9PK5n5BlPJsqqieuptd7WMorAFSaurgUZjIb9eUAONxhKPx9Hb26sGGg0mGo3C5/OpgUaDaYTdVWZDoVAoFApFXVGZDYVCoVAoFHVFDTYUCoVCoVDUFTXYUCgUCoVCUVfUYEOhUCgUCkVdUYMNhUKhUCgUdUUNNhQKhUKhUNQVNdj4GVaNUSTSZvdeXsGPFSGTqfv99re/LVNy5EWdyDGyvOgQr0bIX2ePn41Go5biXI2EF2lzosDKvgt7H283J23C2449Jp2vK28/Vvislke61wOZz8rgbevE7oRoNFp2TeaXvKAcuZcvz6zvsvc78a96wPqXk74n80uCWRwx6yfsPU77CW979nh1mY80g2piO0F0lLfMBuw9fHnVxBG2TL48Oz7SVOpyLukvHNHxvGbwxyDv7++XwuFw6eXLl2VHy7LX+PtnZmYMx8uSo3BFR8fKjl7m6y77rOx452bg5EjpZDJJ7UVsZdZe7NG9Vm2iaVrZscykDWT24o8YbjXbsvz000+lmZkZaoOlpSXbddU0zeCnmqZR+7C2KpWMbUQgZcnambedk6OX+c9qmtYy7UB8j/iKU5kAM78slczjiKyfVBO72PL5z8p8pNk4ie2EZDJZCofDpXA4bNoGZjYQ9a1K4whpvydPngiPQrfykWajMhsCTk5OcHx8bEu8jJf63d7extOnT7G3t4ejoyODYI7H44HH48HR0RG9f35+HsPDw1Q1lrC3t4f+/n7Hpxfu7OwAAH3e3t4enj59iu3t7bLP5vN5gyx7syF2sWN3v99PBbWIWJeZWN3BwQEVTLNqE6/XC03ToOs6FRPr7Oy0rE8+nzcIJ7Ey6K2Gy+XC3bt3qQ36+vqo6qSMYrGI169f4/Lly/RvAwMDVICqs7MT3333Hc068EJ12WwWa2truHHjhrScSv1S13Wsra3R+um6jkKh0DKy293d3fjxxx8NIo12sfJLszgi6yfVxC5WUdjj8eDcuXM4Ojqy9JFm4yS2A2996NGjR7h16xba2toM18xsAMCgyu0EWRwhyrZmJ3xWGrsahRpsMJD0lc/nw/DwsC21PPaLLBqN4uDgANevX0c+n0d7ezt2d3dpKmtnZweLi4vUIVOpFAqFAkZGRsqeu7m5iQ8//JCmxfg05qtXrxAMBsuusYE6Ho8jFovhm2++oZ2dTbXFYjGhhHGjIWneYDCIiYkJx0ehi9QY2dQpa2OrNhkdHcXU1BR8Ph9CoRCi0aihc6+srKC3t7csPdrZ2WlQctzY2EA6nTYMLFsVu6qcLpcLp0+fpgNXMqAoFAooFovw+/1YWFhAOBxGb28vxsbGaDAmctsTExM4e/Zs2bOt/PLhw4eWaXn+y5Oodd67d8801f1LQeaXsjjCwveTamIXC6t0beUjzaKS2A68G8RZfV6k9i2jHnHEKnY1m/dqsMGvafFraUSmN5PJIJFI2A5Mb968wVdffQWv12sYzXZ3d2N4eBg+nw9dXV1YXV3FlStX0N7eTkeeslnXxsYGvv/+e+RyOczOzuLBgwcoFot0hEvkhG/evIlIJGJYnyPBY25uDh988AH9O3vv1NQUxsfH676ux69R8naNRCJUBlzTNEdr2cViEXfu3CkLCN3d3dja2kIul0MgEMD09DSKxaK0TYB30uvpdBqzs7MIBoO0PqxcdSaTAQDcv3+fXuvo6KAB5MWLF/D5fLZnUPWA3+sgsiuRibc76Lx27RpisRgdHF64cIEOblOpFMLhMBYWFrC+vo7JyUna1qurqwgEAqbBT+aXrHx2Op1GMpkUrlezWQ3CysoKRkZG6L1ra2u296dUAr+OX6t1czO/tBNHSL1E/aSS2MWi6zoikYhhkiDzkXpRj9hO+otVdllkAxn1iiOy2NUSNHsdp1URrTeL4KWJZWuCL1++LH355Zelly9fCqXMWblmfp1TtN9DdE0kE2y2Zsqv3bcCTtYZrWScCazdZdfMpLTN/EB2bX9/vzQzM9MSewXMsCMvbgXbXryfkfXmQqEglLQ3k1m38kuRP/P7BNjyedlvJ+v19cBqjxGPzC+t4gh7v5lsvdPY5fQ9Wm3vgJ3YTmwhsi3rU3b2i1ntGXIaR0Tt6TR2NYP3KrNhF9F6M5mx8DOVvr4+DA4O0vTZzs4O8vl8WTqNzCwCgQDcbrdhtpbL5aBpGq5fv46trS10d3djYGDAkE6TpbqXl5fptZ6eHgwODtJUaTabxbNnzwxLDISdnR1kMhnbs28zG9QK0TqjWZm6ruPKlSu2VDk3NjYAAKdOnTL8nW8TArv3w2ytmdR1YGBAeC0SiWBkZKQmszkya6tkF70ZZBZkpuBrp0ySFRkaGqJ/Y+1F7HjmzBlDJi6dTmNwcBDr6+vCdW2ZX4r8mfyNz2r09PTg+PgYh4eHAJynuklGrpFLL7IyRX5pFUdk/aTS2AW8tXkoFLJUCRX5iBWiX37UClFsF5XpcrkwNzdnyECEQiHEYjHMzc3B5XLZtoGMWscRO7HLjHraHYDKbBD4GQI/UiUjR9FMkGQTvF6v4To/OpbNAESjUE3TpKNps9nh/v5+6dKlS2UzHP6+Snbpa5pmeGa1sO8oeq6Z3UUzOvIZ9v15+1i1icxGbDvz97L31dI+MhtUCv+O5B//64bPPvtMuJPezH9425rVl/8VhJVfsm0tsq0oqyGqr9N2IX5Uq9mhyO58nUSZSdG9Zn2XjyOyfsKW5zR2sf2Wr5PMR+xgZoNKsYrt7GfMYrQocyCzgcjupF0qjSNm2Rb210XVxPda251HScwrHJFKpaQzYkV9iMfjSCQS+Id/+IeW+GXF+0CxWMT09LSt7Jmidui6jvHx8aoyBgrn1NvuahlFYQuSVlcDjcZCflWjBhqNJR6Po7e3Vw00Gkw0GoXP51MDjQbTCLurzIZCoVAoFIq6ojIbCoVCoVAo6ooabCgUCoVCoagrarChUCgUCoWirqjBhkKhUCgUirqiBhsKhUKhUCjqihpsMLBaEk5Oa2S1P0QnXbJn9vOns7HXWUE1XmOBFZ/ir/EibbIynVxrxMmJ/Ls4KZMIuInEudh34W3OirTxtgPe+YHIB2Rlsv5jdspq3U/ps4mVX8rg24y9V3ZNZh9Zm/B15e+VldlqyGKFFWaxApD7rJl9eO0c/rm8phHv72Z9gW1Lp7G0Xsj6rQzWBrzNZfaxiqWVxi6zfsv/vZJ3rTt1OSrsFwivo2D3XHleM4A9zZA/dY4/OZGcUCg6tY4/FdFM34Svq6xMO9dIGY3SkWB1E/b390uffvqprVMeWX0DXp+APIdof7DvwOtu8NoFxO4iTQNZmbzOCO9P5G/hcLgUDoebqs9h5ZcyyHuLfFF2TWYfO23Caz6QMmRlthqyWGGFnVgh8lkn9mE1lvi6itrITnxqBT0aWb+VwccjmT6Vk1haTexy0m9l3xnNQGU28O68/LGxMbhcLhSLRSQSCSSTScuZh9vtxtzcHD3kamBggMop7+zsAAA++eQTAG8lr58+fUrll7e3t3Hz5s2yQ1SKxSJev35t0HoYGBgwPeuelYqWlSm7dnJyAgBUw8DlciEQCBjO2q8Ho6Oj9NAkj8eDc+fO2ZJT9vv9VFfD7Xajv7+f1rW7uxs//vijUAPD5XLh7t27VPmyr68Px8fH9P0jkYhQr8OqzHw+j7GxMeoHpGzyXF3X8ejRI9y6dQttbW02LFM/rPxSxt7eHvr7+4UHXcmuyexj1SZerxeapkHX9TL9HFmZrYYsVlhhFisAuc86sc/y8jKGh4fhdrupjtD8/DwAo14Nr7Cr6zqSyaRBy4lwcnKC4+Pjpqofy/qtjKOjI1y8eJH6pcfjgcfjEcYn8rf29nbLWFpp7HLSb7PZLHZ3dx1p0tQbNdiAsUMQ4aJgMIgzZ87Y+uJj2dzcRCAQgMvlQj6fp9LK8XgcsVgM33zzDQ4ODuiA5s2bNzTlSNKqLpcLp0+fpk5EBkNsYGJTlYVCASMjIwAgLVN2jQSXvb09AO8EgpyK+VSDU6Esgq7r2N3dFYrNWSETuHNSZmdnpyHYbmxsIJ1OU/+Zn58vk/duFjI/sGJzcxMffvihMJ0vu2ZlHxa+TUZHRzE1NQWfz4dQKIRoNEq/dGVltjpsrJAhixV2yrBjH/7LiQiRAUBXVxdisRgWFxfhdrsNX6zZbBZXr17F559/bhggkiUan8/XMn4POIsV7e3t2N3dpXbe2dnB4uKiwWfJckgwGKQS805iqZP6OOm37MCxVXivBhv8uha/lri3t4erV68iGo3i4sWLjp8fj8cNX/yEaDSKg4MDzM3N4YMPPjBce/78OdbX15HL5TA2NkZnEteuXUMsFqOOfOHCBepowNsR8NbWFnK5HAKBAKanpw3vIitTdM3lcuGLL77A5OQkurq6MD4+jqGhoTJ1xErg1zZF+zKIwiHpsHYhipSVBDSiSHnt2jVH94nK9Pv96OjoQG9vL7q6uvDixQv4fD60t7fTddVWm33LfETGxsYGvv/+e+RyOczOzuLBgweGQYTomsw+LKI2IQq16XQas7OzCAaDhrVsWX0aDb8/QrZ3RxQrZJjFCivs2If/ciLvAQC5XA4dHR24cuWK4V2ePXuGSCSCx48fl00QiBptJpNBIpGo+/4vq9hO3slJrOju7sbw8DB8Ph+6urqwurqKK1euGHw2EolQJWNN05BKpWzH0kpjl1W/bcWsBvCeDTbcbrdB6ppIBZ86dQptbW34wx/+gMePH6O7u9swereDSCirs7MTDx8+hNfrpWkzdskDAF26IZ8n2Qu2rltbW/jggw9Mv/iJU52cnEjLtKoPO4BZXl7Gf/7nfxrk3ivF7/cbZLD5L91KBYCqEcqqVFBOViYJPLlcDsFgEKdPn0apVEIsFsPDhw/R1dWF3t5erKysYGxsrGkb5+z4pQx2OaS9vd0wo5VdE9mHzAIBcZuQWf3U1BTcbjf8fj80TcPm5qat+jQaXpp8eXm5zL8qFdUzixV27pPZJ5VKIZlMGr6cSJaRDPpu3LiBzs5O7O3tob29Ha9evcL6+roh29HW1mZoT2KPsbGxumdIzWI7odJYQQZNuVwOf/u3f4vXr18LvxPcbjfGxsYMyyGyWFpJfez022KxiAcPHrRcVgN4zwYbZpA1tf7+ftpAy8vLhlQuGemLZirRaFQYPHp6ejA4OEhTZNlsFs+ePUNfXx8tkw2aZmlVMtszG6lubGwAAE6dOiUtU3aNh8y8yPqglQ0qJZvNIhQKmQ40zMoky12VDDTITNnpQMNumSRLMzIygrNnzxq+fDKZDEKhEGKxWFlAlD3v8uXLNRuc2PEDszIHBgYMyyHskofsmpl9yPtbtQm7xs5+cdktsxJIRq6Ws3KzWCEr00ms4LGyD7tfTdROZNJ1cnJC24DsMyDlk2eI6kOuOcmQ1voXW3b6rVWZJAsRCASEvsXvJeKfzcbSSmOXnX67s7ODfD5fUVaj7r+Ua/YO1VZC07SS1+steb3esl3iZCcwu6O+VHq3S5zcR/6xu4vJ9UuXLhl2DpNnispMJpP07/wOc75Mvq6yMmXXlpaWTJ/J2oi/rxpYm4ve18zubF3JP/IZssObvUbqLLrm9Xrprm3Rc4kt7JYps08lu/PNbFANMj8old7tkud9r1Qythl/3eyazD5WbcJft1tmrWxk9xcjdp9nFitKpXf9nv8VgSxWyHy2VJLbh/+VEAsbg/g68fVhr/H1cfpLFDMbVIqs3/KfEf36w+w9WLs6iaWVxq5Syd73SaV2q7XdeZTqq8IRlS4/KKqj0tS7onKqWaZTVE6ly6qK6qi33dUyisIWJK2uBhqNhfzqSA00Gks8Hkdvb68aaDSYaDQKn8+nBhoNphF2V5kNhUKhUCgUdUVlNhQKhUKhUNQVNdhQKBQKhUJRV9RgQ6FQKBQKRV1Rgw2FQqFQKBR1RQ02FAqFQqFQ1BU12PgZVtjM7Gx9M9hz+fnT14hQD/nHX2fv5UWS2Huj0ajhPnLam0h/gdUiMRNeqvtpcTbhtSTsntbI662w78I/k7cB29b8NdauZvYTna4pK1NU12bb3sovZbA24vsJeVfZKbPRaLTMrnZ8lrQb3xcA+SmrbH1F9zYS9j2dnMQr80teF4Q8l/dJUZvJ6iPrJ7xPs3aV9c1mIYulduDjJW9zs2eLfJZvF/6eSttEVmZLUJejwn7hODnlcX9/v/Tpp5/Sk/hk9ySTScMJfuQ0ONE9yWSSnjxHTpUjn+NP/WOfS+rDnjgXDocNp+Ulk8lSOBwuhcNhx6f71ZqlpSV6Yh1fdxm8La3KILb86aefSjMzM7SMpaUlw3PYz4ogvqFpmrR82XNevnxZCofDNTuFtVqc2JL/LPuexJZPnjwxPZWSfIZ9fzs+S2w2MzNjerqvqE2s2qmRvHz5svTll1/S99I0zfbppDJ/0jTN0I/ZPiV7jqw+Vv2Efy+2PZ34UyOQxVK799uJl5qmGexu5rN8u7P3Vdsmsn7SbFRmQwArOW9Fd3c3fvzxR1uy6KxEMPBWp+DmzZvCQ1T8fj8V2yFaBESbIJ/PG7QMSNknJyc4OjrCxYsXqYqgx+OBx+OhGge6ruPRo0e4desW2traLOtcb0ZHR+mhSR6PB+fOnRPKjlcDK1bkcrlw9+5dap++vj5Hwl2rq6vo6OjAwMCA7TJ5NjY2cP78+ZaR3eb90gxWR4NoYiQSCSSTSei6jtHRUaneSzabxdraGm7cuGH4u5XPAsD8/DyGh4cNWj0EszbRdR2FQgG3b99uicPQ3G435ubmaL8dGBiwLaYmw+v1QtM06Lou1ejQdR1ra2u4fPmyZX2c9BOnopWNRhZLrbAbL0VKqyKfLRaLeP36NW0D4K3did5PtW0i6yfNRg02GEiqzOfzVSRZLoJNt7HS2SRQv3nzhqbFzNKquq5jd3eXCu50dnYaxJU2NjaQTqdxdHSE9vZ27O7u0ufs7OxgcXGRBgTijK3yRcdClCbtDNwAYGVlhUqW8+lzNt0ok/IWCXcRhVY+xUm+LM0k6e2UyQf8ZmHmlzLYQTgRkwoGgzhz5ozlAJGoUU5MTODs2bOGa1Y+m0qlTO0pa5O9vT0AwL179xwv0zUCu2JqBDO/HB0dxdTUFHw+H0KhEKLRqHACYzXIldVH1E/I0kQwGMTExIThmqxvNhM+llphN14uLy8blFbNfNblcuH06dPY3t4G8G4AbzbodNImsn7SCrxXgw1+nY3vBEROOJPJIJFI1CQwsdLHU1NTGB8fNwwonj9/jvX1deRyOYyNjWF+ft5wP1EbZB3e7/ejo6ODduYXL17A5/Ohvb0d3d3dGB4ehs/nQ1dXF1ZXV3HlyhW0t7fT9cZGH7/Mr+GK7EqUQPmgZQYrW5/JZAAA9+/fp9dZiedAIIDp6emyzkzUdNkvKlZSOp1OI5lMIh6Po1gs4ocffsDXX39tWj87ZTYyq8Gv87Pr5lZ+KWNvbw9Xr15FNBrFxYsXbd2zurqKQCAg/BKU+SyZqYuyE3baZGVlBSMjI7Q919bWhOvctYJfjzebQBAlULtfDGZ+SZ6laRrS6TRmZ2cRDAaFe8Nkg1xZfUT9BAAikQitj6ZptEyrvlkPrGI7II6lMuzGSz6rIfNZALh27RpisRgdqF24cEGYWXTSJlZltgTNXsdpVdh1PjvYWQtk19xE+0L4tU67Kn77+/ulmZkZ0/XUL7/8slQoFAwKhmYqq82gknVUHqs9Euw6aKkkV7tkIeupvAKmlaqlqEwne1IaCb8WLPscrz5rtieI/YyZqquZujBrO5FCJlG8/NOf/iRtE7P+1Ar7lKrtc8QvRe8k6guy/SGy+tjtJ3b3iTQLp4qovOKrlSq1TPXWTBmW/TxfL6dt4rTMZqAGGwJEDmQl823nC5N3Er4T8huFPvvsM8vOIStX1sFaTercrB52yrSyvWgTqJ13kA0OrDbBiYKFkw2BLDK591ogCl5mZYp8ln8nqy8o2QZZqy8F2RcX3yZ8OaKBkYx6SG5bbVi1UybrlyJ78W0i82NZfez2E1n/q2QiIZJ7rwY7sdSqTLN4aWcwZuWz/P21aJNKBni1tjvPe7WMIoNNOff29mJsbMzWcgNJ3/l8Pjx//hxjY2P0J0l8ai8Wi2FxcZGmfEdGRlAoFOh14F3KbmNjA8+fP8fU1FRZSpZ9Lr9Gy6ZxnbyHHVwuFzo6OvDixYuabeJcXl7G4eEhxsbGHP3smF2a4dUK+Z8xHxwc0I2LJN34/PlzmrZnl3ZYPwiHw/jd735nK+UqK5Ncf/bsWUV7NU6dOoXOzk7k83nbG1llWPmlDOJLrM+SzXekTcbGxqh97bRlvXzW7XZjYmICwWCQtqdsyYWnvb0dHo+Hbt6rlmw2iz//+c+G/Qx2fxZq5pculwu3b9+maXmyX4jdhLu8vGzYgGunPlb9hP0pKR+DZH3TDmRzq91NnFbIYmk1sBumnahgs/bh+141bVIttbY7j1J9VTgilUopmfkmEI/Hlcx8gykWi5ienlYy8w1G13WMj48rmfkGU2+7q8yGwhZkNqwGGo2FZEzUQKOxxONx9Pb2qoFGg4lGoxVlQxTV0Qi7q8yGQqFQKBSKuqIyGwqFQqFQKOqKGmwoFAqFQqGoK2qwoVAoFAqFoq6owYZCoVAoFIq6ogYbCoVCoVAo6ooabPwMfyiTXfEg/oAkXiSJ1wVhr/H38oezkHtlB9BEo1F6iJioPP7gIPYwHrsHCtUTXkvCyQE1/L3su7CHIPH2Y23E2o6/xrcXXx5/L18ue2+rwfq76D3M4G3AC9WZ9SEn/URUH/Z+K38nfmCnvRoNW18nB0vJ/JJA7M9fY/u8LP7YjV2i+9jrsjZpBpXGdsDoQ6LYZGZb4F0s4MuT9SH2Pj4mWsV2vl1qcXBZTanLuaS/cJwc5S07epnXxxBpo5AjdPkyyXHXT548MT2elnzGrHyr+lkdud0IWF0AJ9ohsiOI+SOA2ffky5AdYc3bjtcw4PVzrI6ibhV4LRQnWh38UdjsEft8Gaw/y/zQqk1kR9o78eFma3Tw8aCa4+t5W5K/zczMGJ7J+ih/dHilsUsE6wetEFfMcBrbP/vss9KTJ0+ER+jL5CZI29qRLWDv43WuRBpLbP3Y9tM0rUwjp5ZH7VeLymwIYKW0q+HUqVMAQJVcd3Z2kMlk0N7eTo+dJkqBLpcLgUCAHhU7OjpqOO6ah0hrs8cSi5CpjObzeaHaYCMZHR2lhyZ5PB6cO3fO1lHoe3t76O/vFx64lM/nDUcIE8n6k5MTHB0dGY5u9ng88Hg8wjLJ34gfdHZ24rvvvkM2m6VHFXu9XgBvZxWFQqG1VRd/xuVy4e7du9QGfX19OD4+tjwKvVgs4vXr14Yj1wcGBoTHeTvpQ1Ztsr29jZs3b1Z92NDBwQFtr2bgdrsxNzdH/XJgYMBUWlwG75fAOyn0Tz75xPBZv99Pj5N3u93o7++3dRy1LHbx8KqnrYwTvyTKyCK/41V0dV1HMplEIpFAsVhEJBKhdmex6kPt7e3IZDJYXV0F8DZ+A+/ag4WP7V6vF5qmUUmLWCxGjyBvBdRgg4Gkr3w+n20ZYgB49eoV1V9gU7Uulwtzc3MAUHYOPnGevb09AO8kgu3oMBSLRTx48AATExM4e/as6edEstJsqk0kG91MDg8PcXx8TAcHMjY3N/Hhhx8KU+udnZ200wNvO2U6ncbR0RHa29uxu7tL04s7OztYXFw0DDZIajQYDBok7/1+PxYWFhAOh8s0PEg73rt3r+aaBfVme3sb58+ftzwV1uVy4fTp09je3gbwThuC/cKU9SGzfiJrk2KxiEQigTdv3tBUOJ8eZnUk+JQ1m2E+SFoAACAASURBVEJ3IuneCDY3NxEIBGwPTs38MpVK2Xo3Xdexu7uLvr4++rdKYhfP8vIyhoeHDddkbdIMKo3tZrADvmw2i6tXr+Lzzz+3HLRb9SG3243FxUUkEgl0dZVrLBFEsX10dBRTU1Pw+XxlejUtQbNTK42El7o2S/U5lSNmYVX5yHNIykzTtDJ57kuXLpW8Xm/ps88+K/3+978vK9NMTphVhzVLhVqlje3KR1cLL88usqtTdUjelnzqVtM0Wt7vf/97QyqSlWOemZkxTQ/zdUomk1SymbQdmzpmFRNl7dIoeNlps2UIJz7A9qFLly6VFhYWhD5mR72VV0AWtQl5Dq/aKyuzEin1WsFLk1stgVZSF9Yv+TS7lW2slE/txi6ClZKuVZvUinrGdtE95L3/5V/+hdpFZAtRO8v6ELs8LPJ99rm8Tdn24+NRK/BeDTacwK/H24V1ON75rNYL+TU3Ug+2k/Odiv1nV1aaYLUO2ygqkaHm9wrIgh6/DsqXbbYmWioZB3Z8mewAhx/sOFkbbha1GGzK1oVlfchqrwxpE5EdZXsCZANsq7ZuFLUY9BC78wNK8o8Miksl+1+wTmOX3ec2e68Mj9PYLnpPErPYdhT5pZ22ZvsQ/wzRpEUU20Xt02p2V8soAvj1ePK3r776ynKH7/LysiElres6TbmdnJyYrpfG43EUCoWyNVceso6Yy+WQy+WQTqcxODiI9fV1wxqhmaw0i2wdVoRdGzghm80iFAqZCgCZlTkwMGBYKjFbCtB1HZFIBCMjI2WpyGKxiDt37iAQCAjTw6J1T3aZi23Lnp4eHB8f4/DwEICzJSEryNJXLdPR8XhcKqpnp8xUKoVYLCZcqxf1IRa+n7D3sW1C9jJtbm7Sz5gtP5D2GhgYEJYpW/82e79aL4dFo1GpqJ6dMlm/HB0dpbEgl8tB0zRcv34dW1tb6O7uhq7ruHLlii1BOaexa2dnB/l8XrpXw6pNRJAlj3r8gsXML52WSfa/ED8kz3WyLAaI+1A+n6dLMUdHR2WxVhbb2TaysyTPUk+7A3i/llFkWKWcyciRnwnymQZ+JClbQmDLtLrPLDVod+QrqmslsytN0wyzpmphlztE9TKzO38vPxtg05SiGYBZO7PPtLqXrxPbZrW0kcwGlWCWHbOauZVKxncUzeLM+pCsn1i1CX+d/7WFWZnsMqWoj1lB7q/V7JCvj6jO5H34bIHML1n42awo80H8qJrYJctqyNrEDmY2qBQ7y4nkM6LMjVl78dfNYjtvX1kfsqqvLGNdbXyvtd15lOqrwhGpVErJzDeBeDyuZOYbTLFYxPT0tJKZbzC6rmN8fFzJzDeYettdLaMobEHS6mqg0VjIrynUQKOxxONx9Pb2qoFGg4lGo/D5fGqg0WAaYXeV2VAoFAqFQlFXVGZDoVAoFApFXVGDDYVCoVAoFHVFDTYUCoVCoVDUFTXYUCgUCoVCUVfUYEOhUCgUCkVdUYMNhUKhUCgUdUUNNhiIqmJXVxei0ajt+1hlSVY50eoaOZpYVCY5pptcY+9llVtF97LvUdfjZ2sA/552j4bmbSd6T3L8Lm8fmd1l1whmR3mz5bW66qvML2Xw7VUr+9ipD/kMKZOvC/nHlsu2Zy2P2a8EWZ+2A7Gf2RHyvH0Isrgms4+sTez023ocs18p5D2d+gD7nvw7ymIF7+u8/djrrH34+/h7ZfXhvxdaLf6owcbPkC8qojeSTCZtfUnruo5vv/0WCwsLyOVyWFhYwLfffgtd11EsFvHDDz/Qazdv3sSDBw+oY/n9foPGye7uLnWq1dVVBAIBen12dhbLy8u03DNnzmB9fZ1eZ3VRIpEI/XssFkMsFmt6ZzeDfc/19XX88Y9/tB2AQ6EQMpkMfVf2MBqiQUGuE/sQrYZ0Oo1cLodMJoPXr1/TMmVtArzT7ujv7zfUJZvNUq0Zcm8ikWjql5sZVn4p4/79++jo6KA2AmAIapXYx059SD8bHBykfyMy6Lw2SEdHB1wuV1lb9/f3Y35+vlrzVQxf35s3bxr6tIxoNIqDgwNomia8LrIPII9rMvtYtYlVvzXzg2YQj8cRi8Xw+PFjR/cRXZlPP/0UoVCo7LpVrLh+/Tq9TrRqgLdtwsamQCCA+/fvA0CZ1s36+jo+/vhjtLe3S+tD7D02NkbjWiKRaKmJphps/Izf76dfSERkx0w0jWVjYwPnz5+njrS9vY2nT59ib28PLpcLd+/epdf6+vpwfHxMRXZYiOAREUXr7OzEd999h2w2aylqJSOfz9Pg24qMjo7SExo9Hg/OnTtHbVEpuq6jUCjg9u3bZe9NRLhIUJWJ0fFtArwNsh0dHWXCUu3t7chkMlhdXQXgXPSrkTjxS5ZisYjXr1/j8uXL9G8DAwMGwadK7GOnPvPz8xgeHpYKFeq6jrW1NVo/t9uNubk5etrtwMAACoVCywy8Dw4ObPfpSCRimFDwmNlHFtdk9rFqE6t+a+YHzWB0dBRzc3OOYyARvbRzoqYoVoggsXxsbIwKuCUSCSSTSeHEZHl5GcPDw3C73dL6kHYhgm5EwNDOd1ijUIMNAbquY3d3F319fZafZQMGmX1cv35d2MgiZVKS4gwGg5iYmKDX/H4/FhYWEA6H0dvbi7GxMcOxya9evUIwGBSm6Nh0WiwWw7Vr1yq2RSNxqpS6srKC3t7eslTk3t4eAODevXtlKUUyuwRA7bO4uGirTbLZLNbW1oT2dLvdWFxcRCKRQFdXFw4ODioKcM3ATDGXx+Vy4fTp09je3gbwLnCSL6ha2YevTyqVQqFQwMjIiLR+/MCfx0wttpGwSxN23skOdu1jFddk9pH5CN9vZX7wa8MsVgDAw4cPy5ZYTk5OcHx8bMhUBINBnDlzpmySlc1msbu7K1XVJZBJDYl9JGvlVPm1nrxXgw1+TUu0lkjSUcPDw1J5dpY3b97gq6++gtfrNZ19EClhvgOSJY90Og1N02jaK5VKIRwOY2FhAevr65icnKRfmLzM/M2bNxGJROjImL0+NTWF8fHxpqbz+bVNs/XdSCRS1mHNYFOYmUwGAGgqEng7EBkZGaG2XVtbo1mir776CsDb1HJHRweuXLlisI+oTUha+euvvzaVZCdS3plMBoVCoelr1fz6ryilauaXZly7dg2xWIwG2AsXLqCjowMAamIfvj4kaIqyVPzz2ayGyBa1+nKXwe9l4PcIdHd3Y2tri6bPp6enq/IRu/aximsy+8h8hO+3Vv2kXtiJ7fXALH6zyyFk+YqNe3t7e7h69Sqi0SguXrwofDab1bDC5XLhiy++wOTkJLq6ujA+Po6hoaGKsuF1oy5asr9QZLLJZvDSxOQZvGy0HXnwpaUlWramaYZ6JJNJU8ng/f39UjgcFj7/p59+Ks3MzNRM7rweEGlkp1LULKy0Nm8rtk14W4nai3/u0tJSmWw2LwHNl/ny5ctSOBxuabvb9UsZtbSPqD4iqW6RzLqmaaZS8EtLS47lthvBy5cvS19++aUj+/PvYsc+VnFNZh+Zj4j6rZUfNJNK/d3u9wIbv3lIPCfPYushit+yutqtj6ZpTbc5y3uV2ZDBzrxEKo9kxsLPVPr6+jA4OEhTiDs7O8jn8/T/4/G4LaVUMkPp7Oykf2NTYLK1t+XlZdMUp2xPglPMbFAN2WwWoVDIVG3QTpnEdmR9uKenB8fHxzg8PARQnubVdZ2mLE9OTkxty7YJm0khG2/JBlVS73w+T9dOj46Oamajeuzqt/JLO2WSGe/Q0FDV9jGrD79hTtM0XL9+3bDhLpvN4tmzZ8KsBtkoXIliLsnI1WtXv2hfj9MyrexjFddk9pH5iFm/teMHVpCMXCM3N1Zbpih+E4h/9vX10b0U/f391KZ8/Gb3dVSaHSKZKtkeJ9E9dbV7s0c7rYJohsCOLEUjUgI7mmevk5E//1w2e2E2UyPl2XkuO6Pjr9V6RqdpWlldq30ebx9RVoK3Oz+D4kfw7HW+vvy97AxB1ib883nb8j5Uq1mFzPcqwcov2c/w78jaTuZbTuxjpz7sM/gMhllWY39/v3Tp0qWKZ9jkfrOMiVP4+oieS+zLv7soPonu5+0ji2sy+ziJXTJ/kGVkzTCzQaWIsi1mvsn6Bh+Def+RxQrW7qI4wt7Lt6NZVsOqPmyZlfhsre3OoyTmFY5IpVK2MjWK2hKPxyueoSsqo1gsYnp62jQroKgPuq5jfHzcNNupqA/1trtaRlHYgqTV1UCjsZBfL6iBRmOJx+Po7e1VA40GE41G4fP51ECjwTTC7iqzoVAoFAqFoq6ozIZCoVAoFIq6ogYbCoVCoVAo6ooabCgUCoVCoagrarChUCgUCoWirqjBhkKhUCgUirqiBhsclZzWyJ7Lz5++xuuCEEEe/j721EBeX0F03j+re8GfrknEgWSaGK0C/65OT2u0aq9oNFomVAcY24W/l9jWTDuHra/ItqIyZX7QDKrxEdb3WBvxthHZnb2X2IC3DV8nmY9UWmazYN/VyUm8TuIIea6dOCKrjx0fIT/NFtm1HiffVgIrfudUN4W3LWsDM7sTzOKIyN/Z58piu1l9+L+3ir8bqMtRYb9QyAltmqbZPvVuf3+/9Omnn9JT39gT6HjtA1anhD/f3o5GB6v9wZ4wJzulr5IT/BoJqydAbGn3dFKr9iKaD7wGx9LSkulpnOQ0SpFeBDlVUXbCnqhMmR+0Ak58hP8s65e8NkQymTScZGi3X/G6KTIfqVWZjYD3A5mei9VzWPvwGhgyjQ62vZzUR+QjpB4zMzNl91USSxuBVZyVfdaJ3WVxxCpem8V2q/rw8PpazUZlNhhWV1fR0dFBNTbs0N3djR9//FEoi040D+bn5wEYdUqIRgSRDyZn5ot0OnhFy3w+bzg3n5RNnsmSz+fR0dHRsodBjY6O0kOTPB4Pzp07Vya1bIasvYjM9Y0bNwx/LxaL2NnZQTQaFR5MFolETJV79/b20N/fb3rIk1mZMj9oBez6CKvZ4HK5UCwWkUgkkEwmoes6Ojs78d1331F13VgsRlUndV1HoVCwVCcFyqXiZT5SqzIbgdvtxtzcHPW7gYEBFAoFx7N+8u7Ef7xeLzRNg67rUo0OPo44qY/IR+bn5zE8PCzU36gkljYCVuLdip2dHQCg77e3t4enT59ie3sbgNzusjgiQxbbrerD4kSevlGowcbPkC8Ku1LbdnC5XJibmwMAdHV1IRaLYXFxEW63m34B7e3tAXgn5MOKrxH44NvZ2YlEIkGDwsbGBtLpNA1CbErNiXx4s+EF02TI2qtYLOLBgweYmJjA2bNny8o4PDzE+vq641Tj5uYmPvzwQ2pbNmUvK1PmB82iEh9hAzUR+AoGgzhz5gyOjo7g9/uxsLCAcDiM3t5ejI2N0UEC8fN79+5Jl8yspOJ5H6lFmc1ic3MTgUDA9kCILGsEg0Eq6Q68HYxNTU3B5/MhFAohGo0KT4Hk44hVfWQ+kkqlTCXp6xFLq4UsTfh8PgwPD5vagIUdYMXjccRiMXzzzTc0Rtu1u4iVlRX09vaWLbPIYrtVfVicyNM3ivdqsMGva5FGLhaL+OGHH/D111/XtHHImikA5HI5dHR04MqVK9B1HS6XC1988QUmJyfR1dWF8fFxDA0N0VkZW2c++Pr9fnR0dFBnffHiBXw+Hx2tu91uLC8vI5fLYWpqCuPj4zVTIK0Efo3S7EsmEokYgqgZVu21urqKQCBg2vH39/fx0UcfIZfLIZPJoFAo2N6zsLGxge+//x65XA6zs7N48OABisWitEyZH9QTdu2XXxeuxkf29vZw9epVRKNRXLx4kf49lUohHA5jYWEB6+vrmJycNLT1ysoKRkZGkMvlkE6nsba2Vra/QvaFKPKRWpRZS/h9Emb7Mogqp+jL2oxIJELfQ9M02p5EnTWdTmN2dhbBYLDMn60GcaL6mPkImRiJMkb1iqVWmMV2AlHHzWQySCQSjgad0WgUBwcHmJubwwcffED/bsfuIlhl3EwmAwC4f/8+vSaL7bL6EFoxqwFA7dkolcSqgE4VIsl6Pvv5/f39UjgcLlOONXsmvwZI/ma1rru/v1+amZkRrgG22v4AESLbyZC117/+678K1SqJEiLfJqWSeG1TtNbKf448K5fLOSrTybpxI7DrIyL1WfbdePuw6838OrXIBrI9O2Y+Um2ZzUDkW5U8Y2lpSfhOTpRx7daH9RGRkixRN/3Tn/5UdSytN/y+HtnnRKrQTuxux7ai+whsbJfVh8DvBWwl3qvMhhnsSDOXyyEWiyEUCiGTydCZKpmxONlBDrwdcZPljZOTE+GeDODdzIJd/8xms3j27JnpbIQ8PxKJYGRkRJiOreX+gEptICObzSIUCpkKAInKlLXX0NAQnY2RWeDg4CDW19cRiUTg8XjQ1tZmWL7a3d1FX1+fZV0HBgYMKc7t7W2cP38eXq9XWiYpx44fiKj3rn6Rj4jKJPuK+vv76ax1eXkZ58+fp//PpnTZd+zp6cHx8TEODw8BiJfMlpeXcfHixbKshpWPVFOmDJKRq+XSSzQalYrq2SlTtC+DfW8+rS6LI1b1IbA+QrIE5J+mabh+/Tq2trZw+fJly1hqBcnI1eNXdPy+HlmZPT09GBwcpLGB2JGNFTK724G0pWhvCx/b7dRnZ2cH+Xy+oqxGPe0OQGU2RIh2C4tmdaXSuxkXP8InszN+Fs6OONkZgmhkazYbYctkyxLVp9Y7wTVNKyuz2ufxMyDRbNTs1yOlknx3t2jH9v7+funSpUvCGZdo1sb/usHKtqIyZX5ghR0bOMGOj5DPiK6xNmBtQ+pJrvH1ZW3A+5AsqyHzkWrKtIL4SSW/GJE9TzbjF81eSyWjDZz2ebM4IquPkzgim5lX8ms4MxtUCt+nRRkW8hn+GmsjJ3aXxRE+FvC/YDSL7Vb1qTarUWu78yjVV4UjUqmUkplvAvF4XMnMN5hisYjp6WklM99gdF3H+Pi4kplvMPW2u1pGUdiCpNXVQKOxkAOJ1ECjscTjcfT29qqBRoOJRqPw+XxqoNFgGmF3ldlQKBQKhUJRV1RmQ6FQKBQKRV1Rgw2FQqFQKBR1RQ02FAqFQqFQ1BU12FAoFAqFQlFX1GBDoVAoFApFXVGDDQYidOREnAswnssvOn2N1QbhT4E0K5PXWGBFv2Rl8vexZfIaJSLNjEbD19fpaY1mp2vyuiD8c0Vtwusr8O0ish9rO7ZM/pRV8hNWUVs2g2rqQ+xgdpJsJW3C217m02ZtydeHL68VbM/6kJOTeO30E9KmbByx8lnAaCdR3BOdLMm3F1sf1rdEMa/RVFofWSwlmMVv3j5sW9utTzQaLfNXWb/l/b3Zvl5GXY4K+wXCnpfvRKuDnHyYTCaF9ywtLZme/MifvMeec0+0D0T1k5XpRP9BdNJlo2HfU3aKpAjyrpqmGU7v47ViXr58Wfryyy9pG8jahIdtE9lpiKQtyDPZz/LaI7XQxqiGaupDPvvkyROhDStpE95neb+U+YhVfVhEujiNhPdDO7pHBKt+Qmw2MzNjeKbVCZ58O/Ekk8lSOBwuhcNh2j78SZWymNMqejQEJ/Wx+qwsfvM6V3w8tyqD+DXbD6z6rewk11ZAZTZ+xu/3Ux0Lt9uN/v5+W/oV3d3d+PHHH4V6C8ViETs7O4hGo2WHYPEqjLquI5lMUu2Nzs5OfPfdd8hms2Xn+cvKdIKV5HQjGB0dpYcmeTwenDt3jmqIWLG6uoqOjo4yXYH29nZkMhmsrq4CePueAHDq1Clpm/A4UU/M5/MYGxujzyRtc3JyApfLhbt371I79/X14fj4GCcnJ7bes9ZUU5/R0VHMzc2ZHi5WSZvs7OwAANUF2tvbw9OnT7G9vU3LNPMRq/qwNFt22+12Y25ujpY/MDCAQqFga5Zt1U/m5+cxPDxs0FayQtd1FAoFoXoruf7o0SPcunULbW1t9O/ET0i/IJo5onh5cnKC4+Pjmmgz1YJa1ccqfnu9XmiaZlDJZbVsZPXJZrNYW1vDjRs3DJ9ttTjiFDXYEOBEnEvG4eEhDg8Psb6+XpZqI4Givb0d2WwWV69exeeff06dx+/3Y2FhAeFwGL29vRgbG3N0kuHY2Jh0mcRKcroZOBHKIh3y2rVrZdfcbjcWFxeRSCTQ1dVF5ZhdLpe0TXhEX04rKytU/plNf3Z2dhpE2jY2NpBOp4UDJyLg1iqnsNaqPpW2ST6fR0dHB1wuF+LxOGKxGL755huhsJVTMTW2bq0mu725uYlAIOD4VFjeBqlUSipXb+azRIzw3r17wuUQMoDhJyOnTp0y3E++TNn2Iil9n88nfEajqaY+olhqFb9HR0cxNTUFn8+HUCiEaDRqOJnTrD7FYhEPHjzAxMQEzp49K62XqN8+fPiwoq0AjeC9Gmzw62iitbJisYg7d+7UrIPs7+/jo48+Qi6XQyaTQaFQMHz5P3v2DJFIBI8fPzYE0FQqhXA4jIWFBayvr2NyctLWfgaXy4W5uTmD6uLk5GTZ2l0jsxr8urHoPYjC4cTEhOWXXrFYxA8//ICvv/5a+Fld13HlyhUEAgFqc7atrdoEEH85sWqzmUwGAHD//n16raOjgwb1Fy9ewOfzlc2gUqkUYrGY8Au51vBruGb7iWpRn2rbBHi7Rk0GIR988IHwGXZ9hKdRWQ1+nd9sXwZReTYbIJjB24B80ZtlJ2Q+C7wdiIyMjFC14rW1NWSzWeorogmOy+XCF198gcnJSXR1dWF8fBxDQ0MGJVWiDJvJZJBIJGqqnCvCKrZXUh87sdQsfsfjcWiahnQ6jdnZWQSDQUP/M6vP6uoqAoGA5ZHhon7LqvGm02kkk8m6290RzV7HaSWqUc0T7fMQrRGTdT2Rqia7vsqu//HXZGWK6iVSPXWyN6LeONkjUyqVqybyCoq8rVgbyNqEYNcPZGuk/B4FUu9aKbfWgmrqI9qjUmmbiNQm+Tax8hHZu7Sa3SvdsyOygUhdVKZwy/os3ybE7zc3Nw1KumaKzCz8HgUWdr9ZK1BpfViflcXvo6Ojsn0YVsq4mqYJFcRF6sp2/ZnvQ83mvcpsyGBnXqLRPJmxONlB7vF40NbWZkg3kuUZsi+EpFHJvgw2rcqmJe3sHxGxsbGBtrY2eDwe+rfl5WVcvHjRcVajEhtYkc1mEQqFTAWARGWyszUy4wiFQshkMvQZ+XyermUeHR3Re2VtQtjZ2UE+n5em3MmMkt+bQK5FIhGMjIzQtiQznUpE7Mx+3VENVvVxWmY1bdLT04PBwUHaBtlsFs+ePTP8v8xHZJB+xe6nsQvJyNVydhiNRqWiemZlmtmAnc3mcjlomobr169ja2urrH/zPtvT04Pj42McHh4CeLc889d//deGGX0mk0EoFEIsFhPujyFZGtF+EX6/mR1Ev36pFWb1sVMmG0vtxG82ZouWBPn6uN1uLC8vG7ITg4ODWF9fp/sJ7cYRvg/ZoZ52B6AyGwTRDIEdPZJRPz+iFI1G2VnF/v5+6dKlS4ZZHoE8k1wTza5FdZGVyd/Hz0SqzWpommY6a6r0ebLZk5ndWURZH749+YyTVZuIZgT87J29j20T3j5mMxa7sw47NnCCnfqIZm4iG5jNdqtpE95+Mh+xqk81WQ1Sp1rNynm/E9lBlOUplaz7CYGfQct8lr9u1q9Fv5hg25K3j6yd7WBmg0qxUx/yGVl85u0ti998H+N/NWLHPnxW2qrfss+tJEbX2u48SvVV4YhUKqVk5ptAPB5XMvMNplgsYnp6WsnMNxhd1zE+Pq5k5htMve2ullEUtiBpdTXQaCzkEB810Ggs8Xgcvb29aqDRYKLRKHw+nxpoNJhG2F1lNhQKhUKhUNQVldlQKBQKhUJRV9RgQ6FQKBQKRV1Rgw2FQqFQKBR1RQ02FAqFQqFQ1BU12FAoFAqFQlFX1GBDoVAoFApFXVGDDYZoNFqRYh4rNCYTXuKPguXFg8gRxbyYk0hciBXZ4stk34MtU/TcWh6BXSnkXZzUhX8Xtr14u/LX2fb67W9/axBWqqZN2Hcx86G6HwlsE3J+h8gGZljZgBfcY9+TF4VjyxXdx97Lt4nM3534QTNg6+TEB8z80uqarE0IlfosWy7rQ3bKbDSVxHanfskfMW8W12Sxi7/G9032upmgpahNWoK6nEv6C4QV53EiDPby5cvSl19+SY9D1jSt7PjeZDJZCofDpXA4TJ/JH4stOhKYhRdQ4kWwzASS2GtWZTQDYi+n4lS8nVnRIZH4HIE/rp0VZqumTUgdZO8g8oNm8NNPP5VmZmaoDSoVBiP3mgl7yRAJ4hH49uNFvpaWlmgbyfqtzA+aAfE90n/t+oDML6181qpNKvVZcvS66B2c+EEjqDS287D+ZGV3WVyTxS4etn+Ruj958kQoqSBrk1ZAZTZ+xu/3U7EbIrJjR/zM7XZjbm6Onqg5MDCAQqFAR7K6ruPRo0e4desW2tra6H1EkIqIfblcLgQCAWGZuq5jbW0Nly9fBvBW4IcVliLSxuSZLPl8Hh0dHS178mQkEqF2t0uxWMTr16+pPYC3djcTO2I5OjoyiNB5PB54PB4cHR1V1Sa6rqNQKJhKfZv5QTNwuVy4e/cutUFfXx+Oj4+F/iODt4ETZJLvGxsbOH/+PK2f1+uFpmnQdZ2KiXV2dgKovN82g+7ubvz4448GKXI7yPzSic/yVOOz29vbuHnz5i/ilM9a+Qjrl1Z2N4trTmPXwcEBFY0jQm1mNm/1NlGDDQEiJVC7bG5uGpT/5ufnMTw8XKbAeOrUKQAwqI/GYjGh0/HBt7OzE4lEgg5oNjY2kE6ncXR0RJ9FUmmxWAzXrl0zPG9sbKxl0puV4HK5cPr0aWxvbwN4p5zIDvJevXqFYDBYlk5sb2/H7u4uTcPv7OxgcXERR0dHVbUJuefevXvCtKqZH7QC29vbOH/+vOMj6HkbAMDKygp6e3uly2LZbBa7ucKf5gAAIABJREFUu7tCVV3RAGZ0dBRTU1Pw+XwIhUKIRqPCgCrqt2Z+8EtC5pd2fNasTSr12WKxiEQigTdv3tClOH5py44fNINKYzvvl05iBYud2MUucRYKBYyMjFjWz06bNJv3arDBr7GJOkGxWMSdO3cq+mIgUsvEOcgXuUhbweVy4YsvvsDk5CS6urowPj6OoaGhMuljUfD1+/3o6OignfnFixfw+Xxob28HAINU8dTUFMbHx6HrOlwul0E6OhaLYXJysu4BmF/7rIVk97Vr1xCLxdDV1YVgMIgLFy7QDA4v1Xzz5k1EIhHouo7u7m4MDw/D5/Ohq6sLq6uruHLlCtrb26tqE+BtgB0ZGaHy0Gtra3RPAiD2g3rC75MQDSxTqZRwQGqFmV+ysuQAcP/+/bJ7nWQ1yHtomoZ0Oo3Z2VkEg8GydxH1W5kf1At+zb0WAV/ml1Y+a9Um1fjs8+fPsb6+jlwuh7GxMczPz9sqsx7UO7bzfmk3VoiQxS7gbQZsa2sLuVwOgUAA09PTtgdrZm3SEjR7HaeVkMmLW8GvzfHyw1bS0KVS+do0+ZuVxPX+/n5pZmZG+Ex+fZ6llda0q9k3QO43azer/QHsnhseu23Cr1OT9t/c3HTsB42iGvl1O37J72mxKpPfT1Mqmcubs8+1229lftBIqtk3QBD5pZ1rsj02dn326OiorE1k+zREftBoqontIr8UIbK7nbgmi12i+CR6F1E/abW9M+9VZkOGruu4cuWKqcojmbGIZirRaLRMlZPPImQyGYRCIcRiMczNzZWtkZKsyCeffEL/ls1m8ezZM+mauK7riEQiGBkZEa677uzsIJPJ0KwHy8bGBtra2uDxeOTGsWGDemGnTDI7F6XlgbczadEyAZnpBAIB4SzbSZv09PTg+PgYh4eHAIDDw0McHx/jr//6rx35gQgya6tlOppkC8wUfGVl2vXLWCyGgYEB+jeSMmb3G7EsLy8b9tOwsGvsbKraqt/yz3eyXEQycrXIxNnFTpkiv7RzjW+TSn327NmzCAQC2NzcpM/ml4/NyrRDrX+xZcdHZGXK/JK938zuMqxi18bGBoB3yzZmkD0jdtrEjHr/Uk4NNn5mY2MDz58/x9TUlKMUaDabxZ///GfDGqXdBmPT3AcHB2VfPmZOzqYM+TVsPp0Yi8WwuLgIt9tdluJ1KlvucrnQ0dGBFy9e0P0h1UJsMDU1RW1o56dp7NIM+45AuQ0A0M1arA16e3sxNjZmCECVtonb7cbExATdHxAOh/H111873gch4tSpU+js7DRsCKwG8gXw/Plzupzk5EvVzAZsm4jkqnd2dpDP54WB1WwA43K5cPv2bZp2JuvYN27cACDvtzI/sEN7ezs8Ho+tjcd2IPXx+Xx4/vw5xsbGbO8jkfml7JqsTarx2ZGRERQKBYNtST+y8gMryObfWm30rTS2A/KBtZ02EcU1Wexi92vwzyWxq7e3FysrK/R9yHeNrE3sUGu78yiJeYUjUqmUdEasqA/xeNzx4FBRHcViEdPT07ayJoraoes6xsfHHQ9SFNVRb7urzIbCFmRWpgYajYXMdNRAo7HE43H09vaqgUaDiUajFWVDFNXRCLurzIZCoVAoFIq6ojIbCoVCoVAo6ooabCgUCoVCoagrarChUCgUCoWirqjBhkKhUCgUirqiBhsKhUKhUCjqihpsMESjUXogip2DpQjs4S7sYTG8Jgh/4Bd/6BB/qBK5X3QADXsvf4AYezCM2aFB9T4tzi68jSq1O3/SJa8LwtrWyj7kXv6Z/KFo7L2ya6J3bbY4lR0fMcOsn8hswPs6e69VP+Hbkn2u6N5m+7QMtr5OTuLlbcv6syyOyOzD25W1rVWbyOrDH0zVbF8HKo/tgHm/NfNLka+z5fK2Y+sjaxP+XrND+Ij9nb5n3WnuaemtQzKZpOf3O9Eu4LUeZOfRs1ok/Pn2/Nn25Ez9J0+elGlJkLP6SdlsPXktFNHZ/MlkshQOh0vhcLgqfYZa40Srhbczq7/Aa8Ww+gJW9iGaHyK78RoGrM/w8HoQS0tLFeuQ1Bo7PmKGrJ/I7OOkbfnPyrQ1Wk3/QQavc2FHX4bA2pbV6rCKI07sY6UhxLeJqD48Is2ORlNpbC+V5P3WieYLiSnkv9n72Gs8bJuQuj958sRU54W008zMTNP1aHhUZuNn/H4/PcrY7Xajv7/f1rGt+XzeoPXQ09MDAMJjpVnlQHKdHN1MzrYnZY6OjppqZ3R3d+PHH3+kZbG4XC7cvXuXHiXd19eH4+NjWp6u63j06BFu3bqFtrY2y/drJOQIdJGOCwursUGO8U0kEkgmk9B1He3t7chkMlhdXQVg1Bewsk8kEjE90rqzsxPfffcdstksrYOZyuPBwQG9ViwWsbOzg2g02hKHoVnZQIasnzixjwyR8uuvAbfbjbm5OeoDAwMDBmlxGaOjo/RwMY/Hg3PnzuHo6MgyjjjBiRqvWX14Tk5OcHx8bNmn60mlsb1W/TabzWJ3dxdDQ0MoFot4/fq14fjzgYEB0yPx2TYhKsayQ7fm5+cxPDzsWKOlEajBhgBd17G7u4u+vj7Lz3Z2diKRSNCAsbGxgXQ6XdbxeEluIqyzt7dHr8disZrpMBC2t7cN4lPEGVspkJMUZzAYxMTEhGXHZgMYEVkKBoM4c+YMjo6O4Ha7sbi4iEQiYapxQuDtI8Pv92NhYQHhcFioq8KmjwuFAkZGRgC8Fbg6PDzE+vp6xanceuLEBix8P7Gyz6tXr6gOh9nSjUi6HgAePnxoajtWl6gVUvZ2cSqURSCCaT09PbbiiB37sF+IPGZtIqoPgSwH+Hy+loo3TmK7nX4r80sCO2BwuVw4ffo0tre3AbybOIkGnbI2EZFKpQxxp9V4rwYb/FqaqOMRJVC7HcTv96Ojo4N25hcvXsDn85WN5PmZgcvlwhdffIHJyUl0dXVhfHwcQ0NDFc0EzSCKgteuXaP/DzgT56lVPcz2TwBvswm5XA7pdBqaptlec9/b28PVq1cRjUZx8eJF+ndW5TGTyaBQKAjbmrePnfcIh8NYWFjA+vo6JicnDe/S3d2Nra0t5HI5BAIBTE9P0zL39/fx0UcfURXNQqFQ970F/PqvqDynNiCI+onMPmRWRpREb968iUgkUrZnQZTVGB0dpfel02kkk0n6XL/fb1AnBYD79+87epdawq/Hm+3LICqhTr8YiMozGZRbxRG79nGS1ZDVh0DaLJPJIJFI1F05tx6xHZD3W5lfEkQDhmvXrlFhwWAwiAsXLqCjo6Ns0ClrE9H7x2Ix3L59u3UlDZq9jtNK8OuflcDvFyB/M1vTZNE0rWwtkd8TwiJbf+TvI+/m9XrL/rXSmje/7i+CvAv7fuzaJr9OLdovILMrqQdvF35t1Wp/DlmfF62Fy9ZpG4WVDcww6ydO7COyiZN+YmY7J+vozcLJHhkWu/sNRHGELZu3j8wPZG1itz6yvU2NopLY7rTf8tfslimKebI2ET13aWlJGNsvXbpka59UI3ivMhsy2NmwaOZPZiyyHeRklD8yMmJLlpyFzHRqsdYWj8fLBNNcLhfm5uYMs5xQKIRYLGa6xMBjxwbVQEbnROrYrEyyLt3f30/fb3l52bAUwMqxHx0dGeorso9d2PS0bN2X3Sfi8XjQ1tZmSHXbTeWSz1++fLmmSwRWNjAr06qf2LUP317kb1b9hEh+i2xH/GdgYMD0fieQjFwtZ+XRaFQqqmdWZjabRSgUshTKksURkX3Y/U8iPzBrE7v1qWTvTq1/KWfls2ZlOum3Ir/c2dlBPp+XLoOQzCL7Gas2EcFmWXK5HDRNw/Xr17G1tWU7i1P3Xyg2e7TTKohGhqLMAD/aJKN7s1GkbGbAlimabZhlINgy+RGs6JrX6y0bOVe6S1zTtJqOljVNk47CzezO38vbj29P8p5W9hH5AXk2nx3iMyuXLl0yrQ9/3YndZTaoBDs+Qj4j+kWOWT+R2YcvU2QfO/2E9xG+n9TyVw+kzWo1K+d9QFRn8j6irJFZPHASR5xkTmVtYrc+lbSJmQ0qxSq2s5/h6yrrtzK/lGU12DYRZbjM2sQsOy2ybyUZvlrbnUepviockUqllMx8E4jH40pmvsEUi0VMT08rmfkGo+s6xsfHlcx8g6m33dUyisIWJK2uBhqNhfzCRQ00Gks8Hkdvb68aaDSYaDQKn8+nBhoNphF2V5kNhUKhUCgUdUVlNhQKhUKhUNSVv2h2BRQKRR35538G/u3fml2L1uav/gr4r/8V+PmALIVCUXvUMopC8Wvk5ATo6wN2d5tdk18GZ84A/+f/AH/5l82uiULxq0QtoygUv0b++Z/VQMMJr169tZlCoagLarChUPwa+b//t9k1+OVhQ4hOoVBUhhpsMJDT+5yckMmfyW8myGN2OhsRIePvY0W9RGf987oX7ImDrBYJL3jFllfX0+Jswuum2BUo4zUo2Pv4ayLRL7Zc1rZ8e7J2lZUJ2G+TpgixDQ4Ct2/Tf4mhIfzPM2fov8PPPzdcF/37f3//9/hffr/hvv955gz+l9+P//f3f1/23MTQkOH+w88/p9f+v/PncTI1Zas+fLn/e3RUWi8774Lbt9/apMGw/dpMjI5HFGNYH+L9UuazbJlW/URWV97X2etWsasZsPV1Wh+zWEEg78v2ab7N2O8UPhaI4jBbX/a5duK3qD58vZrSJnU5KuwXCNErePLkiaOTGkW6GzzJZLIUDodL4XC47AQ69pQ3s3P3+dM+ef0VXoeDPfVPdL4/W69W0kWxY0uCpmm2bFcqldt5aWlJekIfeQ5vd1mZsjZh/5s8d2Zmpr6aBbdvl0rAu3+3b5t+tBo/YG3LamDwuhm8DXhbyurDakfw/k3KqejUQwc2qgW8f8v6phWs78nsw+tuyHRK2LbkfZTXc7F7QmWlJxXXEpE/2T1d0yxWEEibzszMlMUGPtaTdrDqb5qm2eqPoueY1adUetcWdp9fa1Rm42dGR0dta4Q4Qdd1PHr0CLdu3UJbW5vh76xss67rSCaTBrl6AiupDgDt7e3IZDJYXV0FYNThODo6MmgZeDweeDyeMsl74K12hUhtsFmQOvKKuTzFYhGvX782SF4PDAwYdDlYDg4OqDZDsVjEzs4OotFo2cFkREuF6BQQDZZ8Pm9ZpqxNiAz4/Pw8gLeaCZlMxvI9G0WlfsD7sN/vRyQSAfBW5bW/v5/qo7jdbszNzVGbDwwMCGW1RfUZHR2lB2t5PB6cO3eO+sre3h76+/t/EQdv8eqp29vbePr0KdXesAuvJCqzT2dnJ7777jtks1lLnRK2n7hcLty9e5fWta+vD8fHx7SP2IWPXY2G1RlxuVwoFotIJBJIJpOW2WtZrCDMz89jeHi4TIvG6/VC0zToui7UfDJD13UUCgVb6q2ifmtWHwBYXV1FR0dHzbSDnKIGGzXg1atXCAaDwnQjaXxeDIf9Ys1ms7h69So+//xzQ4cmqTSfz2d4htvtxuLiIhKJBLq6unBwcEAHSu3t7djd3aUdaWdnB4uLi7Q8Nr1XibR4PSCpwWAwWCZVLcLlcuH06dPY3t4G8C6gsF9ebCqXlfI+PDzE4eEh1tfXy9KUZFDACi/FYjEcHBxYlilrEyKCB4DafXFxsamnsNbCD6ykx2Vic5ubmwgEAjRQ2q3P4eEhjo+P0dPTQ5/z4Ycf0nvtLk00A/bLPBqN4uDgANevX5cK1omQSY/z9vH7/VhYWEA4HEZvby/GxsYMAzOzfsKzvb1dJpz38OFDyyVFPnY1GnawQwTZgsEgzpw5I5yAschiBfB2OcTMZqOjo5iamoLP50MoFEI0GjWczLmysoLe3t6ypRkSe+7duydcFpP1E1l9stks1tbWmhrv36vBBr+OVot1K7fbjeXlZaq2d/PmTUQiEei6TtfTZLOuZ8+eIRKJ4PHjxzRAEIiSXyaTQSKRoE7HqhhmMhkUCgX6Lt3d3RgeHobP50NXVxdWV1dx5coVOrNg6zs1NYXx8fG6KLiy8GuUvKJlJBJBLpdDOp2Gpmm29pFcu3YNsViMDlIuXLhgGOV3d3dja2sLuVwOgUAA09PTtK339/fx0UcfUdsWCgWkUim4XC588cUXmJycRFdXF8bHxzE0NES/IGRlytqErI0DQC6XQ0dHB65cuVJ3u8uo1g/4rAZLsVjEnTt3TL9kiDIpGxTt1IeoKvMD0o2NDXz//ffI5XKYnZ3FgwcPmrZHgN8Hwe//evPmDb766it4vV6aBXICn9VgEdknlUohHA5jYWEB6+vrmJycNPQ/WT8hEGVS9ouKVRlNp9NIJpOG55rFrnphFdv39vZw9epVRKNRXLx40fZzzWIFmYiYZSCIqnI6ncbs7CyCwSCNa36/36C+DQD379+n966srGBkZITadm1tjQ6gzfqJrD7FYhE//PADvv7666ZOcN6rQ71IQ9WTvr4+rK2t4eTkBLFYDCsrK3j48CG9vrKyQqWZX716hfX1dSwuLsLlcmFvbw9tbW10hk1wuVwYGxvD5uYmgLcdp7Ozk0rZ3759G3/3d3+Hw8NDdHd3G9Kquq7jzp07wjTmJ598gt7eXhwdHdXVCUnnssLtdmNsbAz5fN7yfH6+LePxuGl6eGhoCIlEgmaMPv74Y8NSSUdHBy2TBF8C0QywKlPWJgBwfHyM27dvAwBu3LiBQqGAvb29ltB/qMQP5ufnhVkNK/EyO4JyovrIRKJYKe729naaHWzG8iDJYpFMFovX68XU1BRisRj8fj+KxSIKhYLttHaxWMSDBw+EWQ0z+2xubuLmzZu0nWZnZxGLxaifsrD9hFyzI7xIlszM7MHGrnohi+1tbW34wx/+gMePH8PtdtMvbjtLO2axIp/PY2VlBSsrK4bP//nPf8bDhw+RSCQwNTUFt9sNv98PTdOwublZ5rtkqZZdAg6FQnQZ5NSpU2hra8PR0VFZX2P7yfb2tml9bt26hcXFRSwuLhquraysUF9sBO9VZqMayIzF6pcqy8vLOH/+PH7zm99gbm7OMIINhUKIxWKYm5vDb37zG/T399NUMknLs6lltmx+rTWfz9Mvz6OjI2GdyAwzEAgIA4XTvQN2bVAporVNO2WSmZdotgcY9094PB60tbUZlkrM0v1kBi5a/xSVKWsTXddp2vbk5MRR6rzeO8hFfiArM5vN4tmzZ2VZDTa7IxpoRKNRW4JyfH2y2SwdoPOBcWBgwLDPSZTurxSSkavVrLyvrw+Dg4M0g7mzs4N8Pm/IaMrKJJ/n/VxmHwCGLzKZ37H9BHg3O7cSXiT+IOpDVvtERJj9cq8SyJd5f38/fQcSo9l3EpUpixVsZieXy0HTNFy/fh1bW1vo6uoCYLS12X4yEvPIgLOnpwfHx8d0ksIvi7Gw/URWn8uXLxuuxWIxhEIhZDIZg7/U0u5CGrodtYVJJpMlr9dr+Mfu2CU7efmdyWQ3PLnHbJezaFc2+Ru5l901vrS0ZKgLv5vb7Dr/TPY+vq6V7EjWNK106dKlmv2SQtM0Wh/Rc83szrYX/x77+/ulS5cumbYJf53fNW52n6xM/l7+ubx/Ofn1hJkNpEh+aWHHD8hnRNfMfknCv7/X66V15m3O2siqPqyPiD7DXnfk0xa/RiF1tvvLBTuwfiBqT3Kd9w/+l1IsMvvw8YAtU9ZP+Dbh/ZZta77fWsUuuzaq6BdGJrA2kvkuX1dZrODvl9mP9Us+FvDPZK+ztnUSv2W/uDH7NUw97M6iBhsKRySTSWdfeoqawP/00JIG/6zzF4mFjWRf8Ir6wf9sWtEY6m3392rPhqJyyJowAMu0qqJ2ZLNZhMNh+Hw+y+UHKT/8ADx9WtvK/dL59383/v/z529PET11CvF4HFNTU9A07Rfxs9pfC9FoFA8fPmzoXgJFY+yuhNgUil8j/+N/AHfuNLsWvzyUIJtCURfUBlGF4teIkkuvDCXIplDUBTXYUCh+jfz3/w78hVolrQglyKZQ1BwVjRSKXyN/+ZfAxATw+9+/+9tf/RVw7lzz6tSq/Pu/A//2b82uhULxq0YNNhSKXyv/5b8Y//+//be3ezkURtT+FoWi7qhlFIVCoVAoFHVFDTYYyOl9Tk/IZM/lF52+xmqDsCcy8uf586cGkhPdRGf9s9f4+rL6DOwzed2GWunDVAt5Fyd1YQWkZPdGo9EycS62PfhrVm1CRONk4lOie2VlNgP2PSo5NdDshFFZH2LbTNYmvG1lbcL7NN8m7Pu2gt2tYoXde9l3sfJZWZvIfNYsjvDlmdm+3iffOqHS2M7e6yR+83Zl24v3dd4X7PYT/hrfF+qtR+MUNdj4mXg8jlgshsePHzu6jyi2Tk1N4cKFC8LnEkGeXC5HlUDJUeJjY2MGwSLidKlUColEAplMhookEbEeclQ2eebU1BTu3LmDYrFIj4z+9NNPEQqFhHWOxWL06FpSn2ZB1C81TXN0HysgRcSMdnZ2DJ8hx41//PHH9G/ZbBb/+I//iPX1deRyOSwsLODbb7+Fruu22gSAQXyK/C2bzdLjg8n1RCIBXdelZTYLIn5Hji+OxWK2vxCInXg9DFkf0nUd3377LRYWFspsQI5sJv6cyWT+//beL7StJM37/y68FxHpCCuiYZBAWQnFGzAT2ujCDk5jXzRiAl5P77CGEb5ZJRPsi7aTboI16Tfb7nTDzBytdzCdXDikk/RcJBrsxc16DAGvITZBRIY1CgLtZGUhvxisWWhsNfGkZfgt6HfhrkqdUp065+ifnbg+YEh8fE7Veeqpf0/VqS92d3epLLqsTG7dugWPx0PfheSDzxfvBweBWVthdi85krxQKODp06cIBoOm9pGVicxnZe0ILz5ZKBQwMjKiO5LcyEcOglrbdnJvLe03AIyMjFD7kPIikOPCyXVytoWsnpi1IwsLC+jp6UGhUMDi4iIePXp04INrFjXY+JHBwcGaOt5gMIhvv/1WeHZ9uVxGJpNBPB6vOgSLaGiwIj89PT3Y3NykegKRSIQ6djKZRCqVQqlUwubmpk58iqS9t7dHG4I35UCc8fHxmtQvWVgZaQKRVB4dHdX97fb2Ns6dO0crvtfrhdfrxfb2trRMgH1BOZJXIj5FrrndbmSzWSwsLADQ60zI0jwMbG5u6hRzzVhYWIDH46kSEJPVIV6OPp1OY3l5Gblcjmpx3L17F4Be88Gsnuzu7uo0Wnp7e3U6FEZ+cBDI2goz0uk0xsbGquq1mc/KykTms3baEZESrZGPHAS1tu21tt/1IKsnZu0IK8Dp9Xpx6tSpQ9PGAGqw0VS2trawtbWFxcXFqlAjaWBZkZ9EIoGNjQ1d50lmGOFwGG1tbdje3obP59OJTy0tLWFtbc2yY0UikZrD54cJEqoMhUI6OXOijjk8PIyTJ0/q7nG73Xjx4gWdDWQyGTx8+BDb29vSMuHhBdxcLhcePnyIZDKJQCCAjY0N2sDJ0jwo2BAwLx8ug3TeVv+esLGxQWe+JJo1MjKCzc1NqpQKgObn4cOHcLlc0jJxOBw4ceIE0uk0gNeiX8ViEeVyWeoHbxJksvHq1SsaXifLAXZ8lkfms3aYm5vTKdHW6iOHjVrbb8L09LThEtP8/Dw6OjqqlmZk9cROOyITcDsojtRgg19ja8Va4vr6Ot59910aaisWi1hdXYXD4cBHH32Ea9euIRAI4NKlS/jggw90ochcLoeLFy8iHo/j3Llz9PddXV3weDzUWb/77juEQiFT9VbSqLPh82vXrjU91MavUTZqLZEoHZIQJnkuCSeKZmXBYBAXLlxAKBRCIBDAwsIChoaG4Ha7LZUJ8DpEzA5wWMVTUs7Ev2RpHhRsKDwWi+HSpUumyzrlchl/+MMf8Omnn9Z0XP2rV69w9epV+P1+XTSLrDUD+0tUHo8HQ0NDKJVKpmVy+fJlJBIJBAIBhMNhvPfeezRKI/ODZsGvmzdSIfn58+c0hB6JRHD37l3LPitC5rNW4aMa9fpIrTSrba+1/WZVWMmSK2mfurq6dGrgAOgSOWBcT6y2I6VSCePj4xgeHj5UshJHarDBrzW2Yr/C6dOndaE2j8dDQ23svoO5uTn89a9/hc/nw7Fjx+B0OvH111/j/v37CAaDdPRKHItdcw+Hwzhx4gQdbVulvb29JevYbOUqFAoN15pwOByIRCLY2NigM4xYLEajHsvLywiHw3R2wTYEn3zyCXZ3d6ldjcqEUC6Xcf369SoZ9VwuB5/Ph/7+fjgcDkxMTOikomVpHjRnz55FR0eHaaSFzKTC4TACgQAikQidoZlFyPx+P7744gtEIhEMDg6iXC6jWCzC5/PRWRiZCY+OjsLn89FZo6xM2Dr99OlTHD9+HH6/35IfNAN+QD83N9ewBp8sqwKAz+ejERwznzXCzGfNIJEjNqpRj4/UQ7Pa9lrab1HejPaukOUXgqyeAObtCNGwisVih24p/UgNNuqBzFjszFS8Xi+cTqcu1MaG3lnIJrazZ89SB+zu7qaVeG5uDmfOnKlquMgoljQYdlhaWoLT6YTX67X097XYoF6spEnC536/v6rRWVtbQ19fHxYXF6v2hpAIRU9Pj7BDYMsE0M8ERQOmzc1Nupa7vb0tzK9ZmiKavauf3SMhS5MfNCYSCbrRzaxh6+zsRF9fHw3rZjIZbG5u0v+XSiU62Nnb2zNc++bLhIVsnP7ggw9s+YERJCLXyl39ojRJe7CyskJ/t7Kygp6enqo6L7OPCCs+awQpQ3avRj0+wr5Dq5d4RWnW2n7z5PN5PHv2THgfGRSTvS1m9YQgakfYTcS1DDSabvemaMm+gaRSqYrf79f9sJLeRG6al1cnsrzsfefPn6+sr69XKpVKZX19vXL+/Hl6jZXvnZmZob/XNK0qT5qmCa+zabJpsfnk3yWVSlVdsyVZzuSJT7MeWBuI3tXI7vx9RrLIOzs7lWg0SvPL24C/T1Ymoryy+TLKk1maZhgYFRTBAAAgAElEQVTZwBQDCXXeZ0V+QP5G5iOpVEp33awOsdf5d+HvZWXdZWXC3ifLK+8HVuxE6q6obtaCWVvBvg8va8/7EJsnq/YR2cmqzxpd5/PJw/uIFYxsUCtmNqhUXtuBr5u1tt/sNaMyNmoPjOqJWTvC9he1tPGNtjuPGmwobJFKpex3eoq6mZmZsT84NOhEFRwGdrLaoSoaCxmU2R2UK+qj2XY/mseVf/ON0kIA9rUyfvlLSwqhZC0QAO7du3eoNh69zeTzeUSjUYRCIfz2t7+tbx16ZUUdVy6CWZ4gzM7OIhaLQdO0hu8xUhgTj8cxPT2NRCJx6PYcvM20wu5/U6lUKk158mFkbw/o7ARevDjonBwe2tqAP/95X7hL8XahND9qY2JCDcoUigZztDaIfvONGmjwfP/9vl0Ubx82v05SKBSKZnG0Bhv/8z8HnYPDyY+70RVvGf/0T2rAUQs/+9lB50CheOs4mns2CH19wCE4TrflrKwAy8sHnQtFs/nJT4B0GvjjHw86J28O3d37PwqFoqEc7cFGb+/RXJv9/HM12DgqnDlzNH1coVAcKo7WMopCoVAoFIqWowYbCoVCoVAomooabPxIPp+niop2hHx44aX3339fJ2wWj8d1ImTsUbD8vewRxWbPJUfL8mJPvOjZm6DuSt7FznHcZvZhhZnYd7dqH+IPvI4Ga3c2v+zvjfLE/t1BlwcvXGU1PyL7sfey/m6kQRKPx6tswz5XVpZW/J3Nj5Ef8HltVZmw7YzIP4zg/Yu9l7ePnXaEYFYmovJk07XzLgeBUb01g7Uff2y9zC/ZNEXp8eVCfE9WXmZtl8wPau3fGkpTjgo7rFg8UZGcHFjLSWozMzOGxxvzR/fOzMzQ0wnX19crH374IT3Wlr1G7iXP5U/xlB0JLDym+RCdLKlpWkXTNNsnZMrsQ2xJ7CQrR5F9yO9u3LihK0tR+RmV9fr6eiUajVYdyR2NRivRaPRAT0fk/Vt6lDeHzNfYMjA6jZCUM5se7/u87TRNqzommpS9LD92/KCWY7XtwttZ5CNGGPkaf8opX7ayesI+my+TnZ2dypUrV3RHZd+4caNKhuFNOOXTTr1lIT785MkT4UmyMr+UtWvkuVZOppXllfUnMz9gqad/qwcV2RCwt7eHly9f1qTKubGxYSjvvLm5SeWvgX0FP3I6odfrxalTp6gYlc/nw1dffYV8Pq8TGiPPiUQi9BRPItKzJ/iEdWlpCWfOnKFS6IeN8fFxy8JYLDL7BINBfPvtt1XiRSJE9rl79y4uXLigE1UiaRDlzXK5jGQyiVQqJRSvmpub06lhlkol3LlzBx9//DGcTqft920kmUwGAOj75XI5LC8vI51O1/Xcrq4uWpZE6ZIVVMvn83j8+DFGR0d1921vb+PcuXO0DLxeL7xeL60Lfr8fmqahVCpR4SorqqZ2/ICvm82A97V0Oo3l5WUq9FULpM6zyqQ9PT3U7rJ6AhiXCVGQvnv3LoBqsb50Oo2xsbFDf8qn3XrLQsT8jN5R5peydi2Xy6G7u9vSybSy/oT1JzM/YKmnf6sHNdhgIGGvUCiECxcuWO6g2RBVsVhEf38/vcaGthKJBJXR5iEy26Rh7OrqwoMHDxCNRtHR0UElh4H9BiSZTNIw2NLSEtbW1qokwkulEh4/foxf/OIXtm1x2JHZxyoi+6yurlaVIaCvoET9NRwOo62trcru+XweL1680KlhkgHMYRj0sR3r7OwsEokEPvvsM2xsbFi6n0iGy8KxvEImkSMfHh7GyZMndX/rdrvx4sUL2vgTmXJi18HBQcRiMYRCIQwMDCAej+s6ACv5EWG1bjYKtuOIx+PY2NjAyMiIocItz/T0dNWSBhkUsMqkiUSClqWsnsjKxOFwYGpqCgCofR4+fAiXy0U77FevXtF2r5VK0HawU2/tYuaXRqysrOCdd94xXIKS9ScEvu0y8wOg9v6tUajBBsPg4CAKhQKy2SySyaRlaelgMIinT5+iUCigp6cH169fpw0eK3Udi8Vw6dKlqkpJZOKHh4fpTHh1dRXRaBQPHjzA4uIirl27RvPT1dUFj8dDG9jvvvsOoVCoaqR6WKIa/FpjIyS7ZfaxCm8fUkEnJiYMZ7i5XA4XL15EPB7HuXPnhH/DRzXImmqrNTb4dX7RnoWNjQ1MTU3h+PHjlp7JSohns1kAwK1bt3R/Q+Sv2QZtYWEBPT09wsY4GAziwoULCIVCCAQCWFhYwNDQEPXn2dlZaJqGtbU1TE5OIhwO03exkh8jrNTNRvPq1StcvXoVfr/fVkSPtE2FQgFra2tIpVKYnZ2Fw+HARx99hGvXriEQCODSpUv44IMP6KBGVk9kZUL2DgBAoVCAx+PB0NCQzj7Pnz/H4uIiCoUCIpEIjYIcBPx+BX7QaaXe2kXml2YsLS3h3r17KBQKmJycxO3bt2l+Zf0Jez/bdpn5AVB7/9Yo1GBDgMPhQCQSsTzTYyGzWdGSxtmzZ9HR0aEbUROBs1gspqv0KysrGBsbQzAYRDAYxOTkpC6aMT4+ThufcDiMEydO0NEtsD86fvTo0aGIarAdQqFQaEina2YfM0T2WVpawvz8PO30YrEYpqen8f7776NYLMLpdOLrr7/G/fv3EQwGaTmyg7zV1VWkUinqBySMS2alHR0dmJ+fRyQSafomLbaDKhQK1L98Ph+mp6d1HZ4sXGsECdWylMtlXL9+HT09PbScySAuFovRmdXy8jLC4TCdobN5/eSTT7C7uwu3201n0bFYDC6XC11dXdA0DSsC8TRRfqwiqpuNxu/344svvqDRhXK5jGKxaGlJiIUsURHYzmlubg5//etf6TON6slf/vIXaZmQSCuJ9oyOjsLn8+mWfMjSBLDvU8VisfWbDn+EHTgWCgVMTU3B4XDg2LFjluqtXez4pQh2GdztduPly5fCPkPUnxi17TI/YKmnf6sHNdgQIFrbJCN9s3Dh0tISAOg6fgK/7pnP5zEwMFA10CCwzmAUaiVRkf7+ft1sfG5uTrcO3gis2qCRyNK0Yh8jRPbhO2dN0zAyMoKnT5+io6MDPT096O7upo3E3Nwczpw5Q//Prg+T35FwNDv7HhgYQCKRoA2iGWTW1qjBSXt7O/r6+ugSRz6fx7Nnz+j/raZJBhG9P57CS8LU7EADqO4I1tbW0NfXh8XFxarZPYmK9PT06JSF2fI1aiT5/NiBr5vA64hco2aAnZ2d6Ovro0ulmUwGm5ubuj0lVtIUlRdhdnYWxWJRt99IVE/a2tpMy6RUKtGOeW9vj95LBnVsx7qysoKenp6G7Hlp5BdbJK+yeltPmlb8kqe3t1c3MUqn01X5IYj6Eyttu8gPCKL+jdzT1K+yWrod9aCRfIUxMzNT8fv99IffqUt28LJfgVQqr3dlk/vYncNk1zG5xu9K1jRNlyb7NyQ98ns2Xfa558+fr/qKgN/db8cOZmiaJkyzVni78zY0srtV+5AfNs+m9mHyxu8EZ8uMv8Z/JSSilp3gRjaoB9ZvReVJbMj7bCqVMqwnorIU5Zn/KoMvS942snoky4/MD8zqJmsjK18uWIXNr8g25Dr/pQJrW7682Gt8XmX1hEX0RRJvWzZP/HObYSMrX2tYRVZvK5XXNmT9h39H3sdkPmTWrrH5Ye+T9SfkulHbJfMDs/6tUmmO3VnUYOMoUocdrHSoisZj99NgRf3wnxMqWoPRZ9OK5tJsux9tbRSFZcjeEgC4d++eMOQn5JtvgP/3/5qYs7eIv/1b4Je/1Cm15vN5RKNRhEIh/Pa3v23qp5mK18zOziIWi0HTtJZv7D3KxONxTE9PI5FIHPrPat8mWmH3v6lUKpWmPPkw8vnnwM2br/8/MXE0RapaYYe9PaCzE3jxorHPfdtpawP+/Od9xVaFQqF4S1AbRBXN4Ztv1ECjFr7/ft92CoVC8RZxtJdRVlaOZmTD4udZdfE//9P8NN5WBJ/AKRQKxZvM0R5sLC/v/yiaT18fUMMniUeClRXlhwqF4q3maA02BGdfKFpEb+/RjCJZ4fPP1WBDoVC81RytPRv/9E9qwCHiZz876BwoFAqF4i3maEU2fvITIJ0G/vhH4eWtrS3827/9G37yk5/g5z//ufAUUJ7/+q//wn/8x3/ofud0OvHzn/8cTqcTS0tL+O///m967e/+7u/wwQcf4P/8n9em39nZwb//+7+jvb1dd9yyUX7+93//V/dckh4RU0omk/jP//xP+px//Md/hNfrFb9Ad/f+zwGxurqKSCRC/z8yMmJJM4K/DwD9bIscmT0/Pw9gX0X0wYMH9MQ98pkXfx+BfOb7/Plz6b1sXs3SPGyw7whU28AII7ufPXtW9/6EgYEB+skue+97770n/ISafHLK5oe3Lf85qlGZ8Pll0xS9hx071Ar5lHlra8u2jxDbsDYVPfvv//7vhTbgbW7FZ1k7semSvBDYe2V1kyUej+NPf/pTS+oJm18j+4lg30VkHyPf4+sXa3vWB0T5YfNqx2fN6gl/veWfFzfl9I43EHJo0pMnT+o6tGp9fb0SjUYrOzs7lk6LJCf33bhxQ3fqm538iE66JKRSqYM5DKqGg8NEpxgaYee97NiHnOBndMIeeY7ZATiyNKto8WFzvF+2wu47OzuVK1euUD/WNE14Ams0Gq1Eo9Gqk0nJwVr8CYqyMrGSJsGODWqFT4NtK8wgeTc63K0Z7cjMzIzhPfW2OSRfzba5KD9W6ybva3x58c8h5UP+beTDLHxd5A9NlNmS9ydZPSF14yAPqDtayygSBgcHLWtVyOAVP80g0uP8GfZ28iMT0WLlxA87jRBIEmHHPul0GmNjY8IRf1dXF525EDEsI02WWoTNWkUmkwEA6nO5XA7Ly8tIp9MNS4OXwHa5XJiamqL1ore3VyfcVSqVcOfOHXz88cdwOp26Zw0ODtIZmtfrxalTp6ivyMrELE2WVigk82mk02ksLy/rxM2MGB8fl0b8Gt2OlMtlZDIZxONx6wf4WSSfz+Px48cYHR1t6HNFsHpFDoeDCqilUilTfaft7W2dBonX64XX68X29naVf5dKJaRSKap54vf7oWkaSqUS1ewRiaLt7e3h5cuXtM3b3NzUaSsR3RyRSBvvT7J6ksvl0N3dfaAH1KnBRgPJ5/N48eIFVeojRCIRocz36uoqisUi+vv7a0rr/fffRyAQqHoGK7ecSCSocuNhJR6PIxAIIBwOY3h42HLjNj8/j46ODqGkdC32IQ3Rq1ev6L1GonOlUgkvXrzQiWHJ0jxMsAOs2dlZJBIJfPbZZ5aFpGR2J5h13rxwF+kszTp7okbKipcRRGUiS5O9j+04mgXbmcfjcWxsbGBkZMS2iCBPM9qRra0tbG1tYXFxkbZdRKGXQJSMRdeMfKRcLuP27dsYHh6my77NhO3MiVBgOBxGW1ubqcKv2+3GixcvaP3PZDJ4+PAhtre3dROjfD6Pixcv4le/+hVVbx0cHEQsFkMoFMLAwADi8bhuAkNEz0KhkM7vfT6fTqRtaWkJa2trVXk181m+nqysrOCdd96h7d7777+PfD5fg0VrRw02Gggf1eAVPxOJBK5du4Z8Pk9HuxMTEzVFHVg54Z6eHly/fp06KKuyGYvFcOnSpZaptNbC+Pg4VZ7UNM2S6iArW5/NZgEAt27dotfrsc/z58+xuLiIQqGASCSCu3fv6tImyqR85yhL8yAgDZpooAu87vCmpqZw/PhxS880sztg3hASRUrSsZF8mc26iMKxaEBqVCZGabK0IqpBePXqFa5evQq/329pb5IZzWxH1tfX8e6779KyLhaLurJiFWNTqRRVqpX5yMLCAnp6elp+FHkul8PFixcRj8dx7tw5S/cEg0FcuHABoVAIgUAACwsLGBoa0kVenz17hvHxcdy/f183AJ6dnYWmaVhbW8Pk5CTC4bCu/hH7ZbNZJJNJne08Hg8dqH333XcIhUJV0V6ZzxrVk6WlJdy7dw+FQgGTk5O4fft2S9snNdhoEKurq0ilUlVRDZb29nacPn0awH7Bz8/PU0eOxWKYnp6uacRJ0hSF2s6ePYuOjg7TUfxhwOVyIRKJ2J7pERlpI+zah4Rcgf2ZBht6J5useBl1O2m2CrZDKBQKtIH3+XyYnp7WdXi1LPsY2f3u3buGDeHs7CySySTdEEfC3GSW3NHRgfn5eUQiEd2MmGy4i8ViVR2VWZnwabLk83k8evSo6VENAPD7/fjiiy8QiUQwODiIcrmMYrEoDK9bpZntyOnTp+nvHA4HPB6PsG6S5SsRrI+QgVEsFqOz+uXlZYTD4arISKM4duwYnE4nvv76a9y/fx/BYNDWci1bhz755BPs7u7C7XbD7Xbj+++/x+LiIh4+fAiXy4Xt7W04nU5UKhUkk0nEYjG4XC50dXVB0zSsCA5TdDgciEQiuqgimXwVCgWEw2GcOHFC97GCzGdl9YRdnnG73TQK0yrUYMMi5XIZV69eFYbV2XVB2RLA0tISnE4nvF5vVUegaRpGRkbw9OlT2zOspaUlABB+PZPJZJDNZhuyD0Jmg0YgWtu0kia5r9fg0DCr9iENI9sosKF3EoY1G2iYpWkXsuxjtGRhl/b2dvT19dHlhnw+j2fPnumWH6ykKbI7eZaoIYzH41WdPh/9y2azGBgYQCKRoHsN8vk8BgYGhA2oWZmI0mSZm5vTrcuzrK6uIhAI0FlnvXR2dqKvr4/OgDOZDDY3N3UzYrtpNqsd8Xq9cDqddD+JbIlK5D8E1kfYiCKJiPT19WFxcVEX5SEROSsRTjNIne7u7qZt89zcHM6cOaNrq83SJJGznp4euFwuOsAibQPpA9hlOnZgZrRESe4TDfRJhKK/v1/nu0Y+K6snvb29uuWZdDpt2wb1ogYbP0IqeSQSwfPnzxEKhSw37qTR4KMapKMkYWxZo2cnP+w6ayAQoKFw0iGSdTmyJ4GMvOuFzG6+++67hkVKyH6NQCAgXNs0gtiHzJDYClaPffr7+1EsFul14HV4f2lpCc+fP6czM3ZPhyzNejl27Bh8Ph82NzcbMhNxuVz49NNPEY1GEQgEEI1G8S//8i+WOieZ3QF5Q/inP/1Jt5ZvtWGbm5vD1taWbu8TqQtmZSJLUzYwAvZnf16v1/JeFjOCwSCGh4dpFELTNMsKyqQjiMVi9H2sRANqbUccDgc++ugjXLt2jZb18PAwLVd2iY73HzMfMYNMNurdy0Ig9Zet01aWsNj2u6Ojg0akCKOjo0gmk/Q6GfA6HA5MTEwgkUjQNIvFIt0Qy9qOfy7bPonaQ5nPyuoJvzyTTCarNug22u5VHNh3MIo3Ev7TLENa/DnnG40FWxl98qhoHuSzxIP8XPAoYvZZuaI5NNvuKrKhsAQZcduZjSnqh8w+7UTFFPUzOzurm60qWkM8Hq8pGqKoj1bY/W8qlUqlKU9WHG0+/xy4efP1/5UQmzG8ENvEhNKRUSgUbxVH67hyxcGhFHats7y8P9j4278FfvlLpeejUCjeeNRgQ9EcVAdZOysr+z8A8PHHwJ//vK/ro1AoFG8oas+Gojkohd3G8P33wDffHHQuFAqFoi7UYEPRHIjC7sSE2qtRLwd4MJhCoVA0ArWMomgeZ87s7z34/PPXywKA2ixqBr9hVKFQKN50mvJB7RtKKpWq+P1+WxLz5Ft8v99f8fv9lfPnz1NZX/6a3+/XnZVA0iM/rFwxfy8viUy+ifb7/brvomVpmuWnaUjOkeDzZPVMAzP7EKl4vkwqFb3tePvJykSULv9N+szMTNW9tu3egjNK2DzVanPRu9RiH9F1Xp6bTY+Up1l+ZHXzoJD5pRG8v4rsR2wk8itZmbD+zrd7srrA54m99zDanfWhWto8kV/KbMDeY6dMRGVt1C+w9daKjxw0arDxI+TQpCdPntgabIiew3cyVg5J2dnZqUSjUVopNU3TOYqmadS51tfXKx9++CE9YEs02BClaSc/DUXSec7MzFS9l5WGSWYf3pbr6+uVaDRa2dnZqbIB/7cs/DVSoY06Z03TpA2LZbs3ebBB3uPJkyd1H1rF+nut9iGkUqlKNBqtRKPRqsGG1UZT9rd2ntMMZH5pF9bfSV0QHfwmK5OdnZ3KlStXaPpsneKv/fDDD5UbN27o2ie+jEga7L8rlf1yPUi7p1IpnV3s+oGRX8psUE+ZyNojO/WW9ZHDgNqz8SODg4MNOV66FkErADpxoHK5jN3dXd2RtL29vfTY5GAwiG+//VYos/2mMTg4SA9N8nq9OHXqlOlR6Gb24RUR0+k0lpeXkcvlkMlkAOwLsAH7apDLy8tIp9NV6fCCTblcDt3d3cJDnkqlEorFYs3qm62EaFTUe3gPr+5aj31KpRLu3LmDjz/+GE6nsyH54am1bjYKmV/aIZ/P48WLF1QeYXx83PD4bVmZuFwuTE1N0QP6ent7qegg0fQhise8xpLf74emaSiVSlWaRj6fD1999RXy+bxU+6MVsLpVRMMkmUwilUpZ0neS+aXMBrWWiQw79Zb3kcOAGmw0AFZjQCRjzZ5VL5L5DgQCCIfDVBLY4XDgxIkTtAMkFYZVHzVDlqbs2kGytbWFly9fmg6izOzDdipERn1kZASbm5vY3NyEx+OBw+HA7OwsEokEPvvsM53+hahMgH1RtnfeeYdqF7DKmqTD+P3vf09ty4tpHVa71wrfedZjn7t37xrKwwOgqrCBQMBQE0Qku21WN1uJzC/tMDc3hwsXLlg6xVdWJqK/JUJiRCAPgFBDaHBwELFYDKFQqErDo6urCw8ePEA0GhVqirSSvb09vHz5Em63m4r2hcNhtLW1WdJ3kvmlzAYyzMrk+++/RzgcNi0vGXZ8pFUcqcEGL8LVKBXNYDCIp0+folAooKenB9evX0e5XK5StEwkErh27ZrOeYic8NraGjRNo53Q5cuXqZBPOBzGe++9RztJGbI0reTnoCAKh2znLsPMPq9evcLVq1d1MuospLGfmprC8ePHddeMygTY79Du3buHQqGAyclJ3L59m/rQ/Pw8+vv76b2PHz8+ULuzgk+NHuAYRRFqsQ/Jl1GHxCqbrq2tIZVKVQ3kjPJjVDebBS++yKsVm/mlGbXMWGVlQpidndUNxsh7AEChUIDH48HQ0BB9l9nZWWiahrW1NUxOTiIcDtNyXF1dRTQaxYMHD7C4uIhr1641TDnXCLO2PZfL4eLFi4jH4zh37pylZ5r5pcwGZhiVCa+MOzY2hvHxcVsq24cxqgEcscEGX5CNUuVkIQUsUudsb2/H6dOnDfMWiUToLIfN69OnT3H8+PGaQpGyNGXXWkmpVMKlS5dsncsvs4/f78cXX3xBZ1TlchnFYhE+nw8+nw/T09O6xt4ovM6XCbAfnSCDIbfbjZcvX9KyHhgYoMszx44dg9PpFM6eWmV3Xn68kZoHd+/erYoiAPbtUywWkUgkaOSio6MD8/PziEQiwskAkfa2mh8WWd1sFPzAcm5ujtpD5pdWKJfLuH37tu0Zq6xMgP1Ok9feIVHGy5cvA9hXOPX5fMjlcnQpIhaLweVyoaurC5qmYeXHL85WVlYwNjaGYDCIYDCIyclJnbx5MzBq24mfff3117h//z6CwWDV8qgIEi018sudnR2pDcwwKxNCZ2cn2traLNuhVh9pBUdqsFEPZKTPz1R4lpaWAICuefLXnE4nvF5v1TV+zY9ldXUViUSippGqLE3ZNRFWbWCHfD6PgYEBw4GGlTR5+3R2dqKvr48ux2QyGWxubqK9vR3t7e3o6+tDZ2cnTf/Zs2f0/yx8mfT29uoazXQ6jTNnzsDlcqG9vR0vX77E1tYWAPmSkF277+3t4Re/+EXDInFWIDNFUZpGMte12OenP/2prnPOZrMYGBhAIpEQTgZE5WUmFU+Q1U0RRCq9UbNymV9aSZP8vZ12QFYmwH6Ez0jkr1Qq0Y55b2+varmH/T+7DMn/3+4yEYnINSIS53A40NPTg+7ubvrOc3NzOhuI0uQHjUZ+KbOBEWZlwiLKq4xafITQSLsLOegdqocF/jMvo0/6+C9V2E/ZzD5f5Xcla5pm+HkYmx+j3cxWPgeUfZZYyydgmqbZ/5RN8nUFawM7dpfZh78uKzP+XWRlwl/n02XTlH0CbWp3zl7/3//9v0Ib1IrRJ6Oiz/pEeeW/BOKv2bWPKG9Gn74alYkoP7K6aQVyfyO/pJD5JXud/4pAJnfPfxrMv6tRmfD24f2AbxNln1rK2hm7fmtkg3pgbWD1k2sWkV/KbFBrmfDPlPUnfHnJfMQKzbA7i1J9VdhidXXVvsy8UoC1h0AFdrajQ8nMt5hyuYzr168rmfkWU8uyqqJ+mm73pgxhFG8dZMRd0+yaj2yoH1s/932+1hy+pqCQmelhOqfgKEBm/C0/C+iI0wq7q8iGovn87nfA9esHnYs3l4mJ/eiQQqFQvKGoDaKK5qMUYOvjZz876BwoFApFXajIhqI1vHgB/PGPB52LN4/ubjXYUCgUbzxqsKFQKBQKhaKpqGUUhUKhUCgUTUUNNhQKhUKhUDQVNdhgYLUk7JzWyGtQsKf/sVoJ/KmAvI6C0X0yQZ54PC68Rk4i5N+F/X2tIj+NhgifyUS27N5nVCbs+4t0Q3iNBdHJpUana7L38qfw8eny+TUqr2bBv6fVUwNF9iP3WvFZtlxYG8jyw5cl/1ySJ9kps0b1pNXIfMQMYgcj/yCic6xdzcqELU/efqyInch27Luw1/k07dTpZsC+h936ZWYDozKRtSNm9qm1PznsqMHGj6yuriKZTCKbzVLRplu3bpnel8/nqfwyEYpKJpNUdnhoaAgffvghBgYGqu5dWFhAT08PCoUCFhcX8ejRI+rM7DUi1jM3N6e7n4gn8TobrEAQqxOQz+fxu9/9DouLiygUCnjw4AF+85vfNOzo8VogFYa8J/s7GaShZsW5yO9kZdLV1aXTC1lbW0NfXx/VSQNj/qcAACAASURBVLh79y5isRi9HolE6DHXwH5lv3nzZpU+Rz6fx8WLFxGLxfDee+/prpFjz0l5ZLNZ7O7u0rI2Kq9mQd6BvOfa2hru3LljuSMeGBigtmU1V8x8lhyNTe4l2jRW8jMyMkKf+/TpU6qBQpR779+/b5hfo3rSamQ+YgYRDtQ0TXi9VCrhN7/5Dfr6+nS/l5UJ75fd3d1UUr5cLuMPf/gDHjx4QAXBWAE3VmaAL5Nbt27B4/HYrtPNghXjy2azAPaP9TbDzAayMpG1IzL71NOfHHbUYAOvRXcikQgcDgcVGkqlUqYdsdvtRjabxcLCAgC9/gIRBzI6jW1wcJCeTOj1enHq1CmqReDz+fDVV18hn8/T/LFiYfl8Ho8fP8bo6GjVu2QyGcTj8aoTPre3t3Hu3DnaKHi9Xni9XktSy82AV+oslUpIpVKWRJu6urpoZ0XEuYhOgaxMeHhZcr/fD03TaOXm9WoWFhbg8XjQy52AGgwG8e233wq1UEi6pCEnjYnb7ZaWV7MgDS0RRcvlclheXkY6na7ruTKfLZVKKBaLmJiYqBpI1ZOfwcFB6eDMqJ4cBDIfMWN8fFyqEkuk0IkNCbIycblcmJqaon7X29uLYrFIFau//PJLWi86Ozt1YmHpdBpjY2NVbVu5XMbu7q5Op6a3t9eybkizYSXnzTCzgaxMjNoRM/vU058cdtRgA3oHJKPHcDiMtrY2047Y5XLh4cOHSCaTCAQCVLbc7syUF+7q6urCgwcPEI1G0dHRQZUigdfKfsPDwzh58mTVc7a2trC4uFgVpnO73Xjx4gUdQGUyGTx8+PDABhus+iKZ9f3qV78yVEA0olQq4cWLF1Scy2qZiGTJBwcHEYvFEAqFMDAwgHg8Tis36biIEqZViKgTAAQCASQSCTx8+BAul0taXs1ic3MTHo8HDoeDRgY+++wzyx3C/Pw8Ojo6qsLHMp/N5XIAgN///vdV4WEr+SHqm3bsI6snbxOrq6s6eXgWWZnwrKysoKenR9h2sWJhZDL26tUrusRAlgkcDgdOnDhBB4pkgEMGMQcFWZoIhUK4cOGCVB3YCJlgGo9RO2Jmn0b1J4eRIzXY4NfR+HW2XC6HixcvIh6P49y5c5afOTQ0hJ6eHmSzWRSLRdtr7qVSCePj4xgeHqaOvLq6img0igcPHmBxcRHXrl2jjTMJjRqNcNfX1/Huu+/SsGGxWMTq6iqCwSAuXLiAUCiEQCCAhYUFDA0NWRrlN5Nnz55hfHwc9+/ftz3rIyF4tgGxWiZ8VAPQL2lMTk4iHA5jdXWVhlQ//fRT2xEIss4K7C/7eDweDA0N0UGfUXnVC7/2yz+ThIGnpqZw/PhxS89kl6FISJosN8p8FtgfpPT399Pw8OPHj3VLJUb5GRwc1C17pVIpS2F5s3rSDPj1+EYqJIsgs2ZRxAgwLxMCWWoSDViIqjI/yH7+/Dldko1EIjRyd/nyZSQSCQQCAYTDYbz33nt0MHlQEB/KZrNIJpO2l3WMbGCEUTsCyO3TiP7ksHKkBhskDEUaLjJiPHbsGJxOJ77++mvcv38fwWBQN+uWkcvl4PP50N/fD4fDgYmJCZ2UthlG4jcrKysYGxtDMBhEMBjE5OQkkskk/vKXvyCRSCAWi9GR+vLyMsLhMJ3xnT59mkoMOxwOeDweusTANtyffPIJdnd3D2yw4Xa78f3332NxcZHO9Le3t+F0Oi3JgBsJZVkpk3w+j0ePHumiGmTGFovF4HK50NXVBU3TsLKyQqNA4XAYgUAAkUiEzvDNBgYkakUaqtHRUfh8Pjrbl5VXPbBlze6t8Pl8mJ6eht/vp2HgjY0N3TKdFYh8N8HIZ0lDOTAwQMP8pM5tb2/byg9ZMjODdMKyetIMeGnyubm5pi6PLS0tYX5+nk4gYrEYpqen6WZGszIB9jtGI5E/mfAiWXYG9n2KnZ2Tdvbp06c4fvy4bd9qFg6HA5FIxNayjl3xSVk7AkBqn3r7k8PMkRpsGEEaze7ubupMc3NzupAZmbGIZiqbm5s07L+9vW15JsNushLNvtgKQTqftrY23YCJbHBcXFzE+Pg4vF4vnE4n7cj4JQYCiQj09PRYbgxlNqgF0nGQ0C0JKbKhXKM02RmAKCxsViZzc3O6/Sv8vQRSBvzG0kQiQTdKWpk5l0olOoDd29ujaVgpL6OvX2qlvb0dfX19NI18Po9nz57ZTpN06Oz+FZHPkjTZRpNdNrSSH4LsGgs/seDriRXIlxqt3NxoN01+QKlpGkZGRnQbNo3KBHi9aVc00CCzc76TJe0l6TwB4yUYEhEgg2krkIhcI6J7PKL9b7I0jWxgBVE7wiOyT639Sb000+4AlOorC1G+8/v9FU3TdNd++OGHypUrV4Sqp0QhkvwQ5TxyD3uNvc6mR36Iuid/r5Ha6s7OTiUajVbW19fp79bX1yvnz583zU8tCn+aplXOnz+vS68e+DzxKptGdudtztvIqEwqlX37fPjhh8J3IOq2fHnwpFIp3TX+Pr/fr7NTKpXSXWPf06i8zGxQD2yaovIk78O/P/8ebF7NfJa9l09Tlh+2LPlrfH6MykxUT6zaiG8LasXMR9j34euByN9F+ZqZmdH9XlYmvN+xZSrKK5sv/rlsmmyZ1KJWbGSDWpG1BfzfsNfMbCArE1k7YmafWvuTemm03XnUceUKW9gNKSoagyzUrWgORst0iuZitLSsaC7NtrtaRlFYgoTV1UCjtZBDhdRAo7XMzs6io6NDDTRaTDweRygUUgONFtMKu6vIhkKhUCgUiqaiIhsKhUKhUCiaihpsKBQKhUKhaCpqsKFQKBQKhaKpqMGGQqFQKBSKpqIGGwqFQqFQKJqKGmwoFAqFQqFoKmqwwcAKV1k9GpoXdxOpUpIjiEXPZe8negZm+eEFtvh72fQamZ9mQd7H7nHcvOgVe8wu+478Uedm9jGyAX8fm6aZH5DzMlppVxl8fq0cUczbW+RD8Xhc+P4ynxXZleRJZFf22bIy4d+zaccw26DW/PD2M2or+DpkZh9CPB6veiZblvx9vC/wx6uTdJstRGeVWtp2QP+e7DvK/NLMZwHjesI/W1RWRu23zEfY9qeWtrYhNOVc0jcQ/vhp/thfI8yOQZ6ZmTE8ZpocFyw6btZOftbX1yvRaLSys7NT2dnZqVy5coWm98MPP1Ru3LhB81drfpqFpmkVTdMqMzMzto42JscBi47W5W1A0hBd4+1jp0z4NI38QFQGtRzj3CjIscfkHWs5ypvA+mUqlao6rtnIl1if5THLD/EXkqaRLcmx9KlUSpqXVlFPfmT1X1aHZPZhn33lyhWpzUXtESkD/vh/8rwnT5409Ij9Wqmnbf+Hf/iHypMnTypXrlwxPcab9UvZNVk9MfMRWftt9b34+t8qVGQDr8V5iIohUe1LpVJ1jcrL5TIymQzi8bjwxM10Oo2xsbGqE9vs5mdubg4XLlyAy+WiaqlE7jmTySCbzcLtdtecn2YyPj5uWRiLJZfLobu7W3i6o8vlwtTUFH3H3t5eqkgpsw/QHBs4HA58+eWXVBirs7MTL1++pGJLrSaTyQAAVWDN5XJYXl5GOp229ZxSqYTHjx9T5dyuri5alkRkz0i9lvVZnqWlJZw5c0YokpfP5/HixQtLwl7BYBDffvst2tvb7bxW02hWfmqtQ8C+PR8/fozR0VHp321ubupk4gcHB2nd83q9OHXqFBUaHBwcpIraB009bTsR87PSFsj8kr8mqycyHzFrv62yt7eHly9ftlztWw02oDc+URMNh8Noa2ujFUjG999/T6XH2dDV1tYWtra2sLi4WBUyI07/6tUrGt4iIUc7+eEdmUhcA0AgEEAikaDy7bXm5zCysrKCd955x9KSD6tIKbOPFRsQWXlRKNLID3jS6bROUbjVsB3H7OwsEokEPvvsM1uy24B8UGCkNgzIG2Z+AMMjGqTIyuRtYnp62jD0LsPIPuVyGbdv38bw8DBOnjxZdR8bzk8kErh8+bLw+ayC72Gj3rbdKrLBs+yarJ7wyNpvgsxHyDJLKBTChQsXhPW2majBBkMul8PFixcRj8dx7tw5S/fwUtZjY2MYHx+nHdT6+jreffddFAoFZLNZFItF3Trc8+fPsbi4iEKhgEgkQmfcVvPDOzJZYwSAQqEAj8eDoaGhhuSnVvh140ZJdi8tLeHevXsoFAqYnJzE7du3qzqa2dlZFItF9Pf3AzC3D2BsA1ZmPpvNAgBu3boFwNwPWFvIGu5Gwq/h8uu/8XgcGxsbmJqawvHjx209WzYoKJfLuHnzpmGD1siohqxM3iZYKfm1tTWkUilL9Uhmn4WFBfT09BjO3FmfjsViuHTpUpU/l0oljI+PY3h4+ED1kvh9Evygs5a23Sp2ohosZvVEhKz9NvMRcj2bzSKZTDasHbaKGmwAOHbsGJxOJ77++mvcv38fwWCQjnrthpo6OzvR1tZG/3/69Gld1MHj8ehCyyS8BwA+nw/FYhGVSsVSflZXV5FKpXSOTGYZpDMbHR2Fz+dDLperOT/1zhTZBq9QKDRM2CoSidAGzu12Vy1NiJRSzexDnmtmA4fDgZ6eHsO88X4AtF4xl218CoUC7VR8Ph+mp6fh9/tpOHdjYwN+v9/ys+/evSscFJgppYp8lpDP5/Ho0SPDAczt27cNBymAeZm8LZDQu11Y+5RKJSQSCcRiMTrbXV5eRjgcFkZNzp49i46ODl004DCps/KDfbKM08i2XYTML82u1aIobNZ+E2Q+4nA4EIlEbEcy60UNNvC6EnZ3d1OnmJub04W6yYzYbGmBvc/r9cLpdNKOjA2ZkTRXVlbovSTcf/LkSUv5IWuRvCOXSiVaofb29qgz1pof0vFatUEjMUqzt7cXyWSSDgL4pYl4PG6olGpkHys2YJ+RSCTQ29srzDdfXrOzszUPNMisrVFLBO3t7ejr66Oh23w+j2fPnulCubI0yd/zgwISpjZqQGU+C+zb7Ny5c8JZXiaTwebmpnSvhlmZ2IVE5Fo5A7SSpqi8rMDah++c19bW0NfXh8XFReH+D35vUz6fx8DAQFMGGiQi14ivh6y07fWkKfNLo2tm9cQIWfvNI/MRUg/5yUUj7S6kpdtRDzmaplX8fn/F7/dX7eolO3j5ncBkN7HRfeTrBnKd3QFMnml0ryw/ZLeyaFdyKpWi9/n9ft0O6XryQ/J0/vz5mr5cEDEzM6PLK5+ukd1JXsg97G5z/h35d5XZR2YD/j7WdjI/4K+J0pUhs0GtsDYSlSfJs+hLBvbrHhZRWbJ5lvks/0UDC3l/kb3slInRu5rZyMoOfytYyQ95H/5dWdvy98jqkMw+fN7Yr1H4vPJ+wNY9/m/4NEX3yzCyQT3I2tJK5bUNZe0hb0OZX8quyeqJmY/I2m87PmL0tV2j7c6iJOYVtmj1UoBiH9GSkKK51BrqVtTHYVqeOUo02+5qGUVhCRJWVwON1kIO41EDjdYyOzuLjo4ONdBoMfF4HKFQSA00Wkwr7K4iGwqFQqFQKJqKimwoFAqFQqFoKmqwoVAoFAqFoqmowYZCoVAoFIqmogYbCoVCoVAomooabCgUCoVCoWgqarDxI+QTQ6Oz9WWw2h+i0zXZ6+S5/Fn+IvGceDxuKKojS5OcusmfRMhrlBhpZrQSNq92Tmu0Ul6sNojoCGbRiXm8nggvqMbnV3YveZfDaHfWfjLROB7+/Vm7yq4BevsYnUIrKhPefuxzRbY1uvcwCAvWane+veDrCXvdjs+y9/H+KLM78LqsRHWP3GtWznZF5Wqhnradt7uR7fh3NSsvWZkY+exhbEds0ZSjwt5wyOlvRqftsezs7FSuXLlCT0XkT1ecmZmxfPKjpmn09LZUKkWfQ06VI/mRpUn+9smTJ4Yn2LF5Z08NPAhmZmZoHmWnSMoQlZemadJTC1OpVCUajVai0WjVKXxGJ0YS24psur6+Xrlx4wZNjy8j/jkHafcffvihcuPGDZr+zMyM5RMeef9mfVZ2jT89NJVKVaVpVCYsvO1Ez2H/VlY3Ww2f9/X19Uo0GjVtG/jTKHl/J6dKimxm5rMffvghLRv+9FXWdrzPEFuKfIf87smTJ4Yn/9o5UbSR2Gnb+b/ly0/TtKq2Y2ZmxrS8ZGVix2cPuh2xi4psCGBlic1wuVyYmpqih1z19vZS4a5yuYxMJoN4PG56CBavDtjV1UU1CoioDtHwkKVJNA+sHMwiU9hsFYODg/TQJK/Xi1OnTtmWfubLq1QqoVgsYmJiQngIVqlUwp07d/Dxxx/D6XRaTieXy6G7u1t4yJPb7UY2m8XCwgKAfdsC+yJ/PAdtd4fDgS+//JKm39nZWSViJ6JcLmN3d1enidLb24uNjQ3pNWBf1p7VRCFy5CRNq2ViR0RLVk8OAr7c0+k0lpeXdSKAIoiNWAGunp4e2h6k02mMjY0J67zMZ4PBIL799luhNDzxW6J4zGujjI+PCzVUgP06TYTQeMzqZrOx07ZnMhkA+yJ0wL4tl5eXkU6nAQB+vx+apqFUKlHdGZ/PZ1pesjKx47MH3Y7YRQ02GEhoLxQK2ZL9ZWGFu7a2trC1tYXFxUXTkKFMdlsmuMOnaRWZRPhBQRRZRY2fCKPyIo3373//e2EY8+7du9LynZ6eFpbXysoK3nnnHWG42uVy4eHDh0gmkwgEAlS6XSTgdtjszovYGeFwOHDixAna2BJBp2KxCACG18rlMnw+n044b2lpCWtra3TwYFYmZEkxHA5XyZnPz8+jo6PDNEReSz1pJKyybjwex8bGBkZGRoSqnSyk42cFuBKJBB3kJZNJvHr1ii4VsKF3mc/KcDgcmJqaAgAEAgEkEgk8fPiw7pODzepms6ilbd/c3ITH44HD4cDs7CwSiQQ+++wzOoAeHBxELBZDKBTCwMAA4vE4urq6pOUF2CsTmRDkYWtHzDhSgw1+HY1vmIgkdzabRTKZtF0JZmdnUSwW0d/fT3+3vr6Od999lz63WCxWrbHxUQ2WcrmMmzdvGlYQUZpWaOWomF9rFNm1VCphfHy8qiORISuv+fl59Pf3U0XLx48fI5/PU9sbHUHNyrKvra0hlUrpnru0tIR79+6hUChgcnISt2/fpntwiJIjKWdRx9dKu/N7SERru6urq0gkErh8+bKlZ16+fBmJRIJ2/O+99x5tkGXXurq64PF46KDgu+++QygUgtvtNi0TYH8mTcpE0zR6T1dXFy2vbDYLALh165bQFrXUk0bz6tUrXL16FX6/3zAywONwOPDRRx/h2rVrCAQCuHTpEj744AOdaufz58+xuLiIQqGASCRCIxKAsc/KIHsKAKBQKMDj8WBoaKghe16M6mY9NLNtJwPDqakpHD9+nP6eKDmvra1hcnIS4XAYq6urlsrLSpnIfPZNi2oAR2ywwcsqG4X6HA4HIpEIHYlawUgo6/Tp07pwmsfj0c1kyuUybt++LYxqmAlB1SrOlc/n8ejRo5aNitkOoVAoVL1LvQJAovIaGBig4c9jx47B6XSiWCwikUjQyEVHRwfm5+cRiUSEAwOyfMXCLgW43W66/JDL5eDz+dDf3w+Hw4GJiQm8fPkSW1tb9N5W250dOBUKhSrb1iKqx9ahp0+f4vjx47QRlV0DXg8YCoUCwuEwTpw4gUqlYrtMIpGIMBpAwtU8h0XEzu/344svvkAkEsHg4CDK5TKKxSJ8Pp/pvcFgEE+fPkWhUMDc3Bz++te/6u6LRCL03Xw+ny70buSzMkiUkQxCR0dH4fP5TJd8rCCqm3aXTnma0bb7fD5MT0/rBoYkOkUiSrFYDC6XC11dXdA0DSsrKwCslZesTGQ+2+p2pFEcqcGGVUgImG0oyUhftLs6Ho8LHcPr9cLpdOrCafxySCaTwebmZlVUg50piwYaRmlaYW5uDufOnbM9KpbZoFby+TwGBgYMBxpW0uTLq729XdfRk4bzpz/9KaampnQz4YGBASQSCWHjlM/n8ezZM1pevb29uqUAfvlhc3OTNhjb29tV+a3V7mTWZmcXvRlkVmY00LCSJomKiCJysmskitXf34+TJ0/aKhN2bVz03EQigd7eXvq7euoJicg1Kszf2dmJvr4+ukxI6j67bGglTTLjPXv2LB1gkU4O0IfezXxWRqlUooOAvb090+UeKxjVTdYGoi+SGoWobTdKs729HX19fbT+8+0BAJ1NjAYwbHkB5u2Imc/W2o6Y0Uy7A1BfoxBmZmYqfr+f/vC7lcmOYn53NdkJzt7L3s9fZ5/L71qW5cfv99O0ZWmSZ8ryU8sXHwRN0yrnz59v2A5oTdOq8sruVDeyu1l5pVIpes0ov6Kd6exzRfex+RXtwjfKUz12N7JBrZDd8LzdWT8kfyP6YsTo/WXX2DRl/mP0ZZFRmbBpimwuq5tmkPsb+QULm19ReZLrfJvA+hafH77O89eNfFbkB6x9eduyeRK1TyRd/j4+XbO6aWSDWjFrK9i/4a+xPsTnlbcf+46y8qpUjMvESn9ST/sto9F251Gqrwpb1BJ6V9TPYVkKOEqYLWMqmkO9y6qK2mi23dUyisISJKyuBhqthRxIpAYarWV2dhYdHR1qoNFi4vE4QqGQGmi0mFbYXUU2FAqFQqFQNBUV2VAoFAqFQtFU1GBDoVAoFApFU1GDDYVCoVAoFE1FDTYUCoVCoVA0FTXYUFiGlbxvlp5BPB5viVZCPYhkwlnJaDvS4QdFPp/HP//zPx+YKJlCoThaqMEGA9uZGgmmiWA1KIxOuhSdzsaf58/ey3ZefAfG38fnV9QZEnidEjvvSY6bHhkZsXxPvayurjb05Mx6IcfLT05O0mO5g8EgFa5aW1vD6dOnG5rm7OyssJzYchbpQbBlzftlMBjE2bNnhToiCoVC0WjUYONHyCCAFeCycmwrOZZ5bW0NhUIBsVgMN2/erGr0Hz9+jL6+Pt29d+/eRSwWo0c1RyIRKk2+sLCAnp4eem1ychJzc3P03ra2Niq8VCgU6Nn95XIZf/jDH/DgwQMUCgWMjY3pxMLYvGazWezu7h76Wfhhwo5EdbNh9ReICBmRxebLuru7WyfOBQD9/f2GwoAXL15s2JH0CoVCoQYbP9LV1UU7bCLAZUULYHNzUyeqQ874JxoZpVIJd+7cwccffwyn06m71+/3Q9M0lEqlKs0Hn8+Hr776ioboRef5i3A4HPjyyy/pufmdnZ1U5IdIH5NOJ5PJIJvNHnjHyUaUpqenAbyO3kQiEZ2EeDwep9fYTnJ1dZXO3skSwTfffCNc9uEjQ1aWbcg9oVAIy8vLCIfDtiJDfBSCTZNXZyXXyD2xWEwney/KLz8IcrlcmJqaon7Z29urE+cCXotSJRIJ3e+DwSCGh4cxMDCgBqIKhaIxNOUQ9DecnZ2dSjQatXT+fCqVqjoTnz1DX9O0yszMjFDzgdxvpBHAnpPP3sefyS/TmpiZmTHUSuD1K6xC3qkRaJqmyx//bN6+BP692Pt4TYv19fVKNBqt7OzsVH744YfKjRs3qL3Y/xvpyrDpmPmG6DqvZ0DKT6TRsLOzU7ly5UqVDoyRPgerwSArE6Myk70PsaNVLRGFQqEw4khFNvgZrWgvQLlcxs2bN3HhwgVLqnpdXV3weDx05v3dd98hFArB7XbTmbfRccdEeXNtbQ2Tk5MIh8P0ntXVVUSjUTx48ACLi4u4du0andHycspjY2MYHx+vCnuTJR4iE032gQD7y0UejwdDQ0MHFi4nKri1SCV/8MEHKBaLNCpULBZ1CqOnT5+m7+31euH1erG9vY2trS1dZKKjowMPHz7E9vY23XfByrKzS1S1sr29rVNp5CNn7N6KUCiEtbU1y5LbREY+m80imUwKox5EdbK/v7/q2rFjx+D1eoXPDgaDmJ+fh6Zptvb2KBQKBc+RGmzwnTQvYV2r8BLZOFkoFBAOh3HixAlUKhUkEgka/u7o6MD8/DwikQiuXr2KnZ0dJJNJxGIxuFwudHV1QdM0KhW9srKCsbExBINBBINBTE5O6mSJWTo7O9HW1qb7nUgwjcg5k054dHQUPp8PuVzOti0PGpfLBY/Hg6WlJeRyOXg8Hst6LX19fchms7oBRVdXV9Wm3Fo20dqlVCpB0zQkEgm6X6iWDaZkSYSXua5HwC2fz2NgYACxWKzuAZdCoTjaHKnBhoxSqYShoSHDgQbpiIy+NiHPGB8fR39/P06ePKmbJWezWQwMDCCRSOgGOey+EL6jYP8v2z8yNzeHM2fO0M6WRExEgmmlUonOmvf29iztS7GK6IsbGceOHYPT6UQ6nab3kz0bBLfbTfec8PziF79AMplEIpGQRkcymQx2d3fh9XrhdruRzWaxsLBQ9XfNimy43W48e/aM7n/I5/N49uwZOjs7sb29jUqlQvdaLC0tYX19XXe/z+er2m/BI9rXE4/HTQcae3t72N3drdq3w0bWlCCWQqGoFzXY+JGlpSU8f/4csVjM9DNWFnZpZmBgAPF43FLj7HA4MDExgUQiQdMrFosYHR0FsB91KBaL9FoikcDExAQcDkfVchAA2iGSjabPnz9HKBTSbSoMBoP49a9/TZcQQqEQIpFIwzoTsrnV6gDG4XDgo48+wldffYVAIICNjQ1omqb7m2AwiDNnztB3YaMMbPifXwpYX1+n92iaRm3ncrkQj8dpmlbL2QzRBlLy2TFv93A4jF//+tc0anXu3Dl6LZPJVH21dPbsWQCgS3VkqYTdWNrR0YFIJEIHyvl8Hn/60590m2tFA0ES1SKbh8m9d+7cwfz8vKWlRIVCoTBDqb4qbBOPx+H3+6siQKVSCZcuXWqZPLTRslc+n8dvfvMb/Ou//qvlpZWjCLFfIwecCoVCIUJFNhQNIR6PIxQKtWygAewvj2xubuo2hiqss7CwAI/HowYaCoWi6ajBhsIy5DwMfl8F8HqTbCs6LnL+xLVr1xCPx1X0ogby+TwymQxd0nRqKQAAAFVJREFUtlMoFIpmopZRFAqFQqFQNBUV2VAoFAqFQtFU1GBDoVAoFApFU1GDDYVCoVAoFE1FDTYUCoVCoVA0FTXYUCgUCoVC0VTUYEOhUCgUCkVT+f8BcSikxy1eUyUAAAAASUVORK5CYII=)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UuAT_ymD15U7" - }, - "source": [ - "На картинке отмечены наиболее вероятные символы." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "4V4MfFg0RQJg" - }, - "outputs": [], - "source": [ - "sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)\n", - "sampled_indices = tf.squeeze(sampled_indices, axis=-1).numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YqFMUQc_UFgM", - "outputId": "1842194b-d7d7-4702-bca5-6b03f04b9432" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([103, 2, 125, 127, 46, 128, 85, 84, 14, 37, 55, 7, 129,\n", - " 123, 72, 38, 138, 88, 116, 125, 142, 109, 110, 131, 21, 29,\n", - " 15, 99, 118, 48, 15, 143, 106, 139, 28, 115, 119, 8, 41,\n", - " 55, 138, 68, 130, 133, 135, 4, 114, 10, 62, 130, 120, 47,\n", - " 119, 16, 87, 71, 32, 111, 121, 85, 13, 87, 87, 75, 25,\n", - " 91, 41, 83, 51, 106, 100, 133, 3, 9, 37, 36, 103, 61,\n", - " 2, 79, 136, 56, 99, 101, 20, 143, 70, 117, 60, 43, 6,\n", - " 49, 51, 15, 85, 115, 113, 138, 23, 89], dtype=int64)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sampled_indices" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xWcFwPwLSo05", - "outputId": "bed36421-88f9-429f-a9cd-099717127029" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input:\n", - " же прошли. Их мненья, толки, страсти\n", - "Забыты для других. Смотри: вокруг тебя\n", - "Всё новое кипит, былое и\n", - "\n", - "Next Char Predictions:\n", - " Э ухfцИЗ;Vo)чсèWёЛку…гдщDN<Цмh<а–Mйн*aoё»шыэ\"и-vшоgн>КçQепИ:ККôIОaЖkаЧы!,VUЭu ВюpЦШCâлtc(ik<ИйзёFМ\n" - ] - } - ], - "source": [ - "print(\"Input:\\n\", text_from_ids(input_example_batch[0]))\n", - "print()\n", - "print(\"Next Char Predictions:\\n\", text_from_ids(sampled_indices))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LJL0Q0YPY6Ee" - }, - "source": [ - "## Обучение модели" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YCbHQHiaa4Ic" - }, - "source": [ - "\n", - "Можно представить задачу как задачу классификации - по предыдущему состоянию RNN и входу в данный момент времени предсказать класс (очередной символ). " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "trpqTWyvk0nr" - }, - "source": [ - "### Настройка оптимизатора и функции потерь" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UAjbjY03eiQ4" - }, - "source": [ - "В этом случае работает стандартная функция потерь `tf.keras.losses.sparse_categorical_crossentropy`- кроссэнтропия, которая равна минус логарифму предсказанной вероятности для верного класса.\n", - "\n", - "Поскольку модель возвращает логиты, вам необходимо установить флаг `from_logits`." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "id": "ZOeWdgxNFDXq" - }, - "outputs": [], - "source": [ - "loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4HrXTACTdzY-", - "outputId": "f0dbf6f2-738e-48f8-df7c-0bbf60fc17e4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction shape: (64, 100, 144) # (batch_size, sequence_length, vocab_size)\n", - "Mean loss: tf.Tensor(4.970122, shape=(), dtype=float32)\n" - ] - } - ], - "source": [ - "example_batch_mean_loss = loss(target_example_batch, example_batch_predictions)\n", - "print(\"Prediction shape: \", example_batch_predictions.shape, \" # (batch_size, sequence_length, vocab_size)\")\n", - "print(\"Mean loss: \", example_batch_mean_loss)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vkvUIneTFiow" - }, - "source": [ - "Необученная модель не может делать адекватные предсказания. Ее перплексия («коэффициент неопределённости») приблизительно равна размеру словаря. Это говорит о полной неопределенности модели при генерации текста.\n", - "\n", - "Перплексия = exp(кроссэнтропия)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MAJfS5YoFiHf", - "outputId": "0588a9b2-8598-458f-db1d-6923e6a50502" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "perplexity: 144.04443\n" - ] - } - ], - "source": [ - "print('perplexity: ',np.exp(example_batch_mean_loss))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jeOXriLcymww" - }, - "source": [ - "Настраиваем обучение, используя метод `tf.keras.Model.compile`. Используйте `tf.keras.optimizers.Adam` с аргументами по умолчанию и функцией потерь." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "DDl1_Een6rL0" - }, - "outputs": [], - "source": [ - "model.compile(optimizer='adam', loss=loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vPGmAAXmVLGC", - "outputId": "1399a58c-85ff-430d-f67f-942942dec071" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"my_model\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " embedding (Embedding) multiple 2880 \n", - " \n", - " gru (GRU) multiple 289800 \n", - " \n", - " dense (Dense) multiple 43344 \n", - " \n", - "=================================================================\n", - "Total params: 336,024\n", - "Trainable params: 336,024\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "C6XBUUavgF56" - }, - "source": [ - "Используем `tf.keras.callbacks.ModelCheckpoint`, чтобы убедиться, что контрольные точки сохраняются во время обучения:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "id": "W6fWTriUZP-n" - }, - "outputs": [], - "source": [ - "# Directory where the checkpoints will be saved\n", - "checkpoint_dir = './training_checkpoints'\n", - "# Name of the checkpoint files\n", - "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n", - "\n", - "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n", - " filepath=checkpoint_prefix,\n", - " monitor=\"val_loss\",\n", - " save_weights_only=True,\n", - " save_best_only=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3Ky3F_BhgkTW" - }, - "source": [ - "### Обучение!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Обратим внимание, что перед очередным обучением нужно сбросить веса модели. Проще всего это сделать, заново объявив и скомпилировав модель:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'MyModel' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mMyModel\u001b[49m(\n\u001b[0;32m 2\u001b[0m vocab_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(ids_from_chars\u001b[38;5;241m.\u001b[39mget_vocabulary()),\n\u001b[0;32m 3\u001b[0m embedding_dim\u001b[38;5;241m=\u001b[39membedding_dim,\n\u001b[0;32m 4\u001b[0m rnn_units\u001b[38;5;241m=\u001b[39mrnn_units)\n\u001b[0;32m 5\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39mloss)\n", - "\u001b[1;31mNameError\u001b[0m: name 'MyModel' is not defined" - ] - } - ], - "source": [ - "model = MyModel(\n", - " vocab_size=len(ids_from_chars.get_vocabulary()),\n", - " embedding_dim=embedding_dim,\n", - " rnn_units=rnn_units)\n", - "model.compile(optimizer='adam', loss=loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "id": "7yGBE2zxMMHs" - }, - "outputs": [], - "source": [ - "EPOCHS = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UK-hmKjYVoll", - "outputId": "4403459e-c93d-4361-b6b4-d4f4a29797e5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "72/72 [==============================] - 24s 301ms/step - loss: 3.7387 - val_loss: 3.4320\n", - "Epoch 2/5\n", - "72/72 [==============================] - 22s 294ms/step - loss: 3.2194 - val_loss: 2.8863\n", - "Epoch 3/5\n", - "72/72 [==============================] - 22s 292ms/step - loss: 2.8698 - val_loss: 2.7059\n", - "Epoch 4/5\n", - "72/72 [==============================] - 23s 309ms/step - loss: 2.7643 - val_loss: 2.6365\n", - "Epoch 5/5\n", - "72/72 [==============================] - 24s 320ms/step - loss: 2.6941 - val_loss: 2.5800\n" - ] - } - ], - "source": [ - "history = model.fit(train_dataset, validation_data = val_dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UejMtXPmH-U0", - "outputId": "ec06daed-25a1-4ae3-9f9a-cd130e04c216" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9/9 [==============================] - 1s 101ms/step - loss: 2.5769\n", - "eval loss: 2.576871871948242\n", - "perplexity 13.155920322524834\n" - ] - } - ], - "source": [ - "eval_loss = model.evaluate(test_dataset)\n", - "print('eval loss:',eval_loss)\n", - "print('perplexity',np.exp(eval_loss))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kKkD5M6eoSiN" - }, - "source": [ - "## Генерация текста" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oIdQ8c8NvMzV" - }, - "source": [ - "Самый простой способ сгенерировать текст с помощью этой модели — запустить ее в цикле и отслеживать внутреннее состояние модели по мере ее выполнения.\n", - "\n", - "![To generate text the model's output is fed back to the input](https://github.com/tensorflow/text/blob/master/docs/tutorials/images/text_generation_sampling.png?raw=1)\n", - "\n", - "Каждый раз, когда вы вызываете модель, вы передаете некоторый текст и внутреннее состояние. Модель возвращает прогноз для следующего символа и его нового состояния. Передайте предсказание и состояние обратно, чтобы продолжить создание текста.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DjGz1tDkzf-u" - }, - "source": [ - "Создаем модель реализующую один шаг предсказания:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "id": "iSBU1tHmlUSs" - }, - "outputs": [], - "source": [ - "class OneStep(tf.keras.Model):\n", - " def __init__(self, model, chars_from_ids, ids_from_chars, temperature=1.0):\n", - " super().__init__()\n", - " self.temperature = temperature\n", - " self.model = model\n", - " self.chars_from_ids = chars_from_ids\n", - " self.ids_from_chars = ids_from_chars\n", - "\n", - " # Create a mask to prevent \"[UNK]\" from being generated.\n", - " skip_ids = self.ids_from_chars(['[UNK]'])[:, None]\n", - " sparse_mask = tf.SparseTensor(\n", - " # Put a -inf at each bad index.\n", - " values=[-float('inf')]*len(skip_ids),\n", - " indices=skip_ids,\n", - " # Match the shape to the vocabulary\n", - " dense_shape=[len(ids_from_chars.get_vocabulary())])\n", - " self.prediction_mask = tf.sparse.to_dense(sparse_mask)\n", - "\n", - " \n", - " # Этот фрагмент целиком написан с использованием Tensorflow, поэтому его можно выполнять \n", - " # не с помощью интерпретатора языка Python, а через граф операций. Это будет значительно быстрее. \n", - " # Для этого воспользуемся декоратором @tf.function \n", - " @tf.function \n", - " def generate_one_step(self, inputs, states=None,temperature=1.0):\n", - " # Convert strings to token IDs.\n", - " input_chars = tf.strings.unicode_split(inputs, 'UTF-8')\n", - " input_ids = self.ids_from_chars(input_chars).to_tensor()\n", - "\n", - " # Run the model.\n", - " # predicted_logits.shape is [batch, char, next_char_logits]\n", - " predicted_logits, states = self.model(inputs=input_ids, states=states,\n", - " return_state=True)\n", - " # Only use the last prediction.\n", - " predicted_logits = predicted_logits[:, -1, :]\n", - " predicted_logits = predicted_logits/temperature\n", - " # Apply the prediction mask: prevent \"[UNK]\" from being generated.\n", - " predicted_logits = predicted_logits + self.prediction_mask\n", - "\n", - " # Sample the output logits to generate token IDs.\n", - " predicted_ids = tf.random.categorical(predicted_logits, num_samples=1)\n", - " predicted_ids = tf.squeeze(predicted_ids, axis=-1)\n", - "\n", - " # Convert from token ids to characters\n", - " predicted_chars = self.chars_from_ids(predicted_ids)\n", - "\n", - "\n", - " # Return the characters and model state.\n", - " return predicted_chars, states" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "id": "fqMOuDutnOxK" - }, - "outputs": [], - "source": [ - "one_step_model = OneStep(model, chars_from_ids, ids_from_chars)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p9yDoa0G3IgQ" - }, - "source": [ - "Изменяя температуру можно регулировать вариативность текста" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ST7PSyk9t1mT", - "outputId": "ed0dfecc-3d31-4bfe-efeb-d2315044ef06" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "uКочаму,\n", - "Лыт\n", - "Нума Гве!\n", - "àvо етекы ва;\n", - "Хо> вы,\n", - "Муметы,.\n", - "Воцу:\n", - "ОтекаЕдраэмь эзоспосанах…Я кы.\n", - ") маа Почи защоючеты>\n", - "Алаций поши)\"Пры – /й пий лазой\n", - "Еже?\n", - "И,\n", - "В, à*\n", - "logJ\"nДаогей piКо '\n", - "Дя цый,;\n", - "Зази:\n", - "Унумыйй поль ь,\n", - "Тост,,,\n", - "Дежы!Шоны\n", - "H-му,\n", - "iБачуй,,;цо,\n", - "Тахрей\n", - "Ты а;\n", - "Памогой елыны…\n", - "Жебы\n", - "Ве яны!\n", - "Экычигруй,\n", - "И вьчам ре кабощалуй, ль наша водух, ё ко,\n", - "Чячу бемий>\n", - "Елетанех гружара, eалодищи:\n", - "И,\n", - "Гро вишемороцемапа\"oОмицы\n", - "upЛицль: Преща, Оды iH бочавоча!, Ценобый ни\n", - "Вныла,\n", - "Чу:.\n", - "Ввуй,\n", - "eРаДобе по!\n", - "Qтый,\n", - "Чки fХралошумо:\n", - "na-Койкаца –\n", - "Ай\n", - "Мо,\n", - "i)цыденатедубодех выйха\n", - "Полынене на бы.\n", - "Заму рыхасла cИмино Одорафута уга гоЮй вожве,\n", - "ГчаCКазко.\n", - "Мотя выйхоруnАчлатоный\n", - "Рабойх удапи,\n", - "Ты уТашь,\n", - "Обымаекоги вайзоты зой кишаметумилетелы\n", - "Я, —\n", - "yJи, солыFлы!éuХуZетце, ё\n", - "oПробемнанетече ей.\n", - "Ри,\n", - "Шоный.\n", - "По!\n", - "Ностыгу!\n", - "Hv\n", - "________________________________________________________________________________\n", - "\n", - "Run time: 1.620434284210205\n" - ] - } - ], - "source": [ - "T = 0.5 #@param {type:\"slider\", min:0, max:2, step:0.1}\n", - "N = 1000\n", - "\n", - "start = time.time()\n", - "states = None\n", - "next_char = tf.constant(['\\n'])\n", - "result = [next_char]\n", - "\n", - "for n in range(N):\n", - " next_char, states = one_step_model.generate_one_step(next_char, states=states,temperature=T)\n", - " result.append(next_char)\n", - "\n", - "result = tf.strings.join(result)\n", - "end = time.time()\n", - "\n", - "result_text = result[0].numpy().decode('utf-8')\n", - "print(result_text)\n", - "print('_'*80)\n", - "print('\\nRun time:', end - start)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - }, - "id": "VCwWY9xM6KCB", - "outputId": "e709c661-8bbe-4abf-e329-db9af7e6eb55" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
lenlinesmean_line_len
count2.0000002.0000002.000000
mean499.00000035.50000012.123718
std482.24682533.2340191.262820
min158.00000012.00000011.230769
25%328.50000023.75000011.677244
50%499.00000035.50000012.123718
75%669.50000047.25000012.570192
max840.00000059.00000013.016667
\n", - "
" - ], - "text/plain": [ - " len lines mean_line_len\n", - "count 2.000000 2.000000 2.000000\n", - "mean 499.000000 35.500000 12.123718\n", - "std 482.246825 33.234019 1.262820\n", - "min 158.000000 12.000000 11.230769\n", - "25% 328.500000 23.750000 11.677244\n", - "50% 499.000000 35.500000 12.123718\n", - "75% 669.500000 47.250000 12.570192\n", - "max 840.000000 59.000000 13.016667" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "describe_poems(result_text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "db7UJQr9ILfW" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "22rnSwqqICn2" - }, - "source": [ - "По мотивам https://colab.research.google.com/github/tensorflow/text/blob/master/docs/tutorials/text_generation.ipynb" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/lections/OATD_lec_12.pdf b/lections/OATD_lec_12.pdf new file mode 100644 index 0000000..ead1a6b Binary files /dev/null and b/lections/OATD_lec_12.pdf differ diff --git a/lections/OATD_lec_12.pptx b/lections/OATD_lec_12.pptx new file mode 100644 index 0000000..41e0cff Binary files /dev/null and b/lections/OATD_lec_12.pptx differ diff --git a/lections/OATD_lec_7.pdf b/lections/OATD_lec_7.pdf index b5dab50..0b6ab80 100644 Binary files a/lections/OATD_lec_7.pdf and b/lections/OATD_lec_7.pdf differ diff --git a/lections/OATD_lec_7.pptx b/lections/OATD_lec_7.pptx index 34edec8..fed83f6 100644 Binary files a/lections/OATD_lec_7.pptx and b/lections/OATD_lec_7.pptx differ diff --git a/lections/notebooks/lec7_clustering.ipynb b/lections/notebooks/lec7_clustering.ipynb index f212a2f..64ba832 100644 --- a/lections/notebooks/lec7_clustering.ipynb +++ b/lections/notebooks/lec7_clustering.ipynb @@ -2,18 +2,320 @@ "cells": [ { "cell_type": "code", - "execution_count": 150, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "text/html": [ + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " function drop(id) {\n", + " const view = Bokeh.index.get_by_id(id)\n", + " if (view != null) {\n", + " view.model.document.clear()\n", + " Bokeh.index.delete(view)\n", + " }\n", + " }\n", + "\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + "\n", + " // Clean up Bokeh references\n", + " if (id != null) {\n", + " drop(id)\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim()\n", + " drop(id)\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"ef503a75-3e3b-446c-9160-ddfe41cc28eb\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"ef503a75-3e3b-446c-9160-ddfe41cc28eb\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"ef503a75-3e3b-446c-9160-ddfe41cc28eb\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"ef503a75-3e3b-446c-9160-ddfe41cc28eb\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -21,24 +323,50 @@ "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.metrics.pairwise import euclidean_distances\n", + "from sklearn.metrics import silhouette_score\n", + "from bokeh.io import output_notebook, show\n", + "output_notebook()\n", + "from bokeh.models import ColumnDataSource, HoverTool, BoxAnnotation\n", + "from bokeh.models.tools import * \n", + "from bokeh.plotting import figure\n", + "\n", + "from bokeh.transform import factor_cmap, CategoricalColorMapper\n", + "from bokeh.palettes import Category20\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "\n", "#X,y = datasets.make_moons(n_samples=100, random_state = 42, noise = 0.1 )\n", "X,y = datasets.make_blobs(n_samples=100, centers = 6, random_state =45 )\n", "\n", "plt.scatter (X[:,0], X[:,1])\n", - "plt.show()\n" + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 152, - "metadata": { - "scrolled": true - }, + "execution_count": 107, + "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABMIAAAMoCAYAAAAtD0zKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABNbElEQVR4nO3de5xVdb038O8gDAzERU1AHrFDJ5/UrJOKKWpFRWKIIRgIavioYSl0vFQqpeYFQ9SMg5mmeSFDRbzlSbJjetQ0JTUvecns0ZTyAfMCCMN12M8fPrMfBpm9Z83sPXvP/N7v12ter5k9a9Z897r81lqf/Vu/VZPL5XIBAAAAAJ1cl0oXAAAAAADtQRAGAAAAQBIEYQAAAAAkQRAGAAAAQBIEYQAAAAAkQRAGAAAAQBIEYQAAAAAkoWulC2iNjRs3xuuvvx69e/eOmpqaSpcDAAAAQAXlcrl49913Y9CgQdGlS/P9vjpkEPb666/H4MGDK10GAAAAAFVk8eLFscMOOzT7+w4ZhPXu3Tsi3ntzffr0qXA1AAAAAFTSihUrYvDgwfnMqDkdMghrvB2yT58+gjAAAAAAIiKKDqFlsHwAAAAAkiAIAwAAACAJgjAAAAAAkiAIAwAAACAJgjAAAAAAkiAIAwAAACAJgjAAAAAAkiAIAwAAACAJgjAAAAAAkiAIAwAAACAJgjAAAAAAkiAIAwAAACAJgjAAAAAAkpA5CHvwwQfj4IMPjkGDBkVNTU3ccccdTX6fy+XirLPOiu233z7q6upixIgR8dJLLzWZ5u23344jjjgi+vTpE/369Ytjjz02Vq5c2aY3AgAAAACFZA7CVq1aFf/2b/8Wl1122RZ/f+GFF8acOXPiiiuuiEWLFkWvXr1i5MiRsWbNmvw0RxxxRDz33HNxzz33xK9+9at48MEH47jjjmv9uwAAAACAImpyuVyu1X9cUxO33357HHLIIRHxXm+wQYMGxbe+9a349re/HRERy5cvjwEDBsR1110XEydOjBdeeCF23XXXeOyxx2Lo0KEREXH33XfHqFGj4u9//3sMGjSo6P9dsWJF9O3bN5YvXx59+vRpbfkAAAAAdAItzYpKOkbYK6+8EkuWLIkRI0bkX+vbt2/svffe8cgjj0RExCOPPBL9+vXLh2ARESNGjIguXbrEokWLtjjftWvXxooVK5p8AQAAAEAWJQ3ClixZEhERAwYMaPL6gAED8r9bsmRJ9O/fv8nvu3btGttss01+ms3NnDkz+vbtm/8aPHhwKcsGAAAAIAEd4qmR06dPj+XLl+e/Fi9eXOmSAAAAAOhgShqEDRw4MCIili5d2uT1pUuX5n83cODAeOONN5r8fsOGDfH222/np9lc9+7do0+fPk2+AAAAACCLkgZhQ4YMiYEDB8a9996bf23FihWxaNGiGDZsWEREDBs2LJYtWxZPPPFEfpr77rsvNm7cGHvvvXcpywEAAACAvK5Z/2DlypXx17/+Nf/zK6+8Ek899VRss802seOOO8ZJJ50UM2bMiJ122imGDBkSZ555ZgwaNCj/ZMlddtklDjzwwJgyZUpcccUVsX79+pg2bVpMnDixRU+MBAAAAIDWyByEPf744/G5z30u//Mpp5wSERFHHXVUXHfddXHqqafGqlWr4rjjjotly5bF/vvvH3fffXf06NEj/zfz5s2LadOmxRe+8IXo0qVLHHrooTFnzpwSvB0AAAAA2LKaXC6Xq3QRWa1YsSL69u0by5cvN14YAAAAQOJamhV1iKdGAgAAAEBbCcIAAAAASIIgDAAAAIAkCMIAAAAASIIgDAAAAIAkCMIAAAAASIIgDAAAAIAkCMIAAAAASELXShcAFJbL5WL1+oZKlwEAsEV13baKmpqaSpcBAC0iCIMqlsvl4itXPBJPvPpOpUsBANiioR/aOhZ8Y5gwDIAOwa2RUMVWr28QggEAVe3xV9/Rex2ADkOPMOggHj9jRPSs3arSZQAARERE/bqGGDrjt5UuAwAyEYRBB9GzdqvoWWuXBQAAgNZyayQAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJCEkgdhDQ0NceaZZ8aQIUOirq4u/vVf/zXOO++8yOVy+WlyuVycddZZsf3220ddXV2MGDEiXnrppVKXAgAAAAB5JQ/CZs2aFZdffnn8+Mc/jhdeeCFmzZoVF154YVx66aX5aS688MKYM2dOXHHFFbFo0aLo1atXjBw5MtasWVPqcgAAAAAgIiK6lnqGv//972PMmDFx0EEHRUTEv/zLv8SNN94Yf/jDHyLivd5gs2fPjjPOOCPGjBkTERE///nPY8CAAXHHHXfExIkTS10SAAAAAJS+R9i+++4b9957b/zlL3+JiIinn346HnroofjSl74UERGvvPJKLFmyJEaMGJH/m759+8bee+8djzzyyBbnuXbt2lixYkWTLwAAAADIouQ9wk4//fRYsWJF7LzzzrHVVltFQ0NDnH/++XHEEUdERMSSJUsiImLAgAFN/m7AgAH5321u5syZcc4555S6VAAAAAASUvIeYTfffHPMmzcvbrjhhvjjH/8Yc+fOjYsvvjjmzp3b6nlOnz49li9fnv9avHhxCSsGAAAAIAUl7xH2ne98J04//fT8WF8f//jH49VXX42ZM2fGUUcdFQMHDoyIiKVLl8b222+f/7ulS5fGJz/5yS3Os3v37tG9e/dSlwoAAABAQkreI6y+vj66dGk626222io2btwYERFDhgyJgQMHxr333pv//YoVK2LRokUxbNiwUpcDAAAAABFRhh5hBx98cJx//vmx4447xsc+9rF48skn45JLLoljjjkmIiJqamripJNOihkzZsROO+0UQ4YMiTPPPDMGDRoUhxxySKnLAQAAAICIKEMQdumll8aZZ54ZJ5xwQrzxxhsxaNCg+PrXvx5nnXVWfppTTz01Vq1aFccdd1wsW7Ys9t9//7j77rujR48epS4HAAAAACIioiaXy+UqXURWK1asiL59+8by5cujT58+lS4HyqZ+3YbY9azfRETE8+eOjJ61Jc+uAQBaxXkKANWkpVlRyccIAwAAAIBqJAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSUJYg7B//+EcceeSRse2220ZdXV18/OMfj8cffzz/+1wuF2eddVZsv/32UVdXFyNGjIiXXnqpHKUAAAAAQESUIQh75513Yr/99otu3brFr3/963j++efjhz/8YWy99db5aS688MKYM2dOXHHFFbFo0aLo1atXjBw5MtasWVPqcgAAAAAgIiK6lnqGs2bNisGDB8e1116bf23IkCH573O5XMyePTvOOOOMGDNmTERE/PznP48BAwbEHXfcERMnTix1SQAAAABQ+h5hd955ZwwdOjTGjx8f/fv3j9133z2uuuqq/O9feeWVWLJkSYwYMSL/Wt++fWPvvfeORx55ZIvzXLt2baxYsaLJFwAAAABkUfIg7OWXX47LL788dtppp/jNb34Txx9/fPz7v/97zJ07NyIilixZEhERAwYMaPJ3AwYMyP9uczNnzoy+ffvmvwYPHlzqsgEAAADo5EoehG3cuDH22GOP+MEPfhC77757HHfccTFlypS44oorWj3P6dOnx/Lly/NfixcvLmHFAAAAAKSg5EHY9ttvH7vuumuT13bZZZd47bXXIiJi4MCBERGxdOnSJtMsXbo0/7vNde/ePfr06dPkCwAAAACyKHkQtt9++8WLL77Y5LW//OUv8aEPfSgi3hs4f+DAgXHvvffmf79ixYpYtGhRDBs2rNTlAAAAAEBElOGpkSeffHLsu+++8YMf/CAmTJgQf/jDH+LKK6+MK6+8MiIiampq4qSTTooZM2bETjvtFEOGDIkzzzwzBg0aFIccckipywEAAACAiChDELbXXnvF7bffHtOnT49zzz03hgwZErNnz44jjjgiP82pp54aq1atiuOOOy6WLVsW+++/f9x9993Ro0ePUpcDAAAAABFRhiAsImL06NExevToZn9fU1MT5557bpx77rnl+PcAAAAA8D4lHyMMAAAAAKqRIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEiCIAwAAACAJAjCAAAAAEhC10oXAABQjXK5XKzesLrSZUDVql/fsMn3qyNqtqpgNVD96rrWRU1NTaXLgOQJwgAANpPL5WLyryfHU/98qtKlQNXKbewWEedFRMTwmz8bNV3WV7YgqHK799895h44VxgGFSYIAwDYzOoNq4VgUERNl/XRe5fTK10GdBhPvvFkrN6wOnp261npUiBpgjAAgALun3B/1HWtq3QZAHRQqzesjuE3D690GcD/IwgDACigrmudT+8BADoJT40EAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAllD8IuuOCCqKmpiZNOOin/2po1a2Lq1Kmx7bbbxgc+8IE49NBDY+nSpeUuBQAAAICElTUIe+yxx+KnP/1pfOITn2jy+sknnxz/+Z//GQsWLIgHHnggXn/99Rg3blw5SwEAAAAgcWULwlauXBlHHHFEXHXVVbH11lvnX1++fHlcffXVcckll8TnP//52HPPPePaa6+N3//+9/Hoo4+WqxwAAAAAEle2IGzq1Klx0EEHxYgRI5q8/sQTT8T69eubvL7zzjvHjjvuGI888sgW57V27dpYsWJFky8AAAAAyKJrOWZ60003xR//+Md47LHH3ve7JUuWRG1tbfTr16/J6wMGDIglS5ZscX4zZ86Mc845pxylAgAAAJCIkvcIW7x4cZx44okxb9686NGjR0nmOX369Fi+fHn+a/HixSWZLwAAAADpKHkQ9sQTT8Qbb7wRe+yxR3Tt2jW6du0aDzzwQMyZMye6du0aAwYMiHXr1sWyZcua/N3SpUtj4MCBW5xn9+7do0+fPk2+AAAAACCLkt8a+YUvfCH+9Kc/NXnt6KOPjp133jlOO+20GDx4cHTr1i3uvffeOPTQQyMi4sUXX4zXXnsthg0bVupyAAAAACAiyhCE9e7dO3bbbbcmr/Xq1Su23Xbb/OvHHntsnHLKKbHNNttEnz594pvf/GYMGzYs9tlnn1KXAwAAAAARUabB8ov50Y9+FF26dIlDDz001q5dGyNHjoyf/OQnlSgFAAAAgES0SxB2//33N/m5R48ecdlll8Vll13WHv8eAAAAAEo/WD4AAAAAVCNBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkARBGAAAAABJEIQBAAAAkISulS4AAACAbHK5XKzesLrSZdACm64n66zjqOtaFzU1NZUugzIQhAEAAHQguVwuJv96cjz1z6cqXQoZDb95eKVLoIV27797zD1wrjCsE3JrJAAAQAeyesNqIRiU2ZNvPKkHXyelRxgAAEAHdf+E+6Oua12ly4BOY/WG1XrudXKCMAAAgA6qrmtd9OzWs9JlAHQYbo0EAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAmCMAAAAACSIAgDAAAAIAldK10AAFBcLpeL1RtWV7qMZGy6rC339lXXtS5qamoqXQYA0EkJwgCgyuVyuZj868nx1D+fqnQpSRp+8/BKl5CU3fvvHnMPnCsMAwDKouS3Rs6cOTP22muv6N27d/Tv3z8OOeSQePHFF5tMs2bNmpg6dWpsu+228YEPfCAOPfTQWLp0aalLAYBOYfWG1UIwkvHkG0/qhQcAlE3Je4Q98MADMXXq1Nhrr71iw4YN8d3vfjcOOOCAeP7556NXr14REXHyySfHXXfdFQsWLIi+ffvGtGnTYty4cfHwww+XuhwA6FTun3B/1HWtq3QZUHKrN6zW+w4AKLuSB2F33313k5+vu+666N+/fzzxxBPxmc98JpYvXx5XX3113HDDDfH5z38+IiKuvfba2GWXXeLRRx+NffbZp9QlAUCnUde1Lnp261npMgAAoEMq+1Mjly9fHhER22yzTUREPPHEE7F+/foYMWJEfpqdd945dtxxx3jkkUe2OI+1a9fGihUrmnwBAAAAQBZlDcI2btwYJ510Uuy3336x2267RUTEkiVLora2Nvr169dk2gEDBsSSJUu2OJ+ZM2dG375981+DBw8uZ9kAAAAAdEJlDcKmTp0azz77bNx0001tms/06dNj+fLl+a/FixeXqEIAAAAAUlHyMcIaTZs2LX71q1/Fgw8+GDvssEP+9YEDB8a6deti2bJlTXqFLV26NAYOHLjFeXXv3j26d+9erlIBAAAASEDJe4TlcrmYNm1a3H777XHffffFkCFDmvx+zz33jG7dusW9996bf+3FF1+M1157LYYNG1bqcgAAAAAgIsrQI2zq1Klxww03xC9/+cvo3bt3ftyvvn37Rl1dXfTt2zeOPfbYOOWUU2KbbbaJPn36xDe/+c0YNmyYJ0YCAAAAUDYlD8Iuv/zyiIgYPnx4k9evvfba+F//639FRMSPfvSj6NKlSxx66KGxdu3aGDlyZPzkJz8pdSkAAAAAkFfyICyXyxWdpkePHnHZZZfFZZddVup/DwAAAABbVNanRgIAAABAtRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASeha6QKocrlcxPr6SleRrnUNm3xfHxFbVawUIqJbz4iamkpXAQAAQCsJwmheLhdxzciIxYsqXUm6ct0j4tr3vr/oIxE1aytaTvIG7xNxzN3CMAAAgA5KEEbz1tcLwSqsZ83a+FuPwytdBo0WP/reflHbq9KVAAAA0AqCMFrm23+NqO1Z6SqgMtbVR1z8kUpXAQAAQBsJwmiZ2p56wQAAAAAdmqdGAgAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASRCEAQAAAJAEQRgAAAAASeha6QKATiiXi1hfX+kqSmdd/Za/7wy69Yyoqal0FQAAAO1CEAaUVi4Xcc3IiMWLKl1JeVz8kUpXUFqD94k45m5hGAAAkAS3RgKltb6+84ZgndHiRztX7z0AAIAC9AgDyufbf42o7VnpKtiSdfWdr3cbAABAEYIwoHxqe0bU9qp0FQAAABARbo0EAAAAIBF6hAEAJCiXy8XqDasrXUbeprVUU111XeuixgNFAKDTEIQBACQml8vF5F9Pjqf++VSlS9mi4TcPr3QJebv33z3mHjhXGAYAnYRbIwEAErN6w+qqDcGqzZNvPFlVPdQAgLbRIwwAIGH3T7g/6rrWVbqMqrN6w+qq6pkGAJSGIAwAIGF1XeuiZ7eelS4DAKBduDUSAAAAgCQIwgAAAABIgiAMAAAAgCQIwgAAAABIgsHy20MuF7G+vtJVZLeufsvfdxTdekbU1FS6CgAAAKBKCMLKLZeLuGZkxOJFla6kbS7+SKUryG7wPhHH3C0MAwAAACLCrZHlt76+44dgHdXiRztmTzwAAACgLPQIa0/f/mtEbc9KV9H5ravvmD3YAAAAgLIShLWn2p4Rtb0qXQUAGeVyuVi9YXXF/v+m/7uSdURE1HWtixq3nNPOKrEPVnq/s68B1azS50blVOn2v72kfJwRhAFAAblcLib/enI89c+nKl1KREQMv3l4Rf//7v13j7kHzk32xIn2Vw37YCX2O/saUK2qoV1uL5U+7yqnlI8zxggDgAJWb1idxIleSz35xpOd+tNRqk+q+6B9DahWqbbLnU3Kxxk9wgCghe6fcH/Uda2rdBkVsXrD6k79qSgdQwr7oH0N6EhSaJerUS6XizUNa1r1t6s3rI4v3fal/Pet1ZFvrRSEAUAL1XWti57dPPQEKsU+CFBdtMvtr5S3prblg5eOfGulWyMBAAAAOoBquTW1I99aqUcYAAAAQAdTiVtTO8Mt/IIwqlcuF7G+Pvvfravf8vdZdesZ0QG7eQJpaa/Hl7f3o8Q78rgTpKMc+1+59zX7FkDn4dbU1hGEUZ1yuYhrRkYsXtS2+Vz8kdb/7eB9Io65WxgGVK1KPb68PT4F7MjjTpCG9tj/yrGv2bcKa68PF9qqvT+cKBVBLFANBGFUp/X1bQ/B2mrxo+/VUdursnVAa3tHFlKqnpNbojdlu6mWMSLKoXHcCZ9yUq066v5n32pepT5caKuOdIuSIBaoBoIwqt+3/xpR244na+vq29aTDEqpVL0jCyn19q43ZUV0lseXd4ZxJ0hPR9j/7FvFddRwsyMRxALVQBBG9avtqVcW6aqG3pFZ6U1ZEcaIgMqx/3U+HSHc7EgEsUA1EYQBdBTt3TsyK70pAegkOlK42VHGNWvUUWo1nhl0XoIwgI5C70gAYBMdcVyzjtIzrLONZ1bKwLScD2sQQNIeBGEAQLPau6dBJZ+E5uQb6GiMa1Y+nWk8s3IGpqUONjtbAEl1EoRBZ1WOJw22RDmfRpiFJxdCm1W6p0F79xpw8g10ZMY1K43OOJ5ZRwpMO1MASfVKLwhr73CgkqGAICBd7fGkwZao5HhRnlwIbdaRTpxLwck30JF1pHHNqJxqDUw7YwBZ7Vrb67+tvferoQd+WkFYpcOB9g4FBAHp6ohPGiw1Ty6EkqrWE+dScPINQCoEpkSUrtd/a86fqqEHflpBWGrhgCCAiOp/0mCpeXJh6bW0J21re8C2Q+/VtoxzVaoxq6rh06+2cOIMlVOKsfpKOf5eR2/PAFJXyV7/1dADP60gbFOdORwQBLApTxqkLVrbkzZLG1Tm3qulHOeqLb2GquHTL6DjKcdYfW3tAak9o1qU44EunohIatqr13819cBPNwgTDgAU1x49acvce7Vaxrmqhk+/gI6nWtqwTWnPqAbt8UAXT0QkBSn2+k83CAOqTykeZlHKB1R44ERTpe5JW4Heq5UY56qaPv2CatLSnhyt7Z3RGXtetLUNy+VysaZhTav/fvWG1fGl276U/76tOuM6ov1UY0hcjBAZqoMgDKgO5XiYRVtDFg+caKoT9KRN8ROvzqQct8BElPc2mEYu+JtqbU+OLKFyZ+x50ZY2rNS9Z0oR8HfGdURlVPsDXXwoBtVFEEbnl7WXUVt6FLVHD6KWvJ+s76Eaej5V48MsPHACqkZ73AITUfrbYBq19wV/sdAwS/hXjhCvPXpy6HnRVDX2nmnvdVSq/UKwXX0q/UFXlg9qOuO2VW0P9GjUUZcn5ScIq7RS3Aq2uVLeGtacaghOWqKtvYyy9igqdw+i1ryflryHauv5VOmHWXjgBFSdaryIz+LJN56Mt9e83eIeC205ec8aGhYL/8od4pW6J4eeF8VVuvdMJdZRKfcLPdnYVOrbVjU+0KNRR1yem9tSyFgsNBQAFicIq6Ry3Aq2uXJdzFdbcNKc9u5lVO4eROV6P9XW86kT3IIHlE8lL+JzuVxMuWdK/OnNP2X+2/a6ra/UoWG5e+1UuidHIYV6OVS6V11bVPMyL5dS7hd6G7Kpatu2sgYnbW2fqvmDqo6+r7YkZNzSuUVnCADLTRBWSdV4K1hLLX40YtWbhXvtVFuvsXL2MqpED6JSvB89nyqvWK/QLD08q22fgzKp5EV8/fr6VoVgWZXq5L0toWHqPauy9HKodK86smntflHufaI1t5e15XayagloW3tbXalupSvlcqj0ttWa4KSU7VM5PqhqzUM+2vpgj2rZN1obMnb0ALA9dK4grFQXlJW4mKz0rWAR7y2/n4+J+MfjLZu+WIBSbb3GOlsvo872flKUtVdoR9vnoJMrxwl/qS+0WxoaZhk7qXG+1XCRUM5bRqqtlwelU4094kpxe1nWtqMaAtpS3VbXlnazlMuh0ttWa9qtUrZPpX7/ldgvIqpj39hcS845Uv8AK4vOE4SV8oKyEheT1RBqrFvV8hCsJartdjuoNqXuFbqlnpp6ibUr4zikpdIXPKVS6R4ErdWet4xUupdHR9DawcJb+jedva2sxO1l1RDQVsNtddWwHMqhWLvVEdqnSm0f1bhNVPM5R0vb/9b24ixH+995grBSXlCWIsDp6E/2a0sPtc54u11H7m1Ix9CSfa4lvTY33/eqpZfYlvahYvtNhv2lGsbx6ejjOAjx0lXuHgTl2rba85aRar4A2ZL23p/b0mujpRfi1dJWtodyj4NYrQFIe4//WK3LoVQ6WrtVTHtsH519myiH1rb/7TVuanM6TxC2qZaGOLlcxPpNTgTW10f8xyfe+74tF2Wd4cl+1dBDLausF9rlWp/V1tuwWlTTWFjVVEujluxzrem1WQ09M1uyD21pv2nh/lIt4/h05HEcOnqI11FVY/hY6h4E7bVtuWXk/6vE/twevTaae/JqZwzk2xpgtLZ33pbqaM9l29mCG0rL9lGd2qv9L/W5cucMwlpyQVnswqwNF2VlfbJftQ1QX+ZeHpnqyHqhXYn1WYlQolDo016BTzWNhVVNtbRFscC/NT0zy7U/t3YfauH+UupxfEpxoVWui/LNL24qPRZSW09MOsKtVOVa5tUaPpb6QqO9tq1quECqlmCz0qF8W3ptFHsqa2cK5CvZtmyq0HGooy7b5pRrmaekWtq51mjJOUfWc41qeW/VotS99sr5AVbnDMJaojUXZq0JMYpdrHbkAerL3Msjk2pZn82p1O2iWUKfcm5b7TEWVkR5Q5ly1NIWpe612V77c0v2oTbsL8UOwMUusiJKc6FVjovyYhc37TkWUilOTNrjVqpPbPeJuHLEle97/6W6oGzLMq90WFEJnbnXVrUGm5VY5m1p/1rzVNaOuE9UY9uyJaVYtlkexFHO3mntdQzNUk9HC+XK3c6VM2RrzTlHS9rFzhYWt1Vb2v+sD+1p/H+tXfYVDcIuu+yyuOiii2LJkiXxb//2b3HppZfGpz71qfYvpBw9KxoVu1jtyAPUl7mXR6tVcn02amkvrEblCk1K3ZutFIFPucbCiihPKNNetVRae+3PZb7tutgBuDUXWRHVcaHVXk+Caq+eNe3Rlf6Zfz4T+9y4z/teL+cFZWuWeWcOiDZVDb22yqVag82OvMwrPdB3tT2RtFxtS8R773VNw5om9X3pti/lv99cJXqntfWDjUo/TXFT1RbKtVQ527lyh2zlOudo7k6CTVVDiFkq5WoXK/FhUsWCsPnz58cpp5wSV1xxRey9994xe/bsGDlyZLz44ovRv3//9i2mWsbDytLbaPMwpdj4ZuV+f2Xu5ZFJpddnOW+7bYtS9E4sRe3lGgsrojyhTHvVUk2qaX8uo44cPlT6ArE16jf7cGDTk6Vfj/v1Fm9HLaa177McF5RtWeYdOazg/Tpy29JShfbnLV0ItWb7ruR+UU1PJC1329JReqe19YONTVX6GFpNoVxrlbqda88PE0px98Cmir3HLYW41bIesyhnu1iJD5MqFoRdcsklMWXKlDj66KMjIuKKK66Iu+66K6655po4/fTTm0y7du3aWLt2bf7n5cuXR0TEihUr/v9E61ZFrM3F//tFRG1D4QKyTN9e816zIWJjkXk3mvmvzf/u/C38bvrfi8+zLe+zJbWv21B9y7zU826c/q+PFp5mc399JOKtJS0LZcq1jtatinj5sZbX3Kgltbel7m8+FVFb5IJ43eqISz/Zsvl3llrKOe+OtD/HexdkDasb/t/kK2JDtw0tmnZ9/fro1q1bwXmvX7++VfMuNm1bpy9We5a626P2iIh9bnj/BUyjA+Yd8L7XHj28eDu66ftcOHZh9Ojao+D0azasiVG3j4qIzr3MO+q8O0stHbltKcX+/Om5n37fay3Zn7PWUu71+cRrT7So5k09/urjsfTtpUUvyqqtbcn6XlvzPou10Zu2z+WupRqWeWuWSzW1oeVs5yp9PK9fXx9PLX6q4PyyePK1J2Ova/Zq8lo52sSs07dHWxHRsn20lOu/MSPK5XIF51GTKzZFGaxbty569uwZt9xySxxyyCH514866qhYtmxZ/PKXv2wy/dlnnx3nnHNOO1cJAAAAQEeyePHi2GGHHZr9fUV6hL355pvR0NAQAwYMaPL6gAED4s9//vP7pp8+fXqccsop+Z83btwYb7/9dmy77bad5n5bAAAAAFonl8vFu+++G4MGDSo4XYd4amT37t2je/fuTV7r169fZYoBAAAAoOr07du36DRd2qGO9/ngBz8YW221VSxdurTJ60uXLo2BAwdWoiQAAAAAOrmKBGG1tbWx5557xr333pt/bePGjXHvvffGsGHDKlESAAAAAJ1cxW6NPOWUU+Koo46KoUOHxqc+9amYPXt2rFq1Kv8USQAAAAAopYoFYYcddlj885//jLPOOiuWLFkSn/zkJ+Puu+9+3wD6AAAAAFAKNblcLlfpIgAAAACg3CoyRhgAAAAAtDdBGAAAAABJEIQBAAAAkARBGAAAAABJSC4Iu+GGG2LixIlxxBFHxOGHHx433nhjpUtqF2vXri3ZvK6//vr47//+7xg/fnxMmjQpLr/88pLNO6srrrgiJk+eHDfddFOMHj26orV0JrNmzap0CUn5wx/+UOkSaMayZctiyZIllS6jiVK252zZPffcE1OmTImnnnoqIiKuvPLKyhZUZtW0nVdTLR3RrbfeGmPGjIlPf/rTMXbs2Pj9739f6ZIq7sEHH4yXX345jjzyyJgwYUI8+OCDlS4pT3tefgsXLoyFCxfGXXfdFWPHjo2FCxdWuqSIyHbuVw3nifvvv39cddVVsWrVqkqXksmsWbNi0qRJMXfu3Bg/fnyceuqplS6JKtHpgrC777674O8feOCBuOmmm2LevHlxww03xEMPPVRw+mXLlsXjjz8eK1asiOuvvz7++c9/Fpz+97//fdx8883xzDPPlLTuiIjVq1fHL37xi5g1a1bMmzcvVq9e3ey03/rWt+Kwww6L73//+xERcfLJJxecd5b3+dhjj8Vdd90VCxYsiBtvvDH+/Oc/F5x3LpeLf/zjH/mfX3/99YLT33PPPdHSh5ned999MXfu3Lj++uvjV7/6VTz99NMlm3dExJo1a2LevHlx4YUXxp133llw2vr6+njmmWdi48aNceedd8b/+T//p+D0Wdbnpp599tl49NFHW/weWrJtTZgwIf81fvz4+NnPflbyuhsVu8DJur1EZF8mLa1l6dKlsX79+rj22mvj0ksvjbfeeqvZaV9//fW48847Y9WqVTFnzpz405/+VHDe48ePzy/vr371qzFhwoSS1b255557ruDvs+z/69ati4iI+++/P/7zP/8z1q9f36IaWrKOsizvLSn2PrPUPmvWrLj++uvjG9/4Rpxzzjnx3e9+t+C8s263WdqWcrbnrVnmDz/8cMyfPz8efvjhotNm3S8iWr8/l3I7v+aaa+Kiiy6KX/ziF3HfffflA7HmtGY5tvR9tmYZbqrQcsm6nWetJct2nrWWl156qeDvN5X12J/1eL6plhxzs0ybdZnfd9998ctf/jKGDRsWt956a9EPCLPszxEtP8/dsGFDLF26NDZs2BC/+93vYs2aNQWnz3q+naXNvfHGG2PGjBlxySWXxC9+8YuiyyTLsSLLdhiRvT3PemzJsj6z7J8R2dq51u5DLW0Xs9Ty/e9/P55//vl48803o76+Pt58882C8y7ndUuWc7+s54nlbJ8jInbZZZfo379/HHPMMfH1r3+94Hpq67VCoeNW1rblpZdeihtvvDGuv/76WLBgQbz77rsFp8+yPrPWsrli5y1Zj12NSp0rZN1Wsq7/rO1oa/OWzXWKIGz48OH5huLEE08s2FCsXbs27rrrrnjmmWdi4cKFRVfM0UcfHQ899FAceeSR0aNHj/jWt77V7LTf/va34+GHH45f//rXccstt8Q555xTsrojIo4//vjYZptt4qCDDoqtt946pk6d2uy0K1eujPnz58cXv/jF+Pa3v110J8ryPvv06RNvvvlmXHXVVXHLLbcU/WTga1/7Wlx00UVxwgknxIYNG2LGjBkFpz/ttNNizJgxcfbZZ8drr71WcNptt902ampq4hvf+EZERHTv3r1k845474SltrY2/vGPf8Sbb74ZJ554YrPTHn300bFw4cIYN25crFy5Mr75zW8WnHeW9RkRceqpp8YPf/jDuPbaa+OBBx6Ir3/9681Om3Xb6tOnT9x8881x8803x4IFC2LEiBElq7u+vj7/tWrVqjjrrLMKTp9le8myTFpTy8yZM+Occ86JAQMGxNChQwvuF9/85jejvr4+Dj744Bg6dGice+65Bec9dOjQOPjgg2PBggUxatSouPnmm0tW9/PPP5//eu6554ruc1n2/+nTp8cPfvCDeP7552PlypVx/PHHF5x3lnWUZXm35n1mqf1vf/tb/oOTyy+/PN5+++2C887azmVpW8rZnmdd5scff3w8/fTT0atXr3j66afjhBNOKDh9lv0i6/5czu28d+/e0a9fv7j44ovjv/7rv+Kxxx4rOO8syzHr+8zatmRZLlm386y1ZNnOs9YyatSoGDt2bFx33XVFz+WyHvuzHs+zHHOzHp+zLvM333wzHn744Vi9enV06dIlevbs2ey0WffnLOe5kydPjosuuigmT54cjz76aEybNq3gvLPsnxHZ2tznnnsuli5dGv3794/a2tro27dvwXlnOVZk2Q4jsrfnWd5n1vWZZf+MyNbOZd2HsraLWWp54IEH4t13340ePXrExz72sZg8eXLBeZfzuiXLuV+WaSPK2z5HRHTt2jXGjBkT8+fPjzPOOKNg2JL1WiHLcStr2/LWW2/FDTfcEBs3boyHHnqoaBCaZX1mrSXreUuWWsqZK2TdVrKu/yztaNa8pZBOEYRNnjw59tprr7jhhhuKNhQ/+clP4p133omFCxfGO++8Ez/+8Y8LzvuDH/xgnHTSSbHjjjvG+PHjo1+/fs1OW19fH9/5zneiX79+ce6558bSpUtLVndERG1tbYwaNSp22223GDVqVPTo0aPZaRs/zdp///3joIMOijvuuKPgvLO8z/POOy8OOeSQePvtt2PdunVx6aWXFpx3t27dYvbs2fHv//7vcfzxx+dra87ee+8dd955Zxx00EFx/vnnx1e+8pVmp23cEQ8++OCIiBg3blzJ5h0R0dDQEOPHj49cLhfHHHNMbNiwodlp+/btG6effnqsW7cuDj/88BgwYEDBeWdZnxER7777brz00kvxwx/+ME477bTo2rVrs9MeddRRmbat733ve01+Pv/880tW9+677x7Tpk2LqVOnxrRp0+J3v/tdwelra2tj9uzZceKJJxbdXjZfJltttVVJa2loaIiGhoY48MADY9iwYQUvKPr16xcTJ06Mmpqa2HfffeODH/xgwXmfdtppsdNOO8VJJ50U77zzTknr/upXvxq33HJLLFiwIG655Zb43//7fxecPsv+v27dulixYkWccMIJMWnSpPjABz5QcN5Z1lGW5d2a95ml9j/+8Y/xyiuvRMR7nw4XOzA3tnMt2W4jsrUtm7bno0ePLml7nnWZd+3aNU444YQYPXp0nHDCCVFbW1tw+q233rrF+0XW/bmc2/lBBx2U//6CCy4oeuGUZTlmfZ9Z25Ysy2XT7Twiim7nWWvJsp1nrWXEiBFx8803R69eveLII48seNGf9dif9Xie5Xwu67lf1mV+9tlnx6JFi/IfmBTqWZd1f85yntujR4+4+OKLo0+fPvGd73ynaNuSZf+MyNbmnnfeeXHaaaflfx45cmTBeWc5VmTZDhvnHfFee37wwQcXbc+znENnXZ8bNmxo8f4Zka2dy7oPZW0Xs9TSs2fPOOecc2Lbbbctuh1GZD+eZ2lfGs/9TjzxxKLnflmmjShv+xwRccQRR+S/Hzx4cJx99tnNTpv1WiHLcStr2zJnzpzo169f3HLLLbFo0aKCdUdkW59Za8l63pKllnLmClm3lazrP0s7mjVvKaT5K+kO5JhjjolXXnklTj755KK3I/Ts2TOOPPLIFs/7f/7P/xkREbNnz87/fXNyuVxMmTIlPvaxj0VEFG2AGus+5ZRTiqbTERGf+9znYsKECVFTUxM1NTVxyCGHNDvt5z//+Vi4cGHkcrn42c9+FhdeeGHBeTe+zx/96EcRUfh9Fvvfm2s8gOy8885x0kknxQEHHFBw+sZP6vbaa6/Ya6+9YuXKlc1Ou/POOzf5+bOf/WzJ5h0Rsd1228W4cePyAVvjctqSurq6OPzww2PPPfeMr33ta9GlS+Gc+XOf+1wcdthhUVNTExFRdJm++uqr0atXr/zPhXriHX300S3eJyIihgwZ0uTnbbbZpmR1jx8/vsknHo3bWHMax8v46Ec/GieeeGLBE9bNl0mxC6estUycODFmz54dO++8c+y6664F244Pf/jDccQRR8TEiRNjzJgx+XagOQ8++GDssMMO8fe//z2WLVsWDz74YHzmM58pWncul8u3R8058sgjm9xusfn63VyW/f+AAw6Iyy67LPbff/8YNGhQszU32nwd1dfXNztt4/LeZZddYpdddinaVmd9n1lqb7yIvOuuu+JnP/tZ0QvnxltnGrfbYu1clrZl1KhRMWXKlJg6dWoMHz68aC+8j370oxHRsvWZdZkPGTIkvvrVr8Z2220Xb7zxRgwdOrTo9I37xZe//OXYbbfdmp02y7YS0fbtfNP/tbkxY8Y0+blYb4YsyzHr+2xsWyZNmtSitiXLcpkxY0bMnz8/nnrqqfjkJz8Zu+yyS4tqacn6jMi2nV944YWxww47xJFHHhnr1q1rctHVnG7dusX48eNj/PjxTUK0zWU99jcez4cOHdqi43mW89As00ZkX/+N22CjD33oQ81Ou+n+/Oabb8Yee+xRcN5ZznP79+8fEZH/tL5Yz6cs59sR///c8qMf/WjRc8vNzw3Hjh1bcN6bHisGDhwYw4cPLzh943b4la98Jf72t78VnPbLX/5yvPzyy3HWWWfFunXrYs6cOQWn3/wc+otf/GKz02Zdn/3792/x/hmRrZ3b9Jz42GOPLRpsZW0Xsx67It672C50x0OjLNtWRORD25a0L7/97W9j/vz5ccwxx0RDQ0NceeWVcdxxx21x2saxzL74xS/Gf/zHf8TChQtj1KhRzc4763lolvY54r3wtqUarxUiWnbdmOW41di2NPZ4K9a2DB48OAYPHhwRUbSnaUS240XWdi7reUuWWlqTK7T0ei7rtpL1WjGi5cfzrHlLIZ2iR9j+++8fv/3tb2PmzJlFuxhm9eEPfzjGjBkTn/vc52Ls2LHx5S9/udlpDzvssJgyZUo8/vjjMWHChKKN7fXXXx9/+9vf4vXXX4/169cXHbNg0qRJcfPNN8f8+fPjpptuildffbXZaefMmdPkfvhiO2bv3r1j8uTJceutt8bo0aMLnjhlNXz48Pzg+jNmzIgzzjij4PT/8i//kh8A/+CDD47rr7++ZLVccMEFTX6+7LLLCk7/yU9+MhoaGuKnP/1pjB07Nvbaa69mpx05cmQcccQRsc8++8SSJUvedyG1uQ0bNsTXv/71aGhoiIgo+mnP8uXL44ADDsgHYNddd13B6YcMGRKXXnppzJgxo6SD37/22mtRU1MTBx54YDQ0NMQf//jHgtMfcMABTQap3XPPPQtOf8011+S/32233Qp2p/3sZz8bW221VX4AzO22267gvD/72c/GlClT8mPJFboQjojYb7/9YsGCBfGXv/wl7rjjjoKf3HTt2jUaGhqitrY2amtri35a0jhmyeWXXx4LFy4suP/vu++++cFex40blw86mnPLLbc0GdC0WG+W73znOxHx3kEo4r1ePM3p27dvzJkzJ3r06BENDQ3xiU98ouC8Fy5cGAsWLMj/XOhC++WXX47jjz8+Pv7xj0ePHj2KjhGz4447NhkU+iMf+UjB6Xv06BGDBg2Kiy++OObNm1fw0/Kzzz47XnjhhXwb2rifNufPf/5zfpnvtttuTcYX2ZIPfOADUVtbm/+UrdD0t912W5PxqoqNEXf77bfHVVddlT+Z37zd29Sf/vSn6NGjR5x99tmxYcOGost85513jgkTJsQee+wRb7/9dtGToe9973sxb968mDJlStx5550Fb0k69thjY926dfn12dzFQaMPfvCDTR7cUuw2/Y0bN8akSZPihhtuiPHjxxddp1k89NBDsdVWW8X06dOjW7duBQc2/vKXvxw9evTIP+Rl7733LjjvwYMHx7HHHhu/+c1voq6uLn9C35x169Y1GRj42WefbXbaa665Ji6++OL8tlXs1ouFCxfG8OHD4/DDD48777wzfvCDHxSc/rzzzovbbrstf5FaaPyU+fPnx/nnn58fx+mmm24qOO+vfOUrTY4tixcvbnbavfbaq0lbUWxckXHjxsV5550XL7/8crz66qtFQ7mf/vSn8f3vfz/222+/ePfddwu257NmzYrvfve7sccee8SGDRuKDty88847x8qVK2Pu3LlRU1MTo0ePLjh9FkOHDo2zzjornnnmmVi1alXR4GTs2LHR0NCQD4Z22GGHZqf9zGc+EwsXLozHH388xo4dWzRMGDhwYPz3f/93TJo0KSZNmlT0PPSjH/1ofjs/++yzY9KkSQWnz2KrrbaKE044IY4//vior6+PD3/4w81Oe/DBB+cHYh83bly88MILBef9X//1X03GK7vtttsKTn/QQQflt90zzjijYC+Pj3/84/mLyauvvrpomHjffffFl770pXwwWOx2pyVLlsS6devy51qDBg1qdtrRo0dHz54949BDD40LL7yw6LY1ceLEOP744/PtebF28bTTTosDDjggnnjiibjjjjuKfliVxac+9an8dchpp51W9LrlwQcfbHLOVagH4dVXXx0XXXRR3HrrrbFu3bqC4082jm321ltvRZcuXYqGG9tuu2106dIlevfuHQ0NDUWPFf/jf/yP6NWrV3Tt2jVGjx5dtAdhFj/+8Y9jxIgRcc0118RNN90UEydOLDj95mPlFRpTbtSoUfHyyy/HKaecEhMmTCh4bd4au+66a5Nzi0LXoV/4whdiypQp+Xp32mmngvOuq6tr8pC3YuctW2+9dZPjeaHbXW+99dY46aST4oknnoh333236INSli9fHrW1tTFmzJhYuXJlwevQnXbaKaZNmxa//OUvY9KkSUW3ldra2lizZk38/e9/jzVr1sSOO+5YcPpx48Y1eUDRPffc0+y0Bx10UHz605+OnXbaKcaOHRv77LNPwXkX0il6hDUO3ve1r30t+vXrF0cffXSbFsqmGgcdPfXUU+OCCy6Io446Kvbdd98tTjt//vxYu3Zt/OhHP4q+ffvGUUcdVbDHwWOPPRZPP/103HrrrRFR/AC06b2+uVwunnrqqSZdvTf1wAMPxKxZs2Lw4MEtuh/+vvvui/nz58fo0aPjrrvuKtrjIIvHH388nnnmmfzFcLH3uXktjeN/lUKWZdhYS0vX//e///047LDDYrvttov169cX/aS3cf23dLnsuuuu+UEqi23nWd9nFi+99FLcdNNNMWLEiPjtb39b9DaAG2+8MdauXRuXXHJJ9OvXr+h+saXam7u1I2st11xzTVx++eUxY8aMeOutt4oOgJ1lOWat5bnnnovevXvnTygLBQSbblstGeg1y7YSke19Nq7PG2+8Mfr16xeTJ0/OvD6bm3fjPnHLLbdERMvaipbunxFN1399fX3B9Z+1Dc26zLNsL5uOV3X66acXHa8qSy1Z2/5Nt8WGhoai22KW9Z91fWZtQxsHzG3pPppFlvWZdZlnPYZmeZ/l3LYisq3/Z599Nnr37p3/hL3YOE633HJLi48tWbetm2666X3nc4V6nN97770tPm/ZfP0UW/9Za88ia3t+7bXX5tvQt99+u+hF/KbHrWIf9lXT/pylnct6fG489rd0O9/S+m+uZ86mx7jVq1cXPcfJuj831nLaaafFzJkzC26Lm9YyatSoouFz1nau8drv2GOPLcu1X5Y2OstyzNLmlvtarpzXfu1xHrpp21+op2RWWbbFTbfzt99+u+gD5LIu8yzHi6zHiiy1tOZavqVtRcT7l2Ohtuucc86Jww47LHK5XIva3EI6RRDWOHjfmDFjYvHixXH11VeXrDHMMuholgvbiPcGKP/73/8eV111VWy99dZFU+E+ffo0eZpfoQ228X743/72ty26H37zQedL+alA1veZdQD8rLW0dBlGZFv/WQ9YWZdLlu086/vMYvNBJ4v1IMl60pel9qy1ZL3oK2ct5513Xr7LcEThMUuybltZ28Qs77M17VxL5511n8iyf0ZkW/9Z29CsyzzL9rL5eFXFxmXMUkvWtr817VxL13/W9Zl1e8m6j2aRZd5Zl3k532c5t63G2lu6/rO0iRHZji1Zt62s7VyW85asAzdnrT2LrO+znBfx1bQ/Z6k96/vMup1nWf9Zz3Gy7s+NtdTX15e8lnKeE2eVtY3OUkuWNrfc13LlvPZrj/PQll5XZJVlW8y6nWdd5lmOF1mPFVlqae35eUvaiojyHlsKynUCv/vd78o27+effz73wx/+MPfGG2/kcrlc7m9/+1uz095///25Bx54IP/zbbfdVnDeGzduzN1+++25Cy64IDdv3rxcfX19welffvnlJj+/9dZbxcpvsRdeeKHJz/fff3/J5p31fZazlqzLMMv6b3TPPffkvve97xWdLutyybKdl3Nbee2113J33XVX7q233spdfPHFuWeffbbg9Fn3iyy1Z63ljjvuaPLznDlzKlZLa7R028raJmZ5n+Vcn1n3iaz7Z9b1n0XWZV7O7SVLLa1tb1u6LWZZ/1nXZ9btpZzLPMu8sy7zanqf5WxbssrSFmXdtrK2c1nWadb105rzkJbK+j5b04aW65yomo65WafNIsv6z7p+su7P5aylnOfEWWVto8tZSxZZ6y7n9VY1nYdmlWVbzLqdZ13mWdq5rMeKLLVU4/l5KdrcmlyuyOBRAAAAANAJdIrB8gEAAACgGEEYAAAAAEkQhAEAAACQBEEYAAAAAEkQhAEAAACQBEEYAAAAAEkQhAEAAACQhP8L67OhDX3KzcsAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -55,7 +383,7 @@ "\n", "# Реализация иерархической кластеризации при помощи функции linkage\n", "\n", - "mergings = linkage(X, method='single')\n", + "mergings = linkage(X, method='ward')\n", " \n", "# Строим дендрограмму, указав параметры удобные для отображения\n", "\n", @@ -67,32 +395,39 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 1 1 2 1 2 2 1 2\n", - " 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 1 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2\n", - " 2 1 2 2 2 1 2 2 2 2 2 1 1 1 1 2 1 2 1 2 2 2 2 2 2 2]\n" + "[1 2 3 1 5 5 1 6 6 4 1 6 2 2 2 2 2 1 3 4 5 2 3 6 5 4 3 6 5 4 6 6 5 1 4 3 3\n", + " 1 6 6 3 2 1 4 5 3 5 5 1 6 4 2 5 6 2 4 2 4 4 1 1 3 1 4 1 5 2 6 3 6 6 2 3 3\n", + " 1 2 5 5 4 5 2 4 4 3 4 2 3 6 2 3 6 3 5 1 6 3 5 4 4 1]\n" ] } ], "source": [ - "T = fcluster(mergings, 10, 'distance') # distance or maxclust\n", + "T = fcluster(mergings,10, 'distance') # distance or maxclust\n", "print (T)" ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -107,54 +442,75 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ - "def update_cluster_centers(X, c):\n", - " ix = np.where(c==1)\n", - " mu[0,:] = np.mean(X[ix,:], axis=1)\n", - " ix = np.where(c==2)\n", - " mu[1,:] = np.mean(X[ix,:], axis=1)\n", - " ix = np.where(c==3)\n", - " mu[2,:] = np.mean(X[ix,:], axis=1)\n", - " ix = np.where(c==4)\n", - " mu[3,:] = np.mean(X[ix,:], axis=1)\n", + "def update_cluster_centers(X, c, num_clusters):\n", + " for cl in range(1, num_clusters+1):\n", + " ix = np.where(c==cl)\n", + " mu[cl-1,:] = np.mean(X[ix,:], axis=1)\n", " return mu" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[-1.56129966 4.21898437]\n", - " [-7.5371344 -4.96080784]\n", - " [ nan nan]\n", - " [ nan nan]]\n" + "[[ 8.20412426 2.061867 ]\n", + " [-6.74804529 -7.51382813]\n", + " [-4.16045299 1.00683372]]\n" ] } ], "source": [ - "mu = np.array([[0.0,0], [0,0], [0,0], [0,0]])\n", - "mu = update_cluster_centers(X, T)\n", + "num_clusters = len(set(T))\n", + "mu = np.zeros([num_clusters,2])\n", + "mu = update_cluster_centers(X, T, num_clusters)\n", "print(mu)" ] }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -170,21 +526,121 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bokeh plots" + ] + }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "data_df =pd.DataFrame({'x' : X[:,0], \n", + " 'y' : X[:,1], \n", + " 'cluster' : T})\n", + "data_df['cluster']=data_df['cluster'].astype('str')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "palette = Category20[len(data_df['cluster'].unique())]\n", + "color_map = CategoricalColorMapper(factors=data_df['cluster'].unique(),\n", + " palette=palette)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = {\"1d860943-41e7-4f37-b225-2858ad6e6255\":{\"version\":\"3.3.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1339\",\"attributes\":{\"height\":500,\"max_width\":1800,\"sizing_mode\":\"stretch_width\",\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1340\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1341\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1349\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1350\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1342\",\"attributes\":{\"text\":\"Clustering\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1377\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1368\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1369\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1370\"},\"data\":{\"type\":\"map\",\"entries\":[[\"index\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[100],\"dtype\":\"int32\",\"order\":\"little\"}],[\"x\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[100],\"dtype\":\"float64\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[100],\"dtype\":\"float64\",\"order\":\"little\"}],[\"cluster\",{\"type\":\"ndarray\",\"array\":[\"1\",\"1\",\"2\",\"1\",\"3\",\"3\",\"1\",\"3\",\"3\",\"2\",\"1\",\"3\",\"1\",\"1\",\"1\",\"1\",\"1\",\"1\",\"2\",\"2\",\"3\",\"1\",\"2\",\"3\",\"3\",\"2\",\"2\",\"3\",\"3\",\"2\",\"3\",\"3\",\"3\",\"1\",\"2\",\"2\",\"2\",\"1\",\"3\",\"3\",\"2\",\"1\",\"1\",\"2\",\"3\",\"2\",\"3\",\"3\",\"1\",\"3\",\"2\",\"1\",\"3\",\"3\",\"1\",\"2\",\"1\",\"2\",\"2\",\"1\",\"1\",\"2\",\"1\",\"2\",\"1\",\"3\",\"1\",\"3\",\"2\",\"3\",\"3\",\"1\",\"2\",\"2\",\"1\",\"1\",\"3\",\"3\",\"2\",\"3\",\"1\",\"2\",\"2\",\"2\",\"2\",\"1\",\"2\",\"3\",\"1\",\"2\",\"3\",\"2\",\"3\",\"1\",\"3\",\"2\",\"3\",\"2\",\"2\",\"1\"],\"shape\":[100],\"dtype\":\"object\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1378\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1379\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1374\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"type\":\"object\",\"name\":\"CategoricalColorMapper\",\"id\":\"p1338\",\"attributes\":{\"palette\":[\"#1f77b4\",\"#aec7e8\",\"#ff7f0e\"],\"factors\":{\"type\":\"ndarray\",\"array\":[\"1\",\"2\",\"3\"],\"shape\":[3],\"dtype\":\"object\",\"order\":\"little\"}}}},\"line_width\":{\"type\":\"value\",\"value\":3},\"fill_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}},\"hatch_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1375\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"line_width\":{\"type\":\"value\",\"value\":3},\"fill_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1376\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"line_width\":{\"type\":\"value\",\"value\":3},\"fill_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"cluster\",\"transform\":{\"id\":\"p1338\"}},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}},{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1386\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1380\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1381\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1382\"},\"data\":{\"type\":\"map\",\"entries\":[[\"x\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"2da4+YJoIECIqcyV//0awIiWsclNpBDA\"},\"shape\":[3],\"dtype\":\"float64\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"ziAlILR+AEABWCv2KA4ewJP6Zaz9G/A/\"},\"shape\":[3],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1387\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1388\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1383\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"value\",\"value\":\"red\"},\"line_width\":{\"type\":\"value\",\"value\":5},\"fill_color\":{\"type\":\"value\",\"value\":\"red\"},\"hatch_color\":{\"type\":\"value\",\"value\":\"red\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1384\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"value\",\"value\":\"red\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"line_width\":{\"type\":\"value\",\"value\":5},\"fill_color\":{\"type\":\"value\",\"value\":\"red\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"value\",\"value\":\"red\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Scatter\",\"id\":\"p1385\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"value\",\"value\":\"red\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"line_width\":{\"type\":\"value\",\"value\":5},\"fill_color\":{\"type\":\"value\",\"value\":\"red\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"value\",\"value\":\"red\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1348\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1361\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1362\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1363\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1364\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1365\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1366\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1367\"},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1389\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":[[\"x\",\"@x\"],[\"y\",\"@y\"],[\"cluster\",\"@cluster\"]]}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1356\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1357\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1358\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1359\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1351\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1352\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1353\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1354\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1355\",\"attributes\":{\"axis\":{\"id\":\"p1351\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1360\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1356\"}}}]}}]}};\n", + " const render_items = [{\"docid\":\"1d860943-41e7-4f37-b225-2858ad6e6255\",\"roots\":{\"p1339\":\"b65a3fa7-9c23-44a2-8628-c575f4174609\"},\"root_ids\":[\"p1339\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1339" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p=figure(sizing_mode=\"stretch_width\", max_width=1800, height=500, title = 'Clustering')\n", + "source = ColumnDataSource(data_df)\n", + "p.scatter(source=source, x='x', y='y',color={'field': 'cluster', 'transform': color_map}, width = 3)\n", + "p.scatter(mu[:,0],mu[:,1], color = 'red', width = 5)\n", + "\n", + "p.add_tools(HoverTool(tooltips=[(\"x\", \"@x\"), (\"y\", \"@y\"), (\"cluster\", \"@cluster\")]))\n", + "\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Сумма квадратов расстояний до центроида" + ] + }, + { + "cell_type": "code", + "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0 custer dist: [[43.11598405]]\n", - "1 custer dist: [[723.0435087]]\n", - "2 custer dist: 0\n", - "3 custer dist: 0\n", - "Summary of squared cluster dist: [[766.15949275]]\n", - "Mean summary of squared cluster dist: [[191.53987319]]\n" + "0 custer dist: [[140.52532826]]\n", + "1 custer dist: [[284.50246387]]\n", + "2 custer dist: [[445.8365626]]\n", + "Summary of squared cluster dist: [[870.86435472]]\n", + "Mean summary of squared cluster dist: [[290.28811824]]\n" ] } ], @@ -219,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -228,16 +684,16 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1 1 2 1 0 0 1 3 3 2 1 3 1 1 1 1 1 1 2 2 0 1 2 3 0 2 2 3 0 2 3 3 0 1 2 2 2\n", - " 1 3 3 2 1 1 2 0 2 0 0 1 3 2 1 0 3 1 2 1 2 2 1 1 2 1 2 1 0 1 3 2 3 3 1 2 2\n", - " 1 1 0 0 2 0 1 2 2 2 2 1 2 3 1 2 3 2 0 1 3 2 0 2 2 1]\n" + "[1 1 0 1 2 2 1 3 3 0 1 3 1 1 1 1 1 1 0 0 2 1 0 3 2 0 0 3 2 0 3 3 2 1 0 0 0\n", + " 1 3 3 0 1 1 0 2 0 2 2 1 3 0 1 2 3 1 0 1 0 0 1 1 0 1 0 1 2 1 3 0 3 3 1 0 0\n", + " 1 1 2 2 0 2 1 0 0 0 0 1 0 3 1 0 3 0 2 1 3 0 2 0 0 1]\n" ] } ], @@ -250,12 +706,12 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 96, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -272,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -289,28 +745,31 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[6974.827094379589, 2194.619727635032, 870.8643547241177, 472.08573606137327, 259.5795564009951, 176.1282308577753, 153.70943167635173, 142.4483725489951, 125.24089654012067]\n" + "[2194.619727635032, 870.8643547241178, 472.08573606137327, 259.5795564009951, 176.1282308577753, 153.70943167635176, 143.24864846484422, 125.24089654012067]\n" ] } ], "source": [ "inertia = []\n", - "for k in range(1,10):\n", + "silhouette = []\n", + "for k in range(2,10):\n", " kmeans = KMeans(n_clusters=k, random_state=1, n_init=10).fit(X)\n", " inertia.append((kmeans.inertia_))\n", + " all_predictions = kmeans.predict(X)\n", + " silhouette.append(silhouette_score(X, all_predictions))\n", "print (inertia)" ] }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -319,13 +778,13 @@ "" ] }, - "execution_count": 159, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -336,19 +795,52 @@ ], "source": [ "plt.figure(figsize=(5,5))\n", - "plt.plot(range (1,10), inertia, marker='s')\n", + "plt.plot(range (2,10), inertia, marker='s')\n", "\n", "plt.show" ] }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 100, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5,5))\n", + "plt.plot(range (2,10), silhouette, marker='s')\n", + "\n", + "plt.show" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", "text/plain": [ "
" ] @@ -358,7 +850,7 @@ } ], "source": [ - "kmeans = KMeans(n_clusters=6, random_state=1, n_init=10).fit(X)\n", + "kmeans = KMeans(n_clusters=5, random_state=1, n_init=10).fit(X)\n", "plt.scatter (X[:,0], X[:,1], c=kmeans.predict(X))\n", "plt.show()" ] @@ -372,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -381,30 +873,30 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Estimated number of clusters: 4\n", - "Estimated number of noise points: 8\n" + "Estimated number of clusters: 5\n", + "Estimated number of noise points: 21\n" ] }, { "data": { "text/plain": [ - "array([ 1, 0, 1, 0, 2, 2, 3, 3, 3, 1, 3, 3, 0, 3, -1, 3, 3,\n", - " 1, 2, 2, 1, 1, 1, 0, 2, 3, 3, -1, 0, 2, 2, 1, 2, 0,\n", - " 1, -1, 1, 3, 0, 1, 1, 3, 3, 0, 1, 2, 1, 0, 1, 2, 0,\n", - " 2, 2, 0, 3, 1, 2, 2, 1, 0, -1, 2, 0, 0, 3, 1, 3, 0,\n", - " 3, 2, 0, 1, 1, 3, 0, 2, 0, 1, 3, -1, 0, -1, 1, -1, 3,\n", - " 2, 2, 2, 2, 0, 2, 3, -1, 0, 0, 1, 3, 3, 0, 3],\n", + "array([ 0, 0, 1, 0, 2, 2, 0, 3, 3, 4, 0, 3, 0, -1, 0, 0, 0,\n", + " 0, 1, 4, 2, 0, -1, 3, 2, -1, 1, 3, 2, 4, -1, 3, 2, 0,\n", + " 4, 1, 1, -1, -1, -1, -1, -1, 0, 4, 2, 1, -1, 2, 0, 3, 4,\n", + " 0, 2, 3, 0, -1, -1, 4, -1, 0, 0, 1, 0, 4, 0, 2, 0, 3,\n", + " 1, 3, 3, -1, -1, -1, 0, 0, 2, 2, -1, 2, 0, -1, 4, 1, 4,\n", + " 0, 1, -1, 0, 1, 3, 1, 2, 0, -1, 1, 2, 4, -1, 0],\n", " dtype=int64)" ] }, - "execution_count": 137, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -423,12 +915,12 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 104, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGdCAYAAAA8F1jjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABqb0lEQVR4nO3dd3gUVdsG8PvMbLLpPSGUAKGDdEKvUqUoWBAVUVCxV3xVeC282FCxY6/w2QUFBSz0XqX3DgFCem+b3Z3z/bEkErK72cC2JPfvuqJk5szME5Kwz57yHCGllCAiIiLyQoqnAyAiIiKyhYkKEREReS0mKkREROS1mKgQERGR12KiQkRERF6LiQoRERF5LSYqRERE5LWYqBAREZHX0nk6gCulaRqSkpIQHBwMIYSnwyEiIiIHSCmRl5eHevXqQVFs95tU+0QlKSkJcXFxng6DiIiILsOZM2fQoEEDm+erfaISHBwMwPKFhoSEeDgaIiIickRubi7i4uLKXsdtqfaJSulwT0hICBMVIiKiaqayaRucTEtERERei4kKEREReS0mKkREROS1mKgQERGR12KiQkRERF6LiQoRERF5LSYqRERE5LWYqBAREZHXqvYF34iIqHaSUmLvvrM4fz4bQUF+SOjSGHq9j6fDIidjokJERNXOrt2n8ebbfyEpKavsWGCgHnfe0Qc3Xp/ATWprECYqRERUrezbfxZPT/0JZrNW7nhBgQEffbwCxhIzbr2lh4eiI2fjHBUiIqpWPvtiNTRNQkrr5+f83zrk5xe7NyhyGfaoEBGR15FSYsvWE/jt9+04cjQZPj469O3TAn16t8C+fWftXms0mrFm7WGMHNHBTdGSKzFRISIiryKlxAcfLseC37ZDUQQ0zdJ1smDhdvz2245Kr1dVBZlZ+a4Ok9yEiQoREXmVlasOYMFv2wGgLEkp/bMGG+M9FzGbNURFBrssPnIvzlEhIiKvMm/+tkpX7dg77eurQ7++LZ0cFXkKe1SIiMhrmM0ajhxNrrSdEAJClO9xKTX57v4IDNS7IjzyACYqRETkVYSAzRU9AKAoAp06NkJWdiFOnEgtOx4WFoC7J/V36iTarKwCLF2+D2fPZiIwUI/+/VqhVcu6rNPiRkxUiIjIa6iqgnZt47Bv/1mrvSWApRfl6gGtMfya9jh+PBVJ57MRHOyHdm0bQKdTnRbL74t2YvaHy6BpEopiSUx+nrcVXRPiMf35MQgIYK+NO3COChEReZWbx3azmaQoikBIiD8GXt0GQgg0a1YH/fq2RKeOjZyapKzfcATvvv83zGYNUkqYzVpZgbntO07h1dcWOe1ZZB8TFSIi8iq9ejbH3ZP6WT0nhMAjDw6Gn59r9/T5v2/W2xze0TSJjZuO4eTJNJfGQBZMVIiIyOsMvLoNgoP8KqzukVJi1lt/YO++My57dmpqLo4dT4W0M1FGUQTWbzjishjoX0xUiIjI67z/wTIUFBoqTKrVNAmjScPM1xbbHB66UsXFxkrbKIpAkQPt6MoxUSEiIq+SlpaLLVuP20xEpJRITsnBrl2nXfL8mJgQ6PX215qYTBoaN4pyyfOpPCYq5HIGswln8rORVsSS1kRUuTNnMittIwRw6nS6S57v5+eDa4a2L1vpY+3ZlqXKLCrnDl6VqLz22msQQuDxxx/3dCjkBPlGA2buWoFuC9/BgCUfosfv72H00i+x7NxhT4dGRF7M39+30jZSOtbuck2a2Bf164VXSFYURUARAtOeGQW93rUTesnCaxKVbdu24dNPP0X79u09HQo5Qb7RgFtXfoOvj2xBvqmk7PiBrBTcv34+vj36jwejIyJXOX8+Gz/8uBmffb4Kvy/aifz84kqvKSoqQXJyNvILLG1btIhFZGSQ3WtUVUGP7k2dErM1ISH++OD9O3DjDV0ReKFeigDQNSEe775zO3r1bO6yZ1N5XlHwLT8/H+PHj8fnn3+Ol19+2dPh1GhJBTlYlLgfGcWFiA0IxnWN2iLKL9Dpz/ny8BYcykmFdslMuNINxV7cuRRDG7REjD83DiOqCUwmM97/YBkWL9ll6XVQBMxmDR99shwPPzgEo0Z2rHBNckoO5sxdh5WrDsBk0iAE0L17M0y6sw/uuL033nnvb6vPEgK4dlQnhIc7/9+uiwUH++GB+wZi8t39kZtXDH8/H5f24pB1XtGj8tBDD2HkyJEYPHhwpW0NBgNyc3PLfVDlNCnx0o6l6Lf4A7y5dzXmHt2GmbtWoNfv7+PjAxvsLsO7nGd9e2x7hSTlYlICv5zc47RnEpFnffjxCiz5YxcAy8ock0mDlEBJiRlvv/sXVq85VK79uaQs3P/gHKxYuR8mk6WQmpTA1q3H8fCj36Bhw0jcNbFfWdKjUxWoqmUYZtiQdnjw/oEOxSWlRGZmPlJTc8sKtlWVTqciIjyQSYqHeLxH5ccff8SOHTuwbds2h9rPnDkTM2bMcHFUNc/be1djzlHL37GUF22VLiXe3LsavoqKaP8g5JQUo2FQOPrUiYeqXF4eW2A0INNQaLeNEMDxvIzLuj8ReZeMjHwsWrzT5v48QgBfzVmL/v1alhVRm/3BMuTnF1dY2aNpElJqeP2NJfj2/+7H8GvaY9nyfUhOyUFYaAAGDmyDhnGRDsW1bPk+/PDj5rJJt+HhgRgzujNuubkHfHycV8WWXMujicqZM2fw2GOPYdmyZfDz83PommnTpmHKlClln+fm5iIuLs5VIdYIuSXF+PLwFrttXt29AoBlDFYCiPEPwmtdR6F/3aqPAfuqOigQ/yZDVggIBOn47oSoJli/8YjdXlkpgbNnM3HqdDriG0cjJTUHW7edsNP+3+XHnTs3xi3jelQ5pjlz1+H/vt1QrmBcVlYB5sxdj717z+DVl8c6teQ+uY5Hh362b9+O1NRUdO7cGTqdDjqdDmvWrMH7778PnU4Hs9lc4Rq9Xo+QkJByH2TfmuTjKNEq/l1aU/pPTVpRPiav+wlbUxOr/Dy9qsOg+s2h2tld1CQ1XBPXusr3JiLvU1BggOLAbsKFhSXYvTsRr7xa+T45QgCnEy+v1/XkyTT837cbAFTchVlKiX+2n8Jff++9rHuT+3k0URk0aBD27t2LXbt2lX0kJCRg/Pjx2LVrF1SV2a4zFBhLKm90CQnLL/ibe1dd1jMfaN0LgKWH5lKqEOgS1QDdoxte1r2JyLs0qB8BcyVVYoUQ2LzlGJ74z/c4cPBcpfe0LD++vOW/i//YVTafxXoswMLftl/Wvcn9PDr0ExwcjLZt25Y7FhgYiMjIyArH6fI1CXZsPPdSGiS2p59FUkEO6gWGVunaDpH18UmfsXhi00Lkm0qgEwokALPU0DW6IT7qfaPNDb+IqHrp2aMZQkP9kZtbZHWeiqIIXNWmPr77fhMAOFT6XqdT0LNHs8uK59SpdJjN9oeizpytvKgceQePT6Yl1+saHYdGQeE4k59td96ILRmGwionKgAwsF5zbB79OJYkHsDhnFToVR2G1m+J9pH1qnwvIvJePj4qnv7PSDw//RcIUT4RURWBoGA/BAbqoaqKQytvhABGX9cZoaEBVYojLS0Xy1ccQHJKdqVtXb37MjmP1yUqq1ev9nQINY4QAm90uxYTVn8Hs9RgrsJSZAEg9gpqnfjrfHBTkw6XfT0RVQ89ezTD22/ehq/nrMXuPZadjVVVwYD+rXD3Xf3x8KP/5/Dy4BHDO+D+eytfflxYaMCyFfuxdt1hJCZmICMjH0Kg0t5aVRUYOKCNQ7GQ53ldokKukRAdh/mDJ+KdvWuw+vwxSFjmikgJm70sqhDoG9sE0f72K0QSEQFA+3ZxeOet8cjMzEdeXjEio4IQFGhZ0elIuYPIyCC8+9Z41K8fXmnbM2czMeU/3yMjo/weYlLC7gokRRHQ6VTceENCpc8g78BEpRa5KjwWX/Qbh2xDEbJKihClD8CixAN4fvufFdqqQkCv6vBMh0EAgGxDEVKK8hCu92c1WSKyKyIiCBER5d/g9OjeFH/8tdvm3BFFERh+TXuHkhSzWcPUaT8hK6vA4ZhKh52Cgvzw4v9uQIMGEQ5fS57FRKUWCtP7I0zvDwC4rVlnBOp88ebeVUgq/LfKb+fIBvhfl2HwUVQ8tOEXLD13uKzSbPfohniy/dXoEtXAI/ETUfUzZnQXLPlzt9VzQlgmz15rpcy+NRs3HcX55JwqPT8hIR6DB7ZB3z4t4evLl77qhN8twujGbXFto6uwNzMJuUYD4gLD0Dg4Asdz03H9sq9QaCopVw5/W9oZ3LbyG3zZbxz6xDbxYOREVF3Ex0fjuf9eh1dmLrJUx74w4bZ0KOalGTciOtqxuljbd5xyeGJuqav7t8KggVddVuzkWUxUCACgCIEOkfXLHfvf9r9RaCqpMPlWg4SUwNNbF2PdqIcvu9Q+EdUuA/q3RquWdbF4yW7s2pMIRRFI6NwYI0d0rHS35Is5srz5UnEOlt0n78NEhaw6W5CNjamnbJ6XkEgpysP6lJOXVWafiGqn2Ngw3HN3/yu6R+tW9bB4yS6H2iqKQKOGkWjVsu4VPZM8h2+FyarTeVmVthEQOJXHoklE5F4Dr26N4GA/KErly5B9fXR45qlRLDBZjTFRIauCffSVtpGQCHKgHRGRM+n1Pnhpxo3w8dHZTFaEAHr3aoEPP7gDLVrEujlCciYO/ZBVbSPqom5ACM5ftBLoUj6KikH1mrsxKiIii/bt4vDl53djwcJ/sHr1IRgMRsTHR2P4Ne3Rrm0cwsICEBjIN1I1gZD2KuNUA7m5uQgNDUVOTg53UnayX0/uwVNbbe9yen/rXniq/dWV3iffaMC8E7sx7+QupBUXoI5/MMY16Yib4jvAX8cy1kREtZGjr99MVMgmTUrM2PE3vj++A5qUUC7aC3lii66Y2mFQpSt+0ovzcevKb3Eyz7Jdu8S/Oyq3DI3B9wNvR6ivv4u+AiIi8laOvn5zjgpZlW0owtjlc/Dtse2AtCQXGiQkJB5q0xvPdhri0LLkZ7Yuxun8TEigrFB/6Z+P5qbh+X8qVsUlIiIqxUSFrHpww3zszToPoDRBsZAAZh9Yjz/PHKz0Hqfzs7D6/HGbmyCapcSfZw8htSjPSVETEVFNw8m0VMGejCRsSUu0eV5A4KMDGzA8rrXd++xKP1fpszQpsTszCUPqt6xynERUcxw+fB6/LdqBgweT4OOjQ69ezTBqREdERXFvsdqOiQpVsDzpCFShwCytl6eWkDiQnYLkwlzEBtiZAOVg3YKL574QUe3z/Y+b8MWXa6CqomzTwhMnU/HzvK14febNaNc2zsMRkidx6IcqKDabHEodDJrZ7vlu0XGVJiE6oaAzNzckqrW2bjuBL75cAwDldlbWNImSEhOmPTsP+QXFngqPvAATFaqgdVgdmGz0ppQK8tEj1t9+l2xsQAhGNGwN1UbPigKBm+LbI1wfcNmxElH1Nm/+VptF2zRNoqioBEuX7nNzVORNmKhQBcMbtEKIj95mX4giBG5t2gl6tfKRw5cTRqBdRD3LdRfuWJq4dItuiOc6DXVKzERU/UgpsWt3YqWbDO7cfdpNEZE3YqJCFfjpfPBez+uhCsVqb4gKgV9P7sX96+dhQ8pJu/cK9tHjw143YFTDNgjT+yNI54umwVF4u8dozB1wGwu+EVHlnFztKy0tF9u2ncDu3YkoKTE59+bkdJxMS1b1q9sUvw6ZhE8ObsTfZw+XTawVAIxSQ4ahACuTjmLZuSN4sE1vPNlugNX7LD17GI9tWgCTpkGDhIDAkdw0vLRjKZoGR6JtBHc0JaqthBC4qk197Nt/1m6vSru2zpnHlpaeh/dnL8XGTUdRWjUhKEiPW27ugVvG9ah0k0PyDPaokE1Xhcdidq8bsG7Uw/BVVAiUf2NTWh/lowMbsDLpaIXrD2en4pGNv8KomaFduLK0IkuOsRh3rPkeOSVFrv4yiMiLjb2pq80kRQjLBoTXDGt/xc/Jzi7Ew4/+HzZvPoaLSzvl5xvwxVdr8OFHy6/4GeQaTFSoUt8e2w6jZrbZ+6oKgS8Pb6lw/KsjW8tVpL2YJiVyS4rx68m9zgyViKqZ3r1aYPytPQEAqvrvS5KiCPjoVLz4vxsQEnLl22z8NG8LMjLyYbaRFC34bTtOnU6/4ueQ83Hoh2xalXQMHx5Yj50Z9gu3maXEjvSzFY6vOHfEZi0WwJLArEw6ikktu11pqERUjd19V3906dIYC3/bgQMHk+Dro6J37xYYfV1n1KsbdsX3l1Ji8ZJddoeXVFXgz7/24IH7Bl7x88i5mKiQVd8f24Hnt//pcDE2a8XdjJr9Jc4AYNA4kY2IgI4dGqFjh0YuuXdJiQkFBQa7bTTNMsmWvA+HfqiCtKJ8/G/H3wBQNrfEHlUI9IppXOF424hYKHaq06pCoMOFpctERK7i66uDXm//fbmiCISGsqaTN2KiQhXMP7kb0sZGgtaYpcTdLbtXOH5n867Q7NxHkxK3Nu18WTESETlKCIGhg9tCVW2/cTKbNQwd3NaNUZGjmKhQBcdy0+HgNj0AgOc6DUHPOo0rHB9SvwVub9YFAMr1rJTWZpnR5Ro0CYm8oliJiBxxy7ge8PPztboEWQiBPr2bo1UrlkvwRkxUqIIAnS+EA3NTBICuUXGY1ML6ZFghBJ7rOATv9RiDDhH1oAoBH0VFv9im+O7q2zH+QhJDRORqdeuG4f13bkejRlHljquKwIhr2uO5/452eCNVR5nNGnbsOIWVqw5g3/6zVeqppn9xMi1VcE2DVvj++I5K20kA/6SfwbmCHNQPDC07nltSjK+ObMEPx3Yi3VCAANUHYxq3w1vdr0Oj4AgXRk5EZFt8fDS++PQuHDyYhOMnUuHrq0PXhHhERAQ5/VlLl+3Fp5+vRlZWQdmxevXC8fijQ5HQJd7pz6vJhKzmKV5ubi5CQ0ORk5ODkJAQT4dTI2hS4sblX2Nv5nmHKld/0fdmXF2vOQAg01CIm1fMxen8rHLzU1ShIEDng++vvh1twmNdFDkRVVfp6Xn4ffFOrFp9EMXFRsQ3jsbo6zqhV8/mTu/pcLU//9qNWW/9WeG4EJae5jdeG4fOnRq7PzAv4+jrN4d+qAJFCHzZ7xY0Cgp3qL3vRZsTvrJzGRIvSVIAwCw1FJpK8OimBWXdnwXGEhQYS5wXOBFVS0eOJGPSPV/g+x824dy5LGRk5GPHzlN4fvqveO2NJZVuWuhNSkpM+OiTlVbPSWmp6fLRJys4DFQFHPohqyL0Afhp4B3otej9slL51gT76JEQFQfA0puyOPGAzfZmKXEyLxNv7l2NVUnHcDgnFQDQMjQG97Tsjusbt6t275yI6MoYjWZMe24eiopKyiUkpX9etnwfWrWsi+vHeGZOm8lkxsZNR3H0WCp8fVT07NEMzZrVsdl+85Zjdmu2SAmcOJGGU6fSER8f7YqQaxwmKmRTlH8QbmnSCd8f31m2R8+l7mnZA/oLPSrHc9NhslOJttQnBzeWm6p7OCcVT21dhGnblqBFaDQmNE/A9Y3bwUdRnfFlEJEXW7f+cLl5HNbM/3Ubxozu7PY3Mnv2nsGMlxYiK6sAqqpASomv565D506N8MJzY6yW9s/IKIAQQGUdJukZ+UxUHMREpRpKO5uBJZ8tw4FNR6CqCroM7YChEwcgJCLY6c96rtNQZJUU4Y8zB6EKBfLCDshmqeH2Zl3wYJveZW19q5BYWPsdNkkNB7JTMG3bEiw6vR9f9BtXlgQRkfvkZeVj2dw1OLL9OHQ+OnQb0Qm9RneFzsf5v497952Fqiowm22/yTl/PhvZ2YUIDw90+vNtOXU6HU9P/QkmkxkAysW3a3cipj03D7PfnVBhuXNkZGClSUppO3KMx18FZs6ciV9//RWHDh2Cv78/evXqhddffx0tW7b0dGheac28TZh5+3uQmoR24Rdn+7I9+OLThRgy+ybUbVIHXaPj0DzUOZm6r6pidq8bMDkzCQtP7UOGoQCx/iG4Kb59hWe0CY9FpD4AGYbCK37u5tTT+PDAekxpN+CK70VEjtv42za8ctu7MBYbIRQBIYC/56xCbHwMXvv7OdRv5txaI452kri7N+WnnzfDbDZbnR+jaRIHDyZh+/aT6Nq1SblzPbo3Q2Cg3ubwjxBAfONoxDdmb4qjPD6Zds2aNXjooYewefNmLFu2DEajEUOHDkVBgf2uwNroxJ7TePW2d2E2mcuSFHOIDknPxOPoi03wUeZ2PL/9T1zz12e4fdW3SC3Kc9qz20fUwwudh+K9ntdjWsdBVhMhH0XFfa17OeV5GiS+PbodJWazU+5HRJU7tvMkXhz7JkqKSyCl5c2Q2WT5tyY1MR3PDHkJJcXOnQDfsUMju70pQgAN4yIQGnrlOyg7SkqJVasPwWy2t4mhglVrDlY47uurw/33Wt/YsHTVz4P3D+J8vCrweKLy119/YeLEibjqqqvQoUMHzJkzB4mJidi+fbunQ/M6v763xPLu48LvjuYrkPR8MxS1vTDkc9EP/ta0RNyy8hu3r6q5q0W3sgJwqrBsaagKy49ZgOpTpXvlGItxpiDL2SESkQ3z315k+YOV12fNrCHldBrWzNvk1Gf27tUcdeqEWK0YC1jmeoy7ubtbX9jNZg0lJfY3TNU0zWavycgRHfD0f0YgLKz83kGxsWGY+fJYdO7c2Fmh1goeH/q5VE5ODgAgIoKFwS61efH2snc3AJDfJxzG+nqrfadmKZGYn4VfT+3BhOYJbotRCIHnOg3B2PgOmH9yN84W5CBM74/Rjdpia+ppzD6w3u7+P5cqTXKIyPXWL9ha7t+YSwlFYNPv2zBkQn+nPVNVFcx85WZM+c/3yMkpLJvfUTpv5foxXXDNsPZOe54jdDoVUVHBSE+33SsthED9+rZfp64Z1h6DB12FXbsTkZNTiJiYELS9qgF7Ui6DVyUqmqbh8ccfR+/evdG2rfXNoQwGAwyGf7PY3Nzasy23Zio/DJLXN8LyzsfOz/38k7vdmqiUahkWg2c7DSl3rGlwJL46shUFphKHkpV6ASFo6GAtFyK6ckaD0e55qUkYipzfS9u4URTmfnUv/vp7D1avPYTCQgOaxMfgums7oUP7hk5/HgCcOZuJLVuPw2Q0o3nzWHTq2Khcr87oazvhqznrbNY70TSJkcM72H2GTqeyCq0TeFWi8tBDD2Hfvn1Yv369zTYzZ87EjBkz3BiV92jVowW2L9397/yUUB1go7sUsOQwGcVXPrHVWaL9g/B/A27DPWt/cmjC7b2tepbbzJCIXKtx2zic3JsIaaPAmqIqaNK+sUueHRzsh7E3dcPYm6zvHeYsBQUGzHx9MTZuOgohLJOFNU2iXr0wTH9+DJo3s1TOvuH6BKxddxjHT6RanVA78Y4+qF+fb6TcwWv61R9++GEsXrwYq1atQoMGDWy2mzZtGnJycso+zpw548YoPev6R0eUJSkA4JNaAtiZ7KVAoEFQqM3zntA+oh7WXfsI3ux+HUY1bIMgHz2AfzuFSod67miWULbzMhG5x5iHh9tMUgDLJNMRkwe5MSLn0jSJ/z43D5u3HANg+XpKk5Dk5Bw88eT3OH8+GwDg7++Ld966DdeP6QJ/v3/n19WvH45nnhqJOyb0cXv8tZXH9/qRUuKRRx7BggULsHr1ajRv3rxK19e2vX6+nPYdfnx9IRRVQW7XEKQ+1thu+7e6X4cxjdtd9vMMZhO2piWiwFiC+OAItAyLuex72br/H2cOYlHifuSVFKNJSBRuadIJnaLqO/U5RFQ5s9mMl25+GxsWboWAKBv2UFQFmlnDIx/cg+seHObhKC/ftm0n8Mx/f7Z5XlUFRo3shMceGVruuMFgxPnkHPj66lA3NrTCPJNz57KwZu0h5BcY0KB+OK4e0Br+/r4u+RpqEkdfvz2eqDz44IP4/vvv8dtvv5WrnRIaGgp//8qXo9W2RAUAtizZjl/eXYL9Ww7jzJRGKGgVWGGeiiIEOkc2wDcDxsNXrXqFVykl5hzZhtn71yHHWFx2vF14XbzadQQ3FiSqocxmMxZ/sgwL3v8D546eBwB0HNgWPUYl4OSeU0g8lITg8EAMuKU3+t/cC776qq3m86TX31iM5Sv32112HBDgi8W/TXHofiUlJrz1zl9YtnwfFEVAUQRMJg1+fj6Y8vg1GDzoKmeFXiNVm0TF1gzor7/+GhMnTqz0+tqYqFys2GTEG3tW4ccTO2EwW5bT+Soqbm7SEc90GIgA3eVl9bP3r8O7+9ZWOK4IAT9Fh1+HTHJaUTki8j5SShQXGqDqFHwyZS4WfbwUqk6B2aRBKAJSk4hrVR+zVkxHZN3qMVfj2efnY9PmY5W2W7H0GYdW58x8fTGWr9hvc8LtzFfGonu3pjav1zSJvXvPIOl8FgID/dA1Ib5W9cQ4+vrt8cm03EHyyvjpfPBC56F4om0/7M1KhoREu/C6CPH1u+x7phfnY/b+dVbPaVLCoJnw9t41+LjPTZf9DCLybkII+Af6YcH7f2DRx0sBoGzpcuk8lqRj5/G/G2bh/Y2vVItlt3VjQ6Gqwm6PSnR0sENfy7mkLCxbvs/meSEE5v7fepuJyq7dpzHrrT/L5sQAgL+/D26/rTduGefeujHezmsm09KVCfb1Q686jdG7TvwVJSkAsOj0Abt7VZilxPJzR5BTUnRFzyEi72Y2m/Hzm7/bPm/ScGjLURzcctSNUV2+ESM62E1ShBC4dmQnh+61du1hm0XqAMub8EOHzyM1tWIJjf0HzuHpqT8hOTm73PGiIiM+/3I1/u/bDQ7FUFswUaEKUorzoFaSzWuQSC/mNgdENdm5o8lIP5tht42iU7Bj2R43RXRlmsTH4MYbrNeVUhSBRo0iccP1jq02LCgw2E1ULm53qc+/WA1NkzbfEH73/Ubk5HhPaQlP8/jQD3mfaL8gmCsZkhMAIvQBdtsQUfWm2dmDp5SAgNnk/D25igsNWP3jBhzachSKTkXC0A7oPqoz1MtYHHCxB+8fhNg6ofjhx83IzLK82fLxUTF0cFvcO/lqBAToHbpPgwbhMNmp4gsAOp2C6Jjyu9qnpeViz177ZTXMZg2r1x7C6Gs7OxRLTcdEhSoY1bANXtu9wup+HwCgCoEBdZshnIkKUY1Wr2kdBIYGoMDOu3uzyYzWPVo49bm7V+/H9BveQEF2IVSdCghg0cd/o17TOnj1z2evaAdnIQRuvKErxozughMnU2EsMaNhw0gEBVVtyLx/v1aY/eFyFNmo1KuqAgOvboOgwPL3zcquvKdEURRkZbHHuhSHfqiCOv7BuK9VT6vnFCGgU1Q80dZ5e30QkXfy9fPFtQ8Mg7AxxKGoCmLjY5AwzH4p+ao4e/Q8/jviVRTmWubAmU1mmI2WHpvk02l4atAMFBUU27uFQ1RVQfNmsWjTpn6VkxQAOJ+cjejoYKvnFEUgLCwQd99V8d/JyIigSu9tNmuIjqp9q1htYaJCVj3ZbgCebDcAAbryNRKaBkfi+6tvR+vwOh6KjIjcacILN6F9vzYAUC5hUVQFAcH+mLHgaSiK815KFry3BCaTyWqFXM2kIe1MBlb94NnJpj/+vAX33PsVEhOtz9/R6RS8/OJNiI4qn8homoSiiAr7Cl3K10dF//4tbZ6vbTj0Q1YJIfBgm96Y2KIrNqScRP6FyrQdIupx2RxRLeLr54uZfz2LZXPXYNEnS5F0LBkBIf4YNL4vRj88HNENIp36vLXzNkGzt4OzEFj/62aMuMczpfw3bT6Gzz5fZbeNyaThjz93o2ULS2FMo9GMefO34teF/yAz0zKkI4Tlw9p0wMn3DKgwZFSbMVEhuwJ0vhhSn5k9eUZBTgGWfLYcf365Elkp2YioG4bhdw/GyHsHIyC48srV5Bw+vj4YMXkwRkwe7PJnFVeyO7OUEkVWVtK4y48/b7aZYJTSNImly/bioQcGQQiB/z43Dzt2nqpwzaWfh4cH4q6J/TByhPOG0moCJipE5JWyUrLxeN/ncf5EStkwQEFuIT5/5hv8+cVyvL32RYRFe9emm3TlGrdpgCP/HLe6YzEAqDoFTdo1dHNUFkajGXv3nnWorcFgQl5eMdatP1xpkvLEY8PQMC4Sbds2gKpyRsal+DdCRF7pzbs/RvKp1PJzFaSlKuq548l4575PPRccuczoh4fbTFIAS5G5UfcNcWNE/9K0ypdrl1IUgYAAXyz4bbvd3hdFEUhOyUGHDg2ZpNjAvxUi8jrnT6Zg6587bM5V0EwaNv32D1IT09wcGbna1bf2Rp8bKpaQL53Ie+eMcYhv18gToUGv90GjRpXPyRFCoE/vFvDz88HZs5l222qaxKlT6c4KsUbi0A8ReZ1DW47ZrONTSkqJQ1uPIaYhN8esSVRVxXM/PYHfZv+FX99fgpRTlmS0Wad4jHt6DPqPtV46wdlKSkxYs/YQVq85hIICAxo1jMTIkR1x0w3d8NY7f9q9VqdTMOH23hBCwNdXB4PBZLOtogj4+VWfHag9gYkKUTVjLDFiw4KtWPfrFhTlF6NR6wYYee9gNGhRz9OhOY3iYBe4qruyKqXknVRVxQ2Pj8T1j41AXmY+VJ2CwNBAtz0/LT0PTz71A86ezYQQAlJK7D9wFouW7MLYm7ph8KCrsHzFfqvXhoUFYMb069G0SQwAoF/fVli5ar/NPYY0TaJvHy5YsEfIar59saPbRBPVBGlnM/DMkBdx5nASFEVY6jKoCjSzhrtnjsctz4zxdIhOkZWSjVvi7rO7TFXno+LHc58hlIWxyImklLj/wTk4cTLVZnIx5fFhCAryw4KF23HseAoAgSZNojF8WHtcM6x9uRopx0+k4oGH5sBs1irMVVFVgbp1w/HV53dDVwuTbkdfv9mjQlRNaJqG50bNRNLx5AufW/7VK92P5ctp36Fe0zrod5N7usZdKbxOGAaP74fl3661ut+MoggMnTiASQo53Z69Z3D0WIrN80IAP/68Fd/MuRcD+reu9H5Nm8Tg5RdvwosvLURhUUnZhFmzWUPDuEi89urNtTJJqQomKkTVxK6V+3Biz2mb54Ui8MPMBTUiUQGAhz+4Gymn07B79f6yXqPS/3e4ui0eeGeSp0OkGuiff05CVRWYbWzIKCWQlJSFlJQcxMaGOXTPbl2bYN5PD2PFqgM4eiQZPj4qundvhi6dGzu0A3Ntx0SFqJrY+scOqDrV5k61UpM4tvMkctJza0RPg3+gH15f9jy2LNmBpXNWI/1cBqIbRGLYpIHoOrzjFe+iS2SNrQSlYruqzZrw9/fFqBEdgRGXEVQtx0SFqJowlpgAB958GUtsrzCoblRVRa/ruqLXdV09HQrVEq1a1as0WQkJ8UedOtX/zUB1wToqRNVEi4SmZbvI2hJeJxThdVitlehy9erZDJERQTaHZIQQGHNd5wrzStLT87Dtn5PYvTsRJTXozYI3YI8KUTUxYFwvfDxlDgpzi6zuLCsUgdEPD+eQCNEV0OlUvDjjBvzn6R9hMBjLJq2X7u/TqWND3Hbrv/PA0tPz8P4HS7Fh4zGULqINCtJj3NjuuPWWnpyD4gRcnkxUjexYsRfPjXoVmlmD+cLSXSEstdE6D2qHlxdPg48vi0cRXank5Gz8suAfrFx5AIVFJWhQPwKjR3fGNUPblfWm5OQU4v6H5iA9Pc/qnJXR13XGY48MdXfo1Yajr99MVIiqmdMHzmD+24uxZt5GGApL0KBlPYx+8BoMv2cgkxSqUQpyC7H8m7XYu+4AAKBd3zYYPKEfAkMCPByZxWdfrMbP87bY3Zvoy8/uRnw8qydbw0SFiIiqrb3rDuK5a2eiMK+obN8fKSUCgv3x8qJpaNe38homriSlxJgb30NeXrHNNkII3HRDAh64f5AbI6s+WPCNiIiqpbSzGfjviFdQUlRi2TH7ovfTxfnF+O+IV/DVwfcQ3cD+BoGaJrFu/WH8vmgnEs9kIChQj4ED2+DakZ0QFnZlvTJGo9lukgJY4t65O/GKnkNc9UPkMlkp2Th7JAlF+UWeDoWoWln8yVKUFButDqlomkRJsRGLP1lq9x5ms4YZLy3AjJcWYveeRGRk5ON0YgbmzF2Pu+75AqdOX9mOxT4+KvT6yt/rnz6dDmMlq/XIPiYqRE62c+VePNHvedxcdzImtXoMN0bfhbcnf4zM5CxPh0ZULWxYuNXq1gmlNLOGDQu32r3HTz9vwfoNRyztL0p4pJTIzSvCcy/Mtzu3pDJCCHTs0KjSdkajGbvYq3JFmKgQOdHa+ZvwzJCXcGDj4bJjRoMJf89djYe7T0PGeSYrRJUpKTZeURuzWcMvC/6psAlgKU2TSErKxj/bT15uiACALp0bO9SuoMD+EBHZx0SFyEmKCw14656PISErvFPTTBoyzmfh62e/91B0RNVHy25NoepsvzypOgUtuzW1eT45OQdZWQV2n6GqCvbsOXPZMQJAm9b1HGpX18E9gcg6JipETrJu/mYU5hZZippYoZk0rPh+PQpyC90bGFE1M/rBa8rqBFljNmkY/eA1tm/gYI01cYW12Fq3roeGcRF27i/QuFEUWrSIvbIH1XJMVIic5Mzhc9D52K8KayoxIe1MhpsiIqqe2vZpjfHP3ggAUNR/X6ZK/zz+2RvRto/t5cmxdUIRFRVk9xlms4ZOHSufY2KPEAJTnhgOnU6tUIFWUQRUVcGUJ64pW15Nl4fLk4mcJCDY36HJeQEh/m6Ixj1MmhH7czZjR9ZK5BgzEOYTjS4Rg9AmtDtUwVL+dPkmvnQLmndpgvlvL8L+DYcAAG16tsBNU65F7zHd7F6rqgrG3tQNH3+y0sZ5gQYNItCp05UlKgDQvl0c3nt7PD77YhV2XzSU1L5dHO6dfDVatax7xc+o7VjwjchJzh07j4ktHrV5XigCzTrF46Ntr7sxKtcpNhfi6xMzcLboKAQEJCQEFEhoaBzYBnc0fhZ6teYkZeQ5mmYZBlIUxwcBNE1i1lt/4O+le6GqoqzEvRBAVGQw3n7rNtSvF+7Qvc6fz8b8X7dh+Yr9KCwsQd3YUFx3XWdcO7Ij9Pp/q0GnpOYgM7MAkRFBiInh61FlWJmWyANm3v4eVv24weqmgRDAy79PRfeRXdwfmAv8dPpt7M3ZCImKcwkEFHQOvxo3xD3kgciILKSU+Gf7KSxeshOnE/8t+DZsSDsEBuoduseRI8mY8tT3KL6krosQQMuWdfHWG7fC39/XVV9CjcbKtEQe8OQXD0BKiVU/bICiKlAUAZPJDF8/Xzz20eQak6TkGDOwN2cDpI2ZwxIadmatxrC6tyNQF+rm6IgshBDomhCPrgnxl3W92azhhRm/VkhSAMtOykeOJOPruevwIEvkuxQTFSIn8vXzxX+/exwTXhiLtfM2oyCnAPWb18WAW3p7zUZqznC64KDNJKWUBjNOFxxCm9DuboqKyLm2/XMSqam5Ns9rmsSSP3bhron94OfHDUFdxStW/Xz44Ydo3Lgx/Pz80L17d2zdar/iIJG3i2tZH+OfuxH3zroDI+8dUqOSFKD83it221WSzBB5s8NHzkNV7b9MFhUZce5cppsiqp08nqj89NNPmDJlCqZPn44dO3agQ4cOGDZsGFJTUz0dGhHZ0DCwZaVtBBTEBbRwQzREruGjUx1KynU6rnBzJY8P/bz99tuYPHkyJk2aBAD45JNPsGTJEnz11VeYOnWqh6Mjqj2O5e3G+rTfcaJgLzSpIdw3Bpo0o9CcB381CJ3CB6BH5HAE+4Qj3DcGrYK74kjedmg2JtO2De2JEB/bxbCIvF1CQjy++GqN3TbR0cFo0IA/567k0R6VkpISbN++HYMHDy47pigKBg8ejE2bNlm9xmAwIDc3t9wHEV2Z9Wm/4+uTM3AsfzfM0gQJDZklycg2pqFEK0aOMR1rUn/F+0eeQFrxWQDADXEPIlJvvYR4Hb84jK5/nzu/BCKna9E8Fh07NKxQzO1it93Ss9LhIboyHv3bTU9Ph9lsRp06dcodr1OnDpKTk61eM3PmTISGhpZ9xMXFuSNUohrrfNFJ/Hl+DgBYXWpcSkJDsTkf359+40J3uICPqLgsU0AgIXwI/HX2K4MSVQfTnx+Dpk1iAKAsYSlNTMbe2BXXXdvJY7HVFh4f+qmqadOmYcqUKWWf5+bmMlkhugJbMv6GAsXqEM6lNGhINZzFqYID+Dv5GyQXn6rQRkJi8fkvEOkXixbBnV0QMZH7hIYG4KMP7sSmzcewavVB5OUVoX69CIwc0QHNmtWp/AZ0xTyaqERFRUFVVaSkpJQ7npKSgthY65s46fV66PWOFeohosqdKTzsUJJSSkDBnuz1OFN4xG6b1Sm/MFGhGkFVFfTp3QJ9enNyuCd4dOjH19cXXbp0wYoVK8qOaZqGFStWoGfPnh6MjKj20Imq1n+QSDWcgQLbKx0kNJwuPIgiU/6VBUdEtZ7Hh36mTJmCO++8EwkJCejWrRveffddFBQUlK0CIiLXahXSFeeKjjtc80RCIkgX5lBboywBd/uhmio/vxjbd5yCwWBC0ybRaNqUQ0Gu4PFEZdy4cUhLS8MLL7yA5ORkdOzYEX/99VeFCbZE5BpdI4ZgbdpCGLXiSpMVBQrig9qiSVBb7MvZaLdtgBrM8vlUI5nNGr74ag1+XfAPjEZz2fFWLevimadGolGjKA9GV/NwU0IiwumCQ5h78mUYtCLASrJSuityrF8j3NVkBlShw2sH7oZRGqzeT0BB/5gbMCT2NhdHTuR+r89agqXL9uLSV09FEQgM1OPTjyYiNjbMI7FVJ9yUkIgc1iiwFZ5u/Sl2Zq3G8fy90DQNob4RKDTnI8eYjkA1BB3C+6FNSHfoFMuclrENH8MPp98EUH5Zs4BAff8m6B9zg0e+FiJXOnEyFX8v3Wv1nKZJFBYa8MOPm/HE49e4ObKaiz0qRHTZEgsOY03qLzict71s7kqPyOHoHX0dfBWuzqOa55PPVuKXX7fBbLb90unrq8OS36dAUQS2bD2B337fjuPHU+Hn54P+/Vvjums7IToq2I1Reyf2qBDVQsXmAmzO+BPbMpYhz5SNQF0IuoQPRM+oES6ZL9IwsCUmxP8XJs0IkzRCr/hDCNtVPImqu8zMggpDPpcqKTGhsMiAzz5fjSV/7IaiCGia5aIfftyEBQv/wazXb0HrVtYrO1N5rPtLVEPkm7Lx0dGnsTz5B2Qb02CWRuQaM7A69Rd8cORJZJW4bqNPneIDPzWASQrVeFGRQajsx9xP74PVqw9hyR+7AaAsSSn9c3GxEf99bh5KSkyuDPWKFeUXYdWPG7Bw9p/Y+Ns2GEuMHomDPSpENcRvZz9FVklKhZU7EhryTdmYf+Z9TG76soeiI6oZhg5thx9/3mLzvKIIDB3aFvN/2QYhYLX3RdMkcnKKsHrNIQwd0taF0V4eKSV+eWcx5k7/CcUFBgghIKVESGQwHvngbgwY19ut8bBHhagGyClJx8HcrTYrzGrQcKrgAFKLz1Q4l1p8FjuzVmNP9noUmHJcHSpRtda4URSuG2V9fx9VFQgJ9seY6zrjzNlMu0NEqqpg776Kv4/e4Jd3FuPT//wfigssq/pKp7LmZuThldvexcbftrk1HvaoENUAScUnHSrYdrbwGGL8LHtjZZWkYv6Z2ThVsL/svAIVXSIGYmS9u+GjVNxwkIiARx4egrDwAMybvxVFRf8Oh7RrG4cnpwxHaKhjZQ69cai0KL8Ic6f/ZPO8APDZM9+g53UJboufiQpRDWCvnP3FVGFpl2/KxqfH/osCU3a58xrM+CdzOXKNmZjQ+L9e+Q8pkaepqoKJd/TFuLHdsWfvGRgMJsTHRyOuQURZmybx0Th5Ks1mr4rZrKFjh4ZuithxmxfvKOtJsUZK4NyR8zi+6xSadYp3S0wc+iGqARoFtoJOVNYDIlDPvwkAYFP6H8g3ZVsdKpKQOJy3HScv6mkhoor8/X3RvVtT9OvbslySAgA3j+1uM0lRFIHIyCD07dPSDVFWTU56rkNvULLTct0QjQUTFaIawE8NQI/IayBg7x8YiY+OPY2T+fvxT+byckXaLqVAwc6sVc4PlKiWGDL4Koy9qRsASw9MKSEEgoL88NqrN8PHx7GeUHeKaRgFR8qrxTR03zYBHPohqiGGxI5HVkkq9uduttnGqBkw9+TLMMoSu/fSoCHXmOXsEIlqDSEEHrhvIPr0bo7ff9+JY8dT4Ofvi/59W2L48A4IDfHO7Tq7XtMRoVHByEnPs3peUQRaJDRFw1b13RYTExWiGkKn+ODWRk/hp8S3sTdng9U2EhImaYSv0KNEFtu8lwIFoT6RrgqVqNZo1zYO7drGeToMh/n4+uCRDyfj5VvehkD55dWKIqD6qHjwvbvcGhOHfohqECEEkopO2G0jocFH0UPY+fXXoKFzxEBnh0dE1UD/sT0xY8HTqN+ifOXcFglN8eaqGWjdvblb42GPClENY5aVV7vUq/7wUfTINaZbnVDbNrQnGgW0ckV4RFQN9LquK3pem4Dju04hOy0XMQ2j3DrcczH2qBDVMA0CmkOx86utQEGjgFa4r9mraBxYsSqmKnTIMCRjwdmPcDJ/v0MT64io5hFCoFmneCQM7eCxJAVgjwpRjdMzaiT25Wy0eV6Dhh5RwyEhkWY4AwGl3AogszThfPFJJBefwvasFWgVnIBbGv3HJQXgpJT4Z+lu/PH5ciQdS0ZodAgG394PA8b1gq8fC84RESBkNX+75Og20US1yYrkH7Ey9edySUjpn4fG3o7+MTfglzMfYGfWqkor2goIJEQMwZgG9zs1RpPRhFdvexfrftkCRVWgmTUIRUBqEo3aNMCslf9DeIzzd3wmIu/g6Os3h36IaqBBsbdgQuP/Ij6wDRSoUIWKJkFtcWf8c+gfcwNKtGLsylrjUNl9CYntmSucvg/Qty/Nx/pftwIANLMlmZIXdpk9eyQJr976rlOfR0TVE4d+iGqoViEJaBWSYPVcrjETGswO30uDGcfz96J9WB+nxFZSXIKFs/+0Of/FbNKwa9U+nNyXiPi23ldmnIjchz0qRLWQvxpY5WtMmrHyRg46vvs0CnIK7bYRisDOFXud9kwiqp6YqBDVQgFq1edzle4T5AxSs12+v5TAv0NCRFR7MVEhqoWEEFCFYyO/ChQ0DGiJWP9GTnt+fLuG0PvbX9WjaRJX9WYtF6LajokKUS11VWhPu9VpAcuKnwBdCMbGPebUZ/sH+WP4PYOgqNafr+oUNO8cj1bdmjn1uURU/TBRIaql+kaPsbvbsoBAr6hr8XDztxChj3X68++eOR5terawPEv5Nw6hCITFhOL5n590aLt5IqrZuOqHqBYqMuXjZP4+NApsjdMFB6BBgwLLlvMazIjyrY9JTV5AmG+0y2LwC9DjjeUvYMW367D4s2VIPpGCkMhgDLljAEbeOxghkcEuezYRVR8s+EZUyxzI2YKfEt+BSRov9KhISEjoFX+0CumKDmF90Ty4IxShejpUIqrBWPCNiCo4V3gcP5yeBZMsgSVB0cqKvhk1AxILDqFpUHsmKUTkNZioENUi69N+s3lOg4YsYyr252xyY0RERPYxUSGqRQ7kboUG27VJBAQO5m51Y0RERPYxUSGqJaSUMEv71WUlJIxaiZsiIiKqHBMVolpCCIEYvzjA7pJkBbH+jd0WExFRZZioENUiPSJHAHZ3TJZIiBjsrnCIiCrFRIWoFukSMQitQ7ri0l6V0gq119a/F+G+MR6IjIjIOhZ8I6pFMg3n0SGsP4J1ETiatxNZxlQAQHzgVegfcz2aBXf0bIBERJdgokJUC6QVn8OCsx/hdOHBsmN6xR8DY25G3+jr4avqPRgdEZFtHhv6OXXqFO6++27Ex8fD398fTZs2xfTp01FSwhUHRM6UVZKKT49PQ2Lh4XLHDVoRVqb+jBUpP3goMiKiynmsR+XQoUPQNA2ffvopmjVrhn379mHy5MkoKCjAm2++6amwiGqclSk/w2AuhLRRP2V9+u/oHnmNSzYeJCK6Ul6118+sWbPw8ccf48SJEw5fw71+iGwzaUa8uH88zNJks42AgqvrjMWgOuPcGBkR1XaOvn571RyVnJwcRERE2G1jMBhgMBjKPs/NzXV1WETVVpE5326SAliq0eYaM90UERFR1XhNonLs2DHMnj270mGfmTNnYsaMGW6Kiqh681MDoUCFBrOdVhLBujCnPM9kNGHDwm3Y+ucOmEpMaN65CYbeOQAhkcFOuT8R1T5OH/qZOnUqXn/9dbttDh48iFatWpV9fu7cOfTv3x8DBgzAF198Yfdaaz0qcXFxHPohsuHnxHexN3u93T1+nmj5AaL09a7oOeeOncfUYS8j+WQqVJ0CKS1l+318dZj67WPoe0P3K7o/EdUsjg79OD1RSUtLQ0ZGht02TZo0ga+vLwAgKSkJAwYMQI8ePTBnzhwoStUWInGOCpF96YYkfHT0KZRoBqsTartFDMPoBvdd0TNKikswqdVjSD+XCc1c/hlCCAhFYPbmV9GiS9Mreg4R1Rwem6MSHR2N6Ohoh9qeO3cOV199Nbp06YKvv/66ykkKEVUuSl8P9zZ9BfPPzMb54pNlx32EL3pFX4vBdW654mesmbcJqYnpVs9JKaEIgflvL8J/v3v8ip9FRLWLx+aonDt3DgMGDECjRo3w5ptvIi0trexcbCyXSRI5U6x/Yzzc4i2cKzyOVEMifBQ/NAvqAD81wCn337ToHyiKgKZZ76A1mzRsWLjNKc8iotrFY4nKsmXLcOzYMRw7dgwNGjQod86LVkwT1Sj1A5qifoDzh19KikpsJimlTAYjpJQQwvbuzUREl/LYWMvEiRMhpbT6QUTVS9MOjaGotv85EYpA47YNmaQQUZVxUggRXbERkwcDdt5kSE1izCPD3RgREdUUTFSI6IrVaRSNRz6cDADle1aE5aP39d0wdOIAj8RGRNWb1xR8I6LqbdR9Q1CvaR38POs37Fi+B1ICDZrXxfWPjsTI+wZDVVVPh0hE1RATFSJyms6D26Pz4PYwGU0wm8zQ++s9HRIRVXNMVIjI6XQ+Ouh8+M8LEV05zlEhIiIir8VEhYiIiLwWExUiIiLyWkxUiIiIyGsxUSEiIiKvxUSFiIiIvBYTFSIiIvJaTFSIiIjIazFRISIiIq/FRIWIiIi8FhMVIiIi8lpMVIiIiMhrMVEhIiIir8VEhYiIiLwWExUiIiLyWkxUiIiIyGsxUSEiIiKvxUSFiIiIvBYTFSIiIvJaTFSIiIjIazFRISIiIq/FRIWIiIi8ls7TAZBrpSamIf1cJsJiQlGvaaynwyEiIqoSJio11NEdJ/Dpf/4Pu1fvLzvWIqEp7n1jAjoMuMqDkRERETmOQz810OFtx/B43+exd93BcseP7jiBp4e8iK1/7vRQZERERFXDRKUGmv3wlzAZjNDMWrnjUpOQmsQ7930Ks9nsoeiIiIgcx0Slhjl94AwObzsGTZNWz0spkX42A7tX7bd6noiIyJswUalhzp9IdbBdiosjISIiunJMVGqY4Iggp7YjIiLyJK9IVAwGAzp27AghBHbt2uXpcKq1Vt2bITou0m4bvyA/dB3eyU0RERERXT6vSFSefvpp1KtXz9Nh1AiqquLuV8fbbXP7czfBP9DPTRERERFdPo8nKn/++SeWLl2KN99809Oh1BiDxvfFE5/eB/8gSzKi6hRAAD56HSa9fCtufuo6D0dIRETkGI8WfEtJScHkyZOxcOFCBAQEeDKUGmfE5MG4+rY+2LBgK9LOZCAsJgR9b+yBoLBAT4dGRETkMI8lKlJKTJw4Effffz8SEhJw6tQph64zGAwwGAxln+fm5roowurPP9APg2/v5+kwiIiILpvTh36mTp0KIYTdj0OHDmH27NnIy8vDtGnTqnT/mTNnIjQ0tOwjLi7O2V8CEREReQkhpbReGewypaWlISMjw26bJk2a4Oabb8aiRYsghCg7bjaboaoqxo8fj7lz51q91lqPSlxcHHJychASEuKcL8JNDm45ij8+X46kY8kIjgzCwFv7oNfortD5cAsmIiKq2XJzcxEaGlrp67fTExVHJSYmlhu2SUpKwrBhwzB//nx0794dDRo0cOg+jn6h3kTTNLz/4OdY8tlyqDoFZpMGRRXQzBJNOzbG60ufR2hU9fhaiIiILoejr98ee+vesGHDcp8HBVkKkDVt2tThJKW6+uXtxVjy2XIAgNlk2Y9HM1vyxZN7E/HKbe/ijaUveCw+IiIib+Hx5cm1jdlkxry3frd5XjNr2Ll8L07uPe3GqIiIiLyT10yGaNy4MTw0CuVWJ/clIislx24bRVXwz9+7Ed+ukZuiIiIi8k7sUXGz0qEeuwRgMppdHwwREZGXY6LiZg1b1YM+QG+3jWbS0Kp7MzdFRERE5L2YqLiZf5A/ht81EIpq/a9eURXUb14XHa9u6+bIiIiIvA8TFQ+469Vb0bxzE0sNmX/LyEBRFQSG+GP6/CfL1ZchIiKqrZioeIB/kD/eWv0/PPDORDRs1QB6f19ExIbhpinX4tPdb3ESLRER0QUeK/jmLNWx4BsREVFt5+jrN3tUiIiIyGsxUSEiIiKvxUSFiIiIvBYTFSIiIvJaTFSqoZLiEuSk58JsYvVaIiKq2bxmrx+q3Mm9p/HdK79g3S9boJk1+Af5Ydikq3HbszciPCbU0+ERERE5HZcnVxP7NhzCM0NehMlkhnbRfkGKqiCyXjje3/QqoupFeDBCIiIix3F5cg1iNpsxc/x7MJWYyiUpAKCZNWScz8InU+Z4JjgiIiIXYqJSDexYtgepienQNOudX5pJw7pftiArNcfNkREREbkWE5Vq4NS+MzY3MSylmTWcO3reTRERERG5BxOVakAfoIe00ZtyMb8AvRuiISIich8mKtVAj2u7VNomqkEk4ts3dEM0RERE7sNEpRqIiYvCkDv6QyjCZpvbn7sRqqq6MSoiIiLXY6JSTTz28WT0ub4bAEDVqVB1KhRVgRACd84YhxGTB3s4QiIiIudjwbdqwtfPFy/M+w+O7TyJld+vQ25GPmLjYzB04gDExEV5OjwiIiKXYKJSzTTrFI9mneI9HQYREZFbcOiHiIiIvBYTFSIiIvJaTFSIiIjIazFRISIiIq/FRIWIiIi8FhMVIiIi8lpcnuxBUkrAfBzQsgG1PoRatwrXGoGSfwCZC6hxgK41hLBduZaIiKg6YqLiIbJ4FWTem4D56L/HfHtDhDwLoWtm/9rCnyHz3wG0jH8P6loCIS9B+HZ0UcRERETux6EfD5BFiyGz7wfMx8qfKNkMmTEW0njU+oUAZMH/QeY+Vz5JAQDTUcjM2yGNe1wQMRERkWcwUXEzKQ2QudMByAsfFzMDshgyb6b1a7V8Sy+MVRoAE2TuLOcFS0RE5GFMVNyteAUg8+w0MAMlGyDNyVauXQqg2M61GmDcAmlOusIgiYiIvAMTFXcznwGgVtJIAuZzFQ9rKQ5cC8CcehmBEREReR8mKu6mhMEyTONIu0uPRQMwO3Atd1MmIqKaweOJypIlS9C9e3f4+/sjPDwcY8aM8XRIruU3BPZ7RYRlBY/axMq1QwH42rlWAXw6Q+gaXFmMREREXsKjicovv/yCCRMmYNKkSdi9ezc2bNiA2267zZMhuZxQIoDAu22dtfw3aIrVmihCCYEIftzOtQpE8FPOCJOIiMgreKyOislkwmOPPYZZs2bh7rv/feFu06aNp0JyGxH0BCQUoOBzWIZyFMv/RTBEyIsQflfbvjjgbgj4QOa/X35SrhoHEfIyhG8XF0dPRETkPh5LVHbs2IFz585BURR06tQJycnJ6NixI2bNmoW2bdvavM5gMMBgMJR9npub645wnUoIBSL4CcjAiUDxMkDLAXQNAP0gCGFvaAeWnpbAO4GAWwDDBkBmWyrT+iSwMi0REdU4Hhv6OXHiBADgf//7H5577jksXrwY4eHhGDBgADIzM21eN3PmTISGhpZ9xMXFuStkpxNKOETAzRBBkyH8hleapJS7Vugh/AZC+N8A4duVSQoREdVITk9Upk6dCiGE3Y9Dhw5B0ywrX5599lnceOON6NKlC77++msIITBv3jyb9582bRpycnLKPs6cOePsL4GIiIi8hNOHfp588klMnDjRbpsmTZrg/PnzAMrPSdHr9WjSpAkSExNtXqvX66HX650SKxEREXk3pycq0dHRiI6OrrRdly5doNfrcfjwYfTp0wcAYDQacerUKTRq1MjZYREREVE15LHJtCEhIbj//vsxffp0xMXFoVGjRpg1y7JPzdixYz0VlteSWgFQsgmQRYCuKYSPe1dHZSZn4dS+M9D56tCqWzP4+jk+n4aIiOhyeSxRAYBZs2ZBp9NhwoQJKCoqQvfu3bFy5UqEh4d7MiyvIqUGmf8BUPAlgKJ/j+uuggh9DcKnpUufn5WSjQ8f+xrrftkMzWyZVxQUFoibnrwWt067Hori8ZqBRERUgwkp5aVb+FYrubm5CA0NRU5ODkJCQjwdjtNpuS8Dhf9n5YwCiACIyF8hdI1d8uy8rHw83G0qkk+nQTNVLPs/8t7BePyT+1zybCIiqtkcff3m22EvJk2JQOE3Ns5qgCyCzP/QZc//9d0lSD5lPUkBgCWfLcexXSdd9nwiIiImKt6s+DfY/xaZgeIlkLLITpvLt+Tz5WXDPdaoOgV/f7XKJc8mIiICmKh4NWlOR+n+P7aZLJVtnUzTNGQlZ9ttYzZpSElMc/qziYiISjFR8WJCjQZQ2RQiHaCEOf3ZiqIgMDTAbhtVpyI0qubNCyIiIu/h0VU/VAm/0UD+bDsNVMBvFITwc8njh9zRH79//LfNOSpmkxmDJ/RzybPdocRkxpLdhzBvyx6cycxBWIA/ruvcGmO7tUdYgGv+TomIqGrYo+LFhC4OCJho46xqWfUT9JDLnj/2P9chMNgfilrxx0RRBBKGdkD7ftVzt+tiowmTv/oVz81fir1nU5BZUIQTaZl4b+kG3PDeNzib6fzhNCIiqjomKl5OBD8DEfQEIALLn/BpBxHxI4TOdVV8Y+Ki8M66l9CoTQNLLBemywhFYOBtfTH916eq7WaI7/29ATtOnQMAaBet0JcSSM8vwBPfLUY1X7lPRFQjcOjHi0hzCmThd0DRIkDmAbp4iIBbgcDJEIETgZItgFYI6JpB+LRwS0yN2sTh011v4uCWozi24yR89DokDOuI6AaRbnm+KxQYSvDz1j3lEpSLmTWJA0mp2HsmGe0b1nVzdEREdDEmKl5CGg9CZk4AZD6AC3NCjHshc3YDRUsgwj+G0A/wSGxCCLTp0QJtergnOXK14ykZKDaa7LZRhMCO00lMVIiIPIxDPy4kzcmQxcsgi1dCatm220kzZNYDgCxAWZIC/PvnkvWQ+Z+4MtRaxdHhquo6rEVEVJOwR8UFpJYJmTMdMCzDv4mHD6T/jRAh/624SsewFtCS7N0RKPwWMuh+CPHvZoBSmoGSdYDxCCD8AP3Vlgm4ZFeL2CgE++mRV2yw2UaTEj2a8u+SiMjT2KPiZFIrgMy8HTAsR/neESNQ9DNk1v2WBOPia4w7UWnOKLMB89myT7Wi3yBTu0Jm3QuZ/zZk3iuQ6YOhZU9xWaXamkLvo8P4Xh1tltJTFYGE+PpoWTfarXEREVFFTFScrWg+YDoOwGzlpAaUbAQMay45rqLywm6WdlIWQcuYCOQ8dWE+y4X7Qlo+iv+AzH7issOvLe4f2B2DrmoGwJKYAP+uamoYGY43bx3pqdCIiOgiHPpxMln0cyUtVMii+RB+A8uOCH0vyIJKNhdU6gJqHGT2Y4Bxo52GGmBYCWncA+HT3uG4axsfVcU7t43C+iOnMH/bXpzOyEZEYACu69Qawzu0hJ8PfzWIiLwB/zV2NnMq7PeOmAHzJfNRfBIAXWvAdATWe2IAEXi3pafG8LcDQaiQRYuZqFRCUQT6tYpHv1bxng6FiIhs4NCPsylRlTUA1DrljgghIMI/AdT6pUcu/F+1/M//ViBgAmD4Cw5/y+ysMiIiIqou2KNig5QGQMsFlBAIoXf4OhFwE2TeG7Ddq6JB+N9Q8Tq1LhC1CCj6A7J4sWVHZF1TiIBbAJ/OEEJA0wpQ+W7KACC5+sfJDEYTcoqKEeynh7+vj0PXmDUNW46fQWJGNoL99OjfKh5Bfo7/LBEREROVCqTpDGT+h0DxIgBGADpIv5EQQQ9B6BpXfgP/cUDhjxdW6Fw6jKMAPh0B/SCrlwrhDwTcCBFwo/XzusaQsL5B4CVfBWAlGaKKsguLsXD7fuxKTIIqFHRvFoeRHVohUG9ZBn4uKwcfrdiMJbsOwWjWoCoCQ9s2x4ODeqJJTITN+246dhrPzV+K5Jz8smN6nYp7BnTD/Vd3h6KwRgsRkSOErOYbmuTm5iI0NBQ5OTkICQm5ontJ0zHIjFsuFF67OMlQAeEPEfE9hE+ryu9jToPMmQaUrL3oqGLZ6ThkBoQSaPNau/fV8iFTewEotttOBD0KEfTwZT2jNll/5BQe/3aRpUqtsPRVaRIIDfDDZ5OuR5Bej9s+/gH5hhKYtX9/TVRFQK/T4f/uuxmt68VUuO+OU+cw8fN50KSEtd+ue6/uhseG9nbhV0ZE5P0cff1monIRLeMWwLgb1ie0KoCuNZSoBQ7fT5oSAeNOACrg2w1CrfiiVlWy6HfInKdKP7vkrAoEPQUROIlVVStxKi0L17/3DYxmc4W/RUUIBOp90SQmAvvOJMNs5VdEEQJNYyKw4LEJFf6u7/j0Z+w8nWRzLyFVEVg17V5EBgU468shIqp2HH395mTaC6TpGGDcAVurbgANMO2HNB5w+J5C1xDCfzSE/yinJCkAIPyvgwj/AvDpcNFRH0A/DIhaCyXoLiYpDvhu0y6YpWZ1JpEmJfINBuxOPG81SSltczQlA3vPJJc7fj47D9tPnbOZpJRe+9eeI1cSPhFRrcE5KqVMxxxsdxTwaePaWCoh9H0h9H0hzamWYSqlDoTCd+dVsWL/sXLDOZdytJ/xeGpmuY0LswsrrwqsCgVZBYWOPYCIqJZjolJKOPhCL/xdG0cVOKuXpjYqMdvqOasaf9/yv0LRwYEQqKSSjqYhNizYKc8nIqrpOPRTyrcrICqb5OoH+PZySzjkWlfVrwPVzhCZqgj4qPZ/PXx1Knq3aFzuWFRwIPq2jLd7b1+dimHtWlQpXiKi2oqJygVC+EME3mu/UeBdEEqQewIilxrfs6PN+ScAYNYkbu3R0e497ujdGcFW6qI8ObwP9D46KDaSlSeH97N6HRERVcRE5WKB9wEBk2BZqKrCMjJWWh12PETQI56LjZyqb8vGuL1XRwAol1CU/vk/w/vi6ZH9cE//rhDCclynKGUbGI7r3h6PDrXeu9asThS+vX8cOl40dwUA6oQG4ZWbhmL8hecSEVHluDzZCmlKBIp/gzSnAEo0hP8YCF0jp9ybvIeUEn/tPYK563dg35lkCCHQtUkDTOqbgL4tG5e1S8rKxe87DyIlJw+RQQG4tlMbNIoKc+gZp9OzkJiRgxB/Pdo2qANV4XsDIiKAdVSIqkTTJIQAl3YTEbmJo6/fXPVTA0jjfsjiZYAshNA1B/xGcrlyFbGkPRGRd2KiUo1JLQ8y+zGgZD0sc2kEJExA3itA6BsQfkM9HSI5yFBkwLpftuD0/jPwC/RDrzFdEd+2oafDIiLyOA79VFNSSsjMOwHjVqDCRoUCgICI+A7Ct4sHoqOq2Lx4O16b8D4Kcgqh81GhaRKaWUPP6xIw7dtH4R/kPbV7iIichSX0azrjLsC4GRWTFMBSbkxA5n/s3pioyg5sOozp17+BwlxLpVqT0QzNbPmeblmyAy/d/LYnwyMi8jgmKtWULP4LZUunrTIDJesgNZZq92bfvvwLAOsl+zWzhm1/7cLhbQ5u70BEVAMxUamuZAEsQzx2GwGy8r1nyDOK8ouw7a+dZT0o1qg6FWt+3ujGqIiIvAsTlWpK6OJhe6fn0kYhgBLqlnio6oryi+1vCgQAAijMZbJJRLWXRxOVI0eOYPTo0YiKikJISAj69OmDVatWeTKk6sP/etgf+lGAgFsgBBd2eauQyGAEhNifKKuZNdRvUc9NEREReR+PJiqjRo2CyWTCypUrsX37dnTo0AGjRo1CcnKyJ8O6ItKcDpn/MbTM+6BlPQRZ+AOkVuD05wglAiJk+oXPLv02CkCJAEQ4pOms059NzqHz0WH43YOg2Nn8UFEVDLmjnxujIiLyLh5bnpyeno7o6GisXbsWffv2BQDk5eUhJCQEy5Ytw+DBgx26jzctT5bFf0NmT4FlSEaDZQ6JBEQ4RMRXED5XueCZqyDzPwRMey4cufBMKCgbV/AbCRHycq0rApdXbMBv2w/gjz2HkVdkQLM6kbi5ezv0aNrQayrQ5mbm4dGez+L8iZRyc1WEIiA1iUc/vAfXPjDMgxESEbmG15fQl1KidevW6Nu3L959913o9Xq8++67mDVrFg4dOoTw8HCr1xkMBhgMhrLPc3NzERcX5/FERRoPQmbcAEuCculfqQKIEIjo5RCKa2LUSnYBmXcCMKDikmUF8O0FEf6l17xAu1piRjYmfjYPqbn5ACzfEVURMGsSYzq3wUs3DvWaarS5GXmY8/yPWDp3NQxFJQCAph0a4fYXxqLP9d09HB0RkWt4fQl9IQSWL1+OMWPGIDg4GIqiICYmBn/99ZfNJAUAZs6ciRkzZrgxUsfIgrmlf7JyVgNkDlC0EAi8wzUBFM0HUALrdVU0S/Va43bAN8E1z78MUkrsXr0fy79Zi+y0HMTERWHYpKvRsmuzK7qvpkk8NPc3pOcXlPtumDXLZwt3HEDLutG4o0/nK3qOs4REBuPRjyZj8qwJSDuTAb8AX8Q0jPZ0WEREXsHpPSpTp07F66+/brfNwYMH0bJlS4wZMwZGoxHPPvss/P398cUXX+D333/Htm3bULduXavXemuPipbS1ZKM2OPbC0rEHKc/W0oJmdIelt4UW1TA/2Yood6R5BUXGjDjxjfxz9+7oOoUmE1a2f+HThyAKZ/fD1W1N1nYtk3HTuOeL3+126ZOaBCWPX233d2MM/MLsfHoaZSYzWhZNxpX1a9zWfEQEVFFHutRefLJJzFx4kS7bZo0aYKVK1di8eLFyMrKKgvwo48+wrJlyzB37lxMnTrV6rV6vR56vd7ZYTuBqfImssRFzzbCfpICABqgZbno+VX3/oOfY8ey3QAAs0kr9/9lc1cjJi4Kd84Yd1n33nr8LFRFgVmzXZ8kJScf57Jy0TAyrMK5EpMJry1eg/lb98J8UR7ful4MXrv5GjSrE3lZcRERUdU5PVGJjo5GdHTl3daFhZaKqcol72gVRYFm5wXGa+naAsZ/YLu2iQr4tHfRw30AEQ5Ie4mIAugauOj5VZOelInl366F1Kx35kkJ/PreEtwydQz0/lVPSiVkpaXwSp9T8ZjE498txppDJyucO3w+FXd8+hPmP3I76oXXnn2liIg8yWPLk3v27Inw8HDceeed2L17N44cOYKnnnoKJ0+exMiRIz0V1mUTgRNgvwCbBhFwq2ueLQQQcAvsfzvNEP43uuT5VbV96W6bSUqpwtwiHNx89LLu37lRfZgqSXajggJQ30qysf7IaatJCgBo0rKS6Ku1/1xWXEREVHUeS1SioqLw119/IT8/HwMHDkRCQgLWr1+P3377DR06dPBUWJdPPwTwH3/hk4v/WlUAAiJkBoSukcseLwInAWocbBaBC7gLQtfUZc+vClOJA8NkAIwG42Xdv3eLRmgQEQrVxqoeAeD23p2gs1K/5LXFq+3eW5PAz1v2YPvJs6jmG48TEVULHi1bmpCQgL///tuTITiNEAIIeQHwTYAsnAsY9wJQAX0fiMC7IXy7uvb5ShgQ8SNk3kyg+A+UzZlRoiAC7wUC7nTp86uieZcmlbYRikB8+8tL7FRFwYd3XIeJn81HTlExtAsJhSoEzFJi0FXNMKlvxdVP2YXFOJ1e+Twes5S447N5aNugDj6YcB2iQ4IuK04iIqqcx+qoOIu7Cr4d330Kf36xAueOnUdIZDAGjOuNbiM6XfbKFFeSWiZgOg4IPaBrU6Uy+kUFxVjz00ac2pcIfYAeva/vhhZdnN8T82DXZ3B81ymrG/IpqoJeo7ti+vz/XNEzMvILMW/rXizZdQh5xQY0jYnAuB4dMLhNM6s1VPYknsetH//o8P1VRSA+KgLzHx0PHy/8OSAi8mZeX/DNWVydqEgp8emTc/HLu0vKls8qqgLNrKFl16Z49c9nERIR7PTnesLG37fhtQmzUZRXBJ2PCiklzCYNnQa3wws/P4mgsECnPevM4XN4ou/zyMsqKJesKKqCOo2i8d6GlxFeJ8wpzzqanI6zWTkIC/BH+7hYm0uSjySn4/r3vqny/d++bSSGtWtxpWESEdUqTFScZOHsP/HhY19ZPaeoCjoObIvX/37e6c91twObj+CJvs9DalqF1TCKqqBd31aYteJ/Tq1sm34uA7+8swR/z1mF/Kx8hMeGYeTkIbj+sREIDr/y4ZQ9iefx8u8rsf9catmx2NAgPHFNX4zq2KpCe02TGDrrS5zPznP4GYoQGNK2Gd6+bdQVx0tEVJt4fWXa6sBsNuOnNxbaPK+ZNexYtgcn955GfDvXTZR1h+9f+QWA9SW7mlnD7tUHsH/jYbTtXfEF/nJF1Y/EfW/egfvevANSSqcmQfvOJuPOz+fBdMnQUnJOPp756U8YjCbc2LVtuXOKIvDAwB544ddlDj9HkxJ5xZXVsCEiosvl0d2TvV3iwXNIP5dpt42iKtj65y73BOQihiIDtv650+p8kVKqTsXaeZtcFoO9JMVoNmP5/mP4fPVWfLdxJ5Kycyu93xtL1sJk1som0l7q9SWrUVRScVXRDQlX4ZEhvSCEpbfEESdTs5BdWOxQWyIiqhr2qNjhyDJaIRxfbuutDIUlldY1AYCivCI3RFPehiOnMfXnP5FZUARVEdCkxMzFq3F9l6vw/OiB8NVV/BE+m5mD7afO2b1vgcGIFQeOVxgCEkLg/oHdcV2n1li4fT/WHj6FvWeT7d4rJTcP9339K767/xarS56JiOjy8V9VOxq0qAu9v6/dNmaThhYJlS+39WaBYQEIjrA/J0TTNDRoWd9NEVnsTjyPB+cuRFahJUEyaxJSWoanFmzfj+d/sT5Ek3Jhx2R7VEUgJcf2XJR64SF4cHBP/N99Y5EQ38BupVtNAvvOpmDNoROVPpeIiKqGiYod/kH+uOaugVBsvEtWVAV1Gkejy9BqWKDuIqqqYtR9Q2x+nYDlax06cYD7ggLw4fJN0KS0UeoeWLzrEI6nZlQ4FxkYUOm9zZpERFDl7Xx1Onw26Xrofex3PipCYMnuQ5Xej4iIqoaJSiXuevU2NO3QGOKSuhuqToFfoB7T5/+nwn5F1dG4Z8agYev6FZKV0nojD79/N8JjQt0WT05RMTYePW1zjglg6RX5a8+RCscbR4ejTb0Y2ChMCwDQ61QMvqqZQ7HofXR2NzgELJNqsws4T4WIyNmq/yusiwUE++PttS9i8mu3o26TOlBUBUHhgbj2/mH4ZOcsNO9cvYd9SgWGBODddS/h+kdHwD/Yr+x4s85N8OJvz2DUfUPcGk9+sQGVzZoREMixMYn1yRF9L7Sw7sHBPRHs5/iGh/XCQ+wO/6iKQMOoMIfvR0REjmEdFaqgxGBERlIm/AL0Tiu6VlVFJUb0euljlJhsb/QohMDTI/rhjj6drZ5fd/gUpi9YhpScf+esBPr64MHBPXFnn85VWg49d/0OzPpjjdVhqFI/PHgr2sfFOnxPIqLajHVU6LL56n1QN76OR2Pw9/XBdZ1aY8H2/TDbWJGkKgLXdmpt8x59WzbGsqfvxtYTZ3A2MxdhAX7o06Ix/H19qhzPzd3aYfHOgzh0Ps3qcNTN3doxSSEicgEmKuS1HhrcE2sPn0RGfmG5ZEUAkACeHtEP4YH+du+hKgp6NrvyYnz+vj74evJNeOfvDVjwzz4YLvT0RAT6465+CbizT5crfgYREVXEoR/yask5eZj1x1os3Xu0rCejUWQYHhrcEyOtlMF3h/xiA06kZcFHVdCsTiQ3JCQiugzc64dqlMz8QpzNzEGA3gdNYyKdWm7/YgfOpeB4aiYCfH3Qo1lDBOrt19EhIqLLwzkqVKNEBAU4VPfkch1MSsVz85fi0Pm0smN+Pjrc1S8BDwzsUbZMm4iI3IuJCtV6J1IzccenP6PYWH4rhGKjCR+t2Iy8YgOmjhrgmeCIiGo51lGhWu+jFZtgMJlsFpf7dsNOnM3McXNUREQEMFGhWq6wxIil+47aXAINWOq1LN7F8vhERJ7AoR+q0aSUWHXwBL7buBN7z6ZApyq4ulUTTOjTGa3qRiOnsNhukgJY9vFJyytwU8RERHQxJipUY0kpMXPRany3aRdUIWC+MLSzaNdBLNp1EG/eOhJ9WjSGTlFgsrOXjyYlYkIC3RQ1ERFdjEM/VGMt238M323aBQBlSQpg2TlZ0ySe+vEPFBQbcE37FlDtrOqRkHYr4BIRkeswUaEa65v1O6DYqLciAWiaxC//7MeDg3rC39cHqo22k/omoF4Ya/QQEXkCExWqsfacTba5kgewDOnsTkxCo6gwfHf/OLS9ZK+eIL0vHh/WG1Ou6ePqUImIyAbOUaEaSxUCJjvnBQCdYsnVm9WJwvcP3IJjKekXKtP6omuTBvDz4a8IEZEn8V9hqrF6t2iMNYdO2F3V06t543KfN6sThWZ1olwcGREROYpDP1RjTezTGZqNJEURAiH+elzbyTMbGxIRkWOYqFCN1SW+AaZfPxhCoNxEWQEgyM8Xn911A4L89J4LkIiIKsWhH6rRxnZrh25N4jBv6x7sTjwPX52K/q2aYHSXNgj193N7PJqmoSCnEL5+PtD7M0kiIqoMExWq8RpFheE/I/p5NIbiQgPmv7UIv3/0F7JSciAE0GVYR9w27Qa068saLUREtjBRIXKx4kIDnhr4Pxz+5zjkhTkzUgI7lu3B9qW78d/vHsOAcb09GyQRkZfiHBUiF/v5jd9w5KIkpZRm1iClxKxJHyI/m3sJERFZw0SFyIXMZjN+//hvm6uPIAGjwYTl36x1b2BERNUEExUiF8rLzEdOWq7dNoqq4OTe026KiIioenFZovLKK6+gV69eCAgIQFhYmNU2iYmJGDlyJAICAhATE4OnnnoKJpO9WqJE1UNxoQEbf9uG9b9ucai9r7+viyMiIqqeXDaZtqSkBGPHjkXPnj3x5ZdfVjhvNpsxcuRIxMbGYuPGjTh//jzuuOMO+Pj44NVXX3VVWEQuJaXEvDd/x3ev/ILC3CKHrjGbzOhzfXcXR0ZEVD0JKe3s2uYEc+bMweOPP47s7Oxyx//880+MGjUKSUlJqFOnDgDgk08+wTPPPIO0tDT4+jr2DjM3NxehoaHIyclBSAh3uCXP+val+Zg7/SeH2ys6Bc07NcHsza9C2Ni9mYioJnL09dtjc1Q2bdqEdu3alSUpADBs2DDk5uZi//79Nq8zGAzIzc0t90HkDXLSc/Hdy/MrbafqFKg6FQDQrGM8Xl48lUkKEZENHqujkpycXC5JAVD2eXJyss3rZs6ciRkzZrg0NqLLsebnTTCbtErb9bmhB0KjgtH7+u7oNLAtkxQiIjuq1KMydarlnZ+9j0OHDrkqVgDAtGnTkJOTU/Zx5swZlz6PyFGZyVlQdJX/St0ydQwe+eAedB7UjkkKEVElqtSj8uSTT2LixIl22zRp0sShe8XGxmLr1q3ljqWkpJSds0Wv10Ov5x4p5H2i6kdCq6xHRQARsWFuiYeIqCaoUqISHR2N6Ohopzy4Z8+eeOWVV5CamoqYmBgAwLJlyxASEoI2bdo45RlE7tT/5p746PGvYDRYX2KvqAo6D26PiNhwN0dGRFR9uWwybWJiInbt2oXExESYzWbs2rULu3btQn5+PgBg6NChaNOmDSZMmIDdu3fj77//xnPPPYeHHnqIPSZULQWHB2HSS7daPaeoCnS+Otw98zY3R0VEVL25bHnyxIkTMXfu3ArHV61ahQEDBgAATp8+jQceeACrV69GYGAg7rzzTrz22mvQ6Rzv6OHyZPI2iz7+G3On/4Sc9LyyY806xePxT+9Dy4SmHoyMiMh7OPr67fI6Kq7GRIW8kclowt51B5GfXYh6TeugaYfGng6JiMirOPr67bHlyUQ1mc5Hh04D23k6DCKiao+bEhIREZHXYqJCREREXouJChEREXktJipERETktZioEBERkddiokJERERei4kKEREReS0mKkREROS1mKgQERGR16r2lWlLdwDIzc31cCRERETkqNLX7cp28qn2iUpenmXjt7i4OA9HQkRERFWVl5eH0NBQm+er/aaEmqYhKSkJwcHBEEJ4OpwKcnNzERcXhzNnznDTRA/i98F78HvhHfh98A61+fsgpUReXh7q1asHRbE9E6Xa96goioIGDRp4OoxKhYSE1LofQm/E74P34PfCO/D74B1q6/fBXk9KKU6mJSIiIq/FRIWIiIi8FhMVF9Pr9Zg+fTr0er2nQ6nV+H3wHvxeeAd+H7wDvw+Vq/aTaYmIiKjmYo8KEREReS0mKkREROS1mKgQERGR12KiQkRERF6LiYoLvfLKK+jVqxcCAgIQFhZmtU1iYiJGjhyJgIAAxMTE4KmnnoLJZHJvoLVM48aNIYQo9/Haa695Oqxa4cMPP0Tjxo3h5+eH7t27Y+vWrZ4Oqdb53//+V+Hnv1WrVp4Oq8Zbu3Ytrr32WtSrVw9CCCxcuLDceSklXnjhBdStWxf+/v4YPHgwjh496plgvQwTFRcqKSnB2LFj8cADD1g9bzabMXLkSJSUlGDjxo2YO3cu5syZgxdeeMHNkdY+L774Is6fP1/28cgjj3g6pBrvp59+wpQpUzB9+nTs2LEDHTp0wLBhw5Camurp0Gqdq666qtzP//r16z0dUo1XUFCADh064MMPP7R6/o033sD777+PTz75BFu2bEFgYCCGDRuG4uJiN0fqhSS53Ndffy1DQ0MrHP/jjz+koigyOTm57NjHH38sQ0JCpMFgcGOEtUujRo3kO++84+kwap1u3brJhx56qOxzs9ks69WrJ2fOnOnBqGqf6dOnyw4dOng6jFoNgFywYEHZ55qmydjYWDlr1qyyY9nZ2VKv18sffvjBAxF6F/aoeNCmTZvQrl071KlTp+zYsGHDkJubi/3793swsprvtddeQ2RkJDp16oRZs2ZxuM3FSkpKsH37dgwePLjsmKIoGDx4MDZt2uTByGqno0ePol69emjSpAnGjx+PxMRET4dUq508eRLJycnlfj9CQ0PRvXt3/n6gBmxKWJ0lJyeXS1IAlH2enJzsiZBqhUcffRSdO3dGREQENm7ciGnTpuH8+fN4++23PR1ajZWeng6z2Wz15/3QoUMeiqp26t69O+bMmYOWLVvi/PnzmDFjBvr27Yt9+/YhODjY0+HVSqX/3lv7/eBrAeeoVNnUqVMrTES79IP/8LpfVb4vU6ZMwYABA9C+fXvcf//9eOuttzB79mwYDAYPfxVErjd8+HCMHTsW7du3x7Bhw/DHH38gOzsbP//8s6dDI7KKPSpV9OSTT2LixIl22zRp0sShe8XGxlZY9ZCSklJ2jhx3Jd+X7t27w2Qy4dSpU2jZsqULoqOoqCioqlr2810qJSWFP+seFhYWhhYtWuDYsWOeDqXWKv0dSElJQd26dcuOp6SkoGPHjh6KynswUami6OhoREdHO+VePXv2xCuvvILU1FTExMQAAJYtW4aQkBC0adPGKc+oLa7k+7Jr1y4oilL2PSDn8/X1RZcuXbBixQqMGTMGAKBpGlasWIGHH37Ys8HVcvn5+Th+/DgmTJjg6VBqrfj4eMTGxmLFihVliUlubi62bNlic9VobcJExYUSExORmZmJxMREmM1m7Nq1CwDQrFkzBAUFYejQoWjTpg0mTJiAN954A8nJyXjuuefw0EMPcSdNF9m0aRO2bNmCq6++GsHBwdi0aROeeOIJ3H777QgPD/d0eDXalClTcOeddyIhIQHdunXDu+++i4KCAkyaNMnTodUq//nPf3DttdeiUaNGSEpKwvTp06GqKm699VZPh1aj5efnl+u1OnnyJHbt2oWIiAg0bNgQjz/+OF5++WU0b94c8fHxeP7551GvXr2yxL5W8/Syo5rszjvvlAAqfKxataqszalTp+Tw4cOlv7+/jIqKkk8++aQ0Go2eC7qG2759u+zevbsMDQ2Vfn5+snXr1vLVV1+VxcXFng6tVpg9e7Zs2LCh9PX1ld26dZObN2/2dEi1zrhx42TdunWlr6+vrF+/vhw3bpw8duyYp8Oq8VatWmX19eDOO++UUlqWKD///POyTp06Uq/Xy0GDBsnDhw97NmgvIaSU0lNJEhEREZE9XPVDREREXouJChEREXktJipERETktZioEBERkddiokJERERei4kKEREReS0mKkREROS1mKgQERGR12KiQkRERF6LiQoRERF5LSYqRERE5LWYqBAREZHX+n9OiNrGa/W/oAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -449,6 +941,13 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null,